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When it is not found, a full rebuild will be done. -config: c810468e5ce15e612839c964bc8fee3e +config: d4110f82058cef301334e6f3b0981f88 tags: 645f666f9bcd5a90fca523b33c5a78b7 diff --git a/doc/LectureNotes/_build/html/_sources/exercisesweek39.ipynb b/doc/LectureNotes/_build/html/_sources/exercisesweek39.ipynb new file mode 100644 index 000000000..d685e82d8 --- /dev/null +++ b/doc/LectureNotes/_build/html/_sources/exercisesweek39.ipynb @@ -0,0 +1,59 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "f35930ac", + "metadata": { + "editable": true + }, + "source": [ + "\n", + "" + ] + }, + { + "cell_type": "markdown", + "id": "8cb567a0", + "metadata": { + "editable": true + }, + "source": [ + "# Exercises week 39\n", + "**September 25-29, 2023**\n", + "\n", + "Date: **Deadline is Sunday October 1 at midnight**" + ] + }, + { + "cell_type": "markdown", + "id": "934324b3", + "metadata": { + "editable": true + }, + "source": [ + "## Overarching aims of the exercises this week\n", + "\n", + "The aim of the exercises this week is to aid you in getting started\n", + "with writing the report. This will be discussed during the lab\n", + "sessions as well. One of the lab sessions will be recorded.\n", + "\n", + "A general guideline can be found at .\n", + "\n", + "Similarly, an example of an earlier project can be found at \n", + "\n", + "Your task this week is to\n", + "1. Write an abstract for your project\n", + "\n", + "2. Write an introduction\n", + "\n", + "3. Include references\n", + "\n", + "Ashort feedback to the this exercise will be available after the deadline. And you can reuse these elements in your final report." + ] + } + ], + "metadata": {}, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/doc/LectureNotes/_build/html/_sources/week39.ipynb b/doc/LectureNotes/_build/html/_sources/week39.ipynb new file mode 100644 index 000000000..f8ed9613e --- /dev/null +++ b/doc/LectureNotes/_build/html/_sources/week39.ipynb @@ -0,0 +1,4874 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "97c9bb6c", + "metadata": { + "editable": true + }, + "source": [ + "\n", + "" + ] + }, + { + "cell_type": "markdown", + "id": "ade8d870", + "metadata": { + "editable": true + }, + "source": [ + "# Week 39: Optimization and Gradient Methods\n", + "**Morten Hjorth-Jensen**, Department of Physics, University of Oslo and Department of Physics and Astronomy and Facility for Rare Isotope Beams, Michigan State University\n", + "\n", + "Date: **Week 39**" + ] + }, + { + "cell_type": "markdown", + "id": "87cd74c3", + "metadata": { + "editable": true + }, + "source": [ + "## Plan for week 39\n", + "\n", + "**Material for the active learning sessions on Tuesday and Wednesday.**\n", + "\n", + " * Discussions on how to structure your report for the first project\n", + "\n", + " * Exercise for week 39 on how to write the abstract and the introduction of the report and how to include references. \n", + "\n", + " * Work on project 1, in particular resampling methods like cross-validation and bootstrap. **For more discussions of project 1, chapter 5 of Goodfellow et al is a good read, in particular sections 5.1-5.5 and 5.7-5.11**.\n", + "\n", + "These sections summarize neatly what we have done till now and point to what is coming with respect to deep learning. \n", + " * A general guideline can be found at .\n", + "\n", + " \n", + "\n", + "**Material for the lecture on Thursday September 28.**\n", + "\n", + " * Repetition of Logistic regression equations and classification problems and discussion of Gradient methods. Examples on how to implement Logistic Regression and discussion of stochastic gradient descent \n", + "\n", + " * Stochastic Gradient descent with examples and automatic differentiation\n", + "\n", + " * [Video of lecture](https://youtu.be/)\n", + "\n", + " * Whiteboard notes TBA at \n", + "\n", + " * Readings and Videos:\n", + "\n", + " * These lecture notes\n", + "\n", + " * For a good discussion on gradient methods, we would like to recommend Goodfellow et al section 4.3-4.5 and sections 8.3-8.6. We will come back to the latter chapter in our discussion of Neural networks as well. \n", + "\n", + " * [Video on gradient descent](https://www.youtube.com/watch?v=sDv4f4s2SB8)\n", + "\n", + " * [Video on stochastic gradient descent](https://www.youtube.com/watch?v=vMh0zPT0tLI)" + ] + }, + { + "cell_type": "markdown", + "id": "529184e5", + "metadata": { + "editable": true + }, + "source": [ + "## Optimization, the central part of any Machine Learning algortithm\n", + "\n", + "The first few slides here are a repetition from last week. \n", + "\n", + "Almost every problem in machine learning and data science starts with\n", + "a dataset $X$, a model $g(\\beta)$, which is a function of the\n", + "parameters $\\beta$ and a cost function $C(X, g(\\beta))$ that allows\n", + "us to judge how well the model $g(\\beta)$ explains the observations\n", + "$X$. The model is fit by finding the values of $\\beta$ that minimize\n", + "the cost function. Ideally we would be able to solve for $\\beta$\n", + "analytically, however this is not possible in general and we must use\n", + "some approximative/numerical method to compute the minimum." + ] + }, + { + "cell_type": "markdown", + "id": "1a65465a", + "metadata": { + "editable": true + }, + "source": [ + "## Revisiting our Logistic Regression case\n", + "\n", + "In our discussion on Logistic Regression we studied the \n", + "case of\n", + "two classes, with $y_i$ either\n", + "$0$ or $1$. Furthermore we assumed also that we have only two\n", + "parameters $\\beta$ in our fitting, that is we\n", + "defined probabilities" + ] + }, + { + "cell_type": "markdown", + "id": "b67231b3", + "metadata": { + "editable": true + }, + "source": [ + "$$\n", + "\\begin{align*}\n", + "p(y_i=1|x_i,\\boldsymbol{\\beta}) &= \\frac{\\exp{(\\beta_0+\\beta_1x_i)}}{1+\\exp{(\\beta_0+\\beta_1x_i)}},\\nonumber\\\\\n", + "p(y_i=0|x_i,\\boldsymbol{\\beta}) &= 1 - p(y_i=1|x_i,\\boldsymbol{\\beta}),\n", + "\\end{align*}\n", + "$$" + ] + }, + { + "cell_type": "markdown", + "id": "3f5e03db", + "metadata": { + "editable": true + }, + "source": [ + "where $\\boldsymbol{\\beta}$ are the weights we wish to extract from data, in our case $\\beta_0$ and $\\beta_1$." + ] + }, + { + "cell_type": "markdown", + "id": "e83141ae", + "metadata": { + "editable": true + }, + "source": [ + "## The equations to solve\n", + "\n", + "Our compact equations used a definition of a vector $\\boldsymbol{y}$ with $n$\n", + "elements $y_i$, an $n\\times p$ matrix $\\boldsymbol{X}$ which contains the\n", + "$x_i$ values and a vector $\\boldsymbol{p}$ of fitted probabilities\n", + "$p(y_i\\vert x_i,\\boldsymbol{\\beta})$. We rewrote in a more compact form\n", + "the first derivative of the cost function as" + ] + }, + { + "cell_type": "markdown", + "id": "cef5864b", + "metadata": { + "editable": true + }, + "source": [ + "$$\n", + "\\frac{\\partial \\mathcal{C}(\\boldsymbol{\\beta})}{\\partial \\boldsymbol{\\beta}} = -\\boldsymbol{X}^T\\left(\\boldsymbol{y}-\\boldsymbol{p}\\right).\n", + "$$" + ] + }, + { + "cell_type": "markdown", + "id": "2f59bbb1", + "metadata": { + "editable": true + }, + "source": [ + "If we in addition define a diagonal matrix $\\boldsymbol{W}$ with elements \n", + "$p(y_i\\vert x_i,\\boldsymbol{\\beta})(1-p(y_i\\vert x_i,\\boldsymbol{\\beta})$, we can obtain a compact expression of the second derivative as" + ] + }, + { + "cell_type": "markdown", + "id": "3869b3c6", + "metadata": { + "editable": true + }, + "source": [ + "$$\n", + "\\frac{\\partial^2 \\mathcal{C}(\\boldsymbol{\\beta})}{\\partial \\boldsymbol{\\beta}\\partial \\boldsymbol{\\beta}^T} = \\boldsymbol{X}^T\\boldsymbol{W}\\boldsymbol{X}.\n", + "$$" + ] + }, + { + "cell_type": "markdown", + "id": "e4656e92", + "metadata": { + "editable": true + }, + "source": [ + "This defines what is called the Hessian matrix." + ] + }, + { + "cell_type": "markdown", + "id": "b73ae554", + "metadata": { + "editable": true + }, + "source": [ + "## Solving using Newton-Raphson's method\n", + "\n", + "If we can set up these equations, Newton-Raphson's iterative method is normally the method of choice. It requires however that we can compute in an efficient way the matrices that define the first and second derivatives. \n", + "\n", + "Our iterative scheme is then given by" + ] + }, + { + "cell_type": "markdown", + "id": "70a2df05", + "metadata": { + "editable": true + }, + "source": [ + "$$\n", + "\\boldsymbol{\\beta}^{\\mathrm{new}} = \\boldsymbol{\\beta}^{\\mathrm{old}}-\\left(\\frac{\\partial^2 \\mathcal{C}(\\boldsymbol{\\beta})}{\\partial \\boldsymbol{\\beta}\\partial \\boldsymbol{\\beta}^T}\\right)^{-1}_{\\boldsymbol{\\beta}^{\\mathrm{old}}}\\times \\left(\\frac{\\partial \\mathcal{C}(\\boldsymbol{\\beta})}{\\partial \\boldsymbol{\\beta}}\\right)_{\\boldsymbol{\\beta}^{\\mathrm{old}}},\n", + "$$" + ] + }, + { + "cell_type": "markdown", + "id": "2e36f976", + "metadata": { + "editable": true + }, + "source": [ + "or in matrix form as" + ] + }, + { + "cell_type": "markdown", + "id": "7c4959c9", + "metadata": { + "editable": true + }, + "source": [ + "$$\n", + "\\boldsymbol{\\beta}^{\\mathrm{new}} = \\boldsymbol{\\beta}^{\\mathrm{old}}-\\left(\\boldsymbol{X}^T\\boldsymbol{W}\\boldsymbol{X} \\right)^{-1}\\times \\left(-\\boldsymbol{X}^T(\\boldsymbol{y}-\\boldsymbol{p}) \\right)_{\\boldsymbol{\\beta}^{\\mathrm{old}}}.\n", + "$$" + ] + }, + { + "cell_type": "markdown", + "id": "c553379e", + "metadata": { + "editable": true + }, + "source": [ + "The right-hand side is computed with the old values of $\\beta$. \n", + "\n", + "If we can compute these matrices, in particular the Hessian, the above is often the easiest method to implement." + ] + }, + { + "cell_type": "markdown", + "id": "145f9699", + "metadata": { + "editable": true + }, + "source": [ + "## Brief reminder on Newton-Raphson's method\n", + "\n", + "Let us quickly remind ourselves how we derive the above method.\n", + "\n", + "Perhaps the most celebrated of all one-dimensional root-finding\n", + "routines is Newton's method, also called the Newton-Raphson\n", + "method. This method requires the evaluation of both the\n", + "function $f$ and its derivative $f'$ at arbitrary points. \n", + "If you can only calculate the derivative\n", + "numerically and/or your function is not of the smooth type, we\n", + "normally discourage the use of this method." + ] + }, + { + "cell_type": "markdown", + "id": "9a68f686", + "metadata": { + "editable": true + }, + "source": [ + "## The equations\n", + "\n", + "The Newton-Raphson formula consists geometrically of extending the\n", + "tangent line at a current point until it crosses zero, then setting\n", + "the next guess to the abscissa of that zero-crossing. The mathematics\n", + "behind this method is rather simple. Employing a Taylor expansion for\n", + "$x$ sufficiently close to the solution $s$, we have" + ] + }, + { + "cell_type": "markdown", + "id": "d523ab61", + "metadata": { + "editable": true + }, + "source": [ + "\n", + "
\n", + "\n", + "$$\n", + "f(s)=0=f(x)+(s-x)f'(x)+\\frac{(s-x)^2}{2}f''(x) +\\dots.\n", + " \\label{eq:taylornr} \\tag{1}\n", + "$$" + ] + }, + { + "cell_type": "markdown", + "id": "8205fd3a", + "metadata": { + "editable": true + }, + "source": [ + "For small enough values of the function and for well-behaved\n", + "functions, the terms beyond linear are unimportant, hence we obtain" + ] + }, + { + "cell_type": "markdown", + "id": "98d52e46", + "metadata": { + "editable": true + }, + "source": [ + "$$\n", + "f(x)+(s-x)f'(x)\\approx 0,\n", + "$$" + ] + }, + { + "cell_type": "markdown", + "id": "203f65e8", + "metadata": { + "editable": true + }, + "source": [ + "yielding" + ] + }, + { + "cell_type": "markdown", + "id": "dd40195b", + "metadata": { + "editable": true + }, + "source": [ + "$$\n", + "s\\approx x-\\frac{f(x)}{f'(x)}.\n", + "$$" + ] + }, + { + "cell_type": "markdown", + "id": "e13a7340", + "metadata": { + "editable": true + }, + "source": [ + "Having in mind an iterative procedure, it is natural to start iterating with" + ] + }, + { + "cell_type": "markdown", + "id": "30c258c9", + "metadata": { + "editable": true + }, + "source": [ + "$$\n", + "x_{n+1}=x_n-\\frac{f(x_n)}{f'(x_n)}.\n", + "$$" + ] + }, + { + "cell_type": "markdown", + "id": "84f880c9", + "metadata": { + "editable": true + }, + "source": [ + "## Simple geometric interpretation\n", + "\n", + "The above is Newton-Raphson's method. It has a simple geometric\n", + "interpretation, namely $x_{n+1}$ is the point where the tangent from\n", + "$(x_n,f(x_n))$ crosses the $x$-axis. Close to the solution,\n", + "Newton-Raphson converges fast to the desired result. However, if we\n", + "are far from a root, where the higher-order terms in the series are\n", + "important, the Newton-Raphson formula can give grossly inaccurate\n", + "results. For instance, the initial guess for the root might be so far\n", + "from the true root as to let the search interval include a local\n", + "maximum or minimum of the function. If an iteration places a trial\n", + "guess near such a local extremum, so that the first derivative nearly\n", + "vanishes, then Newton-Raphson may fail totally" + ] + }, + { + "cell_type": "markdown", + "id": "b237f214", + "metadata": { + "editable": true + }, + "source": [ + "## Extending to more than one variable\n", + "\n", + "Newton's method can be generalized to systems of several non-linear equations\n", + "and variables. Consider the case with two equations" + ] + }, + { + "cell_type": "markdown", + "id": "b4ea1db2", + "metadata": { + "editable": true + }, + "source": [ + "$$\n", + "\\begin{array}{cc} f_1(x_1,x_2) &=0\\\\\n", + " f_2(x_1,x_2) &=0,\\end{array}\n", + "$$" + ] + }, + { + "cell_type": "markdown", + "id": "8b2b6606", + "metadata": { + "editable": true + }, + "source": [ + "which we Taylor expand to obtain" + ] + }, + { + "cell_type": "markdown", + "id": "e9d217a4", + "metadata": { + "editable": true + }, + "source": [ + "$$\n", + "\\begin{array}{cc} 0=f_1(x_1+h_1,x_2+h_2)=&f_1(x_1,x_2)+h_1\n", + " \\partial f_1/\\partial x_1+h_2\n", + " \\partial f_1/\\partial x_2+\\dots\\\\\n", + " 0=f_2(x_1+h_1,x_2+h_2)=&f_2(x_1,x_2)+h_1\n", + " \\partial f_2/\\partial x_1+h_2\n", + " \\partial f_2/\\partial x_2+\\dots\n", + " \\end{array}.\n", + "$$" + ] + }, + { + "cell_type": "markdown", + "id": "54f87f7c", + "metadata": { + "editable": true + }, + "source": [ + "Defining the Jacobian matrix ${\\bf \\boldsymbol{J}}$ we have" + ] + }, + { + "cell_type": "markdown", + "id": "c2711fc8", + "metadata": { + "editable": true + }, + "source": [ + "$$\n", + "{\\bf \\boldsymbol{J}}=\\left( \\begin{array}{cc}\n", + " \\partial f_1/\\partial x_1 & \\partial f_1/\\partial x_2 \\\\\n", + " \\partial f_2/\\partial x_1 &\\partial f_2/\\partial x_2\n", + " \\end{array} \\right),\n", + "$$" + ] + }, + { + "cell_type": "markdown", + "id": "e0409f1c", + "metadata": { + "editable": true + }, + "source": [ + "we can rephrase Newton's method as" + ] + }, + { + "cell_type": "markdown", + "id": "53651890", + "metadata": { + "editable": true + }, + "source": [ + "$$\n", + "\\left(\\begin{array}{c} x_1^{n+1} \\\\ x_2^{n+1} \\end{array} \\right)=\n", + "\\left(\\begin{array}{c} x_1^{n} \\\\ x_2^{n} \\end{array} \\right)+\n", + "\\left(\\begin{array}{c} h_1^{n} \\\\ h_2^{n} \\end{array} \\right),\n", + "$$" + ] + }, + { + "cell_type": "markdown", + "id": "4e70e7ac", + "metadata": { + "editable": true + }, + "source": [ + "where we have defined" + ] + }, + { + "cell_type": "markdown", + "id": "9336db1d", + "metadata": { + "editable": true + }, + "source": [ + "$$\n", + "\\left(\\begin{array}{c} h_1^{n} \\\\ h_2^{n} \\end{array} \\right)=\n", + " -{\\bf \\boldsymbol{J}}^{-1}\n", + " \\left(\\begin{array}{c} f_1(x_1^{n},x_2^{n}) \\\\ f_2(x_1^{n},x_2^{n}) \\end{array} \\right).\n", + "$$" + ] + }, + { + "cell_type": "markdown", + "id": "2c2ae028", + "metadata": { + "editable": true + }, + "source": [ + "We need thus to compute the inverse of the Jacobian matrix and it\n", + "is to understand that difficulties may\n", + "arise in case ${\\bf \\boldsymbol{J}}$ is nearly singular.\n", + "\n", + "It is rather straightforward to extend the above scheme to systems of\n", + "more than two non-linear equations. In our case, the Jacobian matrix is given by the Hessian that represents the second derivative of cost function." + ] + }, + { + "cell_type": "markdown", + "id": "cdf99885", + "metadata": { + "editable": true + }, + "source": [ + "## Steepest descent\n", + "\n", + "The basic idea of gradient descent is\n", + "that a function $F(\\mathbf{x})$, \n", + "$\\mathbf{x} \\equiv (x_1,\\cdots,x_n)$, decreases fastest if one goes from $\\bf {x}$ in the\n", + "direction of the negative gradient $-\\nabla F(\\mathbf{x})$.\n", + "\n", + "It can be shown that if" + ] + }, + { + "cell_type": "markdown", + "id": "11bb1b41", + "metadata": { + "editable": true + }, + "source": [ + "$$\n", + "\\mathbf{x}_{k+1} = \\mathbf{x}_k - \\gamma_k \\nabla F(\\mathbf{x}_k),\n", + "$$" + ] + }, + { + "cell_type": "markdown", + "id": "5d957768", + "metadata": { + "editable": true + }, + "source": [ + "with $\\gamma_k > 0$.\n", + "\n", + "For $\\gamma_k$ small enough, then $F(\\mathbf{x}_{k+1}) \\leq\n", + "F(\\mathbf{x}_k)$. This means that for a sufficiently small $\\gamma_k$\n", + "we are always moving towards smaller function values, i.e a minimum." + ] + }, + { + "cell_type": "markdown", + "id": "455b420a", + "metadata": { + "editable": true + }, + "source": [ + "## More on Steepest descent\n", + "\n", + "The previous observation is the basis of the method of steepest\n", + "descent, which is also referred to as just gradient descent (GD). One\n", + "starts with an initial guess $\\mathbf{x}_0$ for a minimum of $F$ and\n", + "computes new approximations according to" + ] + }, + { + "cell_type": "markdown", + "id": "aacb8b05", + "metadata": { + "editable": true + }, + "source": [ + "$$\n", + "\\mathbf{x}_{k+1} = \\mathbf{x}_k - \\gamma_k \\nabla F(\\mathbf{x}_k), \\ \\ k \\geq 0.\n", + "$$" + ] + }, + { + "cell_type": "markdown", + "id": "2bb3385b", + "metadata": { + "editable": true + }, + "source": [ + "The parameter $\\gamma_k$ is often referred to as the step length or\n", + "the learning rate within the context of Machine Learning." + ] + }, + { + "cell_type": "markdown", + "id": "21326ef0", + "metadata": { + "editable": true + }, + "source": [ + "## The ideal\n", + "\n", + "Ideally the sequence $\\{\\mathbf{x}_k \\}_{k=0}$ converges to a global\n", + "minimum of the function $F$. In general we do not know if we are in a\n", + "global or local minimum. In the special case when $F$ is a convex\n", + "function, all local minima are also global minima, so in this case\n", + "gradient descent can converge to the global solution. The advantage of\n", + "this scheme is that it is conceptually simple and straightforward to\n", + "implement. However the method in this form has some severe\n", + "limitations:\n", + "\n", + "In machine learing we are often faced with non-convex high dimensional\n", + "cost functions with many local minima. Since GD is deterministic we\n", + "will get stuck in a local minimum, if the method converges, unless we\n", + "have a very good intial guess. This also implies that the scheme is\n", + "sensitive to the chosen initial condition.\n", + "\n", + "Note that the gradient is a function of $\\mathbf{x} =\n", + "(x_1,\\cdots,x_n)$ which makes it expensive to compute numerically." + ] + }, + { + "cell_type": "markdown", + "id": "a41b3cc6", + "metadata": { + "editable": true + }, + "source": [ + "## The sensitiveness of the gradient descent\n", + "\n", + "The gradient descent method \n", + "is sensitive to the choice of learning rate $\\gamma_k$. This is due\n", + "to the fact that we are only guaranteed that $F(\\mathbf{x}_{k+1}) \\leq\n", + "F(\\mathbf{x}_k)$ for sufficiently small $\\gamma_k$. The problem is to\n", + "determine an optimal learning rate. If the learning rate is chosen too\n", + "small the method will take a long time to converge and if it is too\n", + "large we can experience erratic behavior.\n", + "\n", + "Many of these shortcomings can be alleviated by introducing\n", + "randomness. One such method is that of Stochastic Gradient Descent\n", + "(SGD), see below." + ] + }, + { + "cell_type": "markdown", + "id": "a01f11a5", + "metadata": { + "editable": true + }, + "source": [ + "## Convex functions\n", + "\n", + "Ideally we want our cost/loss function to be convex(concave).\n", + "\n", + "First we give the definition of a convex set: A set $C$ in\n", + "$\\mathbb{R}^n$ is said to be convex if, for all $x$ and $y$ in $C$ and\n", + "all $t \\in (0,1)$ , the point $(1 − t)x + ty$ also belongs to\n", + "C. Geometrically this means that every point on the line segment\n", + "connecting $x$ and $y$ is in $C$ as discussed below.\n", + "\n", + "The convex subsets of $\\mathbb{R}$ are the intervals of\n", + "$\\mathbb{R}$. Examples of convex sets of $\\mathbb{R}^2$ are the\n", + "regular polygons (triangles, rectangles, pentagons, etc...)." + ] + }, + { + "cell_type": "markdown", + "id": "076a32fa", + "metadata": { + "editable": true + }, + "source": [ + "## Convex function\n", + "\n", + "**Convex function**: Let $X \\subset \\mathbb{R}^n$ be a convex set. Assume that the function $f: X \\rightarrow \\mathbb{R}$ is continuous, then $f$ is said to be convex if $$f(tx_1 + (1-t)x_2) \\leq tf(x_1) + (1-t)f(x_2) $$ for all $x_1, x_2 \\in X$ and for all $t \\in [0,1]$. If $\\leq$ is replaced with a strict inequaltiy in the definition, we demand $x_1 \\neq x_2$ and $t\\in(0,1)$ then $f$ is said to be strictly convex. For a single variable function, convexity means that if you draw a straight line connecting $f(x_1)$ and $f(x_2)$, the value of the function on the interval $[x_1,x_2]$ is always below the line as illustrated below." + ] + }, + { + "cell_type": "markdown", + "id": "73adde3c", + "metadata": { + "editable": true + }, + "source": [ + "## Conditions on convex functions\n", + "\n", + "In the following we state first and second-order conditions which\n", + "ensures convexity of a function $f$. We write $D_f$ to denote the\n", + "domain of $f$, i.e the subset of $R^n$ where $f$ is defined. For more\n", + "details and proofs we refer to: [S. Boyd and L. Vandenberghe. Convex Optimization. Cambridge University Press](http://stanford.edu/boyd/cvxbook/, 2004).\n", + "\n", + "**First order condition.**\n", + "\n", + "Suppose $f$ is differentiable (i.e $\\nabla f(x)$ is well defined for\n", + "all $x$ in the domain of $f$). Then $f$ is convex if and only if $D_f$\n", + "is a convex set and $$f(y) \\geq f(x) + \\nabla f(x)^T (y-x) $$ holds\n", + "for all $x,y \\in D_f$. This condition means that for a convex function\n", + "the first order Taylor expansion (right hand side above) at any point\n", + "a global under estimator of the function. To convince yourself you can\n", + "make a drawing of $f(x) = x^2+1$ and draw the tangent line to $f(x)$ and\n", + "note that it is always below the graph.\n", + "\n", + "**Second order condition.**\n", + "\n", + "Assume that $f$ is twice\n", + "differentiable, i.e the Hessian matrix exists at each point in\n", + "$D_f$. Then $f$ is convex if and only if $D_f$ is a convex set and its\n", + "Hessian is positive semi-definite for all $x\\in D_f$. For a\n", + "single-variable function this reduces to $f''(x) \\geq 0$. Geometrically this means that $f$ has nonnegative curvature\n", + "everywhere.\n", + "\n", + "This condition is particularly useful since it gives us an procedure for determining if the function under consideration is convex, apart from using the definition." + ] + }, + { + "cell_type": "markdown", + "id": "9f9ab5ff", + "metadata": { + "editable": true + }, + "source": [ + "## More on convex functions\n", + "\n", + "The next result is of great importance to us and the reason why we are\n", + "going on about convex functions. In machine learning we frequently\n", + "have to minimize a loss/cost function in order to find the best\n", + "parameters for the model we are considering. \n", + "\n", + "Ideally we want the\n", + "global minimum (for high-dimensional models it is hard to know\n", + "if we have local or global minimum). However, if the cost/loss function\n", + "is convex the following result provides invaluable information:\n", + "\n", + "**Any minimum is global for convex functions.**\n", + "\n", + "Consider the problem of finding $x \\in \\mathbb{R}^n$ such that $f(x)$\n", + "is minimal, where $f$ is convex and differentiable. Then, any point\n", + "$x^*$ that satisfies $\\nabla f(x^*) = 0$ is a global minimum.\n", + "\n", + "This result means that if we know that the cost/loss function is convex and we are able to find a minimum, we are guaranteed that it is a global minimum." + ] + }, + { + "cell_type": "markdown", + "id": "0b2a482b", + "metadata": { + "editable": true + }, + "source": [ + "## Some simple problems\n", + "\n", + "1. Show that $f(x)=x^2$ is convex for $x \\in \\mathbb{R}$ using the definition of convexity. Hint: If you re-write the definition, $f$ is convex if the following holds for all $x,y \\in D_f$ and any $\\lambda \\in [0,1]$ $\\lambda f(x)+(1-\\lambda)f(y)-f(\\lambda x + (1-\\lambda) y ) \\geq 0$.\n", + "\n", + "2. Using the second order condition show that the following functions are convex on the specified domain.\n", + "\n", + " * $f(x) = e^x$ is convex for $x \\in \\mathbb{R}$.\n", + "\n", + " * $g(x) = -\\ln(x)$ is convex for $x \\in (0,\\infty)$.\n", + "\n", + "3. Let $f(x) = x^2$ and $g(x) = e^x$. Show that $f(g(x))$ and $g(f(x))$ is convex for $x \\in \\mathbb{R}$. Also show that if $f(x)$ is any convex function than $h(x) = e^{f(x)}$ is convex.\n", + "\n", + "4. A norm is any function that satisfy the following properties\n", + "\n", + " * $f(\\alpha x) = |\\alpha| f(x)$ for all $\\alpha \\in \\mathbb{R}$.\n", + "\n", + " * $f(x+y) \\leq f(x) + f(y)$\n", + "\n", + " * $f(x) \\leq 0$ for all $x \\in \\mathbb{R}^n$ with equality if and only if $x = 0$\n", + "\n", + "Using the definition of convexity, try to show that a function satisfying the properties above is convex (the third condition is not needed to show this)." + ] + }, + { + "cell_type": "markdown", + "id": "6566ee55", + "metadata": { + "editable": true + }, + "source": [ + "## Standard steepest descent\n", + "\n", + "Before we proceed, we would like to discuss the approach called the\n", + "**standard Steepest descent** (different from the above steepest descent discussion), which again leads to us having to be able\n", + "to compute a matrix. It belongs to the class of Conjugate Gradient methods (CG).\n", + "\n", + "[The success of the CG method](https://www.cs.cmu.edu/~quake-papers/painless-conjugate-gradient.pdf)\n", + "for finding solutions of non-linear problems is based on the theory\n", + "of conjugate gradients for linear systems of equations. It belongs to\n", + "the class of iterative methods for solving problems from linear\n", + "algebra of the type" + ] + }, + { + "cell_type": "markdown", + "id": "c2e30cc1", + "metadata": { + "editable": true + }, + "source": [ + "$$\n", + "\\boldsymbol{A}\\boldsymbol{x} = \\boldsymbol{b}.\n", + "$$" + ] + }, + { + "cell_type": "markdown", + "id": "5012b398", + "metadata": { + "editable": true + }, + "source": [ + "In the iterative process we end up with a problem like" + ] + }, + { + "cell_type": "markdown", + "id": "ca65d9a9", + "metadata": { + "editable": true + }, + "source": [ + "$$\n", + "\\boldsymbol{r}= \\boldsymbol{b}-\\boldsymbol{A}\\boldsymbol{x},\n", + "$$" + ] + }, + { + "cell_type": "markdown", + "id": "ec9323dd", + "metadata": { + "editable": true + }, + "source": [ + "where $\\boldsymbol{r}$ is the so-called residual or error in the iterative process.\n", + "\n", + "When we have found the exact solution, $\\boldsymbol{r}=0$." + ] + }, + { + "cell_type": "markdown", + "id": "5caf0f7f", + "metadata": { + "editable": true + }, + "source": [ + "## Gradient method\n", + "\n", + "The residual is zero when we reach the minimum of the quadratic equation" + ] + }, + { + "cell_type": "markdown", + "id": "07734ce6", + "metadata": { + "editable": true + }, + "source": [ + "$$\n", + "P(\\boldsymbol{x})=\\frac{1}{2}\\boldsymbol{x}^T\\boldsymbol{A}\\boldsymbol{x} - \\boldsymbol{x}^T\\boldsymbol{b},\n", + "$$" + ] + }, + { + "cell_type": "markdown", + "id": "468dcb52", + "metadata": { + "editable": true + }, + "source": [ + "with the constraint that the matrix $\\boldsymbol{A}$ is positive definite and\n", + "symmetric. This defines also the Hessian and we want it to be positive definite." + ] + }, + { + "cell_type": "markdown", + "id": "b63a89ae", + "metadata": { + "editable": true + }, + "source": [ + "## Steepest descent method\n", + "\n", + "We denote the initial guess for $\\boldsymbol{x}$ as $\\boldsymbol{x}_0$. \n", + "We can assume without loss of generality that" + ] + }, + { + "cell_type": "markdown", + "id": "56a122a3", + "metadata": { + "editable": true + }, + "source": [ + "$$\n", + "\\boldsymbol{x}_0=0,\n", + "$$" + ] + }, + { + "cell_type": "markdown", + "id": "3256eb29", + "metadata": { + "editable": true + }, + "source": [ + "or consider the system" + ] + }, + { + "cell_type": "markdown", + "id": "b6bab858", + "metadata": { + "editable": true + }, + "source": [ + "$$\n", + "\\boldsymbol{A}\\boldsymbol{z} = \\boldsymbol{b}-\\boldsymbol{A}\\boldsymbol{x}_0,\n", + "$$" + ] + }, + { + "cell_type": "markdown", + "id": "7ff39e96", + "metadata": { + "editable": true + }, + "source": [ + "instead." + ] + }, + { + "cell_type": "markdown", + "id": "6678fce9", + "metadata": { + "editable": true + }, + "source": [ + "## Steepest descent method\n", + "One can show that the solution $\\boldsymbol{x}$ is also the unique minimizer of the quadratic form" + ] + }, + { + "cell_type": "markdown", + "id": "853eb11f", + "metadata": { + "editable": true + }, + "source": [ + "$$\n", + "f(\\boldsymbol{x}) = \\frac{1}{2}\\boldsymbol{x}^T\\boldsymbol{A}\\boldsymbol{x} - \\boldsymbol{x}^T \\boldsymbol{x} , \\quad \\boldsymbol{x}\\in\\mathbf{R}^n.\n", + "$$" + ] + }, + { + "cell_type": "markdown", + "id": "7c229917", + "metadata": { + "editable": true + }, + "source": [ + "This suggests taking the first basis vector $\\boldsymbol{r}_1$ (see below for definition) \n", + "to be the gradient of $f$ at $\\boldsymbol{x}=\\boldsymbol{x}_0$, \n", + "which equals" + ] + }, + { + "cell_type": "markdown", + "id": "5c8f310a", + "metadata": { + "editable": true + }, + "source": [ + "$$\n", + "\\boldsymbol{A}\\boldsymbol{x}_0-\\boldsymbol{b},\n", + "$$" + ] + }, + { + "cell_type": "markdown", + "id": "f8a8c317", + "metadata": { + "editable": true + }, + "source": [ + "and \n", + "$\\boldsymbol{x}_0=0$ it is equal $-\\boldsymbol{b}$." + ] + }, + { + "cell_type": "markdown", + "id": "49b64ed0", + "metadata": { + "editable": true + }, + "source": [ + "## Final expressions\n", + "We can compute the residual iteratively as" + ] + }, + { + "cell_type": "markdown", + "id": "857ee939", + "metadata": { + "editable": true + }, + "source": [ + "$$\n", + "\\boldsymbol{r}_{k+1}=\\boldsymbol{b}-\\boldsymbol{A}\\boldsymbol{x}_{k+1},\n", + "$$" + ] + }, + { + "cell_type": "markdown", + "id": "7e5611fa", + "metadata": { + "editable": true + }, + "source": [ + "which equals" + ] + }, + { + "cell_type": "markdown", + "id": "384d5aa2", + "metadata": { + "editable": true + }, + "source": [ + "$$\n", + "\\boldsymbol{b}-\\boldsymbol{A}(\\boldsymbol{x}_k+\\alpha_k\\boldsymbol{r}_k),\n", + "$$" + ] + }, + { + "cell_type": "markdown", + "id": "0ce677be", + "metadata": { + "editable": true + }, + "source": [ + "or" + ] + }, + { + "cell_type": "markdown", + "id": "e97f9044", + "metadata": { + "editable": true + }, + "source": [ + "$$\n", + "(\\boldsymbol{b}-\\boldsymbol{A}\\boldsymbol{x}_k)-\\alpha_k\\boldsymbol{A}\\boldsymbol{r}_k,\n", + "$$" + ] + }, + { + "cell_type": "markdown", + "id": "d7fbeb68", + "metadata": { + "editable": true + }, + "source": [ + "which gives" + ] + }, + { + "cell_type": "markdown", + "id": "293c09b1", + "metadata": { + "editable": true + }, + "source": [ + "$$\n", + "\\alpha_k = \\frac{\\boldsymbol{r}_k^T\\boldsymbol{r}_k}{\\boldsymbol{r}_k^T\\boldsymbol{A}\\boldsymbol{r}_k}\n", + "$$" + ] + }, + { + "cell_type": "markdown", + "id": "af88e065", + "metadata": { + "editable": true + }, + "source": [ + "leading to the iterative scheme" + ] + }, + { + "cell_type": "markdown", + "id": "2757e302", + "metadata": { + "editable": true + }, + "source": [ + "$$\n", + "\\boldsymbol{x}_{k+1}=\\boldsymbol{x}_k+\\alpha_k\\boldsymbol{r}_{k},\n", + "$$" + ] + }, + { + "cell_type": "markdown", + "id": "87aab66b", + "metadata": { + "editable": true + }, + "source": [ + "## Steepest descent example" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "a0e20ff7", + "metadata": { + "collapsed": false, + "editable": true + }, + "outputs": [], + "source": [ + "%matplotlib inline\n", + "\n", + "import numpy as np\n", + "import numpy.linalg as la\n", + "\n", + "import scipy.optimize as sopt\n", + "\n", + "import matplotlib.pyplot as pt\n", + "from mpl_toolkits.mplot3d import axes3d\n", + "\n", + "def f(x):\n", + " return x[0]**2 + 3.0*x[1]**2\n", + "\n", + "def df(x):\n", + " return np.array([2*x[0], 6*x[1]])\n", + "\n", + "fig = pt.figure()\n", + "ax = fig.gca(projection=\"3d\")\n", + "\n", + "xmesh, ymesh = np.mgrid[-3:3:50j,-3:3:50j]\n", + "fmesh = f(np.array([xmesh, ymesh]))\n", + "ax.plot_surface(xmesh, ymesh, fmesh)" + ] + }, + { + "cell_type": "markdown", + "id": "c01b471a", + "metadata": { + "editable": true + }, + "source": [ + "And then as countor plot" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "5b835c85", + "metadata": { + "collapsed": false, + "editable": true + }, + "outputs": [], + "source": [ + "pt.axis(\"equal\")\n", + "pt.contour(xmesh, ymesh, fmesh)\n", + "guesses = [np.array([2, 2./5])]" + ] + }, + { + "cell_type": "markdown", + "id": "d6a3c121", + "metadata": { + "editable": true + }, + "source": [ + "Find guesses" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "19e1d73c", + "metadata": { + "collapsed": false, + "editable": true + }, + "outputs": [], + "source": [ + "x = guesses[-1]\n", + "s = -df(x)" + ] + }, + { + "cell_type": "markdown", + "id": "9f7b2dfc", + "metadata": { + "editable": true + }, + "source": [ + "Run it!" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "7d8247e6", + "metadata": { + "collapsed": false, + "editable": true + }, + "outputs": [], + "source": [ + "def f1d(alpha):\n", + " return f(x + alpha*s)\n", + "\n", + "alpha_opt = sopt.golden(f1d)\n", + "next_guess = x + alpha_opt * s\n", + "guesses.append(next_guess)\n", + "print(next_guess)" + ] + }, + { + "cell_type": "markdown", + "id": "c44006da", + "metadata": { + "editable": true + }, + "source": [ + "What happened?" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "bb8a0fd8", + "metadata": { + "collapsed": false, + "editable": true + }, + "outputs": [], + "source": [ + "pt.axis(\"equal\")\n", + "pt.contour(xmesh, ymesh, fmesh, 50)\n", + "it_array = np.array(guesses)\n", + "pt.plot(it_array.T[0], it_array.T[1], \"x-\")" + ] + }, + { + "cell_type": "markdown", + "id": "3d3c98f0", + "metadata": { + "editable": true + }, + "source": [ + "Note that we did only one iteration here. We can easily add more using our previous guesses." + ] + }, + { + "cell_type": "markdown", + "id": "29e5e792", + "metadata": { + "editable": true + }, + "source": [ + "## Conjugate gradient method\n", + "In the CG method we define so-called conjugate directions and two vectors \n", + "$\\boldsymbol{s}$ and $\\boldsymbol{t}$\n", + "are said to be\n", + "conjugate if" + ] + }, + { + "cell_type": "markdown", + "id": "2b0e0db3", + "metadata": { + "editable": true + }, + "source": [ + "$$\n", + "\\boldsymbol{s}^T\\boldsymbol{A}\\boldsymbol{t}= 0.\n", + "$$" + ] + }, + { + "cell_type": "markdown", + "id": "401dd643", + "metadata": { + "editable": true + }, + "source": [ + "The philosophy of the CG method is to perform searches in various conjugate directions\n", + "of our vectors $\\boldsymbol{x}_i$ obeying the above criterion, namely" + ] + }, + { + "cell_type": "markdown", + "id": "bc29d596", + "metadata": { + "editable": true + }, + "source": [ + "$$\n", + "\\boldsymbol{x}_i^T\\boldsymbol{A}\\boldsymbol{x}_j= 0.\n", + "$$" + ] + }, + { + "cell_type": "markdown", + "id": "c1b7adb4", + "metadata": { + "editable": true + }, + "source": [ + "Two vectors are conjugate if they are orthogonal with respect to \n", + "this inner product. Being conjugate is a symmetric relation: if $\\boldsymbol{s}$ is conjugate to $\\boldsymbol{t}$, then $\\boldsymbol{t}$ is conjugate to $\\boldsymbol{s}$." + ] + }, + { + "cell_type": "markdown", + "id": "18924232", + "metadata": { + "editable": true + }, + "source": [ + "## Conjugate gradient method\n", + "An example is given by the eigenvectors of the matrix" + ] + }, + { + "cell_type": "markdown", + "id": "1764ac31", + "metadata": { + "editable": true + }, + "source": [ + "$$\n", + "\\boldsymbol{v}_i^T\\boldsymbol{A}\\boldsymbol{v}_j= \\lambda\\boldsymbol{v}_i^T\\boldsymbol{v}_j,\n", + "$$" + ] + }, + { + "cell_type": "markdown", + "id": "379d5862", + "metadata": { + "editable": true + }, + "source": [ + "which is zero unless $i=j$." + ] + }, + { + "cell_type": "markdown", + "id": "9587d8bf", + "metadata": { + "editable": true + }, + "source": [ + "## Conjugate gradient method\n", + "Assume now that we have a symmetric positive-definite matrix $\\boldsymbol{A}$ of size\n", + "$n\\times n$. At each iteration $i+1$ we obtain the conjugate direction of a vector" + ] + }, + { + "cell_type": "markdown", + "id": "4c3d0bfb", + "metadata": { + "editable": true + }, + "source": [ + "$$\n", + "\\boldsymbol{x}_{i+1}=\\boldsymbol{x}_{i}+\\alpha_i\\boldsymbol{p}_{i}.\n", + "$$" + ] + }, + { + "cell_type": "markdown", + "id": "4079ca1a", + "metadata": { + "editable": true + }, + "source": [ + "We assume that $\\boldsymbol{p}_{i}$ is a sequence of $n$ mutually conjugate directions. \n", + "Then the $\\boldsymbol{p}_{i}$ form a basis of $R^n$ and we can expand the solution \n", + "$ \\boldsymbol{A}\\boldsymbol{x} = \\boldsymbol{b}$ in this basis, namely" + ] + }, + { + "cell_type": "markdown", + "id": "e5b487a5", + "metadata": { + "editable": true + }, + "source": [ + "$$\n", + "\\boldsymbol{x} = \\sum^{n}_{i=1} \\alpha_i \\boldsymbol{p}_i.\n", + "$$" + ] + }, + { + "cell_type": "markdown", + "id": "b623d7f7", + "metadata": { + "editable": true + }, + "source": [ + "## Conjugate gradient method\n", + "The coefficients are given by" + ] + }, + { + "cell_type": "markdown", + "id": "8520c560", + "metadata": { + "editable": true + }, + "source": [ + "$$\n", + "\\mathbf{A}\\mathbf{x} = \\sum^{n}_{i=1} \\alpha_i \\mathbf{A} \\mathbf{p}_i = \\mathbf{b}.\n", + "$$" + ] + }, + { + "cell_type": "markdown", + "id": "52575016", + "metadata": { + "editable": true + }, + "source": [ + "Multiplying with $\\boldsymbol{p}_k^T$ from the left gives" + ] + }, + { + "cell_type": "markdown", + "id": "1b8a85bd", + "metadata": { + "editable": true + }, + "source": [ + "$$\n", + "\\boldsymbol{p}_k^T \\boldsymbol{A}\\boldsymbol{x} = \\sum^{n}_{i=1} \\alpha_i\\boldsymbol{p}_k^T \\boldsymbol{A}\\boldsymbol{p}_i= \\boldsymbol{p}_k^T \\boldsymbol{b},\n", + "$$" + ] + }, + { + "cell_type": "markdown", + "id": "e53b0f45", + "metadata": { + "editable": true + }, + "source": [ + "and we can define the coefficients $\\alpha_k$ as" + ] + }, + { + "cell_type": "markdown", + "id": "2238e15f", + "metadata": { + "editable": true + }, + "source": [ + "$$\n", + "\\alpha_k = \\frac{\\boldsymbol{p}_k^T \\boldsymbol{b}}{\\boldsymbol{p}_k^T \\boldsymbol{A} \\boldsymbol{p}_k}\n", + "$$" + ] + }, + { + "cell_type": "markdown", + "id": "f00e8864", + "metadata": { + "editable": true + }, + "source": [ + "## Conjugate gradient method and iterations\n", + "\n", + "If we choose the conjugate vectors $\\boldsymbol{p}_k$ carefully, \n", + "then we may not need all of them to obtain a good approximation to the solution \n", + "$\\boldsymbol{x}$. \n", + "We want to regard the conjugate gradient method as an iterative method. \n", + "This will us to solve systems where $n$ is so large that the direct \n", + "method would take too much time.\n", + "\n", + "We denote the initial guess for $\\boldsymbol{x}$ as $\\boldsymbol{x}_0$. \n", + "We can assume without loss of generality that" + ] + }, + { + "cell_type": "markdown", + "id": "7a17895d", + "metadata": { + "editable": true + }, + "source": [ + "$$\n", + "\\boldsymbol{x}_0=0,\n", + "$$" + ] + }, + { + "cell_type": "markdown", + "id": "d4bafcb3", + "metadata": { + "editable": true + }, + "source": [ + "or consider the system" + ] + }, + { + "cell_type": "markdown", + "id": "78a7d2c3", + "metadata": { + "editable": true + }, + "source": [ + "$$\n", + "\\boldsymbol{A}\\boldsymbol{z} = \\boldsymbol{b}-\\boldsymbol{A}\\boldsymbol{x}_0,\n", + "$$" + ] + }, + { + "cell_type": "markdown", + "id": "e3192cbf", + "metadata": { + "editable": true + }, + "source": [ + "instead." + ] + }, + { + "cell_type": "markdown", + "id": "0d99ed55", + "metadata": { + "editable": true + }, + "source": [ + "## Conjugate gradient method\n", + "One can show that the solution $\\boldsymbol{x}$ is also the unique minimizer of the quadratic form" + ] + }, + { + "cell_type": "markdown", + "id": "b9653ede", + "metadata": { + "editable": true + }, + "source": [ + "$$\n", + "f(\\boldsymbol{x}) = \\frac{1}{2}\\boldsymbol{x}^T\\boldsymbol{A}\\boldsymbol{x} - \\boldsymbol{x}^T \\boldsymbol{x} , \\quad \\boldsymbol{x}\\in\\mathbf{R}^n.\n", + "$$" + ] + }, + { + "cell_type": "markdown", + "id": "78441105", + "metadata": { + "editable": true + }, + "source": [ + "This suggests taking the first basis vector $\\boldsymbol{p}_1$ \n", + "to be the gradient of $f$ at $\\boldsymbol{x}=\\boldsymbol{x}_0$, \n", + "which equals" + ] + }, + { + "cell_type": "markdown", + "id": "317355d2", + "metadata": { + "editable": true + }, + "source": [ + "$$\n", + "\\boldsymbol{A}\\boldsymbol{x}_0-\\boldsymbol{b},\n", + "$$" + ] + }, + { + "cell_type": "markdown", + "id": "9bb15157", + "metadata": { + "editable": true + }, + "source": [ + "and \n", + "$\\boldsymbol{x}_0=0$ it is equal $-\\boldsymbol{b}$.\n", + "The other vectors in the basis will be conjugate to the gradient, \n", + "hence the name conjugate gradient method." + ] + }, + { + "cell_type": "markdown", + "id": "ac584971", + "metadata": { + "editable": true + }, + "source": [ + "## Conjugate gradient method\n", + "Let $\\boldsymbol{r}_k$ be the residual at the $k$-th step:" + ] + }, + { + "cell_type": "markdown", + "id": "911f1dfa", + "metadata": { + "editable": true + }, + "source": [ + "$$\n", + "\\boldsymbol{r}_k=\\boldsymbol{b}-\\boldsymbol{A}\\boldsymbol{x}_k.\n", + "$$" + ] + }, + { + "cell_type": "markdown", + "id": "d1472568", + "metadata": { + "editable": true + }, + "source": [ + "Note that $\\boldsymbol{r}_k$ is the negative gradient of $f$ at \n", + "$\\boldsymbol{x}=\\boldsymbol{x}_k$, \n", + "so the gradient descent method would be to move in the direction $\\boldsymbol{r}_k$. \n", + "Here, we insist that the directions $\\boldsymbol{p}_k$ are conjugate to each other, \n", + "so we take the direction closest to the gradient $\\boldsymbol{r}_k$ \n", + "under the conjugacy constraint. \n", + "This gives the following expression" + ] + }, + { + "cell_type": "markdown", + "id": "c79708e8", + "metadata": { + "editable": true + }, + "source": [ + "$$\n", + "\\boldsymbol{p}_{k+1}=\\boldsymbol{r}_k-\\frac{\\boldsymbol{p}_k^T \\boldsymbol{A}\\boldsymbol{r}_k}{\\boldsymbol{p}_k^T\\boldsymbol{A}\\boldsymbol{p}_k} \\boldsymbol{p}_k.\n", + "$$" + ] + }, + { + "cell_type": "markdown", + "id": "535d3e73", + "metadata": { + "editable": true + }, + "source": [ + "## Conjugate gradient method\n", + "We can also compute the residual iteratively as" + ] + }, + { + "cell_type": "markdown", + "id": "ad718f62", + "metadata": { + "editable": true + }, + "source": [ + "$$\n", + "\\boldsymbol{r}_{k+1}=\\boldsymbol{b}-\\boldsymbol{A}\\boldsymbol{x}_{k+1},\n", + "$$" + ] + }, + { + "cell_type": "markdown", + "id": "d0a90fa5", + "metadata": { + "editable": true + }, + "source": [ + "which equals" + ] + }, + { + "cell_type": "markdown", + "id": "860e9217", + "metadata": { + "editable": true + }, + "source": [ + "$$\n", + "\\boldsymbol{b}-\\boldsymbol{A}(\\boldsymbol{x}_k+\\alpha_k\\boldsymbol{p}_k),\n", + "$$" + ] + }, + { + "cell_type": "markdown", + "id": "6d8a72c8", + "metadata": { + "editable": true + }, + "source": [ + "or" + ] + }, + { + "cell_type": "markdown", + "id": "746e6fc0", + "metadata": { + "editable": true + }, + "source": [ + "$$\n", + "(\\boldsymbol{b}-\\boldsymbol{A}\\boldsymbol{x}_k)-\\alpha_k\\boldsymbol{A}\\boldsymbol{p}_k,\n", + "$$" + ] + }, + { + "cell_type": "markdown", + "id": "f76c0e69", + "metadata": { + "editable": true + }, + "source": [ + "which gives" + ] + }, + { + "cell_type": "markdown", + "id": "9aee35ca", + "metadata": { + "editable": true + }, + "source": [ + "$$\n", + "\\boldsymbol{r}_{k+1}=\\boldsymbol{r}_k-\\boldsymbol{A}\\boldsymbol{p}_{k},\n", + "$$" + ] + }, + { + "cell_type": "markdown", + "id": "ce9ce258", + "metadata": { + "editable": true + }, + "source": [ + "## Revisiting our first homework\n", + "\n", + "We will use linear regression as a case study for the gradient descent\n", + "methods. Linear regression is a great test case for the gradient\n", + "descent methods discussed in the lectures since it has several\n", + "desirable properties such as:\n", + "\n", + "1. An analytical solution (recall homework set 1).\n", + "\n", + "2. The gradient can be computed analytically.\n", + "\n", + "3. The cost function is convex which guarantees that gradient descent converges for small enough learning rates\n", + "\n", + "We revisit an example similar to what we had in the first homework set. We had a function of the type" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "f902a0f2", + "metadata": { + "collapsed": false, + "editable": true + }, + "outputs": [], + "source": [ + "x = 2*np.random.rand(m,1)\n", + "y = 4+3*x+np.random.randn(m,1)" + ] + }, + { + "cell_type": "markdown", + "id": "36d883b2", + "metadata": { + "editable": true + }, + "source": [ + "with $x_i \\in [0,1] $ is chosen randomly using a uniform distribution. Additionally we have a stochastic noise chosen according to a normal distribution $\\cal {N}(0,1)$. \n", + "The linear regression model is given by" + ] + }, + { + "cell_type": "markdown", + "id": "cde21ef1", + "metadata": { + "editable": true + }, + "source": [ + "$$\n", + "h_\\beta(x) = \\boldsymbol{y} = \\beta_0 + \\beta_1 x,\n", + "$$" + ] + }, + { + "cell_type": "markdown", + "id": "f2a021d3", + "metadata": { + "editable": true + }, + "source": [ + "such that" + ] + }, + { + "cell_type": "markdown", + "id": "ea0a91e4", + "metadata": { + "editable": true + }, + "source": [ + "$$\n", + "\\boldsymbol{y}_i = \\beta_0 + \\beta_1 x_i.\n", + "$$" + ] + }, + { + "cell_type": "markdown", + "id": "854f3ebb", + "metadata": { + "editable": true + }, + "source": [ + "## Gradient descent example\n", + "\n", + "Let $\\mathbf{y} = (y_1,\\cdots,y_n)^T$, $\\mathbf{\\boldsymbol{y}} = (\\boldsymbol{y}_1,\\cdots,\\boldsymbol{y}_n)^T$ and $\\beta = (\\beta_0, \\beta_1)^T$\n", + "\n", + "It is convenient to write $\\mathbf{\\boldsymbol{y}} = X\\beta$ where $X \\in \\mathbb{R}^{100 \\times 2} $ is the design matrix given by (we keep the intercept here)" + ] + }, + { + "cell_type": "markdown", + "id": "dd282d2d", + "metadata": { + "editable": true + }, + "source": [ + "$$\n", + "X \\equiv \\begin{bmatrix}\n", + "1 & x_1 \\\\\n", + "\\vdots & \\vdots \\\\\n", + "1 & x_{100} & \\\\\n", + "\\end{bmatrix}.\n", + "$$" + ] + }, + { + "cell_type": "markdown", + "id": "fd562029", + "metadata": { + "editable": true + }, + "source": [ + "The cost/loss/risk function is given by (" + ] + }, + { + "cell_type": "markdown", + "id": "25369bc3", + "metadata": { + "editable": true + }, + "source": [ + "$$\n", + "C(\\beta) = \\frac{1}{n}||X\\beta-\\mathbf{y}||_{2}^{2} = \\frac{1}{n}\\sum_{i=1}^{100}\\left[ (\\beta_0 + \\beta_1 x_i)^2 - 2 y_i (\\beta_0 + \\beta_1 x_i) + y_i^2\\right]\n", + "$$" + ] + }, + { + "cell_type": "markdown", + "id": "a222f3ea", + "metadata": { + "editable": true + }, + "source": [ + "and we want to find $\\beta$ such that $C(\\beta)$ is minimized." + ] + }, + { + "cell_type": "markdown", + "id": "1d5fb6f1", + "metadata": { + "editable": true + }, + "source": [ + "## The derivative of the cost/loss function\n", + "\n", + "Computing $\\partial C(\\beta) / \\partial \\beta_0$ and $\\partial C(\\beta) / \\partial \\beta_1$ we can show that the gradient can be written as" + ] + }, + { + "cell_type": "markdown", + "id": "eab2df73", + "metadata": { + "editable": true + }, + "source": [ + "$$\n", + "\\nabla_{\\beta} C(\\beta) = \\frac{2}{n}\\begin{bmatrix} \\sum_{i=1}^{100} \\left(\\beta_0+\\beta_1x_i-y_i\\right) \\\\\n", + "\\sum_{i=1}^{100}\\left( x_i (\\beta_0+\\beta_1x_i)-y_ix_i\\right) \\\\\n", + "\\end{bmatrix} = \\frac{2}{n}X^T(X\\beta - \\mathbf{y}),\n", + "$$" + ] + }, + { + "cell_type": "markdown", + "id": "daee1165", + "metadata": { + "editable": true + }, + "source": [ + "where $X$ is the design matrix defined above." + ] + }, + { + "cell_type": "markdown", + "id": "f2c6f5cc", + "metadata": { + "editable": true + }, + "source": [ + "## The Hessian matrix\n", + "The Hessian matrix of $C(\\beta)$ is given by" + ] + }, + { + "cell_type": "markdown", + "id": "ecce0d08", + "metadata": { + "editable": true + }, + "source": [ + "$$\n", + "\\boldsymbol{H} \\equiv \\begin{bmatrix}\n", + "\\frac{\\partial^2 C(\\beta)}{\\partial \\beta_0^2} & \\frac{\\partial^2 C(\\beta)}{\\partial \\beta_0 \\partial \\beta_1} \\\\\n", + "\\frac{\\partial^2 C(\\beta)}{\\partial \\beta_0 \\partial \\beta_1} & \\frac{\\partial^2 C(\\beta)}{\\partial \\beta_1^2} & \\\\\n", + "\\end{bmatrix} = \\frac{2}{n}X^T X.\n", + "$$" + ] + }, + { + "cell_type": "markdown", + "id": "c4308e5f", + "metadata": { + "editable": true + }, + "source": [ + "This result implies that $C(\\beta)$ is a convex function since the matrix $X^T X$ always is positive semi-definite." + ] + }, + { + "cell_type": "markdown", + "id": "4ee64b17", + "metadata": { + "editable": true + }, + "source": [ + "## Simple program\n", + "\n", + "We can now write a program that minimizes $C(\\beta)$ using the gradient descent method with a constant learning rate $\\gamma$ according to" + ] + }, + { + "cell_type": "markdown", + "id": "57e8db33", + "metadata": { + "editable": true + }, + "source": [ + "$$\n", + "\\beta_{k+1} = \\beta_k - \\gamma \\nabla_\\beta C(\\beta_k), \\ k=0,1,\\cdots\n", + "$$" + ] + }, + { + "cell_type": "markdown", + "id": "c42e4032", + "metadata": { + "editable": true + }, + "source": [ + "We can use the expression we computed for the gradient and let use a\n", + "$\\beta_0$ be chosen randomly and let $\\gamma = 0.001$. Stop iterating\n", + "when $||\\nabla_\\beta C(\\beta_k) || \\leq \\epsilon = 10^{-8}$. **Note that the code below does not include the latter stop criterion**.\n", + "\n", + "And finally we can compare our solution for $\\beta$ with the analytic result given by \n", + "$\\beta= (X^TX)^{-1} X^T \\mathbf{y}$." + ] + }, + { + "cell_type": "markdown", + "id": "4c430cd3", + "metadata": { + "editable": true + }, + "source": [ + "## Gradient Descent Example\n", + "\n", + "Here our simple example" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "9ac6096f", + "metadata": { + "collapsed": false, + "editable": true + }, + "outputs": [], + "source": [ + "\n", + "# Importing various packages\n", + "from random import random, seed\n", + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "from mpl_toolkits.mplot3d import Axes3D\n", + "from matplotlib import cm\n", + "from matplotlib.ticker import LinearLocator, FormatStrFormatter\n", + "import sys\n", + "\n", + "# the number of datapoints\n", + "n = 100\n", + "x = 2*np.random.rand(n,1)\n", + "y = 4+3*x+np.random.randn(n,1)\n", + "\n", + "X = np.c_[np.ones((n,1)), x]\n", + "# Hessian matrix\n", + "H = (2.0/n)* X.T @ X\n", + "# Get the eigenvalues\n", + "EigValues, EigVectors = np.linalg.eig(H)\n", + "print(f\"Eigenvalues of Hessian Matrix:{EigValues}\")\n", + "\n", + "beta_linreg = np.linalg.inv(X.T @ X) @ X.T @ y\n", + "print(beta_linreg)\n", + "beta = np.random.randn(2,1)\n", + "\n", + "eta = 1.0/np.max(EigValues)\n", + "Niterations = 1000\n", + "\n", + "for iter in range(Niterations):\n", + " gradient = (2.0/n)*X.T @ (X @ beta-y)\n", + " beta -= eta*gradient\n", + "\n", + "print(beta)\n", + "xnew = np.array([[0],[2]])\n", + "xbnew = np.c_[np.ones((2,1)), xnew]\n", + "ypredict = xbnew.dot(beta)\n", + "ypredict2 = xbnew.dot(beta_linreg)\n", + "plt.plot(xnew, ypredict, \"r-\")\n", + "plt.plot(xnew, ypredict2, \"b-\")\n", + "plt.plot(x, y ,'ro')\n", + "plt.axis([0,2.0,0, 15.0])\n", + "plt.xlabel(r'$x$')\n", + "plt.ylabel(r'$y$')\n", + "plt.title(r'Gradient descent example')\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "df783e1d", + "metadata": { + "editable": true + }, + "source": [ + "## And a corresponding example using **scikit-learn**" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "98f08f24", + "metadata": { + "collapsed": false, + "editable": true + }, + "outputs": [], + "source": [ + "# Importing various packages\n", + "from random import random, seed\n", + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "from sklearn.linear_model import SGDRegressor\n", + "\n", + "n = 100\n", + "x = 2*np.random.rand(n,1)\n", + "y = 4+3*x+np.random.randn(n,1)\n", + "\n", + "X = np.c_[np.ones((n,1)), x]\n", + "beta_linreg = np.linalg.inv(X.T @ X) @ (X.T @ y)\n", + "print(beta_linreg)\n", + "sgdreg = SGDRegressor(max_iter = 50, penalty=None, eta0=0.1)\n", + "sgdreg.fit(x,y.ravel())\n", + "print(sgdreg.intercept_, sgdreg.coef_)" + ] + }, + { + "cell_type": "markdown", + "id": "50a5ab0d", + "metadata": { + "editable": true + }, + "source": [ + "## Gradient descent and Ridge\n", + "\n", + "We have also discussed Ridge regression where the loss function contains a regularized term given by the $L_2$ norm of $\\beta$," + ] + }, + { + "cell_type": "markdown", + "id": "b35293d4", + "metadata": { + "editable": true + }, + "source": [ + "$$\n", + "C_{\\text{ridge}}(\\beta) = \\frac{1}{n}||X\\beta -\\mathbf{y}||^2 + \\lambda ||\\beta||^2, \\ \\lambda \\geq 0.\n", + "$$" + ] + }, + { + "cell_type": "markdown", + "id": "fed491ee", + "metadata": { + "editable": true + }, + "source": [ + "In order to minimize $C_{\\text{ridge}}(\\beta)$ using GD we adjust the gradient as follows" + ] + }, + { + "cell_type": "markdown", + "id": "a0b4c94e", + "metadata": { + "editable": true + }, + "source": [ + "$$\n", + "\\nabla_\\beta C_{\\text{ridge}}(\\beta) = \\frac{2}{n}\\begin{bmatrix} \\sum_{i=1}^{100} \\left(\\beta_0+\\beta_1x_i-y_i\\right) \\\\\n", + "\\sum_{i=1}^{100}\\left( x_i (\\beta_0+\\beta_1x_i)-y_ix_i\\right) \\\\\n", + "\\end{bmatrix} + 2\\lambda\\begin{bmatrix} \\beta_0 \\\\ \\beta_1\\end{bmatrix} = 2 (\\frac{1}{n}X^T(X\\beta - \\mathbf{y})+\\lambda \\beta).\n", + "$$" + ] + }, + { + "cell_type": "markdown", + "id": "e4f02dce", + "metadata": { + "editable": true + }, + "source": [ + "We can easily extend our program to minimize $C_{\\text{ridge}}(\\beta)$ using gradient descent and compare with the analytical solution given by" + ] + }, + { + "cell_type": "markdown", + "id": "b9f297ff", + "metadata": { + "editable": true + }, + "source": [ + "$$\n", + "\\beta_{\\text{ridge}} = \\left(X^T X + n\\lambda I_{2 \\times 2} \\right)^{-1} X^T \\mathbf{y}.\n", + "$$" + ] + }, + { + "cell_type": "markdown", + "id": "4e1caf44", + "metadata": { + "editable": true + }, + "source": [ + "## The Hessian matrix for Ridge Regression\n", + "The Hessian matrix of Ridge Regression for our simple example is given by" + ] + }, + { + "cell_type": "markdown", + "id": "a046daea", + "metadata": { + "editable": true + }, + "source": [ + "$$\n", + "\\boldsymbol{H} \\equiv \\begin{bmatrix}\n", + "\\frac{\\partial^2 C(\\beta)}{\\partial \\beta_0^2} & \\frac{\\partial^2 C(\\beta)}{\\partial \\beta_0 \\partial \\beta_1} \\\\\n", + "\\frac{\\partial^2 C(\\beta)}{\\partial \\beta_0 \\partial \\beta_1} & \\frac{\\partial^2 C(\\beta)}{\\partial \\beta_1^2} & \\\\\n", + "\\end{bmatrix} = \\frac{2}{n}X^T X+2\\lambda\\boldsymbol{I}.\n", + "$$" + ] + }, + { + "cell_type": "markdown", + "id": "02f05574", + "metadata": { + "editable": true + }, + "source": [ + "This implies that the Hessian matrix is positive definite, hence the stationary point is a\n", + "minimum.\n", + "Note that the Ridge cost function is convex being a sum of two convex\n", + "functions. Therefore, the stationary point is a global\n", + "minimum of this function." + ] + }, + { + "cell_type": "markdown", + "id": "45484749", + "metadata": { + "editable": true + }, + "source": [ + "## Program example for gradient descent with Ridge Regression" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "9973cd20", + "metadata": { + "collapsed": false, + "editable": true + }, + "outputs": [], + "source": [ + "from random import random, seed\n", + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "from mpl_toolkits.mplot3d import Axes3D\n", + "from matplotlib import cm\n", + "from matplotlib.ticker import LinearLocator, FormatStrFormatter\n", + "import sys\n", + "\n", + "# the number of datapoints\n", + "n = 100\n", + "x = 2*np.random.rand(n,1)\n", + "y = 4+3*x+np.random.randn(n,1)\n", + "\n", + "X = np.c_[np.ones((n,1)), x]\n", + "XT_X = X.T @ X\n", + "\n", + "#Ridge parameter lambda\n", + "lmbda = 0.001\n", + "Id = n*lmbda* np.eye(XT_X.shape[0])\n", + "\n", + "# Hessian matrix\n", + "H = (2.0/n)* XT_X+2*lmbda* np.eye(XT_X.shape[0])\n", + "# Get the eigenvalues\n", + "EigValues, EigVectors = np.linalg.eig(H)\n", + "print(f\"Eigenvalues of Hessian Matrix:{EigValues}\")\n", + "\n", + "\n", + "beta_linreg = np.linalg.inv(XT_X+Id) @ X.T @ y\n", + "print(beta_linreg)\n", + "# Start plain gradient descent\n", + "beta = np.random.randn(2,1)\n", + "\n", + "eta = 1.0/np.max(EigValues)\n", + "Niterations = 100\n", + "\n", + "for iter in range(Niterations):\n", + " gradients = 2.0/n*X.T @ (X @ (beta)-y)+2*lmbda*beta\n", + " beta -= eta*gradients\n", + "\n", + "print(beta)\n", + "ypredict = X @ beta\n", + "ypredict2 = X @ beta_linreg\n", + "plt.plot(x, ypredict, \"r-\")\n", + "plt.plot(x, ypredict2, \"b-\")\n", + "plt.plot(x, y ,'ro')\n", + "plt.axis([0,2.0,0, 15.0])\n", + "plt.xlabel(r'$x$')\n", + "plt.ylabel(r'$y$')\n", + "plt.title(r'Gradient descent example for Ridge')\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "1836d4ef", + "metadata": { + "editable": true + }, + "source": [ + "## Using gradient descent methods, limitations\n", + "\n", + "* **Gradient descent (GD) finds local minima of our function**. Since the GD algorithm is deterministic, if it converges, it will converge to a local minimum of our cost/loss/risk function. Because in ML we are often dealing with extremely rugged landscapes with many local minima, this can lead to poor performance.\n", + "\n", + "* **GD is sensitive to initial conditions**. One consequence of the local nature of GD is that initial conditions matter. Depending on where one starts, one will end up at a different local minima. Therefore, it is very important to think about how one initializes the training process. This is true for GD as well as more complicated variants of GD.\n", + "\n", + "* **Gradients are computationally expensive to calculate for large datasets**. In many cases in statistics and ML, the cost/loss/risk function is a sum of terms, with one term for each data point. For example, in linear regression, $E \\propto \\sum_{i=1}^n (y_i - \\mathbf{w}^T\\cdot\\mathbf{x}_i)^2$; for logistic regression, the square error is replaced by the cross entropy. To calculate the gradient we have to sum over *all* $n$ data points. Doing this at every GD step becomes extremely computationally expensive. An ingenious solution to this, is to calculate the gradients using small subsets of the data called \"mini batches\". This has the added benefit of introducing stochasticity into our algorithm.\n", + "\n", + "* **GD is very sensitive to choices of learning rates**. GD is extremely sensitive to the choice of learning rates. If the learning rate is very small, the training process take an extremely long time. For larger learning rates, GD can diverge and give poor results. Furthermore, depending on what the local landscape looks like, we have to modify the learning rates to ensure convergence. Ideally, we would *adaptively* choose the learning rates to match the landscape.\n", + "\n", + "* **GD treats all directions in parameter space uniformly.** Another major drawback of GD is that unlike Newton's method, the learning rate for GD is the same in all directions in parameter space. For this reason, the maximum learning rate is set by the behavior of the steepest direction and this can significantly slow down training. Ideally, we would like to take large steps in flat directions and small steps in steep directions. Since we are exploring rugged landscapes where curvatures change, this requires us to keep track of not only the gradient but second derivatives. The ideal scenario would be to calculate the Hessian but this proves to be too computationally expensive. \n", + "\n", + "* GD can take exponential time to escape saddle points, even with random initialization. As we mentioned, GD is extremely sensitive to initial condition since it determines the particular local minimum GD would eventually reach. However, even with a good initialization scheme, through the introduction of randomness, GD can still take exponential time to escape saddle points." + ] + }, + { + "cell_type": "markdown", + "id": "88975d3d", + "metadata": { + "editable": true + }, + "source": [ + "## Improving gradient descent with momentum\n", + "\n", + "We discuss here some simple examples where we introduce what is called 'memory'about previous steps, or what is normally called momentum gradient descent. The mathematics is explained below in connection with Stochastic gradient descent." + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "id": "56f415e0", + "metadata": { + "collapsed": false, + "editable": true + }, + "outputs": [], + "source": [ + "from numpy import asarray\n", + "from numpy import arange\n", + "from numpy.random import rand\n", + "from numpy.random import seed\n", + "from matplotlib import pyplot\n", + " \n", + "# objective function\n", + "def objective(x):\n", + "\treturn x**2.0\n", + " \n", + "# derivative of objective function\n", + "def derivative(x):\n", + "\treturn x * 2.0\n", + " \n", + "# gradient descent algorithm\n", + "def gradient_descent(objective, derivative, bounds, n_iter, step_size):\n", + "\t# track all solutions\n", + "\tsolutions, scores = list(), list()\n", + "\t# generate an initial point\n", + "\tsolution = bounds[:, 0] + rand(len(bounds)) * (bounds[:, 1] - bounds[:, 0])\n", + "\t# run the gradient descent\n", + "\tfor i in range(n_iter):\n", + "\t\t# calculate gradient\n", + "\t\tgradient = derivative(solution)\n", + "\t\t# take a step\n", + "\t\tsolution = solution - step_size * gradient\n", + "\t\t# evaluate candidate point\n", + "\t\tsolution_eval = objective(solution)\n", + "\t\t# store solution\n", + "\t\tsolutions.append(solution)\n", + "\t\tscores.append(solution_eval)\n", + "\t\t# report progress\n", + "\t\tprint('>%d f(%s) = %.5f' % (i, solution, solution_eval))\n", + "\treturn [solutions, scores]\n", + " \n", + "# seed the pseudo random number generator\n", + "seed(4)\n", + "# define range for input\n", + "bounds = asarray([[-1.0, 1.0]])\n", + "# define the total iterations\n", + "n_iter = 30\n", + "# define the step size\n", + "step_size = 0.1\n", + "# perform the gradient descent search\n", + "solutions, scores = gradient_descent(objective, derivative, bounds, n_iter, step_size)\n", + "# sample input range uniformly at 0.1 increments\n", + "inputs = arange(bounds[0,0], bounds[0,1]+0.1, 0.1)\n", + "# compute targets\n", + "results = objective(inputs)\n", + "# create a line plot of input vs result\n", + "pyplot.plot(inputs, results)\n", + "# plot the solutions found\n", + "pyplot.plot(solutions, scores, '.-', color='red')\n", + "# show the plot\n", + "pyplot.show()" + ] + }, + { + "cell_type": "markdown", + "id": "d3343584", + "metadata": { + "editable": true + }, + "source": [ + "## Same code but now with momentum gradient descent" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "id": "ff1e3778", + "metadata": { + "collapsed": false, + "editable": true + }, + "outputs": [], + "source": [ + "from numpy import asarray\n", + "from numpy import arange\n", + "from numpy.random import rand\n", + "from numpy.random import seed\n", + "from matplotlib import pyplot\n", + " \n", + "# objective function\n", + "def objective(x):\n", + "\treturn x**2.0\n", + " \n", + "# derivative of objective function\n", + "def derivative(x):\n", + "\treturn x * 2.0\n", + " \n", + "# gradient descent algorithm\n", + "def gradient_descent(objective, derivative, bounds, n_iter, step_size, momentum):\n", + "\t# track all solutions\n", + "\tsolutions, scores = list(), list()\n", + "\t# generate an initial point\n", + "\tsolution = bounds[:, 0] + rand(len(bounds)) * (bounds[:, 1] - bounds[:, 0])\n", + "\t# keep track of the change\n", + "\tchange = 0.0\n", + "\t# run the gradient descent\n", + "\tfor i in range(n_iter):\n", + "\t\t# calculate gradient\n", + "\t\tgradient = derivative(solution)\n", + "\t\t# calculate update\n", + "\t\tnew_change = step_size * gradient + momentum * change\n", + "\t\t# take a step\n", + "\t\tsolution = solution - new_change\n", + "\t\t# save the change\n", + "\t\tchange = new_change\n", + "\t\t# evaluate candidate point\n", + "\t\tsolution_eval = objective(solution)\n", + "\t\t# store solution\n", + "\t\tsolutions.append(solution)\n", + "\t\tscores.append(solution_eval)\n", + "\t\t# report progress\n", + "\t\tprint('>%d f(%s) = %.5f' % (i, solution, solution_eval))\n", + "\treturn [solutions, scores]\n", + " \n", + "# seed the pseudo random number generator\n", + "seed(4)\n", + "# define range for input\n", + "bounds = asarray([[-1.0, 1.0]])\n", + "# define the total iterations\n", + "n_iter = 30\n", + "# define the step size\n", + "step_size = 0.1\n", + "# define momentum\n", + "momentum = 0.3\n", + "# perform the gradient descent search with momentum\n", + "solutions, scores = gradient_descent(objective, derivative, bounds, n_iter, step_size, momentum)\n", + "# sample input range uniformly at 0.1 increments\n", + "inputs = arange(bounds[0,0], bounds[0,1]+0.1, 0.1)\n", + "# compute targets\n", + "results = objective(inputs)\n", + "# create a line plot of input vs result\n", + "pyplot.plot(inputs, results)\n", + "# plot the solutions found\n", + "pyplot.plot(solutions, scores, '.-', color='red')\n", + "# show the plot\n", + "pyplot.show()" + ] + }, + { + "cell_type": "markdown", + "id": "c1d70995", + "metadata": { + "editable": true + }, + "source": [ + "## Overview video on Stochastic Gradient Descent\n", + "\n", + "[What is Stochastic Gradient Descent](https://www.youtube.com/watch?v=vMh0zPT0tLI&ab_channel=StatQuestwithJoshStarmer)" + ] + }, + { + "cell_type": "markdown", + "id": "930a5be8", + "metadata": { + "editable": true + }, + "source": [ + "## Batches and mini-batches\n", + "\n", + "In gradient descent we compute the cost function and its gradient for all data points we have.\n", + "\n", + "In large-scale applications such as the [ILSVRC challenge](https://www.image-net.org/challenges/LSVRC/), the\n", + "training data can have on order of millions of examples. Hence, it\n", + "seems wasteful to compute the full cost function over the entire\n", + "training set in order to perform only a single parameter update. A\n", + "very common approach to addressing this challenge is to compute the\n", + "gradient over batches of the training data. For example, a typical batch could contain some thousand examples from\n", + "an entire training set of several millions. This batch is then used to\n", + "perform a parameter update." + ] + }, + { + "cell_type": "markdown", + "id": "0a7fb7ef", + "metadata": { + "editable": true + }, + "source": [ + "## Stochastic Gradient Descent (SGD)\n", + "\n", + "In stochastic gradient descent, the extreme case is the case where we\n", + "have only one batch, that is we include the whole data set.\n", + "\n", + "This process is called Stochastic Gradient\n", + "Descent (SGD) (or also sometimes on-line gradient descent). This is\n", + "relatively less common to see because in practice due to vectorized\n", + "code optimizations it can be computationally much more efficient to\n", + "evaluate the gradient for 100 examples, than the gradient for one\n", + "example 100 times. Even though SGD technically refers to using a\n", + "single example at a time to evaluate the gradient, you will hear\n", + "people use the term SGD even when referring to mini-batch gradient\n", + "descent (i.e. mentions of MGD for “Minibatch Gradient Descent”, or BGD\n", + "for “Batch gradient descent” are rare to see), where it is usually\n", + "assumed that mini-batches are used. The size of the mini-batch is a\n", + "hyperparameter but it is not very common to cross-validate or bootstrap it. It is\n", + "usually based on memory constraints (if any), or set to some value,\n", + "e.g. 32, 64 or 128. We use powers of 2 in practice because many\n", + "vectorized operation implementations work faster when their inputs are\n", + "sized in powers of 2.\n", + "\n", + "In our notes with SGD we mean stochastic gradient descent with mini-batches." + ] + }, + { + "cell_type": "markdown", + "id": "dbff87b0", + "metadata": { + "editable": true + }, + "source": [ + "## Stochastic Gradient Descent\n", + "\n", + "Stochastic gradient descent (SGD) and variants thereof address some of\n", + "the shortcomings of the Gradient descent method discussed above.\n", + "\n", + "The underlying idea of SGD comes from the observation that the cost\n", + "function, which we want to minimize, can almost always be written as a\n", + "sum over $n$ data points $\\{\\mathbf{x}_i\\}_{i=1}^n$," + ] + }, + { + "cell_type": "markdown", + "id": "cd292df5", + "metadata": { + "editable": true + }, + "source": [ + "$$\n", + "C(\\mathbf{\\beta}) = \\sum_{i=1}^n c_i(\\mathbf{x}_i,\n", + "\\mathbf{\\beta}).\n", + "$$" + ] + }, + { + "cell_type": "markdown", + "id": "1b2ffa4e", + "metadata": { + "editable": true + }, + "source": [ + "## Computation of gradients\n", + "\n", + "This in turn means that the gradient can be\n", + "computed as a sum over $i$-gradients" + ] + }, + { + "cell_type": "markdown", + "id": "d0abe4b0", + "metadata": { + "editable": true + }, + "source": [ + "$$\n", + "\\nabla_\\beta C(\\mathbf{\\beta}) = \\sum_i^n \\nabla_\\beta c_i(\\mathbf{x}_i,\n", + "\\mathbf{\\beta}).\n", + "$$" + ] + }, + { + "cell_type": "markdown", + "id": "65c15c60", + "metadata": { + "editable": true + }, + "source": [ + "Stochasticity/randomness is introduced by only taking the\n", + "gradient on a subset of the data called minibatches. If there are $n$\n", + "data points and the size of each minibatch is $M$, there will be $n/M$\n", + "minibatches. We denote these minibatches by $B_k$ where\n", + "$k=1,\\cdots,n/M$." + ] + }, + { + "cell_type": "markdown", + "id": "460354c0", + "metadata": { + "editable": true + }, + "source": [ + "## SGD example\n", + "As an example, suppose we have $10$ data points $(\\mathbf{x}_1,\\cdots, \\mathbf{x}_{10})$ \n", + "and we choose to have $M=5$ minibathces,\n", + "then each minibatch contains two data points. In particular we have\n", + "$B_1 = (\\mathbf{x}_1,\\mathbf{x}_2), \\cdots, B_5 =\n", + "(\\mathbf{x}_9,\\mathbf{x}_{10})$. Note that if you choose $M=1$ you\n", + "have only a single batch with all data points and on the other extreme,\n", + "you may choose $M=n$ resulting in a minibatch for each datapoint, i.e\n", + "$B_k = \\mathbf{x}_k$.\n", + "\n", + "The idea is now to approximate the gradient by replacing the sum over\n", + "all data points with a sum over the data points in one the minibatches\n", + "picked at random in each gradient descent step" + ] + }, + { + "cell_type": "markdown", + "id": "c2a5dfcd", + "metadata": { + "editable": true + }, + "source": [ + "$$\n", + "\\nabla_{\\beta}\n", + "C(\\mathbf{\\beta}) = \\sum_{i=1}^n \\nabla_\\beta c_i(\\mathbf{x}_i,\n", + "\\mathbf{\\beta}) \\rightarrow \\sum_{i \\in B_k}^n \\nabla_\\beta\n", + "c_i(\\mathbf{x}_i, \\mathbf{\\beta}).\n", + "$$" + ] + }, + { + "cell_type": "markdown", + "id": "eeeb0fe8", + "metadata": { + "editable": true + }, + "source": [ + "## The gradient step\n", + "\n", + "Thus a gradient descent step now looks like" + ] + }, + { + "cell_type": "markdown", + "id": "2b49c741", + "metadata": { + "editable": true + }, + "source": [ + "$$\n", + "\\beta_{j+1} = \\beta_j - \\gamma_j \\sum_{i \\in B_k}^n \\nabla_\\beta c_i(\\mathbf{x}_i,\n", + "\\mathbf{\\beta})\n", + "$$" + ] + }, + { + "cell_type": "markdown", + "id": "8ba7b9be", + "metadata": { + "editable": true + }, + "source": [ + "where $k$ is picked at random with equal\n", + "probability from $[1,n/M]$. An iteration over the number of\n", + "minibathces (n/M) is commonly referred to as an epoch. Thus it is\n", + "typical to choose a number of epochs and for each epoch iterate over\n", + "the number of minibatches, as exemplified in the code below." + ] + }, + { + "cell_type": "markdown", + "id": "50da33c0", + "metadata": { + "editable": true + }, + "source": [ + "## Simple example code" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "id": "31bd6a24", + "metadata": { + "collapsed": false, + "editable": true + }, + "outputs": [], + "source": [ + "import numpy as np \n", + "\n", + "n = 100 #100 datapoints \n", + "M = 5 #size of each minibatch\n", + "m = int(n/M) #number of minibatches\n", + "n_epochs = 10 #number of epochs\n", + "\n", + "j = 0\n", + "for epoch in range(1,n_epochs+1):\n", + " for i in range(m):\n", + " k = np.random.randint(m) #Pick the k-th minibatch at random\n", + " #Compute the gradient using the data in minibatch Bk\n", + " #Compute new suggestion for \n", + " j += 1" + ] + }, + { + "cell_type": "markdown", + "id": "0deb8111", + "metadata": { + "editable": true + }, + "source": [ + "Taking the gradient only on a subset of the data has two important\n", + "benefits. First, it introduces randomness which decreases the chance\n", + "that our opmization scheme gets stuck in a local minima. Second, if\n", + "the size of the minibatches are small relative to the number of\n", + "datapoints ($M < n$), the computation of the gradient is much\n", + "cheaper since we sum over the datapoints in the $k-th$ minibatch and not\n", + "all $n$ datapoints." + ] + }, + { + "cell_type": "markdown", + "id": "16d54f02", + "metadata": { + "editable": true + }, + "source": [ + "## When do we stop?\n", + "\n", + "A natural question is when do we stop the search for a new minimum?\n", + "One possibility is to compute the full gradient after a given number\n", + "of epochs and check if the norm of the gradient is smaller than some\n", + "threshold and stop if true. However, the condition that the gradient\n", + "is zero is valid also for local minima, so this would only tell us\n", + "that we are close to a local/global minimum. However, we could also\n", + "evaluate the cost function at this point, store the result and\n", + "continue the search. If the test kicks in at a later stage we can\n", + "compare the values of the cost function and keep the $\\beta$ that\n", + "gave the lowest value." + ] + }, + { + "cell_type": "markdown", + "id": "b300d06b", + "metadata": { + "editable": true + }, + "source": [ + "## Slightly different approach\n", + "\n", + "Another approach is to let the step length $\\gamma_j$ depend on the\n", + "number of epochs in such a way that it becomes very small after a\n", + "reasonable time such that we do not move at all. Such approaches are\n", + "also called scaling. There are many such ways to [scale the learning\n", + "rate](https://towardsdatascience.com/gradient-descent-the-learning-rate-and-the-importance-of-feature-scaling-6c0b416596e1)\n", + "and [discussions here](https://www.jmlr.org/papers/volume23/20-1258/20-1258.pdf). See\n", + "also\n", + "\n", + "for a discussion of different scaling functions for the learning rate." + ] + }, + { + "cell_type": "markdown", + "id": "6bc7778d", + "metadata": { + "editable": true + }, + "source": [ + "## Time decay rate\n", + "\n", + "As an example, let $e = 0,1,2,3,\\cdots$ denote the current epoch and let $t_0, t_1 > 0$ be two fixed numbers. Furthermore, let $t = e \\cdot m + i$ where $m$ is the number of minibatches and $i=0,\\cdots,m-1$. Then the function $$\\gamma_j(t; t_0, t_1) = \\frac{t_0}{t+t_1} $$ goes to zero as the number of epochs gets large. I.e. we start with a step length $\\gamma_j (0; t_0, t_1) = t_0/t_1$ which decays in *time* $t$.\n", + "\n", + "In this way we can fix the number of epochs, compute $\\beta$ and\n", + "evaluate the cost function at the end. Repeating the computation will\n", + "give a different result since the scheme is random by design. Then we\n", + "pick the final $\\beta$ that gives the lowest value of the cost\n", + "function." + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "id": "a60fe5bd", + "metadata": { + "collapsed": false, + "editable": true + }, + "outputs": [], + "source": [ + "import numpy as np \n", + "\n", + "def step_length(t,t0,t1):\n", + " return t0/(t+t1)\n", + "\n", + "n = 100 #100 datapoints \n", + "M = 5 #size of each minibatch\n", + "m = int(n/M) #number of minibatches\n", + "n_epochs = 500 #number of epochs\n", + "t0 = 1.0\n", + "t1 = 10\n", + "\n", + "gamma_j = t0/t1\n", + "j = 0\n", + "for epoch in range(1,n_epochs+1):\n", + " for i in range(m):\n", + " k = np.random.randint(m) #Pick the k-th minibatch at random\n", + " #Compute the gradient using the data in minibatch Bk\n", + " #Compute new suggestion for beta\n", + " t = epoch*m+i\n", + " gamma_j = step_length(t,t0,t1)\n", + " j += 1\n", + "\n", + "print(\"gamma_j after %d epochs: %g\" % (n_epochs,gamma_j))" + ] + }, + { + "cell_type": "markdown", + "id": "2192721f", + "metadata": { + "editable": true + }, + "source": [ + "## Code with a Number of Minibatches which varies\n", + "\n", + "In the code here we vary the number of mini-batches." + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "id": "e404f2c5", + "metadata": { + "collapsed": false, + "editable": true + }, + "outputs": [], + "source": [ + "# Importing various packages\n", + "from math import exp, sqrt\n", + "from random import random, seed\n", + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "\n", + "n = 100\n", + "x = 2*np.random.rand(n,1)\n", + "y = 4+3*x+np.random.randn(n,1)\n", + "\n", + "X = np.c_[np.ones((n,1)), x]\n", + "XT_X = X.T @ X\n", + "theta_linreg = np.linalg.inv(X.T @ X) @ (X.T @ y)\n", + "print(\"Own inversion\")\n", + "print(theta_linreg)\n", + "# Hessian matrix\n", + "H = (2.0/n)* XT_X\n", + "EigValues, EigVectors = np.linalg.eig(H)\n", + "print(f\"Eigenvalues of Hessian Matrix:{EigValues}\")\n", + "\n", + "theta = np.random.randn(2,1)\n", + "eta = 1.0/np.max(EigValues)\n", + "Niterations = 1000\n", + "\n", + "\n", + "for iter in range(Niterations):\n", + " gradients = 2.0/n*X.T @ ((X @ theta)-y)\n", + " theta -= eta*gradients\n", + "print(\"theta from own gd\")\n", + "print(theta)\n", + "\n", + "xnew = np.array([[0],[2]])\n", + "Xnew = np.c_[np.ones((2,1)), xnew]\n", + "ypredict = Xnew.dot(theta)\n", + "ypredict2 = Xnew.dot(theta_linreg)\n", + "\n", + "n_epochs = 50\n", + "M = 5 #size of each minibatch\n", + "m = int(n/M) #number of minibatches\n", + "t0, t1 = 5, 50\n", + "\n", + "def learning_schedule(t):\n", + " return t0/(t+t1)\n", + "\n", + "theta = np.random.randn(2,1)\n", + "\n", + "for epoch in range(n_epochs):\n", + "# Can you figure out a better way of setting up the contributions to each batch?\n", + " for i in range(m):\n", + " random_index = M*np.random.randint(m)\n", + " xi = X[random_index:random_index+M]\n", + " yi = y[random_index:random_index+M]\n", + " gradients = (2.0/M)* xi.T @ ((xi @ theta)-yi)\n", + " eta = learning_schedule(epoch*m+i)\n", + " theta = theta - eta*gradients\n", + "print(\"theta from own sdg\")\n", + "print(theta)\n", + "\n", + "plt.plot(xnew, ypredict, \"r-\")\n", + "plt.plot(xnew, ypredict2, \"b-\")\n", + "plt.plot(x, y ,'ro')\n", + "plt.axis([0,2.0,0, 15.0])\n", + "plt.xlabel(r'$x$')\n", + "plt.ylabel(r'$y$')\n", + "plt.title(r'Random numbers ')\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "fffdbb91", + "metadata": { + "editable": true + }, + "source": [ + "## Replace or not\n", + "\n", + "In the above code, we have use replacement in setting up the\n", + "mini-batches. The discussion\n", + "[here](https://sebastianraschka.com/faq/docs/sgd-methods.html) may be\n", + "useful." + ] + }, + { + "cell_type": "markdown", + "id": "8cce7a0e", + "metadata": { + "editable": true + }, + "source": [ + "## Momentum based GD\n", + "\n", + "The stochastic gradient descent (SGD) is almost always used with a\n", + "*momentum* or inertia term that serves as a memory of the direction we\n", + "are moving in parameter space. This is typically implemented as\n", + "follows" + ] + }, + { + "cell_type": "markdown", + "id": "3154c365", + "metadata": { + "editable": true + }, + "source": [ + "$$\n", + "\\mathbf{v}_{t}=\\gamma \\mathbf{v}_{t-1}+\\eta_{t}\\nabla_\\theta E(\\boldsymbol{\\theta}_t) \\nonumber\n", + "$$" + ] + }, + { + "cell_type": "markdown", + "id": "a2a9ceca", + "metadata": { + "editable": true + }, + "source": [ + "\n", + "
\n", + "\n", + "$$\n", + "\\begin{equation} \n", + "\\boldsymbol{\\theta}_{t+1}= \\boldsymbol{\\theta}_t -\\mathbf{v}_{t},\n", + "\\label{_auto1} \\tag{2}\n", + "\\end{equation}\n", + "$$" + ] + }, + { + "cell_type": "markdown", + "id": "3374c700", + "metadata": { + "editable": true + }, + "source": [ + "where we have introduced a momentum parameter $\\gamma$, with\n", + "$0\\le\\gamma\\le 1$, and for brevity we dropped the explicit notation to\n", + "indicate the gradient is to be taken over a different mini-batch at\n", + "each step. We call this algorithm gradient descent with momentum\n", + "(GDM). From these equations, it is clear that $\\mathbf{v}_t$ is a\n", + "running average of recently encountered gradients and\n", + "$(1-\\gamma)^{-1}$ sets the characteristic time scale for the memory\n", + "used in the averaging procedure. Consistent with this, when\n", + "$\\gamma=0$, this just reduces down to ordinary SGD as discussed\n", + "earlier. An equivalent way of writing the updates is" + ] + }, + { + "cell_type": "markdown", + "id": "893d86fe", + "metadata": { + "editable": true + }, + "source": [ + "$$\n", + "\\Delta \\boldsymbol{\\theta}_{t+1} = \\gamma \\Delta \\boldsymbol{\\theta}_t -\\ \\eta_{t}\\nabla_\\theta E(\\boldsymbol{\\theta}_t),\n", + "$$" + ] + }, + { + "cell_type": "markdown", + "id": "ca2449e8", + "metadata": { + "editable": true + }, + "source": [ + "where we have defined $\\Delta \\boldsymbol{\\theta}_{t}= \\boldsymbol{\\theta}_t-\\boldsymbol{\\theta}_{t-1}$." + ] + }, + { + "cell_type": "markdown", + "id": "3cbd4adb", + "metadata": { + "editable": true + }, + "source": [ + "## More on momentum based approaches\n", + "\n", + "Let us try to get more intuition from these equations. It is helpful\n", + "to consider a simple physical analogy with a particle of mass $m$\n", + "moving in a viscous medium with drag coefficient $\\mu$ and potential\n", + "$E(\\mathbf{w})$. If we denote the particle's position by $\\mathbf{w}$,\n", + "then its motion is described by" + ] + }, + { + "cell_type": "markdown", + "id": "e3f07cbc", + "metadata": { + "editable": true + }, + "source": [ + "$$\n", + "m {d^2 \\mathbf{w} \\over dt^2} + \\mu {d \\mathbf{w} \\over dt }= -\\nabla_w E(\\mathbf{w}).\n", + "$$" + ] + }, + { + "cell_type": "markdown", + "id": "99f2ac0f", + "metadata": { + "editable": true + }, + "source": [ + "We can discretize this equation in the usual way to get" + ] + }, + { + "cell_type": "markdown", + "id": "83336244", + "metadata": { + "editable": true + }, + "source": [ + "$$\n", + "m { \\mathbf{w}_{t+\\Delta t}-2 \\mathbf{w}_{t} +\\mathbf{w}_{t-\\Delta t} \\over (\\Delta t)^2}+\\mu {\\mathbf{w}_{t+\\Delta t}- \\mathbf{w}_{t} \\over \\Delta t} = -\\nabla_w E(\\mathbf{w}).\n", + "$$" + ] + }, + { + "cell_type": "markdown", + "id": "efd3e708", + "metadata": { + "editable": true + }, + "source": [ + "Rearranging this equation, we can rewrite this as" + ] + }, + { + "cell_type": "markdown", + "id": "6c24d65c", + "metadata": { + "editable": true + }, + "source": [ + "$$\n", + "\\Delta \\mathbf{w}_{t +\\Delta t}= - { (\\Delta t)^2 \\over m +\\mu \\Delta t} \\nabla_w E(\\mathbf{w})+ {m \\over m +\\mu \\Delta t} \\Delta \\mathbf{w}_t.\n", + "$$" + ] + }, + { + "cell_type": "markdown", + "id": "853d885b", + "metadata": { + "editable": true + }, + "source": [ + "## Momentum parameter\n", + "\n", + "Notice that this equation is identical to previous one if we identify\n", + "the position of the particle, $\\mathbf{w}$, with the parameters\n", + "$\\boldsymbol{\\theta}$. This allows us to identify the momentum\n", + "parameter and learning rate with the mass of the particle and the\n", + "viscous drag as:" + ] + }, + { + "cell_type": "markdown", + "id": "5ab54645", + "metadata": { + "editable": true + }, + "source": [ + "$$\n", + "\\gamma= {m \\over m +\\mu \\Delta t }, \\qquad \\eta = {(\\Delta t)^2 \\over m +\\mu \\Delta t}.\n", + "$$" + ] + }, + { + "cell_type": "markdown", + "id": "90f1503b", + "metadata": { + "editable": true + }, + "source": [ + "Thus, as the name suggests, the momentum parameter is proportional to\n", + "the mass of the particle and effectively provides inertia.\n", + "Furthermore, in the large viscosity/small learning rate limit, our\n", + "memory time scales as $(1-\\gamma)^{-1} \\approx m/(\\mu \\Delta t)$.\n", + "\n", + "Why is momentum useful? SGD momentum helps the gradient descent\n", + "algorithm gain speed in directions with persistent but small gradients\n", + "even in the presence of stochasticity, while suppressing oscillations\n", + "in high-curvature directions. This becomes especially important in\n", + "situations where the landscape is shallow and flat in some directions\n", + "and narrow and steep in others. It has been argued that first-order\n", + "methods (with appropriate initial conditions) can perform comparable\n", + "to more expensive second order methods, especially in the context of\n", + "complex deep learning models.\n", + "\n", + "These beneficial properties of momentum can sometimes become even more\n", + "pronounced by using a slight modification of the classical momentum\n", + "algorithm called Nesterov Accelerated Gradient (NAG).\n", + "\n", + "In the NAG algorithm, rather than calculating the gradient at the\n", + "current parameters, $\\nabla_\\theta E(\\boldsymbol{\\theta}_t)$, one\n", + "calculates the gradient at the expected value of the parameters given\n", + "our current momentum, $\\nabla_\\theta E(\\boldsymbol{\\theta}_t +\\gamma\n", + "\\mathbf{v}_{t-1})$. This yields the NAG update rule" + ] + }, + { + "cell_type": "markdown", + "id": "d496d988", + "metadata": { + "editable": true + }, + "source": [ + "$$\n", + "\\mathbf{v}_{t}=\\gamma \\mathbf{v}_{t-1}+\\eta_{t}\\nabla_\\theta E(\\boldsymbol{\\theta}_t +\\gamma \\mathbf{v}_{t-1}) \\nonumber\n", + "$$" + ] + }, + { + "cell_type": "markdown", + "id": "258ca1e6", + "metadata": { + "editable": true + }, + "source": [ + "\n", + "
\n", + "\n", + "$$\n", + "\\begin{equation} \n", + "\\boldsymbol{\\theta}_{t+1}= \\boldsymbol{\\theta}_t -\\mathbf{v}_{t}.\n", + "\\label{_auto2} \\tag{3}\n", + "\\end{equation}\n", + "$$" + ] + }, + { + "cell_type": "markdown", + "id": "84278058", + "metadata": { + "editable": true + }, + "source": [ + "One of the major advantages of NAG is that it allows for the use of a larger learning rate than GDM for the same choice of $\\gamma$." + ] + }, + { + "cell_type": "markdown", + "id": "feaee2f4", + "metadata": { + "editable": true + }, + "source": [ + "## Second moment of the gradient\n", + "\n", + "In stochastic gradient descent, with and without momentum, we still\n", + "have to specify a schedule for tuning the learning rates $\\eta_t$\n", + "as a function of time. As discussed in the context of Newton's\n", + "method, this presents a number of dilemmas. The learning rate is\n", + "limited by the steepest direction which can change depending on the\n", + "current position in the landscape. To circumvent this problem, ideally\n", + "our algorithm would keep track of curvature and take large steps in\n", + "shallow, flat directions and small steps in steep, narrow directions.\n", + "Second-order methods accomplish this by calculating or approximating\n", + "the Hessian and normalizing the learning rate by the\n", + "curvature. However, this is very computationally expensive for\n", + "extremely large models. Ideally, we would like to be able to\n", + "adaptively change the step size to match the landscape without paying\n", + "the steep computational price of calculating or approximating\n", + "Hessians.\n", + "\n", + "Recently, a number of methods have been introduced that accomplish\n", + "this by tracking not only the gradient, but also the second moment of\n", + "the gradient. These methods include AdaGrad, AdaDelta, Root Mean Squared Propagation (RMS-Prop), and\n", + "[ADAM](https://arxiv.org/abs/1412.6980)." + ] + }, + { + "cell_type": "markdown", + "id": "218edcf1", + "metadata": { + "editable": true + }, + "source": [ + "## RMS prop\n", + "\n", + "In RMS prop, in addition to keeping a running average of the first\n", + "moment of the gradient, we also keep track of the second moment\n", + "denoted by $\\mathbf{s}_t=\\mathbb{E}[\\mathbf{g}_t^2]$. The update rule\n", + "for RMS prop is given by" + ] + }, + { + "cell_type": "markdown", + "id": "18dbc91c", + "metadata": { + "editable": true + }, + "source": [ + "\n", + "
\n", + "\n", + "$$\n", + "\\begin{equation}\n", + "\\mathbf{g}_t = \\nabla_\\theta E(\\boldsymbol{\\theta}) \n", + "\\label{_auto3} \\tag{4}\n", + "\\end{equation}\n", + "$$" + ] + }, + { + "cell_type": "markdown", + "id": "0bfcf74a", + "metadata": { + "editable": true + }, + "source": [ + "$$\n", + "\\mathbf{s}_t =\\beta \\mathbf{s}_{t-1} +(1-\\beta)\\mathbf{g}_t^2 \\nonumber\n", + "$$" + ] + }, + { + "cell_type": "markdown", + "id": "5fedd6f0", + "metadata": { + "editable": true + }, + "source": [ + "$$\n", + "\\boldsymbol{\\theta}_{t+1}=\\boldsymbol{\\theta}_t - \\eta_t { \\mathbf{g}_t \\over \\sqrt{\\mathbf{s}_t +\\epsilon}}, \\nonumber\n", + "$$" + ] + }, + { + "cell_type": "markdown", + "id": "b210b2a4", + "metadata": { + "editable": true + }, + "source": [ + "where $\\beta$ controls the averaging time of the second moment and is\n", + "typically taken to be about $\\beta=0.9$, $\\eta_t$ is a learning rate\n", + "typically chosen to be $10^{-3}$, and $\\epsilon\\sim 10^{-8} $ is a\n", + "small regularization constant to prevent divergences. Multiplication\n", + "and division by vectors is understood as an element-wise operation. It\n", + "is clear from this formula that the learning rate is reduced in\n", + "directions where the norm of the gradient is consistently large. This\n", + "greatly speeds up the convergence by allowing us to use a larger\n", + "learning rate for flat directions." + ] + }, + { + "cell_type": "markdown", + "id": "34ffacbb", + "metadata": { + "editable": true + }, + "source": [ + "## [ADAM optimizer](https://arxiv.org/abs/1412.6980)\n", + "\n", + "A related algorithm is the ADAM optimizer. In\n", + "[ADAM](https://arxiv.org/abs/1412.6980), we keep a running average of\n", + "both the first and second moment of the gradient and use this\n", + "information to adaptively change the learning rate for different\n", + "parameters. The method isefficient when working with large\n", + "problems involving lots data and/or parameters. It is a combination of the\n", + "gradient descent with momentum algorithm and the RMSprop algorithm\n", + "discussed above.\n", + "\n", + "In addition to keeping a running average of the first and\n", + "second moments of the gradient\n", + "(i.e. $\\mathbf{m}_t=\\mathbb{E}[\\mathbf{g}_t]$ and\n", + "$\\mathbf{s}_t=\\mathbb{E}[\\mathbf{g}^2_t]$, respectively), ADAM\n", + "performs an additional bias correction to account for the fact that we\n", + "are estimating the first two moments of the gradient using a running\n", + "average (denoted by the hats in the update rule below). The update\n", + "rule for ADAM is given by (where multiplication and division are once\n", + "again understood to be element-wise operations below)" + ] + }, + { + "cell_type": "markdown", + "id": "cd03375d", + "metadata": { + "editable": true + }, + "source": [ + "\n", + "
\n", + "\n", + "$$\n", + "\\begin{equation}\n", + "\\mathbf{g}_t = \\nabla_\\theta E(\\boldsymbol{\\theta}) \n", + "\\label{_auto4} \\tag{5}\n", + "\\end{equation}\n", + "$$" + ] + }, + { + "cell_type": "markdown", + "id": "0db5d6e0", + "metadata": { + "editable": true + }, + "source": [ + "$$\n", + "\\mathbf{m}_t = \\beta_1 \\mathbf{m}_{t-1} + (1-\\beta_1) \\mathbf{g}_t \\nonumber\n", + "$$" + ] + }, + { + "cell_type": "markdown", + "id": "84c709d9", + "metadata": { + "editable": true + }, + "source": [ + "$$\n", + "\\mathbf{s}_t =\\beta_2 \\mathbf{s}_{t-1} +(1-\\beta_2)\\mathbf{g}_t^2 \\nonumber\n", + "$$" + ] + }, + { + "cell_type": "markdown", + "id": "e4e47496", + "metadata": { + "editable": true + }, + "source": [ + "$$\n", + "\\boldsymbol{\\mathbf{m}}_t={\\mathbf{m}_t \\over 1-\\beta_1^t} \\nonumber\n", + "$$" + ] + }, + { + "cell_type": "markdown", + "id": "164f27df", + "metadata": { + "editable": true + }, + "source": [ + "$$\n", + "\\boldsymbol{\\mathbf{s}}_t ={\\mathbf{s}_t \\over1-\\beta_2^t} \\nonumber\n", + "$$" + ] + }, + { + "cell_type": "markdown", + "id": "591f4833", + "metadata": { + "editable": true + }, + "source": [ + "$$\n", + "\\boldsymbol{\\theta}_{t+1}=\\boldsymbol{\\theta}_t - \\eta_t { \\boldsymbol{\\mathbf{m}}_t \\over \\sqrt{\\boldsymbol{\\mathbf{s}}_t} +\\epsilon}, \\nonumber\n", + "$$" + ] + }, + { + "cell_type": "markdown", + "id": "e2127e8a", + "metadata": { + "editable": true + }, + "source": [ + "\n", + "
\n", + "\n", + "$$\n", + "\\begin{equation} \n", + "\\label{_auto5} \\tag{6}\n", + "\\end{equation}\n", + "$$" + ] + }, + { + "cell_type": "markdown", + "id": "5cef8b84", + "metadata": { + "editable": true + }, + "source": [ + "where $\\beta_1$ and $\\beta_2$ set the memory lifetime of the first and\n", + "second moment and are typically taken to be $0.9$ and $0.99$\n", + "respectively, and $\\eta$ and $\\epsilon$ are identical to RMSprop.\n", + "\n", + "Like in RMSprop, the effective step size of a parameter depends on the\n", + "magnitude of its gradient squared. To understand this better, let us\n", + "rewrite this expression in terms of the variance\n", + "$\\boldsymbol{\\sigma}_t^2 = \\boldsymbol{\\mathbf{s}}_t -\n", + "(\\boldsymbol{\\mathbf{m}}_t)^2$. Consider a single parameter $\\theta_t$. The\n", + "update rule for this parameter is given by" + ] + }, + { + "cell_type": "markdown", + "id": "505c8905", + "metadata": { + "editable": true + }, + "source": [ + "$$\n", + "\\Delta \\theta_{t+1}= -\\eta_t { \\boldsymbol{m}_t \\over \\sqrt{\\sigma_t^2 + m_t^2 }+\\epsilon}.\n", + "$$" + ] + }, + { + "cell_type": "markdown", + "id": "ca96ddec", + "metadata": { + "editable": true + }, + "source": [ + "## Algorithms and codes for Adagrad, RMSprop and Adam\n", + "\n", + "The algorithms we have implemented are well described in the text by [Goodfellow, Bengio and Courville, chapter 8](https://www.deeplearningbook.org/contents/optimization.html).\n", + "\n", + "The codes which implement these algorithms are discussed after our presentation of automatic differentiation." + ] + }, + { + "cell_type": "markdown", + "id": "fa011176", + "metadata": { + "editable": true + }, + "source": [ + "## Practical tips\n", + "\n", + "* **Randomize the data when making mini-batches**. It is always important to randomly shuffle the data when forming mini-batches. Otherwise, the gradient descent method can fit spurious correlations resulting from the order in which data is presented.\n", + "\n", + "* **Transform your inputs**. Learning becomes difficult when our landscape has a mixture of steep and flat directions. One simple trick for minimizing these situations is to standardize the data by subtracting the mean and normalizing the variance of input variables. Whenever possible, also decorrelate the inputs. To understand why this is helpful, consider the case of linear regression. It is easy to show that for the squared error cost function, the Hessian of the cost function is just the correlation matrix between the inputs. Thus, by standardizing the inputs, we are ensuring that the landscape looks homogeneous in all directions in parameter space. Since most deep networks can be viewed as linear transformations followed by a non-linearity at each layer, we expect this intuition to hold beyond the linear case.\n", + "\n", + "* **Monitor the out-of-sample performance.** Always monitor the performance of your model on a validation set (a small portion of the training data that is held out of the training process to serve as a proxy for the test set. If the validation error starts increasing, then the model is beginning to overfit. Terminate the learning process. This *early stopping* significantly improves performance in many settings.\n", + "\n", + "* **Adaptive optimization methods don't always have good generalization.** Recent studies have shown that adaptive methods such as ADAM, RMSPorp, and AdaGrad tend to have poor generalization compared to SGD or SGD with momentum, particularly in the high-dimensional limit (i.e. the number of parameters exceeds the number of data points). Although it is not clear at this stage why these methods perform so well in training deep neural networks, simpler procedures like properly-tuned SGD may work as well or better in these applications.\n", + "\n", + "Geron's text, see chapter 11, has several interesting discussions." + ] + }, + { + "cell_type": "markdown", + "id": "b91c4543", + "metadata": { + "editable": true + }, + "source": [ + "## Automatic differentiation\n", + "\n", + "[Automatic differentiation (AD)](https://en.wikipedia.org/wiki/Automatic_differentiation), \n", + "also called algorithmic\n", + "differentiation or computational differentiation,is a set of\n", + "techniques to numerically evaluate the derivative of a function\n", + "specified by a computer program. AD exploits the fact that every\n", + "computer program, no matter how complicated, executes a sequence of\n", + "elementary arithmetic operations (addition, subtraction,\n", + "multiplication, division, etc.) and elementary functions (exp, log,\n", + "sin, cos, etc.). By applying the chain rule repeatedly to these\n", + "operations, derivatives of arbitrary order can be computed\n", + "automatically, accurately to working precision, and using at most a\n", + "small constant factor more arithmetic operations than the original\n", + "program.\n", + "\n", + "Automatic differentiation is neither:\n", + "\n", + "* Symbolic differentiation, nor\n", + "\n", + "* Numerical differentiation (the method of finite differences).\n", + "\n", + "Symbolic differentiation can lead to inefficient code and faces the\n", + "difficulty of converting a computer program into a single expression,\n", + "while numerical differentiation can introduce round-off errors in the\n", + "discretization process and cancellation\n", + "\n", + "Python has tools for so-called **automatic differentiation**.\n", + "Consider the following example" + ] + }, + { + "cell_type": "markdown", + "id": "f13065e5", + "metadata": { + "editable": true + }, + "source": [ + "$$\n", + "f(x) = \\sin\\left(2\\pi x + x^2\\right)\n", + "$$" + ] + }, + { + "cell_type": "markdown", + "id": "22937f5e", + "metadata": { + "editable": true + }, + "source": [ + "which has the following derivative" + ] + }, + { + "cell_type": "markdown", + "id": "e1459fe1", + "metadata": { + "editable": true + }, + "source": [ + "$$\n", + "f'(x) = \\cos\\left(2\\pi x + x^2\\right)\\left(2\\pi + 2x\\right)\n", + "$$" + ] + }, + { + "cell_type": "markdown", + "id": "441a109a", + "metadata": { + "editable": true + }, + "source": [ + "Using **autograd** we have" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "id": "9043abae", + "metadata": { + "collapsed": false, + "editable": true + }, + "outputs": [], + "source": [ + "import autograd.numpy as np\n", + "\n", + "# To do elementwise differentiation:\n", + "from autograd import elementwise_grad as egrad \n", + "\n", + "# To plot:\n", + "import matplotlib.pyplot as plt \n", + "\n", + "\n", + "def f(x):\n", + " return np.sin(2*np.pi*x + x**2)\n", + "\n", + "def f_grad_analytic(x):\n", + " return np.cos(2*np.pi*x + x**2)*(2*np.pi + 2*x)\n", + "\n", + "# Do the comparison:\n", + "x = np.linspace(0,1,1000)\n", + "\n", + "f_grad = egrad(f)\n", + "\n", + "computed = f_grad(x)\n", + "analytic = f_grad_analytic(x)\n", + "\n", + "plt.title('Derivative computed from Autograd compared with the analytical derivative')\n", + "plt.plot(x,computed,label='autograd')\n", + "plt.plot(x,analytic,label='analytic')\n", + "\n", + "plt.xlabel('x')\n", + "plt.ylabel('y')\n", + "plt.legend()\n", + "\n", + "plt.show()\n", + "\n", + "print(\"The max absolute difference is: %g\"%(np.max(np.abs(computed - analytic))))" + ] + }, + { + "cell_type": "markdown", + "id": "787d5d78", + "metadata": { + "editable": true + }, + "source": [ + "## Using autograd\n", + "\n", + "Here we\n", + "experiment with what kind of functions Autograd is capable\n", + "of finding the gradient of. The following Python functions are just\n", + "meant to illustrate what Autograd can do, but please feel free to\n", + "experiment with other, possibly more complicated, functions as well." + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "id": "6a677479", + "metadata": { + "collapsed": false, + "editable": true + }, + "outputs": [], + "source": [ + "import autograd.numpy as np\n", + "from autograd import grad\n", + "\n", + "def f1(x):\n", + " return x**3 + 1\n", + "\n", + "f1_grad = grad(f1)\n", + "\n", + "# Remember to send in float as argument to the computed gradient from Autograd!\n", + "a = 1.0\n", + "\n", + "# See the evaluated gradient at a using autograd:\n", + "print(\"The gradient of f1 evaluated at a = %g using autograd is: %g\"%(a,f1_grad(a)))\n", + "\n", + "# Compare with the analytical derivative, that is f1'(x) = 3*x**2 \n", + "grad_analytical = 3*a**2\n", + "print(\"The gradient of f1 evaluated at a = %g by finding the analytic expression is: %g\"%(a,grad_analytical))" + ] + }, + { + "cell_type": "markdown", + "id": "f94a1d32", + "metadata": { + "editable": true + }, + "source": [ + "## Autograd with more complicated functions\n", + "\n", + "To differentiate with respect to two (or more) arguments of a Python\n", + "function, Autograd need to know at which variable the function if\n", + "being differentiated with respect to." + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "id": "c0eb89fd", + "metadata": { + "collapsed": false, + "editable": true + }, + "outputs": [], + "source": [ + "import autograd.numpy as np\n", + "from autograd import grad\n", + "def f2(x1,x2):\n", + " return 3*x1**3 + x2*(x1 - 5) + 1\n", + "\n", + "# By sending the argument 0, Autograd will compute the derivative w.r.t the first variable, in this case x1\n", + "f2_grad_x1 = grad(f2,0)\n", + "\n", + "# ... and differentiate w.r.t x2 by sending 1 as an additional arugment to grad\n", + "f2_grad_x2 = grad(f2,1)\n", + "\n", + "x1 = 1.0\n", + "x2 = 3.0 \n", + "\n", + "print(\"Evaluating at x1 = %g, x2 = %g\"%(x1,x2))\n", + "print(\"-\"*30)\n", + "\n", + "# Compare with the analytical derivatives:\n", + "\n", + "# Derivative of f2 w.r.t x1 is: 9*x1**2 + x2:\n", + "f2_grad_x1_analytical = 9*x1**2 + x2\n", + "\n", + "# Derivative of f2 w.r.t x2 is: x1 - 5:\n", + "f2_grad_x2_analytical = x1 - 5\n", + "\n", + "# See the evaluated derivations:\n", + "print(\"The derivative of f2 w.r.t x1: %g\"%( f2_grad_x1(x1,x2) ))\n", + "print(\"The analytical derivative of f2 w.r.t x1: %g\"%( f2_grad_x1(x1,x2) ))\n", + "\n", + "print()\n", + "\n", + "print(\"The derivative of f2 w.r.t x2: %g\"%( f2_grad_x2(x1,x2) ))\n", + "print(\"The analytical derivative of f2 w.r.t x2: %g\"%( f2_grad_x2(x1,x2) ))" + ] + }, + { + "cell_type": "markdown", + "id": "05d7497d", + "metadata": { + "editable": true + }, + "source": [ + "Note that the grad function will not produce the true gradient of the function. The true gradient of a function with two or more variables will produce a vector, where each element is the function differentiated w.r.t a variable." + ] + }, + { + "cell_type": "markdown", + "id": "24e3ca02", + "metadata": { + "editable": true + }, + "source": [ + "## More complicated functions using the elements of their arguments directly" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "id": "a616e696", + "metadata": { + "collapsed": false, + "editable": true + }, + "outputs": [], + "source": [ + "import autograd.numpy as np\n", + "from autograd import grad\n", + "def f3(x): # Assumes x is an array of length 5 or higher\n", + " return 2*x[0] + 3*x[1] + 5*x[2] + 7*x[3] + 11*x[4]**2\n", + "\n", + "f3_grad = grad(f3)\n", + "\n", + "x = np.linspace(0,4,5)\n", + "\n", + "# Print the computed gradient:\n", + "print(\"The computed gradient of f3 is: \", f3_grad(x))\n", + "\n", + "# The analytical gradient is: (2, 3, 5, 7, 22*x[4])\n", + "f3_grad_analytical = np.array([2, 3, 5, 7, 22*x[4]])\n", + "\n", + "# Print the analytical gradient:\n", + "print(\"The analytical gradient of f3 is: \", f3_grad_analytical)" + ] + }, + { + "cell_type": "markdown", + "id": "f695da56", + "metadata": { + "editable": true + }, + "source": [ + "Note that in this case, when sending an array as input argument, the\n", + "output from Autograd is another array. This is the true gradient of\n", + "the function, as opposed to the function in the previous example. By\n", + "using arrays to represent the variables, the output from Autograd\n", + "might be easier to work with, as the output is closer to what one\n", + "could expect form a gradient-evaluting function." + ] + }, + { + "cell_type": "markdown", + "id": "5ac073ee", + "metadata": { + "editable": true + }, + "source": [ + "## Functions using mathematical functions from Numpy" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "id": "efc8906e", + "metadata": { + "collapsed": false, + "editable": true + }, + "outputs": [], + "source": [ + "import autograd.numpy as np\n", + "from autograd import grad\n", + "def f4(x):\n", + " return np.sqrt(1+x**2) + np.exp(x) + np.sin(2*np.pi*x)\n", + "\n", + "f4_grad = grad(f4)\n", + "\n", + "x = 2.7\n", + "\n", + "# Print the computed derivative:\n", + "print(\"The computed derivative of f4 at x = %g is: %g\"%(x,f4_grad(x)))\n", + "\n", + "# The analytical derivative is: x/sqrt(1 + x**2) + exp(x) + cos(2*pi*x)*2*pi\n", + "f4_grad_analytical = x/np.sqrt(1 + x**2) + np.exp(x) + np.cos(2*np.pi*x)*2*np.pi\n", + "\n", + "# Print the analytical gradient:\n", + "print(\"The analytical gradient of f4 at x = %g is: %g\"%(x,f4_grad_analytical))" + ] + }, + { + "cell_type": "markdown", + "id": "7c587e8f", + "metadata": { + "editable": true + }, + "source": [ + "## More autograd" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "id": "6428be1f", + "metadata": { + "collapsed": false, + "editable": true + }, + "outputs": [], + "source": [ + "import autograd.numpy as np\n", + "from autograd import grad\n", + "def f5(x):\n", + " if x >= 0:\n", + " return x**2\n", + " else:\n", + " return -3*x + 1\n", + "\n", + "f5_grad = grad(f5)\n", + "\n", + "x = 2.7\n", + "\n", + "# Print the computed derivative:\n", + "print(\"The computed derivative of f5 at x = %g is: %g\"%(x,f5_grad(x)))" + ] + }, + { + "cell_type": "markdown", + "id": "8e1de777", + "metadata": { + "editable": true + }, + "source": [ + "## And with loops" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "id": "770ff6aa", + "metadata": { + "collapsed": false, + "editable": true + }, + "outputs": [], + "source": [ + "import autograd.numpy as np\n", + "from autograd import grad\n", + "def f6_for(x):\n", + " val = 0\n", + " for i in range(10):\n", + " val = val + x**i\n", + " return val\n", + "\n", + "def f6_while(x):\n", + " val = 0\n", + " i = 0\n", + " while i < 10:\n", + " val = val + x**i\n", + " i = i + 1\n", + " return val\n", + "\n", + "f6_for_grad = grad(f6_for)\n", + "f6_while_grad = grad(f6_while)\n", + "\n", + "x = 0.5\n", + "\n", + "# Print the computed derivaties of f6_for and f6_while\n", + "print(\"The computed derivative of f6_for at x = %g is: %g\"%(x,f6_for_grad(x)))\n", + "print(\"The computed derivative of f6_while at x = %g is: %g\"%(x,f6_while_grad(x)))" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "id": "b924cc5d", + "metadata": { + "collapsed": false, + "editable": true + }, + "outputs": [], + "source": [ + "import autograd.numpy as np\n", + "from autograd import grad\n", + "# Both of the functions are implementation of the sum: sum(x**i) for i = 0, ..., 9\n", + "# The analytical derivative is: sum(i*x**(i-1)) \n", + "f6_grad_analytical = 0\n", + "for i in range(10):\n", + " f6_grad_analytical += i*x**(i-1)\n", + "\n", + "print(\"The analytical derivative of f6 at x = %g is: %g\"%(x,f6_grad_analytical))" + ] + }, + { + "cell_type": "markdown", + "id": "f0b0e1e9", + "metadata": { + "editable": true + }, + "source": [ + "## Using recursion" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "id": "1585ab28", + "metadata": { + "collapsed": false, + "editable": true + }, + "outputs": [], + "source": [ + "import autograd.numpy as np\n", + "from autograd import grad\n", + "\n", + "def f7(n): # Assume that n is an integer\n", + " if n == 1 or n == 0:\n", + " return 1\n", + " else:\n", + " return n*f7(n-1)\n", + "\n", + "f7_grad = grad(f7)\n", + "\n", + "n = 2.0\n", + "\n", + "print(\"The computed derivative of f7 at n = %d is: %g\"%(n,f7_grad(n)))\n", + "\n", + "# The function f7 is an implementation of the factorial of n.\n", + "# By using the product rule, one can find that the derivative is:\n", + "\n", + "f7_grad_analytical = 0\n", + "for i in range(int(n)-1):\n", + " tmp = 1\n", + " for k in range(int(n)-1):\n", + " if k != i:\n", + " tmp *= (n - k)\n", + " f7_grad_analytical += tmp\n", + "\n", + "print(\"The analytical derivative of f7 at n = %d is: %g\"%(n,f7_grad_analytical))" + ] + }, + { + "cell_type": "markdown", + "id": "2718df1a", + "metadata": { + "editable": true + }, + "source": [ + "Note that if n is equal to zero or one, Autograd will give an error message. This message appears when the output is independent on input." + ] + }, + { + "cell_type": "markdown", + "id": "d8fa5235", + "metadata": { + "editable": true + }, + "source": [ + "## Unsupported functions\n", + "Autograd supports many features. However, there are some functions that is not supported (yet) by Autograd.\n", + "\n", + "Assigning a value to the variable being differentiated with respect to" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "id": "196a52d6", + "metadata": { + "collapsed": false, + "editable": true + }, + "outputs": [], + "source": [ + "import autograd.numpy as np\n", + "from autograd import grad\n", + "def f8(x): # Assume x is an array\n", + " x[2] = 3\n", + " return x*2\n", + "\n", + "f8_grad = grad(f8)\n", + "\n", + "x = 8.4\n", + "\n", + "print(\"The derivative of f8 is:\",f8_grad(x))" + ] + }, + { + "cell_type": "markdown", + "id": "9127a2c5", + "metadata": { + "editable": true + }, + "source": [ + "Here, Autograd tells us that an 'ArrayBox' does not support item assignment. The item assignment is done when the program tries to assign x[2] to the value 3. However, Autograd has implemented the computation of the derivative such that this assignment is not possible." + ] + }, + { + "cell_type": "markdown", + "id": "2b12ed61", + "metadata": { + "editable": true + }, + "source": [ + "## The syntax a.dot(b) when finding the dot product" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "id": "8ced55c8", + "metadata": { + "collapsed": false, + "editable": true + }, + "outputs": [], + "source": [ + "import autograd.numpy as np\n", + "from autograd import grad\n", + "def f9(a): # Assume a is an array with 2 elements\n", + " b = np.array([1.0,2.0])\n", + " return a.dot(b)\n", + "\n", + "f9_grad = grad(f9)\n", + "\n", + "x = np.array([1.0,0.0])\n", + "\n", + "print(\"The derivative of f9 is:\",f9_grad(x))" + ] + }, + { + "cell_type": "markdown", + "id": "92ebdc2b", + "metadata": { + "editable": true + }, + "source": [ + "Here we are told that the 'dot' function does not belong to Autograd's\n", + "version of a Numpy array. To overcome this, an alternative syntax\n", + "which also computed the dot product can be used:" + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "id": "276f763e", + "metadata": { + "collapsed": false, + "editable": true + }, + "outputs": [], + "source": [ + "import autograd.numpy as np\n", + "from autograd import grad\n", + "def f9_alternative(x): # Assume a is an array with 2 elements\n", + " b = np.array([1.0,2.0])\n", + " return np.dot(x,b) # The same as x_1*b_1 + x_2*b_2\n", + "\n", + "f9_alternative_grad = grad(f9_alternative)\n", + "\n", + "x = np.array([3.0,0.0])\n", + "\n", + "print(\"The gradient of f9 is:\",f9_alternative_grad(x))\n", + "\n", + "# The analytical gradient of the dot product of vectors x and b with two elements (x_1,x_2) and (b_1, b_2) respectively\n", + "# w.r.t x is (b_1, b_2)." + ] + }, + { + "cell_type": "markdown", + "id": "7841ad0b", + "metadata": { + "editable": true + }, + "source": [ + "## Recommended to avoid\n", + "The documentation recommends to avoid inplace operations such as" + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "id": "10107989", + "metadata": { + "collapsed": false, + "editable": true + }, + "outputs": [], + "source": [ + "a += b\n", + "a -= b\n", + "a*= b\n", + "a /=b" + ] + }, + { + "cell_type": "markdown", + "id": "4c1139c0", + "metadata": { + "editable": true + }, + "source": [ + "## Using Autograd with OLS\n", + "\n", + "We conclude the part on optmization by showing how we can make codes\n", + "for linear regression and logistic regression using **autograd**. The\n", + "first example shows results with ordinary leats squares." + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "id": "3022af88", + "metadata": { + "collapsed": false, + "editable": true + }, + "outputs": [], + "source": [ + "# Using Autograd to calculate gradients for OLS\n", + "from random import random, seed\n", + "import numpy as np\n", + "import autograd.numpy as np\n", + "import matplotlib.pyplot as plt\n", + "from autograd import grad\n", + "\n", + "def CostOLS(beta):\n", + " return (1.0/n)*np.sum((y-X @ beta)**2)\n", + "\n", + "n = 100\n", + "x = 2*np.random.rand(n,1)\n", + "y = 4+3*x+np.random.randn(n,1)\n", + "\n", + "X = np.c_[np.ones((n,1)), x]\n", + "XT_X = X.T @ X\n", + "theta_linreg = np.linalg.pinv(XT_X) @ (X.T @ y)\n", + "print(\"Own inversion\")\n", + "print(theta_linreg)\n", + "# Hessian matrix\n", + "H = (2.0/n)* XT_X\n", + "EigValues, EigVectors = np.linalg.eig(H)\n", + "print(f\"Eigenvalues of Hessian Matrix:{EigValues}\")\n", + "\n", + "theta = np.random.randn(2,1)\n", + "eta = 1.0/np.max(EigValues)\n", + "Niterations = 1000\n", + "# define the gradient\n", + "training_gradient = grad(CostOLS)\n", + "\n", + "for iter in range(Niterations):\n", + " gradients = training_gradient(theta)\n", + " theta -= eta*gradients\n", + "print(\"theta from own gd\")\n", + "print(theta)\n", + "\n", + "xnew = np.array([[0],[2]])\n", + "Xnew = np.c_[np.ones((2,1)), xnew]\n", + "ypredict = Xnew.dot(theta)\n", + "ypredict2 = Xnew.dot(theta_linreg)\n", + "\n", + "plt.plot(xnew, ypredict, \"r-\")\n", + "plt.plot(xnew, ypredict2, \"b-\")\n", + "plt.plot(x, y ,'ro')\n", + "plt.axis([0,2.0,0, 15.0])\n", + "plt.xlabel(r'$x$')\n", + "plt.ylabel(r'$y$')\n", + "plt.title(r'Random numbers ')\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "04d09021", + "metadata": { + "editable": true + }, + "source": [ + "## Same code but now with momentum gradient descent" + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "id": "71bf4b6d", + "metadata": { + "collapsed": false, + "editable": true + }, + "outputs": [], + "source": [ + "# Using Autograd to calculate gradients for OLS\n", + "from random import random, seed\n", + "import numpy as np\n", + "import autograd.numpy as np\n", + "import matplotlib.pyplot as plt\n", + "from autograd import grad\n", + "\n", + "def CostOLS(beta):\n", + " return (1.0/n)*np.sum((y-X @ beta)**2)\n", + "\n", + "n = 100\n", + "x = 2*np.random.rand(n,1)\n", + "y = 4+3*x#+np.random.randn(n,1)\n", + "\n", + "X = np.c_[np.ones((n,1)), x]\n", + "XT_X = X.T @ X\n", + "theta_linreg = np.linalg.pinv(XT_X) @ (X.T @ y)\n", + "print(\"Own inversion\")\n", + "print(theta_linreg)\n", + "# Hessian matrix\n", + "H = (2.0/n)* XT_X\n", + "EigValues, EigVectors = np.linalg.eig(H)\n", + "print(f\"Eigenvalues of Hessian Matrix:{EigValues}\")\n", + "\n", + "theta = np.random.randn(2,1)\n", + "eta = 1.0/np.max(EigValues)\n", + "Niterations = 30\n", + "\n", + "# define the gradient\n", + "training_gradient = grad(CostOLS)\n", + "\n", + "for iter in range(Niterations):\n", + " gradients = training_gradient(theta)\n", + " theta -= eta*gradients\n", + " print(iter,gradients[0],gradients[1])\n", + "print(\"theta from own gd\")\n", + "print(theta)\n", + "\n", + "# Now improve with momentum gradient descent\n", + "change = 0.0\n", + "delta_momentum = 0.3\n", + "for iter in range(Niterations):\n", + " # calculate gradient\n", + " gradients = training_gradient(theta)\n", + " # calculate update\n", + " new_change = eta*gradients+delta_momentum*change\n", + " # take a step\n", + " theta -= new_change\n", + " # save the change\n", + " change = new_change\n", + " print(iter,gradients[0],gradients[1])\n", + "print(\"theta from own gd wth momentum\")\n", + "print(theta)" + ] + }, + { + "cell_type": "markdown", + "id": "ad417bba", + "metadata": { + "editable": true + }, + "source": [ + "## But noen of these can compete with Newton's method" + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "id": "c394bcef", + "metadata": { + "collapsed": false, + "editable": true + }, + "outputs": [], + "source": [ + "# Using Newton's method\n", + "from random import random, seed\n", + "import numpy as np\n", + "import autograd.numpy as np\n", + "import matplotlib.pyplot as plt\n", + "from autograd import grad\n", + "\n", + "def CostOLS(beta):\n", + " return (1.0/n)*np.sum((y-X @ beta)**2)\n", + "\n", + "n = 100\n", + "x = 2*np.random.rand(n,1)\n", + "y = 4+3*x+np.random.randn(n,1)\n", + "\n", + "X = np.c_[np.ones((n,1)), x]\n", + "XT_X = X.T @ X\n", + "beta_linreg = np.linalg.pinv(XT_X) @ (X.T @ y)\n", + "print(\"Own inversion\")\n", + "print(beta_linreg)\n", + "# Hessian matrix\n", + "H = (2.0/n)* XT_X\n", + "# Note that here the Hessian does not depend on the parameters beta\n", + "invH = np.linalg.pinv(H)\n", + "EigValues, EigVectors = np.linalg.eig(H)\n", + "print(f\"Eigenvalues of Hessian Matrix:{EigValues}\")\n", + "\n", + "beta = np.random.randn(2,1)\n", + "Niterations = 5\n", + "\n", + "# define the gradient\n", + "training_gradient = grad(CostOLS)\n", + "\n", + "for iter in range(Niterations):\n", + " gradients = training_gradient(beta)\n", + " beta -= invH @ gradients\n", + " print(iter,gradients[0],gradients[1])\n", + "print(\"beta from own Newton code\")\n", + "print(beta)" + ] + }, + { + "cell_type": "markdown", + "id": "2e4cf4b5", + "metadata": { + "editable": true + }, + "source": [ + "## Including Stochastic Gradient Descent with Autograd\n", + "In this code we include the stochastic gradient descent approach discussed above. Note here that we specify which argument we are taking the derivative with respect to when using **autograd**." + ] + }, + { + "cell_type": "code", + "execution_count": 31, + "id": "47411bcf", + "metadata": { + "collapsed": false, + "editable": true + }, + "outputs": [], + "source": [ + "# Using Autograd to calculate gradients using SGD\n", + "# OLS example\n", + "from random import random, seed\n", + "import numpy as np\n", + "import autograd.numpy as np\n", + "import matplotlib.pyplot as plt\n", + "from autograd import grad\n", + "\n", + "# Note change from previous example\n", + "def CostOLS(y,X,theta):\n", + " return np.sum((y-X @ theta)**2)\n", + "\n", + "n = 100\n", + "x = 2*np.random.rand(n,1)\n", + "y = 4+3*x+np.random.randn(n,1)\n", + "\n", + "X = np.c_[np.ones((n,1)), x]\n", + "XT_X = X.T @ X\n", + "theta_linreg = np.linalg.pinv(XT_X) @ (X.T @ y)\n", + "print(\"Own inversion\")\n", + "print(theta_linreg)\n", + "# Hessian matrix\n", + "H = (2.0/n)* XT_X\n", + "EigValues, EigVectors = np.linalg.eig(H)\n", + "print(f\"Eigenvalues of Hessian Matrix:{EigValues}\")\n", + "\n", + "theta = np.random.randn(2,1)\n", + "eta = 1.0/np.max(EigValues)\n", + "Niterations = 1000\n", + "\n", + "# Note that we request the derivative wrt third argument (theta, 2 here)\n", + "training_gradient = grad(CostOLS,2)\n", + "\n", + "for iter in range(Niterations):\n", + " gradients = (1.0/n)*training_gradient(y, X, theta)\n", + " theta -= eta*gradients\n", + "print(\"theta from own gd\")\n", + "print(theta)\n", + "\n", + "xnew = np.array([[0],[2]])\n", + "Xnew = np.c_[np.ones((2,1)), xnew]\n", + "ypredict = Xnew.dot(theta)\n", + "ypredict2 = Xnew.dot(theta_linreg)\n", + "\n", + "plt.plot(xnew, ypredict, \"r-\")\n", + "plt.plot(xnew, ypredict2, \"b-\")\n", + "plt.plot(x, y ,'ro')\n", + "plt.axis([0,2.0,0, 15.0])\n", + "plt.xlabel(r'$x$')\n", + "plt.ylabel(r'$y$')\n", + "plt.title(r'Random numbers ')\n", + "plt.show()\n", + "\n", + "n_epochs = 50\n", + "M = 5 #size of each minibatch\n", + "m = int(n/M) #number of minibatches\n", + "t0, t1 = 5, 50\n", + "def learning_schedule(t):\n", + " return t0/(t+t1)\n", + "\n", + "theta = np.random.randn(2,1)\n", + "\n", + "for epoch in range(n_epochs):\n", + "# Can you figure out a better way of setting up the contributions to each batch?\n", + " for i in range(m):\n", + " random_index = M*np.random.randint(m)\n", + " xi = X[random_index:random_index+M]\n", + " yi = y[random_index:random_index+M]\n", + " gradients = (1.0/M)*training_gradient(yi, xi, theta)\n", + " eta = learning_schedule(epoch*m+i)\n", + " theta = theta - eta*gradients\n", + "print(\"theta from own sdg\")\n", + "print(theta)" + ] + }, + { + "cell_type": "markdown", + "id": "f8e30af2", + "metadata": { + "editable": true + }, + "source": [ + "## Same code but now with momentum gradient descent" + ] + }, + { + "cell_type": "code", + "execution_count": 32, + "id": "dd594924", + "metadata": { + "collapsed": false, + "editable": true + }, + "outputs": [], + "source": [ + "# Using Autograd to calculate gradients using SGD\n", + "# OLS example\n", + "from random import random, seed\n", + "import numpy as np\n", + "import autograd.numpy as np\n", + "import matplotlib.pyplot as plt\n", + "from autograd import grad\n", + "\n", + "# Note change from previous example\n", + "def CostOLS(y,X,theta):\n", + " return np.sum((y-X @ theta)**2)\n", + "\n", + "n = 100\n", + "x = 2*np.random.rand(n,1)\n", + "y = 4+3*x+np.random.randn(n,1)\n", + "\n", + "X = np.c_[np.ones((n,1)), x]\n", + "XT_X = X.T @ X\n", + "theta_linreg = np.linalg.pinv(XT_X) @ (X.T @ y)\n", + "print(\"Own inversion\")\n", + "print(theta_linreg)\n", + "# Hessian matrix\n", + "H = (2.0/n)* XT_X\n", + "EigValues, EigVectors = np.linalg.eig(H)\n", + "print(f\"Eigenvalues of Hessian Matrix:{EigValues}\")\n", + "\n", + "theta = np.random.randn(2,1)\n", + "eta = 1.0/np.max(EigValues)\n", + "Niterations = 100\n", + "\n", + "# Note that we request the derivative wrt third argument (theta, 2 here)\n", + "training_gradient = grad(CostOLS,2)\n", + "\n", + "for iter in range(Niterations):\n", + " gradients = (1.0/n)*training_gradient(y, X, theta)\n", + " theta -= eta*gradients\n", + "print(\"theta from own gd\")\n", + "print(theta)\n", + "\n", + "\n", + "n_epochs = 50\n", + "M = 5 #size of each minibatch\n", + "m = int(n/M) #number of minibatches\n", + "t0, t1 = 5, 50\n", + "def learning_schedule(t):\n", + " return t0/(t+t1)\n", + "\n", + "theta = np.random.randn(2,1)\n", + "\n", + "change = 0.0\n", + "delta_momentum = 0.3\n", + "\n", + "for epoch in range(n_epochs):\n", + " for i in range(m):\n", + " random_index = M*np.random.randint(m)\n", + " xi = X[random_index:random_index+M]\n", + " yi = y[random_index:random_index+M]\n", + " gradients = (1.0/M)*training_gradient(yi, xi, theta)\n", + " eta = learning_schedule(epoch*m+i)\n", + " # calculate update\n", + " new_change = eta*gradients+delta_momentum*change\n", + " # take a step\n", + " theta -= new_change\n", + " # save the change\n", + " change = new_change\n", + "print(\"theta from own sdg with momentum\")\n", + "print(theta)" + ] + }, + { + "cell_type": "markdown", + "id": "75c4c29d", + "metadata": { + "editable": true + }, + "source": [ + "## Similar (second order function now) problem but now with AdaGrad" + ] + }, + { + "cell_type": "code", + "execution_count": 33, + "id": "4dd14fc5", + "metadata": { + "collapsed": false, + "editable": true + }, + "outputs": [], + "source": [ + "# Using Autograd to calculate gradients using AdaGrad and Stochastic Gradient descent\n", + "# OLS example\n", + "from random import random, seed\n", + "import numpy as np\n", + "import autograd.numpy as np\n", + "import matplotlib.pyplot as plt\n", + "from autograd import grad\n", + "\n", + "# Note change from previous example\n", + "def CostOLS(y,X,theta):\n", + " return np.sum((y-X @ theta)**2)\n", + "\n", + "n = 1000\n", + "x = np.random.rand(n,1)\n", + "y = 2.0+3*x +4*x*x\n", + "\n", + "X = np.c_[np.ones((n,1)), x, x*x]\n", + "XT_X = X.T @ X\n", + "theta_linreg = np.linalg.pinv(XT_X) @ (X.T @ y)\n", + "print(\"Own inversion\")\n", + "print(theta_linreg)\n", + "\n", + "\n", + "# Note that we request the derivative wrt third argument (theta, 2 here)\n", + "training_gradient = grad(CostOLS,2)\n", + "# Define parameters for Stochastic Gradient Descent\n", + "n_epochs = 50\n", + "M = 5 #size of each minibatch\n", + "m = int(n/M) #number of minibatches\n", + "# Guess for unknown parameters theta\n", + "theta = np.random.randn(3,1)\n", + "\n", + "# Value for learning rate\n", + "eta = 0.01\n", + "# Including AdaGrad parameter to avoid possible division by zero\n", + "delta = 1e-8\n", + "for epoch in range(n_epochs):\n", + " Giter = 0.0\n", + " for i in range(m):\n", + " random_index = M*np.random.randint(m)\n", + " xi = X[random_index:random_index+M]\n", + " yi = y[random_index:random_index+M]\n", + " gradients = (1.0/M)*training_gradient(yi, xi, theta)\n", + " Giter += gradients*gradients\n", + " update = gradients*eta/(delta+np.sqrt(Giter))\n", + " theta -= update\n", + "print(\"theta from own AdaGrad\")\n", + "print(theta)" + ] + }, + { + "cell_type": "markdown", + "id": "be4ce0cd", + "metadata": { + "editable": true + }, + "source": [ + "Running this code we note an almost perfect agreement with the results from matrix inversion." + ] + }, + { + "cell_type": "markdown", + "id": "0b739495", + "metadata": { + "editable": true + }, + "source": [ + "## RMSprop for adaptive learning rate with Stochastic Gradient Descent" + ] + }, + { + "cell_type": "code", + "execution_count": 34, + "id": "ae87789c", + "metadata": { + "collapsed": false, + "editable": true + }, + "outputs": [], + "source": [ + "# Using Autograd to calculate gradients using RMSprop and Stochastic Gradient descent\n", + "# OLS example\n", + "from random import random, seed\n", + "import numpy as np\n", + "import autograd.numpy as np\n", + "import matplotlib.pyplot as plt\n", + "from autograd import grad\n", + "\n", + "# Note change from previous example\n", + "def CostOLS(y,X,theta):\n", + " return np.sum((y-X @ theta)**2)\n", + "\n", + "n = 1000\n", + "x = np.random.rand(n,1)\n", + "y = 2.0+3*x +4*x*x# +np.random.randn(n,1)\n", + "\n", + "X = np.c_[np.ones((n,1)), x, x*x]\n", + "XT_X = X.T @ X\n", + "theta_linreg = np.linalg.pinv(XT_X) @ (X.T @ y)\n", + "print(\"Own inversion\")\n", + "print(theta_linreg)\n", + "\n", + "\n", + "# Note that we request the derivative wrt third argument (theta, 2 here)\n", + "training_gradient = grad(CostOLS,2)\n", + "# Define parameters for Stochastic Gradient Descent\n", + "n_epochs = 50\n", + "M = 5 #size of each minibatch\n", + "m = int(n/M) #number of minibatches\n", + "# Guess for unknown parameters theta\n", + "theta = np.random.randn(3,1)\n", + "\n", + "# Value for learning rate\n", + "eta = 0.01\n", + "# Value for parameter rho\n", + "rho = 0.99\n", + "# Including AdaGrad parameter to avoid possible division by zero\n", + "delta = 1e-8\n", + "for epoch in range(n_epochs):\n", + " Giter = 0.0\n", + " for i in range(m):\n", + " random_index = M*np.random.randint(m)\n", + " xi = X[random_index:random_index+M]\n", + " yi = y[random_index:random_index+M]\n", + " gradients = (1.0/M)*training_gradient(yi, xi, theta)\n", + "\t# Accumulated gradient\n", + "\t# Scaling with rho the new and the previous results\n", + " Giter = (rho*Giter+(1-rho)*gradients*gradients)\n", + "\t# Taking the diagonal only and inverting\n", + " update = gradients*eta/(delta+np.sqrt(Giter))\n", + "\t# Hadamard product\n", + " theta -= update\n", + "print(\"theta from own RMSprop\")\n", + "print(theta)" + ] + }, + { + "cell_type": "markdown", + "id": "76c8872b", + "metadata": { + "editable": true + }, + "source": [ + "## And finally [ADAM](https://arxiv.org/pdf/1412.6980.pdf)" + ] + }, + { + "cell_type": "code", + "execution_count": 35, + "id": "e99dbaa4", + "metadata": { + "collapsed": false, + "editable": true + }, + "outputs": [], + "source": [ + "# Using Autograd to calculate gradients using RMSprop and Stochastic Gradient descent\n", + "# OLS example\n", + "from random import random, seed\n", + "import numpy as np\n", + "import autograd.numpy as np\n", + "import matplotlib.pyplot as plt\n", + "from autograd import grad\n", + "\n", + "# Note change from previous example\n", + "def CostOLS(y,X,theta):\n", + " return np.sum((y-X @ theta)**2)\n", + "\n", + "n = 1000\n", + "x = np.random.rand(n,1)\n", + "y = 2.0+3*x +4*x*x# +np.random.randn(n,1)\n", + "\n", + "X = np.c_[np.ones((n,1)), x, x*x]\n", + "XT_X = X.T @ X\n", + "theta_linreg = np.linalg.pinv(XT_X) @ (X.T @ y)\n", + "print(\"Own inversion\")\n", + "print(theta_linreg)\n", + "\n", + "\n", + "# Note that we request the derivative wrt third argument (theta, 2 here)\n", + "training_gradient = grad(CostOLS,2)\n", + "# Define parameters for Stochastic Gradient Descent\n", + "n_epochs = 50\n", + "M = 5 #size of each minibatch\n", + "m = int(n/M) #number of minibatches\n", + "# Guess for unknown parameters theta\n", + "theta = np.random.randn(3,1)\n", + "\n", + "# Value for learning rate\n", + "eta = 0.01\n", + "# Value for parameters beta1 and beta2, see https://arxiv.org/abs/1412.6980\n", + "beta1 = 0.9\n", + "beta2 = 0.999\n", + "# Including AdaGrad parameter to avoid possible division by zero\n", + "delta = 1e-7\n", + "iter = 0\n", + "for epoch in range(n_epochs):\n", + " first_moment = 0.0\n", + " second_moment = 0.0\n", + " iter += 1\n", + " for i in range(m):\n", + " random_index = M*np.random.randint(m)\n", + " xi = X[random_index:random_index+M]\n", + " yi = y[random_index:random_index+M]\n", + " gradients = (1.0/M)*training_gradient(yi, xi, theta)\n", + " # Computing moments first\n", + " first_moment = beta1*first_moment + (1-beta1)*gradients\n", + " second_moment = beta2*second_moment+(1-beta2)*gradients*gradients\n", + " first_term = first_moment/(1.0-beta1**iter)\n", + " second_term = second_moment/(1.0-beta2**iter)\n", + "\t# Scaling with rho the new and the previous results\n", + " update = eta*first_term/(np.sqrt(second_term)+delta)\n", + " theta -= update\n", + "print(\"theta from own ADAM\")\n", + "print(theta)" + ] + }, + { + "cell_type": "markdown", + "id": "596df570", + "metadata": { + "editable": true + }, + "source": [ + "## And Logistic Regression" + ] + }, + { + "cell_type": "code", + "execution_count": 36, + "id": "6693f042", + "metadata": { + "collapsed": false, + "editable": true + }, + "outputs": [], + "source": [ + "import autograd.numpy as np\n", + "from autograd import grad\n", + "\n", + "def sigmoid(x):\n", + " return 0.5 * (np.tanh(x / 2.) + 1)\n", + "\n", + "def logistic_predictions(weights, inputs):\n", + " # Outputs probability of a label being true according to logistic model.\n", + " return sigmoid(np.dot(inputs, weights))\n", + "\n", + "def training_loss(weights):\n", + " # Training loss is the negative log-likelihood of the training labels.\n", + " preds = logistic_predictions(weights, inputs)\n", + " label_probabilities = preds * targets + (1 - preds) * (1 - targets)\n", + " return -np.sum(np.log(label_probabilities))\n", + "\n", + "# Build a toy dataset.\n", + "inputs = np.array([[0.52, 1.12, 0.77],\n", + " [0.88, -1.08, 0.15],\n", + " [0.52, 0.06, -1.30],\n", + " [0.74, -2.49, 1.39]])\n", + "targets = np.array([True, True, False, True])\n", + "\n", + "# Define a function that returns gradients of training loss using Autograd.\n", + "training_gradient_fun = grad(training_loss)\n", + "\n", + "# Optimize weights using gradient descent.\n", + "weights = np.array([0.0, 0.0, 0.0])\n", + "print(\"Initial loss:\", training_loss(weights))\n", + "for i in range(100):\n", + " weights -= training_gradient_fun(weights) * 0.01\n", + "\n", + "print(\"Trained loss:\", training_loss(weights))" + ] + }, + { + "cell_type": "markdown", + "id": "a40ed853", + "metadata": { + "editable": true + }, + "source": [ + "## Introducing [JAX](https://jax.readthedocs.io/en/latest/)\n", + "\n", + "Presently, instead of using **autograd**, we recommend using [JAX](https://jax.readthedocs.io/en/latest/)\n", + "\n", + "**JAX** is Autograd and [XLA (Accelerated Linear Algebra))](https://www.tensorflow.org/xla),\n", + "brought together for high-performance numerical computing and machine learning research.\n", + "It provides composable transformations of Python+NumPy programs: differentiate, vectorize, parallelize, Just-In-Time compile to GPU/TPU, and more.\n", + "\n", + "Here's a simple example on how you can use **JAX** to compute the derivate of the logistic function." + ] + }, + { + "cell_type": "code", + "execution_count": 37, + "id": "02f88360", + "metadata": { + "collapsed": false, + "editable": true + }, + "outputs": [], + "source": [ + "import jax.numpy as jnp\n", + "from jax import grad, jit, vmap\n", + "\n", + "def sum_logistic(x):\n", + " return jnp.sum(1.0 / (1.0 + jnp.exp(-x)))\n", + "\n", + "x_small = jnp.arange(3.)\n", + "derivative_fn = grad(sum_logistic)\n", + 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0;position:relative}.sd-tab-set>input{opacity:0;position:absolute}.sd-tab-set>input:checked+label{border-color:var(--sd-color-tabs-underline-active);color:var(--sd-color-tabs-label-active)}.sd-tab-set>input:checked+label+.sd-tab-content{display:block}.sd-tab-set>input:not(:checked)+label:hover{color:var(--sd-color-tabs-label-hover);border-color:var(--sd-color-tabs-underline-hover)}.sd-tab-set>input:focus+label{outline-style:auto}.sd-tab-set>input:not(.focus-visible)+label{outline:none;-webkit-tap-highlight-color:transparent}.sd-tab-set>label{border-bottom:.125rem solid transparent;margin-bottom:0;color:var(--sd-color-tabs-label-inactive);border-color:var(--sd-color-tabs-underline-inactive);cursor:pointer;font-size:var(--sd-fontsize-tabs-label);font-weight:700;padding:1em 1.25em .5em;transition:color 250ms;width:auto;z-index:1}html .sd-tab-set>label:hover{color:var(--sd-color-tabs-label-active)}.sd-col>.sd-tab-set{width:100%}.sd-tab-content{box-shadow:0 -0.0625rem 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rgba(178, 206, 245, 0.62);--sd-color-tabs-underline-inactive: transparent;--sd-color-tabs-overline: rgb(222, 222, 222);--sd-color-tabs-underline: rgb(222, 222, 222);--sd-fontsize-tabs-label: 1rem} diff --git a/doc/LectureNotes/_build/html/_sphinx_design_static/design-tabs.js b/doc/LectureNotes/_build/html/_sphinx_design_static/design-tabs.js new file mode 100644 index 000000000..36b38cf0d --- /dev/null +++ b/doc/LectureNotes/_build/html/_sphinx_design_static/design-tabs.js @@ -0,0 +1,27 @@ +var sd_labels_by_text = {}; + +function ready() { + const li = document.getElementsByClassName("sd-tab-label"); + for (const label of li) { + syncId = label.getAttribute("data-sync-id"); + if (syncId) { + label.onclick = onLabelClick; + if (!sd_labels_by_text[syncId]) { + sd_labels_by_text[syncId] = []; + } + sd_labels_by_text[syncId].push(label); + } + } +} + +function onLabelClick() { + // Activate other inputs with the same sync id. + syncId = this.getAttribute("data-sync-id"); + for (label of sd_labels_by_text[syncId]) { + if (label === this) continue; + label.previousElementSibling.checked = true; + } + window.localStorage.setItem("sphinx-design-last-tab", syncId); +} + +document.addEventListener("DOMContentLoaded", ready, false); diff --git a/doc/LectureNotes/_build/html/_static/_sphinx_javascript_frameworks_compat.js b/doc/LectureNotes/_build/html/_static/_sphinx_javascript_frameworks_compat.js new file mode 100644 index 000000000..8549469dc --- /dev/null +++ b/doc/LectureNotes/_build/html/_static/_sphinx_javascript_frameworks_compat.js @@ -0,0 +1,134 @@ +/* + * _sphinx_javascript_frameworks_compat.js + * ~~~~~~~~~~ + * + * Compatability shim for jQuery and underscores.js. + * + * WILL BE REMOVED IN Sphinx 6.0 + * xref RemovedInSphinx60Warning + * + */ + +/** + * select a different prefix for underscore + */ +$u = _.noConflict(); + + +/** + * small helper function to urldecode strings + * + * See https://developer.mozilla.org/en-US/docs/Web/JavaScript/Reference/Global_Objects/decodeURIComponent#Decoding_query_parameters_from_a_URL + */ +jQuery.urldecode = function(x) { + if (!x) { + return x + } + return decodeURIComponent(x.replace(/\+/g, ' ')); +}; + +/** + * small helper function to urlencode strings + */ +jQuery.urlencode = encodeURIComponent; + +/** + * This function returns the parsed url parameters of the + * current request. Multiple values per key are supported, + * it will always return arrays of strings for the value parts. + */ +jQuery.getQueryParameters = function(s) { + if (typeof s === 'undefined') + s = document.location.search; + var parts = s.substr(s.indexOf('?') + 1).split('&'); + var result = {}; + for (var i = 0; i < parts.length; i++) { + var tmp = parts[i].split('=', 2); + var key = jQuery.urldecode(tmp[0]); + var value = jQuery.urldecode(tmp[1]); + if (key in result) + result[key].push(value); + else + result[key] = [value]; + } + return result; +}; + +/** + * highlight a given string on a jquery object by wrapping it in + * span elements with the given class name. + */ +jQuery.fn.highlightText = function(text, className) { + function highlight(node, addItems) { + if (node.nodeType === 3) { + var val = node.nodeValue; + var pos = val.toLowerCase().indexOf(text); + if (pos >= 0 && + !jQuery(node.parentNode).hasClass(className) && + !jQuery(node.parentNode).hasClass("nohighlight")) { + var span; + var isInSVG = jQuery(node).closest("body, svg, foreignObject").is("svg"); + if (isInSVG) { + span = document.createElementNS("http://www.w3.org/2000/svg", "tspan"); + } else { + span = document.createElement("span"); + span.className = className; + } + span.appendChild(document.createTextNode(val.substr(pos, text.length))); + node.parentNode.insertBefore(span, node.parentNode.insertBefore( + document.createTextNode(val.substr(pos + text.length)), + node.nextSibling)); + node.nodeValue = val.substr(0, pos); + if (isInSVG) { + var rect = document.createElementNS("http://www.w3.org/2000/svg", "rect"); + var bbox = node.parentElement.getBBox(); + rect.x.baseVal.value = bbox.x; + rect.y.baseVal.value = bbox.y; + rect.width.baseVal.value = bbox.width; + rect.height.baseVal.value = bbox.height; + rect.setAttribute('class', className); + addItems.push({ + "parent": node.parentNode, + "target": rect}); + } + } + } + else if (!jQuery(node).is("button, select, textarea")) { + jQuery.each(node.childNodes, function() { + highlight(this, addItems); + }); + } + } + var addItems = []; + var result = this.each(function() { + highlight(this, addItems); + }); + for (var i = 0; i < addItems.length; ++i) { + jQuery(addItems[i].parent).before(addItems[i].target); + } + return result; +}; + +/* + * backward compatibility for jQuery.browser + * This will be supported until firefox bug is fixed. + */ +if (!jQuery.browser) { + jQuery.uaMatch = function(ua) { + ua = ua.toLowerCase(); + + var match = /(chrome)[ \/]([\w.]+)/.exec(ua) || + /(webkit)[ \/]([\w.]+)/.exec(ua) || + /(opera)(?:.*version|)[ \/]([\w.]+)/.exec(ua) || + /(msie) ([\w.]+)/.exec(ua) || + ua.indexOf("compatible") < 0 && /(mozilla)(?:.*? rv:([\w.]+)|)/.exec(ua) || + []; + + return { + browser: match[ 1 ] || "", + version: match[ 2 ] || "0" + }; + }; + jQuery.browser = {}; + jQuery.browser[jQuery.uaMatch(navigator.userAgent).browser] = true; +} diff --git a/doc/LectureNotes/_build/html/_static/basic.css b/doc/LectureNotes/_build/html/_static/basic.css index d54be8067..9e364ed34 100644 --- a/doc/LectureNotes/_build/html/_static/basic.css +++ b/doc/LectureNotes/_build/html/_static/basic.css @@ -222,7 +222,7 @@ table.modindextable td { /* -- general body styles --------------------------------------------------- */ div.body { - min-width: 450px; + min-width: 360px; max-width: 800px; } @@ -237,16 +237,6 @@ a.headerlink { visibility: hidden; } -a.brackets:before, -span.brackets > a:before{ - content: "["; -} - -a.brackets:after, -span.brackets > a:after { - content: "]"; -} - h1:hover > a.headerlink, h2:hover > a.headerlink, h3:hover > a.headerlink, @@ -334,12 +324,16 @@ aside.sidebar { p.sidebar-title { font-weight: bold; } +nav.contents, +aside.topic, div.admonition, div.topic, blockquote { clear: left; } /* -- topics ---------------------------------------------------------------- */ +nav.contents, +aside.topic, div.topic { border: 1px solid #ccc; @@ -379,6 +373,9 @@ div.body p.centered { div.sidebar > :last-child, aside.sidebar > :last-child, +nav.contents > :last-child, +aside.topic > :last-child, + div.topic > :last-child, div.admonition > :last-child { margin-bottom: 0; @@ -386,6 +383,9 @@ div.admonition > :last-child { div.sidebar::after, aside.sidebar::after, +nav.contents::after, +aside.topic::after, + div.topic::after, div.admonition::after, blockquote::after { @@ -428,10 +428,6 @@ table.docutils td, table.docutils th { border-bottom: 1px solid #aaa; } -table.footnote td, table.footnote th { - border: 0 !important; -} - th { text-align: left; padding-right: 5px; @@ -615,6 +611,7 @@ ul.simple p { margin-bottom: 0; } +/* Docutils 0.17 and older (footnotes & citations) */ dl.footnote > dt, dl.citation > dt { float: left; @@ -632,6 +629,33 @@ dl.citation > dd:after { clear: both; } +/* Docutils 0.18+ (footnotes & citations) */ +aside.footnote > span, +div.citation > span { + float: left; +} +aside.footnote > span:last-of-type, +div.citation > span:last-of-type { + padding-right: 0.5em; +} +aside.footnote > p { + margin-left: 2em; +} +div.citation > p { + margin-left: 4em; +} +aside.footnote > p:last-of-type, +div.citation > p:last-of-type { + margin-bottom: 0em; +} +aside.footnote > p:last-of-type:after, +div.citation > p:last-of-type:after { + content: ""; + clear: both; +} + +/* Footnotes & citations ends */ + dl.field-list { display: grid; grid-template-columns: fit-content(30%) auto; diff --git a/doc/LectureNotes/_build/html/_static/copybutton.css b/doc/LectureNotes/_build/html/_static/copybutton.css index 40eafe5fc..f1916ec7d 100644 --- a/doc/LectureNotes/_build/html/_static/copybutton.css +++ b/doc/LectureNotes/_build/html/_static/copybutton.css @@ -35,7 +35,8 @@ div.highlight { position: relative; } -.highlight:hover button.copybtn { +/* Show the copybutton */ +.highlight:hover button.copybtn, button.copybtn.success { opacity: 1; } diff --git a/doc/LectureNotes/_build/html/_static/copybutton.js b/doc/LectureNotes/_build/html/_static/copybutton.js index 40ac33108..2ea7ff3e2 100644 --- a/doc/LectureNotes/_build/html/_static/copybutton.js +++ b/doc/LectureNotes/_build/html/_static/copybutton.js @@ -20,7 +20,7 @@ const messages = { }, 'fr' : { 'copy': 'Copier', - 'copy_to_clipboard': 'Copié dans le presse-papier', + 'copy_to_clipboard': 'Copier dans le presse-papier', 'copy_success': 'Copié !', 'copy_failure': 'Échec de la copie', }, @@ -102,18 +102,25 @@ const clearSelection = () => { } } -// Changes tooltip text for two seconds, then changes it back +// Changes tooltip text for a moment, then changes it back +// We want the timeout of our `success` class to be a bit shorter than the +// tooltip and icon change, so that we can hide the icon before changing back. +var timeoutIcon = 2000; +var timeoutSuccessClass = 1500; + const temporarilyChangeTooltip = (el, oldText, newText) => { el.setAttribute('data-tooltip', newText) el.classList.add('success') - setTimeout(() => el.setAttribute('data-tooltip', oldText), 2000) - setTimeout(() => el.classList.remove('success'), 2000) + // Remove success a little bit sooner than we change the tooltip + // So that we can use CSS to hide the copybutton first + setTimeout(() => el.classList.remove('success'), timeoutSuccessClass) + setTimeout(() => el.setAttribute('data-tooltip', oldText), timeoutIcon) } // Changes the copy button icon for two seconds, then changes it back const temporarilyChangeIcon = (el) => { el.innerHTML = iconCheck; - setTimeout(() => {el.innerHTML = iconCopy}, 2000) + setTimeout(() => {el.innerHTML = iconCopy}, timeoutIcon) } const addCopyButtonToCodeCells = () => { @@ -125,7 +132,8 @@ const addCopyButtonToCodeCells = () => { } // Add copybuttons to all of our code cells - const codeCells = document.querySelectorAll('div.highlight pre') + const COPYBUTTON_SELECTOR = 'div.highlight pre'; + const codeCells = document.querySelectorAll(COPYBUTTON_SELECTOR) codeCells.forEach((codeCell, index) => { const id = codeCellId(index) codeCell.setAttribute('id', id) @@ -141,10 +149,25 @@ function escapeRegExp(string) { return string.replace(/[.*+?^${}()|[\]\\]/g, '\\$&'); // $& means the whole matched string } +/** + * Removes excluded text from a Node. + * + * @param {Node} target Node to filter. + * @param {string} exclude CSS selector of nodes to exclude. + * @returns {DOMString} Text from `target` with text removed. + */ +function filterText(target, exclude) { + const clone = target.cloneNode(true); // clone as to not modify the live DOM + if (exclude) { + // remove excluded nodes + clone.querySelectorAll(exclude).forEach(node => node.remove()); + } + return clone.innerText; +} + // Callback when a copy button is clicked. Will be passed the node that was clicked // should then grab the text and replace pieces of text that shouldn't be used in output function formatCopyText(textContent, copybuttonPromptText, isRegexp = false, onlyCopyPromptLines = true, removePrompts = true, copyEmptyLines = true, lineContinuationChar = "", hereDocDelim = "") { - var regexp; var match; @@ -199,7 +222,12 @@ function formatCopyText(textContent, copybuttonPromptText, isRegexp = false, onl var copyTargetText = (trigger) => { var target = document.querySelector(trigger.attributes['data-clipboard-target'].value); - return formatCopyText(target.innerText, '', false, true, true, true, '', '') + + // get filtered text + let exclude = '.linenos'; + + let text = filterText(target, exclude); + return formatCopyText(text, '', false, true, true, true, '', '') } // Initialize with a callback so we can modify the text before copy diff --git a/doc/LectureNotes/_build/html/_static/copybutton_funcs.js b/doc/LectureNotes/_build/html/_static/copybutton_funcs.js index b9168c556..dbe1aaad7 100644 --- a/doc/LectureNotes/_build/html/_static/copybutton_funcs.js +++ b/doc/LectureNotes/_build/html/_static/copybutton_funcs.js @@ -2,10 +2,25 @@ function escapeRegExp(string) { return string.replace(/[.*+?^${}()|[\]\\]/g, '\\$&'); // $& means the whole matched string } +/** + * Removes excluded text from a Node. + * + * @param {Node} target Node to filter. + * @param {string} exclude CSS selector of nodes to exclude. + * @returns {DOMString} Text from `target` with text removed. + */ +export function filterText(target, exclude) { + const clone = target.cloneNode(true); // clone as to not modify the live DOM + if (exclude) { + // remove excluded nodes + clone.querySelectorAll(exclude).forEach(node => node.remove()); + } + return clone.innerText; +} + // Callback when a copy button is clicked. Will be passed the node that was clicked // should then grab the text and replace pieces of text that shouldn't be used in output export function formatCopyText(textContent, copybuttonPromptText, isRegexp = false, onlyCopyPromptLines = true, removePrompts = true, copyEmptyLines = true, lineContinuationChar = "", hereDocDelim = "") { - var regexp; var match; diff --git a/doc/LectureNotes/_build/html/_static/design-style.4045f2051d55cab465a707391d5b2007.min.css b/doc/LectureNotes/_build/html/_static/design-style.4045f2051d55cab465a707391d5b2007.min.css new file mode 100644 index 000000000..3225661c2 --- /dev/null +++ b/doc/LectureNotes/_build/html/_static/design-style.4045f2051d55cab465a707391d5b2007.min.css @@ -0,0 +1 @@ +.sd-bg-primary{background-color:var(--sd-color-primary) !important}.sd-bg-text-primary{color:var(--sd-color-primary-text) !important}button.sd-bg-primary:focus,button.sd-bg-primary:hover{background-color:var(--sd-color-primary-highlight) 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rgba(178, 206, 245, 0.62);--sd-color-tabs-underline-inactive: transparent;--sd-color-tabs-overline: rgb(222, 222, 222);--sd-color-tabs-underline: rgb(222, 222, 222);--sd-fontsize-tabs-label: 1rem} diff --git a/doc/LectureNotes/_build/html/_static/design-tabs.js b/doc/LectureNotes/_build/html/_static/design-tabs.js new file mode 100644 index 000000000..36b38cf0d --- /dev/null +++ b/doc/LectureNotes/_build/html/_static/design-tabs.js @@ -0,0 +1,27 @@ +var sd_labels_by_text = {}; + +function ready() { + const li = document.getElementsByClassName("sd-tab-label"); + for (const label of li) { + syncId = label.getAttribute("data-sync-id"); + if (syncId) { + label.onclick = onLabelClick; + if (!sd_labels_by_text[syncId]) { + sd_labels_by_text[syncId] = []; + } + sd_labels_by_text[syncId].push(label); + } + } +} + +function onLabelClick() { + // Activate other inputs with the same sync id. + syncId = this.getAttribute("data-sync-id"); + for (label of sd_labels_by_text[syncId]) { + if (label === this) continue; + label.previousElementSibling.checked = true; + } + window.localStorage.setItem("sphinx-design-last-tab", syncId); +} + +document.addEventListener("DOMContentLoaded", ready, false); diff --git a/doc/LectureNotes/_build/html/_static/doctools.js b/doc/LectureNotes/_build/html/_static/doctools.js index e509e4834..c3db08d1c 100644 --- a/doc/LectureNotes/_build/html/_static/doctools.js +++ b/doc/LectureNotes/_build/html/_static/doctools.js @@ -2,325 +2,263 @@ * doctools.js * ~~~~~~~~~~~ * - * Sphinx JavaScript utilities for all documentation. + * Base JavaScript utilities for all Sphinx HTML documentation. * * :copyright: Copyright 2007-2022 by the Sphinx team, see AUTHORS. * :license: BSD, see LICENSE for details. * */ +"use strict"; -/** - * select a different prefix for underscore - */ -$u = _.noConflict(); - -/** - * make the code below compatible with browsers without - * an installed firebug like debugger -if (!window.console || !console.firebug) { - var names = ["log", "debug", "info", "warn", "error", "assert", "dir", - "dirxml", "group", "groupEnd", "time", "timeEnd", "count", "trace", - "profile", "profileEnd"]; - window.console = {}; - for (var i = 0; i < names.length; ++i) - window.console[names[i]] = function() {}; -} - */ - -/** - * small helper function to urldecode strings - * - * See https://developer.mozilla.org/en-US/docs/Web/JavaScript/Reference/Global_Objects/decodeURIComponent#Decoding_query_parameters_from_a_URL - */ -jQuery.urldecode = function(x) { - if (!x) { - return x +const _ready = (callback) => { + if (document.readyState !== "loading") { + callback(); + } else { + document.addEventListener("DOMContentLoaded", callback); } - return decodeURIComponent(x.replace(/\+/g, ' ')); }; /** - * small helper function to urlencode strings - */ -jQuery.urlencode = encodeURIComponent; - -/** - * This function returns the parsed url parameters of the - * current request. Multiple values per key are supported, - * it will always return arrays of strings for the value parts. - */ -jQuery.getQueryParameters = function(s) { - if (typeof s === 'undefined') - s = document.location.search; - var parts = s.substr(s.indexOf('?') + 1).split('&'); - var result = {}; - for (var i = 0; i < parts.length; i++) { - var tmp = parts[i].split('=', 2); - var key = jQuery.urldecode(tmp[0]); - var value = jQuery.urldecode(tmp[1]); - if (key in result) - result[key].push(value); - else - result[key] = [value]; - } - return result; -}; - -/** - * highlight a given string on a jquery object by wrapping it in + * highlight a given string on a node by wrapping it in * span elements with the given class name. */ -jQuery.fn.highlightText = function(text, className) { - function highlight(node, addItems) { - if (node.nodeType === 3) { - var val = node.nodeValue; - var pos = val.toLowerCase().indexOf(text); - if (pos >= 0 && - !jQuery(node.parentNode).hasClass(className) && - !jQuery(node.parentNode).hasClass("nohighlight")) { - var span; - var isInSVG = jQuery(node).closest("body, svg, foreignObject").is("svg"); - if (isInSVG) { - span = document.createElementNS("http://www.w3.org/2000/svg", "tspan"); - } else { - span = document.createElement("span"); - span.className = className; - } - span.appendChild(document.createTextNode(val.substr(pos, text.length))); - node.parentNode.insertBefore(span, node.parentNode.insertBefore( +const _highlight = (node, addItems, text, className) => { + if (node.nodeType === Node.TEXT_NODE) { + const val = node.nodeValue; + const parent = node.parentNode; + const pos = val.toLowerCase().indexOf(text); + if ( + pos >= 0 && + !parent.classList.contains(className) && + !parent.classList.contains("nohighlight") + ) { + let span; + + const closestNode = parent.closest("body, svg, foreignObject"); + const isInSVG = closestNode && closestNode.matches("svg"); + if (isInSVG) { + span = document.createElementNS("http://www.w3.org/2000/svg", "tspan"); + } else { + span = document.createElement("span"); + span.classList.add(className); + } + + span.appendChild(document.createTextNode(val.substr(pos, text.length))); + parent.insertBefore( + span, + parent.insertBefore( document.createTextNode(val.substr(pos + text.length)), - node.nextSibling)); - node.nodeValue = val.substr(0, pos); - if (isInSVG) { - var rect = document.createElementNS("http://www.w3.org/2000/svg", "rect"); - var bbox = node.parentElement.getBBox(); - rect.x.baseVal.value = bbox.x; - rect.y.baseVal.value = bbox.y; - rect.width.baseVal.value = bbox.width; - rect.height.baseVal.value = bbox.height; - rect.setAttribute('class', className); - addItems.push({ - "parent": node.parentNode, - "target": rect}); - } + node.nextSibling + ) + ); + node.nodeValue = val.substr(0, pos); + + if (isInSVG) { + const rect = document.createElementNS( + "http://www.w3.org/2000/svg", + "rect" + ); + const bbox = parent.getBBox(); + rect.x.baseVal.value = bbox.x; + rect.y.baseVal.value = bbox.y; + rect.width.baseVal.value = bbox.width; + rect.height.baseVal.value = bbox.height; + rect.setAttribute("class", className); + addItems.push({ parent: parent, target: rect }); } } - else if (!jQuery(node).is("button, select, textarea")) { - jQuery.each(node.childNodes, function() { - highlight(this, addItems); - }); - } + } else if (node.matches && !node.matches("button, select, textarea")) { + node.childNodes.forEach((el) => _highlight(el, addItems, text, className)); } - var addItems = []; - var result = this.each(function() { - highlight(this, addItems); - }); - for (var i = 0; i < addItems.length; ++i) { - jQuery(addItems[i].parent).before(addItems[i].target); - } - return result; }; - -/* - * backward compatibility for jQuery.browser - * This will be supported until firefox bug is fixed. - */ -if (!jQuery.browser) { - jQuery.uaMatch = function(ua) { - ua = ua.toLowerCase(); - - var match = /(chrome)[ \/]([\w.]+)/.exec(ua) || - /(webkit)[ \/]([\w.]+)/.exec(ua) || - /(opera)(?:.*version|)[ \/]([\w.]+)/.exec(ua) || - /(msie) ([\w.]+)/.exec(ua) || - ua.indexOf("compatible") < 0 && /(mozilla)(?:.*? rv:([\w.]+)|)/.exec(ua) || - []; - - return { - browser: match[ 1 ] || "", - version: match[ 2 ] || "0" - }; - }; - jQuery.browser = {}; - jQuery.browser[jQuery.uaMatch(navigator.userAgent).browser] = true; -} +const _highlightText = (thisNode, text, className) => { + let addItems = []; + _highlight(thisNode, addItems, text, className); + addItems.forEach((obj) => + obj.parent.insertAdjacentElement("beforebegin", obj.target) + ); +}; /** * Small JavaScript module for the documentation. */ -var Documentation = { - - init : function() { - this.fixFirefoxAnchorBug(); - this.highlightSearchWords(); - this.initIndexTable(); - if (DOCUMENTATION_OPTIONS.NAVIGATION_WITH_KEYS) { - this.initOnKeyListeners(); - } +const Documentation = { + init: () => { + Documentation.highlightSearchWords(); + Documentation.initDomainIndexTable(); + Documentation.initOnKeyListeners(); }, /** * i18n support */ - TRANSLATIONS : {}, - PLURAL_EXPR : function(n) { return n === 1 ? 0 : 1; }, - LOCALE : 'unknown', + TRANSLATIONS: {}, + PLURAL_EXPR: (n) => (n === 1 ? 0 : 1), + LOCALE: "unknown", // gettext and ngettext don't access this so that the functions // can safely bound to a different name (_ = Documentation.gettext) - gettext : function(string) { - var translated = Documentation.TRANSLATIONS[string]; - if (typeof translated === 'undefined') - return string; - return (typeof translated === 'string') ? translated : translated[0]; + gettext: (string) => { + const translated = Documentation.TRANSLATIONS[string]; + switch (typeof translated) { + case "undefined": + return string; // no translation + case "string": + return translated; // translation exists + default: + return translated[0]; // (singular, plural) translation tuple exists + } }, - ngettext : function(singular, plural, n) { - var translated = Documentation.TRANSLATIONS[singular]; - if (typeof translated === 'undefined') - return (n == 1) ? singular : plural; - return translated[Documentation.PLURALEXPR(n)]; + ngettext: (singular, plural, n) => { + const translated = Documentation.TRANSLATIONS[singular]; + if (typeof translated !== "undefined") + return translated[Documentation.PLURAL_EXPR(n)]; + return n === 1 ? singular : plural; }, - addTranslations : function(catalog) { - for (var key in catalog.messages) - this.TRANSLATIONS[key] = catalog.messages[key]; - this.PLURAL_EXPR = new Function('n', 'return +(' + catalog.plural_expr + ')'); - this.LOCALE = catalog.locale; - }, - - /** - * add context elements like header anchor links - */ - addContextElements : function() { - $('div[id] > :header:first').each(function() { - $('\u00B6'). - attr('href', '#' + this.id). - attr('title', _('Permalink to this headline')). - appendTo(this); - }); - $('dt[id]').each(function() { - $('\u00B6'). - attr('href', '#' + this.id). - attr('title', _('Permalink to this definition')). - appendTo(this); - }); - }, - - /** - * workaround a firefox stupidity - * see: https://bugzilla.mozilla.org/show_bug.cgi?id=645075 - */ - fixFirefoxAnchorBug : function() { - if (document.location.hash && $.browser.mozilla) - window.setTimeout(function() { - document.location.href += ''; - }, 10); + addTranslations: (catalog) => { + Object.assign(Documentation.TRANSLATIONS, catalog.messages); + Documentation.PLURAL_EXPR = new Function( + "n", + `return (${catalog.plural_expr})` + ); + Documentation.LOCALE = catalog.locale; }, /** * highlight the search words provided in the url in the text */ - highlightSearchWords : function() { - var params = $.getQueryParameters(); - var terms = (params.highlight) ? params.highlight[0].split(/\s+/) : []; - if (terms.length) { - var body = $('div.body'); - if (!body.length) { - body = $('body'); - } - window.setTimeout(function() { - $.each(terms, function() { - body.highlightText(this.toLowerCase(), 'highlighted'); - }); - }, 10); - $('

' + _('Hide Search Matches') + '

') - .appendTo($('#searchbox')); - } - }, + highlightSearchWords: () => { + const highlight = + new URLSearchParams(window.location.search).get("highlight") || ""; + const terms = highlight.toLowerCase().split(/\s+/).filter(x => x); + if (terms.length === 0) return; // nothing to do - /** - * init the domain index toggle buttons - */ - initIndexTable : function() { - var togglers = $('img.toggler').click(function() { - var src = $(this).attr('src'); - var idnum = $(this).attr('id').substr(7); - $('tr.cg-' + idnum).toggle(); - if (src.substr(-9) === 'minus.png') - $(this).attr('src', src.substr(0, src.length-9) + 'plus.png'); - else - $(this).attr('src', src.substr(0, src.length-8) + 'minus.png'); - }).css('display', ''); - if (DOCUMENTATION_OPTIONS.COLLAPSE_INDEX) { - togglers.click(); - } + // There should never be more than one element matching "div.body" + const divBody = document.querySelectorAll("div.body"); + const body = divBody.length ? divBody[0] : document.querySelector("body"); + window.setTimeout(() => { + terms.forEach((term) => _highlightText(body, term, "highlighted")); + }, 10); + + const searchBox = document.getElementById("searchbox"); + if (searchBox === null) return; + searchBox.appendChild( + document + .createRange() + .createContextualFragment( + '

' + + '' + + Documentation.gettext("Hide Search Matches") + + "

" + ) + ); }, /** * helper function to hide the search marks again */ - hideSearchWords : function() { - $('#searchbox .highlight-link').fadeOut(300); - $('span.highlighted').removeClass('highlighted'); - var url = new URL(window.location); - url.searchParams.delete('highlight'); - window.history.replaceState({}, '', url); + hideSearchWords: () => { + document + .querySelectorAll("#searchbox .highlight-link") + .forEach((el) => el.remove()); + document + .querySelectorAll("span.highlighted") + .forEach((el) => el.classList.remove("highlighted")); + const url = new URL(window.location); + url.searchParams.delete("highlight"); + window.history.replaceState({}, "", url); }, /** - * make the url absolute + * helper function to focus on search bar */ - makeURL : function(relativeURL) { - return DOCUMENTATION_OPTIONS.URL_ROOT + '/' + relativeURL; + focusSearchBar: () => { + document.querySelectorAll("input[name=q]")[0]?.focus(); }, /** - * get the current relative url + * Initialise the domain index toggle buttons */ - getCurrentURL : function() { - var path = document.location.pathname; - var parts = path.split(/\//); - $.each(DOCUMENTATION_OPTIONS.URL_ROOT.split(/\//), function() { - if (this === '..') - parts.pop(); - }); - var url = parts.join('/'); - return path.substring(url.lastIndexOf('/') + 1, path.length - 1); + initDomainIndexTable: () => { + const toggler = (el) => { + const idNumber = el.id.substr(7); + const toggledRows = document.querySelectorAll(`tr.cg-${idNumber}`); + if (el.src.substr(-9) === "minus.png") { + el.src = `${el.src.substr(0, el.src.length - 9)}plus.png`; + toggledRows.forEach((el) => (el.style.display = "none")); + } else { + el.src = `${el.src.substr(0, el.src.length - 8)}minus.png`; + toggledRows.forEach((el) => (el.style.display = "")); + } + }; + + const togglerElements = document.querySelectorAll("img.toggler"); + togglerElements.forEach((el) => + el.addEventListener("click", (event) => toggler(event.currentTarget)) + ); + togglerElements.forEach((el) => (el.style.display = "")); + if (DOCUMENTATION_OPTIONS.COLLAPSE_INDEX) togglerElements.forEach(toggler); }, - initOnKeyListeners: function() { - $(document).keydown(function(event) { - var activeElementType = document.activeElement.tagName; - // don't navigate when in search box, textarea, dropdown or button - if (activeElementType !== 'TEXTAREA' && activeElementType !== 'INPUT' && activeElementType !== 'SELECT' - && activeElementType !== 'BUTTON' && !event.altKey && !event.ctrlKey && !event.metaKey - && !event.shiftKey) { - switch (event.keyCode) { - case 37: // left - var prevHref = $('link[rel="prev"]').prop('href'); - if (prevHref) { - window.location.href = prevHref; - return false; + initOnKeyListeners: () => { + // only install a listener if it is really needed + if ( + !DOCUMENTATION_OPTIONS.NAVIGATION_WITH_KEYS && + !DOCUMENTATION_OPTIONS.ENABLE_SEARCH_SHORTCUTS + ) + return; + + const blacklistedElements = new Set([ + "TEXTAREA", + "INPUT", + "SELECT", + "BUTTON", + ]); + document.addEventListener("keydown", (event) => { + if (blacklistedElements.has(document.activeElement.tagName)) return; // bail for input elements + if (event.altKey || event.ctrlKey || event.metaKey) return; // bail with special keys + + if (!event.shiftKey) { + switch (event.key) { + case "ArrowLeft": + if (!DOCUMENTATION_OPTIONS.NAVIGATION_WITH_KEYS) break; + + const prevLink = document.querySelector('link[rel="prev"]'); + if (prevLink && prevLink.href) { + window.location.href = prevLink.href; + event.preventDefault(); } break; - case 39: // right - var nextHref = $('link[rel="next"]').prop('href'); - if (nextHref) { - window.location.href = nextHref; - return false; + case "ArrowRight": + if (!DOCUMENTATION_OPTIONS.NAVIGATION_WITH_KEYS) break; + + const nextLink = document.querySelector('link[rel="next"]'); + if (nextLink && nextLink.href) { + window.location.href = nextLink.href; + event.preventDefault(); } break; + case "Escape": + if (!DOCUMENTATION_OPTIONS.ENABLE_SEARCH_SHORTCUTS) break; + Documentation.hideSearchWords(); + event.preventDefault(); } } + + // some keyboard layouts may need Shift to get / + switch (event.key) { + case "/": + if (!DOCUMENTATION_OPTIONS.ENABLE_SEARCH_SHORTCUTS) break; + Documentation.focusSearchBar(); + event.preventDefault(); + } }); - } + }, }; // quick alias for translations -_ = Documentation.gettext; +const _ = Documentation.gettext; -$(document).ready(function() { - Documentation.init(); -}); +_ready(Documentation.init); diff --git a/doc/LectureNotes/_build/html/_static/documentation_options.js b/doc/LectureNotes/_build/html/_static/documentation_options.js index 93b7c24d6..30637825d 100644 --- a/doc/LectureNotes/_build/html/_static/documentation_options.js +++ b/doc/LectureNotes/_build/html/_static/documentation_options.js @@ -1,12 +1,14 @@ var DOCUMENTATION_OPTIONS = { URL_ROOT: document.getElementById("documentation_options").getAttribute('data-url_root'), VERSION: '', - LANGUAGE: 'None', + LANGUAGE: 'en', COLLAPSE_INDEX: false, BUILDER: 'html', FILE_SUFFIX: '.html', LINK_SUFFIX: '.html', HAS_SOURCE: true, SOURCELINK_SUFFIX: '', - NAVIGATION_WITH_KEYS: true + NAVIGATION_WITH_KEYS: true, + SHOW_SEARCH_SUMMARY: true, + ENABLE_SEARCH_SHORTCUTS: false, }; \ No newline at end of file diff --git a/doc/LectureNotes/_build/html/_static/images/logo_deepnote.svg b/doc/LectureNotes/_build/html/_static/images/logo_deepnote.svg new file mode 100644 index 000000000..fa77ebfc2 --- /dev/null +++ b/doc/LectureNotes/_build/html/_static/images/logo_deepnote.svg @@ -0,0 +1 @@ + diff --git a/doc/LectureNotes/_build/html/_static/jquery-3.6.0.js b/doc/LectureNotes/_build/html/_static/jquery-3.6.0.js new file mode 100644 index 000000000..fc6c299b7 --- /dev/null +++ b/doc/LectureNotes/_build/html/_static/jquery-3.6.0.js @@ -0,0 +1,10881 @@ +/*! + * jQuery JavaScript Library v3.6.0 + * https://jquery.com/ + * + * Includes Sizzle.js + * https://sizzlejs.com/ + * + * Copyright OpenJS Foundation and other contributors + * Released under the MIT license + * https://jquery.org/license + * + * Date: 2021-03-02T17:08Z + */ +( function( global, factory ) { + + "use strict"; + + if ( typeof module === "object" && typeof module.exports === "object" ) { + + // For CommonJS and CommonJS-like environments where a proper `window` + // is present, execute the factory and get jQuery. + // For environments that do not have a `window` with a `document` + // (such as Node.js), expose a factory as module.exports. + // This accentuates the need for the creation of a real `window`. + // e.g. var jQuery = require("jquery")(window); + // See ticket #14549 for more info. + module.exports = global.document ? + factory( global, true ) : + function( w ) { + if ( !w.document ) { + throw new Error( "jQuery requires a window with a document" ); + } + return factory( w ); + }; + } else { + factory( global ); + } + +// Pass this if window is not defined yet +} )( typeof window !== "undefined" ? window : this, function( window, noGlobal ) { + +// Edge <= 12 - 13+, Firefox <=18 - 45+, IE 10 - 11, Safari 5.1 - 9+, iOS 6 - 9.1 +// throw exceptions when non-strict code (e.g., ASP.NET 4.5) accesses strict mode +// arguments.callee.caller (trac-13335). But as of jQuery 3.0 (2016), strict mode should be common +// enough that all such attempts are guarded in a try block. +"use strict"; + +var arr = []; + +var getProto = Object.getPrototypeOf; + +var slice = arr.slice; + +var flat = arr.flat ? function( array ) { + return arr.flat.call( array ); +} : function( array ) { + return arr.concat.apply( [], array ); +}; + + +var push = arr.push; + +var indexOf = arr.indexOf; + +var class2type = {}; + +var toString = class2type.toString; + +var hasOwn = class2type.hasOwnProperty; + +var fnToString = hasOwn.toString; + +var ObjectFunctionString = fnToString.call( Object ); + +var support = {}; + +var isFunction = function isFunction( obj ) { + + // Support: Chrome <=57, Firefox <=52 + // In some browsers, typeof returns "function" for HTML elements + // (i.e., `typeof document.createElement( "object" ) === "function"`). + // We don't want to classify *any* DOM node as a function. + // Support: QtWeb <=3.8.5, WebKit <=534.34, wkhtmltopdf tool <=0.12.5 + // Plus for old WebKit, typeof returns "function" for HTML collections + // (e.g., `typeof document.getElementsByTagName("div") === "function"`). (gh-4756) + return typeof obj === "function" && typeof obj.nodeType !== "number" && + typeof obj.item !== "function"; + }; + + +var isWindow = function isWindow( obj ) { + return obj != null && obj === obj.window; + }; + + +var document = window.document; + + + + var preservedScriptAttributes = { + type: true, + src: true, + nonce: true, + noModule: true + }; + + function DOMEval( code, node, doc ) { + doc = doc || document; + + var i, val, + script = doc.createElement( "script" ); + + script.text = code; + if ( node ) { + for ( i in preservedScriptAttributes ) { + + // Support: Firefox 64+, Edge 18+ + // Some browsers don't support the "nonce" property on scripts. + // On the other hand, just using `getAttribute` is not enough as + // the `nonce` attribute is reset to an empty string whenever it + // becomes browsing-context connected. + // See https://github.com/whatwg/html/issues/2369 + // See https://html.spec.whatwg.org/#nonce-attributes + // The `node.getAttribute` check was added for the sake of + // `jQuery.globalEval` so that it can fake a nonce-containing node + // via an object. + val = node[ i ] || node.getAttribute && node.getAttribute( i ); + if ( val ) { + script.setAttribute( i, val ); + } + } + } + doc.head.appendChild( script ).parentNode.removeChild( script ); + } + + +function toType( obj ) { + if ( obj == null ) { + return obj + ""; + } + + // Support: Android <=2.3 only (functionish RegExp) + return typeof obj === "object" || typeof obj === "function" ? + class2type[ toString.call( obj ) ] || "object" : + typeof obj; +} +/* global Symbol */ +// Defining this global in .eslintrc.json would create a danger of using the global +// unguarded in another place, it seems safer to define global only for this module + + + +var + version = "3.6.0", + + // Define a local copy of jQuery + jQuery = function( selector, context ) { + + // The jQuery object is actually just the init constructor 'enhanced' + // Need init if jQuery is called (just allow error to be thrown if not included) + return new jQuery.fn.init( selector, context ); + }; + +jQuery.fn = jQuery.prototype = { + + // The current version of jQuery being used + jquery: version, + + constructor: jQuery, + + // The default length of a jQuery object is 0 + length: 0, + + toArray: function() { + return slice.call( this ); + }, + + // Get the Nth element in the matched element set OR + // Get the whole matched element set as a clean array + get: function( num ) { + + // Return all the elements in a clean array + if ( num == null ) { + return slice.call( this ); + } + + // Return just the one element from the set + return num < 0 ? this[ num + this.length ] : this[ num ]; + }, + + // Take an array of elements and push it onto the stack + // (returning the new matched element set) + pushStack: function( elems ) { + + // Build a new jQuery matched element set + var ret = jQuery.merge( this.constructor(), elems ); + + // Add the old object onto the stack (as a reference) + ret.prevObject = this; + + // Return the newly-formed element set + return ret; + }, + + // Execute a callback for every element in the matched set. + each: function( callback ) { + return jQuery.each( this, callback ); + }, + + map: function( callback ) { + return this.pushStack( jQuery.map( this, function( elem, i ) { + return callback.call( elem, i, elem ); + } ) ); + }, + + slice: function() { + return this.pushStack( slice.apply( this, arguments ) ); + }, + + first: function() { + return this.eq( 0 ); + }, + + last: function() { + return this.eq( -1 ); + }, + + even: function() { + return this.pushStack( jQuery.grep( this, function( _elem, i ) { + return ( i + 1 ) % 2; + } ) ); + }, + + odd: function() { + return this.pushStack( jQuery.grep( this, function( _elem, i ) { + return i % 2; + } ) ); + }, + + eq: function( i ) { + var len = this.length, + j = +i + ( i < 0 ? len : 0 ); + return this.pushStack( j >= 0 && j < len ? [ this[ j ] ] : [] ); + }, + + end: function() { + return this.prevObject || this.constructor(); + }, + + // For internal use only. + // Behaves like an Array's method, not like a jQuery method. + push: push, + sort: arr.sort, + splice: arr.splice +}; + +jQuery.extend = jQuery.fn.extend = function() { + var options, name, src, copy, copyIsArray, clone, + target = arguments[ 0 ] || {}, + i = 1, + length = arguments.length, + deep = false; + + // Handle a deep copy situation + if ( typeof target === "boolean" ) { + deep = target; + + // Skip the boolean and the target + target = arguments[ i ] || {}; + i++; + } + + // Handle case when target is a string or something (possible in deep copy) + if ( typeof target !== "object" && !isFunction( target ) ) { + target = {}; + } + + // Extend jQuery itself if only one argument is passed + if ( i === length ) { + target = this; + i--; + } + + for ( ; i < length; i++ ) { + + // Only deal with non-null/undefined values + if ( ( options = arguments[ i ] ) != null ) { + + // Extend the base object + for ( name in options ) { + copy = options[ name ]; + + // Prevent Object.prototype pollution + // Prevent never-ending loop + if ( name === "__proto__" || target === copy ) { + continue; + } + + // Recurse if we're merging plain objects or arrays + if ( deep && copy && ( jQuery.isPlainObject( copy ) || + ( copyIsArray = Array.isArray( copy ) ) ) ) { + src = target[ name ]; + + // Ensure proper type for the source value + if ( copyIsArray && !Array.isArray( src ) ) { + clone = []; + } else if ( !copyIsArray && !jQuery.isPlainObject( src ) ) { + clone = {}; + } else { + clone = src; + } + copyIsArray = false; + + // Never move original objects, clone them + target[ name ] = jQuery.extend( deep, clone, copy ); + + // Don't bring in undefined values + } else if ( copy !== undefined ) { + target[ name ] = copy; + } + } + } + } + + // Return the modified object + return target; +}; + +jQuery.extend( { + + // Unique for each copy of jQuery on the page + expando: "jQuery" + ( version + Math.random() ).replace( /\D/g, "" ), + + // Assume jQuery is ready without the ready module + isReady: true, + + error: function( msg ) { + throw new Error( msg ); + }, + + noop: function() {}, + + isPlainObject: function( obj ) { + var proto, Ctor; + + // Detect obvious negatives + // Use toString instead of jQuery.type to catch host objects + if ( !obj || toString.call( obj ) !== "[object Object]" ) { + return false; + } + + proto = getProto( obj ); + + // Objects with no prototype (e.g., `Object.create( null )`) are plain + if ( !proto ) { + return true; + } + + // Objects with prototype are plain iff they were constructed by a global Object function + Ctor = hasOwn.call( proto, "constructor" ) && proto.constructor; + return typeof Ctor === "function" && fnToString.call( Ctor ) === ObjectFunctionString; + }, + + isEmptyObject: function( obj ) { + var name; + + for ( name in obj ) { + return false; + } + return true; + }, + + // Evaluates a script in a provided context; falls back to the global one + // if not specified. + globalEval: function( code, options, doc ) { + DOMEval( code, { nonce: options && options.nonce }, doc ); + }, + + each: function( obj, callback ) { + var length, i = 0; + + if ( isArrayLike( obj ) ) { + length = obj.length; + for ( ; i < length; i++ ) { + if ( callback.call( obj[ i ], i, obj[ i ] ) === false ) { + break; + } + } + } else { + for ( i in obj ) { + if ( callback.call( obj[ i ], i, obj[ i ] ) === false ) { + break; + } + } + } + + return obj; + }, + + // results is for internal usage only + makeArray: function( arr, results ) { + var ret = results || []; + + if ( arr != null ) { + if ( isArrayLike( Object( arr ) ) ) { + jQuery.merge( ret, + typeof arr === "string" ? + [ arr ] : arr + ); + } else { + push.call( ret, arr ); + } + } + + return ret; + }, + + inArray: function( elem, arr, i ) { + return arr == null ? -1 : indexOf.call( arr, elem, i ); + }, + + // Support: Android <=4.0 only, PhantomJS 1 only + // push.apply(_, arraylike) throws on ancient WebKit + merge: function( first, second ) { + var len = +second.length, + j = 0, + i = first.length; + + for ( ; j < len; j++ ) { + first[ i++ ] = second[ j ]; + } + + first.length = i; + + return first; + }, + + grep: function( elems, callback, invert ) { + var callbackInverse, + matches = [], + i = 0, + length = elems.length, + callbackExpect = !invert; + + // Go through the array, only saving the items + // that pass the validator function + for ( ; i < length; i++ ) { + callbackInverse = !callback( elems[ i ], i ); + if ( callbackInverse !== callbackExpect ) { + matches.push( elems[ i ] ); + } + } + + return matches; + }, + + // arg is for internal usage only + map: function( elems, callback, arg ) { + var length, value, + i = 0, + ret = []; + + // Go through the array, translating each of the items to their new values + if ( isArrayLike( elems ) ) { + length = elems.length; + for ( ; i < length; i++ ) { + value = callback( elems[ i ], i, arg ); + + if ( value != null ) { + ret.push( value ); + } + } + + // Go through every key on the object, + } else { + for ( i in elems ) { + value = callback( elems[ i ], i, arg ); + + if ( value != null ) { + ret.push( value ); + } + } + } + + // Flatten any nested arrays + return flat( ret ); + }, + + // A global GUID counter for objects + guid: 1, + + // jQuery.support is not used in Core but other projects attach their + // properties to it so it needs to exist. + support: support +} ); + +if ( typeof Symbol === "function" ) { + jQuery.fn[ Symbol.iterator ] = arr[ Symbol.iterator ]; +} + +// Populate the class2type map +jQuery.each( "Boolean Number String Function Array Date RegExp Object Error Symbol".split( " " ), + function( _i, name ) { + class2type[ "[object " + name + "]" ] = name.toLowerCase(); + } ); + +function isArrayLike( obj ) { + + // Support: real iOS 8.2 only (not reproducible in simulator) + // `in` check used to prevent JIT error (gh-2145) + // hasOwn isn't used here due to false negatives + // regarding Nodelist length in IE + var length = !!obj && "length" in obj && obj.length, + type = toType( obj ); + + if ( isFunction( obj ) || isWindow( obj ) ) { + return false; + } + + return type === "array" || length === 0 || + typeof length === "number" && length > 0 && ( length - 1 ) in obj; +} +var Sizzle = +/*! + * Sizzle CSS Selector Engine v2.3.6 + * https://sizzlejs.com/ + * + * Copyright JS Foundation and other contributors + * Released under the MIT license + * https://js.foundation/ + * + * Date: 2021-02-16 + */ +( function( window ) { +var i, + support, + Expr, + getText, + isXML, + tokenize, + compile, + select, + outermostContext, + sortInput, + hasDuplicate, + + // Local document vars + setDocument, + document, + docElem, + documentIsHTML, + rbuggyQSA, + rbuggyMatches, + matches, + contains, + + // Instance-specific data + expando = "sizzle" + 1 * new Date(), + preferredDoc = window.document, + dirruns = 0, + done = 0, + classCache = createCache(), + tokenCache = createCache(), + compilerCache = createCache(), + nonnativeSelectorCache = createCache(), + sortOrder = function( a, b ) { + if ( a === b ) { + hasDuplicate = true; + } + return 0; + }, + + // Instance methods + hasOwn = ( {} ).hasOwnProperty, + arr = [], + pop = arr.pop, + pushNative = arr.push, + push = arr.push, + slice = arr.slice, + + // Use a stripped-down indexOf as it's faster than native + // https://jsperf.com/thor-indexof-vs-for/5 + indexOf = function( list, elem ) { + var i = 0, + len = list.length; + for ( ; i < len; i++ ) { + if ( list[ i ] === elem ) { + return i; + } + } + return -1; + }, + + booleans = "checked|selected|async|autofocus|autoplay|controls|defer|disabled|hidden|" + + "ismap|loop|multiple|open|readonly|required|scoped", + + // Regular expressions + + // http://www.w3.org/TR/css3-selectors/#whitespace + whitespace = "[\\x20\\t\\r\\n\\f]", + + // https://www.w3.org/TR/css-syntax-3/#ident-token-diagram + identifier = "(?:\\\\[\\da-fA-F]{1,6}" + whitespace + + "?|\\\\[^\\r\\n\\f]|[\\w-]|[^\0-\\x7f])+", + + // Attribute selectors: http://www.w3.org/TR/selectors/#attribute-selectors + attributes = "\\[" + whitespace + "*(" + identifier + ")(?:" + whitespace + + + // Operator (capture 2) + "*([*^$|!~]?=)" + whitespace + + + // "Attribute values must be CSS identifiers [capture 5] + // or strings [capture 3 or capture 4]" + "*(?:'((?:\\\\.|[^\\\\'])*)'|\"((?:\\\\.|[^\\\\\"])*)\"|(" + identifier + "))|)" + + whitespace + "*\\]", + + pseudos = ":(" + identifier + ")(?:\\((" + + + // To reduce the number of selectors needing tokenize in the preFilter, prefer arguments: + // 1. quoted (capture 3; capture 4 or capture 5) + "('((?:\\\\.|[^\\\\'])*)'|\"((?:\\\\.|[^\\\\\"])*)\")|" + + + // 2. simple (capture 6) + "((?:\\\\.|[^\\\\()[\\]]|" + attributes + ")*)|" + + + // 3. anything else (capture 2) + ".*" + + ")\\)|)", + + // Leading and non-escaped trailing whitespace, capturing some non-whitespace characters preceding the latter + rwhitespace = new RegExp( whitespace + "+", "g" ), + rtrim = new RegExp( "^" + whitespace + "+|((?:^|[^\\\\])(?:\\\\.)*)" + + whitespace + "+$", "g" ), + + rcomma = new RegExp( "^" + whitespace + "*," + whitespace + "*" ), + rcombinators = new RegExp( "^" + whitespace + "*([>+~]|" + whitespace + ")" + whitespace + + "*" ), + rdescend = new RegExp( whitespace + "|>" ), + + rpseudo = new RegExp( pseudos ), + ridentifier = new RegExp( "^" + identifier + "$" ), + + matchExpr = { + "ID": new RegExp( "^#(" + identifier + ")" ), + "CLASS": new RegExp( "^\\.(" + identifier + ")" ), + "TAG": new RegExp( "^(" + identifier + "|[*])" ), + "ATTR": new RegExp( "^" + attributes ), + "PSEUDO": new RegExp( "^" + pseudos ), + "CHILD": new RegExp( "^:(only|first|last|nth|nth-last)-(child|of-type)(?:\\(" + + whitespace + "*(even|odd|(([+-]|)(\\d*)n|)" + whitespace + "*(?:([+-]|)" + + whitespace + "*(\\d+)|))" + whitespace + "*\\)|)", "i" ), + "bool": new RegExp( "^(?:" + booleans + ")$", "i" ), + + // For use in libraries implementing .is() + // We use this for POS matching in `select` + "needsContext": new RegExp( "^" + whitespace + + "*[>+~]|:(even|odd|eq|gt|lt|nth|first|last)(?:\\(" + whitespace + + "*((?:-\\d)?\\d*)" + whitespace + "*\\)|)(?=[^-]|$)", "i" ) + }, + + rhtml = /HTML$/i, + rinputs = /^(?:input|select|textarea|button)$/i, + rheader = /^h\d$/i, + + rnative = /^[^{]+\{\s*\[native \w/, + + // Easily-parseable/retrievable ID or TAG or CLASS selectors + rquickExpr = /^(?:#([\w-]+)|(\w+)|\.([\w-]+))$/, + + rsibling = /[+~]/, + + // CSS escapes + // http://www.w3.org/TR/CSS21/syndata.html#escaped-characters + runescape = new RegExp( "\\\\[\\da-fA-F]{1,6}" + whitespace + "?|\\\\([^\\r\\n\\f])", "g" ), + funescape = function( escape, nonHex ) { + var high = "0x" + escape.slice( 1 ) - 0x10000; + + return nonHex ? + + // Strip the backslash prefix from a non-hex escape sequence + nonHex : + + // Replace a hexadecimal escape sequence with the encoded Unicode code point + // Support: IE <=11+ + // For values outside the Basic Multilingual Plane (BMP), manually construct a + // surrogate pair + high < 0 ? + String.fromCharCode( high + 0x10000 ) : + String.fromCharCode( high >> 10 | 0xD800, high & 0x3FF | 0xDC00 ); + }, + + // CSS string/identifier serialization + // https://drafts.csswg.org/cssom/#common-serializing-idioms + rcssescape = /([\0-\x1f\x7f]|^-?\d)|^-$|[^\0-\x1f\x7f-\uFFFF\w-]/g, + fcssescape = function( ch, asCodePoint ) { + if ( asCodePoint ) { + + // U+0000 NULL becomes U+FFFD REPLACEMENT CHARACTER + if ( ch === "\0" ) { + return "\uFFFD"; + } + + // Control characters and (dependent upon position) numbers get escaped as code points + return ch.slice( 0, -1 ) + "\\" + + ch.charCodeAt( ch.length - 1 ).toString( 16 ) + " "; + } + + // Other potentially-special ASCII characters get backslash-escaped + return "\\" + ch; + }, + + // Used for iframes + // See setDocument() + // Removing the function wrapper causes a "Permission Denied" + // error in IE + unloadHandler = function() { + setDocument(); + }, + + inDisabledFieldset = addCombinator( + function( elem ) { + return elem.disabled === true && elem.nodeName.toLowerCase() === "fieldset"; + }, + { dir: "parentNode", next: "legend" } + ); + +// Optimize for push.apply( _, NodeList ) +try { + push.apply( + ( arr = slice.call( preferredDoc.childNodes ) ), + preferredDoc.childNodes + ); + + // Support: Android<4.0 + // Detect silently failing push.apply + // eslint-disable-next-line no-unused-expressions + arr[ preferredDoc.childNodes.length ].nodeType; +} catch ( e ) { + push = { apply: arr.length ? + + // Leverage slice if possible + function( target, els ) { + pushNative.apply( target, slice.call( els ) ); + } : + + // Support: IE<9 + // Otherwise append directly + function( target, els ) { + var j = target.length, + i = 0; + + // Can't trust NodeList.length + while ( ( target[ j++ ] = els[ i++ ] ) ) {} + target.length = j - 1; + } + }; +} + +function Sizzle( selector, context, results, seed ) { + var m, i, elem, nid, match, groups, newSelector, + newContext = context && context.ownerDocument, + + // nodeType defaults to 9, since context defaults to document + nodeType = context ? context.nodeType : 9; + + results = results || []; + + // Return early from calls with invalid selector or context + if ( typeof selector !== "string" || !selector || + nodeType !== 1 && nodeType !== 9 && nodeType !== 11 ) { + + return results; + } + + // Try to shortcut find operations (as opposed to filters) in HTML documents + if ( !seed ) { + setDocument( context ); + context = context || document; + + if ( documentIsHTML ) { + + // If the selector is sufficiently simple, try using a "get*By*" DOM method + // (excepting DocumentFragment context, where the methods don't exist) + if ( nodeType !== 11 && ( match = rquickExpr.exec( selector ) ) ) { + + // ID selector + if ( ( m = match[ 1 ] ) ) { + + // Document context + if ( nodeType === 9 ) { + if ( ( elem = context.getElementById( m ) ) ) { + + // Support: IE, Opera, Webkit + // TODO: identify versions + // getElementById can match elements by name instead of ID + if ( elem.id === m ) { + results.push( elem ); + return results; + } + } else { + return results; + } + + // Element context + } else { + + // Support: IE, Opera, Webkit + // TODO: identify versions + // getElementById can match elements by name instead of ID + if ( newContext && ( elem = newContext.getElementById( m ) ) && + contains( context, elem ) && + elem.id === m ) { + + results.push( elem ); + return results; + } + } + + // Type selector + } else if ( match[ 2 ] ) { + push.apply( results, context.getElementsByTagName( selector ) ); + return results; + + // Class selector + } else if ( ( m = match[ 3 ] ) && support.getElementsByClassName && + context.getElementsByClassName ) { + + push.apply( results, context.getElementsByClassName( m ) ); + return results; + } + } + + // Take advantage of querySelectorAll + if ( support.qsa && + !nonnativeSelectorCache[ selector + " " ] && + ( !rbuggyQSA || !rbuggyQSA.test( selector ) ) && + + // Support: IE 8 only + // Exclude object elements + ( nodeType !== 1 || context.nodeName.toLowerCase() !== "object" ) ) { + + newSelector = selector; + newContext = context; + + // qSA considers elements outside a scoping root when evaluating child or + // descendant combinators, which is not what we want. + // In such cases, we work around the behavior by prefixing every selector in the + // list with an ID selector referencing the scope context. + // The technique has to be used as well when a leading combinator is used + // as such selectors are not recognized by querySelectorAll. + // Thanks to Andrew Dupont for this technique. + if ( nodeType === 1 && + ( rdescend.test( selector ) || rcombinators.test( selector ) ) ) { + + // Expand context for sibling selectors + newContext = rsibling.test( selector ) && testContext( context.parentNode ) || + context; + + // We can use :scope instead of the ID hack if the browser + // supports it & if we're not changing the context. + if ( newContext !== context || !support.scope ) { + + // Capture the context ID, setting it first if necessary + if ( ( nid = context.getAttribute( "id" ) ) ) { + nid = nid.replace( rcssescape, fcssescape ); + } else { + context.setAttribute( "id", ( nid = expando ) ); + } + } + + // Prefix every selector in the list + groups = tokenize( selector ); + i = groups.length; + while ( i-- ) { + groups[ i ] = ( nid ? "#" + nid : ":scope" ) + " " + + toSelector( groups[ i ] ); + } + newSelector = groups.join( "," ); + } + + try { + push.apply( results, + newContext.querySelectorAll( newSelector ) + ); + return results; + } catch ( qsaError ) { + nonnativeSelectorCache( selector, true ); + } finally { + if ( nid === expando ) { + context.removeAttribute( "id" ); + } + } + } + } + } + + // All others + return select( selector.replace( rtrim, "$1" ), context, results, seed ); +} + +/** + * Create key-value caches of limited size + * @returns {function(string, object)} Returns the Object data after storing it on itself with + * property name the (space-suffixed) string and (if the cache is larger than Expr.cacheLength) + * deleting the oldest entry + */ +function createCache() { + var keys = []; + + function cache( key, value ) { + + // Use (key + " ") to avoid collision with native prototype properties (see Issue #157) + if ( keys.push( key + " " ) > Expr.cacheLength ) { + + // Only keep the most recent entries + delete cache[ keys.shift() ]; + } + return ( cache[ key + " " ] = value ); + } + return cache; +} + +/** + * Mark a function for special use by Sizzle + * @param {Function} fn The function to mark + */ +function markFunction( fn ) { + fn[ expando ] = true; + return fn; +} + +/** + * Support testing using an element + * @param {Function} fn Passed the created element and returns a boolean result + */ +function assert( fn ) { + var el = document.createElement( "fieldset" ); + + try { + return !!fn( el ); + } catch ( e ) { + return false; + } finally { + + // Remove from its parent by default + if ( el.parentNode ) { + el.parentNode.removeChild( el ); + } + + // release memory in IE + el = null; + } +} + +/** + * Adds the same handler for all of the specified attrs + * @param {String} attrs Pipe-separated list of attributes + * @param {Function} handler The method that will be applied + */ +function addHandle( attrs, handler ) { + var arr = attrs.split( "|" ), + i = arr.length; + + while ( i-- ) { + Expr.attrHandle[ arr[ i ] ] = handler; + } +} + +/** + * Checks document order of two siblings + * @param {Element} a + * @param {Element} b + * @returns {Number} Returns less than 0 if a precedes b, greater than 0 if a follows b + */ +function siblingCheck( a, b ) { + var cur = b && a, + diff = cur && a.nodeType === 1 && b.nodeType === 1 && + a.sourceIndex - b.sourceIndex; + + // Use IE sourceIndex if available on both nodes + if ( diff ) { + return diff; + } + + // Check if b follows a + if ( cur ) { + while ( ( cur = cur.nextSibling ) ) { + if ( cur === b ) { + return -1; + } + } + } + + return a ? 1 : -1; +} + +/** + * Returns a function to use in pseudos for input types + * @param {String} type + */ +function createInputPseudo( type ) { + return function( elem ) { + var name = elem.nodeName.toLowerCase(); + return name === "input" && elem.type === type; + }; +} + +/** + * Returns a function to use in pseudos for buttons + * @param {String} type + */ +function createButtonPseudo( type ) { + return function( elem ) { + var name = elem.nodeName.toLowerCase(); + return ( name === "input" || name === "button" ) && elem.type === type; + }; +} + +/** + * Returns a function to use in pseudos for :enabled/:disabled + * @param {Boolean} disabled true for :disabled; false for :enabled + */ +function createDisabledPseudo( disabled ) { + + // Known :disabled false positives: fieldset[disabled] > legend:nth-of-type(n+2) :can-disable + return function( elem ) { + + // Only certain elements can match :enabled or :disabled + // https://html.spec.whatwg.org/multipage/scripting.html#selector-enabled + // https://html.spec.whatwg.org/multipage/scripting.html#selector-disabled + if ( "form" in elem ) { + + // Check for inherited disabledness on relevant non-disabled elements: + // * listed form-associated elements in a disabled fieldset + // https://html.spec.whatwg.org/multipage/forms.html#category-listed + // https://html.spec.whatwg.org/multipage/forms.html#concept-fe-disabled + // * option elements in a disabled optgroup + // https://html.spec.whatwg.org/multipage/forms.html#concept-option-disabled + // All such elements have a "form" property. + if ( elem.parentNode && elem.disabled === false ) { + + // Option elements defer to a parent optgroup if present + if ( "label" in elem ) { + if ( "label" in elem.parentNode ) { + return elem.parentNode.disabled === disabled; + } else { + return elem.disabled === disabled; + } + } + + // Support: IE 6 - 11 + // Use the isDisabled shortcut property to check for disabled fieldset ancestors + return elem.isDisabled === disabled || + + // Where there is no isDisabled, check manually + /* jshint -W018 */ + elem.isDisabled !== !disabled && + inDisabledFieldset( elem ) === disabled; + } + + return elem.disabled === disabled; + + // Try to winnow out elements that can't be disabled before trusting the disabled property. + // Some victims get caught in our net (label, legend, menu, track), but it shouldn't + // even exist on them, let alone have a boolean value. + } else if ( "label" in elem ) { + return elem.disabled === disabled; + } + + // Remaining elements are neither :enabled nor :disabled + return false; + }; +} + +/** + * Returns a function to use in pseudos for positionals + * @param {Function} fn + */ +function createPositionalPseudo( fn ) { + return markFunction( function( argument ) { + argument = +argument; + return markFunction( function( seed, matches ) { + var j, + matchIndexes = fn( [], seed.length, argument ), + i = matchIndexes.length; + + // Match elements found at the specified indexes + while ( i-- ) { + if ( seed[ ( j = matchIndexes[ i ] ) ] ) { + seed[ j ] = !( matches[ j ] = seed[ j ] ); + } + } + } ); + } ); +} + +/** + * Checks a node for validity as a Sizzle context + * @param {Element|Object=} context + * @returns {Element|Object|Boolean} The input node if acceptable, otherwise a falsy value + */ +function testContext( context ) { + return context && typeof context.getElementsByTagName !== "undefined" && context; +} + +// Expose support vars for convenience +support = Sizzle.support = {}; + +/** + * Detects XML nodes + * @param {Element|Object} elem An element or a document + * @returns {Boolean} True iff elem is a non-HTML XML node + */ +isXML = Sizzle.isXML = function( elem ) { + var namespace = elem && elem.namespaceURI, + docElem = elem && ( elem.ownerDocument || elem ).documentElement; + + // Support: IE <=8 + // Assume HTML when documentElement doesn't yet exist, such as inside loading iframes + // https://bugs.jquery.com/ticket/4833 + return !rhtml.test( namespace || docElem && docElem.nodeName || "HTML" ); +}; + +/** + * Sets document-related variables once based on the current document + * @param {Element|Object} [doc] An element or document object to use to set the document + * @returns {Object} Returns the current document + */ +setDocument = Sizzle.setDocument = function( node ) { + var hasCompare, subWindow, + doc = node ? node.ownerDocument || node : preferredDoc; + + // Return early if doc is invalid or already selected + // Support: IE 11+, Edge 17 - 18+ + // IE/Edge sometimes throw a "Permission denied" error when strict-comparing + // two documents; shallow comparisons work. + // eslint-disable-next-line eqeqeq + if ( doc == document || doc.nodeType !== 9 || !doc.documentElement ) { + return document; + } + + // Update global variables + document = doc; + docElem = document.documentElement; + documentIsHTML = !isXML( document ); + + // Support: IE 9 - 11+, Edge 12 - 18+ + // Accessing iframe documents after unload throws "permission denied" errors (jQuery #13936) + // Support: IE 11+, Edge 17 - 18+ + // IE/Edge sometimes throw a "Permission denied" error when strict-comparing + // two documents; shallow comparisons work. + // eslint-disable-next-line eqeqeq + if ( preferredDoc != document && + ( subWindow = document.defaultView ) && subWindow.top !== subWindow ) { + + // Support: IE 11, Edge + if ( subWindow.addEventListener ) { + subWindow.addEventListener( "unload", unloadHandler, false ); + + // Support: IE 9 - 10 only + } else if ( subWindow.attachEvent ) { + subWindow.attachEvent( "onunload", unloadHandler ); + } + } + + // Support: IE 8 - 11+, Edge 12 - 18+, Chrome <=16 - 25 only, Firefox <=3.6 - 31 only, + // Safari 4 - 5 only, Opera <=11.6 - 12.x only + // IE/Edge & older browsers don't support the :scope pseudo-class. + // Support: Safari 6.0 only + // Safari 6.0 supports :scope but it's an alias of :root there. + support.scope = assert( function( el ) { + docElem.appendChild( el ).appendChild( document.createElement( "div" ) ); + return typeof el.querySelectorAll !== "undefined" && + !el.querySelectorAll( ":scope fieldset div" ).length; + } ); + + /* Attributes + ---------------------------------------------------------------------- */ + + // Support: IE<8 + // Verify that getAttribute really returns attributes and not properties + // (excepting IE8 booleans) + support.attributes = assert( function( el ) { + el.className = "i"; + return !el.getAttribute( "className" ); + } ); + + /* getElement(s)By* + ---------------------------------------------------------------------- */ + + // Check if getElementsByTagName("*") returns only elements + support.getElementsByTagName = assert( function( el ) { + el.appendChild( document.createComment( "" ) ); + return !el.getElementsByTagName( "*" ).length; + } ); + + // Support: IE<9 + support.getElementsByClassName = rnative.test( document.getElementsByClassName ); + + // Support: IE<10 + // Check if getElementById returns elements by name + // The broken getElementById methods don't pick up programmatically-set names, + // so use a roundabout getElementsByName test + support.getById = assert( function( el ) { + docElem.appendChild( el ).id = expando; + return !document.getElementsByName || !document.getElementsByName( expando ).length; + } ); + + // ID filter and find + if ( support.getById ) { + Expr.filter[ "ID" ] = function( id ) { + var attrId = id.replace( runescape, funescape ); + return function( elem ) { + return elem.getAttribute( "id" ) === attrId; + }; + }; + Expr.find[ "ID" ] = function( id, context ) { + if ( typeof context.getElementById !== "undefined" && documentIsHTML ) { + var elem = context.getElementById( id ); + return elem ? [ elem ] : []; + } + }; + } else { + Expr.filter[ "ID" ] = function( id ) { + var attrId = id.replace( runescape, funescape ); + return function( elem ) { + var node = typeof elem.getAttributeNode !== "undefined" && + elem.getAttributeNode( "id" ); + return node && node.value === attrId; + }; + }; + + // Support: IE 6 - 7 only + // getElementById is not reliable as a find shortcut + Expr.find[ "ID" ] = function( id, context ) { + if ( typeof context.getElementById !== "undefined" && documentIsHTML ) { + var node, i, elems, + elem = context.getElementById( id ); + + if ( elem ) { + + // Verify the id attribute + node = elem.getAttributeNode( "id" ); + if ( node && node.value === id ) { + return [ elem ]; + } + + // Fall back on getElementsByName + elems = context.getElementsByName( id ); + i = 0; + while ( ( elem = elems[ i++ ] ) ) { + node = elem.getAttributeNode( "id" ); + if ( node && node.value === id ) { + return [ elem ]; + } + } + } + + return []; + } + }; + } + + // Tag + Expr.find[ "TAG" ] = support.getElementsByTagName ? + function( tag, context ) { + if ( typeof context.getElementsByTagName !== "undefined" ) { + return context.getElementsByTagName( tag ); + + // DocumentFragment nodes don't have gEBTN + } else if ( support.qsa ) { + return context.querySelectorAll( tag ); + } + } : + + function( tag, context ) { + var elem, + tmp = [], + i = 0, + + // By happy coincidence, a (broken) gEBTN appears on DocumentFragment nodes too + results = context.getElementsByTagName( tag ); + + // Filter out possible comments + if ( tag === "*" ) { + while ( ( elem = results[ i++ ] ) ) { + if ( elem.nodeType === 1 ) { + tmp.push( elem ); + } + } + + return tmp; + } + return results; + }; + + // Class + Expr.find[ "CLASS" ] = support.getElementsByClassName && function( className, context ) { + if ( typeof context.getElementsByClassName !== "undefined" && documentIsHTML ) { + return context.getElementsByClassName( className ); + } + }; + + /* QSA/matchesSelector + ---------------------------------------------------------------------- */ + + // QSA and matchesSelector support + + // matchesSelector(:active) reports false when true (IE9/Opera 11.5) + rbuggyMatches = []; + + // qSa(:focus) reports false when true (Chrome 21) + // We allow this because of a bug in IE8/9 that throws an error + // whenever `document.activeElement` is accessed on an iframe + // So, we allow :focus to pass through QSA all the time to avoid the IE error + // See https://bugs.jquery.com/ticket/13378 + rbuggyQSA = []; + + if ( ( support.qsa = rnative.test( document.querySelectorAll ) ) ) { + + // Build QSA regex + // Regex strategy adopted from Diego Perini + assert( function( el ) { + + var input; + + // Select is set to empty string on purpose + // This is to test IE's treatment of not explicitly + // setting a boolean content attribute, + // since its presence should be enough + // https://bugs.jquery.com/ticket/12359 + docElem.appendChild( el ).innerHTML = "" + + ""; + + // Support: IE8, Opera 11-12.16 + // Nothing should be selected when empty strings follow ^= or $= or *= + // The test attribute must be unknown in Opera but "safe" for WinRT + // https://msdn.microsoft.com/en-us/library/ie/hh465388.aspx#attribute_section + if ( el.querySelectorAll( "[msallowcapture^='']" ).length ) { + rbuggyQSA.push( "[*^$]=" + whitespace + "*(?:''|\"\")" ); + } + + // Support: IE8 + // Boolean attributes and "value" are not treated correctly + if ( !el.querySelectorAll( "[selected]" ).length ) { + rbuggyQSA.push( "\\[" + whitespace + "*(?:value|" + booleans + ")" ); + } + + // Support: Chrome<29, Android<4.4, Safari<7.0+, iOS<7.0+, PhantomJS<1.9.8+ + if ( !el.querySelectorAll( "[id~=" + expando + "-]" ).length ) { + rbuggyQSA.push( "~=" ); + } + + // Support: IE 11+, Edge 15 - 18+ + // IE 11/Edge don't find elements on a `[name='']` query in some cases. + // Adding a temporary attribute to the document before the selection works + // around the issue. + // Interestingly, IE 10 & older don't seem to have the issue. + input = document.createElement( "input" ); + input.setAttribute( "name", "" ); + el.appendChild( input ); + if ( !el.querySelectorAll( "[name='']" ).length ) { + rbuggyQSA.push( "\\[" + whitespace + "*name" + whitespace + "*=" + + whitespace + "*(?:''|\"\")" ); + } + + // Webkit/Opera - :checked should return selected option elements + // http://www.w3.org/TR/2011/REC-css3-selectors-20110929/#checked + // IE8 throws error here and will not see later tests + if ( !el.querySelectorAll( ":checked" ).length ) { + rbuggyQSA.push( ":checked" ); + } + + // Support: Safari 8+, iOS 8+ + // https://bugs.webkit.org/show_bug.cgi?id=136851 + // In-page `selector#id sibling-combinator selector` fails + if ( !el.querySelectorAll( "a#" + expando + "+*" ).length ) { + rbuggyQSA.push( ".#.+[+~]" ); + } + + // Support: Firefox <=3.6 - 5 only + // Old Firefox doesn't throw on a badly-escaped identifier. + el.querySelectorAll( "\\\f" ); + rbuggyQSA.push( "[\\r\\n\\f]" ); + } ); + + assert( function( el ) { + el.innerHTML = "" + + ""; + + // Support: Windows 8 Native Apps + // The type and name attributes are restricted during .innerHTML assignment + var input = document.createElement( "input" ); + input.setAttribute( "type", "hidden" ); + el.appendChild( input ).setAttribute( "name", "D" ); + + // Support: IE8 + // Enforce case-sensitivity of name attribute + if ( el.querySelectorAll( "[name=d]" ).length ) { + rbuggyQSA.push( "name" + whitespace + "*[*^$|!~]?=" ); + } + + // FF 3.5 - :enabled/:disabled and hidden elements (hidden elements are still enabled) + // IE8 throws error here and will not see later tests + if ( el.querySelectorAll( ":enabled" ).length !== 2 ) { + rbuggyQSA.push( ":enabled", ":disabled" ); + } + + // Support: IE9-11+ + // IE's :disabled selector does not pick up the children of disabled fieldsets + docElem.appendChild( el ).disabled = true; + if ( el.querySelectorAll( ":disabled" ).length !== 2 ) { + rbuggyQSA.push( ":enabled", ":disabled" ); + } + + // Support: Opera 10 - 11 only + // Opera 10-11 does not throw on post-comma invalid pseudos + el.querySelectorAll( "*,:x" ); + rbuggyQSA.push( ",.*:" ); + } ); + } + + if ( ( support.matchesSelector = rnative.test( ( matches = docElem.matches || + docElem.webkitMatchesSelector || + docElem.mozMatchesSelector || + docElem.oMatchesSelector || + docElem.msMatchesSelector ) ) ) ) { + + assert( function( el ) { + + // Check to see if it's possible to do matchesSelector + // on a disconnected node (IE 9) + support.disconnectedMatch = matches.call( el, "*" ); + + // This should fail with an exception + // Gecko does not error, returns false instead + matches.call( el, "[s!='']:x" ); + rbuggyMatches.push( "!=", pseudos ); + } ); + } + + rbuggyQSA = rbuggyQSA.length && new RegExp( rbuggyQSA.join( "|" ) ); + rbuggyMatches = rbuggyMatches.length && new RegExp( rbuggyMatches.join( "|" ) ); + + /* Contains + ---------------------------------------------------------------------- */ + hasCompare = rnative.test( docElem.compareDocumentPosition ); + + // Element contains another + // Purposefully self-exclusive + // As in, an element does not contain itself + contains = hasCompare || rnative.test( docElem.contains ) ? + function( a, b ) { + var adown = a.nodeType === 9 ? a.documentElement : a, + bup = b && b.parentNode; + return a === bup || !!( bup && bup.nodeType === 1 && ( + adown.contains ? + adown.contains( bup ) : + a.compareDocumentPosition && a.compareDocumentPosition( bup ) & 16 + ) ); + } : + function( a, b ) { + if ( b ) { + while ( ( b = b.parentNode ) ) { + if ( b === a ) { + return true; + } + } + } + return false; + }; + + /* Sorting + ---------------------------------------------------------------------- */ + + // Document order sorting + sortOrder = hasCompare ? + function( a, b ) { + + // Flag for duplicate removal + if ( a === b ) { + hasDuplicate = true; + return 0; + } + + // Sort on method existence if only one input has compareDocumentPosition + var compare = !a.compareDocumentPosition - !b.compareDocumentPosition; + if ( compare ) { + return compare; + } + + // Calculate position if both inputs belong to the same document + // Support: IE 11+, Edge 17 - 18+ + // IE/Edge sometimes throw a "Permission denied" error when strict-comparing + // two documents; shallow comparisons work. + // eslint-disable-next-line eqeqeq + compare = ( a.ownerDocument || a ) == ( b.ownerDocument || b ) ? + a.compareDocumentPosition( b ) : + + // Otherwise we know they are disconnected + 1; + + // Disconnected nodes + if ( compare & 1 || + ( !support.sortDetached && b.compareDocumentPosition( a ) === compare ) ) { + + // Choose the first element that is related to our preferred document + // Support: IE 11+, Edge 17 - 18+ + // IE/Edge sometimes throw a "Permission denied" error when strict-comparing + // two documents; shallow comparisons work. + // eslint-disable-next-line eqeqeq + if ( a == document || a.ownerDocument == preferredDoc && + contains( preferredDoc, a ) ) { + return -1; + } + + // Support: IE 11+, Edge 17 - 18+ + // IE/Edge sometimes throw a "Permission denied" error when strict-comparing + // two documents; shallow comparisons work. + // eslint-disable-next-line eqeqeq + if ( b == document || b.ownerDocument == preferredDoc && + contains( preferredDoc, b ) ) { + return 1; + } + + // Maintain original order + return sortInput ? + ( indexOf( sortInput, a ) - indexOf( sortInput, b ) ) : + 0; + } + + return compare & 4 ? -1 : 1; + } : + function( a, b ) { + + // Exit early if the nodes are identical + if ( a === b ) { + hasDuplicate = true; + return 0; + } + + var cur, + i = 0, + aup = a.parentNode, + bup = b.parentNode, + ap = [ a ], + bp = [ b ]; + + // Parentless nodes are either documents or disconnected + if ( !aup || !bup ) { + + // Support: IE 11+, Edge 17 - 18+ + // IE/Edge sometimes throw a "Permission denied" error when strict-comparing + // two documents; shallow comparisons work. + /* eslint-disable eqeqeq */ + return a == document ? -1 : + b == document ? 1 : + /* eslint-enable eqeqeq */ + aup ? -1 : + bup ? 1 : + sortInput ? + ( indexOf( sortInput, a ) - indexOf( sortInput, b ) ) : + 0; + + // If the nodes are siblings, we can do a quick check + } else if ( aup === bup ) { + return siblingCheck( a, b ); + } + + // Otherwise we need full lists of their ancestors for comparison + cur = a; + while ( ( cur = cur.parentNode ) ) { + ap.unshift( cur ); + } + cur = b; + while ( ( cur = cur.parentNode ) ) { + bp.unshift( cur ); + } + + // Walk down the tree looking for a discrepancy + while ( ap[ i ] === bp[ i ] ) { + i++; + } + + return i ? + + // Do a sibling check if the nodes have a common ancestor + siblingCheck( ap[ i ], bp[ i ] ) : + + // Otherwise nodes in our document sort first + // Support: IE 11+, Edge 17 - 18+ + // IE/Edge sometimes throw a "Permission denied" error when strict-comparing + // two documents; shallow comparisons work. + /* eslint-disable eqeqeq */ + ap[ i ] == preferredDoc ? -1 : + bp[ i ] == preferredDoc ? 1 : + /* eslint-enable eqeqeq */ + 0; + }; + + return document; +}; + +Sizzle.matches = function( expr, elements ) { + return Sizzle( expr, null, null, elements ); +}; + +Sizzle.matchesSelector = function( elem, expr ) { + setDocument( elem ); + + if ( support.matchesSelector && documentIsHTML && + !nonnativeSelectorCache[ expr + " " ] && + ( !rbuggyMatches || !rbuggyMatches.test( expr ) ) && + ( !rbuggyQSA || !rbuggyQSA.test( expr ) ) ) { + + try { + var ret = matches.call( elem, expr ); + + // IE 9's matchesSelector returns false on disconnected nodes + if ( ret || support.disconnectedMatch || + + // As well, disconnected nodes are said to be in a document + // fragment in IE 9 + elem.document && elem.document.nodeType !== 11 ) { + return ret; + } + } catch ( e ) { + nonnativeSelectorCache( expr, true ); + } + } + + return Sizzle( expr, document, null, [ elem ] ).length > 0; +}; + +Sizzle.contains = function( context, elem ) { + + // Set document vars if needed + // Support: IE 11+, Edge 17 - 18+ + // IE/Edge sometimes throw a "Permission denied" error when strict-comparing + // two documents; shallow comparisons work. + // eslint-disable-next-line eqeqeq + if ( ( context.ownerDocument || context ) != document ) { + setDocument( context ); + } + return contains( context, elem ); +}; + +Sizzle.attr = function( elem, name ) { + + // Set document vars if needed + // Support: IE 11+, Edge 17 - 18+ + // IE/Edge sometimes throw a "Permission denied" error when strict-comparing + // two documents; shallow comparisons work. + // eslint-disable-next-line eqeqeq + if ( ( elem.ownerDocument || elem ) != document ) { + setDocument( elem ); + } + + var fn = Expr.attrHandle[ name.toLowerCase() ], + + // Don't get fooled by Object.prototype properties (jQuery #13807) + val = fn && hasOwn.call( Expr.attrHandle, name.toLowerCase() ) ? + fn( elem, name, !documentIsHTML ) : + undefined; + + return val !== undefined ? + val : + support.attributes || !documentIsHTML ? + elem.getAttribute( name ) : + ( val = elem.getAttributeNode( name ) ) && val.specified ? + val.value : + null; +}; + +Sizzle.escape = function( sel ) { + return ( sel + "" ).replace( rcssescape, fcssescape ); +}; + +Sizzle.error = function( msg ) { + throw new Error( "Syntax error, unrecognized expression: " + msg ); +}; + +/** + * Document sorting and removing duplicates + * @param {ArrayLike} results + */ +Sizzle.uniqueSort = function( results ) { + var elem, + duplicates = [], + j = 0, + i = 0; + + // Unless we *know* we can detect duplicates, assume their presence + hasDuplicate = !support.detectDuplicates; + sortInput = !support.sortStable && results.slice( 0 ); + results.sort( sortOrder ); + + if ( hasDuplicate ) { + while ( ( elem = results[ i++ ] ) ) { + if ( elem === results[ i ] ) { + j = duplicates.push( i ); + } + } + while ( j-- ) { + results.splice( duplicates[ j ], 1 ); + } + } + + // Clear input after sorting to release objects + // See https://github.com/jquery/sizzle/pull/225 + sortInput = null; + + return results; +}; + +/** + * Utility function for retrieving the text value of an array of DOM nodes + * @param {Array|Element} elem + */ +getText = Sizzle.getText = function( elem ) { + var node, + ret = "", + i = 0, + nodeType = elem.nodeType; + + if ( !nodeType ) { + + // If no nodeType, this is expected to be an array + while ( ( node = elem[ i++ ] ) ) { + + // Do not traverse comment nodes + ret += getText( node ); + } + } else if ( nodeType === 1 || nodeType === 9 || nodeType === 11 ) { + + // Use textContent for elements + // innerText usage removed for consistency of new lines (jQuery #11153) + if ( typeof elem.textContent === "string" ) { + return elem.textContent; + } else { + + // Traverse its children + for ( elem = elem.firstChild; elem; elem = elem.nextSibling ) { + ret += getText( elem ); + } + } + } else if ( nodeType === 3 || nodeType === 4 ) { + return elem.nodeValue; + } + + // Do not include comment or processing instruction nodes + + return ret; +}; + +Expr = Sizzle.selectors = { + + // Can be adjusted by the user + cacheLength: 50, + + createPseudo: markFunction, + + match: matchExpr, + + attrHandle: {}, + + find: {}, + + relative: { + ">": { dir: "parentNode", first: true }, + " ": { dir: "parentNode" }, + "+": { dir: "previousSibling", first: true }, + "~": { dir: "previousSibling" } + }, + + preFilter: { + "ATTR": function( match ) { + match[ 1 ] = match[ 1 ].replace( runescape, funescape ); + + // Move the given value to match[3] whether quoted or unquoted + match[ 3 ] = ( match[ 3 ] || match[ 4 ] || + match[ 5 ] || "" ).replace( runescape, funescape ); + + if ( match[ 2 ] === "~=" ) { + match[ 3 ] = " " + match[ 3 ] + " "; + } + + return match.slice( 0, 4 ); + }, + + "CHILD": function( match ) { + + /* matches from matchExpr["CHILD"] + 1 type (only|nth|...) + 2 what (child|of-type) + 3 argument (even|odd|\d*|\d*n([+-]\d+)?|...) + 4 xn-component of xn+y argument ([+-]?\d*n|) + 5 sign of xn-component + 6 x of xn-component + 7 sign of y-component + 8 y of y-component + */ + match[ 1 ] = match[ 1 ].toLowerCase(); + + if ( match[ 1 ].slice( 0, 3 ) === "nth" ) { + + // nth-* requires argument + if ( !match[ 3 ] ) { + Sizzle.error( match[ 0 ] ); + } + + // numeric x and y parameters for Expr.filter.CHILD + // remember that false/true cast respectively to 0/1 + match[ 4 ] = +( match[ 4 ] ? + match[ 5 ] + ( match[ 6 ] || 1 ) : + 2 * ( match[ 3 ] === "even" || match[ 3 ] === "odd" ) ); + match[ 5 ] = +( ( match[ 7 ] + match[ 8 ] ) || match[ 3 ] === "odd" ); + + // other types prohibit arguments + } else if ( match[ 3 ] ) { + Sizzle.error( match[ 0 ] ); + } + + return match; + }, + + "PSEUDO": function( match ) { + var excess, + unquoted = !match[ 6 ] && match[ 2 ]; + + if ( matchExpr[ "CHILD" ].test( match[ 0 ] ) ) { + return null; + } + + // Accept quoted arguments as-is + if ( match[ 3 ] ) { + match[ 2 ] = match[ 4 ] || match[ 5 ] || ""; + + // Strip excess characters from unquoted arguments + } else if ( unquoted && rpseudo.test( unquoted ) && + + // Get excess from tokenize (recursively) + ( excess = tokenize( unquoted, true ) ) && + + // advance to the next closing parenthesis + ( excess = unquoted.indexOf( ")", unquoted.length - excess ) - unquoted.length ) ) { + + // excess is a negative index + match[ 0 ] = match[ 0 ].slice( 0, excess ); + match[ 2 ] = unquoted.slice( 0, excess ); + } + + // Return only captures needed by the pseudo filter method (type and argument) + return match.slice( 0, 3 ); + } + }, + + filter: { + + "TAG": function( nodeNameSelector ) { + var nodeName = nodeNameSelector.replace( runescape, funescape ).toLowerCase(); + return nodeNameSelector === "*" ? + function() { + return true; + } : + function( elem ) { + return elem.nodeName && elem.nodeName.toLowerCase() === nodeName; + }; + }, + + "CLASS": function( className ) { + var pattern = classCache[ className + " " ]; + + return pattern || + ( pattern = new RegExp( "(^|" + whitespace + + ")" + className + "(" + whitespace + "|$)" ) ) && classCache( + className, function( elem ) { + return pattern.test( + typeof elem.className === "string" && elem.className || + typeof elem.getAttribute !== "undefined" && + elem.getAttribute( "class" ) || + "" + ); + } ); + }, + + "ATTR": function( name, operator, check ) { + return function( elem ) { + var result = Sizzle.attr( elem, name ); + + if ( result == null ) { + return operator === "!="; + } + if ( !operator ) { + return true; + } + + result += ""; + + /* eslint-disable max-len */ + + return operator === "=" ? result === check : + operator === "!=" ? result !== check : + operator === "^=" ? check && result.indexOf( check ) === 0 : + operator === "*=" ? check && result.indexOf( check ) > -1 : + operator === "$=" ? check && result.slice( -check.length ) === check : + operator === "~=" ? ( " " + result.replace( rwhitespace, " " ) + " " ).indexOf( check ) > -1 : + operator === "|=" ? result === check || result.slice( 0, check.length + 1 ) === check + "-" : + false; + /* eslint-enable max-len */ + + }; + }, + + "CHILD": function( type, what, _argument, first, last ) { + var simple = type.slice( 0, 3 ) !== "nth", + forward = type.slice( -4 ) !== "last", + ofType = what === "of-type"; + + return first === 1 && last === 0 ? + + // Shortcut for :nth-*(n) + function( elem ) { + return !!elem.parentNode; + } : + + function( elem, _context, xml ) { + var cache, uniqueCache, outerCache, node, nodeIndex, start, + dir = simple !== forward ? "nextSibling" : "previousSibling", + parent = elem.parentNode, + name = ofType && elem.nodeName.toLowerCase(), + useCache = !xml && !ofType, + diff = false; + + if ( parent ) { + + // :(first|last|only)-(child|of-type) + if ( simple ) { + while ( dir ) { + node = elem; + while ( ( node = node[ dir ] ) ) { + if ( ofType ? + node.nodeName.toLowerCase() === name : + node.nodeType === 1 ) { + + return false; + } + } + + // Reverse direction for :only-* (if we haven't yet done so) + start = dir = type === "only" && !start && "nextSibling"; + } + return true; + } + + start = [ forward ? parent.firstChild : parent.lastChild ]; + + // non-xml :nth-child(...) stores cache data on `parent` + if ( forward && useCache ) { + + // Seek `elem` from a previously-cached index + + // ...in a gzip-friendly way + node = parent; + outerCache = node[ expando ] || ( node[ expando ] = {} ); + + // Support: IE <9 only + // Defend against cloned attroperties (jQuery gh-1709) + uniqueCache = outerCache[ node.uniqueID ] || + ( outerCache[ node.uniqueID ] = {} ); + + cache = uniqueCache[ type ] || []; + nodeIndex = cache[ 0 ] === dirruns && cache[ 1 ]; + diff = nodeIndex && cache[ 2 ]; + node = nodeIndex && parent.childNodes[ nodeIndex ]; + + while ( ( node = ++nodeIndex && node && node[ dir ] || + + // Fallback to seeking `elem` from the start + ( diff = nodeIndex = 0 ) || start.pop() ) ) { + + // When found, cache indexes on `parent` and break + if ( node.nodeType === 1 && ++diff && node === elem ) { + uniqueCache[ type ] = [ dirruns, nodeIndex, diff ]; + break; + } + } + + } else { + + // Use previously-cached element index if available + if ( useCache ) { + + // ...in a gzip-friendly way + node = elem; + outerCache = node[ expando ] || ( node[ expando ] = {} ); + + // Support: IE <9 only + // Defend against cloned attroperties (jQuery gh-1709) + uniqueCache = outerCache[ node.uniqueID ] || + ( outerCache[ node.uniqueID ] = {} ); + + cache = uniqueCache[ type ] || []; + nodeIndex = cache[ 0 ] === dirruns && cache[ 1 ]; + diff = nodeIndex; + } + + // xml :nth-child(...) + // or :nth-last-child(...) or :nth(-last)?-of-type(...) + if ( diff === false ) { + + // Use the same loop as above to seek `elem` from the start + while ( ( node = ++nodeIndex && node && node[ dir ] || + ( diff = nodeIndex = 0 ) || start.pop() ) ) { + + if ( ( ofType ? + node.nodeName.toLowerCase() === name : + node.nodeType === 1 ) && + ++diff ) { + + // Cache the index of each encountered element + if ( useCache ) { + outerCache = node[ expando ] || + ( node[ expando ] = {} ); + + // Support: IE <9 only + // Defend against cloned attroperties (jQuery gh-1709) + uniqueCache = outerCache[ node.uniqueID ] || + ( outerCache[ node.uniqueID ] = {} ); + + uniqueCache[ type ] = [ dirruns, diff ]; + } + + if ( node === elem ) { + break; + } + } + } + } + } + + // Incorporate the offset, then check against cycle size + diff -= last; + return diff === first || ( diff % first === 0 && diff / first >= 0 ); + } + }; + }, + + "PSEUDO": function( pseudo, argument ) { + + // pseudo-class names are case-insensitive + // http://www.w3.org/TR/selectors/#pseudo-classes + // Prioritize by case sensitivity in case custom pseudos are added with uppercase letters + // Remember that setFilters inherits from pseudos + var args, + fn = Expr.pseudos[ pseudo ] || Expr.setFilters[ pseudo.toLowerCase() ] || + Sizzle.error( "unsupported pseudo: " + pseudo ); + + // The user may use createPseudo to indicate that + // arguments are needed to create the filter function + // just as Sizzle does + if ( fn[ expando ] ) { + return fn( argument ); + } + + // But maintain support for old signatures + if ( fn.length > 1 ) { + args = [ pseudo, pseudo, "", argument ]; + return Expr.setFilters.hasOwnProperty( pseudo.toLowerCase() ) ? + markFunction( function( seed, matches ) { + var idx, + matched = fn( seed, argument ), + i = matched.length; + while ( i-- ) { + idx = indexOf( seed, matched[ i ] ); + seed[ idx ] = !( matches[ idx ] = matched[ i ] ); + } + } ) : + function( elem ) { + return fn( elem, 0, args ); + }; + } + + return fn; + } + }, + + pseudos: { + + // Potentially complex pseudos + "not": markFunction( function( selector ) { + + // Trim the selector passed to compile + // to avoid treating leading and trailing + // spaces as combinators + var input = [], + results = [], + matcher = compile( selector.replace( rtrim, "$1" ) ); + + return matcher[ expando ] ? + markFunction( function( seed, matches, _context, xml ) { + var elem, + unmatched = matcher( seed, null, xml, [] ), + i = seed.length; + + // Match elements unmatched by `matcher` + while ( i-- ) { + if ( ( elem = unmatched[ i ] ) ) { + seed[ i ] = !( matches[ i ] = elem ); + } + } + } ) : + function( elem, _context, xml ) { + input[ 0 ] = elem; + matcher( input, null, xml, results ); + + // Don't keep the element (issue #299) + input[ 0 ] = null; + return !results.pop(); + }; + } ), + + "has": markFunction( function( selector ) { + return function( elem ) { + return Sizzle( selector, elem ).length > 0; + }; + } ), + + "contains": markFunction( function( text ) { + text = text.replace( runescape, funescape ); + return function( elem ) { + return ( elem.textContent || getText( elem ) ).indexOf( text ) > -1; + }; + } ), + + // "Whether an element is represented by a :lang() selector + // is based solely on the element's language value + // being equal to the identifier C, + // or beginning with the identifier C immediately followed by "-". + // The matching of C against the element's language value is performed case-insensitively. + // The identifier C does not have to be a valid language name." + // http://www.w3.org/TR/selectors/#lang-pseudo + "lang": markFunction( function( lang ) { + + // lang value must be a valid identifier + if ( !ridentifier.test( lang || "" ) ) { + Sizzle.error( "unsupported lang: " + lang ); + } + lang = lang.replace( runescape, funescape ).toLowerCase(); + return function( elem ) { + var elemLang; + do { + if ( ( elemLang = documentIsHTML ? + elem.lang : + elem.getAttribute( "xml:lang" ) || elem.getAttribute( "lang" ) ) ) { + + elemLang = elemLang.toLowerCase(); + return elemLang === lang || elemLang.indexOf( lang + "-" ) === 0; + } + } while ( ( elem = elem.parentNode ) && elem.nodeType === 1 ); + return false; + }; + } ), + + // Miscellaneous + "target": function( elem ) { + var hash = window.location && window.location.hash; + return hash && hash.slice( 1 ) === elem.id; + }, + + "root": function( elem ) { + return elem === docElem; + }, + + "focus": function( elem ) { + return elem === document.activeElement && + ( !document.hasFocus || document.hasFocus() ) && + !!( elem.type || elem.href || ~elem.tabIndex ); + }, + + // Boolean properties + "enabled": createDisabledPseudo( false ), + "disabled": createDisabledPseudo( true ), + + "checked": function( elem ) { + + // In CSS3, :checked should return both checked and selected elements + // http://www.w3.org/TR/2011/REC-css3-selectors-20110929/#checked + var nodeName = elem.nodeName.toLowerCase(); + return ( nodeName === "input" && !!elem.checked ) || + ( nodeName === "option" && !!elem.selected ); + }, + + "selected": function( elem ) { + + // Accessing this property makes selected-by-default + // options in Safari work properly + if ( elem.parentNode ) { + // eslint-disable-next-line no-unused-expressions + elem.parentNode.selectedIndex; + } + + return elem.selected === true; + }, + + // Contents + "empty": function( elem ) { + + // http://www.w3.org/TR/selectors/#empty-pseudo + // :empty is negated by element (1) or content nodes (text: 3; cdata: 4; entity ref: 5), + // but not by others (comment: 8; processing instruction: 7; etc.) + // nodeType < 6 works because attributes (2) do not appear as children + for ( elem = elem.firstChild; elem; elem = elem.nextSibling ) { + if ( elem.nodeType < 6 ) { + return false; + } + } + return true; + }, + + "parent": function( elem ) { + return !Expr.pseudos[ "empty" ]( elem ); + }, + + // Element/input types + "header": function( elem ) { + return rheader.test( elem.nodeName ); + }, + + "input": function( elem ) { + return rinputs.test( elem.nodeName ); + }, + + "button": function( elem ) { + var name = elem.nodeName.toLowerCase(); + return name === "input" && elem.type === "button" || name === "button"; + }, + + "text": function( elem ) { + var attr; + return elem.nodeName.toLowerCase() === "input" && + elem.type === "text" && + + // Support: IE<8 + // New HTML5 attribute values (e.g., "search") appear with elem.type === "text" + ( ( attr = elem.getAttribute( "type" ) ) == null || + attr.toLowerCase() === "text" ); + }, + + // Position-in-collection + "first": createPositionalPseudo( function() { + return [ 0 ]; + } ), + + "last": createPositionalPseudo( function( _matchIndexes, length ) { + return [ length - 1 ]; + } ), + + "eq": createPositionalPseudo( function( _matchIndexes, length, argument ) { + return [ argument < 0 ? argument + length : argument ]; + } ), + + "even": createPositionalPseudo( function( matchIndexes, length ) { + var i = 0; + for ( ; i < length; i += 2 ) { + matchIndexes.push( i ); + } + return matchIndexes; + } ), + + "odd": createPositionalPseudo( function( matchIndexes, length ) { + var i = 1; + for ( ; i < length; i += 2 ) { + matchIndexes.push( i ); + } + return matchIndexes; + } ), + + "lt": createPositionalPseudo( function( matchIndexes, length, argument ) { + var i = argument < 0 ? + argument + length : + argument > length ? + length : + argument; + for ( ; --i >= 0; ) { + matchIndexes.push( i ); + } + return matchIndexes; + } ), + + "gt": createPositionalPseudo( function( matchIndexes, length, argument ) { + var i = argument < 0 ? argument + length : argument; + for ( ; ++i < length; ) { + matchIndexes.push( i ); + } + return matchIndexes; + } ) + } +}; + +Expr.pseudos[ "nth" ] = Expr.pseudos[ "eq" ]; + +// Add button/input type pseudos +for ( i in { radio: true, checkbox: true, file: true, password: true, image: true } ) { + Expr.pseudos[ i ] = createInputPseudo( i ); +} +for ( i in { submit: true, reset: true } ) { + Expr.pseudos[ i ] = createButtonPseudo( i ); +} + +// Easy API for creating new setFilters +function setFilters() {} +setFilters.prototype = Expr.filters = Expr.pseudos; +Expr.setFilters = new setFilters(); + +tokenize = Sizzle.tokenize = function( selector, parseOnly ) { + var matched, match, tokens, type, + soFar, groups, preFilters, + cached = tokenCache[ selector + " " ]; + + if ( cached ) { + return parseOnly ? 0 : cached.slice( 0 ); + } + + soFar = selector; + groups = []; + preFilters = Expr.preFilter; + + while ( soFar ) { + + // Comma and first run + if ( !matched || ( match = rcomma.exec( soFar ) ) ) { + if ( match ) { + + // Don't consume trailing commas as valid + soFar = soFar.slice( match[ 0 ].length ) || soFar; + } + groups.push( ( tokens = [] ) ); + } + + matched = false; + + // Combinators + if ( ( match = rcombinators.exec( soFar ) ) ) { + matched = match.shift(); + tokens.push( { + value: matched, + + // Cast descendant combinators to space + type: match[ 0 ].replace( rtrim, " " ) + } ); + soFar = soFar.slice( matched.length ); + } + + // Filters + for ( type in Expr.filter ) { + if ( ( match = matchExpr[ type ].exec( soFar ) ) && ( !preFilters[ type ] || + ( match = preFilters[ type ]( match ) ) ) ) { + matched = match.shift(); + tokens.push( { + value: matched, + type: type, + matches: match + } ); + soFar = soFar.slice( matched.length ); + } + } + + if ( !matched ) { + break; + } + } + + // Return the length of the invalid excess + // if we're just parsing + // Otherwise, throw an error or return tokens + return parseOnly ? + soFar.length : + soFar ? + Sizzle.error( selector ) : + + // Cache the tokens + tokenCache( selector, groups ).slice( 0 ); +}; + +function toSelector( tokens ) { + var i = 0, + len = tokens.length, + selector = ""; + for ( ; i < len; i++ ) { + selector += tokens[ i ].value; + } + return selector; +} + +function addCombinator( matcher, combinator, base ) { + var dir = combinator.dir, + skip = combinator.next, + key = skip || dir, + checkNonElements = base && key === "parentNode", + doneName = done++; + + return combinator.first ? + + // Check against closest ancestor/preceding element + function( elem, context, xml ) { + while ( ( elem = elem[ dir ] ) ) { + if ( elem.nodeType === 1 || checkNonElements ) { + return matcher( elem, context, xml ); + } + } + return false; + } : + + // Check against all ancestor/preceding elements + function( elem, context, xml ) { + var oldCache, uniqueCache, outerCache, + newCache = [ dirruns, doneName ]; + + // We can't set arbitrary data on XML nodes, so they don't benefit from combinator caching + if ( xml ) { + while ( ( elem = elem[ dir ] ) ) { + if ( elem.nodeType === 1 || checkNonElements ) { + if ( matcher( elem, context, xml ) ) { + return true; + } + } + } + } else { + while ( ( elem = elem[ dir ] ) ) { + if ( elem.nodeType === 1 || checkNonElements ) { + outerCache = elem[ expando ] || ( elem[ expando ] = {} ); + + // Support: IE <9 only + // Defend against cloned attroperties (jQuery gh-1709) + uniqueCache = outerCache[ elem.uniqueID ] || + ( outerCache[ elem.uniqueID ] = {} ); + + if ( skip && skip === elem.nodeName.toLowerCase() ) { + elem = elem[ dir ] || elem; + } else if ( ( oldCache = uniqueCache[ key ] ) && + oldCache[ 0 ] === dirruns && oldCache[ 1 ] === doneName ) { + + // Assign to newCache so results back-propagate to previous elements + return ( newCache[ 2 ] = oldCache[ 2 ] ); + } else { + + // Reuse newcache so results back-propagate to previous elements + uniqueCache[ key ] = newCache; + + // A match means we're done; a fail means we have to keep checking + if ( ( newCache[ 2 ] = matcher( elem, context, xml ) ) ) { + return true; + } + } + } + } + } + return false; + }; +} + +function elementMatcher( matchers ) { + return matchers.length > 1 ? + function( elem, context, xml ) { + var i = matchers.length; + while ( i-- ) { + if ( !matchers[ i ]( elem, context, xml ) ) { + return false; + } + } + return true; + } : + matchers[ 0 ]; +} + +function multipleContexts( selector, contexts, results ) { + var i = 0, + len = contexts.length; + for ( ; i < len; i++ ) { + Sizzle( selector, contexts[ i ], results ); + } + return results; +} + +function condense( unmatched, map, filter, context, xml ) { + var elem, + newUnmatched = [], + i = 0, + len = unmatched.length, + mapped = map != null; + + for ( ; i < len; i++ ) { + if ( ( elem = unmatched[ i ] ) ) { + if ( !filter || filter( elem, context, xml ) ) { + newUnmatched.push( elem ); + if ( mapped ) { + map.push( i ); + } + } + } + } + + return newUnmatched; +} + +function setMatcher( preFilter, selector, matcher, postFilter, postFinder, postSelector ) { + if ( postFilter && !postFilter[ expando ] ) { + postFilter = setMatcher( postFilter ); + } + if ( postFinder && !postFinder[ expando ] ) { + postFinder = setMatcher( postFinder, postSelector ); + } + return markFunction( function( seed, results, context, xml ) { + var temp, i, elem, + preMap = [], + postMap = [], + preexisting = results.length, + + // Get initial elements from seed or context + elems = seed || multipleContexts( + selector || "*", + context.nodeType ? [ context ] : context, + [] + ), + + // Prefilter to get matcher input, preserving a map for seed-results synchronization + matcherIn = preFilter && ( seed || !selector ) ? + condense( elems, preMap, preFilter, context, xml ) : + elems, + + matcherOut = matcher ? + + // If we have a postFinder, or filtered seed, or non-seed postFilter or preexisting results, + postFinder || ( seed ? preFilter : preexisting || postFilter ) ? + + // ...intermediate processing is necessary + [] : + + // ...otherwise use results directly + results : + matcherIn; + + // Find primary matches + if ( matcher ) { + matcher( matcherIn, matcherOut, context, xml ); + } + + // Apply postFilter + if ( postFilter ) { + temp = condense( matcherOut, postMap ); + postFilter( temp, [], context, xml ); + + // Un-match failing elements by moving them back to matcherIn + i = temp.length; + while ( i-- ) { + if ( ( elem = temp[ i ] ) ) { + matcherOut[ postMap[ i ] ] = !( matcherIn[ postMap[ i ] ] = elem ); + } + } + } + + if ( seed ) { + if ( postFinder || preFilter ) { + if ( postFinder ) { + + // Get the final matcherOut by condensing this intermediate into postFinder contexts + temp = []; + i = matcherOut.length; + while ( i-- ) { + if ( ( elem = matcherOut[ i ] ) ) { + + // Restore matcherIn since elem is not yet a final match + temp.push( ( matcherIn[ i ] = elem ) ); + } + } + postFinder( null, ( matcherOut = [] ), temp, xml ); + } + + // Move matched elements from seed to results to keep them synchronized + i = matcherOut.length; + while ( i-- ) { + if ( ( elem = matcherOut[ i ] ) && + ( temp = postFinder ? indexOf( seed, elem ) : preMap[ i ] ) > -1 ) { + + seed[ temp ] = !( results[ temp ] = elem ); + } + } + } + + // Add elements to results, through postFinder if defined + } else { + matcherOut = condense( + matcherOut === results ? + matcherOut.splice( preexisting, matcherOut.length ) : + matcherOut + ); + if ( postFinder ) { + postFinder( null, results, matcherOut, xml ); + } else { + push.apply( results, matcherOut ); + } + } + } ); +} + +function matcherFromTokens( tokens ) { + var checkContext, matcher, j, + len = tokens.length, + leadingRelative = Expr.relative[ tokens[ 0 ].type ], + implicitRelative = leadingRelative || Expr.relative[ " " ], + i = leadingRelative ? 1 : 0, + + // The foundational matcher ensures that elements are reachable from top-level context(s) + matchContext = addCombinator( function( elem ) { + return elem === checkContext; + }, implicitRelative, true ), + matchAnyContext = addCombinator( function( elem ) { + return indexOf( checkContext, elem ) > -1; + }, implicitRelative, true ), + matchers = [ function( elem, context, xml ) { + var ret = ( !leadingRelative && ( xml || context !== outermostContext ) ) || ( + ( checkContext = context ).nodeType ? + matchContext( elem, context, xml ) : + matchAnyContext( elem, context, xml ) ); + + // Avoid hanging onto element (issue #299) + checkContext = null; + return ret; + } ]; + + for ( ; i < len; i++ ) { + if ( ( matcher = Expr.relative[ tokens[ i ].type ] ) ) { + matchers = [ addCombinator( elementMatcher( matchers ), matcher ) ]; + } else { + matcher = Expr.filter[ tokens[ i ].type ].apply( null, tokens[ i ].matches ); + + // Return special upon seeing a positional matcher + if ( matcher[ expando ] ) { + + // Find the next relative operator (if any) for proper handling + j = ++i; + for ( ; j < len; j++ ) { + if ( Expr.relative[ tokens[ j ].type ] ) { + break; + } + } + return setMatcher( + i > 1 && elementMatcher( matchers ), + i > 1 && toSelector( + + // If the preceding token was a descendant combinator, insert an implicit any-element `*` + tokens + .slice( 0, i - 1 ) + .concat( { value: tokens[ i - 2 ].type === " " ? "*" : "" } ) + ).replace( rtrim, "$1" ), + matcher, + i < j && matcherFromTokens( tokens.slice( i, j ) ), + j < len && matcherFromTokens( ( tokens = tokens.slice( j ) ) ), + j < len && toSelector( tokens ) + ); + } + matchers.push( matcher ); + } + } + + return elementMatcher( matchers ); +} + +function matcherFromGroupMatchers( elementMatchers, setMatchers ) { + var bySet = setMatchers.length > 0, + byElement = elementMatchers.length > 0, + superMatcher = function( seed, context, xml, results, outermost ) { + var elem, j, matcher, + matchedCount = 0, + i = "0", + unmatched = seed && [], + setMatched = [], + contextBackup = outermostContext, + + // We must always have either seed elements or outermost context + elems = seed || byElement && Expr.find[ "TAG" ]( "*", outermost ), + + // Use integer dirruns iff this is the outermost matcher + dirrunsUnique = ( dirruns += contextBackup == null ? 1 : Math.random() || 0.1 ), + len = elems.length; + + if ( outermost ) { + + // Support: IE 11+, Edge 17 - 18+ + // IE/Edge sometimes throw a "Permission denied" error when strict-comparing + // two documents; shallow comparisons work. + // eslint-disable-next-line eqeqeq + outermostContext = context == document || context || outermost; + } + + // Add elements passing elementMatchers directly to results + // Support: IE<9, Safari + // Tolerate NodeList properties (IE: "length"; Safari: ) matching elements by id + for ( ; i !== len && ( elem = elems[ i ] ) != null; i++ ) { + if ( byElement && elem ) { + j = 0; + + // Support: IE 11+, Edge 17 - 18+ + // IE/Edge sometimes throw a "Permission denied" error when strict-comparing + // two documents; shallow comparisons work. + // eslint-disable-next-line eqeqeq + if ( !context && elem.ownerDocument != document ) { + setDocument( elem ); + xml = !documentIsHTML; + } + while ( ( matcher = elementMatchers[ j++ ] ) ) { + if ( matcher( elem, context || document, xml ) ) { + results.push( elem ); + break; + } + } + if ( outermost ) { + dirruns = dirrunsUnique; + } + } + + // Track unmatched elements for set filters + if ( bySet ) { + + // They will have gone through all possible matchers + if ( ( elem = !matcher && elem ) ) { + matchedCount--; + } + + // Lengthen the array for every element, matched or not + if ( seed ) { + unmatched.push( elem ); + } + } + } + + // `i` is now the count of elements visited above, and adding it to `matchedCount` + // makes the latter nonnegative. + matchedCount += i; + + // Apply set filters to unmatched elements + // NOTE: This can be skipped if there are no unmatched elements (i.e., `matchedCount` + // equals `i`), unless we didn't visit _any_ elements in the above loop because we have + // no element matchers and no seed. + // Incrementing an initially-string "0" `i` allows `i` to remain a string only in that + // case, which will result in a "00" `matchedCount` that differs from `i` but is also + // numerically zero. + if ( bySet && i !== matchedCount ) { + j = 0; + while ( ( matcher = setMatchers[ j++ ] ) ) { + matcher( unmatched, setMatched, context, xml ); + } + + if ( seed ) { + + // Reintegrate element matches to eliminate the need for sorting + if ( matchedCount > 0 ) { + while ( i-- ) { + if ( !( unmatched[ i ] || setMatched[ i ] ) ) { + setMatched[ i ] = pop.call( results ); + } + } + } + + // Discard index placeholder values to get only actual matches + setMatched = condense( setMatched ); + } + + // Add matches to results + push.apply( results, setMatched ); + + // Seedless set matches succeeding multiple successful matchers stipulate sorting + if ( outermost && !seed && setMatched.length > 0 && + ( matchedCount + setMatchers.length ) > 1 ) { + + Sizzle.uniqueSort( results ); + } + } + + // Override manipulation of globals by nested matchers + if ( outermost ) { + dirruns = dirrunsUnique; + outermostContext = contextBackup; + } + + return unmatched; + }; + + return bySet ? + markFunction( superMatcher ) : + superMatcher; +} + +compile = Sizzle.compile = function( selector, match /* Internal Use Only */ ) { + var i, + setMatchers = [], + elementMatchers = [], + cached = compilerCache[ selector + " " ]; + + if ( !cached ) { + + // Generate a function of recursive functions that can be used to check each element + if ( !match ) { + match = tokenize( selector ); + } + i = match.length; + while ( i-- ) { + cached = matcherFromTokens( match[ i ] ); + if ( cached[ expando ] ) { + setMatchers.push( cached ); + } else { + elementMatchers.push( cached ); + } + } + + // Cache the compiled function + cached = compilerCache( + selector, + matcherFromGroupMatchers( elementMatchers, setMatchers ) + ); + + // Save selector and tokenization + cached.selector = selector; + } + return cached; +}; + +/** + * A low-level selection function that works with Sizzle's compiled + * selector functions + * @param {String|Function} selector A selector or a pre-compiled + * selector function built with Sizzle.compile + * @param {Element} context + * @param {Array} [results] + * @param {Array} [seed] A set of elements to match against + */ +select = Sizzle.select = function( selector, context, results, seed ) { + var i, tokens, token, type, find, + compiled = typeof selector === "function" && selector, + match = !seed && tokenize( ( selector = compiled.selector || selector ) ); + + results = results || []; + + // Try to minimize operations if there is only one selector in the list and no seed + // (the latter of which guarantees us context) + if ( match.length === 1 ) { + + // Reduce context if the leading compound selector is an ID + tokens = match[ 0 ] = match[ 0 ].slice( 0 ); + if ( tokens.length > 2 && ( token = tokens[ 0 ] ).type === "ID" && + context.nodeType === 9 && documentIsHTML && Expr.relative[ tokens[ 1 ].type ] ) { + + context = ( Expr.find[ "ID" ]( token.matches[ 0 ] + .replace( runescape, funescape ), context ) || [] )[ 0 ]; + if ( !context ) { + return results; + + // Precompiled matchers will still verify ancestry, so step up a level + } else if ( compiled ) { + context = context.parentNode; + } + + selector = selector.slice( tokens.shift().value.length ); + } + + // Fetch a seed set for right-to-left matching + i = matchExpr[ "needsContext" ].test( selector ) ? 0 : tokens.length; + while ( i-- ) { + token = tokens[ i ]; + + // Abort if we hit a combinator + if ( Expr.relative[ ( type = token.type ) ] ) { + break; + } + if ( ( find = Expr.find[ type ] ) ) { + + // Search, expanding context for leading sibling combinators + if ( ( seed = find( + token.matches[ 0 ].replace( runescape, funescape ), + rsibling.test( tokens[ 0 ].type ) && testContext( context.parentNode ) || + context + ) ) ) { + + // If seed is empty or no tokens remain, we can return early + tokens.splice( i, 1 ); + selector = seed.length && toSelector( tokens ); + if ( !selector ) { + push.apply( results, seed ); + return results; + } + + break; + } + } + } + } + + // Compile and execute a filtering function if one is not provided + // Provide `match` to avoid retokenization if we modified the selector above + ( compiled || compile( selector, match ) )( + seed, + context, + !documentIsHTML, + results, + !context || rsibling.test( selector ) && testContext( context.parentNode ) || context + ); + return results; +}; + +// One-time assignments + +// Sort stability +support.sortStable = expando.split( "" ).sort( sortOrder ).join( "" ) === expando; + +// Support: Chrome 14-35+ +// Always assume duplicates if they aren't passed to the comparison function +support.detectDuplicates = !!hasDuplicate; + +// Initialize against the default document +setDocument(); + +// Support: Webkit<537.32 - Safari 6.0.3/Chrome 25 (fixed in Chrome 27) +// Detached nodes confoundingly follow *each other* +support.sortDetached = assert( function( el ) { + + // Should return 1, but returns 4 (following) + return el.compareDocumentPosition( document.createElement( "fieldset" ) ) & 1; +} ); + +// Support: IE<8 +// Prevent attribute/property "interpolation" +// https://msdn.microsoft.com/en-us/library/ms536429%28VS.85%29.aspx +if ( !assert( function( el ) { + el.innerHTML = ""; + return el.firstChild.getAttribute( "href" ) === "#"; +} ) ) { + addHandle( "type|href|height|width", function( elem, name, isXML ) { + if ( !isXML ) { + return elem.getAttribute( name, name.toLowerCase() === "type" ? 1 : 2 ); + } + } ); +} + +// Support: IE<9 +// Use defaultValue in place of getAttribute("value") +if ( !support.attributes || !assert( function( el ) { + el.innerHTML = ""; + el.firstChild.setAttribute( "value", "" ); + return el.firstChild.getAttribute( "value" ) === ""; +} ) ) { + addHandle( "value", function( elem, _name, isXML ) { + if ( !isXML && elem.nodeName.toLowerCase() === "input" ) { + return elem.defaultValue; + } + } ); +} + +// Support: IE<9 +// Use getAttributeNode to fetch booleans when getAttribute lies +if ( !assert( function( el ) { + return el.getAttribute( "disabled" ) == null; +} ) ) { + addHandle( booleans, function( elem, name, isXML ) { + var val; + if ( !isXML ) { + return elem[ name ] === true ? name.toLowerCase() : + ( val = elem.getAttributeNode( name ) ) && val.specified ? + val.value : + null; + } + } ); +} + +return Sizzle; + +} )( window ); + + + +jQuery.find = Sizzle; +jQuery.expr = Sizzle.selectors; + +// Deprecated +jQuery.expr[ ":" ] = jQuery.expr.pseudos; +jQuery.uniqueSort = jQuery.unique = Sizzle.uniqueSort; +jQuery.text = Sizzle.getText; +jQuery.isXMLDoc = Sizzle.isXML; +jQuery.contains = Sizzle.contains; +jQuery.escapeSelector = Sizzle.escape; + + + + +var dir = function( elem, dir, until ) { + var matched = [], + truncate = until !== undefined; + + while ( ( elem = elem[ dir ] ) && elem.nodeType !== 9 ) { + if ( elem.nodeType === 1 ) { + if ( truncate && jQuery( elem ).is( until ) ) { + break; + } + matched.push( elem ); + } + } + return matched; +}; + + +var siblings = function( n, elem ) { + var matched = []; + + for ( ; n; n = n.nextSibling ) { + if ( n.nodeType === 1 && n !== elem ) { + matched.push( n ); + } + } + + return matched; +}; + + +var rneedsContext = jQuery.expr.match.needsContext; + + + +function nodeName( elem, name ) { + + return elem.nodeName && elem.nodeName.toLowerCase() === name.toLowerCase(); + +} +var rsingleTag = ( /^<([a-z][^\/\0>:\x20\t\r\n\f]*)[\x20\t\r\n\f]*\/?>(?:<\/\1>|)$/i ); + + + +// Implement the identical functionality for filter and not +function winnow( elements, qualifier, not ) { + if ( isFunction( qualifier ) ) { + return jQuery.grep( elements, function( elem, i ) { + return !!qualifier.call( elem, i, elem ) !== not; + } ); + } + + // Single element + if ( qualifier.nodeType ) { + return jQuery.grep( elements, function( elem ) { + return ( elem === qualifier ) !== not; + } ); + } + + // Arraylike of elements (jQuery, arguments, Array) + if ( typeof qualifier !== "string" ) { + return jQuery.grep( elements, function( elem ) { + return ( indexOf.call( qualifier, elem ) > -1 ) !== not; + } ); + } + + // Filtered directly for both simple and complex selectors + return jQuery.filter( qualifier, elements, not ); +} + +jQuery.filter = function( expr, elems, not ) { + var elem = elems[ 0 ]; + + if ( not ) { + expr = ":not(" + expr + ")"; + } + + if ( elems.length === 1 && elem.nodeType === 1 ) { + return jQuery.find.matchesSelector( elem, expr ) ? [ elem ] : []; + } + + return jQuery.find.matches( expr, jQuery.grep( elems, function( elem ) { + return elem.nodeType === 1; + } ) ); +}; + +jQuery.fn.extend( { + find: function( selector ) { + var i, ret, + len = this.length, + self = this; + + if ( typeof selector !== "string" ) { + return this.pushStack( jQuery( selector ).filter( function() { + for ( i = 0; i < len; i++ ) { + if ( jQuery.contains( self[ i ], this ) ) { + return true; + } + } + } ) ); + } + + ret = this.pushStack( [] ); + + for ( i = 0; i < len; i++ ) { + jQuery.find( selector, self[ i ], ret ); + } + + return len > 1 ? jQuery.uniqueSort( ret ) : ret; + }, + filter: function( selector ) { + return this.pushStack( winnow( this, selector || [], false ) ); + }, + not: function( selector ) { + return this.pushStack( winnow( this, selector || [], true ) ); + }, + is: function( selector ) { + return !!winnow( + this, + + // If this is a positional/relative selector, check membership in the returned set + // so $("p:first").is("p:last") won't return true for a doc with two "p". + typeof selector === "string" && rneedsContext.test( selector ) ? + jQuery( selector ) : + selector || [], + false + ).length; + } +} ); + + +// Initialize a jQuery object + + +// A central reference to the root jQuery(document) +var rootjQuery, + + // A simple way to check for HTML strings + // Prioritize #id over to avoid XSS via location.hash (#9521) + // Strict HTML recognition (#11290: must start with <) + // Shortcut simple #id case for speed + rquickExpr = /^(?:\s*(<[\w\W]+>)[^>]*|#([\w-]+))$/, + + init = jQuery.fn.init = function( selector, context, root ) { + var match, elem; + + // HANDLE: $(""), $(null), $(undefined), $(false) + if ( !selector ) { + return this; + } + + // Method init() accepts an alternate rootjQuery + // so migrate can support jQuery.sub (gh-2101) + root = root || rootjQuery; + + // Handle HTML strings + if ( typeof selector === "string" ) { + if ( selector[ 0 ] === "<" && + selector[ selector.length - 1 ] === ">" && + selector.length >= 3 ) { + + // Assume that strings that start and end with <> are HTML and skip the regex check + match = [ null, selector, null ]; + + } else { + match = rquickExpr.exec( selector ); + } + + // Match html or make sure no context is specified for #id + if ( match && ( match[ 1 ] || !context ) ) { + + // HANDLE: $(html) -> $(array) + if ( match[ 1 ] ) { + context = context instanceof jQuery ? context[ 0 ] : context; + + // Option to run scripts is true for back-compat + // Intentionally let the error be thrown if parseHTML is not present + jQuery.merge( this, jQuery.parseHTML( + match[ 1 ], + context && context.nodeType ? context.ownerDocument || context : document, + true + ) ); + + // HANDLE: $(html, props) + if ( rsingleTag.test( match[ 1 ] ) && jQuery.isPlainObject( context ) ) { + for ( match in context ) { + + // Properties of context are called as methods if possible + if ( isFunction( this[ match ] ) ) { + this[ match ]( context[ match ] ); + + // ...and otherwise set as attributes + } else { + this.attr( match, context[ match ] ); + } + } + } + + return this; + + // HANDLE: $(#id) + } else { + elem = document.getElementById( match[ 2 ] ); + + if ( elem ) { + + // Inject the element directly into the jQuery object + this[ 0 ] = elem; + this.length = 1; + } + return this; + } + + // HANDLE: $(expr, $(...)) + } else if ( !context || context.jquery ) { + return ( context || root ).find( selector ); + + // HANDLE: $(expr, context) + // (which is just equivalent to: $(context).find(expr) + } else { + return this.constructor( context ).find( selector ); + } + + // HANDLE: $(DOMElement) + } else if ( selector.nodeType ) { + this[ 0 ] = selector; + this.length = 1; + return this; + + // HANDLE: $(function) + // Shortcut for document ready + } else if ( isFunction( selector ) ) { + return root.ready !== undefined ? + root.ready( selector ) : + + // Execute immediately if ready is not present + selector( jQuery ); + } + + return jQuery.makeArray( selector, this ); + }; + +// Give the init function the jQuery prototype for later instantiation +init.prototype = jQuery.fn; + +// Initialize central reference +rootjQuery = jQuery( document ); + + +var rparentsprev = /^(?:parents|prev(?:Until|All))/, + + // Methods guaranteed to produce a unique set when starting from a unique set + guaranteedUnique = { + children: true, + contents: true, + next: true, + prev: true + }; + +jQuery.fn.extend( { + has: function( target ) { + var targets = jQuery( target, this ), + l = targets.length; + + return this.filter( function() { + var i = 0; + for ( ; i < l; i++ ) { + if ( jQuery.contains( this, targets[ i ] ) ) { + return true; + } + } + } ); + }, + + closest: function( selectors, context ) { + var cur, + i = 0, + l = this.length, + matched = [], + targets = typeof selectors !== "string" && jQuery( selectors ); + + // Positional selectors never match, since there's no _selection_ context + if ( !rneedsContext.test( selectors ) ) { + for ( ; i < l; i++ ) { + for ( cur = this[ i ]; cur && cur !== context; cur = cur.parentNode ) { + + // Always skip document fragments + if ( cur.nodeType < 11 && ( targets ? + targets.index( cur ) > -1 : + + // Don't pass non-elements to Sizzle + cur.nodeType === 1 && + jQuery.find.matchesSelector( cur, selectors ) ) ) { + + matched.push( cur ); + break; + } + } + } + } + + return this.pushStack( matched.length > 1 ? jQuery.uniqueSort( matched ) : matched ); + }, + + // Determine the position of an element within the set + index: function( elem ) { + + // No argument, return index in parent + if ( !elem ) { + return ( this[ 0 ] && this[ 0 ].parentNode ) ? this.first().prevAll().length : -1; + } + + // Index in selector + if ( typeof elem === "string" ) { + return indexOf.call( jQuery( elem ), this[ 0 ] ); + } + + // Locate the position of the desired element + return indexOf.call( this, + + // If it receives a jQuery object, the first element is used + elem.jquery ? elem[ 0 ] : elem + ); + }, + + add: function( selector, context ) { + return this.pushStack( + jQuery.uniqueSort( + jQuery.merge( this.get(), jQuery( selector, context ) ) + ) + ); + }, + + addBack: function( selector ) { + return this.add( selector == null ? + this.prevObject : this.prevObject.filter( selector ) + ); + } +} ); + +function sibling( cur, dir ) { + while ( ( cur = cur[ dir ] ) && cur.nodeType !== 1 ) {} + return cur; +} + +jQuery.each( { + parent: function( elem ) { + var parent = elem.parentNode; + return parent && parent.nodeType !== 11 ? parent : null; + }, + parents: function( elem ) { + return dir( elem, "parentNode" ); + }, + parentsUntil: function( elem, _i, until ) { + return dir( elem, "parentNode", until ); + }, + next: function( elem ) { + return sibling( elem, "nextSibling" ); + }, + prev: function( elem ) { + return sibling( elem, "previousSibling" ); + }, + nextAll: function( elem ) { + return dir( elem, "nextSibling" ); + }, + prevAll: function( elem ) { + return dir( elem, "previousSibling" ); + }, + nextUntil: function( elem, _i, until ) { + return dir( elem, "nextSibling", until ); + }, + prevUntil: function( elem, _i, until ) { + return dir( elem, "previousSibling", until ); + }, + siblings: function( elem ) { + return siblings( ( elem.parentNode || {} ).firstChild, elem ); + }, + children: function( elem ) { + return siblings( elem.firstChild ); + }, + contents: function( elem ) { + if ( elem.contentDocument != null && + + // Support: IE 11+ + // elements with no `data` attribute has an object + // `contentDocument` with a `null` prototype. + getProto( elem.contentDocument ) ) { + + return elem.contentDocument; + } + + // Support: IE 9 - 11 only, iOS 7 only, Android Browser <=4.3 only + // Treat the template element as a regular one in browsers that + // don't support it. + if ( nodeName( elem, "template" ) ) { + elem = elem.content || elem; + } + + return jQuery.merge( [], elem.childNodes ); + } +}, function( name, fn ) { + jQuery.fn[ name ] = function( until, selector ) { + var matched = jQuery.map( this, fn, until ); + + if ( name.slice( -5 ) !== "Until" ) { + selector = until; + } + + if ( selector && typeof selector === "string" ) { + matched = jQuery.filter( selector, matched ); + } + + if ( this.length > 1 ) { + + // Remove duplicates + if ( !guaranteedUnique[ name ] ) { + jQuery.uniqueSort( matched ); + } + + // Reverse order for parents* and prev-derivatives + if ( rparentsprev.test( name ) ) { + matched.reverse(); + } + } + + return this.pushStack( matched ); + }; +} ); +var rnothtmlwhite = ( /[^\x20\t\r\n\f]+/g ); + + + +// Convert String-formatted options into Object-formatted ones +function createOptions( options ) { + var object = {}; + jQuery.each( options.match( rnothtmlwhite ) || [], function( _, flag ) { + object[ flag ] = true; + } ); + return object; +} + +/* + * Create a callback list using the following parameters: + * + * options: an optional list of space-separated options that will change how + * the callback list behaves or a more traditional option object + * + * By default a callback list will act like an event callback list and can be + * "fired" multiple times. + * + * Possible options: + * + * once: will ensure the callback list can only be fired once (like a Deferred) + * + * memory: will keep track of previous values and will call any callback added + * after the list has been fired right away with the latest "memorized" + * values (like a Deferred) + * + * unique: will ensure a callback can only be added once (no duplicate in the list) + * + * stopOnFalse: interrupt callings when a callback returns false + * + */ +jQuery.Callbacks = function( options ) { + + // Convert options from String-formatted to Object-formatted if needed + // (we check in cache first) + options = typeof options === "string" ? + createOptions( options ) : + jQuery.extend( {}, options ); + + var // Flag to know if list is currently firing + firing, + + // Last fire value for non-forgettable lists + memory, + + // Flag to know if list was already fired + fired, + + // Flag to prevent firing + locked, + + // Actual callback list + list = [], + + // Queue of execution data for repeatable lists + queue = [], + + // Index of currently firing callback (modified by add/remove as needed) + firingIndex = -1, + + // Fire callbacks + fire = function() { + + // Enforce single-firing + locked = locked || options.once; + + // Execute callbacks for all pending executions, + // respecting firingIndex overrides and runtime changes + fired = firing = true; + for ( ; queue.length; firingIndex = -1 ) { + memory = queue.shift(); + while ( ++firingIndex < list.length ) { + + // Run callback and check for early termination + if ( list[ firingIndex ].apply( memory[ 0 ], memory[ 1 ] ) === false && + options.stopOnFalse ) { + + // Jump to end and forget the data so .add doesn't re-fire + firingIndex = list.length; + memory = false; + } + } + } + + // Forget the data if we're done with it + if ( !options.memory ) { + memory = false; + } + + firing = false; + + // Clean up if we're done firing for good + if ( locked ) { + + // Keep an empty list if we have data for future add calls + if ( memory ) { + list = []; + + // Otherwise, this object is spent + } else { + list = ""; + } + } + }, + + // Actual Callbacks object + self = { + + // Add a callback or a collection of callbacks to the list + add: function() { + if ( list ) { + + // If we have memory from a past run, we should fire after adding + if ( memory && !firing ) { + firingIndex = list.length - 1; + queue.push( memory ); + } + + ( function add( args ) { + jQuery.each( args, function( _, arg ) { + if ( isFunction( arg ) ) { + if ( !options.unique || !self.has( arg ) ) { + list.push( arg ); + } + } else if ( arg && arg.length && toType( arg ) !== "string" ) { + + // Inspect recursively + add( arg ); + } + } ); + } )( arguments ); + + if ( memory && !firing ) { + fire(); + } + } + return this; + }, + + // Remove a callback from the list + remove: function() { + jQuery.each( arguments, function( _, arg ) { + var index; + while ( ( index = jQuery.inArray( arg, list, index ) ) > -1 ) { + list.splice( index, 1 ); + + // Handle firing indexes + if ( index <= firingIndex ) { + firingIndex--; + } + } + } ); + return this; + }, + + // Check if a given callback is in the list. + // If no argument is given, return whether or not list has callbacks attached. + has: function( fn ) { + return fn ? + jQuery.inArray( fn, list ) > -1 : + list.length > 0; + }, + + // Remove all callbacks from the list + empty: function() { + if ( list ) { + list = []; + } + return this; + }, + + // Disable .fire and .add + // Abort any current/pending executions + // Clear all callbacks and values + disable: function() { + locked = queue = []; + list = memory = ""; + return this; + }, + disabled: function() { + return !list; + }, + + // Disable .fire + // Also disable .add unless we have memory (since it would have no effect) + // Abort any pending executions + lock: function() { + locked = queue = []; + if ( !memory && !firing ) { + list = memory = ""; + } + return this; + }, + locked: function() { + return !!locked; + }, + + // Call all callbacks with the given context and arguments + fireWith: function( context, args ) { + if ( !locked ) { + args = args || []; + args = [ context, args.slice ? args.slice() : args ]; + queue.push( args ); + if ( !firing ) { + fire(); + } + } + return this; + }, + + // Call all the callbacks with the given arguments + fire: function() { + self.fireWith( this, arguments ); + return this; + }, + + // To know if the callbacks have already been called at least once + fired: function() { + return !!fired; + } + }; + + return self; +}; + + +function Identity( v ) { + return v; +} +function Thrower( ex ) { + throw ex; +} + +function adoptValue( value, resolve, reject, noValue ) { + var method; + + try { + + // Check for promise aspect first to privilege synchronous behavior + if ( value && isFunction( ( method = value.promise ) ) ) { + method.call( value ).done( resolve ).fail( reject ); + + // Other thenables + } else if ( value && isFunction( ( method = value.then ) ) ) { + method.call( value, resolve, reject ); + + // Other non-thenables + } else { + + // Control `resolve` arguments by letting Array#slice cast boolean `noValue` to integer: + // * false: [ value ].slice( 0 ) => resolve( value ) + // * true: [ value ].slice( 1 ) => resolve() + resolve.apply( undefined, [ value ].slice( noValue ) ); + } + + // For Promises/A+, convert exceptions into rejections + // Since jQuery.when doesn't unwrap thenables, we can skip the extra checks appearing in + // Deferred#then to conditionally suppress rejection. + } catch ( value ) { + + // Support: Android 4.0 only + // Strict mode functions invoked without .call/.apply get global-object context + reject.apply( undefined, [ value ] ); + } +} + +jQuery.extend( { + + Deferred: function( func ) { + var tuples = [ + + // action, add listener, callbacks, + // ... .then handlers, argument index, [final state] + [ "notify", "progress", jQuery.Callbacks( "memory" ), + jQuery.Callbacks( "memory" ), 2 ], + [ "resolve", "done", jQuery.Callbacks( "once memory" ), + jQuery.Callbacks( "once memory" ), 0, "resolved" ], + [ "reject", "fail", jQuery.Callbacks( "once memory" ), + jQuery.Callbacks( "once memory" ), 1, "rejected" ] + ], + state = "pending", + promise = { + state: function() { + return state; + }, + always: function() { + deferred.done( arguments ).fail( arguments ); + return this; + }, + "catch": function( fn ) { + return promise.then( null, fn ); + }, + + // Keep pipe for back-compat + pipe: function( /* fnDone, fnFail, fnProgress */ ) { + var fns = arguments; + + return jQuery.Deferred( function( newDefer ) { + jQuery.each( tuples, function( _i, tuple ) { + + // Map tuples (progress, done, fail) to arguments (done, fail, progress) + var fn = isFunction( fns[ tuple[ 4 ] ] ) && fns[ tuple[ 4 ] ]; + + // deferred.progress(function() { bind to newDefer or newDefer.notify }) + // deferred.done(function() { bind to newDefer or newDefer.resolve }) + // deferred.fail(function() { bind to newDefer or newDefer.reject }) + deferred[ tuple[ 1 ] ]( function() { + var returned = fn && fn.apply( this, arguments ); + if ( returned && isFunction( returned.promise ) ) { + returned.promise() + .progress( newDefer.notify ) + .done( newDefer.resolve ) + .fail( newDefer.reject ); + } else { + newDefer[ tuple[ 0 ] + "With" ]( + this, + fn ? [ returned ] : arguments + ); + } + } ); + } ); + fns = null; + } ).promise(); + }, + then: function( onFulfilled, onRejected, onProgress ) { + var maxDepth = 0; + function resolve( depth, deferred, handler, special ) { + return function() { + var that = this, + args = arguments, + mightThrow = function() { + var returned, then; + + // Support: Promises/A+ section 2.3.3.3.3 + // https://promisesaplus.com/#point-59 + // Ignore double-resolution attempts + if ( depth < maxDepth ) { + return; + } + + returned = handler.apply( that, args ); + + // Support: Promises/A+ section 2.3.1 + // https://promisesaplus.com/#point-48 + if ( returned === deferred.promise() ) { + throw new TypeError( "Thenable self-resolution" ); + } + + // Support: Promises/A+ sections 2.3.3.1, 3.5 + // https://promisesaplus.com/#point-54 + // https://promisesaplus.com/#point-75 + // Retrieve `then` only once + then = returned && + + // Support: Promises/A+ section 2.3.4 + // https://promisesaplus.com/#point-64 + // Only check objects and functions for thenability + ( typeof returned === "object" || + typeof returned === "function" ) && + returned.then; + + // Handle a returned thenable + if ( isFunction( then ) ) { + + // Special processors (notify) just wait for resolution + if ( special ) { + then.call( + returned, + resolve( maxDepth, deferred, Identity, special ), + resolve( maxDepth, deferred, Thrower, special ) + ); + + // Normal processors (resolve) also hook into progress + } else { + + // ...and disregard older resolution values + maxDepth++; + + then.call( + returned, + resolve( maxDepth, deferred, Identity, special ), + resolve( maxDepth, deferred, Thrower, special ), + resolve( maxDepth, deferred, Identity, + deferred.notifyWith ) + ); + } + + // Handle all other returned values + } else { + + // Only substitute handlers pass on context + // and multiple values (non-spec behavior) + if ( handler !== Identity ) { + that = undefined; + args = [ returned ]; + } + + // Process the value(s) + // Default process is resolve + ( special || deferred.resolveWith )( that, args ); + } + }, + + // Only normal processors (resolve) catch and reject exceptions + process = special ? + mightThrow : + function() { + try { + mightThrow(); + } catch ( e ) { + + if ( jQuery.Deferred.exceptionHook ) { + jQuery.Deferred.exceptionHook( e, + process.stackTrace ); + } + + // Support: Promises/A+ section 2.3.3.3.4.1 + // https://promisesaplus.com/#point-61 + // Ignore post-resolution exceptions + if ( depth + 1 >= maxDepth ) { + + // Only substitute handlers pass on context + // and multiple values (non-spec behavior) + if ( handler !== Thrower ) { + that = undefined; + args = [ e ]; + } + + deferred.rejectWith( that, args ); + } + } + }; + + // Support: Promises/A+ section 2.3.3.3.1 + // https://promisesaplus.com/#point-57 + // Re-resolve promises immediately to dodge false rejection from + // subsequent errors + if ( depth ) { + process(); + } else { + + // Call an optional hook to record the stack, in case of exception + // since it's otherwise lost when execution goes async + if ( jQuery.Deferred.getStackHook ) { + process.stackTrace = jQuery.Deferred.getStackHook(); + } + window.setTimeout( process ); + } + }; + } + + return jQuery.Deferred( function( newDefer ) { + + // progress_handlers.add( ... ) + tuples[ 0 ][ 3 ].add( + resolve( + 0, + newDefer, + isFunction( onProgress ) ? + onProgress : + Identity, + newDefer.notifyWith + ) + ); + + // fulfilled_handlers.add( ... ) + tuples[ 1 ][ 3 ].add( + resolve( + 0, + newDefer, + isFunction( onFulfilled ) ? + onFulfilled : + Identity + ) + ); + + // rejected_handlers.add( ... ) + tuples[ 2 ][ 3 ].add( + resolve( + 0, + newDefer, + isFunction( onRejected ) ? + onRejected : + Thrower + ) + ); + } ).promise(); + }, + + // Get a promise for this deferred + // If obj is provided, the promise aspect is added to the object + promise: function( obj ) { + return obj != null ? jQuery.extend( obj, promise ) : promise; + } + }, + deferred = {}; + + // Add list-specific methods + jQuery.each( tuples, function( i, tuple ) { + var list = tuple[ 2 ], + stateString = tuple[ 5 ]; + + // promise.progress = list.add + // promise.done = list.add + // promise.fail = list.add + promise[ tuple[ 1 ] ] = list.add; + + // Handle state + if ( stateString ) { + list.add( + function() { + + // state = "resolved" (i.e., fulfilled) + // state = "rejected" + state = stateString; + }, + + // rejected_callbacks.disable + // fulfilled_callbacks.disable + tuples[ 3 - i ][ 2 ].disable, + + // rejected_handlers.disable + // fulfilled_handlers.disable + tuples[ 3 - i ][ 3 ].disable, + + // progress_callbacks.lock + tuples[ 0 ][ 2 ].lock, + + // progress_handlers.lock + tuples[ 0 ][ 3 ].lock + ); + } + + // progress_handlers.fire + // fulfilled_handlers.fire + // rejected_handlers.fire + list.add( tuple[ 3 ].fire ); + + // deferred.notify = function() { deferred.notifyWith(...) } + // deferred.resolve = function() { deferred.resolveWith(...) } + // deferred.reject = function() { deferred.rejectWith(...) } + deferred[ tuple[ 0 ] ] = function() { + deferred[ tuple[ 0 ] + "With" ]( this === deferred ? undefined : this, arguments ); + return this; + }; + + // deferred.notifyWith = list.fireWith + // deferred.resolveWith = list.fireWith + // deferred.rejectWith = list.fireWith + deferred[ tuple[ 0 ] + "With" ] = list.fireWith; + } ); + + // Make the deferred a promise + promise.promise( deferred ); + + // Call given func if any + if ( func ) { + func.call( deferred, deferred ); + } + + // All done! + return deferred; + }, + + // Deferred helper + when: function( singleValue ) { + var + + // count of uncompleted subordinates + remaining = arguments.length, + + // count of unprocessed arguments + i = remaining, + + // subordinate fulfillment data + resolveContexts = Array( i ), + resolveValues = slice.call( arguments ), + + // the primary Deferred + primary = jQuery.Deferred(), + + // subordinate callback factory + updateFunc = function( i ) { + return function( value ) { + resolveContexts[ i ] = this; + resolveValues[ i ] = arguments.length > 1 ? slice.call( arguments ) : value; + if ( !( --remaining ) ) { + primary.resolveWith( resolveContexts, resolveValues ); + } + }; + }; + + // Single- and empty arguments are adopted like Promise.resolve + if ( remaining <= 1 ) { + adoptValue( singleValue, primary.done( updateFunc( i ) ).resolve, primary.reject, + !remaining ); + + // Use .then() to unwrap secondary thenables (cf. gh-3000) + if ( primary.state() === "pending" || + isFunction( resolveValues[ i ] && resolveValues[ i ].then ) ) { + + return primary.then(); + } + } + + // Multiple arguments are aggregated like Promise.all array elements + while ( i-- ) { + adoptValue( resolveValues[ i ], updateFunc( i ), primary.reject ); + } + + return primary.promise(); + } +} ); + + +// These usually indicate a programmer mistake during development, +// warn about them ASAP rather than swallowing them by default. +var rerrorNames = /^(Eval|Internal|Range|Reference|Syntax|Type|URI)Error$/; + +jQuery.Deferred.exceptionHook = function( error, stack ) { + + // Support: IE 8 - 9 only + // Console exists when dev tools are open, which can happen at any time + if ( window.console && window.console.warn && error && rerrorNames.test( error.name ) ) { + window.console.warn( "jQuery.Deferred exception: " + error.message, error.stack, stack ); + } +}; + + + + +jQuery.readyException = function( error ) { + window.setTimeout( function() { + throw error; + } ); +}; + + + + +// The deferred used on DOM ready +var readyList = jQuery.Deferred(); + +jQuery.fn.ready = function( fn ) { + + readyList + .then( fn ) + + // Wrap jQuery.readyException in a function so that the lookup + // happens at the time of error handling instead of callback + // registration. + .catch( function( error ) { + jQuery.readyException( error ); + } ); + + return this; +}; + +jQuery.extend( { + + // Is the DOM ready to be used? Set to true once it occurs. + isReady: false, + + // A counter to track how many items to wait for before + // the ready event fires. See #6781 + readyWait: 1, + + // Handle when the DOM is ready + ready: function( wait ) { + + // Abort if there are pending holds or we're already ready + if ( wait === true ? --jQuery.readyWait : jQuery.isReady ) { + return; + } + + // Remember that the DOM is ready + jQuery.isReady = true; + + // If a normal DOM Ready event fired, decrement, and wait if need be + if ( wait !== true && --jQuery.readyWait > 0 ) { + return; + } + + // If there are functions bound, to execute + readyList.resolveWith( document, [ jQuery ] ); + } +} ); + +jQuery.ready.then = readyList.then; + +// The ready event handler and self cleanup method +function completed() { + document.removeEventListener( "DOMContentLoaded", completed ); + window.removeEventListener( "load", completed ); + jQuery.ready(); +} + +// Catch cases where $(document).ready() is called +// after the browser event has already occurred. +// Support: IE <=9 - 10 only +// Older IE sometimes signals "interactive" too soon +if ( document.readyState === "complete" || + ( document.readyState !== "loading" && !document.documentElement.doScroll ) ) { + + // Handle it asynchronously to allow scripts the opportunity to delay ready + window.setTimeout( jQuery.ready ); + +} else { + + // Use the handy event callback + document.addEventListener( "DOMContentLoaded", completed ); + + // A fallback to window.onload, that will always work + window.addEventListener( "load", completed ); +} + + + + +// Multifunctional method to get and set values of a collection +// The value/s can optionally be executed if it's a function +var access = function( elems, fn, key, value, chainable, emptyGet, raw ) { + var i = 0, + len = elems.length, + bulk = key == null; + + // Sets many values + if ( toType( key ) === "object" ) { + chainable = true; + for ( i in key ) { + access( elems, fn, i, key[ i ], true, emptyGet, raw ); + } + + // Sets one value + } else if ( value !== undefined ) { + chainable = true; + + if ( !isFunction( value ) ) { + raw = true; + } + + if ( bulk ) { + + // Bulk operations run against the entire set + if ( raw ) { + fn.call( elems, value ); + fn = null; + + // ...except when executing function values + } else { + bulk = fn; + fn = function( elem, _key, value ) { + return bulk.call( jQuery( elem ), value ); + }; + } + } + + if ( fn ) { + for ( ; i < len; i++ ) { + fn( + elems[ i ], key, raw ? + value : + value.call( elems[ i ], i, fn( elems[ i ], key ) ) + ); + } + } + } + + if ( chainable ) { + return elems; + } + + // Gets + if ( bulk ) { + return fn.call( elems ); + } + + return len ? fn( elems[ 0 ], key ) : emptyGet; +}; + + +// Matches dashed string for camelizing +var rmsPrefix = /^-ms-/, + rdashAlpha = /-([a-z])/g; + +// Used by camelCase as callback to replace() +function fcamelCase( _all, letter ) { + return letter.toUpperCase(); +} + +// Convert dashed to camelCase; used by the css and data modules +// Support: IE <=9 - 11, Edge 12 - 15 +// Microsoft forgot to hump their vendor prefix (#9572) +function camelCase( string ) { + return string.replace( rmsPrefix, "ms-" ).replace( rdashAlpha, fcamelCase ); +} +var acceptData = function( owner ) { + + // Accepts only: + // - Node + // - Node.ELEMENT_NODE + // - Node.DOCUMENT_NODE + // - Object + // - Any + return owner.nodeType === 1 || owner.nodeType === 9 || !( +owner.nodeType ); +}; + + + + +function Data() { + this.expando = jQuery.expando + Data.uid++; +} + +Data.uid = 1; + +Data.prototype = { + + cache: function( owner ) { + + // Check if the owner object already has a cache + var value = owner[ this.expando ]; + + // If not, create one + if ( !value ) { + value = {}; + + // We can accept data for non-element nodes in modern browsers, + // but we should not, see #8335. + // Always return an empty object. + if ( acceptData( owner ) ) { + + // If it is a node unlikely to be stringify-ed or looped over + // use plain assignment + if ( owner.nodeType ) { + owner[ this.expando ] = value; + + // Otherwise secure it in a non-enumerable property + // configurable must be true to allow the property to be + // deleted when data is removed + } else { + Object.defineProperty( owner, this.expando, { + value: value, + configurable: true + } ); + } + } + } + + return value; + }, + set: function( owner, data, value ) { + var prop, + cache = this.cache( owner ); + + // Handle: [ owner, key, value ] args + // Always use camelCase key (gh-2257) + if ( typeof data === "string" ) { + cache[ camelCase( data ) ] = value; + + // Handle: [ owner, { properties } ] args + } else { + + // Copy the properties one-by-one to the cache object + for ( prop in data ) { + cache[ camelCase( prop ) ] = data[ prop ]; + } + } + return cache; + }, + get: function( owner, key ) { + return key === undefined ? + this.cache( owner ) : + + // Always use camelCase key (gh-2257) + owner[ this.expando ] && owner[ this.expando ][ camelCase( key ) ]; + }, + access: function( owner, key, value ) { + + // In cases where either: + // + // 1. No key was specified + // 2. A string key was specified, but no value provided + // + // Take the "read" path and allow the get method to determine + // which value to return, respectively either: + // + // 1. The entire cache object + // 2. The data stored at the key + // + if ( key === undefined || + ( ( key && typeof key === "string" ) && value === undefined ) ) { + + return this.get( owner, key ); + } + + // When the key is not a string, or both a key and value + // are specified, set or extend (existing objects) with either: + // + // 1. An object of properties + // 2. A key and value + // + this.set( owner, key, value ); + + // Since the "set" path can have two possible entry points + // return the expected data based on which path was taken[*] + return value !== undefined ? value : key; + }, + remove: function( owner, key ) { + var i, + cache = owner[ this.expando ]; + + if ( cache === undefined ) { + return; + } + + if ( key !== undefined ) { + + // Support array or space separated string of keys + if ( Array.isArray( key ) ) { + + // If key is an array of keys... + // We always set camelCase keys, so remove that. + key = key.map( camelCase ); + } else { + key = camelCase( key ); + + // If a key with the spaces exists, use it. + // Otherwise, create an array by matching non-whitespace + key = key in cache ? + [ key ] : + ( key.match( rnothtmlwhite ) || [] ); + } + + i = key.length; + + while ( i-- ) { + delete cache[ key[ i ] ]; + } + } + + // Remove the expando if there's no more data + if ( key === undefined || jQuery.isEmptyObject( cache ) ) { + + // Support: Chrome <=35 - 45 + // Webkit & Blink performance suffers when deleting properties + // from DOM nodes, so set to undefined instead + // https://bugs.chromium.org/p/chromium/issues/detail?id=378607 (bug restricted) + if ( owner.nodeType ) { + owner[ this.expando ] = undefined; + } else { + delete owner[ this.expando ]; + } + } + }, + hasData: function( owner ) { + var cache = owner[ this.expando ]; + return cache !== undefined && !jQuery.isEmptyObject( cache ); + } +}; +var dataPriv = new Data(); + +var dataUser = new Data(); + + + +// Implementation Summary +// +// 1. Enforce API surface and semantic compatibility with 1.9.x branch +// 2. Improve the module's maintainability by reducing the storage +// paths to a single mechanism. +// 3. Use the same single mechanism to support "private" and "user" data. +// 4. _Never_ expose "private" data to user code (TODO: Drop _data, _removeData) +// 5. Avoid exposing implementation details on user objects (eg. expando properties) +// 6. Provide a clear path for implementation upgrade to WeakMap in 2014 + +var rbrace = /^(?:\{[\w\W]*\}|\[[\w\W]*\])$/, + rmultiDash = /[A-Z]/g; + +function getData( data ) { + if ( data === "true" ) { + return true; + } + + if ( data === "false" ) { + return false; + } + + if ( data === "null" ) { + return null; + } + + // Only convert to a number if it doesn't change the string + if ( data === +data + "" ) { + return +data; + } + + if ( rbrace.test( data ) ) { + return JSON.parse( data ); + } + + return data; +} + +function dataAttr( elem, key, data ) { + var name; + + // If nothing was found internally, try to fetch any + // data from the HTML5 data-* attribute + if ( data === undefined && elem.nodeType === 1 ) { + name = "data-" + key.replace( rmultiDash, "-$&" ).toLowerCase(); + data = elem.getAttribute( name ); + + if ( typeof data === "string" ) { + try { + data = getData( data ); + } catch ( e ) {} + + // Make sure we set the data so it isn't changed later + dataUser.set( elem, key, data ); + } else { + data = undefined; + } + } + return data; +} + +jQuery.extend( { + hasData: function( elem ) { + return dataUser.hasData( elem ) || dataPriv.hasData( elem ); + }, + + data: function( elem, name, data ) { + return dataUser.access( elem, name, data ); + }, + + removeData: function( elem, name ) { + dataUser.remove( elem, name ); + }, + + // TODO: Now that all calls to _data and _removeData have been replaced + // with direct calls to dataPriv methods, these can be deprecated. + _data: function( elem, name, data ) { + return dataPriv.access( elem, name, data ); + }, + + _removeData: function( elem, name ) { + dataPriv.remove( elem, name ); + } +} ); + +jQuery.fn.extend( { + data: function( key, value ) { + var i, name, data, + elem = this[ 0 ], + attrs = elem && elem.attributes; + + // Gets all values + if ( key === undefined ) { + if ( this.length ) { + data = dataUser.get( elem ); + + if ( elem.nodeType === 1 && !dataPriv.get( elem, "hasDataAttrs" ) ) { + i = attrs.length; + while ( i-- ) { + + // Support: IE 11 only + // The attrs elements can be null (#14894) + if ( attrs[ i ] ) { + name = attrs[ i ].name; + if ( name.indexOf( "data-" ) === 0 ) { + name = camelCase( name.slice( 5 ) ); + dataAttr( elem, name, data[ name ] ); + } + } + } + dataPriv.set( elem, "hasDataAttrs", true ); + } + } + + return data; + } + + // Sets multiple values + if ( typeof key === "object" ) { + return this.each( function() { + dataUser.set( this, key ); + } ); + } + + return access( this, function( value ) { + var data; + + // The calling jQuery object (element matches) is not empty + // (and therefore has an element appears at this[ 0 ]) and the + // `value` parameter was not undefined. An empty jQuery object + // will result in `undefined` for elem = this[ 0 ] which will + // throw an exception if an attempt to read a data cache is made. + if ( elem && value === undefined ) { + + // Attempt to get data from the cache + // The key will always be camelCased in Data + data = dataUser.get( elem, key ); + if ( data !== undefined ) { + return data; + } + + // Attempt to "discover" the data in + // HTML5 custom data-* attrs + data = dataAttr( elem, key ); + if ( data !== undefined ) { + return data; + } + + // We tried really hard, but the data doesn't exist. + return; + } + + // Set the data... + this.each( function() { + + // We always store the camelCased key + dataUser.set( this, key, value ); + } ); + }, null, value, arguments.length > 1, null, true ); + }, + + removeData: function( key ) { + return this.each( function() { + dataUser.remove( this, key ); + } ); + } +} ); + + +jQuery.extend( { + queue: function( elem, type, data ) { + var queue; + + if ( elem ) { + type = ( type || "fx" ) + "queue"; + queue = dataPriv.get( elem, type ); + + // Speed up dequeue by getting out quickly if this is just a lookup + if ( data ) { + if ( !queue || Array.isArray( data ) ) { + queue = dataPriv.access( elem, type, jQuery.makeArray( data ) ); + } else { + queue.push( data ); + } + } + return queue || []; + } + }, + + dequeue: function( elem, type ) { + type = type || "fx"; + + var queue = jQuery.queue( elem, type ), + startLength = queue.length, + fn = queue.shift(), + hooks = jQuery._queueHooks( elem, type ), + next = function() { + jQuery.dequeue( elem, type ); + }; + + // If the fx queue is dequeued, always remove the progress sentinel + if ( fn === "inprogress" ) { + fn = queue.shift(); + startLength--; + } + + if ( fn ) { + + // Add a progress sentinel to prevent the fx queue from being + // automatically dequeued + if ( type === "fx" ) { + queue.unshift( "inprogress" ); + } + + // Clear up the last queue stop function + delete hooks.stop; + fn.call( elem, next, hooks ); + } + + if ( !startLength && hooks ) { + hooks.empty.fire(); + } + }, + + // Not public - generate a queueHooks object, or return the current one + _queueHooks: function( elem, type ) { + var key = type + "queueHooks"; + return dataPriv.get( elem, key ) || dataPriv.access( elem, key, { + empty: jQuery.Callbacks( "once memory" ).add( function() { + dataPriv.remove( elem, [ type + "queue", key ] ); + } ) + } ); + } +} ); + +jQuery.fn.extend( { + queue: function( type, data ) { + var setter = 2; + + if ( typeof type !== "string" ) { + data = type; + type = "fx"; + setter--; + } + + if ( arguments.length < setter ) { + return jQuery.queue( this[ 0 ], type ); + } + + return data === undefined ? + this : + this.each( function() { + var queue = jQuery.queue( this, type, data ); + + // Ensure a hooks for this queue + jQuery._queueHooks( this, type ); + + if ( type === "fx" && queue[ 0 ] !== "inprogress" ) { + jQuery.dequeue( this, type ); + } + } ); + }, + dequeue: function( type ) { + return this.each( function() { + jQuery.dequeue( this, type ); + } ); + }, + clearQueue: function( type ) { + return this.queue( type || "fx", [] ); + }, + + // Get a promise resolved when queues of a certain type + // are emptied (fx is the type by default) + promise: function( type, obj ) { + var tmp, + count = 1, + defer = jQuery.Deferred(), + elements = this, + i = this.length, + resolve = function() { + if ( !( --count ) ) { + defer.resolveWith( elements, [ elements ] ); + } + }; + + if ( typeof type !== "string" ) { + obj = type; + type = undefined; + } + type = type || "fx"; + + while ( i-- ) { + tmp = dataPriv.get( elements[ i ], type + "queueHooks" ); + if ( tmp && tmp.empty ) { + count++; + tmp.empty.add( resolve ); + } + } + resolve(); + return defer.promise( obj ); + } +} ); +var pnum = ( /[+-]?(?:\d*\.|)\d+(?:[eE][+-]?\d+|)/ ).source; + +var rcssNum = new RegExp( "^(?:([+-])=|)(" + pnum + ")([a-z%]*)$", "i" ); + + +var cssExpand = [ "Top", "Right", "Bottom", "Left" ]; + +var documentElement = document.documentElement; + + + + var isAttached = function( elem ) { + return jQuery.contains( elem.ownerDocument, elem ); + }, + composed = { composed: true }; + + // Support: IE 9 - 11+, Edge 12 - 18+, iOS 10.0 - 10.2 only + // Check attachment across shadow DOM boundaries when possible (gh-3504) + // Support: iOS 10.0-10.2 only + // Early iOS 10 versions support `attachShadow` but not `getRootNode`, + // leading to errors. We need to check for `getRootNode`. + if ( documentElement.getRootNode ) { + isAttached = function( elem ) { + return jQuery.contains( elem.ownerDocument, elem ) || + elem.getRootNode( composed ) === elem.ownerDocument; + }; + } +var isHiddenWithinTree = function( elem, el ) { + + // isHiddenWithinTree might be called from jQuery#filter function; + // in that case, element will be second argument + elem = el || elem; + + // Inline style trumps all + return elem.style.display === "none" || + elem.style.display === "" && + + // Otherwise, check computed style + // Support: Firefox <=43 - 45 + // Disconnected elements can have computed display: none, so first confirm that elem is + // in the document. + isAttached( elem ) && + + jQuery.css( elem, "display" ) === "none"; + }; + + + +function adjustCSS( elem, prop, valueParts, tween ) { + var adjusted, scale, + maxIterations = 20, + currentValue = tween ? + function() { + return tween.cur(); + } : + function() { + return jQuery.css( elem, prop, "" ); + }, + initial = currentValue(), + unit = valueParts && valueParts[ 3 ] || ( jQuery.cssNumber[ prop ] ? "" : "px" ), + + // Starting value computation is required for potential unit mismatches + initialInUnit = elem.nodeType && + ( jQuery.cssNumber[ prop ] || unit !== "px" && +initial ) && + rcssNum.exec( jQuery.css( elem, prop ) ); + + if ( initialInUnit && initialInUnit[ 3 ] !== unit ) { + + // Support: Firefox <=54 + // Halve the iteration target value to prevent interference from CSS upper bounds (gh-2144) + initial = initial / 2; + + // Trust units reported by jQuery.css + unit = unit || initialInUnit[ 3 ]; + + // Iteratively approximate from a nonzero starting point + initialInUnit = +initial || 1; + + while ( maxIterations-- ) { + + // Evaluate and update our best guess (doubling guesses that zero out). + // Finish if the scale equals or crosses 1 (making the old*new product non-positive). + jQuery.style( elem, prop, initialInUnit + unit ); + if ( ( 1 - scale ) * ( 1 - ( scale = currentValue() / initial || 0.5 ) ) <= 0 ) { + maxIterations = 0; + } + initialInUnit = initialInUnit / scale; + + } + + initialInUnit = initialInUnit * 2; + jQuery.style( elem, prop, initialInUnit + unit ); + + // Make sure we update the tween properties later on + valueParts = valueParts || []; + } + + if ( valueParts ) { + initialInUnit = +initialInUnit || +initial || 0; + + // Apply relative offset (+=/-=) if specified + adjusted = valueParts[ 1 ] ? + initialInUnit + ( valueParts[ 1 ] + 1 ) * valueParts[ 2 ] : + +valueParts[ 2 ]; + if ( tween ) { + tween.unit = unit; + tween.start = initialInUnit; + tween.end = adjusted; + } + } + return adjusted; +} + + +var defaultDisplayMap = {}; + +function getDefaultDisplay( elem ) { + var temp, + doc = elem.ownerDocument, + nodeName = elem.nodeName, + display = defaultDisplayMap[ nodeName ]; + + if ( display ) { + return display; + } + + temp = doc.body.appendChild( doc.createElement( nodeName ) ); + display = jQuery.css( temp, "display" ); + + temp.parentNode.removeChild( temp ); + + if ( display === "none" ) { + display = "block"; + } + defaultDisplayMap[ nodeName ] = display; + + return display; +} + +function showHide( elements, show ) { + var display, elem, + values = [], + index = 0, + length = elements.length; + + // Determine new display value for elements that need to change + for ( ; index < length; index++ ) { + elem = elements[ index ]; + if ( !elem.style ) { + continue; + } + + display = elem.style.display; + if ( show ) { + + // Since we force visibility upon cascade-hidden elements, an immediate (and slow) + // check is required in this first loop unless we have a nonempty display value (either + // inline or about-to-be-restored) + if ( display === "none" ) { + values[ index ] = dataPriv.get( elem, "display" ) || null; + if ( !values[ index ] ) { + elem.style.display = ""; + } + } + if ( elem.style.display === "" && isHiddenWithinTree( elem ) ) { + values[ index ] = getDefaultDisplay( elem ); + } + } else { + if ( display !== "none" ) { + values[ index ] = "none"; + + // Remember what we're overwriting + dataPriv.set( elem, "display", display ); + } + } + } + + // Set the display of the elements in a second loop to avoid constant reflow + for ( index = 0; index < length; index++ ) { + if ( values[ index ] != null ) { + elements[ index ].style.display = values[ index ]; + } + } + + return elements; +} + +jQuery.fn.extend( { + show: function() { + return showHide( this, true ); + }, + hide: function() { + return showHide( this ); + }, + toggle: function( state ) { + if ( typeof state === "boolean" ) { + return state ? this.show() : this.hide(); + } + + return this.each( function() { + if ( isHiddenWithinTree( this ) ) { + jQuery( this ).show(); + } else { + jQuery( this ).hide(); + } + } ); + } +} ); +var rcheckableType = ( /^(?:checkbox|radio)$/i ); + +var rtagName = ( /<([a-z][^\/\0>\x20\t\r\n\f]*)/i ); + +var rscriptType = ( /^$|^module$|\/(?:java|ecma)script/i ); + + + +( function() { + var fragment = document.createDocumentFragment(), + div = fragment.appendChild( document.createElement( "div" ) ), + input = document.createElement( "input" ); + + // Support: Android 4.0 - 4.3 only + // Check state lost if the name is set (#11217) + // Support: Windows Web Apps (WWA) + // `name` and `type` must use .setAttribute for WWA (#14901) + input.setAttribute( "type", "radio" ); + input.setAttribute( "checked", "checked" ); + input.setAttribute( "name", "t" ); + + div.appendChild( input ); + + // Support: Android <=4.1 only + // Older WebKit doesn't clone checked state correctly in fragments + support.checkClone = div.cloneNode( true ).cloneNode( true ).lastChild.checked; + + // Support: IE <=11 only + // Make sure textarea (and checkbox) defaultValue is properly cloned + div.innerHTML = ""; + support.noCloneChecked = !!div.cloneNode( true ).lastChild.defaultValue; + + // Support: IE <=9 only + // IE <=9 replaces "; + support.option = !!div.lastChild; +} )(); + + +// We have to close these tags to support XHTML (#13200) +var wrapMap = { + + // XHTML parsers do not magically insert elements in the + // same way that tag soup parsers do. So we cannot shorten + // this by omitting or other required elements. + thead: [ 1, "", "
" ], + col: [ 2, "", "
" ], + tr: [ 2, "", "
" ], + td: [ 3, "", "
" ], + + _default: [ 0, "", "" ] +}; + +wrapMap.tbody = wrapMap.tfoot = wrapMap.colgroup = wrapMap.caption = wrapMap.thead; +wrapMap.th = wrapMap.td; + +// Support: IE <=9 only +if ( !support.option ) { + wrapMap.optgroup = wrapMap.option = [ 1, "" ]; +} + + +function getAll( context, tag ) { + + // Support: IE <=9 - 11 only + // Use typeof to avoid zero-argument method invocation on host objects (#15151) + var ret; + + if ( typeof context.getElementsByTagName !== "undefined" ) { + ret = context.getElementsByTagName( tag || "*" ); + + } else if ( typeof context.querySelectorAll !== "undefined" ) { + ret = context.querySelectorAll( tag || "*" ); + + } else { + ret = []; + } + + if ( tag === undefined || tag && nodeName( context, tag ) ) { + return jQuery.merge( [ context ], ret ); + } + + return ret; +} + + +// Mark scripts as having already been evaluated +function setGlobalEval( elems, refElements ) { + var i = 0, + l = elems.length; + + for ( ; i < l; i++ ) { + dataPriv.set( + elems[ i ], + "globalEval", + !refElements || dataPriv.get( refElements[ i ], "globalEval" ) + ); + } +} + + +var rhtml = /<|&#?\w+;/; + +function buildFragment( elems, context, scripts, selection, ignored ) { + var elem, tmp, tag, wrap, attached, j, + fragment = context.createDocumentFragment(), + nodes = [], + i = 0, + l = elems.length; + + for ( ; i < l; i++ ) { + elem = elems[ i ]; + + if ( elem || elem === 0 ) { + + // Add nodes directly + if ( toType( elem ) === "object" ) { + + // Support: Android <=4.0 only, PhantomJS 1 only + // push.apply(_, arraylike) throws on ancient WebKit + jQuery.merge( nodes, elem.nodeType ? [ elem ] : elem ); + + // Convert non-html into a text node + } else if ( !rhtml.test( elem ) ) { + nodes.push( context.createTextNode( elem ) ); + + // Convert html into DOM nodes + } else { + tmp = tmp || fragment.appendChild( context.createElement( "div" ) ); + + // Deserialize a standard representation + tag = ( rtagName.exec( elem ) || [ "", "" ] )[ 1 ].toLowerCase(); + wrap = wrapMap[ tag ] || wrapMap._default; + tmp.innerHTML = wrap[ 1 ] + jQuery.htmlPrefilter( elem ) + wrap[ 2 ]; + + // Descend through wrappers to the right content + j = wrap[ 0 ]; + while ( j-- ) { + tmp = tmp.lastChild; + } + + // Support: Android <=4.0 only, PhantomJS 1 only + // push.apply(_, arraylike) throws on ancient WebKit + jQuery.merge( nodes, tmp.childNodes ); + + // Remember the top-level container + tmp = fragment.firstChild; + + // Ensure the created nodes are orphaned (#12392) + tmp.textContent = ""; + } + } + } + + // Remove wrapper from fragment + fragment.textContent = ""; + + i = 0; + while ( ( elem = nodes[ i++ ] ) ) { + + // Skip elements already in the context collection (trac-4087) + if ( selection && jQuery.inArray( elem, selection ) > -1 ) { + if ( ignored ) { + ignored.push( elem ); + } + continue; + } + + attached = isAttached( elem ); + + // Append to fragment + tmp = getAll( fragment.appendChild( elem ), "script" ); + + // Preserve script evaluation history + if ( attached ) { + setGlobalEval( tmp ); + } + + // Capture executables + if ( scripts ) { + j = 0; + while ( ( elem = tmp[ j++ ] ) ) { + if ( rscriptType.test( elem.type || "" ) ) { + scripts.push( elem ); + } + } + } + } + + return fragment; +} + + +var rtypenamespace = /^([^.]*)(?:\.(.+)|)/; + +function returnTrue() { + return true; +} + +function returnFalse() { + return false; +} + +// Support: IE <=9 - 11+ +// focus() and blur() are asynchronous, except when they are no-op. +// So expect focus to be synchronous when the element is already active, +// and blur to be synchronous when the element is not already active. +// (focus and blur are always synchronous in other supported browsers, +// this just defines when we can count on it). +function expectSync( elem, type ) { + return ( elem === safeActiveElement() ) === ( type === "focus" ); +} + +// Support: IE <=9 only +// Accessing document.activeElement can throw unexpectedly +// https://bugs.jquery.com/ticket/13393 +function safeActiveElement() { + try { + return document.activeElement; + } catch ( err ) { } +} + +function on( elem, types, selector, data, fn, one ) { + var origFn, type; + + // Types can be a map of types/handlers + if ( typeof types === "object" ) { + + // ( types-Object, selector, data ) + if ( typeof selector !== "string" ) { + + // ( types-Object, data ) + data = data || selector; + selector = undefined; + } + for ( type in types ) { + on( elem, type, selector, data, types[ type ], one ); + } + return elem; + } + + if ( data == null && fn == null ) { + + // ( types, fn ) + fn = selector; + data = selector = undefined; + } else if ( fn == null ) { + if ( typeof selector === "string" ) { + + // ( types, selector, fn ) + fn = data; + data = undefined; + } else { + + // ( types, data, fn ) + fn = data; + data = selector; + selector = undefined; + } + } + if ( fn === false ) { + fn = returnFalse; + } else if ( !fn ) { + return elem; + } + + if ( one === 1 ) { + origFn = fn; + fn = function( event ) { + + // Can use an empty set, since event contains the info + jQuery().off( event ); + return origFn.apply( this, arguments ); + }; + + // Use same guid so caller can remove using origFn + fn.guid = origFn.guid || ( origFn.guid = jQuery.guid++ ); + } + return elem.each( function() { + jQuery.event.add( this, types, fn, data, selector ); + } ); +} + +/* + * Helper functions for managing events -- not part of the public interface. + * Props to Dean Edwards' addEvent library for many of the ideas. + */ +jQuery.event = { + + global: {}, + + add: function( elem, types, handler, data, selector ) { + + var handleObjIn, eventHandle, tmp, + events, t, handleObj, + special, handlers, type, namespaces, origType, + elemData = dataPriv.get( elem ); + + // Only attach events to objects that accept data + if ( !acceptData( elem ) ) { + return; + } + + // Caller can pass in an object of custom data in lieu of the handler + if ( handler.handler ) { + handleObjIn = handler; + handler = handleObjIn.handler; + selector = handleObjIn.selector; + } + + // Ensure that invalid selectors throw exceptions at attach time + // Evaluate against documentElement in case elem is a non-element node (e.g., document) + if ( selector ) { + jQuery.find.matchesSelector( documentElement, selector ); + } + + // Make sure that the handler has a unique ID, used to find/remove it later + if ( !handler.guid ) { + handler.guid = jQuery.guid++; + } + + // Init the element's event structure and main handler, if this is the first + if ( !( events = elemData.events ) ) { + events = elemData.events = Object.create( null ); + } + if ( !( eventHandle = elemData.handle ) ) { + eventHandle = elemData.handle = function( e ) { + + // Discard the second event of a jQuery.event.trigger() and + // when an event is called after a page has unloaded + return typeof jQuery !== "undefined" && jQuery.event.triggered !== e.type ? + jQuery.event.dispatch.apply( elem, arguments ) : undefined; + }; + } + + // Handle multiple events separated by a space + types = ( types || "" ).match( rnothtmlwhite ) || [ "" ]; + t = types.length; + while ( t-- ) { + tmp = rtypenamespace.exec( types[ t ] ) || []; + type = origType = tmp[ 1 ]; + namespaces = ( tmp[ 2 ] || "" ).split( "." ).sort(); + + // There *must* be a type, no attaching namespace-only handlers + if ( !type ) { + continue; + } + + // If event changes its type, use the special event handlers for the changed type + special = jQuery.event.special[ type ] || {}; + + // If selector defined, determine special event api type, otherwise given type + type = ( selector ? special.delegateType : special.bindType ) || type; + + // Update special based on newly reset type + special = jQuery.event.special[ type ] || {}; + + // handleObj is passed to all event handlers + handleObj = jQuery.extend( { + type: type, + origType: origType, + data: data, + handler: handler, + guid: handler.guid, + selector: selector, + needsContext: selector && jQuery.expr.match.needsContext.test( selector ), + namespace: namespaces.join( "." ) + }, handleObjIn ); + + // Init the event handler queue if we're the first + if ( !( handlers = events[ type ] ) ) { + handlers = events[ type ] = []; + handlers.delegateCount = 0; + + // Only use addEventListener if the special events handler returns false + if ( !special.setup || + special.setup.call( elem, data, namespaces, eventHandle ) === false ) { + + if ( elem.addEventListener ) { + elem.addEventListener( type, eventHandle ); + } + } + } + + if ( special.add ) { + special.add.call( elem, handleObj ); + + if ( !handleObj.handler.guid ) { + handleObj.handler.guid = handler.guid; + } + } + + // Add to the element's handler list, delegates in front + if ( selector ) { + handlers.splice( handlers.delegateCount++, 0, handleObj ); + } else { + handlers.push( handleObj ); + } + + // Keep track of which events have ever been used, for event optimization + jQuery.event.global[ type ] = true; + } + + }, + + // Detach an event or set of events from an element + remove: function( elem, types, handler, selector, mappedTypes ) { + + var j, origCount, tmp, + events, t, handleObj, + special, handlers, type, namespaces, origType, + elemData = dataPriv.hasData( elem ) && dataPriv.get( elem ); + + if ( !elemData || !( events = elemData.events ) ) { + return; + } + + // Once for each type.namespace in types; type may be omitted + types = ( types || "" ).match( rnothtmlwhite ) || [ "" ]; + t = types.length; + while ( t-- ) { + tmp = rtypenamespace.exec( types[ t ] ) || []; + type = origType = tmp[ 1 ]; + namespaces = ( tmp[ 2 ] || "" ).split( "." ).sort(); + + // Unbind all events (on this namespace, if provided) for the element + if ( !type ) { + for ( type in events ) { + jQuery.event.remove( elem, type + types[ t ], handler, selector, true ); + } + continue; + } + + special = jQuery.event.special[ type ] || {}; + type = ( selector ? special.delegateType : special.bindType ) || type; + handlers = events[ type ] || []; + tmp = tmp[ 2 ] && + new RegExp( "(^|\\.)" + namespaces.join( "\\.(?:.*\\.|)" ) + "(\\.|$)" ); + + // Remove matching events + origCount = j = handlers.length; + while ( j-- ) { + handleObj = handlers[ j ]; + + if ( ( mappedTypes || origType === handleObj.origType ) && + ( !handler || handler.guid === handleObj.guid ) && + ( !tmp || tmp.test( handleObj.namespace ) ) && + ( !selector || selector === handleObj.selector || + selector === "**" && handleObj.selector ) ) { + handlers.splice( j, 1 ); + + if ( handleObj.selector ) { + handlers.delegateCount--; + } + if ( special.remove ) { + special.remove.call( elem, handleObj ); + } + } + } + + // Remove generic event handler if we removed something and no more handlers exist + // (avoids potential for endless recursion during removal of special event handlers) + if ( origCount && !handlers.length ) { + if ( !special.teardown || + special.teardown.call( elem, namespaces, elemData.handle ) === false ) { + + jQuery.removeEvent( elem, type, elemData.handle ); + } + + delete events[ type ]; + } + } + + // Remove data and the expando if it's no longer used + if ( jQuery.isEmptyObject( events ) ) { + dataPriv.remove( elem, "handle events" ); + } + }, + + dispatch: function( nativeEvent ) { + + var i, j, ret, matched, handleObj, handlerQueue, + args = new Array( arguments.length ), + + // Make a writable jQuery.Event from the native event object + event = jQuery.event.fix( nativeEvent ), + + handlers = ( + dataPriv.get( this, "events" ) || Object.create( null ) + )[ event.type ] || [], + special = jQuery.event.special[ event.type ] || {}; + + // Use the fix-ed jQuery.Event rather than the (read-only) native event + args[ 0 ] = event; + + for ( i = 1; i < arguments.length; i++ ) { + args[ i ] = arguments[ i ]; + } + + event.delegateTarget = this; + + // Call the preDispatch hook for the mapped type, and let it bail if desired + if ( special.preDispatch && special.preDispatch.call( this, event ) === false ) { + return; + } + + // Determine handlers + handlerQueue = jQuery.event.handlers.call( this, event, handlers ); + + // Run delegates first; they may want to stop propagation beneath us + i = 0; + while ( ( matched = handlerQueue[ i++ ] ) && !event.isPropagationStopped() ) { + event.currentTarget = matched.elem; + + j = 0; + while ( ( handleObj = matched.handlers[ j++ ] ) && + !event.isImmediatePropagationStopped() ) { + + // If the event is namespaced, then each handler is only invoked if it is + // specially universal or its namespaces are a superset of the event's. + if ( !event.rnamespace || handleObj.namespace === false || + event.rnamespace.test( handleObj.namespace ) ) { + + event.handleObj = handleObj; + event.data = handleObj.data; + + ret = ( ( jQuery.event.special[ handleObj.origType ] || {} ).handle || + handleObj.handler ).apply( matched.elem, args ); + + if ( ret !== undefined ) { + if ( ( event.result = ret ) === false ) { + event.preventDefault(); + event.stopPropagation(); + } + } + } + } + } + + // Call the postDispatch hook for the mapped type + if ( special.postDispatch ) { + special.postDispatch.call( this, event ); + } + + return event.result; + }, + + handlers: function( event, handlers ) { + var i, handleObj, sel, matchedHandlers, matchedSelectors, + handlerQueue = [], + delegateCount = handlers.delegateCount, + cur = event.target; + + // Find delegate handlers + if ( delegateCount && + + // Support: IE <=9 + // Black-hole SVG instance trees (trac-13180) + cur.nodeType && + + // Support: Firefox <=42 + // Suppress spec-violating clicks indicating a non-primary pointer button (trac-3861) + // https://www.w3.org/TR/DOM-Level-3-Events/#event-type-click + // Support: IE 11 only + // ...but not arrow key "clicks" of radio inputs, which can have `button` -1 (gh-2343) + !( event.type === "click" && event.button >= 1 ) ) { + + for ( ; cur !== this; cur = cur.parentNode || this ) { + + // Don't check non-elements (#13208) + // Don't process clicks on disabled elements (#6911, #8165, #11382, #11764) + if ( cur.nodeType === 1 && !( event.type === "click" && cur.disabled === true ) ) { + matchedHandlers = []; + matchedSelectors = {}; + for ( i = 0; i < delegateCount; i++ ) { + handleObj = handlers[ i ]; + + // Don't conflict with Object.prototype properties (#13203) + sel = handleObj.selector + " "; + + if ( matchedSelectors[ sel ] === undefined ) { + matchedSelectors[ sel ] = handleObj.needsContext ? + jQuery( sel, this ).index( cur ) > -1 : + jQuery.find( sel, this, null, [ cur ] ).length; + } + if ( matchedSelectors[ sel ] ) { + matchedHandlers.push( handleObj ); + } + } + if ( matchedHandlers.length ) { + handlerQueue.push( { elem: cur, handlers: matchedHandlers } ); + } + } + } + } + + // Add the remaining (directly-bound) handlers + cur = this; + if ( delegateCount < handlers.length ) { + handlerQueue.push( { elem: cur, handlers: handlers.slice( delegateCount ) } ); + } + + return handlerQueue; + }, + + addProp: function( name, hook ) { + Object.defineProperty( jQuery.Event.prototype, name, { + enumerable: true, + configurable: true, + + get: isFunction( hook ) ? + function() { + if ( this.originalEvent ) { + return hook( this.originalEvent ); + } + } : + function() { + if ( this.originalEvent ) { + return this.originalEvent[ name ]; + } + }, + + set: function( value ) { + Object.defineProperty( this, name, { + enumerable: true, + configurable: true, + writable: true, + value: value + } ); + } + } ); + }, + + fix: function( originalEvent ) { + return originalEvent[ jQuery.expando ] ? + originalEvent : + new jQuery.Event( originalEvent ); + }, + + special: { + load: { + + // Prevent triggered image.load events from bubbling to window.load + noBubble: true + }, + click: { + + // Utilize native event to ensure correct state for checkable inputs + setup: function( data ) { + + // For mutual compressibility with _default, replace `this` access with a local var. + // `|| data` is dead code meant only to preserve the variable through minification. + var el = this || data; + + // Claim the first handler + if ( rcheckableType.test( el.type ) && + el.click && nodeName( el, "input" ) ) { + + // dataPriv.set( el, "click", ... ) + leverageNative( el, "click", returnTrue ); + } + + // Return false to allow normal processing in the caller + return false; + }, + trigger: function( data ) { + + // For mutual compressibility with _default, replace `this` access with a local var. + // `|| data` is dead code meant only to preserve the variable through minification. + var el = this || data; + + // Force setup before triggering a click + if ( rcheckableType.test( el.type ) && + el.click && nodeName( el, "input" ) ) { + + leverageNative( el, "click" ); + } + + // Return non-false to allow normal event-path propagation + return true; + }, + + // For cross-browser consistency, suppress native .click() on links + // Also prevent it if we're currently inside a leveraged native-event stack + _default: function( event ) { + var target = event.target; + return rcheckableType.test( target.type ) && + target.click && nodeName( target, "input" ) && + dataPriv.get( target, "click" ) || + nodeName( target, "a" ); + } + }, + + beforeunload: { + postDispatch: function( event ) { + + // Support: Firefox 20+ + // Firefox doesn't alert if the returnValue field is not set. + if ( event.result !== undefined && event.originalEvent ) { + event.originalEvent.returnValue = event.result; + } + } + } + } +}; + +// Ensure the presence of an event listener that handles manually-triggered +// synthetic events by interrupting progress until reinvoked in response to +// *native* events that it fires directly, ensuring that state changes have +// already occurred before other listeners are invoked. +function leverageNative( el, type, expectSync ) { + + // Missing expectSync indicates a trigger call, which must force setup through jQuery.event.add + if ( !expectSync ) { + if ( dataPriv.get( el, type ) === undefined ) { + jQuery.event.add( el, type, returnTrue ); + } + return; + } + + // Register the controller as a special universal handler for all event namespaces + dataPriv.set( el, type, false ); + jQuery.event.add( el, type, { + namespace: false, + handler: function( event ) { + var notAsync, result, + saved = dataPriv.get( this, type ); + + if ( ( event.isTrigger & 1 ) && this[ type ] ) { + + // Interrupt processing of the outer synthetic .trigger()ed event + // Saved data should be false in such cases, but might be a leftover capture object + // from an async native handler (gh-4350) + if ( !saved.length ) { + + // Store arguments for use when handling the inner native event + // There will always be at least one argument (an event object), so this array + // will not be confused with a leftover capture object. + saved = slice.call( arguments ); + dataPriv.set( this, type, saved ); + + // Trigger the native event and capture its result + // Support: IE <=9 - 11+ + // focus() and blur() are asynchronous + notAsync = expectSync( this, type ); + this[ type ](); + result = dataPriv.get( this, type ); + if ( saved !== result || notAsync ) { + dataPriv.set( this, type, false ); + } else { + result = {}; + } + if ( saved !== result ) { + + // Cancel the outer synthetic event + event.stopImmediatePropagation(); + event.preventDefault(); + + // Support: Chrome 86+ + // In Chrome, if an element having a focusout handler is blurred by + // clicking outside of it, it invokes the handler synchronously. If + // that handler calls `.remove()` on the element, the data is cleared, + // leaving `result` undefined. We need to guard against this. + return result && result.value; + } + + // If this is an inner synthetic event for an event with a bubbling surrogate + // (focus or blur), assume that the surrogate already propagated from triggering the + // native event and prevent that from happening again here. + // This technically gets the ordering wrong w.r.t. to `.trigger()` (in which the + // bubbling surrogate propagates *after* the non-bubbling base), but that seems + // less bad than duplication. + } else if ( ( jQuery.event.special[ type ] || {} ).delegateType ) { + event.stopPropagation(); + } + + // If this is a native event triggered above, everything is now in order + // Fire an inner synthetic event with the original arguments + } else if ( saved.length ) { + + // ...and capture the result + dataPriv.set( this, type, { + value: jQuery.event.trigger( + + // Support: IE <=9 - 11+ + // Extend with the prototype to reset the above stopImmediatePropagation() + jQuery.extend( saved[ 0 ], jQuery.Event.prototype ), + saved.slice( 1 ), + this + ) + } ); + + // Abort handling of the native event + event.stopImmediatePropagation(); + } + } + } ); +} + +jQuery.removeEvent = function( elem, type, handle ) { + + // This "if" is needed for plain objects + if ( elem.removeEventListener ) { + elem.removeEventListener( type, handle ); + } +}; + +jQuery.Event = function( src, props ) { + + // Allow instantiation without the 'new' keyword + if ( !( this instanceof jQuery.Event ) ) { + return new jQuery.Event( src, props ); + } + + // Event object + if ( src && src.type ) { + this.originalEvent = src; + this.type = src.type; + + // Events bubbling up the document may have been marked as prevented + // by a handler lower down the tree; reflect the correct value. + this.isDefaultPrevented = src.defaultPrevented || + src.defaultPrevented === undefined && + + // Support: Android <=2.3 only + src.returnValue === false ? + returnTrue : + returnFalse; + + // Create target properties + // Support: Safari <=6 - 7 only + // Target should not be a text node (#504, #13143) + this.target = ( src.target && src.target.nodeType === 3 ) ? + src.target.parentNode : + src.target; + + this.currentTarget = src.currentTarget; + this.relatedTarget = src.relatedTarget; + + // Event type + } else { + this.type = src; + } + + // Put explicitly provided properties onto the event object + if ( props ) { + jQuery.extend( this, props ); + } + + // Create a timestamp if incoming event doesn't have one + this.timeStamp = src && src.timeStamp || Date.now(); + + // Mark it as fixed + this[ jQuery.expando ] = true; +}; + +// jQuery.Event is based on DOM3 Events as specified by the ECMAScript Language Binding +// https://www.w3.org/TR/2003/WD-DOM-Level-3-Events-20030331/ecma-script-binding.html +jQuery.Event.prototype = { + constructor: jQuery.Event, + isDefaultPrevented: returnFalse, + isPropagationStopped: returnFalse, + isImmediatePropagationStopped: returnFalse, + isSimulated: false, + + preventDefault: function() { + var e = this.originalEvent; + + this.isDefaultPrevented = returnTrue; + + if ( e && !this.isSimulated ) { + e.preventDefault(); + } + }, + stopPropagation: function() { + var e = this.originalEvent; + + this.isPropagationStopped = returnTrue; + + if ( e && !this.isSimulated ) { + e.stopPropagation(); + } + }, + stopImmediatePropagation: function() { + var e = this.originalEvent; + + this.isImmediatePropagationStopped = returnTrue; + + if ( e && !this.isSimulated ) { + e.stopImmediatePropagation(); + } + + this.stopPropagation(); + } +}; + +// Includes all common event props including KeyEvent and MouseEvent specific props +jQuery.each( { + altKey: true, + bubbles: true, + cancelable: true, + changedTouches: true, + ctrlKey: true, + detail: true, + eventPhase: true, + metaKey: true, + pageX: true, + pageY: true, + shiftKey: true, + view: true, + "char": true, + code: true, + charCode: true, + key: true, + keyCode: true, + button: true, + buttons: true, + clientX: true, + clientY: true, + offsetX: true, + offsetY: true, + pointerId: true, + pointerType: true, + screenX: true, + screenY: true, + targetTouches: true, + toElement: true, + touches: true, + which: true +}, jQuery.event.addProp ); + +jQuery.each( { focus: "focusin", blur: "focusout" }, function( type, delegateType ) { + jQuery.event.special[ type ] = { + + // Utilize native event if possible so blur/focus sequence is correct + setup: function() { + + // Claim the first handler + // dataPriv.set( this, "focus", ... ) + // dataPriv.set( this, "blur", ... ) + leverageNative( this, type, expectSync ); + + // Return false to allow normal processing in the caller + return false; + }, + trigger: function() { + + // Force setup before trigger + leverageNative( this, type ); + + // Return non-false to allow normal event-path propagation + return true; + }, + + // Suppress native focus or blur as it's already being fired + // in leverageNative. + _default: function() { + return true; + }, + + delegateType: delegateType + }; +} ); + +// Create mouseenter/leave events using mouseover/out and event-time checks +// so that event delegation works in jQuery. +// Do the same for pointerenter/pointerleave and pointerover/pointerout +// +// Support: Safari 7 only +// Safari sends mouseenter too often; see: +// https://bugs.chromium.org/p/chromium/issues/detail?id=470258 +// for the description of the bug (it existed in older Chrome versions as well). +jQuery.each( { + mouseenter: "mouseover", + mouseleave: "mouseout", + pointerenter: "pointerover", + pointerleave: "pointerout" +}, function( orig, fix ) { + jQuery.event.special[ orig ] = { + delegateType: fix, + bindType: fix, + + handle: function( event ) { + var ret, + target = this, + related = event.relatedTarget, + handleObj = event.handleObj; + + // For mouseenter/leave call the handler if related is outside the target. + // NB: No relatedTarget if the mouse left/entered the browser window + if ( !related || ( related !== target && !jQuery.contains( target, related ) ) ) { + event.type = handleObj.origType; + ret = handleObj.handler.apply( this, arguments ); + event.type = fix; + } + return ret; + } + }; +} ); + +jQuery.fn.extend( { + + on: function( types, selector, data, fn ) { + return on( this, types, selector, data, fn ); + }, + one: function( types, selector, data, fn ) { + return on( this, types, selector, data, fn, 1 ); + }, + off: function( types, selector, fn ) { + var handleObj, type; + if ( types && types.preventDefault && types.handleObj ) { + + // ( event ) dispatched jQuery.Event + handleObj = types.handleObj; + jQuery( types.delegateTarget ).off( + handleObj.namespace ? + handleObj.origType + "." + handleObj.namespace : + handleObj.origType, + handleObj.selector, + handleObj.handler + ); + return this; + } + if ( typeof types === "object" ) { + + // ( types-object [, selector] ) + for ( type in types ) { + this.off( type, selector, types[ type ] ); + } + return this; + } + if ( selector === false || typeof selector === "function" ) { + + // ( types [, fn] ) + fn = selector; + selector = undefined; + } + if ( fn === false ) { + fn = returnFalse; + } + return this.each( function() { + jQuery.event.remove( this, types, fn, selector ); + } ); + } +} ); + + +var + + // Support: IE <=10 - 11, Edge 12 - 13 only + // In IE/Edge using regex groups here causes severe slowdowns. + // See https://connect.microsoft.com/IE/feedback/details/1736512/ + rnoInnerhtml = /\s*$/g; + +// Prefer a tbody over its parent table for containing new rows +function manipulationTarget( elem, content ) { + if ( nodeName( elem, "table" ) && + nodeName( content.nodeType !== 11 ? content : content.firstChild, "tr" ) ) { + + return jQuery( elem ).children( "tbody" )[ 0 ] || elem; + } + + return elem; +} + +// Replace/restore the type attribute of script elements for safe DOM manipulation +function disableScript( elem ) { + elem.type = ( elem.getAttribute( "type" ) !== null ) + "/" + elem.type; + return elem; +} +function restoreScript( elem ) { + if ( ( elem.type || "" ).slice( 0, 5 ) === "true/" ) { + elem.type = elem.type.slice( 5 ); + } else { + elem.removeAttribute( "type" ); + } + + return elem; +} + +function cloneCopyEvent( src, dest ) { + var i, l, type, pdataOld, udataOld, udataCur, events; + + if ( dest.nodeType !== 1 ) { + return; + } + + // 1. Copy private data: events, handlers, etc. + if ( dataPriv.hasData( src ) ) { + pdataOld = dataPriv.get( src ); + events = pdataOld.events; + + if ( events ) { + dataPriv.remove( dest, "handle events" ); + + for ( type in events ) { + for ( i = 0, l = events[ type ].length; i < l; i++ ) { + jQuery.event.add( dest, type, events[ type ][ i ] ); + } + } + } + } + + // 2. Copy user data + if ( dataUser.hasData( src ) ) { + udataOld = dataUser.access( src ); + udataCur = jQuery.extend( {}, udataOld ); + + dataUser.set( dest, udataCur ); + } +} + +// Fix IE bugs, see support tests +function fixInput( src, dest ) { + var nodeName = dest.nodeName.toLowerCase(); + + // Fails to persist the checked state of a cloned checkbox or radio button. + if ( nodeName === "input" && rcheckableType.test( src.type ) ) { + dest.checked = src.checked; + + // Fails to return the selected option to the default selected state when cloning options + } else if ( nodeName === "input" || nodeName === "textarea" ) { + dest.defaultValue = src.defaultValue; + } +} + +function domManip( collection, args, callback, ignored ) { + + // Flatten any nested arrays + args = flat( args ); + + var fragment, first, scripts, hasScripts, node, doc, + i = 0, + l = collection.length, + iNoClone = l - 1, + value = args[ 0 ], + valueIsFunction = isFunction( value ); + + // We can't cloneNode fragments that contain checked, in WebKit + if ( valueIsFunction || + ( l > 1 && typeof value === "string" && + !support.checkClone && rchecked.test( value ) ) ) { + return collection.each( function( index ) { + var self = collection.eq( index ); + if ( valueIsFunction ) { + args[ 0 ] = value.call( this, index, self.html() ); + } + domManip( self, args, callback, ignored ); + } ); + } + + if ( l ) { + fragment = buildFragment( args, collection[ 0 ].ownerDocument, false, collection, ignored ); + first = fragment.firstChild; + + if ( fragment.childNodes.length === 1 ) { + fragment = first; + } + + // Require either new content or an interest in ignored elements to invoke the callback + if ( first || ignored ) { + scripts = jQuery.map( getAll( fragment, "script" ), disableScript ); + hasScripts = scripts.length; + + // Use the original fragment for the last item + // instead of the first because it can end up + // being emptied incorrectly in certain situations (#8070). + for ( ; i < l; i++ ) { + node = fragment; + + if ( i !== iNoClone ) { + node = jQuery.clone( node, true, true ); + + // Keep references to cloned scripts for later restoration + if ( hasScripts ) { + + // Support: Android <=4.0 only, PhantomJS 1 only + // push.apply(_, arraylike) throws on ancient WebKit + jQuery.merge( scripts, getAll( node, "script" ) ); + } + } + + callback.call( collection[ i ], node, i ); + } + + if ( hasScripts ) { + doc = scripts[ scripts.length - 1 ].ownerDocument; + + // Reenable scripts + jQuery.map( scripts, restoreScript ); + + // Evaluate executable scripts on first document insertion + for ( i = 0; i < hasScripts; i++ ) { + node = scripts[ i ]; + if ( rscriptType.test( node.type || "" ) && + !dataPriv.access( node, "globalEval" ) && + jQuery.contains( doc, node ) ) { + + if ( node.src && ( node.type || "" ).toLowerCase() !== "module" ) { + + // Optional AJAX dependency, but won't run scripts if not present + if ( jQuery._evalUrl && !node.noModule ) { + jQuery._evalUrl( node.src, { + nonce: node.nonce || node.getAttribute( "nonce" ) + }, doc ); + } + } else { + DOMEval( node.textContent.replace( rcleanScript, "" ), node, doc ); + } + } + } + } + } + } + + return collection; +} + +function remove( elem, selector, keepData ) { + var node, + nodes = selector ? jQuery.filter( selector, elem ) : elem, + i = 0; + + for ( ; ( node = nodes[ i ] ) != null; i++ ) { + if ( !keepData && node.nodeType === 1 ) { + jQuery.cleanData( getAll( node ) ); + } + + if ( node.parentNode ) { + if ( keepData && isAttached( node ) ) { + setGlobalEval( getAll( node, "script" ) ); + } + node.parentNode.removeChild( node ); + } + } + + return elem; +} + +jQuery.extend( { + htmlPrefilter: function( html ) { + return html; + }, + + clone: function( elem, dataAndEvents, deepDataAndEvents ) { + var i, l, srcElements, destElements, + clone = elem.cloneNode( true ), + inPage = isAttached( elem ); + + // Fix IE cloning issues + if ( !support.noCloneChecked && ( elem.nodeType === 1 || elem.nodeType === 11 ) && + !jQuery.isXMLDoc( elem ) ) { + + // We eschew Sizzle here for performance reasons: https://jsperf.com/getall-vs-sizzle/2 + destElements = getAll( clone ); + srcElements = getAll( elem ); + + for ( i = 0, l = srcElements.length; i < l; i++ ) { + fixInput( srcElements[ i ], destElements[ i ] ); + } + } + + // Copy the events from the original to the clone + if ( dataAndEvents ) { + if ( deepDataAndEvents ) { + srcElements = srcElements || getAll( elem ); + destElements = destElements || getAll( clone ); + + for ( i = 0, l = srcElements.length; i < l; i++ ) { + cloneCopyEvent( srcElements[ i ], destElements[ i ] ); + } + } else { + cloneCopyEvent( elem, clone ); + } + } + + // Preserve script evaluation history + destElements = getAll( clone, "script" ); + if ( destElements.length > 0 ) { + setGlobalEval( destElements, !inPage && getAll( elem, "script" ) ); + } + + // Return the cloned set + return clone; + }, + + cleanData: function( elems ) { + var data, elem, type, + special = jQuery.event.special, + i = 0; + + for ( ; ( elem = elems[ i ] ) !== undefined; i++ ) { + if ( acceptData( elem ) ) { + if ( ( data = elem[ dataPriv.expando ] ) ) { + if ( data.events ) { + for ( type in data.events ) { + if ( special[ type ] ) { + jQuery.event.remove( elem, type ); + + // This is a shortcut to avoid jQuery.event.remove's overhead + } else { + jQuery.removeEvent( elem, type, data.handle ); + } + } + } + + // Support: Chrome <=35 - 45+ + // Assign undefined instead of using delete, see Data#remove + elem[ dataPriv.expando ] = undefined; + } + if ( elem[ dataUser.expando ] ) { + + // Support: Chrome <=35 - 45+ + // Assign undefined instead of using delete, see Data#remove + elem[ dataUser.expando ] = undefined; + } + } + } + } +} ); + +jQuery.fn.extend( { + detach: function( selector ) { + return remove( this, selector, true ); + }, + + remove: function( selector ) { + return remove( this, selector ); + }, + + text: function( value ) { + return access( this, function( value ) { + return value === undefined ? + jQuery.text( this ) : + this.empty().each( function() { + if ( this.nodeType === 1 || this.nodeType === 11 || this.nodeType === 9 ) { + this.textContent = value; + } + } ); + }, null, value, arguments.length ); + }, + + append: function() { + return domManip( this, arguments, function( elem ) { + if ( this.nodeType === 1 || this.nodeType === 11 || this.nodeType === 9 ) { + var target = manipulationTarget( this, elem ); + target.appendChild( elem ); + } + } ); + }, + + prepend: function() { + return domManip( this, arguments, function( elem ) { + if ( this.nodeType === 1 || this.nodeType === 11 || this.nodeType === 9 ) { + var target = manipulationTarget( this, elem ); + target.insertBefore( elem, target.firstChild ); + } + } ); + }, + + before: function() { + return domManip( this, arguments, function( elem ) { + if ( this.parentNode ) { + this.parentNode.insertBefore( elem, this ); + } + } ); + }, + + after: function() { + return domManip( this, arguments, function( elem ) { + if ( this.parentNode ) { + this.parentNode.insertBefore( elem, this.nextSibling ); + } + } ); + }, + + empty: function() { + var elem, + i = 0; + + for ( ; ( elem = this[ i ] ) != null; i++ ) { + if ( elem.nodeType === 1 ) { + + // Prevent memory leaks + jQuery.cleanData( getAll( elem, false ) ); + + // Remove any remaining nodes + elem.textContent = ""; + } + } + + return this; + }, + + clone: function( dataAndEvents, deepDataAndEvents ) { + dataAndEvents = dataAndEvents == null ? false : dataAndEvents; + deepDataAndEvents = deepDataAndEvents == null ? dataAndEvents : deepDataAndEvents; + + return this.map( function() { + return jQuery.clone( this, dataAndEvents, deepDataAndEvents ); + } ); + }, + + html: function( value ) { + return access( this, function( value ) { + var elem = this[ 0 ] || {}, + i = 0, + l = this.length; + + if ( value === undefined && elem.nodeType === 1 ) { + return elem.innerHTML; + } + + // See if we can take a shortcut and just use innerHTML + if ( typeof value === "string" && !rnoInnerhtml.test( value ) && + !wrapMap[ ( rtagName.exec( value ) || [ "", "" ] )[ 1 ].toLowerCase() ] ) { + + value = jQuery.htmlPrefilter( value ); + + try { + for ( ; i < l; i++ ) { + elem = this[ i ] || {}; + + // Remove element nodes and prevent memory leaks + if ( elem.nodeType === 1 ) { + jQuery.cleanData( getAll( elem, false ) ); + elem.innerHTML = value; + } + } + + elem = 0; + + // If using innerHTML throws an exception, use the fallback method + } catch ( e ) {} + } + + if ( elem ) { + this.empty().append( value ); + } + }, null, value, arguments.length ); + }, + + replaceWith: function() { + var ignored = []; + + // Make the changes, replacing each non-ignored context element with the new content + return domManip( this, arguments, function( elem ) { + var parent = this.parentNode; + + if ( jQuery.inArray( this, ignored ) < 0 ) { + jQuery.cleanData( getAll( this ) ); + if ( parent ) { + parent.replaceChild( elem, this ); + } + } + + // Force callback invocation + }, ignored ); + } +} ); + +jQuery.each( { + appendTo: "append", + prependTo: "prepend", + insertBefore: "before", + insertAfter: "after", + replaceAll: "replaceWith" +}, function( name, original ) { + jQuery.fn[ name ] = function( selector ) { + var elems, + ret = [], + insert = jQuery( selector ), + last = insert.length - 1, + i = 0; + + for ( ; i <= last; i++ ) { + elems = i === last ? this : this.clone( true ); + jQuery( insert[ i ] )[ original ]( elems ); + + // Support: Android <=4.0 only, PhantomJS 1 only + // .get() because push.apply(_, arraylike) throws on ancient WebKit + push.apply( ret, elems.get() ); + } + + return this.pushStack( ret ); + }; +} ); +var rnumnonpx = new RegExp( "^(" + pnum + ")(?!px)[a-z%]+$", "i" ); + +var getStyles = function( elem ) { + + // Support: IE <=11 only, Firefox <=30 (#15098, #14150) + // IE throws on elements created in popups + // FF meanwhile throws on frame elements through "defaultView.getComputedStyle" + var view = elem.ownerDocument.defaultView; + + if ( !view || !view.opener ) { + view = window; + } + + return view.getComputedStyle( elem ); + }; + +var swap = function( elem, options, callback ) { + var ret, name, + old = {}; + + // Remember the old values, and insert the new ones + for ( name in options ) { + old[ name ] = elem.style[ name ]; + elem.style[ name ] = options[ name ]; + } + + ret = callback.call( elem ); + + // Revert the old values + for ( name in options ) { + elem.style[ name ] = old[ name ]; + } + + return ret; +}; + + +var rboxStyle = new RegExp( cssExpand.join( "|" ), "i" ); + + + +( function() { + + // Executing both pixelPosition & boxSizingReliable tests require only one layout + // so they're executed at the same time to save the second computation. + function computeStyleTests() { + + // This is a singleton, we need to execute it only once + if ( !div ) { + return; + } + + container.style.cssText = "position:absolute;left:-11111px;width:60px;" + + "margin-top:1px;padding:0;border:0"; + div.style.cssText = + "position:relative;display:block;box-sizing:border-box;overflow:scroll;" + + "margin:auto;border:1px;padding:1px;" + + "width:60%;top:1%"; + documentElement.appendChild( container ).appendChild( div ); + + var divStyle = window.getComputedStyle( div ); + pixelPositionVal = divStyle.top !== "1%"; + + // Support: Android 4.0 - 4.3 only, Firefox <=3 - 44 + reliableMarginLeftVal = roundPixelMeasures( divStyle.marginLeft ) === 12; + + // Support: Android 4.0 - 4.3 only, Safari <=9.1 - 10.1, iOS <=7.0 - 9.3 + // Some styles come back with percentage values, even though they shouldn't + div.style.right = "60%"; + pixelBoxStylesVal = roundPixelMeasures( divStyle.right ) === 36; + + // Support: IE 9 - 11 only + // Detect misreporting of content dimensions for box-sizing:border-box elements + boxSizingReliableVal = roundPixelMeasures( divStyle.width ) === 36; + + // Support: IE 9 only + // Detect overflow:scroll screwiness (gh-3699) + // Support: Chrome <=64 + // Don't get tricked when zoom affects offsetWidth (gh-4029) + div.style.position = "absolute"; + scrollboxSizeVal = roundPixelMeasures( div.offsetWidth / 3 ) === 12; + + documentElement.removeChild( container ); + + // Nullify the div so it wouldn't be stored in the memory and + // it will also be a sign that checks already performed + div = null; + } + + function roundPixelMeasures( measure ) { + return Math.round( parseFloat( measure ) ); + } + + var pixelPositionVal, boxSizingReliableVal, scrollboxSizeVal, pixelBoxStylesVal, + reliableTrDimensionsVal, reliableMarginLeftVal, + container = document.createElement( "div" ), + div = document.createElement( "div" ); + + // Finish early in limited (non-browser) environments + if ( !div.style ) { + return; + } + + // Support: IE <=9 - 11 only + // Style of cloned element affects source element cloned (#8908) + div.style.backgroundClip = "content-box"; + div.cloneNode( true ).style.backgroundClip = ""; + support.clearCloneStyle = div.style.backgroundClip === "content-box"; + + jQuery.extend( support, { + boxSizingReliable: function() { + computeStyleTests(); + return boxSizingReliableVal; + }, + pixelBoxStyles: function() { + computeStyleTests(); + return pixelBoxStylesVal; + }, + pixelPosition: function() { + computeStyleTests(); + return pixelPositionVal; + }, + reliableMarginLeft: function() { + computeStyleTests(); + return reliableMarginLeftVal; + }, + scrollboxSize: function() { + computeStyleTests(); + return scrollboxSizeVal; + }, + + // Support: IE 9 - 11+, Edge 15 - 18+ + // IE/Edge misreport `getComputedStyle` of table rows with width/height + // set in CSS while `offset*` properties report correct values. + // Behavior in IE 9 is more subtle than in newer versions & it passes + // some versions of this test; make sure not to make it pass there! + // + // Support: Firefox 70+ + // Only Firefox includes border widths + // in computed dimensions. (gh-4529) + reliableTrDimensions: function() { + var table, tr, trChild, trStyle; + if ( reliableTrDimensionsVal == null ) { + table = document.createElement( "table" ); + tr = document.createElement( "tr" ); + trChild = document.createElement( "div" ); + + table.style.cssText = "position:absolute;left:-11111px;border-collapse:separate"; + tr.style.cssText = "border:1px solid"; + + // Support: Chrome 86+ + // Height set through cssText does not get applied. + // Computed height then comes back as 0. + tr.style.height = "1px"; + trChild.style.height = "9px"; + + // Support: Android 8 Chrome 86+ + // In our bodyBackground.html iframe, + // display for all div elements is set to "inline", + // which causes a problem only in Android 8 Chrome 86. + // Ensuring the div is display: block + // gets around this issue. + trChild.style.display = "block"; + + documentElement + .appendChild( table ) + .appendChild( tr ) + .appendChild( trChild ); + + trStyle = window.getComputedStyle( tr ); + reliableTrDimensionsVal = ( parseInt( trStyle.height, 10 ) + + parseInt( trStyle.borderTopWidth, 10 ) + + parseInt( trStyle.borderBottomWidth, 10 ) ) === tr.offsetHeight; + + documentElement.removeChild( table ); + } + return reliableTrDimensionsVal; + } + } ); +} )(); + + +function curCSS( elem, name, computed ) { + var width, minWidth, maxWidth, ret, + + // Support: Firefox 51+ + // Retrieving style before computed somehow + // fixes an issue with getting wrong values + // on detached elements + style = elem.style; + + computed = computed || getStyles( elem ); + + // getPropertyValue is needed for: + // .css('filter') (IE 9 only, #12537) + // .css('--customProperty) (#3144) + if ( computed ) { + ret = computed.getPropertyValue( name ) || computed[ name ]; + + if ( ret === "" && !isAttached( elem ) ) { + ret = jQuery.style( elem, name ); + } + + // A tribute to the "awesome hack by Dean Edwards" + // Android Browser returns percentage for some values, + // but width seems to be reliably pixels. + // This is against the CSSOM draft spec: + // https://drafts.csswg.org/cssom/#resolved-values + if ( !support.pixelBoxStyles() && rnumnonpx.test( ret ) && rboxStyle.test( name ) ) { + + // Remember the original values + width = style.width; + minWidth = style.minWidth; + maxWidth = style.maxWidth; + + // Put in the new values to get a computed value out + style.minWidth = style.maxWidth = style.width = ret; + ret = computed.width; + + // Revert the changed values + style.width = width; + style.minWidth = minWidth; + style.maxWidth = maxWidth; + } + } + + return ret !== undefined ? + + // Support: IE <=9 - 11 only + // IE returns zIndex value as an integer. + ret + "" : + ret; +} + + +function addGetHookIf( conditionFn, hookFn ) { + + // Define the hook, we'll check on the first run if it's really needed. + return { + get: function() { + if ( conditionFn() ) { + + // Hook not needed (or it's not possible to use it due + // to missing dependency), remove it. + delete this.get; + return; + } + + // Hook needed; redefine it so that the support test is not executed again. + return ( this.get = hookFn ).apply( this, arguments ); + } + }; +} + + +var cssPrefixes = [ "Webkit", "Moz", "ms" ], + emptyStyle = document.createElement( "div" ).style, + vendorProps = {}; + +// Return a vendor-prefixed property or undefined +function vendorPropName( name ) { + + // Check for vendor prefixed names + var capName = name[ 0 ].toUpperCase() + name.slice( 1 ), + i = cssPrefixes.length; + + while ( i-- ) { + name = cssPrefixes[ i ] + capName; + if ( name in emptyStyle ) { + return name; + } + } +} + +// Return a potentially-mapped jQuery.cssProps or vendor prefixed property +function finalPropName( name ) { + var final = jQuery.cssProps[ name ] || vendorProps[ name ]; + + if ( final ) { + return final; + } + if ( name in emptyStyle ) { + return name; + } + return vendorProps[ name ] = vendorPropName( name ) || name; +} + + +var + + // Swappable if display is none or starts with table + // except "table", "table-cell", or "table-caption" + // See here for display values: https://developer.mozilla.org/en-US/docs/CSS/display + rdisplayswap = /^(none|table(?!-c[ea]).+)/, + rcustomProp = /^--/, + cssShow = { position: "absolute", visibility: "hidden", display: "block" }, + cssNormalTransform = { + letterSpacing: "0", + fontWeight: "400" + }; + +function setPositiveNumber( _elem, value, subtract ) { + + // Any relative (+/-) values have already been + // normalized at this point + var matches = rcssNum.exec( value ); + return matches ? + + // Guard against undefined "subtract", e.g., when used as in cssHooks + Math.max( 0, matches[ 2 ] - ( subtract || 0 ) ) + ( matches[ 3 ] || "px" ) : + value; +} + +function boxModelAdjustment( elem, dimension, box, isBorderBox, styles, computedVal ) { + var i = dimension === "width" ? 1 : 0, + extra = 0, + delta = 0; + + // Adjustment may not be necessary + if ( box === ( isBorderBox ? "border" : "content" ) ) { + return 0; + } + + for ( ; i < 4; i += 2 ) { + + // Both box models exclude margin + if ( box === "margin" ) { + delta += jQuery.css( elem, box + cssExpand[ i ], true, styles ); + } + + // If we get here with a content-box, we're seeking "padding" or "border" or "margin" + if ( !isBorderBox ) { + + // Add padding + delta += jQuery.css( elem, "padding" + cssExpand[ i ], true, styles ); + + // For "border" or "margin", add border + if ( box !== "padding" ) { + delta += jQuery.css( elem, "border" + cssExpand[ i ] + "Width", true, styles ); + + // But still keep track of it otherwise + } else { + extra += jQuery.css( elem, "border" + cssExpand[ i ] + "Width", true, styles ); + } + + // If we get here with a border-box (content + padding + border), we're seeking "content" or + // "padding" or "margin" + } else { + + // For "content", subtract padding + if ( box === "content" ) { + delta -= jQuery.css( elem, "padding" + cssExpand[ i ], true, styles ); + } + + // For "content" or "padding", subtract border + if ( box !== "margin" ) { + delta -= jQuery.css( elem, "border" + cssExpand[ i ] + "Width", true, styles ); + } + } + } + + // Account for positive content-box scroll gutter when requested by providing computedVal + if ( !isBorderBox && computedVal >= 0 ) { + + // offsetWidth/offsetHeight is a rounded sum of content, padding, scroll gutter, and border + // Assuming integer scroll gutter, subtract the rest and round down + delta += Math.max( 0, Math.ceil( + elem[ "offset" + dimension[ 0 ].toUpperCase() + dimension.slice( 1 ) ] - + computedVal - + delta - + extra - + 0.5 + + // If offsetWidth/offsetHeight is unknown, then we can't determine content-box scroll gutter + // Use an explicit zero to avoid NaN (gh-3964) + ) ) || 0; + } + + return delta; +} + +function getWidthOrHeight( elem, dimension, extra ) { + + // Start with computed style + var styles = getStyles( elem ), + + // To avoid forcing a reflow, only fetch boxSizing if we need it (gh-4322). + // Fake content-box until we know it's needed to know the true value. + boxSizingNeeded = !support.boxSizingReliable() || extra, + isBorderBox = boxSizingNeeded && + jQuery.css( elem, "boxSizing", false, styles ) === "border-box", + valueIsBorderBox = isBorderBox, + + val = curCSS( elem, dimension, styles ), + offsetProp = "offset" + dimension[ 0 ].toUpperCase() + dimension.slice( 1 ); + + // Support: Firefox <=54 + // Return a confounding non-pixel value or feign ignorance, as appropriate. + if ( rnumnonpx.test( val ) ) { + if ( !extra ) { + return val; + } + val = "auto"; + } + + + // Support: IE 9 - 11 only + // Use offsetWidth/offsetHeight for when box sizing is unreliable. + // In those cases, the computed value can be trusted to be border-box. + if ( ( !support.boxSizingReliable() && isBorderBox || + + // Support: IE 10 - 11+, Edge 15 - 18+ + // IE/Edge misreport `getComputedStyle` of table rows with width/height + // set in CSS while `offset*` properties report correct values. + // Interestingly, in some cases IE 9 doesn't suffer from this issue. + !support.reliableTrDimensions() && nodeName( elem, "tr" ) || + + // Fall back to offsetWidth/offsetHeight when value is "auto" + // This happens for inline elements with no explicit setting (gh-3571) + val === "auto" || + + // Support: Android <=4.1 - 4.3 only + // Also use offsetWidth/offsetHeight for misreported inline dimensions (gh-3602) + !parseFloat( val ) && jQuery.css( elem, "display", false, styles ) === "inline" ) && + + // Make sure the element is visible & connected + elem.getClientRects().length ) { + + isBorderBox = jQuery.css( elem, "boxSizing", false, styles ) === "border-box"; + + // Where available, offsetWidth/offsetHeight approximate border box dimensions. + // Where not available (e.g., SVG), assume unreliable box-sizing and interpret the + // retrieved value as a content box dimension. + valueIsBorderBox = offsetProp in elem; + if ( valueIsBorderBox ) { + val = elem[ offsetProp ]; + } + } + + // Normalize "" and auto + val = parseFloat( val ) || 0; + + // Adjust for the element's box model + return ( val + + boxModelAdjustment( + elem, + dimension, + extra || ( isBorderBox ? "border" : "content" ), + valueIsBorderBox, + styles, + + // Provide the current computed size to request scroll gutter calculation (gh-3589) + val + ) + ) + "px"; +} + +jQuery.extend( { + + // Add in style property hooks for overriding the default + // behavior of getting and setting a style property + cssHooks: { + opacity: { + get: function( elem, computed ) { + if ( computed ) { + + // We should always get a number back from opacity + var ret = curCSS( elem, "opacity" ); + return ret === "" ? "1" : ret; + } + } + } + }, + + // Don't automatically add "px" to these possibly-unitless properties + cssNumber: { + "animationIterationCount": true, + "columnCount": true, + "fillOpacity": true, + "flexGrow": true, + "flexShrink": true, + "fontWeight": true, + "gridArea": true, + "gridColumn": true, + "gridColumnEnd": true, + "gridColumnStart": true, + "gridRow": true, + "gridRowEnd": true, + "gridRowStart": true, + "lineHeight": true, + "opacity": true, + "order": true, + "orphans": true, + "widows": true, + "zIndex": true, + "zoom": true + }, + + // Add in properties whose names you wish to fix before + // setting or getting the value + cssProps: {}, + + // Get and set the style property on a DOM Node + style: function( elem, name, value, extra ) { + + // Don't set styles on text and comment nodes + if ( !elem || elem.nodeType === 3 || elem.nodeType === 8 || !elem.style ) { + return; + } + + // Make sure that we're working with the right name + var ret, type, hooks, + origName = camelCase( name ), + isCustomProp = rcustomProp.test( name ), + style = elem.style; + + // Make sure that we're working with the right name. We don't + // want to query the value if it is a CSS custom property + // since they are user-defined. + if ( !isCustomProp ) { + name = finalPropName( origName ); + } + + // Gets hook for the prefixed version, then unprefixed version + hooks = jQuery.cssHooks[ name ] || jQuery.cssHooks[ origName ]; + + // Check if we're setting a value + if ( value !== undefined ) { + type = typeof value; + + // Convert "+=" or "-=" to relative numbers (#7345) + if ( type === "string" && ( ret = rcssNum.exec( value ) ) && ret[ 1 ] ) { + value = adjustCSS( elem, name, ret ); + + // Fixes bug #9237 + type = "number"; + } + + // Make sure that null and NaN values aren't set (#7116) + if ( value == null || value !== value ) { + return; + } + + // If a number was passed in, add the unit (except for certain CSS properties) + // The isCustomProp check can be removed in jQuery 4.0 when we only auto-append + // "px" to a few hardcoded values. + if ( type === "number" && !isCustomProp ) { + value += ret && ret[ 3 ] || ( jQuery.cssNumber[ origName ] ? "" : "px" ); + } + + // background-* props affect original clone's values + if ( !support.clearCloneStyle && value === "" && name.indexOf( "background" ) === 0 ) { + style[ name ] = "inherit"; + } + + // If a hook was provided, use that value, otherwise just set the specified value + if ( !hooks || !( "set" in hooks ) || + ( value = hooks.set( elem, value, extra ) ) !== undefined ) { + + if ( isCustomProp ) { + style.setProperty( name, value ); + } else { + style[ name ] = value; + } + } + + } else { + + // If a hook was provided get the non-computed value from there + if ( hooks && "get" in hooks && + ( ret = hooks.get( elem, false, extra ) ) !== undefined ) { + + return ret; + } + + // Otherwise just get the value from the style object + return style[ name ]; + } + }, + + css: function( elem, name, extra, styles ) { + var val, num, hooks, + origName = camelCase( name ), + isCustomProp = rcustomProp.test( name ); + + // Make sure that we're working with the right name. We don't + // want to modify the value if it is a CSS custom property + // since they are user-defined. + if ( !isCustomProp ) { + name = finalPropName( origName ); + } + + // Try prefixed name followed by the unprefixed name + hooks = jQuery.cssHooks[ name ] || jQuery.cssHooks[ origName ]; + + // If a hook was provided get the computed value from there + if ( hooks && "get" in hooks ) { + val = hooks.get( elem, true, extra ); + } + + // Otherwise, if a way to get the computed value exists, use that + if ( val === undefined ) { + val = curCSS( elem, name, styles ); + } + + // Convert "normal" to computed value + if ( val === "normal" && name in cssNormalTransform ) { + val = cssNormalTransform[ name ]; + } + + // Make numeric if forced or a qualifier was provided and val looks numeric + if ( extra === "" || extra ) { + num = parseFloat( val ); + return extra === true || isFinite( num ) ? num || 0 : val; + } + + return val; + } +} ); + +jQuery.each( [ "height", "width" ], function( _i, dimension ) { + jQuery.cssHooks[ dimension ] = { + get: function( elem, computed, extra ) { + if ( computed ) { + + // Certain elements can have dimension info if we invisibly show them + // but it must have a current display style that would benefit + return rdisplayswap.test( jQuery.css( elem, "display" ) ) && + + // Support: Safari 8+ + // Table columns in Safari have non-zero offsetWidth & zero + // getBoundingClientRect().width unless display is changed. + // Support: IE <=11 only + // Running getBoundingClientRect on a disconnected node + // in IE throws an error. + ( !elem.getClientRects().length || !elem.getBoundingClientRect().width ) ? + swap( elem, cssShow, function() { + return getWidthOrHeight( elem, dimension, extra ); + } ) : + getWidthOrHeight( elem, dimension, extra ); + } + }, + + set: function( elem, value, extra ) { + var matches, + styles = getStyles( elem ), + + // Only read styles.position if the test has a chance to fail + // to avoid forcing a reflow. + scrollboxSizeBuggy = !support.scrollboxSize() && + styles.position === "absolute", + + // To avoid forcing a reflow, only fetch boxSizing if we need it (gh-3991) + boxSizingNeeded = scrollboxSizeBuggy || extra, + isBorderBox = boxSizingNeeded && + jQuery.css( elem, "boxSizing", false, styles ) === "border-box", + subtract = extra ? + boxModelAdjustment( + elem, + dimension, + extra, + isBorderBox, + styles + ) : + 0; + + // Account for unreliable border-box dimensions by comparing offset* to computed and + // faking a content-box to get border and padding (gh-3699) + if ( isBorderBox && scrollboxSizeBuggy ) { + subtract -= Math.ceil( + elem[ "offset" + dimension[ 0 ].toUpperCase() + dimension.slice( 1 ) ] - + parseFloat( styles[ dimension ] ) - + boxModelAdjustment( elem, dimension, "border", false, styles ) - + 0.5 + ); + } + + // Convert to pixels if value adjustment is needed + if ( subtract && ( matches = rcssNum.exec( value ) ) && + ( matches[ 3 ] || "px" ) !== "px" ) { + + elem.style[ dimension ] = value; + value = jQuery.css( elem, dimension ); + } + + return setPositiveNumber( elem, value, subtract ); + } + }; +} ); + +jQuery.cssHooks.marginLeft = addGetHookIf( support.reliableMarginLeft, + function( elem, computed ) { + if ( computed ) { + return ( parseFloat( curCSS( elem, "marginLeft" ) ) || + elem.getBoundingClientRect().left - + swap( elem, { marginLeft: 0 }, function() { + return elem.getBoundingClientRect().left; + } ) + ) + "px"; + } + } +); + +// These hooks are used by animate to expand properties +jQuery.each( { + margin: "", + padding: "", + border: "Width" +}, function( prefix, suffix ) { + jQuery.cssHooks[ prefix + suffix ] = { + expand: function( value ) { + var i = 0, + expanded = {}, + + // Assumes a single number if not a string + parts = typeof value === "string" ? value.split( " " ) : [ value ]; + + for ( ; i < 4; i++ ) { + expanded[ prefix + cssExpand[ i ] + suffix ] = + parts[ i ] || parts[ i - 2 ] || parts[ 0 ]; + } + + return expanded; + } + }; + + if ( prefix !== "margin" ) { + jQuery.cssHooks[ prefix + suffix ].set = setPositiveNumber; + } +} ); + +jQuery.fn.extend( { + css: function( name, value ) { + return access( this, function( elem, name, value ) { + var styles, len, + map = {}, + i = 0; + + if ( Array.isArray( name ) ) { + styles = getStyles( elem ); + len = name.length; + + for ( ; i < len; i++ ) { + map[ name[ i ] ] = jQuery.css( elem, name[ i ], false, styles ); + } + + return map; + } + + return value !== undefined ? + jQuery.style( elem, name, value ) : + jQuery.css( elem, name ); + }, name, value, arguments.length > 1 ); + } +} ); + + +function Tween( elem, options, prop, end, easing ) { + return new Tween.prototype.init( elem, options, prop, end, easing ); +} +jQuery.Tween = Tween; + +Tween.prototype = { + constructor: Tween, + init: function( elem, options, prop, end, easing, unit ) { + this.elem = elem; + this.prop = prop; + this.easing = easing || jQuery.easing._default; + this.options = options; + this.start = this.now = this.cur(); + this.end = end; + this.unit = unit || ( jQuery.cssNumber[ prop ] ? "" : "px" ); + }, + cur: function() { + var hooks = Tween.propHooks[ this.prop ]; + + return hooks && hooks.get ? + hooks.get( this ) : + Tween.propHooks._default.get( this ); + }, + run: function( percent ) { + var eased, + hooks = Tween.propHooks[ this.prop ]; + + if ( this.options.duration ) { + this.pos = eased = jQuery.easing[ this.easing ]( + percent, this.options.duration * percent, 0, 1, this.options.duration + ); + } else { + this.pos = eased = percent; + } + this.now = ( this.end - this.start ) * eased + this.start; + + if ( this.options.step ) { + this.options.step.call( this.elem, this.now, this ); + } + + if ( hooks && hooks.set ) { + hooks.set( this ); + } else { + Tween.propHooks._default.set( this ); + } + return this; + } +}; + +Tween.prototype.init.prototype = Tween.prototype; + +Tween.propHooks = { + _default: { + get: function( tween ) { + var result; + + // Use a property on the element directly when it is not a DOM element, + // or when there is no matching style property that exists. + if ( tween.elem.nodeType !== 1 || + tween.elem[ tween.prop ] != null && tween.elem.style[ tween.prop ] == null ) { + return tween.elem[ tween.prop ]; + } + + // Passing an empty string as a 3rd parameter to .css will automatically + // attempt a parseFloat and fallback to a string if the parse fails. + // Simple values such as "10px" are parsed to Float; + // complex values such as "rotate(1rad)" are returned as-is. + result = jQuery.css( tween.elem, tween.prop, "" ); + + // Empty strings, null, undefined and "auto" are converted to 0. + return !result || result === "auto" ? 0 : result; + }, + set: function( tween ) { + + // Use step hook for back compat. + // Use cssHook if its there. + // Use .style if available and use plain properties where available. + if ( jQuery.fx.step[ tween.prop ] ) { + jQuery.fx.step[ tween.prop ]( tween ); + } else if ( tween.elem.nodeType === 1 && ( + jQuery.cssHooks[ tween.prop ] || + tween.elem.style[ finalPropName( tween.prop ) ] != null ) ) { + jQuery.style( tween.elem, tween.prop, tween.now + tween.unit ); + } else { + tween.elem[ tween.prop ] = tween.now; + } + } + } +}; + +// Support: IE <=9 only +// Panic based approach to setting things on disconnected nodes +Tween.propHooks.scrollTop = Tween.propHooks.scrollLeft = { + set: function( tween ) { + if ( tween.elem.nodeType && tween.elem.parentNode ) { + tween.elem[ tween.prop ] = tween.now; + } + } +}; + +jQuery.easing = { + linear: function( p ) { + return p; + }, + swing: function( p ) { + return 0.5 - Math.cos( p * Math.PI ) / 2; + }, + _default: "swing" +}; + +jQuery.fx = Tween.prototype.init; + +// Back compat <1.8 extension point +jQuery.fx.step = {}; + + + + +var + fxNow, inProgress, + rfxtypes = /^(?:toggle|show|hide)$/, + rrun = /queueHooks$/; + +function schedule() { + if ( inProgress ) { + if ( document.hidden === false && window.requestAnimationFrame ) { + window.requestAnimationFrame( schedule ); + } else { + window.setTimeout( schedule, jQuery.fx.interval ); + } + + jQuery.fx.tick(); + } +} + +// Animations created synchronously will run synchronously +function createFxNow() { + window.setTimeout( function() { + fxNow = undefined; + } ); + return ( fxNow = Date.now() ); +} + +// Generate parameters to create a standard animation +function genFx( type, includeWidth ) { + var which, + i = 0, + attrs = { height: type }; + + // If we include width, step value is 1 to do all cssExpand values, + // otherwise step value is 2 to skip over Left and Right + includeWidth = includeWidth ? 1 : 0; + for ( ; i < 4; i += 2 - includeWidth ) { + which = cssExpand[ i ]; + attrs[ "margin" + which ] = attrs[ "padding" + which ] = type; + } + + if ( includeWidth ) { + attrs.opacity = attrs.width = type; + } + + return attrs; +} + +function createTween( value, prop, animation ) { + var tween, + collection = ( Animation.tweeners[ prop ] || [] ).concat( Animation.tweeners[ "*" ] ), + index = 0, + length = collection.length; + for ( ; index < length; index++ ) { + if ( ( tween = collection[ index ].call( animation, prop, value ) ) ) { + + // We're done with this property + return tween; + } + } +} + +function defaultPrefilter( elem, props, opts ) { + var prop, value, toggle, hooks, oldfire, propTween, restoreDisplay, display, + isBox = "width" in props || "height" in props, + anim = this, + orig = {}, + style = elem.style, + hidden = elem.nodeType && isHiddenWithinTree( elem ), + dataShow = dataPriv.get( elem, "fxshow" ); + + // Queue-skipping animations hijack the fx hooks + if ( !opts.queue ) { + hooks = jQuery._queueHooks( elem, "fx" ); + if ( hooks.unqueued == null ) { + hooks.unqueued = 0; + oldfire = hooks.empty.fire; + hooks.empty.fire = function() { + if ( !hooks.unqueued ) { + oldfire(); + } + }; + } + hooks.unqueued++; + + anim.always( function() { + + // Ensure the complete handler is called before this completes + anim.always( function() { + hooks.unqueued--; + if ( !jQuery.queue( elem, "fx" ).length ) { + hooks.empty.fire(); + } + } ); + } ); + } + + // Detect show/hide animations + for ( prop in props ) { + value = props[ prop ]; + if ( rfxtypes.test( value ) ) { + delete props[ prop ]; + toggle = toggle || value === "toggle"; + if ( value === ( hidden ? "hide" : "show" ) ) { + + // Pretend to be hidden if this is a "show" and + // there is still data from a stopped show/hide + if ( value === "show" && dataShow && dataShow[ prop ] !== undefined ) { + hidden = true; + + // Ignore all other no-op show/hide data + } else { + continue; + } + } + orig[ prop ] = dataShow && dataShow[ prop ] || jQuery.style( elem, prop ); + } + } + + // Bail out if this is a no-op like .hide().hide() + propTween = !jQuery.isEmptyObject( props ); + if ( !propTween && jQuery.isEmptyObject( orig ) ) { + return; + } + + // Restrict "overflow" and "display" styles during box animations + if ( isBox && elem.nodeType === 1 ) { + + // Support: IE <=9 - 11, Edge 12 - 15 + // Record all 3 overflow attributes because IE does not infer the shorthand + // from identically-valued overflowX and overflowY and Edge just mirrors + // the overflowX value there. + opts.overflow = [ style.overflow, style.overflowX, style.overflowY ]; + + // Identify a display type, preferring old show/hide data over the CSS cascade + restoreDisplay = dataShow && dataShow.display; + if ( restoreDisplay == null ) { + restoreDisplay = dataPriv.get( elem, "display" ); + } + display = jQuery.css( elem, "display" ); + if ( display === "none" ) { + if ( restoreDisplay ) { + display = restoreDisplay; + } else { + + // Get nonempty value(s) by temporarily forcing visibility + showHide( [ elem ], true ); + restoreDisplay = elem.style.display || restoreDisplay; + display = jQuery.css( elem, "display" ); + showHide( [ elem ] ); + } + } + + // Animate inline elements as inline-block + if ( display === "inline" || display === "inline-block" && restoreDisplay != null ) { + if ( jQuery.css( elem, "float" ) === "none" ) { + + // Restore the original display value at the end of pure show/hide animations + if ( !propTween ) { + anim.done( function() { + style.display = restoreDisplay; + } ); + if ( restoreDisplay == null ) { + display = style.display; + restoreDisplay = display === "none" ? "" : display; + } + } + style.display = "inline-block"; + } + } + } + + if ( opts.overflow ) { + style.overflow = "hidden"; + anim.always( function() { + style.overflow = opts.overflow[ 0 ]; + style.overflowX = opts.overflow[ 1 ]; + style.overflowY = opts.overflow[ 2 ]; + } ); + } + + // Implement show/hide animations + propTween = false; + for ( prop in orig ) { + + // General show/hide setup for this element animation + if ( !propTween ) { + if ( dataShow ) { + if ( "hidden" in dataShow ) { + hidden = dataShow.hidden; + } + } else { + dataShow = dataPriv.access( elem, "fxshow", { display: restoreDisplay } ); + } + + // Store hidden/visible for toggle so `.stop().toggle()` "reverses" + if ( toggle ) { + dataShow.hidden = !hidden; + } + + // Show elements before animating them + if ( hidden ) { + showHide( [ elem ], true ); + } + + /* eslint-disable no-loop-func */ + + anim.done( function() { + + /* eslint-enable no-loop-func */ + + // The final step of a "hide" animation is actually hiding the element + if ( !hidden ) { + showHide( [ elem ] ); + } + dataPriv.remove( elem, "fxshow" ); + for ( prop in orig ) { + jQuery.style( elem, prop, orig[ prop ] ); + } + } ); + } + + // Per-property setup + propTween = createTween( hidden ? dataShow[ prop ] : 0, prop, anim ); + if ( !( prop in dataShow ) ) { + dataShow[ prop ] = propTween.start; + if ( hidden ) { + propTween.end = propTween.start; + propTween.start = 0; + } + } + } +} + +function propFilter( props, specialEasing ) { + var index, name, easing, value, hooks; + + // camelCase, specialEasing and expand cssHook pass + for ( index in props ) { + name = camelCase( index ); + easing = specialEasing[ name ]; + value = props[ index ]; + if ( Array.isArray( value ) ) { + easing = value[ 1 ]; + value = props[ index ] = value[ 0 ]; + } + + if ( index !== name ) { + props[ name ] = value; + delete props[ index ]; + } + + hooks = jQuery.cssHooks[ name ]; + if ( hooks && "expand" in hooks ) { + value = hooks.expand( value ); + delete props[ name ]; + + // Not quite $.extend, this won't overwrite existing keys. + // Reusing 'index' because we have the correct "name" + for ( index in value ) { + if ( !( index in props ) ) { + props[ index ] = value[ index ]; + specialEasing[ index ] = easing; + } + } + } else { + specialEasing[ name ] = easing; + } + } +} + +function Animation( elem, properties, options ) { + var result, + stopped, + index = 0, + length = Animation.prefilters.length, + deferred = jQuery.Deferred().always( function() { + + // Don't match elem in the :animated selector + delete tick.elem; + } ), + tick = function() { + if ( stopped ) { + return false; + } + var currentTime = fxNow || createFxNow(), + remaining = Math.max( 0, animation.startTime + animation.duration - currentTime ), + + // Support: Android 2.3 only + // Archaic crash bug won't allow us to use `1 - ( 0.5 || 0 )` (#12497) + temp = remaining / animation.duration || 0, + percent = 1 - temp, + index = 0, + length = animation.tweens.length; + + for ( ; index < length; index++ ) { + animation.tweens[ index ].run( percent ); + } + + deferred.notifyWith( elem, [ animation, percent, remaining ] ); + + // If there's more to do, yield + if ( percent < 1 && length ) { + return remaining; + } + + // If this was an empty animation, synthesize a final progress notification + if ( !length ) { + deferred.notifyWith( elem, [ animation, 1, 0 ] ); + } + + // Resolve the animation and report its conclusion + deferred.resolveWith( elem, [ animation ] ); + return false; + }, + animation = deferred.promise( { + elem: elem, + props: jQuery.extend( {}, properties ), + opts: jQuery.extend( true, { + specialEasing: {}, + easing: jQuery.easing._default + }, options ), + originalProperties: properties, + originalOptions: options, + startTime: fxNow || createFxNow(), + duration: options.duration, + tweens: [], + createTween: function( prop, end ) { + var tween = jQuery.Tween( elem, animation.opts, prop, end, + animation.opts.specialEasing[ prop ] || animation.opts.easing ); + animation.tweens.push( tween ); + return tween; + }, + stop: function( gotoEnd ) { + var index = 0, + + // If we are going to the end, we want to run all the tweens + // otherwise we skip this part + length = gotoEnd ? animation.tweens.length : 0; + if ( stopped ) { + return this; + } + stopped = true; + for ( ; index < length; index++ ) { + animation.tweens[ index ].run( 1 ); + } + + // Resolve when we played the last frame; otherwise, reject + if ( gotoEnd ) { + deferred.notifyWith( elem, [ animation, 1, 0 ] ); + deferred.resolveWith( elem, [ animation, gotoEnd ] ); + } else { + deferred.rejectWith( elem, [ animation, gotoEnd ] ); + } + return this; + } + } ), + props = animation.props; + + propFilter( props, animation.opts.specialEasing ); + + for ( ; index < length; index++ ) { + result = Animation.prefilters[ index ].call( animation, elem, props, animation.opts ); + if ( result ) { + if ( isFunction( result.stop ) ) { + jQuery._queueHooks( animation.elem, animation.opts.queue ).stop = + result.stop.bind( result ); + } + return result; + } + } + + jQuery.map( props, createTween, animation ); + + if ( isFunction( animation.opts.start ) ) { + animation.opts.start.call( elem, animation ); + } + + // Attach callbacks from options + animation + .progress( animation.opts.progress ) + .done( animation.opts.done, animation.opts.complete ) + .fail( animation.opts.fail ) + .always( animation.opts.always ); + + jQuery.fx.timer( + jQuery.extend( tick, { + elem: elem, + anim: animation, + queue: animation.opts.queue + } ) + ); + + return animation; +} + +jQuery.Animation = jQuery.extend( Animation, { + + tweeners: { + "*": [ function( prop, value ) { + var tween = this.createTween( prop, value ); + adjustCSS( tween.elem, prop, rcssNum.exec( value ), tween ); + return tween; + } ] + }, + + tweener: function( props, callback ) { + if ( isFunction( props ) ) { + callback = props; + props = [ "*" ]; + } else { + props = props.match( rnothtmlwhite ); + } + + var prop, + index = 0, + length = props.length; + + for ( ; index < length; index++ ) { + prop = props[ index ]; + Animation.tweeners[ prop ] = Animation.tweeners[ prop ] || []; + Animation.tweeners[ prop ].unshift( callback ); + } + }, + + prefilters: [ defaultPrefilter ], + + prefilter: function( callback, prepend ) { + if ( prepend ) { + Animation.prefilters.unshift( callback ); + } else { + Animation.prefilters.push( callback ); + } + } +} ); + +jQuery.speed = function( speed, easing, fn ) { + var opt = speed && typeof speed === "object" ? jQuery.extend( {}, speed ) : { + complete: fn || !fn && easing || + isFunction( speed ) && speed, + duration: speed, + easing: fn && easing || easing && !isFunction( easing ) && easing + }; + + // Go to the end state if fx are off + if ( jQuery.fx.off ) { + opt.duration = 0; + + } else { + if ( typeof opt.duration !== "number" ) { + if ( opt.duration in jQuery.fx.speeds ) { + opt.duration = jQuery.fx.speeds[ opt.duration ]; + + } else { + opt.duration = jQuery.fx.speeds._default; + } + } + } + + // Normalize opt.queue - true/undefined/null -> "fx" + if ( opt.queue == null || opt.queue === true ) { + opt.queue = "fx"; + } + + // Queueing + opt.old = opt.complete; + + opt.complete = function() { + if ( isFunction( opt.old ) ) { + opt.old.call( this ); + } + + if ( opt.queue ) { + jQuery.dequeue( this, opt.queue ); + } + }; + + return opt; +}; + +jQuery.fn.extend( { + fadeTo: function( speed, to, easing, callback ) { + + // Show any hidden elements after setting opacity to 0 + return this.filter( isHiddenWithinTree ).css( "opacity", 0 ).show() + + // Animate to the value specified + .end().animate( { opacity: to }, speed, easing, callback ); + }, + animate: function( prop, speed, easing, callback ) { + var empty = jQuery.isEmptyObject( prop ), + optall = jQuery.speed( speed, easing, callback ), + doAnimation = function() { + + // Operate on a copy of prop so per-property easing won't be lost + var anim = Animation( this, jQuery.extend( {}, prop ), optall ); + + // Empty animations, or finishing resolves immediately + if ( empty || dataPriv.get( this, "finish" ) ) { + anim.stop( true ); + } + }; + + doAnimation.finish = doAnimation; + + return empty || optall.queue === false ? + this.each( doAnimation ) : + this.queue( optall.queue, doAnimation ); + }, + stop: function( type, clearQueue, gotoEnd ) { + var stopQueue = function( hooks ) { + var stop = hooks.stop; + delete hooks.stop; + stop( gotoEnd ); + }; + + if ( typeof type !== "string" ) { + gotoEnd = clearQueue; + clearQueue = type; + type = undefined; + } + if ( clearQueue ) { + this.queue( type || "fx", [] ); + } + + return this.each( function() { + var dequeue = true, + index = type != null && type + "queueHooks", + timers = jQuery.timers, + data = dataPriv.get( this ); + + if ( index ) { + if ( data[ index ] && data[ index ].stop ) { + stopQueue( data[ index ] ); + } + } else { + for ( index in data ) { + if ( data[ index ] && data[ index ].stop && rrun.test( index ) ) { + stopQueue( data[ index ] ); + } + } + } + + for ( index = timers.length; index--; ) { + if ( timers[ index ].elem === this && + ( type == null || timers[ index ].queue === type ) ) { + + timers[ index ].anim.stop( gotoEnd ); + dequeue = false; + timers.splice( index, 1 ); + } + } + + // Start the next in the queue if the last step wasn't forced. + // Timers currently will call their complete callbacks, which + // will dequeue but only if they were gotoEnd. + if ( dequeue || !gotoEnd ) { + jQuery.dequeue( this, type ); + } + } ); + }, + finish: function( type ) { + if ( type !== false ) { + type = type || "fx"; + } + return this.each( function() { + var index, + data = dataPriv.get( this ), + queue = data[ type + "queue" ], + hooks = data[ type + "queueHooks" ], + timers = jQuery.timers, + length = queue ? queue.length : 0; + + // Enable finishing flag on private data + data.finish = true; + + // Empty the queue first + jQuery.queue( this, type, [] ); + + if ( hooks && hooks.stop ) { + hooks.stop.call( this, true ); + } + + // Look for any active animations, and finish them + for ( index = timers.length; index--; ) { + if ( timers[ index ].elem === this && timers[ index ].queue === type ) { + timers[ index ].anim.stop( true ); + timers.splice( index, 1 ); + } + } + + // Look for any animations in the old queue and finish them + for ( index = 0; index < length; index++ ) { + if ( queue[ index ] && queue[ index ].finish ) { + queue[ index ].finish.call( this ); + } + } + + // Turn off finishing flag + delete data.finish; + } ); + } +} ); + +jQuery.each( [ "toggle", "show", "hide" ], function( _i, name ) { + var cssFn = jQuery.fn[ name ]; + jQuery.fn[ name ] = function( speed, easing, callback ) { + return speed == null || typeof speed === "boolean" ? + cssFn.apply( this, arguments ) : + this.animate( genFx( name, true ), speed, easing, callback ); + }; +} ); + +// Generate shortcuts for custom animations +jQuery.each( { + slideDown: genFx( "show" ), + slideUp: genFx( "hide" ), + slideToggle: genFx( "toggle" ), + fadeIn: { opacity: "show" }, + fadeOut: { opacity: "hide" }, + fadeToggle: { opacity: "toggle" } +}, function( name, props ) { + jQuery.fn[ name ] = function( speed, easing, callback ) { + return this.animate( props, speed, easing, callback ); + }; +} ); + +jQuery.timers = []; +jQuery.fx.tick = function() { + var timer, + i = 0, + timers = jQuery.timers; + + fxNow = Date.now(); + + for ( ; i < timers.length; i++ ) { + timer = timers[ i ]; + + // Run the timer and safely remove it when done (allowing for external removal) + if ( !timer() && timers[ i ] === timer ) { + timers.splice( i--, 1 ); + } + } + + if ( !timers.length ) { + jQuery.fx.stop(); + } + fxNow = undefined; +}; + +jQuery.fx.timer = function( timer ) { + jQuery.timers.push( timer ); + jQuery.fx.start(); +}; + +jQuery.fx.interval = 13; +jQuery.fx.start = function() { + if ( inProgress ) { + return; + } + + inProgress = true; + schedule(); +}; + +jQuery.fx.stop = function() { + inProgress = null; +}; + +jQuery.fx.speeds = { + slow: 600, + fast: 200, + + // Default speed + _default: 400 +}; + + +// Based off of the plugin by Clint Helfers, with permission. +// https://web.archive.org/web/20100324014747/http://blindsignals.com/index.php/2009/07/jquery-delay/ +jQuery.fn.delay = function( time, type ) { + time = jQuery.fx ? jQuery.fx.speeds[ time ] || time : time; + type = type || "fx"; + + return this.queue( type, function( next, hooks ) { + var timeout = window.setTimeout( next, time ); + hooks.stop = function() { + window.clearTimeout( timeout ); + }; + } ); +}; + + +( function() { + var input = document.createElement( "input" ), + select = document.createElement( "select" ), + opt = select.appendChild( document.createElement( "option" ) ); + + input.type = "checkbox"; + + // Support: Android <=4.3 only + // Default value for a checkbox should be "on" + support.checkOn = input.value !== ""; + + // Support: IE <=11 only + // Must access selectedIndex to make default options select + support.optSelected = opt.selected; + + // Support: IE <=11 only + // An input loses its value after becoming a radio + input = document.createElement( "input" ); + input.value = "t"; + input.type = "radio"; + support.radioValue = input.value === "t"; +} )(); + + +var boolHook, + attrHandle = jQuery.expr.attrHandle; + +jQuery.fn.extend( { + attr: function( name, value ) { + return access( this, jQuery.attr, name, value, arguments.length > 1 ); + }, + + removeAttr: function( name ) { + return this.each( function() { + jQuery.removeAttr( this, name ); + } ); + } +} ); + +jQuery.extend( { + attr: function( elem, name, value ) { + var ret, hooks, + nType = elem.nodeType; + + // Don't get/set attributes on text, comment and attribute nodes + if ( nType === 3 || nType === 8 || nType === 2 ) { + return; + } + + // Fallback to prop when attributes are not supported + if ( typeof elem.getAttribute === "undefined" ) { + return jQuery.prop( elem, name, value ); + } + + // Attribute hooks are determined by the lowercase version + // Grab necessary hook if one is defined + if ( nType !== 1 || !jQuery.isXMLDoc( elem ) ) { + hooks = jQuery.attrHooks[ name.toLowerCase() ] || + ( jQuery.expr.match.bool.test( name ) ? boolHook : undefined ); + } + + if ( value !== undefined ) { + if ( value === null ) { + jQuery.removeAttr( elem, name ); + return; + } + + if ( hooks && "set" in hooks && + ( ret = hooks.set( elem, value, name ) ) !== undefined ) { + return ret; + } + + elem.setAttribute( name, value + "" ); + return value; + } + + if ( hooks && "get" in hooks && ( ret = hooks.get( elem, name ) ) !== null ) { + return ret; + } + + ret = jQuery.find.attr( elem, name ); + + // Non-existent attributes return null, we normalize to undefined + return ret == null ? undefined : ret; + }, + + attrHooks: { + type: { + set: function( elem, value ) { + if ( !support.radioValue && value === "radio" && + nodeName( elem, "input" ) ) { + var val = elem.value; + elem.setAttribute( "type", value ); + if ( val ) { + elem.value = val; + } + return value; + } + } + } + }, + + removeAttr: function( elem, value ) { + var name, + i = 0, + + // Attribute names can contain non-HTML whitespace characters + // https://html.spec.whatwg.org/multipage/syntax.html#attributes-2 + attrNames = value && value.match( rnothtmlwhite ); + + if ( attrNames && elem.nodeType === 1 ) { + while ( ( name = attrNames[ i++ ] ) ) { + elem.removeAttribute( name ); + } + } + } +} ); + +// Hooks for boolean attributes +boolHook = { + set: function( elem, value, name ) { + if ( value === false ) { + + // Remove boolean attributes when set to false + jQuery.removeAttr( elem, name ); + } else { + elem.setAttribute( name, name ); + } + return name; + } +}; + +jQuery.each( jQuery.expr.match.bool.source.match( /\w+/g ), function( _i, name ) { + var getter = attrHandle[ name ] || jQuery.find.attr; + + attrHandle[ name ] = function( elem, name, isXML ) { + var ret, handle, + lowercaseName = name.toLowerCase(); + + if ( !isXML ) { + + // Avoid an infinite loop by temporarily removing this function from the getter + handle = attrHandle[ lowercaseName ]; + attrHandle[ lowercaseName ] = ret; + ret = getter( elem, name, isXML ) != null ? + lowercaseName : + null; + attrHandle[ lowercaseName ] = handle; + } + return ret; + }; +} ); + + + + +var rfocusable = /^(?:input|select|textarea|button)$/i, + rclickable = /^(?:a|area)$/i; + +jQuery.fn.extend( { + prop: function( name, value ) { + return access( this, jQuery.prop, name, value, arguments.length > 1 ); + }, + + removeProp: function( name ) { + return this.each( function() { + delete this[ jQuery.propFix[ name ] || name ]; + } ); + } +} ); + +jQuery.extend( { + prop: function( elem, name, value ) { + var ret, hooks, + nType = elem.nodeType; + + // Don't get/set properties on text, comment and attribute nodes + if ( nType === 3 || nType === 8 || nType === 2 ) { + return; + } + + if ( nType !== 1 || !jQuery.isXMLDoc( elem ) ) { + + // Fix name and attach hooks + name = jQuery.propFix[ name ] || name; + hooks = jQuery.propHooks[ name ]; + } + + if ( value !== undefined ) { + if ( hooks && "set" in hooks && + ( ret = hooks.set( elem, value, name ) ) !== undefined ) { + return ret; + } + + return ( elem[ name ] = value ); + } + + if ( hooks && "get" in hooks && ( ret = hooks.get( elem, name ) ) !== null ) { + return ret; + } + + return elem[ name ]; + }, + + propHooks: { + tabIndex: { + get: function( elem ) { + + // Support: IE <=9 - 11 only + // elem.tabIndex doesn't always return the + // correct value when it hasn't been explicitly set + // https://web.archive.org/web/20141116233347/http://fluidproject.org/blog/2008/01/09/getting-setting-and-removing-tabindex-values-with-javascript/ + // Use proper attribute retrieval(#12072) + var tabindex = jQuery.find.attr( elem, "tabindex" ); + + if ( tabindex ) { + return parseInt( tabindex, 10 ); + } + + if ( + rfocusable.test( elem.nodeName ) || + rclickable.test( elem.nodeName ) && + elem.href + ) { + return 0; + } + + return -1; + } + } + }, + + propFix: { + "for": "htmlFor", + "class": "className" + } +} ); + +// Support: IE <=11 only +// Accessing the selectedIndex property +// forces the browser to respect setting selected +// on the option +// The getter ensures a default option is selected +// when in an optgroup +// eslint rule "no-unused-expressions" is disabled for this code +// since it considers such accessions noop +if ( !support.optSelected ) { + jQuery.propHooks.selected = { + get: function( elem ) { + + /* eslint no-unused-expressions: "off" */ + + var parent = elem.parentNode; + if ( parent && parent.parentNode ) { + parent.parentNode.selectedIndex; + } + return null; + }, + set: function( elem ) { + + /* eslint no-unused-expressions: "off" */ + + var parent = elem.parentNode; + if ( parent ) { + parent.selectedIndex; + + if ( parent.parentNode ) { + parent.parentNode.selectedIndex; + } + } + } + }; +} + +jQuery.each( [ + "tabIndex", + "readOnly", + "maxLength", + "cellSpacing", + "cellPadding", + "rowSpan", + "colSpan", + "useMap", + "frameBorder", + "contentEditable" +], function() { + jQuery.propFix[ this.toLowerCase() ] = this; +} ); + + + + + // Strip and collapse whitespace according to HTML spec + // https://infra.spec.whatwg.org/#strip-and-collapse-ascii-whitespace + function stripAndCollapse( value ) { + var tokens = value.match( rnothtmlwhite ) || []; + return tokens.join( " " ); + } + + +function getClass( elem ) { + return elem.getAttribute && elem.getAttribute( "class" ) || ""; +} + +function classesToArray( value ) { + if ( Array.isArray( value ) ) { + return value; + } + if ( typeof value === "string" ) { + return value.match( rnothtmlwhite ) || []; + } + return []; +} + +jQuery.fn.extend( { + addClass: function( value ) { + var classes, elem, cur, curValue, clazz, j, finalValue, + i = 0; + + if ( isFunction( value ) ) { + return this.each( function( j ) { + jQuery( this ).addClass( value.call( this, j, getClass( this ) ) ); + } ); + } + + classes = classesToArray( value ); + + if ( classes.length ) { + while ( ( elem = this[ i++ ] ) ) { + curValue = getClass( elem ); + cur = elem.nodeType === 1 && ( " " + stripAndCollapse( curValue ) + " " ); + + if ( cur ) { + j = 0; + while ( ( clazz = classes[ j++ ] ) ) { + if ( cur.indexOf( " " + clazz + " " ) < 0 ) { + cur += clazz + " "; + } + } + + // Only assign if different to avoid unneeded rendering. + finalValue = stripAndCollapse( cur ); + if ( curValue !== finalValue ) { + elem.setAttribute( "class", finalValue ); + } + } + } + } + + return this; + }, + + removeClass: function( value ) { + var classes, elem, cur, curValue, clazz, j, finalValue, + i = 0; + + if ( isFunction( value ) ) { + return this.each( function( j ) { + jQuery( this ).removeClass( value.call( this, j, getClass( this ) ) ); + } ); + } + + if ( !arguments.length ) { + return this.attr( "class", "" ); + } + + classes = classesToArray( value ); + + if ( classes.length ) { + while ( ( elem = this[ i++ ] ) ) { + curValue = getClass( elem ); + + // This expression is here for better compressibility (see addClass) + cur = elem.nodeType === 1 && ( " " + stripAndCollapse( curValue ) + " " ); + + if ( cur ) { + j = 0; + while ( ( clazz = classes[ j++ ] ) ) { + + // Remove *all* instances + while ( cur.indexOf( " " + clazz + " " ) > -1 ) { + cur = cur.replace( " " + clazz + " ", " " ); + } + } + + // Only assign if different to avoid unneeded rendering. + finalValue = stripAndCollapse( cur ); + if ( curValue !== finalValue ) { + elem.setAttribute( "class", finalValue ); + } + } + } + } + + return this; + }, + + toggleClass: function( value, stateVal ) { + var type = typeof value, + isValidValue = type === "string" || Array.isArray( value ); + + if ( typeof stateVal === "boolean" && isValidValue ) { + return stateVal ? this.addClass( value ) : this.removeClass( value ); + } + + if ( isFunction( value ) ) { + return this.each( function( i ) { + jQuery( this ).toggleClass( + value.call( this, i, getClass( this ), stateVal ), + stateVal + ); + } ); + } + + return this.each( function() { + var className, i, self, classNames; + + if ( isValidValue ) { + + // Toggle individual class names + i = 0; + self = jQuery( this ); + classNames = classesToArray( value ); + + while ( ( className = classNames[ i++ ] ) ) { + + // Check each className given, space separated list + if ( self.hasClass( className ) ) { + self.removeClass( className ); + } else { + self.addClass( className ); + } + } + + // Toggle whole class name + } else if ( value === undefined || type === "boolean" ) { + className = getClass( this ); + if ( className ) { + + // Store className if set + dataPriv.set( this, "__className__", className ); + } + + // If the element has a class name or if we're passed `false`, + // then remove the whole classname (if there was one, the above saved it). + // Otherwise bring back whatever was previously saved (if anything), + // falling back to the empty string if nothing was stored. + if ( this.setAttribute ) { + this.setAttribute( "class", + className || value === false ? + "" : + dataPriv.get( this, "__className__" ) || "" + ); + } + } + } ); + }, + + hasClass: function( selector ) { + var className, elem, + i = 0; + + className = " " + selector + " "; + while ( ( elem = this[ i++ ] ) ) { + if ( elem.nodeType === 1 && + ( " " + stripAndCollapse( getClass( elem ) ) + " " ).indexOf( className ) > -1 ) { + return true; + } + } + + return false; + } +} ); + + + + +var rreturn = /\r/g; + +jQuery.fn.extend( { + val: function( value ) { + var hooks, ret, valueIsFunction, + elem = this[ 0 ]; + + if ( !arguments.length ) { + if ( elem ) { + hooks = jQuery.valHooks[ elem.type ] || + jQuery.valHooks[ elem.nodeName.toLowerCase() ]; + + if ( hooks && + "get" in hooks && + ( ret = hooks.get( elem, "value" ) ) !== undefined + ) { + return ret; + } + + ret = elem.value; + + // Handle most common string cases + if ( typeof ret === "string" ) { + return ret.replace( rreturn, "" ); + } + + // Handle cases where value is null/undef or number + return ret == null ? "" : ret; + } + + return; + } + + valueIsFunction = isFunction( value ); + + return this.each( function( i ) { + var val; + + if ( this.nodeType !== 1 ) { + return; + } + + if ( valueIsFunction ) { + val = value.call( this, i, jQuery( this ).val() ); + } else { + val = value; + } + + // Treat null/undefined as ""; convert numbers to string + if ( val == null ) { + val = ""; + + } else if ( typeof val === "number" ) { + val += ""; + + } else if ( Array.isArray( val ) ) { + val = jQuery.map( val, function( value ) { + return value == null ? "" : value + ""; + } ); + } + + hooks = jQuery.valHooks[ this.type ] || jQuery.valHooks[ this.nodeName.toLowerCase() ]; + + // If set returns undefined, fall back to normal setting + if ( !hooks || !( "set" in hooks ) || hooks.set( this, val, "value" ) === undefined ) { + this.value = val; + } + } ); + } +} ); + +jQuery.extend( { + valHooks: { + option: { + get: function( elem ) { + + var val = jQuery.find.attr( elem, "value" ); + return val != null ? + val : + + // Support: IE <=10 - 11 only + // option.text throws exceptions (#14686, #14858) + // Strip and collapse whitespace + // https://html.spec.whatwg.org/#strip-and-collapse-whitespace + stripAndCollapse( jQuery.text( elem ) ); + } + }, + select: { + get: function( elem ) { + var value, option, i, + options = elem.options, + index = elem.selectedIndex, + one = elem.type === "select-one", + values = one ? null : [], + max = one ? index + 1 : options.length; + + if ( index < 0 ) { + i = max; + + } else { + i = one ? index : 0; + } + + // Loop through all the selected options + for ( ; i < max; i++ ) { + option = options[ i ]; + + // Support: IE <=9 only + // IE8-9 doesn't update selected after form reset (#2551) + if ( ( option.selected || i === index ) && + + // Don't return options that are disabled or in a disabled optgroup + !option.disabled && + ( !option.parentNode.disabled || + !nodeName( option.parentNode, "optgroup" ) ) ) { + + // Get the specific value for the option + value = jQuery( option ).val(); + + // We don't need an array for one selects + if ( one ) { + return value; + } + + // Multi-Selects return an array + values.push( value ); + } + } + + return values; + }, + + set: function( elem, value ) { + var optionSet, option, + options = elem.options, + values = jQuery.makeArray( value ), + i = options.length; + + while ( i-- ) { + option = options[ i ]; + + /* eslint-disable no-cond-assign */ + + if ( option.selected = + jQuery.inArray( jQuery.valHooks.option.get( option ), values ) > -1 + ) { + optionSet = true; + } + + /* eslint-enable no-cond-assign */ + } + + // Force browsers to behave consistently when non-matching value is set + if ( !optionSet ) { + elem.selectedIndex = -1; + } + return values; + } + } + } +} ); + +// Radios and checkboxes getter/setter +jQuery.each( [ "radio", "checkbox" ], function() { + jQuery.valHooks[ this ] = { + set: function( elem, value ) { + if ( Array.isArray( value ) ) { + return ( elem.checked = jQuery.inArray( jQuery( elem ).val(), value ) > -1 ); + } + } + }; + if ( !support.checkOn ) { + jQuery.valHooks[ this ].get = function( elem ) { + return elem.getAttribute( "value" ) === null ? "on" : elem.value; + }; + } +} ); + + + + +// Return jQuery for attributes-only inclusion + + +support.focusin = "onfocusin" in window; + + +var rfocusMorph = /^(?:focusinfocus|focusoutblur)$/, + stopPropagationCallback = function( e ) { + e.stopPropagation(); + }; + +jQuery.extend( jQuery.event, { + + trigger: function( event, data, elem, onlyHandlers ) { + + var i, cur, tmp, bubbleType, ontype, handle, special, lastElement, + eventPath = [ elem || document ], + type = hasOwn.call( event, "type" ) ? event.type : event, + namespaces = hasOwn.call( event, "namespace" ) ? event.namespace.split( "." ) : []; + + cur = lastElement = tmp = elem = elem || document; + + // Don't do events on text and comment nodes + if ( elem.nodeType === 3 || elem.nodeType === 8 ) { + return; + } + + // focus/blur morphs to focusin/out; ensure we're not firing them right now + if ( rfocusMorph.test( type + jQuery.event.triggered ) ) { + return; + } + + if ( type.indexOf( "." ) > -1 ) { + + // Namespaced trigger; create a regexp to match event type in handle() + namespaces = type.split( "." ); + type = namespaces.shift(); + namespaces.sort(); + } + ontype = type.indexOf( ":" ) < 0 && "on" + type; + + // Caller can pass in a jQuery.Event object, Object, or just an event type string + event = event[ jQuery.expando ] ? + event : + new jQuery.Event( type, typeof event === "object" && event ); + + // Trigger bitmask: & 1 for native handlers; & 2 for jQuery (always true) + event.isTrigger = onlyHandlers ? 2 : 3; + event.namespace = namespaces.join( "." ); + event.rnamespace = event.namespace ? + new RegExp( "(^|\\.)" + namespaces.join( "\\.(?:.*\\.|)" ) + "(\\.|$)" ) : + null; + + // Clean up the event in case it is being reused + event.result = undefined; + if ( !event.target ) { + event.target = elem; + } + + // Clone any incoming data and prepend the event, creating the handler arg list + data = data == null ? + [ event ] : + jQuery.makeArray( data, [ event ] ); + + // Allow special events to draw outside the lines + special = jQuery.event.special[ type ] || {}; + if ( !onlyHandlers && special.trigger && special.trigger.apply( elem, data ) === false ) { + return; + } + + // Determine event propagation path in advance, per W3C events spec (#9951) + // Bubble up to document, then to window; watch for a global ownerDocument var (#9724) + if ( !onlyHandlers && !special.noBubble && !isWindow( elem ) ) { + + bubbleType = special.delegateType || type; + if ( !rfocusMorph.test( bubbleType + type ) ) { + cur = cur.parentNode; + } + for ( ; cur; cur = cur.parentNode ) { + eventPath.push( cur ); + tmp = cur; + } + + // Only add window if we got to document (e.g., not plain obj or detached DOM) + if ( tmp === ( elem.ownerDocument || document ) ) { + eventPath.push( tmp.defaultView || tmp.parentWindow || window ); + } + } + + // Fire handlers on the event path + i = 0; + while ( ( cur = eventPath[ i++ ] ) && !event.isPropagationStopped() ) { + lastElement = cur; + event.type = i > 1 ? + bubbleType : + special.bindType || type; + + // jQuery handler + handle = ( dataPriv.get( cur, "events" ) || Object.create( null ) )[ event.type ] && + dataPriv.get( cur, "handle" ); + if ( handle ) { + handle.apply( cur, data ); + } + + // Native handler + handle = ontype && cur[ ontype ]; + if ( handle && handle.apply && acceptData( cur ) ) { + event.result = handle.apply( cur, data ); + if ( event.result === false ) { + event.preventDefault(); + } + } + } + event.type = type; + + // If nobody prevented the default action, do it now + if ( !onlyHandlers && !event.isDefaultPrevented() ) { + + if ( ( !special._default || + special._default.apply( eventPath.pop(), data ) === false ) && + acceptData( elem ) ) { + + // Call a native DOM method on the target with the same name as the event. + // Don't do default actions on window, that's where global variables be (#6170) + if ( ontype && isFunction( elem[ type ] ) && !isWindow( elem ) ) { + + // Don't re-trigger an onFOO event when we call its FOO() method + tmp = elem[ ontype ]; + + if ( tmp ) { + elem[ ontype ] = null; + } + + // Prevent re-triggering of the same event, since we already bubbled it above + jQuery.event.triggered = type; + + if ( event.isPropagationStopped() ) { + lastElement.addEventListener( type, stopPropagationCallback ); + } + + elem[ type ](); + + if ( event.isPropagationStopped() ) { + lastElement.removeEventListener( type, stopPropagationCallback ); + } + + jQuery.event.triggered = undefined; + + if ( tmp ) { + elem[ ontype ] = tmp; + } + } + } + } + + return event.result; + }, + + // Piggyback on a donor event to simulate a different one + // Used only for `focus(in | out)` events + simulate: function( type, elem, event ) { + var e = jQuery.extend( + new jQuery.Event(), + event, + { + type: type, + isSimulated: true + } + ); + + jQuery.event.trigger( e, null, elem ); + } + +} ); + +jQuery.fn.extend( { + + trigger: function( type, data ) { + return this.each( function() { + jQuery.event.trigger( type, data, this ); + } ); + }, + triggerHandler: function( type, data ) { + var elem = this[ 0 ]; + if ( elem ) { + return jQuery.event.trigger( type, data, elem, true ); + } + } +} ); + + +// Support: Firefox <=44 +// Firefox doesn't have focus(in | out) events +// Related ticket - https://bugzilla.mozilla.org/show_bug.cgi?id=687787 +// +// Support: Chrome <=48 - 49, Safari <=9.0 - 9.1 +// focus(in | out) events fire after focus & blur events, +// which is spec violation - http://www.w3.org/TR/DOM-Level-3-Events/#events-focusevent-event-order +// Related ticket - https://bugs.chromium.org/p/chromium/issues/detail?id=449857 +if ( !support.focusin ) { + jQuery.each( { focus: "focusin", blur: "focusout" }, function( orig, fix ) { + + // Attach a single capturing handler on the document while someone wants focusin/focusout + var handler = function( event ) { + jQuery.event.simulate( fix, event.target, jQuery.event.fix( event ) ); + }; + + jQuery.event.special[ fix ] = { + setup: function() { + + // Handle: regular nodes (via `this.ownerDocument`), window + // (via `this.document`) & document (via `this`). + var doc = this.ownerDocument || this.document || this, + attaches = dataPriv.access( doc, fix ); + + if ( !attaches ) { + doc.addEventListener( orig, handler, true ); + } + dataPriv.access( doc, fix, ( attaches || 0 ) + 1 ); + }, + teardown: function() { + var doc = this.ownerDocument || this.document || this, + attaches = dataPriv.access( doc, fix ) - 1; + + if ( !attaches ) { + doc.removeEventListener( orig, handler, true ); + dataPriv.remove( doc, fix ); + + } else { + dataPriv.access( doc, fix, attaches ); + } + } + }; + } ); +} +var location = window.location; + +var nonce = { guid: Date.now() }; + +var rquery = ( /\?/ ); + + + +// Cross-browser xml parsing +jQuery.parseXML = function( data ) { + var xml, parserErrorElem; + if ( !data || typeof data !== "string" ) { + return null; + } + + // Support: IE 9 - 11 only + // IE throws on parseFromString with invalid input. + try { + xml = ( new window.DOMParser() ).parseFromString( data, "text/xml" ); + } catch ( e ) {} + + parserErrorElem = xml && xml.getElementsByTagName( "parsererror" )[ 0 ]; + if ( !xml || parserErrorElem ) { + jQuery.error( "Invalid XML: " + ( + parserErrorElem ? + jQuery.map( parserErrorElem.childNodes, function( el ) { + return el.textContent; + } ).join( "\n" ) : + data + ) ); + } + return xml; +}; + + +var + rbracket = /\[\]$/, + rCRLF = /\r?\n/g, + rsubmitterTypes = /^(?:submit|button|image|reset|file)$/i, + rsubmittable = /^(?:input|select|textarea|keygen)/i; + +function buildParams( prefix, obj, traditional, add ) { + var name; + + if ( Array.isArray( obj ) ) { + + // Serialize array item. + jQuery.each( obj, function( i, v ) { + if ( traditional || rbracket.test( prefix ) ) { + + // Treat each array item as a scalar. + add( prefix, v ); + + } else { + + // Item is non-scalar (array or object), encode its numeric index. + buildParams( + prefix + "[" + ( typeof v === "object" && v != null ? i : "" ) + "]", + v, + traditional, + add + ); + } + } ); + + } else if ( !traditional && toType( obj ) === "object" ) { + + // Serialize object item. + for ( name in obj ) { + buildParams( prefix + "[" + name + "]", obj[ name ], traditional, add ); + } + + } else { + + // Serialize scalar item. + add( prefix, obj ); + } +} + +// Serialize an array of form elements or a set of +// key/values into a query string +jQuery.param = function( a, traditional ) { + var prefix, + s = [], + add = function( key, valueOrFunction ) { + + // If value is a function, invoke it and use its return value + var value = isFunction( valueOrFunction ) ? + valueOrFunction() : + valueOrFunction; + + s[ s.length ] = encodeURIComponent( key ) + "=" + + encodeURIComponent( value == null ? "" : value ); + }; + + if ( a == null ) { + return ""; + } + + // If an array was passed in, assume that it is an array of form elements. + if ( Array.isArray( a ) || ( a.jquery && !jQuery.isPlainObject( a ) ) ) { + + // Serialize the form elements + jQuery.each( a, function() { + add( this.name, this.value ); + } ); + + } else { + + // If traditional, encode the "old" way (the way 1.3.2 or older + // did it), otherwise encode params recursively. + for ( prefix in a ) { + buildParams( prefix, a[ prefix ], traditional, add ); + } + } + + // Return the resulting serialization + return s.join( "&" ); +}; + +jQuery.fn.extend( { + serialize: function() { + return jQuery.param( this.serializeArray() ); + }, + serializeArray: function() { + return this.map( function() { + + // Can add propHook for "elements" to filter or add form elements + var elements = jQuery.prop( this, "elements" ); + return elements ? jQuery.makeArray( elements ) : this; + } ).filter( function() { + var type = this.type; + + // Use .is( ":disabled" ) so that fieldset[disabled] works + return this.name && !jQuery( this ).is( ":disabled" ) && + rsubmittable.test( this.nodeName ) && !rsubmitterTypes.test( type ) && + ( this.checked || !rcheckableType.test( type ) ); + } ).map( function( _i, elem ) { + var val = jQuery( this ).val(); + + if ( val == null ) { + return null; + } + + if ( Array.isArray( val ) ) { + return jQuery.map( val, function( val ) { + return { name: elem.name, value: val.replace( rCRLF, "\r\n" ) }; + } ); + } + + return { name: elem.name, value: val.replace( rCRLF, "\r\n" ) }; + } ).get(); + } +} ); + + +var + r20 = /%20/g, + rhash = /#.*$/, + rantiCache = /([?&])_=[^&]*/, + rheaders = /^(.*?):[ \t]*([^\r\n]*)$/mg, + + // #7653, #8125, #8152: local protocol detection + rlocalProtocol = /^(?:about|app|app-storage|.+-extension|file|res|widget):$/, + rnoContent = /^(?:GET|HEAD)$/, + rprotocol = /^\/\//, + + /* Prefilters + * 1) They are useful to introduce custom dataTypes (see ajax/jsonp.js for an example) + * 2) These are called: + * - BEFORE asking for a transport + * - AFTER param serialization (s.data is a string if s.processData is true) + * 3) key is the dataType + * 4) the catchall symbol "*" can be used + * 5) execution will start with transport dataType and THEN continue down to "*" if needed + */ + prefilters = {}, + + /* Transports bindings + * 1) key is the dataType + * 2) the catchall symbol "*" can be used + * 3) selection will start with transport dataType and THEN go to "*" if needed + */ + transports = {}, + + // Avoid comment-prolog char sequence (#10098); must appease lint and evade compression + allTypes = "*/".concat( "*" ), + + // Anchor tag for parsing the document origin + originAnchor = document.createElement( "a" ); + +originAnchor.href = location.href; + +// Base "constructor" for jQuery.ajaxPrefilter and jQuery.ajaxTransport +function addToPrefiltersOrTransports( structure ) { + + // dataTypeExpression is optional and defaults to "*" + return function( dataTypeExpression, func ) { + + if ( typeof dataTypeExpression !== "string" ) { + func = dataTypeExpression; + dataTypeExpression = "*"; + } + + var dataType, + i = 0, + dataTypes = dataTypeExpression.toLowerCase().match( rnothtmlwhite ) || []; + + if ( isFunction( func ) ) { + + // For each dataType in the dataTypeExpression + while ( ( dataType = dataTypes[ i++ ] ) ) { + + // Prepend if requested + if ( dataType[ 0 ] === "+" ) { + dataType = dataType.slice( 1 ) || "*"; + ( structure[ dataType ] = structure[ dataType ] || [] ).unshift( func ); + + // Otherwise append + } else { + ( structure[ dataType ] = structure[ dataType ] || [] ).push( func ); + } + } + } + }; +} + +// Base inspection function for prefilters and transports +function inspectPrefiltersOrTransports( structure, options, originalOptions, jqXHR ) { + + var inspected = {}, + seekingTransport = ( structure === transports ); + + function inspect( dataType ) { + var selected; + inspected[ dataType ] = true; + jQuery.each( structure[ dataType ] || [], function( _, prefilterOrFactory ) { + var dataTypeOrTransport = prefilterOrFactory( options, originalOptions, jqXHR ); + if ( typeof dataTypeOrTransport === "string" && + !seekingTransport && !inspected[ dataTypeOrTransport ] ) { + + options.dataTypes.unshift( dataTypeOrTransport ); + inspect( dataTypeOrTransport ); + return false; + } else if ( seekingTransport ) { + return !( selected = dataTypeOrTransport ); + } + } ); + return selected; + } + + return inspect( options.dataTypes[ 0 ] ) || !inspected[ "*" ] && inspect( "*" ); +} + +// A special extend for ajax options +// that takes "flat" options (not to be deep extended) +// Fixes #9887 +function ajaxExtend( target, src ) { + var key, deep, + flatOptions = jQuery.ajaxSettings.flatOptions || {}; + + for ( key in src ) { + if ( src[ key ] !== undefined ) { + ( flatOptions[ key ] ? target : ( deep || ( deep = {} ) ) )[ key ] = src[ key ]; + } + } + if ( deep ) { + jQuery.extend( true, target, deep ); + } + + return target; +} + +/* Handles responses to an ajax request: + * - finds the right dataType (mediates between content-type and expected dataType) + * - returns the corresponding response + */ +function ajaxHandleResponses( s, jqXHR, responses ) { + + var ct, type, finalDataType, firstDataType, + contents = s.contents, + dataTypes = s.dataTypes; + + // Remove auto dataType and get content-type in the process + while ( dataTypes[ 0 ] === "*" ) { + dataTypes.shift(); + if ( ct === undefined ) { + ct = s.mimeType || jqXHR.getResponseHeader( "Content-Type" ); + } + } + + // Check if we're dealing with a known content-type + if ( ct ) { + for ( type in contents ) { + if ( contents[ type ] && contents[ type ].test( ct ) ) { + dataTypes.unshift( type ); + break; + } + } + } + + // Check to see if we have a response for the expected dataType + if ( dataTypes[ 0 ] in responses ) { + finalDataType = dataTypes[ 0 ]; + } else { + + // Try convertible dataTypes + for ( type in responses ) { + if ( !dataTypes[ 0 ] || s.converters[ type + " " + dataTypes[ 0 ] ] ) { + finalDataType = type; + break; + } + if ( !firstDataType ) { + firstDataType = type; + } + } + + // Or just use first one + finalDataType = finalDataType || firstDataType; + } + + // If we found a dataType + // We add the dataType to the list if needed + // and return the corresponding response + if ( finalDataType ) { + if ( finalDataType !== dataTypes[ 0 ] ) { + dataTypes.unshift( finalDataType ); + } + return responses[ finalDataType ]; + } +} + +/* Chain conversions given the request and the original response + * Also sets the responseXXX fields on the jqXHR instance + */ +function ajaxConvert( s, response, jqXHR, isSuccess ) { + var conv2, current, conv, tmp, prev, + converters = {}, + + // Work with a copy of dataTypes in case we need to modify it for conversion + dataTypes = s.dataTypes.slice(); + + // Create converters map with lowercased keys + if ( dataTypes[ 1 ] ) { + for ( conv in s.converters ) { + converters[ conv.toLowerCase() ] = s.converters[ conv ]; + } + } + + current = dataTypes.shift(); + + // Convert to each sequential dataType + while ( current ) { + + if ( s.responseFields[ current ] ) { + jqXHR[ s.responseFields[ current ] ] = response; + } + + // Apply the dataFilter if provided + if ( !prev && isSuccess && s.dataFilter ) { + response = s.dataFilter( response, s.dataType ); + } + + prev = current; + current = dataTypes.shift(); + + if ( current ) { + + // There's only work to do if current dataType is non-auto + if ( current === "*" ) { + + current = prev; + + // Convert response if prev dataType is non-auto and differs from current + } else if ( prev !== "*" && prev !== current ) { + + // Seek a direct converter + conv = converters[ prev + " " + current ] || converters[ "* " + current ]; + + // If none found, seek a pair + if ( !conv ) { + for ( conv2 in converters ) { + + // If conv2 outputs current + tmp = conv2.split( " " ); + if ( tmp[ 1 ] === current ) { + + // If prev can be converted to accepted input + conv = converters[ prev + " " + tmp[ 0 ] ] || + converters[ "* " + tmp[ 0 ] ]; + if ( conv ) { + + // Condense equivalence converters + if ( conv === true ) { + conv = converters[ conv2 ]; + + // Otherwise, insert the intermediate dataType + } else if ( converters[ conv2 ] !== true ) { + current = tmp[ 0 ]; + dataTypes.unshift( tmp[ 1 ] ); + } + break; + } + } + } + } + + // Apply converter (if not an equivalence) + if ( conv !== true ) { + + // Unless errors are allowed to bubble, catch and return them + if ( conv && s.throws ) { + response = conv( response ); + } else { + try { + response = conv( response ); + } catch ( e ) { + return { + state: "parsererror", + error: conv ? e : "No conversion from " + prev + " to " + current + }; + } + } + } + } + } + } + + return { state: "success", data: response }; +} + +jQuery.extend( { + + // Counter for holding the number of active queries + active: 0, + + // Last-Modified header cache for next request + lastModified: {}, + etag: {}, + + ajaxSettings: { + url: location.href, + type: "GET", + isLocal: rlocalProtocol.test( location.protocol ), + global: true, + processData: true, + async: true, + contentType: "application/x-www-form-urlencoded; charset=UTF-8", + + /* + timeout: 0, + data: null, + dataType: null, + username: null, + password: null, + cache: null, + throws: false, + traditional: false, + headers: {}, + */ + + accepts: { + "*": allTypes, + text: "text/plain", + html: "text/html", + xml: "application/xml, text/xml", + json: "application/json, text/javascript" + }, + + contents: { + xml: /\bxml\b/, + html: /\bhtml/, + json: /\bjson\b/ + }, + + responseFields: { + xml: "responseXML", + text: "responseText", + json: "responseJSON" + }, + + // Data converters + // Keys separate source (or catchall "*") and destination types with a single space + converters: { + + // Convert anything to text + "* text": String, + + // Text to html (true = no transformation) + "text html": true, + + // Evaluate text as a json expression + "text json": JSON.parse, + + // Parse text as xml + "text xml": jQuery.parseXML + }, + + // For options that shouldn't be deep extended: + // you can add your own custom options here if + // and when you create one that shouldn't be + // deep extended (see ajaxExtend) + flatOptions: { + url: true, + context: true + } + }, + + // Creates a full fledged settings object into target + // with both ajaxSettings and settings fields. + // If target is omitted, writes into ajaxSettings. + ajaxSetup: function( target, settings ) { + return settings ? + + // Building a settings object + ajaxExtend( ajaxExtend( target, jQuery.ajaxSettings ), settings ) : + + // Extending ajaxSettings + ajaxExtend( jQuery.ajaxSettings, target ); + }, + + ajaxPrefilter: addToPrefiltersOrTransports( prefilters ), + ajaxTransport: addToPrefiltersOrTransports( transports ), + + // Main method + ajax: function( url, options ) { + + // If url is an object, simulate pre-1.5 signature + if ( typeof url === "object" ) { + options = url; + url = undefined; + } + + // Force options to be an object + options = options || {}; + + var transport, + + // URL without anti-cache param + cacheURL, + + // Response headers + responseHeadersString, + responseHeaders, + + // timeout handle + timeoutTimer, + + // Url cleanup var + urlAnchor, + + // Request state (becomes false upon send and true upon completion) + completed, + + // To know if global events are to be dispatched + fireGlobals, + + // Loop variable + i, + + // uncached part of the url + uncached, + + // Create the final options object + s = jQuery.ajaxSetup( {}, options ), + + // Callbacks context + callbackContext = s.context || s, + + // Context for global events is callbackContext if it is a DOM node or jQuery collection + globalEventContext = s.context && + ( callbackContext.nodeType || callbackContext.jquery ) ? + jQuery( callbackContext ) : + jQuery.event, + + // Deferreds + deferred = jQuery.Deferred(), + completeDeferred = jQuery.Callbacks( "once memory" ), + + // Status-dependent callbacks + statusCode = s.statusCode || {}, + + // Headers (they are sent all at once) + requestHeaders = {}, + requestHeadersNames = {}, + + // Default abort message + strAbort = "canceled", + + // Fake xhr + jqXHR = { + readyState: 0, + + // Builds headers hashtable if needed + getResponseHeader: function( key ) { + var match; + if ( completed ) { + if ( !responseHeaders ) { + responseHeaders = {}; + while ( ( match = rheaders.exec( responseHeadersString ) ) ) { + responseHeaders[ match[ 1 ].toLowerCase() + " " ] = + ( responseHeaders[ match[ 1 ].toLowerCase() + " " ] || [] ) + .concat( match[ 2 ] ); + } + } + match = responseHeaders[ key.toLowerCase() + " " ]; + } + return match == null ? null : match.join( ", " ); + }, + + // Raw string + getAllResponseHeaders: function() { + return completed ? responseHeadersString : null; + }, + + // Caches the header + setRequestHeader: function( name, value ) { + if ( completed == null ) { + name = requestHeadersNames[ name.toLowerCase() ] = + requestHeadersNames[ name.toLowerCase() ] || name; + requestHeaders[ name ] = value; + } + return this; + }, + + // Overrides response content-type header + overrideMimeType: function( type ) { + if ( completed == null ) { + s.mimeType = type; + } + return this; + }, + + // Status-dependent callbacks + statusCode: function( map ) { + var code; + if ( map ) { + if ( completed ) { + + // Execute the appropriate callbacks + jqXHR.always( map[ jqXHR.status ] ); + } else { + + // Lazy-add the new callbacks in a way that preserves old ones + for ( code in map ) { + statusCode[ code ] = [ statusCode[ code ], map[ code ] ]; + } + } + } + return this; + }, + + // Cancel the request + abort: function( statusText ) { + var finalText = statusText || strAbort; + if ( transport ) { + transport.abort( finalText ); + } + done( 0, finalText ); + return this; + } + }; + + // Attach deferreds + deferred.promise( jqXHR ); + + // Add protocol if not provided (prefilters might expect it) + // Handle falsy url in the settings object (#10093: consistency with old signature) + // We also use the url parameter if available + s.url = ( ( url || s.url || location.href ) + "" ) + .replace( rprotocol, location.protocol + "//" ); + + // Alias method option to type as per ticket #12004 + s.type = options.method || options.type || s.method || s.type; + + // Extract dataTypes list + s.dataTypes = ( s.dataType || "*" ).toLowerCase().match( rnothtmlwhite ) || [ "" ]; + + // A cross-domain request is in order when the origin doesn't match the current origin. + if ( s.crossDomain == null ) { + urlAnchor = document.createElement( "a" ); + + // Support: IE <=8 - 11, Edge 12 - 15 + // IE throws exception on accessing the href property if url is malformed, + // e.g. http://example.com:80x/ + try { + urlAnchor.href = s.url; + + // Support: IE <=8 - 11 only + // Anchor's host property isn't correctly set when s.url is relative + urlAnchor.href = urlAnchor.href; + s.crossDomain = originAnchor.protocol + "//" + originAnchor.host !== + urlAnchor.protocol + "//" + urlAnchor.host; + } catch ( e ) { + + // If there is an error parsing the URL, assume it is crossDomain, + // it can be rejected by the transport if it is invalid + s.crossDomain = true; + } + } + + // Convert data if not already a string + if ( s.data && s.processData && typeof s.data !== "string" ) { + s.data = jQuery.param( s.data, s.traditional ); + } + + // Apply prefilters + inspectPrefiltersOrTransports( prefilters, s, options, jqXHR ); + + // If request was aborted inside a prefilter, stop there + if ( completed ) { + return jqXHR; + } + + // We can fire global events as of now if asked to + // Don't fire events if jQuery.event is undefined in an AMD-usage scenario (#15118) + fireGlobals = jQuery.event && s.global; + + // Watch for a new set of requests + if ( fireGlobals && jQuery.active++ === 0 ) { + jQuery.event.trigger( "ajaxStart" ); + } + + // Uppercase the type + s.type = s.type.toUpperCase(); + + // Determine if request has content + s.hasContent = !rnoContent.test( s.type ); + + // Save the URL in case we're toying with the If-Modified-Since + // and/or If-None-Match header later on + // Remove hash to simplify url manipulation + cacheURL = s.url.replace( rhash, "" ); + + // More options handling for requests with no content + if ( !s.hasContent ) { + + // Remember the hash so we can put it back + uncached = s.url.slice( cacheURL.length ); + + // If data is available and should be processed, append data to url + if ( s.data && ( s.processData || typeof s.data === "string" ) ) { + cacheURL += ( rquery.test( cacheURL ) ? "&" : "?" ) + s.data; + + // #9682: remove data so that it's not used in an eventual retry + delete s.data; + } + + // Add or update anti-cache param if needed + if ( s.cache === false ) { + cacheURL = cacheURL.replace( rantiCache, "$1" ); + uncached = ( rquery.test( cacheURL ) ? "&" : "?" ) + "_=" + ( nonce.guid++ ) + + uncached; + } + + // Put hash and anti-cache on the URL that will be requested (gh-1732) + s.url = cacheURL + uncached; + + // Change '%20' to '+' if this is encoded form body content (gh-2658) + } else if ( s.data && s.processData && + ( s.contentType || "" ).indexOf( "application/x-www-form-urlencoded" ) === 0 ) { + s.data = s.data.replace( r20, "+" ); + } + + // Set the If-Modified-Since and/or If-None-Match header, if in ifModified mode. + if ( s.ifModified ) { + if ( jQuery.lastModified[ cacheURL ] ) { + jqXHR.setRequestHeader( "If-Modified-Since", jQuery.lastModified[ cacheURL ] ); + } + if ( jQuery.etag[ cacheURL ] ) { + jqXHR.setRequestHeader( "If-None-Match", jQuery.etag[ cacheURL ] ); + } + } + + // Set the correct header, if data is being sent + if ( s.data && s.hasContent && s.contentType !== false || options.contentType ) { + jqXHR.setRequestHeader( "Content-Type", s.contentType ); + } + + // Set the Accepts header for the server, depending on the dataType + jqXHR.setRequestHeader( + "Accept", + s.dataTypes[ 0 ] && s.accepts[ s.dataTypes[ 0 ] ] ? + s.accepts[ s.dataTypes[ 0 ] ] + + ( s.dataTypes[ 0 ] !== "*" ? 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"ajaxSuccess" : "ajaxError", + [ jqXHR, s, isSuccess ? success : error ] ); + } + + // Complete + completeDeferred.fireWith( callbackContext, [ jqXHR, statusText ] ); + + if ( fireGlobals ) { + globalEventContext.trigger( "ajaxComplete", [ jqXHR, s ] ); + + // Handle the global AJAX counter + if ( !( --jQuery.active ) ) { + jQuery.event.trigger( "ajaxStop" ); + } + } + } + + return jqXHR; + }, + + getJSON: function( url, data, callback ) { + return jQuery.get( url, data, callback, "json" ); + }, + + getScript: function( url, callback ) { + return jQuery.get( url, undefined, callback, "script" ); + } +} ); + +jQuery.each( [ "get", "post" ], function( _i, method ) { + jQuery[ method ] = function( url, data, callback, type ) { + + // Shift arguments if data argument was omitted + if ( isFunction( data ) ) { + type = type || callback; + callback = data; + data = undefined; + } + + // The url can be an options object (which then must have .url) + return jQuery.ajax( jQuery.extend( { + url: url, + type: method, + dataType: type, + data: data, + success: callback + }, jQuery.isPlainObject( url ) && url ) ); + }; +} ); + +jQuery.ajaxPrefilter( function( s ) { + var i; + for ( i in s.headers ) { + if ( i.toLowerCase() === "content-type" ) { + s.contentType = s.headers[ i ] || ""; + } + } +} ); + + +jQuery._evalUrl = function( url, options, doc ) { + return jQuery.ajax( { + url: url, + + // Make this explicit, since user can override this through ajaxSetup (#11264) + type: "GET", + dataType: "script", + cache: true, + async: false, + global: false, + + // Only evaluate the response if it is successful (gh-4126) + // dataFilter is not invoked for failure responses, so using it instead + // of the default converter is kludgy but it works. + converters: { + "text script": function() {} + }, + dataFilter: function( response ) { + jQuery.globalEval( response, options, doc ); + } + } ); +}; + + +jQuery.fn.extend( { + wrapAll: function( html ) { + var wrap; + + if ( this[ 0 ] ) { + if ( isFunction( html ) ) { + html = html.call( this[ 0 ] ); + } + + // The elements to wrap the target around + wrap = jQuery( html, this[ 0 ].ownerDocument ).eq( 0 ).clone( true ); + + if ( this[ 0 ].parentNode ) { + wrap.insertBefore( this[ 0 ] ); + } + + wrap.map( function() { + var elem = this; + + while ( elem.firstElementChild ) { + elem = elem.firstElementChild; + } + + return elem; + } ).append( this ); + } + + return this; + }, + + wrapInner: function( html ) { + if ( isFunction( html ) ) { + return this.each( function( i ) { + jQuery( this ).wrapInner( html.call( this, i ) ); + } ); + } + + return this.each( function() { + var self = jQuery( this ), + contents = self.contents(); + + if ( contents.length ) { + contents.wrapAll( html ); + + } else { + self.append( html ); + } + } ); + }, + + wrap: function( html ) { + var htmlIsFunction = isFunction( html ); + + return this.each( function( i ) { + jQuery( this ).wrapAll( htmlIsFunction ? html.call( this, i ) : html ); + } ); + }, + + unwrap: function( selector ) { + this.parent( selector ).not( "body" ).each( function() { + jQuery( this ).replaceWith( this.childNodes ); + } ); + return this; + } +} ); + + +jQuery.expr.pseudos.hidden = function( elem ) { + return !jQuery.expr.pseudos.visible( elem ); +}; +jQuery.expr.pseudos.visible = function( elem ) { + return !!( elem.offsetWidth || elem.offsetHeight || elem.getClientRects().length ); +}; + + + + +jQuery.ajaxSettings.xhr = function() { + try { + return new window.XMLHttpRequest(); + } catch ( e ) {} +}; + +var xhrSuccessStatus = { + + // File protocol always yields status code 0, assume 200 + 0: 200, + + // Support: IE <=9 only + // #1450: sometimes IE returns 1223 when it should be 204 + 1223: 204 + }, + xhrSupported = jQuery.ajaxSettings.xhr(); + +support.cors = !!xhrSupported && ( "withCredentials" in xhrSupported ); +support.ajax = xhrSupported = !!xhrSupported; + +jQuery.ajaxTransport( function( options ) { + var callback, errorCallback; + + // Cross domain only allowed if supported through XMLHttpRequest + if ( support.cors || xhrSupported && !options.crossDomain ) { + return { + send: function( headers, complete ) { + var i, + xhr = options.xhr(); + + xhr.open( + options.type, + options.url, + options.async, + options.username, + options.password + ); + + // Apply custom fields if provided + if ( options.xhrFields ) { + for ( i in options.xhrFields ) { + xhr[ i ] = options.xhrFields[ i ]; + } + } + + // Override mime type if needed + if ( options.mimeType && xhr.overrideMimeType ) { + xhr.overrideMimeType( options.mimeType ); + } + + // X-Requested-With header + // For cross-domain requests, seeing as conditions for a preflight are + // akin to a jigsaw puzzle, we simply never set it to be sure. + // (it can always be set on a per-request basis or even using ajaxSetup) + // For same-domain requests, won't change header if already provided. + if ( !options.crossDomain && !headers[ "X-Requested-With" ] ) { + headers[ "X-Requested-With" ] = "XMLHttpRequest"; + } + + // Set headers + for ( i in headers ) { + xhr.setRequestHeader( i, headers[ i ] ); + } + + // Callback + callback = function( type ) { + return function() { + if ( callback ) { + callback = errorCallback = xhr.onload = + xhr.onerror = xhr.onabort = xhr.ontimeout = + xhr.onreadystatechange = null; + + if ( type === "abort" ) { + xhr.abort(); + } else if ( type === "error" ) { + + // Support: IE <=9 only + // On a manual native abort, IE9 throws + // errors on any property access that is not readyState + if ( typeof xhr.status !== "number" ) { + complete( 0, "error" ); + } else { + complete( + + // File: protocol always yields status 0; see #8605, #14207 + xhr.status, + xhr.statusText + ); + } + } else { + complete( + xhrSuccessStatus[ xhr.status ] || xhr.status, + xhr.statusText, + + // Support: IE <=9 only + // IE9 has no XHR2 but throws on binary (trac-11426) + // For XHR2 non-text, let the caller handle it (gh-2498) + ( xhr.responseType || "text" ) !== "text" || + typeof xhr.responseText !== "string" ? 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DefaultType(){return{}}static get NAME(){throw new Error('You have to implement the static method "NAME", for each component!')}_getConfig(t){return t=this._mergeConfigObj(t),t=this._configAfterMerge(t),this._typeCheckConfig(t),t}_configAfterMerge(t){return t}_mergeConfigObj(t,e){const i=Mt(e)?be.getDataAttribute(e,"config"):{};return{...this.constructor.Default,..."object"==typeof i?i:{},...Mt(e)?be.getDataAttributes(e):{},..."object"==typeof t?t:{}}}_typeCheckConfig(t,e=this.constructor.DefaultType){for(const n of Object.keys(e)){const s=e[n],o=t[n],r=Mt(o)?"element":null==(i=o)?`${i}`:Object.prototype.toString.call(i).match(/\s([a-z]+)/i)[1].toLowerCase();if(!new RegExp(s).test(r))throw new TypeError(`${this.constructor.NAME.toUpperCase()}: Option "${n}" provided type "${r}" but expected type "${s}".`)}var i}}class ye extends ve{constructor(t,e){super(),(t=Ht(t))&&(this._element=t,this._config=this._getConfig(e),ge.set(this._element,this.constructor.DATA_KEY,this))}dispose(){ge.remove(this._element,this.constructor.DATA_KEY),ue.off(this._element,this.constructor.EVENT_KEY);for(const t of Object.getOwnPropertyNames(this))this[t]=null}_queueCallback(t,e,i=!0){Xt(t,e,i)}_getConfig(t){return t=this._mergeConfigObj(t,this._element),t=this._configAfterMerge(t),this._typeCheckConfig(t),t}static getInstance(t){return ge.get(Ht(t),this.DATA_KEY)}static getOrCreateInstance(t,e={}){return this.getInstance(t)||new this(t,"object"==typeof e?e:null)}static get VERSION(){return"5.2.3"}static get DATA_KEY(){return`bs.${this.NAME}`}static get EVENT_KEY(){return`.${this.DATA_KEY}`}static eventName(t){return`${t}${this.EVENT_KEY}`}}const we=(t,e="hide")=>{const i=`click.dismiss${t.EVENT_KEY}`,n=t.NAME;ue.on(document,i,`[data-bs-dismiss="${n}"]`,(function(i){if(["A","AREA"].includes(this.tagName)&&i.preventDefault(),Wt(this))return;const s=Pt(this)||this.closest(`.${n}`);t.getOrCreateInstance(s)[e]()}))},Ae=".bs.alert",Ee=`close${Ae}`,Ce=`closed${Ae}`;class Te extends ye{static get NAME(){return"alert"}close(){if(ue.trigger(this._element,Ee).defaultPrevented)return;this._element.classList.remove("show");const t=this._element.classList.contains("fade");this._queueCallback((()=>this._destroyElement()),this._element,t)}_destroyElement(){this._element.remove(),ue.trigger(this._element,Ce),this.dispose()}static jQueryInterface(t){return this.each((function(){const e=Te.getOrCreateInstance(this);if("string"==typeof t){if(void 0===e[t]||t.startsWith("_")||"constructor"===t)throw new TypeError(`No method named "${t}"`);e[t](this)}}))}}we(Te,"close"),Kt(Te);const Oe='[data-bs-toggle="button"]';class xe extends ye{static get 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NAME(){return"swipe"}dispose(){ue.off(this._element,Le)}_start(t){this._supportPointerEvents?this._eventIsPointerPenTouch(t)&&(this._deltaX=t.clientX):this._deltaX=t.touches[0].clientX}_end(t){this._eventIsPointerPenTouch(t)&&(this._deltaX=t.clientX-this._deltaX),this._handleSwipe(),Qt(this._config.endCallback)}_move(t){this._deltaX=t.touches&&t.touches.length>1?0:t.touches[0].clientX-this._deltaX}_handleSwipe(){const t=Math.abs(this._deltaX);if(t<=40)return;const e=t/this._deltaX;this._deltaX=0,e&&Qt(e>0?this._config.rightCallback:this._config.leftCallback)}_initEvents(){this._supportPointerEvents?(ue.on(this._element,Ie,(t=>this._start(t))),ue.on(this._element,Ne,(t=>this._end(t))),this._element.classList.add("pointer-event")):(ue.on(this._element,De,(t=>this._start(t))),ue.on(this._element,$e,(t=>this._move(t))),ue.on(this._element,Se,(t=>this._end(t))))}_eventIsPointerPenTouch(t){return this._supportPointerEvents&&("pen"===t.pointerType||"touch"===t.pointerType)}static isSupported(){return"ontouchstart"in document.documentElement||navigator.maxTouchPoints>0}}const He=".bs.carousel",Be=".data-api",We="next",Fe="prev",ze="left",qe="right",Re=`slide${He}`,Ve=`slid${He}`,Ye=`keydown${He}`,Ke=`mouseenter${He}`,Qe=`mouseleave${He}`,Xe=`dragstart${He}`,Ue=`load${He}${Be}`,Ge=`click${He}${Be}`,Je="carousel",Ze="active",ti=".active",ei=".carousel-item",ii=ti+ei,ni={ArrowLeft:qe,ArrowRight:ze},si={interval:5e3,keyboard:!0,pause:"hover",ride:!1,touch:!0,wrap:!0},oi={interval:"(number|boolean)",keyboard:"boolean",pause:"(string|boolean)",ride:"(boolean|string)",touch:"boolean",wrap:"boolean"};class ri extends ye{constructor(t,e){super(t,e),this._interval=null,this._activeElement=null,this._isSliding=!1,this.touchTimeout=null,this._swipeHelper=null,this._indicatorsElement=ke.findOne(".carousel-indicators",this._element),this._addEventListeners(),this._config.ride===Je&&this.cycle()}static get Default(){return si}static get DefaultType(){return oi}static get NAME(){return"carousel"}next(){this._slide(We)}nextWhenVisible(){!document.hidden&&Bt(this._element)&&this.next()}prev(){this._slide(Fe)}pause(){this._isSliding&&jt(this._element),this._clearInterval()}cycle(){this._clearInterval(),this._updateInterval(),this._interval=setInterval((()=>this.nextWhenVisible()),this._config.interval)}_maybeEnableCycle(){this._config.ride&&(this._isSliding?ue.one(this._element,Ve,(()=>this.cycle())):this.cycle())}to(t){const e=this._getItems();if(t>e.length-1||t<0)return;if(this._isSliding)return void ue.one(this._element,Ve,(()=>this.to(t)));const i=this._getItemIndex(this._getActive());if(i===t)return;const n=t>i?We:Fe;this._slide(n,e[t])}dispose(){this._swipeHelper&&this._swipeHelper.dispose(),super.dispose()}_configAfterMerge(t){return t.defaultInterval=t.interval,t}_addEventListeners(){this._config.keyboard&&ue.on(this._element,Ye,(t=>this._keydown(t))),"hover"===this._config.pause&&(ue.on(this._element,Ke,(()=>this.pause())),ue.on(this._element,Qe,(()=>this._maybeEnableCycle()))),this._config.touch&&Me.isSupported()&&this._addTouchEventListeners()}_addTouchEventListeners(){for(const t of ke.find(".carousel-item img",this._element))ue.on(t,Xe,(t=>t.preventDefault()));const t={leftCallback:()=>this._slide(this._directionToOrder(ze)),rightCallback:()=>this._slide(this._directionToOrder(qe)),endCallback:()=>{"hover"===this._config.pause&&(this.pause(),this.touchTimeout&&clearTimeout(this.touchTimeout),this.touchTimeout=setTimeout((()=>this._maybeEnableCycle()),500+this._config.interval))}};this._swipeHelper=new Me(this._element,t)}_keydown(t){if(/input|textarea/i.test(t.target.tagName))return;const e=ni[t.key];e&&(t.preventDefault(),this._slide(this._directionToOrder(e)))}_getItemIndex(t){return this._getItems().indexOf(t)}_setActiveIndicatorElement(t){if(!this._indicatorsElement)return;const e=ke.findOne(ti,this._indicatorsElement);e.classList.remove(Ze),e.removeAttribute("aria-current");const i=ke.findOne(`[data-bs-slide-to="${t}"]`,this._indicatorsElement);i&&(i.classList.add(Ze),i.setAttribute("aria-current","true"))}_updateInterval(){const t=this._activeElement||this._getActive();if(!t)return;const e=Number.parseInt(t.getAttribute("data-bs-interval"),10);this._config.interval=e||this._config.defaultInterval}_slide(t,e=null){if(this._isSliding)return;const i=this._getActive(),n=t===We,s=e||Ut(this._getItems(),i,n,this._config.wrap);if(s===i)return;const o=this._getItemIndex(s),r=e=>ue.trigger(this._element,e,{relatedTarget:s,direction:this._orderToDirection(t),from:this._getItemIndex(i),to:o});if(r(Re).defaultPrevented)return;if(!i||!s)return;const a=Boolean(this._interval);this.pause(),this._isSliding=!0,this._setActiveIndicatorElement(o),this._activeElement=s;const l=n?"carousel-item-start":"carousel-item-end",c=n?"carousel-item-next":"carousel-item-prev";s.classList.add(c),qt(s),i.classList.add(l),s.classList.add(l),this._queueCallback((()=>{s.classList.remove(l,c),s.classList.add(Ze),i.classList.remove(Ze,c,l),this._isSliding=!1,r(Ve)}),i,this._isAnimated()),a&&this.cycle()}_isAnimated(){return this._element.classList.contains("slide")}_getActive(){return ke.findOne(ii,this._element)}_getItems(){return ke.find(ei,this._element)}_clearInterval(){this._interval&&(clearInterval(this._interval),this._interval=null)}_directionToOrder(t){return Yt()?t===ze?Fe:We:t===ze?We:Fe}_orderToDirection(t){return Yt()?t===Fe?ze:qe:t===Fe?qe:ze}static jQueryInterface(t){return this.each((function(){const e=ri.getOrCreateInstance(this,t);if("number"!=typeof t){if("string"==typeof t){if(void 0===e[t]||t.startsWith("_")||"constructor"===t)throw new TypeError(`No method named "${t}"`);e[t]()}}else e.to(t)}))}}ue.on(document,Ge,"[data-bs-slide], 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e=Nt(t),i=ke.find(e).filter((t=>t===this._element));null!==e&&i.length&&this._triggerArray.push(t)}this._initializeChildren(),this._config.parent||this._addAriaAndCollapsedClass(this._triggerArray,this._isShown()),this._config.toggle&&this.toggle()}static get Default(){return bi}static get DefaultType(){return vi}static get NAME(){return"collapse"}toggle(){this._isShown()?this.hide():this.show()}show(){if(this._isTransitioning||this._isShown())return;let t=[];if(this._config.parent&&(t=this._getFirstLevelChildren(".collapse.show, .collapse.collapsing").filter((t=>t!==this._element)).map((t=>yi.getOrCreateInstance(t,{toggle:!1})))),t.length&&t[0]._isTransitioning)return;if(ue.trigger(this._element,li).defaultPrevented)return;for(const e of t)e.hide();const e=this._getDimension();this._element.classList.remove(pi),this._element.classList.add(gi),this._element.style[e]=0,this._addAriaAndCollapsedClass(this._triggerArray,!0),this._isTransitioning=!0;const i=`scroll${e[0].toUpperCase()+e.slice(1)}`;this._queueCallback((()=>{this._isTransitioning=!1,this._element.classList.remove(gi),this._element.classList.add(pi,fi),this._element.style[e]="",ue.trigger(this._element,ci)}),this._element,!0),this._element.style[e]=`${this._element[i]}px`}hide(){if(this._isTransitioning||!this._isShown())return;if(ue.trigger(this._element,hi).defaultPrevented)return;const t=this._getDimension();this._element.style[t]=`${this._element.getBoundingClientRect()[t]}px`,qt(this._element),this._element.classList.add(gi),this._element.classList.remove(pi,fi);for(const t of this._triggerArray){const e=Pt(t);e&&!this._isShown(e)&&this._addAriaAndCollapsedClass([t],!1)}this._isTransitioning=!0,this._element.style[t]="",this._queueCallback((()=>{this._isTransitioning=!1,this._element.classList.remove(gi),this._element.classList.add(pi),ue.trigger(this._element,di)}),this._element,!0)}_isShown(t=this._element){return t.classList.contains(fi)}_configAfterMerge(t){return t.toggle=Boolean(t.toggle),t.parent=Ht(t.parent),t}_getDimension(){return this._element.classList.contains("collapse-horizontal")?"width":"height"}_initializeChildren(){if(!this._config.parent)return;const t=this._getFirstLevelChildren(_i);for(const e of t){const t=Pt(e);t&&this._addAriaAndCollapsedClass([e],this._isShown(t))}}_getFirstLevelChildren(t){const e=ke.find(mi,this._config.parent);return ke.find(t,this._config.parent).filter((t=>!e.includes(t)))}_addAriaAndCollapsedClass(t,e){if(t.length)for(const i of t)i.classList.toggle("collapsed",!e),i.setAttribute("aria-expanded",e)}static jQueryInterface(t){const e={};return"string"==typeof t&&/show|hide/.test(t)&&(e.toggle=!1),this.each((function(){const i=yi.getOrCreateInstance(this,e);if("string"==typeof t){if(void 0===i[t])throw new TypeError(`No method named "${t}"`);i[t]()}}))}}ue.on(document,ui,_i,(function(t){("A"===t.target.tagName||t.delegateTarget&&"A"===t.delegateTarget.tagName)&&t.preventDefault();const e=Nt(this),i=ke.find(e);for(const t of i)yi.getOrCreateInstance(t,{toggle:!1}).toggle()})),Kt(yi);const wi="dropdown",Ai=".bs.dropdown",Ei=".data-api",Ci="ArrowUp",Ti="ArrowDown",Oi=`hide${Ai}`,xi=`hidden${Ai}`,ki=`show${Ai}`,Li=`shown${Ai}`,Di=`click${Ai}${Ei}`,$i=`keydown${Ai}${Ei}`,Si=`keyup${Ai}${Ei}`,Ii="show",Ni='[data-bs-toggle="dropdown"]:not(.disabled):not(:disabled)',Pi=`${Ni}.${Ii}`,ji=".dropdown-menu",Mi=Yt()?"top-end":"top-start",Hi=Yt()?"top-start":"top-end",Bi=Yt()?"bottom-end":"bottom-start",Wi=Yt()?"bottom-start":"bottom-end",Fi=Yt()?"left-start":"right-start",zi=Yt()?"right-start":"left-start",qi={autoClose:!0,boundary:"clippingParents",display:"dynamic",offset:[0,2],popperConfig:null,reference:"toggle"},Ri={autoClose:"(boolean|string)",boundary:"(string|element)",display:"string",offset:"(array|string|function)",popperConfig:"(null|object|function)",reference:"(string|element|object)"};class Vi extends ye{constructor(t,e){super(t,e),this._popper=null,this._parent=this._element.parentNode,this._menu=ke.next(this._element,ji)[0]||ke.prev(this._element,ji)[0]||ke.findOne(ji,this._parent),this._inNavbar=this._detectNavbar()}static get Default(){return qi}static get DefaultType(){return Ri}static get NAME(){return wi}toggle(){return this._isShown()?this.hide():this.show()}show(){if(Wt(this._element)||this._isShown())return;const t={relatedTarget:this._element};if(!ue.trigger(this._element,ki,t).defaultPrevented){if(this._createPopper(),"ontouchstart"in document.documentElement&&!this._parent.closest(".navbar-nav"))for(const t of[].concat(...document.body.children))ue.on(t,"mouseover",zt);this._element.focus(),this._element.setAttribute("aria-expanded",!0),this._menu.classList.add(Ii),this._element.classList.add(Ii),ue.trigger(this._element,Li,t)}}hide(){if(Wt(this._element)||!this._isShown())return;const t={relatedTarget:this._element};this._completeHide(t)}dispose(){this._popper&&this._popper.destroy(),super.dispose()}update(){this._inNavbar=this._detectNavbar(),this._popper&&this._popper.update()}_completeHide(t){if(!ue.trigger(this._element,Oi,t).defaultPrevented){if("ontouchstart"in document.documentElement)for(const t of[].concat(...document.body.children))ue.off(t,"mouseover",zt);this._popper&&this._popper.destroy(),this._menu.classList.remove(Ii),this._element.classList.remove(Ii),this._element.setAttribute("aria-expanded","false"),be.removeDataAttribute(this._menu,"popper"),ue.trigger(this._element,xi,t)}}_getConfig(t){if("object"==typeof(t=super._getConfig(t)).reference&&!Mt(t.reference)&&"function"!=typeof t.reference.getBoundingClientRect)throw new TypeError(`${wi.toUpperCase()}: Option "reference" provided type "object" without a required "getBoundingClientRect" method.`);return t}_createPopper(){if(void 0===e)throw new TypeError("Bootstrap's dropdowns require Popper (https://popper.js.org)");let t=this._element;"parent"===this._config.reference?t=this._parent:Mt(this._config.reference)?t=Ht(this._config.reference):"object"==typeof this._config.reference&&(t=this._config.reference);const i=this._getPopperConfig();this._popper=Dt(t,this._menu,i)}_isShown(){return this._menu.classList.contains(Ii)}_getPlacement(){const t=this._parent;if(t.classList.contains("dropend"))return Fi;if(t.classList.contains("dropstart"))return zi;if(t.classList.contains("dropup-center"))return"top";if(t.classList.contains("dropdown-center"))return"bottom";const e="end"===getComputedStyle(this._menu).getPropertyValue("--bs-position").trim();return t.classList.contains("dropup")?e?Hi:Mi:e?Wi:Bi}_detectNavbar(){return null!==this._element.closest(".navbar")}_getOffset(){const{offset:t}=this._config;return"string"==typeof t?t.split(",").map((t=>Number.parseInt(t,10))):"function"==typeof t?e=>t(e,this._element):t}_getPopperConfig(){const t={placement:this._getPlacement(),modifiers:[{name:"preventOverflow",options:{boundary:this._config.boundary}},{name:"offset",options:{offset:this._getOffset()}}]};return(this._inNavbar||"static"===this._config.display)&&(be.setDataAttribute(this._menu,"popper","static"),t.modifiers=[{name:"applyStyles",enabled:!1}]),{...t,..."function"==typeof this._config.popperConfig?this._config.popperConfig(t):this._config.popperConfig}}_selectMenuItem({key:t,target:e}){const i=ke.find(".dropdown-menu .dropdown-item:not(.disabled):not(:disabled)",this._menu).filter((t=>Bt(t)));i.length&&Ut(i,e,t===Ti,!i.includes(e)).focus()}static jQueryInterface(t){return this.each((function(){const e=Vi.getOrCreateInstance(this,t);if("string"==typeof t){if(void 0===e[t])throw new TypeError(`No method named "${t}"`);e[t]()}}))}static clearMenus(t){if(2===t.button||"keyup"===t.type&&"Tab"!==t.key)return;const e=ke.find(Pi);for(const i of e){const e=Vi.getInstance(i);if(!e||!1===e._config.autoClose)continue;const n=t.composedPath(),s=n.includes(e._menu);if(n.includes(e._element)||"inside"===e._config.autoClose&&!s||"outside"===e._config.autoClose&&s)continue;if(e._menu.contains(t.target)&&("keyup"===t.type&&"Tab"===t.key||/input|select|option|textarea|form/i.test(t.target.tagName)))continue;const o={relatedTarget:e._element};"click"===t.type&&(o.clickEvent=t),e._completeHide(o)}}static dataApiKeydownHandler(t){const e=/input|textarea/i.test(t.target.tagName),i="Escape"===t.key,n=[Ci,Ti].includes(t.key);if(!n&&!i)return;if(e&&!i)return;t.preventDefault();const s=this.matches(Ni)?this:ke.prev(this,Ni)[0]||ke.next(this,Ni)[0]||ke.findOne(Ni,t.delegateTarget.parentNode),o=Vi.getOrCreateInstance(s);if(n)return t.stopPropagation(),o.show(),void o._selectMenuItem(t);o._isShown()&&(t.stopPropagation(),o.hide(),s.focus())}}ue.on(document,$i,Ni,Vi.dataApiKeydownHandler),ue.on(document,$i,ji,Vi.dataApiKeydownHandler),ue.on(document,Di,Vi.clearMenus),ue.on(document,Si,Vi.clearMenus),ue.on(document,Di,Ni,(function(t){t.preventDefault(),Vi.getOrCreateInstance(this).toggle()})),Kt(Vi);const Yi=".fixed-top, .fixed-bottom, .is-fixed, .sticky-top",Ki=".sticky-top",Qi="padding-right",Xi="margin-right";class Ui{constructor(){this._element=document.body}getWidth(){const t=document.documentElement.clientWidth;return Math.abs(window.innerWidth-t)}hide(){const t=this.getWidth();this._disableOverFlow(),this._setElementAttributes(this._element,Qi,(e=>e+t)),this._setElementAttributes(Yi,Qi,(e=>e+t)),this._setElementAttributes(Ki,Xi,(e=>e-t))}reset(){this._resetElementAttributes(this._element,"overflow"),this._resetElementAttributes(this._element,Qi),this._resetElementAttributes(Yi,Qi),this._resetElementAttributes(Ki,Xi)}isOverflowing(){return this.getWidth()>0}_disableOverFlow(){this._saveInitialAttribute(this._element,"overflow"),this._element.style.overflow="hidden"}_setElementAttributes(t,e,i){const n=this.getWidth();this._applyManipulationCallback(t,(t=>{if(t!==this._element&&window.innerWidth>t.clientWidth+n)return;this._saveInitialAttribute(t,e);const s=window.getComputedStyle(t).getPropertyValue(e);t.style.setProperty(e,`${i(Number.parseFloat(s))}px`)}))}_saveInitialAttribute(t,e){const i=t.style.getPropertyValue(e);i&&be.setDataAttribute(t,e,i)}_resetElementAttributes(t,e){this._applyManipulationCallback(t,(t=>{const i=be.getDataAttribute(t,e);null!==i?(be.removeDataAttribute(t,e),t.style.setProperty(e,i)):t.style.removeProperty(e)}))}_applyManipulationCallback(t,e){if(Mt(t))e(t);else for(const i of ke.find(t,this._element))e(i)}}const Gi="backdrop",Ji="show",Zi=`mousedown.bs.${Gi}`,tn={className:"modal-backdrop",clickCallback:null,isAnimated:!1,isVisible:!0,rootElement:"body"},en={className:"string",clickCallback:"(function|null)",isAnimated:"boolean",isVisible:"boolean",rootElement:"(element|string)"};class nn extends ve{constructor(t){super(),this._config=this._getConfig(t),this._isAppended=!1,this._element=null}static get Default(){return tn}static get DefaultType(){return en}static get NAME(){return Gi}show(t){if(!this._config.isVisible)return void Qt(t);this._append();const e=this._getElement();this._config.isAnimated&&qt(e),e.classList.add(Ji),this._emulateAnimation((()=>{Qt(t)}))}hide(t){this._config.isVisible?(this._getElement().classList.remove(Ji),this._emulateAnimation((()=>{this.dispose(),Qt(t)}))):Qt(t)}dispose(){this._isAppended&&(ue.off(this._element,Zi),this._element.remove(),this._isAppended=!1)}_getElement(){if(!this._element){const t=document.createElement("div");t.className=this._config.className,this._config.isAnimated&&t.classList.add("fade"),this._element=t}return this._element}_configAfterMerge(t){return t.rootElement=Ht(t.rootElement),t}_append(){if(this._isAppended)return;const t=this._getElement();this._config.rootElement.append(t),ue.on(t,Zi,(()=>{Qt(this._config.clickCallback)})),this._isAppended=!0}_emulateAnimation(t){Xt(t,this._getElement(),this._config.isAnimated)}}const sn=".bs.focustrap",on=`focusin${sn}`,rn=`keydown.tab${sn}`,an="backward",ln={autofocus:!0,trapElement:null},cn={autofocus:"boolean",trapElement:"element"};class hn extends ve{constructor(t){super(),this._config=this._getConfig(t),this._isActive=!1,this._lastTabNavDirection=null}static get Default(){return ln}static get DefaultType(){return cn}static get NAME(){return"focustrap"}activate(){this._isActive||(this._config.autofocus&&this._config.trapElement.focus(),ue.off(document,sn),ue.on(document,on,(t=>this._handleFocusin(t))),ue.on(document,rn,(t=>this._handleKeydown(t))),this._isActive=!0)}deactivate(){this._isActive&&(this._isActive=!1,ue.off(document,sn))}_handleFocusin(t){const{trapElement:e}=this._config;if(t.target===document||t.target===e||e.contains(t.target))return;const i=ke.focusableChildren(e);0===i.length?e.focus():this._lastTabNavDirection===an?i[i.length-1].focus():i[0].focus()}_handleKeydown(t){"Tab"===t.key&&(this._lastTabNavDirection=t.shiftKey?an:"forward")}}const dn=".bs.modal",un=`hide${dn}`,fn=`hidePrevented${dn}`,pn=`hidden${dn}`,gn=`show${dn}`,mn=`shown${dn}`,_n=`resize${dn}`,bn=`click.dismiss${dn}`,vn=`mousedown.dismiss${dn}`,yn=`keydown.dismiss${dn}`,wn=`click${dn}.data-api`,An="modal-open",En="show",Cn="modal-static",Tn={backdrop:!0,focus:!0,keyboard:!0},On={backdrop:"(boolean|string)",focus:"boolean",keyboard:"boolean"};class xn extends ye{constructor(t,e){super(t,e),this._dialog=ke.findOne(".modal-dialog",this._element),this._backdrop=this._initializeBackDrop(),this._focustrap=this._initializeFocusTrap(),this._isShown=!1,this._isTransitioning=!1,this._scrollBar=new Ui,this._addEventListeners()}static get Default(){return Tn}static get DefaultType(){return On}static get NAME(){return"modal"}toggle(t){return this._isShown?this.hide():this.show(t)}show(t){this._isShown||this._isTransitioning||ue.trigger(this._element,gn,{relatedTarget:t}).defaultPrevented||(this._isShown=!0,this._isTransitioning=!0,this._scrollBar.hide(),document.body.classList.add(An),this._adjustDialog(),this._backdrop.show((()=>this._showElement(t))))}hide(){this._isShown&&!this._isTransitioning&&(ue.trigger(this._element,un).defaultPrevented||(this._isShown=!1,this._isTransitioning=!0,this._focustrap.deactivate(),this._element.classList.remove(En),this._queueCallback((()=>this._hideModal()),this._element,this._isAnimated())))}dispose(){for(const t of[window,this._dialog])ue.off(t,dn);this._backdrop.dispose(),this._focustrap.deactivate(),super.dispose()}handleUpdate(){this._adjustDialog()}_initializeBackDrop(){return new nn({isVisible:Boolean(this._config.backdrop),isAnimated:this._isAnimated()})}_initializeFocusTrap(){return new hn({trapElement:this._element})}_showElement(t){document.body.contains(this._element)||document.body.append(this._element),this._element.style.display="block",this._element.removeAttribute("aria-hidden"),this._element.setAttribute("aria-modal",!0),this._element.setAttribute("role","dialog"),this._element.scrollTop=0;const e=ke.findOne(".modal-body",this._dialog);e&&(e.scrollTop=0),qt(this._element),this._element.classList.add(En),this._queueCallback((()=>{this._config.focus&&this._focustrap.activate(),this._isTransitioning=!1,ue.trigger(this._element,mn,{relatedTarget:t})}),this._dialog,this._isAnimated())}_addEventListeners(){ue.on(this._element,yn,(t=>{if("Escape"===t.key)return this._config.keyboard?(t.preventDefault(),void this.hide()):void this._triggerBackdropTransition()})),ue.on(window,_n,(()=>{this._isShown&&!this._isTransitioning&&this._adjustDialog()})),ue.on(this._element,vn,(t=>{ue.one(this._element,bn,(e=>{this._element===t.target&&this._element===e.target&&("static"!==this._config.backdrop?this._config.backdrop&&this.hide():this._triggerBackdropTransition())}))}))}_hideModal(){this._element.style.display="none",this._element.setAttribute("aria-hidden",!0),this._element.removeAttribute("aria-modal"),this._element.removeAttribute("role"),this._isTransitioning=!1,this._backdrop.hide((()=>{document.body.classList.remove(An),this._resetAdjustments(),this._scrollBar.reset(),ue.trigger(this._element,pn)}))}_isAnimated(){return this._element.classList.contains("fade")}_triggerBackdropTransition(){if(ue.trigger(this._element,fn).defaultPrevented)return;const t=this._element.scrollHeight>document.documentElement.clientHeight,e=this._element.style.overflowY;"hidden"===e||this._element.classList.contains(Cn)||(t||(this._element.style.overflowY="hidden"),this._element.classList.add(Cn),this._queueCallback((()=>{this._element.classList.remove(Cn),this._queueCallback((()=>{this._element.style.overflowY=e}),this._dialog)}),this._dialog),this._element.focus())}_adjustDialog(){const t=this._element.scrollHeight>document.documentElement.clientHeight,e=this._scrollBar.getWidth(),i=e>0;if(i&&!t){const t=Yt()?"paddingLeft":"paddingRight";this._element.style[t]=`${e}px`}if(!i&&t){const t=Yt()?"paddingRight":"paddingLeft";this._element.style[t]=`${e}px`}}_resetAdjustments(){this._element.style.paddingLeft="",this._element.style.paddingRight=""}static jQueryInterface(t,e){return this.each((function(){const i=xn.getOrCreateInstance(this,t);if("string"==typeof t){if(void 0===i[t])throw new TypeError(`No method named "${t}"`);i[t](e)}}))}}ue.on(document,wn,'[data-bs-toggle="modal"]',(function(t){const e=Pt(this);["A","AREA"].includes(this.tagName)&&t.preventDefault(),ue.one(e,gn,(t=>{t.defaultPrevented||ue.one(e,pn,(()=>{Bt(this)&&this.focus()}))}));const i=ke.findOne(".modal.show");i&&xn.getInstance(i).hide(),xn.getOrCreateInstance(e).toggle(this)})),we(xn),Kt(xn);const kn=".bs.offcanvas",Ln=".data-api",Dn=`load${kn}${Ln}`,$n="show",Sn="showing",In="hiding",Nn=".offcanvas.show",Pn=`show${kn}`,jn=`shown${kn}`,Mn=`hide${kn}`,Hn=`hidePrevented${kn}`,Bn=`hidden${kn}`,Wn=`resize${kn}`,Fn=`click${kn}${Ln}`,zn=`keydown.dismiss${kn}`,qn={backdrop:!0,keyboard:!0,scroll:!1},Rn={backdrop:"(boolean|string)",keyboard:"boolean",scroll:"boolean"};class Vn extends ye{constructor(t,e){super(t,e),this._isShown=!1,this._backdrop=this._initializeBackDrop(),this._focustrap=this._initializeFocusTrap(),this._addEventListeners()}static get Default(){return qn}static get DefaultType(){return Rn}static get NAME(){return"offcanvas"}toggle(t){return this._isShown?this.hide():this.show(t)}show(t){this._isShown||ue.trigger(this._element,Pn,{relatedTarget:t}).defaultPrevented||(this._isShown=!0,this._backdrop.show(),this._config.scroll||(new Ui).hide(),this._element.setAttribute("aria-modal",!0),this._element.setAttribute("role","dialog"),this._element.classList.add(Sn),this._queueCallback((()=>{this._config.scroll&&!this._config.backdrop||this._focustrap.activate(),this._element.classList.add($n),this._element.classList.remove(Sn),ue.trigger(this._element,jn,{relatedTarget:t})}),this._element,!0))}hide(){this._isShown&&(ue.trigger(this._element,Mn).defaultPrevented||(this._focustrap.deactivate(),this._element.blur(),this._isShown=!1,this._element.classList.add(In),this._backdrop.hide(),this._queueCallback((()=>{this._element.classList.remove($n,In),this._element.removeAttribute("aria-modal"),this._element.removeAttribute("role"),this._config.scroll||(new Ui).reset(),ue.trigger(this._element,Bn)}),this._element,!0)))}dispose(){this._backdrop.dispose(),this._focustrap.deactivate(),super.dispose()}_initializeBackDrop(){const t=Boolean(this._config.backdrop);return new nn({className:"offcanvas-backdrop",isVisible:t,isAnimated:!0,rootElement:this._element.parentNode,clickCallback:t?()=>{"static"!==this._config.backdrop?this.hide():ue.trigger(this._element,Hn)}:null})}_initializeFocusTrap(){return new hn({trapElement:this._element})}_addEventListeners(){ue.on(this._element,zn,(t=>{"Escape"===t.key&&(this._config.keyboard?this.hide():ue.trigger(this._element,Hn))}))}static jQueryInterface(t){return this.each((function(){const e=Vn.getOrCreateInstance(this,t);if("string"==typeof t){if(void 0===e[t]||t.startsWith("_")||"constructor"===t)throw new TypeError(`No method named "${t}"`);e[t](this)}}))}}ue.on(document,Fn,'[data-bs-toggle="offcanvas"]',(function(t){const e=Pt(this);if(["A","AREA"].includes(this.tagName)&&t.preventDefault(),Wt(this))return;ue.one(e,Bn,(()=>{Bt(this)&&this.focus()}));const i=ke.findOne(Nn);i&&i!==e&&Vn.getInstance(i).hide(),Vn.getOrCreateInstance(e).toggle(this)})),ue.on(window,Dn,(()=>{for(const t of ke.find(Nn))Vn.getOrCreateInstance(t).show()})),ue.on(window,Wn,(()=>{for(const t of ke.find("[aria-modal][class*=show][class*=offcanvas-]"))"fixed"!==getComputedStyle(t).position&&Vn.getOrCreateInstance(t).hide()})),we(Vn),Kt(Vn);const Yn=new Set(["background","cite","href","itemtype","longdesc","poster","src","xlink:href"]),Kn=/^(?:(?:https?|mailto|ftp|tel|file|sms):|[^#&/:?]*(?:[#/?]|$))/i,Qn=/^data:(?:image\/(?:bmp|gif|jpeg|jpg|png|tiff|webp)|video\/(?:mpeg|mp4|ogg|webm)|audio\/(?:mp3|oga|ogg|opus));base64,[\d+/a-z]+=*$/i,Xn=(t,e)=>{const i=t.nodeName.toLowerCase();return e.includes(i)?!Yn.has(i)||Boolean(Kn.test(t.nodeValue)||Qn.test(t.nodeValue)):e.filter((t=>t instanceof RegExp)).some((t=>t.test(i)))},Un={"*":["class","dir","id","lang","role",/^aria-[\w-]*$/i],a:["target","href","title","rel"],area:[],b:[],br:[],col:[],code:[],div:[],em:[],hr:[],h1:[],h2:[],h3:[],h4:[],h5:[],h6:[],i:[],img:["src","srcset","alt","title","width","height"],li:[],ol:[],p:[],pre:[],s:[],small:[],span:[],sub:[],sup:[],strong:[],u:[],ul:[]},Gn={allowList:Un,content:{},extraClass:"",html:!1,sanitize:!0,sanitizeFn:null,template:"
"},Jn={allowList:"object",content:"object",extraClass:"(string|function)",html:"boolean",sanitize:"boolean",sanitizeFn:"(null|function)",template:"string"},Zn={entry:"(string|element|function|null)",selector:"(string|element)"};class ts extends ve{constructor(t){super(),this._config=this._getConfig(t)}static get Default(){return Gn}static get DefaultType(){return Jn}static get NAME(){return"TemplateFactory"}getContent(){return Object.values(this._config.content).map((t=>this._resolvePossibleFunction(t))).filter(Boolean)}hasContent(){return this.getContent().length>0}changeContent(t){return this._checkContent(t),this._config.content={...this._config.content,...t},this}toHtml(){const t=document.createElement("div");t.innerHTML=this._maybeSanitize(this._config.template);for(const[e,i]of Object.entries(this._config.content))this._setContent(t,i,e);const e=t.children[0],i=this._resolvePossibleFunction(this._config.extraClass);return i&&e.classList.add(...i.split(" ")),e}_typeCheckConfig(t){super._typeCheckConfig(t),this._checkContent(t.content)}_checkContent(t){for(const[e,i]of Object.entries(t))super._typeCheckConfig({selector:e,entry:i},Zn)}_setContent(t,e,i){const n=ke.findOne(i,t);n&&((e=this._resolvePossibleFunction(e))?Mt(e)?this._putElementInTemplate(Ht(e),n):this._config.html?n.innerHTML=this._maybeSanitize(e):n.textContent=e:n.remove())}_maybeSanitize(t){return this._config.sanitize?function(t,e,i){if(!t.length)return t;if(i&&"function"==typeof i)return i(t);const n=(new window.DOMParser).parseFromString(t,"text/html"),s=[].concat(...n.body.querySelectorAll("*"));for(const t of s){const i=t.nodeName.toLowerCase();if(!Object.keys(e).includes(i)){t.remove();continue}const n=[].concat(...t.attributes),s=[].concat(e["*"]||[],e[i]||[]);for(const e of n)Xn(e,s)||t.removeAttribute(e.nodeName)}return n.body.innerHTML}(t,this._config.allowList,this._config.sanitizeFn):t}_resolvePossibleFunction(t){return"function"==typeof t?t(this):t}_putElementInTemplate(t,e){if(this._config.html)return e.innerHTML="",void e.append(t);e.textContent=t.textContent}}const es=new Set(["sanitize","allowList","sanitizeFn"]),is="fade",ns="show",ss=".modal",os="hide.bs.modal",rs="hover",as="focus",ls={AUTO:"auto",TOP:"top",RIGHT:Yt()?"left":"right",BOTTOM:"bottom",LEFT:Yt()?"right":"left"},cs={allowList:Un,animation:!0,boundary:"clippingParents",container:!1,customClass:"",delay:0,fallbackPlacements:["top","right","bottom","left"],html:!1,offset:[0,0],placement:"top",popperConfig:null,sanitize:!0,sanitizeFn:null,selector:!1,template:'',title:"",trigger:"hover focus"},hs={allowList:"object",animation:"boolean",boundary:"(string|element)",container:"(string|element|boolean)",customClass:"(string|function)",delay:"(number|object)",fallbackPlacements:"array",html:"boolean",offset:"(array|string|function)",placement:"(string|function)",popperConfig:"(null|object|function)",sanitize:"boolean",sanitizeFn:"(null|function)",selector:"(string|boolean)",template:"string",title:"(string|element|function)",trigger:"string"};class ds extends ye{constructor(t,i){if(void 0===e)throw new TypeError("Bootstrap's tooltips require Popper (https://popper.js.org)");super(t,i),this._isEnabled=!0,this._timeout=0,this._isHovered=null,this._activeTrigger={},this._popper=null,this._templateFactory=null,this._newContent=null,this.tip=null,this._setListeners(),this._config.selector||this._fixTitle()}static get Default(){return cs}static get DefaultType(){return hs}static get NAME(){return"tooltip"}enable(){this._isEnabled=!0}disable(){this._isEnabled=!1}toggleEnabled(){this._isEnabled=!this._isEnabled}toggle(){this._isEnabled&&(this._activeTrigger.click=!this._activeTrigger.click,this._isShown()?this._leave():this._enter())}dispose(){clearTimeout(this._timeout),ue.off(this._element.closest(ss),os,this._hideModalHandler),this._element.getAttribute("data-bs-original-title")&&this._element.setAttribute("title",this._element.getAttribute("data-bs-original-title")),this._disposePopper(),super.dispose()}show(){if("none"===this._element.style.display)throw new Error("Please use show on visible elements");if(!this._isWithContent()||!this._isEnabled)return;const t=ue.trigger(this._element,this.constructor.eventName("show")),e=(Ft(this._element)||this._element.ownerDocument.documentElement).contains(this._element);if(t.defaultPrevented||!e)return;this._disposePopper();const i=this._getTipElement();this._element.setAttribute("aria-describedby",i.getAttribute("id"));const{container:n}=this._config;if(this._element.ownerDocument.documentElement.contains(this.tip)||(n.append(i),ue.trigger(this._element,this.constructor.eventName("inserted"))),this._popper=this._createPopper(i),i.classList.add(ns),"ontouchstart"in document.documentElement)for(const t of[].concat(...document.body.children))ue.on(t,"mouseover",zt);this._queueCallback((()=>{ue.trigger(this._element,this.constructor.eventName("shown")),!1===this._isHovered&&this._leave(),this._isHovered=!1}),this.tip,this._isAnimated())}hide(){if(this._isShown()&&!ue.trigger(this._element,this.constructor.eventName("hide")).defaultPrevented){if(this._getTipElement().classList.remove(ns),"ontouchstart"in document.documentElement)for(const t of[].concat(...document.body.children))ue.off(t,"mouseover",zt);this._activeTrigger.click=!1,this._activeTrigger[as]=!1,this._activeTrigger[rs]=!1,this._isHovered=null,this._queueCallback((()=>{this._isWithActiveTrigger()||(this._isHovered||this._disposePopper(),this._element.removeAttribute("aria-describedby"),ue.trigger(this._element,this.constructor.eventName("hidden")))}),this.tip,this._isAnimated())}}update(){this._popper&&this._popper.update()}_isWithContent(){return Boolean(this._getTitle())}_getTipElement(){return this.tip||(this.tip=this._createTipElement(this._newContent||this._getContentForTemplate())),this.tip}_createTipElement(t){const e=this._getTemplateFactory(t).toHtml();if(!e)return null;e.classList.remove(is,ns),e.classList.add(`bs-${this.constructor.NAME}-auto`);const i=(t=>{do{t+=Math.floor(1e6*Math.random())}while(document.getElementById(t));return t})(this.constructor.NAME).toString();return e.setAttribute("id",i),this._isAnimated()&&e.classList.add(is),e}setContent(t){this._newContent=t,this._isShown()&&(this._disposePopper(),this.show())}_getTemplateFactory(t){return this._templateFactory?this._templateFactory.changeContent(t):this._templateFactory=new ts({...this._config,content:t,extraClass:this._resolvePossibleFunction(this._config.customClass)}),this._templateFactory}_getContentForTemplate(){return{".tooltip-inner":this._getTitle()}}_getTitle(){return this._resolvePossibleFunction(this._config.title)||this._element.getAttribute("data-bs-original-title")}_initializeOnDelegatedTarget(t){return this.constructor.getOrCreateInstance(t.delegateTarget,this._getDelegateConfig())}_isAnimated(){return this._config.animation||this.tip&&this.tip.classList.contains(is)}_isShown(){return this.tip&&this.tip.classList.contains(ns)}_createPopper(t){const e="function"==typeof this._config.placement?this._config.placement.call(this,t,this._element):this._config.placement,i=ls[e.toUpperCase()];return Dt(this._element,t,this._getPopperConfig(i))}_getOffset(){const{offset:t}=this._config;return"string"==typeof t?t.split(",").map((t=>Number.parseInt(t,10))):"function"==typeof t?e=>t(e,this._element):t}_resolvePossibleFunction(t){return"function"==typeof t?t.call(this._element):t}_getPopperConfig(t){const e={placement:t,modifiers:[{name:"flip",options:{fallbackPlacements:this._config.fallbackPlacements}},{name:"offset",options:{offset:this._getOffset()}},{name:"preventOverflow",options:{boundary:this._config.boundary}},{name:"arrow",options:{element:`.${this.constructor.NAME}-arrow`}},{name:"preSetPlacement",enabled:!0,phase:"beforeMain",fn:t=>{this._getTipElement().setAttribute("data-popper-placement",t.state.placement)}}]};return{...e,..."function"==typeof this._config.popperConfig?this._config.popperConfig(e):this._config.popperConfig}}_setListeners(){const t=this._config.trigger.split(" ");for(const e of t)if("click"===e)ue.on(this._element,this.constructor.eventName("click"),this._config.selector,(t=>{this._initializeOnDelegatedTarget(t).toggle()}));else if("manual"!==e){const t=e===rs?this.constructor.eventName("mouseenter"):this.constructor.eventName("focusin"),i=e===rs?this.constructor.eventName("mouseleave"):this.constructor.eventName("focusout");ue.on(this._element,t,this._config.selector,(t=>{const e=this._initializeOnDelegatedTarget(t);e._activeTrigger["focusin"===t.type?as:rs]=!0,e._enter()})),ue.on(this._element,i,this._config.selector,(t=>{const e=this._initializeOnDelegatedTarget(t);e._activeTrigger["focusout"===t.type?as:rs]=e._element.contains(t.relatedTarget),e._leave()}))}this._hideModalHandler=()=>{this._element&&this.hide()},ue.on(this._element.closest(ss),os,this._hideModalHandler)}_fixTitle(){const t=this._element.getAttribute("title");t&&(this._element.getAttribute("aria-label")||this._element.textContent.trim()||this._element.setAttribute("aria-label",t),this._element.setAttribute("data-bs-original-title",t),this._element.removeAttribute("title"))}_enter(){this._isShown()||this._isHovered?this._isHovered=!0:(this._isHovered=!0,this._setTimeout((()=>{this._isHovered&&this.show()}),this._config.delay.show))}_leave(){this._isWithActiveTrigger()||(this._isHovered=!1,this._setTimeout((()=>{this._isHovered||this.hide()}),this._config.delay.hide))}_setTimeout(t,e){clearTimeout(this._timeout),this._timeout=setTimeout(t,e)}_isWithActiveTrigger(){return Object.values(this._activeTrigger).includes(!0)}_getConfig(t){const e=be.getDataAttributes(this._element);for(const t of Object.keys(e))es.has(t)&&delete e[t];return t={...e,..."object"==typeof t&&t?t:{}},t=this._mergeConfigObj(t),t=this._configAfterMerge(t),this._typeCheckConfig(t),t}_configAfterMerge(t){return t.container=!1===t.container?document.body:Ht(t.container),"number"==typeof t.delay&&(t.delay={show:t.delay,hide:t.delay}),"number"==typeof t.title&&(t.title=t.title.toString()),"number"==typeof t.content&&(t.content=t.content.toString()),t}_getDelegateConfig(){const t={};for(const e in this._config)this.constructor.Default[e]!==this._config[e]&&(t[e]=this._config[e]);return t.selector=!1,t.trigger="manual",t}_disposePopper(){this._popper&&(this._popper.destroy(),this._popper=null),this.tip&&(this.tip.remove(),this.tip=null)}static jQueryInterface(t){return this.each((function(){const e=ds.getOrCreateInstance(this,t);if("string"==typeof t){if(void 0===e[t])throw new TypeError(`No method named "${t}"`);e[t]()}}))}}Kt(ds);const us={...ds.Default,content:"",offset:[0,8],placement:"right",template:'',trigger:"click"},fs={...ds.DefaultType,content:"(null|string|element|function)"};class ps extends ds{static get Default(){return us}static get DefaultType(){return fs}static get NAME(){return"popover"}_isWithContent(){return this._getTitle()||this._getContent()}_getContentForTemplate(){return{".popover-header":this._getTitle(),".popover-body":this._getContent()}}_getContent(){return this._resolvePossibleFunction(this._config.content)}static jQueryInterface(t){return this.each((function(){const e=ps.getOrCreateInstance(this,t);if("string"==typeof t){if(void 0===e[t])throw new TypeError(`No method named "${t}"`);e[t]()}}))}}Kt(ps);const gs=".bs.scrollspy",ms=`activate${gs}`,_s=`click${gs}`,bs=`load${gs}.data-api`,vs="active",ys="[href]",ws=".nav-link",As=`${ws}, .nav-item > ${ws}, .list-group-item`,Es={offset:null,rootMargin:"0px 0px -25%",smoothScroll:!1,target:null,threshold:[.1,.5,1]},Cs={offset:"(number|null)",rootMargin:"string",smoothScroll:"boolean",target:"element",threshold:"array"};class Ts extends ye{constructor(t,e){super(t,e),this._targetLinks=new Map,this._observableSections=new 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e=this._observableSections.get(t.target.hash);if(e){t.preventDefault();const i=this._rootElement||window,n=e.offsetTop-this._element.offsetTop;if(i.scrollTo)return void i.scrollTo({top:n,behavior:"smooth"});i.scrollTop=n}})))}_getNewObserver(){const t={root:this._rootElement,threshold:this._config.threshold,rootMargin:this._config.rootMargin};return new IntersectionObserver((t=>this._observerCallback(t)),t)}_observerCallback(t){const e=t=>this._targetLinks.get(`#${t.target.id}`),i=t=>{this._previousScrollData.visibleEntryTop=t.target.offsetTop,this._process(e(t))},n=(this._rootElement||document.documentElement).scrollTop,s=n>=this._previousScrollData.parentScrollTop;this._previousScrollData.parentScrollTop=n;for(const o of t){if(!o.isIntersecting){this._activeTarget=null,this._clearActiveClass(e(o));continue}const t=o.target.offsetTop>=this._previousScrollData.visibleEntryTop;if(s&&t){if(i(o),!n)return}else s||t||i(o)}}_initializeTargetsAndObservables(){this._targetLinks=new Map,this._observableSections=new Map;const t=ke.find(ys,this._config.target);for(const e of t){if(!e.hash||Wt(e))continue;const t=ke.findOne(e.hash,this._element);Bt(t)&&(this._targetLinks.set(e.hash,e),this._observableSections.set(e.hash,t))}}_process(t){this._activeTarget!==t&&(this._clearActiveClass(this._config.target),this._activeTarget=t,t.classList.add(vs),this._activateParents(t),ue.trigger(this._element,ms,{relatedTarget:t}))}_activateParents(t){if(t.classList.contains("dropdown-item"))ke.findOne(".dropdown-toggle",t.closest(".dropdown")).classList.add(vs);else for(const e of ke.parents(t,".nav, .list-group"))for(const t of ke.prev(e,As))t.classList.add(vs)}_clearActiveClass(t){t.classList.remove(vs);const e=ke.find(`${ys}.${vs}`,t);for(const t of e)t.classList.remove(vs)}static jQueryInterface(t){return this.each((function(){const e=Ts.getOrCreateInstance(this,t);if("string"==typeof t){if(void 0===e[t]||t.startsWith("_")||"constructor"===t)throw new TypeError(`No method named "${t}"`);e[t]()}}))}}ue.on(window,bs,(()=>{for(const t of ke.find('[data-bs-spy="scroll"]'))Ts.getOrCreateInstance(t)})),Kt(Ts);const Os=".bs.tab",xs=`hide${Os}`,ks=`hidden${Os}`,Ls=`show${Os}`,Ds=`shown${Os}`,$s=`click${Os}`,Ss=`keydown${Os}`,Is=`load${Os}`,Ns="ArrowLeft",Ps="ArrowRight",js="ArrowUp",Ms="ArrowDown",Hs="active",Bs="fade",Ws="show",Fs=":not(.dropdown-toggle)",zs='[data-bs-toggle="tab"], [data-bs-toggle="pill"], [data-bs-toggle="list"]',qs=`.nav-link${Fs}, .list-group-item${Fs}, [role="tab"]${Fs}, ${zs}`,Rs=`.${Hs}[data-bs-toggle="tab"], .${Hs}[data-bs-toggle="pill"], .${Hs}[data-bs-toggle="list"]`;class Vs extends ye{constructor(t){super(t),this._parent=this._element.closest('.list-group, .nav, [role="tablist"]'),this._parent&&(this._setInitialAttributes(this._parent,this._getChildren()),ue.on(this._element,Ss,(t=>this._keydown(t))))}static get NAME(){return"tab"}show(){const t=this._element;if(this._elemIsActive(t))return;const e=this._getActiveElem(),i=e?ue.trigger(e,xs,{relatedTarget:t}):null;ue.trigger(t,Ls,{relatedTarget:e}).defaultPrevented||i&&i.defaultPrevented||(this._deactivate(e,t),this._activate(t,e))}_activate(t,e){t&&(t.classList.add(Hs),this._activate(Pt(t)),this._queueCallback((()=>{"tab"===t.getAttribute("role")?(t.removeAttribute("tabindex"),t.setAttribute("aria-selected",!0),this._toggleDropDown(t,!0),ue.trigger(t,Ds,{relatedTarget:e})):t.classList.add(Ws)}),t,t.classList.contains(Bs)))}_deactivate(t,e){t&&(t.classList.remove(Hs),t.blur(),this._deactivate(Pt(t)),this._queueCallback((()=>{"tab"===t.getAttribute("role")?(t.setAttribute("aria-selected",!1),t.setAttribute("tabindex","-1"),this._toggleDropDown(t,!1),ue.trigger(t,ks,{relatedTarget:e})):t.classList.remove(Ws)}),t,t.classList.contains(Bs)))}_keydown(t){if(![Ns,Ps,js,Ms].includes(t.key))return;t.stopPropagation(),t.preventDefault();const e=[Ps,Ms].includes(t.key),i=Ut(this._getChildren().filter((t=>!Wt(t))),t.target,e,!0);i&&(i.focus({preventScroll:!0}),Vs.getOrCreateInstance(i).show())}_getChildren(){return ke.find(qs,this._parent)}_getActiveElem(){return this._getChildren().find((t=>this._elemIsActive(t)))||null}_setInitialAttributes(t,e){this._setAttributeIfNotExists(t,"role","tablist");for(const t of e)this._setInitialAttributesOnChild(t)}_setInitialAttributesOnChild(t){t=this._getInnerElement(t);const e=this._elemIsActive(t),i=this._getOuterElement(t);t.setAttribute("aria-selected",e),i!==t&&this._setAttributeIfNotExists(i,"role","presentation"),e||t.setAttribute("tabindex","-1"),this._setAttributeIfNotExists(t,"role","tab"),this._setInitialAttributesOnTargetPanel(t)}_setInitialAttributesOnTargetPanel(t){const e=Pt(t);e&&(this._setAttributeIfNotExists(e,"role","tabpanel"),t.id&&this._setAttributeIfNotExists(e,"aria-labelledby",`#${t.id}`))}_toggleDropDown(t,e){const i=this._getOuterElement(t);if(!i.classList.contains("dropdown"))return;const n=(t,n)=>{const s=ke.findOne(t,i);s&&s.classList.toggle(n,e)};n(".dropdown-toggle",Hs),n(".dropdown-menu",Ws),i.setAttribute("aria-expanded",e)}_setAttributeIfNotExists(t,e,i){t.hasAttribute(e)||t.setAttribute(e,i)}_elemIsActive(t){return t.classList.contains(Hs)}_getInnerElement(t){return t.matches(qs)?t:ke.findOne(qs,t)}_getOuterElement(t){return t.closest(".nav-item, .list-group-item")||t}static jQueryInterface(t){return this.each((function(){const e=Vs.getOrCreateInstance(this);if("string"==typeof t){if(void 0===e[t]||t.startsWith("_")||"constructor"===t)throw new TypeError(`No method named "${t}"`);e[t]()}}))}}ue.on(document,$s,zs,(function(t){["A","AREA"].includes(this.tagName)&&t.preventDefault(),Wt(this)||Vs.getOrCreateInstance(this).show()})),ue.on(window,Is,(()=>{for(const t of ke.find(Rs))Vs.getOrCreateInstance(t)})),Kt(Vs);const Ys=".bs.toast",Ks=`mouseover${Ys}`,Qs=`mouseout${Ys}`,Xs=`focusin${Ys}`,Us=`focusout${Ys}`,Gs=`hide${Ys}`,Js=`hidden${Ys}`,Zs=`show${Ys}`,to=`shown${Ys}`,eo="hide",io="show",no="showing",so={animation:"boolean",autohide:"boolean",delay:"number"},oo={animation:!0,autohide:!0,delay:5e3};class ro extends ye{constructor(t,e){super(t,e),this._timeout=null,this._hasMouseInteraction=!1,this._hasKeyboardInteraction=!1,this._setListeners()}static get Default(){return oo}static get DefaultType(){return so}static get NAME(){return"toast"}show(){ue.trigger(this._element,Zs).defaultPrevented||(this._clearTimeout(),this._config.animation&&this._element.classList.add("fade"),this._element.classList.remove(eo),qt(this._element),this._element.classList.add(io,no),this._queueCallback((()=>{this._element.classList.remove(no),ue.trigger(this._element,to),this._maybeScheduleHide()}),this._element,this._config.animation))}hide(){this.isShown()&&(ue.trigger(this._element,Gs).defaultPrevented||(this._element.classList.add(no),this._queueCallback((()=>{this._element.classList.add(eo),this._element.classList.remove(no,io),ue.trigger(this._element,Js)}),this._element,this._config.animation)))}dispose(){this._clearTimeout(),this.isShown()&&this._element.classList.remove(io),super.dispose()}isShown(){return this._element.classList.contains(io)}_maybeScheduleHide(){this._config.autohide&&(this._hasMouseInteraction||this._hasKeyboardInteraction||(this._timeout=setTimeout((()=>{this.hide()}),this._config.delay)))}_onInteraction(t,e){switch(t.type){case"mouseover":case"mouseout":this._hasMouseInteraction=e;break;case"focusin":case"focusout":this._hasKeyboardInteraction=e}if(e)return void this._clearTimeout();const i=t.relatedTarget;this._element===i||this._element.contains(i)||this._maybeScheduleHide()}_setListeners(){ue.on(this._element,Ks,(t=>this._onInteraction(t,!0))),ue.on(this._element,Qs,(t=>this._onInteraction(t,!1))),ue.on(this._element,Xs,(t=>this._onInteraction(t,!0))),ue.on(this._element,Us,(t=>this._onInteraction(t,!1)))}_clearTimeout(){clearTimeout(this._timeout),this._timeout=null}static jQueryInterface(t){return this.each((function(){const e=ro.getOrCreateInstance(this,t);if("string"==typeof t){if(void 0===e[t])throw new TypeError(`No method named 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The require scope\nvar __webpack_require__ = {};\n\n","// define getter functions for harmony exports\n__webpack_require__.d = (exports, definition) => {\n\tfor(var key in definition) {\n\t\tif(__webpack_require__.o(definition, key) && !__webpack_require__.o(exports, key)) {\n\t\t\tObject.defineProperty(exports, key, { enumerable: true, get: definition[key] });\n\t\t}\n\t}\n};","__webpack_require__.o = (obj, prop) => (Object.prototype.hasOwnProperty.call(obj, prop))","// define __esModule on exports\n__webpack_require__.r = (exports) => {\n\tif(typeof Symbol !== 'undefined' && Symbol.toStringTag) {\n\t\tObject.defineProperty(exports, Symbol.toStringTag, { value: 'Module' });\n\t}\n\tObject.defineProperty(exports, '__esModule', { value: true });\n};","export var top = 'top';\nexport var bottom = 'bottom';\nexport var right = 'right';\nexport var left = 'left';\nexport var auto = 'auto';\nexport var basePlacements = [top, bottom, right, left];\nexport var start = 'start';\nexport var end = 'end';\nexport var clippingParents = 'clippingParents';\nexport var viewport = 'viewport';\nexport var popper = 'popper';\nexport var reference = 'reference';\nexport var variationPlacements = /*#__PURE__*/basePlacements.reduce(function (acc, placement) {\n return acc.concat([placement + \"-\" + start, placement + \"-\" + end]);\n}, []);\nexport var placements = /*#__PURE__*/[].concat(basePlacements, [auto]).reduce(function (acc, placement) {\n return acc.concat([placement, placement + \"-\" + start, placement + \"-\" + end]);\n}, []); // modifiers that need to read the DOM\n\nexport var beforeRead = 'beforeRead';\nexport var read = 'read';\nexport var afterRead = 'afterRead'; // pure-logic modifiers\n\nexport var beforeMain = 'beforeMain';\nexport var main = 'main';\nexport var afterMain = 'afterMain'; // modifier with the purpose to write to the DOM (or write into a framework state)\n\nexport var beforeWrite = 'beforeWrite';\nexport var write = 'write';\nexport var afterWrite = 'afterWrite';\nexport var modifierPhases = [beforeRead, read, afterRead, beforeMain, main, afterMain, beforeWrite, write, afterWrite];","export default function getNodeName(element) {\n return element ? (element.nodeName || '').toLowerCase() : null;\n}","export default function getWindow(node) {\n if (node == null) {\n return window;\n }\n\n if (node.toString() !== '[object Window]') {\n var ownerDocument = node.ownerDocument;\n return ownerDocument ? ownerDocument.defaultView || window : window;\n }\n\n return node;\n}","import getWindow from \"./getWindow.js\";\n\nfunction isElement(node) {\n var OwnElement = getWindow(node).Element;\n return node instanceof OwnElement || node instanceof Element;\n}\n\nfunction isHTMLElement(node) {\n var OwnElement = getWindow(node).HTMLElement;\n return node instanceof OwnElement || node instanceof HTMLElement;\n}\n\nfunction isShadowRoot(node) {\n // IE 11 has no ShadowRoot\n if (typeof ShadowRoot === 'undefined') {\n return false;\n }\n\n var OwnElement = getWindow(node).ShadowRoot;\n return node instanceof OwnElement || node instanceof ShadowRoot;\n}\n\nexport { isElement, isHTMLElement, isShadowRoot };","import getNodeName from \"../dom-utils/getNodeName.js\";\nimport { isHTMLElement } from \"../dom-utils/instanceOf.js\"; // This modifier takes the styles prepared by the `computeStyles` modifier\n// and applies them to the HTMLElements such as popper and arrow\n\nfunction applyStyles(_ref) {\n var state = _ref.state;\n Object.keys(state.elements).forEach(function (name) {\n var style = state.styles[name] || {};\n var attributes = state.attributes[name] || {};\n var element = state.elements[name]; // arrow is optional + virtual elements\n\n if (!isHTMLElement(element) || !getNodeName(element)) {\n return;\n } // Flow doesn't support to extend this property, but it's the most\n // effective way to apply styles to an HTMLElement\n // $FlowFixMe[cannot-write]\n\n\n Object.assign(element.style, style);\n Object.keys(attributes).forEach(function (name) {\n var value = attributes[name];\n\n if (value === false) {\n element.removeAttribute(name);\n } else {\n element.setAttribute(name, value === true ? '' : value);\n }\n });\n });\n}\n\nfunction effect(_ref2) {\n var state = _ref2.state;\n var initialStyles = {\n popper: {\n position: state.options.strategy,\n left: '0',\n top: '0',\n margin: '0'\n },\n arrow: {\n position: 'absolute'\n },\n reference: {}\n };\n Object.assign(state.elements.popper.style, initialStyles.popper);\n state.styles = initialStyles;\n\n if (state.elements.arrow) {\n Object.assign(state.elements.arrow.style, initialStyles.arrow);\n }\n\n return function () {\n Object.keys(state.elements).forEach(function (name) {\n var element = state.elements[name];\n var attributes = state.attributes[name] || {};\n var styleProperties = Object.keys(state.styles.hasOwnProperty(name) ? state.styles[name] : initialStyles[name]); // Set all values to an empty string to unset them\n\n var style = styleProperties.reduce(function (style, property) {\n style[property] = '';\n return style;\n }, {}); // arrow is optional + virtual elements\n\n if (!isHTMLElement(element) || !getNodeName(element)) {\n return;\n }\n\n Object.assign(element.style, style);\n Object.keys(attributes).forEach(function (attribute) {\n element.removeAttribute(attribute);\n });\n });\n };\n} // eslint-disable-next-line import/no-unused-modules\n\n\nexport default {\n name: 'applyStyles',\n enabled: true,\n phase: 'write',\n fn: applyStyles,\n effect: effect,\n requires: ['computeStyles']\n};","import { auto } from \"../enums.js\";\nexport default function getBasePlacement(placement) {\n return placement.split('-')[0];\n}","export var max = Math.max;\nexport var min = Math.min;\nexport var round = Math.round;","export default function getUAString() {\n var uaData = navigator.userAgentData;\n\n if (uaData != null && uaData.brands && Array.isArray(uaData.brands)) {\n return uaData.brands.map(function (item) {\n return item.brand + \"/\" + item.version;\n }).join(' ');\n }\n\n return navigator.userAgent;\n}","import getUAString from \"../utils/userAgent.js\";\nexport default function isLayoutViewport() {\n return !/^((?!chrome|android).)*safari/i.test(getUAString());\n}","import { isElement, isHTMLElement } from \"./instanceOf.js\";\nimport { round } from \"../utils/math.js\";\nimport getWindow from \"./getWindow.js\";\nimport isLayoutViewport from \"./isLayoutViewport.js\";\nexport default function getBoundingClientRect(element, includeScale, isFixedStrategy) {\n if (includeScale === void 0) {\n includeScale = false;\n }\n\n if (isFixedStrategy === void 0) {\n isFixedStrategy = false;\n }\n\n var clientRect = element.getBoundingClientRect();\n var scaleX = 1;\n var scaleY = 1;\n\n if (includeScale && isHTMLElement(element)) {\n scaleX = element.offsetWidth > 0 ? round(clientRect.width) / element.offsetWidth || 1 : 1;\n scaleY = element.offsetHeight > 0 ? round(clientRect.height) / element.offsetHeight || 1 : 1;\n }\n\n var _ref = isElement(element) ? getWindow(element) : window,\n visualViewport = _ref.visualViewport;\n\n var addVisualOffsets = !isLayoutViewport() && isFixedStrategy;\n var x = (clientRect.left + (addVisualOffsets && visualViewport ? visualViewport.offsetLeft : 0)) / scaleX;\n var y = (clientRect.top + (addVisualOffsets && visualViewport ? visualViewport.offsetTop : 0)) / scaleY;\n var width = clientRect.width / scaleX;\n var height = clientRect.height / scaleY;\n return {\n width: width,\n height: height,\n top: y,\n right: x + width,\n bottom: y + height,\n left: x,\n x: x,\n y: y\n };\n}","import getBoundingClientRect from \"./getBoundingClientRect.js\"; // Returns the layout rect of an element relative to its offsetParent. Layout\n// means it doesn't take into account transforms.\n\nexport default function getLayoutRect(element) {\n var clientRect = getBoundingClientRect(element); // Use the clientRect sizes if it's not been transformed.\n // Fixes https://github.com/popperjs/popper-core/issues/1223\n\n var width = element.offsetWidth;\n var height = element.offsetHeight;\n\n if (Math.abs(clientRect.width - width) <= 1) {\n width = clientRect.width;\n }\n\n if (Math.abs(clientRect.height - height) <= 1) {\n height = clientRect.height;\n }\n\n return {\n x: element.offsetLeft,\n y: element.offsetTop,\n width: width,\n height: height\n };\n}","import { isShadowRoot } from \"./instanceOf.js\";\nexport default function contains(parent, child) {\n var rootNode = child.getRootNode && child.getRootNode(); // First, attempt with faster native method\n\n if (parent.contains(child)) {\n return true;\n } // then fallback to custom implementation with Shadow DOM support\n else if (rootNode && isShadowRoot(rootNode)) {\n var next = child;\n\n do {\n if (next && parent.isSameNode(next)) {\n return true;\n } // $FlowFixMe[prop-missing]: need a better way to handle this...\n\n\n next = next.parentNode || next.host;\n } while (next);\n } // Give up, the result is false\n\n\n return false;\n}","import getWindow from \"./getWindow.js\";\nexport default function getComputedStyle(element) {\n return getWindow(element).getComputedStyle(element);\n}","import getNodeName from \"./getNodeName.js\";\nexport default function isTableElement(element) {\n return ['table', 'td', 'th'].indexOf(getNodeName(element)) >= 0;\n}","import { isElement } from \"./instanceOf.js\";\nexport default function getDocumentElement(element) {\n // $FlowFixMe[incompatible-return]: assume body is always available\n return ((isElement(element) ? element.ownerDocument : // $FlowFixMe[prop-missing]\n element.document) || window.document).documentElement;\n}","import getNodeName from \"./getNodeName.js\";\nimport getDocumentElement from \"./getDocumentElement.js\";\nimport { isShadowRoot } from \"./instanceOf.js\";\nexport default function getParentNode(element) {\n if (getNodeName(element) === 'html') {\n return element;\n }\n\n return (// this is a quicker (but less type safe) way to save quite some bytes from the bundle\n // $FlowFixMe[incompatible-return]\n // $FlowFixMe[prop-missing]\n element.assignedSlot || // step into the shadow DOM of the parent of a slotted node\n element.parentNode || ( // DOM Element detected\n isShadowRoot(element) ? element.host : null) || // ShadowRoot detected\n // $FlowFixMe[incompatible-call]: HTMLElement is a Node\n getDocumentElement(element) // fallback\n\n );\n}","import getWindow from \"./getWindow.js\";\nimport getNodeName from \"./getNodeName.js\";\nimport getComputedStyle from \"./getComputedStyle.js\";\nimport { isHTMLElement, isShadowRoot } from \"./instanceOf.js\";\nimport isTableElement from \"./isTableElement.js\";\nimport getParentNode from \"./getParentNode.js\";\nimport getUAString from \"../utils/userAgent.js\";\n\nfunction getTrueOffsetParent(element) {\n if (!isHTMLElement(element) || // https://github.com/popperjs/popper-core/issues/837\n getComputedStyle(element).position === 'fixed') {\n return null;\n }\n\n return element.offsetParent;\n} // `.offsetParent` reports `null` for fixed elements, while absolute elements\n// return the containing block\n\n\nfunction getContainingBlock(element) {\n var isFirefox = /firefox/i.test(getUAString());\n var isIE = /Trident/i.test(getUAString());\n\n if (isIE && isHTMLElement(element)) {\n // In IE 9, 10 and 11 fixed elements containing block is always established by the viewport\n var elementCss = getComputedStyle(element);\n\n if (elementCss.position === 'fixed') {\n return null;\n }\n }\n\n var currentNode = getParentNode(element);\n\n if (isShadowRoot(currentNode)) {\n currentNode = currentNode.host;\n }\n\n while (isHTMLElement(currentNode) && ['html', 'body'].indexOf(getNodeName(currentNode)) < 0) {\n var css = getComputedStyle(currentNode); // This is non-exhaustive but covers the most common CSS properties that\n // create a containing block.\n // https://developer.mozilla.org/en-US/docs/Web/CSS/Containing_block#identifying_the_containing_block\n\n if (css.transform !== 'none' || css.perspective !== 'none' || css.contain === 'paint' || ['transform', 'perspective'].indexOf(css.willChange) !== -1 || isFirefox && css.willChange === 'filter' || isFirefox && css.filter && css.filter !== 'none') {\n return currentNode;\n } else {\n currentNode = currentNode.parentNode;\n }\n }\n\n return null;\n} // Gets the closest ancestor positioned element. Handles some edge cases,\n// such as table ancestors and cross browser bugs.\n\n\nexport default function getOffsetParent(element) {\n var window = getWindow(element);\n var offsetParent = getTrueOffsetParent(element);\n\n while (offsetParent && isTableElement(offsetParent) && getComputedStyle(offsetParent).position === 'static') {\n offsetParent = getTrueOffsetParent(offsetParent);\n }\n\n if (offsetParent && (getNodeName(offsetParent) === 'html' || getNodeName(offsetParent) === 'body' && getComputedStyle(offsetParent).position === 'static')) {\n return window;\n }\n\n return offsetParent || getContainingBlock(element) || window;\n}","export default function getMainAxisFromPlacement(placement) {\n return ['top', 'bottom'].indexOf(placement) >= 0 ? 'x' : 'y';\n}","import { max as mathMax, min as mathMin } from \"./math.js\";\nexport function within(min, value, max) {\n return mathMax(min, mathMin(value, max));\n}\nexport function withinMaxClamp(min, value, max) {\n var v = within(min, value, max);\n return v > max ? max : v;\n}","import getFreshSideObject from \"./getFreshSideObject.js\";\nexport default function mergePaddingObject(paddingObject) {\n return Object.assign({}, getFreshSideObject(), paddingObject);\n}","export default function getFreshSideObject() {\n return {\n top: 0,\n right: 0,\n bottom: 0,\n left: 0\n };\n}","export default function expandToHashMap(value, keys) {\n return keys.reduce(function (hashMap, key) {\n hashMap[key] = value;\n return hashMap;\n }, {});\n}","import getBasePlacement from \"../utils/getBasePlacement.js\";\nimport getLayoutRect from \"../dom-utils/getLayoutRect.js\";\nimport contains from \"../dom-utils/contains.js\";\nimport getOffsetParent from \"../dom-utils/getOffsetParent.js\";\nimport getMainAxisFromPlacement from \"../utils/getMainAxisFromPlacement.js\";\nimport { within } from \"../utils/within.js\";\nimport mergePaddingObject from \"../utils/mergePaddingObject.js\";\nimport expandToHashMap from \"../utils/expandToHashMap.js\";\nimport { left, right, basePlacements, top, bottom } from \"../enums.js\";\nimport { isHTMLElement } from \"../dom-utils/instanceOf.js\"; // eslint-disable-next-line import/no-unused-modules\n\nvar toPaddingObject = function toPaddingObject(padding, state) {\n padding = typeof padding === 'function' ? padding(Object.assign({}, state.rects, {\n placement: state.placement\n })) : padding;\n return mergePaddingObject(typeof padding !== 'number' ? padding : expandToHashMap(padding, basePlacements));\n};\n\nfunction arrow(_ref) {\n var _state$modifiersData$;\n\n var state = _ref.state,\n name = _ref.name,\n options = _ref.options;\n var arrowElement = state.elements.arrow;\n var popperOffsets = state.modifiersData.popperOffsets;\n var basePlacement = getBasePlacement(state.placement);\n var axis = getMainAxisFromPlacement(basePlacement);\n var isVertical = [left, right].indexOf(basePlacement) >= 0;\n var len = isVertical ? 'height' : 'width';\n\n if (!arrowElement || !popperOffsets) {\n return;\n }\n\n var paddingObject = toPaddingObject(options.padding, state);\n var arrowRect = getLayoutRect(arrowElement);\n var minProp = axis === 'y' ? top : left;\n var maxProp = axis === 'y' ? bottom : right;\n var endDiff = state.rects.reference[len] + state.rects.reference[axis] - popperOffsets[axis] - state.rects.popper[len];\n var startDiff = popperOffsets[axis] - state.rects.reference[axis];\n var arrowOffsetParent = getOffsetParent(arrowElement);\n var clientSize = arrowOffsetParent ? axis === 'y' ? arrowOffsetParent.clientHeight || 0 : arrowOffsetParent.clientWidth || 0 : 0;\n var centerToReference = endDiff / 2 - startDiff / 2; // Make sure the arrow doesn't overflow the popper if the center point is\n // outside of the popper bounds\n\n var min = paddingObject[minProp];\n var max = clientSize - arrowRect[len] - paddingObject[maxProp];\n var center = clientSize / 2 - arrowRect[len] / 2 + centerToReference;\n var offset = within(min, center, max); // Prevents breaking syntax highlighting...\n\n var axisProp = axis;\n state.modifiersData[name] = (_state$modifiersData$ = {}, _state$modifiersData$[axisProp] = offset, _state$modifiersData$.centerOffset = offset - center, _state$modifiersData$);\n}\n\nfunction effect(_ref2) {\n var state = _ref2.state,\n options = _ref2.options;\n var _options$element = options.element,\n arrowElement = _options$element === void 0 ? '[data-popper-arrow]' : _options$element;\n\n if (arrowElement == null) {\n return;\n } // CSS selector\n\n\n if (typeof arrowElement === 'string') {\n arrowElement = state.elements.popper.querySelector(arrowElement);\n\n if (!arrowElement) {\n return;\n }\n }\n\n if (process.env.NODE_ENV !== \"production\") {\n if (!isHTMLElement(arrowElement)) {\n console.error(['Popper: \"arrow\" element must be an HTMLElement (not an SVGElement).', 'To use an SVG arrow, wrap it in an HTMLElement that will be used as', 'the arrow.'].join(' '));\n }\n }\n\n if (!contains(state.elements.popper, arrowElement)) {\n if (process.env.NODE_ENV !== \"production\") {\n console.error(['Popper: \"arrow\" modifier\\'s `element` must be a child of the popper', 'element.'].join(' '));\n }\n\n return;\n }\n\n state.elements.arrow = arrowElement;\n} // eslint-disable-next-line import/no-unused-modules\n\n\nexport default {\n name: 'arrow',\n enabled: true,\n phase: 'main',\n fn: arrow,\n effect: effect,\n requires: ['popperOffsets'],\n requiresIfExists: ['preventOverflow']\n};","export default function getVariation(placement) {\n return placement.split('-')[1];\n}","import { top, left, right, bottom, end } from \"../enums.js\";\nimport getOffsetParent from \"../dom-utils/getOffsetParent.js\";\nimport getWindow from \"../dom-utils/getWindow.js\";\nimport getDocumentElement from \"../dom-utils/getDocumentElement.js\";\nimport getComputedStyle from \"../dom-utils/getComputedStyle.js\";\nimport getBasePlacement from \"../utils/getBasePlacement.js\";\nimport getVariation from \"../utils/getVariation.js\";\nimport { round } from \"../utils/math.js\"; // eslint-disable-next-line import/no-unused-modules\n\nvar unsetSides = {\n top: 'auto',\n right: 'auto',\n bottom: 'auto',\n left: 'auto'\n}; // Round the offsets to the nearest suitable subpixel based on the DPR.\n// Zooming can change the DPR, but it seems to report a value that will\n// cleanly divide the values into the appropriate subpixels.\n\nfunction roundOffsetsByDPR(_ref, win) {\n var x = _ref.x,\n y = _ref.y;\n var dpr = win.devicePixelRatio || 1;\n return {\n x: round(x * dpr) / dpr || 0,\n y: round(y * dpr) / dpr || 0\n };\n}\n\nexport function mapToStyles(_ref2) {\n var _Object$assign2;\n\n var popper = _ref2.popper,\n popperRect = _ref2.popperRect,\n placement = _ref2.placement,\n variation = _ref2.variation,\n offsets = _ref2.offsets,\n position = _ref2.position,\n gpuAcceleration = _ref2.gpuAcceleration,\n adaptive = _ref2.adaptive,\n roundOffsets = _ref2.roundOffsets,\n isFixed = _ref2.isFixed;\n var _offsets$x = offsets.x,\n x = _offsets$x === void 0 ? 0 : _offsets$x,\n _offsets$y = offsets.y,\n y = _offsets$y === void 0 ? 0 : _offsets$y;\n\n var _ref3 = typeof roundOffsets === 'function' ? roundOffsets({\n x: x,\n y: y\n }) : {\n x: x,\n y: y\n };\n\n x = _ref3.x;\n y = _ref3.y;\n var hasX = offsets.hasOwnProperty('x');\n var hasY = offsets.hasOwnProperty('y');\n var sideX = left;\n var sideY = top;\n var win = window;\n\n if (adaptive) {\n var offsetParent = getOffsetParent(popper);\n var heightProp = 'clientHeight';\n var widthProp = 'clientWidth';\n\n if (offsetParent === getWindow(popper)) {\n offsetParent = getDocumentElement(popper);\n\n if (getComputedStyle(offsetParent).position !== 'static' && position === 'absolute') {\n heightProp = 'scrollHeight';\n widthProp = 'scrollWidth';\n }\n } // $FlowFixMe[incompatible-cast]: force type refinement, we compare offsetParent with window above, but Flow doesn't detect it\n\n\n offsetParent = offsetParent;\n\n if (placement === top || (placement === left || placement === right) && variation === end) {\n sideY = bottom;\n var offsetY = isFixed && offsetParent === win && win.visualViewport ? win.visualViewport.height : // $FlowFixMe[prop-missing]\n offsetParent[heightProp];\n y -= offsetY - popperRect.height;\n y *= gpuAcceleration ? 1 : -1;\n }\n\n if (placement === left || (placement === top || placement === bottom) && variation === end) {\n sideX = right;\n var offsetX = isFixed && offsetParent === win && win.visualViewport ? win.visualViewport.width : // $FlowFixMe[prop-missing]\n offsetParent[widthProp];\n x -= offsetX - popperRect.width;\n x *= gpuAcceleration ? 1 : -1;\n }\n }\n\n var commonStyles = Object.assign({\n position: position\n }, adaptive && unsetSides);\n\n var _ref4 = roundOffsets === true ? roundOffsetsByDPR({\n x: x,\n y: y\n }, getWindow(popper)) : {\n x: x,\n y: y\n };\n\n x = _ref4.x;\n y = _ref4.y;\n\n if (gpuAcceleration) {\n var _Object$assign;\n\n return Object.assign({}, commonStyles, (_Object$assign = {}, _Object$assign[sideY] = hasY ? '0' : '', _Object$assign[sideX] = hasX ? '0' : '', _Object$assign.transform = (win.devicePixelRatio || 1) <= 1 ? \"translate(\" + x + \"px, \" + y + \"px)\" : \"translate3d(\" + x + \"px, \" + y + \"px, 0)\", _Object$assign));\n }\n\n return Object.assign({}, commonStyles, (_Object$assign2 = {}, _Object$assign2[sideY] = hasY ? y + \"px\" : '', _Object$assign2[sideX] = hasX ? x + \"px\" : '', _Object$assign2.transform = '', _Object$assign2));\n}\n\nfunction computeStyles(_ref5) {\n var state = _ref5.state,\n options = _ref5.options;\n var _options$gpuAccelerat = options.gpuAcceleration,\n gpuAcceleration = _options$gpuAccelerat === void 0 ? true : _options$gpuAccelerat,\n _options$adaptive = options.adaptive,\n adaptive = _options$adaptive === void 0 ? true : _options$adaptive,\n _options$roundOffsets = options.roundOffsets,\n roundOffsets = _options$roundOffsets === void 0 ? true : _options$roundOffsets;\n\n if (process.env.NODE_ENV !== \"production\") {\n var transitionProperty = getComputedStyle(state.elements.popper).transitionProperty || '';\n\n if (adaptive && ['transform', 'top', 'right', 'bottom', 'left'].some(function (property) {\n return transitionProperty.indexOf(property) >= 0;\n })) {\n console.warn(['Popper: Detected CSS transitions on at least one of the following', 'CSS properties: \"transform\", \"top\", \"right\", \"bottom\", \"left\".', '\\n\\n', 'Disable the \"computeStyles\" modifier\\'s `adaptive` option to allow', 'for smooth transitions, or remove these properties from the CSS', 'transition declaration on the popper element if only transitioning', 'opacity or background-color for example.', '\\n\\n', 'We recommend using the popper element as a wrapper around an inner', 'element that can have any CSS property transitioned for animations.'].join(' '));\n }\n }\n\n var commonStyles = {\n placement: getBasePlacement(state.placement),\n variation: getVariation(state.placement),\n popper: state.elements.popper,\n popperRect: state.rects.popper,\n gpuAcceleration: gpuAcceleration,\n isFixed: state.options.strategy === 'fixed'\n };\n\n if (state.modifiersData.popperOffsets != null) {\n state.styles.popper = Object.assign({}, state.styles.popper, mapToStyles(Object.assign({}, commonStyles, {\n offsets: state.modifiersData.popperOffsets,\n position: state.options.strategy,\n adaptive: adaptive,\n roundOffsets: roundOffsets\n })));\n }\n\n if (state.modifiersData.arrow != null) {\n state.styles.arrow = Object.assign({}, state.styles.arrow, mapToStyles(Object.assign({}, commonStyles, {\n offsets: state.modifiersData.arrow,\n position: 'absolute',\n adaptive: false,\n roundOffsets: roundOffsets\n })));\n }\n\n state.attributes.popper = Object.assign({}, state.attributes.popper, {\n 'data-popper-placement': state.placement\n });\n} // eslint-disable-next-line import/no-unused-modules\n\n\nexport default {\n name: 'computeStyles',\n enabled: true,\n phase: 'beforeWrite',\n fn: computeStyles,\n data: {}\n};","import getWindow from \"../dom-utils/getWindow.js\"; // eslint-disable-next-line import/no-unused-modules\n\nvar passive = {\n passive: true\n};\n\nfunction effect(_ref) {\n var state = _ref.state,\n instance = _ref.instance,\n options = _ref.options;\n var _options$scroll = options.scroll,\n scroll = _options$scroll === void 0 ? true : _options$scroll,\n _options$resize = options.resize,\n resize = _options$resize === void 0 ? true : _options$resize;\n var window = getWindow(state.elements.popper);\n var scrollParents = [].concat(state.scrollParents.reference, state.scrollParents.popper);\n\n if (scroll) {\n scrollParents.forEach(function (scrollParent) {\n scrollParent.addEventListener('scroll', instance.update, passive);\n });\n }\n\n if (resize) {\n window.addEventListener('resize', instance.update, passive);\n }\n\n return function () {\n if (scroll) {\n scrollParents.forEach(function (scrollParent) {\n scrollParent.removeEventListener('scroll', instance.update, passive);\n });\n }\n\n if (resize) {\n window.removeEventListener('resize', instance.update, passive);\n }\n };\n} // eslint-disable-next-line import/no-unused-modules\n\n\nexport default {\n name: 'eventListeners',\n enabled: true,\n phase: 'write',\n fn: function fn() {},\n effect: effect,\n data: {}\n};","var hash = {\n left: 'right',\n right: 'left',\n bottom: 'top',\n top: 'bottom'\n};\nexport default function getOppositePlacement(placement) {\n return placement.replace(/left|right|bottom|top/g, function (matched) {\n return hash[matched];\n });\n}","var hash = {\n start: 'end',\n end: 'start'\n};\nexport default function getOppositeVariationPlacement(placement) {\n return placement.replace(/start|end/g, function (matched) {\n return hash[matched];\n });\n}","import getWindow from \"./getWindow.js\";\nexport default function getWindowScroll(node) {\n var win = getWindow(node);\n var scrollLeft = win.pageXOffset;\n var scrollTop = win.pageYOffset;\n return {\n scrollLeft: scrollLeft,\n scrollTop: scrollTop\n };\n}","import getBoundingClientRect from \"./getBoundingClientRect.js\";\nimport getDocumentElement from \"./getDocumentElement.js\";\nimport getWindowScroll from \"./getWindowScroll.js\";\nexport default function getWindowScrollBarX(element) {\n // If has a CSS width greater than the viewport, then this will be\n // incorrect for RTL.\n // Popper 1 is broken in this case and never had a bug report so let's assume\n // it's not an issue. I don't think anyone ever specifies width on \n // anyway.\n // Browsers where the left scrollbar doesn't cause an issue report `0` for\n // this (e.g. Edge 2019, IE11, Safari)\n return getBoundingClientRect(getDocumentElement(element)).left + getWindowScroll(element).scrollLeft;\n}","import getComputedStyle from \"./getComputedStyle.js\";\nexport default function isScrollParent(element) {\n // Firefox wants us to check `-x` and `-y` variations as well\n var _getComputedStyle = getComputedStyle(element),\n overflow = _getComputedStyle.overflow,\n overflowX = _getComputedStyle.overflowX,\n overflowY = _getComputedStyle.overflowY;\n\n return /auto|scroll|overlay|hidden/.test(overflow + overflowY + overflowX);\n}","import getParentNode from \"./getParentNode.js\";\nimport isScrollParent from \"./isScrollParent.js\";\nimport getNodeName from \"./getNodeName.js\";\nimport { isHTMLElement } from \"./instanceOf.js\";\nexport default function getScrollParent(node) {\n if (['html', 'body', '#document'].indexOf(getNodeName(node)) >= 0) {\n // $FlowFixMe[incompatible-return]: assume body is always available\n return node.ownerDocument.body;\n }\n\n if (isHTMLElement(node) && isScrollParent(node)) {\n return node;\n }\n\n return getScrollParent(getParentNode(node));\n}","import getScrollParent from \"./getScrollParent.js\";\nimport getParentNode from \"./getParentNode.js\";\nimport getWindow from \"./getWindow.js\";\nimport isScrollParent from \"./isScrollParent.js\";\n/*\ngiven a DOM element, return the list of all scroll parents, up the list of ancesors\nuntil we get to the top window object. This list is what we attach scroll listeners\nto, because if any of these parent elements scroll, we'll need to re-calculate the\nreference element's position.\n*/\n\nexport default function listScrollParents(element, list) {\n var _element$ownerDocumen;\n\n if (list === void 0) {\n list = [];\n }\n\n var scrollParent = getScrollParent(element);\n var isBody = scrollParent === ((_element$ownerDocumen = element.ownerDocument) == null ? void 0 : _element$ownerDocumen.body);\n var win = getWindow(scrollParent);\n var target = isBody ? [win].concat(win.visualViewport || [], isScrollParent(scrollParent) ? scrollParent : []) : scrollParent;\n var updatedList = list.concat(target);\n return isBody ? updatedList : // $FlowFixMe[incompatible-call]: isBody tells us target will be an HTMLElement here\n updatedList.concat(listScrollParents(getParentNode(target)));\n}","export default function rectToClientRect(rect) {\n return Object.assign({}, rect, {\n left: rect.x,\n top: rect.y,\n right: rect.x + rect.width,\n bottom: rect.y + rect.height\n });\n}","import { viewport } from \"../enums.js\";\nimport getViewportRect from \"./getViewportRect.js\";\nimport getDocumentRect from \"./getDocumentRect.js\";\nimport listScrollParents from \"./listScrollParents.js\";\nimport getOffsetParent from \"./getOffsetParent.js\";\nimport getDocumentElement from \"./getDocumentElement.js\";\nimport getComputedStyle from \"./getComputedStyle.js\";\nimport { isElement, isHTMLElement } from \"./instanceOf.js\";\nimport getBoundingClientRect from \"./getBoundingClientRect.js\";\nimport getParentNode from \"./getParentNode.js\";\nimport contains from \"./contains.js\";\nimport getNodeName from \"./getNodeName.js\";\nimport rectToClientRect from \"../utils/rectToClientRect.js\";\nimport { max, min } from \"../utils/math.js\";\n\nfunction getInnerBoundingClientRect(element, strategy) {\n var rect = getBoundingClientRect(element, false, strategy === 'fixed');\n rect.top = rect.top + element.clientTop;\n rect.left = rect.left + element.clientLeft;\n rect.bottom = rect.top + element.clientHeight;\n rect.right = rect.left + element.clientWidth;\n rect.width = element.clientWidth;\n rect.height = element.clientHeight;\n rect.x = rect.left;\n rect.y = rect.top;\n return rect;\n}\n\nfunction getClientRectFromMixedType(element, clippingParent, strategy) {\n return clippingParent === viewport ? rectToClientRect(getViewportRect(element, strategy)) : isElement(clippingParent) ? getInnerBoundingClientRect(clippingParent, strategy) : rectToClientRect(getDocumentRect(getDocumentElement(element)));\n} // A \"clipping parent\" is an overflowable container with the characteristic of\n// clipping (or hiding) overflowing elements with a position different from\n// `initial`\n\n\nfunction getClippingParents(element) {\n var clippingParents = listScrollParents(getParentNode(element));\n var canEscapeClipping = ['absolute', 'fixed'].indexOf(getComputedStyle(element).position) >= 0;\n var clipperElement = canEscapeClipping && isHTMLElement(element) ? getOffsetParent(element) : element;\n\n if (!isElement(clipperElement)) {\n return [];\n } // $FlowFixMe[incompatible-return]: https://github.com/facebook/flow/issues/1414\n\n\n return clippingParents.filter(function (clippingParent) {\n return isElement(clippingParent) && contains(clippingParent, clipperElement) && getNodeName(clippingParent) !== 'body';\n });\n} // Gets the maximum area that the element is visible in due to any number of\n// clipping parents\n\n\nexport default function getClippingRect(element, boundary, rootBoundary, strategy) {\n var mainClippingParents = boundary === 'clippingParents' ? getClippingParents(element) : [].concat(boundary);\n var clippingParents = [].concat(mainClippingParents, [rootBoundary]);\n var firstClippingParent = clippingParents[0];\n var clippingRect = clippingParents.reduce(function (accRect, clippingParent) {\n var rect = getClientRectFromMixedType(element, clippingParent, strategy);\n accRect.top = max(rect.top, accRect.top);\n accRect.right = min(rect.right, accRect.right);\n accRect.bottom = min(rect.bottom, accRect.bottom);\n accRect.left = max(rect.left, accRect.left);\n return accRect;\n }, getClientRectFromMixedType(element, firstClippingParent, strategy));\n clippingRect.width = clippingRect.right - clippingRect.left;\n clippingRect.height = clippingRect.bottom - clippingRect.top;\n clippingRect.x = clippingRect.left;\n clippingRect.y = clippingRect.top;\n return clippingRect;\n}","import getWindow from \"./getWindow.js\";\nimport getDocumentElement from \"./getDocumentElement.js\";\nimport getWindowScrollBarX from \"./getWindowScrollBarX.js\";\nimport isLayoutViewport from \"./isLayoutViewport.js\";\nexport default function getViewportRect(element, strategy) {\n var win = getWindow(element);\n var html = getDocumentElement(element);\n var visualViewport = win.visualViewport;\n var width = html.clientWidth;\n var height = html.clientHeight;\n var x = 0;\n var y = 0;\n\n if (visualViewport) {\n width = visualViewport.width;\n height = visualViewport.height;\n var layoutViewport = isLayoutViewport();\n\n if (layoutViewport || !layoutViewport && strategy === 'fixed') {\n x = visualViewport.offsetLeft;\n y = visualViewport.offsetTop;\n }\n }\n\n return {\n width: width,\n height: height,\n x: x + getWindowScrollBarX(element),\n y: y\n };\n}","import getDocumentElement from \"./getDocumentElement.js\";\nimport getComputedStyle from \"./getComputedStyle.js\";\nimport getWindowScrollBarX from \"./getWindowScrollBarX.js\";\nimport getWindowScroll from \"./getWindowScroll.js\";\nimport { max } from \"../utils/math.js\"; // Gets the entire size of the scrollable document area, even extending outside\n// of the `` and `` rect bounds if horizontally scrollable\n\nexport default function getDocumentRect(element) {\n var _element$ownerDocumen;\n\n var html = getDocumentElement(element);\n var winScroll = getWindowScroll(element);\n var body = (_element$ownerDocumen = element.ownerDocument) == null ? void 0 : _element$ownerDocumen.body;\n var width = max(html.scrollWidth, html.clientWidth, body ? body.scrollWidth : 0, body ? body.clientWidth : 0);\n var height = max(html.scrollHeight, html.clientHeight, body ? body.scrollHeight : 0, body ? body.clientHeight : 0);\n var x = -winScroll.scrollLeft + getWindowScrollBarX(element);\n var y = -winScroll.scrollTop;\n\n if (getComputedStyle(body || html).direction === 'rtl') {\n x += max(html.clientWidth, body ? body.clientWidth : 0) - width;\n }\n\n return {\n width: width,\n height: height,\n x: x,\n y: y\n };\n}","import getBasePlacement from \"./getBasePlacement.js\";\nimport getVariation from \"./getVariation.js\";\nimport getMainAxisFromPlacement from \"./getMainAxisFromPlacement.js\";\nimport { top, right, bottom, left, start, end } from \"../enums.js\";\nexport default function computeOffsets(_ref) {\n var reference = _ref.reference,\n element = _ref.element,\n placement = _ref.placement;\n var basePlacement = placement ? getBasePlacement(placement) : null;\n var variation = placement ? getVariation(placement) : null;\n var commonX = reference.x + reference.width / 2 - element.width / 2;\n var commonY = reference.y + reference.height / 2 - element.height / 2;\n var offsets;\n\n switch (basePlacement) {\n case top:\n offsets = {\n x: commonX,\n y: reference.y - element.height\n };\n break;\n\n case bottom:\n offsets = {\n x: commonX,\n y: reference.y + reference.height\n };\n break;\n\n case right:\n offsets = {\n x: reference.x + reference.width,\n y: commonY\n };\n break;\n\n case left:\n offsets = {\n x: reference.x - element.width,\n y: commonY\n };\n break;\n\n default:\n offsets = {\n x: reference.x,\n y: reference.y\n };\n }\n\n var mainAxis = basePlacement ? getMainAxisFromPlacement(basePlacement) : null;\n\n if (mainAxis != null) {\n var len = mainAxis === 'y' ? 'height' : 'width';\n\n switch (variation) {\n case start:\n offsets[mainAxis] = offsets[mainAxis] - (reference[len] / 2 - element[len] / 2);\n break;\n\n case end:\n offsets[mainAxis] = offsets[mainAxis] + (reference[len] / 2 - element[len] / 2);\n break;\n\n default:\n }\n }\n\n return offsets;\n}","import getClippingRect from \"../dom-utils/getClippingRect.js\";\nimport getDocumentElement from \"../dom-utils/getDocumentElement.js\";\nimport getBoundingClientRect from \"../dom-utils/getBoundingClientRect.js\";\nimport computeOffsets from \"./computeOffsets.js\";\nimport rectToClientRect from \"./rectToClientRect.js\";\nimport { clippingParents, reference, popper, bottom, top, right, basePlacements, viewport } from \"../enums.js\";\nimport { isElement } from \"../dom-utils/instanceOf.js\";\nimport mergePaddingObject from \"./mergePaddingObject.js\";\nimport expandToHashMap from \"./expandToHashMap.js\"; // eslint-disable-next-line import/no-unused-modules\n\nexport default function detectOverflow(state, options) {\n if (options === void 0) {\n options = {};\n }\n\n var _options = options,\n _options$placement = _options.placement,\n placement = _options$placement === void 0 ? state.placement : _options$placement,\n _options$strategy = _options.strategy,\n strategy = _options$strategy === void 0 ? state.strategy : _options$strategy,\n _options$boundary = _options.boundary,\n boundary = _options$boundary === void 0 ? clippingParents : _options$boundary,\n _options$rootBoundary = _options.rootBoundary,\n rootBoundary = _options$rootBoundary === void 0 ? viewport : _options$rootBoundary,\n _options$elementConte = _options.elementContext,\n elementContext = _options$elementConte === void 0 ? popper : _options$elementConte,\n _options$altBoundary = _options.altBoundary,\n altBoundary = _options$altBoundary === void 0 ? false : _options$altBoundary,\n _options$padding = _options.padding,\n padding = _options$padding === void 0 ? 0 : _options$padding;\n var paddingObject = mergePaddingObject(typeof padding !== 'number' ? padding : expandToHashMap(padding, basePlacements));\n var altContext = elementContext === popper ? reference : popper;\n var popperRect = state.rects.popper;\n var element = state.elements[altBoundary ? altContext : elementContext];\n var clippingClientRect = getClippingRect(isElement(element) ? element : element.contextElement || getDocumentElement(state.elements.popper), boundary, rootBoundary, strategy);\n var referenceClientRect = getBoundingClientRect(state.elements.reference);\n var popperOffsets = computeOffsets({\n reference: referenceClientRect,\n element: popperRect,\n strategy: 'absolute',\n placement: placement\n });\n var popperClientRect = rectToClientRect(Object.assign({}, popperRect, popperOffsets));\n var elementClientRect = elementContext === popper ? popperClientRect : referenceClientRect; // positive = overflowing the clipping rect\n // 0 or negative = within the clipping rect\n\n var overflowOffsets = {\n top: clippingClientRect.top - elementClientRect.top + paddingObject.top,\n bottom: elementClientRect.bottom - clippingClientRect.bottom + paddingObject.bottom,\n left: clippingClientRect.left - elementClientRect.left + paddingObject.left,\n right: elementClientRect.right - clippingClientRect.right + paddingObject.right\n };\n var offsetData = state.modifiersData.offset; // Offsets can be applied only to the popper element\n\n if (elementContext === popper && offsetData) {\n var offset = offsetData[placement];\n Object.keys(overflowOffsets).forEach(function (key) {\n var multiply = [right, bottom].indexOf(key) >= 0 ? 1 : -1;\n var axis = [top, bottom].indexOf(key) >= 0 ? 'y' : 'x';\n overflowOffsets[key] += offset[axis] * multiply;\n });\n }\n\n return overflowOffsets;\n}","import getOppositePlacement from \"../utils/getOppositePlacement.js\";\nimport getBasePlacement from \"../utils/getBasePlacement.js\";\nimport getOppositeVariationPlacement from \"../utils/getOppositeVariationPlacement.js\";\nimport detectOverflow from \"../utils/detectOverflow.js\";\nimport computeAutoPlacement from \"../utils/computeAutoPlacement.js\";\nimport { bottom, top, start, right, left, auto } from \"../enums.js\";\nimport getVariation from \"../utils/getVariation.js\"; // eslint-disable-next-line import/no-unused-modules\n\nfunction getExpandedFallbackPlacements(placement) {\n if (getBasePlacement(placement) === auto) {\n return [];\n }\n\n var oppositePlacement = getOppositePlacement(placement);\n return [getOppositeVariationPlacement(placement), oppositePlacement, getOppositeVariationPlacement(oppositePlacement)];\n}\n\nfunction flip(_ref) {\n var state = _ref.state,\n options = _ref.options,\n name = _ref.name;\n\n if (state.modifiersData[name]._skip) {\n return;\n }\n\n var _options$mainAxis = options.mainAxis,\n checkMainAxis = _options$mainAxis === void 0 ? true : _options$mainAxis,\n _options$altAxis = options.altAxis,\n checkAltAxis = _options$altAxis === void 0 ? true : _options$altAxis,\n specifiedFallbackPlacements = options.fallbackPlacements,\n padding = options.padding,\n boundary = options.boundary,\n rootBoundary = options.rootBoundary,\n altBoundary = options.altBoundary,\n _options$flipVariatio = options.flipVariations,\n flipVariations = _options$flipVariatio === void 0 ? true : _options$flipVariatio,\n allowedAutoPlacements = options.allowedAutoPlacements;\n var preferredPlacement = state.options.placement;\n var basePlacement = getBasePlacement(preferredPlacement);\n var isBasePlacement = basePlacement === preferredPlacement;\n var fallbackPlacements = specifiedFallbackPlacements || (isBasePlacement || !flipVariations ? [getOppositePlacement(preferredPlacement)] : getExpandedFallbackPlacements(preferredPlacement));\n var placements = [preferredPlacement].concat(fallbackPlacements).reduce(function (acc, placement) {\n return acc.concat(getBasePlacement(placement) === auto ? computeAutoPlacement(state, {\n placement: placement,\n boundary: boundary,\n rootBoundary: rootBoundary,\n padding: padding,\n flipVariations: flipVariations,\n allowedAutoPlacements: allowedAutoPlacements\n }) : placement);\n }, []);\n var referenceRect = state.rects.reference;\n var popperRect = state.rects.popper;\n var checksMap = new Map();\n var makeFallbackChecks = true;\n var firstFittingPlacement = placements[0];\n\n for (var i = 0; i < placements.length; i++) {\n var placement = placements[i];\n\n var _basePlacement = getBasePlacement(placement);\n\n var isStartVariation = getVariation(placement) === start;\n var isVertical = [top, bottom].indexOf(_basePlacement) >= 0;\n var len = isVertical ? 'width' : 'height';\n var overflow = detectOverflow(state, {\n placement: placement,\n boundary: boundary,\n rootBoundary: rootBoundary,\n altBoundary: altBoundary,\n padding: padding\n });\n var mainVariationSide = isVertical ? isStartVariation ? right : left : isStartVariation ? bottom : top;\n\n if (referenceRect[len] > popperRect[len]) {\n mainVariationSide = getOppositePlacement(mainVariationSide);\n }\n\n var altVariationSide = getOppositePlacement(mainVariationSide);\n var checks = [];\n\n if (checkMainAxis) {\n checks.push(overflow[_basePlacement] <= 0);\n }\n\n if (checkAltAxis) {\n checks.push(overflow[mainVariationSide] <= 0, overflow[altVariationSide] <= 0);\n }\n\n if (checks.every(function (check) {\n return check;\n })) {\n firstFittingPlacement = placement;\n makeFallbackChecks = false;\n break;\n }\n\n checksMap.set(placement, checks);\n }\n\n if (makeFallbackChecks) {\n // `2` may be desired in some cases – research later\n var numberOfChecks = flipVariations ? 3 : 1;\n\n var _loop = function _loop(_i) {\n var fittingPlacement = placements.find(function (placement) {\n var checks = checksMap.get(placement);\n\n if (checks) {\n return checks.slice(0, _i).every(function (check) {\n return check;\n });\n }\n });\n\n if (fittingPlacement) {\n firstFittingPlacement = fittingPlacement;\n return \"break\";\n }\n };\n\n for (var _i = numberOfChecks; _i > 0; _i--) {\n var _ret = _loop(_i);\n\n if (_ret === \"break\") break;\n }\n }\n\n if (state.placement !== firstFittingPlacement) {\n state.modifiersData[name]._skip = true;\n state.placement = firstFittingPlacement;\n state.reset = true;\n }\n} // eslint-disable-next-line import/no-unused-modules\n\n\nexport default {\n name: 'flip',\n enabled: true,\n phase: 'main',\n fn: flip,\n requiresIfExists: ['offset'],\n data: {\n _skip: false\n }\n};","import getVariation from \"./getVariation.js\";\nimport { variationPlacements, basePlacements, placements as allPlacements } from \"../enums.js\";\nimport detectOverflow from \"./detectOverflow.js\";\nimport getBasePlacement from \"./getBasePlacement.js\";\nexport default function computeAutoPlacement(state, options) {\n if (options === void 0) {\n options = {};\n }\n\n var _options = options,\n placement = _options.placement,\n boundary = _options.boundary,\n rootBoundary = _options.rootBoundary,\n padding = _options.padding,\n flipVariations = _options.flipVariations,\n _options$allowedAutoP = _options.allowedAutoPlacements,\n allowedAutoPlacements = _options$allowedAutoP === void 0 ? allPlacements : _options$allowedAutoP;\n var variation = getVariation(placement);\n var placements = variation ? flipVariations ? variationPlacements : variationPlacements.filter(function (placement) {\n return getVariation(placement) === variation;\n }) : basePlacements;\n var allowedPlacements = placements.filter(function (placement) {\n return allowedAutoPlacements.indexOf(placement) >= 0;\n });\n\n if (allowedPlacements.length === 0) {\n allowedPlacements = placements;\n\n if (process.env.NODE_ENV !== \"production\") {\n console.error(['Popper: The `allowedAutoPlacements` option did not allow any', 'placements. Ensure the `placement` option matches the variation', 'of the allowed placements.', 'For example, \"auto\" cannot be used to allow \"bottom-start\".', 'Use \"auto-start\" instead.'].join(' '));\n }\n } // $FlowFixMe[incompatible-type]: Flow seems to have problems with two array unions...\n\n\n var overflows = allowedPlacements.reduce(function (acc, placement) {\n acc[placement] = detectOverflow(state, {\n placement: placement,\n boundary: boundary,\n rootBoundary: rootBoundary,\n padding: padding\n })[getBasePlacement(placement)];\n return acc;\n }, {});\n return Object.keys(overflows).sort(function (a, b) {\n return overflows[a] - overflows[b];\n });\n}","import { top, bottom, left, right } from \"../enums.js\";\nimport detectOverflow from \"../utils/detectOverflow.js\";\n\nfunction getSideOffsets(overflow, rect, preventedOffsets) {\n if (preventedOffsets === void 0) {\n preventedOffsets = {\n x: 0,\n y: 0\n };\n }\n\n return {\n top: overflow.top - rect.height - preventedOffsets.y,\n right: overflow.right - rect.width + preventedOffsets.x,\n bottom: overflow.bottom - rect.height + preventedOffsets.y,\n left: overflow.left - rect.width - preventedOffsets.x\n };\n}\n\nfunction isAnySideFullyClipped(overflow) {\n return [top, right, bottom, left].some(function (side) {\n return overflow[side] >= 0;\n });\n}\n\nfunction hide(_ref) {\n var state = _ref.state,\n name = _ref.name;\n var referenceRect = state.rects.reference;\n var popperRect = state.rects.popper;\n var preventedOffsets = state.modifiersData.preventOverflow;\n var referenceOverflow = detectOverflow(state, {\n elementContext: 'reference'\n });\n var popperAltOverflow = detectOverflow(state, {\n altBoundary: true\n });\n var referenceClippingOffsets = getSideOffsets(referenceOverflow, referenceRect);\n var popperEscapeOffsets = getSideOffsets(popperAltOverflow, popperRect, preventedOffsets);\n var isReferenceHidden = isAnySideFullyClipped(referenceClippingOffsets);\n var hasPopperEscaped = isAnySideFullyClipped(popperEscapeOffsets);\n state.modifiersData[name] = {\n referenceClippingOffsets: referenceClippingOffsets,\n popperEscapeOffsets: popperEscapeOffsets,\n isReferenceHidden: isReferenceHidden,\n hasPopperEscaped: hasPopperEscaped\n };\n state.attributes.popper = Object.assign({}, state.attributes.popper, {\n 'data-popper-reference-hidden': isReferenceHidden,\n 'data-popper-escaped': hasPopperEscaped\n });\n} // eslint-disable-next-line import/no-unused-modules\n\n\nexport default {\n name: 'hide',\n enabled: true,\n phase: 'main',\n requiresIfExists: ['preventOverflow'],\n fn: hide\n};","import getBasePlacement from \"../utils/getBasePlacement.js\";\nimport { top, left, right, placements } from \"../enums.js\"; // eslint-disable-next-line import/no-unused-modules\n\nexport function distanceAndSkiddingToXY(placement, rects, offset) {\n var basePlacement = getBasePlacement(placement);\n var invertDistance = [left, top].indexOf(basePlacement) >= 0 ? -1 : 1;\n\n var _ref = typeof offset === 'function' ? offset(Object.assign({}, rects, {\n placement: placement\n })) : offset,\n skidding = _ref[0],\n distance = _ref[1];\n\n skidding = skidding || 0;\n distance = (distance || 0) * invertDistance;\n return [left, right].indexOf(basePlacement) >= 0 ? {\n x: distance,\n y: skidding\n } : {\n x: skidding,\n y: distance\n };\n}\n\nfunction offset(_ref2) {\n var state = _ref2.state,\n options = _ref2.options,\n name = _ref2.name;\n var _options$offset = options.offset,\n offset = _options$offset === void 0 ? [0, 0] : _options$offset;\n var data = placements.reduce(function (acc, placement) {\n acc[placement] = distanceAndSkiddingToXY(placement, state.rects, offset);\n return acc;\n }, {});\n var _data$state$placement = data[state.placement],\n x = _data$state$placement.x,\n y = _data$state$placement.y;\n\n if (state.modifiersData.popperOffsets != null) {\n state.modifiersData.popperOffsets.x += x;\n state.modifiersData.popperOffsets.y += y;\n }\n\n state.modifiersData[name] = data;\n} // eslint-disable-next-line import/no-unused-modules\n\n\nexport default {\n name: 'offset',\n enabled: true,\n phase: 'main',\n requires: ['popperOffsets'],\n fn: offset\n};","import computeOffsets from \"../utils/computeOffsets.js\";\n\nfunction popperOffsets(_ref) {\n var state = _ref.state,\n name = _ref.name;\n // Offsets are the actual position the popper needs to have to be\n // properly positioned near its reference element\n // This is the most basic placement, and will be adjusted by\n // the modifiers in the next step\n state.modifiersData[name] = computeOffsets({\n reference: state.rects.reference,\n element: state.rects.popper,\n strategy: 'absolute',\n placement: state.placement\n });\n} // eslint-disable-next-line import/no-unused-modules\n\n\nexport default {\n name: 'popperOffsets',\n enabled: true,\n phase: 'read',\n fn: popperOffsets,\n data: {}\n};","import { top, left, right, bottom, start } from \"../enums.js\";\nimport getBasePlacement from \"../utils/getBasePlacement.js\";\nimport getMainAxisFromPlacement from \"../utils/getMainAxisFromPlacement.js\";\nimport getAltAxis from \"../utils/getAltAxis.js\";\nimport { within, withinMaxClamp } from \"../utils/within.js\";\nimport getLayoutRect from \"../dom-utils/getLayoutRect.js\";\nimport getOffsetParent from \"../dom-utils/getOffsetParent.js\";\nimport detectOverflow from \"../utils/detectOverflow.js\";\nimport getVariation from \"../utils/getVariation.js\";\nimport getFreshSideObject from \"../utils/getFreshSideObject.js\";\nimport { min as mathMin, max as mathMax } from \"../utils/math.js\";\n\nfunction preventOverflow(_ref) {\n var state = _ref.state,\n options = _ref.options,\n name = _ref.name;\n var _options$mainAxis = options.mainAxis,\n checkMainAxis = _options$mainAxis === void 0 ? true : _options$mainAxis,\n _options$altAxis = options.altAxis,\n checkAltAxis = _options$altAxis === void 0 ? false : _options$altAxis,\n boundary = options.boundary,\n rootBoundary = options.rootBoundary,\n altBoundary = options.altBoundary,\n padding = options.padding,\n _options$tether = options.tether,\n tether = _options$tether === void 0 ? true : _options$tether,\n _options$tetherOffset = options.tetherOffset,\n tetherOffset = _options$tetherOffset === void 0 ? 0 : _options$tetherOffset;\n var overflow = detectOverflow(state, {\n boundary: boundary,\n rootBoundary: rootBoundary,\n padding: padding,\n altBoundary: altBoundary\n });\n var basePlacement = getBasePlacement(state.placement);\n var variation = getVariation(state.placement);\n var isBasePlacement = !variation;\n var mainAxis = getMainAxisFromPlacement(basePlacement);\n var altAxis = getAltAxis(mainAxis);\n var popperOffsets = state.modifiersData.popperOffsets;\n var referenceRect = state.rects.reference;\n var popperRect = state.rects.popper;\n var tetherOffsetValue = typeof tetherOffset === 'function' ? tetherOffset(Object.assign({}, state.rects, {\n placement: state.placement\n })) : tetherOffset;\n var normalizedTetherOffsetValue = typeof tetherOffsetValue === 'number' ? {\n mainAxis: tetherOffsetValue,\n altAxis: tetherOffsetValue\n } : Object.assign({\n mainAxis: 0,\n altAxis: 0\n }, tetherOffsetValue);\n var offsetModifierState = state.modifiersData.offset ? state.modifiersData.offset[state.placement] : null;\n var data = {\n x: 0,\n y: 0\n };\n\n if (!popperOffsets) {\n return;\n }\n\n if (checkMainAxis) {\n var _offsetModifierState$;\n\n var mainSide = mainAxis === 'y' ? top : left;\n var altSide = mainAxis === 'y' ? bottom : right;\n var len = mainAxis === 'y' ? 'height' : 'width';\n var offset = popperOffsets[mainAxis];\n var min = offset + overflow[mainSide];\n var max = offset - overflow[altSide];\n var additive = tether ? -popperRect[len] / 2 : 0;\n var minLen = variation === start ? referenceRect[len] : popperRect[len];\n var maxLen = variation === start ? -popperRect[len] : -referenceRect[len]; // We need to include the arrow in the calculation so the arrow doesn't go\n // outside the reference bounds\n\n var arrowElement = state.elements.arrow;\n var arrowRect = tether && arrowElement ? getLayoutRect(arrowElement) : {\n width: 0,\n height: 0\n };\n var arrowPaddingObject = state.modifiersData['arrow#persistent'] ? state.modifiersData['arrow#persistent'].padding : getFreshSideObject();\n var arrowPaddingMin = arrowPaddingObject[mainSide];\n var arrowPaddingMax = arrowPaddingObject[altSide]; // If the reference length is smaller than the arrow length, we don't want\n // to include its full size in the calculation. If the reference is small\n // and near the edge of a boundary, the popper can overflow even if the\n // reference is not overflowing as well (e.g. virtual elements with no\n // width or height)\n\n var arrowLen = within(0, referenceRect[len], arrowRect[len]);\n var minOffset = isBasePlacement ? referenceRect[len] / 2 - additive - arrowLen - arrowPaddingMin - normalizedTetherOffsetValue.mainAxis : minLen - arrowLen - arrowPaddingMin - normalizedTetherOffsetValue.mainAxis;\n var maxOffset = isBasePlacement ? -referenceRect[len] / 2 + additive + arrowLen + arrowPaddingMax + normalizedTetherOffsetValue.mainAxis : maxLen + arrowLen + arrowPaddingMax + normalizedTetherOffsetValue.mainAxis;\n var arrowOffsetParent = state.elements.arrow && getOffsetParent(state.elements.arrow);\n var clientOffset = arrowOffsetParent ? mainAxis === 'y' ? arrowOffsetParent.clientTop || 0 : arrowOffsetParent.clientLeft || 0 : 0;\n var offsetModifierValue = (_offsetModifierState$ = offsetModifierState == null ? void 0 : offsetModifierState[mainAxis]) != null ? _offsetModifierState$ : 0;\n var tetherMin = offset + minOffset - offsetModifierValue - clientOffset;\n var tetherMax = offset + maxOffset - offsetModifierValue;\n var preventedOffset = within(tether ? mathMin(min, tetherMin) : min, offset, tether ? mathMax(max, tetherMax) : max);\n popperOffsets[mainAxis] = preventedOffset;\n data[mainAxis] = preventedOffset - offset;\n }\n\n if (checkAltAxis) {\n var _offsetModifierState$2;\n\n var _mainSide = mainAxis === 'x' ? top : left;\n\n var _altSide = mainAxis === 'x' ? bottom : right;\n\n var _offset = popperOffsets[altAxis];\n\n var _len = altAxis === 'y' ? 'height' : 'width';\n\n var _min = _offset + overflow[_mainSide];\n\n var _max = _offset - overflow[_altSide];\n\n var isOriginSide = [top, left].indexOf(basePlacement) !== -1;\n\n var _offsetModifierValue = (_offsetModifierState$2 = offsetModifierState == null ? void 0 : offsetModifierState[altAxis]) != null ? _offsetModifierState$2 : 0;\n\n var _tetherMin = isOriginSide ? _min : _offset - referenceRect[_len] - popperRect[_len] - _offsetModifierValue + normalizedTetherOffsetValue.altAxis;\n\n var _tetherMax = isOriginSide ? _offset + referenceRect[_len] + popperRect[_len] - _offsetModifierValue - normalizedTetherOffsetValue.altAxis : _max;\n\n var _preventedOffset = tether && isOriginSide ? withinMaxClamp(_tetherMin, _offset, _tetherMax) : within(tether ? _tetherMin : _min, _offset, tether ? _tetherMax : _max);\n\n popperOffsets[altAxis] = _preventedOffset;\n data[altAxis] = _preventedOffset - _offset;\n }\n\n state.modifiersData[name] = data;\n} // eslint-disable-next-line import/no-unused-modules\n\n\nexport default {\n name: 'preventOverflow',\n enabled: true,\n phase: 'main',\n fn: preventOverflow,\n requiresIfExists: ['offset']\n};","export default function getAltAxis(axis) {\n return axis === 'x' ? 'y' : 'x';\n}","import getBoundingClientRect from \"./getBoundingClientRect.js\";\nimport getNodeScroll from \"./getNodeScroll.js\";\nimport getNodeName from \"./getNodeName.js\";\nimport { isHTMLElement } from \"./instanceOf.js\";\nimport getWindowScrollBarX from \"./getWindowScrollBarX.js\";\nimport getDocumentElement from \"./getDocumentElement.js\";\nimport isScrollParent from \"./isScrollParent.js\";\nimport { round } from \"../utils/math.js\";\n\nfunction isElementScaled(element) {\n var rect = element.getBoundingClientRect();\n var scaleX = round(rect.width) / element.offsetWidth || 1;\n var scaleY = round(rect.height) / element.offsetHeight || 1;\n return scaleX !== 1 || scaleY !== 1;\n} // Returns the composite rect of an element relative to its offsetParent.\n// Composite means it takes into account transforms as well as layout.\n\n\nexport default function getCompositeRect(elementOrVirtualElement, offsetParent, isFixed) {\n if (isFixed === void 0) {\n isFixed = false;\n }\n\n var isOffsetParentAnElement = isHTMLElement(offsetParent);\n var offsetParentIsScaled = isHTMLElement(offsetParent) && isElementScaled(offsetParent);\n var documentElement = getDocumentElement(offsetParent);\n var rect = getBoundingClientRect(elementOrVirtualElement, offsetParentIsScaled, isFixed);\n var scroll = {\n scrollLeft: 0,\n scrollTop: 0\n };\n var offsets = {\n x: 0,\n y: 0\n };\n\n if (isOffsetParentAnElement || !isOffsetParentAnElement && !isFixed) {\n if (getNodeName(offsetParent) !== 'body' || // https://github.com/popperjs/popper-core/issues/1078\n isScrollParent(documentElement)) {\n scroll = getNodeScroll(offsetParent);\n }\n\n if (isHTMLElement(offsetParent)) {\n offsets = getBoundingClientRect(offsetParent, true);\n offsets.x += offsetParent.clientLeft;\n offsets.y += offsetParent.clientTop;\n } else if (documentElement) {\n offsets.x = getWindowScrollBarX(documentElement);\n }\n }\n\n return {\n x: rect.left + scroll.scrollLeft - offsets.x,\n y: rect.top + scroll.scrollTop - offsets.y,\n width: rect.width,\n height: rect.height\n };\n}","import getWindowScroll from \"./getWindowScroll.js\";\nimport getWindow from \"./getWindow.js\";\nimport { isHTMLElement } from \"./instanceOf.js\";\nimport getHTMLElementScroll from \"./getHTMLElementScroll.js\";\nexport default function getNodeScroll(node) {\n if (node === getWindow(node) || !isHTMLElement(node)) {\n return getWindowScroll(node);\n } else {\n return getHTMLElementScroll(node);\n }\n}","export default function getHTMLElementScroll(element) {\n return {\n scrollLeft: element.scrollLeft,\n scrollTop: element.scrollTop\n };\n}","import { modifierPhases } from \"../enums.js\"; // source: https://stackoverflow.com/questions/49875255\n\nfunction order(modifiers) {\n var map = new Map();\n var visited = new Set();\n var result = [];\n modifiers.forEach(function (modifier) {\n map.set(modifier.name, modifier);\n }); // On visiting object, check for its dependencies and visit them recursively\n\n function sort(modifier) {\n visited.add(modifier.name);\n var requires = [].concat(modifier.requires || [], modifier.requiresIfExists || []);\n requires.forEach(function (dep) {\n if (!visited.has(dep)) {\n var depModifier = map.get(dep);\n\n if (depModifier) {\n sort(depModifier);\n }\n }\n });\n result.push(modifier);\n }\n\n modifiers.forEach(function (modifier) {\n if (!visited.has(modifier.name)) {\n // check for visited object\n sort(modifier);\n }\n });\n return result;\n}\n\nexport default function orderModifiers(modifiers) {\n // order based on dependencies\n var orderedModifiers = order(modifiers); // order based on phase\n\n return modifierPhases.reduce(function (acc, phase) {\n return acc.concat(orderedModifiers.filter(function (modifier) {\n return modifier.phase === phase;\n }));\n }, []);\n}","import getCompositeRect from \"./dom-utils/getCompositeRect.js\";\nimport getLayoutRect from \"./dom-utils/getLayoutRect.js\";\nimport listScrollParents from \"./dom-utils/listScrollParents.js\";\nimport getOffsetParent from \"./dom-utils/getOffsetParent.js\";\nimport getComputedStyle from \"./dom-utils/getComputedStyle.js\";\nimport orderModifiers from \"./utils/orderModifiers.js\";\nimport debounce from \"./utils/debounce.js\";\nimport validateModifiers from \"./utils/validateModifiers.js\";\nimport uniqueBy from \"./utils/uniqueBy.js\";\nimport getBasePlacement from \"./utils/getBasePlacement.js\";\nimport mergeByName from \"./utils/mergeByName.js\";\nimport detectOverflow from \"./utils/detectOverflow.js\";\nimport { isElement } from \"./dom-utils/instanceOf.js\";\nimport { auto } from \"./enums.js\";\nvar INVALID_ELEMENT_ERROR = 'Popper: Invalid reference or popper argument provided. They must be either a DOM element or virtual element.';\nvar INFINITE_LOOP_ERROR = 'Popper: An infinite loop in the modifiers cycle has been detected! The cycle has been interrupted to prevent a browser crash.';\nvar DEFAULT_OPTIONS = {\n placement: 'bottom',\n modifiers: [],\n strategy: 'absolute'\n};\n\nfunction areValidElements() {\n for (var _len = arguments.length, args = new Array(_len), _key = 0; _key < _len; _key++) {\n args[_key] = arguments[_key];\n }\n\n return !args.some(function (element) {\n return !(element && typeof element.getBoundingClientRect === 'function');\n });\n}\n\nexport function popperGenerator(generatorOptions) {\n if (generatorOptions === void 0) {\n generatorOptions = {};\n }\n\n var _generatorOptions = generatorOptions,\n _generatorOptions$def = _generatorOptions.defaultModifiers,\n defaultModifiers = _generatorOptions$def === void 0 ? [] : _generatorOptions$def,\n _generatorOptions$def2 = _generatorOptions.defaultOptions,\n defaultOptions = _generatorOptions$def2 === void 0 ? DEFAULT_OPTIONS : _generatorOptions$def2;\n return function createPopper(reference, popper, options) {\n if (options === void 0) {\n options = defaultOptions;\n }\n\n var state = {\n placement: 'bottom',\n orderedModifiers: [],\n options: Object.assign({}, DEFAULT_OPTIONS, defaultOptions),\n modifiersData: {},\n elements: {\n reference: reference,\n popper: popper\n },\n attributes: {},\n styles: {}\n };\n var effectCleanupFns = [];\n var isDestroyed = false;\n var instance = {\n state: state,\n setOptions: function setOptions(setOptionsAction) {\n var options = typeof setOptionsAction === 'function' ? setOptionsAction(state.options) : setOptionsAction;\n cleanupModifierEffects();\n state.options = Object.assign({}, defaultOptions, state.options, options);\n state.scrollParents = {\n reference: isElement(reference) ? listScrollParents(reference) : reference.contextElement ? listScrollParents(reference.contextElement) : [],\n popper: listScrollParents(popper)\n }; // Orders the modifiers based on their dependencies and `phase`\n // properties\n\n var orderedModifiers = orderModifiers(mergeByName([].concat(defaultModifiers, state.options.modifiers))); // Strip out disabled modifiers\n\n state.orderedModifiers = orderedModifiers.filter(function (m) {\n return m.enabled;\n }); // Validate the provided modifiers so that the consumer will get warned\n // if one of the modifiers is invalid for any reason\n\n if (process.env.NODE_ENV !== \"production\") {\n var modifiers = uniqueBy([].concat(orderedModifiers, state.options.modifiers), function (_ref) {\n var name = _ref.name;\n return name;\n });\n validateModifiers(modifiers);\n\n if (getBasePlacement(state.options.placement) === auto) {\n var flipModifier = state.orderedModifiers.find(function (_ref2) {\n var name = _ref2.name;\n return name === 'flip';\n });\n\n if (!flipModifier) {\n console.error(['Popper: \"auto\" placements require the \"flip\" modifier be', 'present and enabled to work.'].join(' '));\n }\n }\n\n var _getComputedStyle = getComputedStyle(popper),\n marginTop = _getComputedStyle.marginTop,\n marginRight = _getComputedStyle.marginRight,\n marginBottom = _getComputedStyle.marginBottom,\n marginLeft = _getComputedStyle.marginLeft; // We no longer take into account `margins` on the popper, and it can\n // cause bugs with positioning, so we'll warn the consumer\n\n\n if ([marginTop, marginRight, marginBottom, marginLeft].some(function (margin) {\n return parseFloat(margin);\n })) {\n console.warn(['Popper: CSS \"margin\" styles cannot be used to apply padding', 'between the popper and its reference element or boundary.', 'To replicate margin, use the `offset` modifier, as well as', 'the `padding` option in the `preventOverflow` and `flip`', 'modifiers.'].join(' '));\n }\n }\n\n runModifierEffects();\n return instance.update();\n },\n // Sync update – it will always be executed, even if not necessary. This\n // is useful for low frequency updates where sync behavior simplifies the\n // logic.\n // For high frequency updates (e.g. `resize` and `scroll` events), always\n // prefer the async Popper#update method\n forceUpdate: function forceUpdate() {\n if (isDestroyed) {\n return;\n }\n\n var _state$elements = state.elements,\n reference = _state$elements.reference,\n popper = _state$elements.popper; // Don't proceed if `reference` or `popper` are not valid elements\n // anymore\n\n if (!areValidElements(reference, popper)) {\n if (process.env.NODE_ENV !== \"production\") {\n console.error(INVALID_ELEMENT_ERROR);\n }\n\n return;\n } // Store the reference and popper rects to be read by modifiers\n\n\n state.rects = {\n reference: getCompositeRect(reference, getOffsetParent(popper), state.options.strategy === 'fixed'),\n popper: getLayoutRect(popper)\n }; // Modifiers have the ability to reset the current update cycle. The\n // most common use case for this is the `flip` modifier changing the\n // placement, which then needs to re-run all the modifiers, because the\n // logic was previously ran for the previous placement and is therefore\n // stale/incorrect\n\n state.reset = false;\n state.placement = state.options.placement; // On each update cycle, the `modifiersData` property for each modifier\n // is filled with the initial data specified by the modifier. This means\n // it doesn't persist and is fresh on each update.\n // To ensure persistent data, use `${name}#persistent`\n\n state.orderedModifiers.forEach(function (modifier) {\n return state.modifiersData[modifier.name] = Object.assign({}, modifier.data);\n });\n var __debug_loops__ = 0;\n\n for (var index = 0; index < state.orderedModifiers.length; index++) {\n if (process.env.NODE_ENV !== \"production\") {\n __debug_loops__ += 1;\n\n if (__debug_loops__ > 100) {\n console.error(INFINITE_LOOP_ERROR);\n break;\n }\n }\n\n if (state.reset === true) {\n state.reset = false;\n index = -1;\n continue;\n }\n\n var _state$orderedModifie = state.orderedModifiers[index],\n fn = _state$orderedModifie.fn,\n _state$orderedModifie2 = _state$orderedModifie.options,\n _options = _state$orderedModifie2 === void 0 ? {} : _state$orderedModifie2,\n name = _state$orderedModifie.name;\n\n if (typeof fn === 'function') {\n state = fn({\n state: state,\n options: _options,\n name: name,\n instance: instance\n }) || state;\n }\n }\n },\n // Async and optimistically optimized update – it will not be executed if\n // not necessary (debounced to run at most once-per-tick)\n update: debounce(function () {\n return new Promise(function (resolve) {\n instance.forceUpdate();\n resolve(state);\n });\n }),\n destroy: function destroy() {\n cleanupModifierEffects();\n isDestroyed = true;\n }\n };\n\n if (!areValidElements(reference, popper)) {\n if (process.env.NODE_ENV !== \"production\") {\n console.error(INVALID_ELEMENT_ERROR);\n }\n\n return instance;\n }\n\n instance.setOptions(options).then(function (state) {\n if (!isDestroyed && options.onFirstUpdate) {\n options.onFirstUpdate(state);\n }\n }); // Modifiers have the ability to execute arbitrary code before the first\n // update cycle runs. They will be executed in the same order as the update\n // cycle. This is useful when a modifier adds some persistent data that\n // other modifiers need to use, but the modifier is run after the dependent\n // one.\n\n function runModifierEffects() {\n state.orderedModifiers.forEach(function (_ref3) {\n var name = _ref3.name,\n _ref3$options = _ref3.options,\n options = _ref3$options === void 0 ? {} : _ref3$options,\n effect = _ref3.effect;\n\n if (typeof effect === 'function') {\n var cleanupFn = effect({\n state: state,\n name: name,\n instance: instance,\n options: options\n });\n\n var noopFn = function noopFn() {};\n\n effectCleanupFns.push(cleanupFn || noopFn);\n }\n });\n }\n\n function cleanupModifierEffects() {\n effectCleanupFns.forEach(function (fn) {\n return fn();\n });\n effectCleanupFns = [];\n }\n\n return instance;\n };\n}\nexport var createPopper = /*#__PURE__*/popperGenerator(); // eslint-disable-next-line import/no-unused-modules\n\nexport { detectOverflow };","export default function debounce(fn) {\n var pending;\n return function () {\n if (!pending) {\n pending = new Promise(function (resolve) {\n Promise.resolve().then(function () {\n pending = undefined;\n resolve(fn());\n });\n });\n }\n\n return pending;\n };\n}","export default function mergeByName(modifiers) {\n var merged = modifiers.reduce(function (merged, current) {\n var existing = merged[current.name];\n merged[current.name] = existing ? Object.assign({}, existing, current, {\n options: Object.assign({}, existing.options, current.options),\n data: Object.assign({}, existing.data, current.data)\n }) : current;\n return merged;\n }, {}); // IE11 does not support Object.values\n\n return Object.keys(merged).map(function (key) {\n return merged[key];\n });\n}","import { popperGenerator, detectOverflow } from \"./createPopper.js\";\nimport eventListeners from \"./modifiers/eventListeners.js\";\nimport popperOffsets from \"./modifiers/popperOffsets.js\";\nimport computeStyles from \"./modifiers/computeStyles.js\";\nimport applyStyles from \"./modifiers/applyStyles.js\";\nimport offset from \"./modifiers/offset.js\";\nimport flip from \"./modifiers/flip.js\";\nimport preventOverflow from \"./modifiers/preventOverflow.js\";\nimport arrow from \"./modifiers/arrow.js\";\nimport hide from \"./modifiers/hide.js\";\nvar defaultModifiers = [eventListeners, popperOffsets, computeStyles, applyStyles, offset, flip, preventOverflow, arrow, hide];\nvar createPopper = /*#__PURE__*/popperGenerator({\n defaultModifiers: defaultModifiers\n}); // eslint-disable-next-line import/no-unused-modules\n\nexport { createPopper, popperGenerator, defaultModifiers, detectOverflow }; // eslint-disable-next-line import/no-unused-modules\n\nexport { createPopper as createPopperLite } from \"./popper-lite.js\"; // eslint-disable-next-line import/no-unused-modules\n\nexport * from \"./modifiers/index.js\";","import { popperGenerator, detectOverflow } from \"./createPopper.js\";\nimport eventListeners from \"./modifiers/eventListeners.js\";\nimport popperOffsets from \"./modifiers/popperOffsets.js\";\nimport computeStyles from \"./modifiers/computeStyles.js\";\nimport applyStyles from \"./modifiers/applyStyles.js\";\nvar defaultModifiers = [eventListeners, popperOffsets, computeStyles, applyStyles];\nvar createPopper = /*#__PURE__*/popperGenerator({\n defaultModifiers: defaultModifiers\n}); // eslint-disable-next-line import/no-unused-modules\n\nexport { createPopper, popperGenerator, defaultModifiers, detectOverflow };","/*!\n * Bootstrap v5.2.3 (https://getbootstrap.com/)\n * Copyright 2011-2022 The Bootstrap Authors (https://github.com/twbs/bootstrap/graphs/contributors)\n * Licensed under MIT (https://github.com/twbs/bootstrap/blob/main/LICENSE)\n */\nimport * as Popper from '@popperjs/core';\n\n/**\n * --------------------------------------------------------------------------\n * Bootstrap (v5.2.3): util/index.js\n * Licensed under MIT (https://github.com/twbs/bootstrap/blob/main/LICENSE)\n * --------------------------------------------------------------------------\n */\nconst MAX_UID = 1000000;\nconst MILLISECONDS_MULTIPLIER = 1000;\nconst TRANSITION_END = 'transitionend'; // Shout-out Angus Croll (https://goo.gl/pxwQGp)\n\nconst toType = object => {\n if (object === null || object === undefined) {\n return `${object}`;\n }\n\n return Object.prototype.toString.call(object).match(/\\s([a-z]+)/i)[1].toLowerCase();\n};\n/**\n * Public Util API\n */\n\n\nconst getUID = prefix => {\n do {\n prefix += Math.floor(Math.random() * MAX_UID);\n } while (document.getElementById(prefix));\n\n return prefix;\n};\n\nconst getSelector = element => {\n let selector = element.getAttribute('data-bs-target');\n\n if (!selector || selector === '#') {\n let hrefAttribute = element.getAttribute('href'); // The only valid content that could double as a selector are IDs or classes,\n // so everything starting with `#` or `.`. If a \"real\" URL is used as the selector,\n // `document.querySelector` will rightfully complain it is invalid.\n // See https://github.com/twbs/bootstrap/issues/32273\n\n if (!hrefAttribute || !hrefAttribute.includes('#') && !hrefAttribute.startsWith('.')) {\n return null;\n } // Just in case some CMS puts out a full URL with the anchor appended\n\n\n if (hrefAttribute.includes('#') && !hrefAttribute.startsWith('#')) {\n hrefAttribute = `#${hrefAttribute.split('#')[1]}`;\n }\n\n selector = hrefAttribute && hrefAttribute !== '#' ? hrefAttribute.trim() : null;\n }\n\n return selector;\n};\n\nconst getSelectorFromElement = element => {\n const selector = getSelector(element);\n\n if (selector) {\n return document.querySelector(selector) ? selector : null;\n }\n\n return null;\n};\n\nconst getElementFromSelector = element => {\n const selector = getSelector(element);\n return selector ? document.querySelector(selector) : null;\n};\n\nconst getTransitionDurationFromElement = element => {\n if (!element) {\n return 0;\n } // Get transition-duration of the element\n\n\n let {\n transitionDuration,\n transitionDelay\n } = window.getComputedStyle(element);\n const floatTransitionDuration = Number.parseFloat(transitionDuration);\n const floatTransitionDelay = Number.parseFloat(transitionDelay); // Return 0 if element or transition duration is not found\n\n if (!floatTransitionDuration && !floatTransitionDelay) {\n return 0;\n } // If multiple durations are defined, take the first\n\n\n transitionDuration = transitionDuration.split(',')[0];\n transitionDelay = transitionDelay.split(',')[0];\n return (Number.parseFloat(transitionDuration) + Number.parseFloat(transitionDelay)) * MILLISECONDS_MULTIPLIER;\n};\n\nconst triggerTransitionEnd = element => {\n element.dispatchEvent(new Event(TRANSITION_END));\n};\n\nconst isElement = object => {\n if (!object || typeof object !== 'object') {\n return false;\n }\n\n if (typeof object.jquery !== 'undefined') {\n object = object[0];\n }\n\n return typeof object.nodeType !== 'undefined';\n};\n\nconst getElement = object => {\n // it's a jQuery object or a node element\n if (isElement(object)) {\n return object.jquery ? object[0] : object;\n }\n\n if (typeof object === 'string' && object.length > 0) {\n return document.querySelector(object);\n }\n\n return null;\n};\n\nconst isVisible = element => {\n if (!isElement(element) || element.getClientRects().length === 0) {\n return false;\n }\n\n const elementIsVisible = getComputedStyle(element).getPropertyValue('visibility') === 'visible'; // Handle `details` element as its content may falsie appear visible when it is closed\n\n const closedDetails = element.closest('details:not([open])');\n\n if (!closedDetails) {\n return elementIsVisible;\n }\n\n if (closedDetails !== element) {\n const summary = element.closest('summary');\n\n if (summary && summary.parentNode !== closedDetails) {\n return false;\n }\n\n if (summary === null) {\n return false;\n }\n }\n\n return elementIsVisible;\n};\n\nconst isDisabled = element => {\n if (!element || element.nodeType !== Node.ELEMENT_NODE) {\n return true;\n }\n\n if (element.classList.contains('disabled')) {\n return true;\n }\n\n if (typeof element.disabled !== 'undefined') {\n return element.disabled;\n }\n\n return element.hasAttribute('disabled') && element.getAttribute('disabled') !== 'false';\n};\n\nconst findShadowRoot = element => {\n if (!document.documentElement.attachShadow) {\n return null;\n } // Can find the shadow root otherwise it'll return the document\n\n\n if (typeof element.getRootNode === 'function') {\n const root = element.getRootNode();\n return root instanceof ShadowRoot ? root : null;\n }\n\n if (element instanceof ShadowRoot) {\n return element;\n } // when we don't find a shadow root\n\n\n if (!element.parentNode) {\n return null;\n }\n\n return findShadowRoot(element.parentNode);\n};\n\nconst noop = () => {};\n/**\n * Trick to restart an element's animation\n *\n * @param {HTMLElement} element\n * @return void\n *\n * @see https://www.charistheo.io/blog/2021/02/restart-a-css-animation-with-javascript/#restarting-a-css-animation\n */\n\n\nconst reflow = element => {\n element.offsetHeight; // eslint-disable-line no-unused-expressions\n};\n\nconst getjQuery = () => {\n if (window.jQuery && !document.body.hasAttribute('data-bs-no-jquery')) {\n return window.jQuery;\n }\n\n return null;\n};\n\nconst DOMContentLoadedCallbacks = [];\n\nconst onDOMContentLoaded = callback => {\n if (document.readyState === 'loading') {\n // add listener on the first call when the document is in loading state\n if (!DOMContentLoadedCallbacks.length) {\n document.addEventListener('DOMContentLoaded', () => {\n for (const callback of DOMContentLoadedCallbacks) {\n callback();\n }\n });\n }\n\n DOMContentLoadedCallbacks.push(callback);\n } else {\n callback();\n }\n};\n\nconst isRTL = () => document.documentElement.dir === 'rtl';\n\nconst defineJQueryPlugin = plugin => {\n onDOMContentLoaded(() => {\n const $ = getjQuery();\n /* istanbul ignore if */\n\n if ($) {\n const name = plugin.NAME;\n const JQUERY_NO_CONFLICT = $.fn[name];\n $.fn[name] = plugin.jQueryInterface;\n $.fn[name].Constructor = plugin;\n\n $.fn[name].noConflict = () => {\n $.fn[name] = JQUERY_NO_CONFLICT;\n return plugin.jQueryInterface;\n };\n }\n });\n};\n\nconst execute = callback => {\n if (typeof callback === 'function') {\n callback();\n }\n};\n\nconst executeAfterTransition = (callback, transitionElement, waitForTransition = true) => {\n if (!waitForTransition) {\n execute(callback);\n return;\n }\n\n const durationPadding = 5;\n const emulatedDuration = getTransitionDurationFromElement(transitionElement) + durationPadding;\n let called = false;\n\n const handler = ({\n target\n }) => {\n if (target !== transitionElement) {\n return;\n }\n\n called = true;\n transitionElement.removeEventListener(TRANSITION_END, handler);\n execute(callback);\n };\n\n transitionElement.addEventListener(TRANSITION_END, handler);\n setTimeout(() => {\n if (!called) {\n triggerTransitionEnd(transitionElement);\n }\n }, emulatedDuration);\n};\n/**\n * Return the previous/next element of a list.\n *\n * @param {array} list The list of elements\n * @param activeElement The active element\n * @param shouldGetNext Choose to get next or previous element\n * @param isCycleAllowed\n * @return {Element|elem} The proper element\n */\n\n\nconst getNextActiveElement = (list, activeElement, shouldGetNext, isCycleAllowed) => {\n const listLength = list.length;\n let index = list.indexOf(activeElement); // if the element does not exist in the list return an element\n // depending on the direction and if cycle is allowed\n\n if (index === -1) {\n return !shouldGetNext && isCycleAllowed ? list[listLength - 1] : list[0];\n }\n\n index += shouldGetNext ? 1 : -1;\n\n if (isCycleAllowed) {\n index = (index + listLength) % listLength;\n }\n\n return list[Math.max(0, Math.min(index, listLength - 1))];\n};\n\n/**\n * --------------------------------------------------------------------------\n * Bootstrap (v5.2.3): dom/event-handler.js\n * Licensed under MIT (https://github.com/twbs/bootstrap/blob/main/LICENSE)\n * --------------------------------------------------------------------------\n */\n/**\n * Constants\n */\n\nconst namespaceRegex = /[^.]*(?=\\..*)\\.|.*/;\nconst stripNameRegex = /\\..*/;\nconst stripUidRegex = /::\\d+$/;\nconst eventRegistry = {}; // Events storage\n\nlet uidEvent = 1;\nconst customEvents = {\n mouseenter: 'mouseover',\n mouseleave: 'mouseout'\n};\nconst nativeEvents = new Set(['click', 'dblclick', 'mouseup', 'mousedown', 'contextmenu', 'mousewheel', 'DOMMouseScroll', 'mouseover', 'mouseout', 'mousemove', 'selectstart', 'selectend', 'keydown', 'keypress', 'keyup', 'orientationchange', 'touchstart', 'touchmove', 'touchend', 'touchcancel', 'pointerdown', 'pointermove', 'pointerup', 'pointerleave', 'pointercancel', 'gesturestart', 'gesturechange', 'gestureend', 'focus', 'blur', 'change', 'reset', 'select', 'submit', 'focusin', 'focusout', 'load', 'unload', 'beforeunload', 'resize', 'move', 'DOMContentLoaded', 'readystatechange', 'error', 'abort', 'scroll']);\n/**\n * Private methods\n */\n\nfunction makeEventUid(element, uid) {\n return uid && `${uid}::${uidEvent++}` || element.uidEvent || uidEvent++;\n}\n\nfunction getElementEvents(element) {\n const uid = makeEventUid(element);\n element.uidEvent = uid;\n eventRegistry[uid] = eventRegistry[uid] || {};\n return eventRegistry[uid];\n}\n\nfunction bootstrapHandler(element, fn) {\n return function handler(event) {\n hydrateObj(event, {\n delegateTarget: element\n });\n\n if (handler.oneOff) {\n EventHandler.off(element, event.type, fn);\n }\n\n return fn.apply(element, [event]);\n };\n}\n\nfunction bootstrapDelegationHandler(element, selector, fn) {\n return function handler(event) {\n const domElements = element.querySelectorAll(selector);\n\n for (let {\n target\n } = event; target && target !== this; target = target.parentNode) {\n for (const domElement of domElements) {\n if (domElement !== target) {\n continue;\n }\n\n hydrateObj(event, {\n delegateTarget: target\n });\n\n if (handler.oneOff) {\n EventHandler.off(element, event.type, selector, fn);\n }\n\n return fn.apply(target, [event]);\n }\n }\n };\n}\n\nfunction findHandler(events, callable, delegationSelector = null) {\n return Object.values(events).find(event => event.callable === callable && event.delegationSelector === delegationSelector);\n}\n\nfunction normalizeParameters(originalTypeEvent, handler, delegationFunction) {\n const isDelegated = typeof handler === 'string'; // todo: tooltip passes `false` instead of selector, so we need to check\n\n const callable = isDelegated ? delegationFunction : handler || delegationFunction;\n let typeEvent = getTypeEvent(originalTypeEvent);\n\n if (!nativeEvents.has(typeEvent)) {\n typeEvent = originalTypeEvent;\n }\n\n return [isDelegated, callable, typeEvent];\n}\n\nfunction addHandler(element, originalTypeEvent, handler, delegationFunction, oneOff) {\n if (typeof originalTypeEvent !== 'string' || !element) {\n return;\n }\n\n let [isDelegated, callable, typeEvent] = normalizeParameters(originalTypeEvent, handler, delegationFunction); // in case of mouseenter or mouseleave wrap the handler within a function that checks for its DOM position\n // this prevents the handler from being dispatched the same way as mouseover or mouseout does\n\n if (originalTypeEvent in customEvents) {\n const wrapFunction = fn => {\n return function (event) {\n if (!event.relatedTarget || event.relatedTarget !== event.delegateTarget && !event.delegateTarget.contains(event.relatedTarget)) {\n return fn.call(this, event);\n }\n };\n };\n\n callable = wrapFunction(callable);\n }\n\n const events = getElementEvents(element);\n const handlers = events[typeEvent] || (events[typeEvent] = {});\n const previousFunction = findHandler(handlers, callable, isDelegated ? handler : null);\n\n if (previousFunction) {\n previousFunction.oneOff = previousFunction.oneOff && oneOff;\n return;\n }\n\n const uid = makeEventUid(callable, originalTypeEvent.replace(namespaceRegex, ''));\n const fn = isDelegated ? bootstrapDelegationHandler(element, handler, callable) : bootstrapHandler(element, callable);\n fn.delegationSelector = isDelegated ? handler : null;\n fn.callable = callable;\n fn.oneOff = oneOff;\n fn.uidEvent = uid;\n handlers[uid] = fn;\n element.addEventListener(typeEvent, fn, isDelegated);\n}\n\nfunction removeHandler(element, events, typeEvent, handler, delegationSelector) {\n const fn = findHandler(events[typeEvent], handler, delegationSelector);\n\n if (!fn) {\n return;\n }\n\n element.removeEventListener(typeEvent, fn, Boolean(delegationSelector));\n delete events[typeEvent][fn.uidEvent];\n}\n\nfunction removeNamespacedHandlers(element, events, typeEvent, namespace) {\n const storeElementEvent = events[typeEvent] || {};\n\n for (const handlerKey of Object.keys(storeElementEvent)) {\n if (handlerKey.includes(namespace)) {\n const event = storeElementEvent[handlerKey];\n removeHandler(element, events, typeEvent, event.callable, event.delegationSelector);\n }\n }\n}\n\nfunction getTypeEvent(event) {\n // allow to get the native events from namespaced events ('click.bs.button' --> 'click')\n event = event.replace(stripNameRegex, '');\n return customEvents[event] || event;\n}\n\nconst EventHandler = {\n on(element, event, handler, delegationFunction) {\n addHandler(element, event, handler, delegationFunction, false);\n },\n\n one(element, event, handler, delegationFunction) {\n addHandler(element, event, handler, delegationFunction, true);\n },\n\n off(element, originalTypeEvent, handler, delegationFunction) {\n if (typeof originalTypeEvent !== 'string' || !element) {\n return;\n }\n\n const [isDelegated, callable, typeEvent] = normalizeParameters(originalTypeEvent, handler, delegationFunction);\n const inNamespace = typeEvent !== originalTypeEvent;\n const events = getElementEvents(element);\n const storeElementEvent = events[typeEvent] || {};\n const isNamespace = originalTypeEvent.startsWith('.');\n\n if (typeof callable !== 'undefined') {\n // Simplest case: handler is passed, remove that listener ONLY.\n if (!Object.keys(storeElementEvent).length) {\n return;\n }\n\n removeHandler(element, events, typeEvent, callable, isDelegated ? handler : null);\n return;\n }\n\n if (isNamespace) {\n for (const elementEvent of Object.keys(events)) {\n removeNamespacedHandlers(element, events, elementEvent, originalTypeEvent.slice(1));\n }\n }\n\n for (const keyHandlers of Object.keys(storeElementEvent)) {\n const handlerKey = keyHandlers.replace(stripUidRegex, '');\n\n if (!inNamespace || originalTypeEvent.includes(handlerKey)) {\n const event = storeElementEvent[keyHandlers];\n removeHandler(element, events, typeEvent, event.callable, event.delegationSelector);\n }\n }\n },\n\n trigger(element, event, args) {\n if (typeof event !== 'string' || !element) {\n return null;\n }\n\n const $ = getjQuery();\n const typeEvent = getTypeEvent(event);\n const inNamespace = event !== typeEvent;\n let jQueryEvent = null;\n let bubbles = true;\n let nativeDispatch = true;\n let defaultPrevented = false;\n\n if (inNamespace && $) {\n jQueryEvent = $.Event(event, args);\n $(element).trigger(jQueryEvent);\n bubbles = !jQueryEvent.isPropagationStopped();\n nativeDispatch = !jQueryEvent.isImmediatePropagationStopped();\n defaultPrevented = jQueryEvent.isDefaultPrevented();\n }\n\n let evt = new Event(event, {\n bubbles,\n cancelable: true\n });\n evt = hydrateObj(evt, args);\n\n if (defaultPrevented) {\n evt.preventDefault();\n }\n\n if (nativeDispatch) {\n element.dispatchEvent(evt);\n }\n\n if (evt.defaultPrevented && jQueryEvent) {\n jQueryEvent.preventDefault();\n }\n\n return evt;\n }\n\n};\n\nfunction hydrateObj(obj, meta) {\n for (const [key, value] of Object.entries(meta || {})) {\n try {\n obj[key] = value;\n } catch (_unused) {\n Object.defineProperty(obj, key, {\n configurable: true,\n\n get() {\n return value;\n }\n\n });\n }\n }\n\n return obj;\n}\n\n/**\n * --------------------------------------------------------------------------\n * Bootstrap (v5.2.3): dom/data.js\n * Licensed under MIT (https://github.com/twbs/bootstrap/blob/main/LICENSE)\n * --------------------------------------------------------------------------\n */\n\n/**\n * Constants\n */\nconst elementMap = new Map();\nconst Data = {\n set(element, key, instance) {\n if (!elementMap.has(element)) {\n elementMap.set(element, new Map());\n }\n\n const instanceMap = elementMap.get(element); // make it clear we only want one instance per element\n // can be removed later when multiple key/instances are fine to be used\n\n if (!instanceMap.has(key) && instanceMap.size !== 0) {\n // eslint-disable-next-line no-console\n console.error(`Bootstrap doesn't allow more than one instance per element. Bound instance: ${Array.from(instanceMap.keys())[0]}.`);\n return;\n }\n\n instanceMap.set(key, instance);\n },\n\n get(element, key) {\n if (elementMap.has(element)) {\n return elementMap.get(element).get(key) || null;\n }\n\n return null;\n },\n\n remove(element, key) {\n if (!elementMap.has(element)) {\n return;\n }\n\n const instanceMap = elementMap.get(element);\n instanceMap.delete(key); // free up element references if there are no instances left for an element\n\n if (instanceMap.size === 0) {\n elementMap.delete(element);\n }\n }\n\n};\n\n/**\n * --------------------------------------------------------------------------\n * Bootstrap (v5.2.3): dom/manipulator.js\n * Licensed under MIT (https://github.com/twbs/bootstrap/blob/main/LICENSE)\n * --------------------------------------------------------------------------\n */\nfunction normalizeData(value) {\n if (value === 'true') {\n return true;\n }\n\n if (value === 'false') {\n return false;\n }\n\n if (value === Number(value).toString()) {\n return Number(value);\n }\n\n if (value === '' || value === 'null') {\n return null;\n }\n\n if (typeof value !== 'string') {\n return value;\n }\n\n try {\n return JSON.parse(decodeURIComponent(value));\n } catch (_unused) {\n return value;\n }\n}\n\nfunction normalizeDataKey(key) {\n return key.replace(/[A-Z]/g, chr => `-${chr.toLowerCase()}`);\n}\n\nconst Manipulator = {\n setDataAttribute(element, key, value) {\n element.setAttribute(`data-bs-${normalizeDataKey(key)}`, value);\n },\n\n removeDataAttribute(element, key) {\n element.removeAttribute(`data-bs-${normalizeDataKey(key)}`);\n },\n\n getDataAttributes(element) {\n if (!element) {\n return {};\n }\n\n const attributes = {};\n const bsKeys = Object.keys(element.dataset).filter(key => key.startsWith('bs') && !key.startsWith('bsConfig'));\n\n for (const key of bsKeys) {\n let pureKey = key.replace(/^bs/, '');\n pureKey = pureKey.charAt(0).toLowerCase() + pureKey.slice(1, pureKey.length);\n attributes[pureKey] = normalizeData(element.dataset[key]);\n }\n\n return attributes;\n },\n\n getDataAttribute(element, key) {\n return normalizeData(element.getAttribute(`data-bs-${normalizeDataKey(key)}`));\n }\n\n};\n\n/**\n * --------------------------------------------------------------------------\n * Bootstrap (v5.2.3): util/config.js\n * Licensed under MIT (https://github.com/twbs/bootstrap/blob/main/LICENSE)\n * --------------------------------------------------------------------------\n */\n/**\n * Class definition\n */\n\nclass Config {\n // Getters\n static get Default() {\n return {};\n }\n\n static get DefaultType() {\n return {};\n }\n\n static get NAME() {\n throw new Error('You have to implement the static method \"NAME\", for each component!');\n }\n\n _getConfig(config) {\n config = this._mergeConfigObj(config);\n config = this._configAfterMerge(config);\n\n this._typeCheckConfig(config);\n\n return config;\n }\n\n _configAfterMerge(config) {\n return config;\n }\n\n _mergeConfigObj(config, element) {\n const jsonConfig = isElement(element) ? Manipulator.getDataAttribute(element, 'config') : {}; // try to parse\n\n return { ...this.constructor.Default,\n ...(typeof jsonConfig === 'object' ? jsonConfig : {}),\n ...(isElement(element) ? Manipulator.getDataAttributes(element) : {}),\n ...(typeof config === 'object' ? config : {})\n };\n }\n\n _typeCheckConfig(config, configTypes = this.constructor.DefaultType) {\n for (const property of Object.keys(configTypes)) {\n const expectedTypes = configTypes[property];\n const value = config[property];\n const valueType = isElement(value) ? 'element' : toType(value);\n\n if (!new RegExp(expectedTypes).test(valueType)) {\n throw new TypeError(`${this.constructor.NAME.toUpperCase()}: Option \"${property}\" provided type \"${valueType}\" but expected type \"${expectedTypes}\".`);\n }\n }\n }\n\n}\n\n/**\n * --------------------------------------------------------------------------\n * Bootstrap (v5.2.3): base-component.js\n * Licensed under MIT (https://github.com/twbs/bootstrap/blob/main/LICENSE)\n * --------------------------------------------------------------------------\n */\n/**\n * Constants\n */\n\nconst VERSION = '5.2.3';\n/**\n * Class definition\n */\n\nclass BaseComponent extends Config {\n constructor(element, config) {\n super();\n element = getElement(element);\n\n if (!element) {\n return;\n }\n\n this._element = element;\n this._config = this._getConfig(config);\n Data.set(this._element, this.constructor.DATA_KEY, this);\n } // Public\n\n\n dispose() {\n Data.remove(this._element, this.constructor.DATA_KEY);\n EventHandler.off(this._element, this.constructor.EVENT_KEY);\n\n for (const propertyName of Object.getOwnPropertyNames(this)) {\n this[propertyName] = null;\n }\n }\n\n _queueCallback(callback, element, isAnimated = true) {\n executeAfterTransition(callback, element, isAnimated);\n }\n\n _getConfig(config) {\n config = this._mergeConfigObj(config, this._element);\n config = this._configAfterMerge(config);\n\n this._typeCheckConfig(config);\n\n return config;\n } // Static\n\n\n static getInstance(element) {\n return Data.get(getElement(element), this.DATA_KEY);\n }\n\n static getOrCreateInstance(element, config = {}) {\n return this.getInstance(element) || new this(element, typeof config === 'object' ? config : null);\n }\n\n static get VERSION() {\n return VERSION;\n }\n\n static get DATA_KEY() {\n return `bs.${this.NAME}`;\n }\n\n static get EVENT_KEY() {\n return `.${this.DATA_KEY}`;\n }\n\n static eventName(name) {\n return `${name}${this.EVENT_KEY}`;\n }\n\n}\n\n/**\n * --------------------------------------------------------------------------\n * Bootstrap (v5.2.3): util/component-functions.js\n * Licensed under MIT (https://github.com/twbs/bootstrap/blob/main/LICENSE)\n * --------------------------------------------------------------------------\n */\n\nconst enableDismissTrigger = (component, method = 'hide') => {\n const clickEvent = `click.dismiss${component.EVENT_KEY}`;\n const name = component.NAME;\n EventHandler.on(document, clickEvent, `[data-bs-dismiss=\"${name}\"]`, function (event) {\n if (['A', 'AREA'].includes(this.tagName)) {\n event.preventDefault();\n }\n\n if (isDisabled(this)) {\n return;\n }\n\n const target = getElementFromSelector(this) || this.closest(`.${name}`);\n const instance = component.getOrCreateInstance(target); // Method argument is left, for Alert and only, as it doesn't implement the 'hide' method\n\n instance[method]();\n });\n};\n\n/**\n * --------------------------------------------------------------------------\n * Bootstrap (v5.2.3): alert.js\n * Licensed under MIT (https://github.com/twbs/bootstrap/blob/main/LICENSE)\n * --------------------------------------------------------------------------\n */\n/**\n * Constants\n */\n\nconst NAME$f = 'alert';\nconst DATA_KEY$a = 'bs.alert';\nconst EVENT_KEY$b = `.${DATA_KEY$a}`;\nconst EVENT_CLOSE = `close${EVENT_KEY$b}`;\nconst EVENT_CLOSED = `closed${EVENT_KEY$b}`;\nconst CLASS_NAME_FADE$5 = 'fade';\nconst CLASS_NAME_SHOW$8 = 'show';\n/**\n * Class definition\n */\n\nclass Alert extends BaseComponent {\n // Getters\n static get NAME() {\n return NAME$f;\n } // Public\n\n\n close() {\n const closeEvent = EventHandler.trigger(this._element, EVENT_CLOSE);\n\n if (closeEvent.defaultPrevented) {\n return;\n }\n\n this._element.classList.remove(CLASS_NAME_SHOW$8);\n\n const isAnimated = this._element.classList.contains(CLASS_NAME_FADE$5);\n\n this._queueCallback(() => this._destroyElement(), this._element, isAnimated);\n } // Private\n\n\n _destroyElement() {\n this._element.remove();\n\n EventHandler.trigger(this._element, EVENT_CLOSED);\n this.dispose();\n } // Static\n\n\n static jQueryInterface(config) {\n return this.each(function () {\n const data = Alert.getOrCreateInstance(this);\n\n if (typeof config !== 'string') {\n return;\n }\n\n if (data[config] === undefined || config.startsWith('_') || config === 'constructor') {\n throw new TypeError(`No method named \"${config}\"`);\n }\n\n data[config](this);\n });\n }\n\n}\n/**\n * Data API implementation\n */\n\n\nenableDismissTrigger(Alert, 'close');\n/**\n * jQuery\n */\n\ndefineJQueryPlugin(Alert);\n\n/**\n * --------------------------------------------------------------------------\n * Bootstrap (v5.2.3): button.js\n * Licensed under MIT (https://github.com/twbs/bootstrap/blob/main/LICENSE)\n * --------------------------------------------------------------------------\n */\n/**\n * Constants\n */\n\nconst NAME$e = 'button';\nconst DATA_KEY$9 = 'bs.button';\nconst EVENT_KEY$a = `.${DATA_KEY$9}`;\nconst DATA_API_KEY$6 = '.data-api';\nconst CLASS_NAME_ACTIVE$3 = 'active';\nconst SELECTOR_DATA_TOGGLE$5 = '[data-bs-toggle=\"button\"]';\nconst EVENT_CLICK_DATA_API$6 = `click${EVENT_KEY$a}${DATA_API_KEY$6}`;\n/**\n * Class definition\n */\n\nclass Button extends BaseComponent {\n // Getters\n static get NAME() {\n return NAME$e;\n } // Public\n\n\n toggle() {\n // Toggle class and sync the `aria-pressed` attribute with the return value of the `.toggle()` method\n this._element.setAttribute('aria-pressed', this._element.classList.toggle(CLASS_NAME_ACTIVE$3));\n } // Static\n\n\n static jQueryInterface(config) {\n return this.each(function () {\n const data = Button.getOrCreateInstance(this);\n\n if (config === 'toggle') {\n data[config]();\n }\n });\n }\n\n}\n/**\n * Data API implementation\n */\n\n\nEventHandler.on(document, EVENT_CLICK_DATA_API$6, SELECTOR_DATA_TOGGLE$5, event => {\n event.preventDefault();\n const button = event.target.closest(SELECTOR_DATA_TOGGLE$5);\n const data = Button.getOrCreateInstance(button);\n data.toggle();\n});\n/**\n * jQuery\n */\n\ndefineJQueryPlugin(Button);\n\n/**\n * --------------------------------------------------------------------------\n * Bootstrap (v5.2.3): dom/selector-engine.js\n * Licensed under MIT (https://github.com/twbs/bootstrap/blob/main/LICENSE)\n * --------------------------------------------------------------------------\n */\n/**\n * Constants\n */\n\nconst SelectorEngine = {\n find(selector, element = document.documentElement) {\n return [].concat(...Element.prototype.querySelectorAll.call(element, selector));\n },\n\n findOne(selector, element = document.documentElement) {\n return Element.prototype.querySelector.call(element, selector);\n },\n\n children(element, selector) {\n return [].concat(...element.children).filter(child => child.matches(selector));\n },\n\n parents(element, selector) {\n const parents = [];\n let ancestor = element.parentNode.closest(selector);\n\n while (ancestor) {\n parents.push(ancestor);\n ancestor = ancestor.parentNode.closest(selector);\n }\n\n return parents;\n },\n\n prev(element, selector) {\n let previous = element.previousElementSibling;\n\n while (previous) {\n if (previous.matches(selector)) {\n return [previous];\n }\n\n previous = previous.previousElementSibling;\n }\n\n return [];\n },\n\n // TODO: this is now unused; remove later along with prev()\n next(element, selector) {\n let next = element.nextElementSibling;\n\n while (next) {\n if (next.matches(selector)) {\n return [next];\n }\n\n next = next.nextElementSibling;\n }\n\n return [];\n },\n\n focusableChildren(element) {\n const focusables = ['a', 'button', 'input', 'textarea', 'select', 'details', '[tabindex]', '[contenteditable=\"true\"]'].map(selector => `${selector}:not([tabindex^=\"-\"])`).join(',');\n return this.find(focusables, element).filter(el => !isDisabled(el) && isVisible(el));\n }\n\n};\n\n/**\n * --------------------------------------------------------------------------\n * Bootstrap (v5.2.3): util/swipe.js\n * Licensed under MIT (https://github.com/twbs/bootstrap/blob/main/LICENSE)\n * --------------------------------------------------------------------------\n */\n/**\n * Constants\n */\n\nconst NAME$d = 'swipe';\nconst EVENT_KEY$9 = '.bs.swipe';\nconst EVENT_TOUCHSTART = `touchstart${EVENT_KEY$9}`;\nconst EVENT_TOUCHMOVE = `touchmove${EVENT_KEY$9}`;\nconst EVENT_TOUCHEND = `touchend${EVENT_KEY$9}`;\nconst EVENT_POINTERDOWN = `pointerdown${EVENT_KEY$9}`;\nconst EVENT_POINTERUP = `pointerup${EVENT_KEY$9}`;\nconst POINTER_TYPE_TOUCH = 'touch';\nconst POINTER_TYPE_PEN = 'pen';\nconst CLASS_NAME_POINTER_EVENT = 'pointer-event';\nconst SWIPE_THRESHOLD = 40;\nconst Default$c = {\n endCallback: null,\n leftCallback: null,\n rightCallback: null\n};\nconst DefaultType$c = {\n endCallback: '(function|null)',\n leftCallback: '(function|null)',\n rightCallback: '(function|null)'\n};\n/**\n * Class definition\n */\n\nclass Swipe extends Config {\n constructor(element, config) {\n super();\n this._element = element;\n\n if (!element || !Swipe.isSupported()) {\n return;\n }\n\n this._config = this._getConfig(config);\n this._deltaX = 0;\n this._supportPointerEvents = Boolean(window.PointerEvent);\n\n this._initEvents();\n } // Getters\n\n\n static get Default() {\n return Default$c;\n }\n\n static get DefaultType() {\n return DefaultType$c;\n }\n\n static get NAME() {\n return NAME$d;\n } // Public\n\n\n dispose() {\n EventHandler.off(this._element, EVENT_KEY$9);\n } // Private\n\n\n _start(event) {\n if (!this._supportPointerEvents) {\n this._deltaX = event.touches[0].clientX;\n return;\n }\n\n if (this._eventIsPointerPenTouch(event)) {\n this._deltaX = event.clientX;\n }\n }\n\n _end(event) {\n if (this._eventIsPointerPenTouch(event)) {\n this._deltaX = event.clientX - this._deltaX;\n }\n\n this._handleSwipe();\n\n execute(this._config.endCallback);\n }\n\n _move(event) {\n this._deltaX = event.touches && event.touches.length > 1 ? 0 : event.touches[0].clientX - this._deltaX;\n }\n\n _handleSwipe() {\n const absDeltaX = Math.abs(this._deltaX);\n\n if (absDeltaX <= SWIPE_THRESHOLD) {\n return;\n }\n\n const direction = absDeltaX / this._deltaX;\n this._deltaX = 0;\n\n if (!direction) {\n return;\n }\n\n execute(direction > 0 ? this._config.rightCallback : this._config.leftCallback);\n }\n\n _initEvents() {\n if (this._supportPointerEvents) {\n EventHandler.on(this._element, EVENT_POINTERDOWN, event => this._start(event));\n EventHandler.on(this._element, EVENT_POINTERUP, event => this._end(event));\n\n this._element.classList.add(CLASS_NAME_POINTER_EVENT);\n } else {\n EventHandler.on(this._element, EVENT_TOUCHSTART, event => this._start(event));\n EventHandler.on(this._element, EVENT_TOUCHMOVE, event => this._move(event));\n EventHandler.on(this._element, EVENT_TOUCHEND, event => this._end(event));\n }\n }\n\n _eventIsPointerPenTouch(event) {\n return this._supportPointerEvents && (event.pointerType === POINTER_TYPE_PEN || event.pointerType === POINTER_TYPE_TOUCH);\n } // Static\n\n\n static isSupported() {\n return 'ontouchstart' in document.documentElement || navigator.maxTouchPoints > 0;\n }\n\n}\n\n/**\n * --------------------------------------------------------------------------\n * Bootstrap (v5.2.3): carousel.js\n * Licensed under MIT (https://github.com/twbs/bootstrap/blob/main/LICENSE)\n * --------------------------------------------------------------------------\n */\n/**\n * Constants\n */\n\nconst NAME$c = 'carousel';\nconst DATA_KEY$8 = 'bs.carousel';\nconst EVENT_KEY$8 = `.${DATA_KEY$8}`;\nconst DATA_API_KEY$5 = '.data-api';\nconst ARROW_LEFT_KEY$1 = 'ArrowLeft';\nconst ARROW_RIGHT_KEY$1 = 'ArrowRight';\nconst TOUCHEVENT_COMPAT_WAIT = 500; // Time for mouse compat events to fire after touch\n\nconst ORDER_NEXT = 'next';\nconst ORDER_PREV = 'prev';\nconst DIRECTION_LEFT = 'left';\nconst DIRECTION_RIGHT = 'right';\nconst EVENT_SLIDE = `slide${EVENT_KEY$8}`;\nconst EVENT_SLID = `slid${EVENT_KEY$8}`;\nconst EVENT_KEYDOWN$1 = `keydown${EVENT_KEY$8}`;\nconst EVENT_MOUSEENTER$1 = `mouseenter${EVENT_KEY$8}`;\nconst EVENT_MOUSELEAVE$1 = `mouseleave${EVENT_KEY$8}`;\nconst EVENT_DRAG_START = `dragstart${EVENT_KEY$8}`;\nconst EVENT_LOAD_DATA_API$3 = `load${EVENT_KEY$8}${DATA_API_KEY$5}`;\nconst EVENT_CLICK_DATA_API$5 = `click${EVENT_KEY$8}${DATA_API_KEY$5}`;\nconst CLASS_NAME_CAROUSEL = 'carousel';\nconst CLASS_NAME_ACTIVE$2 = 'active';\nconst CLASS_NAME_SLIDE = 'slide';\nconst CLASS_NAME_END = 'carousel-item-end';\nconst CLASS_NAME_START = 'carousel-item-start';\nconst CLASS_NAME_NEXT = 'carousel-item-next';\nconst CLASS_NAME_PREV = 'carousel-item-prev';\nconst SELECTOR_ACTIVE = '.active';\nconst SELECTOR_ITEM = '.carousel-item';\nconst SELECTOR_ACTIVE_ITEM = SELECTOR_ACTIVE + SELECTOR_ITEM;\nconst SELECTOR_ITEM_IMG = '.carousel-item img';\nconst SELECTOR_INDICATORS = '.carousel-indicators';\nconst SELECTOR_DATA_SLIDE = '[data-bs-slide], [data-bs-slide-to]';\nconst SELECTOR_DATA_RIDE = '[data-bs-ride=\"carousel\"]';\nconst KEY_TO_DIRECTION = {\n [ARROW_LEFT_KEY$1]: DIRECTION_RIGHT,\n [ARROW_RIGHT_KEY$1]: DIRECTION_LEFT\n};\nconst Default$b = {\n interval: 5000,\n keyboard: true,\n pause: 'hover',\n ride: false,\n touch: true,\n wrap: true\n};\nconst DefaultType$b = {\n interval: '(number|boolean)',\n // TODO:v6 remove boolean support\n keyboard: 'boolean',\n pause: '(string|boolean)',\n ride: '(boolean|string)',\n touch: 'boolean',\n wrap: 'boolean'\n};\n/**\n * Class definition\n */\n\nclass Carousel extends BaseComponent {\n constructor(element, config) {\n super(element, config);\n this._interval = null;\n this._activeElement = null;\n this._isSliding = false;\n this.touchTimeout = null;\n this._swipeHelper = null;\n this._indicatorsElement = SelectorEngine.findOne(SELECTOR_INDICATORS, this._element);\n\n this._addEventListeners();\n\n if (this._config.ride === CLASS_NAME_CAROUSEL) {\n this.cycle();\n }\n } // Getters\n\n\n static get Default() {\n return Default$b;\n }\n\n static get DefaultType() {\n return DefaultType$b;\n }\n\n static get NAME() {\n return NAME$c;\n } // Public\n\n\n next() {\n this._slide(ORDER_NEXT);\n }\n\n nextWhenVisible() {\n // FIXME TODO use `document.visibilityState`\n // Don't call next when the page isn't visible\n // or the carousel or its parent isn't visible\n if (!document.hidden && isVisible(this._element)) {\n this.next();\n }\n }\n\n prev() {\n this._slide(ORDER_PREV);\n }\n\n pause() {\n if (this._isSliding) {\n triggerTransitionEnd(this._element);\n }\n\n this._clearInterval();\n }\n\n cycle() {\n this._clearInterval();\n\n this._updateInterval();\n\n this._interval = setInterval(() => this.nextWhenVisible(), this._config.interval);\n }\n\n _maybeEnableCycle() {\n if (!this._config.ride) {\n return;\n }\n\n if (this._isSliding) {\n EventHandler.one(this._element, EVENT_SLID, () => this.cycle());\n return;\n }\n\n this.cycle();\n }\n\n to(index) {\n const items = this._getItems();\n\n if (index > items.length - 1 || index < 0) {\n return;\n }\n\n if (this._isSliding) {\n EventHandler.one(this._element, EVENT_SLID, () => this.to(index));\n return;\n }\n\n const activeIndex = this._getItemIndex(this._getActive());\n\n if (activeIndex === index) {\n return;\n }\n\n const order = index > activeIndex ? ORDER_NEXT : ORDER_PREV;\n\n this._slide(order, items[index]);\n }\n\n dispose() {\n if (this._swipeHelper) {\n this._swipeHelper.dispose();\n }\n\n super.dispose();\n } // Private\n\n\n _configAfterMerge(config) {\n config.defaultInterval = config.interval;\n return config;\n }\n\n _addEventListeners() {\n if (this._config.keyboard) {\n EventHandler.on(this._element, EVENT_KEYDOWN$1, event => this._keydown(event));\n }\n\n if (this._config.pause === 'hover') {\n EventHandler.on(this._element, EVENT_MOUSEENTER$1, () => this.pause());\n EventHandler.on(this._element, EVENT_MOUSELEAVE$1, () => this._maybeEnableCycle());\n }\n\n if (this._config.touch && Swipe.isSupported()) {\n this._addTouchEventListeners();\n }\n }\n\n _addTouchEventListeners() {\n for (const img of SelectorEngine.find(SELECTOR_ITEM_IMG, this._element)) {\n EventHandler.on(img, EVENT_DRAG_START, event => event.preventDefault());\n }\n\n const endCallBack = () => {\n if (this._config.pause !== 'hover') {\n return;\n } // If it's a touch-enabled device, mouseenter/leave are fired as\n // part of the mouse compatibility events on first tap - the carousel\n // would stop cycling until user tapped out of it;\n // here, we listen for touchend, explicitly pause the carousel\n // (as if it's the second time we tap on it, mouseenter compat event\n // is NOT fired) and after a timeout (to allow for mouse compatibility\n // events to fire) we explicitly restart cycling\n\n\n this.pause();\n\n if (this.touchTimeout) {\n clearTimeout(this.touchTimeout);\n }\n\n this.touchTimeout = setTimeout(() => this._maybeEnableCycle(), TOUCHEVENT_COMPAT_WAIT + this._config.interval);\n };\n\n const swipeConfig = {\n leftCallback: () => this._slide(this._directionToOrder(DIRECTION_LEFT)),\n rightCallback: () => this._slide(this._directionToOrder(DIRECTION_RIGHT)),\n endCallback: endCallBack\n };\n this._swipeHelper = new Swipe(this._element, swipeConfig);\n }\n\n _keydown(event) {\n if (/input|textarea/i.test(event.target.tagName)) {\n return;\n }\n\n const direction = KEY_TO_DIRECTION[event.key];\n\n if (direction) {\n event.preventDefault();\n\n this._slide(this._directionToOrder(direction));\n }\n }\n\n _getItemIndex(element) {\n return this._getItems().indexOf(element);\n }\n\n _setActiveIndicatorElement(index) {\n if (!this._indicatorsElement) {\n return;\n }\n\n const activeIndicator = SelectorEngine.findOne(SELECTOR_ACTIVE, this._indicatorsElement);\n activeIndicator.classList.remove(CLASS_NAME_ACTIVE$2);\n activeIndicator.removeAttribute('aria-current');\n const newActiveIndicator = SelectorEngine.findOne(`[data-bs-slide-to=\"${index}\"]`, this._indicatorsElement);\n\n if (newActiveIndicator) {\n newActiveIndicator.classList.add(CLASS_NAME_ACTIVE$2);\n newActiveIndicator.setAttribute('aria-current', 'true');\n }\n }\n\n _updateInterval() {\n const element = this._activeElement || this._getActive();\n\n if (!element) {\n return;\n }\n\n const elementInterval = Number.parseInt(element.getAttribute('data-bs-interval'), 10);\n this._config.interval = elementInterval || this._config.defaultInterval;\n }\n\n _slide(order, element = null) {\n if (this._isSliding) {\n return;\n }\n\n const activeElement = this._getActive();\n\n const isNext = order === ORDER_NEXT;\n const nextElement = element || getNextActiveElement(this._getItems(), activeElement, isNext, this._config.wrap);\n\n if (nextElement === activeElement) {\n return;\n }\n\n const nextElementIndex = this._getItemIndex(nextElement);\n\n const triggerEvent = eventName => {\n return EventHandler.trigger(this._element, eventName, {\n relatedTarget: nextElement,\n direction: this._orderToDirection(order),\n from: this._getItemIndex(activeElement),\n to: nextElementIndex\n });\n };\n\n const slideEvent = triggerEvent(EVENT_SLIDE);\n\n if (slideEvent.defaultPrevented) {\n return;\n }\n\n if (!activeElement || !nextElement) {\n // Some weirdness is happening, so we bail\n // todo: change tests that use empty divs to avoid this check\n return;\n }\n\n const isCycling = Boolean(this._interval);\n this.pause();\n this._isSliding = true;\n\n this._setActiveIndicatorElement(nextElementIndex);\n\n this._activeElement = nextElement;\n const directionalClassName = isNext ? CLASS_NAME_START : CLASS_NAME_END;\n const orderClassName = isNext ? CLASS_NAME_NEXT : CLASS_NAME_PREV;\n nextElement.classList.add(orderClassName);\n reflow(nextElement);\n activeElement.classList.add(directionalClassName);\n nextElement.classList.add(directionalClassName);\n\n const completeCallBack = () => {\n nextElement.classList.remove(directionalClassName, orderClassName);\n nextElement.classList.add(CLASS_NAME_ACTIVE$2);\n activeElement.classList.remove(CLASS_NAME_ACTIVE$2, orderClassName, directionalClassName);\n this._isSliding = false;\n triggerEvent(EVENT_SLID);\n };\n\n this._queueCallback(completeCallBack, activeElement, this._isAnimated());\n\n if (isCycling) {\n this.cycle();\n }\n }\n\n _isAnimated() {\n return this._element.classList.contains(CLASS_NAME_SLIDE);\n }\n\n _getActive() {\n return SelectorEngine.findOne(SELECTOR_ACTIVE_ITEM, this._element);\n }\n\n _getItems() {\n return SelectorEngine.find(SELECTOR_ITEM, this._element);\n }\n\n _clearInterval() {\n if (this._interval) {\n clearInterval(this._interval);\n this._interval = null;\n }\n }\n\n _directionToOrder(direction) {\n if (isRTL()) {\n return direction === DIRECTION_LEFT ? ORDER_PREV : ORDER_NEXT;\n }\n\n return direction === DIRECTION_LEFT ? ORDER_NEXT : ORDER_PREV;\n }\n\n _orderToDirection(order) {\n if (isRTL()) {\n return order === ORDER_PREV ? DIRECTION_LEFT : DIRECTION_RIGHT;\n }\n\n return order === ORDER_PREV ? DIRECTION_RIGHT : DIRECTION_LEFT;\n } // Static\n\n\n static jQueryInterface(config) {\n return this.each(function () {\n const data = Carousel.getOrCreateInstance(this, config);\n\n if (typeof config === 'number') {\n data.to(config);\n return;\n }\n\n if (typeof config === 'string') {\n if (data[config] === undefined || config.startsWith('_') || config === 'constructor') {\n throw new TypeError(`No method named \"${config}\"`);\n }\n\n data[config]();\n }\n });\n }\n\n}\n/**\n * Data API implementation\n */\n\n\nEventHandler.on(document, EVENT_CLICK_DATA_API$5, SELECTOR_DATA_SLIDE, function (event) {\n const target = getElementFromSelector(this);\n\n if (!target || !target.classList.contains(CLASS_NAME_CAROUSEL)) {\n return;\n }\n\n event.preventDefault();\n const carousel = Carousel.getOrCreateInstance(target);\n const slideIndex = this.getAttribute('data-bs-slide-to');\n\n if (slideIndex) {\n carousel.to(slideIndex);\n\n carousel._maybeEnableCycle();\n\n return;\n }\n\n if (Manipulator.getDataAttribute(this, 'slide') === 'next') {\n carousel.next();\n\n carousel._maybeEnableCycle();\n\n return;\n }\n\n carousel.prev();\n\n carousel._maybeEnableCycle();\n});\nEventHandler.on(window, EVENT_LOAD_DATA_API$3, () => {\n const carousels = SelectorEngine.find(SELECTOR_DATA_RIDE);\n\n for (const carousel of carousels) {\n Carousel.getOrCreateInstance(carousel);\n }\n});\n/**\n * jQuery\n */\n\ndefineJQueryPlugin(Carousel);\n\n/**\n * --------------------------------------------------------------------------\n * Bootstrap (v5.2.3): collapse.js\n * Licensed under MIT (https://github.com/twbs/bootstrap/blob/main/LICENSE)\n * --------------------------------------------------------------------------\n */\n/**\n * Constants\n */\n\nconst NAME$b = 'collapse';\nconst DATA_KEY$7 = 'bs.collapse';\nconst EVENT_KEY$7 = `.${DATA_KEY$7}`;\nconst DATA_API_KEY$4 = '.data-api';\nconst EVENT_SHOW$6 = `show${EVENT_KEY$7}`;\nconst EVENT_SHOWN$6 = `shown${EVENT_KEY$7}`;\nconst EVENT_HIDE$6 = `hide${EVENT_KEY$7}`;\nconst EVENT_HIDDEN$6 = `hidden${EVENT_KEY$7}`;\nconst EVENT_CLICK_DATA_API$4 = `click${EVENT_KEY$7}${DATA_API_KEY$4}`;\nconst CLASS_NAME_SHOW$7 = 'show';\nconst CLASS_NAME_COLLAPSE = 'collapse';\nconst CLASS_NAME_COLLAPSING = 'collapsing';\nconst CLASS_NAME_COLLAPSED = 'collapsed';\nconst CLASS_NAME_DEEPER_CHILDREN = `:scope .${CLASS_NAME_COLLAPSE} .${CLASS_NAME_COLLAPSE}`;\nconst CLASS_NAME_HORIZONTAL = 'collapse-horizontal';\nconst WIDTH = 'width';\nconst HEIGHT = 'height';\nconst SELECTOR_ACTIVES = '.collapse.show, .collapse.collapsing';\nconst SELECTOR_DATA_TOGGLE$4 = '[data-bs-toggle=\"collapse\"]';\nconst Default$a = {\n parent: null,\n toggle: true\n};\nconst DefaultType$a = {\n parent: '(null|element)',\n toggle: 'boolean'\n};\n/**\n * Class definition\n */\n\nclass Collapse extends BaseComponent {\n constructor(element, config) {\n super(element, config);\n this._isTransitioning = false;\n this._triggerArray = [];\n const toggleList = SelectorEngine.find(SELECTOR_DATA_TOGGLE$4);\n\n for (const elem of toggleList) {\n const selector = getSelectorFromElement(elem);\n const filterElement = SelectorEngine.find(selector).filter(foundElement => foundElement === this._element);\n\n if (selector !== null && filterElement.length) {\n this._triggerArray.push(elem);\n }\n }\n\n this._initializeChildren();\n\n if (!this._config.parent) {\n this._addAriaAndCollapsedClass(this._triggerArray, this._isShown());\n }\n\n if (this._config.toggle) {\n this.toggle();\n }\n } // Getters\n\n\n static get Default() {\n return Default$a;\n }\n\n static get DefaultType() {\n return DefaultType$a;\n }\n\n static get NAME() {\n return NAME$b;\n } // Public\n\n\n toggle() {\n if (this._isShown()) {\n this.hide();\n } else {\n this.show();\n }\n }\n\n show() {\n if (this._isTransitioning || this._isShown()) {\n return;\n }\n\n let activeChildren = []; // find active children\n\n if (this._config.parent) {\n activeChildren = this._getFirstLevelChildren(SELECTOR_ACTIVES).filter(element => element !== this._element).map(element => Collapse.getOrCreateInstance(element, {\n toggle: false\n }));\n }\n\n if (activeChildren.length && activeChildren[0]._isTransitioning) {\n return;\n }\n\n const startEvent = EventHandler.trigger(this._element, EVENT_SHOW$6);\n\n if (startEvent.defaultPrevented) {\n return;\n }\n\n for (const activeInstance of activeChildren) {\n activeInstance.hide();\n }\n\n const dimension = this._getDimension();\n\n this._element.classList.remove(CLASS_NAME_COLLAPSE);\n\n this._element.classList.add(CLASS_NAME_COLLAPSING);\n\n this._element.style[dimension] = 0;\n\n this._addAriaAndCollapsedClass(this._triggerArray, true);\n\n this._isTransitioning = true;\n\n const complete = () => {\n this._isTransitioning = false;\n\n this._element.classList.remove(CLASS_NAME_COLLAPSING);\n\n this._element.classList.add(CLASS_NAME_COLLAPSE, CLASS_NAME_SHOW$7);\n\n this._element.style[dimension] = '';\n EventHandler.trigger(this._element, EVENT_SHOWN$6);\n };\n\n const capitalizedDimension = dimension[0].toUpperCase() + dimension.slice(1);\n const scrollSize = `scroll${capitalizedDimension}`;\n\n this._queueCallback(complete, this._element, true);\n\n this._element.style[dimension] = `${this._element[scrollSize]}px`;\n }\n\n hide() {\n if (this._isTransitioning || !this._isShown()) {\n return;\n }\n\n const startEvent = EventHandler.trigger(this._element, EVENT_HIDE$6);\n\n if (startEvent.defaultPrevented) {\n return;\n }\n\n const dimension = this._getDimension();\n\n this._element.style[dimension] = `${this._element.getBoundingClientRect()[dimension]}px`;\n reflow(this._element);\n\n this._element.classList.add(CLASS_NAME_COLLAPSING);\n\n this._element.classList.remove(CLASS_NAME_COLLAPSE, CLASS_NAME_SHOW$7);\n\n for (const trigger of this._triggerArray) {\n const element = getElementFromSelector(trigger);\n\n if (element && !this._isShown(element)) {\n this._addAriaAndCollapsedClass([trigger], false);\n }\n }\n\n this._isTransitioning = true;\n\n const complete = () => {\n this._isTransitioning = false;\n\n this._element.classList.remove(CLASS_NAME_COLLAPSING);\n\n this._element.classList.add(CLASS_NAME_COLLAPSE);\n\n EventHandler.trigger(this._element, EVENT_HIDDEN$6);\n };\n\n this._element.style[dimension] = '';\n\n this._queueCallback(complete, this._element, true);\n }\n\n _isShown(element = this._element) {\n return element.classList.contains(CLASS_NAME_SHOW$7);\n } // Private\n\n\n _configAfterMerge(config) {\n config.toggle = Boolean(config.toggle); // Coerce string values\n\n config.parent = getElement(config.parent);\n return config;\n }\n\n _getDimension() {\n return this._element.classList.contains(CLASS_NAME_HORIZONTAL) ? WIDTH : HEIGHT;\n }\n\n _initializeChildren() {\n if (!this._config.parent) {\n return;\n }\n\n const children = this._getFirstLevelChildren(SELECTOR_DATA_TOGGLE$4);\n\n for (const element of children) {\n const selected = getElementFromSelector(element);\n\n if (selected) {\n this._addAriaAndCollapsedClass([element], this._isShown(selected));\n }\n }\n }\n\n _getFirstLevelChildren(selector) {\n const children = SelectorEngine.find(CLASS_NAME_DEEPER_CHILDREN, this._config.parent); // remove children if greater depth\n\n return SelectorEngine.find(selector, this._config.parent).filter(element => !children.includes(element));\n }\n\n _addAriaAndCollapsedClass(triggerArray, isOpen) {\n if (!triggerArray.length) {\n return;\n }\n\n for (const element of triggerArray) {\n element.classList.toggle(CLASS_NAME_COLLAPSED, !isOpen);\n element.setAttribute('aria-expanded', isOpen);\n }\n } // Static\n\n\n static jQueryInterface(config) {\n const _config = {};\n\n if (typeof config === 'string' && /show|hide/.test(config)) {\n _config.toggle = false;\n }\n\n return this.each(function () {\n const data = Collapse.getOrCreateInstance(this, _config);\n\n if (typeof config === 'string') {\n if (typeof data[config] === 'undefined') {\n throw new TypeError(`No method named \"${config}\"`);\n }\n\n data[config]();\n }\n });\n }\n\n}\n/**\n * Data API implementation\n */\n\n\nEventHandler.on(document, EVENT_CLICK_DATA_API$4, SELECTOR_DATA_TOGGLE$4, function (event) {\n // preventDefault only for elements (which change the URL) not inside the collapsible element\n if (event.target.tagName === 'A' || event.delegateTarget && event.delegateTarget.tagName === 'A') {\n event.preventDefault();\n }\n\n const selector = getSelectorFromElement(this);\n const selectorElements = SelectorEngine.find(selector);\n\n for (const element of selectorElements) {\n Collapse.getOrCreateInstance(element, {\n toggle: false\n }).toggle();\n }\n});\n/**\n * jQuery\n */\n\ndefineJQueryPlugin(Collapse);\n\n/**\n * --------------------------------------------------------------------------\n * Bootstrap (v5.2.3): dropdown.js\n * Licensed under MIT (https://github.com/twbs/bootstrap/blob/main/LICENSE)\n * --------------------------------------------------------------------------\n */\n/**\n * Constants\n */\n\nconst NAME$a = 'dropdown';\nconst DATA_KEY$6 = 'bs.dropdown';\nconst EVENT_KEY$6 = `.${DATA_KEY$6}`;\nconst DATA_API_KEY$3 = '.data-api';\nconst ESCAPE_KEY$2 = 'Escape';\nconst TAB_KEY$1 = 'Tab';\nconst ARROW_UP_KEY$1 = 'ArrowUp';\nconst ARROW_DOWN_KEY$1 = 'ArrowDown';\nconst RIGHT_MOUSE_BUTTON = 2; // MouseEvent.button value for the secondary button, usually the right button\n\nconst EVENT_HIDE$5 = `hide${EVENT_KEY$6}`;\nconst EVENT_HIDDEN$5 = `hidden${EVENT_KEY$6}`;\nconst EVENT_SHOW$5 = `show${EVENT_KEY$6}`;\nconst EVENT_SHOWN$5 = `shown${EVENT_KEY$6}`;\nconst EVENT_CLICK_DATA_API$3 = `click${EVENT_KEY$6}${DATA_API_KEY$3}`;\nconst EVENT_KEYDOWN_DATA_API = `keydown${EVENT_KEY$6}${DATA_API_KEY$3}`;\nconst EVENT_KEYUP_DATA_API = `keyup${EVENT_KEY$6}${DATA_API_KEY$3}`;\nconst CLASS_NAME_SHOW$6 = 'show';\nconst CLASS_NAME_DROPUP = 'dropup';\nconst CLASS_NAME_DROPEND = 'dropend';\nconst CLASS_NAME_DROPSTART = 'dropstart';\nconst CLASS_NAME_DROPUP_CENTER = 'dropup-center';\nconst CLASS_NAME_DROPDOWN_CENTER = 'dropdown-center';\nconst SELECTOR_DATA_TOGGLE$3 = '[data-bs-toggle=\"dropdown\"]:not(.disabled):not(:disabled)';\nconst SELECTOR_DATA_TOGGLE_SHOWN = `${SELECTOR_DATA_TOGGLE$3}.${CLASS_NAME_SHOW$6}`;\nconst SELECTOR_MENU = '.dropdown-menu';\nconst SELECTOR_NAVBAR = '.navbar';\nconst SELECTOR_NAVBAR_NAV = '.navbar-nav';\nconst SELECTOR_VISIBLE_ITEMS = '.dropdown-menu .dropdown-item:not(.disabled):not(:disabled)';\nconst PLACEMENT_TOP = isRTL() ? 'top-end' : 'top-start';\nconst PLACEMENT_TOPEND = isRTL() ? 'top-start' : 'top-end';\nconst PLACEMENT_BOTTOM = isRTL() ? 'bottom-end' : 'bottom-start';\nconst PLACEMENT_BOTTOMEND = isRTL() ? 'bottom-start' : 'bottom-end';\nconst PLACEMENT_RIGHT = isRTL() ? 'left-start' : 'right-start';\nconst PLACEMENT_LEFT = isRTL() ? 'right-start' : 'left-start';\nconst PLACEMENT_TOPCENTER = 'top';\nconst PLACEMENT_BOTTOMCENTER = 'bottom';\nconst Default$9 = {\n autoClose: true,\n boundary: 'clippingParents',\n display: 'dynamic',\n offset: [0, 2],\n popperConfig: null,\n reference: 'toggle'\n};\nconst DefaultType$9 = {\n autoClose: '(boolean|string)',\n boundary: '(string|element)',\n display: 'string',\n offset: '(array|string|function)',\n popperConfig: '(null|object|function)',\n reference: '(string|element|object)'\n};\n/**\n * Class definition\n */\n\nclass Dropdown extends BaseComponent {\n constructor(element, config) {\n super(element, config);\n this._popper = null;\n this._parent = this._element.parentNode; // dropdown wrapper\n // todo: v6 revert #37011 & change markup https://getbootstrap.com/docs/5.2/forms/input-group/\n\n this._menu = SelectorEngine.next(this._element, SELECTOR_MENU)[0] || SelectorEngine.prev(this._element, SELECTOR_MENU)[0] || SelectorEngine.findOne(SELECTOR_MENU, this._parent);\n this._inNavbar = this._detectNavbar();\n } // Getters\n\n\n static get Default() {\n return Default$9;\n }\n\n static get DefaultType() {\n return DefaultType$9;\n }\n\n static get NAME() {\n return NAME$a;\n } // Public\n\n\n toggle() {\n return this._isShown() ? this.hide() : this.show();\n }\n\n show() {\n if (isDisabled(this._element) || this._isShown()) {\n return;\n }\n\n const relatedTarget = {\n relatedTarget: this._element\n };\n const showEvent = EventHandler.trigger(this._element, EVENT_SHOW$5, relatedTarget);\n\n if (showEvent.defaultPrevented) {\n return;\n }\n\n this._createPopper(); // If this is a touch-enabled device we add extra\n // empty mouseover listeners to the body's immediate children;\n // only needed because of broken event delegation on iOS\n // https://www.quirksmode.org/blog/archives/2014/02/mouse_event_bub.html\n\n\n if ('ontouchstart' in document.documentElement && !this._parent.closest(SELECTOR_NAVBAR_NAV)) {\n for (const element of [].concat(...document.body.children)) {\n EventHandler.on(element, 'mouseover', noop);\n }\n }\n\n this._element.focus();\n\n this._element.setAttribute('aria-expanded', true);\n\n this._menu.classList.add(CLASS_NAME_SHOW$6);\n\n this._element.classList.add(CLASS_NAME_SHOW$6);\n\n EventHandler.trigger(this._element, EVENT_SHOWN$5, relatedTarget);\n }\n\n hide() {\n if (isDisabled(this._element) || !this._isShown()) {\n return;\n }\n\n const relatedTarget = {\n relatedTarget: this._element\n };\n\n this._completeHide(relatedTarget);\n }\n\n dispose() {\n if (this._popper) {\n this._popper.destroy();\n }\n\n super.dispose();\n }\n\n update() {\n this._inNavbar = this._detectNavbar();\n\n if (this._popper) {\n this._popper.update();\n }\n } // Private\n\n\n _completeHide(relatedTarget) {\n const hideEvent = EventHandler.trigger(this._element, EVENT_HIDE$5, relatedTarget);\n\n if (hideEvent.defaultPrevented) {\n return;\n } // If this is a touch-enabled device we remove the extra\n // empty mouseover listeners we added for iOS support\n\n\n if ('ontouchstart' in document.documentElement) {\n for (const element of [].concat(...document.body.children)) {\n EventHandler.off(element, 'mouseover', noop);\n }\n }\n\n if (this._popper) {\n this._popper.destroy();\n }\n\n this._menu.classList.remove(CLASS_NAME_SHOW$6);\n\n this._element.classList.remove(CLASS_NAME_SHOW$6);\n\n this._element.setAttribute('aria-expanded', 'false');\n\n Manipulator.removeDataAttribute(this._menu, 'popper');\n EventHandler.trigger(this._element, EVENT_HIDDEN$5, relatedTarget);\n }\n\n _getConfig(config) {\n config = super._getConfig(config);\n\n if (typeof config.reference === 'object' && !isElement(config.reference) && typeof config.reference.getBoundingClientRect !== 'function') {\n // Popper virtual elements require a getBoundingClientRect method\n throw new TypeError(`${NAME$a.toUpperCase()}: Option \"reference\" provided type \"object\" without a required \"getBoundingClientRect\" method.`);\n }\n\n return config;\n }\n\n _createPopper() {\n if (typeof Popper === 'undefined') {\n throw new TypeError('Bootstrap\\'s dropdowns require Popper (https://popper.js.org)');\n }\n\n let referenceElement = this._element;\n\n if (this._config.reference === 'parent') {\n referenceElement = this._parent;\n } else if (isElement(this._config.reference)) {\n referenceElement = getElement(this._config.reference);\n } else if (typeof this._config.reference === 'object') {\n referenceElement = this._config.reference;\n }\n\n const popperConfig = this._getPopperConfig();\n\n this._popper = Popper.createPopper(referenceElement, this._menu, popperConfig);\n }\n\n _isShown() {\n return this._menu.classList.contains(CLASS_NAME_SHOW$6);\n }\n\n _getPlacement() {\n const parentDropdown = this._parent;\n\n if (parentDropdown.classList.contains(CLASS_NAME_DROPEND)) {\n return PLACEMENT_RIGHT;\n }\n\n if (parentDropdown.classList.contains(CLASS_NAME_DROPSTART)) {\n return PLACEMENT_LEFT;\n }\n\n if (parentDropdown.classList.contains(CLASS_NAME_DROPUP_CENTER)) {\n return PLACEMENT_TOPCENTER;\n }\n\n if (parentDropdown.classList.contains(CLASS_NAME_DROPDOWN_CENTER)) {\n return PLACEMENT_BOTTOMCENTER;\n } // We need to trim the value because custom properties can also include spaces\n\n\n const isEnd = getComputedStyle(this._menu).getPropertyValue('--bs-position').trim() === 'end';\n\n if (parentDropdown.classList.contains(CLASS_NAME_DROPUP)) {\n return isEnd ? PLACEMENT_TOPEND : PLACEMENT_TOP;\n }\n\n return isEnd ? PLACEMENT_BOTTOMEND : PLACEMENT_BOTTOM;\n }\n\n _detectNavbar() {\n return this._element.closest(SELECTOR_NAVBAR) !== null;\n }\n\n _getOffset() {\n const {\n offset\n } = this._config;\n\n if (typeof offset === 'string') {\n return offset.split(',').map(value => Number.parseInt(value, 10));\n }\n\n if (typeof offset === 'function') {\n return popperData => offset(popperData, this._element);\n }\n\n return offset;\n }\n\n _getPopperConfig() {\n const defaultBsPopperConfig = {\n placement: this._getPlacement(),\n modifiers: [{\n name: 'preventOverflow',\n options: {\n boundary: this._config.boundary\n }\n }, {\n name: 'offset',\n options: {\n offset: this._getOffset()\n }\n }]\n }; // Disable Popper if we have a static display or Dropdown is in Navbar\n\n if (this._inNavbar || this._config.display === 'static') {\n Manipulator.setDataAttribute(this._menu, 'popper', 'static'); // todo:v6 remove\n\n defaultBsPopperConfig.modifiers = [{\n name: 'applyStyles',\n enabled: false\n }];\n }\n\n return { ...defaultBsPopperConfig,\n ...(typeof this._config.popperConfig === 'function' ? this._config.popperConfig(defaultBsPopperConfig) : this._config.popperConfig)\n };\n }\n\n _selectMenuItem({\n key,\n target\n }) {\n const items = SelectorEngine.find(SELECTOR_VISIBLE_ITEMS, this._menu).filter(element => isVisible(element));\n\n if (!items.length) {\n return;\n } // if target isn't included in items (e.g. when expanding the dropdown)\n // allow cycling to get the last item in case key equals ARROW_UP_KEY\n\n\n getNextActiveElement(items, target, key === ARROW_DOWN_KEY$1, !items.includes(target)).focus();\n } // Static\n\n\n static jQueryInterface(config) {\n return this.each(function () {\n const data = Dropdown.getOrCreateInstance(this, config);\n\n if (typeof config !== 'string') {\n return;\n }\n\n if (typeof data[config] === 'undefined') {\n throw new TypeError(`No method named \"${config}\"`);\n }\n\n data[config]();\n });\n }\n\n static clearMenus(event) {\n if (event.button === RIGHT_MOUSE_BUTTON || event.type === 'keyup' && event.key !== TAB_KEY$1) {\n return;\n }\n\n const openToggles = SelectorEngine.find(SELECTOR_DATA_TOGGLE_SHOWN);\n\n for (const toggle of openToggles) {\n const context = Dropdown.getInstance(toggle);\n\n if (!context || context._config.autoClose === false) {\n continue;\n }\n\n const composedPath = event.composedPath();\n const isMenuTarget = composedPath.includes(context._menu);\n\n if (composedPath.includes(context._element) || context._config.autoClose === 'inside' && !isMenuTarget || context._config.autoClose === 'outside' && isMenuTarget) {\n continue;\n } // Tab navigation through the dropdown menu or events from contained inputs shouldn't close the menu\n\n\n if (context._menu.contains(event.target) && (event.type === 'keyup' && event.key === TAB_KEY$1 || /input|select|option|textarea|form/i.test(event.target.tagName))) {\n continue;\n }\n\n const relatedTarget = {\n relatedTarget: context._element\n };\n\n if (event.type === 'click') {\n relatedTarget.clickEvent = event;\n }\n\n context._completeHide(relatedTarget);\n }\n }\n\n static dataApiKeydownHandler(event) {\n // If not an UP | DOWN | ESCAPE key => not a dropdown command\n // If input/textarea && if key is other than ESCAPE => not a dropdown command\n const isInput = /input|textarea/i.test(event.target.tagName);\n const isEscapeEvent = event.key === ESCAPE_KEY$2;\n const isUpOrDownEvent = [ARROW_UP_KEY$1, ARROW_DOWN_KEY$1].includes(event.key);\n\n if (!isUpOrDownEvent && !isEscapeEvent) {\n return;\n }\n\n if (isInput && !isEscapeEvent) {\n return;\n }\n\n event.preventDefault(); // todo: v6 revert #37011 & change markup https://getbootstrap.com/docs/5.2/forms/input-group/\n\n const getToggleButton = this.matches(SELECTOR_DATA_TOGGLE$3) ? this : SelectorEngine.prev(this, SELECTOR_DATA_TOGGLE$3)[0] || SelectorEngine.next(this, SELECTOR_DATA_TOGGLE$3)[0] || SelectorEngine.findOne(SELECTOR_DATA_TOGGLE$3, event.delegateTarget.parentNode);\n const instance = Dropdown.getOrCreateInstance(getToggleButton);\n\n if (isUpOrDownEvent) {\n event.stopPropagation();\n instance.show();\n\n instance._selectMenuItem(event);\n\n return;\n }\n\n if (instance._isShown()) {\n // else is escape and we check if it is shown\n event.stopPropagation();\n instance.hide();\n getToggleButton.focus();\n }\n }\n\n}\n/**\n * Data API implementation\n */\n\n\nEventHandler.on(document, EVENT_KEYDOWN_DATA_API, SELECTOR_DATA_TOGGLE$3, Dropdown.dataApiKeydownHandler);\nEventHandler.on(document, EVENT_KEYDOWN_DATA_API, SELECTOR_MENU, Dropdown.dataApiKeydownHandler);\nEventHandler.on(document, EVENT_CLICK_DATA_API$3, Dropdown.clearMenus);\nEventHandler.on(document, EVENT_KEYUP_DATA_API, Dropdown.clearMenus);\nEventHandler.on(document, EVENT_CLICK_DATA_API$3, SELECTOR_DATA_TOGGLE$3, function (event) {\n event.preventDefault();\n Dropdown.getOrCreateInstance(this).toggle();\n});\n/**\n * jQuery\n */\n\ndefineJQueryPlugin(Dropdown);\n\n/**\n * --------------------------------------------------------------------------\n * Bootstrap (v5.2.3): util/scrollBar.js\n * Licensed under MIT (https://github.com/twbs/bootstrap/blob/main/LICENSE)\n * --------------------------------------------------------------------------\n */\n/**\n * Constants\n */\n\nconst SELECTOR_FIXED_CONTENT = '.fixed-top, .fixed-bottom, .is-fixed, .sticky-top';\nconst SELECTOR_STICKY_CONTENT = '.sticky-top';\nconst PROPERTY_PADDING = 'padding-right';\nconst PROPERTY_MARGIN = 'margin-right';\n/**\n * Class definition\n */\n\nclass ScrollBarHelper {\n constructor() {\n this._element = document.body;\n } // Public\n\n\n getWidth() {\n // https://developer.mozilla.org/en-US/docs/Web/API/Window/innerWidth#usage_notes\n const documentWidth = document.documentElement.clientWidth;\n return Math.abs(window.innerWidth - documentWidth);\n }\n\n hide() {\n const width = this.getWidth();\n\n this._disableOverFlow(); // give padding to element to balance the hidden scrollbar width\n\n\n this._setElementAttributes(this._element, PROPERTY_PADDING, calculatedValue => calculatedValue + width); // trick: We adjust positive paddingRight and negative marginRight to sticky-top elements to keep showing fullwidth\n\n\n this._setElementAttributes(SELECTOR_FIXED_CONTENT, PROPERTY_PADDING, calculatedValue => calculatedValue + width);\n\n this._setElementAttributes(SELECTOR_STICKY_CONTENT, PROPERTY_MARGIN, calculatedValue => calculatedValue - width);\n }\n\n reset() {\n this._resetElementAttributes(this._element, 'overflow');\n\n this._resetElementAttributes(this._element, PROPERTY_PADDING);\n\n this._resetElementAttributes(SELECTOR_FIXED_CONTENT, PROPERTY_PADDING);\n\n this._resetElementAttributes(SELECTOR_STICKY_CONTENT, PROPERTY_MARGIN);\n }\n\n isOverflowing() {\n return this.getWidth() > 0;\n } // Private\n\n\n _disableOverFlow() {\n this._saveInitialAttribute(this._element, 'overflow');\n\n this._element.style.overflow = 'hidden';\n }\n\n _setElementAttributes(selector, styleProperty, callback) {\n const scrollbarWidth = this.getWidth();\n\n const manipulationCallBack = element => {\n if (element !== this._element && window.innerWidth > element.clientWidth + scrollbarWidth) {\n return;\n }\n\n this._saveInitialAttribute(element, styleProperty);\n\n const calculatedValue = window.getComputedStyle(element).getPropertyValue(styleProperty);\n element.style.setProperty(styleProperty, `${callback(Number.parseFloat(calculatedValue))}px`);\n };\n\n this._applyManipulationCallback(selector, manipulationCallBack);\n }\n\n _saveInitialAttribute(element, styleProperty) {\n const actualValue = element.style.getPropertyValue(styleProperty);\n\n if (actualValue) {\n Manipulator.setDataAttribute(element, styleProperty, actualValue);\n }\n }\n\n _resetElementAttributes(selector, styleProperty) {\n const manipulationCallBack = element => {\n const value = Manipulator.getDataAttribute(element, styleProperty); // We only want to remove the property if the value is `null`; the value can also be zero\n\n if (value === null) {\n element.style.removeProperty(styleProperty);\n return;\n }\n\n Manipulator.removeDataAttribute(element, styleProperty);\n element.style.setProperty(styleProperty, value);\n };\n\n this._applyManipulationCallback(selector, manipulationCallBack);\n }\n\n _applyManipulationCallback(selector, callBack) {\n if (isElement(selector)) {\n callBack(selector);\n return;\n }\n\n for (const sel of SelectorEngine.find(selector, this._element)) {\n callBack(sel);\n }\n }\n\n}\n\n/**\n * --------------------------------------------------------------------------\n * Bootstrap (v5.2.3): util/backdrop.js\n * Licensed under MIT (https://github.com/twbs/bootstrap/blob/main/LICENSE)\n * --------------------------------------------------------------------------\n */\n/**\n * Constants\n */\n\nconst NAME$9 = 'backdrop';\nconst CLASS_NAME_FADE$4 = 'fade';\nconst CLASS_NAME_SHOW$5 = 'show';\nconst EVENT_MOUSEDOWN = `mousedown.bs.${NAME$9}`;\nconst Default$8 = {\n className: 'modal-backdrop',\n clickCallback: null,\n isAnimated: false,\n isVisible: true,\n // if false, we use the backdrop helper without adding any element to the dom\n rootElement: 'body' // give the choice to place backdrop under different elements\n\n};\nconst DefaultType$8 = {\n className: 'string',\n clickCallback: '(function|null)',\n isAnimated: 'boolean',\n isVisible: 'boolean',\n rootElement: '(element|string)'\n};\n/**\n * Class definition\n */\n\nclass Backdrop extends Config {\n constructor(config) {\n super();\n this._config = this._getConfig(config);\n this._isAppended = false;\n this._element = null;\n } // Getters\n\n\n static get Default() {\n return Default$8;\n }\n\n static get DefaultType() {\n return DefaultType$8;\n }\n\n static get NAME() {\n return NAME$9;\n } // Public\n\n\n show(callback) {\n if (!this._config.isVisible) {\n execute(callback);\n return;\n }\n\n this._append();\n\n const element = this._getElement();\n\n if (this._config.isAnimated) {\n reflow(element);\n }\n\n element.classList.add(CLASS_NAME_SHOW$5);\n\n this._emulateAnimation(() => {\n execute(callback);\n });\n }\n\n hide(callback) {\n if (!this._config.isVisible) {\n execute(callback);\n return;\n }\n\n this._getElement().classList.remove(CLASS_NAME_SHOW$5);\n\n this._emulateAnimation(() => {\n this.dispose();\n execute(callback);\n });\n }\n\n dispose() {\n if (!this._isAppended) {\n return;\n }\n\n EventHandler.off(this._element, EVENT_MOUSEDOWN);\n\n this._element.remove();\n\n this._isAppended = false;\n } // Private\n\n\n _getElement() {\n if (!this._element) {\n const backdrop = document.createElement('div');\n backdrop.className = this._config.className;\n\n if (this._config.isAnimated) {\n backdrop.classList.add(CLASS_NAME_FADE$4);\n }\n\n this._element = backdrop;\n }\n\n return this._element;\n }\n\n _configAfterMerge(config) {\n // use getElement() with the default \"body\" to get a fresh Element on each instantiation\n config.rootElement = getElement(config.rootElement);\n return config;\n }\n\n _append() {\n if (this._isAppended) {\n return;\n }\n\n const element = this._getElement();\n\n this._config.rootElement.append(element);\n\n EventHandler.on(element, EVENT_MOUSEDOWN, () => {\n execute(this._config.clickCallback);\n });\n this._isAppended = true;\n }\n\n _emulateAnimation(callback) {\n executeAfterTransition(callback, this._getElement(), this._config.isAnimated);\n }\n\n}\n\n/**\n * --------------------------------------------------------------------------\n * Bootstrap (v5.2.3): util/focustrap.js\n * Licensed under MIT (https://github.com/twbs/bootstrap/blob/main/LICENSE)\n * --------------------------------------------------------------------------\n */\n/**\n * Constants\n */\n\nconst NAME$8 = 'focustrap';\nconst DATA_KEY$5 = 'bs.focustrap';\nconst EVENT_KEY$5 = `.${DATA_KEY$5}`;\nconst EVENT_FOCUSIN$2 = `focusin${EVENT_KEY$5}`;\nconst EVENT_KEYDOWN_TAB = `keydown.tab${EVENT_KEY$5}`;\nconst TAB_KEY = 'Tab';\nconst TAB_NAV_FORWARD = 'forward';\nconst TAB_NAV_BACKWARD = 'backward';\nconst Default$7 = {\n autofocus: true,\n trapElement: null // The element to trap focus inside of\n\n};\nconst DefaultType$7 = {\n autofocus: 'boolean',\n trapElement: 'element'\n};\n/**\n * Class definition\n */\n\nclass FocusTrap extends Config {\n constructor(config) {\n super();\n this._config = this._getConfig(config);\n this._isActive = false;\n this._lastTabNavDirection = null;\n } // Getters\n\n\n static get Default() {\n return Default$7;\n }\n\n static get DefaultType() {\n return DefaultType$7;\n }\n\n static get NAME() {\n return NAME$8;\n } // Public\n\n\n activate() {\n if (this._isActive) {\n return;\n }\n\n if (this._config.autofocus) {\n this._config.trapElement.focus();\n }\n\n EventHandler.off(document, EVENT_KEY$5); // guard against infinite focus loop\n\n EventHandler.on(document, EVENT_FOCUSIN$2, event => this._handleFocusin(event));\n EventHandler.on(document, EVENT_KEYDOWN_TAB, event => this._handleKeydown(event));\n this._isActive = true;\n }\n\n deactivate() {\n if (!this._isActive) {\n return;\n }\n\n this._isActive = false;\n EventHandler.off(document, EVENT_KEY$5);\n } // Private\n\n\n _handleFocusin(event) {\n const {\n trapElement\n } = this._config;\n\n if (event.target === document || event.target === trapElement || trapElement.contains(event.target)) {\n return;\n }\n\n const elements = SelectorEngine.focusableChildren(trapElement);\n\n if (elements.length === 0) {\n trapElement.focus();\n } else if (this._lastTabNavDirection === TAB_NAV_BACKWARD) {\n elements[elements.length - 1].focus();\n } else {\n elements[0].focus();\n }\n }\n\n _handleKeydown(event) {\n if (event.key !== TAB_KEY) {\n return;\n }\n\n this._lastTabNavDirection = event.shiftKey ? TAB_NAV_BACKWARD : TAB_NAV_FORWARD;\n }\n\n}\n\n/**\n * --------------------------------------------------------------------------\n * Bootstrap (v5.2.3): modal.js\n * Licensed under MIT (https://github.com/twbs/bootstrap/blob/main/LICENSE)\n * --------------------------------------------------------------------------\n */\n/**\n * Constants\n */\n\nconst NAME$7 = 'modal';\nconst DATA_KEY$4 = 'bs.modal';\nconst EVENT_KEY$4 = `.${DATA_KEY$4}`;\nconst DATA_API_KEY$2 = '.data-api';\nconst ESCAPE_KEY$1 = 'Escape';\nconst EVENT_HIDE$4 = `hide${EVENT_KEY$4}`;\nconst EVENT_HIDE_PREVENTED$1 = `hidePrevented${EVENT_KEY$4}`;\nconst EVENT_HIDDEN$4 = `hidden${EVENT_KEY$4}`;\nconst EVENT_SHOW$4 = `show${EVENT_KEY$4}`;\nconst EVENT_SHOWN$4 = `shown${EVENT_KEY$4}`;\nconst EVENT_RESIZE$1 = `resize${EVENT_KEY$4}`;\nconst EVENT_CLICK_DISMISS = `click.dismiss${EVENT_KEY$4}`;\nconst EVENT_MOUSEDOWN_DISMISS = `mousedown.dismiss${EVENT_KEY$4}`;\nconst EVENT_KEYDOWN_DISMISS$1 = `keydown.dismiss${EVENT_KEY$4}`;\nconst EVENT_CLICK_DATA_API$2 = `click${EVENT_KEY$4}${DATA_API_KEY$2}`;\nconst CLASS_NAME_OPEN = 'modal-open';\nconst CLASS_NAME_FADE$3 = 'fade';\nconst CLASS_NAME_SHOW$4 = 'show';\nconst CLASS_NAME_STATIC = 'modal-static';\nconst OPEN_SELECTOR$1 = '.modal.show';\nconst SELECTOR_DIALOG = '.modal-dialog';\nconst SELECTOR_MODAL_BODY = '.modal-body';\nconst SELECTOR_DATA_TOGGLE$2 = '[data-bs-toggle=\"modal\"]';\nconst Default$6 = {\n backdrop: true,\n focus: true,\n keyboard: true\n};\nconst DefaultType$6 = {\n backdrop: '(boolean|string)',\n focus: 'boolean',\n keyboard: 'boolean'\n};\n/**\n * Class definition\n */\n\nclass Modal extends BaseComponent {\n constructor(element, config) {\n super(element, config);\n this._dialog = SelectorEngine.findOne(SELECTOR_DIALOG, this._element);\n this._backdrop = this._initializeBackDrop();\n this._focustrap = this._initializeFocusTrap();\n this._isShown = false;\n this._isTransitioning = false;\n this._scrollBar = new ScrollBarHelper();\n\n this._addEventListeners();\n } // Getters\n\n\n static get Default() {\n return Default$6;\n }\n\n static get DefaultType() {\n return DefaultType$6;\n }\n\n static get NAME() {\n return NAME$7;\n } // Public\n\n\n toggle(relatedTarget) {\n return this._isShown ? this.hide() : this.show(relatedTarget);\n }\n\n show(relatedTarget) {\n if (this._isShown || this._isTransitioning) {\n return;\n }\n\n const showEvent = EventHandler.trigger(this._element, EVENT_SHOW$4, {\n relatedTarget\n });\n\n if (showEvent.defaultPrevented) {\n return;\n }\n\n this._isShown = true;\n this._isTransitioning = true;\n\n this._scrollBar.hide();\n\n document.body.classList.add(CLASS_NAME_OPEN);\n\n this._adjustDialog();\n\n this._backdrop.show(() => this._showElement(relatedTarget));\n }\n\n hide() {\n if (!this._isShown || this._isTransitioning) {\n return;\n }\n\n const hideEvent = EventHandler.trigger(this._element, EVENT_HIDE$4);\n\n if (hideEvent.defaultPrevented) {\n return;\n }\n\n this._isShown = false;\n this._isTransitioning = true;\n\n this._focustrap.deactivate();\n\n this._element.classList.remove(CLASS_NAME_SHOW$4);\n\n this._queueCallback(() => this._hideModal(), this._element, this._isAnimated());\n }\n\n dispose() {\n for (const htmlElement of [window, this._dialog]) {\n EventHandler.off(htmlElement, EVENT_KEY$4);\n }\n\n this._backdrop.dispose();\n\n this._focustrap.deactivate();\n\n super.dispose();\n }\n\n handleUpdate() {\n this._adjustDialog();\n } // Private\n\n\n _initializeBackDrop() {\n return new Backdrop({\n isVisible: Boolean(this._config.backdrop),\n // 'static' option will be translated to true, and booleans will keep their value,\n isAnimated: this._isAnimated()\n });\n }\n\n _initializeFocusTrap() {\n return new FocusTrap({\n trapElement: this._element\n });\n }\n\n _showElement(relatedTarget) {\n // try to append dynamic modal\n if (!document.body.contains(this._element)) {\n document.body.append(this._element);\n }\n\n this._element.style.display = 'block';\n\n this._element.removeAttribute('aria-hidden');\n\n this._element.setAttribute('aria-modal', true);\n\n this._element.setAttribute('role', 'dialog');\n\n this._element.scrollTop = 0;\n const modalBody = SelectorEngine.findOne(SELECTOR_MODAL_BODY, this._dialog);\n\n if (modalBody) {\n modalBody.scrollTop = 0;\n }\n\n reflow(this._element);\n\n this._element.classList.add(CLASS_NAME_SHOW$4);\n\n const transitionComplete = () => {\n if (this._config.focus) {\n this._focustrap.activate();\n }\n\n this._isTransitioning = false;\n EventHandler.trigger(this._element, EVENT_SHOWN$4, {\n relatedTarget\n });\n };\n\n this._queueCallback(transitionComplete, this._dialog, this._isAnimated());\n }\n\n _addEventListeners() {\n EventHandler.on(this._element, EVENT_KEYDOWN_DISMISS$1, event => {\n if (event.key !== ESCAPE_KEY$1) {\n return;\n }\n\n if (this._config.keyboard) {\n event.preventDefault();\n this.hide();\n return;\n }\n\n this._triggerBackdropTransition();\n });\n EventHandler.on(window, EVENT_RESIZE$1, () => {\n if (this._isShown && !this._isTransitioning) {\n this._adjustDialog();\n }\n });\n EventHandler.on(this._element, EVENT_MOUSEDOWN_DISMISS, event => {\n // a bad trick to segregate clicks that may start inside dialog but end outside, and avoid listen to scrollbar clicks\n EventHandler.one(this._element, EVENT_CLICK_DISMISS, event2 => {\n if (this._element !== event.target || this._element !== event2.target) {\n return;\n }\n\n if (this._config.backdrop === 'static') {\n this._triggerBackdropTransition();\n\n return;\n }\n\n if (this._config.backdrop) {\n this.hide();\n }\n });\n });\n }\n\n _hideModal() {\n this._element.style.display = 'none';\n\n this._element.setAttribute('aria-hidden', true);\n\n this._element.removeAttribute('aria-modal');\n\n this._element.removeAttribute('role');\n\n this._isTransitioning = false;\n\n this._backdrop.hide(() => {\n document.body.classList.remove(CLASS_NAME_OPEN);\n\n this._resetAdjustments();\n\n this._scrollBar.reset();\n\n EventHandler.trigger(this._element, EVENT_HIDDEN$4);\n });\n }\n\n _isAnimated() {\n return this._element.classList.contains(CLASS_NAME_FADE$3);\n }\n\n _triggerBackdropTransition() {\n const hideEvent = EventHandler.trigger(this._element, EVENT_HIDE_PREVENTED$1);\n\n if (hideEvent.defaultPrevented) {\n return;\n }\n\n const isModalOverflowing = this._element.scrollHeight > document.documentElement.clientHeight;\n const initialOverflowY = this._element.style.overflowY; // return if the following background transition hasn't yet completed\n\n if (initialOverflowY === 'hidden' || this._element.classList.contains(CLASS_NAME_STATIC)) {\n return;\n }\n\n if (!isModalOverflowing) {\n this._element.style.overflowY = 'hidden';\n }\n\n this._element.classList.add(CLASS_NAME_STATIC);\n\n this._queueCallback(() => {\n this._element.classList.remove(CLASS_NAME_STATIC);\n\n this._queueCallback(() => {\n this._element.style.overflowY = initialOverflowY;\n }, this._dialog);\n }, this._dialog);\n\n this._element.focus();\n }\n /**\n * The following methods are used to handle overflowing modals\n */\n\n\n _adjustDialog() {\n const isModalOverflowing = this._element.scrollHeight > document.documentElement.clientHeight;\n\n const scrollbarWidth = this._scrollBar.getWidth();\n\n const isBodyOverflowing = scrollbarWidth > 0;\n\n if (isBodyOverflowing && !isModalOverflowing) {\n const property = isRTL() ? 'paddingLeft' : 'paddingRight';\n this._element.style[property] = `${scrollbarWidth}px`;\n }\n\n if (!isBodyOverflowing && isModalOverflowing) {\n const property = isRTL() ? 'paddingRight' : 'paddingLeft';\n this._element.style[property] = `${scrollbarWidth}px`;\n }\n }\n\n _resetAdjustments() {\n this._element.style.paddingLeft = '';\n this._element.style.paddingRight = '';\n } // Static\n\n\n static jQueryInterface(config, relatedTarget) {\n return this.each(function () {\n const data = Modal.getOrCreateInstance(this, config);\n\n if (typeof config !== 'string') {\n return;\n }\n\n if (typeof data[config] === 'undefined') {\n throw new TypeError(`No method named \"${config}\"`);\n }\n\n data[config](relatedTarget);\n });\n }\n\n}\n/**\n * Data API implementation\n */\n\n\nEventHandler.on(document, EVENT_CLICK_DATA_API$2, SELECTOR_DATA_TOGGLE$2, function (event) {\n const target = getElementFromSelector(this);\n\n if (['A', 'AREA'].includes(this.tagName)) {\n event.preventDefault();\n }\n\n EventHandler.one(target, EVENT_SHOW$4, showEvent => {\n if (showEvent.defaultPrevented) {\n // only register focus restorer if modal will actually get shown\n return;\n }\n\n EventHandler.one(target, EVENT_HIDDEN$4, () => {\n if (isVisible(this)) {\n this.focus();\n }\n });\n }); // avoid conflict when clicking modal toggler while another one is open\n\n const alreadyOpen = SelectorEngine.findOne(OPEN_SELECTOR$1);\n\n if (alreadyOpen) {\n Modal.getInstance(alreadyOpen).hide();\n }\n\n const data = Modal.getOrCreateInstance(target);\n data.toggle(this);\n});\nenableDismissTrigger(Modal);\n/**\n * jQuery\n */\n\ndefineJQueryPlugin(Modal);\n\n/**\n * --------------------------------------------------------------------------\n * Bootstrap (v5.2.3): offcanvas.js\n * Licensed under MIT (https://github.com/twbs/bootstrap/blob/main/LICENSE)\n * --------------------------------------------------------------------------\n */\n/**\n * Constants\n */\n\nconst NAME$6 = 'offcanvas';\nconst DATA_KEY$3 = 'bs.offcanvas';\nconst EVENT_KEY$3 = `.${DATA_KEY$3}`;\nconst DATA_API_KEY$1 = '.data-api';\nconst EVENT_LOAD_DATA_API$2 = `load${EVENT_KEY$3}${DATA_API_KEY$1}`;\nconst ESCAPE_KEY = 'Escape';\nconst CLASS_NAME_SHOW$3 = 'show';\nconst CLASS_NAME_SHOWING$1 = 'showing';\nconst CLASS_NAME_HIDING = 'hiding';\nconst CLASS_NAME_BACKDROP = 'offcanvas-backdrop';\nconst OPEN_SELECTOR = '.offcanvas.show';\nconst EVENT_SHOW$3 = `show${EVENT_KEY$3}`;\nconst EVENT_SHOWN$3 = `shown${EVENT_KEY$3}`;\nconst EVENT_HIDE$3 = `hide${EVENT_KEY$3}`;\nconst EVENT_HIDE_PREVENTED = `hidePrevented${EVENT_KEY$3}`;\nconst EVENT_HIDDEN$3 = `hidden${EVENT_KEY$3}`;\nconst EVENT_RESIZE = `resize${EVENT_KEY$3}`;\nconst EVENT_CLICK_DATA_API$1 = `click${EVENT_KEY$3}${DATA_API_KEY$1}`;\nconst EVENT_KEYDOWN_DISMISS = `keydown.dismiss${EVENT_KEY$3}`;\nconst SELECTOR_DATA_TOGGLE$1 = '[data-bs-toggle=\"offcanvas\"]';\nconst Default$5 = {\n backdrop: true,\n keyboard: true,\n scroll: false\n};\nconst DefaultType$5 = {\n backdrop: '(boolean|string)',\n keyboard: 'boolean',\n scroll: 'boolean'\n};\n/**\n * Class definition\n */\n\nclass Offcanvas extends BaseComponent {\n constructor(element, config) {\n super(element, config);\n this._isShown = false;\n this._backdrop = this._initializeBackDrop();\n this._focustrap = this._initializeFocusTrap();\n\n this._addEventListeners();\n } // Getters\n\n\n static get Default() {\n return Default$5;\n }\n\n static get DefaultType() {\n return DefaultType$5;\n }\n\n static get NAME() {\n return NAME$6;\n } // Public\n\n\n toggle(relatedTarget) {\n return this._isShown ? this.hide() : this.show(relatedTarget);\n }\n\n show(relatedTarget) {\n if (this._isShown) {\n return;\n }\n\n const showEvent = EventHandler.trigger(this._element, EVENT_SHOW$3, {\n relatedTarget\n });\n\n if (showEvent.defaultPrevented) {\n return;\n }\n\n this._isShown = true;\n\n this._backdrop.show();\n\n if (!this._config.scroll) {\n new ScrollBarHelper().hide();\n }\n\n this._element.setAttribute('aria-modal', true);\n\n this._element.setAttribute('role', 'dialog');\n\n this._element.classList.add(CLASS_NAME_SHOWING$1);\n\n const completeCallBack = () => {\n if (!this._config.scroll || this._config.backdrop) {\n this._focustrap.activate();\n }\n\n this._element.classList.add(CLASS_NAME_SHOW$3);\n\n this._element.classList.remove(CLASS_NAME_SHOWING$1);\n\n EventHandler.trigger(this._element, EVENT_SHOWN$3, {\n relatedTarget\n });\n };\n\n this._queueCallback(completeCallBack, this._element, true);\n }\n\n hide() {\n if (!this._isShown) {\n return;\n }\n\n const hideEvent = EventHandler.trigger(this._element, EVENT_HIDE$3);\n\n if (hideEvent.defaultPrevented) {\n return;\n }\n\n this._focustrap.deactivate();\n\n this._element.blur();\n\n this._isShown = false;\n\n this._element.classList.add(CLASS_NAME_HIDING);\n\n this._backdrop.hide();\n\n const completeCallback = () => {\n this._element.classList.remove(CLASS_NAME_SHOW$3, CLASS_NAME_HIDING);\n\n this._element.removeAttribute('aria-modal');\n\n this._element.removeAttribute('role');\n\n if (!this._config.scroll) {\n new ScrollBarHelper().reset();\n }\n\n EventHandler.trigger(this._element, EVENT_HIDDEN$3);\n };\n\n this._queueCallback(completeCallback, this._element, true);\n }\n\n dispose() {\n this._backdrop.dispose();\n\n this._focustrap.deactivate();\n\n super.dispose();\n } // Private\n\n\n _initializeBackDrop() {\n const clickCallback = () => {\n if (this._config.backdrop === 'static') {\n EventHandler.trigger(this._element, EVENT_HIDE_PREVENTED);\n return;\n }\n\n this.hide();\n }; // 'static' option will be translated to true, and booleans will keep their value\n\n\n const isVisible = Boolean(this._config.backdrop);\n return new Backdrop({\n className: CLASS_NAME_BACKDROP,\n isVisible,\n isAnimated: true,\n rootElement: this._element.parentNode,\n clickCallback: isVisible ? clickCallback : null\n });\n }\n\n _initializeFocusTrap() {\n return new FocusTrap({\n trapElement: this._element\n });\n }\n\n _addEventListeners() {\n EventHandler.on(this._element, EVENT_KEYDOWN_DISMISS, event => {\n if (event.key !== ESCAPE_KEY) {\n return;\n }\n\n if (!this._config.keyboard) {\n EventHandler.trigger(this._element, EVENT_HIDE_PREVENTED);\n return;\n }\n\n this.hide();\n });\n } // Static\n\n\n static jQueryInterface(config) {\n return this.each(function () {\n const data = Offcanvas.getOrCreateInstance(this, config);\n\n if (typeof config !== 'string') {\n return;\n }\n\n if (data[config] === undefined || config.startsWith('_') || config === 'constructor') {\n throw new TypeError(`No method named \"${config}\"`);\n }\n\n data[config](this);\n });\n }\n\n}\n/**\n * Data API implementation\n */\n\n\nEventHandler.on(document, EVENT_CLICK_DATA_API$1, SELECTOR_DATA_TOGGLE$1, function (event) {\n const target = getElementFromSelector(this);\n\n if (['A', 'AREA'].includes(this.tagName)) {\n event.preventDefault();\n }\n\n if (isDisabled(this)) {\n return;\n }\n\n EventHandler.one(target, EVENT_HIDDEN$3, () => {\n // focus on trigger when it is closed\n if (isVisible(this)) {\n this.focus();\n }\n }); // avoid conflict when clicking a toggler of an offcanvas, while another is open\n\n const alreadyOpen = SelectorEngine.findOne(OPEN_SELECTOR);\n\n if (alreadyOpen && alreadyOpen !== target) {\n Offcanvas.getInstance(alreadyOpen).hide();\n }\n\n const data = Offcanvas.getOrCreateInstance(target);\n data.toggle(this);\n});\nEventHandler.on(window, EVENT_LOAD_DATA_API$2, () => {\n for (const selector of SelectorEngine.find(OPEN_SELECTOR)) {\n Offcanvas.getOrCreateInstance(selector).show();\n }\n});\nEventHandler.on(window, EVENT_RESIZE, () => {\n for (const element of SelectorEngine.find('[aria-modal][class*=show][class*=offcanvas-]')) {\n if (getComputedStyle(element).position !== 'fixed') {\n Offcanvas.getOrCreateInstance(element).hide();\n }\n }\n});\nenableDismissTrigger(Offcanvas);\n/**\n * jQuery\n */\n\ndefineJQueryPlugin(Offcanvas);\n\n/**\n * --------------------------------------------------------------------------\n * Bootstrap (v5.2.3): util/sanitizer.js\n * Licensed under MIT (https://github.com/twbs/bootstrap/blob/main/LICENSE)\n * --------------------------------------------------------------------------\n */\nconst uriAttributes = new Set(['background', 'cite', 'href', 'itemtype', 'longdesc', 'poster', 'src', 'xlink:href']);\nconst ARIA_ATTRIBUTE_PATTERN = /^aria-[\\w-]*$/i;\n/**\n * A pattern that recognizes a commonly useful subset of URLs that are safe.\n *\n * Shout-out to Angular https://github.com/angular/angular/blob/12.2.x/packages/core/src/sanitization/url_sanitizer.ts\n */\n\nconst SAFE_URL_PATTERN = /^(?:(?:https?|mailto|ftp|tel|file|sms):|[^#&/:?]*(?:[#/?]|$))/i;\n/**\n * A pattern that matches safe data URLs. Only matches image, video and audio types.\n *\n * Shout-out to Angular https://github.com/angular/angular/blob/12.2.x/packages/core/src/sanitization/url_sanitizer.ts\n */\n\nconst DATA_URL_PATTERN = /^data:(?:image\\/(?:bmp|gif|jpeg|jpg|png|tiff|webp)|video\\/(?:mpeg|mp4|ogg|webm)|audio\\/(?:mp3|oga|ogg|opus));base64,[\\d+/a-z]+=*$/i;\n\nconst allowedAttribute = (attribute, allowedAttributeList) => {\n const attributeName = attribute.nodeName.toLowerCase();\n\n if (allowedAttributeList.includes(attributeName)) {\n if (uriAttributes.has(attributeName)) {\n return Boolean(SAFE_URL_PATTERN.test(attribute.nodeValue) || DATA_URL_PATTERN.test(attribute.nodeValue));\n }\n\n return true;\n } // Check if a regular expression validates the attribute.\n\n\n return allowedAttributeList.filter(attributeRegex => attributeRegex instanceof RegExp).some(regex => regex.test(attributeName));\n};\n\nconst DefaultAllowlist = {\n // Global attributes allowed on any supplied element below.\n '*': ['class', 'dir', 'id', 'lang', 'role', ARIA_ATTRIBUTE_PATTERN],\n a: ['target', 'href', 'title', 'rel'],\n area: [],\n b: [],\n br: [],\n col: [],\n code: [],\n div: [],\n em: [],\n hr: [],\n h1: [],\n h2: [],\n h3: [],\n h4: [],\n h5: [],\n h6: [],\n i: [],\n img: ['src', 'srcset', 'alt', 'title', 'width', 'height'],\n li: [],\n ol: [],\n p: [],\n pre: [],\n s: [],\n small: [],\n span: [],\n sub: [],\n sup: [],\n strong: [],\n u: [],\n ul: []\n};\nfunction sanitizeHtml(unsafeHtml, allowList, sanitizeFunction) {\n if (!unsafeHtml.length) {\n return unsafeHtml;\n }\n\n if (sanitizeFunction && typeof sanitizeFunction === 'function') {\n return sanitizeFunction(unsafeHtml);\n }\n\n const domParser = new window.DOMParser();\n const createdDocument = domParser.parseFromString(unsafeHtml, 'text/html');\n const elements = [].concat(...createdDocument.body.querySelectorAll('*'));\n\n for (const element of elements) {\n const elementName = element.nodeName.toLowerCase();\n\n if (!Object.keys(allowList).includes(elementName)) {\n element.remove();\n continue;\n }\n\n const attributeList = [].concat(...element.attributes);\n const allowedAttributes = [].concat(allowList['*'] || [], allowList[elementName] || []);\n\n for (const attribute of attributeList) {\n if (!allowedAttribute(attribute, allowedAttributes)) {\n element.removeAttribute(attribute.nodeName);\n }\n }\n }\n\n return createdDocument.body.innerHTML;\n}\n\n/**\n * --------------------------------------------------------------------------\n * Bootstrap (v5.2.3): util/template-factory.js\n * Licensed under MIT (https://github.com/twbs/bootstrap/blob/main/LICENSE)\n * --------------------------------------------------------------------------\n */\n/**\n * Constants\n */\n\nconst NAME$5 = 'TemplateFactory';\nconst Default$4 = {\n allowList: DefaultAllowlist,\n content: {},\n // { selector : text , selector2 : text2 , }\n extraClass: '',\n html: false,\n sanitize: true,\n sanitizeFn: null,\n template: '
'\n};\nconst DefaultType$4 = {\n allowList: 'object',\n content: 'object',\n extraClass: '(string|function)',\n html: 'boolean',\n sanitize: 'boolean',\n sanitizeFn: '(null|function)',\n template: 'string'\n};\nconst DefaultContentType = {\n entry: '(string|element|function|null)',\n selector: '(string|element)'\n};\n/**\n * Class definition\n */\n\nclass TemplateFactory extends Config {\n constructor(config) {\n super();\n this._config = this._getConfig(config);\n } // Getters\n\n\n static get Default() {\n return Default$4;\n }\n\n static get DefaultType() {\n return DefaultType$4;\n }\n\n static get NAME() {\n return NAME$5;\n } // Public\n\n\n getContent() {\n return Object.values(this._config.content).map(config => this._resolvePossibleFunction(config)).filter(Boolean);\n }\n\n hasContent() {\n return this.getContent().length > 0;\n }\n\n changeContent(content) {\n this._checkContent(content);\n\n this._config.content = { ...this._config.content,\n ...content\n };\n return this;\n }\n\n toHtml() {\n const templateWrapper = document.createElement('div');\n templateWrapper.innerHTML = this._maybeSanitize(this._config.template);\n\n for (const [selector, text] of Object.entries(this._config.content)) {\n this._setContent(templateWrapper, text, selector);\n }\n\n const template = templateWrapper.children[0];\n\n const extraClass = this._resolvePossibleFunction(this._config.extraClass);\n\n if (extraClass) {\n template.classList.add(...extraClass.split(' '));\n }\n\n return template;\n } // Private\n\n\n _typeCheckConfig(config) {\n super._typeCheckConfig(config);\n\n this._checkContent(config.content);\n }\n\n _checkContent(arg) {\n for (const [selector, content] of Object.entries(arg)) {\n super._typeCheckConfig({\n selector,\n entry: content\n }, DefaultContentType);\n }\n }\n\n _setContent(template, content, selector) {\n const templateElement = SelectorEngine.findOne(selector, template);\n\n if (!templateElement) {\n return;\n }\n\n content = this._resolvePossibleFunction(content);\n\n if (!content) {\n templateElement.remove();\n return;\n }\n\n if (isElement(content)) {\n this._putElementInTemplate(getElement(content), templateElement);\n\n return;\n }\n\n if (this._config.html) {\n templateElement.innerHTML = this._maybeSanitize(content);\n return;\n }\n\n templateElement.textContent = content;\n }\n\n _maybeSanitize(arg) {\n return this._config.sanitize ? sanitizeHtml(arg, this._config.allowList, this._config.sanitizeFn) : arg;\n }\n\n _resolvePossibleFunction(arg) {\n return typeof arg === 'function' ? arg(this) : arg;\n }\n\n _putElementInTemplate(element, templateElement) {\n if (this._config.html) {\n templateElement.innerHTML = '';\n templateElement.append(element);\n return;\n }\n\n templateElement.textContent = element.textContent;\n }\n\n}\n\n/**\n * --------------------------------------------------------------------------\n * Bootstrap (v5.2.3): tooltip.js\n * Licensed under MIT (https://github.com/twbs/bootstrap/blob/main/LICENSE)\n * --------------------------------------------------------------------------\n */\n/**\n * Constants\n */\n\nconst NAME$4 = 'tooltip';\nconst DISALLOWED_ATTRIBUTES = new Set(['sanitize', 'allowList', 'sanitizeFn']);\nconst CLASS_NAME_FADE$2 = 'fade';\nconst CLASS_NAME_MODAL = 'modal';\nconst CLASS_NAME_SHOW$2 = 'show';\nconst SELECTOR_TOOLTIP_INNER = '.tooltip-inner';\nconst SELECTOR_MODAL = `.${CLASS_NAME_MODAL}`;\nconst EVENT_MODAL_HIDE = 'hide.bs.modal';\nconst TRIGGER_HOVER = 'hover';\nconst TRIGGER_FOCUS = 'focus';\nconst TRIGGER_CLICK = 'click';\nconst TRIGGER_MANUAL = 'manual';\nconst EVENT_HIDE$2 = 'hide';\nconst EVENT_HIDDEN$2 = 'hidden';\nconst EVENT_SHOW$2 = 'show';\nconst EVENT_SHOWN$2 = 'shown';\nconst EVENT_INSERTED = 'inserted';\nconst EVENT_CLICK$1 = 'click';\nconst EVENT_FOCUSIN$1 = 'focusin';\nconst EVENT_FOCUSOUT$1 = 'focusout';\nconst EVENT_MOUSEENTER = 'mouseenter';\nconst EVENT_MOUSELEAVE = 'mouseleave';\nconst AttachmentMap = {\n AUTO: 'auto',\n TOP: 'top',\n RIGHT: isRTL() ? 'left' : 'right',\n BOTTOM: 'bottom',\n LEFT: isRTL() ? 'right' : 'left'\n};\nconst Default$3 = {\n allowList: DefaultAllowlist,\n animation: true,\n boundary: 'clippingParents',\n container: false,\n customClass: '',\n delay: 0,\n fallbackPlacements: ['top', 'right', 'bottom', 'left'],\n html: false,\n offset: [0, 0],\n placement: 'top',\n popperConfig: null,\n sanitize: true,\n sanitizeFn: null,\n selector: false,\n template: '
' + '
' + '
' + '
',\n title: '',\n trigger: 'hover focus'\n};\nconst DefaultType$3 = {\n allowList: 'object',\n animation: 'boolean',\n boundary: '(string|element)',\n container: '(string|element|boolean)',\n customClass: '(string|function)',\n delay: '(number|object)',\n fallbackPlacements: 'array',\n html: 'boolean',\n offset: '(array|string|function)',\n placement: '(string|function)',\n popperConfig: '(null|object|function)',\n sanitize: 'boolean',\n sanitizeFn: '(null|function)',\n selector: '(string|boolean)',\n template: 'string',\n title: '(string|element|function)',\n trigger: 'string'\n};\n/**\n * Class definition\n */\n\nclass Tooltip extends BaseComponent {\n constructor(element, config) {\n if (typeof Popper === 'undefined') {\n throw new TypeError('Bootstrap\\'s tooltips require Popper (https://popper.js.org)');\n }\n\n super(element, config); // Private\n\n this._isEnabled = true;\n this._timeout = 0;\n this._isHovered = null;\n this._activeTrigger = {};\n this._popper = null;\n this._templateFactory = null;\n this._newContent = null; // Protected\n\n this.tip = null;\n\n this._setListeners();\n\n if (!this._config.selector) {\n this._fixTitle();\n }\n } // Getters\n\n\n static get Default() {\n return Default$3;\n }\n\n static get DefaultType() {\n return DefaultType$3;\n }\n\n static get NAME() {\n return NAME$4;\n } // Public\n\n\n enable() {\n this._isEnabled = true;\n }\n\n disable() {\n this._isEnabled = false;\n }\n\n toggleEnabled() {\n this._isEnabled = !this._isEnabled;\n }\n\n toggle() {\n if (!this._isEnabled) {\n return;\n }\n\n this._activeTrigger.click = !this._activeTrigger.click;\n\n if (this._isShown()) {\n this._leave();\n\n return;\n }\n\n this._enter();\n }\n\n dispose() {\n clearTimeout(this._timeout);\n EventHandler.off(this._element.closest(SELECTOR_MODAL), EVENT_MODAL_HIDE, this._hideModalHandler);\n\n if (this._element.getAttribute('data-bs-original-title')) {\n this._element.setAttribute('title', this._element.getAttribute('data-bs-original-title'));\n }\n\n this._disposePopper();\n\n super.dispose();\n }\n\n show() {\n if (this._element.style.display === 'none') {\n throw new Error('Please use show on visible elements');\n }\n\n if (!(this._isWithContent() && this._isEnabled)) {\n return;\n }\n\n const showEvent = EventHandler.trigger(this._element, this.constructor.eventName(EVENT_SHOW$2));\n const shadowRoot = findShadowRoot(this._element);\n\n const isInTheDom = (shadowRoot || this._element.ownerDocument.documentElement).contains(this._element);\n\n if (showEvent.defaultPrevented || !isInTheDom) {\n return;\n } // todo v6 remove this OR make it optional\n\n\n this._disposePopper();\n\n const tip = this._getTipElement();\n\n this._element.setAttribute('aria-describedby', tip.getAttribute('id'));\n\n const {\n container\n } = this._config;\n\n if (!this._element.ownerDocument.documentElement.contains(this.tip)) {\n container.append(tip);\n EventHandler.trigger(this._element, this.constructor.eventName(EVENT_INSERTED));\n }\n\n this._popper = this._createPopper(tip);\n tip.classList.add(CLASS_NAME_SHOW$2); // If this is a touch-enabled device we add extra\n // empty mouseover listeners to the body's immediate children;\n // only needed because of broken event delegation on iOS\n // https://www.quirksmode.org/blog/archives/2014/02/mouse_event_bub.html\n\n if ('ontouchstart' in document.documentElement) {\n for (const element of [].concat(...document.body.children)) {\n EventHandler.on(element, 'mouseover', noop);\n }\n }\n\n const complete = () => {\n EventHandler.trigger(this._element, this.constructor.eventName(EVENT_SHOWN$2));\n\n if (this._isHovered === false) {\n this._leave();\n }\n\n this._isHovered = false;\n };\n\n this._queueCallback(complete, this.tip, this._isAnimated());\n }\n\n hide() {\n if (!this._isShown()) {\n return;\n }\n\n const hideEvent = EventHandler.trigger(this._element, this.constructor.eventName(EVENT_HIDE$2));\n\n if (hideEvent.defaultPrevented) {\n return;\n }\n\n const tip = this._getTipElement();\n\n tip.classList.remove(CLASS_NAME_SHOW$2); // If this is a touch-enabled device we remove the extra\n // empty mouseover listeners we added for iOS support\n\n if ('ontouchstart' in document.documentElement) {\n for (const element of [].concat(...document.body.children)) {\n EventHandler.off(element, 'mouseover', noop);\n }\n }\n\n this._activeTrigger[TRIGGER_CLICK] = false;\n this._activeTrigger[TRIGGER_FOCUS] = false;\n this._activeTrigger[TRIGGER_HOVER] = false;\n this._isHovered = null; // it is a trick to support manual triggering\n\n const complete = () => {\n if (this._isWithActiveTrigger()) {\n return;\n }\n\n if (!this._isHovered) {\n this._disposePopper();\n }\n\n this._element.removeAttribute('aria-describedby');\n\n EventHandler.trigger(this._element, this.constructor.eventName(EVENT_HIDDEN$2));\n };\n\n this._queueCallback(complete, this.tip, this._isAnimated());\n }\n\n update() {\n if (this._popper) {\n this._popper.update();\n }\n } // Protected\n\n\n _isWithContent() {\n return Boolean(this._getTitle());\n }\n\n _getTipElement() {\n if (!this.tip) {\n this.tip = this._createTipElement(this._newContent || this._getContentForTemplate());\n }\n\n return this.tip;\n }\n\n _createTipElement(content) {\n const tip = this._getTemplateFactory(content).toHtml(); // todo: remove this check on v6\n\n\n if (!tip) {\n return null;\n }\n\n tip.classList.remove(CLASS_NAME_FADE$2, CLASS_NAME_SHOW$2); // todo: on v6 the following can be achieved with CSS only\n\n tip.classList.add(`bs-${this.constructor.NAME}-auto`);\n const tipId = getUID(this.constructor.NAME).toString();\n tip.setAttribute('id', tipId);\n\n if (this._isAnimated()) {\n tip.classList.add(CLASS_NAME_FADE$2);\n }\n\n return tip;\n }\n\n setContent(content) {\n this._newContent = content;\n\n if (this._isShown()) {\n this._disposePopper();\n\n this.show();\n }\n }\n\n _getTemplateFactory(content) {\n if (this._templateFactory) {\n this._templateFactory.changeContent(content);\n } else {\n this._templateFactory = new TemplateFactory({ ...this._config,\n // the `content` var has to be after `this._config`\n // to override config.content in case of popover\n content,\n extraClass: this._resolvePossibleFunction(this._config.customClass)\n });\n }\n\n return this._templateFactory;\n }\n\n _getContentForTemplate() {\n return {\n [SELECTOR_TOOLTIP_INNER]: this._getTitle()\n };\n }\n\n _getTitle() {\n return this._resolvePossibleFunction(this._config.title) || this._element.getAttribute('data-bs-original-title');\n } // Private\n\n\n _initializeOnDelegatedTarget(event) {\n return this.constructor.getOrCreateInstance(event.delegateTarget, this._getDelegateConfig());\n }\n\n _isAnimated() {\n return this._config.animation || this.tip && this.tip.classList.contains(CLASS_NAME_FADE$2);\n }\n\n _isShown() {\n return this.tip && this.tip.classList.contains(CLASS_NAME_SHOW$2);\n }\n\n _createPopper(tip) {\n const placement = typeof this._config.placement === 'function' ? this._config.placement.call(this, tip, this._element) : this._config.placement;\n const attachment = AttachmentMap[placement.toUpperCase()];\n return Popper.createPopper(this._element, tip, this._getPopperConfig(attachment));\n }\n\n _getOffset() {\n const {\n offset\n } = this._config;\n\n if (typeof offset === 'string') {\n return offset.split(',').map(value => Number.parseInt(value, 10));\n }\n\n if (typeof offset === 'function') {\n return popperData => offset(popperData, this._element);\n }\n\n return offset;\n }\n\n _resolvePossibleFunction(arg) {\n return typeof arg === 'function' ? arg.call(this._element) : arg;\n }\n\n _getPopperConfig(attachment) {\n const defaultBsPopperConfig = {\n placement: attachment,\n modifiers: [{\n name: 'flip',\n options: {\n fallbackPlacements: this._config.fallbackPlacements\n }\n }, {\n name: 'offset',\n options: {\n offset: this._getOffset()\n }\n }, {\n name: 'preventOverflow',\n options: {\n boundary: this._config.boundary\n }\n }, {\n name: 'arrow',\n options: {\n element: `.${this.constructor.NAME}-arrow`\n }\n }, {\n name: 'preSetPlacement',\n enabled: true,\n phase: 'beforeMain',\n fn: data => {\n // Pre-set Popper's placement attribute in order to read the arrow sizes properly.\n // Otherwise, Popper mixes up the width and height dimensions since the initial arrow style is for top placement\n this._getTipElement().setAttribute('data-popper-placement', data.state.placement);\n }\n }]\n };\n return { ...defaultBsPopperConfig,\n ...(typeof this._config.popperConfig === 'function' ? this._config.popperConfig(defaultBsPopperConfig) : this._config.popperConfig)\n };\n }\n\n _setListeners() {\n const triggers = this._config.trigger.split(' ');\n\n for (const trigger of triggers) {\n if (trigger === 'click') {\n EventHandler.on(this._element, this.constructor.eventName(EVENT_CLICK$1), this._config.selector, event => {\n const context = this._initializeOnDelegatedTarget(event);\n\n context.toggle();\n });\n } else if (trigger !== TRIGGER_MANUAL) {\n const eventIn = trigger === TRIGGER_HOVER ? this.constructor.eventName(EVENT_MOUSEENTER) : this.constructor.eventName(EVENT_FOCUSIN$1);\n const eventOut = trigger === TRIGGER_HOVER ? this.constructor.eventName(EVENT_MOUSELEAVE) : this.constructor.eventName(EVENT_FOCUSOUT$1);\n EventHandler.on(this._element, eventIn, this._config.selector, event => {\n const context = this._initializeOnDelegatedTarget(event);\n\n context._activeTrigger[event.type === 'focusin' ? TRIGGER_FOCUS : TRIGGER_HOVER] = true;\n\n context._enter();\n });\n EventHandler.on(this._element, eventOut, this._config.selector, event => {\n const context = this._initializeOnDelegatedTarget(event);\n\n context._activeTrigger[event.type === 'focusout' ? TRIGGER_FOCUS : TRIGGER_HOVER] = context._element.contains(event.relatedTarget);\n\n context._leave();\n });\n }\n }\n\n this._hideModalHandler = () => {\n if (this._element) {\n this.hide();\n }\n };\n\n EventHandler.on(this._element.closest(SELECTOR_MODAL), EVENT_MODAL_HIDE, this._hideModalHandler);\n }\n\n _fixTitle() {\n const title = this._element.getAttribute('title');\n\n if (!title) {\n return;\n }\n\n if (!this._element.getAttribute('aria-label') && !this._element.textContent.trim()) {\n this._element.setAttribute('aria-label', title);\n }\n\n this._element.setAttribute('data-bs-original-title', title); // DO NOT USE IT. Is only for backwards compatibility\n\n\n this._element.removeAttribute('title');\n }\n\n _enter() {\n if (this._isShown() || this._isHovered) {\n this._isHovered = true;\n return;\n }\n\n this._isHovered = true;\n\n this._setTimeout(() => {\n if (this._isHovered) {\n this.show();\n }\n }, this._config.delay.show);\n }\n\n _leave() {\n if (this._isWithActiveTrigger()) {\n return;\n }\n\n this._isHovered = false;\n\n this._setTimeout(() => {\n if (!this._isHovered) {\n this.hide();\n }\n }, this._config.delay.hide);\n }\n\n _setTimeout(handler, timeout) {\n clearTimeout(this._timeout);\n this._timeout = setTimeout(handler, timeout);\n }\n\n _isWithActiveTrigger() {\n return Object.values(this._activeTrigger).includes(true);\n }\n\n _getConfig(config) {\n const dataAttributes = Manipulator.getDataAttributes(this._element);\n\n for (const dataAttribute of Object.keys(dataAttributes)) {\n if (DISALLOWED_ATTRIBUTES.has(dataAttribute)) {\n delete dataAttributes[dataAttribute];\n }\n }\n\n config = { ...dataAttributes,\n ...(typeof config === 'object' && config ? config : {})\n };\n config = this._mergeConfigObj(config);\n config = this._configAfterMerge(config);\n\n this._typeCheckConfig(config);\n\n return config;\n }\n\n _configAfterMerge(config) {\n config.container = config.container === false ? document.body : getElement(config.container);\n\n if (typeof config.delay === 'number') {\n config.delay = {\n show: config.delay,\n hide: config.delay\n };\n }\n\n if (typeof config.title === 'number') {\n config.title = config.title.toString();\n }\n\n if (typeof config.content === 'number') {\n config.content = config.content.toString();\n }\n\n return config;\n }\n\n _getDelegateConfig() {\n const config = {};\n\n for (const key in this._config) {\n if (this.constructor.Default[key] !== this._config[key]) {\n config[key] = this._config[key];\n }\n }\n\n config.selector = false;\n config.trigger = 'manual'; // In the future can be replaced with:\n // const keysWithDifferentValues = Object.entries(this._config).filter(entry => this.constructor.Default[entry[0]] !== this._config[entry[0]])\n // `Object.fromEntries(keysWithDifferentValues)`\n\n return config;\n }\n\n _disposePopper() {\n if (this._popper) {\n this._popper.destroy();\n\n this._popper = null;\n }\n\n if (this.tip) {\n this.tip.remove();\n this.tip = null;\n }\n } // Static\n\n\n static jQueryInterface(config) {\n return this.each(function () {\n const data = Tooltip.getOrCreateInstance(this, config);\n\n if (typeof config !== 'string') {\n return;\n }\n\n if (typeof data[config] === 'undefined') {\n throw new TypeError(`No method named \"${config}\"`);\n }\n\n data[config]();\n });\n }\n\n}\n/**\n * jQuery\n */\n\n\ndefineJQueryPlugin(Tooltip);\n\n/**\n * --------------------------------------------------------------------------\n * Bootstrap (v5.2.3): popover.js\n * Licensed under MIT (https://github.com/twbs/bootstrap/blob/main/LICENSE)\n * --------------------------------------------------------------------------\n */\n/**\n * Constants\n */\n\nconst NAME$3 = 'popover';\nconst SELECTOR_TITLE = '.popover-header';\nconst SELECTOR_CONTENT = '.popover-body';\nconst Default$2 = { ...Tooltip.Default,\n content: '',\n offset: [0, 8],\n placement: 'right',\n template: '
' + '
' + '

' + '
' + '
',\n trigger: 'click'\n};\nconst DefaultType$2 = { ...Tooltip.DefaultType,\n content: '(null|string|element|function)'\n};\n/**\n * Class definition\n */\n\nclass Popover extends Tooltip {\n // Getters\n static get Default() {\n return Default$2;\n }\n\n static get DefaultType() {\n return DefaultType$2;\n }\n\n static get NAME() {\n return NAME$3;\n } // Overrides\n\n\n _isWithContent() {\n return this._getTitle() || this._getContent();\n } // Private\n\n\n _getContentForTemplate() {\n return {\n [SELECTOR_TITLE]: this._getTitle(),\n [SELECTOR_CONTENT]: this._getContent()\n };\n }\n\n _getContent() {\n return this._resolvePossibleFunction(this._config.content);\n } // Static\n\n\n static jQueryInterface(config) {\n return this.each(function () {\n const data = Popover.getOrCreateInstance(this, config);\n\n if (typeof config !== 'string') {\n return;\n }\n\n if (typeof data[config] === 'undefined') {\n throw new TypeError(`No method named \"${config}\"`);\n }\n\n data[config]();\n });\n }\n\n}\n/**\n * jQuery\n */\n\n\ndefineJQueryPlugin(Popover);\n\n/**\n * --------------------------------------------------------------------------\n * Bootstrap (v5.2.3): scrollspy.js\n * Licensed under MIT (https://github.com/twbs/bootstrap/blob/main/LICENSE)\n * --------------------------------------------------------------------------\n */\n/**\n * Constants\n */\n\nconst NAME$2 = 'scrollspy';\nconst DATA_KEY$2 = 'bs.scrollspy';\nconst EVENT_KEY$2 = `.${DATA_KEY$2}`;\nconst DATA_API_KEY = '.data-api';\nconst EVENT_ACTIVATE = `activate${EVENT_KEY$2}`;\nconst EVENT_CLICK = `click${EVENT_KEY$2}`;\nconst EVENT_LOAD_DATA_API$1 = `load${EVENT_KEY$2}${DATA_API_KEY}`;\nconst CLASS_NAME_DROPDOWN_ITEM = 'dropdown-item';\nconst CLASS_NAME_ACTIVE$1 = 'active';\nconst SELECTOR_DATA_SPY = '[data-bs-spy=\"scroll\"]';\nconst SELECTOR_TARGET_LINKS = '[href]';\nconst SELECTOR_NAV_LIST_GROUP = '.nav, .list-group';\nconst SELECTOR_NAV_LINKS = '.nav-link';\nconst SELECTOR_NAV_ITEMS = '.nav-item';\nconst SELECTOR_LIST_ITEMS = '.list-group-item';\nconst SELECTOR_LINK_ITEMS = `${SELECTOR_NAV_LINKS}, ${SELECTOR_NAV_ITEMS} > ${SELECTOR_NAV_LINKS}, ${SELECTOR_LIST_ITEMS}`;\nconst SELECTOR_DROPDOWN = '.dropdown';\nconst SELECTOR_DROPDOWN_TOGGLE$1 = '.dropdown-toggle';\nconst Default$1 = {\n offset: null,\n // TODO: v6 @deprecated, keep it for backwards compatibility reasons\n rootMargin: '0px 0px -25%',\n smoothScroll: false,\n target: null,\n threshold: [0.1, 0.5, 1]\n};\nconst DefaultType$1 = {\n offset: '(number|null)',\n // TODO v6 @deprecated, keep it for backwards compatibility reasons\n rootMargin: 'string',\n smoothScroll: 'boolean',\n target: 'element',\n threshold: 'array'\n};\n/**\n * Class definition\n */\n\nclass ScrollSpy extends BaseComponent {\n constructor(element, config) {\n super(element, config); // this._element is the observablesContainer and config.target the menu links wrapper\n\n this._targetLinks = new Map();\n this._observableSections = new Map();\n this._rootElement = getComputedStyle(this._element).overflowY === 'visible' ? null : this._element;\n this._activeTarget = null;\n this._observer = null;\n this._previousScrollData = {\n visibleEntryTop: 0,\n parentScrollTop: 0\n };\n this.refresh(); // initialize\n } // Getters\n\n\n static get Default() {\n return Default$1;\n }\n\n static get DefaultType() {\n return DefaultType$1;\n }\n\n static get NAME() {\n return NAME$2;\n } // Public\n\n\n refresh() {\n this._initializeTargetsAndObservables();\n\n this._maybeEnableSmoothScroll();\n\n if (this._observer) {\n this._observer.disconnect();\n } else {\n this._observer = this._getNewObserver();\n }\n\n for (const section of this._observableSections.values()) {\n this._observer.observe(section);\n }\n }\n\n dispose() {\n this._observer.disconnect();\n\n super.dispose();\n } // Private\n\n\n _configAfterMerge(config) {\n // TODO: on v6 target should be given explicitly & remove the {target: 'ss-target'} case\n config.target = getElement(config.target) || document.body; // TODO: v6 Only for backwards compatibility reasons. Use rootMargin only\n\n config.rootMargin = config.offset ? `${config.offset}px 0px -30%` : config.rootMargin;\n\n if (typeof config.threshold === 'string') {\n config.threshold = config.threshold.split(',').map(value => Number.parseFloat(value));\n }\n\n return config;\n }\n\n _maybeEnableSmoothScroll() {\n if (!this._config.smoothScroll) {\n return;\n } // unregister any previous listeners\n\n\n EventHandler.off(this._config.target, EVENT_CLICK);\n EventHandler.on(this._config.target, EVENT_CLICK, SELECTOR_TARGET_LINKS, event => {\n const observableSection = this._observableSections.get(event.target.hash);\n\n if (observableSection) {\n event.preventDefault();\n const root = this._rootElement || window;\n const height = observableSection.offsetTop - this._element.offsetTop;\n\n if (root.scrollTo) {\n root.scrollTo({\n top: height,\n behavior: 'smooth'\n });\n return;\n } // Chrome 60 doesn't support `scrollTo`\n\n\n root.scrollTop = height;\n }\n });\n }\n\n _getNewObserver() {\n const options = {\n root: this._rootElement,\n threshold: this._config.threshold,\n rootMargin: this._config.rootMargin\n };\n return new IntersectionObserver(entries => this._observerCallback(entries), options);\n } // The logic of selection\n\n\n _observerCallback(entries) {\n const targetElement = entry => this._targetLinks.get(`#${entry.target.id}`);\n\n const activate = entry => {\n this._previousScrollData.visibleEntryTop = entry.target.offsetTop;\n\n this._process(targetElement(entry));\n };\n\n const parentScrollTop = (this._rootElement || document.documentElement).scrollTop;\n const userScrollsDown = parentScrollTop >= this._previousScrollData.parentScrollTop;\n this._previousScrollData.parentScrollTop = parentScrollTop;\n\n for (const entry of entries) {\n if (!entry.isIntersecting) {\n this._activeTarget = null;\n\n this._clearActiveClass(targetElement(entry));\n\n continue;\n }\n\n const entryIsLowerThanPrevious = entry.target.offsetTop >= this._previousScrollData.visibleEntryTop; // if we are scrolling down, pick the bigger offsetTop\n\n if (userScrollsDown && entryIsLowerThanPrevious) {\n activate(entry); // if parent isn't scrolled, let's keep the first visible item, breaking the iteration\n\n if (!parentScrollTop) {\n return;\n }\n\n continue;\n } // if we are scrolling up, pick the smallest offsetTop\n\n\n if (!userScrollsDown && !entryIsLowerThanPrevious) {\n activate(entry);\n }\n }\n }\n\n _initializeTargetsAndObservables() {\n this._targetLinks = new Map();\n this._observableSections = new Map();\n const targetLinks = SelectorEngine.find(SELECTOR_TARGET_LINKS, this._config.target);\n\n for (const anchor of targetLinks) {\n // ensure that the anchor has an id and is not disabled\n if (!anchor.hash || isDisabled(anchor)) {\n continue;\n }\n\n const observableSection = SelectorEngine.findOne(anchor.hash, this._element); // ensure that the observableSection exists & is visible\n\n if (isVisible(observableSection)) {\n this._targetLinks.set(anchor.hash, anchor);\n\n this._observableSections.set(anchor.hash, observableSection);\n }\n }\n }\n\n _process(target) {\n if (this._activeTarget === target) {\n return;\n }\n\n this._clearActiveClass(this._config.target);\n\n this._activeTarget = target;\n target.classList.add(CLASS_NAME_ACTIVE$1);\n\n this._activateParents(target);\n\n EventHandler.trigger(this._element, EVENT_ACTIVATE, {\n relatedTarget: target\n });\n }\n\n _activateParents(target) {\n // Activate dropdown parents\n if (target.classList.contains(CLASS_NAME_DROPDOWN_ITEM)) {\n SelectorEngine.findOne(SELECTOR_DROPDOWN_TOGGLE$1, target.closest(SELECTOR_DROPDOWN)).classList.add(CLASS_NAME_ACTIVE$1);\n return;\n }\n\n for (const listGroup of SelectorEngine.parents(target, SELECTOR_NAV_LIST_GROUP)) {\n // Set triggered links parents as active\n // With both - -
-

14.14. Batch Normalization

+ +
+

14.14. Batch Normalization#

Batch Normalization aims to address the vanishing/exploding gradients problems, and more generally the problem that the distribution of each layer’s inputs changes during training, as the parameters of the previous layers change.

@@ -2911,27 +1933,27 @@ learn the optimal scale and mean of the inputs for each layer. In order to zero-center and normalize the inputs, the algorithm needs to estimate the inputs’ mean and standard deviation. It does so by evaluating the mean and standard deviation of the inputs over the current mini-batch, from this the name batch normalization.

-
-
-

14.15. Dropout

+ +
+

14.15. Dropout#

It is a fairly simple algorithm: at every training step, every neuron (including the input neurons but excluding the output neurons) has a probability \(p\) of being temporarily dropped out, meaning it will be entirely ignored during this training step, but it may be active during the next step.

The hyperparameter \(p\) is called the dropout rate, and it is typically set to 50%. After training, the neurons are not dropped anymore. It is viewed as one of the most popular regularization techniques.

-
-
-

14.16. Gradient Clipping

+ +
+

14.16. Gradient Clipping#

A popular technique to lessen the exploding gradients problem is to simply clip the gradients during backpropagation so that they never exceed some threshold (this is mostly useful for recurrent neural networks).

This technique is called Gradient Clipping.

In general however, Batch Normalization is preferred.

-
-
-

14.17. A top-down perspective on Neural networks

+ +
+

14.17. A top-down perspective on Neural networks#

The first thing we would like to do is divide the data into two or three parts. A training set, a validation or dev (development) set, and a test set. The test set is the data on which we want to make @@ -2963,9 +1985,9 @@ the test data. The difference between the performance of the algorithm on these two validation sets quantifies the train-test mismatch. This can serve as another important diagnostic when using DNNs for supervised learning.

-
-
-

14.18. Limitations of supervised learning with deep networks

+ +
+

14.18. Limitations of supervised learning with deep networks#

Like all statistical methods, supervised learning using neural networks has important limitations. This is especially important when one seeks to apply these methods, especially to physics problems. Like @@ -2981,8 +2003,8 @@ features).

  • Many problems are not about prediction. In natural science we are often interested in learning something about the underlying distribution that generates the data. In this case, it is often difficult to cast these ideas in a supervised learning setting. While the problems are related, it is possible to make good predictions with a wrong model. The model might or might not be useful for understanding the underlying science.

    • Some of these remarks are particular to DNNs, others are shared by all supervised learning methods. This motivates the use of unsupervised methods which in part circumvent these problems.

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    previous

    -

    13. Neural networks

    -
    + + + + + +     + +
    - - -
    -

    - - By Morten Hjorth-Jensen
    - - © Copyright 2021.
    -

    -
    - + + +
    +
    +
    + Contents +
    +
    -
    -
    + + + + +
    + +
    - +
    + +

    +By Morten Hjorth-Jensen +

    +
    + +
    + +

    + + © Copyright 2022. +
    + +

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    \ No newline at end of file diff --git a/doc/LectureNotes/_build/html/chapter11.html b/doc/LectureNotes/_build/html/chapter11.html index 92d140dec..357146930 100644 --- a/doc/LectureNotes/_build/html/chapter11.html +++ b/doc/LectureNotes/_build/html/chapter11.html @@ -1,50 +1,61 @@ + - + + + - + + 15. Solving Differential Equations with Deep Learning — Applied Data Analysis and Machine Learning - - - + + + + + + + + + - - - - + + + + + - - - - - + - + - - - - + + + + + + + + - - + + + - - - - - - + + - - - + + + - + + + + +
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    Initial cost: 457.256
-
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    /Users/mhjensen/miniforge3/envs/myenv/lib/python3.9/site-packages/numpy/core/fromnumeric.py:3208: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray.
-  return asarray(a).size
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    Final cost: 0.00310113
-The max absolute difference between the solutions is: 0.000464088
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    -_images/chapter11_79_3.png -
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    15.7.1. Comparing with a numerical scheme

    +
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    15.7.1. Comparing with a numerical scheme#

    The Poisson equation is possible to solve using Taylor series to approximate the second derivative.

    Using Taylor series, the second derivative can be expressed as

    @@ -1816,7 +1659,7 @@ g''(x) = \frac{g(x + \Delta x) - 2g(x) + g(x-\Delta x)}{\Delta x^2} + E_{\Delta g''(x) \approx \frac{g(x + \Delta x) - 2g(x) + g(x-\Delta x)}{\Delta x^2} \end{equation} \]
    -

    If \(x_i = i \Delta x = x_{i-1} + \Delta x\) and \(g_i = g(x_i)\) for \(i = 1,\dots N_x - 2\) with \(N_x\) being the number of values for \(x\), (15) becomes

    +

    If \(x_i = i \Delta x = x_{i-1} + \Delta x\) and \(g_i = g(x_i)\) for \(i = 1,\dots N_x - 2\) with \(N_x\) being the number of values for \(x\), (15) becomes

    \[\begin{split} \begin{aligned} @@ -2073,27 +1916,11 @@ f(x_{N_x - 2})
    -
    -
    Initial cost: 457.256
-
    -
    /Users/mhjensen/miniforge3/envs/myenv/lib/python3.9/site-packages/numpy/core/fromnumeric.py:3208: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray.
-  return asarray(a).size
-
    -
    -
    Final cost: 0.00310113
-The max absolute difference between the analytical solution and DNN Autograd: 0.000464088
-The max absolute difference between the analytical solution and numerical scheme: 0.00266858
-
    -
    -_images/chapter11_91_3.png -_images/chapter11_91_4.png -
    - - - -
    -

    15.8. Partial Differential Equations

    + + +
    +

    15.8. Partial Differential Equations#

    A partial differential equation (PDE) has a solution here the function is defined by multiple variables. The equation may involve all kinds of combinations of which variables the function is differentiated with @@ -2108,8 +1935,8 @@ respect to.

    \end{equation} \]

    where \(f\) is an expression involving all kinds of possible mixed derivatives of \(g(x_1,\dots,x_N)\) up to an order \(n\). In order for the solution to be unique, some additional conditions must also be given.

    -
    -

    15.8.1. Type of problem

    +
    +

    15.8.1. Type of problem#

    The problem our network must solve for, is similar to the ODE case. We must have a trial solution \(g_t\) at hand.

    For instance, the trial solution could be expressed as

    @@ -2122,13 +1949,13 @@ We must have a trial solution \(g_t\)

    where \(h_1(x_1,\dots,x_N)\) is a function that ensures \(g_t(x_1,\dots,x_N)\) satisfies some given conditions. The neural network \(N(x_1,\dots,x_N,P)\) has weights and biases described by \(P\) and \(h_2(x_1,\dots,x_N,N(x_1,\dots,x_N,P))\) is an expression using the output from the neural network in some way.

    The role of the function \(h_2(x_1,\dots,x_N,N(x_1,\dots,x_N,P))\), is to ensure that the output of \(N(x_1,\dots,x_N,P)\) is zero when \(g_t(x_1,\dots,x_N)\) is evaluated at the values of \(x_1,\dots,x_N\) where the given conditions must be satisfied. The function \(h_1(x_1,\dots,x_N)\) should alone make \(g_t(x_1,\dots,x_N)\) satisfy the conditions.

    -
    -
    -

    15.8.2. Network requirements

    + +
    +

    15.8.2. Network requirements#

    The network tries then the minimize the cost function following the same ideas as described for the ODE case, but now with more than one variables to consider. The concept still remains the same; find a set -of parameters \(P\) such that the expression \(f\) in (17) is as +of parameters \(P\) such that the expression \(f\) in (17) is as close to zero as possible.

    As for the ODE case, the cost function is the mean squared error that the network must try to minimize. The cost function for the network to @@ -2147,10 +1974,10 @@ C\left(\boldsymbol{x}, P\right) = f\left( \left( \boldsymbol{x}, \frac{\partial \[ C\left(X, P \right) = \sum_{i=1}^M f\left( \left( \boldsymbol{x}_i, \frac{\partial g(\boldsymbol{x}_i) }{\partial x_1}, \dots , \frac{\partial g(\boldsymbol{x}_i) }{\partial x_N}, \frac{\partial g(\boldsymbol{x}_i) }{\partial x_1\partial x_2}, \, \dots \, , \frac{\partial^n g(\boldsymbol{x}_i) }{\partial x_N^n} \right) \right)^2. \]

    - - -
    -

    15.9. Example: The diffusion equation

    + + +
    +

    15.9. Example: The diffusion equation#

    In one spatial dimension, the equation reads

    \[ @@ -2317,8 +2144,8 @@ mixed derivatives of \(g(x,t)\)
    -
    -

    15.9.1. Setting up the network using Autograd; The full program

    +
    +

    15.9.1. Setting up the network using Autograd; The full program#

    Having set up the network, along with the trial solution and cost function, we can now see how the deep neural network performs by comparing the results to the analytical solution.

    The analytical solution of our problem is

    @@ -2558,190 +2385,11 @@ Using TensorFlow results in a much better execution time. Try it!

    -
    -
    /Users/mhjensen/miniforge3/envs/myenv/lib/python3.9/site-packages/numpy/core/fromnumeric.py:3208: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray.
-  return asarray(a).size
-
    -
    Initial cost:  41.05505310046363
-
    -
    -
    ---------------------------------------------------------------------------
-KeyboardInterrupt                         Traceback (most recent call last)
-Input In [9], in <cell line: 129>()
-    140 num_iter = 250
-    141 lmb = 0.01
---> 143 P = solve_pde_deep_neural_network(x,t, num_hidden_neurons, num_iter, lmb)
-    145 ## Store the results
-    146 g_dnn_ag = np.zeros((Nx, Nt))
-
-Input In [9], in solve_pde_deep_neural_network(x, t, num_neurons, num_iter, lmb)
-    118 # Let the update be done num_iter times
-    119 for i in range(num_iter):
---> 120     cost_grad =  cost_function_grad(P, x , t)
-    122     for l in range(N_hidden+1):
-    123         P[l] = P[l] - lmb * cost_grad[l]
-
-File ~/miniforge3/envs/myenv/lib/python3.9/site-packages/autograd/wrap_util.py:20, in unary_to_nary.<locals>.nary_operator.<locals>.nary_f(*args, **kwargs)
-     18 else:
-     19     x = tuple(args[i] for i in argnum)
----> 20 return unary_operator(unary_f, x, *nary_op_args, **nary_op_kwargs)
-
-File ~/miniforge3/envs/myenv/lib/python3.9/site-packages/autograd/differential_operators.py:25, in grad(fun, x)
-     18 @unary_to_nary
-     19 def grad(fun, x):
-     20     """
-     21     Returns a function which computes the gradient of `fun` with respect to
-     22     positional argument number `argnum`. The returned function takes the same
-     23     arguments as `fun`, but returns the gradient instead. The function `fun`
-     24     should be scalar-valued. The gradient has the same type as the argument."""
----> 25     vjp, ans = _make_vjp(fun, x)
-     26     if not vspace(ans).size == 1:
-     27         raise TypeError("Grad only applies to real scalar-output functions. "
-     28                         "Try jacobian, elementwise_grad or holomorphic_grad.")
-
-File ~/miniforge3/envs/myenv/lib/python3.9/site-packages/autograd/core.py:10, in make_vjp(fun, x)
-      8 def make_vjp(fun, x):
-      9     start_node = VJPNode.new_root()
----> 10     end_value, end_node =  trace(start_node, fun, x)
-     11     if end_node is None:
-     12         def vjp(g): return vspace(x).zeros()
-
-File ~/miniforge3/envs/myenv/lib/python3.9/site-packages/autograd/tracer.py:10, in trace(start_node, fun, x)
-      8 with trace_stack.new_trace() as t:
-      9     start_box = new_box(x, t, start_node)
----> 10     end_box = fun(start_box)
-     11     if isbox(end_box) and end_box._trace == start_box._trace:
-     12         return end_box._value, end_box._node
-
-File ~/miniforge3/envs/myenv/lib/python3.9/site-packages/autograd/wrap_util.py:15, in unary_to_nary.<locals>.nary_operator.<locals>.nary_f.<locals>.unary_f(x)
-     13 else:
-     14     subargs = subvals(args, zip(argnum, x))
----> 15 return fun(*subargs, **kwargs)
-
-Input In [9], in cost_function(P, x, t)
-     78 g_t = g_trial(point,P)
-     79 g_t_jacobian = g_t_jacobian_func(point,P)
----> 80 g_t_hessian = g_t_hessian_func(point,P)
-     82 g_t_dt = g_t_jacobian[1]
-     83 g_t_d2x = g_t_hessian[0][0]
-
-File ~/miniforge3/envs/myenv/lib/python3.9/site-packages/autograd/wrap_util.py:20, in unary_to_nary.<locals>.nary_operator.<locals>.nary_f(*args, **kwargs)
-     18 else:
-     19     x = tuple(args[i] for i in argnum)
----> 20 return unary_operator(unary_f, x, *nary_op_args, **nary_op_kwargs)
-
-File ~/miniforge3/envs/myenv/lib/python3.9/site-packages/autograd/differential_operators.py:78, in hessian(fun, x)
-     75 @unary_to_nary
-     76 def hessian(fun, x):
-     77     "Returns a function that computes the exact Hessian."
----> 78     return jacobian(jacobian(fun))(x)
-
-File ~/miniforge3/envs/myenv/lib/python3.9/site-packages/autograd/wrap_util.py:20, in unary_to_nary.<locals>.nary_operator.<locals>.nary_f(*args, **kwargs)
-     18 else:
-     19     x = tuple(args[i] for i in argnum)
----> 20 return unary_operator(unary_f, x, *nary_op_args, **nary_op_kwargs)
-
-File ~/miniforge3/envs/myenv/lib/python3.9/site-packages/autograd/differential_operators.py:61, in jacobian(fun, x)
-     59 jacobian_shape = ans_vspace.shape + vspace(x).shape
-     60 grads = map(vjp, ans_vspace.standard_basis())
----> 61 return np.reshape(np.stack(grads), jacobian_shape)
-
-File ~/miniforge3/envs/myenv/lib/python3.9/site-packages/autograd/numpy/numpy_wrapper.py:88, in stack(arrays, axis)
-     83 def stack(arrays, axis=0):
-     84     # this code is basically copied from numpy/core/shape_base.py's stack
-     85     # we need it here because we want to re-implement stack in terms of the
-     86     # primitives defined in this file
----> 88     arrays = [array(arr) for arr in arrays]
-     89     if not arrays:
-     90         raise ValueError('need at least one array to stack')
-
-File ~/miniforge3/envs/myenv/lib/python3.9/site-packages/autograd/numpy/numpy_wrapper.py:88, in <listcomp>(.0)
-     83 def stack(arrays, axis=0):
-     84     # this code is basically copied from numpy/core/shape_base.py's stack
-     85     # we need it here because we want to re-implement stack in terms of the
-     86     # primitives defined in this file
----> 88     arrays = [array(arr) for arr in arrays]
-     89     if not arrays:
-     90         raise ValueError('need at least one array to stack')
-
-File ~/miniforge3/envs/myenv/lib/python3.9/site-packages/autograd/core.py:14, in make_vjp.<locals>.vjp(g)
----> 14 def vjp(g): return backward_pass(g, end_node)
-
-File ~/miniforge3/envs/myenv/lib/python3.9/site-packages/autograd/core.py:21, in backward_pass(g, end_node)
-     19 for node in toposort(end_node):
-     20     outgrad = outgrads.pop(node)
----> 21     ingrads = node.vjp(outgrad[0])
-     22     for parent, ingrad in zip(node.parents, ingrads):
-     23         outgrads[parent] = add_outgrads(outgrads.get(parent), ingrad)
-
-File ~/miniforge3/envs/myenv/lib/python3.9/site-packages/autograd/core.py:67, in defvjp.<locals>.vjp_argnums.<locals>.<lambda>(g)
-     64         raise NotImplementedError(
-     65             "VJP of {} wrt argnum 0 not defined".format(fun.__name__))
-     66     vjp = vjpfun(ans, *args, **kwargs)
----> 67     return lambda g: (vjp(g),)
-     68 elif L == 2:
-     69     argnum_0, argnum_1 = argnums
-
-File ~/miniforge3/envs/myenv/lib/python3.9/site-packages/autograd/numpy/numpy_vjps.py:423, in matmul_vjp_1.<locals>.<lambda>(g)
-    421 A_ndim = anp.ndim(A)
-    422 B_meta = anp.metadata(B)
---> 423 return lambda g: matmul_adjoint_1(A, g, A_ndim, B_meta)
-
-File ~/miniforge3/envs/myenv/lib/python3.9/site-packages/autograd/numpy/numpy_vjps.py:410, in matmul_adjoint_1(A, G, A_ndim, B_meta)
-    408 else:  # We need to swap the last two axes of A
-    409     A = anp.swapaxes(A, A_ndim - 2, A_ndim - 1)
---> 410 result = anp.matmul(A, G)
-    411 if B_is_vec:
-    412     result = anp.squeeze(result, anp.ndim(G) - 1)
-
-File ~/miniforge3/envs/myenv/lib/python3.9/site-packages/autograd/tracer.py:45, in primitive.<locals>.f_wrapped(*args, **kwargs)
-     43     argnums = tuple(argnum    for argnum, _   in boxed_args)
-     44     ans = f_wrapped(*argvals, **kwargs)
----> 45     node = node_constructor(ans, f_wrapped, argvals, kwargs, argnums, parents)
-     46     return new_box(ans, trace, node)
-     47 else:
-
-File ~/miniforge3/envs/myenv/lib/python3.9/site-packages/autograd/core.py:36, in VJPNode.__init__(self, value, fun, args, kwargs, parent_argnums, parents)
-     33     fun_name = getattr(fun, '__name__', fun)
-     34     raise NotImplementedError("VJP of {} wrt argnums {} not defined"
-     35                               .format(fun_name, parent_argnums))
----> 36 self.vjp = vjpmaker(parent_argnums, value, args, kwargs)
-
-File ~/miniforge3/envs/myenv/lib/python3.9/site-packages/autograd/core.py:77, in defvjp.<locals>.vjp_argnums(argnums, ans, args, kwargs)
-     74         raise NotImplementedError(
-     75             "VJP of {} wrt argnums 0, 1 not defined".format(fun.__name__))
-     76     vjp_0 = vjp_0_fun(ans, *args, **kwargs)
----> 77     vjp_1 = vjp_1_fun(ans, *args, **kwargs)
-     78     return lambda g: (vjp_0(g), vjp_1(g))
-     79 else:
-
-File ~/miniforge3/envs/myenv/lib/python3.9/site-packages/autograd/numpy/numpy_vjps.py:422, in matmul_vjp_1(ans, A, B)
-    420 def matmul_vjp_1(ans, A, B):
-    421     A_ndim = anp.ndim(A)
---> 422     B_meta = anp.metadata(B)
-    423     return lambda g: matmul_adjoint_1(A, g, A_ndim, B_meta)
-
-File ~/miniforge3/envs/myenv/lib/python3.9/site-packages/autograd/tracer.py:61, in notrace_primitive.<locals>.f_wrapped(*args, **kwargs)
-     58 @wraps(f_raw)
-     59 def f_wrapped(*args, **kwargs):
-     60     argvals = map(getval, args)
----> 61     return f_raw(*argvals, **kwargs)
-
-File ~/miniforge3/envs/myenv/lib/python3.9/site-packages/autograd/numpy/numpy_wrapper.py:148, in metadata(A)
-    146 @notrace_primitive
-    147 def metadata(A):
---> 148     return _np.shape(A), _np.ndim(A), _np.result_type(A), _np.iscomplexobj(A)
-
-KeyboardInterrupt: 
-
    -
    -
    - - - -
    -

    15.10. Solving the wave equation with Neural Networks

    + + +
    +

    15.10. Solving the wave equation with Neural Networks#

    The wave equation is

    \[ @@ -2783,7 +2431,7 @@ g(x,0) &= u(x), &x\in[0,1] \\ \end{split}\]

    In this example, let \(c = 1\) and \(u(x) = \sin(\pi x)\) and \(v(x) = -\pi\sin(\pi x)\).

    Setting up the network is done in similar matter as for the example of solving the diffusion equation. -The only things we have to change, is the trial solution such that it satisfies the conditions from (20) and the cost function.

    +The only things we have to change, is the trial solution such that it satisfies the conditions from (20) and the cost function.

    The trial solution becomes slightly different since we have other conditions than in the example of solving the diffusion equation. Here, a possible trial solution \(g_t(x,t)\) is

    \[ @@ -3026,17 +2674,17 @@ g(x,t) = \sin(\pi x)\cos(\pi t) - \sin(\pi x)\sin(\pi t)
    - - - + + - + - - -
    - - -
    -

    previous

    -

    14. Building a Feed Forward Neural Network

    -
    + + + + + +     + +
    - - -
    -

    - - By Morten Hjorth-Jensen
    - - © Copyright 2021.
    -

    -
    - + + +
    +
    +
    + Contents +
    +
    -
    -
    + + + + +
    + +
    - +
    + +

    +By Morten Hjorth-Jensen +

    +
    + +
    + +

    + + © Copyright 2022. +
    + +

    + +
    + +
    + +
    + +
    + +
    + +
    +
    + + + + + + + + + + +
    +
    \ No newline at end of file diff --git a/doc/LectureNotes/_build/html/chapter12.html b/doc/LectureNotes/_build/html/chapter12.html index f969e3b51..a4609262f 100644 --- a/doc/LectureNotes/_build/html/chapter12.html +++ b/doc/LectureNotes/_build/html/chapter12.html @@ -1,50 +1,61 @@ + - + + + - + + 16. Convolutional Neural Networks — Applied Data Analysis and Machine Learning - - - + + + + + + + + + - - - - + + + + + - - - - - + - + - - - - + + + + + + + + - - + + + - - - - - - + + - - - + + + - + + + + +
    -
    -
    - -
    -
    - + + - + - - -
    - - -
    -

    previous

    -

    15. Solving Differential Equations with Deep Learning

    -
    + + + + + +     + +
    - - -
    -

    - - By Morten Hjorth-Jensen
    - - © Copyright 2021.
    -

    -
    - + + +
    +
    +
    + Contents +
    +
    -
    -
    + + + + +
    + +
    - +
    + +

    +By Morten Hjorth-Jensen +

    +
    + +
    + +

    + + © Copyright 2022. +
    + +

    + +
    + +
    + +
    + +
    + +
    + +
    +
    + + + + + + + + + + +
    +
    \ No newline at end of file diff --git a/doc/LectureNotes/_build/html/chapter13.html b/doc/LectureNotes/_build/html/chapter13.html index 81f1db1a4..2117cc6f2 100644 --- a/doc/LectureNotes/_build/html/chapter13.html +++ b/doc/LectureNotes/_build/html/chapter13.html @@ -1,50 +1,61 @@ + - + + + - + + 17. Recurrent neural networks: Overarching view — Applied Data Analysis and Machine Learning - - - + + + + + + + + + - - - - + + + + + - - - - - + - + - - - - + + + + + + + + - - + + + - - - - - - + + - - - + + + - + + + + +
    -
    -
    - -

    How can we understand this?

    Let us write out the values of the coefficients \(\beta_i\) as functions @@ -2802,228 +2402,6 @@ large variance (normally for higher orders in the polynomial).

    -
    -
    - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
    beta
    00.986699
    1-0.606760
    21.280573
    3-0.850164
    40.000000
    -
    - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
    beta
    00.978553
    1-0.511888
    21.051418
    3-0.701370
    40.000000
    -
    - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
    beta
    00.946957
    1-0.162246
    20.221921
    3-0.167787
    40.000000
    -
    - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
    beta
    00.906747
    10.017665
    2-0.029483
    3-0.053849
    40.000000
    -
    - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
    beta
    00.718165
    10.156956
    20.040102
    3-0.001880
    40.000000
    -

    As an exercise, repeat these calculations with ordinary least squares only with and without noise. Calculate thereafter the variance of the @@ -3032,9 +2410,9 @@ noise. Here we recommend to use \(\si added noise (which follows a normal distribution with mean value zero). Comment your results. If you have a large noise term, do the parameters \(\beta_j\) vary more as function of model complexity? And what about their variance?

    - -
    -

    4.14. Linking Bayes’ Theorem with Ridge and Lasso Regression

    + +
    +

    4.14. Linking Bayes’ Theorem with Ridge and Lasso Regression#

    We have seen that Ridge regression suppresses those features which have a small singular value. This corresponds to a feature which exhibits a large variance in the parameters \(\beta_j\). @@ -3120,8 +2498,8 @@ C(\boldsymbol{\beta})=\frac{\vert\vert (\boldsymbol{y}-\boldsymbol{X}\boldsymbol \(\sigma^2=1/(2\lambda)\). Thus, increasing the variance means decreasing \(\lambda\) and shrinking the variance means increasing \(\lambda\). When we increase \(\lambda\), this corresponds to shrinking the role of less important features (small singular values).

    -
    - + + - + - - -
    - - -
    -

    previous

    -

    3. Linear Regression

    -
    + + + + + +     + +
    - - -
    -

    - - By Morten Hjorth-Jensen
    - - © Copyright 2021.
    -

    -
    - + + +
    +
    +
    + Contents +
    +
    -
    -
    + + + + +
    + +
    - +
    + +

    +By Morten Hjorth-Jensen +

    +
    + +
    + +

    + + © Copyright 2022. +
    + +

    + +
    + +
    + +
    + +
    + +
    + +
    +
    + + + + + + + + + + +
    +
    \ No newline at end of file diff --git a/doc/LectureNotes/_build/html/chapter3.html b/doc/LectureNotes/_build/html/chapter3.html index 2194b7987..9a6d63e7f 100644 --- a/doc/LectureNotes/_build/html/chapter3.html +++ b/doc/LectureNotes/_build/html/chapter3.html @@ -1,50 +1,61 @@ + - + + + - + + 5. Resampling Methods — Applied Data Analysis and Machine Learning - - - + + + + + + + + + - - - - + + + + + - - - - - + - + - - - - + + + + + + + + - - + + + - - - - - - + + - - - + + + - + + + + +
    -
    -
    - - -
    -
    Degree of polynomial:   1
-Mean squared error on training data: 439230.69504801
-Mean squared error on test data: 481979.17861098
-Degree of polynomial:   2
-Mean squared error on training data: 115822.95008046
-Mean squared error on test data: 123711.53703498
-Degree of polynomial:   3
-Mean squared error on training data: 9011.85263220
-Mean squared error on test data: 10913.84780262
-Degree of polynomial:   4
-Mean squared error on training data: 303.47610036
-Mean squared error on test data: 426.30787294
-Degree of polynomial:   5
-Mean squared error on training data: 3.80354994
-Mean squared error on test data: 5.98822371
-Degree of polynomial:   6
-Mean squared error on training data: 3.66204648
-Mean squared error on test data: 8.14812206
-Degree of polynomial:   7
-Mean squared error on training data: 0.47075725
-Mean squared error on test data: 2.00607783
-
    -
    Degree of polynomial:   8
-Mean squared error on training data: 0.04912436
-Mean squared error on test data: 0.21596432
-Degree of polynomial:   9
-Mean squared error on training data: 0.02522069
-Mean squared error on test data: 0.08576932
-Degree of polynomial:  10
-Mean squared error on training data: 0.02511518
-Mean squared error on test data: 1.20015436
-Degree of polynomial:  11
-Mean squared error on training data: 0.01640891
-Mean squared error on test data: 1.35533773
-Degree of polynomial:  12
-Mean squared error on training data: 0.00813803
-Mean squared error on test data: 0.17446471
-Degree of polynomial:  13
-Mean squared error on training data: 0.00759119
-Mean squared error on test data: 1.08131003
-
    -
    -
    Degree of polynomial:  14
-Mean squared error on training data: 0.00472199
-Mean squared error on test data: 0.81333804
-Degree of polynomial:  15
-Mean squared error on training data: 0.00410478
-Mean squared error on test data: 92.09172409
-Degree of polynomial:  16
-Mean squared error on training data: 0.00315593
-Mean squared error on test data: 234.38533185
-Degree of polynomial:  17
-Mean squared error on training data: 0.00242999
-Mean squared error on test data: 1271.35771826
-Degree of polynomial:  18
-Mean squared error on training data: 0.00228742
-Mean squared error on test data: 108.27092910
-Degree of polynomial:  19
-Mean squared error on training data: 0.00156376
-Mean squared error on test data: 1371.99051150
-Degree of polynomial:  20
-Mean squared error on training data: 0.00137818
-Mean squared error on test data: 1887.86252988
-
    -
    -
    Degree of polynomial:  21
-Mean squared error on training data: 0.00118508
-Mean squared error on test data: 14859.69908626
-Degree of polynomial:  22
-Mean squared error on training data: 0.00092647
-Mean squared error on test data: 876.51191552
-Degree of polynomial:  23
-Mean squared error on training data: 0.00085889
-Mean squared error on test data: 5594.60815105
-Degree of polynomial:  24
-Mean squared error on training data: 0.00084705
-Mean squared error on test data: 1277.61702282
-Degree of polynomial:  25
-Mean squared error on training data: 0.00079129
-Mean squared error on test data: 128664.31650694
-Degree of polynomial:  26
-Mean squared error on training data: 0.00076905
-Mean squared error on test data: 19003.94822514
-Degree of polynomial:  27
-Mean squared error on training data: 0.00068946
-Mean squared error on test data: 2379.66219404
-
    -
    -
    Degree of polynomial:  28
-Mean squared error on training data: 0.00062595
-Mean squared error on test data: 4082.19983530
-Degree of polynomial:  29
-Mean squared error on training data: 0.00060705
-Mean squared error on test data: 3250.17647619
-
    -
    -
    /var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_7650/626635268.py:73: RuntimeWarning: divide by zero encountered in log10
-  plt.plot(polynomial, np.log10(trainingerror), label='Training Error')
-/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_7650/626635268.py:74: RuntimeWarning: divide by zero encountered in log10
-  plt.plot(polynomial, np.log10(testerror), label='Test Error')
-
    -
    -_images/chapter3_69_6.png -
    -
    -
    -
    -

    5.5. Cross-validation

    + +
    +

    5.5. Cross-validation#

    When the repetitive splitting of the data set is done randomly, samples may accidently end up in a fast majority of the splits in either training or test set. Such samples may have an unbalanced @@ -1674,7 +1370,7 @@ cross-validation (LOOCV).

    \end{align*} \]

    For the various values of \(k\)

    -
      +

      1. shuffle the dataset randomly.

      2. Split the dataset into \(k\) groups.

      3. For each unique group:

        4. @@ -1683,7 +1379,7 @@ cross-validation (LOOCV).

        b. Take the remaining groups as a training data set

        c. Fit a model on the training set and evaluate it on the test set

        d. Retain the evaluation score and discard the model

        -
          +

          1. Summarize the model using the sample of model evaluation scores

          The code here uses Ridge regression with cross-validation (CV) resampling and \(k\)-fold CV in order to fit a specific polynomial.

          @@ -1781,9 +1477,6 @@ cross-validation (LOOCV).

          -
          -_images/chapter3_75_0.png -

          More examples of the application of cross-validation follow here.

          @@ -1858,18 +1551,11 @@ cross-validation (LOOCV).

          -
          -
          /var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_7650/3817475779.py:63: RuntimeWarning: divide by zero encountered in log10
-  plt.plot(polynomial, np.log10(estimated_mse_sklearn), label='Test Error')
-
          -
          -_images/chapter3_77_1.png -

          Note that we have kept the intercept in the first column of design matrix \(\boldsymbol{X}\). When we call the corresponding Scikit-Learn function we need thus to set the intercept to False. Libraries like Scikit-Learn normally scale the design matrix and do not fit intercept. See the discussions below.

          - -
          -

          5.6. More on Rescaling data

          + +
          +

          5.6. More on Rescaling data#

          We end this chapter by adding some words on scaling and how to deal with the intercept for regression cases.

          When you are comparing your own code with for example Scikit-Learn’s library, there are some technicalities to keep in mind. The examples @@ -1930,11 +1616,6 @@ on your eventual new data set before making a prediction. If we translate this

          -
          -
          '\n#Model training, we compute the mean value of y and X\ny_train_mean = np.mean(y_train)\nX_train_mean = np.mean(X_train,axis=0)\nX_train = X_train - X_train_mean\ny_train = y_train - y_train_mean\n\n# The we fit our model with the training data\ntrained_model = some_model.fit(X_train,y_train)\n\n\n#Model prediction, we need also to transform our data set used for the prediction.\nX_test = X_test - X_train_mean #Use mean from training data\ny_pred = trained_model(X_test)\ny_pred = y_pred + y_train_mean\n'
-
          -
          -

          Let us try to understand what this may imply mathematically when we subtract the mean values, also known as zero centering. For @@ -2118,26 +1799,6 @@ Note also that we do not split the data into training and test.

          -
          -
          True beta: [2, 0.5, 3.7]
-Fitted beta: [2.08376632 0.19569961 3.97898392]
-Sklearn fitted beta: [2.08376632 0.19569961 3.97898392]
-MSE with intercept column
-0.004113634617443139
-MSE with intercept column from SKL
-0.004113634617443147
-Manual intercept: 2.083766322923899
-Fitted beta (wiothout intercept): [0.19569961 3.97898392]
-Sklearn intercept: 2.0837663229239043
-Sklearn fitted beta (without intercept): [0.19569961 3.97898392]
-MSE with Manual intercept
-0.00411363461744314
-MSE with Sklearn intercept
-0.004113634617443131
-
          -
          -_images/chapter3_112_1.png -

          The intercept is the value of our output/target variable when all our features are zero and our function crosses the \(y\)-axis (for a one-dimensional case).

          @@ -2240,99 +1901,6 @@ intercept.

          -
          -
          Beta values for own Ridge implementation
-[ 1.03032441e+00  6.28336218e-02 -6.24175744e-01  5.21169159e-02
-  2.80847477e-01  2.12552073e-01  8.13220608e-02 -1.69634577e-02
- -6.50846112e-02 -7.38962192e-02 -5.94226022e-02 -3.50227564e-02
- -9.80609616e-03  1.08299273e-02  2.41882037e-02  2.93492130e-02
-  2.64742912e-02  1.63249532e-02 -5.01831251e-05 -2.15098090e-02]
-Beta values for Scikit-Learn Ridge implementation
-[ 1.03032441e+00  6.28336218e-02 -6.24175744e-01  5.21169159e-02
-  2.80847477e-01  2.12552073e-01  8.13220608e-02 -1.69634577e-02
- -6.50846112e-02 -7.38962192e-02 -5.94226022e-02 -3.50227564e-02
- -9.80609615e-03  1.08299273e-02  2.41882037e-02  2.93492130e-02
-  2.64742912e-02  1.63249532e-02 -5.01831207e-05 -2.15098090e-02]
-MSE values for own Ridge implementation
-4.3632959215700067e-07
-MSE values for Scikit-Learn Ridge implementation
-4.363295916323784e-07
-Beta values for own Ridge implementation
-[ 1.03630548 -0.01963611 -0.37900111 -0.07062318  0.12182967  0.16343471
-  0.13003291  0.07490892  0.02365049 -0.01449782 -0.03814292 -0.04909093
- -0.05009826 -0.04389027 -0.03279636 -0.01866537 -0.00289724  0.01348565
-  0.02976145  0.04543942]
-Beta values for Scikit-Learn Ridge implementation
-[ 1.03630548 -0.01963611 -0.37900111 -0.07062318  0.12182967  0.16343471
-  0.13003291  0.07490892  0.02365049 -0.01449782 -0.03814292 -0.04909093
- -0.05009826 -0.04389027 -0.03279636 -0.01866537 -0.00289724  0.01348565
-  0.02976145  0.04543942]
-MSE values for own Ridge implementation
-5.194042827197027e-06
-MSE values for Scikit-Learn Ridge implementation
-5.1940428268204826e-06
-Beta values for own Ridge implementation
-[ 1.04220758 -0.10931453 -0.17641709 -0.06020587  0.02208512  0.05789007
-  0.06491736  0.05785343  0.04537385  0.03196357  0.01969145  0.00934499
-  0.00107405 -0.00526348 -0.00992331 -0.01318643 -0.01531845 -0.01655318
- -0.01708852 -0.01708781]
-Beta values for Scikit-Learn Ridge implementation
-[ 1.04220758 -0.10931453 -0.17641709 -0.06020587  0.02208512  0.05789007
-  0.06491736  0.05785343  0.04537385  0.03196357  0.01969145  0.00934499
-  0.00107405 -0.00526348 -0.00992331 -0.01318643 -0.01531845 -0.01655318
- -0.01708852 -0.01708781]
-MSE values for own Ridge implementation
-2.0940821989643363e-05
-MSE values for Scikit-Learn Ridge implementation
-2.094082198961999e-05
-Beta values for own Ridge implementation
-[ 1.01219292 -0.06043581 -0.10391807 -0.05651951 -0.01898855  0.00312361
-  0.01463049  0.01975848  0.02123176  0.02068067  0.01905883  0.01691985
-  0.01458337  0.01223198  0.00996754  0.00784393  0.00588657  0.00410387
-  0.00249435  0.00105081]
-Beta values for Scikit-Learn Ridge implementation
-[ 1.01219292 -0.06043581 -0.10391807 -0.05651951 -0.01898855  0.00312361
-  0.01463049  0.01975848  0.02123176  0.02068067  0.01905883  0.01691985
-  0.01458337  0.01223198  0.00996754  0.00784393  0.00588657  0.00410387
-  0.00249435  0.00105081]
-MSE values for own Ridge implementation
-0.0003153514830957865
-MSE values for Scikit-Learn Ridge implementation
-0.00031535148309580783
-Beta values for own Ridge implementation
-[ 8.38916861e-01  1.31276579e-01  8.97497404e-03 -1.72271878e-02
- -2.11744554e-02 -1.91492986e-02 -1.57201944e-02 -1.23002365e-02
- -9.30466214e-03 -6.81048318e-03 -4.78184120e-03 -3.15130074e-03
- -1.84923989e-03 -8.13661243e-04  7.46984697e-06  6.56636616e-04
-  1.16805821e-03  1.56912044e-03  1.88168312e-03  2.12318726e-03]
-Beta values for Scikit-Learn Ridge implementation
-[ 8.38916861e-01  1.31276579e-01  8.97497404e-03 -1.72271878e-02
- -2.11744554e-02 -1.91492986e-02 -1.57201944e-02 -1.23002365e-02
- -9.30466214e-03 -6.81048318e-03 -4.78184120e-03 -3.15130074e-03
- -1.84923989e-03 -8.13661243e-04  7.46984697e-06  6.56636616e-04
-  1.16805821e-03  1.56912044e-03  1.88168312e-03  2.12318726e-03]
-MSE values for own Ridge implementation
-0.015072388895177157
-MSE values for Scikit-Learn Ridge implementation
-0.0150723888951771
-Beta values for own Ridge implementation
-[0.37396662 0.14174745 0.0764924  0.04892055 0.03447512 0.02586427
- 0.02024962 0.01633913 0.01347916 0.0113104  0.0096208  0.00827728
- 0.00719176 0.00630331 0.00556826 0.0049544  0.00443743 0.0039987
- 0.0036237  0.003301  ]
-Beta values for Scikit-Learn Ridge implementation
-[0.37396662 0.14174745 0.0764924  0.04892055 0.03447512 0.02586427
- 0.02024962 0.01633913 0.01347916 0.0113104  0.0096208  0.00827728
- 0.00719176 0.00630331 0.00556826 0.0049544  0.00443743 0.0039987
- 0.0036237  0.003301  ]
-MSE values for own Ridge implementation
-0.26409315307910036
-MSE values for Scikit-Learn Ridge implementation
-0.26409315307910025
-
          -
          -_images/chapter3_120_1.png -

          The results here agree when we force Scikit-Learn’s Ridge function to include the first column in our design matrix. We see that the results agree very well. Here we have thus explicitely included the intercept column in the design matrix. @@ -2421,121 +1989,6 @@ Let us see how we can change this code by zero centering.

          -
          -
          Beta values for own Ridge implementation
-[ 3.43579948e-02 -5.43330971e-01 -3.10141413e-03  2.47116868e-01
-  2.18613217e-01  1.02054837e-01 -4.25617658e-04 -5.90475506e-02
- -7.68534263e-02 -6.68929213e-02 -4.24906604e-02 -1.40927184e-02
-  1.11482289e-02  2.88529063e-02  3.67047975e-02  3.38135733e-02
-  2.02198703e-02 -3.46383925e-03 -3.63025821e-02]
-Beta values for Scikit-Learn Ridge implementation
-[ 3.43579948e-02 -5.43330971e-01 -3.10141413e-03  2.47116868e-01
-  2.18613217e-01  1.02054837e-01 -4.25617654e-04 -5.90475506e-02
- -7.68534263e-02 -6.68929213e-02 -4.24906604e-02 -1.40927184e-02
-  1.11482289e-02  2.88529063e-02  3.67047975e-02  3.38135733e-02
-  2.02198702e-02 -3.46383925e-03 -3.63025821e-02]
-Intercept from own implementation:
-1.0330308045188872
-Intercept from Scikit-Learn Ridge implementation
-1.0330308045183219
-MSE values for own Ridge implementation
-3.1392559591206444e-06
-MSE values for Scikit-Learn Ridge implementation
-3.1392559585048734e-06
-Beta values for own Ridge implementation
-[-0.05807125 -0.29822833 -0.08551306  0.08156108  0.13679863  0.12333649
-  0.08251519  0.03815288  0.00111756 -0.02498832 -0.04010697 -0.04566964
- -0.04355837 -0.03562355 -0.02348765 -0.00848904  0.00831018  0.0260906
-  0.04423486]
-Beta values for Scikit-Learn Ridge implementation
-[-0.05807125 -0.29822833 -0.08551306  0.08156108  0.13679863  0.12333649
-  0.08251519  0.03815288  0.00111756 -0.02498832 -0.04010697 -0.04566964
- -0.04355837 -0.03562355 -0.02348765 -0.00848904  0.00831018  0.0260906
-  0.04423486]
-Intercept from own implementation:
-1.041148729430595
-Intercept from Scikit-Learn Ridge implementation
-1.041148729430523
-MSE values for own Ridge implementation
-1.96013048502692e-05
-MSE values for Scikit-Learn Ridge implementation
-1.960130485007504e-05
-Beta values for own Ridge implementation
-[-0.1416398  -0.14021063 -0.05383795  0.01367553  0.04784395  0.05796251
-  0.05447415  0.044613    0.03267527  0.02098261  0.01066519  0.00217499
- -0.00440346 -0.00917248 -0.01231917 -0.01405935 -0.0146081  -0.01416528
- -0.01290947]
-Beta values for Scikit-Learn Ridge implementation
-[-0.1416398  -0.14021063 -0.05383795  0.01367553  0.04784395  0.05796251
-  0.05447415  0.044613    0.03267527  0.02098261  0.01066519  0.00217499
- -0.00440346 -0.00917248 -0.01231917 -0.01405935 -0.0146081  -0.01416528
- -0.01290947]
-Intercept from own implementation:
-1.0495569966278295
-Intercept from Scikit-Learn Ridge implementation
-1.0495569966278269
-MSE values for own Ridge implementation
-5.4959161509377256e-05
-MSE values for Scikit-Learn Ridge implementation
-5.495916150936645e-05
-Beta values for own Ridge implementation
-[-0.13535942 -0.08593216 -0.03568439 -0.0036367   0.01397146  0.02229529
-  0.02503753  0.0245528   0.02228115  0.01908936  0.01549377  0.01179792
-  0.00817631  0.00472512  0.00149311 -0.00149956 -0.00424967 -0.00676387
- -0.00905423]
-Beta values for Scikit-Learn Ridge implementation
-[-0.13535942 -0.08593216 -0.03568439 -0.0036367   0.01397146  0.02229529
-  0.02503753  0.0245528   0.02228115  0.01908936  0.01549377  0.01179792
-  0.00817631  0.00472512  0.00149311 -0.00149956 -0.00424967 -0.00676387
- -0.00905423]
-Intercept from own implementation:
-1.0399676689527966
-Intercept from Scikit-Learn Ridge implementation
-1.0399676689527975
-MSE values for own Ridge implementation
-7.571105947979352e-05
-MSE values for Scikit-Learn Ridge implementation
-7.571105947979394e-05
-Beta values for own Ridge implementation
-[-0.05100875 -0.04063602 -0.02723445 -0.01713366 -0.0100706  -0.00517114
- -0.00174276  0.00068734  0.00243186  0.00369758  0.00462287  0.0053018
-  0.00579953  0.006162    0.00642221  0.00660427  0.00672607  0.0068011
-  0.00683964]
-Beta values for Scikit-Learn Ridge implementation
-[-0.05100875 -0.04063602 -0.02723445 -0.01713366 -0.0100706  -0.00517114
- -0.00174276  0.00068734  0.00243186  0.00369758  0.00462287  0.0053018
-  0.00579953  0.006162    0.00642221  0.00660427  0.00672607  0.0068011
-  0.00683964]
-Intercept from own implementation:
-0.999955585168597
-Intercept from Scikit-Learn Ridge implementation
-0.999955585168597
-MSE values for own Ridge implementation
-0.0007698473260556344
-MSE values for Scikit-Learn Ridge implementation
-0.0007698473260556325
-Beta values for own Ridge implementation
-[-0.00834567 -0.00803064 -0.00673407 -0.00554552 -0.00458878 -0.0038335
- -0.00323332 -0.00274989 -0.0023548  -0.00202756 -0.00175331 -0.00152117
- -0.001323   -0.0011526  -0.00100519 -0.00087697 -0.00076495 -0.00066668
- -0.00058016]
-Beta values for Scikit-Learn Ridge implementation
-[-0.00834567 -0.00803064 -0.00673407 -0.00554552 -0.00458878 -0.0038335
- -0.00323332 -0.00274989 -0.0023548  -0.00202756 -0.00175331 -0.00152117
- -0.001323   -0.0011526  -0.00100519 -0.00087697 -0.00076495 -0.00066668
- -0.00058016]
-Intercept from own implementation:
-0.9637117593816477
-Intercept from Scikit-Learn Ridge implementation
-0.9637117593816477
-MSE values for own Ridge implementation
-0.0023813163025848865
-MSE values for Scikit-Learn Ridge implementation
-0.002381316302584886
-
          -
          -_images/chapter3_122_1.png -

          We see here, when compared to the code which includes explicitely the intercept column, that our MSE value is actually smaller. This is @@ -2546,9 +1999,9 @@ centered matrix and/or vector that enter the fitting procedure. Note also that the problem with the intercept occurs mainly in these type of polynomial fitting problem.

          The next example is indeed an example where all these discussions about the role of intercept are not present.

          - -
          -

          5.7. More complicated Example: The Ising model

          + +
          +

          5.7. More complicated Example: The Ising model#

          The one-dimensional Ising model with nearest neighbor interaction, no external field and a constant coupling constant \(J\) is given by

          @@ -2747,15 +2200,6 @@ linear system as an equation would reduce this down to
          -
          -
          /var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_7650/4162706317.py:6: MatplotlibDeprecationWarning: Auto-removal of grids by pcolor() and pcolormesh() is deprecated since 3.5 and will be removed two minor releases later; please call grid(False) first.
-  cb = fig.colorbar(im)
-/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_7650/4162706317.py:7: UserWarning: FixedFormatter should only be used together with FixedLocator
-  cb.ax.set_yticklabels(cb.ax.get_yticklabels(), fontsize=18)
-
          -
          -_images/chapter3_154_1.png -

          It is interesting to note that OLS considers both \(J_{j, j + 1} = -0.5\) and \(J_{j, j - 1} = -0.5\) as @@ -2893,15 +2337,6 @@ with the form utilized in linear regression, viz.

          -
          -
          /var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_7650/3777801602.py:6: MatplotlibDeprecationWarning: Auto-removal of grids by pcolor() and pcolormesh() is deprecated since 3.5 and will be removed two minor releases later; please call grid(False) first.
-  cb = fig.colorbar(im)
-/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_7650/3777801602.py:7: UserWarning: FixedFormatter should only be used together with FixedLocator
-  cb.ax.set_yticklabels(cb.ax.get_yticklabels(), fontsize=18)
-
          -
          -_images/chapter3_172_1.png -

          The results agree perfectly with our previous discussion where we used our own code.

          Having explored the ordinary least squares we move on to ridge @@ -2935,15 +2370,6 @@ cost function is given by

          -
          -
          /var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_7650/438060758.py:9: MatplotlibDeprecationWarning: Auto-removal of grids by pcolor() and pcolormesh() is deprecated since 3.5 and will be removed two minor releases later; please call grid(False) first.
-  cb = fig.colorbar(im)
-/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_7650/438060758.py:10: UserWarning: FixedFormatter should only be used together with FixedLocator
-  cb.ax.set_yticklabels(cb.ax.get_yticklabels(), fontsize=18)
-
          -
          -_images/chapter3_175_1.png -

          In the Least Absolute Shrinkage and Selection Operator (LASSO)-method we get a third cost function.

          @@ -2972,15 +2398,6 @@ cost function is given by

          -
          -
          /var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_7650/3544313922.py:8: MatplotlibDeprecationWarning: Auto-removal of grids by pcolor() and pcolormesh() is deprecated since 3.5 and will be removed two minor releases later; please call grid(False) first.
-  cb = fig.colorbar(im)
-/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_7650/3544313922.py:9: UserWarning: FixedFormatter should only be used together with FixedLocator
-  cb.ax.set_yticklabels(cb.ax.get_yticklabels(), fontsize=18)
-
          -
          -_images/chapter3_179_1.png -

          It is quite striking how LASSO breaks the symmetry of the coupling constant as opposed to ridge and OLS. We get a sparse solution with @@ -3027,51 +2444,6 @@ constant as opposed to ridge and OLS. We get a sparse solution with -

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-  model = cd_fast.enet_coordinate_descent(
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          We see that LASSO reaches a good solution for low values of \(\lambda\), but will “wither” when we increase \(\lambda\) too @@ -3118,16 +2490,13 @@ testing set that is close to the accuracy of the training set.

          -
          -_images/chapter3_183_0.png -

          From the above figure we can see that LASSO with \(\lambda = 10^{-2}\) achieves a very good accuracy on the test set. This by far surpasses the other models for all values of \(\lambda\).

          - -
          -

          5.8. Exercises and Projects

          + +
          +

          5.8. Exercises and Projects#

          The main aim of this project is to study in more detail various regression methods, including the Ordinary Least Squares (OLS) method, The total score is 100 points. Each subtask has its own final score.

          @@ -3210,18 +2579,9 @@ which polynomial fits the data best.

          -
          -
          /var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_7650/3980313467.py:9: MatplotlibDeprecationWarning: Calling gca() with keyword arguments was deprecated in Matplotlib 3.4. Starting two minor releases later, gca() will take no keyword arguments. The gca() function should only be used to get the current axes, or if no axes exist, create new axes with default keyword arguments. To create a new axes with non-default arguments, use plt.axes() or plt.subplot().
-  ax = fig.gca(projection='3d')
-/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_7650/3980313467.py:37: MatplotlibDeprecationWarning: Auto-removal of grids by pcolor() and pcolormesh() is deprecated since 3.5 and will be removed two minor releases later; please call grid(False) first.
-  fig.colorbar(surf, shrink=0.5, aspect=5)
-
          -_images/chapter3_188_1.png -
          - -
          -

          5.8.1. Exercise: Ordinary Least Square (OLS) on the Franke function

          +
          +

          5.8.1. Exercise: Ordinary Least Square (OLS) on the Franke function#

          We will generate our own dataset for a function \(\mathrm{FrankeFunction}(x,y)\) with \(x,y \in [0,1]\). The function \(f(x,y)\) is the Franke function. You should explore also the addition @@ -3264,9 +2624,9 @@ is no explicit recipe for how much data should be included as training data and say test data. An accepted rule of thumb is to use approximately \(2/3\) to \(4/5\) of the data as training data.

          You can easily reuse the solutions to your exercises from week 35 and week 36.

          -
          -
          -

          5.8.2. Exercise: Bias-variance trade-off and resampling techniques

          + +
          +

          5.8.2. Exercise: Bias-variance trade-off and resampling techniques#

          Our aim here is to study the bias-variance trade-off by implementing the bootstrap resampling technique.

          With a code which does OLS and includes resampling techniques, we will now discuss the bias-variance trade-off in the context of @@ -3313,9 +2673,9 @@ studying the MSE value as function of the complexity of your model.

          of your model complexity (the degree of the polynomial) and the number of data points, and possibly also your training and test data using the bootstrap resampling method.

          Note also that when you calculate the bias, in all applications you don’t know the function values \(f_i\). You would hence replace them with the actual data points \(y_i\).

          -
          -
          -

          5.8.3. Exercise: Cross-validation as resampling techniques, adding more complexity

          + +
          +

          5.8.3. Exercise: Cross-validation as resampling techniques, adding more complexity#

          The aim here is to write your own code for another widely popular resampling technique, the so-called cross-validation method. Again, before you start with cross-validation approach, you should scale your @@ -3328,9 +2688,9 @@ from the test folds. You can compare your own code with that from you got from your bootstrap code. Comment your results. Try \(5-10\) folds. You can also compare your own cross-validation code with the one provided by Scikit-Learn.

          -
          -
          -

          5.8.4. Exercise: Ridge Regression on the Franke function with resampling

          + +
          +

          5.8.4. Exercise: Ridge Regression on the Franke function with resampling#

          Write your own code for the Ridge method, either using matrix inversion or the singular value decomposition as done in the previous exercise. Perform the same bootstrap analysis as in the @@ -3339,25 +2699,25 @@ analyze your results with those obtained in exercises 1-3. Study the dependence on \(\lambda\).

          Study also the bias-variance trade-off as function of various values of the parameter \(\lambda\). For the bias-variance trade-off, use the bootstrap resampling method. Comment your results.

          -
          -
          -

          5.8.5. Exercise: Lasso Regression on the Franke function with resampling

          + +
          +

          5.8.5. Exercise: Lasso Regression on the Franke function with resampling#

          This exercise is essentially a repeat of the previous two ones, but now with Lasso regression. Write either your own code (difficult and optional) or, in this case, you can also use the functionalities of Scikit-Learn (recommended). Give a critical discussion of the three methods and a judgement of which model fits the data best. Perform here as well an analysis of the bias-variance trade-off using the bootstrap resampling technique and an analysis of the mean squared error using cross-validation.

          -
          -
          -

          5.8.6. Exercise: Analysis of real data

          + +
          +

          5.8.6. Exercise: Analysis of real data#

          With our codes functioning and having been tested properly on a simpler function we are now ready to look at real data. We will essentially repeat in this exercise what was done in exercises 1-5. However, we need first to download the data and prepare properly the inputs to our codes. We are going to download digital terrain data from the website https://earthexplorer.usgs.gov/,

          -

          Or, if you prefer, we have placed selected datafiles at https://github.com/CompPhysics/MachineLearning/tree/master/doc/Projects/2021/Project1/DataFiles

          +

          Or, if you prefer, we have placed selected datafiles at CompPhysics/MachineLearning

          In order to obtain data for a specific region, you need to register as a user (free) at this website and then decide upon which area you want to fetch the digital terrain data from. In order to be able to read @@ -3371,16 +2731,6 @@ Python program using

          -
          -
          ---------------------------------------------------------------------------
-NameError                                 Traceback (most recent call last)
-Input In [31], in <cell line: 1>()
-----> 1 scipy.misc.imread
-
-NameError: name 'scipy' is not defined
-
          -
          -

          Here is a simple part of a Python code which reads and plots the data from such files

          @@ -3420,9 +2770,9 @@ model fits the data best.

          At the end, you should present a critical evaluation of your results and discuss the applicability of these regression methods to the type of data presented here (either the terrain data we propose or other data sets).

          - - - + + + - + - - -
          - - -
          -

          previous

          -

          4. Ridge and Lasso Regression

          -
          + + + + + +           + +
          - - -
          -

          - - By Morten Hjorth-Jensen
          - - © Copyright 2021.
          -

          -
          - + + +
          +
          +
          + Contents +
          +
          -
          -
          + + + + +
          + +
          - +
          + +

          +By Morten Hjorth-Jensen +

          +
          + +
          + +

          + + © Copyright 2022. +
          + +

          + +
          + +
          + +
          + +
          + +
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          +
          \ No newline at end of file diff --git a/doc/LectureNotes/_build/html/chapter4.html b/doc/LectureNotes/_build/html/chapter4.html index eb5cd3b1c..630e99757 100644 --- a/doc/LectureNotes/_build/html/chapter4.html +++ b/doc/LectureNotes/_build/html/chapter4.html @@ -1,50 +1,61 @@ + - + + + - + + 6. Logistic Regression — Applied Data Analysis and Machine Learning - - - + + + + + + + + + - - - - + + + + + - - - - - + - + - - - - + + + + + + + + - - + + + - - - - - - + + - - - + + + - + + + + +
          -
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          - -
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          -_images/chapter4_57_0.png -_images/chapter4_57_1.png -

          In the above example we note two things. In the first plot we display the overlap of benign and malignant tumors as functions of the various @@ -1092,7 +972,7 @@ the classical Principal Component Analysis (PCA) theorem with applications. This will be discussed later this semester (week 43).

          Here we present a further way to present our results in terms of a so-called confusion matrix, the cumulative gain and the ROC curve. This way of displaying our data are based upon different ways to classify our possible outcomes. Before we proceed we need some definitions.

          -
            +

            1. TP: true positive or in other words, something equivalent with a proper classification

            2. TN: true negative, which is equivalent with a correct rejection

            3. FP: false positive, or in simpler words something that is equivalent with a false alarm

              4. @@ -1153,33 +1033,9 @@ Based on this we can then define the accuracy score as the sum of correctly pred -
              -
              (426, 30)
-(143, 30)
-Test set accuracy with Logistic Regression: 0.94
-Test set accuracy Logistic Regression with scaled data: 0.96
-[1.         1.         1.         1.         1.         1.
- 1.         1.         0.92857143 0.92857143]
-Test set accuracy with Logistic Regression  and scaled data: 0.96
-
              -
              -
              /Users/mhjensen/miniforge3/envs/myenv/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:814: ConvergenceWarning: lbfgs failed to converge (status=1):
-STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
-
-Increase the number of iterations (max_iter) or scale the data as shown in:
-    https://scikit-learn.org/stable/modules/preprocessing.html
-Please also refer to the documentation for alternative solver options:
-    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
-  n_iter_i = _check_optimize_result(
-
              -
              -_images/chapter4_64_2.png -_images/chapter4_64_3.png -_images/chapter4_64_4.png -
              - - + + - + - - -
              - - -
              -

              previous

              -

              5. Resampling Methods

              -
              + + + + + +               + +
              - - -
              -

              - - By Morten Hjorth-Jensen
              - - © Copyright 2021.
              -

              -
              - + + +
              +
              +
              + Contents +
              +
              -
              -
              + + + + +
              + +
              - +
              + +

              +By Morten Hjorth-Jensen +

              +
              + +
              + +

              + + © Copyright 2022. +
              + +

              + +
              + +
              + +
              + +
              + +
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              + + + + + + + + + + +
              +
              \ No newline at end of file diff --git a/doc/LectureNotes/_build/html/chapter5.html b/doc/LectureNotes/_build/html/chapter5.html index a5a6f181e..a9d12411e 100644 --- a/doc/LectureNotes/_build/html/chapter5.html +++ b/doc/LectureNotes/_build/html/chapter5.html @@ -1,50 +1,61 @@ + - + + + - + + 8. Support Vector Machines, overarching aims — Applied Data Analysis and Machine Learning - - - + + + + + + + + + - - - - + + + + + - - - - - + - + - - - - + + + + + + + + - - + + + - - - - - - + + - - - + + + - + + + + +
              -
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              +
              + +
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              +By Morten Hjorth-Jensen +

              +
              + +
              + +

              + + © Copyright 2022. +
              + +

              + +
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              \ No newline at end of file diff --git a/doc/LectureNotes/_build/html/chapter6.html b/doc/LectureNotes/_build/html/chapter6.html index 05e5bf3e6..c3f7944ef 100644 --- a/doc/LectureNotes/_build/html/chapter6.html +++ b/doc/LectureNotes/_build/html/chapter6.html @@ -1,50 +1,61 @@ + - + + + - + + 9. Decision trees, overarching aims — Applied Data Analysis and Machine Learning - - - + + + + + + + + + - - - - + + + + + - - - - - + - + - - - - + + + + + + + + - - + + + - - - - - - + + - - - + + + - + + + + +
              -
              -
              - - -
              -

              9.6. Entropy and the ID3 algorithm

              + + +
              +

              9.6. Entropy and the ID3 algorithm#

              The ID3 algorithm learns decision trees by constructing them in a top down way, beginning with the question which attribute should be tested at the root of the tree?

              -
                +

                1. Each instance attribute is evaluated using a statistical test to determine how well it alone classifies the training examples.

                2. The best attribute is selected and used as the test at the root node of the tree.

                3. A descendant of the root node is then created for each possible value of this attribute.

                  4. @@ -1455,8 +1184,8 @@ examples.

                  training examples according to their target classification.

                  The ID3 algorithm uses this information gain measure to select among the candidate attributes at each step while growing the tree.

                  -
                  -

                  9.6.1. Cancer Data again now with Decision Trees and other Methods

                  +
                  +

                  9.6.1. Cancer Data again now with Decision Trees and other Methods#

                  import matplotlib.pyplot as plt
@@ -1504,9 +1233,9 @@ attributes at each step while growing the tree.
                  -
                  -
                  -

                  9.6.2. Another example, the moons again

                  +
                  +
                  +

                  9.6.2. Another example, the moons again#

                  from __future__ import division, print_function, unicode_literals
@@ -1703,10 +1432,10 @@ attributes at each step while growing the tree.
                  -
                  -
                  -
                  -

                  9.7. Pros and cons of trees, pros

                  +
              + +
              +

              9.7. Pros and cons of trees, pros#

              • White box, easy to interpret model. Some people believe that decision trees more closely mirror human decision-making than do the regression and classification approaches discussed earlier (think of support vector machines)

              • Trees are very easy to explain to people. In fact, they are even easier to explain than linear regression!

                • @@ -1716,8 +1445,8 @@ attributes at each step while growing the tree.

              • Can model interactions between the different descriptive features

              • Trees can be displayed graphically, and are easily interpreted even by a non-expert (especially if they are small)

              -
              -

              9.7.1. Disadvantages

              +
              +

              9.7.1. Disadvantages#

              • Unfortunately, trees generally do not have the same level of predictive accuracy as some of the other regression and classification approaches

              • If continuous features are used the tree may become quite large and hence less interpretable

                • @@ -1730,9 +1459,9 @@ attributes at each step while growing the tree.

                However, by aggregating many decision trees, using methods like bagging, random forests, and boosting, the predictive performance of trees can be substantially improved.

                -
              -
              -
              + + + -
              + - - -
              - - -
              -

              previous

              -

              8. Support Vector Machines, overarching aims

              -
              + + + + + +               + +
              - - -
              -

              - - By Morten Hjorth-Jensen
              - - © Copyright 2021.
              -

              -
              - + + +
              +
              +
              + Contents +
              +
              -
              -
              + + + + +
              + +
              - +
              + +

              +By Morten Hjorth-Jensen +

              +
              + +
              + +

              + + © Copyright 2022. +
              + +

              + +
              + +
              + +
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              +
              \ No newline at end of file diff --git a/doc/LectureNotes/_build/html/chapter7.html b/doc/LectureNotes/_build/html/chapter7.html index d613340fb..37ec75d61 100644 --- a/doc/LectureNotes/_build/html/chapter7.html +++ b/doc/LectureNotes/_build/html/chapter7.html @@ -1,50 +1,61 @@ + - + + + - + + 10. Ensemble Methods: From a Single Tree to Many Trees and Extreme Boosting, Meet the Jungle of Methods — Applied Data Analysis and Machine Learning - - - + + + + + + + + + - - - - + + + + + - - - - - + - + - - - - + + + + + + + + - - + + + - - - - - - + + - - - + + + - + + + + +
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              \ No newline at end of file diff --git a/doc/LectureNotes/_build/html/chapter8.html b/doc/LectureNotes/_build/html/chapter8.html index 2ce06ffd3..85b37f315 100644 --- a/doc/LectureNotes/_build/html/chapter8.html +++ b/doc/LectureNotes/_build/html/chapter8.html @@ -1,50 +1,61 @@ + - + + + - + + 11. Basic ideas of the Principal Component Analysis (PCA) — Applied Data Analysis and Machine Learning - - - + + + + + + + + + - - - - + + + + + - - - - - + - + - - - - + + + + + + + + - - + + + - - - - - - + + - - - + + + - + + + + +
              -
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              - - -
              -
              Eigenvalues of Covariance matrix
-5.185584177293881
-0.7780841853755783
-First eigenvector
-[0.85044503 0.52606392]
-Second eigenvector
-[-0.52606392  0.85044503]
-
              -
              -
              Eigenvector of largest eigenvalue
-[0.85044503 0.52606392]
-
              -
              -

              This code does not contain all the above elements, but it shows how we can use Scikit-Learn to extract the eigenvector which corresponds to the largest eigenvalue. Try to address the questions we pose before the above code. Try also to change the values of the covariance matrix by making one of the diagonal elements much larger than the other. What do you observe then?

              -
              -
              -
              -

              11.4. Classical PCA Theorem

              + + +
              +

              11.4. Classical PCA Theorem#

              We assume now that we have a design matrix \(\boldsymbol{X}\) which has been centered as discussed above. For the sake of simplicity we skip the overline symbol. The matrix is defined in terms of the various column @@ -1242,9 +1040,9 @@ discussion in chapter 12.2 of Murphy’s text has also a nice link with the Singular Value Decomposition theorem. For categorical data, see chapter 12.4 and discussion therein.

              For more details, see for example Vidal, Ma and Sastry, chapter 2.

              -
              - -
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              - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
              01234
              0-1.5744650.2591531.1973700.1474000.649382
              10.6895190.137652-1.0257090.210340-0.076938
              2-0.2827270.351636-0.5392611.2166830.340782
              30.070889-0.6148081.074067-0.038300-1.450257
              41.7942821.458078-0.207545-0.442600-0.147420
              51.1123830.6474731.4058900.073598-0.276263
              60.397700-1.526744-0.7120181.2162900.418506
              7-0.2806471.106095-1.646283-0.956563-1.564374
              8-0.369139-0.7516990.051649-0.2131030.967809
              9-1.557795-1.0668370.401842-1.2137431.138775
              -
                   0    1    2    3    4
-0  0.0  0.0  0.0  0.0  0.0
-1  0.0  0.0  0.0  0.0  0.0
-2  0.0  0.0  0.0  0.0  0.0
-3  0.0  0.0  0.0  0.0  0.0
-4  0.0  0.0  0.0  0.0  0.0
-5  0.0  0.0  0.0  0.0  0.0
-6  0.0  0.0  0.0  0.0  0.0
-7  0.0  0.0  0.0  0.0  0.0
-8  0.0  0.0  0.0  0.0  0.0
-9  0.0  0.0  0.0  0.0  0.0
-[[-1.5378811   0.94639099]
- [ 0.86145244 -0.89288636]
- [-0.00445655 -0.81633628]
- [ 0.07145103  1.00433417]
- [ 2.03707133  0.48476997]
- [ 0.72174172  1.4557763 ]
- [-0.55854694 -1.60673226]
- [ 1.6999536  -0.43766686]
- [-1.10405456 -0.31718909]
- [-2.18673098  0.17953942]]
-
              -
              -

              PCA assumes that the dataset is centered around the origin. Scikit-Learn’s PCA classes take care of centering the data for you. However, if you implement PCA yourself (as in the preceding example), or if you use other libraries, don’t @@ -1427,9 +1092,9 @@ Selecting this hyperplane ensures that the projection will preserve as much vari - -

              -

              11.6. PCA and scikit-learn

              + +
              +

              11.6. PCA and scikit-learn#

              Scikit-Learn’s PCA class implements PCA using SVD decomposition just like we did before. The following code applies PCA to reduce the dimensionality of the dataset down to two dimensions (note that it automatically takes care of centering the data):

              @@ -1443,20 +1108,6 @@ that it automatically takes care of centering the data):

              -
              -
              [[ 1.5378811  -0.94639099]
- [-0.86145244  0.89288636]
- [ 0.00445655  0.81633628]
- [-0.07145103 -1.00433417]
- [-2.03707133 -0.48476997]
- [-0.72174172 -1.4557763 ]
- [ 0.55854694  1.60673226]
- [-1.6999536   0.43766686]
- [ 1.10405456  0.31718909]
- [ 2.18673098 -0.17953942]]
-
              -
              -

              After fitting the PCA transformer to the dataset, you can access the principal components using the components variable (note that it contains the PCs as horizontal vectors, so, for example, the first @@ -1467,18 +1118,13 @@ principal component is equal to

              -
              -
              array([-0.62373464, -0.5303329 ,  0.317367  ,  0.01873344,  0.47815203])
-
              -
              -

              Another very useful piece of information is the explained variance ratio of each principal component, available via the \(explained\_variance\_ratio\) variable. It indicates the proportion of the dataset’s variance that lies along the axis of each principal component.

              - -
              -

              11.7. Back to the Cancer Data

              + +
              +

              11.7. Back to the Cancer Data#

              We can now repeat the above but applied to real data, in this case our breast cancer data. Here we compute performance scores on the training data using logistic regression.

              @@ -1514,23 +1160,6 @@ Here we compute performance scores on the training data using logistic regressio
              -
              -
              Train set accuracy from Logistic Regression: 0.95
-Train set accuracy scaled data: 0.99
-Train set accuracy scaled and PCA data: 0.96
-
              -
              -
              /Users/mhjensen/miniforge3/envs/myenv/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:814: ConvergenceWarning: lbfgs failed to converge (status=1):
-STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
-
-Increase the number of iterations (max_iter) or scale the data as shown in:
-    https://scikit-learn.org/stable/modules/preprocessing.html
-Please also refer to the documentation for alternative solver options:
-    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
-  n_iter_i = _check_optimize_result(
-
              -
              -

              We see that our training data after the PCA decomposition has a performance similar to the non-scaled data.

              Instead of arbitrarily choosing the number of dimensions to reduce down to, it is generally preferable to @@ -1560,23 +1189,23 @@ a float between 0.0 and 1.0, indicating the ratio of variance you wish to preser -

              -

              11.7.1. Incremental PCA

              +
              +

              11.7.1. Incremental PCA#

              One problem with the preceding implementation of PCA is that it requires the whole training set to fit in memory in order for the SVD algorithm to run. Fortunately, Incremental PCA (IPCA) algorithms have been developed: you can split the training set into mini-batches and feed an IPCA algorithm one minibatch at a time. This is useful for large training sets, and also to apply PCA online (i.e., on the fly, as new instances arrive).

              -
              -
              -

              11.7.2. Randomized PCA

              + +
              +

              11.7.2. Randomized PCA#

              Scikit-Learn offers yet another option to perform PCA, called Randomized PCA. This is a stochastic algorithm that quickly finds an approximation of the first d principal components. Its computational complexity is \(O(m \times d^2)+O(d^3)\), instead of \(O(m \times n^2) + O(n^3)\), so it is dramatically faster than the previous algorithms when \(d\) is much smaller than \(n\).

              -
              -
              -

              11.7.3. Kernel PCA

              + +
              +

              11.7.3. Kernel PCA#

              The kernel trick is a mathematical technique that implicitly maps instances into a very high-dimensional space (called the feature space), enabling nonlinear classification and regression with Support Vector Machines. Recall that a linear decision boundary in the high-dimensional feature @@ -1595,10 +1224,10 @@ For example, the following code uses Scikit-Learn’s KernelPCA class to perform

              - - -
              -

              11.8. Other techniques

              + + +
              +

              11.8. Other techniques#

              There are many other dimensionality reduction techniques, several of which are available in Scikit-Learn.

              Here are some of the most popular:

                @@ -1607,8 +1236,8 @@ For example, the following code uses Scikit-Learn’s KernelPCA class to perform

              • t-Distributed Stochastic Neighbor Embedding (t-SNE) reduces dimensionality while trying to keep similar instances close and dissimilar instances apart. It is mostly used for visualization, in particular to visualize clusters of instances in high-dimensional space (e.g., to visualize the MNIST images in 2D).

              • Linear Discriminant Analysis (LDA) is actually a classification algorithm, but during training it learns the most discriminative axes between the classes, and these axes can then be used to define a hyperplane onto which to project the data. The benefit is that the projection will keep classes as far apart as possible, so LDA is a good technique to reduce dimensionality before running another classification algorithm such as a Support Vector Machine (SVM) classifier discussed in the SVM lectures.

              -
              - + + - + - - -
              - - -
              -

              previous

              -

              10. Ensemble Methods: From a Single Tree to Many Trees and Extreme Boosting, Meet the Jungle of Methods

              -
              + + + + + +               + +
              - - -
              -

              - - By Morten Hjorth-Jensen
              - - © Copyright 2021.
              -

              -
              - + + +
              +
              +
              + Contents +
              +
              -
              -
              + + + + +
              + +
              - +
              + +

              +By Morten Hjorth-Jensen +

              +
              + +
              + +

              + + © Copyright 2022. +
              + +

              + +
              + +
              + +
              + +
              + +
              + +
              +
              + + + + + + + + + + +
              +
              \ No newline at end of file diff --git a/doc/LectureNotes/_build/html/chapter9.html b/doc/LectureNotes/_build/html/chapter9.html index dafec4bb9..dd0efd8a0 100644 --- a/doc/LectureNotes/_build/html/chapter9.html +++ b/doc/LectureNotes/_build/html/chapter9.html @@ -1,50 +1,61 @@ + - + + + - + + 13. Neural networks — Applied Data Analysis and Machine Learning - - - + + + + + + + + + - - - - + + + + + - - - - - + - + - - - - + + + + + + + + - - + + + - - - - - - + + - - - + + + - + + + + +
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              \ No newline at end of file diff --git a/doc/LectureNotes/_build/html/chapteroptimization.html b/doc/LectureNotes/_build/html/chapteroptimization.html index 3e60c521e..2ec431c29 100644 --- a/doc/LectureNotes/_build/html/chapteroptimization.html +++ b/doc/LectureNotes/_build/html/chapteroptimization.html @@ -1,50 +1,61 @@ + - + + + - + + 7. Optimization, the central part of any Machine Learning algortithm — Applied Data Analysis and Machine Learning - - - + + + + + + + + + - - - - + + + + + - - - - - + - + - - - - + + + + + + + + - - + + + - - - - - - + + - - - + + + - + + + + +
              -
              -
              - - -
              -
              [0.33196524 4.36593124]
-[[4.09963157]
- [2.99760425]]
-[[4.09963157]
- [2.99760425]]
-
              -
              -_images/chapteroptimization_123_1.png -

              Alternatively, we can use Scikit-Learn as done here

              @@ -1348,13 +1189,6 @@ when \(||\nabla_\beta C(\beta_k) || \
              -
              -
              [[4.21639245]
- [2.82985482]]
-[4.15172236] [2.79589065]
-
              -
              -

              We have also discussed Ridge regression where the loss function contains a regularized term given by the \(L_2\) norm of \(\beta\),

              @@ -1421,19 +1255,10 @@ C_{\text{ridge}}(\beta) = \frac{1}{n}||X\beta -\mathbf{y}||^2 + \lambda ||\beta|
              -
              -
              [[3.98919197]
- [2.94015598]]
-[[3.94925462]
- [2.97087408]]
-
              -_images/chapteroptimization_132_1.png -
              - - -
              -

              7.6. Using gradient descent methods, limitations

              + +
              +

              7.6. Using gradient descent methods, limitations#

              • Gradient descent (GD) finds local minima of our function. Since the GD algorithm is deterministic, if it converges, it will converge to a local minimum of our cost/loss/risk function. Because in ML we are often dealing with extremely rugged landscapes with many local minima, this can lead to poor performance.

              • GD is sensitive to initial conditions. One consequence of the local nature of GD is that initial conditions matter. Depending on where one starts, one will end up at a different local minima. Therefore, it is very important to think about how one initializes the training process. This is true for GD as well as more complicated variants of GD.

                • @@ -1442,9 +1267,9 @@ C_{\text{ridge}}(\beta) = \frac{1}{n}||X\beta -\mathbf{y}||^2 + \lambda ||\beta|

              • GD treats all directions in parameter space uniformly. Another major drawback of GD is that unlike Newton’s method, the learning rate for GD is the same in all directions in parameter space. For this reason, the maximum learning rate is set by the behavior of the steepest direction and this can significantly slow down training. Ideally, we would like to take large steps in flat directions and small steps in steep directions. Since we are exploring rugged landscapes where curvatures change, this requires us to keep track of not only the gradient but second derivatives. The ideal scenario would be to calculate the Hessian but this proves to be too computationally expensive.

              • GD can take exponential time to escape saddle points, even with random initialization. As we mentioned, GD is extremely sensitive to initial condition since it determines the particular local minimum GD would eventually reach. However, even with a good initialization scheme, through the introduction of randomness, GD can still take exponential time to escape saddle points.

              -
              -
              -

              7.7. Stochastic Gradient Descent (SGD)

              + +
              +

              7.7. Stochastic Gradient Descent (SGD)#

              In stochastic gradient descent, the extreme case is the case where we have only one batch, that is we include the whole data set.

              This process is called Stochastic Gradient @@ -1590,15 +1415,10 @@ function.

              -
              -
              gamma_j after 500 epochs: 9.97108e-05
-
              -
              -

              We note that we have defined several hyperparameters. These are now the number of epochs, the number of mini-batches and the parameters \(t_0\) and \(t_1\).

              -
              -

              7.7.1. Program for stochastic gradient

              +
              +

              7.7.1. Program for stochastic gradient#

              # Importing various packages
@@ -1673,30 +1493,15 @@ function.
              -
              -
              Own inversion
-[[4.5424657 ]
- [2.40026896]]
-Eigenvalues of Hessian Matrix:[0.29830651 3.89658408]
-theta from own gd
-[[4.5424657 ]
- [2.40026896]]
-theta from own sdg
-[[4.55687658]
- [2.41165766]]
-
              -
              -_images/chapteroptimization_148_1.png -

              In the above code, we have use replacement in setting up the mini-batches. The discussion here may be useful. More material will be added later.

              -
              - -
              -

              7.8. Momentum based GD

              + + +
              +

              7.8. Momentum based GD#

              The stochastic gradient descent (SGD) is almost always used with a momentum or inertia term that serves as a memory of the direction we are moving in parameter space. This is typically implemented as @@ -1811,8 +1616,8 @@ Hessians.

              this by tracking not only the gradient, but also the second moment of the gradient. These methods include AdaGrad, AdaDelta, Root Mean Squared Propagation (RMS-Prop), and ADAM.

              -
              -

              7.8.1. RMS prop

              +
              +

              7.8.1. RMS prop#

              In RMS prop, in addition to keeping a running average of the first moment of the gradient, we also keep track of the second moment denoted by \(\mathbf{s}_t=\mathbb{E}[\mathbf{g}_t^2]\). The update rule @@ -1843,9 +1648,9 @@ is clear from this formula that the learning rate is reduced in directions where the norm of the gradient is consistently large. This greatly speeds up the convergence by allowing us to use a larger learning rate for flat directions.

              -
              -
              -

              7.8.2. ADAM optimizer

              +
              +
              +

              7.8.2. ADAM optimizer#

              A related algorithm is the ADAM optimizer. In ADAM, we keep a running average of both the first and second moment of the gradient and use this information to adaptively change the learning rate for different @@ -1908,19 +1713,19 @@ update rule for this parameter is given by

              \[ \Delta \theta_{t+1}= -\eta_t { \boldsymbol{m}_t \over \sqrt{\sigma_t^2 + m_t^2 }+\epsilon}. \]
              - - -
              -

              7.9. Practical tips

              + + +
              +

              7.9. Practical tips#

              • Randomize the data when making mini-batches. It is always important to randomly shuffle the data when forming mini-batches. Otherwise, the gradient descent method can fit spurious correlations resulting from the order in which data is presented.

              • Transform your inputs. Learning becomes difficult when our landscape has a mixture of steep and flat directions. One simple trick for minimizing these situations is to standardize the data by subtracting the mean and normalizing the variance of input variables. Whenever possible, also decorrelate the inputs. To understand why this is helpful, consider the case of linear regression. It is easy to show that for the squared error cost function, the Hessian of the cost function is just the correlation matrix between the inputs. Thus, by standardizing the inputs, we are ensuring that the landscape looks homogeneous in all directions in parameter space. Since most deep networks can be viewed as linear transformations followed by a non-linearity at each layer, we expect this intuition to hold beyond the linear case.

              • Monitor the out-of-sample performance. Always monitor the performance of your model on a validation set (a small portion of the training data that is held out of the training process to serve as a proxy for the test set. If the validation error starts increasing, then the model is beginning to overfit. Terminate the learning process. This early stopping significantly improves performance in many settings.

              • Adaptive optimization methods don’t always have good generalization. Recent studies have shown that adaptive methods such as ADAM, RMSPorp, and AdaGrad tend to have poor generalization compared to SGD or SGD with momentum, particularly in the high-dimensional limit (i.e. the number of parameters exceeds the number of data points). Although it is not clear at this stage why these methods perform so well in training deep neural networks, simpler procedures like properly-tuned SGD may work as well or better in these applications.

              -
              -
              -

              7.10. Automatic differentiation

              + +
              +

              7.10. Automatic differentiation#

              Automatic differentiation (AD), also called algorithmic differentiation or computational differentiation,is a set of @@ -1994,12 +1799,6 @@ f'(x) = \cos\left(2\pi x + x^2\right)\left(2\pi + 2x\right)

              -
              -_images/chapteroptimization_188_0.png -
              The max absolute difference is: 1.77636e-15
-
              -
              -

              Here we experiment with what kind of functions Autograd is capable @@ -2028,12 +1827,6 @@ experiment with other, possibly more complicated, functions as well.

              -
              -
              The gradient of f1 evaluated at a = 1 using autograd is: 3
-The gradient of f1 evaluated at a = 1 by finding the analytic expression is: 3
-
              -
              -

              To differentiate with respect to two (or more) arguments of a Python function, Autograd need to know at which variable the function if @@ -2076,17 +1869,6 @@ being differentiated with respect to.

              -
              -
              Evaluating at x1 = 1, x2 = 3
-------------------------------
-The derivative of f2 w.r.t x1: 12
-The analytical derivative of f2 w.r.t x1: 12
-
-The derivative of f2 w.r.t x2: -4
-The analytical derivative of f2 w.r.t x2: -4
-
              -
              -

              Note that the grad function will not produce the true gradient of the function. The true gradient of a function with two or more variables will produce a vector, where each element is the function differentiated w.r.t a variable.

              @@ -2111,12 +1893,6 @@ The analytical derivative of f2 w.r.t x2: -4
              -
              -
              The computed gradient of f3 is:  [ 2.  3.  5.  7. 88.]
-The analytical gradient of f3 is:  [ 2.  3.  5.  7. 88.]
-
              -
              -

              Note that in this case, when sending an array as input argument, the output from Autograd is another array. This is the true gradient of @@ -2146,12 +1922,6 @@ could expect form a gradient-evaluting function.

              -
              -
              The computed derivative of f4 at x = 2.7 is: 13.8759
-The analytical gradient of f4 at x = 2.7 is: 13.8759
-
              -
              -
              @@ -2172,11 +1942,6 @@ The analytical gradient of f4 at x = 2.7 is: 13.8759
              -
              -
              The computed derivative of f5 at x = 2.7 is: 5.4
-
              -
              -
              @@ -2207,12 +1972,6 @@ The analytical gradient of f4 at x = 2.7 is: 13.8759
              -
              -
              The computed derivative of f6_for at x = 0.5 is: 3.95703
-The computed derivative of f6_while at x = 0.5 is: 3.95703
-
              -
              -
              @@ -2228,11 +1987,6 @@ The computed derivative of f6_while at x = 0.5 is: 3.95703
              -
              -
              The analytical derivative of f6 at x = 0.5 is: 3.95703
-
              -
              -
              @@ -2266,12 +2020,6 @@ The computed derivative of f6_while at x = 0.5 is: 3.95703
              -
              -
              The computed derivative of f7 at n = 2 is: 1
-The analytical derivative of f7 at n = 2 is: 1
-
              -
              -

              Note that if n is equal to zero or one, Autograd will give an error message. This message appears when the output is independent on input.

              Autograd supports many features. However, there are some functions that is not supported (yet) by Autograd.

              @@ -2294,11 +2042,6 @@ The analytical derivative of f7 at n = 2 is: 1 -
              -
              '\nimport autograd.numpy as np\nfrom autograd import grad\ndef f8(x): # Assume x is an array\n    x[2] = 3\n    return x*2\n\nf8_grad = grad(f8)\n\nx = 8.4\n\nprint("The derivative of f8 is:",f8_grad(x))\n'
-
              -
              -

              Here, Autograd tells us that an ‘ArrayBox’ does not support item assignment. The item assignment is done when the program tries to assign x[2] to the value 3. However, Autograd has implemented the computation of the derivative such that this assignment is not possible.

              @@ -2317,60 +2060,6 @@ The analytical derivative of f7 at n = 2 is: 1
              -
              -
              ---------------------------------------------------------------------------
-AttributeError                            Traceback (most recent call last)
-Input In [23], in <cell line: 11>()
-      7 f9_grad = grad(f9)
-      9 x = np.array([1.0,0.0])
----> 11 print("The derivative of f9 is:",f9_grad(x))
-
-File ~/miniforge3/envs/myenv/lib/python3.9/site-packages/autograd/wrap_util.py:20, in unary_to_nary.<locals>.nary_operator.<locals>.nary_f(*args, **kwargs)
-     18 else:
-     19     x = tuple(args[i] for i in argnum)
----> 20 return unary_operator(unary_f, x, *nary_op_args, **nary_op_kwargs)
-
-File ~/miniforge3/envs/myenv/lib/python3.9/site-packages/autograd/differential_operators.py:25, in grad(fun, x)
-     18 @unary_to_nary
-     19 def grad(fun, x):
-     20     """
-     21     Returns a function which computes the gradient of `fun` with respect to
-     22     positional argument number `argnum`. The returned function takes the same
-     23     arguments as `fun`, but returns the gradient instead. The function `fun`
-     24     should be scalar-valued. The gradient has the same type as the argument."""
----> 25     vjp, ans = _make_vjp(fun, x)
-     26     if not vspace(ans).size == 1:
-     27         raise TypeError("Grad only applies to real scalar-output functions. "
-     28                         "Try jacobian, elementwise_grad or holomorphic_grad.")
-
-File ~/miniforge3/envs/myenv/lib/python3.9/site-packages/autograd/core.py:10, in make_vjp(fun, x)
-      8 def make_vjp(fun, x):
-      9     start_node = VJPNode.new_root()
----> 10     end_value, end_node =  trace(start_node, fun, x)
-     11     if end_node is None:
-     12         def vjp(g): return vspace(x).zeros()
-
-File ~/miniforge3/envs/myenv/lib/python3.9/site-packages/autograd/tracer.py:10, in trace(start_node, fun, x)
-      8 with trace_stack.new_trace() as t:
-      9     start_box = new_box(x, t, start_node)
----> 10     end_box = fun(start_box)
-     11     if isbox(end_box) and end_box._trace == start_box._trace:
-     12         return end_box._value, end_box._node
-
-File ~/miniforge3/envs/myenv/lib/python3.9/site-packages/autograd/wrap_util.py:15, in unary_to_nary.<locals>.nary_operator.<locals>.nary_f.<locals>.unary_f(x)
-     13 else:
-     14     subargs = subvals(args, zip(argnum, x))
----> 15 return fun(*subargs, **kwargs)
-
-Input In [23], in f9(a)
-      3 def f9(a): # Assume a is an array with 2 elements
-      4     b = np.array([1.0,2.0])
-----> 5     return a.dot(b)
-
-AttributeError: 'ArrayBox' object has no attribute 'dot'
-
              -
              -

              Here we are told that the ‘dot’ function does not belong to Autograd’s version of a Numpy array. To overcome this, an alternative syntax @@ -2406,16 +2095,16 @@ which also computed the dot product can be used:

              - -
              -

              7.11. Replace or not

              + +
              +

              7.11. Replace or not#

              In the above code, we have use replacement in setting up the mini-batches. The discussion here may be useful.

              -
              -
              -

              7.12. Using Autograd

              + +
              +

              7.12. Using Autograd#

              We conclude the part on optmization by showing how we can make codes for linear regression and logistic regression using autograd. The first example shows results with ordinary leats squares.

              @@ -2474,9 +2163,9 @@ first example shows results with ordinary leats squares.

              - -
              -

              7.13. Same code but now with momentum gradient descent

              + +
              +

              7.13. Same code but now with momentum gradient descent#

              # Using Autograd to calculate gradients for OLS
@@ -2582,9 +2271,9 @@ However, if we can invert the Hessian matrix, this is the preferred approach, as
              -
              -
              -

              7.14. Including Stochastic Gradient Descent with Autograd

              +
              +
              +

              7.14. Including Stochastic Gradient Descent with Autograd#

              In this code we include the stochastic gradient descent approach discussed above. Note here that we specify which argument we are taking the derivative with respect to when using autograd.

              @@ -2739,8 +2428,8 @@ However, if we can invert the Hessian matrix, this is the preferred approach, as
              -
              -

              7.14.1. Similar (second order function now) problem but now with AdaGrad

              +
              +

              7.14.1. Similar (second order function now) problem but now with AdaGrad#

              # Using Autograd to calculate gradients using AdaGrad and Stochastic Gradient descent
@@ -2866,10 +2555,10 @@ However, if we can invert the Hessian matrix, this is the preferred approach, as
              -
              -
              -
              -

              7.15. Introducing JAX

              + + +
              +

              7.15. Introducing JAX#

              Presently, instead of using autograd, we recommend using JAX

              JAX is Autograd and XLA (Accelerated Linear Algebra)), brought together for high-performance numerical computing and machine learning research. @@ -2890,8 +2579,8 @@ It provides composable transformations of Python+NumPy programs: differentiate,

              - - + + - + - - -
              - - -
              -

              previous

              -

              6. Logistic Regression

              -
              + + + + + +               + +
              - - -
              -

              - - By Morten Hjorth-Jensen
              - - © Copyright 2021.
              -

              -
              - + + +
              +
              +
              + Contents +
              +
              -
              -
              + + + + +
              + +
              - +
              + +

              +By Morten Hjorth-Jensen +

              +
              + +
              + +

              + + © Copyright 2022. +
              + +

              + +
              + +
              + +
              + +
              + +
              + +
              +
              + + + + + + + + + + +
              +
              \ No newline at end of file diff --git a/doc/LectureNotes/_build/html/clustering.html b/doc/LectureNotes/_build/html/clustering.html index 86656c216..f05309dbf 100644 --- a/doc/LectureNotes/_build/html/clustering.html +++ b/doc/LectureNotes/_build/html/clustering.html @@ -1,50 +1,61 @@ + - + + + - + + 12. Clustering and Unsupervised Learning — Applied Data Analysis and Machine Learning - - - + + + + + + + + + - - - - + + + + + - - - - - + - + - - - - + + + + + + + + - - + + + - - - - - - + + - - - + + + - + + + + +
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              \ No newline at end of file diff --git a/doc/LectureNotes/_build/html/exercisesweek34.html b/doc/LectureNotes/_build/html/exercisesweek34.html index 6650984ac..ff75c928d 100644 --- a/doc/LectureNotes/_build/html/exercisesweek34.html +++ b/doc/LectureNotes/_build/html/exercisesweek34.html @@ -1,50 +1,61 @@ + - + + + - + + Exercises week 34 — Applied Data Analysis and Machine Learning - - - + + + + + + + + + - - - - + + + + + - - - - - + - + - - - - + + + + + + + + - - + + + - - - - - - + + - - - + + + - + + + + +
              -
              -
              - -
              -
              -
              The intercept alpha: 
- [2.18780801]
-Coefficient beta : 
- [[4.72228205]]
-Mean squared error: 0.37
-Variance score: 0.83
-Mean squared log error: 0.01
-Mean absolute error: 0.47
-
              -
              -_images/week34_103_1.png -

              The function coef gives us the parameter \(\beta\) of our fit while intercept yields \(\alpha\). Depending on the constant in front of the normal distribution, we get values near or far from \(\alpha =2\) and \(\beta =5\). Try to play around with different parameters in front of the normal distribution. The function meansquarederror gives us the mean square error, a risk metric corresponding to the expected value of the squared (quadratic) error or loss defined as

              @@ -3072,9 +1827,9 @@ H_{\delta}(\boldsymbol{a})=\left\{\begin{array}{cc}\frac{1}{2} \boldsymbol{a}^{2

              Here \(\boldsymbol{a}=\boldsymbol{y} - \boldsymbol{\tilde{y}}\).

              We will discuss in more detail these and other functions in the various lectures and lab sessions.

              - -
              -

              To our real data: nuclear binding energies. Brief reminder on masses and binding energies

              + +
              +

              To our real data: nuclear binding energies. Brief reminder on masses and binding energies#

              Let us now dive into nuclear physics and remind ourselves briefly about some basic features about binding energies. A basic quantity which can be measured for the ground states of nuclei is the atomic mass \(M(N, Z)\) of the neutral atom with @@ -3135,9 +1890,9 @@ to the experimental data.

              We could also add a so-called pairing term, which is a correction term that arises from the tendency of proton pairs and neutron pairs to occur. An even number of particles is more stable than an odd number.

              -
              -
              -

              Organizing our data

              + +
              +

              Organizing our data#

              Let us start with reading and organizing our data. We start with the compilation of masses and binding energies from 2016. After having downloaded this file to our own computer, we are now ready to read the file and start structuring our data.

              @@ -3220,11 +1975,6 @@ data) to actually open the file and simply take a look at it!

              -
              -
              '                                                                                                                         \nThis is taken from the data file of the mass 2016 evaluation.                                                               \nAll files are 3436 lines long with 124 character per line.                                                                  \n       Headers are 39 lines long.                                                                                           \n   col 1     :  Fortran character control: 1 = page feed  0 = line feed                                                     \n   format    :  a1,i3,i5,i5,i5,1x,a3,a4,1x,f13.5,f11.5,f11.3,f9.3,1x,a2,f11.3,f9.3,1x,i3,1x,f12.5,f11.5                     \n   These formats are reflected in the pandas widths variable below, see the statement                                       \n   widths=(1,3,5,5,5,1,3,4,1,13,11,11,9,1,2,11,9,1,3,1,12,11,1),                                                            \n   Pandas has also a variable header, with length 39 in this case.                                                          \n'
-
              -
              -

              The data we are interested in are in columns 2, 3, 4 and 11, giving us the number of neutrons, protons, mass numbers and binding energies, @@ -3253,40 +2003,6 @@ covert them into the pandas DataFrame structure.

              -
              -
              ---------------------------------------------------------------------------
-ValueError                                Traceback (most recent call last)
-Input In [30], in <cell line: 2>()
-      1 # Read the experimental data with Pandas
-----> 2 Masses = pd.read_fwf(infile, usecols=(2,3,4,6,11),
-      3               names=('N', 'Z', 'A', 'Element', 'Ebinding'),
-      4               widths=(1,3,5,5,5,1,3,4,1,13,11,11,9,1,2,11,9,1,3,1,12,11,1),
-      5               header=39,
-      6               index_col=False)
-      8 # Extrapolated values are indicated by '#' in place of the decimal place, so
-      9 # the Ebinding column won't be numeric. Coerce to float and drop these entries.
-     10 Masses['Ebinding'] = pd.to_numeric(Masses['Ebinding'], errors='coerce')
-
-File ~/miniforge3/envs/myenv/lib/python3.9/site-packages/pandas/util/_decorators.py:311, in deprecate_nonkeyword_arguments.<locals>.decorate.<locals>.wrapper(*args, **kwargs)
-    305 if len(args) > num_allow_args:
-    306     warnings.warn(
-    307         msg.format(arguments=arguments),
-    308         FutureWarning,
-    309         stacklevel=stacklevel,
-    310     )
---> 311 return func(*args, **kwargs)
-
-File ~/miniforge3/envs/myenv/lib/python3.9/site-packages/pandas/io/parsers/readers.py:871, in read_fwf(filepath_or_buffer, colspecs, widths, infer_nrows, **kwds)
-    869                     len_index = len(index_col)
-    870         if len(names) + len_index != len(colspecs):
---> 871             raise ValueError("Length of colspecs must match length of names")
-    873 kwds["colspecs"] = colspecs
-    874 kwds["infer_nrows"] = infer_nrows
-
-ValueError: Length of colspecs must match length of names
-
              -
              -

              We have now read in the data, grouped them according to the variables we are interested in. We see how easy it is to reorganize the data using pandas. If we @@ -3361,9 +2077,9 @@ Now we can print measures of how our fit is doing, the coefficients from the fit - -

              -

              And what about using neural networks?

              + +
              +

              And what about using neural networks?#

              The seaborn package allows us to visualize data in an efficient way. Note that we use scikit-learn’s multi-layer perceptron (or feed forward neural network) functionality.

              @@ -3401,10 +2117,10 @@ functionality.

              - - -
              -

              A first summary

              + + +
              +

              A first summary#

              The aim behind these introductory words was to present to you various Python libraries and their functionalities, in particular libraries like numpy, pandas, xarray and matplotlib and other that make our life much easier @@ -3413,9 +2129,9 @@ in handling various data sets and visualizing data.

              Scikit-Learn allows us with few lines of code to implement popular Machine Learning algorithms for supervised learning. Later we will meet Tensorflow, a powerful library for deep learning. Now it is time to dive more into the details of various methods. We will start with linear regression and try to take a deeper look at what it entails.

              -
              -
              -

              Why Linear Regression (aka Ordinary Least Squares and family)

              + +
              +

              Why Linear Regression (aka Ordinary Least Squares and family)#

              Fitting a continuous function with linear parameterization in terms of the parameters \(\boldsymbol{\beta}\).

              • Method of choice for fitting a continuous function!

                • @@ -3430,9 +2146,9 @@ Now it is time to dive more into the details of various methods. We will start w

              For more discussions of Ridge and Lasso regression, Wessel van Wieringen’s article is highly recommended. Similarly, Mehta et al’s article is also recommended.

              -
              -
              -

              Regression analysis, overarching aims

              + +
              +

              Regression analysis, overarching aims#

              Regression modeling deals with the description of the sampling distribution of a given random variable \(y\) and how it varies as function of another variable or a set of such variables \(\boldsymbol{x} =[x_0, x_1,\dots, x_{n-1}]^T\). The first variable is called the dependent, the outcome or the response variable while the set of variables \(\boldsymbol{x}\) is called the independent variable, or the predictor variable or the explanatory variable, or simply just the inputs.

              A regression model aims at finding a likelihood function \(p(\boldsymbol{y}\vert \boldsymbol{x})\) or in the more traditional sense a function \(\boldsymbol{y}(\boldsymbol{x})\), that is the conditional distribution for \(\boldsymbol{y}\) with a given \(\boldsymbol{x}\). The estimation of \(p(\boldsymbol{y}\vert \boldsymbol{x})\) is made using a data set with

              @@ -3442,9 +2158,9 @@ The first variable is called the dependent, the outcome

            4. \(p\) so-called explanatory (independent or predictor or feature) variables \(\boldsymbol{x}_i=[x_{i0}, x_{i1}, \dots, x_{ip-1}]\) with \(i = 0, 1, 2, \dots, n-1\) and explanatory variables running from \(0\) to \(p-1\). See below for more explicit examples.

              5. The goal of the regression analysis is to extract/exploit relationship between \(\boldsymbol{y}\) and \(\boldsymbol{x}\) in order to infer specific dependencies, approximations to the likelihood functions, functional relationships and to make predictions, making fits and many other things.

              -
              -
              -

              Regression analysis, overarching aims II

              + +
              +

              Regression analysis, overarching aims II#

              Consider an experiment in which \(p\) characteristics/features of \(n\) samples are measured. The data from this experiment, for various explanatory variables \(p\) are normally represented by a matrix
              \(\mathbf{X}\).

              @@ -3460,9 +2176,9 @@ between \(\boldsymbol{X}\) and the linear regression model where \(\boldsymbol{\beta} = [\beta_0, \ldots, \beta_{p-1}]^{T}\) are the regression parameters.

              Linear regression gives us a set of analytical equations for the parameters \(\beta_j\).

              -
              -
              -

              Examples

              + +
              +

              Examples#

              In order to understand the relation among the predictors (or features or properties) \(p\), the set of data \(n\) and the target (outcome, output etc) \(\boldsymbol{y}\), consider the model we discussed for describing nuclear binding energies.

              There we assumed that we could parametrize the data using a polynomial approximation based on the liquid drop model. @@ -3476,9 +2192,9 @@ This gives \(p=0,1,2,3,4\). Fu \(p\times n\) matrix \(\boldsymbol{X}\).

              Here the predictors are based on a model we have made. A popular data set which is widely encountered in ML applications is the so-called credit card default data from Taiwan. The data set contains data on \(n=30000\) credit card holders with predictors like gender, marital status, age, profession, education, etc. In total there are \(24\) such predictors or attributes leading to a design matrix of dimensionality \(24 \times 30000\). This is however a classification problem and we will come back to it when we discuss Logistic Regression.

              -
              -
              -

              General linear models and linear algebra

              + +
              +

              General linear models and linear algebra#

              Before we proceed let us study a case where we aim at fitting a set of data \(\boldsymbol{y}=[y_0,y_1,\dots,y_{n-1}]\). We could think of these data as a result of an experiment or a complicated numerical experiment. These data are functions of a series of variables \(\boldsymbol{x}=[x_0,x_1,\dots,x_{n-1}]\), that is \(y_i = y(x_i)\) with \(i=0,1,2,\dots,n-1\). The variables \(x_i\) could represent physical quantities like time, temperature, position etc. We assume that \(y(x)\) is a smooth function.

              Since obtaining these data points may not be trivial, we want to use these data to fit a function which can allow us to make predictions for values of \(y\) which are not in the present set. The perhaps simplest approach is to assume we can parametrize our function in terms of a polynomial of degree \(n-1\) with \(n\) points, that is

              @@ -3486,9 +2202,9 @@ so-called \(\epsilon_i\) is the error in our approximation.

              -
              -
              +
              +

              Rewriting the fitting procedure as a linear algebra problem#

              For every set of values \(y_i,x_i\) we have thus the corresponding set of equations

              \[\begin{split} @@ -3500,9 +2216,9 @@ y_2&=\beta_0+\beta_1x_2^1+\beta_2x_2^2+\dots+\beta_{n-1}x_2^{n-1}+\epsilon_2 y_{n-1}&=\beta_0+\beta_1x_{n-1}^1+\beta_2x_{n-1}^2+\dots+\beta_{n-1}x_{n-1}^{n-1}+\epsilon_{n-1}.\\ \end{align*} \end{split}\]
              -
              -
              -

              Rewriting the fitting procedure as a linear algebra problem, more details

              + +
              +

              Rewriting the fitting procedure as a linear algebra problem, more details#

              Defining the vectors

              \[ @@ -3536,9 +2252,9 @@ y_{n-1}&=\beta_0+\beta_1x_{n-1}^1+\beta_2x_{n-1}^2+\dots+\beta_{n-1}x_{n-1}^ \boldsymbol{y} = \boldsymbol{X}\boldsymbol{\beta}+\boldsymbol{\epsilon}. \]

              The above design matrix is called a Vandermonde matrix.

              -
              -
              -

              Generalizing the fitting procedure as a linear algebra problem

              + +
              +

              Generalizing the fitting procedure as a linear algebra problem#

              We are obviously not limited to the above polynomial expansions. We could replace the various powers of \(x\) with elements of Fourier series or instead of \(x_i^j\) we could have \(\cos{(j x_i)}\) or \(\sin{(j @@ -3557,9 +2273,9 @@ y_{n-1}&=\beta_0x_{n-1,0}+\beta_1x_{n-1,2}+\beta_2x_{n-1,2}+\dots+\beta_{n-1 \end{align*} \end{split}\]

              Note that we have \(p=n\) here. The matrix is symmetric. This is generally not the case!

              - -
              -

              Generalizing the fitting procedure as a linear algebra problem

              + +
              +

              Generalizing the fitting procedure as a linear algebra problem#

              We redefine in turn the matrix \(\boldsymbol{X}\) as

              \[\begin{split} @@ -3578,9 +2294,9 @@ x_{n-1,0}& x_{n-1,1} &x_{n-1,2}& \dots & \dots &x_{n-1,n-1}\ \boldsymbol{y} = \boldsymbol{X}\boldsymbol{\beta}+\boldsymbol{\epsilon}. \]

              The left-hand side of this equation is kwown. Our error vector \(\boldsymbol{\epsilon}\) and the parameter vector \(\boldsymbol{\beta}\) are our unknow quantities. How can we obtain the optimal set of \(\beta_i\) values?

              -
              -
              -

              Optimizing our parameters

              + +
              +

              Optimizing our parameters#

              We have defined the matrix \(\boldsymbol{X}\) via the equations

              \[\begin{split} @@ -3597,9 +2313,9 @@ y_{n-1}&=\beta_0x_{n-1,0}+\beta_1x_{n-1,2}+\beta_2x_{n-1,2}+\dots+\beta_{n-1

              As we noted above, we stayed with a system with the design matrix \(\boldsymbol{X}\in {\mathbb{R}}^{n\times n}\), that is we have \(p=n\). For reasons to come later (algorithmic arguments) we will hereafter define our matrix as \(\boldsymbol{X}\in {\mathbb{R}}^{n\times p}\), with the predictors refering to the column numbers and the entries \(n\) being the row elements.

              -
              -
              -

              Our model for the nuclear binding energies

              +
              +
              +

              Our model for the nuclear binding energies#

              In our introductory notes we looked at the so-called liquid drop model. Let us remind ourselves about what we did by looking at the code.

              We restate the parts of the code we are most interested in.

              @@ -3683,9 +2399,9 @@ our matrix as \(\boldsymbol{X}\in {\m \boldsymbol{\tilde{y}}= \boldsymbol{X}\boldsymbol{\beta}, \]

              throughout these lectures.

              -
              -
              -

              Optimizing our parameters, more details

              + +
              +

              Optimizing our parameters, more details#

              With the above we use the design matrix to define the approximation \(\boldsymbol{\tilde{y}}\) via the unknown quantity \(\boldsymbol{\beta}\) as

              \[ @@ -3709,9 +2425,9 @@ the function \(C\) as

              C(\boldsymbol{\beta})=\frac{1}{2n}\sum_{i=0}^{n-1}\left(y_i-\tilde{y}_i\right)^2, \]

              since when taking the first derivative with respect to the unknown parameters \(\beta\), the factor of \(2\) cancels out.

              -
              -
              -

              Interpretations and optimizing our parameters

              + +
              +

              Interpretations and optimizing our parameters#

              The function

              \[ @@ -3752,9 +2468,9 @@ will treat \(y_i\) as our exac \[ \frac{\partial C(\boldsymbol{\beta})}{\partial \boldsymbol{\beta}} = 0 = \boldsymbol{X}^T\left( \boldsymbol{y}-\boldsymbol{X}\boldsymbol{\beta}\right). \]
              -
              -
              -

              Interpretations and optimizing our parameters

              + +
              +

              Interpretations and optimizing our parameters#

              We can rewrite

              \[ @@ -3781,14 +2497,14 @@ regression or support vector machines, exhibit dimensionalities which allow for the usage of direct linear algebra methods such as LU decomposition or Singular Value Decomposition (SVD) for finding the inverse of the matrix \(\boldsymbol{X}^T\boldsymbol{X}\).

              Small question: Do you think the example we have at hand here (the nuclear binding energies) can lead to problems in inverting the matrix \(\boldsymbol{X}^T\boldsymbol{X}\)? What kind of problems can we expect?

              -
              -
              +
              +

              Some useful matrix and vector expressions#

              +

              See the handwritten notes at CompPhysics/MachineLearning

              These notes will be discussed during one of the lectures.

              -
              -
              -

              Interpretations and optimizing our parameters

              + +
              +

              Interpretations and optimizing our parameters#

              The residuals \(\boldsymbol{\epsilon}\) are in turn given by

              \[ @@ -3806,9 +2522,9 @@ allow for the usage of direct linear algebra methods such as LU \]

              meaning that the solution for \(\boldsymbol{\beta}\) is the one which minimizes the residuals. Later we will link this with the maximum likelihood approach.

              Let us now return to our nuclear binding energies and simply code the above equations.

              -
              -
              -

              Own code for Ordinary Least Squares

              + +
              +

              Own code for Ordinary Least Squares#

              It is rather straightforward to implement the matrix inversion and obtain the parameters \(\boldsymbol{\beta}\). After having defined the matrix \(\boldsymbol{X}\) we simply need to write

              @@ -3849,9 +2565,9 @@ write

              - -
              -

              Adding error analysis and training set up

              + +
              +

              Adding error analysis and training set up#

              We can easily test our fit by computing the \(R2\) score that we discussed in connection with the functionality of Scikit-Learn in the introductory slides. Since we are not using Scikit-Learn here we can define our own \(R2\) function as

              @@ -3892,9 +2608,9 @@ Since we are not using Scikit-Learn here we can define our own
              - -
              -

              The \(\chi^2\) function

              + +
              +

              The \(\chi^2\) function#

              Normally, the response (dependent or outcome) variable \(y_i\) is the outcome of a numerical experiment or another type of experiment and is thus only an approximation to the true value. It is then always @@ -3910,9 +2626,9 @@ as

              \chi^2(\boldsymbol{\beta})=\frac{1}{n}\sum_{i=0}^{n-1}\frac{\left(y_i-\tilde{y}_i\right)^2}{\sigma_i^2}=\frac{1}{n}\left\{\left(\boldsymbol{y}-\boldsymbol{\tilde{y}}\right)^T\frac{1}{\boldsymbol{\Sigma^2}}\left(\boldsymbol{y}-\boldsymbol{\tilde{y}}\right)\right\}, \]

              where the matrix \(\boldsymbol{\Sigma}\) is a diagonal matrix with \(\sigma_i\) as matrix elements.

              - -
              -

              The \(\chi^2\) function

              + +
              +

              The \(\chi^2\) function#

              In order to find the parameters \(\beta_i\) we will then minimize the spread of \(\chi^2(\boldsymbol{\beta})\) by requiring

              \[ @@ -3929,9 +2645,9 @@ as

              \frac{\partial \chi^2(\boldsymbol{\beta})}{\partial \boldsymbol{\beta}} = 0 = \boldsymbol{A}^T\left( \boldsymbol{b}-\boldsymbol{A}\boldsymbol{\beta}\right). \]

              where we have defined the matrix \(\boldsymbol{A} =\boldsymbol{X}/\boldsymbol{\Sigma}\) with matrix elements \(a_{ij} = x_{ij}/\sigma_i\) and the vector \(\boldsymbol{b}\) with elements \(b_i = y_i/\sigma_i\).

              -
              -
              -

              The \(\chi^2\) function

              + +
              +

              The \(\chi^2\) function#

              We can rewrite

              \[ @@ -3947,9 +2663,9 @@ as

              \[ \boldsymbol{\beta} =\left(\boldsymbol{A}^T\boldsymbol{A}\right)^{-1}\boldsymbol{A}^T\boldsymbol{b}. \]
              -
              -
              -

              The \(\chi^2\) function

              + +
              +

              The \(\chi^2\) function#

              If we then introduce the matrix

              \[ @@ -3970,9 +2686,9 @@ as

              \[ \sigma^2(\beta_j) = \left(\sum_{k=0}^{p-1}h_{jk}\sum_{i=0}^{n-1}a_{ik}\right)\left(\sum_{l=0}^{p-1}h_{jl}\sum_{m=0}^{n-1}a_{ml}\right) = h_{jj}! \]
              -
              -
              -

              The \(\chi^2\) function

              + +
              +

              The \(\chi^2\) function#

              The first step here is to approximate the function \(y\) with a first-order polynomial, that is we write

              \[ @@ -3988,9 +2704,9 @@ y=y(x) \rightarrow y(x_i) \approx \beta_0+\beta_1 x_i. \[ \frac{\partial \chi^2(\boldsymbol{\beta})}{\partial \beta_1} = -\frac{2}{n}\left[ \sum_{i=0}^{n-1}x_i\left(\frac{y_i-\beta_0-\beta_1x_{i}}{\sigma_i^2}\right)\right]=0. \]
              -
              -
              -

              The \(\chi^2\) function

              + +
              +

              The \(\chi^2\) function#

              For a linear fit (a first-order polynomial) we don’t need to invert a matrix!!
              Defining

              @@ -4026,9 +2742,9 @@ Defining

              often from both being underdetermined and overdetermined in the unknown coefficients \(\beta_i\). A better approach is to use the Singular Value Decomposition (SVD) method discussed next week.

              -
              -
              -

              Fitting an Equation of State for Dense Nuclear Matter

              +
              +
              +

              Fitting an Equation of State for Dense Nuclear Matter#

              Before we continue, let us introduce yet another example. We are going to fit the nuclear equation of state using results from many-body calculations. The equation of state we have made available here, as function of @@ -4043,9 +2759,9 @@ before, with the same initializations and declarations. We use also instead of our own matrix inversion implementation. Furthermore, we sneak in Ridge regression (to be discussed below) which includes a hyperparameter \(\lambda\), also to be explained below.

              -
              -
              -

              The code

              + +
              +

              The code#

              # Common imports
@@ -4142,9 +2858,9 @@ to the data.
              
 
              We note also that there is a small deviation between the
 standard OLS and the Ridge regression at higher densities. We discuss this in more detail
 below.
              
-
              -
              -

              Splitting our Data in Training and Test data

              +
              +
              +

              Splitting our Data in Training and Test data#

              It is normal in essentially all Machine Learning studies to split the data in a training set and a test set (sometimes also an additional validation set). Scikit-Learn has an own function for this. There @@ -4225,13 +2941,13 @@ but now splitting the data into a training set and a test set.

              - -
              -

              Exercises

              + +
              +

              Exercises#

              Here are three possible exercises for week 34

              -
              -
              -

              Exercise 1: Setting up various Python environments

              + +
              +

              Exercise 1: Setting up various Python environments#

              The first exercise here is of a mere technical art. We want you to have

              • git as a version control software and to establish a user account on a provider like GitHub. Other providers like GitLab etc are equally fine. You can also use the University of Oslo GitHub facilities.

                • @@ -4247,7 +2963,7 @@ on Python.

                If you have Python installed (we recommend Python3) and you feel pretty familiar with installing different packages, we recommend that you install the following Python packages via pip as

                -
                  +

                  1. pip install numpy scipy matplotlib ipython scikit-learn sympy pandas pillow

                  For Tensorflow, we recommend following the instructions in the text of @@ -4257,12 +2973,12 @@ you install the following Python packages via pip as

                  For OSX users we recommend, after having installed Xcode, to install brew. Brew allows for a seamless installation of additional software via for example

                  -
                    +

                    1. brew install python3

                    For Linux users, with its variety of distributions like for example the widely popular Ubuntu distribution, you can use pip as well and simply install Python as

                    -
                      +

                      1. sudo apt-get install python3 (or python for Python2.7)

                      If you don’t want to perform these operations separately and venture @@ -4285,9 +3001,9 @@ distribution for scientific and analytic computing distribution and analysis environment, available for free and under a commercial license.

                      We recommend using Anaconda if you are not too familiar with setting paths in a terminal environment.

                      -
              -
              -

              Exercise 2: making your own data and exploring scikit-learn

              + +
              +

              Exercise 2: making your own data and exploring scikit-learn#

              We will generate our own dataset for a function \(y(x)\) where \(x \in [0,1]\) and defined by random numbers computed with the uniform distribution. The function \(y\) is a quadratic polynomial in \(x\) with added stochastic noise according to the normal distribution \(\cal {N}(0,1)\). The following simple Python instructions define our \(x\) and \(y\) values (with 100 data points).

              @@ -4298,7 +3014,7 @@ The following simple Python instructions define our +

              1. Write your own code (following the examples under the regression notes) for computing the parametrization of the data set fitting a second-order polynomial.

              2. Use thereafter scikit-learn (see again the examples in the regression slides) and compare with your own code.

              3. Using scikit-learn, compute also the mean square error, a risk metric corresponding to the expected value of the squared (quadratic) error defined as

                4. @@ -4321,9 +3037,9 @@ R^2(\boldsymbol{y}, \tilde{\boldsymbol{y}}) = 1 - \frac{\sum_{i=0}^{n - 1} (y_i \]

              You can use the functionality included in scikit-learn. If you feel for it, you can use your own program and define functions which compute the above two functions. Discuss the meaning of these results. Try also to vary the coefficient in front of the added stochastic noise term and discuss the quality of the fits.

              -
              -
              -

              Exercise 3: Split data in test and training data

              + +
              +

              Exercise 3: Split data in test and training data#

              In this exercise we want you to to compute the MSE for the training data and the test data as function of the complexity of a polynomial, that is the degree of a given polynomial.

              @@ -4348,8 +3064,8 @@ Write thereafter (using either scikit-learn or your matrix inve and perform an ordinary least squares fitting and compute the mean squared error for the training data and the test data. These calculations should apply to a model given by a fifth-order polynomial.

              c) Add now a model which allows you to make polynomials up to degree \(15\). Perform a standard OLS fitting of the training data and compute the MSE for the training and test data and plot both test and training data MSE as functions of the polynomial degree. Compare what you see with Figure 2.11 of Hastie et al. Comment your results. For which polynomial degree do you find an optimal MSE (smallest value)?

              -
              - + + - + - - -
              - - -
              -

              previous

              -

              Exercises week 34

              -
              + + + + + +               + +
              - - -
              -

              - - By Morten Hjorth-Jensen
              - - © Copyright 2021.
              -

              -
              - + + +
              +
              +
              + Contents +
              +
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              -
              + + + + +
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              +By Morten Hjorth-Jensen +

              +
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              + + © Copyright 2022. +
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              \ No newline at end of file diff --git a/doc/LectureNotes/_build/html/week35.html b/doc/LectureNotes/_build/html/week35.html index 11c1e9409..0590e16d6 100644 --- a/doc/LectureNotes/_build/html/week35.html +++ b/doc/LectureNotes/_build/html/week35.html @@ -1,50 +1,61 @@ + - + + + - + + Week 35: From Ordinary Linear Regression to Ridge and Lasso Regression — Applied Data Analysis and Machine Learning - - - + + + + + + + + + - - - - + + + + + - - - - - + - + - - - - + + + + + + + + - - + + + - - - - - - + + - - - + + + - + + + + +
              -
              -
              - - -
              -

              What does centering (subtracting the mean values) mean mathematically?

              + +
              +

              What does centering (subtracting the mean values) mean mathematically?#

              Let us try to understand what this may imply mathematically when we subtract the mean values, also known as zero centering. For simplicity, we will focus on ordinary regression, as done in the above example.

              @@ -2569,9 +1564,9 @@ When we take the derivative with respect to -

              Further Manipulations

              +
              +
              +

              Further Manipulations#

              Let us special first to the case where we have only two parameters \(\beta_0\) and \(\beta_1\). Our result for \(\beta_0\) simplifies then to

              @@ -2608,9 +1603,9 @@ n\beta_0 = \sum_{i=0}^{n-1}y_i - \sum_{i=0}^{n-1} X_{i1} \beta_1. \[ C(\boldsymbol{\beta}) = (\boldsymbol{\tilde{y}} - \tilde{X}\boldsymbol{\beta})^T(\boldsymbol{\tilde{y}} - \tilde{X}\boldsymbol{\beta}). \]
              -
              -
              -

              Wrapping it up

              + +
              +

              Wrapping it up#

              If we minimize with respect to \(\boldsymbol{\beta}\) we have then

              \[ @@ -2624,9 +1619,9 @@ and \(\tilde{X}_{ij} = X_{ij} - \frac \hat{\boldsymbol{\beta}} = (\tilde{X}^T\tilde{X} + \lambda I)^{-1}\tilde{X}^T\boldsymbol{\tilde{y}}. \]

              What does this mean? And why do we insist on all this? Let us look at some examples.

              -
              -
              -

              Linear Regression code, Intercept handling first

              + +
              +

              Linear Regression code, Intercept handling first#

              This code shows a simple first-order fit to a data set using the above transformed data, where we consider the role of the intercept first, by either excluding it or including it (code example thanks to Øyvind Sigmundson Schøyen). Here our scaling of the data is done by subtracting the mean values only. Note also that we do not split the data into training and test.

              @@ -2720,26 +1715,6 @@ Note also that we do not split the data into training and test.

              -
              -
              True beta: [2, 0.5, 3.7]
-Fitted beta: [2.08376632 0.19569961 3.97898392]
-Sklearn fitted beta: [2.08376632 0.19569961 3.97898392]
-MSE with intercept column
-0.004113634617443139
-MSE with intercept column from SKL
-0.004113634617443147
-Manual intercept: 2.083766322923899
-Fitted beta (wiothout intercept): [0.19569961 3.97898392]
-Sklearn intercept: 2.0837663229239043
-Sklearn fitted beta (without intercept): [0.19569961 3.97898392]
-MSE with Manual intercept
-0.00411363461744314
-MSE with Sklearn intercept
-0.004113634617443131
-
              -
              -_images/week35_181_1.png -

              The intercept is the value of our output/target variable when all our features are zero and our function crosses the \(y\)-axis (for a one-dimensional case).

              @@ -2772,9 +1747,9 @@ since it focuses only on the remaining quantities. If we however bring back the intercept, we will get an MSE which then contains the intercept. This becomes more important when we discuss Ridge and Lasso regression next week.

              - -
              -

              The Boston housing data example

              + +
              +

              The Boston housing data example#

              The Boston housing
              data set was originally a part of UCI Machine Learning Repository and has been removed now. The data set is now included in Scikit-Learn’s @@ -2782,7 +1757,7 @@ library. There are 506 samples and 13 feature (predictor) variables in this data set. The objective is to predict the value of prices of the house using the features (predictors) listed here.

              The features/predictors are

              -
                +

                1. CRIM: Per capita crime rate by town

                2. ZN: Proportion of residential land zoned for lots over 25000 square feet

                3. INDUS: Proportion of non-retail business acres per town

                  4. @@ -2797,9 +1772,9 @@ the house using the features (predictors) listed here.

                4. LSTAT: Percentage of lower status of the population

                5. MEDV: Median value of owner-occupied homes in USD 1000s

                -
              -
              -

              Housing data, the code

              + +
              +

              Housing data, the code#

              We start by importing the libraries

              @@ -2825,49 +1800,6 @@ the house using the features (predictors) listed here.

              -
              -
              /Users/mhjensen/miniforge3/envs/myenv/lib/python3.9/site-packages/sklearn/utils/deprecation.py:87: FutureWarning: Function load_boston is deprecated; `load_boston` is deprecated in 1.0 and will be removed in 1.2.
-
-    The Boston housing prices dataset has an ethical problem. You can refer to
-    the documentation of this function for further details.
-
-    The scikit-learn maintainers therefore strongly discourage the use of this
-    dataset unless the purpose of the code is to study and educate about
-    ethical issues in data science and machine learning.
-
-    In this special case, you can fetch the dataset from the original
-    source::
-
-        import pandas as pd
-        import numpy as np
-
-
-        data_url = "http://lib.stat.cmu.edu/datasets/boston"
-        raw_df = pd.read_csv(data_url, sep="\s+", skiprows=22, header=None)
-        data = np.hstack([raw_df.values[::2, :], raw_df.values[1::2, :2]])
-        target = raw_df.values[1::2, 2]
-
-    Alternative datasets include the California housing dataset (i.e.
-    :func:`~sklearn.datasets.fetch_california_housing`) and the Ames housing
-    dataset. You can load the datasets as follows::
-
-        from sklearn.datasets import fetch_california_housing
-        housing = fetch_california_housing()
-
-    for the California housing dataset and::
-
-        from sklearn.datasets import fetch_openml
-        housing = fetch_openml(name="house_prices", as_frame=True)
-
-    for the Ames housing dataset.
-    
-  warnings.warn(msg, category=FutureWarning)
-
              -
              -
              dict_keys(['data', 'target', 'feature_names', 'DESCR', 'filename', 'data_module'])
-
              -
              -

              Then we invoke Pandas

              @@ -2887,25 +1819,6 @@ the house using the features (predictors) listed here.

              -
              -
              CRIM       0
-ZN         0
-INDUS      0
-CHAS       0
-NOX        0
-RM         0
-AGE        0
-DIS        0
-RAD        0
-TAX        0
-PTRATIO    0
-B          0
-LSTAT      0
-MEDV       0
-dtype: int64
-
              -
              -

              We can then visualize the data

              @@ -2919,13 +1832,6 @@ dtype: int64
              -
              -
              /Users/mhjensen/miniforge3/envs/myenv/lib/python3.9/site-packages/seaborn/distributions.py:2619: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).
-  warnings.warn(msg, FutureWarning)
-
              -
              -_images/week35_199_1.png -

              It is now useful to look at the correlation matrix

              @@ -2938,12 +1844,6 @@ dtype: int64
              -
              -
              <AxesSubplot:>
-
              -
              -_images/week35_201_1.png -

              From the above coorelation plot we can see that MEDV is strongly correlated to LSTAT and RM. We see also that RAD and TAX are stronly correlated, but we don’t include this in our features together to avoid multi-colinearity

              @@ -2964,9 +1864,6 @@ dtype: int64
              -
              -_images/week35_203_0.png -

              Now we start training our model

              @@ -2992,14 +1889,6 @@ dtype: int64
              -
              -
              (404, 2)
-(102, 2)
-(404,)
-(102,)
-
              -
              -

              Then we use the linear regression functionality from Scikit-Learn

              @@ -3038,20 +1927,6 @@ dtype: int64
              -
              -
              The model performance for training set
---------------------------------------
-RMSE is 5.637129335071195
-R2 score is 0.6300745149331701
-
-
-The model performance for testing set
---------------------------------------
-RMSE is 5.137400784702911
-R2 score is 0.6628996975186953
-
              -
              -
              @@ -3062,16 +1937,13 @@ R2 score is 0.6628996975186953
              -
              -_images/week35_210_0.png
              - - -
              -

              Material for lecture Thursday, August 31

              -
              -
              -

              Mathematical Interpretation of Ordinary Least Squares

              + +
              +

              Material for lecture Thursday, August 31#

              +
              +
              +

              Mathematical Interpretation of Ordinary Least Squares#

              What is presented here is a mathematical analysis of various regression algorithms (ordinary least squares, Ridge and Lasso Regression). The analysis is based on an important algorithm in linear algebra, the so-called Singular Value Decomposition (SVD).

              We have shown that in ordinary least squares the optimal parameters \(\beta\) are given by

              @@ -3096,18 +1968,18 @@ R2 score is 0.6628996975186953 \]

              The matrix \(\boldsymbol{A}\) has the important property that \(\boldsymbol{A}^2=\boldsymbol{A}\). This is the definition of a projection matrix. We can then interpret our optimal model \(\tilde{\boldsymbol{y}}\) as being represented by an orthogonal projection of \(\boldsymbol{y}\) onto a space defined by the column vectors of \(\boldsymbol{X}\). In our case here the matrix \(\boldsymbol{A}\) is a square matrix. If it is a general rectangular matrix we have an oblique projection matrix.

              -
              -
              -

              Residual Error

              + +
              +

              Residual Error#

              We have defined the residual error as

              \[ \boldsymbol{\epsilon}=\boldsymbol{y}-\tilde{\boldsymbol{y}}=\left[\boldsymbol{I}-\boldsymbol{X}\left(\boldsymbol{X}^T\boldsymbol{X}\right)^{-1}\boldsymbol{X}^T\right]\boldsymbol{y}. \]

              The residual errors are then the projections of \(\boldsymbol{y}\) onto the orthogonal component of the space defined by the column vectors of \(\boldsymbol{X}\).

              -
              -
              -

              Simple case

              + +
              +

              Simple case#

              If the matrix \(\boldsymbol{X}\) is an orthogonal (or unitary in case of complex values) matrix, we have

              \[ @@ -3124,9 +1996,9 @@ We can then interpret our optimal model -

              The singular value decomposition

              +
              +
              +

              The singular value decomposition#

              The examples we have looked at so far are cases where we normally can invert the matrix \(\boldsymbol{X}^T\boldsymbol{X}\). Using a polynomial expansion where we fit of various functions leads to row vectors of the design matrix which are essentially orthogonal due @@ -3152,9 +2024,9 @@ to the covariance matrix (and thereby the correlation matrix) and in turn the variance of a given quantity. It plays also an important role in the principal component analysis where high-dimensional data can be reduced to the statistically relevant features.

              -
              -
              -

              Linear Regression Problems

              + +
              +

              Linear Regression Problems#

              One of the typical problems we encounter with linear regression, in particular when the matrix \(\boldsymbol{X}\) (our so-called design matrix) is high-dimensional, are problems with near singular or singular matrices. The column vectors of \(\boldsymbol{X}\) @@ -3196,9 +2068,9 @@ that the inverse of the matrix \(\bol \end{split}\]

              We see easily that \(\mbox{det}(\boldsymbol{X}) = x_{11} x_{22} - x_{12} x_{21} = 1 \times (-1) - 1 \times (-1) = 0\). Hence, \(\mathbf{X}\) is singular and its inverse is undefined. This is equivalent to saying that the matrix \(\boldsymbol{X}\) has at least an eigenvalue which is zero.

              - -
              -

              Fixing the singularity

              + +
              +

              Fixing the singularity#

              If our design matrix \(\boldsymbol{X}\) which enters the linear regression problem

              @@ -3220,9 +2092,9 @@ the regression parameters \(\beta_i\) \boldsymbol{X}^{T} \boldsymbol{X} \rightarrow \boldsymbol{X}^{T} \boldsymbol{X}+\lambda \boldsymbol{I}, \]

              where \(\boldsymbol{I}\) is the identity matrix. When we discuss Ridge regression this is actually what we end up evaluating. The parameter \(\lambda\) is called a hyperparameter. More about this later.

              - -
              -

              Basic math of the SVD

              + +
              +

              Basic math of the SVD#

              From standard linear algebra we know that a square matrix \(\boldsymbol{X}\) can be diagonalized if and only it is a so-called normal matrix, that is if \(\boldsymbol{X}\in {\mathbb{R}}^{n\times n}\) we have \(\boldsymbol{X}\boldsymbol{X}^T=\boldsymbol{X}^T\boldsymbol{X}\) or if \(\boldsymbol{X}\in {\mathbb{C}}^{n\times n}\) we have \(\boldsymbol{X}\boldsymbol{X}^{\dagger}=\boldsymbol{X}^{\dagger}\boldsymbol{X}\). @@ -3252,9 +2124,9 @@ The matrix has then a set of eigenpairs

              \end{split}\]

              is not diagonalizable, it is a so-called defective matrix. It is easy to see that the condition \(\boldsymbol{X}\boldsymbol{X}^T=\boldsymbol{X}^T\boldsymbol{X}\) is not fulfilled.

              - -
              -

              The SVD, a Fantastic Algorithm

              + +
              +

              The SVD, a Fantastic Algorithm#

              However, and this is the strength of the SVD algorithm, any general matrix \(\boldsymbol{X}\) can be decomposed in terms of a diagonal matrix and two orthogonal/unitary matrices. The Singular Value Decompostion @@ -3290,9 +2162,9 @@ masses and the equation of state this is indeed the case, while for the Ising model we have \(p > n\). These are often cases that lead to near singular or singular matrices.

              The columns of \(\boldsymbol{U}\) are called the left singular vectors while the columns of \(\boldsymbol{V}\) are the right singular vectors.

              -
              -
              -

              Economy-size SVD

              + +
              +

              Economy-size SVD#

              If we assume that \(n > p\), then our matrix \(\boldsymbol{U}\) has dimension \(n \times n\). The last \(n-p\) columns of \(\boldsymbol{U}\) become however irrelevant in our calculations since they are multiplied with the @@ -3307,15 +2179,15 @@ the decomposition.

              If \(p > n\), then only the first \(n\) columns of \(\boldsymbol{V}\) are computed and \(\boldsymbol{\Sigma}\) has dimension \(n\times n\). The \(n=p\) case is obvious, we retain the full SVD. In general the economy-size SVD leads to less FLOPS and still conserving the desired accuracy.

              -
              -
              -

              Codes for the SVD

              + +
              +

              Codes for the SVD#

              import numpy as np
 # SVD inversion
 def SVD(A):
-    ''' Takes as input a numpy matrix A and returns inv(A) based on singular value decomposition (SVD).
+    ''' Takes as input a numpy matrix A and returns inv(A) based on singular value decomposition (SVD).
     SVD is numerically more stable than the inversion algorithms provided by
     numpy and scipy.linalg at the cost of being slower.
     '''
@@ -3344,25 +2216,6 @@ In general the economy-size SVD leads to less FLOPS and still conserving the des
              -
              -
              [[ 1. -1.]
- [ 1. -1.]]
-test U
-[[0. 0.]
- [0. 0.]]
-test VT
-[[0. 0.]
- [0. 0.]]
-[[-0.70710678 -0.70710678]
- [-0.70710678  0.70710678]]
-[2.00000000e+00 3.35470445e-17]
-[[-0.70710678  0.70710678]
- [ 0.70710678  0.70710678]]
-[[-3.33066907e-16  4.44089210e-16]
- [ 0.00000000e+00  2.22044605e-16]]
-
              -
              -

              The matrix \(\boldsymbol{X}\) has columns that are linearly dependent. The first column is the row-wise sum of the other two columns. The rank of a @@ -3372,9 +2225,9 @@ independent columns, in this case just \(\boldsymbol{X}^T\boldsymbol{X}\) results in the program terminating due to a singular matrix.

              -
              -
              -

              Note about SVD Calculations

              + +
              +

              Note about SVD Calculations#

              The \(U\), \(S\), and \(V\) matrices returned from the svd() function cannot be multiplied directly.

              As you can see from the code, the \(S\) vector must be converted into a @@ -3385,9 +2238,9 @@ matrix.

              If you wish to include the zero singular values, you will need to resize the matrices and set up a diagonal matrix as done in the above example

              -
              -
              -

              Mathematics of the SVD and implications

              + +
              +

              Mathematics of the SVD and implications#

              Let us take a closer look at the mathematics of the SVD and the various implications for machine learning studies.

              Our starting point is our design matrix \(\boldsymbol{X}\) of dimension \(n\times p\)

              @@ -3414,9 +2267,9 @@ x_{n-1,0} & x_{n-1,1} & x_{n-1,2}& \dots & \dots x_{n-1,p-1}\\ \sigma_0 > \sigma_1 > \sigma_2 > \dots > \sigma_{p-1} > 0. \]

              All values beyond \(p-1\) are all zero.

              -
              -
              -

              Example Matrix

              + +
              +

              Example Matrix#

              As an example, consider the following \(3\times 2\) example for the matrix \(\boldsymbol{\Sigma}\)

              \[\begin{split} @@ -3467,9 +2320,9 @@ x_{n-1,0} & x_{n-1,1} & x_{n-1,2}& \dots & \dots x_{n-1,p-1}\\

              is a \(3\times 3 \) matrix. The last row and column of this last matrix contain only zeros. This will have important consequences for our SVD decomposition of the design matrix.

              -
              -
              -

              Setting up the Matrix to be inverted

              +
              +
              +

              Setting up the Matrix to be inverted#

              The matrix that may cause problems for us is \(\boldsymbol{X}^T\boldsymbol{X}\). Using the SVD we can rewrite this matrix as

              \[ @@ -3503,9 +2356,9 @@ vectors of the matrix \(\boldsymbol{U \(p-1\), that is \(\boldsymbol{\tilde{y}}\ne \boldsymbol{y}\). We can thus not use the orthogonality relation for the matrix \(\boldsymbol{U}\). This can already be when we multiply the matrices \(\boldsymbol{\Sigma}^T\boldsymbol{U}^T\).

              -
              -
              -

              Further properties (important for our analyses later)

              +
              +
              +

              Further properties (important for our analyses later)#

              Let us study again \(\boldsymbol{X}^T\boldsymbol{X}\) in terms of our SVD,

              \[ @@ -3546,9 +2399,9 @@ always refer to the number of features in our data set, while the number of rows represents the number of data inputs. Note that in other texts you may find the opposite notation. This has consequences for the definition of for example the covariance matrix and its relation to the SVD.

              -
              -
              -

              Meet the Covariance Matrix

              +
              +
              +

              Meet the Covariance Matrix#

              Before we move on to a discussion of Ridge and Lasso regression, we want to show an important example of the above.

              We have already noted that the matrix \(\boldsymbol{X}^T\boldsymbol{X}\) in ordinary least squares is proportional to the second derivative of the cost @@ -3567,9 +2420,9 @@ function, that is we have

              the covariance matrix. This means also that we can use the SVD to find the eigenvalues of the covariance matrix and the Hessian matrix in terms of the singular values. Let us develop these arguments, as they will play an important role in our machine learning studies.

              -
              -
              -

              Introducing the Covariance and Correlation functions

              + +
              +

              Introducing the Covariance and Correlation functions#

              Before we discuss the link between for example Ridge regression and the singular value decomposition, we need to remind ourselves about the definition of the covariance and the correlation function. These are quantities that play a central role in machine learning methods.

              Suppose we have defined two vectors @@ -3605,9 +2458,9 @@ and covariance. It also partially corrects the bias in the estimation of the population standard deviation. If you use a library like Scikit-Learn or nunmpy’s function to calculate the covariance, this quantity will be computed with a factor \(1/(n-1)\).

              -
              -
              -

              Covariance and Correlation Matrix

              + +
              +

              Covariance and Correlation Matrix#

              The covariance takes values between zero and infinity and may thus lead to problems with loss of numerical precision for particularly large values. It is common to scale the covariance matrix by @@ -3628,9 +2481,9 @@ and \(\boldsymbol{y}\) as

              \end{bmatrix}, \end{split}\]

              In the above example this is the function we constructed using pandas.

              - -
              -

              Correlation Function and Design/Feature Matrix

              + +
              +

              Correlation Function and Design/Feature Matrix#

              In our derivation of the various regression algorithms like Ordinary Least Squares or Ridge regression we defined the design/feature matrix \(\boldsymbol{X}\) as

              @@ -3683,9 +2536,9 @@ covariance matrix for the vectors \(\ \mathrm{corr}[\boldsymbol{x}_{p-1},\boldsymbol{x}_0] & \mathrm{corr}[\boldsymbol{x}_{p-1},\boldsymbol{x}_1] & \mathrm{corr}[\boldsymbol{x}_{p-1},\boldsymbol{x}_{2}] & \dots & \dots & 1\\ \end{bmatrix}, \end{split}\]
              -
              -
              -

              Covariance Matrix Examples

              + +
              +

              Covariance Matrix Examples#

              The Numpy function np.cov calculates the covariance elements using the factor \(1/(n-1)\) instead of \(1/n\) since it assumes we do not have the exact mean values. The following simple function uses the @@ -3718,18 +2571,10 @@ covariance matrix through the np.linalg.eig() function.

              -
              -
              0.10790125813226321
-4.340071371496255
-[[ 1.04193203  3.08165104]
- [ 3.08165104 10.18383522]]
-
              -
              - - -
              -

              Correlation Matrix

              + +
              +

              Correlation Matrix#

              The previous example can be converted into the correlation matrix by simply scaling the matrix elements with the variances. We should also subtract the mean values for each column. This leads to the following @@ -3761,22 +2606,14 @@ a more brute force way. Here we scale the mean values for each column of the des

              -
              -
              0.08881497884574564
-1.7086067479626619
-[[1.         0.66080313]
- [0.66080313 1.        ]]
-
              -
              -

              We see that the matrix elements along the diagonal are one as they should be and that the matrix is symmetric. Furthermore, diagonalizing this matrix we easily see that it is a positive definite matrix.

              The above procedure with numpy can be made more compact if we use pandas.

              - -
              -

              Correlation Matrix with Pandas

              + +
              +

              Correlation Matrix with Pandas#

              We whow here how we can set up the correlation matrix using pandas, as done in this simple code

              @@ -3797,39 +2634,11 @@ this matrix we easily see that it is a positive definite matrix.

              -
              -
              [[-0.40620066 -2.01265755]
- [ 0.01458611  0.37737221]
- [-1.0895387  -3.65442354]
- [ 0.2338675   1.12044974]
- [ 0.4676059   1.54393936]
- [-0.65891389 -3.16304863]
- [-0.1715252   0.39197698]
- [ 0.71142161  2.95511792]
- [ 0.39214397  0.13069442]
- [ 0.50655336  2.3105791 ]]
-          0         1
-0 -0.406201 -2.012658
-1  0.014586  0.377372
-2 -1.089539 -3.654424
-3  0.233868  1.120450
-4  0.467606  1.543939
-5 -0.658914 -3.163049
-6 -0.171525  0.391977
-7  0.711422  2.955118
-8  0.392144  0.130694
-9  0.506553  2.310579
-          0         1
-0  1.000000  0.952387
-1  0.952387  1.000000
-
              -
              -

              We expand this model to the Franke function discussed above.

              - -
              -

              Correlation Matrix with Pandas and the Franke function

              + +
              +

              Correlation Matrix with Pandas and the Franke function#

              # Common imports
@@ -3878,43 +2687,6 @@ this matrix we easily see that it is a positive definite matrix.
              -
              -
                   0         1         2         3         4         5         6         7   \
-0   0.0  0.000000  0.000000  0.000000  0.000000  0.000000  0.000000  0.000000   
-1   0.0  0.078974  0.081276  0.075889  0.077168  0.078540  0.065410  0.066474   
-2   0.0  0.081276  0.084076  0.078336  0.079946  0.081655  0.067707  0.069028   
-3   0.0  0.075889  0.078336  0.077460  0.078986  0.080616  0.069452  0.070737   
-4   0.0  0.077168  0.079946  0.078986  0.080764  0.082653  0.070986  0.072476   
-5   0.0  0.078540  0.081655  0.080616  0.082653  0.084809  0.072621  0.074323   
-6   0.0  0.065410  0.067707  0.069452  0.070986  0.072621  0.064074  0.065378   
-7   0.0  0.066474  0.069028  0.070737  0.072476  0.074323  0.065378  0.066854   
-8   0.0  0.067637  0.070457  0.072132  0.074084  0.076150  0.066787  0.068441   
-9   0.0  0.068906  0.072000  0.073644  0.075816  0.078110  0.068307  0.070146   
-10  0.0  0.055734  0.057835  0.060872  0.062337  0.063894  0.057393  0.058645   
-11  0.0  0.056683  0.058996  0.062016  0.063653  0.065390  0.058552  0.059951   
-12  0.0  0.057722  0.060254  0.063260  0.065077  0.066999  0.059807  0.061359   
-13  0.0  0.058854  0.061614  0.064609  0.066612  0.068727  0.061163  0.062874   
-14  0.0  0.060083  0.063080  0.066066  0.068264  0.070582  0.062624  0.064501   
-
-          8         9         10        11        12        13        14  
-0   0.000000  0.000000  0.000000  0.000000  0.000000  0.000000  0.000000  
-1   0.067637  0.068906  0.055734  0.056683  0.057722  0.058854  0.060083  
-2   0.070457  0.072000  0.057835  0.058996  0.060254  0.061614  0.063080  
-3   0.072132  0.073644  0.060872  0.062016  0.063260  0.064609  0.066066  
-4   0.074084  0.075816  0.062337  0.063653  0.065077  0.066612  0.068264  
-5   0.076150  0.078110  0.063894  0.065390  0.066999  0.068727  0.070582  
-6   0.066787  0.068307  0.057393  0.058552  0.059807  0.061163  0.062624  
-7   0.068441  0.070146  0.058645  0.059951  0.061359  0.062874  0.064501  
-8   0.070213  0.072111  0.059993  0.061452  0.063019  0.064699  0.066500  
-9   0.072111  0.074210  0.061443  0.063061  0.064793  0.066647  0.068629  
-10  0.059993  0.061443  0.052305  0.053417  0.054617  0.055910  0.057300  
-11  0.061452  0.063061  0.053417  0.054655  0.055987  0.057418  0.058952  
-12  0.063019  0.064793  0.054617  0.055987  0.057457  0.059031  0.060716  
-13  0.064699  0.066647  0.055910  0.057418  0.059031  0.060756  0.062599  
-14  0.066500  0.068629  0.057300  0.058952  0.060716  0.062599  0.064606  
-
              -
              -

              We note here that the covariance is zero for the first rows and columns since all matrix elements in the design matrix were set to one @@ -3923,9 +2695,9 @@ columns since all matrix elements in the design matrix were set to one cause problems when we set up the correlation matrix. We can simply drop these elements and construct a correlation matrix without these elements.

              -
              -
              -

              Rewriting the Covariance and/or Correlation Matrix

              + +
              +

              Rewriting the Covariance and/or Correlation Matrix#

              We can rewrite the covariance matrix in a more compact form in terms of the design/feature matrix \(\boldsymbol{X}\) as

              \[ @@ -3958,9 +2730,9 @@ x_{01}x_{00}+x_{11}x_{10} & x_{01}^2+x_{11}^2\\ \end{split}\]

              where we wrote $\(\boldsymbol{C}[\boldsymbol{x}_0,\boldsymbol{x}_1] = \boldsymbol{C}[\boldsymbol{x}]\)\( to indicate that this is the covariance of the vectors \)\boldsymbol{x}\( of the design/feature matrix \)\boldsymbol{X}$.

              It is easy to generalize this to a matrix \(\boldsymbol{X}\in {\mathbb{R}}^{n\times p}\).

              -
              -
              -

              Linking with the SVD

              + +
              +

              Linking with the SVD#

              We saw earlier that

              \[ @@ -3991,9 +2763,9 @@ x_{01}x_{00}+x_{11}x_{10} & x_{01}^2+x_{11}^2\\ \[ \left(\boldsymbol{X}^T\boldsymbol{X}\right)\boldsymbol{V}=\boldsymbol{V}\tilde{\boldsymbol{\Sigma}}^2. \]
              -
              -
              -

              What does it mean?

              + +
              +

              What does it mean?#

              This means the vectors \(\boldsymbol{v}_i\) of the orthogonal matrix \(\boldsymbol{V}\) are the eigenvectors of the matrix \(\boldsymbol{X}^T\boldsymbol{X}\) with eigenvalues given by the singular values squared, that is

              @@ -4022,9 +2794,9 @@ matrix. Every singular value of \(\bo root of an eigenvalue of \(\boldsymbol{X}^T\boldsymbol{X}\). If the matrix \(\boldsymbol{X}\) is self-adjoint, the singular values of \(\boldsymbol{X}\) are equal to the absolute value of the eigenvalues of \(\boldsymbol{X}\).

              -
              -
              -

              And finally \(\boldsymbol{X}\boldsymbol{X}^T\)

              + +
              +

              And finally \(\boldsymbol{X}\boldsymbol{X}^T\)#

              For \(\boldsymbol{X}\boldsymbol{X}^T\) we found

              \[ @@ -4052,9 +2824,9 @@ measure how much correlations are contained in the rows of \(\boldsymbol{X}\), the quantity of interest for us are the non-zero singular values and the column vectors of \(\boldsymbol{V}\).

              -
              -
              -

              Ridge and LASSO Regression

              +
              +
              +

              Ridge and LASSO Regression#

              Let us remind ourselves about the expression for the standard Mean Squared Error (MSE) which we used to define our cost function and the equations for the ordinary least squares (OLS) method, that is our optimization problem is

              @@ -4100,9 +2872,9 @@ C(\boldsymbol{X},\boldsymbol{\beta})=\frac{1}{n}\vert\vert \boldsymbol{y}-\bolds \[ \vert\vert \boldsymbol{x}\vert\vert_1 = \sum_i \vert x_i\vert. \]
              -
              -
              -

              Deriving the Ridge Regression Equations

              + +
              +

              Deriving the Ridge Regression Equations#

              Using the matrix-vector expression for Ridge regression and dropping the parameter \(1/n\) in front of the standard means squared error equation, we have

              \[ @@ -4152,9 +2924,9 @@ We have already analyzed the OLS solutions in terms of the eigenvectors (the col \tilde{\boldsymbol{y}}_{\mathrm{Ridge}}=\boldsymbol{X}\boldsymbol{\beta}_{\mathrm{Ridge}} = \boldsymbol{U\Sigma V^T}\left(\boldsymbol{V}\boldsymbol{\Sigma}^2\boldsymbol{V}^T+\lambda\boldsymbol{I} \right)^{-1}(\boldsymbol{U\Sigma V^T})^T\boldsymbol{y}=\sum_{j=0}^{p-1}\boldsymbol{u}_j\boldsymbol{u}_j^T\frac{\sigma_j^2}{\sigma_j^2+\lambda}\boldsymbol{y}, \]

              with the vectors \(\boldsymbol{u}_j\) being the columns of \(\boldsymbol{U}\) from the SVD of the matrix \(\boldsymbol{X}\).

              -
              -
              -

              Interpreting the Ridge results

              + +
              +

              Interpreting the Ridge results#

              Since \(\lambda \geq 0\), it means that compared to OLS, we have

              \[ @@ -4166,9 +2938,9 @@ orthonormal basis \(\boldsymbol{U}\)< eigenvalues ordered in a descending way, that is \(\sigma_i \geq \sigma_{i+1}\).

              For small eigenvalues \(\sigma_i\) it means that their contributions become less important, a fact which can be used to reduce the number of degrees of freedom. More about this when we have covered the material on a statistical interpretation of various linear regression methods.

              -
              -
              -

              More interpretations

              +
              +
              +

              More interpretations#

              For the sake of simplicity, let us assume that the design matrix is orthonormal, that is

              \[ @@ -4190,9 +2962,9 @@ infinity.

              We will come back to more interpreations after we have gone through some of the statistical analysis part.

              For more discussions of Ridge and Lasso regression, Wessel van Wieringen’s article is highly recommended. Similarly, Mehta et al’s article is also recommended.

              -
              -
              -

              Deriving the Lasso Regression Equations

              +
              +
              +

              Deriving the Lasso Regression Equations#

              Using the matrix-vector expression for Lasso regression, we have the following cost function

              \[ @@ -4219,8 +2991,8 @@ C(\boldsymbol{X},\boldsymbol{\beta})=\frac{1}{n}\left\{(\boldsymbol{y}-\boldsymb \boldsymbol{X}^T\boldsymbol{X}\boldsymbol{\beta}+\lambda sgn(\boldsymbol{\beta})=2\boldsymbol{X}^T\boldsymbol{y}. \]

              This equation does not lead to a nice analytical equation as in either Ridge regression or ordinary least squares. This equation can however be solved by using standard convex optimization algorithms using for example the Python package CVXOPT. We will discuss this later.

              -
              - + + - + - - -
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              previous

              -

              Exercises week 35

              -
              + + + + + +               + +
              - - -
              -

              - - By Morten Hjorth-Jensen
              - - © Copyright 2021.
              -

              -
              - + + +
              +
              +
              + Contents +
              +
              -
              -
              + + + + +
              + +
              - +
              + +

              +By Morten Hjorth-Jensen +

              +
              + +
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              + + © Copyright 2022. +
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              + +
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              \ No newline at end of file diff --git a/doc/LectureNotes/_build/html/week36.html b/doc/LectureNotes/_build/html/week36.html index d9983eaef..108d88e44 100644 --- a/doc/LectureNotes/_build/html/week36.html +++ b/doc/LectureNotes/_build/html/week36.html @@ -1,50 +1,61 @@ + - + + + - + + Week 36: Statistical interpretation of Linear Regression and Resampling techniques — Applied Data Analysis and Machine Learning - - - + + + + + + + + + - - - - + + + + + - - - - - + - + - - - - + + + + + + + + - - + + + - - - - - - + + - - - + + + - + + + + +
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              previous

              -

              Exercises week 36

              -
              + + + + + +               + +
              -
              -
              -
              -

              - - By Morten Hjorth-Jensen
              - - © Copyright 2021.
              -

              -
              - + + +
              +
              +
              + Contents +
              +
              -
              -
              + + + + +
              + +
              - +
              + +

              +By Morten Hjorth-Jensen +

              +
              + +
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              + + © Copyright 2022. +
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              + +
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              \ No newline at end of file diff --git a/doc/LectureNotes/_build/html/week37.html b/doc/LectureNotes/_build/html/week37.html index 5c06a8ff8..7e955490c 100644 --- a/doc/LectureNotes/_build/html/week37.html +++ b/doc/LectureNotes/_build/html/week37.html @@ -1,50 +1,61 @@ + - + + + - + + Week 37: Statistical interpretations and Resampling Methods — Applied Data Analysis and Machine Learning - - - + + + + + + + + + - - - - + + + + + - - - - - + - + - - - - + + + + + + + + - - + + + - - - - - - + + - - - + + + - + + + + +
              -
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              -
              [1.79934087 0.47179152 5.01549939]
-[1.79909592 0.47176716 5.01550546]
-  
-test MSE of OLS:
-1.13943111290393
-  
-test MSE of Ridge
-1.1395235273363686
-
              -
              -_images/week37_176_1.png -

              In this example we do not include the intercept and we scale the data by subtracting the mean values. This follows the discussion in the lecture material. see also the weekly slides for week 36. @@ -2867,21 +2170,6 @@ and \(\tilde{X}_{ij} = X_{ij} - \frac

              -
              -
              [0.47179152 5.01549939]
-[0.47176783 5.01542292]
-1.7993408651198877
-1.7995707762668065
-  
-test MSE of OLS:
-1.1394311129039245
-  
-test MSE of Ridge
-1.1395084586525954
-
              -
              -_images/week37_208_1.png -

              Finally, instead of using our own function we repeat the same example using the standardscaler functionality of the library @@ -2950,23 +2238,9 @@ using the standardscaler functionality of the library -

              -
              ---------------------------------------------------------------------------
-NameError                                 Traceback (most recent call last)
-Input In [11], in <cell line: 34>()
-     32 ypredictOLS = OLS.predict(X_test_scaled)
-     33 linear_model.Ridge(Lambda)
----> 34 RegRidge.fit(X_train_scaled,y_train_scaled)
-     35 ypredictRidge = RegRidge.predict(X_test_scaled)
-     36 betaOLS = OLS.coef_
-
-NameError: name 'RegRidge' is not defined
-
              -
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              - - + + - + - - -
              - - -
              -

              previous

              -

              Exercises week 37

              -
              + + + + + +               + +
              - - -
              -

              - - By Morten Hjorth-Jensen
              - - © Copyright 2021.
              -

              -
              - + + +
              +
              +
              + Contents +
              +
              -
              -
              + + + + +
              + +
              - +
              + +

              +By Morten Hjorth-Jensen +

              +
              + +
              + +

              + + © Copyright 2022. +
              + +

              + +
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              \ No newline at end of file diff --git a/doc/LectureNotes/_build/html/week38.html b/doc/LectureNotes/_build/html/week38.html index cb4de6f2c..6a43181c4 100644 --- a/doc/LectureNotes/_build/html/week38.html +++ b/doc/LectureNotes/_build/html/week38.html @@ -1,50 +1,61 @@ + - + + + - + + Week 38: Logistic Regression and Optimization — Applied Data Analysis and Machine Learning - - - + + + + + + + + + - - - - + + + + + - - - - - + - + - - - - + + + + + + + + - - + + + - - - - - - + + - - - + + + - + + + + +
              -
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              Here we have performed a rather data greedy calculation as function of the regularization parameter \(\lambda\). There is no resampling here. The latter can easily be added by employing the function RidgeCV instead of just calling the Ridge function. For RidgeCV we need to pass the array of \(\lambda\) values. By inspecting the figure we can in turn determine which is the optimal regularization parameter. This becomes however less functional in the long run.

              -
              -
              -
              -
              GridSearchCV(estimator=Ridge(),
-             param_grid={'alpha': array([1.00000000e-04, 4.64158883e-04, 2.15443469e-03, 1.00000000e-02,
-       4.64158883e-02, 2.15443469e-01, 1.00000000e+00, 4.64158883e+00,
-       2.15443469e+01, 1.00000000e+02])})
-Best estimated lambda-value: 100.0
-MSE score: 1.0892144853354966
-R2 score: -0.0038332550504751595
-
              -
              -

              By default the grid search function includes cross validation with five folds. The Scikit-Learn documentation contains more information on how to set the different parameters.

              If we take out the random noise, running the above codes results in \(\lambda=0\) yielding the best fit.

              - - -
              -
              RandomizedSearchCV(estimator=Ridge(), n_iter=100,
-                   param_distributions={'alpha': <scipy.stats._distn_infrastructure.rv_frozen object at 0x1309f4ca0>})
-Best estimated lambda-value: 0.9849967686928113
-MSE score: 1.0853136633465326
-R2 score: -0.0002382102844775691
-
              -
              - - -
              -

              Wisconsin Cancer Data

              + +
              +

              Wisconsin Cancer Data#

              We show here how we can use a simple regression case on the breast cancer data using Logistic regression as our algorithm for classification.

              @@ -2029,27 +1362,10 @@ classification.

              -
              -
              (426, 30)
-(143, 30)
-Test set accuracy with Logistic Regression: 0.94
-
              -
              /Users/mhjensen/miniforge3/envs/myenv/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:814: ConvergenceWarning: lbfgs failed to converge (status=1):
-STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
-
-Increase the number of iterations (max_iter) or scale the data as shown in:
-    https://scikit-learn.org/stable/modules/preprocessing.html
-Please also refer to the documentation for alternative solver options:
-    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
-  n_iter_i = _check_optimize_result(
-
              -
              -
              - - -
              -

              Using the correlation matrix

              + +
              +

              Using the correlation matrix#

              In addition to the above scores, we could also study the covariance (and the correlation matrix). We use Pandas to compute the correlation matrix.

              @@ -2091,14 +1407,10 @@ We use Pandas to compute the correlation matrix.

              -
              -_images/week38_82_0.png -_images/week38_82_1.png
              - - -
              -

              Discussing the correlation data

              + +
              +

              Discussing the correlation data#

              In the above example we note two things. In the first plot we display the overlap of benign and malignant tumors as functions of the various features in the Wisconsing breast cancer data set. We see that for @@ -2129,9 +1441,9 @@ matrix.

              features are of relevance and which are not. This leads us to the classical Principal Component Analysis (PCA) theorem with applications. This will be discussed later this semester (week 43).

              -
              -
              -

              Other measures in classification studies: Cancer Data again

              + +
              +

              Other measures in classification studies: Cancer Data again#

              import matplotlib.pyplot as plt
@@ -2169,112 +1481,10 @@ applications. This will be discussed later this semester (
-
              (426, 30)
-(143, 30)
-[1.         0.86666667 1.         0.92857143 1.         0.85714286
- 1.         0.92857143 0.92857143 1.        ]
-Test set accuracy with Logistic Regression: 0.94
-
              
 
              
-
              /Users/mhjensen/miniforge3/envs/myenv/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:814: ConvergenceWarning: lbfgs failed to converge (status=1):
-STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
-
-Increase the number of iterations (max_iter) or scale the data as shown in:
-    https://scikit-learn.org/stable/modules/preprocessing.html
-Please also refer to the documentation for alternative solver options:
-    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
-  n_iter_i = _check_optimize_result(
-/Users/mhjensen/miniforge3/envs/myenv/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:814: ConvergenceWarning: lbfgs failed to converge (status=1):
-STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
-
-Increase the number of iterations (max_iter) or scale the data as shown in:
-    https://scikit-learn.org/stable/modules/preprocessing.html
-Please also refer to the documentation for alternative solver options:
-    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
-  n_iter_i = _check_optimize_result(
-/Users/mhjensen/miniforge3/envs/myenv/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:814: ConvergenceWarning: lbfgs failed to converge (status=1):
-STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
-
-Increase the number of iterations (max_iter) or scale the data as shown in:
-    https://scikit-learn.org/stable/modules/preprocessing.html
-Please also refer to the documentation for alternative solver options:
-    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
-  n_iter_i = _check_optimize_result(
-/Users/mhjensen/miniforge3/envs/myenv/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:814: ConvergenceWarning: lbfgs failed to converge (status=1):
-STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
-
-Increase the number of iterations (max_iter) or scale the data as shown in:
-    https://scikit-learn.org/stable/modules/preprocessing.html
-Please also refer to the documentation for alternative solver options:
-    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
-  n_iter_i = _check_optimize_result(
-/Users/mhjensen/miniforge3/envs/myenv/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:814: ConvergenceWarning: lbfgs failed to converge (status=1):
-STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
-
-Increase the number of iterations (max_iter) or scale the data as shown in:
-    https://scikit-learn.org/stable/modules/preprocessing.html
-Please also refer to the documentation for alternative solver options:
-    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
-  n_iter_i = _check_optimize_result(
-/Users/mhjensen/miniforge3/envs/myenv/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:814: ConvergenceWarning: lbfgs failed to converge (status=1):
-STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
-
-Increase the number of iterations (max_iter) or scale the data as shown in:
-    https://scikit-learn.org/stable/modules/preprocessing.html
-Please also refer to the documentation for alternative solver options:
-    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
-  n_iter_i = _check_optimize_result(
-/Users/mhjensen/miniforge3/envs/myenv/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:814: ConvergenceWarning: lbfgs failed to converge (status=1):
-STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
-
-Increase the number of iterations (max_iter) or scale the data as shown in:
-    https://scikit-learn.org/stable/modules/preprocessing.html
-Please also refer to the documentation for alternative solver options:
-    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
-  n_iter_i = _check_optimize_result(
-/Users/mhjensen/miniforge3/envs/myenv/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:814: ConvergenceWarning: lbfgs failed to converge (status=1):
-STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
-
-Increase the number of iterations (max_iter) or scale the data as shown in:
-    https://scikit-learn.org/stable/modules/preprocessing.html
-Please also refer to the documentation for alternative solver options:
-    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
-  n_iter_i = _check_optimize_result(
-/Users/mhjensen/miniforge3/envs/myenv/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:814: ConvergenceWarning: lbfgs failed to converge (status=1):
-STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
-
-Increase the number of iterations (max_iter) or scale the data as shown in:
-    https://scikit-learn.org/stable/modules/preprocessing.html
-Please also refer to the documentation for alternative solver options:
-    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
-  n_iter_i = _check_optimize_result(
-/Users/mhjensen/miniforge3/envs/myenv/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:814: ConvergenceWarning: lbfgs failed to converge (status=1):
-STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
-
-Increase the number of iterations (max_iter) or scale the data as shown in:
-    https://scikit-learn.org/stable/modules/preprocessing.html
-Please also refer to the documentation for alternative solver options:
-    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
-  n_iter_i = _check_optimize_result(
-/Users/mhjensen/miniforge3/envs/myenv/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:814: ConvergenceWarning: lbfgs failed to converge (status=1):
-STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
-
-Increase the number of iterations (max_iter) or scale the data as shown in:
-    https://scikit-learn.org/stable/modules/preprocessing.html
-Please also refer to the documentation for alternative solver options:
-    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
-  n_iter_i = _check_optimize_result(
-
              
-
              
-_images/week38_89_2.png
-_images/week38_89_3.png
-_images/week38_89_4.png
-
              -
              -
              -
              +
              +

              Optimization, the central part of any Machine Learning algortithm#

              Overview Video, why do we care about gradient methods?

              Almost every problem in machine learning and data science starts with a dataset \(X\), a model \(g(\beta)\), which is a function of the @@ -2284,9 +1494,9 @@ us to judge how well the model \(g(\b the cost function. Ideally we would be able to solve for \(\beta\) analytically, however this is not possible in general and we must use some approximative/numerical method to compute the minimum.

              -
              -
              -

              Revisiting our Logistic Regression case

              + +
              +

              Revisiting our Logistic Regression case#

              In our discussion on Logistic Regression we studied the case of two classes, with \(y_i\) either @@ -2301,9 +1511,9 @@ p(y_i=0|x_i,\boldsymbol{\beta}) &= 1 - p(y_i=1|x_i,\boldsymbol{\beta}), \end{align*} \end{split}\]

              where \(\boldsymbol{\beta}\) are the weights we wish to extract from data, in our case \(\beta_0\) and \(\beta_1\).

              - -
              -

              The equations to solve

              + +
              +

              The equations to solve#

              Our compact equations used a definition of a vector \(\boldsymbol{y}\) with \(n\) elements \(y_i\), an \(n\times p\) matrix \(\boldsymbol{X}\) which contains the \(x_i\) values and a vector \(\boldsymbol{p}\) of fitted probabilities @@ -2320,9 +1530,9 @@ the first derivative of the cost function as

              \frac{\partial^2 \mathcal{C}(\boldsymbol{\beta})}{\partial \boldsymbol{\beta}\partial \boldsymbol{\beta}^T} = \boldsymbol{X}^T\boldsymbol{W}\boldsymbol{X}. \]

              This defines what is called the Hessian matrix.

              - -
              -

              Solving using Newton-Raphson’s method

              + +
              +

              Solving using Newton-Raphson’s method#

              If we can set up these equations, Newton-Raphson’s iterative method is normally the method of choice. It requires however that we can compute in an efficient way the matrices that define the first and second derivatives.

              Our iterative scheme is then given by

              @@ -2336,9 +1546,9 @@ the first derivative of the cost function as

              \]

              The right-hand side is computed with the old values of \(\beta\).

              If we can compute these matrices, in particular the Hessian, the above is often the easiest method to implement.

              -
              -
              -

              Brief reminder on Newton-Raphson’s method

              + +
              +

              Brief reminder on Newton-Raphson’s method#

              Let us quickly remind ourselves how we derive the above method.

              Perhaps the most celebrated of all one-dimensional root-finding routines is Newton’s method, also called the Newton-Raphson @@ -2347,9 +1557,9 @@ function \(f\) and its derivat If you can only calculate the derivative numerically and/or your function is not of the smooth type, we normally discourage the use of this method.

              -
              -
              -

              The equations

              + +
              +

              The equations#

              The Newton-Raphson formula consists geometrically of extending the tangent line at a current point until it crosses zero, then setting the next guess to the abscissa of that zero-crossing. The mathematics @@ -2378,9 +1588,9 @@ s\approx x-\frac{f(x)}{f'(x)}. \[ x_{n+1}=x_n-\frac{f(x_n)}{f'(x_n)}. \]

              - -
              -

              Simple geometric interpretation

              + +
              +

              Simple geometric interpretation#

              The above is Newton-Raphson’s method. It has a simple geometric interpretation, namely \(x_{n+1}\) is the point where the tangent from \((x_n,f(x_n))\) crosses the \(x\)-axis. Close to the solution, @@ -2392,9 +1602,9 @@ from the true root as to let the search interval include a local maximum or minimum of the function. If an iteration places a trial guess near such a local extremum, so that the first derivative nearly vanishes, then Newton-Raphson may fail totally

              -
              -
              -

              Extending to more than one variable

              + +
              +

              Extending to more than one variable#

              Newton’s method can be generalized to systems of several non-linear equations and variables. Consider the case with two equations

              @@ -2440,9 +1650,9 @@ is to understand that difficulties may arise in case \({\bf \boldsymbol{J}}\) is nearly singular.

              It is rather straightforward to extend the above scheme to systems of more than two non-linear equations. In our case, the Jacobian matrix is given by the Hessian that represents the second derivative of cost function.

              -
              -
              -

              Steepest descent

              +
              +
              +

              Steepest descent#

              The basic idea of gradient descent is that a function \(F(\mathbf{x})\), \(\mathbf{x} \equiv (x_1,\cdots,x_n)\), decreases fastest if one goes from \(\bf {x}\) in the @@ -2456,9 +1666,9 @@ direction of the negative gradient \(

              For \(\gamma_k\) small enough, then \(F(\mathbf{x}_{k+1}) \leq F(\mathbf{x}_k)\). This means that for a sufficiently small \(\gamma_k\) we are always moving towards smaller function values, i.e a minimum.

              -
              -
              -

              More on Steepest descent

              + +
              +

              More on Steepest descent#

              The previous observation is the basis of the method of steepest descent, which is also referred to as just gradient descent (GD). One starts with an initial guess \(\mathbf{x}_0\) for a minimum of \(F\) and @@ -2469,9 +1679,9 @@ computes new approximations according to

              \]

              The parameter \(\gamma_k\) is often referred to as the step length or the learning rate within the context of Machine Learning.

              - -
              -

              The ideal

              + +
              +

              The ideal#

              Ideally the sequence \(\{\mathbf{x}_k \}_{k=0}\) converges to a global minimum of the function \(F\). In general we do not know if we are in a global or local minimum. In the special case when \(F\) is a convex @@ -2487,9 +1697,9 @@ have a very good intial guess. This also implies that the scheme is sensitive to the chosen initial condition.

              Note that the gradient is a function of \(\mathbf{x} = (x_1,\cdots,x_n)\) which makes it expensive to compute numerically.

              -
              -
              -

              The sensitiveness of the gradient descent

              + +
              +

              The sensitiveness of the gradient descent#

              The gradient descent method is sensitive to the choice of learning rate \(\gamma_k\). This is due to the fact that we are only guaranteed that \(F(\mathbf{x}_{k+1}) \leq @@ -2500,9 +1710,9 @@ large we can experience erratic behavior.

              Many of these shortcomings can be alleviated by introducing randomness. One such method is that of Stochastic Gradient Descent (SGD), to be discussed next week.

              -
              -
              -

              Convex functions

              + +
              +

              Convex functions#

              Ideally we want our cost/loss function to be convex(concave).

              First we give the definition of a convex set: A set \(C\) in \(\mathbb{R}^n\) is said to be convex if, for all \(x\) and \(y\) in \(C\) and @@ -2512,13 +1722,13 @@ connecting \(x\) and The convex subsets of \(\mathbb{R}\) are the intervals of \(\mathbb{R}\). Examples of convex sets of \(\mathbb{R}^2\) are the regular polygons (triangles, rectangles, pentagons, etc…).

              -
              -
              -

              Convex function

              + +
              +

              Convex function#

              Convex function: Let \(X \subset \mathbb{R}^n\) be a convex set. Assume that the function \(f: X \rightarrow \mathbb{R}\) is continuous, then \(f\) is said to be convex if $\(f(tx_1 + (1-t)x_2) \leq tf(x_1) + (1-t)f(x_2) \)\( for all \)x_1, x_2 \in X\( and for all \)t \in [0,1]\(. If \)\leq\( is replaced with a strict inequaltiy in the definition, we demand \)x_1 \neq x_2\( and \)t\in(0,1)\( then \)f\( is said to be strictly convex. For a single variable function, convexity means that if you draw a straight line connecting \)f(x_1)\( and \)f(x_2)\(, the value of the function on the interval \)[x_1,x_2]$ is always below the line as illustrated below.

              -
              -
              -

              Conditions on convex functions

              + +
              +

              Conditions on convex functions#

              In the following we state first and second-order conditions which ensures convexity of a function \(f\). We write \(D_f\) to denote the domain of \(f\), i.e the subset of \(R^n\) where \(f\) is defined. For more @@ -2540,9 +1750,9 @@ Hessian is positive semi-definite for all \(f''(x) \geq 0\). Geometrically this means that \(f\) has nonnegative curvature everywhere.

              This condition is particularly useful since it gives us an procedure for determining if the function under consideration is convex, apart from using the definition.

              -
              -
              -

              More on convex functions

              + +
              +

              More on convex functions#

              The next result is of great importance to us and the reason why we are going on about convex functions. In machine learning we frequently have to minimize a loss/cost function in order to find the best @@ -2556,10 +1766,10 @@ is convex the following result provides invaluable information:

              is minimal, where \(f\) is convex and differentiable. Then, any point \(x^*\) that satisfies \(\nabla f(x^*) = 0\) is a global minimum.

              This result means that if we know that the cost/loss function is convex and we are able to find a minimum, we are guaranteed that it is a global minimum.

              -
              -
              -

              Some simple problems

              -
                + +
                +

                Some simple problems#

                +

                1. Show that \(f(x)=x^2\) is convex for \(x \in \mathbb{R}\) using the definition of convexity. Hint: If you re-write the definition, \(f\) is convex if the following holds for all \(x,y \in D_f\) and any \(\lambda \in [0,1]\) \(\lambda f(x)+(1-\lambda)f(y)-f(\lambda x + (1-\lambda) y ) \geq 0\).

                2. Using the second order condition show that the following functions are convex on the specified domain.

                @@ -2567,7 +1777,7 @@ is minimal, where \(f\) is con

              1. \(f(x) = e^x\) is convex for \(x \in \mathbb{R}\).

              2. \(g(x) = -\ln(x)\) is convex for \(x \in (0,\infty)\).

                3. -
                  +

                  1. Let \(f(x) = x^2\) and \(g(x) = e^x\). Show that \(f(g(x))\) and \(g(f(x))\) is convex for \(x \in \mathbb{R}\). Also show that if \(f(x)\) is any convex function than \(h(x) = e^{f(x)}\) is convex.

                  2. A norm is any function that satisfy the following properties

                  @@ -2577,14 +1787,14 @@ is minimal, where \(f\) is con

                1. \(f(x) \leq 0\) for all \(x \in \mathbb{R}^n\) with equality if and only if \(x = 0\)

                  2. Using the definition of convexity, try to show that a function satisfying the properties above is convex (the third condition is not needed to show this).

                  -
              -
              -

              Revisiting our first homework

              + +
              +

              Revisiting our first homework#

              We will use linear regression as a case study for the gradient descent methods. Linear regression is a great test case for the gradient descent methods discussed in the lectures since it has several desirable properties such as:

              -
                +

                1. An analytical solution (recall homework set 1).

                2. The gradient can be computed analytically.

                3. The cost function is convex which guarantees that gradient descent converges for small enough learning rates

                  4. @@ -2597,17 +1807,6 @@ desirable properties such as:

              -
              -
              ---------------------------------------------------------------------------
-NameError                                 Traceback (most recent call last)
-Input In [13], in <cell line: 1>()
-----> 1 x = 2*np.random.rand(m,1)
-      2 y = 4+3*x+np.random.randn(m,1)
-
-NameError: name 'm' is not defined
-
              -
              -

              with \(x_i \in [0,1] \) is chosen randomly using a uniform distribution. Additionally we have a stochastic noise chosen according to a normal distribution \(\cal {N}(0,1)\). The linear regression model is given by

              @@ -2620,9 +1819,9 @@ h_\beta(x) = \boldsymbol{y} = \beta_0 + \beta_1 x, \[ \boldsymbol{y}_i = \beta_0 + \beta_1 x_i. \] - -
              -

              Gradient descent example

              + +
              +

              Gradient descent example#

              Let \(\mathbf{y} = (y_1,\cdots,y_n)^T\), \(\mathbf{\boldsymbol{y}} = (\boldsymbol{y}_1,\cdots,\boldsymbol{y}_n)^T\) and \(\beta = (\beta_0, \beta_1)^T\)

              It is convenient to write \(\mathbf{\boldsymbol{y}} = X\beta\) where \(X \in \mathbb{R}^{100 \times 2} \) is the design matrix given by (we keep the intercept here)

              @@ -2639,9 +1838,9 @@ X \equiv \begin{bmatrix} C(\beta) = \frac{1}{n}||X\beta-\mathbf{y}||_{2}^{2} = \frac{1}{n}\sum_{i=1}^{100}\left[ (\beta_0 + \beta_1 x_i)^2 - 2 y_i (\beta_0 + \beta_1 x_i) + y_i^2\right] \]

              and we want to find \(\beta\) such that \(C(\beta)\) is minimized.

              -
              -
              -

              The derivative of the cost/loss function

              + +
              +

              The derivative of the cost/loss function#

              Computing \(\partial C(\beta) / \partial \beta_0\) and \(\partial C(\beta) / \partial \beta_1\) we can show that the gradient can be written as

              \[\begin{split} @@ -2650,9 +1849,9 @@ C(\beta) = \frac{1}{n}||X\beta-\mathbf{y}||_{2}^{2} = \frac{1}{n}\sum_{i=1}^{100 \end{bmatrix} = \frac{2}{n}X^T(X\beta - \mathbf{y}), \end{split}\]

              where \(X\) is the design matrix defined above.

              -
              -
              -

              The Hessian matrix

              + +
              +

              The Hessian matrix#

              The Hessian matrix of \(C(\beta)\) is given by

              \[\begin{split} @@ -2662,9 +1861,9 @@ C(\beta) = \frac{1}{n}||X\beta-\mathbf{y}||_{2}^{2} = \frac{1}{n}\sum_{i=1}^{100 \end{bmatrix} = \frac{2}{n}X^T X. \end{split}\]

              This result implies that \(C(\beta)\) is a convex function since the matrix \(X^T X\) always is positive semi-definite.

              -
              -
              -

              Simple program

              + +
              +

              Simple program#

              We can now write a program that minimizes \(C(\beta)\) using the gradient descent method with a constant learning rate \(\gamma\) according to

              \[ @@ -2675,9 +1874,9 @@ C(\beta) = \frac{1}{n}||X\beta-\mathbf{y}||_{2}^{2} = \frac{1}{n}\sum_{i=1}^{100 when \(||\nabla_\beta C(\beta_k) || \leq \epsilon = 10^{-8}\). Note that the code below does not include the latter stop criterion.

              And finally we can compare our solution for \(\beta\) with the analytic result given by \(\beta= (X^TX)^{-1} X^T \mathbf{y}\).

              -
              -
              -

              Gradient Descent Example

              +
              +
              +

              Gradient Descent Example#

              Here our simple example

              @@ -2730,9 +1929,9 @@ when \(||\nabla_\beta C(\beta_k) || \
              - -
              -

              And a corresponding example using scikit-learn

              + +
              +

              And a corresponding example using scikit-learn#

              # Importing various packages
@@ -2755,9 +1954,9 @@ when \(||\nabla_\beta C(\beta_k) || \
              -
              -
              -

              Gradient descent and Ridge

              +
              +
              +

              Gradient descent and Ridge#

              We have also discussed Ridge regression where the loss function contains a regularized term given by the \(L_2\) norm of \(\beta\),

              \[ @@ -2775,9 +1974,9 @@ C_{\text{ridge}}(\beta) = \frac{1}{n}||X\beta -\mathbf{y}||^2 + \lambda ||\beta| \[ \beta_{\text{ridge}} = \left(X^T X + n\lambda I_{2 \times 2} \right)^{-1} X^T \mathbf{y}. \]
              -
              -
              -

              The Hessian matrix for Ridge Regression

              + +
              +

              The Hessian matrix for Ridge Regression#

              The Hessian matrix of Ridge Regression for our simple example is given by

              \[\begin{split} @@ -2791,9 +1990,9 @@ minimum. Note that the Ridge cost function is convex being a sum of two convex functions. Therefore, the stationary point is a global minimum of this function.

              -
              -
              -

              Program example for gradient descent with Ridge Regression

              +
              +
              +

              Program example for gradient descent with Ridge Regression#

              from random import random, seed
@@ -2850,9 +2049,9 @@ minimum of this function.
              -
              -
              -

              Using gradient descent methods, limitations

              +
              +
              +

              Using gradient descent methods, limitations#

              • Gradient descent (GD) finds local minima of our function. Since the GD algorithm is deterministic, if it converges, it will converge to a local minimum of our cost/loss/risk function. Because in ML we are often dealing with extremely rugged landscapes with many local minima, this can lead to poor performance.

              • GD is sensitive to initial conditions. One consequence of the local nature of GD is that initial conditions matter. Depending on where one starts, one will end up at a different local minima. Therefore, it is very important to think about how one initializes the training process. This is true for GD as well as more complicated variants of GD.

                • @@ -2861,12 +2060,12 @@ minimum of this function.

              • GD treats all directions in parameter space uniformly. Another major drawback of GD is that unlike Newton’s method, the learning rate for GD is the same in all directions in parameter space. For this reason, the maximum learning rate is set by the behavior of the steepest direction and this can significantly slow down training. Ideally, we would like to take large steps in flat directions and small steps in steep directions. Since we are exploring rugged landscapes where curvatures change, this requires us to keep track of not only the gradient but second derivatives. The ideal scenario would be to calculate the Hessian but this proves to be too computationally expensive.

              • GD can take exponential time to escape saddle points, even with random initialization. As we mentioned, GD is extremely sensitive to initial condition since it determines the particular local minimum GD would eventually reach. However, even with a good initialization scheme, through the introduction of randomness, GD can still take exponential time to escape saddle points.

              -
              -
              -

              Challenge yourself the coming weekend

              + +
              +

              Challenge yourself the coming weekend#

              Write a code which implements gradient descent for a logistic regression example.

              -
              - + + - + - - -
              - - -
              -

              previous

              -

              Exercises week 38

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              + + + + + +               + +
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              - - By Morten Hjorth-Jensen
              - - © Copyright 2021.
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              + Contents +
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              +By Morten Hjorth-Jensen +

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              + + © Copyright 2022. +
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              Week 39: Optimization and Gradient Methods

              + +
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              + +
              +

              Contents

              +
              + +
              +
              +
              + + + + +
              + + +
              +

              Week 39: Optimization and Gradient Methods#

              +

              Morten Hjorth-Jensen, Department of Physics, University of Oslo and Department of Physics and Astronomy and Facility for Rare Isotope Beams, Michigan State University

              +

              Date: Week 39

              +
              +

              Plan for week 39#

              +

              Material for the active learning sessions on Tuesday and Wednesday.

              +
                +

              • Discussions on how to structure your report for the first project

                • +

              • Exercise for week 39 on how to write the abstract and the introduction of the report and how to include references.

                • +

              • Work on project 1, in particular resampling methods like cross-validation and bootstrap. For more discussions of project 1, chapter 5 of Goodfellow et al is a good read, in particular sections 5.1-5.5 and 5.7-5.11.

                • +
              +

              These sections summarize neatly what we have done till now and point to what is coming with respect to deep learning.

              + +

              Material for the lecture on Thursday September 28.

              +
                +

              • Repetition of Logistic regression equations and classification problems and discussion of Gradient methods. Examples on how to implement Logistic Regression and discussion of stochastic gradient descent

                • +

              • Stochastic Gradient descent with examples and automatic differentiation

                • +

              • Video of lecture

                • +

              • Whiteboard notes TBA at CompPhysics/MachineLearning

                • +

              • Readings and Videos:

                + +
                • +
              +
              +
              +

              Optimization, the central part of any Machine Learning algortithm#

              +

              The first few slides here are a repetition from last week.

              +

              Almost every problem in machine learning and data science starts with +a dataset \(X\), a model \(g(\beta)\), which is a function of the +parameters \(\beta\) and a cost function \(C(X, g(\beta))\) that allows +us to judge how well the model \(g(\beta)\) explains the observations +\(X\). The model is fit by finding the values of \(\beta\) that minimize +the cost function. Ideally we would be able to solve for \(\beta\) +analytically, however this is not possible in general and we must use +some approximative/numerical method to compute the minimum.

              +
              +
              +

              Revisiting our Logistic Regression case#

              +

              In our discussion on Logistic Regression we studied the +case of +two classes, with \(y_i\) either +\(0\) or \(1\). Furthermore we assumed also that we have only two +parameters \(\beta\) in our fitting, that is we +defined probabilities

              +
              +\[\begin{split} +\begin{align*} +p(y_i=1|x_i,\boldsymbol{\beta}) &= \frac{\exp{(\beta_0+\beta_1x_i)}}{1+\exp{(\beta_0+\beta_1x_i)}},\nonumber\\ +p(y_i=0|x_i,\boldsymbol{\beta}) &= 1 - p(y_i=1|x_i,\boldsymbol{\beta}), +\end{align*} +\end{split}\]
              +

              where \(\boldsymbol{\beta}\) are the weights we wish to extract from data, in our case \(\beta_0\) and \(\beta_1\).

              +
              +
              +

              The equations to solve#

              +

              Our compact equations used a definition of a vector \(\boldsymbol{y}\) with \(n\) +elements \(y_i\), an \(n\times p\) matrix \(\boldsymbol{X}\) which contains the +\(x_i\) values and a vector \(\boldsymbol{p}\) of fitted probabilities +\(p(y_i\vert x_i,\boldsymbol{\beta})\). We rewrote in a more compact form +the first derivative of the cost function as

              +
              +\[ +\frac{\partial \mathcal{C}(\boldsymbol{\beta})}{\partial \boldsymbol{\beta}} = -\boldsymbol{X}^T\left(\boldsymbol{y}-\boldsymbol{p}\right). +\]
              +

              If we in addition define a diagonal matrix \(\boldsymbol{W}\) with elements +\(p(y_i\vert x_i,\boldsymbol{\beta})(1-p(y_i\vert x_i,\boldsymbol{\beta})\), we can obtain a compact expression of the second derivative as

              +
              +\[ +\frac{\partial^2 \mathcal{C}(\boldsymbol{\beta})}{\partial \boldsymbol{\beta}\partial \boldsymbol{\beta}^T} = \boldsymbol{X}^T\boldsymbol{W}\boldsymbol{X}. +\]
              +

              This defines what is called the Hessian matrix.

              +
              +
              +

              Solving using Newton-Raphson’s method#

              +

              If we can set up these equations, Newton-Raphson’s iterative method is normally the method of choice. It requires however that we can compute in an efficient way the matrices that define the first and second derivatives.

              +

              Our iterative scheme is then given by

              +
              +\[ +\boldsymbol{\beta}^{\mathrm{new}} = \boldsymbol{\beta}^{\mathrm{old}}-\left(\frac{\partial^2 \mathcal{C}(\boldsymbol{\beta})}{\partial \boldsymbol{\beta}\partial \boldsymbol{\beta}^T}\right)^{-1}_{\boldsymbol{\beta}^{\mathrm{old}}}\times \left(\frac{\partial \mathcal{C}(\boldsymbol{\beta})}{\partial \boldsymbol{\beta}}\right)_{\boldsymbol{\beta}^{\mathrm{old}}}, +\]
              +

              or in matrix form as

              +
              +\[ +\boldsymbol{\beta}^{\mathrm{new}} = \boldsymbol{\beta}^{\mathrm{old}}-\left(\boldsymbol{X}^T\boldsymbol{W}\boldsymbol{X} \right)^{-1}\times \left(-\boldsymbol{X}^T(\boldsymbol{y}-\boldsymbol{p}) \right)_{\boldsymbol{\beta}^{\mathrm{old}}}. +\]
              +

              The right-hand side is computed with the old values of \(\beta\).

              +

              If we can compute these matrices, in particular the Hessian, the above is often the easiest method to implement.

              +
              +
              +

              Brief reminder on Newton-Raphson’s method#

              +

              Let us quickly remind ourselves how we derive the above method.

              +

              Perhaps the most celebrated of all one-dimensional root-finding +routines is Newton’s method, also called the Newton-Raphson +method. This method requires the evaluation of both the +function \(f\) and its derivative \(f'\) at arbitrary points. +If you can only calculate the derivative +numerically and/or your function is not of the smooth type, we +normally discourage the use of this method.

              +
              +
              +

              The equations#

              +

              The Newton-Raphson formula consists geometrically of extending the +tangent line at a current point until it crosses zero, then setting +the next guess to the abscissa of that zero-crossing. The mathematics +behind this method is rather simple. Employing a Taylor expansion for +\(x\) sufficiently close to the solution \(s\), we have

              + +
              +
              +\[ +f(s)=0=f(x)+(s-x)f'(x)+\frac{(s-x)^2}{2}f''(x) +\dots. + \label{eq:taylornr} \tag{1} +\]
              +

              For small enough values of the function and for well-behaved +functions, the terms beyond linear are unimportant, hence we obtain

              +
              +\[ +f(x)+(s-x)f'(x)\approx 0, +\]
              +

              yielding

              +
              +\[ +s\approx x-\frac{f(x)}{f'(x)}. +\]
              +

              Having in mind an iterative procedure, it is natural to start iterating with

              +
              +\[ +x_{n+1}=x_n-\frac{f(x_n)}{f'(x_n)}. +\]
              +
              +
              +

              Simple geometric interpretation#

              +

              The above is Newton-Raphson’s method. It has a simple geometric +interpretation, namely \(x_{n+1}\) is the point where the tangent from +\((x_n,f(x_n))\) crosses the \(x\)-axis. Close to the solution, +Newton-Raphson converges fast to the desired result. However, if we +are far from a root, where the higher-order terms in the series are +important, the Newton-Raphson formula can give grossly inaccurate +results. For instance, the initial guess for the root might be so far +from the true root as to let the search interval include a local +maximum or minimum of the function. If an iteration places a trial +guess near such a local extremum, so that the first derivative nearly +vanishes, then Newton-Raphson may fail totally

              +
              +
              +

              Extending to more than one variable#

              +

              Newton’s method can be generalized to systems of several non-linear equations +and variables. Consider the case with two equations

              +
              +\[\begin{split} +\begin{array}{cc} f_1(x_1,x_2) &=0\\ + f_2(x_1,x_2) &=0,\end{array} +\end{split}\]
              +

              which we Taylor expand to obtain

              +
              +\[\begin{split} +\begin{array}{cc} 0=f_1(x_1+h_1,x_2+h_2)=&f_1(x_1,x_2)+h_1 + \partial f_1/\partial x_1+h_2 + \partial f_1/\partial x_2+\dots\\ + 0=f_2(x_1+h_1,x_2+h_2)=&f_2(x_1,x_2)+h_1 + \partial f_2/\partial x_1+h_2 + \partial f_2/\partial x_2+\dots + \end{array}. +\end{split}\]
              +

              Defining the Jacobian matrix \({\bf \boldsymbol{J}}\) we have

              +
              +\[\begin{split} +{\bf \boldsymbol{J}}=\left( \begin{array}{cc} + \partial f_1/\partial x_1 & \partial f_1/\partial x_2 \\ + \partial f_2/\partial x_1 &\partial f_2/\partial x_2 + \end{array} \right), +\end{split}\]
              +

              we can rephrase Newton’s method as

              +
              +\[\begin{split} +\left(\begin{array}{c} x_1^{n+1} \\ x_2^{n+1} \end{array} \right)= +\left(\begin{array}{c} x_1^{n} \\ x_2^{n} \end{array} \right)+ +\left(\begin{array}{c} h_1^{n} \\ h_2^{n} \end{array} \right), +\end{split}\]
              +

              where we have defined

              +
              +\[\begin{split} +\left(\begin{array}{c} h_1^{n} \\ h_2^{n} \end{array} \right)= + -{\bf \boldsymbol{J}}^{-1} + \left(\begin{array}{c} f_1(x_1^{n},x_2^{n}) \\ f_2(x_1^{n},x_2^{n}) \end{array} \right). +\end{split}\]
              +

              We need thus to compute the inverse of the Jacobian matrix and it +is to understand that difficulties may +arise in case \({\bf \boldsymbol{J}}\) is nearly singular.

              +

              It is rather straightforward to extend the above scheme to systems of +more than two non-linear equations. In our case, the Jacobian matrix is given by the Hessian that represents the second derivative of cost function.

              +
              +
              +

              Steepest descent#

              +

              The basic idea of gradient descent is +that a function \(F(\mathbf{x})\), +\(\mathbf{x} \equiv (x_1,\cdots,x_n)\), decreases fastest if one goes from \(\bf {x}\) in the +direction of the negative gradient \(-\nabla F(\mathbf{x})\).

              +

              It can be shown that if

              +
              +\[ +\mathbf{x}_{k+1} = \mathbf{x}_k - \gamma_k \nabla F(\mathbf{x}_k), +\]
              +

              with \(\gamma_k > 0\).

              +

              For \(\gamma_k\) small enough, then \(F(\mathbf{x}_{k+1}) \leq +F(\mathbf{x}_k)\). This means that for a sufficiently small \(\gamma_k\) +we are always moving towards smaller function values, i.e a minimum.

              +
              +
              +

              More on Steepest descent#

              +

              The previous observation is the basis of the method of steepest +descent, which is also referred to as just gradient descent (GD). One +starts with an initial guess \(\mathbf{x}_0\) for a minimum of \(F\) and +computes new approximations according to

              +
              +\[ +\mathbf{x}_{k+1} = \mathbf{x}_k - \gamma_k \nabla F(\mathbf{x}_k), \ \ k \geq 0. +\]
              +

              The parameter \(\gamma_k\) is often referred to as the step length or +the learning rate within the context of Machine Learning.

              +
              +
              +

              The ideal#

              +

              Ideally the sequence \(\{\mathbf{x}_k \}_{k=0}\) converges to a global +minimum of the function \(F\). In general we do not know if we are in a +global or local minimum. In the special case when \(F\) is a convex +function, all local minima are also global minima, so in this case +gradient descent can converge to the global solution. The advantage of +this scheme is that it is conceptually simple and straightforward to +implement. However the method in this form has some severe +limitations:

              +

              In machine learing we are often faced with non-convex high dimensional +cost functions with many local minima. Since GD is deterministic we +will get stuck in a local minimum, if the method converges, unless we +have a very good intial guess. This also implies that the scheme is +sensitive to the chosen initial condition.

              +

              Note that the gradient is a function of \(\mathbf{x} = +(x_1,\cdots,x_n)\) which makes it expensive to compute numerically.

              +
              +
              +

              The sensitiveness of the gradient descent#

              +

              The gradient descent method +is sensitive to the choice of learning rate \(\gamma_k\). This is due +to the fact that we are only guaranteed that \(F(\mathbf{x}_{k+1}) \leq +F(\mathbf{x}_k)\) for sufficiently small \(\gamma_k\). The problem is to +determine an optimal learning rate. If the learning rate is chosen too +small the method will take a long time to converge and if it is too +large we can experience erratic behavior.

              +

              Many of these shortcomings can be alleviated by introducing +randomness. One such method is that of Stochastic Gradient Descent +(SGD), see below.

              +
              +
              +

              Convex functions#

              +

              Ideally we want our cost/loss function to be convex(concave).

              +

              First we give the definition of a convex set: A set \(C\) in +\(\mathbb{R}^n\) is said to be convex if, for all \(x\) and \(y\) in \(C\) and +all \(t \in (0,1)\) , the point \((1 − t)x + ty\) also belongs to +C. Geometrically this means that every point on the line segment +connecting \(x\) and \(y\) is in \(C\) as discussed below.

              +

              The convex subsets of \(\mathbb{R}\) are the intervals of +\(\mathbb{R}\). Examples of convex sets of \(\mathbb{R}^2\) are the +regular polygons (triangles, rectangles, pentagons, etc…).

              +
              +
              +

              Convex function#

              +

              Convex function: Let \(X \subset \mathbb{R}^n\) be a convex set. Assume that the function \(f: X \rightarrow \mathbb{R}\) is continuous, then \(f\) is said to be convex if $\(f(tx_1 + (1-t)x_2) \leq tf(x_1) + (1-t)f(x_2) \)\( for all \)x_1, x_2 \in X\( and for all \)t \in [0,1]\(. If \)\leq\( is replaced with a strict inequaltiy in the definition, we demand \)x_1 \neq x_2\( and \)t\in(0,1)\( then \)f\( is said to be strictly convex. For a single variable function, convexity means that if you draw a straight line connecting \)f(x_1)\( and \)f(x_2)\(, the value of the function on the interval \)[x_1,x_2]$ is always below the line as illustrated below.

              +
              +
              +

              Conditions on convex functions#

              +

              In the following we state first and second-order conditions which +ensures convexity of a function \(f\). We write \(D_f\) to denote the +domain of \(f\), i.e the subset of \(R^n\) where \(f\) is defined. For more +details and proofs we refer to: [S. Boyd and L. Vandenberghe. Convex Optimization. Cambridge University Press](http://stanford.edu/boyd/cvxbook/, 2004).

              +

              First order condition.

              +

              Suppose \(f\) is differentiable (i.e \(\nabla f(x)\) is well defined for +all \(x\) in the domain of \(f\)). Then \(f\) is convex if and only if \(D_f\) +is a convex set and $\(f(y) \geq f(x) + \nabla f(x)^T (y-x) \)\( holds +for all \)x,y \in D_f\(. This condition means that for a convex function +the first order Taylor expansion (right hand side above) at any point +a global under estimator of the function. To convince yourself you can +make a drawing of \)f(x) = x^2+1\( and draw the tangent line to \)f(x)$ and +note that it is always below the graph.

              +

              Second order condition.

              +

              Assume that \(f\) is twice +differentiable, i.e the Hessian matrix exists at each point in +\(D_f\). Then \(f\) is convex if and only if \(D_f\) is a convex set and its +Hessian is positive semi-definite for all \(x\in D_f\). For a +single-variable function this reduces to \(f''(x) \geq 0\). Geometrically this means that \(f\) has nonnegative curvature +everywhere.

              +

              This condition is particularly useful since it gives us an procedure for determining if the function under consideration is convex, apart from using the definition.

              +
              +
              +

              More on convex functions#

              +

              The next result is of great importance to us and the reason why we are +going on about convex functions. In machine learning we frequently +have to minimize a loss/cost function in order to find the best +parameters for the model we are considering.

              +

              Ideally we want the +global minimum (for high-dimensional models it is hard to know +if we have local or global minimum). However, if the cost/loss function +is convex the following result provides invaluable information:

              +

              Any minimum is global for convex functions.

              +

              Consider the problem of finding \(x \in \mathbb{R}^n\) such that \(f(x)\) +is minimal, where \(f\) is convex and differentiable. Then, any point +\(x^*\) that satisfies \(\nabla f(x^*) = 0\) is a global minimum.

              +

              This result means that if we know that the cost/loss function is convex and we are able to find a minimum, we are guaranteed that it is a global minimum.

              +
              +
              +

              Some simple problems#

              +
                +

              1. Show that \(f(x)=x^2\) is convex for \(x \in \mathbb{R}\) using the definition of convexity. Hint: If you re-write the definition, \(f\) is convex if the following holds for all \(x,y \in D_f\) and any \(\lambda \in [0,1]\) \(\lambda f(x)+(1-\lambda)f(y)-f(\lambda x + (1-\lambda) y ) \geq 0\).

                2. +

              2. Using the second order condition show that the following functions are convex on the specified domain.

                3. +
              +
                +

              • \(f(x) = e^x\) is convex for \(x \in \mathbb{R}\).

                • +

              • \(g(x) = -\ln(x)\) is convex for \(x \in (0,\infty)\).

                • +
              +
                +

              1. Let \(f(x) = x^2\) and \(g(x) = e^x\). Show that \(f(g(x))\) and \(g(f(x))\) is convex for \(x \in \mathbb{R}\). Also show that if \(f(x)\) is any convex function than \(h(x) = e^{f(x)}\) is convex.

                2. +

              2. A norm is any function that satisfy the following properties

                3. +
              +
                +

              • \(f(\alpha x) = |\alpha| f(x)\) for all \(\alpha \in \mathbb{R}\).

                • +

              • \(f(x+y) \leq f(x) + f(y)\)

                • +

              • \(f(x) \leq 0\) for all \(x \in \mathbb{R}^n\) with equality if and only if \(x = 0\)

                • +
              +

              Using the definition of convexity, try to show that a function satisfying the properties above is convex (the third condition is not needed to show this).

              +
              +
              +

              Standard steepest descent#

              +

              Before we proceed, we would like to discuss the approach called the +standard Steepest descent (different from the above steepest descent discussion), which again leads to us having to be able +to compute a matrix. It belongs to the class of Conjugate Gradient methods (CG).

              +

              The success of the CG method +for finding solutions of non-linear problems is based on the theory +of conjugate gradients for linear systems of equations. It belongs to +the class of iterative methods for solving problems from linear +algebra of the type

              +
              +\[ +\boldsymbol{A}\boldsymbol{x} = \boldsymbol{b}. +\]
              +

              In the iterative process we end up with a problem like

              +
              +\[ +\boldsymbol{r}= \boldsymbol{b}-\boldsymbol{A}\boldsymbol{x}, +\]
              +

              where \(\boldsymbol{r}\) is the so-called residual or error in the iterative process.

              +

              When we have found the exact solution, \(\boldsymbol{r}=0\).

              +
              +
              +

              Gradient method#

              +

              The residual is zero when we reach the minimum of the quadratic equation

              +
              +\[ +P(\boldsymbol{x})=\frac{1}{2}\boldsymbol{x}^T\boldsymbol{A}\boldsymbol{x} - \boldsymbol{x}^T\boldsymbol{b}, +\]
              +

              with the constraint that the matrix \(\boldsymbol{A}\) is positive definite and +symmetric. This defines also the Hessian and we want it to be positive definite.

              +
              +
              +

              Steepest descent method#

              +

              We denote the initial guess for \(\boldsymbol{x}\) as \(\boldsymbol{x}_0\). +We can assume without loss of generality that

              +
              +\[ +\boldsymbol{x}_0=0, +\]
              +

              or consider the system

              +
              +\[ +\boldsymbol{A}\boldsymbol{z} = \boldsymbol{b}-\boldsymbol{A}\boldsymbol{x}_0, +\]
              +

              instead.

              +
              +
              +

              Steepest descent method#

              +

              One can show that the solution \(\boldsymbol{x}\) is also the unique minimizer of the quadratic form

              +
              +\[ +f(\boldsymbol{x}) = \frac{1}{2}\boldsymbol{x}^T\boldsymbol{A}\boldsymbol{x} - \boldsymbol{x}^T \boldsymbol{x} , \quad \boldsymbol{x}\in\mathbf{R}^n. +\]
              +

              This suggests taking the first basis vector \(\boldsymbol{r}_1\) (see below for definition) +to be the gradient of \(f\) at \(\boldsymbol{x}=\boldsymbol{x}_0\), +which equals

              +
              +\[ +\boldsymbol{A}\boldsymbol{x}_0-\boldsymbol{b}, +\]
              +

              and +\(\boldsymbol{x}_0=0\) it is equal \(-\boldsymbol{b}\).

              +
              +
              +

              Final expressions#

              +

              We can compute the residual iteratively as

              +
              +\[ +\boldsymbol{r}_{k+1}=\boldsymbol{b}-\boldsymbol{A}\boldsymbol{x}_{k+1}, +\]
              +

              which equals

              +
              +\[ +\boldsymbol{b}-\boldsymbol{A}(\boldsymbol{x}_k+\alpha_k\boldsymbol{r}_k), +\]
              +

              or

              +
              +\[ +(\boldsymbol{b}-\boldsymbol{A}\boldsymbol{x}_k)-\alpha_k\boldsymbol{A}\boldsymbol{r}_k, +\]
              +

              which gives

              +
              +\[ +\alpha_k = \frac{\boldsymbol{r}_k^T\boldsymbol{r}_k}{\boldsymbol{r}_k^T\boldsymbol{A}\boldsymbol{r}_k} +\]
              +

              leading to the iterative scheme

              +
              +\[ +\boldsymbol{x}_{k+1}=\boldsymbol{x}_k+\alpha_k\boldsymbol{r}_{k}, +\]
              +
              +
              +

              Steepest descent example#

              +
              +
              +
              %matplotlib inline
+
+import numpy as np
+import numpy.linalg as la
+
+import scipy.optimize as sopt
+
+import matplotlib.pyplot as pt
+from mpl_toolkits.mplot3d import axes3d
+
+def f(x):
+    return x[0]**2 + 3.0*x[1]**2
+
+def df(x):
+    return np.array([2*x[0], 6*x[1]])
+
+fig = pt.figure()
+ax = fig.gca(projection="3d")
+
+xmesh, ymesh = np.mgrid[-3:3:50j,-3:3:50j]
+fmesh = f(np.array([xmesh, ymesh]))
+ax.plot_surface(xmesh, ymesh, fmesh)
+
              +
              +
              +
              +
              ---------------------------------------------------------------------------
+ModuleNotFoundError                       Traceback (most recent call last)
+Cell In[1], line 1
+----> 1 get_ipython().run_line_magic('matplotlib', 'inline')
+      3 import numpy as np
+      4 import numpy.linalg as la
+
+File ~/miniforge3/lib/python3.9/site-packages/IPython/core/interactiveshell.py:2432, in InteractiveShell.run_line_magic(self, magic_name, line, _stack_depth)
+   2430     kwargs['local_ns'] = self.get_local_scope(stack_depth)
+   2431 with self.builtin_trap:
+-> 2432     result = fn(*args, **kwargs)
+   2434 # The code below prevents the output from being displayed
+   2435 # when using magics with decorator @output_can_be_silenced
+   2436 # when the last Python token in the expression is a ';'.
+   2437 if getattr(fn, magic.MAGIC_OUTPUT_CAN_BE_SILENCED, False):
+
+File ~/miniforge3/lib/python3.9/site-packages/IPython/core/magics/pylab.py:99, in PylabMagics.matplotlib(self, line)
+     97     print("Available matplotlib backends: %s" % backends_list)
+     98 else:
+---> 99     gui, backend = self.shell.enable_matplotlib(args.gui.lower() if isinstance(args.gui, str) else args.gui)
+    100     self._show_matplotlib_backend(args.gui, backend)
+
+File ~/miniforge3/lib/python3.9/site-packages/IPython/core/interactiveshell.py:3606, in InteractiveShell.enable_matplotlib(self, gui)
+   3585 def enable_matplotlib(self, gui=None):
+   3586     """Enable interactive matplotlib and inline figure support.
+   3587 
+   3588     This takes the following steps:
+   (...)
+   3604         display figures inline.
+   3605     """
+-> 3606     from matplotlib_inline.backend_inline import configure_inline_support
+   3608     from IPython.core import pylabtools as pt
+   3609     gui, backend = pt.find_gui_and_backend(gui, self.pylab_gui_select)
+
+File ~/miniforge3/lib/python3.9/site-packages/matplotlib_inline/__init__.py:1
+----> 1 from . import backend_inline, config  # noqa
+      2 __version__ = "0.1.6"  # noqa
+
+File ~/miniforge3/lib/python3.9/site-packages/matplotlib_inline/backend_inline.py:6
+      1 """A matplotlib backend for publishing figures via display_data"""
+      3 # Copyright (c) IPython Development Team.
+      4 # Distributed under the terms of the BSD 3-Clause License.
+----> 6 import matplotlib
+      7 from matplotlib import colors
+      8 from matplotlib.backends import backend_agg
+
+ModuleNotFoundError: No module named 'matplotlib'
+
              +
              +
              +
              +

              And then as countor plot

              +
              +
              +
              pt.axis("equal")
+pt.contour(xmesh, ymesh, fmesh)
+guesses = [np.array([2, 2./5])]
+
              +
              +
              +
              +

              Find guesses

              +
              +
              +
              x = guesses[-1]
+s = -df(x)
+
              +
              +
              +
              +

              Run it!

              +
              +
              +
              def f1d(alpha):
+    return f(x + alpha*s)
+
+alpha_opt = sopt.golden(f1d)
+next_guess = x + alpha_opt * s
+guesses.append(next_guess)
+print(next_guess)
+
              +
              +
              +
              +

              What happened?

              +
              +
              +
              pt.axis("equal")
+pt.contour(xmesh, ymesh, fmesh, 50)
+it_array = np.array(guesses)
+pt.plot(it_array.T[0], it_array.T[1], "x-")
+
              +
              +
              +
              +

              Note that we did only one iteration here. We can easily add more using our previous guesses.

              +
              +
              +

              Conjugate gradient method#

              +

              In the CG method we define so-called conjugate directions and two vectors +\(\boldsymbol{s}\) and \(\boldsymbol{t}\) +are said to be +conjugate if

              +
              +\[ +\boldsymbol{s}^T\boldsymbol{A}\boldsymbol{t}= 0. +\]
              +

              The philosophy of the CG method is to perform searches in various conjugate directions +of our vectors \(\boldsymbol{x}_i\) obeying the above criterion, namely

              +
              +\[ +\boldsymbol{x}_i^T\boldsymbol{A}\boldsymbol{x}_j= 0. +\]
              +

              Two vectors are conjugate if they are orthogonal with respect to +this inner product. Being conjugate is a symmetric relation: if \(\boldsymbol{s}\) is conjugate to \(\boldsymbol{t}\), then \(\boldsymbol{t}\) is conjugate to \(\boldsymbol{s}\).

              +
              +
              +

              Conjugate gradient method#

              +

              An example is given by the eigenvectors of the matrix

              +
              +\[ +\boldsymbol{v}_i^T\boldsymbol{A}\boldsymbol{v}_j= \lambda\boldsymbol{v}_i^T\boldsymbol{v}_j, +\]
              +

              which is zero unless \(i=j\).

              +
              +
              +

              Conjugate gradient method#

              +

              Assume now that we have a symmetric positive-definite matrix \(\boldsymbol{A}\) of size +\(n\times n\). At each iteration \(i+1\) we obtain the conjugate direction of a vector

              +
              +\[ +\boldsymbol{x}_{i+1}=\boldsymbol{x}_{i}+\alpha_i\boldsymbol{p}_{i}. +\]
              +

              We assume that \(\boldsymbol{p}_{i}\) is a sequence of \(n\) mutually conjugate directions. +Then the \(\boldsymbol{p}_{i}\) form a basis of \(R^n\) and we can expand the solution +\( \boldsymbol{A}\boldsymbol{x} = \boldsymbol{b}\) in this basis, namely

              +
              +\[ +\boldsymbol{x} = \sum^{n}_{i=1} \alpha_i \boldsymbol{p}_i. +\]
              +
              +
              +

              Conjugate gradient method#

              +

              The coefficients are given by

              +
              +\[ +\mathbf{A}\mathbf{x} = \sum^{n}_{i=1} \alpha_i \mathbf{A} \mathbf{p}_i = \mathbf{b}. +\]
              +

              Multiplying with \(\boldsymbol{p}_k^T\) from the left gives

              +
              +\[ +\boldsymbol{p}_k^T \boldsymbol{A}\boldsymbol{x} = \sum^{n}_{i=1} \alpha_i\boldsymbol{p}_k^T \boldsymbol{A}\boldsymbol{p}_i= \boldsymbol{p}_k^T \boldsymbol{b}, +\]
              +

              and we can define the coefficients \(\alpha_k\) as

              +
              +\[ +\alpha_k = \frac{\boldsymbol{p}_k^T \boldsymbol{b}}{\boldsymbol{p}_k^T \boldsymbol{A} \boldsymbol{p}_k} +\]
              +
              +
              +

              Conjugate gradient method and iterations#

              +

              If we choose the conjugate vectors \(\boldsymbol{p}_k\) carefully, +then we may not need all of them to obtain a good approximation to the solution +\(\boldsymbol{x}\). +We want to regard the conjugate gradient method as an iterative method. +This will us to solve systems where \(n\) is so large that the direct +method would take too much time.

              +

              We denote the initial guess for \(\boldsymbol{x}\) as \(\boldsymbol{x}_0\). +We can assume without loss of generality that

              +
              +\[ +\boldsymbol{x}_0=0, +\]
              +

              or consider the system

              +
              +\[ +\boldsymbol{A}\boldsymbol{z} = \boldsymbol{b}-\boldsymbol{A}\boldsymbol{x}_0, +\]
              +

              instead.

              +
              +
              +

              Conjugate gradient method#

              +

              One can show that the solution \(\boldsymbol{x}\) is also the unique minimizer of the quadratic form

              +
              +\[ +f(\boldsymbol{x}) = \frac{1}{2}\boldsymbol{x}^T\boldsymbol{A}\boldsymbol{x} - \boldsymbol{x}^T \boldsymbol{x} , \quad \boldsymbol{x}\in\mathbf{R}^n. +\]
              +

              This suggests taking the first basis vector \(\boldsymbol{p}_1\) +to be the gradient of \(f\) at \(\boldsymbol{x}=\boldsymbol{x}_0\), +which equals

              +
              +\[ +\boldsymbol{A}\boldsymbol{x}_0-\boldsymbol{b}, +\]
              +

              and +\(\boldsymbol{x}_0=0\) it is equal \(-\boldsymbol{b}\). +The other vectors in the basis will be conjugate to the gradient, +hence the name conjugate gradient method.

              +
              +
              +

              Conjugate gradient method#

              +

              Let \(\boldsymbol{r}_k\) be the residual at the \(k\)-th step:

              +
              +\[ +\boldsymbol{r}_k=\boldsymbol{b}-\boldsymbol{A}\boldsymbol{x}_k. +\]
              +

              Note that \(\boldsymbol{r}_k\) is the negative gradient of \(f\) at +\(\boldsymbol{x}=\boldsymbol{x}_k\), +so the gradient descent method would be to move in the direction \(\boldsymbol{r}_k\). +Here, we insist that the directions \(\boldsymbol{p}_k\) are conjugate to each other, +so we take the direction closest to the gradient \(\boldsymbol{r}_k\)
              +under the conjugacy constraint. +This gives the following expression

              +
              +\[ +\boldsymbol{p}_{k+1}=\boldsymbol{r}_k-\frac{\boldsymbol{p}_k^T \boldsymbol{A}\boldsymbol{r}_k}{\boldsymbol{p}_k^T\boldsymbol{A}\boldsymbol{p}_k} \boldsymbol{p}_k. +\]
              +
              +
              +

              Conjugate gradient method#

              +

              We can also compute the residual iteratively as

              +
              +\[ +\boldsymbol{r}_{k+1}=\boldsymbol{b}-\boldsymbol{A}\boldsymbol{x}_{k+1}, +\]
              +

              which equals

              +
              +\[ +\boldsymbol{b}-\boldsymbol{A}(\boldsymbol{x}_k+\alpha_k\boldsymbol{p}_k), +\]
              +

              or

              +
              +\[ +(\boldsymbol{b}-\boldsymbol{A}\boldsymbol{x}_k)-\alpha_k\boldsymbol{A}\boldsymbol{p}_k, +\]
              +

              which gives

              +
              +\[ +\boldsymbol{r}_{k+1}=\boldsymbol{r}_k-\boldsymbol{A}\boldsymbol{p}_{k}, +\]
              +
              +
              +

              Revisiting our first homework#

              +

              We will use linear regression as a case study for the gradient descent +methods. Linear regression is a great test case for the gradient +descent methods discussed in the lectures since it has several +desirable properties such as:

              +
                +

              1. An analytical solution (recall homework set 1).

                2. +

              2. The gradient can be computed analytically.

                3. +

              3. The cost function is convex which guarantees that gradient descent converges for small enough learning rates

                4. +
              +

              We revisit an example similar to what we had in the first homework set. We had a function of the type

              +
              +
              +
              x = 2*np.random.rand(m,1)
+y = 4+3*x+np.random.randn(m,1)
+
              +
              +
              +
              +

              with \(x_i \in [0,1] \) is chosen randomly using a uniform distribution. Additionally we have a stochastic noise chosen according to a normal distribution \(\cal {N}(0,1)\). +The linear regression model is given by

              +
              +\[ +h_\beta(x) = \boldsymbol{y} = \beta_0 + \beta_1 x, +\]
              +

              such that

              +
              +\[ +\boldsymbol{y}_i = \beta_0 + \beta_1 x_i. +\]
              +
              +
              +

              Gradient descent example#

              +

              Let \(\mathbf{y} = (y_1,\cdots,y_n)^T\), \(\mathbf{\boldsymbol{y}} = (\boldsymbol{y}_1,\cdots,\boldsymbol{y}_n)^T\) and \(\beta = (\beta_0, \beta_1)^T\)

              +

              It is convenient to write \(\mathbf{\boldsymbol{y}} = X\beta\) where \(X \in \mathbb{R}^{100 \times 2} \) is the design matrix given by (we keep the intercept here)

              +
              +\[\begin{split} +X \equiv \begin{bmatrix} +1 & x_1 \\ +\vdots & \vdots \\ +1 & x_{100} & \\ +\end{bmatrix}. +\end{split}\]
              +

              The cost/loss/risk function is given by (

              +
              +\[ +C(\beta) = \frac{1}{n}||X\beta-\mathbf{y}||_{2}^{2} = \frac{1}{n}\sum_{i=1}^{100}\left[ (\beta_0 + \beta_1 x_i)^2 - 2 y_i (\beta_0 + \beta_1 x_i) + y_i^2\right] +\]
              +

              and we want to find \(\beta\) such that \(C(\beta)\) is minimized.

              +
              +
              +

              The derivative of the cost/loss function#

              +

              Computing \(\partial C(\beta) / \partial \beta_0\) and \(\partial C(\beta) / \partial \beta_1\) we can show that the gradient can be written as

              +
              +\[\begin{split} +\nabla_{\beta} C(\beta) = \frac{2}{n}\begin{bmatrix} \sum_{i=1}^{100} \left(\beta_0+\beta_1x_i-y_i\right) \\ +\sum_{i=1}^{100}\left( x_i (\beta_0+\beta_1x_i)-y_ix_i\right) \\ +\end{bmatrix} = \frac{2}{n}X^T(X\beta - \mathbf{y}), +\end{split}\]
              +

              where \(X\) is the design matrix defined above.

              +
              +
              +

              The Hessian matrix#

              +

              The Hessian matrix of \(C(\beta)\) is given by

              +
              +\[\begin{split} +\boldsymbol{H} \equiv \begin{bmatrix} +\frac{\partial^2 C(\beta)}{\partial \beta_0^2} & \frac{\partial^2 C(\beta)}{\partial \beta_0 \partial \beta_1} \\ +\frac{\partial^2 C(\beta)}{\partial \beta_0 \partial \beta_1} & \frac{\partial^2 C(\beta)}{\partial \beta_1^2} & \\ +\end{bmatrix} = \frac{2}{n}X^T X. +\end{split}\]
              +

              This result implies that \(C(\beta)\) is a convex function since the matrix \(X^T X\) always is positive semi-definite.

              +
              +
              +

              Simple program#

              +

              We can now write a program that minimizes \(C(\beta)\) using the gradient descent method with a constant learning rate \(\gamma\) according to

              +
              +\[ +\beta_{k+1} = \beta_k - \gamma \nabla_\beta C(\beta_k), \ k=0,1,\cdots +\]
              +

              We can use the expression we computed for the gradient and let use a +\(\beta_0\) be chosen randomly and let \(\gamma = 0.001\). Stop iterating +when \(||\nabla_\beta C(\beta_k) || \leq \epsilon = 10^{-8}\). Note that the code below does not include the latter stop criterion.

              +

              And finally we can compare our solution for \(\beta\) with the analytic result given by +\(\beta= (X^TX)^{-1} X^T \mathbf{y}\).

              +
              +
              +

              Gradient Descent Example#

              +

              Here our simple example

              +
              +
              +
              # Importing various packages
+from random import random, seed
+import numpy as np
+import matplotlib.pyplot as plt
+from mpl_toolkits.mplot3d import Axes3D
+from matplotlib import cm
+from matplotlib.ticker import LinearLocator, FormatStrFormatter
+import sys
+
+# the number of datapoints
+n = 100
+x = 2*np.random.rand(n,1)
+y = 4+3*x+np.random.randn(n,1)
+
+X = np.c_[np.ones((n,1)), x]
+# Hessian matrix
+H = (2.0/n)* X.T @ X
+# Get the eigenvalues
+EigValues, EigVectors = np.linalg.eig(H)
+print(f"Eigenvalues of Hessian Matrix:{EigValues}")
+
+beta_linreg = np.linalg.inv(X.T @ X) @ X.T @ y
+print(beta_linreg)
+beta = np.random.randn(2,1)
+
+eta = 1.0/np.max(EigValues)
+Niterations = 1000
+
+for iter in range(Niterations):
+    gradient = (2.0/n)*X.T @ (X @ beta-y)
+    beta -= eta*gradient
+
+print(beta)
+xnew = np.array([[0],[2]])
+xbnew = np.c_[np.ones((2,1)), xnew]
+ypredict = xbnew.dot(beta)
+ypredict2 = xbnew.dot(beta_linreg)
+plt.plot(xnew, ypredict, "r-")
+plt.plot(xnew, ypredict2, "b-")
+plt.plot(x, y ,'ro')
+plt.axis([0,2.0,0, 15.0])
+plt.xlabel(r'$x$')
+plt.ylabel(r'$y$')
+plt.title(r'Gradient descent example')
+plt.show()
+
              +
              +
              +
              +
              +
              +

              And a corresponding example using scikit-learn#

              +
              +
              +
              # Importing various packages
+from random import random, seed
+import numpy as np
+import matplotlib.pyplot as plt
+from sklearn.linear_model import SGDRegressor
+
+n = 100
+x = 2*np.random.rand(n,1)
+y = 4+3*x+np.random.randn(n,1)
+
+X = np.c_[np.ones((n,1)), x]
+beta_linreg = np.linalg.inv(X.T @ X) @ (X.T @ y)
+print(beta_linreg)
+sgdreg = SGDRegressor(max_iter = 50, penalty=None, eta0=0.1)
+sgdreg.fit(x,y.ravel())
+print(sgdreg.intercept_, sgdreg.coef_)
+
              +
              +
              +
              +
              +
              +

              Gradient descent and Ridge#

              +

              We have also discussed Ridge regression where the loss function contains a regularized term given by the \(L_2\) norm of \(\beta\),

              +
              +\[ +C_{\text{ridge}}(\beta) = \frac{1}{n}||X\beta -\mathbf{y}||^2 + \lambda ||\beta||^2, \ \lambda \geq 0. +\]
              +

              In order to minimize \(C_{\text{ridge}}(\beta)\) using GD we adjust the gradient as follows

              +
              +\[\begin{split} +\nabla_\beta C_{\text{ridge}}(\beta) = \frac{2}{n}\begin{bmatrix} \sum_{i=1}^{100} \left(\beta_0+\beta_1x_i-y_i\right) \\ +\sum_{i=1}^{100}\left( x_i (\beta_0+\beta_1x_i)-y_ix_i\right) \\ +\end{bmatrix} + 2\lambda\begin{bmatrix} \beta_0 \\ \beta_1\end{bmatrix} = 2 (\frac{1}{n}X^T(X\beta - \mathbf{y})+\lambda \beta). +\end{split}\]
              +

              We can easily extend our program to minimize \(C_{\text{ridge}}(\beta)\) using gradient descent and compare with the analytical solution given by

              +
              +\[ +\beta_{\text{ridge}} = \left(X^T X + n\lambda I_{2 \times 2} \right)^{-1} X^T \mathbf{y}. +\]
              +
              +
              +

              The Hessian matrix for Ridge Regression#

              +

              The Hessian matrix of Ridge Regression for our simple example is given by

              +
              +\[\begin{split} +\boldsymbol{H} \equiv \begin{bmatrix} +\frac{\partial^2 C(\beta)}{\partial \beta_0^2} & \frac{\partial^2 C(\beta)}{\partial \beta_0 \partial \beta_1} \\ +\frac{\partial^2 C(\beta)}{\partial \beta_0 \partial \beta_1} & \frac{\partial^2 C(\beta)}{\partial \beta_1^2} & \\ +\end{bmatrix} = \frac{2}{n}X^T X+2\lambda\boldsymbol{I}. +\end{split}\]
              +

              This implies that the Hessian matrix is positive definite, hence the stationary point is a +minimum. +Note that the Ridge cost function is convex being a sum of two convex +functions. Therefore, the stationary point is a global +minimum of this function.

              +
              +
              +

              Program example for gradient descent with Ridge Regression#

              +
              +
              +
              from random import random, seed
+import numpy as np
+import matplotlib.pyplot as plt
+from mpl_toolkits.mplot3d import Axes3D
+from matplotlib import cm
+from matplotlib.ticker import LinearLocator, FormatStrFormatter
+import sys
+
+# the number of datapoints
+n = 100
+x = 2*np.random.rand(n,1)
+y = 4+3*x+np.random.randn(n,1)
+
+X = np.c_[np.ones((n,1)), x]
+XT_X = X.T @ X
+
+#Ridge parameter lambda
+lmbda  = 0.001
+Id = n*lmbda* np.eye(XT_X.shape[0])
+
+# Hessian matrix
+H = (2.0/n)* XT_X+2*lmbda* np.eye(XT_X.shape[0])
+# Get the eigenvalues
+EigValues, EigVectors = np.linalg.eig(H)
+print(f"Eigenvalues of Hessian Matrix:{EigValues}")
+
+
+beta_linreg = np.linalg.inv(XT_X+Id) @ X.T @ y
+print(beta_linreg)
+# Start plain gradient descent
+beta = np.random.randn(2,1)
+
+eta = 1.0/np.max(EigValues)
+Niterations = 100
+
+for iter in range(Niterations):
+    gradients = 2.0/n*X.T @ (X @ (beta)-y)+2*lmbda*beta
+    beta -= eta*gradients
+
+print(beta)
+ypredict = X @ beta
+ypredict2 = X @ beta_linreg
+plt.plot(x, ypredict, "r-")
+plt.plot(x, ypredict2, "b-")
+plt.plot(x, y ,'ro')
+plt.axis([0,2.0,0, 15.0])
+plt.xlabel(r'$x$')
+plt.ylabel(r'$y$')
+plt.title(r'Gradient descent example for Ridge')
+plt.show()
+
              +
              +
              +
              +
              +
              +

              Using gradient descent methods, limitations#

              +
                +

              • Gradient descent (GD) finds local minima of our function. Since the GD algorithm is deterministic, if it converges, it will converge to a local minimum of our cost/loss/risk function. Because in ML we are often dealing with extremely rugged landscapes with many local minima, this can lead to poor performance.

                • +

              • GD is sensitive to initial conditions. One consequence of the local nature of GD is that initial conditions matter. Depending on where one starts, one will end up at a different local minima. Therefore, it is very important to think about how one initializes the training process. This is true for GD as well as more complicated variants of GD.

                • +

              • Gradients are computationally expensive to calculate for large datasets. In many cases in statistics and ML, the cost/loss/risk function is a sum of terms, with one term for each data point. For example, in linear regression, \(E \propto \sum_{i=1}^n (y_i - \mathbf{w}^T\cdot\mathbf{x}_i)^2\); for logistic regression, the square error is replaced by the cross entropy. To calculate the gradient we have to sum over all \(n\) data points. Doing this at every GD step becomes extremely computationally expensive. An ingenious solution to this, is to calculate the gradients using small subsets of the data called “mini batches”. This has the added benefit of introducing stochasticity into our algorithm.

                • +

              • GD is very sensitive to choices of learning rates. GD is extremely sensitive to the choice of learning rates. If the learning rate is very small, the training process take an extremely long time. For larger learning rates, GD can diverge and give poor results. Furthermore, depending on what the local landscape looks like, we have to modify the learning rates to ensure convergence. Ideally, we would adaptively choose the learning rates to match the landscape.

                • +

              • GD treats all directions in parameter space uniformly. Another major drawback of GD is that unlike Newton’s method, the learning rate for GD is the same in all directions in parameter space. For this reason, the maximum learning rate is set by the behavior of the steepest direction and this can significantly slow down training. Ideally, we would like to take large steps in flat directions and small steps in steep directions. Since we are exploring rugged landscapes where curvatures change, this requires us to keep track of not only the gradient but second derivatives. The ideal scenario would be to calculate the Hessian but this proves to be too computationally expensive.

                • +

              • GD can take exponential time to escape saddle points, even with random initialization. As we mentioned, GD is extremely sensitive to initial condition since it determines the particular local minimum GD would eventually reach. However, even with a good initialization scheme, through the introduction of randomness, GD can still take exponential time to escape saddle points.

                • +
              +
              +
              +

              Improving gradient descent with momentum#

              +

              We discuss here some simple examples where we introduce what is called ‘memory’about previous steps, or what is normally called momentum gradient descent. The mathematics is explained below in connection with Stochastic gradient descent.

              +
              +
              +
              from numpy import asarray
+from numpy import arange
+from numpy.random import rand
+from numpy.random import seed
+from matplotlib import pyplot
+ 
+# objective function
+def objective(x):
+	return x**2.0
+ 
+# derivative of objective function
+def derivative(x):
+	return x * 2.0
+ 
+# gradient descent algorithm
+def gradient_descent(objective, derivative, bounds, n_iter, step_size):
+	# track all solutions
+	solutions, scores = list(), list()
+	# generate an initial point
+	solution = bounds[:, 0] + rand(len(bounds)) * (bounds[:, 1] - bounds[:, 0])
+	# run the gradient descent
+	for i in range(n_iter):
+		# calculate gradient
+		gradient = derivative(solution)
+		# take a step
+		solution = solution - step_size * gradient
+		# evaluate candidate point
+		solution_eval = objective(solution)
+		# store solution
+		solutions.append(solution)
+		scores.append(solution_eval)
+		# report progress
+		print('>%d f(%s) = %.5f' % (i, solution, solution_eval))
+	return [solutions, scores]
+ 
+# seed the pseudo random number generator
+seed(4)
+# define range for input
+bounds = asarray([[-1.0, 1.0]])
+# define the total iterations
+n_iter = 30
+# define the step size
+step_size = 0.1
+# perform the gradient descent search
+solutions, scores = gradient_descent(objective, derivative, bounds, n_iter, step_size)
+# sample input range uniformly at 0.1 increments
+inputs = arange(bounds[0,0], bounds[0,1]+0.1, 0.1)
+# compute targets
+results = objective(inputs)
+# create a line plot of input vs result
+pyplot.plot(inputs, results)
+# plot the solutions found
+pyplot.plot(solutions, scores, '.-', color='red')
+# show the plot
+pyplot.show()
+
              +
              +
              +
              +
              +
              +

              Same code but now with momentum gradient descent#

              +
              +
              +
              from numpy import asarray
+from numpy import arange
+from numpy.random import rand
+from numpy.random import seed
+from matplotlib import pyplot
+ 
+# objective function
+def objective(x):
+	return x**2.0
+ 
+# derivative of objective function
+def derivative(x):
+	return x * 2.0
+ 
+# gradient descent algorithm
+def gradient_descent(objective, derivative, bounds, n_iter, step_size, momentum):
+	# track all solutions
+	solutions, scores = list(), list()
+	# generate an initial point
+	solution = bounds[:, 0] + rand(len(bounds)) * (bounds[:, 1] - bounds[:, 0])
+	# keep track of the change
+	change = 0.0
+	# run the gradient descent
+	for i in range(n_iter):
+		# calculate gradient
+		gradient = derivative(solution)
+		# calculate update
+		new_change = step_size * gradient + momentum * change
+		# take a step
+		solution = solution - new_change
+		# save the change
+		change = new_change
+		# evaluate candidate point
+		solution_eval = objective(solution)
+		# store solution
+		solutions.append(solution)
+		scores.append(solution_eval)
+		# report progress
+		print('>%d f(%s) = %.5f' % (i, solution, solution_eval))
+	return [solutions, scores]
+ 
+# seed the pseudo random number generator
+seed(4)
+# define range for input
+bounds = asarray([[-1.0, 1.0]])
+# define the total iterations
+n_iter = 30
+# define the step size
+step_size = 0.1
+# define momentum
+momentum = 0.3
+# perform the gradient descent search with momentum
+solutions, scores = gradient_descent(objective, derivative, bounds, n_iter, step_size, momentum)
+# sample input range uniformly at 0.1 increments
+inputs = arange(bounds[0,0], bounds[0,1]+0.1, 0.1)
+# compute targets
+results = objective(inputs)
+# create a line plot of input vs result
+pyplot.plot(inputs, results)
+# plot the solutions found
+pyplot.plot(solutions, scores, '.-', color='red')
+# show the plot
+pyplot.show()
+
              +
              +
              +
              +
              +
              +

              Overview video on Stochastic Gradient Descent#

              +

              What is Stochastic Gradient Descent

              +
              +
              +

              Batches and mini-batches#

              +

              In gradient descent we compute the cost function and its gradient for all data points we have.

              +

              In large-scale applications such as the ILSVRC challenge, the +training data can have on order of millions of examples. Hence, it +seems wasteful to compute the full cost function over the entire +training set in order to perform only a single parameter update. A +very common approach to addressing this challenge is to compute the +gradient over batches of the training data. For example, a typical batch could contain some thousand examples from +an entire training set of several millions. This batch is then used to +perform a parameter update.

              +
              +
              +

              Stochastic Gradient Descent (SGD)#

              +

              In stochastic gradient descent, the extreme case is the case where we +have only one batch, that is we include the whole data set.

              +

              This process is called Stochastic Gradient +Descent (SGD) (or also sometimes on-line gradient descent). This is +relatively less common to see because in practice due to vectorized +code optimizations it can be computationally much more efficient to +evaluate the gradient for 100 examples, than the gradient for one +example 100 times. Even though SGD technically refers to using a +single example at a time to evaluate the gradient, you will hear +people use the term SGD even when referring to mini-batch gradient +descent (i.e. mentions of MGD for “Minibatch Gradient Descent”, or BGD +for “Batch gradient descent” are rare to see), where it is usually +assumed that mini-batches are used. The size of the mini-batch is a +hyperparameter but it is not very common to cross-validate or bootstrap it. It is +usually based on memory constraints (if any), or set to some value, +e.g. 32, 64 or 128. We use powers of 2 in practice because many +vectorized operation implementations work faster when their inputs are +sized in powers of 2.

              +

              In our notes with SGD we mean stochastic gradient descent with mini-batches.

              +
              +
              +

              Stochastic Gradient Descent#

              +

              Stochastic gradient descent (SGD) and variants thereof address some of +the shortcomings of the Gradient descent method discussed above.

              +

              The underlying idea of SGD comes from the observation that the cost +function, which we want to minimize, can almost always be written as a +sum over \(n\) data points \(\{\mathbf{x}_i\}_{i=1}^n\),

              +
              +\[ +C(\mathbf{\beta}) = \sum_{i=1}^n c_i(\mathbf{x}_i, +\mathbf{\beta}). +\]
              +
              +
              +

              Computation of gradients#

              +

              This in turn means that the gradient can be +computed as a sum over \(i\)-gradients

              +
              +\[ +\nabla_\beta C(\mathbf{\beta}) = \sum_i^n \nabla_\beta c_i(\mathbf{x}_i, +\mathbf{\beta}). +\]
              +

              Stochasticity/randomness is introduced by only taking the +gradient on a subset of the data called minibatches. If there are \(n\) +data points and the size of each minibatch is \(M\), there will be \(n/M\) +minibatches. We denote these minibatches by \(B_k\) where +\(k=1,\cdots,n/M\).

              +
              +
              +

              SGD example#

              +

              As an example, suppose we have \(10\) data points \((\mathbf{x}_1,\cdots, \mathbf{x}_{10})\) +and we choose to have \(M=5\) minibathces, +then each minibatch contains two data points. In particular we have +\(B_1 = (\mathbf{x}_1,\mathbf{x}_2), \cdots, B_5 = +(\mathbf{x}_9,\mathbf{x}_{10})\). Note that if you choose \(M=1\) you +have only a single batch with all data points and on the other extreme, +you may choose \(M=n\) resulting in a minibatch for each datapoint, i.e +\(B_k = \mathbf{x}_k\).

              +

              The idea is now to approximate the gradient by replacing the sum over +all data points with a sum over the data points in one the minibatches +picked at random in each gradient descent step

              +
              +\[ +\nabla_{\beta} +C(\mathbf{\beta}) = \sum_{i=1}^n \nabla_\beta c_i(\mathbf{x}_i, +\mathbf{\beta}) \rightarrow \sum_{i \in B_k}^n \nabla_\beta +c_i(\mathbf{x}_i, \mathbf{\beta}). +\]
              +
              +
              +

              The gradient step#

              +

              Thus a gradient descent step now looks like

              +
              +\[ +\beta_{j+1} = \beta_j - \gamma_j \sum_{i \in B_k}^n \nabla_\beta c_i(\mathbf{x}_i, +\mathbf{\beta}) +\]
              +

              where \(k\) is picked at random with equal +probability from \([1,n/M]\). An iteration over the number of +minibathces (n/M) is commonly referred to as an epoch. Thus it is +typical to choose a number of epochs and for each epoch iterate over +the number of minibatches, as exemplified in the code below.

              +
              +
              +

              Simple example code#

              +
              +
              +
              import numpy as np 
+
+n = 100 #100 datapoints 
+M = 5   #size of each minibatch
+m = int(n/M) #number of minibatches
+n_epochs = 10 #number of epochs
+
+j = 0
+for epoch in range(1,n_epochs+1):
+    for i in range(m):
+        k = np.random.randint(m) #Pick the k-th minibatch at random
+        #Compute the gradient using the data in minibatch Bk
+        #Compute new suggestion for 
+        j += 1
+
              +
              +
              +
              +

              Taking the gradient only on a subset of the data has two important +benefits. First, it introduces randomness which decreases the chance +that our opmization scheme gets stuck in a local minima. Second, if +the size of the minibatches are small relative to the number of +datapoints (\(M < n\)), the computation of the gradient is much +cheaper since we sum over the datapoints in the \(k-th\) minibatch and not +all \(n\) datapoints.

              +
              +
              +

              When do we stop?#

              +

              A natural question is when do we stop the search for a new minimum? +One possibility is to compute the full gradient after a given number +of epochs and check if the norm of the gradient is smaller than some +threshold and stop if true. However, the condition that the gradient +is zero is valid also for local minima, so this would only tell us +that we are close to a local/global minimum. However, we could also +evaluate the cost function at this point, store the result and +continue the search. If the test kicks in at a later stage we can +compare the values of the cost function and keep the \(\beta\) that +gave the lowest value.

              +
              +
              +

              Slightly different approach#

              +

              Another approach is to let the step length \(\gamma_j\) depend on the +number of epochs in such a way that it becomes very small after a +reasonable time such that we do not move at all. Such approaches are +also called scaling. There are many such ways to scale the learning +rate +and discussions here. See +also +https://towardsdatascience.com/learning-rate-schedules-and-adaptive-learning-rate-methods-for-deep-learning-2c8f433990d1 +for a discussion of different scaling functions for the learning rate.

              +
              +
              +

              Time decay rate#

              +

              As an example, let \(e = 0,1,2,3,\cdots\) denote the current epoch and let \(t_0, t_1 > 0\) be two fixed numbers. Furthermore, let \(t = e \cdot m + i\) where \(m\) is the number of minibatches and \(i=0,\cdots,m-1\). Then the function $\(\gamma_j(t; t_0, t_1) = \frac{t_0}{t+t_1} \)\( goes to zero as the number of epochs gets large. I.e. we start with a step length \)\gamma_j (0; t_0, t_1) = t_0/t_1\( which decays in *time* \)t$.

              +

              In this way we can fix the number of epochs, compute \(\beta\) and +evaluate the cost function at the end. Repeating the computation will +give a different result since the scheme is random by design. Then we +pick the final \(\beta\) that gives the lowest value of the cost +function.

              +
              +
              +
              import numpy as np 
+
+def step_length(t,t0,t1):
+    return t0/(t+t1)
+
+n = 100 #100 datapoints 
+M = 5   #size of each minibatch
+m = int(n/M) #number of minibatches
+n_epochs = 500 #number of epochs
+t0 = 1.0
+t1 = 10
+
+gamma_j = t0/t1
+j = 0
+for epoch in range(1,n_epochs+1):
+    for i in range(m):
+        k = np.random.randint(m) #Pick the k-th minibatch at random
+        #Compute the gradient using the data in minibatch Bk
+        #Compute new suggestion for beta
+        t = epoch*m+i
+        gamma_j = step_length(t,t0,t1)
+        j += 1
+
+print("gamma_j after %d epochs: %g" % (n_epochs,gamma_j))
+
              +
              +
              +
              +
              +
              +

              Code with a Number of Minibatches which varies#

              +

              In the code here we vary the number of mini-batches.

              +
              +
              +
              # Importing various packages
+from math import exp, sqrt
+from random import random, seed
+import numpy as np
+import matplotlib.pyplot as plt
+
+n = 100
+x = 2*np.random.rand(n,1)
+y = 4+3*x+np.random.randn(n,1)
+
+X = np.c_[np.ones((n,1)), x]
+XT_X = X.T @ X
+theta_linreg = np.linalg.inv(X.T @ X) @ (X.T @ y)
+print("Own inversion")
+print(theta_linreg)
+# Hessian matrix
+H = (2.0/n)* XT_X
+EigValues, EigVectors = np.linalg.eig(H)
+print(f"Eigenvalues of Hessian Matrix:{EigValues}")
+
+theta = np.random.randn(2,1)
+eta = 1.0/np.max(EigValues)
+Niterations = 1000
+
+
+for iter in range(Niterations):
+    gradients = 2.0/n*X.T @ ((X @ theta)-y)
+    theta -= eta*gradients
+print("theta from own gd")
+print(theta)
+
+xnew = np.array([[0],[2]])
+Xnew = np.c_[np.ones((2,1)), xnew]
+ypredict = Xnew.dot(theta)
+ypredict2 = Xnew.dot(theta_linreg)
+
+n_epochs = 50
+M = 5   #size of each minibatch
+m = int(n/M) #number of minibatches
+t0, t1 = 5, 50
+
+def learning_schedule(t):
+    return t0/(t+t1)
+
+theta = np.random.randn(2,1)
+
+for epoch in range(n_epochs):
+# Can you figure out a better way of setting up the contributions to each batch?
+    for i in range(m):
+        random_index = M*np.random.randint(m)
+        xi = X[random_index:random_index+M]
+        yi = y[random_index:random_index+M]
+        gradients = (2.0/M)* xi.T @ ((xi @ theta)-yi)
+        eta = learning_schedule(epoch*m+i)
+        theta = theta - eta*gradients
+print("theta from own sdg")
+print(theta)
+
+plt.plot(xnew, ypredict, "r-")
+plt.plot(xnew, ypredict2, "b-")
+plt.plot(x, y ,'ro')
+plt.axis([0,2.0,0, 15.0])
+plt.xlabel(r'$x$')
+plt.ylabel(r'$y$')
+plt.title(r'Random numbers ')
+plt.show()
+
              +
              +
              +
              +
              +
              +

              Replace or not#

              +

              In the above code, we have use replacement in setting up the +mini-batches. The discussion +here may be +useful.

              +
              +
              +

              Momentum based GD#

              +

              The stochastic gradient descent (SGD) is almost always used with a +momentum or inertia term that serves as a memory of the direction we +are moving in parameter space. This is typically implemented as +follows

              +
              +\[ +\mathbf{v}_{t}=\gamma \mathbf{v}_{t-1}+\eta_{t}\nabla_\theta E(\boldsymbol{\theta}_t) \nonumber +\]
              + +
              +
              +\[ +\begin{equation} +\boldsymbol{\theta}_{t+1}= \boldsymbol{\theta}_t -\mathbf{v}_{t}, +\label{_auto1} \tag{2} +\end{equation} +\]
              +

              where we have introduced a momentum parameter \(\gamma\), with +\(0\le\gamma\le 1\), and for brevity we dropped the explicit notation to +indicate the gradient is to be taken over a different mini-batch at +each step. We call this algorithm gradient descent with momentum +(GDM). From these equations, it is clear that \(\mathbf{v}_t\) is a +running average of recently encountered gradients and +\((1-\gamma)^{-1}\) sets the characteristic time scale for the memory +used in the averaging procedure. Consistent with this, when +\(\gamma=0\), this just reduces down to ordinary SGD as discussed +earlier. An equivalent way of writing the updates is

              +
              +\[ +\Delta \boldsymbol{\theta}_{t+1} = \gamma \Delta \boldsymbol{\theta}_t -\ \eta_{t}\nabla_\theta E(\boldsymbol{\theta}_t), +\]
              +

              where we have defined \(\Delta \boldsymbol{\theta}_{t}= \boldsymbol{\theta}_t-\boldsymbol{\theta}_{t-1}\).

              +
              +
              +

              More on momentum based approaches#

              +

              Let us try to get more intuition from these equations. It is helpful +to consider a simple physical analogy with a particle of mass \(m\) +moving in a viscous medium with drag coefficient \(\mu\) and potential +\(E(\mathbf{w})\). If we denote the particle’s position by \(\mathbf{w}\), +then its motion is described by

              +
              +\[ +m {d^2 \mathbf{w} \over dt^2} + \mu {d \mathbf{w} \over dt }= -\nabla_w E(\mathbf{w}). +\]
              +

              We can discretize this equation in the usual way to get

              +
              +\[ +m { \mathbf{w}_{t+\Delta t}-2 \mathbf{w}_{t} +\mathbf{w}_{t-\Delta t} \over (\Delta t)^2}+\mu {\mathbf{w}_{t+\Delta t}- \mathbf{w}_{t} \over \Delta t} = -\nabla_w E(\mathbf{w}). +\]
              +

              Rearranging this equation, we can rewrite this as

              +
              +\[ +\Delta \mathbf{w}_{t +\Delta t}= - { (\Delta t)^2 \over m +\mu \Delta t} \nabla_w E(\mathbf{w})+ {m \over m +\mu \Delta t} \Delta \mathbf{w}_t. +\]
              +
              +
              +

              Momentum parameter#

              +

              Notice that this equation is identical to previous one if we identify +the position of the particle, \(\mathbf{w}\), with the parameters +\(\boldsymbol{\theta}\). This allows us to identify the momentum +parameter and learning rate with the mass of the particle and the +viscous drag as:

              +
              +\[ +\gamma= {m \over m +\mu \Delta t }, \qquad \eta = {(\Delta t)^2 \over m +\mu \Delta t}. +\]
              +

              Thus, as the name suggests, the momentum parameter is proportional to +the mass of the particle and effectively provides inertia. +Furthermore, in the large viscosity/small learning rate limit, our +memory time scales as \((1-\gamma)^{-1} \approx m/(\mu \Delta t)\).

              +

              Why is momentum useful? SGD momentum helps the gradient descent +algorithm gain speed in directions with persistent but small gradients +even in the presence of stochasticity, while suppressing oscillations +in high-curvature directions. This becomes especially important in +situations where the landscape is shallow and flat in some directions +and narrow and steep in others. It has been argued that first-order +methods (with appropriate initial conditions) can perform comparable +to more expensive second order methods, especially in the context of +complex deep learning models.

              +

              These beneficial properties of momentum can sometimes become even more +pronounced by using a slight modification of the classical momentum +algorithm called Nesterov Accelerated Gradient (NAG).

              +

              In the NAG algorithm, rather than calculating the gradient at the +current parameters, \(\nabla_\theta E(\boldsymbol{\theta}_t)\), one +calculates the gradient at the expected value of the parameters given +our current momentum, \(\nabla_\theta E(\boldsymbol{\theta}_t +\gamma +\mathbf{v}_{t-1})\). This yields the NAG update rule

              +
              +\[ +\mathbf{v}_{t}=\gamma \mathbf{v}_{t-1}+\eta_{t}\nabla_\theta E(\boldsymbol{\theta}_t +\gamma \mathbf{v}_{t-1}) \nonumber +\]
              + +
              +
              +\[ +\begin{equation} +\boldsymbol{\theta}_{t+1}= \boldsymbol{\theta}_t -\mathbf{v}_{t}. +\label{_auto2} \tag{3} +\end{equation} +\]
              +

              One of the major advantages of NAG is that it allows for the use of a larger learning rate than GDM for the same choice of \(\gamma\).

              +
              +
              +

              Second moment of the gradient#

              +

              In stochastic gradient descent, with and without momentum, we still +have to specify a schedule for tuning the learning rates \(\eta_t\) +as a function of time. As discussed in the context of Newton’s +method, this presents a number of dilemmas. The learning rate is +limited by the steepest direction which can change depending on the +current position in the landscape. To circumvent this problem, ideally +our algorithm would keep track of curvature and take large steps in +shallow, flat directions and small steps in steep, narrow directions. +Second-order methods accomplish this by calculating or approximating +the Hessian and normalizing the learning rate by the +curvature. However, this is very computationally expensive for +extremely large models. Ideally, we would like to be able to +adaptively change the step size to match the landscape without paying +the steep computational price of calculating or approximating +Hessians.

              +

              Recently, a number of methods have been introduced that accomplish +this by tracking not only the gradient, but also the second moment of +the gradient. These methods include AdaGrad, AdaDelta, Root Mean Squared Propagation (RMS-Prop), and +ADAM.

              +
              +
              +

              RMS prop#

              +

              In RMS prop, in addition to keeping a running average of the first +moment of the gradient, we also keep track of the second moment +denoted by \(\mathbf{s}_t=\mathbb{E}[\mathbf{g}_t^2]\). The update rule +for RMS prop is given by

              + +
              +
              +\[ +\begin{equation} +\mathbf{g}_t = \nabla_\theta E(\boldsymbol{\theta}) +\label{_auto3} \tag{4} +\end{equation} +\]
              +
              +\[ +\mathbf{s}_t =\beta \mathbf{s}_{t-1} +(1-\beta)\mathbf{g}_t^2 \nonumber +\]
              +
              +\[ +\boldsymbol{\theta}_{t+1}=\boldsymbol{\theta}_t - \eta_t { \mathbf{g}_t \over \sqrt{\mathbf{s}_t +\epsilon}}, \nonumber +\]
              +

              where \(\beta\) controls the averaging time of the second moment and is +typically taken to be about \(\beta=0.9\), \(\eta_t\) is a learning rate +typically chosen to be \(10^{-3}\), and \(\epsilon\sim 10^{-8} \) is a +small regularization constant to prevent divergences. Multiplication +and division by vectors is understood as an element-wise operation. It +is clear from this formula that the learning rate is reduced in +directions where the norm of the gradient is consistently large. This +greatly speeds up the convergence by allowing us to use a larger +learning rate for flat directions.

              +
              +
              +

              ADAM optimizer#

              +

              A related algorithm is the ADAM optimizer. In +ADAM, we keep a running average of +both the first and second moment of the gradient and use this +information to adaptively change the learning rate for different +parameters. The method isefficient when working with large +problems involving lots data and/or parameters. It is a combination of the +gradient descent with momentum algorithm and the RMSprop algorithm +discussed above.

              +

              In addition to keeping a running average of the first and +second moments of the gradient +(i.e. \(\mathbf{m}_t=\mathbb{E}[\mathbf{g}_t]\) and +\(\mathbf{s}_t=\mathbb{E}[\mathbf{g}^2_t]\), respectively), ADAM +performs an additional bias correction to account for the fact that we +are estimating the first two moments of the gradient using a running +average (denoted by the hats in the update rule below). The update +rule for ADAM is given by (where multiplication and division are once +again understood to be element-wise operations below)

              + +
              +
              +\[ +\begin{equation} +\mathbf{g}_t = \nabla_\theta E(\boldsymbol{\theta}) +\label{_auto4} \tag{5} +\end{equation} +\]
              +
              +\[ +\mathbf{m}_t = \beta_1 \mathbf{m}_{t-1} + (1-\beta_1) \mathbf{g}_t \nonumber +\]
              +
              +\[ +\mathbf{s}_t =\beta_2 \mathbf{s}_{t-1} +(1-\beta_2)\mathbf{g}_t^2 \nonumber +\]
              +
              +\[ +\boldsymbol{\mathbf{m}}_t={\mathbf{m}_t \over 1-\beta_1^t} \nonumber +\]
              +
              +\[ +\boldsymbol{\mathbf{s}}_t ={\mathbf{s}_t \over1-\beta_2^t} \nonumber +\]
              +
              +\[ +\boldsymbol{\theta}_{t+1}=\boldsymbol{\theta}_t - \eta_t { \boldsymbol{\mathbf{m}}_t \over \sqrt{\boldsymbol{\mathbf{s}}_t} +\epsilon}, \nonumber +\]
              + +
              +
              +\[ +\begin{equation} +\label{_auto5} \tag{6} +\end{equation} +\]
              +

              where \(\beta_1\) and \(\beta_2\) set the memory lifetime of the first and +second moment and are typically taken to be \(0.9\) and \(0.99\) +respectively, and \(\eta\) and \(\epsilon\) are identical to RMSprop.

              +

              Like in RMSprop, the effective step size of a parameter depends on the +magnitude of its gradient squared. To understand this better, let us +rewrite this expression in terms of the variance +\(\boldsymbol{\sigma}_t^2 = \boldsymbol{\mathbf{s}}_t - +(\boldsymbol{\mathbf{m}}_t)^2\). Consider a single parameter \(\theta_t\). The +update rule for this parameter is given by

              +
              +\[ +\Delta \theta_{t+1}= -\eta_t { \boldsymbol{m}_t \over \sqrt{\sigma_t^2 + m_t^2 }+\epsilon}. +\]
              +
              +
              +

              Algorithms and codes for Adagrad, RMSprop and Adam#

              +

              The algorithms we have implemented are well described in the text by Goodfellow, Bengio and Courville, chapter 8.

              +

              The codes which implement these algorithms are discussed after our presentation of automatic differentiation.

              +
              +
              +

              Practical tips#

              +
                +

              • Randomize the data when making mini-batches. It is always important to randomly shuffle the data when forming mini-batches. Otherwise, the gradient descent method can fit spurious correlations resulting from the order in which data is presented.

                • +

              • Transform your inputs. Learning becomes difficult when our landscape has a mixture of steep and flat directions. One simple trick for minimizing these situations is to standardize the data by subtracting the mean and normalizing the variance of input variables. Whenever possible, also decorrelate the inputs. To understand why this is helpful, consider the case of linear regression. It is easy to show that for the squared error cost function, the Hessian of the cost function is just the correlation matrix between the inputs. Thus, by standardizing the inputs, we are ensuring that the landscape looks homogeneous in all directions in parameter space. Since most deep networks can be viewed as linear transformations followed by a non-linearity at each layer, we expect this intuition to hold beyond the linear case.

                • +

              • Monitor the out-of-sample performance. Always monitor the performance of your model on a validation set (a small portion of the training data that is held out of the training process to serve as a proxy for the test set. If the validation error starts increasing, then the model is beginning to overfit. Terminate the learning process. This early stopping significantly improves performance in many settings.

                • +

              • Adaptive optimization methods don’t always have good generalization. Recent studies have shown that adaptive methods such as ADAM, RMSPorp, and AdaGrad tend to have poor generalization compared to SGD or SGD with momentum, particularly in the high-dimensional limit (i.e. the number of parameters exceeds the number of data points). Although it is not clear at this stage why these methods perform so well in training deep neural networks, simpler procedures like properly-tuned SGD may work as well or better in these applications.

                • +
              +

              Geron’s text, see chapter 11, has several interesting discussions.

              +
              +
              +

              Automatic differentiation#

              +

              Automatic differentiation (AD), +also called algorithmic +differentiation or computational differentiation,is a set of +techniques to numerically evaluate the derivative of a function +specified by a computer program. AD exploits the fact that every +computer program, no matter how complicated, executes a sequence of +elementary arithmetic operations (addition, subtraction, +multiplication, division, etc.) and elementary functions (exp, log, +sin, cos, etc.). By applying the chain rule repeatedly to these +operations, derivatives of arbitrary order can be computed +automatically, accurately to working precision, and using at most a +small constant factor more arithmetic operations than the original +program.

              +

              Automatic differentiation is neither:

              +
                +

              • Symbolic differentiation, nor

                • +

              • Numerical differentiation (the method of finite differences).

                • +
              +

              Symbolic differentiation can lead to inefficient code and faces the +difficulty of converting a computer program into a single expression, +while numerical differentiation can introduce round-off errors in the +discretization process and cancellation

              +

              Python has tools for so-called automatic differentiation. +Consider the following example

              +
              +\[ +f(x) = \sin\left(2\pi x + x^2\right) +\]
              +

              which has the following derivative

              +
              +\[ +f'(x) = \cos\left(2\pi x + x^2\right)\left(2\pi + 2x\right) +\]
              +

              Using autograd we have

              +
              +
              +
              import autograd.numpy as np
+
+# To do elementwise differentiation:
+from autograd import elementwise_grad as egrad 
+
+# To plot:
+import matplotlib.pyplot as plt 
+
+
+def f(x):
+    return np.sin(2*np.pi*x + x**2)
+
+def f_grad_analytic(x):
+    return np.cos(2*np.pi*x + x**2)*(2*np.pi + 2*x)
+
+# Do the comparison:
+x = np.linspace(0,1,1000)
+
+f_grad = egrad(f)
+
+computed = f_grad(x)
+analytic = f_grad_analytic(x)
+
+plt.title('Derivative computed from Autograd compared with the analytical derivative')
+plt.plot(x,computed,label='autograd')
+plt.plot(x,analytic,label='analytic')
+
+plt.xlabel('x')
+plt.ylabel('y')
+plt.legend()
+
+plt.show()
+
+print("The max absolute difference is: %g"%(np.max(np.abs(computed - analytic))))
+
              +
              +
              +
              +
              +
              +

              Using autograd#

              +

              Here we +experiment with what kind of functions Autograd is capable +of finding the gradient of. The following Python functions are just +meant to illustrate what Autograd can do, but please feel free to +experiment with other, possibly more complicated, functions as well.

              +
              +
              +
              import autograd.numpy as np
+from autograd import grad
+
+def f1(x):
+    return x**3 + 1
+
+f1_grad = grad(f1)
+
+# Remember to send in float as argument to the computed gradient from Autograd!
+a = 1.0
+
+# See the evaluated gradient at a using autograd:
+print("The gradient of f1 evaluated at a = %g using autograd is: %g"%(a,f1_grad(a)))
+
+# Compare with the analytical derivative, that is f1'(x) = 3*x**2 
+grad_analytical = 3*a**2
+print("The gradient of f1 evaluated at a = %g by finding the analytic expression is: %g"%(a,grad_analytical))
+
              +
              +
              +
              +
              +
              +

              Autograd with more complicated functions#

              +

              To differentiate with respect to two (or more) arguments of a Python +function, Autograd need to know at which variable the function if +being differentiated with respect to.

              +
              +
              +
              import autograd.numpy as np
+from autograd import grad
+def f2(x1,x2):
+    return 3*x1**3 + x2*(x1 - 5) + 1
+
+# By sending the argument 0, Autograd will compute the derivative w.r.t the first variable, in this case x1
+f2_grad_x1 = grad(f2,0)
+
+# ... and differentiate w.r.t x2 by sending 1 as an additional arugment to grad
+f2_grad_x2 = grad(f2,1)
+
+x1 = 1.0
+x2 = 3.0 
+
+print("Evaluating at x1 = %g, x2 = %g"%(x1,x2))
+print("-"*30)
+
+# Compare with the analytical derivatives:
+
+# Derivative of f2 w.r.t x1 is: 9*x1**2 + x2:
+f2_grad_x1_analytical = 9*x1**2 + x2
+
+# Derivative of f2 w.r.t x2 is: x1 - 5:
+f2_grad_x2_analytical = x1 - 5
+
+# See the evaluated derivations:
+print("The derivative of f2 w.r.t x1: %g"%( f2_grad_x1(x1,x2) ))
+print("The analytical derivative of f2 w.r.t x1: %g"%( f2_grad_x1(x1,x2) ))
+
+print()
+
+print("The derivative of f2 w.r.t x2: %g"%( f2_grad_x2(x1,x2) ))
+print("The analytical derivative of f2 w.r.t x2: %g"%( f2_grad_x2(x1,x2) ))
+
              +
              +
              +
              +

              Note that the grad function will not produce the true gradient of the function. The true gradient of a function with two or more variables will produce a vector, where each element is the function differentiated w.r.t a variable.

              +
              +
              +

              More complicated functions using the elements of their arguments directly#

              +
              +
              +
              import autograd.numpy as np
+from autograd import grad
+def f3(x): # Assumes x is an array of length 5 or higher
+    return 2*x[0] + 3*x[1] + 5*x[2] + 7*x[3] + 11*x[4]**2
+
+f3_grad = grad(f3)
+
+x = np.linspace(0,4,5)
+
+# Print the computed gradient:
+print("The computed gradient of f3 is: ", f3_grad(x))
+
+# The analytical gradient is: (2, 3, 5, 7, 22*x[4])
+f3_grad_analytical = np.array([2, 3, 5, 7, 22*x[4]])
+
+# Print the analytical gradient:
+print("The analytical gradient of f3 is: ", f3_grad_analytical)
+
              +
              +
              +
              +

              Note that in this case, when sending an array as input argument, the +output from Autograd is another array. This is the true gradient of +the function, as opposed to the function in the previous example. By +using arrays to represent the variables, the output from Autograd +might be easier to work with, as the output is closer to what one +could expect form a gradient-evaluting function.

              +
              +
              +

              Functions using mathematical functions from Numpy#

              +
              +
              +
              import autograd.numpy as np
+from autograd import grad
+def f4(x):
+    return np.sqrt(1+x**2) + np.exp(x) + np.sin(2*np.pi*x)
+
+f4_grad = grad(f4)
+
+x = 2.7
+
+# Print the computed derivative:
+print("The computed derivative of f4 at x = %g is: %g"%(x,f4_grad(x)))
+
+# The analytical derivative is: x/sqrt(1 + x**2) + exp(x) + cos(2*pi*x)*2*pi
+f4_grad_analytical = x/np.sqrt(1 + x**2) + np.exp(x) + np.cos(2*np.pi*x)*2*np.pi
+
+# Print the analytical gradient:
+print("The analytical gradient of f4 at x = %g is: %g"%(x,f4_grad_analytical))
+
              +
              +
              +
              +
              +
              +

              More autograd#

              +
              +
              +
              import autograd.numpy as np
+from autograd import grad
+def f5(x):
+    if x >= 0:
+        return x**2
+    else:
+        return -3*x + 1
+
+f5_grad = grad(f5)
+
+x = 2.7
+
+# Print the computed derivative:
+print("The computed derivative of f5 at x = %g is: %g"%(x,f5_grad(x)))
+
              +
              +
              +
              +
              +
              +

              And with loops#

              +
              +
              +
              import autograd.numpy as np
+from autograd import grad
+def f6_for(x):
+    val = 0
+    for i in range(10):
+        val = val + x**i
+    return val
+
+def f6_while(x):
+    val = 0
+    i = 0
+    while i < 10:
+        val = val + x**i
+        i = i + 1
+    return val
+
+f6_for_grad = grad(f6_for)
+f6_while_grad = grad(f6_while)
+
+x = 0.5
+
+# Print the computed derivaties of f6_for and f6_while
+print("The computed derivative of f6_for at x = %g is: %g"%(x,f6_for_grad(x)))
+print("The computed derivative of f6_while at x = %g is: %g"%(x,f6_while_grad(x)))
+
              +
              +
              +
              +
              +
              +
              import autograd.numpy as np
+from autograd import grad
+# Both of the functions are implementation of the sum: sum(x**i) for i = 0, ..., 9
+# The analytical derivative is: sum(i*x**(i-1)) 
+f6_grad_analytical = 0
+for i in range(10):
+    f6_grad_analytical += i*x**(i-1)
+
+print("The analytical derivative of f6 at x = %g is: %g"%(x,f6_grad_analytical))
+
              +
              +
              +
              +
              +
              +

              Using recursion#

              +
              +
              +
              import autograd.numpy as np
+from autograd import grad
+
+def f7(n): # Assume that n is an integer
+    if n == 1 or n == 0:
+        return 1
+    else:
+        return n*f7(n-1)
+
+f7_grad = grad(f7)
+
+n = 2.0
+
+print("The computed derivative of f7 at n = %d is: %g"%(n,f7_grad(n)))
+
+# The function f7 is an implementation of the factorial of n.
+# By using the product rule, one can find that the derivative is:
+
+f7_grad_analytical = 0
+for i in range(int(n)-1):
+    tmp = 1
+    for k in range(int(n)-1):
+        if k != i:
+            tmp *= (n - k)
+    f7_grad_analytical += tmp
+
+print("The analytical derivative of f7 at n = %d is: %g"%(n,f7_grad_analytical))
+
              +
              +
              +
              +

              Note that if n is equal to zero or one, Autograd will give an error message. This message appears when the output is independent on input.

              +
              +
              +

              Unsupported functions#

              +

              Autograd supports many features. However, there are some functions that is not supported (yet) by Autograd.

              +

              Assigning a value to the variable being differentiated with respect to

              +
              +
              +
              import autograd.numpy as np
+from autograd import grad
+def f8(x): # Assume x is an array
+    x[2] = 3
+    return x*2
+
+f8_grad = grad(f8)
+
+x = 8.4
+
+print("The derivative of f8 is:",f8_grad(x))
+
              +
              +
              +
              +

              Here, Autograd tells us that an ‘ArrayBox’ does not support item assignment. The item assignment is done when the program tries to assign x[2] to the value 3. However, Autograd has implemented the computation of the derivative such that this assignment is not possible.

              +
              +
              +

              The syntax a.dot(b) when finding the dot product#

              +
              +
              +
              import autograd.numpy as np
+from autograd import grad
+def f9(a): # Assume a is an array with 2 elements
+    b = np.array([1.0,2.0])
+    return a.dot(b)
+
+f9_grad = grad(f9)
+
+x = np.array([1.0,0.0])
+
+print("The derivative of f9 is:",f9_grad(x))
+
              +
              +
              +
              +

              Here we are told that the ‘dot’ function does not belong to Autograd’s +version of a Numpy array. To overcome this, an alternative syntax +which also computed the dot product can be used:

              +
              +
              +
              import autograd.numpy as np
+from autograd import grad
+def f9_alternative(x): # Assume a is an array with 2 elements
+    b = np.array([1.0,2.0])
+    return np.dot(x,b) # The same as x_1*b_1 + x_2*b_2
+
+f9_alternative_grad = grad(f9_alternative)
+
+x = np.array([3.0,0.0])
+
+print("The gradient of f9 is:",f9_alternative_grad(x))
+
+# The analytical gradient of the dot product of vectors x and b with two elements (x_1,x_2) and (b_1, b_2) respectively
+# w.r.t x is (b_1, b_2).
+
              +
              +
              +
              +
              + +
              +

              Using Autograd with OLS#

              +

              We conclude the part on optmization by showing how we can make codes +for linear regression and logistic regression using autograd. The +first example shows results with ordinary leats squares.

              +
              +
              +
              # Using Autograd to calculate gradients for OLS
+from random import random, seed
+import numpy as np
+import autograd.numpy as np
+import matplotlib.pyplot as plt
+from autograd import grad
+
+def CostOLS(beta):
+    return (1.0/n)*np.sum((y-X @ beta)**2)
+
+n = 100
+x = 2*np.random.rand(n,1)
+y = 4+3*x+np.random.randn(n,1)
+
+X = np.c_[np.ones((n,1)), x]
+XT_X = X.T @ X
+theta_linreg = np.linalg.pinv(XT_X) @ (X.T @ y)
+print("Own inversion")
+print(theta_linreg)
+# Hessian matrix
+H = (2.0/n)* XT_X
+EigValues, EigVectors = np.linalg.eig(H)
+print(f"Eigenvalues of Hessian Matrix:{EigValues}")
+
+theta = np.random.randn(2,1)
+eta = 1.0/np.max(EigValues)
+Niterations = 1000
+# define the gradient
+training_gradient = grad(CostOLS)
+
+for iter in range(Niterations):
+    gradients = training_gradient(theta)
+    theta -= eta*gradients
+print("theta from own gd")
+print(theta)
+
+xnew = np.array([[0],[2]])
+Xnew = np.c_[np.ones((2,1)), xnew]
+ypredict = Xnew.dot(theta)
+ypredict2 = Xnew.dot(theta_linreg)
+
+plt.plot(xnew, ypredict, "r-")
+plt.plot(xnew, ypredict2, "b-")
+plt.plot(x, y ,'ro')
+plt.axis([0,2.0,0, 15.0])
+plt.xlabel(r'$x$')
+plt.ylabel(r'$y$')
+plt.title(r'Random numbers ')
+plt.show()
+
              +
              +
              +
              +
              +
              +

              Same code but now with momentum gradient descent#

              +
              +
              +
              # Using Autograd to calculate gradients for OLS
+from random import random, seed
+import numpy as np
+import autograd.numpy as np
+import matplotlib.pyplot as plt
+from autograd import grad
+
+def CostOLS(beta):
+    return (1.0/n)*np.sum((y-X @ beta)**2)
+
+n = 100
+x = 2*np.random.rand(n,1)
+y = 4+3*x#+np.random.randn(n,1)
+
+X = np.c_[np.ones((n,1)), x]
+XT_X = X.T @ X
+theta_linreg = np.linalg.pinv(XT_X) @ (X.T @ y)
+print("Own inversion")
+print(theta_linreg)
+# Hessian matrix
+H = (2.0/n)* XT_X
+EigValues, EigVectors = np.linalg.eig(H)
+print(f"Eigenvalues of Hessian Matrix:{EigValues}")
+
+theta = np.random.randn(2,1)
+eta = 1.0/np.max(EigValues)
+Niterations = 30
+
+# define the gradient
+training_gradient = grad(CostOLS)
+
+for iter in range(Niterations):
+    gradients = training_gradient(theta)
+    theta -= eta*gradients
+    print(iter,gradients[0],gradients[1])
+print("theta from own gd")
+print(theta)
+
+# Now improve with momentum gradient descent
+change = 0.0
+delta_momentum = 0.3
+for iter in range(Niterations):
+    # calculate gradient
+    gradients = training_gradient(theta)
+    # calculate update
+    new_change = eta*gradients+delta_momentum*change
+    # take a step
+    theta -= new_change
+    # save the change
+    change = new_change
+    print(iter,gradients[0],gradients[1])
+print("theta from own gd wth momentum")
+print(theta)
+
              +
              +
              +
              +
              +
              +

              But noen of these can compete with Newton’s method#

              +
              +
              +
              # Using Newton's method
+from random import random, seed
+import numpy as np
+import autograd.numpy as np
+import matplotlib.pyplot as plt
+from autograd import grad
+
+def CostOLS(beta):
+    return (1.0/n)*np.sum((y-X @ beta)**2)
+
+n = 100
+x = 2*np.random.rand(n,1)
+y = 4+3*x+np.random.randn(n,1)
+
+X = np.c_[np.ones((n,1)), x]
+XT_X = X.T @ X
+beta_linreg = np.linalg.pinv(XT_X) @ (X.T @ y)
+print("Own inversion")
+print(beta_linreg)
+# Hessian matrix
+H = (2.0/n)* XT_X
+# Note that here the Hessian does not depend on the parameters beta
+invH = np.linalg.pinv(H)
+EigValues, EigVectors = np.linalg.eig(H)
+print(f"Eigenvalues of Hessian Matrix:{EigValues}")
+
+beta = np.random.randn(2,1)
+Niterations = 5
+
+# define the gradient
+training_gradient = grad(CostOLS)
+
+for iter in range(Niterations):
+    gradients = training_gradient(beta)
+    beta -= invH @ gradients
+    print(iter,gradients[0],gradients[1])
+print("beta from own Newton code")
+print(beta)
+
              +
              +
              +
              +
              +
              +

              Including Stochastic Gradient Descent with Autograd#

              +

              In this code we include the stochastic gradient descent approach discussed above. Note here that we specify which argument we are taking the derivative with respect to when using autograd.

              +
              +
              +
              # Using Autograd to calculate gradients using SGD
+# OLS example
+from random import random, seed
+import numpy as np
+import autograd.numpy as np
+import matplotlib.pyplot as plt
+from autograd import grad
+
+# Note change from previous example
+def CostOLS(y,X,theta):
+    return np.sum((y-X @ theta)**2)
+
+n = 100
+x = 2*np.random.rand(n,1)
+y = 4+3*x+np.random.randn(n,1)
+
+X = np.c_[np.ones((n,1)), x]
+XT_X = X.T @ X
+theta_linreg = np.linalg.pinv(XT_X) @ (X.T @ y)
+print("Own inversion")
+print(theta_linreg)
+# Hessian matrix
+H = (2.0/n)* XT_X
+EigValues, EigVectors = np.linalg.eig(H)
+print(f"Eigenvalues of Hessian Matrix:{EigValues}")
+
+theta = np.random.randn(2,1)
+eta = 1.0/np.max(EigValues)
+Niterations = 1000
+
+# Note that we request the derivative wrt third argument (theta, 2 here)
+training_gradient = grad(CostOLS,2)
+
+for iter in range(Niterations):
+    gradients = (1.0/n)*training_gradient(y, X, theta)
+    theta -= eta*gradients
+print("theta from own gd")
+print(theta)
+
+xnew = np.array([[0],[2]])
+Xnew = np.c_[np.ones((2,1)), xnew]
+ypredict = Xnew.dot(theta)
+ypredict2 = Xnew.dot(theta_linreg)
+
+plt.plot(xnew, ypredict, "r-")
+plt.plot(xnew, ypredict2, "b-")
+plt.plot(x, y ,'ro')
+plt.axis([0,2.0,0, 15.0])
+plt.xlabel(r'$x$')
+plt.ylabel(r'$y$')
+plt.title(r'Random numbers ')
+plt.show()
+
+n_epochs = 50
+M = 5   #size of each minibatch
+m = int(n/M) #number of minibatches
+t0, t1 = 5, 50
+def learning_schedule(t):
+    return t0/(t+t1)
+
+theta = np.random.randn(2,1)
+
+for epoch in range(n_epochs):
+# Can you figure out a better way of setting up the contributions to each batch?
+    for i in range(m):
+        random_index = M*np.random.randint(m)
+        xi = X[random_index:random_index+M]
+        yi = y[random_index:random_index+M]
+        gradients = (1.0/M)*training_gradient(yi, xi, theta)
+        eta = learning_schedule(epoch*m+i)
+        theta = theta - eta*gradients
+print("theta from own sdg")
+print(theta)
+
              +
              +
              +
              +
              +
              +

              Same code but now with momentum gradient descent#

              +
              +
              +
              # Using Autograd to calculate gradients using SGD
+# OLS example
+from random import random, seed
+import numpy as np
+import autograd.numpy as np
+import matplotlib.pyplot as plt
+from autograd import grad
+
+# Note change from previous example
+def CostOLS(y,X,theta):
+    return np.sum((y-X @ theta)**2)
+
+n = 100
+x = 2*np.random.rand(n,1)
+y = 4+3*x+np.random.randn(n,1)
+
+X = np.c_[np.ones((n,1)), x]
+XT_X = X.T @ X
+theta_linreg = np.linalg.pinv(XT_X) @ (X.T @ y)
+print("Own inversion")
+print(theta_linreg)
+# Hessian matrix
+H = (2.0/n)* XT_X
+EigValues, EigVectors = np.linalg.eig(H)
+print(f"Eigenvalues of Hessian Matrix:{EigValues}")
+
+theta = np.random.randn(2,1)
+eta = 1.0/np.max(EigValues)
+Niterations = 100
+
+# Note that we request the derivative wrt third argument (theta, 2 here)
+training_gradient = grad(CostOLS,2)
+
+for iter in range(Niterations):
+    gradients = (1.0/n)*training_gradient(y, X, theta)
+    theta -= eta*gradients
+print("theta from own gd")
+print(theta)
+
+
+n_epochs = 50
+M = 5   #size of each minibatch
+m = int(n/M) #number of minibatches
+t0, t1 = 5, 50
+def learning_schedule(t):
+    return t0/(t+t1)
+
+theta = np.random.randn(2,1)
+
+change = 0.0
+delta_momentum = 0.3
+
+for epoch in range(n_epochs):
+    for i in range(m):
+        random_index = M*np.random.randint(m)
+        xi = X[random_index:random_index+M]
+        yi = y[random_index:random_index+M]
+        gradients = (1.0/M)*training_gradient(yi, xi, theta)
+        eta = learning_schedule(epoch*m+i)
+        # calculate update
+        new_change = eta*gradients+delta_momentum*change
+        # take a step
+        theta -= new_change
+        # save the change
+        change = new_change
+print("theta from own sdg with momentum")
+print(theta)
+
              +
              +
              +
              +
              +
              +

              Similar (second order function now) problem but now with AdaGrad#

              +
              +
              +
              # Using Autograd to calculate gradients using AdaGrad and Stochastic Gradient descent
+# OLS example
+from random import random, seed
+import numpy as np
+import autograd.numpy as np
+import matplotlib.pyplot as plt
+from autograd import grad
+
+# Note change from previous example
+def CostOLS(y,X,theta):
+    return np.sum((y-X @ theta)**2)
+
+n = 1000
+x = np.random.rand(n,1)
+y = 2.0+3*x +4*x*x
+
+X = np.c_[np.ones((n,1)), x, x*x]
+XT_X = X.T @ X
+theta_linreg = np.linalg.pinv(XT_X) @ (X.T @ y)
+print("Own inversion")
+print(theta_linreg)
+
+
+# Note that we request the derivative wrt third argument (theta, 2 here)
+training_gradient = grad(CostOLS,2)
+# Define parameters for Stochastic Gradient Descent
+n_epochs = 50
+M = 5   #size of each minibatch
+m = int(n/M) #number of minibatches
+# Guess for unknown parameters theta
+theta = np.random.randn(3,1)
+
+# Value for learning rate
+eta = 0.01
+# Including AdaGrad parameter to avoid possible division by zero
+delta  = 1e-8
+for epoch in range(n_epochs):
+    Giter = 0.0
+    for i in range(m):
+        random_index = M*np.random.randint(m)
+        xi = X[random_index:random_index+M]
+        yi = y[random_index:random_index+M]
+        gradients = (1.0/M)*training_gradient(yi, xi, theta)
+        Giter += gradients*gradients
+        update = gradients*eta/(delta+np.sqrt(Giter))
+        theta -= update
+print("theta from own AdaGrad")
+print(theta)
+
              +
              +
              +
              +

              Running this code we note an almost perfect agreement with the results from matrix inversion.

              +
              +
              +

              RMSprop for adaptive learning rate with Stochastic Gradient Descent#

              +
              +
              +
              # Using Autograd to calculate gradients using RMSprop  and Stochastic Gradient descent
+# OLS example
+from random import random, seed
+import numpy as np
+import autograd.numpy as np
+import matplotlib.pyplot as plt
+from autograd import grad
+
+# Note change from previous example
+def CostOLS(y,X,theta):
+    return np.sum((y-X @ theta)**2)
+
+n = 1000
+x = np.random.rand(n,1)
+y = 2.0+3*x +4*x*x# +np.random.randn(n,1)
+
+X = np.c_[np.ones((n,1)), x, x*x]
+XT_X = X.T @ X
+theta_linreg = np.linalg.pinv(XT_X) @ (X.T @ y)
+print("Own inversion")
+print(theta_linreg)
+
+
+# Note that we request the derivative wrt third argument (theta, 2 here)
+training_gradient = grad(CostOLS,2)
+# Define parameters for Stochastic Gradient Descent
+n_epochs = 50
+M = 5   #size of each minibatch
+m = int(n/M) #number of minibatches
+# Guess for unknown parameters theta
+theta = np.random.randn(3,1)
+
+# Value for learning rate
+eta = 0.01
+# Value for parameter rho
+rho = 0.99
+# Including AdaGrad parameter to avoid possible division by zero
+delta  = 1e-8
+for epoch in range(n_epochs):
+    Giter = 0.0
+    for i in range(m):
+        random_index = M*np.random.randint(m)
+        xi = X[random_index:random_index+M]
+        yi = y[random_index:random_index+M]
+        gradients = (1.0/M)*training_gradient(yi, xi, theta)
+	# Accumulated gradient
+	# Scaling with rho the new and the previous results
+        Giter = (rho*Giter+(1-rho)*gradients*gradients)
+	# Taking the diagonal only and inverting
+        update = gradients*eta/(delta+np.sqrt(Giter))
+	# Hadamard product
+        theta -= update
+print("theta from own RMSprop")
+print(theta)
+
              +
              +
              +
              +
              +
              +

              And finally ADAM#

              +
              +
              +
              # Using Autograd to calculate gradients using RMSprop  and Stochastic Gradient descent
+# OLS example
+from random import random, seed
+import numpy as np
+import autograd.numpy as np
+import matplotlib.pyplot as plt
+from autograd import grad
+
+# Note change from previous example
+def CostOLS(y,X,theta):
+    return np.sum((y-X @ theta)**2)
+
+n = 1000
+x = np.random.rand(n,1)
+y = 2.0+3*x +4*x*x# +np.random.randn(n,1)
+
+X = np.c_[np.ones((n,1)), x, x*x]
+XT_X = X.T @ X
+theta_linreg = np.linalg.pinv(XT_X) @ (X.T @ y)
+print("Own inversion")
+print(theta_linreg)
+
+
+# Note that we request the derivative wrt third argument (theta, 2 here)
+training_gradient = grad(CostOLS,2)
+# Define parameters for Stochastic Gradient Descent
+n_epochs = 50
+M = 5   #size of each minibatch
+m = int(n/M) #number of minibatches
+# Guess for unknown parameters theta
+theta = np.random.randn(3,1)
+
+# Value for learning rate
+eta = 0.01
+# Value for parameters beta1 and beta2, see https://arxiv.org/abs/1412.6980
+beta1 = 0.9
+beta2 = 0.999
+# Including AdaGrad parameter to avoid possible division by zero
+delta  = 1e-7
+iter = 0
+for epoch in range(n_epochs):
+    first_moment = 0.0
+    second_moment = 0.0
+    iter += 1
+    for i in range(m):
+        random_index = M*np.random.randint(m)
+        xi = X[random_index:random_index+M]
+        yi = y[random_index:random_index+M]
+        gradients = (1.0/M)*training_gradient(yi, xi, theta)
+        # Computing moments first
+        first_moment = beta1*first_moment + (1-beta1)*gradients
+        second_moment = beta2*second_moment+(1-beta2)*gradients*gradients
+        first_term = first_moment/(1.0-beta1**iter)
+        second_term = second_moment/(1.0-beta2**iter)
+	# Scaling with rho the new and the previous results
+        update = eta*first_term/(np.sqrt(second_term)+delta)
+        theta -= update
+print("theta from own ADAM")
+print(theta)
+
              +
              +
              +
              +
              +
              +

              And Logistic Regression#

              +
              +
              +
              import autograd.numpy as np
+from autograd import grad
+
+def sigmoid(x):
+    return 0.5 * (np.tanh(x / 2.) + 1)
+
+def logistic_predictions(weights, inputs):
+    # Outputs probability of a label being true according to logistic model.
+    return sigmoid(np.dot(inputs, weights))
+
+def training_loss(weights):
+    # Training loss is the negative log-likelihood of the training labels.
+    preds = logistic_predictions(weights, inputs)
+    label_probabilities = preds * targets + (1 - preds) * (1 - targets)
+    return -np.sum(np.log(label_probabilities))
+
+# Build a toy dataset.
+inputs = np.array([[0.52, 1.12,  0.77],
+                   [0.88, -1.08, 0.15],
+                   [0.52, 0.06, -1.30],
+                   [0.74, -2.49, 1.39]])
+targets = np.array([True, True, False, True])
+
+# Define a function that returns gradients of training loss using Autograd.
+training_gradient_fun = grad(training_loss)
+
+# Optimize weights using gradient descent.
+weights = np.array([0.0, 0.0, 0.0])
+print("Initial loss:", training_loss(weights))
+for i in range(100):
+    weights -= training_gradient_fun(weights) * 0.01
+
+print("Trained loss:", training_loss(weights))
+
              +
              +
              +
              +
              +
              +

              Introducing JAX#

              +

              Presently, instead of using autograd, we recommend using JAX

              +

              JAX is Autograd and XLA (Accelerated Linear Algebra)), +brought together for high-performance numerical computing and machine learning research. +It provides composable transformations of Python+NumPy programs: differentiate, vectorize, parallelize, Just-In-Time compile to GPU/TPU, and more.

              +

              Here’s a simple example on how you can use JAX to compute the derivate of the logistic function.

              +
              +
              +
              import jax.numpy as jnp
+from jax import grad, jit, vmap
+
+def sum_logistic(x):
+  return jnp.sum(1.0 / (1.0 + jnp.exp(-x)))
+
+x_small = jnp.arange(3.)
+derivative_fn = grad(sum_logistic)
+print(derivative_fn(x_small))
+
              +
              +
              +
              +
              +
              + + + + +
              + + + + + + + + +
              + + + +
              + +
              +
              + Contents +
              +
              + +
              + + +
              +
              + +
              + +
              + +

              +By Morten Hjorth-Jensen +

              + +
              + +
              + +

              + + © Copyright 2022. +
              + +

              + +
              + +
              + +
              + +
              + +
              + +
              +
              + + +
              +
              +
              + + + + + +
              +
              + + \ No newline at end of file diff --git a/doc/LectureNotes/_build/jupyter_execute/chapter1.ipynb b/doc/LectureNotes/_build/jupyter_execute/chapter1.ipynb index f470b9c6d..e339ffdc5 100644 --- a/doc/LectureNotes/_build/jupyter_execute/chapter1.ipynb +++ b/doc/LectureNotes/_build/jupyter_execute/chapter1.ipynb @@ -342,18 +342,20 @@ }, "outputs": [ { - "data": { - "image/png": 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- "text/plain": [ - "
              " - ] - }, - "metadata": { - "filenames": { - "image/png": "/Users/mhjensen/Teaching/MachineLearning/doc/LectureNotes/_build/jupyter_execute/chapter1_9_0.png" - } - }, - "output_type": "display_data" + "ename": "ModuleNotFoundError", + "evalue": "No module named 'matplotlib'", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mModuleNotFoundError\u001b[0m Traceback (most recent call last)", + "Cell \u001b[0;32mIn[1], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \u001b[43mget_ipython\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mrun_line_magic\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mmatplotlib\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43minline\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m)\u001b[49m\n\u001b[1;32m 3\u001b[0m \u001b[38;5;66;03m# Importing various packages\u001b[39;00m\n\u001b[1;32m 4\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mnumpy\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m \u001b[38;5;21;01mnp\u001b[39;00m\n", + "File \u001b[0;32m~/miniforge3/lib/python3.9/site-packages/IPython/core/interactiveshell.py:2432\u001b[0m, in \u001b[0;36mInteractiveShell.run_line_magic\u001b[0;34m(self, magic_name, line, _stack_depth)\u001b[0m\n\u001b[1;32m 2430\u001b[0m kwargs[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mlocal_ns\u001b[39m\u001b[38;5;124m'\u001b[39m] \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mget_local_scope(stack_depth)\n\u001b[1;32m 2431\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mbuiltin_trap:\n\u001b[0;32m-> 2432\u001b[0m result \u001b[38;5;241m=\u001b[39m \u001b[43mfn\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 2434\u001b[0m \u001b[38;5;66;03m# The code below prevents the output from being displayed\u001b[39;00m\n\u001b[1;32m 2435\u001b[0m \u001b[38;5;66;03m# when using magics with decorator @output_can_be_silenced\u001b[39;00m\n\u001b[1;32m 2436\u001b[0m \u001b[38;5;66;03m# when the last Python token in the expression is a ';'.\u001b[39;00m\n\u001b[1;32m 2437\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mgetattr\u001b[39m(fn, magic\u001b[38;5;241m.\u001b[39mMAGIC_OUTPUT_CAN_BE_SILENCED, \u001b[38;5;28;01mFalse\u001b[39;00m):\n", + "File \u001b[0;32m~/miniforge3/lib/python3.9/site-packages/IPython/core/magics/pylab.py:99\u001b[0m, in \u001b[0;36mPylabMagics.matplotlib\u001b[0;34m(self, line)\u001b[0m\n\u001b[1;32m 97\u001b[0m \u001b[38;5;28mprint\u001b[39m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mAvailable matplotlib backends: \u001b[39m\u001b[38;5;132;01m%s\u001b[39;00m\u001b[38;5;124m\"\u001b[39m \u001b[38;5;241m%\u001b[39m backends_list)\n\u001b[1;32m 98\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m---> 99\u001b[0m gui, backend \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mshell\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43menable_matplotlib\u001b[49m\u001b[43m(\u001b[49m\u001b[43margs\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mgui\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mlower\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mif\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[38;5;28;43misinstance\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43margs\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mgui\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mstr\u001b[39;49m\u001b[43m)\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01melse\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43margs\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mgui\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 100\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_show_matplotlib_backend(args\u001b[38;5;241m.\u001b[39mgui, backend)\n", + "File \u001b[0;32m~/miniforge3/lib/python3.9/site-packages/IPython/core/interactiveshell.py:3606\u001b[0m, in \u001b[0;36mInteractiveShell.enable_matplotlib\u001b[0;34m(self, gui)\u001b[0m\n\u001b[1;32m 3585\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21menable_matplotlib\u001b[39m(\u001b[38;5;28mself\u001b[39m, gui\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mNone\u001b[39;00m):\n\u001b[1;32m 3586\u001b[0m \u001b[38;5;250m \u001b[39m\u001b[38;5;124;03m\"\"\"Enable interactive matplotlib and inline figure support.\u001b[39;00m\n\u001b[1;32m 3587\u001b[0m \n\u001b[1;32m 3588\u001b[0m \u001b[38;5;124;03m This takes the following steps:\u001b[39;00m\n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 3604\u001b[0m \u001b[38;5;124;03m display figures inline.\u001b[39;00m\n\u001b[1;32m 3605\u001b[0m \u001b[38;5;124;03m \"\"\"\u001b[39;00m\n\u001b[0;32m-> 3606\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mmatplotlib_inline\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mbackend_inline\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m configure_inline_support\n\u001b[1;32m 3608\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mIPython\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mcore\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m pylabtools \u001b[38;5;28;01mas\u001b[39;00m pt\n\u001b[1;32m 3609\u001b[0m gui, backend \u001b[38;5;241m=\u001b[39m pt\u001b[38;5;241m.\u001b[39mfind_gui_and_backend(gui, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mpylab_gui_select)\n", + "File \u001b[0;32m~/miniforge3/lib/python3.9/site-packages/matplotlib_inline/__init__.py:1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01m.\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m backend_inline, config \u001b[38;5;66;03m# noqa\u001b[39;00m\n\u001b[1;32m 2\u001b[0m __version__ \u001b[38;5;241m=\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m0.1.6\u001b[39m\u001b[38;5;124m\"\u001b[39m \u001b[38;5;66;03m# noqa\u001b[39;00m\n", + "File \u001b[0;32m~/miniforge3/lib/python3.9/site-packages/matplotlib_inline/backend_inline.py:6\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[38;5;124;03m\"\"\"A matplotlib backend for publishing figures via display_data\"\"\"\u001b[39;00m\n\u001b[1;32m 3\u001b[0m \u001b[38;5;66;03m# Copyright (c) IPython Development Team.\u001b[39;00m\n\u001b[1;32m 4\u001b[0m \u001b[38;5;66;03m# Distributed under the terms of the BSD 3-Clause License.\u001b[39;00m\n\u001b[0;32m----> 6\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mmatplotlib\u001b[39;00m\n\u001b[1;32m 7\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mmatplotlib\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m colors\n\u001b[1;32m 8\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mmatplotlib\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mbackends\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m backend_agg\n", + "\u001b[0;31mModuleNotFoundError\u001b[0m: No module named 'matplotlib'" + ] } ], "source": [ @@ -512,22 +514,7 @@ "collapsed": false, "editable": true }, - "outputs": [ - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
              " - ] - }, - "metadata": { - "filenames": { - "image/png": "/Users/mhjensen/Teaching/MachineLearning/doc/LectureNotes/_build/jupyter_execute/chapter1_17_0.png" - } - }, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "import numpy as np\n", "import matplotlib.pyplot as plt\n", @@ -577,36 +564,7 @@ "collapsed": false, "editable": true }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "The intercept alpha: \n", - " [1.89563529]\n", - "Coefficient beta : \n", - " [[5.26512016]]\n", - "Mean squared error: 0.27\n", - "Variance score: 0.87\n", - "Mean squared log error: 0.01\n", - "Mean absolute error: 0.41\n" - ] - }, - { - "data": { - "image/png": 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\n", 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\n", 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              " - ] - }, - "metadata": { - "filenames": { - "image/png": "/Users/mhjensen/Teaching/MachineLearning/doc/LectureNotes/_build/jupyter_execute/chapter1_33_0.png" - } - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "0.0049999999999999845\n" - ] - } - ], + "outputs": [], "source": [ "import matplotlib.pyplot as plt\n", "import numpy as np\n", @@ -1188,18 +1124,7 @@ "collapsed": false, "editable": true }, - "outputs": [ - { - "data": { - "text/plain": [ - "' \\nThis is taken from the data file of the mass 2016 evaluation. \\nAll files are 3436 lines long with 124 character per line. \\n Headers are 39 lines long. \\n col 1 : Fortran character control: 1 = page feed 0 = line feed \\n format : a1,i3,i5,i5,i5,1x,a3,a4,1x,f13.5,f11.5,f11.3,f9.3,1x,a2,f11.3,f9.3,1x,i3,1x,f12.5,f11.5 \\n These formats are reflected in the pandas widths variable below, see the statement \\n widths=(1,3,5,5,5,1,3,4,1,13,11,11,9,1,2,11,9,1,3,1,12,11,1), \\n Pandas has also a variable header, with length 39 in this case. \\n'" - ] - }, - "execution_count": 7, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "\"\"\" \n", "This is taken from the data file of the mass 2016 evaluation. \n", @@ -1234,21 +1159,7 @@ "collapsed": false, "editable": true }, - "outputs": [ - { - "ename": "ValueError", - "evalue": "Length of colspecs must match length of names", - "output_type": "error", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mValueError\u001b[0m Traceback (most recent call last)", - "Input \u001b[0;32mIn [8]\u001b[0m, in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[38;5;66;03m# Read the experimental data with Pandas\u001b[39;00m\n\u001b[0;32m----> 2\u001b[0m Masses \u001b[38;5;241m=\u001b[39m \u001b[43mpd\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mread_fwf\u001b[49m\u001b[43m(\u001b[49m\u001b[43minfile\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43musecols\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m2\u001b[39;49m\u001b[43m,\u001b[49m\u001b[38;5;241;43m3\u001b[39;49m\u001b[43m,\u001b[49m\u001b[38;5;241;43m4\u001b[39;49m\u001b[43m,\u001b[49m\u001b[38;5;241;43m6\u001b[39;49m\u001b[43m,\u001b[49m\u001b[38;5;241;43m11\u001b[39;49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 3\u001b[0m \u001b[43m \u001b[49m\u001b[43mnames\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mN\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mZ\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mA\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mElement\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mEbinding\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 4\u001b[0m \u001b[43m \u001b[49m\u001b[43mwidths\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m1\u001b[39;49m\u001b[43m,\u001b[49m\u001b[38;5;241;43m3\u001b[39;49m\u001b[43m,\u001b[49m\u001b[38;5;241;43m5\u001b[39;49m\u001b[43m,\u001b[49m\u001b[38;5;241;43m5\u001b[39;49m\u001b[43m,\u001b[49m\u001b[38;5;241;43m5\u001b[39;49m\u001b[43m,\u001b[49m\u001b[38;5;241;43m1\u001b[39;49m\u001b[43m,\u001b[49m\u001b[38;5;241;43m3\u001b[39;49m\u001b[43m,\u001b[49m\u001b[38;5;241;43m4\u001b[39;49m\u001b[43m,\u001b[49m\u001b[38;5;241;43m1\u001b[39;49m\u001b[43m,\u001b[49m\u001b[38;5;241;43m13\u001b[39;49m\u001b[43m,\u001b[49m\u001b[38;5;241;43m11\u001b[39;49m\u001b[43m,\u001b[49m\u001b[38;5;241;43m11\u001b[39;49m\u001b[43m,\u001b[49m\u001b[38;5;241;43m9\u001b[39;49m\u001b[43m,\u001b[49m\u001b[38;5;241;43m1\u001b[39;49m\u001b[43m,\u001b[49m\u001b[38;5;241;43m2\u001b[39;49m\u001b[43m,\u001b[49m\u001b[38;5;241;43m11\u001b[39;49m\u001b[43m,\u001b[49m\u001b[38;5;241;43m9\u001b[39;49m\u001b[43m,\u001b[49m\u001b[38;5;241;43m1\u001b[39;49m\u001b[43m,\u001b[49m\u001b[38;5;241;43m3\u001b[39;49m\u001b[43m,\u001b[49m\u001b[38;5;241;43m1\u001b[39;49m\u001b[43m,\u001b[49m\u001b[38;5;241;43m12\u001b[39;49m\u001b[43m,\u001b[49m\u001b[38;5;241;43m11\u001b[39;49m\u001b[43m,\u001b[49m\u001b[38;5;241;43m1\u001b[39;49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 5\u001b[0m \u001b[43m \u001b[49m\u001b[43mheader\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;241;43m39\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[1;32m 6\u001b[0m \u001b[43m \u001b[49m\u001b[43mindex_col\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mFalse\u001b[39;49;00m\u001b[43m)\u001b[49m\n\u001b[1;32m 8\u001b[0m \u001b[38;5;66;03m# Extrapolated values are indicated by '#' in place of the decimal place, so\u001b[39;00m\n\u001b[1;32m 9\u001b[0m \u001b[38;5;66;03m# the Ebinding column won't be numeric. Coerce to float and drop these entries.\u001b[39;00m\n\u001b[1;32m 10\u001b[0m Masses[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mEbinding\u001b[39m\u001b[38;5;124m'\u001b[39m] \u001b[38;5;241m=\u001b[39m pd\u001b[38;5;241m.\u001b[39mto_numeric(Masses[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mEbinding\u001b[39m\u001b[38;5;124m'\u001b[39m], errors\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mcoerce\u001b[39m\u001b[38;5;124m'\u001b[39m)\n", - "File \u001b[0;32m~/miniforge3/envs/myenv/lib/python3.9/site-packages/pandas/util/_decorators.py:311\u001b[0m, in \u001b[0;36mdeprecate_nonkeyword_arguments..decorate..wrapper\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m 305\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mlen\u001b[39m(args) \u001b[38;5;241m>\u001b[39m num_allow_args:\n\u001b[1;32m 306\u001b[0m warnings\u001b[38;5;241m.\u001b[39mwarn(\n\u001b[1;32m 307\u001b[0m msg\u001b[38;5;241m.\u001b[39mformat(arguments\u001b[38;5;241m=\u001b[39marguments),\n\u001b[1;32m 308\u001b[0m \u001b[38;5;167;01mFutureWarning\u001b[39;00m,\n\u001b[1;32m 309\u001b[0m stacklevel\u001b[38;5;241m=\u001b[39mstacklevel,\n\u001b[1;32m 310\u001b[0m )\n\u001b[0;32m--> 311\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mfunc\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n", - "File \u001b[0;32m~/miniforge3/envs/myenv/lib/python3.9/site-packages/pandas/io/parsers/readers.py:871\u001b[0m, in \u001b[0;36mread_fwf\u001b[0;34m(filepath_or_buffer, colspecs, widths, infer_nrows, **kwds)\u001b[0m\n\u001b[1;32m 869\u001b[0m len_index \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mlen\u001b[39m(index_col)\n\u001b[1;32m 870\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mlen\u001b[39m(names) \u001b[38;5;241m+\u001b[39m len_index \u001b[38;5;241m!=\u001b[39m \u001b[38;5;28mlen\u001b[39m(colspecs):\n\u001b[0;32m--> 871\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mLength of colspecs must match length of names\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[1;32m 873\u001b[0m kwds[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mcolspecs\u001b[39m\u001b[38;5;124m\"\u001b[39m] \u001b[38;5;241m=\u001b[39m colspecs\n\u001b[1;32m 874\u001b[0m kwds[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124minfer_nrows\u001b[39m\u001b[38;5;124m\"\u001b[39m] \u001b[38;5;241m=\u001b[39m infer_nrows\n", - "\u001b[0;31mValueError\u001b[0m: Length of colspecs must match length of names" - ] - } - ], + "outputs": [], "source": [ "# Read the experimental data with Pandas\n", "Masses = pd.read_fwf(infile, usecols=(2,3,4,6,11),\n", @@ -5129,7 +5040,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.9.10" + "version": "3.9.18" } }, "nbformat": 4, diff --git a/doc/LectureNotes/_build/jupyter_execute/chapter10.ipynb b/doc/LectureNotes/_build/jupyter_execute/chapter10.ipynb index 3d8c9ceb8..32c9f88d0 100644 --- a/doc/LectureNotes/_build/jupyter_execute/chapter10.ipynb +++ b/doc/LectureNotes/_build/jupyter_execute/chapter10.ipynb @@ -537,27 +537,20 @@ }, "outputs": [ { - "name": "stdout", - "output_type": "stream", - "text": [ - "inputs = (n_inputs, pixel_width, pixel_height) = (1797, 8, 8)\n", - "labels = (n_inputs) = (1797,)\n", - "X = (n_inputs, n_features) = (1797, 64)\n" + "ename": "ModuleNotFoundError", + "evalue": "No module named 'matplotlib'", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mModuleNotFoundError\u001b[0m Traceback (most recent call last)", + "Cell \u001b[0;32mIn[1], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \u001b[43mget_ipython\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mrun_line_magic\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mmatplotlib\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43minline\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m)\u001b[49m\n\u001b[1;32m 3\u001b[0m \u001b[38;5;66;03m# import necessary packages\u001b[39;00m\n\u001b[1;32m 4\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mnumpy\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m \u001b[38;5;21;01mnp\u001b[39;00m\n", + "File \u001b[0;32m~/miniforge3/lib/python3.9/site-packages/IPython/core/interactiveshell.py:2432\u001b[0m, in \u001b[0;36mInteractiveShell.run_line_magic\u001b[0;34m(self, magic_name, line, _stack_depth)\u001b[0m\n\u001b[1;32m 2430\u001b[0m kwargs[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mlocal_ns\u001b[39m\u001b[38;5;124m'\u001b[39m] \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mget_local_scope(stack_depth)\n\u001b[1;32m 2431\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mbuiltin_trap:\n\u001b[0;32m-> 2432\u001b[0m result \u001b[38;5;241m=\u001b[39m \u001b[43mfn\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 2434\u001b[0m \u001b[38;5;66;03m# The code below prevents the output from being displayed\u001b[39;00m\n\u001b[1;32m 2435\u001b[0m \u001b[38;5;66;03m# when using magics with decorator @output_can_be_silenced\u001b[39;00m\n\u001b[1;32m 2436\u001b[0m \u001b[38;5;66;03m# when the last Python token in the expression is a ';'.\u001b[39;00m\n\u001b[1;32m 2437\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mgetattr\u001b[39m(fn, magic\u001b[38;5;241m.\u001b[39mMAGIC_OUTPUT_CAN_BE_SILENCED, \u001b[38;5;28;01mFalse\u001b[39;00m):\n", + "File \u001b[0;32m~/miniforge3/lib/python3.9/site-packages/IPython/core/magics/pylab.py:99\u001b[0m, in \u001b[0;36mPylabMagics.matplotlib\u001b[0;34m(self, line)\u001b[0m\n\u001b[1;32m 97\u001b[0m \u001b[38;5;28mprint\u001b[39m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mAvailable matplotlib backends: \u001b[39m\u001b[38;5;132;01m%s\u001b[39;00m\u001b[38;5;124m\"\u001b[39m \u001b[38;5;241m%\u001b[39m backends_list)\n\u001b[1;32m 98\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m---> 99\u001b[0m gui, backend \u001b[38;5;241m=\u001b[39m 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3606\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mmatplotlib_inline\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mbackend_inline\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m configure_inline_support\n\u001b[1;32m 3608\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mIPython\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mcore\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m pylabtools \u001b[38;5;28;01mas\u001b[39;00m pt\n\u001b[1;32m 3609\u001b[0m gui, backend \u001b[38;5;241m=\u001b[39m pt\u001b[38;5;241m.\u001b[39mfind_gui_and_backend(gui, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mpylab_gui_select)\n", + "File \u001b[0;32m~/miniforge3/lib/python3.9/site-packages/matplotlib_inline/__init__.py:1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01m.\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m backend_inline, config \u001b[38;5;66;03m# noqa\u001b[39;00m\n\u001b[1;32m 2\u001b[0m __version__ \u001b[38;5;241m=\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m0.1.6\u001b[39m\u001b[38;5;124m\"\u001b[39m \u001b[38;5;66;03m# noqa\u001b[39;00m\n", + "File \u001b[0;32m~/miniforge3/lib/python3.9/site-packages/matplotlib_inline/backend_inline.py:6\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[38;5;124;03m\"\"\"A matplotlib backend for publishing figures via display_data\"\"\"\u001b[39;00m\n\u001b[1;32m 3\u001b[0m \u001b[38;5;66;03m# Copyright (c) IPython Development Team.\u001b[39;00m\n\u001b[1;32m 4\u001b[0m \u001b[38;5;66;03m# Distributed under the terms of the BSD 3-Clause License.\u001b[39;00m\n\u001b[0;32m----> 6\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mmatplotlib\u001b[39;00m\n\u001b[1;32m 7\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mmatplotlib\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m colors\n\u001b[1;32m 8\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mmatplotlib\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mbackends\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m backend_agg\n", + "\u001b[0;31mModuleNotFoundError\u001b[0m: No module named 'matplotlib'" ] - }, - { - "data": { - "image/png": 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              " - ] - }, - "metadata": { - "filenames": { - "image/png": "/Users/mhjensen/Teaching/MachineLearning/doc/LectureNotes/_build/jupyter_execute/chapter10_39_1.png" - } - }, - "output_type": "display_data" } ], "source": [ @@ -635,16 +628,7 @@ "collapsed": false, "editable": true }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Number of training images: 1437\n", - "Number of test images: 360\n" - ] - } - ], + "outputs": [], "source": [ "from sklearn.model_selection import train_test_split\n", "\n", @@ -858,24 +842,7 @@ "collapsed": false, "editable": true }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "probabilities = (n_inputs, n_categories) = (1437, 10)\n", - "probability that image 0 is in category 0,1,2,...,9 = \n", - "[5.41511965e-04 2.17174962e-03 8.84355903e-03 1.44970586e-03\n", - " 1.10378326e-04 5.08318298e-09 2.03256632e-04 1.92507116e-03\n", - " 9.84443254e-01 3.11507992e-04]\n", - "probabilities sum up to: 1.0\n", - "\n", - "predictions = (n_inputs) = (1437,)\n", - "prediction for image 0: 8\n", - "correct label for image 0: 6\n" - ] - } - ], + "outputs": [], "source": [ "# setup the feed-forward pass, subscript h = hidden layer\n", "\n", @@ -1065,30 +1032,7 @@ "collapsed": false, "editable": true }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Old accuracy on training data: 0.1440501043841336\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_7604/953065564.py:4: RuntimeWarning: overflow encountered in exp\n", - " return 1/(1 + np.exp(-x))\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "New accuracy on training data: 0.09951287404314545\n" - ] - } - ], + "outputs": [], "source": [ "# to categorical turns our integer vector into a onehot representation\n", "from sklearn.metrics import accuracy_score\n", @@ -1321,15 +1265,7 @@ "collapsed": false, "editable": true }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Accuracy score on test set: 0.9444444444444444\n" - ] - } - ], + "outputs": [], "source": [ "epochs = 100\n", "batch_size = 100\n", @@ -1370,718 +1306,7 @@ "collapsed": false, "editable": true }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Learning rate = 1e-05\n", - "Lambda = 1e-05\n", - "Accuracy score on test set: 0.11666666666666667\n", - "\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Learning rate = 1e-05\n", - "Lambda = 0.0001\n", - "Accuracy score on test set: 0.20833333333333334\n", - "\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Learning rate = 1e-05\n", - "Lambda = 0.001\n", - "Accuracy score on test set: 0.12222222222222222\n", - "\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Learning rate = 1e-05\n", - "Lambda = 0.01\n", - "Accuracy score on test set: 0.14722222222222223\n", - "\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Learning rate = 1e-05\n", - "Lambda = 0.1\n", - "Accuracy score on test set: 0.17777777777777778\n", - "\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Learning rate = 1e-05\n", - "Lambda = 1.0\n", - "Accuracy score on test set: 0.16111111111111112\n", - "\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Learning rate = 1e-05\n", - "Lambda = 10.0\n", - "Accuracy score on test set: 0.20277777777777778\n", - "\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Learning rate = 0.0001\n", - "Lambda = 1e-05\n", - "Accuracy score on test set: 0.5305555555555556\n", - "\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Learning rate = 0.0001\n", - "Lambda = 0.0001\n", - "Accuracy score on test set: 0.5944444444444444\n", - "\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Learning rate = 0.0001\n", - "Lambda = 0.001\n", - "Accuracy score on test set: 0.5888888888888889\n", - "\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Learning rate = 0.0001\n", - "Lambda = 0.01\n", - "Accuracy score on test set: 0.6111111111111112\n", - "\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Learning rate = 0.0001\n", - "Lambda = 0.1\n", - "Accuracy score on test set: 0.5222222222222223\n", - "\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Learning rate = 0.0001\n", - "Lambda = 1.0\n", - "Accuracy score on test set: 0.5555555555555556\n", - "\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Learning rate = 0.0001\n", - "Lambda = 10.0\n", - "Accuracy score on test set: 0.8055555555555556\n", - "\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Learning rate = 0.001\n", - "Lambda = 1e-05\n", - "Accuracy score on test set: 0.85\n", - "\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Learning rate = 0.001\n", - "Lambda = 0.0001\n", - "Accuracy score on test set: 0.85\n", - "\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Learning rate = 0.001\n", - "Lambda = 0.001\n", - "Accuracy score on test set: 0.875\n", - "\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Learning rate = 0.001\n", - "Lambda = 0.01\n", - "Accuracy score on test set: 0.8666666666666667\n", - "\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Learning rate = 0.001\n", - "Lambda = 0.1\n", - "Accuracy score on test set: 0.8638888888888889\n", - "\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Learning rate = 0.001\n", - "Lambda = 1.0\n", - "Accuracy score on test set: 0.9555555555555556\n", - "\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Learning rate = 0.001\n", - "Lambda = 10.0\n", - "Accuracy score on test set: 0.925\n", - "\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Learning rate = 0.01\n", - "Lambda = 1e-05\n", - "Accuracy score on test set: 0.9472222222222222\n", - "\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Learning rate = 0.01\n", - "Lambda = 0.0001\n", - "Accuracy score on test set: 0.9277777777777778\n", - "\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Learning rate = 0.01\n", - "Lambda = 0.001\n", - "Accuracy score on test set: 0.9472222222222222\n", - "\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Learning rate = 0.01\n", - "Lambda = 0.01\n", - "Accuracy score on test set: 0.9305555555555556\n", - "\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Learning rate = 0.01\n", - "Lambda = 0.1\n", - "Accuracy score on test set: 0.9555555555555556\n", - "\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Learning rate = 0.01\n", - "Lambda = 1.0\n", - "Accuracy score on test set: 0.7694444444444445\n", - "\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Learning rate = 0.01\n", - "Lambda = 10.0\n", - "Accuracy score on test set: 0.19166666666666668\n", - "\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_7604/953065564.py:4: RuntimeWarning: overflow encountered in exp\n", - " return 1/(1 + np.exp(-x))\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Learning rate = 0.1\n", - "Lambda = 1e-05\n", - "Accuracy score on test set: 0.10555555555555556\n", - "\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_7604/953065564.py:4: RuntimeWarning: overflow encountered in exp\n", - " return 1/(1 + np.exp(-x))\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Learning rate = 0.1\n", - "Lambda = 0.0001\n", - "Accuracy score on test set: 0.08611111111111111\n", - "\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_7604/953065564.py:4: RuntimeWarning: overflow encountered in exp\n", - " return 1/(1 + np.exp(-x))\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Learning rate = 0.1\n", - "Lambda = 0.001\n", - "Accuracy score on test set: 0.10555555555555556\n", - "\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_7604/953065564.py:4: RuntimeWarning: overflow encountered in exp\n", - " return 1/(1 + np.exp(-x))\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Learning rate = 0.1\n", - "Lambda = 0.01\n", - "Accuracy score on test set: 0.08888888888888889\n", - "\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_7604/953065564.py:4: RuntimeWarning: overflow encountered in exp\n", - " return 1/(1 + np.exp(-x))\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Learning rate = 0.1\n", - "Lambda = 0.1\n", - "Accuracy score on test set: 0.08611111111111111\n", - "\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_7604/953065564.py:4: RuntimeWarning: overflow encountered in exp\n", - " return 1/(1 + np.exp(-x))\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Learning rate = 0.1\n", - "Lambda = 1.0\n", - "Accuracy score on test set: 0.08888888888888889\n", - "\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_7604/953065564.py:4: RuntimeWarning: overflow encountered in exp\n", - " return 1/(1 + np.exp(-x))\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Learning rate = 0.1\n", - "Lambda = 10.0\n", - "Accuracy score on test set: 0.09166666666666666\n", - "\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_7604/953065564.py:4: RuntimeWarning: overflow encountered in exp\n", - " return 1/(1 + np.exp(-x))\n", - "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_7604/1630775253.py:43: RuntimeWarning: overflow encountered in exp\n", - " exp_term = np.exp(self.z_o)\n", - "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_7604/1630775253.py:44: RuntimeWarning: invalid value encountered in true_divide\n", - " self.probabilities = exp_term / np.sum(exp_term, axis=1, keepdims=True)\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Learning rate = 1.0\n", - "Lambda = 1e-05\n", - "Accuracy score on test set: 0.07777777777777778\n", - "\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_7604/953065564.py:4: RuntimeWarning: overflow encountered in exp\n", - " return 1/(1 + np.exp(-x))\n", - "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_7604/1630775253.py:43: RuntimeWarning: overflow encountered in exp\n", - " exp_term = np.exp(self.z_o)\n", - "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_7604/1630775253.py:44: RuntimeWarning: invalid value encountered in true_divide\n", - " self.probabilities = exp_term / np.sum(exp_term, axis=1, keepdims=True)\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Learning rate = 1.0\n", - "Lambda = 0.0001\n", - "Accuracy score on test set: 0.07777777777777778\n", - "\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_7604/953065564.py:4: RuntimeWarning: overflow encountered in exp\n", - " return 1/(1 + np.exp(-x))\n", - "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_7604/1630775253.py:43: RuntimeWarning: overflow encountered in exp\n", - " exp_term = np.exp(self.z_o)\n", - "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_7604/1630775253.py:44: RuntimeWarning: invalid value encountered in true_divide\n", - " self.probabilities = exp_term / np.sum(exp_term, axis=1, keepdims=True)\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Learning rate = 1.0\n", - "Lambda = 0.001\n", - "Accuracy score on test set: 0.07777777777777778\n", - "\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_7604/953065564.py:4: RuntimeWarning: overflow encountered in exp\n", - " return 1/(1 + np.exp(-x))\n", - "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_7604/1630775253.py:43: RuntimeWarning: overflow encountered in exp\n", - " exp_term = np.exp(self.z_o)\n", - "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_7604/1630775253.py:44: RuntimeWarning: invalid value encountered in true_divide\n", - " self.probabilities = exp_term / np.sum(exp_term, axis=1, keepdims=True)\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Learning rate = 1.0\n", - "Lambda = 0.01\n", - "Accuracy score on test set: 0.07777777777777778\n", - "\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_7604/953065564.py:4: RuntimeWarning: overflow encountered in exp\n", - " return 1/(1 + np.exp(-x))\n", - "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_7604/1630775253.py:43: RuntimeWarning: overflow encountered in exp\n", - " exp_term = np.exp(self.z_o)\n", - "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_7604/1630775253.py:44: RuntimeWarning: invalid value encountered in true_divide\n", - " self.probabilities = exp_term / np.sum(exp_term, axis=1, keepdims=True)\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Learning rate = 1.0\n", - "Lambda = 0.1\n", - "Accuracy score on test set: 0.07777777777777778\n", - "\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_7604/953065564.py:4: RuntimeWarning: overflow encountered in exp\n", - " return 1/(1 + np.exp(-x))\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Learning rate = 1.0\n", - "Lambda = 1.0\n", - "Accuracy score on test set: 0.10555555555555556\n", - "\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_7604/953065564.py:4: RuntimeWarning: overflow encountered in exp\n", - " return 1/(1 + np.exp(-x))\n", - "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_7604/1630775253.py:43: RuntimeWarning: overflow encountered in exp\n", - " exp_term = np.exp(self.z_o)\n", - "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_7604/1630775253.py:44: RuntimeWarning: invalid value encountered in true_divide\n", - " self.probabilities = exp_term / np.sum(exp_term, axis=1, keepdims=True)\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Learning rate = 1.0\n", - "Lambda = 10.0\n", - "Accuracy score on test set: 0.07777777777777778\n", - "\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_7604/953065564.py:4: RuntimeWarning: overflow encountered in exp\n", - " return 1/(1 + np.exp(-x))\n", - "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_7604/1630775253.py:43: RuntimeWarning: overflow encountered in exp\n", - " exp_term = np.exp(self.z_o)\n", - "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_7604/1630775253.py:44: RuntimeWarning: invalid value encountered in true_divide\n", - " self.probabilities = exp_term / np.sum(exp_term, axis=1, keepdims=True)\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Learning rate = 10.0\n", - "Lambda = 1e-05\n", - "Accuracy score on test set: 0.07777777777777778\n", - "\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_7604/953065564.py:4: RuntimeWarning: overflow encountered in exp\n", - " return 1/(1 + np.exp(-x))\n", - "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_7604/1630775253.py:43: RuntimeWarning: overflow encountered in exp\n", - " exp_term = np.exp(self.z_o)\n", - "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_7604/1630775253.py:44: RuntimeWarning: invalid value encountered in true_divide\n", - " self.probabilities = exp_term / np.sum(exp_term, axis=1, keepdims=True)\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Learning rate = 10.0\n", - "Lambda = 0.0001\n", - "Accuracy score on test set: 0.07777777777777778\n", - "\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_7604/953065564.py:4: RuntimeWarning: overflow encountered in exp\n", - " return 1/(1 + np.exp(-x))\n", - "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_7604/1630775253.py:43: RuntimeWarning: overflow encountered in exp\n", - " exp_term = np.exp(self.z_o)\n", - "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_7604/1630775253.py:44: RuntimeWarning: invalid value encountered in true_divide\n", - " self.probabilities = exp_term / np.sum(exp_term, axis=1, keepdims=True)\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Learning rate = 10.0\n", - "Lambda = 0.001\n", - "Accuracy score on test set: 0.07777777777777778\n", - "\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_7604/953065564.py:4: RuntimeWarning: overflow encountered in exp\n", - " return 1/(1 + np.exp(-x))\n", - "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_7604/1630775253.py:43: RuntimeWarning: overflow encountered in exp\n", - " exp_term = np.exp(self.z_o)\n", - "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_7604/1630775253.py:44: RuntimeWarning: invalid value encountered in true_divide\n", - " self.probabilities = exp_term / np.sum(exp_term, axis=1, keepdims=True)\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Learning rate = 10.0\n", - "Lambda = 0.01\n", - "Accuracy score on test set: 0.07777777777777778\n", - "\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_7604/953065564.py:4: RuntimeWarning: overflow encountered in exp\n", - " return 1/(1 + np.exp(-x))\n", - "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_7604/1630775253.py:43: RuntimeWarning: overflow encountered in exp\n", - " exp_term = np.exp(self.z_o)\n", - "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_7604/1630775253.py:44: RuntimeWarning: invalid value encountered in true_divide\n", - " self.probabilities = exp_term / np.sum(exp_term, axis=1, keepdims=True)\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Learning rate = 10.0\n", - "Lambda = 0.1\n", - "Accuracy score on test set: 0.07777777777777778\n", - "\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_7604/953065564.py:4: RuntimeWarning: overflow encountered in exp\n", - " return 1/(1 + np.exp(-x))\n", - "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_7604/1630775253.py:43: RuntimeWarning: overflow encountered in exp\n", - " exp_term = np.exp(self.z_o)\n", - "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_7604/1630775253.py:44: RuntimeWarning: invalid value encountered in true_divide\n", - " self.probabilities = exp_term / np.sum(exp_term, axis=1, keepdims=True)\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Learning rate = 10.0\n", - "Lambda = 1.0\n", - "Accuracy score on test set: 0.07777777777777778\n", - "\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_7604/953065564.py:4: RuntimeWarning: overflow encountered in exp\n", - " return 1/(1 + np.exp(-x))\n", - "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_7604/1630775253.py:43: RuntimeWarning: overflow encountered in exp\n", - " exp_term = np.exp(self.z_o)\n", - "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_7604/1630775253.py:44: RuntimeWarning: invalid value encountered in true_divide\n", - " self.probabilities = exp_term / np.sum(exp_term, axis=1, keepdims=True)\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Learning rate = 10.0\n", - "Lambda = 10.0\n", - "Accuracy score on test set: 0.07777777777777778\n", - "\n" - ] - } - ], + "outputs": [], "source": [ "eta_vals = np.logspace(-5, 1, 7)\n", "lmbd_vals = np.logspace(-5, 1, 7)\n", @@ -2123,52 +1348,7 @@ "collapsed": false, "editable": true }, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_7604/953065564.py:4: RuntimeWarning: overflow encountered in exp\n", - " return 1/(1 + np.exp(-x))\n", - "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_7604/953065564.py:4: RuntimeWarning: overflow encountered in exp\n", - " return 1/(1 + np.exp(-x))\n", - "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_7604/953065564.py:4: RuntimeWarning: overflow encountered in exp\n", - " return 1/(1 + np.exp(-x))\n", - "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_7604/953065564.py:4: RuntimeWarning: overflow encountered in exp\n", - " return 1/(1 + np.exp(-x))\n", - "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_7604/953065564.py:4: RuntimeWarning: overflow encountered in exp\n", - " return 1/(1 + np.exp(-x))\n" - ] - }, - { - "data": { - "image/png": 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pktqGV4Yxcegct2vDDVoF88SQTgRVCbSfI6at5edxy5xat3KtMoyeP4inW31E1InzDvOmbR5K4eIB6dZ5rME7XHC3Om5RhScGtCeoUnGiL1xm0ayN/Pz9CqfWrRxSitEznufpjqOIOnXRYV7TttXp/lwbylQoyoWzsSz/dRs/f7+ClGRrNhxF7tXgjmo8MehugiqXIPr8ZRbNWMfP4/92at3KNUozenZ/nm43gqiTFxzmlalYjKdfu4fajSuRkmxl16YjfD98IafDz2eyNfcS2rQyvfpd+f6L4/c5m5n1w6pMl/f09KDL481of28dipUowJnIGP7+cwezflhNSop7tdlbkVtHwWrbtu115y9fvjzD6adPnwbsQcS/FS9enIiIiHTLFytWjDNnzmC1WvHw8AAgNjaW6Ohozp07l275zCgAcYHqjSrx/rQXWTlvE1M+nk/NxlXo9faDmMwmZn6+6LrrVqhRhqEzB+Dplf5P1fKBBrz1w3P8/sMKpnw8n0LFAnj8jQcYseAVXmz9IcmJKdl1SLlKSO0yvD+qOyuW7mLK+L+oUSeIJ59vg9lkYkYmJ+HnXu5A+3vqMG3iSvbvOUXlaiV5/NlWFA8syOhhvzosmz/Ah/dHdSewVEEXHE3uVL1hRd6f+gIr529myscLqNm4Mr3eehCT2czM0U604en98fTySDev5f0NeGtSX36fvIIpHy+wt+HX72fEvJd5se0wt2nD1RuU572Jz7Dyt238OHIRNRpWpNeQTphNJmaOTd8F/m8Vqpfigx+ezbB+Cxb1p3DxAL79YB5hW486zIu5cDkrDyHXq143iPfGPc7KP3by41dLqVG/PL1eao/ZbGLmt/+77roVqgXywfheGdZxvaaVefvLHqz8Yyc/fL6Y8lVL8OTAuyhQKB/ffLQwm44m96lerxzvjX+SlYv+4cfRi6nRoDy9Bnewt+Fv/rruuhWCS/LBhKczrN+iJQswatYLnDhyhhGDpmPx8aLXoLv5aHIfnu80iiQ3OUdkJqR2Wd4f3Z0VS3Yz5eu/qFE3iCf7pX7/TVqZ4TrPvdKB9vfWZdqEFezffZLKwaV4vO+d9u+/Dxe4+Agkp115eNxisThM9/b2Jjo6Ot3y99xzD+PHj+fjjz9m8ODBWK1WPvjgA0wmU4bPjGRGAYgL9Hz1Pg7vDGfk85MA2LJ8Nx5eHjzyUkfmfr2UpITkdOt4enlw/7NteOKNBzKcD/DYK/eycckOxrw8NW1a+IHTfLX8LRrfXZvVv27NngPKZXo804rD+08z8r35AGxedwhPTw8e6dWcOdPXpfuCyh/gwz2dQ5k4Zhmzp64FYPumIwD0eak9k8YuI/piHABN76jG8690wNfX8T+mu+k55D4O7wpnZL/UNvzXlTbcgbnfXKcN92nDE68/QFJCxielx16+h41LdzLmlWlp08IPRvDV0rdofFdtVi90kzY8sAOH95zks4H2etiyIgxPLw8e7teWud//j6TETOr3yZY8/kqnTM8RlWqUAWDtnzvS3VV2Nz36teHw3gg+e/0XALasPoCnp5mHn2nF3MmrM7yQ9fTy4P4eTXm8f7tML3TbP9SAMxHRjHztZ2w2g23rDlKwsD8P9WrOdyN+x5pBj2pe1GNAew7vPcVnr8wEYMvKfXh6evBw39bMnbQy8/p9ojmPD7w70zb8+IC7iL+cyJtPfEdi6jKR4ed579snqVKrLLs3H8m+g7oN9Hj2Tg7vO83Id+cCsHndQfv335MtmDNtbQbff77c0yWUiV8tY/ZPa4B/ff8NvItJY5amff/J7SWzHo4b8fHxAezPglz5N0BiYiK+vr7pli9Xrhxjxozh3XffZdq0afj4+PD4449Ts2ZN/P39nd6vngHJZl4WT2o1r8qa3xwvpFb/ugW//D7UbFolw/Uatq9Fj1fvY+bni5j0wZx0800mE1v/t4dFUxzvcJw4GAlAyfLFs+gIcjcvLw9qNyjP6r/3Okxf9dce/PJ5U7NuULp1/Px9+H3uZtav2ucw/eRxe9dhYOlCAOTz9+adTx9hx5ajvDlgarrtuIurbXibw/TVC7fg5+9DzSaZtOF2tegx5F5mjl7EpKFz0803mUxsXbGHRT9e24ajAChZvlgWHUHu5mXxoHaTyqz5c4fD9NW/b7fXb6OKGa7XsHUIPQZ2YNaYpUwanvGd9oo1SnMpOs7tgw8vLw9qN6rImmW7HaavXrLLfp5oUD7D9RreUZUe/dow67v/MWlUxumGXhYPEuKTsP1rGJqYi5fxsnjim887y44hN/OyeFC7cSXWLNnlMH31nzvsbbhhJm24VTA9+rdn1tfLmTQy457UZnfXYvEvm9KCD4ADu07Qs/kwtw8+Mv3+W77b3q7rlUu3jp+/N7/P2cz6lWEO008ePwtAYJlC2VfgPMKKKVf+3KorqVdRUVEO06OioggMDMxwnVatWrFixQpWrVrF+vXrefnllwkPD6d8+fJO71cBSDYLLF8Ui7cXJw9FOkw/ddj+hy5dqUSG6+3fdpRedV5n5ueLMryDZhgG37/zC+v/cHweofl99QA4tvdkVhQ/1wssXQiLxTMteLjiVGpucJmgIunWiTx1kbEjFnHi2DXPIrQOJjnZmratxIRknn30az77YAExbnxHKLBcZm34DHCDNlzvDWaOXoQ1g7xiwzD4/t3Z6dvwvaltOOxUVhQ/1wsMKoqXtycnU+vzilPH7BcEpStmHIjt/+c4vZoNZebYpZneZa8UUprY6Dje/vYpZu8azty9I3h97BMUyuCZkLwssGxhvCyenDx6zXki9f966fJFM1xv/86T9Go/kpnf/g+rNeM6Xjh9PaXKFaFL75bky+9DcO2yPPh4czauCCM2+ubGxb9dBZYtYq/fI9e24dT6rZBZ/YbTq9XHzPzmrwzbcIkyhfAP8CXy5Hn6vf8Qsza9z4LdH/P+d09RrGTBLD+O203a99+xm/z+++T39N9/bUJITk5Jty3J+4KDg/H392fDhg1p02JiYtizZw+hoaHplt+yZQs9e/YkKSmJYsWK4ePjw8aNG7lw4QLNmjVzer85noKVkpLCkiVL2Lx5M6dOnSIpKQlfX18CAwMJDQ2lffv2eHrmeDFvmX8BPwDiLiU4TI+LtX/2y5++ewvgXMTFm95XqYrFeeaDrhzYfoxNy3bdeIU8wD+/vbsw7nKiw/S4OPtnPyfvQLZoXZ22neowf+YGYlP/VikptnQnaXd04zbsk24dgHOnL970vkpVLM4z73flwD/u04bzBdjPAVfq84q42NQ27J9J/Uamz829VsWQ0hQNLMifM9Yzb+IKgiqXoOfgjnz684u82PEzEuOdz9e9neVLO09cU8eX7cfv55/xeeJcVMwNt71j42FmT1zFM6905JlXOgJwcM9JRgyZ9V+KfFu52oavOQ9fvlEbvn79FihsT+foPaQT+3aEM2LQdAoU8eepVzoyYtpzPH/PKBLjM07dcgeZf/+ltmtnv//aVKdtp9rMn3H1+0/ch8VioWfPnnz22WcULlyY0qVLM3LkSAIDA2nfvj1Wq5Xz58+TP39+fHx8qFSpEgcOHODjjz/m6aefJjw8nFdffZVu3bpRtmxZp/ebo1f2x48fp0+fPkRGRhISEkLx4sUpUKAAiYmJ7N27lzlz5jBmzBgmTJhAqVKlcrKot8xktneLGUbGb4kxbFmTH1y2aiAfzx1McmIKw54cn+n+8hqT6Ur9Zjzf5kQ9tGhTndc+7MzOrUeZNNa5UYfcyY3bcNa0tbJVAvl4ziCSk5IZ9pT7tGFzav1m1oidacOZ+fyVGSQnJnNot71HdPfGwxzbf5pRc1+iXZeG/D51zS1v+3ZytY4znm/7D224//sP0v6h+kz/5i+2rz9EYJlC9HyxHcO+e4o3ek90SB3Kq8ymG7ThW6xfr9SH0i+ei2VYvx/TzgkRx84yenZ/2jzQgD9mrr+lbecFNzo3O/X91zaE14Z1YefWY0wac/0BL8TOZuTOUbD+iwEDBpCSksLbb79NQkICDRs2ZOLEiVgsFk6cOEHbtm0ZPnw4nTt3pmDBgnz33XcMHz6c++67j0KFCtGtWzeef/75m9pnjgYgH3zwAWXKlGH27Nnkz58/3fyYmBgGDRrE0KFDGT9+fA6U8L+7nNoFf21Px5U7Qpdj/nsXfe0W1Xjnx+eJj03gjc6jiUzN5XQHl6/chb/mTo+fn/3ztXfkrtX5sSY8M6A9O7Ye5f2XZ5LsZsNmOuNytD397NqejrQ2fCmL2vDk54iPTeSNrqOJPO4+PU+xqeeAa+8SX7krHxdz63ckrx35CmDP5iPERsdTIeT2vKlzK67c1U13nshnH1wi7tL1zxOZKVI8gA5dQ5n13Qp+GmO/ebFz0xH27zzJ+F9f4q7ODVg4Pe9fIMdeyqQNp9b3tb2nzrpyZ3/TijCHi+yw7ce5FB1Hperu04YzcvlKu/a/9vsvtV3f6PuvR1Oeeekudmw5yvuDZ+j7z415eHgwZMgQhgwZkm5emTJl2LfP8ZnZOnXqMHPmzP+0zxwNQLZs2cKsWbMyDD7A/iKUIUOG0KNHDxeXLOucOhKFNcVKqWvyuEtVtD8kfnxf+jGWb8adXRrx8rinOHkokrcf/pKzp9zrYdNTJ85jTbFRqkxhh+mlyto/H7smJ/nf+r3SkQcebcSKpbsY+d58nXwzceromdQ27DiwwZU2nSVteMyT9jb8yJecvYX0w9tZxLGzWFOslLzmOYRS5eyfjx84fUvbzRfgS/MOtQnbdpTjBxyf3/G0eBBz3n2G4Y04ft5ex+Ucc+JLpebIHz8UldFqN1SsZAHMZjN7th1zmH7sYCTRFy5TrnLGz0flNRHHzmVcv6mfjx+MzGi1G2/3+DmsVhtelvSXKp6eHiRmMDqcOzl14oL93Fzmmnq/8v13OPN23W9IJx7o1pgVS3Yx8t25+v4Tl8vRh9ADAgLSPXV/rVOnTjkMC3a7SU5MYefaAzS/t77D9Bb3N+DSxcvs23rro3g0bFeTId/0Zu/GQwzuMMLtgg+A5CQrO7cdo3nrYIfpLduEcCkmnn27M34Y/6kX2vLAo42YM20dH785Ryff60hOTGHnugM0v6eew/QW92VRGx73FHs3HWJwp0/dLviA1PrdcIjmHeo4TG9xT10uRcexb/vxW9puSlIKLwzrysP92jlMb3pXLXx8LexYf/CWy3y7SU5KYefmozRvV8Nheou7anIpOp59O8NvabtXApsa14yiVbp8UQoUysfpE+7xorzkpBR2bjpC87trOUxv0aG2vQ3/c2ttOCEuid2bj9D8rlp4Wa6+I6Ru08r45vNm9yb3HgUrOSnF/v3XprrD9JZta1z/++/FdjzQrTFzpq7l4zd+0fffTcrp0a6yehSsnJKjPSBdu3bljTfeYMCAATRu3JiSJUtisVhISkoiMjKSjRs38tlnn9G1a9ecLOZ/NmPU7wyfN4i3fujL4mlrCGlUia7972LSB3NISkjGL78PQdVKEXEkiuhzsU5t08vbk4Ff9iIuNoEZn/9OUDXHN1iePXXBbQKS6ZNW8sm4J3hreFcWL9xOSO2ydH28GRPHLCMpMQW/fBaCKhQj4sQFoi/GUbFqCR55ojn79pxk5bLdBNcs7bC940fOpD2cKnYzPv+d4XMG8dbEviyevoaQhhXp+uJdTBo6196G/X0IqlaSiKNnbq4Nf/FEahteRFBVx+H+zp664DYBycwxS/l4+vO8+c2TLJm1geoNytOlb2smDV9IUmIyfv7eBFUJJOLYWaKd7LlITEhm9vi/eGzg3Vw8c4ktK/ZSPrgUPQd1YMPy3WxfvT+bjyp3mfnt33w8sTdvju7OkrlbqF43iC69WzLp88Wp5wlvgioVJyL8PNFOvqQx+sJl5v+0lq5PtQRg29qDFC9VkB792hB56gJ/zt6UnYeUq8wct4yPf3yWN8f0ZMkvm6hevzxd+rRi0qeL7PXr701Q5RJEHD/ndBsG+OGzP/h02nMMnfA0cyasoGDR/PR+tRNh24+xfvnuG28gj5s+cSWffP0Eb414hMULthJSJ4iuTzRj4lfLrrbrCsWIOHE+9fsvkEd6NWff7pOsXLqb4JplHLZn//67tZREkZuRowFI//79MZvNjBgxgri49MOc5suXjx49evDSSy/lQOmyzj+rwhjWazyPv34/7/7Uj3MRF5nw3mzmjrM/8FW5dhCfLhzCqBd+YOmMtU5tM6RRZYqkDkM4fO7gdPOnjviVqSPc4y28/2w+yoev/czjz97JeyMf5dyZS0z4ailzpq0DoHK1koz89kk++2A+S3/7hxatq2M2m6gWUpovf3gm3faG9J3Mjq3H0k13Z/+s2sewJ8fz+Gv38+6Pz9vb8PtzmPt1ahuuE8SnC15h1Is/sHTmOqe2GdKoEkUCCwIwfM6gdPOnfrqQqZ+6SRtee4CP+v5Az8Edeff7pzkbeZGJH/3K3O//B0ClmmX59OcXGTV4Ostmb3R6u1NH/8mFs5e45/Hm3PdkCy5diGPR9LVMzeSdFnnZPxsO89FL0+n5YlveHdOTs5ExTPzsT+ZOXg1ApZBSfDqlD6PenM2y+c6/AHPCyD84ezqaTo82psuTLTh/5hJb1xxgypdLif0Pz+/cbv5Zf4iPXviJni/dxbvjn+Ts6WgmjviduRPt7/mpVKM0n057nlGvzmLZ3M1Obzds2zFe6zGeXi934K1xT5AYn8y6ZbuYMPy3/zR4QF7xz6YjfPjqLB7v25r3RnXnXFQME75cypzUl+xWDi7JyO+e4rP357F04XZatKmO2WymWo3SfDmlT7rtDXn2B3ZsOerioxB3ZDJywVAzycnJ7N27l8jISOLj4/Hx8SEwMJDg4OB0r4b/LzoUTv+fTbKOUanMjReS/8R0xD3e75JTTH5+OV2EvM9fdZztktz72YjsZiugNpzdFm/5IKeLkKlNx8vndBEy1DDoaE4X4abkihdseHl5Ubt27ZwuhoiIiIiIZDO9CV1ERERERFwmV/SAiIiIiIjkdnnxRYQ5QT0gIiIiIiLiMgpARERERETEZZSCJSIiIiLihNvxpX+5kXpARERERETEZRSAiIiIiIiIyygFS0RERETECVZD9+6zgmpRRERERERcRgGIiIiIiIi4jFKwREREREScYNO9+yyhWhQREREREZdRACIiIiIiIi6jFCwRERERESfoRYRZQz0gIiIiIiLiMgpARERERETEZZSCJSIiIiLiBL2IMGuoFkVERERExGUUgIiIiIiIiMsoBUtERERExAk2jYKVJdQDIiIiIiIiLqMAREREREREXEYpWCIiIiIiTrDq3n2WUC2KiIiIiIjLKAARERERERGXUQqWiIiIiIgT9CLCrKFaFBERERERl1EAIiIiIiIiLqMULBERERERJ9h07z5LqBZFRERERMRlFICIiIiIiIjLKAVLRERERMQJVsOU00XIE9QDIiIiIiIiLqMAREREREREXEYBiIiIiIiIuIyeARERERERcYJV9+6zhGpRRERERERcRgGIiIiIiIi4jFKwREREREScYDN07z4rqBZFRERERMRlFICIiIiIiIjLKAVLRERERMQJGgUra6gWRURERETEZRSAiIiIiIiIyygFS0RERETECVbDlNNFyBPUAyIiIiIiIi6jAERERERERFxGKVgiIiIiIk6w6d59lnCrACTsw+CcLkKeZi6UmNNFyPMMo0JOFyFP87RYc7oIeV7Kad+cLkKeZ/O15XQR8rSBzZfmdBFEbnsK40RERERExGXcqgdERERERORWWQ3du88KqkUREREREXEZBSAiIiIiIuIySsESEREREXGCDb2IMCuoB0RERERERFxGAYiIiIiIiLiMUrBERERERJygUbCyhmpRRERERERcRgGIiIiIiIi4jFKwREREREScYNW9+yyhWhQREREREZdRACIiIiIiIi6jFCwRERERESfYDL2IMCuoB0RERERERFxGAYiIiIiIiLiMUrBERERERJygUbCyhmpRRERERERcRgGIiIiIiIi4jFKwREREREScYDN07z4rqBZFRERERNyUzWbjq6++omXLltSpU4fevXtz7NixTJc/c+YMgwcPpnHjxjRu3JiXXnqJ06dP39Q+FYCIiIiIiLipr7/+mpkzZzJs2DBmzZqFyWSiT58+JCUlZbj8oEGDiIiI4IcffuCHH37g9OnT9OvX76b2qQBERERERMQJVky58udWJSUlMWnSJPr370+rVq0IDg5m9OjRREZGsnTp0nTLx8TEsGnTJvr06UNISAghISE8++yz7N69mwsXLji9XwUgIiIiIiJuKCwsjMuXL9OkSZO0aQEBAYSEhLBp06Z0y3t7e+Pn58f8+fOJjY0lNjaWBQsWUL58eQoUKOD0fvUQuoiIiIiIG7ry7EbJkiUdphcvXpyIiIh0y3t7e/PRRx8xdOhQQkNDMZlMFCtWjKlTp2I2O9+voQBERERERMQJuXUUrLZt2153/vLlyzOcHh8fD4DFYnGY7u3tTXR0dLrlDcNg37591KtXj2eeeQar1cro0aN54YUXmDFjBv7+/k6VVwGIiIiIiIgb8vHxAezPglz5N0BiYiK+vr7plv/999+ZPn06f//9d1qwMX78eFq3bs2cOXPo1auXU/tVACIiIiIichvLrIfjRq6kXkVFRREUFJQ2PSoqiuDg4HTLb9myhQoVKjj0dBQoUIAKFSpw9OhRp/ebO/uRRERERERymZwe7SqrR8EKDg7G39+fDRs2pE2LiYlhz549hIaGplu+ZMmSHDt2jMTExLRp8fHxnDhxgnLlyjm9XwUgIiIiIiJuyGKx0LNnTz777DOWL19OWFgYgwYNIjAwkPbt22O1Wjlz5gwJCQkAPPjggwAMHDiQsLCwtOUtFgudO3d2er8KQERERERE3NSAAQPo2rUrb7/9Nt27d8fDw4OJEydisViIiIigRYsWLFq0CLCPjjV9+nQMw6BXr1489dRTeHl5MWPGDAICApzep8kwDCO7Dii3KT9uVE4XIU8zF0q88ULynxjGrXezyo15Wqw5XYQ8L+V0+ocaJWvZfG05XYQ8bWDz9C9nk6w1qPqSnC5Cpj7afW9OFyFDb9X4LaeLcFPUAyIiIiIiIi6jAERERERERFxGw/CKiIiIiDjBmktfRHi7US2KiIiIiIjLKAARERERERGXUQqWiIiIiIgTbP/hpX9ylXpARERERETEZRSAiIiIiIiIyygFS0RERETECRoFK2uoFkVERERExGUUgIiIiIiIiMsoBUtERERExAk2Q6NgZQX1gIiIiIiIiMsoABEREREREZdRCpaIiIiIiBOsunefJRSAuEiroPK83Lg5VQoV4VxCPNN3/cPXWzdmunylgoVZ3uOpdNMPXThP2+k/pH3uFlKL3nXqExRQgJOXLjF113Z+2LEtW44hN7ujZEVert2KygWKcj4xjukHtvLNnnWZLl8xoAjL7u2bbvqh6HO0//3btM9dKtTimepNKJ+/EFHxscw7spOxu9aQYtiy5ThysztKVuTlOndQJbWOpx3Yxje7r1fHhVl+X8Z13O6379I+d6lYiz7VG6fV8dzDu9yyjluWqMigmndSOaAY5xPjmHF4C9+Grb3uOncGVubFGi2pVqA4FxPjWXwyjFE7/ybempxuWX9PCwvvepYxu1cy99iO7DqMXO2OoPK83KQ5VQoX4Xx8PNN2/cM3WzI/D1csVJjlPTM+D7ebevU83KFSFZ5r0JCKhQpzKTGJtSeOMWLNKs7Gx2XLceRWrcqU55UGLe3fc/FxTAv7h6//2ZDp8pUKFOavh59ON/3gxXO0nT3JYbk3GrWiScmypNhsbDh9gmEb/ib8UnS2HEdudnxrPJumXeRCeDI+BcyE3J2fel0CMJkyfi7BZjX4Z34Me5fFEnfeSoGSntTrWoDKLfJluHxSnI2fXzpFaLeCBLf1z85DETenAMQF6geW4vtOD/LbwX2M2rCG0JKleaVJC0wmE+O2ZHxyDilaDIBu82aRaLWmTU9ISUn7d88adRh2Zzu+2bKRVSeOUbdEIG81vxNfLy++vs6Xal5Tv2hpvrvjYX4/vodRO1YQWqwsL9e5E5PJxNe7M76ACylUAoDuy6aSaL1apwn/+veT1RryboP2LDq+l0+2LaeQtx8Da7ekWsHiPL9qTvYeVC5Tv2hpvm/Vld+P72XUPytpWLwMr9RphRkT425Qx92WTnOo40SHOg7lvdD2LDq2l+Hb/qKwtx8Da7UguFAxnls5N3sPKhepV6QM41s8yqLwPYze9T9CiwYxuGZrzJj4JmxNhuu0KVmFr5s/zPyjO/lsx99UDijKy7VaU9jbj8Eb5jssW8DLh/EtHqVMvoLZfzC5VP3AUnx/74P8fmAfo9avoWGp0rzStAVmk4lxm29wHp47i8SUq+fhf7fhTpWrMq7jfUzb+Q+j1q2hqJ8fgxo3Z9pDD3PfrKkk/ev8nZc1KF6KCe0789vhMD7bsoqGJcowJLQlZpOJsdvXZ7hOSJHiADz6+0wSUzI+D5fMl5859z3G4ejzDPj7N3w8PXmlQUumdniYu+ZOdvhb5HWnwxL48+MoKjXPR8MeBTm9N4GN0y5iGNDg4QIZrrN55kW2zYmhwSMFCKzuzeF1cSz77CwmM1Rq5hiEJFyy8ufHZ4g94x5tVnKWAhAXGNiwKXvORjF42R8ArDh+FC+zmefrN2LC9i0ZnkBDihYnPCaa9adOZLrd5xo04rcD+xixfhUAa08cp2KBQjxZq55bBSADarVk78VIXl63EICVEYfxMpt5LqQpE8M2Zly/BYsTHnuRDVHHM9ym2WRiQM0WrIo4zIur56VN33X+NEvufZYWgeVZffpothxPbvRSrRbsvRDJ4LVX69jTZOa5Gk2YkFkdFypxwzp+qVYLVkUc4YXV89Om7zwXwdL73KuO+4e0ZO/F0wzZuACAVZGH8TSbeTa4GZP2byDRlr5+36p7F4tPhPH6ZvvfZP2Zo3iYTDxRpRE+Hp5pF3FtS1Xlnbp34+fp5boDyoVeatSUvWejGLzUfh5eefwonmYzzzVoxIRtmZyHixUnPDqaDSczPw/3b9iEv44e5u3/LUubdujCeRY82pO25Svyx6EDWX8wudBL9Zux51wUg1YsAmDFCXv9Pl+7Md/v3Jxx/RYpTvili6yPCM90u4PrNyc2OYnHFv2c1qbDL0UzsX1nahctwabIk9lzQLnQ5pnRFKlgoe2gogAE1ffFlgLb50RT5/78eHqnTw0KW3aZyi3zEdqtIABl6vhy9nASuxddcghAjmyIY82E8yQnGC45ltuZRsHKGkpky2YWsweNS5dh8eGDDtMXHdqPv8VCo1KlM1wvpGgx9pyNuu62ey2cw/B1KxymJdlsWDw8/luhbyMWsweNiwexOHyfw/Q/jofh7+VNw2JlM1yveqES7LkQmel2i/rko6C3L8tPOv7dDsac5VxCHK1LV/nvhb9NWMweNC4RxJ/p6ngf/l7eNMqkjkOcreMTjhdoB2POcS4hjjalK//3wt8GLGYPGhcrx5ITjvX754m9qW04KN06IQVLEORfiJ8ObnKYPuXgJtr+MS7tQi2/lzfjmnVlw5lj9F41I/sOIpezmD1oXKYMfx5y/P/8x8H/dh42AavDjzFzl2NK25GLFwAIKlDwP5X7dmExe9CkZFn+PLrfYfqiI6n1G1gmw/VCChdn97kz19323eWr8PO+nQ69IjvPRtJoxjduFXxYkw1O7UqgYhM/h+kVm/mRnGAQsScx4/VSDCx+jhfMPgEeJFy6muKaGGtjyYgzlKrpwz3vFc/6wotkQD0g2axsgQJ4e3hy+OJ5h+lHoy8CUKFgIVaFH0u3Xkix4hw4f465XbpTo2hxYpISmb13N6M2riHFZj9xHLpwdZsFvH3oULEKXaqF8O22Tem2l1eV9S+It4cnR2Kuqd9L9guACgGFWX36SLr1QgqV4ED0WWbf9QQ1CgUSk5TAnMM7+HzHSlIMGzFJCSTbrJTJ59itHeDlQwGLD2XzZdzdnRel1fGla+o49modr8qwjotzIPosc+56ghqFSxCTlMDswzv5/J9r6tj/mjq2pNaxf8FsO6bcpGy+glg8PDkae85h+rHU+i3vX5jVkYcd5lUvGAjYU1W+a/4oTUuUJ9GawoJjuxixYxlJNnsKRUJKMh3/HM+R2POU9nOfNnutK+fhI5mdhwtlch4uaj8Pz+nanRrFihOTmMjssN18vt5+HjaAj1avSLfe3ZXsNyj2nz+b5ceSGwUFpNZvzAWH6UdTP1csUIhVJ4+mWy+kSHEOXjzLvPt6EFIk9XvuwC5GbV5NimGjrH8BCnj7EB4bzYfN2nFfxWB8Pb1Yfeoo76xZxqnLl1xxeLlCzOkUbClQoJRjT2aBkvbLuOhTyZSt55tuvdr3B7B9TjTlGvoRGOzN0U1xhG+Np/HjBdOW8fQ28eiYUhQs7UVMpPuktEnOUgCSzQpYvAGITUpymH459bO/xZJunaK+fhTzy4fNMPhk7UpOxV6iWZkgnqvfkJL58zNw6SKH5RsElmJOl+4A7Ig6zZSd7vMQeoDFB4DYZMe7P5dT7J/ze3qnW6eoTz6K+fpjMwxGbP+bU3ExNCtRnr4hTSiZL4BBa38lwZrC78f28njVBhyIPsPi8P0U8fHj3QbtSbFZ8fNM/3fLq67W8TVtOLXO/b0yaMM+fv+q4/9x6nI0zQJT69gvfR3vv3iWxSf2U9Tbj3dD7XXs6yYpQ/m9MqnflCv1m74NF/a23wX9utnDLDy+i4n711O7cCkG1LiDIj5+DFxvTxtMNmwciT2fbn13E+B9i+fhfPbz8Ii1Kzl16RLNygbRt0FDSvrnZ9CSRenWAShfoCBvNG/FzqhI/nc0fWCeF105R1y6tn5T23SG5whfP4r75cMwDIZvWsGp2BialyrHc3UaUSpffl763+8U9rVfUL/RsBXbz0TQ/+/fKOrrx6uhLZl5TzfunjuZ+JT0Ay7kRYmX7Tcer+3N8PK1J7IkxWWcOlXrnvyc3pPAoqFXe/KC2+aj7kNXb0h4eJkoWNo9zrdZwabkoSyhACSbXRmZIrOsSiODGZeSkuix4BcOX7xARKz9Ds+GUydIsloZ0qQFYzavd+j9CI+J5tF5syiRz59BjZrx68M9eeCXaW4xAouZ69evLYM5l5ITeXz5dA5fOkdEnL1+N0YdJ8mWwit17mTsrjUcijnH25v+IMmWwvDG9zCiyb3EpSTx3Z71+Hp6EZeSlG67eZU59fvOyKixArYMpl9KTqLn8hkcjrlaxxuiwkmyWnmlbqu0On5r458kWq180qQTn5ruIS4liW/3bMDX09NtLizMaecI5+vXy2xPs1x6MoyRO/8CYMOZY5gwMaR2G77ctUKBx7+k1XEmJwpbJufhnvN/4fCF9OfhV5q2YOwmx/MwQKVChfnpwa4kWVPo98evmZ6X8hpz2ghMN3GOSLI/13E4+jwRqT0ZG06fIMlmZUhoS8ZsX5fWzs/GX6bvsvlpWz8afYH5D/TkocohTA/7J6sPJ1fK7Px7hSmDa2JrssGCN08Td8HKHc8XpmBpLyL2JrLtl2g8fc/T4pnC2VRakRtTGJfNYpJS78RfcwcoX+odt0uJ6fM2E60prDlxPO1L74q/j9nTMEKKFHOYHhV3mQ2nTvDrgTB6LZxDSf/8PBpSK8uOITeLSU4A0t9hy5fa83EpKZP6jTyadmF8xd+pz3tUTx29KS4lmdc3LKL2L59x9+/f0XDOl3y1azWl8xXgYlJClh9LbnWlDV97Jz5f6udLyZnU8en0dfzXqSt1bM8zvlLHtX4exV2/fU/o7K/4amdqHSfGZ/mx5EZpbfia3rorbTg2OX1bu9L79HeE4zMNq04fAq6maIldTOp59tqejhueh8PTn4f/Omo/D1cv6ngeblK6LHO6dsdmGPSY9wsnYmKyrPy5XXTilfPwteeI1PpNTn/DJtGawppTx9KCjyv+Op5av4WLp/Wg/H3iiENos+1MBNGJCWmjaLkD73z2y7XkeMdAJDn+Ss9I+su5w2vjOHc0mbaDixJyd35K1fShwcMFaPJkIXb9dolzR93nRprkPjneA/L4449nOn71tX788cdsLk3WOx59kRSbjXIFCzpML5/6cOKBC+fSrVOxYCGali7Lgv1hDmkZPh72P9f5hHjyeXnRrkIltkee5lhqHjPA8ZhoohMTKOWfP8uPJTc6dumCvX7zF3KYXj7184GY9DnYFfIXpmmJcvx6dDex/+rJ8ElN+bmQYO85alOqMtFJCWw5e4ID0fbtFPH2o6RfALvPn86W48mNrtRx+Wvr2N/++WB0+jqumL8wTQLL8euRa+rYI7WOU4OLNqUrE50Uz5YzJ9PX8XUeYM9LjsemtmF/x/q98vlgBm34aGrvhsXsOOCEp9l+EZKQwXtA3Nmx1PNw+WseCr/y+eD5jM/DTcqU5dd915yHPe3n4QsJVwPk+6sGM7JdB45cvMCTC+Zw+nJs1h9ELnb8Umr9BhR0mF4+IPU8fCGDc0SBQjQtGcSCQ3szrN/zCfEci7mI1WbDO4OBVTzNZoehe/O6gEAvTGaIjnD8vx0dYa+DQmXTp1BdOmOfFxjsGBiWqmH/fOFEMkXKu086cVaxahSsLJHjPSBNmzZl06ZNnDt3jtKlS1/353aUaLWy8dQJOlR0HDWpU6WqRCcksD0y/YVsiXz+fHRnezpWruow/d4q1biUlMjOM5FYDYNPW99N33oNHZapXbwEhXx82Xv2+iOL5BVJNisbo45zd9lqDtM7BgUTnRTPP+dOpVsn0C8/wxp1pGNQsMP0e4Oqcyk5kZ2pwcVjVerxRv02Dss8FdwQq2Hjr2tGx8rLMq/jakQnxrP9XES6dUr45eejRh3S13G59HX8Zr22DstcqeNrRyDLq5JsVjadPc5dZRzrqkOZ6vY2fD59G9505jiXU5K4N6iGw/S2paqSbLOy/Zz7jA7kjCSrlY0nT6Q9HH5Fx8rXOQ/7+/NR6+uch6PsAfKd5Sowqn1Htkac4uHZM9wu+IDU77nT4XQo71hXnSpUJToxge1n0tdvoF9+Pm5xF50qXFO/FVPr9+xp4lKS2Rh5gg7lqzgE281LBZHPy8LG05kPj5zXeFpMlKzhzZH1cQ7pWIfXxmHJZ6Z4lfSBRKEy9qDk2hGyToelZmYUz/F70OLGcrz19evXDz8/P7766iu+/fZbypTJeLi+29mYzeuZ9sDDjLv7Xn7Zu4v6gaV4tl5DPlm3kkRrCv5eFqoULsKx6IucT4hnw6kTrDtxnHeat8LP04tDF87TpnwFnqxdn4/XrkhLJxi/bRP9Q5twMSGe1anvAHmpkf2dI7+E7crho3adcbvX8FObxxjb4iF+OfQP9YuVoU/1JozY/pe9fj0tVC5QlOOxFzmfGMeGqOOsizzGW/Xb4etp4XDMWVqXqkyvag0Zvm15WkrM5H2b+bFNd96p345lJw/QtER5+tVozje71xJ++WLOHrSLjd21lqltuzOuxYP8fHgHDYqW4dmQJozY9vc1dXyB84nx9jo+fYy3G7S1t+GYc7QuXZknq4UyfOtfxKSmsE0J28yPbbvxToN2LDtxgGaB5XihZjO+3r2W8NiLOXvQLvT1ntVMadWDr5p0ZvbRf6hfpAzPVGvKyB3LSbSl1m9AMXv9JsURZ03my10reLNue6KTElhyMoz6RcrQJ7gZUw5s5HxS3n/+62aN3byeqQ8+zLgO9/Lz3l00CCzFs/UbMmLN1fNw5cJFOH7lPHzSfh5+u2Ur/Lzs5+HW5SvwZJ36DF9jPw9bPDz4pM1dXE5KYtzmDVQqVMRhn6djL7lNQDJm23qmdXqEr9vcz8/7d9KgRCn61m7EJxtXXP2eK1iEY5fs9bv+dDjrTh3n7cat8fP04mD0edqUrchTNRrw8cb/paV+frppFTPveZTJd3fhu52bKOrrx+uNWrE16hRLj7vHTYorGjxcgIXvRbF05FmC2/pzOiyR7fNjaPJEQTy9zSTF2bgQnkxAoCe+BTwo19CX4lUtLP/iLA27FaRgGS+i9iey5ZdoyjX0pUTV9ANciLiKybjRk00u8swzz1CwYEE+++yzbNtH+XGjsm3bN3J3hcoMbNSMioUKERkby4+7tjNh+xYAmpQqw8yHHuWV5X8yO2w3APktFgY2bMZdFStT3C8fx2IuMumfrczcszNtmyagR4069KxVh/IFCnIxIYE/Dx9k1IbV6UYjcQVzoYzHIXeFu8pUZWCtO6gQUJjI+Ev8tH8LE8PsL2NsXDyIGe16MmTdQuYcsddffi9vXqrVkvZlqlLc159jly7ww75NzDq03WG795UL4YUazSnrX5CTl6OZemArP+7f7OrDS2PkYNfvXWWqMqh2S3sdx13ip/1bmfCvOp7ZvgevrPuNOYf/XcctuKvsv+o4bBMzDzk+NHpfuRBerNnsah3v38qU/VtcfnwAnpacewNw+1LVGFDjDirmL0Jk/CWmHtrMpP32N3Q3KlaOaXc+zmsbf2XusavvnOhSvg69qzamvH9hIhNimXV4K9+Frc3wUeDSfgX43z39023D1VJOpx8q1FXuqliZQY2bUSH1PPzTzu1M2GZva41Ll2Fm50d5ZemfzPnXefilRqnn4Xz5OBZ9kR/+2crM3fY23rRMWaY/9Eim+/tiw1q+3Lgu+w/sGjZf240XygZ3l6vCoAbNqVigEJGXY/lx7za+32k/XzYpWZZZ93Tj5RWLmH0gtX69LAys35y7y1emmK8/xy9dZOKuLczc59g+GxQvxZDQltQtXpL4lGSWHDvIRxuuBimuNrD50hzZL8CR9XFsmnGRiyeTyVfEk5od81PnwQAATu5MYOE7kdzZvwjBbf0BSIqzsXHqRQ6viyMx1kpACS+qts5H7fsD8PBK/30SE5nC9L4nHbaREwZVX5Jj+76Rl7Z1z+kiZOjLerfXu55yTQASGRnJnj17aN26dbbtIycDEHeQkwGIu8jJAMQd5GQA4i5yMgBxFzkVgLiLnAxA3IUCkJt3uwUgOZ6CdUWJEiUoUaJEThdDRERERESyUa4JQEREREREcjObkePjN+UJqkUREREREXEZBSAiIiIiIuIySsESEREREXGCFQ0GkxXUAyIiIiIiIi6jAERERERERFxGKVgiIiIiIk6w6X1cWUI9ICIiIiIi4jIKQERERERExGWUgiUiIiIi4gS9iDBrqBZFRERERMRlFICIiIiIiIjLKAVLRERERMQJNr2IMEuoB0RERERERFxGAYiIiIiIiLiMUrBERERERJxg1YsIs4R6QERERERExGUUgIiIiIiIiMsoBUtERERExAl6EWHWUC2KiIiIiIjLKAARERERERGXUQqWiIiIiIgTbBoFK0uoB0RERERERFxGAYiIiIiIiLiMUrBERERERJxgQylYWUE9ICIiIiIi4jIKQERERERExGWUgiUiIiIi4gSNgpU11AMiIiIiIiIuowBERERERERcRilYIiIiIiJOsBm6d58VVIsiIiIiIuIyCkBERERERMRllIIlIiIiIuIEjYKVNdQDIiIiIiIiLqMAREREREREXEYpWCIiIiIiTrChFKysoB4QERERERFxGQUgIiIiIiLiMkrBEhERERFxgkbByhrqAREREREREZdRACIiIiIiIi6jFCwREREREScoBStrqAdERERERERcRgGIiIiIiIi4jFKwREREREScoBSsrKEeEBERERERcRkFICIiIiIibspms/HVV1/RsmVL6tSpQ+/evTl27FiGy44ZM4Zq1apl+PPGG284vU+TYRhGVh1AbrfgcN2cLoLIf1LYHJvTRcjTkvHI6SLkeVZD972yW1tfa04XIU87Z7uc00XI84qVOpnTRcjUPSsH5HQRMvT7HV/d8rpjx45l+vTpDB8+nBIlSjBy5EjCw8P57bffsFgsDstevnyZuLg4h2mzZ89m/PjxzJo1i+DgYKf2qW8CERERERE3lJSUxKRJk+jfvz+tWrUiODiY0aNHExkZydKlS9Mtny9fPooVK5b2Ex8fz7fffsvrr7/udPABCkBERERERNxSWFgYly9fpkmTJmnTAgICCAkJYdOmTTdc/5NPPqFKlSo8+uijN7VfjYIlIiIiIuIEG7lzFKy2bdted/7y5csznH769GkASpYs6TC9ePHiREREXHebO3fuZPny5UyZMgWz+eb6NNQDIiIiIiLihuLj4wHSPevh7e1NYmLiddedPHkyderUceg9cZZ6QEREREREbmOZ9XDciI+PD2B/FuTKvwESExPx9fXNdL24uDiWLl3Ke++9d0v7VQAiIiIiIuKEvPYiwiupV1FRUQQFBaVNj4qKuu5D5atWrcJms9G+fftb2q9SsERERERE3FBwcDD+/v5s2LAhbVpMTAx79uwhNDQ00/W2bNlCjRo1CAgIuKX9qgdERERERMQNWSwWevbsyWeffUbhwoUpXbo0I0eOJDAwkPbt22O1Wjl//jz58+d3SNEKCwujatWqt7xf9YCIiIiIiDjBZphy5c9/MWDAALp27crbb79N9+7d8fDwYOLEiVgsFiIiImjRogWLFi1yWOfs2bMULFjwlvepN6GL3Eb0JvTspTehZz+9CT376U3o2UtvQs9+uflN6O3/Nyini5ChpXeOzuki3BR9E4iIiIiIiMvoGRARERERESfktVGwcop6QERERERExGUUgIiIiIiIiMsoBUtERERExAlKwcoa6gERERERERGXUQAiIiIiIiIuoxQsEREREREnGErByhLqAREREREREZdRACIiIiIiIi6jFCwRERERESfYUApWVlAPiIiIiIiIuIwCEBERERERcRmlYImIiIiIOEEvIswa6gERERERERGXUQAiIiIiIiIuoxQsEREREREn6EWEWUM9ICIiIiIi4jIKQERERERExGWUgiUiIiIi4gSNgpU11AMiIiIiIiIuowBERERERERcRilYIiIiIiJO0ChYWUM9ICIiIiIi4jIKQERERERExGWUgiUiIiIi4gSNgpU11AMiIiIiIiIuowBERERERERcRilYIiIiIiJOMIycLkHeoB4QERERERFxGQUgIiIiIiLiMkrBEhERERFxgg2NgpUV1AMiIiIiIiIuowBERERERERcRilYIiIiIiJOMPQiwiyhHhAREREREXEZBSAiIiIiIuIySsFykX2brfz5YzJRxw3yFTDRpJMHrR/xxGTKuCvPajVYOSeFjYutxJwzKFraROtHPKnbyvFPNvSxeGIvpF//nWk+5C/sPt2Eqt/st2sTzJtiJuI4+BeAO+8x6PioQSZVjNUKi2ebWP2niYvnoERp6PioQaM7Hd/itG0tLJxmJvIEBBSCpu0MOj1q4OnlgoPKRXZvgl+nQMRxyF8AWt4Ddz/Kdet32WxY8ydEn4Pipe3Lh95pn3/uNLzdK/P9NW0PT7yS5YeRq+3ZbLBwisHp1DbcopOJux7luueJ5bNh3WKD6HNQrDTc9aiJBq0clz8dbjB/gsHBnWD2gMq1oHMfE0VLutc5YtUG+HIiHDoKhQpCt/uhT4/M23BSEoydDAuXwIVoqBgET3WD+9o7Lnf4GHw2HjZuB09PCK0Nr70AZUtl7/HkRus3evD9RAtHj5kpWNDgwfuS6flY8nXreNIUC0uWenIx2kS5IBvdH0nmrvYpDsv9+psnP8/24lSEmRIlDB66P5mHu2S+XXdmUwpWllAA4gJH91iZ/EESde7woMMTHhzZbWPxlBQMG7TtnvFV1tKpKfz9cwrtHvOkfIiZnautTP8kGbPZRO2WHgBcumAQewHufdaLcsGOnVl+Adl+WLmG6jf7HdwNY94307CVwUO9DA7sNjFvsgmbDe59LOPXwv76k4lFs0zc18OgcojBltUmvhtuxuxhJbSlfZndW+DrofbtdultcPKoibk/mLh0EXq86D6vmz20G755Hxq0gvt72ev718lg2KDjYxmv89tPsHgWdOoBlUJg22qYONx+AVy/JQQUhiFfpF9vxULYsgKadcjGA8qFDu8x+PZ9g/p3wH29TBzaZfDbFAPDMNGhe8brLPrJYMnP0PExExVDYPsagx+GG5jNUK+l/SLkwhmDzwcblCgDvV4zkZwIv00xGPumwZvjweLtHhcr23bBC29Ch9bw0tOwZSd8MQFsBjz3eMbrDP4AVqyzBx1N6sPeA/D+KHsw8kRX+zIRUfDYi1ChLIx8BxIT7UHOM6/Agh/Ax9t1x5jTdu4y8/pbPrRtnUKfp5PYsdOD7yZasBnQq2dyhuu896EPa9d50P3RZBrUt3LggJmRn3tzMdrEI13t68xb4MmoL3zo0T2Jhg2S2LPXzLhvLCQkwBOZbFfkv1IA4gLLpqVQqqKJbkMsAFQL9cCWAn//ksIdnT3xyuALatOSFOrd6UH7HvYL6Cr1PDh1KIG1v6WkXSCfOmQDoFYzM4VKuG82neo3+y2cZqZsRXjmVXtQULOhgTUF/vjZxF1dDCwZXASsXmyicWuD+3va1wmpbxB+yODvX82EtrTX7ZolJgoXt2/X7AE1GhjEXIRl80w8+pyBp5ucoX6fBmUqwlOv2j/XaAi2FFj8M7TtQob1u24xNGwN9/a0f65eH8IPwYpf7QGIlwUqVndc59h+e/DxwJNQuWa2HlKus2iqQZmK0OtV+//lkFATVquNpT8btOmccaCwbom9R6lTT/u84PomThyysXKhkRaA/P6TgY8v9B9uwuJjn1YkEL593+D4Afep53GTIbgyfPq2/XPLxpCSAt9PgycfSR8o7NkPy1ebGPiMQd/UAKVZKPj6wKhv4cG7ISA/jJ0E+fxg0uf2eQBlSkK/N2FXGITWcdkh5rgfplioUtnGO28mAtCkkZWUFJg63UK3h5PxvqaO9x8ws2q1J88+nZgWSDRsYMXHx+Cbb73p2CEZ/3wwdYaFNncm8/yzSQCENrASfsLMnHleCkAk27j3VZULpCQZHNpho2ZzD4fptVp4kBQPR3bZMlzPmgzefo7T/AqYiIu5elf41GEbvv649cWx6jf7JSfBvh1Qv4Vjj0SDlgaJ8SYO7Mp4vZRk8L2mjv0DIPbSv5cx4e1tv2t/Rf4A+/SEuCw6gFwuOQkO7IB6LRyn12sJifFw8Dr165NB/V6+lPHyhgEzxkBgWWjb+b+X+3aSnGRPj6rT3DHIqNfCRGI8HLqJOs4XAJdj7P82DIN/1kDTu68GHwDlqpr4eLqZyjXdo/cjKcmeHtX+Dsfpd7eCuHgTW3akX+fwMfvv1s0cpzesa19nwzZ7m126Crp0uhp8ANQMhpVz3Sv4SEqCbf94cEdLx9Sp1q1SiI838c8Oj3TrHD1m/+5q3szqML1uHRvxCSa2brOvM2pEPP36Jjks4+kJScnu0X5vlmHkzp/bjXtfWbnAudP2O8VFSztWdZFS9v/YZ05m3GpaPuTJluVW9m22knDZYOtfKezfbKN+26snmVOHDHz9Tfz4YSLvdonn7YfimTY8iZjzt2FLvEWq3+x35rQ9IChR2vG4i6fmX0eeyPhLqn1ng7XLTOzaBPGXYf1fJnZthqZtr26nzf02Ik/Bn7+YiIuFQ3th2XwTtRoZ+LtJmtvZ0/YL3eKlHacXS63fqBMZr9e2M2xYZn92JP4ybPwL9myGRm0zXn7T3/YekEeedwz43MG5G9XxyYzXa9PZxMbl9mdH4i8bbPrLYO9maNTW3ubPRdrrvkgJmDXWxqsP2xh4n43x79k4H+U+54nwU5CcbKJcWcfpQWXsv4+Gp1+nUEH775On028L4GSEfd6lWBOlA2HoaGhyH9RpD8+/Dqcis/QQcr1TESaSk00ElXG8qVa6tP1z+In0l3OFCtrbYMRpx3P0qVP2zxERZkwmKF/OIDDQwDAgJgYW/u7J4iWedH5QvR+SfXI8weHIkSP89ttvREdH07JlS1q1auUwPzY2lo8++ojhw4fnUAn/m4TL9hPAtXfRrtx9T4zL+Euq+f2eHNltY+I7V+9KNLzLgzu7Xn2m4dRhG9FnDRp18KDlQ55EHjdYMjWZ8a/aGDjW2+GOXF6l+s1+8bH239f2Zlyp8/hMeiraPmBwYJeJL96+erXb4m4bHR6++jepVgc6PGwwe4KZ2RPs04IqG/R5PeOeq7zoSv1e24ZvVL93PmDvHRn79tVpze6Gux7OePllc6BSDajqRneNr4jLpI6vnCcy621rdb+9d+Trt6+22aZ3QbuH7f/3Y6Pt0+ZPMihfDZ563f780q8/GHz1msEb34C3G5wnYlLr1//a3iJf++/Yy+nXaVgXypYy+Ogr8PGBWsEQdhBGjQez2SAuAc5ftC876luoXR1GvQvnLsDo7+DJgTB/Evj5ZtNB5TKxsfZ25JfPcbpfap1fzqCO69axUqqUjS/HeOPjnUj1YCsHD5n55jtvzGaDhATH5XfuMtNvgH2D1apa6fqQAhDJPjkagGzZsoWnn36aEiVKYBgG06ZNo127dowaNQqLxZ7Pn5CQwPz582/bAMS4wXWUKYM+qJQkg2+GJHLpgkHn/l4UK2Pi6G4bf81KweKbxAPP2evmkcFeeHqZKF3ZvpEKNSGwnImvX0liyzIrTe/N8fgy26l+s58t9dors9FQMqrj5CQY8bKZ6Avw+AAbgWXtwciimSa8faH78/aN/vSViTVLTNz7mI3gugZnT5v49ScTX7xl5uVPbHj7pN92XmPcoH7NmdTvqJch5gI8NgBKlLVfKP8xE7x97b0c/3ZoN4QfhOfey9qy3y5uVMcZTU9OMvjiFYOYC9Ctv8lex7th8UwDb18bXZ83Y029PgsoBM+8Y8Jstm+oWCkYNchg03JocU82HFAucytt2OIF34+Et0ZA78Gp9VbE4K0B9ofT/XwgObV+ixaCrz68up2g0tC9n4mFSw0evT+LDyaXsqV+12UWzmZ0Hvbygs8/jWf4pz4MfMUeqRUpYmNg/0TeG+qDzzXn11IlDcaMjuPMWTOTJlt4+jlfJnwTT+HC7tOb5wy9iDBr5OgV1KhRo+jatStvv22/hffHH3/w1ltv8dxzz/Htt9/i5XX7j8Pp429vqInX3GG78tnHL31D3rnGSsQRgz4fW6hSz373uFJtD3z9Tcz/OplGd9soWcFMuerp8yjK1/DAJx+cOuIed5BVv9nvyh23a+/EX7lrfG3PCMDW1SZOHDExeLiVkPr2adVqG/j5w/RxZlp2sJIvP6z6w0SnbgYP9kr9gqtjUKGawXt9PViz2ESbB/L+F59vav1eexc+Ia0Np19n22o4eQQGDLc/fA5QtTb4+sOscdC8A5SucHX5ravAzx9qNsr68t8O/DKp4yvnCd9r7ioDbF9jr+MXPzYRXN9+HqlS277sL18bNO1gpPWghISSFnwAVKhuwtff4MRhg8wvGfOO/P7239f2dFyOt//2z6B+AcqVgalj4NwFg4vR9s+nz4DNZqJAgEG+1Ppt2dgxiKlbAwL8DfYeyNrjyM38U+v48jVtOC71s3++jM+VZUobjPsyngsXTETHQJkyBlFRJmw2EwEBjusULWpQtKgB2KhR3Uq3x/1YuMgz0xG2RP6LHH0GZN++ffTs2TPtc8eOHfn+++/Ztm0br776ag6WLOsUKWnCbIazEY4XrOdO2f/jFw9K/+V0ITV3uHyI45+nYi3758jjNuJjDTYtTiHymON2DcP+TES+gLz/pQeqX1coXsqeEhF1yvGYo1JztUuVS//Fdy7K/rtyDcfp1Wrblz11zMT5KPudpMo1HNcvXR78AwxOHsuS4ud6xUrZL66u1OcVZ1I/lyyXfp3zqfVb6Zr6rVLb/jvimrrbuQHqNAMP9+i0S6doah2fyaSOA4PSr3M+9RmDipnU8enjUKyk/c5zSgbXZ9YU+0hk7iCoFHh4GBy/5lma46nPL1Uqn36dhET4dQmciIAihezLeHrC7n32+SFV7e/5MJsNkjKo3xSrew3BW7q0DQ+zwcmTjt9bVz6XL5f+plhiIixe6smpCBOFChmUL2fg6QH79tvXqVrFRlwcLFnqyYmTjuf30qUN8ueHqCg9KizZI0dblr+/PxcuOL7lrUGDBowcOZLFixfftmlX/+ZlMVGhlplda6wY/xqmYOdqK77+EFQt/Z+geBn7tGtHcDq6x/65cKAZD0+YNy6Zv39xHBFj9zobyYlQqbZ7nDRUv9nPywJVa8HWNSaHkTa2rDLh529QoVr6dQLL2he8doSsg7vtX3JFA420wObAzmtf6gaxMSaKBmbpYeRaXhb7i+u2r3EcyWTbKnuPRvkM69f++9oRsg7vtv/+d91djrFfaF8brLgTL4sptY4Nh/PEttUGvv5Q7jp1fO0IWVfquEggePuaqFQD/lljT9m6Yt82g6QE3GYULG9v+8sBl650bMOLV9h7KmpXT7+OlycM+xJ+Xnh1mtUKU+dCUGmDKhXsw+82SN1u0r8GaVq3xT5SVoPa2XdMuY23BerUsbJiladDHf+9whN/f4OQ6ukDEE9PGP2lN7/+djWbxGqFOXMtlClto2IFG2YzfDLSm2kzHKPlvWFmYmJMVK5kvXazbs8wTLny53aTo/fDWrVqxdChQ3n//fcJCQlJS7lq164db775JsOGDSMiIiIni5gl2nbz5Ps3k5j6cRIN7/Lk2F4bK+ak0LG3/R0VCZcNIo8bFClpwr+giZAmZoKqmZgxMom7enpRrKyJ8H02ls9IoXpjc9pFdauuniyfkYJ/wWSqNTATcdRg2dRkghuZ01KL3IHqN/vd85iNz183M/4jMy3utnFoj4nFs010edr+DpD4y3DqOBQvCfkLQt0mUDHYYMIIM/c/blCyrMHhfSZ+n26iThODisH27bZ7yGDxbPuJM6S+wbkoEwunmihc3OCOjnk//eqKjo/BV6/DhI+g6d1weA8snQ0PPk1a/Uak3nHPXxBqN4HywfDDCLj3cfvF8tF98Md0qJU674qTR+2/S2Zwl9+d3N3dxNg3DCZ9ZNDkbjiyx/6W8wd6m7B4m4i/bH9DetGSkL+gKa0ep3xqcM/j9udsjobBnzMMajaG8tXs7fb+p0x89ZrBN+8atO0Cly7AgkkG5YPtfwt38dwT0HswDHoPOneCbbth0kx4ua+9pyL2Mhw8an9+o3BB8PCA7g/Aj7OhRFGoWA6mzbW/0HDsR1dTrgb1gV4Doe9r8NSj9ofQR30LtUMM2jTPwQPOAb16JjPwFR/e+cCHezoms2u3BzNmefH8s0l4e9sfRD9yzEzpUjYKFbTX8UMPJPPzHC+KFTUoF2Rjznwvdu4yM3xYAmazfQCAHt2TmfyTFwUKGITWtxJ+wsSkKRYqV7LSqWPKDcslcitMhpFzowdHR0czaNAg1q1bx7fffssddzgOIj59+nQ+/vhjrFYre/fu/c/7W3C47n/exq3atcbKkqnJnDlhUKCoiab3etCqiz3gOrTDyrevJfHIYC9C29tjwoTLBn9OSWbnGivxl6BwoIkG7eyjMXl62b/4bDaD9b9bWfd7CuciDPLlh7qtPbmrZ8Yv38vL3KV+C5tjc2S/AFvXwIKfzESegIJFoPV9Bnd3tZ8+wv6Bz1714KmXbTS/yz4t/jLMm2xiy2oTly/ZL56btjO4q7OBZ+oNOcOwv3Rwxe8mzkZCgcJQo77BQ08a5C/o+mNMJucCy+1r7G83jzwBBYrAnfdBu9S3Qe//B0a/Ck+8bB+FCez1++tk+/Mgly/ZL5ybtLMPz+v5r8fntqyACR/De99nnGrkalYj53oP/1lj8PtPBlEn7XV8x30m2nax/1/e/4995Kqeg000ucs+Lf6ywcIpBttXQ1xqHTdqa6JNZ9LOE2B/y/rCyQZH99kDxtpN4aE+Jvz8c+Y80dY3Z+5aL10JY3+AI+H2oOKxh+xBA8DGbdBroImPXzd4qKN9WnKK/QWGvy6G6Ev2Fxn26wXNGzpud9su+OJ72LHXfsHctgW8+rz9RYU54ZwtgyGnXGTFKg8mTbZwPNxM0aIGnR9Mpvsj9hy1rds9GDDIlzdfS6BTB3vgkJICk6ZY+HOJJ5diTFSubOOpJ5Jo1PBqG7HZYMFCT+Yt8OLESTMBAQatWqbQp3dS2rMnrlasVCZjY+cCtX7NnaN57Lz/g5wuwk3J0QDkiuPHj1OoUCHy509/Njly5AhLliyhb9++/3k/ORmAiGSFnAxA3EFOBiDuIicDEHeRUwGIu8jJAMRd5OYApMaC93O6CBna/cD7OV2Em5IrHkkMCsr8tlyFChWyJPgQEREREZGcp1tRIiIiIiLiMrmiB0REREREJLfL+QcX8gb1gIiIiIiIiMsoABEREREREZdRCpaIiIiIiBNux5f+5UbqAREREREREZdRACIiIiIiIi6jFCwREREREScoBStrqAdERERERERcRgGIiIiIiIi4jFKwREREREScoPcQZg31gIiIiIiIiMsoABEREREREZdRCpaIiIiIiBM0ClbWUA+IiIiIiIi4jAIQERERERFxGaVgiYiIiIg4Q8NgZQn1gIiIiIiIiMsoABEREREREZdRCpaIiIiIiBM0ClbWUA+IiIiIiIi4jAIQERERERFxGaVgiYiIiIg4wdAoWFlCPSAiIiIiIuIyCkBERERERMRlFICIiIiIiDjBMEy58ue/sNlsfPXVV7Rs2ZI6derQu3dvjh07lunyycnJjBo1ipYtW1K3bl169uzJ3r17b2qfCkBERERERNzU119/zcyZMxk2bBizZs3CZDLRp08fkpKSMlz+/fffZ/bs2Xz44YfMmTOHggUL0qdPHy5duuT0PhWAiIiIiIi4oaSkJCZNmkT//v1p1aoVwcHBjB49msjISJYuXZpu+fDwcGbPns3w4cO58847qVSpEh9//DEWi4Vdu3Y5vV+NgiUiIiIi4ow89iLCsLAwLl++TJMmTdKmBQQEEBISwqZNm7jnnnscll+9ejUBAQHccccdDsv/9ddfN7VfBSAiIiIiIrextm3bXnf+8uXLM5x++vRpAEqWLOkwvXjx4kRERKRb/ujRo5QtW5YlS5bw3XffERkZSUhICK+//jqVKlVyurxKwRIRERERcUPx8fEAWCwWh+ne3t4kJiamWz42Npbjx4/z9ddfM3jwYL755hs8PT157LHHOHfunNP7VQ+IiIiIiIgTcuuLCDPr4bgRHx8fwP4syJV/AyQmJuLr65tueS8vLy5dusTo0aPTejxGjx5Nq1atmDdvHs8884xT+1UPiIiIiIiIG7qSehUVFeUwPSoqisDAwHTLBwYG4unp6ZBu5ePjQ9myZTlx4oTT+1UAIiIiIiLihoKDg/H392fDhg1p02JiYtizZw+hoaHplg8NDSUlJYWdO3emTUtISCA8PJxy5co5vV+lYImIiIiIOCOXpmDdKovFQs+ePfnss88oXLgwpUuXZuTIkQQGBtK+fXusVivnz58nf/78+Pj4EBoaSrNmzXjttdcYOnQoBQsW5KuvvsLDw4MHHnjA6f2qB0RERERExE0NGDCArl278vbbb9O9e3c8PDyYOHEiFouFiIgIWrRowaJFi9KWHzNmDI0aNeLFF1+ka9euxMbG8uOPP1K4cGGn92kyjNz6OE3WW3C4bk4XQeQ/KWyOzeki5GnJeOR0EfI8q6H7Xtmtra81p4uQp52zXc7pIuR5xUqdzOkiZKri9I9zuggZOvzYmzldhJuiFCwREREREScYeexFhDlFt6JERERERMRlFICIiIiIiIjLKAVLRERERMQZbvPkdPZSD4iIiIiIiLiMAhAREREREXEZpWCJiIiIiDhBo2BlDfWAiIiIiIiIyygAERERERERl1EKloiIiIiIMzQKVpZQD4iIiIiIiLiMW/WAdPCNyeki5GkxRmJOFyHPCzB553QR8rRYIyGni5DnFTL75XQR8rxXI+vldBHytF1PVM3pIuR5f/6T0yWQ7OZWAYiIiIiIyK3TKFhZQSlYIiIiIiLiMgpARERERETEZZSCJSIiIiLiDI2ClSXUAyIiIiIiIi6jAERERERERFxGKVgiIiIiIs5QClaWUA+IiIiIiIi4jAIQERERERFxGaVgiYiIiIg4w9CLCLOCekBERERERMRlFICIiIiIiIjLKAVLRERERMQJhkbByhLqAREREREREZdRACIiIiIiIi6jFCwREREREWcoBStLqAdERERERERcRgGIiIiIiIi4jFKwREREREScoRcRZgn1gIiIiIiIiMsoABEREREREZdRCpaIiIiIiBNMGgUrS6gHREREREREXEYBiIiIiIiIuIxSsEREREREnKEUrCyhHhAREREREXEZBSAiIiIiIuIySsESEREREXGGXkSYJdQDIiIiIiIiLqMAREREREREXEYpWCIiIiIiztAoWFlCPSAiIiIiIuIyCkBERERERMRllIIlIiIiIuIMpWBlCfWAiIiIiIiIyygAERERERERl1EKloiIiIiIM5SClSXUAyIiIiIiIi6jAERERERERFxGKVgiIiIiIs4wTDldgjxBPSAiIiIiIuIyCkBERERERMRllIIlIiIiIuIEk0bByhLqAREREREREZdRACIiIiIiIi6jFCwREREREWcoBStLqAdERERERERcRj0gLrJ6g4mxE80cPmaiUEF4+H4bTz9mw5TJcNJJSfDNZDO/LTVzMRoqBEGvR63c094x9G79kCfnLqTfyF9zkilaJBsOJJdav9GD7ydaOHrMTMGCBg/el0zPx5KvW7+TplhYstSTi9EmygXZ6P5IMne1T3FY7tffPPl5thenIsyUKGHw0P3JPNwl8+3mZWrD2Wv9Rg++m+jFkdQ2/NB9KTx+gzY8cYoXi//Vhh97JJm721sdlluQ2oZPRpgITG3Dj3RJccs2vGoDfDkRDh2FQgWh2/3QpwfXreOxk2HhErgQDRWD4KlucF97x+UOH4PPxsPG7eDpCaG14bUXoGyp7D2e3CZqewxhMyO4dCIB7wBPyrUvSuUHi2PKoILD/3eO7V+HZ7qtuv2CKBKSj+Uv7s10mbJ3FqZuv6AsKfvtokGzyvR6sR1BFYsRfSGORb9sYtaklU6tW7l6Kb746Vmevv8LIk9ddJjX/v56dOnVnFJlC3P+bCzLFm5nxvf/w5piy4ajEFEA4hLbd5kY8JYHHVobvPi0lW07TYyZYMZmg2cfz/g/96tDPVi5zkSvR200rm+w96CJoaM8uBBto2dX+zrnzsO5CyaGvGCldojjRV2BAtl+WLnGzl1mXn/Lh7atU+jzdBI7dnrw3UQLNgN69UzOcJ33PvRh7ToPuj+aTIP6Vg4cMDPyc28uRpt4pKt9nXkLPBn1hQ89uifRsEESe/aaGfeNhYQEeCKT7eZVasPZa+cuM6++5U3b1laefTqRf3aa+XaiFzYDnsykrb37oTdr1nnw2KPJhNa3sv+AB59+7s3F6CQe7WoPpOcu8OSzL7zp2T2JRg2s7N7rwdhvLCQkmDL9v5FXbdsFL7wJHVrDS0/Dlp3wxQSwGfDc4xmvM/gDWLHOHnQ0qQ97D8D7o+zByBNd7ctERMFjL0KFsjDyHUhMtAc5z7wCC34AH2/XHWNOOr/vMhtHHKFUs4IEdyvJ+bDLhM2MwDAMqnYOTLd88foFaDHMx2GaAez4NpzkOCvF6wfg6WumxbAq6dY9svgsp9ZepGybwtl1OLlS9Tplef+rHqxcvIspY5dRs145evVvi8lsYuaEFdddt0LVQIaO7Ymnl0e6eQ881oTnX7uHVUt2MeHzxRQo5EfP59tQoWoJPhw0I7sOR9ycAhAXGD/FTHBlg4/fst+ZbNHYIMUKk6abeeIRW7ovqL0H4K/VZvo/Y6VPT/uFWpNQA18fGD3ezP132wjID3sP2O8qtW1po1T687vb+GGKhSqVbbzzZiIATRpZSUmBqdMtdHs4Ge9r6nf/ATOrVnvy7NOJaYFEwwZWfHwMvvnWm44dkvHPB1NnWGhzZzLPP5sEQGgDK+EnzMyZ5+V2AYjacPaaOMWLKpVtvJeuDXvRPYM2vO+AmZWrPen7dFJaINGwgQ0fH4Ovv7XQqUMK/vngpxletL0zhX7P2pcJbWAj/ISJ2fM83S4AGTcZgivDp2/bP7dsDCkp8P00ePKR9IHCnv2wfLWJgc8Y9E0NUJqFgq8PjPoWHrwbAvLD2EmQzw8mfW6fB1CmJPR7E3aFQWgdlx1ijto/+zQFyvtSv385AIrXDcCWYnBwfhSV7i2Oh8Ux49s7wBPvAMdLkMOLznDpRAIthlVJm1eoaj6HZS4eiuPU2otU716SIsH+2XhEuU/P51pzeN9pRr41B4Ataw/i4eXBI71bMvenNSQlpqRbx9PTg/sfa8wT/dpmON9sNtHzudZsWXeQj4bMSpt+YM8pvps3gHpNKrFt/aHsOyhxWzf1DMiZM2d4//33efrpp3nttdeYPHkymzdvJj4+PrvKd9tLSoJN2020bel4d7d9K4O4eBNbd6Tvmj58zD6tVTPHO8uhdWzEJ5jYtN0+f99BE/n9Dbe+cEtKgm3/eHBHS8cTa+tWKcTHm/hnR/q7PUeP2Zt982aOqSp1U+t36zb7OqNGxNOvb5LDMp6ekJTsXrkrasPZ60obbtXSsT22bmUlLt7E9gza8LHU+m3RzLHd16tjdWjDo0ckqA1jr+ON26H9HY7T724FcfEmtuxIv87hY/bfrZs5Tm9Y177Ohm1gGLB0FXTpdDX4AKgZDCvnuk/wYU22cW53LIGNHbstSzUpiDXBxrm9sTfcRsLFZMJmRlD+rqIUqpIvw2UMw2DnhBPkL+NNxXuLZUnZbxdeXh7UCq3AmuV7HKavXrobv3ze1KxfLsP1GrasSo++rZk5YSWTvliSbn7BIv7kL+DHhhVhDtOPHz7DxfOXaXxHtaw7CJF/uakekDfffJPVq1dTpUoVTpw4wcKFCzEMA7PZTMWKFalZsya1atWiVq1aBAcH4+XllV3lvm2ciIDkZBPlyjpevAWVtn8+Fm6iWUPHeYUL2n+fOm2iasWr88JP2S8aTkaYAIN9B00E5IdB73iwYasJqxVaNTUY8qKVYm6SO38qwkRysomgMo4XuqVL2z+HnzDTqKHjhV2hgvY6jThtolLFf20rtX4jIsyYTFbKl7MvZxhw6RKsWOXJ4iWedO/mXneO1YazV2ZtuExaGzbRuKHjOgXT2rCZShWvtu+Tp8xp2zSZSNeG/7fKkz+XePKYm7Xh8FOZtOEy9t9Hw6H5NXVcqKD998nTULWS47YATkbY512KNVE60GDoaFj0F8QnQLMG8M4gKFUie44nt4mLTMKWYuBf0rEbyS/QAsDliES4QTC2b9ZpTGYI7pb53YiTay5y8VAcTd+rhMnsXkF0YJnCWCyenDx2zmH6qeP2z6XLFWXruvQ9Fft3n6BXx8+JjYmn/f310s2/fCmBlGQrJUoVcpjun9+H/AE+BJYulG4dd6cXEWaNmwpAtm3bxpAhQ+jduzcAcXFx7N69m507d7Jz5042bdrEvHnzALBYLOzYkcFtpWskJiZy4MABKleujI+PD3v37mXq1KlERkZSpUoVevXqRWDg7Xt79FKs/STp7+c43c/X/js2Lv06oXUMypQy+OQrD3y8rdQMNth3yMQX33pgNhvEJ9iXCztoIvIMdLnXoOfDNo4cg3GTPOj9kiezvk9J20deFptav37X3DDzS63vy5fTr1O3jpVSpWx8OcYbH+9EqgdbOXjIzDffeWM2GyQkOC6/c5eZfgPsG6xW1UrXh9zr4k1tOHtdqd98+Ry/1a624fQXWvXq2ChVysboMZbUNmzjwCEzX39nSW3Djuvs3GXmuQH2ygx2wzYck3oD/to2nO9KG87gPNGwLpQtZfDRV+DjA7WCIewgjBoPZrNBXAKcv2hfdtS3ULs6jHoXzl2A0d/BkwNh/iTcog0nx9mDYE9fx966K59T4q7/IHNidDInVp6n0n3F8cqX+WXJoYVRFKqWj6I18v/HEt9+/PPbu9jiYh2/oOLi7D2cfvkyftjoXNSl6243MSGZlUt2cV+3xhw7FMXa5XsoWNif517rREqKDW9f3UiW7HFTAYi3tzchISFpn/38/GjYsCENG169dXTx4kV27NjBrl27bri9Q4cO8eSTT3LmzBlKlSrFsGHD6NevH2XKlKFSpUosW7aMuXPnMn36dCpVqnTD7eVGtivn3Uxu1mR0E8fLC8Z/msK7n3rw7Mv2P1GxIgav9bfy6lCPtK7+D1+3YrEYVE99Rq9BbahU3kqv/p4sXGLm0Qfy/ugVV+o3s3thpgySDL284PNP4xn+qQ8DX7FfHRQpYmNg/0TeG+qDj+NzkZQqaTBmdBxnzpqZNNnC08/5MuGbeAoXdo/bIGrD2cu4xTb8xacJfPSpNwNS23DRIjYG9k/i3aHe+Pg4ts2SJQ3GjY7nzFkTE1Lb8MRv4insJs/wGqnVkdloV+YM6tjiBd+PhLdGQO/B9hWLFTF4a4D94XQ/H0hOjeOKFoKvPry6naDS0L2fiYVLDR69P4sPJjeyXangTObfINn72PJzGDao0CnztKrzYbHEHImn4ZAKt1bG29yVHh8jk68dI7MZTvjqw19JTkph4HsPMPiDh0iIT+KXH1bj7eNFYrx73awQ17mpAKRdu3bs2bOHJk2aZLpMwYIFueOOO7jjjjsyXeaKTz/9lHr16tGvXz8mTpzI888/z/3338/QoUMxmUykpKTw6quvMnz4cCZMmHAzRc018vvbTwqXr7lLHJf62Ix/Js/QBZWByV9ZOXfBSnSM/Qvt9Bmw2UwUyG/fZp0a6U849WoZ5M9nsP9glh1Crnal/tLVb+pn/3wZn5TLlDYY92U8Fy6YiI6BMmUMoqJM2GwmAgIc1yla1KBoUQOwUaO6lW6P+7Fwkfs8xKs2nL380+rX8erNmTb8zZcJnL8AMTGma9qw47LFihoUK2rfTkj1BB593JdfF3llOsJWXpM/tY1e29Nx+UobzviRA8qVgalj4NwFg4vR9s9pbTjAIF9qj0rLxo5BTN0aEOBvsPdA1h5HbuWVL7WnI94x3fXKZy+/9M8x/VvE+miK1cmf7qH0fzu1PhqvfB4UrxeQ6TJ52eVL9p4PP/9r0tz8LA7zb0VCfBKj35/PNyMWUaJUQU6fukBifDJ3PVifHScO33qh8yrDvdL/sstNPYTepUsX/vjjDw4ezJorg40bNzJw4ECCg4N57bXXSExMpHv37mljhnt6evLcc8+xZcuWLNlfTihbCjzMBsdPOjbYK58rlUt/cZGQCL8tMXEiAooUgorl7A+O7tlnX6d6VYOYSzBvkYlDRx3XNQxIToGCbjKEaenSNjzMBidPOjblK5/Ll0t/Bz0xERYv9eRUhIlChQzKlzPw9IB9++3rVK1iIy4Oliz15MQ1f7fSpQ3y54eoKPd5h6facPYqXdrAw2xw4po2fOVzhUza8J9LPTgVYaJwIdLacFhqG65WxcrlOFi81CNdGy6T1obd50s0qBR4eBgcP+k4/fgJ++9K5dOvk5AIvy4hrQ1XKm9vw7v32eeHVLX/3zCbDZIyiONSrO4zBK9fCW9MZrh82nHAg7jUz/5lfDJaDYD4c0nEHI2nVNOC191H1NZoAhsWwOzpPu32306Fn8eaYqVUWcduy1JB9ofljh8+c8vbbnRHVULqBpEQn8SxQ1EkxidToHA+igUGcDAs4j+VWyQzN3UV9cgjj7Br1y4efvhh3njjDRYtWsSxY8dueec+Pj4kpCbcFy1alEceeQTva8abjImJIX/+2zff09sb6tcxWL7S5NB1unSFffSfmtXTX7x5ecLwLz2Ys/Dqn8dqhRnzzASVNqhcwZ6C8dFoDyZOd7yz9PdqEwmJJhrWdY/0IG8L1KljZcUqT4f6/XuFJ/7+BiHV01+8eXrC6C+9+fW3q7mtVivMmWuhTGkbFSvYMJvhk5HeTJthcVh3b5iZmBgTlStZr91snqU2nL3sbdjG/1Z5XNOGPch/nTb8+ZfeLPjt6h1jqxVmz/VKbcMGHmYYPtKbqTMcc7j3pLXhvJ/edoW3t/3lgEtXOqawLF5h76moXT39Ol6eMOxL+Hnh1WlWK0ydax+AoUoF+/C7DVK3m/Sva+91W+wjZTWonX3HlJt4WMwUru7P6Q0XHVKBTq2/iFc+DwpV9st03YsH7V19hatl0g0FJMWmcPl0EoWDM18mr0tOSmHn1mM0bxviML1F+xpcioln364Tt7ztex5uRJ/BdztMe6hHU2xWgw0r9t3ydkWu56ZSsIYNG8bevXvZvXs3f/zxB/PmzcNkMpEvXz5CQkKoWbMmr776qtPba9GiBR9++CHDhg2jUqVKDB06NG2eYRhs3LiRDz74gHbt2t1MMXOdZx+38ezLHrzyvgcPdrLxzy4Tk2eaGdjX/v6E2Mtw6KiJsqUNChcEDw945EEb02abKV4UKpQzmDnPzPadJr78yIrZbB/y8anuNr790YMihQyaNzI4cNjEN5PN3NHERpNQ97h4A/vLBge+4sM7H/hwT8dkdu32YMYsL55/Nglvb/uD6EeOmSldykahgvb6feiBZH6e40WxogblgmzMme/Fzl1mhg9LwGy2P3Tao3syk3/yokABg9D6VsJPmJg0xULlSlY6dUw/nnpepjacvZ7smcRLr/jw9gfe3NsxhZ27zUyf5UW/Z5MzbcOdH0hmVmobLh9kY3ZqG/5kWGJaG+7ZPZkfUttww/pWjp8wM3GKhSqVrNzjZm34uSeg92AY9B507gTbdsOkmfByX9La8MGj9lTBK224+wPw42woUdTeizdtrv2FhmM/uppyNagP9BoIfV+Dpx61P4Q+6luoHWLQpnkOHrCLVe1SgnUfHmLL6KOUbV2EC/suc2hhFNV7lMLDYiY5zkrsiQT8Ar0dUq1ijidg9jKRLzDz7qKY4/YbldfrSXEHM77/H8O/fZK3Rj7K4vlbCakbRNdezZn0xRKSElPwy+dNUMViRJw4T/SFDEYHycSC6ev4ePyT9B3SkfUrwqjbsCLdnmnFrIkrOX3yQjYe0W3Kfb6aspXJuMUnl2w2G4cOHWL37t3s2rWLPXv2EBYWxtatW53exvnz53nuuecoW7Yso0aNcpj3+++/8/LLL9OyZUtGjx6Nf2aJ5jchMaLijRfKJstXmfj6Bw+OhkPxotDtQRu9HrXfgdy0zcTTgzz58LUUHuho/3Mkp8D4yWYWLjETHQPBlQ369rI5DHdqs8HPC8zMWmDmxCkoEACd2tno91T6F8O5QoyR6PqdplqxyoNJky0cDzdTtKhB5weT6f6IPS9i63YPBgzy5c3XEujUwX7RlZICk6ZY+HOJJ5diTFSubOOpJ5Ichuy12WDBQk/mLfDixEkzAQEGrVqm0Kd3UqbPPWS3AFPO5XS4QxuONZJuvFA2WbHKgwmTLRwPN1EstQ0/9oi9vW7dbubFQb689Voi9/yrDU+c4sWfSzyJiTFRpbKNp55IpvE1bXj+Qk/mLvDixEn78013trTybA624ULmzO+GZ7elK2HsD3Ak3B5UPPaQPWgA2LgNeg008fHrBg91tE9LTrG/wPDXxRB9yf4iw3690g/Zu20XfPE97NhrD/zatoBXn7e/qDAnvBqZfrhVV4jYeJF9P5/m8qlEfAp7Uf7uolS6rzgAZ3dfYt0Hh6jbryxl77w6xvaOCeGc3hDNXd/XzHS7p9ZeYMsXx7hzdDD5S+d8ELLriao5tu9mbarz+PNtKF2+KOeiYlg4awNzf1wLQO3Q8nw68WlGvTOXpb9uS7du+/vr8fKHnenVcRSRpy46zLuzQy26P3snJUoVJCoimt9+3sCvMza44pAy9Oc/H+bYvm+k4hef53QRMnR44OCcLsJNueUAJCOGYaQ9v3EzLl68SMGCBR2mnT9/nqioKIKDg7OodDkbgLiDnAxA3EVOBiDuICcDEHeRkwGIu8ipAMRd5GQA4i4UgNy82y0AuakUrBu5leADSBd8ABQuXJjC7jJGpIiIiIjkfkrByhLuM5SPiIiIiIjkOAUgIiIiIiLiMlmagiUiIiIikleZlIKVJdQDIiIiIiLipmw2G1999RUtW7akTp069O7d+7rv+Zs3bx7VqlVL93Mz7wZUD4iIiIiIiJv6+uuvmTlzJsOHD6dEiRKMHDmSPn368Ntvv2GxWNItv2/fPho1asTnnzuOCHYzg0epB0RERERExBlGLv25RUlJSUyaNIn+/fvTqlUrgoODGT16NJGRkSxdujTDdfbv309wcDDFihVz+PHw8HB6vwpARERERETcUFhYGJcvX6ZJkyZp0wICAggJCWHTpk0ZrrNv3z4qV678n/arFCwRERERkdtY27Ztrzt/+fLlGU4/ffo0ACVLlnSYXrx4cSIiItItf/78ec6ePcumTZv46aefuHjxInXq1OGVV16hQoUKTpdXPSAiIiIiIs7I6VSrLE7Bio+PB0j3rIe3tzeJiYnplt+/fz8AHh4ejBgxgtGjRxMXF8djjz3G2bNnnd6vekBERERERG5jmfVw3IiPjw9gfxbkyr8BEhMT8fX1Tbd8kyZN2LhxIwUKFEibNm7cOFq3bs3cuXN59tlnndqvekBERERERNzQldSrqKgoh+lRUVEEBgZmuM6/gw8APz8/ypQpQ2RkpNP7VQAiIiIiIuIEk5E7f25VcHAw/v7+bNiwIW1aTEwMe/bsITQ0NN3y06dPp3HjxiQkJKRNi42N5ejRozf1YLoCEBERERERN2SxWOjZsyefffYZy5cvJywsjEGDBhEYGEj79u2xWq2cOXMmLeBo3bo1hmHw6quvcuDAAXbu3En//v0pXLgwDz30kNP7VQAiIiIiIuKmBgwYQNeuXXn77bfp3r07Hh4eTJw4EYvFQkREBC1atGDRokWAPWVrypQpXL58me7du/Pkk0+SP39+fvzxR4dnSG7EZBjGf+i4ub0kRlTM6SLkaTFG+tESJGsFmLxzugh5WqyRlNNFyPMKmf1yugh53quR9XK6CHnarieq5nQR8rw///kwp4uQqcqfjs7pImTo4KuDcroIN0U9ICIiIiIi4jIKQERERERExGX0HhAREREREWe4zYML2Us9ICIiIiIi4jIKQERERERExGWUgiUiIiIi4oT/8tI/uUo9ICIiIiIi4jIKQERERERExGWUgiUiIiIi4gylYGUJ9YCIiIiIiIjLKAARERERERGXUQqWiIiIiIgTNApW1lAPiIiIiIiIuIwCEBERERERcRmlYImIiIiIOEMpWFlCPSAiIiIiIuIyCkBERERERMRllIIlIiIiIuIMpWBlCfWAiIiIiIiIyygAERERERERl1EKloiIiIiIE/QiwqyhHhAREREREXEZBSAiIiIiIuIyCkBERERERMRlFICIiIiIiIjLKAARERERERGX0ShYIiIiIiLO0ChYWUI9ICIiIiIi4jIKQERERERExGWUgiUiIiIi4gS9iDBrqAdERERERERcRgGIiIiIiIi4jFKwREREREScoRSsLOFWAcgD1VrldBHyNFt8fE4XIc8z+/rmdBHyNFtCYk4XIe8zbDldgrzPpOSG7GQyH87pIojc9nSWEhERERERl3GrHhARERERkVumFKwsoR4QERERERFxGQUgIiIiIiLiMkrBEhERERFxgl5EmDXUAyIiIiIiIi6jAERERERERFxGKVgiIiIiIs5QClaWUA+IiIiIiIi4jAIQERERERFxGaVgiYiIiIg4QaNgZQ31gIiIiIiIiMsoABEREREREZdRCpaIiIiIiDOUgpUl1AMiIiIiIiIuowBERERERERcRilYIiIiIiLOUApWllAPiIiIiIiIuIwCEBERERERcRmlYImIiIiIOEEvIswa6gERERERERGXUQAiIiIiIiIuoxQsERERERFnKAUrS6gHREREREREXEYBiIiIiIiIuIxSsEREREREnKEUrCyhHhAREREREXEZBSAiIiIiIuIySsESEREREXGCXkSYNdQDIiIiIiIiLqMAREREREREXEYpWCIiIiIizlAKVpZQD4iIiIiIiLiMAhAREREREXEZpWCJiIiIiDhBo2BlDfWAiIiIiIiIyygAERERERERl1EKloiIiIiIM5SClSXUAyIiIiIiIi6jAERERERERFxGAYiIiIiIiLiMAhAREREREWcYufTnP7DZbHz11Ve0bNmSOnXq0Lt3b44dO+bUugsXLqRatWqcOHHipvapAERERERExE19/fXXzJw5k2HDhjFr1ixMJhN9+vQhKSnpuuudPHmSDz744Jb2qQBERERERMQNJSUlMWnSJPr370+rVq0IDg5m9OjRREZGsnTp0kzXs9lsDBkyhBo1atzSfhWAiIiIiIg4wZRLf25VWFgYly9fpkmTJmnTAgICCAkJYdOmTZmuN378eJKTk+nbt+8t7VfvARERERERcUOnT58GoGTJkg7TixcvTkRERIbr7Nixg0mTJjF79mwiIyNvab8KQEREREREbmNt27a97vzly5dnOD0+Ph4Ai8XiMN3b25vo6Oh0y8fFxfHKK6/wyiuvUL58+VsOQJSC5SIN2tXkq/+9y/zT45myaySPDr7H6XUr1y3Hb+e+p0RQkVuanxeF3lWHses/4tfoKfx0cAzdXn3ghuu0fawF320fycKYH5m463M69G6dbplm94cybsPHLLgwmR/2fkHPt7vg6eXhsEy+An70H9ObmeHjWXBhMl+sGkrdO28tB/J2ojactULvqs3YdcP49eIP/HTgS7q9ev8N12n7WHO+2/4pC6MnM3HXZ3R46s50yzS7P5Rx6z9iwflJ/LDnc3q+3TldG67euAqfLnmLXy/+wM8nvuGVCX0pHFgwaw4sl9A5IvupDWev0Pa1GbPmQxacn8SP+7/k0SE3rt823Zvz3dYR/HrhBybsGJlh/bZ//A6+3fIJCy/+wJSw0Tz+Thc8PB3rt2Ltcnw4fwizwr/hl5Pj+fi316lct3zWHNjtLqdHu8riUbB8fHwA0j1wnpiYiK+vb7rlhw0bRvny5enWrdut7xT1gLhE9UaVeX/mS6ycu5Epw+ZSs2lVer3bGZPZxMzPfrvuuhVqlmXoL4Pw9Mr4T3Wj+XlRSNOqfDBvCCt+Wcfkd3+mZvNqPPnho5jMJmZ8Mj/DdVp2acyQH/oxf8yfbFr8E80eaMjgb/uSFJ/EXzPWAFC/bS3e/WUwK35ex8Q3Z1ChVlme+rAbBYoFMO6lHwAwm0189NvrFC9blAlvTONCZDQP9e/IsIWv0b/Z2xzZedxV1eBSasNZK6RJFT6Y+4q9Db+X2oaHPpLahhdkuE7LLo0ZMul5extesoNm94cy+NtnSUpI/lcbrsm7Pw9kxS/rmfjWDCrUDOKpDx+hQNEAxg2cDEC10EqMXPY24WEnGfn0eJLik+jyUie+WPE+zzV8g7iYeFdVQ7bROSL7qQ1nr5AmVXh/zsusmL2eKe//Qo3m1Xjyg4cxm03MGJFJ/XZuxJCJzzF/7GI2L/mHZveHMuibPiTGJ/H3zLUAPPjC3Tw/6glWztnA92/MoEDR/Dz+Thf7efjRLwAoWbE4ny17h4PbjzD6ue8xbAZdBnZi1F/v8kLjtzhxIOO0HMlZmfVw3MiV1KuoqCiCgoLSpkdFRREcHJxu+Tlz5mCxWKhXrx4AVqsVgHvvvZf777+foUOHOrVf9/nGz0E933iAwzuPM/LZ7wHYsmwXHp4ePDKoE3PHLiYpITndOp5eHtzftx1PvP3QLc3Py3q+3YVD/xzl0yfHAbB5yT94eHny6KsPMOeL3zOsj/+3d+/xOdf/H8efwzZmm/DF2JxPsxxSyqGtJV+pyKH4SomEDgolQ86+FLEovlR8R/rJl5wlktTX6SuHhJhRjrHZnHc+2K7fHzvU5RpddF2fz3btcb/ddru193W9r8/r8+rd1V7X+/X5XC9M6K5tK3bp46GfSZJ+3HRQPmW99fzYbnn/43u098OKO3NJ7/X+l7KyLNq3+WeVqVBGTw1+Qh+/9Zkyr2fqkWeDVb9ZbQ144O28PyQObj2iT/ZN1X1/b+wyf1zciDXsWNlr+LSm9vlIkrT3m4Mq7l5c3cM6asUH62+yhrtp28rd+jhskaScNVyutJ4f8/Qf1nBozhqenbOGD6lMRV89NehxfTz0/5R5PVM93u6kpKvJCmv7jhKvJkmS9m0+pPmHwvWPoU/q07FfGJQF5+E9wvlYw8713KindOLAaU17MSe/mw6qRIni+sfQJ7Xiw/zz23t8N21fuVufDMvJ77c/y6est3qNeVrfL/mfihVzU89RT+nHb3/WO8/NzJv3y76Tmrd/qu5t01D7Nh9S59ceU3pKusZ0Dldacpokaf9/D+uzYx+q04BHNfvNhQZkAEYJDAyUt7e3du3alVeAxMfHKzIyUj179rR5/jfffGP1+4EDBxQWFqa5c+eqdu3adh+XFiwnc/cooUbB9bVj7Y9W49vX7JWXTyk1bFUv33n3P9pYz43opCXh6zR/7LLbftxVuXuUUOPQIO1YtdtqfNvKH+TlU0qNghvYzKlUvYKq1q+iHatt5/jX8ZN/3ezq392zhFKTUpWV9fteZvzFBHl4usvLJ3uLMrhLcx3cesTqj4iMtAy9ePebWj7j1jsBhRVr2LGy13AD2/W4YnfOGrb9xKlS9b+par0q2rHa+o4k21buzlnDftmv7emu1OS0W67haoH+OvS/o3l/uEnZa/jo3hNq/kRTh52nWXiPcD7WsHO5e5RQ44caaPuaG3K1Kju/DW+RX9s5u1Sltp/86/jprkpl5FPOWz98tc/qOWeizunqhXg98Hh27n6LOqflH3yVV3xIUlpKui6eu6zKtSo56jQLLTdLwfy5Ux4eHurZs6fCw8O1efNmRUVF6c0335Sfn5/atm2rzMxMXbhwQampqZKk6tWrW/1UqpS9JqpUqaLy5e1vo6YAcTK/GhXk4emuc79aX6QTfSL7d/86fvnOO7bvpHo3CtOS8HXKvJ5524+7Kr9aFeXh6W6zBRydk1//epVt5lQL9Jck2znHs+cE5PxxsXbORvnXraxuQzqodBkvBTavoy6DHteu9fuUcCX7f3S1m1TXqcO/qcugx/XZsZnakPK5Zu+erEYhtn/UuArWsGP9vobPW43nrsfcP3b/KG8NH7txDWe/xu9r+Bv51/H7fQ0/UEddBj6mXet/ylvD1y7Ey696BZtjVK5VUX41bMcLG94jnI817Fx+NbPze85mPebkKp/33Kr1s/N77ib/TgLqVlbS1WRdz7hukzvvu7zkU7Z0Xu7Wzdus5TO+snqOfx0/1bg7QKcO//YXzgwF1aBBg9S1a1eNHj1aPXr0UPHixRURESEPDw/FxMQoODhY69evd+gxC2wL1pNPPqm5c+fa3BassPG+y0uSlJxg3ZOanJBdSeZ+onOjSzFXb/m6f/a4q/K+q7Qk2fT45ubXy8f2gqnSuf8ObpiTkjvHN3vOgS2RWha+Vv3f66n+72VvO/7y00lNfn5W3pwyFXz10NPNlXAlSfNGfK7U5DQ9M6yTJq9/W4OCx+jEgdOOOM0ChTXsWN5l/mQN+95iDSfcuIZTreYc2BKpZe9/qf5TnlX/Kc9KylnDvf6VN2fjwi0a8slLeiX8eX3x/peyZFn01ODHVS3QXyU8Cuz/EuzGe4TzsYad60/fc/PJr/dN1/Dvc9JS0rVl+Q968tW2OnXkrP63Zo/uqlBGr77/vK5nZKpkac984/Es5aGh/35FaSkZWjNn4187ORRIxYsXV1hYmMLCwmweCwgI0NGjR286t3nz5rd8/GZM/S919erVN33s9OnT2rBhg8qVKydJ6ty5szFBOZibW/bXw1gs+e+PWbL+wr5ZEeRWLDef+T9uycqyGSt2szm5/25y5gye00+P9n5Yn7+zUj99d0h+NSuo19huevertzX80UlKS0mXu0cJlb6rtAa2Gq2L5y5Lkg5tj9LCox+q+9COVn+IuArWsGP9voZvls/81nCx/OfkreHs8cGz++rR3qH6/N1V2Wu4RkX1Gvu03l03QsPbvaO0lHR9veC/Ku3rpV7juuqpQY8rKytL21bu1ldzv1W7Pg875iRNxHuE87GGncstL1f5P56Vz3tu3r+TG2+H5JY7J/vfyczX5ysj7bre/Kif3vrkJaUmpeqL6V/J08tTqUlpupGXTymNXz5E9e6rqX/+4wNdOHv5Ds/KhfC/PIcwtQCZMGFCXk9Zfm9kU6dOlZT9B1BhLUCSriVLsv3ULfdT4yQXuFuHkZKuZueztO+N+cz+Pb98JubMufFTo1LeOf8OrqWofJWyerzvI1oyZbUWjs++gPHgVunY3uOauz9c7fq01to5G5WckKIzUdF5f1hIUkpiqiJ3HlPtJjUcc5IFDGvYsZKuZbeR3HQNX8tvDWfPsV3DnjlzknPWcGsteW+NFo7Pvqbm4NYjOvbjcc39aaravfCw1n6UffHgig/Xa/XsjapSu5LiLyXo2sUEhUW8ooTLSSrseI9wPtawcyXl5uom77nJOe/J1nPyf5/+4xqWpNSkNM14ZZ4+euszVar2N50/fVFpyWlq1ztUMSfjrOZWCCinf64KU0BdP73z3Czt2vCTA84OyGbqNSArV65UUFCQmjdvri1btigqKirvp1SpUtq0aZOioqJ05MgRM8P8S6JPxinzeqaq1KpoNV4l50KuM1HRZoRVaEUfj83OZ23rC+Gq1Mn+/XTkWZs5Z49l59hmTs7vp4+cVcVqf1OxYsV0+H/HrJ5z6vBZXbsYr+pBAZKkc7+el4enbd1e3L240lLTbcZdAWvYsaKP5+SztnUf9x/X441y++Zt5/jlzDmnilVvtYYT8tZw3Xtr6sHO9yvzeqZ+OxqtaxcT8sZ/3X/SAWdoLt4jnI817FzRJ+LyX8O5uYo6ZzPn9/zmv4bP5Mxp/nhTBbWsp9SkNJ0+ck5pyWkqU8FXFQLK6defTuXNq9mwqj7c+k9VCCivUR2n6n9r9zrs/ADJ5AKkZs2aWrp0qRo3bqxOnTo5/AKXgiAj7bp+3nFMD3a8z2o8uFMzJVxJ0tEfT5gUWeGUkZahn7cd0YNdHrAaD3mqhRKuJOronl9t5kQfj1X08ViFPNXCZs5vR6MVd+aion89r8zrmTZ3FwmoV1ll/uar8zmfDO3ZsF+1mtRQ1cAqec/xKeetu1vV16HtUY46zQKFNexY2Ws4Sg92vt9qPOTpB7Lzuee4zZzf1/CN6/4B/XYsZw0fv9Ua9tH5U9lruElokEYsfE2ly3jlPefeNg1V4+6q2rGm8P+RwXuE87GGnSsjLUM/b4/Sg51uyG+XW+T3RKyiT8QqpEvzG+Y0z8uvJLXv30b9Jz9r9ZynBj6mrMws7dqQfXesCgHlNPmrt2WxWDSk9QQd3Fp4PwR2CrO/cNDBX0RoFtOv1ipRooSGDBmikJAQDR8+XJs3b9b48ePNDsuh/jPtS01eO1SjFg7QxkXbFNS8jroOfkzzxy5TemqGvHxKqlpgFcWcuKBrlxLMDrfAW/zuKk3ZOEqj//OGNn76XwW1rKdub3VQxNuLc/JZStWC/BVzPDbvk7HP312psIhXlXA5QTu//FEtn7xPD/+jpSb1+ECSdO1iglbO3KBub3WQJO379qAqVq+g50c/rdjTF7Qh4jtJ0qpZG/Ro71BNWjNcC8YuVWpiqp4d9ZQsFouWvf+lKfkwAmvYsRZPXqUpX4/U6P8M/n0ND+mgiJFLfl/DDfwVc+KPa3hVdovJpUTtXPejWna4Tw93a6lJz34o6Q9rOOcb6vd9+7MqVvubnh/9VM4a/l6StHnxdj0zrKPG/Gewlk1fpwoB5fXytJ46tOOovs/5LobCjvcI52MNO9fiKas1Zf3bGvX5IG1cuEVBLeuq65D2ihh18/wunrxaQ+e9rPjLCfph3T616HCvQru1sPrOj9VzNmryuhF6ZVpP7fxqn+55+G49M6yTlkxbq/MnL0iSXn2/l8pWKqMPX4+Ql28pBT5QJ29+cnxK3m4K8Fe4WW52FZkJ4uPjNWHCBO3du1eXLl3Shg0bVLVqVYe9/mO+fRz2WrerVYd79fzIzvKv66dL0Vf05bzvtPJf2XeTaBxcX1PXj9D7r/xbmxbbvnm2ffZBvfVxP/VuOFSxZy7d9uNGyUox7lqABzvdr+fHdVVAvSq6dO6y1n70jVZ8kH3bwMYPBSl881hN6/uRNn22JW9O+/5t1PXNDqpQtbxiTsRpydQ12vz5NqvX7TLocXXo/3dVqllRl2Ouat+3B7VgzJK8N3hJ+pt/OfWb/KyatbtH7h4ldGhHlOYOW5Rva4ejFStle/cToxSJNZxqexGmszzYqZmeH9tVAfUq69K5K1r78Tda8UH2LnDjhxoo/Nsxmtb3Y236v615c9r3eyRnDZdTzMk4LZm6Vps/3271ul0GPqYOL/1dlWpUyFnDP2vB2KVWa7hu05p6eVpP1WlaQ0lXk7Vt1W4tHL9MKYmpzj9xi+0Fys5QVN8jJEluxjQ3FNU1nHvBt7O16thMz495Oju/0Vf05cebtOLD3/M77ZvRCu//iVV+n+j3iLq+0V4VAsop5uQFLZ22VpsXW+f34X+01LMjOqtSjQqKO3NRX37ybd61NSXci2vN5fkq4Z7/59MHtkZq2KPvOOmMf7cx9XOnH+NONRk0w+wQ8nVg5ptmh3BbClQBkmv16tVauXKlwsPDVbFixT+fYCczC5CiwMgCpKgyswApCowsQIosgwqQIs2gAqSoMqoAKcoKcgFyz8CCWYDsn1W4ChDTW7Dy07lz50J71ysAAAAAN8fHJAAAAAAMUyB3QAAAAIACp8BduFA4sQMCAAAAwDAUIAAAAAAMQwsWAAAAYAc3WrAcgh0QAAAAAIahAAEAAABgGFqwAAAAAHvQguUQ7IAAAAAAMAwFCAAAAADD0IIFAAAA2IG7YDkGOyAAAAAADEMBAgAAAMAwtGABAAAA9qAFyyHYAQEAAABgGAoQAAAAAIahBQsAAACwBy1YDsEOCAAAAADDUIAAAAAAMAwtWAAAAIAd+CJCx2AHBAAAAIBhKEAAAAAAGIYWLAAAAMAetGA5BDsgAAAAAAxDAQIAAADAMLRgAQAAAHZws9CD5QjsgAAAAAAwDAUIAAAAAMPQggUAAADYgw4sh2AHBAAAAIBhKEAAAAAAGIYWLAAAAMAObrRgOQQ7IAAAAAAMQwECAAAAwDC0YAEAAAD2oAXLIdgBAQAAAGAYChAAAAAAhqEFCwAAALADd8FyDHZAAAAAABiGAgQAAACAYWjBAgAAAOxBC5ZDsAMCAAAAwDAUIAAAAAAMQwsWAAAAYAfuguUY7IAAAAAAMAwFCAAAAADD0IIFAAAA2IMWLIdgBwQAAACAYYrUDkhmYqLZIQB/CWsYwJ8p7l3K7BBcGu/DwF9XpAoQAAAA4E5xFyzHoAULAAAAgGEoQAAAAAAYhhYsAAAAwB4WerAcgR0QAAAAAIahAAEAAABgGFqwAAAAADtwFyzHYAcEAAAAgGEoQAAAAAAYhhYsAAAAwB60YDkEOyAAAAAADEMBAgAAAMAwtGABAAAAdnDLMjsC18AOCAAAAADDUIAAAAAAMAwtWAAAAIA9uAuWQ7ADAgAAAMAwFCAAAAAADEMLFgAAAGAHN1qwHIIdEAAAAKCIysrK0syZMxUSEqImTZroxRdf1OnTp2/6/EOHDql3795q2rSpWrRoobFjxyo+Pv62jkkBAgAAABRRc+bM0ZIlSzRp0iQtXbpUbm5u6t+/v9LT022eGxcXpz59+qhatWpatWqV5syZo3379mn48OG3dUwKEAAAAMAeFkvB/LlD6enpmj9/vgYOHKjQ0FAFBgZqxowZio2N1aZNm2yef+7cOYWEhGjcuHGqUaOG7r33XnXr1k07d+68reNSgAAAAABFUFRUlJKSktSiRYu8MV9fXwUFBWnPnj02z2/atKmmT5+uEiWyLyP/9ddftWrVKj344IO3dVwuQgcAAAAKsTZt2tzy8c2bN+c7fv78eUlS5cqVrcYrVqyomJiYW75mu3btdOrUKfn7+2vOnDm3ES07IAAAAIBd3CwF8+dOpaSkSJI8PDysxj09PZWWlnbLueHh4Vq0aJEqVKigXr16KSkpye7jsgMCAAAAFGI32+H4MyVLlpSUfS1I7j9LUlpamkqVKnXLuY0aNZIkzZo1S6Ghodq0aZM6d+5s13HZAQEAAACKoNzWq7i4OKvxuLg4+fn52Tz/+PHj2rJli9VYxYoVVaZMGcXGxtp9XAoQAAAAwB6WAvpzhwIDA+Xt7a1du3bljcXHxysyMlLNmjWzef62bds0ePBgJSYm5o2dOXNGV65cUe3ate0+LgUIAAAAUAR5eHioZ8+eCg8P1+bNmxUVFaU333xTfn5+atu2rTIzM3XhwgWlpqZKkjp16iQfHx+FhYXpl19+0d69ezVo0CA1btxYrVu3tvu4FCAAAABAETVo0CB17dpVo0ePVo8ePVS8eHFFRETIw8NDMTExCg4O1vr16yVJZcuW1WeffaasrCz16NFDr732moKCghQREaHixYvbfUw3i+UvfHtJIdO2WDezQwAAwKmKe3ubHYJLy/xD6wmcY1PWMrNDuKmQLuFmh5CvbauGmh3CbWEHBAAAAIBhKEAAAAAAGIbvAQEAAADsUXSuXHAqdkAAAAAAGIYCBAAAAIBhaMECAAAA7OBGB5ZDsAMCAAAAwDAUIAAAAAAMQwsWAAAAYA9asByCHRAAAAAAhqEAAQAAAGAYWrAAAAAAO3AXLMdgBwQAAACAYShAAAAAABiGFiwAAADAHln0YDkCOyAAAAAADMMOiBM0a3eP+kx8RtWCAnTtQrzWffKNlkxZfcs5bZ4L0TMjuqhyrUqKO3NRy8LXaEPEd5KkStUraNHJOTedu3HB9wrvm/14jYbV1P+9ngpsXlcZaRn68ZsDmjd8ka7GXXPY+RUE5Ni5yK/zkWPnIr/muO/vDdV79FOqFlhF1y4maP38/2rp9K/smlvnnur6YPNo9W06QrFnLt32466GNQxXRgHiYEEt6+mfa4Zry9L/acGYJWoYHKg+k3qoWLFiWvzuynznPNS1hYYtfF2rZq7X3q/3q1XnBzRk3qtKS0nXd4u363LMFQ1qOdJmXscBjym0eyttmJ/95lK20l0K/26c4s5cVHif2fL08lC/KT317vqRGthipDKvZzr13I1Cjp2L/DofOXYu8muOBg/U0fglg7V15W4tnLRSDVvWU++xT8mtmJuWhK+75dyaDavqn8veVAn3/P8s+bPHXQ1ruACjA8shisZ/yQZ6fmw3Hd9/Su/1niVJ2rtxv0q4F1f34Z21fPo6paem28x5YWIPbVv+gz4esjB7zjcH5FPWW73Gd9d3i7crI/26juz6xWpOvftqKbR7K80ftViHd0RJklp2bKYyf/PVwBYjFXMiVpKUeDVZkzeM0t2t6uvg1khnnrphyLFzkV/nI8fORX7N0fPtTjrx8xlNe2meJOnHbw+peIni+sebT2jlvzYqPTXDZk4J9+Lq+PLf1Wt0lzt63FWxhuHquAbEgdw9Sqjxw3dr+6pdVuNbl/8gL59SahQSaDOnUvUKqlq/is2cbSt2yr+On/zrVs73WANn99eZI2e1csbvW9vuntn1ZHJ8St5Y/MV4SZJvee87O6kChhw7F/l1PnLsXOTXHO4eJdQouL52rP3Ranz7mr3y8imlhq3q5Tvv/kcb67kRnbQkfJ3mj11224+7ItYwigIKEAeqXKuSPDzdde5YtNV49K/nJUkB9arYzKnWwF+SdPZYjNX4ubw5tm8arXsEK/CBOprzxgJlZWXljW/5Yqcunrus12f1VTm/u+RXo6L6T31el6Iva9/mQ3/t5AoIcuxc5Nf5yLFzkV9z+NWokJ33X2OtxqNzPkH3r+OX77xj+06qd6MwLQlfl29rz5897opYwwWbm6Vg/hQ2prZgLV++XB07dpSHh0fe2A8//KD58+fr/Pnzqlu3rl599VXVqVPHxCjtV/qu0pKkpD98aiBJyQnZv3v5lrKZ450zJ/mGOSkJqTlzvGzmdHvrSR3aHqWDW6y3Qa/GXdPM1+Zp5OI39HD3VpKk+MuJCntkvJLjk+/gjAoecuxc5Nf5yLFzkV9zeN+VnaPcPOdKzs2hT8l8512KuXrL1/2zx10RaxhFgak7IGPGjFFCQkLe79u3b1efPn2UlZWl4OBgXbhwQU8//bT27dtnYpT2K1bMLfsfLPmXoln53DvaLWeO5YY5brkv9YdPJSTp7lb1VffeWvoifI3Na7XuEazxK8O0c+1ejWg3UWM7v6czkWc1ZeNoVa1v+4lJYUSOnYv8Oh85di7yaw43t/xzmMvCdyfYjTWMosDUAuTG/1DmzJmjXr166d///reGDRumzz77TM8884zCw8NNivD2JF5NkmT7SYOXT/anFUnXbD85SLyanDPH+hONkt4l850T0rWF4i8navf6n2xeq9e4bjq846jeffYD/bjpYN6bR3pqhl6Y2OMOz6pgIcfORX6djxw7F/k1R26OcvOcK3fn48ZP83FzrOECzmIpmD+FTIG6BuT06dPq1KmT1Vj37t0VGVk47rgQfTxWmdczVeWGXtfc389EnrWZc/Zodo/njf2xub+fvmFOi/b36X9rdufbC1uxegVF7jxqNZaWkq6je46rxt0Bt3k2BRM5di7y63zk2LnIrzmiT8Zl571WRavxKrUqSZLOREXnNw35YA2jKDC1AMndss1Vo0YNJSdbV+lXrlyRj4+PkWHdsYy0DB3cekTBXZpbjT/UtYUSriQqavevNnOij59X9PHzCnm6pdV4yNMt9dvRaMWduZg35lPWW/51K+vwjqM3vowk6beoc2r4oPXdMdw93VX33po6fzLuTk+rQCHHzkV+nY8cOxf5NUdG2nX9vOOYHux4n9V4cKdmSriSpKM/njApssKHNYyiwNSL0C0Wi9q0aaOaNWuqdu3a8vDw0LRp07Ro0SK5u7tr3759mjBhgkJDQ80M87YsfmeF3ts0RmOWDtHXC75TUKv66ja0o/494nOlp6bLy6eUqgcFKPp4rK7l3Nbu80krFLbgNcVfTtDOtXvVsmMzPdy9lSZ2n2712jUbVZNk+0lGroVjl2r8qjCNWTpEG+Z/J3fPEnr6jQ4q719Ok3vOdO6JG4gcOxf5dT5y7Fzk1xz/mfalJq8dqlELB2jjom0Kal5HXQc/pvljlyk9NUNePiVVLbCKYk5c0LVLCX/+gkUYa7jgKox3nCqITN0B+e677zRjxgw99thjysrK0oULF3T48GFlZmZvCfbt21deXl566623zAzztuz//pD+2fV9BdSvovGrhqnNsyGaN+z/tCx8rSSpzr01NXPnu2re/t68Od8s/K8+eOUT3ff3xpqwKkxNQu/We71maeuynVavfVelMpKkxCuJ+R5755d7Nar9ZJWvUlbjV4bpzbmvKDUpTQObv60jPxxz0hkbjxw7F/l1PnLsXOTXHAe2HtGknrMVUNdPYxcPVOtuLfTv0V9o+cyvJUl1mlTXB5vH6IF2jU2OtOBjDcPVuVludssKk2RkZMjd3V2SdPToUdWrV8+mVetOtS3WzSGvAwBAQVXcmy+Lc6bMxPz/cIfjbMoquF862brde2aHkK/vNw43O4TbYmoLVn5yiw9Jql+/vomRAAAAAH9QoD62L7wK1F2wAAAAALg2ChAAAAAAhilwLVgAAABAQeRWsC6dLrTYAQEAAABgGAoQAAAAAIahBQsAAACwR5bZAbgGdkAAAAAAGIYCBAAAAIBhaMECAAAA7MBdsByDHRAAAAAAhqEAAQAAAGAYWrAAAAAAe9CB5RDsgAAAAAAwDAUIAAAAAMPQggUAAADYg7tgOQQ7IAAAAAAMQwECAAAAwDC0YAEAAAB2cKMDyyHYAQEAAABgGAoQAAAAAIahBQsAAACwB3fBcgh2QAAAAAAYhgIEAAAAgGFowQIAAADs4JZldgSugR0QAAAAAIahAAEAAABgGFqwAAAAAHtwFyyHYAcEAAAAgGEoQAAAAAAYhhY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              " - ] - }, - "metadata": { - "filenames": { - "image/png": "/Users/mhjensen/Teaching/MachineLearning/doc/LectureNotes/_build/jupyter_execute/chapter10_59_2.png" - } - }, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "# visual representation of grid search\n", "# uses seaborn heatmap, you can also do this with matplotlib imshow\n", @@ -2235,584 +1415,7 @@ "collapsed": false, "editable": true }, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/Users/mhjensen/miniforge3/envs/myenv/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.\n", - " warnings.warn(\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Learning rate = 1e-05\n", - "Lambda = 1e-05\n", - "Accuracy score on test set: 0.18333333333333332\n", - "\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/Users/mhjensen/miniforge3/envs/myenv/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.\n", - " warnings.warn(\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Learning rate = 1e-05\n", - "Lambda = 0.0001\n", - "Accuracy score on test set: 0.18611111111111112\n", - "\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/Users/mhjensen/miniforge3/envs/myenv/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.\n", - " warnings.warn(\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Learning rate = 1e-05\n", - "Lambda = 0.001\n", - "Accuracy score on test set: 0.13055555555555556\n", - "\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/Users/mhjensen/miniforge3/envs/myenv/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.\n", - " warnings.warn(\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Learning rate = 1e-05\n", - "Lambda = 0.01\n", - "Accuracy score on test set: 0.24444444444444444\n", - "\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/Users/mhjensen/miniforge3/envs/myenv/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.\n", - " warnings.warn(\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Learning rate = 1e-05\n", - "Lambda = 0.1\n", - "Accuracy score on test set: 0.23333333333333334\n", - "\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/Users/mhjensen/miniforge3/envs/myenv/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.\n", - " warnings.warn(\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Learning rate = 1e-05\n", - "Lambda = 1.0\n", - "Accuracy score on test set: 0.12777777777777777\n", - "\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/Users/mhjensen/miniforge3/envs/myenv/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.\n", - " warnings.warn(\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Learning rate = 1e-05\n", - "Lambda = 10.0\n", - "Accuracy score on test set: 0.1527777777777778\n", - "\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/Users/mhjensen/miniforge3/envs/myenv/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.\n", - " warnings.warn(\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Learning rate = 0.0001\n", - "Lambda = 1e-05\n", - "Accuracy score on test set: 0.9111111111111111\n", - "\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/Users/mhjensen/miniforge3/envs/myenv/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.\n", - " warnings.warn(\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Learning rate = 0.0001\n", - "Lambda = 0.0001\n", - "Accuracy score on test set: 0.8888888888888888\n", - "\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/Users/mhjensen/miniforge3/envs/myenv/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.\n", - " warnings.warn(\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Learning rate = 0.0001\n", - "Lambda = 0.001\n", - "Accuracy score on test set: 0.8722222222222222\n", - "\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/Users/mhjensen/miniforge3/envs/myenv/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.\n", - " warnings.warn(\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Learning rate = 0.0001\n", - "Lambda = 0.01\n", - "Accuracy score on test set: 0.8305555555555556\n", - "\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/Users/mhjensen/miniforge3/envs/myenv/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.\n", - " warnings.warn(\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Learning rate = 0.0001\n", - "Lambda = 0.1\n", - "Accuracy score on test set: 0.8888888888888888\n", - "\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/Users/mhjensen/miniforge3/envs/myenv/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.\n", - " warnings.warn(\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Learning rate = 0.0001\n", - "Lambda = 1.0\n", - "Accuracy score on test set: 0.8805555555555555\n", - "\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/Users/mhjensen/miniforge3/envs/myenv/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.\n", - " warnings.warn(\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Learning rate = 0.0001\n", - "Lambda = 10.0\n", - "Accuracy score on test set: 0.8944444444444445\n", - "\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/Users/mhjensen/miniforge3/envs/myenv/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.\n", - " warnings.warn(\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Learning rate = 0.001\n", - "Lambda = 1e-05\n", - "Accuracy score on test set: 0.975\n", - "\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/Users/mhjensen/miniforge3/envs/myenv/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.\n", - " warnings.warn(\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Learning rate = 0.001\n", - "Lambda = 0.0001\n", - "Accuracy score on test set: 0.9777777777777777\n", - "\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/Users/mhjensen/miniforge3/envs/myenv/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.\n", - " warnings.warn(\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Learning rate = 0.001\n", - "Lambda = 0.001\n", - "Accuracy score on test set: 0.9805555555555555\n", - "\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/Users/mhjensen/miniforge3/envs/myenv/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.\n", - " warnings.warn(\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Learning rate = 0.001\n", - "Lambda = 0.01\n", - "Accuracy score on test set: 0.9861111111111112\n", - "\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/Users/mhjensen/miniforge3/envs/myenv/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.\n", - " warnings.warn(\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Learning rate = 0.001\n", - "Lambda = 0.1\n", - "Accuracy score on test set: 0.9805555555555555\n", - "\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/Users/mhjensen/miniforge3/envs/myenv/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.\n", - " warnings.warn(\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Learning rate = 0.001\n", - "Lambda = 1.0\n", - "Accuracy score on test set: 0.9777777777777777\n", - "\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/Users/mhjensen/miniforge3/envs/myenv/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.\n", - " warnings.warn(\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Learning rate = 0.001\n", - "Lambda = 10.0\n", - "Accuracy score on test set: 0.9444444444444444\n", - "\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/Users/mhjensen/miniforge3/envs/myenv/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.\n", - " warnings.warn(\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Learning rate = 0.01\n", - "Lambda = 1e-05\n", - "Accuracy score on test set: 0.9861111111111112\n", - "\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Learning rate = 0.01\n", - "Lambda = 0.0001\n", - "Accuracy score on test set: 0.9888888888888889\n", - "\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Learning rate = 0.01\n", - "Lambda = 0.001\n", - "Accuracy score on test set: 0.9888888888888889\n", - "\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Learning rate = 0.01\n", - "Lambda = 0.01\n", - "Accuracy score on test set: 0.9861111111111112\n", - "\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Learning rate = 0.01\n", - "Lambda = 0.1\n", - "Accuracy score on test set: 0.9888888888888889\n", - "\n", - "Learning rate = 0.01\n", - "Lambda = 1.0\n", - "Accuracy score on test set: 0.9722222222222222\n", - "\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Learning rate = 0.01\n", - "Lambda = 10.0\n", - "Accuracy score on test set: 0.9527777777777777\n", - "\n", - "Learning rate = 0.1\n", - "Lambda = 1e-05\n", - "Accuracy score on test set: 0.9027777777777778\n", - "\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Learning rate = 0.1\n", - "Lambda = 0.0001\n", - "Accuracy score on test set: 0.8583333333333333\n", - "\n", - "Learning rate = 0.1\n", - "Lambda = 0.001\n", - "Accuracy score on test set: 0.8722222222222222\n", - "\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Learning rate = 0.1\n", - "Lambda = 0.01\n", - "Accuracy score on test set: 0.9055555555555556\n", - "\n", - "Learning rate = 0.1\n", - "Lambda = 0.1\n", - "Accuracy score on test set: 0.8805555555555555\n", - "\n", - "Learning rate = 0.1\n", - "Lambda = 1.0\n", - "Accuracy score on test set: 0.8722222222222222\n", - "\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Learning rate = 0.1\n", - "Lambda = 10.0\n", - "Accuracy score on test set: 0.8666666666666667\n", - "\n", - "Learning rate = 1.0\n", - "Lambda = 1e-05\n", - "Accuracy score on test set: 0.08611111111111111\n", - "\n", - "Learning rate = 1.0\n", - "Lambda = 0.0001\n", - "Accuracy score on test set: 0.10555555555555556\n", - "\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Learning rate = 1.0\n", - "Lambda = 0.001\n", - "Accuracy score on test set: 0.10555555555555556\n", - "\n", - "Learning rate = 1.0\n", - "Lambda = 0.01\n", - "Accuracy score on test set: 0.17777777777777778\n", - "\n", - "Learning rate = 1.0\n", - "Lambda = 0.1\n", - "Accuracy score on test set: 0.08333333333333333\n", - "\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Learning rate = 1.0\n", - "Lambda = 1.0\n", - "Accuracy score on test set: 0.08888888888888889\n", - "\n", - "Learning rate = 1.0\n", - "Lambda = 10.0\n", - "Accuracy score on test set: 0.09444444444444444\n", - "\n", - "Learning rate = 10.0\n", - "Lambda = 1e-05\n", - "Accuracy score on test set: 0.17222222222222222\n", - "\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Learning rate = 10.0\n", - "Lambda = 0.0001\n", - "Accuracy score on test set: 0.11666666666666667\n", - "\n", - "Learning rate = 10.0\n", - "Lambda = 0.001\n", - "Accuracy score on test set: 0.10555555555555556\n", - "\n", - "Learning rate = 10.0\n", - "Lambda = 0.01\n", - "Accuracy score on test set: 0.1388888888888889\n", - "\n", - "Learning rate = 10.0\n", - "Lambda = 0.1\n", - "Accuracy score on test set: 0.11388888888888889\n", - "\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Learning rate = 10.0\n", - "Lambda = 1.0\n", - "Accuracy score on test set: 0.10555555555555556\n", - "\n", - "Learning rate = 10.0\n", - "Lambda = 10.0\n", - "Accuracy score on test set: 0.09444444444444444\n", - "\n" - ] - } - ], + "outputs": [], "source": [ "from sklearn.neural_network import MLPClassifier\n", "# store models for later use\n", @@ -2850,36 +1453,7 @@ "collapsed": false, "editable": true }, - "outputs": [ - { - "data": { - "image/png": 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AAMAyJCAAAAAALEMJFgAAAOAFp4xAN+GqwAgIAAAAAMuQgAAAAACwDCVYAAAAgBdcPIjQJxgBAQAAAGAZEhAAAAAAlqEEyyL1m1ZS30faqUyFIjpzKkHzpq/WtAm/Zbu+w2FX97ubqv1NdVSkeD4dPxKrJfM26psJvyktzZlp/fA8Ifro24c1+eOftXDun348kpypfotrdPcTnVWmUlGdORmveVNW6ptPfvZq20rVS2rMt4+qX4e3dPTQKUlS0ZIFNPGXodlu89OM1Rrz7Lc+afuVon6barr7mRtV5poonTkRp3lf/qZv3v/Jq20r1SqtMd8/rX5Nh+nowZPZrnf/8O669f62uj7qEV81+4rRsFEF3duvlcqWLawzpxP03dx1mvL1imzXdzhsuu3269Sxcy0VKRKp48fOavGiLZry9e9KSzv/qKzSZQrp/gfaqHadsnKmObVx4wF9PHaxoqNPW3BUOV/9ZpXV97EO6X1zvOZ984emjV+a7foOh13d+zZT+5vrqUixfDp+5IyWzNugbz5bmmXffDVr0KmO7n2lp8pUK6Uzx2L1/Sc/aeobsy+6TbveLdTz2VsVVaGYju4/rm9Hz9H88Us81ml6c0P1fqGHSlcpoZMxp7Vo8q+a+vpspaWmZazz3NePq03PZpn2/2qvMfpl2u8+Ob6cqH7Lqrr7yetVpnIxnTkZp3lfrdA3Hy32attKNUppzMxB6tfmtYzfunNKVSiqfkNvVK3rKiktzanNf+zRuFfnKOZA9v11bsUsWL5BAmKBa2uX1rB379TSBZs18cPFqlG3rPo+2k6GzdDUz7L+oXtgyPXqcFMdff3pr/pryyFVujZKfR5so2JR+TRm+ByPdSPyhmn4u3eqWIkCVhxOjnNt3bJ6+eN7tHTeBn055kdVb1BefQd3ks1maOpHSy66bfmqURo+7j45guwey08di9UTt32Qaf0bezdRyxtq66dvV/v0GHK6axuU18tfPKClc9fpyze/V/VGFdT32ZvcMX53wUW3LV+tpIZPejhTjC9Uo3Elde3X2oetvnJUq15Sr7x6m375eas+H/+ratQsrfv6t5ZhM/T15KxPph5+tIM6dqqpyZOWa8f2aFWuXEx339NCxYrl1ehR8yRJRYpE6r3379KBAyf12sg5Cg526L5+rfTm6J7qf+9nSklJy3LfucW1tcto2Pt9tPTHTZr4/kLVqFdWfQd2kGGzaeq4X7Lc5oFnuqhD17r6+tOf9dfmg6p0bQn1eaidikXl15iXZ1l7AAFUrck1GjHnGf067Xd9/uJU1WheVfeO7CWbzaavX5uZ5TYtezTWkImPatZ787Tmxz/V9JZGGjzuISUnpmjJ18skSfXa19LLM57Sr9N+1/ihX6l8zTK679Veyl8knz54bHzGvirWKadFk5Zq7tgfPT7j4M5o/x10gF1br5xeHneflv7wp758e76qNyyvvk9d7+6HP1x00W3LX1tCwyf0z7IfLhyVX29Pf0wH9xzVm49PVnBokPo+eb1e/fJBPdR5lFKSU/11SMjFSEAs0OeBNtqzI0ajXnB3ymt/3yW7w6bb722hmZN+V0qy50lARN4wdenRQBPeXajpE5dLkv78Y48kqf8TnTThvYU6cypBktS4dVU9NOQGhYUHW3hEOUvvx9prz7bDGv30NEnS2t/+cl8dvr+NZk5Ymim+kuQIsqvrXc101+MdlZKUuXNNTXFq+5/7PZZVrlFKLW+orYnv/Kgta//2y7HkVL2fvEF7thzU6McmSpLW/rxVDoddtz3aUTM/WZJlDB1BdnW9r7XueuZGpSSmXHT/IWHBGjymj07GnFGRkrkvkb67bwvt3nVEb7z2nSRp9R975LDb1OvOJpr+zR+ZEoXIyFDd1LWexn2yRN9MWyVJWr/ub0nSAw+107hPf9GZMwnqe29LJSSm6Oknv1Zy+r+DmJjTeuXV21SlSpQ2bTpg3UHmQH0eaqs922M06rnpkqS1y3fK7rDr9n4tNfPLZVn3zbc31IQxCzT9C/cJ85+r0vvmJ6/XhP8tyOibr3Z3vXSbdv/5t97s+74kac2CP+UIsuuOZ27R9He+V0pS5n/z97zSS79NX6mPB7v7kTU/bVBkgQjdPeyOjASk0z1tdHT/cb1x1/tyuVxat2ij8hfNp26Pd9FHT3whZ5pTIWHBKlk5SlPfmKVtq3Zad9AB1ntQR/dv3eCvJUlrl25398MPttXMz37NMlFwBNnVtW9z3TX4+iz7aUm66/FOSoxP1nN9PlZy+jpHDpzQy+P6qXKtUtqyeq//Dgq5FveA+FlQkF01G5TT8sXbPJYvW7RF4XlCVKNe2Uzb5IkI0Q/T12jlL9s9lh/cd0KSVLxkQfd6kaF68e2e2rh2r55/+Es/HUHOFhRsV63rKmr5T5s9li/7cZPCI0JUo2H5LLdr2Kqqej/WXtM+WqIJo+Z79VmPDLtFB3Yf1azPsy+duxoFBTtUq0llLZ/3p8fyZd+vV3hEqGpcVzHL7Rq2q67eT16vae/+qAmvzslynXMGvNxNJ4/GauG07EuOrlZBQXbVrlNGv/22w2P50l+3Kzw8RDVrlc60TZ48Ifpu7jr9/rvnydfB9PK2qBL5JUktWlbR/B82ZCQfkvTXjhjd0eP9XJ98BAXZVbNheS1fvMVj+bKfNqf3zeUybZMnMlQ/fLM6+765VEG/tTcnCQp2qFbr6lo2a5XH8qXTVyo8Mkw1W1TNtE2xskVUukqJTNv8NmOFSlYqrpKVo9z7DnEoKT5ZLtf5MsIzx2MVHBKk8MgwSVKFWmVlt9u0+8+/fXxkOZf7t66Slv+40WP5svkb3P1wo2x+61pfq94DO2nah4s04c3vs1ynaedaWvDNqozkQ5J2bjqoPo2Hk3xkwSkjR/650pCA+FnxUgUUHOzQoX3HPZYf3u8+UShZplCmbY4cPq0PX/s+40ftnGbtrlVqalrGvpITU/VAt/f19ouzFHs6d1x1u1Dx0oUUFOzQob8viG967EqWK5Lldn9tOqC+rV/X1I+WyOn897rt1jfVUZXaZfTxyLly5bI5+IqXLaSgkCAd2nPUY/nhv49JkkpWKJbldn/9uU99G72kqe8ukPMf9yRcqG7Lqmp3WyONeWJyroutJEVF5VdwsEMHL6i1PpReo12qdOaT2piYM3rvfwsybdOiRRWlpjp18MBJFS+eTxERoYqJOaOBgzpp1pzHNf+nIRr52m0qWjSv/w7oClG8VEF333xh33HgXN9RONM2Rw6d0oevztXBC7Zp1q6aR998tYuqUEzBIUE69Ndhj+WHd8VIkkpdUyLTNmWuLSlJOviXZ4nUoYxt3AnInA9/VMnKUbrtqa7Kky9c115XWd0GddGqH9bp7Kk4Se7yK0m68cGOmnZ4nOYlTdE7v45Q1UaVfHeQOUzx0oUUFOLQob3HPJYfTv8ulixfNMvt/tp4QH1bjNTUDxdl2Q8XK1VQEXnDdOTgST08opumrXtFc7a/qWGf9VOR9AsZgD8EvAQrLS1NP/30k9asWaPDhw8rJSVFYWFhKl68uBo0aKAOHTrI4Qh4My9bRPoVm4T4ZI/lCQnu4enwiFCv9tOsXTW161Jbs79eqbizSZKktDRnpiQlt8kT6Y5fQlySx/Jz8Q6PCMlyuxNHYi/pc7r3a6Uta/ZqU3opXG6SJ2+4JCnh7AUxjkuPcWTW3+ETMWf+dd/hkaF6/J3emvTWD5kSnNwiIr0PSEi4oI9IdL/OE571d/hCLVpWUfuONTVzxmrFxSWpZCl3KduAB9pox/bDGvnKHBUoEK5+A9ro7TG9NaDfZ0rKpiQjN4jImx73C/vm+PS+OY93cW/Wvrra3VRHs79aobjYpH/f4CqQJ38eSVJ8bKLH8oSz7tfhecMybRORvk3CBdskpvcr4en9zIZftuibUXN0/1t36f637pIk7Vy3R6/1fjdjm3MJSEhosF7tNUZ5C0Wq5zO3aNSSYRrY5Dnt3eRZPns1yJMe00v/rbt4P5yvUIQk6b5nbtSODfv15qBJylcoUvc+fYPe/PphPXT9aCX/SwktcDkCema/f/9+DRgwQEeOHFG1atVUtGhR5cuXT8nJydq2bZtmzJih999/X5999plKlMh8ReVKYNjcw2JmNhd2TS+u+DZvX01DXuuhTWv36fN3F/qyeVc8W3p8lU0YfXFFvVq9sqpUvaSGP/jFf97XlejfY5z96Ma/eWBEDx0/fFqzPr34ZAFXs3/rI1zZvfEPLVtV1dDnu2rjhv367FP37G9B6TebnjoVr5dfnJGx/0OHTumDsfeofYca+v679f/9AK5QhnEu7lnHN7vl/9S8Q3UNeeN2bVrztz4fc/HJGK4m5/uErGOUVb97/nvu+Z5xblfp/cigj+9Xp3vaaPLI6Vq/eJOiyhfV3cPu0Ovzn9eQ9sOVnJiiGWN+0NJvV2j9kvOlt+sXb9IXf72vO5/rrld7jfmvh5jj+Ou37lw/cfr4WY188IuM/z7Rfx/XmFmD1PaW+po/JfeVxl6My7zyyp1yooAmIMOHD1epUqU0ffp0RUZGZno/NjZWTzzxhEaMGKGPP/44AC387+LPXRG64GpaePpN4/FxF79i1q1PU/V7oqM2rvlbwx//WqmpuWuax39z7orjhVd/zsX7wqtFl6N551o6ezpBq3/d/u8rX4XizqR/hy8Y6TgX84TLvOrbqH0Ntbq5vgZ2fkuGzZAhI+NH1ma3yXSZXp0EXuni0r+j4RdMJBEe5o5vfFxypm3+qcdtjXT/g2214c/9evGFbzP6iHOjrKtX7fY4T9y29bDOnk1UxUpZl87lFvHnrrznueB7nSfY4/3sdLu7mfoN7qyNq/dq+MDJuapvjjsdL+n8qMU55+7RiD+TuSQ4Lr1M+MLRkdD0EcD4MwkqVKKgbujfTlNen6WJL7knFdn461btWL1b4za9o873tdWcD3/Uwb8O6+AF5V/xZxK0Zfl2Vaid+b7Kq0Fc+sjRhVUTGb91//J9zc65EZTVv2z36G+3/7lPZ88kqGK1K/PiL3K+gCYga9eu1bRp07JMPiQpb968evrpp9W7d2+LW+Y7hw+ckjPNqRJlPOu4z73ev+dYVptJkh565gbd3Kuxfl2wSaNfmJmrfuC8Fb3/hJxpTkWV9azXLlHWfW/N/l3/vaynUZtrtWLRlovex3A1i953zB3jC+6nKZH+ev9lTnvZ/Ma6CgkL1ie/vpDpvR8Ovq+F01bqnccnXda+rySHD5+S0+lSyQtm/zr3et9F7it4dGBH3dqtgX5eslVvvv6dRx9xbr9BQZm7eYfDnuun1jx84GTWfXPp9L7jIiWBDw29UTff2US//rhRo5+bnuv65sO7j7hjV6m4x/Jzr/dvPZhpm4M73AlDyUrFPW4eL5m+zb6tB1W0TGHZbDZtWe55sefvLQd05nisylZ3T8jQ+o6mij0Rp3WLPG/IDgkLVuzxs//t4HKo6H3pv3UX3JtUIv31/l1HLnO/x939REjW/URyLi7ThH8F9Cb0vHnz6ujRi58gHj58WKGh3t0nkROlpqRp07p9ata2msfy5u2r62xsonZsztxRS9K9j7XXzb0aa+ak3/X6M9/muh84b6WmpGnT6r1q1rGGx/LmnWvq7JkE7djw32qBI/KFqWS5wrlu2t1/Sk1O06aVu9Tshtoey5vfWFdnTydox/p9l7XfyaN/0MDOb3r8mT/ZPRXnwM5vavLoH/5z268EqSlObdywXy1aVvFY3rJVVZ09m6jt2w5nuV2/Aa11a7cGmv7NKo0cMTtTH5GUmKpNmw6oecsqGWUWklS3XjmFhQVr48bcPQtWakqaNq39W83aV/dY3rxjDXffvCmbvnlQR918ZxPN/HKZXn96Wq7sm1OTU7Vx6TY1v/U6j+UtezTW2VNx2v7HrkzbHN4do8O7Y9SiexOP5S26N9GBHYd1dP9xHd4VI2eaUzVbXOuxTqlrSihf4byK2es+yb7poU4aOHaAHP9IrguVKKjqzapqw6+es5pdLVJT0rTpjz1q1qmmx/Lm19d2/9b9eXm/dUkJKdqy2r3foODz/USdppUVlieEWbCyEOjZrq6WWbACOgLSo0cPDR06VAMHDtR1112nqKgoBQcHKyUlRUeOHNEff/yh0aNHq0ePHoFs5n82Zdyvev2Tvnp+1O1aMHu9qtUurR59m2nCuwuVkpym8DwhKlOhiKIPntSZUwmqUKW4bru3uf7ackhLf9qsqjVLeexv/55jmW6czM2mjl2s1yYO0HPv9dFP01fr2npl1b1/K00YNd8d34gQlalUTNH7T+jMyfhL2nf5Ku6ZWS736tLVYur/ftRr3zym5z7tp5+mrtC1DSqo+8PtNWHkHKUkpSo8IlRlrimu6H3HdeZEnFf7PHrwZKanojdq704kd/7HxPFKM3nSco16+069NOxW/Thvg6rVKKXbezbWuE+WKCUlTeHhwSpbrrAOHzqtM2cSVLFSUfXs1UTbtx/WL79s07UXlEns+/u4EhJSNP7TX/T2/3rrtTfu0LfTVqpAwTwacH9bbd16SCt+zz3PT8jOlE9/0evj7tXzb/fUglnrVK1OGfW4p7kmjFlwvm+uWFTRB06k981Ruu2+Fvpr80EtXbBZVS+YInn/7qO5pm/++tUZenPhi3px2mD9+PkSVWtaRbc91VWfPfuVUpJSFB4ZprLVSunw7iM6c9w96cdXI2fo6c8fUezJs1oxd42adG2g1nc01St3vCPJPd3uzHd/0G1PdZUkrV24UcXKFlGfl27TkX3HNG+c+4nfk1+Zrtd/fEEvz3hKcz78UZEFI3T3y7fp7Kk4fTt6bmACYoGpHyzUa5Mf1HMf3q2fvvlD19Yvp+73t9aEN39QSnJq+m9dcUXvP35Jv3Wfv/WD3pryiEZMGKAZ435R/sKRuu/ZG7V9/T6tXLT533cAXAbDDGCRtWma+vDDD/X5558rISFzzWiePHnUu3dvDRo0SDbbfx+s6Vznpf+8j8vVtM21uuuhNipZrrBOHI3Vd9P+0MxJ7icc12pQTm99dp/efmmmFs79U3c91Fa9H2id7b6G9J+gjWv+9lhWrER+TZw3OGMfgWDEB24GmKYdqqvPwI4qVaGIjh85o+8nr9DMCe6nzNdsVEFvffWg3n5mmhbNXJtp2/bd6uvJN+9Q39av62j61KfntLi+lp57r48GdBqlgxcpl7NM3KUlUL7U9Pra6vNUF5WqWFTHY87o+8+XauYn7hOCmk0q662Zj+vtQZO06JuVmbZtf3tjPfnuXerb8MVMScc/9X7yBvV5qouuj3rEb8dxMSlVSgbkcyWpWfNrdM+9LVWqdEEdP35Wc2ev1bff/CFJql2njN75Xx+99cZ3WvDjJt1zb0vd1bd5tvsa/PhkbUi/Ilqtekn1699aVa8toeTkVC1f9pc+/mjxv95b4i9BJwL3Hc5K07bVdNcj7c73zVNWauaX7gfA1mpQXm993l9vvzBdC+es112PtFPvB9tmu68h936mjWsCf8XYueUvSz6n2S2NdPew21WqSgmdOHRSc8f+qOnvuJ81UatVNb3983CNuvdD/TTxl4xtutzfXrc92VVFShdS9J6jmvrGLC2avNRjv7cOukE3PtBRxcsX1cnoU1q7cKM+f35KRiIjuZ+Y3ufFHqpQq6xcLpfWLNigcc9M1rED/p8K2VE287N5rNK0Y031eaKTSpUv6v6tm7RMMz/7VZJU87qKemvqI3r7qSlaNGN1pm3bd2+oJ0f3Ut/mr2T6rbu2Xjn1feoGValTRsmJKVqxcLM+e3Xuv94L5S/z974TkM/1xrr9ZQLdhCzVK3NlXbgLaAJyTmpqqrZt26YjR44oMTFRoaGhKl68uKpWrargYN894TuQCUhuEMgEJNcIYAKSGwQyAcktcloCcjWyKgHJrQKZgOQWOTkBWb2/XKCbkKWGZf4OdBMuSY54wEZQUJBq1aoV6GYAAAAA8DOehA4AAADAMjliBAQAAADI6XgQoW8wAgIAAADAMiQgAAAAACxDCRYAAADghSvxoX85ESMgAAAAACxDAgIAAADAMpRgAQAAAF5wmly79wWiCAAAAMAyJCAAAAAALEMJFgAAAOAFF9fufYIoAgAAALAMCQgAAAAAy1CCBQAAAHiBBxH6BiMgAAAAACxDAgIAAADAMpRgAQAAAF7gQYS+QRQBAAAAWIYEBAAAAIBlKMECAAAAvOBiFiyfYAQEAAAAgGVIQAAAAABYhhIsAAAAwAtOrt37BFEEAAAAYBkSEAAAAACWoQQLAAAA8AIPIvQNoggAAADAMiQgAAAAACxDCRYAAADgBRfX7n2CKAIAAACwDAkIAAAAAMtQggUAAAB4wWkagW7CVYEREAAAAACWIQEBAAAAYBkSEAAAAACW4R4QAAAAwAtOrt37BFEEAAAAYBkSEAAAAACWoQQLAAAA8ILL5Nq9LxBFAAAAAJYhAQEAAABgGUqwAAAAAC8wC5ZvEEUAAAAAliEBAQAAAGAZSrAAAAAALzhNI9BNuCowAgIAAADAMiQgAAAAACxDCRYAAADgBRfX7n0iVyUgQ2dPDXQTrmpJZq76OgVEkhkc6CZc1ZJcQYFuwlWvdNCJQDfhqnfWFRroJlzVnNoW6CYAVzzSOAAAAACW4ZI1AAAA4AWnybV7XyCKAAAAACxDAgIAAADAMpRgAQAAAF5wiQcR+gIjIAAAAAAsQwICAAAAwDKUYAEAAABeYBYs3yCKAAAAACxDAgIAAADAMpRgAQAAAF5wcu3eJ4giAAAAAMuQgAAAAACwDCVYAAAAgBdcJg8i9AVGQAAAAABYhgQEAAAAgGUowQIAAAC8wCxYvkEUAQAAAFiGBAQAAACAZSjBAgAAALzgMrl27wtEEQAAAIBlSEAAAAAAWIYSLAAAAMALTvEgQl9gBAQAAACAZUhAAAAAAFiGEiwAAADAC8yC5RtEEQAAAIBlSEAAAAAAWIYSLAAAAMALzILlG4yAAAAAALAMCQgAAAAAy1CCBQAAAHiBWbB8gygCAAAAsAwJCAAAAADLUIIFAAAAeMFJCZZPEEUAAAAAliEBAQAAAGAZSrAAAAAAL7h4EKFPMAICAAAAwDIkIAAAAAAsQwkWAAAA4AVmwfINoggAAADAMiQgAAAAACxDCRYAAADgBZfJLFi+wAgIAAAAAMuQgAAAAACwDCVYAAAAgBecXLv3CRIQi2xeLc2ZKB3eL0Xmk1p1kTrfIRnZlBKmpkjfTZZWLZbiYqXipaWOPaTr2ma9fmK8NOIh6aY+UtOO/juOK8nWNaZ+mOhSzH4pIp/U7AZDHe4wZGQT9NQUU/Mnm1qzxFR8rFS0tNS2u6GGbelsJGnHGqd+/DJVR/ebypPPUOMb7GpzuyPbeDqdppbOSNMfC5yKPWGqcElDbW53qE4rz25nzcI0/TojTScOm4osaKh+O7va9XLI7shddbY716Rp8aQkHTvgUnheQw1vCFaL24IvGt/lM1K0bmGqzp5wqVAJm1rcHqKaLYM81tu2IlW/TEnWiUMuRRQwVLute7+OoNwVX0natNrQzC9sOrzfUGQ+qXUXl7r0dF20H549yaYVi22Ki5WiSkudezjVpJ3psd6a3wzN+8am6AOGwsKlanVN3dbfqXwFLDioHI5+2P+2rXFp3kSXjuw3FZFPanqDTe3usGUb47QUUz9OdmntEldGjNt0t6s+MYaFSEAssHuL9OEwqUEr6ea+0q4t0uwvJJdL6nJn1tuMe13auMqddFStIx3YLU1+V4o7I7W71XPd+Fjpg2HSiSOGJDOLveU+e7aaGjfMpbotDXXpa2jPZlM/TDRlmlKnXll3yhPfcGnzKvePXZU6hg7uNjXtPVPxsS61viV3d8x/b3Xqi+Epqt3Srs5327V3i0sLJqbJdEntegVluc3CyWn6+Zs0tb/ToXLVbNq0zKmv30iVzWaoVgu7JGnZ7DTN/SRVNZvb1KVfkOJjTS2cnKbovS71fSnEykMMqP1b0/T1Kwmq0SJI7e4K1b6taVr8ZbJMl9SqZ9Zx+PmrZP32bYpa9wpRmWvt2vp7qr59M1E2m1S9ufu/ya71aZr6aqJqtHCow72hOvK3U4snJiv+jEs3PhRm5SEG3M4tht592a5GrUx1u8epvza7kxHTlG6605XlNh+/ZteGVYY69XCpWl1T+3cZmviuXWdjXep4q3ub1UsNjR3pUOsuTnXr61LsaWnWRLveGuLQsA/TFBRs4UHmMPTD/rd3q0vjhzlVp6WhG/ratXezqXkTXTJNqUMve5bbfPmGU1tWmWrT3abKdQwd2m3qm/ecios11eqWrLcBfI0ExALffSWVriD1G+J+XaOh5EyTfvxG6tBdCr7g/GL/LunP3w3dco+pG3q5l1WrJ4WESjM+k5p0kMIj3Mv//F2a+pGUnGjd8VwJfpzsUskK0t1D3D9Y1RoYcjpdWvSNqTbdTAWHeP74HdhlauPv0o33GOrY071NlXqGgkNdmjPeVKP2psIjct8V43MWfZWmEhUM9XzafTZVpYFdrjTp52/T1LKbQ0EhmWOz+qc01W1tV4fe7pPhynXtOrw7Sb9/n6ZaLexyOU0t/CpVlevadNfz5/8RlKpk09sPJuuvdU5dUy93/Bj+PCVZxSvY1P0pd1JQuYFDrjTpt+nJanprcJbxXb8wVbVaBanNne7YVazrUPRul1Z9n5KRgKxfmKJ8RQx1fypMNruhSnUdij9tasXsFF0/IDRXjTLNmWxTmYqm7n/GKUmq2dCU0ynNm2ZTp+6uTP3wvl3Sut9t6n6vUzf2cicb1euZCgmVvvnMpuYdXAqPkOZ+ZVetRi71HXQ+iSle2qlXHnPoz5WGGrbMvReF6If9b8Fkl0pWMNRniPt07toGktMpLf7GpVbdbJlifHCXqU2/m7rhHps69HT3r1XqScGh0nfjXWrU3qYwYnxRzILlG1xO8LPUFOmvjVLd5p7L67eQkhMN7dyceZvo/e6/azX2XH5NTSk5ydCODe7XCXHSR69IVWpJg17zfduvVKkppnZukmo38+wk6jQ3lJwo7c4i5kcOuE8SalznuU2lmoZSkqSdG/zW3BwvLcXU7o0u1WjmmQzUbG5XSqK0d3PWV4+dqVJIuOey8HyGEmLdsT57WkqMk6pd57nfYmVtypNX2vaH02fHkJOlpZr6e6NT1zbxHEmq3jxIKYnSvi1ZxyEtq/jmNZR49vwJrzNVCgo1ZLMbHus403LXRYvUFGnHRkP1m3kmAw1amEpKNPTXpswnFIf3u5fVbuz5/a5Sy6XkJEPb/jTkcknV67nU6gbPdYqXcn/O0ejce6JCP+x/aSmmdm0yVfOCGNdOj/GezZmT33Mxrn6d5+lfxZq29Bjn3oQZ1iIB8bPjMVJaqqFiJT2XFynh/vvIwczbROZz/33iiOfyo9Hn9ym5R06Gfyrd+7QUkdd3bb7SnYhxn3gVLenZKZ+L+bFDmTvYiHzudS+M+fHoc/vMvZ3yiRhTzjSpcEnP7qJQCXfMsoqnJLW41aG1i53ascappHhT65ak6a81LtVr5044wvJINrt08qjn9glnTSXGSSdzScxPRbuyjG/BKPfrE4eyTvCa3hKsP5ekaueaNCUlmNrwc6p2rU1T7bbnE5lGNwbr5GGXls1IVmKcqQPb07RyTooqN3AoPDL3nBwfO9cPl/L8ThUr4X4dcyhzLPKe64djPN87l1QcjzFks0k9H3CpXlPP/a5d5l6nVLnc8R3OCv2w/2UX48IX6Zsj0r/XJ494vnci2v06t/S7CDxKsPwsIc79d+gFVyrPvU5KyLzNNbWkwlGmpo51JxnlrpEO7pFmjpcMm6nkJPd6jiD3zenwlJhNzEMuEvNKNaVCUdKMj1wKDrGpzDXS4b3S3AkuGTYpJcm/bc7JkuLdP0jZxTM5IesfrGZdHdq7xaXxL6ZkLGvY0a7WPdwnyMGhhmq3tOv3uWkqXsZQ9aZ2xZ0xNffjVNkcUmouiXlienxDwj1PIoIzvq9Zx/e6rsHatyVNk14+/4Wu1yFIzbufryUqX8uuZt2D9dOEZP00IVmSFFXRptuG5K77PxLi3LENu4R+uEotU0WiTH011q7gUKfKX2PqwB5D335m9+iHLxRzSPpmnF1lK5mq2TD3nszRD/tfYty5vsNz+cViXLGmoUJR0qyPnAoOkcpcY+jQXlPfTXASYy+5rsJr9y6XSx988IG+/fZbxcbGqn79+nr55ZdVtmzZLNc/duyYXn/9dS1fvlyS1LhxYw0dOlTFixf3+jNJQPzMTP/9yW6WFSOL77EjSHr8VWniO9KYZ90b5itoqufD0qevue8FQfbOxVzZxTyL5Y4gQw+/atPX77j04VD3Fee8BaXuD9n0xesuBefimJtZX4DPkNV3OC3F1EdPJ+vsKVPdHgtSkVKG/t7i0pJpaQoOS9HND7rvJen2WJAcQdL0d1P17f9SFRQitb7NodRkKSiXxPxyvq9pqabGD4lX3ClTNz0aqsKlbNq/xaml3yQrOMzQDQ+4g/fdB0lavyhVrXoGq0Jth04dcennr5L15UsJuufVcAWH5o5RkH/th7PsE6QnX0vThLftGvWM+6cyf0FTdz7s1Eev2bPshw/vl0Y/65AjSHrkxTTZrr7zFK/RD/uf67K+14YeeNWhqe849dFQd3ln3oLSrQ/Z9eXrTmKcS40dO1ZTp07V66+/rmLFimnUqFEaMGCAvv/+ewUHZ55J44knnpDT6dTnn38uSRo+fLgefvhhzZw50+vPJAHxs/A87r8vvBJx7vWFV+TOKVpSevptKfZ0+lSEJaVTxyTTZShPZO69quaNsGxinpz+OjRP1tsVKWFo0Gi7zqbHvEhJ6fQx9wl4nkj/tTenC02/ITE5u3iGZ/6V27Tcqei9pga8FqzKdd0lVxVr2RUWYWj22FQ16uRSVHmbQsIM3fZEsLo+aOrUUVMFixkKDjW0+qckVayVO87ewvKci6/nv+uUjO9r5vhuXZ6mI3td6jsyXBXrurvx8jUdCo0w9MNHSarfKUhhEYbWLkhVi9uD1e4u91lFeUklK9v14SPxWr8wVdfdlDumaArP445tYrzn8ox+OJs+oVhJaeg7TsWecirurPv1yaPp/XBez/9e2/409MEIu0LDpKffTFORKF8fxZWFftj/zvUd2cU4u+91kRKGHhvt0NnTphJipcL/iHFuKs2EW0pKiiZMmKCnn35arVq1kiSNGTNGLVq00MKFC9WlSxeP9WNjY7V69Wp99NFHqlatmiTp/vvv18MPP6xTp06pQAHv5h8PeAJy1113ZTtX9YW+/PJLP7fG94qUkGw2U0cPey4/lv46KovRrZRkad0yqVJ1qXBxKW9+9/J9O91/l6nkt+ZeFQqXkGw26fhhU/+8/HYu5sXLZP6+pSSb2rDMVIXqhgoVNxSZ3718/073SUapSrm3Uy4U5a51Px7tknT+hvETh92xKZpFPE+l39dRrppnElGhpvv1kf3uBGTrKqfCI6Ry1e0qXta9n7jTps4cM1WyUu5IQApE2WSzSSejPYeazr0uUiZzHE4fdb9XpprnDfzlarhfH93vUr4ihkwz8zrFytkVntfQ0f254yZ/SSqa0Q97TlV+5LD7O1eibOaLOinJ7ud7VK5uqkiUlDf9N/Xvne5tylY6v82KJYbGj7areElp8GtpKljEf8dypaAf9j/PGJ937nWxbGK8cZmp8hfE+MBOd59CjP+d8yqbBWv79u2Kj49X48bnZz7KmzevqlWrptWrV2dKQEJCQhQeHq7Zs2erUaNGkqQ5c+aoXLlyypcvn9efG/Bf+CZNmmj16tU6ceKESpYsedE/V6KgYKlyTWn98n8MSUta+5sUHmGqfJXM2zgc0pQPpaXzzi9zOaUlc6SiJUyVKOf3Zl/RgoINVawpbVhuyvxH0P9cZiosQiqbTcynjzW1fN759V1OU0vnulSkhBRVzoKG51BBwYbK17Rp83KnRzw3LXMqLEIqUyVzN1K0lHvZhTNk/b3V/bpgcff7K+el6fvPUj3W+W12mgybdG2jgHdPlggKNlS2hl1bf0/ziO+WZakKzSOVuibzVMSF0+N74QxZ+7e5XxcoZlOhEu7E5sJ1jh90KiHWVP5iuSO+krsfvqamqbXLDY9+eM1vhsIjTFWokjkBcTikyR/a9cu883FyOaXFc2wqWsJUyXLuZRv+MPTZW3ZVqmbquf+RfJxDP+x/QcGGKtQ0tPGCGG9Ij3GZKplPlB0OacZYp1bMO983u5ymls11qXAJqXg5K1qOnCQmxj2zUVSU57Bt0aJFFR0dnWn9kJAQvfrqq/rjjz/UoEEDNWzYUH/++afGjRsn2yXUnQZ8BOThhx9WeHi43nvvPX3yyScqVapUoJvkc13ulMY8K33yqtSsk7Rnq/TTdKlbP/dN5onx7ql3i0RJkfndMwO1vlFaNFvKX0iKKiP9PNf9QMOHhylX1xV7q1Mvmz4c6tLnr7rUuJNNe7eaWjLdVNf7DAWHGEqMNxWzXyocJUXmd09T2vxGQ7/MNpW/sEvFShv67TuX9m6RBrxsk812dV3xuFTtejo07rkUTX4tRQ07OrRvm0u/zkjT9fe5nwGSFG/qyH5ThaIMReQ3VK2xTWWqGJoyKkUd+wSpSGlDB3a4tHhKmq69zpaRtDTv6tBnL6Ro7scpqtbYrl0bXPp5Wpra3O5Qoajc80Vv1TNEE59P0DevJ6puxyAd2ObU8pkp6nBviDu+CaaO7XeqYJRNefLZVPU6h0pVsWvG6ES16R2iwqVsOrjDqaXTklWlkfs9SWp8c7CWz3BPAlCxjkNnjrr085Rk5StiqEGn3FF+dc5Nd7o0+lm7xo60q0Unl3ZtNfTjtzbd1s+V0Q8f3m+oSJSpvPnd/XDbm1xaOMumAoWlEmVMLZ5j084thgYOd8pmc0/v+8U7doWGSzf1cil6v2c/UaCwmasTEvph/+vYy6aPhjo18VWnrkuP8c/TXbrxPvczQJLiTcXsN1U4vW92x9imX2e7lK+wVKy0oWXfubR3i6n7XrYT4ytYu3btLvr+4sWLs1yemOiek/3Cez1CQkJ05syZTOubpqkdO3aobt266t+/v5xOp8aMGaNHHnlEU6ZMUUREhFftNcx/ps0B1L9/f+XPn1+jR4/222f8+vc1ftv3v1m/XJo7yT3tbv5CUuub3E85l6QdG6S3hxi650lTTTu6l6WlSd9PllYskhLOSqUqSjf2lqrXz3r/x2Ok5/p67sNqSWbA81kPG5abmj/JpSOH3DFvcZOhtt3dJ7U7N5h6/xmXeg82dF1H9zJnmqn5k02tXmwq/qxUqoLUqbdN19bPOR1ykhm4k8bNy536aXKqjh00la+woSY32tWqu3tGq90bnfrkmRTdPjhIDTq4vwdJ8aZ+nJiqTcudSjwrFSxuqH57u1rc6pAj6HxM1/+SpiVT0nTyiKkCRQ016eJQs5sD811KcmX9VHcrbP09VT9/lazjB13KW8hQoxuD1aybe0arvRvT9PnQBN36eKjqdnB/B5ISTC2emKStv6cp8aypAsVtqt02SE1vDc6Ir2maWjEnRWvmp+pUjEuRBQ1VrOtQ+74hypMvMAle6aATAflcyT097uxJdsUclAoUktp2dalzD/eV4O0bDL35tEP9nkpT847un8W0NGnOJJt+X2RT/FmpTEVTXXu7VKOB+/2t642MG9SzcnMfp265+19mcfCDs66ccyfx1dgPOwNfPOJh43KXfpzk1NFDUr5CUvObbGrT3X0RYtcGlz58xqleg+1q9I8YL5js0urFLiWclUpWMNSxt01V6+ec47qhfBYPiskhBq3vFegmZGnzU0cv+n52CciCBQs0cOBAbdiwQaGh5/uOQYMGKSUlRR999JHH+t9//72GDx+un3/+OSPZOHPmjNq0aaNBgwapb9++XrU3xyQgR44c0datW9WmTRu/fUYgE5DcIKclIFejQCYguUEgE5DcIpAJSG6RkxKQq1FOS0CuRiQgl+7dulMua7uNGzfqtttu08KFC1WmTJmM5b169VLVqlX18ssve6w/fPhwbdmyRd98843H8u7du6tWrVqZ1s9OjvlXVKxYMb8mHwAAAADOq1q1qiIiIrRq1aqMZbGxsdq6dasaNGiQaf2oqCjt27dPycnJGcsSExN18ODBbJ8bkpUck4AAAAAAOZnLtOXIP5crODhYffr00ejRo7V48WJt375dTzzxhIoXL64OHTrI6XTq2LFjSkpyP6XylltukSQ9/vjj2r59e8b6wcHB6tatm9efSwICAAAA5FIDBw5Ujx499MILL6hXr16y2+0aP368goODFR0drebNm2vePPfUrEWLFtXXX38t0zTVt29f3XvvvQoKCtKUKVOUN29erz8zx9wDYgXuAfEv7gHxP+4B8S/uAfE/7gHxP+4B8S/uAfG/nHwPyGPrege6CVl6v95XgW7CJeGMEQAAAPCCUzlnRrYrGWk8AAAAAMuQgAAAAACwDCVYAAAAgBdcJiVYvsAICAAAAADLkIAAAAAAsAwlWAAAAIAX/stD/3AeUQQAAABgGRIQAAAAAJahBAsAAADwgosHEfoEIyAAAAAALEMCAgAAAMAylGABAAAAXnDyIEKfYAQEAAAAgGVIQAAAAABYhhIsAAAAwAs8iNA3iCIAAAAAy5CAAAAAALAMJVgAAACAF1zMguUTjIAAAAAAsAwJCAAAAADLUIIFAAAAeMElSrB8gREQAAAAAJYhAQEAAABgGUqwAAAAAC8wC5ZvMAICAAAAwDIkIAAAAAAsQwkWAAAA4AWXybV7XyCKAAAAACxDAgIAAADAMpRgAQAAAF5gFizfYAQEAAAAgGVIQAAAAABYhhIsAAAAwAsuUYLlC4yAAAAAALAMCQgAAAAAy1CCBQAAAHiBWbB8gxEQAAAAAJYhAQEAAABgGUqwAAAAAC9QguUbjIAAAAAAsAwJCAAAAADLUIIFAAAAeIESLN9gBAQAAACAZUhAAAAAAFgmV5VgNQt1BboJV7mUQDfgqmdTWqCbAPxHuepnJyBSzaRAN+GqdspFfHMzSrB8gxEQAAAAAJYhAQEAAABgGcbCAQAAAC+4RAmWLzACAgAAAMAyJCAAAAAALEMJFgAAAOAFZsHyDUZAAAAAAFiGBAQAAACAZSjBAgAAALxACZZvMAICAAAAwDIkIAAAAAAsQwkWAAAA4AVKsHyDERAAAAAAliEBAQAAAGAZSrAAAAAAL1CC5RuMgAAAAACwDAkIAAAAAMtQggUAAAB4waQEyycYAQEAAABgGRIQAAAAAJahBAsAAADwgkuUYPkCIyAAAAAALEMCAgAAAMAylGABAAAAXuBBhL7BCAgAAAAAy5CAAAAAALAMJVgAAACAF3gQoW8wAgIAAADAMiQgAAAAACxDCRYAAADgBWbB8g1GQAAAAABYhgQEAAAAgGUowQIAAAC8wCxYvsEICAAAAADLkIAAAAAAsAwlWAAAAIAXmAXLNxgBAQAAAGAZEhAAAAAAlqEECwAAAPCCaQa6BVcHRkAAAAAAWIYEBAAAAIBlKMECAAAAvOASs2D5AiMgAAAAACxDAgIAAADAMpRgAQAAAF4weRChTzACAgAAAMAyJCAAAAAALEMCkoNFH5Uad7Hpj/WBbsnVixj7V/RRqVEXEV8/Isb+R4y9s2yVoZ7329Wok0Od7nDos69sF31oW0qK9O6nNnW4zaGGHR26vb9DPyzMXN4yZ76hW+9xqEEHhzr3dOjDz21KTfPjgeRgq/6w6/4Hw9Xx+gjd3jOPJn8d/K8x/nRcsHrckUcdOkeo3/3hWrgoc/X9vv02DX0+TNffGKGbbonQ8y+G6vBhSo2y4jKNHPnnSsM9IDnU4Rjp/qdtOht35X2prhTE2L8OxUgDnlZ6fHl0rD8QY/8jxt75c7Ohgc/b1bmNqUf7ObV+k6H3P7PJ5ZLuv8uV5TZDRti1dIWhvne4dF09U9t2GRrxtl2nzrjUp4d7m8nTbXrrA7s6tHJp8EMunT4jjf3crp27Df1vpNPKQwy4zZtteu6FMLVpnaZ+9yVr0ya7PhsfLNMl3dUnJctthr8SqhUrHep5e4rq1XNq506b3n4nVGfOJKtH91RJ0tGjhh4dGK7SpVx68flEJScbGj8hRE8NCdfn4+MVEmLlUSK3IAHJYVwuac6PhkZ9xEmxvxBj/3K5pNk/Sm99FOiWXL2Isf8R40vz8USbqlYy9drz7qSg+XWm0pzShK9tuvt2l0IvOIndtlNassymx/o7NaCPO9lo3MBUWKg05mObunZyKU+4e79NGrj09vDzyUa1a9J06z1BWrHGpSYNck9S+MWXIapU0aUXnkuSJF3XyKk0p/TVlGDdfltKpkThr502LVsepP79knVXb3eC0qC+U6Fh0iefhKhTp1RFRkgTvghReJipd0YnKDTUvW1UlEvPvRCm7Tvsql0rdyV6sAYlWDnMjt3SiDGGbu5k6o3ns75qhP+GGPvXjt3S8DHSLZ2kN58PdGuuTsTY/4ix91JSpNV/GmrXwjMZ6NDKVEKioXUbM1/s2bPPvaxVU88+uEFtlxKTDK3+09CJU1LsWUOtmnrut2I5qUA+U7+uyD0XkVJSpD832NWyhWftWauWaUpMNLRxoz3TNvv2u0/xmjbx3KZOLacSkwytX++QaUq//ebQDTekZiQfklS1ikszv40n+ciCaebMP1caRkBymKhi0vyvXCpelHpjfyHG/hVVTFrwlYivHxFj/yPG3jsYLaWmGipb2vMsqExJ9+t9Bww1bej5XsH87r8Pxxi6psL59w6k33dwKNq9jcNu6nCM5+fFnnX/ORSdexKQw9E2paYaKl3KM2ErVdL9+sBBmxo29EwW8udzxzUmxlDFCv/YV3qMo2MMxcQYios3VLyYS2PeDdGSn4OUlCjVr+/UE4OSVKzYFXhmiytCwEdA9u7dq/fff18jR47Ur7/+mun9uLg4DR06NAAtC4z8ed0/ePAfYuxfxNf/iLH/EWPvnbuPLiLcc3l4mPvvuITM2zSobapUCVNvvGfXyrWG4uKltRsN/e8Tu2w2U4lJUlio1KmNqSmzbJo1z1DsWWnvfve9Iw6HlJjk5wPLQeLi3H+H5/FMCMLSYx6fkDkZq1PbqRJRLr33QajWrrMrPl7asNGuj8eFyGYzlZRk6PRp93afjAvR8eOGXno+UU8/laRdu216fHC4EhP9eljIxQKagKxdu1a33nqrvv/+ey1dulQPPvigHnvsMaWknL+ZKikpSbNnzw5cIwEAQLZc5y7KZzMgYctieVCQ9PFbaSpe1NT9TzrUtEuQhgy365H73Ffxw9LLgV4c7NSNHUwNG2VX85uCdMf9DtWubqp6VTNjndzg3MPvjEuM8ai3ElS0iEuDnwrXDTdFavgroep3r/scKzTUVGqae8MCBUy9MjxJDRs61bFDmoa/nKjD0TYtXBTkl+O5kpmmkSP/XGkCWoL19ttvq0ePHnrhhRckSfPnz9fzzz+vBx98UJ988omCgvjiAwCQk0VGuK/Kx18w0pGQfvU8IiLr7cqUkr54z6kTp5w6EyuVKSnFHJNcLkP5It37DA+Xhg9x6plHpcNHpBLF3SMrs+c7VCoq95QHRaSPfMTHe55oJqbHPE+erGNRqqSp999N1KlThs7EGipVyqVjRw25XIbyRpoKD3dvd12jNNn+cUm6ejWXIiJM7dwV8EIZXKUC+s3asWOH+vTpk/H6+uuv17hx47R+/XoNGTIkgC0DAADeKF1CsttM7T/keXJ87nXFsplPjpOSpe9/MnQwWipUQKpQVnI4pK073Ntce417m19/N7R+k6HwcKlSeXfyceKUFHP0/Dq5QYmSLtltpg5dEOODh9ynceXKZZ5QJTlZ+mmhQ9HRhgoUMFWurEsOu7TjL/cN69dUdqlECZdsNlOpqZmvoDvTxBS88JuAJiARERE6deqUx7L69etr1KhRWrBggV5//fUAtQwAAHgjJESqV9vU4qWGx2w8C381FBlhqsa1mROFIIf0+rt2zfju/GmI0ylNmWVTmZKmKpV3L/t2rk1vf+R5qjJ5uk12m9SqSe6ZxTAkWKpVy6mly4I8YvzrUociIkxdWzXzbFUOh/Tue6H67vvz1SROpzRzVpBKlnSpfHmXwsOkWjWdWvqbQ/+oftfadXYlJhmqVZNZsC4U6FKrq6UEK6AJSKtWrTRixAht2LBBqampGcvbt2+v5557ThMnTtSIESMC2EIAAPBv7r/LpU3bDD01zK7fVhn6YLxNX0y1qX8f9zNA4uKlDVsMnTztXt9ul26/xaWvZtg0ZaZNK9caevJlu/7cZGjIo86McqA7u7u0catNb75v06p17ocbjv/Krr53uFSqRMAONyDu7pOibdtsenl4qFausmv8hGBNnRasPncmKyREio+Xtmy1ZdxYbrdLN9+coukzgzVzVpDWrrPrpWGh2rzZrsceScqI8YD+yTpxwtAzQ8O0cpVd83906JVXQ1XtWqeaNc2lj5yH3wU0AXnyySdVoEAB9ezZUytWrPB4r0+fPnrppZe0ZMmSALUOAAB447p6pt4Z4dTfBww9/oJdPyyyafCDLt3b0z1Kse0vQ3c94tBv/3h2x8P3unTXbS59PtWmQc/bdeq09OGbTrVscv4Sf9OGpt54MU0r19r02FC7Fi216dmBTg26P/eMfpxTr55TI4Yl6cBBm154KUwLFwfpoQeS1aun+wLuXzvtevjRPFqx8vwzQe67J0W390jRlGnBev6FMJ05Y+jN1xPVpPH5kY0a1V3639sJcpnSS8PC9NHHIWraJE1vvZkge+bHiwA+YZhm4B9fsn//fhUoUECRkZGZ3tu7d69++uknPfDAA//5c9JiKv3nfQCBZAv8zNkAcrhUk6vW/nTKlYvm/w2Q4iUPB7oJ2ao+Z1igm5ClLTcPC3QTLkmOeBBhmTJlsn2vfPnyPkk+AAAAAAQel1MBAAAAWCZHjIAAAAAAOV3gb1y4OjACAgAAAMAyJCAAAAAALEMJFgAAAOCFK/GhfzkRIyAAAAAALEMCAgAAAMAylGABAAAAXqAEyzcYAQEAAABgGRIQAAAAAJahBAsAAADwAs8h9A1GQAAAAABYhgQEAAAAgGUowQIAAAC8wCxYvsEICAAAAADLkIAAAAAAsAwlWAAAAIA3mAbLJxgBAQAAAGAZEhAAAAAAlqEECwAAAPACs2D5BiMgAAAAACxDAgIAAADAMpRgAQAAAF4wmQXLJxgBAQAAAGAZEhAAAAAAlqEECwAAAPACs2D5BiMgAAAAACxDAgIAAADAMpRgAQAAAN6gBMsnGAEBAAAAYBkSEAAAAACWoQQLAAAA8AIPIvQNRkAAAAAAWIYEBAAAAIBlKMECAAAAvEEJlk8wAgIAAADAMiQgAAAAACxDCRYAAADgBZMHEfoEIyAAAAAALEMCAgAAAMAylGABAAAA3mAWLJ9gBAQAAACAZUhAAAAAAFiGEiwAAADAC8yC5RuMgAAAAACwDAkIAAAAAMtQggUAAAB4g1mwfIIREAAAAACWyVUjIDbyLQDAVc5u8FvnT6lcAgf+M3opAAAAwCtGDv1z+Vwul9577z21aNFCtWvX1n333ad9+/Zlu35qaqrefvtttWjRQnXq1FGfPn20bdu2S/pMEhAAAAAglxo7dqymTp2qkSNHatq0aTIMQwMGDFBKSkqW6w8bNkzTp0/XK6+8ohkzZih//vwaMGCAzp496/VnkoAAAAAAuVBKSoomTJigxx57TK1atVLVqlU1ZswYHTlyRAsXLsy0/oEDBzR9+nS9/vrrat26tSpWrKjXXntNwcHB2rx5s9efSwICAAAAeMPMoX8u0/bt2xUfH6/GjRtnLMubN6+qVaum1atXZ1p/2bJlyps3r1q2bOmx/pIlS9SkSROvP5cEBAAAAMiFYmJiJElRUVEey4sWLaro6OhM6//9998qXbq0fvrpJ3Xr1k3NmjXTgAEDtHv37kv63Fw1CxYAAABwtWnXrt1F31+8eHGWyxMTEyVJwcHBHstDQkJ05syZTOvHxcVp//79Gjt2rIYMGaK8efPqo48+0p133ql58+apUKFCXrWXERAAAADAG4EutfJxCVZoaKgkZbrhPDk5WWFhYZnWDwoK0tmzZzVmzBg1b95ctWrV0pgxYyRJs2bN8vpzGQEBAAAArmDZjXD8m3OlV0ePHlWZMmUylh89elRVq1bNtH7x4sXlcDhUsWLFjGWhoaEqXbq0Dh486PXnMgICAAAA5EJVq1ZVRESEVq1albEsNjZWW7duVYMGDTKt36BBA6WlpWnTpk0Zy5KSknTgwAGVLVvW689lBAQAAADwhvnfHvqX0wQHB6tPnz4aPXq0ChYsqJIlS2rUqFEqXry4OnToIKfTqZMnTyoyMlKhoaFq0KCBmjZtqmeeeUYjRoxQ/vz59d5778lut+vmm2/2+nMZAQEAAAByqYEDB6pHjx564YUX1KtXL9ntdo0fP17BwcGKjo5W8+bNNW/evIz133//fTVq1EiPPvqoevToobi4OH355ZcqWLCg159pmKb5H25dubK4Yq4JdBMAAPArl1yBbsJVLdoZH+gmXPVKl8w8/WtOUe7ztwLdhCz9fe+QQDfhklCCBQAAAHgh91y29y9KsAAAAABYhgQEAAAAgGUowQIAAAC8QQmWTzACAgAAAMAyJCAAAAAALEMJFgAAAOCNq+xBhIHCCAgAAAAAy5CAAAAAALAMJVgAAACAFwxmwfIJRkAAAAAAWIYEBAAAAIBlKMECAAAAvEEJlk8wAgIAAADAMiQgAAAAACxDCRYAAADgDR5E6BOMgAAAAACwDAkIAAAAAMtQggUAAAB4g1mwfIIREAAAAACWIQEBAAAAYBlKsAAAAABvUILlE4yAAAAAALAMCQgAAAAAy1CCBQAAAHiDEiyfYAQEAAAAgGVIQAAAAABYhhIsAAAAwBumEegWXBUYAQEAAABgGRIQAAAAAJahBAsAAADwgsEsWD7BCAgAAAAAy5CAAAAAALAMJVgAAACANyjB8glGQAAAAABYhgQkB4s+KjXqIv2xPtAtuXoRY/8ivv5HjP2PGHvnt1XS7ffbVL+jTe1vt2ncZEPmRa4Wp6RIYz411K6HTfU62NS9n03fL8z8jIVZ8w3dfI9NdTvY1PEOmz6YYCg1zY8HkoP98YddDz+YR12uj9SdPSP09dfB/xrjz8aFqNcdEbqhc6QeuD+PFi/KXPyyf79NLz4fpq43RurWWyL08othOnyY513AfyjByqEOxUgDnpbOxhlivM8/iLF/EV//I8b+R4y9s36z9OhzNl3fxtRj/Uyt22To3c8MuUzpgbuyjttTw236dYV0T09TjeuZ2rbT0LC3DZ06I93Vw73NpOmG3njfpo6tTD31oEunzhj68HNDf+029N6rLisPMeC2bLbrpRfC1bp1qu65L0mbNzn0+fgQmS6pd5+ULLd59ZUwrVzp0G23p6huvTTt2mnXmHfCdOZMsrp1d29z9KihQQPDVbqUS889n6jkZOnzCSF6dkgejRsfp5AQK48SuQUJSA7jckmzf5Te+ijQLbl6EWP/Ir7+R4z9jxhfmrFf2FS1kvTGC+7EocV1ptLSpM++MtT3dlOhF5zEbvtLWrzM0KD+Lt2fnqA0aWAqLFR6+xNDN3cylSdcGvuFoaYNTI0ZcS7ZMFWtiqmb+9r1+2qpaUMLDzLAvvwyRBUruvTsc0mSpEaNnHI6palTQtTjtpRMicLOnTYtXx6k+/ol6c7e7mSjfn2nQsNMjfskVB07pSgiQpr4RYjCw6S3RicoNNS9bfEol156IVx/7bCrZi2nlYeJXOKSSrCOHTumYcOGqV+/fnrmmWf0xRdfaM2aNUpMTPRX+3KdHbul4WOkWzpJbz4f6NZcnYixfxFf/yPG/keMvZeSIq3+U2rf0nOko2MrUwmJhtZuzLzN7n3u8p7WTT23aVjHVGKioT/WSydOSbFnjUzrVConFchn6tcVuadEKCVF2rjBruYtUj2Wt2iZqsREQ5s22jNts3+/+xSvcRPPerXatZxKSjL053qHTFNa9luQrr8hJSP5kKQqVVya9m0cyQf85pJGQJ577jktW7ZMlStX1sGDB/Xdd9/JNE3ZbDZVqFBBNWrUUM2aNVWzZk1VrVpVQUFB/mr3VSuqmLTgK6l4UeqN/YUY+xfx9T9i7H/E2HsHDkupqYbKlfYsiSpTyv333wcMNWvomUQUzO9+fShGuqbi+eX7D7v/PhhtqFkjUw67qUMxnp935qwUG+deJ7eUxUVH25SaaqhUKc8Ylyzpfn3woE0NGnomC/nzuWNzJMamChXOb3f4sDsxiYmxKSbGUHy8oWLFTL33bqh+/tmhpERD9eun6bFBSSpWLHfE91LwIELfuKQEZP369Xr66ad13333SZISEhK0ZcsWbdq0SZs2bdLq1as1a9YsSVJwcLA2bszisscFkpOTtXPnTlWqVEmhoaHatm2bJk+erCNHjqhy5crq27evihcvfhmHdmXKn1dS3kC34upGjP2L+PofMfY/Yuy9s3HuvyPCPZfnCXP/HR+feZsGdaTSJUy9/p5NYaEu1agq7dgljfnYJpvNVGKSFBYqdW5r6utZhiqVk9q1NHXylPT6+zY57FJikj+PKmeJj3OP9oTn8Tz7DU+PeUJC5tGgWrWdiopy6cMPQhUSmqgqVZzas9uuz8aFyGYzlZQknTnt3u6zcSGqUtWp559P1OnTNo3/LERPDc6jTz+LU1iYf48NudMlJSAhISGqVq1axuvw8HA1bNhQDRueL8I8ffq0Nm7cqM2bN//r/nbv3q177rlHx44dU4kSJTRy5Eg9/PDDKlWqlCpWrKhFixZp5syZ+vrrr1WxYsV/3R8AALCWK/2c2MimIsrIotg7OEj6ZJRLL75pU7/B7vKhIoVMDR3o0lPDbQpLLwd6abCp4CDppVGGXnzLprBQU/f1MpWUZGSskxv8a4yzWB4UJL3xVrxGvxWmIU/lkSQVKuTSI48maeQrYQoNlVLT3BvmL2Bq2PBE2WyS5FSJki4NfDSPFi8K0o03pWbeOfAfXVIC0r59e23dulWNGzfOdp38+fOrZcuWatmy5b/u76233lLdunX18MMPa/z48XrooYfUtWtXjRgxQoZhKC0tTUOGDNHrr7+uzz777FKaCgAALJA3wv133AUjHfHpt4dG5sl6u7KlpC/fd+nEKen0GffrmGOSy2UoX173GXeecOmVZ0w9+5ip6CNSieJSeJg0c56hRiVyTy1MRPrIR0K8Z6aRkOD+O0+erGNRsqSpMe8m6NQpQ7Gx7hKuo0cNuVyGIiNNhYe7t2vUKC09+XCrVs2piAhTu3bZJZGAeDBzz71H/nRJN6F3795d8+fP165du3zy4X/88Ycef/xxVa1aVc8884ySk5PVq1cvGempvMPh0IMPPqi1a9f65PMAAIBvlS4h2e2m9h/yPDHbf9D9d8VymU+Ok5Kl734ydDBaKlRAqlhOcjikLTvc71e7xr3NL79L6za5E5FK5d3Jx4lTUsxRqVplfx5VzlKipEs2m6lDhzxP2869Llsu85TEycnSooVBio42VKCAqbJlXbLbpZ1/uUecKld2qkQJ935Ts8gx0tKkkJDck+TBWpeUgNx+++3avHmzbrvtNg0dOlTz5s3Tvn37LvvDQ0NDlZTkLuIsXLiwbr/9doVcMI9cbGysIiMjL/szAACA/4SESPVrSYuWej548KdfDeWNMFXz2szbBDmkV9819O1355MWp1P6eqZNZUqaqlzeveybuTaNHut5qjLpW0N2m9Sqae45OQ4OlmrVcmrZModHjH9bGqSICFNVq2aercrhkN5/L1Q/fB+csczplGbPClbJkk6VK+9SWJhUo6ZTy34LUso/HiWybp1dSUmGatZkFiz4xyWVYI0cOVLbtm3Tli1bNH/+fM2aNUuGYShPnjyqVq2aatSooSFDhni9v+bNm+uVV17RyJEjVbFiRY0YMSLjPdM09ccff2j48OFq3779pTQTAABY6IG7Xeo/2KbBL9vU7QaX1m8x9PlUQ4MfcD8DJC5e2v23VLqkVDC/ZLdLPW82NWm6oaKFpYplTX0906b1m6X3X3VllAP17u7S/U/Z9fr7hto0M7VqnaFxX9nUv7dLpUsE8oit17tPsoY8Ha5Xhoep8/Wp2rLFrm+mBav/gGSFhLhv9t+3z64SJVzKn9+U3S51vTlFM2cEq3Bhl8qUdWnOrGBt3mzXiJGJGTHu3z9ZTw4O1/NDw3Xb7Sk6dcrQuHEhqnptmpo0zaWPnL+Y3JP3+tUlJSA9evTI+P8ul0u7d+/Wli1btHnzZm3dulVTp069pARk6NChevDBBzV27Fi9/fbbHu/NmzdPTz75pFq0aKHBgwdfSjMBAICFGteT/jfCpQ8/t+mxF2wqVlh66iFT99zhPlvb+pd07+N2jXzWpVuvdy975D5Thk2aMMXQmbOGqlaSPnrTpWb/eLhgs4bSWy+69MkkQ9O/M1SimPTcQJd6d899Z4F16zn18rBETZwYopdfClOhwqbufyBZt93uHrrYudOupwbn0dNDEtWps7umqu89yTIMadq0EJ2NNVSxklOvvZ7gMWVvtepOjX47QRMmhGj4sDCFhJhq1jxNDzyYJHvmx4sAPmGYpumzf8WmaWbcv3EpTp8+rfz583ssO3nypI4ePaqqVav6qHWSK+Yan+0LAICcyKXM9wPAd6KdWcwrDJ8qXTI60E3IVoX/vRPoJmRpz+NX1sX6SxoB+TeXk3xIypR8SFLBggVVsGDB/9giAAAAwEdy3+CbX1zSTegAAAAA8F+QgAAAAACwjE9LsAAAAICrlUEJlk8wAgIAAADAMiQgAAAAACxDCRYAAADgDUqwfIIREAAAAACWIQEBAAAAYBlKsAAAAABvUILlE4yAAAAAALAMCQgAAAAAy1CCBQAAAHiBBxH6BiMgAAAAACxDAgIAAADAMpRgAQAAAN4wjUC34KrACAgAAAAAy5CAAAAAALAMJVgAAACAN5gFyycYAQEAAABgGRIQAAAAAJahBAsAAADwAg8i9A1GQAAAAABYhgQEAAAAgGUowQIAAAC8QQmWTzACAgAAAMAyJCAAAAAALEMJFgAAAOAFZsHyDUZAAAAAAFiGBAQAAACAZSjBAgAAALxBCZZPMAICAAAAwDIkIAAAAAAsQwkWAAAA4A1KsHyCERAAAAAAliEBAQAAAGAZSrAAAAAAL/AgQt9gBAQAAACAZUhAAAAAAFiGBAQAAACAZUhAAAAAAFiGBAQAAACAZZgFCwAAAPAGs2D5BCMgAAAAACxDAgIAAADAMpRgAQAAAF7gQYS+wQgIAAAAAMuQgAAAAACwDCVYAAAAgDcowfKJXJWArExOC3QTrmoxafkC3YSrXh5bcqCbcFWLtCUFugnAf+Y0KW7wp03JVQPdhKvew4FuAPyOXgoAAACAZXLVCAgAAABw2SjB8glGQAAAAABYhgQEAAAAgGUowQIAAAC8wIMIfYMREAAAAACWIQEBAAAAYBlKsAAAAABvUILlE4yAAAAAALAMCQgAAAAAy1CCBQAAAHiBWbB8gxEQAAAAAJYhAQEAAABgGUqwAAAAAG9QguUTjIAAAAAAsAwJCAAAAADLUIIFAAAAeIMSLJ9gBAQAAACAZUhAAAAAAFiGEiwAAADACzyI0DcYAQEAAABgGRIQAAAAAJahBAsAAADwBiVYPsEICAAAAADLkIAAAAAAsAwlWAAAAIA3KMHyCUZAAAAAAFiGBAQAAACAZSjBAgAAALzAgwh9gxEQAAAAAJYhAQEAAABgGUqwAAAAAG9QguUTjIAAAAAAsAwJCAAAAADLUIIFAAAAeIFZsHyDERAAAAAAliEBAQAAAGAZSrAAAAAAb1CC5ROMgAAAAACwDAkIAAAAAMuQgAAAAACwDPeAAAAAAN7gHhCfYAQEAAAAgGVIQAAAAABYhhIsAAAAwAtGoBtwlWAEBAAAAIBlSEAAAAAAWIYSLItsWm1o5hc2Hd5vKDKf1LqLS116umRkM5aXmiLNnmTTisU2xcVKUaWlzj2catLOc/qFNb8ZmveNTdEHDIWFS9Xqmrqtv1P5ClhwUDnIjjVOLfwyVUcPuJQnr6HrbnCo1e0OGdkE2Ok09duMNK35KU2xJ0wVLmmo9W1BqtXK85/E2oVp+m1mqk4cNhVZ0FDddna17Rkku4NB2K1rTP0w0aWY/VJEPqnZDYY63GFkG/PUFFPzJ5tas8RUfKxUtLTUtruhhm25DiLRR1iBGPvX5tXSrIk2Raf3Ca27mLr+DvOi8Z072dDKxYbiYqXipaVOPUw1bpv1NEOJ8dKwh2zq2sdUs465cyqiv9cmacVXZ3Vyf5rC8tlUs3O4GvSIyLbfdTlNrZ0Vpy0LExR/0qX8Jexq2CNS17QI81hv3N0xSjjtyrR9/4nFlKeA3S/HcsXKnV89nyMBscDOLYbefdmuRq1MdbvHqb82u38ETVO66c7M/+Al6ePX7NqwylCnHi5Vq2tq/y5DE9+162ysSx1vdW+zeqmhsSMdat3FqW59XYo9Lc2aaNdbQxwa9mGagoItPMgA2rfVqUkjklWzhV0d7g7Rvi1O/fRlqkxTatMzKMttFk9O1S/fpqltryCVq2bT5uVOTXkzRYZdqtnc/c9i+exUff9pqmo0t+v6+xyKjzW16KtUxew1ddeLIVYeYo6zZ6upccNcqtvSUJe+hvZsNvXDRFOmKXXqlfUP4cQ3XNq8yp10VKlj6OBuU9PeMxUf61LrW3J3EkIf4X/E2L92bZHeH2ZTw1ambu1raucWQ7O+MORySTfemfUZ26ev27RxldSxh6lr65jav9vQpHcNxZ2R2t/quU1crPTBMJtOHDGUW88AD29L0XevntQ1zcPUpHekDm9L0e+Tz8o0pUa3R2a5zcopZ7Vmepwa3RGpEtcGa9fviZo/6pQMm1S5mTsJiT/lVMJpl1r0y6uoKp5f2NDI3N03w39IQCwwZ7JNZSqauv8ZpySpZkNTTqc0b5pNnbq7FHzBuey+XdK6323qfq9TN/Zy/8hVr2cqJFT65jObmndwKTxCmvuVXbUaudR30Pkfz+KlnXrlMYf+XGmoYcvc0Ukv/jpVURVsuuNpdyCrNLDL6ZR++TZVzW91KCgk8wnxmoVO1W5lV/ve7gSlUl27Du92aeX3aarZ3CGX09Tir1NVqa5NvZ87/x+oZGWb/vdgknauc6pyvdx7VejHyS6VrCDdPcT941StgSGn06VF35hq081U8AUxP7DL1MbfpRvvMdSxp3ubKvUMBYe6NGe8qUbtTYVH5N5RJfoI/yPG/vXdVzaVriD1H+I+3hoNTTnTpPnfGOrY3cwU3/27pPW/G7r1Hpe69HJvUy09vtM/M9S0g6nwCPe663+XpnxkU3KilUeU86yaelZFygep02D30Fq5+qFypUlrZsSp3s0RcmTxW7d1UYKqtAxT417uBKVMnRAd25OqjfPiMxKQY3tSJUmVmoQqb1FOC2ENUls/S02Rdmw0VL+Z549QgxamkhIN/bUpc4dxeL97We3GnlflqtRyKTnJ0LY/3VeVqtdzqdUNnusUL+X+nKPRueNkLi3V1J6NLlVv6pkM1GhuV0qitHdL1lc201JNhYZ7xig8r5QQ645f3GlTiXHStdd57rdYGZvy5JW2/+H04VFcWVJTTO3cJNVu5hm/Os0NJSdKuzdn3ubIgfSTkus8t6lU01BKkrRzg9+am+PRR/gfMfYvd3yles0941u/hankREM7s+gTojPi67nNNTVNJScZ2p7eJyTESWNfsalKLVNPvJZ1f54bpKWaOrQpWRWbhHosr9QsVKmJpg5tTc5yO2eqqeALfutC89qUGHs+lsf2piokj0Hy4SXDzJl/rjQkIH52LEZKSzVUrJTnt6NYCffrmEOZf6Dy5nP/fSLG871zP2bHYwzZbFLPB1yq19Rzv2uXudcpVe4K/DZehpPR7qtshUt6xqpwlPurffxQ1j9YLW4N0roladqxxqmkBFPrf07TzrUu1W3r7oBD8xiy2aVTRzzjmHjWnZhcuDw3OREjOVOlohfEvEgJ99/HDmWOTUQ+97onjnguPx59bp+5N570Ef5HjP0rI74lPY+3aHqfcORg5vhG5HOve/yCPuFYep9wPD3uwSHSK5+61O9pUxF5fdvuK0lsTJqcaVKBEp5JQv4o9+vTh7K+KFb35ght+zlRf69NUnKCS9t/SdC+dcm6tk14xjrH96YqJMKm7187qY96Rmvs7dGaP+qk4k/m3gtt8L8cm+7edNNN+vTTTxUVFRXopvwnCXHuTjQs3HN5aPrrpITM21SpZapIlKmvxtoVHOpU+WtMHdhj6NvP7DJsppKTsv6smEPSN+PsKlvJVM2GueOHLzHefZwXjmYEp8c3OYv4SlKTrg7t3eLUFy+dv2rUoKNdLXu4S7KCQw3VamnXiu/SVKysTdWb2BV3xtR3n6TI5pBSknJHfLOSGOf+O/SC73TIRb7TlWpKhaKkGR+5FBxiU5lrpMN7pbkTXDJsUko23+ncgD7C/4ixf53rE7KLb2KW8ZWKRJmaOtamkBCXyl0jHdgjTR9v84ivI8h9c3pul5z+Wxcc7nndODjM/d1OTsj6YlvtG/Po8JYUzRl+MmNZtfbhqt8tIuP1sT1pijvhVI2O4ap7cx6dPJCmlV+f1fTnjuvO/xVRUCjXquF7AU1AZs+ene17+/bt0/z581WwYEFJ0i233GJNo3zMTP/9yW4WkKyWO4KkJ19L04S37Rr1jPs/Uf6Cpu582KmPXrMrJDTzNof3S6OfdcgRJD3yYppsuaS/MM/1uZcQ37RUU588naS4U6ZueTRIRUrbtG+LSz9PS1VwaIpuetB9E94tjwbLEZSime+maMb/pKAQqWWPIKUmuROU3Orcd/pSYu4IMvTwqzZ9/Y5LHw51/0fLW1Dq/pBNX7zuUnAW3+ncgj7C/4ixf7n+Lb5ZxMERJD3+qktfvGPT28+6S13zFTTV62GXPnnNlmV8czMzI8hZv59VjNNSTU1/9rjiT7nU9uF8KlDKocNbU7T6mzgFhxlqNcA9zNdhUH7ZgwwVrei+AFeyeogKlXHo22dPaNuSRNW6IY8/DunKlTuuK/hdQBOQ4cOHKynJfZnDNDP/F33rrbckSYZhXLEJSHge93ElxnsuP3fFLSybf9fFSkpD33Eq9pRTcWfdr08elUyXoTx5PWO17U9DH4ywKzRMevrNNBW5sgeNLklYxLmrP54xSUmPb2gW8d28zKmYvab6vRqiSnXdP3wVatoVmkea+1GqGnZyqHh5m0LCDHV/PEQ3PmDq9FFTBYoZCg41tPanNBWMyr0JyLnv7IVXjZMvEnNJKlLC0KDRdp097Z6Gt0hJ6fQxdxKZJ+sJXHIF+gj/I8b+FZ4evwtHOjLie8HIyDnFSkrPvO2eOSwuNj2+x9LjG8lZ3j+FRLgzjJQLRjpSEt1xCgnPnIHs+j1Rx/9O060jCqlMHfcsAKVqhCgkj02/fHJG1TuEq3C5IEVVzTxVW4lqIQrOY+jY36m+PhRAUoDvAZk5c6aqVaum6667Tr/++qu2b9+e8ScsLEwLFy7U9u3btW3btkA28z8pWkKy2UwdPex5wnok/XWJspk72ZRk6fdFho5FS3kLSCXKSHa79PdO9zZlK53fZsUSQ28/Z1eBQtLz/0tTVC4bqi4Y5a7DPnHYM47Ho92ddNEymb/ip4+61y1bzfO98jXdycjR/e5tt61y6u8tToWEGSpW1qbgUENxp02dOW6qRMVccmkzC4VLSDabdPyCmB877P67eJnMyVlKsqnVi106EWMqMr+h4mUM2e2G9u90v1+qUu5N6Ogj/I8Y+1d28T2a3idkF98Viw0di5Hy5j8f333pfUKZSiQg/5SvuEOGTToT7XlfxunoNElSwTKZryefPepet8S1nglGyRru1ycPpCk5zqUtCxN0Yr9nomGaplypUlje3PtbB/8K6DerfPnymjZtmmrVqqWbb75Z8+bNC2Rz/CIo2D2rx9rlhv45yLPmN0PhEaYqVMncyToc0uQP7fpl3vn/PC6ntHiOTUVLmCpZzr1swx+GPnvLrkrVTD33vzQVLOLng8mBgoINlath0+bfnR6jaJuXORUaIZW+JvNXvEhp94/khTNk7dvq7qwLFHe/v2pequaN9+yUl89OlWGTrm2Ue6fgDQo2VLGmtGG56RHzP5eZCouQylbJvI3DIU0fa2r5vPPru5ymls51qUgJKaqcBQ3Poegj/I8Y+5c7vtK6C+K7Nj2+5bPpE77+0NDSeeeTFpdTWnJBfOHmCDZUsnqwdq1I9Oh3dy1PUkgeQ8UrZx7FKFDKnZRcOENW9LYUSVLeYnbZgqSfPz6tNTPiPNbZsypJaSmmStXI3c+8ypKZQ/9cYQJ+E7rD4dDgwYPVokULPfPMM1q8eLGGDRsW6Gb51E13ujT6WbvGjrSrRSeXdm019OO3Nt3Wzz33fGK8e8rHIlGm8uaXbHap7U0uLZxlU4HCUokyphbPsWnnFkMDhztls7mnPfziHbtCw6WberkypjQ8p0BhM9f8ELbtGaTxzyfr69dT1KCDQ/u2OfXbjDR1vjdIQSGGkhJMHd3vUsEomyLyGbr2OrtKV7Hpm1HJat8nSEVK2XRgh0s/T01Nf8+dXDS9OUifv5Cs7z5JUbXr7Nq9walfvklTq9scKhiVu68Kdepl04dDXfr8VZcad7Jp71ZTS6ab6nqfoeAQQ4nxpmL2S4WjpMj8hmx2Q81vNPTLbFP5C7tUrLSh375zae8WacDLNtlsuXcERKKPsAIx9q8ud7r0zrM2ffyqTc07ubR7q6EF0w1172f+I75S0SgpMr87vm1uNLVotqEChaSoMqaWzLVp1xbp0WGuXHP/zKVodHukZr50QvPePKXqHcIVvS1Fa2fFqXnfvHKEGEpOcOnk/jTli7IrPJ9dFRqFqvg1QVrwzmk17hWpAqUcivnLfQ9I+UYhKn6NO2mp3y1Cf0yLU3h+m8rWC9Xxv1O1aspZlWsQklG6BfiaYWZ180WAxMbGavjw4VqzZo1OnDih+fPnq3Rp341l/76vgs/2danWLjM0e5JdMQelAoWktl1d6tzDfQV++wZDbz7tUL+n0tS8o/s/R1qaNGeSTb8vsin+rFSmoqmuvV2q0cD9/tb1RsaNkVm5uY9Tt9xt7ZzpMWn5LP28f9rye5oWTU7VsYOm8hY21ORGh1p0c99Qt2ejU+OeTVaPJ4JVv4M7ZkkJpn6amKrNy51KPGuqYHFDdds51PxWhxxB508i/vwlTT9PTdWpI6byFzXUuItDTbtm/XR1K+SxZT3XeyBsWG5q/iSXjhyS8heSWtxkqG1391nDzg2m3n/Gpd6DDV3X0b3MmWZq/mRTqxebij8rlaogdept07X1c07yEWkL3HRcuaGPCLTcEmOnGZiz93XL3fE6ctDdJ7S5yVSnHu5Ybd8gjR5i171PutTsH/H9brKhFYsMxZ+VSleUburtUvX6We//eIz0bF/PfQTCpuTA1djtWpGolV+f1elDacpTyK7aN+RRvVvdM1od3JSsGc+fUIdB+VWtnfvGm+QEl1ZMOqtdKxKVdNalfMUdqtomTPVujpA9/bfOdJnaOD9Bm+bH63RMmsIibe6HF96ZN8uHG1rh4So/B+RzvVF74JhANyFLG957ItBNuCQ5KgE5Z/bs2Zo5c6ZGjx6tokWL+my/gUxAcoNAJiC5RU5KQK5GgUxAAF8JVAKSWwQyAcktcnICUuexnJmA/Pn+5ScgLpdLH3zwgb799lvFxsaqfv36evnll1W2bNl/3fa7777TU089pcWLF6tUqVJef2aO7KVuueUWffnllz5NPgAAAAB4Gjt2rKZOnaqRI0dq2rRpMgxDAwYMUEpKykW3O3TokIYPH35Zn5kjExAAAAAA/pWSkqIJEyboscceU6tWrVS1alWNGTNGR44c0cKFC7PdzuVy6emnn1b16tUv63NJQAAAAABvBHq2Kx/PgrV9+3bFx8ercePGGcvy5s2ratWqafXq1dlu9/HHHys1NVUPPPDAZX1uwGfBAgAAAHD52rVrd9H3Fy9enOXymJgYSVJUlOfTU4sWLaro6Ogst9m4caMmTJig6dOn68iRI5fRWkZAAAAAgFwpMTFRkhQc7PksmZCQECUnZ574JiEhQU899ZSeeuoplStX7rI/lxEQAAAAwAtGjps71i27EY5/ExoaKsl9L8i5/y9JycnJCgsLy7T+yJEjVa5cOfXs2fPyGpqOBAQAAADIhc6VXh09elRlypTJWH706FFVrVo10/ozZsxQcHCw6tatK0lyOp2SpBtvvFFdu3bViBEjvPpcEhAAAAAgF6pataoiIiK0atWqjAQkNjZWW7duVZ8+fTKt/9NPP3m83rBhg55++ml9+umnqlixotefSwICAAAAeCOHlmBdruDgYPXp00ejR49WwYIFVbJkSY0aNUrFixdXhw4d5HQ6dfLkSUVGRio0NDTTwwnP3cReokQJFSpUyOvP5SZ0AAAAIJcaOHCgevTooRdeeEG9evWS3W7X+PHjFRwcrOjoaDVv3lzz5s3z6WcapmleZblc9n7fVyHQTbiqxaTlC3QTrnp5bJlnpIDvRNqSAt0E4D9zmlxb9KdNyaUD3YSr3sNVfg50E7JV9+ExgW5CltaPfSLQTbgklGABAAAAXsips2BdabhMAgAAAMAyJCAAAAAALEMJFgAAAOANSrB8ghEQAAAAAJYhAQEAAABgGUqwAAAAAG9QguUTjIAAAAAAsAwJCAAAAADLUIIFAAAAeIEHEfoGIyAAAAAALEMCAgAAAMAylGABAAAA3qAEyycYAQEAAABgGRIQAAAAAJahBAsAAADwgmFSg+ULjIAAAAAAsAwJCAAAAADLUIIFAAAAeIMKLJ9gBAQAAACAZUhAAAAAAFiGEiwAAADACwYlWD7BCAgAAAAAy5CAAAAAALAMJVgAAACANyjB8glGQAAAAABYhgQEAAAAgGUowQIAAAC8wCxYvsEICAAAAADLkIAAAAAAsAwlWAAAAIA3KMHyCUZAAAAAAFiGBAQAAACAZSjBAgAAALzALFi+wQgIAAAAAMuQgAAAAACwDCVYAAAAgDcowfIJRkAAAAAAWCZXjYAMr9Ik0E0AkIPZoooFuglXPVf0kUA34epncG3Rn4ygXHXqFBAPxwa6BfA3/hUBAAAAXmAWLN/gMgkAAAAAy5CAAAAAALAMJVgAAACAN0xqsHyBERAAAAAAliEBAQAAAGAZSrAAAAAALzALlm8wAgIAAADAMiQgAAAAACxDCRYAAADgDUqwfIIREAAAAACWIQEBAAAAYBlKsAAAAAAvGK5At+DqwAgIAAAAAMuQgAAAAACwDCVYAAAAgDeYBcsnGAEBAAAAYBkSEAAAAACWoQQLAAAA8IJBCZZPMAICAAAAwDIkIAAAAAAsQwkWAAAA4A2TGixfYAQEAAAAgGVIQAAAAABYhhIsAAAAwAvMguUbjIAAAAAAsAwJCAAAAADLUIIFAAAAeIMSLJ9gBAQAAACAZUhAAAAAAFiGEiwAAADAC8yC5RuMgAAAAACwDAkIAAAAAMtQggUAAAB4w6QGyxcYAQEAAABgGRIQAAAAAJahBAsAAADwArNg+QYjIAAAAAAsQwICAAAAwDKUYAEAAADeoATLJxgBAQAAAGAZEhAAAAAAlqEECwAAAPACs2D5BiMgAAAAACxDAgIAAADAMpRgAQAAAN5wUYPlC4yAAAAAALAMCUiANOhQS+8vf0VzTk7Ql3+9qzue7ur1tpXqltMPZyeqWNnCfmzhlY8Y+xfx9a36Lavq3TlPaNbWN/TFshd0+0PtvN62Uo1S+u6vUSpaskDGsqIlC2j+3ney/fPEWz39cRhXHL7H/tWgQ029v2y45pz4TF/uGKM7nrrJ620r1S2nH2I/V7EyxPec+u1r6L1fXtLsmI81cfMo3TG4i9fbVqpTVt+fGKdiZQpleq9Z1/p69+cXNePgWE3a+rae/Kif8hfJ68umAx4owQqAao0ra9iMJ/Xr9JWaOOxbVW9WRfcMv002m6Epb8656LYVapbRK7OeliOI/3QXQ4z9i/j61rX1yunlcfdp6Q9/6su356t6w/Lq+9T1stkMTf1w0UW3LX9tCQ2f0F+OILvH8lPHYvXEre9mWv/Gu5upZZc6+umbVT49hisR32P/qta4soZNH+yO7/Dpqt60iu4Z3sMd37fmXnTbCjXL6JWZTxLff7i2USUNmzpIS2f+oYkjZ6pGk2vU96VuMmyGpo7+/qLblq9RWiO+fSLLeLa4pYGe//IR/TD+Z018ZaYKFM2ru567VW9+P0SPthym1OQ0fx3SlYkKLJ/gX3YA9H6+m/Zs2KdR930kSVqzcKMcDrtuf+omzXh3nlKSUjNt4wiy6+aHO+nul3soJTHF6iZfcYixfxFf3+o9qKP2bDus0YO/liStXbpdDoddtz3YVjM/+1UpyVnHs2vf5rpr8PVZxjs1xantf+7zWFa5Zim17FJHE0fP05Y1e/1zMFcQvsf+1fu5W7Rn4z6N6veJJGnNwk1yBNl1+1M3asZ78y8S3466+6XuxPcCfYberD2b9mvU/eMkSWsXbZbdYdftT9ygmR8syDaeXR9or7tfuDXL9yXpziFd9ceCDXr/iS8zlh34K0bv/fKSrutcR8vmrPHPASFXowTLYkHBDtVqea2WzVntsfy3WX8oPDJMNZpXzXK7hp3rqPfz3TT1zTka/8JUK5p6xSLG/kV8fSso2K5a11XS8h83eixfNn+DwiNCVaNR+Sy3a9j6WvUe2EnTPlykCW9e/OrnOY+80kMHdh/VrPG//ud2X+n4HvvX+fh6nrxmxLdZlSy3a9i5jno/d6umvjlX41+YZkVTrwhBwQ7VbF5Fy+eu9Vi+bM4adzybXpPldg071lLvZ2/W1NHfa8JL32Z63zAMrft5i+Z97tknHNwVI0mKKl/UR0cAeCIBsVjx8kUVHBKkQzujPZYf3u3+x16qUvEst/tr7R71rTJIU96cI2eay+/tvJIRY/8ivr5VvHQhBYU4dGjvMY/lh/8+Lkkqmc0JwF8bD6hvi5Ga+uEir+LZums9ValdRh8PnyUXs7jwPfaz8/GN8Vh+ePcRSVKpyheJb9UnNOWtucT3H4qXK+KO564jHssP73G/Lpnd93XdXvWt+bSmjv5ezjRnpvdN09S456dp5bz1Hsub3VRfkrRv20FfNP+qYpg588+VJqAlWNOnT1fXrl0VHBycsWzlypWaMGGCYmJiVLlyZT300EOqVKlSAFvpWxH5wyVJCWcTPZYnnE2SJIXnDctyuxOHT/m3YVcRYuxfxNe38qTHKyEuyWN5QnyyJCk8IiTL7U4cOXNJn9N9QGttWb1Hm1btvoxWXn34HvtXRnxjs4lvJPG9FP/6fY0MzXK7E9GnL/mzSlQspv4jb9fO9X9r9U+bLnl7wBsBHQF58cUXdfbs2YzXy5Yt07333iuXy6XmzZvr2LFj6t69u9atWxfAVvqWYXOH3MwmW+XK5H9HjP2L+PqWzWa4/48f41mtfjlVqlFK08f9/J/3dbXge+xfRvr32swmwK7sAo8sGcbF42n66Pta+poovfn9EKUmp2nk3R9m+3nAfxXQEZALv9hjx47V3XffraFDh2Yse/311zV69Gh9/fXXVjfPL+JPx0vKfPXn3NWLhDMJlrfpakOM/Yv4+lZc+hXi8AjPK5jhedwjH+eucP4Xza+vrbOnE7T6523/eV9XC77H/hV/2h2/C0eSzsc3MdM2yF58+vcxu+9rfOx/j2etFlX14uRHlRiXpKE3j9KRfcf/8z6vSiRlPpGj7gHZt2+fbr75Zo9ld9xxh7Zu3RqgFvne4T1H5UxzqkTFYh7LS1R012/u234oEM26qhBj/yK+vhW974ScaU5FlfN81kGJ9Nf7L6j5vhyN2lbTip82UVP/D3yP/Sv7+Lpf79tGfC/F4b3p8azgeU9YiQrueO7ffvg/7b/1bY316qwndSL6lAZ3eDXjJnTAXwKagJwbUjynXLlySkjwvOp06tQpRUZGWtksv0pNTtWmZdvV7OaGHstb3NpIZ0/Fa8dq6rP/K2LsX8TXt1JT0rTpjz1q1qmmx/Lm19fW2TMJ2vHn/v+0/4h84SpZvoi2rP37P+3nasP32L/c8d2hZjc38FieEd81xPdSpCanadPyv9Ssa32P5c1vbuCO59o9l73vhh1r6elP+mvbql0a3PE1Hec+HFggoAmIaZpq166dbr31Vj311FMKDg7WqFGjlJrqnqt63bp1Gj58uFq1ahXIZvrc12/MVtVGFfX8VwPVoGNt3f1yD/UY3EVT35qjlKRUhUeGqWqjSspX+OpJvKxGjP2L+PrW1A8WqkqdMnruw7vVoFVV3TW4s7rf31rTxi5WSnKqwiNCVLVOWeUrmOeS912+apQkaf9OrmheiO+xf339xhxVbVhRz3/1mBp0rKW7X+quHk/coKmj5qbHN1RVG1Ukvl6aMuo7VWlQQc9PfFgNOtTU3S/cqh6DOmva29+fj2fDCspXyPt4BoU49Pj79yjhbJKmjP5OZapEqWrDChl/Cpco4McjujIFerarq2UWrIAmIEuWLNGYMWPUuXNnuVwuHTt2TFu2bJHT6Z4qrl+/fgoP/397dx4WVfn/f/w1KIuIuJSKQpoKLrhi7mtmZpktflpNTXMtTDO3Sk3ii5lbKpp7mJkL5l6u2abpJ02t0MR9XxD8uCEugMLvDxR/E6CjDucww/NxXXNdcnPuM++5Z7yH97nf5xxP9evXz8ww7S7q12iFvR4uv/IlFLLwfT3xekN9+dF8LRq3UpLkH/SowjeEqs4zQSZH6rgY4+zF+NpX1O8H9Ok7X8uvbDENndZZzV54TBGffa/F09NOGi9X2U/jlr6n2s0C73nfhR72kiQlUHOfAZ/j7BW1PlphbSfIL8BHId/20ROvN9CXgyK1aNwqSZJ/jUcVvv4T1Xm6hrmBOoioDbs1rP0k+QX4aOi8Xmr2Sj19OeRbLZqwRpLkX720xv/0seq0rGbzPgPrBuihEoVVoHB+fbZ8gMb/9LHV4+mOTbLr5SCXs6TmsEscJCcny9XVVZK0d+9elS9fPkOp1v1q6dHOLvsB4JxcShS/+0Z4ICkxD35OC+7CkqNO73Q6FldTr9+TK6yJ/8rsELLUrOVIs0PI1C9rPzA7hHuS4/4X3Uo+JKlChczvlAoAAAAYLkcdtndcHCYBAAAAYBgSEAAAAACGyXElWAAAAEBOZMlZp047LFZAAAAAABiGBAQAAACAYSjBAgAAAGyRYnYAzoEVEAAAAACGIQEBAAAAYBhKsAAAAAAbcBUs+2AFBAAAAIBhSEAAAAAAGIYSLAAAAMAWVGDZBSsgAAAAAAxDAgIAAADAMJRgAQAAALbgKlh2wQoIAAAAAMOQgAAAAAAwDCVYAAAAgA0sVGDZBSsgAAAAAAxDAgIAAADAMJRgAQAAALbgKlh2wQoIAAAAAMOQgAAAAAAwDCVYAAAAgA0sKWZH4BxYAQEAAABgGBIQAAAAAIahBAsAAACwBVfBsgtWQAAAAAAYhgQEAAAAgGEowQIAAABsQQWWXbACAgAAAMAwJCAAAAAADEMJFgAAAGADC1fBsgtWQAAAAAAYhgQEAAAAgGEowQIAAABsQQmWXbACAgAAAMAwJCAAAAAADEMJFgAAAGCLFLMDcA6sgAAAAAAwDAkIAAAAAMNQggUAAADYgBsR2gcrIAAAAAAMQwICAAAAwDCUYAEAAAC2oATLLlgBAQAAAGAYEhAAAAAAhqEECwAAALAFJVh2kasSEJcypcwOwandOHDY7BCcniVPHrNDcG6Mb7ZzKehtdghOL+VivNkhOLXU5OtmhwA4PEqwAAAAABgmV62AAAAAAPctxewAnAMrIAAAAAAMQwICAAAAwDCUYAEAAAA2sHAVLLtgBQQAAACAYUhAAAAAABiGBAQAAACwRWpqznw8gJSUFE2YMEGNGzdW9erV1blzZx09ejTL7ffv36/u3burbt26ql+/vnr37q1Tp07d03OSgAAAAAC51OTJkxUZGalhw4ZpwYIFslgs6tatm5KSkjJse/78eb311lvKnz+/5syZoxkzZuj8+fPq2rWrEhMTbX5OEhAAAAAgF0pKStLMmTPVq1cvNW3aVBUrVtS4ceMUGxurdevWZdj+xx9/1NWrVzVixAgFBASoSpUqGj16tA4ePKg///zT5uclAQEAAABsYXaplZ1LsPbs2aPLly+rXr166W3e3t4KDAzU1q1bM2xfv359TZo0Se7u7hl+d/HiRZufl8vwAgAAAA6sefPmd/z9Tz/9lGn76dOnJUklSpSwai9WrJhiYmIybO/n5yc/Pz+rtmnTpsnd3V21a9e2OV5WQAAAAIBc6OrVq5IkNzc3q3Z3d3ebzumYPXu25s2bp759++qhhx6y+XlZAQEAAABskUNvRJjVCsfdeHh4SEo7F+TWvyUpMTFR+fLly7JfamqqwsPDNWXKFPXo0UOdOnW6p+dlBQQAAADIhW6VXsXFxVm1x8XFycfHJ9M+ycnJGjBggKZOnaqBAweqb9++9/y8JCAAAABALlSxYkV5eXlpy5Yt6W3x8fGKjo5WrVq1Mu0zcOBArVmzRp9//rm6dOlyX89LCRYAAABgixSzA7AvNzc3tW/fXmPGjFGRIkXk6+ur0aNHy8fHRy1atNCNGzd07tw5FShQQB4eHlqyZIlWrVqlgQMHqk6dOjpz5kz6vm5tYwtWQAAAAIBcqnfv3nr55Zc1ZMgQtW3bVnny5FFERITc3NwUExOjRo0aadWqVZKkFStWSJJGjRqlRo0aWT1ubWMLS2pqDj2bJhs8U+kjs0NwajcOHDY7BKdnyZPH7BCcmotfSbNDcH6XEsyOwOmlXIw3OwTnZuHYbXZbe/Ubs0PI0tOVB5sdQqbW7PrU7BDuCSVYAAAAgA0suee4fbYijQcAAABgGBIQAAAAAIahBAsAAACwBSVYdsEKCAAAAADDkIAAAAAAMAwlWAAAAIAtUijBsgdWQAAAAAAYhgQEAAAAgGEowQIAAABswVWw7IIVEAAAAACGIQEBAAAAYBhKsAAAAABbUIJlF6yAAAAAADAMCQgAAAAAw1CCBQAAANiCEiy7YAUEAAAAgGFIQAAAAAAYhhIsAAAAwBYplGDZAysgAAAAAAxDAgIAAADAMJRgAQAAALZITTE7AqfACggAAAAAw5CAAAAAADAMJVgAAACALbgRoV2wAgIAAADAMCQgAAAAAAxDCRYAAABgC25EaBesgBjksUblFb6wp5b+GapZPw3Uq92a2tzXP7Ckvt8xTMVKFsrwuydfrKkp372n5X//n75aN0Dt331SefI6/9ta66nq+mLzp/ru4tf65sBEvT7whbv2af5GI03/e7S+j5+tiH/G6unOzTJs0+LNppr+12ituDRbs/dNUIehLytP3jxZ7rP+c4/ph+RIVWsS+ECvxxHValFNEzeFafm5mZq9L1yvDXje5r7+QY9q5aWvVbz0w9kYoWN5rEkFhS/traU7P9Ws9YP06tsZP59Z8a/sq+93j1Ax38LpbcV8C2v1gdFZPt4f8Wp2vIwc7bFmgQpf+6GWHg7XrG3D9Grvljb39a9WSt+f+ELFHilyx+26/9/LWh075UFDdRrME9mvVouqmrgxVMvPfqnZe8fptf7P2dzXP+hRrYz/SsVLMcYwFisgBqhUo5RCJnXQhjU7NTt8nSo/Vlod+zwlFxeLIqf9ese+ZSr4KHRqJ+V1zfhH8AsdGujtQc/ptzU7FTF6tbwL51f7d59UmQo+Cus1J5tejfkC65dX6NIBWr/wd80a+q2qNKygTmGvyeJi0fwRyzLt0/iluhrwVbCWTVyjrWu/UYMXaqvvtB5Kupqkn+dvkiS92OsZBY/tqA2LNmvGh3Pl/XABvTn0ZZWtWkqhr4zNsM8CRbz03uRu2flSc6zAegH6ZHE/rV+0WV9/slCVG1ZQp9BX5OJi0fyRy+/Yt2zVUgpbOkB5XZl+bqkUVFohUztpw6oozR63VpUfe1Qd+z4tF4tFkVN+vmPfMhVLKPTLLhnmiPNn4vX+yxMzbN+6fQM1aVVdPyz8w66vIaerVKusQma/ow3Lt2v2iO9Uua6/On70fNo8PH7NHfuWCfRV6NzgTOfh/1+Vev56vqvtiaOzY57IfoH1AvTJor5pYxy6SJUbVFCn0JfTxnjUd3fsW7ZqKYUt6ccYwxR86gzQrmdzHdoTozEffCtJ2r5xn/LmzaNXuj2uJbM2KinxeoY+eV3z6Pl29dWhd4tMf+/iYlG74Ob6c9N+DX9/Xnr7gV0nNW3F+wpq4K+//nsg+16UidoPeUkHo45oVKdJkqRtP0Qpj2tevTbwBS0ev1JJ15Iz9OkU+pp+W7xFU/vPliRtX7dDBQp7qcPQV/Tz/E1ycbGo/ZCXtH3dDg1rOz693/4/D+nLHZ+rZvOq+vOnnVb77DWxi24k38i+F5qDtRv8Hx2KOqrRndOO9G5bt0N58+bRq/2f0+LwVZm+B3ld8+iF4JZ6M+RlJV1NMjrkHK1d7xY6tPuUxvSPlCRt37A3bY7o0UxLZm7Ieo54s6E69GmZ6XgnJ93Qnr+PWbUFVPFTk1bV9fXna7Rr+5FseS05Vbv+z+rQrhMa8+4sSdL2X6KVN6+LXunVUkum/pTlZ/b5Lo+rwwfPK+nanT+z7p5u6hv+ps6dvqCivndeJcktmCeyX7tBL+rQjqMa3WWaJGnbup3K65pHr/ZvrcUTVt9hjJ/Sm0NfYozvB1fBsgvnr9UxmatrHlWrU1ab1u2yat+49h955ndXlcfKZNqvdpMKatezuRZM+0UzP1+d4feFHvJSgUKe2vLLbqv2YwfjdPFcguo0rWi/F5GDuLrlVbWmgdq01Pro7W9LNsuzQD5VbVQpQ5/ipYvqkQoltWlZxj6+/j7yDSihQsULybuIlzav3G61zbHdJ3XhTLzqPlvTqr3pK/VV88mqmvHRXDu9Msfh6pZX1ZpU0sblW63af1v6hzwL5FOVRpl/9mo/XUPtBv9HkSOXK2JIpBGhOgRXtzyqVrecNv3wj1X7xjU75OnloSq1y2bar3bTimrXq4UWTP5JM0evsum5eoa20fGDcVr61YYHjtuRuLrlVbUGAdq08i+r9o0r/kob47r+mfar/WQVtev/rBaEr9bMYcvu+BzdQl7Subh4rYv83V5hOzTmiex3e4y3WbWnj3HDCpn2q/10DbUb1EaRI79TxJAFRoQKZEACks18HikiV7e8Onn0f1btp46l/ez7aOZ1l/t2nlDHJ0cpctqvunE9JcPvL1+6puvJN6xqviXJy9tDXt755ONXOEMfZ+BTtpjc3F11Yn+MVfupA7GSJN/yJTL0KVXRV5Iy9jmY1scvoIQuX7is68nXVbx0UattvArlV4HC+eXz6O32QsUK6t0Jb2lK3691LubCA78mR+NTJu09OJlhPE9Lkvz8fTLtt2/7IXWs8J7mj1ye6Wc6t/J55KG0OeLwGav2U0fPSpJ8y2Q1RxxXx6bDFTnlZ5vG8/HnaqhC9VKaOmy5UnLZSZQ+pR+Wq7urTh6Ms2o/dXPMfcsVy7Tfvr+OqGOtIYocv0Y3rme92hnUpKKav1pX496bnevGNivME9nv9hiftmq//d12hzGu+L7mj/qOMYZpTC/BioqK0pYtW9S9e3dJ0ubNmzVr1iydOHFCpUqVUufOnVWrVi2To7x/+b3zSZKuJFyzar9yOW3Z09PLPdN+Z+Pi77jfxGvJ2rB6h55vV1/HDsTqvz/uUsEiXnp70HO6fj1FHvnc7BB9zuNVKL8k6Ur8Vav2K5fSfvYskC9Dn/yFPDPtc/VWH+98SryapPULf9fzwS11NPqENi3bqkLFvPXO2I66nnxDHvk90vv1mdJNuzfv109zf8uVJ5973RrPS/9+D9I+457eGd8DSTp76nz2Buagbs8RiVbtVy6n/ezp5ZGhjySdjb3zHPFvL3Vtql3bDmvnlkP3EaVjy18wi3n45s+ZzRuSdPb0xbvu27OAh/qM66BvRn6vk4fi7rp9bsE8kf28svhuSx/jrD7XjPGDoQTLLkxdAVmzZo3atm2rP/5IK4355Zdf9NZbbyk1NVVNmzZVcnKyOnbsqF9++cXMMB+Ii8WS9o8sPq8PcrRsYugy/fz9X3ov7D9auCVEXyzupd1/H9O+f07ompPWdVpc0sYzq///qSkZj+a4ZNXn5ntzq0948Jf6ed5GvT+tu5acidDkPz7T7s37tW/bQV27nDaht+jQRFUaVdT44C/t8Gock8UlbdrI6j3gCPC9uT1HZD5u9hjPwJqPyr+ynxZ9uf6B9+WI7j7G938UuEfYK/pfzHktnXbniwXkNswT2e/292EWn2v+UEYOZuoKyBdffKF3331XwcHBkqQpU6bo7bff1nvvvZe+zZQpUzRhwgQ1a+aYVxZJuHWU/V8rHZ7501Yo/n1E7l5cu5Kk8UOWaOrwFSpWspBiT55X4tVkPfXSY9px/Nz9B52DXb5wRdLto8a33DrSc/lfR4IkKeFmn38fcct388jy5Ytpfa5dTtTY7tM0+f1ZKl66qGKPnNG1K4lq2elxxRyO00MlC+vtz9/U9IFzdCHuolzyuMglT9qXrEseF7m4WHLFl+rlC5clZTy65lkgbTyvXLxieEyO7PYcYb3S4Zk/bc64dTTzQTR6pqouXbiirb/uvvvGTighPosxvvnzlfj7G+M6Laqo6Yu11LvlCFlcLLLIkn7AwyWPi1JTUrP849DZMU9kv8tZfLfdHuOM34dATmFqAnLs2DE999zt61WfOHFCLVtaX5e9devWmjLFca+pHnPsnG5cv6ESpR6yai9585rbxw7e/5J9nccrKuHiVUX/dVTHDqTtp2CR/CrqU1AHok/df9A52KmDsbpx/YZKlitu1V7SP+3no9EnMvQ5sS9tLEqWK66Dfx+53efmPo7uTutTt1VNXbqQoOj/7kvfT6Gi3ir6yEM68Ndh1XyymgoU9lK/GW+r34y3rZ5j1A9DdPrIGb0Z0Ms+LzQHO3UoLvP3oFxavfHRPSfNCMthxRw9mzZHlP7XHHHz52M3z296EHWaVdLv6/7JtfXeMUfOpI1xGetzPUqWSTu369i+mMy63VWj1jXlns9N0zYMzfC7lacmaV3k7xr73uz72rejY57IflmP8a3vNsY4W+TSgwr2ZmoJ1iOPPKL162+XBFSqVEl79uyx2mbHjh0qXrz4v7s6jOSk69q57Ygatqhi1d6oZRVdunhVe3ccv+99t3qtrroObGXV9uKbDZVyI1V/OOmRzuTEZO38bbcatqlj1d74P/V06XyC9m7NeOnhUwdjdepgrBr/p16GPsf3nlLczQsCPNv9SXUf2d5qmza9WynlRoo2r/xTm1dsV896g6we4cEzJEnhwTM0tM0oe77UHCs5MVk7N+5RwxdqW7U3blNHl85f1t6tB02KzDElJ13Xzq2H1bBlVav2Rk9X06WLV7Q36lgWPW3jVTCffB8tql1/Hnmg/Tiy5MTr2rn5gBo+W8OqvVHrIF26cEV7/zpyX/udM2aFej/1mdVj9Te/SZJ6P/WZ5oxZ8YCROy7mieyXNsZ71fAF6/Nk08d4G2OMnMvUFZBu3bpp8ODBOn36tFq3bq3g4GB9+OGHSkxMVEBAgKKiojRp0iS9++67Zob5wCKn/qzhM7to0Lg39MOSbaoUVFovdW6smZ+vUVLidXnmd1cp/2KKOXZOF89ftnm/333zX30a0Vk9PmqtzT9Hq3q9cnq9RzMtmP6rTp9w3pPM5g1fqhFrB2vI/D5aO+tXBdYvr1f6tVbER/OUdC1ZngXyqVSgr2IOxuri/y5JkuYOX6IBEe/o0rlL+v377ar/3GN6/NX6Vvf8WP7FGn22epDe/vxNbf5+u2o0q6y2H76oyJHLdPpw2grTpXMJVrHku3ly+vG9MTryz/0nk45m3ohlGrHqIw2e21trv16vwPoBernvs4oYHHn7Pajkq5hDt98DZC1y0o8aPru7Bk1srx8WblWlmo/qpW5NNXPUqrQ5wstdpfyLK+bYWV08Z/scIUllKqRdGe7WKmluFTlutYYv7K1BM7rqh/m/q1LtsnqpZwvNDFuW9pn18lCpCiUUc+SMLp5NuPsOJcUdP6e4f5W71mmRduL6/gdMHJ0B80T2mzdiuUas+kCD5/ZKG+N6AXr5/VaKGLLg5hh73BzjOMYYOYqpKyAvvviihg8frtWrV+vFF19U+/btdfz4cYWEhOiNN97QF198oS5duqhTp05mhvnAorYc0qfvzZVfmYc19IsOata6hiJGr9bimWlHysoFltS4yGDVbpr5Nbuz8ud/92tEv0gFNfDXJ1M6qmGLKpoy7DvNGrc2O15GjvH3r7sU9uo4+VUooZDF/fRE24aa8cFcLRybdrTRP6iMJmwcpjqtbt+7Y93s9QoPnqGazavqk8X9VK1JoEZ2mqQNizanb7P9xx0a3n6Cajavqv9bPlCN2tTVpD5faSbXos8g6tdohb0eLr/yJRSy8H098XpDffnRfC0at1KS5B/0qMI3hKrOM0EmR+oYojYf1Kc9v5FfmWIaOrWTmj0fpIiRK7X45knj5Sr7atyiXqr9eMb73NxNoYe9JEkJubzmPmrjXn3aebr8/Itr6KweavZSHUWELtHiyeskSeWqPaJxqwaq9pNV7rIn2Ip5IvtFrY9WWNsJ8gvwUci3ffTE6w305aBILRqXdm8g/xqPKnz9J6rzdA1zA3UmKSk58+FgLKk55Ay5Q4cO6ciRI0pISJCrq6t8fHwUGBgod/fML1N7P56p9JHd9oWMbhw4bHYITs+SJ4/ZITg1F7+SZofg/C7ZtrqA+5dy8d4u0Yx7ZOEWatlt7dVvzA4hS8+U6Gl2CJlaHTPJ7BDuien3AbmlbNmyKls28zv+AgAAAHAOOSYBAQAAAHK0nFE45PBYRwQAAABgGBIQAAAAAIahBAsAAACwBSVYdsEKCAAAAADDkIAAAAAAMAwlWAAAAIAtUijBsgdWQAAAAAAYhgQEAAAAgGEowQIAAABskJqaYnYIToEVEAAAAACGIQEBAAAAYBhKsAAAAABbcBUsu2AFBAAAAIBhSEAAAAAAGIYSLAAAAMAWqZRg2QMrIAAAAAAMQwICAAAAwDCUYAEAAAC2SOFGhPbACggAAAAAw5CAAAAAADAMJVgAAACALbgKll2wAgIAAADAMCQgAAAAAAxDCRYAAABgg1SugmUXrIAAAAAAMAwJCAAAAADDUIIFAAAA2IKrYNkFKyAAAAAADEMCAgAAAMAwlGABAAAAtkihBMseWAEBAAAAYBgSEAAAAACGoQQLAAAAsEUqNyK0B1ZAAAAAABiGBAQAAACAYSjBAgAAAGyQylWw7IIVEAAAAACGIQEBAAAAYBhKsAAAAABbcBUsu2AFBAAAAIBhSEAAAAAAGIYSLAAAAMAGXAXLPlgBAQAAAGAYEhAAAAAAhqEECwAAALAFV8GyC1ZAAAAAABiGBAQAAACAYSypqamczg8AAADAEKyAAAAAADAMCQgAAAAAw5CAAAAAADAMCQgAAAAAw5CAAAAAADAMCQgAAAAAw5CAAAAAADAMCQgAAAAAw5CAAAAAADAMCQgAAAAAw5CAAAAAADAMCQgAAAAAw5CAAAAAADAMCUgOk5KSogkTJqhx48aqXr26OnfurKNHj5odltOaPHmyOnToYHYYTuXChQsaOnSomjRpopo1a6pt27batm2b2WE5lbNnz2rAgAGqV6+egoKC1L17dx04cMDssJzS4cOHFRQUpCVLlpgdilM5efKkKlSokOGxcOFCs0NzKsuWLVOrVq1UtWpVPfvss1q9erXZIQGSSEBynMmTJysyMlLDhg3TggULZLFY1K1bNyUlJZkdmtOZNWuWJkyYYHYYTqdv376KiorS2LFjtWjRIlWuXFldunTRwYMHzQ7Nabzzzjs6fvy4ZsyYoUWLFsnDw0OdOnXS1atXzQ7NqSQnJ6t///66cuWK2aE4nb1798rd3V2//fabNm7cmP547rnnzA7NaSxfvlyDBg3Sa6+9phUrVqhVq1bq27ev/vrrL7NDA0hAcpKkpCTNnDlTvXr1UtOmTVWxYkWNGzdOsbGxWrdundnhOY3Y2Fh17dpV4eHhKlOmjNnhOJWjR49q06ZNCgkJUa1atVS2bFkNHjxYxYsX14oVK8wOzymcP39efn5+CgsLU9WqVVWuXDkFBwfrzJkz2r9/v9nhOZWJEycqf/78ZofhlPbt26cyZcqoWLFiKlq0aPrDw8PD7NCcQmpqqsLDw9WxY0d17NhRpUuXVs+ePdWgQQP98ccfZocHkIDkJHv27NHly5dVr1699DZvb28FBgZq69atJkbmXHbt2qWCBQvqu+++U/Xq1c0Ox6kULlxY06dPV5UqVdLbLBaLUlNTdfHiRRMjcx6FCxfW2LFjFRAQIEn63//+p4iICPn4+Mjf39/k6JzH1q1btWDBAo0cOdLsUJzS3r17+bxmo0OHDunkyZMZVpQiIiLUo0cPk6ICbstrdgC47fTp05KkEiVKWLUXK1ZMMTExZoTklJ544gk98cQTZofhlLy9vdW0aVOrttWrV+vYsWNq1KiRSVE5r48//ljffvut3NzcNGXKFHl6epodklOIj4/XwIEDNWTIkAzzMexj3759Klq0qN544w0dOXJEpUuXVnBwsBo3bmx2aE7hyJEjkqQrV66oS5cuio6Olp+fn9555x2+/5AjsAKSg9yq33Zzc7Nqd3d3V2JiohkhAQ9k+/btGjRokJo3b86XXjbo2LGjFi9erOeff149e/bUrl27zA7JKXzyySeqUaMG5yNkk6SkJB05ckQJCQnq06ePpk+frqpVq6pbt276/fffzQ7PKSQkJEiSPvjgA7Vu3VozZ85Uw4YNFRwczBgjR2AFJAe5VfualJRkVQebmJiofPnymRUWcF9+/PFH9e/fX9WrV9fYsWPNDscp3SphCQsL099//605c+bos88+Mzkqx7Zs2TJt27ZN33//vdmhOC03Nzdt3bpVefPmTT/gVqVKFR08eFARERGqX7++yRE6PldXV0lSly5d1KZNG0lSpUqVFB0dra+++ooxhulYAclBbi31x8XFWbXHxcXJx8fHjJCA+zJnzhz16tVLTZo00YwZMzix1I7Onj2rFStW6MaNG+ltLi4uKleuXIa5A/du8eLFOnv2rB5//HEFBQUpKChIkhQSEqJnn33W5Oich6enZ4bV/vLlyys2NtakiJzLrb8Zypcvb9Xu7++vEydOmBESYIUEJAepWLGivLy8tGXLlvS2+Ph4RUdHq1atWiZGBthu3rx5CgsLU7t27TR+/PgMf2TgwcTFxalfv35WV7JJTk5WdHS0ypUrZ2JkzmHMmDFatWqVli1blv6QpN69e2v69OnmBuck9uzZo6CgoAz3B/rnn384Md1OAgMDlT9/fkVFRVm179u3T6VKlTIpKuA2SrByEDc3N7Vv315jxoxRkSJF5Ovrq9GjR8vHx0ctWrQwOzzgrg4fPqzhw4erRYsW6tGjh86ePZv+Ow8PDxUoUMDE6JxDxYoV1ahRI4WGhmrYsGHy9vbW1KlTFR8fr06dOpkdnsMrXrx4pu0PPfSQfH19DY7GOZUvX14BAQEKDQ1VSEiIChcurG+//VZ///23Fi1aZHZ4TsHDw0Ndu3bVpEmTVLx4cVWrVk0rV67Upk2bNGvWLLPDA0hAcprevXvr+vXrGjJkiK5du6batWsrIiKCo8hwCGvXrlVycrLWrVuX4d41bdq00YgRI0yKzHlYLBaNHz9en3/+ufr06aNLly6pVq1amjt3rkqWLGl2eMBdubi4aOrUqRozZoz69Omj+Ph4BQYG6quvvlKFChXMDs9pBAcHK1++fOn3EytXrpw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- "text/plain": [ - "
              " - ] - }, - "metadata": { - "filenames": { - "image/png": "/Users/mhjensen/Teaching/MachineLearning/doc/LectureNotes/_build/jupyter_execute/chapter10_63_1.png" - } - }, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "# optional\n", "# visual representation of grid search\n", @@ -2969,16 +1543,7 @@ "collapsed": false, "editable": true }, - "outputs": [ - { - "ename": "SyntaxError", - "evalue": "invalid syntax (2259440937.py, line 1)", - "output_type": "error", - "traceback": [ - "\u001b[0;36m Input \u001b[0;32mIn [12]\u001b[0;36m\u001b[0m\n\u001b[0;31m conda create -n tf tensorflow\u001b[0m\n\u001b[0m ^\u001b[0m\n\u001b[0;31mSyntaxError\u001b[0m\u001b[0;31m:\u001b[0m invalid syntax\n" - ] - } - ], + "outputs": [], "source": [ "conda create -n tf tensorflow\n", "conda activate tf" @@ -3755,7 +2320,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.9.10" + "version": "3.9.18" } }, "nbformat": 4, diff --git a/doc/LectureNotes/_build/jupyter_execute/chapter11.ipynb b/doc/LectureNotes/_build/jupyter_execute/chapter11.ipynb index 9115e9414..502da1094 100644 --- a/doc/LectureNotes/_build/jupyter_execute/chapter11.ipynb +++ b/doc/LectureNotes/_build/jupyter_execute/chapter11.ipynb @@ -782,33 +782,20 @@ }, "outputs": [ { - "name": "stdout", - "output_type": "stream", - "text": [ - "Initial cost: 367.01\n" + "ename": "ModuleNotFoundError", + "evalue": "No module named 'matplotlib'", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mModuleNotFoundError\u001b[0m Traceback (most recent call last)", + "Cell \u001b[0;32mIn[1], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \u001b[43mget_ipython\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mrun_line_magic\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mmatplotlib\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43minline\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m)\u001b[49m\n\u001b[1;32m 3\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mautograd\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mnumpy\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m \u001b[38;5;21;01mnp\u001b[39;00m\n\u001b[1;32m 4\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mautograd\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m grad, elementwise_grad\n", + "File \u001b[0;32m~/miniforge3/lib/python3.9/site-packages/IPython/core/interactiveshell.py:2432\u001b[0m, in \u001b[0;36mInteractiveShell.run_line_magic\u001b[0;34m(self, magic_name, line, _stack_depth)\u001b[0m\n\u001b[1;32m 2430\u001b[0m kwargs[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mlocal_ns\u001b[39m\u001b[38;5;124m'\u001b[39m] \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mget_local_scope(stack_depth)\n\u001b[1;32m 2431\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mbuiltin_trap:\n\u001b[0;32m-> 2432\u001b[0m result \u001b[38;5;241m=\u001b[39m \u001b[43mfn\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 2434\u001b[0m \u001b[38;5;66;03m# The code below prevents the output from being displayed\u001b[39;00m\n\u001b[1;32m 2435\u001b[0m \u001b[38;5;66;03m# when using magics with decorator @output_can_be_silenced\u001b[39;00m\n\u001b[1;32m 2436\u001b[0m \u001b[38;5;66;03m# when the last Python token in the expression is a ';'.\u001b[39;00m\n\u001b[1;32m 2437\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mgetattr\u001b[39m(fn, magic\u001b[38;5;241m.\u001b[39mMAGIC_OUTPUT_CAN_BE_SILENCED, \u001b[38;5;28;01mFalse\u001b[39;00m):\n", + "File \u001b[0;32m~/miniforge3/lib/python3.9/site-packages/IPython/core/magics/pylab.py:99\u001b[0m, in \u001b[0;36mPylabMagics.matplotlib\u001b[0;34m(self, line)\u001b[0m\n\u001b[1;32m 97\u001b[0m \u001b[38;5;28mprint\u001b[39m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mAvailable matplotlib backends: \u001b[39m\u001b[38;5;132;01m%s\u001b[39;00m\u001b[38;5;124m\"\u001b[39m \u001b[38;5;241m%\u001b[39m backends_list)\n\u001b[1;32m 98\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m---> 99\u001b[0m gui, backend \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mshell\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43menable_matplotlib\u001b[49m\u001b[43m(\u001b[49m\u001b[43margs\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mgui\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mlower\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mif\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[38;5;28;43misinstance\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43margs\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mgui\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mstr\u001b[39;49m\u001b[43m)\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01melse\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43margs\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mgui\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 100\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_show_matplotlib_backend(args\u001b[38;5;241m.\u001b[39mgui, backend)\n", + "File \u001b[0;32m~/miniforge3/lib/python3.9/site-packages/IPython/core/interactiveshell.py:3606\u001b[0m, in \u001b[0;36mInteractiveShell.enable_matplotlib\u001b[0;34m(self, gui)\u001b[0m\n\u001b[1;32m 3585\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21menable_matplotlib\u001b[39m(\u001b[38;5;28mself\u001b[39m, gui\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mNone\u001b[39;00m):\n\u001b[1;32m 3586\u001b[0m \u001b[38;5;250m \u001b[39m\u001b[38;5;124;03m\"\"\"Enable interactive matplotlib and inline figure support.\u001b[39;00m\n\u001b[1;32m 3587\u001b[0m \n\u001b[1;32m 3588\u001b[0m \u001b[38;5;124;03m This takes the following steps:\u001b[39;00m\n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 3604\u001b[0m \u001b[38;5;124;03m display figures inline.\u001b[39;00m\n\u001b[1;32m 3605\u001b[0m \u001b[38;5;124;03m \"\"\"\u001b[39;00m\n\u001b[0;32m-> 3606\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mmatplotlib_inline\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mbackend_inline\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m configure_inline_support\n\u001b[1;32m 3608\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mIPython\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mcore\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m pylabtools \u001b[38;5;28;01mas\u001b[39;00m pt\n\u001b[1;32m 3609\u001b[0m gui, backend \u001b[38;5;241m=\u001b[39m pt\u001b[38;5;241m.\u001b[39mfind_gui_and_backend(gui, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mpylab_gui_select)\n", + "File \u001b[0;32m~/miniforge3/lib/python3.9/site-packages/matplotlib_inline/__init__.py:1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01m.\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m backend_inline, config \u001b[38;5;66;03m# noqa\u001b[39;00m\n\u001b[1;32m 2\u001b[0m __version__ \u001b[38;5;241m=\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m0.1.6\u001b[39m\u001b[38;5;124m\"\u001b[39m \u001b[38;5;66;03m# noqa\u001b[39;00m\n", + "File \u001b[0;32m~/miniforge3/lib/python3.9/site-packages/matplotlib_inline/backend_inline.py:6\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[38;5;124;03m\"\"\"A matplotlib backend for publishing figures via display_data\"\"\"\u001b[39;00m\n\u001b[1;32m 3\u001b[0m \u001b[38;5;66;03m# Copyright (c) IPython Development Team.\u001b[39;00m\n\u001b[1;32m 4\u001b[0m \u001b[38;5;66;03m# Distributed under the terms of the BSD 3-Clause License.\u001b[39;00m\n\u001b[0;32m----> 6\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mmatplotlib\u001b[39;00m\n\u001b[1;32m 7\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mmatplotlib\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m colors\n\u001b[1;32m 8\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mmatplotlib\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mbackends\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m backend_agg\n", + "\u001b[0;31mModuleNotFoundError\u001b[0m: No module named 'matplotlib'" ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Final cost: 0.0666807\n", - "Max absolute difference: 0.0437499\n" - ] - }, - { - "data": { - "image/png": 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\n", 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              " - ] - }, - "metadata": { - "filenames": { - "image/png": "/Users/mhjensen/Teaching/MachineLearning/doc/LectureNotes/_build/jupyter_execute/chapter11_50_2.png" - } - }, - "output_type": "display_data" } ], "source": [ @@ -982,44 +969,7 @@ "collapsed": false, "editable": true }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Initial cost: 324.246\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/Users/mhjensen/miniforge3/envs/myenv/lib/python3.9/site-packages/numpy/core/fromnumeric.py:3208: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray.\n", - " return asarray(a).size\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Final cost: 0.119936\n" - ] - }, - { - "data": { - "image/png": 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- "text/plain": [ - "
              " - ] - }, - "metadata": { - "filenames": { - "image/png": "/Users/mhjensen/Teaching/MachineLearning/doc/LectureNotes/_build/jupyter_execute/chapter11_52_3.png" - } - }, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "import autograd.numpy as np\n", "from autograd import grad, elementwise_grad\n", @@ -1286,45 +1236,7 @@ "collapsed": false, "editable": true }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Initial cost: 0.221805\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/Users/mhjensen/miniforge3/envs/myenv/lib/python3.9/site-packages/numpy/core/fromnumeric.py:3208: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray.\n", - " return asarray(a).size\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Final cost: 0.000417932\n", - "The max absolute difference between the solutions is: 0.00424909\n" - ] - }, - { - "data": { - "image/png": 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              " - ] - }, - "metadata": { - "filenames": { - "image/png": "/Users/mhjensen/Teaching/MachineLearning/doc/LectureNotes/_build/jupyter_execute/chapter11_58_3.png" - } - }, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "import autograd.numpy as np\n", "from autograd import grad, elementwise_grad\n", @@ -1609,61 +1521,7 @@ "collapsed": false, "editable": true }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Initial cost: 0.221805\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/Users/mhjensen/miniforge3/envs/myenv/lib/python3.9/site-packages/numpy/core/fromnumeric.py:3208: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray.\n", - " return asarray(a).size\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Final cost: 0.000417932\n", - "The max absolute difference between the solutions is: 0.00424909\n", - "Max absolute difference between Euler method and analytical: 0.011225\n", - "Max absolute difference between deep neural network and analytical: 0.00424909\n" - ] - }, - { - "data": { - "image/png": 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VKmDFRMaDe+3atSYlJcWcPXvWzJ8/34SEhBh/f38TExNjoqKiDGBef/11p8sePHjQeHt7myeeeMKxrF27dgYwP//8c6brGjZsmPH19XVatmjRIgOYSZMmOS2fM2eOUwE0Jn1n5Orqanbu3Ok0NmPn2rt3b6flo0aNMoB5+OGHnZb37dvXBAcHZ3ufpKWlmZSUFDNjxgzj6upqTp06len2rVu3zukytWrVMl27dnX8PH78eAOYpUuXZns9s2bNMoD59ttvnZZv2LDBAI4/xLPLWKZMGVO3bl2TlpbmWH727FkTGhpqWrZs6ViWcf98/fXX2W4vQ8bj4YEHHnBaPmnSJAOYo0ePGmOMOXDggHFzc8tUZM+ePWvCwsLM7bff7lh2tQWscuXKTn8UZSU1NdWkpKSYO++80zRs2NBp3dUUsNKlS5v4+HjHspiYGOPi4mImTpzoWNa1a1dTtmxZExcX53T5hx56yHh5eTkeHxm/t4tfZDK0bt3a3HjjjY6fq1SpYv7zn/8YFxcXs2rVKmPMv4U644/U3HhuZNzOBx980MTGxprWrVubiIgIEx0dfcX757XXXjOA0x88l3rqqaeyfD7cf//9xmazOeW59IX9nXfeMYDTmFOnThlPT0/z2GOPOZZlV8By8jz8z3/+Y2w2W6Y/Drt27ZqjAnap1NRUk5ycbKpWrWoeffRRx/KM51iPHj2cxn/11VcGcPqj7VIxMTEGMAMGDLjsdffv398A5tixY8aYKxcwY4wpXbq0qVmzpuPnjD+msjpVrlz5ste/Z88e4+rqmumP1oudPn3a8Yf+xQ4cOGA8PT3NoEGDHMuGDRuWaf+XkpJiQkJCDOD4480YY2JjY42rq6sZPXq0Y1nG7b/55pudruvXX381gHnppZeyzGi3201KSorZv3+/Acz333+f6f557rnnnC6T09fA06dPGy8vr2wzXamAGWOMr69vlvuvAQMGGE9PT3PgwAGn5d27dzc+Pj6XfZ5ebrt5sc/PiYx9eMeOHZ3ur4zXgbp16zr9U2D9+vUGMLNmzbrsNhMSEoyvr695++23HcsvLWDGpP+uPTw8HM8nY/7dt2bsk40xpmfPnk6vUxe7dJ82cuRI4+7ubrZt25aTu8CR+Xpey/J6P53TAmaMMbVr187yMZ5VAWvevLkJDQ01Z8+edSxLTU01derUMWXLljV2u93peq70+CxKdAhiMdO8eXPc3d3x9/enV69ehIWFsXDhQkqXLs38+fOx2WzccccdpKamOk5hYWHUr18/02E8QUFB3HjjjTm63uXLlwNk+tDmbbfdhq+vLz///LPT8nr16mU7gUSvXr2cfq5ZsyZApsO8atasyalTp5wOQ9yyZQs33XQTJUuWxNXVFXd3d4YOHUpaWhp///230+XDwsJo2rRpplwXH/awcOFCqlWrluUH4DPMnz+fEiVK0Lt3b6f7tUGDBoSFhV328KidO3dy5MgRhgwZgovLv09XPz8/br31VtauXcu5c+eyvfyV3HTTTZluH/x7SOrixYtJTU1l6NChTtm9vLxo167dNc02dfF1u7u7Z1r+9ddf06pVK/z8/HBzc8Pd3Z1PP/0006FNV6NDhw74+/s7fi5dujShoaGO23nhwgV+/vlnbr75Znx8fJxua48ePbhw4UKmw2Ky0rFjR3799VfOnz/P/v372b17NwMGDKBBgwYsXboUgGXLllG+fHmqVq0K5O5zY+/evbRo0YL4+HjWrl1L/fr1r5g545DK22+/na+++irLWVGXL19OrVq1Mj0fhg8fjjHGcRuyMnjwYDw9PZ0OS5k1axZJSUmMGDHiivly8jxctWoVderUoVatWk7jBg4ceMXtA6SmpjJhwgRq1aqFh4cHbm5ueHh4sGvXriwfd1d63lwPYwxAlocDXekyl1q2bBkbNmxwOl1p5r2lS5eSlpbGgw8+mO2YqKgozp8/n+kxW65cOW688cZMj1mbzUaPHj0cP7u5uVGlShXCw8Np2LChY3lwcLDT8/Jil35GuWXLllSoUIEVK1Y4lh0/fpz77ruPcuXKOfYdFSpUAMjy93jxYcBAjl8Do6KiuHDhQraZrsfy5cvp2LEj5cqVc1o+fPhwzp07l+XhdFcjP/b5H374IY0aNcLLy8vxe/j555+z/B307NkTV1fXbPMAJCQk8OSTT1KlShXc3Nxwc3PDz8+PxMTEK74u3H///QBOh2G/99571K1bl7Zt217xtmRl4cKFdOjQwfG3R3Zy87Usr/fTeSExMZF169bRr18/p8nYXF1dGTJkCIcOHcp06HZe7lsLGhWwYmbGjBls2LCBLVu2cOTIEf744w/Hh72PHTuGMYbSpUvj7u7udFq7dm2m4+ivZiat2NhY3NzcMn3I3mazERYWRmxsbI63HRwc7PSzh4fHZZdfuHABSD82uk2bNhw+fJi3336bNWvWsGHDBsdnfM6fP+90+ZIlS2a6bk9PT6dxJ06cuOLsSMeOHePMmTN4eHhkul9jYmIy3a8Xy7hfsro/ypQpg91u5/Tp05e9/su59DZmTIKRcRszjme/4YYbMmWfM2fOZbNfSVa3ae7cudx+++1ERETwxRdfEBUVxYYNGxg5cqTj93gtrvS7jI2NJTU1lXfffTfT7cz4wzEnt7VTp04kJSXxyy+/sHTpUkqVKkXDhg3p1KkTy5YtA+Dnn392Kuy5+dxYv349f//9N/3798/xrF1t27Zl3rx5jj+6ypYtS506dZg1a5ZTxuwegxnrsxMcHMxNN93EjBkzSEtLA9KP92/atCm1a9e+Yr6cPA9jY2MzTUABZLksK6NHj+bZZ5+lb9++/Pjjj6xbt44NGzZQv379TPuFrDJd+rzJSqlSpfDx8WHv3r2XzbJv3z58fHwy7c+yk5iYSGxsrON3cbH69evTpEkTp1OdOnUuu72Mz6Ve7vFzpf3SpY8HHx8fvLy8nJZ5eHhkeRs9PDyyfK6HhYVluSzjuux2O126dGHu3Lk88cQT/Pzzz6xfv97xj5OsfjeX5s/pa2DGdWaX6Xpcz3MtJ/J6n//GG29w//3306xZM7799lvWrl3Lhg0b6Nat2zU/lwYNGsR7773HXXfdxeLFi1m/fj0bNmwgJCTkss85SN8H9O/fn48++oi0tDT++OMP1qxZc12ThOXkdT+3X8vyej+dF06fPo0x5qoyXcu+tbDSLIjFTM2aNZ1m0rpYqVKlsNlsrFmzJsvZ6C5ddjX/oS1ZsiSpqamcOHHC6Q9NYwwxMTGO/+5cy7Zzat68eSQmJjJ37lyn/1Jez/c5hYSEcOjQocuOyfgw6aJFi7Jcf/E7M5fK2BkdPXo007ojR47g4uJCUFDQVSS+OhkzqH3zzTdX/M+ul5cXcXFxmZZn94Kd1e/4iy++oGLFisyZM8dpfVJS0tXEvmpBQUGO/8pl95//ihUrXnE7zZo1w8/Pj2XLlrFv3z46duyIzWajY8eOvP7662zYsIEDBw44FbDcfG7079+fsLAwxo4di91u55lnnrliZoA+ffrQp08fkpKSWLt2LRMnTmTQoEFERkbSokULSpYsme1jELjiTHsjRozg66+/ZunSpZQvX54NGzbwwQcf5ChbTpQsWdLxh+PFYmJicnT5L774gqFDhzJhwgSn5SdPnqREiRK5ERFXV1c6dOjAokWLOHToUJZ/wB06dIhNmzbRvXt3p3cFLmfBggWkpaXl6LunciLjMXjo0KFM78JkuNJ+KS9mXszqdxkTE0OVKlWA9Ikrfv/9d6ZNm8awYcMcY3bv3p3tNi99LuX0NTDj9meX6eJJh67W9T7XrtfV7POz8sUXX9C+fftMz++zZ89eU564uDjmz5/P888/z1NPPeVYnpSUxKlTp3K0jUceeYTPP/+c77//nkWLFlGiRInrmvU5J6/7efFalpf76Yx/kCQlJTk99q/nn6xBQUG4uLhY+nguyPQOmDj06tULYwyHDx/O9F/TJk2aULdu3WvedseOHYH0ndLFvv32WxITEx3r81LGTvDinYsx5oozxF1O9+7d+fvvvy/71n6vXr2IjY0lLS0ty/u1evXq2V62evXqRERE8OWXXzodZpSYmMi3337rmBkxr3Tt2hU3Nzf++eefLLNfXOYjIyP5+++/nV5gYmNj+e2333J8fTabDQ8PD6cXrJiYmOuaOSonfHx86NChA1u2bKFevXpZ3s6MP7ou9x85d3d32rZty9KlS1m+fDmdO3cGoE2bNri5ufHMM884ClmG3H5uPPPMM7z11ls899xzjBkz5qou6+npSbt27XjllVcAHLOedezYkW3btrF582an8TNmzMBms9GhQ4fLbrdLly5EREQwdepUpk6dipeXV44PD8yJdu3asXXrVrZt2+a0fPbs2Tm6vM1my/QH94IFC3L9S+rHjBmDMYYHHnjA8W5ghrS0NO6//36MMTn+vR04cIDHH3+cwMBA7r333lzJ2KVLF1xdXS9bkFu0aIG3t3emx+yhQ4cch9DltpkzZzr9/Ntvv7F//35H8cxq/w5c1eyQOX0NbN68OV5eXtlmyolL38XN0LFjR5YvX55p5rcZM2bg4+NzxWm+s9tuTl3NPj8rWT2X/vjjj2s+dNJms2GMybTNKVOmZHoOZadx48a0bNmSV155hZkzZzJ8+PBM33t6Nfdb9+7dWbFixWVnPs3L17K82E9n/NPgjz/+cFr+448/Znn9ObmvfH19adasGXPnznUab7fb+eKLLyhbtmyx/q5SvQMmDq1ateKee+5hxIgRbNy4kbZt2+Lr68vRo0f55ZdfqFu3ruN46qvVuXNnunbtypNPPkl8fDytWrXijz/+4Pnnn6dhw4YMGTIkl29N1hk8PDwYOHAgTzzxBBcuXOCDDz64rkP4Ro0axZw5c+jTpw9PPfUUTZs25fz586xatYpevXrRoUMHBgwYwMyZM+nRowePPPIITZs2xd3dnUOHDrFixQr69OnDzTffnOX2XVxcmDRpEoMHD6ZXr17ce++9JCUl8eqrr3LmzJkcT1d9rSIjIxk/fjxjx45lz549dOvWjaCgII4dO8b69evx9fV1TIM+ZMgQPvroI+644w7uvvtuYmNjmTRpEgEBATm+vowp1h944AH69evHwYMHefHFFwkPD2fXrl15dTMBePvtt2ndujVt2rTh/vvvJzIykrNnz7J7925+/PFHR8muXLky3t7ezJw5k5o1a+Ln50eZMmUch1R07NiRxx57DMDxTpe3tzctW7ZkyZIl1KtXj9DQUMf15sVz45FHHsHPz4977rmHhIQE3nnnnWzfOXvuuec4dOgQHTt2pGzZspw5c4a3334bd3d3x/fJPfroo8yYMYOePXsyfvx4KlSowIIFC5g8eTL333//FV9EXV1dGTp0KG+88QYBAQHccsstWX5B8LUaNWoUn332Gd27d2f8+PGULl2aL7/80vFVEBd/fjIrvXr1Ytq0adSoUYN69eqxadMmXn311Vz/8tVWrVrx1ltvMWrUKFq3bs1DDz1E+fLlHV/EvG7dOt566y1atmyZ6bJbt251fB7n+PHjrFmzhqlTp+Lq6sp3332X5Xeobdq0Kcv7uVatWtk+LyMjI3n66ad58cUXOX/+PAMHDiQwMJBt27Zx8uRJXnjhBUqUKMGzzz7L008/zdChQxk4cCCxsbG88MILeHl58fzzz1//nXWJjRs3ctddd3Hbbbdx8OBBxo4dS0REBA888AAANWrUoHLlyjz11FMYYwgODubHH390fPYyJ3L6GhgUFMTjjz/OSy+95JRp3LhxOT4EsW7duqxcuZIff/yR8PBw/P39qV69Os8//zzz58+nQ4cOPPfccwQHBzNz5kwWLFjApEmTrvi8yW67OXU1+/ys9OrVixdffJHnn3+edu3asXPnTsaPH0/FihVJTU3NcY4MAQEBtG3blldffZVSpUoRGRnJqlWr+PTTT6/q3elHHnnE8RUtGY+Zi9WtW5e5c+fywQcf0LhxY1xcXLItm+PHj2fhwoW0bduWp59+mrp163LmzBkWLVrE6NGjqVGjRq6/luX1frpHjx4EBwdz5513Mn78eNzc3Jg2bRoHDx7M8r6aPXs2c+bMoVKlSnh5eWX7D/qJEyfSuXNnOnTowOOPP46HhweTJ09m69atzJo1K0+Odio08n3aD7FETmbSyvDZZ5+ZZs2aGV9fX+Pt7W0qV65shg4d6jQ1cLt27Uzt2rWzvHxWsyAaY8z58+fNk08+aSpUqGDc3d1NeHi4uf/++52mETYmfUagnj17Zrp8drP8ZXfbMmb1uXia2h9//NHUr1/feHl5mYiICPOf//zHLFy4MNPMSdndvktn9DMmfUasRx55xJQvX964u7ub0NBQ07NnT6fpq1NSUsxrr73muG4/Pz9To0YNc++995pdu3Zlup5LzZs3zzRr1sx4eXkZX19f07FjR/Prr7/m6P7JSnb3WVazSGVcf4cOHUxAQIDx9PQ0FSpUMP369TPLli1zGjd9+nRTs2ZN4+XlZWrVqmXmzJmT7SyIr776apbZ/vvf/5rIyEjj6elpatasaT755JMsZ2i6mlkQM6Znv9Ll9+7da0aOHGkiIiKMu7u7CQkJMS1btsw009qsWbNMjRo1jLu7e6bZpH7//XcDmKpVqzpd5uWXXzaA0wxvGa73uZHd7Zw1a5Zxc3MzI0aMcJpF82Lz58833bt3NxEREcbDw8OEhoaaHj16OL7iIMP+/fvNoEGDTMmSJY27u7upXr26efXVVzNt99L7I8Pff//tmIkvq1lDs5sFMafPw61bt5pOnToZLy8vExwcbO68804zffp0A5jff/89y9ue4fTp0+bOO+80oaGhxsfHx7Ru3dqsWbMm08ye2T3Hspr963KioqJMv379TOnSpY2bm5sJDQ01t9xyi/ntt98yjc24XzJOGb+jdu3amQkTJpjjx49nuszlZkHM7v6/1IwZM8wNN9zg2F81bNgw0+2bMmWKqVevnvHw8DCBgYGmT58+mWaizO71ILvf7aWP8Yzbv2TJEjNkyBBTokQJxwyMl+47t23bZjp37mz8/f1NUFCQue2228yBAweynfHt4teGi+XkNdBut5uJEyeacuXKGQ8PD1OvXj3z448/Zjsb7KWio6NNq1atjI+PT6aZE//880/Tu3dvExgYaDw8PEz9+vVz/NjKbrt5tc+/VFJSknn88cdNRESE8fLyMo0aNTLz5s27qteBS39fhw4dMrfeeqsJCgoy/v7+plu3bmbr1q2Z9uHZ3ZaMXJ6enqZbt25Z5j516pTp16+fKVGihLHZbE6vN1nt0w4ePGhGjhxpwsLCjLu7uylTpoy5/fbbnWZbzM3XsvzYT69fv960bNnS+Pr6moiICPP888+bKVOmZNov79u3z3Tp0sX4+/sbwPF7zW4/uGbNGnPjjTc6nk/Nmzc3P/74o9OYq318FgU2Y7KZPklERKQQu+eee5g1axaxsbGOSXmkcJk2bRojRoxgw4YNVzz8TSQ7P/74IzfddBMLFixwmpFTxCo6BFFERAq98ePHU6ZMGSpVqkRCQgLz589nypQpPPPMMypfIsXUtm3b2L9/P4899hgNGjSge/fuVkcSAVTARESkCHB3d+fVV1/l0KFDpKamUrVqVd544w0eeeQRq6OJiEUeeOABfv31Vxo1asT06dOL92eOpEDRIYgiIiIiIiL5RNPQi4iIiIiI5BMVMBERERERkXyiAiYiIiIiIpJPNAnHNbLb7Rw5cgR/f399qFNEREREpBgzxnD27FnKlCmDi8vl3+NSAbtGR44coVy5clbHEBERERGRAuLgwYOULVv2smNUwK6Rv78/kH4nBwQEWJxGRERERESsEh8fT7ly5Rwd4XJUwK5RxmGHAQEBKmAiIiIiIpKjjyZpEg4REREREZF8ogImIiIiIiKST1TARERERERE8ok+AyYiIiIikkfS0tJISUmxOoZcJ1dXV9zc3HLl66dUwERERERE8kBCQgKHDh3CGGN1FMkFPj4+hIeH4+HhcV3bUQETEREREcllaWlpHDp0CB8fH0JCQnLlnROxhjGG5ORkTpw4wd69e6lateoVv2z5clTARERERERyWUpKCsYYQkJC8Pb2tjqOXCdvb2/c3d3Zv38/ycnJeHl5XfO2NAmHiIiIiEge0TtfRcf1vOvltJ1c2YqIiIiIiIhckQqYiIiIiIhIPlEBExERERGRfNW+fXtGjRp1XdvYt28fNpuN6OjoXMkE6YeMzps3L9e2lxVNwiEiIiIiIgXa8OHDOXPmjFM5KleuHEePHqVUqVLWBbsGKmAiIiIiIlLouLq6EhYWZnWMq6ZDEEVERERE8pgxhnPJqZacrvaLoBctWkTr1q0pUaIEJUuWpFevXvzzzz/Av4f9zZ07lw4dOuDj40P9+vWJiopyXD42NpaBAwdStmxZfHx8qFu3LrNmzcr2+saPH0/dunUzLW/cuDHPPfcc48aNY/r06Xz//ffYbDZsNhsrV67M8hDEv/76i549exIQEIC/vz9t2rRxZN+wYQOdO3emVKlSBAYG0q5dOzZv3nxV901u0DtgIiIiIiJ57HxKGrWeW2zJdW8b3xUfj5z/2Z+YmMjo0aOpW7cuiYmJPPfcc9x8881ORWfs2LG89tprVK1albFjxzJw4EB2796Nm5sbFy5coHHjxjz55JMEBASwYMEChgwZQqVKlWjWrFmm6xs5ciQvvPACGzZs4IYbbgDgjz/+YMuWLXz99deEhoayfft24uPjmTp1KgDBwcEcOXLEaTuHDx+mbdu2tG/fnuXLlxMQEMCvv/5KamoqAGfPnmXYsGG88847ALz++uv06NGDXbt24e/vf1X36fVQARMREREREYdbb73V6edPP/2U0NBQtm3bhp+fHwCPP/44PXv2BOCFF16gdu3a7N69mxo1ahAREcHjjz/uuPz//d//sWjRIr7++ussC1jZsmXp2rUrU6dOdRSwqVOn0q5dOypVqgSkfxFyUlLSZQ85fP/99wkMDGT27Nm4u7sDUK1aNcf6G2+80Wn8Rx99RFBQEKtWraJXr145vn+ulwqYiIiIiEge83Z3Zdv4rpZd99X4559/ePbZZ1m7di0nT57EbrcDcODAAWrVqgVAvXr1HOPDw8MBOH78ODVq1CAtLY3//ve/zJkzh8OHD5OUlERSUhK+vr7ZXufdd9/NyJEjeeONN3B1dWXmzJm8/vrrV5U7OjqaNm3aOMrXpY4fP85zzz3H8uXLOXbsGGlpaZw7d44DBw5c1fVcLxUwEREREZE8ZrPZruowQCv17t2bcuXK8cknn1CmTBnsdjt16tQhOTnZMebikmOz2QAcRe3111/nzTff5K233qJu3br4+voyatQop8tndZ2enp589913eHp6kpSUlOmduCvx9va+7Prhw4dz4sQJ3nrrLSpUqICnpyctWrS4bK68UDgeBSIiIiIikudiY2PZvn07H330EW3atAHgl19+uaptrFmzhj59+nDHHXcA6cVs165d1KxZM9vLuLm5MWzYMKZOnYqnpycDBgzAx8fHsd7Dw4O0tLTLXm+9evWYPn06KSkpWb4LtmbNGiZPnkyPHj0AOHjwICdPnryq25YbNAuiiIiIiIgAEBQURMmSJfn444/ZvXs3y5cvZ/To0Ve1jSpVqrB06VJ+++03tm/fzr333ktMTMwVL3fXXXexfPlyFi5cyMiRI53WRUZG8scff7Bz505OnjxJSkpKpss/9NBDxMfHM2DAADZu3MiuXbv4/PPP2blzpyPX559/zvbt21m3bh2DBw++4rtmeUEFTEREREREAHBxcWH27Nls2rSJOnXq8Oijj/Lqq69e1TaeffZZGjVqRNeuXWnfvj1hYWH07dv3iperWrUqLVu2pHr16pkm67j77rupXr06TZo0ISQkhF9//TXT5UuWLMny5ctJSEigXbt2NG7cmE8++cTxbthnn33G6dOnadiwIUOGDOHhhx8mNDT0qm5bbrCZq/1iAAEgPj6ewMBA4uLiCAgIsDqOiIiIiBQgFy5cYO/evVSsWBEvLy+r4xQKxhhq1KjBvffee9XvuuWHy/1Or6Yb6DNgIiIiIiJiqePHj/P5559z+PBhRowYYXWcPKUCJiIiIiIilipdujSlSpXi448/JigoyOo4eUoFTERERERELFWcPhWlSThERERERETyiQqYiIiIiIhIPlEBExERERERySeWFrDVq1fTu3dvypQpg81mY968eZcdP3fuXDp37kxISAgBAQG0aNGCxYsXZxr37bffUqtWLTw9PalVqxbfffddpjGTJ092TCHZuHFj1qxZk1s3S0REREREJEuWFrDExETq16/Pe++9l6Pxq1evpnPnzvz0009s2rSJDh060Lt3b7Zs2eIYExUVRf/+/RkyZAi///47Q4YM4fbbb2fdunWOMXPmzGHUqFGMHTuWLVu20KZNG7p3786BAwdy/TaKiIiIiIhkKDBfxGyz2fjuu+9y9C3ZF6tduzb9+/fnueeeA6B///7Ex8ezcOFCx5hu3boRFBTErFmzAGjWrBmNGjXigw8+cIypWbMmffv2ZeLEiTm63oL0RcxnTsaw46f3qXPzY/j5l7A0i4iIiIjoi5iLotz6IuZC/Rkwu93O2bNnCQ4OdiyLioqiS5cuTuO6du3Kb7/9BkBycjKbNm3KNKZLly6OMVlJSkoiPj7e6VRQxH7QneZ73mHrgg+tjiIiIiIiIpdRqAvY66+/TmJiIrfffrtjWUxMDKVLl3YaV7p0aWJiYgA4efIkaWlplx2TlYkTJxIYGOg4lStXLhdvyfU5UbU/ABE7p2NPS7M4jYiIiIiIZKfQFrBZs2Yxbtw45syZQ2hoqNM6m83m9LMxJtOynIy52JgxY4iLi3OcDh48eJ23IPfU6Xkf8caHcuYIW1d9Y3UcERERERHJRqEsYHPmzOHOO+/kq6++olOnTk7rwsLCMr2Tdfz4ccc7XqVKlcLV1fWyY7Li6elJQECA06mg8PMvwdawvgC4rPvg8oNFREREJP8ZA8mJ1pyuYsqH9u3b8/DDD/PEE08QHBxMWFgY48aNc6y32WxMmTKFm2++GR8fH6pWrcoPP/yQB3dY0eVmdYCrNWvWLEaOHMmsWbPo2bNnpvUtWrRg6dKlPProo45lS5YsoWXLlgB4eHjQuHFjli5dys033+wYs3TpUvr06ZP3NyCPVOg2irRps6iTtIUD29dTvmZTqyOJiIiISIaUczChjDXX/fQR8PDN8fDp06czevRo1q1bR1RUFMOHD6dVq1Z07twZgBdeeIFJkybx6quv8u677zJ48GD279/vNC+DZM/Sd8ASEhKIjo4mOjoagL179xIdHe2YDn7MmDEMHTrUMX7WrFkMHTqU119/nebNmxMTE0NMTAxxcXGOMY888ghLlizhlVdeYceOHbzyyissW7aMUaNGOcaMHj2aKVOm8Nlnn7F9+3YeffRRDhw4wH333ZcvtzsvRFSszha/tgAcX/q2xWlEREREpLCqV68ezz//PFWrVmXo0KE0adKEn3/+2bF++PDhDBw4kCpVqjBhwgQSExNZv369hYkLF0vfAdu4cSMdOnRw/Dx69GgAhg0bxrRp0zh69KjTd3N99NFHpKam8uCDD/Lggw86lmeMB2jZsiWzZ8/mmWee4dlnn6Vy5crMmTOHZs2aOcb379+f2NhYxo8fz9GjR6lTpw4//fQTFSpUyONbnLc8Wz8Ii1dRN3Yx8SePElAq3OpIIiIiIgLg7pP+TpRV130V6tWr5/RzeHg4x48fz3K9r68v/v7+Tuvl8iwtYO3bt+dyX0OWUaoyrFy5Mkfb7devH/369bvsmAceeIAHHnggR9srLOo068zOZVWpnraL3xe8Q9NhOftOMxERERHJYzbbVR0GaCV3d3enn202G3a7Pcfr5fIK5SQckjWbiwun6t4NQKW9s0hNOm9xIhERERERuZgKWBHTsNswjhNMKU6zdel0q+OIiIiIiMhFVMCKGC8vL3aUHwBAQPTHVzXtqIiIiIiI5K1CNw29XFmNnv/H+clTqJT6D3s2LaVSky5WRxIRERGRQiCrORfmzZvnOJ/V/A1nzpzJu0BFkN4BK4JCS5dhU4muACSsesfiNCIiIiIikkEFrIgqeePDANSJ/4XYgzstTiMiIiIiIqACVmTVrN+Uze6NcbEZ9i98y+o4IiIiIiKCCliRltTkHgCqHfmOpMTTFqcREREREREVsCKsScfb2EtZ/DjPjoUfWh1HREREpNjJatIKKZxy63epAlaEubu5sr/qUABCt03DpKVanEhERESkeHB1dQUgOTnZ4iSSW86dOweAu7v7dW1H09AXcfV63suZN98m3B7Drl+/oWrbAVZHEhERESny3Nzc8PHx4cSJE7i7u+Piovc9CitjDOfOneP48eOUKFHCUa6vlQpYERdcogQrQvrS4eRMTNQHoAImIiIikudsNhvh4eHs3buX/fv3Wx1HckGJEiUICwu77u2ogBUD5bo9Qsrns6l2Pprjf28gtNoNVkcSERERKfI8PDyoWrWqDkMsAtzd3a/7na8MKmDFQJUq1fnVpw2tzq8kZskbhFabZXUkERERkWLBxcUFLy8vq2NIAaKDUYsJ15YPAlDj5BLOnTpscRoRERERkeJJBayYuKFVZ/50qY4Hqez+6W2r44iIiIiIFEsqYMWEq4uN47XvBKDcP7MxKectTiQiIiIiUvyogBUjN3QfylFTkiATx66fp1kdR0RERESk2FEBK0YCfLz5s2z6NPRemz4GfTO7iIiIiEi+UgErZqp3f5BzxpPyKXs4Er3Y6jgiIiIiIsWKClgxU6FsBFEB3QCIX/GOxWlERERERIoXFbBiKLD9QwBUi/uNhCM7LE4jIiIiIlJ8qIAVQ40b3cBatya42Az7f3rT6jgiIiIiIsWGClgxZLPZSGx4DwAVD80j7dxpixOJiIiIiBQPKmDFVMtOt7KLcvhwgd2LPrA6joiIiIhIsaACVkx5e7qxq+JQAIL/mgZpqdYGEhEREREpBlTAirEGPe8i1vgTknaMQ2u/sTqOiIiIiEiRpwJWjJUpFcz6kn0BSPn1PWvDiIiIiIgUAypgxVyZTg+RbFypeO5Pzuxaa3UcEREREZEiTQWsmKtXszq/eLYD4NjSt6wNIyIiIiJSxKmAFXM2mw2a3wdA5eNLSDlz2OJEIiIiIiJFlwqY0KptJzZTEzfS2PvTW1bHEREREREpslTABE83Vw7XGAFA2K7ZkHzO4kQiIiIiIkWTCpgA0Lz7EA6aEAJMPAdWTbM6joiIiIhIkaQCJgCEBPqwOaw/AO4bPgJjLE4kIiIiIlL0qICJQ9Vu95FgvAhP3sepPxdbHUdEREREpMhRAROHWhXLscq3GwBnlr9lbRgRERERkSJIBUyc+LV5ALuxUelMFElHt1sdR0RERESkSFEBEyetmt7AL643AHBw4ZsWpxERERERKVpUwMSJm6sLZ+rdDUDZA/Mw505ZnEhEREREpOhQAZNM2nXuyzYTiRdJHFj6gdVxRERERESKDBUwySTQ14PtFQYD4P/HVEhLsTiRiIiIiEjRoAImWarf/U5OmECC005wcv3XVscRERERESkSVMAkS1XCS7K6RB8ALvzynsVpRERERESKBhUwyVbpGx8gybhRNvEvzu2JsjqOiIiIiEihpwIm2WpZtwbL3dsBcGyxpqQXEREREbleKmCSLRcXG6k33AtA+WNLsZ8+YHEiEREREZHCTQVMLuvG9h1ZSx1csXNw8TtWxxERERERKdRUwOSyfD3d2F9lKAClds6C5ESLE4mIiIiIFF4qYHJFLbsPZp8pja9J4PiaaVbHEREREREptFTA5IrKlfRjbchtANjWfwB2u8WJREREREQKJxUwyZFKne8l3vgQknSQhL8WWh1HRERERKRQUgGTHLmhWjmWenUF4PRyTcYhIiIiInItVMAkR2w2G56t7iPN2Ch3ei2pR/+yOpKIiIiISKGjAiY51qnFDaywNQPgiL6YWURERETkqqmASY55ubtyos6dAITtmweJJ60NJCIiIiJSyKiAyVW5sXNv/rBXwoMUYpZ/aHUcEREREZFCRQVMrkrpQG+iIwYC4P37Z5CabHEiEREREZHCQwVMrlr9rsM5ZkoQmBpL3KavrI4jIiIiIlJoqIDJVasfGcoyvz4AXFj9LhhjcSIRERERkcJBBUyuSal293LBuFM6cQfJe3+zOo6IiIiISKGgAibX5MbGNVnk2h6A40s1Jb2IiIiISE6ogMk1cXd14XyjuwEIP/oz5tReixOJiIiIiBR8KmByzbp16MAaez1csROz7F2r44iIiIiIFHgqYHLNgnw92FVpCAAldsyGpLMWJxIRERERKdhUwOS6tOx6O//Yw/G2J3L6t2lWxxERERERKdBUwOS61AgvwargW9N/WPsh2NOsDSQiIiIiUoCpgMl1i7zxLs4YX4KSDpG07Ser44iIiIiIFFgqYHLd2teJZIFHVwBOL3/b4jQiIiIiIgWXCphcNxcXG27N7iHVuBB2agP2I79bHUlEREREpEBSAZNc0aN1E5bSHIDjy/QumIiIiIhIVlTAJFf4e7lzqMYIAEru+QESjlucSERERESk4FEBk1zTuXNPNtur4E4Kp1Z9aHUcEREREZECRwVMck1kKV/Wl+4PgMeWzyDlgsWJREREREQKFhUwyVV1Ow3hiAnGL/U057Z8ZXUcEREREZECRQVMclXLamEs8O4NwPnV74IxFicSERERESk4VMAkV9lsNoJb380540nJhL9J27vG6kgiIiIiIgWGCpjkuh5NazHf1g6Ak0vftDiNiIiIiEjBoQImuc7bw5W4+ncBEHJ0BcT+Y3EiEREREZGCQQVM8kTPDm1ZYW+AC4bYFe9aHUdEREREpEBQAZM8UaaEN1vLDQbAd9tsuBBncSIREREREeupgEmeadn5Vnbay+JlP0/i2qlWxxERERERsZwKmOSZRhWCWRJwCwBpUR9CWqrFiURERERErKUCJnnGZrNRof1wThk/ApKOkrJ9gdWRREREREQspQImeapbg4rMc+0KQNyKty1OIyIiIiJiLRUwyVMebi6YJneRbFwpFbsJc3iL1ZFERERERCyjAiZ5rk/bxiw0LQA4tVzvgomIiIhI8WVpAVu9ejW9e/emTJky2Gw25s2bd9nxR48eZdCgQVSvXh0XFxdGjRqVaUz79u2x2WyZTj179nSMGTduXKb1YWFhuXzrJEMpP0/2VBkGQOA/P0L8UYsTiYiIiIhYw9IClpiYSP369XnvvfdyND4pKYmQkBDGjh1L/fr1sxwzd+5cjh496jht3boVV1dXbrvtNqdxtWvXdhr3559/Xvftkex16dSV9fbquJHK2V8/sjqOiIiIiIgl3Ky88u7du9O9e/ccj4+MjOTtt9MPYfvss8+yHBMcHOz08+zZs/Hx8clUwNzc3PSuVz6qXSaQ14Nvo+mZl3DdNBU6PQnu3lbHEhERERHJV0X+M2CffvopAwYMwNfX12n5rl27KFOmDBUrVmTAgAHs2bPnsttJSkoiPj7e6SRXp3bHgRy0h+CTeoaULbOtjiMiIiIiku+KdAFbv349W7du5a677nJa3qxZM2bMmMHixYv55JNPiImJoWXLlsTGxma7rYkTJxIYGOg4lStXLq/jFzmda0cwzyP9s3iJq98DYyxOJCIiIiKSv4p0Afv000+pU6cOTZs2dVrevXt3br31VurWrUunTp1YsCD9C4KnT5+e7bbGjBlDXFyc43Tw4ME8zV4UubrY8G85kgTjRYmE3Zg9K62OJCIiIiKSr4psATt37hyzZ8/O9O5XVnx9falbty67du3KdoynpycBAQFOJ7l6N7eoxTzaA3Dm57cszSIiIiIikt+KbAH76quvSEpK4o477rji2KSkJLZv3054eHg+JCveAr3dOVFrBHZjI+jISjiZfekVERERESlqLC1gCQkJREdHEx0dDcDevXuJjo7mwIEDQPphf0OHDnW6TMb4hIQETpw4QXR0NNu2bcu07U8//ZS+fftSsmTJTOsef/xxVq1axd69e1m3bh39+vUjPj6eYcOG5f6NlExuurE1P9sbAXB21bsWpxERERERyT+WTkO/ceNGOnTo4Ph59OjRAAwbNoxp06Zx9OhRRxnL0LBhQ8f5TZs28eWXX1KhQgX27dvnWP7333/zyy+/sGTJkiyv99ChQwwcOJCTJ08SEhJC8+bNWbt2LRUqVMjFWyfZqRzix1fhA+h8fBOef82B7uPAJ/iKlxMRERERKexsxmgqumsRHx9PYGAgcXFx+jzYNVi54xilv+xETZcDJLV/Ds/2j1kdSURERETkmlxNNyiynwGTgq1ttVB+8OkLQGrUR5CWYm0gEREREZF8oAImlnBxsVG29R2cMAH4Jh3Dvu1HqyOJiIiIiOQ5FTCxTN8bKvONrQsAZ1e+bXEaEREREZG8pwImlvH1dCO54QiSjBuBsdFwcIPVkURERERE8pQKmFjq1raN+NHeEoB4TUkvIiIiIkWcCphYqmyQDzsqpH9Ztu/u+RB32OJEIiIiIiJ5RwVMLNf5xk5EpdXClTQu/PqB1XFERERERPKMCphYrmnFYJYG3JL+w+bpkJxobSARERERkTyiAiaWs9ls1Gx/G/vspfFKjSctepbVkURERERE8oQKmBQIvRuU42vXHgCcW/M+2O0WJxIRERERyX0qYFIgeLm74tV0GPHGG/+ze+Cfn62OJCIiIiKS61TApMDo37om39g7AHB25TsWpxERERERyX0qYFJghPp7cbjaUNKMDf/Dq+H4dqsjiYiIiIjkKhUwKVD6dGjBEnsTAM6ved/iNCIiIiIiuUsFTAqUemVL8Gup2wFw/+srSIy1OJGIiIiISO5RAZMCp3n7nvxhr4ibPYmUDZ9ZHUdEREREJNeogEmB07VOOHM9bgIgNepjSE22OJGIiIiISO5QAZMCx93VhbCWAzluSuCddByzbZ7VkUREREREcoUKmBRI/ZtV5kvTBYDEVe+AMRYnEhERERG5fipgUiAF+XqQUGcIF4w7frF/wsF1VkcSEREREbluKmBSYN3eriHfpbUG4Nzqdy1OIyIiIiJy/VTApMCqVtqf3yMGAOC1+yc4c8DiRCIiIiIi10cFTAq0zu07sCatDi7YSfntQ6vjiIiIiIhcFxUwKdA6VA9lvk9fAMzmGZB01tpAIiIiIiLXQQVMCjQXFxs12tzCP/ZwPFLPYt/ypdWRRERERESumQqYFHj9mpTnS1sPAC788j7Y7RYnEhERERG5NipgUuD5e7nj2nAgccYHn4T9sGux1ZFERERERK6JCpgUCoPb1GJWWkdAU9KLiIiISOGlAiaFQoWSvuypOJBU44LP4V8hZqvVkURERERErpoKmBQafds1Y5G9KQDJv75vcRoRERERkaunAiaFRovKJVkWcCsArlu/hoQTFicSEREREbk6KmBSaNhsNpq368YWexVcTQppGz61OpKIiIiIyFVRAZNCpW/DCOa49gIgde0nkJpkcSIRERERkZxTAZNCxcvdlVI33MZRE4xn0knY+q3VkUREREREckwFTAqdwa0q83laFwDOr3kPjLE4kYiIiIhIzqiASaETHuhNbI1BnDceeMf+Bft/tTqSiIiIiEiOqIBJodS/bT2+TWsDQNIv71mcRkREREQkZ1TApFBqVD6IqJDbAPDYvQhO7bE4kYiIiIjIlamASaHVpV1bVqbVx4Yhbe1HVscREREREbkiFTAptLrXCedbj5sAsG/+HC7EW5xIREREROTyVMCk0PJwc6Fai5vYZY/APTURs+VzqyOJiIiIiFyWCpgUaoOaV2CG6Q5A8q8fgD3N4kQiIiIiItlTAZNCraSfJ2l1bue08cMz4SDs/MnqSCIiIiIi2VIBk0LvjjY1mZnWEdCU9CIiIiJSsKmASaFXq0wAf0XcRopxxfPwWjgSbXUkEREREZEsqYBJkdC37Q0ssDcDIPW3yRanERERERHJmgqYFAmdapZmgU9fAGx/fQtnY6wNJCIiIiKSBRUwKRJcXWw0a92ZDfZquJpUzIYpVkcSEREREclEBUyKjNualGMmPQFIXfcppFywOJGIiIiIiDMVMCkyAr3dKdGoL4dMKdyTTsGfX1kdSURERETEiQqYFClDWlVhemoXAJJ/eR+MsTiRiIiIiMi/VMCkSKkc4sfhSreRaDzxOLUD9q6yOpKIiIiIiIMKmBQ5A9rU5eu0dgCk/qovZhYRERGRgkMFTIqcNlVLsSLwFuzGhts/S+HkbqsjiYiIiIgAKmBSBNlsNjq3acnP9oYA2Nd9aHEiEREREZF0KmBSJN3SKII5rr0AMJu/gPOnLU4kIiIiIqICJkWUj4cblW/oznZ7OVzTzsPmGVZHEhERERFRAZOia0jLSKaldQcgJepDSEu1OJGIiIiIFHcqYFJklQ3y4Vz1WzhpAnBPOAI7frQ6koiIiIgUcypgUqQNaVOdmWmdAEj59X2L04iIiIhIcacCJkXaDZFBbAi5mWTjivuRDXBok9WRRERERKQYUwGTIs1ms3Fz60b8aG8JgD1K74KJiIiIiHVUwKTI61U/nLkeN6X/sO17iDtsbSARERERKbZUwKTI83RzpUnz9qy118TFpMKGKVZHEhEREZFiSgVMioXBzcsz3Z4+JX3qhs8g+ZzFiURERESkOFIBk2Ih1N8Lnzq92G8PxS3pDPwx2+pIIiIiIlIMqYBJsTG8dRWmpXUFIPW3yWC3W5xIRERERIobFTApNuqWDWR3mT6cNd64ndoFe5ZbHUlEREREihkVMClWBratw1dp7QFI+01T0ouIiIhI/lIBk2KlS63SLPK5Cbux4bpnORzfYXUkERERESlGVMCkWHFzdaFjq2YssTcBwKz70OJEIiIiIlKcqIBJsTPghnJ8QQ8A7NGz4NwpixOJiIiISHGhAibFTgkfD8o37MRWeySuaRdg46dWRxIRERGRYkIFTIqlEa0qMiX1f++CrXkL4o9aG0hEREREigUVMCmWqpb251Slm9hir4JLSgIsftrqSCIiIiJSDKiASbF1V9sqjE0ZSZqxwV9zYfcyqyOJiIiISBGnAibFVpuqpQipegNT07qlL1jwOKSctzaUiIiIiBRpKmBSbNlsNp7tVYt37bdx1ATD6b2w5g2rY4mIiIhIEaYCJsValVA/+rWsyQspQwEwv74FJ3dZG0pEREREiiwVMCn2Hu5YlQ1erVie1gBbWjIsGA3GWB1LRERERIogFTAp9gK93flPtxo8lzqcC7jD3tXw59dWxxIRERGRIkgFTAS4rUk5SpSpwjspt6QvWPw0nD9tbSgRERERKXJUwEQAVxcbz/euzSdpPdllj4DEE/Dzi1bHEhEREZEiRgVM5H9uiAyme/3yPJMyEgCz8TM4tNHiVCIiIiJSlKiAiVxkTI8a/OFWh2/S2mLDwPxRkJZqdSwRERERKSJUwEQuEh7ozQPtKzMhZRDx+EHMn7DhE6tjiYiIiEgRoQImcom721bCJ6g0E1IGpC9Y/hLEH7E2lIiIiIgUCZYWsNWrV9O7d2/KlCmDzWZj3rx5lx1/9OhRBg0aRPXq1XFxcWHUqFGZxkybNg2bzZbpdOHCBadxkydPpmLFinh5edG4cWPWrFmTi7dMCjMvd1fG9qjJnLT2bDZVITkBFj1ldSwRERERKQIsLWCJiYnUr1+f9957L0fjk5KSCAkJYezYsdSvXz/bcQEBARw9etTp5OXl5Vg/Z84cRo0axdixY9myZQtt2rShe/fuHDhw4LpvkxQN3eqE0bxSCE8n30kaLrDte9i11OpYIiIiIlLIWVrAunfvzksvvcQtt9ySo/GRkZG8/fbbDB06lMDAwGzH2Ww2wsLCnE4Xe+ONN7jzzju56667qFmzJm+99RblypXjgw8+uK7bI0WHzWbjud61+JvyfJraPX3hgscg5by1wURERESkUCuSnwFLSEigQoUKlC1bll69erFlyxbHuuTkZDZt2kSXLl2cLtOlSxd+++23bLeZlJREfHy800mKtprhAQxuVoG3Um/lhK0UnNkPq1+zOpaIiIiIFGJFroDVqFGDadOm8cMPPzBr1iy8vLxo1aoVu3btAuDkyZOkpaVRunRpp8uVLl2amJiYbLc7ceJEAgMDHady5crl6e2QgmF052q4e/szNmlI+oJf34YTO60NJSIiIiKFVpErYM2bN+eOO+6gfv36tGnThq+++opq1arx7rvvOo2z2WxOPxtjMi272JgxY4iLi3OcDh48mCf5pWAJ8vXgsS7VWGJvwioagz0l/VBEY6yOJiIiIiKFUJErYJdycXHhhhtucLwDVqpUKVxdXTO923X8+PFM74pdzNPTk4CAAKeTFA+DmpaneukAxiYNJdnmCfvWwB9zrI4lIiIiIoVQkS9gxhiio6MJDw8HwMPDg8aNG7N0qfOMdkuXLqVly5ZWRJQCzs3Vhed71+KQCeHNlP9NGLN4LJw7ZW0wERERESl03Ky88oSEBHbv3u34ee/evURHRxMcHEz58uUZM2YMhw8fZsaMGY4x0dHRjsueOHGC6OhoPDw8qFWrFgAvvPACzZs3p2rVqsTHx/POO+8QHR3N+++/79jG6NGjGTJkCE2aNKFFixZ8/PHHHDhwgPvuuy9/brgUOi2rlKJb7TA++as7g7x+o9y5/fDzC9D7baujiYiIiEghYmkB27hxIx06dHD8PHr0aACGDRvGtGnTOHr0aKbv5mrYsKHj/KZNm/jyyy+pUKEC+/btA+DMmTPcc889xMTEEBgYSMOGDVm9ejVNmzZ1XK5///7ExsYyfvx4jh49Sp06dfjpp5+oUKFCHt5aKezG9qzJ8p3HGZ04jK89x8OmadBgMJRresXLioiIiIgA2IzRbALXIj4+nsDAQOLi4vR5sGLktcU7eW/Fbt73nULPtOVQug7cswpcLf1fhoiIiIhY6Gq6QZH/DJhIbnqgQ2XCArx4JvF2LrgFwrGtsO5Dq2OJiIiISCGhAiZyFXw83BjTowanCeCl5AHpC1dMgLhD1gYTERERkUJBBUzkKt1UvwyNKwQxM7kNe7zrQEoiLHrK6lgiIiIiUgiogIlcJZvNxrjetcHmwv1n7sDY3GD7j7BzkdXRRERERKSAUwETuQZ1ywZye+Ny7DTlmevVJ33hwv9A8jlrg4mIiIhIgaYCJnKNHu9aHX9PN5453ZNE73A4cwBWv2p1LBEREREpwFTARK5RiL8nD3esynm8eDZpaPrC396B49utDSYiIiIiBZYKmMh1GNYykkohvsw9V5+dJdqCPRXmjwZ9vZ6IiIiIZEEFTOQ6eLi58GyvWgDcfeI27G7ecOA3iP7S4mQiIiIiUhCpgIlcpw7VQ7mxRigH0krytd8d6QuXPgvnTlkbTEREREQKHBUwkVzwTM+auLvaGBvThoTAanAuFpY9b3UsERERESlgVMBEckGlED9GtKpIKm6MSR6ZvnDzDDiw1tpgIiIiIlKgqICJ5JL/u7EKpfw8+fF0eXaE901fOP9RSEuxNJeIiIiIFBwqYCK5xN/LnSe6VQfgriO9sXsFw/FtsPYDi5OJiIiISEGhAiaSi/o1Kku9soEcSvLm65L3pi9cORHOHLQ2mIiIiIgUCCpgIrnIxcXG871rA/DUnjokhDWFlHOw8EmLk4mIiIhIQaACJpLLGlcI4uaGERhj45mUkRgXN9i5AHb8ZHU0EREREbGYCphIHniyWw18PFyZdziAXZWHpy9c+AQkJ1qaS0RERESspQImkgfCAr14sEMVAO7adyP2wPIQdxBWvWJxMhERERGxkgqYSB65s3VFygf7cOAszAt/JH1h1PtwbJu1wURERETEMipgInnEy92VsT1rAvDU1gjOVeoO9lRYMBrsdovTiYiIiIgVVMBE8lCXWqVpVaUkyal2xqcNBXdfOBAF0TOtjiYiIiIiFlABE8lDNlv6tPSuLjZm7zTsrftw+oqlz0JirLXhRERERCTfqYCJ5LFqpf0Z0rwCAA/ubooJrQ3nT8Oy5yxOJiIiIiL5TQVMJB882qkaQT7ubDt+noWR//tS5i1fwP7frA0mIiIiIvlKBUwkHwT6uDO6S3UAxmzwJqne0PQV80dDarKFyUREREQkP6mAieSTQU3LUyPMn7jzKbxuHwg+peDEdlj7vtXRRERERCSfqICJ5BNXFxvjbqoNwJRNpznSbGz6ipWvwOn9FiYTERERkfyiAiaSj5pXKknPuuHYDYzeUQNToRWknoeFT4AxVscTERERkTymAiaSz8b0qIGnmwtr955mTbWnwcUd/l4EOxZYHU1ERERE8pgKmEg+Kxvkw73tKgMwZk0yqc3/L33FwicgKcHCZCIiIiKS11TARCxwf7vKhAd6cfjMeT7mFihRAeIPw6r/Wh1NRERERPKQCpiIBbw9XBnToyYA76w5RGy7l9NXRE2GmK0WJhMRERGRvKQCJmKR3vXCaRoZzIUUOy/sKAs1bwKTBvMfBbvd6ngiIiIikgdUwEQsYrPZeK53LWw2+OH3I0TXeQo8/ODQetjyudXxRERERCQPqICJWKhORCADbigPwNifT2Fv/3T6iqXPQeJJC5OJiIiISF5QAROx2ONdquHv5cZfR+L5yqU7hNWFC2dgybNWRxMRERGRXKYCJmKxkn6ejOpUDYBXl/5DQufXABv8/iXs+8XacCIiIiKSq1TARAqAoS0qUCXUj9jEZN7cHgBNRqSvmD8aUpOtDSciIiIiuUYFTKQAcHd14dletQCY/ts+9tQbDb4hcHInRL1rcToRERERyS0qYCIFRLtqIXSqGUqq3TBu2VFMl5fSV6yaBKf2WhtORERERHKFCphIAfJMz1p4uLqw+u8TLHdvD5FtIPUCLHwCjLE6noiIiIhcJxUwkQIkspQvI1tXBODFBdtJ7v4auHrAriWw/UeL04mIiIjI9VIBEylgHrqxCiH+nuyLPcdnO9yh1aj0FQufhKSzlmYTERERkeujAiZSwPh5uvFktxoAvPvzLo43eACCIuHsEVgx0dpwIiIiInJdVMBECqBbGkZQv1wJEpPTmPTzAejxevqKdR/C0T+sDSciIiIi10wFTKQAcnGxMa53+rT032w6RLRXE6h9M5g0mP8o2O0WJxQRERGRa6ECJlJANSwfxC2NIgAY98Nf2LtMAA9/OLwRNk+zNpyIiIiIXBMVMJEC7KluNfD1cCX64Bnm/WOHG59JX7FsHCQctzSbiIiIiFw9FTCRAiw0wIuHbqwKwH8X7iCh/nAIrw8X4mDJs9aGExEREZGrpgImUsCNbB1JhZI+HD+bxPur9kGvNwEb/DEb9q62Op6IiIiIXAUVMJECztPNlWd6pk/I8emavezzrAE33Jm+cv5oSE2yMJ2IiIiIXA0VMJFCoFPNUNpULUVymp2Xf9oONz4LvqEQuwt+e8fqeCIiIiKSQypgIoWAzWbj+d61cHOxsXTbMdYcSoFu//tS5tWvwak91gYUERERkRxRARMpJKqE+jO0RSQAL/y4jZSaN0PFdpB6AX76DxhjbUARERERuSIVMJFC5JFOVQn29WD38QS+WHcAer4Brh6wexlsm2d1PBERERG5AhUwkUIk0Nudx7tUB+DNpX8T61UOWo9OX7loDFyItzCdiIiIiFyJCphIIdP/hnLUCg8g/kIqry/9G1o/CsGV4OxRWDHB6ngiIiIichkqYCKFjKuLjXE31QZg1voD/HUiCXq+nr5y/UdwJNq6cCIiIiJyWSpgIoVQ04rB9KoXjjHpE3KYSh2gzq1g7DD/UbCnWR1RRERERLKgAiZSSD3doyZe7i6s33uKBX8eha4TwDMAjmyGTVOtjiciIiIiWVABEymkypTw5v52VQCYsGA75z1D0r+gGWDZeDh7zMJ0IiIiIpIVFTCRQuyetpWIKOHNkbgLfLjqH7jhTghvAElxsGSs1fFERERE5BIqYCKFmLeHK0/3qAnAh6v+4XB8MvR+C2wu8OfXsGelpflERERExJkKmEgh16NuGM0qBpOUamfCT9uhTEO44e70lfNHQ8oFawOKiIiIiIMKmEghZ7PZeL53bVxssOCPo6zdEws3jgW/0nDqH/j1basjioiIiMj/qICJFAG1ygQwsGl5IH1a+jSPAOg2MX3lmtch9h8L04mIiIhIBhUwkSLisS7VCfByY/vReGZvOAC1b4HKN0JaEvz0OBhjdUQRERGRYk8FTKSICPb1YHTnagC8tngncedTocdr4OoJ/yyHv+ZanFBEREREVMBEipDBzStQNdSP0+dSeHPZ31CyMrR5LH3lojFwIc7agCIiIiLFnAqYSBHi7urC871rA/D52v3sOnYWWo+CklUg4Rgsf8nagCIiIiLFnAqYSBHTumoputQqTZrdMH7+NoyrB/R8PX3lhilweLO1AUVERESKMRUwkSLomZ618HB1Yc2ukyzddgwqtYe6t4Gxw/xHwZ5mdUQRERGRYkkFTKQIKl/Sh7vaVATgpQXbuZCSBl1eBs9AOBoNGz61NqCIiIhIMaUCJlJEPdihCqUDPDlw6hyf/boX/EtDp+fSVy5/Ec7GWBtQREREpBhSARMponw93Xiqew0A3lu+m2PxF6DxCIhoDEnxsPhpixOKiIiIFD8qYCJFWN8GETQqX4JzyWm8snAHuLhCrzfB5gJbv4XdP1sdUURERKRYUQETKcJsNptjWvq5Ww6z+cBpCK8PTe9NH/DT45BywcKEIiIiIsWLCphIEVe/XAlua1wWgBd++Au73UCHp8E/HE7tgV/etDihiIiISPGhAiZSDPynW3X8PN34/VAc324+BF4B0G1i+spf3oCTu60NKCIiIlJMqICJFAOh/l483LEKAK8s2snZCylQqy9U6QRpybBgNBhjbUgRERGRYkAFTKSYGN6yIhVL+XIyIYn3VuwGmw16vApuXrB3VfqkHCIiIiKSp1TARIoJDzcXnu1VE4DPftnL3pOJEFwJ2j6ePmDRGDh/xrqAIiIiIsWACphIMXJjjdK0rx5CSprhpfnb0he2fBhKVoXE4+lf0CwiIiIieUYFTKSYeaZnLdxcbPy84zgrdx4HN0/o9Ub6yg2fwqFN1gYUERERKcJUwESKmSqhfgxvGQnA+PnbSEmzQ8W2UG8AYGD+KEhLtTKiiIiISJGlAiZSDD3cqSql/DzYcyKR6b/tS1/Y5SXwCoSYP2DDFEvziYiIiBRVlhaw1atX07t3b8qUKYPNZmPevHmXHX/06FEGDRpE9erVcXFxYdSoUZnGfPLJJ7Rp04agoCCCgoLo1KkT69evdxozbtw4bDab0yksLCwXb5lIwRbg5c5/ulYH4O1luziZkAR+IdBpXPqA5S9B/BHrAoqIiIgUUZYWsMTEROrXr897772Xo/FJSUmEhIQwduxY6tevn+WYlStXMnDgQFasWEFUVBTly5enS5cuHD582Glc7dq1OXr0qOP0559/XvftESlM+jUuR52IAM4mpfLa4p3pCxsNh7I3QPLZ9FkRRURERCRX2YwpGN++arPZ+O677+jbt2+Oxrdv354GDRrw1ltvXXZcWloaQUFBvPfeewwdOhRIfwds3rx5REdHX3Pe+Ph4AgMDiYuLIyAg4Jq3I2KljftO0e/DKGw2+PGh1tSJCISYP+GjdmDSYPC3ULWT1TFFRERECrSr6QZF/jNg586dIyUlheDgYKflu3btokyZMlSsWJEBAwawZ8+ey24nKSmJ+Ph4p5NIYdckMpg+DcpgDIz74S+MMRBWF5rdlz7gp8cg5by1IUVERESKkCJfwJ566ikiIiLo1Onf/+I3a9aMGTNmsHjxYj755BNiYmJo2bIlsbGx2W5n4sSJBAYGOk7lypXLj/giee6p7jXwdndl4/7T/PD7/z731WEM+JeB0/tgzeuW5hMREREpSop0AZs0aRKzZs1i7ty5eHl5OZZ3796dW2+9lbp169KpUycWLFgAwPTp07Pd1pgxY4iLi3OcDh48mOf5RfJDeKA3D7SvDMB/F+7gXHIqePpD91fSB/zyFpz427qAIiIiIkVIkS1gr732GhMmTGDJkiXUq1fvsmN9fX2pW7cuu3btynaMp6cnAQEBTieRouLutpUoG+TN0bgLfLjyn/SFNXtD1S5gT4EFo6FgfFxUREREpFArkgXs1Vdf5cUXX2TRokU0adLkiuOTkpLYvn074eHh+ZBOpODxcnflmZ41Afho9R4OnjoHNhv0eBXcvGDfGvjjK4tTioiIiBR+lhawhIQEoqOjHbMR7t27l+joaA4cOACkH/aXMXNhhozxCQkJnDhxgujoaLZt2+ZYP2nSJJ555hk+++wzIiMjiYmJISYmhoSEBMeYxx9/nFWrVrF3717WrVtHv379iI+PZ9iwYXl/o0UKqK61w2hZuSRJqXYm/LQ9fWFQJLR7Iv384qfh/GnL8omIiIgUBZZOQ79y5Uo6dOiQafmwYcOYNm0aw4cPZ9++faxcudKxzmazZRpfoUIF9u3bB0BkZCT79+/PNOb5559n3LhxAAwYMIDVq1dz8uRJQkJCaN68OS+++CK1atXKcXZNQy9F0Y6YeHq8vQa7gS/vbkbLyqUgNRk+bA0nd0KTkdDrTatjioiIiBQoV9MNCsz3gBU2KmBSVD33/VZmRO2nRpg/8/+vNW6uLrDvF5jWE7DBnUuh3A1WxxQREREpMPQ9YCJyzUZ3rkYJH3d2xJxl1vr0w4GJbA31BwEG5j8KaamWZhQREREprFTARMRJCR8PRneuBsDrS//mzLnk9BVdXgSvEnDsT1j/kXUBRURERAoxFTARyWRQ0/LUCPPnzLkU3lz6v+8A8y0FnV9IP79iAsQdti6giIiISCGlAiYimbi5uvBc7/RJab5Yd4CdMWfTVzQcCmWbQnICLHxC3w0mIiIicpVUwEQkSy0rl6J7nTDS7IYXfvwLYwy4uKTPgujiBjvmw/qPrY4pIiIiUqiogIlItp7uURMPNxd++yeWxX/FpC8MqwOdX0w/v/hp2P+bdQFFREREChkVMBHJVrlgH+5tWwmAlxZs50JKWvqK5vdD3dvAngpfDYP4IxamFBERESk8VMBE5LLub1+Z8EAvDp0+z5Q1e9IX2mzQ+20oXQcSj8NXQyE1ydqgIiIiIoWACpiIXJaPhxtPda8BwPsr/uFo3Pn0FR6+0P9z8AqEQxtg0VMWphQREREpHFTAROSKbqpfhiYVgjifksYrC3f8uyK4Etz6KWCDjZ/B5s8tyygiIiJSGKiAicgV2Ww2xt1UG5sN5kUfYdP+U/+urNoZOoxNP7/gMTi82ZqQIiIiIoWACpiI5EidiED6NykHwLgftmG3X/QdYG0eg+o9IC0J5gyBxJMWpRQREREp2FTARCTHHu9aHX9PN/48HMfXmw7+u8LFBW7+EEpWgfhD8M0ISEu1LqiIiIhIAaUCJiI5VsrPk0c6VQXglUU7iYm78O9Kr0DoPxM8/GDvavh5nDUhRURERAowFTARuSpDW0RSMzyAU4nJPPTlZlLS7P+uDK0BfSenn//tXdj6rTUhRURERAooFTARuSoebi5MHtwIf083Nu4/zaRFO5wH1OoDrUaln//+ITi2Ld8zioiIiBRUKmAictUqlvLl1dvqAfDJmr0s2nrUeUDH56BSe0g5B3MGw/kz+Z5RREREpCBSARORa9KtTjh3ta4IwH++/oN9JxP/XeniCrd+BoHl4dQemHsP2O3ZbElERESk+FABE5Fr9mT3GjSpEMTZpFTun7mZCylp/670LQn9Pwc3L9i1GFZPsi6oiIiISAGhAiYi18zd1YX3BjWipK8H24/G8/z3fzkPKNMAer2Zfn7lRNi5KN8zioiIiBQkKmAicl3CAr14Z2BDbDaYs/EgX2086DygwSC44e7083Pvgdh/8j+kiIiISAGhAiYi161VlVKM7lQNgGfnbWXbkXjnAV0nQLnmkBQHswdDUoIFKUVERESspwImIrniwQ5VaF89hKRUOw/M3ET8hZR/V7p5wO3TwS8MTmyHHx4CY6wLKyIiImIRFTARyRUuLjbevL0BZQK92Bd7jie/+QNzccnyD4PbZ4CLO/z1XfoXNYuIiIgUMypgIpJrgnw9eH9wI9xdbSzcGsNnv+5zHlC+GXSbmH5+2fOwZ1W+ZxQRERGxkgqYiOSqhuWDeKZnLQAm/rSdTftPOQ+44S6oPwiMHb4ZAWcOZrEVERERkaJJBUxEct3QFhXoVS+cVLvhwZlbiE1I+nelzQa93oDw+nAuFubcASkXrAsrIiIiko9UwEQk19lsNv57az0qhfgSE3+BUXOiSbNf9Hkwd2/o/wV4B8PRaFjwmCblEBERkWJBBUxE8oSfpxsf3tEYb3dX1uw6yTs/73IeUKI89PsMbC4Q/QVs/MyaoCIiIiL5SAVMRPJMtdL+vHxzHQDeWb6LVX+fcB5QuQN0fD79/MIn4eD6fE4oIiIikr9UwEQkT93SqCyDmpXHGBg1ewtHzpx3HtDqEajVB+wp8NVQOHvMmqAiIiIi+UAFTETy3HO9alEnIoDT51J48MvNJKfa/11ps0Gf9yGkBpw9Cl8Pg7SU7DcmIiIiUoipgIlInvNyd+WDwY0J8HJjy4EzTFy43XmApz/0nwmeAXAgChaPtSaoiIiISB5TARORfFEu2IfXb28AwNRf97Hgj6POA0pVgZs/Sj+//iP4fXb+BhQRERHJBypgIpJvOtcqzX3tKgPwxDe/88+JBOcBNXpA2yfSz//4CBz9PZ8TioiIiOQtFTARyVePd6lG04rBJCan8cAXmzmfnOY8oP0YqNoFUi+kf0nzuVPWBBURERHJAypgIpKv3FxdeG9gQ0r5ebLz2FnGzvsTc/GXMLu4wC0fQ1BFOHMAvr0T7GnZb1BERESkEFEBE5F8FxrgxbsDG+Jig7mbDzNnw0HnAd5B0P8LcPeBf5bD8pesCSoiIiKSy1TARMQSLSqX5PGu1QF47oe/2Ho4znlAWB246d3087+8Adt+yOeEIiIiIrnvugtYUlJSbuQQkWLovraV6VgjlORUOw/M3Ezc+Uu+/6tuP2j+YPr5effDiZ35H1JEREQkF111AVu8eDHDhw+ncuXKuLu74+Pjg7+/P+3atePll1/myJEjeZFTRIogFxcbr99en7JB3hw4dY7Hv/7d+fNgAJ3HQ2QbSE6A2YPhQrw1YUVERERyQY4L2Lx586hevTrDhg3DxcWF//znP8ydO5fFixfz6aef0q5dO5YtW0alSpW47777OHHiRF7mFpEiooSPB5MHN8LD1YWl247xyZo9zgNc3aDfVAiIgNhd6e+E2e3WhBURERG5TjaT6d/NWWvatCnPPvssPXv2xMUl+952+PBh3n77bUqXLs1jjz2Wa0ELmvj4eAIDA4mLiyMgIMDqOCKF3hdr9/PMvK24utiYdXdzmlYMdh5waBNM7QZpyXDjs9D2cWuCioiIiFziarpBjguYOFMBE8ldxhgenRPNvOgjhPp7suDhNoT4ezoP2jQdfnwYsMHgb6BqJ0uyioiIiFzsarrBNU3CMX78eM6dO5dp+fnz5xk/fvy1bFJEijmbzcbLN9elaqgfx88m8fCsLaTZL/n/UONh0Hg4YNK/H+zUXiuiioiIiFyza3oHzNXVlaNHjxIaGuq0PDY2ltDQUNLSiv6XpuodMJG8sfv4WW5671fOJafxUIcqjqnqHVKTYGoPOLwRSteFO5eAh481YUVERETIh3fAjDHYbLZMy3///XeCg4OzuISISM5UCfXnv7fWA+C9FbtZvuOY8wA3T7h9BviGwLE/4cdHQEdSi4iISCFxVQUsKCiI4OBgbDYb1apVIzg42HEKDAykc+fO3H777XmVVUSKiZvql2FoiwoAPDrndw6dvuSQ58AIuG0a2Fzhz69g3Uf5H1JERETkGlzVIYjTp0/HGMPIkSN56623CAwMdKzz8PAgMjKSFi1a5EnQgkaHIIrkraTUNG7/MIrfD8VRv2wgX93XAk83V+dBUZNh8Zj0IjbsR4hsZU1YERERKdbyfBbEVatW0apVK9zc3K45ZGGnAiaS9w6dPkfPd34h7nwKQ1tUYHyfOs4DjIG5d8OfX6cfknjvaggoY01YERERKbby5DNgiYmJjvPt2rW7Yvm6eLyIyLUoG+TDW/0bADAjaj/fRx92HmCzQe93oHQdSDwBc4akT9IhIiIiUkDluIBVqVKFCRMmcOTIkWzHGGNYunQp3bt355133smVgCJSvHWoEcpDHaoAMGbun+w+ftZ5gIcP9P8CvALTZ0Zc+KQFKUVERERyJseHIO7cuZNnnnmGH374gQYNGtCkSRPKlCmDl5cXp0+fZtu2bURFReHu7s6YMWO45557cHV1vfKGCykdgiiSf9LshiGfruO3f2KpEurH9w+2wtfzknfhdy2Dmf0AAze9C42GWpJVREREip88/QzYoUOH+Oqrr1izZg379u3j/PnzlCpVioYNG9K1a1d69OiBi8s1zW5fqKiAieSvE2eT6PnOGo6fTaJvgzK82b9B5q/DWP0qLH8JXD1gxCIo29iasCIiIlKs5PkkHKICJmKF9XtPMfCTtaTZDS/1rcMdzSs4D7DbYc4dsHMBBETAPavAL8SasCIiIlJs5HkBGz16dNYbs9nw8vKiSpUq9OnTp0h/KbMKmIg1Pl79DxN+2oGHqwvf3N+CemVLOA+4EAef3AixuyGyDQyZB67Fd8ZWERERyXt5XsA6dOjA5s2bSUtLo3r16hhj2LVrF66urtSoUYOdO3dis9n45ZdfqFWr1jXfkIJMBUzEGsYY7v18E0u2HSOihDcLHm5NCR8P50HHd8CUjpCcAC0egq4vWxNWREREioU8mYb+Yn369KFTp04cOXKETZs2sXnzZg4fPkznzp0ZOHAghw8fpm3btjz66KPXdANERLJjs9l49bb6lA/24fCZ8zz21e/Y7Zf8Hym0BvSdnH4+6j3485v8DyoiIiKShWt6BywiIoKlS5dmenfrr7/+okuXLhw+fJjNmzfTpUsXTp48mWthCxK9AyZira2H47jlg99ITrXzRLfqPNC+SuZBy8bBL2+Cuw/ctQxK1873nCIiIlL05fk7YHFxcRw/fjzT8hMnThAfHw9AiRIlSE5OvpbNi4hcUZ2IQMbflF6oXlu8k6h/YjMPuvFZqNQBUs7B7MFw/nQ+pxQRERFxds2HII4cOZLvvvuOQ4cOcfjwYb777jvuvPNO+vbtC8D69eupVq1abmYVEXHS/4Zy3NqoLHYD/zdrC8fjLzgPcHGFfp9BYHk4vRfm3pM+U6KIiIiIRa6pgH300Ud07NiRAQMGUKFCBcqXL8+AAQPo2LEjH374IQA1atRgypQpuRpWRORiNpuNl/rWoUaYPycTknho1hZS0y4pWD7B0P9zcPOCXUtg1X+tCSsiIiLCdX4PWEJCAnv27MEYQ+XKlfHz88vNbAWaPgMmUnDsOZHATe/9SkJSKve1q8xT3WtkHhQ9C+bdl35+4Gyo3j1/Q4qIiEiRleefAcvg5+dHvXr1qF+/frEqXyJSsFQK8eOVW+sB8OGqf1i67VjmQQ0GQtN70s/PvQdi/8nHhCIiIiLprquAiYgUFD3rhTOiVSQAj30VzYHYc5kHdXkZyjWHpHiYPQiSEvI3pIiIiBR7KmAiUmSM6V6ThuVLEH8hlQe+3MSFlDTnAW4ecPt08AuDEzvg+wfh2o/CFhEREblqKmAiUmR4uLnw/qBGBPm4s/VwPOPnb8s8yD8Mbp8BLu6wbR789k6+5xQREZHiSwVMRIqUMiW8eWtAQ2w2+HLdAeZuPpR5UPlm0P1/syEuGwd7VuZnRBERESnGVMBEpMhpVy2Eh2+sCsDY77ayM+Zs5kFN7oQGg8HY4esRcOZAPqcUERGR4kgFTESKpIc7VqVN1VKcT0nj/pmbSEhKdR5gs0HP1yG8Ppw/BXPugJTz1oQVERGRYkMFTESKJFcXG2/1b0BYgBd7TiTy5Ld/kOlrD929of8X4B0MR3+HBY9pUg4RERHJUypgIlJklfTz5P3BDXFzsbHgj6PMiNqfeVCJ8tDvM7C5QPRM2Php/gcVERGRYkMFTESKtMYVghnToyYALy3YxpYDpzMPqtwBOo1LP7/wKTiwLv8CioiISLGiAiYiRd7IVpF0rxNGSprhwZmbOZ2YnHlQy4ehVl+wp8BXQ+FsTL7nFBERkaJPBUxEijybzcakfvWoWMqXI3EXGDUnGrvdXDoI+rwPITUgIQa+GgapWRQ1ERERkeugAiYixYK/lzuTBzfC082FVX+f4P0VuzMP8vSD/jPBMwAOroUlY/M/qIiIiBRpKmAiUmzUDA/gpb51AHhj2d/8sutk5kGlqsDNH6WfX/8xRM/Kx4QiIiJS1KmAiUixcluTcvRvUg5j4JHZW4iJu5B5UI0e0O7J9PPzR8GR6PyMKCIiIkWYCpiIFDsv9KlNzfAAYhOTeejLzaSk2TMPavcUVO0CqRdgzhA4dyr/g4qIiEiRowImIsWOl7srHwxuhL+nGxv3n2bSoh2ZB7m4wC0fQ1BFiDsA34wEe1r+hxUREZEiRQVMRIqlyFK+vHpbfQA+WbOXRVuPZh7kHQQDZoK7D+xZActfzOeUIiIiUtSogIlIsdWtThh3t6kIwH++/oN9JxMzDypdG256N/38L2/Ctu/zMaGIiIgUNSpgIlKsPdGtBk0qBHE2KZX7Z27mQkoWhxnW7QctHko/P+8BOJ7FIYsiIiIiOaACJiLFmrurC+8NakRJXw+2H43n+e//ynpgpxcgsg0kJ8CcwXAhLn+DioiISJGgAiYixV5YoBfvDGyIiw3mbDzIVxsPZh7k6gb9pkJABMTuhu/uB3sWsyeKiIiIXIYKmIgI0KpKKUZ3rgbAs/O2su1IfOZBfiFw++fg6gE7F8Avr+dzShERESnsVMBERP7ngfZVaF89hKRUOw/M3ET8hZTMg8o2hp7/K17LX4ZdS/M3pIiIiBRqlhaw1atX07t3b8qUKYPNZmPevHmXHX/06FEGDRpE9erVcXFxYdSoUVmO+/bbb6lVqxaenp7UqlWL7777LtOYyZMnU7FiRby8vGjcuDFr1qzJhVskIoWZi4uNN29vQEQJb/bFnuPJb/7AGJN5YKOh0Hg4YODbO+HUnvyOKiIiIoWUpQUsMTGR+vXr89577+VofFJSEiEhIYwdO5b69etnOSYqKor+/fszZMgQfv/9d4YMGcLtt9/OunXrHGPmzJnDqFGjGDt2LFu2bKFNmzZ0796dAwcO5MrtEpHCK8jXg/cHN8Ld1cbCrTF89uu+rAd2nwQRTdIn45gzBJKzmMJeRERE5BI2k+W/d/OfzWbju+++o2/fvjka3759exo0aMBbb73ltLx///7Ex8ezcOFCx7Ju3boRFBTErFmzAGjWrBmNGjXigw8+cIypWbMmffv2ZeLEiTm6/vj4eAIDA4mLiyMgICBHlxGRwmNG1D6e+/4v3FxszLm3OY0rBGceFHcYPm4HiSeg7m1wyydgs+V/WBEREbHU1XSDIvcZsKioKLp06eK0rGvXrvz2228AJCcns2nTpkxjunTp4hiTlaSkJOLj451OIlJ0DWlegd71y5BqNzw4cwuxCUmZBwVGwG3TweYKf34N6z7M/6AiIiJSqBS5AhYTE0Pp0qWdlpUuXZqYmBgATp48SVpa2mXHZGXixIkEBgY6TuXKlcv98CJSYNhsNibeUpfKIb7ExF9g1Jxo0uxZHDAQ2Qq6vpx+fvFY2PdL/gYVERGRQqXIFTBI/8PpYsaYTMtyMuZiY8aMIS4uznE6eDCL7wkSkSLFz9OND+5ojLe7K2t2neTtn3dlPbDZfemHIJo0+Hp4+qGJIiIiIlkocgUsLCws0ztZx48fd7zjVapUKVxdXS87Jiuenp4EBAQ4nUSk6KtW2p+Jt9QF4N3lu1i583jmQTYb9H4HStdN/zzYV0MhNYtDFkVERKTYK3IFrEWLFixd6vy9PEuWLKFly5YAeHh40Lhx40xjli5d6hgjInKxvg0jGNysPMbAo3OiOXzmfOZBHj7Q/3PwKgGHN8LCJ/I9p4iIiBR8lhawhIQEoqOjiY6OBmDv3r1ER0c7poMfM2YMQ4cOdbpMxviEhAROnDhBdHQ027Ztc6x/5JFHWLJkCa+88go7duzglVdeYdmyZU7fGTZ69GimTJnCZ599xvbt23n00Uc5cOAA9913X57fZhEpnJ7tVYu6EYGcPpfCgzM3k5xqzzwouCLc+ilgg03TYNP0/I4pIiIiBZyl09CvXLmSDh06ZFo+bNgwpk2bxvDhw9m3bx8rV650rMvqc1oVKlRg3759jp+/+eYbnnnmGfbs2UPlypV5+eWXueWWW5wuM3nyZCZNmsTRo0epU6cOb775Jm3bts1xdk1DL1L8HDx1jp7vrCH+QirDW0Yy7qbaWQ9c/SosfwlcPWDEIijbOH+DioiISL66mm5QYL4HrLBRARMpnpZtO8ZdMzYC8N6ghvSqVybzILsdvhoCO+ZDQATcswr8QvI5qYiIiOSXYv09YCIiealTrdLc374yAE9+8wf/nEjIPMjFBfp+ACWrQPzh9JkR01LzN6iIiIgUSCpgIiJX6bHO1WhWMZjE5DTu/2IT55KzKFdeATDgS/Dwg/2/wLLn8z+oiIiIFDgqYCIiV8nN1YV3BzUkxN+Tv48l8Mx3W8nyaO6Q6unvhAFEvQd/fpO/QUVERKTAUQETEbkGof5evDuwIS42mLvlMLM3ZPPl7LVugtaPpp///iGI2Zp/IUVERKTAUQETEblGzSuV5D9dawDw/A9/sfVwXNYDb3wWKnWA1PMwZzAkxuZjShERESlIVMBERK7DvW0r0almKMmpdu6fuYm4cymZB7m4Qr/PoER5OL0PPu8D50/ne1YRERGxngqYiMh1cHGx8fptDSgb5M3BU+d57Ovfs/48mE8wDP4WfEMg5k/4oh8knc3/wCIiImIpFTARkesU6OPOB4Mb4+HqwrLtx/h49Z6sB4ZUgyHzwDsIDm+EL/tD8rl8zSoiIiLWUgETEckFdcsG8vxNtQCYtHgn6/Zk8zmvsDpwx1zwDID9v8LsQZByIR+TioiIiJVUwEREcsmgpuW5uWEEaXbDQ7O2cPxsNsUqohEM/gbcfWHPiv99UXMWnx0TERGRIkcFTEQkl9hsNl6+uQ7VSvtx4mwSj8yKJjXNnvXg8s1g4Cxw84K/F8K3d0FaFl/oLCIiIkWKCpiISC7y8XBj8uDG+Hi4ErUnljeX/Z394ErtoP8X4OIO2+bBDw+BPZvCJiIiIkWCCpiISC6rEurHf2+tB8D7K/5h+Y5j2Q+u2hlumwo2V/h9FiwYDVnNoigiIiJFggqYiEgeuKl+GYa1qADAo3N+5+Cpy8x2WLM33PIxYINNU2Hx0yphIiIiRZQKmIhIHnm6Z03qlytB3PkUHvxyM0mpadkPrtsPbno3/fzaybD8pfwJKSIiIvlKBUxEJI94urny/qCGlPBx549Dcbw0f/vlL9BoCPR4Lf38mtdg9at5H1JERETylQqYiEgeKhvkw5v9GwDw+dr9fB99+PIXaHo3dH4x/fzylyDq/bwNKCIiIvlKBUxEJI91qB7K/91YBYCnvv2T3w+eufwFWj0M7Z9OP7/4adjwad4GFBERkXyjAiYikg9GdapGm6qlOJ+Sxh2frrtyCWv3BLQalX5+wWiI/jKvI4qIiEg+UAETEckHri42PryjMTdEBnH2Qip3fLqOPw6dyf4CNht0GgfN7kv/+fsHYeu3+RFVRERE8pAKmIhIPvH1dGPaiKaOEjZ4Sg5KWLf/QqNhYOww9x7YsSDf8oqIiEjuUwETEclHvp5uTB3RlCYV/vdO2JR1/HkoLvsL2GzQ602o1x/sqfD1cNi9LN/yioiISO5SARMRyWd+nm5MG5lewuIvpDJ4ytrLlzAXV+gzGWreBGnJMHsw7F2Tf4FFREQk16iAiYhYIKOENf5fCbvj03VsPXyZEubqBrd+ClW7QuoF+LI/HFyff4FFREQkV6iAiYhYxM/Tjen/K2Fx51MYPOUKJczNA26fAZXaQ0oifNEPjkTnV1wRERHJBSpgIiIW8vN0Y9qIG2hUvkTOSpi7Fwz4Esq3gKQ4+PxmOLYt/wKLiIjIdVEBExGxmL+XO9NHNs15CfPwhUFfQZlGcP4UzOgDJ3fnX2ARERG5ZipgIiIFwFWXMK8AuONbKF0XEo/DjJvg9L58yysiIiLXRgVMRKSAyChhDf9Xwu74dB1/HblMCfMJhiHfQanqEH8Ypt8EcYfzL7CIiIhcNRUwEZEC5OISduZc+jthly1hfiEw9HsIqghn9qe/E5ZwPP8Ci4iIyFVRARMRKWAC/lfCGpT7t4RtOxJ/mQuEw7AfILAcxO5O/0zYuVP5F1hERERyTAVMRKQACvByZ8ad/5awQVPWXr6ElSif/k6YXxgc3waf94XzZ/IrroiIiOSQCpiISAGVUcLqO94Ju0IJK1k5/Z0wn1Jw9HeYeRskJeRfYBEREbkiFTARkQIswMudGSPTS9jp/5Ww7UcvU8JCqsPQeeBVAg6th1kDIOV8fsUVERGRK1ABExEp4AK9/1fCygZy+lwKgz65QgkLqwtD5oKHP+xbA3PugNSk/AssIiIi2VIBExEpBAK93ZlxZzNHCRs8ZR07Yi5TwiIaw+Cvwd0Hdi+Db0ZCWkr+BRYREZEsqYCJiBQSGSWsXtlATiUmM+iTK5SwCi1g4Cxw9YQd8+G7e8Geln+BRUREJBMVMBGRQiTQ253PLylhO2POZn+BSu2h/xfg4g5bv4Uf/g/s9nzLKyIiIs5UwERECplAb3c+H9mMuhHpJWzgJ2svX8KqdYF+n4LNFaJnwk+PgzH5F1hEREQcVMBERAqhQB93vrjz3xI26EolrFYfuPlDwAYbP4Ulz6iEiYiIWEAFTESkkMooYXUiAoj9Xwn7+9hlSli92+Gmd9LPR70HKybkT1ARERFxUAETESnEAn3cmXlnc0cJG/jxFUpYo6HQfVL6+dWTYM3r+RNUREREABUwEZFCL+OdsNpl/n0nbNflSlize6HTC+nnfx4Paz/In6AiIiKiAiYiUhSU8PFg5l3pJexkQvrEHJctYa1HQbun0s8vego2Ts2XnCIiIsWdCpiISBGRuYStu3wJa/8UtHw4/fz8R+H32fkTVEREpBhTARMRKUIySlit8ABOJiQx8JN17D6eTQmz2aDzeLjhbsDAvPvhr+/yNa+IiEhxowImIlLEXFrCBnx8hRLWfRI0vAOMHb696//bu+8oqer7/+PP2c4u7NKWhaX3IkqVJiggQlBRYscodsUa9WssMYmaGE1MYvKzN2yxV0Rjw0JTLCAoIkjvZVnKLnXr/P4YWFgpsgh3dpfn45w5cj9zZ+Y959yD8+Lzue8P/Ph+sAVLknQIMYBJUiVUIyUSwtqWCmEbd39yTAwMuQ8OPx2KC+GVc2HeJ8EWLEnSIcIAJkmVVI2UBF4oFcK+2EsIi4Whj0DbIVCUDy+eDQs/C7ZgSZIOAQYwSarEts+Etalbbds9YXsJYbFxcOqT0HIgFG6BF86ApZODLViSpErOACZJlVzNlAReuKQHbepWY/WGSAibt3oPISwuAc54FpoeDfkb4blTYMV3wRYsSVIlZgCTpEPALiHssb2EsPgqcNaL0LAHbM2B/w6FrFmB1itJUmVlAJOkQ8TOISzr50JYYlX4zSuQ2Qk2r4FnT4I184ItWJKkSsgAJkmHkJo73RO2PYTN31MIS0qDc96AjPawcRU8cxKsWxRswZIkVTIGMEk6xNSqmsjzF3endca2EPb4XkJYck04dxTUbgW5SyMzYbnLA61XkqTKxAAmSYegWlUTeeGSSAhblRsJYQuyN+3+5KrpMPwtqNEE1i2MzIRtzAqyXEmSKg0DmCQdompVTeT5S7rTKqMqq3LzOOuxSXsOYamZMHw0pDaANXPg2aGweW2g9UqSVBkYwCTpEFa7aiIvXNKjJIQNe+wLFu4phNVoDOeNhqoZkDUj0qJ+a06wBUuSVMEZwCTpELdzCFuZu5Wz9hbCajWPzIQl14LlU+H50yFvD/ePSZKkXRjAJEklIaxlnX0IYXXawLlvRrokLvkSXhoGBVuCLViSpArKACZJAnYNYcMe/4JFa/YQwup1iLSoT6gKC8bDy+dCYV6wBUuSVAEZwCRJJdKrRUJYizpVWZETmQnbYwhr0BXOfgXiqsDcMfDahVBUGGzBkiRVMAYwSVIp6dUSeXFfQ1iTo2DYCxCbCLPegVEjoLgo2IIlSapADGCSpF1EZsK60zw9hRU5Wxn22BcsXrN59yc37w9nPAsxcTD9VXj7t1BcHGzBkiRVEAYwSdJu1amWxIuX9qB5egrLc7Zy1mOT9hzCWv8KTh0JoRiY+l94/yYIh4MtWJKkCsAAJknao5+GsGGP72Um7LChMPQRIARfPQZj/mQIkyTpJwxgkqS9qlMtiRcv6UGz9BSWrd/CsMe/YMnaPYSwDmfCif+O/Pnz+2Dc34MrVJKkCsAAJkn6WXVSk3hppxB21mN7CWFdL4Bf/S3y57F3w8T/BFanJEnlnQFMkrRPSkJY7X0IYT0uh2Nvi/z5o9vgy8eCK1SSpHLMACZJ2md1UpN46dJ9DGF9roejb4z8+b3fwTfPBleoJEnllAFMklQmdVIjjTn2KYT1+z30vCry59HXwHevBFeoJEnlkAFMklRmGdtCWNPaOxpzLF23mxAWCsHAO6HrRUAY3hwBP7wVeL2SJJUXBjBJ0n7J2LYcsWntFJaui8yE7TGEHf9P6PgbCBfBaxfB7A+CL1iSpHLAACZJ2m8ZqZEW9T8bwmJi4KT74bBToLgAXj4X5o8NvF5JkqLNACZJ+kXqpkVCWJNaySxdF1mOuGz9ll1PjImFUx6D1idAUR68OAwWTQq+YEmSosgAJkn6xeqmRe4Ja1IrmSVrt3DWY5N2H8Ji4+H0p6D5sVCwGZ4/HZZNCb5gSZKixAAmSTog6qVV4cVLe9D450JYXCKc+Rw06QP5G+C/p8DK6cEXLElSFBjAJEkHTL20Kry0Uwgb9tgXLN9dCEtIhmEvQYNusHU9PDsUVv8YdLmSJAXOACZJOqDqpVXhxUsiIWzx2s2ctacQllgVznkN6nWEzdnwzEmwZl7g9UqSFCQDmCTpgMusHglhjWpGQtiwx/cQwpLS4Nw3oU472LgSnj0Z1i8JvmBJkgJiAJMkHRSZ1SPLERvVTGbRmkgIW5GzmxCWXBOGvwW1WkDOEnhmCOSuCL5gSZICYACTJB00mdUjjTka1qzCojWR5Yi7DWFV68Dw0VC9MaxbEJkJ25QdfMGSJB1kBjBJ0kFVv3oVXrq0Z0kIG7anEJZWH84bDan1IfvHSGOOLesCr1eSpIPJACZJOuh2DmELt4WwlTlbdz2xRpPITFhKHVg1HZ47FbbmBl6vJEkHiwFMkhSI+tsaczSoEQlhZz02afchrHaLyD1hVWpGNml+4UzI3xR8wZIkHQQGMElSYBrUSOalS3eEsGGP72EmLKNdpDtiYhos/hxeOhsKdnOeJEkVjAFMkhSonUPYguxNDHv8C1bl7iZcZXaEc16HhKowfyw8dwpsXht0uZIkHVBRDWDjx49nyJAhZGZmEgqFGDVq1M++Zty4cXTp0oWkpCSaNWvGI488Uur5vn37EgqFdnmccMIJJefcfvvtuzxft27dA/31JEl70KBGMi9e0oP61SMh7KzH9hDCGh4JZ78Ciamw6DMYORDWzg++YEmSDpCoBrBNmzbRoUMHHnjggX06f8GCBRx//PH06dOHqVOn8vvf/55rrrmG119/veScN954gxUrVpQ8vv/+e2JjYzn99NNLvddhhx1W6rzp06cf0O8mSdq7hjUjM2HbQ9iwPYWwJkfBhR9AWkNYMweeGABLvgq+YEmSDoC4aH744MGDGTx48D6f/8gjj9CoUSP+85//ANC2bVsmT57MP//5T0499VQAatasWeo1L730EsnJybsEsLi4OGe9JCnKtoewsx77gvnbQthLl/agTmpS6RMz2sHFH0UacqyYBk+fCKc8Cof9Oip1S5K0vyrUPWCTJk1i4MCBpcYGDRrE5MmTKSgo2O1rRo4cyVlnnUVKSkqp8Tlz5pCZmUnTpk0566yzmD9/70ta8vLyyM3NLfWQJP1yO8+Ezd+2HDFrdzNh1erCBe9Cq8FQlAevng8T/wPhcNAlS5K03ypUAFu5ciUZGRmlxjIyMigsLCQ7O3uX87/66iu+//57Lr744lLj3bt359lnn+WDDz7g8ccfZ+XKlfTq1Ys1a9bs8bPvvvtu0tLSSh4NGzY8MF9KkrRrCHt8DyEsIQXOeh66j4gcf3QbvHMdFBUGW7AkSfupQgUwgFAoVOo4vO1fPn86DpHZr/bt29OtW7dS44MHD+bUU0/l8MMPZ8CAAfzvf/8D4Jlnntnj595yyy3k5OSUPJYsWfJLv4okaScNa+5ozDF/daQ7YtaG3YSwmFgY/Hf41d+AEEx5Cl48E/I2BF6zJEllVaECWN26dVm5cmWpsaysLOLi4qhVq1ap8c2bN/PSSy/tMvu1OykpKRx++OHMmTNnj+ckJiaSmppa6iFJOrAa1YqEsMy0JOatjtwTttsQBtDj8shsWFwVmPsRPPkryFkWbMGSJJVRhQpgPXv2ZMyYMaXGPvzwQ7p27Up8fHyp8VdeeYW8vDzOOeecn33fvLw8Zs6cSb169Q5ovZKksmtUK5mXLu25byGszQmR+8JS6sCq7+GJY2HFd8EWLElSGUQ1gG3cuJFp06Yxbdo0INJmftq0aSxevBiILPsbPnx4yfkjRoxg0aJFXH/99cycOZMnn3ySkSNHcsMNN+zy3iNHjmTo0KG7zIwB3HDDDYwbN44FCxbw5Zdfctppp5Gbm8t55513cL6oJKlMGtVK5sVLe1BvWwg7+/Evd39PGED9znDJx5DeFjasgKcGw+wPgy1YkqR9FNUANnnyZDp16kSnTp0AuP766+nUqRN/+tOfAFixYkVJGANo2rQp7777LmPHjqVjx4785S9/4b777itpQb/d7NmzmThxIhdddNFuP3fp0qUMGzaM1q1bc8opp5CQkMAXX3xB48aND9I3lSSVVeNaKby0LYTNzdrICfdP5OuFa3d/cvVGcOH70PQYyN8YuSfs6yeCLViSpH0QCoft37s/cnNzSUtLIycnx/vBJOkgWrxmMxc/+zWzV20kNibELYPbcFHvprttvkRhfqQr4rTnIse9roYBf4aYCrXiXpJUwZQlG/h/JElSudaoVjJvXnEUJ3XIpKg4zJ3/m8lVL05lY95uWs/HJcDJD0D/P0SOP78fXj0PCrYEW7QkSXtgAJMklXspiXH8v7M6cvuQdsTFhPjfdysY+uBnzM3aTev5UAiO/h2c8gTEJsDM0fD0ibBxdfCFS5L0EwYwSVKFEAqFOP+oprx8WQ8yUhOZm7WRkx/4jHe+W777FxxxOgx/C6rUgGWTIx0SV88OtmhJkn7CACZJqlC6NK7JO1f3oUezmmzKL+KqF6by57d/oKCoeNeTG/eCiz6CGk1h/SIYOQAWTgy+aEmStjGASZIqnPRqiTx3UXdGHNMcgCc/W8DZj3+x+1b1tVvAxR9Bg26wNQeeHQrfvhxswZIkbWMAkyRVSHGxMdw8uA2PntuFaolxfL1wHcffN5Ev56/Z9eSU2nDeaGg3FIoL4M1LYezfwUbAkqSAGcAkSRXaoMPq8tZVR9E6oxrZG/M4+4kveXz8fHbZZSW+Cpz2FBx1beR47F0w6opI63pJkgJiAJMkVXjN0qvy5pW9GNox0qr+r+/O5MoXvtm1VX1MDBx3Bwz5fxCKhW9fgOdOgS3rolO4JOmQYwCTJFUKyQlx/PvMjvz55MOIjw3x7vSVnPTAROas2k2r+i7nw29egYRqsHACjBwI6xYGXbIk6RBkAJMkVRqhUIjhPZvw8mU9qZuaxPzVmzj5wc94+9vdtKpvMQAufB9S60P2bHhiACydEnzRkqRDigFMklTpdG5Ug3eu6U2v5rXYnF/E1S9O5Y63Z5Bf+JNW9XXbRzok1j0cNq2Gp0+AmW9Hp2hJ0iHBACZJqpRqV03k2Qu7cXnfSKv6pz5byLDHv2DVT1vVp2bCBe9By4FQuAVePhc+f8AOiZKkg8IAJkmqtOJiY7jpV214bFur+imL1nHCfROYNO8nreoTq8FZL8KRFwNh+PBWePcGKCrc7ftKkrS/DGCSpEpv4GF1efvq3rSpW43sjfmcM/JLHh03r3Sr+tg4OP6fMPCvQAi+fgJeOhvyNkatbklS5WMAkyQdEprUTuHNK47ilE71KSoOc/d7s7j8uW/YsLVgx0mhEPS6Cs54FuKSYM4H8NRgyF0RvcIlSZWKAUySdMiokhDLv87owF+Gtic+NsT7M1Zy8gOfMfunrerbnQTn/w+Sa8PK7+CJY2HVjOgULUmqVAxgkqRDSigU4twejXnlsp7US0tifvYmTn7gM96atqz0iQ26Rjok1m4Fuctg5CCY+1F0ipYkVRoGMEnSIalToxq8c3VvereozZaCIn770jRuH/2TVvU1m8JFH0KTPpC/AZ4/AyY/Fb2iJUkVngFMknTIqlU1kWcu7MZV/VoA8PTnCznrsUmsyNmy46QqNeCcN+CIsyBcBO9cC2Nug+Li3b+pJEl7YQCTJB3SYmNC3DCoNU8M70q1pDi+WbyeE++byOdzs3ecFJcAv34E+t4SOf7sP/D6hVCwdbfvKUnSnhjAJEkCBrTL4J2re9O2XiprNkVa1T88dqdW9aEQ9L0Zhj4CMfEw40149iTYtGbvbyxJ0k4MYJIkbdO4VgpvXN6LUzs3oDgMf39/Fpf9dwq5O7eq7zgMzn0TktJgyZeRDonZc6NXtCSpQjGASZK0kyoJsfzz9CP466/bkxAbw4c/rOKk+ycya2XujpOa9oGLxkD1RrBuAYwcAIs+j17RkqQKwwAmSdJPhEIhftO9Ma+O6En96lVYuGYzQx/8jDenLt1xUnpruPhjqN8FtqyDZ0+G716NXtGSpArBACZJ0h50aFidt6/uTZ+WtdlaUMx1L3/LH0d9v6NVfdU6cN470HYIFOXDGxfD+H/A9vvGJEn6CQOYJEl7UTMlgacv6MY1/SOt6v/7xSLOeHQSy9dva1WfkAynPws9r4ocf3InjL4Kigr28I6SpEOZAUySpJ8RGxPi+oGtefL8rqQmxTFtyXpOvH8in21vVR8TA4P+Csf/E0IxMPU5eO5U2LI+qnVLksofA5gkSfuof5sM3rm6D+3qpbJ2Uz7njvySBz+dS3HxtiWH3S6BYS9BfAosGAdP/grWL45u0ZKkcsUAJklSGTSqlcwbV/Ti9C6RVvX/+OBHLv3vFHK2bFty2GoQXPgeVKsHq2fCEwNg2TfRLVqSVG4YwCRJKqOk+FjuOe0I7j7lcBJiY/ho5ipOemAiM1dsa1Vfr0OkQ2JGe9i4Cp4+AWa9G92iJUnlggFMkqT9EAqFGNatEa9dHmlVv2jNZn790Ge8PmVbq/q0+nDBe9D8WCjYDC+dDV88Et2iJUlRZwCTJOkXOKJBdd65ujdHt0pna0Ex//fqt9z65nTyCosgKRXOfhm6nA+E4f2b4L2bobgo2mVLkqLEACZJ0i9UIyWBp84/kt8e25JQCJ7/cjFnPPoFy9Zvgdh4OPE/MOCOyMlfPgwvnwP5m6JasyQpOgxgkiQdALExIa47rhVPnnckaVXi+XbJek68bwIT5qyGUAh6XwunPw2xifDju/DU8bBhVbTLliQFzAAmSdIB1K9NHd65ujft66eybnMBw5/8igc+mRNpVX/Yr+G8tyG5FqyYBk8cC1kzo12yJClABjBJkg6whjWTeW1EL87s2pBwGP754Wwu/e9kcjYXQKPucPFHUKsF5CyBkQNh3qfRLlmSFBADmCRJB0FSfCx/P+0I/n7q4STExfDRzCyGPDCRGctzoGYzuGgMNOoFebnw/Gkw9blolyxJCoABTJKkg+jMIxvxxuW9aFCjCovXbuaUhz7ntSlLIbkmDB8Fh58OxYXw1pXw8V8gHI52yZKkg8gAJknSQda+fhrvXN2bfq3TySss5oZXv+WWN6azNRwHpzwOR/8ucuKEf8LrF0NhXnQLliQdNAYwSZICUD05gZHnHcl1A1oRCsGLXy3mjEcnsXT9Fuj/Bzj5QYiJg+9fg2eHwua10S5ZknQQGMAkSQpITEyI3w5oyVPnH0n15Hi+W5rDifdPZNzs1dDpHDjndUhMhcWfwxMDYM28aJcsSTrADGCSJAWsb+s6vH1Vbw6vn8b6zQWc/9RX3PfxHIqbHAMXfQhpDWHtPBh5HCz+MtrlSpIOIAOYJElR0LBmMq+O6Mmwbo0Ih+HeMbO56JmvWV+1OVz8MWR2gs1r4Jkh8P0b0S5XknSAGMAkSYqSpPhY7j7lcO457QgS42L49MfVnHj/RL7PTYLz/wetj4eiPHjtApj4bzskSlIlYACTJCnKzujakNcv70XDmlVYum4Lpzz8Oa98uxbOfA66Xx456aPb4e3fQlFBVGuVJP0yBjBJksqB9vXTeOeqPvRvU4f8wmJufP07bn5zBlsH/BUG3wOhGPjmGXjhTNiaG+1yJUn7yQAmSVI5kZYczxPDu/J/x0Va1b/09RJOf2QSS1qeC2c+D/HJMO9jePJXkLM02uVKkvaDAUySpHIkJibE1ce25JkLulEjOZ7py3IY8sBExoa6wgXvQtUMyJoRaVO/4ttolytJKiMDmCRJ5dDRrdJ5++redGgQaVV/wdNf858fUii+cAykt4UNK+DJwTD7g2iXKkkqAwOYJEnlVIMaybwyoie/6R5pVf+fj+ZwwahVrD/7HWjWFwo2wYtnwVePR7tUSdI+MoBJklSOJcbF8tdfH86/Tu9AYlwM42av5oRHv2N635HQ6VwIF8O7N8AHt0JxcbTLlST9DAOYJEkVwKldGvDmFUfRuFYyy9Zv4dTHvualur+D/n+MnDDpAXh1OORvjm6hkqS9MoBJklRBtMtMZfRVvRnQNtKq/uY3v+fGrOPIH/o4xCbAzLfhmRNhY1a0S5Uk7YEBTJKkCiStSjyPnduV3w1qTUwIXpm8lF+Pr8eqoS9DlRqwbAo8cSys/jHapUqSdsMAJklSBRMTE+LKfi149sLu1ExJYMbyXAa+UcgX/V6GGk1h/WIYeRwsGB/tUiVJP2EAkySpgurdsjbvXN2bDg2rk7OlgLPeyOahFo8SbtAdtubAf0+Bb56FcDjapUqStjGASZJUgWVWr8Irl/Xg3B6NAbhnQjYX8Ufy2wyF4gIYfTW8diFsWR/VOiVJEQYwSZIquMS4WP4ytD33ntGBpPgYPpmbS/8F57Ki640QioUZb8AjvWHRpGiXKkmHPAOYJEmVxCmdI63qm9RKZmlOHsdM6szbRz5NuEYTyFkCTx8Pn94FRYXRLlWSDlkGMEmSKpG29VJ566reHNcug/yiYq4eH8sZoXtY0+LUyKbN4/4OTw2GdQujXaokHZIMYJIkVTJpVeJ59Jwu/GVoe6olxfH18kK6zjiVFxvdTjihGiz9Ch7pA9+9Gu1SJemQYwCTJKkSiokJcW6Pxnzyf305pXN9wmG4ZXYrTij4G6trdIK8XHjjYnjjUtiaG+1yJemQYQCTJKkSS6+WyL1ndOSVy3rSOqMaP2ypQY8V1/NSym8Ih2Lgu5cjDTqWfB3tUiXpkGAAkyTpENCtaU3euaY3fzihLUkJCdy85gTOyPsj6xPqwvpF8OQgGPcPKC6KdqmSVKkZwCRJOkTEx8ZwcZ9mfPx/fTnxiHp8XdyaPrl38n6oN4SL4NM74ekTYf2SaJcqSZWWAUySpENM3bQkHji7M89d1J302umM2HI51+VfzpZQFVj8OTxyFHz/RrTLlKRKyQAmSdIhqnfL2rx3bR9+N6gN78Uew6CtdzGtuDlszYHXLoC3roS8jdEuU5IqFQOYJEmHsMS4WK7s14Ix1x1D67ZHcFr+bdxfOJRiQjD1OcKPHg3Lvol2mZJUaRjAJEkSDWsm8/jwrjx2fg9eST2PYXl/YHm4JqG18wiPPA4m/geKi6NdpiRVeAYwSZJUon+bDMZcdwzd+w3h5MJ7+F9RN0LFhfDRbRQ9ezLkLo92iZJUoRnAJElSKUnxsVw/sDWvXHc8Lze5kxsLLmFzOJHYheMpeKAnzHwn2iVKUoVlAJMkSbvVtHYKz1zYjX5n/R8XJPyT6cVNiM9fDy//ho2vXQX5m6NdoiRVOAYwSZK0R6FQiMGH1+PJG87m3e7/5dGiIQBU/f6/rP1PT/KXTotugZJUwRjAJEnSz0pJjOOmE46g/1UPc2fNu1gVrk7NzQvhiWOZP/oeG3RI0j4KhcPhcLSLqIhyc3NJS0sjJyeH1NTUaJcjSVJgwuEw7305g+QPrqVv+GsAfkjuSu1znqJOZqMoVydJwStLNnAGTJIklUkoFOL4Hu3pfOO7vNPwd2wJJ9Bu82RiHz2KD958hoIiZ8MkaU+cAdtPzoBJkhQxd8bXxL15CU0KFwDwVsKJZJ7+D45smRnlyiQpGM6ASZKkwLQ47Ega3TiJH5ueA8DJ+e9Q7b8DuefZN1i9IS/K1UlS+eIM2H5yBkySpF1t+P49GHU51QrXkReO51+hc2kw8Bp+06MJsTGhaJcnSQeFM2CSJCkqqrUfTLVrvyK3QV8SQwX8niep/94FDL//HaYuXhft8iQp6gxgkiTpwKpah9SLRlH8q79TFJPAsbFT+c/aK/nPIw9zyxvTWbcpP9oVSlLUGMAkSdKBFwoR02MEsZd+SmGt1qSHcngm4e80++YuBv1zDC9/vZjiYu+CkHToMYBJkqSDp2574kaMgyMvAeCSuHd5qugWHnvjfU575HNmLM+JcoGSFCwDmCRJOrjiq8AJ/4RhLxFOrsVhMYt4J+FW2ix7nSH3T+D20TPI3VoQ7SolKRAGMEmSFIzWgwld/jk060eVUD53xY/k4bh/M+rz6Rz7r3GMmroMmzNLquwMYJIkKTjV6sI5b8DAv0JMPINiJ/NRlVtosekbrn15GsMe/4I5qzZEu0pJOmgMYJIkKVgxMdDrKrjkY6jVktrhtbyQcBe/T3iJKfOzGPz/JnD3uzPZlFcY7Uol6YCLagAbP348Q4YMITMzk1AoxKhRo372NePGjaNLly4kJSXRrFkzHnnkkVLPP/3004RCoV0eW7duLXXeQw89RNOmTUlKSqJLly5MmDDhQH41SZL0c+p1gMvGQZfzCRHm0pjRjEm9k4bh5Tw6fj4D7h3He9NXuCxRUqUS1QC2adMmOnTowAMPPLBP5y9YsIDjjz+ePn36MHXqVH7/+99zzTXX8Prrr5c6LzU1lRUrVpR6JCUllTz/8ssvc+2113LrrbcydepU+vTpw+DBg1m8ePEB/X6SJOlnJKTAkP8HZ/wXkqrTJH82Y1L+yGWpn7EiZwuXP/8N5z31NQuyN0W7Ukk6IELhcvLPSqFQiDfffJOhQ4fu8ZybbrqJ0aNHM3PmzJKxESNG8O233zJp0iQgMgN27bXXsn79+j2+T/fu3encuTMPP/xwyVjbtm0ZOnQod9999z7Vm5ubS1paGjk5OaSmpu7TayRJ0l7kLIM3L4OFkVUpP9Y6lt+sHEZ2UTIJsTGMOKYZV/RrQVJ8bJQLlaTSypINKtQ9YJMmTWLgwIGlxgYNGsTkyZMpKNjRvnbjxo00btyYBg0acOKJJzJ16tSS5/Lz85kyZcou7zNw4EA+//zzPX52Xl4eubm5pR6SJOkASqsPw9+CY2+DmDhar/mYL2r8iUsarSC/qJj7PpnLcf8ex8czV0W7UknabxUqgK1cuZKMjIxSYxkZGRQWFpKdnQ1AmzZtePrppxk9ejQvvvgiSUlJHHXUUcyZMweA7OxsioqKdvs+K1eu3ONn33333aSlpZU8GjZseIC/nSRJIiYW+lwPF30INZsRt3E5v1/9Oz7qOIEGqXEsWbuFi56ZzCXPTmbpus3RrlaSyqxCBTCILFXc2fYVlNvHe/TowTnnnEOHDh3o06cPr7zyCq1ateL+++//2ff56djObrnlFnJyckoeS5YsORBfR5Ik7U79LnDZeOj4G0LhYlrMephxtf/OTd0TiYsJMeaHVQy4dxwPfjqXvMKiaFcrSfusQgWwunXr7jJLlZWVRVxcHLVq1drta2JiYjjyyCNLZsBq165NbGzsbt/np7NiO0tMTCQ1NbXUQ5IkHUSJ1WDoQ3Dak5CYRuzyKVw+83wmDs6ie9OabC0o5h8f/Mjg/0xg4pzsaFcrSfukQgWwnj17MmbMmFJjH374IV27diU+Pn63rwmHw0ybNo169eoBkJCQQJcuXXZ5nzFjxtCrV6+DU7gkSdp/7U+FyydCo56Qv4G6H/+Wl2o/yQOnNKd21UTmZ2/inJFfcuUL37AyZ+vPv58kRVFUA9jGjRuZNm0a06ZNAyJt5qdNm1bSDv6WW25h+PDhJeePGDGCRYsWcf311zNz5kyefPJJRo4cyQ033FByzh133MEHH3zA/PnzmTZtGhdddBHTpk1jxIgRJedcf/31PPHEEzz55JPMnDmT6667jsWLF5c6R5IklSPVG8F570C/WyEUS2j6K5z4+emMG5bM+b2aEBOC/323gmP/NZYnJsynoKg42hVL0m5FtQ392LFj6dev3y7j5513Hk8//TTnn38+CxcuZOzYsSXPjRs3juuuu44ZM2aQmZnJTTfdVCo4XXfddbzxxhusXLmStLQ0OnXqxO23307Pnj1LfcZDDz3EPffcw4oVK2jfvj3//ve/Ofroo/e5dtvQS5IUJUu+gtcvgvWLIRQLfW9mRvOL+ePomXyzeD0ArTOq8Zeh7enWtGZ0a5V0SChLNig3+4BVNAYwSZKiaGsO/O8GmP5K5LhRT4qHPsqr80L87b1ZrNsc2Z7mlM71uWVwW9KrJUaxWEmVXaXdB0ySJAmApDQ49XH49WOQUA0WTyLm0T6cWeVrPvm/vgzr1ohQCN74Zhn9/zWW/05aSFGx/+YsKfqcAdtPzoBJklROrF0Ar18MyyZHjjv+Bgb/nWlZRfxh1HS+X5YLwOH10/jL0PZ0bFg9erVKqpRcghgAA5gkSeVIUQGM+zuM/ycQhhpN4dSRFGV25oUvF3HPBz+yYWshoRCcdWQjbhzUmhopCdGuWlIlYQALgAFMkqRyaOFn8MalkLsUYuIiXROP+i2rNxVy93szeeObZQDUSI7n5sFtOL1LQ2JiQlEuWlJFZwALgAFMkqRyass6ePta+GFU5LhJH/j1o5BWn68WrOWPo77nx1UbAOjcqDp/GdqewzLTolaupIrPABYAA5gkSeVYOAzTnod3b4SCTZBUHU66H9qdREFRMc98vpB/j5nNpvwiYkIwvGcTrh/YitSk+GhXLqkCMoAFwAAmSVIFsGZeZM+w5VMjx53Pg1/dDQkprMzZyp3/+4F3vlsBQHq1RG49vi0nd8wkFHJZoqR9ZwALgAFMkqQKojAfPv0rfPb/gDDUagmnPgGZHQGYOCebP731PfOzNwHQo1lN/nxye1plVItezZIqFANYAAxgkiRVMPPHwZuXwYYVEBMPA26DHldCTAx5hUU8MWEB938yh60FxcSE4PjD63F53+beHybpZxnAAmAAkySpAtq8FkZfDbPeiRw36we/fgSq1QVgydrN3Pm/H/hgxqqSl/Rrnc6V/VrQtUnNaFQsqQIwgAXAACZJUgUVDsOUp+H9W6BwCyTXgpMfhNaDS075YXkuD4+bx/++W07xtl9K3ZrW5Mp+LTi6ZW3vEZNUigEsAAYwSZIquNU/Rhp0rJweOT7yEhj4F4ivUnLKwuxNPDp+Hq9NWUpBUeQn02GZqVzZrwWDDqtLrHuIScIAFggDmCRJlUBhHnz8Z5j0QOQ4vW2kQUfd9qVOW5mzlScmzOf5LxezpaAIgGa1UxjRtzlDO9YnIS4m6MollSMGsAAYwCRJqkTmfgRvXg6bsiA2EQbcDt0vg5jYUqet3ZTP058v5JnPF5KzpQCAzLQkLj26GWce2YgqCbG7eXNJlZ0BLAAGMEmSKpmNq+GtK2HOB5HjjPbwq79B0z67nppXyAtfLuLxCQtYvSEPgFopCVzYuynn9GhMWhU3dJYOJQawABjAJEmqhMJhmPxkZFni1vWRsXYnw8A7oXqjXU7fWlDEa1OW8uj4eSxZuwWAaolxnNOzMRce1ZT0aokBFi8pWgxgATCASZJUiW1eC5/cCVOegnAxxCVBr2ug97WQkLLL6YVFxbzz3QoeGjuX2as2ApAYF8NZRzbkkqOb0aBGcsBfQFKQDGABMIBJknQIWPk9vH8zLJwQOU6tD8f9GdqfCrtpRV9cHOajmat4cOw8vl2yHoC4mBAnd6zP5X2b0aJOtQCLlxQUA1gADGCSJB0iwmGYORo++APkLI6MNeoZuT8ss+MeXhJm0rw1PDh2Lp/NXQNE8tqvDqvLFX1bcHiDtICKlxQEA1gADGCSJB1iCrbA5/fDhHsjGzgTgs7nQv8/QdX0Pb5s2pL1PPTpXD78YVXJWJ+WtbmyXwu6N63pps5SJWAAC4ABTJKkQ1TOUhhzG3z/WuQ4MQ363hTZyDkuYY8vm71qA4+Mncdb3y6nqDjy86tzo+pc2a8F/dvUMYhJFZgBLAAGMEmSDnGLJsH7N8GKbyPHtVpGliW2HLDXly1Zu5lHx8/jlclLyS8sBqBN3Wpc3rc5Jxxej7hYN3WWKhoDWAAMYJIkieIimPpcpG395uzIWKtfwaC7oFbzvb40a8NWRk5cwHOTFrEpvwiAxrWSGXFMc07pXJ/EODd1lioKA1gADGCSJKnE1hwYdw98+QgUF0JMPPS4HI7+HSTt/XdCzuYCnpm0kKc+W8C6zQUAZKQmckmfZgzr1oiUxLggvoGkX8AAFgADmCRJ2sXq2fDBLTD3o8hxSh0YcBt0OBti9r60cHN+IS9+tYTHx89nZe5WAKonx3NBr6ac16sx1ZP3fH+ZpOgygAXAACZJknYrHIY5H8L7t8DaeZGxzM4w+B5oeOTPvjyvsIhRU5fx8Nh5LFyzGYCUhFh+06MxF/duSp3UpINZvaT9YAALgAFMkiTtVWF+ZEniuHsgf0Nk7IgzYcAdkFrvZ19eVBzm3ekreGjsPGauyAUgITaG07s24LKjm9OoVvLBrF5SGRjAAmAAkyRJ+2TDqkiTjmnPRY7jU6DP9dDzKoj/+dmscDjM2B9X8+Cnc5m8aB0AsTEhhhxRj8v7tqB13WoHs3pJ+8AAFgADmCRJKpNlU+C9m2HpV5HjGk1g4F+hzQmwj3uAfbVgLQ9+Opdxs1eXjB3XLoMr+janU6MaB6FoSfvCABYAA5gkSSqzcBi+ewU+ug02rIiMNesb2T+sTtt9fpvvl+Xw0Ni5vPf9Srb/kuvVvBZX9mtBr+a13NRZCpgBLAAGMEmStN/yNsLEe+Hz+6EoH0KxcOTF0O8WqLLvM1nzVm/kkbHzeHPqMgqLIz/pOjRI44p+LTiubQYxMQYxKQgGsAAYwCRJ0i+2dgF8+AeY9U7kuEpN6H8rdLkAYvZ9I+Zl67fw+Pj5vPT1YrYWFAPQsk5VrujXnCFHZBIXu/cW+JJ+GQNYAAxgkiTpgJn3aaRt/eqZkeOM9pFliU37lOltsjfm8dRnC3j280VsyCsEoEGNKlx2THNO79KApPh9D3WS9p0BLAAGMEmSdEAVFcLkJ+HTv8LW9ZGxdifDwDuheqMyvVXu1gKe+2IRIycsYM2mfABqV03k4j5N+U33RlRLij/AxUuHNgNYAAxgkiTpoNi0JhLCpjwF4WKIS4Je10DvayEhpUxvtSW/iFcmL+Gx8fNZtn4LAKlJcZzfqwnnH9WUmikJB+ELSIceA1gADGCSJOmgWvk9vH8zLJwQOU6tD8f9Gdqfus9t67crKCrmrWnLeXjsXOat3gRAlfhYhnVrxCVHN6VeWpUDXb10SDGABcAAJkmSDrpwGGaOhg/+ADmLI2ONesLgv0O9DmV+u+LiMB/+sJIHP53H9GU5AMTHhjilUwNG9G1O09plm2GTFGEAC4ABTJIkBaZgS6Rl/YR7oXALEILOw+HYP0FK7TK/XTgcZsKcbB4aO5cv5q8FICYExx9ejyv6tqBdpr9tpLIwgAXAACZJkgKXsxTG3AbfvxY5TkyDvjdBt0shdv8aa0xZtJaHPp3Hx7OySsb6tU7nyn4t6Nqk5oGoWqr0DGABMIBJkqSoWTQJ3rsRVn4XOa7dCgbdDS0H7PdbzlyRy8Nj5/HOd8vZtqcz3ZrW5Mp+LTi6ZW1CZbzvTDqUGMACYACTJElRVVwEU5+Dj/8Mm7MjY61+BYPuglrN9/ttF2Zv4tHx83h9yjLyiyKbOh+WmcqV/Vow6LC6xMYYxKSfMoAFwAAmSZLKhS3rYfw/4MtHoLgQYuKhx+Vw9O8gaf9/o6zM2coTE+bz/JeL2VJQBECz2imM6NucoR3rkxAXc4C+gFTxGcACYACTJEnlyurZ8MEtMPejyHFKHRhwG3Q4G2L2Pyyt25TP058v5OnPF5KzpQCAzLQkLj26GWce2YgqCbEHonqpQjOABcAAJkmSyp1wGOZ8CO/fAmvnRcYyO8Pge6Dhkb/orTfmFfLCl4t4fMICVm/IA6BWSgIX9m7KOT0ak1Zl/5qASJWBASwABjBJklRuFebDlw/DuH9A/obI2BFnwYDbIbXeL3rrrQVFvP7NUh4ZN48la7cAUC0xjnN7NubC3k2pXTXxFxYvVTwGsAAYwCRJUrm3YVWkSce05yLH8Slw9P9BjyshPukXvXVhUTH/m76CBz+dy+xVGwFIiIvhV4fV5bQuDTiqRW0bduiQYQALgAFMkiRVGMumwHs3wdKvI8c1msDAv0KbE+AXtpcvLg7z8awsHvx0LtOWrC8Zr5uaxCmd63NqlwY0T6/6iz5DKu8MYAEwgEmSpAqluBimvwof3QYbVkTGmvWFX/0N6rT9xW8fDof5flkur01ZwlvfLmf95oKS5zo1qs5pXRpw4hGZ3iumSskAFgADmCRJqpDyNsLEe+Hz+6EoH0KxcOTF0O8WqFLjwHxEYRGfzMzitSlLGTt7NUXbdnZOiIth0LYlir1doqhKxAAWAAOYJEmq0NYugA//ALPeiRxXqQn9b4UuF0DMgWstn7VhK6OnLefVyUv5cdWGkvGM1ERO6dyAUzs3oEUdlyiqYjOABcAAJkmSKoV5n0ba1q+eGTnOaB9Zlti0zwH9mHA4zIzlubw2ZSmjpi0rtUSxY8PIEsUhR2SSluwSRVU8BrAAGMAkSVKlUVQIk0fCp3+FrTmRsXYnw8A7oXqjA/5xeYVFfDorskTx0x9LL1Ec2C6D07o0oE/LdJcoqsIwgAXAACZJkiqdTWsiIWzKUxAuhrgkOOq3cNS1kJB8UD5y9YY83pq2jNemLGXWyh1LFOtUS+TXnetzepcGtKhT7aB8tnSgGMACYACTJEmV1srp8N7NsGhi5Di1Phz3Z2h/6i9uW78nOy9RfGvaMtbttESxw7Yliie5RFHllAEsAAYwSZJUqYXD8MNb8OEfIWdxZKxRTxj8d6jX4aB+dH5hMZ+ULFHM2rFEMTaG4w7L4LTODejTsjZxsTEHtQ5pXxnAAmAAkyRJh4SCLZGW9RPuhcItQAg6D4dj/wQptQ/6x+9piWJ6tURO6VSf07o0oGWGSxQVXQawABjAJEnSISVnKYy5Db5/LXKcmAZ9b4Jul0JsMMsCZyzP2bZEcTlrN+WXjHdokBbpotghk+rJCYHUIu3MABYAA5gkSTokLZoE790IK7+LHNduBYPuhpYDAishv7CYT3/ctkRxVhaFOy1RHNCuDqd1acDRLdNdoqjAGMACYACTJEmHrOIimPpf+PjPsHlNZKzVr2DQXVCreaClZG/M461py3l9ylJ+WJFbMp5eLZFfb1ui2MolijrIDGABMIBJkqRD3pb1MO4e+OpRKC6EmDhoexIceRE0PuqgdUzckxnLc3h9yjJGTVtWaoniEduXKB6RSY0UlyjqwDOABcAAJkmStM3q2fDBLTD3ox1jtVtD1wuhw1lQpXqg5eQXFjN22xLFT36yRPHYtpElise0comiDhwDWAAMYJIkST+x4lv4eiRMfxUKNkfG4pMj+4d1vRDqdw68pDUb8xj97XJem7KUGct3LFGsXTWRX3fK5LQuDWld1yWK+mUMYAEwgEmSJO3B1hz47pVIGFs9c8d4ZifoelEkkCUkB17WD8tzef2bpYyauow1Oy1RPLx+ZIniSR1coqj9YwALgAFMkiTpZ4TDsPgLmDwysqlz0bbQk5gGHYdFZsXSWwdeVkFRMWN/XM1rU5bw8cwdSxTjY0Mc2yYjskSxdTrxLlHUPjKABcAAJkmSVAabsmHqczD5SVi/aMd4kz7Q9QJoMwTigp992r5E8fVvlvL9sp2XKCYwtGN9TuvagDZ1/a2nvTOABcAAJkmStB+Ki2HeJ5FZsdnvQ7g4Mp5SBzqfC53PgxqNo1LazBW5vD5lKaOmLSN7444liu3rp3Jq5wac3LE+NV2iqN0wgAXAACZJkvQL5SyFKc/AN8/CxpXbBkPQcmCklX2LARATG3hZBUXFjPtxNa9NWcrHs1ZRULRjiWL/NnU4rUtD+rpEUTsxgAXAACZJknSAFBXAj+9GmnYsGLdjPK0RdDkPOg+HqnWiUtraTfmMnraM136yRLFWSgJDt2303LaevwUPdQawABjAJEmSDoLsuTDlqcj9YlvXR8Zi4qHtiZEOik16B77B83azVkaWKL45tfQSxcMyty9RzKRW1cSo1KboMoAFwAAmSZJ0EBVsgRmjIveKLf16x3gUN3guKa2omPGzI0sUP5q5Y4liXMz2JYoN6NemjksUDyEGsAAYwCRJkgKy4rtI98TvXoGCTZGxuCpw+KmRWbEobPC83bpN+SUbPU9fllMyXislgZM7RpYotsv0t2JlZwALgAFMkiQpYFtz4buXI2Es64cd4/U6Rpp2tD8VElKiVt6PKzfw+jdLeeObZWRvzCsZb1svldO6RJYo1naJYqVkAAuAAUySJClKwmFY8mWkaccPo0pv8NzhrMgSxTptolZeYVEx4+dsW6L4Qxb5RZFW+3ExIfptX6LYug4JcS5RrCwMYAEwgEmSJJUDm7Jh2vORWbF1C3eMN+4d2eC57UlR2eB5u3Wb8nn7u8gSxe+W7liiWDMlgZM7ZnJalwYclpkWtfp0YBjAAmAAkyRJKkeKi2H+JzD5qUhL+5INntOh07nQ5fyobfC83exVG3h9ylLemLqM1Rt2LFFsU7caJxxej/5t69CuXiqhKHV51P4zgAXAACZJklRO5SyLbO78zTOwYcW2wRC0PC7StKPlcVHZ4Hm7wqJiJszJ5rUpSxnzw6qSJYoA9dKS6NemDse2qUOv5rWpkhC9OrXvDGABMIBJkiSVc0UF8ON7keWJ8z/dMZ7WMLLBc6fhUC0jevUB6zfn8+70lXwyaxUT52aztWBHGEuMi6FX81r0b5tB/zZ1qF+9ShQr1d4YwAJgAJMkSapA1syLBLFpz8OWdZGxmDhoc2Kkg2KTPlHb4Hm7rQVFTJq/hk9mZvHJrCyWrd9S6vk2davRv00djm1bh44NaxAb41LF8sIAFgADmCRJUgVUsDXSOfHrkbD0qx3jtVpGuid2HAZVakStvO3C4TA/rtrAJ7Oy+GRmFt8sXkfxTr/aayTH0691Hfq3rUOflumkVYmPXrEygAXBACZJklTBrZy+Y4Pn/I2Rsbgqkf3Eul4Y2eC5nDTEWLspn3Gzs/hk1mrG/pjFhq2FJc/FxoQ4skkNjm2TQf+2dWhWO8VGHgEzgAXAACZJklRJ5G2IbPD89ZOQNWPHeL0OkaYdh58W1Q2ef6qgqJgpi9ZFZsdmZTE3a2Op5xvXSo4sVWyTQbemNd1vLAAGsAAYwCRJkiqZcBiWfAWTR8KMUVC0rVV8YupOGzy3jWqJu7NozaaSMPbF/DUUFO34eV81MY4+LWvTr00d+rWuQ3q1xChWWnkZwAJgAJMkSarENq3ZaYPnBTvGGx8VCWJth0Bc+QszG/MKmTgnm09mreKTWavJ3phX6vkODatzbJs69G9Th8My3XPsQDGABcAAJkmSdAgoLoYFYyNNO358D8JFkfHk2tB5+wbPTaJY4J4VF4eZviynZHZs+rKcUs9npCbSv00d+rfJ4KgWtUhOiItSpRWfASwABjBJkqRDTO7yyAbPU56BDcu3DYagxYBIK/uWA6O6wfPPWZW7lU9nZfHxrCwmzslmS0FRyXMJ2/cc2zY71qBGchQrrXgMYAEwgEmSJB2iigph9vuRe8XmfbJjPLVBZEasc/Q3eP45WwuK+HLBWj6ZuYqPZ2WxdF3pPcdaZ1Sjf9tIGOvUsDpxsTby2BsDWAAMYJIkSWLNPJjyNEx9DrasjYzFxEGbEyIdFJseXW5a2e9JOBxmTtbGkj3HJi9aW2rPserJ8fRtlU7/thkc0zKdtGT3HPupsmSDqEbZ8ePHM2TIEDIzMwmFQowaNepnXzNu3Di6dOlCUlISzZo145FHHin1/OOPP06fPn2oUaMGNWrUYMCAAXz11Velzrn99tsJhUKlHnXr1j2QX02SJEmHglrNYeBf4PqZ8OvHoGF3KC6EH96CZ0+CB46ESQ/BlnXRrnSPQqEQrTKqMeKY5rwyoiff/PE4/t9ZHTm5YyZpVeJZv7mAUdOWc82LU+l85xjOeHQSj46bx9ysDTiXU3ZRvdNu06ZNdOjQgQsuuIBTTz31Z89fsGABxx9/PJdccgnPPfccn332GVdccQXp6eklrx87dizDhg2jV69eJCUlcc899zBw4EBmzJhB/fr1S97rsMMO46OPPio5jo0tv+t1JUmSVM7FJ0GHMyOPld9v2+D5ZVgzBz64BT6+Y9sGzxeVqw2ed6d6cgInd6zPyR3rU1hUzDeL1/PxrFV8OiuL2as28tWCtXy1YC13vzeLRjWTS+4b696sJolx/qb+OeVmCWIoFOLNN99k6NChezznpptuYvTo0cycObNkbMSIEXz77bdMmjRpt68pKiqiRo0aPPDAAwwfPhyIzICNGjWKadOm7Xe9LkGUJEnSXuVtgOmvRjZ4XjV9x3jdIyJNO9qfBolVo1fffliydjOfbGvk8cW8NeQXFZc8l5wQS5+WtTm2TQZ926RTp1pSFCsNVlmyQYXqNTlp0iQGDhxYamzQoEGMHDmSgoIC4uN3XY+6efNmCgoKqFmzZqnxOXPmkJmZSWJiIt27d+euu+6iWbNme/zsvLw88vJ27KOQm5v7C7+NJEmSKrXEapE9w7pcAEu/jsyKff8GrPwO3v4tfPhHOOLMyDkZ7aJd7T5pWDOZ83o14bxeTdiUV8hnc7NL2txnbcjjgxmr+GDGKgCOaJBG/zZ1OLZNBodlphITU35n/YJUoQLYypUrycgo3VEmIyODwsJCsrOzqVev3i6vufnmm6lfvz4DBgwoGevevTvPPvssrVq1YtWqVdx555306tWLGTNmUKtWrd1+9t13380dd9xxYL+QJEmSKr9QCBp2izwG3QXTXoiEsbXz4OvHI4+qGZDeGmq3jvw3vTWkt4GU9HK7XDElMY6Bh9Vl4GF1KS4OM2N57rYwtopvl+bw3bbHfz6aQ51qifRrXYf+bevQu0VtUhIrVAw5oCrUEsRWrVpxwQUXcMstt5SMffbZZ/Tu3ZsVK1bs0kjjnnvu4W9/+xtjx47liCOO2OP7btq0iebNm3PjjTdy/fXX7/ac3c2ANWzY0CWIkiRJKrviYlgwLhLEZv1vxwbPP5VUPRLE0ncKZrVbQ1qDchvMALI2bGXsrNV8MiuLCXNWsyl/pz3HYmPo0bwWx267d6xhzYq/51ilXYJYt25dVq5cWWosKyuLuLi4XWau/vnPf3LXXXfx0Ucf7TV8AaSkpHD44YczZ86cPZ6TmJhIYmLi/hcvSZIkbRcTA837RR55GyB7Nqz+cafHLFi3ELauhyVfRB47S6gKtVtGwlntVjtCWo0m5WIz6DrVkjjjyIaccWRD8gqL+GrBWj6eGVmquHjtZsbPXs342au5bfQMWtapSv+2kaWKnRtV/j3HKlQA69mzJ2+//XapsQ8//JCuXbuWuv/rH//4B3feeScffPABXbt2/dn3zcvLY+bMmfTp0+eA1yxJkiTtVWI1qN8l8thZwRZYM3dHKMve9t818yB/IyyfGnnsLDYxEsxKQtm2/9ZsDnEJwX2nnSTGxdKnZTp9WqZz25B2zFu9iU9mreLjmVlMXrSOOVkbmZO1kUfHzSetSjzHtErn2LZ1OKZVOtWTo1PzwRTVJYgbN25k7ty5AHTq1Il7772Xfv36UbNmTRo1asQtt9zCsmXLePbZZ4FIG/r27dtz2WWXcckllzBp0iRGjBjBiy++WNKG/p577uGPf/wjL7zwAkcddVTJZ1WtWpWqVSNdZm644QaGDBlCo0aNyMrK4s4772TcuHFMnz6dxo0b71PtdkGUJElSVBQVwNoFkVmy7J1mzbLnQOGW3b8mFAs1m5VexpjeOhLUEqK3BDBncwHj5qzm01lZfPpjFus3F5Q8FxOCro1r0r9tZKliyzpVCZXTZZdlyQZRDWBjx46lX79+u4yfd955PP3005x//vksXLiQsWPHljw3btw4rrvuOmbMmEFmZiY33XQTI0aMKHm+SZMmLFq0aJf3vO2227j99tsBOOussxg/fjzZ2dmkp6fTo0cP/vKXv9Cu3b53nzGASZIkqVwpLoacxTuWMK6evS2kzYa8vXTwrt5o16WMtVtBleqBlQ5QVBxm6uJ1fDwri09nZTFr5YZSzzeoUSVy31jbDLo3rUlSfPSXWm5XYQJYRWYAkyRJUoUQDsOGFaXvL8veFs42r9nz66rW3bX5R3obSKkdSAOQpes28+m2Pcc+n7eG/MIde45ViY+ld8vaHNumDid1zCQ5Ibp3VhnAAmAAkyRJUoW3Kbv0/WXbHxuW7/k1VWruupQxvTWk1j9owWxzfiGfz13Dx9va3K/KjXQnT4iNYeqfjot6W3sDWAAMYJIkSaq0tuZE7ilbPat0E5B1i4A9xIeEajs6M6bvtJyxeuMD2pkxHA7zw4pcPpmZxbrNBfxpSPQ3sTaABcAAJkmSpENO/uadOjPu1ARk7XwoLtz9a2ITt91f1qr0vWY1m0WtM+OBVmn3AZMkSZIURQnJUO+IyGNnhfmREPbTpYzZs6EoD1ZNjzx2FhMXCWElzT+2zZzVahnVzowHmzNg+8kZMEmSJOlnFBfB+kU/CWXb/pu/cQ8vCu3ozFgya9Y68uektEDL31cuQQyAAUySJEnaT+Ew5C7fcY9Z9k4dGres2/PrqtXbtflHw+4QGx9c7bthAAuAAUySJEk6wMLhSGfG7J/sZbb6R9i4ctfzQzHw+xUQnxR8rTvxHjBJkiRJFU8oBFXTI48mvUs/t2X9tv3LdtrLrHBr1MNXWRnAJEmSJJV/VapDw26RRwUWE+0CJEmSJOlQYQCTJEmSpIAYwCRJkiQpIAYwSZIkSQqIAUySJEmSAmIAkyRJkqSAGMAkSZIkKSAGMEmSJEkKiAFMkiRJkgJiAJMkSZKkgBjAJEmSJCkgBjBJkiRJCogBTJIkSZICYgCTJEmSpIAYwCRJkiQpIAYwSZIkSQqIAUySJEmSAmIAkyRJkqSAGMAkSZIkKSAGMEmSJEkKiAFMkiRJkgJiAJMkSZKkgBjAJEmSJCkgBjBJkiRJCogBTJIkSZICYgCTJEmSpIAYwCRJkiQpIAYwSZIkSQqIAUySJEmSAmIAkyRJkqSAGMAkSZIkKSAGMEmSJEkKSFy0C6iowuEwALm5uVGuRJIkSVI0bc8E2zPC3hjA9tOGDRsAaNiwYZQrkSRJklQebNiwgbS0tL2eEwrvS0zTLoqLi1m+fDnVqlUjFApFtZbc3FwaNmzIkiVLSE1NjWotqhi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              " - ] - }, - "metadata": { - "filenames": { - "image/png": "/Users/mhjensen/Teaching/MachineLearning/doc/LectureNotes/_build/jupyter_execute/chapter11_66_4.png" - } - }, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "# Assume that all function definitions from the example program using Autograd\n", "# are located here.\n", @@ -1895,45 +1753,7 @@ "collapsed": false, "editable": true }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Initial cost: 457.256\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/Users/mhjensen/miniforge3/envs/myenv/lib/python3.9/site-packages/numpy/core/fromnumeric.py:3208: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray.\n", - " return asarray(a).size\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Final cost: 0.00310113\n", - "The max absolute difference between the solutions is: 0.000464088\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
              " - ] - }, - "metadata": { - "filenames": { - "image/png": "/Users/mhjensen/Teaching/MachineLearning/doc/LectureNotes/_build/jupyter_execute/chapter11_79_3.png" - } - }, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "import autograd.numpy as np\n", "from autograd import grad, elementwise_grad\n", @@ -2276,60 +2096,7 @@ "collapsed": false, "editable": true }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Initial cost: 457.256\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/Users/mhjensen/miniforge3/envs/myenv/lib/python3.9/site-packages/numpy/core/fromnumeric.py:3208: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray.\n", - " return asarray(a).size\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Final cost: 0.00310113\n", - "The max absolute difference between the analytical solution and DNN Autograd: 0.000464088\n", - "The max absolute difference between the analytical solution and numerical scheme: 0.00266858\n" - ] - }, - { - "data": { - "image/png": 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\n", 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              " - ] - }, - "metadata": { - "filenames": { - "image/png": "/Users/mhjensen/Teaching/MachineLearning/doc/LectureNotes/_build/jupyter_execute/chapter11_91_4.png" - } - }, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "import autograd.numpy as np\n", "from autograd import grad, elementwise_grad\n", @@ -2995,58 +2762,7 @@ "collapsed": false, "editable": true }, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/Users/mhjensen/miniforge3/envs/myenv/lib/python3.9/site-packages/numpy/core/fromnumeric.py:3208: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray.\n", - " return asarray(a).size\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Initial cost: 41.05505310046363\n" - ] - }, - { - "ename": "KeyboardInterrupt", - "evalue": "", - "output_type": "error", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mKeyboardInterrupt\u001b[0m Traceback (most recent call last)", - "Input \u001b[0;32mIn [9]\u001b[0m, in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[1;32m 140\u001b[0m num_iter \u001b[38;5;241m=\u001b[39m \u001b[38;5;241m250\u001b[39m\n\u001b[1;32m 141\u001b[0m lmb \u001b[38;5;241m=\u001b[39m \u001b[38;5;241m0.01\u001b[39m\n\u001b[0;32m--> 143\u001b[0m P \u001b[38;5;241m=\u001b[39m \u001b[43msolve_pde_deep_neural_network\u001b[49m\u001b[43m(\u001b[49m\u001b[43mx\u001b[49m\u001b[43m,\u001b[49m\u001b[43mt\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mnum_hidden_neurons\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mnum_iter\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mlmb\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 145\u001b[0m \u001b[38;5;66;03m## Store the results\u001b[39;00m\n\u001b[1;32m 146\u001b[0m g_dnn_ag \u001b[38;5;241m=\u001b[39m np\u001b[38;5;241m.\u001b[39mzeros((Nx, Nt))\n", - "Input \u001b[0;32mIn [9]\u001b[0m, in \u001b[0;36msolve_pde_deep_neural_network\u001b[0;34m(x, t, num_neurons, num_iter, lmb)\u001b[0m\n\u001b[1;32m 118\u001b[0m \u001b[38;5;66;03m# Let the update be done num_iter times\u001b[39;00m\n\u001b[1;32m 119\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m i \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mrange\u001b[39m(num_iter):\n\u001b[0;32m--> 120\u001b[0m cost_grad \u001b[38;5;241m=\u001b[39m \u001b[43mcost_function_grad\u001b[49m\u001b[43m(\u001b[49m\u001b[43mP\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mx\u001b[49m\u001b[43m \u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mt\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 122\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m l \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mrange\u001b[39m(N_hidden\u001b[38;5;241m+\u001b[39m\u001b[38;5;241m1\u001b[39m):\n\u001b[1;32m 123\u001b[0m P[l] \u001b[38;5;241m=\u001b[39m P[l] \u001b[38;5;241m-\u001b[39m lmb \u001b[38;5;241m*\u001b[39m cost_grad[l]\n", - "File \u001b[0;32m~/miniforge3/envs/myenv/lib/python3.9/site-packages/autograd/wrap_util.py:20\u001b[0m, in \u001b[0;36munary_to_nary..nary_operator..nary_f\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m 18\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m 19\u001b[0m x \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mtuple\u001b[39m(args[i] \u001b[38;5;28;01mfor\u001b[39;00m i \u001b[38;5;129;01min\u001b[39;00m argnum)\n\u001b[0;32m---> 20\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43munary_operator\u001b[49m\u001b[43m(\u001b[49m\u001b[43munary_f\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mx\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mnary_op_args\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mnary_op_kwargs\u001b[49m\u001b[43m)\u001b[49m\n", - "File \u001b[0;32m~/miniforge3/envs/myenv/lib/python3.9/site-packages/autograd/differential_operators.py:25\u001b[0m, in \u001b[0;36mgrad\u001b[0;34m(fun, x)\u001b[0m\n\u001b[1;32m 18\u001b[0m \u001b[38;5;129m@unary_to_nary\u001b[39m\n\u001b[1;32m 19\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mgrad\u001b[39m(fun, x):\n\u001b[1;32m 20\u001b[0m \u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m 21\u001b[0m \u001b[38;5;124;03m Returns a function which computes the gradient of `fun` with respect to\u001b[39;00m\n\u001b[1;32m 22\u001b[0m \u001b[38;5;124;03m positional argument number `argnum`. The returned function takes the same\u001b[39;00m\n\u001b[1;32m 23\u001b[0m \u001b[38;5;124;03m arguments as `fun`, but returns the gradient instead. The function `fun`\u001b[39;00m\n\u001b[1;32m 24\u001b[0m \u001b[38;5;124;03m should be scalar-valued. The gradient has the same type as the argument.\"\"\"\u001b[39;00m\n\u001b[0;32m---> 25\u001b[0m vjp, ans \u001b[38;5;241m=\u001b[39m \u001b[43m_make_vjp\u001b[49m\u001b[43m(\u001b[49m\u001b[43mfun\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mx\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 26\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m vspace(ans)\u001b[38;5;241m.\u001b[39msize \u001b[38;5;241m==\u001b[39m \u001b[38;5;241m1\u001b[39m:\n\u001b[1;32m 27\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mTypeError\u001b[39;00m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mGrad only applies to real scalar-output functions. \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m 28\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mTry jacobian, elementwise_grad or holomorphic_grad.\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n", - "File \u001b[0;32m~/miniforge3/envs/myenv/lib/python3.9/site-packages/autograd/core.py:10\u001b[0m, in \u001b[0;36mmake_vjp\u001b[0;34m(fun, x)\u001b[0m\n\u001b[1;32m 8\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mmake_vjp\u001b[39m(fun, x):\n\u001b[1;32m 9\u001b[0m start_node \u001b[38;5;241m=\u001b[39m VJPNode\u001b[38;5;241m.\u001b[39mnew_root()\n\u001b[0;32m---> 10\u001b[0m end_value, end_node \u001b[38;5;241m=\u001b[39m \u001b[43mtrace\u001b[49m\u001b[43m(\u001b[49m\u001b[43mstart_node\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mfun\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mx\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 11\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m end_node \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m 12\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mvjp\u001b[39m(g): \u001b[38;5;28;01mreturn\u001b[39;00m vspace(x)\u001b[38;5;241m.\u001b[39mzeros()\n", - "File \u001b[0;32m~/miniforge3/envs/myenv/lib/python3.9/site-packages/autograd/tracer.py:10\u001b[0m, in \u001b[0;36mtrace\u001b[0;34m(start_node, fun, x)\u001b[0m\n\u001b[1;32m 8\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m trace_stack\u001b[38;5;241m.\u001b[39mnew_trace() \u001b[38;5;28;01mas\u001b[39;00m t:\n\u001b[1;32m 9\u001b[0m start_box \u001b[38;5;241m=\u001b[39m new_box(x, t, start_node)\n\u001b[0;32m---> 10\u001b[0m end_box \u001b[38;5;241m=\u001b[39m \u001b[43mfun\u001b[49m\u001b[43m(\u001b[49m\u001b[43mstart_box\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 11\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m isbox(end_box) \u001b[38;5;129;01mand\u001b[39;00m end_box\u001b[38;5;241m.\u001b[39m_trace \u001b[38;5;241m==\u001b[39m start_box\u001b[38;5;241m.\u001b[39m_trace:\n\u001b[1;32m 12\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m end_box\u001b[38;5;241m.\u001b[39m_value, end_box\u001b[38;5;241m.\u001b[39m_node\n", - "File \u001b[0;32m~/miniforge3/envs/myenv/lib/python3.9/site-packages/autograd/wrap_util.py:15\u001b[0m, in \u001b[0;36munary_to_nary..nary_operator..nary_f..unary_f\u001b[0;34m(x)\u001b[0m\n\u001b[1;32m 13\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m 14\u001b[0m subargs \u001b[38;5;241m=\u001b[39m subvals(args, \u001b[38;5;28mzip\u001b[39m(argnum, x))\n\u001b[0;32m---> 15\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mfun\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43msubargs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n", - "Input \u001b[0;32mIn [9]\u001b[0m, in \u001b[0;36mcost_function\u001b[0;34m(P, x, t)\u001b[0m\n\u001b[1;32m 78\u001b[0m g_t \u001b[38;5;241m=\u001b[39m g_trial(point,P)\n\u001b[1;32m 79\u001b[0m g_t_jacobian \u001b[38;5;241m=\u001b[39m g_t_jacobian_func(point,P)\n\u001b[0;32m---> 80\u001b[0m g_t_hessian \u001b[38;5;241m=\u001b[39m \u001b[43mg_t_hessian_func\u001b[49m\u001b[43m(\u001b[49m\u001b[43mpoint\u001b[49m\u001b[43m,\u001b[49m\u001b[43mP\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 82\u001b[0m g_t_dt \u001b[38;5;241m=\u001b[39m g_t_jacobian[\u001b[38;5;241m1\u001b[39m]\n\u001b[1;32m 83\u001b[0m g_t_d2x \u001b[38;5;241m=\u001b[39m g_t_hessian[\u001b[38;5;241m0\u001b[39m][\u001b[38;5;241m0\u001b[39m]\n", - "File \u001b[0;32m~/miniforge3/envs/myenv/lib/python3.9/site-packages/autograd/wrap_util.py:20\u001b[0m, in \u001b[0;36munary_to_nary..nary_operator..nary_f\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m 18\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m 19\u001b[0m x \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mtuple\u001b[39m(args[i] \u001b[38;5;28;01mfor\u001b[39;00m i \u001b[38;5;129;01min\u001b[39;00m argnum)\n\u001b[0;32m---> 20\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43munary_operator\u001b[49m\u001b[43m(\u001b[49m\u001b[43munary_f\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mx\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mnary_op_args\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mnary_op_kwargs\u001b[49m\u001b[43m)\u001b[49m\n", - "File \u001b[0;32m~/miniforge3/envs/myenv/lib/python3.9/site-packages/autograd/differential_operators.py:78\u001b[0m, in \u001b[0;36mhessian\u001b[0;34m(fun, x)\u001b[0m\n\u001b[1;32m 75\u001b[0m \u001b[38;5;129m@unary_to_nary\u001b[39m\n\u001b[1;32m 76\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mhessian\u001b[39m(fun, x):\n\u001b[1;32m 77\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mReturns a function that computes the exact Hessian.\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m---> 78\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mjacobian\u001b[49m\u001b[43m(\u001b[49m\u001b[43mjacobian\u001b[49m\u001b[43m(\u001b[49m\u001b[43mfun\u001b[49m\u001b[43m)\u001b[49m\u001b[43m)\u001b[49m\u001b[43m(\u001b[49m\u001b[43mx\u001b[49m\u001b[43m)\u001b[49m\n", - "File \u001b[0;32m~/miniforge3/envs/myenv/lib/python3.9/site-packages/autograd/wrap_util.py:20\u001b[0m, in \u001b[0;36munary_to_nary..nary_operator..nary_f\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m 18\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m 19\u001b[0m x \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mtuple\u001b[39m(args[i] \u001b[38;5;28;01mfor\u001b[39;00m i \u001b[38;5;129;01min\u001b[39;00m argnum)\n\u001b[0;32m---> 20\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43munary_operator\u001b[49m\u001b[43m(\u001b[49m\u001b[43munary_f\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mx\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mnary_op_args\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mnary_op_kwargs\u001b[49m\u001b[43m)\u001b[49m\n", - "File \u001b[0;32m~/miniforge3/envs/myenv/lib/python3.9/site-packages/autograd/differential_operators.py:61\u001b[0m, in \u001b[0;36mjacobian\u001b[0;34m(fun, x)\u001b[0m\n\u001b[1;32m 59\u001b[0m jacobian_shape \u001b[38;5;241m=\u001b[39m ans_vspace\u001b[38;5;241m.\u001b[39mshape \u001b[38;5;241m+\u001b[39m vspace(x)\u001b[38;5;241m.\u001b[39mshape\n\u001b[1;32m 60\u001b[0m grads \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mmap\u001b[39m(vjp, ans_vspace\u001b[38;5;241m.\u001b[39mstandard_basis())\n\u001b[0;32m---> 61\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m np\u001b[38;5;241m.\u001b[39mreshape(\u001b[43mnp\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mstack\u001b[49m\u001b[43m(\u001b[49m\u001b[43mgrads\u001b[49m\u001b[43m)\u001b[49m, jacobian_shape)\n", - "File \u001b[0;32m~/miniforge3/envs/myenv/lib/python3.9/site-packages/autograd/numpy/numpy_wrapper.py:88\u001b[0m, in \u001b[0;36mstack\u001b[0;34m(arrays, axis)\u001b[0m\n\u001b[1;32m 83\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mstack\u001b[39m(arrays, axis\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m0\u001b[39m):\n\u001b[1;32m 84\u001b[0m \u001b[38;5;66;03m# this code is basically copied from numpy/core/shape_base.py's stack\u001b[39;00m\n\u001b[1;32m 85\u001b[0m \u001b[38;5;66;03m# we need it here because we want to re-implement stack in terms of the\u001b[39;00m\n\u001b[1;32m 86\u001b[0m \u001b[38;5;66;03m# primitives defined in this file\u001b[39;00m\n\u001b[0;32m---> 88\u001b[0m arrays \u001b[38;5;241m=\u001b[39m [array(arr) \u001b[38;5;28;01mfor\u001b[39;00m arr \u001b[38;5;129;01min\u001b[39;00m arrays]\n\u001b[1;32m 89\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m arrays:\n\u001b[1;32m 90\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mneed at least one array to stack\u001b[39m\u001b[38;5;124m'\u001b[39m)\n", - "File \u001b[0;32m~/miniforge3/envs/myenv/lib/python3.9/site-packages/autograd/numpy/numpy_wrapper.py:88\u001b[0m, in \u001b[0;36m\u001b[0;34m(.0)\u001b[0m\n\u001b[1;32m 83\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mstack\u001b[39m(arrays, axis\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m0\u001b[39m):\n\u001b[1;32m 84\u001b[0m \u001b[38;5;66;03m# this code is basically copied from numpy/core/shape_base.py's stack\u001b[39;00m\n\u001b[1;32m 85\u001b[0m \u001b[38;5;66;03m# we need it here because we want to re-implement stack in terms of the\u001b[39;00m\n\u001b[1;32m 86\u001b[0m \u001b[38;5;66;03m# primitives defined in this file\u001b[39;00m\n\u001b[0;32m---> 88\u001b[0m arrays \u001b[38;5;241m=\u001b[39m [array(arr) \u001b[38;5;28;01mfor\u001b[39;00m arr \u001b[38;5;129;01min\u001b[39;00m arrays]\n\u001b[1;32m 89\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m arrays:\n\u001b[1;32m 90\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mneed at least one array to stack\u001b[39m\u001b[38;5;124m'\u001b[39m)\n", - "File \u001b[0;32m~/miniforge3/envs/myenv/lib/python3.9/site-packages/autograd/core.py:14\u001b[0m, in \u001b[0;36mmake_vjp..vjp\u001b[0;34m(g)\u001b[0m\n\u001b[0;32m---> 14\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mvjp\u001b[39m(g): \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mbackward_pass\u001b[49m\u001b[43m(\u001b[49m\u001b[43mg\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mend_node\u001b[49m\u001b[43m)\u001b[49m\n", - "File \u001b[0;32m~/miniforge3/envs/myenv/lib/python3.9/site-packages/autograd/core.py:21\u001b[0m, in \u001b[0;36mbackward_pass\u001b[0;34m(g, end_node)\u001b[0m\n\u001b[1;32m 19\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m node \u001b[38;5;129;01min\u001b[39;00m toposort(end_node):\n\u001b[1;32m 20\u001b[0m outgrad \u001b[38;5;241m=\u001b[39m outgrads\u001b[38;5;241m.\u001b[39mpop(node)\n\u001b[0;32m---> 21\u001b[0m ingrads \u001b[38;5;241m=\u001b[39m \u001b[43mnode\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mvjp\u001b[49m\u001b[43m(\u001b[49m\u001b[43moutgrad\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;241;43m0\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 22\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m parent, ingrad \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mzip\u001b[39m(node\u001b[38;5;241m.\u001b[39mparents, ingrads):\n\u001b[1;32m 23\u001b[0m outgrads[parent] \u001b[38;5;241m=\u001b[39m add_outgrads(outgrads\u001b[38;5;241m.\u001b[39mget(parent), ingrad)\n", - "File \u001b[0;32m~/miniforge3/envs/myenv/lib/python3.9/site-packages/autograd/core.py:67\u001b[0m, in \u001b[0;36mdefvjp..vjp_argnums..\u001b[0;34m(g)\u001b[0m\n\u001b[1;32m 64\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mNotImplementedError\u001b[39;00m(\n\u001b[1;32m 65\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mVJP of \u001b[39m\u001b[38;5;132;01m{}\u001b[39;00m\u001b[38;5;124m wrt argnum 0 not defined\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;241m.\u001b[39mformat(fun\u001b[38;5;241m.\u001b[39m\u001b[38;5;18m__name__\u001b[39m))\n\u001b[1;32m 66\u001b[0m vjp \u001b[38;5;241m=\u001b[39m vjpfun(ans, \u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n\u001b[0;32m---> 67\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;01mlambda\u001b[39;00m g: (\u001b[43mvjp\u001b[49m\u001b[43m(\u001b[49m\u001b[43mg\u001b[49m\u001b[43m)\u001b[49m,)\n\u001b[1;32m 68\u001b[0m \u001b[38;5;28;01melif\u001b[39;00m L \u001b[38;5;241m==\u001b[39m \u001b[38;5;241m2\u001b[39m:\n\u001b[1;32m 69\u001b[0m argnum_0, argnum_1 \u001b[38;5;241m=\u001b[39m argnums\n", - "File \u001b[0;32m~/miniforge3/envs/myenv/lib/python3.9/site-packages/autograd/numpy/numpy_vjps.py:423\u001b[0m, in \u001b[0;36mmatmul_vjp_1..\u001b[0;34m(g)\u001b[0m\n\u001b[1;32m 421\u001b[0m A_ndim \u001b[38;5;241m=\u001b[39m anp\u001b[38;5;241m.\u001b[39mndim(A)\n\u001b[1;32m 422\u001b[0m B_meta \u001b[38;5;241m=\u001b[39m anp\u001b[38;5;241m.\u001b[39mmetadata(B)\n\u001b[0;32m--> 423\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;01mlambda\u001b[39;00m g: \u001b[43mmatmul_adjoint_1\u001b[49m\u001b[43m(\u001b[49m\u001b[43mA\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mg\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mA_ndim\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mB_meta\u001b[49m\u001b[43m)\u001b[49m\n", - "File \u001b[0;32m~/miniforge3/envs/myenv/lib/python3.9/site-packages/autograd/numpy/numpy_vjps.py:410\u001b[0m, in \u001b[0;36mmatmul_adjoint_1\u001b[0;34m(A, G, A_ndim, B_meta)\u001b[0m\n\u001b[1;32m 408\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m: \u001b[38;5;66;03m# We need to swap the last two axes of A\u001b[39;00m\n\u001b[1;32m 409\u001b[0m A \u001b[38;5;241m=\u001b[39m anp\u001b[38;5;241m.\u001b[39mswapaxes(A, A_ndim \u001b[38;5;241m-\u001b[39m \u001b[38;5;241m2\u001b[39m, A_ndim \u001b[38;5;241m-\u001b[39m \u001b[38;5;241m1\u001b[39m)\n\u001b[0;32m--> 410\u001b[0m result \u001b[38;5;241m=\u001b[39m \u001b[43manp\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mmatmul\u001b[49m\u001b[43m(\u001b[49m\u001b[43mA\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mG\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 411\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m B_is_vec:\n\u001b[1;32m 412\u001b[0m result \u001b[38;5;241m=\u001b[39m anp\u001b[38;5;241m.\u001b[39msqueeze(result, anp\u001b[38;5;241m.\u001b[39mndim(G) \u001b[38;5;241m-\u001b[39m \u001b[38;5;241m1\u001b[39m)\n", - "File \u001b[0;32m~/miniforge3/envs/myenv/lib/python3.9/site-packages/autograd/tracer.py:45\u001b[0m, in \u001b[0;36mprimitive..f_wrapped\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m 43\u001b[0m argnums \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mtuple\u001b[39m(argnum \u001b[38;5;28;01mfor\u001b[39;00m argnum, _ \u001b[38;5;129;01min\u001b[39;00m boxed_args)\n\u001b[1;32m 44\u001b[0m ans \u001b[38;5;241m=\u001b[39m f_wrapped(\u001b[38;5;241m*\u001b[39margvals, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n\u001b[0;32m---> 45\u001b[0m node \u001b[38;5;241m=\u001b[39m \u001b[43mnode_constructor\u001b[49m\u001b[43m(\u001b[49m\u001b[43mans\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mf_wrapped\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43margvals\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mkwargs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43margnums\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mparents\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 46\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m new_box(ans, trace, node)\n\u001b[1;32m 47\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n", - "File \u001b[0;32m~/miniforge3/envs/myenv/lib/python3.9/site-packages/autograd/core.py:36\u001b[0m, in \u001b[0;36mVJPNode.__init__\u001b[0;34m(self, value, fun, args, kwargs, parent_argnums, parents)\u001b[0m\n\u001b[1;32m 33\u001b[0m fun_name \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mgetattr\u001b[39m(fun, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124m__name__\u001b[39m\u001b[38;5;124m'\u001b[39m, fun)\n\u001b[1;32m 34\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mNotImplementedError\u001b[39;00m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mVJP of \u001b[39m\u001b[38;5;132;01m{}\u001b[39;00m\u001b[38;5;124m wrt argnums \u001b[39m\u001b[38;5;132;01m{}\u001b[39;00m\u001b[38;5;124m not defined\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m 35\u001b[0m \u001b[38;5;241m.\u001b[39mformat(fun_name, parent_argnums))\n\u001b[0;32m---> 36\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mvjp \u001b[38;5;241m=\u001b[39m \u001b[43mvjpmaker\u001b[49m\u001b[43m(\u001b[49m\u001b[43mparent_argnums\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mvalue\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n", - "File \u001b[0;32m~/miniforge3/envs/myenv/lib/python3.9/site-packages/autograd/core.py:77\u001b[0m, in \u001b[0;36mdefvjp..vjp_argnums\u001b[0;34m(argnums, ans, args, kwargs)\u001b[0m\n\u001b[1;32m 74\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mNotImplementedError\u001b[39;00m(\n\u001b[1;32m 75\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mVJP of \u001b[39m\u001b[38;5;132;01m{}\u001b[39;00m\u001b[38;5;124m wrt argnums 0, 1 not defined\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;241m.\u001b[39mformat(fun\u001b[38;5;241m.\u001b[39m\u001b[38;5;18m__name__\u001b[39m))\n\u001b[1;32m 76\u001b[0m vjp_0 \u001b[38;5;241m=\u001b[39m vjp_0_fun(ans, \u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n\u001b[0;32m---> 77\u001b[0m vjp_1 \u001b[38;5;241m=\u001b[39m \u001b[43mvjp_1_fun\u001b[49m\u001b[43m(\u001b[49m\u001b[43mans\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 78\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;01mlambda\u001b[39;00m g: (vjp_0(g), vjp_1(g))\n\u001b[1;32m 79\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n", - "File \u001b[0;32m~/miniforge3/envs/myenv/lib/python3.9/site-packages/autograd/numpy/numpy_vjps.py:422\u001b[0m, in \u001b[0;36mmatmul_vjp_1\u001b[0;34m(ans, A, B)\u001b[0m\n\u001b[1;32m 420\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mmatmul_vjp_1\u001b[39m(ans, A, B):\n\u001b[1;32m 421\u001b[0m A_ndim \u001b[38;5;241m=\u001b[39m anp\u001b[38;5;241m.\u001b[39mndim(A)\n\u001b[0;32m--> 422\u001b[0m B_meta \u001b[38;5;241m=\u001b[39m \u001b[43manp\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mmetadata\u001b[49m\u001b[43m(\u001b[49m\u001b[43mB\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 423\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;01mlambda\u001b[39;00m g: matmul_adjoint_1(A, g, A_ndim, B_meta)\n", - "File \u001b[0;32m~/miniforge3/envs/myenv/lib/python3.9/site-packages/autograd/tracer.py:61\u001b[0m, in \u001b[0;36mnotrace_primitive..f_wrapped\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m 58\u001b[0m \u001b[38;5;129m@wraps\u001b[39m(f_raw)\n\u001b[1;32m 59\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mf_wrapped\u001b[39m(\u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs):\n\u001b[1;32m 60\u001b[0m argvals \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mmap\u001b[39m(getval, args)\n\u001b[0;32m---> 61\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mf_raw\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margvals\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n", - "File \u001b[0;32m~/miniforge3/envs/myenv/lib/python3.9/site-packages/autograd/numpy/numpy_wrapper.py:148\u001b[0m, in \u001b[0;36mmetadata\u001b[0;34m(A)\u001b[0m\n\u001b[1;32m 146\u001b[0m \u001b[38;5;129m@notrace_primitive\u001b[39m\n\u001b[1;32m 147\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mmetadata\u001b[39m(A):\n\u001b[0;32m--> 148\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m _np\u001b[38;5;241m.\u001b[39mshape(A), _np\u001b[38;5;241m.\u001b[39mndim(A), _np\u001b[38;5;241m.\u001b[39mresult_type(A), _np\u001b[38;5;241m.\u001b[39miscomplexobj(A)\n", - "\u001b[0;31mKeyboardInterrupt\u001b[0m: " - ] - } - ], + "outputs": [], "source": [ "import autograd.numpy as np\n", "from autograd import jacobian,hessian,grad\n", @@ -3681,7 +3397,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.9.10" + "version": "3.9.18" } }, "nbformat": 4, diff --git a/doc/LectureNotes/_build/jupyter_execute/chapter12.ipynb b/doc/LectureNotes/_build/jupyter_execute/chapter12.ipynb index 67a50bdf0..0300f86fa 100644 --- a/doc/LectureNotes/_build/jupyter_execute/chapter12.ipynb +++ b/doc/LectureNotes/_build/jupyter_execute/chapter12.ipynb @@ -624,18 +624,20 @@ }, "outputs": [ { - "data": { - "image/png": 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              " - ] - }, - "metadata": { - "filenames": { - "image/png": "/Users/mhjensen/Teaching/MachineLearning/doc/LectureNotes/_build/jupyter_execute/chapter12_35_0.png" - } - }, - "output_type": "display_data" + "ename": "ModuleNotFoundError", + "evalue": "No module named 'matplotlib'", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mModuleNotFoundError\u001b[0m Traceback (most recent call last)", + "Cell \u001b[0;32mIn[1], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \u001b[43mget_ipython\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mrun_line_magic\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mmatplotlib\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43minline\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m)\u001b[49m\n\u001b[1;32m 3\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mnumpy\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m \u001b[38;5;21;01mnp\u001b[39;00m\n\u001b[1;32m 4\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mmath\u001b[39;00m\n", + "File \u001b[0;32m~/miniforge3/lib/python3.9/site-packages/IPython/core/interactiveshell.py:2432\u001b[0m, in \u001b[0;36mInteractiveShell.run_line_magic\u001b[0;34m(self, magic_name, line, _stack_depth)\u001b[0m\n\u001b[1;32m 2430\u001b[0m kwargs[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mlocal_ns\u001b[39m\u001b[38;5;124m'\u001b[39m] \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mget_local_scope(stack_depth)\n\u001b[1;32m 2431\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mbuiltin_trap:\n\u001b[0;32m-> 2432\u001b[0m result \u001b[38;5;241m=\u001b[39m \u001b[43mfn\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 2434\u001b[0m \u001b[38;5;66;03m# The code below prevents the output from being displayed\u001b[39;00m\n\u001b[1;32m 2435\u001b[0m \u001b[38;5;66;03m# when using magics with decorator @output_can_be_silenced\u001b[39;00m\n\u001b[1;32m 2436\u001b[0m \u001b[38;5;66;03m# when the last Python token in the expression is a ';'.\u001b[39;00m\n\u001b[1;32m 2437\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mgetattr\u001b[39m(fn, magic\u001b[38;5;241m.\u001b[39mMAGIC_OUTPUT_CAN_BE_SILENCED, \u001b[38;5;28;01mFalse\u001b[39;00m):\n", + "File \u001b[0;32m~/miniforge3/lib/python3.9/site-packages/IPython/core/magics/pylab.py:99\u001b[0m, in \u001b[0;36mPylabMagics.matplotlib\u001b[0;34m(self, line)\u001b[0m\n\u001b[1;32m 97\u001b[0m \u001b[38;5;28mprint\u001b[39m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mAvailable matplotlib backends: \u001b[39m\u001b[38;5;132;01m%s\u001b[39;00m\u001b[38;5;124m\"\u001b[39m \u001b[38;5;241m%\u001b[39m backends_list)\n\u001b[1;32m 98\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m---> 99\u001b[0m gui, backend \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mshell\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43menable_matplotlib\u001b[49m\u001b[43m(\u001b[49m\u001b[43margs\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mgui\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mlower\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mif\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[38;5;28;43misinstance\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43margs\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mgui\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mstr\u001b[39;49m\u001b[43m)\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01melse\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43margs\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mgui\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 100\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_show_matplotlib_backend(args\u001b[38;5;241m.\u001b[39mgui, backend)\n", + "File \u001b[0;32m~/miniforge3/lib/python3.9/site-packages/IPython/core/interactiveshell.py:3606\u001b[0m, in \u001b[0;36mInteractiveShell.enable_matplotlib\u001b[0;34m(self, gui)\u001b[0m\n\u001b[1;32m 3585\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21menable_matplotlib\u001b[39m(\u001b[38;5;28mself\u001b[39m, gui\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mNone\u001b[39;00m):\n\u001b[1;32m 3586\u001b[0m \u001b[38;5;250m \u001b[39m\u001b[38;5;124;03m\"\"\"Enable interactive matplotlib and inline figure support.\u001b[39;00m\n\u001b[1;32m 3587\u001b[0m \n\u001b[1;32m 3588\u001b[0m \u001b[38;5;124;03m This takes the following steps:\u001b[39;00m\n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 3604\u001b[0m \u001b[38;5;124;03m display figures inline.\u001b[39;00m\n\u001b[1;32m 3605\u001b[0m \u001b[38;5;124;03m \"\"\"\u001b[39;00m\n\u001b[0;32m-> 3606\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mmatplotlib_inline\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mbackend_inline\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m configure_inline_support\n\u001b[1;32m 3608\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mIPython\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mcore\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m pylabtools \u001b[38;5;28;01mas\u001b[39;00m pt\n\u001b[1;32m 3609\u001b[0m gui, backend \u001b[38;5;241m=\u001b[39m pt\u001b[38;5;241m.\u001b[39mfind_gui_and_backend(gui, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mpylab_gui_select)\n", + "File \u001b[0;32m~/miniforge3/lib/python3.9/site-packages/matplotlib_inline/__init__.py:1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01m.\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m backend_inline, config \u001b[38;5;66;03m# noqa\u001b[39;00m\n\u001b[1;32m 2\u001b[0m __version__ \u001b[38;5;241m=\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m0.1.6\u001b[39m\u001b[38;5;124m\"\u001b[39m \u001b[38;5;66;03m# noqa\u001b[39;00m\n", + "File \u001b[0;32m~/miniforge3/lib/python3.9/site-packages/matplotlib_inline/backend_inline.py:6\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[38;5;124;03m\"\"\"A matplotlib backend for publishing figures via display_data\"\"\"\u001b[39;00m\n\u001b[1;32m 3\u001b[0m \u001b[38;5;66;03m# Copyright (c) IPython Development Team.\u001b[39;00m\n\u001b[1;32m 4\u001b[0m \u001b[38;5;66;03m# Distributed under the terms of the BSD 3-Clause License.\u001b[39;00m\n\u001b[0;32m----> 6\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mmatplotlib\u001b[39;00m\n\u001b[1;32m 7\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mmatplotlib\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m colors\n\u001b[1;32m 8\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mmatplotlib\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mbackends\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m backend_agg\n", + "\u001b[0;31mModuleNotFoundError\u001b[0m: No module named 'matplotlib'" + ] } ], "source": [ @@ -907,22 +909,7 @@ "collapsed": false, "editable": true }, - "outputs": [ - { - "data": { - "image/png": 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bd9NfeyQ4XgpZ5zwvZGuabVPN6g62gw9AZiQCybjeZ5F1gSRrNpRwPhQgfChYY17Cnpe+L+t8zAvXmg6fUvu+hwu+15ZrPVNuqL7nxZJ5XoIGOcOaLhySlZNcjMOr6NkeCv/aiEzWfDEvpNdGdJ6X3MGHi7DuIJAzEPK8MIXsWkso6wJJ1mwo4fS8MJ6e3KyRxed5CYyXQdaknheAa03n6l4xel7oNtSc58XweV6Y1nQUkjUbRhzOoDuATwFZ0lgM1msjNs8LQKboDWeqNEAmZ4A25gXKNhKx4WdmcD0U4RQ8Dse1L2GyTTWsMBlkzbqmbd6LQbCm854XMjkjYLxQrGcEUqVdMmkr5kXEBqnrF3ynJz9gl8x4CWWiEcja+kqSS858a5rTa+sVA+QxEgHw1nkB25ounGTNhpFA5UYmQtVISR4K1kBSsF0bgfPaiC6OizRVmrHMQijbqMRDiULYUCSRdYEkazaMOLyeF0P2UPh1XtgCdkOeF4KTKq3nhUvRG//gQyZnNiMRCMS8sBWpA1KGTNYFkqzZUJI7pRI9EeBUQLwBu954LQyF8WKJ1zSVR4A1iJTMSAQQ8rwwZdFZl8trG4VkzYYRh7PQFAjLTucr7HLJ2jcCSOTsj5NMznTxAaENlQdrkW80yiBngLZInQFbde7CSdZsKMkHgjER9rz0fW8AwOt5Adkp1QZc7EzQrWnqK2dW44Us2yhU4JJgTUeAZOUkF0PcVVoxL8XBj3khkTNr+j/A1WzU0Ab7k3m4gHCFXSJxG7Lq3FFI1mwYySogNkXPqIDYu0pbkpOTZb02UsxLUaDMgAllG/HIm646dwSSNRtGaAtN8fXMyNd54ZI1XcyLr1a45MxmvBjS6tye1zYLiUHO6nlh64sWhWTNhhBDe3oi9LwYVs8L1ynVGs56OuGu0gSy9nUHF+Hq3ARyBo4oUlfisURAnhcRGzbgjmTCgi9jgDVgl83zYok9L0zGC2+8HJ/XlvXaSJ4XERu8nWEDfWBIzn7+Y0wWsJsfOYecDa3nBVRr2pBmKoItqwtAbj2wFakL7ysssi4MGS+lhvTaCOA6pQJ5j4AlMxT9Wg0scibtKs3mecl3pLdg2lG9ejps10acHnJD1hctCsmaDSHGfyik6OMmlyrN5nnhc7HzBqFTrengGJmMF0LdwVqkjq0vWhSSNRtGHM5UaYDP9csa88InZ9aq0VyyNiHjhUvWfAZ51ngxZHJWkToRG4bz3toLuuNy/fp1XshknVNAlkTOvpJkM8gtWWYXqfHC1gATQDhVmkh/ONkrZ5a+aFEgWTkJJhGl1DmWUf7aqOvYH+xj0Cl6kK5pkK1pVuOFUHewXhvRebgikLwZsUEaCMaogHKeC6bGjF5WF6ec+QJ2uWJeTPbKGQCX8QKu6zkA4TovJR5KFNjKLEQheTMiw7B6XsDnEfA9L0RHp5CRSPK4WtJrI7Y1bYIbP5PxQmiQhxozEq3r3L5Cc+UcgeTNiAwbuEtlglEB5QN2ea6NQhuqwyFn2msjsvYAofXAZLyAy0gEcETMCxF+U1cSD1cESFZOcslXyaR6JCjb2ltKz4sNlJYikbO/HnjkDPCtaQPOayMvMDoLgZwBhI0XqmXN5bWNQvJmxAZpzEu4SibHMsp3O+aRdfiUSnJ68mNeeOQMMMa8BMfII2sLm085JpAzgCP0NI+sFfMiYoO2MSNbQS8EY154Tql0VxlAoEgdj5wB9mwjHv3BuaZZs40U8yJiIhd0l2JT9KGaGKUdS+F4A6XKNgoZiSyC5uxtBAsYv9loaYdSCMZhDdgNepoJBA2EDHIi2yVgJJLIOQIyXkoN6+kJZKdU8Hpe2ORMe21EVjzNGAPXEq5p8Abs0nle/HVBIucIJG9GZBjDGXTHuKnmY1545AyALz7Aj8XgkjPbmjZgNcj5rpzDxguP9SLPi4gPVtcvLBzDpYAspaLniw/IpaTTeV7YjBdDaryAcFOlLVJHdvCJQPJmxAZxiW+qPjAI1Hkh6irNGBjNG7DL1a/LwFA2G2UzEgGE2rgw2eRM6zkqyZsRGaydYRljXvIVU4jkbPPxAYZEzjCcndLZNlVWzwvIYosAhK+NiHwvdE1dI5C8GbFBGvMCwntr/wF2eZQPpZFI63nhkjVvzAuXnAGE67zwqA95XkR8sKY7AnwZAy6h5wXgi3lhbQ8AkK1pA7iE10YA4ZrOGS+GTM6WLLYoAiQrJ7kY4v4kbAqIsreRDbrYSRSQk6vzQnREBV9wtBfzQtjyAmRGIhBuD1DioUSDTM4RSN6MyLDB/wuYFFBI0XNsqnlFX9pxRMEzErPQNGbMXYUSCRpAMCqKZU2zXhupt1FxoJNzBJI3IzJ467wQxrwQutiDAbs0jytrzIvl8giwBuyyFQMEQBuwSyfnCCRvRmSEr414Hgq24EYgH0jKpOgR9HCReF5ygdF010bgWtNewK43TqqWF+QBu0zLWgG7IjaM4QzYZexPQlmkLljnhUTOuXHyeV64ZG1Mvp2rpapdFLxyLulQIsNXpI5nPUdFxkuJMQa0/UnYrHrG9gDMp1RGzwvTmjbIX4VyeV74rpzDRep41jVTAHpUkjcjOgznvTWhAqIsUgfGInWcqdJshmIw5oXJ8wIQxmKEYl54yLcHkOdF9DKsQXcAl6IHAsYLkaK31vKdnvyAXSY1zxjzkj/4cHleQNcXTUXq+h7JmxEZQdcvk/HClpkBBK+NeLRPaEMlCdjNXxvxrGfA815QbarBInVMBjkYDfKc8eJS+V7o5ByB5M2IDGNIr40IFRC7kch3bcSj5IEjxksga2MAa3OeF6bCi3wHH9Y6L0yexKgkb0ZkJKM/Ccd9KmOqtIXl8gYAtDEvoV2JYE0HdQfdtRHbpkqYKu1dOZMZiRFI3ozIMKT9SdjiAwDOgN1QnRcSObNmGwEB7wWBrHm9tny6I3xtxAGlkRiB5M2IDNr+JDbQpIzkwbCU8QF8CsiSel5CxhaBrINF6qg6pTN6BEKeFw5ZM+qOKCRvRmSEso1obHqEg9ZIHoyc8cIUi8HWLBAAbcyLZTNeTN6bSBXzgkDJNILrOQCBcfKE61JmKkYg1hmtWrUKc+bMwYgRI2CMwQsvvHDMz69cuRLGmG6vt956K85hlhxK1y9h0B1tzAtZbJGfKk1yQs0Ryo4iWNOhVGkibyIYrzMYY15AKOcIpOP8yw8cOIBzzjkHX/rSl3DFFVcU/L2NGzeiurra/3no0KFxDK9PwBrzAvA9GPlsIxLtk4XNSES22SjdtRHIjBdq3cG2pgN1XkgIe21JDj4RiNV4mTVrFmbNmhX5e7W1tRg4cGDvD6gPEuxPwqeAyB4MwxewG1JAJP1JDGmROrZsIyB/0cwShwEcUWaBZE3n1oOX+cch63D3bhI5R6BPmr3nnnsu6urqcMkll+CVV1455mfb29vR1tYWerHBWX+EL+gu52JnKp7G6frNKnoiOQN8nheAtbcR4ZoO9TYq8VgKhFLOEehTM6qrq8Ojjz6KhoYGPPfcc5gwYQIuueQSrFq16qjfWbBgAWpqavzXqFGjijjikycZdV761DI6KqxGIp2L3eEM2KXLNgoG+xPFvAT7dTHIGUA45qXEQ4kCm46OQqzXRlGZMGECJkyY4P88depUbN26Fffffz8uvPDCHr8zf/581NfX+z+3tbVRGTCsvY3CHgESlyRjwC6hkQjkT6lMGLY6L7RNXQnXNGHALgA673gU+vyMpkyZgnfeeeeov6+oqEB1dXXoxYSngBg9AnwKiDVgly7dkdTzEqqVQmCQ8x58CFN4CXsbMeroKPT5GTU1NaGurq7Uw4iNYH8Spk3VU0BcVj1lqjRjSnrO80IkZw+b/V8OOQebuvLFvHCtacbeRsEyCzR90SIQ67XR/v378Yc//MH/edOmTVi/fj0GDRqE0047DfPnz8f27dvxxBNPAAAWLlyIMWPGYOLEiejo6MCTTz6JhoYGNDQ0xDnMkqKYl+JhCa8zwnVeOOQM0mwjky30Zo1DkQMTag9AF/PCeeXswCJT4qEUCmMV9CjEarysXbsWM2bM8H/OxaZce+21WLp0KZqbm7Flyxb/9x0dHZg3bx62b9+OqqoqTJw4EcuWLcPs2bPjHGZpoXX9Ehovhv16jkTRO16dF6aaGB5ccmZtzAjL22yUrT0A3fVcBGI1Xi6++OJj/h+9dOnS0M933nkn7rzzzjiH1OdgjXkBYxYMqZHIpoAMoYcLyF9zWRY5h4rUsbUH4FrTwZgXFrxMRTI5RyB5MyIj2J+E5jIVR5RpInkwXN/zQiRnxv4kDt96DsMhZ6/AZc7zwiNr7n5dPMs6ZCQ6JHKOQPJmREbY9Ut0emIMuiPMNmK8nmMspQ6EY15YcC1hzAujRyBYpI5kXQevnJMYsJu8GZHhBd1lMwaYFBBhIKn1A0mJ5MwYdEfb24gr5gUAXEN48AHhwYexzgujhysCyZsRGV66oweb6zd/smZR9nwxLwjVxOCQc26YbNlGuV3J0qxnAIxXzoxB6MhnG7GIOtTbiGpNF4aMlxLjxbxk/29wuU5PxnApIN46L1ynJxu8NmLR9AhU2CWRMxAM2OWRM3vALoukQ7pDMS+itwmW+GZKd2SMeckXT2NRP5wudpO9NgJAtanmx8ohZyDvJeLSHYSZiqEidRxrOhgvp5gX0fuQ1nkB+DwCwaA7JvjkHPDEEa1px0+V5vAkAsFMRR6vLUB4bRQoUseCl6lIZiRGIHkzIiPYn4Tq9MQYsEtoJIbTSlkUfWA9EMna97yQrGcgf23EpDsARoOcr6u0PC8idvINA4kUkAWdVU9ZYZfQSAxfG/HIOmckUqVKkwbs0sa8GBcs1gtjvFwUkjcjMkJ1XqhSpfkeDMqYF8LYIr9IHcBlvPhXLyQeLrAG+5PHvJBYL4wHnygkb0ZkhJqrESl6xkJT+VgGJjnzGYkhtUK0ppk9L0zXRpRrmrbOS85I5DHIC4Vk5SQXz/NCWKSO0CPAeD1nwdcewDikxgthzItfeJFmR1WqdLFg9I5HIXkzIiPU24jmscgpoCwsVj2joieMDwh7LnhkDTJjHACsJTTICdc0Y28jgFDOEUjejMgwMH5/EjbPC9uD4XteqDZUxtMTp+fFr7DLYowj2B6AR86UsRjkvY1o5ByB5M2IDENa54U66I5JzoTXc6FqnkTH1Hz9HxI5gzf9n25NE8a8WFg4RsaLiJF8lUyijAHG/iSM7QGCp1SSLBgD1mwjxuBGvq7SAPKbKsmazg2Tqs6LAnZFnIRiXlhM+ixsLkk/VZos24itSB1rwC7I1jMQCPan2VJzlV+51nTQ88KipxWwK2LF623EmG3EV3o635iRQ/kAnArIAOiyfF4uvz0AkVr01zSV7uBb0+GYFw4Yy1lEIXkzIoM35oVQAYEx5oUvtshb03xZMGwB6EBAdzB5E6GYl2JAaSRGIHkzIiNYYZepuRrjg8HZHgB0QXesBjkI4wPyFXZ55MyoO8LGC4n1AsW8iBgxxvgKyLo8D4W1Lt2m6td5oXH8cqakGxjKLBjKInWEcmYsvEhZpI7RSIxA8mZERtjzwqOAQr5TkgeD0fMCypR01mujbMwLi5xBWjWaOFXaMF0bMdbTiUDyZkQGq4s9lLFD45LMeV545AzwnZ5YDXI6bwAANztWpiJ1AN+aDmUbkUBpJEYgeTMiI9iYkanOC6fnJQWArz0Am6IPNxvlkXU+kJvFGGe9NuJb05TXRuA0yAsleTMiJO/6Le04omAZjRfSHlL5+iMcm6oBa+0iPkXPaLx4a4JrTefj5UATsBuup8OzpgsleTMihNPFHrw2IllGhDEvnJ4X0pgXv7cRh5wBTjkze16M4TBcAFI5RyB5MyKE89qIz3ixfldpIkVPWGjKK7zIZ5AzxgcwergYDfLQtRGJqCnlHIHkzYgSPgWEUFo3ievXbw/AI+dwQS8OOYdbXvAYL5S9jfyq0TwHn3BTVxZZe+P0ehux6I9AFXQaHV04Ml76AIzpjgYBZUli1VvGxoyUdV5IjRd/U+KQMxD02rJsqJxrmr/CrowXEQO+i52oSiZjtlHe88IjZ8paDaTp/6AsUpdreUGyo4I0FoOwtxFAKOcIJG9GhOQ8AlS1GghjXvyMASJFD9JaDYyBpIyl1Bm9tpT1Rxg9L+BrLRKF5M2IEEYFFPa8cCj7fBYJj5wZT6msAbtsXdIB0qtQWKTYNtWg8ULie/Gu5/jWdKEkb0aEMCp6ZGNeLAyN8eK7fonkzBgfEA7Y5VD0AJ+cAc7YolCwP4usGbONGK+cI5C8GRHiN2YkUkDG5auJwXhtFM7M4JC1AeBavk01n23EIWeA1GtLWSOKs6mrjBcRK4wBu34TO6IUPItsewCmayPCjAGvPQDfpspmJAL53kZMcg7pOZI1HY554TBgKGOLIpC8GRHin56INtWcsmTyvOTjAziUD8AZdEfbbJQy24hPzpzB/owBu2oPIGKGcVPN9wciWkKBtvYsMPYnCdV5IZI1ZW8jQt0RCnhlkXVAd7BIOhwvR+LhigDJykk2zKcny/RQUGZm8AWS0vY2IlT0lDEvjDWiCD0vgGJeRMxQKvpcEzuqJZSLeSHSPmBUQJyp0ozxAYxXzob42og3Xo5EzhFI3owo4VP0YOyZkR0qU6p0qEgdiaxpexsRel58iIL9w4YWiawNX2+jcKo0iZwjIOOlD5DvT8KjgAzhtZFlPD0R1mqg7W1E6E1kLLxoXF7PC9O1kYrUidjxDQCq0xNhnRfGPjCUReo4r418Re9wyBkIxrzwrOnQtS3JmvaNF0OUKg1dG4mYcRnvrX1Di2gJEZ5SGWs1GARTpTkUPRD0yHHIGeD0cMHydaQPjZNE1tbyeW2jkLwZEeIrIJdH0ecMAK5rI8YKu4xF6riD0A2R58UlzKBj7IsWHieHrD3dwXXwiULyZkRI/o6965if60v4MS9ES4gy5oXw9GRgKD0CvpyZ1jThtZFfZoGwLxoAmkMm45VzFJI3I0IoPS+E1Uj9mBcm4wV8Coi3wm4u5oVkQwXya4JIzr7Xlmn7CT17LIdMvoNPFJI3I0IspQLKdpVmOTkBfiAm1bURYcwLQHpt5Ms5VdqBRCB38GEyyHOJCVS6g9DzAvBdOUeBRxsmGEYXO2NaKaPnhfX0ROl5IVT0ljHmhd7zwiFrawHH8B18CiV5MyIkn27MY9Hnr414FH2+PUBphxEFxnvrcJE6ImGTyRlgjXnJ/odKdwSzjThkHRol0ZoulOTNiBBGFzsYPS9Orj0Aj5wpY15g4Fq+Ne0QxnHlnj+mqtEmd+XMpDsoU6U5r5wLJXkzIiR3SrVEReo4KzfyxQcw9iehDdgldLEzXjn7MSNEcg6OlcVQZMxUjEKsM1q1ahXmzJmDESNGwBiDF1544bjfaWxsxKRJk1BZWYlx48Zh0aJFcQ6xj5Bzn3I8FEAwVZrH9ctZ58UGTk8csvaujbIQydqv80IiZwCc2Ua+7mAisCZI1nSoRhSRni6UWI2XAwcO4JxzzsEPf/jDgj6/adMmzJ49G9OnT0dTUxPuvvtu3HrrrWhoaIhzmCWHuT0Ay4YKINCfhEfOXtAdl6wNDOVVKKOLndHzYvwClzxyDj17liNVOum9jdJx/uWzZs3CrFmzCv78okWLcNppp2HhwoUAgDPPPBNr167F/fffjyuuuKLH77S3t6O9vd3/ua2t7aTGXAr8e2uis4ixuVRpoofiyKA7AmOAMuaFvqs0h5yBYNArj+7IrwkeOcN4hRcNLJHnRddGRWPNmjWYOXNm6L1LL70Ua9euRWdnZ4/fWbBgAWpqavzXqFGjijHUXkWKvljwBd0BhDEv4Ix5YZMzEKwRxbGhAoEyCwSHhyB0wdGE8XJR6FMzamlpwbBhw0LvDRs2DJlMBrt27erxO/Pnz0dra6v/2rp1azGG2qtYQhc7CGNeQpVTSWTtBd1xuX5pexuRyRkg3FABUNZ5QdDY4pA1Y1+0KMR6bXQiHBksZ48TRFdRUYGKiorYxxUnjM3VDHFaqfcDj6z5Tk+G0vPCuab55OyXWSCSM8BXUyfpMS99akbDhw9HS0tL6L0dO3YgnU5j8ODBJRpV/DB6Xgzj6Ym0VgPbFR1vzAtXVhcQNACI5OwHvPLIGQhWMyYJ2FXMS/GYOnUqVqxYEXrv5ZdfxuTJk1FWVlaiUcUPo6L3x8r0UDAaLyC8NgLntVFO0Rui3kZ+7SIiORt5XooCY42oKMQ6o/3792P9+vVYv349AC8Vev369diyZQsAL17lmmuu8T8/d+5cbN68GfX19diwYQOWLFmCxYsXY968eXEOs+QwNmbMeQOYgu4MGGNe+BSQMaTXRjk5OxxyBgCXMGA3H/PCozuAvJ5mKXLpxbxwHXyiEGvMy9q1azFjxgz/5/r6egDAtddei6VLl6K5udk3ZABg7NixWL58Oe644w489NBDGDFiBB588MGjpkknBc50R64NFQCsEzhRkyh7ymsjBLKNSLDWBkoVMI2d7+DDqDsAvn5dNjhOMlkXQqzGy8UXXxwW4BEsXbq023sXXXQR3njjjRhH1fdgc0cCrIFgjNdGfN2OGWNegh4u4/BcG+WrRnPI2YOwSB1AV8046Z6X5M2IEj4F5N9bM51Sg6udRNah/iQksvZKeZEZLwhWXuaQswdX+i4QbM/BJGc+z0u4zguXrAtBxksfgE3RA+AM2AXhtREYY174itSFjESimBfGInV+jSiyDTV/UOPINgL4dEcUkjcjQjgDdvmC7kK1glhkTdrWni3bKHg9Z4jkzOi1zRe4ZJJzQE+7HIaihYUh64sWBa7Vk1A4PS98Fr0xBq7lkrWFpQvYBfjWNGNWF8B68OGTs0fWUCS5orOkB59CSd6MCOFUQHwPBed1Bt+magwojURf0TNdG1E2deXz2gJ8V3SMuiMKyZsRIXSBYOAsNMVYf4QxY8CTM5dBbi3gELrY81VfeXQHyNZzjnwfKY6YF8Z4uSgkb0aE+MWPSBQ9EHSd8ih6gK8Vg7VAynApINau0vmYF55U6bwKJ5KzH7DLsZ5zsBmKXu0iTkOxEJI3I0IssQJieig4r40C4ySRdbjOC4ui5/NwAaR1XnyvLdfBh60goDwvIn4Yu0qD8NqIsNtxKDiQRNkzyhkI9jbiWdOM8XKOP1YeOQN5Y8shiS9SzIuIHZfslOrB+VD4sRgsCiiYlkkia8/DRRbcGMrq4jASgWAcBoecAU/WANfBB8iP19J4yDkzFQsleTMixGaLpzFmDDApesbrjJCRRaKAGGNewj2kiGJe/OePQ85A8IqLR3cAfIaiPC8ifpivjYiWEOOmCjeQ2cCigBh7GyHY24hEzuAM9gep54VNTzP2RYsC2epJJmyKHkCgPQDPQ8HYMBCkMS9scmbsIeXB5kkMeC5I1nMONq9tKAidak0XhoyXPgBj0B3jXSplICltzAuXnEP1dBwiRZ/zvDBdG/m9gTjWs48va5Y6L1bXRiJechY9y10qEMgYIHooGDdVWL5rI+96LhvcSCLnYMwLU50XNg8XECxwSWQkgk9PK+ZFxA+h5yVfJZNNAZHJmrLOS8DD5bLIOaDoqWJessH+JBuqB2eqNJue9mJe+A6ZhZK8GRFiKTMGcsqSZwlRtgewhNdGyJ9SaTwvgd5GTHVe2JoFAswVdrmCo0NxXGSyLoTkzYiQfAoex0MB5JUlkwKizDai9LzwyTnoYmcyXqzDFUQKJCBgl6ikhYwXES9k7kiAuT0AmaxDxguHsvcCo8liXsAZhM7Y2wiEBx8AfHra2kCzUTJZF0DyZkQIWyAYwFk/wACwluukmjMSXbJHlc/zErg2oop54SqcBnBeOQMBDzmJ54XxyjkKyZsRIfkTCIeiB4K9jXgyMxhjXnLjzLusCSCspxMqUkel6PlSpUHotQUCsYmWI1U6nKlIpD8KhGv1JJR8BgyHRQ+AtkgdWx8pxj4wxgCuzV0bkcjZBsp4EcmareorEDz48OgOAFzrAvK8iGJA2NaeMT4gWH+ERtm7fJ4XxsBoCwvH8HkEXPClSjPqDoAwsYKwwGUUkjcjQhij2CnvrQ1f2frcVSJTzAvn9RzppurwpUqzXhuBLVU61FqETNYFkLwZMcIWxY5AKXUi12+w/giLrPNGIpecQSZnIJhWyiNrTjnzeRODsATshpu6csr6WMh46QPks414FBBr5UY+j0AurZRH+VDWeUHQIOdZ077uYNlQgXy8GZGcAb4idSFPPpmsCyF5MyLED9glUkCMhaYYN9V8thHPoxpsgGlJ2gPQ9oGh21A5C1wCCHjIOfS0cXVtJOKGOmCXKFUahra3EZ/nhUvOFpYz5iVYe4RlU/XXBM+aBoJ1XjhSpS34mrpGIXkzIoRuQ0Xw2ohHATGmSvsdeMkeVbbYIs/zQnhtFCyox7KmKY1EBNLSOeSsbCMRO/5dKlHGAGPMC2MKb67QFFNwI+P1XLBIHZNBHh4ryaZKmm1Ep6cJ+6JFIXkzIsSyWfQIxrzwLCHGTTW3JpjiAyh7G9F24A1c25LIOidnpjUNgC7mhbEvWhTIVk9CoQy641P01DEvpJ4XluJplrTOSygWimxNs22ouWfQIfG85A4OTDWiopDMWZGRDwTjeCgAUHaVBqXnhS8zI1hPx7L0gQFpzEtwrCRrmtFIBMBXj4vw4BMFstWTUNjckQBAGB8QinkhiQ/wexsRKSCvwm5uTZd2LIXC29uIz3gBOIPQqdYFgJyc5XkR8WG4NlQgGNxIlCod2lQ5FL3xexvxPKqMgdEWltLzEko3JpE1pdcWhEXqcrqD6IAZBa7Vk1DoGn6BNFUafCm8IAxuZAyMZo15gcPneWHUHR5cfaT8YoAJ3eaTOSs2GIvUERaaMgawlmtTNUQxI0HYjMRQqjTRmub0vBAaiSDMCvVrRCUTrtWTUPKnap5lxnhKZSxSR5kqHejezZUqTXidEYp54VjThtJIBF9WKGGwfxSSOSs22B4KkBovgZ47LKdU1owBl8zDZQE4hm9Nh1Q4iaxZY17AlhXqcuqOQmFbPYkk1BmW5PTkECogY/iuM0B6b80W3BiOeSFS9g7htVFu83dY1zSHjmbVHYWSzFmxwez6JVNAdNlGpK5fPiORs6u0gUEXmZcrH1vEI2cA+dhENs8LkzEeAbLVk0wsY9Cd/wBzLSG+ayO+Oi9AwNhikTNtqjSfQU4bi0HneSGtp1MgyZwVGdRVMok8L16dFzLjBZyK3pIFRntdpQk9L4RB6JTXc0DeeKHxvPA1dY0Cz1OaZAiNF4fQ8+LVeSE7pbp8KelAXs402UZgDULnu6Jj7EgPBD0YHHJmrBEVhWTOig4+48V3nTo8mypj8TRD63nhMhI9zwvfpuqtaS5Zs9Z5obs2Is1ULBSy1ZNQQu5TkgcjO07D1B4glCpNImfLeW/tkhX0skEVT7SpMqb/M3q4APC1cfGNFzI5F0gyZ0WGi4ABQKKA+OMDOOScd/1ynZ7YrjK4PS9csqat80KW/s9Y4DIKyZwVG8y1Gog21XDMC8fpKd8egOtRZTReGANJw7WLSNY04cEHCNR5ofO88KznKHCtnoRiGFOlbe7aiGcJeYo+C4mc4XJ6XvxqpCRy9rpK8xkv3lbKZSjms3WY5Az4jRlJ5CzjRcROaGMiOT0xutg9tckV3JivksmlgPJN7DjknIxUaQ5Zs3pewOZ58YP9uXRHoRRl9Tz88MMYO3YsKisrMWnSJKxevfqon125ciWMMd1eb731VjGGWiL4Yl5YXexsih6kmRls3gCA0yA3YMw24rtyBpCvsEsiZ99rm1AfReyzeuaZZ3D77bfjnnvuQVNTE6ZPn45Zs2Zhy5Ytx/zexo0b0dzc7L/Gjx8f91BLhqGOeeF5MDxFz7Wp+qnSZArI5rLQSDyJ4ZgXHlkz9utilDMAOs+Lkefl5HjggQfw5S9/Gddffz3OPPNMLFy4EKNGjcIjjzxyzO/V1tZi+PDh/iuV4knJjYox4O1PwpQqbQxd/RHW/iRsG2o45oVnU6VMlVa2UVHIj5NMzgUS66w6Ojqwbt06zJw5M/T+zJkz8dprrx3zu+eeey7q6upwySWX4JVXXjnq59rb29HW1hZ6sWEIYzEor40AuGRGIqOHC+DzcHGnSrPqDh45A8FsIw45q87LSbBr1y50dXVh2LBhofeHDRuGlpaWHr9TV1eHRx99FA0NDXjuuecwYcIEXHLJJVi1alWPn1+wYAFqamr816hRo3p9HrFDGIvB2NaeM+aFUwHla0twyNkCSBm+TTV0FUpyncFoJHpwXRvlG2DyHDCjkC7GP2KOEJ61ttt7OSZMmIAJEyb4P0+dOhVbt27F/fffjwsvvLDb5+fPn4/6+nr/57a2NjoDhrE/ieMFCcAQbarMTezYFBDberZuYJxEm6p3Fcola8amrgDoYl78ApdEOjoKsc5qyJAhSKVS3bwsO3bs6OaNORZTpkzBO++80+PvKioqUF1dHXqx4XU75nL9OoQKyIAv5oX13ppNzqFxEhkvQPAqlGNT9XUH2ZrmyzZKtucl1tVTXl6OSZMmYcWKFaH3V6xYgWnTphX89zQ1NaGurq63h9enYPMIgPTems3FzlriO1/nhUPONrgeyJQ928Enn/7PJWd5XvoWsV8b1dfX4+qrr8bkyZMxdepUPProo9iyZQvmzp0LwLv22b59O5544gkAwMKFCzFmzBhMnDgRHR0dePLJJ9HQ0ICGhoa4h1oyGFN4c/fWTBV2GWOLaIvU+RV2SRQ97bUR35pmDUKnM14U83JyXHnlldi9eze+/e1vo7m5GWeffTaWL1+O0aNHAwCam5tDNV86Ojowb948bN++HVVVVZg4cSKWLVuG2bNnxz3UkqH+JMWBs6AXn5yBrKfIAiwBu6zXRoayPQBfmQUAdKnSrMH+hVKUgN0bb7wRN954Y4+/W7p0aejnO++8E3feeWcRRtV3YAzYzSmgowVe90VCYyWRM6vnxYdFzqFx8sias0hdzvNS2nFEJhfzQmKQ540sNkEXRjJNMjK8gF0uBcSY7sjoeWGND/BjdEjkbGk9L3zXRg6pNzHveeHwjvsxL2xyLpBkzooMxk2VMd2RMT7A8e+teeQM8GUb8aZKM+qO3MGH9NqIJObFWHKv7XHgeUqTDKHrlzFgl7GUuj9OIjkD+SBBmlMqq+eFuM4L05UzgIDxwiFnVt1RKMmcFRmMmyqj69eLD8idUkk2VdJ0RzrPC6vxAkJvImF1bgB8dV7keRFxQ+n6zZZSN0QKiFHRG9uV/QOZAjJccmZNlQ6n/3MY5HlvHJGcAf+ai+XaSDEvInYYN1XHEt5bE8a8GNYidb5q4ZAzckYiQGUoMjd1ZTr4AAhkG3EYL/mYFzI5F0gyZ0UGc50Xpntrxuu5/ObP9ajSFanLjtOF4TJeKHUHZxA6a8xLUovUka2eZGJgAv1JOB4Mh7FIneGLxch7XrgUkGW7NiKND6D02hLqDgD+eB0SObP2RSuUZM6KjFDMC4lLEoyp0uBrYsfaQwpk69n6nhcuOTPGy8H32nLJmu8Z9GA7+BQK5/8bCYOy/gjhpspYDJD13pptQ7XZmBc+zwvfmmbMVAQCV6Ek10a013MFksxZ0cFXq8EhjMVgPqUyxWEAwTovJHIm9ryw6Y7cpkoXsOtwFamDro1E3HAqoFzGAM+m6vWQysIiZ9r+JFzXczRG1hEw9kXzvbZkazo3WraYF10biVhhcv1aa30FxHZvzVakjtX1y+Z5yRWpY/O8AKAL9s+3ByCTNVudl1ywP5mRWChkqyeZsPU2sjbYmJGnzgtjbBFrnZf8xsQhZ9q00lAcF8emyhgvB4D32ohNzgWSzFmRwXZtZAE4htHzwhfcCNJTas7YYvO8sJ1S2Q4+QCDmhWxN5+LOHBKD3PfaJnSbT+asyGDLGLDWUlbJZPa8sMUHhIwAAo+AcTmvjdgOPkDAc0EULwfkjS0WgzzveeGSc6FwPakJJbyp9n1Fb8Hp+vWCG8lOqZYz5iW0Lihkzep54Tr4AEHdwXPlDAS8iSTXRrRXzgWSzFmRweb6DcW8UHle+BQ97bURuIwX63K62BnT/1kDdg1ZewDWGlGFksxZkWEMV50XC85sI8pS6qwFvYKuagZZ57KNyFzslGuasC8agHx7ABLPS/7gQybnAuHSiAmGSQFZG2zMyLOEGE+prAooHPNCIGs/rZRnPQNHxLyQbKp+HJfDdW3kN2YkuNoHAtdGZGu6UJI5KzLCmyrJg0HZ28jQKXrWTTUUz0CwpmmzjYyhM8j9gw/dmuaKeWG9ci6UZM6KDLagOy/mhdXzwhMYDfDGB/B5XjiNF4DLawsE1jTRwQfIZ1ayxLyAtCN9oXCtnoTClu7oxbxw9idhPaXSZQywZRsR9zZiM8gZDz4eXJ4X1oNPoSRzVmSw9ScJxbwQLSHPSMxCIGcgUFOC7vDE5nnxukqzxRYZGIBIdwDBVGkyWftF6jiMF5DWiCoUnp0nwRjD1Z8kWOeFyfMSinkhkLNHTgFxBTdaOs8L57URZeFFUo+AZU2VJpNzoSRzVmQYsv4kwcaMTArIMxLJro1Iq2SyVdilvTYCn/HCePABAjEvDOsZQSOLS3cUCtfqSSh0ReqQL1LHZNUzn1KZ5AwoYLdYMKb/G8KDD5B/BlmujVRhV8QPWcAuEIh5IepPwpbVBQSK1JE9qsZxqK5CQepiZ7wK9YP96WTNdW2krtKiKDBtquFUabJYDLpTai6tlMdIBAivM0g9LwBXvByQ1x1shmIutZvG86Ku0iJuPI8A0aYa6G3EdG/NmFYKVs8L2xVdrj0AmfHCfG3EpDsAxt5GqvMiYoZN0VtYpAxfrQY6bwB4Y168tHSiTZW1kjH41jTrtRFvbyMyORdIMmdFBl2dFzf/8Bqi/iTMp1Q2BUQXX5St88JnJAZjXko7lkJhXdPwK+xyCFq9jUTshDZVggfDBsbIdXoKKvq+L2cA+XFSyfkIbyLBms6NkS3mhdObyOe1BYLXRgzrGfCfO10bibhgO6Vat8v/M1W2Edn1HMBb0Ist/R8u5ymV0ZvoX7uQxbzkjACemBdO3VEoyZwVGWyBpDagJJmyjShPqZb09GTYvFy5ayMuOTOuadZMxVyndJaYF9Z4uUJJ5qzIMIH/ZVBA1g2MkUjZh+MD+r6cgXxwI5OcgVwcVxYGWbuk10aEazof81LacUTF+J4XDuNFvY1E/BiyayObvzZickkynlLzJgCPnAHC6wzSOi90HemtTUC2Ud+XM8B75VwoyZwVGWybqglkGzE9GHTpuwjcW5PFB7Ctad4Ku1xyDhW4JMpUBPJ1aRyKa1DVeRFFgO2UGox5oTJewNUAE+CtkmnIvImsnhfPa0ukOxCIGSHSHQD88bJcG8nzImKHrT9JOOaFZwlRZhuxpkqDzctFaiSCa01ba/1NlbXCLsu1UX49cMm5UJI5KzKM4epPYsEZ8wJwebgA3tMTnaGYC9gllDNTVlfQ82LItp9cdhSP54Xz4FMoyZwVGXw1MXg9L0weLiCogLiuM9hqF5lcqjTZtRGbnIMxL2xxXLnmqDSp0n4cF9eaLhSy1ZNM2E6p4ZgXngfDGEPl4QLyCoivJgaZQU4a3MgYL+fk+qKxGS9kjRlpY4sKJJmzIsOwFfTKjrHLkil6cBmJQFAB8cnaWqY1rZiX4sDaWiQY80KwngGoMaMoCkxZMDnPi0u4fJh6SHlwxmIAXGs639uIT875q66+L+dgU1e2NW3Zso0sr+4ohGTOihAm128u5oUuPoAw5oXV9ct2FZoP2OVb064l0h3B1iKkdV5YjJe8Mcu1pguFSyMmFDpF73teuB4KtuBGINhcjVHWRJuqH7DLpRLZ1nSwzIJhW9Nk10b5gF0uI7FQuJ7UhMJa54Xb80KigIjbA/gQrGmW9dANMm9isLUIm/GSW9QsdV78ejpkYi4ULo2YUOiuM0irkfIFNwYyG8gyM9iyYJjbAzDpjrDnhcsjYNi6SpMWuCyUZM6KDLZNlTZgl7K3Eee9Ndt1hmHNNjJcLS9sKOaFTNZkMS+srUUKJZmzIoPtlGoV81I08p4XtlMql0Ge97ywrWkuObO2FgH42gP4RhaZkVgoRZnVww8/jLFjx6KyshKTJk3C6tWrj/n5xsZGTJo0CZWVlRg3bhwWLVpUjGGWjNCm6mZKO5hC6PLGSKfogxtqVyfFSTXlt2LgkjUQOPERrGljSQN2gwefrs7SDqYQbH4tsHlebLbCbspYwO06zqdLj+PHF3HJuVBin9UzzzyD22+/Hffccw+ampowffp0zJo1C1u2bOnx85s2bcLs2bMxffp0NDU14e6778att96KhoaGuIdaMowBttsh3g/rnwbeXQlk2ks6pqPSuh2D1vy/AIBmDCnxYKJhAOywA9FpU8ChPcDKBcDBPaUeVs90HAR+sxh/lfkDAKC9/4gSDygaxpj8ml75v4Cdb4fbSvQVXBf4cxPO2vwkAOD9stoSDygaId3x1jLv1XmotIM6Gvt3ouL/fAMA8J4dCOOUlXhAESmvQZut8v78s68B+3eUdjxHI9MO/P5ZnHVoHQDgUL/hJR5QPKTj/gceeOABfPnLX8b1118PAFi4cCF+/vOf45FHHsGCBQu6fX7RokU47bTTsHDhQgDAmWeeibVr1+L+++/HFVdcEfdwS8YzXTNwXdWr+ODhPwBPfAauSeNgVR06y6rRWTYAnWWnwHXK4Zo0rEnBOqnsn7M/H8MLYo7rYXDhuBk4thOO2wnjZuC4nf7Pqa52VLTvRuXhnSjP7EN/eNV1F5jr8e+9KoV4McbgfVTj/sznMb/sR0Djd4HG7+JQxVC0V5yKTNqTcybdz5era1Len53cn499fXNsWVsY2+XJ1u2EYzP+n43NwOnqQEXnXlQe3omK9j3+ldG/Zz6BUadO6EVJxI8B8M+ZL+DCsjdRsX0t8NB56Ez1w6Gq4ciUnYLOrKxdk4Z1vHWck691UrA4uTVtkJNzBiawlp3s2k5nDqCyfRcqD+9Eym3HKQC22SF4edAX8YneFUWsGBi85J6HdalzMCnzW+DHX4CFg4P9RqCjrDq7ngegK1UR0h3e2s7pjqOfYaPpjqysQ2u7HZU53dHZhvLst77e+fd42OHyJpqyCtzXeS2+X74I+M1jwG8ew+HyQWivGJzV0QOQSfXLrucYdUdQbwT0dnlHKyrbd6KifTcc24U0gP/qugAHB0/tVTn0FWI1Xjo6OrBu3TrcddddofdnzpyJ1157rcfvrFmzBjNnzgy9d+mll2Lx4sXo7OxEWVnYWm9vb0d7e95L0dbW1kujLx4VaQcdKMNV++sxL/0f+GTq16jBQZxycGuph9aNLmuw3v4VHsx8Fv/T76xSDycS6ZSnLB/t+hQOoxzXpF7GB51mVLXvRFX7zhKPrjvNdhB+nJmBh7s+gyUpLtdvedrBJluHKw/NR336J7jAeQsVXQdRtv/dUg+tG4dsOX7hfgjfy1yJD5fXlHo4kShPO7Bw8KUDN6M+/RPMSa3BYLMP/Q9uQ/9SD64H/tsdg0cyl+EXOA8O2bVzWcpBgzsd6ABuSC/D6WYbKjv2oLKj73lvd9lqPNt1IX6Q+Sz+uYxLdxRKrMbLrl270NXVhWHDhoXeHzZsGFpaWnr8TktLS4+fz2Qy2LVrF+rq6kK/W7BgAe67777eHXiRufTs4Vi7+X28f3AoluEeLLMWg7t2YnDmPfSzB1DlHkA/9wDSNgMHXUjZriP+e5IxBcYggzQypgxdJoUMypAxaXSZMmSQQqcpR1tqIPamBmNPaijaHc91etc5XFcZ1ZVl+OqlE/CrTXuwCX+H+/B3qHQPoK5zG/q7+1BlPTlXuIfhwD2KrN2TyjXoMmlPtkgHZJzOvp/CAacae1OD8X5qMNqcgYAxuKKmEueNGdRbYigKN0wfB9e16HSH4t8wFUtsF4ZntqGm631/PVfZg3BsF1Lo6vG/J4M1TnYdp3wZB9d3u1OJ1tSp/pruMmmMTzu4dtqY3hFAkZg+fgg+N2kkduxrxy/wVfzCWtS4e1Cbafbl7OmOTm/9+jJ2/TXtnMyazuqO3LrOICvjgMz3pQZirzMIu9NDcdjxTKp7Th+KFJnnpaaqDPNmTsCv/1SLf8YVKHcPYURmK/p37fP1dKV7KDbd4YbkmpU5sjrEpHDQnILW1CC8nxqMvanBgDGYdUo5PjZh2PH/ckJivzYCuhcjstYes0BRT5/v6X0AmD9/Purr6/2f29raMGrUqJMZbtGpHVCJH1x1bqmH8RfBTTP+CjfNKPUoks/5Ywfh/LFcBhcjAyrLcP/fnlPqYfzFcPPHxpd6CCJLrMbLkCFDkEqlunlZduzY0c27kmP48OE9fj6dTmPw4MHdPl9RUYGKioreG7QQQggh+jSxXoaVl5dj0qRJWLFiRej9FStWYNq0aT1+Z+rUqd0+//LLL2Py5Mnd4l2EEEII8ZdH7JE89fX1+Ld/+zcsWbIEGzZswB133IEtW7Zg7ty5ALxrn2uuucb//Ny5c7F582bU19djw4YNWLJkCRYvXox58+bFPVQhhBBCEBB7zMuVV16J3bt349vf/jaam5tx9tlnY/ny5Rg9ejQAoLm5OVTzZezYsVi+fDnuuOMOPPTQQxgxYgQefPDBRKdJCyGEEKJwjLUEZUYj0NbWhpqaGrS2tqK6urrUwxFCCCFEAUTZv5OZAC6EEEKIxCLjRQghhBBUyHgRQgghBBUyXoQQQghBhYwXIYQQQlAh40UIIYQQVMh4EUIIIQQVMl6EEEIIQYWMFyGEEEJQIeNFCCGEEFTIeBFCCCEEFTJehBBCCEGFjBchhBBCUCHjRQghhBBUyHgRQgghBBUyXoQQQghBhYwXIYQQQlAh40UIIYQQVMh4EUIIIQQVMl6EEEIIQYWMFyGEEEJQIeNFCCGEEFTIeBFCCCEEFTJehBBCCEGFjBchhBBCUCHjRQghhBBUyHgRQgghBBUyXoQQQghBhYwXIYQQQlAh40UIIYQQVMh4EUIIIQQVMl6EEEIIQYWMFyGEEEJQIeNFCCGEEFTIeBFCCCEEFTJehBBCCEGFjBchhBBCUCHjRQghhBBUyHgRQgghBBUyXoQQQghBhYwXIYQQQlAh40UIIYQQVMh4EUIIIQQVMl6EEEIIQYWMFyGEEEJQIeNFCCGEEFTIeBFCCCEEFTJehBBCCEGFjBchhBBCUCHjRQghhBBUyHgRQgghBBWxGi/vv/8+rr76atTU1KCmpgZXX3019u7de8zvXHfddTDGhF5TpkyJc5hCCCGEICId51/+hS98Adu2bcNLL70EAPiHf/gHXH311fjP//zPY37vk5/8JB5//HH/5/Ly8jiHKYQQQggiYjNeNmzYgJdeegmvv/46LrjgAgDAY489hqlTp2Ljxo2YMGHCUb9bUVGB4cOHF/TvtLe3o7293f+5tbUVANDW1nYSoxdCCCFEMcnt29ba43/YxsTixYttTU1Nt/dramrskiVLjvq9a6+91tbU1NihQ4fa8ePH2+uvv96+9957R/38vffeawHopZdeeumll14JeG3duvW4NkZsnpeWlhbU1tZ2e7+2thYtLS1H/d6sWbPwt3/7txg9ejQ2bdqEb3zjG/jYxz6GdevWoaKiotvn58+fj/r6ev9n13WxZ88eDB48GMaY3plMlra2NowaNQpbt25FdXV1r/7dIo/kXDwk6+IgORcHybl4xCFray327duHESNGHPezkY2Xb33rW7jvvvuO+Znf/OY3ANCj8WCtPaZRceWVV/p/PvvsszF58mSMHj0ay5Ytw2c/+9lun6+oqOhm1AwcOPCY4ztZqqur9WAUAcm5eEjWxUFyLg6Sc/HobVnX1NQU9LnIxsvNN9+Mq6666pifGTNmDH73u9/hvffe6/a7nTt3YtiwYQX/e3V1dRg9ejTeeeedqEMVQgghRAKJbLwMGTIEQ4YMOe7npk6ditbWVvz617/G+eefDwD41a9+hdbWVkybNq3gf2/37t3YunUr6urqog5VCCGEEAkktjovZ555Jj75yU/ihhtuwOuvv47XX38dN9xwAz796U+HMo3OOOMMPP/88wCA/fv3Y968eVizZg3+9Kc/YeXKlZgzZw6GDBmCv/mbv4lrqAVTUVGBe++9t8fYG9F7SM7FQ7IuDpJzcZCci0epZW2sLSQn6cTYs2cPbr31Vrz44osAgMsuuww//OEPQzEpxhg8/vjjuO6663Do0CFcfvnlaGpqwt69e1FXV4cZM2bgO9/5DkaNGhXXMIUQQghBRKzGixBCCCFEb6PeRkIIIYSgQsaLEEIIIaiQ8SKEEEIIKmS8CCGEEIIKGS9H8PDDD2Ps2LGorKzEpEmTsHr16mN+vrGxEZMmTUJlZSXGjRuHRYsWFWmk3ESR83PPPYdPfOITGDp0KKqrqzF16lT8/Oc/L+JouYm6pnP88pe/RDqdxoc+9KF4B5gQosq5vb0d99xzD0aPHo2Kigp88IMfxJIlS4o0Wl6iyvmpp57COeecg379+qGurg5f+tKXsHv37iKNlpNVq1Zhzpw5GDFiBIwxeOGFF477naLvhcftfvQXxI9//GNbVlZmH3vsMfvmm2/a2267zfbv399u3ry5x8+/++67tl+/fva2226zb775pn3sscdsWVmZffbZZ4s8ci6iyvm2226z3/3ud+2vf/1r+/bbb9v58+fbsrIy+8YbbxR55HxElXWOvXv32nHjxtmZM2fac845pziDJeZE5HzZZZfZCy64wK5YscJu2rTJ/upXv7K//OUvizhqPqLKefXq1dZxHPuDH/zAvvvuu3b16tV24sSJ9vLLLy/yyLlYvny5veeee2xDQ4MFYJ9//vljfr4Ue6GMlwDnn3++nTt3bui9M844w9511109fv7OO++0Z5xxRui9r3zlK3bKlCmxjTEJRJVzT5x11ln2vvvu6+2hJY4TlfWVV15pv/71r9t7771XxksBRJXzz372M1tTU2N3795djOElhqhy/t73vmfHjRsXeu/BBx+0I0eOjG2MSaMQ46UUe6GujbJ0dHRg3bp1mDlzZuj9mTNn4rXXXuvxO2vWrOn2+UsvvRRr165FZ2dnbGNl5kTkfCSu62Lfvn0YNGhQHENMDCcq68cffxx//OMfce+998Y9xERwInJ+8cUXMXnyZPzLv/wLPvCBD+D000/HvHnzcOjQoWIMmZITkfO0adOwbds2LF++HNZavPfee3j22WfxqU99qhhD/ouhFHth5N5GSWXXrl3o6urq1jRy2LBhaGlp6fE7LS0tPX4+k8lg165d6sfUAyci5yP5/ve/jwMHDuDzn/98HENMDCci63feeQd33XUXVq9ejXRa6qEQTkTO7777Ll599VVUVlbi+eefx65du3DjjTdiz549ins5Cici52nTpuGpp57ClVdeicOHDyOTyeCyyy7Dv/7rvxZjyH8xlGIvlOflCIwxoZ+ttd3eO97ne3pfhIkq5xw/+tGP8K1vfQvPPPMMamtr4xpeoihU1l1dXfjCF76A++67D6effnqxhpcYoqxp13VhjMFTTz2F888/H7Nnz8YDDzyApUuXyvtyHKLI+c0338Stt96Kb37zm1i3bh1eeuklbNq0CXPnzi3GUP+iKPZeqKNVliFDhiCVSnWz4Hfs2NHNoswxfPjwHj+fTqcxePDg2MbKzInIOcczzzyDL3/5y/jJT36Cj3/843EOMxFElfW+ffuwdu1aNDU14eabbwbgbbLWWqTTabz88sv42Mc+VpSxM3Eia7qurg4f+MAHUFNT47935plnwlqLbdu2Yfz48bGOmZETkfOCBQvwkY98BF/96lcBAH/913+N/v37Y/r06finf/onecd7iVLshfK8ZCkvL8ekSZOwYsWK0PsrVqzAtGnTevzO1KlTu33+5ZdfxuTJk1FWVhbbWJk5ETkDnsfluuuuw9NPP6376gKJKuvq6mr8/ve/x/r16/3X3LlzMWHCBKxfvx4XXHBBsYZOxYms6Y985CP485//jP379/vvvf3223AcByNHjox1vKyciJwPHjwIxwlvc6lUCkDeMyBOnpLshbGFAhOSS8NbvHixffPNN+3tt99u+/fvb//0pz9Za62966677NVXX+1/Ppcedscdd9g333zTLl68WKnSBRBVzk8//bRNp9P2oYcess3Nzf5r7969pZoCDVFlfSTKNiqMqHLet2+fHTlypP3c5z5n/+d//sc2Njba8ePH2+uvv75UU6Agqpwff/xxm06n7cMPP2z/+Mc/2ldffdVOnjzZnn/++aWaAgX79u2zTU1NtqmpyQKwDzzwgG1qavJT0vvCXijj5QgeeughO3r0aFteXm4//OEP28bGRv931157rb3oootCn1+5cqU999xzbXl5uR0zZox95JFHijxiTqLI+aKLLrIAur2uvfba4g+ckKhrOoiMl8KJKucNGzbYj3/847aqqsqOHDnS1tfX24MHDxZ51HxElfODDz5ozzrrLFtVVWXr6ursF7/4Rbtt27Yij5qLV1555Zg6ty/shcZa+c6EEEIIwYNiXoQQQghBhYwXIYQQQlAh40UIIYQQVMh4EUIIIQQVMl6EEEIIQYWMFyGEEEJQIeNFCCGEEFTIeBFCCCEEFTJehBBCCEGFjBchhBBCUCHjRQghhBBU/P9Ux/93+gNmOAAAAABJRU5ErkJggg==\n", 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              " - ] - }, - "metadata": { - "filenames": { - "image/png": "/Users/mhjensen/Teaching/MachineLearning/doc/LectureNotes/_build/jupyter_execute/chapter12_51_0.png" - } - }, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "import numpy as np\n", "import math\n", @@ -1221,30 +1208,7 @@ "collapsed": false, "editable": true }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "inputs = (n_inputs, pixel_width, pixel_height, depth) = (1797, 8, 8, 1)\n", - "labels = (n_inputs) = (1797,)\n" - ] - }, - { - "data": { - "image/png": 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              " - ] - }, - "metadata": { - "filenames": { - "image/png": "/Users/mhjensen/Teaching/MachineLearning/doc/LectureNotes/_build/jupyter_execute/chapter12_67_1.png" - } - }, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "# import necessary packages\n", "import numpy as np\n", @@ -1296,45 +1260,7 @@ "collapsed": false, "editable": true }, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/Users/mhjensen/miniforge3/envs/myenv/lib/python3.9/site-packages/jax/_src/lib/__init__.py:32: UserWarning: JAX on Mac ARM machines is experimental and minimally tested. Please see https://github.com/google/jax/issues/5501 in the event of problems.\n", - " warnings.warn(\"JAX on Mac ARM machines is experimental and minimally tested. \"\n" - ] - }, - { - "ename": "AttributeError", - "evalue": "module 'jaxlib.pocketfft' has no attribute 'pocketfft'", - "output_type": "error", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mAttributeError\u001b[0m Traceback (most recent call last)", - "Input \u001b[0;32mIn [4]\u001b[0m, in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mtensorflow\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mkeras\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m datasets, layers, models\n\u001b[1;32m 2\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mtensorflow\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mkeras\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mlayers\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m Input\n\u001b[1;32m 3\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mtensorflow\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mkeras\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mmodels\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m Sequential \u001b[38;5;66;03m#This allows appending layers to existing models\u001b[39;00m\n", - "File \u001b[0;32m~/miniforge3/envs/myenv/lib/python3.9/site-packages/tensorflow/__init__.py:51\u001b[0m, in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[1;32m 49\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01m_api\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mv2\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m autograph\n\u001b[1;32m 50\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01m_api\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mv2\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m bitwise\n\u001b[0;32m---> 51\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01m_api\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mv2\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m compat\n\u001b[1;32m 52\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01m_api\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mv2\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m config\n\u001b[1;32m 53\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01m_api\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mv2\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m data\n", - "File \u001b[0;32m~/miniforge3/envs/myenv/lib/python3.9/site-packages/tensorflow/_api/v2/compat/__init__.py:37\u001b[0m, in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[1;32m 3\u001b[0m \u001b[38;5;124;03m\"\"\"Compatibility functions.\u001b[39;00m\n\u001b[1;32m 4\u001b[0m \n\u001b[1;32m 5\u001b[0m \u001b[38;5;124;03mThe `tf.compat` module contains two sets of compatibility functions.\u001b[39;00m\n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 32\u001b[0m \n\u001b[1;32m 33\u001b[0m \u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m 35\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01msys\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m \u001b[38;5;21;01m_sys\u001b[39;00m\n\u001b[0;32m---> 37\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01m.\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m v1\n\u001b[1;32m 38\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01m.\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m v2\n\u001b[1;32m 39\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mtensorflow\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mpython\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mcompat\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mcompat\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m forward_compatibility_horizon\n", - "File \u001b[0;32m~/miniforge3/envs/myenv/lib/python3.9/site-packages/tensorflow/_api/v2/compat/v1/__init__.py:30\u001b[0m, in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[1;32m 28\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01m.\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m autograph\n\u001b[1;32m 29\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01m.\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m bitwise\n\u001b[0;32m---> 30\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01m.\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m compat\n\u001b[1;32m 31\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01m.\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m config\n\u001b[1;32m 32\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01m.\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m data\n", - "File \u001b[0;32m~/miniforge3/envs/myenv/lib/python3.9/site-packages/tensorflow/_api/v2/compat/v1/compat/__init__.py:37\u001b[0m, in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[1;32m 3\u001b[0m \u001b[38;5;124;03m\"\"\"Compatibility functions.\u001b[39;00m\n\u001b[1;32m 4\u001b[0m \n\u001b[1;32m 5\u001b[0m \u001b[38;5;124;03mThe `tf.compat` module contains two sets of compatibility functions.\u001b[39;00m\n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 32\u001b[0m \n\u001b[1;32m 33\u001b[0m \u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m 35\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01msys\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m \u001b[38;5;21;01m_sys\u001b[39;00m\n\u001b[0;32m---> 37\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01m.\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m v1\n\u001b[1;32m 38\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01m.\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m v2\n\u001b[1;32m 39\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mtensorflow\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mpython\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mcompat\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mcompat\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m forward_compatibility_horizon\n", - "File \u001b[0;32m~/miniforge3/envs/myenv/lib/python3.9/site-packages/tensorflow/_api/v2/compat/v1/compat/v1/__init__.py:47\u001b[0m, in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[1;32m 45\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mtensorflow\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01m_api\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mv2\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mcompat\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mv1\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m layers\n\u001b[1;32m 46\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mtensorflow\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01m_api\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mv2\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mcompat\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mv1\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m linalg\n\u001b[0;32m---> 47\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mtensorflow\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01m_api\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mv2\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mcompat\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mv1\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m lite\n\u001b[1;32m 48\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mtensorflow\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01m_api\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mv2\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mcompat\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mv1\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m logging\n\u001b[1;32m 49\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mtensorflow\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01m_api\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mv2\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mcompat\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mv1\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m lookup\n", - "File \u001b[0;32m~/miniforge3/envs/myenv/lib/python3.9/site-packages/tensorflow/_api/v2/compat/v1/lite/__init__.py:9\u001b[0m, in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[1;32m 6\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01msys\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m \u001b[38;5;21;01m_sys\u001b[39;00m\n\u001b[1;32m 8\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01m.\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m constants\n\u001b[0;32m----> 9\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01m.\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m experimental\n\u001b[1;32m 10\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mtensorflow\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mlite\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mpython\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mlite\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m Interpreter\n\u001b[1;32m 11\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mtensorflow\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mlite\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mpython\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mlite\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m OpHint\n", - "File \u001b[0;32m~/miniforge3/envs/myenv/lib/python3.9/site-packages/tensorflow/_api/v2/compat/v1/lite/experimental/__init__.py:8\u001b[0m, in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[1;32m 3\u001b[0m \u001b[38;5;124;03m\"\"\"Public API for tf.lite.experimental namespace.\u001b[39;00m\n\u001b[1;32m 4\u001b[0m \u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m 6\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01msys\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m \u001b[38;5;21;01m_sys\u001b[39;00m\n\u001b[0;32m----> 8\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01m.\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m authoring\n\u001b[1;32m 9\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mtensorflow\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mlite\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mpython\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01manalyzer\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m ModelAnalyzer \u001b[38;5;28;01mas\u001b[39;00m Analyzer\n\u001b[1;32m 10\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mtensorflow\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mlite\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mpython\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mlite\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m OpResolverType\n", - "File \u001b[0;32m~/miniforge3/envs/myenv/lib/python3.9/site-packages/tensorflow/_api/v2/compat/v1/lite/experimental/authoring/__init__.py:8\u001b[0m, in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[1;32m 3\u001b[0m \u001b[38;5;124;03m\"\"\"Public API for tf.lite.experimental.authoring namespace.\u001b[39;00m\n\u001b[1;32m 4\u001b[0m \u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m 6\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01msys\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m \u001b[38;5;21;01m_sys\u001b[39;00m\n\u001b[0;32m----> 8\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mtensorflow\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mlite\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mpython\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mauthoring\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mauthoring\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m compatible\n", - "File \u001b[0;32m~/miniforge3/envs/myenv/lib/python3.9/site-packages/tensorflow/lite/python/authoring/authoring.py:43\u001b[0m, in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[1;32m 39\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mfunctools\u001b[39;00m\n\u001b[1;32m 42\u001b[0m \u001b[38;5;66;03m# pylint: disable=g-import-not-at-top\u001b[39;00m\n\u001b[0;32m---> 43\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mtensorflow\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mlite\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mpython\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m convert\n\u001b[1;32m 44\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mtensorflow\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mlite\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mpython\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m lite\n\u001b[1;32m 45\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mtensorflow\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mlite\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mpython\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mmetrics\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m converter_error_data_pb2\n", - "File \u001b[0;32m~/miniforge3/envs/myenv/lib/python3.9/site-packages/tensorflow/lite/python/convert.py:29\u001b[0m, in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[1;32m 26\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01msix\u001b[39;00m\n\u001b[1;32m 28\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mtensorflow\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mlite\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mpython\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m lite_constants\n\u001b[0;32m---> 29\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mtensorflow\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mlite\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mpython\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m util\n\u001b[1;32m 30\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mtensorflow\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mlite\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mpython\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m wrap_toco\n\u001b[1;32m 31\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mtensorflow\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mlite\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mpython\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mconvert_phase\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m Component\n", - "File \u001b[0;32m~/miniforge3/envs/myenv/lib/python3.9/site-packages/tensorflow/lite/python/util.py:51\u001b[0m, in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[1;32m 47\u001b[0m \u001b[38;5;66;03m# Jax functions used by TFLite\u001b[39;00m\n\u001b[1;32m 48\u001b[0m \u001b[38;5;66;03m# pylint: disable=g-import-not-at-top\u001b[39;00m\n\u001b[1;32m 49\u001b[0m \u001b[38;5;66;03m# pylint: disable=unused-import\u001b[39;00m\n\u001b[1;32m 50\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m---> 51\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mjax\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m xla_computation \u001b[38;5;28;01mas\u001b[39;00m _xla_computation\n\u001b[1;32m 52\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mImportError\u001b[39;00m:\n\u001b[1;32m 53\u001b[0m _xla_computation \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m\n", - "File \u001b[0;32m~/miniforge3/envs/myenv/lib/python3.9/site-packages/jax/__init__.py:116\u001b[0m, in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[1;32m 40\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01m_src\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mconfig\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m (\n\u001b[1;32m 41\u001b[0m config \u001b[38;5;28;01mas\u001b[39;00m config,\n\u001b[1;32m 42\u001b[0m enable_checks \u001b[38;5;28;01mas\u001b[39;00m enable_checks,\n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 51\u001b[0m numpy_rank_promotion \u001b[38;5;28;01mas\u001b[39;00m numpy_rank_promotion,\n\u001b[1;32m 52\u001b[0m )\n\u001b[1;32m 53\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01m_src\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mapi\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m (\n\u001b[1;32m 54\u001b[0m ad, \u001b[38;5;66;03m# TODO(phawkins): update users to avoid this.\u001b[39;00m\n\u001b[1;32m 55\u001b[0m checkpoint \u001b[38;5;28;01mas\u001b[39;00m checkpoint,\n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 114\u001b[0m xla_computation \u001b[38;5;28;01mas\u001b[39;00m xla_computation,\n\u001b[1;32m 115\u001b[0m )\n\u001b[0;32m--> 116\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mexperimental\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mmaps\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m soft_pmap \u001b[38;5;28;01mas\u001b[39;00m soft_pmap\n\u001b[1;32m 117\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mversion\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m __version__ \u001b[38;5;28;01mas\u001b[39;00m __version__\n\u001b[1;32m 119\u001b[0m \u001b[38;5;66;03m# These submodules are separate because they are in an import cycle with\u001b[39;00m\n\u001b[1;32m 120\u001b[0m \u001b[38;5;66;03m# jax and rely on the names imported above.\u001b[39;00m\n", - "File \u001b[0;32m~/miniforge3/envs/myenv/lib/python3.9/site-packages/jax/experimental/maps.py:26\u001b[0m, in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[1;32m 23\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mfunctools\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m wraps, partial, partialmethod\n\u001b[1;32m 24\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01menum\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m Enum\n\u001b[0;32m---> 26\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m numpy \u001b[38;5;28;01mas\u001b[39;00m jnp\n\u001b[1;32m 27\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m core\n\u001b[1;32m 28\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m linear_util \u001b[38;5;28;01mas\u001b[39;00m lu\n", - "File \u001b[0;32m~/miniforge3/envs/myenv/lib/python3.9/site-packages/jax/numpy/__init__.py:19\u001b[0m, in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[38;5;66;03m# Copyright 2018 Google LLC\u001b[39;00m\n\u001b[1;32m 2\u001b[0m \u001b[38;5;66;03m#\u001b[39;00m\n\u001b[1;32m 3\u001b[0m \u001b[38;5;66;03m# Licensed under the Apache License, Version 2.0 (the \"License\");\u001b[39;00m\n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 17\u001b[0m \n\u001b[1;32m 18\u001b[0m \u001b[38;5;66;03m# flake8: noqa: F401\u001b[39;00m\n\u001b[0;32m---> 19\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01m.\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m fft \u001b[38;5;28;01mas\u001b[39;00m fft\n\u001b[1;32m 20\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01m.\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m linalg \u001b[38;5;28;01mas\u001b[39;00m linalg\n\u001b[1;32m 22\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mjax\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01minterpreters\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mxla\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m DeviceArray \u001b[38;5;28;01mas\u001b[39;00m DeviceArray\n", - "File \u001b[0;32m~/miniforge3/envs/myenv/lib/python3.9/site-packages/jax/numpy/fft.py:17\u001b[0m, in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[38;5;66;03m# Copyright 2020 Google LLC\u001b[39;00m\n\u001b[1;32m 2\u001b[0m \u001b[38;5;66;03m#\u001b[39;00m\n\u001b[1;32m 3\u001b[0m \u001b[38;5;66;03m# Licensed under the Apache License, Version 2.0 (the \"License\");\u001b[39;00m\n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 14\u001b[0m \n\u001b[1;32m 15\u001b[0m \u001b[38;5;66;03m# flake8: noqa: F401\u001b[39;00m\n\u001b[0;32m---> 17\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mjax\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01m_src\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mnumpy\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mfft\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m (\n\u001b[1;32m 18\u001b[0m ifft \u001b[38;5;28;01mas\u001b[39;00m ifft,\n\u001b[1;32m 19\u001b[0m ifft2 \u001b[38;5;28;01mas\u001b[39;00m ifft2,\n\u001b[1;32m 20\u001b[0m ifftn \u001b[38;5;28;01mas\u001b[39;00m ifftn,\n\u001b[1;32m 21\u001b[0m ifftshift \u001b[38;5;28;01mas\u001b[39;00m ifftshift,\n\u001b[1;32m 22\u001b[0m ihfft \u001b[38;5;28;01mas\u001b[39;00m ihfft,\n\u001b[1;32m 23\u001b[0m irfft \u001b[38;5;28;01mas\u001b[39;00m irfft,\n\u001b[1;32m 24\u001b[0m irfft2 \u001b[38;5;28;01mas\u001b[39;00m irfft2,\n\u001b[1;32m 25\u001b[0m irfftn \u001b[38;5;28;01mas\u001b[39;00m irfftn,\n\u001b[1;32m 26\u001b[0m fft \u001b[38;5;28;01mas\u001b[39;00m fft,\n\u001b[1;32m 27\u001b[0m fft2 \u001b[38;5;28;01mas\u001b[39;00m fft2,\n\u001b[1;32m 28\u001b[0m fftfreq \u001b[38;5;28;01mas\u001b[39;00m fftfreq,\n\u001b[1;32m 29\u001b[0m fftn \u001b[38;5;28;01mas\u001b[39;00m fftn,\n\u001b[1;32m 30\u001b[0m fftshift \u001b[38;5;28;01mas\u001b[39;00m fftshift,\n\u001b[1;32m 31\u001b[0m hfft \u001b[38;5;28;01mas\u001b[39;00m hfft,\n\u001b[1;32m 32\u001b[0m rfft \u001b[38;5;28;01mas\u001b[39;00m rfft,\n\u001b[1;32m 33\u001b[0m rfft2 \u001b[38;5;28;01mas\u001b[39;00m rfft2,\n\u001b[1;32m 34\u001b[0m rfftfreq \u001b[38;5;28;01mas\u001b[39;00m rfftfreq,\n\u001b[1;32m 35\u001b[0m rfftn \u001b[38;5;28;01mas\u001b[39;00m rfftn,\n\u001b[1;32m 36\u001b[0m )\n\u001b[1;32m 38\u001b[0m \u001b[38;5;66;03m# Module initialization is encapsulated in a function to avoid accidental\u001b[39;00m\n\u001b[1;32m 39\u001b[0m \u001b[38;5;66;03m# namespace pollution.\u001b[39;00m\n\u001b[1;32m 40\u001b[0m _NOT_IMPLEMENTED \u001b[38;5;241m=\u001b[39m []\n", - "File \u001b[0;32m~/miniforge3/envs/myenv/lib/python3.9/site-packages/jax/_src/numpy/fft.py:19\u001b[0m, in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[1;32m 16\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01moperator\u001b[39;00m\n\u001b[1;32m 17\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mnumpy\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m \u001b[38;5;21;01mnp\u001b[39;00m\n\u001b[0;32m---> 19\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mjax\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m lax\n\u001b[1;32m 20\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mjax\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01m_src\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mlib\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m xla_client\n\u001b[1;32m 21\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mjax\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01m_src\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mutil\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m safe_zip\n", - "File \u001b[0;32m~/miniforge3/envs/myenv/lib/python3.9/site-packages/jax/lax/__init__.py:332\u001b[0m, in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[1;32m 299\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mjax\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01m_src\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mlax\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mlax\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m (_reduce_sum, _reduce_max, _reduce_min, _reduce_or,\n\u001b[1;32m 300\u001b[0m _reduce_and, _reduce_window_sum, _reduce_window_max,\n\u001b[1;32m 301\u001b[0m _reduce_window_min, _reduce_window_prod,\n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 306\u001b[0m _upcast_fp16_for_computation, _broadcasting_shape_rule,\n\u001b[1;32m 307\u001b[0m _eye, _tri, _delta, _ones, _zeros, _dilate_shape)\n\u001b[1;32m 308\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mjax\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01m_src\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mlax\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mcontrol_flow\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m (\n\u001b[1;32m 309\u001b[0m associative_scan \u001b[38;5;28;01mas\u001b[39;00m associative_scan,\n\u001b[1;32m 310\u001b[0m cond \u001b[38;5;28;01mas\u001b[39;00m cond,\n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 330\u001b[0m while_p \u001b[38;5;28;01mas\u001b[39;00m while_p,\n\u001b[1;32m 331\u001b[0m )\n\u001b[0;32m--> 332\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mjax\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01m_src\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mlax\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mfft\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m (\n\u001b[1;32m 333\u001b[0m fft \u001b[38;5;28;01mas\u001b[39;00m fft,\n\u001b[1;32m 334\u001b[0m fft_p \u001b[38;5;28;01mas\u001b[39;00m fft_p,\n\u001b[1;32m 335\u001b[0m )\n\u001b[1;32m 336\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mjax\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01m_src\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mlax\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mparallel\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m (\n\u001b[1;32m 337\u001b[0m all_gather \u001b[38;5;28;01mas\u001b[39;00m all_gather,\n\u001b[1;32m 338\u001b[0m all_to_all \u001b[38;5;28;01mas\u001b[39;00m all_to_all,\n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 355\u001b[0m xeinsum \u001b[38;5;28;01mas\u001b[39;00m xeinsum,\n\u001b[1;32m 356\u001b[0m )\n\u001b[1;32m 357\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mjax\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01m_src\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mlax\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mother\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m (\n\u001b[1;32m 358\u001b[0m conv_general_dilated_patches \u001b[38;5;28;01mas\u001b[39;00m conv_general_dilated_patches\n\u001b[1;32m 359\u001b[0m )\n", - "File \u001b[0;32m~/miniforge3/envs/myenv/lib/python3.9/site-packages/jax/_src/lax/fft.py:145\u001b[0m, in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[1;32m 143\u001b[0m batching\u001b[38;5;241m.\u001b[39mprimitive_batchers[fft_p] \u001b[38;5;241m=\u001b[39m fft_batching_rule\n\u001b[1;32m 144\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m pocketfft:\n\u001b[0;32m--> 145\u001b[0m xla\u001b[38;5;241m.\u001b[39mbackend_specific_translations[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mcpu\u001b[39m\u001b[38;5;124m'\u001b[39m][fft_p] \u001b[38;5;241m=\u001b[39m \u001b[43mpocketfft\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mpocketfft\u001b[49m\n", - "\u001b[0;31mAttributeError\u001b[0m: module 'jaxlib.pocketfft' has no attribute 'pocketfft'" - ] - } - ], + "outputs": [], "source": [ "from tensorflow.keras import datasets, layers, models\n", "from tensorflow.keras.layers import Input\n", @@ -1673,7 +1599,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.9.10" + "version": "3.9.18" } }, "nbformat": 4, diff --git a/doc/LectureNotes/_build/jupyter_execute/chapter13.ipynb b/doc/LectureNotes/_build/jupyter_execute/chapter13.ipynb index 23ad81342..58b249e1d 100644 --- a/doc/LectureNotes/_build/jupyter_execute/chapter13.ipynb +++ b/doc/LectureNotes/_build/jupyter_execute/chapter13.ipynb @@ -60,40 +60,19 @@ }, "outputs": [ { - "name": "stderr", - "output_type": "stream", - "text": [ - "/Users/mhjensen/miniforge3/envs/myenv/lib/python3.9/site-packages/jax/_src/lib/__init__.py:32: UserWarning: JAX on Mac ARM machines is experimental and minimally tested. Please see https://github.com/google/jax/issues/5501 in the event of problems.\n", - " warnings.warn(\"JAX on Mac ARM machines is experimental and minimally tested. \"\n" - ] - }, - { - "ename": "AttributeError", - "evalue": "module 'jaxlib.pocketfft' has no attribute 'pocketfft'", + "ename": "ModuleNotFoundError", + "evalue": "No module named 'matplotlib'", "output_type": "error", "traceback": [ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mAttributeError\u001b[0m Traceback (most recent call last)", - "Input \u001b[0;32mIn [1]\u001b[0m, in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[1;32m 5\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mnumpy\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m \u001b[38;5;21;01mnp\u001b[39;00m\n\u001b[1;32m 6\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mmatplotlib\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mpyplot\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m \u001b[38;5;21;01mplt\u001b[39;00m\n\u001b[0;32m----> 7\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mtensorflow\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m \u001b[38;5;21;01mtf\u001b[39;00m\n\u001b[1;32m 8\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mtensorflow\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mkeras\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m datasets, layers, models\n\u001b[1;32m 9\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mtensorflow\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mkeras\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mlayers\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m Input\n", - "File \u001b[0;32m~/miniforge3/envs/myenv/lib/python3.9/site-packages/tensorflow/__init__.py:51\u001b[0m, in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[1;32m 49\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01m_api\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mv2\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m autograph\n\u001b[1;32m 50\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01m_api\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mv2\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m bitwise\n\u001b[0;32m---> 51\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01m_api\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mv2\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m compat\n\u001b[1;32m 52\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01m_api\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mv2\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m config\n\u001b[1;32m 53\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01m_api\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mv2\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m data\n", - "File \u001b[0;32m~/miniforge3/envs/myenv/lib/python3.9/site-packages/tensorflow/_api/v2/compat/__init__.py:37\u001b[0m, in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[1;32m 3\u001b[0m \u001b[38;5;124;03m\"\"\"Compatibility functions.\u001b[39;00m\n\u001b[1;32m 4\u001b[0m \n\u001b[1;32m 5\u001b[0m \u001b[38;5;124;03mThe `tf.compat` module contains two sets of compatibility functions.\u001b[39;00m\n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 32\u001b[0m \n\u001b[1;32m 33\u001b[0m \u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m 35\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01msys\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m \u001b[38;5;21;01m_sys\u001b[39;00m\n\u001b[0;32m---> 37\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01m.\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m v1\n\u001b[1;32m 38\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01m.\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m v2\n\u001b[1;32m 39\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mtensorflow\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mpython\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mcompat\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mcompat\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m forward_compatibility_horizon\n", - "File \u001b[0;32m~/miniforge3/envs/myenv/lib/python3.9/site-packages/tensorflow/_api/v2/compat/v1/__init__.py:30\u001b[0m, in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[1;32m 28\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01m.\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m autograph\n\u001b[1;32m 29\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01m.\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m bitwise\n\u001b[0;32m---> 30\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01m.\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m compat\n\u001b[1;32m 31\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01m.\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m config\n\u001b[1;32m 32\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01m.\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m data\n", - "File \u001b[0;32m~/miniforge3/envs/myenv/lib/python3.9/site-packages/tensorflow/_api/v2/compat/v1/compat/__init__.py:37\u001b[0m, in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[1;32m 3\u001b[0m \u001b[38;5;124;03m\"\"\"Compatibility functions.\u001b[39;00m\n\u001b[1;32m 4\u001b[0m \n\u001b[1;32m 5\u001b[0m \u001b[38;5;124;03mThe `tf.compat` module contains two sets of compatibility functions.\u001b[39;00m\n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 32\u001b[0m \n\u001b[1;32m 33\u001b[0m \u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m 35\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01msys\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m \u001b[38;5;21;01m_sys\u001b[39;00m\n\u001b[0;32m---> 37\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01m.\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m v1\n\u001b[1;32m 38\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01m.\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m v2\n\u001b[1;32m 39\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mtensorflow\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mpython\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mcompat\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mcompat\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m forward_compatibility_horizon\n", - "File \u001b[0;32m~/miniforge3/envs/myenv/lib/python3.9/site-packages/tensorflow/_api/v2/compat/v1/compat/v1/__init__.py:47\u001b[0m, in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[1;32m 45\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mtensorflow\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01m_api\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mv2\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mcompat\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mv1\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m layers\n\u001b[1;32m 46\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mtensorflow\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01m_api\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mv2\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mcompat\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mv1\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m linalg\n\u001b[0;32m---> 47\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mtensorflow\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01m_api\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mv2\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mcompat\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mv1\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m lite\n\u001b[1;32m 48\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mtensorflow\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01m_api\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mv2\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mcompat\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mv1\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m logging\n\u001b[1;32m 49\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mtensorflow\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01m_api\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mv2\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mcompat\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mv1\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m lookup\n", - "File \u001b[0;32m~/miniforge3/envs/myenv/lib/python3.9/site-packages/tensorflow/_api/v2/compat/v1/lite/__init__.py:9\u001b[0m, in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[1;32m 6\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01msys\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m \u001b[38;5;21;01m_sys\u001b[39;00m\n\u001b[1;32m 8\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01m.\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m constants\n\u001b[0;32m----> 9\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01m.\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m experimental\n\u001b[1;32m 10\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mtensorflow\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mlite\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mpython\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mlite\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m Interpreter\n\u001b[1;32m 11\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mtensorflow\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mlite\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mpython\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mlite\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m OpHint\n", - "File \u001b[0;32m~/miniforge3/envs/myenv/lib/python3.9/site-packages/tensorflow/_api/v2/compat/v1/lite/experimental/__init__.py:8\u001b[0m, in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[1;32m 3\u001b[0m \u001b[38;5;124;03m\"\"\"Public API for tf.lite.experimental namespace.\u001b[39;00m\n\u001b[1;32m 4\u001b[0m \u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m 6\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01msys\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m \u001b[38;5;21;01m_sys\u001b[39;00m\n\u001b[0;32m----> 8\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01m.\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m authoring\n\u001b[1;32m 9\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mtensorflow\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mlite\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mpython\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01manalyzer\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m ModelAnalyzer \u001b[38;5;28;01mas\u001b[39;00m Analyzer\n\u001b[1;32m 10\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mtensorflow\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mlite\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mpython\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mlite\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m OpResolverType\n", - "File \u001b[0;32m~/miniforge3/envs/myenv/lib/python3.9/site-packages/tensorflow/_api/v2/compat/v1/lite/experimental/authoring/__init__.py:8\u001b[0m, in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[1;32m 3\u001b[0m \u001b[38;5;124;03m\"\"\"Public API for tf.lite.experimental.authoring namespace.\u001b[39;00m\n\u001b[1;32m 4\u001b[0m \u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m 6\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01msys\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m \u001b[38;5;21;01m_sys\u001b[39;00m\n\u001b[0;32m----> 8\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mtensorflow\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mlite\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mpython\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mauthoring\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mauthoring\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m compatible\n", - "File \u001b[0;32m~/miniforge3/envs/myenv/lib/python3.9/site-packages/tensorflow/lite/python/authoring/authoring.py:43\u001b[0m, in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[1;32m 39\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mfunctools\u001b[39;00m\n\u001b[1;32m 42\u001b[0m \u001b[38;5;66;03m# pylint: disable=g-import-not-at-top\u001b[39;00m\n\u001b[0;32m---> 43\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mtensorflow\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mlite\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mpython\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m convert\n\u001b[1;32m 44\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mtensorflow\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mlite\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mpython\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m lite\n\u001b[1;32m 45\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mtensorflow\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mlite\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mpython\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mmetrics\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m converter_error_data_pb2\n", - "File \u001b[0;32m~/miniforge3/envs/myenv/lib/python3.9/site-packages/tensorflow/lite/python/convert.py:29\u001b[0m, in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[1;32m 26\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01msix\u001b[39;00m\n\u001b[1;32m 28\u001b[0m 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_xla_computation\n\u001b[1;32m 52\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mImportError\u001b[39;00m:\n\u001b[1;32m 53\u001b[0m _xla_computation \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m\n", - "File \u001b[0;32m~/miniforge3/envs/myenv/lib/python3.9/site-packages/jax/__init__.py:116\u001b[0m, in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[1;32m 40\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01m_src\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mconfig\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m (\n\u001b[1;32m 41\u001b[0m config \u001b[38;5;28;01mas\u001b[39;00m config,\n\u001b[1;32m 42\u001b[0m enable_checks \u001b[38;5;28;01mas\u001b[39;00m enable_checks,\n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 51\u001b[0m numpy_rank_promotion \u001b[38;5;28;01mas\u001b[39;00m numpy_rank_promotion,\n\u001b[1;32m 52\u001b[0m )\n\u001b[1;32m 53\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01m_src\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mapi\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m (\n\u001b[1;32m 54\u001b[0m ad, \u001b[38;5;66;03m# TODO(phawkins): update users to avoid this.\u001b[39;00m\n\u001b[1;32m 55\u001b[0m checkpoint \u001b[38;5;28;01mas\u001b[39;00m checkpoint,\n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 114\u001b[0m xla_computation \u001b[38;5;28;01mas\u001b[39;00m xla_computation,\n\u001b[1;32m 115\u001b[0m )\n\u001b[0;32m--> 116\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mexperimental\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mmaps\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m soft_pmap \u001b[38;5;28;01mas\u001b[39;00m soft_pmap\n\u001b[1;32m 117\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mversion\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m __version__ \u001b[38;5;28;01mas\u001b[39;00m __version__\n\u001b[1;32m 119\u001b[0m \u001b[38;5;66;03m# These submodules are separate because they are in an import cycle with\u001b[39;00m\n\u001b[1;32m 120\u001b[0m \u001b[38;5;66;03m# jax and rely on the names imported above.\u001b[39;00m\n", - "File \u001b[0;32m~/miniforge3/envs/myenv/lib/python3.9/site-packages/jax/experimental/maps.py:26\u001b[0m, in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[1;32m 23\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mfunctools\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m wraps, partial, partialmethod\n\u001b[1;32m 24\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01menum\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m Enum\n\u001b[0;32m---> 26\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m numpy \u001b[38;5;28;01mas\u001b[39;00m jnp\n\u001b[1;32m 27\u001b[0m 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2.0 (the \"License\");\u001b[39;00m\n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 14\u001b[0m \n\u001b[1;32m 15\u001b[0m \u001b[38;5;66;03m# flake8: noqa: F401\u001b[39;00m\n\u001b[0;32m---> 17\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mjax\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01m_src\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mnumpy\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mfft\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m (\n\u001b[1;32m 18\u001b[0m ifft \u001b[38;5;28;01mas\u001b[39;00m ifft,\n\u001b[1;32m 19\u001b[0m ifft2 \u001b[38;5;28;01mas\u001b[39;00m ifft2,\n\u001b[1;32m 20\u001b[0m ifftn \u001b[38;5;28;01mas\u001b[39;00m ifftn,\n\u001b[1;32m 21\u001b[0m ifftshift \u001b[38;5;28;01mas\u001b[39;00m ifftshift,\n\u001b[1;32m 22\u001b[0m ihfft \u001b[38;5;28;01mas\u001b[39;00m ihfft,\n\u001b[1;32m 23\u001b[0m irfft \u001b[38;5;28;01mas\u001b[39;00m irfft,\n\u001b[1;32m 24\u001b[0m irfft2 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accidental\u001b[39;00m\n\u001b[1;32m 39\u001b[0m \u001b[38;5;66;03m# namespace pollution.\u001b[39;00m\n\u001b[1;32m 40\u001b[0m _NOT_IMPLEMENTED \u001b[38;5;241m=\u001b[39m []\n", - "File \u001b[0;32m~/miniforge3/envs/myenv/lib/python3.9/site-packages/jax/_src/numpy/fft.py:19\u001b[0m, in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[1;32m 16\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01moperator\u001b[39;00m\n\u001b[1;32m 17\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mnumpy\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m \u001b[38;5;21;01mnp\u001b[39;00m\n\u001b[0;32m---> 19\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mjax\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m lax\n\u001b[1;32m 20\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mjax\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01m_src\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mlib\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m xla_client\n\u001b[1;32m 21\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mjax\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01m_src\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mutil\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m safe_zip\n", - "File \u001b[0;32m~/miniforge3/envs/myenv/lib/python3.9/site-packages/jax/lax/__init__.py:332\u001b[0m, in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[1;32m 299\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mjax\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01m_src\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mlax\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mlax\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m (_reduce_sum, _reduce_max, _reduce_min, _reduce_or,\n\u001b[1;32m 300\u001b[0m _reduce_and, _reduce_window_sum, _reduce_window_max,\n\u001b[1;32m 301\u001b[0m _reduce_window_min, _reduce_window_prod,\n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 306\u001b[0m _upcast_fp16_for_computation, _broadcasting_shape_rule,\n\u001b[1;32m 307\u001b[0m _eye, _tri, _delta, _ones, _zeros, _dilate_shape)\n\u001b[1;32m 308\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mjax\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01m_src\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mlax\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mcontrol_flow\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m (\n\u001b[1;32m 309\u001b[0m associative_scan \u001b[38;5;28;01mas\u001b[39;00m associative_scan,\n\u001b[1;32m 310\u001b[0m cond \u001b[38;5;28;01mas\u001b[39;00m cond,\n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 330\u001b[0m while_p \u001b[38;5;28;01mas\u001b[39;00m while_p,\n\u001b[1;32m 331\u001b[0m )\n\u001b[0;32m--> 332\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m 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355\u001b[0m xeinsum \u001b[38;5;28;01mas\u001b[39;00m xeinsum,\n\u001b[1;32m 356\u001b[0m )\n\u001b[1;32m 357\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mjax\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01m_src\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mlax\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mother\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m (\n\u001b[1;32m 358\u001b[0m conv_general_dilated_patches \u001b[38;5;28;01mas\u001b[39;00m conv_general_dilated_patches\n\u001b[1;32m 359\u001b[0m )\n", - "File \u001b[0;32m~/miniforge3/envs/myenv/lib/python3.9/site-packages/jax/_src/lax/fft.py:145\u001b[0m, in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[1;32m 143\u001b[0m batching\u001b[38;5;241m.\u001b[39mprimitive_batchers[fft_p] \u001b[38;5;241m=\u001b[39m fft_batching_rule\n\u001b[1;32m 144\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m pocketfft:\n\u001b[0;32m--> 145\u001b[0m xla\u001b[38;5;241m.\u001b[39mbackend_specific_translations[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mcpu\u001b[39m\u001b[38;5;124m'\u001b[39m][fft_p] \u001b[38;5;241m=\u001b[39m \u001b[43mpocketfft\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mpocketfft\u001b[49m\n", - "\u001b[0;31mAttributeError\u001b[0m: module 'jaxlib.pocketfft' has no attribute 'pocketfft'" + "\u001b[0;31mModuleNotFoundError\u001b[0m Traceback (most recent call last)", + "Cell \u001b[0;32mIn[1], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \u001b[43mget_ipython\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mrun_line_magic\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mmatplotlib\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43minline\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m)\u001b[49m\n\u001b[1;32m 3\u001b[0m \u001b[38;5;66;03m# Start importing packages\u001b[39;00m\n\u001b[1;32m 4\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mpandas\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m \u001b[38;5;21;01mpd\u001b[39;00m\n", + "File \u001b[0;32m~/miniforge3/lib/python3.9/site-packages/IPython/core/interactiveshell.py:2432\u001b[0m, in \u001b[0;36mInteractiveShell.run_line_magic\u001b[0;34m(self, magic_name, line, _stack_depth)\u001b[0m\n\u001b[1;32m 2430\u001b[0m kwargs[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mlocal_ns\u001b[39m\u001b[38;5;124m'\u001b[39m] \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mget_local_scope(stack_depth)\n\u001b[1;32m 2431\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mbuiltin_trap:\n\u001b[0;32m-> 2432\u001b[0m result \u001b[38;5;241m=\u001b[39m \u001b[43mfn\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 2434\u001b[0m \u001b[38;5;66;03m# The code below prevents the output from being displayed\u001b[39;00m\n\u001b[1;32m 2435\u001b[0m \u001b[38;5;66;03m# when using magics with decorator @output_can_be_silenced\u001b[39;00m\n\u001b[1;32m 2436\u001b[0m \u001b[38;5;66;03m# when the last Python token in the expression is a ';'.\u001b[39;00m\n\u001b[1;32m 2437\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mgetattr\u001b[39m(fn, magic\u001b[38;5;241m.\u001b[39mMAGIC_OUTPUT_CAN_BE_SILENCED, \u001b[38;5;28;01mFalse\u001b[39;00m):\n", + "File \u001b[0;32m~/miniforge3/lib/python3.9/site-packages/IPython/core/magics/pylab.py:99\u001b[0m, in \u001b[0;36mPylabMagics.matplotlib\u001b[0;34m(self, line)\u001b[0m\n\u001b[1;32m 97\u001b[0m \u001b[38;5;28mprint\u001b[39m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mAvailable matplotlib backends: \u001b[39m\u001b[38;5;132;01m%s\u001b[39;00m\u001b[38;5;124m\"\u001b[39m \u001b[38;5;241m%\u001b[39m backends_list)\n\u001b[1;32m 98\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m---> 99\u001b[0m gui, backend \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mshell\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43menable_matplotlib\u001b[49m\u001b[43m(\u001b[49m\u001b[43margs\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mgui\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mlower\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mif\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[38;5;28;43misinstance\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43margs\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mgui\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mstr\u001b[39;49m\u001b[43m)\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01melse\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43margs\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mgui\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 100\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_show_matplotlib_backend(args\u001b[38;5;241m.\u001b[39mgui, backend)\n", + "File \u001b[0;32m~/miniforge3/lib/python3.9/site-packages/IPython/core/interactiveshell.py:3606\u001b[0m, in \u001b[0;36mInteractiveShell.enable_matplotlib\u001b[0;34m(self, gui)\u001b[0m\n\u001b[1;32m 3585\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21menable_matplotlib\u001b[39m(\u001b[38;5;28mself\u001b[39m, gui\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mNone\u001b[39;00m):\n\u001b[1;32m 3586\u001b[0m \u001b[38;5;250m \u001b[39m\u001b[38;5;124;03m\"\"\"Enable interactive matplotlib and inline figure support.\u001b[39;00m\n\u001b[1;32m 3587\u001b[0m \n\u001b[1;32m 3588\u001b[0m \u001b[38;5;124;03m This takes the following steps:\u001b[39;00m\n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 3604\u001b[0m \u001b[38;5;124;03m display figures inline.\u001b[39;00m\n\u001b[1;32m 3605\u001b[0m \u001b[38;5;124;03m \"\"\"\u001b[39;00m\n\u001b[0;32m-> 3606\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mmatplotlib_inline\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mbackend_inline\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m configure_inline_support\n\u001b[1;32m 3608\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mIPython\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mcore\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m pylabtools \u001b[38;5;28;01mas\u001b[39;00m pt\n\u001b[1;32m 3609\u001b[0m gui, backend \u001b[38;5;241m=\u001b[39m pt\u001b[38;5;241m.\u001b[39mfind_gui_and_backend(gui, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mpylab_gui_select)\n", + "File \u001b[0;32m~/miniforge3/lib/python3.9/site-packages/matplotlib_inline/__init__.py:1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01m.\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m backend_inline, config \u001b[38;5;66;03m# noqa\u001b[39;00m\n\u001b[1;32m 2\u001b[0m __version__ \u001b[38;5;241m=\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m0.1.6\u001b[39m\u001b[38;5;124m\"\u001b[39m \u001b[38;5;66;03m# noqa\u001b[39;00m\n", + "File \u001b[0;32m~/miniforge3/lib/python3.9/site-packages/matplotlib_inline/backend_inline.py:6\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[38;5;124;03m\"\"\"A matplotlib backend for publishing figures via display_data\"\"\"\u001b[39;00m\n\u001b[1;32m 3\u001b[0m \u001b[38;5;66;03m# Copyright (c) IPython Development Team.\u001b[39;00m\n\u001b[1;32m 4\u001b[0m \u001b[38;5;66;03m# Distributed under the terms of the BSD 3-Clause License.\u001b[39;00m\n\u001b[0;32m----> 6\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mmatplotlib\u001b[39;00m\n\u001b[1;32m 7\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mmatplotlib\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m colors\n\u001b[1;32m 8\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mmatplotlib\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mbackends\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m backend_agg\n", + "\u001b[0;31mModuleNotFoundError\u001b[0m: No module named 'matplotlib'" ] } ], @@ -1916,7 +1895,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.9.10" + "version": "3.9.18" } }, "nbformat": 4, diff --git a/doc/LectureNotes/_build/jupyter_execute/chapter2.ipynb b/doc/LectureNotes/_build/jupyter_execute/chapter2.ipynb index 38a0fb505..0ff8d6a7b 100644 --- a/doc/LectureNotes/_build/jupyter_execute/chapter2.ipynb +++ b/doc/LectureNotes/_build/jupyter_execute/chapter2.ipynb @@ -748,13 +748,13 @@ " [2 4 5]\n", " [3 5 6]]\n", "test U\n", - "[[-2.22044605e-16 -7.49932427e-16 -8.23408962e-16]\n", - " [-7.49932427e-16 0.00000000e+00 4.77954956e-17]\n", - " [-8.23408962e-16 4.77954956e-17 2.22044605e-16]]\n", + "[[ 4.44089210e-16 -4.69484813e-16 -6.67314874e-16]\n", + " [-4.69484813e-16 -4.44089210e-16 -1.54041041e-16]\n", + " [-6.67314874e-16 -1.54041041e-16 1.11022302e-16]]\n", "test VT\n", - "[[ 3.33066907e-16 -7.32066545e-17 3.32714903e-16]\n", - " [-7.32066545e-17 0.00000000e+00 -1.82997013e-16]\n", - " [ 3.32714903e-16 -1.82997013e-16 -3.33066907e-16]]\n", + "[[ 2.22044605e-16 3.78156479e-17 1.85278920e-16]\n", + " [ 3.78156479e-17 0.00000000e+00 -6.33166055e-17]\n", + " [ 1.85278920e-16 -6.33166055e-17 -1.11022302e-16]]\n", "[[0. 0. 0.]\n", " [0. 0. 0.]\n", " [0. 0. 0.]]\n" @@ -1798,10 +1798,10 @@ "name": "stdout", "output_type": "stream", "text": [ - "0.003788445263115483\n", - "3.859628021353642\n", - "[[0.93725291 2.8143782 ]\n", - " [2.8143782 9.64008343]]\n" + "0.015836015068980684\n", + "4.146630977737321\n", + "[[0.88786042 2.4773343 ]\n", + " [2.4773343 7.74767365]]\n" ] } ], @@ -1845,10 +1845,10 @@ "name": "stdout", "output_type": "stream", "text": [ - "0.07783152589039466\n", - "2.06282378894371\n", - "[[1. 0.66124684]\n", - " [0.66124684 1. ]]\n" + "0.07636331150360104\n", + "1.4231233000637726\n", + "[[1. 0.68477696]\n", + " [0.68477696 1. ]]\n" ] } ], @@ -1902,33 +1902,14 @@ }, "outputs": [ { - "name": "stdout", - "output_type": "stream", - "text": [ - "[[-0.15499979 -0.6924788 ]\n", - " [-0.50456256 -2.98248681]\n", - " [ 1.97264311 4.4533029 ]\n", - " [ 0.03286166 -0.27108228]\n", - " [-0.97421755 -3.08891672]\n", - " [-0.3744637 0.62223681]\n", - " [ 1.18655084 4.67392051]\n", - " [-0.51719273 -1.14648903]\n", - " [-0.51793145 -1.51975002]\n", - " [-0.14868784 -0.04825655]]\n", - " 0 1\n", - "0 -0.155000 -0.692479\n", - "1 -0.504563 -2.982487\n", - "2 1.972643 4.453303\n", - "3 0.032862 -0.271082\n", - "4 -0.974218 -3.088917\n", - "5 -0.374464 0.622237\n", - "6 1.186551 4.673921\n", - "7 -0.517193 -1.146489\n", - "8 -0.517931 -1.519750\n", - "9 -0.148688 -0.048257\n", - " 0 1\n", - "0 1.000000 0.929612\n", - "1 0.929612 1.000000\n" + "ename": "ModuleNotFoundError", + "evalue": "No module named 'pandas'", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mModuleNotFoundError\u001b[0m Traceback (most recent call last)", + "Cell \u001b[0;32mIn[7], line 2\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mnumpy\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m \u001b[38;5;21;01mnp\u001b[39;00m\n\u001b[0;32m----> 2\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mpandas\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m \u001b[38;5;21;01mpd\u001b[39;00m\n\u001b[1;32m 3\u001b[0m n \u001b[38;5;241m=\u001b[39m \u001b[38;5;241m10\u001b[39m\n\u001b[1;32m 4\u001b[0m x \u001b[38;5;241m=\u001b[39m np\u001b[38;5;241m.\u001b[39mrandom\u001b[38;5;241m.\u001b[39mnormal(size\u001b[38;5;241m=\u001b[39mn)\n", + "\u001b[0;31mModuleNotFoundError\u001b[0m: No module named 'pandas'" ] } ], @@ -1967,47 +1948,7 @@ "collapsed": false, "editable": true }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - " 0 1 2 3 4 5 6 7 \\\n", - "0 0.0 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 \n", - "1 0.0 0.079729 0.074983 0.077280 0.078995 0.080168 0.068546 0.070577 \n", - "2 0.0 0.074983 0.071314 0.073127 0.075035 0.076519 0.065106 0.067183 \n", - "3 0.0 0.077280 0.073127 0.080979 0.082487 0.083517 0.075422 0.077387 \n", - "4 0.0 0.078995 0.075035 0.082487 0.084199 0.085447 0.076612 0.078725 \n", - "5 0.0 0.080168 0.076519 0.083517 0.085447 0.086942 0.077411 0.079671 \n", - "6 0.0 0.068546 0.065106 0.075422 0.076612 0.077411 0.072522 0.074203 \n", - "7 0.0 0.070577 0.067183 0.077387 0.078725 0.079671 0.074203 0.076007 \n", - "8 0.0 0.072638 0.069315 0.079368 0.080867 0.081977 0.075887 0.077825 \n", - "9 0.0 0.074671 0.071455 0.081315 0.082991 0.084282 0.077536 0.079615 \n", - "10 0.0 0.060425 0.057463 0.068676 0.069583 0.070161 0.067515 0.068912 \n", - "11 0.0 0.062197 0.059245 0.070486 0.071501 0.072183 0.069129 0.070625 \n", - "12 0.0 0.064048 0.061113 0.072365 0.073497 0.074293 0.070797 0.072398 \n", - "13 0.0 0.065974 0.063066 0.074310 0.075569 0.076491 0.072515 0.074230 \n", - "14 0.0 0.067967 0.065101 0.076313 0.077711 0.078771 0.074276 0.076112 \n", - "\n", - " 8 9 10 11 12 13 14 \n", - "0 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 \n", - "1 0.072638 0.074671 0.060425 0.062197 0.064048 0.065974 0.067967 \n", - "2 0.069315 0.071455 0.057463 0.059245 0.061113 0.063066 0.065101 \n", - "3 0.079368 0.081315 0.068676 0.070486 0.072365 0.074310 0.076313 \n", - "4 0.080867 0.082991 0.069583 0.071501 0.073497 0.075569 0.077711 \n", - "5 0.081977 0.084282 0.070161 0.072183 0.074293 0.076491 0.078771 \n", - "6 0.075887 0.077536 0.067515 0.069129 0.070797 0.072515 0.074276 \n", - "7 0.077825 0.079615 0.068912 0.070625 0.072398 0.074230 0.076112 \n", - "8 0.079786 0.081728 0.070305 0.072122 0.074007 0.075958 0.077970 \n", - "9 0.081728 0.083834 0.071659 0.073585 0.075587 0.077666 0.079814 \n", - "10 0.070305 0.071659 0.063876 0.065268 0.066700 0.068170 0.069669 \n", - "11 0.072122 0.073585 0.065268 0.066742 0.068261 0.069823 0.071420 \n", - "12 0.074007 0.075587 0.066700 0.068261 0.069873 0.071534 0.073236 \n", - "13 0.075958 0.077666 0.068170 0.069823 0.071534 0.073300 0.075115 \n", - "14 0.077970 0.079814 0.069669 0.071420 0.073236 0.075115 0.077050 \n" - ] - } - ], + "outputs": [], "source": [ "# Common imports\n", "import numpy as np\n", @@ -3552,31 +3493,7 @@ "collapsed": false, "editable": true }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[2. 2.]\n", - "Training MSE for OLS\n", - "3.0\n" - ] - }, - { - "data": { - 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\n", 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\n", 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-912,12 +912,19 @@ }, "outputs": [ { - "name": "stdout", - "output_type": "stream", - "text": [ - "Bootstrap Statistics :\n", - "original bias std. error\n", - " 99.9169 14.873 99.9169 0.147934\n" + "ename": "ModuleNotFoundError", + "evalue": "No module named 'matplotlib'", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mModuleNotFoundError\u001b[0m Traceback (most recent call last)", + "Cell \u001b[0;32mIn[2], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \u001b[43mget_ipython\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mrun_line_magic\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mmatplotlib\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43minline\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m)\u001b[49m\n\u001b[1;32m 3\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mnumpy\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m \u001b[38;5;21;01mnp\u001b[39;00m\n\u001b[1;32m 4\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mtime\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m time\n", + "File \u001b[0;32m~/miniforge3/lib/python3.9/site-packages/IPython/core/interactiveshell.py:2432\u001b[0m, in \u001b[0;36mInteractiveShell.run_line_magic\u001b[0;34m(self, magic_name, line, _stack_depth)\u001b[0m\n\u001b[1;32m 2430\u001b[0m kwargs[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mlocal_ns\u001b[39m\u001b[38;5;124m'\u001b[39m] \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mget_local_scope(stack_depth)\n\u001b[1;32m 2431\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mbuiltin_trap:\n\u001b[0;32m-> 2432\u001b[0m result \u001b[38;5;241m=\u001b[39m \u001b[43mfn\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 2434\u001b[0m \u001b[38;5;66;03m# The code below prevents the output from being displayed\u001b[39;00m\n\u001b[1;32m 2435\u001b[0m \u001b[38;5;66;03m# when using magics with decorator @output_can_be_silenced\u001b[39;00m\n\u001b[1;32m 2436\u001b[0m \u001b[38;5;66;03m# when the last Python token in the expression is a ';'.\u001b[39;00m\n\u001b[1;32m 2437\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mgetattr\u001b[39m(fn, magic\u001b[38;5;241m.\u001b[39mMAGIC_OUTPUT_CAN_BE_SILENCED, \u001b[38;5;28;01mFalse\u001b[39;00m):\n", + "File \u001b[0;32m~/miniforge3/lib/python3.9/site-packages/IPython/core/magics/pylab.py:99\u001b[0m, in \u001b[0;36mPylabMagics.matplotlib\u001b[0;34m(self, line)\u001b[0m\n\u001b[1;32m 97\u001b[0m \u001b[38;5;28mprint\u001b[39m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mAvailable matplotlib backends: \u001b[39m\u001b[38;5;132;01m%s\u001b[39;00m\u001b[38;5;124m\"\u001b[39m \u001b[38;5;241m%\u001b[39m backends_list)\n\u001b[1;32m 98\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m---> 99\u001b[0m gui, backend \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mshell\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43menable_matplotlib\u001b[49m\u001b[43m(\u001b[49m\u001b[43margs\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mgui\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mlower\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mif\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[38;5;28;43misinstance\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43margs\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mgui\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mstr\u001b[39;49m\u001b[43m)\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01melse\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43margs\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mgui\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 100\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_show_matplotlib_backend(args\u001b[38;5;241m.\u001b[39mgui, backend)\n", + "File \u001b[0;32m~/miniforge3/lib/python3.9/site-packages/IPython/core/interactiveshell.py:3606\u001b[0m, in \u001b[0;36mInteractiveShell.enable_matplotlib\u001b[0;34m(self, gui)\u001b[0m\n\u001b[1;32m 3585\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21menable_matplotlib\u001b[39m(\u001b[38;5;28mself\u001b[39m, gui\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mNone\u001b[39;00m):\n\u001b[1;32m 3586\u001b[0m \u001b[38;5;250m \u001b[39m\u001b[38;5;124;03m\"\"\"Enable interactive matplotlib and inline figure support.\u001b[39;00m\n\u001b[1;32m 3587\u001b[0m \n\u001b[1;32m 3588\u001b[0m \u001b[38;5;124;03m This takes the following steps:\u001b[39;00m\n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 3604\u001b[0m \u001b[38;5;124;03m display figures inline.\u001b[39;00m\n\u001b[1;32m 3605\u001b[0m \u001b[38;5;124;03m \"\"\"\u001b[39;00m\n\u001b[0;32m-> 3606\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mmatplotlib_inline\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mbackend_inline\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m configure_inline_support\n\u001b[1;32m 3608\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mIPython\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mcore\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m pylabtools \u001b[38;5;28;01mas\u001b[39;00m pt\n\u001b[1;32m 3609\u001b[0m gui, backend \u001b[38;5;241m=\u001b[39m pt\u001b[38;5;241m.\u001b[39mfind_gui_and_backend(gui, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mpylab_gui_select)\n", + "File \u001b[0;32m~/miniforge3/lib/python3.9/site-packages/matplotlib_inline/__init__.py:1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01m.\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m backend_inline, config \u001b[38;5;66;03m# noqa\u001b[39;00m\n\u001b[1;32m 2\u001b[0m __version__ \u001b[38;5;241m=\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m0.1.6\u001b[39m\u001b[38;5;124m\"\u001b[39m \u001b[38;5;66;03m# noqa\u001b[39;00m\n", + "File \u001b[0;32m~/miniforge3/lib/python3.9/site-packages/matplotlib_inline/backend_inline.py:6\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[38;5;124;03m\"\"\"A matplotlib backend for publishing figures via display_data\"\"\"\u001b[39;00m\n\u001b[1;32m 3\u001b[0m \u001b[38;5;66;03m# Copyright (c) IPython Development Team.\u001b[39;00m\n\u001b[1;32m 4\u001b[0m \u001b[38;5;66;03m# Distributed under the terms of the BSD 3-Clause License.\u001b[39;00m\n\u001b[0;32m----> 6\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mmatplotlib\u001b[39;00m\n\u001b[1;32m 7\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mmatplotlib\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m colors\n\u001b[1;32m 8\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mmatplotlib\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mbackends\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m backend_agg\n", + "\u001b[0;31mModuleNotFoundError\u001b[0m: No module named 'matplotlib'" ] } ], @@ -972,22 +979,7 @@ "collapsed": false, "editable": true }, - "outputs": [ - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
              " - ] - }, - "metadata": { - "filenames": { - "image/png": "/Users/mhjensen/Teaching/MachineLearning/doc/LectureNotes/_build/jupyter_execute/chapter3_51_0.png" - } - }, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "# the histogram of the bootstrapped data (normalized data if density = True)\n", "n, binsboot, patches = plt.hist(t, 50, density=True, facecolor='red', alpha=0.75)\n", @@ -1172,32 +1164,7 @@ "collapsed": false, "editable": true }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Error: 0.013121574062587286\n", - "Bias^2: 0.012073649469946107\n", - "Var: 0.0010479245926411787\n", - "0.013121574062587286 >= 0.012073649469946107 + 0.0010479245926411787 = 0.013121574062587286\n" - ] - }, - { - "data": { - "image/png": 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nfd0X39LSQnFxMYWFhWzbto3f/va3XHrppXzrW99i8eLFAIwZM4b29nb+/Oc/c/jwYV544QX++te/upxn5MiR1NfX89FHH1FeXk5jYyMjRowgLCzMPG7lypU88sgjAa3r888/53e/+x0HDhzgL3/5C//973+5/fbbAbjwwgs55ZRTuPrqq9m2bRtbtmxh8eLFnHPOOcyYMaPTP4uRI0eyfv16CgsLKS8vB/RamTVr1pCfn8+2bdv4+OOPuyR4BEHwjGF2Niol2qU2JNOPC6tN1bj3zV0yj0boMkGLkfr6enbs2MGOHTsAyM/PZ8eOHWYdwn333WdeSAGuuuoqkpOTue6668jLy2P9+vX8/Oc/5/rrr/dawNpb9HVf/OrVq8nMzGTkyJHMnz+fTz75hKeeeop33nnHrK+YOnUqTz75JI8//jiTJ0/mpZdeYunSpS7nmT17NjfddBNXXnklqamp/O53vyM1NZUVK1bw3//+l9zcXB577DGeeOKJgNb1f//3f3z11VdMmzaNRx55hD/84Q9cdNFFAKa3TGJiImeffTYXXngho0eP5tVXX+3Sz+Lhhx/myJEjjBkzhtTUVABsNhu33norEydOZP78+YwfP57ly5d36XkEQeiIsw28M0ZHTaGXyMjTH3/jc5CezKMRAkXRgpkPje5rcd5553XY/sMf/pAVK1Zw7bXXcuTIEZc2z3379vGzn/2Mzz//nOTkZK644goeffTRgMVIbW0t8fHx1NTUdEjZNDc3k5+fz6hRo4iI6Fw6ZfXuIh56N88lzJgZH8GShblDri9+5MiR3HHHHS4dPAOB7ngfCMJQ5b43d/LylmPcdv5J3DVvvLn9uY1HWLJyDxdNSudv17hGPm2qxvRH1lLd5H+q75++N5VLpw7r9nUL/R9f129ngm7tPffcc/GlX5zNugwmTJjA2rVrg32qXkP64gVBGMq4d9IYGDUjnroOt+RXBiREoOt1dwdK6lh/oIwfzh5JqFWmmAxGetxnZKAgffGCIAwFbKrW4cbL8BjpmKbx7sIaaJdhQlRol+vulryzh02HK8iMj+SSU4ZWtHqoIGJEcOHIkSN9vQRBEHoITynp9Nhwyuv1dv9RdsMzA0OMlNe30tJuIzzE4RUUaLTjutmjuhRl1jSNPSdqAMgrqhExMkiReJcgCMIQwFsLbond0CwxKpTocNf708SoUCJC9ctEsdtxRjeiL+IirMwYmdglI7SS2hZqm/XBod+U1PvZWxioSGREEARhAKJpGrXN7cRHhvrd19foC4OGVlsH23dFUciKj+RweQOF1U3kJDsiJ0Y34k0vbvP+vBpc/c8vzO870xhwoKTO/P83pSJGBisSGREEQRiA3PvGLqY89AG7jtf43dff6AuA1nbVYwtupr2915PXyPzJmZwzLqXD9rAQ/dLS0OJqZNgZIzRnMXK0ooHmto7miMLAR8SIIAjCAOOro5W8ulV3Qd5+rMrv/l0ZfZEV772IVdM09hfr0YpfXDSeP31vKi/dcDpxEZ6D7p0xQnMWI6rmMGgTBhciRgRBEAYQqqrx8HuOoZRVDf7ba7sy+sJwYT3hIbKyq7CG4tpmosKsXH/mKC6dOgyLRTELYj0RrBHafnudiDH66pvSOh97CwMVqRkRBEEYQLy78wRfH6s2v69q9D/40ig2La5p9lo3khQd5rEFN8v0GukYGflgTwkA545LIaLgMzi6gcSiGq63NrBfHcHn2sno8qNjN00g0RpV1Thoj4zMyEnkyyNVUsQ6SJHIyBDgwQcfZOrUqX29DEEQukhzm43frd4PwDB7xCIQMeJr9IXBbeeN8diC68tr5IO8YgDmHX4cXrgU1v+eCd/8nQdCX+LFsKVkUgEoXGddxRmWPCw4JnUHEq0prG6iodVGqFVhbm464Jq2EQYPIkb6kGuvvRZFUVAUhdDQUEaPHs3dd99NQ4PkRAVhKGFTNTYdqvDbAvuvDfkUVjeRFR/BzeeOAaCywb8YAb3Y9JkfnEqGl3bcZ9Yf9lhYmuWlgPVIeQMHSuoJoZ3zbJ93OE5RIFmpBeBsy25eCXuUDeG3Md+yJeABpEZKZnRKDLmZ+uT3g9JRMyiRNI2BaoOjG6G+BGLSIWc2WKz+j+si8+fP59///jdtbW189tln3HjjjTQ0NPDMM8+47NfW1kZoqP8WPkEQBhaBzsYqq2th+ScHAbhnwQRi7UWivgbVuTN/ciaqCrf8p2M7bmltCze/uI1nrp7C/Jh887MwM2MmAHUt7dQ2txEXoX8Ord2jC5fTLfuIVzzfQMUpDaBBDXpLcAaVLA9dxtenjg7ICM0ojh2XEcvYdN0d9oi9oyYitOc/n4XeQyIjAHkrYdlkeO5b8MYN+r/LJuvbe5jw8HAyMjLIzs7mqquu4uqrr+btt982UyvPPvsso0ePJjw8HE3TqKmp4cc//jFpaWnExcVx/vnn8/XXX7uc87HHHiM9PZ3Y2FhuuOEGmpsDq6QXBKF38WZE5qkF9sm1B2hotTElO4GFp2SREBUGBB4ZAT0C88j/8jw+pgEXWbYw7Y2zXT4Lo5+ZSnyYHqkxoiP1Le288+U3AMyzbPX6fPHoIqVG08WIRdEjJtP2PA6H1sGu1yH/M/1m0APf2FMy49JiSIsNJy4iBFWD/HKJHg82RIzkrYTXFkPtCdfttUX69l4QJM5ERkbS1qbf6Rw8eJDXXnuNN954gx07dgBwySWXUFxczKpVq/jqq6849dRTueCCC6is1CvTX3vtNZYsWcJvfvMbtm7dSmZmJsuXL+/V1yAIgn98GZE5t8C221Re2HSEV78sAODXl0zEYlFIsouR6gBqRgx8+Y1cZNnC8tBlpGoVrg/UFpHZpj/3iZomPswrYd6Tn7K7XCWMVuZZfYgRe8SklihzmwJQW6jXmPi5+dtviJGMWBRFYWx6LCB1I4ORoS1GVBusvgd8fRysvterau9utmzZwn/+8x8uuOACAFpbW3nhhReYNm0ap5xyCp988gm7du3iv//9LzNmzGDs2LE88cQTJCQk8PrrrwOwbNkyrr/+em688UbGjx/Po48+Sm5ubq+sXxCEwPFnRGa0wH7v75v59Tt7UDW4ckY2M0bqtRaJdjHS0GqjpT2wzyjvHSwaS0KfB/TohftjwxRdoCx5Zzc3Pr+VEzXNZMcq/Dv092Qq3lt03SMjXvFw82dTNbM+ZJxdhIyzp2o81Y18U1LH0vf30tja7vu5hH7J0BYjRzd2jIi4oOkK/ujGHlvCe++9R0xMDBEREcyaNYuzzz6bP//5zwDk5OSQmppq7vvVV19RX19PcnIyMTEx5ld+fj6HDh0CYO/evcyaNcvlOdy/FwSh7wnUiGzr0SpCrQq//lYuj11+srk9NiLErLsItG7EuYPFgkoGusg4WTlMllLpQYjoZNrFSEFlE1aLwk3njOGD/7uQOQm+vULi7JERo2bEOx1v/goqG2lpVwkPsTAiSY+snJTmPTJy75u7+Nunh3lre6Gf5xL6I0O7gLW+pHv36wTnnXcezzzzDKGhoWRlZbkUqUZHu/4Bq6pKZmYm69at63CehISEHlujIAjdT6BGZJnxEfxj8QwmD4t32W6xKCREhlLR0EplQyvpcf7PZ/iNTKlbzwOhz/Obth/wPzWZb1s3+DxumuUgL9rmMiVZZenV55CbFac/sOB38No1Xo8LODICuNz8jTrLFBwnpenRkE2HKiiv14f6uXuNnKhu4qujuhOtJ9t6of8ztMVITHr37tcJoqOjOemkkwLa99RTT6W4uJiQkBBGjhzpcZ+JEyeyefNmFi9ebG7bvHlzdyxVEIRuJBAjsshQC2vuOJs4L8PwEqPDqGhopSrAIlYrKs+P/oiT8v4CQImWCEC64ttS/nLrZ5xu2UvW5f/GYggRgNxFcMUL8O5t0NTxHEbNSGBixI795u9AsS5GYsJDOPPxj11SWofLG3j360IWThkGwP92Ogp9y+xTiIWBxdBO0+TMhrgsvNsAKRA3TN+vH3DhhRcya9YsLrvsMtasWcORI0fYuHEjv/rVr9i6VS8iu/3223n22Wd59tlnOXDgAEuWLGHPnj19vHJBENwJxIjskUsnexUigFnEWhVImiZvJfx+DGP3/gXF3tVSgqsY8T4tRmF4fDiWkXM6PpS7CH5+CK55B87+OUxYBOG6YDEjI37TNE7Yb/4O2OtCvvBSW/Ozl3eY3Ubv7XKIESN6IgwshrYYsVhh/uP2b9w/Duzfz3+sV/xGAkFRFFatWsXZZ5/N9ddfz7hx4/je977HkSNHSE/X/4CvvPJKHnjgAe655x6mT5/O0aNHufnmm/t45YIgeMKXEVlCVCjfmZHt8/iEKF2oVPrrqMlbqadTnKIXmgYlWgIAaejbPYuiAD4LLVYYcy6c/yv43gtwzxH44XvEn38HALWR2bB4JcRmdniWfDWD1bYZaJrrzd/+4lrfrwm92+hIeYOLPX6ZiJEBiaJpWmCjE/uQ2tpa4uPjqampIS4uzuWx5uZm8vPzGTVqFBERgeVgO5C3Uu+qcS5mjRum//HlLurCyoXeolveB4LQR9hUjS35lZTWNbPjWDX//vwI509I49lrT/N53L1v7OSVL4/xf3PH8bMLxnreSbXprbNuxfrVWjRTW/4BwP7wxYQrXrpQuvBZmF/ewHlPrCM6zMqeh+c7rBQAIw7z3ZYH+FKbwNKQf/L9q66D3EW02VQm/Hp1QJN9vz9zBC9vKSApOozKhlYy4yPYdN8FQa9V6Bl8Xb+dGdo1Iwa5i2DCJX3iwCoIgmC1KMwakwzA5sN658rEzFi/x5nGZ74iI166Bo16kUTqOgqRi36rfw528bMw3p5iami10WZTCc1dBFc873LzV6ClAfA76w0sGDmPBHSr+UCECMBn35QBcNXMETz9yUHK61vQNA1F8e/wKvQfRIwYWKww6qy+XoUgCEOcvCK9cNOYxeKLpGj9Yu+ztddLN6DX4tW4YXD6Td1yMxYX4bjE1Da1kRwT7nLzp9WVUPlKJKhQ1aLwxAf7efSykzkQxGTe41V6u/EPzsjh6U8O0mbTqGlqM4WaMDAY2jUjgiAI/Qibqpm1ErlZ3kPaBgFZwnvpBjTESJq7GOnGOrkQq4WYcF2Q1DY7RV/sN3/14y6lzTHIl5e+KGB3YY3pvBoZavVa3AsQE66vc/aYZDLiI8xIjBSxDjxEjAiCIPQT8ssbaG5TiQqzkpMU5Xf/gCzhza5BV0pxj4xY4LvPdXudnCEQapo6Rm+qGvRtkaFWFk3JQtPggXd2m4LskpMzAO/dRkYm51un6AMFU2PDASiV9t4Bh4gRQRCEfkJekX4RnpARiyWAqbaJ0QF007h0DTow0zT2Thq+82+YdFlwCw6AOB9ixFh3UnQY9188kagwK9sKqvlwbykAi6YO89htFGrVfzaNrTZCLAoXTdJFS0qMLs7Ea2TgMWhqRgZAU5DQg8jvXxgM5J3QxcjETP8pGnDMp6lu8OMzYpqT3Q5NuoW70dabHtYK332hxzoH4yP1y4xHMdKgi4ak6DAy4iO47YKxPPb+PrN4dVx6LBnxEczNzTC7jdJiI3hr+3Fe23ocgDPHppjpqlS7q215feDDA4X+wYAXI4Z9emNjI5GRkX28GqGvaG3VP3ysVumAEgYue4sCrxcB/SIOUNfSTmu7SliIj2C3UTh6ZAPkf0bJlnFQA2mXPwa5HdM43YWvNE2lXUQl2l/H9XNG8drWYxwuayAuIoT0OD3tYkVlliUPrCVgSWdPmmO93zrF8X+JjAxcBrwYsVqtJCQkUFqqh/WioqKkpWuIoaoqZWVlREVFERIy4N/SwhDGSNPkBhgZiYsIxaLotRPVja2k+ZtPY7HC6HNg9DmUbvkIaCY93n9tSlcwxEitx5oRe5rGbt4WFmLhkUsn88Nnt3Dm2BT9s9yDD9TY8HOBHxNmtTA311Gga9SMSAHrwGNQfHJnZOj5QkOQCEMPi8XCiBEjRIgKA5ayuhbK6lpQFBif4d9jBOzD8qJ0s6+qxjb/YsSOqmpmkWdGgMd0Fl+RkQpDjESHm9vmnJTChnvO191lTZM01zTsrOYNLLRMZsq0M8zzA6TE6OeRyMjAY1CIEUVRyMzMJC0tjba2wEZpC4OLsLAwLBapxxYGLkaKZlRKNFFhAX40qzYSQtupBCoPbYW0swNqy61sbKVd1VAUR2qjp4iLsIsRD14oZmQk2nX+TkZ8hO4cu/oePE3MCVPa+XPYX6DgLVAvNl+zERkRMTLwGBRixMBqtUrNgCAIAxIjRRNo8Sp73ob//R9JNbdymPFUr/4NfHGL3jnjpxi1pFYfPJcSE06ItWdFfHyU/24ao2bEBS/OsQ40qC3U97MbVqbGSJpmoCK3koIgCP2AvcHUi3zwa/jvD6GxnARFNwir1GL1i/dri/X0hg9Ka/WLtVEg2pOYNSPNngpYdTGS7EmMeHGO9bWfERmpaGhFDdBOXugfiBgRBEHoBxhtvX7FyO63YeNT5rdJdjFSjVFnosHqe/U0hxeMyEh6bM8PlfTlM2KkaRI9Wbd7cY71tV9SdBiKojvZVvmbZCz0K0SMCIIg9CI2VWPToQre2VHIpkMV2FSN5jYbh8r0eSw+23pVG6y6y2VTIvpxlZpT0auRvvBCiT0yEmjBa1fw2drrZHrWAdM51ltRuqLP0cmZbW4JtVpMYVMmqZoBxaCqGREEQejPrN5dxEPv5lFU02xuy4yP4NrZOaianq5Ii/WROjm6ERorXDYl2iMjVVqM674+0hwldfbISC+madzFSLtNNbd5rBkxnGNfW4wuSJzTLnaB4mGOTmpMOJUNrZTVtTAho5tehNDjSGREEAShF1i9u4ibX9zmIkQAimuaWfr+fkAvXvXZnu5BYBiRkSrc2oF9pDlKjTRNL0ZG6prbTWdVgOqmNjQNFAUSIkM9H5y7CK54HuIyXbfHZenbPRTqpsTqwkaKWAcWQYuR9evXs3DhQrKyslAUhbfffjvgYz///HNCQkKYOnVqsE8rCIIwYLGpGg+9m+ehSdX1fn9Cph9/EQ8Cw2NkJCrFJX3hTkkfFLAC1DkVsRr1IvGRob47enIXwR274YfvweX/0v+9Y5fXjqFU8RoZkAQtRhoaGpgyZQpPP/10UMfV1NSwePFiLrjggmCfUhAEod/hqfbDG1vyKztERDwR5q/N1sMEXqOA1SUycskffPqNFNsjI2m9UMAaarUQFaavxTlVU2m6rwbgc2Kx6u27J39H/9fHa3O4sEoB60Ai6JqRBQsWsGDBgqCf6Cc/+QlXXXUVVqs1qGiKIAhCf8Nb7ceShbnMn5zZYf/SOv9CBCA2ws9HsksdhS5+Eow0jVHAOvs2n9N3222qmcLojTQN6NGPxlabRzHisV6kC4gL68CkV2pG/v3vf3Po0CGWLFkS0P4tLS3U1ta6fAmCIPQHfNV+3PziNlbvLupwTKARiMnD4v3vZNZR6BESIzJSSzTN314B8x7xeXh5fSuaBlaL4tnfowcwXVidxYivTpouIC6sA5MeFyPffPMN9957Ly+99FLAQ8yWLl1KfHy8+ZWdnd3DqxQEQfBPILUfD72b1yFlM3NUEpnxEV6bVAFCLAqzx6QEtpDcRayeu5ZbQh9mSesPzWc/+91Yj2LImRIzRROOxdI7s5wcw/LazW1VwaRpgiBFXFgHJD0qRmw2G1dddRUPPfQQ48aNC/i4++67j5qaGvPr2LFjPbhKQRCEwPBX+6EBRTXNbMmvdNlutSgsWZgLeHfNmD85A2uA4mD17iJufulrVtWdxLvabPOspXUtXqMzBqYY6aUUDXg2PqvooTSNREYGJj0qRurq6ti6dSs//elPCQkJISQkhIcffpivv/6akJAQPv74Y4/HhYeHExcX5/IlCILQ1wRa++Fpv/mTM3nmB6fqQ+DcUID7Lp4Y0Ll9RWcMPEVnDErsF+l0X34m3Ywnr5EqX1bwXcCIjFQ2ttJuU7v13ELP0aOmZ3Fxcezatctl2/Lly/n44495/fXXGTVqVE8+vSAIQrcSaO2Ht/3mT85kbm4GW/IrKa1rZt3+Mt7aXsiZY1MYlhAZ0LmDic7MGpPc4fHe9Bgx8CRGKht9GJ51gaToMCwKqJpeJNubESCh8wQtRurr6zl48KD5fX5+Pjt27CApKYkRI0Zw3333UVhYyPPPP4/FYmHy5Mkux6elpREREdFhuyAIQn/HqP0ormn2GJlQgIz4CGaOSvJ6DqtFYdaYZGyqxmPv7wPgytMCr4sLJjpTWN3E6t3FfOfU4cRHhWJTNXYdrwGgpV3FpmoBp4a6gkcx0qBHaJKivRiedRKrRSE5JpyyuhZK61pEjAwQgk7TbN26lWnTpjFt2jQA7rrrLqZNm8YDDzwAQFFREQUFBd27SkEQhH6Ar9oP4/slC3MDusCv/6aMoppmEqNCmZsb4FA4govOLF21l0fey+P/PfM5L2w6wpmPf8y6A2UAvLb1GGc+/rHfgtfuID5Sv++tdUnT2CMj3VzAClLEOhAJWoyce+65aJrW4WvFihUArFixgnXr1nk9/sEHH2THjh2dXK4gCELf4q32IyM+gmd+cKpHnxFPvPalXph/2bRhhId4N/Fyx19njoLueXLayEQ2HdLn2Bwua+DX7+wJqh25O4mP8hQZMWpGur92RYpYBx4yKE8QBCFI3Gs/0mL11Iy/iIhN1diSX8nhsno+yNPnzFwxw56iUW36ILz6Et32PWe2R6dRIzpz84vbOoyPM1iyMJejlY1UNLQSFmJBVTXaPRS0auji5aF385ibG3g3T7C4p2maWm00tdkASOzmNA1ASoxM7h1oiBgRBEHoBEbtR6B4cm0NtSocrWhgYtU6WH0P1J5wHBCXpbutepjBMn9yJn+56lR+9c5uM8IAEB5i4U/fm8r8yZm8vEVPl49JjWZvUZ3XdfkreO0O3E3PDMOzUKtCTHj3X4ZMS/g6sYQfKMjUXkEQhB7Gm2trm03j7f/8Fe21xa5CBKC2SLd9z1vp8XyP/C/PRYgAZCVEmmkiw+tkRFJUQGsMtDC2M5imZ/ZBeabhWXSY7ynFncQclieRkQGDiBFBEIQexJcviILKA6HPo/nydF19r57CseNN2ADklzeY9R+GGJme472zx5meHJrncGBtQ1U1x1yaHiheBefIiIiRgYKIEUEQhB7Ely/I6ZZ9ZCmVPj6INagt1GtJCNzwrKCikcLqJkIsCt+fmR1QwauvduSuYjiwqhrUt7Y7Jvb20GwciYwMPESMCIIg9CC+0h8XKl8FdpJ6vdjVn+EZ6PUfr3yp14tMGhZPbERot7Ujd5aIUCvhIfrlpqaxrccm9hqkSDfNgEPEiCAIQg/iLf2hoPL/QjYEdpIY3Yck0LqO7QXVAJxuj3Z0VztyV3DuqKlq7BkreAMjMlLT1EZLu83P3kJ/QLppBEEQehBvrq2nW/aRrHjvcjGJStHbfAm8ruNoRYP+3CMdqZfOtiN3F/GRoZTWtVDb1OYYktdDNSPxkaGEWBTaVY2K+layArTaF/oOiYwIgiD0IM6urc6kUR3YCU65wvQb8Wd4BpAQFcqJmmYUBU4b6VoHYrQjXzp1GLPGJPeaEAG3yEgP14xYLIq4sA4wRIwIgiD0MEaaJDLUYWJWSkJgB4+/2PyvLzt6c/f0WPNfw/m0P+AsRnq6gBUgJdZufCZ1IwMCESOCIAi9wPzJmZyUFgPA9XNGcvt1P0SLy8K7rFAgbpiZonE+j6f6D6NAdG9RLUCPdsd0Bk81Iz0pRsyOGhEjAwKpGREEQegFbKrGgRK9RmRxdjkjm/fCqdfCuqXQwdjdLlDmP+bREt5T/cdrW4/x1vZCapvbgf4nRuI8REZ6qmYEnLxG3NI0hiV/X9TNCN4RMSIIgtALHKlooKVdJZJWRrx1KSh28RGZCCjQVOnYOS5LFyIerOAN3O3oP9pb4vL4zJH9U4xUN7VR1ag7sfZomsZDZMSTJX9mfARLFub2SkeR4B0RI4IgCL3Avi8/BqyMUwqwKE5RkKZqQINz74fkMT6H5PnC2bNjVEo0aXE956jqC2+RByNNc7yqCZt9aF9PDMkzcERG9CiM4VzrbhhnTC7urRZnwTMiRgRBEHoa1cb+LR8AC5hoKXB70D47d9tzcMeuoEWIgXPKo6+iIr4iD4YYOVKutx3HhIcQHtK51xoIzpERX861vTW5WPCNFLAKgiD0NOufYG9rKgDjlWMednC1fe8MiU6dM6f1Qb2It5k5RuThUKleL3O8qhHo2agIOCIjZfUtfp1rnScXC32DiBFBEIROYFM1Nh2q4J0dhWw6VGGmHjqg2uCL5ezTRgAwQXGPjDhRX+L9MT84p2lO72Ux4i/yAPDa1uOAPp8GICk6vEfXlGYXIyeqmyiobAjomJ6cXCz4RtI0giAIQbJq5wl+9c5uKhvazG1eCyGPbqS+sZljWhoAEyyeIiN27LbvnSEnOQpFgVHJ0QxP7F3H0UAiD4brqkFSD3ugjEyOZnRqNIfLGsg74XC6taAy07KPNKopJYEt6gRU+315T04uFnwjYkQQBCEIlq7K42/r8ztsL/JWCFlfwn4tG4AMKkhU6j2fODKpg6dIMGTGR/LOrXNIiQlHUXq37qEzEYWeGpJnYLEoXDd7JL9+Zw+fHiglIy6cqfWf8UDo82QpjnTMCS2Jh9sW83Xs2f2uHXooIWkaQRCEAFm1s8ijEDHQ0AshXVI2MensU3UxMt5XVOT0mzpdvGpwyvCEPpnD0pmIQlIPeowYfPvU4cRGhHCkopEbckpZHrqMDFzrQjKoZHnoMpafelyKV/sQESOCIAgBYFM1fvXObr/7dSiEzJnNvtCJgI96kcgkOPvu7lhmn+BvZo4CZMSFE2Z1XHKSYnpejESHh/C903QhuH5fMYoC7nrDooCiKEzb87he39NJ6prbKKpp6spyhzQiRgRBEAJgS36l6RzqD5e0hcXK/lg9/TLRW2Rk4Z+6HBXpS3zNzDG+f3DRJOIiHZUBvREZAVg8ayQWBT5rG89BdZjHfZRu6Gb6/j82c+7v18lgvk4iYkQQBCEAgqmLcE5baJrG3lq9WHN8rNs54obBFS/4dFodKHibmZMRH2HW0RgurNDzNSMG2UlRzB2uRzz+bbvI986d7GZSVY29RXW0tKum5b9BwF1XQxwpYBUEQQiAQOsikqJDXQohT9Q0U9fcTohFYcwda6Bws37R66TTan/G08wc59kv8U5iJLmXxAjAdVNjWXOskTdtZ/GLkFdJULy0+naym6m6qc0UGSeqHYJT7OcDRyIjgiAIAWDURfjj0UsnuxRC7i/Wp+ielBZDWFgojDoLTv6O/u8gEiIGxsycS6cOY9aYZJefRXwfREYATj/jLCaGFNJMOK/YzvOwh+cJyYHinJopqtbrRvyZwK3eXdSp5xqsiBgRBEEIAKMuwle/xazRSVx8SpbLtr1Feth+QkZsD65uYOAsRnqrZgRAsYZw3Uw9EvF8+zzaNedLn+8JyYHgLEZO1DQFZALXoetqiCNiRBAEIUCMuoj0OFf30Ogw/SKWV1RHTWOby2P7inUxMj4jzue5h0JtgSFGLIqrMOkNFi24mOQIjROk8KE63fFAXBZc8XyX6naMYXygp2nEfj54pGZEEAQhCOZPzmRseiwX/OFTwkIsPHfdTKbnJLLwzxvYX1LHX95ex/0n15g1IfuK9DTNhEzvkZGhUltgCJDEqDAsvezpERFq5f/NGM0/N+Tz6Um/YP6ptd1Wt1Ne5xQZqW4KuNhZ7OcdSGREEAQhSCrsd8JZ8RHMGpNMWIiFeydXA/DPnc3c/PJONj77C5r/OJXDZXpkZKKXyMhQqi0wxUgv1os4M2NkIgDbqyK6tW7HJU1T3URqTGBzd8R+3oGIEUEQhCAx7miNybDkreTcDYu5wvoJKhbeV0/nqrZfcUHZ/2HTFOLDtA6pHQhswNxgqi0wxEhv1os4MzVbFyMHSupoaGnvtvNWOKVpGlptTMyM82sClxkfIfbzTogYEQRBCJIye1g+LTZCd+1cfQ+KovG70H+wJuwXXG39kCiaKSQVgPHaYRRN7XCeoVZbcM74VM4YncQPZuX0yfNnxEeQEReBqsGuwhrXB1Ub5H8Gu17X/w3CjdXd6KykrtmvCdyShbliP++E1IwIgiAESaldjKTGhuuunbUnzMfGW47zG8uz3BPyMm/YzmadOoUbeB+OTtPTAi7nGVq1BWmxEbzy41l9uoap2Qms3lPMjmPVnDE6Wd+YtxJW3+PyeyQuC+Y/HlBhq7sYKapuNoud3WuBMgZhLVB3IGJEEAQhSEprncSIF9fOOKWJ60LWcB1r9A0e9gu0ZkBqC7qPqSPsYqSgWt+QtxJeWwzuybLaIn17AJ02RjdNSkwY5fWtFNq9RvyZwAkOJE0jCIIQJGX1RpomPHDXTg/7BTJgTmoLupdp2QkA7DhWbabYOggRcGxbfa/PlI2maWZk5ORh8QAuA/N8mcAJDkSMCIIgBElprR52T4uL0FtD47LoWB1g4N3dM5ABc1Jb0L2cPDweq0WhuLaZ4rwNrqmZDvgfoFff0k5Lu2o/dwLgagkvBEbQYmT9+vUsXLiQrKwsFEXh7bff9rn/m2++ydy5c0lNTSUuLo5Zs2axZs2azq5XEAShzzEKWFNjwvXW0PmP2x/xIil8uHsGMmBO6D6iwkIYl657vuw4WhHYQT4G6BkpmqgwK2NSowG9vVcIjqBrRhoaGpgyZQrXXXcdl19+ud/9169fz9y5c/ntb39LQkIC//73v1m4cCFffPEF06ZN69SiBaGvsKma5H+HOG02lcpG/QKUVr0dKsr0FMx3V6CtuQ/F6U5bi8tCmf+Y35oDqS3oXaZmJ7C3qJbttbHMD+QAH6m4CnuKJiUmnKyESEC3hBeCI2gxsmDBAhYsWBDw/suWLXP5/re//S3vvPMO7777rogRYUAxVFwyBd9U1LeiaWDFRtKrl4Ki1xY0RaTzcNs15LdGkEY1pSRwrHkKv1ZPDuiCZ9QWCD3PtOwEXt5SwI7aaJoiMwhvLMaT7lM1aInKINLHAL1yU4yEmWKkuKYZVdV63WV2INPrNSOqqlJXV0dSkveCrJaWFmpra12+BKEvGUoumYJvSr/+AIAUarAojsLHiKYSftP+BPHUs1KdzWY1lxO1bfL+6IdMHZEAwK7CWpa0LgZ04eGM8f1DbYux+bhUlpmdNOGkx4ZjUaDNpnVo9xV80+ti5A9/+AMNDQ1cccUVXvdZunQp8fHx5ld2dnYvrlAQXBlqLplDDZuqcfkzG7l+xZdomp/foWqj7NO/A5CmVLs8pCh6hcjS0H9iQS9olPdH/2RMagwx4SE0ttp4rWEqN7fdQTGuN8jFJHNz2x28Uj+Vpav28vf1hzyey5hLkxwTTojVQnqcXvtzwoeZndCRXvUZefnll3nwwQd55513SEtL87rffffdx1133WV+X1tbK4JE6DOCccmUMPvAo6imia+OVgF6MaJp8e6J/M8obdE/Nt3FCOiCJIl6TrfksUmdDMj7oz9itSicMjyejYf0AtY16kzWtsxgpmWfmWLbok5Atd+v/2tDPhpw8cmZDE+McjlXRYNRzKxb3GfGR1BU08yJ6iam2tuIBf/0mhh59dVXueGGG/jvf//LhRde6HPf8PBwwsMDGzQkCD3NUHPJ7C36SzFwdWOb+f9vSut8i5GvnqWUBABSPYgRg1lKHpuY7LJN3h/9i6nZCaYYAVCxsFnN9bivEdM6Ut7YQYyU19nTNPb3TVZCJNsKql06avrLe70/0yti5OWXX+b666/n5Zdf5pJLLumNpxSEbkNcMruf/lQMXNXoGHJ2sLSe2WNSPO+o2uDgR5Rp3wUgjWrvJ/VwnZH3R/9i2gh9aF6IRcGmah7TsACRoRaa2vS0W355PWeOdX1/lDt10wCOjhq710h/eq/3Z4KuGamvr2fHjh3s2LEDgPz8fHbs2EFBQQGgp1gWL15s7v/yyy+zePFi/vCHP3DGGWdQXFxMcXExNTU1nk4vCJ3CpmpsOlTBOzsK2XSoolvz8+KS2b30t2LgKufISEm99x2PboTWekq1BMB3ZGST0x22vD/6J0YKxRAi3v6+DSEC8Nj7+zq8Pw0xkhytp2my7H4xRTVN/e693p8JWoxs3bqVadOmmW25d911F9OmTeOBBx4AoKioyBQmAH/7299ob2/n1ltvJTMz0/y6/fbbu+klCEOd1buLOPPxj/n+PzZz+ys7+P4/NnPm4x932x+6uGR2H/2xGLjaKTLyTWmd9x3txlf+xEi9Fs4Xquv7Rd4f/Y/U2HCGJUSiAXdcOLaD6RyABZUwHGK1obW9g4ioqHdN02TaIyOF1U397r3enwk6TXPuuef6rDhfsWKFy/fr1q0L9ikEIWCMOw/3d6Rx59FdDpYygbN76I/FwFUNjovNwVIfkRG78VWZXYx4KmAF+Fv7t8zCR3l/9G+mjkigsLqJUKuFDfecz5b8Soprmnjkf3s5rWkDt4S8w6WtvzH3H60UMU45zkPvRjA3N4M2m0pdSzvglKaJ18VIQUUj1U1tHZ/UjhQ2uyJTe4UBi7+7bAX9zmNubka33JWKS2bX6Y/FwM41I+X1rVQ2tJJkD7m7kDMbLTaLsuYEwLMY0SKTmH31Y5zU0CbvjwHAtOwE/reziB3Hqk3TuU2HKjitaQPPhC5jrW06ALE0UEc0J7Rk1obezS11sCV/KtlJuvAIs1qIi9Avp1kJeoTFlxBxRgqbdWRQnjBgCeYuu7uQCZxdI9AiztSYcP63s4hjlY09vCLXNA34iI5YrNSc/zithAK66ZkrCsrCPzFrbJq8PwYIU50m+BoR/9LaBpaEPg/ALm00AHMt27Bio5lwSklgSegLlNY2mHNpUmLCUBT9d50UHUZ4SOCXVils1hExIgxY+uNdtuCbQIuBVQ1u/c827n9rV4+vybmAFXzXjZRlnQdAvNJIhOJ0XNwwuOJ5vzNohP7F5GHxhIdYKKtrIa9Id/o+qXEXWUolFgW+1sYAcKrlAMOVMgAKyCBLqeCkxl3mXJrkGEc7uKIoZkdNUnSoFL4HiIgRYcAiLbcDj0CLgQ+U6IKgJ6afundeGWma0Sn6xFVfHTWldrfNtNQ0+OF7cPm/9H/v2CVCZAASEWrl3PGpAKzeXQzAxFg9GqdpsFPVIyNTLIcZoZQCcFRNN/dznkvjTKa9GHbRlCxACt8DQcSIMGDp6ZbbnmwX7gztNpUn1x5g06EAx573U4xiYPfuhYz4CLPguMCenqlrbu/W5/bUebXnhH5HfNpI/X3iq4jViLKlxobDqLPg5O/o/1qs3bpOofdYYC8uXrVL75CxxGYAcFRLp4YYwmhlvFLASEUXK0e0DHO/cqe5NM4YkZHU2Ai/73VBRwpYhQGLcZd984vbUMClkLWrdx790ajoi/xKnvroG97fVcTau87pkzV0F/6KgY/1gBjx1nlliMyIUP3ezGeaxoiM+HJpFQYU509MI8xq4VBZA9+U1DE2ZzbEZbGjahQAucpRwhQbOYre2n1US9fTcjmzKdu1D3C09RoYXiOF1U3cet5JUvgeABIZEQY0gdxlB0t/NSoy7soPlzfQ0m7rkzV0J76KgY/axUhTm412m+rtFAHjq/PKwLgzLqltocZLJ0RprV2MxEnqb7AQFxHKWXZX1VW7ivUo1/zHzRTNVIs+IM9FjMx/DCxWKhr0yEiyW/eVERkpsqcZpfDdPxIZEQYcmqaZlevQvS23vd0uHAyVdj8Mm6qRX97AhIy4Xn3+3kJVNZcumvqWdhKiPLTaBoG/zivQR8EnRYdR2dDKwdI6pud0TO8ZNSOpMRIZGUzMn5zBR/tKeX93EbdfOBZyF/F1kgXK4BTLYQBGGmIkZCTaxEtQcEzsdZ9nlOlmCS/4RyIjwoDi92v2cdpvPmR/sWsovbvuPPqiXThQqhocLagHfNmWD3DK6ltoaXdEQ7ojVRNoR5WRfvFWxGqmaeJEjAwm5uamE2JR2Fdcx+GyetpsKnuq9TqgKd/+OVz+L7Kv+SuKAnVtCpX2v0X3uTQGw+xeIydqur8Ae7AiYkQYUKzeXUx5fSu/W72vR87fn9uFK51ty0t82JYPcArcvEW6Q4wE2lEVH6l7iKw/UOaxYNmlgFUYNCREhZkuqO/vLuZASR3NbSqx4SGMmnIOnPwdIsaeTYY9PWekEY00jbsYybS7sNY1t1PXHJj52VBHxIgwYNA0zYxafLSvlO0FVd12bqNzJtCLfF+0C7tGRgavGDla4S5Guv5h7q/zCsCi6EXCAKt2F3ucbyQFrIOXi0/W68ve313EzuO6od3Jw+OxOEVZc5KjADha0UC7TTXbwpPdWnujw0NMYesvPSjoiBgRBgx1Le00tjoKN59ce6Bbzuvc7vn0J4d87tuXRkWVDc6Rke5P03gr2uxteiIy4svfxMA9EOJesNzcZqPWvpZU8a4ZdMzLTceiwO7CWt7beQKAKXaHVoORyboXzZGyeir3foam6SI2MaJja3emU0eN4B8RI8KAodh+hxERaiHEovDZN+UB12548wzx1jnjib42KnKeoXKkons7ap7fdIQpD33A/3ae6HNvFXcL+PqW7mnv9dZ55Q33yapGVCQ8xDGHRBg8JMeEc/ooPVXz+UHdy2fK8HiXfXLsYqTg89coe+1nACRp1VifOhnyVrrsO8zsqJHISCDIX5QwYDAEw8jkaE7NSeQ/XxTwxAf7efXHZ7h017jjzTPk15dM5JH/7fXZ7ulMX09grXSaLqtqcLisgYmZ3dNRs/WInvL6xRs7aWhxiJy+8FY5WtEAQIhFoV3VujXn7t559eqXx9hoN5GzoDLTso/t6km0EIaCiobFLFgOs88bSY0N9/l+EwYuF5+cwabDDlNB98hITtMewMqR1jgqQvS/vRSlBmqL4LXFLiMBMo0iVomMBIRERoQBg9Gznxkfwc/OP4mwEAtb8ivNuxhP+PIMueU/2wOKiPz0vJN4+UdnsOGe8/tMiGiaZkZGhifqd1zdWTdinMtZiEDfeKsUVOq/57HpsQBmaqS7cO68MoajnaQcY0v4zbwS9ijTLXr67/6Ql7jIsgXQC1fL7MWrUi8yeLloUgaGzkyNDTcLVgFQbeTs/BOge42Uo0dNUpRazDja6ntB1f+Gssz2XhEjgSBiRBgwGMIhIz6SzPhIrpo5AoA/rN1vXlSc8ecZEihj02P63KiotrndTJkY9SrdVTdiUzWvFujuqYqeprG13WyXzLVHfbrbEt6Zsjpd4C22riVF0QXZWKVQf0xL4JnQZVxk2UJabIRjLo3Uiwxa0uIimJGTCMCU4QmuEbCjG8lp3A1AJXHkq/qNiWN6swa1hXB0I+CoL9lbHPxNQ38bRdEbiBgRBgxGzYhRGHbLeWOICLWwvaCadfvLOuwfiNFVIPSHi4/RSRMdZmVyln5H5su2PBi25FfS7uPDrje9VYzi1ehwK/Utenqmp1ojbapGTYUe8UnEIcZOsouRg9owAB4Ke4GZOfFmzYi09Q5ubjhzFCEWhcumZbk+UF9CjNJsio+vtLGAPU3jth84Zh3tK66l2qneyx+e5id56uwabIgYEQYMRbVGZEQXB2mxESyeNRKA363Z3+HuoateIP1pxLfhMZIYHcY4e/qiuyIjxbWBhZF7w1vl7e26EGhosbFmj/6h/sa2453+IFZVjfve3Mm/NuR3eGzLoTLiNH1IXqLi+FmOsxwH4BttOBYFMqjAemyTwwpexMigZv7kTL75zQK+dYqbGInRp/Xm2Afm7VBPAjyIEft+qbHhnJQWg6bB5sOBCfn+OoqiNxAxInSKvggjFtvdDLPshkIAN50zhriIEPYW1fLql8dc9u+OiEZ/GfFtREaSosMYlx4D6B01zW1d76iJCQsNaL+ejhCt3l3EXz893GF7c5va6Q/i/SV1vLzlGL9fs69DKs925HNqNd03IkFxRJmMNM1xLYVGzS486ktMMSbuq4MfjwXK9gF6xoyaRvS/h2RqjaPMAXoGZ4zWb2Q2H/Y/aTuQtHJvpUv7AhEjQtD0VRjRUTPiuCgmRYdxx4XjAHjig/3UNDpC+v6MrozIx/KrTiU6zNUnwKLA01dN6zcjvg2PkcSoMFJjw4mLCDE7arrKyJQon4/3RoTI+CD2RWc+iIvt0bTmNtUc926QSjXV6FEm58hIklJHMjVoWDis2X//MemU1UuaZkhjH6CXYyl12axHRuyfMvYBegazRusD+AIRI/15FEVvIGJECIpVO4u4qQ/CiPUt7WYho7tPxDWzchibFkNlQyt//NBhhObL6MrZM+TiUzK5MFcPrX7rlExi7Rf6iNCORkZ9RaVTZERRFEeqphvqRqoavddk9Ja3Sk99EJfWOs7pbj6VkT2aNru7gXPNCDjqRl61ncvr1gW8XTWSwir9+P5QQyT0EbmLGHnGt102pSo1EJfl0tZrcPpoo26kzsVB2RP9eRRFbyBiRAiYVTtP8NOXt3l8rKfDiEbxamxECDHhrvY4oVYLSxZOAuCFzUd57ctjZvpobm6GR6OrjPgInvnBqWbko8J+13zBxDSumJENwH+3Hu/219FZzJoR+/Tasd1YN1LZ0GL+311vuP+ceoqe+iA26jwAjle5mqnVpp4KQBitRNLi8phRN/KCbR53N1zDHa/tNEWbpGmGNjlTznL5PuWKp+COXR2ECOgza8am6WnVL/J9R0cCFbmDVQyL6ZkQEKt3F3HLf7b73Mf57tUYOtVduHfSuHPm2BSmZsez41gNv3hjp7ndMO3acM/5ptFVWqyecnC+0zc7JWIi+O6MOP61IZ+P9pVQUd9Ccj8YF++oGdHrO4y6ke7wGnFOX0SFhfCPxTO8/px6ip76IC5xEi9GZAPVBkc3Un20FIjQoyJuL/E662oqtVgaEiegJo9D0zRUTWP6iMRBezEQAmNksmta85uwXFKx4C2OesboZL4prWfz4Uqfot5IKxfXNHusG1HQbw76Q0F9TyBiRPBLIPl8Z3oijFhkL17NcCpedWb17iJ2HKvpsN1IH/m7uzdHgceGMSEjjlOGx7PzeA1v7zjBDWeO6oZX0DUM99XEaD0y4kjTdEdkxCFGGlrbOWN0Uq87jM4clURabLjp5eGJztStuEZGmnTL7tX3QO0JqmwnA/eRaG2CyCRocqSARsW085dLZsCky4J9KcIgZ/PhChQc0eBrnt3i06l41phkXth81G/diJFWvvnFbS7nh74fRdEbSJpG8Euwfh2B3DlqmkZFfUvAKR0zMhLX8dy+xFIg6aN2m2qmQVLtUZDvTh8OwH+3HvNoqNbbGO6rSUaaxh76PdoNHTUV9Y4LtqbpxZ69jdWi8IMzRvjcpzMfxCVO4qbw2BHdsrtWH4JWhf4zTNBqUJoq4dz74fJ/wQ/fQ7n7gAgRoQNG6637J4KvmjlDQO8rrnMR/p7wNj+pt9KlfYlERgS/BBPp8Hf32tJuY9WuIlZ8foSvj9dw0zljuHfBBL/ndfcYcSaY4kdP6aPKhlY0Tb8gGjUZi6YM45H/7WVfcR17TtQyeVh8h+N6E+fWXtA7OuIjQ6lpauNwWQO5WZ2fUVPh9gHZ0NpOZFjvF+8aUa+wEAut7a6CaOm3J3fqg7jMqYD1eHEJhDouI9WavZOGOkCBbc/puX9L/ylcFvoP/lpvFeCRlbuYG/UN1oZS3W8kZzYpMeGMS4/hQEk9XxyuYMHJvt/H7vOTejNd2peIGBH8EkyO3Nvda0V9Cy9sPsqLmwvMlAjA2rzigMRIsTGXpn4P5Jfovfz2i0ZpXbM55CyNag5qmeRpI3EvBPAmqozUQHJ0GBb72uOjQrloUgbvfn2CN7Yd73MxYkRuDDGid9TE8OWRKr4preuaGHFreW1ssWEPGvQqBRV6gel3pw/nW6dkUVrXzP1v7aKhxWa6WQaDqmouaZ9CWzxaCObsETMyotTjYuU96iwPZxOGOv5ueuZZtrCk5Xmszzt1fMVlwfzHOWP0aA6U1LM5ADECjvlJQwlJ0wh+MQqrfGFRYPlV3sOIV//zC5Z9+A3l9S2kx4Vz87ljADhc3kCDtxHxqg3yP4PV91F0UC+ezdj2JDz3LVg22RzZPaFqHRvCb+OVsEd5KuxpVoX/ko/D7mIMhS6n85ZtMetF3ApVF56iv5Y1u4v7NFXTblOpaXKtGQFHR01Xi1jdQ8cNrT03C8YXhhV8TnKUOcjOiFR1ZlheVWOri819A5FUO6msak3/v0tbr93KWxDc8RUhvsiyhWdCl5GBW+u5fZrvrDDdzC9QJ9ahiIgRwS/Ofh3eePr707j4FM9CpLG1nX32YVF/vHIKG+45n3vmTyAtNhxN02c3dCBvpS44nvsWbF5OkU2PTGQq9j9mY2T3B79m3Ke3kqG4/pGPtpTwYfjP+U3IPwlDv9j+47OO7p7g6KRJcTOzOntcKlFhVk7UNPP18Y7Fsb1FTVObKaQSIh1uqUbdyIEutvdWNLgWjTb2sRgZkeToVoiN0F9vZ4blldiLV1MiNFKoBqBQSzEfrzLSNE7uq4aVtyC44y1CbEFlSejz+v87BIXtwy33PALojsAvbT46ZIbfBYOIESEg5k/O5O554zpsz4yP4K8/OJWL3ec4OHGkXL/IJESF8v+mDSfUqr/tJtlTC3tOOIkR1QbrHofXrjELDZu0MNMp0yE6NP1r09MoaB7fyIoCV4d8zKqw+wHIO1FNy6r79WiL6ij6NFpbU90iIxGhVs6bkAbA+304E8IoXo2PDCXE6nilRkeNt4m7gaCqmhkZSYnRoxANLYEXxN784ldc9pfPu0XAOMRItLkt1u4pU98JMWLcyaa2n2C4og9SPO4sRow0DfV4svIWBGe8OTrPtOwjS6n0IEQMNJLrDxBj0cXxL9/ePWSG3wWDiBEhYNzbav9y1alsuOd85k/O9Dmr5kiFbllujNQ2MOow9hTaxUjeSvjjJFj3W5f9ijW9XiCKZuJwNa5C89/5MUY5QSTNaFgo3PxGhzSPIzIS1uHYBZMzAH2A29vbj3fbHU1RTRO7CwOLthhtvUnRrusbm971jprqpjaMlzMsUY9IBCosKupbeH93MTuOVfPmtkL/B/igrrnNFEXZSY73WWxEiPl4sBhtvelqGcOUcgCOa6nm42aaxqgZcbPyFgRnvDk6p9mjbv5IVV2jt0Nh+F0wiBgRAuZohesclJzkKKwWxe+smvxy/bhRKa5ixIiM7D5RowuD1xZDXcc/zCK7GMlQKumM/YWiQLb9zviYpkc6qD2hP1/eSrNmxD0yAnq9Bugh/zte/brb7miuX7GVy/7yeYefqSccc2lcB9qlxoSTEBWKqsGhss5FRwz31biIEOLtKaBAIyP7ix3pjX9/no/aBZF2rFIvUE6KDjNTM+AsRjqTprEPtlOqzMiIS5oGpzTNGbd4dNAUBGc8td6WkhDQsaGK63t4KAy/CwYRI0LAHKlwjUqU1bUENPL6SLnnyMikLD0ycqCkjtb37wePTXNQjC5GMpXOF385xEiq6wOr76XcCOe71Yys3l3Ena9+3XE9XbyjKa1tZm9RLe2qxt4iD/UyblS5ddIYKIpi1o10NlVjpKiSY8LNYYGBRkb2OxXOHipr4LOD5Z1aA0BBpf4eca4XAYgxxIi3ImcfGJ006VQz3EdkJIF6GH9x8IsWhiTzJ2ey4Z7zeflHZ/Cn703l9ut+iBaXheZlJKeqwQktmW+0YR0eG+zD74JBxIgQMAX2u3ijdbektjmgkddGZMR9OuzwxEjiIkJos2l8U+P9rWhERjIVDw6GSmBv4WxFn7R53EWM6O2cZVXVgGtkpCfHeW8rqDL/f7yqyceeOs4Te90xUisf7i3pVArJOHdydBhRYfqFv6HVERnxlX4zIiMRofrv4N+f5wf13M54Kl4F5wLW4NM0ZmQkUjPFiBEZadOs1KE/V2JcjNSKCEFhtN5eOnUYs8amsWPSvfaRAa77Gd8/1HaNl8o2ncE6/C4YRIwIAWNERibb0yvbC6oDMhszLMvdIyOKopj+GHvUkV7PY9SMZLq3zaHArJ8GtHbjYtQhMgKUN+oX35Sa3bDrdcj/jC2HynpsnPdXRx1i5Fhlo489ddwNzwxW7y7iw7xiAN79uqhTKSTDfTUpOozocHtkxB6F8Jd+Mzqkfnb+WBQF1u0v63SExrsYCTJNY7SD73qd0nL9d54249IONSPV6O9FBZX4+Q9IrYjQaWyqxi3bhnNz2x1mFNegmGRubruDNepMn+eQeUdieiYESHVjq+l1cWpOIl8frzHnxfjDOG6kW80IwOSseDYfriRPy/F6vKNmxCkyEjdMLzjMXQTDT4N3b4OmKi9ngOFmZCTNZXubZqWqRY/0pK68GhT9Yjo1Ip2LLN/3+yHy+cGyoN0RXcRIIJERY2KvkxjxZ0sdqHW04b6aHBPuEhnxd/6/XD2Nb+xpmnm56WwvqObDvSWs2JjPo5ed7Pd53TlqF7ojkrsQGXGaOwNQ2vwUkEJaJAy7fCm8DLVEU6tFmu6rcWEK1skLg16vIBgYZmhFzGRtywzTfLGUBLaoE1B93PMP9uF3wRB0ZGT9+vUsXLiQrKwsFEXh7bff9nvMp59+yvTp04mIiGD06NH89a9/7cxahT7EiIqkx4Wbd68t7f47WQySosPMAklnJg2zF7FaxtNhdKqdYk13IsyMaNcLDX/4nuvI7txF8PND+myRyESP5/BcM6JQEaHPQwmhnXgcxaQRzaU8E7qMiyxbfL6upz85xPRH1vKnDw8ElCJpbrOxu9BRJxJUZMSepunOFJLhvpocHWbWjNS3tPs9/5KVe2hotRFmtTAyJZrr54wE4I2vCqlpDD6lYqSrshPdxIjR2uuvZsQogLYLEVVTKLMXFqZ/fAfRoY4C4MIL/kLV/L8AkBjbUSALQjA4p1hULGxWc1mpzmazmouKBQsqZ1jyWGTZaN4UwdAYfhcMQYuRhoYGpkyZwtNPPx3Q/vn5+Vx88cWcddZZbN++nfvvv5/bbruNN954I+jFCn2H0fWRkxxtFnqqmuax795AwWHS5T5228AoYt2rjUTVFDwJEiNNk3Hd8zB/qW7X7R5Wt1jh3Ht0UfLD93TREuWwUzZqRiqJo0ELtz+PRpmqi6FkarEojsuvggYK/Cb0WS61bGC6sh9vBbbVTW388cNvmP7oWr8pkt2FNbTaVELsHz7Hq5r8urtWNrq6rwYzi8cfhuFZckwYUfYL/7HKRr/nL6vTRczo1GhCrRZmjUlmQkYsTW02XvmywO/zupxP0zhht/sfluDaPh5Qmka16RERp99PFTG02QO/KdTA6nsZnqifuzDlTKriJwKQ4KEORxCCwVeK5SLLFhd36NusbwGQQvWQGH4XDEGLkQULFvDoo4/y7W9/O6D9//rXvzJixAiWLVvGxIkTufHGG7n++ut54okngl6s0HcYxmUjk6PMQs/y+laPfffO3589Ti8Y9JSiARidEk14iIWGdoUjC56DONc/zJbYEZRjd19NCOAu1mLVxcr8pXD3N6YwiYuOIt5u+31cS9VnRpx7P+XN9hSN0tHzwwKkKLX8KWw5b4Q/xGnKfvtr8xwRqm5s89tlY6RozhybgqJAU5utw6A6dxw1I7qwC7TYLZD9jMhIklNkpLYp8MjGhAw93aEoCtfPGQXAcxuPmC3RgVDZ0EpLu4qiQHq8a0dTQA6sRzeaERGDUi0BgGRqCFPaobaQYWH6e/h4VSPVRuorqmO0ThCCwZsZmieLeKMIP0mpY8OiehEiTvR4AeumTZuYN2+ey7aLLrqIrVu30tbm+UOvpaWF2tpaly+hb3GOjBi26WV1LX5HXoda9QvcqGTPQiLEamFipr2INXIG3LFbFxD2Ue6lP9wIQHiIhYRgLxxuwmR4iv48x877s57mSR5DmaYLnRQPYsSdhdZNAJys5HtN32j4TpEYYmTW6GQy4vSfmb9UTZVbN02gxW6B7Fdhuq+Gm5GRYBpyxmc4BvQtmppFUnQYJ2qa+SAv8BkvRhQmJSac8BDXiFdAkREP3jQlmp6uS1Mc9TnDw/XoS2F1E1VGtEkiI0IX8WSG5s0i3rAnKNKSsb53h4sT9FCnx8VIcXEx6emu8x7S09Npb2+nvNyzL8HSpUuJj483v7Kzs3t6mYIfnF1UjTRNfUs7ja3tHfruX/7RGaYzq3mcl8gIuNnCGwLi5O/AqLMoqtMvGpnxESidcTwzsFjJTteLV4+FjdKfJyadMnvUJVWp9nuKi6xfArBTG8PDIc/yl5BlzLLsxuIWKfGWItE0zWzrnZ6TaNZH+CpibW1XTY8No5vG252YgYL+8wqkKM5s7Y1xREZCrIrf8xtppvEZjsFzEaFWrjxN/1tdvbvY73MbGCmaLA/DGGPC/Tiw5q2E1fd12FxqipFqc9uwJH2tx6uaTO8WSdMI3YH7TZk3i3gjMlJHFHWNTbBeMgQGvdLa634RMXLk3i4u9913HzU1NebXsWPHenyNgm+Mboec5Chiw0MID9HfOuX22gGXvvsxyWZB1hEv7qvOGHUje050jE4YHTvukZfOYNiMG26f5MymLFQ3IkrBf2QkXalmkqJ7aWzSTuaSkC28HPZbdobfwM+sb7iIEk8pkoLKRsrrWwm1KkweFs9w+3qOV3mPjBjpBIsCcfaUhTdbaufvAymKs6mai6Ga0U3T2GrzeX4NvV4IXCMjALn2KFexj5oTd0wx4lYvAo7X3NKu0upeMG0UrTZ2vKkxXDHTlSqMuTPDRuuvqbC6ieoGIzIiaRqhe3C+Kbt7drzHfaKVFuLshfLFWhJsfEqiI3Z6XIxkZGRQXOx6l1RaWkpISAjJyckejwkPDycuLs7lS+g76prbzHB+TnIUiqKY0ZGyeu8XnVq347zhHBlxL+Y0QviZ8R0vVMGSnWREIuwXf4uV8vQ5gOeaEU+cZdkFwHqbo301Rmnh/0Lf4OvwH7HA8gXgOUVipGgmD4snItTKcCMyUumIjLibjBlW9YlRYVicxIW/9FggueiqxlZzGnBSlKvPiK/z//qSiaia3uniHs1It6eeimsDFyO+fseGAyu4RUc8FK06Y6ZpjLkh8x9juKfISLRERoTuw7gpmzFpotd9jOhIkZYErfUSHbHT4z4js2bN4t1333XZ9sEHHzBjxgxCQ+WuZCBgREVSYhxzQ1Jjwzle1WR2Vbig2uDoRo4cLQUiSHGbN+LO+IxYrBaFyoZWimubXS5Kxh12t0RGzIu/IxJRbkkBKkmJsoDvOlJAFyN/tS1ig3oymobLrJxYpYnloX/iRetlzBzV0V7cECPTRyTa1+MaGVm9u4iH3s1z6WQxilYTPVw050/OZG5uBve/tYtXvzzG2WNT+Pd1MwNuEzSKVxOi9GnA7g6sxvm35FdSWtdMWqye+nlvp14sOi4jtkN0M8NJjGiaFlBqrdCMjHT8HVstClFhVhpbbdS3tJNsuOR6KFp1xihgTQ9vg+88D7mLGGYvzK1saOWEPeImkRGhR8iZDZEJ0FTd4aFMpZL92giK7JYFfPEMnH33kDfeCzoyUl9fz44dO9ixYwegt+7u2LGDggK9ne++++5j8eLF5v433XQTR48e5a677mLv3r08++yz/Otf/+Luu+/unlcg9DiOFI0j1WJ01JTZ79xN8lbqE3Gf+xb5H/0DgJGt+80JuZ6ICLWaM1b2FLoWKxtpmsxuECNma6dTO60xsTf1ij/phbPf/gdEpXg9x3TLASJooZREDmjDPe7zA/VtrGvu1Z1AnUKwphjJsYuRJIc48jbjx5jY6w2rRWHWaP1Drc2mBeVXYLb12oWOGRlxmk3jKf1m2MCPt3fSOJMWp78vWttV0+zOH8Zr9pSmAaci1sYW012V/E99ntOIjKROX2T60cRHhprnOlCsd1ZJAavQI1iscPotHh8yIiMnDDHSVKWL6yFO0GJk69atTJs2jWnTpgFw1113MW3aNB544AEAioqKTGECMGrUKFatWsW6deuYOnUqjzzyCE899RSXX355N70Eoac5YnbSOFItqU4dNSZuxlNHtQwARqoF5oRcb+Q6T/C1Y1M10168prGty5MtjbRIXUu7eaE0BsWlxkXqhbOnXAHf+iPeDNgilDZOt+wF4DO1o9OootiP/OKv8Ny34I+TYN3j1G173byIu4uRwuomHly5x0vCQedYZaPX129cxE8E6Ihr4DA803+XRmSkzaZ1rM9wwngdEzyIkYhQq9n1FGiqpqjat+A0omq1Ly3Wf6Zv3ADrf+/znGVGZCTZtYjX8DFptbceB92hJQiBcvbdENaxVs7oqHGxjq8PvPtssBK0GDn33HPRNK3D14oVKwBYsWIF69atcznmnHPOYdu2bbS0tJCfn89NN93UHWsXegmzrTfJKTLiLkY85PCPqLoYGaXYa4ZW3+u1WMsoYt1dWMumQxU8/O4eTvvNhxwq05/7D2sPBD13xZ3IMCsp9ojOscomWtptpihJcRqSR+4iuOJ5iPTcjXKWZTfgWYx0oK4I1v2WHW/9EQ3ItlSQdvwDQE9phFgU2mwaxbUtPk/T0q56NTEzLuJFNc2oQQg2504agKgwR5jYOTriXsdizKQZl95RjIBTqiaAItZ2m2qKFq+REVV/vvoG/261AJoGpdhrRsad5vLYcDeHV4mMCD2GxQqzb++wORO3yAhATHqH/YYaMptG8IthBe88dbeDGPGQw883IiNKMcaEXI5u1CMQbhhFrJ/sK+HDvZ7vEoKdu+KJ7KRIyutbOFbVaF6EQ61KR6v63EUw4RK9uGzzcmiuNh86y7ITgC/UibRoIYQr/oe4faWNA2A6e+G1Z+CK57HmLiIrIZKCykYUVE73M9PCm4lZRnwEiqKnRioaWs3fjT+ch+TpPwcLYSEWWttVGlptJER5rmMx8BQZAb2IdV9xHaV+BJb+mlpQNf134Dw12US1EVN7ABhvTtn1RxWxpvtqalzHSdHOiBgRepSz79ZrQpzmZpmRES0JvdMrS6ZGI1N7hQBwNjwz6FAzsn9Vh+OOuIgRO17CkUao3ubjxj7YuSuecC5iNYRUSky450JLw2L+F4f1uTd2xinHSaOKZsL5Sh0X0PMa+023HNA32KNERrvxr0JeNC2jXwl7lK/Cb+I26xuk2LtBzlF2cFL9No+RpVCrhXR7947RJhsIzkPyDAyvkcaWdq91LAabD1d43J5urxsJJE1j1ASlx0W4dAuZHN1IXLv+QV6nBdZRVRI9HtBFVliI60ecsxgJD7EQGTa0iwaFHsZihYVPuWxydNPYIyPzHxvyxasgYkTwQ1OrjRL7He5IDzUj5XUt+gVy56sux9Vo0VSh3zm7iBEP4UibqvG7NfsDWk8wc1c8YXqNVDWabbN+IwmGKLniBYhMRFHgTHuLbyCpGpumsEM9CYBTLd/gHCXKRh/gV4fbHbtSz12hbzDRotdfXRqykUkf/gB+P8a19ka1Qf5nZNqtzk9UNRAozkPyDIy6kbpm78PyDLyJwowg2nsLq+0pGqODSrXBoXXw8aPw0aNweB2xiv7a6vEhRs76ucO1d9FLAKR5+L06z76RqIjQK+Qu0j874rIAR2SknkhqL3veMfBziCNpGsEnBfY22PjIUBe3SqPGoqyuBe3TJ1AaXe+SjRRNGlVEK/boSVSKx3Ckv8Fvngh0Pos7Rs2A3pbsiIwEhJG6yf+Ms955nTdL4TP1FO7hVZ+HfaMNp44oomlivOJk4FdXxPCi9cACjmtpHo+ttI+6T0Svm6CpCl67Rv9wA71Op/YEWa0/YzuzOLHyIQj5VkAfcO41I+DoqPnqaJXf34khCmeNcfULSrfXsJQGEhkx23rDYd3jsPFP0OoqqGK4GoA6zTXlUq1F8/9aH2aWZQ+/HX2Omf4r2ar/jA3PE2eca0akeFXoNYzPjqMbiaovIf51qGmB4szzEBctHYmMCD5x2MC7XgiMaEKrTaV23Z86HucpRXPKFR7DkZ0RFoHOZ3HHOU1jRkYCFSOgr3/Mucz5kW5UtEfLoULzXDthsEMdA8AUyyFCFKculYYyslsP6etRUz0eW2U/d5JS5/rAytt0UWKv0xlmtAs2hfrtXDIob3CtGQFHZKSkC8P4Ao6MqDaKjh0GIDPvWVj32w5CBDAjI7VuNSNfqBPJ1zJ52XY+xQmnmtsNkekxMpIokRGhj3AadZGZqP9dB5NWHeyIGBG8YlM11h/Q0wjR4SEuIfmIUKvp2WAMm3Mm3+iksTiJkfEdjcAgOGERzNwVT2SbFuxNlBqRkdjgL0ppsRFMyIhFw8Krk/7GwTGL0fDsB1qo6UJjtGJ0Aun25ESnMlzRf77HNc9ipNKe6krCTYw0V7l828G7wEfnknlupyF5BkbNSErVDs6w5BFCO7Msu7kr5DXutLxGCK7Fup5+d6YLa42HAlZ7WonV98ETYynM0/0VslTvXVKx6B/Y7pGRfE0vYtaw8Od1h81uH6OLx1NkJDEq1OwaSoyWyIjQNxgdcMGMTRjsSJpG8Ih7F8XGQxWc+fjHLFmYa3aypIbbqGvW3S5PwrWTpkNkJG6Y14pxY/BbcU2zzxqFYOaueCMrIRKLorfK7ivSL/BBRUacyE6MZF9xHb/bBr9jPhdZkng47AXScU1ZFdvbTDOUSsermP8YRCaSrZQCUEQybZqVUMUhIJq0MJrR15boHhlxf10uYsR35xJAm02l2j651oyM5K0k6theYDJx+/7LK2EfY9MUrIr+WynVEvhjyxVYULnJ8g7vxFzBzJx4XVzUl+j1QDmzTRFQ0dBCW1sbocc36y3Oh9bBgf+5uFIaRXzG+j0Ri+eakfyQ0Rja6KUvCnjpC72+JtSq/4wbWtqxqa5GcIqiMCwhkm9K62VIntBnZJreQCJGDESMCB0wuijchYFLa63lS1IbDnGYcebkW2ecxYgGKD4qxo3Bbze/uM0cxOaJjPgIFzHUGUKtFjLjIymsbmJnYTUAKQG2wjqzencRa/eWumxbo87kw+YZnGbZx31nJjAlqhK2raC4XI/ipFOlF7HNf0zPIas2UuOiCS9rpYUwirRkRiiOcxpRkTDaiMb3h9YwRR8W5+Jd4MNIyZjNoij2dIXdsC66/WYAGtEFhcXpt7Ff1R1nRyrF/CLsv9ylrsL6hzCXtkXiski+6HFCLCG0qxply84iq2Gv13UUafrPJtOXGFGMyEikXqiaNgFi0vnqTRs0d0zrtNlbsv698Qir9xR3eM8MT9TFiFjBC32FMdOpSNI0JpKmEVywqZrXLgpj27q3n0V77RpSVf0CUu4hTXNE07tmRirFPNn2HVarp3XYxxlvg9mSokO5Yc5IXv7RGWy45/wuCREDo26guU2v3wg2MmL8jDw+hoUv1Fxu+no0trN/ge22XRyNOgWAlhk3Ybttp6O41GJFWfC4mao55paqqXIqXnXuPNY8/HKMyEIZibRo9nsMH0ZKRidNYlQYVlTTsC5K0UVPgz0i4/y8+7URgN7aDBDS3uAqRABqi7D8dzFpofp5iuu8W8I3a6FU2IVsIJGROqJg9Dlw8new5ZzJ4XL/nUNFdgHtbJY3Y6QugCZneZ6sKgg9TYa9eyyYgZKDHYmMCC7462xRUPlF23JQIFWpBhzW2wZVWgw16LNmwmllue0y0t7NY25uhs/0irfBbJ1NyXgjOzHKpTU42MiIv5+R0X789McHeeXLAop0R3t+tRn+svdT1zv13EVkZ73HocKOYsTspHFK0Wiafv4aYoinAcUuEROpIxw9wlKsJZMTH+LTSMmlrdfJsM6IwDRqHestCu1RlxznomSPr14hvbWAE4w1Z8R4wkjRRNFMPN6Fhdnaq8SYr2nd/lKCsZp56N08zp+QzldHqxiWEMFfrjqVeZMyAj+BIHQjRmSkQwGrfcioc9pzqHiQiBgRXPDX2XKr9W2SFP3q6hAjrneYRltvBhU83v59bFi8toG6Ywxm60mMIlaDgFt77QTa/fPHDw902ObJRTY7eyQUHuX4iMugaodZU2H4tDh30lQRw31tNwLw17A/gT2xpSh6quawlsUJLZmc+ff5/BAzh+TFhEF9obk9Cn17Ax3FSIWmNyGmKrUdHnNF0+tjNPyIEUeKxtdw3xijgNWaYL6mvCJ/a3BejS4Oz1j6kVm0C3oRYVfTfoLQGTKcRjiY063zVpqt+iZxWTD/8SHhRSJpGsEFX50tFlSuD1ltfp+KPtSujASX/Yx6kRZCWaPONLd31hsEOs5H6crQvGwnr4mwEAtxEcFp8s62FYNnF1nDFfRY3FT4+WE2zFnBba0/5Y/t3wagXIvjqbbL+H7r/cxo+Str1JmsUWfy5cxlEOe4kJpFrDPv9/vh5TIkzymdE614j4xU2h0RkvyKEUhX9PRNsea966lQS3FZtzdiI3WxWN+umLN3Wtq8D/LzhrMQAYcw7Mq8I0HoDJn2NE1jq43a5vYOQ0ZNaosCbtUf6EhkRHDBV2fLTMs+Eu1REfAeGdlqtz6vcrPz6exF3NN8lK7c1RrTckGvF/FoBe+DQLt/vOHsIjtrTLK5nmOVjWCxYh19Nis/cnR6HNBG8KRtRIfz2MYvhPnXmGHdzK1xsL+NE1ET/K7BxfAsZ4p+B1ZbRBSuNSPOlNsjI8kELkZ8Rkbw0UkTFgNjLoDTbiB22CxYshZNg4bWdmIjQumOzJ2eUNKFob8UoiB0J5FhVhKjQqlqbKOoqoF4tyGjDuzv0tX36qZpgzhlI5ERwQWjs8UTF1q2unyfqtgjI041IzZNYbVtpst+XfEG8TYfpSt3tc7zSTrTSePrZxTM5cyIFJlGbFV6OsIQO95w+Xk6GSllDR8JwIka/xX6Fc6GZxarHgoG0y230UeaJtn+e/eOQoZdqJbgXYwYnT9GJ01zWCKccQv88D24twCufB5Gn0NEeBhhVv2jqq5Z7+U1hjfqz9Z5ujpeQBA6i1HEWvTNto4RERecWvUHMSJGhA7Mn5zJ8qunuXzIX2TZwg1OKRpwiJFK4rBp+t5btfFmOB+65g0SSGdPZ4bmpcdFmF4UqTGd85owu3/iXMVMRnwEd144NqBzGJEio4alrK6F5jZbQGLH08/TmLtyotp/OsxM0xj1MrmL4IrniYrSz9Fgpmn051A1xaxhSfbpeaLvnz75HMB3msYQIwdtWXyv9Vds/+4WmL9UF1dud4AxEY6ZOQD59k6am84Z3aEDqzN0JYUoCJ3BbO+t8Cfu7XgYRjqYEDEieGTysAQ0IMSi8IfvTOKphFc67JNELQoqKhYqiEMD3rbNcdknIz7CpVgzGALtWgn2rtZqUcwLt98heT6YPzmTz++9wLQ///UlE9lwz/n89PyxZMZHeL1jd48UxUeGEhOuX2yf33SETYcqmJubwdi0mA7H+vp5ZpliJJDISMcheeQuIvryvwDQGH+SHqH4VSksXkntuO/Qbs/qJtnTNFpkEkS6RT7isuCK50k/X/crKcGzGKnSYtitjgLgPW02R2NPZeYYzy60gOn2W9fchqZpphj5zvThbLjnfF7+0RlcNjWrw3FJAbqsdqUOSBA6Q2aC3YW1vePfuUc2Lx/UtSNSMyJ4ZM8J/YIzPiOWy5OPQWPHds4QRSWZOsqJp0xLIIVaPgk/HxrhR2eN4vwJ6V1qzQ30brUzd7XZSVEcqWgMupPGHatFYWx6DMW1zcRHhZmv1TBxc8dTZGPNnmKa23Tn1d+u2gfotuwNrfq2Ry+bTGxEiN9WZ+PD7UR1k6NC3wuVnsQIEBWhf99giXa4t44+h7eLRsCuPKzY+HnbTZSSwLGwKfx6YS7zY/I7tCJmtOgRjAYtgrrvv8vhg3t5Z+NOKrU4ikniC3UCmtO9kL/ImSlGWtopq2+hvqUdi6L/Ho0OrDNGJ7HnRC3flNbz3enD+fapw5mek8g5v//Ea32Pgi7wOjteQBA6i1HEeoJUe82Wr1QNDPbaEYmMCB4xWidzM+N8OnmmOBWx7sj9BcWNCtFhVv5v3nhmjUnuUlFgoHernbmrnWk3vpqanRD0se4YUZbjVY46BiONY6SDDNwjG0ZNTLtbqskQIhefnMEPzsjh0qnD/P48s+wfbg1Ghb4PjCGByW5pqmj7oLzGFoct/erdRTxoN3mzYWWlOpvNai4natu4+aWvWd1wEpz8HZf0SnR4CLH2aE9J4qlMueQnzPz+r/gi9kI2q7kuQuSp7031GzmLDdcjHHXN7Rwu06MiwxOjCA9xfCgrisJjl5/Md6cP59cLc5k1JpmwEIuZ8nL/yXXHeAFB6CxGXVhRbbNZs+WbwV07IpERwSN59sjIpKw4n06eqUoN+zS9iHWj7TSglQsmphMR2nXl7q9rxd9drU3VvBqo/fT8k/j+6SO6HBkBhxgprHJNj8yfnEli1B5K61q4e944puckuazBV02MwfaC6g7zVbwRGWYlKTqMyoZWTlQ3ER/pOUXR2q6atRfJ0a6v3xgi19Da7rJGT/jrRkmPj6CutJ6S2mZOSotxMbXbcayKx1fvJzEqlEVTh/n8XYFzzUgbDfaoy6iU6A5rmp6TxPQc1/eDIQzdO7K6Y7yAIHQWZ68Rchfpxdubl/s9Tq05MSijCCJGBI/sNSIjWfGQM8Js/XRvP0ulGoDS8BGsOqZfyBZM7h5nS18za/zd1fprB1YUpVuECMBwp0nAzrTZVMrsEYgrTsvuEMHxVxMD+gfVH9ceYM5JKQGlvDLjI0wxMjHTUUjc2q6y9UglH+8r5eP9+vwbq0XpIFii7dGM5jbVFAiB1u24m9Wlx4VzsLTeZTKpkVJpamu3rzcyoNZtI02zp7CW4lr95zwyuaMY8UZvufsKQqAYkcyiarvx2fiLAxIj1W/fzdGSZqZd9MOeXmKvMhgFltBFqhtbKbQXQU5o2Q173oJTr8VxL+zA6Kj5NGYBx6uaiAy1cu74tG5bi7eZNb4KOXuiHdgXwxL01txCt8LRsroWNE0vAk6J7ih8Aq11efqTg3z/H5s58/GP/a7dvYhVVTWWrzvI9EfWctU/v+CfG/I5XNZAiEXhmjNysLhdjI3ICEBja3uX6naM6b0lHh4zOn5CLIrH31VRTTM3vbiNVTv1PHqVvcblP1sK+HifPsvnre3Hg/pdGkIokJSXIPQ0xmdaU5uNmqY2yJmNFpeF5qdZPUGrZcrG29i+5rneWGavIZERoQNGvcgISzlxr1zleCAyCdBchqOlRmpQD1tK9D+gc8enEhnWvcVVwdzV+msH7gmTK8O35ER1k0tKxRiClR4X0eGiD8HXuniykndnmNNo8qqGVu56bQef7Ncv3snRYZw7Po3zJ6Rx1rgU4iI6pnHCQyxYLQo2VaOx1daluh2jy6jEQ2SlyO6FcrCs3mea6qcvb+eGY9Xma3Cmtrnd789DEPorEaGOtGpRTTObDzewrvEH/Fb7HRq4GPuVagnE0UCE0oZFAVWDzE0PYbvgaqwhg+MyPjhehdCt5G3fBFjI5bDrA4YIOfd+SB4DMemkVo+EV3eauyw4uWcuCoHOrOlKWqGzpMdFEGJRaFc1SuuazSp54yKcHuc5HRSsk2sgYirL3lGz8WA5K3ecoLC6ifAQCw9fOonvTs/2KIqcURSFqDArdc3tNLS0m2v09jP1VbdjREY8TSY1IiONrbYOjzmjavCPz/J97iMOqsJAxUirvvf1CZavO4TGVKosd/Db0H8RqrXzrm0Wr9nO5WvtJC60fMU/w/4A6EIlgwr2fLGGSXMu6eNX0T1ImkZwRbWRt1tvSc21HHV70H7J3PYcTPp/MOosUuMcbqZhIRbOn9B9KZrO0JPtwN6wWhSzrda5iNW4gBvixNNx3jo9vOHPW8V4rq+P11BY3cTI5CjeumUOV542wq8QMTA7alo7b8AGTmma2pYOjwXihRII4qAqDGSMjpoXNh81b0jWqDO5tOURZrYs55ftN/K1dhIA69VTaNNco85NVYUMFkSMCK4c3Uheiy4ochV3MQLu7WVpTqZhZ49NNc27+oqebAf2haO913GRLXFK03jDW02MP7yJKee5OwsmZ7DyZ2eSmxXncV9vRIXbO2rsXSvzJ2eabpHO+DO0M15TiYfIiL/C3WARB1VhIGLcPLi34h8njWbCGasc51chLxJDI62Eclhz/VuLTBzWa2vtaSRNI7jQXFPCQU13ssy1HPG+o917xLkjpbu6aLpCV9uBO8vwxCig0qWI1UhPZMT77tpxron5/GAZT39yyO/zeRNTpwyL56fnnUR2UiRXzMgOeggguEZGDFra9Sm5j337ZCLDrAF1oxjpqdK6FpdaGlXVzJqR1Nhwyuo6Rk6CRRxUhYGIEVHtiMY/Qp/gfGU7Vgustp3GVm08e7UcxnMcVYNSJZkJp1/Uq+vtSSQyIrhwsDWFdkJIoI5MfIS+7d4j8ZGhjE6NJiUmnAsnevcj6S18pT560uTKk/GZcfef4SVN44xRE3Pn3PFBWcm7Y7Eo3H3ReK48bUSnhAh49hqpbNS7Wc6fmBZwN0pqTDgWRT/eGMwHUN7QQptNQ1HgQS8poEDpyhBGQehrvA/EVHjddg6KvVjVSJnnqTkY/ohFs5YMmuJVEDEiuJGHPi8k13IUz9cyBeKG6bbf6AWP79w6hw/uPJv4qMDmgPQ0nWkH7irDEr2naTJ8pGnc6Ssx5YzhNWK4sFY3tqLZPwCTogIfLBhitZiRs5IahxgxilfTYyO45JQsll81jc68HHFQFQY6c8akMCkrjojQjpfiNepMbm67g2KSmGhPme/VRlCqJPP17KcGnc/I4JFVQreQV1wPwCTlKHizGpv/mMtshFgPLaJ9TW+bXBntvUYBq6ZpptlXMGIE+t4x1D0yYgzVS4gKJcQa3P1LRnwEpXUtFNc2czLxAByt0O3cjRD1xadk8TQKt/zH+yyfq2Zm89KWYx3OLQ6qwkAmLS6C/912lumNBK6fuB+oM1nbMoOfTmyEvbAz5BRS7ssj48SXsOt1l3lQAx0RI4ILhg187qwFcOAT1+FNcVm6EMld1EerC45A24G7g+FOxmeaplHd2GbWWaR5ae31RV86hrrXjJhzbKIDj4oY6LUcNWb9jE3V+Ounesv49BGOib8Xn5LJXy3eBdick1JcxMjffjCdC3PTJSIiDAr83YCcOz6Npx9YTU0rVD51NmkN+x0Hx2Xps20GyOeyN0SMCCaqqjkG5M04Fy7erXfNuE1kFTqSER+BRdELPcvqW6io16MJSdFhnZ7T05tiyhn3bhpzwm8n7PON4t1Suxh5besx9hbVEhcRwi3nneSyry8Bpqp6jYmmQVxECPMmpXe6JkYQ+iP+bkBGx2kcrFHYUxtBmvNHSm0RvLYYrnh+QAsSESOCybGqRupb2gkLsTA6NRosFscYecEnYSEW0uN0c7DCqiaqm9qA4FM0/QH3yIghrDoTGTFef3FNM7XNbTyxRr+ju/3CcSR5OJ83AWaxKMSEh1DX3M6o1BgRIsKgxOsNiGpjYvMODjKNvdoIzuNrpwftdoir74UJlwzYG0YpYBVMjBTN+PRYQoOsDRCc6kaqm0z31WD9Q/oD7pGRCiNNExO8GEm1+9DsKqzh/jd3UdHQyujUaBbPygn6XLH2wtrRHqb1CsKg5uhGcm37AMhTR3rYQfd/sh35vFeX1Z3IFUcw2WPUi2QGZ5Il6DgbnxWZVvADT4x0iIwYaRoPw/58sXp3EY+/r3+A7iuu472d+lC7BZMzOiV2jULpUSJGhKFGfYlLR403HvqP/2Ga/RURI4LJZwfLAZiSndC3Cxmg6MZnekeN0dbr3Ueg/9Khm8ZI0wQRGTG6Ayob2zo89pdPDnXqAzMlVn/+cekxQR8rCAOamHRyLQUA5GuZNGme/xYPNEb3yGTy3qBTYmT58uWMGjWKiIgIpk+fzmeffeZz/5deeokpU6YQFRVFZmYm1113HRUVFZ1asNCRw2X1vLKlAFX1Pm6tsqGVVbuKvO5zorqJr49VoyhwYW7fzpcZqDi8Rhod7qsDMTLi5jNSGWRkxNfkZHAM+7P5eL964v6LJ3Lvggn9wlxPEHqVnNmkxkWRQg0qFvZr2S4PaxqUa3FsVccBnfv76muCFiOvvvoqd9xxB7/85S/Zvn07Z511FgsWLKCgoMDj/hs2bGDx4sXccMMN7Nmzh//+9798+eWX3HjjjV1evKBz/1u7uPfNXbzy5TGv+/z8v19zy0vbeG7TEY+Pr9lTDMCMnMQuW2vbVI1Nhyp4Z0chmw5VDLg/is5ipGkKq5tMj5H0QRAZKbe7p3oqOPVEMJOTg2FSVjw3nTMmaK8TQRjwWKwoCx5noj06sld1TdUoCqQotawPv4N5li0Dcnhk0H/VTz75JDfccAM33ngjEydOZNmyZWRnZ/PMM8943H/z5s2MHDmS2267jVGjRnHmmWfyk5/8hK1bt3Z58QK02VS2F1QD8MqXngXh8apGPt5fCsB/vihA0zqKg9W7dTFy0aSuzZdZvbuIMx//mO//YzO3v7KD7/9jM2c+PnDzmMEw3MmFtXgAp2nMyIhbN01KgGmavpicLAiDntxF5E6cBECe5rkAPINKngldxkWWLXyRX8GPn9/qMi+rPxOUGGltbeWrr75i3rx5LtvnzZvHxo0bPR4ze/Zsjh8/zqpVq9A0jZKSEl5//XUuueQSr8/T0tJCbW2ty5fgmf3Fdaa51s7jNew5UdNhn9e2HjftvL8prWebXbwYlNe38OURXUXP78KwO6NOwP2uuLimecDmMYMhyx4ZaWy1UW2vlRiIBaxmZKSlnTabSo29TTlQn5G+mpwsCIOdiZNPBWCnOhpPAWfDA3BJ6At8vLeED/JK+Pun/gdv9geCEiPl5eXYbDbS011ztunp6RQXF3s8Zvbs2bz00ktceeWVhIWFkZGRQUJCAn/+85+9Ps/SpUuJj483v7Kzs73uO9TZedxVfLzmlqqxqRr/3apvM+7SX3WLoKzNK0HV4ORh8WYRZrD4qhMwtg3EPGYwRIRazVZWgMhQK3ERA8/Kx7mbpspeL2JRICEyMNt/Y3JyZ4f9CYLgmdwsvdPxoDaMjtOrdCwKpFPJ4VL9Jv6jfaUeo+H9jU4lX90NhzRN82pClJeXx2233cYDDzzAV199xerVq8nPz+emm27yev777ruPmpoa8+vYMe+1EEOdr49VAzBluD73463thTS3OUa/rz9QRlFNMwlRofz+O1MAePfrIuqaHV0ORoom2KiIc23Iis/ze6ROYKBh1I2AfsEdiOZcps9Iaztl9Y56EUuA1uv9YdifIAxGRqdEE2bRaCCSY1qq1/32a9nU2z/ij1c1cbC0vpdW2HmCEiMpKSlYrdYOUZDS0tIO0RKDpUuXMmfOHH7+859zyimncNFFF7F8+XKeffZZioo8h+3Dw8OJi4tz+RI88/XxagBuPncMwxIiqW1uN8UFwMtb9CjIt6cNZ85JyYxOjaapzWZ6PtQ0tbHxkN7SG0y9iHttyCP/2xvQcYO9TsCoG4GBmaIBR2RE0xwTdoP1GOmLycmCMNgJsVoYn6TfLHirGwH4yt5VY/DRvtIeXVd3EJQYCQsLY/r06axdu9Zl+9q1a5k9e7bHYxobG7FYXJ/GatV/mAMhdNSfaWxt50BJHQDTRiTy3enDAXj6k4NsOlRBUXWT+Sb83sxsFEXhe6fpKS+j8+bjfSW02TTGpsVwUlpg/g3eakMCYbDXCWQ5RUasFmVApqUinWbpFFQ2AoF30jgzf3ImG+45n5d/dAZ/+t5UXv7RGWy453wRIoLQBSaM0P9+8lTPYkTVYL16CuCoIfl47yATIwB33XUX//znP3n22WfZu3cvd955JwUFBWba5b777mPx4sXm/gsXLuTNN9/kmWee4fDhw3z++efcdtttzJw5k6ysrO57JUOQPSdqUTVIjwtne0EV/7FHQQ6W1vP9f2zmvCfWYVM1po1IYFx6LADfPnU4IRaFr49Vs7eoNugUjT8PCW8MhTqB1buLeGWLox5nw8HyAdlJZLEoZhHrMbsY6YwVPDhmbVw6dRizxiRLakYQusDq3UWsySsB9MiI+72O8f1n6sku3289Wkl1Y2tvLbNTBC1GrrzySpYtW8bDDz/M1KlTWb9+PatWrSInR1dpRUVFLp4j1157LU8++SRPP/00kydP5rvf/S7jx4/nzTff7L5XMUQx6kUy4iO4+cVtlNa1uDzebO+y2VdcZ14QU2LCmZurp9RWfH6ETw+UAYGnaPx5SHhiKNQJGNGi2uZ2l+0DtZMoyp6qMcRISicm9gqC0H24f8Z8rk6mGNebu2KSubb1FzTj+veqavDJ/rJeW2tnULQBkCupra0lPj6empoaqR9x4mcvb+fdr08QGx5CXUu73/3/as/Vr9tfyrX//tLcnp0UyfqfnxdQseU7Owq5/ZUdQa0zMz6CJQtzB2143qZqnPn4x15FmoIuGDfcc/6AEWPn/P4TjlY0MjYthm9K67lr7jhuu2BsXy9LEIYk3j5jFFROt+wjjWpKSWCLOgHVS4xhzphkXvrRGb2xXBcCvX4PvL5DwcSIjAQiREBvrZ2bm8FZY1PJio/ghP2NPX9SRsBdH4HWfPz6komkxIaTFqunZgbKRbgzBOM46nE8eD/EjIxUdS1NIwhC1/H2GaNhYbOaa35vQSWdSkpIIoMKSkk0xcn2Y9W021TTwdimamzJr6S0rrlffE6LGBmgVDa0msWFgeJ8QfzujGz+9NE3QHAtvYaHRHFNs8e6ESMKcO2cUYNagDgzGB1Ho+01I81teqov2G4aQRC6j0A+Oy6ybGFJ6PPc0Ho3JVoSD4S+wFTLQZa0LmatNpPGVhvbj1Vz2sgkVu8u4qF381wETl9HsGXIwwBlp72l15PduIKKFRspVHOGJQ8LqvmY8aa+4rRsYsJDGJMazbTsxICfVzwkOjIYHUejwl3vUyQyIgh9h7/PjossW3gmdBlRWhP7NH1uzQzLATKo5G9hy8gJ0f2dPtpb2m+dskWMDFC+PqY7r55uj1QYXGTZwufht7Et/Cd8Hn4br4Q9yobw27jIsgVwvKmHJUTy4V3n8MbNswM2szIQDwlXBqPjqBEZMUjuRGuvM0N1eKIgdAe+PmMUVJaEPg/A19pYNCyMUEpIU6rN1t6fhf8P0K0c+qtTtqRpBihGZGRKdgLzJ2dw84vbmGdXx+DoLwfH8KT7Q3/BzFEXO7Z3YYjb/MmZzM3N6Fc5x77CiBbd/OI2FHD5Qx+o0SKjZsQg0Lk0nuiPIWFBGEj4+owJo510qrAosFUdD8AM5YD5uEWBC9vXY1Wu4UCJbyfWvqxvk8jIAETTNNN5VRcjmTxz9RQeCtPVsfs1zzE86XmsTimbriIeEg4GW7QoOtwRGQm1Kp2esdNfQ8KCMNDw9hnTQhgfq9MA2KrpzqszLPtd9klQGpieYiNQ+qK+TSIjAwD3qufhiZGU17cSYlHIzdRbpeZXvARUepudhEWByKZiOLoRRp3Ve4sfQgymaJFzZCQpOqxTM3b8DU9UcHR4DcSfkSD0Nu6fMWvzSnhvZxH/ts3nXMvXbFdPAuA0NzECcP7oKLaUtXTY7om+qG8TMdLP8RTiTojSp6dOyIwlItQKeSth3W8DO2F9SU8sU7BjRIsGOs41I0md7KQZjC3PgtDXOH/GzBiZxPu7i9moTuZN21k0E0489YxRTjgdoUBcFuefMYPHvvjc57mNbsi+qG+TNE0/xluIu7pRH8eYGBUGqg1W3xP4SWM8DzQUBGecu2lSOtlJMxhbngWhPzEsIZKLJumf6Y+2/wDQu2gsihGPtEcc5z/GmPR4wqyOS35/64YUMdJPCWQGzM7j1diOfA61J3zs5UTcMMjxPNBQEJxxjox0tpNmMLY8C0J/4/o5owCoIwqA6c4pmrgsuOJ5yF2E1aIwPEkf5HnHBSf1u/o2SdP0UwKZAVPT1M6hw4cY53MvJ+Y/Bhar//2EIY9zZKSzaZpADfIGUsuzIPQ3puckcvKweHYV6nYPp118A8RepkfBc2a7fOaPSIricFkD6fGRbLjn/H5V3yZipJ8ScIhbSwhMjJx7P+Qu6tKahKGDS2TER5rGl6X0YGx5FoT+hqIoXDdnJHe99jVhVgsnn3Y2hHq+6cxJ0qMnRysa+119m4iRfkqgoWvryDmwKwtqi8BbUiduGJx9d/ctThj0OHfTeKsZCcQ/xGhHdN8vQ3xGBKHbWDgli53HaxiTFqM3NXhhRHI0AAWVDb21tIARMdJP8RfiBrur55hUmP84vLYYvN1/SnpGCBJnnxFPaRqjuNr9vWn4hzjnngdTy7Mg9EdCrRYeXDTJ734j7JGRYOea9QZSwNpP8TUDxsAMcecu0ouU4tzuMp2KlwQhGJwjI+5pGn/+IdDRUloM8gSh78lJdqRpNK1/jWSQyEg/xluIOzrMyh+umOIa4s5dBBMu0U3N6ks8Fi8JQqA4R0ZS3CIj4h8iCAOT7ERdjNQ1t1Pd2EZiF2dOdSciRvo5Roh71a4ibntlO5oGb986h7HpsR13tljFXVXoFmLCvUdGxD9EEAYmkWFW0mLDKa1roaCyUcSIEBxWi8I3JXVoGswanexZiAhCNxIbEcrd88YRYrUQHe76MSH+IYIwcMlJjqK0roWjlY1MyU7o6+WYiBgZALTZVF758hgAV58xoo9XIwwVfnr+WI/bxT9EEAYu2UlRfHmkioKK/tVRIwWsA4CP9pZSWtdCSkwY83Iz+no5whDHV3G1+IcIQj9DtUH+Z7Drdcj/jJxE3YW1v3XUSGRkAPCfL44C8N0RDYQd+1wKU4U+R/xDBGEAkLcSbfU9KE4jQ0aEXQJczdEKESOCDyrqWwgNsRAXoU/mLfhiJeu/0YXH9w/+HA6X6i278x+Xll2hTxH/EEHox+StRHttMRqaSwQzu/kAAAUlFX2zLi+IGOlHvLOjkDtf3YGqQWxECMMi21CrTwDZnGXZyQhLqb5jbZFuciYeIkIf098spQVBAFQbTe/+nHBNw/3eYKRSAkBxo0ZzSysR4f2jo0bESB/hPtPDalH4+X93YvhE1TW3s69ZAbIB+IH1Q6ejNUCB1ffq3iKSshEEQRDs2I58TmRTsUfHzGRqiaaJBiIp2PM54049r/cX6AERI32Ap5keFgVUDebmpvPkFVMozvuc4289QKGWQpTSwjzLVrezaFBbqJucibeIIAiCYMfXNHdFgWyllH1aDlsOnGDcqb26NK+IGOllvM30MCIi3zolg9iIUGJDyxlr/dr/CetLun2NgiAIwsDF3zT3HKWEfVoOBxpjem1N/pDW3l7E10wPg8fe36/P9IhJD+ykge4nCIIgDAmsI+dwQktC9XKxGaHo9YelocN6cVW+ETHSi/ib6QFQUtPIvk2roK4IolLwPiZPgbhhepuvIAiCINiZOSaVp0JvBOggSFRNT9MAtNn6z7A8SdP0Iv5mdVxk2cKS0OfJWlvp50x2gTL/MSleFQRBEFywWhTOvex6bvlPKw+EPk8WjmtKMcmstOk3sf3J+EzESC/ia1bHRZYtPBO6LLATxWXpQkTaegVBEAQPzJ+cCVfdxHdXziG7/mvSqKaUBI7FTOEn88ey9Z08CiobUVUNSz/wBhIx0ot4m+lhQWVJ6PP6/729J6JSYP5SiM0UB1ZBEATBLw5jwukuxoSqpvHQu3tpaVcpq28hPa7vh1pKzUgv4jzTw5mZln1kKZXehQhAY7kuREadJUJEEARBCAjDmPDSqcOYNSYZq0Uh1GohK0EXIP3FFl7ESC9jzPSIDHX86NOoDuxgaeMVBEEQuoERSVEAHO0n03tFjPQB8ydnMiZN7+++/syR/OSSADtipI1XEARB6AZGJEUDcKyfFLGKGOkDVFXjYGk9ANecMZJJs+brRanSxisIgiD0MLb2diLqCgDYuf8gtvb2Pl6RiJE+4VhVI81tKuEhFj1UZrHqU3iBjoJE2ngFQRCE7mH7mucof3Qcpx1cBkD1iW8of3Qc29c816fr6pQYWb58OaNGjSIiIoLp06fz2Wef+dy/paWFX/7yl+Tk5BAeHs6YMWN49tlnO7Xg7samamw6VME7OwrZdKhCdz/tYQ6U6FGRMakxjnHruYv0Kbxxma47x2XJdF5BEAShy2xf8xxTNt5GqlbBCPv03mNaGqlaBVM23tangiTo1t5XX32VO+64g+XLlzNnzhz+9re/sWDBAvLy8hgxYoTHY6644gpKSkr417/+xUknnURpaSnt/SAs5GlgXWZ8BEsW5uo92j3EgZI6AMZnxLo+kLtIn8J7dKNerBqTLm28giAIQpextbeTtekhQLeQGIHuwlpBPI1EEE0zIzf9EtsFV2MN6X3Xj6AjI08++SQ33HADN954IxMnTmTZsmVkZ2fzzDPPeNx/9erVfPrpp6xatYoLL7yQkSNHMnPmTGbP7tv6B2Ngnbs9e3FNMze/uI3Vu4t67LkNMTI2tBx2vQ75n4Fq0x+0WPX23ZO/I228giAIQrew74s1pFNhWkjEKU0kol+LjmrpKAokUseW5+7vk/UFJUZaW1v56quvmDdvnsv2efPmsXHjRo/HrFy5khkzZvC73/2OYcOGMW7cOO6++26ampq8Pk9LSwu1tbUuX92Jr4F1xraH3s3rsZTNgaOFAIzb/ht44wZ47luwbDLkreyR5xMEQRCGNk1VhR22OadqDCYce6lPClqDEiPl5eXYbDbS011bTNPT0ykuLvZ4zOHDh9mwYQO7d+/mrbfeYtmyZbz++uvceuutXp9n6dKlxMfHm1/Z2dnBLNMv/gbWaUBRTTNb8v3NiAme9t0rOVSl/6LHK8ccD9QWwWuLRZAIgiAI3U5kYscJvcb03qOa45qeSD37vljTa+sy6FQBq6K4dnxomtZhm4GqqiiKwksvvcTMmTO5+OKLefLJJ1mxYoXX6Mh9991HTU2N+XXs2DGP+3UWfwPrgt0vYFQbR1c9QSuhRNLMMKXc6UF7FGb1vY6UjSAIgiB0AxNOv4hqol225dgjI0edIiMA4YdW9dq6DIISIykpKVit1g5RkNLS0g7REoPMzEyGDRtGfHy8uW3ixIlomsbx48c9HhMeHk5cXJzLV3fia2BdZ/YLmKMb+aYuDICxSiEWxT0NpEFtoV7AKgiCIAjdhDUkhOOp57tsuzrkIz4Mu5sHQl5w2T7m8Iu9HqUPSoyEhYUxffp01q5d67J97dq1XgtS58yZw4kTJ6ivrze3HThwAIvFwvDhwzux5K5jDKzzYTFGZrw+UKhbqS9hv6a/5nEWz0IM4MChg73aaiwIgiAMfnLnLHT5PlOp5CTLCSKUto4793KUPug0zV133cU///lPnn32Wfbu3cudd95JQUEBN910E6CnWBYvXmzuf9VVV5GcnMx1111HXl4e69ev5+c//znXX389kZGR3fdKgsB5YJ0XizGWLMx1eIB0FzHpHFDtYkTxLkYe+Lic21/Zwff/sZkzH/+4Rzt7BEEQhKGBJT4roP0U6PUofdBi5Morr2TZsmU8/PDDTJ06lfXr17Nq1SpycnIAKCoqoqCgwNw/JiaGtWvXUl1dzYwZM7j66qtZuHAhTz31VPe9ik5gDKzLiHdNxWTER/DMD07tGZ+RnNl8o4wCYKwHMaJqcEJLZos6wdzWG63GgiAIwhAgZzbEZXnsJPVILw5nVTRN6/d5gNraWuLj46mpqen2+hGbqrElv5LSumbSYvXUjK+IiKpqXP/cl7TbNFZcdxoh1sD1XGu7Su6vV9GuKXwefptLAasKoMHNbXewRp3pcpyCLpI23HN+90drBEEQhKFD3kp47ZrA9v3he7rfVRcI9Po95GfTWC0Ks8Ykc+nUYcwak+z3Yn+orJ51+8vYcLCcD/KCU41HKhpo1xRiQjWy4sJcHivWkj0KEejZVmNBEARhCJG7CL77HKqPy7+qQVNkRq8OZx3yYiRYthVUmf9fsfFIUMeazquZiSh37tZV5+X/YsOcFZzZ8iePQsSZbm81FgRBEIYctomX8kvrXaiaLjycMb5/qG0xtl6UCCJGgmR7QbX5/y35lewtCtwd9kCxLkbGpcW62L5bR5/tU6UadHursSAIgjDk2JJfycsNp3Jz2x0U49o1WowepX+lfmqvRuN7fxrOAMcQIykx4ZTXt/DcxiM8dvkpAR1rTOsd5zYgz2g1Lq5p9lhYZNSMdHursSAIgjDkMKLsa9SZrG2ZwUzLPtKoppQEtqgTzJvj3ozGS2QkCOqa2zhQqkc3Hr50EgBv7yikurE1oOONY8elx7hs77NWY0EQBGHI4RxlV7GwWc1lpTqbzWquS5S+N6PxIkaC4OtjNWgaDE+MZMHkDCZmxtHcpvLql/7t6pvbbBwpbwBgXHpsh8f7pNVYEARBGHL0mfGnDyRNEwTb7cWr00YkoigK187O4Z43dvH8pqPceNZon5GLw2UNqBrER4aSFhvucZ/5kzOZm5sRVKuxIAiCIASDEY2/+cVtKOBSHtBX0XgRI0Gw/Vg1AKeOSADg0qnDWPr+Pgqrm/hwbwkXTcoA4Otj1Tz2/j52Fdagahqqppm27uPSY7wOFQRHq7EgCIIg9BRGNP6hd/NcpthnxEewZGFur0fjRYwEiKZpLpERgIhQK1eels3fPj3McxuPMGV4Ar9bvZc3t5/wep4LJnoeKCgIgiAIvUl/isaLGAmQoxWNVDW2ERZiITfT4SJ3zRk5/GP9YTYequC8339IU7v+S7zc8ik/CllFdGw8nHMPlnFzCQ+xkBLjOUUjCIIgCL1Nf4nGixgJEMPsbHJWHGEhjrrf4YlRXDgxnQ/ySmhqV5iu7OeB0BeYYjkMgNZwHGXVYoh5Xne+EwRBEATBBREjAWL4ixgpGmd+ffF4Yg7/j3Ntm1ho2YRzSYiChoaCsvpemHCJbnYmCIIgCIKJiJEA2X7MqBdJ6PBYdt0OnuRJ8KIzFDTHOOYuDh0SBEEQhMGG+IwEQFOrjb1FumHZqR4iI2pdcUDnCXQ/QRAEQRhKiBgJgJ3Hq7GpGulx4WQ6m5KpNsj/jMID2wM6z966qB5aoSAIgiAMXCRN44RN1Ty2OBn+ItOydbMzVBt8+jvY9DS01pNtP17TwJOFiKrpw4cORp3MpF57NYIgCIIwMBAxYmf17qIO5i+ZdvMXh79IAuSthLdvgtYGj+dxFySOcczXcG1cdE8tXxAEQRAGLCJG0IXIzS9u6zAxt7immZte3EZchP5jmqbugdeu8XoeRdHFiMs5SObhtmvYGXu2TN0VBEEQBA8MeTFiUzUeejevgxABh19/bXM7IRaFk7/6pd/zGVGRp9ovY6M6mS/t45ifkam7giAIguCRIV/AuiW/0iU1443RkY1E1hcEfN6D6nA2q7mkxUfJ1F1BEARB8MGQj4yU1vkXIgCnN38GoYGf94rzZvD90WfI1F1BEARB8MOQFyNpsRFeH5ugHGWqcohKYrjeujrwk0alcOYFi8RtVRAEQRACYMiLkZmjksiMj6C4ptmlbsSCyrNhvyeDSoIObFz8BxEigiAIghAgQ75mxGpRWLIwt8P2mZZ9ZCmdECKzb4PJl3XL2gRBEARhKDDkxQjA/MmZPPODUwmzOn4caVQHdQ4tPBa++xzMe6SbVycIgiAIgxsRI3bmT84kOSYMgDsuHMtPLpkd0HFvtM/h+633c6b2LKu103tyiYIgCIIwKBExYqel3UZxrd5Z84Mzcpg0az7EZQGe8zSqBie0ZH7efjOb1MmcqG3j5he3sXp3US+uWhAEQRAGPiJG7ByrbELTIDrMSnJ0mF6AOv9x+6OugsTZ4l21/wiN4teH3s3DpnqyUBMEQRAEwRMiRuwcq2wEIDspSh+GB5C7CK54HuJcDcuKSebmtjtYo8502a4BRTXNbMmv7I0lC4IgCMKgYMi39hocrdAH3+UkR7k+kLsIJlwCRzeydc9enthYwxa7xbs3AjVSEwRBEARBxIjJUXtkJCfZw2RdixVGnUWbmsvmDZv9nsuXkZogCIIgCK5ImsZOQYUuRkYkRXndxzBI82Y9ogCZ8REynVcQBEEQgkDEiB1HZMS7GHE2SHMXJMb3S2Q6ryAIgiAEhYgRQFU1CuxiZERDHux6HfI/A9XWYV/DIC0j3jUVkxEfIdN5BUEQBKETSM0IUFrXQmu7ihUbWW/9P1DsIiQuS2/vzV3ksv/8yZnMzc1gS34lpXXNpMVGyHReQRAEQegknYqMLF++nFGjRhEREcH06dP57LPPAjru888/JyQkhKlTp3bmaXuMo9s+AGCYUk6o4hQNqS2C1xZD3soOx1gtCrPGJHPp1GHMGpMsQkQQBEEQOknQYuTVV1/ljjvu4Je//CXbt2/nrLPOYsGCBRQUFPg8rqamhsWLF3PBBRd0erE9gmrj6MbXAchRStwetJuXrb7XY8pGEARBEISuE7QYefLJJ7nhhhu48cYbmThxIsuWLSM7O5tnnnnG53E/+clPuOqqq5g1a1anF9sjHN1IQaM+k2aEUuphBw1qC+Hoxt5dlyAIgiAMEYISI62trXz11VfMmzfPZfu8efPYuNH7xfrf//43hw4dYsmSJQE9T0tLC7W1tS5fPUZ9CUe1dMCbGHHsJwiCIAhC9xOUGCkvL8dms5Genu6yPT09neLiYo/HfPPNN9x777289NJLhIQEVi+7dOlS4uPjza/s7OxglhkcMekUaGmApzSN636CIAiCIHQ/nSpgNWe32NE0rcM2AJvNxlVXXcVDDz3EuHHjAj7/fffdR01Njfl17NixziwzMHJmU4DejjvCoxhRIG4Y5Mzu8IhN1dh0qIJ3dhSy6VCFDMgTBEEQhE4QVGtvSkoKVqu1QxSktLS0Q7QEoK6ujq1bt7J9+3Z++tOfAqCqKpqmERISwgcffMD555/f4bjw8HDCw8ODWVqnqW1VqdJ0C/gRSpnbo3aBNf8x3RLeidW7i3jo3TyKahxzaDLjI1iyMFe8RgRBEAQhCIKKjISFhTF9+nTWrl3rsn3t2rXMnt0xchAXF8euXbvYsWOH+XXTTTcxfvx4duzYwemnn9611XcDhg18SoRGTHyi64NxWfrUXjefkdW7i7j5xW0uQgSguKaZm1/cxurdRT26ZkEQBEEYTARtenbXXXdxzTXXMGPGDGbNmsXf//53CgoKuOmmmwA9xVJYWMjzzz+PxWJh8uTJLsenpaURERHRYXtfcdQuRrLTEuGm3XrXTH2JXiOSM7tDRMSmajz0bh6eEjIaeizloXfzmJubId4jgiAIghAAQYuRK6+8koqKCh5++GGKioqYPHkyq1atIicnB4CioiK/niP9iaOVDQDkJEWZ03l9sSW/skNExBkNKKppZkt+JbPGJHfnUgVBEARhUNIpO/hbbrmFW265xeNjK1as8Hnsgw8+yIMPPtiZp+0RjhkzaZKjA9q/tM67EOnMfoIgCIIw1Bnyg/KMNE1Okvdpvc6kxUb43ymI/QRBEARhqCNixBAj/7+9e4+Nsl7zAP6d6WXKpTNcepkWsFTUIlQMtJYOoYBAaxE5BRNB5HSHYIwQwRRN3AKJQLKGQgTWDVA0shijQlfbeiBgpSf2gkuRQoZQnIDusUD30FKL9GJZQNpn/xg7OsylM0Nnhnfm+0nehP76e2eep0+gD++8v9870r1mJCN5BPRa542GCpZVNRnJIwYiPCIioqAXsk/t7ekVnPifNlzt+D8AwKhhg9w6r9Lcglt3HT+npu921Y0LJvDmVSIiIjeF5JWRivPNmL71G+T/5ynI78tiFu35736X5PYt6W2/+ZvD7w8bHIHiv07hPiNEREQeCLlmxNEeIYNwCxm/VuGjzz5BRcP/OjzP1ZLePppwNbIn6Ac4YiIiouAWUs3IvQ3F46pLAIDp6vP4j8hdOBj5b5hcOgM93//N7tz+lvQCQEvnbZxq/GWAoyYiIgpuIdWM/LmheEZ9Cga1GYDtA/Ji5TrUnxsB8yGbc7mkl4iIyDdCqhnpaxTU6MXGiI+tT+t9SNVqnWO977SiEOj940ZVLuklIiLyjZBqRvoahQz1BSSqfsHa8FLsiNiDLHWDzTwVBOj8p2Vr+N9lJI9Agi4KztbIcEkvERGRd0KqGelrKOLQDgCYqL6M58O+RbK6xfEJv/7x8U2YWoWNCyYAgF1DwiW9RERE3gupZqSvoWjFMPdOGBoPwHLja90/ruP23V4UzH0M8fdseqbXRXFJLxERkZdCbtOz3NQEYOlSXCstttys6vBChgrQJgJJ01BxvhmbD5ttVtLotRqsnfsoxsYMQVy05aMZXhEhIiLyTkhdGemT+8RoxLywEyqVCuLsQ5fcIlSYW+32JAGAa5238e9//xGacDUM40ayESEiIroPIdmMAEDYxDyoFn8Mlfaej1a0icDij9EzfoHTTc76xjYfNqOn19U2aERERNSfkPuYxsaEvwDj51tWzfx6zXKPSNI0QB2GU/+47nKTMwHQ3HELpxp/gWHcSP/FTEREFGRCuxkBAHUYkJxlN8xNzoiIiPwjZD+m6Q83OSMiIvIPNiNOcJMzIiIi/2Az4gQ3OSMiIvIPNiMu5KYmoPivU6DXcZMzIiIiX+ENrP3ITU1A9gQ9TjX+gtauW9zkjIiIaICxGXFDmFrF5btEREQ+ErrNSG+Pw/1FiIiIyL9CsxkxHwIq/hXovPrHmDYRyN1q2QiNiIiI/Cb0bmA1HwL+619sGxEA6Gy2jJsPBSYuIiKiEBVazUhvj+WKiKsnzlQUWuYRERGRX4RWM3L5hP0VERsCdP7TMo+IiIj8IrSakV+vDew8IiIium+h1YwMjR/YeURERHTfQqsZSZpmWTXj6okz2lGWeUREROQXodWMqMMsy3cBOH3iTG4R9xshIiLyo9BqRgDLPiKLPwa09zxXRptoGec+I0RERH4VmpueTfgLMH4+d2AlIiJ6AIRmMwJYGo/krEBHQUREFPJC72MaIiIieqCwGSEiIqKA8qoZ2bNnD5KTkxEVFYW0tDQcP37c6dyysjJkZ2cjNjYWWq0WBoMBX3/9tdcBExERUXDxuBkpKSlBQUEBNmzYAJPJhKysLMybNw9XrlxxOL+2thbZ2dk4evQozpw5g6effhoLFiyAyWS67+CJiIhI+VQi4uipcU5NnToVU6ZMQXFxsXXs8ccfx8KFC7Flyxa3XmPixIlYsmQJ3n77bbfmd3Z2QqfToaOjA1qt1pNwiYiIKEDc/f3t0ZWRO3fu4MyZM8jJybEZz8nJwYkT7j1crre3F11dXRgxYoTTObdv30ZnZ6fNQURERMHJo2akra0NPT09iI+3fXZLfHw8Wlpa3HqN7du3o7u7G4sXL3Y6Z8uWLdDpdNZjzJgxnoRJRERECuLVDawqle1W6iJiN+bIgQMHsGnTJpSUlCAuLs7pvHXr1qGjo8N6NDU1eRMmERERKYBHm57FxMQgLCzM7ipIa2ur3dWSe5WUlODll1/G559/jrlz57qcq9FooNFoPAmNiIiIFMqjZiQyMhJpaWmorKzEokWLrOOVlZXIy8tzet6BAwewYsUKHDhwAPPnz/c4yL57bHnvCBERkXL0/d7ud62MeOjgwYMSEREh+/btE7PZLAUFBTJkyBC5dOmSiIgUFhZKfn6+df5nn30m4eHhsnv3bmlubrYe7e3tbr9nU1OTAODBgwcPHjx4KPBoampy+Xve46W9gGXTs23btqG5uRmpqanYuXMnZsyYAQBYvnw5Ll26hOrqagDArFmzUFNTY/caRqMRH330kVvv19vbi6tXryI6Otqte1Pc1dnZiTFjxqCpqSlolwwzR+UL9vyA4M8x2PMDmGMw8EV+IoKuri4kJiZCrXZ+m6pXzUiwCIX9S5ij8gV7fkDw5xjs+QHMMRgEMj8+m4aIiIgCis0IERERBVRINyMajQYbN24M6mXEzFH5gj0/IPhzDPb8AOYYDAKZX0jfM0JERESBF9JXRoiIiCjw2IwQERFRQLEZISIiooBiM0JEREQBFXLNyDvvvINp06Zh8ODBGDZsmFvniAg2bdqExMREDBo0CLNmzcL333/v20Dvw40bN5Cfnw+dTgedTof8/Hy0t7e7PGf58uVQqVQ2R2Zmpn8C7seePXuQnJyMqKgopKWl4fjx4y7n19TUIC0tDVFRUXj44Yexd+9eP0XqPU9yrK6utquVSqXChQsX/Bix+2pra7FgwQIkJiZCpVLhyy+/7PccpdXQ0xyVVsMtW7bgqaeeQnR0NOLi4rBw4UJcvHix3/OUUkdv8lNaDYuLizFp0iRotVpotVoYDAZ89dVXLs/xZ/1Crhm5c+cOXnjhBaxatcrtc7Zt24YdO3Zg165dqK+vh16vR3Z2Nrq6unwYqfdeeuklnD17FhUVFaioqMDZs2eRn5/f73m5ublobm62HkePHvVDtK6VlJSgoKAAGzZsgMlkQlZWFubNm4crV644nN/Y2Ihnn30WWVlZMJlMWL9+PV5//XWUlpb6OXL3eZpjn4sXL9rU69FHH/VTxJ7p7u7Gk08+iV27drk1X4k19DTHPkqpYU1NDV577TWcPHkSlZWVuHv3LnJyctDd3e30HCXV0Zv8+iilhqNHj0ZRURFOnz6N06dPY/bs2cjLy3P6H2u/18+zx+QFj/3794tOp+t3Xm9vr+j1eikqKrKO3bp1S3Q6nezdu9eHEXrHbDYLADl58qR1rK6uTgDIhQsXnJ5nNBolLy/PDxF6JiMjQ1auXGkzNn78eCksLHQ4/6233pLx48fbjL366quSmZnpsxjvl6c5VlVVCQC5ceOGH6IbWACkvLzc5Rwl1vDP3MlRyTUUEWltbRUAUlNT43SOkuvoTn5Kr6GIyPDhw+XDDz90+D1/1y/krox4qrGxES0tLcjJybGOaTQazJw5EydOnAhgZI7V1dVBp9Nh6tSp1rHMzEzodLp+462urkZcXBwee+wxvPLKK2htbfV1uC7duXMHZ86csfnZA0BOTo7TXOrq6uzmP/PMMzh9+jR+++03n8XqLW9y7DN58mQkJCRgzpw5qKqq8mWYfqW0Gt4Ppdawo6MDADBixAinc5RcR3fy66PEGvb09ODgwYPo7u6GwWBwOMff9WMz0o+WlhYAQHx8vM14fHy89XsPkpaWFsTFxdmNx8XFuYx33rx5+PTTT/HNN99g+/btqK+vx+zZs3H79m1fhutSW1sbenp6PPrZt7S0OJx/9+5dtLW1+SxWb3mTY0JCAj744AOUlpairKwMKSkpmDNnDmpra/0Rss8prYbeUHINRQRvvPEGpk+fjtTUVKfzlFpHd/NTYg0bGhowdOhQaDQarFy5EuXl5ZgwYYLDuf6uX/iAv2IAbNq0CZs3b3Y5p76+Hunp6V6/h0qlsvlaROzGfMndHAH7WIH+412yZIn1z6mpqUhPT0dSUhKOHDmC559/3suoB4anP3tH8x2NP0g8yTElJQUpKSnWrw0GA5qamvDuu+9ixowZPo3TX5RYQ08ouYarV6/GuXPn8O233/Y7V4l1dDc/JdYwJSUFZ8+eRXt7O0pLS2E0GlFTU+O0IfFn/YKiGVm9ejVefPFFl3PGjh3r1Wvr9XoAli4xISHBOt7a2mrXNfqSuzmeO3cO165ds/vezz//7FG8CQkJSEpKwo8//uhxrAMlJiYGYWFhdlcIXP3s9Xq9w/nh4eEYOXKkz2L1ljc5OpKZmYlPPvlkoMMLCKXVcKAooYZr1qzBoUOHUFtbi9GjR7ucq8Q6epKfIw96DSMjI/HII48AANLT01FfX4/33nsP77//vt1cf9cvKJqRmJgYxMTE+OS1k5OTodfrUVlZicmTJwOwfM5fU1ODrVu3+uQ9HXE3R4PBgI6ODpw6dQoZGRkAgO+++w4dHR2YNm2a2+93/fp1NDU12TRg/hYZGYm0tDRUVlZi0aJF1vHKykrk5eU5PMdgMODw4cM2Y8eOHUN6ejoiIiJ8Gq83vMnREZPJFNBaDSSl1XCgPMg1FBGsWbMG5eXlqK6uRnJycr/nKKmO3uTnyINcQ0dExOlH8X6vn09ui32AXb58WUwmk2zevFmGDh0qJpNJTCaTdHV1WeekpKRIWVmZ9euioiLR6XRSVlYmDQ0NsnTpUklISJDOzs5ApNCv3NxcmTRpktTV1UldXZ088cQT8txzz9nM+XOOXV1d8uabb8qJEyeksbFRqqqqxGAwyKhRowKe48GDByUiIkL27dsnZrNZCgoKZMiQIXLp0iURESksLJT8/Hzr/J9++kkGDx4sa9euFbPZLPv27ZOIiAj54osvApVCvzzNcefOnVJeXi4//PCDnD9/XgoLCwWAlJaWBioFl7q6uqx/zwDIjh07xGQyyeXLl0UkOGroaY5Kq+GqVatEp9NJdXW1NDc3W4+bN29a5yi5jt7kp7Qarlu3Tmpra6WxsVHOnTsn69evF7VaLceOHRORwNcv5JoRo9EoAOyOqqoq6xwAsn//fuvXvb29snHjRtHr9aLRaGTGjBnS0NDg/+DddP36dVm2bJlER0dLdHS0LFu2zG752Z9zvHnzpuTk5EhsbKxERETIQw89JEajUa5cueL/4B3YvXu3JCUlSWRkpEyZMsVmuZ3RaJSZM2fazK+urpbJkydLZGSkjB07VoqLi/0csec8yXHr1q0ybtw4iYqKkuHDh8v06dPlyJEjAYjaPX1LIO89jEajiARHDT3NUWk1dJTbvf9OKrmO3uSntBquWLHC+m9MbGyszJkzx9qIiAS+fiqR3+9IISIiIgoALu0lIiKigGIzQkRERAHFZoSIiIgCis0IERERBRSbESIiIgooNiNEREQUUGxGiIiIKKDYjBAREVFAsRkhIiKigGIzQkRERAHFZoSIiIgCis0IERERBdT/A5AXg33lDaS1AAAAAElFTkSuQmCC\n", - "text/plain": [ - "
              " - ] - }, - "metadata": { - "filenames": { - "image/png": "/Users/mhjensen/Teaching/MachineLearning/doc/LectureNotes/_build/jupyter_execute/chapter3_65_1.png" - } - }, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "import matplotlib.pyplot as plt\n", "import numpy as np\n", @@ -1263,110 +1230,7 @@ "collapsed": false, "editable": true }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Polynomial degree: 0\n", - "Error: 0.32149601703519115\n", - "Bias^2: 0.3123314713548606\n", - "Var: 0.009164545680330616\n", - "0.32149601703519115 >= 0.3123314713548606 + 0.009164545680330616 = 0.3214960170351912\n", - "Polynomial degree: 1\n", - "Error: 0.08426840630693412\n", - "Bias^2: 0.0796891867672603\n", - "Var: 0.004579219539673834\n", - "0.08426840630693412 >= 0.0796891867672603 + 0.004579219539673834 = 0.08426840630693413\n", - "Polynomial degree: 2\n", - "Error: 0.10398646080125037\n", - "Bias^2: 0.10077114273548984\n", - "Var: 0.0032153180657605116\n", - "0.10398646080125037 >= 0.10077114273548984 + 0.0032153180657605116 = 0.10398646080125036\n", - "Polynomial degree: 3\n", - "Error: 0.06547790180152352\n", - "Bias^2: 0.062082386342319454\n", - "Var: 0.0033955154592040923\n", - "0.06547790180152352 >= 0.062082386342319454 + 0.0033955154592040923 = 0.06547790180152355\n", - "Polynomial degree: 4\n", - "Error: 0.06844519414009445\n", - "Bias^2: 0.06453579006728322\n", - "Var: 0.003909404072811221\n", - "0.06844519414009445 >= 0.06453579006728322 + 0.003909404072811221 = 0.06844519414009444\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Polynomial degree: 5\n", - "Error: 0.05227921801205679\n", - "Bias^2: 0.04818727730430286\n", - "Var: 0.004091940707753925\n", - "0.05227921801205679 >= 0.04818727730430286 + 0.004091940707753925 = 0.05227921801205679\n", - "Polynomial degree: 6\n", - "Error: 0.03781367141738902\n", - "Bias^2: 0.03365768507152769\n", - "Var: 0.0041559863458613296\n", - "0.03781367141738902 >= 0.03365768507152769 + 0.0041559863458613296 = 0.03781367141738902\n", - "Polynomial degree: 7\n", - "Error: 0.027609773491022394\n", - "Bias^2: 0.022999498260366198\n", - "Var: 0.004610275230656182\n", - "0.027609773491022394 >= 0.022999498260366198 + 0.004610275230656182 = 0.02760977349102238\n", - "Polynomial degree: 8\n", - "Error: 0.017355848195593312\n", - "Bias^2: 0.010331721306655165\n", - "Var: 0.007024126888938144\n", - "0.017355848195593312 >= 0.010331721306655165 + 0.007024126888938144 = 0.01735584819559331\n", - "Polynomial degree: 9\n", - "Error: 0.026605727637184558\n", - "Bias^2: 0.010018312644139219\n", - "Var: 0.016587414993045335\n", - "0.026605727637184558 >= 0.010018312644139219 + 0.016587414993045335 = 0.026605727637184554\n", - "Polynomial degree: 10\n", - "Error: 0.021592704588021178\n", - "Bias^2: 0.010516485576646504\n", - "Var: 0.01107621901137467\n", - "0.021592704588021178 >= 0.010516485576646504 + 0.01107621901137467 = 0.021592704588021174\n", - "Polynomial degree: 11\n", - "Error: 0.07160048164232538\n", - "Bias^2: 0.014436800088896381\n", - "Var: 0.05716368155342902\n", - "0.07160048164232538 >= 0.014436800088896381 + 0.05716368155342902 = 0.0716004816423254\n", - "Polynomial degree: 12\n", - "Error: 0.11547777218876518\n", - "Bias^2: 0.016285782696017142\n", - "Var: 0.09919198949274803\n", - "0.11547777218876518 >= 0.016285782696017142 + 0.09919198949274803 = 0.11547777218876518\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Polynomial degree: 13\n", - "Error: 0.2284246870217162\n", - "Bias^2: 0.01975416527168255\n", - "Var: 0.20867052175003364\n", - "0.2284246870217162 >= 0.01975416527168255 + 0.20867052175003364 = 0.2284246870217162\n" - ] - }, - { - "data": { - "image/png": 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              " - ] - }, - "metadata": { - "filenames": { - "image/png": "/Users/mhjensen/Teaching/MachineLearning/doc/LectureNotes/_build/jupyter_execute/chapter3_66_3.png" - } - }, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "import matplotlib.pyplot as plt\n", "import numpy as np\n", @@ -1460,49 +1324,7 @@ "collapsed": false, "editable": true }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "============================\n", - "Underfitting vs. Overfitting\n", - "============================\n", - "\n", - "This example demonstrates the problems of underfitting and overfitting and\n", - "how we can use linear regression with polynomial features to approximate\n", - "nonlinear functions. The plot shows the function that we want to approximate,\n", - "which is a part of the cosine function. In addition, the samples from the\n", - "real function and the approximations of different models are displayed. The\n", - "models have polynomial features of different degrees. We can see that a\n", - "linear function (polynomial with degree 1) is not sufficient to fit the\n", - "training samples. This is called **underfitting**. A polynomial of degree 4\n", - "approximates the true function almost perfectly. However, for higher degrees\n", - "the model will **overfit** the training data, i.e. it learns the noise of the\n", - "training data.\n", - "We evaluate quantitatively **overfitting** / **underfitting** by using\n", - "cross-validation. We calculate the mean squared error (MSE) on the validation\n", - "set, the higher, the less likely the model generalizes correctly from the\n", - "training data.\n", - "\n" - ] - }, - { - "data": { - "image/png": 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data: 426.30787294\n", - "Degree of polynomial: 5\n", - "Mean squared error on training data: 3.80354994\n", - "Mean squared error on test data: 5.98822371\n", - "Degree of polynomial: 6\n", - "Mean squared error on training data: 3.66204648\n", - "Mean squared error on test data: 8.14812206\n", - "Degree of polynomial: 7\n", - "Mean squared error on training data: 0.47075725\n", - "Mean squared error on test data: 2.00607783\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Degree of polynomial: 8\n", - "Mean squared error on training data: 0.04912436\n", - "Mean squared error on test data: 0.21596432\n", - "Degree of polynomial: 9\n", - "Mean squared error on training data: 0.02522069\n", - "Mean squared error on test data: 0.08576932\n", - "Degree of polynomial: 10\n", - "Mean squared error on training data: 0.02511518\n", - "Mean squared error on test data: 1.20015436\n", - "Degree of polynomial: 11\n", - "Mean squared error on training data: 0.01640891\n", - "Mean squared error on test data: 1.35533773\n", - "Degree of polynomial: 12\n", - "Mean squared error on training data: 0.00813803\n", - "Mean squared error on test data: 0.17446471\n", - "Degree of polynomial: 13\n", - "Mean squared error on training data: 0.00759119\n", - "Mean squared error on test data: 1.08131003\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Degree of polynomial: 14\n", - "Mean squared error on training data: 0.00472199\n", - "Mean squared error on test data: 0.81333804\n", - "Degree of polynomial: 15\n", - "Mean squared error on training data: 0.00410478\n", - "Mean squared error on test data: 92.09172409\n", - "Degree of polynomial: 16\n", - "Mean squared error on training data: 0.00315593\n", - "Mean squared error on test data: 234.38533185\n", - "Degree of polynomial: 17\n", - "Mean squared error on training data: 0.00242999\n", - "Mean squared error on test data: 1271.35771826\n", - "Degree of polynomial: 18\n", - "Mean squared error on training data: 0.00228742\n", - "Mean squared error on test data: 108.27092910\n", - "Degree of polynomial: 19\n", - "Mean squared error on training data: 0.00156376\n", - "Mean squared error on test data: 1371.99051150\n", - "Degree of polynomial: 20\n", - "Mean squared error on training data: 0.00137818\n", - "Mean squared error on test data: 1887.86252988\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Degree of polynomial: 21\n", - "Mean squared error on training data: 0.00118508\n", - "Mean squared error on test data: 14859.69908626\n", - "Degree of polynomial: 22\n", - "Mean squared error on training data: 0.00092647\n", - "Mean squared error on test data: 876.51191552\n", - "Degree of polynomial: 23\n", - "Mean squared error on training data: 0.00085889\n", - "Mean squared error on test data: 5594.60815105\n", - "Degree of polynomial: 24\n", - "Mean squared error on training data: 0.00084705\n", - "Mean squared error on test data: 1277.61702282\n", - "Degree of polynomial: 25\n", - "Mean squared error on training data: 0.00079129\n", - "Mean squared error on test data: 128664.31650694\n", - "Degree of polynomial: 26\n", - "Mean squared error on training data: 0.00076905\n", - "Mean squared error on test data: 19003.94822514\n", - "Degree of polynomial: 27\n", - "Mean squared error on training data: 0.00068946\n", - "Mean squared error on test data: 2379.66219404\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Degree of polynomial: 28\n", - "Mean squared error on training data: 0.00062595\n", - "Mean squared error on test data: 4082.19983530\n", - "Degree of polynomial: 29\n", - "Mean squared error on training data: 0.00060705\n", - "Mean squared error on test data: 3250.17647619\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_7650/626635268.py:73: RuntimeWarning: divide by zero encountered in log10\n", - " plt.plot(polynomial, np.log10(trainingerror), label='Training Error')\n", - "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_7650/626635268.py:74: RuntimeWarning: divide by zero encountered in log10\n", - " plt.plot(polynomial, np.log10(testerror), label='Test Error')\n" - ] - }, - { - "data": { - "image/png": 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\n", 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\n", 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cNW7nFkAhAY08HSv0Hi8XW/w8IRwvrT6ByOQMjPr+GL4c2RpPBhn/F68x+WrfBcSlZqGWvTXeG1i69TzM3w3jOwfgm/0XMWtTDHbVrwU3BxuZKn28zLxCjP3hOC7dyoO9jRJBXveFF08nNPZy4hgsE2S0bWMHDhxAeno6/Pz8YGVlBSsrK1y+fBnTpk1DgwYNyn2fSqWCs7NzqQcRmY9zqcWtNA3qOMDWuuIzPVztbfDTK+3RvYk78gu1GL/2FDZFXq2uMs1OwvVsfL4nEUDxLJuyvvCn9mqMxp6OuJmjxjubY4x2AUR1kQav/ngSiek58HRW4c+pXbHptY5YOKQlxnX0R4fAOgw0JspoQ82LL76I6OhoREVF6R4+Pj6YPn06du7cKXd5RCSTeN2ie/qPi7GzUeKb0W3xTGhdaLQCU38+g+8OXDR0iWZHoxWYviEahRqBnk09MDCk7JmottZKLB3eClYKCb//nYZfo67VcKWPp733cz+WdBtOKiusHhcGHwterM7cyNr9lJOTg/Pnz+ueJyUlISoqCm5ubvDz80Pt2rVLvd7a2hpeXl5o0qRJTZdKREaiIjOfHsVaqcCnw0JQy94GPxxKwoLtcci8W4ipvRrr1dVtSX44mIQzVzLgZGuFBYNbPPI8Na/rgsk9GmHp7gTM/fVvPBHgBm8X4wkNH+2Iw/boVFgrJXz9Yhs09WZrvjmRtaXm5MmTCA0NRWhoKABg6tSpCA0Nxdy5c+Usi4iMWEVnPj2KQiFhztNNMb1P8X+QvthzHvN/i4WWt1V4SNLNXHyyKx4A8O5TTeHlYvvY97zWrSFCfF2RlV+EGRuijaYb6vuDSfjuYPGCgR8PDUGHQPNeU8cSydpS061bN70u9kuXLlVfMURk9Io0WiRezwFQsZlPjyJJEl7vHghnWyvM+TUWa45cRlZ+ET4e2rJGpuLeLdBg7q9/4/e/09C1iTsGt6qLro3dYWNlPKMCtFqBmRuioS7SolNgHQxvW7FJF1b3WsOe+vwADiTexH+OJePF9vWrudpH2x6digXbzwIAZvYNwuDQurLWQ9XDeP72EBE9RtLNXBRotHCwUaJeLcN0abwY3gDLnmsFpULC5tMpmPRTZLXfxyj5Vh6GrDyMX05dRY66CNujUzF+7UmEffQn/m9zDI4n3TaKVqP/HLuM45duw95GiYVDHt3t9KBAD0fM7BsEAPhoexwu3cytrjIf63jSbbz1cxSEAEaH18fErgGy1ULVi6GGiExGyfo0jb2coFAYbvzL4NC6+HpUG9hYKbD77HW8tPoEctRFBjv+/fbFp2PAioOIS81CbQcbfPZcCF7u5A93JxUy8grx32PJGP71EXResheL/zin626raVdu52HR78V3fp7VL6hSKzGP7dAA4QG1cbdQg6k/R8ly+4DE69l4Zc0JFBRp0aeZJ+YNaMaxU2aMoYaITMa5Kg4SfpSewZ5YMy4MDjZKHL5wCyO/O4aMvAKDHV+rFfjir0SMW30CmXcLEeLrim2TO+GZ0HqY83Qwjs7ugf+8/ASGtqkHR5UVUjLuYuW+C+izbD/6LtuPryIu4FrGXYPV8yhCCPzf5hjkFWgQ1sANo56oXNeRQiHh42Et4aiyQmRyBr7ef8HAlT7a9ax8jF11Aln5RWhTvxaWjwiF0oBhmIwPQw0RmYyqTOeuiPCGtfHf8e3ham+NM1cy8NzXR5Gux71+ypOVX4hXfzyJT3cnQAjghSf88POE9qVmBSkVEjo1qoNPhoXg5Ls98e8XWqNXsCeslRLOpWVj0e/n0HHxHjz39RH873gyMvMKq1xXeX45eRUHEm9CZaXAomdbVKlVrF4te8y7d/fuz3YnIC61Zm5dk51fiLGrTiAl4y4C6jjgu9Ft9VrXiEyTJIxlWHo1ycrKgouLCzIzM7kQH5GJ67hoD1Iy7mL9q+3xREDtx7+hkhKuZ2PUd8eQnq2Gn5s9fnrliUrfCDM+LRsT/3MKSTdzYWOlwIJBzTG8XcVXOc/IK8COmDRsiUrB8aTbuu02SgW6NXHH4NC698KPYf6Pej0rHz2XRiA7vwj/1z8Ir3ZpWOVjCiEwfu0p/Bl3HUFeTvj1jY5QWVVfwCgo0uKl1Sdw8PxN1HFUYfNrHXgjUxNUme9vttQQkUnIyi9Eyr3ul+rofrpfY08nbJjYAX5u9ki+nYehXx1G4nX9x7ZsPXMNg/99CEk3c1HX1Q4bJobrFWiA4pWQi1t2wnFo1pOY2TcIQV5OKNBosevsdbz2UyS6LNmL7w5crPI4ICEE3tkcg+z8IoTUc8FLHf2rdLwSkiRh4ZAWcHOwwbm0bCz/M9Egxy2LEAKzNkbj4PmbsLdRYtXYdgw0FoShhohMQsK9ridvF1u42Ff/DQX9attjw8RwNPZ0xPUsNYZ/fQRnrmRU6L2FGi0+2HYW//rfadwt1KBTYB1s/VcntKznWqWa6rraYVK3hvhjShf8MaUzJnZtiDqOKqRm5mPB9jiEL/wLi34/V+kus9/OXMOfcemwVkpYMjTEoFPb3Z1U+OiZ5gCAryIu4NTlOwY79v0+3hmPTadToFRI+HJka7So51Itn0PGiaGGiEzCOQMsuqcvD2dbrH81HCG+rriTV4gXvj2KIxduPfI9N7LVGPXdMXx/b5G3Sd0aYs1LYQa/uWOQlzNm9QvCwZndsWhICwS4OyA7vwhfRVxAp8V7MWPDGZxPr3jr0s0cNeb/FgsA+NeTjarlPPdt7o0hoXWhFcC0n6OQV2DYGWY/Hr2ML/cVD0ZeNKQFujXxMOjxyfgx1BCRSajOmU+PUsvBBj+98gQ6NKyN3AINxqw6jj/PXi/ztZHJdzDgi4M4lnQbDjZKfDWqNWb2DarWGTe21kqMCPPDn291xTcvtkHb+rVQoNHi55NX0XPpfryy5gSOJ91+7EKn836LxZ28QjT1dsakblUfR1Pu5wxsBm8XW1y69c+UcUPYFZuGeb/+DaD4xprDKrhQIJkXhhoiMgnVPfPpURxVVvhhbDv0CvZEQZEWE/5zCr9Gpej2CyHwn6OX8dzXR5CWlY+G7g749Y1O6Nvcu8ZqVCgk9G7mhQ2TOmDjpHD0DvaEJAF/xqVj+NdH8MyXh/HH36llrhXzx99p2B6dCqVCwsdDWxps0HFZXOyssWRoSwDA2iOXcSDxRpWPGZl8B5PXnYZWAM+H+eJfTwZW+ZhkmhhqiMjoCSF03U9B3jUfaoDiFpEvR7bW3eF7yvoo/Hj0MvILNZixIRrvbvkbhRqBvs288OsbnRDo4ShLnQDQpr4bvhndFn9O7Yrnw/xgY6VA1JUMTPxPJHp8ug//uVc3UDy7as69Fo4JXQLQvG71j0Hp3Mgdo8OL176Z/kt0laanX7yRg5dXn0B+oRZPBnngg0HNubieBeOUbiIyeikZd9Fx0R5YKSScfb+vrPdH0moF5m+NxdojlwEA9WrZ4eqdu1BIwPQ+QZjYNcDovlRvZKux5vAlrD1yCVn5xeNYajvYYEyHBkhMz8HWM9fQ0N0B2yd3rrG1XPIKitB/+QFcupUHhQQoJAkKSYIkAdL9z3HvuaL498Wvke69BsjOL0JegQYh9Vzwv1fbw95G1lsakgFV5vubP30iMnrn7i3YFujhKPsNHxUKCe8NbAYXO2t8sec8rt65i1r21ljxQmt0NNK7Prs7qfB2nyaY1K0h1p+4gu8PJiEl4y6W7k4AUBwalgwNqdHF6extrLD0uVZ44dujyC/UQisEgMr9HzvA3QHfj23HQEMMNURk/OSY+fQokiRhWu8mqOtqh+NJtzG1d2PUq2X8a6E4qKzwUid/vBheHztiUvF1xEWcTc3ChC4N0aZ+rRqvp7VfLUTO6YWc/CJoBSAgoBXQ3cxTK4qfi/t+FSXbtcWvF6L4uqjOcUBkOhhqiMjo6cbT1PDMp8cZEeaHEWF+cpehN2ulAoNa1cXAEB9cz1LD01klWy32NlZsYSGD4ZVEREYvXjed2zhaasyFJEnwcrGVuwwig2F7HREZNXWRBhdu5AKQb+YTEZkGhhoiMmoX0nOh0Qo421rBy5mtCkRUPoYaIjJqupWEvZ2Nbqo0ERkXhhoiMmpyriRMRKaFoYaIjFqckc58IiLjw1BDREatZOE9DhImosdhqCEio3U7twDp2WoAQGNPhhoiejSGGiIyWiWDhP3c7OGo4rJaRPRoDDVEZLTijez2CERk3BhqiMhonUstDjVNGWqIqAIYaojIaJ27XtJSw5lPRPR4DDVEZJS0WoGEkuncnPlERBXAUENERin5dh7uFmqgslKgQW0HucshIhPAUENERqlk5lNjTycoFbw9AhE9HkMNERmlc5z5RER6YqghIqNUMvOJ93wioopiqCEioxR/nfd8IiL9MNQQkdHJKyjCpVu5ADjziYgqjqGGiIxO4vUcCAHUcbRBHUeV3OUQkYlgqCEio1My84ldT0SkD4YaIjI6nPlERJXBUENERoczn4ioMhhqiMioCCHY/URElcJQQ0RG5Ua2GnfyCqGQgEaejnKXQ0QmhKGGiIxKyXiaBnUcYGutlLkaIjIlDDVEZFRKup6asuuJiPTEUENERoUzn4ioshhqiMiocOYTEVUWQw0RGY1CjRbn03MAAE292f1ERPphqCEio3HpZi4KNFo42ChR19VO7nKIyMQw1BCR0Yi7bzyNQiHJXA0RmRqGGiIyGvH3Zj414cwnIqoEhhoiMholg4SbenOQMBHpj6GGiIyGbjq3J0MNEelP1lCzf/9+DBgwAD4+PpAkCVu2bNHtKywsxMyZM9GiRQs4ODjAx8cHo0ePxrVr1+QrmIiqTVZ+IVIy7gLgPZ+IqHJkDTW5ubkICQnBihUrHtqXl5eHyMhIzJkzB5GRkdi0aRMSEhIwcOBAGSolouqWcK+VxtvFFi721jJXQ0SmyErOD+/Xrx/69etX5j4XFxfs3r271LYvvvgCYWFhSE5Ohp+fX5nvU6vVUKvVuudZWVmGK5iIqk3JzCcuukdElWVSY2oyMzMhSRJcXV3Lfc3ChQvh4uKie/j6+tZcgURUaZz5RERVZTKhJj8/H7NmzcILL7wAZ+fy/9GbPXs2MjMzdY8rV67UYJVEVFmc+UREVSVr91NFFRYWYsSIEdBqtfjyyy8f+VqVSgWVSlVDlRGRIQghEM8bWRJRFRl9qCksLMTw4cORlJSEPXv2PLKVhohMU0rGXWSri2CtlBBQx1HucojIRBl1qCkJNImJidi7dy9q164td0lEVA1KWmkaujvCxspkesWJyMjIGmpycnJw/vx53fOkpCRERUXBzc0NPj4+GDp0KCIjI7Ft2zZoNBqkpaUBANzc3GBjYyNX2URkYOc484mIDEDWUHPy5El0795d93zq1KkAgDFjxmD+/Pn47bffAACtWrUq9b69e/eiW7duNVUmEVUz3UrCnPlERFUga6jp1q0bhBDl7n/UPiIyH+dSi6dzB3HmExFVATuviUhW6iINLt7MBcDuJyKqGoYaIpLV+fQcaLQCLnbW8HK2lbscIjJhDDVEVCkarcDyPxOx9sglFBRpK32c+9enkSTJUOURkQUy6indRGS8tkVfw2d/JgAAfjiYhNn9m6J3sKfewaRkkHBTdj0RURWxpYaIKmXDqasAAKVCwqVbeZjw4yk8/+1R/J2SqddxdNO5vTnziYiqhqGGiPR2LeMuDp6/CQDY+kYnvNE9ECorBY5evI0BKw7i7V/O4HpWfoWOVTLzibdHIKKqYqghIr1tPp0CIYAwfzcE+zjj7T5NsOftbhjUygdCFLfidPt4H5b/mYi7BZpyj3M7twDp2WoAQBNPhhoiqhqGGiLSixBC1/U0rE093fa6rnZYPiIUm1/rgNZ+rrhbqMFnfyag+yf7sCnyKrTah9edOpdW3Erj52YPBxWH+BFR1TDUEJFeTl2+g6SbubC3UaJ/C++H9of61cLGSR3wxfOhqOtqh7SsfEz9+QwGf3kIx5Nul3rtuVTeHoGIDIehhoj0UtJK07+Fd7mtK5IkYUCID/6a1hUz+jaBo8oK0VczMfzrI5j0n1NIvpUH4J/p3Aw1RGQIbO8logrLKyjCtuhUAMDQ+7qeymNrrcRr3QIxrI0vlu5OwPoTyfj97zT8FZeOcR0b4MzVDACc+UREhsGWGiKqsJ2xachRF8HXzQ5hDdwq/D53JxUWDmmBHW92RqfAOijQaPH1/ov33ciSLTVEVHUMNURUYb+cLO56GtraFwqF/qv/Bnk548eXw/DD2LYIcHcAADiprNCgtoNB6yQiy8TuJyKqkKt38nD4wi0AwJDWdSt9HEmS8GSQJzo3cse26GvwcraDshIBiYjoQQw1RFQhG0+lAAA6NKwNXzf7Kh/PWqnAM6GPH5dDRFRR7H4iosfSagU2RF4BULEBwkREcmCoIaLHOn7pNq7cvgtHlRX6NveSuxwiojIx1BDRY5WsTfNUC2/Y27DXmoiME0MNET1SrroIO2KK16YZ1pZdT0RkvBhqiOiRdsSkIq9AA/86DmhTv5bc5RARlYuhhoge6Zd7XU9D29SDJHHqNREZL4YaIirX5Vu5OJ50G5IEPBNa+bVpiIhqAkMNEZVrY2Tx2jSdAuvAx9VO5mqIiB6NoYaIyqTVCmy8r+uJiMjYVXhu5m+//ab3wXv16gU7O/7vjsgUHb14CykZd+Fka4U+zbg2DREZvwqHmsGDB+t1YEmSkJiYiICAAH1rIiIjUDJAeECID2ytlTJXQ0T0eHp1P6WlpUGr1VboYW9f9XvDEJE8svML8fvf99amYdcTEZmICoeaMWPG6NWVNGrUKDg7O1eqKCKS1/boVOQXatHQ3QGtfF3lLoeIqEIq3P20atUqvQ68cuVKvYshIuNQcluEYW19uTYNEZkMg85+EkIgPT3dkIckohp28UYOTl6+AwXXpiEiE6NXqLG3t8eNGzd0z/v27YvU1FTd8/T0dHh7exuuOiKqcRsji1tpujZ2h6ezrczVEBFVnF6hJj8/H0II3fNDhw7h7t27pV5z/34iMi0arcDGU8UL7g1t4ytzNURE+jH44nvsfycyXYfO30RaVj5c7KzRM9hD7nKIiPTCFYWJSKdkbZpBrXygsuLaNERkWvQKNZIklWqJefA5EZmuzLuF2BmbBgAYxq4nIjJBFZ7SDRSPl2ncuLEuyOTk5CA0NBQKhUK3n4hM09Yz11BQpEUTTyc0r8s1pojI9OgVavRdq4aITMc/a9PUYwssEZkkvULNmDFjqqsOIpLR+fRsRF3JgFIhYVArrk1DRKZJr1BTlvz8fKxfvx65ubno1asXGjVqZIi6iKgGlQwQ7t7EA+5OKpmrISKqHL1CzfTp01FQUIDly5cDAAoKChAeHo7Y2FjY29tjxowZ2L17N8LDw6ulWCIyvCKNFpsjS9am4c0rich06TX76ffff0ePHj10z3/66SdcvnwZiYmJuHPnDoYNG4YFCxYYvEgiqj4HEm8iPVsNNwcbPBnEtWmIyHTpFWqSk5MRHByse75r1y4MHToU9evXhyRJePPNN3H69GmDF0lE1WfDfWvT2Fhx6SoiMl16/QumUChKTds+evQo2rdvr3vu6uqKO3fuGK46IqpWGXkF2H32OgCuTUNEpk+vUBMUFIStW7cCAGJjY5GcnIzu3bvr9l++fBmenp6GrZCIqs1vZ66hQKNFsLczgn24Ng0RmTa9Bwo///zz2L59O2JjY9G/f3/4+/vr9u/YsQNhYWEGL5KIqscvJ/9Zm4aIyNTp1VLz7LPPYseOHWjZsiXeeustrF+/vtR+e3t7vPbaawYtkIiqR3xaNmJSMmGt5No0RGQe9F6npmfPnujZs2eZ++bNm1flgoioZmyLvgYA6NbEA24ONjJXQ0RUdXqFmuTk5Aq9zs/Pr1LFEFHNEEJge0wqAODplt4yV0NEZBh6hZr7x8+UzIK6/x4xQghIkgSNRmOg8oioOiRcz8HFG7mwUSq4Ng0RmQ29Qo0kSahXrx7Gjh2LAQMGwMqqyndZICIZ7LjXStOlcR042VrLXA0RkWHoNVD46tWrmDRpEtavX4+nnnoKP/74I2xsbBASElLqUVH79+/HgAED4OPjA0mSsGXLllL7hRCYP38+fHx8YGdnh27duiE2NlafkomoDL//XRxq+rdg1xMRmQ+9Qo2XlxdmzpyJuLg4bNiwAXfu3METTzyB9u3b49tvv4VWq9Xrw3NzcxESEoIVK1aUuX/JkiVYunQpVqxYgRMnTsDLywu9evVCdna2Xp9DRP84n56NhOs5sFZK6NGU60oRkfmQxP1LBFfC9evX8fzzzyMiIgI3btyAm5tb5QqRJGzevBmDBw8GUNxK4+PjgylTpmDmzJkAALVaDU9PTyxevBgTJkwo8zhqtRpqtVr3PCsrC76+vsjMzISzMxcXI/rir0R8ujsB3Zu4Y9U4ritFRMYpKysLLi4uen1/V/pGL4cPH8Yrr7yCxo0bIycnB//+97/h6upa2cM9JCkpCWlpaejdu7dum0qlQteuXXH48OFy37dw4UK4uLjoHr6+XPqd6H47/k4DAPRj1xMRmRm9Qk1qaioWL16MoKAgPPPMM3B2dsbhw4dx/PhxTJw4EQqF4W6Gl5ZW/A/vg7dd8PT01O0ry+zZs5GZmal7XLlyxWA1EZm6pJu5iEvNgpVCQu9gdj0RkXnRa/pS/fr14ePjgzFjxmDgwIGwtraGRqNBdHR0qde1bNnSYAXeP2Uc+GfaeHlUKhVUKpXBPp/InJTMegpvWBuu9lxwj4jMi16hpqioCMnJyfjggw+wYMECAMCDQ3IMtU6Nl5cXgOIWG2/vf5rJ09PTedNMokoqmfX0FLueiMgM6RVqkpKSqquOh/j7+8PLywu7d+9GaGgoAKCgoAARERFYvHhxjdVBZC6Sb+Xh75QsKBUSejfzkrscIiKD07v7yZBycnJw/vx53fOkpCRERUXBzc0Nfn5+mDJlCj766CM0atQIjRo1wkcffQR7e3u88MILBq2DyBKUtNK0D3DjvZ6IyCxVONRER0ejefPmFR4MHBsbiyZNmjxy1eGTJ0+ie/fuuudTp04FAIwZMwarV6/GjBkzcPfuXbz22mu6NXF27doFJyenipZNRPfoZj01Z9cTEZmnCq9To1QqkZaWBnd39wod2NnZGVFRUQgICKhSgVVVmXnuRObm6p08dFq8F5IEHP+/nnB34mB6IjJulfn+rnBLjRACc+bMgb29fYVeX1BQUNFDE1E1++NeK01YAzcGGiIyWxUONV26dEF8fHyFDxweHg47O7tKFUVEhlUylZv3eiIic1bhULNv375qLIOIqktq5l1EJmdAkoC+zTnriYjMl+GWACYio1TS9dS2fi14OtvKXA0RUfXRO9ScOHECI0eOhL+/P+zs7GBvbw9/f3+MHDkSJ0+erI4aiagKfo/hrCcisgx6rVOzZcsWDB8+HD169MCbb74JT09PCCGQnp6OXbt2oWPHjvj5558xaNCg6qqXiPSQnpWPE5dvA2DXExGZvwpP6QaA5s2bY9SoUZg1a1aZ+xcvXoy1a9ciNjbWYAVWFad0kyVbe+QS5v4ai1A/V2x+raPc5RARVVhlvr/16n46f/48hgwZUu7+wYMH48KFC/ockoiqkW7WE7ueiMgC6BVqGjZsiC1btpS7/9dff5V9sT0iKnYjW43jSex6IiLLodeYmvfffx8jRoxAREQEevfuDU9PT0iShLS0NOzevRu7du3CunXrqqtWItLDrrNp0AogpJ4LfN0qtmgmEZEp0yvUPPvss9i/fz+WL1+OpUuXIi2teFaFl5cXwsPDERERgfDw8GoplIj0o5v1xAX3iMhC6BVqgOKVghlciIzb7dwCHLl4CwDQj11PRGQhuPgekRnafTYNGq1AMx9n1K/tIHc5REQ1wqChJi4ujgOFiYzA9ntdT7zXExFZEoOGmoKCAly+fNmQhyQiPWXkFeDw+ZsA2PVERJZFrzE1U6dOfeT+GzduVKkYIqq63Wevo0grEOTlhAB3R7nLISKqMXqFmuXLl6NVq1blruyXk5NjkKKIqPJ+/5tdT0RkmfQKNY0aNcJbb72FUaNGlbk/KioKbdq0MUhhRKS/rPxCHEgsbjHt34JdT0RkWfQaU9OmTRucOnWq3P2SJEGPW0kRkYH9FXcdhRqBRh6OCPRwkrscIqIapVdLzaeffgq1Wl3u/pCQEGi12ioXRUSVs4ML7hGRBdMr1Hh5sTmbyFhl5xciIoFdT0Rkubj4HpGZ2HMuHQVFWgTUcUATT3Y9EZHl0fs2CQBQq1YtSJL00HZJkmBra4vAwECMHTsW48aNq3KBRFQxv9+34F5Zfz+JiMxdpULN3Llz8eGHH6Jfv34ICwuDEAInTpzAH3/8gddffx1JSUmYNGkSioqKMH78eEPXTEQPyFUXYW98OgCgH7ueiMhCVSrUHDx4EAsWLMDEiRNLbf/666+xa9cubNy4ES1btsTnn3/OUENUA/bF34C6SIv6te0R7F32OlJEROauUmNqdu7ciZ49ez60vUePHti5cycAoH///rh48WLVqiOiCtnxdyoAoF9zdj0RkeWqVKhxc3PD1q1bH9q+detWuLm5AQByc3Ph5MTBikTV7W6BBnviirueOOuJiCxZpbqf5syZg0mTJmHv3r0ICwuDJEk4fvw4duzYga+++goAsHv3bnTt2tWgxRLRwyIS0nG3UIO6rnZoUddF7nKIiGRTqVAzfvx4BAcHY8WKFdi0aROEEAgKCkJERAQ6dOgAAJg2bZpBCyWisu3QzXryYtcTEVm0SoUaAOjYsSM6duxoyFqISE/5hRr8FXcdAG9gSURU6VCj0WiwZcsWxMXFQZIkBAcHY+DAgVAqlYasj4ge4UDiTeQWaODjYotWvq5yl0NEJKtKhZrz58+jf//+SElJQZMmTSCEQEJCAnx9fbF9+3Y0bNjQ0HUSURl+jyme9dSXs56IiCo3+2ny5Mlo2LAhrly5gsjISJw+fRrJycnw9/fH5MmTDV0jEZVBXaTBbl3XE2c9ERFVqqUmIiICR48e1U3fBoDatWtj0aJFHGdDVEM2R6YgO78IHk4qtParJXc5RESyq1RLjUqlQnZ29kPbc3JyYGNjU+WiiOjR1hy+hNmbYwAAw9v6QqFg1xMRUaVCzdNPP41XX30Vx44dgxACQggcPXoUEydOxMCBAw1dIxHdI4TA4j/OYd5vsRACGPmEH97q1VjusoiIjEKlQs3nn3+Ohg0bIjw8HLa2trC1tUWHDh0QGBiIZcuWGbhEIgKAQo0W0345g5X7LgAApvVqjAWDm0PJVhoiIgCVHFPj6uqKX3/9FefPn0dcXByEEAgODkZgYKCh6yMiFN+Fe9JPkdifcANKhYSPnmmO59r5yV0WEZFRqXComTp16iP379u3T/f7pUuXVrogIirtRrYaL60+gZiUTNhaK/DlyNZ4MshT7rKIiIxOhUPN6dOnK/Q6rpVBZDiXbuZizKrjuHwrD7XsrfHD2HYI5UwnIqIyVTjU7N27tzrrIKIHRF/NwLhVJ3ArtwD1atlh7UthCHB3lLssIiKjVenbJBBR9dkXn47XfopEXoEGzXycsWpcO3g42cpdFhGRUWOoITIyG05dxayN0SjSCnQKrIOVo1rDydZa7rKIiIweQw2RkRBC4Mt9F/DxzngAwKBWPvh4aAhsrCq18gIRkcVhqCEyAhqtwHtbY7H2yGUAwKtdAjCrbxBXCiYi0gNDDZHM8gs1eGt9FH7/Ow0AMOfpYLzcyV/mqoiITA9DDZGMMvMKMf7HkziedBs2SgU+HR6CASE+cpdFRGSSGGqIZHIjW42R3x1FwvUcOKms8PXoNujQsI7cZRERmSyGGiKZrNx3AQnXc+DhpMLqcWEI9nGWuyQiIpNm1NMqioqK8O6778Lf3x92dnYICAjA+++/D61WK3dpRFW2P/EGAOC9gc0YaIiIDMCoW2oWL16Mr776CmvWrEGzZs1w8uRJjBs3Di4uLnjzzTflLo+o0lIz7+J8eg4UEtjlRERkIEYdao4cOYJBgwbhqaeeAgA0aNAA//vf/3Dy5EmZKyOqmkPnbwEAWtRzhYs9F9YjIjIEo+5+6tSpE/766y8kJCQAAM6cOYODBw+if//+5b5HrVYjKyur1IPI2By81/XUOZCtNEREhmLULTUzZ85EZmYmgoKCoFQqodFo8OGHH+L5558v9z0LFy7Ee++9V4NVEulHCIGD91pqOjLUEBEZjFG31Kxfvx7/+c9/8N///heRkZFYs2YNPvnkE6xZs6bc98yePRuZmZm6x5UrV2qwYqLHi7+ejZs5athZK9G6vqvc5RARmQ2jbqmZPn06Zs2ahREjRgAAWrRogcuXL2PhwoUYM2ZMme9RqVRQqVQ1WSaRXg4m3gQAhPm7QWWllLkaIiLzYdQtNXl5eVAoSpeoVCo5pZtM2sHzxaGmcyN2PRERGZJRt9QMGDAAH374Ifz8/NCsWTOcPn0aS5cuxUsvvSR3aUSVoi7S4NjF2wCATgw1REQGZdSh5osvvsCcOXPw2muvIT09HT4+PpgwYQLmzp0rd2lElXI6OQN3CzWo46hCE08nucshIjIrRh1qnJycsGzZMixbtkzuUogMomQ8TafA2pAkSeZqiIjMi1GPqSEyNyXjaTiVm4jI8BhqiGpIZl4hoq9mAOB4GiKi6sBQQ1RDjly8Ba0AGro7wNvFTu5yiIjMDkMNUQ05eP7erREauctcCRGReWKoIaohJYOEOZ6GiKh6MNQQ1YArt/Nw6VYelAoJ7QPc5C6HiMgsMdQQ1YBD92Y9tfJ1hZOttczVEBGZJ4YaohpQMpW7E7ueiIiqDUMNUTXTagUOX7gFgPd7IiKqTgw1RNXsbGoWbucWwFFlhRBfV7nLISIyWww1RNWspOupfYAbrJX8K0dEVF34LyxRNTvEWyMQEdUIhhqiapRfqMHxpNsAOJ6GiKi6MdQQVaOTl+5AXaSFp7MKDd0d5S6HiMisMdQQVaN/pnK7Q5IkmashIjJvDDVE1ajkfk+dGtWWuRIiIvPHUENUTW7nFiD2WhYADhImIqoJDDVE1eTwhZsQAgjycoKHk63c5RARmT2GGqJqwqncREQ1i6GGqBoIIXAg8d4gYU7lJiKqEQw1RNUg+XYert65C2ulhCf83eQuh4jIIjDUEFWDklaa1n61YG9jJXM1RESWgaGGqBocLOl64ngaIqIaw1BDZGAarcDhCxxPQ0RU0xhqiAwsJiUTWflFcLK1Qou6LnKXQ0RkMRhqiAysZCp3h4a1YaXkXzEioprCf3GJDOxA4r1bI3A8DRFRjWKoITKgvIIiRF7OAAB0auQubzFERBaGoYbIgI4n3UaBRou6rnZoUNte7nKIiCwKQw2RAZWMp+kUWAeSJMlcDRGRZWGoITIg3hqBiEg+DDVEBnIjW41zadkAimc+ERFRzWKoITKQkgX3mvk4o7ajSuZqiIgsD0MNkYEc4K0RiIhkxVBDZABCiH8GCXM8DRGRLBhqiAzgwo1cpGbmw8ZKgXYN3OQuh4jIIjHUEBlASStNuwa1YGutlLkaIiLLxFBDZAD/jKfhKsJERHJhqCGqoiKNFkcv3gLAQcJERHJiqCGqojNXM5CjLoKrvTWa+TjLXQ4RkcViqCGqooOJxa00HRvWgULBWyMQEcmFoYaoig6evwGAU7mJiOTGUENUBTnqIpxOzgDA8TRERHJjqCGqgmMXb6FIK1C/tj183ezlLoeIyKIx1BBVQclU7o5spSEikh1DDVEVlCy615mhhohIdgw1RJWUlpmPxPQcSBIQ3rC23OUQEVk8K7kLIJLLsYu38L/jyQAAK6UCVgoJSoV071cFrJTFv7//ecl+K4WE+Os5AICWdV3gam8j5x+FiIjAUEMWSqsVmLExGpdv5VX5WJzKTURkHBhqyCIdv3Qbl2/lwVFlhTd7NIJGCGi0AkUagSKtFkXaf55rtFoUagU0GnFv+z/P7VVKjOnQQO4/DhERwQRCTUpKCmbOnInff/8dd+/eRePGjfH999+jTZs2cpdGJuznk1cAAANCvDG+S4DM1RARkSEYdai5c+cOOnbsiO7du+P333+Hh4cHLly4AFdXV7lLIxOWnV+IHTGpAIBhbX1lroaIiAzFqEPN4sWL4evri1WrVum2NWjQQL6CyCxsi05FfqEWgR6OCPV1lbscIiIyEKOe0v3bb7+hbdu2GDZsGDw8PBAaGopvv/32ke9Rq9XIysoq9SC63/oTxV1Pw9vWgyTxBpRERObCqEPNxYsXsXLlSjRq1Ag7d+7ExIkTMXnyZKxdu7bc9yxcuBAuLi66h68vuxfoHwnXsxF1JQNWCgnPhNaTuxwiIjIgSQgh5C6iPDY2Nmjbti0OHz6s2zZ58mScOHECR44cKfM9arUaarVa9zwrKwu+vr7IzMyEs7NztddMxu3D7Wfx7YEk9A72xDej28pdDhERlSMrKwsuLi56fX8bdUuNt7c3goODS21r2rQpkpOTy32PSqWCs7NzqQcRABRqtNgUmQIAGM4BwkREZseoQ03Hjh0RHx9faltCQgLq168vU0X/yFEXIfNuodxlkB7+ikvHrdwCuDup0K2Ju9zlEBGRgRn17Ke33noLHTp0wEcffYThw4fj+PHj+Oabb/DNN9/IXRp+jUrBO5v/hoeTCo08HRHo7ohATyc08nBEoIcjajvYcBCqkfnl3to0z7auByulUed5IiKqBKMONe3atcPmzZsxe/ZsvP/++/D398eyZcswcuRIuUtDakY+ACA9W430bDUOnb9Van8te2s08nBCQw9HNPJwLA4+Ho7wcrZl2JHB9ax87I1PBwAMa8sBwkRE5sioBwobQmUGGlX42PmFuJCeg8T0HN2vienZuHrnLso7q44qKwTea81p5OGIp0N8UNfVzqB10cNW7ruAxX+cQ9v6tbBhUge5yyEioseozPe3UbfUGDtnW2uE+tVCqF+tUtvvFmhw4UYOLtzIQeL14qBzPj0Hl27lIUddhKgrGYi6kgEAWP5XImb1C8KoJ+pDoWALTnUQQui6njhAmIjIfDHUVAM7GyWa13VB87oupbYXFGlx6VYuzqcXh519Cek4nZyBub/GYnt0KhY/2xIN6jjIVLX5Onn5Di7ezIW9jRJPtfSWuxwiIqomHC1Zg2ysFGjs6YT+LbzxZs9G2DixA94b2Az2NkocS7qNvsv34/uDSdBozbpHsMb9fG8F4adbesNBxRxPRGSuGGpkpFBIGNOhAXZO6YIODWsjv1CLD7adxfCvj+DCjRy5yzMLOeoibL9380p2PRERmTeGGiPg62aPn155Ah8+0xyOKiucunwH/ZYfwFcRF1Ck0cpdnknbEZ2KvAINAtwd0KZ+rce/gYiITBZDjZGQJAkjn6iPnW91QedGdVBQpMWi38/h2ZWHkXA9W+7yTNb6+wYIcyo9EZF5Y6gxMnVd7bD2pTAsebYlnGytcOZqJp7+/CBW7ElEIVtt9HI+PQenLt+BUiFhSGhducshIqJqxlBjhCRJwvB2vtj9Vlc8GeSBAo0Wn+xKwOB/H8LZa1lyl2cyfjlV3ErTvYk7PJxtZa6GiIiqG0ONEfNyscX3Y9ris+dC4GJnjdhrWRi44iA+252AgiK22jxKoUaLjaeKb145jAOEiYgsAkONkZMkCc+E1sPuqV3Qp5knirQCy/9KxMAVB/F3Sqbc5RmtiPgbuJmjRh1HGzwZ5CF3OUREVAMYakyEh5MtvhrVBl88Hwo3BxucS8vGoH8fwqpDSXKXZpRKBggPaV0P1rx5JRGRReC/9iZEkiQMCPHB7re64KmW3tBoBRZsj2OLzQPSs/Ox59y9m1e24c0riYgsBUONCartqMK/X2itCzYzNkRzZtR9tpxOgUYrEOrnikaeTnKXQ0RENYShxoTNH9AMLnbWOJuahW8PXJS7HKMghMDPJ68C4ArCRESWhqHGhLk7qTDn6WAAwLI/E3GRt1ZAZHIGzqfnwM5aiad580oiIovCUGPinm1dV7cC8axNMdBa+M0wf7k3QLh/C2842VrLXA0REdUkhhoTJ0kSPnqmBeyslTiedBv/O5Esd0myySsowtYz1wAAw9tygDARkaVhqDEDvm72eLtPEwDAoh3nkJaZL3NF8tgRk4bcAg0a1LZHmL+b3OUQEVENY6gxE2M7NEArX1dkq4vw7pa/IYTldUP9fK/raRhvXklEZJEYasyEUiFh8bMtYa2U8GfcdWyPSZW7pBqVdDMXx5NuQyEBz7Zm1xMRkSViqDEjTbycMKlbIABg/m+xuJNbIHNFNadkgHDXxu7wcuHNK4mILBFDjZl5vXtDBHo44mZOARZsj5O7nHIVabSY8ONJPPnpPnx34CJy1EVVOtbGSK5NQ0Rk6RhqzIzKSonFz7aEJAEbI69if8INuUsq05f7LmBn7HVcvJGLBdvjEL7wLyzcEYfUzLt6H+tA4k1cz1LDzcEGPZp6VkO1RERkChhqzFCb+rUwJrwBAGD2phjkVqEVpDpEXcnA8r8SAQAvtq+PAHcHZOcX4ev9F9F58V5MXR+Fs9eyKny89SeKu56eCa0LGyte0kRElorfAGZqep8mqOtqh5SMu/hkV7zc5ejkqoswZd1paLQCA0J88P6gZvjzra74fkxbPOHvhiKtwKbTKej/+QG8+P0x7E+48ciZXLdy1Pgz7joAdj0REVk6hhoz5aCywofPNAcArD58CZHJd2SuqNiC7Wdx6VYefFxssWBQc0iSBIVCQo+mnlg/IRy/vt4RT7f0hkIq7lYa/cNx9Ft+ABtOXUVB0cM37dx8OgVFWoGQei5o4sWbVxIRWTKGGjPWrYkHhoTWhRDArI3RZYaCmrQrNg3/O34FkgR8MjwELvYP38YgxNcVK15ojYjp3TGuYwPY2yhxLi0bb/9yBp2X7MHKfReQebcQQMnNK/9Zm4aIiCybJMx8lbasrCy4uLggMzMTzs7OcpdT4+7kFqDn0gjcyi3AlJ6NMKVnY1nqSM/OR99lB3A7twATugRgdv+mFXpfZl4hfjp+GasPXUJ6thoA4GCjxHPt/NC2QS289lMkVFYKnHi3J5x5ryciIrNRme9vttSYuVoONpg3sBkA4N97zyPhenaN1yCEwPRfonE7twDB3s6Y2rviwcrF3hqvdQvEgZnd8cmwEDTxdEJugQY/HErCaz9FAii+eSUDDRERMdRYgAEtvdEjyAOFGoGZG6OhqeE7ea89chkRCTegslJg+YhWUFkp9T6GykqJoW3q4Y8pnbHmpTB0Cqyj2/d8mJ8hyyUiIhNlJXcBVP0kScKCZ5rj2NL9OJ2cgbVHLmFcR/8a+ezE69n4aEfxIoCz+wWhkWfVBvNKkoSujd3RtbE7zqVlITu/CO0a8OaVRETElhqL4e1ih5n9ggAAH++Mx9U7edX+meoiDd5cFwV1kRZdG7tjTIcGBj1+kJczAw0REekw1FiQkWF+CGvghrwCDf5vc/XfyXvp7gScTc1CLXtrfDy0Je+cTURE1YqhxoIoFBIWPtsCNlYK7E+4gc2nU6rts45cuIVv9l8EACx6tiU8nHmTSSIiql4MNRamobsj3uzRCADw/razuJmjNvhnZOYVYtrPURACGNHOF32aeRn8M4iIiB7EUGOBXu0SgKbezsjIK8SUdVFIydD/JpKPMufXv3EtMx8NattjztPBBj02ERFReRhqLJC1UoElz7aEtVLCwfM30f2TffhoRxwy8gqqfOwtp1Pw25lrUCokfPZcKzioOMGOiIhqBkONhWpRzwUbJ3XAE/5uKCjS4pv9F9F5yV6s3HcB+YWaSh3z6p08zNnyNwBg8pONEOpXy5AlExERPRJDjQVrWc8V615tj1Xj2iHIywnZ+UVY/Mc5dPt4H34+cQVFmorfK0qjFZi6/gyy1UVo7eeK17s3rMbKiYiIHsZQY+EkSUL3Jh7YPrkzPh0WgrqudkjLyseMjdHot/wAdp+9XqGp31/vv4Djl27DwUaJz55rBSslLy0iIqpZ/OYhAIBSIeHZNvXw17SuePeppnC1t0Zieg7Grz2J4V8fwanLt8t9798pmVi6KwEAMG9gM9Sv7VBTZRMREekw1FApttZKvNI5ABHTu+O1bg1ha63AiUt38OzKI3h17UmcTy99Q8y7BRpMXncaRVqBfs29MKxNPZkqJyIiS8dQQ2VysbPGjL5B2Pd2d4xo5wuFBOw6ex29P9uPWRujkZaZDwD4aEccLt7IhYeTCh8904KrBhMRkWwkUd1r5cssKysLLi4uyMzMhLOzs9zlmKzz6dlY8kc8dp29DgBQWSnQv4W3blXiH18OQ+dG7nKWSEREZqQy399sqaEKCfRwwjej22LjpHC0rV8L6iKtLtC81NGfgYaIiGTHldFIL23qu+GXieH4Ky4dn+9JvNdN1UTusoiIiBhqSH+SJKFnsCd6BnvKXQoREZEOu5+IiIjILDDUEBERkVlgqCEiIiKzYFKhZuHChZAkCVOmTJG7FCIiIjIyJhNqTpw4gW+++QYtW7aUuxQiIiIyQiYRanJycjBy5Eh8++23qFWr1iNfq1arkZWVVepBRERE5s8kQs3rr7+Op556Cj179nzsaxcuXAgXFxfdw9fXtwYqJCIiIrkZfahZt24dIiMjsXDhwgq9fvbs2cjMzNQ9rly5Us0VEhERkTEw6sX3rly5gjfffBO7du2Cra1thd6jUqmgUqmquTIiIiIyNkZ9Q8stW7bgmWeegVKp1G3TaDSQJAkKhQJqtbrUvrLwhpZERESmpzLf30bdUtOjRw/ExMSU2jZu3DgEBQVh5syZjw00REREZDmMOtQ4OTmhefPmpbY5ODigdu3aD20nIiIiy2b0A4WJiIiIKsKoW2rKsm/fPrlLICIiIiNkcqFGXyXjoLkIHxERkeko+d7WZz6T2Yea7OxsAOAifERERCYoOzsbLi4uFXqtUU/pNgStVotr167ByckJkiTptmdlZcHX1xdXrlzhVO9K4jmsOp7DquH5qzqew6rh+au68s6hEALZ2dnw8fGBQlGxIcBm31KjUChQr169cvc7OzvzQqwinsOq4zmsGp6/quM5rBqev6or6xxWtIWmBGc/ERERkVlgqCEiIiKzYLGhRqVSYd68ebxPVBXwHFYdz2HV8PxVHc9h1fD8VZ0hz6HZDxQmIiIiy2CxLTVERERkXhhqiIiIyCww1BAREZFZYKghIiIis2CxoebLL7+Ev78/bG1t0aZNGxw4cEDukkzG/PnzIUlSqYeXl5fcZRmt/fv3Y8CAAfDx8YEkSdiyZUup/UIIzJ8/Hz4+PrCzs0O3bt0QGxsrT7FG6nHncOzYsQ9dk+3bt5enWCO0cOFCtGvXDk5OTvDw8MDgwYMRHx9f6jW8DstXkfPHa/DRVq5ciZYtW+oW2AsPD8fvv/+u22+o688iQ8369esxZcoUvPPOOzh9+jQ6d+6Mfv36ITk5We7STEazZs2Qmpqqe8TExMhdktHKzc1FSEgIVqxYUeb+JUuWYOnSpVixYgVOnDgBLy8v9OrVS3ffMnr8OQSAvn37lromd+zYUYMVGreIiAi8/vrrOHr0KHbv3o2ioiL07t0bubm5utfwOixfRc4fwGvwUerVq4dFixbh5MmTOHnyJJ588kkMGjRIF1wMdv0JCxQWFiYmTpxYaltQUJCYNWuWTBWZlnnz5omQkBC5yzBJAMTmzZt1z7VarfDy8hKLFi3SbcvPzxcuLi7iq6++kqFC4/fgORRCiDFjxohBgwbJUo8pSk9PFwBERESEEILXob4ePH9C8BqsjFq1aonvvvvOoNefxbXUFBQU4NSpU+jdu3ep7b1798bhw4dlqsr0JCYmwsfHB/7+/hgxYgQuXrwod0kmKSkpCWlpaaWuR5VKha5du/J61NO+ffvg4eGBxo0bY/z48UhPT5e7JKOVmZkJAHBzcwPA61BfD56/ErwGK0aj0WDdunXIzc1FeHi4Qa8/iws1N2/ehEajgaenZ6ntnp6eSEtLk6kq0/LEE09g7dq12LlzJ7799lukpaWhQ4cOuHXrltylmZySa47XY9X069cPP/30E/bs2YNPP/0UJ06cwJNPPgm1Wi13aUZHCIGpU6eiU6dOaN68OQBeh/oo6/wBvAYrIiYmBo6OjlCpVJg4cSI2b96M4OBgg15/Zn+X7vJIklTquRDioW1Utn79+ul+36JFC4SHh6Nhw4ZYs2YNpk6dKmNlpovXY9U899xzut83b94cbdu2Rf369bF9+3YMGTJExsqMzxtvvIHo6GgcPHjwoX28Dh+vvPPHa/DxmjRpgqioKGRkZGDjxo0YM2YMIiIidPsNcf1ZXEtNnTp1oFQqH0p/6enpD6VEqhgHBwe0aNECiYmJcpdickpmjfF6NCxvb2/Ur1+f1+QD/vWvf+G3337D3r17Ua9ePd12XocVU975KwuvwYfZ2NggMDAQbdu2xcKFCxESEoLly5cb9PqzuFBjY2ODNm3aYPfu3aW27969Gx06dJCpKtOmVqsRFxcHb29vuUsxOf7+/vDy8ip1PRYUFCAiIoLXYxXcunULV65c4TV5jxACb7zxBjZt2oQ9e/bA39+/1H5eh4/2uPNXFl6DjyeEgFqtNuz1Z6BBzCZl3bp1wtraWnz//ffi7NmzYsqUKcLBwUFcunRJ7tJMwrRp08S+ffvExYsXxdGjR8XTTz8tnJyceP7KkZ2dLU6fPi1Onz4tAIilS5eK06dPi8uXLwshhFi0aJFwcXERmzZtEjExMeL5558X3t7eIisrS+bKjcejzmF2draYNm2aOHz4sEhKShJ79+4V4eHhom7dujyH90yaNEm4uLiIffv2idTUVN0jLy9P9xpeh+V73PnjNfh4s2fPFvv37xdJSUkiOjpa/N///Z9QKBRi165dQgjDXX8WGWqEEOLf//63qF+/vrCxsRGtW7cuNTWPHu25554T3t7ewtraWvj4+IghQ4aI2NhYucsyWnv37hUAHnqMGTNGCFE8nXbevHnCy8tLqFQq0aVLFxETEyNv0UbmUecwLy9P9O7dW7i7uwtra2vh5+cnxowZI5KTk+Uu22iUde4AiFWrVulew+uwfI87f7wGH++ll17Sfee6u7uLHj166AKNEIa7/iQhhKhkyxERERGR0bC4MTVERERknhhqiIiIyCww1BAREZFZYKghIiIis8BQQ0RERGaBoYaIiIjMAkMNERERmQWGGiIiIjILDDVEhH379kGSJGRkZMhdymOtXr0arq6uer2nQYMGWLZsmV7vGTt2LAYPHqzXe4hIXgw1RGZg7NixkCQJkiTB2toaAQEBePvtt5Gbmyt3aQb33HPPISEhQe4yiMgIWcldABEZRt++fbFq1SoUFhbiwIEDeOWVV5Cbm4uVK1fKXZpB2dnZwc7OTu4yDEKj0UCSJCgU/P8lkSHwbxKRmVCpVPDy8oKvry9eeOEFjBw5Elu2bAEAqNVqTJ48GR4eHrC1tUWnTp1w4sSJMo+Tm5sLZ2dnbNiwodT2rVu3wsHBAdnZ2bh06RIkScKmTZvQvXt32NvbIyQkBEeOHCn1no0bN6JZs2ZQqVRo0KABPv3001L7GzRogAULFmD06NFwdHRE/fr18euvv+LGjRsYNGgQHB0d0aJFC5w8eVL3nge7ny5cuIBBgwbB09MTjo6OaNeuHf7880+9zp1Go8HUqVPh6uqK2rVrY8aMGXjwtnhCCCxZsgQBAQGws7NDSEjIQ+fot99+Q6NGjWBnZ4fu3btjzZo1pbr1Smrftm0bgoODoVKpcPnyZRQUFGDGjBmoW7cuHBwc8MQTT2Dfvn2ljn348GF06dIFdnZ28PX1xeTJk82yJY6oKhhqiMyUnZ0dCgsLAQAzZszAxo0bsWbNGkRGRiIwMBB9+vTB7du3H3qfg4MDRowYgVWrVpXavmrVKgwdOhROTk66be+88w7efvttREVFoXHjxnj++edRVFQEADh16hSGDx+OESNGICYmBvPnz8ecOXOwevXqUsf97LPP0LFjR5w+fRpPPfUUXnzxRYwePRqjRo3S1Tp69OiHQkaJnJwc9O/fH3/++SdOnz6NPn36YMCAAUhOTq7wufr000/xww8/4Pvvv8fBgwdx+/ZtbN68udRr3n33XaxatQorV65EbGws3nrrLYwaNQoREREAgEuXLmHo0KEYPHgwoqKiMGHCBLzzzjsPfVZeXh4WLlyI7777DrGxsfDw8MC4ceNw6NAhrFu3DtHR0Rg2bBj69u2LxMREAEBMTAz69OmDIUOGIDo6GuvXr8fBgwfxxhtvVPjPSGQRDHRXcSKS0ZgxY8SgQYN0z48dOyZq164thg8fLnJycoS1tbX46aefdPsLCgqEj4+PWLJkiRBCiL179woA4s6dO7r3K5VKkZKSIoQQ4saNG8La2lrs27dPCCFEUlKSACC+++473TFjY2MFABEXFyeEEOKFF14QvXr1KlXn9OnTRXBwsO55/fr1xahRo3TPU1NTBQAxZ84c3bYjR44IACI1NVUIIcSqVauEi4vLI89HcHCw+OKLL0p9zmeffVbu6729vcWiRYt0zwsLC0W9evV05zQnJ0fY2tqKw4cPl3rfyy+/LJ5//nkhhBAzZ84UzZs3L7X/nXfeKXVeV61aJQCIqKgo3WvOnz8vJEnSnesSPXr0ELNnzxZCCPHiiy+KV199tdT+AwcOCIVCIe7evfuIM0FkWdhSQ2Qmtm3bBkdHR9ja2iI8PBxdunTBF198gQsXLqCwsBAdO3bUvdba2hphYWGIi4sr81hhYWFo1qwZ1q5dCwD48ccf4efnhy5dupR6XcuWLXW/9/b2BgCkp6cDAOLi4kp9JgB07NgRiYmJ0Gg0ZR7D09MTANCiRYuHtpUc90G5ubmYMWMGgoOD4erqCkdHR5w7d67CLTWZmZlITU1FeHi4bpuVlRXatm2re3727Fnk5+ejV69ecHR01D3Wrl2LCxcuAADi4+PRrl27UscOCwt76PNsbGxK/ZkjIyMhhEDjxo1LHTsiIkJ37FOnTmH16tWl9vfp0wdarRZJSUkV+nMSWQIOFCYyE927d8fKlSthbW0NHx8fWFtbAwBSU1MBAJIklXq9EOKhbfd75ZVXsGLFCsyaNQurVq3CuHHjHnp9yWfcf3ytVlvu8UUZXUhlHeNRx33Q9OnTsXPnTnzyyScIDAyEnZ0dhg4dioKCgnL/bPoq+ezt27ejbt26pfapVCoAFf/z2tnZlXqdVquFUqnEqVOnoFQqS73W0dFR95oJEyZg8uTJDx3Pz8+vEn8iIvPEUENkJhwcHBAYGPjQ9sDAQNjY2ODgwYN44YUXAACFhYU4efIkpkyZUu7xRo0ahRkzZuDzzz9HbGwsxowZo1c9wcHBOHjwYKlthw8fRuPGjR/68q6KAwcOYOzYsXjmmWcAFI+xuXTpUoXf7+LiAm9vbxw9elTXElVUVIRTp06hdevWAKAb1JucnIyuXbuWeZygoCDs2LGj1Lb7BziXJzQ0FBqNBunp6ejcuXOZr2ndujViY2PL/PkS0T8YaojMnIODAyZNmoTp06fDzc0Nfn5+WLJkCfLy8vDyyy+X+75atWphyJAhmD59Onr37o169erp9bnTpk1Du3bt8MEHH+C5557DkSNHsGLFCnz55ZdV/SOVEhgYiE2bNmHAgAGQJAlz5swpt1WnPG+++SYWLVqERo0aoWnTpli6dGmphQidnJzw9ttv46233oJWq0WnTp2QlZWFw4cPw9HREWPGjMGECROwdOlSzJw5Ey+//DKioqJ0g6If1SLWuHFjjBw5EqNHj8ann36K0NBQ3Lx5E3v27EGLFi3Qv39/zJw5E+3bt8frr7+O8ePHw8HBAXFxcdi9eze++OKLypw2IrPEMTVEFmDRokV49tln8eKLL6J169Y4f/48du7ciVq1aj3yfS+//DIKCgrw0ksv6f2ZrVu3xs8//4x169ahefPmmDt3Lt5//32MHTu2kn+Ksn322WeoVasWOnTogAEDBqBPnz66FpaKmjZtGkaPHo2xY8ciPDwcTk5OupafEh988AHmzp2LhQsXomnTpujTpw+2bt0Kf39/AIC/vz82bNiATZs2oWXLlli5cqVu9lNJF1V5Vq1ahdGjR2PatGlo0qQJBg4ciGPHjsHX1xdA8bijiIgIJCYmonPnzggNDcWcOXN045iIqJgkyur0JSIC8NNPP+HNN9/EtWvXYGNjI3c5JufDDz/EV199hStXrshdCpFFYPcTET0kLy8PSUlJWLhwISZMmMBAU0Fffvkl2rVrh9q1a+PQoUP4+OOPuZYMUQ1i9xMRPWTJkiVo1aoVPD09MXv2bLnLMRmJiYkYNGgQgoOD8cEHH2DatGmYP3++3GURWQx2PxEREZFZYEsNERERmQWGGiIiIjILDDVERERkFhhqiIiIyCww1BAREZFZYKghIiIis8BQQ0RERGaBoYaIiIjMwv8DWEr4FeupTssAAAAASUVORK5CYII=\n", - "text/plain": [ - "
              " - ] - }, - "metadata": { - "filenames": { - "image/png": "/Users/mhjensen/Teaching/MachineLearning/doc/LectureNotes/_build/jupyter_execute/chapter3_77_1.png" - } - }, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "# Common imports\n", "import os\n", @@ -2216,18 +1858,7 @@ "collapsed": false, "editable": true }, - "outputs": [ - { - "data": { - "text/plain": [ - "'\\n#Model training, we compute the mean value of y and X\\ny_train_mean = np.mean(y_train)\\nX_train_mean = np.mean(X_train,axis=0)\\nX_train = X_train - X_train_mean\\ny_train = y_train - y_train_mean\\n\\n# The we fit our model with the training data\\ntrained_model = some_model.fit(X_train,y_train)\\n\\n\\n#Model prediction, we need also to transform our data set used for the prediction.\\nX_test = X_test - X_train_mean #Use mean from training data\\ny_pred = trained_model(X_test)\\ny_pred = y_pred + y_train_mean\\n'" - ] - }, - "execution_count": 10, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "\"\"\"\n", "#Model training, we compute the mean value of y and X\n", @@ -2615,43 +2246,7 @@ "collapsed": false, "editable": true }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "True beta: [2, 0.5, 3.7]\n", - "Fitted beta: [2.08376632 0.19569961 3.97898392]\n", - "Sklearn fitted beta: [2.08376632 0.19569961 3.97898392]\n", - "MSE with intercept column\n", - "0.004113634617443139\n", - "MSE with intercept column from SKL\n", - "0.004113634617443147\n", - "Manual intercept: 2.083766322923899\n", - "Fitted beta (wiothout intercept): [0.19569961 3.97898392]\n", - "Sklearn intercept: 2.0837663229239043\n", - "Sklearn fitted beta (without intercept): [0.19569961 3.97898392]\n", - "MSE with Manual intercept\n", - "0.00411363461744314\n", - "MSE with Sklearn intercept\n", - "0.004113634617443131\n" - ] - }, - { - "data": { - "image/png": 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              " - ] - }, - "metadata": { - "filenames": { - "image/png": "/Users/mhjensen/Teaching/MachineLearning/doc/LectureNotes/_build/jupyter_execute/chapter3_112_1.png" - } - }, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "import numpy as np\n", "import matplotlib.pyplot as plt\n", @@ -2843,116 +2438,7 @@ "collapsed": false, "editable": true }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Beta values for own Ridge implementation\n", - "[ 1.03032441e+00 6.28336218e-02 -6.24175744e-01 5.21169159e-02\n", - " 2.80847477e-01 2.12552073e-01 8.13220608e-02 -1.69634577e-02\n", - " -6.50846112e-02 -7.38962192e-02 -5.94226022e-02 -3.50227564e-02\n", - " -9.80609616e-03 1.08299273e-02 2.41882037e-02 2.93492130e-02\n", - " 2.64742912e-02 1.63249532e-02 -5.01831251e-05 -2.15098090e-02]\n", - "Beta values for Scikit-Learn Ridge implementation\n", - "[ 1.03032441e+00 6.28336218e-02 -6.24175744e-01 5.21169159e-02\n", - " 2.80847477e-01 2.12552073e-01 8.13220608e-02 -1.69634577e-02\n", - " -6.50846112e-02 -7.38962192e-02 -5.94226022e-02 -3.50227564e-02\n", - " -9.80609615e-03 1.08299273e-02 2.41882037e-02 2.93492130e-02\n", - " 2.64742912e-02 1.63249532e-02 -5.01831207e-05 -2.15098090e-02]\n", - "MSE values for own Ridge implementation\n", - "4.3632959215700067e-07\n", - "MSE values for Scikit-Learn Ridge implementation\n", - "4.363295916323784e-07\n", - "Beta values for own Ridge implementation\n", - "[ 1.03630548 -0.01963611 -0.37900111 -0.07062318 0.12182967 0.16343471\n", - " 0.13003291 0.07490892 0.02365049 -0.01449782 -0.03814292 -0.04909093\n", - " -0.05009826 -0.04389027 -0.03279636 -0.01866537 -0.00289724 0.01348565\n", - " 0.02976145 0.04543942]\n", - "Beta values for Scikit-Learn Ridge implementation\n", - "[ 1.03630548 -0.01963611 -0.37900111 -0.07062318 0.12182967 0.16343471\n", - " 0.13003291 0.07490892 0.02365049 -0.01449782 -0.03814292 -0.04909093\n", - " -0.05009826 -0.04389027 -0.03279636 -0.01866537 -0.00289724 0.01348565\n", - " 0.02976145 0.04543942]\n", - "MSE values for own Ridge implementation\n", - "5.194042827197027e-06\n", - "MSE values for Scikit-Learn Ridge implementation\n", - "5.1940428268204826e-06\n", - "Beta values for own Ridge implementation\n", - "[ 1.04220758 -0.10931453 -0.17641709 -0.06020587 0.02208512 0.05789007\n", - " 0.06491736 0.05785343 0.04537385 0.03196357 0.01969145 0.00934499\n", - " 0.00107405 -0.00526348 -0.00992331 -0.01318643 -0.01531845 -0.01655318\n", - " -0.01708852 -0.01708781]\n", - "Beta values for Scikit-Learn Ridge implementation\n", - "[ 1.04220758 -0.10931453 -0.17641709 -0.06020587 0.02208512 0.05789007\n", - " 0.06491736 0.05785343 0.04537385 0.03196357 0.01969145 0.00934499\n", - " 0.00107405 -0.00526348 -0.00992331 -0.01318643 -0.01531845 -0.01655318\n", - " -0.01708852 -0.01708781]\n", - "MSE values for own Ridge implementation\n", - "2.0940821989643363e-05\n", - "MSE values for Scikit-Learn Ridge implementation\n", - "2.094082198961999e-05\n", - "Beta values for own Ridge implementation\n", - "[ 1.01219292 -0.06043581 -0.10391807 -0.05651951 -0.01898855 0.00312361\n", - " 0.01463049 0.01975848 0.02123176 0.02068067 0.01905883 0.01691985\n", - " 0.01458337 0.01223198 0.00996754 0.00784393 0.00588657 0.00410387\n", - " 0.00249435 0.00105081]\n", - "Beta values for Scikit-Learn Ridge implementation\n", - "[ 1.01219292 -0.06043581 -0.10391807 -0.05651951 -0.01898855 0.00312361\n", - " 0.01463049 0.01975848 0.02123176 0.02068067 0.01905883 0.01691985\n", - " 0.01458337 0.01223198 0.00996754 0.00784393 0.00588657 0.00410387\n", - " 0.00249435 0.00105081]\n", - "MSE values for own Ridge implementation\n", - "0.0003153514830957865\n", - "MSE values for Scikit-Learn Ridge implementation\n", - "0.00031535148309580783\n", - "Beta values for own Ridge implementation\n", - "[ 8.38916861e-01 1.31276579e-01 8.97497404e-03 -1.72271878e-02\n", - " -2.11744554e-02 -1.91492986e-02 -1.57201944e-02 -1.23002365e-02\n", - " -9.30466214e-03 -6.81048318e-03 -4.78184120e-03 -3.15130074e-03\n", - " -1.84923989e-03 -8.13661243e-04 7.46984697e-06 6.56636616e-04\n", - " 1.16805821e-03 1.56912044e-03 1.88168312e-03 2.12318726e-03]\n", - "Beta values for Scikit-Learn Ridge implementation\n", - "[ 8.38916861e-01 1.31276579e-01 8.97497404e-03 -1.72271878e-02\n", - " -2.11744554e-02 -1.91492986e-02 -1.57201944e-02 -1.23002365e-02\n", - " -9.30466214e-03 -6.81048318e-03 -4.78184120e-03 -3.15130074e-03\n", - " -1.84923989e-03 -8.13661243e-04 7.46984697e-06 6.56636616e-04\n", - " 1.16805821e-03 1.56912044e-03 1.88168312e-03 2.12318726e-03]\n", - "MSE values for own Ridge implementation\n", - "0.015072388895177157\n", - "MSE values for Scikit-Learn Ridge implementation\n", - "0.0150723888951771\n", - "Beta values for own Ridge implementation\n", - "[0.37396662 0.14174745 0.0764924 0.04892055 0.03447512 0.02586427\n", - " 0.02024962 0.01633913 0.01347916 0.0113104 0.0096208 0.00827728\n", - " 0.00719176 0.00630331 0.00556826 0.0049544 0.00443743 0.0039987\n", - " 0.0036237 0.003301 ]\n", - "Beta values for Scikit-Learn Ridge implementation\n", - "[0.37396662 0.14174745 0.0764924 0.04892055 0.03447512 0.02586427\n", - " 0.02024962 0.01633913 0.01347916 0.0113104 0.0096208 0.00827728\n", - " 0.00719176 0.00630331 0.00556826 0.0049544 0.00443743 0.0039987\n", - " 0.0036237 0.003301 ]\n", - "MSE values for own Ridge implementation\n", - "0.26409315307910036\n", - "MSE values for Scikit-Learn Ridge implementation\n", - "0.26409315307910025\n" - ] - }, - { - "data": { - "image/png": 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\n", 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              " - ] - }, - "metadata": { - "filenames": { - "image/png": "/Users/mhjensen/Teaching/MachineLearning/doc/LectureNotes/_build/jupyter_execute/chapter3_120_1.png" - } - }, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "import numpy as np\n", "import pandas as pd\n", @@ -3042,138 +2528,7 @@ "collapsed": false, "editable": true }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Beta values for own Ridge implementation\n", - "[ 3.43579948e-02 -5.43330971e-01 -3.10141413e-03 2.47116868e-01\n", - " 2.18613217e-01 1.02054837e-01 -4.25617658e-04 -5.90475506e-02\n", - " -7.68534263e-02 -6.68929213e-02 -4.24906604e-02 -1.40927184e-02\n", - " 1.11482289e-02 2.88529063e-02 3.67047975e-02 3.38135733e-02\n", - " 2.02198703e-02 -3.46383925e-03 -3.63025821e-02]\n", - "Beta values for Scikit-Learn Ridge implementation\n", - "[ 3.43579948e-02 -5.43330971e-01 -3.10141413e-03 2.47116868e-01\n", - " 2.18613217e-01 1.02054837e-01 -4.25617654e-04 -5.90475506e-02\n", - " -7.68534263e-02 -6.68929213e-02 -4.24906604e-02 -1.40927184e-02\n", - " 1.11482289e-02 2.88529063e-02 3.67047975e-02 3.38135733e-02\n", - " 2.02198702e-02 -3.46383925e-03 -3.63025821e-02]\n", - "Intercept from own implementation:\n", - "1.0330308045188872\n", - "Intercept from Scikit-Learn Ridge implementation\n", - "1.0330308045183219\n", - "MSE values for own Ridge implementation\n", - "3.1392559591206444e-06\n", - "MSE values for Scikit-Learn Ridge implementation\n", - "3.1392559585048734e-06\n", - "Beta values for own Ridge implementation\n", - "[-0.05807125 -0.29822833 -0.08551306 0.08156108 0.13679863 0.12333649\n", - " 0.08251519 0.03815288 0.00111756 -0.02498832 -0.04010697 -0.04566964\n", - " -0.04355837 -0.03562355 -0.02348765 -0.00848904 0.00831018 0.0260906\n", - " 0.04423486]\n", - "Beta values for Scikit-Learn Ridge implementation\n", - "[-0.05807125 -0.29822833 -0.08551306 0.08156108 0.13679863 0.12333649\n", - " 0.08251519 0.03815288 0.00111756 -0.02498832 -0.04010697 -0.04566964\n", - " -0.04355837 -0.03562355 -0.02348765 -0.00848904 0.00831018 0.0260906\n", - " 0.04423486]\n", - "Intercept from own implementation:\n", - "1.041148729430595\n", - "Intercept from Scikit-Learn Ridge implementation\n", - "1.041148729430523\n", - "MSE values for own Ridge implementation\n", - "1.96013048502692e-05\n", - "MSE values for Scikit-Learn Ridge implementation\n", - "1.960130485007504e-05\n", - "Beta values for own Ridge implementation\n", - "[-0.1416398 -0.14021063 -0.05383795 0.01367553 0.04784395 0.05796251\n", - " 0.05447415 0.044613 0.03267527 0.02098261 0.01066519 0.00217499\n", - " -0.00440346 -0.00917248 -0.01231917 -0.01405935 -0.0146081 -0.01416528\n", - " -0.01290947]\n", - "Beta values for Scikit-Learn Ridge implementation\n", - "[-0.1416398 -0.14021063 -0.05383795 0.01367553 0.04784395 0.05796251\n", - " 0.05447415 0.044613 0.03267527 0.02098261 0.01066519 0.00217499\n", - " -0.00440346 -0.00917248 -0.01231917 -0.01405935 -0.0146081 -0.01416528\n", - " -0.01290947]\n", - "Intercept from own implementation:\n", - "1.0495569966278295\n", - "Intercept from Scikit-Learn Ridge implementation\n", - "1.0495569966278269\n", - "MSE values for own Ridge implementation\n", - "5.4959161509377256e-05\n", - "MSE values for Scikit-Learn Ridge implementation\n", - "5.495916150936645e-05\n", - "Beta values for own Ridge implementation\n", - "[-0.13535942 -0.08593216 -0.03568439 -0.0036367 0.01397146 0.02229529\n", - " 0.02503753 0.0245528 0.02228115 0.01908936 0.01549377 0.01179792\n", - " 0.00817631 0.00472512 0.00149311 -0.00149956 -0.00424967 -0.00676387\n", - " -0.00905423]\n", - "Beta values for Scikit-Learn Ridge implementation\n", - "[-0.13535942 -0.08593216 -0.03568439 -0.0036367 0.01397146 0.02229529\n", - " 0.02503753 0.0245528 0.02228115 0.01908936 0.01549377 0.01179792\n", - " 0.00817631 0.00472512 0.00149311 -0.00149956 -0.00424967 -0.00676387\n", - " -0.00905423]\n", - "Intercept from own implementation:\n", - "1.0399676689527966\n", - "Intercept from Scikit-Learn Ridge implementation\n", - "1.0399676689527975\n", - "MSE values for own Ridge implementation\n", - "7.571105947979352e-05\n", - "MSE values for Scikit-Learn Ridge implementation\n", - "7.571105947979394e-05\n", - "Beta values for own Ridge implementation\n", - "[-0.05100875 -0.04063602 -0.02723445 -0.01713366 -0.0100706 -0.00517114\n", - " -0.00174276 0.00068734 0.00243186 0.00369758 0.00462287 0.0053018\n", - " 0.00579953 0.006162 0.00642221 0.00660427 0.00672607 0.0068011\n", - " 0.00683964]\n", - "Beta values for Scikit-Learn Ridge implementation\n", - "[-0.05100875 -0.04063602 -0.02723445 -0.01713366 -0.0100706 -0.00517114\n", - " -0.00174276 0.00068734 0.00243186 0.00369758 0.00462287 0.0053018\n", - " 0.00579953 0.006162 0.00642221 0.00660427 0.00672607 0.0068011\n", - " 0.00683964]\n", - "Intercept from own implementation:\n", - "0.999955585168597\n", - "Intercept from Scikit-Learn Ridge implementation\n", - "0.999955585168597\n", - "MSE values for own Ridge implementation\n", - "0.0007698473260556344\n", - "MSE values for Scikit-Learn Ridge implementation\n", - "0.0007698473260556325\n", - "Beta values for own Ridge implementation\n", - "[-0.00834567 -0.00803064 -0.00673407 -0.00554552 -0.00458878 -0.0038335\n", - " -0.00323332 -0.00274989 -0.0023548 -0.00202756 -0.00175331 -0.00152117\n", - " -0.001323 -0.0011526 -0.00100519 -0.00087697 -0.00076495 -0.00066668\n", - " -0.00058016]\n", - "Beta values for Scikit-Learn Ridge implementation\n", - "[-0.00834567 -0.00803064 -0.00673407 -0.00554552 -0.00458878 -0.0038335\n", - " -0.00323332 -0.00274989 -0.0023548 -0.00202756 -0.00175331 -0.00152117\n", - " -0.001323 -0.0011526 -0.00100519 -0.00087697 -0.00076495 -0.00066668\n", - " -0.00058016]\n", - "Intercept from own implementation:\n", - "0.9637117593816477\n", - "Intercept from Scikit-Learn Ridge implementation\n", - "0.9637117593816477\n", - "MSE values for own Ridge implementation\n", - "0.0023813163025848865\n", - "MSE values for Scikit-Learn Ridge implementation\n", - "0.002381316302584886\n" - ] - }, - { - "data": { - "image/png": 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\n", 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              " - ] - }, - "metadata": { - "filenames": { - "image/png": "/Users/mhjensen/Teaching/MachineLearning/doc/LectureNotes/_build/jupyter_execute/chapter3_122_1.png" - } - }, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "import numpy as np\n", "import pandas as pd\n", @@ -3720,32 +3075,7 @@ "collapsed": false, "editable": true }, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_7650/4162706317.py:6: MatplotlibDeprecationWarning: Auto-removal of grids by pcolor() and pcolormesh() is deprecated since 3.5 and will be removed two minor releases later; please call grid(False) first.\n", - " cb = fig.colorbar(im)\n", - "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_7650/4162706317.py:7: UserWarning: FixedFormatter should only be used together with FixedLocator\n", - " cb.ax.set_yticklabels(cb.ax.get_yticklabels(), fontsize=18)\n" - ] - }, - { - "data": { - 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              " - ] - }, - "metadata": { - "filenames": { - "image/png": "/Users/mhjensen/Teaching/MachineLearning/doc/LectureNotes/_build/jupyter_execute/chapter3_154_1.png" - } - }, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "fig = plt.figure(figsize=(20, 14))\n", "im = plt.imshow(J, **cmap_args)\n", @@ -4032,32 +3362,7 @@ "collapsed": false, "editable": true }, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_7650/3777801602.py:6: MatplotlibDeprecationWarning: Auto-removal of grids by pcolor() and pcolormesh() is deprecated since 3.5 and will be removed two minor releases later; please call grid(False) first.\n", - " cb = fig.colorbar(im)\n", - "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_7650/3777801602.py:7: UserWarning: FixedFormatter should only be used together with FixedLocator\n", - " cb.ax.set_yticklabels(cb.ax.get_yticklabels(), fontsize=18)\n" - ] - }, - { - "data": { - "image/png": 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lLM9zzz0Xa9eujUGDBr1qVNz+++8fp556ahx++OHrPCH56aefjoiIww8/fJ2HYWyxxRbxxS9+Mfbdd99485vf/KrbpP/ZbbfdFk899VT069cvfvjDH8YOO+zQ81pjY2NccMEFccABB0RbW9t6b+GNiPjUpz4V7373u9dZhrPPPjsiukYWbuqnpX7605/ueXjDlltuGYVCIR599NGe0Zff+c531vmb1tXVxZlnnhn/8i//EhERF1988Xp/79FHHx3ve9/7eka5NTU1xWGHHRYRXSPBvv3tb68zEu0DH/hAzwjUJ598stfLP3r06Lj++ut7bjOO6LoV+s9//nNccskl8dGPfjT22WefOP/883u2kVf60Y9+FKtXr47ddtstLrzwwnUeZHHggQfGaaedFhERv/71r3u9TBERv/nNb+I//uM/orq6Or72ta/Fe9/73kz9N6Uf/ehHsXz58jj00EPjS1/6UgwcOLDntbFjx8all14ajY2NMW3atLjnnnt6Xrv//vujpqYmPvzhD79qRN6ECRPi5JNPjogN76/69esXn/3sZ3s+t+t70Ezq7R0A2HQU9gB4w9hnn32iX79+r/r5hAkTIiLWmffrnnvuidbW1pg4cWLsvPPO6/1973nPeyKia6L+F198MSIidt5553jooYfioYceii233PJVfVpbW3uKfWvWrFnv733Tm970muvy8MMPv+q/v/71rzF37tzYf//94+yzz4477rijZxm7zZkzp+cWue4HDfyzgw46KLbccsuewknEP96P0aNHx8EHH/yqPltssUW8//3v3+gyr2+9Sl2eMWPGRE1NTaxYsSLOOeecnqJdt9NPPz2+853vxNvf/vaen3UXmK644oq4+eab1/l7jxgxIn784x/Hl770paivr9/oetx1110R0TV3WneR9p9137o6derU9c4nd8ghh7zqZ93bYURXIXVTqaqqWu/t2N1FvSlTpsQee+yx3r4nnXRSREQ8//zz6y3srG9b6C7cbbfddut9QnV3Yb25ubl3K/CykSNHxmWXXRa33357fPazn4299957nb/VqlWr4pprrokjjzzyVdtD97p+8IMf7Jl78pU+8pGPxK233ho//elPe708t9xyS/y///f/orOzM77+9a/Hu971rkzrs6l1z1e3oc/RsGHDYr/99ouIdecq/PrXvx6PP/54nHXWWevt1/1U4NbW1lfNQxgRsdNOO0VDQ8NGly3l9g4AbFrm2APgDWPEiBHr/Xl3ceCVD3LofpLsggULNjj/U/EVD6WYOXNmDB06dJ3fOWvWrHjiiSdi9uzZ8cILL8T06dPjmWee6ZnAfn0XyRHxmiPfImKdBz20trbGQw89FF/72tfiqaeeioULF8YBBxwQw4YNe1W/Vz4h9/TTT9/g7+9exu455Lr7TZo0aYN9Jk+evNFlXt96lbo8Q4cOjZNPPjm+//3vx29+85v4zW9+E1tttVXsvffesf/++8eBBx74qpFDH//4x+O2226LxYsXx9lnnx01NTWxyy67xL777hsHHnhg7Lrrrr2aX617dNGGCr6vfK2joyOef/75V70369sWX1mk6ujoeM3l6K2BAweut1jZ/V5ubD3GjRsXjY2N0dzcHM8991w0NTWt8/qoUaNe1ad7lOT6Rm698vXiBh7q8lrGjRsXp556apx66qnR2toajz/+eNx///1x0003xdy5c2PZsmVx2mmnxe233x51dXWxdu3aWLhwYUTEOqMrX6mxsTG23377Xi/D0qVL4wtf+ELPZ7i7sL+5vPTSSz0PUbn00kvjJz/5yXrbdbfp/tt3q66ujtbW1vjzn/8cM2fOjBdeeCFmzZoVTz/9dMyfP7+nXWdn56sKo73ZX6Xc3gGATUthD4A3jFfelvlaukdZNTc3r3OL7oa8csTJY489FhdffHHPUy+7bbnllnHQQQfFk08+GXPmzNng73qtEWP/rK6uLvbbb7/Ydddd49hjj41nn302TjjhhLjmmmvWGRUTse6oxN6sV3f77tsbNzYyZ323DL/S+tar1OWJiDjrrLNi8uTJ8bOf/SymTZsWixcvjltuuSVuueWWqKmpiXe84x3xn//5nz23XY4aNSpuuumm+MEPfhC33XZbLFy4sOfBCd/73vdi6623jnPPPTfe+ta3bnQZukeavfJ2zn/2yvfipZdeetXrr7Utllr0Wp/1jVKN6N16REQMGDAgmpub17se3aO51md9I+M2tbq6uthzzz1jzz33jNNPPz2+/vWvx5VXXhnz5s2LP/zhD/H2t789li9f3tP+tUaW9VZbW1vU1tbGoYceGnfccUdcfPHFceCBB25wBGdvLV68OD71qU+t97WDDjooPvGJT6z3tVeOftzQLbOv9MrPUVtbW3zve9+LX/ziF+u8V9XV1dHU1BRTpkzZ6JOpN7R9vVLK7R0A2LQU9gDIpe6CxeGHHx7f+c53et1vxowZ8dGPfjRaWlpi4sSJ8YEPfCB22GGHmDBhQs+olQ996EMbLeyVqrGxMb797W/HBz7wgVi2bFmcccYZ8etf/3qd4kt3YWPw4MHx4IMP9vp3d/+Ojd0+ub7Cz2spdXm6ve1tb4u3ve1t0dzc3PM03HvuuSdmzpzZc7vt97///Z72Q4cOjXPPPTfOPffceOaZZ2Lq1KnxwAMPxH333Rdz586NT33qU/HLX/4ypkyZssHMAQMGRESs9xbbbq8s9Ha3f6PpzXq88vXNtR6XXHJJ3HLLLbH33nvHf//3f2+wXU1NTZx99tnxf//3f7FgwYKekZWv3P5L2UbXp7a2Nr73ve/FfvvtF0cffXQ89dRT8e///u9x9dVXl/RU3W5r167dYIF722233WC/V67jLbfc8qqRlRvzn//5n3HDDTdEdXV1/Mu//Eu8+c1vju233z7GjRsX9fX1cf/992+0sAcAVDZz7AGQS9ttt11ErHur6D9bs2ZNTJ06NV544YWeW8muvvrqaGlpifHjx8f1118fJ510Uuy7777r3IrWfVvg62H8+PHx2c9+NiK6ioz//NCD7vVavnx5LF68eIO/Z9q0aTFjxoyeB0l0Fwo2Nhron+c1641Sl6elpSWefvrpnszGxsY49NBD45xzzonf/va3Pe/B3Xff3VOYWrhwYTzwwAM9v2PSpElx/PHHx/e+97248847Y+utt46Ojo649dZbN7rM48ePj4iIv/3tbxts89e//jUiIgqFwnrnmXsj6M16zJgxI1avXh0RGy8svZ46Oztj1qxZcdddd0VbW9tG21ZVVfXcEt99K/DAgQN7frahz/OiRYvimGOOibPOOus1C53dv/uggw6KmpqauOCCC6K6ujoefPDB+PnPf55l1V5lzJgx8cwzz6z3v6985Ssb7Ddw4MCeW++nT5++wXbPPPNMPPXUU7FixYqI6PpM3HjjjRER8aUvfSnOP//8ePe73x077LBDzwjbBQsWlLVOAEC+KewBkEsHHXRQVFdXx8yZM+P+++9fb5urrroqjj/++HjPe97T8yCM7jmsJkyYsN7bFO+///6YN29eRLx+80odd9xxseeee0ZExM9//vN1RgBNmDChp0Dzs5/9bL39//KXv8Rxxx0X73jHO+LRRx+NiK6HJNTW1sb8+fPjvvvue1WftWvXxm9+85vMy1rq8lx77bXxnve8J84+++z13sa377779vzv9vb2aG9vj/e+971xwgknxB/+8IdXtR82bFhP8XJDcx92634QwF133RUvvPDCett0z3G22267rfN00jeS7vV4/PHHNzhKrPvJ0CNHjtzo/Iqvp3e+851RVVUVixYtWmf05fp0z2NZW1vb86CIiOh58vWGnnp72223xWOPPRaPPfbYa96a/M8mT54cJ554YkR0PT14Q9vEPytnZN/6dD/I5Gc/+9l6t+FVq1bFCSecEO9973vj6quvjoiIefPm9Xx+1jfXYmdnZ9xwww09/99ceADQ9yjsAVCWVatWxdKlSzf63+sxP9PWW28dH/zgByMi4jOf+UzPk1Ajui52r7vuurjkkksioquQ1j2nWvcItPvvvz+mTZvW06e9vT1uvfXWdZ482T1ybFMrFApx/vnnR21tbXR2dsZ//Md/RGtra8/rZ555ZkREXH755fHDH/5wndemTZvW8/puu+0We++9d0R0Fb4+/OEPR0TEOeecs04haNmyZfHpT3+65NuLS1meI488Mmpra+PZZ5+NCy+8sGdUWUTXgw2++c1vRkTErrvuGltuuWXU1NTEO9/5zoiI+PKXvxyPP/74Osvwu9/9rqdg2V0E2pAjjjgiJk2aFGvXro1TTjllnZGKzc3N8R//8R9x3333RU1NTXzuc58r6T1JYffdd4+DDjooIiI+9alPrXMrdGtra3znO9+JX/3qVxER8fnPf36TF6J6a8KECXHCCSdERNdtuWefffarRqW1tbXFHXfcESeddFK0t7fHiSeeGKNHj+55/eSTT45+/frFtGnT4vzzz1/nidR//OMfe7aXj3/84yUt46c+9anYdtttY/Xq1XHuuef2ap/UfRt695cB5Tr11FOjoaEh/vKXv8TZZ58dS5cu7Xlt7ty5ceqpp8ayZctiiy22iOOOOy4iukZhVldXR0TED3/4w3Xel3nz5sWZZ565zn5sQ0/yBgAqlzn2ACjLxp6U2u2hhx56XUZFnXvuubFw4cK4++6747TTTovhw4fHiBEjYu7cuT0XzYcffnh8+tOf7ulz0kknxa233hrLli2L4447LsaNGxcDBgyIOXPmxIoVK6KhoSF23333eOSRR17XW9wmTJgQ//qv/xqXXHJJTJ8+Pb7//e/3TMr/zne+M2bNmhXf/e534+KLL44f/OAHMW7cuFi6dGlPkWG77baLSy+9dJ3f+ZnPfCaeeuqpmDp1ahx77LE96/b3v/892tvbY/LkyfHEE0/0FAp6q5TlGT58eFx44YVx9tlnx09+8pO4/vrrY+zYsdHR0RGzZ8+OtWvXxpZbbhlf/vKXe/qcddZZ8Ze//CWefPLJ+OAHPxhbb711bLnllrFo0aJYtGhRREQce+yxr1nYq6mpiUsvvTROOeWUmDlzZrznPe/peS+6bxeur6+P//7v/+4ZOflGddFFF8UnPvGJeOSRR+KjH/1obL311jFkyJB47rnnorm5Oaqrq+PTn/50T1F0c/nCF74Q1dXVcdVVV8XNN98cN998c2y11VYxfPjwaG9vjxdeeCFWr14dhUIhjj/++J5bsbtNnDgxvvrVr8bnP//5uOaaa+LGG2+M8ePHx4svvtjz1Nf3v//9PcXrrOrr6+P888+PE088MaZOnRo///nPe4pnG7LTTjvF3XffHbfccks888wzseeee8Z//dd/lZQf0VWk+9a3vhVnnXVW3HrrrXH77bfHxIkTo62tLWbNmhXt7e3R0NAQl19++Tq3K3/sYx+LK664Im699da45557YuzYsfHSSy/F888/H8ViMd7ylrfEX/7yl2hvb48FCxbE4MGDS15GACB/FPYAyK1+/frFZZddFr/97W/jhhtuiCeeeCKeeuqpGDBgQLzlLW+J97///XHUUUet8/TP0aNHx8033xyXXnpp/PnPf4758+dHdXV1jBo1Ko466qg44YQTYu7cuXHCCSfEgw8+GKtXr95kT+r8Z//6r/8av/3tb2PGjBlx+eWXx5FHHhnbb799RHQVTPfff//46U9/GtOmTYunn346amtrY6eddoq3ve1tccIJJ7zqYQn19fVx5ZVXxs9+9rO4+eabY9asWVEoFGLPPfeM0047LR555JF44oknMj/Vt9TlOeqoo2LkyJHxs5/9LB577LGYMWNG1NbWxrbbbhuHHHJInHjiiT3zrEV0Pfzhpz/9aVx99dVx5513xqxZs2LhwoWx5ZZbxmGHHRbHHHNMz+2Mr2XMmDHx61//On7xi1/EbbfdFjNmzIgFCxbEqFGj4oADDugp6r7RDR48OH7605/GjTfeGDfffHM888wzsXjx4hgxYkQcccQRcdxxx8VOO+20uRczCoVCnH322fGBD3wgbrrppnjwwQdj7ty58fe//z1qa2tj5MiRsc8++8T73ve+2GWXXdb7O4488siYNGlSXHnllfGnP/0pnnnmmejXr1+85S1viWOPPTaOPPLIspZx7733jqOPPjquu+66Xj0l95RTTolFixbFHXfcEbNmzcp8C/D6HHTQQfG///u/cdVVV8W9994bzz33XHR0dMTWW28d++23X5x00kmvWqazzz47dtlll/jJT34Szz33XDzzzDMxaNCgnvfz3e9+dxx//PHx0EMPxd133x077LBD2csJAORHoej59QDQJ3z1q1+NK6+8Mo455pj40pe+tLkXBwAAKJM59gCgAjz33HNx8MEHx4knnrjOHHjdisVi3HvvvRERb4gRXgAAQPkU9gCgAmyzzTaxdu3a+POf/xwXX3zxOg/+WLVqVfzXf/1X/P3vf48hQ4bEEUccsRmXFAAA2FTcigsAFeK2226Lz3zmM9HR0REDBgxY52EVLS0tMXDgwPjud7/b8+RaAAAg3xT2AKCCzJw5M6666qr4y1/+0vM00VGjRsVBBx0UH/nIR2L06NGbeQkBAIBNRWEPAAAAAHLIHHsAAAAAkEM1m3sBAAAAAIB/eOyxx+Izn/nMBl+/8847I6LCCnudnRGtrb1vXyhE1NV19cl6Q3Jzc7b25Rg2MMNKlalYW5csq6MjWVS0t2drX8620a9ftvblKHSmexPbi9VJcmqqOpPkRERpG2GhEFFd3dU3w8bRWqzNnlWiuqqMG3wZOqvSHUaqium291Wrs2/vVVURDQ0Rq1d3HY96a8CAzFElq2pbmyyrWJduZ1hY2/LajTaRzrr6zH0KhezHku5+lSjL56Nc1W2VuW2kVFVIt4DtHWk2+uo0pzQRUfrft5Rto6oz3fG/WJ3u+F9oS3ct1FZIdy1U21nCMblQiKitjWhry7aB1KVbr6Q7+ap0NyCuWJlm/7TFFklXq2J0trfHitmzN/dibFKDxo6NrbbaqldtK6qw19oa8dhjvW/f0BCxyy4Rzz7bdSGWxTXXZGtfju98clayrLZxTcmylixJFhWzZmVrP2BAxK67Rjz9dMRLL2Xru+ee2dqXo275i8myXiwOT5Izon/CqnkpG2FdXcSYMRELFmT6JmFW+/jsWSVqqp+XLKt58NhkWQPbVyTLuvHWIZn7DBkS8a53Rdx9d8TSpb3vd/TRmaNK1vD035JltU3eI1lW3cMPJ8tq2W3fTO0LhYj+/SNaWrJfpNdU1FnaP6T8cnTI0+m2jebJ2baNqqqIxsau84yU18FZDaxvS5Y1b0Ga4sPIkUliIiL7l8sRpe83GpYvyh5WouLIdA9iKmQ9kS/DCzXproXGLy/hmNy/f8SOO0bMmBGxZk3v++22W/asUr2Y7vokBg9OFnXNNWn2T8cdFzFoUJKoirJi9uz4zoQJm3sxNqlPzZgRo8eP7xmVtzFqwQAAAACQQwp7AAAAAJBDFXqTBwAAAAB9QV8etZa0sLdmzZq44oor4n//939jzpw5MWDAgJg8eXJ89KMfjYMOOijlogAAAABAriUr7K1evTpOPPHEeOyxx6K2tja23377WL58edx3331x3333xRlnnBGf/OQnUy0OAAAAAORastGK559/fjz22GOx4447xh133BE33nhj3H333fHVr341ampq4rvf/W786U9/SrU4AAAAAJBrSQp7s2fPjptvvjmqqqri4osvjlGjRvW89t73vjdOPvnkiIj47ne/m2JxAAAAAKgQVRX2X9Z1f93ddNNN0dHREbvttltMnDjxVa9/+MMfjoiIhx9+OObNm5dikQAAAAAg15IU9h599NGIiHjTm9603tdHjBgRW2+9dURETJ06NcUiAQAAAECuJSnsPf/88xERMXbs2A226S7szZo1K8UiAQAAAECuJXkq7osvvhgREUOGDNlgm8GDB0dExLJly0rOKRQiGhp6376+ft1/sxgzJnufkvXrlyyqUEgWFbW16bIGDMjWvn//df/NIuV7GDXJHmwdNcVEQVXJnukTUVeXvU/3hptxA+5XnT2qZAk/XCn/XFGd7k3cyOFqgwYOXPff3kq6zyhlp1aipOuVdSdfhqzr1d2+lPcj6XuYUMKPctJtI+v+sLt90v1oKRJuiKkOXyk/W+V89jP3TXlynVLCa6G6lPunUo7J5VzAppLw+iTlh3nYsDQ5SY+RFeaNfjh9PSX51LW0tERERN1GLqL7vbzD7m5birq6iF12yd5v++2z9yklp3TbJktKeTqw1VZv/Kympk27HJtcKdWHEg1NltSYLCkay8gaMSJT83Sf4oiIUa/dZBNJ+NeKiEHJkt71rtL7HnjgpluOTW7HHZNFJb283HXXZFGllkbfyNdgqQ1K91FOum2Uuj/M8qX05pHu0zwq3eErmXJqbZn3G/3TnVwnvYDeNt1ZVMqxGxFlHJO3227TLcamlvD6JKVjjtncSwAblqSwV11dHZ2dnVHYSEW9WOwaDlRVxteWra0Rzz7b+/b19V1Fvb//PSJrPfG3v83Wvhyf/5fnk2W1jU534Fy+PFlUZH0mS//+XUW9Z5+NWLMmW9+ddsrWvhy1q5Ymy3qxmOYgPbRfc5KciChtI6yt7SrqLVwY0dbW627Pd6Q7Vdy2bn6yrOYtEhYRO1Yky7r13uyVh4EDu4p6f/xjxMqVve932GGZo0rWf9ZTybLaJiYsIj75WLKsNU3ZCkWFQtf5RktLRDHjyOeUgx5SWr06XdagWem2jeYJ2baNqqquot7q1RGdna/TQm0Cjf16f6wr1/wlaYqIqUbeRES0t2fvU+p+o3/z4uxhJeocmrCI+EK6a6E51QmLiKtKOCbX13cV9Z57LtsF7KRJ2bNKlfIib4stkkX96sY0+6d3vKO8sQf0TUlOGRsaGmLFihWxdu3aDbZpbW2NiH+M3CtFsVjayWJLS/Z+c+ZkzynZRt63TS3rRUc5MtREyvbSS6X1W7Mme9+U72FJZ4ulRqVar9qEVzcv73dK0taWqf/adH+qiEK6D1fSi9GOjmRRS8uoma9cma1/0n1G1m8qypB0vUrdyZeg1PUqFrP3TfoeJpTwo5x02yh1f9jZ+cYu7KXcEFOdG6b8bJWTlXm/kfLkOqWE10KtKb9QKeeY3NKS9JieScLrk5Qf5iVL0uQkPUZSMZKMot5yyy0jImL5Rqr33XPrDR2a7oY/AAAAAMirJN9JjB8/PmbNmhVzNjLMbe7cuRERMW7cuBSLBAAAAEDOFaLyHp6R5dEwSdZ915cnLn700UfX+/rChQtj3suToO2+++4pFgkAAAAAci1JYe+II46IiIipU6fGzJkzX/X6z3/+84iI2GuvvWLMmLTPIgIAAACAPEpS2Bs3bly8613vio6OjjjjjDPi+ef/8WSjm266Ka644oqIiDjttNNSLA4AAAAA5F6y5/588YtfjGeffTaeffbZOPLII6OpqSlWrlzZM7feWWedFfvuu2+qxQEAAACgAlTaHHtZJCvsbbnllnHttdfGj370o/jtb38bM2bMiJqamthrr73iIx/5SBx++OGpFgUAAAAAci9ZYS8ioqGhIc4444w444wzUsYCAAAAQMXpy6MVAQAAACC3ko7YAwAAAIBNqS+PWuvL6w4AAAAAuVVRI/ZWr4646abetx89OmKXXSLuuSdi3rxsWeedl619OT5zQVOyrE9/OllUNDeny9q38fFsHfr3j4jtY9eGv0cU1mTqunDZlGxZZVi7dniyrLHT70qS03nwoUlyIiJaagZm7lMoRPSPiDVDx0Sx2Pt+Tc2LMmeVamX92GRZ9fXJomLmnCHJso4+OnufQqHr38MOi0zbxvTp2bNKNWXMmGRZdc1Lk2U93rhvsqwpLRnXq7o6ov+g6N+6IqKjI1vflB+whBvikJQnACNHJot64ols7QcMiNh114gZMyJeeilb330nr8zWoRwt6aLGzpmWJGd2pNtnDBuWLCqW1o9OljVkzuxkWZHw2NU+J1lURGNj9j79+nX929DQdXzppWenpxvP0zQy3bHrwUfqkmWdfHKanNraNDlUFiP2AAAAACCHKmrEHgAAAAB9RyEqb9RaIUPbSlt3AAAAAOgTFPYAAAAAIIcU9gAAAAAgh8yxBwAAAEBu9eVRa3153QEAAAAgtxT2AAAAACCHFPYAAAAAIIcU9gAAAAAghzw8AwAAAIDc6suj1vryugMAAABAbinsAQAAAEAOKewBAAAAQA6ZYw8AAACA3Cps7gXYjIzYAwAAAIAcUtgDAAAAgBxS2AMAAACAHDLHHgAAAAC5VIiI6s29EJtYljkDjdgDAAAAgBxS2AMAAACAHFLYAwAAAIAcMsceAAAAALnVl0et9eV1BwAAAIDcUtgDAAAAgBxS2AMAAACAHCoUi8Xi5l6ITaW9PeLFF3vfvqYmYujQrj7t7dmyvv3tbO3Lcc456bI+97l0WZdcki4rq0IhorY2oq0tIusnpG7JvNdnodZnyZJkUa07TEmSU9e+OklORERrTUPmPqVuG8uWZY4q2Yi1s5NlzY6xybLGTr8rWVYMG5a9T//+EdtvH/H3v0esWdPrbj95NM1nKyLio29Nt39aPXh0sqyGmtZkWTF9erb29fUR48dHzJwZ0dKSre/gwdnalyPriVAZWkem22/U1XQmy8p8TK6piRgyJGLp0szv/+MLhmfLKkOG3VnZdtwxTU5NwlnGG6KE85pCoeuYsmZNtpONrPunciTcPz3ZnG6fsdMOCfcZpex3Sz0RTbnRNzcni5rXPDBZ1ujGlWmCGhsjqoy/ymrlzJnxiwkTNvdibFLHzpgRA8eP71VbD88AAAAAILf6cjm0L687AAAAAOSWwh4AAAAA5JDCHgAAAADkkDn2AAAAAMitvjxqrS+vOwAAAADklsIeAAAAAOSQwh4AAAAA5JA59gAAAADIpUJU3qi1Qoa2lbbuAAAAANAnKOwBAAAAQA4p7AEAAABADpljDwAAAIDc6suj1vryugMAAABAbinsAQAAAEAOKewBAAAAQA6ZYw8AAACA3OrLo9b68roDAAAAQG4p7AEAAABADinsAQAAAEAOKewBAAAAQA55eAYAAAAAuVXY3AuwGRmxBwAAAAA5pLAHAAAAADmksAcAAAAAOWSOPQAAAAByqRAR1Zt7ITaxLHMGForFYvF1W5LEisWItrbety8UImpru/pkfRdaWrK1L8cXvpAu67JvtybL+sYldcmyPnPi0mwdqqsjBg2KWLEioqMjU9c/PT0kW1YZ9t0h43qVY9q0JDEPDnp7kpyIiLf0fzx7p/79I7bfPuLvf49Ys6bX3Vp3mJI9q0R1c2Ymy1o9cnyyrIZYnSzryVkNmfvU10eMHx8xc2a2Y8ROO3RmzsqD1vZ0NwXUJPyasrk5W/uqqojGxq5+nRn/1APr0x2TV7enOyY3tK9MlhX19cmiOmuyv4dVVdm3i4iIqvZ028bMOem2jfGDE53XJNwuSlIodJ1vrFmT6SLl8enZj12lmjJsXrKszDveMsyub0qWNXZYCec1JW4brTXpto26SLd/enJ6uv3TTvFkmqCJEyPq0q1XpWieOTNumTBhcy/GJvXuGTOicXzvrrXcigsAAAAAOaSwBwAAAAA5ZI49AAAAAHKrL49a68vrDgAAAAC5pbAHAAAAADmksAcAAAAAOWSOPQAAAAByqy+PWuvL6w4AAAAAuaWwBwAAAAA5pLAHAAAAADlkjj0AAAAAcqkQlTdqrZChbaWtOwAAAAD0CQp7AAAAAJBDCnsAAAAAkEMKewAAAACQQx6eAQAAAEBu9eVRa3153QEAAAAgtxT2AAAAACCHFPYAAAAAIIfMsQcAAABAbvXlUWt9ed0BAAAAILcU9gAAAAAghxT2AAAAACCHzLEHAAAAQG715VFrfXndAQAAACC3FPYAAAAAIIcq6lbcjo6IBQt63762NmLUqIglSyLa2rJlDRuWrX05/uM/0mV9/ot1ybK+8pVkUXHSyUMytR87NuK88yLO++agmD07W9aVF8zL1qEMC9tGJ8uaNejtSXK22ipJTJeRE7P3KRS6/h0zJqJY7HW3uvbV2bNKVZNu197w6J+SZbXuuW+yrJ1GLs3eqbo6IgbF+KErug5IvbS6Jdv+qRxz5iSLikGD0mWN2CLd52tgY30JvaqisaEze7flzSVklaZh8OBkWbEg3XrdNW1gsqy9987WvlCI6N8/Yu3aTIeTiIhoaEn3HrYk3EelOn79cVpDkpyIiN12y96nqiqiMSKaO/pHZ4Zdx5TBGU9ay7C0fmyyrCH1JRyTS9SyJFlUxAMPZO/T2Bix114Rf/1rRHPv9wM1Bx+aPatECxenu55MeMob8xp3SpIzvKrCijQkYZsBAAAAILcKm3sBNiO34gIAAABADinsAQAAAEAOKewBAAAAQA4p7AEAAABADnl4BgAAAAC5VIiI6s29EJtYloeBGLEHAAAAADmksAcAAAAAOaSwBwAAAAA5ZI49AAAAAHKrL49a68vrDgAAAAC5pbAHAAAAADmksAcAAAAAOWSOPQAAAAByqy+PWuvL6w4AAAAAuaWwBwAAAAA5pLAHAAAAADlkjj0AAAAAcqkQlTdqrZChbaWtOwAAAAD0CQp7AAAAAJBDCnsAAAAAkEPm2AMAAAAgt/ryqLW+vO4AAAAAkFsKewAAAACQQwp7AAAAAJBD5tgDAAAAgDeQefPmxfHHH7/B1++8886IqLDCXrEY0d7e+/ZVL49X7OjI1i8iomHWk9k6lGPcTsmiLjpnabKsj544JFnWVVeV1u8//zN7n9NOH11aWAm++tVkUfGWN3emCcr6YSzDs7MaMvfp1y9i220jnl/UP9au7X2/5ubMUSXbY+LgZFmtI8cmy5ozJ1lU1Ndn3z/V1kZsFRGLWwdFW1vv+41unp05q1QTJ6b7e02fniwqttoq+2e5VE88ka19//4R228f8fcZVbFmTba+UyYPztahDKtb0t3EsTzSHScHDEgWFdOmZWvf2Bixxx4RTz2V/RgxcWK6c6ihQ5NFxeqagUlyairqCugV6uuTRQ1pbE2WFQvSnUSNHJnus/Vk+6GZ+9TXR4yPiJnD9oqWxt732+kPd2XOKtWI3XZLlzUuw5tQptkL6pJlUZq+fDtqpR7WAAAAACCXRo8e3TMqb2P6clETAAAAAHJLYQ8AAAAAcsituAAAAADkVl8etZa0sHfooYfG3LlzN9rmoYceioED00ycCwAAAAB5laywt2rVqpg7d25UV1fHrrvuusF21dXVqRYJAAAAAHIrWWHv6aefjoiIbbfdNn7xi1+kigUAAACAipS8sNfU1JQqEgAAAIAKVnj5v0qSZX2SzS/YXdjbfvvtU0UCAAAAQMVKXtibNGlSqkgAAAAAqFhJbsXt6OiI6dOnR0TE8OHD46qrropp06bFqlWrYsSIEXHwwQfHEUccEVVVffkBxQAAAADQe0kKe7NmzYqWlpaIiPjYxz4WL7300jqv33TTTfHjH/84vve978Xw4cNLzikUIurqet++tnbdf7OpL6VTSQopbxZP+FTibbdNFpXUNtuky6rIWnjCDb5fv+x9uvcxWfY1EREdHdmzSpZww0i5f8r6npejlONCTc26//ZaoaSD0BteKZ+vPOjfP1v77vfhjf5+pPwsl3beVZqGhnRZWdere1vKuk2VklWOlOcaqbbDAQPS5ESU9v5198ncN/MBqAwVutNIub3Xl3A5Wep5aDQ2Zg8rVcLryZTbYcr9LqVJuOW94RSKxWLx9Q753//93/jMZz4TERF77713nHHGGbHzzjvH2rVr449//GNcdNFFsXjx4thxxx3jV7/6VdSVePVWLCYuggEAAACw2bTMnBmPTJiwuRdjk9p9xoyoHz++V22TfK0zatSoOP7446OqqirOOeecnltu+/fvH0cddVTssssu8b73vS+eeuqpuP766+PDH/5wSTkdHRELFvS+fW1txIgREQsXRrS1Zcsa0zozW4cyrBnVuz/mptC/dUWyrP+4eFCyrP/+7+x9qqoiOjuz9/vKV7L3KdWnPpUuq7GhhDejFAmHtj0/L/tXb3V1EaNGRcyfH9Ha2vt+q1dnjirZjts0J8tq65fuG+CFC5NFlTS6qqYmYsstI5Yti2hv732/rdrnZw8rUeeIUcmyXnghWVTSkdIzZmRr369fxNixEbNnR6xdm63v9hMS7XcjYs3adMNUmtPtojKdF5Yr67lk//4RO+4Y8dRTEWvWZOtbqXcHpBph+eyzaXIiIkq5zqyq6novVq/Odi7a2Lo0e1ipttgiXdaSJcmimrdId5xctCh7n7q6iDFjIubMyXYeOn7J1OxhpUo5p37CYdnzl6QZsrfVVmkH31IZkmwye+yxR+yxxx4bfH277baLd7/73fGrX/0qfv/735dc2CsWs+3gurW1ldDv5VuLU3j9x1S+QsqiyvPJopJKeTFbSuHxDS/hBp/1IvuVWluz9c960VaWhBtGyv1TKfv3UpVzIdvenvECvz1jNSAnyvl8vZGV+lleuzbxfiCjlJ/lrAWwcqT8UqXUbX7NmuzFzpTvYcq76lJth/80K9DrqpxDcmdnxv5ZvlUqV4XuNFKeW5dzOdnamrF/ym9UUs4/k3A7TLnfhazeMLXgHXfcMSIi5syZs5mXBAAAAIC8qMTp53sr2bp3dnZG60aGW3RP9Vdj3CkAAAAAvKYkhb3jjjsuJk+eHBdddNEG2/ztb3+LiIiJEyemWCQAAAAAyLUkhb3tt98+Ojo64ne/+100r+f+/rlz58Zvf/vbiIh4xzvekWKRAAAAACDXkhT2TjzxxKirq4uFCxfGWWedFYsXL+557emnn46Pf/zjsXr16njzm98cb3/721MsEgAAAADkWpIJ7caNGxcXX3xxnH322fHHP/4xDjnkkNhuu+2ivb09Zs6cGRERkydPjksuuSSqynkUIQAAAAB9Sl+uJCV7UsXhhx8e22+/ffz4xz+O+++/P5577rmor6+P3XffPd71rnfFhz70IQ/OAAAAAIBeSlpJGz9+fHzpS19KGQkAAAAAFakvj1YEAAAAgNxy7ysAAAAAuVSIyhu1VsjQttLWHQAAAAD6hEKxWCxu7oXYZDo7I1paet++UIjo3z9izZqIjG/DyvaGjAtXuoEti5Jl/Wn68GRZ++6wNFnWp84bkqn9mDERn/98xEUXRcyZky3rOxeszNahDJ/64sBkWeedlyZnyODONEER2f+4ERG1tRGjRkXMnx/R1tbrbjPbx2bPKtGYMcmioq453ee4tTHb57gcde2rs3cq9Zhy333Zs0rUevDbk2XVTX8yWVZMnJgsqrOmLnOfqqquU5TM/VpK2A5LleX8qVzLl6fLSrlDLOUhcCVuHJ0Jv5tvb08WFUuWpMkZ3ZjuXK21Pvu5WqHQdbrR1pb5EiWZBQvSZQ0enC6ruTld1ugHbsjeafDgiEMPjbjrrmz70r33zp5VqpQPxHz00XRZe+6ZJmfQoIjq6jRZFWTtzJnxxIQJm3sxNqnJM2ZEv/Hje9XWiD0AAAAAyCFz7AEAAACQW3151FpfXncAAAAAyC2FPQAAAADIIYU9AAAAAMghc+wBAAAAkFt9edRaX153AAAAAMgthT0AAAAAyCGFPQAAAADIIXPsAQAAAJBLhai8UWuFDG0rbd0BAAAAoE9Q2AMAAACAHFLYAwAAAIAcUtgDAAAAgBzy8AwAAAAAcivLwyYqjRF7AAAAAJBDCnsAAAAAkEMKewAAAACQQ+bYAwAAACC3qjf3AmxGRuwBAAAAQA4p7AEAAABADinsAQAAAEAOmWMPAAAAgNzqy6PW+vK6AwAAAEBuKewBAAAAQA4p7AEAAABADhWKxWJxcy/EplIsRrS19b59oRBRW9vVJ+u70NycrX05hiyfmSyrc9z4ZFlPPJEsKurrs7Xv1y9i220jnn8+Yu3abH1/9rNs7ctx/hdbk2Wde15dkpy3vjVJTERE7LBD9j61tRFbbRWxeHG2/c3IkdmzSrV8ebqsIc2z04UNHpwuqxRVVRGNjV0HiM7OXnf70xMDX8eFWlcp23yphtSsTJb17IJ072HT4EXZOtTURAwZErF0aUR7e7a+KT/MjY0VmTWvOd22MW1atvaDBkUcdFDEPfdErFiRre9R7+r9PqZsc+Yki5odY5PkjK3P+Dkuw+yW4Zn71NZGjBoVMX/+G/dco25BuuN/qu0iImLYsGRRUVPCbPelXr+m/HutHpbu77VgQbKoGD9ydZqg+vquc0oyaZs5M2ZNmLC5F2OTGjdjRtSO7119xhYDAAAAADmksAcAAAAAOaSwBwAAAAA5pLAHAAAAADlUwpSdAAAAAPDG0JdHrfXldQcAAACA3FLYAwAAAIAcUtgDAAAAgBwyxx4AAAAAudWXR6315XUHAAAAgNxS2AMAAACAHFLYAwAAAIAcMsceAAAAALnVl0et9eV1BwAAAIDcUtgDAAAAgBxS2AMAAACAHDLHHgAAAAC5VIjKG7VWyNC20tYdAAAAAPoEhT0AAAAAyCGFPQAAAADIIXPsAQAAAJBbWeakqzRG7AEAAABADinsAQAAAEAOKewBAAAAQA4p7AEAAABADnl4BgAAAAC5Vb25F2AzqqjCXnt7xJw5vW9fVxcxZkzEwoURra3ZssYv+FO2DmVYOXnfZFkDf3NDsqyaHd6fLGvixNL6bbNN9j6f/nRpWaU49ZN1ybIuv3hlkpwPf2JgkpyIiJ//rLPEnlWx1dBsfVe3pBsgvXx5sqiIwWOTRQ2pWZ0sK5qbs/epefmQ2tradUDqpX33bsyeVaJ5C9Jth50j032Wm8ak2zZWtg/P1L6qKqIxIprrhkRn1rOukdmyytGYbjOMBQvSZY2OecmyjjpiWLYOhUJE1MZB+7ZFFIsZ0xKewtckzOr9rrPMnFRBEWNjdgm9aiNiVIyK+RHR1utez85Kd0xuGpNxey/DnEeTRcWwdKsVLS3Z+1RVRdTWRqxdG9GZ4VS07q9/zR5Woobd0+0zxi+YlSwrZpXwByvFPvtE9O+fJouK4VZcAAAAAMghhT0AAAAAyKGKuhUXAAAAgL6lL49a68vrDgAAAAC5pbAHAAAAADmksAcAAAAAOWSOPQAAAAByqRCVN2qtkKFtpa07AAAAAPQJCnsAAAAAkEMKewAAAACQQ+bYAwAAACC3+vKotb687gAAAACQWwp7AAAAAJBDCnsAAAAAkEPm2AMAAAAgt/ryqLW+vO4AAAAAkFsKewAAAACQQwp7AAAAAJBDCnsAAAAAkEMengEAAABALhWi8katFTK0rbR1BwAAAIA+QWEPAAAAAHJIYQ8AAAAAcsgcewAAAADkVl8etdaX1x0AAAAAcquiRuzVRluMjxcy9KiLiDExJuZERGu2sN12y9a+DI31yaLi4XHvTxfWki6qasmibB1qaiKGDImq5Usj2tszdV3ePDxbVhk+/vFkUfH+Ewcmybn++iQxERHxjW9l/25j+PCIj3wk4mc/r4pFGTarT3wic1TJxo/rTBf29NPpskaOTBa1tCb757i6OmJQRKyoHhIdWTouzxxVsvqEx5Pp09NlNS15NFnWkpH7ZmpfVxfR2BixfHlEa8ZTjfHDVmbrUI72dBvH6MHZjqtlaUm40bdkPLGpqoqorY1YuzaiM9t+e+GaNMfkiIgRy5cnyxo7bnCaoOlL0uRExLxhUzL3qa2J2CoiFteMirZi7/s1Dc54vluOB55IFrXDbocmy2p4+uFkWa2T98jcp/DyYzL79YsoZtg2/rTlOzNnlWrf9tnJsmLvvdNlLUm036irS5NDRTFiDwAAAAByqKJG7AEAAADQt/TlUWt9ed0BAAAAILcU9gAAAAAghxT2AAAAACCHzLEHAAAAQG715VFrfXndAQAAACC3FPYAAAAAIIcU9gAAAAAgh8yxBwAAAEAuFaLyRq0VMrSttHUHAAAAgD5BYQ8AAAAAckhhDwAAAABySGEPAAAAAHLIwzMAAAAAyK2+PGqtL687AAAAAOSWwh4AAAAA5JDCHgAAAADkkDn2AAAAAMitvjxqrS+vOwAAAADklsIeAAAAAOSQwh4AAAAA5JA59gAAAADIrb48aq0vrzsAAAAA5JbCHgAAAADkkFtxAQAAAOANZN68eXH88cdv8PU777wzIiqtsFcoRNRkWKXq6n/8m6VfRHTWN2RqX4729mRRsUf71HRhkycni5q9ZHim9rW1EaMiYv7aIdHWli1rfM3sbB3KUDNqbLKsG67vTJJz4VfSDSQ+95xS16kqPvLhbH3TrleyqFg5ZqdkWQPblybLGtLYmr1ToRARtTGooS2iWOx1t86auuxZJVq8OFlUjBuXLmte477JsmoyHpPLONVIa/r0ZFHzBqfbb4wcme58rWrJomwdujeI1tbMJ3sjCi3ZssowuzHd36sx0WoNaWxMExQRoxtXZu9UVRURjbFV/+aIfhnON2rSrVfsv3+yqCEtJbyHpRo5MllUSwnbe1VV13XK2rURnRk2jWHDsmeVrL4+WdS8BenOr0c3L08TNHjwP04e6LVCxMvn4n3TG/kUEwAAAAD6nNGjR/eMytsYc+wBAAAAQA4p7AEAAABADinsAQAAAEAObbLC3i9/+cuYNGlSXHfddRtss2zZsrjwwgvjsMMOi8mTJ8e+++4bn/zkJ+PRRx/dVIsBAAAAQF9SU1NZ/2WwSQp7jz/+eFx00UUbbbNkyZI45phj4uqrr44XX3wxmpqaolAoxB133BEf/vCH4/rrr98UiwIAAAAAfULZhb0HH3wwTj755HjppZc22u6ss86K2bNnx3777Rf33HNP3HDDDXHvvffGZz/72ejo6IjzzjsvZsyYUe7iAAAAAECfUHJhb+3atfHd7343Pvaxj8WKFSs22vbBBx+MqVOnRkNDQ1x88cUxaNCgrvCqqjj11FPj3e9+d7S1tcVll11W6uIAAAAAQJ+S7cbdlz3//PNxwgknxPz586O6ujo+/elPx3XXXRdz585db/sbb7wxIiIOO+ywGDJkyKteP/bYY+OWW26JO++8M1paWqK+vr6UxQIAAACgLykUMs9L94ZXKPS6aUkj9hYsWBDz58+P3XbbLX71q1/FaaedttH2jzzySEREvOlNb1rv61OmTImamppYvXp1PPHEE6UsEgAAAAD0KSUV9kaOHBmXX355XHvttTF58uSNtu3s7Iw5c+ZERMTYsWPX26a2tjZGjBgRERHPPfdcKYsEAAAAAH1KSWMVt91229h222171XbFihXR3t4eEbHe23C7DR48OObOnRvLli0rZZEAAAAAoE953W9Cbmlp6fnfdXV1G2zXr1+/V7UvSW1t79t234P9Br8XO8Ot1eVraEiXlXDFsmwWEeVuGhnDypAuKZ2XB+9WnEpdr6qyn62eQXV1uqxy909Jd9y9l/Jwl/ItyLqPL0fW9SrreJLyA5ZwfuOUf6+ksv6Ru/dpKfdtJUj590r2VmzkmmSTK+Vz3N0na9+UO96UWSn3hQk3+JSbxsuX2mkkPNmoTXm6lepNfIOeQ+bCG7yu83p63de86hV7ncJGNtJisfiq9plVV0eMGpW931ZbZe6S8lo26YXza9xanVej+pfWr4RNIyJK2AZLlC4pItVW//GPJ4l5WRnrlPGDmZv1yqixMVlURAxKGVa6jBcFKXfxQ4cmDEuotH11WqUtY8IPWMIPcw7+XKXZyJ0pGzXojb1vS3uukcigMZt7CXon5Rfub2Qpq8sJ94XlJGXdNNKer5W4LyxB2uNJ7+5YhM3hdS/sDRgwoOd/t7a2brBd92v9yqmEd3RELF7c+/Y1NV1n2osXR7x8u3BvdY5Id5rT0ZEsKmqfSfjwkgkTkkXNX56tslfGphGjYn62DmWYn7KIOKIzSc6PfpyuzPHxj5W4TlVVEZ3Z+uZivUrQvDphEbFjRbKski+mamsj2toydemsTnfBknK2i4ED02UtX54uK+sxoazjyRbN2TqUY9GiZFGLtxifLCtlMbtq+dJsHaqru4p6K1akPdnLaP7adBfpqepYg1bNSRMUETF4cPY+VVVdb8bq1dnON1KO/kw5Mmbt2nRZa9Yki2ruX8LgkhI3jRdfzBxVsm23yLgvLMPijoRFxNXPpwkaPbqCh7bzennd98gNDQ1RV1cXra2tG50/r/u1oeWegWW8oIqIrjPtUvol8vJgxjRWr06XlXDFSv3zlrZppNuW3rhbbekWLtzcS/D6qNT1yljfLE/KC99S9k+vHJWedMfde1kLS+VI+RakPISX+h6WdDxJ+QErdyqUDNrS3fWbVqkbR0dH2g9nRik/X8l28xsZbLDJlfM57uzM1j/lbT4pd/Ip94UJN/jOMsazZN00UtZGo3+6/Vlbyl1nqjfxDXoOyRtbkltxt9tuu3jmmWd6no77z9ra2mLRy98Ujxs37vVeJAAAAAAqQaFQeXPsZZhvMcnXOrvuumtERDz66KPrff3xxx+P9vb26NevX+y0004pFgkAAAAAci1JYe/II4+MiIjbb789lq9nIpxf/OIXERHxjne8I+oTPpUNAAAAAPIqSWFvn332iTe96U2xatWqOP3002PJkiUREdHZ2Rk//OEP45Zbbona2to45ZRTUiwOAAAAAORekpuQC4VCfPWrX43jjjsupk2bFoccckhsv/32sWjRoli8eHEUCoW48MILY0LCp6QCAAAAkHPm2Etjm222id/85jdxwgknxIgRI+LZZ5+NtWvXxgEHHBBXXXVVHHXUUakWBQAAAAByb5OVNO+6667XbDNkyJA499xz49xzz91UsQAAAADQJyUbsQcAAAAAbDoKewAAAACQQxU2uyAAAAAAfUqlPTwjg4pa89bOmpjVMrbX7fsVI7aNiOdbR8XatdmymlpWZ+tQhllzGpJlTdxzr2RZVcuXJsuaPj3be9jYGDFqVMQLL0Q0N2fLah/X+22wXOPGJYtK5pOfTJd14VeyD1oeMSLi4x+P+NGPq2Lhwt73O/eczsxZpTr1E+kGY3/608miYty4IenC2rN3KRQi+tdGrGmvjWKx9/0aatJtGyO2aEmWVcp7WKrRS6Yny1o5bkqm9lUvfxy32CKiM+ufOusBqByNjcmiRse8ZFmdMTpZVub3sPtpd/37R6adRkS01qQ7N1zyRLKoaE+03xiyfHmaoIhYOWx85j5VEdEYEc3RGFl2GwOXp/tspdxnxIIF6bImTkwWNbCUa6Hq6ogYFI0dKyI6Onrdbc2gdOdQncOGJ8tqmZUsKhYOakqSM7Sqwoo0JOFWXAAAAADIIYU9AAAAAMghozwBAAAAyKdCofLm2OuesqMXjNgDAAAAgBxS2AMAAACAHFLYAwAAAIAcqrCbkAEAAADoUyptjr0MjNgDAAAAgBxS2AMAAACAHFLYAwAAAIAc6rs3IQMAAACQb4VC5c2xVyj0uqkRewAAAACQQwp7AAAAAJBDCnsAAAAAkEMVdhMyAAAAAH1Kpc2xl4ERewAAAACQQwp7AAAAAJBDCnsAAAAAkEMKewAAAACQQ313dkEAAAAA8q1QqLyHZxQKvW5qxB4AAAAA5JDCHgAAAADkkMIeAAAAAORQhd2EDAAAAECfUmlz7GVgxB4AAAAA5JDCHgAAAADkUKFYLBY390JsMu3tEfPm9b59bW3EqFER8+dHtLVlilo5eGzGhSvdwAXPJsuqWMOGZWtfXR0xaFDEihURHR2Zuq6sGZItKyeWLEmTM25cmpyIiKroLLFjVURntr6nfiLd9yiXf7/E9SrBuV9Mt14XnteaLCva27P3KRQi+vePWLMmIsuhtaUle1apUn2QI7Lvd8vQ2phuv1u3YHa2DmWca0R9fbb25WhsTBY1e0lDsqx+/ZJFxYgtVmfrUOo+IyLi0UeztS/DvHH7Jssa3TIzTVDC27VaR2a/ZigUunYdbW3ZNo2nn84cVbIpO6Q7Jq9ur0uW1VBTmecaDz+dbr+7x8gM1+NlenzJ6GRZO+yQJqe2tuvPTEZz5kQcddTmXopN6+abI8aM6VXTvnsTMgAAAAD5VihU3hx7GSq8bsUFAAAAgBxS2AMAAACAHFLYAwAAAIAcqrCbkAEAAADoUyptjr0MjNgDAAAAgBxS2AMAAACAHFLYAwAAAIAc6rs3IQMAAACQb4VC5c2xVyj0uqkRewAAAACQQwp7AAAAAJBDCnsAAAAAkEMKewAAAACQQxU2uyAAAAAAfUqlPTwjAyP2AAAAACCHFPYAAAAAIIcU9gAAAAAgh/ruTcgAAAAA5FuhUHlz7BUKvW5qxB4AAAAA5JDCHgAAAADkkMIeAAAAAORQhd2EDAAAAECfUmlz7GVgxB4AAAAA5JDCHgAAAADkkMIeAAAAAORQ370JGQAAAIB8KxQqb469QqHXTStqzTsKNbGicWyv21dXRwyKiBUNo6KjI1vWkPrWbB3K8HhLU7KsiROTRUXDo39KlrVyZLb3sKoqojEimqsHRWfvP08RETGwsTNbh3IsX54uqmZIkpyqp59MkhMRsXLMTpn7VFVFNDZGNK+uis4Mf+pPfzpzVMnO/WK6wdgXXpBue3//0XXJsq6/vrSsqojo7Nc/W5/6+pKySjJ4cLqs6dOTRdUtWZIsK8aNy9a++6Rs2LCIYjFb35aWbO3L8OSshmRZOzXOTpbVuVXvzwvL1dqe7T0sFCJqI6Ktpn/mTaNu2LBsHcowemTC85onmpPEtO4wJUlORER7e/Y+hUJEbW1X3yzbxpQxS7OHlao93bGroT7dJevK5nTnGgObSzh21dZG9O8f0dwc0dbW6257TCxhQyxVmo9xRERMnpwua9asNDnbbNP1Z4Ys3IoLAAAAADmksAcAAAAAOaSwBwAAAAA5VFFz7AEAAADQx1TawzMyMGIPAAAAAHJIYQ8AAAAAckhhDwAAAAByqO/ehAwAAABAvhUKlTfHXqHQ66ZG7AEAAABADinsAQAAAEAOKewBAAAAQA5V2E3IAAAAAPQZ5tgDAAAAAPJGYQ8AAAAAckhhDwAAAAByqMJuQgYAAACgT6m0OfYyMGIPAAAAAHJIYQ8AAAAAckhhDwAAAAByqO/ehAwAAABAvhUKlTfHXqHQ66ZG7AEAAABADinsAQAAAEAOKewBAAAAQA4p7AEAAABADlXY7IIAAAAA9CmV9vCMDIzYAwAAAIAcqqiSZnVVMYYMLmbsVRWDtujMnDVvQV3mPqWa0jI1WVY0j0sW1brnvsmy2puzta+u7vq3o6Prv0xaWjJ2KEPCbyXGNi5NE1Q/LE1ORAxsL2GdqqsjYlA0dqzItHGMGzcke1aJLjyvNVnW+49Oty+84frs++pSXX5F9u+9hg6N+MAHIm68MeLFF3vfb//9033HNmZMsqgYmC4qYuTIdFl33JGt/cCBEQccEPHAAxErV2bq2vq2d2bLKkN7e7Ko6BwzNllW1ROPJ8tqnzglU/tCIaK2tuu9L2Y8fa1L+QebPj1dVmNjkpi6SHecfGBa9uNkY2PEHntEPPVURHOGc9gDd0t3XrhwVUOyrK3qk0XFwOkPJ8tavcMemfsUChH9I2JN41aZ9xupNAxL9weras52XC3H+HFp9k9RKEREIU0WFcOIPQAAAADIoYoasQcAAABAH1IoVN4ce4Xej9w0Yg8AAAAAckhhDwAAAABySGEPAAAAAHKowm5CBgAAAKBPqbQ59jIwYg8AAAAAckhhDwAAAABySGEPAAAAAHKo796EDAAAAEC+FQqVN8deodDrpkbsAQAAAEAOKewBAAAAQA4p7AEAAABADlXYTcgAAAAA9CmVNsdeBkbsAQAAAEAOKewBAAAAQA4p7AEAAABADinsAQAAAEAO9d3ZBQEAAADIt0Kh8h6eUSj0uqkRewAAAACQQwp7AAAAAJBDCnsAAAAAkEMVdhMyAAAAAH1Kpc2xl4ERewAAAACQQwp7AAAAAJBDfXesIgAAAAC8Ac2bNy+OP/74Db5+5513RkSFFfbWtBTiz38u9Lp9Y2PEXntFTJ1WFc3N2bJ22y1b+7IMG5Ys6sklw5Nl7bT82WRZbYOaMrUvFrv+bW/v+i+T9owbUzkGD06XtWBBkpjVw8YmyYmIaKhpzd6p8PI+pqHhHxtKb2TdjsqReaMt3fXX1yXLuvyKdIPMTz25s8SeVfGB92Xr+5nPpVuviy9OFhWdE7Ptd8uxfHmyqBiy++7ZOtTWdv27ww4RbW2ZutYtmZctqwxTJo9MlpVyHxXjxiWLmjUrW/v6+ojx4yPmz49oacnWd8yYnbJ1KEPWc+RyVFenyRlx311pgiLiwL33zt6pUIiI/rHHjmuynWvU1GfPKtGI5nT7p6XLRyfLGjJmTLKshvaV2TtVVUVEY/TvaI7o7P35xsPTB2bPKtEejbOSZT3Znu5cI9XhpL4+oqr3JQ26FQqVN8deofcbQoWtOQAAAADk2+jRo3tG5W2MOfYAAAAAIIcU9gAAAAAgh9yKCwAAAEB+VdocexlssjX/5S9/Gf/1X/8VF1xwQXzwgx981ettbW2x++67R9tGJo7eYostYtq0aZtqkQAAAACgYm2Swt7jjz8eF1100UbbzJgxI9ra2qJ///6x4447rrfNgAEDNsXiAAAAAEDFK7uw9+CDD8YZZ5wRL7300kbbPf300xERseeee8YVV1xRbiwAAAAA9GklF/bWrl0bl19+eVx22WXR0dHxmu27C3tNTU2lRgIAAADAPxQKlTfHXqHQ66YlPRX3+eefj8MPPzwuueSSiIj49Kc/HVtvvfVG+yjsAQAAAMCmU1Jhb8GCBTF//vzYbbfd4le/+lWcdtppr9lHYQ8AAAAANp2SxiqOHDkyLr/88jjooIN61X7hwoWxbNmyqK6ujgEDBsRll10Wjz32WLS0tMSYMWPibW97W69/FwAAAABQYmFv2223jW233bbX7btH6xUKhXj3u98da9euXef16667Lg4++OD4+te/Ho2NjaUsEgAAAAD0KUlmF3zmmWciIqK9vT3e+ta3ximnnBLbb799rFq1Km6//fb4+te/Hn/4wx/irLPOih/+8Icl5xQKEVnqgg0N6/6bRXV19j4lq6tLFlWfLCkiqvsli8o6j2b337ekv3Mh4aSdGSbULFttbZKYlKtUdliG/knXK5KGJTN06OZegtfHa0xRSy8kPSZn3Rd2H4AqbULncqTcIVaVNOtMSeoznkR1n96VcpqXcLVSHf4jIuF6pRxIUMr23t0na98KPC+MSLyPT3N53KWUDb67T8a+/ftnjypZv3TXePUVetlFifrwuVahWCwWN8UvOvTQQ2Pu3LlxwQUXxAc/+MF1Xrvnnnvi7rvvjmHDhsUnP/nJV/WdNm1afOQjH4lisZjpFt9/Viz6wAEAAAD0GcuXR1x55eZeik3rpJMiBg/uVdMkJc2DDjpoo8W6PffcM/bdd9+4//774/e//33Jhb21ayMef7z37RsaIiZPjnjiiYjVq7NlTZqUrX05Bq2akyxrZuuYZFnjq59PlvViY+9vHY/o+mZw8OCu/UNHR7asoYWl2TqUY4st0mUtWZIkZs3gUUlyIiL617SV1rG2NqItW9817em+2e4fa5JldfZL9xXwjTcmi4oPvK+ztI5VVRGd2fp+/Zvpht6cdVayqKRWrUqXNah1cbYONTURW24ZsWxZRHv767NQm0LKIbFZD6zl+KfpX15PMxdlGwVWVxcxZkzEnDkRra3ZsoYPz9a+HGvSHVKSjdgbOmNqmqCIiF12yd6nUOgaAtrS0jVyobdSjlZZvjxZ1Iq6rZJlDepIeB5f6nDdhoaui9cM5xtPvZBulOqODemu8WZ2ZLvGK8eoRJco/fqlHZVNZXjDjFXccccd4/777485c0ovYhWLEc3N2futXp29X8pz0sxne2VoaUkWFVGT7mS7vcR7jDs6SrgOKyS8cNs0A257J2Mhq1QpV6mksFcOC87QP+l6RdKwZF58cXMvwetj7tzNvQT5l/SYXOq+sL092X70DS/lDjFjob0cpZ5DtbZm75twtZJutsluuSzlgqFU5WzvxWK2/hV4XhgR0ZHyVtyUX8CUU4jt7My0I0hZoI/qdNd4LRV62QVZJSvsdXR0RLFYjJoN7MA6X94xbeh1AAAAAFhHoVB5c+xlmGcuySDPQw89NHbeeef46U9/usE2Tz75ZERETJw4McUiAQAAAECuJSnsTZgwIYrFYtx8883Rvp7hzY8//ng8+OCDERHxjne8I8UiAQAAAECuJSnsnXLKKVEoFOLJJ5+ML37xi9H8inktHnzwwfi3f/u3KBaLcdRRR8UupUwwCwAAAAB9TJKbkPfaa68499xz4ytf+UrceOON8dvf/ja22267WLVqVc/DMg466KC44IILUiwOAAAAAJWi0ubYyyDZmn/0ox+N3XbbLa6++up46KGHYvr06TFgwIDYe++94/3vf38cddRRUcgwOSAAAAAA9GWbrLB31113vWabKVOmxNe//vVNFQkAAAAAfVaSOfYAAAAAgE2r796EDAAAAEC+FQqVN8dehqnqjNgDAAAAgByqqJJmTU3EuHG9b19X1/Xv6NERra3Zsl5+mG8Stz46PlnWhz6ULCqiZmKyqBFLFmXrUKiJiCExtLA0otCere8TT2RrX4YnRx6aLGvixLFJclqak8RERET94LqS+lVFRGd1baY+DTWdJWWVZHlLsqiq+vpkWfvvn+67qM98LnvW1ltHfPazEV//ZlXMndv7ft+4ON22cdrp6d7DlFPqJtwMI2oas7Wvevk9798/ol+/TF1b6wdmyyrDgoTnNWOHZTyuliPht/cTM57WdH8Rv802EcVitr51c2Zm61CGdFthpPsw77lnmpyIiJYSjsnV1V37jNbWiI6OTb9MOdOYcbdbluXpohauyf7pqqmJGNoY8eLaxmjPsCvdY+LKzFklW57uoLxTe7p94dKWNNfkGU8VICKM2AMAAACAXFLYAwAAAIAcqqhbcQEAAADoQzw8AwAAAADIG4U9AAAAAMghhT0AAAAAyKEKuwkZAAAAgD6l0ubYy8CIPQAAAADIIYU9AAAAAMghhT0AAAAAyKG+exMyAAAAAPlWKFTeHHuFQq+bGrEHAAAAADmksAcAAAAAOaSwBwAAAAA5VGE3IQMAAADQp1TaHHsZGLEHAAAAADmksAcAAAAAOaSwBwAAAAA51HdvQgYAAAAg3wqFyptjr1DodVMj9gAAAAAghxT2AAAAACCHFPYAAAAAIIcU9gAAAAAghypsdkEAAAAA+pRKe3hGBkbsAQAAAEAOKewBAAAAQA4VisVicXMvxCbT3h4xb17v29fWRowaFTF/fkRbW7asMWOytS9HS0uyqNaahmRZ7e3JojKPyi0UujaPtraIrJ+QJUuytS9HY2O6rFR/r/r6NDkREatWZe9TUxMxdGjEiy9me09GbLE6e1ip5sxJlzVxYrKolc3pvosq9bNVVRXR2Zmtz+mnl5ZVisu+l3HhyvD+o9P9vX75y2RRUTfr2Wwd+vWL2HbbiOefj1i7NlPXhYOasmWVYUTt0mRZrY1DkmWl3DY+esSibB1qaiKGDIlYujTzQfbZ5cOzZZVhwYJkUTF4cJqcKSMz/q3KsLI++9+qqqrrONTcnO2YMrA93ec46S1vKc9rxo1LFrWyPft1V6nbRnNz5qiSjW6fnSyrc8zYZFlVjz6cJmjnnbvOHcjmpZci7rxzcy/FpnXYYREDBvSqad+9CRkAAACAfCsUKm+OvUKh103digsAAAAAOaSwBwAAAAA5pLAHAAAAADlUYTchAwAAANCnVNocexkYsQcAAAAAOaSwBwAAAAA5pLAHAAAAADnUd29CBgAAACDfCoXKm2OvUOh1UyP2AAAAACCHFPYAAAAAIIcU9gAAAAAghyrsJmQAAAAA+pRKm2MvAyP2AAAAACCHFPYAAAAAIIcU9gAAAAAghxT2AAAAACCH+u7sggAAAADkW6FQeQ/PKBR63dSIPQAAAADIIYU9AAAAAMghhT0AAAAAyKEKuwkZAAAAgD6l0ubYy8CIPQAAAADIIYU9AAAAAMghhT0AAAAAyKG+exMyAAAAAPlWKFTeHHuFQu+bFovF4uu4KEm1t0e8+GLv29fURAwd2tWnvT1b1ogtW7N1KENr1CXLamlJFhVr1qTLWrs2W/va2ohRoyLmz49oa8vWd2zNvGwdytHYmCxqZQxMktPcnCQmIiKGDcvep1Do2j7a2iKy7D3r2ldnDytVyg/ykiXpshLqnNhUUr+qqojOzmx9Uv65PvKRdFk3XJ/xjSjDz3+Z7gaEDx+xNFuH6uqIQYMiVqyI6OjI1vfRR7O1L8Pjww5NljVl8OxkWQv7jU2WNeKlmdk61NVFjBkTMWdORGvG88qUFy+lHCxLlegkYGnN8CQ5ERELFmTvU18fMX58xMyZ2Y4R48ZlzypV1muncgysT3fd9bs/pLvuevveK7N3qqrqOv9vbs52wpHwfG3lsPHJsga2Zzwml+HxOUOS5EyaFNGvX5KoyrJ2bcTf/ra5l2LT2nnnXm8MbsUFAAAAgBxS2AMAAACAHKqwm5ABAAAA6FMqbY69DIzYAwAAAIAcUtgDAAAAgBxS2AMAAACAHOq7NyEDAAAAkG+FQuXNsVco9LqpEXsAAAAAkEMKewAAAACQQwp7AAAAAJBDCnsAAAAAkEMVNrsgAAAAAH1KpT08IwMj9gAAAAAghxT2AAAAACCHFPYAAAAAIIf67k3IAAAAAORboVB5c+wVCr1uasQeAAAAAOSQwh4AAAAA5JDCHgAAAADkUIXdhAwAAABAn2GOPQAAAAAgbxT2AAAAACCHFPYAAAAAIIcq7CZkAAAAAPqUSptjLwMj9gAAAAAghxT2AAAAACCHCsVisbi5F2KTWbMm4s9/7n37xsaIvfaKmDo1ork5U9TsiYdmXLjS9euXLCpGbNWZLqy9/Y2bVShE9O/ftU29gT8i85Y3JMsaPTLRtvGHP6TJiYh5O2T/HNfWRmy1VcTixRFtbb3vN3rJ45mzStW6w5RkWXWznk2WFSNHJota2j4wc5/q6ohBgyJWrIjo6Oh9v/r6zFElS3mHwvXXp8v68IfSHbs+f06270S33jrizDMjvv3tiLlzs2WdeGK29uVIeUieskNrsqzZC+qSZT39dLb2W2wRsc8+Xaeuq1Zl6/v2kemOKUvHpDumpDKkfVG6sFI+XKWebCQ8Tj75dLrxITsNnpcsa2n96GRZQ2Jp9k4lnmzMaxmSPatEKc95Oyen2z/NmpUmZ5ttunYBZNTeHrEo4b49heHDe33ybsQeAAAAAORQ351dEAAAAIB8KxQq7+EZhUKvmxqxBwAAAAA5pLAHAAAAADmksAcAAAAAOVRhNyEDAAAA0KdU2hx7GRixBwAAAAA5pLAHAAAAADmksAcAAAAAOdR3b0IGAAAAIN8KhcqbY69Q6HVTI/YAAAAAIIcU9gAAAAAghxT2AAAAACCHKuwmZAAAAAD6lEqbYy8DI/YAAAAAIIcU9gAAAAAghxT2AAAAACCH+u5NyAAAAADkW6FQeXPsFQq9bmrEHgAAAADkkMIeAAAAAOSQwh4AAAAA5JDCHgAAAADkUIXNLggAAABAn1JpD8/IwIg9AAAAAMghhT0AAAAAyKG+O1YRAAAAAN6A5s2bF8cff/wGX7/zzjsjIqJQLBaLqRbq9dbeHvHii71vX1MTMXRoV5/29mxZI9bOztahHEuWJIuaOXiPZFnDhiWLyvwW1tVFjBkTMWdORGtrtr5jxmRrX46U0wjMmZMmp7k5TU5ERGNj9j61tRGjRkXMnx/R1tb7foMHZ88q1cDlCfdPI0emy7rjjnRZu++evU9tbcRWW0UsXpxt4yhlQyzVggXpshLu5D//lSHJsi76Smf2TlVVEZ3Z+/3neelurDj/xJnJspKeACxfni4r62e5ujpi0KCIFSsiOjoyde0cnG6br2pZnSxr4aqGJDkjtsx4cleORx/N3qehIWLy5IgnnohY3fv3//+W7JU9q0QHH5wsKhqWpDuvWdo4NllWfX32PoVCRP/+EWvWRGS5im9oXpQ9rFQJzzUWjpiSLGvEViUc/0tRKHT9RybFYrbPRB4UChHz5/eusGfEHgAAAAC8gYwePbqneLcx5tgDAAAAgBxS2AMAAACAHHIrLgAAAAC5lfW5CW90tbW9b1tyYW/BggVx5ZVXxr333hvz5s2LiIgxY8bEwQcfHCeddFIMHTr0VX2WLVsWl112Wdx5552xcOHCGDhwYOyxxx5x8sknx2677VbqogAAAABAn1NSYW/atGlx2mmnxcqVK6O6ujrGjh0bnZ2d8dxzz8X06dPjpptuiiuuuCJ22GGHnj5LliyJY489NmbPnh39+/ePpqamWLhwYdxxxx1x1113xfnnnx9HH330JlsxAAAAAKhkmefYW7lyZZxxxhmxcuXKOOCAA+IPf/hD3HbbbfG73/0ubr/99thjjz1i8eLFcfrpp8fatWt7+p111lkxe/bs2G+//eKee+6JG264Ie6999747Gc/Gx0dHXHeeefFjBkzNunKAQAAAEClylzYu+GGG2Lp0qUxfPjw+Na3vhXDhw/veW2bbbaJ733vezFo0KCYM2dO3HbbbRER8eCDD8bUqVOjoaEhLr744hg0aFBXeFVVnHrqqfHud7872tra4rLLLttEqwUAAABApSsWu+bYq6T/isXer3/mwt6DDz4YERGHHHJINDY2vur1IUOGxO677x4REX/9618jIuLGG2+MiIjDDjsshgwZ8qo+xx57bERE3HnnndHS0pJ1kQAAAACgz8k8x95pp50Whx9+eGy33XYbbFN8ubTY2dkZERGPPPJIRES86U1vWm/7KVOmRE1NTaxevTqeeOKJ2HPPPbMuFgAAAAD0KZkLe1OmTIkpU6Zs8PWlS5fG1KlTIyJi4sSJ0dnZGXPmzImIiLFjx663T21tbYwYMSLmzp0bzz33nMIeAAAAALyGkp6KuzFf/vKXY82aNdG/f/84/PDDY8WKFdHe3h4Rsd7bcLsNHjw45s6dG8uWLSs5u1CIqMmwRtXV6/6bSWdtCZ1K1L9/sqi6umRRUZX5RvDSZV2v2tp1/82iUMjeJw9KeS9KUV+fJieitHXq3sdk2ddEpN3ek/2xItJu8AMHpsuq1I2jX790WSUdXEuz9dbJopIaNSphWKWeAKTcH2bd5rvfh6QHiBIk3M9n3X2WLOWxq6Ehe5/uk6GMJ0UpD5NJz3cTfo4THrpKeg+7+2Tum+zDFUmvXVOuFm98L5ed+qRN+lG49NJL49Zbb42IiH/7t3+LoUOHxvz583ter9vISWO/ly82ypljr6oqYujQ7P0GDy4lLeHZdsIz+zHJktJaz3SQvTJixKZdjjxLeoGZA1tttbmXYCMaK/SPdcABm3sJemfLLTf3EmxYqTvDN7gzz0yZVmIRpoTizWmnlRZVmgo9A8jDNr/FFpm7JC0FJrxIH5osKmHBd/Lk0vtOnJip+f6lJ72x9U93XjMoWVJ5Mn8R3n/DA2w2uY0M5tnUSrj0L8Mb/EsY+rRNVti75JJL4rvf/W5ERBx66KFxyimnRETXk2+7FTby1UL3vHxVZXxr2dkZsXx579tXV3cV9ZYvj+joyJY1tHX+azfaVLKsVJnmbLFjsqzSCqqlyfoW1tZ2FfUWLoxoa8vWN2UxMOW3igsXpslZsyZNTkRp1yo1NV1FvcWLs30rVMJ1W8kaVyXcPw0bli7rgQfSZe2wQ/Y+NTVdRb1ly7JtHAkvmuPFF9NlJdzJf/uqdJdiZ57Rmb1TVVXXSUpGl/0g3UXEae+ekywr6QnAqlXpsrKOzKqq6jo4rFqVefvo3CLdNl+1Nt2B+cXVafaHQwdmPLkrxzPPZO9TX99V1Js+PSLDoIf7lpdRRMxoA1Onvy76L093XrOiIV0RsZSB0oVC1+bR0pLtiZn91yzNHlaqhOcaLw7ZPlnW0C1LOP6XolCo3FvAeN2UXdhrb2+P888/P6699tqIiNh///3jW9/6Vk8Rb8CAAT1tW1tbN/h7ul/rV8ZtQt2POM6qo6OEflmrPeVIWOloTXiXVgnXOCXbyKa3UW1t2ftmOcjmSapNPuWDscsZvt/enu09Sbm9J90/pdzgV65Ml1XOe5h140h5e+zatemysn5jVoa5c5NFJTU/YY2+5ANlKVLuEFPuD0vd5js7k35eMku4n092G1XKY9fq1aX3bWnJ1D/lYTLp+W7Cz3HKj2I572GxmLF/ynsUE1679uVbL+GVyirsNTc3x6c+9am4//77IyLiHe94R3z1q19d55bbhoaGqKuri9bW1o3On9f92tBS7qUFAAAAgD6m5MLeggUL4pRTTolnn302IiI+/vGPx9lnn/2q222rqqpiu+22i2eeeabn6bj/rK2tLRYtWhQREePGjSt1kQAAAADoQ0q9e/ONLMuo3JImb1m0aFEcf/zx8eyzz0Z1dXWcd9558fnPf36Dc+jtuuuuERHx6KOPrvf1xx9/PNrb26Nfv36x0047lbJIAAAAANCnZC7stba2xic+8YmYPXt21NbWxre//e049thjN9rnyCOPjIiI22+/PZav5ykGv/jFLyKi61be+syP+AEAAACAvidzYe+HP/xh/O1vf4uIiP/6r/+Kt73tba/ZZ5999ok3velNsWrVqjj99NNjyZIlERHR2dkZP/zhD+OWW26J2tranifpAgAAAAAbl2mOvdbW1rj66qu7OtbUxA033BA33HDDBtsfdNBB8YlPfCIKhUJ89atfjeOOOy6mTZsWhxxySGy//faxaNGiWLx4cRQKhbjwwgtjwoQJ5a0NAAAAAH1Kpc2xl0Wmwt6zzz4bK1asiIiI9vb2ePjhhzfaftttt+3539tss0385je/ie9///tx1113xbPPPhv9+/ePAw44IE4++eTYe++9S1h8AAAAAOibMhX2Jk+eHM8880zJYUOGDIlzzz03zj333JJ/BwAAAABQ4lNxAQAAAIDNK9OIPQAAAAB4oygWK2+OvWKx922N2AMAAACAHCoUi1nqgG9wnZ0Rzc29b19VFdHY2NWnszNbVn19tvblyLJO5Uq5XimzlizJ1r6mJmLIkIilS7OX/hsbs7Uvx7RpyaIWTjowSc6I2qVJciKi6++cVan7jZSf41LWq1QJP8et9QOTZdUtmZe9U21txFZbRSxeHNHW1uturcNGZ88q0bJlyaJixN/uSpb15MhDk2X98pfZ2o8aFXHaaRGXXRYxf362vuefl/HcpAyf+Vy673ovvjhZVCxenC5rxItPZutQXx8xfnzEzJkRLS3Z+o4Zk619GWYuSbfvHf/EzWmCdtstTU5ErB42NnOfQiGif/+INWuyjdhomJVxGyzHuHHJop6d05Asq2lYwvPQrJ/7iJLPNZKeG6bMWr48XdawYWlyGhu7rjfIpL09YtGizb0Um9bw4b3/ONliAAAAACCHzLEHAAAAQG5V2hx7WRixBwAAAAA5pLAHAAAAADmksAcAAAAAOWSOPQAAAAByqVisvDn2sjwV3Yg9AAAAAMghhT0AAAAAyCGFPQAAAADIIYU9AAAAAMghD88AAAAAIJc8PAMAAAAAyB2FPQAAAADIIYU9AAAAAMghc+wBAAAAkFuVNsdeFkbsAQAAAEAOKewBAAAAQA4p7AEAAABADpljDwAAAIBcKhYrb469YrH3bY3YAwAAAIAcUtgDAAAAgBxS2AMAAACAHDLHHgAAAAC5VWlz7GVhxB4AAAAA5JDCHgAAAADkkMIeAAAAAORQoVgsFjf3QmwqnZ0Rzc29b19VFdHY2NWnszNb1sAlM7N1KEd9fbKolY2jk2UNbF+aLGt1/ZBM7QuFiP79I9asicj6CWloX5mtQzlaWpJFdQ4bniSnqr01SU5EREyfnr1PfX3E+PERM2dme/8bG7NnlWrYsGRRT85qSJaVct6MKZMzHhS6VVVlPqDMnpPuO7axjen2u4/PybbfzYspjRmP/3V1EWPGRMyZE9Gabf/2mUvGZ8sqwzcuLnGbL8Fpp6fb5s8+O1lUjB9XwntYwj4jImLp8nTvYU3CGbkH1ic6B0i5UqUcvAqFiNraiLa2bCeiTzyRPSsPdtghWdQfp6U7rzlw/3TnGrFkSWlZpZgzJ1lU6+Q9kmXVta9OE1Rf3/U3JpO1ayP+9rfNvRSb1s47R/Tr17u2Hp4BAAAAQC4Vi5X38Iws3+soBQMAAABADinsAQAAAEAOKewBAAAAQA6ZYw8AAACA3Kq0OfayMGIPAAAAAHJIYQ8AAAAAckhhDwAAAAByyBx7AAAAAORSsVh5c+wVi71va8QeAAAAAOSQwh4AAAAA5JDCHgAAAADkkDn2AAAAAMitSptjLwsj9gAAAAAghxT2AAAAACCHFPYAAAAAIIfMsQcAAABALhWLlTfHXrHY+7ZG7AEAAABADinsAQAAAEAOKewBAAAAQA4p7AEAAABADnl4BgAAAAC5VWkPz8jCiD0AAAAAyCGFPQAAAADIIYU9AAAAAMghc+wBAAAAkEvFYuXNsVcs9r5tRRX2CoWI+vps7SMi+vXL9qZFRMSsWRk7lG7lnocmyxoYK5NlRXNzsqglzUMyta+tjejfP2L58oi2tmxZY9uXZOtQjpEjk0Wl+nM1NtalCYqIBYN3ytyntjZiq4hYvMX4aMuwvxkd8zJnlWr2koZkWTs1zk6W1TlmbLKsks4MCoWIqqqIjo5MB5Wxw9KdhbTWZNsXlmPK4HTbRsp9YbQMy9a+6uWbIwYPjujszNT14ouzRZXjtNPT3cRx2feyvQ/leP/R6dbrkkuyZdXWRmy1VcTiF6syn2uMHrw6W4dyJDxfW7hmeJKcF19MEhMRpZ3X1NZGjBoVMX9JbaZtY2xjY+asUt01pylZ1qGRbnufODFZVERLS/Y+hULXRcratZnONVY3pvlsRUTU75Yuq25OwnONVFWjbbb5x7kD9JItBgAAAABySGEPAAAAAHKoom7FBQAAAKBvqbQ59rIwYg8AAAAAckhhDwAAAABySGEPAAAAAHLIHHsAAAAA5FKxWHlz7BWLvW9rxB4AAAAA5JDCHgAAAADkkMIeAAAAAOSQOfYAAAAAyK1Km2MvCyP2AAAAACCHFPYAAAAAIIcU9gAAAAAghxT2AAAAACCHPDwDAAAAgFwqFivv4RnFYu/bGrEHAAAAADmksAcAAAAAOaSwBwAAAAA5ZI49AAAAAHKr0ubYy8KIPQAAAADIIYU9AAAAAMghhT0AAAAAyCFz7AEAAACQS8Vi5c2xVyz2vq0RewAAAACQQwp7AAAAAJBDFXUrbmFtS9Q9/njvOzQ0REyeHLXPPBGxenW2sN12y9a+DI2NyaKiMwYmy7rv0XRZNRm39AEDIkaNili0KOKll7L1Hbbb+GwdyrBkSbKoGFu/KFFSfaKciJEjS98Ghw7N1r4zRpeclVW/xcmionOrscmyqp7IsH8v17hx2ftUVUXU1kasXRvR2dn7fll3UGX45S+TRcXhh6fbNtYuSBYVYyNjWG1t14F81aqItrZMXRevSXecPPvsZFHx/qPTfa98w/UZPotl+vw52dZr660jzjwz4uc/j5g7N1vWV77SkK1DGarq0x2Xt2hJkzNiq3TbxdLl2bf36uqufxsaIjo6et+vc0xT5qxS7TkyWVQsbUm3vQ8enCwqoqWEDb66OqJ//4jW1kwbR01jwn3GklTXDBEPL0l3rrHHmETrVWXsFdlVVGEPAAAAgL7DHHsAAAAAQO4o7AEAAABADinsAQAAAEAOKewBAAAAQA55eAYAAAAAuVVpD8/Iwog9AAAAAMghhT0AAAAAyCGFPQAAAADIIXPsAQAAAJBLxWLlzbFXLPa+rRF7AAAAAJBDCnsAAAAAkEMKewAAAACQQ+bYAwAAACC3Km2OvSyM2AMAAACAHFLYAwAAAIAcUtgDAAAAgBwyxx4AAAAAuVQsVt4ce8Vi79sasQcAAAAAOaSwBwAAAAA5pLAHAAAAADlkjj0AAAAAcqvS5tjLwog9AAAAAMghhT0AAAAAyCGFPQAAAADIIXPsAQAAAMAbyLx58+L444/f4Ot33nlnRFRYYa+zrj5aJu/V6/aFQkT/iFgzYXIUi9myHnggW/tyHLp/a7Ks1e11ybL23z9ZVDz6aLb2dXX/+LejI1vf+vps7cuRdb3KMfaIwenCEqlasih7p5qaiCFDomr50mwztDY2Zs8q0YgtkkVFa3tDsqz2iVOSZc2alb1PfX3E+MaImYsao6Wl9/0mTsyeVaqPHlHCNl+q5uZkUb+bPj5Z1tg9M36Wq6u7/m1oyHxAGbHgyWxZ5dhhh2RRl1yS7oaRz5+TLuuir3SW0Ksqzjwje79rr0u3XkcemS4r1cTn7e3p1mlILC2hV3VEDIpBsSIiMuw3nl5QQlZpBg4blixrZf3wZFkNy+cly4qaMi7FOzoyfWDqlqRbryeXj06WNXhwsqiIBYk+X4MG/ePcgV4rFivv4RlZalQVVdgDAAAAgLwbPXp0z6i8jTHHHgAAAADkkMIeAAAAAOSQW3EBAAAAyK1Km2Mvi5ILewsWLIgrr7wy7r333pg3r2syzjFjxsTBBx8cJ510UgwdOnSd9m1tbbH77rtHW1vbBn/nFltsEdOmTSt1kQAAAACgzyipsDdt2rQ47bTTYuXKlVFdXR1jx46Nzs7OeO6552L69Olx0003xRVXXBE7vOIJazNmzIi2trbo379/7Ljjjuv9vQMGDChtLQAAAACgj8lc2Fu5cmWcccYZsXLlyjjggAPiwgsvjOHDux5B/sILL8TnP//5ePjhh+P000+P//u//4t+/fpFRMTTTz8dERF77rlnXHHFFZtwFQAAAACg78lc2Lvhhhti6dKlMXz48PjWt74VjY2NPa9ts8028b3vfS+OOOKImDNnTtx2223xnve8JyL+UdhramraRIsOAAAAQF9WLFbeHHvFYu/bZn4q7oMPPhgREYcccsg6Rb1uQ4YMid133z0iIv7617/2/FxhDwAAAAA2ncwj9k477bQ4/PDDY7vttttgm+LLpcXOzs6enynsAQAAAMCmk7mwN2XKlJgyZcoGX1+6dGlMnTo1IiImTpwYERH/v737j7Kzqu/F/55fyUwSkhgCARJ+JIQgGEPkh4uCVxBFrAhFwF4RsFXA5Q+833Kx9S6/tlJKe5ErvVhsuRfUVu1tpVgo6L2WlmAV41eQpZgbqSLEgEkkIRnyY0wmycyc7x8hkQDBec6EPTxnXq+1WEfn7J3Pfs7ZZz/7fM5+9rN69eo8/fTT6ejoyMSJE3PTTTflhz/8Yfr7+zNr1qycfvrpOeWUU5o8BAAAAAAYe5q6K+6L+dM//dNs2bIlPT09OeOMM5L8arVeW1tbzjrrrGzdunW3OrfddltOPfXUXH/99S94eW8VbW3Vy1aps9MIm1lNMw18+YcqqqenWvnu7t0fX66mTCkYrBU7R2cTQ2BHx+6Pw9WKr1/KHlbJWM189seN2/1xuIp2jWb6fLOqvhAjsM8+xUJV/+y3t+/+WMXL/STUpK6ucrFmziwXq6RXvKJcrGa6brOqfryaVfKYmjqoZseNkmNGwfNJ0fer5ADVTN+owTy0ZDcsONWo/oWyWUU7fGtptT32qmhrNKpsyffi/uqv/iqf/vSnkyRXXnll3ve+9yVJbr755lx//fVJkre85S257LLLcsQRR2TTpk25++67c/311+eXv/xlXv/61+eWW25pOn6j0bLfnQEAAAB4jnXrkj/909Fuxd71//6/yb77Dq/sXvup5TOf+UxuvPHGJMlpp52Wyy67bNdzRx55ZC644IJMnz49l19++a6/jx8/PhdeeGGOPPLIXHTRRfnWt76Vb37zm01flttoJP39wy/f1rbjF4X+/mp3HEmSZ90X5CX32tdsLxZry0C5X6nGjy8WKj/5SbXy3d3J7NnJz35WrU8lyZFHVis/EvfdVy7WKSeV64fFbNpUvU5Hx46lkhs2JIODw69X6le+wrZ3ljuukr/C/eIX1euMG5fMmpWsWJFs2zb8egcfXD1Ws7o29ZYLtnlzsVD/389nFYv1G0dvqFahvX3HksJNm5Jn7T08LOvWVSs/EocdVizUU+vKrUb4u78rFir/z4crvr/Jjv5RtV8k+Zd7yr2GJ51ULFSl0+pIlFoZmCSTBiuOGUnz40bJMWPq1GKh+sZNKxZr0panisVqesVeM/PQUh+uJMs27VcsVskVe7O2/LRMoMMOK7tylJYw4sTewMBArr766tx6661Jkte97nW54YYb0vaspXOnnHLKiybrjj/++Jx00klZvHhx7rnnnhHtt9fM+sNGo3q9vr7qcZq29xZVvpxCFbVlS3P1+vubr1vChibmik1rxc4xkkzR4GC1+q34+qXsYZWMVTWh/2zbtlWrX7RrlMyOVslujlAzOfqmNfvlaGioet2RdMSXse0FfydaubJcrJKefrpcrCbyjk0rlXsoeoXPSA6q6rhRcswoeD4ZKriLRNEBaiQTgKrz0ILvV4ueusp9KSw56NIyRjRM9vX15T/9p/+UxYsXJ0ne+ta35pOf/GTGNZE6P+qoo7J48eKsWLFiJE0CAAAAYIxoNFpvj70quf+mE3tPPvlkLrvssjzyyCNJkksuuSS///u/v9tKvWcbHBxMo9FI5x42Wh16JjO9p+cBAAAAgF9paoOONWvW5OKLL84jjzySjo6OXHXVVfmDP/iDPSb1TjvttLzqVa/Kl770pT3+mw8//HCSZO7cuc00CQAAAADGlMqJvW3btuX9739/nnjiiXR1deXTn/50Lrjgghetc/jhh6fRaOSuu+7KwAusj1yyZEnuv//+JDsu5wUAAAAAXlzlxN4tt9ySH/3oR0mST3ziEzn99NN/bZ3LLrssbW1tefjhh/Pxj388fc+688T999+fD37wg2k0Gjn77LPz6le/umqTAAAAAGDMqbSh3bZt2/KFL3xhR8XOztx+++25/fbb91j+lFNOyfvf//689rWvzcc+9rFce+21ueOOO/L1r389s2fPzqZNm3bdLOOUU07JNddcM4JDAQAAAGCsabWbZ1RRKbH3yCOPZMOGDUmSgYGBfP/733/R8oceeuiu//3ud787CxcuzBe+8IV873vfy6OPPpqJEyfmxBNPzLnnnpuzzz57j3v0AQAAAAC7q5TYmz9/fn7yk580HWzBggW5/vrrm64PAAAAAOzQ1F1xAQAAAIDRVWnFHgAAAAC8XDQarbfHXqMx/LJW7AEAAABADbXUir32tkYmdFdIa+6olZ7xQ5VjnbZwfeU6zVry42nFYi2Yu7lYrKS7WKQDDqiWw+7q2vE4fXqyfXu1WO0D26pVGIGzX9dXLNbmgTL9cNOmImGSJDMK3q9nW+eEYrHGPfidcrGmTy8Xq+DPcLNmHV25Tvszw8z++ydDFU4r41YsqxyrWY8MzCkWa153f7FYbz5gSbFYQ1MXVK7TnmRonynV63WWm6b1ri/3W+9BU8vNNa69ttzYe+tt1V7DV7wiefObk3+5pz1PP10t1n98R/W5a7OuvqZc3/ij8x8uEqf3gOpjfNP6mpirdXUlU6YkmzdXm4jOnVs9VpOWrRhXLNbUgt9Yl/UfVCzWnO5V1SvtvNnk4GC15UkF51Dr1xcLlZMWljuf3Pvd6uf/ZvzGEUlPkUi0Eiv2AAAAAKCGWmrFHgAAAABjS6vtsVeFFXsAAAAAUEMSewAAAABQQxJ7AAAAAFBD9tgDAAAAoJYajdbbY6/RGH5ZK/YAAAAAoIYk9gAAAACghiT2AAAAAKCG7LEHAAAAQG212h57VVixBwAAAAA1JLEHAAAAADUksQcAAAAANSSxBwAAAAA15OYZAAAAANRSo9F6N89oNIZf1oo9AAAAAKghiT0AAAAAqCGJPQAAAACoIXvsAQAAAFBL9tgDAAAAAGpHYg8AAAAAakhiDwAAAABqyB57AAAAANRWq+2xV4UVewAAAABQQxJ7AAAAAFBDEnsAAAAAUEMttcdeI23ZPtA27PJtbUlXe7J9sD2NRrVY4yZNqti65nV3FwuVh5dPKBar5DXwhx1WrXz7Mynvnp5k/PhqdYc6x1WrMAIlM/MTBjaWibPhySJxkuSJ7nmV63R1JQcm+cXWadm+ffj11i6tHKppBxx2UrFYBx0wVCxWHn20WKi+vup1urqSSZOSLVtSqW9Mrh6qaU+W+3hl3vHTi8XqnXRIsVjT+jdXq9DWlvT0pH3rllSdbCxbW653TC/3djX3AWtSe8FJ1G/+ZrWz8s65xkknJUMVh9Krryk3A/ijj5cb50963dFF4nznn8vMaZIkBxxQvU7bM99npk+vNG5sHig3By05Zkwe6C0Wa9r65cViZf786nV29o2pUyv1jdVPl+sbJ816olis1ZvKnf8XLiwTZ1y5t6qlNBqtt8delWmjFXsAAAAAUEMSewAAAABQQxJ7AAAAAFBDEnsAAAAAUEMtdfMMAAAAAMaWVrt5RhVW7AEAAABADUnsAQAAAEANSewBAAAAQA3ZYw8AAACAWmo0Wm+PvUZj+GWt2AMAAACAGpLYAwAAAIAaktgDAAAAgBqyxx4AAAAAtdVqe+xVYcUeAAAAANSQxB4AAAAA1JDEHgAAAADUkD32AAAAAKilRqP19thrNIZf1oo9AAAAAKghiT0AAAAAqCGJPQAAAACoIXvsAQAAAFBbrbbHXhVW7AEAAABADUnsAQAAAEANSewBAAAAQA1J7AEAAABADbl5BgAAAAC11Gi03s0zGo3hl22pxN7AQLJixfDLjxuXzJqVrF6dbNtWLdac5d+uVmEE5p16arFYRRX85PX2jatUvqNjx+Pg4I7/qnjqqWrlR2JwcFqxWP39ZeLMOaBMnCSZ1EQX3Nk3Jkyo1jdKnmgO6l9WLtjSvnKxJk0qFmrn+1xFe/uvHivV7+6uHqxJU6cWC5X0FewbnROKhVq9qVqszs5k355k3eaeyuPAnKV3VaswEm95S7FQq7fsXyzWPoXOXUn1cX4kc40/Ov/hahVG4KTXHV0s1ne+PVQkzu3/NLlInCQ595wRHFPFk9Gmp5sPVdUrXlEu1sb+cvPd7vnlYj3dxPvV2Znsu2+ybmNXpTFnw4bqsZo1Y9b0crH61hSLtaq/zLlrcrnhiRbiUlwAAAAAqCGJPQAAAACooZa6FBcAAACAsaXV9tirwoo9AAAAAKghiT0AAAAAqCGJPQAAAACoIXvsAQAAAFBLjUbr7bHXaAy/rBV7AAAAAFBDEnsAAAAAUEMSewAAAABQQ/bYAwAAAKC2Wm2PvSqs2AMAAACAGpLYAwAAAIAaktgDAAAAgBqyxx4AAAAAtdRotN4ee43G8MtasQcAAAAANSSxBwAAAAA1JLEHAAAAADUksQcAAAAANeTmGQAAAADUVqvdPKMKK/YAAAAAoIYk9gAAAACghiT2AAAAAKCG7LEHAAAAQC01Gq23x16jMfyyVuwBAAAAQA211Iq9zs5k1qzhl29r2/E4Y0a1bGiSLOk7rVqFEVgwsK1YrJJp7nu/O6FYrOnTq5Xv6UmmTEnWrEm2bKlWd8ErC75f/f3lYnUX6hvLV5SJk2TapEnVK40bl0yZlSmbViTbhv9eT1u/vnqsZlXt8COw7ZULisUal3KfrRnfvrd6pUmTkn1fm30feyDp6xt+veOPrx6rSQs61xSL1du5f7FY0wbKHVdeMbVa+ba2JF3Zd/L26pONhQurlR+JznJTwnXrioXKjP2GisUaGKj2e3n7M8U7On41Jx2u3gOOrlZhBL7zzxuLxbr9nyYXiXPuOeX6xX/+SPV1FDNnJldemVz/39uzcuXw633kI5VDNa3Kaa5OsQ4ZWFYs1j4HzKlcZ+dYMWFCtVPKjMFVlWM1rbPcPLTknPegFBo32tqSVDwpMOZZsQcAAAAANdRSK/YAAAAAGFtabY+9KqzYAwAAAIAaktgDAAAAgBqS2AMAAACAGrLHHgAAAAC11Gi03h57Ve58bcUeAAAAANSQxB4AAAAA1JDEHgAAAADUkD32AAAAAKgle+wBAAAAALUjsQcAAAAANSSxBwAAAAA1JLEHAAAAADXk5hkAAAAA1Far3TyjCiv2AAAAAKCGJPYAAAAAoIYk9gAAAACghuyxBwAAAEAtNRqtt8deozH8slbsAQAAAEANSewBAAAAQA1J7AEAAABADdljDwAAAIDaarU99qqwYg8AAAAAakhiDwAAAABqyKW4AAAAAPAysmrVqlx88cV7fH7RokVJWiyx12hUu666rS3p6tpRp9GoFmvBK7dVqzASBS8WH+qeUCzWK19ZLFTll7Cra8fjpEnJ+PHV6g51jqtWYQRWrC8X65DuNUXirJ6xoEicJJnRs7F6pfZnFjpPnZoMDQ272sbpc6rHalJ3d7FQRfey+O6D5fr76088sXqltrYdj69+dbWTSn9/9VhN2ti9f7FYT64oFirTphbsiA89VK38hAnJ/PnJT36SbN5cqerm+a+tFmsEJgyUm9dMmlTus9y7vtzFKdPSW61CR0eSKZk0uCEZHKxWt6+vWvmROOCAYqHOPWf459WR+M8fKdcv/vxTzR5Te668olrdd11U7rg+85lioYrNQZOkt7PcfK2ziVPXzmno4GClaWiW9R9UPViTDih4Sp6wYlmxWL1Ty/SNKVOeOT1QUSONqkmdl722YZdsqcQeAAAAANTdQQcdtGtV3ouxxx4AAAAA1JDEHgAAAADUkMQeAAAAANRQU3vsLV++PDfffHO+853vZO3atZkyZUqOOeaYXHjhhTn55JNfsM7TTz+dm266KYsWLcrq1aszefLkHHvssbn00kuzcOHCkRwDAAAAAGNWxRtRvewNP11XecXefffdl9/6rd/KP/7jP2b9+vU5/PDD097enkWLFuW9731vPvnJTz6vztq1a/Pbv/3b+cIXvpB169Zl3rx5aWtry7/+67/mXe96V77yla9UbQYAAAAAjGmVEnu9vb258sor09/fnzPPPDP33Xdf7rzzztx333351Kc+lY6Ojnz+85/P3XffvVu9K664Ik888UROPvnkfPOb38ztt9+e++67L1deeWUGBwdz1VVX5bHHHturBwYAAAAAraxSYu8rX/lKNmzYkJkzZ+baa6/NPvvss+u5s846K+94xzuSJF/+8pd3/f3+++/PAw88kAkTJuRTn/pUpkyZsiNwe3ve97735ayzzsr27dtz00037Y3jAQAAAIAxodIeezNnzszb3va2HHXUURk3btzznj/yyCOTJKtWrdr1tzvuuCNJ8sY3vjHTpk17Xp0LLrggX/3qV7No0aL09/enu7u70gEAAAAAMFY10np77HUkaRtWyUqJvTPPPDNnnnnmHp9funRpkuTQQw/d9bcf/OAHSZLjjjvuBessWLAgnZ2d2bx5c5YuXZrjjz++SpMAAAAAYEyqfPOMF7Jx48Z85jOfye23357Ozs5cdtllSZKhoaGsWLEiSXLIIYe8YN2urq7MmDEjSfKzn/1sbzQHAAAAAFpepRV7z3X33XfnxhtvzOOPP55t27blwAMPzFVXXZUTTjghSbJhw4YMDAwkyQtehrvT1KlTs3Llyjz99NMjaQ4AAAAAjBkjSuwtWbIkP/3pT3f9/w0bNuTee+/N8ccfn0mTJqW/v3/Xcy+0J99O48ePT5LdyjerbXiXIO9WtkqdpgKNVMlYBXV1lYtV9SXs7Nz98eWq5GtY6sXobBQJs0N7E4uWd9apWHevLI8eplYdniZNKherqQNr9qTS0VE9VpOa6fLNKrplbsnBcMKEauV3vhBNvCBFT/8Fg5V8uwp+vLJjL5wKmjyfJHl5T6JqYObM0W7BS+Oww8rFKvvZKjchL3lcBaeheZGv4ntd0SGj4IGV6hstOOQW1Gp77A1fW6PRaPpr9JNPPpnJkyenr68vixcvznXXXZfe3t7Mnz8/t956a9atW5fXv/71SZKvf/3rmTNnzgv+O+985zvzgx/8IB/+8Idz+eWXN9ucNBo+CAAAAABjxbJlQzn88C2j3Yy96rHHejJnzvCy+CP6+eOAAw5IkkyYMCFvf/vbc8wxx+Scc87J0qVLc9ddd+XNb37zrrLbtm3b47+z87mdK/ea1WgkVRb9tbXt+AG9v39H3Sp6OrdXqzASz1zOXMLQ+J5isdatKxaq8kvY2Znst1/y1FPV6z6zZWQRq1eXi3Xg+N4icdY19nzZ/t627/i+6pXa23es2Nm8ORkaGna1vpRbbjbCobSSgsNT/v3fy8U69qgmJgbNnlRe5Py4t/V1TCkWa82aYqEyZ5+nygWrOvB2dydz5yaPPlptkpJky+Hzq8UagZLzml+sLbfarOoCy5GYkg3VKrS3J/vsk2zaVOl8kmTHOaiU6dPLxSq0JOb6/15u+fKVV1R8b3dqb6/cLz728XLH9dGPFguVKYNl5qBJsqGj3Dy0me7e5DQ069dXj9WsffctF6tn3YpisTbsM6tInH32KXuFBa1hr65rnjNnTk4//fR87WtfywMPPJBzzjkn48aNy7Zt2150/7ydz+27F0aBZtYfNhpN1Gt+oWN1JWMVtL0GudGBgbLtrKpo2zrKZHAGSnb3riYn28mO2VSFGdUIIlXWqsNTXxN52KaN5MCqnlQGy102MFRwVfte2F1j+LoLDobNJlT6+yvXLXr6Lxis5Lmr4McrTV8CNDRUvaElX8QWnIeuXDnaLXhpLF9eLlbRz1bBXxFLHtZIrjSrOA0t+Rti2SGj4IGV6vMtOORSQKVc8Pr167N06dL09u75V5OZz2xa8dRTT6W9vT2zZ89Okl13x32u7du3Z80zP+sfVnJjCAAAAABawFCL/Td8lRJ7559/fs4777zcfvvteyyz8pmfwGY8cz3iMccckyR56KGHXrD8kiVLMjAwkPHjx+foo4+u0hwAAAAAGLMqJfZOPvnkJMltt92W7S9wCcCKFStyzz33JElOO+20JMlv/uZvJknuvvvurH+Bi/v//u//Pkny1re+Nd1Fb6EHAAAAAPVVKbF36aWXpru7O8uXL8+VV1652yW5Dz/8cC655JL09/fnhBNOyBvf+MYkyW/8xm/kuOOOy6ZNm/KhD30oa9euTZIMDQ3llltuyVe/+tV0dXXlsssu24uHBQAAAACtrdLNMw4++ODccMMNueKKK3L33XfnG9/4RmbPnp2tW7dm+TO7tS5cuDB/8Rd/kbZndgNta2vLJz/5yVx44YV58MEH84Y3vCFHHHFE1qxZk6eeeiptbW35sz/7sxx++OF7/eAAAAAAaGWNlL39TQnDv5NK5Rspv+ENb8idd96Z//gf/2P222+/LFu2LOvWrctxxx2XP/7jP87f/u3fZtq03W8TfvDBB+ef/umf8ju/8zuZMWNGHnnkkWzdujX/4T/8h/zN3/xNzj777KrNAAAAAIAxrdKKvZ0OPfTQXH311ZXqTJs2LR/72MfysY99rJmQAAAAAMCzVF6xBwAAAACMPok9AAAAAKihpi7FBQAAAICXh1a7ecbwtVRir31oIBPWrhp+ha6upOfA9Kz/RbJ9e6VYqzoPqdi65h3Uv6JYrB/3zykW6+juZcViZerUauU7OpJMyYETNiSD1QaIjX3Tfn2hvWTWrGKh8sij+xeJM2/qmiJxkiSdk6rXeeaO3+noSNqHv+h58voKY9MILVl+ULFYC2b1Fov1+oUFT1md3dXr7OwbnZ1JY/h3sSpp8kC59+uww8qNhek+oFio//NQtc/X5MnJ65J8e/38bNxYLdZblz9crcJI9PcXC3XIpCbG3iYNzZpXLFZ+/GS18t3dyZQpybp11V//uXOrlR+BzQPjisXa9HSZOB/5SJk4SfKui6pfIHXYYcmf/VnysY+3Z/ny4df7u78dqhyrWf/5I+Uu/Hr/+8vMQZOyc+sJzcwNu7qSSftl0panKn1/ndw5UD1Wk4a6y31PTme5ueG0H3+nTKBjj006mpiHMqa5FBcAAAAAakhiDwAAAABqqKUuxQUAAABgrBm7e+xZsQcAAAAANSSxBwAAAAA1JLEHAAAAADVkjz0AAAAAaqqR1ttjrzHsklbsAQAAAEANSewBAAAAQA1J7AEAAABADdljDwAAAIAaGxrtBowaK/YAAAAAoIYk9gAAAACghiT2AAAAAKCG7LEHAAAAQI0NjnYDRo0VewAAAABQQxJ7AAAAAFBDEnsAAAAAUEMSewAAAABQQ26eAQAAAEBNNdJ6N89oDLukFXsAAAAAUEMSewAAAABQQxJ7AAAAAFBD9tgDAAAAoMZabY+94bNiDwAAAABqSGIPAAAAAGqordFoDP8eui9zjcaO/6pob0+GhqrHal/xRPVKzervLxdr0qRioXq7DyoWq+phtbUlXV3J9u3V+9Ty5dXKj8S8zmXlgk2fXibOgw+WiZMkr3td9TrNdo6Sn+Pu7nKxBgaKhVq9aUKxWDMGV1Wv1NWV7Ldf8tRTO/rHy1HBMX5jJheLtWJFsVA57LBq5dvakp6eZMuW6ueTCdlcrcJI/PjHxULdu/7YYrGOP75YqEzuX1OtQmdnMm1a0ttbeSxd1rd/tVgjUOr0n5Q7ffX1lYnTrI6OZMqUZMOGZLDClWXXXPPStem5/vxTTXyBatJ/+r1ya1H+4pqNxWLloYeq15k0KTn22OT736/UkXvnv756rCatX18sVOZML/d+LVtbZl5z8ME7ppRUs2zZ9hx++OOj3Yy96rHHDs2cOcPrDPbYAwAAAKCmGmm9PfaG/4uwS3EBAAAAoIYk9gAAAACghiT2AAAAAKCG7LEHAAAAQI2Vu6nPy40VewAAAABQQxJ7AAAAAFBDEnsAAAAAUEP22AMAAACgxgZHuwGjxoo9AAAAAKghiT0AAAAAqCGJPQAAAACoIYk9AAAAAKghN88AAAAAoKYaab2bZzSGXdKKPQAAAACoIYk9AAAAAKghiT0AAAAAqCF77AEAAABQY622x97wWbEHAAAAADUksQcAAAAANSSxBwAAAAA1ZI89AAAAAGqqkdbbY68x7JJW7AEAAABADUnsAQAAAEANSewBAAAAQA3ZYw8AAACAGhsa7QaMmrZGozH8Hfle7gYGkjVrhl++qyvZb7/kqaeS7dsrhdo89aCKjWved79bLFROO2xZuWCzZpWLtXZttfI16Rvr1xcLlUcfLRNn4cIycZJkcjZWr9TenkyalPT1JUMVTh5PPlk9VpM2z5pXLNaE7nIn0KGCi8yb+Wx1dCRTpiQbNiSDFfbunTSpeqxmjXv04XLB5s4tF6vqGD8SAwPVynd1JQcemPziF5XPJ4/0H1It1gjMm7W5WKySevsnFIvVWfHn8mZPJ0n1bjgS09JbLNbGzmlF4pScPx3SXeG7yU6dncm0aUlvb6U3+5H1+1eP1aTPfKZYqPzFDeXmGn9+Q7m5xvnnV6/T7Cml5Fxj2qRtxWItWzGuWKw5ax8oE2jBgqS7u0ysFrJsWX8OP/yHo92Mveqxx47JnDnD6wsuxQUAAACAGpLYAwAAAIAaktgDAAAAgBpy8wwAAAAAaqzCJtctxoo9AAAAAKghiT0AAAAAqCGJPQAAAACoIXvsAQAAAFBTjbTeHnuNYZe0Yg8AAAAAakhiDwAAAABqSGIPAAAAAGrIHnsAAAAA1Fir7bE3fFbsAQAAAEANSewBAAAAQA1J7AEAAABADdljDwAAAIAas8ceAAAAAFAjEnsAAAAAUEMSewAAAABQQ/bYAwAAAKCmGkmGRrsRe1lj2CWt2AMAAACAGpLYAwAAAIAaktgDAAAAgBqS2AMAAACAGnLzDAAAAABqbHC0GzBqrNgDAAAAgBpqazQaw7+H7svc0FDS3z/88m1tSU9PsmVLUvVVePLJauVH4oADysUqqeRrOHFitfKdncm++ybr1iUDA9Xqbt1arfxITJ9eLtaE7kK3D//ud8vESZLDDqtep6sr2W+/5Kmnku3bh1+v5Ae5aqcdgY3944rFmvzo94vFyqxZ1et0dibTpiW9vUXfg0omTSoW6l++PaFYrOOPLxaqso6OZMqUZMOGZLDiD8nT0vvSNOoFfGvptGKx5s4tFipTp5aLNWH9qmoVmj2fJFnWf1C1WCMwZ325sXfb/GOLxBm3YlmROEnSO3VO5TrNjhvd3ZVDNW3CwMZisf78s5OLxfrPv1dovpvk3b9bfY3NoYcmf/InyR/+YfL448Ovd801lUM1benScrHeemK582SlRMNI7L//jjkllSxbtjmHH/6t0W7GXvXYY6/PnDnDm09bsQcAAAAANSQVDAAAAECN2WMPAAAAAKgRiT0AAAAAqCGJPQAAAACoIXvsAQAAAFBTjbTeHnuNYZe0Yg8AAAAAakhiDwAAAABqSGIPAAAAAGrIHnsAAAAA1NjQaDdg1FixBwAAAAA1JLEHAAAAADUksQcAAAAANWSPPQAAAABqbHC0GzBqrNgDAAAAgBqS2AMAAACAGpLYAwAAAIAaktgDAAAAgBpy8wwAAAAAaqqR1rt5RmPYJa3YAwAAAIAaktgDAAAAgBqS2AMAAACAGrLHHgAAAAA11mp77A2fFXsAAAAAUEMSewAAAABQQy7FBQAAAICXkVWrVuXiiy/e4/OLFi1K0mKJvfZt/ZmwdMnwK0yYkMyfn57HliabN1eKNaez4EvXfUC5WNOnFws1p+/HxWJtPmBBpfJtbTseJ0xIGo1qsWZ88x+qVRiJ888vFmpzf5kFvhNOPLFInCTZ2Ff9mNrbk0lJ+nr2y9D44debvL63cqymdXcXCzW5b22xWJtfeWyxWBMGNlav1P5Mfxo3Lqlwjli9ZXL1WE3qGSgWKm8+sYnXsFkD5Q5sc/e0SuV3nk/Gjat+Psn6/ooVmvf61w0Vi5X+csdVNFbVuWFHx68eK3aOOd2rqsUaifnzi4V6+ukycfY5YE6ZQEk6mxiedp5OOjp+NYYMx4T1BfvFo48WC3X++a8vFuvdv1vugrYv/k2z4257/uSPq9V97YnljuuBrzxRLNbGzkOKxRooNL2e0pZ0lAnVYhppvT32hj83aKnEHgAAAADU3UEHHbRrVd6LscceAAAAANSQxB4AAAAA1JBLcQEAAACosYL7Bb/MNJXYW758eW6++eZ85zvfydq1azNlypQcc8wxufDCC3PyySc/r/z27dvzmte8Jtu3b9/jv7nPPvvkwQcfbKY5AAAAADDmVE7s3Xfffbn88svT39+fnp6eHH744ent7c2iRYuyaNGivPe9781HP/rR3eo89thj2b59e3p6enLUUUe94L87ceLE5o4AAAAAAMagSom93t7eXHnllenv78+ZZ56ZP/7jP84+++yTJPnqV7+aj370o/n85z+fhQsX5owzzthV78c//nGS5Pjjj89nP/vZvdh8AAAAABibKt084ytf+Uo2bNiQmTNn5tprr92V1EuSs846K+94xzuSJF/+8pd3q7czsTdv3ryRthcAAAAAnmWwxf4bvkor9mbOnJm3ve1tOeqoozJu3LjnPX/kkUcmSVatWrXb3yX2AAAAAGDvqpTYO/PMM3PmmWfu8fmlS5cmSQ499NDd/i6xBwAAAAB7V1N3xX2ujRs35otf/GJuv/32dHZ25rLLLtv13OrVq/P000+no6MjEydOzE033ZQf/vCH6e/vz6xZs3L66afnlFNO2RvNAAAAAIAxY0SJvbvvvjs33nhjHn/88Wzbti0HHnhgrrrqqpxwwgm7yuxcrdfW1pazzjorW7du3e3fuO2223Lqqafm+uuvz6RJk0bSHAAAAAAYM0aU2FuyZEl++tOf7vr/GzZsyL333pvjjz9+V5LuJz/5SZJkYGAgb3rTm3LZZZfliCOOyKZNm3L33Xfn+uuvz7/927/liiuuyC233DKS5iRtbcmECcMv3929+2MVHR3V6zSrq6tcrLa2crF6eoqFqnpYO8s39XJMndpEpZe/kl2jlPZKtw/avU7luiXHjJJvVsHxqWgfLNg5OvfK2vnhaeawRhCtXKiCn6+i55OS5/+SSn6YS469Ve1sWzNtLPkaFoxVajws+fIVnWuUHDMKLsgoeVjP2VGqZTyzFX4ZBd+wkvOaUqeTVvzOVUYjVW848fLXGHbJtkajMfzSz/Hkk09m8uTJ6evry+LFi3Pdddelt7c38+fPz6233prOzs5885vfzDe+8Y1Mnz49l19++fP+jQcffDAXXXRRGo1Gbr755pFdltto+CQAAAAAjBHLlm3M4Yf/w2g3Y6967LHfzpw5k4dVdkSJvedatmxZzjnnnGzdujX/9b/+15x77rnDqvfe9743ixcvzm//9m/nT/7kT5pvwNatybNWEP5a3d3J3LnJo48m/f3VYpX8BXj69HKxSq42W768WKgts46oVL6tbUf36O/fkS+uoue+f6lWYSTe9KZiobZsLfOTWM/4oSJxkqRvc/Vjam/fsTB48+ZkqEJTJw1uqByraS9w1/KXTF9fsVBbJu1XLFbPYBPH1WTnWLe13KqH8eOLhcqklOsbGSz3C+2WcVMqlR/R+aTvqWoVRmLffcvFes62LC+pbdvKxaraDzs6kilTkg0bqtct2OdLzg3XbSyz0qfKBT4j1cxb1fRcY0vBMePnPy8W6hcHHlss1l/9VbFQ+ZM/bnLO295erWMkufh3yi1t+9J1vygWq2+fA4vFKjXs7rNP6SssWsNYT+zt1QXvc+bMyemnn56vfe1reeCBB4ad2DvqqKOyePHirFixYmQNaDR2nAGr6u+vXq/ktVPbt5eLtffyvL/eli3FQjV7WI1GE3XXr28u2Mtcya5RSsU50fPqVqpf8ktYyTer4PhUtA8W7BwDA82HqqrslZ3lkvQlP19Fzyclz/8llfwwlxx7m/0wDw5Wr1ty4Cj4fpU6rJJdsOhco+SYUfCHvZKH9fjj5WKV9MzuWGUUfMNG8vmqqtTppBW/c/HSq5SdWr9+fVasWJGDDjoo06ZNe8EyM2fOTJI89dSvfjEaHBxMo9FI5x6SYUPPfCL39DwAAAAAvLBW22Nv+Cot8jz//PNz3nnn5fbbb99jmZUrVyZJZsyYkSQ57bTT8qpXvSpf+tKX9ljn4YcfTpLMnTu3SnMAAAAAYMyqlNg7+eSTkyS33XZbtr/AEtsVK1bknnvuSbIjoZckhx9+eBqNRu66664MvMD6+iVLluT+++9Pkrz1rW+t1noAAAAAGKMqJfYuvfTSdHd3Z/ny5bnyyivT29u767mHH344l1xySfr7+3PCCSfkjW98Y5LksssuS1tbWx5++OF8/OMfT9+z9mO4//7788EPfjCNRiNnn312Xv3qV++lwwIAAACA1lZpU7uDDz44N9xwQ6644orcfffd+cY3vpHZs2dn69atWf7MHU4XLlyYv/iLv0hbW1uS5LWvfW0+9rGP5dprr80dd9yRr3/965k9e3Y2bdq062YZp5xySq655pq9e2QAAAAAtLhGWm+PveHfSaXy3Sre8IY35M4778znPve5fPvb386yZcvS3d2d4447LmeffXbOO++8dD3ntnvvfve7s3DhwnzhC1/I9773vTz66KOZOHFiTjzxxJx77rk5++yzdyUCAQAAAIBfr6nb0B566KG5+uqrK9VZsGBBrr/++mbCAQAAAADPUWmPPQAAAADg5aGpFXsAAAAA8PIwNNoNGDVW7AEAAABADbXWir3x45Pjj69e7+ijK1d55NFyOdF53b3FYg11jisWa+CVC4rFmvDQAxUrTEjmz0/PY0uTzZur1T311GrlR+KZu1GXMOGZu1i/1L6V1xeJkyQHHFC9zvjxyaRJybp1ydatw6+3Zcq06sGatHJ5sVA5du5AuWAFff/RyZXr9PQkRx2V/PvPJ2XLluHXO3buxsqxmrWqr/pxNWty/9pisVZ1zykW66CBNdUqdHYmPdPSs6U3Gaj4eeksOE1bW+792jxp/2KxOidNKBZr3NpV1SrsvHHc4GD1vlG1/Aisfrrc3HDDhjJxZgxWfK9GYFn/QZXrjBu3Y66xfn2ybdvw603uLNcveueXm69NKhYpueaacrFee2L175NHHpl86UvJxb/Tnp/8ZPj1HvhuuZVMX/zbQ4rFetObioXKQdMrfBhHor0riRuLUo0VewAAAABQQxJ7AAAAAFBDrXUpLgAAAABjzOBoN2DUWLEHAAAAADUksQcAAAAANSSxBwAAAAA1ZI89AAAAAGqqkdbbY68x7JJW7AEAAABADUnsAQAAAEANSewBAAAAQA3ZYw8AAACAGmu1PfaGz4o9AAAAAKghiT0AAAAAqCGJPQAAAACoIXvsAQAAAFBj9tgDAAAAAGpEYg8AAAAAakhiDwAAAABqyB57AAAAANRUI623x15j2CWt2AMAAACAGpLYAwAAAIAaktgDAAAAgBqS2AMAAACAGnLzDAAAAABqbGi0GzBqrNgDAAAAgBqS2AMAAACAGmqtS3EHBpKf/3z45ceNS2bNSlatSrZtqxRq3tzDqrVtJFb0FQvVPnVqsVh9feXyytPmzq1WoaNjx+PBByeDg9Xq9pV7v7bNmlMs1rju7iJxXj/wRJE4SZJmjqmzM8m0HLpPb9IzMOxqQ9P3rx6rSTMGVxWLlXLdPROml+mDSXLspOXVK40fn+TQHDXh8aRj6/DrrS93XAdlfbFYG6eXG58OWr6kWKzKenqSadOSdeuSLVuq1Z0166Vp0wtZvrxYqO6F5cbD9rVrisV6eP1Blcp3dydz9kuWbdov/f3VYq1fX638SJw0q9x5ecas6WUCdRaKk+SA4U8Vdmlr2/G4775JozH8ekPdh1QP1qT1y4uFypxZ1b6njcR3vzuuWKwHvtLEZ6urK8mB+dJ1v0i2bx92tS/+bbm+8e6Lyl0O+d5Ly32f/PxHHi0TaO7cHXkKqKC1EnsAAAAAjDEVF+S0EJfiAgAAAEANSewBAAAAQA1J7AEAAABADdljDwAAAICaaqT19tgb/t2TrNgDAAAAgBqS2AMAAACAGpLYAwAAAIAassceAAAAADXWanvsDZ8VewAAAABQQxJ7AAAAAFBDEnsAAAAAUEP22AMAAACgphpJhka7EXtZY9glrdgDAAAAgBqS2AMAAACAGpLYAwAAAIAaktgDAAAAgBpy8wwAAAAAamxwtBswaqzYAwAAAIAaktgDAAAAgBqS2AMAAACAGrLHHgAAAAA1Zo89AAAAAKBGJPYAAAAAoIYk9gAAAACghuyxBwAAAEBNNdJ6e+w1hl2ypRJ7Qx1d6T9gzrDLt7UlPUm27DsrjeG/ZkmSR5dWKz8Ss2YdUixW/5PFQmXt2nKxuudOq1R+V98YN6Vy35iwtFznWN5frm/MW7u8TKATTywTJ8mqJ6svWu5qS/ZL8tTgtGwfGH69/uWVQzWtr++gYrHmzy8WKu19G4vFenhgXuU63Z3JnCTLBg9Nf4W+cfTAssqxmjV02PDPkSM1eX1vsVhD8xcUi/XUU9XKd3Ym+yZZN+2IDFToF0kyY325vrFt/rHFYo1b8USxWN9fW+48OXVqtfLjxu3+WMVJCzdXr9Sk1ZvKvYYz+taUCTR9epk4SSasaOJzPG5cMmtWetatSLZtG369znJf7eZMn1os1rIVk4vFeuuJ5c5dGzurf7ba25NJSfr2OTBDQ8Ov96Y3VQ7VtPdeWu6iwM9/tsKLMEJXX3N0kTgf/nDyiibOC4xtLsUFAAAAgBqS2AMAAACAGmqpS3EBAAAAGGtabY+94bNiDwAAAABqSGIPAAAAAGpIYg8AAAAAasgeewAAAADU2NBoN2DUWLEHAAAAADUksQcAAAAANSSxBwAAAAA1JLEHAAAAADXk5hkAAAAA1FQjyeBoN2Ivawy7pBV7AAAAAFBDEnsAAAAAUEMSewAAAABQQ/bYAwAAAKDGWm2PveGzYg8AAAAAakhiDwAAAABqSGIPAAAAAGrIHnsAAAAA1FQjrbfHXmPYJa3YAwAAAIAaktgDAAAAgBqS2AMAAACAGrLHHgAAAAA11mp77A2fFXsAAAAAUEMSewAAAABQQy11KW5fX/LlLw+//L77Juedl/yf/5OsW1ct1vsu2lytwghsHJhQLNZB6x8uFqt/0tHFYj35ZLXy48Yls2bt6BfbtlWrO2f+/GoVRmBe58ZisdLXXSbO2rVl4iQ5qG999Urjxyc5NPttfjzZunXY1VZPmVc9VpNmzSoWKsuXl4s157BJxWIddlj1Om1tOx4PPDBpDP/u9Ontn1M9WJOmPfT9YrGWdB5bLNak9cVCZc5hQ03Uas++r2iiXs/0JmI1Z9xAuXlNBgaKhTp21ppisSpPNnp6khyRWVt+mmzZUqnqvd9dUC3WCCxcWCxUVvXvXyTOQWnmc9yc3qnVx/iOjmRKkg37zMpghSvLpv34O5VjNWvZwCHFYs1Z+0CxWOkuN4kaaGJq3dGx43FwMJX6xkHTK36hGYHPf+TRYrGuvqbc98k/+nihcaOtLUlbmVi0DCv2AAAAAKCGWmrFHgAAAABjjZtnAAAAAAA1IrEHAAAAADUksQcAAAAANWSPPQAAAABqqpEUvON5GY1hl7RiDwAAAABqSGIPAAAAAGpIYg8AAAAAasgeewAAAADU2OBoN2DUWLEHAAAAADUksQcAAAAANSSxBwAAAAA1ZI89AAAAAGrMHnsAAAAAQI1I7AEAAABADUnsAQAAAEAN2WMPAAAAgJpqpPX22GsMu6QVewAAAABQQxJ7AAAAAFBDEnsAAAAAUEMSewAAAABQQ26eAQAAAECNDY12A0aNFXsAAAAAUEMSewAAAABQQ22NRqMx2o3YW4aGkr6+4Zfv6EgmTkx++ctkcLBarMmTyi3zHCqYf20f2FYs1va2ccViVdXWlnR2JgMDSdVPSFd7xc40Em1t5WJt314mTmfBHQKqfvCTHa95V9eO16NC5xhoL9ffOzqKhcrAQLlYXZ3lTldDjeY+W+3tO85FVZQ8C3cMbC0Wa2vGF4vVXvBnyqb6YVtbc29060zRdtfM2Nuskp2j6oDY3v6r80nFgWPLULnP17iC07VSXb6zo9xna3Co+vmkre1X55Mqr0nH9v7KsZq1vaO7WKyuwXLHVXIeOthWPVbTfaO94Pmk1HeGJE//stwA9YqpBV/Dkt/xWsTAwGCeeOKp0W7GXnXIIftlzZrVufjii/dYZtGiRUlabI+99vZk8uTq9SZObCpaM5WaUnRZZcHZW1exSM1r7txeMKtS0vhyXyKKGUkGrKtaD26pwfZZKr4MI1RuktM+glAl8wiVdZT7HLfgiPGMJjtHM5P0Vp3Yv6w/JCPQ7DmliYG0p7lI7FLuszWSqUblj0rJZFuxSEm6yh1XSSP5xlB9GC14Pin4ffIVRdeJtOg5uUV0dnZkzpwDRrsZe91TTw0vWdlSK/YAAAAAYKxo0Z9MAQAAAKC1SewBAAAAQA1J7AEAAABADUnsAQAAAEANSewBAAAAQA1J7AEAAABADUnsAQAAAEANSewBAAAAQA1J7AEAAABADUnsAQAAAEANSewBAAAAQA1J7AEAAABADXWOdgNK27JlSz772c/mf//v/50VK1Zk4sSJmT9/ft797nfnlFNOGe3mMYpOO+20rFy58kXLfO9738vkyZMLtYjR9uUvfzmf+MQncs011+Qd73jHC5Z5+umnc9NNN2XRokVZvXp1Jk+enGOPPTaXXnppFi5cWLbBFPPr+sb27dvzmte8Jtu3b9/jv7HPPvvkwQcffCmbyUvsySefzOc///ncd999WbVqVZJk1qxZOfXUU/Pe9743++677/PqGDPGhqp9w5gxNixfvjw333xzvvOd72Tt2rWZMmVKjjnmmFx44YU5+eSTX7COMWNsqNo3jBnAs42pxN7mzZvzu7/7u/nhD3+Yrq6uHHHEEVm/fn2+/e1v59vf/nY+/OEP5/LLLx/tZjIKNm3alJUrV6ajoyPHHHPMHst1dHQUbBWjacmSJbnuuutetMzatWtzwQUX5IknnkhPT0/mzZuX1atX51//9V9z77335uqrr875559fqMWUMpy+8dhjj2X79u3p6enJUUcd9YJlJk6c+FI0j0IefPDBfOADH8jGjRvT0dGRQw45JENDQ/nZz36WRx99NHfeeWc++9nP5pWvfOWuOsaMsaGZvmHMaH333XdfLr/88vT396enpyeHH354ent7s2jRoixatCjvfe9789GPfnS3OsaMsaGZvmHMAHbTGEM++tGPNubNm9f4rd/6rcaqVat2/f2OO+5oHH300Y158+Y1Fi9ePIotZLQ88MADjXnz5jXe8pa3jHZTeBn47ne/2zjhhBMa8+bNa8ybN6/xD//wDy9Y7qKLLmrMmzev8Z73vKexfv36RqPRaAwODjb+5//8n4158+Y1XvWqVzUeffTRkk3nJTbcvnHHHXc05s2b17jkkksKt5ASNmzY0DjxxBN3vcerV6/e9dwTTzzReOc739mYN29e47TTTmv09/fves6Y0fqa7RvGjNa2bt26XeeOK664orFx48Zdz911112No446qjFv3rzGP//zP+9Wz5jR+prtG8YM4NnGzB57TzzxRO666660t7fnU5/6VA488MBdz51zzjm59NJLkyQ33njjaDWRUfTjH/84STJv3rxRbgmjaevWrbnxxhvznve8Jxs2bHjRsvfff38eeOCBTJgwIZ/61KcyZcqUJEl7e3ve97735ayzzsr27dtz0003lWg6L7EqfSMxprS622+/Pb29vdl///1zww03ZP/999/13MEHH5y//Mu/zJQpU7JixYr88z//cxJjxljRTN9IjBmt7itf+Uo2bNiQmTNn5tprr80+++yz67mzzjpr15YOX/7yl3f93ZgxNjTTNxJjBrC7MZPYu/POOzM4OJiFCxdm7ty5z3v+Xe96V5Lk+9///q69UBg7dp4cjzjiiFFuCaPl8ccfzxlnnJHPfOYzSZLf+73fy8yZM/dY/o477kiSvPGNb8y0adOe9/wFF1yQJFm0aFH6+/tfghZTStW+kZhwt7r7778/SfKGN7whkyZNet7z06ZNy2te85okyf/9v/83iTFjrGimbyTGjFY3c+bMvO1tb8u73vWujBs37nnPH3nkkUmy23cQY8bY0EzfSIwZwO7GzB57Dz30UJLkuOOOe8HnZ8yYkZkzZ2blypV54IEHcs4555RrHKNu58lx58mTsefJJ5/ML37xiyxcuDB/+Id/mPnz5+e2227bY/kf/OAHSfY8pixYsCCdnZ3ZvHlzli5dmuOPP/4laTcvvap9IzHhbnUf+MAHcsYZZ2T27Nl7LNNoNJIkQ0NDSYwZY0UzfSMxZrS6M888M2eeeeYen1+6dGmS5NBDD931N2PG2NBM30iMGcDuxkxi7/HHH0+SHHLIIXssszOxt3z58kKt4uVgcHAwjz76aJJk//33z9/8zd/kwQcfzKZNmzJjxoyceuqpectb3pL29jGzwHVMOuCAA3LzzTcP6+7YQ0NDWbFiRZI9jyldXV2ZMWNGVq5cmZ/97Gcm3DVWpW8kyerVq/P000+no6MjEydOzE033ZQf/vCH6e/vz6xZs3L66ae7C3vNLViwIAsWLNjj8729vXnggQeSJHPnzjVmjCFV+0ZizBjLNm7cmC9+8Yu5/fbb09nZmcsuuyyJeQZ77huJMQN4vjGT2Fu3bl2SvOBS9p2mTp2aZMdt5Rk7li9fvusShve85z355S9/udvzd955Z/76r/86f/mXf7nbXjm0lkMPPfR5v4buyYYNGzIwMJDk148pK1euNKbUXJW+kfzqV/S2tracddZZ2bp1627P33bbbTn11FNz/fXXv+CletTfn/7pn2bLli3p6enJGWecYcxgl+f2jcSYMRbdfffdufHGG/P4449n27ZtOfDAA3PVVVflhBNOSGKeMZb9ur6RGDOA5xszS5B2Jm5eaO+CncaPH79bWcaGnSfHJHn1q1+d//W//lceeuih3H///flv/+2/Zb/99suSJUvyvve9L9u2bRvFlvJy8ewxwpjCc/3kJz9JkgwMDOQNb3hD/vEf/zFLlizJ4sWL80d/9EeZOHFi/u3f/i1XXHHFKLeUl8Jf/dVf5Wtf+1qS5IMf/GD23XdfYwZJXrhvJMaMsWjJkiX56U9/umteuWHDhtx7773p6+tLYp4xlv26vpEYM4DnGzMr9jo6OjI0NJS2trY9ltm554lLLseWAw88MBdffHHa29vzX/7Lf9n1/vf09OTss8/Oq1/96rz97W/Pv//7v+crX/nKrhutMHY9e4wwpvBcRx55ZC644IJMnz49l19++a6/jx8/PhdeeGGOPPLIXHTRRfnWt76Vb37zmy6XaSGf+cxncuONNyZJTjvttF2XThkz2FPfSIwZY9HFF1+cD33oQ+nr68vixYtz3XXX5dZbb82PfvSj3HrrrcaMMezX9Y3Ozk5jBvA8YyaxN2HChGzYsOF5S5WfbecvIzt//WJsOPbYY3Psscfu8fnZs2fnrLPOyj/8wz/knnvukdgjEydO3PW/X2wVpzFlbDrllFNedBJ9/PHH56STTsrixYtzzz33mHC3gIGBgVx99dW59dZbkySve93rcsMNN+z6Qm7MGLt+Xd9IjBlj0QEHHJBkx/eTt7/97TnmmGNyzjnnZOnSpbnrrrvy5je/eVdZY8bY8uv6xrnnnmvMAJ5nzPy884pXvCJJsn79+j2W2bk/xc5LI2Cno446Kkl2bWTM2DZhwoRdl8a82L42xhT2xJjSOvr6+vK+971vV+LmrW99a2666abdvmgbM8am4fSN4TJmtLY5c+bk9NNPT5I88MADxgx2eW7fGC5jBowtYyaxN2fOnCQvPritXLkySXLYYYeVaBIvI0NDQy/6i+jOSx06O8fMIldeRHt7e2bPnp1kz2PK9u3bs2bNmiTGlLFocHBw18bnL2RoaCiJMaXunnzyyVxwwQVZvHhxkuSSSy7Jn//5nz9vTyxjxtgz3L6xkzGjta1fvz5Lly5Nb2/vHsvMnDkzSfLUU08ZM8aQqn1jJ2MG8GxjJrF3zDHHJEkeeuihF3x+9erVWbVqVZLkNa95Talm8TJw4YUXZv78+bnuuuv2WOZHP/pRkmTu3LmlmsXL3K8bU5YsWZKBgYGMHz8+Rx99dMGWMdpOO+20vOpVr8qXvvSlPZZ5+OGHkxhT6mzNmjW5+OKL88gjj6SjoyNXXXVV/uAP/mCP+2EZM8aOqn3DmNH6zj///Jx33nm5/fbb91hm5wKDGTNmJDFmjBXN9A1jBvBcYyax95a3vCXJjiXMy5Yte97zf/d3f5ckee1rX5tZs2YVbRuj64gjjsjg4GD+5V/+Zbc7Tu20cuXKfP3rX0+y4zIaSJLf/M3fTJLcfffdL3iJ/9///d8n2dFnuru7SzaNUXb44Yen0WjkrrvuesFf05csWZL7778/iTGlrrZt25b3v//9eeKJJ9LV1ZVPf/rTueCCC160jjFjbGimbxgzWt/JJ5+cJLntttuyffv25z2/YsWK3HPPPUl2JG0SY8ZY0UzfMGYAzzVmEnuHHXZY3va2t2VwcDAf/vCH8/jjj+967s4778xnP/vZJMkHPvCB0Woio+R3f/d3M27cuKxevTpXXHHFbsvcf/zjH+eSSy7J5s2bc8IJJ+y2mTFj22/8xm/kuOOOy6ZNm/KhD30oa9euTbLj0odbbrklX/3qV9PV1bXbnQ8ZGy677LK0tbXl4Ycfzsc//vHdfjC4//7788EPfjCNRmPXXbepn1tuuWXXSu5PfOITu/Y/ejHGjLGhmb5hzGh9l156abq7u7N8+fJceeWVu112+fDDD+eSSy5Jf39/TjjhhLzxjW9MYswYK5rpG8YM4LnaGjs3DxsDnn766bz73e/edWnEvHnzsnHjxl3Lm6+44oq8//3vH+VWMhruvvvu/P7v/362bt2arq6uzJ49OwMDA7tWd86fPz+f+9znMnXq1NFtKEWddtppWblyZa655pq84x3veN7zP//5z3PhhRdm9erVGTduXI444oisWbMmTz31VNra2nLdddfl7LPPHoWW81L7dX3ji1/8Yq699toMDg6mu7s7s2fPzqZNm3btlXTKKafkxhtvdCfDGtq2bVte97rXZcOGDens7MyCBQtetPwpp5yya25hzGhtI+kbxozW941vfCNXXHFFtmzZknHjxmX27NnZunVrli9fniRZuHBhbrrppkybNm1XHWPG2NBM3zBmAM82phJ7SbJ58+Z87nOfy9e//vX8/Oc/T2dnZ+bPn5+LLrooZ5xxxmg3j1G0bNmy/PVf/3UWL16cNWvWpLu7O3Pnzs3b3va2vPOd77T57Bj065I3SdLb25v/8T/+R+699948+eST6enpyTHHHJNLL700J554YuEWU8pw+saSJUvyhS98Id/73vfS29ubiRMn5pWvfGXOPffcnH322Xvcb4uXt6VLl+a8884bdvm3v/3tufbaa3f9f2NG6xpp3zBmtL7HH388n/vc5/Ltb39711xz3rx5Ofvss3Peeeelq6vreXWMGWNDM33DmAHsNOYSewAAAADQCsbMHnsAAAAA0Eok9gAAAACghiT2AAAAAKCGJPYAAAAAoIYk9gAAAACghiT2AAAAAKCGJPYAAAAAoIYk9gAAAACghiT2AAAAAKCGJPYAAAAAoIYk9gAAAACghiT2AAAAAKCGJPYAAAAAoIb+f8Tv1+Z7Y8RGAAAAAElFTkSuQmCC\n", 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              " - ] - }, - "metadata": { - "filenames": { - "image/png": "/Users/mhjensen/Teaching/MachineLearning/doc/LectureNotes/_build/jupyter_execute/chapter3_172_1.png" - } - }, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "fig = plt.figure(figsize=(20, 14))\n", "im = plt.imshow(J_sk, **cmap_args)\n", @@ -4111,32 +3416,7 @@ "collapsed": false, "editable": true }, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_7650/438060758.py:9: MatplotlibDeprecationWarning: Auto-removal of grids by pcolor() and pcolormesh() is deprecated since 3.5 and will be removed two minor releases later; please call grid(False) first.\n", - " cb = fig.colorbar(im)\n", - "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_7650/438060758.py:10: UserWarning: FixedFormatter should only be used together with FixedLocator\n", - " cb.ax.set_yticklabels(cb.ax.get_yticklabels(), fontsize=18)\n" - ] - }, - { - "data": { - "image/png": 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              " - ] - }, - "metadata": { - "filenames": { - "image/png": "/Users/mhjensen/Teaching/MachineLearning/doc/LectureNotes/_build/jupyter_execute/chapter3_175_1.png" - } - }, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "_lambda = 0.1\n", "clf_ridge = skl.Ridge(alpha=_lambda).fit(X_train, y_train)\n", @@ -4198,32 +3478,7 @@ "collapsed": false, "editable": true }, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_7650/3544313922.py:8: MatplotlibDeprecationWarning: Auto-removal of grids by pcolor() and pcolormesh() is deprecated since 3.5 and will be removed two minor releases later; please call grid(False) first.\n", - " cb = fig.colorbar(im)\n", - "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_7650/3544313922.py:9: UserWarning: FixedFormatter should only be used together with FixedLocator\n", - " cb.ax.set_yticklabels(cb.ax.get_yticklabels(), fontsize=18)\n" - ] - }, - { - "data": { - "image/png": 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kzJgxSV4IhW677bb88Y9/zLXXXpv58+fn//7v//KVr3wl3/72t5Ok+2c+ZMiQpaoSX2xJ78Te+u1vf9sddD3zzDNZuHBh3c+yMt17773df15SFbkszz//fJK8pFpy8ODBeeqpp3L77bdn6tSpmTZtWh544IH89a9/7a46ffE/Ly/W0/5a4h9furHEkpB9WRWWAACvFII9AGigqqpy8cUX54c//GFmzpzZ/flarZbhw4fnXe96V66++uqX3PeWt7wlV1xxRc4///z84Q9/yDPPPJMbb7wxN954Y0455ZTssMMOOfnkk7P55psneaHS6Yc//GF++tOf5sorr8yUKVPywAMP5IEHHsh//Md/ZLXVVsvhhx+eI444IrVabanqtNVXX73H9S8Jyp555pmV9S1ZKmB8sSVr6s16lqzpxcHeqquuutx5X1xN93JZffXVs8cee2SPPfbIcccdl2OOOSa33nprfvWrX+XEE0/MOuuskzlz5iRJhg4dutLmXbBgQTbYYIMMGzYskyZNygknnJArrrhiqeCyL2688cYejz0fccQR2XXXXZd57cVVb7052vri8U888US+9a1v5de//vVSlaqrrrpqXve616WzszP/+7//2+PX+mdvcV7R7wkAQH8S7AFAA33/+9/PmWeemSTZZ5998pa3vCWbb755RowYkaFDh2bq1KnLDPaSF/rcffvb386iRYtyxx13ZMKECbnlllsyadKk/O///m8++tGP5je/+U330cRBgwblsMMOy2GHHZbHHnss//M//5MJEybkj3/8Y5588sl897vfzSqrrJLDDjtsqVBp3rx5Pb4BdUl11MoMoXoydOjQzJ07d7lHIV/cI7ARa1qWI444Ivfee2+OPfbYHHDAAT2OW3PNNXPyySfn7W9/e7q6uvLQQw9lnXXW6f55rcywdNiwYfnxj3+ctra2vPOd78zdd9+dc889N8cee+wKfd2nnnqqx2DuxT0h/9GSZ1xrrbW6j3z3xvPPP59DDz00999/f9Zaa60ceOCB2XbbbTNy5MhssskmaW9vz3e+853lBnsAAM1Mjz0AaJBFixblhz/8YZIXjiN+5zvfybvf/e687nWv6w6lHnvssZfc19nZmYceeii33XZbkhcqjMaOHZujjz46P/3pT/PTn/40tVotTzzxRG655ZYkLwRet99+e3dvvQ022CAHHHBATjnllPzhD3/I7rvvniTdIeImm2zSXbl011139fgMS64t6fn3clrS4+zPf/7zP13PmmuuuVS1XiPNnz8/06ZN6+4JuDwvPha65CjwkmPUzz77bKZNm7bM+8aPH59DDjkkp512Wq/WtMMOO2TjjTfOhhtumE9/+tNJkh/84Afdfe766j3veU/uueeeZX685z3v6fG+4cOHJ0nmzJmTJ554osdxEydOzP333999jPiGG27I/fffnwEDBuTSSy/Npz/96bz1rW/N8OHDu/sjLuufGQCAViHYA4AGmT17dp599tkkPfeNu/zyy7v/vKS317333pu3ve1tOfTQQ5cZiowePbo7GFzSZ+xLX/pSPvjBD+b8889/yfiBAwfmDW94Q5IXQsPkhYqqN77xjUmSH//4x8tc2yOPPNIdXv3jSyleDkvCx1/+8pdLVeYtsXDhwlxyySVJXng7an9Z8jKI3/72t90vfejJkrcJjxgxoruf3siRI7tf6PGLX/ximfddddVVufXWWzNr1qy613fIIYdk1KhRWbRoUU444YSljrMuT61WS9K3XoP/aOTIkd1h8H/8x38sc8z//u//5uCDD84+++yT22+/PUm6g86hQ4e+pI9k8sLbo//whz8k+fteBgBoJYI9AFhB8+bNy6xZs5b7UVVV1llnnay11lpJkosuumipsGrWrFk56aSTct1113V/bknV0lZbbZWOjo50dnbmM5/5zFIVSgsXLsx3vvOdzJ8/P0OGDMnYsWOTJO9617uSJJdeeml++ctfLhXO3HvvvfnJT36SJEv1RDvmmGMyYMCA/OlPf8r/+3//b6m+e3fffXfGjRuX559/PltttdVyj5yuLAceeGDWX3/9PPnkk/nkJz+ZRx55pPvaU089lU996lOZMmVKhg4dusJHTFfEAQcckNGjR6erqytHHHFEzjrrrJcEsPPnz8+PfvSjfO1rX0tbW1u+8IUvdPf4q9VqOeqoo5Ik559/fi6//PLun1dnZ2fOO++8/Pa3v82AAQPy0Y9+tO71tbW15etf/3oGDhzYfSS3N5Ycn50xY0bdcy7Lpz71qSTJeeedl/PPP3+pF3pMnDix+/qoUaPypje9Kcnfqzbnzp2biy++eKl9fPvtt+ewww7r7lH43HPPrZR1AgCURI89AFhBy3vL5xK33XZb1lhjjXzqU5/KV7/61dx6663Zdddds9lmm2XhwoV56KGHsnjx4myzzTZ59NFHM3v27Dz22GPdlX3f+c538qEPfSi33npr3vrWt2ajjTbKqquummnTpuXpp59Oe3t7Tj755O7jnW9729vygQ98IJdddlm+8IUv5Fvf+lZe85rXZP78+Xn44YdTVVW22267HHHEEd1rHD16dL7xjW/kxBNPzGWXXZZrrrkmI0eOzLPPPtv9htqOjo6cddZZGTRo0MvwnVzaGmuskXPPPTef+MQnMnny5LztbW/L5ptvngEDBuTee+/NokWLstZaa+X0009fZjVXowwaNCjnnXdePvvZz+bGG2/MmWeembPOOisbbbRR1l577TzzzDPdP98hQ4bk61//enbbbbelvsb73ve+3Hfffbnwwgtz4okn5rvf/W422GCDTJs2LXPmzEl7e3tOOumkbLXVVn1a4xZbbJFPfvKTOeuss/KDH/wgb33rW7P11lsv955tttkmt912W04++eRccsklOeigg/K+972vT/Mnyb777pupU6fmzDPPzOmnn54f/OAH2WyzzTJr1qxMnz49yQtHds8+++zue/bYY4+MHj06kydPzje/+c2cf/75WX/99fPEE0/k8ccfT61Wy0477ZRbbrklM2fOTFVV3ZWGAACtQMUeADTQQQcdlIsuuig777xzVl999dx777156qmnsv322+fLX/5yLrvssu4qut///vfd922++ea56qqrcuCBB2bDDTfMjBkzct9992WNNdbIe9/73lx99dXZb7/9lprrq1/9ak455ZS88Y1vTFdXV+65557MmTMnO+ywQ7785S/nZz/72VJvlU1eqD67+uqr84EPfCCvetWrcu+992b27NkZM2ZMvvzlL+eKK67oPkLaCNtss02uu+66HHXUUdliiy3yyCOPZOrUqRk+fHiOOOKIXHPNNf16DHeJNdZYI+edd14uvvjiHHTQQdliiy3yzDPP5K9//WueeuqpbLXVVjnmmGPy61//usdqxxNOOCEXXnhh9txzz1RVlbvvvjvt7e15+9vfnksvvTTvf//7V2iNn/zkJ7PFFlv0+kjuN7/5zey8884ZMGBAHnzwwUydOnWF5k9eCMEvvfTS7LfffllttdVy9913Z/bs2dlmm23yqU99Kr/4xS+WenFLe3t7Lrroonz2s5/N1ltvneeeey5TpkzJgAEDss8+++Q//uM/cvbZZ2fw4MGZM2dOr964CwDQTGrVymicAgAAAAA0lIo9AAAAACiQYA8AAAAACiTYAwAAAIACCfYAAAAAoECCPQAAAAAokGAPAAAAAAo0oL8XAAAAAAD83R133JHPfOYzPV4fP358kmYL9qqqYVM98ODchs01YviaDZsLAAAA+LtG/ff/xhuvnoED2xsyVzPpWrw4cx9+uL+XsVKtuckmefWrX92rsbWqamAa9nKrqvrDvba2pKur7qlq7afVfU9fVZ2fb9hcvEgf9wYtwN6gJ/YGy2Jf0BN7g57YG/TE3ugXtfYzGjLP/fePy4gRazVkrmYy+4EH8r2RI/t7GSvVv9x/f9YeMaJXY/XYAwAAAIACCfYAAAAAoEDN1WMPAAAAgJbSylVrDQ32nnvuuVxwwQX5z//8z0ybNi1Dhw7Ntttum4985CPZddddG7kUAAAAAChaw4K9Z599Nh/96Edzxx13ZODAgdliiy0yZ86c/OlPf8qf/vSnHHvssTnmmGMatRwAAAAAKFrDqhVPPvnk3HHHHdl6663z29/+NldddVV+//vf51vf+lYGDBiQM888M7fcckujlgMAAAAARWtIsPfwww/nmmuuSVtbW04//fS85jWv6b52wAEH5PDDD0+SnHnmmY1YDgAAAABNoq3JPup99pfd1Vdfnc7OzowaNSqbb775S64fdNBBSZJJkyZlxowZjVgSAAAAABStIcHe7bffniTZYYcdlnl9/fXXz4YbbpgkufXWWxuxJAAAAAAoWkOCvYceeihJsskmm/Q4ZkmwN3Xq1EYsCQAAAACK1pC34j711FNJknXWWafHMWuttVaSZPbs2Y1Y0gobPXr9/l4CAAAA8DIbPXq9hswzaFB7Q+ZpRg17M+wrUEOCvQULFiRJBg0a1OOYwYMHLzW2z9r68OPswz2TJh1W/zyUpy/7idZgb9ATe4NlsS/oib1BT+wNemJvNNykSYf29xKgRw0J9trb29PV1ZVardbjmKqqkiRtK/pLqqurvvFtbfXfk2TM2IvrvqevJk30S6Rf9HFv0ALsDXpib7As9gU9sTfoib1BT+yNfjFm7E8aMs8117wnG220ekPmonk0JNgbMmRI5s6dm+eff77HMQsXLkzy98q9V7rJkx/v7yUAAAAAL7PJk2c2ZJ6FCzsbMg/NpSE1vGuvvXaSZM6cOT2OWdJbb911123EkgAAAACgaA2p2BsxYkSmTp2aadOm9Thm+vTpSZLNNtusEUsCAAAAoHC1NN/LM3puZPdSDXn27bffPkly++23L/P6448/nhkzZiRJRo8e3YglAQAAAEDRGhLsvf3tb0+S3HrrrXnggQdecv1nP/tZkuQNb3hDNtpoo0YsCQAAAACK1pBgb7PNNss73/nOdHZ25thjj81DDz3Ufe3qq6/OBRdckCQ58sgjG7EcAAAAACheQ3rsJcmJJ56YKVOmZMqUKXnHO96Rjo6OPP3009299Y477rjstNNOjVoOAAAAAE2g2Xrs1aNhwd7aa6+dSy+9ND/84Q/zq1/9Kvfff38GDBiQN7zhDfnwhz+cvffeu1FLAQAAAIDiNSzYS5IhQ4bk2GOPzbHHHtvIaQEAAACg6bRytSIAAAAAFKuhFXsAAAAAsDK1ctVaKz87AAAAABSrqSr2uqpa5s+v9Xp8W1uy2mrJ/Gfb0tVV31xV5+frXF3f1dpPadhcVecXGzYXAAAAvNJVncc3ZqJa7/MMWELFHgAAAAAUqKkq9gAAAABoHbU0X9VaPbWbzfbsAAAAANASBHsAAAAAUCDBHgAAAAAUSI89AAAAAIrVylVrrfzsAAAAAFAswR4AAAAAFEiwBwAAAAAFEuwBAAAAQIG8PAMAAACAYrVy1VorPzsAAAAAFEuwBwAAAAAFEuwBAAAAQIH02AMAAACgWLX+XkA/UrEHAAAAAAUS7AEAAABAgQR7AAAAAFAgPfYAAAAAKFItSXt/L2Ilq6dnoIo9AAAAACiQYA8AAAAACiTYAwAAAIAC6bEHAAAAQLFauWqtlZ8dAAAAAIol2AMAAACAAgn2AAAAAKBATdVjr61WZY3VqnrvympDuuqea8ZjjctEq84vNmyuWvvnGzZX1Xlaw+YCAAAAaDZNFewBAAAA0Fpa+ThqKz87AAAAABRLsAcAAAAABRLsAQAAAECB9NgDAAAAoFitXLXWys8OAAAAAMUS7AEAAABAgQR7AAAAAFAgPfYAAAAAKFItzVe1VqtjbLM9OwAAAAC0BMEeAAAAABRIsAcAAAAABdJjDwAAAIBitXLVWis/OwAAAAAUS7AHAAAAAAUS7AEAAABAgfTYAwAAAKBYrVy11srPDgAAAADFEuwBAAAAQIEEewAAAABQIMEeAAAAABTIyzMAAAAAKFatvxfQj1TsAQAAAECBBHsAAAAAUCDBHgAAAAAUSI89AAAAAIpUS9Le34tYyerpGSjY66NhG3Q1bK6uBhZWVp2nNWyuWvu/NGyuqvN7DZsLAAAAoBEcxQUAAACAAgn2AAAAAKBAjuICAAAAUKxWrlpr5WcHAAAAgGIJ9gAAAACgQII9AAAAACiQHnsAAAAAFKuVq9Za+dkBAAAAoFiCPQAAAAAokGAPAAAAAAqkxx4AAAAARaql+arWanWMbbZnBwAAAICWINgDAAAAgAIJ9gAAAACgQII9AAAAACiQl2cAAAAAUKxWrlpr5WcHAAAAgGIJ9gAAAACgQII9AAAAACiQHnsAAAAAFKuVq9Za+dkBAAAAoFiCPQAAAAAokGAPAAAAAAqkxx4AAAAAxWrlqrVWfnYAAAAAKJZgDwAAAAAK5ChuAdrS1bC5nl3QuKy36vxew+aqtR9d1/jRozfOpElfypixp2by5Efqurfq/H5d4wEAAAD6QrAHAAAAQLFq/b2AfuQoLgAAAAAUSLAHAAAAAAUS7AEAAABAgQR7AAAAAFAgL88AAAAAoEi1JO39vYiVrJ6XgajYAwAAAIACCfYAAAAAoECCPQAAAAAokB57AAAAABSrlavWWvnZAQAAAKBYgj0AAAAAKJBgDwAAAAAKpMceAAAAAMVq5aq1Vn52AAAAACiWYA8AAAAACiTYAwAAAIAC6bEHAAAAQJFqab6qtVodY5vt2QEAAACgJQj2AAAAAKBAgj0AAAAAKJAeewAAAAAUq5Wr1lr52QEAAACgWII9AAAAACiQYA8AAAAACqTHHgAAAAC8gsyYMSOHHHJIj9fHjx+fRLDHPxiySlfD5nr8icYVjFad3+/TfZMmnlD3PbX2z/dprr6oOk9r2FwAAADwStTKx1EFewAAAADwCjJs2LDuqrzlaeVQEwAAAACKJdgDAAAAgAI5igsAAABAsVq5aq2hwd4ee+yR6dOnL3fMbbfdljXWWKNBKwIAAACAMjUs2Js3b16mT5+e9vb2bL/99j2Oa29vb9SSAAAAAKBYDQv27r777iTJpptumksuuaRR0wIAAABAU2p4sNfR0dGoKQEAAABoYrW/fTSTep6nYf0FlwR7W2yxRaOmBAAAAICm1fBgb8stt2zUlAAAAADQtBpyFLezszP33XdfkmS99dbLRRddlIkTJ2bevHlZf/31s9tuu+Xtb3972tpa+QXFAAAAANB7DQn2pk6dmgULFiRJDjvssDzzzDNLXb/66qtz4YUX5vvf/37WW2+9RiyJV4ABDevw2FijR2/Y30sAAACAltHe3wvoRw2JVpYcw02S173udTn22GPz2te+Ns8//3z++Mc/5rTTTsudd96ZT3ziE7nssssyaNCgvk/Wl6o/lYL9Yt11GzlbH3/GfdgbkyYd17e5KIvfG/TE3mBZ7At6Ym/QE3uDntgbwIs0JNh7zWtek0MOOSRtbW054YQTuo/crrrqqtl///3zute9Lu9+97vz17/+NVdccUUOOuigvk/W1VXf+La2+u9hpXhqduP+D2ndtfvwM+7j3hgz9t/rn6uPJk38VMPm4kX83qAn9gbLYl/QE3uDntgb9MTeaG612gsfUIeGBHtjxozJmDFjerw+fPjw7Lfffrnssstyww03rFiwRzEWL+7vFbw8Jk+e3t9LAAAAAFrAK6bL2dZbb50kmTZtWj+vBAAAAIBStPIB9YY9e1dXVxYuXNjj9aqqkiQDmvWNCgAAAACwEjUk2Dv44IOz7bbb5rTTTutxzJ///Ockyeabb96IJQEAAABA0RoS7G2xxRbp7OzMb37zm8yfP/8l16dPn55f/epXSZJ99tmnEUsCAAAAgKI1JNj76Ec/mkGDBuXxxx/PcccdlyeeeKL72t13352Pf/zjefbZZ/P6178+b3vb2xqxJAAAAAAoWkMa2m222WY5/fTT87nPfS5//OMfs/vuu2f48OFZvHhxHnjggSTJtttum7POOittba3c8hAAAACAerRyktSwN1Xsvffe2WKLLXLhhRfm5ptvzoMPPphVVlklo0ePzjvf+c586EMf8uIMAAAAAOilhiZpI0aMyNe+9rVGTgkAAAAATamVqxUBAAAAoFjOvgIAAABQpFqar2qtVsfYZnt2AAAAAGgJKvboN+u/uqthc82aU1+G3d6erLlmMndeWzo765ur6jytvhtWQK396IbNVXV+v2FzAQAAAP+cij0AAAAAKJCKPQAAAACK1cpVa6387AAAAABQLMEeAAAAABRIsAcAAAAABdJjDwAAAIBitXLVWis/OwAAAAAUS7AHAAAAAAUS7AEAAABAgfTYAwAAAKBItTRf1VqtjrHN9uwAAAAA0BIEewAAAABQIMEeAAAAABRIsAcAAAAABfLyDAAAAACKVc/LJpqNij0AAAAAKJBgDwAAAAAKJNgDAAAAgALpsQcAAABAsdr7ewH9SMUeAAAAABRIsAcAAAAABRLsAQAAAECB9NgDAAAAoFitXLXWys8OAAAAAMUS7AEAAABAgQR7AAAAAFAgPfZoCeus1dWHu9qy5ur137dwcePy8qrz+w2bq9Z+YkPmqTq/3pB5AAAAKF8tzVe1VqtjbLM9OwAAAAC0BMEeAAAAABRIsAcAAAAABRLsAQAAAECBvDwDAAAAgGK1ctVaKz87AAAAABRLsAcAAAAABRLsAQAAAECB9NgDAAAAoFitXLXWys8OAAAAAMUS7AEAAABAgQR7AAAAAFAgPfYAAAAAKFYrV6218rMDAAAAQLEEewAAAABQIMEeAAAAABRIjz0AAAAAilRL81Wt1eoY22zPDgAAAAAtQbAHAAAAAAUS7AEAAABAgfTYAwAAAKBY9fSkazYq9gAAAACgQII9AAAAACiQYA8AAAAACiTYAwAAAIACeXkGAAAAAMVq7+8F9CPBHqxkgwZ0NWyux59oXNFt1fn1hsxTaz+iIfMkSdV5bsPmAgAAgJXNUVwAAAAAKJBgDwAAAAAK5CguAAAAAMVq5aq1Vn52AAAAACiWYA8AAAAACiTYAwAAAIAC6bEHAAAAQJFqab6qtVodY5vt2QEAAACgJQj2AAAAAKBAgj0AAAAAKJAeewAAAAAUq5Wr1lr52QEAAACgWII9AAAAACiQYA8AAAAACqTHHgAAAADFauWqtVZ+dgAAAAAolmAPAAAAAAok2AMAAACAAgn2AAAAAKBAXp4BAAAAQJFqab6qtVodY5vt2QEAAACgJQj2AAAAAKBAgj0AAAAAKJAeewAAAAAUq5Wr1lr52QEAAACgWCr2oGDrv7qrYXN1NejvAarOcxsyT5LU2o+v+57RozfKpEnHZ8zY72Ty5Gm9vq/qPKPuuQAAAGB5VOwBAAAAQIFU7AEAAABQrFauWmvlZwcAAACAYgn2AAAAAKBAgj0AAAAAKJAeewAAAAAUq5Wr1lr52QEAAACgWII9AAAAACiQYA8AAAAACqTHHgAAAABFqqX5qtZqdYxttmcHAAAAgJYg2AMAAACAAgn2AAAAAKBAgj0AAAAAKJCXZwAAAABQrFauWmvlZwcAAACAYgn2AAAAAKBAgj0AAAAAKJAeewAAAAAUq5Wr1lr52QEAAACgWII9AAAAACiQYA8AAAAACqTHHgAAAADFauWqtVZ+dgAAAAAolmAPAAAAAArkKC4AAAAAvILMmDEjhxxySI/Xx48fn0SwB/RSW7oaMs/CxY0rJK46z+jzvZMmHlfX+Fp7feNXRNX5nYbNBQAA0J9qSVKr9fcy+o1gDwAAAABeQYYNG9Zdlbc8euwBAAAAQIEEewAAAABQIMEeAAAAABRopQV7P//5z7Plllvm8ssv73HM7Nmz881vfjN77rlntt122+y000455phjcvvtt6+sZQAAAADQSgYMaK6POqyUYO/OO+/MaaedttwxTz75ZD7wgQ/k4osvzlNPPZWOjo7UarX89re/zUEHHZQrrrhiZSwFAAAAAFrCCgd7EyZMyOGHH55nnnlmueOOO+64PPzww9l5551z44035sorr8xNN92U448/Pp2dnTnppJNy//33r+hyAAAAAKAl9DnYe/7553PmmWfmsMMOy9y5c5c7dsKECbn11lszZMiQnH766VlzzTVfmLytLZ/4xCey3377ZdGiRTnnnHP6uhwAAAAAaCn1Hdz9m4ceeiiHHnpoHn300bS3t+fTn/50Lr/88kyfPn2Z46+66qokyZ577pl11lnnJdcPPPDAXHvttRk/fnwWLFiQVVZZpS/LAgAAAKCV1Gp196V7xavVej20TxV7jz32WB599NGMGjUql112WY488sjljp88eXKSZIcddljm9e222y4DBgzIs88+m7vuuqsvSwIAAACAltKnYG+DDTbIeeedl0svvTTbbrvtcsd2dXVl2rRpSZJNNtlkmWMGDhyY9ddfP0ny4IMP9mVJAAAAANBS+lSruOmmm2bTTTft1di5c+dm8eLFSbLMY7hLrLXWWpk+fXpmz57dlyUBAAAAQEt52Q8hL1iwoPvPgwYN6nHc4MGDXzIeaD11tBIoyujRG/X3EgAAAJpTs/XYq8PL/uRtbX8/7Vtbzn+xV1X1kvF9nLAx99Aa7I2GG9jQb/kKTFbn3pg06XN9n4uy+L3BstgX9MTeoCf2Bj2xN4AXedmDvaFDh3b/eeHChT2OW3JtSeVen3V11Te+ra3+e2gN9ka/WNTZuH9RGdjex59vH/bGmLFn9G2uPpg08fiGzcU/8HuDZbEv6Im9QU/sDXpibzS3Wq15jzDxsnnZg70hQ4Zk0KBBWbhw4XL75y25tu66677cSwJewf5WvNt0Jk+e1t9LAAAAoMk05Cju8OHDc88993S/HfcfLVq0KDNnzkySbLbZZi/3kgAAAABoBrVa8/XYq6NysyFn3rbffvskye23377M63feeWcWL16cwYMHZ5tttmnEkgAAAACgaA0J9t7xjnckSa6//vrMmTPnJdcvueSSJMk+++yTVVZZpRFLAgAAAICiNSTY23HHHbPDDjtk3rx5Ofroo/Pkk08mSbq6unL++efn2muvzcCBAzNu3LhGLAcAAAAAiteQQ8i1Wi3f+ta3cvDBB2fixInZfffds8UWW2TmzJl54oknUqvV8s1vfjMjR45sxHIAAAAAaAZ67DXGxhtvnF/+8pc59NBDs/7662fKlCl5/vnn8+Y3vzkXXXRR9t9//0YtBQAAAACKV6uqqurvRaw0VfXCRz3a2pKurpdnPZTN3ugXCxc37O8bMmhAH3++fdgbtfbj+jZXH1Sd32nYXPwDvzdYFvuCntgb9MTeoCf2RnOr1eqq1OJvHnooef3r+3sVK9dttyWbbtqroY37L2gAAAAAYKUR7AEAAABAgZqsuyAAAAAALaXZXp5Rh9Z9cuAVqc997/qgq49Fy219uLeRfe9q7cc3bK6q84yGzQUAAMDSHMUFAAAAgAIJ9gAAAACgQI7iAgAAAFCmWq35euzVar0eqmIPAAAAAAok2AMAAACAAgn2AAAAAKBATXYIGQAAAICW0mw99uqgYg8AAAAACiTYAwAAAIACCfYAAAAAoECtewgZAAAAgLLVas3XY69W6/VQFXsAAAAAUCDBHgAAAAAUSLAHAAAAAAVqskPIAAAAALSUZuuxVwcVewAAAABQIMEeAAAAABRIsAcAAAAABRLsAQAAAECBWre7IAAAAABlq9Wa7+UZtVqvh6rYAwAAAIACCfYAAAAAoECCPQAAAAAoUJMdQgYAAACgpTRbj706qNgDAAAAgAIJ9gAAAACgQK1bqwi0vLZ09fnOeu/tauDfo1SdZzRsrlr7Fxs2V9V5SsPmAgAAKIFgDwAAAIAy1WrN12OvVuv1UEdxAQAAAKBAgj0AAAAAKJBgDwAAAAAK1GSHkAEAAABoKc3WY68OKvYAAAAAoECCPQAAAAAokGAPAAAAAArUuoeQAQAAAChbrdZ8PfZqtV4PVbEHAAAAAAUS7AEAAABAgQR7AAAAAFAgwR4AAAAAFKjJugsCAAAA0FKa7eUZdVCxBwAAAAAFEuwBAAAAQIEEewAAAABQoNY9hAwAAABA2Wq15uuxV6v1eqiKPQAAAAAokGAPAAAAAAok2AMAAACAAjXZIWQAAAAAWkqz9dirg4o9AAAAACiQYA8AAAAACiTYAwAAAIACte4hZAAAAADKVqs1X4+9Wq3XQ5vsyQFemdrS1bC5uhpYjF11ntKwuWrthzRsrqrzJw2bCwAAoK8cxQUAAACAAgn2AAAAAKBAgj0AAAAAKJAeewAAAACUq9lenlEHFXsAAAAAUCDBHgAAAAAUSLAHAAAAAAVq3UPIAAAAAJStVmu+Hnu1Wq+HqtgDAAAAgAIJ9gAAAACgQII9AAAAAChQkx1CBgAAAKBl6LEHAAAAAJRGsAcAAAAABRLsAQAAAECBmuwQMgAAAAAtpdl67NVBxR4AAAAAFEiwBwAAAAAFEuwBAAAAQIFa9xAyAAAAAGWr1Zqvx16t1uuhKvYAAAAAoECCPQAAAAAokGAPAAAAAAok2AMAAACAAjVZd0EAAAAAWkqzvTyjDir2AAAAAKBArRtpAjSptnT19xJeFlXnTxo2V63963XfM3r0Bpk06RMZM/aCTJ78WK/vqzpPrHsuAACARMUeAAAAABRJxR4AAAAAZarVmq/HXq3W66Eq9gAAAACgQII9AAAAACiQYA8AAAAACtRkh5ABAAAAaCnN1mOvDir2AAAAAKBAgj0AAAAAKJBgDwAAAAAK1LqHkAEAAAAoW63WfD32arVeD1WxBwAAAAAFEuwBAAAAQIEEewAAAABQoCY7hAwAAABAS2m2Hnt1ULEHAAAAAAUS7AEAAABAgQR7AAAAAFAgwR4AAAAAFKh1uwsCAAAAULZarflenlGr9Xqoij0AAAAAKJBgDwAAAAAKJNgDAAAAgAI12SFkAAAAAFpKs/XYq4OKPQAAAAAokGAPAAAAAArUurWKAAAAAPAKNGPGjBxyyCE9Xh8/fnwSwR4AhehqYJF51Xlin++dNPHwusbX2r/Y57nqVXWe0rC5AACgIWq15uuxV6v1emiTPTkAAAAAlG3YsGHdVXnLo8ceAAAAABRIsAcAAAAABXIUFwAAAIByNVuPvTqstCf/+c9/nq985Sv5+te/nve///0vub5o0aKMHj06ixYt6vFrrL766pk4ceLKWhIAAAAANK2VEuzdeeedOe2005Y75v7778+iRYuy6qqrZuutt17mmKFDh66M5QAAAABA01vhYG/ChAk59thj88wzzyx33N13350kGTt2bC644IIVnRYAAAAAWlqfg73nn38+5513Xs4555x0dnb+0/FLgr2Ojo6+TgkAAAAAf1erNV+PvVqt10P79Fbchx56KHvvvXfOOuusJMmnP/3pbLjhhsu9R7AHAAAAACtPn4K9xx57LI8++mhGjRqVyy67LEceeeQ/vUewBwAAAAArT59qFTfYYIOcd9552XXXXXs1/vHHH8/s2bPT3t6eoUOH5pxzzskdd9yRBQsWZKONNspee+3V668FAAAAAPQx2Nt0002z6aab9nr8kmq9Wq2W/fbbL88///xS1y+//PLstttuOeOMM7Laaqv1ZUkAAAAA0FIa0l3wnnvuSZIsXrw4b33rWzNu3LhsscUWmTdvXq6//vqcccYZ+cMf/pDjjjsu559/fiOWBACvCKNHD+vvJQAAQNma7eUZdWjIk2+55ZY58MAD86pXvSrHHHNM9+cHDx6cgw8+OFtuuWU+/OEP549//GNuvPHGFTuW29aHtoF9uYfWYG/QE3uj4Rr7HV+B2ercG5Mmfarvc1EOvzPoib1BT+wNemJvAC/SkGBv1113XW5YN3bs2Oy00065+eabc8MNN6xYsNfVVd/4trb676E12Bv0xN7oF10NjPba0sefbx/2xpixZ/Ztrj6YNPHYhs3Fi/idQU/sDXpib9ATe6O51WovfEAdXjG1iltvvXVuvvnmTJs2rb+XAgANM3nyjP5eAgAAUKiGBXudnZ2pqioDejj33PW3v3Xo6ToAAAAALKVWa74ee3VUbjbkXNMee+yR1772tfnJT37S45i//OUvSZLNN9+8EUsCAAAAgKI1JNgbOXJkqqrKNddck8WLF7/k+p133pkJEyYkSfbZZ59GLAkAAAAAitaQYG/cuHGp1Wr5y1/+khNPPDHz58/vvjZhwoQcddRRqaoq+++/f173utc1YkkAAAAAULSGHEJ+wxvekC996Us59dRTc9VVV+VXv/pVhg8fnnnz5nW/LGPXXXfN17/+9UYsBwAAAIBm0Ww99urQsCf/yEc+klGjRuXiiy/Obbfdlvvuuy9Dhw7Nm970prznPe/J/vvvn5rXOgMAAABAr6y0YO93v/vdPx2z3Xbb5YwzzlhZUwIAAABAy2pIjz0AAAAAYOVq3UPIAAAAAJStVmu+Hnt1tKpTsQcAAAAABWqySBOAZtWWrobN9fT8+v/eq60tWW21ZP6zbemqY6lV5yl1z9VXtfYvNmyuRj4XAAC0KhV7AAAAAFAgwR4AAAAAFMhRXAAAAADK5OUZAAAAAEBpBHsAAAAAUCDBHgAAAAAUqMkOIQMAAADQUpqtx14dVOwBAAAAQIEEewAAAABQIMEeAAAAABSodQ8hAwAAAFC2Wq35euzVar0eqmIPAAAAAAok2AMAAACAAgn2AAAAAKBATXYIGQAAAICW0mw99uqgYg8AAAAACiTYAwAAAIACCfYAAAAAoECtewgZAAAAgLLVas3XY69W6/VQFXsAAAAAUCDBHgAAAAAUSLAHAAAAAAUS7AEAAABAgZqsuyAAAAAALaXZXp5RBxV7AAAAAFAgwR4AAAAAFKh1axUBoAdrrNbVxzvbstqQ+u5duLhxf8dWdZ7SsLlq7f/SsLmqzu81bC4AAHglEewBAAAAUKZarfl67NVqvR7qKC4AAAAAFEiwBwAAAAAFEuwBAAAAQIGa7BAyAAAAAC2l2Xrs1UHFHgAAAAAUSLAHAAAAAAUS7AEAAABAgVr3EDIAAAAAZavVmq/HXq3W66Eq9gAAAACgQII9AAAAACiQYA8AAAAACtRkh5ABAAAAaCnN1mOvDir2AAAAAKBAgj0AAAAAKJBgDwAAAAAKJNgDAAAAgAK1bndBAAAAAMpWqzXfyzNqtV4PVbEHAAAAAAUS7AEAAABAgQR7AAAAAFCgJjuEDAAAAEBLabYee3VQsQcAAAAABRLsAQAAAECBBHsAAAAAUKDWPYQMAAAAQNlqtebrsVer9Xpokz05AJRl0ICuhs01a07jCvWrzu81bK5a+1cbNlfV+ZWGzQUAAP+Mo7gAAAAAUCDBHgAAAAAUyFFcAAAAAMrVbD326qBiDwAAAAAKJNgDAAAAgAIJ9gAAAACgQK17CBkAAACAstVqzddjr1br9VAVewAAAABQIMEeAAAAABRIsAcAAAAABRLsAQAAAECBmqy7IAAAAAAtpdlenlEHFXsAAAAAUCDBHgAAAAAUSLAHAAAAAAVq3UPIAAAAAJStVmu+Hnu1Wq+HqtgDAAAAgAIJ9gAAAACgQII9AAAAAChQkx1CBgAAAKBl6LEHAAAAAJRGsAcAAAAABRLsAQAAAECBmuwQMgAAAAAtpdl67NVBxR4AAAAAFEiwBwAAAAAFat1aRQBoMeus1dWwuRYubtzfHVadX2nYXLX2E+saP3r0sEyadEzGjD07kyfPqOveqvPrdY0HAKD1qNgDAAAAgAKp2AMAAACgTLVa8708o1br9VAVewAAAABQIMEeAAAAABRIsAcAAAAABWqyQ8gAAAAAtJRm67FXBxV7AAAAAFAgwR4AAAAAFEiwBwAAAAAFat1DyAAAAACUrVZrvh57tVqvh6rYAwAAAIACCfYAAAAAoECCPQAAAAAoUJMdQgYAAACgpTRbj706qNgDAAAAgAIJ9gAAAACgQII9AAAAAChQ6x5CBgAAAKBstVrz9dir1Xo9VMUeAAAAABRIsAcAAAAABRLsAQAAAECBBHsAAAAAUKAm6y4IAAAAQEtptpdn1EHFHgAAAAAUSLAHAAAAAAVq3VpFAAAAAHgFmjFjRg455JAer48fPz6JYA8AeBkMGtDVsLmeXdC4AwhV59f7dN+kiUfVfU+t/cQ+zdUXfX0uAID+VqWWKrX+XsZKVc/TCPYAAAAA4BVk2LBh3VV5y6PHHgAAAAAUSLAHAAAAAAVyFBcAAACAYi1e3N8rWLkGDuz92D4He4899lh+9KMf5aabbsqMGTOSJBtttFF22223fOxjH8u66677kntmz56dc845J+PHj8/jjz+eNdZYI2PGjMnhhx+eUaNG9XUpAAAAANByalVVVfXeNHHixBx55JF5+umn097enk022SRdXV2ZNm1aOjs78+pXvzoXXHBBttpqq+57nnzyyRx44IF5+OGHs+qqq2bEiBF5/PHH8+STT6a9vT0nn3xy3ve+963Y01TVCx/1aGtLuhr35j4KYm/QE3uDntgb/aKRb8Udskoffr593BfeitsC/M6gJ/YGPbE3mlut9sIHdamqZNGi/l7FyjVwYO+3Qt3/Jvz000/n2GOPzdNPP503v/nN+cMf/pBf//rX+c1vfpPrr78+Y8aMyRNPPJGjjz46zz//fPd9xx13XB5++OHsvPPOufHGG3PllVfmpptuyvHHH5/Ozs6cdNJJuf/+++tdDgAAAAC0pLqDvSuvvDKzZs3Keuutl+9+97tZb731uq9tvPHG+f73v58111wz06ZNy69//eskyYQJE3LrrbdmyJAhOf3007Pmmmu+MHlbWz7xiU9kv/32y6JFi3LOOeespMcCAAAAoNlV1Qs99prpo57DqHUHexMmTEiS7L777llttdVecn2dddbJ6NGjkyT/93//lyS56qqrkiR77rln1llnnZfcc+CBByZJxo8fnwULFtS7JAAAAABoOXW/POPII4/M3nvvneHDh/c4Zknbvq6/nf2fPHlykmSHHXZY5vjtttsuAwYMyLPPPpu77rorY8eOrXdZAAAAANBS6g72tttuu2y33XY9Xp81a1ZuvfXWJMnmm2/e/VKNJNlkk02Wec/AgQOz/vrrZ/r06XnwwQcFewAAAADwT9Qd7P0z3/jGN/Lcc89l1VVXzd577525c+dm8eLFSbLMY7hLrLXWWpk+fXpmz569spcEADSxZn153OjRw/p7CQAARfhb7NSSVmqwd/bZZ+e6665Lkhx11FFZd9118+ijj3ZfHzRoUI/3Dh48OElWvMdeW91tA/t2D63B3qAn9gY9sTcabtVVGzlbH3++fdgXkyYd07e5KIvfGfTE3qAn9gbwIist2DvrrLNy5plnJkn22GOPjBs3LskLb75doracv1Jf0pevbUV/Sf2tr1+vtbXVfw+twd6gJ/YGPbE3+sVzzzfuP3BWHdyHn28f98WYsWfXP1cfTZp4VMPm4kX8zqAn9gY9sTeaW63WvEcReNmscLC3ePHinHzyybn00kuTJLvssku++93vdod4Q4cO7R67cOHCHr/OkmtLKvcAAHrjb3832HQmT57R30sAAOAVboWCvfnz5+df/uVfcvPNNydJ9tlnn3zrW99a6sjtkCFDMmjQoCxcuHC5/fOWXFt33XVXZEkAAAAA0BL6HOw99thjGTduXKZMmZIk+fjHP57Pfe5zLzlu29bWluHDh+eee+7pfjvuP1q0aFFmzpyZJNlss836uiQAAAAAWkhVNd/LM+o5kdKnpjQzZ87MIYcckilTpqS9vT0nnXRSPv/5z/fYQ2/77bdPktx+++3LvH7nnXdm8eLFGTx4cLbZZpu+LAkAAAAAWkrdwd7ChQtzxBFH5OGHH87AgQPz7//+7znwwAOXe8873vGOJMn111+fOXPmvOT6JZdckuSFo7yrrLJKvUsCAAAAgJZTd7B3/vnn589//nOS5Ctf+Ur22muvf3rPjjvumB122CHz5s3L0UcfnSeffDJJ0tXVlfPPPz/XXnttBg4c2P0mXQAAAABg+WpV1fuTuwsXLswuu+ySuXPnZsCAAdluu+2WO37XXXfNEUcckSR55JFHcvDBB+fxxx/PoEGDssUWW2TmzJl54oknUqvVctppp2X//fdfsaepqvpfjed14fTE3qAn9gY9sTf6xbML+tRZpE+GrNKHn28f90Wt/cT65+qjqvPrDZuLF/E7g57YG/TE3mhutdoLH9SlszN56qn+XsXKte66SXt778bW9fKMKVOmZO7cuUmSxYsXZ9KkScsdv+mmm3b/eeONN84vf/nLnHvuufnd736XKVOmZNVVV82b3/zmHH744XnTm95Uz1IAAAAAoKXVVbH3iqdij5XJ3qAn9gY9sTf6hYq9Fadir5/4nUFP7A16Ym80NxV7fdLqFXuN+zdhAAAAAGClqesoLgAAAAC8UlRVsnhxf69i5arnMKqKPQAAAAAokIo9AKBofep710ddffg70bY+3tfIvne19iMaNlfVeW7D5gIAaHYq9gAAAACgQCr2AAAAAChWs/XYq4eKPQAAAAAokGAPAAAAAAok2AMAAACAAumxBwAAAECRqqr5euxVVe/HqtgDAAAAgAIJ9gAAAACgQII9AAAAACiQYA8AAAAACuTlGQAAAAAUycszAAAAAIDiCPYAAAAAoECCPQAAAAAokB57AAAAABSr2Xrs1UPFHgAAAAAUSLAHAAAAAAUS7AEAAABAgfTYAwAAAKBIVdV8PfaqqvdjVewBAAAAQIEEewAAAABQIMEeAAAAABRIjz0AAAAAitVsPfbqoWIPAAAAAAok2AMAAACAAgn2AAAAAKBAeuwBAPRSW7r6dFff7mucqvPchs1Vaz+xYXNVnV9v2FwAAP1BsAcAAABAkaqq+V6eUVW9H+soLgAAAAAUSLAHAAAAAAUS7AEAAABAgfTYAwAAAKBYzdZjrx4q9gAAAACgQII9AAAAACiQYA8AAAAACqTHHgAAAABFqqrm67FXVb0fq2IPAAAAAAok2AMAAACAAgn2AAAAAKBAeuwBAAAAUKxm67FXDxV7AAAAAFAgwR4AAAAAFEiwBwAAAAAF0mMPAAAAgCJVVfP12Kuq3o9VsQcAAAAABRLsAQAAAECBBHsAAAAAUCDBHgAAAAAUyMszAAAAAChWs708ox4q9gAAAACgQII9AAAAACiQYA8AAAAACqTHHgAAAABFqqrm67FXVb0fK9gDAGhxCxc37hBH1fn1hs1Va/94w+aqOn/YsLkAAJZwFBcAAAAACiTYAwAAAIACOYoLAAAAQLGarcdePVTsAQAAAECBBHsAAAAAUCDBHgAAAAAUSI89AAAAAIpUVc3XY6+qej9WxR4AAAAAFEiwBwAAAAAFEuwBAAAAQIH02AMAAACgWM3WY68eKvYAAAAAoECCPQAAAAAokGAPAAAAAAok2AMAAACAAnl5BgAAAABFqqrme3lGVfV+rIo9AAAAACiQYA8AAAAACiTYAwAAAIAC6bEHAAAAQLGarcdePVTsAQAAAECBBHsAAAAAUCDBHgAAAAAUSI89AAAAAIpUVc3XY6+qej9WxR4AAAAAFEiwBwAAAAAFchQXAKDFDRrQ1bC5uhr498pV5w8bNlet/fN1jR89esNMmnRcxoz990yePL2ue6vO0+oaDwA0L8EeAAAAAEXSYw8AAAAAKI5gDwAAAAAKJNgDAAAAgAIJ9gAAAACgQF6eAQAAAECxmu3lGfVQsQcAAAAABRLsAQAAAECBBHsAAAAAUCA99gAAAAAoUlU1X4+9qur9WBV7AAAAAFAgwR4AAAAAFEiwBwAAAAAF0mMPAAAAgGI1W4+9eqjYAwAAAIACCfYAAAAAoECCPQAAAAAokB57AAAAABSpqpqvx15V9X6sij0AAAAAKJBgDwAAAAAKJNgDAAAAgALpsQcAAABAsZqtx149VOwBAAAAQIEEewAAAABQIMEeAAAAABRIjz0AAAAAeAWZMWNGDjnkkB6vjx8/PolgDwCABmpLV38v4WVRdZ7Wp/smTfxU3ffU2r/ep7n6ouo8sWFzAUBfVFXzvTyjqno/VrAHAAAAAK8gw4YN667KWx499gAAAACgQII9AAAAACiQo7gAAAAAFKvZeuzVo8/B3mOPPZYf/ehHuemmmzJjxowkyUYbbZTddtstH/vYx7LuuusuNX7RokUZPXp0Fi1a1OPXXH311TNx4sS+LgkAAAAAWkafgr2JEyfmyCOPzNNPP5329vZssskm6erqyoMPPpj77rsvV199dS644IJstdVW3ffcf//9WbRoUVZdddVsvfXWy/y6Q4cO7dtTAAAAAECLqTvYe/rpp3Psscfm6aefzpvf/OZ885vfzHrrrZckeeSRR/L5z38+kyZNytFHH53/+q//yuDBg5Mkd999d5Jk7NixueCCC1biIwAAAABA66k72Lvyyisza9asrLfeevnud7+b1VZbrfvaxhtvnO9///t5+9vfnmnTpuXXv/513vWudyX5e7DX0dGxkpYOAAAAQCurqubrsVdVvR9b91txJ0yYkCTZfffdlwr1llhnnXUyevToJMn//d//dX9esAcAAAAAK0/dFXtHHnlk9t577wwfPrzHMdXfosWurq7uzwn2AAAAAGDlqTvY22677bLddtv1eH3WrFm59dZbkySbb755kuTxxx/P7Nmz097enqFDh+acc87JHXfckQULFmSjjTbKXnvtlV133bWPjwAAAAAAradPb8Vdnm984xt57rnnsuqqq2bvvfdO8vdqvVqtlv322y/PP//8Uvdcfvnl2W233XLGGWcs83gvAADwgtGjN+jvJQDAK0qz9dirx0oN9s4+++xcd911SZKjjjoq6667bpLknnvuSZIsXrw4b33rWzNu3LhsscUWmTdvXq6//vqcccYZ+cMf/pDjjjsu559//ootoq3utoF9u4fWYG/QE3uDntgbLIt9QU/6sDcmTfrEy7AQXnH83qAn9gbwIist2DvrrLNy5plnJkn22GOPjBs3rvvalltumQMPPDCvetWrcswxx3R/fvDgwTn44IOz5ZZb5sMf/nD++Mc/5sYbb1yxY7kv6uvXK21t9d9Da7A36Im9QU/sDZbFvqAnfdwbY8Ze8DIsZtkmTTy8YXPxIn5v0BN7o7nVai98QB1WONhbvHhxTj755Fx66aVJkl122SXf/e53U3vRZtx1112XG9aNHTs2O+20U26++ebccMMN+u0BAEAPJk9+rL+XAAC8QqxQsDd//vz8y7/8S26++eYkyT777JNvfetbGTRoUN1fa+utt87NN9+cadOmrciSAAAAAGgRVdV8Pfaqqvdj+xzsPfbYYxk3blymTJmSJPn4xz+ez33uc0tV6r1YZ2dnqqrKgAHLnrLrb+XEPV0HAAAAAP6uT103Z86cmUMOOSRTpkxJe3t7TjrppHz+85/vMdTbY4898trXvjY/+clPevyaf/nLX5Ikm2++eV+WBAAAAAAtpe5gb+HChTniiCPy8MMPZ+DAgfn3f//3HHjggcu9Z+TIkamqKtdcc00WL6M+8s4778yECROSvHCcFwAAAABYvrqDvfPPPz9//vOfkyRf+cpXstdee/3Te8aNG5darZa//OUvOfHEEzN//vzuaxMmTMhRRx2Vqqqy//7753Wve129SwIAAACAllOrqt635Fu4cGF22WWXzJ07NwMGDMh222233PG77rprjjjiiCTJj3/845x66qnp7OzMKquskuHDh2fevHndL8vYddddc+aZZ2bw4MF9f5qqqq/DYOJ14fTM3qAn9gY9sTdYFvuCnvRxb9Tav/4yLGbZqs4TGzYXL+L3Bj2xN5pbrfbCB3V58snky1/u71WsXCefnLzqVb0bW9ebKqZMmZK5c+cmSRYvXpxJkyYtd/ymm27a/eePfOQjGTVqVC6++OLcdtttue+++zJ06NC86U1vynve857sv//+PfboAwAAAACWVlewt+222+aee+7p82TbbbddzjjjjD7fDwAAAAC8oE9vxQUAAAAA+lddFXsAAAAA8EpRVcnixf29ipWrntdHqNgDAAAAgAKp2AMAgBW0cHF9f19eqyUD25JFnW11/a180tg31dba/6Vhc1Wd32vYXADQLFTsAQAAAECBVOwBAAAAUKxm67FXDxV7AAAAAFAgwR4AAAAAFEiwBwAAAAAF0mMPAAAAgCJVVfP12Kuq3o9VsQcAAAAABRLsAQAAAECBBHsAAAAAUCA99gAAAAAoVrP12KuHij0AAAAAKJBgDwAAAAAKJNgDAAAAgAIJ9gAAAACgQF6eAQAAAECRqqr5Xp5RVb0fq2IPAAAAAAok2AMAAACAAgn2AAAAAKBAeuwBAAAAUCQ99gAAAACA4gj2AAAAAKBAgj0AAAAAKJAeewAAAAAUq9l67NVDxR4AAAAAFEiwBwAAAAAFEuwBAAAAQIH02AMAgBU0aEBXH+5qy8D2+u/rauDfzVed32vYXLX2jzdknqrzhw2ZB4DGqKrm67FXVb0fq2IPAAAAAAok2AMAAACAAgn2AAAAAKBAgj0AAAAAKJCXZwAAAABQrGZ7eUY9VOwBAAAAQIEEewAAAABQIMEeAAAAABRIjz0AAAAAilRVzddjr6p6P1bFHgAAAAAUSLAHAAAAAAUS7AEAAABAgfTYAwAAAKBYzdZjrx4q9gAAAACgQII9AAAAACiQYA8AAAAACqTHHgAAAABFqqrm67FXVb0fq2IPAAAAAAok2AMAAACAAgn2AAAAAKBAeuwBAAAAUKxm67FXDxV7AAAAAFAgwR4AAAAAFEiwBwAAAAAFEuwBAAAAQIG8PAMAAACAIlVV8708o6p6P1awBwAABWlLV8Pm6mrgAZ+q84cNmafWfkZD5kmSqvP4hs0FQGtyFBcAAAAACiTYAwAAAIACOYoLAAAAQLGarcdePVTsAQAAAECBBHsAAAAAUCDBHgAAAAAUSI89AAAAAIpUVc3XY6+qej9WxR4AAAAAFEiwBwAAAAAFEuwBAAAAQIH02AMAAACgWM3WY68eKvYAAAAAoECCPQAAAAAokGAPAAAAAAqkxx4AAAAARaqq5uuxV1W9H6tiDwAAAAAKJNgDAAAAgAIJ9gAAAACgQII9AAAAACiQl2cAAAAAUKxme3lGPVTsAQAAAECBBHsAAAAAUCDBHgAAAAAUSI89AAAAAIpUVc3XY6+qej9WxR4AAAAAFEjFHgAAsExt6WrYXM8uaEzNQdV5fEPmSZJa+9F13zN69MaZNOlLGTP21Eye/Eiv76s6v1/3XACUT8UeAAAAABRIxR4AAAAAxWq2Hnv1ULEHAAAAAAUS7AEAAABAgQR7AAAAAFAgPfYAAAAAKFJVNV+Pvarq/VgVewAAAABQIMEeAAAAABRIsAcAAAAABdJjDwAAAIAi6bEHAAAAABRHsAcAAAAABRLsAQAAAECBBHsAAAAAUCAvzwAAAACgWM328ox6qNgDAAAAgAIJ9gAAAACgQII9AAAAACiQHnsAAAAAFKmqmq/HXlX1fqyKPQAAAAAokGAPAAAAAAok2AMAAACAAumxBwAAAECxmq3HXj1U7AEAAABAgQR7AAAAAFAgR3EBAAAA4BVkxowZOeSQQ3q8Pn78+CSCPQAA4BVgyCpdDZmnq4GHlqrO7/f53kkTT6hrfK39iD7PVa+q89yGzQXwz1Wpqqq/F7GS1Xo9UrAHAAAAAK8gw4YN667KWx499gAAAACgQII9AAAAACiQYA8AAAAACtSnHntTp07Neeedl1tuuSVPPvlk1lxzzWy//fY5+OCDs/POOy/zntmzZ+ecc87J+PHj8/jjj2eNNdbImDFjcvjhh2fUqFEr8gwAAAAAtKzO/l7AStb7uK7uir2bbrop73rXu/KLX/wic+bMyciRI9PW1pbx48fnYx/7WL71rW+95J4nn3wyH/jAB3LxxRfnqaeeSkdHR2q1Wn7729/moIMOyhVXXFHvMgAAAACgpdUV7M2aNSvHH398FixYkH333Tc33XRTrr766tx00005/fTT097enh/96Ee5/vrrl7rvuOOOy8MPP5ydd945N954Y6688srcdNNNOf7449PZ2ZmTTjop999//0p9MAAAAABoZnUFe1dccUXmzp2bDTfcMKeeempWX3317mv77bdf3v/+9ydJfv7zn3d/fsKECbn11lszZMiQnH766VlzzTVfmLitLZ/4xCey3377ZdGiRTnnnHNWxvMAAAAAQEuoq8fehhtumHe+853ZeuutM2jQoJdc33LLLZMkM2bM6P7cVVddlSTZc889s84667zkngMPPDDXXnttxo8fnwULFmSVVVap6wEAAAAAaFVVmq/HXnuSWq9G1hXs7bvvvtl33317vH7XXXclSTbddNPuz02ePDlJssMOOyzznu222y4DBgzIs88+m7vuuitjx46tZ0kAAAAA0JLqfnnGsjz99NM566yzcuWVV2bAgAEZN25ckqSrqyvTpk1LkmyyySbLvHfgwIFZf/31kyQPPvjgylgOAAAAADS9uir2/tH111+fM888Mw899FAWLlyY17zmNTnppJPy+te/Pkkyd+7cLF68OEmWeQx3ibXWWivTp0/P7NmzV2Q5AAAAANAyVijYu/POO3Pvvfd2/++5c+fmd7/7XcaOHZvVVlstCxYs6L62rJ58SwwePDhJlhoPAABA74wevewTUgCtodl67PXeCgV7hxxySI4++ujMnz8/N998c0477bRceuml+fOf/5xLL700bW1/P+lbq/Xc9K+qqiRZanyf9eVrrIx5aU72Bj2xN+iJvcGy2Bf0xN5ouMZ+x1dgtjr3xqRJJ/Z9Lsri9wbwIisU7G2wwQZJkiFDhuTd7353tt9++xxwwAG56667cs011+Rtb3tb99iFCxf2+HWWXFtSubdCurrqG9/WVv89tAZ7g57YG/TE3mBZ7At6Ym/0i64GRntt6ePPtw97Y8zYb/Ztrj6YNPFLDZuLf+D3RnOr1V74gDqsULD3j0aMGJG99tor1113XW699dYccMABGTRoUBYuXLjc/nlLrq277rorczkAAAAtYfLkh/t7CQD0g7r+umrOnDm56667MmvWrB7HbLjhhkmSJ554Im1tbRk+fHiSdL8d9x8tWrQoM2fOTJJsttlm9SwHAAAAgJbX1WQfvVdXsPe+970v733ve3PllVf2OGb69OlJkvXXXz9Jsv322ydJbr/99mWOv/POO7N48eIMHjw422yzTT3LAQAAAICWVVewt/POOydJLr/88ixatOgl16dNm5YbbrghSbLHHnskSd7xjnckSa6//vrMmTPnJfdccsklSZJ99tknq6yySj3LAQAAAICWVVewd/jhh2eVVVbJ1KlTc/zxxy91JPcvf/lLPv7xj2fBggV5/etfnz333DNJsuOOO2aHHXbIvHnzcvTRR+fJJ59MknR1deX888/Ptddem4EDB2bcuHEr8bEAAAAAoLnVqqqq6rnh97//fY477rg899xzGTRoUIYPH57nn38+U6dOTZKMGjUq55xzTtZZZ53uex555JEcfPDBefzxxzNo0KBsscUWmTlzZp544onUarWcdtpp2X///Vf8aarqhY96eKsQPbE36Im9QU/sDZbFvqAn9ka/aNa34tbaj+jbXH1QdZ7bsLn4B35vNDdvxe2TBx7ozMiR8/p7GSvV/fevnhEj2ns1tu5gL0keeuih/PCHP8yf/vSnzJw5M6ussko6Ojqy//77573vfW8GDhz4kntmzZqVc889N7/73e/y2GOPZdVVV83222+fww8/PG9605vqXcKyCfZYmewNemJv0BN7g2WxL+iJvdEvBHsrTrDXj/zeaG6CvT4R7PUh2HvFEuyxMtkb9MTeoCf2BstiX9ATe6NfCPZWnGCvH/m90dwEe33S6sFe4/5fDQAAAABYaQR7AAAAAFCgAf29AAAAAADou87+XkC/EewBAAAto8997/qgr/382vpwbyP73tXaj2/YXFXnGQ2bC6BEjuICAAAAQIEEewAAAABQIEdxAQAAAChY6/bYU7EHAAAAAAUS7AEAAABAgQR7AAAAAFAgPfYAAAAAKFSV5uuxV/V6pIo9AAAAACiQYA8AAAAACiTYAwAAAIAC6bEHAAAAQMG6+nsB/UbFHgAAAAAUSLAHAAAAAAUS7AEAAABAgfTYAwAAAKBgnf29gH6jYg8AAAAACiTYAwAAAIACCfYAAAAAoECCPQAAAAAokJdnAAAAAFCoKs338oyq1yNV7AEAAABAgQR7AAAAAFAgwR4AAAAAFEiPPQAAAAAK1mw99npPxR4AAAAAFEiwBwAAAAAFchQXAADgZdCWrj7fWe+9zy5oXM1G1XlGw+aqtR/RsLmqznMbNhfAyiLYAwAAAKBQVZqvx17V65GO4gIAAABAgQR7AAAAAFAgwR4AAAAAFEiPPQAAAAAK1teXFZVPxR4AAAAAFEiwBwAAAAAFEuwBAAAAQIH02AMAAACgYJ39vYB+o2IPAAAAAAok2AMAAACAAgn2AAAAAKBAgj0AAAAAKJCXZwAAAABQqCrN9/KMqtcjVewBAAAAQIEEewAAAABQIMEeAAAAABRIjz0AAAAACtZsPfZ6T8UeAAAAABRIsAcAAAAABRLsAQAAAECB9NgDAAAAoFBVmq/HXtXrkSr2AAAAAKBAgj0AAAAAKJBgDwAAAAAKpMceAAAAAAXr6u8F9BvBHgAAQOGGrNK4/6jtauDBr6rz3IbNVWs/vmFzVZ1nNGwuoLk5igsAAAAABRLsAQAAAECBBHsAAAAAUCA99gAAAAAoWGd/L6DfqNgDAAAAgAIJ9gAAAACgQII9AAAAACiQHnsAAAAAFKpK8/XYq3o9UsUeAAAAABRIsAcAAAAABRLsAQAAAECB9NgDAAAAoGDN1mOv91TsAQAAAECBBHsAAAAAUCDBHgAAAAAUSI89AAAAAAqmxx4AAAAAUBDBHgAAAAAUSLAHAAAAAAXSYw8AAACAQlVJuvp7EStZ1euRKvYAAAAAoECCPQAAAAAokGAPAAAAAAok2AMAAACAAnl5BgAAAAAF6+zvBfQbFXsAAAAAUCAVewAAAPRaW7oaNldXA2tRqs4zGjZXrf24uu8ZPXqjTJr0uYwZe0YmT57W6/uqzu/UPRdQDhV7AAAAAFAgFXsAAAAAFEyPPQAAAACgIII9AAAAACiQYA8AAAAACqTHHgAAAACFqtJ8PfaqXo9UsQcAAAAABRLsAQAAAECBBHsAAAAAUCA99gAAAAAoWFd/L6DfqNgDAAAAgAIJ9gAAAACgQII9AAAAACiQHnsAAAAAFKyzvxfQb1TsAQAAAECBBHsAAAAAUCDBHgAAAAAUSLAHAAAAAAXy8gwAAAAAClWl+V6eUfV6pIo9AAAAACiQYA8AAAAACiTYAwAAAIAC6bEHAAAAQMGarcde76nYAwAAAIACCfYAAAAAoECO4gIAAADAK8iMGTNyyCGH9Hh9/PjxSQR7AAAAvEK1pathc3U18EBb1fmdPt87aeLxdY2vtR/d57nqVXV+v2Fzwd9Vab4ee1WvRwr2AAAAAOAVZNiwYd1Vecujxx4AAAAAFEiwBwAAAAAFchQXAAAAgII1rh/nK02fgr2pU6fmvPPOyy233JInn3wya665ZrbffvscfPDB2XnnnV8yftGiRRk9enQWLVrU49dcffXVM3HixL4sBwAAAABaTt3B3k033ZRjjjkmCxYsyKqrrpqRI0dm1qxZGT9+fMaPH5+Pfexj+cIXvrDUPffff38WLVqUVVddNVtvvfUyv+7QoUP79gQAAAAA0ILqCvZmzZqV448/PgsWLMi+++6br371q1l99dWTJNdee22+8IUv5Ec/+lFGjRqVvffeu/u+u+++O0kyduzYXHDBBStx+QAAAADQmup6ecYVV1yRuXPnZsMNN8ypp57aHeolyX777Zf3v//9SZKf//znS923JNjr6OhY0fUCAAAAwIt0NtlH79VVsbfhhhvmne98Z7beeusMGjToJde33HLLJMmMGTOW+rxgDwAAAABWrrqCvX333Tf77rtvj9fvuuuuJMmmm2661OcFewAAAACwcvXprbj/6Omnn86Pf/zjXHnllRkwYEDGjRvXfe3xxx/P7Nmz097enqFDh+acc87JHXfckQULFmSjjTbKXnvtlV133XVlLAPg/7d390Fa1vX+wN8LC4KUdrREw0yeVk0UNGk62QyGmZ4Sjoo1EdqkoJMP/SbG6eEPOzk9TAwnz3QOGeVDnWx6UDo6cjpTnER7cJtApmwjelICZA3QVhZIedjd6/eHP+6fCLfuLnjfXvf9es04I3t9r70+u3z47L2f+3t9LgAAAGgaB9XYW7ZsWRYtWpT169dn9+7dOe6443LTTTdl6tSplTV7d+u1tLRkxowZ2bVr1z6fY8mSJTnnnHNy880351WvetXBhAMAAAAATeOgGnsdHR3585//XPlzd3d3HnjggZx11lmVJt0f//jHJElPT0/e+c535qqrrsrEiROzffv2LFu2LDfffHN+8pOfZP78+bntttsOJhwAAADgec444w31DgFeZkUG+sCJV76i3ytbiqLo/+oX2LRpU4444ojs2LEj7e3tWbhwYbq6ujJp0qTcddddaW1tzU9/+tM8+OCDee1rX5vrr79+v8+xatWqXHbZZSmKIrfeeuvB3ZZbFElLy+DPBwAAAKA01q7dlvHj7653GIfUY4+9L+PGHdGvtQfV2HuhtWvX5qKLLsquXbvyhS98IZdcckm/zrvyyivT3t6e973vffnsZz87+ACK4rn/BmLIkKSvb/DXpHHJDaqRG1QjNzgQeUE1coNq5EZd9GVIza41JIP8+x1Ebpx51oLBXWsQfrXqkzW7VkNqabFZaRCavbF3SB6esde4ceNy3nnn5Qc/+EFWrlzZ78beKaeckvb29mzcuPFQhgMAAABN7de/frzeIQAvowE19rZu3ZqNGzfm9a9/fY466qgDrhkzZkyS5Mknn6x8rLe3N0VRpLX1wJfr+3/vOFQ7DgAAAAAH1mgz9vpvQHuNL7300syaNSv33HNP1TWdnZ1JktGjRydJpk+fnlNPPTXf+ta3qp6zZs2aJMmECRMGEg4AAAAANK0BNfbOPvvsJMmSJUuyZ8+e/Y5v3Lgx999/f5LnGnpJMn78+BRFkaVLl6anp2e/czo6OrJixYokybvf/e6BRQ8AAAAATWpAjb158+ZlxIgRWbduXW644YZ0dXVVjq1ZsyZz587Nzp07M3Xq1Jx77rlJkquuuiotLS1Zs2ZNbrzxxuzYsaNyzooVK3LttdemKIrMnDkzp5122iH6sgAAAACgsQ34qbgPPvhg5s+fn2effTbDhw/P2LFjs2vXrqxbty5JMmXKlCxevHifGXx33nlnFixYkN7e3owYMSJjx47N9u3bKw/LmDZtWhYtWpTDDjvs4L4aT8XlUJIbVCM3qEZucCDygmrkBtXIjbpo1Kfitgy9bnDXGoSi95aaXasheSruoKxd253x479d7zAOqccem5Nx447s19oBN/aSZP369bnjjjvy0EMPZcuWLRkxYkTa2toyc+bMzJo1K8OGDdvvnI6Ojnzzm9/Mww8/nK6urowaNSonn3xyLrnkksycOTMthyJ5NfY4lOQG1cgNqpEbHIi8oBq5QTVyoy409g6ext5B0tgbFI29QTT2XrE09jiU5AbVyA2qkRsciLygGrlBNXKjLjT2Dp7G3kHS2BuUZm/s1a5yAQAAAACHTGu9AwAAAACAwWveXc527AEAAABACdmxBwAAQNMb9Ny7QRjsPL8hgzi3lnPvWoZ+vGbXKnoX1uxa8Epmxx4AAAAAlJDGHgAAAACUkFtxAQAAACix3noHUDd27AEAAABACWnsAQAAAEAJaewBAAAAQAmZsQcAAABASRVpvBl7Rb9X2rEHAAAAACWksQcAAAAAJaSxBwAAAAAlZMYeAAAAACXWaDP2+s+OPQAAAAAoIY09AAAAACghjT0AAAAAKCEz9gAAAAAoMTP2AAAAAIAS0dgDAAAAgBLS2AMAAACAEjJjDwAAAICSKtJ4M/aKfq+0Yw8AAAAASkhjDwAAAABKSGMPAAAAAEpIYw8AAAAASsjDMwAAAAAosb56B1A3duwBAAAAQAlp7AEAAABACbkVFwAAAGpoyKBvGxwy4HP7arifp+hdWLNrtQz9PzW7VtH7HzW7FgyUxh4AAAAAJdZb7wDqxq24AAAAAFBCGnsAAAAAUEIaewAAAABQQmbsAQAAAFBSRRpvxl7R75V27AEAAABACWnsAQAAAEAJaewBAAAAQAmZsQcAAABAiTXajL3+s2MPAAAAAEpIYw8AAAAASkhjDwAAAABKyIw9AAAAAEqqSNJX7yAOsaLfK+3YAwAAAIAS0tgDAAAAgBLS2AMAAACAEtLYAwAAAIAS8vAMAAAAAEqst94B1I0dewAAAABQQhp7AAAAAFBCGnsAAAAAUEJm7AEAAABQYmbsAQAAAAAlorEHAAAAACWksQcAAAAAJWTGHgAAAAAlVaTxZuwV/V6psQcAAAANakj6anatvhreFFj0/kfNrtUy9IaaXOexx27MuHFH1+RaNA634gIAAABACWnsAQAAAEAJuRUXAAAAgBJrtBl7/WfHHgAAAACUkMYeAAAAAJSQxh4AAAAAlJAZewAAAACUWF+9A6gbO/YAAAAAoIQ09gAAAACghDT2AAAAAKCENPYAAAAAoIQ8PAMAAACAkiqS9NY7iEOs6PdKO/YAAAAAoIQ09gAAAACghDT2AAAAAKCEzNgDAAAAoMQabcZe/9mxBwAAAAAlpLEHAAAAACWksQcAAAAAJWTGHgAAAAAlVaTxZuwV/V5pxx4AAAAAlJDGHgAAAACUkMYeAAAAAJSQGXsAAAAAlFijzdjrPzv2AAAAAKCENPYAAAAAoIQa6lbcPT0tefzxln6vHz48Of74ZOMTQ7J798CuNe7EvgFGBwAAAI1rSGr3e3JfDfcpFb031+ZCLf3vZ8BeduwBAAAAQAk11I49AAAAAJqNh2cAAAAAACWisQcAAAAAJaSxBwAAAAAlZMYeAAAAACVVJDV8InNtFP1eacceAAAAAJSQxh4AAAAAlJDGHgAAAACUkBl7AAAAAJRYb70DqBs79gAAAACghDT2AAAAAKCENPYAAAAAoITM2AMAAACgxMzYAwAAAABKRGMPAAAAAEpIYw8AAAAASsiMPQAAAABKqkjjzdgr+r3Sjj0AAAAAKCGNPQAAAAAoIY09AAAAACghjT0AAAAAKCEPzwAAAACgxPrqHUDd2LEHAAAAACWksQcAAAAAJdRSFEVR7yAOlaJIenr6v76lJWltfe6cgX4XhrU2zLeNalpaBp4YNAe5QTVygwORF1QjN6hGblCN3Kgo0lKza7Wkht/zltp9XY2ip6c3GzY8We8wDqkTTnhdtmzZnMsvv7zqmuXLlydpsBl7LS3JsGEDP691UN8F/9iagqJKNXKDauQGByIvqEZuUI3coBq5kaTWv5H7nr+StbYOzbhxx9Y7jEPuySf716xsqB17AAAAANAszNgDAAAAgBLS2AMAAACAEtLYAwAAAIAS0tgDAAAAgBLS2AMAAACAEtLYAwAAAIAS0tgDAAAAgBLS2AMAAACAEtLYAwAAAIAS0tgDAAAAgBLS2AMAAACAEtLYAwAAAIASaq13ALX27LPP5vbbb8///M//ZOPGjRk1alQmTZqUD37wg5k2bVq9w6OOpk+fns7Ozhdd8/DDD+eII46oUUTU2/e+9718+tOfzuc+97m8973vPeCap59+OosXL87y5cuzefPmHHHEETnzzDMzb968TJkypbYBUzMvlRt79uzJGWeckT179lT9HK9+9auzatWqlzNMXmabNm3K17/+9fz85z/PE088kSQ5/vjjc8455+TKK6/M0Ucfvd85akZzGGhuqBnNYd26dbn11lvzi1/8Ik899VSOPPLITJ48OXPmzMnZZ599wHPUjOYw0NxQM4Dna6rG3jPPPJMPfehD+c1vfpNhw4Zl4sSJ2bp1ax566KE89NBD+chHPpLrr7++3mFSB9u3b09nZ2eGDh2ayZMnV103dOjQGkZFPXV0dGThwoUvuuapp57K7Nmzs2HDhowcOTJtbW3ZvHlzfvzjH+eBBx7IZz7zmVx66aU1ipha6U9uPPbYY9mzZ09GjhyZU0455YBrRo0a9XKER42sWrUq11xzTbZt25ahQ4fmhBNOSF9fX/7yl7/k0UcfzX333Zfbb789J598cuUcNaM5DCY31IzG9/Of/zzXX399du7cmZEjR2b8+PHp6urK8uXLs3z58lx55ZX5xCc+sc85akZzGExuqBnAPoom8olPfKJoa2sr/vmf/7l44oknKh+/9957ize96U1FW1tb0d7eXscIqZeVK1cWbW1txQUXXFDvUHgF+OUvf1lMnTq1aGtrK9ra2oq77777gOsuu+yyoq2trbjiiiuKrVu3FkVRFL29vcXXvva1oq2trTj11FOLRx99tJah8zLrb27ce++9RVtbWzF37twaR0gtdHd3F29961srf8ebN2+uHNuwYUPx/ve/v2hrayumT59e7Ny5s3JMzWh8g80NNaOx/e1vf6v87Jg/f36xbdu2yrGlS5cWp5xyStHW1lb86Ec/2uc8NaPxDTY31Azg+Zpmxt6GDRuydOnSDBkyJF/84hdz3HHHVY5ddNFFmTdvXpJk0aJF9QqROvrDH/6QJGlra6tzJNTTrl27smjRolxxxRXp7u5+0bUrVqzIypUrc/jhh+eLX/xijjzyyCTJkCFDcvXVV2fGjBnZs2dPFi9eXIvQeZkNJDcSNaXR3XPPPenq6soxxxyTL33pSznmmGMqx97whjfklltuyZFHHpmNGzfmRz/6URI1o1kMJjcSNaPRff/73093d3fGjBmTBQsW5NWvfnXl2IwZMyojHb73ve9VPq5mNIfB5EaiZgD7aprG3n333Zfe3t5MmTIlEyZM2O/4Bz7wgSTJr371q8osFJrH3h+OEydOrHMk1Mv69etz/vnn58tf/nKS5KMf/WjGjBlTdf29996bJDn33HNz1FFH7Xd89uzZSZLly5dn586dL0PE1MpAcyPxgrvRrVixIknyjne8I6961av2O37UUUfljDPOSJL89re/TaJmNIvB5EaiZjS6MWPG5MILL8wHPvCBDB8+fL/jJ510UpLs8zuImtEcBpMbiZoB7KtpZuw98sgjSZI3v/nNBzw+evTojBkzJp2dnVm5cmUuuuii2gVH3e394bj3hyfNZ9OmTfnrX/+aKVOm5FOf+lQmTZqUJUuWVF3/61//Okn1mnL66aentbU1zzzzTFavXp2zzjrrZYmbl99AcyPxgrvRXXPNNTn//PMzduzYqmuKokiS9PX1JVEzmsVgciNRMxrde97znrznPe+penz16tVJkje+8Y2Vj6kZzWEwuZGoGcC+mqaxt379+iTJCSecUHXN3sbeunXrahQVrwS9vb159NFHkyTHHHNM/vM//zOrVq3K9u3bM3r06Jxzzjm54IILMmRI02xwbUrHHntsbr311n49Hbuvry8bN25MUr2mDBs2LKNHj05nZ2f+8pe/eMFdYgPJjSTZvHlznn766QwdOjSjRo3K4sWL85vf/CY7d+7M8ccfn/POO89T2Evu9NNPz+mnn171eFdXV1auXJkkmTBhgprRRAaaG4ma0cy2bduWO++8M/fcc09aW1tz1VVXJfE6g+q5kagZwP6aprH3t7/9LUkOuJV9r9e85jVJnnusPM1j3bp1lVsYrrjiivz973/f5/h9992Xb3zjG7nlllv2mZVDY3njG9+437uh1XR3d6enpyfJS9eUzs5ONaXkBpIbyf9/F72lpSUzZszIrl279jm+ZMmSnHPOObn55psPeKse5ff5z38+zz77bEaOHJnzzz9fzaDihbmRqBnNaNmyZVm0aFHWr1+f3bt357jjjstNN92UqVOnJvE6o5m9VG4kagawv6bZgrS3cXOg2QV7HXbYYfuspTns/eGYJKeddlq+/e1v55FHHsmKFSvyr//6r3nd616Xjo6OXH311dm9e3cdI+WV4vk1Qk3hhf74xz8mSXp6evKOd7wj//Vf/5WOjo60t7fnX/7lXzJq1Kj85Cc/yfz58+scKS+Hr3zlK/nBD36QJLn22mtz9NFHqxkkOXBuJGpGM+ro6Mif//znyuvK7u7uPPDAA9mxY0cSrzOa2UvlRqJmAPtrmh17Q4cOTV9fX1paWqqu2TvzxC2XzeW4447L5ZdfniFDhuSTn/xk5e9/5MiRmTlzZk477bRcfPHF+f3vf5/vf//7lQet0LyeXyPUFF7opJNOyuzZs/Pa1742119/feXjhx12WObMmZOTTjopl112WX72s5/lpz/9qdtlGsiXv/zlLFq0KEkyffr0yq1TagbVciNRM5rR5Zdfnuuuuy47duxIe3t7Fi5cmLvuuiu/+93vctddd6kZTeylcqO1tVXNAPbTNI29ww8/PN3d3fttVX6+ve+M7H33i+Zw5pln5swzz6x6fOzYsZkxY0buvvvu3H///Rp7ZNSoUZX/f7FdnGpKc5o2bdqLvog+66yz8ra3vS3t7e25//77veBuAD09PfnMZz6Tu+66K0ny9re/PV/60pcqv5CrGc3rpXIjUTOa0bHHHpvkud9PLr744kyePDkXXXRRVq9enaVLl+Zd73pXZa2a0VxeKjcuueQSNQPYT9O8vfMP//APSZKtW7dWXbN3PsXeWyNgr1NOOSVJKoOMaW6HH3545daYF5tro6ZQjZrSOHbs2JGrr7660rh597vfncWLF+/zi7aa0Zz6kxv9pWY0tnHjxuW8885LkqxcuVLNoOKFudFfagY0l6Zp7I0bNy7Jixe3zs7OJMmJJ55Yi5B4Benr63vRd0T33urQ2to0m1x5EUOGDMnYsWOTVK8pe/bsyZYtW5KoKc2ot7e3Mvj8QPr6+pKoKWW3adOmzJ49O+3t7UmSuXPn5t/+7d/2m4mlZjSf/ubGXmp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              " - ] - }, - "metadata": { - "filenames": { - "image/png": "/Users/mhjensen/Teaching/MachineLearning/doc/LectureNotes/_build/jupyter_execute/chapter3_183_0.png" - } - }, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "fig = plt.figure(figsize=(20, 14))\n", "\n", @@ -4587,32 +3707,7 @@ "collapsed": false, "editable": true }, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_7650/3980313467.py:9: MatplotlibDeprecationWarning: Calling gca() with keyword arguments was deprecated in Matplotlib 3.4. Starting two minor releases later, gca() will take no keyword arguments. The gca() function should only be used to get the current axes, or if no axes exist, create new axes with default keyword arguments. To create a new axes with non-default arguments, use plt.axes() or plt.subplot().\n", - " ax = fig.gca(projection='3d')\n", - "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_7650/3980313467.py:37: MatplotlibDeprecationWarning: Auto-removal of grids by pcolor() and pcolormesh() is deprecated since 3.5 and will be removed two minor releases later; please call grid(False) first.\n", - " fig.colorbar(surf, shrink=0.5, aspect=5)\n" - ] - }, - { - "data": { - "image/png": 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aF76Py+VyPy6s3xevy8LkLfOFIfpy0tLSCtyOi7quLpyPlL/2wuZz8bxsNpskeawPb9dvYbVFR0frtttu07JlyzRixAhlZGTo+++/18SJEwscX5ALz7XP2z4urDUtLU0ul8v9yVCes2fPKjo6WmlpaapVq1aJLZM3CtuupKLtS2rUqKHFixdr1qxZevfddxUXF6fGjRvrueee05/+9CdJJbv8NptNiYmJmjVrlpYvX64PPvhA1atX1yOPPKKBAwfKZrO5t7e///3vCgkJcb9fTk6OoqOj832KAxQVgRd/CGfPntUvv/yizz77LN9zQ4YM0cqVKzVq1ChFRUUpJCREp0+fzjcuPT3dI0DkHbkaPXq0OnXq5FU9eUfSTp06le89iqpZs2aaOnWqMjIy9Omnn2ratGl65JFHtG7dugJ/AZaUM2fOeDzOW4YLP0K22+35rnFclI9YLycqKirfOruwhouP/BbXkiVLdMstt1z2C09Fldfvi7eri9dlYTp27KjJkyfrxx9/VI8ePQoc8/vvv2vXrl1q3bq1wsPDFRUVpWPHjuUb5+26ytvOT58+7fGFuStZ5yW5fnv16qX169frq6++0pEjR3TXXXeV6OX0oqKilJaWVug1svOU9DZTFIXtR6Si70tiYmL02muvaezYsVq7dq2mTZump556SitXrlRMTEyJL3/FihU1YsQIPffcc9q8ebNmzZqlCRMmKCIiQg888IB7e3vzzTfzfToBXAlOacAfwqeffqq2bdsW+Nydd96pU6dOafXq1QoKCtKNN96on376Sbm5ue4x586dy3fO4y233CK73V7gdW7Hjx9/yXN4GzZsqIiICCUnJ3tM//nnn4u0PCtXrtTatWslnT9K+MADD+jvf/+7MjIy3Od75h0dyQue+/btK9Y1eS92cY3btm2TdP60gjzR0dH5fglfeJ7lhYKDg901HjlyRD/88EOh733rrbfq8OHD+Y4qbtmyRREREe6P2a+Uw+FwH5nMc+HH3N5q3ry57HZ7vptoXPht+UupX7++7rvvPv3nP/8p9Cjvq6++qilTpig8PFzS+XWVnZ3tPl0nz5YtW2S324sczG699VZJ0tatWz2m5z3Oe94bJbl+O3bsqEqVKmnJkiVavHhxkc+LLqpbb71V6enp+W4EcfDgQT3//PPu/URJLVPeuel5PxPJycnav39/gWOrV6+uWrVq5duujh49WqQ/pjZu3KiPP/5Y0vlTmrp27ao33nhDubm52rt3r6SSXf4TJ05o/Pjxks4f7W3VqpXefvttRUZGun8W8rannTt3erzW6XRq5MiR+uWXXy67XEBBCLz4Q1i0aJHuvPPOAp+74447ZLPZ3F9eGz58uE6cOOG+C1beF0MuPmpUp04dDRo0SPPmzXMHV8uy9OGHH2r9+vW6/vrrC62nYsWKGjx4sNatW+f+clNGRobefffdIi3PgQMHNGPGDPcRQ5fLpe+++05Vq1ZV/fr1JUmVK1dWeHi4+yjf9OnT3SH5Sqxbt859ruzp06c1ffp01atXz+PIY9u2bXX27Fl9+eWXks6f7pB32bGL1a5d213jRx995P4FXJChQ4cqIiJCEydOdP+iXb9+vb788kuNHDmyyKcIXM4dd9yhpKQkd5A4ffq03nvvvWLP79prr1WPHj20ePFidwA9fvy45s2bV+R5/N///Z9atGihIUOGePzRkZGRoTfeeEObNm3Sa6+95p4+cOBA1a1bV5MmTXIfXd+2bZuWLFmiQYMGqU6dOkV6327duqlFixZKSEhw/6Fx8OBBvffee+rSpYv7y3reKMn1GxISom7durm3r5I+Kpi3HseNG6fMzExJ54+evvLKK6pevbo7oJbUMtWuXVs2m839MzF+/Ph8f2zksdlsGjZsmHbs2OE+ApuTk6PJkycrLCzssu919OhRvfvuux6nI23atEnly5d3n7Ndkst/7tw5ffTRRx7n4e7YsUOZmZnuL++2adNGXbp00dtvv61Dhw5JOn/qWXx8vA4dOlSsc50BSbJZF3/uCBgkLS1NgwYN0q5du9SoUSONHj3a48jW7t27NWrUKO3du1dOp1MNGzZUXFyc9u3bp7feekuZmZmqUaOGHn74YXcQS0xM9HiPxMREffjhh3I6nSpXrpzq16+v559//rJfAHO5XHr77bf10UcfKSIiQpUqVdJzzz2nAQMGKDo6Wtdcc40SExPd1+E9e/asrrvuOv3jH/9QhQoVNGfOHG3btk0hISFyOp269tprNWLECI8v4SxcuFDvvvuuIiIiFB0drbi4OC1cuFBLlizRoUOHVLduXVWsWNHjhgVTpkxxX4e3bt266tSpkypXrqwlS5Zo3759evHFF7Vjxw7t3btXv/76a77r8OaZPn26Fi1apIoVKyo2NladO3fWsGHD3PMcPXq0JGnt2rWaMGGCypcvr/Lly2vixInasGGD+zq8NWrU0M0336y4uDhJ58NWXFyce9nzrsP7l7/8xaPneb8s69atq4ULF2ratGn65JNPdPToUV133XUaMmRIoacHZGRkaOLEifrvf/+rKlWqKCoqyh366tatq549e6pJkyaaPHmydu3apejoaLVt21ZxcXEaNGiQdu7c6e5XXFyc6tev73Ed3sqVK6tKlSrq37+/nn76adWoUUPNmzfX1KlTL7vNLF26VIsXL9aZM2fc5wq3adNGjzzyiPuqHXlSU1MVFxenjRs3KiwsTEFBQXr44Yc9vtyYd93kvHo7d+7s8aWlvPWRdx3e0NBQOZ1O3XvvvXriiSfcnyQ888wz2rJli1JTU9WoUSP94x//UGpqquLj49197NSpk1566aVird/o6Gi9/vrrBf4huXv3bnXv3l0vv/yy+vbte8l1KEn//Oc/tX79eve2MHToUFWvXl3//Oc/3e938803Kz4+XtL5P07efPNNJSUl6aqrrlJQUJD+8pe/aPDgwe7zfktymRISErRkyRJVqFBB9erV0+TJky95msaCBQs0a9Ys2Ww2VapUSYMHD9Ybb7yhjIwM1ahRQ/Pnz9fw4cPd1+Ft1KiRnnjiCTVt2lTvvfeevvvuOwUFBcnlcqlKlSp69tlnPT6xKanlf+SRRzRr1iytW7fOfR59UFCQBgwY4PGzmJOTo3/9619asWKFQkJCFBISohYtWui5555znzsOeIvACxRRt27dVKtWLfcNHQAEBofDodtuu01ffPGFT89fB1B2cUoDcJGtW7d6XHVBOv+Fq8OHD1/yNAUA/vG///1P7du3J+wCKBSBF7jI6dOnNXv2bPe1I10ul958800FBwfroYce8nN1ACRp5syZWrFihfv23kU5lQHAH5ffL0vmcDiUkJCg2bNn64svvijwGpt5kpKSNHfuXJ09e1bZ2dmKiIjQX//6V91www2lWDFMV79+fd1+++0aMmSIwsLCdPr0aTVq1Ejz588v8t2bAPhWRESEJkyYoBkzZqhTp05q2bKlv0sCEMD8eg7v4cOHNXLkSF177bVatmyZ1q5de8nA27lzZz3++ON68MEHJZ2/4P+CBQu0atUq9y0vAQAAgAv59ZSGs2fPatKkSR737b6UJk2aeFxjsX///jp16lS+W2QCAAAAefx6SkNsbKwkFXg3oIJMmTLF43HedQZzcnJKtjAAAAAYo0x/aW3Lli0KDw9Xx44d/V0KAAAAAlSZDbyWZemdd97R8OHDvb6XO5ceBgAA+OPw+1UaiishIUHVqlXT4MGDvX7tyZOZstttlx9YDEFBdkVGllN6+jk5nS6fvAf8g96ajf6ai96ai96aK6+3JaVMBt6PPvpI27dv1/Tp04v1epfLksvl26O8TqdLubn88JmI3pqN/pqL3pqL3uJyytwpDZ988olWrVqlhIQEhYaGKiUlhas0AAAAoFABfYR3zJgxys3N1RtvvCFJWr9+veLi4jRhwgTt2bNHkrRjxw4dP35c7du392epAAAACFB+DbwOh0OPPvqo0tPTJUnPP/+8qlevrvj4eElSdna2cnNz3ePHjBmjtLQ0DRw40GM+Q4cOLb2iAQAAUKb49U5r/nL8+BmfzTs42K6oqAilpWVyPpFh6K3Z6K+56K256K258npbUsrcObwAAACANwi8AAAAMBqBFwAAAEYj8AIAAMBoBF4AAAAYjcALAAAAoxF4AQAAYDQCLwAAAIxG4AUAAIDRCLwAAAAwGoEXAAAARiPwAgAAwGgEXgAAABiNwAsAAACjEXgBAABgNAIvAAAAjEbgBQAAgNEIvAAAADAagRcAAABGI/ACAADAaAReAAAAGI3ACwAAAKMReAEAAGA0Ai8AAACMRuAFAACA0Qi8AAAAMBqBFwAAAEYj8AIAAMBoBF4AAAAYjcALAAAAoxF4AQAAYDQCLwAAAIxG4AUAAIDRCLwAAAAwGoEXAAAARiPwAgAAwGgEXgAAABiNwAsAAACjEXgBAABgNAIvAAAAjEbgBQAAgNEIvAAAADAagRcAAABGI/ACAADAaAReAAAAGI3ACwAAAKMReAEAAGA0Ai8AAACMRuAFAACA0Qi8AAAAMBqBFwAAAEYj8AIAAMBoBF4AAAAYLdjfBTgcDiUkJGj27Nn64osvVLt27UuO37x5syZNmqTQ0FA5HA6NGjVKLVu2LKVqcaETp89p9eYUZWblKiI8WJ1b1lHlq8r5u6yAsPdwmuat3qNz2bkqFxasfp0bqH7tKH+X5TPebgu+Hl+c+td8f0S5lqVgm02dbq51yfn7sr+Bti4Dbby3697b3gba8noz/vvdx/TB53uUk+NUSEiQBnZpoJsbVi903v/98ZA+XLtPTqeloCCb+tx5nTq2qFvo+OKse2+W1dv6fd1bbwXatu/r+gNl3kVlsyzLKtV3vMDhw4c1cuRIXXvttVq2bJnWrl17ycB75MgRde/eXW+//bbatGmjb7/9Vk899ZRWrFihWrVqFfl9jx8/UxLlFyg42K6oqAilpWUqN9fls/fxJ6fLpTmrdmn7LyeUfjbHPT2yfIiaxlTWoK6NFGQ378ODovTWkZurl2d9q99PZeV7rurV4XrlsdYKDfb735klxtttwdfjfV2/L/sbaOsy0MZ7u+4DrX5fjj/nyNHIhK+VlZN/vxQeYlfcs7eoXGiIe1pGlkPPvbVBrgJ++9tt0tTht6pCeKh7mq/Xvbf1l/X9SCDtd4pTT2nNO+93bknxayo5e/asJk2apJ49exZpfGJiomJiYtSmTRtJUuvWrVWvXj3NmzfPl2XiInNW7dLXycc8Nl5JSj+bo6+Tj2nOp7v9VJn/FbZTkqTfT2Xp5VnflXJFvuXttuDr8b6u35f9DbR1GWjjvV33gVa/L8cXFhYlKSvHpZEJSR7TCgu7kuSypOfe+tpjmq/Xvbf1l/X9SCDtd4pTT6DM21t+DbyxsbG65pprijw+KSlJTZs29ZjWtGlTJSUlFfIKlLQTp89p+y8nLjlm+y+pOnH6XClVFDj2Hk4rdKeU5/ipc9p7OK2UKvItb7cFX4/3lrfz92V/A21dBtp4b9d9oNXvy/Hf7z5WaFjMk5Xj1Pe7j0k6fxpDYWE3j8uy9N8fD0ny/br3tv6yvh8JpP1OceoJlHkXR5n63DklJUXR0dEe06Kjo5WSkuLVfOx2m4KD7T75FxR0fpUGBflm/v7+t+b7I/n+UrtYemaO1v5wxO+1lnZv56/ec9ltz5I0f80evy+LP7YFX4/3df2+7G+grctAG+/tug+0+n05fu7nl183kjT38/Pr5sO1+4o0/sO1+0pl3Xtbf1nfjwTSfsfX6+dK5533O7eklKmTCbOyshQaGuoxLTQ0VFlZl/7r52KVKkXIZrOVZGn5REaa+eWt3CKe8p3rUomeexNICuttdhHP2XbkuIxYN95uC74e7y1v5+/L/gbaugy08d6u+0Cr35fjc4q4bnJzz68bp7No83Y6rVJZ997WX9b3I4G035F8+zs90PJCmQq84eHhcjgcHtMcDofCw8O9ms/Jk5my230TeIOC7IqMLKf09HNyOs370lpwEf9QCLZLaWmZPq6mdF2ut2HBRftrNDTEbsS68XZb8PV4b3k7f1/2N9DWZaCN93bdB1r9vhwfEmxXlsN5+bHB59dNUJBNrtzLB5GgIFuprHtv6y/r+5FA2u9Ivv2dfqXzzvudW1LK1CkNderUUWpqqse01NRU1alTx6v5uFyWcnNdPvmXF4ScTt/M39//Ot1cS5HlQy65fiMjQnTnTbX8Xmtp97Zv5waX3fZskvp2auD3ZfHHtuDr8b6u35f9DbR1GWjjvV33gVa/L8cP6HL5dSNJA7qcXzd97ryuSOP73Hldqax7b+sv6/uRQNrv+Hr9XOm8S/qgYZkKvO3atVNycrLHtOTkZLVv395PFf3xVL6qnJrGVL7kmKb1Kv8hr8dbv3aUql596U8bqlwdbsz1eL3dFnw93lvezt+X/Q20dRlo471d94FWvy/H39ywusJDLv2rPDzE7r6ebccWdXW5DzjtNrmvx+vrde9t/WV9PxJI+53i1BMo8y6OgA68Y8aM0QsvvOB+PGDAAO3bt0/ffXf+EhybN2/Wvn371K9fP3+V+Ic0qGsj3dK0hiIjPP9yi4wI0S1NqmtQ10Z+qsz/XnmstapeXU4X/z6x6f9fL9Ek3m4Lvh7v6/p92d9AW5eBNt7bdR9o9ftyfNyztyg8JEgFybuO7YWmDr9V9kI+bs67Du+FfL3uva2/rO9HAmm/U5x6AmXe3vLrjSccDoceffRRpaena9euXbrxxhtVvXp1xcfHS5Kef/555ebmuh9L//9OayEhIXI4HBo9erTXd1rjxhMlI+/OKWezchURHqLOreqoUqR351OXJd70Nu+OOFnZuSofFqJ+dzVQTK2rS6dQP/B2W/D1+OLUv+b7I3Ja588n63Rz7UvO35f9DbR1GWjjvV333vY20JbXm/F5dyrLzXEqNCRIA++OVYvYaoXO++I7rfXtVF9/al74KYLFWffeLKu39fu6t94KtG3f1/X7et55v3NLil8Dr78QeFEc9NZs9Ndc9NZc9NZcJR14A/qUBgAAAOBKEXgBAABgNAIvAAAAjEbgBQAAgNEIvAAAADAagRcAAABGI/ACAADAaAReAAAAGI3ACwAAAKMReAEAAGA0Ai8AAACMRuAFAACA0Qi8AAAAMBqBFwAAAEYj8AIAAMBoBF4AAAAYjcALAAAAoxF4AQAAYDQCLwAAAIxG4AUAAIDRCLwAAAAwGoEXAAAARiPwAgAAwGgEXgAAABiNwAsAAACjEXgBAABgNAIvAAAAjEbgBQAAgNEIvAAAADAagRcAAABGI/ACAADAaAReAAAAGI3ACwAAAKMReAEAAGA0Ai8AAACMRuAFAACA0Qi8AAAAMBqBFwAAAEYj8AIAAMBoBF4AAAAYjcALAAAAoxF4AQAAYDQCLwAAAIxG4AUAAIDRCLwAAAAwGoEXAAAARiPwAgAAwGgEXgAAABiNwAsAAACjEXgBAABgNAIvAAAAjEbgBQAAgNEIvAAAADBasL8LWL16td555x2Fh4fLbrdr7NixatCgQYFjLcvSO++8o88++0yRkZE6e/asevfurd69e5dy1QAAACgr/Bp4t23bplGjRmnx4sWKiYnRsmXL9Oijj2rVqlWqUKFCvvGLFi3S7NmztWrVKlWrVk1Hjx7VPffco2rVqqljx46lvwAAAAAIeH49pWHmzJnq0KGDYmJiJEndu3eX0+nUsmXLChy/a9cuxcTEqFq1apKkGjVqqF69etqwYUNplQwAAIAyxq+Bd+PGjWratKn7sd1uV+PGjZWUlFTg+DvvvFP79u3T7t27JZ0PwHv27FF0dHSp1AsAAICyx2+nNKSlpenMmTP5wmp0dLS2b99e4Gvat2+vCRMmaODAgapUqZL279+vli1b6uGHHy6NkgEAAFAG+S3wZmVlSZJCQ0M9poeGhrqfu9j69ev18ssva/bs2WrSpIlSUlL06aefKjw83Kv3ttttstttxSv8MoKC7B7/hTnordnor7norbnorblKuqd+C7x5IdXhcHhMdzgchQbYKVOmqHPnzmrSpIkkqU6dOjpw4IDGjRuncePGFfm9K1WKkM3mm8CbJzKynE/nD/+ht2ajv+ait+ait7gcvwXeqKgoVaxYUampqR7TU1NTVadOnQJfc/DgQf35z3/2mFa7dm3NmTPHq8B78mSmT4/wRkaWU3r6OTmdLp+8B/yD3pqN/pqL3pqL3porr7clxa+XJWvbtq2Sk5Pdjy3L0s6dO/Xkk08WOL5atWo6fvy4x7Tjx48rLCzMq/d1uSy5XJb3BXvB6XQpN5cfPhPRW7PRX3PRW3PRW1yOX096GTJkiL788kvt379fkrRixQrZ7Xb16NFDkjRmzBi98MIL7vG9evXSqlWr9Ouvv0qSjhw5olWrVuU76gsAAADk8esR3mbNmmnixIkaOXKk+05rs2fPdt90Ijs7W7m5ue7xjz76qGw2m5555hmFh4crIyNDDz30kJ5++ml/LQIAAAACnM2yLN9+th+Ajh8/47N5BwfbFRUVobS0TD5eMQy9NRv9NRe9NRe9NVdeb0sK1/EAAACA0Qi8AAAAMBqBFwAAAEYj8AIAAMBoBF4AAAAYjcALAAAAoxF4AQAAYDQCLwAAAIxG4AUAAIDRCLwAAAAwGoEXAAAARiPwAgAAwGgEXgAAABiNwAsAAACjEXgBAABgNAIvAAAAjEbgBQAAgNEIvAAAADAagRcAAABGI/ACAADAaAReAAAAGI3ACwAAAKMReAEAAGA0Ai8AAACMRuAFAACA0Qi8AAAAMBqBFwAAAEYj8AIAAMBoBF4AAAAYjcALAAAAoxF4AQAAYDQCLwAAAIxG4AUAAIDRCLwAAAAwGoEXAAAARiPwAgAAwGgEXgAAABiNwAsAAACjEXgBAABgNAIvAAAAjEbgBQAAgNEIvAAAADAagRcAAABGI/ACAADAaAReAAAAGI3ACwAAAKMReAEAAGA0Ai8AAACMRuAFAACA0Qi8AAAAMBqBFwAAAEYj8AIAAMBoBF4AAAAYLdjfBaxevVrvvPOOwsPDZbfbNXbsWDVo0KDQ8SdPnlRcXJwOHTqkzMxMORwOPf300+ratWspVg0AAICywq9HeLdt26ZRo0Zp8uTJWrBgge6//349+uijysjIKHC8w+HQI488olatWikxMVFLlizRbbfdpu3bt5dy5QAAACgr/Bp4Z86cqQ4dOigmJkaS1L17dzmdTi1btqzA8R9//LHCwsLUo0cP97THH39c999/fylUCwAAgLLIr4F348aNatq0qfux3W5X48aNlZSUVOD4L774Qq1atfKYVqlSJV133XU+rRMAAABll9/O4U1LS9OZM2cUHR3tMT06OrrQUxR+/vlntWzZUmPHjtXu3bsVEhKirl276qGHHpLNZiuNsgEAAFDG+C3wZmVlSZJCQ0M9poeGhrqfu1h6erreffddTZ8+Xf/85z914MAB9e3bV2fOnNGQIUOK/N52u012u28CclCQ3eO/MAe9NRv9NRe9NRe9NVdJ97RYgffMmTPaunWrTp8+rauuuko33nijKlas6NU8wsPDJZ3/ItqFHA6H+7mL2Ww2NWvWTB06dJAkXXvtterVq5c++OADrwJvpUoRPj8iHBlZzqfzh//QW7PRX3PRW3PRW1yOV4HX4XDo9ddf17///W85nU739KCgIPXu3VujR4/Od8S2MFFRUapYsaJSU1M9pqempqpOnToFvqZGjRqqXr26x7SaNWsqNTVVWVlZhQbli508menTI7yRkeWUnn5OTqfLJ+8B/6C3ZqO/5qK35qK35srrbUnxKvA+88wz2rhxo+644w41b95cFStW1OnTp/XDDz9o4cKFSklJ0YwZM4o8v7Zt2yo5Odn92LIs7dy5U08++WSB41u2bKnDhw97TEtNTVVUVFSRw64kuVyWXC6ryOOLw+l0KTeXHz4T0Vuz0V9z0Vtz0VtcTpED76pVq7R7924tXbq0wBtD7Nq1S48//rg+/fRT/fnPfy7SPIcMGaJBgwZp//79qlevnlasWCG73e6+7NiYMWOUm5urN954Q5I0cOBA9e7dW9u2bVOzZs106tQpLV++XP379y/qYgAAAOAPpsiBd8WKFXrttdcKvQtao0aN9Oqrr2r+/PlFDrzNmjXTxIkTNXLkSPed1mbPnq0KFSpIkrKzs5Wbm+vxHtOmTdMrr7yi4OBgOZ1OPfjggxo8eHBRFwMAAAB/MDbLsor02X6PHj0KvSFEccb50/HjZ3w27+Bgu6KiIpSWlsnHK4aht2ajv+ait+ait+bK621JKfI1H/KOupbUOAAAAKA0FDnw2u1FG1rUcQAAAEBpKPI5vD/99JMGDBhw2XG7du26ooIAAACAkuTVZcmKeLovAAAAEDCKHHgbNWqkxMTEy47jEmEAAAAIJEU+4XbChAklOg4AAAAoDUUOvDZb0W7FW9RxAAAAQGkocuAdM2ZMiY4DAAAASgNXaQAAAIDRvLpormVZHv9++umnfNMAAACAQHJFV2no379/gdMAAACAQFHkI7yTJk3KN62gL6gVNA4AAADwlyIH3ho1apToOAAAAKA0eHUOLwAAAFDWFDnwDhs2rETHAQAAAKWhyF9aS05O1vTp0z2uxHDkyJF803bs2FGyFQIAAABXoMiB99dff1VCQkK+6RdP405rAAAACCRFDrytWrXKdwmygnBZMgAAAASSIp/DO3DgwBIdBwAAAJSGIgfem266SdOmTdO0adP0888/53t+4sSJOn36tDp16lSiBQIAAABXosiBd+3atZo2bZr279+v8PDwfM9v2rRJ/fr1U3p6eokWCAAAAFyJIgfe9evX66WXXlJcXJzq1q2b7/klS5aobdu2mj17dokWCAAAAFyJIgfekydPqm/fvpcc89e//lXffPPNFRcFAAAAlJQiB96QkJDLXnIsLCxMoaGhV1wUAAAAUFJK/NbCLperpGcJAAAAFFuRA294eLj27dt3yTF79uxRuXLlrrgoAAAAoKQUOfA+/PDDeuqpp7Rt27YCn9+6dauGDh3KdXgBAAAQUIp8p7Xbb79dmzZt0oMPPqhrrrlGsbGxKl++vM6ePauff/5Zhw4d0hNPPKHbbrvNl/UCAAAAXily4JWkF198UU2aNNF7772nNWvWyLIs2Ww23XDDDZo6daq6dOniqzoBAACAYvEq8ErSPffco3vuuUdZWVk6ffq0rrrqqgJvRAEAAAAEAq8Db57w8HCCLgAAAAJeiV+WDAAAAAgkBF4AAAAYjcALAAAAoxF4AQAAYDQCLwAAAIxG4AUAAIDRCLwAAAAwGoEXAAAARiPwAgAAwGgEXgAAABiNwAsAAACjEXgBAABgNAIvAAAAjEbgBQAAgNEIvAAAADAagRcAAABGI/ACAADAaAReAAAAGI3ACwAAAKMReAEAAGA0Ai8AAACMRuAFAACA0fweeFevXq2ePXvq4YcfVr9+/bRnz54ivW79+vVq2LChlixZ4uMKAQAAUJYF+/PNt23bplGjRmnx4sWKiYnRsmXL9Oijj2rVqlWqUKFCoa87e/aspk6dWnqFAgAAoMzy6xHemTNnqkOHDoqJiZEkde/eXU6nU8uWLbvk6+Lj49WnT59SqBAAAABlnV8D78aNG9W0aVP3Y7vdrsaNGyspKanQ1+zcuVPbtm1T7969S6NEAAAAlHF+C7xpaWk6c+aMoqOjPaZHR0crJSWlwNe4XC698sorevnll2Wz2UqjTAAAAJRxfjuHNysrS5IUGhrqMT00NNT93MXmzZunm266SY0aNbqi97bbbbLbfROYg4LsHv+FOeit2eivueitueituUq6p34LvOHh4ZIkh8PhMd3hcLifu9Bvv/2mjz/+WAsXLrzi965UKcLnR4gjI8v5dP7wH3prNvprLnprLnqLy/Fb4I2KilLFihWVmprqMT01NVV16tTJN37Dhg2SpCeeeMJj+owZM7R06VINHz5cLVu2LNJ7nzyZ6dMjvJGR5ZSefk5Op8sn7wH/oLdmo7/morfmorfmyuttSfHrZcnatm2r5ORk92PLsrRz5049+eST+cb26tVLvXr18pjWsGFDDRkyRD179vTqfV0uSy6XVbyii8jpdCk3lx8+E9Fbs9Ffc9Fbc9FbXI5fT3oZMmSIvvzyS+3fv1+StGLFCtntdvXo0UOSNGbMGL3wwgt+rBAAAABlnV+P8DZr1kwTJ07UyJEjFR4eLrvdrtmzZ7tvOpGdna3c3Nx8r5sxY4a++uor9/8vXbpUiYmJpVo7AAAAygabZVm+/Ww/AB0/fsZn8w4OtisqKkJpaZl8vGIYems2+msuemsuemuuvN6WFK7jAQAAAKMReAEAAGA0Ai8AAACMRuAFAACA0Qi8AAAAMBqBFwAAAEYj8AIAAMBoBF4AAAAYjcALAAAAoxF4AQAAYDQCLwAAAIxG4AUAAIDRCLwAAAAwGoEXAAAARiPwAgAAwGgEXgAAABiNwAsAAACjEXgBAABgNAIvAAAAjEbgBQAAgNEIvAAAADAagRcAAABGI/ACAADAaAReAAAAGI3ACwAAAKMReAEAAGA0Ai8AAACMRuAFAACA0Qi8AAAAMBqBFwAAAEYj8AIAAMBoBF4AAAAYjcALAAAAoxF4AQAAYDQCLwAAAIxG4AUAAIDRCLwAAAAwGoEXAAAARiPwAgAAwGgEXgAAABiNwAsAAACjEXgBAABgNAIvAAAAjEbgBQAAgNEIvAAAADAagRcAAABGI/ACAADAaAReAAAAGI3ACwAAAKMReAEAAGA0Ai8AAACMRuAFAACA0Qi8AAAAMBqBFwAAAEYL9ncBq1ev1jvvvKPw8HDZ7XaNHTtWDRo0KHBsUlKS5s6dq7Nnzyo7O1sRERH661//qhtuuKGUqwYAAEBZ4dcjvNu2bdOoUaM0efJkLViwQPfff78effRRZWRkFDh+7NixuuOOOzR37lwtXLhQN954ox555BGdOHGilCsHAABAWeHXwDtz5kx16NBBMTExkqTu3bvL6XRq2bJlBY5v0qSJ7r//fvfj/v3769SpU0pKSiqNcgEAAFAG+TXwbty4UU2bNnU/ttvtaty4caEBdsqUKbLb/3/JYWFhkqScnBzfFgoAAIAyy2/n8KalpenMmTOKjo72mB4dHa3t27cXaR5btmxReHi4Onbs6NV72+022e02r15TVEFBdo//whz01mz011z01lz01lwl3VO/Bd6srCxJUmhoqMf00NBQ93OXYlmW3nnnHQ0fPlyVKlXy6r0rVYqQzeabwJsnMrKcT+cP/6G3ZqO/5qK35qK3uBy/Bd7w8HBJksPh8JjucDjcz11KQkKCqlWrpsGDB3v93idPZvr0CG9kZDmlp5+T0+nyyXvAP+it2eivueitueitufJ6W1L8FnijoqJUsWJFpaamekxPTU1VnTp1Lvnajz76SNu3b9f06dOL9d4ulyWXyyrWa4vK6XQpN5cfPhPRW7PRX3PRW3PRW1yOX096adu2rZKTk92PLcvSzp071b59+0Jf88knn2jVqlVKSEhQaGioUlJSuEoDAAAACuXXwDtkyBB9+eWX2r9/vyRpxYoVstvt6tGjhyRpzJgxeuGFF9zj169fr7i4OD399NPas2ePtm/frq+//lrff/+9P8oHAABAGeDXO601a9ZMEydO1MiRI913Wps9e7YqVKggScrOzlZubq57/JgxY5SWlqaBAwd6zGfo0KGlWjcAAADKDptlWb49mTUAHT9+xmfzDg62KyoqQmlpmZxPZBh6azb6ay56ay56a6683pYULlwHAAAAoxF4AQAAYDQCLwAAAIxG4AUAAIDRCLwAAAAwGoEXAAAARiPwAgAAwGgEXgAAABiNwAsAAACjEXgBAABgNAIvAAAAjEbgBQAAgNEIvAAAADAagRcAAABGI/ACAADAaAReAAAAGI3ACwAAAKMReAEAAGA0Ai8AAACMRuAFAACA0Qi8AAAAMBqBFwAAAEYj8AIAAMBoBF4AAAAYjcALAAAAoxF4AQAAYDQCLwAAAIxG4AUAAIDRCLwAAAAwGoEXAAAARiPwAgAAwGgEXgAAABiNwAsAAACjEXgBAABgNAIvAAAAjEbgBQAAgNEIvAAAADAagRcAAABGI/ACAADAaAReAAAAGI3ACwAAAKMReAEAAGA0Ai8AAACMRuAFAACA0Qi8AAAAMBqBFwAAAEYj8AIAAMBoBF4AAAAYjcALAAAAoxF4AQAAYDQCLwAAAIxG4AUAAIDRCLwAAAAwWrC/C1i9erXeeecdhYeHy263a+zYsWrQoEGh4zdv3qxJkyYpNDRUDodDo0aNUsuWLUux4pKz93Ca5q3eo3PZuSoXFqx+nRuofu2oEpv/idPntHpzijKzchURHqzOLeuo8lXlSmx8oNXjzXhv1/3ew2mav3qPsnNdCgu2q+9lxn+/+5g++HyPcnKcCgkJ0sAuDXRzw+olNt7b+n1dj7e98rb+0pj/mu+PKNeyFGyzqdPNtUpsW/vvj4f04dp9cjotBQXZ1OfO69SxRd0SW1ZfbzuB1ltf76d8vV/2lq+X15d8vR8JNIFWf6DV4282y7Isf735tm3bNHDgQC1evFgxMTFatmyZ3nzzTa1atUoVKlTIN/7IkSPq3r273n77bbVp00bffvutnnrqKa1YsUK1atUq8vseP36mJBfDQ3CwXVFREUpLy1RurqvAMY7cXL0861v9fior33NVrw7XK4+1Vmhw8f8WcbpcmrNql7b/ckLpZ3Pc0yPLh6hpTGUN6tpIQXZ7sccHWj3ejPd23Xs7/pwjRyMTvlZWTv7eh4fYFffsLSoXGlLs8YFWj7e98rb+QJu/N+Mzshx67q0NchWwh7XbpKnDb1WF8NBi1+Lrbacsr/uLBcJ+2Vu+3i/7kq/3IxcqSm99LdB6FWj1FFdeb0uKX5d45syZ6tChg2JiYiRJ3bt3l9Pp1LJlywocn5iYqJiYGLVp00aS1Lp1a9WrV0/z5s0rrZJLRGE7VUn6/VSWXp713RXNf86qXfo6+ZjHhi5J6Wdz9HXyMc35dPcVjQ+0erwZ7+2693Z8YTt5ScrKcWlkQtIVjQ+0erztlbf1B9r8vRlfWNiVJJclPffW11dUi6+3nbK87ovD1/tlb/l6eX3J1/uRQBNo9QdaPYHCr4F348aNatq0qfux3W5X48aNlZSUVOD4pKQkj/GS1LRp00LHB6K9h9MK3anmOX7qnPYeTivW/E+cPqftv5y45Jjtv6TqxOlzxRofaPV4M97bde/t+O93Hyt0J58nK8ep73cfK9b4QKvH2155W3+gzd+b8f/98VChYTePy7L03x8PFasWX287ZXndF4ev98ve8vXy+pKv9yOBJtDqD7R6AonfAm9aWprOnDmj6Ohoj+nR0dFKSUkp8DUpKSlejS+M3W5TcLDdJ/+Cgs6v0qCggp+fv3rPZeuzJM1fs6dY77/m+yP5/qq7WHpmjtb+cKRY4wOtHm/Ge7vuvR0/9/PLj5ekuZ8Xb3yg1eNtr7ytP9Dm7834D9fuu2wtkvTh2n3FqsXX205ZXveBuF/29X4zkP75ej/ibW//aL0KtHqu5F9eb0uK3760lpV1/q/p0NBQj+mhoaHu5wp6jTfjC1OpUoRsNptXr/FWZGTBJ4ZnF/EcI0eOq1jnruQW8ZTsXJcUFRXh9fhAq8eb8d6ue2/H5xRxfG5u8cYHWj3e9srb+gNt/t6MdzqLNtbptIpVi6+3nbK87i+1n/LXftlbvt4v+5Kv9yOFKay3vhZovQq0egKJ3wJveHi4JMnhcHhMdzgc7ucKeo034wtz8mSm7HbfBN6gILsiI8spPf2cnM78P/hhwUX7iyU0xK60tEyv3z+4iEE+2C6lpWV6PT7Q6vFmvLfr3tvxIcF2ZTmcl68luHjjA60eb3vlbf2BNn9vxgcF2eTKvfwvnqAgW7Fq8fW2U5bXfUH7KX/vl73l6/2yL/l6P3Kxy/XW1wKtV4FWz5XI621J8dspDVFRUapYsaJSU1M9pqempqpOnToFvqZOnTpejS+My2UpN9flk395P3BOZ8HP9+1c+CXX8tgk9e3UoFjv3+nmWoosH3LJ+UdGhOjOm2oVa3yg1ePNeG/XvbfjB3S5/HhJGtCleOMDrR5ve+Vt/YE2f2/G97nzusvWIkl97ryuWLX4etspy+s+EPfLvt5vBtI/X+9HvO3tH61XgVbPlfwr6T9g/PqltbZt2yo5Odn92LIs7dy5U+3bty9wfLt27TzGS1JycnKh4wNR/dpRqnr1pY9IV7k6vNjXfax8VTk1jal8yTFN61V2X4vP2/GBVo83471d996Ov7lhdYWHXPpHKjzE7r4OpbfjA60eb3vlbf2BNn9vxndsUVeX+xDJbpP7erze1uLrbacsr/vi8PV+2Vu+Xl5f8vV+JNAEWv2BVk8g8WvgHTJkiL788kvt379fkrRixQrZ7Xb16NFDkjRmzBi98MIL7vEDBgzQvn379N135y8Ps3nzZu3bt0/9+vUr9dqvxCuPtVbVq8vp4t+HNv3/6z1eiUFdG+mWpjUUGeH5V15kRIhuaVJdg7o2uqLxgVaPN+O9Xffejo979haFhwTlXwn6/9efvJLxgVaPt73ytv5Am78346cOv1X2Qj5ezLsO75XU4uttpyyv++Lw9X7ZW75eXl/y9X4k0ARa/YFWT6Dw640npEvfae35559Xbm6u4uPj3ePz7rQWEhIih8Oh0aNHe32nNX/feCJP3h19srJzVT4sRP3uaqCYWleXWC15d1k5m5WriPAQdW5VR5UiCz+K4e34QKvHm/Hervu8O605cl0KCwlSv86XHp93h6HcHKdCQ4I08O5YtYitVmLjva3f1/V42ytv6y+N+a/5/oic1vlz2zrdXLvEtrWL77TWt1N9/al54adhebusvt52Aq23xdlPBdJ+2Vu+3i/7kq/3I1Jg3HgiT6D1KtDq8VZeb0uK3wOvPwRK4EXZQm/NRn/NRW/NRW/NVdKBN/DvLQcAAABcAQIvAAAAjEbgBQAAgNEIvAAAADAagRcAAABGI/ACAADAaAReAAAAGI3ACwAAAKMReAEAAGA0Ai8AAACMRuAFAACA0Qi8AAAAMBqBFwAAAEYj8AIAAMBoBF4AAAAYjcALAAAAoxF4AQAAYDQCLwAAAIxG4AUAAIDRCLwAAAAwGoEXAAAARiPwAgAAwGgEXgAAABiNwAsAAACjEXgBAABgNAIvAAAAjGazLMvydxEAAACAr3CEFwAAAEYj8AIAAMBoBF4AAAAYjcALAAAAoxF4AQAAYDQCLwAAAIxG4AUAAIDRCLwAAAAwGoEXAAAARgv2dwFl1apVq7Ro0SI5nU5lZGSoZs2aGjVqlOrUqeMe89FHH2nhwoUKCwtTZGSkxo0bp2rVqvmxalzOmjVr9PHHH8vhcCgrK0vZ2dl67LHH1LVrV49x9LbsS0xM1Pjx4zV37ly1adPGPZ3elk0JCQlas2aNIiMj3dMqVKigd955x/2Y3pZtR44c0RtvvKGTJ08qLS1NISEhGjVqlNq2bSuJ/pZVd999t6pUqeIx7dixY6patarmz58vqYR6a6FYGjdubH311VeWZVmW0+m0Ro8ebd11111WVlaWZVmW9fnnn1vt27e3UlNTLcuyrISEBOvee++1nE6n32rG5Q0ePNhaunSp+/HatWutRo0aWbt27XJPo7dl37Fjx6yOHTtasbGx1jfffOOeTm/Lrvj4eI9eXozelm0nTpyw7rjjDispKcmyLMtyuVzW8OHDrcTERMuy6G9Z1q9fv3zTnn32WWvevHmWZZVcbzmloZjuuOMO3XrrrZIku92uvn376sCBA9qxY4ck6V//+pd69OihypUrS5IGDhyoPXv26Msvv/Rbzbi8ESNG6J577nE/bt26tVwulw4dOuSeRm/LvvHjx2vIkCH5ptNbc9Hbsm3WrFlq1qyZ2rVrJ0my2WwaNWqUOnbsKIn+lmWvvfaax+NTp07p66+/dv8uLqneEniLKT4+3uNxWFiYJCknJ0enT5/Wjh071LRpU/fzFStW1LXXXqukpKRSrRPeadKkiYKDz5/pk5OTo9mzZ6t+/fpq3769JNFbA6xbt07BwcG67bbbPKbTW3PR27Lviy++UKtWrTym1axZU7Vr16a/ZdyFp4JK0n/+8x/96U9/0lVXXVWiveUc3hKyZcsWVa1aVTfddJN2794tSYqOjvYYEx0drZSUFH+UBy/985//1MqVK1W/fn3Nnj1bERERkuTuH70tm86ePaspU6Zo9uzZcjgcHs/R27Jv8eLFmjZtmnJycnTNNdfomWeeUd26deltGXf27FmlpKTIsiyNHDlSR44cUbly5dS7d2/dfffd9NcwS5Ys0XPPPSepZPfLHOEtAQ6HQ7Nnz9ZLL72kkJAQZWVlSZJCQ0M9xoWGhrqfQ2AbO3asNm3apHbt2qlPnz76/fffJYnelnFvvfWWHnroIVWtWjXfc/S2bKtRo4ZuuOEGvf/++1qwYIFq166tnj176rfffqO3ZdyZM2ckSVOnTtXgwYP10UcfacSIERo1apRWrVpFfw2yd+9epaam6pZbbpFUsvtlAm8JePnll9WlSxd16dJFkhQeHi5J+Y4gORwO93MIfEFBQXr22WdlWZbef/99SfS2LNu5c6e2bt2qPn36FPg8vS3b7r//fg0aNEjBwcGy2+16+umnFRYWpgULFtDbMs5ms0mSOnbsqMaNG0uSmjVrps6dO2vOnDn01yBLlixRjx49ZLefj6cl2VsC7xWaPHmygoKCNGLECPe0vPNRUlNTPcampqbmO1cFgeXiHyq73a5rrrlG+/btk0Rvy7L169crOztbAwcOVP/+/fX8889LOv+Fif79+8vlckmit6YICgpSrVq1dOjQIX5uy7hKlSopNDRU1atX95hes2ZNHT58mP4awul0auXKlerZs6d7Wkn2lsB7BWbMmKEjR45o3LhxstlsSk5OVnJysq666irdcMMNSk5Odo/NyMjQgQMH3F9+QmC68Actz/Hjx90fgdPbsuuZZ57R0qVLlZiYqMTERL355puSpL/97W9KTExUs2bN6G0ZNn78+HzTfv/9d1WvXp2f2zIuODhYzZs31/Hjxz2mp6amqkaNGvTXEBs2bFDdunV1zTXXuKeVZG8JvMX04YcfasWKFRowYIB27Nih7du3a/369fr5558lSU899ZSWLVumkydPSpLmzp2rBg0aqEOHDv4sG5exd+9e/fe//3U/Xr58ufbv368ePXq4p9Fbc9HbsmvdunVau3at+/HHH3+sEydO6P7775dEb8u6xx9/XGvWrHF/UenIkSNas2aN+vfvL4n+mmDp0qUFHnQqqd7aLMuySqTSP5CMjAy1atXK/RHohSZMmOBu2Icffqh///vf7juDvPLKK/k+kkFgSUxM1H/+8x/Z7XZ3f5988kn3tR7z0Nuy7dVXX9XWrVu1detWNWrUSDExMZoyZYokeltWrVy5Uh9//LEsy1JOTo5CQkI0fPhwtWzZ0j2G3pZty5cvd5+z63Q69eCDD7r/oJHob1mWnp6uzp07a926de6rIl2oJHpL4AUAAIDROKUBAAAARiPwAgAAwGgEXgAAABiNwAsAAACjEXgBAABgNAIvAAAAjEbgBQAAgNEIvAAQwFatWqWGDRtq3bp1/i4FAMosAi8ABLDFixcrKChIH3/8sb9LAYAyi8ALAAHq119/1bFjx9StWzf973//0/Hjx/1dEgCUScH+LgAAULAlS5bovvvuU7NmzbRs2TItXbpUQ4YMcT+fk5OjSZMmafXq1apZs6aqVq2qBg0aKD4+Xq1bt9bw4cPVsmVLZWRkaPLkyfrhhx8UGRmp3NxcDRo0SHfffbcflw4ASg+BFwACkGVZWrFihRYsWKDKlSurbt26Wrx4sUfgnTJlilatWqWPP/5YNWvW1G+//aaHHnpIkpSYmOiez5NPPqny5ctr0aJFCg0N1a5du/TAAw8oODhYnTp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- "text/plain": [ - "
              " - ] - }, - "metadata": { - "filenames": { - "image/png": "/Users/mhjensen/Teaching/MachineLearning/doc/LectureNotes/_build/jupyter_execute/chapter4_8_1.png" - } - }, - "output_type": "display_data" + "ename": "ModuleNotFoundError", + "evalue": "No module named 'matplotlib'", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mModuleNotFoundError\u001b[0m Traceback (most recent call last)", + "Cell \u001b[0;32mIn[1], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \u001b[43mget_ipython\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mrun_line_magic\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mmatplotlib\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43minline\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m)\u001b[49m\n\u001b[1;32m 3\u001b[0m \u001b[38;5;66;03m# Common imports\u001b[39;00m\n\u001b[1;32m 4\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mos\u001b[39;00m\n", + "File \u001b[0;32m~/miniforge3/lib/python3.9/site-packages/IPython/core/interactiveshell.py:2432\u001b[0m, in \u001b[0;36mInteractiveShell.run_line_magic\u001b[0;34m(self, magic_name, line, _stack_depth)\u001b[0m\n\u001b[1;32m 2430\u001b[0m kwargs[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mlocal_ns\u001b[39m\u001b[38;5;124m'\u001b[39m] \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mget_local_scope(stack_depth)\n\u001b[1;32m 2431\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mbuiltin_trap:\n\u001b[0;32m-> 2432\u001b[0m result \u001b[38;5;241m=\u001b[39m \u001b[43mfn\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 2434\u001b[0m \u001b[38;5;66;03m# The code below prevents the output from being displayed\u001b[39;00m\n\u001b[1;32m 2435\u001b[0m \u001b[38;5;66;03m# when using magics with decorator @output_can_be_silenced\u001b[39;00m\n\u001b[1;32m 2436\u001b[0m \u001b[38;5;66;03m# when the last Python token in the expression is a ';'.\u001b[39;00m\n\u001b[1;32m 2437\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mgetattr\u001b[39m(fn, magic\u001b[38;5;241m.\u001b[39mMAGIC_OUTPUT_CAN_BE_SILENCED, \u001b[38;5;28;01mFalse\u001b[39;00m):\n", + "File \u001b[0;32m~/miniforge3/lib/python3.9/site-packages/IPython/core/magics/pylab.py:99\u001b[0m, in \u001b[0;36mPylabMagics.matplotlib\u001b[0;34m(self, line)\u001b[0m\n\u001b[1;32m 97\u001b[0m \u001b[38;5;28mprint\u001b[39m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mAvailable matplotlib backends: \u001b[39m\u001b[38;5;132;01m%s\u001b[39;00m\u001b[38;5;124m\"\u001b[39m \u001b[38;5;241m%\u001b[39m backends_list)\n\u001b[1;32m 98\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m---> 99\u001b[0m gui, backend \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mshell\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43menable_matplotlib\u001b[49m\u001b[43m(\u001b[49m\u001b[43margs\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mgui\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mlower\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mif\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[38;5;28;43misinstance\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43margs\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mgui\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mstr\u001b[39;49m\u001b[43m)\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01melse\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43margs\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mgui\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 100\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_show_matplotlib_backend(args\u001b[38;5;241m.\u001b[39mgui, backend)\n", + "File \u001b[0;32m~/miniforge3/lib/python3.9/site-packages/IPython/core/interactiveshell.py:3606\u001b[0m, in \u001b[0;36mInteractiveShell.enable_matplotlib\u001b[0;34m(self, gui)\u001b[0m\n\u001b[1;32m 3585\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21menable_matplotlib\u001b[39m(\u001b[38;5;28mself\u001b[39m, gui\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mNone\u001b[39;00m):\n\u001b[1;32m 3586\u001b[0m \u001b[38;5;250m \u001b[39m\u001b[38;5;124;03m\"\"\"Enable interactive matplotlib and inline figure support.\u001b[39;00m\n\u001b[1;32m 3587\u001b[0m \n\u001b[1;32m 3588\u001b[0m \u001b[38;5;124;03m This takes the following steps:\u001b[39;00m\n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 3604\u001b[0m \u001b[38;5;124;03m display figures inline.\u001b[39;00m\n\u001b[1;32m 3605\u001b[0m \u001b[38;5;124;03m \"\"\"\u001b[39;00m\n\u001b[0;32m-> 3606\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mmatplotlib_inline\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mbackend_inline\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m configure_inline_support\n\u001b[1;32m 3608\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mIPython\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mcore\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m pylabtools \u001b[38;5;28;01mas\u001b[39;00m pt\n\u001b[1;32m 3609\u001b[0m gui, backend \u001b[38;5;241m=\u001b[39m pt\u001b[38;5;241m.\u001b[39mfind_gui_and_backend(gui, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mpylab_gui_select)\n", + "File \u001b[0;32m~/miniforge3/lib/python3.9/site-packages/matplotlib_inline/__init__.py:1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01m.\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m backend_inline, config \u001b[38;5;66;03m# noqa\u001b[39;00m\n\u001b[1;32m 2\u001b[0m __version__ \u001b[38;5;241m=\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m0.1.6\u001b[39m\u001b[38;5;124m\"\u001b[39m \u001b[38;5;66;03m# noqa\u001b[39;00m\n", + "File \u001b[0;32m~/miniforge3/lib/python3.9/site-packages/matplotlib_inline/backend_inline.py:6\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[38;5;124;03m\"\"\"A matplotlib backend for publishing figures via display_data\"\"\"\u001b[39;00m\n\u001b[1;32m 3\u001b[0m \u001b[38;5;66;03m# Copyright (c) IPython Development Team.\u001b[39;00m\n\u001b[1;32m 4\u001b[0m \u001b[38;5;66;03m# Distributed under the terms of the BSD 3-Clause License.\u001b[39;00m\n\u001b[0;32m----> 6\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mmatplotlib\u001b[39;00m\n\u001b[1;32m 7\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mmatplotlib\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m colors\n\u001b[1;32m 8\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mmatplotlib\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mbackends\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m backend_agg\n", + "\u001b[0;31mModuleNotFoundError\u001b[0m: No module named 'matplotlib'" + ] } ], "source": [ @@ -403,22 +275,7 @@ "collapsed": false, "editable": true }, - "outputs": [ - { - "data": { - "image/png": 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\n", 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\n", - "text/plain": [ - "
              " - ] - }, - "metadata": { - "filenames": { - "image/png": "/Users/mhjensen/Teaching/MachineLearning/doc/LectureNotes/_build/jupyter_execute/chapter4_18_2.png" - } - }, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "\"\"\"The sigmoid function (or the logistic curve) is a\n", "function that takes any real number, z, and outputs a number (0,1).\n", @@ -1090,32 +904,7 @@ "collapsed": false, "editable": true }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "(426, 30)\n", - "(143, 30)\n", - "Test set accuracy with Logistic Regression: 0.94\n", - "Test set accuracy Logistic Regression with scaled data: 0.96\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/Users/mhjensen/miniforge3/envs/myenv/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:814: ConvergenceWarning: lbfgs failed to converge (status=1):\n", - "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", - "\n", - "Increase the number of iterations (max_iter) or scale the data as shown in:\n", - " https://scikit-learn.org/stable/modules/preprocessing.html\n", - "Please also refer to the documentation for alternative solver options:\n", - " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", - " n_iter_i = _check_optimize_result(\n" - ] - } - ], + "outputs": [], "source": [ "import matplotlib.pyplot as plt\n", "import numpy as np\n", @@ -1163,36 +952,7 @@ "collapsed": false, "editable": true }, - "outputs": [ - { - "data": { - "image/png": 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              " - ] - }, - "metadata": { - "filenames": { - "image/png": "/Users/mhjensen/Teaching/MachineLearning/doc/LectureNotes/_build/jupyter_execute/chapter4_57_1.png" - } - }, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "import matplotlib.pyplot as plt\n", "import numpy as np\n", @@ -1334,77 +1094,7 @@ "collapsed": false, "editable": true }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "(426, 30)\n", - "(143, 30)\n", - "Test set accuracy with Logistic Regression: 0.94\n", - "Test set accuracy Logistic Regression with scaled data: 0.96\n", - "[1. 1. 1. 1. 1. 1.\n", - " 1. 1. 0.92857143 0.92857143]\n", - "Test set accuracy with Logistic Regression and scaled data: 0.96\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/Users/mhjensen/miniforge3/envs/myenv/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:814: ConvergenceWarning: lbfgs failed to converge (status=1):\n", - "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", - "\n", - "Increase the number of iterations (max_iter) or scale the data as shown in:\n", - " https://scikit-learn.org/stable/modules/preprocessing.html\n", - "Please also refer to the documentation for alternative solver options:\n", - " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", - " n_iter_i = _check_optimize_result(\n" - ] - }, - { - "data": { - "image/png": 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8PT3x+uuv44svvoCHhweCg4PxySefGNbxBWD495s+fbphbeZq1arh008/RXJyMmbNmmU4Ju3atcMnn3yCJUuWYOfOncjJyTFMRejWrRuGDBnyXN/TP2srX7lyBVZWVtDr9fD29kb//v3RsmXLRx4/Z84crF+/Hk5OTqhSpQrat2+PCRMmwNfXF+3atcPIkSMxePBg4Xvq37//E9cL7ty5M/z9/fHzzz8/9v6VK1fit99+g06ng52dHSpVqoR3331XmL9NRMaBjS8RERERWQROdSAiIiIii8DGl4iIiIgsguyNb0FBAWbMmIEaNWoYdup5mlOnTqF3797o378/evfuzatmiYiIiOi5yNr43r59GwMGDMC9e/eEq3WfJD4+HqNGjcKkSZOwatUqvPfeexg1apRw9TIRERER0ePI2vjm5OTgu+++Q/fu3Z/r8StXroS/vz8aNWoE4MHySH5+foaleIiIiIiInkTWxrdKlSqG3Xaex5EjR1CrVi2hVqtWLRw5cqSooxERERGRmZF9jm9hxMXFGXZ/+oeHh8dz7chERERERJZNLXeAwsjLy3tkv3Zra2vk5eUV6utIklSs+9ITEVmqv4/FIGxdhNwx5KMugE3141DaZT/7sUT0VL/3mVfkX9OkGl9bW1tha1TgwaoQtra2hfo6CoUCGRm50On0RRmPjJBKpYSzsx2Pt4Xg8ZZXfoEOq7ZfljuGfJRa2FQ9xaaX6AWkxaTAxccNCmXxTkYwqcbXx8cHycnJQi05ORk+Pj6F/lo6nR5aLf8wWgoeb8vC4y2PbcdikJYlDk682bYyPFwLNzjxvFQqJRwdbJCVnV/sH3S0eg2ydBlPvF8CcDR1N+7ki49xU3ugvmtzADzL+LKUSgVsbayQl6+BXs9NZ82FVqPBnytXYfeaDegxYhja9+oG4MHxLg4m1fg2adIEZ8+eFWoXL15E06ZNZUpEREQAkJFTgO3HYoRaoF8ptG9Q+IGJ56VWK+Hm5oDU1Oxi/aBzKjECv15ZhwK9plDPK2Xrhkn1RsHVxqWYklmWkjreVHIuXbqI0ImjcOnSBQDAn8tW4N1Bo1GpUmWo1cUz8mvUF7dNmTIF77//vuH2wIEDERkZiZMnTwJ4sJlFZGQk+vfvL1dEIiICsPVwNPIKHq7HrgDQq1WAfIGKyPmkS1h+eU2hm15HKweMDx7OppfoMbRaLWbN+hGvvBJiaHrVajXefXcyKlb0K9bXlnXEt6CgAMOGDUNGxoNTQ++++y7KlCmDWbNmAQDy8/Oh1WoNjy9XrhwWLFiA7777DlZWVigoKMDChQtRrlw5WfITERFwLy0X+86KGwk1DvSCr5eTTImKxo3UW1hyaTX0UuFGF21VNggNHg5P+9LFlIzIdN26dRPjxo3C6dMnDbXq1WsgLGwhatUKKvbXV0iSZJETZXiqxDLw1Jhl+ffxPhARj12nbiM7t3AjdVR4uflaZOc9HKRQqxSYNqIxPFztivV1i/PnOy7zDn4+Mx95usKtGuRlXxr9q/eCv0vFIs1D/H1u6vR6PX75ZRGmTv0Mubm5AAClUolx4yZi8uSPYGNjIzz+n+Nd1Exqji8R0fO4FpuKxVuvyB3DYrWpW77Ym97idC8nCXMiFj/S9Db2ro9+VXs8dTlMpcKoZxASyWbJkgX4+OMPDLf9/Pwxe/YCNGzYqERz8CeUiMyKJElYs+eG3DEslp2NGp2aVpQ7xgtLy09HWMRiZGqyhHptj0D0q9oDKqUKSoXyif8R0eP16zfQMH936NAR2Lv3cIk3vQBHfInIzBw5n4DI+CcvO0XFRwFg4KtV4WhnJXeUF5KtycGciCVIyUsV6pVd/TEksB9USpVMyYhMj1arhVr9sM10cHDA3LmLkJ2djZCQ1rLlYuNLRGZDq9NjxTZxAwVPNzv0bVtZpkSWQwHA18sJbk42z3ysMcrXFWDeuV9wJ/uuUPdxKodRQYNhpTLNZp5IDlu3bsEXX3yCdes2w8/P31CvX7+hjKkeYONLRGZj/9l43EkWd83qERKA4EoeMiUiU6DVa7H4wkpEZYjrEHvaeWBc7WGwUxfPBhxE5iYtLRUffTQZ69evBQBMmDAGmzdvg0plPGdLOCGJiMxCXoEWmw9GCTU/b2fUr8olpejJ9JIeKy6vxeX714S6q40LQoNHwMnaUaZkRKZl797dCAlpYmh6AcDd3QO5uTkypnoUR3yJyCz8fSIOGdnidrm9Wwc89Qp8smySJGHd9S04fe+cUHdQ2yM0eDjc7dxkSkZkOrKysvDll59i+fIlhpqzswumTfsOvXr1NbrfwWx8icjkpWcXYMfxWKEWFOCOqr5sXOjJtkXvxoH4I0LNWmWNMbWHwtvBS6ZURKbj2LGjmDBhNKKjH55tCwlpjZkz56JsWePcXIxTHYjI5G05HIV8zb+2y1UAPc1gu1wqPvtvH8a2qF1CTaVQYWTNgfBz8ZUpFZHpCAubiS5dXjM0vfb29pg+/Uf8/vtmo216AY74EpGJS7yfgwMRd4Ra8yBvlC/NuZn0eCfvnsW6638INQUUGFSjL6q7V5EpFZFpqVmzFv7Z/LdBg0aYPXs+/P2Nf8CBjS8RmbQN4ZHQ6R/uvG6tVqJ7S+P/5UvyuJRyFSuurH2k3rtKV9Tzqi1DIiLT1KpVG4waNQ6enl4YO3a8Ua3c8DRsfInIZEXeScepa0lC7Y0W/nB3sYVWq5cpFRmrW+nRWHRhJfSS+N7o5PcqWpZvIlMqIuN348Z1rFq1HF988ZVwsdr//veNjKleDBtfIjJJkiRh3b5IoeZgq0bPNpWhydfIlIqMVXxWAuae+wUavfjeaF2+OV6r2EamVETGTa/XY9Giefj66y+Rl5cHPz9/DB48TO5YL4UXtxGRSTofmYLrcWlCrXNzPzjaW8sTiIxWcm4K5kQsRq42V6g38KqL7pU7Gd1yS0TGIDY2Bt27d8Knn05BXl4eAGD16hXQ6037bBobXyIyOXq9hPXh4mivu7MN2tYvL1MiMlbp+ZmYHbEY6QWZQr2mezUMqN4LSgX/DBL9myRJWLVqOUJCmuDIkUOG+qhRY7Flyw4olab9M8OpDkRUbDRaHVbvuo7L0anCBWgvS6eXHtmsoltLf1irTePiCioZOZpczDm3GMm5KUI9wKUihtXsD5WS7xeif0tMvIt33x2PXbv+NtR8fHwxa9Y8NGvWQsZkRYeNLxEVm9/23MSBcwnF/jo+no5oHFim2F+HTEeBToP555chPkt8/5Vz9MbooCGwVnFKDNG/bd68AR988C5SU1MNtbfeGoipU6fByclZxmRFi40vERWLO8nZCI+IL5HX6tUqAErO06T/p9PrsPTSKkSmRwl1D9tSGFd7OOyt7GRKRmS8du/eaWh6PT298OOPs/DKKx1kTlX0THuiBhEZrQ3hkZCKbnbDE7WsXRaBfqWK/4XIJOglPVZdXYcLyVeEurO1E8bXGQEXGyeZkhEZt6+/no6yZcuhS5fuOHDgmFk2vQBHfImoGNy4nYazN5KFWkhwWdSrUrpIX6eUsy283e15Vb6FuJudiEPxx5Hz/6szKBSAtY0aBflaw4es9PwMXE29ITzPTm2L0ODh8LBzL+nIREYpMzMDV69eQYMGjQw1FxdX7Np1AKVLF+3vaWPDxpeIipQkSfh9302h5mhnhd6tK8HOhr9y6MXEZMRh5tkFyNcVPPvB/2KltMLooCEo5+hdTMmITMvhwwcxYcIYZGRk4MCBY/D2Lmu4z9ybXoBTHYioiJ29kYzI+Ayh9kazimx66YUlZt/D3HNLC930KhVKDK/ZH5Vc/YopGZHpyM3NxSeffIBu3ToiLi4W6elp+OijyXLHKnH8S0RERUan12PDf9bXLe1qi9Z1ysmUiExdal4aZkcsRpYmu1DPU0CBAdV7o6ZH9WJKRmQ6zpw5hdDQUbh58+E0oKZNm+OLL76SMZU82PgSUZE5eD4BCSk5Qq17ywCoVTy5RIWXVZCN2RGLkZqfJtT9nH0R4OoHpVIBW1sr5OVpoP/XOtFqpRo13avBz6VCCScmMi4FBQX48cfpmDnzR+h0OgCAjY0NPv74c4wcOdbkN6N4EWx8iahI5Bfo8MdBcfmoCmWc0KC6p0yJyJTlafMw99xSJObcE+oVnHwQGjwctmpbqNVKuLk5IDU1G1qtaW+jSlTUrly5jNDQUbhw4ZyhVqdOXcyevQBVqlSVMZm82PgSUZHYeTIW6f/ZTa0319elF6DRa7HowkrEZMYJdS97T4ytPRS2aluZkhGZBo1Gg379eiI+/jYAQK1WY9KkDzBx4iSo1Zbd+lneGDcRFbmMnAJsPx4r1Gr6l0L1ilxflwpHL+mx/NJvjyxJ5mbjivHBw+Fo7SBTMiLTYWVlha+//g4AUK1adezYsReTJn1g8U0vwBFfIioCfx6ORl6BznBbAaBnSIB8gcgkSZKENdc24WzSBaHuaOWA0ODhcLN1lScYkZHT6/XIzc2Fg8PDD4avv94JCxf+gtde6whbW54l+QdHfInopdxLzcH+s+LWxI0Dy8DXiztkUeFsvfU3Dt85LtRsVNYYW3soyjhwrjjR48TH30bv3t0QGjoK0n+2y+zatQeb3v9g40tEL2XjgVvQ/fuKepUC3Vpy3VQqnL2xB7AjZq9QUytUGFVrMCo4+8iUish4SZKEtWt/RUhIExw4sA9//bUFGzb8Lncso8epDkT0wqISMnDiinjVfdt65eHhYidTIjJFxxNOY8PNrUJNAQWGBPZD1VKVZEpFZLySkpLw3nsTsX37w58bb++y8PAw/53XXhYbXyJ6LvkaHVb9fQ2XY1Kh0z1YOipPoxMeY2+jRscmFWVIR6bqQvJlrLq67pH6m9W6I9izlgyJiIzbX3/9ifffn4jk5GRDrWfPPpg27Tu4urrJmMw0sPEloufy667rOHzx7lMf07FJBTjaWZVQIjJ1N1JvYcnFVdBL4hq8XQI6oFnZRjKlIjJO/2wxvG7dGkPN3d0d338/E506dZYxmWlh40tEz3Q7KQuHLiQ89TFuTjZoW698CSUiUxeXeQfzzy+DRq8V6m19W6K9byt5QhEZqcTERLz6aivcufPwQuLXXuuIH36YCU9PXvhZGLy4jYieaf3+SPznYmGBWqXEsI7VYW2lKrlQZLLu5SRhTsRi5OnyhHpj7/roFtARCm56QiTw9PREnTr1AABOTs6YNWseli//lU3vC+CILxE91bXYVJyPTBFqbeuWR90qHgAAhUIBXy9H2NtyigM9W1p+OsIiFiNTkyXUgzwC0a9qDza9RI+hUCjw/fc/w8pKjc8++x/Kl+dKJy+KjS8RPZEkSfh9X6RQc7a3Qo9W/rC15q8PKpwcTQ7mRCxBSl6qUK/s6o+hgf2gUvKMAVF+fj6mT/8a9es3xOuvdzLUPTw8sHDhMvmCmQn+5SKiJzp1LQlRCRlCrXNzPza9VGj5ugLMO/8L7mSLF0j6OJbFqKDBsFLxjAHRhQvnEBo6CleuXIaHhwcaNGiE0qW5RFlR4hxfInosrU6PDeHiaK+Xmx1a1i4rUyIyVVq9FosvrMSt9Bih7mnngXHBw2Gn5s5SZNm0Wi1+/PE7vPpqa1y5chkAkJGRgZMnjz/jmVRYHLYhoscKj7iDe6m5Qq1HSADUKn5epuenl/RYeeV3XL5/Tai7WDsjNHg4nKwdZUpGZBxu3LiO0NCROHv2jKEWGFgLc+YsRI0agTImM0/8C0ZEj8jN12LL4Sih5l/WGfWq8pQbPT9JkrD+xhacSowQ6vZqO4QGD4e7XSl5ghEZAb1ejwUL5qBt2+aGplepVOKdd97D33/vY9NbTDjiS0SP+PtELDJzNEKtV6sAXnFPhbItejfCbx8RatZKK4ytPRRlHcvIlIpIfvHxtxEaOgqHDx801AICKiEsbAHq1WsgYzLzxxFfIhKkZ+Xj7xNxQq12gDuq+nIrTHp++28fxraoXUJNpVBhRK2B8HOpIFMqIuOgUChw8eIFw+0RI0Zjz55DbHpLABtfIhJsORyNfI3OcFuhAHq2CpAxEZmaU3fPYt31P4SaAgoMqtEHNdyrypSKyHiULVsO33zzPcqX98HGjVvx9dffwd7eXu5YFoGNLxEZ3L2fg/CIO0KtWS1vlCvNC5Do+VxKuYblV9Y+Uu9dpSvqeQWXfCAiI7B16xakp6cJtR49euPgwRNo3rylPKEsFBtfIjLYEB4J/b/2JrZSK9G1uZ+MiciU3EqPxqILK6CX9EK9k9+raFm+iUypiORz/34KRo0agqFD++Pjjz8Q7lMoFHBwcJApmeVi40tEAIDI+HScvpYk1NrX90EpZ66xSs8Wn5WAued+gUYvXhTZqnwzvFaxjUypiOSze/ffaNmyMTZt2gAA+P3333Dq1AmZUxEbXyKCJElYt++mUHOwVeP1xr4yJSJTkpx7H3MiFiNXK6773MCrDnpUfoOrgZBFycrKxLvvjke/fr1w714iAMDV1RXz5y/hxWtGgMuZERHO3UzB9dvpQq1T04qwt+U2svR0GQWZmB2xCOkFmUK9pns1DKjeG0oFx1fIchw5cggTJoxBbOzDXQrbtm2Pn34KQ5ky3jImo3+w8SWycDq9Huv/szWxu7Mt2tQtL1MiMhU5mlyERSxGcm6KUPd3qYhhNftDpVTJlIyoZOXm5mLatKlYuHAupP+/TsLBwRFTp05D//6DeNbDiLDxJbJwhy/cxZ3kbKHWvaU/rNQcqaMnK9BpMP/8MsRnJQj1co7eGBM0BNYqa5mSEZW8rVv/wIIFcwy3mzRphlmz5qFChYryhaLH4l82IguWr9Fh88FbQs3X0xGNAr1kSkSmQKfXYemlVYhMF7e19rAthXG1h8Heyk6mZETy6NmzD1q3bgsbGxtMnToNmzb9xabXSHHEl8iC7T4Vh7SsAqHWs3UAlDwtR0+gl/RYfXU9LiRfEerO1k4YX2cEXGycZUpGVHLu3bsHT09Pw22FQoGff56DjIwMVK1aTcZk9Cwc8SWyUFm5Gmw7FiPUalR0Q00/d5kSkbGTJAmbbv6F43dPC3U7tS1Cg4fDw47vHTJvOp0OYWEzUa9eIPbv3yvc5+1dlk2vCWDjS2Shth6JRm6+Tqj1alVJpjRkCnbG7MPeuINCzUqpxuigISjnyCvWybxFRd1C166vY+rUT5Gfn4+JE8c+shsbGT82vkQWKDktF3vP3BZqjWt4oUIZJ5kSkbE7FH8MW27tEGpKhRLDaw5AJVfu7kfmS5IkLFu2BK1bN8Px40cBPJja0K1bT9jYcIMfU8M5vkQWaOPBW9DqHm5NrFYp0K2lv4yJyJiduXcea65teqQ+oHpv1PSoLkMiopKRkHAHb789Dvv27THUKlSoiNmz56Nx46YyJqMXxRFfIgsTczcTxy4lCrXWdcqjtCuvxKdHXbl/Hcsu/QYJklDvWbkzGpapK1MqouIlSRLWrVuDli0bC03voEHDsG/fETa9JowjvkQWZv1+cWtiOxsVOjWtIFMaMmbRGbFYeGEFdJI4F7xDxbZo7dNcplRExW/GjOn47rtphttlynjj55/D0KZNexlTUVHgiC+RBbkUdR+XolOFWodGFeBkz80GSHQ3OxFzI5aiQCcud9eiXBN09HtFplREJaNHj96wt3cw/P+BA8fY9JoJjvgSWQi9JGHdf0Z7XR2t0b6Bj0yJyFjdz0vF7IjFyNbmCPV6nrXRu0oXbr9KZs/Pzx/Tp8+Avb093nijq9xxqAhxxJfIQhy/nIjYxCyh1rWFP2ysVDIlImOUWZCFsIjFSMtPF+rVS1XBwBp9oFTwzwaZlwMH9qN7907IyhJ/P/bp049NrxnibzAiC6DR6rHpgLg1sbe7PZrVKiNTIjJGedo8zD23BIk5SUK9orMvRtQaCLWSJwnJfOTk5GDKlPfQs2dnHDp0AFOnfip3JCoB/C1GZAH2nY1HcnqeUOvZKgAqJT/70gManQYLLqxAbGa8UC/j4IUxtYfARsV54GQ+Tp48jvHjR+PWrUhDLTIyEgUFBbC25nvdnPGvHpGZy8nTYuuRaKFWubwLgit5yBOIjI5Or8Mvl3/D9VRxDngpWzeMDx4ORysHmZIRFa38/Hx89dUXeOONVw1Nr52dHb755nusW7eZTa8F4IgvkZnbfjwGWbkaodardSVeoEQAHqxXuubaRpxLuijUHa0cEBo8HK42LjIlIypaFy9eQGjoKFy+/PC9Xq9eA4SFzUdAQGUZk1FJ4ogvkRlLzczHrpNxQq1eldKoVI7NDD3wR+R2HEk4KdRsVTYYFzwMXvalZUpFVLTmzp2NV19tZWh6rays8PHHn+PPP/9m02thOOJLZMY2H7yFAq3ecFupUKB7CLcmpgd2x4ZjV+x+oaZWqjEqaDB8ncrLE4qoGEiSBI3mwZmvGjVqIixsAWrWrCVzKpIDG18iMxWfnI1DFxKEWsvgsvB253xNAo7eOYlNN/8SagooMDTwLVRxC5ApFVHxGD16HHbv/hv16zfEe+99CBsbG7kjkUzY+BKZqQ37IyFJD29bWynRuVlF2fKQ8TiXdBGrr65/pP5WtZ6oXTpQhkRERScuLhYHD4ajX78BhppKpcK6dX9ArWbbY+n4DiAyQ9fj0hBxM1movdrAF66OHOWwdNdTI7H00q+QIAn1bpU6oknZBjKlInp5kiTht99W4ZNPPkR2dhYCAiqjUaPGhvvZ9BLAi9uIzI4kSVi3T1yWysneCq818pUpERmL2MzbWHB+GbR6rVBv79sK7XxDZEpF9PISExMxYEAfvP32OGRlZUKSJEyf/pXcscgIsfElMjOnryUh8k6GUOvczA92NhztsGSJOUmYE7EEebp8od7UuwG6BHSQKRXRy/vjj41o2bIhdu7cYaj16dMPy5atljEVGSvZ/xLu2rUL8+bNg62tLZRKJT7//HNUrvz4pUUkScK8efOwY8cOODs7IycnB3369EGfPn1KODWRcdLq9NgQHinUPF3tEBJcVqZEZAxS89Iw++wiZGmyhXpw6ZroW7U713Qmk5Saeh8ffjgJmzZtMNQ8PEpjxoxZ6NCho4zJyJjJ2vieP38ekydPxoYNG+Dv74/Nmzdj2LBh2LZtGxwdHR95/Pr167FkyRJs27YNXl5eSEhIQKdOneDl5YVWrVqV/DdAZGQOnk9AYmquUOse4g+1iid3LFWWJhth55YgNT9NqFdxq4TBNd6ESqmSJxjRS9izZyfefjsUiYl3DbVOnbrgu+9+gocHd6WkJ5P1r+GiRYsQEhICf/8H64p27twZOp0Omzdvfuzjr169Cn9/f3h5eQEAvL294efnh0OHDpVUZCKjlVegxR+HooSan7cTGlTzlCkRyS1Pm495537B3exEoe7rVB6jag2ElcpKpmREL06v12P69K8NTa+Liyvmzl2EJUtWsOmlZ5K18T169Chq1Xq4gLRSqURgYCCOHDny2Me3bdsWkZGRuHbtGoAHjfCNGzf4RicCsPNEHDKyC4Rar1bcmthSafVaLL64EtEZsULdy740xtYeClu1rUzJiF6OUqnE7NkLYGNjg9at2+LAgWPo2bMPf9fRc5FtqkNqaioyMzMfaVo9PDxw4cKFxz6nadOm+OabbzBo0CCUKlUKUVFRqF+/Pvr161fo11fx1K9F+Oc4m/vxzsguwI4TYoNTu5I7aga4y5RIHpZyvJ9FL+mx8vLvuHL/ulB3s3HB2/VGws3OWaZkRYvH2zLk5eXh3r1E+Pn5AXhwvAMDa2D37nDUqBHIhtdMFdfPtWyNb15eHgDA2tpaqFtbWxvu+699+/bhs88+w5IlS1CzZk3ExcVh+/btsLUt/MiFs7Nd4UOTyTL34712XyTyCnSG2woFMLxrENzcLHOXNnM/3k8jSRIWn/4Np+5GCHUnawd82mYiyjt7yxOsGFny8TZ3p06dwsCBA6FUKnHq1CkAD4938+aN5IxGJkq2xvefZrWgQDw1W1BQ8MRG9qeffkL79u1Rs2ZNAICPjw+io6Pxv//9D//73/8K9foZGbnQ6fQvkJxMiUqlhLOznVkf77v3c7DjaLRQa17LGy62KqSmZj/+SWbKEo73s2y5uQO7bh0UajYqa4yrMwwOOmezek/weJsvjUaDH36Yjh9//B463YMP9VOmfIKffvqBx9tC/PPzXdRka3zd3Nzg5OSE5GRxd6nk5GT4+Pg89jkxMTHo0EFcb7J8+fJYtmxZoRtfnU4PrZY/OJbCnI/373tvQqd/uAuXWqVE52Z+Zvv9Pg9zPt5Psy/uEP66tVuoqRQqjKw1CD4O5c3238RSj7e5unr1CkJDR+H8+QhDLSgoGL179wXA400vR9aJUY0bN8bFixcNtyVJwuXLl9G0adPHPt7LywtJSUlCLSkpCTY23IaVLNOtOxk4dfWeUGtXvzzcXXjhkqU5cfcM1t/YItQUUGBw4JuoVurxa6MTGROdToc5c2ahffuWhqZXpVLhvfc+xPbte1CtWnV5A5JZkLXxHTlyJMLDwxEV9WAJpi1btkCpVKJr164AgClTpuD99983PL5Hjx7Ytm0b7ty5AwCIj4/Htm3bHhkFJrIEj9ua2MFWjY5NKsiUiORyMfkKVl75/ZF636rdUNczSIZERIUTHR2Fbt064ssvP0F+/oPdBatWrYYdO/Zi8uSPYGXFpfeoaMi6gUVQUBCmT5+OSZMmGXZuW7JkiWHzivz8fGi1D/eUHzZsGBQKBcaNGwdbW1tkZWWhb9++GDt2rFzfApFszkem4FpcmlDr2KQiHGz5B8KS3EyLwuKLK6GXxFO/nf1fQ/NyjWVKRfT8MjLS0b59CNLT0wAACoUCo0eHYsqUT1/o4nWip1FIkiQ9+2HmJzU1m3OELIBarYSbm4PZHW+9XsLnv5xAfNLDC5XcnW0wbWRjWKktdycucz3eTxKflYCfzsxDrlZcCaeNTwt0r9TJ7Jd5srTjbc6+//4bfP/9N/D1rYjZs+ehSZNmjzyGx9uy/HO8i/zrFvlXJKJid+TiXaHpBYCuLfwtuum1NEk5KQiLWPxI09uoTD10q9TR7JteMl2SJEGSJCiVD2dbvv32e7CyssLw4aPg6OgkYzoyd1z1m8jEFGh02HTwllArX9oRTQLLyJSISlp6fgbCIhYhoyBTqNfyqI63qvWEUsFf7WSckpOTMWzYQPz00/dC3crKCm+//R6bXip2/O1IZGL2nL6N1Mx8odardQCUSo7wWYIcTQ7CIhYjOe++UA9w8cPQwP5QKTnqT8Zpx45taNmyEbZu/QMzZkwXlisjKimc6kBkQrJyNfjraIxQq17BDTX9SsmUiEpSga4A884vw53su0K9nKM3xtQeDGsVL2wk45ORkY5PPvkQa9asNtScnZ2RnJz0lGcRFQ82vkQm5K+j0cjJ1wq1nq0COJ/TAuj0Oiy+uAq30qOFemk7d4QGD4edmtv2kvE5eDAcEyeOxe3bcYbaq692wA8/zIKXl5eMychSsfElMhHJ6bnYc/q2UGtY3RN+3s4yJaKSopf0WHnld1xKuSrUXaydEBo8As7WnBdJxiUnJwdfffU5Fi9eYKg5Ojrh66+no2/ft/hhnWTDxpfIRGw6EAWt7uHqgyqlAt1b+suYiEqCJEnYcONPnEw8K9Tt1HYIDR4BDztOcyHjEh0dhTff7IHIyIcb7DRv3hIzZ86Fj4+vjMmI2PgSmYTYxEwcuyTO62xVpxw83exlSkQlZUf0Xuy/fVioWSutMLb2UJR15EoeZHzKlPE27LRma2uLTz/9EsOGjRKWLyOSC9+FRCZg/f5I/HunGVtrFd5oVlGuOFRCDtw+iq1Rfws1pUKJ4bUGwt+FW1OTcbK1tUVY2AI0atQEe/cexogRY9j0ktHgO5HIyF2Ovo+LUeLSVR0a+cLZ3lqmRFQSTidG4Pfrm4WaAgoMqt4Hge5V5QlF9B9arRazZv2I69evCfWgoGBs2bIDlSpVlikZ0eOx8SUyYnpJwrr9kULNxdEarzTgPDlzdjnlGpZfXgsJ4o7yPat0Rv0ydWRKRSSKjLyBN954FV999QVCQ0dCo9EI9/MCNjJGbHyJjNjJK/cQc1fcnatLcz/YWHOTAnMVlR6DRRdWQCfphPrrfu3RqnwzmVIRPaTX67F48Xy0adMcp0+fBACcP38OR48efsYzieTHi9uIjJRWp8fGA+Jor7e7PVoEecuUiIrbnay7mHtuKQr04shZSPmmeL1iO5lSET10+3YcJk4ci4MHww01Pz9/zJ69AA0bNpIxGdHz4YgvkZHadzYeSWl5Qq1HSABUvEjELKXk3kdYxGLkaHOFen2vYPSs3JmnjUlWkiRhzZrVCAlpIjS9w4aNxN69h9n0ksngiC+REcrN1+LPw9FCrVI5F9Sp7CFPICpWmQVZmB2xCOkFGUK9hntVDKjeG0oFP+yQfO7du4f33puAHTu2GWrlypXHzz/PQUhIaxmTERUef5sSGaHtx2OQlSue7u7VmlsTm6NcbR7mRCxGUm6KUPd3qYARNQdAreT4BMnr1q1I/P33dsPtPn36ITz8KJteMklsfImMTGpmPnaeiBNqdSp7oHJ5V3kCUbHR6DRYcH4Z4rLuCPWyDmUwJmgIrFVcso7k17hxE4wZMx4eHqWxfPlvmD17PpydXeSORfRC2PgSGZk/DkWhQKs33FYoHsztJfOi0+uw9NKvuJF2S6i727phXPAw2FtxVz6Sx4kTx6HX64Xahx9+ggMHjqNDh44ypSIqGmx8iYzIneRsHDwvjv61CCqLsh4OMiWi4iBJEn69ugHnky8JdScrR4QGj4CrDUfTqORlZWXhvffeRqdO7bFgwVzhPltbW3h48BoDMn1sfImMyIbwSEj/2rPAWq1El+Z+8gWiIidJEjbd/AvH7p4S6rYqW4wLHg5PezYXVPKOHTuK1q2bYsWKpQCAadO+RHR0lMypiIoer5ogMhI3bqfh7I1kofZKQx+4OdnIlIiKw67Y/dgTd0CoWSnVGB00GD5OZWVKRZYqLy8P3377FebNmw3p/z9129vb44svvkaFChXlDUdUDNj4EhkBSZKwbp+4WYWjnRU6NKogUyIqDofjj+OPyO1CTalQYljN/qjs5i9TKrJU589HIDR0FK5evWKoNWzYGLNnz4efH9+PZJ7Y+BIZgbM3knEzPl2ovdGsIuxs+CNqLs7eu4Dfrm18pN6/Wi/U8qghQyKyVBqNBjNnzsCPP34HrVYLALC2tsaHH36KMWNCoVJxS3QyX/yrSiQznV6PDeHiaG9pV1u0rlNOpkRU1K7dv4lll36FBEmo96j8Bhp515MpFVmquXNn4bvvphlu16pVG2FhC1C9Oj+AkfnjxW1EMjt4PgEJKTlCrXvLAKhV/PE0BzEZcVhwYRm0kk6ov1ahDdr4tJApFVmyYcNGoUKFilCpVHj33cnYvn0Pm16yGBzxJZJRfoEOfxwUr5yuUMYJDap7ypSIitLd7HuYe24p8nUFQr152Ubo5P+qTKnI0uTl5cHW1tZw29HREfPnL4FSqUSdOjzjQJaFQ0pEMtp5Mhbp2WJT1KtVAJTcmtjkpealISxiMbI02UK9Tula6FO1G7efpmInSRJWrPgF9evXemRpsnr1GrDpJYvExpdIJhk5Bdh+PFao1fQrhRoVS8mUiIpKVkE2ZkcsRmp+mlCv5lYZgwLfhFLBX71UvO7eTUC/fj3x3nsTce9eIiZMGPPIbmxEloi/fYlk8ufhaOQVPJz3qQDQsxW3JjZ1edo8zD23FIk594R6BWcfjKg1EFZKzjCj4iNJEjZs+B0tWzbCnj27DPVKlSojPz9fxmRExoG/gYlkcC81B/vPxgu1xoFl4OvlJFMiKgoavRYLL6xATGacUC9j74mxQUNhq+ZmJFR8UlJSMHnyO/jzz82GmqenF376aTbat39NvmBERoQjvkQy2HjgFnT6h0tbqVUKdGvJrYlNmV7SY9ml33At9aZQd7NxRWjwcDhaO8iUjCzB339vR8uWjYSmt2vX7jhw4BibXqJ/4YgvUQmLSsjAiSviafC29crDw8VOpkT0siRJwpprGxGRdEGoO1o5YHzwcLjZusoTjCzC119/iZkzZxhuu7m5Yfr0H9G1aw8ZUxEZJ474EpUgSZKwfr+4WYW9jRodm1SUJxAViT9v/Y3Dd04INRuVNcbVHgYvBy5NR8WradPmhv9v3/5VHDhwnE0v0RNwxJeoBF2Muo8rMalCrWOTCnC0s5IpERWGRqfBX1G7cC31BnTSgyvk9ZIeCdmJwuPUChVGBw2Gr3N5OWKShWndui3GjZuISpUqo1+/AVwqj+gp2PgSlRC9XsK6feJor5uTDdrWY3NkCnR6HZZcWo0LyZef+jgFFBhS8y1UcatUQsnIkpw+fRK///4bvv12htDgfv75/2RMRWQ62PgSlZCjl+7idlKWUOvWwh/WViqZEtHzkiQJv17d8MymFwD6VeuB4NI1SyAVWZKCggLMmPEtZs78EXq9HjVq1MSgQUPljkVkcjjHl6gEaLQ6bD54S6iVK+2ApjXLyJSInpckSdh08y8cu3vqqY9TQIFulTqiadmGJZSMLMXly5fw2mtt8NNPPxg2odiyZRMkSXrGM4novzjiS1QC9pyOR0qGuHh8z5AAKJWci2fsdsXux564A0LNSqlGt0qdYKe2BfCg6S3n6I2yjvwgQ0VHp9NhzpyZmD79a2g0GgCAWq3G++9Pwfjx73AuL9ELYONLVMyy8zT462i0UKvq44qgAHd5AtFzOxx/HH9EbhdqSoUSw2r2Ry2PGjKlIktw69ZNhIaOxqlTD1cLqV69BsLCFqJWrSAZkxGZNk51ICpmfx2NQXaeVqj1al2JozVG7uy9C/jt2sZH6v2r9WLTS8VGkiQsWbIQbdo0NzS9SqUS48e/g507w9n0Er0kjvgSFaOU9DzsPnVbqNWv5gn/ss4yJaLncfX+DSy79CskiHMoe1TqhEbe9WRKRZZAoVDg9OmTyMnJAQBUrOiHsLCFaNiwkczJiMwDR3yJitHmQ7eg1ekNt1VKBXqE+MuYiJ4lJiMOCy8sh1bSCfVXK7RBG9+WMqUiS/LNN9+jbNlyGDJkOPbtO8Kml6gIccSXqJjcvpeFIxfuCrWQ4LLwcrOXKRE9y93se5hzbgnydQVCvVnZRnjD/1WZUpE5S0pKwo0b14Td11xcXHHgwDE4O7vImIzIPHHEl6iYrA+PFE6U21ir0LmZn2x56OlS89IwO2IRsjU5Qr1O6VroW7Ub52RTkdu6dQtatmyIQYP64e7dBOE+Nr1ExYONL1ExuBqTivORKUKtQ0NfODtYy5SIniarIBuzIxYjLT9dqFdzq4xBgW9CqeCvSio6aWmpGDt2BIYO7Y+UlBSkp6fhiy8+kTsWkUXgVAeiIiZJEtbtvynUnB2s8UpDH5kS0dPkafMw99xSJObcE+oVnH0wotZAWCn5a5KKzr59e/D22+OQkHDHUHv99Tfwv/99K2MqIsvB3+hERezk1XuISsgUal2a+8HWmj9uxkaj12LhhRWIyYwT6l72nhgbNBS2ahuZkpG5ycrKwtSpn2LZsiWGmrOzC6ZN+w69evXlVBqiEsK/xERFSKvTY2O4uDWxVyl7tAjylikRPYle0mPZpd9wLVUcnXezccX44OFwtHaQKRmZm2PHjmLChNGIjo4y1EJCWmPmzLkoW7acjMmILA8bX6IiFB5xB/fScoVaj5b+UKs4R9SYSJKENdc2IiLpglB3tHJAaPBwuNm6yhOMzE5ubi6GDx+Ie/cSAQD29vb4/POvMHjwMI7yEsmAf42JikhuvhZbDkcJtYCyzqhXtbRMiehJttzagcN3Tgg1G5U1xtYeijIOnjKlInNkZ2eH6dN/BAA0aNAIe/cexpAhw9n0EsmEI75ERWTH8Vhk5miEGrcmNj67Y8OxM2afUFMrVBhVazAqOPMCRHo5Wq0Wubk5cHJ6uDtjx45vYNWqtWjb9hWoVCoZ0xERR3yJikB6Vj7+Phkr1IIreaCKj6s8geixjiWcwqabfwk1BRQYEtgPVUtVkikVmYvr16+hY8d2ePvtUEiSuN31K690YNNLZATY+BIVgT8OR6NA83BrYoUC6NEqQMZE9F/nky5h9dX1j9T7VeuBYM9aMiQic6HX6zF/fhjatWuBs2fP4M8/N2PjxnVyxyKix+BUB6KXlJCSjQMRd4Ra81reKOfBVQGMxY3USCy5tBp6SS/Uuwa8jqZlG8qUisxBbGwMJkwYgyNHDhlqlSpVhp+fv4ypiOhJOOJL9JI2ht+C/l+nNa3VSnRtwT96xiIuMx7zzy+HVq8V6m19W6J9hVbyhCKTJ0kSVq1ajpCQJkLTO2rUWOzZcwh169aXMR0RPQlHfIlews34dJy+niTU2jfwgZsTNz4wBvdykjAnYgnydHlCvbF3fXQL6ChTKjJ1iYl38e6747Fr19+Gmo+PL2bNmodmzVrImIyInoWNL9ELkiQJ6/aJmx842lmhQ6MKMiWif0vLT8fsiMXI1GQJ9SCPQPSr2oOrbdALuX07Dm3bNkdqaqqh9tZbAzF16jRhJQciMk6c6kD0giJuJuPG7XSh1qlpRdjb8vOk3LI1OQiLWIz7ealCvbKrP4YG9oNKyavr6cWUK1ceTZo0BwB4enph1aq1+OmnMDa9RCaCf6GJXoBOr8f6/ZFCzcPFFq3rcPtRueVp8zH7zBIkZCcKdR+nchgVNBhWKiuZkpE5UCgU+OGHmXB3d8fHH3+OUqXc5Y5ERIXAEV+iF3D4wl0kpOQItW4t/WGl5o+UnLR6LWYcXoio9Bih7mnngXG1h8FObStTMjJFmZkZePfd8dixY5tQ9/DwwIwZs9j0EpkgjvgSFVK+RofNB28JNV8vRzSq4SVTIgIAvaTHiotrcO7uZaHuauOC0OARcLJ2lCkZmaLDhw9iwoQxiIuLxY4d21C/fkN4eHjIHYuIXhKHp4gKafepOKRlFQi1Xq0qQcmLpWQjSRLWXd+Ck3cjhLqD2h6hwcPhbucmTzAyObm5ufjkkw/QrVtHxMXFGmqXLl2QORkRFQWO+BIVQmZOAbYdE0+jB1Z0Q6BfKZkSEQBsi9qFA/FHhJq10gpjag+BtwNH4un5nDlzCqGho3Dz5g1DrWnT5pg5cy4qVKgoXzAiKjJsfIkKYeuRGOTm64Raz1aVZEpDALA/7jC2Re8WaiqFCiNqDYSfC5eWo2crKCjAjz9Ox8yZP0Kne/DzbWNjg48//hwjR46FUsmTo0Tmgo0v0XNKSsvF3jO3hVrjQC9UKOMkUyI6cfcM1t34Q6gpoMCQWm+ihntVmVKRKYmMvIGRI4fiwoVzhlpwcB2EhS1ElSp8DxGZG36MJXpOmw7cgk7/cGtitUqB7tyaWDYXk69g5ZXfH6kPq9cHDcoEl3wgMkl2dvaIiYkGAKjVanzwwcf466/dbHqJzBQbX6LnEHM3E8cui+vCtqlbHh6udjIlsmyRadFYfHEV9JJeqHeu9BpeqRQiUyoyRWXLlsO0ad+hatVq2LFjLyZN+gBWVlzrmchcsfEleg7r9otbE9vZqNGpaUV5wli4+KwEzDv/CzR6jVBvXb45XvdrK1MqMgWSJGH16hVIT08T6r169cWePYcQFBQsSy4iKjlsfIme4WJUCi5Hi1vfvt7YF452HBUqacm5KZgTsRi52lyh3sCrLrpX7gQFl5SjJ4iPv43evbvinXdC8cknHwr3KRQKWFtby5SMiEoSG1+ip9BLEtbvE7cmdnOyQbv6PjIlslzp+ZmYHbEY6QWZQr2mezUMqN4LSgV/ndGjJEnC2rW/IiSkCcLD9wEA1q79FRcvcl1eIkvEVR2InuL4pUTE3ssSal2a+8HGSiVTIsuUo8nFnHOLkZybItQDXCpiWM3+UCl5POhRSUlJeO+9idi+fauh5u1dFj/9FIaaNWvJmIyI5MIhEqIn0Gh12HhA3Jq4rIcDmtUqI1Miy1SgK8D8878gPitBqJdz9MbooCGwVvEUNT3qr7/+REhII6Hp7dmzD8LDj6JNm3YyJiMiOck+4rtr1y7MmzcPtra2UCqV+Pzzz1G5cuUnPv7+/fuYMWMGYmNjkZ2djYKCAowdOxavv/56CaYmS7DvTDxSMvKEWs+QAKi4mH2J0el1WHJxNSLTo4W6h20pjKs9DPZWXFWDROnpafjoo8lYt26Noebu7o7vv5+JTp06y5iMiIyBrH/Bz58/j8mTJ+OHH37Ar7/+ip49e2LYsGHIysp67OMLCgowZMgQNGjQACtXrsTGjRvRokULXLjAuVpUtHLyNPjzSLRQq1LeBbUrucsTyALpJT1WXlmHiylXhLqztRPG1xkBFxtnmZKRMdu6dYvQ9L72WkeEhx9n00tEAGRufBctWoSQkBD4+z/YBKBz587Q6XTYvHnzYx+/bt062NjYoGvXrobaiBEj0LNnzxJIS5Zk27FYZOdphVqv1pW4akAJkSQJG29sxcnEM0LdTm2L0ODh8LDjBxB6vH79BiAkpDWcnJwxa9Y8LF/+Kzw9PeWORURGQtbG9+jRo6hV6+EFBkqlEoGBgThy5MhjH79z5040aNBAqJUqVQoBAQHFmpMsy/2MPOw6FSfU6lUtjYByLjIlsjx/x+zFvtuHhJqV0gqjg4agnKO3TKnIGEVHRwu3FQoFZs2ah/Dwo+jb9y1+WCUigWyNb2pqKjIzM+Hh4SHUPTw8EBcX99jnXL9+Hba2tvj888/Rt29fDBgwAL/99hskSXrs44lexOZDUdBoH+4IplQo0COEH65KysH4Y/jz1t9CTalQYnjN/qjk6idTKjI2+fn5+OKLT1GpUiXs379PuM/buyzKl+eSg0T0KNkubsvLe3DR0H8XDbe2tjbc918ZGRlYsGAB5syZgy+//BLR0dF46623kJmZiZEjRxbq9VUqXqBkCf45zs97vG8nZeHwBXH1gFZ1y6G8p2ORZ6NHnb57DmuvbXqkPrhmXwSXCXzm8wt7vMk0nT9/DmPGjMCVK5cBAOPHj8bhwyfg7MyzMuaMP9+WpbiOs2yNr62tLYAHF6z9W0FBgeG+/1IoFAgKCkJISAgAoGLFiujRoweWL19e6MbX2ZlXg1uS5z3eszdewL9PINhaqzC4UyDcnB//nqSic/7uFSy5+CskiGdwBtfphdeqtCjU1+LPt3nSarX49ttv8eWXX0KrfTAH39raGuPHj0f58l5QqbiesyXgzze9DNkaXzc3Nzg5OSE5OVmoJycnw8fn8aeovL29UaaMuIZq2bJlkZycjLy8vCc2zI+TkZELnU7/7AeSSVOplHB2tnuu4301JhUnLycKtdca+QI6HVJTs4szpsWLSovBT6cXQKfXCfWO/u3QpHSj5/73L8zxJtNy/fo1jB07CmfOnDLUatWqhdWrV6NChUrIyHj8mUIyH/z5tiz/HO+iJus6vo0bN8bFixcNtyVJwuXLlzF69OjHPr5+/fq4ffu2UEtOToabm1uhml4A0On00Gr5g2MpnnW8JUnCmj03hJqzvRXa1/fh+6SYJWQnYvaZJcjXiWd/WpRrgg4V2r/Qvz9/vs2HXq/H4sXz8dVXXximwSmVSkyc+C4++OAjeHm5ITU1m8fbgvDnm16GrBNlRo4cifDwcERFRQEAtmzZAqVSaViubMqUKXj//fcNjx80aBDOnz+P8+fPAwDS0tLwxx9/YMCAASWenczL6WtJuHUnQ6i90cwPdjay7/Fi1lJyUxEWsRjZ2hyhXs+zNnpX6cIr8glffPEJPvnkQ0PTGxBQCX/9tQtTpnz2yDUiRETPIutf9aCgIEyfPh2TJk0y7Ny2ZMkSODo+uJAoPz/fMI8LAKpVq4awsDBMnToVarUaOp0OvXv3xtChQ+X6FsgMaHV6bAiPFGqebnYICS4rUyLLkFmQhbBzi5CWny7Uq5eqgoE1+kCp4AUsBAwePAwrVixFTk4ORowYjY8//gL29vZyxyIiE6WQLHQtMJ4aswxqtRJubg5PPd77ztzGyp3XhdqYrjXRoBoXvS8uudo8zDq7ALGZ8ULdz9kX4+uMhI3qxUbynud4k3GTJOmRkf4NG36Hp6cXWrQIEeo83paFx9uy/HO8ixqHVMii5RVo8cfhaKHm5+2M+lVLyxPIAmh0Giw8v/yRptfbwQtjag994aaXTN8ff2zE66+3RXa2eDFjjx69H2l6iYheBBtfsmh/n4hDRrZ4UVXv1gGcW1pMdHodfrn0K66niVNLStm6ITR4OByseArbEqWm3seoUUMwYsRgnD59Cv/732dyRyIiM8Urd8hipWcXYMfxWKEWFOCOqr5uMiUyb5Ik4bdrG3Eu+ZJQd7RywPjg4XC14eYDlmj37r/xzjvjkZh411BLSUmBTqfjurxEVOTY+JLF2nI4Cvmah+vGKhRAz1bcmri4/BG5HUcTTgo1W5UNQoOHw9OeU0ssTVZWJj7//GOsXLnMUHN1dcW3385At249edaFiIoFG1+ySIn3c3Ag4o5Qa1bTG+VLc2vi4rArZj92xe4XamqlGqOCBsPHqZw8oUg2R44cwoQJYxAbG2OotWnTDj//PAdlynjLmIyIzB0bX7JIG8IjodM/XNDESq1E1xZ+MiYyX0funMDmyG1CTQEFhga+hSpuHGG3NF9//SVmzfoR/ywoZG/vgKlTp2HAgMEc5SWiYsfGlyxO5J10nLqWJNTa1SuPUs6F2/2Pni0i6SJ+vbrhkfpb1XqidulAGRKR3FxcXA1Nb5MmzTBz5lxUrMgPnURUMtj4kkWRJAnr9okrCjjYqvF6kwoyJTJf11Nv4pdLv0KCuFR4t0od0aRsA5lSkdzGjAnF/v170bZte4waNRZKJRcXIqKSw8aXLMq5yBRcj0sTah2bVISDrZU8gcxUbMZtLDi/HFq9Vqi/UqE12vlyPVZLceXKZRw7dgRDhgw31FQqFdat28xpDUQkCza+ZDH0egkb9oujve7ONmhbjxdXFaXE7HuYc24J8nT5Qr2pd0N09n9NplRUknQ6HebNC8O33/4PWq0WgYG10LBhI8P9bHqJSC48x0QW4/DFBMQniztCdWvpDys11wotKql5aZgdsRhZGvHfObh0TbxZrTsbHgsQFXULXbu+jqlTP0VBQQH0ej1mz/5R7lhERADY+JKFKNDosPlglFDz8XRE48AyMiUyP1mabIRFLEZqfppQr+JWCYMD+0Gp4K8bcyZJEpYtW4LWrZvh+PGjAB6M7I4ZMx6LFi2XOR0R0QOc6kAWYefJOKRmiqfee7UKgJIjkEUiT5uPueeW4m7OPaHu61Qeo2oNhJWSv2rMWULCHbz99jjs27fHUPP1rYjZs+ehSZNmMiYjIhJxCIbMXkZ2AbYejhZq1Su4IdCvlDyBzIxGr8WiCysQkxEn1L3sS2Ns7aGwVXOZOHMlSRLWrVuDli0bC03vwIFDsX//ETa9RGR0OAxDZm/dnuvIyRdXF+jVOoDzTYuAXtJj+eU1uJp6Q6i72rggNHg4nKy5E5450+l0WLBgLtLT0wAAZcp44+efw9CmTXt5gxERPQFHfMmsJaXlYushcW5voxpeqFjGWaZE5kOSJKy9tgln750X6g5W9hgfPBylbN1kSkYlRa1WIyxsAWxsbNCjR28cOHCMTS8RGTWO+JJZ27A/Elqd3nBbpVSgW0t/GROZj61RO3HoznGhZq2yxrjaw1DGwUumVFScMjLSkZycDH//h1tNV6tWHeHhx4QaEZGx4ogvma3YxEwcvXhXqLWuUw6ernYyJTIfe+MOYkf0HqGmVqgwqtYgVHD2kSkVFacDB/YjJKQJhgx5C/n54oWibHqJyFSw8SWztW5/pLBZrq21Cp2aVZQrjtk4nnAaG278KdQUUGBwYD9UK1VZplRUXHJycjBlynvo2bMz4uNv48qVy/jpp+/kjkVE9EI41YHM0qXo+7gUdV+odWhcAc721jIlMg8Xki9j1dV1j9TfrNoddTxryZCIitPJk8cxfvxo3Lr1cMfDFi1C0L//YPlCERG9BDa+ZHb0koT1+8StiV0drfFKfZ6Cfxk306Kw5OIq6CW9UO/s/xqalWv0hGeRKcrPz8f333+DsLCfodc/ON52dnb47LOpGDJkBJRKniwkItPExpfMzokriYhJzBRq3VoGwMaaWxO/qLjMO5h37hdo9OKycG18WuCVCq1lSkXF4eLFCwgNHYXLly8aavXqNUBY2HwEBHAqCxGZtiL/2L506dKi/pJEz02j1WNj+C2hVt7TES2DvWVKZPqSclIw59xi5OnyhHqjMvXQrVJHrodsRpKTk9GpU3tD02tlZYWPP/4cf/75N5teIjILLzzie+LECVy5cgVZWVmQpIeXEG3atAlDhw4tknBEhbX/bDyS08UGbVDHGlApldDq9U94Fj1Jen4GZkcsQmZBllCv5VEDb1XrCaWCp7zNiYeHB8aMGY8ZM6ajRo2aCAtbgJo1OXebiMzHCzW+X331FX7//XdUqlQJDg4Own2ZmZlPeBZR8crJ0+LPI9FCrXJ5FzQKLIO0tBx5QpmwHE0OwiIWIyVPvEiwkqsfhga+BZWSU0dMnV6vhyRJUKkeHst3352MUqVKYeDAobCxsZExHRFR0XuhxvfgwYPYv38/SpUq9ch9U6ZMeelQRC9i+/EYZOVqhFrftpV5Kv4FFOgKMO/8L7iTLa6DXN6xLEYHDYa1ykqmZFRU4uJiMXHiWDRv3hLvvjvZULeyssKIEWNkTEZEVHxe6Dylv7//Y5tegI0vySM1Mx+7TsYJtbpVSqOyj6s8gUyYTq/DoosrcSs9RqiXtnPHuOBhsFNzAxBTJkkSfv11JUJCmuDQoQP44YdvceHC+Wc/kYjIDLxQ49unTx8sWbIEiYmJwvxeAAgNDS2SYESF8cehWyjQPpzDq1Qo0COEWxMXll7SY8WVtbicck2ou1g7YXzwCDhbO8mUjIpCYmIiBgzog7ffHoesrAfT0ry8yiAnh1OBiMgyvNBUh9GjRwMAfvjhhyINQ/Qi4pOzcfB8glBrUdsb3u4OT3gGPY4kSVh/YwtOJUYIdTu1HUKDR8Dd7vFnecg0bNmyCZMnv4P79x/O2e7b9y189dW3cHZ2kTEZEVHJeaHGt1q1avjoo48eqUuShG+++ealQxEVxob9kfj3iQdrKyW6NPeTL5CJ2h69G+G3jwg1a6UVxtYeirKOZWRKRS8rNfU+pkx5Dxs3rjfUPDxKY8aMWejQoaOMyYiISt4LNb5jxoxBw4YNH3vfpEmTXioQUWFcj0tDxM1kofZKA1+4OvJq9MI4cPsI/oraJdSUCiWG1xoIf5cKMqWil3X16hX06tUFiYkPL1Ls1KkLvvvuJ3h4eMiYjIhIHi80x/fVV1994n1nzpx54TBEhSFJEtbtvynUHO2s0KGRr0yJTNOpxAj8fv0PoaaAAoOq90Gge1WZUlFRqFjRDy4uD6YxuLi4Yu7cRViyZAWbXiKyWM894nvx4kU4ODjAz88PYWFhT3zcli1bMHHixCIJR/Q0Z64nIzI+Q6h1blYRdjbcift5XUq5huWX10CCeJFq7ypdUL9MHZlSUVGxtbVFWNgCfP/9N/jhh5nw9i4rdyQiIlk9d4cQGhoKf39/LF26FMuXL0e1atUe+zhuYEElQafXY0N4pFDzdLVDqzrlZEpkem6lx2DxhRXQS+KOdh392qNl+aYypaIXlZeXh+nTv8abb/ZHlSoPR+qDg+ti9ep1MiYjIjIez9347ty507C7T1BQEJYsWfLYxw0bNqxokhE9xcFzCbh7X1yCqXuIP9QqbqH7PO5k3cW8c0tRoBc3/Agp3wwdKraTKRW9qIiIMwgNHYXr16/hyJGD+Ouv3VCreeaDiOi/nrtLsLa2NjS+T2p6AS5xRsUvv0CHPw5FCbWKZZxQv5qnTIlMS3LufYRFLEKONleo1/cKRs/Kb3CnOxOi0WgwffrX6NChLa5ff7D28uXLl3D27GmZkxERGaciHx7j/F4qbn+fjEV6doFQ69W6EpRs2J4poyATYRGLkF4gTkmq4V4VA6r3hlLBEXNTcfXqFXTo0BYzZkyHTqcDAAQFBWPXrgNo0KCRzOmIiIzTC50Ly8zMxDfffIODBw8iOTn52U8gKiIZ2QXYfjxWqNX0L4XqFdxkSmQ6crW5mBOxBEm5KULd36UCRtQcALWSp8ZNgU6nw/z5c/Dtt/9Dfn4+AEClUuGdd97HO++8DysrK5kTEhEZrxf6Szdt2jSo1Wp88803+OGHH/DRRx+hoKAAO3fuhI0N10+l4vPn4WjkF+gMtxUAerWqJF8gE1Gg02D++WW4nXVHqJd1KIMxQUNgrbKWKRkVRnR0FCZMGINjxx5uNFK1ajWEhS1A7dpchYOI6FleqPGNiorCmjVrAAALFy40bGbRvHlzjBs3rujSEf1LYmoO9kfEC7UmNcvAx9NRpkSmQafXYeml1biZJs6LdrcthXHBw2BvZS9TMiqshIQ7OH78KABAoVBg9OhQTJnyKWxtbWVORkRkGl5oQp+19cPRoYKCAmg0D68Mj4+Pf9xTiF7apgO3oNM/XG9WrVKiWwt/GRMZP72kx+qr63Eh+bJQd7J2RGjwcLjauMiUjF5EkybNMGrUOPj6VsTmzdvw5Zdfs+klIiqEF76SZfXq1SgoKEClSpUwadIkbNmyBVOmTIFSyYtjqOhFJWTgxJV7Qq1dvfJwd+Ef/SeRJAmbbv6F43fFK/zt1LYIrT0cnvbcvcuYSZKEvXt3Qa8X11n+6KPPsH//YTRp0kymZEREpuuFutSxY8fi9u3bSE9Px7hx4xAZGYnJkyfj4MGD+OSTT4o6I1k4SZKwbp+4NbG9jRqvN6kgUyLTsCtmP/bGHRRqVko1RgcNQXkn7uBlzJKTkzFs2ED07dsDCxfOFe6ztbWFo6OTTMmIiEybQpIk6dkPe7bU1FS4uZnOlfWpqdnQavXPfiDJ7nxkCn5ed06o9WodgA6Nnt34qtVKuLk5WNzxPhx/HL9e2yDUlAolRtYaiFoeNWRKVfzM4Xjv2LEN7747HsnJSQAAGxsbnDhxjtsNP4Y5HG96fjzeluWf413kX/d5H3jz5k3MmzcPANC5c2eEhIQAANq0aYOEhAQADy622Lhx4xO3MyYqLL1ewvr94mhvKWcbtKtXXqZExu/MvfP47drGR+r9q/Uy66bX1GVkpOOTTz7EmjWrDbVSpUrhu+9+YtNLRFREnnuqw9atW3H48GHUr18fQUFBhrqbmxumTZuGr7/+Gt27d8e6ddwTnorO0Ut3cTspW6h1a+EPK7VKpkTG7er9G1h+6TdIEE/k9Kj8Bhp515MpFT3LwYPhaNWqqdD0vvpqB4SHH0fnzt1kTEZEZF6ee8T3yJEjWLx4MWrWrCnU7e3t0a3bg1/Mbdu2xZAhQ4o2IVksjVaHTQdvCbXypR3QJLCMTImMW0xGHBZcWA6tpBPqr1VogzY+LWRKRU+Tk5ODr776HIsXLzDUHB2d8PXX09G371vcPpqIqIg9d+OrUqkeaXoBYNKkSYb/d3Fx4S9qKjK7T9/G/Yx8odazVSUolXyP/dfd7HuYc24JCnTiVs7NyzZCJ/9XZUpFzzJr1gyh6W3evCVmzpwLHx9fGVMREZmv557q8O+1e/8tODhYuO3gUPQTkcnyZOVq8NeRGKFWzdcVtfxLyZTIeN3PS8XsiEXI1uQI9TqeQehTtRs/jBqx0NC34etbEba2tvj66+lYv34Lm14iomL03CO+Wq0WGo3mqfvA5+fnG/aOJ3oZ247GICdfK9R6ta7EJu4/MguyEBaxGGn56UK9mltlDKrRF0oF19U2JllZmcJSZI6OTli06Bc4OTmjUqXKMiYjIrIMz/1XMSQkBN99991TH/Ptt9+iVatWL5uJLFxKeh52n74t1BpU84Sft7NMiYxTnjYPc88tRWJOklCv4OyDEbUGwkr5QjuSUzHQarWYOXMG6tevhZiYaOG+OnXqseklIiohz/2XcfDgwRg0aBC6deuGjh07IiAgAHZ2dsjNzcWNGzfw119/wc7ODlOmTCnOvGQBNh+8Ba3u4RqNKqUC3UO4NfG/afRaLLiwArGZ4geEMvaeGFt7KGzVNjIlo/+6desmxo0bhdOnTwIAJk4ci40bt3KXSyIiGTx342ttbY1ly5Zh5syZmD9/PrKysgz3OTo6olevXpg4ceIT5wITPY+4e1k4cvGuUGsVXA5ebvYyJTI+ekmPZZd+xfVUcX1jNxtXhAYPh6MV59kbA71ej19+WYSpUz9Dbm4uAECpVKJ+/YbQ6XRsfImIZFCoc6E2NjaYPHkyJk2ahKioKGRkZMDZ2Rl+fn5QqbiuKr289fsjhRVobaxVeKNZRbniGB1JkvDb1Y2ISLoo1B2tHDA+eDjcbF3lCUaC27fjMHHiOBw8uN9Q8/Pzx+zZC9CwYSO5YhERWbwXmgSoUqlQqVKlos5CFu5KTCou3EoRah0a+cLZgWcR/rHl1g4cSTgh1GxU1hhXexi8HDxlSkX/kCQJa9f+io8//gCZmRmG+tChI/Dpp1O56g0Rkcx49QsZBb0kYd0+8dS9i4M1Xm3ApZ3+sTs2HDtj9gk1tUKF0UGD4evMLZyNweTJ72L58iWG22XLlsPMmXMREtJaxlRERPQPTjIjo3Dq6j1E380Ual2a+8HGmlNoAOBowilsuvmXUFNAgSE130IVN559MRYdOrxu+P8+ffohPPwom14iIiPCEV+SnVanx4bwSKHmVcoeLWp7y5TIuJxLuoRfr65/pN6vWk8El350N0WST5s27fH22+8hOLguXn+9k9xxiIjoPzjiS7LbfzYeSWl5Qq1niD9UvOodN1IjsfTSauglvVDvGvA6mpZtIFMqAoC9e3fjnXdCIUmSUP/oo8/Y9BIRGSmO+JKscvO12HI4WqgFlHNG3Sql5QlkROIy4zH//DJo9eIOdu19W6F9hVbyhCJkZWXhyy8/NczlrVu3PgYMGCxvKCIiei4cUiNZbT8ei6xcjVDr1YpbEyfmJCEsYjHydOIW4E28G6BLQAeZUtGxY0fRunVT4QK2vXt3y5iIiIgKg40vySYtKx87T8YKteBKHqji4ypPICORlp+OsIjFyNJkC/XaHoF4s2p3i/9QIIe8vDx88cUn6NLlNcOWw/b29pg+/UcsXbpS3nBERPTcONWBZLPlUBQKNA/nrioUQI9WATImkl+2JgezIxbjfl6qUK/s6o8hgf2gUnKVi5J2/nwEQkNH4erVK4Zaw4aNMWvWPPj7W/b7lYjI1HDEl2SRkJKNA+cShFqLIG+U87DcBf7zdQWYd24p7mYnCnUfp3IYFTQYViormZJZJkmS8MMP3+K119oYml5ra2t89tn/8Mcf29n0EhGZII74kiw2hN+C/l9Xw1urlejS3F/GRPLS6rVYdGEFojLEqR+e9h4YV3sY7NS2MiWzXAqFApGRN6HVPri4sFat2ggLW4Dq1WvInIyIiF4UR3ypxN28nY4z15OEWvsGPnBzspEpkbz0kh4rLq/FlfvXhbqrjQtCa4+Ak7WjTMnom2++h4+PL959dzK2b9/DppeIyMRxxJdKlCRJ+H2/uDWxo50VOjSqIFMieUmShHXX/8Dpe+eEuoPaHqHBw+Fu5yZTMssTHR2FmJhoYac1V1c3HDx4Avb29jImIyKiosIRXypRETeScfN2ulB7o2lF2Nta5mewv6J24UD8UaFmrbLGmNpD4e3gJVMqyyJJElas+AWtWjXFiBGDcPeuOPecTS8Rkflg40slRqfXY/1/tib2cLFFqzrlZEokr31xh7A9WlwDVqVQYWStgfBz8ZUplWW5ezcB/fr1xHvvTUROTjbS0tIwffrXcsciIqJiwsaXSsyh8wlISMkRat1b+sNKbXlvwxN3z2D9jS1CTQEFBtXoi+qlqsiUynJIkoSNG9ehZctG2LNnl6Hev/8g/O9/38iYjIiIipNlnl+mEpdfoMPmQ1FCrYKXExrWsLzT+ReTr2Dlld8fqfep2hX1vGrLkMiypKSk4IMP3sWWLZsMNU9PL/z002y0b/+ajMmIiKi4Wd5QG8li16k4pGcVCLWerQOgtLBdyCLTorH44iroJb1Qf8P/VbQo10SmVJbj77+3o2XLRkLT27Vrdxw4cIxNLxGRBeCILxW7zJwCbD8eI9QC/UohsGIpmRLJIz4rAfPO/wKNXiPUW/s0x6sV2siUynJkZWXinXfGITk5GQDg5uaG6dN/RNeuPWRORkREJUX2Ed9du3ahe/fu6NevH/r3748bN2481/P27duHqlWrYuPGjcWckF7Wn0eikZuvM9xWAOhlYVsTJ+emICxiMXK1uUK9YZm66F6pExQWNvItB0dHJ3z33c8AgPbtX8WBA8fZ9BIRWRhZR3zPnz+PyZMnY8OGDfD398fmzZsxbNgwbNu2DY6OT160PycnBz///HPJBaUXdi8tF/vOxAu1xoFe8PVykilRyUvPz8Tss4uQUZAp1Gu6V0f/ar2gVMj++dMs5eTkQKMpgIuLq6HWqVNnbNy4Fc2ateCHDSIiCyTrX9xFixYhJCQE/v4Ptqrt3LkzdDodNm/e/NTnzZo1C2+++WYJJKSXtenALej0D7cmVqsU6NbCcrYmztHkYs65xUjOuy/UA1z8MKxmf6iUKpmSmbdTp06ibdvmmDRpIqR/bY0NAM2bt2TTS0RkoWRtfI8ePYpatWoZbiuVSgQGBuLIkSNPfM7ly5dx/vx59OnTpyQi0kuIvpuB45cThVqbuuXh4WonU6KSVaArwPzzvyA+S9wQoZyjN0YHDYa1ykqmZOaroKAAH3/8MV57rS0iI29iy5ZN2LRpvdyxiIjISMg21SE1NRWZmZnw8PAQ6h4eHrhw4cJjn6PX6zF16lR88cUXLz1io1Lx9HJxkiQJ6/ffEmr2Nmp0aeEHdQmu2/vPcS7p463T67D0/GpEpkcL9dJ27phYbwScbRxKNI8luHTpIsaOHSH8/qhbtz6Cg4NL9D1HJUeun2+SB4+3ZSmu4yxb45uXlwcAsLa2FurW1taG+/5r1apVqFu3LqpVq/bSr+/sbBmjjnI5c/UeLkeLp/d7tasC33JusuQpyeOtl/SYc3w5LiRfEequts74rM1EeDmWLrEslkCn0+H777/HZ599Bo3mwYoZarUaX3zxBT744AOo1Vy8xtzx97ll4fGmlyHbXwRbW1sAD05N/ltBQYHhvn9LTEzEunXrsHbt2iJ5/YyMXOh0+mc/kApNL0lYskUctXdzskGLml5ITc0u0SwqlRLOznYldrwlScLv17bgYOwJoW6vtsP4OsNhrbEv8X8DcxYZeRNjx47CyZPHDbWaNWti7tyFqFkzCJmZ+QDy5QtIxaqkf75JXjzeluWf413UZGt83dzc4OTkZFhT8x/Jycnw8fF55PGHDh0CAIwaNUqoL1y4EJs2bcLEiRNRv3795359nU4PrZY/OMXhyMUExCZmCbWuzf2gVChk+zcvqeO9I3oP9sYeFGpWSiuMDhqCMnZl+J4rQpGRN9C2bQvk5DzYBlupVGL8+Lcxffo05ORo+W9tQfj73LLweNPLkPUcYOPGjXHx4kXDbUmScPnyZYwePfqRx/bo0QM9eohrblatWhUjR45E9+7diz0rPR+NVodNB8S5veU8HNCslrdMiUrOwfij+PPW30JNqVBieM3+CHCtKE8oM+bvXwkhIW2wfftWVKzoh7CwhWjatAlsbGyQk6OVOx4RERkhWWeIjxw5EuHh4YiKigIAbNmyBUqlEl27dgUATJkyBe+//76MCamw9p6JR0qGeGq5R6sAKJXmvXzU6cRzWHtt8yP1gdX7oKZH9ZIPZAEUCgV++GEmxo6dgH37jqBhw0ZyRyIiIiMn64hvUFAQpk+fjkmTJsHW1hZKpRJLliwxbF6Rn58PrfbRkZuFCxfi4MGDhv/ftGkTVq5cWaLZ6VE5eRpsPRIt1Kr4uKJ2gLs8gUrIlZTrWH55DSSI68X2qtwFDcrUkSmVeUlKSsJ7703Em2/2x2uvvW6oly5dGl988ZWMyYiIyJQopP+u7m4hUlOzOUeoiK3bfxPbj8UKtY8H1kNAWReZEgFqtRJubg7Fdryj0mMw6+xCFOg1Qr1DxXbo5P9Kkb+eJdq6dQvef38iUlJSULq0Jw4cOA5398d/mCru403GhcfbsvB4W5Z/jndR42J4VCTuZ+Rh96nbQq1+1dKyNr3F7U7WXcw798sjTW/Lck3Q0a+9TKnMR1paKsaOHYGhQ/sjJSUFACBJety6dVPmZEREZKrY+FKR2HwwCpp/fQJXKRXoERIgY6LilZKbijnnliBbmyPU63nWRq8qXbgl7kvat28PQkKaYP36h8sXvv76GwgPP44GDTiXl4iIXgxXdqeXdjspC4cvitvytgwuC69S9jIlKl6ZBVkIO7cIafnpQr16qSoYWKMPlAp+nnxRWVlZmDr1UyxbtsRQc3Z2wbRp36FXr778QEFERC+FjS+9tPX7I/HvmeI21ip0buYnX6BilKvNw5xzS3AvR1x/2s/ZFyNqDYRayR+pF3XhwjkMGzYQ0dFRhlpISGv8/PMclCtXXsZkRERkLvhXml7KtdhUnI9MEWqvNfSFi4P1E55hujQ6DRacX4a4zHih7u3ghTG1h8JGZX7fc0kqVcod9+8/2Oba3t4en332PwwZMpyjvEREVGR4TpZemCRJ+H1fpFBztrfCKw0e3XnP1On0Ovxy6VfcSBM353C3dUNo8HA4WJnntI6SVK5ceXz99XQ0aNAIe/cextChI9j0EhFRkWLjSy/s1LUkRCVkCLXOzf1gZ2NeJxIkScKv1zbgXPIloe5k5YjQ4OFwtTHflSuKi1arxbx5YcjIEOdJ9+79JrZs2QF/f/O9MJKIiORjXh0KlRitTo8N4eJor5ebHVrWLitTouKzOXIbjiWcEmq2KluMCx4GT/vSMqUyXdevX8P48aNw9uwZXL16GTNnzjXcp1AooFKpZExHRETmjCO+9EIOnLuDe6m5Qq1HSADUKvN6S+2K2Y/dseFCTa1UY3TQIPg4lZMplWnS6/VYsGAO2rVrgbNnzwAAfv/9N0RG3pA5GRERWQqO+FKh5eZrseVQlFDzL+uMelXNa/TzyJ0T2By5TagpFUoMC3wLld14Kr4wYmNjMGHCGBw5cshQq1SpMsLCFiAgoLKMyYiIyJKY1/AclYi/T8QiI0fcraxXqwCzuhApIukifr264ZH6W9V6Iqh0oAyJTJMkSVi1ajlCQpoITe+oUWOxZ88h1K1bX8Z0RERkaTjiS4WSnpWPv0/ECbXaAe6o6usmU6Kidz31Jn65uBoSJKHevVInNPZmo/a8EhPv4t13x2PXrr8NNR8fX8yaNQ/NmrWQMRkREVkqjvhSoWw5HI18jc5wW6EAerYyn9P+MRlxmH9+GbSSTqi/UqE12vq2lCmVadq+/S+h6X3rrYHYv/8Im14iIpING196bnfv5yA84o5Qa1bLG+VKO8qUqGglZt/D3HNLka8rEOrNyjZEZ//XZEplugYOHIKWLVvD09MLq1atxU8/hcHJyVnuWEREZME41YGe24bwSOj/tTexlVqJrs3NY2vi1Lw0zI5YjCxNtlAPLl0Lfat2N6v5y8Xl+vVrqFKlquG2UqnEnDkLYGVlhVKl3GVMRkRE9ABHfOm5RMan4/S1JKHWvr4PSjnbypSo6GQVZGN2xGKk5qcJ9apulTA48E0oFfwxeZqsrEy8++54tGjREAcO7Bfu8/Iqw6aXiIiMBv+i0zNJkoR1+24KNQdbNV5v7CtToqKTp83D3HNLkZhzT6hXcPLByFoDYaXkSZGnOXz4IFq1aopVq5ZDkiRMnDgWWVmZcsciIiJ6LP5Vp2c6F5mC67fFrWU7Na0Ie1srmRIVDY1ei0UXViImU1ylwsu+NMbWHgpbtemPZheX3NxcTJv2JRYseLjrmoODI959dzIcHMxjzjcREZkfNr70VHq9hA37xa2J3Z1t0aZueZkSFQ29pMfyy2twNVXcNczNxhXjg0fA0dpBpmTG7+zZ0wgNHYUbN64bak2aNMOsWfNQoUJF+YIRERE9AxtfeqrDFxIQnyxe8NW9pT+s1KY7S0aSJKy9tgln750X6g5W9ggNHg43W1d5ghm5goIC/PjjdMyc+SN0ugfLvdnY2ODjjz/HyJFjoVSa7nuCiIgsAxtfeqJ8jQ6b/7M1sa+nIxoFesmUqGhsvfU3Dt05LtRsVNYYV3sYyjh4ypTK+E2Z8h5WrlxmuB0cXAdhYQuFlRyIiIiMGYdo6Il2n4pDama+UOvZOgBKE17aa2/sAeyI2SvU1AoVRtYahArOPjKlMg3jxk2Evb091Go1PvjgY/z11242vUREZFI44kuPlZWrwbZjsUKtRkU31PQz3aWpjiecxoabW4WaAgoMCeyHaqUqy5TKeOl0OqhUKsNtf/8AzJw5F35+/ggKCpYvGBER0QviiC891tYj0cjN1wq1Xq0qyZTm5Z27dwmrrq57pP5mte4I9qwlQyLjJUkSli5dhPbtQ5CdLc7v7tKlO5teIiIyWWx86RHJabnYe+a2UGtcwwsVyjjJlOjlXL53A4vOr4Re0gv1Lv4d0KxsI5lSGac7d+LRu3dXfPjhJFy8eB5fffW53JGIiIiKDBtfesTGg7eg1T3cmlilVKBbS38ZE724uIx4TD80Fxq9OHrd1qcl2ldoJU8oIyRJEn7//Te0bNkY4eH7DHWdTgfpX9tUExERmTLO8SVBzN1MHLuUKNRa1y2H0q52MiV6cfdykjHrzGLkavKEeuMy9dGtUkcoTPgivaKUlJSE999/G9u2/WmolSnjjZ9/noM2bdrJmIyIiKhoccSXBOv3i1sT29mo8EbTivKEeQlp+ekIi1iEjAJx+9xaHjXQr1oPNr3/76+//kRISCOh6e3RozcOHDjGppeIiMwOR3zJ4FLUfVyKThVqHRpVgJO9tUyJXkyOJgdzIpYgJU/8Xiq7+mNo4FtQKVVPeKZlmTRpgrAur7u7O7777me88UYX+UIREREVIza+BADQSxLW/We019XRGu0bmNbatvm6Asw7/wvuZN8V6j5O5TAqaBCsVVYyJTM+FSs+nLf92msd8cMPM+HpyQ08iIjIfLHxJQDAicuJiE3MEmpdW/jDxsp0Rke1ei0WX1iJW+kxQt3b0RMT6g6Hncr05ikXp7Fjx+Po0UPo3Lkb+vTpx+kfRERk9tj4EjRaPTYeuCXUvN3t0axWGZkSFZ5e0mPlld9x+f41oe5q44yPW02AVYEttFr9E55t/k6cOI5z585gxIgxhppKpcLq1evY8BIRkcVg40vYdzYeyeniygc9WwVApTSNax8lScL6G1twKjFCqNur7TCx3kh4OrgjtSD78U82c/n5+Zg+/WvMnTsLABAcXBcNGjxcu5hNLxERWRLT6Gyo2OTkabH1SLRQq1zeBcGVPOQJ9AK2R+9G+O0jQs1aaYUxtYeirKPpjFoXtQsXzuGVV0IQFvYz9Ho99Ho9li5dJHcsIiIi2bDxtXDbj8cgK1cj1Hq1rmQyI4Hht4/gr6hdQk2lUGFErYHwd6kgUyp5abVa/Pjjd3j11da4cuUyAMDKygqffPIFwsIWyJyOiIhIPpzqYMFSM/Ox62ScUKtXpTQqlXORKVHhnLp7Fuuu/yHUFFBgYI0+qOFeVaZU8rpx4zrGjx+FM2dOG2qBgbUQFrYAgYE1ZUxGREQkP474WrDNB2+h4F8XfCkVCnQPMY2tiS+lXMPyK2shQdxOt3eVrqjvFSxPKBnp9XosXDgXbds2NzS9SqUS77zzHv7+ex+bXiIiInDE12LFJ2fj0IUEodaytje83R1kSvT8bqVHY9GFFdBL4ioNnfxeQcvyTWRKJS+dToe1a39DXt6DixQDAiohLGwB6tVrIHMyIiIi48ERXwu1YX8kpH8NllpbKdG5uZ98gZ5TfFYC5p77BRq9OC+5VflmeK1iW5lSyc/KygphYQtga2uLESNGY8+eQ2x6iYiI/oMjvhboelwaIm4mC7VXG/jC1dFGpkTPJzn3PuZELEauNleoN/Cqgx6V3zCZC/KKQmLiXWRmZqJSpcqGWvXqNXD8eAS8vcvKmIyIiMh4ccTXwkiShHX7xK2Jneyt8FojX5kSPZ+MgkzMjliE9IJMoR7oXg0DqveGUmE5b+U//tiIli0bYdiwgcjPzxfuY9NLRET0ZJbTLRAA4Mz1JETeyRBqnZv5wc7GeAf/c7W5mBOxBMm5KULd36UihtfsD5XSdLZVfhmpqfcxatQQjBgxGKmpqbhy5RJmz/5J7lhEREQmw3i7HSpyWp0e68PFrYk9Xe0QEmy8o4QFOg3mn1+G21l3hHpZhzIYEzQY1iprmZKVrN27/8Y774xHYuJdQ61z524YMmSEjKmIiIhMCxtfC3LwfAIS7+cIte4h/lCrjHPgX6fXYemlVbiZFiXU3W1LITR4OOyt7GVKVnKysjLx+ecfY+XKZYaaq6srvv12Brp162lR85qJiIheFhtfC5FXoMUfh8QG0s/bCQ2qecqU6On0kh6rr67HheQrQt3J2hHjg0fAxcZZpmQl58iRQ5gwYQxiY2MMtTZt2uHnn+egTBlvGZMRERGZJja+FmLniThkZBcItV6tjHNrYkmSsOnmXzh+97RQt1PbIrT2cJS2d5cpWclJSLiDXr26QKN5sGybvb0Dpk6dhgEDBhvlMSMiIjIFxnmOm4pURnYBtp+IFWpBAe6oVsFNpkRPtzNmH/bGHRRqVko1RgcNQXkn452PXJS8vcti/Ph3AABNmjTD/v1HMHDgEDa9REREL4EjvhZgy+Eo5BfoDLcVAHqEBMgX6CkOxR/Dlls7hJpSocTwmgNQydX4N9h4URqNBgqFAmr1wx/Jd9+djAoVKqJPn35QKvkZlYiI6GXxr6mZS7yfg/AIcUWEpjXLwMfTUaZET3bm3nmsubbpkfqA6r1R06O6DIlKxpUrl/Haa20QFvazULe2tsabb/Zn00tERFRE+BfVzG08cAs6/cO9idUqJbq28Jcx0eNdvX8Dyy/9BgmSUO9ZuTMalqkrU6ripdPpEBY2E+3bt8SFC+fw/fff4OLFC3LHIiIiMluc6mDGohIycPLqPaHWrn55uLvYypTo8aIzYrHgwnJoJZ1Qf61iW7T2aS5TquIVFXULEyaMwfHjRw01f3/jnH5CRERkLjjia6YetzWxg60aHZtUkCnR493NTsTcc0tRoBNXnGherjE6+b0iU6riI0kSli1bgtatmxmaXoVCgTFjxmPXrgOoWbOWzAmJiIjMF0d8zdSFWym4Gpsm1Do2qQgHWyt5Aj3G/bxUzI5YjGyNuKlGXc8g9KnS1exWMEhIuIO33x6Hffv2GGq+vhUxe/Y8NGnSTMZkREREloGNrxnS6yWs2x8p1NydbdC2XjmZEj0qsyALYRGLkZafLtSrl6qCQTX6Qqkwr5MRERFn0KtXV6SnpxlqAwcOxRdffAVHR+O70JCIiMgcsfE1Q0cu3kV8UrZQ69rCH1ZqlUyJRHnaPMw9twSJOUlCvaKzL4bXHAC10vzellWrVoeXlxfS09NQpow3fv45DG3atJc7FhERkUUxr2E1QoFGh00Hbwm18qUd0SSwjEyJRBqdBgsurEBsZrxQL+PghTG1h8BWbSNTsuJlZ2eHsLAF6NmzDw4cOMaml4iISAZsfM3MntO3kZqZL9R6tQ6AUin/fFm9pMeyy7/heqp40Z2bjStCaw+Do5WDTMmKVkZGOiZNmoCbN28I9eDgupg7dxFcXY1zxzwiIiJzZ37nlC1YVq4Gfx2NEWrVK7ihpl8pmRI9JEkSfru6ERFJF4W6o5UDxtcZATdbV3mCFbEDB/Zj4sSxiI+/jUuXLmDr1l3CbmxEREQkH474mpG/jkYjJ18r1Hq2CjCK1RG23NqBIwknhJqtygbjag+Dl31pmVIVnZycHEyZ8h569uyM+PjbAIAbN27g6tUrMicjIiKif7DxNRPJ6bnYc/q2UGtY3RN+3s4yJXpod2w4dsbsE2pqpRqjggbD17m8TKmKzsmTx9GmTTMsWbLQUGvRIgTh4Ue5Li8REZER4TlYM7H5YBS0uofb/aqUCnRvKf/WxEfvnMSmm38JNQUUGBrYD1XcTHunsvz8fHz//TcIC/sZer0ewIOL2D799EsMHToSSiU/VxIRERkTNr5mIDYxE0cv3hVqreqUg6ebvUyJHjiXdBGrr65/pP5WtZ6oXbqmDImKzqVLFzFu3EhcvvxwznK9evURFrYAAQGVZUxGRERET8LG1wysD4+E9K/bttYqvNGsolxxAADXUyOx9NKvkIRkQLdKHdGkbAOZUhWd9PQ0XLlyCQBgZWWFyZM/wrhxE3khGxERkRHjX2kTdyX6Pi7eui/UOjTyhbO9tUyJgNjM21hwfhm0evFCu/a+rdDON0SmVEWradPmGDlyLA4eDEdY2ALO5SUiIjIBbHxNmF6S8Pt/tiZ2cbTGKw18ZUoEJOYkYU7EEuTpxLWEm3o3QJeADjKlejl6vR5//rkZb7zRVZi3+/HHn0OhUMDGxjw33SAiIjI3vPrGhJ28cg8xdzOFWpfmfrCxlmdr4rT8dIRFLEaWRtwuuXbpmuhbtbtRLKtWWHFxsejZszNGjBiMxYvnC/fZ2tqy6SUiIjIhbHxNlFanx8YD4mivt7s9WgR5y5InW5OD2RGLcT8vVahXcQ3AkBpvQqWUpxl/UZIk4ddfVyIkpAkOHToAAPj66y+RnJwsczIiIiJ6UZzqYKL2nY1HUlqeUOsREgCVDEto5WnzMffcUtzNThTqvk7lMDJoEKxUViWe6WUkJiZi0qTx2Llzh6FWrlx5zJw5Fx4eHjImIyIiopfBEV8TlJuvxZ+Ho4VapXIuqFO55JsyrV6LxRdXIjojVqh72ntgbO1hsFPblniml7FlyyaEhDQSmt4+ffohPPwoWrZsJV8wIiIiemkc8TVB24/HIitXI9R6tS75rYn1kh4rLq/FlfvXhbqrjQvGB4+Ak7VjieZ5Gamp9zFlynvYuPHhusMeHqUxY8YsdOjQUcZkREREVFQ44mti0rLysfOkOLpap7IHKpd3LdEckiRh7fXNOH3vnFB3sLLH+ODhKGXrVqJ5XtbMmT8KTW+nTl1w4MBxNr1ERERmhI2vifnjUBQKNHrDbYXiwdzekvZX1E4cij8m1KxV1hhbeyjKOHiVeJ6X9d57H8DXtwJcXFwxd+4iLFmygvN5iYiIzAynOpiQhJRsHDyXINRaBJVFWQ+HEs2xL+4QtkfvEWoqhQqjag1CRWf51hAujPv3U1CqlLvhtqOjE5YuXYnSpT3h7V1WxmRERERUXGRvfHft2oV58+bB1tYWSqUSn3/+OSpXrvzYxx45cgQrVqxATk4O8vPz4eDggPfeew81atQo4dTyWL8/Enrp4RbA1molujT3K9EMJ+6ewfobW4SaAgoMDnwT1Uo9/rgZk7y8PEybNhW//roSe/cegq9vBcN9QUHB8gUjIiKiYifrVIfz589j8uTJ+OGHH/Drr7+iZ8+eGDZsGLKysh77+M8//xxt2rTBihUrsHbtWtSuXRtDhgxBSkpKCScveTdup+HsDXEN2Vca+sDNqeQ2ULiYfAUrr/z+SL1P1W6o6xlUYjleVETEGbRr1wLz54chIyMdEyaMgV6vf/YTiYiIyCzI2vguWrQIISEh8Pf3BwB07twZOp0Omzdvfuzja9asiZ49expuDxgwAGlpaThy5EhJxJWNJElYt0/crMLRzgodGlV4wjOK3s20KCy+uBJ6SWwU3/B/DS3KNS6xHC9Co9Hg22+/RocObXH9+jUAgI2NDV55pQOkf42gExERkXmTtfE9evQoatWqZbitVCoRGBj4xEb2p59+gvJfGzT8s12sRqN57OPNxdkbybgZny7U3mhWEXY2JTNTJT4rAfPP/wKNXivU2/i0wKsVWpdIhhd19eoVNGnSBN999w10Oh2AB1Madu06gLFjx0OlMq0d5YiIiOjFyTbHNzU1FZmZmY9cOe/h4YELFy4819eIiIiAra0tWrVqVejXV6lMY0ELnV6PDeHiaK+nqx3aN/CBugS+h6ScZIRFLEauVtwlrrF3PfSq9gaUCuP8d9TpdJg7NwzTpk1Ffn4+AEClUmHSpMmYNGkyrKxMazc5ej7//Fybys83vRweb8vC421Zius4y9b45uU9aKSsra2FurW1teG+p5EkCfPmzcPEiRNRqlSpQr++s7NdoZ8jhx1Ho5GQkiPUBnWqgdIeTsX+2qm56Zh9eDEyCjKFer2ytTCh2RColcY7Wtq3b1+sXbvWcLt69epYsWIF6tevL2MqKimm8vNNRYPH27LweNPLkK3xtbV9sJVtQUGBUC8oKDDc9zSzZ8+Gl5cXhg4d+kKvn5GRC53OuC9syi/QYdWOK0KtorcTAiu4IjU1u1hfO1uTgxkn5yExW7ygrpKrHwZX74fM9Gd/OJFT9+59sHbtWigUCrzzzjuYPPljWFvbFPu/G8lLpVLC2dnOJH6+6eXxeFsWHm/L8s/xLmqyNb5ubm5wcnJCcrLYWCUnJ8PHx+epz12zZg0uXLiAOXPmvPDr63R6aLXG/YOz7VgM0rPEDwY9QwKg10nQo/guyirQFSAsYinis8Q1g8s5emN00GAoJZXR/9u1bt0O778/BSEhIejY8VWkpmYbfWYqOqbw801Fh8fbsvB408uQdaJM48aNcfHiRcNtSZJw+fJlNG3a9InP2bp1K7Zt24bZs2fD2toacXFxZrmqQ0ZOAbYfixFqNf1KoUbFwk/rKAydXoclF1fhVnq0UPewc0do8HDYqY3rFJMkSdiw4XeMHTvikRUa3n9/Cpo2bS5TMiIiIjI2sja+I0eORHh4OKKiogAAW7ZsgVKpRNeuXQEAU6ZMwfvvv294/L59+zBjxgyMHTsWN27cwIULF3D48GGcPn1ajvjFauvhaOQV6Ay3FQB6tirerYn1kh4rr6zDxZSrQt3F2gnjg0fA2br45xUXRkpKCoYPH4QxY4Zj/fq1WL16hdyRiIiIyIjJunNbUFAQpk+fjkmTJhl2bluyZAkcHR0BAPn5+dBqHy6hNWXKFKSmpmLQoEHC1wkNDS3R3MXtXlou9p2NF2qNA8vA16v4Gk9JkrDhxp84mXhGqNup7TAueDg87Ip3pLmw/v57O959dzySku4ZamfPnkH//oOe8iwiIiKyZArJQlfwN+Y5n/P/uIgTVx42dGqVAtNGNoaHS/FNM9getQdbo/4WalZKK0yoMwL+LhWL7XULKyMjHZ988iHWrFltqJUqVQrfffcTOnfu9sjj1Wol3NwcjPp4U9Hh8bYsPN6WhcfbsvxzvIv86xb5V6SXEpWQITS9ANC2XvlibXoP3D76SNOrVCgxotYAo2p6Dx4Mx8SJY3H7dpyh9sorr2HGjAcrfBARERE9DRtfIyJJEtbvFzersLdRo2OTisX2mqcTI/D79c1CTQEFBlXvg0D3asX2uoWh0+nw6acfYvHiBYaao6MTvvrqW7z5Zn8oFAoZ0xEREZGpYONrRC5G3ceVmFSh1rFJBTjaFc8uY5dTrmH55bWQ/rM0Ws8qnVG/TJ1iec0XoVKpcP/+fcPt5s1bYubMufDx8ZUxFREREZkaNr5GQq+XsG6fONrr5mSDtvXKF8vrRaXHYNGFFdBJOqH+ul97tCrfrFhe82V88833OHPmFEaMGI1hw0ZBqeSWlURERFQ4bHyNxNFLd3E7KUuodWvhD2urot8W+E7WXcw79wsK9BqhHlK+KV6v2K7IX6+wLl26iMTEBLRp095Qc3MrhcOHT8HKqnhGv4mIiMj8cdjMCGi0Omw+eEuolSvtgKY1yxT5a6Xk3kdYxGJka3OEen2vYPSs3FnW+bJarRYzZ87AK6+EYMyY4UhMvCvcz6aXiIiIXgYbXyOw53Q8UjLyhVrPkAAolUXbhGYWZCEsYjHSCzKEeo1SVTGgem8oFfK9HW7duok33ngVX3/9JTQaDVJTUzFz5gzZ8hAREZH5YeMrs+w8Df46Gi3Uqvq4IijAvUhfJ1ebhzkRi3EvN1mo+zlXwPBaA6BWyjPrRa/XY8mSBWjduhlOnz4JAFAqlRg//h18/vlXsmQiIiIi88Q5vjLbdjQG2XlaodardaUinXKg0Wmw4PwyxGXdEeplHcpgTO0hsFFZF9lrFcbt23GYOHEcDh7cb6j5+flj9uwFaNiwkSyZiIiIyHyx8ZXR/Yw87Dp1W6jVr+YJ/7LORfYaOr0OSy/9ihtp4hxid1s3jAseBgcr+yJ7reclSRLWrv0VH3/8ATIzH067GDp0BD79dCocHIp+pxYiIiJjlZ+fh19/XYmTJ49DpVJBo9HAxsYWLVuGoE2b9nBzK4VJkybg0qXz6NXrTQwbNkqWnOfORWDOnJ9hbW2NgoICjBs3EbVrG8/yp8+Dja+MNh28Ba3u4baLKqUCPUL8i+zrS5KEX69uwPnkS0LdycoRocEj4GrjUmSvVRhpaan44ouPDU1v2bLlMHPmXISEtJYlDxERkVzy8/MwYcIY1K4djFmz5kOtftCahYfvw+efT0FaWhqGDRuFGTNmITR0pGw5795NwOTJE/HNNzNQt259nD17GpMnv43ly9egTBlv2XIVFuf4yuT2vSwcuSCuWhASXBZebkU3Arsp8i8cu3tKqNmqbDEueDg87T2K7HUKy82tFL7//mcAQO/ebyI8/CibXiIiskhLliyARlOAMWMmGJpeAAgJaY2uXXvKmEy0bt0a+PpWRN269QEAderUg69vBaxfv1bmZIXDEV+ZrA+PFPZLs7FWoXMzvyL7+rti9mNP7AGhZqVUY3TQYPg4lS2y13keaWkPdqNzdXUz1N54oyv+/nsf6tSpV6JZiIjIcuj1ErLyNM9+YBFytLV67lWZtFottmzZhP79Bz/22p6+ffsjKyvzic8/ffokli1bDEmSoNFo4OtbARMmTIKTkxMAID09Dd9//w1SUpKgUqlhZ2eHwYNHIDCwJvLz8/Hzz98jMvImbGxsoFAo8OabA9CkyeM3sTp16gRq1w4WatWqBeLUqRPP9b0aCza+Mrgak4rzkSlCrUNDXzg7FM1FZofvHMfmyG1CTalQYmjgW6jsVnRTKZ7H3r278c47oWjUqDEWLlwm3Meml4iIisvJq/eweuc1ZOSUbOPrbG+Ft16pigbVPJ/52NjYaGRlZaFixccPfJUpUwbAk9f0P3r0MFq2bI1evfpCkiR8993XmD37R3z00ecAgEWL5sPGxgbz5i0FACxcOBfHjh1GYGBNrF+/BnFxsYa/zX/+uRl79+56YuN75048WrduK9Tc3d1x5078M79PY8KpDiVMkiSs239TqDk7WOOVhj5F8vUj7l3Ab1c3PlLvX60XgkoHFslrPI+srCy8//476Nu3OxIS7mDz5o3YvHlDib0+ERFZtmXbr5Z40wsAGTkaLNt+9bkem5X1YMdWO7sXm+bYt+9b6Ny5GwBAoVCgVau2OH78iOH+5OR7SE1NRUFBAQCgV6830b79awCApKQkZGZmIjv7QYZ27V5F7979nvha+fl5sLYWB+isrKyQn5/3QtnlwhHfEnby6j1EJYinLbo0qwhb65c/FNfu38Qvl36FJEyiAHpU6oRG3iU3unrs2FGMHz8KMTHRhlqrVm3QsGHjEstARERk7JycHqzilJub+0LP12p1+Omn7xAdHQW1Wo2srCykpDw8o9y//2BMmfIeunfviNat2+GNN7qgSpVqAIAePXrjyJGD6N69I0JC2qBDh05PPRNrY2NraKD/8c/qE6aEI74lSKvTY2O4uKyYVyl7tKj98nNuYzLisODCMmglnVB/tUIbtPFt+dJf/3nk5eXhyy8/RZcurxmaXnt7e0yf/iPWrt2EsmXLlUgOIiKiwR2qwdm+5Le6d7a3wuAO1Z7rsb6+FeDo6ISYmKgXeq333puAzMwMzJo1H2FhCzFhwrvC/TVrBmH9+i2YMOFdxMZGY9iwAdi4cR0AwMfHF7/+ugEff/wlMjMzMWHCaMybN/uJr1W2bDncvy9O00xJSTG5v+0c8S1B4RF3cC9N/FTXo6U/1KqX+/xxN/se5p5binyd+EmsWdlGeMP/1Zf62s/r/PkIhIaOwtWrVwy1hg0bY9asefD3DyiRDERERP9oUM0T9aqUNuqL21QqFbp164k9e3ahX7+Bj1zgNnny2/D1rYjQ0LcfeW5a2v+1d+dhVVXrA8e/Bw6HWQVBIRAEFS0ViUxzSE3TUnNInDLR1FIc0puaZlbezHLKVMRQ1LIkb4VTWjZYmplDzlNmGoLgDAoycw6c/fvDH0d3gnIYBd7P8/Tce9699t7vZqHP6zprr5VMbOw5Bg8eapqCkJOj3hBr584dtG3bji5dutKlS1eWLl3Mpk3r6NOnHwcP7sfPrxHt2nWgXbsOrF//NcuWhTF69Kv55tq8+eOcOHFcFTt9+hTNm7co1LM+KGTEt4xkZuewebf6X3T1HqrGYw1di3XdpKxkwo6uJM2Qroo/6tqUgQ2fL9Ed4Ary558nefbZjqaiV6fT8c477/HNN99L0SuEEKLcWFhoqGanK9P/Clv05hk27BVsbGwIDw81Fa6KorBu3ZfExMQwaFBwvudVr16dmjVrcujQAVNs587tqjZRUf9TrbpgNObi5eUNwI8/bmX79m13HDOajuWnX78XOH8+hqNHDwNw7NgRzp+PpW/fAWY9b3nTKIqi3L9Z5ZOUlE5OjvH+DUvIpl3n2Lw7VhV748VA/OrUKPI10/TpfHQ4nKsZ11TxRk4NCGk2DCuLshnQVxSFoUNf4IcfttK0aTPCwpbz8MOPlMm970ertcDJyb7M+1uUD+nvqkX6u2qpzP2dnZ3N2rWfs3//XiwttRgMBnx8fBk27BVq1761qkPezm0ODo506dKVkSPHcOzYURYtmofRqODu7o6bmzvr1n1FQEAg7703lwMH9rFhQxRarZbc3BycnWvy2mtTcXFxYd++PURGrjbloNPpmDBhMt7edQvM89ixIyxduhgrKyv0ej3jxv2n1HZuy+vvkiaFbxm4mZbNG8v3kW24Pf82oL4L4/v6F/maWTlZhB5ZwfnUeFXc27EO4x8diY3WusjXvh9FUe4aSb527RqRkasZN+4/d731WZ4q81+U4m7S31WL9HfVIv1dtZRW4StTHcrAN7tjVUWvRgNBHYo+BcBgzGHFiTV3Fb217WoxptnwUi16Y2Nj6N27Gz/99L0qXqtWLSZOnPJAFb1CCCGEEHeSl9tK2eXr6fx29JIq1rapOx4uRftXjFEx8tmf/+N00llV3Mm6Bq8GvIyDruT/dQS3RnnXrFnNO++8SUZGOtHR//Dbb/twdq5ZKvcTQgghhChpUviWsg07z2G8YzaJTmtB7yeLtnuaoih8+fdGjiScUMUdrOwZF/AyTjY1ipNqga5cucxrr43jl19uT4K3trbm0qVLUvgKIYQQosKQqQ6l6J+LNzl0JkEV6/x4HZwcizYVYcu5H9l96Q9VzNpSx5hmw3Gzv//WiOZSFIUNG6Jo166lqugdPHgoO3fupUmTpiV+TyGEEEKI0iIjvqVEURSidqi3Jra30dK1ZcFLhdzLL3G/8eN59TIlWo0lo5q+hHe1ktnu+E7Xr19n6tSJbN680RSrVas2CxcuMW13KIQQQghRkUjhW0qO/XOdsxduqmI9WtfFzsb8H/m+ywfZ8M+3qpgGDcMaD6Khc/1i5Znv/fbtZcSIYBISbi+T1rt3H+bMWSBTG4QQQghRYUnhWwpyjUbW7YxWxVyq2/BUoKfZ1zqReIovTq+7Kz6oURABtUpnqoGnpydZWVkAODk5MXfuR/TuHVQq9xJCCCGEKCsyx7cU7D5xhUuJ6p3Unm/ni5XWvB/32aRzrDoZiVFRr1fYu143Wj9UelsEenrW4f335/L001347bc/pOgVQgghRKUghW8JyzbksmnXOVXMq7YDLR+pbdZ14lMvsez4agxG9b7bnbza0dm7Q3HTNMnIyGDu3PdJTU1RxQcMGMQXX0SZdowRQgghhKjoZKpDCfv5YDzJaXpVrF+H+lhoCr9397WMBJYeXUlWbpYq/oR7c56v171E8gQ4dOgA48aNIjr6Hy5fvsSiRUtNx/69M5sQQgghSkd2dhZr167hwIE/sLS0xGAwYG1tQ7t27enYsTNOTs6mLYv79XuBESNGlUueRqORr79eS0REOB9+uJjAwOblkkdxyIhvCUrN0LN133lVrHFdJxr7OBf6GsnZNwk7upJUQ5oq7u/SmEENg0qkINXr9cyePZPu3TsTHX1r5YkNG6KIj48r9rWFEEIIUXjZ2VmMHz+azMwMQkOXsWTJcpYt+4Q+ffqxZMlCNmyIAmDBglDq1/crtzxTUlJ47bWxnD9/Hr0+u9zyKC4pfEvQt3vOk5mdq4r17VD4VRfSDRksPbqK61lJqniDGr4MbzwISwvLYud46tSfPPtsRxYu/BCj8dbc4cDAx/jll9+pU8er2NcXQgghROGtWrUcg0HP6NHj0WpvfxHfvv1T9O7dtxwzU8vKymT06FcZMmRYeadSLDLVoYQkJGey/fAFVeyJxrXxdnMs1PnZuXrCj33KpfQrqngdRw9G+b+ElaVVsfLLzc1l6dLFzJ37PgaDAQCtVsvrr0/j1VdfU/1hE0IIISoDo2Ik3ZBRpve0t7LDQlO4ccWcnBw2b97I4MEv5fuN7sCBg0lLSy3w/EOHDrB69UoURcFgMODl5c348ZNwdLxVe9y8mcz8+bO5fj0BS0sttra2vPTSKzRu3ITs7GwWLZpPdPQ/WFtbo9FoeOGFYFq1apPvvWrVqk2tWrW5fPlSoZ7tQSXVTgnZ+Ns5co23tybWWmroU8itiXOMOaw8sYaYFPU0iVq2LoxtNgJbrU2xcjt37h/GjQvh4MH9ptjDDz9CWNhymjZtVqxrCyGEEA+iw9eO8/Xfm+6aOljaHK0c6N+wN4G1/O/bNi4ulrS0NOrW9cn3uJubG1DwS+Z79+6mXbun6NdvIIqiMG/e+yxZ8hFvvjkDgBUrlmFtbU14+CcARER8zL59u2ncuAnr1n1JfHwcERGrAdiyZRPbt28rsPCtLKTwLQHnr6Sy79RVVaxjoCcuNWzve65RMbLmr685deNvVbyGdXXGBbyCo86h2Pnt2LHdVPRaWFgwduwEpkx5E2vrom2dLIQQQjzo1p5eR2ZO1v0blrBUQxprT68rVOGblnarKLe1tSvSvQYOfBFHx2rArZfSO3ToxAcf/Nd0PDHxGnq9Ab1ej06no1+/F0yrOCUkJJCamkp6ehr29g48/fQzNGr0SJHyqEik8C0BUb+qtya2tbake6v7b02sKArrzm7m4NWjqri91o5xAS9T09apRPIbNuxltm79lvj484SFRdCiRcsSua4QQgghii6vaM3MzCzS+Tk5uSxcOI/Y2Bi0Wi1paWlcv37ddHzw4JeYNm0yffp056mnnqZHj174+TUCICioP3v27KJPn+60b9+Rrl2f49FHHyv+Qz3g5OW2Yvoz5ganYtUvo3V7whtHO919z90a+zM7L+xRxXQWVoxuNgx3e/PW/c2jKApHjhxSxSwsLAgPX8mOHXuk6BVCCFElDGrUF0er4n9rai5HKwcGNSrcS2leXt44ODhy/nxMke41efJ4UlNTCA1dRlhYBOPHT1Qdb9LEn3XrNjN+/ETi4mIZMSLYtEpEnTperF27nunT3yU1NZXx40MID19SpDwqEhnxLQajotw12uvkaM3Tzevc99xfL+xma8w2VcxSY8krTYfgU/3+o8X5SUhIYPLkCXz//besX7+FJ59sbzpWq1atIl1TCCGEqIgCa/kT4NrkgX65zdLSkuef78svv2xj0KAhd73gNmXKf/Dyqsu4cf+569zk5GRiY88xePBQdLpbg205OepNr3bu3EHbtu3o0qUrXbp0ZenSxWzatI4+ffpx8OB+/Pwa0a5dB9q168D69V+zbFkYo0e/WrQHryBkxLcY/jh1lbir6knzvdr6YG1172XHDlw5QtSZb1QxDRqGPjKAR2o2LFIu3323hfbtW/L9998CMGHCmCJ/dSKEEEJUBhYaCxx1DmX6X2GL3jzDhr2CjY0N4eGhpsJVURTWrfuSmJgYBg0Kzve86tWrU7NmTQ4dOmCK7dy5XdUmKup/qhfbjcZcvLxuDa79+ONWtm/fdscxo+lYZaZRFEW5f7PKJykpnZwcY5HPN+QYmb5iH4k3b0+cf8jFnneHP46lRcG/9H9eP82y46sxKup7D/B7nnaerczO4+bNZKZNe511674yxVxcXJg/fzHdu/cw+3qVjVZrgZOTfbH7W1QM0t9Vi/R31VKZ+zs7O5u1az9n//69WFpqMRgM+Pj4MmzYK9SufWtVh7yd2xwcHOnSpSsjR47h2LGjLFo0D6NRwd3dHTc3d9at+4qAgEDee28uBw7sY8OGKLRaLbm5OTg71+S116bi4uLCvn17iIxcbcpBp9MxYcJkvL3rFpjnm2++TmJiAqdOnaR+fT8cHBxYvDgcS8vi7zPwb3n9XdKk8C2in/bH8eV29TSH8UH+BDRwKfCcczdjCT2yAoPRoIo/5/MMXX06mZ3Djh2/8J//jFWtqdetWw/mz1+Eq6ur2derjCrzX5TibtLfVYv0d9Ui/V21lFbhK3N8iyAjy8CWPbGqmJ9ndZrVr1ngORfTLvPxsU/vKnqf8mzLs3U7mnX/tLQ0Zs58m9WrV5li1apV54MP5tGv38AS2dZYCCGEEKKykcK3CLbuiyM9Sz2BvN9T9QssOBMzb7D06Eoyc9Rzbh+vHUifBs+ZXahOnjyeDRvWmT63b/8UixYtxcPD06zrCCGEEEJUJfJym5lupGSx7WC8KvZYQ1fqeVTPt32KPpUlR1dwU6/ecrBJzUYEP9zP7EnwAFOmvImtrS12dnbMmbOAr7/eJEWvEEIIIcR9yIivmTb9HoPhjrlFFhoNQe3r5ds2w5BJ2NGVJGZeV8XrVa/LiCaDsbQo3GRwg8GAlZWV6bOvb32WLl3BI480xtc3/3sLIYQQQgg1GfE1w8WENHafuKyKtQ94CDfnu7ca1OcaWHZ8NRfT1O09HNwJ8R+GzvL+G1zk5OTw0Ufz6NSpLRkZ6nUIn3uupxS9QgghhBBmkMLXDOt3nuPONTCsrSzp2abuXe1yjbl88mck0TfVO7G42DgzttkI7Kxs73uvs2fP0L3708yZM4vTp//i/ff/W8zshRBCCCGqNil8C+lMfDJH/0lUxZ5pUYfqDtaqmFExEnk6ihOJf6ni1XSOvProK1S3rnbP+xiNRpYvX0qnTm05cuQwcGvLYUfHalTRleeEEEIIIUqEzPEtBEVRiNqhXrO3mp0Vz7Twuqvdhn++Zf+Vw6q4rdaGcQEv42Jb8HJnAHFx5xk/fjR79vxuitWv34AlS5bx2GOPF/MphBBCCCGqNhnxLYRDfycQfSlFFevRxgdba/W/G348v4Md8b+rYlYWVoT4D8PDwb3A6yuKQmTkZ7Rv30pV9I4cOZqff94lRa8QQgghRAmQEd/7yMk1sn5ntCpWy8mW9gEPqWK7Lu5jy7kfVDELjQUvNxlM/Ro+BV5fURSGDw/mu+82m2J16nixePHHtG3brgSeQAghhBAPolOnTvLxx6EcPXoYLy9vnJ1rkpmZSVpaKh07dubll0NKZTvgOy1bFsbPP/+Im5s7YWERACQkXGP48MF88kkkrq61SvX+ZU0K3/vYdewSV5PUG08Eta+H1vL2YPnha8f56u+Nd50b/HB/mrg8fM/razQaHn000FT4vvjiEGbO/ABHx3vPBRZCCCFExfbII00IC4ugbdvmDB78Et269QDg9Om/GDXqJVxcXAkK6l+qOYSEjMPKyoojRw6ZYjqdDi8vb3S6+69AVdFI4XsPWfocvtkdq4r5uFejeUNX0+e/bpxh9Z//Q0H94lnfBj1p4RZYqPuMHTuBI0cO88ILL9KlS9di5y2EEEKIiqtRo4fx9a3H4cMHSr3wzU/16jVYunRFmd+3LEjhew8/7o8nJV2vivV/qp5pi+HYlDgiTnxOrpKratO1bieeqtM232v+/POPnDlzhjFjXjXFLC0t+fTTyBLOXgghhBAVVW5urqneOHToAKtXr0RRFAwGA15e3owfPwlHR0cAbt5MZv782Vy/noClpRZbW1teeukVGjduAkBcXCyLFn1IWloalpYWNGrUmJCQsVhb29x135SUm7z55uucOnWSyZOn0a1bD9av/4r1679Gr9czfPhIfvllGxcuxPHcc70JDn7JdG5GRjqLFy/gzJnT2Ns74OjoyIQJr+Pm5lb6P7BCkpfbCnAzXc8Pf8SpYv71atLQywmAK+lX+fjoJ+hz1YXxkx6t6O7T5a7rpaWlMnHiqwwa1I+ZM9/m4MH9pZe8EEIIISqs3bt3ERd3nu7dewGwd+9u2rV7irCwCJYt+wStVsuSJR+Z2q9YsQxra2vCwz8hLCyCBg0asm/fbgCys7OZOPFV2rXrQETEapYsiSA+/jyhoR/le+9q1aoTFhaBs/PtlaiCggYwePBLJCXdQKPRsGBBKO+/P5+IiKVcvHjB1G7OnFlkZ2ezalUkYWERNGr0CJMnjyc3Nze/W5ULGfEtwObdMWQbbneUBujb4dZOadczk1hydCXpOerd1B6r1Yz+fr1M/0LLs3v3LiZMGENc3Hng1lq9UVFf0rx5i9J9CCGEEKKKCw8PY9mysPu28/dvxpo1X6liwcEDOH782H3PDQkZx+jR44qcI0Bk5Gq2bt1CSspNLl68QHDwMFq2bAXAwIEvmt790Wg0dOjQiQ8++K/p3MTEa+j1BvR6PTqdjn79XiA19dZqVNu2/UBKSgo9e/YBQKvV0r17T/773+lMmDDZrHm8iqLQufOzwK3lVh0cHPnnn7N4eHhy8eIFtm/fxsqVn2NhcWtctVevIFasCOfIkUMPTM0jhW8+rt7I4Lejl1Sx1k3d8HR1IFWfRtixFSRn31Qdf9jZjyGPDMBCc3sQPTMzkw8+eJflyz82xeztHZg58wMGDx5aug8hhBBCCFJTU7h8+dJ92z30kMddscTExEKdm1dkFsedL7clJyczffrrnDlzmrlzF5KTk8vChfOIjY1Bq9WSlpbG9evXVedOmzaZPn2689RTT9OjRy/8/BoBEBMTjdGYy/jxIab2er0eV9daXL+eiLu7epWqe6lRwwmt9nbpaGdnR0ZGuuk+AIsXf4il5e02bm7uJCcnFeEnUjqk8M3H+t/OkWu8/bKaldaC55/0JSsni4+PreJahnoHN59qXrzSdAhai9s/ziNHDjFu3CjOnj1jirVq1YbQ0HC8veuW+jMIIYQQAhwdqxWquHNxcck3VphzS3olpho1atC37wDefvsNzp+PZfr0KXh7exMaugydTsfhwwdVhWyTJv6sW7eZnTt38N13mxkxIpjXXptCnz79gFsvq+UtVVYceSO5eTQazV27yr799nv5/iPiQSGF77+cu5TCwdPXVLGnH/PE0d6Sj49/SlzqRdUxd/vajG42HGvLW18VGAwGFiyYy+LFC0xzWqytrZk+fQYjR46565dGCCGEEKVn9OiiT0P499SHspQ3apqUdIPY2HMMHjzUNC0hJydH1Xbnzh20bduOLl260qVLV5YuXcymTevo06cfvr71Wb/+a9LT07C3dzCdP3fuLKZOfUs1glscPj63Xv6PizuvKnxXrlxG587PPjCDflKF3SG/rYntbbQ884Qnn576H2eS1MecbZwYF/Ay9lZ2qmv89NMPpqI3IOBRfvnld0JCxknRK4QQQoj7MhgMfP/9Fjw9vWjSxJ+aNWty6NAB0/GdO7er2kdF/U/10rzRmIuXlzcAnTs/i6trbSIjPzMd//rrtWg0mhIregE8PDzp1KkLa9d+TnZ2NgAnThzj11+34+HhWWL3KS6N8u8x6ioiKSmdnByjKnbsn0QWrzuuivXrUI8b1fez5/IBVdzByp5Jj42hlp0r/3bq1J906/Y048ZNYMKESVhZWZX8A4hC0WotcHKyz7e/ReUj/V21SH9XLZWxv/Pbuc1oNJKSkoKXlzejR79KnTpeHDt2lEWL5mE0Kri7u+Pm5s66dV8REBDIe+/N5cCBfWzYEIVWqyU3Nwdn55q89tpU0/SN+Pg4Fi36kISEq1SrVh0vL29efXUitra2pp3b0tJS8fcP4K233jUtZ+bm5k5QUH9sbe2IjFzNlSuXadbsURYuXMqkSeM5cuQgbm7uDBkynGef7U5GRgZhYQs5evQwNWu6YGtrx/jxE/H0rGP2zyavv0uaFL7/z2hUmPHpfi4mpJtiNatZ06pzMr/E71Sda2NpzYTAUXg5enLuXDQGg4GGDRup2ty4cV21FIgoH5XxL0pRMOnvqkX6u2qR/q5aSqvwle/e/9/uk5dVRS9Aw8du3FX0ai20jPJ/iToOHnzyyQo6dmzDqFHD0evV6/lK0SuEEEII8WCRwhfQG3LZtCtGFXP1TeBI+i5VTIOG4Y1fxCHThv79e/PGG5PIyMjg1KmTLFu2tCxTFkIIIYQQZpJVHYCfD10gKTXb9NmixlXSXY7e1W5QwyD+3nGcoDenkJJyex3foUNHMHz4K2WRqhBCCCGEKKIqX/imZRr4bu9502cLx+tYNziGgnrqc0fnNkRMX8zWrVtMMTc3dxYtWkrHjk+XWb5CCCGEEKJoqnzh++2eWDKzb62Hp7G7ic7vMGjUk+ZdYux5e9xrJCbe3rgiKKg/s2fPp0YNpzLNVwghhBBCFE2VLnwTb2ay/fAFADQ26Vg3PITGMlfVxk+py5y3ppvW5a1Zsybz5i2iR49eZZ6vEEIIIYQouir9ctvG32LIyVXAKgtdwwNorNQrMwS4NuHVjiFMmDARgGef7c7OnX9I0SuEEEIIUQFV2RHfuKup7PvzCmj1WDc8iIV1FgA5WQYsrCxp5OLHS4+8gIXGgokTp9K4sT/PPdcTjUZTzpkLIYQQQoiiqLIjvl/98g+KRQ7WfoewsEsDIPHvK2x7I4qrP8UwqukQrCxv7bim0+no0aOXFL1CCCGEEBVYuY/4btu2jfDwcGxsbLCwsGDGjBk0aNCgwPYHDx5k3rx56HQ69Ho9U6ZMoXnz5mbd89iZBE7EJKDzO4KFw01yDbn8GXWAv7ccA0Xhj7W/8s/QszRp0rS4jyeEEEIIIR4Q5Vr4Hj9+nClTprB+/Xp8fX3ZtGkTI0aMYOvWrTg4ONzV/uLFi4waNYqPP/6Yli1bsn//fkaNGsXmzZvx8PAo9H0//e4EVr7Hsax+neTYRP5Yup2U+Bum482aPYqdnV2JPKMQQgghhHgwlOtUhxUrVtC+fXt8fX0B6NmzJ7m5uWzatCnf9mvWrMHX15eWLVsC0KJFC3x8fIiMjDTrvnGW+7CocYlTGw7x8/QNpqJXa2XFW2/9ly1bfsTXt17RH0wIIYQQQjxwyrXw3bt3L02b3p5OYGFhQePGjdmzZ0++7ffs2aNqD9C0adMC2xckM/cEO97ZxJ9fH0DJvbVmb4OHG7Ltp52MHz8RS0tLM59ECCGEEEI86Mqt8E1KSiI1NRUXFxdV3MXFhfj4+HzPiY+PN6t9QX6aGsWN6GsAaDQagkNGsGPbbho3bmLWdYQQQgghRMVRbnN8s7JuLR+m0+lUcZ1OZzqW3znmtC+IvasjqZeScXCvzvsfzSf4mUFmnS8qDktLC9X/ispN+rtqkf6uWqS/q5bS6udyK3xtbGwA0OvVm0bo9XrTsfzOMad9QVIuJpnVXlR81arZlncKogxJf1ct0t9Vi/S3KI5y+2eTk5MTjo6OJCYmquKJiYnUqVMn33Pq1KljVnshhBBCCCHylOv3BU888QQnT540fVYUhVOnTtG6det827dq1UrVHuDkyZMFthdCCCGEECJPuRa+I0eOZOfOncTExACwefNmLCws6N27NwDTpk3j9ddfN7UfMmQI0dHRHDhwALi1mUV0dDSDBw8u89yFEEIIIUTFUq4bWPj7+zN37lwmTZpk2rlt1apVps0rsrOzycnJMbX38PBg+fLlzJs3DysrK/R6PREREWZtXiGEEEIIIaomjaIoSnknIYQQQgghRGmTNUGEEEIIIUSVIIWvEEIIIYSoEqTwFUIIIYQQVYIUvkIIIYQQokoo11UdSsu2bdsIDw83rRQxY8YMGjRoUGD7gwcPMm/ePHQ6HXq9nilTptC8efMyzFgUhzn9vWfPHj7//HMyMjLIzs7G3t6eyZMn88gjj5Rx1qKozP3znWfHjh2EhIQwe/Zs+vTpUwaZipJgbn/fuHGDBQsWEBcXR3p6Onq9njFjxtCtW7cyzFoUlTn9rSgK4eHh/PDDD1SrVo2MjAwGDBjAgAEDyjhrUVR6vZ4lS5awatUqfvrpJzw9Pe/ZvkTqNaWSOXbsmBIQEKBER0criqIoGzduVJ588kklNTU13/YXLlxQAgMDlX379imKoih//PGHEhgYqFy4cKHMchZFZ25/P/3008pXX31l+rxo0SKlRYsWSmJiYpnkK4rH3P7Ok56ervTs2VPx8/NT1q9fXxapihJgbn9nZ2crPXv2VDZu3GiKzZkzR5kzZ05ZpCuKydz+/vrrr5XAwEDlypUriqIoyqVLl5TAwEBlx44dZZWyKIb4+Hilf//+ypQpUxQ/Pz8lPj7+nu1Lql6rdFMdVqxYQfv27fH19QWgZ8+e5ObmsmnTpnzbr1mzBl9fX1q2bAlAixYt8PHxITIysqxSFsVgbn83adKEvn37mj4HBweTnJzMnj17yiJdUUzm9nee0NBQXnjhhTLIUJQkc/s7KioKa2tr0yZIAK+88orqz7x4cJnb36dPn8bX15fatWsD4O7ujo+PD7///ntZpSyKISMjg3nz5hX6G7iSqtcqXeG7d+9emjZtavpsYWFB48aNCyxs9uzZo2oP0LRpUymEKghz+3vhwoVYWNz+tbe2tgbAYDCUbqKiRJjb3wCnTp3i+PHj8vVnBWRuf//00088/vjjqpizszP16tUr1TxFyTC3vzt16kR0dDR///03cKsQPnv2LC4uLmWSrygePz8/vL29C92+pOq1SlX4JiUlkZqaetcvvYuLC/Hx8fmeEx8fb1Z78eAoSn//29GjR7GxsaFDhw6lkKEoSUXpb6PRyMyZM3nnnXfQaDRlkaYoIUXp7zNnzmBjY8OMGTMYOHAgwcHB/O9//0ORfZoeeEXp79atWzN79myGDh1Kt27deP755/H392fQoEFlkbIoYyVVr1Wql9uysrIA0Ol0qrhOpzMdy+8cc9qLB0dR+vtOyv+/GDFhwgScnZ1LJUdRcorS35GRkQQGBtKoUaNSz0+UrKL0d0pKCsuXL2fp0qW8++67xMbG8uKLL5KamsrIkSNLPWdRdEXp7x07dvDOO++watUqmjRpQnx8PN9//z02Njalnq8oeyVVr1WqEd+8X3a9Xq+K6/X6Av8g2NjYmNVePDiK0t93WrJkCbVr12b48OGlkp8oWeb299WrV4mKimLcuHFlkp8oWUX5863RaPD396d9+/YA1K1bl6CgID777LPSTVYUW1H6e+HChXTu3JkmTZoAUKdOHWJjY3nvvfdKN1lRLkqqXqtUha+TkxOOjo4kJiaq4omJidSpUyffc+rUqWNWe/HgKEp/5/nyyy85ceIEs2fPLs0URQkyt7/zXnAZNWoUwcHBBAcHAxAREUFwcDAHDx4s/aRFkRXlz7e7uztubm6q2EMPPURiYqJ8i/eAK0p/nz9/Hg8PD1XM09OTH3/8sdTyFOWnpOq1SjXVAeCJJ57g5MmTps+KonDq1ClCQkLybd+qVSuOHDmiip08eZLWrVuXap6iZJjb3wDffvstW7duJSIiAp1OR3x8PPHx8dLnFYA5/R0UFERQUJAq1rBhQ0aOHCnr+FYQ5v75bt68ORcuXFDFEhMTcXJykm/xKgBz+7t27dokJCSoYgkJCaaXlkXlUlL1WqUa8QUYOXIkO3fuJCYmBoDNmzdjYWFhWt5m2rRpvP7666b2Q4YMITo6mgMHDgC3FkeOjo5m8ODBZZ67MJ+5/b1jxw4WLFjAmDFjOHv2LCdOnGD37t0cOnSoPNIXZjK3v0XFZm5/Dx06lOPHj3P8+HEAkpOT+eabb0yj/eLBZm5/BwUFsXXrVi5dugTAxYsX2bp1K127di3z3EXJK616rdKN+Pr7+zN37lwmTZpk2vll1apVODg4AJCdnU1OTo6pvYeHB8uXL2fevHlYWVmh1+uJiIi46+sT8WAyt7+nTZtGUlISQ4cOVV1H5oFWDOb2d56IiAh27dpl+v8bN25kzZo1ZZq7MJ+5/d2oUSPCwsKYOXMmWq2W3Nxc+vfvL/P4Kwhz+3vEiBFoNBrGjh2LjY0NaWlpDBw4kDFjxpTXIwgz6PV6RowYQUpKCgATJ07Ezc2N0NBQoPTqNY0i67wIIYQQQogqoNJNdRBCCCGEECI/UvgKIYQQQogqQQpfIYQQQghRJUjhK4QQQgghqgQpfIUQQgghRJUgha8QQgghhKgSpPAVQgghhBBVQqXbwEIIUTkcP36c+fPns3//fnx8fHB1dSU7O5vMzExefPFFBg4cWN4pFsqGDRvw8PCgZcuW5Z1KoaSkpDBlyhSSkpJIS0ujb9++DBs2rLzTuq9p06bx22+/8eSTTzJnzpzyTkcI8YCSwlcI8UDy9/dnzZo1NGzYkJEjR9KnTx8ADh06RHBwMI6OjnTv3r2cs7y/jRs30qJFiwpT+H7++eekp6fz1VdfkZSUxM8//1zeKRXK7NmzeeONN8o7DSHEA06mOgghKpTHHnuMBg0a8OOPP5Z3KpXSxYsXTVuAOjk50a9fv3LOSAghSo6M+AohKpycnBysrKwAOHfuHO+//z6pqalYWFjQtGlTJk2ahI2NDZGRkURGRqLX6xk3bhzff/89x44do1evXkyfPp3Y2FhmzZrFjRs3sLKywsnJiZCQEAICAgA4evQo8+bNw2g0oigK7dq1IyQkBEtLSxYuXMiWLVvw8PCgffv27Nq1i0uXLjF27Fh69+4NwJQpU/jrr7+4ePGiacrGzJkz+euvv/joo49IT09HURScnZ15++23cXNzMz3jkSNHmDFjBlqtFmdnZ9q2bcvs2bNp0aIFs2bNwtvb+57PXpBNmzbx6aefotPpMBgMjBo1iq5duwIwffp0fvvtNwCCg4N58sknGTly5F3X2L17N6Ghoeh0OrKysmjWrBkTJ07Ezs7uvs82ffp0fv31V1q3bk2tWrU4cuQIN2/eZPbs2aSnp7NmzRrOnj1Lv379TPdesGAB3333HQ899BCPPvooR48eJT4+nmeffZYpU6ZgYVHwGM6GDRv4/PPPsbOzw2g0MmLECDp37lzI3zQhRKWjCCHEA8zPz09Zv3696fN3332nNGzYUPn999+VrKws5amnnlLWrl2rKIqi6PV65eWXX1befvttU/v169cr/v7+yhdffKEoiqLs3btX+fDDD5Xs7GylY8eOSnh4uKIoimI0GpW33npLmTVrlqIoipKYmKgEBgYqv/76q6IoipKWlqb06tVLWb58uenaoaGhSkBAgLJnzx5FURTl559/VgICApTU1FRTm8GDByuhoaGqZ1qzZo3ywQcfmD6HhYUpwcHBps+pqalKixYtlFWrVimKoigZGRlK//79FT8/P1Obwjz7v+3atUsJCAhQoqOjFUVRlNOnTytNmzZVDh48aGozdepUZerUqQVew2AwKIGBgaZnTk9PV7p06aLEx8cX6tny7tGiRQslNjZWURRF+eijj5TnnntOiYyMVBRFUf755x+lUaNGSlxcnOmc0NBQ5eGHH1a2bt2qKIqiXLt2TWnTpo2yZs2aAnP/9ddflRYtWiiXL19WFEVRYmJilGbNmimHDx8u8PmEEJWbTHUQQjzwIiIiCA4Opn///qxfv56IiAjatGnDli1bSE5OZsCAAQBYWVkRFBTE+vXr0ev1pvNzc3NNX9k/8cQTTJo0iS1btnDt2jWGDBkCgEajYdiwYTRp0gSAyMhI3N3dad++PQD29vb06NGDL774QpWbi4sLrVq1AqBly5ZkZGQQFxd3z+fp1q0br776qulz165d2b9/P1lZWQB8++23ZGRkMGjQIABsbW3p27ev6hqFffY7LVu2jE6dOuHr6wtAw4YNadu2LcuXL79nvndKT08nLS2Ny5cvA2BnZ8fChQtxcXEp1LPlefjhh/H29gYgMDCQM2fO0KlTJwDq1atH9erV+fvvv1XnuLq6mkanXV1d6d69O2vWrCkw14iICLp3724aba5bty4tW7Zk7dq1hX5eIUTlIlMdhBAPvDtfbrvT2bNnMRqNDB061BTLzs6mdu3aXLt2DU9PTwBq1qxpmhpx57murq7Y2dmZYr6+vqai8OzZsyQkJBAcHGw6np6ejlarxWAwmK7n6upqOm5vbw9AWlraPZ9Ho9EQGhrK8ePH0Wq16PV6FEXh+vXreHh4cO7cOVxdXVVTFh566KEiPfu/z3niiSdUMW9vb3744Yd75nun6tWrM2rUKKZPn05kZCQ9evSgT58+plzv92x57vy55Z17Z8zOzo7U1FTVvf/9M/Dy8iIuLk7VH/9+3qtXr6r6MCkpCWtr60I/rxCicpHCVwhRoTk5Od1z1A/A0tLyrpiiKPe9doMGDcy6tkajKdS1p06dys2bN1m1ahUODg5cuHCBTp06mc5TFMV0rXspzLMXRmHudaeJEyfSv39/Nm3axGeffcbKlSv56quv8PT0vO+z5cmvT/4d+/c59/v8bxqNhp49ezJ+/HhzHk8IUYnJVAchRIXl5+dHQkKCaoTVYDDwxhtvkJOTU6hzMzMzTbHz58+zZcsW0/HY2FiMRqPp+PXr15k5c6ZZOd5ZVOa98HXgwAHatWuHg4ODKec71a9fn2vXrqmmB1y6dCnf/M159gYNGhAbG6uKnT9/ngYNGhT6edLS0ti1axeenp6mFwatra356aefAO77bMVx5coV1ef4+Hi8vLzyHe2FW88bExOjiu3bt0+mOghRhUnhK4SosHr06EHt2rWJiIgwxT777DM0Gg1a7b2/0OrRowe1atUyjZgajUZCQ0NNhfDgwYPJysoiKioKuDW6+PHHH+Ps7GxWjs7Ozty8eROA/v37k56eTv369dm/f7+pQM0rGvM899xz2NnZmQq0rKwsNm/eXOxnDwkJ4ZdffuHcuXMA/P333/z++++MGjWq0M+TnJzMzJkzycjIMMVyc3Px8fEBuO+zFUdycrJpGbuEhAS+++471TSGfwsJCWH79u2cPn0agIyMDBYuXGiaziKEqHo0SmG+7xNCiDL2753bPD09Wbly5V3t8pYku3r1KtWrV8fX15c33ngDOzs7NmzYQEREBBcvXiQgIIAJEybQvHlz07kxMTHMmjWLpKQkrKysaNu2rerFrOPHjzNnzhwyMzOxtbWlefPmTJgwAUtLSyIiIvjyyy9JSUmhY8eOvP3224wZM4b9+/fTqFEjpkyZQps2bTh48CBvvfUWNWrU4PHHH2fSpEmcPXuWGTNmcOPGDXx8fPD19WXlypU0a9aM9957j4YNG6qWM6tduzbt2rVj1qxZ/Pnnn4V69oJs2rSJTz75BGtrawwGAyNHjqRbt27A7aXG4NZ853ffffeuIjEjI4OPPvqIw4cPY29vT0ZGBs8884xp6bH7Pdu6devYunUrAL169TLttHb69GlatGjBkiVLmDhxIgcOHMDDw8M0v3vJkiXs37+fNm3asG/fPmJjY1XLmeXt3AbQoUMH3n//fQC++eYbVq5cib29PRqNhhdeeIGePXsW+vdQCFG5SOErhBAPoBs3bqhGl7ds2cKSJUtKdAS1IskrfEtiTrMQouqSqQ5CCPEAevHFF7lx4wYAer2eqKgoGakUQohikhFfIYR4AM2fP589e/bg4OBAVlYWrVu3ZuzYseh0uvJOrczl7dyWkpLC448/Tnh4eHmnJISooKTwFUIIIYQQVYJMdRBCCCGEEFWCFL5CCCGEEKJKkMJXCCGEEEJUCVL4CiGEEEKIKkEKXyGEEEIIUSVI4SuEEEIIIaoEKXyFEEIIIUSVIIWvEEIIIYSoEqTwFUIIIYQQVcL/Ab6WsjAl4F2PAAAAAElFTkSuQmCC\n", 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              " - ] - }, - "metadata": { - "filenames": { - "image/png": "/Users/mhjensen/Teaching/MachineLearning/doc/LectureNotes/_build/jupyter_execute/chapter4_64_4.png" - } - }, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "import matplotlib.pyplot as plt\n", "import numpy as np\n", @@ -1464,7 +1154,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.9.10" + "version": "3.9.18" } }, "nbformat": 4, diff --git a/doc/LectureNotes/_build/jupyter_execute/chapter5.ipynb b/doc/LectureNotes/_build/jupyter_execute/chapter5.ipynb index 12b1fb151..8c3154897 100644 --- a/doc/LectureNotes/_build/jupyter_execute/chapter5.ipynb +++ b/doc/LectureNotes/_build/jupyter_execute/chapter5.ipynb @@ -54,27 +54,20 @@ }, "outputs": [ { - "name": "stdout", - "output_type": "stream", - "text": [ - "LinearSVC: [0.28475098] [[1.05364854 1.09903804]]\n", - "SVC: [0.31896852] [[1.1203284 1.02625193]]\n", - "SGDClassifier(alpha=0.00200): [0.117] [[0.77714169 0.72981762]]\n" + "ename": "ModuleNotFoundError", + "evalue": "No module named 'matplotlib'", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mModuleNotFoundError\u001b[0m Traceback (most recent call last)", + "Cell \u001b[0;32mIn[1], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \u001b[43mget_ipython\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mrun_line_magic\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mmatplotlib\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43minline\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m)\u001b[49m\n\u001b[1;32m 3\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01msklearn\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m datasets\n\u001b[1;32m 4\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01msklearn\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01msvm\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m SVC, LinearSVC\n", + "File \u001b[0;32m~/miniforge3/lib/python3.9/site-packages/IPython/core/interactiveshell.py:2432\u001b[0m, in \u001b[0;36mInteractiveShell.run_line_magic\u001b[0;34m(self, magic_name, line, _stack_depth)\u001b[0m\n\u001b[1;32m 2430\u001b[0m kwargs[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mlocal_ns\u001b[39m\u001b[38;5;124m'\u001b[39m] \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mget_local_scope(stack_depth)\n\u001b[1;32m 2431\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mbuiltin_trap:\n\u001b[0;32m-> 2432\u001b[0m result \u001b[38;5;241m=\u001b[39m \u001b[43mfn\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 2434\u001b[0m \u001b[38;5;66;03m# The code below prevents the output from being displayed\u001b[39;00m\n\u001b[1;32m 2435\u001b[0m \u001b[38;5;66;03m# when using magics with decorator @output_can_be_silenced\u001b[39;00m\n\u001b[1;32m 2436\u001b[0m \u001b[38;5;66;03m# when the last Python token in the expression is a ';'.\u001b[39;00m\n\u001b[1;32m 2437\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mgetattr\u001b[39m(fn, magic\u001b[38;5;241m.\u001b[39mMAGIC_OUTPUT_CAN_BE_SILENCED, \u001b[38;5;28;01mFalse\u001b[39;00m):\n", + "File \u001b[0;32m~/miniforge3/lib/python3.9/site-packages/IPython/core/magics/pylab.py:99\u001b[0m, in \u001b[0;36mPylabMagics.matplotlib\u001b[0;34m(self, line)\u001b[0m\n\u001b[1;32m 97\u001b[0m \u001b[38;5;28mprint\u001b[39m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mAvailable matplotlib backends: \u001b[39m\u001b[38;5;132;01m%s\u001b[39;00m\u001b[38;5;124m\"\u001b[39m \u001b[38;5;241m%\u001b[39m backends_list)\n\u001b[1;32m 98\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m---> 99\u001b[0m gui, backend \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mshell\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43menable_matplotlib\u001b[49m\u001b[43m(\u001b[49m\u001b[43margs\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mgui\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mlower\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mif\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[38;5;28;43misinstance\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43margs\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mgui\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mstr\u001b[39;49m\u001b[43m)\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01melse\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43margs\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mgui\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 100\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_show_matplotlib_backend(args\u001b[38;5;241m.\u001b[39mgui, backend)\n", + "File \u001b[0;32m~/miniforge3/lib/python3.9/site-packages/IPython/core/interactiveshell.py:3606\u001b[0m, in \u001b[0;36mInteractiveShell.enable_matplotlib\u001b[0;34m(self, gui)\u001b[0m\n\u001b[1;32m 3585\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21menable_matplotlib\u001b[39m(\u001b[38;5;28mself\u001b[39m, gui\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mNone\u001b[39;00m):\n\u001b[1;32m 3586\u001b[0m \u001b[38;5;250m \u001b[39m\u001b[38;5;124;03m\"\"\"Enable interactive matplotlib and inline figure support.\u001b[39;00m\n\u001b[1;32m 3587\u001b[0m \n\u001b[1;32m 3588\u001b[0m \u001b[38;5;124;03m This takes the following steps:\u001b[39;00m\n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 3604\u001b[0m \u001b[38;5;124;03m display figures inline.\u001b[39;00m\n\u001b[1;32m 3605\u001b[0m \u001b[38;5;124;03m \"\"\"\u001b[39;00m\n\u001b[0;32m-> 3606\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mmatplotlib_inline\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mbackend_inline\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m configure_inline_support\n\u001b[1;32m 3608\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mIPython\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mcore\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m pylabtools \u001b[38;5;28;01mas\u001b[39;00m pt\n\u001b[1;32m 3609\u001b[0m gui, backend \u001b[38;5;241m=\u001b[39m pt\u001b[38;5;241m.\u001b[39mfind_gui_and_backend(gui, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mpylab_gui_select)\n", + "File \u001b[0;32m~/miniforge3/lib/python3.9/site-packages/matplotlib_inline/__init__.py:1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01m.\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m backend_inline, config \u001b[38;5;66;03m# noqa\u001b[39;00m\n\u001b[1;32m 2\u001b[0m __version__ \u001b[38;5;241m=\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m0.1.6\u001b[39m\u001b[38;5;124m\"\u001b[39m \u001b[38;5;66;03m# noqa\u001b[39;00m\n", + "File \u001b[0;32m~/miniforge3/lib/python3.9/site-packages/matplotlib_inline/backend_inline.py:6\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[38;5;124;03m\"\"\"A matplotlib backend for publishing figures via display_data\"\"\"\u001b[39;00m\n\u001b[1;32m 3\u001b[0m \u001b[38;5;66;03m# Copyright (c) IPython Development Team.\u001b[39;00m\n\u001b[1;32m 4\u001b[0m \u001b[38;5;66;03m# Distributed under the terms of the BSD 3-Clause License.\u001b[39;00m\n\u001b[0;32m----> 6\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mmatplotlib\u001b[39;00m\n\u001b[1;32m 7\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mmatplotlib\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m colors\n\u001b[1;32m 8\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mmatplotlib\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mbackends\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m backend_agg\n", + "\u001b[0;31mModuleNotFoundError\u001b[0m: No module named 'matplotlib'" ] - }, - { - "data": { - "image/png": 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\n", 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              " - ] - }, - "metadata": { - "filenames": { - "image/png": "/Users/mhjensen/Teaching/MachineLearning/doc/LectureNotes/_build/jupyter_execute/chapter5_129_4.png" - } - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Phi(-1.0, -2) = [0.74081822]\n", - "Phi(-1.0, 1) = [0.30119421]\n" - ] - }, - { - "data": { - "image/png": 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- "text/plain": [ - "
              " - ] - }, - "metadata": { - "filenames": { - "image/png": "/Users/mhjensen/Teaching/MachineLearning/doc/LectureNotes/_build/jupyter_execute/chapter5_129_6.png" - } - }, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "from __future__ import division, print_function, unicode_literals\n", "\n", @@ -1800,7 +1691,7 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": null, "metadata": { "collapsed": false, "editable": true @@ -1932,21 +1823,12 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": null, "metadata": { "collapsed": false, "editable": true }, - "outputs": [ - { - "ename": "SyntaxError", - "evalue": "invalid character '’' (U+2019) (3974140161.py, line 5)", - "output_type": "error", - "traceback": [ - "\u001b[0;36m Input \u001b[0;32mIn [5]\u001b[0;36m\u001b[0m\n\u001b[0;31m P = matrix(numpy.diag([1,0]), tc=’d’)\u001b[0m\n\u001b[0m ^\u001b[0m\n\u001b[0;31mSyntaxError\u001b[0m\u001b[0;31m:\u001b[0m invalid character '’' (U+2019)\n" - ] - } - ], + "outputs": [], "source": [ "# Import the necessary packages\n", "import numpy\n", @@ -2007,7 +1889,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.9.10" + "version": "3.9.18" } }, "nbformat": 4, diff --git a/doc/LectureNotes/_build/jupyter_execute/chapter6.ipynb b/doc/LectureNotes/_build/jupyter_execute/chapter6.ipynb index 12c0a8057..83fb0823c 100644 --- a/doc/LectureNotes/_build/jupyter_execute/chapter6.ipynb +++ b/doc/LectureNotes/_build/jupyter_execute/chapter6.ipynb @@ -82,42 +82,20 @@ }, "outputs": [ { - "name": "stdout", - "output_type": "stream", - "text": [ - "2nd degree coefficients:\n", - "zero power: 4.0618352118150005\n", - "first power: 0.004339135748752641\n", - "second power: 1.9543538503067644e-05\n" + "ename": "ModuleNotFoundError", + "evalue": "No module named 'matplotlib'", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mModuleNotFoundError\u001b[0m Traceback (most recent call last)", + "Cell \u001b[0;32mIn[1], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \u001b[43mget_ipython\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mrun_line_magic\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mmatplotlib\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43minline\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m)\u001b[49m\n\u001b[1;32m 3\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mnumpy\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m \u001b[38;5;21;01mnp\u001b[39;00m\n\u001b[1;32m 4\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mmatplotlib\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mpyplot\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m \u001b[38;5;21;01mplt\u001b[39;00m\n", + "File \u001b[0;32m~/miniforge3/lib/python3.9/site-packages/IPython/core/interactiveshell.py:2432\u001b[0m, in \u001b[0;36mInteractiveShell.run_line_magic\u001b[0;34m(self, magic_name, line, _stack_depth)\u001b[0m\n\u001b[1;32m 2430\u001b[0m kwargs[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mlocal_ns\u001b[39m\u001b[38;5;124m'\u001b[39m] \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mget_local_scope(stack_depth)\n\u001b[1;32m 2431\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mbuiltin_trap:\n\u001b[0;32m-> 2432\u001b[0m result \u001b[38;5;241m=\u001b[39m \u001b[43mfn\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 2434\u001b[0m \u001b[38;5;66;03m# The code below prevents the output from being displayed\u001b[39;00m\n\u001b[1;32m 2435\u001b[0m \u001b[38;5;66;03m# when using magics with decorator @output_can_be_silenced\u001b[39;00m\n\u001b[1;32m 2436\u001b[0m \u001b[38;5;66;03m# when the last Python token in the expression is a ';'.\u001b[39;00m\n\u001b[1;32m 2437\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mgetattr\u001b[39m(fn, magic\u001b[38;5;241m.\u001b[39mMAGIC_OUTPUT_CAN_BE_SILENCED, \u001b[38;5;28;01mFalse\u001b[39;00m):\n", + "File \u001b[0;32m~/miniforge3/lib/python3.9/site-packages/IPython/core/magics/pylab.py:99\u001b[0m, in \u001b[0;36mPylabMagics.matplotlib\u001b[0;34m(self, line)\u001b[0m\n\u001b[1;32m 97\u001b[0m \u001b[38;5;28mprint\u001b[39m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mAvailable matplotlib backends: \u001b[39m\u001b[38;5;132;01m%s\u001b[39;00m\u001b[38;5;124m\"\u001b[39m \u001b[38;5;241m%\u001b[39m backends_list)\n\u001b[1;32m 98\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m---> 99\u001b[0m gui, backend \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mshell\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43menable_matplotlib\u001b[49m\u001b[43m(\u001b[49m\u001b[43margs\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mgui\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mlower\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mif\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[38;5;28;43misinstance\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43margs\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mgui\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mstr\u001b[39;49m\u001b[43m)\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01melse\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43margs\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mgui\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 100\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_show_matplotlib_backend(args\u001b[38;5;241m.\u001b[39mgui, backend)\n", + "File \u001b[0;32m~/miniforge3/lib/python3.9/site-packages/IPython/core/interactiveshell.py:3606\u001b[0m, in \u001b[0;36mInteractiveShell.enable_matplotlib\u001b[0;34m(self, gui)\u001b[0m\n\u001b[1;32m 3585\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21menable_matplotlib\u001b[39m(\u001b[38;5;28mself\u001b[39m, gui\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mNone\u001b[39;00m):\n\u001b[1;32m 3586\u001b[0m \u001b[38;5;250m \u001b[39m\u001b[38;5;124;03m\"\"\"Enable interactive matplotlib and inline figure support.\u001b[39;00m\n\u001b[1;32m 3587\u001b[0m \n\u001b[1;32m 3588\u001b[0m \u001b[38;5;124;03m This takes the following steps:\u001b[39;00m\n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 3604\u001b[0m \u001b[38;5;124;03m display figures inline.\u001b[39;00m\n\u001b[1;32m 3605\u001b[0m \u001b[38;5;124;03m \"\"\"\u001b[39;00m\n\u001b[0;32m-> 3606\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mmatplotlib_inline\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mbackend_inline\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m configure_inline_support\n\u001b[1;32m 3608\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mIPython\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mcore\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m pylabtools \u001b[38;5;28;01mas\u001b[39;00m pt\n\u001b[1;32m 3609\u001b[0m gui, backend \u001b[38;5;241m=\u001b[39m pt\u001b[38;5;241m.\u001b[39mfind_gui_and_backend(gui, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mpylab_gui_select)\n", + "File \u001b[0;32m~/miniforge3/lib/python3.9/site-packages/matplotlib_inline/__init__.py:1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01m.\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m backend_inline, config \u001b[38;5;66;03m# noqa\u001b[39;00m\n\u001b[1;32m 2\u001b[0m __version__ \u001b[38;5;241m=\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m0.1.6\u001b[39m\u001b[38;5;124m\"\u001b[39m \u001b[38;5;66;03m# noqa\u001b[39;00m\n", + "File \u001b[0;32m~/miniforge3/lib/python3.9/site-packages/matplotlib_inline/backend_inline.py:6\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[38;5;124;03m\"\"\"A matplotlib backend for publishing figures via display_data\"\"\"\u001b[39;00m\n\u001b[1;32m 3\u001b[0m \u001b[38;5;66;03m# Copyright (c) IPython Development Team.\u001b[39;00m\n\u001b[1;32m 4\u001b[0m \u001b[38;5;66;03m# Distributed under the terms of the BSD 3-Clause License.\u001b[39;00m\n\u001b[0;32m----> 6\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mmatplotlib\u001b[39;00m\n\u001b[1;32m 7\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mmatplotlib\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m colors\n\u001b[1;32m 8\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mmatplotlib\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mbackends\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m backend_agg\n", + "\u001b[0;31mModuleNotFoundError\u001b[0m: No module named 'matplotlib'" ] - }, - { - "data": { - "image/png": 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\n", 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\n", 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              " - ] - }, - "metadata": { - "filenames": { - "image/png": "/Users/mhjensen/Teaching/MachineLearning/doc/LectureNotes/_build/jupyter_execute/chapter6_1_2.png" - } - }, - "output_type": "display_data" } ], "source": [ @@ -530,122 +508,12 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": null, "metadata": { "collapsed": false, "editable": true }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - " mean radius mean texture mean perimeter mean area mean smoothness \\\n", - "0 17.99 10.38 122.80 1001.0 0.11840 \n", - "1 20.57 17.77 132.90 1326.0 0.08474 \n", - "2 19.69 21.25 130.00 1203.0 0.10960 \n", - "3 11.42 20.38 77.58 386.1 0.14250 \n", - "4 20.29 14.34 135.10 1297.0 0.10030 \n", - ".. ... ... ... ... ... \n", - "564 21.56 22.39 142.00 1479.0 0.11100 \n", - "565 20.13 28.25 131.20 1261.0 0.09780 \n", - "566 16.60 28.08 108.30 858.1 0.08455 \n", - "567 20.60 29.33 140.10 1265.0 0.11780 \n", - "568 7.76 24.54 47.92 181.0 0.05263 \n", - "\n", - " mean compactness mean concavity mean concave points mean symmetry \\\n", - "0 0.27760 0.30010 0.14710 0.2419 \n", - "1 0.07864 0.08690 0.07017 0.1812 \n", - "2 0.15990 0.19740 0.12790 0.2069 \n", - "3 0.28390 0.24140 0.10520 0.2597 \n", - "4 0.13280 0.19800 0.10430 0.1809 \n", - ".. ... ... ... ... \n", - "564 0.11590 0.24390 0.13890 0.1726 \n", - "565 0.10340 0.14400 0.09791 0.1752 \n", - "566 0.10230 0.09251 0.05302 0.1590 \n", - "567 0.27700 0.35140 0.15200 0.2397 \n", - "568 0.04362 0.00000 0.00000 0.1587 \n", - "\n", - " mean fractal dimension ... worst radius worst texture \\\n", - "0 0.07871 ... 25.380 17.33 \n", - "1 0.05667 ... 24.990 23.41 \n", - "2 0.05999 ... 23.570 25.53 \n", - "3 0.09744 ... 14.910 26.50 \n", - "4 0.05883 ... 22.540 16.67 \n", - ".. ... ... ... ... \n", - "564 0.05623 ... 25.450 26.40 \n", - "565 0.05533 ... 23.690 38.25 \n", - "566 0.05648 ... 18.980 34.12 \n", - "567 0.07016 ... 25.740 39.42 \n", - "568 0.05884 ... 9.456 30.37 \n", - "\n", - " worst perimeter worst area worst smoothness worst compactness \\\n", - "0 184.60 2019.0 0.16220 0.66560 \n", - "1 158.80 1956.0 0.12380 0.18660 \n", - "2 152.50 1709.0 0.14440 0.42450 \n", - "3 98.87 567.7 0.20980 0.86630 \n", - "4 152.20 1575.0 0.13740 0.20500 \n", - ".. ... ... ... ... \n", - "564 166.10 2027.0 0.14100 0.21130 \n", - "565 155.00 1731.0 0.11660 0.19220 \n", - "566 126.70 1124.0 0.11390 0.30940 \n", - "567 184.60 1821.0 0.16500 0.86810 \n", - "568 59.16 268.6 0.08996 0.06444 \n", - "\n", - " worst concavity worst concave points worst symmetry \\\n", - "0 0.7119 0.2654 0.4601 \n", - "1 0.2416 0.1860 0.2750 \n", - "2 0.4504 0.2430 0.3613 \n", - "3 0.6869 0.2575 0.6638 \n", - "4 0.4000 0.1625 0.2364 \n", - ".. ... ... ... \n", - "564 0.4107 0.2216 0.2060 \n", - "565 0.3215 0.1628 0.2572 \n", - "566 0.3403 0.1418 0.2218 \n", - "567 0.9387 0.2650 0.4087 \n", - "568 0.0000 0.0000 0.2871 \n", - "\n", - " worst fractal dimension \n", - "0 0.11890 \n", - "1 0.08902 \n", - "2 0.08758 \n", - "3 0.17300 \n", - "4 0.07678 \n", - ".. ... \n", - "564 0.07115 \n", - "565 0.06637 \n", - "566 0.07820 \n", - "567 0.12400 \n", - "568 0.07039 \n", - "\n", - "[569 rows x 30 columns]\n", - " malignant benign\n", - "0 1 0\n", - "1 1 0\n", - "2 1 0\n", - "3 1 0\n", - "4 1 0\n", - ".. ... ...\n", - "564 1 0\n", - "565 1 0\n", - "566 1 0\n", - "567 1 0\n", - "568 0 1\n", - "\n", - "[569 rows x 2 columns]\n" - ] - }, - { - "data": { - "text/plain": [ - "0" - ] - }, - "execution_count": 2, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "import os\n", "from sklearn.datasets import load_breast_cancer\n", @@ -684,23 +552,12 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": null, "metadata": { "collapsed": false, "editable": true }, - "outputs": [ - { - "data": { - "text/plain": [ - "0" - ] - }, - "execution_count": 3, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "# Common imports\n", "import numpy as np\n", @@ -739,53 +596,12 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": null, "metadata": { "collapsed": false, "editable": true }, - "outputs": [ - { - "data": { - "text/plain": [ - "[Text(0.5, 0.9166666666666666, 'X[2] <= 2.45\\ngini = 0.667\\nsamples = 150\\nvalue = [50, 50, 50]'),\n", - " Text(0.4230769230769231, 0.75, 'gini = 0.0\\nsamples = 50\\nvalue = [50, 0, 0]'),\n", - " Text(0.5769230769230769, 0.75, 'X[3] <= 1.75\\ngini = 0.5\\nsamples = 100\\nvalue = [0, 50, 50]'),\n", - " Text(0.3076923076923077, 0.5833333333333334, 'X[2] <= 4.95\\ngini = 0.168\\nsamples = 54\\nvalue = [0, 49, 5]'),\n", - " Text(0.15384615384615385, 0.4166666666666667, 'X[3] <= 1.65\\ngini = 0.041\\nsamples = 48\\nvalue = [0, 47, 1]'),\n", - " Text(0.07692307692307693, 0.25, 'gini = 0.0\\nsamples = 47\\nvalue = [0, 47, 0]'),\n", - " Text(0.23076923076923078, 0.25, 'gini = 0.0\\nsamples = 1\\nvalue = [0, 0, 1]'),\n", - " 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0.0\\nsamples = 43\\nvalue = [0, 0, 43]')]" - ] - }, - "execution_count": 4, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
              " - ] - }, - "metadata": { - "filenames": { - "image/png": "/Users/mhjensen/Teaching/MachineLearning/doc/LectureNotes/_build/jupyter_execute/chapter6_24_1.png" - } - }, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "from sklearn.datasets import load_iris\n", "from sklearn import tree\n", @@ -806,27 +622,12 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": null, "metadata": { "collapsed": false, "editable": true }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "|--- petal width (cm) <= 0.80\n", - "| |--- class: 0\n", - "|--- petal width (cm) > 0.80\n", - "| |--- petal width (cm) <= 1.75\n", - "| | |--- class: 1\n", - "| |--- petal width (cm) > 1.75\n", - "| | |--- class: 2\n", - "\n" - ] - } - ], + "outputs": [], "source": [ "from sklearn.datasets import load_iris\n", "from sklearn.tree import DecisionTreeClassifier\n", @@ -986,24 +787,12 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": null, "metadata": { "collapsed": false, "editable": true }, - "outputs": [ - { - "ename": "FileNotFoundError", - "evalue": "[Errno 2] No such file or directory: 'DataFiles/rideclass.csv'", - "output_type": "error", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mFileNotFoundError\u001b[0m Traceback (most recent call last)", - "Input \u001b[0;32mIn [6]\u001b[0m, in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[1;32m 34\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21msave_fig\u001b[39m(fig_id):\n\u001b[1;32m 35\u001b[0m plt\u001b[38;5;241m.\u001b[39msavefig(image_path(fig_id) \u001b[38;5;241m+\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m.png\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;28mformat\u001b[39m\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mpng\u001b[39m\u001b[38;5;124m'\u001b[39m)\n\u001b[0;32m---> 37\u001b[0m infile \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mopen\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43mdata_path\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mrideclass.csv\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mr\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m)\u001b[49m\n\u001b[1;32m 39\u001b[0m \u001b[38;5;66;03m# Read the experimental data with Pandas\u001b[39;00m\n\u001b[1;32m 40\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mIPython\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mdisplay\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m display\n", - "\u001b[0;31mFileNotFoundError\u001b[0m: [Errno 2] No such file or directory: 'DataFiles/rideclass.csv'" - ] - } - ], + "outputs": [], "source": [ "# Common imports\n", "import numpy as np\n", @@ -1534,7 +1323,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.9.10" + "version": "3.9.18" } }, "nbformat": 4, diff --git a/doc/LectureNotes/_build/jupyter_execute/chapter7.ipynb b/doc/LectureNotes/_build/jupyter_execute/chapter7.ipynb index 23bdfcee5..09663bf62 100644 --- a/doc/LectureNotes/_build/jupyter_execute/chapter7.ipynb +++ b/doc/LectureNotes/_build/jupyter_execute/chapter7.ipynb @@ -86,7 +86,7 @@ "traceback": [ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)", - "Input \u001b[0;32mIn [1]\u001b[0m, in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[1;32m 1\u001b[0m heads_proba \u001b[38;5;241m=\u001b[39m \u001b[38;5;241m0.51\u001b[39m\n\u001b[0;32m----> 2\u001b[0m coin_tosses \u001b[38;5;241m=\u001b[39m (\u001b[43mnp\u001b[49m\u001b[38;5;241m.\u001b[39mrandom\u001b[38;5;241m.\u001b[39mrand(\u001b[38;5;241m10000\u001b[39m, \u001b[38;5;241m10\u001b[39m) \u001b[38;5;241m<\u001b[39m heads_proba)\u001b[38;5;241m.\u001b[39mastype(np\u001b[38;5;241m.\u001b[39mint32)\n\u001b[1;32m 3\u001b[0m cumulative_heads_ratio \u001b[38;5;241m=\u001b[39m np\u001b[38;5;241m.\u001b[39mcumsum(coin_tosses, axis\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m0\u001b[39m) \u001b[38;5;241m/\u001b[39m np\u001b[38;5;241m.\u001b[39marange(\u001b[38;5;241m1\u001b[39m, \u001b[38;5;241m10001\u001b[39m)\u001b[38;5;241m.\u001b[39mreshape(\u001b[38;5;241m-\u001b[39m\u001b[38;5;241m1\u001b[39m, \u001b[38;5;241m1\u001b[39m)\n\u001b[1;32m 4\u001b[0m plt\u001b[38;5;241m.\u001b[39mfigure(figsize\u001b[38;5;241m=\u001b[39m(\u001b[38;5;241m8\u001b[39m,\u001b[38;5;241m3.5\u001b[39m))\n", + "Cell \u001b[0;32mIn[1], line 2\u001b[0m\n\u001b[1;32m 1\u001b[0m heads_proba \u001b[38;5;241m=\u001b[39m \u001b[38;5;241m0.51\u001b[39m\n\u001b[0;32m----> 2\u001b[0m coin_tosses \u001b[38;5;241m=\u001b[39m (\u001b[43mnp\u001b[49m\u001b[38;5;241m.\u001b[39mrandom\u001b[38;5;241m.\u001b[39mrand(\u001b[38;5;241m10000\u001b[39m, \u001b[38;5;241m10\u001b[39m) \u001b[38;5;241m<\u001b[39m heads_proba)\u001b[38;5;241m.\u001b[39mastype(np\u001b[38;5;241m.\u001b[39mint32)\n\u001b[1;32m 3\u001b[0m cumulative_heads_ratio \u001b[38;5;241m=\u001b[39m np\u001b[38;5;241m.\u001b[39mcumsum(coin_tosses, axis\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m0\u001b[39m) \u001b[38;5;241m/\u001b[39m np\u001b[38;5;241m.\u001b[39marange(\u001b[38;5;241m1\u001b[39m, \u001b[38;5;241m10001\u001b[39m)\u001b[38;5;241m.\u001b[39mreshape(\u001b[38;5;241m-\u001b[39m\u001b[38;5;241m1\u001b[39m, \u001b[38;5;241m1\u001b[39m)\n\u001b[1;32m 4\u001b[0m plt\u001b[38;5;241m.\u001b[39mfigure(figsize\u001b[38;5;241m=\u001b[39m(\u001b[38;5;241m8\u001b[39m,\u001b[38;5;241m3.5\u001b[39m))\n", "\u001b[0;31mNameError\u001b[0m: name 'np' is not defined" ] } @@ -1597,7 +1597,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.9.10" + "version": "3.9.18" } }, "nbformat": 4, diff --git a/doc/LectureNotes/_build/jupyter_execute/chapter8.ipynb b/doc/LectureNotes/_build/jupyter_execute/chapter8.ipynb index e3b7593db..fa0a99fe6 100644 --- a/doc/LectureNotes/_build/jupyter_execute/chapter8.ipynb +++ b/doc/LectureNotes/_build/jupyter_execute/chapter8.ipynb @@ -295,10 +295,10 @@ "name": "stdout", "output_type": "stream", "text": [ - "0.03822007730337899\n", - "3.989393825676568\n", - "[[0.90044211 2.80659468]\n", - " [2.80659468 9.73667253]]\n" + "-0.08860271693417623\n", + "3.7557911271214808\n", + "[[ 1.08361608 3.3060763 ]\n", + " [ 3.3060763 10.96612219]]\n" ] } ], @@ -340,10 +340,10 @@ "name": "stdout", "output_type": "stream", "text": [ - "0.07864140367329474\n", - "2.0511284278960114\n", - "[[1. 0.68282072]\n", - " [0.68282072 1. ]]\n" + "0.07620892676178488\n", + "1.732950730376058\n", + "[[1. 0.64673189]\n", + " [0.64673189 1. ]]\n" ] } ], @@ -394,33 +394,14 @@ }, "outputs": [ { - "name": "stdout", - "output_type": "stream", - "text": [ - "[[ 0.21424133 -1.00310568]\n", - " [ 1.84955363 5.92472578]\n", - " [ 0.43773101 1.60738434]\n", - " [ 0.94910711 2.53617977]\n", - " [-1.00106025 -3.87889126]\n", - " [-0.2544356 -0.48005485]\n", - " [-0.72117282 -1.4229538 ]\n", - " [-0.48783291 0.5970842 ]\n", - " [-0.17565241 -0.84643086]\n", - " [-0.8104791 -3.03393764]]\n", - " 0 1\n", - "0 0.214241 -1.003106\n", - "1 1.849554 5.924726\n", - "2 0.437731 1.607384\n", - "3 0.949107 2.536180\n", - "4 -1.001060 -3.878891\n", - "5 -0.254436 -0.480055\n", - "6 -0.721173 -1.422954\n", - "7 -0.487833 0.597084\n", - "8 -0.175652 -0.846431\n", - "9 -0.810479 -3.033938\n", - " 0 1\n", - "0 1.00000 0.93503\n", - "1 0.93503 1.00000\n" + "ename": "ModuleNotFoundError", + "evalue": "No module named 'pandas'", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mModuleNotFoundError\u001b[0m Traceback (most recent call last)", + "Cell \u001b[0;32mIn[3], line 2\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mnumpy\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m \u001b[38;5;21;01mnp\u001b[39;00m\n\u001b[0;32m----> 2\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mpandas\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m \u001b[38;5;21;01mpd\u001b[39;00m\n\u001b[1;32m 3\u001b[0m n \u001b[38;5;241m=\u001b[39m \u001b[38;5;241m10\u001b[39m\n\u001b[1;32m 4\u001b[0m x \u001b[38;5;241m=\u001b[39m np\u001b[38;5;241m.\u001b[39mrandom\u001b[38;5;241m.\u001b[39mnormal(size\u001b[38;5;241m=\u001b[39mn)\n", + "\u001b[0;31mModuleNotFoundError\u001b[0m: No module named 'pandas'" ] } ], @@ -449,52 +430,12 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": null, "metadata": { "collapsed": false, "editable": true }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - " 0 1 2 3 4 5 6 7 \\\n", - "0 0.0 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 \n", - "1 0.0 0.082232 0.082339 0.080863 0.080028 0.078987 0.072334 0.071321 \n", - "2 0.0 0.082339 0.085298 0.084636 0.085154 0.085104 0.077849 0.077521 \n", - "3 0.0 0.080863 0.084636 0.085290 0.086290 0.086690 0.079860 0.079858 \n", - "4 0.0 0.080028 0.085154 0.086290 0.088026 0.088995 0.081970 0.082385 \n", - "5 0.0 0.078987 0.085104 0.086690 0.088995 0.090424 0.083319 0.084079 \n", - "6 0.0 0.072334 0.077849 0.079860 0.081970 0.083319 0.077182 0.077910 \n", - "7 0.0 0.071321 0.077521 0.079858 0.082385 0.084079 0.077910 0.078901 \n", - "8 0.0 0.070302 0.077012 0.079649 0.082509 0.084487 0.078333 0.079546 \n", - "9 0.0 0.069312 0.076408 0.079319 0.082449 0.084666 0.078555 0.079958 \n", - "10 0.0 0.063977 0.070119 0.072959 0.075620 0.077491 0.072176 0.073337 \n", - "11 0.0 0.063115 0.069617 0.072682 0.075590 0.077676 0.072384 0.073717 \n", - "12 0.0 0.062295 0.069072 0.072344 0.075455 0.077724 0.072474 0.073955 \n", - "13 0.0 0.061529 0.068521 0.071982 0.075263 0.077690 0.072492 0.074102 \n", - "14 0.0 0.060822 0.067984 0.071619 0.075045 0.077611 0.072468 0.074193 \n", - "\n", - " 8 9 10 11 12 13 14 \n", - "0 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 \n", - "1 0.070302 0.069312 0.063977 0.063115 0.062295 0.061529 0.060822 \n", - "2 0.077012 0.076408 0.070119 0.069617 0.069072 0.068521 0.067984 \n", - "3 0.079649 0.079319 0.072959 0.072682 0.072344 0.071982 0.071619 \n", - "4 0.082509 0.082449 0.075620 0.075590 0.075455 0.075263 0.075045 \n", - "5 0.084487 0.084666 0.077491 0.077676 0.077724 0.077690 0.077611 \n", - "6 0.078333 0.078555 0.072176 0.072384 0.072474 0.072492 0.072468 \n", - "7 0.079546 0.079958 0.073337 0.073717 0.073955 0.074102 0.074193 \n", - "8 0.080384 0.080965 0.074161 0.074692 0.075064 0.075330 0.075529 \n", - "9 0.080965 0.081699 0.074752 0.075418 0.075911 0.076288 0.076588 \n", - "10 0.074161 0.074752 0.068701 0.069233 0.069621 0.069911 0.070139 \n", - "11 0.074692 0.075418 0.069233 0.069886 0.070381 0.070769 0.071086 \n", - "12 0.075064 0.075911 0.069621 0.070381 0.070975 0.071453 0.071853 \n", - "13 0.075330 0.076288 0.069911 0.070769 0.071453 0.072016 0.072493 \n", - "14 0.075529 0.076588 0.070139 0.071086 0.071853 0.072493 0.073045 \n" - ] - } - ], + "outputs": [], "source": [ "# Common imports\n", "import numpy as np\n", @@ -788,7 +729,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": null, "metadata": { "collapsed": false, "editable": true @@ -854,7 +795,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": null, "metadata": { "collapsed": false, "editable": true @@ -905,24 +846,12 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": null, "metadata": { "collapsed": false, "editable": true }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - " 0 1\n", - "0 3.965838 1.971865\n", - "1 1.971865 1.997830\n", - "[[3.96583833 1.97186457]\n", - " [1.97186457 1.99783004]]\n" - ] - } - ], + "outputs": [], "source": [ "print(df.cov())\n", "print(np.cov(X_centered.T))" @@ -938,36 +867,12 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": null, "metadata": { "collapsed": false, "editable": true }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Centered covariance using own code\n", - "[[3.96583833 1.97186457]\n", - " [1.97186457 1.99783004]]\n" - ] - }, - { - "data": { - "image/png": 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0.0 0.0 0.0 0.0 0.0\n", - "9 0.0 0.0 0.0 0.0 0.0\n", - "[[-1.5378811 0.94639099]\n", - " [ 0.86145244 -0.89288636]\n", - " [-0.00445655 -0.81633628]\n", - " [ 0.07145103 1.00433417]\n", - " [ 2.03707133 0.48476997]\n", - " [ 0.72174172 1.4557763 ]\n", - " [-0.55854694 -1.60673226]\n", - " [ 1.6999536 -0.43766686]\n", - " [-1.10405456 -0.31718909]\n", - " [-2.18673098 0.17953942]]\n" - ] - } - ], + "outputs": [], "source": [ "import numpy as np\n", "import pandas as pd\n", @@ -1431,7 +1156,7 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": null, "metadata": { "collapsed": false, "editable": true @@ -1455,29 +1180,12 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": null, "metadata": { "collapsed": false, "editable": true }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[[ 1.5378811 -0.94639099]\n", - " [-0.86145244 0.89288636]\n", - " [ 0.00445655 0.81633628]\n", - " [-0.07145103 -1.00433417]\n", - " [-2.03707133 -0.48476997]\n", - " [-0.72174172 -1.4557763 ]\n", - " [ 0.55854694 1.60673226]\n", - " [-1.6999536 0.43766686]\n", - " [ 1.10405456 0.31718909]\n", - " [ 2.18673098 -0.17953942]]\n" - ] - } - ], + "outputs": [], "source": [ "#thereafter we do a PCA with Scikit-learn\n", "from sklearn.decomposition import PCA\n", @@ -1497,23 +1205,12 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": null, "metadata": { "collapsed": false, "editable": true }, - "outputs": [ - { - "data": { - "text/plain": [ - "array([-0.62373464, -0.5303329 , 0.317367 , 0.01873344, 0.47815203])" - ] - }, - "execution_count": 13, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "pca.components_.T[:, 0]" ] @@ -1533,36 +1230,12 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": null, "metadata": { "collapsed": false, "editable": true }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Train set accuracy from Logistic Regression: 0.95\n", - "Train set accuracy scaled data: 0.99\n", - "Train set accuracy scaled and PCA data: 0.96\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/Users/mhjensen/miniforge3/envs/myenv/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:814: ConvergenceWarning: lbfgs failed to converge (status=1):\n", - "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", - "\n", - "Increase the number of iterations (max_iter) or scale the data as shown in:\n", - " https://scikit-learn.org/stable/modules/preprocessing.html\n", - "Please also refer to the documentation for alternative solver options:\n", - " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", - " n_iter_i = _check_optimize_result(\n" - ] - } - ], + "outputs": [], "source": [ "import matplotlib.pyplot as plt\n", "import numpy as np\n", @@ -1611,7 +1284,7 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": null, "metadata": { "collapsed": false, "editable": true @@ -1635,7 +1308,7 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": null, "metadata": { "collapsed": false, "editable": true @@ -1682,7 +1355,7 @@ }, { "cell_type": "code", - "execution_count": 17, + "execution_count": null, "metadata": { "collapsed": false, "editable": true @@ -1725,7 +1398,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.9.10" + "version": "3.9.18" } }, "nbformat": 4, diff --git a/doc/LectureNotes/_build/jupyter_execute/chapter9.ipynb b/doc/LectureNotes/_build/jupyter_execute/chapter9.ipynb index 099f10d42..3ae8e8703 100644 --- a/doc/LectureNotes/_build/jupyter_execute/chapter9.ipynb +++ b/doc/LectureNotes/_build/jupyter_execute/chapter9.ipynb @@ -577,60 +577,20 @@ }, "outputs": [ { - "data": { - "image/png": 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\n", 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\n", 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\n", 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              " - ] - }, - "metadata": { - "filenames": { - "image/png": "/Users/mhjensen/Teaching/MachineLearning/doc/LectureNotes/_build/jupyter_execute/chapter9_29_3.png" - } - }, - "output_type": "display_data" + "ename": "ModuleNotFoundError", + "evalue": "No module named 'matplotlib'", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mModuleNotFoundError\u001b[0m Traceback (most recent call last)", + "Cell \u001b[0;32mIn[1], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \u001b[43mget_ipython\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mrun_line_magic\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mmatplotlib\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43minline\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m)\u001b[49m\n\u001b[1;32m 3\u001b[0m \u001b[38;5;124;03m\"\"\"The sigmoid function (or the logistic curve) is a \u001b[39;00m\n\u001b[1;32m 4\u001b[0m \u001b[38;5;124;03mfunction that takes any real number, z, and outputs a number (0,1).\u001b[39;00m\n\u001b[1;32m 5\u001b[0m \u001b[38;5;124;03mIt is useful in neural networks for assigning weights on a relative scale.\u001b[39;00m\n\u001b[1;32m 6\u001b[0m \u001b[38;5;124;03mThe value z is the weighted sum of parameters involved in the learning algorithm.\"\"\"\u001b[39;00m\n\u001b[1;32m 8\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mnumpy\u001b[39;00m\n", + "File \u001b[0;32m~/miniforge3/lib/python3.9/site-packages/IPython/core/interactiveshell.py:2432\u001b[0m, in \u001b[0;36mInteractiveShell.run_line_magic\u001b[0;34m(self, magic_name, line, _stack_depth)\u001b[0m\n\u001b[1;32m 2430\u001b[0m kwargs[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mlocal_ns\u001b[39m\u001b[38;5;124m'\u001b[39m] \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mget_local_scope(stack_depth)\n\u001b[1;32m 2431\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mbuiltin_trap:\n\u001b[0;32m-> 2432\u001b[0m result \u001b[38;5;241m=\u001b[39m \u001b[43mfn\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 2434\u001b[0m \u001b[38;5;66;03m# The code below prevents the output from being displayed\u001b[39;00m\n\u001b[1;32m 2435\u001b[0m \u001b[38;5;66;03m# when using magics with decorator @output_can_be_silenced\u001b[39;00m\n\u001b[1;32m 2436\u001b[0m \u001b[38;5;66;03m# when the last Python token in the expression is a ';'.\u001b[39;00m\n\u001b[1;32m 2437\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mgetattr\u001b[39m(fn, magic\u001b[38;5;241m.\u001b[39mMAGIC_OUTPUT_CAN_BE_SILENCED, \u001b[38;5;28;01mFalse\u001b[39;00m):\n", + "File \u001b[0;32m~/miniforge3/lib/python3.9/site-packages/IPython/core/magics/pylab.py:99\u001b[0m, in \u001b[0;36mPylabMagics.matplotlib\u001b[0;34m(self, line)\u001b[0m\n\u001b[1;32m 97\u001b[0m \u001b[38;5;28mprint\u001b[39m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mAvailable matplotlib backends: \u001b[39m\u001b[38;5;132;01m%s\u001b[39;00m\u001b[38;5;124m\"\u001b[39m \u001b[38;5;241m%\u001b[39m backends_list)\n\u001b[1;32m 98\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m---> 99\u001b[0m gui, backend \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mshell\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43menable_matplotlib\u001b[49m\u001b[43m(\u001b[49m\u001b[43margs\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mgui\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mlower\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mif\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[38;5;28;43misinstance\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43margs\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mgui\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mstr\u001b[39;49m\u001b[43m)\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01melse\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43margs\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mgui\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 100\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_show_matplotlib_backend(args\u001b[38;5;241m.\u001b[39mgui, backend)\n", + "File \u001b[0;32m~/miniforge3/lib/python3.9/site-packages/IPython/core/interactiveshell.py:3606\u001b[0m, in \u001b[0;36mInteractiveShell.enable_matplotlib\u001b[0;34m(self, gui)\u001b[0m\n\u001b[1;32m 3585\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21menable_matplotlib\u001b[39m(\u001b[38;5;28mself\u001b[39m, gui\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mNone\u001b[39;00m):\n\u001b[1;32m 3586\u001b[0m \u001b[38;5;250m \u001b[39m\u001b[38;5;124;03m\"\"\"Enable interactive matplotlib and inline figure support.\u001b[39;00m\n\u001b[1;32m 3587\u001b[0m \n\u001b[1;32m 3588\u001b[0m \u001b[38;5;124;03m This takes the following steps:\u001b[39;00m\n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 3604\u001b[0m \u001b[38;5;124;03m display figures inline.\u001b[39;00m\n\u001b[1;32m 3605\u001b[0m \u001b[38;5;124;03m \"\"\"\u001b[39;00m\n\u001b[0;32m-> 3606\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mmatplotlib_inline\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mbackend_inline\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m configure_inline_support\n\u001b[1;32m 3608\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mIPython\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mcore\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m pylabtools \u001b[38;5;28;01mas\u001b[39;00m pt\n\u001b[1;32m 3609\u001b[0m gui, backend \u001b[38;5;241m=\u001b[39m pt\u001b[38;5;241m.\u001b[39mfind_gui_and_backend(gui, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mpylab_gui_select)\n", + "File \u001b[0;32m~/miniforge3/lib/python3.9/site-packages/matplotlib_inline/__init__.py:1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01m.\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m backend_inline, config \u001b[38;5;66;03m# noqa\u001b[39;00m\n\u001b[1;32m 2\u001b[0m __version__ \u001b[38;5;241m=\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m0.1.6\u001b[39m\u001b[38;5;124m\"\u001b[39m \u001b[38;5;66;03m# noqa\u001b[39;00m\n", + "File \u001b[0;32m~/miniforge3/lib/python3.9/site-packages/matplotlib_inline/backend_inline.py:6\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[38;5;124;03m\"\"\"A matplotlib backend for publishing figures via display_data\"\"\"\u001b[39;00m\n\u001b[1;32m 3\u001b[0m \u001b[38;5;66;03m# Copyright (c) IPython Development Team.\u001b[39;00m\n\u001b[1;32m 4\u001b[0m \u001b[38;5;66;03m# Distributed under the terms of the BSD 3-Clause License.\u001b[39;00m\n\u001b[0;32m----> 6\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mmatplotlib\u001b[39;00m\n\u001b[1;32m 7\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mmatplotlib\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m colors\n\u001b[1;32m 8\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mmatplotlib\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mbackends\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m backend_agg\n", + "\u001b[0;31mModuleNotFoundError\u001b[0m: No module named 'matplotlib'" + ] } ], "source": [ @@ -1345,7 +1305,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.9.10" + "version": "3.9.18" } }, "nbformat": 4, diff --git a/doc/LectureNotes/_build/jupyter_execute/chapteroptimization.ipynb b/doc/LectureNotes/_build/jupyter_execute/chapteroptimization.ipynb index a53571646..0ff5de13f 100644 --- a/doc/LectureNotes/_build/jupyter_execute/chapteroptimization.ipynb +++ b/doc/LectureNotes/_build/jupyter_execute/chapteroptimization.ipynb @@ -921,36 +921,20 @@ }, "outputs": [ { - "name": "stderr", - "output_type": "stream", - "text": [ - "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_7714/483257001.py:18: MatplotlibDeprecationWarning: Calling gca() with keyword arguments was deprecated in Matplotlib 3.4. Starting two minor releases later, gca() will take no keyword arguments. The gca() function should only be used to get the current axes, or if no axes exist, create new axes with default keyword arguments. To create a new axes with non-default arguments, use plt.axes() or plt.subplot().\n", - " ax = fig.gca(projection=\"3d\")\n" + "ename": "ModuleNotFoundError", + "evalue": "No module named 'matplotlib'", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mModuleNotFoundError\u001b[0m Traceback (most recent call last)", + "Cell \u001b[0;32mIn[1], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \u001b[43mget_ipython\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mrun_line_magic\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mmatplotlib\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43minline\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m)\u001b[49m\n\u001b[1;32m 3\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mnumpy\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m \u001b[38;5;21;01mnp\u001b[39;00m\n\u001b[1;32m 4\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mnumpy\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mlinalg\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m \u001b[38;5;21;01mla\u001b[39;00m\n", + "File \u001b[0;32m~/miniforge3/lib/python3.9/site-packages/IPython/core/interactiveshell.py:2432\u001b[0m, in \u001b[0;36mInteractiveShell.run_line_magic\u001b[0;34m(self, magic_name, line, _stack_depth)\u001b[0m\n\u001b[1;32m 2430\u001b[0m kwargs[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mlocal_ns\u001b[39m\u001b[38;5;124m'\u001b[39m] \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mget_local_scope(stack_depth)\n\u001b[1;32m 2431\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mbuiltin_trap:\n\u001b[0;32m-> 2432\u001b[0m result \u001b[38;5;241m=\u001b[39m \u001b[43mfn\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 2434\u001b[0m \u001b[38;5;66;03m# The code below prevents the output from being displayed\u001b[39;00m\n\u001b[1;32m 2435\u001b[0m \u001b[38;5;66;03m# when using magics with decorator @output_can_be_silenced\u001b[39;00m\n\u001b[1;32m 2436\u001b[0m \u001b[38;5;66;03m# when the last Python token in the expression is a ';'.\u001b[39;00m\n\u001b[1;32m 2437\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mgetattr\u001b[39m(fn, magic\u001b[38;5;241m.\u001b[39mMAGIC_OUTPUT_CAN_BE_SILENCED, \u001b[38;5;28;01mFalse\u001b[39;00m):\n", + "File \u001b[0;32m~/miniforge3/lib/python3.9/site-packages/IPython/core/magics/pylab.py:99\u001b[0m, in \u001b[0;36mPylabMagics.matplotlib\u001b[0;34m(self, line)\u001b[0m\n\u001b[1;32m 97\u001b[0m \u001b[38;5;28mprint\u001b[39m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mAvailable matplotlib backends: \u001b[39m\u001b[38;5;132;01m%s\u001b[39;00m\u001b[38;5;124m\"\u001b[39m \u001b[38;5;241m%\u001b[39m backends_list)\n\u001b[1;32m 98\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m---> 99\u001b[0m gui, backend \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mshell\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43menable_matplotlib\u001b[49m\u001b[43m(\u001b[49m\u001b[43margs\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mgui\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mlower\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mif\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[38;5;28;43misinstance\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43margs\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mgui\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mstr\u001b[39;49m\u001b[43m)\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01melse\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43margs\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mgui\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 100\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_show_matplotlib_backend(args\u001b[38;5;241m.\u001b[39mgui, backend)\n", + "File \u001b[0;32m~/miniforge3/lib/python3.9/site-packages/IPython/core/interactiveshell.py:3606\u001b[0m, in \u001b[0;36mInteractiveShell.enable_matplotlib\u001b[0;34m(self, gui)\u001b[0m\n\u001b[1;32m 3585\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21menable_matplotlib\u001b[39m(\u001b[38;5;28mself\u001b[39m, gui\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mNone\u001b[39;00m):\n\u001b[1;32m 3586\u001b[0m \u001b[38;5;250m \u001b[39m\u001b[38;5;124;03m\"\"\"Enable interactive matplotlib and inline figure support.\u001b[39;00m\n\u001b[1;32m 3587\u001b[0m \n\u001b[1;32m 3588\u001b[0m \u001b[38;5;124;03m This takes the following steps:\u001b[39;00m\n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 3604\u001b[0m \u001b[38;5;124;03m display figures inline.\u001b[39;00m\n\u001b[1;32m 3605\u001b[0m \u001b[38;5;124;03m \"\"\"\u001b[39;00m\n\u001b[0;32m-> 3606\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mmatplotlib_inline\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mbackend_inline\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m configure_inline_support\n\u001b[1;32m 3608\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mIPython\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mcore\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m pylabtools \u001b[38;5;28;01mas\u001b[39;00m pt\n\u001b[1;32m 3609\u001b[0m gui, backend \u001b[38;5;241m=\u001b[39m pt\u001b[38;5;241m.\u001b[39mfind_gui_and_backend(gui, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mpylab_gui_select)\n", + "File \u001b[0;32m~/miniforge3/lib/python3.9/site-packages/matplotlib_inline/__init__.py:1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01m.\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m backend_inline, config \u001b[38;5;66;03m# noqa\u001b[39;00m\n\u001b[1;32m 2\u001b[0m __version__ \u001b[38;5;241m=\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m0.1.6\u001b[39m\u001b[38;5;124m\"\u001b[39m \u001b[38;5;66;03m# noqa\u001b[39;00m\n", + "File \u001b[0;32m~/miniforge3/lib/python3.9/site-packages/matplotlib_inline/backend_inline.py:6\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[38;5;124;03m\"\"\"A matplotlib backend for publishing figures via display_data\"\"\"\u001b[39;00m\n\u001b[1;32m 3\u001b[0m \u001b[38;5;66;03m# Copyright (c) IPython Development Team.\u001b[39;00m\n\u001b[1;32m 4\u001b[0m \u001b[38;5;66;03m# Distributed under the terms of the BSD 3-Clause License.\u001b[39;00m\n\u001b[0;32m----> 6\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mmatplotlib\u001b[39;00m\n\u001b[1;32m 7\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mmatplotlib\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m colors\n\u001b[1;32m 8\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mmatplotlib\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mbackends\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m backend_agg\n", + "\u001b[0;31mModuleNotFoundError\u001b[0m: No module named 'matplotlib'" ] - }, - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 1, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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\n", 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\n", 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              " - ] - }, - "metadata": { - "filenames": { - "image/png": "/Users/mhjensen/Teaching/MachineLearning/doc/LectureNotes/_build/jupyter_execute/chapteroptimization_63_0.png" - } - }, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "pt.axis(\"equal\")\n", "pt.contour(xmesh, ymesh, fmesh)\n", @@ -1060,15 +1029,7 @@ "collapsed": false, "editable": true }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[ 1.33333333 -0.26666667]\n" - ] - } - ], + "outputs": [], "source": [ "def f1d(alpha):\n", " return f(x + alpha*s)\n", @@ -1097,32 +1058,7 @@ "collapsed": false, "editable": true }, - "outputs": [ - { - "data": { - "text/plain": [ - "[]" - ] - }, - "execution_count": 5, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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\n", 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              " - ] - }, - "metadata": { - "filenames": { - "image/png": "/Users/mhjensen/Teaching/MachineLearning/doc/LectureNotes/_build/jupyter_execute/chapteroptimization_132_1.png" - } - }, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "from random import random, seed\n", "import numpy as np\n", @@ -2319,15 +2194,7 @@ "collapsed": false, "editable": true }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "gamma_j after 500 epochs: 9.97108e-05\n" - ] - } - ], + "outputs": [], "source": [ "import numpy as np \n", "\n", @@ -2383,38 +2250,7 @@ "collapsed": false, "editable": true }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Own inversion\n", - "[[4.5424657 ]\n", - " [2.40026896]]\n", - "Eigenvalues of Hessian Matrix:[0.29830651 3.89658408]\n", - "theta from own gd\n", - "[[4.5424657 ]\n", - " [2.40026896]]\n", - "theta from own sdg\n", - "[[4.55687658]\n", - " [2.41165766]]\n" - ] - }, - { - "data": { - "image/png": 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\n", 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\n", - "text/plain": [ - "
              " - ] - }, - "metadata": { - "filenames": { - "image/png": "/Users/mhjensen/Teaching/MachineLearning/doc/LectureNotes/_build/jupyter_execute/chapteroptimization_188_0.png" - } - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "The max absolute difference is: 1.77636e-15\n" - ] - } - ], + "outputs": [], "source": [ "import autograd.numpy as np\n", "\n", @@ -3182,16 +2996,7 @@ "collapsed": false, "editable": true }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "The gradient of f1 evaluated at a = 1 using autograd is: 3\n", - "The gradient of f1 evaluated at a = 1 by finding the analytic expression is: 3\n" - ] - } - ], + "outputs": [], "source": [ "import autograd.numpy as np\n", "from autograd import grad\n", @@ -3232,21 +3037,7 @@ "collapsed": false, "editable": true }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Evaluating at x1 = 1, x2 = 3\n", - "------------------------------\n", - "The derivative of f2 w.r.t x1: 12\n", - "The analytical derivative of f2 w.r.t x1: 12\n", - "\n", - "The derivative of f2 w.r.t x2: -4\n", - "The analytical derivative of f2 w.r.t x2: -4\n" - ] - } - ], + "outputs": [], "source": [ "import autograd.numpy as np\n", "from autograd import grad\n", @@ -3301,16 +3092,7 @@ "collapsed": false, "editable": true }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "The computed gradient of f3 is: [ 2. 3. 5. 7. 88.]\n", - "The analytical gradient of f3 is: [ 2. 3. 5. 7. 88.]\n" - ] - } - ], + "outputs": [], "source": [ "import autograd.numpy as np\n", "from autograd import grad\n", @@ -3354,16 +3136,7 @@ "collapsed": false, "editable": true }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "The computed derivative of f4 at x = 2.7 is: 13.8759\n", - "The analytical gradient of f4 at x = 2.7 is: 13.8759\n" - ] - } - ], + "outputs": [], "source": [ "import autograd.numpy as np\n", "from autograd import grad\n", @@ -3392,15 +3165,7 @@ "collapsed": false, "editable": true }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "The computed derivative of f5 at x = 2.7 is: 5.4\n" - ] - } - ], + "outputs": [], "source": [ "import autograd.numpy as np\n", "from autograd import grad\n", @@ -3426,16 +3191,7 @@ "collapsed": false, "editable": true }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "The computed derivative of f6_for at x = 0.5 is: 3.95703\n", - "The computed derivative of f6_while at x = 0.5 is: 3.95703\n" - ] - } - ], + "outputs": [], "source": [ "import autograd.numpy as np\n", "from autograd import grad\n", @@ -3471,15 +3227,7 @@ "collapsed": false, "editable": true }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "The analytical derivative of f6 at x = 0.5 is: 3.95703\n" - ] - } - ], + "outputs": [], "source": [ "import autograd.numpy as np\n", "from autograd import grad\n", @@ -3500,16 +3248,7 @@ "collapsed": false, "editable": true }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "The computed derivative of f7 at n = 2 is: 1\n", - "The analytical derivative of f7 at n = 2 is: 1\n" - ] - } - ], + "outputs": [], "source": [ "import autograd.numpy as np\n", "from autograd import grad\n", @@ -3562,18 +3301,7 @@ "collapsed": false, "editable": true }, - "outputs": [ - { - "data": { - "text/plain": [ - "'\\nimport autograd.numpy as np\\nfrom autograd import grad\\ndef f8(x): # Assume x is an array\\n x[2] = 3\\n return x*2\\n\\nf8_grad = grad(f8)\\n\\nx = 8.4\\n\\nprint(\"The derivative of f8 is:\",f8_grad(x))\\n'" - ] - }, - "execution_count": 22, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "\"\"\"\n", "import autograd.numpy as np\n", @@ -3608,25 +3336,7 @@ "collapsed": false, "editable": true }, - "outputs": [ - { - "ename": "AttributeError", - "evalue": "'ArrayBox' object has no attribute 'dot'", - "output_type": "error", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mAttributeError\u001b[0m Traceback (most recent call last)", - "Input \u001b[0;32mIn [23]\u001b[0m, in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[1;32m 7\u001b[0m f9_grad \u001b[38;5;241m=\u001b[39m grad(f9)\n\u001b[1;32m 9\u001b[0m x \u001b[38;5;241m=\u001b[39m np\u001b[38;5;241m.\u001b[39marray([\u001b[38;5;241m1.0\u001b[39m,\u001b[38;5;241m0.0\u001b[39m])\n\u001b[0;32m---> 11\u001b[0m \u001b[38;5;28mprint\u001b[39m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mThe derivative of f9 is:\u001b[39m\u001b[38;5;124m\"\u001b[39m,\u001b[43mf9_grad\u001b[49m\u001b[43m(\u001b[49m\u001b[43mx\u001b[49m\u001b[43m)\u001b[49m)\n", - "File \u001b[0;32m~/miniforge3/envs/myenv/lib/python3.9/site-packages/autograd/wrap_util.py:20\u001b[0m, in \u001b[0;36munary_to_nary..nary_operator..nary_f\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m 18\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m 19\u001b[0m x \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mtuple\u001b[39m(args[i] \u001b[38;5;28;01mfor\u001b[39;00m i \u001b[38;5;129;01min\u001b[39;00m argnum)\n\u001b[0;32m---> 20\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43munary_operator\u001b[49m\u001b[43m(\u001b[49m\u001b[43munary_f\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mx\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mnary_op_args\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mnary_op_kwargs\u001b[49m\u001b[43m)\u001b[49m\n", - "File \u001b[0;32m~/miniforge3/envs/myenv/lib/python3.9/site-packages/autograd/differential_operators.py:25\u001b[0m, in \u001b[0;36mgrad\u001b[0;34m(fun, x)\u001b[0m\n\u001b[1;32m 18\u001b[0m \u001b[38;5;129m@unary_to_nary\u001b[39m\n\u001b[1;32m 19\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mgrad\u001b[39m(fun, x):\n\u001b[1;32m 20\u001b[0m \u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m 21\u001b[0m \u001b[38;5;124;03m Returns a function which computes the gradient of `fun` with respect to\u001b[39;00m\n\u001b[1;32m 22\u001b[0m \u001b[38;5;124;03m positional argument number `argnum`. The returned function takes the same\u001b[39;00m\n\u001b[1;32m 23\u001b[0m \u001b[38;5;124;03m arguments as `fun`, but returns the gradient instead. The function `fun`\u001b[39;00m\n\u001b[1;32m 24\u001b[0m \u001b[38;5;124;03m should be scalar-valued. The gradient has the same type as the argument.\"\"\"\u001b[39;00m\n\u001b[0;32m---> 25\u001b[0m vjp, ans \u001b[38;5;241m=\u001b[39m \u001b[43m_make_vjp\u001b[49m\u001b[43m(\u001b[49m\u001b[43mfun\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mx\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 26\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m vspace(ans)\u001b[38;5;241m.\u001b[39msize \u001b[38;5;241m==\u001b[39m \u001b[38;5;241m1\u001b[39m:\n\u001b[1;32m 27\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mTypeError\u001b[39;00m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mGrad only applies to real scalar-output functions. \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m 28\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mTry jacobian, elementwise_grad or holomorphic_grad.\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n", - "File \u001b[0;32m~/miniforge3/envs/myenv/lib/python3.9/site-packages/autograd/core.py:10\u001b[0m, in \u001b[0;36mmake_vjp\u001b[0;34m(fun, x)\u001b[0m\n\u001b[1;32m 8\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mmake_vjp\u001b[39m(fun, x):\n\u001b[1;32m 9\u001b[0m start_node \u001b[38;5;241m=\u001b[39m VJPNode\u001b[38;5;241m.\u001b[39mnew_root()\n\u001b[0;32m---> 10\u001b[0m end_value, end_node \u001b[38;5;241m=\u001b[39m \u001b[43mtrace\u001b[49m\u001b[43m(\u001b[49m\u001b[43mstart_node\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mfun\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mx\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 11\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m end_node \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m 12\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mvjp\u001b[39m(g): \u001b[38;5;28;01mreturn\u001b[39;00m vspace(x)\u001b[38;5;241m.\u001b[39mzeros()\n", - "File \u001b[0;32m~/miniforge3/envs/myenv/lib/python3.9/site-packages/autograd/tracer.py:10\u001b[0m, in \u001b[0;36mtrace\u001b[0;34m(start_node, fun, x)\u001b[0m\n\u001b[1;32m 8\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m trace_stack\u001b[38;5;241m.\u001b[39mnew_trace() \u001b[38;5;28;01mas\u001b[39;00m t:\n\u001b[1;32m 9\u001b[0m start_box \u001b[38;5;241m=\u001b[39m new_box(x, t, start_node)\n\u001b[0;32m---> 10\u001b[0m end_box \u001b[38;5;241m=\u001b[39m \u001b[43mfun\u001b[49m\u001b[43m(\u001b[49m\u001b[43mstart_box\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 11\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m isbox(end_box) \u001b[38;5;129;01mand\u001b[39;00m end_box\u001b[38;5;241m.\u001b[39m_trace \u001b[38;5;241m==\u001b[39m start_box\u001b[38;5;241m.\u001b[39m_trace:\n\u001b[1;32m 12\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m end_box\u001b[38;5;241m.\u001b[39m_value, end_box\u001b[38;5;241m.\u001b[39m_node\n", - "File \u001b[0;32m~/miniforge3/envs/myenv/lib/python3.9/site-packages/autograd/wrap_util.py:15\u001b[0m, in \u001b[0;36munary_to_nary..nary_operator..nary_f..unary_f\u001b[0;34m(x)\u001b[0m\n\u001b[1;32m 13\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m 14\u001b[0m subargs \u001b[38;5;241m=\u001b[39m subvals(args, \u001b[38;5;28mzip\u001b[39m(argnum, x))\n\u001b[0;32m---> 15\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mfun\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43msubargs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n", - "Input \u001b[0;32mIn [23]\u001b[0m, in \u001b[0;36mf9\u001b[0;34m(a)\u001b[0m\n\u001b[1;32m 3\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mf9\u001b[39m(a): \u001b[38;5;66;03m# Assume a is an array with 2 elements\u001b[39;00m\n\u001b[1;32m 4\u001b[0m b \u001b[38;5;241m=\u001b[39m np\u001b[38;5;241m.\u001b[39marray([\u001b[38;5;241m1.0\u001b[39m,\u001b[38;5;241m2.0\u001b[39m])\n\u001b[0;32m----> 5\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43ma\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mdot\u001b[49m(b)\n", - "\u001b[0;31mAttributeError\u001b[0m: 'ArrayBox' object has no attribute 'dot'" - ] - } - ], + "outputs": [], "source": [ "import autograd.numpy as np\n", "from autograd import grad\n", @@ -4324,7 +4034,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.9.10" + "version": "3.9.18" } }, "nbformat": 4, diff --git a/doc/LectureNotes/_build/jupyter_execute/clustering.ipynb b/doc/LectureNotes/_build/jupyter_execute/clustering.ipynb index c72bd0283..57a7b064c 100644 --- a/doc/LectureNotes/_build/jupyter_execute/clustering.ipynb +++ b/doc/LectureNotes/_build/jupyter_execute/clustering.ipynb @@ -283,40 +283,19 @@ }, "outputs": [ { - "name": "stderr", - "output_type": "stream", - "text": [ - "/Users/mhjensen/miniforge3/envs/myenv/lib/python3.9/site-packages/jax/_src/lib/__init__.py:32: UserWarning: JAX on Mac ARM machines is experimental and minimally tested. Please see https://github.com/google/jax/issues/5501 in the event of problems.\n", - " warnings.warn(\"JAX on Mac ARM machines is experimental and minimally tested. \"\n" - ] - }, - { - "ename": "AttributeError", - "evalue": "module 'jaxlib.pocketfft' has no attribute 'pocketfft'", + "ename": "ModuleNotFoundError", + "evalue": "No module named 'matplotlib'", "output_type": "error", "traceback": [ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mAttributeError\u001b[0m Traceback (most recent call last)", - "Input \u001b[0;32mIn [1]\u001b[0m, in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[1;32m 3\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mtime\u001b[39;00m\n\u001b[1;32m 4\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mnumpy\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m \u001b[38;5;21;01mnp\u001b[39;00m\n\u001b[0;32m----> 5\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mtensorflow\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m \u001b[38;5;21;01mtf\u001b[39;00m\n\u001b[1;32m 6\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mmatplotlib\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m image\n\u001b[1;32m 7\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mmatplotlib\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mpyplot\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m \u001b[38;5;21;01mplt\u001b[39;00m\n", - "File \u001b[0;32m~/miniforge3/envs/myenv/lib/python3.9/site-packages/tensorflow/__init__.py:51\u001b[0m, in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[1;32m 49\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01m_api\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mv2\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m autograph\n\u001b[1;32m 50\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01m_api\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mv2\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m bitwise\n\u001b[0;32m---> 51\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01m_api\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mv2\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m compat\n\u001b[1;32m 52\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01m_api\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mv2\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m config\n\u001b[1;32m 53\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01m_api\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mv2\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m data\n", - "File \u001b[0;32m~/miniforge3/envs/myenv/lib/python3.9/site-packages/tensorflow/_api/v2/compat/__init__.py:37\u001b[0m, in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[1;32m 3\u001b[0m \u001b[38;5;124;03m\"\"\"Compatibility functions.\u001b[39;00m\n\u001b[1;32m 4\u001b[0m \n\u001b[1;32m 5\u001b[0m \u001b[38;5;124;03mThe `tf.compat` module contains two sets of compatibility functions.\u001b[39;00m\n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 32\u001b[0m \n\u001b[1;32m 33\u001b[0m \u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m 35\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01msys\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m \u001b[38;5;21;01m_sys\u001b[39;00m\n\u001b[0;32m---> 37\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01m.\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m v1\n\u001b[1;32m 38\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01m.\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m v2\n\u001b[1;32m 39\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mtensorflow\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mpython\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mcompat\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mcompat\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m forward_compatibility_horizon\n", - "File \u001b[0;32m~/miniforge3/envs/myenv/lib/python3.9/site-packages/tensorflow/_api/v2/compat/v1/__init__.py:30\u001b[0m, in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[1;32m 28\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01m.\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m autograph\n\u001b[1;32m 29\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01m.\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m bitwise\n\u001b[0;32m---> 30\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01m.\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m compat\n\u001b[1;32m 31\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01m.\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m config\n\u001b[1;32m 32\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01m.\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m data\n", - "File \u001b[0;32m~/miniforge3/envs/myenv/lib/python3.9/site-packages/tensorflow/_api/v2/compat/v1/compat/__init__.py:37\u001b[0m, in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[1;32m 3\u001b[0m \u001b[38;5;124;03m\"\"\"Compatibility functions.\u001b[39;00m\n\u001b[1;32m 4\u001b[0m \n\u001b[1;32m 5\u001b[0m \u001b[38;5;124;03mThe `tf.compat` module contains two sets of compatibility functions.\u001b[39;00m\n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 32\u001b[0m \n\u001b[1;32m 33\u001b[0m \u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m 35\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01msys\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m \u001b[38;5;21;01m_sys\u001b[39;00m\n\u001b[0;32m---> 37\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01m.\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m v1\n\u001b[1;32m 38\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01m.\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m v2\n\u001b[1;32m 39\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mtensorflow\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mpython\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mcompat\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mcompat\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m forward_compatibility_horizon\n", - "File \u001b[0;32m~/miniforge3/envs/myenv/lib/python3.9/site-packages/tensorflow/_api/v2/compat/v1/compat/v1/__init__.py:47\u001b[0m, in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[1;32m 45\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mtensorflow\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01m_api\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mv2\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mcompat\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mv1\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m layers\n\u001b[1;32m 46\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mtensorflow\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01m_api\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mv2\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mcompat\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mv1\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m linalg\n\u001b[0;32m---> 47\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mtensorflow\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01m_api\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mv2\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mcompat\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mv1\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m lite\n\u001b[1;32m 48\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mtensorflow\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01m_api\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mv2\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mcompat\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mv1\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m logging\n\u001b[1;32m 49\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mtensorflow\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01m_api\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mv2\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mcompat\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mv1\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m lookup\n", - "File \u001b[0;32m~/miniforge3/envs/myenv/lib/python3.9/site-packages/tensorflow/_api/v2/compat/v1/lite/__init__.py:9\u001b[0m, in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[1;32m 6\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01msys\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m \u001b[38;5;21;01m_sys\u001b[39;00m\n\u001b[1;32m 8\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01m.\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m constants\n\u001b[0;32m----> 9\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01m.\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m experimental\n\u001b[1;32m 10\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mtensorflow\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mlite\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mpython\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mlite\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m Interpreter\n\u001b[1;32m 11\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mtensorflow\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mlite\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mpython\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mlite\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m OpHint\n", - "File \u001b[0;32m~/miniforge3/envs/myenv/lib/python3.9/site-packages/tensorflow/_api/v2/compat/v1/lite/experimental/__init__.py:8\u001b[0m, in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[1;32m 3\u001b[0m \u001b[38;5;124;03m\"\"\"Public API for tf.lite.experimental namespace.\u001b[39;00m\n\u001b[1;32m 4\u001b[0m \u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m 6\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01msys\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m \u001b[38;5;21;01m_sys\u001b[39;00m\n\u001b[0;32m----> 8\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01m.\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m authoring\n\u001b[1;32m 9\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mtensorflow\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mlite\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mpython\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01manalyzer\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m ModelAnalyzer \u001b[38;5;28;01mas\u001b[39;00m Analyzer\n\u001b[1;32m 10\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mtensorflow\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mlite\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mpython\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mlite\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m OpResolverType\n", - "File \u001b[0;32m~/miniforge3/envs/myenv/lib/python3.9/site-packages/tensorflow/_api/v2/compat/v1/lite/experimental/authoring/__init__.py:8\u001b[0m, in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[1;32m 3\u001b[0m \u001b[38;5;124;03m\"\"\"Public API for tf.lite.experimental.authoring namespace.\u001b[39;00m\n\u001b[1;32m 4\u001b[0m \u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m 6\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01msys\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m \u001b[38;5;21;01m_sys\u001b[39;00m\n\u001b[0;32m----> 8\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mtensorflow\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mlite\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mpython\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mauthoring\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mauthoring\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m compatible\n", - "File \u001b[0;32m~/miniforge3/envs/myenv/lib/python3.9/site-packages/tensorflow/lite/python/authoring/authoring.py:43\u001b[0m, in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[1;32m 39\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mfunctools\u001b[39;00m\n\u001b[1;32m 42\u001b[0m \u001b[38;5;66;03m# pylint: disable=g-import-not-at-top\u001b[39;00m\n\u001b[0;32m---> 43\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mtensorflow\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mlite\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mpython\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m convert\n\u001b[1;32m 44\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mtensorflow\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mlite\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mpython\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m lite\n\u001b[1;32m 45\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mtensorflow\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mlite\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mpython\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mmetrics\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m converter_error_data_pb2\n", - "File \u001b[0;32m~/miniforge3/envs/myenv/lib/python3.9/site-packages/tensorflow/lite/python/convert.py:29\u001b[0m, in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[1;32m 26\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01msix\u001b[39;00m\n\u001b[1;32m 28\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mtensorflow\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mlite\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mpython\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m lite_constants\n\u001b[0;32m---> 29\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mtensorflow\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mlite\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mpython\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m util\n\u001b[1;32m 30\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mtensorflow\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mlite\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mpython\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m wrap_toco\n\u001b[1;32m 31\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m 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_xla_computation\n\u001b[1;32m 52\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mImportError\u001b[39;00m:\n\u001b[1;32m 53\u001b[0m _xla_computation \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m\n", - "File \u001b[0;32m~/miniforge3/envs/myenv/lib/python3.9/site-packages/jax/__init__.py:116\u001b[0m, in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[1;32m 40\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01m_src\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mconfig\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m (\n\u001b[1;32m 41\u001b[0m config \u001b[38;5;28;01mas\u001b[39;00m config,\n\u001b[1;32m 42\u001b[0m enable_checks \u001b[38;5;28;01mas\u001b[39;00m enable_checks,\n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 51\u001b[0m numpy_rank_promotion \u001b[38;5;28;01mas\u001b[39;00m numpy_rank_promotion,\n\u001b[1;32m 52\u001b[0m )\n\u001b[1;32m 53\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m 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\u001b[38;5;28;01mas\u001b[39;00m __version__\n\u001b[1;32m 119\u001b[0m \u001b[38;5;66;03m# These submodules are separate because they are in an import cycle with\u001b[39;00m\n\u001b[1;32m 120\u001b[0m \u001b[38;5;66;03m# jax and rely on the names imported above.\u001b[39;00m\n", - "File \u001b[0;32m~/miniforge3/envs/myenv/lib/python3.9/site-packages/jax/experimental/maps.py:26\u001b[0m, in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[1;32m 23\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mfunctools\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m wraps, partial, partialmethod\n\u001b[1;32m 24\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01menum\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m Enum\n\u001b[0;32m---> 26\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m numpy \u001b[38;5;28;01mas\u001b[39;00m jnp\n\u001b[1;32m 27\u001b[0m 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2.0 (the \"License\");\u001b[39;00m\n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 14\u001b[0m \n\u001b[1;32m 15\u001b[0m \u001b[38;5;66;03m# flake8: noqa: F401\u001b[39;00m\n\u001b[0;32m---> 17\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mjax\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01m_src\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mnumpy\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mfft\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m (\n\u001b[1;32m 18\u001b[0m ifft \u001b[38;5;28;01mas\u001b[39;00m ifft,\n\u001b[1;32m 19\u001b[0m ifft2 \u001b[38;5;28;01mas\u001b[39;00m ifft2,\n\u001b[1;32m 20\u001b[0m ifftn \u001b[38;5;28;01mas\u001b[39;00m ifftn,\n\u001b[1;32m 21\u001b[0m ifftshift \u001b[38;5;28;01mas\u001b[39;00m ifftshift,\n\u001b[1;32m 22\u001b[0m ihfft \u001b[38;5;28;01mas\u001b[39;00m ihfft,\n\u001b[1;32m 23\u001b[0m irfft \u001b[38;5;28;01mas\u001b[39;00m irfft,\n\u001b[1;32m 24\u001b[0m irfft2 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accidental\u001b[39;00m\n\u001b[1;32m 39\u001b[0m \u001b[38;5;66;03m# namespace pollution.\u001b[39;00m\n\u001b[1;32m 40\u001b[0m _NOT_IMPLEMENTED \u001b[38;5;241m=\u001b[39m []\n", - "File \u001b[0;32m~/miniforge3/envs/myenv/lib/python3.9/site-packages/jax/_src/numpy/fft.py:19\u001b[0m, in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[1;32m 16\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01moperator\u001b[39;00m\n\u001b[1;32m 17\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mnumpy\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m \u001b[38;5;21;01mnp\u001b[39;00m\n\u001b[0;32m---> 19\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mjax\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m lax\n\u001b[1;32m 20\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mjax\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01m_src\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mlib\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m xla_client\n\u001b[1;32m 21\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mjax\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01m_src\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mutil\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m safe_zip\n", - "File \u001b[0;32m~/miniforge3/envs/myenv/lib/python3.9/site-packages/jax/lax/__init__.py:332\u001b[0m, in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[1;32m 299\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mjax\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01m_src\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mlax\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mlax\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m (_reduce_sum, _reduce_max, _reduce_min, _reduce_or,\n\u001b[1;32m 300\u001b[0m _reduce_and, _reduce_window_sum, _reduce_window_max,\n\u001b[1;32m 301\u001b[0m _reduce_window_min, _reduce_window_prod,\n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 306\u001b[0m _upcast_fp16_for_computation, _broadcasting_shape_rule,\n\u001b[1;32m 307\u001b[0m _eye, _tri, _delta, _ones, _zeros, _dilate_shape)\n\u001b[1;32m 308\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mjax\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01m_src\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mlax\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mcontrol_flow\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m (\n\u001b[1;32m 309\u001b[0m associative_scan \u001b[38;5;28;01mas\u001b[39;00m associative_scan,\n\u001b[1;32m 310\u001b[0m cond \u001b[38;5;28;01mas\u001b[39;00m cond,\n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 330\u001b[0m while_p \u001b[38;5;28;01mas\u001b[39;00m while_p,\n\u001b[1;32m 331\u001b[0m )\n\u001b[0;32m--> 332\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mjax\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01m_src\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mlax\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mfft\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m (\n\u001b[1;32m 333\u001b[0m fft \u001b[38;5;28;01mas\u001b[39;00m fft,\n\u001b[1;32m 334\u001b[0m fft_p \u001b[38;5;28;01mas\u001b[39;00m fft_p,\n\u001b[1;32m 335\u001b[0m )\n\u001b[1;32m 336\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mjax\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01m_src\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mlax\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mparallel\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m (\n\u001b[1;32m 337\u001b[0m all_gather \u001b[38;5;28;01mas\u001b[39;00m all_gather,\n\u001b[1;32m 338\u001b[0m all_to_all \u001b[38;5;28;01mas\u001b[39;00m all_to_all,\n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 355\u001b[0m xeinsum \u001b[38;5;28;01mas\u001b[39;00m xeinsum,\n\u001b[1;32m 356\u001b[0m )\n\u001b[1;32m 357\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mjax\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01m_src\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mlax\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mother\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m (\n\u001b[1;32m 358\u001b[0m conv_general_dilated_patches \u001b[38;5;28;01mas\u001b[39;00m conv_general_dilated_patches\n\u001b[1;32m 359\u001b[0m )\n", - "File \u001b[0;32m~/miniforge3/envs/myenv/lib/python3.9/site-packages/jax/_src/lax/fft.py:145\u001b[0m, in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[1;32m 143\u001b[0m batching\u001b[38;5;241m.\u001b[39mprimitive_batchers[fft_p] \u001b[38;5;241m=\u001b[39m fft_batching_rule\n\u001b[1;32m 144\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m pocketfft:\n\u001b[0;32m--> 145\u001b[0m xla\u001b[38;5;241m.\u001b[39mbackend_specific_translations[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mcpu\u001b[39m\u001b[38;5;124m'\u001b[39m][fft_p] \u001b[38;5;241m=\u001b[39m \u001b[43mpocketfft\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mpocketfft\u001b[49m\n", - "\u001b[0;31mAttributeError\u001b[0m: module 'jaxlib.pocketfft' has no attribute 'pocketfft'" + "\u001b[0;31mModuleNotFoundError\u001b[0m Traceback (most recent call last)", + "Cell \u001b[0;32mIn[1], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \u001b[43mget_ipython\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mrun_line_magic\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mmatplotlib\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43minline\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m)\u001b[49m\n\u001b[1;32m 3\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mtime\u001b[39;00m\n\u001b[1;32m 4\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mnumpy\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m \u001b[38;5;21;01mnp\u001b[39;00m\n", + "File \u001b[0;32m~/miniforge3/lib/python3.9/site-packages/IPython/core/interactiveshell.py:2432\u001b[0m, in \u001b[0;36mInteractiveShell.run_line_magic\u001b[0;34m(self, magic_name, line, _stack_depth)\u001b[0m\n\u001b[1;32m 2430\u001b[0m kwargs[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mlocal_ns\u001b[39m\u001b[38;5;124m'\u001b[39m] \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mget_local_scope(stack_depth)\n\u001b[1;32m 2431\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mbuiltin_trap:\n\u001b[0;32m-> 2432\u001b[0m result \u001b[38;5;241m=\u001b[39m \u001b[43mfn\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 2434\u001b[0m \u001b[38;5;66;03m# The code below prevents the output from being displayed\u001b[39;00m\n\u001b[1;32m 2435\u001b[0m \u001b[38;5;66;03m# when using magics with decorator @output_can_be_silenced\u001b[39;00m\n\u001b[1;32m 2436\u001b[0m \u001b[38;5;66;03m# when the last Python token in the expression is a ';'.\u001b[39;00m\n\u001b[1;32m 2437\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mgetattr\u001b[39m(fn, magic\u001b[38;5;241m.\u001b[39mMAGIC_OUTPUT_CAN_BE_SILENCED, \u001b[38;5;28;01mFalse\u001b[39;00m):\n", + "File \u001b[0;32m~/miniforge3/lib/python3.9/site-packages/IPython/core/magics/pylab.py:99\u001b[0m, in \u001b[0;36mPylabMagics.matplotlib\u001b[0;34m(self, line)\u001b[0m\n\u001b[1;32m 97\u001b[0m \u001b[38;5;28mprint\u001b[39m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mAvailable matplotlib backends: \u001b[39m\u001b[38;5;132;01m%s\u001b[39;00m\u001b[38;5;124m\"\u001b[39m \u001b[38;5;241m%\u001b[39m backends_list)\n\u001b[1;32m 98\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m---> 99\u001b[0m gui, backend \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mshell\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43menable_matplotlib\u001b[49m\u001b[43m(\u001b[49m\u001b[43margs\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mgui\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mlower\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mif\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[38;5;28;43misinstance\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43margs\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mgui\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mstr\u001b[39;49m\u001b[43m)\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01melse\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43margs\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mgui\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 100\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_show_matplotlib_backend(args\u001b[38;5;241m.\u001b[39mgui, backend)\n", + "File \u001b[0;32m~/miniforge3/lib/python3.9/site-packages/IPython/core/interactiveshell.py:3606\u001b[0m, in \u001b[0;36mInteractiveShell.enable_matplotlib\u001b[0;34m(self, gui)\u001b[0m\n\u001b[1;32m 3585\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21menable_matplotlib\u001b[39m(\u001b[38;5;28mself\u001b[39m, gui\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mNone\u001b[39;00m):\n\u001b[1;32m 3586\u001b[0m \u001b[38;5;250m \u001b[39m\u001b[38;5;124;03m\"\"\"Enable interactive matplotlib and inline figure support.\u001b[39;00m\n\u001b[1;32m 3587\u001b[0m \n\u001b[1;32m 3588\u001b[0m \u001b[38;5;124;03m This takes the following steps:\u001b[39;00m\n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 3604\u001b[0m \u001b[38;5;124;03m display figures inline.\u001b[39;00m\n\u001b[1;32m 3605\u001b[0m \u001b[38;5;124;03m \"\"\"\u001b[39;00m\n\u001b[0;32m-> 3606\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mmatplotlib_inline\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mbackend_inline\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m configure_inline_support\n\u001b[1;32m 3608\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mIPython\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mcore\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m pylabtools \u001b[38;5;28;01mas\u001b[39;00m pt\n\u001b[1;32m 3609\u001b[0m gui, backend \u001b[38;5;241m=\u001b[39m pt\u001b[38;5;241m.\u001b[39mfind_gui_and_backend(gui, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mpylab_gui_select)\n", + "File \u001b[0;32m~/miniforge3/lib/python3.9/site-packages/matplotlib_inline/__init__.py:1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01m.\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m backend_inline, config \u001b[38;5;66;03m# noqa\u001b[39;00m\n\u001b[1;32m 2\u001b[0m __version__ \u001b[38;5;241m=\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m0.1.6\u001b[39m\u001b[38;5;124m\"\u001b[39m \u001b[38;5;66;03m# noqa\u001b[39;00m\n", + "File \u001b[0;32m~/miniforge3/lib/python3.9/site-packages/matplotlib_inline/backend_inline.py:6\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[38;5;124;03m\"\"\"A matplotlib backend for publishing figures via display_data\"\"\"\u001b[39;00m\n\u001b[1;32m 3\u001b[0m \u001b[38;5;66;03m# Copyright (c) IPython Development Team.\u001b[39;00m\n\u001b[1;32m 4\u001b[0m \u001b[38;5;66;03m# Distributed under the terms of the BSD 3-Clause License.\u001b[39;00m\n\u001b[0;32m----> 6\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mmatplotlib\u001b[39;00m\n\u001b[1;32m 7\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mmatplotlib\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m colors\n\u001b[1;32m 8\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mmatplotlib\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mbackends\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m backend_agg\n", + "\u001b[0;31mModuleNotFoundError\u001b[0m: No module named 'matplotlib'" ] } ], @@ -692,7 +671,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.9.10" + "version": "3.9.18" } }, "nbformat": 4, diff --git a/doc/LectureNotes/_build/jupyter_execute/exercisesweek34.ipynb b/doc/LectureNotes/_build/jupyter_execute/exercisesweek34.ipynb index 53b974273..6bc6c9558 100644 --- a/doc/LectureNotes/_build/jupyter_execute/exercisesweek34.ipynb +++ b/doc/LectureNotes/_build/jupyter_execute/exercisesweek34.ipynb @@ -135,7 +135,7 @@ "traceback": [ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)", - "Input \u001b[0;32mIn [1]\u001b[0m, in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0m x \u001b[38;5;241m=\u001b[39m \u001b[43mnp\u001b[49m\u001b[38;5;241m.\u001b[39mrandom\u001b[38;5;241m.\u001b[39mrand(\u001b[38;5;241m100\u001b[39m,\u001b[38;5;241m1\u001b[39m)\n\u001b[1;32m 2\u001b[0m y \u001b[38;5;241m=\u001b[39m \u001b[38;5;241m2.0\u001b[39m\u001b[38;5;241m+\u001b[39m\u001b[38;5;241m5\u001b[39m\u001b[38;5;241m*\u001b[39mx\u001b[38;5;241m*\u001b[39mx\u001b[38;5;241m+\u001b[39m\u001b[38;5;241m0.1\u001b[39m\u001b[38;5;241m*\u001b[39mnp\u001b[38;5;241m.\u001b[39mrandom\u001b[38;5;241m.\u001b[39mrandn(\u001b[38;5;241m100\u001b[39m,\u001b[38;5;241m1\u001b[39m)\n", + "Cell \u001b[0;32mIn[1], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m x \u001b[38;5;241m=\u001b[39m \u001b[43mnp\u001b[49m\u001b[38;5;241m.\u001b[39mrandom\u001b[38;5;241m.\u001b[39mrand(\u001b[38;5;241m100\u001b[39m,\u001b[38;5;241m1\u001b[39m)\n\u001b[1;32m 2\u001b[0m y \u001b[38;5;241m=\u001b[39m \u001b[38;5;241m2.0\u001b[39m\u001b[38;5;241m+\u001b[39m\u001b[38;5;241m5\u001b[39m\u001b[38;5;241m*\u001b[39mx\u001b[38;5;241m*\u001b[39mx\u001b[38;5;241m+\u001b[39m\u001b[38;5;241m0.1\u001b[39m\u001b[38;5;241m*\u001b[39mnp\u001b[38;5;241m.\u001b[39mrandom\u001b[38;5;241m.\u001b[39mrandn(\u001b[38;5;241m100\u001b[39m,\u001b[38;5;241m1\u001b[39m)\n", "\u001b[0;31mNameError\u001b[0m: name 'np' is not defined" ] } @@ -319,7 +319,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.9.10" + "version": "3.9.18" } }, "nbformat": 4, diff --git a/doc/LectureNotes/_build/jupyter_execute/exercisesweek35.ipynb b/doc/LectureNotes/_build/jupyter_execute/exercisesweek35.ipynb index 0b4eb4f75..e8be6f76c 100644 --- a/doc/LectureNotes/_build/jupyter_execute/exercisesweek35.ipynb +++ b/doc/LectureNotes/_build/jupyter_execute/exercisesweek35.ipynb @@ -403,7 +403,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.9.10" + "version": "3.9.18" } }, "nbformat": 4, diff --git a/doc/LectureNotes/_build/jupyter_execute/exercisesweek39.ipynb b/doc/LectureNotes/_build/jupyter_execute/exercisesweek39.ipynb new file mode 100644 index 000000000..7a94380ac --- /dev/null +++ b/doc/LectureNotes/_build/jupyter_execute/exercisesweek39.ipynb @@ -0,0 +1,59 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "f35930ac", + "metadata": { + "editable": true + }, + "source": [ + "\n", + "" + ] + }, + { + "cell_type": "markdown", + "id": "8cb567a0", + "metadata": { + "editable": true + }, + "source": [ + "# Exercises week 39\n", + "**September 25-29, 2023**\n", + "\n", + "Date: **Deadline is Sunday October 1 at midnight**" + ] + }, + { + "cell_type": "markdown", + "id": "934324b3", + "metadata": { + "editable": true + }, + "source": [ + "## Overarching aims of the exercises this week\n", + "\n", + "The aim of the exercises this week is to aid you in getting started\n", + "with writing the report. This will be discussed during the lab\n", + "sessions as well. One of the lab sessions will be recorded.\n", + "\n", + "A general guideline can be found at .\n", + "\n", + "Similarly, an example of an earlier project can be found at \n", + "\n", + "Your task this week is to\n", + "1. Write an abstract for your project\n", + "\n", + "2. Write an introduction\n", + "\n", + "3. Include references\n", + "\n", + "Ashort feedback to the this exercise will be available after the deadline. And you can reuse these elements in your final report." + ] + } + ], + "metadata": {}, + "nbformat": 4, + "nbformat_minor": 5 +} \ No newline at end of file diff --git a/doc/LectureNotes/_build/jupyter_execute/linalg.ipynb b/doc/LectureNotes/_build/jupyter_execute/linalg.ipynb index 64a0a00c0..be4c6b3dd 100644 --- a/doc/LectureNotes/_build/jupyter_execute/linalg.ipynb +++ b/doc/LectureNotes/_build/jupyter_execute/linalg.ipynb @@ -225,8 +225,8 @@ "name": "stdout", "output_type": "stream", "text": [ - "[-0.28587856 -0.73509675 0.35559283 -0.62819534 -0.15254784 -0.44792888\n", - " 1.5393681 -0.81784072 0.53476918 1.02055673]\n" + "[-0.1223405 0.96602098 -0.42045733 2.00897328 0.96178833 -0.30742211\n", + " 0.23867951 -1.84775931 -0.3804507 -0.47953891]\n" ] } ], @@ -662,26 +662,26 @@ "name": "stdout", "output_type": "stream", "text": [ - "[[0.99218609 0.13120126 0.8260149 0.70761641 0.8634474 0.47202234\n", - " 0.09862064 0.8970168 0.40947888 0.19121744]\n", - " [0.93969474 0.4378299 0.23405814 0.52872817 0.00229179 0.41050604\n", - " 0.846471 0.78027207 0.11551515 0.18573652]\n", - " [0.80515205 0.40984037 0.61970354 0.03608726 0.90793026 0.45494697\n", - " 0.41718305 0.77288474 0.94404545 0.09218919]\n", - " [0.24566623 0.18632646 0.82113506 0.01710429 0.88981061 0.77756468\n", - " 0.94073014 0.89779302 0.97936584 0.69051742]\n", - " [0.84924359 0.43533824 0.62420578 0.02185737 0.36336962 0.10056192\n", - " 0.19468989 0.96562567 0.770356 0.4756968 ]\n", - " [0.05851756 0.43471448 0.80088524 0.62083489 0.88778045 0.56821272\n", - " 0.7133574 0.10148978 0.54861905 0.45917334]\n", - " [0.15939618 0.47756853 0.08791994 0.87602519 0.11470382 0.67142502\n", - " 0.7600498 0.81709481 0.85895314 0.33874172]\n", - " [0.21561586 0.65011691 0.1897755 0.5538822 0.75747652 0.27898999\n", - " 0.35128114 0.27674224 0.47513129 0.59366475]\n", - " [0.5916751 0.82365318 0.16026316 0.533142 0.93265898 0.05881845\n", - " 0.10326551 0.95604667 0.8617319 0.74199799]\n", - " [0.21089449 0.0402861 0.12611267 0.49197115 0.59203799 0.74577306\n", - " 0.14491591 0.71135895 0.84723978 0.03416188]]\n" + "[[0.69846205 0.78851232 0.08308983 0.5305063 0.12815515 0.72072214\n", + " 0.67232869 0.76697449 0.58848856 0.9904943 ]\n", + " [0.75838023 0.80529041 0.6484163 0.10609258 0.41344691 0.48942943\n", + " 0.92662338 0.88338811 0.12254643 0.81520361]\n", + " [0.55042787 0.34048788 0.25737687 0.7565813 0.30552745 0.25529596\n", + " 0.44101674 0.95844886 0.86670783 0.3992021 ]\n", + " [0.62482386 0.98232786 0.5248769 0.48823898 0.944291 0.88340778\n", + " 0.96704933 0.5255844 0.38941883 0.29239286]\n", + " [0.65680942 0.65466708 0.96617085 0.42214481 0.99932624 0.96546053\n", + " 0.48953049 0.26725306 0.57937269 0.36135213]\n", + " [0.73623167 0.23284855 0.36113667 0.61429341 0.91951571 0.84545016\n", + " 0.37857094 0.76168988 0.96457138 0.61999335]\n", + " [0.89635517 0.52058686 0.41240055 0.94738767 0.74171774 0.96592507\n", + " 0.63880898 0.44499556 0.21026792 0.92965555]\n", + " [0.30230092 0.9347861 0.56187437 0.3860215 0.58467645 0.27058589\n", + " 0.67628977 0.01494766 0.30681968 0.30491305]\n", + " [0.00860818 0.97140208 0.86478154 0.68302636 0.72968415 0.5859337\n", + " 0.88898063 0.25024282 0.07427349 0.65046291]\n", + " [0.01924293 0.54705652 0.01611258 0.47220243 0.36688196 0.47846644\n", + " 0.17072424 0.48396466 0.7900928 0.28490342]]\n" ] } ], @@ -800,13 +800,13 @@ "name": "stdout", "output_type": "stream", "text": [ - "0.041129694365319464\n", - "4.025325212488352\n", - "0.010687707010849668\n", - "[[ 1.23319332 3.73704762 4.12446443]\n", - " [ 3.73704762 12.59095724 12.81409445]\n", - " [ 4.12446443 12.81409445 20.04983109]]\n", - "[30.70850263 0.10254846 3.06293056]\n" + "-0.0017018997030592974\n", + "3.9531810558360623\n", + "-0.018173592942955955\n", + "[[ 1.06833761 3.33062366 3.10248906]\n", + " [ 3.33062366 11.28977247 10.01231809]\n", + " [ 3.10248906 10.01231809 13.75855891]]\n", + "[23.52658256 0.07563718 2.51444926]\n" ] } ], @@ -838,32 +838,20 @@ }, "outputs": [ { - "name": "stdout", - "output_type": "stream", - "text": [ - "[[1. 0. 0. 0.]\n", - " [0. 1. 0. 0.]\n", - " [0. 0. 1. 0.]\n", - " [0. 0. 0. 1.]]\n", - " (0, 0)\t1.0\n", - " (1, 1)\t1.0\n", - " (2, 2)\t1.0\n", - " (3, 3)\t1.0\n" + "ename": "ModuleNotFoundError", + "evalue": "No module named 'matplotlib'", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mModuleNotFoundError\u001b[0m Traceback (most recent call last)", + "Cell \u001b[0;32mIn[15], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \u001b[43mget_ipython\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mrun_line_magic\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mmatplotlib\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43minline\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m)\u001b[49m\n\u001b[1;32m 3\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mnumpy\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m \u001b[38;5;21;01mnp\u001b[39;00m\n\u001b[1;32m 4\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mmatplotlib\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mpyplot\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m \u001b[38;5;21;01mplt\u001b[39;00m\n", + "File \u001b[0;32m~/miniforge3/lib/python3.9/site-packages/IPython/core/interactiveshell.py:2432\u001b[0m, in \u001b[0;36mInteractiveShell.run_line_magic\u001b[0;34m(self, magic_name, line, _stack_depth)\u001b[0m\n\u001b[1;32m 2430\u001b[0m kwargs[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mlocal_ns\u001b[39m\u001b[38;5;124m'\u001b[39m] \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mget_local_scope(stack_depth)\n\u001b[1;32m 2431\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mbuiltin_trap:\n\u001b[0;32m-> 2432\u001b[0m result \u001b[38;5;241m=\u001b[39m \u001b[43mfn\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 2434\u001b[0m \u001b[38;5;66;03m# The code below prevents the output from being displayed\u001b[39;00m\n\u001b[1;32m 2435\u001b[0m \u001b[38;5;66;03m# when using magics with decorator @output_can_be_silenced\u001b[39;00m\n\u001b[1;32m 2436\u001b[0m \u001b[38;5;66;03m# when the last Python token in the expression is a ';'.\u001b[39;00m\n\u001b[1;32m 2437\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mgetattr\u001b[39m(fn, magic\u001b[38;5;241m.\u001b[39mMAGIC_OUTPUT_CAN_BE_SILENCED, \u001b[38;5;28;01mFalse\u001b[39;00m):\n", + "File \u001b[0;32m~/miniforge3/lib/python3.9/site-packages/IPython/core/magics/pylab.py:99\u001b[0m, in \u001b[0;36mPylabMagics.matplotlib\u001b[0;34m(self, line)\u001b[0m\n\u001b[1;32m 97\u001b[0m \u001b[38;5;28mprint\u001b[39m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mAvailable matplotlib backends: \u001b[39m\u001b[38;5;132;01m%s\u001b[39;00m\u001b[38;5;124m\"\u001b[39m \u001b[38;5;241m%\u001b[39m backends_list)\n\u001b[1;32m 98\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m---> 99\u001b[0m gui, backend \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mshell\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43menable_matplotlib\u001b[49m\u001b[43m(\u001b[49m\u001b[43margs\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mgui\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mlower\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mif\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[38;5;28;43misinstance\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43margs\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mgui\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mstr\u001b[39;49m\u001b[43m)\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01melse\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43margs\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mgui\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 100\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_show_matplotlib_backend(args\u001b[38;5;241m.\u001b[39mgui, backend)\n", + "File \u001b[0;32m~/miniforge3/lib/python3.9/site-packages/IPython/core/interactiveshell.py:3606\u001b[0m, in \u001b[0;36mInteractiveShell.enable_matplotlib\u001b[0;34m(self, gui)\u001b[0m\n\u001b[1;32m 3585\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21menable_matplotlib\u001b[39m(\u001b[38;5;28mself\u001b[39m, gui\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mNone\u001b[39;00m):\n\u001b[1;32m 3586\u001b[0m \u001b[38;5;250m \u001b[39m\u001b[38;5;124;03m\"\"\"Enable interactive matplotlib and inline figure support.\u001b[39;00m\n\u001b[1;32m 3587\u001b[0m \n\u001b[1;32m 3588\u001b[0m \u001b[38;5;124;03m This takes the following steps:\u001b[39;00m\n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 3604\u001b[0m \u001b[38;5;124;03m display figures inline.\u001b[39;00m\n\u001b[1;32m 3605\u001b[0m \u001b[38;5;124;03m \"\"\"\u001b[39;00m\n\u001b[0;32m-> 3606\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mmatplotlib_inline\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mbackend_inline\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m configure_inline_support\n\u001b[1;32m 3608\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mIPython\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mcore\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m pylabtools \u001b[38;5;28;01mas\u001b[39;00m pt\n\u001b[1;32m 3609\u001b[0m gui, backend \u001b[38;5;241m=\u001b[39m pt\u001b[38;5;241m.\u001b[39mfind_gui_and_backend(gui, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mpylab_gui_select)\n", + "File \u001b[0;32m~/miniforge3/lib/python3.9/site-packages/matplotlib_inline/__init__.py:1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01m.\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m backend_inline, config \u001b[38;5;66;03m# noqa\u001b[39;00m\n\u001b[1;32m 2\u001b[0m __version__ \u001b[38;5;241m=\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m0.1.6\u001b[39m\u001b[38;5;124m\"\u001b[39m \u001b[38;5;66;03m# noqa\u001b[39;00m\n", + "File \u001b[0;32m~/miniforge3/lib/python3.9/site-packages/matplotlib_inline/backend_inline.py:6\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[38;5;124;03m\"\"\"A matplotlib backend for publishing figures via display_data\"\"\"\u001b[39;00m\n\u001b[1;32m 3\u001b[0m \u001b[38;5;66;03m# Copyright (c) IPython Development Team.\u001b[39;00m\n\u001b[1;32m 4\u001b[0m \u001b[38;5;66;03m# Distributed under the terms of the BSD 3-Clause License.\u001b[39;00m\n\u001b[0;32m----> 6\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mmatplotlib\u001b[39;00m\n\u001b[1;32m 7\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mmatplotlib\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m colors\n\u001b[1;32m 8\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mmatplotlib\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mbackends\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m backend_agg\n", + "\u001b[0;31mModuleNotFoundError\u001b[0m: No module named 'matplotlib'" ] - }, - { - "data": { - "image/png": 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              " - ] - }, - "metadata": { - "filenames": { - "image/png": "/Users/mhjensen/Teaching/MachineLearning/doc/LectureNotes/_build/jupyter_execute/linalg_44_1.png" - } - }, - "output_type": "display_data" } ], "source": [ @@ -902,25 +890,7 @@ "collapsed": false, "editable": true }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "The test matrix:[[ 1. 2. 3.]\n", - " [ 4. 5. 6.]\n", - " [ 7. 8. 9.]\n", - " [10. 11. 12.]]\n", - "This is the total mean summed over all elements:6.5\n", - "This is the mean for each column:[[5.5 6.5 7.5]]\n", - "This is the mean value for each row:[[ 2.]\n", - " [ 5.]\n", - " [ 8.]\n", - " [11.]]\n", - "This is the mean value for each row with keepdims false:[ 2. 5. 8. 11.]\n" - ] - } - ], + "outputs": [], "source": [ "\"\"\"\n", "Simple code that tests various numpy functions\n", @@ -960,19 +930,7 @@ "collapsed": false, "editable": true }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Flatten the matrix:[ 1. 2. 3. 4. 5. 6. 7. 8. 9. 10. 11. 12.]\n", - "Reshape the matrix to a one-dim array:[ 1. 2. 3. 4. 5. 6. 7. 8. 9. 10. 11. 12.]\n", - "[ 1. 4. 7. 10. 2. 5. 8. 11. 3. 6. 9. 12.]\n", - "[ 1. 4. 7. 10. 2. 5. 8. 11. 3. 6. 9. 12.]\n", - "[ 1. 2. 3. 4. 5. 6. 7. 8. 9. 10. 11. 12.]\n" - ] - } - ], + "outputs": [], "source": [ "# Ravel return a contiguous flattened array.\n", "print(f\"Flatten the matrix:{np.ravel(a)}\")\n", @@ -2126,7 +2084,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.9.10" + "version": "3.9.18" } }, "nbformat": 4, diff --git a/doc/LectureNotes/_build/jupyter_execute/project1.ipynb b/doc/LectureNotes/_build/jupyter_execute/project1.ipynb index 9654f79a0..a692327b8 100644 --- a/doc/LectureNotes/_build/jupyter_execute/project1.ipynb +++ b/doc/LectureNotes/_build/jupyter_execute/project1.ipynb @@ -140,26 +140,20 @@ }, "outputs": [ { - "name": "stderr", - "output_type": "stream", - "text": [ - "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_22981/39730396.py:11: MatplotlibDeprecationWarning: Calling gca() with keyword arguments was deprecated in Matplotlib 3.4. Starting two minor releases later, gca() will take no keyword arguments. The gca() function should only be used to get the current axes, or if no axes exist, create new axes with default keyword arguments. To create a new axes with non-default arguments, use plt.axes() or plt.subplot().\n", - " ax = fig.gca(projection='3d')\n" + "ename": "ModuleNotFoundError", + "evalue": "No module named 'matplotlib'", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mModuleNotFoundError\u001b[0m Traceback (most recent call last)", + "Cell \u001b[0;32mIn[1], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \u001b[43mget_ipython\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mrun_line_magic\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mmatplotlib\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43minline\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m)\u001b[49m\n\u001b[1;32m 3\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mmpl_toolkits\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mmplot3d\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m Axes3D\n\u001b[1;32m 4\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mmatplotlib\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mpyplot\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m \u001b[38;5;21;01mplt\u001b[39;00m\n", + "File \u001b[0;32m~/miniforge3/lib/python3.9/site-packages/IPython/core/interactiveshell.py:2432\u001b[0m, in \u001b[0;36mInteractiveShell.run_line_magic\u001b[0;34m(self, magic_name, line, _stack_depth)\u001b[0m\n\u001b[1;32m 2430\u001b[0m kwargs[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mlocal_ns\u001b[39m\u001b[38;5;124m'\u001b[39m] \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mget_local_scope(stack_depth)\n\u001b[1;32m 2431\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mbuiltin_trap:\n\u001b[0;32m-> 2432\u001b[0m result \u001b[38;5;241m=\u001b[39m \u001b[43mfn\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 2434\u001b[0m \u001b[38;5;66;03m# The code below prevents the output from being displayed\u001b[39;00m\n\u001b[1;32m 2435\u001b[0m \u001b[38;5;66;03m# when using magics with decorator @output_can_be_silenced\u001b[39;00m\n\u001b[1;32m 2436\u001b[0m \u001b[38;5;66;03m# when the last Python token in the expression is a ';'.\u001b[39;00m\n\u001b[1;32m 2437\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mgetattr\u001b[39m(fn, magic\u001b[38;5;241m.\u001b[39mMAGIC_OUTPUT_CAN_BE_SILENCED, \u001b[38;5;28;01mFalse\u001b[39;00m):\n", + "File \u001b[0;32m~/miniforge3/lib/python3.9/site-packages/IPython/core/magics/pylab.py:99\u001b[0m, in \u001b[0;36mPylabMagics.matplotlib\u001b[0;34m(self, line)\u001b[0m\n\u001b[1;32m 97\u001b[0m \u001b[38;5;28mprint\u001b[39m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mAvailable matplotlib backends: \u001b[39m\u001b[38;5;132;01m%s\u001b[39;00m\u001b[38;5;124m\"\u001b[39m \u001b[38;5;241m%\u001b[39m backends_list)\n\u001b[1;32m 98\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m---> 99\u001b[0m gui, backend \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mshell\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43menable_matplotlib\u001b[49m\u001b[43m(\u001b[49m\u001b[43margs\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mgui\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mlower\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mif\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[38;5;28;43misinstance\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43margs\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mgui\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mstr\u001b[39;49m\u001b[43m)\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01melse\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43margs\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mgui\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 100\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_show_matplotlib_backend(args\u001b[38;5;241m.\u001b[39mgui, backend)\n", + "File \u001b[0;32m~/miniforge3/lib/python3.9/site-packages/IPython/core/interactiveshell.py:3606\u001b[0m, in \u001b[0;36mInteractiveShell.enable_matplotlib\u001b[0;34m(self, gui)\u001b[0m\n\u001b[1;32m 3585\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21menable_matplotlib\u001b[39m(\u001b[38;5;28mself\u001b[39m, gui\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mNone\u001b[39;00m):\n\u001b[1;32m 3586\u001b[0m \u001b[38;5;250m \u001b[39m\u001b[38;5;124;03m\"\"\"Enable interactive matplotlib and inline figure support.\u001b[39;00m\n\u001b[1;32m 3587\u001b[0m \n\u001b[1;32m 3588\u001b[0m \u001b[38;5;124;03m This takes the following steps:\u001b[39;00m\n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 3604\u001b[0m \u001b[38;5;124;03m display figures inline.\u001b[39;00m\n\u001b[1;32m 3605\u001b[0m \u001b[38;5;124;03m \"\"\"\u001b[39;00m\n\u001b[0;32m-> 3606\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mmatplotlib_inline\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mbackend_inline\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m configure_inline_support\n\u001b[1;32m 3608\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mIPython\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mcore\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m pylabtools \u001b[38;5;28;01mas\u001b[39;00m pt\n\u001b[1;32m 3609\u001b[0m gui, backend \u001b[38;5;241m=\u001b[39m pt\u001b[38;5;241m.\u001b[39mfind_gui_and_backend(gui, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mpylab_gui_select)\n", + "File \u001b[0;32m~/miniforge3/lib/python3.9/site-packages/matplotlib_inline/__init__.py:1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01m.\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m backend_inline, config \u001b[38;5;66;03m# noqa\u001b[39;00m\n\u001b[1;32m 2\u001b[0m __version__ \u001b[38;5;241m=\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m0.1.6\u001b[39m\u001b[38;5;124m\"\u001b[39m \u001b[38;5;66;03m# noqa\u001b[39;00m\n", + "File \u001b[0;32m~/miniforge3/lib/python3.9/site-packages/matplotlib_inline/backend_inline.py:6\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[38;5;124;03m\"\"\"A matplotlib backend for publishing figures via display_data\"\"\"\u001b[39;00m\n\u001b[1;32m 3\u001b[0m \u001b[38;5;66;03m# Copyright (c) IPython Development Team.\u001b[39;00m\n\u001b[1;32m 4\u001b[0m \u001b[38;5;66;03m# Distributed under the terms of the BSD 3-Clause License.\u001b[39;00m\n\u001b[0;32m----> 6\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mmatplotlib\u001b[39;00m\n\u001b[1;32m 7\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mmatplotlib\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m colors\n\u001b[1;32m 8\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mmatplotlib\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mbackends\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m backend_agg\n", + "\u001b[0;31mModuleNotFoundError\u001b[0m: No module named 'matplotlib'" ] - }, - { - "data": { - "image/png": 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\n", 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              " - ] - }, - "metadata": { - "filenames": { - "image/png": "/Users/mhjensen/Teaching/MachineLearning/doc/LectureNotes/_build/jupyter_execute/project1_6_1.png" - } - }, - "output_type": "display_data" } ], "source": [ @@ -732,19 +726,7 @@ "collapsed": false, "editable": true }, - "outputs": [ - { - "ename": "NameError", - "evalue": "name 'scipy' is not defined", - "output_type": "error", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)", - "Input \u001b[0;32mIn [2]\u001b[0m, in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \u001b[43mscipy\u001b[49m\u001b[38;5;241m.\u001b[39mmisc\u001b[38;5;241m.\u001b[39mimread\n", - "\u001b[0;31mNameError\u001b[0m: name 'scipy' is not defined" - ] - } - ], + "outputs": [], "source": [ "scipy.misc.imread" ] @@ -937,7 +919,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.9.10" + "version": "3.9.18" } }, "nbformat": 4, diff --git a/doc/LectureNotes/_build/jupyter_execute/statistics.ipynb b/doc/LectureNotes/_build/jupyter_execute/statistics.ipynb index d905662e4..7b3c02138 100644 --- a/doc/LectureNotes/_build/jupyter_execute/statistics.ipynb +++ b/doc/LectureNotes/_build/jupyter_execute/statistics.ipynb @@ -415,18 +415,20 @@ }, "outputs": [ { - "data": { - "image/png": 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- "text/plain": [ - "
              " - ] - }, - "metadata": { - "filenames": { - "image/png": "/Users/mhjensen/Teaching/MachineLearning/doc/LectureNotes/_build/jupyter_execute/statistics_32_0.png" - } - }, - "output_type": "display_data" + "ename": "ModuleNotFoundError", + "evalue": "No module named 'matplotlib'", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mModuleNotFoundError\u001b[0m Traceback (most recent call last)", + "Cell \u001b[0;32mIn[1], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \u001b[43mget_ipython\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mrun_line_magic\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mmatplotlib\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43minline\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m)\u001b[49m\n\u001b[1;32m 3\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mnumpy\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m \u001b[38;5;21;01mnp\u001b[39;00m\n\u001b[1;32m 4\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mmath\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m acos, exp, sqrt\n", + "File \u001b[0;32m~/miniforge3/lib/python3.9/site-packages/IPython/core/interactiveshell.py:2432\u001b[0m, in \u001b[0;36mInteractiveShell.run_line_magic\u001b[0;34m(self, magic_name, line, _stack_depth)\u001b[0m\n\u001b[1;32m 2430\u001b[0m kwargs[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mlocal_ns\u001b[39m\u001b[38;5;124m'\u001b[39m] \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mget_local_scope(stack_depth)\n\u001b[1;32m 2431\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mbuiltin_trap:\n\u001b[0;32m-> 2432\u001b[0m result \u001b[38;5;241m=\u001b[39m \u001b[43mfn\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 2434\u001b[0m \u001b[38;5;66;03m# The code below prevents the output from being displayed\u001b[39;00m\n\u001b[1;32m 2435\u001b[0m \u001b[38;5;66;03m# when using magics with decorator @output_can_be_silenced\u001b[39;00m\n\u001b[1;32m 2436\u001b[0m \u001b[38;5;66;03m# when the last Python token in the expression is a ';'.\u001b[39;00m\n\u001b[1;32m 2437\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mgetattr\u001b[39m(fn, magic\u001b[38;5;241m.\u001b[39mMAGIC_OUTPUT_CAN_BE_SILENCED, \u001b[38;5;28;01mFalse\u001b[39;00m):\n", + "File \u001b[0;32m~/miniforge3/lib/python3.9/site-packages/IPython/core/magics/pylab.py:99\u001b[0m, in \u001b[0;36mPylabMagics.matplotlib\u001b[0;34m(self, line)\u001b[0m\n\u001b[1;32m 97\u001b[0m \u001b[38;5;28mprint\u001b[39m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mAvailable matplotlib backends: \u001b[39m\u001b[38;5;132;01m%s\u001b[39;00m\u001b[38;5;124m\"\u001b[39m \u001b[38;5;241m%\u001b[39m backends_list)\n\u001b[1;32m 98\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m---> 99\u001b[0m gui, backend \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mshell\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43menable_matplotlib\u001b[49m\u001b[43m(\u001b[49m\u001b[43margs\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mgui\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mlower\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mif\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[38;5;28;43misinstance\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43margs\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mgui\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mstr\u001b[39;49m\u001b[43m)\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01melse\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43margs\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mgui\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 100\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_show_matplotlib_backend(args\u001b[38;5;241m.\u001b[39mgui, backend)\n", + "File \u001b[0;32m~/miniforge3/lib/python3.9/site-packages/IPython/core/interactiveshell.py:3606\u001b[0m, in \u001b[0;36mInteractiveShell.enable_matplotlib\u001b[0;34m(self, gui)\u001b[0m\n\u001b[1;32m 3585\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21menable_matplotlib\u001b[39m(\u001b[38;5;28mself\u001b[39m, gui\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mNone\u001b[39;00m):\n\u001b[1;32m 3586\u001b[0m \u001b[38;5;250m \u001b[39m\u001b[38;5;124;03m\"\"\"Enable interactive matplotlib and inline figure support.\u001b[39;00m\n\u001b[1;32m 3587\u001b[0m \n\u001b[1;32m 3588\u001b[0m \u001b[38;5;124;03m This takes the following steps:\u001b[39;00m\n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 3604\u001b[0m \u001b[38;5;124;03m display figures inline.\u001b[39;00m\n\u001b[1;32m 3605\u001b[0m \u001b[38;5;124;03m \"\"\"\u001b[39;00m\n\u001b[0;32m-> 3606\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mmatplotlib_inline\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mbackend_inline\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m configure_inline_support\n\u001b[1;32m 3608\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mIPython\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mcore\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m pylabtools \u001b[38;5;28;01mas\u001b[39;00m pt\n\u001b[1;32m 3609\u001b[0m gui, backend \u001b[38;5;241m=\u001b[39m pt\u001b[38;5;241m.\u001b[39mfind_gui_and_backend(gui, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mpylab_gui_select)\n", + "File \u001b[0;32m~/miniforge3/lib/python3.9/site-packages/matplotlib_inline/__init__.py:1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01m.\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m backend_inline, config \u001b[38;5;66;03m# noqa\u001b[39;00m\n\u001b[1;32m 2\u001b[0m __version__ \u001b[38;5;241m=\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m0.1.6\u001b[39m\u001b[38;5;124m\"\u001b[39m \u001b[38;5;66;03m# noqa\u001b[39;00m\n", + "File \u001b[0;32m~/miniforge3/lib/python3.9/site-packages/matplotlib_inline/backend_inline.py:6\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[38;5;124;03m\"\"\"A matplotlib backend for publishing figures via display_data\"\"\"\u001b[39;00m\n\u001b[1;32m 3\u001b[0m \u001b[38;5;66;03m# Copyright (c) IPython Development Team.\u001b[39;00m\n\u001b[1;32m 4\u001b[0m \u001b[38;5;66;03m# Distributed under the terms of the BSD 3-Clause License.\u001b[39;00m\n\u001b[0;32m----> 6\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mmatplotlib\u001b[39;00m\n\u001b[1;32m 7\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mmatplotlib\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m colors\n\u001b[1;32m 8\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mmatplotlib\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mbackends\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m backend_agg\n", + "\u001b[0;31mModuleNotFoundError\u001b[0m: No module named 'matplotlib'" + ] } ], "source": [ @@ -1339,35 +1341,7 @@ "collapsed": false, "editable": true }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2.0543653729690416\n", - "[[ 8.75352505 9.52812155 6.26350283 2.23593659 5.0058263 12.34270689\n", - " 3.69987647 18.4524458 5.51178587 5.67743788]\n", - " [ 9.52812155 10.37126183 6.8177581 2.43379387 5.44879019 13.43490889\n", - " 4.0272773 20.08529655 5.99952195 6.17983246]\n", - " [ 6.26350283 6.8177581 4.48179077 1.59990347 3.58187211 8.83170827\n", - " 2.64741194 13.2034747 3.94390673 4.06243748]\n", - " [ 2.23593659 2.43379387 1.59990347 0.57113133 1.27865175 3.15273101\n", - " 0.94506946 4.71335815 1.40789038 1.45020332]\n", - " [ 5.0058263 5.44879019 3.58187211 1.27865175 2.86265211 7.05835037\n", - " 2.11582635 10.55229041 3.1519922 3.24672263]\n", - " [12.34270689 13.43490889 8.83170827 3.15273101 7.05835037 17.40355028\n", - " 5.21692581 26.01844727 7.77176703 8.00534085]\n", - " [ 3.69987647 4.0272773 2.64741194 0.94506946 2.11582635 5.21692581\n", - " 1.56383695 7.79934594 2.32968167 2.39969826]\n", - " [18.4524458 20.08529655 13.2034747 4.71335815 10.55229041 26.01844727\n", - " 7.79934594 38.89778737 11.61885405 11.96804878]\n", - " [ 5.51178587 5.99952195 3.94390673 1.40789038 3.1519922 7.77176703\n", - " 2.32968167 11.61885405 3.47057708 3.57488231]\n", - " [ 5.67743788 6.17983246 4.06243748 1.45020332 3.24672263 8.00534085\n", - " 2.39969826 11.96804878 3.57488231 3.68232235]]\n" - ] - } - ], + "outputs": [], "source": [ "# Importing various packages\n", "from math import exp, sqrt\n", @@ -1996,23 +1970,7 @@ "collapsed": false, "editable": true }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "0.1999902359008225\n", - "4.487000566129411\n", - "1.3211480515121596\n", - "1.0120561022775605 9.785157020098348 24.0365416391166\n", - "2.9740940821517547 3.838584583553216 11.184094793957168\n", - "[[ 1.0120561 2.97409408 3.83858458]\n", - " [ 2.97409408 9.78515702 11.18409479]\n", - " [ 3.83858458 11.18409479 24.03654164]]\n", - "[30.9410965 0.08502185 3.80763641]\n" - ] - } - ], + "outputs": [], "source": [ "# Importing various packages\n", "from math import exp, sqrt\n", @@ -2635,22 +2593,7 @@ "collapsed": false, "editable": true }, - "outputs": [ - { - "data": { - "image/png": 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\n", 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              " - ] - }, - "metadata": { - "filenames": { - "image/png": "/Users/mhjensen/Teaching/MachineLearning/doc/LectureNotes/_build/jupyter_execute/statistics_188_1.png" - } - }, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "# Importing various packages\n", "from math import exp, sqrt\n", @@ -2837,7 +2758,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.9.10" + "version": "3.9.18" } }, "nbformat": 4, diff --git a/doc/LectureNotes/_build/jupyter_execute/week34.ipynb b/doc/LectureNotes/_build/jupyter_execute/week34.ipynb index 2d131abfd..f91a2fc32 100644 --- a/doc/LectureNotes/_build/jupyter_execute/week34.ipynb +++ b/doc/LectureNotes/_build/jupyter_execute/week34.ipynb @@ -985,8 +985,8 @@ "name": "stdout", "output_type": "stream", "text": [ - "[-1.79544237 -0.19322111 0.14623399 0.65045925 -0.73133982 -1.90092179\n", - " 0.45359479 1.04800573 -1.16171326 1.30577095]\n" + "[ 0.90148776 -0.63784548 0.61907743 -0.95073282 0.3897131 0.88123172\n", + " 0.68407077 1.02936353 -0.85538578 -1.44188631]\n" ] } ], @@ -1424,26 +1424,36 @@ "name": "stdout", "output_type": "stream", "text": [ - "[[0.79725436 0.93166184 0.53462883 0.92250132 0.79313278 0.4185877\n", - " 0.21601417 0.21037404 0.98496212 0.89482188]\n", - " [0.15696119 0.72299619 0.71846708 0.7506246 0.74409239 0.1280756\n", - " 0.5316382 0.91236018 0.24479698 0.3435384 ]\n", - " [0.5342745 0.2369833 0.47356208 0.16499193 0.60378214 0.9920084\n", - " 0.80788856 0.36452684 0.23309661 0.33829607]\n", - " [0.7803076 0.17074761 0.15484848 0.82187419 0.69965466 0.85754328\n", - " 0.35700089 0.70856363 0.44993812 0.48802709]\n", - " [0.06875458 0.23106527 0.28230186 0.40805354 0.54632096 0.11033412\n", - " 0.51220693 0.07752967 0.58908017 0.78263078]\n", - " [0.62686358 0.14793475 0.11341291 0.46164895 0.32542732 0.42090072\n", - " 0.25033968 0.33199489 0.25137033 0.17009925]\n", - " [0.74260356 0.49932184 0.99035178 0.5146617 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2.86689196e-01\n", + " 4.68794083e-01 9.14515024e-01]\n", + " [2.35728987e-01 5.58997922e-01 1.81586331e-01 7.01875673e-02\n", + " 4.86286714e-01 1.91892059e-02 1.99830074e-01 1.38493791e-01\n", + " 5.80880777e-01 9.24801038e-01]]\n" ] } ], @@ -1557,13 +1567,13 @@ "name": "stdout", "output_type": "stream", "text": [ - "-0.13314597157635316\n", - "3.42745728579187\n", - "-0.1549258803307933\n", - "[[ 1.0457893 3.16754123 2.39769983]\n", - " [ 3.16754123 10.50453066 7.29238177]\n", - " [ 2.39769983 7.29238177 8.3431023 ]]\n", - "[17.73772319 0.07333967 2.0823594 ]\n" + "0.19672699443907488\n", + "4.695047732333614\n", + "0.5139271024913812\n", + "[[0.9118261 2.86708608 1.95090925]\n", + " [2.86708608 9.97030515 6.26396287]\n", + " [1.95090925 6.26396287 6.36590624]]\n", + "[15.50830953 0.07327648 1.66645148]\n" ] } ], @@ -1595,32 +1605,20 @@ }, "outputs": [ { - "name": "stdout", - "output_type": "stream", - "text": [ - "[[1. 0. 0. 0.]\n", - " [0. 1. 0. 0.]\n", - " [0. 0. 1. 0.]\n", - " [0. 0. 0. 1.]]\n", - " (0, 0)\t1.0\n", - " (1, 1)\t1.0\n", - " (2, 2)\t1.0\n", - " (3, 3)\t1.0\n" + "ename": "ModuleNotFoundError", + "evalue": "No module named 'matplotlib'", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mModuleNotFoundError\u001b[0m Traceback (most recent call last)", + "Cell \u001b[0;32mIn[16], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \u001b[43mget_ipython\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mrun_line_magic\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mmatplotlib\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43minline\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m)\u001b[49m\n\u001b[1;32m 3\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mnumpy\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m \u001b[38;5;21;01mnp\u001b[39;00m\n\u001b[1;32m 4\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mmatplotlib\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mpyplot\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m \u001b[38;5;21;01mplt\u001b[39;00m\n", + "File \u001b[0;32m~/miniforge3/lib/python3.9/site-packages/IPython/core/interactiveshell.py:2432\u001b[0m, in \u001b[0;36mInteractiveShell.run_line_magic\u001b[0;34m(self, magic_name, line, _stack_depth)\u001b[0m\n\u001b[1;32m 2430\u001b[0m kwargs[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mlocal_ns\u001b[39m\u001b[38;5;124m'\u001b[39m] \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mget_local_scope(stack_depth)\n\u001b[1;32m 2431\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mbuiltin_trap:\n\u001b[0;32m-> 2432\u001b[0m result \u001b[38;5;241m=\u001b[39m \u001b[43mfn\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 2434\u001b[0m \u001b[38;5;66;03m# The code below prevents the output from being displayed\u001b[39;00m\n\u001b[1;32m 2435\u001b[0m \u001b[38;5;66;03m# when using magics with decorator @output_can_be_silenced\u001b[39;00m\n\u001b[1;32m 2436\u001b[0m \u001b[38;5;66;03m# when the last Python token in the expression is a ';'.\u001b[39;00m\n\u001b[1;32m 2437\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mgetattr\u001b[39m(fn, magic\u001b[38;5;241m.\u001b[39mMAGIC_OUTPUT_CAN_BE_SILENCED, \u001b[38;5;28;01mFalse\u001b[39;00m):\n", + "File \u001b[0;32m~/miniforge3/lib/python3.9/site-packages/IPython/core/magics/pylab.py:99\u001b[0m, in \u001b[0;36mPylabMagics.matplotlib\u001b[0;34m(self, line)\u001b[0m\n\u001b[1;32m 97\u001b[0m \u001b[38;5;28mprint\u001b[39m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mAvailable matplotlib backends: \u001b[39m\u001b[38;5;132;01m%s\u001b[39;00m\u001b[38;5;124m\"\u001b[39m \u001b[38;5;241m%\u001b[39m backends_list)\n\u001b[1;32m 98\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m---> 99\u001b[0m gui, backend \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mshell\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43menable_matplotlib\u001b[49m\u001b[43m(\u001b[49m\u001b[43margs\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mgui\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mlower\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mif\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[38;5;28;43misinstance\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43margs\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mgui\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mstr\u001b[39;49m\u001b[43m)\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01melse\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43margs\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mgui\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 100\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_show_matplotlib_backend(args\u001b[38;5;241m.\u001b[39mgui, backend)\n", + "File \u001b[0;32m~/miniforge3/lib/python3.9/site-packages/IPython/core/interactiveshell.py:3606\u001b[0m, in \u001b[0;36mInteractiveShell.enable_matplotlib\u001b[0;34m(self, gui)\u001b[0m\n\u001b[1;32m 3585\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21menable_matplotlib\u001b[39m(\u001b[38;5;28mself\u001b[39m, gui\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mNone\u001b[39;00m):\n\u001b[1;32m 3586\u001b[0m \u001b[38;5;250m \u001b[39m\u001b[38;5;124;03m\"\"\"Enable interactive matplotlib and inline figure support.\u001b[39;00m\n\u001b[1;32m 3587\u001b[0m \n\u001b[1;32m 3588\u001b[0m \u001b[38;5;124;03m This takes the following steps:\u001b[39;00m\n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 3604\u001b[0m \u001b[38;5;124;03m display figures inline.\u001b[39;00m\n\u001b[1;32m 3605\u001b[0m \u001b[38;5;124;03m \"\"\"\u001b[39;00m\n\u001b[0;32m-> 3606\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mmatplotlib_inline\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mbackend_inline\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m configure_inline_support\n\u001b[1;32m 3608\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mIPython\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mcore\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m pylabtools \u001b[38;5;28;01mas\u001b[39;00m pt\n\u001b[1;32m 3609\u001b[0m gui, backend \u001b[38;5;241m=\u001b[39m pt\u001b[38;5;241m.\u001b[39mfind_gui_and_backend(gui, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mpylab_gui_select)\n", + "File \u001b[0;32m~/miniforge3/lib/python3.9/site-packages/matplotlib_inline/__init__.py:1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01m.\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m backend_inline, config \u001b[38;5;66;03m# noqa\u001b[39;00m\n\u001b[1;32m 2\u001b[0m __version__ \u001b[38;5;241m=\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m0.1.6\u001b[39m\u001b[38;5;124m\"\u001b[39m \u001b[38;5;66;03m# noqa\u001b[39;00m\n", + "File \u001b[0;32m~/miniforge3/lib/python3.9/site-packages/matplotlib_inline/backend_inline.py:6\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[38;5;124;03m\"\"\"A matplotlib backend for publishing figures via display_data\"\"\"\u001b[39;00m\n\u001b[1;32m 3\u001b[0m \u001b[38;5;66;03m# Copyright (c) IPython Development Team.\u001b[39;00m\n\u001b[1;32m 4\u001b[0m \u001b[38;5;66;03m# Distributed under the terms of the BSD 3-Clause License.\u001b[39;00m\n\u001b[0;32m----> 6\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mmatplotlib\u001b[39;00m\n\u001b[1;32m 7\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mmatplotlib\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m colors\n\u001b[1;32m 8\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mmatplotlib\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mbackends\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m backend_agg\n", + "\u001b[0;31mModuleNotFoundError\u001b[0m: No module named 'matplotlib'" ] - }, - { - "data": { - "image/png": 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\n", 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\n", - "text/plain": [ - "
              " - ] - }, - "metadata": { - "filenames": { - "image/png": "/Users/mhjensen/Teaching/MachineLearning/doc/LectureNotes/_build/jupyter_execute/week34_86_3.png" - } - }, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "df.columns = ['First', 'Second', 'Third', 'Fourth', 'Fifth']\n", "df.index = np.arange(10)\n", @@ -2571,23 +1856,7 @@ "collapsed": false, "editable": true }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[[ 0 1 2 3]\n", - " [ 4 5 6 7]\n", - " [ 8 9 10 11]\n", - " [12 13 14 15]]\n", - " 0 1 2 3\n", - "0 0 1 2 3\n", - "1 4 5 6 7\n", - "2 8 9 10 11\n", - "3 12 13 14 15\n" - ] - } - ], + "outputs": [], "source": [ "b = np.arange(16).reshape((4,4))\n", "print(b)\n", @@ -2690,22 +1959,7 @@ "collapsed": false, "editable": true }, - "outputs": [ - { - "data": { - "image/png": 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\n", 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              " - ] - }, - "metadata": { - "filenames": { - "image/png": "/Users/mhjensen/Teaching/MachineLearning/doc/LectureNotes/_build/jupyter_execute/week34_101_0.png" - } - }, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "import numpy as np\n", "import matplotlib.pyplot as plt\n", @@ -2923,36 +2162,7 @@ "collapsed": false, "editable": true }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "The intercept alpha: \n", - " [2.18780801]\n", - "Coefficient beta : \n", - " [[4.72228205]]\n", - "Mean squared error: 0.37\n", - "Variance score: 0.83\n", - "Mean squared log error: 0.01\n", - "Mean absolute error: 0.47\n" - ] - }, - { - "data": { - "image/png": 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              " - ] - }, - "metadata": { - "filenames": { - "image/png": "/Users/mhjensen/Teaching/MachineLearning/doc/LectureNotes/_build/jupyter_execute/week34_103_1.png" - } - }, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "import numpy as np \n", "import matplotlib.pyplot as plt \n", @@ -3471,18 +2681,7 @@ "collapsed": false, "editable": true }, - "outputs": [ - { - "data": { - "text/plain": [ - "' \\nThis is taken from the data file of the mass 2016 evaluation. \\nAll files are 3436 lines long with 124 character per line. \\n Headers are 39 lines long. \\n col 1 : Fortran character control: 1 = page feed 0 = line feed \\n format : a1,i3,i5,i5,i5,1x,a3,a4,1x,f13.5,f11.5,f11.3,f9.3,1x,a2,f11.3,f9.3,1x,i3,1x,f12.5,f11.5 \\n These formats are reflected in the pandas widths variable below, see the statement \\n widths=(1,3,5,5,5,1,3,4,1,13,11,11,9,1,2,11,9,1,3,1,12,11,1), \\n Pandas has also a variable header, with length 39 in this case. \\n'" - ] - }, - "execution_count": 29, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "\"\"\" \n", "This is taken from the data file of the mass 2016 evaluation. \n", @@ -3517,21 +2716,7 @@ "collapsed": false, "editable": true }, - "outputs": [ - { - "ename": "ValueError", - "evalue": "Length of colspecs must match length of names", - "output_type": "error", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mValueError\u001b[0m Traceback (most recent call last)", - "Input \u001b[0;32mIn [30]\u001b[0m, in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[38;5;66;03m# Read the experimental data with Pandas\u001b[39;00m\n\u001b[0;32m----> 2\u001b[0m Masses \u001b[38;5;241m=\u001b[39m \u001b[43mpd\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mread_fwf\u001b[49m\u001b[43m(\u001b[49m\u001b[43minfile\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43musecols\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m2\u001b[39;49m\u001b[43m,\u001b[49m\u001b[38;5;241;43m3\u001b[39;49m\u001b[43m,\u001b[49m\u001b[38;5;241;43m4\u001b[39;49m\u001b[43m,\u001b[49m\u001b[38;5;241;43m6\u001b[39;49m\u001b[43m,\u001b[49m\u001b[38;5;241;43m11\u001b[39;49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 3\u001b[0m \u001b[43m \u001b[49m\u001b[43mnames\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mN\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mZ\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mA\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mElement\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mEbinding\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 4\u001b[0m \u001b[43m \u001b[49m\u001b[43mwidths\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m1\u001b[39;49m\u001b[43m,\u001b[49m\u001b[38;5;241;43m3\u001b[39;49m\u001b[43m,\u001b[49m\u001b[38;5;241;43m5\u001b[39;49m\u001b[43m,\u001b[49m\u001b[38;5;241;43m5\u001b[39;49m\u001b[43m,\u001b[49m\u001b[38;5;241;43m5\u001b[39;49m\u001b[43m,\u001b[49m\u001b[38;5;241;43m1\u001b[39;49m\u001b[43m,\u001b[49m\u001b[38;5;241;43m3\u001b[39;49m\u001b[43m,\u001b[49m\u001b[38;5;241;43m4\u001b[39;49m\u001b[43m,\u001b[49m\u001b[38;5;241;43m1\u001b[39;49m\u001b[43m,\u001b[49m\u001b[38;5;241;43m13\u001b[39;49m\u001b[43m,\u001b[49m\u001b[38;5;241;43m11\u001b[39;49m\u001b[43m,\u001b[49m\u001b[38;5;241;43m11\u001b[39;49m\u001b[43m,\u001b[49m\u001b[38;5;241;43m9\u001b[39;49m\u001b[43m,\u001b[49m\u001b[38;5;241;43m1\u001b[39;49m\u001b[43m,\u001b[49m\u001b[38;5;241;43m2\u001b[39;49m\u001b[43m,\u001b[49m\u001b[38;5;241;43m11\u001b[39;49m\u001b[43m,\u001b[49m\u001b[38;5;241;43m9\u001b[39;49m\u001b[43m,\u001b[49m\u001b[38;5;241;43m1\u001b[39;49m\u001b[43m,\u001b[49m\u001b[38;5;241;43m3\u001b[39;49m\u001b[43m,\u001b[49m\u001b[38;5;241;43m1\u001b[39;49m\u001b[43m,\u001b[49m\u001b[38;5;241;43m12\u001b[39;49m\u001b[43m,\u001b[49m\u001b[38;5;241;43m11\u001b[39;49m\u001b[43m,\u001b[49m\u001b[38;5;241;43m1\u001b[39;49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 5\u001b[0m \u001b[43m \u001b[49m\u001b[43mheader\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;241;43m39\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[1;32m 6\u001b[0m \u001b[43m \u001b[49m\u001b[43mindex_col\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mFalse\u001b[39;49;00m\u001b[43m)\u001b[49m\n\u001b[1;32m 8\u001b[0m \u001b[38;5;66;03m# Extrapolated values are indicated by '#' in place of the decimal place, so\u001b[39;00m\n\u001b[1;32m 9\u001b[0m \u001b[38;5;66;03m# the Ebinding column won't be numeric. Coerce to float and drop these entries.\u001b[39;00m\n\u001b[1;32m 10\u001b[0m Masses[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mEbinding\u001b[39m\u001b[38;5;124m'\u001b[39m] \u001b[38;5;241m=\u001b[39m pd\u001b[38;5;241m.\u001b[39mto_numeric(Masses[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mEbinding\u001b[39m\u001b[38;5;124m'\u001b[39m], errors\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mcoerce\u001b[39m\u001b[38;5;124m'\u001b[39m)\n", - "File \u001b[0;32m~/miniforge3/envs/myenv/lib/python3.9/site-packages/pandas/util/_decorators.py:311\u001b[0m, in \u001b[0;36mdeprecate_nonkeyword_arguments..decorate..wrapper\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m 305\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mlen\u001b[39m(args) \u001b[38;5;241m>\u001b[39m num_allow_args:\n\u001b[1;32m 306\u001b[0m warnings\u001b[38;5;241m.\u001b[39mwarn(\n\u001b[1;32m 307\u001b[0m msg\u001b[38;5;241m.\u001b[39mformat(arguments\u001b[38;5;241m=\u001b[39marguments),\n\u001b[1;32m 308\u001b[0m \u001b[38;5;167;01mFutureWarning\u001b[39;00m,\n\u001b[1;32m 309\u001b[0m stacklevel\u001b[38;5;241m=\u001b[39mstacklevel,\n\u001b[1;32m 310\u001b[0m )\n\u001b[0;32m--> 311\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mfunc\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n", - "File \u001b[0;32m~/miniforge3/envs/myenv/lib/python3.9/site-packages/pandas/io/parsers/readers.py:871\u001b[0m, in \u001b[0;36mread_fwf\u001b[0;34m(filepath_or_buffer, colspecs, widths, infer_nrows, **kwds)\u001b[0m\n\u001b[1;32m 869\u001b[0m len_index \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mlen\u001b[39m(index_col)\n\u001b[1;32m 870\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mlen\u001b[39m(names) \u001b[38;5;241m+\u001b[39m len_index \u001b[38;5;241m!=\u001b[39m \u001b[38;5;28mlen\u001b[39m(colspecs):\n\u001b[0;32m--> 871\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mLength of colspecs must match length of names\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[1;32m 873\u001b[0m kwds[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mcolspecs\u001b[39m\u001b[38;5;124m\"\u001b[39m] \u001b[38;5;241m=\u001b[39m colspecs\n\u001b[1;32m 874\u001b[0m kwds[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124minfer_nrows\u001b[39m\u001b[38;5;124m\"\u001b[39m] \u001b[38;5;241m=\u001b[39m infer_nrows\n", - "\u001b[0;31mValueError\u001b[0m: Length of colspecs must match length of names" - ] - } - ], + "outputs": [], "source": [ "# Read the experimental data with Pandas\n", "Masses = pd.read_fwf(infile, usecols=(2,3,4,6,11),\n", @@ -5967,7 +5152,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.9.10" + "version": "3.9.18" } }, "nbformat": 4, diff --git a/doc/LectureNotes/_build/jupyter_execute/week35.ipynb b/doc/LectureNotes/_build/jupyter_execute/week35.ipynb index 71e93c686..fa5c6c0ea 100644 --- a/doc/LectureNotes/_build/jupyter_execute/week35.ipynb +++ b/doc/LectureNotes/_build/jupyter_execute/week35.ipynb @@ -1519,7 +1519,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "0.9952714635635723\n" + "0.9947950318641428\n" ] } ], @@ -1550,7 +1550,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "0.009039197557276654\n" + "0.010272505059533654\n" ] } ], @@ -1585,31 +1585,23 @@ "name": "stdout", "output_type": "stream", "text": [ - "[6.11430166e-02 7.59428035e-03 7.64295978e-03 5.34442654e-02\n", - " 2.53861506e-02 7.46543658e-02 2.79339933e-02 1.32946226e-02\n", - " 3.80320028e-03 2.40474117e-02 4.36028477e-02 3.24382528e-02\n", - " 1.26022420e-02 5.06848116e-03 1.96793569e-02 1.04826413e-04\n", - " 9.25580178e-03 1.80413812e-02 1.22519792e-02 1.01505087e-02\n", - " 2.67180442e-02 6.71316703e-02 7.68415876e-03 3.33174939e-02\n", - " 1.43570786e-02 8.43459933e-03 4.11046407e-03 9.10453697e-04\n", - " 4.33142087e-03 7.64507671e-02 2.19696497e-02 1.18460525e-02\n", - " 4.35665906e-02 3.62818572e-02 1.61018347e-03 3.78738205e-02\n", - " 4.57031517e-03 3.11279984e-02 1.07745809e-02 7.09848429e-03\n", - " 2.30006341e-03 4.62535124e-02 6.70493970e-02 6.26624267e-03\n", - " 4.23399553e-02 1.88690267e-02 7.46184816e-03 1.75934097e-02\n", - " 3.60820168e-02 1.09322328e-02 3.16749353e-02 2.30626515e-02\n", - " 5.76196895e-04 1.51000420e-02 1.95456434e-03 1.22138768e-02\n", - " 2.52945186e-03 2.12804129e-03 3.32695433e-03 1.90886410e-02\n", - " 1.84291049e-02 1.78104004e-02 6.34498455e-02 1.50032818e-02\n", - " 3.43525003e-02 1.52033394e-03 3.95921014e-02 7.67148008e-02\n", - " 2.40893740e-02 6.75638425e-02 1.22105472e-02 7.72706640e-03\n", - " 3.00721135e-02 8.77081499e-02 1.40684484e-02 4.57081718e-02\n", - " 3.51190686e-02 7.42092776e-02 4.03105744e-02 1.08994018e-02\n", - " 1.37071826e-02 3.69106251e-02 1.94966662e-03 1.71112140e-02\n", - " 3.12669602e-03 3.01610677e-02 1.72139847e-02 2.75703556e-02\n", - " 2.23333773e-02 2.76892410e-03 8.05886306e-02 1.15545946e-01\n", - " 4.88029792e-02 4.59648068e-02 1.98750794e-02 2.69178261e-03\n", - " 1.07925220e-02 1.13151043e-03 5.00620642e-03 3.69871367e-03]\n" + "[0.01027068 0.05313438 0.04538489 0.01836177 0.02080937 0.01800647\n", + " 0.01875198 0.00467902 0.00753502 0.02005707 0.00232645 0.01531078\n", + " 0.03829802 0.00785147 0.04770861 0.04729106 0.01202626 0.05597075\n", + " 0.00759075 0.01536802 0.02241641 0.00274849 0.0097933 0.01440849\n", + " 0.05803407 0.03087398 0.0131244 0.0291341 0.03955471 0.04581495\n", + " 0.0096887 0.02838502 0.00331854 0.00917575 0.02232857 0.01026639\n", + " 0.01719879 0.04680163 0.03691034 0.05657464 0.01277062 0.02319192\n", + " 0.03410211 0.0114457 0.03498633 0.0345711 0.00908896 0.05115386\n", + " 0.01253218 0.00362237 0.03169296 0.06165423 0.0072295 0.00869224\n", + " 0.03849245 0.05269447 0.01539813 0.04350167 0.05006792 0.00862396\n", + " 0.02270315 0.0141736 0.00537237 0.02931659 0.02817395 0.025761\n", + " 0.05417012 0.01227 0.00370021 0.02342548 0.00361816 0.0400444\n", + " 0.02198873 0.00914991 0.01461902 0.04424899 0.00910613 0.0099676\n", + " 0.02701179 0.03971778 0.04524779 0.05279081 0.01519497 0.00134839\n", + " 0.04233422 0.01660753 0.05817493 0.0167955 0.01299388 0.00265975\n", + " 0.02189559 0.04726103 0.01705804 0.10695767 0.06613465 0.04663936\n", + " 0.00250484 0.02495131 0.01816794 0.01905766]\n" ] } ], @@ -1660,18 +1652,19 @@ }, "outputs": [ { - "name": "stdout", - "output_type": "stream", - "text": [ - "[ 2.04038959 -0.81343612 7.94304103 -3.83770135 1.70073443]\n", - "Training R2\n", - "0.9954441212986554\n", - "Training MSE\n", - "0.008653750612762521\n", - "Test R2\n", - "0.99255249100346\n", - "Test MSE\n", - "0.016970019460234076\n" + "ename": "ModuleNotFoundError", + "evalue": "No module named 'matplotlib'", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mModuleNotFoundError\u001b[0m Traceback (most recent call last)", + "Cell \u001b[0;32mIn[7], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \u001b[43mget_ipython\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mrun_line_magic\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mmatplotlib\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43minline\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m)\u001b[49m\n\u001b[1;32m 3\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mos\u001b[39;00m\n\u001b[1;32m 4\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mnumpy\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m \u001b[38;5;21;01mnp\u001b[39;00m\n", + "File \u001b[0;32m~/miniforge3/lib/python3.9/site-packages/IPython/core/interactiveshell.py:2432\u001b[0m, in \u001b[0;36mInteractiveShell.run_line_magic\u001b[0;34m(self, magic_name, line, _stack_depth)\u001b[0m\n\u001b[1;32m 2430\u001b[0m kwargs[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mlocal_ns\u001b[39m\u001b[38;5;124m'\u001b[39m] \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mget_local_scope(stack_depth)\n\u001b[1;32m 2431\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mbuiltin_trap:\n\u001b[0;32m-> 2432\u001b[0m result \u001b[38;5;241m=\u001b[39m \u001b[43mfn\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 2434\u001b[0m \u001b[38;5;66;03m# The code below prevents the output from being displayed\u001b[39;00m\n\u001b[1;32m 2435\u001b[0m \u001b[38;5;66;03m# when using magics with decorator @output_can_be_silenced\u001b[39;00m\n\u001b[1;32m 2436\u001b[0m \u001b[38;5;66;03m# when the last Python token in the expression is a ';'.\u001b[39;00m\n\u001b[1;32m 2437\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mgetattr\u001b[39m(fn, magic\u001b[38;5;241m.\u001b[39mMAGIC_OUTPUT_CAN_BE_SILENCED, \u001b[38;5;28;01mFalse\u001b[39;00m):\n", + "File \u001b[0;32m~/miniforge3/lib/python3.9/site-packages/IPython/core/magics/pylab.py:99\u001b[0m, in \u001b[0;36mPylabMagics.matplotlib\u001b[0;34m(self, line)\u001b[0m\n\u001b[1;32m 97\u001b[0m \u001b[38;5;28mprint\u001b[39m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mAvailable matplotlib backends: \u001b[39m\u001b[38;5;132;01m%s\u001b[39;00m\u001b[38;5;124m\"\u001b[39m \u001b[38;5;241m%\u001b[39m backends_list)\n\u001b[1;32m 98\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m---> 99\u001b[0m gui, backend \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mshell\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43menable_matplotlib\u001b[49m\u001b[43m(\u001b[49m\u001b[43margs\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mgui\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mlower\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mif\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[38;5;28;43misinstance\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43margs\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mgui\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mstr\u001b[39;49m\u001b[43m)\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01melse\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43margs\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mgui\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 100\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_show_matplotlib_backend(args\u001b[38;5;241m.\u001b[39mgui, backend)\n", + "File \u001b[0;32m~/miniforge3/lib/python3.9/site-packages/IPython/core/interactiveshell.py:3606\u001b[0m, in \u001b[0;36mInteractiveShell.enable_matplotlib\u001b[0;34m(self, gui)\u001b[0m\n\u001b[1;32m 3585\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21menable_matplotlib\u001b[39m(\u001b[38;5;28mself\u001b[39m, gui\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mNone\u001b[39;00m):\n\u001b[1;32m 3586\u001b[0m \u001b[38;5;250m \u001b[39m\u001b[38;5;124;03m\"\"\"Enable interactive matplotlib and inline figure support.\u001b[39;00m\n\u001b[1;32m 3587\u001b[0m \n\u001b[1;32m 3588\u001b[0m \u001b[38;5;124;03m This takes the following steps:\u001b[39;00m\n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 3604\u001b[0m \u001b[38;5;124;03m display figures inline.\u001b[39;00m\n\u001b[1;32m 3605\u001b[0m \u001b[38;5;124;03m \"\"\"\u001b[39;00m\n\u001b[0;32m-> 3606\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mmatplotlib_inline\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mbackend_inline\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m configure_inline_support\n\u001b[1;32m 3608\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mIPython\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mcore\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m pylabtools \u001b[38;5;28;01mas\u001b[39;00m pt\n\u001b[1;32m 3609\u001b[0m gui, backend \u001b[38;5;241m=\u001b[39m pt\u001b[38;5;241m.\u001b[39mfind_gui_and_backend(gui, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mpylab_gui_select)\n", + "File \u001b[0;32m~/miniforge3/lib/python3.9/site-packages/matplotlib_inline/__init__.py:1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01m.\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m backend_inline, config \u001b[38;5;66;03m# noqa\u001b[39;00m\n\u001b[1;32m 2\u001b[0m __version__ \u001b[38;5;241m=\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m0.1.6\u001b[39m\u001b[38;5;124m\"\u001b[39m \u001b[38;5;66;03m# noqa\u001b[39;00m\n", + "File \u001b[0;32m~/miniforge3/lib/python3.9/site-packages/matplotlib_inline/backend_inline.py:6\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[38;5;124;03m\"\"\"A matplotlib backend for publishing figures via display_data\"\"\"\u001b[39;00m\n\u001b[1;32m 3\u001b[0m \u001b[38;5;66;03m# Copyright (c) IPython Development Team.\u001b[39;00m\n\u001b[1;32m 4\u001b[0m \u001b[38;5;66;03m# Distributed under the terms of the BSD 3-Clause License.\u001b[39;00m\n\u001b[0;32m----> 6\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mmatplotlib\u001b[39;00m\n\u001b[1;32m 7\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mmatplotlib\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m colors\n\u001b[1;32m 8\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mmatplotlib\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mbackends\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m backend_agg\n", + "\u001b[0;31mModuleNotFoundError\u001b[0m: No module named 'matplotlib'" ] } ], @@ -1928,416 +1921,7 @@ "collapsed": false, "editable": true }, - "outputs": [ - { - "data": { - "text/html": [ - "
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- "text/plain": [ - "
              " - ] - }, - "metadata": { - "filenames": { - "image/png": "/Users/mhjensen/Teaching/MachineLearning/doc/LectureNotes/_build/jupyter_execute/week35_146_0.png" - } - }, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "import matplotlib.pyplot as plt\n", "import numpy as np\n", @@ -2537,40 +2106,7 @@ "collapsed": false, "editable": true }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "MSE before scaling: 0.00\n", - "R2 score before scaling 1.00\n", - "Feature min values before scaling:\n", - " [1.00000000e+00 6.97906022e-03 2.43639284e-03 4.87072815e-05\n", - " 1.70037324e-05 5.93601008e-06 3.39931051e-07 1.18670072e-07\n", - " 4.14277718e-08 1.44624525e-08 2.37239927e-09 8.28205578e-10\n", - " 2.89126914e-10 1.00934327e-10 3.52362157e-11 1.65571174e-11\n", - " 5.78009660e-12 2.01783414e-12 7.04426744e-13 2.45915671e-13\n", - " 8.58492636e-14]\n", - "Feature max values before scaling:\n", - " [1. 0.99970894 0.99978365 0.99941797 0.99949266 0.99956735\n", - " 0.99912709 0.99920175 0.99927642 0.9993511 0.99883628 0.99891093\n", - " 0.99898558 0.99906023 0.99913489 0.99854557 0.99862019 0.99869482\n", - " 0.99876945 0.99884409 0.99891873]\n", - "Feature min values after scaling:\n", - " [ 0. -1.71761101 -1.75770568 -1.12330033 -1.12591227 -1.12871842\n", - " -0.88613493 -0.884669 -0.88323026 -0.88182591 -0.75269037 -0.75050135\n", - " -0.74829661 -0.74607851 -0.74384949 -0.6652177 -0.66294408 -0.66064822\n", - " -0.65833132 -0.65599456 -0.6536392 ]\n", - "Feature max values after scaling:\n", - " [0. 1.71737253 1.75576555 2.20916295 2.23971032 2.26995402\n", - " 2.60543038 2.63162342 2.65743689 2.68286725 2.94273542 2.96631321\n", - " 2.98947894 3.01222822 3.034557 3.24159785 3.26297455 3.28391978\n", - " 3.30442964 3.32450054 3.34412923]\n", - "MSE after scaling: 0.00\n", - "R2 score for scaled data: 1.00\n" - ] - } - ], + "outputs": [], "source": [ "# Common imports\n", "import os\n", @@ -3105,43 +2641,7 @@ "collapsed": false, "editable": true }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "True beta: [2, 0.5, 3.7]\n", - "Fitted beta: [2.08376632 0.19569961 3.97898392]\n", - "Sklearn fitted beta: [2.08376632 0.19569961 3.97898392]\n", - "MSE with intercept column\n", - "0.004113634617443139\n", - "MSE with intercept column from SKL\n", - "0.004113634617443147\n", - "Manual intercept: 2.083766322923899\n", - "Fitted beta (wiothout intercept): [0.19569961 3.97898392]\n", - "Sklearn intercept: 2.0837663229239043\n", - "Sklearn fitted beta (without intercept): [0.19569961 3.97898392]\n", - "MSE with Manual intercept\n", - "0.00411363461744314\n", - "MSE with Sklearn intercept\n", - "0.004113634617443131\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
              " - ] - }, - "metadata": { - "filenames": { - "image/png": "/Users/mhjensen/Teaching/MachineLearning/doc/LectureNotes/_build/jupyter_execute/week35_181_1.png" - } - }, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "import numpy as np\n", "import matplotlib.pyplot as plt\n", @@ -3414,60 +2914,7 @@ "collapsed": false, "editable": true }, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/Users/mhjensen/miniforge3/envs/myenv/lib/python3.9/site-packages/sklearn/utils/deprecation.py:87: FutureWarning: Function load_boston is deprecated; `load_boston` is deprecated in 1.0 and will be removed in 1.2.\n", - "\n", - " The Boston housing prices dataset has an ethical problem. You can refer to\n", - " the documentation of this function for further details.\n", - "\n", - " The scikit-learn maintainers therefore strongly discourage the use of this\n", - " dataset unless the purpose of the code is to study and educate about\n", - " ethical issues in data science and machine learning.\n", - "\n", - " In this special case, you can fetch the dataset from the original\n", - " source::\n", - "\n", - " import pandas as pd\n", - " import numpy as np\n", - "\n", - "\n", - " data_url = \"http://lib.stat.cmu.edu/datasets/boston\"\n", - " raw_df = pd.read_csv(data_url, sep=\"\\s+\", skiprows=22, header=None)\n", - " data = np.hstack([raw_df.values[::2, :], raw_df.values[1::2, :2]])\n", - " target = raw_df.values[1::2, 2]\n", - "\n", - " Alternative datasets include the California housing dataset (i.e.\n", - " :func:`~sklearn.datasets.fetch_california_housing`) and the Ames housing\n", - " dataset. You can load the datasets as follows::\n", - "\n", - " from sklearn.datasets import fetch_california_housing\n", - " housing = fetch_california_housing()\n", - "\n", - " for the California housing dataset and::\n", - "\n", - " from sklearn.datasets import fetch_openml\n", - " housing = fetch_openml(name=\"house_prices\", as_frame=True)\n", - "\n", - " for the Ames housing dataset.\n", - " \n", - " warnings.warn(msg, category=FutureWarning)\n" - ] - }, - { - "data": { - "text/plain": [ - "dict_keys(['data', 'target', 'feature_names', 'DESCR', 'filename', 'data_module'])" - ] - }, - "execution_count": 16, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "from sklearn.datasets import load_boston\n", "\n", @@ -3521,32 +2968,7 @@ "collapsed": false, "editable": true }, - "outputs": [ - { - "data": { - "text/plain": [ - "CRIM 0\n", - "ZN 0\n", - "INDUS 0\n", - "CHAS 0\n", - "NOX 0\n", - "RM 0\n", - "AGE 0\n", - "DIS 0\n", - "RAD 0\n", - "TAX 0\n", - "PTRATIO 0\n", - "B 0\n", - "LSTAT 0\n", - "MEDV 0\n", - "dtype: int64" - ] - }, - "execution_count": 18, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "# check for missing values in all the columns\n", "boston.isnull().sum()" @@ -3570,30 +2992,7 @@ "collapsed": false, "editable": true }, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/Users/mhjensen/miniforge3/envs/myenv/lib/python3.9/site-packages/seaborn/distributions.py:2619: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n", - " warnings.warn(msg, FutureWarning)\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
              " - ] - }, - "metadata": { - "filenames": { - "image/png": "/Users/mhjensen/Teaching/MachineLearning/doc/LectureNotes/_build/jupyter_execute/week35_201_1.png" - } - }, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "# compute the pair wise correlation for all columns \n", "correlation_matrix = boston.corr().round(2)\n", @@ -3673,22 +3047,7 @@ "collapsed": false, "editable": true }, - "outputs": [ - { - "data": { - "image/png": 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- "text/plain": [ - "
              " - ] - }, - "metadata": { - "filenames": { - "image/png": "/Users/mhjensen/Teaching/MachineLearning/doc/LectureNotes/_build/jupyter_execute/week35_203_0.png" - } - }, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "plt.figure(figsize=(20, 5))\n", "\n", @@ -3747,18 +3106,7 @@ "collapsed": false, "editable": true }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "(404, 2)\n", - "(102, 2)\n", - "(404,)\n", - "(102,)\n" - ] - } - ], + "outputs": [], "source": [ "from sklearn.model_selection import train_test_split\n", "\n", @@ -3789,24 +3137,7 @@ "collapsed": false, "editable": true }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "The model performance for training set\n", - "--------------------------------------\n", - "RMSE is 5.637129335071195\n", - "R2 score is 0.6300745149331701\n", - "\n", - "\n", - "The model performance for testing set\n", - "--------------------------------------\n", - "RMSE is 5.137400784702911\n", - "R2 score is 0.6628996975186953\n" - ] - } - ], + "outputs": [], "source": [ "from sklearn.linear_model import LinearRegression\n", "from sklearn.metrics import mean_squared_error, r2_score\n", @@ -3849,22 +3180,7 @@ "collapsed": false, "editable": true }, - "outputs": [ - { - "data": { - "image/png": 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              " - ] - }, - "metadata": { - "filenames": { - "image/png": "/Users/mhjensen/Teaching/MachineLearning/doc/LectureNotes/_build/jupyter_execute/week35_210_0.png" - } - }, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "# plotting the y_test vs y_pred\n", "# ideally should have been a straight line\n", @@ -4531,29 +3847,7 @@ "collapsed": false, "editable": true }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[[ 1. -1.]\n", - " [ 1. -1.]]\n", - "test U\n", - "[[0. 0.]\n", - " [0. 0.]]\n", - "test VT\n", - "[[0. 0.]\n", - " [0. 0.]]\n", - "[[-0.70710678 -0.70710678]\n", - " [-0.70710678 0.70710678]]\n", - "[2.00000000e+00 3.35470445e-17]\n", - "[[-0.70710678 0.70710678]\n", - " [ 0.70710678 0.70710678]]\n", - "[[-3.33066907e-16 4.44089210e-16]\n", - " [ 0.00000000e+00 2.22044605e-16]]\n" - ] - } - ], + "outputs": [], "source": [ "import numpy as np\n", "# SVD inversion\n", @@ -5585,18 +4879,7 @@ "collapsed": false, "editable": true }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "0.10790125813226321\n", - "4.340071371496255\n", - "[[ 1.04193203 3.08165104]\n", - " [ 3.08165104 10.18383522]]\n" - ] - } - ], + "outputs": [], "source": [ "# Importing various packages\n", "import numpy as np\n", @@ -5634,18 +4917,7 @@ "collapsed": false, "editable": true }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "0.08881497884574564\n", - "1.7086067479626619\n", - "[[1. 0.66080313]\n", - " [0.66080313 1. ]]\n" - ] - } - ], + "outputs": [], "source": [ "import numpy as np\n", "n = 100\n", @@ -5704,38 +4976,7 @@ "collapsed": false, "editable": true }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[[-0.40620066 -2.01265755]\n", - " [ 0.01458611 0.37737221]\n", - " [-1.0895387 -3.65442354]\n", - " [ 0.2338675 1.12044974]\n", - " [ 0.4676059 1.54393936]\n", - " [-0.65891389 -3.16304863]\n", - " [-0.1715252 0.39197698]\n", - " [ 0.71142161 2.95511792]\n", - " [ 0.39214397 0.13069442]\n", - " [ 0.50655336 2.3105791 ]]\n", - " 0 1\n", - "0 -0.406201 -2.012658\n", - "1 0.014586 0.377372\n", - "2 -1.089539 -3.654424\n", - "3 0.233868 1.120450\n", - "4 0.467606 1.543939\n", - "5 -0.658914 -3.163049\n", - "6 -0.171525 0.391977\n", - "7 0.711422 2.955118\n", - "8 0.392144 0.130694\n", - "9 0.506553 2.310579\n", - " 0 1\n", - "0 1.000000 0.952387\n", - "1 0.952387 1.000000\n" - ] - } - ], + "outputs": [], "source": [ "import numpy as np\n", "import pandas as pd\n", @@ -5781,47 +5022,7 @@ "collapsed": false, "editable": true }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - " 0 1 2 3 4 5 6 7 \\\n", - "0 0.0 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 \n", - "1 0.0 0.078974 0.081276 0.075889 0.077168 0.078540 0.065410 0.066474 \n", - "2 0.0 0.081276 0.084076 0.078336 0.079946 0.081655 0.067707 0.069028 \n", - "3 0.0 0.075889 0.078336 0.077460 0.078986 0.080616 0.069452 0.070737 \n", - "4 0.0 0.077168 0.079946 0.078986 0.080764 0.082653 0.070986 0.072476 \n", - "5 0.0 0.078540 0.081655 0.080616 0.082653 0.084809 0.072621 0.074323 \n", - "6 0.0 0.065410 0.067707 0.069452 0.070986 0.072621 0.064074 0.065378 \n", - "7 0.0 0.066474 0.069028 0.070737 0.072476 0.074323 0.065378 0.066854 \n", - "8 0.0 0.067637 0.070457 0.072132 0.074084 0.076150 0.066787 0.068441 \n", - "9 0.0 0.068906 0.072000 0.073644 0.075816 0.078110 0.068307 0.070146 \n", - "10 0.0 0.055734 0.057835 0.060872 0.062337 0.063894 0.057393 0.058645 \n", - "11 0.0 0.056683 0.058996 0.062016 0.063653 0.065390 0.058552 0.059951 \n", - "12 0.0 0.057722 0.060254 0.063260 0.065077 0.066999 0.059807 0.061359 \n", - "13 0.0 0.058854 0.061614 0.064609 0.066612 0.068727 0.061163 0.062874 \n", - "14 0.0 0.060083 0.063080 0.066066 0.068264 0.070582 0.062624 0.064501 \n", - "\n", - " 8 9 10 11 12 13 14 \n", - "0 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 \n", - "1 0.067637 0.068906 0.055734 0.056683 0.057722 0.058854 0.060083 \n", - "2 0.070457 0.072000 0.057835 0.058996 0.060254 0.061614 0.063080 \n", - "3 0.072132 0.073644 0.060872 0.062016 0.063260 0.064609 0.066066 \n", - "4 0.074084 0.075816 0.062337 0.063653 0.065077 0.066612 0.068264 \n", - "5 0.076150 0.078110 0.063894 0.065390 0.066999 0.068727 0.070582 \n", - "6 0.066787 0.068307 0.057393 0.058552 0.059807 0.061163 0.062624 \n", - "7 0.068441 0.070146 0.058645 0.059951 0.061359 0.062874 0.064501 \n", - "8 0.070213 0.072111 0.059993 0.061452 0.063019 0.064699 0.066500 \n", - "9 0.072111 0.074210 0.061443 0.063061 0.064793 0.066647 0.068629 \n", - "10 0.059993 0.061443 0.052305 0.053417 0.054617 0.055910 0.057300 \n", - "11 0.061452 0.063061 0.053417 0.054655 0.055987 0.057418 0.058952 \n", - "12 0.063019 0.064793 0.054617 0.055987 0.057457 0.059031 0.060716 \n", - "13 0.064699 0.066647 0.055910 0.057418 0.059031 0.060756 0.062599 \n", - "14 0.066500 0.068629 0.057300 0.058952 0.060716 0.062599 0.064606 \n" - ] - } - ], + "outputs": [], "source": [ "# Common imports\n", "import numpy as np\n", @@ -6905,7 +6106,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.9.10" + "version": "3.9.18" } }, "nbformat": 4, diff --git a/doc/LectureNotes/_build/jupyter_execute/week36.ipynb b/doc/LectureNotes/_build/jupyter_execute/week36.ipynb index d15ac0234..f0646a41d 100644 --- a/doc/LectureNotes/_build/jupyter_execute/week36.ipynb +++ b/doc/LectureNotes/_build/jupyter_execute/week36.ipynb @@ -397,8 +397,8 @@ "traceback": [ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[0;31mAttributeError\u001b[0m Traceback (most recent call last)", - "Input \u001b[0;32mIn [1]\u001b[0m, in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mnumpy\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m \u001b[38;5;21;01mnp\u001b[39;00m\n\u001b[1;32m 2\u001b[0m X \u001b[38;5;241m=\u001b[39m np\u001b[38;5;241m.\u001b[39marray( [ [\u001b[38;5;241m1\u001b[39m,\u001b[38;5;241m2\u001b[39m,\u001b[38;5;241m3\u001b[39m],[\u001b[38;5;241m2\u001b[39m,\u001b[38;5;241m4\u001b[39m,\u001b[38;5;241m5\u001b[39m],[\u001b[38;5;241m3\u001b[39m,\u001b[38;5;241m5\u001b[39m,\u001b[38;5;241m6\u001b[39m]])\n\u001b[0;32m----> 3\u001b[0m Xinv \u001b[38;5;241m=\u001b[39m \u001b[43mnp\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mlinlag\u001b[49m\u001b[38;5;241m.\u001b[39mpinv(X)\n", - "File \u001b[0;32m~/miniforge3/envs/myenv/lib/python3.9/site-packages/numpy/__init__.py:313\u001b[0m, in \u001b[0;36m__getattr__\u001b[0;34m(attr)\u001b[0m\n\u001b[1;32m 310\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mtesting\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m Tester\n\u001b[1;32m 311\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m Tester\n\u001b[0;32m--> 313\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mAttributeError\u001b[39;00m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mmodule \u001b[39m\u001b[38;5;132;01m{!r}\u001b[39;00m\u001b[38;5;124m has no attribute \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m 314\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;132;01m{!r}\u001b[39;00m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;241m.\u001b[39mformat(\u001b[38;5;18m__name__\u001b[39m, attr))\n", + "Cell \u001b[0;32mIn[1], line 3\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mnumpy\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m \u001b[38;5;21;01mnp\u001b[39;00m\n\u001b[1;32m 2\u001b[0m X \u001b[38;5;241m=\u001b[39m np\u001b[38;5;241m.\u001b[39marray( [ [\u001b[38;5;241m1\u001b[39m,\u001b[38;5;241m2\u001b[39m,\u001b[38;5;241m3\u001b[39m],[\u001b[38;5;241m2\u001b[39m,\u001b[38;5;241m4\u001b[39m,\u001b[38;5;241m5\u001b[39m],[\u001b[38;5;241m3\u001b[39m,\u001b[38;5;241m5\u001b[39m,\u001b[38;5;241m6\u001b[39m]])\n\u001b[0;32m----> 3\u001b[0m Xinv \u001b[38;5;241m=\u001b[39m \u001b[43mnp\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mlinlag\u001b[49m\u001b[38;5;241m.\u001b[39mpinv(X)\n", + "File \u001b[0;32m~/miniforge3/lib/python3.9/site-packages/numpy/__init__.py:313\u001b[0m, in \u001b[0;36m__getattr__\u001b[0;34m(attr)\u001b[0m\n\u001b[1;32m 310\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mtesting\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m Tester\n\u001b[1;32m 311\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m Tester\n\u001b[0;32m--> 313\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mAttributeError\u001b[39;00m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mmodule \u001b[39m\u001b[38;5;132;01m{!r}\u001b[39;00m\u001b[38;5;124m has no attribute \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m 314\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;132;01m{!r}\u001b[39;00m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;241m.\u001b[39mformat(\u001b[38;5;18m__name__\u001b[39m, attr))\n", "\u001b[0;31mAttributeError\u001b[0m: module 'numpy' has no attribute 'linlag'" ] } @@ -4260,7 +4260,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.9.10" + "version": "3.9.18" } }, "nbformat": 4, diff --git a/doc/LectureNotes/_build/jupyter_execute/week37.ipynb b/doc/LectureNotes/_build/jupyter_execute/week37.ipynb index 5586b0735..2e79f07db 100644 --- a/doc/LectureNotes/_build/jupyter_execute/week37.ipynb +++ b/doc/LectureNotes/_build/jupyter_execute/week37.ipynb @@ -2018,12 +2018,19 @@ }, "outputs": [ { - "name": "stdout", - "output_type": "stream", - "text": [ - "Bootstrap Statistics :\n", - "original bias std. error\n", - " 99.971 15.2789 99.9715 0.152907\n" + "ename": "ModuleNotFoundError", + "evalue": "No module named 'matplotlib'", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mModuleNotFoundError\u001b[0m Traceback (most recent call last)", + "Cell \u001b[0;32mIn[1], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \u001b[43mget_ipython\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mrun_line_magic\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mmatplotlib\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43minline\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m)\u001b[49m\n\u001b[1;32m 3\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mnumpy\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m \u001b[38;5;21;01mnp\u001b[39;00m\n\u001b[1;32m 4\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mtime\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m time\n", + "File \u001b[0;32m~/miniforge3/lib/python3.9/site-packages/IPython/core/interactiveshell.py:2432\u001b[0m, in \u001b[0;36mInteractiveShell.run_line_magic\u001b[0;34m(self, magic_name, line, _stack_depth)\u001b[0m\n\u001b[1;32m 2430\u001b[0m kwargs[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mlocal_ns\u001b[39m\u001b[38;5;124m'\u001b[39m] \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mget_local_scope(stack_depth)\n\u001b[1;32m 2431\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mbuiltin_trap:\n\u001b[0;32m-> 2432\u001b[0m result \u001b[38;5;241m=\u001b[39m \u001b[43mfn\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 2434\u001b[0m \u001b[38;5;66;03m# The code below prevents the output from being displayed\u001b[39;00m\n\u001b[1;32m 2435\u001b[0m \u001b[38;5;66;03m# when using magics with decorator @output_can_be_silenced\u001b[39;00m\n\u001b[1;32m 2436\u001b[0m \u001b[38;5;66;03m# when the last Python token in the expression is a ';'.\u001b[39;00m\n\u001b[1;32m 2437\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mgetattr\u001b[39m(fn, magic\u001b[38;5;241m.\u001b[39mMAGIC_OUTPUT_CAN_BE_SILENCED, \u001b[38;5;28;01mFalse\u001b[39;00m):\n", + "File \u001b[0;32m~/miniforge3/lib/python3.9/site-packages/IPython/core/magics/pylab.py:99\u001b[0m, in \u001b[0;36mPylabMagics.matplotlib\u001b[0;34m(self, line)\u001b[0m\n\u001b[1;32m 97\u001b[0m \u001b[38;5;28mprint\u001b[39m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mAvailable matplotlib backends: \u001b[39m\u001b[38;5;132;01m%s\u001b[39;00m\u001b[38;5;124m\"\u001b[39m \u001b[38;5;241m%\u001b[39m backends_list)\n\u001b[1;32m 98\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m---> 99\u001b[0m gui, backend \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mshell\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43menable_matplotlib\u001b[49m\u001b[43m(\u001b[49m\u001b[43margs\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mgui\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mlower\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mif\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[38;5;28;43misinstance\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43margs\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mgui\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mstr\u001b[39;49m\u001b[43m)\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01melse\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43margs\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mgui\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 100\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_show_matplotlib_backend(args\u001b[38;5;241m.\u001b[39mgui, backend)\n", + "File \u001b[0;32m~/miniforge3/lib/python3.9/site-packages/IPython/core/interactiveshell.py:3606\u001b[0m, in \u001b[0;36mInteractiveShell.enable_matplotlib\u001b[0;34m(self, gui)\u001b[0m\n\u001b[1;32m 3585\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21menable_matplotlib\u001b[39m(\u001b[38;5;28mself\u001b[39m, gui\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mNone\u001b[39;00m):\n\u001b[1;32m 3586\u001b[0m \u001b[38;5;250m \u001b[39m\u001b[38;5;124;03m\"\"\"Enable interactive matplotlib and inline figure support.\u001b[39;00m\n\u001b[1;32m 3587\u001b[0m \n\u001b[1;32m 3588\u001b[0m \u001b[38;5;124;03m This takes the following steps:\u001b[39;00m\n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 3604\u001b[0m \u001b[38;5;124;03m display figures inline.\u001b[39;00m\n\u001b[1;32m 3605\u001b[0m \u001b[38;5;124;03m \"\"\"\u001b[39;00m\n\u001b[0;32m-> 3606\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mmatplotlib_inline\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mbackend_inline\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m configure_inline_support\n\u001b[1;32m 3608\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mIPython\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mcore\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m pylabtools \u001b[38;5;28;01mas\u001b[39;00m pt\n\u001b[1;32m 3609\u001b[0m gui, backend \u001b[38;5;241m=\u001b[39m pt\u001b[38;5;241m.\u001b[39mfind_gui_and_backend(gui, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mpylab_gui_select)\n", + "File \u001b[0;32m~/miniforge3/lib/python3.9/site-packages/matplotlib_inline/__init__.py:1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01m.\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m backend_inline, config \u001b[38;5;66;03m# noqa\u001b[39;00m\n\u001b[1;32m 2\u001b[0m __version__ \u001b[38;5;241m=\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m0.1.6\u001b[39m\u001b[38;5;124m\"\u001b[39m \u001b[38;5;66;03m# noqa\u001b[39;00m\n", + "File \u001b[0;32m~/miniforge3/lib/python3.9/site-packages/matplotlib_inline/backend_inline.py:6\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[38;5;124;03m\"\"\"A matplotlib backend for publishing figures via display_data\"\"\"\u001b[39;00m\n\u001b[1;32m 3\u001b[0m \u001b[38;5;66;03m# Copyright (c) IPython Development Team.\u001b[39;00m\n\u001b[1;32m 4\u001b[0m \u001b[38;5;66;03m# Distributed under the terms of the BSD 3-Clause License.\u001b[39;00m\n\u001b[0;32m----> 6\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mmatplotlib\u001b[39;00m\n\u001b[1;32m 7\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mmatplotlib\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m colors\n\u001b[1;32m 8\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mmatplotlib\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mbackends\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m backend_agg\n", + "\u001b[0;31mModuleNotFoundError\u001b[0m: No module named 'matplotlib'" ] } ], @@ -2086,22 +2093,7 @@ "collapsed": false, "editable": true }, - "outputs": [ - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
              " - ] - }, - "metadata": { - "filenames": { - "image/png": "/Users/mhjensen/Teaching/MachineLearning/doc/LectureNotes/_build/jupyter_execute/week37_144_0.png" - } - }, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "# the histogram of the bootstrapped data (normalized data if density = True)\n", "n, binsboot, patches = plt.hist(t, 50, density=True, facecolor='red', alpha=0.75)\n", @@ -2312,32 +2304,7 @@ "collapsed": false, "editable": true }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Error: 0.013121574062587286\n", - "Bias^2: 0.012073649469946107\n", - "Var: 0.0010479245926411787\n", - "0.013121574062587286 >= 0.012073649469946107 + 0.0010479245926411787 = 0.013121574062587286\n" - ] - }, - { - "data": { - "image/png": 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\n", 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              " - ] - }, - "metadata": { - "filenames": { - "image/png": "/Users/mhjensen/Teaching/MachineLearning/doc/LectureNotes/_build/jupyter_execute/week37_160_1.png" - } - }, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "import matplotlib.pyplot as plt\n", "import numpy as np\n", @@ -2413,110 +2380,7 @@ "collapsed": false, "editable": true }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Polynomial degree: 0\n", - "Error: 0.32149601703519115\n", - "Bias^2: 0.3123314713548606\n", - "Var: 0.009164545680330616\n", - "0.32149601703519115 >= 0.3123314713548606 + 0.009164545680330616 = 0.3214960170351912\n", - "Polynomial degree: 1\n", - "Error: 0.08426840630693412\n", - "Bias^2: 0.0796891867672603\n", - "Var: 0.004579219539673834\n", - "0.08426840630693412 >= 0.0796891867672603 + 0.004579219539673834 = 0.08426840630693413\n", - "Polynomial degree: 2\n", - "Error: 0.10398646080125037\n", - "Bias^2: 0.10077114273548984\n", - "Var: 0.0032153180657605116\n", - "0.10398646080125037 >= 0.10077114273548984 + 0.0032153180657605116 = 0.10398646080125036\n", - "Polynomial degree: 3\n", - "Error: 0.06547790180152352\n", - "Bias^2: 0.062082386342319454\n", - "Var: 0.0033955154592040923\n", - "0.06547790180152352 >= 0.062082386342319454 + 0.0033955154592040923 = 0.06547790180152355\n", - "Polynomial degree: 4\n", - "Error: 0.06844519414009445\n", - "Bias^2: 0.06453579006728322\n", - "Var: 0.003909404072811221\n", - "0.06844519414009445 >= 0.06453579006728322 + 0.003909404072811221 = 0.06844519414009444\n", - "Polynomial degree: 5\n", - "Error: 0.05227921801205679\n", - "Bias^2: 0.04818727730430286\n", - "Var: 0.004091940707753925\n", - "0.05227921801205679 >= 0.04818727730430286 + 0.004091940707753925 = 0.05227921801205679\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Polynomial degree: 6\n", - "Error: 0.03781367141738902\n", - "Bias^2: 0.03365768507152769\n", - "Var: 0.0041559863458613296\n", - "0.03781367141738902 >= 0.03365768507152769 + 0.0041559863458613296 = 0.03781367141738902\n", - "Polynomial degree: 7\n", - "Error: 0.027609773491022394\n", - "Bias^2: 0.022999498260366198\n", - "Var: 0.004610275230656182\n", - "0.027609773491022394 >= 0.022999498260366198 + 0.004610275230656182 = 0.02760977349102238\n", - "Polynomial degree: 8\n", - "Error: 0.017355848195593312\n", - "Bias^2: 0.010331721306655165\n", - "Var: 0.007024126888938144\n", - "0.017355848195593312 >= 0.010331721306655165 + 0.007024126888938144 = 0.01735584819559331\n", - "Polynomial degree: 9\n", - "Error: 0.026605727637184558\n", - "Bias^2: 0.010018312644139219\n", - "Var: 0.016587414993045335\n", - "0.026605727637184558 >= 0.010018312644139219 + 0.016587414993045335 = 0.026605727637184554\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Polynomial degree: 10\n", - "Error: 0.021592704588021178\n", - "Bias^2: 0.010516485576646504\n", - "Var: 0.01107621901137467\n", - "0.021592704588021178 >= 0.010516485576646504 + 0.01107621901137467 = 0.021592704588021174\n", - "Polynomial degree: 11\n", - "Error: 0.07160048164232538\n", - "Bias^2: 0.014436800088896381\n", - "Var: 0.05716368155342902\n", - "0.07160048164232538 >= 0.014436800088896381 + 0.05716368155342902 = 0.0716004816423254\n", - "Polynomial degree: 12\n", - "Error: 0.11547777218876518\n", - "Bias^2: 0.016285782696017142\n", - "Var: 0.09919198949274803\n", - "0.11547777218876518 >= 0.016285782696017142 + 0.09919198949274803 = 0.11547777218876518\n", - "Polynomial degree: 13\n", - "Error: 0.2284246870217162\n", - "Bias^2: 0.01975416527168255\n", - "Var: 0.20867052175003364\n", - "0.2284246870217162 >= 0.01975416527168255 + 0.20867052175003364 = 0.2284246870217162\n" - ] - }, - { - "data": { - "image/png": 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              " - ] - }, - "metadata": { - "filenames": { - "image/png": "/Users/mhjensen/Teaching/MachineLearning/doc/LectureNotes/_build/jupyter_execute/week37_162_3.png" - } - }, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "import matplotlib.pyplot as plt\n", "import numpy as np\n", @@ -2622,49 +2486,7 @@ "collapsed": false, "editable": true }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "============================\n", - "Underfitting vs. Overfitting\n", - "============================\n", - "\n", - "This example demonstrates the problems of underfitting and overfitting and\n", - "how we can use linear regression with polynomial features to approximate\n", - "nonlinear functions. The plot shows the function that we want to approximate,\n", - "which is a part of the cosine function. In addition, the samples from the\n", - "real function and the approximations of different models are displayed. The\n", - "models have polynomial features of different degrees. We can see that a\n", - "linear function (polynomial with degree 1) is not sufficient to fit the\n", - "training samples. This is called **underfitting**. A polynomial of degree 4\n", - "approximates the true function almost perfectly. However, for higher degrees\n", - "the model will **overfit** the training data, i.e. it learns the noise of the\n", - "training data.\n", - "We evaluate quantitatively **overfitting** / **underfitting** by using\n", - "cross-validation. We calculate the mean squared error (MSE) on the validation\n", - "set, the higher, the less likely the model generalizes correctly from the\n", - "training data.\n", - "\n" - ] - }, - { - "data": { - "image/png": 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\n", 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              " - ] - }, - "metadata": { - "filenames": { - "image/png": "/Users/mhjensen/Teaching/MachineLearning/doc/LectureNotes/_build/jupyter_execute/week37_169_0.png" - } - }, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "import numpy as np\n", "import matplotlib.pyplot as plt\n", @@ -2938,149 +2745,7 @@ "collapsed": false, "editable": true }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Degree of polynomial: 1\n", - "Mean squared error on training data: 446033.51374050\n", - "Mean squared error on test data: 455173.80460179\n", - "Degree of polynomial: 2\n", - "Mean squared error on training data: 114550.54637219\n", - "Mean squared error on test data: 129963.83146596\n", - "Degree of polynomial: 3\n", - "Mean squared error on training data: 9054.61775176\n", - "Mean squared error on test data: 10572.87627342\n", - "Degree of polynomial: 4\n", - "Mean squared error on training data: 302.15313054\n", - "Mean squared error on test data: 433.26292364\n", - "Degree of polynomial: 5\n", - "Mean squared error on training data: 3.64316192\n", - "Mean squared error on test data: 7.23528337\n", - "Degree of polynomial: 6\n", - "Mean squared error on training data: 3.56589683\n", - "Mean squared error on test data: 10.50427787\n", - "Degree of polynomial: 7\n", - "Mean squared error on training data: 0.47313680\n", - "Mean squared error on test data: 1.53738247\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Degree of polynomial: 8\n", - "Mean squared error on training data: 0.04926746\n", - "Mean squared error on test data: 0.14629156\n", - "Degree of polynomial: 9\n", - "Mean squared error on training data: 0.02546675\n", - "Mean squared error on test data: 0.11202337\n", - "Degree of polynomial: 10\n", - "Mean squared error on training data: 0.02424794\n", - "Mean squared error on test data: 0.22467274\n", - "Degree of polynomial: 11\n", - "Mean squared error on training data: 0.01594452\n", - "Mean squared error on test data: 1.07641937\n", - "Degree of polynomial: 12\n", - "Mean squared error on training data: 0.00805074\n", - "Mean squared error on test data: 0.04295757\n", - "Degree of polynomial: 13\n", - "Mean squared error on training data: 0.00781918\n", - "Mean squared error on test data: 0.56965674\n", - "Degree of polynomial: 14\n", - "Mean squared error on training data: 0.00465099\n", - "Mean squared error on test data: 0.28443039\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Degree of polynomial: 15\n", - "Mean squared error on training data: 0.00420072\n", - "Mean squared error on test data: 568.47202442\n", - "Degree of polynomial: 16\n", - "Mean squared error on training data: 0.00325450\n", - "Mean squared error on test data: 48.97690235\n", - "Degree of polynomial: 17\n", - "Mean squared error on training data: 0.00242954\n", - "Mean squared error on test data: 2.52775466\n", - "Degree of polynomial: 18\n", - "Mean squared error on training data: 0.00219194\n", - "Mean squared error on test data: 429.23643365\n", - "Degree of polynomial: 19\n", - "Mean squared error on training data: 0.00154860\n", - "Mean squared error on test data: 238.16356503\n", - "Degree of polynomial: 20\n", - "Mean squared error on training data: 0.00140849\n", - "Mean squared error on test data: 1345.68592431\n", - "Degree of polynomial: 21\n", - "Mean squared error on training data: 0.00119699\n", - "Mean squared error on test data: 1836.21110005\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Degree of polynomial: 22\n", - "Mean squared error on training data: 0.00092904\n", - "Mean squared error on test data: 1182.64316482\n", - "Degree of polynomial: 23\n", - "Mean squared error on training data: 0.00089187\n", - "Mean squared error on test data: 3886.35846425\n", - "Degree of polynomial: 24\n", - "Mean squared error on training data: 0.00083346\n", - "Mean squared error on test data: 1346.92651068\n", - "Degree of polynomial: 25\n", - "Mean squared error on training data: 0.00079910\n", - "Mean squared error on test data: 7697.35412147\n", - "Degree of polynomial: 26\n", - "Mean squared error on training data: 0.00075597\n", - "Mean squared error on test data: 1078.81597834\n", - "Degree of polynomial: 27\n", - "Mean squared error on training data: 0.00068088\n", - "Mean squared error on test data: 3189.20355156\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Degree of polynomial: 28\n", - "Mean squared error on training data: 0.00063364\n", - "Mean squared error on test data: 692.24085321\n", - "Degree of polynomial: 29\n", - "Mean squared error on training data: 0.00063862\n", - "Mean squared error on test data: 3073.63180447\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_10727/626635268.py:73: RuntimeWarning: divide by zero encountered in log10\n", - " plt.plot(polynomial, np.log10(trainingerror), label='Training Error')\n", - "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_10727/626635268.py:74: RuntimeWarning: divide by zero encountered in log10\n", - " plt.plot(polynomial, np.log10(testerror), label='Test Error')\n" - ] - }, - { - "data": { - "image/png": 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\n", 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              " - ] - }, - "metadata": { - "filenames": { - "image/png": "/Users/mhjensen/Teaching/MachineLearning/doc/LectureNotes/_build/jupyter_execute/week37_171_6.png" - } - }, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "# Common imports\n", "import os\n", @@ -3192,30 +2857,7 @@ "collapsed": false, "editable": true }, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_10727/3817475779.py:63: RuntimeWarning: divide by zero encountered in log10\n", - " plt.plot(polynomial, np.log10(estimated_mse_sklearn), label='Test Error')\n" - ] - }, - { - "data": { - "image/png": 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\n", 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              " - ] - }, - "metadata": { - "filenames": { - "image/png": "/Users/mhjensen/Teaching/MachineLearning/doc/LectureNotes/_build/jupyter_execute/week37_174_1.png" - } - }, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "# Common imports\n", "import os\n", @@ -3320,36 +2962,7 @@ "collapsed": false, "editable": true }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[1.79934087 0.47179152 5.01549939]\n", - "[1.79909592 0.47176716 5.01550546]\n", - " \n", - "test MSE of OLS:\n", - "1.13943111290393\n", - " \n", - "test MSE of Ridge\n", - "1.1395235273363686\n" - ] - }, - { - "data": { - "image/png": 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\u001b[43mRegRidge\u001b[49m\u001b[38;5;241m.\u001b[39mfit(X_train_scaled,y_train_scaled)\n\u001b[1;32m 35\u001b[0m ypredictRidge \u001b[38;5;241m=\u001b[39m RegRidge\u001b[38;5;241m.\u001b[39mpredict(X_test_scaled)\n\u001b[1;32m 36\u001b[0m betaOLS \u001b[38;5;241m=\u001b[39m OLS\u001b[38;5;241m.\u001b[39mcoef_\n", - "\u001b[0;31mNameError\u001b[0m: name 'RegRidge' is not defined" - ] - } - ], + "outputs": [], "source": [ "from sklearn import linear_model\n", "np.random.seed(2018)\n", @@ -3990,7 +3560,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.9.10" + "version": "3.9.18" } }, "nbformat": 4, diff --git a/doc/LectureNotes/_build/jupyter_execute/week38.ipynb b/doc/LectureNotes/_build/jupyter_execute/week38.ipynb index 502d4850c..edb418296 100644 --- a/doc/LectureNotes/_build/jupyter_execute/week38.ipynb +++ b/doc/LectureNotes/_build/jupyter_execute/week38.ipynb @@ -197,18 +197,20 @@ }, "outputs": [ { - "data": { - "image/png": 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              " - ] - }, - "metadata": { - "filenames": { - "image/png": "/Users/mhjensen/Teaching/MachineLearning/doc/LectureNotes/_build/jupyter_execute/week38_10_0.png" - } - }, - "output_type": "display_data" + "ename": "ModuleNotFoundError", + "evalue": "No module named 'matplotlib'", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mModuleNotFoundError\u001b[0m Traceback (most recent call last)", + "Cell \u001b[0;32mIn[1], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \u001b[43mget_ipython\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mrun_line_magic\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mmatplotlib\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43minline\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m)\u001b[49m\n\u001b[1;32m 3\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mnumpy\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m \u001b[38;5;21;01mnp\u001b[39;00m\n\u001b[1;32m 4\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mmatplotlib\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mpyplot\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m \u001b[38;5;21;01mplt\u001b[39;00m\n", + "File \u001b[0;32m~/miniforge3/lib/python3.9/site-packages/IPython/core/interactiveshell.py:2432\u001b[0m, in \u001b[0;36mInteractiveShell.run_line_magic\u001b[0;34m(self, magic_name, line, _stack_depth)\u001b[0m\n\u001b[1;32m 2430\u001b[0m kwargs[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mlocal_ns\u001b[39m\u001b[38;5;124m'\u001b[39m] \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mget_local_scope(stack_depth)\n\u001b[1;32m 2431\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mbuiltin_trap:\n\u001b[0;32m-> 2432\u001b[0m result \u001b[38;5;241m=\u001b[39m \u001b[43mfn\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 2434\u001b[0m \u001b[38;5;66;03m# The code below prevents the output from being displayed\u001b[39;00m\n\u001b[1;32m 2435\u001b[0m \u001b[38;5;66;03m# when using magics with decorator @output_can_be_silenced\u001b[39;00m\n\u001b[1;32m 2436\u001b[0m \u001b[38;5;66;03m# when the last Python token in the expression is a ';'.\u001b[39;00m\n\u001b[1;32m 2437\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mgetattr\u001b[39m(fn, magic\u001b[38;5;241m.\u001b[39mMAGIC_OUTPUT_CAN_BE_SILENCED, \u001b[38;5;28;01mFalse\u001b[39;00m):\n", + "File \u001b[0;32m~/miniforge3/lib/python3.9/site-packages/IPython/core/magics/pylab.py:99\u001b[0m, in \u001b[0;36mPylabMagics.matplotlib\u001b[0;34m(self, line)\u001b[0m\n\u001b[1;32m 97\u001b[0m \u001b[38;5;28mprint\u001b[39m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mAvailable matplotlib backends: \u001b[39m\u001b[38;5;132;01m%s\u001b[39;00m\u001b[38;5;124m\"\u001b[39m \u001b[38;5;241m%\u001b[39m backends_list)\n\u001b[1;32m 98\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m---> 99\u001b[0m gui, backend \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mshell\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43menable_matplotlib\u001b[49m\u001b[43m(\u001b[49m\u001b[43margs\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mgui\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mlower\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mif\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[38;5;28;43misinstance\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43margs\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mgui\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mstr\u001b[39;49m\u001b[43m)\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01melse\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43margs\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mgui\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 100\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_show_matplotlib_backend(args\u001b[38;5;241m.\u001b[39mgui, backend)\n", + "File \u001b[0;32m~/miniforge3/lib/python3.9/site-packages/IPython/core/interactiveshell.py:3606\u001b[0m, in \u001b[0;36mInteractiveShell.enable_matplotlib\u001b[0;34m(self, gui)\u001b[0m\n\u001b[1;32m 3585\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21menable_matplotlib\u001b[39m(\u001b[38;5;28mself\u001b[39m, gui\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mNone\u001b[39;00m):\n\u001b[1;32m 3586\u001b[0m \u001b[38;5;250m \u001b[39m\u001b[38;5;124;03m\"\"\"Enable interactive matplotlib and inline figure support.\u001b[39;00m\n\u001b[1;32m 3587\u001b[0m \n\u001b[1;32m 3588\u001b[0m \u001b[38;5;124;03m This takes the following steps:\u001b[39;00m\n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 3604\u001b[0m \u001b[38;5;124;03m display figures inline.\u001b[39;00m\n\u001b[1;32m 3605\u001b[0m \u001b[38;5;124;03m \"\"\"\u001b[39;00m\n\u001b[0;32m-> 3606\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mmatplotlib_inline\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mbackend_inline\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m configure_inline_support\n\u001b[1;32m 3608\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mIPython\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mcore\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m pylabtools \u001b[38;5;28;01mas\u001b[39;00m pt\n\u001b[1;32m 3609\u001b[0m gui, backend \u001b[38;5;241m=\u001b[39m pt\u001b[38;5;241m.\u001b[39mfind_gui_and_backend(gui, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mpylab_gui_select)\n", + "File \u001b[0;32m~/miniforge3/lib/python3.9/site-packages/matplotlib_inline/__init__.py:1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01m.\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m backend_inline, config \u001b[38;5;66;03m# noqa\u001b[39;00m\n\u001b[1;32m 2\u001b[0m __version__ \u001b[38;5;241m=\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m0.1.6\u001b[39m\u001b[38;5;124m\"\u001b[39m \u001b[38;5;66;03m# noqa\u001b[39;00m\n", + "File \u001b[0;32m~/miniforge3/lib/python3.9/site-packages/matplotlib_inline/backend_inline.py:6\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[38;5;124;03m\"\"\"A matplotlib backend for publishing figures via display_data\"\"\"\u001b[39;00m\n\u001b[1;32m 3\u001b[0m \u001b[38;5;66;03m# Copyright (c) IPython Development Team.\u001b[39;00m\n\u001b[1;32m 4\u001b[0m \u001b[38;5;66;03m# Distributed under the terms of the BSD 3-Clause License.\u001b[39;00m\n\u001b[0;32m----> 6\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mmatplotlib\u001b[39;00m\n\u001b[1;32m 7\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mmatplotlib\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m colors\n\u001b[1;32m 8\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mmatplotlib\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mbackends\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m backend_agg\n", + "\u001b[0;31mModuleNotFoundError\u001b[0m: No module named 'matplotlib'" + ] } ], "source": [ @@ -518,152 +520,7 @@ "collapsed": false, "editable": true }, - "outputs": [ - { - "data": { - "text/html": [ - "
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\n", 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\n", 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KS0v13HPPaebMmR2+fl1do5EztOnpXVVf36xwOBLvchBjjLdZGG+zMN5mMXm8MzJSOtSvUwba0tJSVVVVacmSJafs6/P51K9fP+3evfu03iMSsRWJ2GdaoquFwxG1tpr1hTAZ420WxtssjLdZGO/2dbrFGC+99JIqKipUVFSkpKQk1dTURO1yMH/+/OPO+fTTT5WVlXUuywQAAEAnEddAO3fuXM2ZM8c5fu2117Ro0SLNmDFDH374oaqqqvTmm2/q7bffdvps3LhRr776qnP8/PPP68CBA8ctRQAAAIAZYrrkIBgMaurUqaqvr5ckzZo1S1lZWVq8eLEkqaWlRa2trU7/uXPnKhAI6I477oi6zj333OP8fubMmVq5cqVWrFihUCikxMREPfPMM7rwwgtj+VEAAADQSSXYtm3kQtL9+xviXcI55/dbyshIUSDQyBocAzDeZmG8zcJ4m8Xk8e7ZM61D/TrdGloAAADgdBBoAQAA4GoEWgAAALgagRYAAACuRqAFAACAqxFoAQAA4GoEWgAAALgagRYAAACuRqAFAACAqxFoAQAA4GoEWgAAALgagRYAAACuRqAFAACAqxFoAQAA4GoEWgAAALgagRYAAACuRqAFAACAqxFoAQAA4GoEWgAAALgagRYAAACuRqAFAACAqxFoAQAA4GoEWgAAALgagRYAAACuRqAFAACAqxFoAQAA4GoEWgAAALgagRYAAACuRqAFAACAqxFoAQAA4GoEWgAAALgagRYAAACuRqAFAACAqxFoAQAA4GoEWgAAALgagRYAAACuRqAFAACAqxFoAQAA4GoEWgAAALgagRYAAACuRqAFAACAq8U80AaDQS1atEiXXXaZ9uzZc8r+W7Zs0YQJEzRp0iRNmDBBW7ZsOa7P+vXrNX78eN1yyy2aNGmSPvzww1iUDgAAABfwx/Lie/bs0ezZszV48GCFw+FT9v/kk080ffp0LV26VCNHjtQf//hHTZ8+XevWrVO/fv0kSdu3b1dBQYHKysqUnZ2t8vJyTZ06VRUVFUpNTY3lxwEAAEAnFNMZ2qamJhUWFmr8+PEd6r969WplZ2dr5MiRkqSrrrpKF1xwgX7xi184fZYvX67c3FxlZ2dLksaNG6dwOKzy8vKzXj8AAAA6v5jO0F588cWSpL1793aof2VlpUaMGBHVlpOTo8rKSud48+bNuuuuu5xjy7I0dOhQVVZWatKkSWehagBwr/rGoJoPt8a7DMSYz28ppAR99tlhhVsj8S4HMWbqeJ+fltThvjENtKerpqZGX//616PaMjMzVVNTI0kKBAJqaGhQZmbmcX2qqqpO670sK0GWlfD5CnYZn8+K+hXexnibxeezVLbxQ614eUe8SwGAs6LX+V1V8m9f61DfThVoDx8+rKSk6DSelJSkw4cPO6+3tbXXp6O6d09RQoJZgbZNenrXeJeAc4jxNsdbf9oX7xIA4Kw52BjscN9OFWiTk5MVDEYXHwwGlZyc7Lze1tZen46qq2s0coY2Pb2r6uubFQ6b8yMLUzHeZvH5LEUitiTpov7ddMPoQXGuCLFkWZa6dk1Sc3NQkQjfb68zdbwHZ6V3uG+nCrQDBgxQbW1tVFttba0GDBggScrIyFBaWtpJ+3RUJGI7f/ibJhyOqNWgNTimY7zNc35qF33hwsxTd4Rr+f2WMjJSFAg08v02AON9ap1qcd3o0aNVXV0d1VZdXa0xY8Y4x6NGjYrqY9u2duzYEdUHAEwUsY/8I92wHz4BQHwD7dy5czVnzhzn+Pbbb9fOnTv11ltvSTrykIWdO3dG7V6Qn5+vTZs2adeuXZKkdevWybIs5eXlndPaAaDTMfOHTgAQ2yUHwWBQU6dOVX19vSRp1qxZysrK0uLFiyVJLS0tam09ur1Mv3799NRTT6mwsFCJiYkKBoNatmyZ81AFSRo+fLgWLlyo2bNnKzk5WZZlqaSkhIcqADDe0RlapmgBmCXBtm0j/02/f39DvEs451iDYxbG2yx+v6VHVm7RRzUHNWpob+X/v6HxLgkxxPfbLCaPd8+eaR3q16nW0AIAzlzb/ESCmKEFYBYCLQB4RNvP21hxAMA0BFoA8AhnhpZAC8AwBFoA8AhnhpYlBwAMQ6AFAI9ghhaAqQi0AOARbVvWEGgBmIZACwAecXSGlkQLwCwEWgDwiKNraAHALARaAPAIZmgBmIpACwAe4Tz3kTwLwDAEWgDwiLZAa5FoARiGQAsAHmGLRbQAzESgBQCPiPDoWwCGItACgFe03RTGFC0AwxBoAcAjmKEFYCoCLQB4BY++BWAoAi0AeMTRGVoSLQCzEGgBwDPa1tACgFkItADgEczQAjAVgRYAvIKbwgAYikALAB4R4aYwAIYi0AKAR7Q9+pZVtABMQ6AFAI+w/55oLfIsAMMQaAHAI+xTdwEATyLQAoBHHJ2hZYoWgFkItADgEc4aWvIsAMMQaAHAI2xnlwMSLQCzEGgBwCOYoAVgKgItAHiEHWEfWgBmItACgEc4M7QkWgCGIdACgEfYPCkMgKEItADgEW27HCSwihaAYQi0AOARzNACMBWBFgA8gl0OAJiKQAsAHuEsOWCKFoBhCLQA4AG285gwMUULwDgEWgDwgGPzrMUMLQDDEGgBwANs2afuBAAeRaAFAA+InqGNXx0AEA8EWgDwgGMDLft2ATANgRYAPODYm8LIswBM44/1G6xfv17FxcVKTk6WZVmaN2+ehgwZcsK+t91223FtgUBAhw4d0uuvvy5JKioq0oYNG5Senu70SU1NVXFxcUzqBwA3iJqgjVsVABAfMQ2027dvV0FBgcrKypSdna3y8nJNnTpVFRUVSk1NPeE5q1evjjpeuHDhcX0eeughjRw5MiY1A4AbRc/QEmkBmCWmSw6WL1+u3NxcZWdnS5LGjRuncDis8vLyE/ZfsGBB1HE4HNaLL76ob37zm7EsEwBcj21oAZgspoF28+bNysnJOfpmlqWhQ4eqsrLyhP0HDBgQdfz73/9effv21UUXXRTLMgHA9aICLTO0AAwTsyUHgUBADQ0NyszMjGrPzMxUVVVVh66xdu1ajR8//rj2srIyPfnkkwqFQho0aJDuvvtuDRw48LTqs6wEWYbtbePzWVG/wtsYb7O0HpNofb4E+f2Mu5fx/TYL431qMQu0hw8fliQlJSVFtSclJTmvncxnn32mzZs369FHH41q79Onj9LS0rRgwQJZlqUlS5Zo/Pjxevnll9W7d+8O19e9e4qxsxjp6V3jXQLOIcbbDA1NQef3qanJyshIiWM1OFf4fpuF8W5fzAJtcnKyJCkYDEa1B4NB57WTeemll3TNNdcoLS0tqv3mm2+OOp4xY4ZKS0v13HPPaebMmR2ur66u0cgZ2vT0rqqvb1Y4HIl3OYgxxtssTS2tR3/f1KJAoDGO1SDW+H6bxeTx7ug/zmMWaDMyMpSWlqba2tqo9tra2uPWyp7ICy+8oNmzZ5+yn8/nU79+/bR79+7Tqi8SsRWJmPmoyHA4otZWs74QJmO8zXDsGNsRmzE3BN9vszDe7YvpYoxRo0apurraObZtWzt27NCYMWNOet7OnTtVV1enUaNGHffa/Pnzj2v79NNPlZWV9fkLBgAvMOuHTwAQ20Cbn5+vTZs2adeuXZKkdevWybIs5eXlSZLmzp2rOXPmHHfe2rVrlZeXd8I1rhs3btSrr77qHD///PM6cODAcUsRAMAkkWNuCrMMvT8AgLli+mCF4cOHa+HChZo9e7bzpLCSkhLnoQotLS1qbW2NOqdt79lnn332hNecOXOmVq5cqRUrVigUCikxMVHPPPOMLrzwwlh+FADo3MxcQQUAkqQE+9jHyxhk//6GeJdwzvn9ljIyUhQINLIGxwCMt1nqm4K6f/EbkqQZecM04tJeca4IscT32ywmj3fPnmmn7qQYLzkAAAAAYo1ACwAeEOFJYQAMRqAFAC84ZvUYeRaAaQi0AOABdtQMbfzqAIB4INACgAfYx2xzkMBGtAAMQ6AFAA9ghhaAyQi0AOABx+6/SKAFYBoCLQB4gB11UxiJFoBZCLQA4AFRSw7iVwYAxAWBFgA8gBlaACYj0AKAB0Q9w5w8C8AwBFoA8IBjlxzwBzsA0/DnHgB4gM2+XQAMRqAFAA+ImqElzwIwDIEWADzAjl5FCwBGIdACgAdErzhgihaAWQi0AOABLKEFYDICLQB4APvQAjAZgRYAPIY4C8A0BFoA8IAIM7QADEagBQAvYA0tAIMRaAHAAyIEWgAGI9ACgCccs+SAVbQADEOgBQAPYNsuACYj0AKABxy7bRcAmIZACwAecGyetZiiBWAYAi0AeEDU/Cx5FoBhCLQA4AE8KQyAyQi0AOABUTeFxa8MAIgLAi0AeIB97LZdJFoAhiHQAoAHRG/bRaIFYBYCLQB4APvQAjAZgRYAPCBqyUEc6wCAeCDQAoAHsOQAgMkItADgAVHbdsWxDgCIBwItAHgMM7QATEOgBQAPiHBTGACDEWgBwAt4UhgAgxFoAcADjr0pDABMQ6AFAA84Ns9aTNACMAyBFgA8wObJCgAM5o/1G6xfv17FxcVKTk6WZVmaN2+ehgwZcsK+a9eu1bJly9SzZ8+o9uXLlys5OfmMrgkAJiDPAjBZTAPt9u3bVVBQoLKyMmVnZ6u8vFxTp05VRUWFUlNTT3hOfn6+xo8ff1avCQBexz60AEwW0yUHy5cvV25urrKzsyVJ48aNUzgcVnl5eae6JgC43bFraNnlAIBpYhpoN2/erJycnKNvZlkaOnSoKisrO9U1AcDtomZoybMADBOzJQeBQEANDQ3KzMyMas/MzFRVVVW7573++usqLy9XKBRSr169NH36dF122WWf65onYlkJsgy7Fdjns6J+hbcx3mY5dlY20e+T38+4exnfb7Mw3qcWs0B7+PBhSVJSUlJUe1JSkvPaP+rRo4cGDRqkxx57TElJSSorK9OECRP061//WpdddtkZXbM93bunGPtjufT0rvEuAecQ422G5ORE5/cZGefpvGOO4V18v83CeLcvZoG2bVeCYDAY1R4MBqN2LDhWbm6ucnNzneNvfvObeu6551RSUqJFixad0TXbU1fXaOQMbXp6V9XXNyscjsS7HMQY422WpuaQ8/vPPmtSS3PMN7FBHPH9NovJ452RkdKhfjH7Ey8jI0NpaWmqra2Naq+trdWAAQM6fJ2BAweqpqbmrF5TkiIRW5GImY/WCYcjam016wthMsbbDMf+JRdutdVqMeYm4PttFsa7fTFdjDFq1ChVV1c7x7Zta8eOHRozZswJ+y9atEjNzc1Rbfv27VNWVtYZXxMATMA+tABMFtNAm5+fr02bNmnXrl2SpHXr1smyLOXl5UmS5s6dqzlz5jj9t23bpjVr1jjHb775prZu3arvfOc7Hb4mAJiIXQ4AmCymi6yGDx+uhQsXavbs2c5TvUpKSpwHILS0tKi1tdXpP23aND377LN65ZVXFA6HZdu2ioqKNHr06A5fEwBMZ+oNrwDMlWBHPQDcHPv3N8S7hHPO77eUkZGiQKCRNTgGYLzN8vq2T7TqlQ8kScvmXCs/2/t4Gt9vs5g83j17pnWoH3/iAYDHWMzQAjAMgRYAPCASdVdY/OoAgHgg0AKAF5BnARiMQAsAHhCJ2raLSAvALARaAPCEI4mWLAvARARaAPCAtiW0CSw4AGAgAi0AeIATaMmzAAxEoAUAD2jbUpxAC8BEBFoA8IC2e8K4IQyAiQi0AOABzgxtnOsAgHgg0AKABxxdQ0ukBWAeAi0AeABraAGYjEALAB5wdA1tXMsAgLgg0AKAB7APLQCTEWgBwANYcgDAZARaAPAAmzUHAAxGoAUAD2jLsxZ5FoCBCLQA4AG2M0ULAOYh0AKAF/w9z1osOQBgIAItAHhARM42BwBgHAItAHgBM7QADEagBQAPiLCEFoDBCLQA4AnsQwvAXARaAPCACE8KA2AwAi0AeAFPCgNgMAItAHhA2za0CSRaAAYi0AKAB/DkWwAmI9ACgAe0PSmMPAvARARaAPAAlhwAMBmBFgA8wOamMAAGI9ACgAccXUNLogVgHgItAHiAs+QgvmUAQFwQaAHAA1hyAMBkBFoA8ABuCgNgMgItAHiALWZoAZiLQAsAHtA2QwsAJiLQAoAHtAVaiylaAAYi0AKAB9hscwDAYARaAPAQZmgBmIhACwAeEGERLQCDEWgBwAvYtguAwfyxfoP169eruLhYycnJsixL8+bN05AhQ07Yt7KyUqtWrVJTU5NaWlqUkpKiBx54QJdddpnTp6ioSBs2bFB6errTlpqaquLi4lh/FADotCJ//5U8C8BEMQ2027dvV0FBgcrKypSdna3y8nJNnTpVFRUVSk1NPa7/vHnz9L3vfU8TJkyQJP3sZz/T5MmTVVFRoR49ejj9HnroIY0cOTKWpQOAu7Q9KSzOZQBAPMR0ycHy5cuVm5ur7OxsSdK4ceMUDodVXl5+wv7Dhg3TzTff7BzfdtttOnjwoCorK2NZJgC4Hk8KA2CymAbazZs3Kycn5+ibWZaGDh3abkB94oknZFlHS+rSpYskKRQKxbJMAHA9nhQGwGQxW3IQCATU0NCgzMzMqPbMzExVVVV16Brbtm1TcnKyrr322qj2srIyPfnkkwqFQho0aJDuvvtuDRw48LTqs6wEWZZZf/L7fFbUr/A2xts0R/48sxIS5Pcz5l7H99ssjPepxSzQHj58WJKUlJQU1Z6UlOS8djK2bau4uFj33Xefunfv7rT36dNHaWlpWrBggSzL0pIlSzR+/Hi9/PLL6t27d4fr6949xdgfzaWnd413CTiHGG8ztP1Fl5joU0ZGSpyrwbnC99ssjHf7YhZok5OTJUnBYDCqPRgMOq+dTFFRkXr37q0pU6ZEtR+7xlaSZsyYodLSUj333HOaOXNmh+urq2s0coY2Pb2r6uubFQ5HTn0CXI3xNksoFJYkhcMRBQKNca4Gscb32ywmj3dH/4Ees0CbkZGhtLQ01dbWRrXX1tZqwIABJz23tLRUVVVVWrJkySnfx+fzqV+/ftq9e/dp1ReJ2IpEzNyIPByOqLXVrC+EyRhvMxz7YAXG2xx8v83CeLcvposxRo0aperqaufYtm3t2LFDY8aMafecl156SRUVFSoqKlJSUpJqamqibiKbP3/+ced8+umnysrKOrvFA4CL2GzbBcBgMQ20+fn52rRpk3bt2iVJWrdunSzLUl5eniRp7ty5mjNnjtP/tdde06JFizRjxgx9+OGHqqqq0ptvvqm3337b6bNx40a9+uqrzvHzzz+vAwcOHLcUAQBMwrZdAEwW0wcrDB8+XAsXLtTs2bOdJ4WVlJQ4D1VoaWlRa2ur03/u3LkKBAK64447oq5zzz33OL+fOXOmVq5cqRUrVigUCikxMVHPPPOMLrzwwlh+FADo1I4G2vjWAQDxkGDbtpELSffvb4h3Ceec328pIyNFgUAja3AMwHib5fFfb1P1/9XpCxf10H03fyHe5SDG+H6bxeTx7tkzrUP92NAMADzg6NQEU7QAzEOgBQAPaAu0hu1GCACSCLQA4AnO6jECLQADEWgBwEMs7goDYCACLQB4QMTM+3sBQBKBFgC8wVlDywwtAPMQaAHAAyKsoQVgMAItAHgITwoDYCICLQB4QIQJWgAGI9ACgCccSbRM0AIwEYEWADzg6BJaEi0A8xBoAcADnEBLngVgIAItAHhA25PCCLQATESgBQAPaHusArscADARgRYAPMCZoY1zHQAQDwRaAPCAo2toibQAzEOgBQAPYA0tAJMRaAHAA1hDC8BkBFoA8ACbJ4UBMBiBFgA8gCUHAExGoAUAD7CPrjmIax0AEA8EWgDwgLY8a5FnARiIQAsAHmA7U7QAYB4CLQB4wd/zrMWSAwAGItACgAdE2OYAgMEItADgIexDC8BEBFoA8IC2GVriLAATEWgBwAvaVhwwQwvAQARaAPAAtqEFYDICLQB4AE8KA2AyAi0AeMDRTQ5ItADMQ6AFAA9gyQEAkxFoAcADbHY5AGAwAi0AeIDNLgcADEagBQAP4KYwACYj0AKABxxdQ0uiBWAeAi0AeABraAGYjEALAB5wdA1tfOsAgHgg0AKAB3BTGACTEWgBwANscVMYAHP5Y/0G69evV3FxsZKTk2VZlubNm6chQ4a023/Lli0qLCxUUlKSgsGgCgoKNGLEiM91TQDwurYZWlbRAjBRTAPt9u3bVVBQoLKyMmVnZ6u8vFxTp05VRUWFUlNTj+v/ySefaPr06Vq6dKlGjhypP/7xj5o+fbrWrVunfv36ndE1AcAIfw+0FnkWgIFiuuRg+fLlys3NVXZ2tiRp3LhxCofDKi8vP2H/1atXKzs7WyNHjpQkXXXVVbrgggv0i1/84oyvCQAmiDiLaONbBwDEQ0wD7ebNm5WTk3P0zSxLQ4cOVWVl5Qn7V1ZWRvWXpJycnKj+p3tNADCJxSJaAAaK2ZKDQCCghoYGZWZmRrVnZmaqqqrqhOfU1NTo61//+nH9a2pqzvia7dlcvffojIYhfL4EpaR0UWNji8Jhsz67iRhvs0QijDEAc8Us0B4+fFiSlJSUFNWelJTkvHaic07W/0yu2Z7lL+04rf4A4AY+nyW/nw1svM7ns6J+hbcx3qcWs0CbnJwsSQoGg1HtwWDQee1E55ys/5lcEwBMkXZekkYP76uMjJR4l4JzJD29a7xLwDnEeLcvZoE2IyNDaWlpqq2tjWqvra3VgAEDTnjOgAEDTtr/TK7ZnuLZucbdPOHzWUpL66qGhmaFw5F4l4MYY7zN4vNZ6tkjVU2NLQoEGuNdDmLM57OUnt5V9fV8v01g8nh39B/oMd22a9SoUaqurnaObdvWjh07dOedd56w/+jRo7V169aoturqao0ZM+aMr9meLom+0+rvBX6/pdSuiQodDqqVG0c8j/E2i99vye+zFA5H1Npq1l94JmO8zcJ4ty+mizHy8/O1adMm7dq1S5K0bt06WZalvLw8SdLcuXM1Z84cp//tt9+unTt36q233pJ05CELO3fu1KRJkzp8TQAAAJglpjO0w4cP18KFCzV79mznqV4lJSXOAxBaWlrU2trq9O/Xr5+eeuopFRYWKjExUcFgUMuWLXMeqtCRawIAAMAsCbZt2N5Vf7d/f0O8Szjn/H5LGRkpCgQa+ZGFARhvszDeZmG8zWLyePfsmdahfuz/AAAAAFcj0AIAAMDVCLQAAABwNQItAAAAXI1ACwAAAFcj0AIAAMDVCLQAAABwNQItAAAAXI1ACwAAAFcj0AIAAMDVCLQAAABwNQItAAAAXI1ACwAAAFcj0AIAAMDVCLQAAABwNQItAAAAXI1ACwAAAFcj0AIAAMDVCLQAAABwNQItAAAAXI1ACwAAAFcj0AIAAMDVCLQAAABwNQItAAAAXI1ACwAAAFcj0AIAAMDVCLQAAABwNQItAAAAXI1ACwAAAFcj0AIAAMDVCLQAAABwNQItAAAAXI1ACwAAAFcj0AIAAMDVCLQAAABwNQItAAAAXI1ACwAAAFcj0AIAAMDVCLQAAABwNQItAAAAXI1ACwAAAFfzx/LiwWBQhYWFeueddyRJV1xxhQoKCpSUlHTC/s3NzSotLdWGDRtkWZYOHTqkq666Svfee69SUlKcfl//+tfVs2fPqHO/8Y1v6JZbbondhwEAAECnFNNAW1hYqI8++kjPP/+8JGnatGkqLCzUww8/fML+7733npYvX661a9cqKytLDQ0NuuWWWxQIBFRYWOj069mzp1avXh3L0gEAAOASMVtyEAgEVFpaqsmTJ8vn88nn8+m73/2uSktLdfDgwROek5qaqttuu01ZWVmSpLS0NOXl5emVV15ROByOVakAAABwsZgF2i1btigUCiknJ8dpy8nJUSgU0pYtW054zqWXXqq77rorqq1Lly6KRCKKRCKxKhUAAAAuFrMlBzU1NfL7/erevbvT1r17d/l8PtXU1HT4Olu3btV1112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\n", - "text/plain": [ - "
              " - ] - }, - "metadata": { - "filenames": { - "image/png": "/Users/mhjensen/Teaching/MachineLearning/doc/LectureNotes/_build/jupyter_execute/week38_32_2.png" - } - }, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "\"\"\"The sigmoid function (or the logistic curve) is a\n", "function that takes any real number, z, and outputs a number (0,1).\n", @@ -1474,22 +1273,7 @@ "collapsed": false, "editable": true }, - "outputs": [ - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
              " - ] - }, - "metadata": { - "filenames": { - "image/png": "/Users/mhjensen/Teaching/MachineLearning/doc/LectureNotes/_build/jupyter_execute/week38_72_0.png" - } - }, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "import numpy as np\n", "import pandas as pd\n", @@ -1571,21 +1355,7 @@ "collapsed": false, "editable": true }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "GridSearchCV(estimator=Ridge(),\n", - " param_grid={'alpha': array([1.00000000e-04, 4.64158883e-04, 2.15443469e-03, 1.00000000e-02,\n", - " 4.64158883e-02, 2.15443469e-01, 1.00000000e+00, 4.64158883e+00,\n", - " 2.15443469e+01, 1.00000000e+02])})\n", - "Best estimated lambda-value: 100.0\n", - "MSE score: 1.0892144853354966\n", - "R2 score: -0.0038332550504751595\n" - ] - } - ], + "outputs": [], "source": [ "import numpy as np\n", "from sklearn.model_selection import train_test_split\n", @@ -1673,19 +1443,7 @@ "collapsed": false, "editable": true }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "RandomizedSearchCV(estimator=Ridge(), n_iter=100,\n", - " param_distributions={'alpha': })\n", - "Best estimated lambda-value: 0.9849967686928113\n", - "MSE score: 1.0853136633465326\n", - "R2 score: -0.0002382102844775691\n" - ] - } - ], + "outputs": [], "source": [ "import numpy as np\n", "from sklearn.model_selection import train_test_split\n", @@ -1754,31 +1512,7 @@ "collapsed": false, "editable": true }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "(426, 30)\n", - "(143, 30)\n", - "Test set accuracy with Logistic Regression: 0.94\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/Users/mhjensen/miniforge3/envs/myenv/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:814: ConvergenceWarning: lbfgs failed to converge (status=1):\n", - "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", - "\n", - "Increase the number of iterations (max_iter) or scale the data as shown in:\n", - " https://scikit-learn.org/stable/modules/preprocessing.html\n", - "Please also refer to the documentation for alternative solver options:\n", - " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", - " n_iter_i = _check_optimize_result(\n" - ] - } - ], + "outputs": [], "source": [ "import matplotlib.pyplot as plt\n", "import numpy as np\n", @@ -1819,36 +1553,7 @@ "collapsed": false, "editable": true }, - "outputs": [ - { - "data": { - "image/png": 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              " - ] - }, - "metadata": { - "filenames": { - "image/png": "/Users/mhjensen/Teaching/MachineLearning/doc/LectureNotes/_build/jupyter_execute/week38_82_1.png" - } - }, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "import matplotlib.pyplot as plt\n", "import numpy as np\n", @@ -1977,155 +1682,7 @@ "collapsed": false, "editable": true }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "(426, 30)\n", - "(143, 30)\n", - "[1. 0.86666667 1. 0.92857143 1. 0.85714286\n", - " 1. 0.92857143 0.92857143 1. ]\n", - "Test set accuracy with Logistic Regression: 0.94\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/Users/mhjensen/miniforge3/envs/myenv/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:814: ConvergenceWarning: lbfgs failed to converge (status=1):\n", - "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", - "\n", - "Increase the number of iterations (max_iter) or scale the data as shown in:\n", - " https://scikit-learn.org/stable/modules/preprocessing.html\n", - "Please also refer to the documentation for alternative solver options:\n", - " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", - " n_iter_i = _check_optimize_result(\n", - "/Users/mhjensen/miniforge3/envs/myenv/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:814: ConvergenceWarning: lbfgs failed to converge (status=1):\n", - "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", - "\n", - "Increase the number of iterations (max_iter) or scale the data as shown in:\n", - " https://scikit-learn.org/stable/modules/preprocessing.html\n", - "Please also refer to the documentation for alternative solver options:\n", - " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", - " n_iter_i = _check_optimize_result(\n", - "/Users/mhjensen/miniforge3/envs/myenv/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:814: ConvergenceWarning: lbfgs failed to converge (status=1):\n", - "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", - "\n", - "Increase the number of iterations (max_iter) or scale the data as shown in:\n", - " https://scikit-learn.org/stable/modules/preprocessing.html\n", - "Please also refer to the documentation for alternative solver options:\n", - " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", - " n_iter_i = _check_optimize_result(\n", - "/Users/mhjensen/miniforge3/envs/myenv/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:814: ConvergenceWarning: lbfgs failed to converge (status=1):\n", - "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", - "\n", - "Increase the number of iterations (max_iter) or scale the data as shown in:\n", - " https://scikit-learn.org/stable/modules/preprocessing.html\n", - "Please also refer to the documentation for alternative solver options:\n", - " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", - " n_iter_i = _check_optimize_result(\n", - "/Users/mhjensen/miniforge3/envs/myenv/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:814: ConvergenceWarning: lbfgs failed to converge (status=1):\n", - "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", - "\n", - "Increase the number of iterations (max_iter) or scale the data as shown in:\n", - " https://scikit-learn.org/stable/modules/preprocessing.html\n", - "Please also refer to the documentation for alternative solver options:\n", - " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", - " n_iter_i = _check_optimize_result(\n", - "/Users/mhjensen/miniforge3/envs/myenv/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:814: ConvergenceWarning: lbfgs failed to converge (status=1):\n", - "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", - "\n", - "Increase the number of iterations (max_iter) or scale the data as shown in:\n", - " https://scikit-learn.org/stable/modules/preprocessing.html\n", - "Please also refer to the documentation for alternative solver options:\n", - " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", - " n_iter_i = _check_optimize_result(\n", - "/Users/mhjensen/miniforge3/envs/myenv/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:814: ConvergenceWarning: lbfgs failed to converge (status=1):\n", - "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", - "\n", - "Increase the number of iterations (max_iter) or scale the data as shown in:\n", - " https://scikit-learn.org/stable/modules/preprocessing.html\n", - "Please also refer to the documentation for alternative solver options:\n", - " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", - " n_iter_i = _check_optimize_result(\n", - "/Users/mhjensen/miniforge3/envs/myenv/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:814: ConvergenceWarning: lbfgs failed to converge (status=1):\n", - "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", - "\n", - "Increase the number of iterations (max_iter) or scale the data as shown in:\n", - " https://scikit-learn.org/stable/modules/preprocessing.html\n", - "Please also refer to the documentation for alternative solver options:\n", - " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", - " n_iter_i = _check_optimize_result(\n", - "/Users/mhjensen/miniforge3/envs/myenv/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:814: ConvergenceWarning: lbfgs failed to converge (status=1):\n", - "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", - "\n", - "Increase the number of iterations (max_iter) or scale the data as shown in:\n", - " https://scikit-learn.org/stable/modules/preprocessing.html\n", - "Please also refer to the documentation for alternative solver options:\n", - " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", - " n_iter_i = _check_optimize_result(\n", - "/Users/mhjensen/miniforge3/envs/myenv/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:814: ConvergenceWarning: lbfgs failed to converge (status=1):\n", - "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", - "\n", - "Increase the number of iterations (max_iter) or scale the data as shown in:\n", - " https://scikit-learn.org/stable/modules/preprocessing.html\n", - "Please also refer to the documentation for alternative solver options:\n", - " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", - " n_iter_i = _check_optimize_result(\n", - "/Users/mhjensen/miniforge3/envs/myenv/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:814: ConvergenceWarning: lbfgs failed to converge (status=1):\n", - "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", - "\n", - "Increase the number of iterations (max_iter) or scale the data as shown in:\n", - " https://scikit-learn.org/stable/modules/preprocessing.html\n", - "Please also refer to the documentation for alternative solver options:\n", - " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", - " n_iter_i = _check_optimize_result(\n" - ] - }, - { - "data": { - "image/png": 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AxcUF/v7++PTTTw3r+AIw/P/Nnj3bsDZztWrV8NlnnyExMRHz5s0znJN27drh008/xYoVK7Bnzx5kZWUZpiL06NEDw4YNe6nP6dHayteuXYOZmRn0ej3c3d0xcOBAtGzZ8onjFyxYgE2bNsHOzg5VqlRB+/btMXHiRHh6eqJdu3YYNWoUhg4dKnxOAwcOfOZ6wV27doW3tzd++umnpz6/du1a/Pbbb9DpdLCyskKlSpXw/vvvC/O3icg4sPElIiIiIpPAqQ5EREREZBLY+BIRERGRSZC98c3Ly8OcOXNQo0YNw049z3PmzBn07dsXAwcORN++fXnXLBERERG9FFkb35iYGAwaNAj3798X7tZ9ltjYWIwePRqTJ0/GunXr8MEHH2D06NHC3ctERERERE8ja+OblZWFb7/9Fj179nyp49euXQtvb280atQIwMPlkby8vAxL8RARERERPYusjW+VKlUMu+28jOPHj6NWrVpCrVatWjh+/HhBRyMiIiKiEkb2Ob75ER0dbdj96REXF5eX2pGJiIiIiEybWu4A+ZGTk/PEfu3m5ubIycnJ18eRJKlQ96UnItP055E7WLrtktwxqLhR58Gi+ikorTJffCyRCfm936IC/5jFqvG1tLQUtkYFHq4KYWlpma+Po1AokJaWDZ1OX5DxyAipVErY21vxfJsIOc93dq4Wv+25XqTvSSWAUguLKmfY9JLJS4lMgoOHExTKwp2MUKwaXw8PDyQmJgq1xMREeHh45Ptj6XR6aLVshEwFz7dpkeN8/3ksHOlZGqE2qEMVONpZFGkOU6JSKWFrY4GMzNxi8YetJElI16VCLz1cxUgCcCJ5H+7mpgnHOaqd0cCxBQBemfw3pVIBSwsz5ORqoNdz09mSQqvR4M+167Bv/Wb0GhmI9n16AHh4vgtDsWp8mzRpgvPnzwu1y5cvo2nTpjIlIiICktNzsecf8V6DOpVd0LpueZkSmQa1WgknJxskJ2ca/R+2SdkPsPDiz7iXGf/c45wsHDG53mg4WToWTbBipDidb3o5V65cRtCk0bhy5eEUsT9XrcH7Q8agUqXKUKsLZ+TXqG9umzp1Kj788EPD48GDByMsLAynT58G8HAzi7CwMAwcOFCuiERE+ONoOPL+9YtYoQB6BfjImIiMSVpeOuaHLnth02trZoMJdUay6aUST6vVYt68H9ChQ4Ch6VWr1Xj//SmoWNGrUN9b1hHfvLw8BAYGIi3t4WWe999/H2XKlMG8efMAALm5udBqtYbjy5UrhyVLluDbb7+FmZkZ8vLysHTpUpQrV06W/EREdxMzceTiXaHWwq8syrrYyJSIjEm2NhsLQ1cgITvpucdZqiwwvnYg3KxLF1EyInncuXMb48ePxtmzpw216tVrIDh4KWrV8iv091dIkmSSE2V4qcQ08NKYafn3+T4cGou9Z2KQma158QtfQ3auFpk5j/9AN1crMWt0Ezhxbm+hM/bv7zydBgsuLMftlPDnHudq7YKB1frCx7Fi0QQrpoz9fNPz6fV6/PzzMsyY8Tmys7MBAEqlEuPHT8KUKZ/AwkL8mfnofBe0YjXHl4joZdyISsbyHddkee8ODT3Y9JqAe5nxuP7gNrSS9pnHXH9w64mm19myFN6vNxb25naGmlJh1LMOiQrEihVLMG3aR4bHXl7emD9/CRo2bFSkOdj4ElGJIkkS1u+/Jct721qZoWOjl9+NkoqnCwmXseLyL9D9/+oML8vO3BZB/iPgaOFQSMmIjNeAAYOxbNliRESEY/jwkfjssxmwsSn6KWFsfImoRDl+MQ5hsWkvPrCAKRUKDHmzGqws+GO1JLuZfBsrr/ya76bXSm2JoNoj4Grt8uKDiUoArVYLtfrxz0MbGxssXLgMmZmZCAhoLVsu/oQmohJDq9Njzc6rQs3VyQr921Yu1PdVKgBPNzs42nKKQ0kWlRaDJRdXQ6t/9vSGpzFTqjHGbxjK25UtpGRExmXHju344otPsXHjNnh5eRvq9es3lDHVQ2x8iajECAm9i7uJ4g5YvQJ84F+Jo2z0euIz72PBhRXI0eUKdU+78rA1e/blWmszK7T2aI6K9p6FHZFIdikpyfjkkynYtGkDAGDixLHYtm0nVCqVzMkeY+NLRCVCTp4W2w7fEWpe7vaoX5XLQ9Gz6SU9QmKO40rS9eeO5N7LvI8MjfhHlX/pWgis+Q5vTiMCcODAPrz3XhDi4h4v7+js7ILs7CzY2to955VFi40vEZUIf/8TjdTMPKHWt7UPFApu+0pPJ0kSNt3ajpCY4/l+bVWnShjq+zabXjJ5GRkZ+PLLz7B69QpDzd7eATNnfos+ffob3c9gNr5EZPSOXYrD3jPRz12TNyVDbHr9fJxR1dOpsKNRMbYzYt8rNb2eduUxqtZgmCn5K5RM28mTJzBx4hhERDxeti8goDXmzl2IsmWNc3MxftcSkVG7HpmMFX/lb01ehQLo3YpbBtOzHYo5hp3he/P9OncbN4yvHQhLtWUhpCIqPoKD5+J///scj/ZBs7a2xvTpX2Ho0ECjG+X9Nza+RGS09JKE3w/ezvfrmvu5o3xp20JIRMWRXtIL83dDEy5j480/hGMUUOCNim1gZ/7srxsbtTX8SvvCQmVeaFmJiouaNWsZmt4GDRph/vzF8PY2/gEHNr5EZLTOXL+PiHvp+XqNu4sN+rauVEiJqDiRJAl/he/BvqjD0Oifv3V13yrd0bJ8kyJKRlT8tWrVBqNHj4erqxvGjZtgVCs3PA8bXyIySlqdHptDwoSaWylrvP2cNXltrNSo5+uOjPQcaLX6wo5IRm7Hnb+xO/LAC4/r4vUGm16i57h16ybWrVuNL774SpjG8L//zZIx1ath40tERunQ+VgkpOQItd4B3vDzcX7ma9RqJczUxWPUgQrXgajDL9X0ti7fHG9WbFMEiYiKH71ej2XLFuHrr79ETk4OvLy8MXRooNyxXgvXYSEio5Odq8X2YxFCzaecPepW4Zq89GKn4s5i8+0dLzwuoHwz9KzcxahvxCGSS1RUJHr27ILPPpuKnJyHgxC//LIGen3xvprGEV8iMjq7T0Uh4z9Ll/VpVYkNCr3QpcSrWHd94xP1vlW6w9e5muGxtdoK1mZWRRmNqFiQJAm//LIGn302FZmZGYb66NHj8Mkn06FUFu8xUza+RCSrmIQM/LbvFu49yDLU0v6zEYV/JRdU8XAs4mRU3NxKvoMVl9dBL4kjUt18OiKgfFOZUhEVH/Hx9/D++xOwd+/fhpqHhyfmzVuEZs1ayJis4LDxJSLZ5Gp0+GnjBTxIy33mMQoF0Itr8tILRKffxeKLq6D5z7bDbT1bor1nK3lCERUj27ZtxkcfvY/k5GRD7Z13BmPGjJmws7OXMVnBYuNLRLLZdyb6uU0vALTwc0c5F5siSkTFUXxmAhaELkeOTrwZsrF7ffTw6cwpMkQvYd++PYam19XVDT/8MA8dOnSUOVXBK94TNYio2ErPysPOk5HPPcatlDV6BnC0l57tQXYK5p5dinRNhlD3c/HFgKq92PQSvaSvv56NsmXLoVu3njh8+GSJbHoBjvgSkUx2HI9Edq5OqAV2rg4Hm4e7YlmYq+Dlbg+1in+f00P3MuNxNPYUsrTZAB5Og4lMj0ZSTrJwXGVHbwz3HQCVkkvbET1Nenoarl+/hgYNGhlqDg6O2Lv3MEqXLtmr57DxJaIil5CSjQPnYoRa4xpuaFbLXaZEZOwi06Ix9/wS5Orynnuch105jPYbCjOVWRElIypejh07gokTxyItLQ2HD5+Eu3tZw3MlvekFONWBiGSw9cgd6PSS4bFapUCPlt4yJiJjFp95HwsvrHxh0+tq5YLxtQNhpbYsomRExUd2djY+/fQj9OjRGdHRUUhNTcEnn0yRO1aR44gvERWpyHvpOHklXqi1rlMepR25pio9KTknBfNDlyNDk/nc4xwtHBDkPxJ25rZFlIyo+Dh37gyCgkbj9u1bhlrTps3xxRdfyZhKHmx8iahIbTp0W3hsZaFCl6YVZEpDxiwjLxPzQ5cjOTdFqHvZe8LH0QtKpQKWlmZQ6czQyK0+m16i/8jLy8MPP8zG3Lk/QKd7eE+FhYUFpk2bjlGjxhX7zSheBRtfIioyV8If4EqEeCNSp8YVYGdtLlMiMlY52hwsvLAS8Vn3hXoFOw8E+Y+ApdoSarUSTk42SE7OhFZbvLdRJSpo165dRVDQaFy6dMFQq1OnLubPX4IqVarKmExebHyJqEjoJQkb/zPa62RngXb1PWRKRMZKo9di2aW1iEyPFupu1q4YV3s4LDmHl+i5NBoNBgzojdjYhzcRq9VqTJ78ESZNmgy12rRbP9Mb4yYiWZy6Go+oeHGt1W7NvWBhxiWn6DG9pMfqK7/hevItoe5k4YgJ/iNga87NTIhexMzMDF9//S0AoFq16ti9+wAmT/7I5JtegCO+RFQENFo9th6+I9TKutigWa0yMiUiYyRJEjbc2IrzCZeEuq2ZDYL8R8DJ0lGeYERGTq/XIzs7GzY2j/8w7NSpC5Yu/RlvvtkZlpa8SvIIR3yJqNAdPBeDxFRxO9leAd5QmeCNFfRsO+78jaN3Twk1C5U5xtUejjI2rjKlIjJusbEx6Nu3B4KCRkOSJOG57t17sen9D/7WIaJClZWjwZ/HI4Ra5fIO8K/kIk8gMkoHog5jd+QBoaZWqDC61lBUsOc8cKL/kiQJGzb8ioCAJjh8+CD++ms7Nm/+Xe5YRo9THYioUO08GYXMHK1Q69O6EhQKhUyJyNicijuLzbd3CDUFFBjmOwBVS1WSKRWR8UpISMAHH0zCrl2Pv2/c3cvCxaXk77z2utj4ElGBiYpPx697b+LegyxDLSNbbHrrVSmNSuUcijoaGalLiVex7vrGJ+pvV+sJf9daMiQiMm5//fUnPvxwEhITEw213r37YebMb+Ho6CRjsuKBjS8RFYjcPB1+2ngBKRnP3lZWqVCgZwC3JqaHbiXfwYrL66CXxDV4u/l0RLOyjWRKRWScHm0xvHHjekPN2dkZ3303F126dJUxWfHCxpeICsTfp6Oe2/QCQEv/snB35nJUBESn38Xii6ug0YtXBNp6tkR7z1byhCIyUvHx8XjjjVa4ezfWUHvzzc74/vu5cHXljZ/5wZvbiOi1pWXmYdepqOceU87FBj1bcrSXgPtZiVhwYTlydOJKH43d66OHT2fO/yb6D1dXV9SpUw8AYGdnj3nzFmH16l/Z9L4CjvgS0Wv781gEcvN0hscKAKO6+sLe2gwAYGGuRoUytly+jJCSm4rg0GVIzxM3M/Fz8cWAqr3Y9BI9hUKhwHff/QQzMzU+//x/KF+eK528Kja+RPRa4pOzcCg0Vqg1rVkGjWq4yZSIjFWWJgsLQlcgKSdZqFd29MZw3wFQKbmLH1Fubi5mz/4a9es3RKdOXQx1FxcXLF26Sr5gJQQbXyJ6LVtC7kCnf7xoulqlRPcWnNJAolxdHhZd/Bl3M+8JdQ/bshjtNwRmKjOZkhEZj0uXLiAoaDSuXbsKFxcXNGjQCKVLc4mygsTrjkT0ysLj0nD6+n2h1q5eeTg7cKcgekyr12L5pbW4kxop1F2tXDDefwSs1FYyJSMyDlqtFj/88C3eeKM1rl27CgBIS0vD6dOnXvBKyi+O+BLRK5EkCRsP3hZq1hZqdGpSQaZEZIz0kh5rr/2Oqw9uCHUHc3sE+Y+AnbmtTMmIjMOtWzcRFDQK58+fM9R8fWthwYKlqFHDV8ZkJRNHfInolVy6k4TrUSlCrXPTCrC14iVrekiSJGy6tR1n4kOFurXaCkH+I+BsVUqeYERGQK/XY8mSBWjbtrmh6VUqlXjvvQ/w998H2fQWEo74ElG+6fUSNh4KE2ql7C3Qrl55mRKRMdoZsQ8hMceFmrnSDONqD0dZ2zIypSKSX2xsDIKCRuPYsSOGmo9PJQQHL0G9eg1kTFbyccSXiPLt+OV7iE3IFGo9WnjDTM278umhkJjj2Bm+V6ipFCqMrDUYXg6cDkOmTaFQ4PLlS4bHI0eOwf79R9n0FgE2vkSUL3kaHbYeuSPUype2QRNfjuDRQ2funcfGm38INQUUGFKjH2o4V5UpFZHxKFu2HGbN+g7ly3tgy5Yd+Prrb2FtbS13LJPAxpeI8mX/uRgkp+cKtd6tKkGp5MYDBFxJuoHV1zZAgiTU+1bpjnpu/vKEIpLZjh3bkZqaItR69eqLI0f+QfPmLeUJZaLY+BLRS8vI1uCv4+KSVNU8HVHLmzcpEXAnNQLLLq2BXtIL9S5eHdCyfBOZUhHJ58GDJIwePQzDhw/EtGkfCc8pFArY2NjIlMx0sfElope280QksnK1Qq1P60rcZpYQmxGHhRd+hkavEeqtyjfDmxXbypSKSD779v2Nli0bY+vWzQCA33//DWfO/CNzKuKqDkT0VOFxafh1703ce5BlqP236W1QzRVe7vZFHY1klpidhFsp4dDrdQAAPSTsCt+LbG22cFwDtzroVfkt/mFEJiUjIx2ff/4J1q1bbag5Ojrim2/m8OY1I8DGl4iekJ2rxdxNF5GWmffMY1RKBXoGcGtiU3Mp8SpWXF4HjV773ONqOlfDoOp9oVTwwiKZjuPHj2LixLGIino8Jaxt2/b48cdglCnjLmMyeoSNLxE9YfepqOc2vQDQyr8c3Jx4F7IpuZV856WaXm+HigisORAqJZe3I9OQnZ2NmTNnYOnShZCkhzd22tjYYsaMmRg4cAivehgRNr5EJEjJyMXfp6Oee4yHqy26t/QqokRkDKLT72LxxVUvbHrL2bpjrN8wmKvMiygZkfx27PgDS5YsMDxu0qQZ5s1bhAoVKsoXip6KjS8RCbYfDUee5vFd+QoFMK57TdhZP2xkLM1VKF/alsuXmZD7WQlYELocObocoV7G2hVWaisAD79OytqUwVveb8LazEqOmESy6d27HzZt2oDjx49i2rTpGDVqHJRKTvMxRmx8icggLikThy/ECbXmtdxRr6qrTIlIbim5qQgOXY50TYZQ93PxxQhOZyATdf/+fbi6Pv65qFAo8NNPC5CWloaqVavJmIxehH+OEJHB5pA70EuPNx4wVyvRvQVvYDNVWZosLAhdgaScZKFe2dEbw30HsOklk6PT6RAcPBf16vni0KEDwnPu7mXZ9BYDbHyJCABwOyYV524mCLX2DTzgZGchUyKSU64uD4su/oy7mfeEuoddOYz2GwozlZlMyYjkER5+B927d8KMGZ8hNzcXkyaNe2I3NjJ+nOpAZIKyc7VYtes6rkUmQ6d/OMKr0eqEY2ws1ejYqIIc8UhmWr0Wyy+txZ1UcZc+VysXjK8dCCu1pUzJiIqeJElYvXolvvjiU2RlZQJ4OLWhR4/esLDg90Jxw8aXyASt23MDp6/ff+4xbzWtCGtL/ogwNXpJj7XXfsfVBzeEuoO5PYL8R8DO3FamZERFLy7uLt59dzwOHtxvqFWoUBHz5y9G48ZNZUxGr4pTHYhMTOS9dJy4Ev/cY1wcLNG6bvkiSkTGQpIkbLq1HWfiQ4W6tdoKQf4j4GxVSp5gREVMkiRs3LgeLVs2FpreIUMCcfDgcTa9xRiHc4hMzKZDt5/7vLmZEoGdq8NMzb+LTc3OiH0IiTku1MyVZhhXezjK2paRKRVR0ZszZza+/Xam4XGZMu746adgtGnTXsZUVBDY+BKZkCvhD3AlQrxDv0MDD9SrWhrAw3lrHqVtYWHOu/VNzaGYY9gZvleoqRQqjKw1GF4OnOtNpqVXr74IDp6LrKxM9OrVF7NmfQdHRye5Y1EBYONLZCL0koSN/xntdbQ1R4+W3rAwY6Nrys7cO4+NN/8QagooMKRGP9RwripTKiL5eHl5Y/bsObC2tsZbb3WXOw4VIF7LJDIRp67GIype3ISgews2vabuStINrL624Yl63yrdUc/Nv+gDERWxw4cPoWfPLsjIEH8+9us3gE1vCcTGl8gEaLR6bD18R6i5O1ujWS3O2zRld1IjsOzSGuglvVDv4vUGWpZvIlMqoqKRlZWFqVM/QO/eXXH06GHMmPGZ3JGoCHCqA5EJOHguBompOUKtdysfqLiXvMmKzYjDwgs/Q6PXCPVW5ZvhzYptZEpFVDROnz6FCRPG4M6dMEMtLCwMeXl5MDc3lzEZFTb+1iMq4bJytPjzeIRQq1zeAf6VXOQJRLJLzH6ABaHLka3NFuoN3OqgV+W3oFAoZEpGVLhyc3Px1Vdf4K233jA0vVZWVpg16zts3LiNTa8J4IgvUQm361QkMnO0Qq1P60psbkxUWl465ocuQ2peulCv6VwNg6r3hVLB8RAqmS5fvoSgoNG4evWyoVavXgMEBy+Gj09lGZNRUeJPOKISLDk9F3tPRwu1elVKo1I5B5kSkZyyNNkIDl2OxOwkoe7tUBGBNQdCpeSNjlQyLVw4H2+80crQ9JqZmWHatOn488+/2fSaGI74EpVg247cQZ728Y1LSoUCPQO8ZUxEcsnTabD44irEZsQJ9XK27hjrNwzmKl7ipZJLkiRoNA/ns9eoURPBwUtQs2YtmVORHNj4EpVQsYmZOHpJbHJa+peFu7ONTIlILjq9DiuvrENYarhQd7EshfG1A2FtZiVTMqKiMWbMeOzb9zfq12+IDz74GBYWFnJHIpmw8SUqoTYfCoMkPX5sbqZEt2YVZctD8tBLevxyfRMuJV4T6vbmdphQZyQcLOxlSkZUOKKjo3DkSAgGDBhkqKlUKmzc+AfUarY9po5fAUQl0M3oFITeThRqbzTwhIMtRzlMiSRJ2Hr7L5y6d1aoW6ktEeQ/Ai5WzjIlIyp4kiTht9/W4dNPP0ZmZgZ8fCqjUaPGhufZ9BLAm9uIShxJkrDxoLg1sZ21Gd5s5ClTIpLLnsiDOBB9RKiZKdUY4zcM5WzdZUpFVPDi4+MxaFA/vPvueGRkpEOSJMye/ZXcscgIsfElKmHO3khA2N00oda1mResLDjaYUqOxp7E9ju7hZpSocSImoNQydFLplREBe+PP7agZcuG2LPn8dd7v34DsGrVLzKmImMl+2/CvXv3YtGiRbC0tIRSqcT06dNRufLTlxaRJAmLFi3C7t27YW9vj6ysLPTr1w/9+vUr4tRExkmr02NzSJhQc3W0QoB/WZkSkRzO3b+I9Te2PlEfVL0varpUlyERUcFLTn6Ajz+ejK1bNxtqLi6lMWfOPHTs2FnGZGTMZG18L168iClTpmDz5s3w9vbGtm3bEBgYiJ07d8LW1vaJ4zdt2oQVK1Zg586dcHNzQ1xcHLp06QI3Nze0atWq6D8BIiNz5MJdxCeLu3H1DPCGWsWLO6bi2oObWHXlN0iQhHrvyl3RsExdmVIRFaz9+/fg3XeDEB9/z1Dr0qUbvv32R7i4cFdKejZZfxsuW7YMAQEB8PZ+uK5o165dodPpsG3btqcef/36dXh7e8PNzQ0A4O7uDi8vLxw9erSoIhMZrZw8Lf44FiHUvNzt0KCaqzyBqMhFpEVh6aU10Ek6od6xYlu09mguUyqigqXX6zF79teGptfBwRELFy7DihVr2PTSC8na+J44cQK1aj1eQFqpVMLX1xfHjx9/6vFt27ZFWFgYbty4AeBhI3zr1i1+oRMB2PNPNNIy84Ran1bcmthU3MuMx8LQlcjTiV8DLco1QWevDjKlIip4SqUS8+cvgYWFBVq3bovDh0+id+9+/FlHL0W2qQ7JyclIT09/oml1cXHBpUuXnvqapk2bYtasWRgyZAhKlSqF8PBw1K9fHwMGDMj3+6t46dckPDrPJf18p2XmYfc/UUKtdiVn1PQxreWqTOV8/9eD7GQEhy5HpjZLqNcv448BNXpAqSiZ/x+mer5NTU5ODu7fj4eX18ObMlUqJXx9a2DfvhDUqOHLhreEKqzva9ka35ycHACAubm4Taa5ubnhuf86ePAgPv/8c6xYsQI1a9ZEdHQ0du3aBUtLy3y/v709dyoyJSX9fG84GIacvMeXtxUKYER3Pzg5meYubSX9fP9bWk465p9YjuTcVKFeu0x1vN88EGqV7PcwFzpTOt+m5syZMxg8eDCUSiXOnDkD4PH5bt68kZzRqJiS7Sfio2Y1L0+8LJeXl/fMRvbHH39E+/btUbNmTQCAh4cHIiIi8L///Q//+9//8vX+aWnZ0On0r5CcihOVSgl7e6sSfb7vPcjC7hMRQq15LXc4WKqQnJwpTyiZmML5/rccbQ7mnFmMu+nxQt3LwROBNQYiPS0XQK484YqAqZ1vU6LRaPD997Pxww/fQad7+Ef91Kmf4scfv+f5NhGPvr8LmmyNr5OTE+zs7JCYKO4ulZiYCA8Pj6e+JjIyEh07dhRq5cuXx6pVq/Ld+Op0emi1/MYxFSX5fP9+4DZ0+sd38KtVSnRt5lViP9+XUZLP9yManQYLL65CVFqMUC9j44YxfsOgglmJ/z94xBTOtym5fv0agoJG4+LFUEPNz88fffv2B8DzTa9H1olRjRs3xuXLlw2PJUnC1atX0bRp06ce7+bmhoSEBKGWkJAACwtuw0qm6c7dNJy5fl+otatfHs4O+Z/+Q8WHTq/Dz1d/w81kcYe+UpZOmOA/ArZmpjnFhYo3nU6HBQvmoX37loamV6VS4YMPPsauXftRrRrXoKbXJ2vjO2rUKISEhCA8PBwAsH37diiVSnTv3h0AMHXqVHz44YeG43v16oWdO3fi7t27AIDY2Fjs3LnziVFgIlPwtK2JbSzV6NykgkyJqChIkoT1N7bgQsJloW5rZoMg/xFwtHCQKRnRq4uICEePHp3x5ZefIjf34fScqlWrYffuA5gy5ROYmZnJnJBKClnvevDz88Ps2bMxefJkw85tK1asMGxekZubC61Wazg+MDAQCoUC48ePh6WlJTIyMtC/f3+MGzdOrk+BSDYXw5JwIzpFqHVuUhE2lvwFUZL9EbYLx+NOCzVLlQXG+wfCzbq0TKmIXl1aWiratw9AamoKAEChUGDMmCBMnfrZK928TvQ8CkmSpBcfVvIkJ2dyjpAJUKuVcHKyKXHnW6+XMP3nfxCb8PjmNWd7C8wc1RhmapWMyeRVUs/3I/uiQrD19l9CTa1UY3ztQFRx8pEplXxK+vk2Jd99NwvffTcLnp4VMX/+IjRp0uyJY3i+Tcuj813gH7fAPyIRFbrjl+8JTS8AdG/hbdJNb0l34u7pJ5peBRQY7vuOSTa9VHxJkgRJkqBUPp5t+e67H8DMzAwjRoyGra2djOmopOOq30TFTJ5Gh61H7gi18qVt0cS3jEyJqLBdSLiMX65veqL+TrXeqF3aV4ZERK8mMTERgYGD8eOP3wl1MzMzvPvuB2x6qdCx8SUqZvafjUFyurg2a5/WPlAquXtRSXQzOQwrr/wKCeKstB6VOqNJ2QYypSLKv927d6Jly0bYseMPzJkzW1iujKiocKoDUTGSka3BXycihVr1Ck6o6VVKpkRUmKLSY7Dk4ipo9Vqh3t6zFdp5BsiUiih/0tJS8emnH2P9+l8MNXt7eyQmJjznVUSFg40vUTHy14kIZOWKTVCf1j7cq74Eis9KwILQFcjRiaP7Td0boJsPl3Ck4uHIkRBMmjQOMTHRhtobb3TE99/Pg5ubm4zJyFSx8SUqJhJTs7H/rLhLV8PqrqhYxl6mRFRYknNSMP/8MmRoxBsY/UvXRP+qPfmHDhm9rKwsfPXVdCxfvsRQs7W1w9dfz0b//u/wa5hkw8aXqJjYejgcWt3jeZ4qpQI9W3rLmIgKQ4YmE8EXViA5N0WoV3GqhKE13oZKyZU7yLhFRITj7bd7ISzs8QY7zZu3xNy5C+Hh4SljMiI2vkTFQlR8Ok5euSfUWtUpB1cna5kSUWHI0eZi0YWfcS8zXqh72pXH6FqDYabi5iRk/MqUcTfstGZpaYnPPvsSgYGjheXLiOTCr0KiYmDToTDhnn5LcxXealZRrjhUCLR6LZZfXouItCih7mZdGuNqD4elmjtYUfFgaWmJ4OAlaNSoCQ4cOIaRI8ey6SWjwa9EIiN3NeIBLoc/EGodG3nC3tpcpkRU0PSSHmuubsC1BzeFuqOFA4L8R8DO3FamZETPp9VqMW/eD7h584ZQ9/Pzx/btu1GpUmWZkhE9HRtfIiOmlyRsPBQm1BxszdGhAefJlRSSJGHDzW04e/+CULcxs8YE/xEoZekkUzKi5wsLu4W33noDX331BYKCRkGj0QjP8wY2MkZsfImM2Olr9xF5L12odWvuBQtz3uBUUvwVvgdHY08KNXOVOcbVHo4yNlzuiYyPXq/H8uWL0aZNc5w9exoAcPHiBZw4cUzmZEQvxpvbiIyUVqfHlsPiaK+7szVa+LnLlIgK2sHoo9gVsV+oqRQqjK41BBXtOapPxicmJhqTJo3DkSMhhpqXlzfmz1+Chg0byZiM6OVwxJfISB08H4uElByh1ivAByreJFIi/HPvHDbd2i7UFFBgqO/bqFaK8yLJuEiShPXrf0FAQBOh6Q0MHIUDB46x6aVigyO+REYoO1eLP49FCLVK5RxQp7KLPIGoQF1OvIa1135/ot6/ag/UdfWTIRHRs92/fx8ffDARu3fvNNTKlSuPn35agICA1jImI8o/Dh0RGaFdpyKRkS3eKMKtiUuG2ynhWH55LfSSXqh39X4Tzcs1likV0bPduROGv//eZXjcr98AhIScYNNLxRIbXyIjk5yeiz3/RAu1OpVdULm8ozyBqMDEZsRh8cWfodFrhXobjxboUIFNBBmnxo2bYOzYCXBxKY3Vq3/D/PmLYW/vIHcsolfCxpfIyPxxNBx52sejgQrFw7m9VLwlZCUhOHQ5srXivO1GZeqhR6XOHM0no/HPP6eg14tXJD7++FMcPnwKHTt2likVUcFg40tkRO4mZuLIxbtCrYVfWZR1sZEpERWE1Nw0BIcuQ1qeuDRdLZfqeKdabygV/FFM8svIyMAHH7yLLl3aY8mShcJzlpaWcHHhPQZU/PGnLZER2RwSBulfexObq5Xo1txLvkD02rI0WQgOXY7EHHH3PR8HLwz3HQiVkmsyk/xOnjyB1q2bYs2alQCAmTO/REREuMypiAoeV3UgMhK3YlJw/laiUOvQ0ANOdhYyJaLXlafLw6KLq3A3855QL2frjrG1h8JcZSZTMqKHcnJy8M03X2HRovmQ/v+vbmtra3zxxdeoUKGivOGICgEbXyIjIEkSNh4UN6uwtTJDx0YVZEpEr0un12H55XW4kxoh1EtbOSPIfwSs1FbyBCP6fxcvhiIoaDSuX79mqDVs2Bjz5y+Gl5e3jMmICg8bXyIjcP5WIm7Hpgq1t5pVhJUFv0WLI72kx9prv+NK0nWh7mBuhyD/kbA3t5MpGRGg0Wgwd+4c/PDDt9BqH64wYm5ujo8//gxjxwZBpeL0Gyq5+FuVSGY6vR6bQ8TR3tKOlmhdp5xMieh1SJKEzbf+xOn480LdSm2FIP+RcLEqJVMyoocWLpyHb7+daXhcq1ZtBAcvQfXqNWRMRVQ0eHMbkcyOXIxDXFKWUOvZ0gdqFb89i6PdEQdwKOaYUDNXmmFc7eEoa1tGplREjwUGjkaFChWhUqnw/vtTsGvXfja9ZDI44ksko9w8Hf44It45XaGMHRpUd5UpEb2OwzEnsCP8b6GmVCgxotZgeDtwvjbJIycnB5aWlobHtra2WLx4BZRKJerUqSdjMqKixyElIhntOR2F1Mw8oda3lQ+U3Myg2DkbH4rfb24TagooMKR6P/g6V5UnFJk0SZKwZs3PqF+/1hNLk9Wr14BNL5kkNr5EMknLysOuU1FCraZXKVSvyDmgxc3VpBtYfXUDJEhCvXeVrqhfpo5MqciU3bsXhwEDeuODDybh/v14TJw49ond2IhMERtfIpn8eSwCOXk6w2MFgN6tuDVxcROeGolll9ZAJ+mEeiev9mhVvplMqchUSZKEzZt/R8uWjbB//15DvVKlysjNzZUxGZFx4BxfIhncT87CofOxQq2xbxl4unGZq+LkbsY9LLywEnl6jVAPKN8UnSq2kykVmaqkpCRMmfIe/vxzm6Hm6uqGH3+cj/bt35QvGJER4YgvkQy2HL4Dnf7xZXG1SoEeLbk1cXGSlP0AwaHLkaXNFur13fzRu3JXKDhPm4rQ33/vQsuWjYSmt3v3njh8+CSbXqJ/4YgvURELj0vDP9fuC7W29crDxYE7eRUX6XkZmB+6DKl5aUK9hnNVDKreF0oFxxSo6Hz99ZeYO3eO4bGTkxNmz/4B3bv3kjEVkXHiT2eiIiRJEjYdEjersLZQo3OTivIEonzL1uZgQehyJGQnCXVvhwoYWXMQ1EqOJ1DRatq0ueHf7du/gcOHT7HpJXoG/oQmKkKXwx/gWmSyUOvcpAJsrcxkSkT5odFpsOTiKkRn3BXqZW3KYKzfMJirzGVKRqasdeu2GD9+EipVqowBAwZxmg3Rc7DxJSoier2EjQfF0V4nOwu0rVdepkSUHzq9Diuv/IpbKXeEurOlE8b7B8LazFqmZGRKzp49jd9//w3ffDNHaHCnT/+fjKmIig82vkRF5MSVe4hJyBBqPVp4w9xMJVMielmSJOHX65txMfGKULczs0WQ/0g4WjjIlIxMRV5eHubM+QZz5/4AvV6PGjVqYsiQ4XLHIip2OMeXqAhotDpsOyKOFJYvbYOmNcvIlIheliRJ2Hr7L5y8d0aoW6osMd5/BFytXWRKRqbi6tUrePPNNvjxx+8Nm1Bs374VkiS94JVE9F9sfImKwP6zsUhKExeP793KB0ol5+IZu71Rh7A/+rBQM1OqMcZvKDzsysqUikyBTqfDvHk/oH37lrh8+SIAQK1WY+rUz7Bhw1bO5SV6BZzqQFTIMnM0+OtEhFCr6uGIWt7O8gSil3Ys9hT+CNsl1JQKJQJrDkRlJ2+ZUpEpuHPnNoKCxuDMmX8MterVayA4eClq1fKTMRlR8cYRX6JC9teJSGTmaIVan9aVOFpj5ELvX8JvN7Y8UR9YrQ9qudSQIRGZAkmSsGLFUrRp09zQ9CqVSkyY8B727Alh00v0mjjiS1SIklJzsO9MjFCrX80V3mXtZUpEL+PGg9v4+cqvkCDOoexV+S00cq8nUyoyBQqFAmfPnkZWVhYAoGJFLwQHL0XDho1kTkZUMnDEl6gQbTt6B1qd3vBYpVSgVwAvkRuzyLRoLLm0ClpJJ9TfrNAGbTxayJSKTMmsWd+hbNlyGDZsBA4ePM6ml6gAccSXqJDE3M/A8Uv3hFqAf1m4OXG9V2N1L/M+Fl5YiVxdnlBvXrYRuni/IVMqKskSEhJw69YNYfc1BwdHHD58Evb2XCaPqKBxxJeokGwKCRMulFuYq9C1mZdseej5knNSEBy6HBmaTKFep3Qt9Kvag3OyqcDt2LEdLVs2xJAhA3DvXpzwHJteosLBxpeoEFyPTMbFsCSh1rGhJ+xtuKWtMcrIy8T80OVIzk0R6tWcKmOI79tQKvijkgpOSkoyxo0bieHDByIpKQmpqSn44otP5Y5FZBI41YGogEmShI2Hbgs1extzdGjoIVMiep4cbQ4WXliJ+Kz7Qr2CvQdG1hoMMyV/TFLBOXhwP959dzzi4u4aap06vYX//e8bGVMRmQ7+RCcqYKev30d4XLpQ69bcC5bm/HYzNhq9FksvrUFkerRQL2PtinF+w2GptpApGZU0GRkZmDHjM6xatcJQs7d3wMyZ36JPn/6cSkNURPibmKgAaXV6bAkRtyZ2K2WNFn7uMiWiZ9FLeqy68htuJIuj804WjgjyHwFbcxuZklFJc/LkCUycOAYREeGGWkBAa8yduxBly5aTMRmR6WHjS1SAQkLv4n5KtlDr1dIbahXniBoTSZKw/sZWhCZcEuq2ZjaY4D8CTpaO8gSjEic7OxsjRgzG/fvxAABra2tMn/4Vhg4N5CgvkQz425iogGTnarH9WLhQ8ylrj3pVS8uUiJ7lzzt/49jdU0LNQmWO8bUD4WbjKlMqKomsrKwwe/YPAIAGDRrhwIFjGDZsBJteIplwxJeogOw+FYX0LI1Q49bExmd/1GH8HXlAqKkVKozxGwpP+/IypaKSQqvVIjs7C3Z2j3dn7Nz5LaxbtwFt23aASqWSMR0RccSXqACkZOTi79NRQs2/kguqeDjKE4ie6mTcGWy5vUOoKaDAMN8BqOJUSaZUVFLcvHkDnTu3w7vvBkGSxO2uO3ToyKaXyAiw8SUqANuPRSBP83hrYoUC6NXKR8ZE9F8XE67gl+ubnqgPqNYL/q61ZEhEJYVer8fixcFo164Fzp8/hz//3IYtWzbKHYuInoJTHYheU1xSJg6H3hVqzWu5o5wLVwUwFreSw7Diyi/QS3qh3t2nE5qWbShTKioJoqIiMXHiWBw/ftRQq1SpMry8vGVMRUTPwhFfote0JeQO9P+6rGmuVqJ7C/7SMxbR6bFYfHE1tHqtUG/nGYD2FVrJE4qKPUmSsG7dagQENBGa3tGjx2H//qOoW7e+jOmI6Fk44kv0Gm7HpuLszQSh1r6BB5zsuPGBMbiflYAFoSuQo8sR6k3cG6C7TyeZUlFxFx9/D++/PwF79/5tqHl4eGLevEVo1qyFjMmI6EXY+BK9IkmSsPGguPmBrZUZOjaqIFMi+reU3FQEhy5HuiZDqNd28cXbVXtytQ16JTEx0WjbtjmSk5MNtXfeGYwZM2YKKzkQkXHiVAeiVxR6OxG3YlKFWpemFWFtyb8n5ZapycKC0BVIykkW6pUdvTHMdwBUSt5dT6+mXLnyaNKkOQDA1dUN69ZtwI8/BrPpJSom+Bua6BXo9HpsOhQm1FwcLNG6DrcflVuONhfB51bgbuY9oe5hVw6j/YbCTGUmUzIqCRQKBb7/fi6cnZ0xbdp0lCrlLHckIsoHjvgSvYJjl+4hLilLqPVo6Q0zNb+l5KTVa/HDsaW4kxop1F2tXDC+diCs1JYyJaPiKD09De+/PwG7d+8U6i4uLpgzZx6bXqJiiCO+RPmUq9Fh25E7Qs3TzRaNarjJlIgAQC/psebyeoTeuyrUHS0cEOQ/EnbmtjIlo+Lo2LEjmDhxLKKjo7B7907Ur98QLi4ucsciotfE4SmifNp7OhopGXlCrU+rSlDyZinZSJKEjTe34/S9UKFuo7ZGkP8IOFs5yROMip3s7Gx8+ulH6NGjM6Kjowy1K1cuyZyMiAoCR3yJ8iE9Kw+7TomX0X0rOsHXq5RMiQgAdobvxeHY40LNXGWOsbWHw92GI/H0cs6dO4OgoNG4ffuWoda0aXPMnbsQFSpUlC8YERUYNr5E+bDjeCSyc3VCrXerSjKlIQA4FH0MOyP2CTWVQoVRNQfDy8FTplRUnOTl5eGHH2Zj7twfoNM9/P62sLDAtGnTMWrUOCiVvDhKVFKw8SV6SQkp2ThwLkaoNfZ1Q4UydjIlotP3zmPjrT+EmgIKDK/1Nqo7V5EpFRUnYWG3MGrUcFy6dMFQ8/evg+DgpahSpaqMyYioMPDPWKKXtPXwHej0j7cmVqsU6MmtiWVzJek61lzb8EQ9sF5/1C/jX/SBqFiysrJGZGQEAECtVuOjj6bhr7/2seklKqHY+BK9hMh76Th5NV6otalbHi6OVjIlMm13UiOw7NJa6CW9UO9a6U10qNRSplRUHJUtWw4zZ36LqlWrYffuA5g8+SOYmXGtZ6KSio0v0UvYeEjcmtjKQo0uTSvKE8bExWbEYeGFn6HRa4R66/LN0cmrrUypqDiQJAm//LIGqakpQr1Pn/7Yv/8o/Pz8ZclFREWHjS/RC1wOT8LVCHHr206NPWFrxVGhopaYnYQFocuRrc0W6g3c6qJn5S5QcEk5eobY2Bj07dsd770XhE8//Vh4TqFQwNzcXKZkRFSU2PgSPYdekrDpoLg1sZOdBdrV95ApkelKzU3H/NDlSM1LF+o1nathUPU+UCr444yeJEkSNmz4FQEBTRASchAAsGHDr7h8mevyEpkirupA9BynrsQj6n6GUOvW3AsWZiqZEpmmLE02FlxYjsTsJKHu41ARgTUHQqXk+aAnJSQk4IMPJmHXrh2Gmrt7Wfz4YzBq1qwlYzIikguHSIieQaPVYcthcWvisi42aFarjEyJTFOeLg+LL/6M2Iw4oV7O1h1j/IbBXMVL1PSkv/76EwEBjYSmt3fvfggJOYE2bdrJmIyI5CT7iO/evXuxaNEiWFpaQqlUYvr06ahcufIzj3/w4AHmzJmDqKgoZGZmIi8vD+PGjUOnTp2KMDWZggPnYpGUliPUegf4QMXF7IuMTq/Disu/ICw1Qqi7WDljfO0RsDbjqhokSk1NwSefTMHGjesNNWdnZ3z33Vx06dJVxmREZAxk/Q1+8eJFTJkyBd9//z1+/fVX9O7dG4GBgcjIyHjq8Xl5eRg2bBgaNGiAtWvXYsuWLWjRogUuXeJcLSpYWTka7DgeIdSqlHdA7UrO8gQyQXpJj3XXN+Jy0jWhbm9uhwn+I+BgwY1D6Ek7dmwXmt433+yMkJBTbHqJCIDMje+yZcsQEBAAb++HmwB07doVOp0O27Zte+rxGzduhIWFBbp3726ojRw5Er179y6CtGRKdp6MQmaOVqj1aV2JqwYUEUmSsOX2Dvxz75xQt1JbIsh/BFys+AcIPd2AAYMQENAadnb2mDdvEVav/hWurq5yxyIiIyFr43vixAnUqvX4BgOlUglfX18cP378qcfv2bMHDRo0EGqlSpWCj49PoeYk0/IgLQd7z0QLtXpVS8OnnINMiUzP35EHcTD6qFAzU5phjN8wlLN1lykVGaOIiAjhsUKhwLx5ixAScgL9+7/DP1aJSCBb45ucnIz09HS4uLgIdRcXF0RHRz/1NTdv3oSlpSWmT5+O/v37Y9CgQfjtt98gSdJTjyd6FduOhkOjfbwjmFKhQK8A/nFVVI7EnsSfd3YLNaVCiRE1B6KSo5dMqcjY5Obm4osvPkOlSpVw6NBB4Tl397IoX55LDhLRk2S7uS0n5+FNQ/9dNNzc3Nzw3H+lpaVhyZIlWLBgAb788ktERETgnXfeQXp6OkaNGpWv91epeIOSKXh0nl/2fMckZODYJXH1gFZ1y6G8q22BZ6Mnnb13ARtubH2iPrRmf/iX8X3h6/N7vql4unjxAsaOHYlr164CACZMGINjx/6BvT2vypRk/P42LYV1nmVrfC0tLQE8vGHt3/Ly8gzP/ZdCoYCfnx8CAgIAABUrVkSvXr2wevXqfDe+9va8G9yUvOz5nr/lEv59AcHSXIWhb/nCye7pX5NUcC7eu4YVl3+FBPEKztA6ffBmlRb5+lj8/i6ZtFotvvnmG3z55ZfQah/OwTc3N8eECRNQvrwbVCqu52wK+P1Nr0O2xtfJyQl2dnZITEwU6omJifDwePolKnd3d5QpI66hWrZsWSQmJiInJ+eZDfPTpKVlQ6fTv/hAKtZUKiXs7a1e6nxfj0zG6avxQu3NRp6AVofk5MzCjGnywlMi8ePZJdDpdUK9s3d7NCnd6KX///Nzvql4uXnzBsaNG41z584YarVq1cIvv/yCChUqIS3t6VcKqeTg97dpeXS+C5qs6/g2btwYly9fNjyWJAlXr17FmDFjnnp8/fr1ERMTI9QSExPh5OSUr6YXAHQ6PbRafuOYihedb0mSsH7/LaFmb22G9vU9+HVSyOIy4zH/3Ark6sSrPy3LNUHHCu1e6f+f398lh16vx/Lli/HVV18YpsEplUpMmvQ+PvroE7i5OSE5OZPn24Tw+5teh6wTZUaNGoWQkBCEh4cDALZv3w6lUmlYrmzq1Kn48MMPDccPGTIEFy9exMWLFwEAKSkp+OOPPzBo0KAiz04ly9kbCbhzN02ovdXMC1YWsu/xUqIlZScjOHQ5MrVZQr2ea230qdKNd+QTvvjiU3z66ceGptfHpxL++msvpk79/Il7RIiIXkTW3+p+fn6YPXs2Jk+ebNi5bcWKFbC1fXgjUW5urmEeFwBUq1YNwcHBmDFjBtRqNXQ6Hfr27Yvhw4fL9SlQCaDV6bE5JEyouTpZIcC/rEyJTEN6XgaCLyxDSm6qUK9eqgoG1+gHpYI3sBAwdGgg1qxZiaysLIwcOQbTpn0Ba2truWMRUTGlkEx0LTBeGjMNarUSTk42zz3fB8/FYO2em0JtbPeaaFCNi94XlmxtDuadX4Ko9Fih7mXviQl1RsFC9WojeS9zvsm4SZL0xEj/5s2/w9XVDS1aBAh1nm/TwvNtWh6d74LGIRUyaTl5WvxxLEKoebnbo37V0vIEMgEanQZLL65+oul1t3HD2NrDX7nppeLvjz+2oFOntsjMFG9m7NWr7xNNLxHRq2DjSybt73+ikZYp3lTVt7UP55YWEp1eh5+v/IqbKeLUklKWTgjyHwEbM17CNkXJyQ8wevQwjBw5FGfPnsH//ve53JGIqITinTtkslIz87D7VJRQ8/NxRlVPJ5kSlWySJOG3G1twIfGKULc1s8EE/xFwtODmA6Zo376/8d57ExAff89QS0pKgk6n47q8RFTg2PiSydp+LBy5msfrxioUQO9W3Jq4sPwRtgsn4k4LNUuVJYL8R8DVmlNLTE1GRjqmT5+GtWtXGWqOjo745ps56NGjN6+6EFGhYONLJin+QRYOh94Vas1quqN8aW5NXBj2Rh7C3qhDQk2tVGOM3xB42JWTJxTJ5vjxo5g4cSyioiINtTZt2uGnnxagTBl3GZMRUUnHxpdM0uaQMOj0jxc0MVMr0b2Fl4yJSq7jd09jW9hOoaZUKBHo+w4qO3GE3dR8/fWXmDfvBzxaUMja2gYzZszEoEFDOcpLRIWOjS+ZnLC7qThzI0GotatXHqXs87f7H73YhYTL+PX6pifq71TrDb/SvjIkIrk5ODgamt4mTZph7tyFqFiRf3QSUdFg40smRZIkbDworihgY6lGpyYVZEpUct1Mvo2VV36FBHGp8B6VOqOxe32ZUpHcxo4NwqFDB9C2bXuMHj0OSiUXFyKiosPGl0zKhbAk3IxOEWqdm1SEjaWZPIFKqKi0GCy5uBpavVaod6jQGu08uR6rqbh27SpOnjyOYcNGGGoqlQobN27jtAYikgUbXzIZer2EzYfE0V5newu0rcebqwpSfOZ9LLiwAjm6XKHe1L0hunq/KVMqKko6nQ6LFgXjm2/+B61WC1/fWmjYsJHheTa9RCQXXmMik3HschxiE8UdoXq09IaZmmuFFpTknBTMD12ODI34/+xfuibertaTDY8JCA+/g+7dO2HGjM+Ql5cHvV6P+fN/kDsWEREANr5kIvI0Omw7Ei7UPFxt0di3jEyJSp4MTSaCQ5cjOTdFqFd1qoShvgOgVPDHTUkmSRJWrVqB1q2b4dSpEwAejuyOHTsBy5atljkdEdFDnOpAJmHP6Wgkp4uX3vu08oGSI5AFIkebi4UXVuJe1n2h7mlXHqNqDYaZkj9qSrK4uLt4993xOHhwv6Hm6VkR8+cvQpMmzWRMRkQk4hAMlXhpmXnYcSxCqFWv4ARfr1LyBCphNHotll1ag8i0aKHuZl0a42sHwlLNZeJKKkmSsHHjerRs2VhoegcPHo5Dh46z6SUio8NhGCrxNu6/iaxccXWBPq19ON+0AOglPdZcXY/rybeEupOFIyb4j4StuY1Myago6HQ6LFmyEKmpKQCAMmXc8dNPwWjTpr28wYiInoEjvlSiJaRkY8dRcW5voxpuqFjGXqZEJYckSdhwYyvO3b8o1G3MrBHkPwJOlo7yBKMio1arERy8BBYWFujVqy8OHz7JppeIjBpHfKlE23woDFqd3vBYpVSgR0tvGROVHDvC9+Do3VNCzVxljvG1A1HGxlWmVFSY0tJSkZiYCG/vx1tNV6tWHSEhJ4UaEZGx4ogvlVhR8ek4cfmeUGtdpxxcHa1kSlRyHIg+gt0R+4WaWqHC6FpDUMHeQ6ZUVJgOHz6EgIAmGDbsHeTmijeKsuklouKCjS+VWBsPhQmb5Vqaq9ClWUW54pQYp+LOYvOtP4WaAgoM9R2AaqUqy5SKCktWVhamTv0AvXt3RWxsDK5du4off/xW7lhERK+EUx2oRLoS8QBXwh8ItY6NK8De2lymRCXDpcSrWHd94xP1t6v2RB3XWjIkosJ0+vQpTJgwBnfuPN7xsEWLAAwcOFS+UEREr4GNL5U4eknCpoPi1sSOtuboUJ+X4F/H7ZRwrLi8DnpJL9S7eXdEs3KNnvEqKo5yc3Px3XezEBz8E/T6h+fbysoKn38+A8OGjYRSyYuFRFQ8sfGlEuefa/GIjE8Xaj1a+sDCnFsTv6qY9LtYfPFnaPTisnBtPVqifYVW8oSiQnH58iUEBY3G1auXDbV69RogOHgxfHw4lYWIircC/7N95cqVBf0hiV6aRqvHlpA7Qq28qy1a+rvLlKj4S8hKQvCF5cjW5gj1xmXqo0elzlwPuQRJTExEly7tDU2vmZkZpk2bjj///JtNLxGVCK884vvPP//g2rVryMjIgCQ9voVo69atGD58eIGEI8qvQ+djkZgqNmhDOteASqmEVq9/xqvoWVJz0zA/dBnS8zKEei2XGhhQrReb3hLGxcUFY8dOwJw5s1GjRk0EBy9BzZqcu01EJccrNb5fffUVfv/9d1SqVAk2NuLOTOnp6c94FVHhysrR4s/jEUKtcnkHNPItg5SULHlCFWNZmiwEhy5HUo54k2AlRy8M930HKiWnjhR3er0ekiRBpXp8Lt9/fwpKlSqFwYOHw8LCQsZ0REQF75Ua3yNHjuDQoUMoVarUE89NnTr1tUMRvYpdpyKRka0Rav3bVuao5CvI0+Vh0cWfcTdTXAe5vG1ZjPEbCnOVmUzJqKBER0dh0qRxaN68Jd5/f4qhbmZmhpEjx8qYjIio8LzSHF9vb++nNr0AG1+SR3J6LvaejhZqdauURmUPR3kCFWM6vQ7LLq/FndRIoV7ayhnj/QNhpeYGIMWZJEn49de1CAhogqNHD+P777/BpUsXX/xCIqIS4JUa3379+mHFihWIj48X5vcCQFBQUIEEI8qPP47eQZ728RxepUKBXgHcmji/9JIea65twNWkG0LdwdweE/xHwt7cTqZkVBDi4+MxaFA/vPvueGRkPJyW5uZWBllZnApERKbhlaY6jBkzBgDw/fffF2gYolcRm5iJIxfjhFqL2u5wd7Z5xivoaSRJwqZbf+JMfKhQt1ZbIch/BJytnn6Vh4qH7du3YsqU9/DgweM52/37v4OvvvoG9vYOMiYjIio6r9T4VqtWDZ988skTdUmSMGvWrNcORZQfmw+F4d8XHszNlOjW3Eu+QMXU7oj9CIk5JtTMlWYYW3s4ytqWkSkVva7k5AeYOvUDbNmyyVBzcSmNOXPmoWPHzjImIyIqeq/U+I4dOxYNGzZ86nOTJ09+rUBE+XEzOgWhtxOFWocGnnC05d3o+XE45jh2hO8RaiqFCiNrDYa3QwWZUtHrun79Gvr06Yb4+Mc3KXbp0g3ffvsjXFxcZExGRCSPV5rj+8YbbzzzuXPnzr1yGKL8kCQJGw/dFmq2Vmbo2MhTpkTF05n4UPx+8w+hpoACg2v0Qw3nqjKlooJQsaIXHBweTmNwcHDEwoXLsGLFGja9RGSyXnrE9/Lly7CxsYGXlxeCg4Ofedz27dsxadKkAglH9DznbiYiLDZNqHVtVhFWFtyJ+2VdSbqB1VfXQ4J4k2rfKt1Q381fnlBUYCwtLREcvATffTcL338/F+7uZeWOREQkq5fuEIKCguDt7Y2VK1di9erVqFat2lOP4wYWVBR0ej02h4QJNVdHK7SqU06mRMXPndRILL+0BnpJ3NGus1d7tCzfVKZU9KpycnIwe/bXePvtgahS5fFIvb9/Xfzyy0YZkxERGY+Xbnz37Nlj2N3Hz88PK1aseOpxgYGBBZOM6DmOXIjDvQfiEkw9A7yhVr3S7B2TczfjHhZdWIk8vbjhR0D5ZuhYsZ1MqehVhYaeQ1DQaNy8eQPHjx/BX3/tg1rNKx9ERP/10l2Cubm5ofF9VtMLcIkzKny5eTr8cTRcqFUsY4f61VxlSlS8JGY/QHDoMmRps4V6fTd/9K78Fne6K0Y0Gg1mz/4aHTu2xc2bD9devnr1Cs6fPytzMiIi41Tgw2Oc30uF7e/TUUjNzBNqfVpXgpIN2wul5aUjOHQZUvPEKUk1nKticPV+UCo4Yl5cXL9+DR07tsWcObOh0+kAAH5+/ti79zAaNGgkczoiIuP0StfC0tPTMWvWLBw5cgSJiYkvfgFRAUnLzMOuU1FCrZa3M6pXcJIpUfGRrc3GwtAVSMhOEureDhUxsuYgqJQqmZJRfuh0OixevADffPM/5ObmAgBUKhXee+9DvPfehzAzM5M5IRGR8XqlxnfmzJlQq9WYNWsWvv/+e3zyySfIy8vDnj17YGHB9VOp8Px5LAK5eTrDYwWA3q185AtUTOTpNFh8cRWiM+4K9bI2ZTDWbyjMVeYyJaP8iIgIx8SJY3Hy5HFDrWrVaggOXoLatevImIyIqHh4pcY3PDwc69evBwAsXbrUsJlF8+bNMX78+IJLR/Qv8clZOBQaK9Sa1CwDD1dbmRIVDzq9Diuv/ILbKeK8aGfLUgjyHwFrM2uZklF+xcXdxalTJwAACoUCY8YEYerUz2BpaSlzMiKi4uGVJvSZmz8eHcrLy4NG8/jO8NjY2Ke9hOi1bQm5A53+8XqzapUSPVp4y5jI+OklPX65vgmXEq8KdTtzWwT5j4CDhb1MyehVNGnSDKNHj4enZ0Vs27YTX375NZteIqJ8eOU7WX755Rfk5eWhUqVKmDx5MrZv346pU6dCqeTNMVTwwuPScPr6faHWrl55ODvwl/6zSJKErbf/wql74h3+VmpLBNUeAVdr7t5lzCRJwoEDe6HXi+ssf/LJ5zh06BiaNGkmUzIiouLrlbrUcePGISYmBqmpqRg/fjzCwsIwZcoUHDlyBJ9++mlBZyQTJ0kSNh4Utya2tlCjU5MKMiUqHvZGHsKB6CNCzUypxhi/YShvxx28jFliYiICAwejf/9eWLp0ofCcpaUlbG3tZEpGRFS8KSRJkl582IslJyfDyan43FmfnJwJrVb/4gNJdhfDkvDTxgtCrU9rH3Rs9OLGV61WwsnJxuTO97HYU/j1xmahplQoMarWYNRyqSFTqsJXEs737t078f77E5CYmAAAsLCwwD//XOB2w09REs43vTyeb9Py6HwX+Md92QNv376NRYsWAQC6du2KgIAAAECbNm0QFxcH4OHNFlu2bHnmdsZE+aXXS9h0SBztLWVvgXb1ysuUyPidv38Jv93Y8kR9YLU+JbrpLe7S0lLx6acfY/36Xwy1UqVK4dtvf2TTS0RUQF56qsOOHTtw7Ngx1K9fH35+foa6k5MTZs6cia+//ho9e/bExo3cE54Kzokr9xCTkCnUerTwhpmaa84+zfUHt7Dqyq+QIF7I6V25Kxq515MpFb3IkSMhaNWqqdD0vvFGR4SEnELXrj1kTEZEVLK89Ijv8ePHsXz5ctSsWVOoW1tbo0ePhz+Y27Zti2HDhhVsQjJZGq0OW4/cEWrlS9ugiW8ZmRIZt8i0aCy9tBpaSSfU36zYFq09msuUip4nKysLX301HcuXLzHUbG3t8PXXs9G//zvcPpqIqIC9dOOrUqmeaHoBYPLkyYZ/Ozg48Ac1FZh9Z2PwIC1XqPVuVQlKJb/G/ute5n0suLACuTpxK+fmZRuhi1cHmVLRi8ybN0doeps3b4m5cxfCw8NTxlRERCXXS091+Pfavf/m7+8vPLaxKfiJyGR6MrI1+Ot4pFCr5umIWt6lZEpkvB7kJGN+6DJkarKEeh1XP/Sr2oN/jBqxoKB34elZEZaWlvj669nYtGk7m14iokL00iO+Wq0WGo3mufvA5+bmGvaOJ3odO09EIitXK9T6tK7EJu4/0vMyEBy6HCm5qUK9mlNlDKnRH0oF19U2JhkZ6cJSZLa2dli27GfY2dmjUqXKMiYjIjINL/1bMSAgAN9+++1zj/nmm2/QqlWr181EJi4pNQf7zsYItQbVXOHlzl3G/i1Hm4OFF1YiPitBqFew98DIWoNhpnylHcmpEGi1WsydOwf169dCZGSE8FydOvXY9BIRFZGX/s04dOhQDBkyBD169EDnzp3h4+MDKysrZGdn49atW/jrr79gZWWFqVOnFmZeMgHbjtyBVvd4jUaVUoGeAdya+N80ei2WXFqDqHTxD4Qy1q4YV3s4LNUWMiWj/7pz5zbGjx+Ns2dPAwAmTRqHLVt2cJdLIiIZvHTja25ujlWrVmHu3LlYvHgxMjIyDM/Z2tqiT58+mDRp0jPnAhO9jOj7GTh++Z5Qa+VfDm5O1jIlMj56SY9VV37FzWRxfWMnC0cE+Y+ArRnn2RsDvV6Pn39ehhkzPkd2djYAQKlUon79htDpdGx8iYhkkK9roRYWFpgyZQomT56M8PBwpKWlwd7eHl5eXlCpuK4qvb5Nh8KEFWgtzFV4q1lFueIYHUmSsP7GFoQmXBbqtmY2mFBnJJwsHeUJRoKYmGhMmjQeR44cMtS8vLwxf/4SNGzYSK5YREQm75UmAapUKlSqVKmgs5CJuxaZjEt3koRax0aesLfhVYRHtt/ZjWN3/xFqlioLjK8dCDfr0jKlokckScKGDb9i2rSPkJ6eZqgPHz4Sn302g6veEBHJjHe/kFHQSxI2HhQv3TvYmOONBlza6ZF9USHYE3lQqKkVKoz2GwJPe27hbAymTHkfq1evMDwuW7Yc5s5diICA1jKmIiKiRzjJjIzCmev3EXEvXah1a+4FC3NOoQGAE3FnsPX2X0JNAQWG1XwHVZx49cVYdOzYyfDvfv0GICTkBJteIiIjwhFfkp1Wp8fmkDChVqaUNVrUdpcpkXG5kHAFv17f9ER9QLXe8C/95G6KJJ82bdrj3Xc/gL9/XXTq1EXuOERE9B8c8SXZHTofi4SUHKHWK8AHKt71jlvJYVh55RfoJb1Q7+7TCU3LNpApFQHAgQP78N57QZAkSah/8snnbHqJiIwUR3xJVtm5Wmw/FiHUfMrZo24VF3kCGZHo9FgsvrgKWr24g117z1ZoX6GVPKEIGRkZ+PLLzwxzeevWrY9Bg4bKG4qIiF4Kh9RIVrtORSEjWyPU+rTi1sT3sxIQHLocOTpxC/Cm7g3QzaejTKno5MkTaN26qXAD24ED+2RMRERE+cHGl2STkpGLPaejhJp/JRdU8XCUJ5CRSMlNxfzQ5cjQZAr12qVron/Vnib/R4EccnJy8MUXn6JbtzcNWw5bW1tj9uwfsHLlWnnDERHRS+NUB5LN9qPhyNM8nruqUAC9WvnImEh+mZosBIcux4OcZKFexdEHw2q8DZWSq1wUtYsXQxEUNBrXr18z1Bo2bIx58xbB29u0v16JiIobjviSLOKSMnH4QpxQa+HnjnIuprvAf64uD4surERcZrxQ97Arh1F+Q2CmMpMpmWmSJAnff/8N3nyzjaHpNTc3x+ef/w9//LGLTS8RUTHEEV+SxeaQO9D/6254c7US3Zp7y5hIXlq9FssurUF4mjj1w9XaBeNrB8JKbSlTMtOlUCgQFnYbWu3Dmwtr1aqN4OAlqF69hszJiIjoVXHEl4rc7ZhUnLuZINTaN/CAk52FTInkpZf0WHN1A649uCnUHS0cEFR7JOzMbWVKRrNmfQcPD0+8//4U7Nq1n00vEVExxxFfKlKSJOH3Q+LWxLZWZujYqIJMieQlSRI23vwDZ+9fEOo2amsE+Y+As5WTTMlMT0REOCIjI4Sd1hwdnXDkyD+wtraWMRkRERUUjvhSkQq9lYjbMalC7a2mFWFtaZp/g/0VvheHY08INXOVOcbWHg53GzeZUpkWSZKwZs3PaNWqKUaOHIJ798S552x6iYhKDja+VGR0ej02/WdrYhcHS7SqU06mRPI6GH0UuyLENWBVChVG1RoMLwdPmVKZlnv34jBgQG988MEkZGVlIiUlBbNnfy13LCIiKiRsfKnIHL0Yh7ikLKHWs6U3zNSm92X4z71z2HRru1BTQIGhvm+jeqkqMqUyHZIkYcuWjWjZshH2799rqA8cOAT/+98sGZMREVFhMs3ry1TkcvN02HY0XKhVcLNDwxqmdzn/cuI1rL32+xP1flV7oK6rnwyJTEtSUhI++uh9bN++1VBzdXXDjz/OR/v2b8qYjIiICpvpDbWRLPaciUZqRp5Q693aB0oT24UsLCUCyy+vg17SC/W3vN9Ai3KNZUplOv7+exdatmwkNL3du/fE4cMn2fQSEZkAjvhSoUvPysOuk5FCzderFHwrlpIpkTxiM+Kw6OLP0Og1Qr21R3O8UaGNTKlMR0ZGOt57bzwSExMBAE5OTpg9+wd0795L5mRERFRUZB/x3bt3L3r27IkBAwZg4MCBuHXr1ku97uDBg6hatSq2bNlSyAnpdf15PAI5eTrDYwWAPia2NXFidhKCQ5cjW5st1BuWqYuelbpAYWIj33KwtbXDt9/+BABo3/4NHD58ik0vEZGJkXXE9+LFi5gyZQo2b94Mb29vbNu2DYGBgdi5cydsbZ+9aH9WVhZ++umnogtKr+x+SjYOnosVao193eDpZidToqKXmpuO+eeXIS0vXajXdK6OgdX6QKmQ/e/PEikrKwsaTR4cHBwNtS5dumLLlh1o1qwF/9ggIjJBsv7GXbZsGQICAuDt/XCr2q5du0Kn02Hbtm3Pfd28efPw9ttvF0FCel1bD9+BTv94a2K1SoEeLUxna+IsTTYWXFiOxJwHQt3HwQuBNQdCpVTJlKxkO3PmNNq2bY7JkydB+tfW2ADQvHlLNr1ERCZK1sb3xIkTqFWrluGxUqmEr68vjh8//szXXL16FRcvXkS/fv2KIiK9hoh7aTh1NV6otalbHi6OVjIlKlp5ujwsvvgzYjPEDRHK2bpjjN9QmKvMZEpWcuXl5WHatGl48822CAu7je3bt2Lr1k1yxyIiIiMh21SH5ORkpKenw8XFRai7uLjg0qVLT32NXq/HjBkz8MUXX7z2iI1KxcvLhUmSJGw6dEeoWVuo0a2FF9RFuG7vo/Nc1Odbp9dh5cVfEJYaIdRLWzljUr2RsLewKdI8puDKlcsYN26k8POjbt368Pf3L9KvOSo6cn1/kzx4vk1LYZ1n2RrfnJwcAIC5ublQNzc3Nzz3X+vWrUPdunVRrVq1135/e3vTGHWUy7nr93E1Qry836ddFXiWc5IlT1Geb72kx4JTq3Ep8ZpQd7J0wPQ278LV1uUZr6RXodPp8N133+Hzzz+HRvNwxQy1Wo0vvvgCH330EdRqLl5T0vHnuWnh+abXIdtvBEtLSwAPL03+W15enuG5f4uPj8fGjRuxYcOGAnn/tLRs6HT6Fx9I+aaXJKzYLo7aO9lZoEVNNyQnZxZpFpVKCXt7qyI735Ik4fcb23Ek6h+hbq22woQ6I2CmsSry/4OSLCzsNsaNG43Tp08ZajVr1sTChUtRs6Yf0tNzAeTKF5AKVVF/f5O8eL5Ny6PzXdBka3ydnJxgZ2dnWFPzkcTERHh4eDxx/NGjRwEAo0ePFupLly7F1q1bMWnSJNSvX/+l31+n00Or5TdOYTh+OQ5R8RlCrXtzLygVCtn+z4vqfO+O2I8DUUeEmpnSDGP8hsHNyo1fcwUoLOwW2rZtgaysh9tgK5VKTJjwLmbPnomsLC3/r00If56bFp5veh2yXgNs3LgxLl++bHgsSRKuXr2KMWPGPHFsr1690KuXuOZm1apVMWrUKPTs2bPQs9LL0Wh12HpYnNtbzsUGzWq5y5So6ByJPYE/7/wt1JQKJUbUHAgfx4ryhCrBvL0rISCgDXbt2oGKFb0QHLwUTZs2gYWFBbKytHLHIyIiIyTrDPFRo0YhJCQE4eHhAIDt27dDqVSie/fuAICpU6fiww8/lDEh5deBc7FIShMvLfdq5QOlsmQvH3U2/gI23Nj2RH1w9X6o6VK96AOZAIVCge+/n4tx4ybi4MHjaNiwkdyRiIjIyMk64uvn54fZs2dj8uTJsLS0hFKpxIoVKwybV+Tm5kKrfXLkZunSpThy5Ijh31u3bsXatWuLNDs9KStHgx3HI4RaFQ9H1PZxlidQEbmWdBOrr66HBHG92D6Vu6FBmToypSpZEhIS8MEHk/D22wPx5pudDPXSpUvjiy++kjEZEREVJwrpv6u7m4jk5EzOESpgGw/dxq6TUUJt2uB68CnrIFMiQK1WwsnJptDOd3hqFOadX4I8vUaod6rYDp29OxT4+5miHTu248MPJyEpKQmlS7vi8OFTcHZ++h9ThX2+ybjwfJsWnm/T8uh8FzQuhkcF4kFaDvadiRFq9auWlrXpLWxxmfFYdGHlE01vy3JN0cmrvUypSo6UlGSMGzcSw4cPRFJSEgBAkvS4c+e2zMmIiKi4YuNLBWLbkXBo/vUXuEqpQK8AHxkTFa6k7GQEhy5HpjZLqNd380efKl25Je5rOnhwPwICmmDTpsfLF3bq9BZCQk6hQQPO5SUiolfDld3ptcUkZODYZXFb3pb+ZeFWylqmRIUrPS8DwReWISU3VahXL1UFg6r3hVLBvydfVUZGBmbM+AyrVq0w1OztHTBz5rfo06c//6AgIqLXwsaXXtumQ2H490xxC3MVujbzki9QIcrW5mDBhRW4nyWuP+1l74mRtQZDreS31Ku6dOkCAgMHIyIi3FALCGiNn35agHLlysuYjIiISgr+lqbXciMqGRfDkoTamw094WBj/oxXFF8anQZLLq5CdHqsUHe3ccPY2sNhoSp5n3NRKlXKGQ8ePNzm2traGp9//j8MGzaCo7xERFRgeE2WXpkkSfj9YJhQs7c2Q4cGT+68V9zp9Dr8fOVX3EoRN+dwtnRCkP8I2JiVzGkdRalcufL4+uvZaNCgEQ4cOIbhw0ey6SUiogLFxpde2ZkbCQiPSxNqXZt7wcqiZF1IkCQJv97YjAuJV4S6nZktgvxHwNGi5K5cUVi0Wi0WLQpGWpo4T7pv37exfftueHuX3BsjiYhIPiWrQ6Eio9XpsTlEHO11c7JCy9plZUpUeLaF7cTJuDNCzVJlifH+gXC1Li1TquLr5s0bmDBhNM6fP4fr169i7tyFhucUCgVUKpWM6YiIqCTjiC+9ksMX7uJ+crZQ6xXgA7WqZH1J7Y08hH1RIULNTKnGGL+h8LArJ1Oq4kmv12PJkgVo164Fzp8/BwD4/fffEBZ2S+ZkRERkKjjiS/mWnavF9qPhQs27rD3qVS1Zo5/H7/6DbWE7hZpSocRw33dQ2clbplTFU1RUJCZOHIvjx48aapUqVUZw8BL4+FSWMRkREZmSkjU8R0Xi73+ikJYl7lbWp5VPiboRKTThMn69vvmJ+jvVesOvtK8MiYonSZKwbt1qBAQ0EZre0aPHYf/+o6hbt76M6YiIyNRwxJfyJTUjF3//Ey3Uavs4o6qnk0yJCt7N5Nv4+fIvkCAJ9Z6VuqCxOxu1lxUffw/vvz8Be/f+bah5eHhi3rxFaNashYzJiIjIVHHEl/Jl+7EI5Gp0hscKBdC7Vcm5Az8yLRqLL66CVtIJ9Q4VWqOtZ0uZUhVPu3b9JTS977wzGIcOHWfTS0REsmHjSy/t3oMshITeFWrNarmjXGlbmRIVrPjM+1h4YSVydXlCvVnZhujq/aZMqYqvwYOHoWXL1nB1dcO6dRvw44/BsLOzlzsWERGZME51oJe2OSQM+n/tTWymVqJ785KxNXFyTgrmhy5HhiZTqPuXroX+VXuWqPnLheXmzRuoUqWq4bFSqcSCBUtgZmaGUqWcZUxGRET0EEd86aWExabi7I0Eoda+vgdK2VvKlKjgZGgyERy6HMm5KUK9qlMlDPV9G0oFv02eJyMjHe+/PwEtWjTE4cOHhOfc3Mqw6SUiIqPB3+j0QpIkYePB20LNxlKNTo09ZUpUcHK0uVh4YSXuZd0X6hXsPDCq1mCYKXlR5HmOHTuCVq2aYt261ZAkCZMmjUNGRrrcsYiIiJ6Kv9XphS7cTsLNGHFr2S5NK8La0kymRAVDo9di2aU1iEwTV6lws3bFuNrDYaku/qPZhSU7OxszZ36JJUse77pmY2OL99+fAhubkjHnm4iISh42vvRcer2ETf/ZmtjZ3hJt6paXKVHB0Et6rL66HteTxV3DnCwcMcF/BGzNbWRKZvzOnz+LoKDRuHXrpqHWpEkzzJu3CBUqVJQvGBER0Quw8aXnOnYpDncTxRu+erb0hpm6+M6SkSQJG25sxfn7F4W6jZk1gvxHwMnSUZ5gRi4vLw8//DAbc+f+AJ3u4XJvFhYWmDZtOkaNGgelsvh+TRARkWlg40vPlKvRYdt/tib2dLVFI183mRIVjB13/sbRu6eEmoXKHONrB6KMjatMqYzf1KkfYO3aVYbH/v51EBy8VFjJgYiIyJhxiIaead+ZaCSn5wq13q19oCzGS3sdiDqM3ZEHhJpaocKoWkNQwd5DplTFw/jxk2BtbQ21Wo2PPpqGv/7ax6aXiIiKFY740lNlZGuw82SUUKtR0Qk1vYrv0lSn4s5i8+0dQk0BBYb5DkC1UpVlSmW8dDodVCqV4bG3tw/mzl0ILy9v+Pn5yxeMiIjoFXHEl55qx/EIZOdqhVqfVpVkSvP6Lty/gnXXNz5Rf7taT/i71pIhkfGSJAkrVy5D+/YByMwU53d369aTTS8RERVbbHzpCYkp2ThwLkaoNa7hhgpl7GRK9HquJdzCsotroZf0Qr2bT0c0K9tIplTG6e7dWPTt2x0ffzwZly9fxFdfTZc7EhERUYFh40tP2HLkDrS6x1sTq1UK9GjpLWOiVxedFotvjiyERi+OXrf1bIn2nq3kCWWEJEnC77//hpYtGyMk5KChrtPpIP1rm2oiIqLijHN8SRB5Lx0nr8QLtdZ1yqO0o5VMiV7d/axEzDu3HNmaHKHeuEx99PDpDEUxvkmvICUkJODDD9/Fzp1/Gmplyrjjp58WoE2bdjImIyIiKlgc8SXBpkPi1sRWFip0aVpBpjSvLiU3FcGhy5CWJ26fW8ulBgZU68Wm9//99defCAhoJDS9vXr1xeHDJ9n0EhFRicMRXzK4Ev4AVyKShVrHRhVgZ20uU6JXk6XJwoLQFUjKET+Xyo7eGO77DlRK1TNeaVomT54orMvr7OyMb7/9CW+91U2+UERERIWIjS8BAPSShI3/Ge11tDVH+wbFa23bXF0eFl38GXcz7wl1D7tyGO03BOYqM5mSGZ+KFR/P237zzc74/vu5cHXlBh5ERFRysfElAMA/V+MRFZ8h1Lq38IaFWfEZHdXqtVh+aS3upEYKdXdbV0ysOwJWquI3T7kwjRs3ASdOHEXXrj3Qr98ATv8gIqISj40vQaPVY8vhO0LN3dkazWqVkSlR/uklPdZe+x1XH9wQ6o4W9pjWaiLM8iyh1eqf8eqS759/TuHChXMYOXKsoaZSqfDLLxvZ8BIRkclg40s4eD4Wianiyge9W/lApSwe9z5KkoRNt7bjTHyoULdWW2FSvVFwtXFGcl7m019cwuXm5mL27K+xcOE8AIC/f100aPB47WI2vUREZEqKR2dDhSYrR4sdxyOEWuXyDvCv5CJPoFewK2IfQmKOCzVzpRnG1R6OsrbFZ9S6oF26dAEdOgQgOPgn6PV66PV6rFy5TO5YREREsmHja+J2nYpERrZGqPVpXanYjASGxBzHX+F7hZpKocLIWoPh5VD8lmErCFqtFj/88C3eeKM1rl27CgAwMzPDp59+geDgJTKnIyIikg+nOpiw5PRc7D0dLdTqVSmNSuUcZEqUP2funcfGm38INQUUGFyjH2o4V5Uplbxu3bqJCRNG49y5s4aar28tBAcvga9vTRmTERERyY8jviZs25E7yPvXDV9KhQI9A4rH1sRXkm5g9bUNkCBup9u3SnfUd/OXJ5SM9Ho9li5diLZtmxuaXqVSiffe+wB//32QTS8RERE44muyYhMzcfRSnFBrWdsd7s42MiV6eXdSI7Ds0hroJXGVhi5eHdCyfBOZUslLp9Nhw4bfkJPz8CZFH59KCA5egnr1GsicjIiIyHhwxNdEbT4UBulfg6XmZkp0be4lX6CXFJsRh4UXfoZGL85LblW+Gd6s2FamVPIzMzNDcPASWFpaYuTIMdi//yibXiIiov/giK8JuhmdgtDbiULtjQaecLS1kCnRy0nMfoAFocuRrc0W6g3c6qBX5beKzQ15BSE+/h7S09NRqVJlQ6169Ro4dSoU7u5lZUxGRERkvDjia2IkScLGg+LWxHbWZnizkadMiV5OWl46gkOXITUvXajXdK6GQdX7QqkwnS/lP/7YgpYtGyEwcDByc3OF59j0EhERPZvpdAsEADh3MwFhd9OEWtdmXrCyMN7B/2xtNhaErkBCdpJQ93aoiMCaA6FSFp9tlV9HcvIDjB49DCNHDkVycjKuXbuC+fN/lDsWERFRsWG83Q4VOK1Oj00h4tbEro5WCPA33lHCPJ0Giy+uQkzGXaFe1qYMxvoNg7nKXKZkRWvfvr/x3nsTEB9/z1Dr2rUHhg0bKWMqIiKi4oWNrwk5cjEO8Q+yhFrPAG+oVcY58K/T67DyyjrcTgkX6s6WpRDkPwLWZlYyJSs6GRnpmD59GtauXWWoOTo64ptv5qBHj94mNa+ZiIjodbHxNRE5eVr8cVRsIL3c7dCgmqtMiZ5PL+nxy/VNuJR4Tajbmdtigv9IOFjYy5Ss6Bw/fhQTJ45FVFSkodamTTv89NMClCnjLmMyIiKi4omNr4nY80800jLzhFqfVsa5NbEkSdh6+y+cundWqFupLRFUewRKWzvLlKzoxMXdRZ8+3aDRPFy2zdraBjNmzMSgQUON8pwREREVB8Z5jZsKVFpmHnb9EyXU/HycUa2Ck0yJnm9P5EEciD4i1MyUaozxG4bydsY7H7kgubuXxYQJ7wEAmjRphkOHjmPw4GFseomIiF4DR3xNwPZj4cjN0xkeKwD0DvCRL9BzHI09ie13dgs1pUKJETUHoZKj8W+w8ao0Gg0UCgXU6sffku+/PwUVKlREv34DoFTyb1QiIqLXxd+mJVz8gyyEhIorIjStWQblXW1lSvRs5+5fxPobW5+oD6reFzVdqsuQqGhcu3YVb77ZBsHBPwl1c3NzvP32QDa9REREBYS/UUu4LYfvQKd/vDexWqVE9xbeMiZ6uusPbmH1ld8gQRLqvSt3RcMydWVKVbh0Oh2Cg+eiffuWuHTpAr77bhYuX74kdywiIqISi1MdSrDwuDScvn5fqLWrXx7ODpYyJXq6iLQoLLm0GlpJJ9TfrNgWrT2ay5SqcIWH38HEiWNx6tQJQ83b2zinnxAREZUUHPEtoZ62NbGNpRqdm1SQKdHT3cuMx8ILK5GnE1ecaF6uMbp4dZApVeGRJAmrVq1A69bNDE2vQqHA2LETsHfvYdSsWUvmhERERCUXR3xLqEt3knA9KkWodW5SETaWZvIEeooHOcmYH7ocmRpxU426rn7oV6V7iVvBIC7uLt59dzwOHtxvqHl6VsT8+YvQpEkzGZMRERGZBja+JZBeL2HjoTCh5mxvgbb1ysmU6EnpeRkIDl2OlNxUoV69VBUMqdEfSkXJuhgRGnoOffp0R2pqiqE2ePBwfPHFV7C1Nb4bDYmIiEoiNr4l0PHL9xCbkCnUurfwhplaJVMiUY42BwsvrEB8VoJQr2jviRE1B0GtLHlfllWrVoebmxtSU1NQpow7fvopGG3atJc7FhERkUkpWcNqhDyNDluP3BFq5UvboolvGZkSiTQ6DZZcWoOo9FihXsbGDWNrD4Ol2kKmZIXLysoKwcFL0Lt3Pxw+fJJNLxERkQzY+JYw+8/GIDk9V6j1ae0DpVL++bJ6SY9VV3/DzWTxprtSlk6Y4D8CtmY2MiUrWGlpqZg8eSJu374l1P3962LhwmVwdDTOHfOIiIhKupJ3TdmEZWRr8NeJSKFWvYITanqVkinRY5Ik4bfrWxCacFmo25rZIMh/BBwtHGRKVrAOHz6ESZPGITY2BleuXMKOHXuF3diIiIhIPhzxLUH+OhGBrFytUOvdyscoVkfYfmc3jsf9I9QsVRYY7x8IN+vSMqUqOFlZWZg69QP07t0VsbExAIBbt27h+vVrMicjIiKiR9j4lhCJqdnYfzZGqDWs7govd3uZEj22LyoEeyIPCjW1Uo3RfkPhaVdeplQF5/TpU2jTphlWrFhqqLVoEYCQkBNcl5eIiMiI8BpsCbHtSDi0usfb/aqUCvRsKf/WxCfunsbW238JNQUUGO47AFWcivdOZbm5ufjuu1kIDv4Jer0ewMOb2D777EsMHz4KSiX/riQiIjImbHxLgKj4dJy4fE+otapTDq5O1jIleuhCwmX8cn3TE/V3qvVG7dI1ZUhUcK5cuYzx40fh6tXHc5br1auP4OAl8PGpLGMyIiIiehY2viXAppAwSP96bGmuwlvNKsoVBwBwMzkMK6/8CklIBvSo1BlNyjaQKVXBSU1NwbVrVwAAZmZmmDLlE4wfP4k3shERERkx/pYu5q5FPMDlOw+EWsdGnrC3NpcpERCVHoMlF1dBqxdvtGvv2QrtPANkSlWwmjZtjlGjxuHIkRAEBy/hXF4iIqJigI1vMaaXJPz+n62JHWzN0aGBp0yJgPisBCwIXYEcnbiWcFP3Bujm01GmVK9Hr9fjzz+34a23ugvzdqdNmw6FQgELi5K56QYREVFJw7tvirHT1+4j8l66UOvW3AsW5vJsTZySm4rg0OXI0IjbJfuXron+VXsaxbJq+RUdHYXevbti5MihWL58sfCcpaUlm14iIqJihI1vMaXV6bHlsDja6+5sjRZ+7rLkydRkYX7ocjzISRbqVRx9MLTG21Ap5WnGX5UkSfj117UICGiCo0cPAwC+/vpLJCYmypyMiIiIXhWnOhRTB8/HIiElR6j1CvCBSoYltHK0uVh4YSXuZcYLdU+7chjlNwRmKrMiz/Q64uPjMXnyBOzZs9tQK1euPObOXQgXFxcZkxEREdHr4IhvMZSdq8WfxyKEWqVyDqhTueibMq1ei+WX1yIiLUqou1q7YFztQFipLYs80+vYvn0rAgIaCU1vv34DEBJyAi1btpIvGBEREb02jvgWQ7tORSIjWyPU+rQu+q2J9ZIea65uwLUHN4W6o4UDJviPhJ25bZHmeR3JyQ8wdeoH2LLl8brDLi6lMWfOPHTs2FnGZERERFRQOOJbzKRk5GLPP9FCrU5lF1Qu71ikOSRJwoab23D2/gWhbmNmjQn+I1DK0qlI87yuuXN/EJreLl264fDhU2x6iYiIShA2vsXMH0fDkafVGx4rFA/n9ha1v8L34GjsSaFmrjLHuNrDUcbGrcjzvK4PPvgInp4V4ODgiIULl2HFijWcz0tERFTCcKpDMRKXlIkjF+KEWgu/sijrYlOkOQ5GH8WuiP1CTaVQYXStIahoL98awvnx4EESSpVyNjy2tbXDypVrUbq0K9zdy8qYjIiIiAqL7I3v3r17sWjRIlhaWkKpVGL69OmoXLnyU489fvw41qxZg6ysLOTm5sLGxgYffPABatSoUcSp5bHpUBj00uMtgM3VSnRr7lWkGf65dw6bbm0XagooMNT3bVQr9fTzZkxycnIwc+YM/PrrWhw4cBSenhUMz/n5+csXjIiIiAqdrFMdLl68iClTpuD777/Hr7/+it69eyMwMBAZGRlPPX769Olo06YN1qxZgw0bNqB27doYNmwYkpKSijh50bsVk4Lzt8Q1ZDs09ICTXdFtoHA58RrWXvv9iXr/qj1Q19WvyHK8qtDQc2jXrgUWLw5GWloqJk4cC71e/+IXEhERUYkga+O7bNkyBAQEwNvbGwDQtWtX6HQ6bNu27anH16xZE7179zY8HjRoEFJSUnD8+PGiiCsbSZKw8aC4WYWtlRk6NqrwjFcUvNsp4Vh+eS30ktgovuX9JpqXa1xkOV6FRqPBN998jY4d2+LmzRsAAAsLC3To0BHSv0bQiYiIqGSTtfE9ceIEatWqZXisVCrh6+v7zEb2xx9/hPJfGzQ82i5Wo9E89fiS4vytRNyOTRVqbzWrCCuLopmpEpsRh8UXf4ZGrxXqbTxa4I0KrYskw6u6fv0amjRpgm+/nQWdTgfg4ZSGvXsPY9y4CVCpiteOckRERPTqZJvjm5ycjPT09CfunHdxccGlS5de6mOEhobC0tISrVq1yvf7q1TFY0ELnV6PzSHiaK+roxXaN/CAugg+h4SsRASHLke2VtwlrrF7PfSp9haUCuP8f9TpdFi4MBgzZ85Abm4uAEClUmHy5CmYPHkKzMyK125y9HIefV8Xl+9vej0836aF59u0FNZ5lq3xzcl52EiZm5sLdXNzc8NzzyNJEhYtWoRJkyahVKlS+X5/e3urfL9GDrtPRCAuKUuoDelSA6Vd7Ar9vZOzUzH/2HKk5aUL9Xpla2Fis2FQK413tLR///7YsGGD4XH16tWxZs0a1K9fX8ZUVFSKy/c3FQyeb9PC802vQ7bG19Ly4Va2eXl5Qj0vL8/w3PPMnz8fbm5uGD58+Cu9f1paNnQ6476xKTdPh3W7rwm1iu528K3giOTkzEJ97yxNNuacXoj4TPGGukqOXhhafQDSU1/8x4mcevbshw0bNkChUOC9997DlCnTYG5uUej/byQvlUoJe3urYvH9Ta+P59u08Hyblkfnu6DJ1vg6OTnBzs4OiYliY5WYmAgPD4/nvnb9+vW4dOkSFixY8Mrvr9PpodUa9zfOzpORSM0Q/zDoHeADvU6CHoV3U1aeLg/BoSsQkyGuGVzO1h1j/IZCKamM/v+udet2+PDDqQgICEDnzm8gOTnT6DNTwSkO399UcHi+TQvPN70OWSfKNG7cGJcvXzY8liQJV69eRdOmTZ/5mh07dmDnzp2YP38+zM3NER0dXSJXdUjLysOuk5FCraZXKdSomP9pHfmh0+uw4vI6hKVGCPXSVs4I8h8BK7VxXWKSJAmbN/+OceNGPrFCw4cfTkXTps1lSkZERETGRtbGd9SoUQgJCUF4eDgAYPv27VAqlejevTsAYOrUqfjwww8Nxx88eBBz5szBuHHjcOvWLVy6dAnHjh3D2bNn5YhfqHYci0BOns7wWAGgd6vC3ZpYL+mx9tpGXE66LtQdzO0Q5D8S9uaFP684P5KSkjBixBCMHTsCmzZtwC+/rJE7EhERERkxWXdu8/Pzw+zZszF58mTDzm0rVqyAra0tACA3Nxda7eMltKZOnYrk5GQMGTJE+DhBQUFFmruw3U/JxsHzsUKtsW8ZeLoVXuMpSRI23/oTp+PPCXUrtRXG+4+Ai1XhjjTn199/78L7709AQsJ9Q+38+XMYOHDIc15FREREpkwhmegK/sY853PxH5fxz7XHDZ1apcDMUY3h4lB40wx2he/HjvC/hZqZ0gwT64yEt0PFQnvf/EpLS8Wnn36M9et/MdRKlSqFb7/9EV279njieLVaCScnG6M+31RweL5NC8+3aeH5Ni2PzneBf9wC/4j0WsLj0oSmFwDa1itfqE3v4ZgTTzS9SoUSI2sNMqqm98iREEyaNA4xMdGGWocOb2LOnIcrfBARERE9DxtfIyJJEjYdEjersLZQo3OTioX2nmfjQ/H7zW1CTQEFhlTvB1/naoX2vvmh0+nw2WcfY/nyJYaara0dvvrqG7z99kAoFAoZ0xEREVFxwcbXiFwOf4BrkclCrXOTCrC1Kpxdxq4l3cTqqxsg/WdptN5VuqJ+mTqF8p6vQqVS4cGDB4bHzZu3xNy5C+Hh4SljKiIiIipu2PgaCb1ewsaD4mivk50F2tYrXyjvF54aiaWXVkMn6YR6J6/2aFW+WaG85+uYNes7nDt3BiNHjkFg4GgoldyykoiIiPKHja+ROHHlHmISMoRajxbeMDcr+G2B72bcw6ILPyNPrxHqAeWbolPFdgX+fvl15cplxMfHoU2b9oaak1MpHDt2BmZmhTP6TURERCUfh82MgEarw7Yjd4RaudI2aFqzTIG/V1L2AwSHLkemNkuo13fzR+/KXWWdL6vVajF37hx06BCAsWNHID7+nvA8m14iIiJ6HWx8jcD+s7FISssVar0DfKBUFmwTmp6XgeDQ5UjNSxPqNUpVxaDqfaFUyPflcOfObbz11hv4+usvodFokJycjLlz58iWh4iIiEoeNr4yy8zR4K8TEUKtqocj/HycC/R9srU5WBC6HPezE4W6l30FjKg1CGqlPLNe9Ho9VqxYgtatm+Hs2dMAAKVSiQkT3sP06V/JkomIiIhKJs7xldnOE5HIzNEKtT6tKxXolAONToMlF1chOuOuUC9rUwZjaw+Dhcq8wN4rP2JiojFp0ngcOXLIUPPy8sb8+UvQsGEjWTIRERFRycXGV0YP0nKw90yMUKtfzRXeZe0L7D10eh1WXvkVt1LEOcTOlk4Y7x8IGzPrAnuvlyVJEjZs+BXTpn2E9PTH0y6GDx+Jzz6bARubgt+phYiIyFjl5ubg11/X4vTpU1CpVNBoNLCwsETLlgFo06Y9nJxKYfLkibhy5SL69HkbgYGjZcl54UIoFiz4Cebm5sjLy8P48ZNQu7bxLH/6Mtj4ymjrkTvQ6h5vu6hSKtArwLvAPr4kSfj1xmZcTLwi1O3MbBHkPxKOFg4F9l75kZKSjC++mGZoesuWLYe5cxciIKC1LHmIiIjkkpubg4kTx6J2bX/Mm7cYavXD1iwk5CCmT5+KlJQUBAaOxpw58xAUNEq2nPfuxWHKlEmYNWsO6tatj/Pnz2LKlHexevV6lCnjLluu/OIcX5nE3M/A8UviqgUB/mXh5lRwI7DbwnbiZNwZoWapssR4/xFwtXYpsPfJLyenUvjuu58AAH37vo2QkBNseomIyCStWLEEGk0exo6daGh6ASAgoDW6d+8tYzLRxo3r4elZEXXr1gcA1KlTD56eFbBp0waZk+UPR3xlsikkTNgvzcJcha7NvArs4++NPIR9USFCzUypxhi/ofCwK1tg7/MyUlIe7kbn6OhkqL31Vnf8/fdB1KlTr0izEBGR6dDrJWTkaF58YAGytTR76VWZtFottm/fioEDhz713p7+/QciIyP9ma8/e/Y0Vq1aDkmSoNFo4OlZARMnToadnR0AIDU1Bd99NwtJSQlQqdSwsrLC0KEj4etbE7m5ufjpp+8QFnYbFhYWUCgUePvtQWjS5OmbWJ058w9q1/YXatWq+eLMmX9e6nM1Fmx8ZXA9MhkXw5KEWseGnrC3KZibzI7dPYVtYTuFmlKhRGDNgajsVHBTKV7GgQP78N57QWjUqDGWLl0lPMeml4iICsvp6/fxy54bSMsq2sbX3toM73SoigbVXF94bFRUBDIyMlCx4tMHvsqUKQPg2Wv6nzhxDC1btkafPv0hSRK+/fZrzJ//Az75ZDoAYNmyxbCwsMCiRSsBAEuXLsTJk8fg61sTmzatR3R0lOF3859/bsOBA3uf2fjevRuL1q3bCjVnZ2fcvRv7ws/TmHCqQxGTJAkbD90WavY25ujQ0KNAPn7o/Uv47fqWJ+oDq/VBLZcaBfIeLyMjIwMffvge+vfvibi4u9i2bQu2bdtcZO9PRESmbdWu60Xe9AJAWpYGq3Zdf6ljMzIe7thqZfVq0xz7938HXbv2AAAoFAq0atUWp04dNzyfmHgfycnJyMvLAwD06fM22rd/EwCQkJCA9PR0ZGY+zNCu3Rvo23fAM98rNzcH5ubiAJ2ZmRlyc3NeKbtcOOJbxE5fv4/wOPGyRbdmFWFp/vqn4saD2/j5yq+QhEkUQK9KXdDIvehGV0+ePIEJE0YjMjLCUGvVqg0aNmxcZBmIiIiMnZ3dw1WcsrOzX+n1Wq0OP/74LSIiwqFWq5GRkYGkpMdXlAcOHIqpUz9Az56d0bp1O7z1VjdUqVINANCrV18cP34EPXt2RkBAG3Ts2OW5V2ItLCwNDfQjj1afKE444luEtDo9toSIy4q5lbJGi9qvP+c2Mi0aSy6tglbSCfU3KrRBG8+Wr/3xX0ZOTg6+/PIzdOv2pqHptba2xuzZP2DDhq0oW7ZckeQgIiIa2rEa7K2Lfqt7e2szDO1Y7aWO9fSsAFtbO0RGhr/Se33wwUSkp6dh3rzFCA5eiokT3xeer1nTD5s2bcfEie8jKioCgYGDsGXLRgCAh4cnfv11M6ZN+xLp6emYOHEMFi2a/8z3Klu2HB48EKdpJiUlFbvf7RzxLUIhoXdxP0X8q65XS2+oVa/398e9zPtYeGElcnXiX2LNyjbCW95vvNbH/r/27jssiqv7A/h3C0sXQRCQJqiAikiIYg1YorFrxC5o1ARQERM1qDHGN0Zjiw1QFDUxgZgookQjiT1YkIAKYokNaXZQkM4u7P39wY/RERCWXs7nefK87565M3OGiz7Hu3furay4uFh4errj9u3/uJiDQ0/4+PjDwqJdneRACCGElOhu3RrvW+o16JfbRCIRPv54HE6fPokpU6aVesHN2/tzmJq2hafn56XOzcjIQGLiA7i4TOemIBQW8jfECg8/i759HTF48FAMHjwU27ZtRWjoQYwdOx6XL0fB0tIajo794OjYDyEhB7Bjhx9mz55XZq7dunXH9etxvNjt27fQrZtDpZ61oaAR3zqSV1CIIxf5/6Jr16YF3rfSq9Z10/Mz4Be7G9myHF78Pb0umGT1cY3uAFeemzdvYMiQAVzRK5FI8M033+GPP/6iopcQQki9EQoFaKEmqdP/Klv0lpgx4zOoqKjA39+HK1wZYzh48HckJCRgyhTXMs/T0tJCq1atcOVKNBcLDz/DaxMc/Btv1QW5vAimpmYAgOPHw3DmzMk3jsm5Y2UZP34ykpISEBt7FQBw7VoMkpISMW7cRIWet74JGGOs4mZNT3p6DgoL5RU3rCGh5x/gyMVEXmzJVHtYmrSs8jWzpTnYdNUfz3Kf8+LW2h3g0XUGlIR1M6DPGMP06ZPx999h6NKlK/z8dqJjx7p7ke5dxGIhtLXV67y/Sf2g/m5eqL+bl6bc3wUFBdi37xdERV2CSCSGTCaDubkFZsz4DPr6xas6lOzcpqGhicGDh8LNbQ6uXYvFli3rIZczGBoawsDAEAcP7oednT2++24doqMjcehQMMRiMYqKCqGj0wpffLEYurq6iIyMQFDQXi4HiUSC+fMXwcysbbl5XrsWg23btkJJSQlSqRSenp/X2s5tJf1d06jwrQOvsguwZGckCmSv59/atdeF1zjbKl8zvzAfPjG7kJSVwoubtTCBl50bVMTKVb52RRhjpUaSnz9/jqCgvfD0/LzUW5/1qSn/RUlKo/5uXqi/mxfq7+altgpfmupQB/64mMgregUCwLlf1acAyOSF2HU9sFTRq6/WGnNsZ9Zq0ZuYmIAxY4bhxIm/ePHWrVtjwQLvBlX0EkIIIYS8iV5uq2VPXuTgXOxjXqxvF0MY6VbtXzFyJsfPN3/D7fR7vLi2ckvMs/sUGpKa/9cRUDzKGxi4F9988xVyc3MQH38f585FQkenVa3cjxBCCCGkplHhW8sOhT+A/I3ZJBKxEGM+qNruaYwx/H7nMGJSr/PiGkrq8LT7FNoqLauTarmePn2CL77wxOnTryfBKysr4/Hjx1T4EkIIIaTRoKkOtej+o1e4cjeVFxvU3QTamlWbinD0wXFcfPwvL6YskmBO15kwUK94a0RFMcZw6FAwHB178IpeF5fpCA+/BBubLjV+T0IIIYSQ2kIjvrWEMYbgs/ytidVVxBjao/ylQt7ldPI5HE/iL1MiFojg3uUTmLWome2O3/TixQssXrwAR44c5mKtW+tj82ZfbrtDQgghhJDGhArfWnLt/gvce/iKFxvZuy3UVBT/kf/75AoO3f+TFxNAgBmdp8BKp3218ixLZOQlzJrlitTU18ukjRkzFmvXbqSpDYQQQghptKjwrQVFcjkOhsfzYrpaKuhvb6zwta6n3ULQ7eBS8SnWzrBrXTtTDYyNjZGfnw8A0NbWxrp1mzBmjHOt3IsQQgghpK7QHN9acPH6UzxO4++k9rGjBZTEiv2476U/wJ4bQZAz/nqFY9oNQ+82tbdFoLGxCVavXocPPxyMc+f+paKXEEIIIU0CFb41rEBWhNDzD3gxU30N9Oikr9B1UrIeY0fcXsjk/H23B5o6YpBZv+qmycnNzcW6dauRlZXJi0+cOAW//hrM7RhDCCGEENLY0VSHGnbqcgoysqW82Ph+7SEUVH7v7ue5qdgWuxv5Rfm8eE/Dbvi43fAayRMArlyJhqenO+Lj7+PJk8fYsmUbd+ztndkIIYQQUjsKCvKxb18goqP/hUgkgkwmg7KyChwdnTBgwCBoa+twWxaPHz8Zs2a510uecrkcBw7sQ0CAP374YSvs7bvVSx7VQSO+NSgrV4qwyCRerHNbbXQ216n0NTIKXsEvdjeyZNm8uK1uZ0yxcq6RglQqlWLNmpUYPnwQ4uOLV544dCgYKSnJ1b42IYQQQiqvoCAfXl6zkZeXCx+fHfD13YkdO37E2LHj4eu7GYcOFb/ns3GjD9q3t6y3PDMzM/HFF3ORlJQEqbSg3vKoLip8a9CfEUnIKyjixcb1q/yqCzmyXGyL3YMX+em8eIeWFpjZeQpEQlG1c7x16yaGDBmAzZt/gFxePHfY3v59nD59ASYmptW+PiGEEEIqb8+enZDJpJg92wti8esv4p2c+mPMmHH1mBlffn4eZs+eh2nTZtR3KtVCUx1qSGpGHs5cfciL9eysDzMDzUqdX1Akhf+1n/A45ykvbqJpBHfbT6AkUqpWfkVFRdi2bSvWrVsNmUwGABCLxfjyy6WYN+8L3h82QgghpCmQMzlyZLl1ek91JTUIBZUbVywsLMSRI4fh4vJJmd/oTprkguzsrHLPv3IlGnv37gZjDDKZDKamZvDyWghNzeLa49WrDGzYsAYvXqRCJBJDVVUVn3zyGTp3tkFBQQG2bNmA+Pj7UFZWhkAgwOTJrujVq0+Z92rdWh+tW+vjyZPHlXq2hoqqnRpy+NwDFMlfb00sFgkwtpJbExfKC7H7eiASMvnTJFqr6mJu11lQFatUK7cHD+7D09MDly9HcbGOHTvBz28nunTpWq1rE0IIIQ3R1edxOHAntNTUwdqmqaSBCVZjYN/atsK2ycmJyM7ORtu25mUeNzAwAFD+S+aXLl2Eo2N/jB8/CYwxrF+/Gr6+m/DVVysAALt27YCysjL8/X8EAAQEbEdk5EV07myDgwd/R0pKMgIC9gIAjh4NxZkzJ8stfJsKKnxrQNLTLETeesaLDbA3hm5L1QrPlTM5Av87gFsv7/DiLZW14Gn3GTQlGtXO7+zZM1zRKxQKMXfufHh7fwVl5aptnUwIIYQ0dPtuH0ReYX7FDWtYliwb+24frFThm51dXJSrqqpV6V6TJk2FpmYLAMUvpffrNxDff/8/7nha2nNIpTJIpVJIJBKMHz+ZW8UpNTUVWVlZyMnJhrq6Bj788CNYW3eqUh6NCRW+NSD4H/7WxKrKIgzvVfHWxIwxHLx3BJefxfLi6mI1eNp9ilaq2jWS34wZnyIs7E+kpCTBzy8ADg49auS6hBBCCKm6kqI1Ly+vSucXFhZh8+b1SExMgFgsRnZ2Nl68eMEdd3H5BEuXLsLYscPRv/+HGDlyNCwtrQEAzs4TEBFxHmPHDoeT0wAMHToC7733fvUfqoGjl9uq6WbCS9xK5L+MNqynGTTVJBWeG5Z4CuEPI3gxiVAJs7vOgKG6Yuv+lmCMISbmCi8mFArh778bZ89GUNFLCCGkWZhiPQ6aStX/1lRRmkoamGJduZfSTE3NoKGhiaSkhCrda9EiL2RlZcLHZwf8/ALg5bWAd9zGxhYHDx6Bl9cCJCcnYtYsV26VCBMTU+zbF4Jly75FVlYWvLw84O/vW6U8GhMa8a0GOWOlRnu1NZXxYTeTCs/95+FFhCWc5MVEAhE+6zIN5loVjxaXJTU1FYsWzcdff/2JkJCj+OADJ+5Y69atq3RNQgghpDGyb20LOz2bBv1ym0gkwscfj8Pp0ycxZcq0Ui+4eXt/DlPTtvD0/LzUuRkZGUhMfAAXl+mQSIoH2woL+ZtehYefRd++jhg8eCgGDx6Kbdu2IjT0IMaOHY/Ll6NgaWkNR8d+cHTsh5CQA9ixww+zZ8+r2oM3EjTiWw3/3nqG5Gf8SfOj+5pDWendy45FP41B8N0/eDEBBJjeaSI6tbKqUi7Hjh2Fk1MP/PXXnwCA+fPnVPmrE0IIIaQpEAqE0JRo1Ol/lS16S8yY8RlUVFTg7+/DFa6MMRw8+DsSEhIwZYprmedpaWmhVatWuHIlmouFh5/htQkO/o33YrtcXgRT0+LBtePHw3DmzMk3jsm5Y02ZgDHGKm7W9KSn56CwUF7l82WFcizbFYm0V68nzrfRVce3M7tDJCz/l/7mi9vYEbcXcsa/90TLj+Fo3EvhPF69ysDSpV/i4MH9XExXVxcbNmzF8OEjFb5eUyMWC6GtrV7t/iaNA/V380L93bw05f4uKCjAvn2/ICrqEkQiMWQyGczNLTBjxmfQ1y9e1aFk5zYNDU0MHjwUbm5zcO1aLLZsWQ+5nMHQ0BAGBoY4eHA/7Ozs8d136xAdHYlDh4IhFotRVFQIHZ1W+OKLxdDV1UVkZASCgvZyOUgkEsyfvwhmZm3LzfOrr75EWloqbt26gfbtLaGhoYGtW/0hElV/n4G3lfR3TaPCt4pORCXj9zP8aQ5ezraw66Bb7jkPXiXCJ2YXZHIZLz7C/CMMNR+ocA5nz57G55/P5a2pN2zYSGzYsAV6enoKX68pasp/UZLSqL+bF+rv5oX6u3mprcKX5vhWQW6+DEcjEnkxS2MtdG3fqtxzHmU/wfZrP5Uqevsb98WQtgMUun92djZWrlyOvXv3cLEWLbTw/ffrMX78pBrZ1pgQQgghpKmhwrcKwiKTkZPPn0A+vn/7cgvOtLyX2Ba7G3mF/Dm33fXtMbbDCIUL1UWLvHDo0EHus5NTf2zZsg1GRsYKXYcQQgghpDmhl9sU9DIzHycvp/Bi71vpoZ2RVpntM6VZ8I3dhVdS/paDNq2s4dpxvMKT4AHA2/srqKqqQk1NDWvXbsSBA6FU9BJCCCGEVIBGfBUUeiEBsjfmFgkFAjg7tSuzba4sD36xu5GW94IXb6fVFrNsXCASVm4yuEwmg5KSEvfZwqI9tm3bhU6dOsPCoux7E0IIIYQQPhrxVcCj1GxcvP6EF3OyawMDndJbDUqLZNgRtxePsvntjTQM4WE7AxJRxRtcFBYWYtOm9Rg4sC9yc/nrEI4YMYqKXkIIIYQQBVDhq4CQ8Ad4cw0MZSURRvVpW6pdkbwIP94MQvwr/k4suio6mNt1FtSUVCu81717dzF8+IdYu3YVbt/+D6tX/6+a2RNCCCGENG9U+FbS3ZQMxN5P48U+cjCBloYyLyZncgTdDsb1tP948RYSTcx77zNoKbd4533kcjl27tyGgQP7IibmKoDiLYc1NVugma48RwghhBBSI2iObyUwxhB8lr9mbws1JXzkYFqq3aH7fyLq6VVeXFWsAk+7T6GrWv5yZwCQnJwEL6/ZiIi4wMXat+8AX98deP/97tV8CkIIIYSQ5o1GfCvhyp1UxD/O5MVG9jGHqjL/3w3Hk87ibMoFXkxJqAQP2xkw0jAs9/qMMQQF/Qwnp168otfNbTZOnTpPRS8hhBBCSA2gEd8KFBbJERIez4u11laFk10bXuzCo0gcffA3LyYUCPGpjQvatzQv9/qMMcyc6Ypjx45wMRMTU2zduh19+zrWwBMQQgghpCG6desGtm/3QWzsVZiamkFHpxXy8vKQnZ2FAQMG4dNPPWplO+A37djhh1OnjsPAwBB+fgEAgNTU55g50wU//hgEPb3WtXr/ukaFbwXOX3uMZ+n8jSecndpBLHo9WH71eRx+v3O41LmuHSfARrfjO68vEAjw3nv2XOE7deo0rFz5PTQ13z0XmBBCCCGNW6dONvDzC0Dfvt3g4vIJhg0bCQC4ffs/uLt/Al1dPTg7T6jVHDw8PKGkpISYmCtcTCKRwNTUDBJJxStQNTZU+L5DvrQQf1xM5MXMDVugm5Ue9/m/l3ex9+ZvYOC/eDauwyg4GNhX6j5z585HTMxVTJ48FYMHD6123oQQQghpvKytO8LCoh2uXo2u9cK3LFpaLbFt2646v29doML3HY5HpSAzR8qLTejfjttiODEzGQHXf0ERK+K1Gdp2IPqb9C3zmqdOHcfdu3cxZ848LiYSifDTT0E1nD0hhBBCGquioiKu3rhyJRp79+4GYwwymQympmbw8loITU1NAMCrVxnYsGENXrxIhUgkhqqqKj755DN07mwDAEhOTsSWLT8gOzsbIpEQ1tad4eExF8rKKqXum5n5Cl999SVu3bqBRYuWYtiwkQgJ2Y+QkAOQSqWYOdMNp0+fxMOHyRgxYgxcXT/hzs3NzcHWrRtx9+5tqKtrQFNTE/PnfwkDA4Pa/4FVEr3cVo5XOVL8/W8yL2bbrhWsTLUBAE9znmF77I+QFvEL4w+MemG4+eBS18vOzsKCBfMwZcp4rFy5HJcvR9Ve8oQQQghptC5ePI/k5CQMHz4aAHDp0kU4OvaHn18Aduz4EWKxGL6+m7j2u3btgLKyMvz9f4SfXwA6dLBCZORFAEBBQQEWLJgHR8d+CAjYC1/fAKSkJMHHZ1OZ927RQgt+fgHQ0Xm9EpWz80S4uHyC9PSXEAgE2LjRB6tXb0BAwDY8evSQa7d27SoUFBRgz54g+PkFwNq6ExYt8kJRUVFZt6oXNOJbjiMXE1Age91RAgDj+hXvlPYiLx2+sbuRU8jfTe391l0xwXI09y+0Ehcvnsf8+XOQnJwEoHit3uDg39Gtm0PtPgQhhBDSzPn7+2HHDr8K29nadkVg4H5ezNV1IuLirlV4roeHJ2bP9qxyjgAQFLQXYWFHkZn5Co8ePYSr6wz06NELADBp0lTu3R+BQIB+/Qbi++//x52blvYcUqkMUqkUEokE48dPRlZW8WpUJ0/+jczMTIwaNRYAIBaLMXz4KPzvf8swf/4ihebxMsYwaNAQAMXLrWpoaOL+/XswMjLGo0cPcebMSeze/QuEwuJx1dGjnbFrlz9iYq40mJqHCt8yPHuZi3Oxj3mx3l0MYKyngSxpNvyu7UJGwSve8Y46lpjWaSKEgteD6Hl5efj++2+xc+d2LqauroGVK7+Hi8v02n0IQgghhCArKxNPnjyusF2bNkalYmlpaZU6t6TIrI43X27LyMjAsmVf4u7d21i3bjMKC4uwefN6JCYmQCwWIzs7Gy9evOCdu3TpIowdOxz9+3+IkSNHw9LSGgCQkBAPubwIXl4eXHupVAo9vdZ48SINhob8VarepWVLbYjFr0tHNTU15ObmcPcBgK1bf4BI9LqNgYEhMjLSq/ATqR1U+JYh5NwDFMlfv6ymJBbi4w8skF+Yj+3X9uB5Ln8HN/MWpvisyzSIha9/nDExV+Dp6Y579+5ysV69+sDHxx9mZm1r/RkIIYQQAmhqtqhUcaerq1tmrDLn1vRKTC1btsS4cROxfPkSJCUlYtkyb5iZmcHHZwckEgmuXr3MK2RtbGxx8OARhIefxbFjRzBrliu++MIbY8eOB1D8slrJUmXVUTKSW0IgEJTaVXb58u/K/EdEQ0GF71sePM7E5dvPebEP3zeGproI2+N+QnLWI94xQ3V9zO46E8qi4q8KZDIZNm5ch61bN3JzWpSVlbFs2Qq4uc0p9UtDCCGEkNoze3bVpyG8PfWhLpWMmqanv0Ri4gO4uEznpiUUFhby2oaHn0Xfvo4YPHgoBg8eim3btiI09CDGjh0PC4v2CAk5gJycbKira3Dnr1u3CosXf80bwa0Oc/Pil/+Tk5N4he/u3TswaNCQBjPoR1XYG8ramlhdRYyPehrjp1u/4W46/5iOijY87T6FupIa7xonTvzNFb12du/h9OkL8PDwpKKXEEIIIRWSyWT466+jMDY2hY2NLVq1aoUrV6K54+HhZ3jtg4N/4700L5cXwdTUDAAwaNAQ6OnpIyjoZ+74gQP7IBAIaqzoBQAjI2MMHDgY+/b9goKCAgDA9evX8M8/Z2BkZFxj96kuAXt7jLqZSE/PQWGhnBe7dj8NWw/G8WLj+7XDS60oRDyJ5sU1lNSx8P05aK2mh7fdunUTw4Z9CE/P+Zg/fyGUlJRq/gFIpYjFQmhrq5fZ36Tpof5uXqi/m5em2N9l7dwml8uRmZkJU1MzzJ49DyYmprh2LRZbtqyHXM5gaGgIAwNDHDy4H3Z29vjuu3WIjo7EoUPBEIvFKCoqhI5OK3zxxWJu+kZKSjK2bPkBqanP0KKFFkxNzTBv3gKoqqpyO7dlZ2fB1tYOX3/9LbecmYGBIZydJ0BVVQ1BQXvx9OkTdO36HjZv3oaFC70QE3MZBgaGmDZtJoYMGY7c3Fz4+W1GbOxVtGqlC1VVNXh5LYCxsYnCP5uS/q5pVPj+P7mcYcVPUXiUmsPFWrVQRq9BGTidEs47V0WkjPn27jDVNMaDB/GQyWSwsrLmtXn58gVvKRBSP5riX5SkfNTfzQv1d/NC/d281FbhS9+9/7+LN57wil4AsHr/ZamiVywUw932E5hoGOHHH3dhwIA+cHefCamUv54vFb2EEEIIIQ0LFb4ApLIihJ5P4MX0LFIRk3OeFxNAgJmdp0IjTwUTJozBkiULkZubi1u3bmDHjm11mTIhhBBCCFEQreoA4NSVh0jPKuA+C1s+Q45ubKl2U6yccedsHJy/8kZm5ut1fKdPn4WZMz+ri1QJIYQQQkgVNfvCNztPhmOXkrjPQs0XUO5wDQz8qc8DdPogYNlWhIUd5WIGBobYsmUbBgz4sM7yJYQQQgghVdPsC98/IxKRV1C8Hp5A7RUkllcBAX/SvG6COpZ7foG0tNcbVzg7T8CaNRvQsqV2neZLCCGEEEKqplkXvmmv8nDm6kMAgEAlB8pWVyAQFfHaWLK2WPv1Mm5d3latWmH9+i0YOXJ0nedLCCGEEEKqrlm/3Hb4XAIKixiglA+JVTQESvyVGez0bDBvgAfmz18AABgyZDjCw/+lopcQQgghpBFqtiO+yc+yEHnzKSCWQtnqMoTK+QCAwnwZhEoiWOta4pNOkyEUCLFgwWJ07myLESNGQSAQ1HPmhBBCCCGkKprtiO/+0/fBhIVQtrwCoVo2ACDtzlOcXBKMZycS4N5lGpRExTuuSSQSjBw5mopeQgghhJBGrN5HfE+ePAl/f3+oqKhAKBRixYoV6NChQ7ntL1++jPXr10MikUAqlcLb2xvdunVT6J7X7qbiekIqJJYxEGq8QpGsCDeDo3Hn6DWAMfy77x/cn34PNjZdqvt4hBBCCCGkgajXwjcuLg7e3t4ICQmBhYUFQkNDMWvWLISFhUFDQ6NU+0ePHsHd3R3bt29Hjx49EBUVBXd3dxw5cgRGRkaVvu9Px65DySIOIq0XyEhMw7/bziAz5SV3vGvX96CmplYjz0gIIYQQQhqGep3qsGvXLjg5OcHCwgIAMGrUKBQVFSE0NLTM9oGBgbCwsECPHj0AAA4ODjA3N0dQUJBC900WRULY8jFuHbqCU8sOcUWvWEkJX3/9Pxw9ehwWFu2q/mCEEEIIIaTBqdfC99KlS+jS5fV0AqFQiM6dOyMiIqLM9hEREbz2ANClS5dy25cnr+g6zn4TipsHosGKitfs7dDRCidPhMPLawFEIpGCT0IIIYQQQhq6eit809PTkZWVBV1dXV5cV1cXKSkpZZ6TkpKiUPvynFgcjJfxzwEAAoEArh6zcPbkRXTubKPQdQghhBBCSONRb3N88/OLlw+TSCS8uEQi4Y6VdY4i7cujrqeJrMcZ0DDUwupNG+D60RSFzieNh0gk5P0vadqov5sX6u/mhfq7eamtfq63wldFRQUAIJXyN42QSqXcsbLOUaR9eTIfpSvUnjR+LVqo1ncKpA5Rfzcv1N/NC/U3qY56+2eTtrY2NDU1kZaWxounpaXBxMSkzHNMTEwUak8IIYQQQkiJev2+oGfPnrhx4wb3mTGGW7duoXfv3mW279WrF689ANy4caPc9oQQQgghhJSo18LXzc0N4eHhSEhIAAAcOXIEQqEQY8aMAQAsXboUX375Jdd+2rRpiI+PR3R0NIDizSzi4+Ph4uJS57kTQgghhJDGpV43sLC1tcW6deuwcOFCbue2PXv2cJtXFBQUoLCwkGtvZGSEnTt3Yv369VBSUoJUKkVAQIBCm1cQQgghhJDmScAYY/WdBCGEEEIIIbWN1gQhhBBCCCHNAhW+hBBCCCGkWaDClxBCCCGENAtU+BJCCCGEkGahXld1qC0nT56Ev78/t1LEihUr0KFDh3LbX758GevXr4dEIoFUKoW3tze6detWhxmT6lCkvyMiIvDLL78gNzcXBQUFUFdXx6JFi9CpU6c6zppUlaJ/vkucPXsWHh4eWLNmDcaOHVsHmZKaoGh/v3z5Ehs3bkRycjJycnIglUoxZ84cDBs2rA6zJlWlSH8zxuDv74+///4bLVq0QG5uLiZOnIiJEyfWcdakqqRSKXx9fbFnzx6cOHECxsbG72xfI/Uaa2KuXbvG7OzsWHx8PGOMscOHD7MPPviAZWVlldn+4cOHzN7enkVGRjLGGPv333+Zvb09e/jwYZ3lTKpO0f7+8MMP2f79+7nPW7ZsYQ4ODiwtLa1O8iXVo2h/l8jJyWGjRo1ilpaWLCQkpC5SJTVA0f4uKChgo0aNYocPH+Zia9euZWvXrq2LdEk1KdrfBw4cYPb29uzp06eMMcYeP37M7O3t2dmzZ+sqZVINKSkpbMKECczb25tZWlqylJSUd7avqXqtyU112LVrF5ycnGBhYQEAGDVqFIqKihAaGlpm+8DAQFhYWKBHjx4AAAcHB5ibmyMoKKiuUibVoGh/29jYYNy4cdxnV1dXZGRkICIioi7SJdWkaH+X8PHxweTJk+sgQ1KTFO3v4OBgKCsrc5sgAcBnn33G+zNPGi5F+/v27duwsLCAvr4+AMDQ0BDm5ua4cOFCXaVMqiE3Nxfr16+v9DdwNVWvNbnC99KlS+jSpQv3WSgUonPnzuUWNhEREbz2ANClSxcqhBoJRft78+bNEApf/9orKysDAGQyWe0mSmqEov0NALdu3UJcXBx9/dkIKdrfJ06cQPfu3XkxHR0dtGvXrlbzJDVD0f4eOHAg4uPjcefOHQDFhfC9e/egq6tbJ/mS6rG0tISZmVml29dUvdakCt/09HRkZWWV+qXX1dVFSkpKmeekpKQo1J40HFXp77fFxsZCRUUF/fr1q4UMSU2qSn/L5XKsXLkS33zzDQQCQV2kSWpIVfr77t27UFFRwYoVKzBp0iS4urrit99+A6N9mhq8qvR37969sWbNGkyfPh3Dhg3Dxx9/DFtbW0yZMqUuUiZ1rKbqtSb1clt+fj4AQCKR8OISiYQ7VtY5irQnDUdV+vtN7P9fjJg/fz50dHRqJUdSc6rS30FBQbC3t4e1tXWt50dqVlX6OzMzEzt37sS2bdvw7bffIjExEVOnTkVWVhbc3NxqPWdSdVXp77Nnz+Kbb77Bnj17YGNjg5SUFPz1119QUVGp9XxJ3aupeq1JjfiW/LJLpVJeXCqVlvsHQUVFRaH2pOGoSn+/ydfXF/r6+pg5c2at5EdqlqL9/ezZMwQHB8PT07NO8iM1qyp/vgUCAWxtbeHk5AQAaNu2LZydnfHzzz/XbrKk2qrS35s3b8agQYNgY2MDADAxMUFiYiK+++672k2W1IuaqteaVOGrra0NTU1NpKWl8eJpaWkwMTEp8xwTExOF2pOGoyr9XeL333/H9evXsWbNmtpMkdQgRfu75AUXd3d3uLq6wtXVFQAQEBAAV1dXXL58ufaTJlVWlT/fhoaGMDAw4MXatGmDtLQ0+havgatKfyclJcHIyIgXMzY2xvHjx2stT1J/aqpea1JTHQCgZ8+euHHjBveZMYZbt27Bw8OjzPa9evVCTEwML3bjxg307t27VvMkNUPR/gaAP//8E2FhYQgICIBEIkFKSgpSUlKozxsBRfrb2dkZzs7OvJiVlRXc3NxoHd9GQtE/3926dcPDhw95sbS0NGhra9O3eI2Aov2tr6+P1NRUXiw1NZV7aZk0LTVVrzWpEV8AcHNzQ3h4OBISEgAAR44cgVAo5Ja3Wbp0Kb788kuu/bRp0xAfH4/o6GgAxYsjx8fHw8XFpc5zJ4pTtL/Pnj2LjRs3Ys6cObh37x6uX7+Oixcv4sqVK/WRPlGQov1NGjdF+3v69OmIi4tDXFwcACAjIwN//PEHN9pPGjZF+9vZ2RlhYWF4/PgxAODRo0cICwvD0KFD6zx3UvNqq15rciO+tra2WLduHRYuXMjt/LJnzx5oaGgAAAoKClBYWMi1NzIyws6dO7F+/XooKSlBKpUiICCg1NcnpGFStL+XLl2K9PR0TJ8+nXcdmgfaOCja3yUCAgJw/vx57v8fPnwYgYGBdZo7UZyi/W1tbQ0/Pz+sXLkSYrEYRUVFmDBhAs3jbyQU7e9Zs2ZBIBBg7ty5UFFRQXZ2NiZNmoQ5c+bU1yMQBUilUsyaNQuZmZkAgAULFsDAwAA+Pj4Aaq9eEzBa54UQQgghhDQDTW6qAyGEEEIIIWWhwpcQQgghhDQLVPgSQgghhJBmgQpfQgghhBDSLFDhSwghhBBCmgUqfAkhhBBCSLNAhS8hhBBCCGkWmtwGFoSQpiEuLg4bNmxAVFQUzM3Noaenh4KCAuTl5WHq1KmYNGlSfadYKYcOHYKRkRF69OhR36lUSmZmJry9vZGeno7s7GyMGzcOM2bMqO+0KrR06VKcO3cOH3zwAdauXVvf6RBCGigqfAkhDZKtrS0CAwNhZWUFNzc3jB07FgBw5coVuLq6QlNTE8OHD6/nLCt2+PBhODg4NJrC95dffkFOTg7279+P9PR0nDp1qr5TqpQ1a9ZgyZIl9Z0GIaSBo6kOhJBG5f3330eHDh1w/Pjx+k6lSXr06BG3Bai2tjbGjx9fzxkRQkjNoRFfQkijU1hYCCUlJQDAgwc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Examples on how to implement Logistic Regression and discussion of stochastic gradient descent \n", + "\n", + " * Stochastic Gradient descent with examples and automatic differentiation\n", + "\n", + " * [Video of lecture](https://youtu.be/)\n", + "\n", + " * Whiteboard notes TBA at \n", + "\n", + " * Readings and Videos:\n", + "\n", + " * These lecture notes\n", + "\n", + " * For a good discussion on gradient methods, we would like to recommend Goodfellow et al section 4.3-4.5 and sections 8.3-8.6. We will come back to the latter chapter in our discussion of Neural networks as well. \n", + "\n", + " * [Video on gradient descent](https://www.youtube.com/watch?v=sDv4f4s2SB8)\n", + "\n", + " * [Video on stochastic gradient descent](https://www.youtube.com/watch?v=vMh0zPT0tLI)" + ] + }, + { + "cell_type": "markdown", + "id": "529184e5", + "metadata": { + "editable": true + }, + "source": [ + "## Optimization, the central part of any Machine Learning algortithm\n", + "\n", + "The first few slides here are a repetition from last week. \n", + "\n", + "Almost every problem in machine learning and data science starts with\n", + "a dataset $X$, a model $g(\\beta)$, which is a function of the\n", + "parameters $\\beta$ and a cost function $C(X, g(\\beta))$ that allows\n", + "us to judge how well the model $g(\\beta)$ explains the observations\n", + "$X$. The model is fit by finding the values of $\\beta$ that minimize\n", + "the cost function. Ideally we would be able to solve for $\\beta$\n", + "analytically, however this is not possible in general and we must use\n", + "some approximative/numerical method to compute the minimum." + ] + }, + { + "cell_type": "markdown", + "id": "1a65465a", + "metadata": { + "editable": true + }, + "source": [ + "## Revisiting our Logistic Regression case\n", + "\n", + "In our discussion on Logistic Regression we studied the \n", + "case of\n", + "two classes, with $y_i$ either\n", + "$0$ or $1$. Furthermore we assumed also that we have only two\n", + "parameters $\\beta$ in our fitting, that is we\n", + "defined probabilities" + ] + }, + { + "cell_type": "markdown", + "id": "b67231b3", + "metadata": { + "editable": true + }, + "source": [ + "$$\n", + "\\begin{align*}\n", + "p(y_i=1|x_i,\\boldsymbol{\\beta}) &= \\frac{\\exp{(\\beta_0+\\beta_1x_i)}}{1+\\exp{(\\beta_0+\\beta_1x_i)}},\\nonumber\\\\\n", + "p(y_i=0|x_i,\\boldsymbol{\\beta}) &= 1 - p(y_i=1|x_i,\\boldsymbol{\\beta}),\n", + "\\end{align*}\n", + "$$" + ] + }, + { + "cell_type": "markdown", + "id": "3f5e03db", + "metadata": { + "editable": true + }, + "source": [ + "where $\\boldsymbol{\\beta}$ are the weights we wish to extract from data, in our case $\\beta_0$ and $\\beta_1$." + ] + }, + { + "cell_type": "markdown", + "id": "e83141ae", + "metadata": { + "editable": true + }, + "source": [ + "## The equations to solve\n", + "\n", + "Our compact equations used a definition of a vector $\\boldsymbol{y}$ with $n$\n", + "elements $y_i$, an $n\\times p$ matrix $\\boldsymbol{X}$ which contains the\n", + "$x_i$ values and a vector $\\boldsymbol{p}$ of fitted probabilities\n", + "$p(y_i\\vert x_i,\\boldsymbol{\\beta})$. We rewrote in a more compact form\n", + "the first derivative of the cost function as" + ] + }, + { + "cell_type": "markdown", + "id": "cef5864b", + "metadata": { + "editable": true + }, + "source": [ + "$$\n", + "\\frac{\\partial \\mathcal{C}(\\boldsymbol{\\beta})}{\\partial \\boldsymbol{\\beta}} = -\\boldsymbol{X}^T\\left(\\boldsymbol{y}-\\boldsymbol{p}\\right).\n", + "$$" + ] + }, + { + "cell_type": "markdown", + "id": "2f59bbb1", + "metadata": { + "editable": true + }, + "source": [ + "If we in addition define a diagonal matrix $\\boldsymbol{W}$ with elements \n", + "$p(y_i\\vert x_i,\\boldsymbol{\\beta})(1-p(y_i\\vert x_i,\\boldsymbol{\\beta})$, we can obtain a compact expression of the second derivative as" + ] + }, + { + "cell_type": "markdown", + "id": "3869b3c6", + "metadata": { + "editable": true + }, + "source": [ + "$$\n", + "\\frac{\\partial^2 \\mathcal{C}(\\boldsymbol{\\beta})}{\\partial \\boldsymbol{\\beta}\\partial \\boldsymbol{\\beta}^T} = \\boldsymbol{X}^T\\boldsymbol{W}\\boldsymbol{X}.\n", + "$$" + ] + }, + { + "cell_type": "markdown", + "id": "e4656e92", + "metadata": { + "editable": true + }, + "source": [ + "This defines what is called the Hessian matrix." + ] + }, + { + "cell_type": "markdown", + "id": "b73ae554", + "metadata": { + "editable": true + }, + "source": [ + "## Solving using Newton-Raphson's method\n", + "\n", + "If we can set up these equations, Newton-Raphson's iterative method is normally the method of choice. It requires however that we can compute in an efficient way the matrices that define the first and second derivatives. \n", + "\n", + "Our iterative scheme is then given by" + ] + }, + { + "cell_type": "markdown", + "id": "70a2df05", + "metadata": { + "editable": true + }, + "source": [ + "$$\n", + "\\boldsymbol{\\beta}^{\\mathrm{new}} = \\boldsymbol{\\beta}^{\\mathrm{old}}-\\left(\\frac{\\partial^2 \\mathcal{C}(\\boldsymbol{\\beta})}{\\partial \\boldsymbol{\\beta}\\partial \\boldsymbol{\\beta}^T}\\right)^{-1}_{\\boldsymbol{\\beta}^{\\mathrm{old}}}\\times \\left(\\frac{\\partial \\mathcal{C}(\\boldsymbol{\\beta})}{\\partial \\boldsymbol{\\beta}}\\right)_{\\boldsymbol{\\beta}^{\\mathrm{old}}},\n", + "$$" + ] + }, + { + "cell_type": "markdown", + "id": "2e36f976", + "metadata": { + "editable": true + }, + "source": [ + "or in matrix form as" + ] + }, + { + "cell_type": "markdown", + "id": "7c4959c9", + "metadata": { + "editable": true + }, + "source": [ + "$$\n", + "\\boldsymbol{\\beta}^{\\mathrm{new}} = \\boldsymbol{\\beta}^{\\mathrm{old}}-\\left(\\boldsymbol{X}^T\\boldsymbol{W}\\boldsymbol{X} \\right)^{-1}\\times \\left(-\\boldsymbol{X}^T(\\boldsymbol{y}-\\boldsymbol{p}) \\right)_{\\boldsymbol{\\beta}^{\\mathrm{old}}}.\n", + "$$" + ] + }, + { + "cell_type": "markdown", + "id": "c553379e", + "metadata": { + "editable": true + }, + "source": [ + "The right-hand side is computed with the old values of $\\beta$. \n", + "\n", + "If we can compute these matrices, in particular the Hessian, the above is often the easiest method to implement." + ] + }, + { + "cell_type": "markdown", + "id": "145f9699", + "metadata": { + "editable": true + }, + "source": [ + "## Brief reminder on Newton-Raphson's method\n", + "\n", + "Let us quickly remind ourselves how we derive the above method.\n", + "\n", + "Perhaps the most celebrated of all one-dimensional root-finding\n", + "routines is Newton's method, also called the Newton-Raphson\n", + "method. This method requires the evaluation of both the\n", + "function $f$ and its derivative $f'$ at arbitrary points. \n", + "If you can only calculate the derivative\n", + "numerically and/or your function is not of the smooth type, we\n", + "normally discourage the use of this method." + ] + }, + { + "cell_type": "markdown", + "id": "9a68f686", + "metadata": { + "editable": true + }, + "source": [ + "## The equations\n", + "\n", + "The Newton-Raphson formula consists geometrically of extending the\n", + "tangent line at a current point until it crosses zero, then setting\n", + "the next guess to the abscissa of that zero-crossing. The mathematics\n", + "behind this method is rather simple. Employing a Taylor expansion for\n", + "$x$ sufficiently close to the solution $s$, we have" + ] + }, + { + "cell_type": "markdown", + "id": "d523ab61", + "metadata": { + "editable": true + }, + "source": [ + "\n", + "
              \n", + "\n", + "$$\n", + "f(s)=0=f(x)+(s-x)f'(x)+\\frac{(s-x)^2}{2}f''(x) +\\dots.\n", + " \\label{eq:taylornr} \\tag{1}\n", + "$$" + ] + }, + { + "cell_type": "markdown", + "id": "8205fd3a", + "metadata": { + "editable": true + }, + "source": [ + "For small enough values of the function and for well-behaved\n", + "functions, the terms beyond linear are unimportant, hence we obtain" + ] + }, + { + "cell_type": "markdown", + "id": "98d52e46", + "metadata": { + "editable": true + }, + "source": [ + "$$\n", + "f(x)+(s-x)f'(x)\\approx 0,\n", + "$$" + ] + }, + { + "cell_type": "markdown", + "id": "203f65e8", + "metadata": { + "editable": true + }, + "source": [ + "yielding" + ] + }, + { + "cell_type": "markdown", + "id": "dd40195b", + "metadata": { + "editable": true + }, + "source": [ + "$$\n", + "s\\approx x-\\frac{f(x)}{f'(x)}.\n", + "$$" + ] + }, + { + "cell_type": "markdown", + "id": "e13a7340", + "metadata": { + "editable": true + }, + "source": [ + "Having in mind an iterative procedure, it is natural to start iterating with" + ] + }, + { + "cell_type": "markdown", + "id": "30c258c9", + "metadata": { + "editable": true + }, + "source": [ + "$$\n", + "x_{n+1}=x_n-\\frac{f(x_n)}{f'(x_n)}.\n", + "$$" + ] + }, + { + "cell_type": "markdown", + "id": "84f880c9", + "metadata": { + "editable": true + }, + "source": [ + "## Simple geometric interpretation\n", + "\n", + "The above is Newton-Raphson's method. It has a simple geometric\n", + "interpretation, namely $x_{n+1}$ is the point where the tangent from\n", + "$(x_n,f(x_n))$ crosses the $x$-axis. Close to the solution,\n", + "Newton-Raphson converges fast to the desired result. However, if we\n", + "are far from a root, where the higher-order terms in the series are\n", + "important, the Newton-Raphson formula can give grossly inaccurate\n", + "results. For instance, the initial guess for the root might be so far\n", + "from the true root as to let the search interval include a local\n", + "maximum or minimum of the function. If an iteration places a trial\n", + "guess near such a local extremum, so that the first derivative nearly\n", + "vanishes, then Newton-Raphson may fail totally" + ] + }, + { + "cell_type": "markdown", + "id": "b237f214", + "metadata": { + "editable": true + }, + "source": [ + "## Extending to more than one variable\n", + "\n", + "Newton's method can be generalized to systems of several non-linear equations\n", + "and variables. Consider the case with two equations" + ] + }, + { + "cell_type": "markdown", + "id": "b4ea1db2", + "metadata": { + "editable": true + }, + "source": [ + "$$\n", + "\\begin{array}{cc} f_1(x_1,x_2) &=0\\\\\n", + " f_2(x_1,x_2) &=0,\\end{array}\n", + "$$" + ] + }, + { + "cell_type": "markdown", + "id": "8b2b6606", + "metadata": { + "editable": true + }, + "source": [ + "which we Taylor expand to obtain" + ] + }, + { + "cell_type": "markdown", + "id": "e9d217a4", + "metadata": { + "editable": true + }, + "source": [ + "$$\n", + "\\begin{array}{cc} 0=f_1(x_1+h_1,x_2+h_2)=&f_1(x_1,x_2)+h_1\n", + " \\partial f_1/\\partial x_1+h_2\n", + " \\partial f_1/\\partial x_2+\\dots\\\\\n", + " 0=f_2(x_1+h_1,x_2+h_2)=&f_2(x_1,x_2)+h_1\n", + " \\partial f_2/\\partial x_1+h_2\n", + " \\partial f_2/\\partial x_2+\\dots\n", + " \\end{array}.\n", + "$$" + ] + }, + { + "cell_type": "markdown", + "id": "54f87f7c", + "metadata": { + "editable": true + }, + "source": [ + "Defining the Jacobian matrix ${\\bf \\boldsymbol{J}}$ we have" + ] + }, + { + "cell_type": "markdown", + "id": "c2711fc8", + "metadata": { + "editable": true + }, + "source": [ + "$$\n", + "{\\bf \\boldsymbol{J}}=\\left( \\begin{array}{cc}\n", + " \\partial f_1/\\partial x_1 & \\partial f_1/\\partial x_2 \\\\\n", + " \\partial f_2/\\partial x_1 &\\partial f_2/\\partial x_2\n", + " \\end{array} \\right),\n", + "$$" + ] + }, + { + "cell_type": "markdown", + "id": "e0409f1c", + "metadata": { + "editable": true + }, + "source": [ + "we can rephrase Newton's method as" + ] + }, + { + "cell_type": "markdown", + "id": "53651890", + "metadata": { + "editable": true + }, + "source": [ + "$$\n", + "\\left(\\begin{array}{c} x_1^{n+1} \\\\ x_2^{n+1} \\end{array} \\right)=\n", + "\\left(\\begin{array}{c} x_1^{n} \\\\ x_2^{n} \\end{array} \\right)+\n", + "\\left(\\begin{array}{c} h_1^{n} \\\\ h_2^{n} \\end{array} \\right),\n", + "$$" + ] + }, + { + "cell_type": "markdown", + "id": "4e70e7ac", + "metadata": { + "editable": true + }, + "source": [ + "where we have defined" + ] + }, + { + "cell_type": "markdown", + "id": "9336db1d", + "metadata": { + "editable": true + }, + "source": [ + "$$\n", + "\\left(\\begin{array}{c} h_1^{n} \\\\ h_2^{n} \\end{array} \\right)=\n", + " -{\\bf \\boldsymbol{J}}^{-1}\n", + " \\left(\\begin{array}{c} f_1(x_1^{n},x_2^{n}) \\\\ f_2(x_1^{n},x_2^{n}) \\end{array} \\right).\n", + "$$" + ] + }, + { + "cell_type": "markdown", + "id": "2c2ae028", + "metadata": { + "editable": true + }, + "source": [ + "We need thus to compute the inverse of the Jacobian matrix and it\n", + "is to understand that difficulties may\n", + "arise in case ${\\bf \\boldsymbol{J}}$ is nearly singular.\n", + "\n", + "It is rather straightforward to extend the above scheme to systems of\n", + "more than two non-linear equations. In our case, the Jacobian matrix is given by the Hessian that represents the second derivative of cost function." + ] + }, + { + "cell_type": "markdown", + "id": "cdf99885", + "metadata": { + "editable": true + }, + "source": [ + "## Steepest descent\n", + "\n", + "The basic idea of gradient descent is\n", + "that a function $F(\\mathbf{x})$, \n", + "$\\mathbf{x} \\equiv (x_1,\\cdots,x_n)$, decreases fastest if one goes from $\\bf {x}$ in the\n", + "direction of the negative gradient $-\\nabla F(\\mathbf{x})$.\n", + "\n", + "It can be shown that if" + ] + }, + { + "cell_type": "markdown", + "id": "11bb1b41", + "metadata": { + "editable": true + }, + "source": [ + "$$\n", + "\\mathbf{x}_{k+1} = \\mathbf{x}_k - \\gamma_k \\nabla F(\\mathbf{x}_k),\n", + "$$" + ] + }, + { + "cell_type": "markdown", + "id": "5d957768", + "metadata": { + "editable": true + }, + "source": [ + "with $\\gamma_k > 0$.\n", + "\n", + "For $\\gamma_k$ small enough, then $F(\\mathbf{x}_{k+1}) \\leq\n", + "F(\\mathbf{x}_k)$. This means that for a sufficiently small $\\gamma_k$\n", + "we are always moving towards smaller function values, i.e a minimum." + ] + }, + { + "cell_type": "markdown", + "id": "455b420a", + "metadata": { + "editable": true + }, + "source": [ + "## More on Steepest descent\n", + "\n", + "The previous observation is the basis of the method of steepest\n", + "descent, which is also referred to as just gradient descent (GD). One\n", + "starts with an initial guess $\\mathbf{x}_0$ for a minimum of $F$ and\n", + "computes new approximations according to" + ] + }, + { + "cell_type": "markdown", + "id": "aacb8b05", + "metadata": { + "editable": true + }, + "source": [ + "$$\n", + "\\mathbf{x}_{k+1} = \\mathbf{x}_k - \\gamma_k \\nabla F(\\mathbf{x}_k), \\ \\ k \\geq 0.\n", + "$$" + ] + }, + { + "cell_type": "markdown", + "id": "2bb3385b", + "metadata": { + "editable": true + }, + "source": [ + "The parameter $\\gamma_k$ is often referred to as the step length or\n", + "the learning rate within the context of Machine Learning." + ] + }, + { + "cell_type": "markdown", + "id": "21326ef0", + "metadata": { + "editable": true + }, + "source": [ + "## The ideal\n", + "\n", + "Ideally the sequence $\\{\\mathbf{x}_k \\}_{k=0}$ converges to a global\n", + "minimum of the function $F$. In general we do not know if we are in a\n", + "global or local minimum. In the special case when $F$ is a convex\n", + "function, all local minima are also global minima, so in this case\n", + "gradient descent can converge to the global solution. The advantage of\n", + "this scheme is that it is conceptually simple and straightforward to\n", + "implement. However the method in this form has some severe\n", + "limitations:\n", + "\n", + "In machine learing we are often faced with non-convex high dimensional\n", + "cost functions with many local minima. Since GD is deterministic we\n", + "will get stuck in a local minimum, if the method converges, unless we\n", + "have a very good intial guess. This also implies that the scheme is\n", + "sensitive to the chosen initial condition.\n", + "\n", + "Note that the gradient is a function of $\\mathbf{x} =\n", + "(x_1,\\cdots,x_n)$ which makes it expensive to compute numerically." + ] + }, + { + "cell_type": "markdown", + "id": "a41b3cc6", + "metadata": { + "editable": true + }, + "source": [ + "## The sensitiveness of the gradient descent\n", + "\n", + "The gradient descent method \n", + "is sensitive to the choice of learning rate $\\gamma_k$. This is due\n", + "to the fact that we are only guaranteed that $F(\\mathbf{x}_{k+1}) \\leq\n", + "F(\\mathbf{x}_k)$ for sufficiently small $\\gamma_k$. The problem is to\n", + "determine an optimal learning rate. If the learning rate is chosen too\n", + "small the method will take a long time to converge and if it is too\n", + "large we can experience erratic behavior.\n", + "\n", + "Many of these shortcomings can be alleviated by introducing\n", + "randomness. One such method is that of Stochastic Gradient Descent\n", + "(SGD), see below." + ] + }, + { + "cell_type": "markdown", + "id": "a01f11a5", + "metadata": { + "editable": true + }, + "source": [ + "## Convex functions\n", + "\n", + "Ideally we want our cost/loss function to be convex(concave).\n", + "\n", + "First we give the definition of a convex set: A set $C$ in\n", + "$\\mathbb{R}^n$ is said to be convex if, for all $x$ and $y$ in $C$ and\n", + "all $t \\in (0,1)$ , the point $(1 − t)x + ty$ also belongs to\n", + "C. Geometrically this means that every point on the line segment\n", + "connecting $x$ and $y$ is in $C$ as discussed below.\n", + "\n", + "The convex subsets of $\\mathbb{R}$ are the intervals of\n", + "$\\mathbb{R}$. Examples of convex sets of $\\mathbb{R}^2$ are the\n", + "regular polygons (triangles, rectangles, pentagons, etc...)." + ] + }, + { + "cell_type": "markdown", + "id": "076a32fa", + "metadata": { + "editable": true + }, + "source": [ + "## Convex function\n", + "\n", + "**Convex function**: Let $X \\subset \\mathbb{R}^n$ be a convex set. Assume that the function $f: X \\rightarrow \\mathbb{R}$ is continuous, then $f$ is said to be convex if $$f(tx_1 + (1-t)x_2) \\leq tf(x_1) + (1-t)f(x_2) $$ for all $x_1, x_2 \\in X$ and for all $t \\in [0,1]$. If $\\leq$ is replaced with a strict inequaltiy in the definition, we demand $x_1 \\neq x_2$ and $t\\in(0,1)$ then $f$ is said to be strictly convex. For a single variable function, convexity means that if you draw a straight line connecting $f(x_1)$ and $f(x_2)$, the value of the function on the interval $[x_1,x_2]$ is always below the line as illustrated below." + ] + }, + { + "cell_type": "markdown", + "id": "73adde3c", + "metadata": { + "editable": true + }, + "source": [ + "## Conditions on convex functions\n", + "\n", + "In the following we state first and second-order conditions which\n", + "ensures convexity of a function $f$. We write $D_f$ to denote the\n", + "domain of $f$, i.e the subset of $R^n$ where $f$ is defined. For more\n", + "details and proofs we refer to: [S. Boyd and L. Vandenberghe. Convex Optimization. Cambridge University Press](http://stanford.edu/boyd/cvxbook/, 2004).\n", + "\n", + "**First order condition.**\n", + "\n", + "Suppose $f$ is differentiable (i.e $\\nabla f(x)$ is well defined for\n", + "all $x$ in the domain of $f$). Then $f$ is convex if and only if $D_f$\n", + "is a convex set and $$f(y) \\geq f(x) + \\nabla f(x)^T (y-x) $$ holds\n", + "for all $x,y \\in D_f$. This condition means that for a convex function\n", + "the first order Taylor expansion (right hand side above) at any point\n", + "a global under estimator of the function. To convince yourself you can\n", + "make a drawing of $f(x) = x^2+1$ and draw the tangent line to $f(x)$ and\n", + "note that it is always below the graph.\n", + "\n", + "**Second order condition.**\n", + "\n", + "Assume that $f$ is twice\n", + "differentiable, i.e the Hessian matrix exists at each point in\n", + "$D_f$. Then $f$ is convex if and only if $D_f$ is a convex set and its\n", + "Hessian is positive semi-definite for all $x\\in D_f$. For a\n", + "single-variable function this reduces to $f''(x) \\geq 0$. Geometrically this means that $f$ has nonnegative curvature\n", + "everywhere.\n", + "\n", + "This condition is particularly useful since it gives us an procedure for determining if the function under consideration is convex, apart from using the definition." + ] + }, + { + "cell_type": "markdown", + "id": "9f9ab5ff", + "metadata": { + "editable": true + }, + "source": [ + "## More on convex functions\n", + "\n", + "The next result is of great importance to us and the reason why we are\n", + "going on about convex functions. In machine learning we frequently\n", + "have to minimize a loss/cost function in order to find the best\n", + "parameters for the model we are considering. \n", + "\n", + "Ideally we want the\n", + "global minimum (for high-dimensional models it is hard to know\n", + "if we have local or global minimum). However, if the cost/loss function\n", + "is convex the following result provides invaluable information:\n", + "\n", + "**Any minimum is global for convex functions.**\n", + "\n", + "Consider the problem of finding $x \\in \\mathbb{R}^n$ such that $f(x)$\n", + "is minimal, where $f$ is convex and differentiable. Then, any point\n", + "$x^*$ that satisfies $\\nabla f(x^*) = 0$ is a global minimum.\n", + "\n", + "This result means that if we know that the cost/loss function is convex and we are able to find a minimum, we are guaranteed that it is a global minimum." + ] + }, + { + "cell_type": "markdown", + "id": "0b2a482b", + "metadata": { + "editable": true + }, + "source": [ + "## Some simple problems\n", + "\n", + "1. Show that $f(x)=x^2$ is convex for $x \\in \\mathbb{R}$ using the definition of convexity. Hint: If you re-write the definition, $f$ is convex if the following holds for all $x,y \\in D_f$ and any $\\lambda \\in [0,1]$ $\\lambda f(x)+(1-\\lambda)f(y)-f(\\lambda x + (1-\\lambda) y ) \\geq 0$.\n", + "\n", + "2. Using the second order condition show that the following functions are convex on the specified domain.\n", + "\n", + " * $f(x) = e^x$ is convex for $x \\in \\mathbb{R}$.\n", + "\n", + " * $g(x) = -\\ln(x)$ is convex for $x \\in (0,\\infty)$.\n", + "\n", + "3. Let $f(x) = x^2$ and $g(x) = e^x$. Show that $f(g(x))$ and $g(f(x))$ is convex for $x \\in \\mathbb{R}$. Also show that if $f(x)$ is any convex function than $h(x) = e^{f(x)}$ is convex.\n", + "\n", + "4. A norm is any function that satisfy the following properties\n", + "\n", + " * $f(\\alpha x) = |\\alpha| f(x)$ for all $\\alpha \\in \\mathbb{R}$.\n", + "\n", + " * $f(x+y) \\leq f(x) + f(y)$\n", + "\n", + " * $f(x) \\leq 0$ for all $x \\in \\mathbb{R}^n$ with equality if and only if $x = 0$\n", + "\n", + "Using the definition of convexity, try to show that a function satisfying the properties above is convex (the third condition is not needed to show this)." + ] + }, + { + "cell_type": "markdown", + "id": "6566ee55", + "metadata": { + "editable": true + }, + "source": [ + "## Standard steepest descent\n", + "\n", + "Before we proceed, we would like to discuss the approach called the\n", + "**standard Steepest descent** (different from the above steepest descent discussion), which again leads to us having to be able\n", + "to compute a matrix. It belongs to the class of Conjugate Gradient methods (CG).\n", + "\n", + "[The success of the CG method](https://www.cs.cmu.edu/~quake-papers/painless-conjugate-gradient.pdf)\n", + "for finding solutions of non-linear problems is based on the theory\n", + "of conjugate gradients for linear systems of equations. It belongs to\n", + "the class of iterative methods for solving problems from linear\n", + "algebra of the type" + ] + }, + { + "cell_type": "markdown", + "id": "c2e30cc1", + "metadata": { + "editable": true + }, + "source": [ + "$$\n", + "\\boldsymbol{A}\\boldsymbol{x} = \\boldsymbol{b}.\n", + "$$" + ] + }, + { + "cell_type": "markdown", + "id": "5012b398", + "metadata": { + "editable": true + }, + "source": [ + "In the iterative process we end up with a problem like" + ] + }, + { + "cell_type": "markdown", + "id": "ca65d9a9", + "metadata": { + "editable": true + }, + "source": [ + "$$\n", + "\\boldsymbol{r}= \\boldsymbol{b}-\\boldsymbol{A}\\boldsymbol{x},\n", + "$$" + ] + }, + { + "cell_type": "markdown", + "id": "ec9323dd", + "metadata": { + "editable": true + }, + "source": [ + "where $\\boldsymbol{r}$ is the so-called residual or error in the iterative process.\n", + "\n", + "When we have found the exact solution, $\\boldsymbol{r}=0$." + ] + }, + { + "cell_type": "markdown", + "id": "5caf0f7f", + "metadata": { + "editable": true + }, + "source": [ + "## Gradient method\n", + "\n", + "The residual is zero when we reach the minimum of the quadratic equation" + ] + }, + { + "cell_type": "markdown", + "id": "07734ce6", + "metadata": { + "editable": true + }, + "source": [ + "$$\n", + "P(\\boldsymbol{x})=\\frac{1}{2}\\boldsymbol{x}^T\\boldsymbol{A}\\boldsymbol{x} - \\boldsymbol{x}^T\\boldsymbol{b},\n", + "$$" + ] + }, + { + "cell_type": "markdown", + "id": "468dcb52", + "metadata": { + "editable": true + }, + "source": [ + "with the constraint that the matrix $\\boldsymbol{A}$ is positive definite and\n", + "symmetric. This defines also the Hessian and we want it to be positive definite." + ] + }, + { + "cell_type": "markdown", + "id": "b63a89ae", + "metadata": { + "editable": true + }, + "source": [ + "## Steepest descent method\n", + "\n", + "We denote the initial guess for $\\boldsymbol{x}$ as $\\boldsymbol{x}_0$. \n", + "We can assume without loss of generality that" + ] + }, + { + "cell_type": "markdown", + "id": "56a122a3", + "metadata": { + "editable": true + }, + "source": [ + "$$\n", + "\\boldsymbol{x}_0=0,\n", + "$$" + ] + }, + { + "cell_type": "markdown", + "id": "3256eb29", + "metadata": { + "editable": true + }, + "source": [ + "or consider the system" + ] + }, + { + "cell_type": "markdown", + "id": "b6bab858", + "metadata": { + "editable": true + }, + "source": [ + "$$\n", + "\\boldsymbol{A}\\boldsymbol{z} = \\boldsymbol{b}-\\boldsymbol{A}\\boldsymbol{x}_0,\n", + "$$" + ] + }, + { + "cell_type": "markdown", + "id": "7ff39e96", + "metadata": { + "editable": true + }, + "source": [ + "instead." + ] + }, + { + "cell_type": "markdown", + "id": "6678fce9", + "metadata": { + "editable": true + }, + "source": [ + "## Steepest descent method\n", + "One can show that the solution $\\boldsymbol{x}$ is also the unique minimizer of the quadratic form" + ] + }, + { + "cell_type": "markdown", + "id": "853eb11f", + "metadata": { + "editable": true + }, + "source": [ + "$$\n", + "f(\\boldsymbol{x}) = \\frac{1}{2}\\boldsymbol{x}^T\\boldsymbol{A}\\boldsymbol{x} - \\boldsymbol{x}^T \\boldsymbol{x} , \\quad \\boldsymbol{x}\\in\\mathbf{R}^n.\n", + "$$" + ] + }, + { + "cell_type": "markdown", + "id": "7c229917", + "metadata": { + "editable": true + }, + "source": [ + "This suggests taking the first basis vector $\\boldsymbol{r}_1$ (see below for definition) \n", + "to be the gradient of $f$ at $\\boldsymbol{x}=\\boldsymbol{x}_0$, \n", + "which equals" + ] + }, + { + "cell_type": "markdown", + "id": "5c8f310a", + "metadata": { + "editable": true + }, + "source": [ + "$$\n", + "\\boldsymbol{A}\\boldsymbol{x}_0-\\boldsymbol{b},\n", + "$$" + ] + }, + { + "cell_type": "markdown", + "id": "f8a8c317", + "metadata": { + "editable": true + }, + "source": [ + "and \n", + "$\\boldsymbol{x}_0=0$ it is equal $-\\boldsymbol{b}$." + ] + }, + { + "cell_type": "markdown", + "id": "49b64ed0", + "metadata": { + "editable": true + }, + "source": [ + "## Final expressions\n", + "We can compute the residual iteratively as" + ] + }, + { + "cell_type": "markdown", + "id": "857ee939", + "metadata": { + "editable": true + }, + "source": [ + "$$\n", + "\\boldsymbol{r}_{k+1}=\\boldsymbol{b}-\\boldsymbol{A}\\boldsymbol{x}_{k+1},\n", + "$$" + ] + }, + { + "cell_type": "markdown", + "id": "7e5611fa", + "metadata": { + "editable": true + }, + "source": [ + "which equals" + ] + }, + { + "cell_type": "markdown", + "id": "384d5aa2", + "metadata": { + "editable": true + }, + "source": [ + "$$\n", + "\\boldsymbol{b}-\\boldsymbol{A}(\\boldsymbol{x}_k+\\alpha_k\\boldsymbol{r}_k),\n", + "$$" + ] + }, + { + "cell_type": "markdown", + "id": "0ce677be", + "metadata": { + "editable": true + }, + "source": [ + "or" + ] + }, + { + "cell_type": "markdown", + "id": "e97f9044", + "metadata": { + "editable": true + }, + "source": [ + "$$\n", + "(\\boldsymbol{b}-\\boldsymbol{A}\\boldsymbol{x}_k)-\\alpha_k\\boldsymbol{A}\\boldsymbol{r}_k,\n", + "$$" + ] + }, + { + "cell_type": "markdown", + "id": "d7fbeb68", + "metadata": { + "editable": true + }, + "source": [ + "which gives" + ] + }, + { + "cell_type": "markdown", + "id": "293c09b1", + "metadata": { + "editable": true + }, + "source": [ + "$$\n", + "\\alpha_k = \\frac{\\boldsymbol{r}_k^T\\boldsymbol{r}_k}{\\boldsymbol{r}_k^T\\boldsymbol{A}\\boldsymbol{r}_k}\n", + "$$" + ] + }, + { + "cell_type": "markdown", + "id": "af88e065", + "metadata": { + "editable": true + }, + "source": [ + "leading to the iterative scheme" + ] + }, + { + "cell_type": "markdown", + "id": "2757e302", + "metadata": { + "editable": true + }, + "source": [ + "$$\n", + "\\boldsymbol{x}_{k+1}=\\boldsymbol{x}_k+\\alpha_k\\boldsymbol{r}_{k},\n", + "$$" + ] + }, + { + "cell_type": "markdown", + "id": "87aab66b", + "metadata": { + "editable": true + }, + "source": [ + "## Steepest descent example" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "a0e20ff7", + "metadata": { + "collapsed": false, + "editable": true + }, + "outputs": [ + { + "ename": "ModuleNotFoundError", + "evalue": "No module named 'matplotlib'", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mModuleNotFoundError\u001b[0m Traceback (most recent call last)", + "Cell \u001b[0;32mIn[1], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \u001b[43mget_ipython\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mrun_line_magic\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mmatplotlib\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43minline\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m)\u001b[49m\n\u001b[1;32m 3\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mnumpy\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m \u001b[38;5;21;01mnp\u001b[39;00m\n\u001b[1;32m 4\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mnumpy\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mlinalg\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m \u001b[38;5;21;01mla\u001b[39;00m\n", + "File \u001b[0;32m~/miniforge3/lib/python3.9/site-packages/IPython/core/interactiveshell.py:2432\u001b[0m, in \u001b[0;36mInteractiveShell.run_line_magic\u001b[0;34m(self, magic_name, line, _stack_depth)\u001b[0m\n\u001b[1;32m 2430\u001b[0m kwargs[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mlocal_ns\u001b[39m\u001b[38;5;124m'\u001b[39m] \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mget_local_scope(stack_depth)\n\u001b[1;32m 2431\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mbuiltin_trap:\n\u001b[0;32m-> 2432\u001b[0m result \u001b[38;5;241m=\u001b[39m \u001b[43mfn\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 2434\u001b[0m \u001b[38;5;66;03m# The code below prevents the output from being displayed\u001b[39;00m\n\u001b[1;32m 2435\u001b[0m \u001b[38;5;66;03m# when using magics with decorator @output_can_be_silenced\u001b[39;00m\n\u001b[1;32m 2436\u001b[0m \u001b[38;5;66;03m# when the last Python token in the expression is a ';'.\u001b[39;00m\n\u001b[1;32m 2437\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mgetattr\u001b[39m(fn, magic\u001b[38;5;241m.\u001b[39mMAGIC_OUTPUT_CAN_BE_SILENCED, \u001b[38;5;28;01mFalse\u001b[39;00m):\n", + "File \u001b[0;32m~/miniforge3/lib/python3.9/site-packages/IPython/core/magics/pylab.py:99\u001b[0m, in \u001b[0;36mPylabMagics.matplotlib\u001b[0;34m(self, line)\u001b[0m\n\u001b[1;32m 97\u001b[0m \u001b[38;5;28mprint\u001b[39m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mAvailable matplotlib backends: \u001b[39m\u001b[38;5;132;01m%s\u001b[39;00m\u001b[38;5;124m\"\u001b[39m \u001b[38;5;241m%\u001b[39m backends_list)\n\u001b[1;32m 98\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m---> 99\u001b[0m gui, backend \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mshell\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43menable_matplotlib\u001b[49m\u001b[43m(\u001b[49m\u001b[43margs\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mgui\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mlower\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mif\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[38;5;28;43misinstance\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43margs\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mgui\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mstr\u001b[39;49m\u001b[43m)\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01melse\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43margs\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mgui\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 100\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_show_matplotlib_backend(args\u001b[38;5;241m.\u001b[39mgui, backend)\n", + "File \u001b[0;32m~/miniforge3/lib/python3.9/site-packages/IPython/core/interactiveshell.py:3606\u001b[0m, in \u001b[0;36mInteractiveShell.enable_matplotlib\u001b[0;34m(self, gui)\u001b[0m\n\u001b[1;32m 3585\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21menable_matplotlib\u001b[39m(\u001b[38;5;28mself\u001b[39m, gui\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mNone\u001b[39;00m):\n\u001b[1;32m 3586\u001b[0m \u001b[38;5;250m \u001b[39m\u001b[38;5;124;03m\"\"\"Enable interactive matplotlib and inline figure support.\u001b[39;00m\n\u001b[1;32m 3587\u001b[0m \n\u001b[1;32m 3588\u001b[0m \u001b[38;5;124;03m This takes the following steps:\u001b[39;00m\n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 3604\u001b[0m \u001b[38;5;124;03m display figures inline.\u001b[39;00m\n\u001b[1;32m 3605\u001b[0m \u001b[38;5;124;03m \"\"\"\u001b[39;00m\n\u001b[0;32m-> 3606\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mmatplotlib_inline\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mbackend_inline\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m configure_inline_support\n\u001b[1;32m 3608\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mIPython\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mcore\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m pylabtools \u001b[38;5;28;01mas\u001b[39;00m pt\n\u001b[1;32m 3609\u001b[0m gui, backend \u001b[38;5;241m=\u001b[39m pt\u001b[38;5;241m.\u001b[39mfind_gui_and_backend(gui, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mpylab_gui_select)\n", + "File \u001b[0;32m~/miniforge3/lib/python3.9/site-packages/matplotlib_inline/__init__.py:1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01m.\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m backend_inline, config \u001b[38;5;66;03m# noqa\u001b[39;00m\n\u001b[1;32m 2\u001b[0m __version__ \u001b[38;5;241m=\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m0.1.6\u001b[39m\u001b[38;5;124m\"\u001b[39m \u001b[38;5;66;03m# noqa\u001b[39;00m\n", + "File \u001b[0;32m~/miniforge3/lib/python3.9/site-packages/matplotlib_inline/backend_inline.py:6\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[38;5;124;03m\"\"\"A matplotlib backend for publishing figures via display_data\"\"\"\u001b[39;00m\n\u001b[1;32m 3\u001b[0m \u001b[38;5;66;03m# Copyright (c) IPython Development Team.\u001b[39;00m\n\u001b[1;32m 4\u001b[0m \u001b[38;5;66;03m# Distributed under the terms of the BSD 3-Clause License.\u001b[39;00m\n\u001b[0;32m----> 6\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mmatplotlib\u001b[39;00m\n\u001b[1;32m 7\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mmatplotlib\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m colors\n\u001b[1;32m 8\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mmatplotlib\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mbackends\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m backend_agg\n", + "\u001b[0;31mModuleNotFoundError\u001b[0m: No module named 'matplotlib'" + ] + } + ], + "source": [ + "%matplotlib inline\n", + "\n", + "import numpy as np\n", + "import numpy.linalg as la\n", + "\n", + "import scipy.optimize as sopt\n", + "\n", + "import matplotlib.pyplot as pt\n", + "from mpl_toolkits.mplot3d import axes3d\n", + "\n", + "def f(x):\n", + " return x[0]**2 + 3.0*x[1]**2\n", + "\n", + "def df(x):\n", + " return np.array([2*x[0], 6*x[1]])\n", + "\n", + "fig = pt.figure()\n", + "ax = fig.gca(projection=\"3d\")\n", + "\n", + "xmesh, ymesh = np.mgrid[-3:3:50j,-3:3:50j]\n", + "fmesh = f(np.array([xmesh, ymesh]))\n", + "ax.plot_surface(xmesh, ymesh, fmesh)" + ] + }, + { + "cell_type": "markdown", + "id": "c01b471a", + "metadata": { + "editable": true + }, + "source": [ + "And then as countor plot" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "5b835c85", + "metadata": { + "collapsed": false, + "editable": true + }, + "outputs": [], + "source": [ + "pt.axis(\"equal\")\n", + "pt.contour(xmesh, ymesh, fmesh)\n", + "guesses = [np.array([2, 2./5])]" + ] + }, + { + "cell_type": "markdown", + "id": "d6a3c121", + "metadata": { + "editable": true + }, + "source": [ + "Find guesses" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "19e1d73c", + "metadata": { + "collapsed": false, + "editable": true + }, + "outputs": [], + "source": [ + "x = guesses[-1]\n", + "s = -df(x)" + ] + }, + { + "cell_type": "markdown", + "id": "9f7b2dfc", + "metadata": { + "editable": true + }, + "source": [ + "Run it!" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "7d8247e6", + "metadata": { + "collapsed": false, + "editable": true + }, + "outputs": [], + "source": [ + "def f1d(alpha):\n", + " return f(x + alpha*s)\n", + "\n", + "alpha_opt = sopt.golden(f1d)\n", + "next_guess = x + alpha_opt * s\n", + "guesses.append(next_guess)\n", + "print(next_guess)" + ] + }, + { + "cell_type": "markdown", + "id": "c44006da", + "metadata": { + "editable": true + }, + "source": [ + "What happened?" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "bb8a0fd8", + "metadata": { + "collapsed": false, + "editable": true + }, + "outputs": [], + "source": [ + "pt.axis(\"equal\")\n", + "pt.contour(xmesh, ymesh, fmesh, 50)\n", + "it_array = np.array(guesses)\n", + "pt.plot(it_array.T[0], it_array.T[1], \"x-\")" + ] + }, + { + "cell_type": "markdown", + "id": "3d3c98f0", + "metadata": { + "editable": true + }, + "source": [ + "Note that we did only one iteration here. We can easily add more using our previous guesses." + ] + }, + { + "cell_type": "markdown", + "id": "29e5e792", + "metadata": { + "editable": true + }, + "source": [ + "## Conjugate gradient method\n", + "In the CG method we define so-called conjugate directions and two vectors \n", + "$\\boldsymbol{s}$ and $\\boldsymbol{t}$\n", + "are said to be\n", + "conjugate if" + ] + }, + { + "cell_type": "markdown", + "id": "2b0e0db3", + "metadata": { + "editable": true + }, + "source": [ + "$$\n", + "\\boldsymbol{s}^T\\boldsymbol{A}\\boldsymbol{t}= 0.\n", + "$$" + ] + }, + { + "cell_type": "markdown", + "id": "401dd643", + "metadata": { + "editable": true + }, + "source": [ + "The philosophy of the CG method is to perform searches in various conjugate directions\n", + "of our vectors $\\boldsymbol{x}_i$ obeying the above criterion, namely" + ] + }, + { + "cell_type": "markdown", + "id": "bc29d596", + "metadata": { + "editable": true + }, + "source": [ + "$$\n", + "\\boldsymbol{x}_i^T\\boldsymbol{A}\\boldsymbol{x}_j= 0.\n", + "$$" + ] + }, + { + "cell_type": "markdown", + "id": "c1b7adb4", + "metadata": { + "editable": true + }, + "source": [ + "Two vectors are conjugate if they are orthogonal with respect to \n", + "this inner product. Being conjugate is a symmetric relation: if $\\boldsymbol{s}$ is conjugate to $\\boldsymbol{t}$, then $\\boldsymbol{t}$ is conjugate to $\\boldsymbol{s}$." + ] + }, + { + "cell_type": "markdown", + "id": "18924232", + "metadata": { + "editable": true + }, + "source": [ + "## Conjugate gradient method\n", + "An example is given by the eigenvectors of the matrix" + ] + }, + { + "cell_type": "markdown", + "id": "1764ac31", + "metadata": { + "editable": true + }, + "source": [ + "$$\n", + "\\boldsymbol{v}_i^T\\boldsymbol{A}\\boldsymbol{v}_j= \\lambda\\boldsymbol{v}_i^T\\boldsymbol{v}_j,\n", + "$$" + ] + }, + { + "cell_type": "markdown", + "id": "379d5862", + "metadata": { + "editable": true + }, + "source": [ + "which is zero unless $i=j$." + ] + }, + { + "cell_type": "markdown", + "id": "9587d8bf", + "metadata": { + "editable": true + }, + "source": [ + "## Conjugate gradient method\n", + "Assume now that we have a symmetric positive-definite matrix $\\boldsymbol{A}$ of size\n", + "$n\\times n$. At each iteration $i+1$ we obtain the conjugate direction of a vector" + ] + }, + { + "cell_type": "markdown", + "id": "4c3d0bfb", + "metadata": { + "editable": true + }, + "source": [ + "$$\n", + "\\boldsymbol{x}_{i+1}=\\boldsymbol{x}_{i}+\\alpha_i\\boldsymbol{p}_{i}.\n", + "$$" + ] + }, + { + "cell_type": "markdown", + "id": "4079ca1a", + "metadata": { + "editable": true + }, + "source": [ + "We assume that $\\boldsymbol{p}_{i}$ is a sequence of $n$ mutually conjugate directions. \n", + "Then the $\\boldsymbol{p}_{i}$ form a basis of $R^n$ and we can expand the solution \n", + "$ \\boldsymbol{A}\\boldsymbol{x} = \\boldsymbol{b}$ in this basis, namely" + ] + }, + { + "cell_type": "markdown", + "id": "e5b487a5", + "metadata": { + "editable": true + }, + "source": [ + "$$\n", + "\\boldsymbol{x} = \\sum^{n}_{i=1} \\alpha_i \\boldsymbol{p}_i.\n", + "$$" + ] + }, + { + "cell_type": "markdown", + "id": "b623d7f7", + "metadata": { + "editable": true + }, + "source": [ + "## Conjugate gradient method\n", + "The coefficients are given by" + ] + }, + { + "cell_type": "markdown", + "id": "8520c560", + "metadata": { + "editable": true + }, + "source": [ + "$$\n", + "\\mathbf{A}\\mathbf{x} = \\sum^{n}_{i=1} \\alpha_i \\mathbf{A} \\mathbf{p}_i = \\mathbf{b}.\n", + "$$" + ] + }, + { + "cell_type": "markdown", + "id": "52575016", + "metadata": { + "editable": true + }, + "source": [ + "Multiplying with $\\boldsymbol{p}_k^T$ from the left gives" + ] + }, + { + "cell_type": "markdown", + "id": "1b8a85bd", + "metadata": { + "editable": true + }, + "source": [ + "$$\n", + "\\boldsymbol{p}_k^T \\boldsymbol{A}\\boldsymbol{x} = \\sum^{n}_{i=1} \\alpha_i\\boldsymbol{p}_k^T \\boldsymbol{A}\\boldsymbol{p}_i= \\boldsymbol{p}_k^T \\boldsymbol{b},\n", + "$$" + ] + }, + { + "cell_type": "markdown", + "id": "e53b0f45", + "metadata": { + "editable": true + }, + "source": [ + "and we can define the coefficients $\\alpha_k$ as" + ] + }, + { + "cell_type": "markdown", + "id": "2238e15f", + "metadata": { + "editable": true + }, + "source": [ + "$$\n", + "\\alpha_k = \\frac{\\boldsymbol{p}_k^T \\boldsymbol{b}}{\\boldsymbol{p}_k^T \\boldsymbol{A} \\boldsymbol{p}_k}\n", + "$$" + ] + }, + { + "cell_type": "markdown", + "id": "f00e8864", + "metadata": { + "editable": true + }, + "source": [ + "## Conjugate gradient method and iterations\n", + "\n", + "If we choose the conjugate vectors $\\boldsymbol{p}_k$ carefully, \n", + "then we may not need all of them to obtain a good approximation to the solution \n", + "$\\boldsymbol{x}$. \n", + "We want to regard the conjugate gradient method as an iterative method. \n", + "This will us to solve systems where $n$ is so large that the direct \n", + "method would take too much time.\n", + "\n", + "We denote the initial guess for $\\boldsymbol{x}$ as $\\boldsymbol{x}_0$. \n", + "We can assume without loss of generality that" + ] + }, + { + "cell_type": "markdown", + "id": "7a17895d", + "metadata": { + "editable": true + }, + "source": [ + "$$\n", + "\\boldsymbol{x}_0=0,\n", + "$$" + ] + }, + { + "cell_type": "markdown", + "id": "d4bafcb3", + "metadata": { + "editable": true + }, + "source": [ + "or consider the system" + ] + }, + { + "cell_type": "markdown", + "id": "78a7d2c3", + "metadata": { + "editable": true + }, + "source": [ + "$$\n", + "\\boldsymbol{A}\\boldsymbol{z} = \\boldsymbol{b}-\\boldsymbol{A}\\boldsymbol{x}_0,\n", + "$$" + ] + }, + { + "cell_type": "markdown", + "id": "e3192cbf", + "metadata": { + "editable": true + }, + "source": [ + "instead." + ] + }, + { + "cell_type": "markdown", + "id": "0d99ed55", + "metadata": { + "editable": true + }, + "source": [ + "## Conjugate gradient method\n", + "One can show that the solution $\\boldsymbol{x}$ is also the unique minimizer of the quadratic form" + ] + }, + { + "cell_type": "markdown", + "id": "b9653ede", + "metadata": { + "editable": true + }, + "source": [ + "$$\n", + "f(\\boldsymbol{x}) = \\frac{1}{2}\\boldsymbol{x}^T\\boldsymbol{A}\\boldsymbol{x} - \\boldsymbol{x}^T \\boldsymbol{x} , \\quad \\boldsymbol{x}\\in\\mathbf{R}^n.\n", + "$$" + ] + }, + { + "cell_type": "markdown", + "id": "78441105", + "metadata": { + "editable": true + }, + "source": [ + "This suggests taking the first basis vector $\\boldsymbol{p}_1$ \n", + "to be the gradient of $f$ at $\\boldsymbol{x}=\\boldsymbol{x}_0$, \n", + "which equals" + ] + }, + { + "cell_type": "markdown", + "id": "317355d2", + "metadata": { + "editable": true + }, + "source": [ + "$$\n", + "\\boldsymbol{A}\\boldsymbol{x}_0-\\boldsymbol{b},\n", + "$$" + ] + }, + { + "cell_type": "markdown", + "id": "9bb15157", + "metadata": { + "editable": true + }, + "source": [ + "and \n", + "$\\boldsymbol{x}_0=0$ it is equal $-\\boldsymbol{b}$.\n", + "The other vectors in the basis will be conjugate to the gradient, \n", + "hence the name conjugate gradient method." + ] + }, + { + "cell_type": "markdown", + "id": "ac584971", + "metadata": { + "editable": true + }, + "source": [ + "## Conjugate gradient method\n", + "Let $\\boldsymbol{r}_k$ be the residual at the $k$-th step:" + ] + }, + { + "cell_type": "markdown", + "id": "911f1dfa", + "metadata": { + "editable": true + }, + "source": [ + "$$\n", + "\\boldsymbol{r}_k=\\boldsymbol{b}-\\boldsymbol{A}\\boldsymbol{x}_k.\n", + "$$" + ] + }, + { + "cell_type": "markdown", + "id": "d1472568", + "metadata": { + "editable": true + }, + "source": [ + "Note that $\\boldsymbol{r}_k$ is the negative gradient of $f$ at \n", + "$\\boldsymbol{x}=\\boldsymbol{x}_k$, \n", + "so the gradient descent method would be to move in the direction $\\boldsymbol{r}_k$. \n", + "Here, we insist that the directions $\\boldsymbol{p}_k$ are conjugate to each other, \n", + "so we take the direction closest to the gradient $\\boldsymbol{r}_k$ \n", + "under the conjugacy constraint. \n", + "This gives the following expression" + ] + }, + { + "cell_type": "markdown", + "id": "c79708e8", + "metadata": { + "editable": true + }, + "source": [ + "$$\n", + "\\boldsymbol{p}_{k+1}=\\boldsymbol{r}_k-\\frac{\\boldsymbol{p}_k^T \\boldsymbol{A}\\boldsymbol{r}_k}{\\boldsymbol{p}_k^T\\boldsymbol{A}\\boldsymbol{p}_k} \\boldsymbol{p}_k.\n", + "$$" + ] + }, + { + "cell_type": "markdown", + "id": "535d3e73", + "metadata": { + "editable": true + }, + "source": [ + "## Conjugate gradient method\n", + "We can also compute the residual iteratively as" + ] + }, + { + "cell_type": "markdown", + "id": "ad718f62", + "metadata": { + "editable": true + }, + "source": [ + "$$\n", + "\\boldsymbol{r}_{k+1}=\\boldsymbol{b}-\\boldsymbol{A}\\boldsymbol{x}_{k+1},\n", + "$$" + ] + }, + { + "cell_type": "markdown", + "id": "d0a90fa5", + "metadata": { + "editable": true + }, + "source": [ + "which equals" + ] + }, + { + "cell_type": "markdown", + "id": "860e9217", + "metadata": { + "editable": true + }, + "source": [ + "$$\n", + "\\boldsymbol{b}-\\boldsymbol{A}(\\boldsymbol{x}_k+\\alpha_k\\boldsymbol{p}_k),\n", + "$$" + ] + }, + { + "cell_type": "markdown", + "id": "6d8a72c8", + "metadata": { + "editable": true + }, + "source": [ + "or" + ] + }, + { + "cell_type": "markdown", + "id": "746e6fc0", + "metadata": { + "editable": true + }, + "source": [ + "$$\n", + "(\\boldsymbol{b}-\\boldsymbol{A}\\boldsymbol{x}_k)-\\alpha_k\\boldsymbol{A}\\boldsymbol{p}_k,\n", + "$$" + ] + }, + { + "cell_type": "markdown", + "id": "f76c0e69", + "metadata": { + "editable": true + }, + "source": [ + "which gives" + ] + }, + { + "cell_type": "markdown", + "id": "9aee35ca", + "metadata": { + "editable": true + }, + "source": [ + "$$\n", + "\\boldsymbol{r}_{k+1}=\\boldsymbol{r}_k-\\boldsymbol{A}\\boldsymbol{p}_{k},\n", + "$$" + ] + }, + { + "cell_type": "markdown", + "id": "ce9ce258", + "metadata": { + "editable": true + }, + "source": [ + "## Revisiting our first homework\n", + "\n", + "We will use linear regression as a case study for the gradient descent\n", + "methods. Linear regression is a great test case for the gradient\n", + "descent methods discussed in the lectures since it has several\n", + "desirable properties such as:\n", + "\n", + "1. An analytical solution (recall homework set 1).\n", + "\n", + "2. The gradient can be computed analytically.\n", + "\n", + "3. The cost function is convex which guarantees that gradient descent converges for small enough learning rates\n", + "\n", + "We revisit an example similar to what we had in the first homework set. We had a function of the type" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "f902a0f2", + "metadata": { + "collapsed": false, + "editable": true + }, + "outputs": [], + "source": [ + "x = 2*np.random.rand(m,1)\n", + "y = 4+3*x+np.random.randn(m,1)" + ] + }, + { + "cell_type": "markdown", + "id": "36d883b2", + "metadata": { + "editable": true + }, + "source": [ + "with $x_i \\in [0,1] $ is chosen randomly using a uniform distribution. Additionally we have a stochastic noise chosen according to a normal distribution $\\cal {N}(0,1)$. \n", + "The linear regression model is given by" + ] + }, + { + "cell_type": "markdown", + "id": "cde21ef1", + "metadata": { + "editable": true + }, + "source": [ + "$$\n", + "h_\\beta(x) = \\boldsymbol{y} = \\beta_0 + \\beta_1 x,\n", + "$$" + ] + }, + { + "cell_type": "markdown", + "id": "f2a021d3", + "metadata": { + "editable": true + }, + "source": [ + "such that" + ] + }, + { + "cell_type": "markdown", + "id": "ea0a91e4", + "metadata": { + "editable": true + }, + "source": [ + "$$\n", + "\\boldsymbol{y}_i = \\beta_0 + \\beta_1 x_i.\n", + "$$" + ] + }, + { + "cell_type": "markdown", + "id": "854f3ebb", + "metadata": { + "editable": true + }, + "source": [ + "## Gradient descent example\n", + "\n", + "Let $\\mathbf{y} = (y_1,\\cdots,y_n)^T$, $\\mathbf{\\boldsymbol{y}} = (\\boldsymbol{y}_1,\\cdots,\\boldsymbol{y}_n)^T$ and $\\beta = (\\beta_0, \\beta_1)^T$\n", + "\n", + "It is convenient to write $\\mathbf{\\boldsymbol{y}} = X\\beta$ where $X \\in \\mathbb{R}^{100 \\times 2} $ is the design matrix given by (we keep the intercept here)" + ] + }, + { + "cell_type": "markdown", + "id": "dd282d2d", + "metadata": { + "editable": true + }, + "source": [ + "$$\n", + "X \\equiv \\begin{bmatrix}\n", + "1 & x_1 \\\\\n", + "\\vdots & \\vdots \\\\\n", + "1 & x_{100} & \\\\\n", + "\\end{bmatrix}.\n", + "$$" + ] + }, + { + "cell_type": "markdown", + "id": "fd562029", + "metadata": { + "editable": true + }, + "source": [ + "The cost/loss/risk function is given by (" + ] + }, + { + "cell_type": "markdown", + "id": "25369bc3", + "metadata": { + "editable": true + }, + "source": [ + "$$\n", + "C(\\beta) = \\frac{1}{n}||X\\beta-\\mathbf{y}||_{2}^{2} = \\frac{1}{n}\\sum_{i=1}^{100}\\left[ (\\beta_0 + \\beta_1 x_i)^2 - 2 y_i (\\beta_0 + \\beta_1 x_i) + y_i^2\\right]\n", + "$$" + ] + }, + { + "cell_type": "markdown", + "id": "a222f3ea", + "metadata": { + "editable": true + }, + "source": [ + "and we want to find $\\beta$ such that $C(\\beta)$ is minimized." + ] + }, + { + "cell_type": "markdown", + "id": "1d5fb6f1", + "metadata": { + "editable": true + }, + "source": [ + "## The derivative of the cost/loss function\n", + "\n", + "Computing $\\partial C(\\beta) / \\partial \\beta_0$ and $\\partial C(\\beta) / \\partial \\beta_1$ we can show that the gradient can be written as" + ] + }, + { + "cell_type": "markdown", + "id": "eab2df73", + "metadata": { + "editable": true + }, + "source": [ + "$$\n", + "\\nabla_{\\beta} C(\\beta) = \\frac{2}{n}\\begin{bmatrix} \\sum_{i=1}^{100} \\left(\\beta_0+\\beta_1x_i-y_i\\right) \\\\\n", + "\\sum_{i=1}^{100}\\left( x_i (\\beta_0+\\beta_1x_i)-y_ix_i\\right) \\\\\n", + "\\end{bmatrix} = \\frac{2}{n}X^T(X\\beta - \\mathbf{y}),\n", + "$$" + ] + }, + { + "cell_type": "markdown", + "id": "daee1165", + "metadata": { + "editable": true + }, + "source": [ + "where $X$ is the design matrix defined above." + ] + }, + { + "cell_type": "markdown", + "id": "f2c6f5cc", + "metadata": { + "editable": true + }, + "source": [ + "## The Hessian matrix\n", + "The Hessian matrix of $C(\\beta)$ is given by" + ] + }, + { + "cell_type": "markdown", + "id": "ecce0d08", + "metadata": { + "editable": true + }, + "source": [ + "$$\n", + "\\boldsymbol{H} \\equiv \\begin{bmatrix}\n", + "\\frac{\\partial^2 C(\\beta)}{\\partial \\beta_0^2} & \\frac{\\partial^2 C(\\beta)}{\\partial \\beta_0 \\partial \\beta_1} \\\\\n", + "\\frac{\\partial^2 C(\\beta)}{\\partial \\beta_0 \\partial \\beta_1} & \\frac{\\partial^2 C(\\beta)}{\\partial \\beta_1^2} & \\\\\n", + "\\end{bmatrix} = \\frac{2}{n}X^T X.\n", + "$$" + ] + }, + { + "cell_type": "markdown", + "id": "c4308e5f", + "metadata": { + "editable": true + }, + "source": [ + "This result implies that $C(\\beta)$ is a convex function since the matrix $X^T X$ always is positive semi-definite." + ] + }, + { + "cell_type": "markdown", + "id": "4ee64b17", + "metadata": { + "editable": true + }, + "source": [ + "## Simple program\n", + "\n", + "We can now write a program that minimizes $C(\\beta)$ using the gradient descent method with a constant learning rate $\\gamma$ according to" + ] + }, + { + "cell_type": "markdown", + "id": "57e8db33", + "metadata": { + "editable": true + }, + "source": [ + "$$\n", + "\\beta_{k+1} = \\beta_k - \\gamma \\nabla_\\beta C(\\beta_k), \\ k=0,1,\\cdots\n", + "$$" + ] + }, + { + "cell_type": "markdown", + "id": "c42e4032", + "metadata": { + "editable": true + }, + "source": [ + "We can use the expression we computed for the gradient and let use a\n", + "$\\beta_0$ be chosen randomly and let $\\gamma = 0.001$. Stop iterating\n", + "when $||\\nabla_\\beta C(\\beta_k) || \\leq \\epsilon = 10^{-8}$. **Note that the code below does not include the latter stop criterion**.\n", + "\n", + "And finally we can compare our solution for $\\beta$ with the analytic result given by \n", + "$\\beta= (X^TX)^{-1} X^T \\mathbf{y}$." + ] + }, + { + "cell_type": "markdown", + "id": "4c430cd3", + "metadata": { + "editable": true + }, + "source": [ + "## Gradient Descent Example\n", + "\n", + "Here our simple example" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "9ac6096f", + "metadata": { + "collapsed": false, + "editable": true + }, + "outputs": [], + "source": [ + "\n", + "# Importing various packages\n", + "from random import random, seed\n", + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "from mpl_toolkits.mplot3d import Axes3D\n", + "from matplotlib import cm\n", + "from matplotlib.ticker import LinearLocator, FormatStrFormatter\n", + "import sys\n", + "\n", + "# the number of datapoints\n", + "n = 100\n", + "x = 2*np.random.rand(n,1)\n", + "y = 4+3*x+np.random.randn(n,1)\n", + "\n", + "X = np.c_[np.ones((n,1)), x]\n", + "# Hessian matrix\n", + "H = (2.0/n)* X.T @ X\n", + "# Get the eigenvalues\n", + "EigValues, EigVectors = np.linalg.eig(H)\n", + "print(f\"Eigenvalues of Hessian Matrix:{EigValues}\")\n", + "\n", + "beta_linreg = np.linalg.inv(X.T @ X) @ X.T @ y\n", + "print(beta_linreg)\n", + "beta = np.random.randn(2,1)\n", + "\n", + "eta = 1.0/np.max(EigValues)\n", + "Niterations = 1000\n", + "\n", + "for iter in range(Niterations):\n", + " gradient = (2.0/n)*X.T @ (X @ beta-y)\n", + " beta -= eta*gradient\n", + "\n", + "print(beta)\n", + "xnew = np.array([[0],[2]])\n", + "xbnew = np.c_[np.ones((2,1)), xnew]\n", + "ypredict = xbnew.dot(beta)\n", + "ypredict2 = xbnew.dot(beta_linreg)\n", + "plt.plot(xnew, ypredict, \"r-\")\n", + "plt.plot(xnew, ypredict2, \"b-\")\n", + "plt.plot(x, y ,'ro')\n", + "plt.axis([0,2.0,0, 15.0])\n", + "plt.xlabel(r'$x$')\n", + "plt.ylabel(r'$y$')\n", + "plt.title(r'Gradient descent example')\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "df783e1d", + "metadata": { + "editable": true + }, + "source": [ + "## And a corresponding example using **scikit-learn**" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "98f08f24", + "metadata": { + "collapsed": false, + "editable": true + }, + "outputs": [], + "source": [ + "# Importing various packages\n", + "from random import random, seed\n", + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "from sklearn.linear_model import SGDRegressor\n", + "\n", + "n = 100\n", + "x = 2*np.random.rand(n,1)\n", + "y = 4+3*x+np.random.randn(n,1)\n", + "\n", + "X = np.c_[np.ones((n,1)), x]\n", + "beta_linreg = np.linalg.inv(X.T @ X) @ (X.T @ y)\n", + "print(beta_linreg)\n", + "sgdreg = SGDRegressor(max_iter = 50, penalty=None, eta0=0.1)\n", + "sgdreg.fit(x,y.ravel())\n", + "print(sgdreg.intercept_, sgdreg.coef_)" + ] + }, + { + "cell_type": "markdown", + "id": "50a5ab0d", + "metadata": { + "editable": true + }, + "source": [ + "## Gradient descent and Ridge\n", + "\n", + "We have also discussed Ridge regression where the loss function contains a regularized term given by the $L_2$ norm of $\\beta$," + ] + }, + { + "cell_type": "markdown", + "id": "b35293d4", + "metadata": { + "editable": true + }, + "source": [ + "$$\n", + "C_{\\text{ridge}}(\\beta) = \\frac{1}{n}||X\\beta -\\mathbf{y}||^2 + \\lambda ||\\beta||^2, \\ \\lambda \\geq 0.\n", + "$$" + ] + }, + { + "cell_type": "markdown", + "id": "fed491ee", + "metadata": { + "editable": true + }, + "source": [ + "In order to minimize $C_{\\text{ridge}}(\\beta)$ using GD we adjust the gradient as follows" + ] + }, + { + "cell_type": "markdown", + "id": "a0b4c94e", + "metadata": { + "editable": true + }, + "source": [ + "$$\n", + "\\nabla_\\beta C_{\\text{ridge}}(\\beta) = \\frac{2}{n}\\begin{bmatrix} \\sum_{i=1}^{100} \\left(\\beta_0+\\beta_1x_i-y_i\\right) \\\\\n", + "\\sum_{i=1}^{100}\\left( x_i (\\beta_0+\\beta_1x_i)-y_ix_i\\right) \\\\\n", + "\\end{bmatrix} + 2\\lambda\\begin{bmatrix} \\beta_0 \\\\ \\beta_1\\end{bmatrix} = 2 (\\frac{1}{n}X^T(X\\beta - \\mathbf{y})+\\lambda \\beta).\n", + "$$" + ] + }, + { + "cell_type": "markdown", + "id": "e4f02dce", + "metadata": { + "editable": true + }, + "source": [ + "We can easily extend our program to minimize $C_{\\text{ridge}}(\\beta)$ using gradient descent and compare with the analytical solution given by" + ] + }, + { + "cell_type": "markdown", + "id": "b9f297ff", + "metadata": { + "editable": true + }, + "source": [ + "$$\n", + "\\beta_{\\text{ridge}} = \\left(X^T X + n\\lambda I_{2 \\times 2} \\right)^{-1} X^T \\mathbf{y}.\n", + "$$" + ] + }, + { + "cell_type": "markdown", + "id": "4e1caf44", + "metadata": { + "editable": true + }, + "source": [ + "## The Hessian matrix for Ridge Regression\n", + "The Hessian matrix of Ridge Regression for our simple example is given by" + ] + }, + { + "cell_type": "markdown", + "id": "a046daea", + "metadata": { + "editable": true + }, + "source": [ + "$$\n", + "\\boldsymbol{H} \\equiv \\begin{bmatrix}\n", + "\\frac{\\partial^2 C(\\beta)}{\\partial \\beta_0^2} & \\frac{\\partial^2 C(\\beta)}{\\partial \\beta_0 \\partial \\beta_1} \\\\\n", + "\\frac{\\partial^2 C(\\beta)}{\\partial \\beta_0 \\partial \\beta_1} & \\frac{\\partial^2 C(\\beta)}{\\partial \\beta_1^2} & \\\\\n", + "\\end{bmatrix} = \\frac{2}{n}X^T X+2\\lambda\\boldsymbol{I}.\n", + "$$" + ] + }, + { + "cell_type": "markdown", + "id": "02f05574", + "metadata": { + "editable": true + }, + "source": [ + "This implies that the Hessian matrix is positive definite, hence the stationary point is a\n", + "minimum.\n", + "Note that the Ridge cost function is convex being a sum of two convex\n", + "functions. Therefore, the stationary point is a global\n", + "minimum of this function." + ] + }, + { + "cell_type": "markdown", + "id": "45484749", + "metadata": { + "editable": true + }, + "source": [ + "## Program example for gradient descent with Ridge Regression" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "9973cd20", + "metadata": { + "collapsed": false, + "editable": true + }, + "outputs": [], + "source": [ + "from random import random, seed\n", + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "from mpl_toolkits.mplot3d import Axes3D\n", + "from matplotlib import cm\n", + "from matplotlib.ticker import LinearLocator, FormatStrFormatter\n", + "import sys\n", + "\n", + "# the number of datapoints\n", + "n = 100\n", + "x = 2*np.random.rand(n,1)\n", + "y = 4+3*x+np.random.randn(n,1)\n", + "\n", + "X = np.c_[np.ones((n,1)), x]\n", + "XT_X = X.T @ X\n", + "\n", + "#Ridge parameter lambda\n", + "lmbda = 0.001\n", + "Id = n*lmbda* np.eye(XT_X.shape[0])\n", + "\n", + "# Hessian matrix\n", + "H = (2.0/n)* XT_X+2*lmbda* np.eye(XT_X.shape[0])\n", + "# Get the eigenvalues\n", + "EigValues, EigVectors = np.linalg.eig(H)\n", + "print(f\"Eigenvalues of Hessian Matrix:{EigValues}\")\n", + "\n", + "\n", + "beta_linreg = np.linalg.inv(XT_X+Id) @ X.T @ y\n", + "print(beta_linreg)\n", + "# Start plain gradient descent\n", + "beta = np.random.randn(2,1)\n", + "\n", + "eta = 1.0/np.max(EigValues)\n", + "Niterations = 100\n", + "\n", + "for iter in range(Niterations):\n", + " gradients = 2.0/n*X.T @ (X @ (beta)-y)+2*lmbda*beta\n", + " beta -= eta*gradients\n", + "\n", + "print(beta)\n", + "ypredict = X @ beta\n", + "ypredict2 = X @ beta_linreg\n", + "plt.plot(x, ypredict, \"r-\")\n", + "plt.plot(x, ypredict2, \"b-\")\n", + "plt.plot(x, y ,'ro')\n", + "plt.axis([0,2.0,0, 15.0])\n", + "plt.xlabel(r'$x$')\n", + "plt.ylabel(r'$y$')\n", + "plt.title(r'Gradient descent example for Ridge')\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "1836d4ef", + "metadata": { + "editable": true + }, + "source": [ + "## Using gradient descent methods, limitations\n", + "\n", + "* **Gradient descent (GD) finds local minima of our function**. Since the GD algorithm is deterministic, if it converges, it will converge to a local minimum of our cost/loss/risk function. Because in ML we are often dealing with extremely rugged landscapes with many local minima, this can lead to poor performance.\n", + "\n", + "* **GD is sensitive to initial conditions**. One consequence of the local nature of GD is that initial conditions matter. Depending on where one starts, one will end up at a different local minima. Therefore, it is very important to think about how one initializes the training process. This is true for GD as well as more complicated variants of GD.\n", + "\n", + "* **Gradients are computationally expensive to calculate for large datasets**. In many cases in statistics and ML, the cost/loss/risk function is a sum of terms, with one term for each data point. For example, in linear regression, $E \\propto \\sum_{i=1}^n (y_i - \\mathbf{w}^T\\cdot\\mathbf{x}_i)^2$; for logistic regression, the square error is replaced by the cross entropy. To calculate the gradient we have to sum over *all* $n$ data points. Doing this at every GD step becomes extremely computationally expensive. An ingenious solution to this, is to calculate the gradients using small subsets of the data called \"mini batches\". This has the added benefit of introducing stochasticity into our algorithm.\n", + "\n", + "* **GD is very sensitive to choices of learning rates**. GD is extremely sensitive to the choice of learning rates. If the learning rate is very small, the training process take an extremely long time. For larger learning rates, GD can diverge and give poor results. Furthermore, depending on what the local landscape looks like, we have to modify the learning rates to ensure convergence. Ideally, we would *adaptively* choose the learning rates to match the landscape.\n", + "\n", + "* **GD treats all directions in parameter space uniformly.** Another major drawback of GD is that unlike Newton's method, the learning rate for GD is the same in all directions in parameter space. For this reason, the maximum learning rate is set by the behavior of the steepest direction and this can significantly slow down training. Ideally, we would like to take large steps in flat directions and small steps in steep directions. Since we are exploring rugged landscapes where curvatures change, this requires us to keep track of not only the gradient but second derivatives. The ideal scenario would be to calculate the Hessian but this proves to be too computationally expensive. \n", + "\n", + "* GD can take exponential time to escape saddle points, even with random initialization. As we mentioned, GD is extremely sensitive to initial condition since it determines the particular local minimum GD would eventually reach. However, even with a good initialization scheme, through the introduction of randomness, GD can still take exponential time to escape saddle points." + ] + }, + { + "cell_type": "markdown", + "id": "88975d3d", + "metadata": { + "editable": true + }, + "source": [ + "## Improving gradient descent with momentum\n", + "\n", + "We discuss here some simple examples where we introduce what is called 'memory'about previous steps, or what is normally called momentum gradient descent. The mathematics is explained below in connection with Stochastic gradient descent." + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "id": "56f415e0", + "metadata": { + "collapsed": false, + "editable": true + }, + "outputs": [], + "source": [ + "from numpy import asarray\n", + "from numpy import arange\n", + "from numpy.random import rand\n", + "from numpy.random import seed\n", + "from matplotlib import pyplot\n", + " \n", + "# objective function\n", + "def objective(x):\n", + "\treturn x**2.0\n", + " \n", + "# derivative of objective function\n", + "def derivative(x):\n", + "\treturn x * 2.0\n", + " \n", + "# gradient descent algorithm\n", + "def gradient_descent(objective, derivative, bounds, n_iter, step_size):\n", + "\t# track all solutions\n", + "\tsolutions, scores = list(), list()\n", + "\t# generate an initial point\n", + "\tsolution = bounds[:, 0] + rand(len(bounds)) * (bounds[:, 1] - bounds[:, 0])\n", + "\t# run the gradient descent\n", + "\tfor i in range(n_iter):\n", + "\t\t# calculate gradient\n", + "\t\tgradient = derivative(solution)\n", + "\t\t# take a step\n", + "\t\tsolution = solution - step_size * gradient\n", + "\t\t# evaluate candidate point\n", + "\t\tsolution_eval = objective(solution)\n", + "\t\t# store solution\n", + "\t\tsolutions.append(solution)\n", + "\t\tscores.append(solution_eval)\n", + "\t\t# report progress\n", + "\t\tprint('>%d f(%s) = %.5f' % (i, solution, solution_eval))\n", + "\treturn [solutions, scores]\n", + " \n", + "# seed the pseudo random number generator\n", + "seed(4)\n", + "# define range for input\n", + "bounds = asarray([[-1.0, 1.0]])\n", + "# define the total iterations\n", + "n_iter = 30\n", + "# define the step size\n", + "step_size = 0.1\n", + "# perform the gradient descent search\n", + "solutions, scores = gradient_descent(objective, derivative, bounds, n_iter, step_size)\n", + "# sample input range uniformly at 0.1 increments\n", + "inputs = arange(bounds[0,0], bounds[0,1]+0.1, 0.1)\n", + "# compute targets\n", + "results = objective(inputs)\n", + "# create a line plot of input vs result\n", + "pyplot.plot(inputs, results)\n", + "# plot the solutions found\n", + "pyplot.plot(solutions, scores, '.-', color='red')\n", + "# show the plot\n", + "pyplot.show()" + ] + }, + { + "cell_type": "markdown", + "id": "d3343584", + "metadata": { + "editable": true + }, + "source": [ + "## Same code but now with momentum gradient descent" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "id": "ff1e3778", + "metadata": { + "collapsed": false, + "editable": true + }, + "outputs": [], + "source": [ + "from numpy import asarray\n", + "from numpy import arange\n", + "from numpy.random import rand\n", + "from numpy.random import seed\n", + "from matplotlib import pyplot\n", + " \n", + "# objective function\n", + "def objective(x):\n", + "\treturn x**2.0\n", + " \n", + "# derivative of objective function\n", + "def derivative(x):\n", + "\treturn x * 2.0\n", + " \n", + "# gradient descent algorithm\n", + "def gradient_descent(objective, derivative, bounds, n_iter, step_size, momentum):\n", + "\t# track all solutions\n", + "\tsolutions, scores = list(), list()\n", + "\t# generate an initial point\n", + "\tsolution = bounds[:, 0] + rand(len(bounds)) * (bounds[:, 1] - bounds[:, 0])\n", + "\t# keep track of the change\n", + "\tchange = 0.0\n", + "\t# run the gradient descent\n", + "\tfor i in range(n_iter):\n", + "\t\t# calculate gradient\n", + "\t\tgradient = derivative(solution)\n", + "\t\t# calculate update\n", + "\t\tnew_change = step_size * gradient + momentum * change\n", + "\t\t# take a step\n", + "\t\tsolution = solution - new_change\n", + "\t\t# save the change\n", + "\t\tchange = new_change\n", + "\t\t# evaluate candidate point\n", + "\t\tsolution_eval = objective(solution)\n", + "\t\t# store solution\n", + "\t\tsolutions.append(solution)\n", + "\t\tscores.append(solution_eval)\n", + "\t\t# report progress\n", + "\t\tprint('>%d f(%s) = %.5f' % (i, solution, solution_eval))\n", + "\treturn [solutions, scores]\n", + " \n", + "# seed the pseudo random number generator\n", + "seed(4)\n", + "# define range for input\n", + "bounds = asarray([[-1.0, 1.0]])\n", + "# define the total iterations\n", + "n_iter = 30\n", + "# define the step size\n", + "step_size = 0.1\n", + "# define momentum\n", + "momentum = 0.3\n", + "# perform the gradient descent search with momentum\n", + "solutions, scores = gradient_descent(objective, derivative, bounds, n_iter, step_size, momentum)\n", + "# sample input range uniformly at 0.1 increments\n", + "inputs = arange(bounds[0,0], bounds[0,1]+0.1, 0.1)\n", + "# compute targets\n", + "results = objective(inputs)\n", + "# create a line plot of input vs result\n", + "pyplot.plot(inputs, results)\n", + "# plot the solutions found\n", + "pyplot.plot(solutions, scores, '.-', color='red')\n", + "# show the plot\n", + "pyplot.show()" + ] + }, + { + "cell_type": "markdown", + "id": "c1d70995", + "metadata": { + "editable": true + }, + "source": [ + "## Overview video on Stochastic Gradient Descent\n", + "\n", + "[What is Stochastic Gradient Descent](https://www.youtube.com/watch?v=vMh0zPT0tLI&ab_channel=StatQuestwithJoshStarmer)" + ] + }, + { + "cell_type": "markdown", + "id": "930a5be8", + "metadata": { + "editable": true + }, + "source": [ + "## Batches and mini-batches\n", + "\n", + "In gradient descent we compute the cost function and its gradient for all data points we have.\n", + "\n", + "In large-scale applications such as the [ILSVRC challenge](https://www.image-net.org/challenges/LSVRC/), the\n", + "training data can have on order of millions of examples. Hence, it\n", + "seems wasteful to compute the full cost function over the entire\n", + "training set in order to perform only a single parameter update. A\n", + "very common approach to addressing this challenge is to compute the\n", + "gradient over batches of the training data. For example, a typical batch could contain some thousand examples from\n", + "an entire training set of several millions. This batch is then used to\n", + "perform a parameter update." + ] + }, + { + "cell_type": "markdown", + "id": "0a7fb7ef", + "metadata": { + "editable": true + }, + "source": [ + "## Stochastic Gradient Descent (SGD)\n", + "\n", + "In stochastic gradient descent, the extreme case is the case where we\n", + "have only one batch, that is we include the whole data set.\n", + "\n", + "This process is called Stochastic Gradient\n", + "Descent (SGD) (or also sometimes on-line gradient descent). This is\n", + "relatively less common to see because in practice due to vectorized\n", + "code optimizations it can be computationally much more efficient to\n", + "evaluate the gradient for 100 examples, than the gradient for one\n", + "example 100 times. Even though SGD technically refers to using a\n", + "single example at a time to evaluate the gradient, you will hear\n", + "people use the term SGD even when referring to mini-batch gradient\n", + "descent (i.e. mentions of MGD for “Minibatch Gradient Descent”, or BGD\n", + "for “Batch gradient descent” are rare to see), where it is usually\n", + "assumed that mini-batches are used. The size of the mini-batch is a\n", + "hyperparameter but it is not very common to cross-validate or bootstrap it. It is\n", + "usually based on memory constraints (if any), or set to some value,\n", + "e.g. 32, 64 or 128. We use powers of 2 in practice because many\n", + "vectorized operation implementations work faster when their inputs are\n", + "sized in powers of 2.\n", + "\n", + "In our notes with SGD we mean stochastic gradient descent with mini-batches." + ] + }, + { + "cell_type": "markdown", + "id": "dbff87b0", + "metadata": { + "editable": true + }, + "source": [ + "## Stochastic Gradient Descent\n", + "\n", + "Stochastic gradient descent (SGD) and variants thereof address some of\n", + "the shortcomings of the Gradient descent method discussed above.\n", + "\n", + "The underlying idea of SGD comes from the observation that the cost\n", + "function, which we want to minimize, can almost always be written as a\n", + "sum over $n$ data points $\\{\\mathbf{x}_i\\}_{i=1}^n$," + ] + }, + { + "cell_type": "markdown", + "id": "cd292df5", + "metadata": { + "editable": true + }, + "source": [ + "$$\n", + "C(\\mathbf{\\beta}) = \\sum_{i=1}^n c_i(\\mathbf{x}_i,\n", + "\\mathbf{\\beta}).\n", + "$$" + ] + }, + { + "cell_type": "markdown", + "id": "1b2ffa4e", + "metadata": { + "editable": true + }, + "source": [ + "## Computation of gradients\n", + "\n", + "This in turn means that the gradient can be\n", + "computed as a sum over $i$-gradients" + ] + }, + { + "cell_type": "markdown", + "id": "d0abe4b0", + "metadata": { + "editable": true + }, + "source": [ + "$$\n", + "\\nabla_\\beta C(\\mathbf{\\beta}) = \\sum_i^n \\nabla_\\beta c_i(\\mathbf{x}_i,\n", + "\\mathbf{\\beta}).\n", + "$$" + ] + }, + { + "cell_type": "markdown", + "id": "65c15c60", + "metadata": { + "editable": true + }, + "source": [ + "Stochasticity/randomness is introduced by only taking the\n", + "gradient on a subset of the data called minibatches. If there are $n$\n", + "data points and the size of each minibatch is $M$, there will be $n/M$\n", + "minibatches. We denote these minibatches by $B_k$ where\n", + "$k=1,\\cdots,n/M$." + ] + }, + { + "cell_type": "markdown", + "id": "460354c0", + "metadata": { + "editable": true + }, + "source": [ + "## SGD example\n", + "As an example, suppose we have $10$ data points $(\\mathbf{x}_1,\\cdots, \\mathbf{x}_{10})$ \n", + "and we choose to have $M=5$ minibathces,\n", + "then each minibatch contains two data points. In particular we have\n", + "$B_1 = (\\mathbf{x}_1,\\mathbf{x}_2), \\cdots, B_5 =\n", + "(\\mathbf{x}_9,\\mathbf{x}_{10})$. Note that if you choose $M=1$ you\n", + "have only a single batch with all data points and on the other extreme,\n", + "you may choose $M=n$ resulting in a minibatch for each datapoint, i.e\n", + "$B_k = \\mathbf{x}_k$.\n", + "\n", + "The idea is now to approximate the gradient by replacing the sum over\n", + "all data points with a sum over the data points in one the minibatches\n", + "picked at random in each gradient descent step" + ] + }, + { + "cell_type": "markdown", + "id": "c2a5dfcd", + "metadata": { + "editable": true + }, + "source": [ + "$$\n", + "\\nabla_{\\beta}\n", + "C(\\mathbf{\\beta}) = \\sum_{i=1}^n \\nabla_\\beta c_i(\\mathbf{x}_i,\n", + "\\mathbf{\\beta}) \\rightarrow \\sum_{i \\in B_k}^n \\nabla_\\beta\n", + "c_i(\\mathbf{x}_i, \\mathbf{\\beta}).\n", + "$$" + ] + }, + { + "cell_type": "markdown", + "id": "eeeb0fe8", + "metadata": { + "editable": true + }, + "source": [ + "## The gradient step\n", + "\n", + "Thus a gradient descent step now looks like" + ] + }, + { + "cell_type": "markdown", + "id": "2b49c741", + "metadata": { + "editable": true + }, + "source": [ + "$$\n", + "\\beta_{j+1} = \\beta_j - \\gamma_j \\sum_{i \\in B_k}^n \\nabla_\\beta c_i(\\mathbf{x}_i,\n", + "\\mathbf{\\beta})\n", + "$$" + ] + }, + { + "cell_type": "markdown", + "id": "8ba7b9be", + "metadata": { + "editable": true + }, + "source": [ + "where $k$ is picked at random with equal\n", + "probability from $[1,n/M]$. An iteration over the number of\n", + "minibathces (n/M) is commonly referred to as an epoch. Thus it is\n", + "typical to choose a number of epochs and for each epoch iterate over\n", + "the number of minibatches, as exemplified in the code below." + ] + }, + { + "cell_type": "markdown", + "id": "50da33c0", + "metadata": { + "editable": true + }, + "source": [ + "## Simple example code" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "id": "31bd6a24", + "metadata": { + "collapsed": false, + "editable": true + }, + "outputs": [], + "source": [ + "import numpy as np \n", + "\n", + "n = 100 #100 datapoints \n", + "M = 5 #size of each minibatch\n", + "m = int(n/M) #number of minibatches\n", + "n_epochs = 10 #number of epochs\n", + "\n", + "j = 0\n", + "for epoch in range(1,n_epochs+1):\n", + " for i in range(m):\n", + " k = np.random.randint(m) #Pick the k-th minibatch at random\n", + " #Compute the gradient using the data in minibatch Bk\n", + " #Compute new suggestion for \n", + " j += 1" + ] + }, + { + "cell_type": "markdown", + "id": "0deb8111", + "metadata": { + "editable": true + }, + "source": [ + "Taking the gradient only on a subset of the data has two important\n", + "benefits. First, it introduces randomness which decreases the chance\n", + "that our opmization scheme gets stuck in a local minima. Second, if\n", + "the size of the minibatches are small relative to the number of\n", + "datapoints ($M < n$), the computation of the gradient is much\n", + "cheaper since we sum over the datapoints in the $k-th$ minibatch and not\n", + "all $n$ datapoints." + ] + }, + { + "cell_type": "markdown", + "id": "16d54f02", + "metadata": { + "editable": true + }, + "source": [ + "## When do we stop?\n", + "\n", + "A natural question is when do we stop the search for a new minimum?\n", + "One possibility is to compute the full gradient after a given number\n", + "of epochs and check if the norm of the gradient is smaller than some\n", + "threshold and stop if true. However, the condition that the gradient\n", + "is zero is valid also for local minima, so this would only tell us\n", + "that we are close to a local/global minimum. However, we could also\n", + "evaluate the cost function at this point, store the result and\n", + "continue the search. If the test kicks in at a later stage we can\n", + "compare the values of the cost function and keep the $\\beta$ that\n", + "gave the lowest value." + ] + }, + { + "cell_type": "markdown", + "id": "b300d06b", + "metadata": { + "editable": true + }, + "source": [ + "## Slightly different approach\n", + "\n", + "Another approach is to let the step length $\\gamma_j$ depend on the\n", + "number of epochs in such a way that it becomes very small after a\n", + "reasonable time such that we do not move at all. Such approaches are\n", + "also called scaling. There are many such ways to [scale the learning\n", + "rate](https://towardsdatascience.com/gradient-descent-the-learning-rate-and-the-importance-of-feature-scaling-6c0b416596e1)\n", + "and [discussions here](https://www.jmlr.org/papers/volume23/20-1258/20-1258.pdf). See\n", + "also\n", + "\n", + "for a discussion of different scaling functions for the learning rate." + ] + }, + { + "cell_type": "markdown", + "id": "6bc7778d", + "metadata": { + "editable": true + }, + "source": [ + "## Time decay rate\n", + "\n", + "As an example, let $e = 0,1,2,3,\\cdots$ denote the current epoch and let $t_0, t_1 > 0$ be two fixed numbers. Furthermore, let $t = e \\cdot m + i$ where $m$ is the number of minibatches and $i=0,\\cdots,m-1$. Then the function $$\\gamma_j(t; t_0, t_1) = \\frac{t_0}{t+t_1} $$ goes to zero as the number of epochs gets large. I.e. we start with a step length $\\gamma_j (0; t_0, t_1) = t_0/t_1$ which decays in *time* $t$.\n", + "\n", + "In this way we can fix the number of epochs, compute $\\beta$ and\n", + "evaluate the cost function at the end. Repeating the computation will\n", + "give a different result since the scheme is random by design. Then we\n", + "pick the final $\\beta$ that gives the lowest value of the cost\n", + "function." + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "id": "a60fe5bd", + "metadata": { + "collapsed": false, + "editable": true + }, + "outputs": [], + "source": [ + "import numpy as np \n", + "\n", + "def step_length(t,t0,t1):\n", + " return t0/(t+t1)\n", + "\n", + "n = 100 #100 datapoints \n", + "M = 5 #size of each minibatch\n", + "m = int(n/M) #number of minibatches\n", + "n_epochs = 500 #number of epochs\n", + "t0 = 1.0\n", + "t1 = 10\n", + "\n", + "gamma_j = t0/t1\n", + "j = 0\n", + "for epoch in range(1,n_epochs+1):\n", + " for i in range(m):\n", + " k = np.random.randint(m) #Pick the k-th minibatch at random\n", + " #Compute the gradient using the data in minibatch Bk\n", + " #Compute new suggestion for beta\n", + " t = epoch*m+i\n", + " gamma_j = step_length(t,t0,t1)\n", + " j += 1\n", + "\n", + "print(\"gamma_j after %d epochs: %g\" % (n_epochs,gamma_j))" + ] + }, + { + "cell_type": "markdown", + "id": "2192721f", + "metadata": { + "editable": true + }, + "source": [ + "## Code with a Number of Minibatches which varies\n", + "\n", + "In the code here we vary the number of mini-batches." + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "id": "e404f2c5", + "metadata": { + "collapsed": false, + "editable": true + }, + "outputs": [], + "source": [ + "# Importing various packages\n", + "from math import exp, sqrt\n", + "from random import random, seed\n", + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "\n", + "n = 100\n", + "x = 2*np.random.rand(n,1)\n", + "y = 4+3*x+np.random.randn(n,1)\n", + "\n", + "X = np.c_[np.ones((n,1)), x]\n", + "XT_X = X.T @ X\n", + "theta_linreg = np.linalg.inv(X.T @ X) @ (X.T @ y)\n", + "print(\"Own inversion\")\n", + "print(theta_linreg)\n", + "# Hessian matrix\n", + "H = (2.0/n)* XT_X\n", + "EigValues, EigVectors = np.linalg.eig(H)\n", + "print(f\"Eigenvalues of Hessian Matrix:{EigValues}\")\n", + "\n", + "theta = np.random.randn(2,1)\n", + "eta = 1.0/np.max(EigValues)\n", + "Niterations = 1000\n", + "\n", + "\n", + "for iter in range(Niterations):\n", + " gradients = 2.0/n*X.T @ ((X @ theta)-y)\n", + " theta -= eta*gradients\n", + "print(\"theta from own gd\")\n", + "print(theta)\n", + "\n", + "xnew = np.array([[0],[2]])\n", + "Xnew = np.c_[np.ones((2,1)), xnew]\n", + "ypredict = Xnew.dot(theta)\n", + "ypredict2 = Xnew.dot(theta_linreg)\n", + "\n", + "n_epochs = 50\n", + "M = 5 #size of each minibatch\n", + "m = int(n/M) #number of minibatches\n", + "t0, t1 = 5, 50\n", + "\n", + "def learning_schedule(t):\n", + " return t0/(t+t1)\n", + "\n", + "theta = np.random.randn(2,1)\n", + "\n", + "for epoch in range(n_epochs):\n", + "# Can you figure out a better way of setting up the contributions to each batch?\n", + " for i in range(m):\n", + " random_index = M*np.random.randint(m)\n", + " xi = X[random_index:random_index+M]\n", + " yi = y[random_index:random_index+M]\n", + " gradients = (2.0/M)* xi.T @ ((xi @ theta)-yi)\n", + " eta = learning_schedule(epoch*m+i)\n", + " theta = theta - eta*gradients\n", + "print(\"theta from own sdg\")\n", + "print(theta)\n", + "\n", + "plt.plot(xnew, ypredict, \"r-\")\n", + "plt.plot(xnew, ypredict2, \"b-\")\n", + "plt.plot(x, y ,'ro')\n", + "plt.axis([0,2.0,0, 15.0])\n", + "plt.xlabel(r'$x$')\n", + "plt.ylabel(r'$y$')\n", + "plt.title(r'Random numbers ')\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "fffdbb91", + "metadata": { + "editable": true + }, + "source": [ + "## Replace or not\n", + "\n", + "In the above code, we have use replacement in setting up the\n", + "mini-batches. The discussion\n", + "[here](https://sebastianraschka.com/faq/docs/sgd-methods.html) may be\n", + "useful." + ] + }, + { + "cell_type": "markdown", + "id": "8cce7a0e", + "metadata": { + "editable": true + }, + "source": [ + "## Momentum based GD\n", + "\n", + "The stochastic gradient descent (SGD) is almost always used with a\n", + "*momentum* or inertia term that serves as a memory of the direction we\n", + "are moving in parameter space. This is typically implemented as\n", + "follows" + ] + }, + { + "cell_type": "markdown", + "id": "3154c365", + "metadata": { + "editable": true + }, + "source": [ + "$$\n", + "\\mathbf{v}_{t}=\\gamma \\mathbf{v}_{t-1}+\\eta_{t}\\nabla_\\theta E(\\boldsymbol{\\theta}_t) \\nonumber\n", + "$$" + ] + }, + { + "cell_type": "markdown", + "id": "a2a9ceca", + "metadata": { + "editable": true + }, + "source": [ + "\n", + "
              \n", + "\n", + "$$\n", + "\\begin{equation} \n", + "\\boldsymbol{\\theta}_{t+1}= \\boldsymbol{\\theta}_t -\\mathbf{v}_{t},\n", + "\\label{_auto1} \\tag{2}\n", + "\\end{equation}\n", + "$$" + ] + }, + { + "cell_type": "markdown", + "id": "3374c700", + "metadata": { + "editable": true + }, + "source": [ + "where we have introduced a momentum parameter $\\gamma$, with\n", + "$0\\le\\gamma\\le 1$, and for brevity we dropped the explicit notation to\n", + "indicate the gradient is to be taken over a different mini-batch at\n", + "each step. We call this algorithm gradient descent with momentum\n", + "(GDM). From these equations, it is clear that $\\mathbf{v}_t$ is a\n", + "running average of recently encountered gradients and\n", + "$(1-\\gamma)^{-1}$ sets the characteristic time scale for the memory\n", + "used in the averaging procedure. Consistent with this, when\n", + "$\\gamma=0$, this just reduces down to ordinary SGD as discussed\n", + "earlier. An equivalent way of writing the updates is" + ] + }, + { + "cell_type": "markdown", + "id": "893d86fe", + "metadata": { + "editable": true + }, + "source": [ + "$$\n", + "\\Delta \\boldsymbol{\\theta}_{t+1} = \\gamma \\Delta \\boldsymbol{\\theta}_t -\\ \\eta_{t}\\nabla_\\theta E(\\boldsymbol{\\theta}_t),\n", + "$$" + ] + }, + { + "cell_type": "markdown", + "id": "ca2449e8", + "metadata": { + "editable": true + }, + "source": [ + "where we have defined $\\Delta \\boldsymbol{\\theta}_{t}= \\boldsymbol{\\theta}_t-\\boldsymbol{\\theta}_{t-1}$." + ] + }, + { + "cell_type": "markdown", + "id": "3cbd4adb", + "metadata": { + "editable": true + }, + "source": [ + "## More on momentum based approaches\n", + "\n", + "Let us try to get more intuition from these equations. It is helpful\n", + "to consider a simple physical analogy with a particle of mass $m$\n", + "moving in a viscous medium with drag coefficient $\\mu$ and potential\n", + "$E(\\mathbf{w})$. If we denote the particle's position by $\\mathbf{w}$,\n", + "then its motion is described by" + ] + }, + { + "cell_type": "markdown", + "id": "e3f07cbc", + "metadata": { + "editable": true + }, + "source": [ + "$$\n", + "m {d^2 \\mathbf{w} \\over dt^2} + \\mu {d \\mathbf{w} \\over dt }= -\\nabla_w E(\\mathbf{w}).\n", + "$$" + ] + }, + { + "cell_type": "markdown", + "id": "99f2ac0f", + "metadata": { + "editable": true + }, + "source": [ + "We can discretize this equation in the usual way to get" + ] + }, + { + "cell_type": "markdown", + "id": "83336244", + "metadata": { + "editable": true + }, + "source": [ + "$$\n", + "m { \\mathbf{w}_{t+\\Delta t}-2 \\mathbf{w}_{t} +\\mathbf{w}_{t-\\Delta t} \\over (\\Delta t)^2}+\\mu {\\mathbf{w}_{t+\\Delta t}- \\mathbf{w}_{t} \\over \\Delta t} = -\\nabla_w E(\\mathbf{w}).\n", + "$$" + ] + }, + { + "cell_type": "markdown", + "id": "efd3e708", + "metadata": { + "editable": true + }, + "source": [ + "Rearranging this equation, we can rewrite this as" + ] + }, + { + "cell_type": "markdown", + "id": "6c24d65c", + "metadata": { + "editable": true + }, + "source": [ + "$$\n", + "\\Delta \\mathbf{w}_{t +\\Delta t}= - { (\\Delta t)^2 \\over m +\\mu \\Delta t} \\nabla_w E(\\mathbf{w})+ {m \\over m +\\mu \\Delta t} \\Delta \\mathbf{w}_t.\n", + "$$" + ] + }, + { + "cell_type": "markdown", + "id": "853d885b", + "metadata": { + "editable": true + }, + "source": [ + "## Momentum parameter\n", + "\n", + "Notice that this equation is identical to previous one if we identify\n", + "the position of the particle, $\\mathbf{w}$, with the parameters\n", + "$\\boldsymbol{\\theta}$. This allows us to identify the momentum\n", + "parameter and learning rate with the mass of the particle and the\n", + "viscous drag as:" + ] + }, + { + "cell_type": "markdown", + "id": "5ab54645", + "metadata": { + "editable": true + }, + "source": [ + "$$\n", + "\\gamma= {m \\over m +\\mu \\Delta t }, \\qquad \\eta = {(\\Delta t)^2 \\over m +\\mu \\Delta t}.\n", + "$$" + ] + }, + { + "cell_type": "markdown", + "id": "90f1503b", + "metadata": { + "editable": true + }, + "source": [ + "Thus, as the name suggests, the momentum parameter is proportional to\n", + "the mass of the particle and effectively provides inertia.\n", + "Furthermore, in the large viscosity/small learning rate limit, our\n", + "memory time scales as $(1-\\gamma)^{-1} \\approx m/(\\mu \\Delta t)$.\n", + "\n", + "Why is momentum useful? SGD momentum helps the gradient descent\n", + "algorithm gain speed in directions with persistent but small gradients\n", + "even in the presence of stochasticity, while suppressing oscillations\n", + "in high-curvature directions. This becomes especially important in\n", + "situations where the landscape is shallow and flat in some directions\n", + "and narrow and steep in others. It has been argued that first-order\n", + "methods (with appropriate initial conditions) can perform comparable\n", + "to more expensive second order methods, especially in the context of\n", + "complex deep learning models.\n", + "\n", + "These beneficial properties of momentum can sometimes become even more\n", + "pronounced by using a slight modification of the classical momentum\n", + "algorithm called Nesterov Accelerated Gradient (NAG).\n", + "\n", + "In the NAG algorithm, rather than calculating the gradient at the\n", + "current parameters, $\\nabla_\\theta E(\\boldsymbol{\\theta}_t)$, one\n", + "calculates the gradient at the expected value of the parameters given\n", + "our current momentum, $\\nabla_\\theta E(\\boldsymbol{\\theta}_t +\\gamma\n", + "\\mathbf{v}_{t-1})$. This yields the NAG update rule" + ] + }, + { + "cell_type": "markdown", + "id": "d496d988", + "metadata": { + "editable": true + }, + "source": [ + "$$\n", + "\\mathbf{v}_{t}=\\gamma \\mathbf{v}_{t-1}+\\eta_{t}\\nabla_\\theta E(\\boldsymbol{\\theta}_t +\\gamma \\mathbf{v}_{t-1}) \\nonumber\n", + "$$" + ] + }, + { + "cell_type": "markdown", + "id": "258ca1e6", + "metadata": { + "editable": true + }, + "source": [ + "\n", + "
              \n", + "\n", + "$$\n", + "\\begin{equation} \n", + "\\boldsymbol{\\theta}_{t+1}= \\boldsymbol{\\theta}_t -\\mathbf{v}_{t}.\n", + "\\label{_auto2} \\tag{3}\n", + "\\end{equation}\n", + "$$" + ] + }, + { + "cell_type": "markdown", + "id": "84278058", + "metadata": { + "editable": true + }, + "source": [ + "One of the major advantages of NAG is that it allows for the use of a larger learning rate than GDM for the same choice of $\\gamma$." + ] + }, + { + "cell_type": "markdown", + "id": "feaee2f4", + "metadata": { + "editable": true + }, + "source": [ + "## Second moment of the gradient\n", + "\n", + "In stochastic gradient descent, with and without momentum, we still\n", + "have to specify a schedule for tuning the learning rates $\\eta_t$\n", + "as a function of time. As discussed in the context of Newton's\n", + "method, this presents a number of dilemmas. The learning rate is\n", + "limited by the steepest direction which can change depending on the\n", + "current position in the landscape. To circumvent this problem, ideally\n", + "our algorithm would keep track of curvature and take large steps in\n", + "shallow, flat directions and small steps in steep, narrow directions.\n", + "Second-order methods accomplish this by calculating or approximating\n", + "the Hessian and normalizing the learning rate by the\n", + "curvature. However, this is very computationally expensive for\n", + "extremely large models. Ideally, we would like to be able to\n", + "adaptively change the step size to match the landscape without paying\n", + "the steep computational price of calculating or approximating\n", + "Hessians.\n", + "\n", + "Recently, a number of methods have been introduced that accomplish\n", + "this by tracking not only the gradient, but also the second moment of\n", + "the gradient. These methods include AdaGrad, AdaDelta, Root Mean Squared Propagation (RMS-Prop), and\n", + "[ADAM](https://arxiv.org/abs/1412.6980)." + ] + }, + { + "cell_type": "markdown", + "id": "218edcf1", + "metadata": { + "editable": true + }, + "source": [ + "## RMS prop\n", + "\n", + "In RMS prop, in addition to keeping a running average of the first\n", + "moment of the gradient, we also keep track of the second moment\n", + "denoted by $\\mathbf{s}_t=\\mathbb{E}[\\mathbf{g}_t^2]$. The update rule\n", + "for RMS prop is given by" + ] + }, + { + "cell_type": "markdown", + "id": "18dbc91c", + "metadata": { + "editable": true + }, + "source": [ + "\n", + "
              \n", + "\n", + "$$\n", + "\\begin{equation}\n", + "\\mathbf{g}_t = \\nabla_\\theta E(\\boldsymbol{\\theta}) \n", + "\\label{_auto3} \\tag{4}\n", + "\\end{equation}\n", + "$$" + ] + }, + { + "cell_type": "markdown", + "id": "0bfcf74a", + "metadata": { + "editable": true + }, + "source": [ + "$$\n", + "\\mathbf{s}_t =\\beta \\mathbf{s}_{t-1} +(1-\\beta)\\mathbf{g}_t^2 \\nonumber\n", + "$$" + ] + }, + { + "cell_type": "markdown", + "id": "5fedd6f0", + "metadata": { + "editable": true + }, + "source": [ + "$$\n", + "\\boldsymbol{\\theta}_{t+1}=\\boldsymbol{\\theta}_t - \\eta_t { \\mathbf{g}_t \\over \\sqrt{\\mathbf{s}_t +\\epsilon}}, \\nonumber\n", + "$$" + ] + }, + { + "cell_type": "markdown", + "id": "b210b2a4", + "metadata": { + "editable": true + }, + "source": [ + "where $\\beta$ controls the averaging time of the second moment and is\n", + "typically taken to be about $\\beta=0.9$, $\\eta_t$ is a learning rate\n", + "typically chosen to be $10^{-3}$, and $\\epsilon\\sim 10^{-8} $ is a\n", + "small regularization constant to prevent divergences. Multiplication\n", + "and division by vectors is understood as an element-wise operation. It\n", + "is clear from this formula that the learning rate is reduced in\n", + "directions where the norm of the gradient is consistently large. This\n", + "greatly speeds up the convergence by allowing us to use a larger\n", + "learning rate for flat directions." + ] + }, + { + "cell_type": "markdown", + "id": "34ffacbb", + "metadata": { + "editable": true + }, + "source": [ + "## [ADAM optimizer](https://arxiv.org/abs/1412.6980)\n", + "\n", + "A related algorithm is the ADAM optimizer. In\n", + "[ADAM](https://arxiv.org/abs/1412.6980), we keep a running average of\n", + "both the first and second moment of the gradient and use this\n", + "information to adaptively change the learning rate for different\n", + "parameters. The method isefficient when working with large\n", + "problems involving lots data and/or parameters. It is a combination of the\n", + "gradient descent with momentum algorithm and the RMSprop algorithm\n", + "discussed above.\n", + "\n", + "In addition to keeping a running average of the first and\n", + "second moments of the gradient\n", + "(i.e. $\\mathbf{m}_t=\\mathbb{E}[\\mathbf{g}_t]$ and\n", + "$\\mathbf{s}_t=\\mathbb{E}[\\mathbf{g}^2_t]$, respectively), ADAM\n", + "performs an additional bias correction to account for the fact that we\n", + "are estimating the first two moments of the gradient using a running\n", + "average (denoted by the hats in the update rule below). The update\n", + "rule for ADAM is given by (where multiplication and division are once\n", + "again understood to be element-wise operations below)" + ] + }, + { + "cell_type": "markdown", + "id": "cd03375d", + "metadata": { + "editable": true + }, + "source": [ + "\n", + "
              \n", + "\n", + "$$\n", + "\\begin{equation}\n", + "\\mathbf{g}_t = \\nabla_\\theta E(\\boldsymbol{\\theta}) \n", + "\\label{_auto4} \\tag{5}\n", + "\\end{equation}\n", + "$$" + ] + }, + { + "cell_type": "markdown", + "id": "0db5d6e0", + "metadata": { + "editable": true + }, + "source": [ + "$$\n", + "\\mathbf{m}_t = \\beta_1 \\mathbf{m}_{t-1} + (1-\\beta_1) \\mathbf{g}_t \\nonumber\n", + "$$" + ] + }, + { + "cell_type": "markdown", + "id": "84c709d9", + "metadata": { + "editable": true + }, + "source": [ + "$$\n", + "\\mathbf{s}_t =\\beta_2 \\mathbf{s}_{t-1} +(1-\\beta_2)\\mathbf{g}_t^2 \\nonumber\n", + "$$" + ] + }, + { + "cell_type": "markdown", + "id": "e4e47496", + "metadata": { + "editable": true + }, + "source": [ + "$$\n", + "\\boldsymbol{\\mathbf{m}}_t={\\mathbf{m}_t \\over 1-\\beta_1^t} \\nonumber\n", + "$$" + ] + }, + { + "cell_type": "markdown", + "id": "164f27df", + "metadata": { + "editable": true + }, + "source": [ + "$$\n", + "\\boldsymbol{\\mathbf{s}}_t ={\\mathbf{s}_t \\over1-\\beta_2^t} \\nonumber\n", + "$$" + ] + }, + { + "cell_type": "markdown", + "id": "591f4833", + "metadata": { + "editable": true + }, + "source": [ + "$$\n", + "\\boldsymbol{\\theta}_{t+1}=\\boldsymbol{\\theta}_t - \\eta_t { \\boldsymbol{\\mathbf{m}}_t \\over \\sqrt{\\boldsymbol{\\mathbf{s}}_t} +\\epsilon}, \\nonumber\n", + "$$" + ] + }, + { + "cell_type": "markdown", + "id": "e2127e8a", + "metadata": { + "editable": true + }, + "source": [ + "\n", + "
              \n", + "\n", + "$$\n", + "\\begin{equation} \n", + "\\label{_auto5} \\tag{6}\n", + "\\end{equation}\n", + "$$" + ] + }, + { + "cell_type": "markdown", + "id": "5cef8b84", + "metadata": { + "editable": true + }, + "source": [ + "where $\\beta_1$ and $\\beta_2$ set the memory lifetime of the first and\n", + "second moment and are typically taken to be $0.9$ and $0.99$\n", + "respectively, and $\\eta$ and $\\epsilon$ are identical to RMSprop.\n", + "\n", + "Like in RMSprop, the effective step size of a parameter depends on the\n", + "magnitude of its gradient squared. To understand this better, let us\n", + "rewrite this expression in terms of the variance\n", + "$\\boldsymbol{\\sigma}_t^2 = \\boldsymbol{\\mathbf{s}}_t -\n", + "(\\boldsymbol{\\mathbf{m}}_t)^2$. Consider a single parameter $\\theta_t$. The\n", + "update rule for this parameter is given by" + ] + }, + { + "cell_type": "markdown", + "id": "505c8905", + "metadata": { + "editable": true + }, + "source": [ + "$$\n", + "\\Delta \\theta_{t+1}= -\\eta_t { \\boldsymbol{m}_t \\over \\sqrt{\\sigma_t^2 + m_t^2 }+\\epsilon}.\n", + "$$" + ] + }, + { + "cell_type": "markdown", + "id": "ca96ddec", + "metadata": { + "editable": true + }, + "source": [ + "## Algorithms and codes for Adagrad, RMSprop and Adam\n", + "\n", + "The algorithms we have implemented are well described in the text by [Goodfellow, Bengio and Courville, chapter 8](https://www.deeplearningbook.org/contents/optimization.html).\n", + "\n", + "The codes which implement these algorithms are discussed after our presentation of automatic differentiation." + ] + }, + { + "cell_type": "markdown", + "id": "fa011176", + "metadata": { + "editable": true + }, + "source": [ + "## Practical tips\n", + "\n", + "* **Randomize the data when making mini-batches**. It is always important to randomly shuffle the data when forming mini-batches. Otherwise, the gradient descent method can fit spurious correlations resulting from the order in which data is presented.\n", + "\n", + "* **Transform your inputs**. Learning becomes difficult when our landscape has a mixture of steep and flat directions. One simple trick for minimizing these situations is to standardize the data by subtracting the mean and normalizing the variance of input variables. Whenever possible, also decorrelate the inputs. To understand why this is helpful, consider the case of linear regression. It is easy to show that for the squared error cost function, the Hessian of the cost function is just the correlation matrix between the inputs. Thus, by standardizing the inputs, we are ensuring that the landscape looks homogeneous in all directions in parameter space. Since most deep networks can be viewed as linear transformations followed by a non-linearity at each layer, we expect this intuition to hold beyond the linear case.\n", + "\n", + "* **Monitor the out-of-sample performance.** Always monitor the performance of your model on a validation set (a small portion of the training data that is held out of the training process to serve as a proxy for the test set. If the validation error starts increasing, then the model is beginning to overfit. Terminate the learning process. This *early stopping* significantly improves performance in many settings.\n", + "\n", + "* **Adaptive optimization methods don't always have good generalization.** Recent studies have shown that adaptive methods such as ADAM, RMSPorp, and AdaGrad tend to have poor generalization compared to SGD or SGD with momentum, particularly in the high-dimensional limit (i.e. the number of parameters exceeds the number of data points). Although it is not clear at this stage why these methods perform so well in training deep neural networks, simpler procedures like properly-tuned SGD may work as well or better in these applications.\n", + "\n", + "Geron's text, see chapter 11, has several interesting discussions." + ] + }, + { + "cell_type": "markdown", + "id": "b91c4543", + "metadata": { + "editable": true + }, + "source": [ + "## Automatic differentiation\n", + "\n", + "[Automatic differentiation (AD)](https://en.wikipedia.org/wiki/Automatic_differentiation), \n", + "also called algorithmic\n", + "differentiation or computational differentiation,is a set of\n", + "techniques to numerically evaluate the derivative of a function\n", + "specified by a computer program. AD exploits the fact that every\n", + "computer program, no matter how complicated, executes a sequence of\n", + "elementary arithmetic operations (addition, subtraction,\n", + "multiplication, division, etc.) and elementary functions (exp, log,\n", + "sin, cos, etc.). By applying the chain rule repeatedly to these\n", + "operations, derivatives of arbitrary order can be computed\n", + "automatically, accurately to working precision, and using at most a\n", + "small constant factor more arithmetic operations than the original\n", + "program.\n", + "\n", + "Automatic differentiation is neither:\n", + "\n", + "* Symbolic differentiation, nor\n", + "\n", + "* Numerical differentiation (the method of finite differences).\n", + "\n", + "Symbolic differentiation can lead to inefficient code and faces the\n", + "difficulty of converting a computer program into a single expression,\n", + "while numerical differentiation can introduce round-off errors in the\n", + "discretization process and cancellation\n", + "\n", + "Python has tools for so-called **automatic differentiation**.\n", + "Consider the following example" + ] + }, + { + "cell_type": "markdown", + "id": "f13065e5", + "metadata": { + "editable": true + }, + "source": [ + "$$\n", + "f(x) = \\sin\\left(2\\pi x + x^2\\right)\n", + "$$" + ] + }, + { + "cell_type": "markdown", + "id": "22937f5e", + "metadata": { + "editable": true + }, + "source": [ + "which has the following derivative" + ] + }, + { + "cell_type": "markdown", + "id": "e1459fe1", + "metadata": { + "editable": true + }, + "source": [ + "$$\n", + "f'(x) = \\cos\\left(2\\pi x + x^2\\right)\\left(2\\pi + 2x\\right)\n", + "$$" + ] + }, + { + "cell_type": "markdown", + "id": "441a109a", + "metadata": { + "editable": true + }, + "source": [ + "Using **autograd** we have" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "id": "9043abae", + "metadata": { + "collapsed": false, + "editable": true + }, + "outputs": [], + "source": [ + "import autograd.numpy as np\n", + "\n", + "# To do elementwise differentiation:\n", + "from autograd import elementwise_grad as egrad \n", + "\n", + "# To plot:\n", + "import matplotlib.pyplot as plt \n", + "\n", + "\n", + "def f(x):\n", + " return np.sin(2*np.pi*x + x**2)\n", + "\n", + "def f_grad_analytic(x):\n", + " return np.cos(2*np.pi*x + x**2)*(2*np.pi + 2*x)\n", + "\n", + "# Do the comparison:\n", + "x = np.linspace(0,1,1000)\n", + "\n", + "f_grad = egrad(f)\n", + "\n", + "computed = f_grad(x)\n", + "analytic = f_grad_analytic(x)\n", + "\n", + "plt.title('Derivative computed from Autograd compared with the analytical derivative')\n", + "plt.plot(x,computed,label='autograd')\n", + "plt.plot(x,analytic,label='analytic')\n", + "\n", + "plt.xlabel('x')\n", + "plt.ylabel('y')\n", + "plt.legend()\n", + "\n", + "plt.show()\n", + "\n", + "print(\"The max absolute difference is: %g\"%(np.max(np.abs(computed - analytic))))" + ] + }, + { + "cell_type": "markdown", + "id": "787d5d78", + "metadata": { + "editable": true + }, + "source": [ + "## Using autograd\n", + "\n", + "Here we\n", + "experiment with what kind of functions Autograd is capable\n", + "of finding the gradient of. The following Python functions are just\n", + "meant to illustrate what Autograd can do, but please feel free to\n", + "experiment with other, possibly more complicated, functions as well." + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "id": "6a677479", + "metadata": { + "collapsed": false, + "editable": true + }, + "outputs": [], + "source": [ + "import autograd.numpy as np\n", + "from autograd import grad\n", + "\n", + "def f1(x):\n", + " return x**3 + 1\n", + "\n", + "f1_grad = grad(f1)\n", + "\n", + "# Remember to send in float as argument to the computed gradient from Autograd!\n", + "a = 1.0\n", + "\n", + "# See the evaluated gradient at a using autograd:\n", + "print(\"The gradient of f1 evaluated at a = %g using autograd is: %g\"%(a,f1_grad(a)))\n", + "\n", + "# Compare with the analytical derivative, that is f1'(x) = 3*x**2 \n", + "grad_analytical = 3*a**2\n", + "print(\"The gradient of f1 evaluated at a = %g by finding the analytic expression is: %g\"%(a,grad_analytical))" + ] + }, + { + "cell_type": "markdown", + "id": "f94a1d32", + "metadata": { + "editable": true + }, + "source": [ + "## Autograd with more complicated functions\n", + "\n", + "To differentiate with respect to two (or more) arguments of a Python\n", + "function, Autograd need to know at which variable the function if\n", + "being differentiated with respect to." + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "id": "c0eb89fd", + "metadata": { + "collapsed": false, + "editable": true + }, + "outputs": [], + "source": [ + "import autograd.numpy as np\n", + "from autograd import grad\n", + "def f2(x1,x2):\n", + " return 3*x1**3 + x2*(x1 - 5) + 1\n", + "\n", + "# By sending the argument 0, Autograd will compute the derivative w.r.t the first variable, in this case x1\n", + "f2_grad_x1 = grad(f2,0)\n", + "\n", + "# ... and differentiate w.r.t x2 by sending 1 as an additional arugment to grad\n", + "f2_grad_x2 = grad(f2,1)\n", + "\n", + "x1 = 1.0\n", + "x2 = 3.0 \n", + "\n", + "print(\"Evaluating at x1 = %g, x2 = %g\"%(x1,x2))\n", + "print(\"-\"*30)\n", + "\n", + "# Compare with the analytical derivatives:\n", + "\n", + "# Derivative of f2 w.r.t x1 is: 9*x1**2 + x2:\n", + "f2_grad_x1_analytical = 9*x1**2 + x2\n", + "\n", + "# Derivative of f2 w.r.t x2 is: x1 - 5:\n", + "f2_grad_x2_analytical = x1 - 5\n", + "\n", + "# See the evaluated derivations:\n", + "print(\"The derivative of f2 w.r.t x1: %g\"%( f2_grad_x1(x1,x2) ))\n", + "print(\"The analytical derivative of f2 w.r.t x1: %g\"%( f2_grad_x1(x1,x2) ))\n", + "\n", + "print()\n", + "\n", + "print(\"The derivative of f2 w.r.t x2: %g\"%( f2_grad_x2(x1,x2) ))\n", + "print(\"The analytical derivative of f2 w.r.t x2: %g\"%( f2_grad_x2(x1,x2) ))" + ] + }, + { + "cell_type": "markdown", + "id": "05d7497d", + "metadata": { + "editable": true + }, + "source": [ + "Note that the grad function will not produce the true gradient of the function. The true gradient of a function with two or more variables will produce a vector, where each element is the function differentiated w.r.t a variable." + ] + }, + { + "cell_type": "markdown", + "id": "24e3ca02", + "metadata": { + "editable": true + }, + "source": [ + "## More complicated functions using the elements of their arguments directly" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "id": "a616e696", + "metadata": { + "collapsed": false, + "editable": true + }, + "outputs": [], + "source": [ + "import autograd.numpy as np\n", + "from autograd import grad\n", + "def f3(x): # Assumes x is an array of length 5 or higher\n", + " return 2*x[0] + 3*x[1] + 5*x[2] + 7*x[3] + 11*x[4]**2\n", + "\n", + "f3_grad = grad(f3)\n", + "\n", + "x = np.linspace(0,4,5)\n", + "\n", + "# Print the computed gradient:\n", + "print(\"The computed gradient of f3 is: \", f3_grad(x))\n", + "\n", + "# The analytical gradient is: (2, 3, 5, 7, 22*x[4])\n", + "f3_grad_analytical = np.array([2, 3, 5, 7, 22*x[4]])\n", + "\n", + "# Print the analytical gradient:\n", + "print(\"The analytical gradient of f3 is: \", f3_grad_analytical)" + ] + }, + { + "cell_type": "markdown", + "id": "f695da56", + "metadata": { + "editable": true + }, + "source": [ + "Note that in this case, when sending an array as input argument, the\n", + "output from Autograd is another array. This is the true gradient of\n", + "the function, as opposed to the function in the previous example. By\n", + "using arrays to represent the variables, the output from Autograd\n", + "might be easier to work with, as the output is closer to what one\n", + "could expect form a gradient-evaluting function." + ] + }, + { + "cell_type": "markdown", + "id": "5ac073ee", + "metadata": { + "editable": true + }, + "source": [ + "## Functions using mathematical functions from Numpy" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "id": "efc8906e", + "metadata": { + "collapsed": false, + "editable": true + }, + "outputs": [], + "source": [ + "import autograd.numpy as np\n", + "from autograd import grad\n", + "def f4(x):\n", + " return np.sqrt(1+x**2) + np.exp(x) + np.sin(2*np.pi*x)\n", + "\n", + "f4_grad = grad(f4)\n", + "\n", + "x = 2.7\n", + "\n", + "# Print the computed derivative:\n", + "print(\"The computed derivative of f4 at x = %g is: %g\"%(x,f4_grad(x)))\n", + "\n", + "# The analytical derivative is: x/sqrt(1 + x**2) + exp(x) + cos(2*pi*x)*2*pi\n", + "f4_grad_analytical = x/np.sqrt(1 + x**2) + np.exp(x) + np.cos(2*np.pi*x)*2*np.pi\n", + "\n", + "# Print the analytical gradient:\n", + "print(\"The analytical gradient of f4 at x = %g is: %g\"%(x,f4_grad_analytical))" + ] + }, + { + "cell_type": "markdown", + "id": "7c587e8f", + "metadata": { + "editable": true + }, + "source": [ + "## More autograd" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "id": "6428be1f", + "metadata": { + "collapsed": false, + "editable": true + }, + "outputs": [], + "source": [ + "import autograd.numpy as np\n", + "from autograd import grad\n", + "def f5(x):\n", + " if x >= 0:\n", + " return x**2\n", + " else:\n", + " return -3*x + 1\n", + "\n", + "f5_grad = grad(f5)\n", + "\n", + "x = 2.7\n", + "\n", + "# Print the computed derivative:\n", + "print(\"The computed derivative of f5 at x = %g is: %g\"%(x,f5_grad(x)))" + ] + }, + { + "cell_type": "markdown", + "id": "8e1de777", + "metadata": { + "editable": true + }, + "source": [ + "## And with loops" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "id": "770ff6aa", + "metadata": { + "collapsed": false, + "editable": true + }, + "outputs": [], + "source": [ + "import autograd.numpy as np\n", + "from autograd import grad\n", + "def f6_for(x):\n", + " val = 0\n", + " for i in range(10):\n", + " val = val + x**i\n", + " return val\n", + "\n", + "def f6_while(x):\n", + " val = 0\n", + " i = 0\n", + " while i < 10:\n", + " val = val + x**i\n", + " i = i + 1\n", + " return val\n", + "\n", + "f6_for_grad = grad(f6_for)\n", + "f6_while_grad = grad(f6_while)\n", + "\n", + "x = 0.5\n", + "\n", + "# Print the computed derivaties of f6_for and f6_while\n", + "print(\"The computed derivative of f6_for at x = %g is: %g\"%(x,f6_for_grad(x)))\n", + "print(\"The computed derivative of f6_while at x = %g is: %g\"%(x,f6_while_grad(x)))" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "id": "b924cc5d", + "metadata": { + "collapsed": false, + "editable": true + }, + "outputs": [], + "source": [ + "import autograd.numpy as np\n", + "from autograd import grad\n", + "# Both of the functions are implementation of the sum: sum(x**i) for i = 0, ..., 9\n", + "# The analytical derivative is: sum(i*x**(i-1)) \n", + "f6_grad_analytical = 0\n", + "for i in range(10):\n", + " f6_grad_analytical += i*x**(i-1)\n", + "\n", + "print(\"The analytical derivative of f6 at x = %g is: %g\"%(x,f6_grad_analytical))" + ] + }, + { + "cell_type": "markdown", + "id": "f0b0e1e9", + "metadata": { + "editable": true + }, + "source": [ + "## Using recursion" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "id": "1585ab28", + "metadata": { + "collapsed": false, + "editable": true + }, + "outputs": [], + "source": [ + "import autograd.numpy as np\n", + "from autograd import grad\n", + "\n", + "def f7(n): # Assume that n is an integer\n", + " if n == 1 or n == 0:\n", + " return 1\n", + " else:\n", + " return n*f7(n-1)\n", + "\n", + "f7_grad = grad(f7)\n", + "\n", + "n = 2.0\n", + "\n", + "print(\"The computed derivative of f7 at n = %d is: %g\"%(n,f7_grad(n)))\n", + "\n", + "# The function f7 is an implementation of the factorial of n.\n", + "# By using the product rule, one can find that the derivative is:\n", + "\n", + "f7_grad_analytical = 0\n", + "for i in range(int(n)-1):\n", + " tmp = 1\n", + " for k in range(int(n)-1):\n", + " if k != i:\n", + " tmp *= (n - k)\n", + " f7_grad_analytical += tmp\n", + "\n", + "print(\"The analytical derivative of f7 at n = %d is: %g\"%(n,f7_grad_analytical))" + ] + }, + { + "cell_type": "markdown", + "id": "2718df1a", + "metadata": { + "editable": true + }, + "source": [ + "Note that if n is equal to zero or one, Autograd will give an error message. This message appears when the output is independent on input." + ] + }, + { + "cell_type": "markdown", + "id": "d8fa5235", + "metadata": { + "editable": true + }, + "source": [ + "## Unsupported functions\n", + "Autograd supports many features. However, there are some functions that is not supported (yet) by Autograd.\n", + "\n", + "Assigning a value to the variable being differentiated with respect to" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "id": "196a52d6", + "metadata": { + "collapsed": false, + "editable": true + }, + "outputs": [], + "source": [ + "import autograd.numpy as np\n", + "from autograd import grad\n", + "def f8(x): # Assume x is an array\n", + " x[2] = 3\n", + " return x*2\n", + "\n", + "f8_grad = grad(f8)\n", + "\n", + "x = 8.4\n", + "\n", + "print(\"The derivative of f8 is:\",f8_grad(x))" + ] + }, + { + "cell_type": "markdown", + "id": "9127a2c5", + "metadata": { + "editable": true + }, + "source": [ + "Here, Autograd tells us that an 'ArrayBox' does not support item assignment. The item assignment is done when the program tries to assign x[2] to the value 3. However, Autograd has implemented the computation of the derivative such that this assignment is not possible." + ] + }, + { + "cell_type": "markdown", + "id": "2b12ed61", + "metadata": { + "editable": true + }, + "source": [ + "## The syntax a.dot(b) when finding the dot product" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "id": "8ced55c8", + "metadata": { + "collapsed": false, + "editable": true + }, + "outputs": [], + "source": [ + "import autograd.numpy as np\n", + "from autograd import grad\n", + "def f9(a): # Assume a is an array with 2 elements\n", + " b = np.array([1.0,2.0])\n", + " return a.dot(b)\n", + "\n", + "f9_grad = grad(f9)\n", + "\n", + "x = np.array([1.0,0.0])\n", + "\n", + "print(\"The derivative of f9 is:\",f9_grad(x))" + ] + }, + { + "cell_type": "markdown", + "id": "92ebdc2b", + "metadata": { + "editable": true + }, + "source": [ + "Here we are told that the 'dot' function does not belong to Autograd's\n", + "version of a Numpy array. To overcome this, an alternative syntax\n", + "which also computed the dot product can be used:" + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "id": "276f763e", + "metadata": { + "collapsed": false, + "editable": true + }, + "outputs": [], + "source": [ + "import autograd.numpy as np\n", + "from autograd import grad\n", + "def f9_alternative(x): # Assume a is an array with 2 elements\n", + " b = np.array([1.0,2.0])\n", + " return np.dot(x,b) # The same as x_1*b_1 + x_2*b_2\n", + "\n", + "f9_alternative_grad = grad(f9_alternative)\n", + "\n", + "x = np.array([3.0,0.0])\n", + "\n", + "print(\"The gradient of f9 is:\",f9_alternative_grad(x))\n", + "\n", + "# The analytical gradient of the dot product of vectors x and b with two elements (x_1,x_2) and (b_1, b_2) respectively\n", + "# w.r.t x is (b_1, b_2)." + ] + }, + { + "cell_type": "markdown", + "id": "7841ad0b", + "metadata": { + "editable": true + }, + "source": [ + "## Recommended to avoid\n", + "The documentation recommends to avoid inplace operations such as" + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "id": "10107989", + "metadata": { + "collapsed": false, + "editable": true + }, + "outputs": [], + "source": [ + "a += b\n", + "a -= b\n", + "a*= b\n", + "a /=b" + ] + }, + { + "cell_type": "markdown", + "id": "4c1139c0", + "metadata": { + "editable": true + }, + "source": [ + "## Using Autograd with OLS\n", + "\n", + "We conclude the part on optmization by showing how we can make codes\n", + "for linear regression and logistic regression using **autograd**. The\n", + "first example shows results with ordinary leats squares." + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "id": "3022af88", + "metadata": { + "collapsed": false, + "editable": true + }, + "outputs": [], + "source": [ + "# Using Autograd to calculate gradients for OLS\n", + "from random import random, seed\n", + "import numpy as np\n", + "import autograd.numpy as np\n", + "import matplotlib.pyplot as plt\n", + "from autograd import grad\n", + "\n", + "def CostOLS(beta):\n", + " return (1.0/n)*np.sum((y-X @ beta)**2)\n", + "\n", + "n = 100\n", + "x = 2*np.random.rand(n,1)\n", + "y = 4+3*x+np.random.randn(n,1)\n", + "\n", + "X = np.c_[np.ones((n,1)), x]\n", + "XT_X = X.T @ X\n", + "theta_linreg = np.linalg.pinv(XT_X) @ (X.T @ y)\n", + "print(\"Own inversion\")\n", + "print(theta_linreg)\n", + "# Hessian matrix\n", + "H = (2.0/n)* XT_X\n", + "EigValues, EigVectors = np.linalg.eig(H)\n", + "print(f\"Eigenvalues of Hessian Matrix:{EigValues}\")\n", + "\n", + "theta = np.random.randn(2,1)\n", + "eta = 1.0/np.max(EigValues)\n", + "Niterations = 1000\n", + "# define the gradient\n", + "training_gradient = grad(CostOLS)\n", + "\n", + "for iter in range(Niterations):\n", + " gradients = training_gradient(theta)\n", + " theta -= eta*gradients\n", + "print(\"theta from own gd\")\n", + "print(theta)\n", + "\n", + "xnew = np.array([[0],[2]])\n", + "Xnew = np.c_[np.ones((2,1)), xnew]\n", + "ypredict = Xnew.dot(theta)\n", + "ypredict2 = Xnew.dot(theta_linreg)\n", + "\n", + "plt.plot(xnew, ypredict, \"r-\")\n", + "plt.plot(xnew, ypredict2, \"b-\")\n", + "plt.plot(x, y ,'ro')\n", + "plt.axis([0,2.0,0, 15.0])\n", + "plt.xlabel(r'$x$')\n", + "plt.ylabel(r'$y$')\n", + "plt.title(r'Random numbers ')\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "04d09021", + "metadata": { + "editable": true + }, + "source": [ + "## Same code but now with momentum gradient descent" + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "id": "71bf4b6d", + "metadata": { + "collapsed": false, + "editable": true + }, + "outputs": [], + "source": [ + "# Using Autograd to calculate gradients for OLS\n", + "from random import random, seed\n", + "import numpy as np\n", + "import autograd.numpy as np\n", + "import matplotlib.pyplot as plt\n", + "from autograd import grad\n", + "\n", + "def CostOLS(beta):\n", + " return (1.0/n)*np.sum((y-X @ beta)**2)\n", + "\n", + "n = 100\n", + "x = 2*np.random.rand(n,1)\n", + "y = 4+3*x#+np.random.randn(n,1)\n", + "\n", + "X = np.c_[np.ones((n,1)), x]\n", + "XT_X = X.T @ X\n", + "theta_linreg = np.linalg.pinv(XT_X) @ (X.T @ y)\n", + "print(\"Own inversion\")\n", + "print(theta_linreg)\n", + "# Hessian matrix\n", + "H = (2.0/n)* XT_X\n", + "EigValues, EigVectors = np.linalg.eig(H)\n", + "print(f\"Eigenvalues of Hessian Matrix:{EigValues}\")\n", + "\n", + "theta = np.random.randn(2,1)\n", + "eta = 1.0/np.max(EigValues)\n", + "Niterations = 30\n", + "\n", + "# define the gradient\n", + "training_gradient = grad(CostOLS)\n", + "\n", + "for iter in range(Niterations):\n", + " gradients = training_gradient(theta)\n", + " theta -= eta*gradients\n", + " print(iter,gradients[0],gradients[1])\n", + "print(\"theta from own gd\")\n", + "print(theta)\n", + "\n", + "# Now improve with momentum gradient descent\n", + "change = 0.0\n", + "delta_momentum = 0.3\n", + "for iter in range(Niterations):\n", + " # calculate gradient\n", + " gradients = training_gradient(theta)\n", + " # calculate update\n", + " new_change = eta*gradients+delta_momentum*change\n", + " # take a step\n", + " theta -= new_change\n", + " # save the change\n", + " change = new_change\n", + " print(iter,gradients[0],gradients[1])\n", + "print(\"theta from own gd wth momentum\")\n", + "print(theta)" + ] + }, + { + "cell_type": "markdown", + "id": "ad417bba", + "metadata": { + "editable": true + }, + "source": [ + "## But noen of these can compete with Newton's method" + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "id": "c394bcef", + "metadata": { + "collapsed": false, + "editable": true + }, + "outputs": [], + "source": [ + "# Using Newton's method\n", + "from random import random, seed\n", + "import numpy as np\n", + "import autograd.numpy as np\n", + "import matplotlib.pyplot as plt\n", + "from autograd import grad\n", + "\n", + "def CostOLS(beta):\n", + " return (1.0/n)*np.sum((y-X @ beta)**2)\n", + "\n", + "n = 100\n", + "x = 2*np.random.rand(n,1)\n", + "y = 4+3*x+np.random.randn(n,1)\n", + "\n", + "X = np.c_[np.ones((n,1)), x]\n", + "XT_X = X.T @ X\n", + "beta_linreg = np.linalg.pinv(XT_X) @ (X.T @ y)\n", + "print(\"Own inversion\")\n", + "print(beta_linreg)\n", + "# Hessian matrix\n", + "H = (2.0/n)* XT_X\n", + "# Note that here the Hessian does not depend on the parameters beta\n", + "invH = np.linalg.pinv(H)\n", + "EigValues, EigVectors = np.linalg.eig(H)\n", + "print(f\"Eigenvalues of Hessian Matrix:{EigValues}\")\n", + "\n", + "beta = np.random.randn(2,1)\n", + "Niterations = 5\n", + "\n", + "# define the gradient\n", + "training_gradient = grad(CostOLS)\n", + "\n", + "for iter in range(Niterations):\n", + " gradients = training_gradient(beta)\n", + " beta -= invH @ gradients\n", + " print(iter,gradients[0],gradients[1])\n", + "print(\"beta from own Newton code\")\n", + "print(beta)" + ] + }, + { + "cell_type": "markdown", + "id": "2e4cf4b5", + "metadata": { + "editable": true + }, + "source": [ + "## Including Stochastic Gradient Descent with Autograd\n", + "In this code we include the stochastic gradient descent approach discussed above. Note here that we specify which argument we are taking the derivative with respect to when using **autograd**." + ] + }, + { + "cell_type": "code", + "execution_count": 31, + "id": "47411bcf", + "metadata": { + "collapsed": false, + "editable": true + }, + "outputs": [], + "source": [ + "# Using Autograd to calculate gradients using SGD\n", + "# OLS example\n", + "from random import random, seed\n", + "import numpy as np\n", + "import autograd.numpy as np\n", + "import matplotlib.pyplot as plt\n", + "from autograd import grad\n", + "\n", + "# Note change from previous example\n", + "def CostOLS(y,X,theta):\n", + " return np.sum((y-X @ theta)**2)\n", + "\n", + "n = 100\n", + "x = 2*np.random.rand(n,1)\n", + "y = 4+3*x+np.random.randn(n,1)\n", + "\n", + "X = np.c_[np.ones((n,1)), x]\n", + "XT_X = X.T @ X\n", + "theta_linreg = np.linalg.pinv(XT_X) @ (X.T @ y)\n", + "print(\"Own inversion\")\n", + "print(theta_linreg)\n", + "# Hessian matrix\n", + "H = (2.0/n)* XT_X\n", + "EigValues, EigVectors = np.linalg.eig(H)\n", + "print(f\"Eigenvalues of Hessian Matrix:{EigValues}\")\n", + "\n", + "theta = np.random.randn(2,1)\n", + "eta = 1.0/np.max(EigValues)\n", + "Niterations = 1000\n", + "\n", + "# Note that we request the derivative wrt third argument (theta, 2 here)\n", + "training_gradient = grad(CostOLS,2)\n", + "\n", + "for iter in range(Niterations):\n", + " gradients = (1.0/n)*training_gradient(y, X, theta)\n", + " theta -= eta*gradients\n", + "print(\"theta from own gd\")\n", + "print(theta)\n", + "\n", + "xnew = np.array([[0],[2]])\n", + "Xnew = np.c_[np.ones((2,1)), xnew]\n", + "ypredict = Xnew.dot(theta)\n", + "ypredict2 = Xnew.dot(theta_linreg)\n", + "\n", + "plt.plot(xnew, ypredict, \"r-\")\n", + "plt.plot(xnew, ypredict2, \"b-\")\n", + "plt.plot(x, y ,'ro')\n", + "plt.axis([0,2.0,0, 15.0])\n", + "plt.xlabel(r'$x$')\n", + "plt.ylabel(r'$y$')\n", + "plt.title(r'Random numbers ')\n", + "plt.show()\n", + "\n", + "n_epochs = 50\n", + "M = 5 #size of each minibatch\n", + "m = int(n/M) #number of minibatches\n", + "t0, t1 = 5, 50\n", + "def learning_schedule(t):\n", + " return t0/(t+t1)\n", + "\n", + "theta = np.random.randn(2,1)\n", + "\n", + "for epoch in range(n_epochs):\n", + "# Can you figure out a better way of setting up the contributions to each batch?\n", + " for i in range(m):\n", + " random_index = M*np.random.randint(m)\n", + " xi = X[random_index:random_index+M]\n", + " yi = y[random_index:random_index+M]\n", + " gradients = (1.0/M)*training_gradient(yi, xi, theta)\n", + " eta = learning_schedule(epoch*m+i)\n", + " theta = theta - eta*gradients\n", + "print(\"theta from own sdg\")\n", + "print(theta)" + ] + }, + { + "cell_type": "markdown", + "id": "f8e30af2", + "metadata": { + "editable": true + }, + "source": [ + "## Same code but now with momentum gradient descent" + ] + }, + { + "cell_type": "code", + "execution_count": 32, + "id": "dd594924", + "metadata": { + "collapsed": false, + "editable": true + }, + "outputs": [], + "source": [ + "# Using Autograd to calculate gradients using SGD\n", + "# OLS example\n", + "from random import random, seed\n", + "import numpy as np\n", + "import autograd.numpy as np\n", + "import matplotlib.pyplot as plt\n", + "from autograd import grad\n", + "\n", + "# Note change from previous example\n", + "def CostOLS(y,X,theta):\n", + " return np.sum((y-X @ theta)**2)\n", + "\n", + "n = 100\n", + "x = 2*np.random.rand(n,1)\n", + "y = 4+3*x+np.random.randn(n,1)\n", + "\n", + "X = np.c_[np.ones((n,1)), x]\n", + "XT_X = X.T @ X\n", + "theta_linreg = np.linalg.pinv(XT_X) @ (X.T @ y)\n", + "print(\"Own inversion\")\n", + "print(theta_linreg)\n", + "# Hessian matrix\n", + "H = (2.0/n)* XT_X\n", + "EigValues, EigVectors = np.linalg.eig(H)\n", + "print(f\"Eigenvalues of Hessian Matrix:{EigValues}\")\n", + "\n", + "theta = np.random.randn(2,1)\n", + "eta = 1.0/np.max(EigValues)\n", + "Niterations = 100\n", + "\n", + "# Note that we request the derivative wrt third argument (theta, 2 here)\n", + "training_gradient = grad(CostOLS,2)\n", + "\n", + "for iter in range(Niterations):\n", + " gradients = (1.0/n)*training_gradient(y, X, theta)\n", + " theta -= eta*gradients\n", + "print(\"theta from own gd\")\n", + "print(theta)\n", + "\n", + "\n", + "n_epochs = 50\n", + "M = 5 #size of each minibatch\n", + "m = int(n/M) #number of minibatches\n", + "t0, t1 = 5, 50\n", + "def learning_schedule(t):\n", + " return t0/(t+t1)\n", + "\n", + "theta = np.random.randn(2,1)\n", + "\n", + "change = 0.0\n", + "delta_momentum = 0.3\n", + "\n", + "for epoch in range(n_epochs):\n", + " for i in range(m):\n", + " random_index = M*np.random.randint(m)\n", + " xi = X[random_index:random_index+M]\n", + " yi = y[random_index:random_index+M]\n", + " gradients = (1.0/M)*training_gradient(yi, xi, theta)\n", + " eta = learning_schedule(epoch*m+i)\n", + " # calculate update\n", + " new_change = eta*gradients+delta_momentum*change\n", + " # take a step\n", + " theta -= new_change\n", + " # save the change\n", + " change = new_change\n", + "print(\"theta from own sdg with momentum\")\n", + "print(theta)" + ] + }, + { + "cell_type": "markdown", + "id": "75c4c29d", + "metadata": { + "editable": true + }, + "source": [ + "## Similar (second order function now) problem but now with AdaGrad" + ] + }, + { + "cell_type": "code", + "execution_count": 33, + "id": "4dd14fc5", + "metadata": { + "collapsed": false, + "editable": true + }, + "outputs": [], + "source": [ + "# Using Autograd to calculate gradients using AdaGrad and Stochastic Gradient descent\n", + "# OLS example\n", + "from random import random, seed\n", + "import numpy as np\n", + "import autograd.numpy as np\n", + "import matplotlib.pyplot as plt\n", + "from autograd import grad\n", + "\n", + "# Note change from previous example\n", + "def CostOLS(y,X,theta):\n", + " return np.sum((y-X @ theta)**2)\n", + "\n", + "n = 1000\n", + "x = np.random.rand(n,1)\n", + "y = 2.0+3*x +4*x*x\n", + "\n", + "X = np.c_[np.ones((n,1)), x, x*x]\n", + "XT_X = X.T @ X\n", + "theta_linreg = np.linalg.pinv(XT_X) @ (X.T @ y)\n", + "print(\"Own inversion\")\n", + "print(theta_linreg)\n", + "\n", + "\n", + "# Note that we request the derivative wrt third argument (theta, 2 here)\n", + "training_gradient = grad(CostOLS,2)\n", + "# Define parameters for Stochastic Gradient Descent\n", + "n_epochs = 50\n", + "M = 5 #size of each minibatch\n", + "m = int(n/M) #number of minibatches\n", + "# Guess for unknown parameters theta\n", + "theta = np.random.randn(3,1)\n", + "\n", + "# Value for learning rate\n", + "eta = 0.01\n", + "# Including AdaGrad parameter to avoid possible division by zero\n", + "delta = 1e-8\n", + "for epoch in range(n_epochs):\n", + " Giter = 0.0\n", + " for i in range(m):\n", + " random_index = M*np.random.randint(m)\n", + " xi = X[random_index:random_index+M]\n", + " yi = y[random_index:random_index+M]\n", + " gradients = (1.0/M)*training_gradient(yi, xi, theta)\n", + " Giter += gradients*gradients\n", + " update = gradients*eta/(delta+np.sqrt(Giter))\n", + " theta -= update\n", + "print(\"theta from own AdaGrad\")\n", + "print(theta)" + ] + }, + { + "cell_type": "markdown", + "id": "be4ce0cd", + "metadata": { + "editable": true + }, + "source": [ + "Running this code we note an almost perfect agreement with the results from matrix inversion." + ] + }, + { + "cell_type": "markdown", + "id": "0b739495", + "metadata": { + "editable": true + }, + "source": [ + "## RMSprop for adaptive learning rate with Stochastic Gradient Descent" + ] + }, + { + "cell_type": "code", + "execution_count": 34, + "id": "ae87789c", + "metadata": { + "collapsed": false, + "editable": true + }, + "outputs": [], + "source": [ + "# Using Autograd to calculate gradients using RMSprop and Stochastic Gradient descent\n", + "# OLS example\n", + "from random import random, seed\n", + "import numpy as np\n", + "import autograd.numpy as np\n", + "import matplotlib.pyplot as plt\n", + "from autograd import grad\n", + "\n", + "# Note change from previous example\n", + "def CostOLS(y,X,theta):\n", + " return np.sum((y-X @ theta)**2)\n", + "\n", + "n = 1000\n", + "x = np.random.rand(n,1)\n", + "y = 2.0+3*x +4*x*x# +np.random.randn(n,1)\n", + "\n", + "X = np.c_[np.ones((n,1)), x, x*x]\n", + "XT_X = X.T @ X\n", + "theta_linreg = np.linalg.pinv(XT_X) @ (X.T @ y)\n", + "print(\"Own inversion\")\n", + "print(theta_linreg)\n", + "\n", + "\n", + "# Note that we request the derivative wrt third argument (theta, 2 here)\n", + "training_gradient = grad(CostOLS,2)\n", + "# Define parameters for Stochastic Gradient Descent\n", + "n_epochs = 50\n", + "M = 5 #size of each minibatch\n", + "m = int(n/M) #number of minibatches\n", + "# Guess for unknown parameters theta\n", + "theta = np.random.randn(3,1)\n", + "\n", + "# Value for learning rate\n", + "eta = 0.01\n", + "# Value for parameter rho\n", + "rho = 0.99\n", + "# Including AdaGrad parameter to avoid possible division by zero\n", + "delta = 1e-8\n", + "for epoch in range(n_epochs):\n", + " Giter = 0.0\n", + " for i in range(m):\n", + " random_index = M*np.random.randint(m)\n", + " xi = X[random_index:random_index+M]\n", + " yi = y[random_index:random_index+M]\n", + " gradients = (1.0/M)*training_gradient(yi, xi, theta)\n", + "\t# Accumulated gradient\n", + "\t# Scaling with rho the new and the previous results\n", + " Giter = (rho*Giter+(1-rho)*gradients*gradients)\n", + "\t# Taking the diagonal only and inverting\n", + " update = gradients*eta/(delta+np.sqrt(Giter))\n", + "\t# Hadamard product\n", + " theta -= update\n", + "print(\"theta from own RMSprop\")\n", + "print(theta)" + ] + }, + { + "cell_type": "markdown", + "id": "76c8872b", + "metadata": { + "editable": true + }, + "source": [ + "## And finally [ADAM](https://arxiv.org/pdf/1412.6980.pdf)" + ] + }, + { + "cell_type": "code", + "execution_count": 35, + "id": "e99dbaa4", + "metadata": { + "collapsed": false, + "editable": true + }, + "outputs": [], + "source": [ + "# Using Autograd to calculate gradients using RMSprop and Stochastic Gradient descent\n", + "# OLS example\n", + "from random import random, seed\n", + "import numpy as np\n", + "import autograd.numpy as np\n", + "import matplotlib.pyplot as plt\n", + "from autograd import grad\n", + "\n", + "# Note change from previous example\n", + "def CostOLS(y,X,theta):\n", + " return np.sum((y-X @ theta)**2)\n", + "\n", + "n = 1000\n", + "x = np.random.rand(n,1)\n", + "y = 2.0+3*x +4*x*x# +np.random.randn(n,1)\n", + "\n", + "X = np.c_[np.ones((n,1)), x, x*x]\n", + "XT_X = X.T @ X\n", + "theta_linreg = np.linalg.pinv(XT_X) @ (X.T @ y)\n", + "print(\"Own inversion\")\n", + "print(theta_linreg)\n", + "\n", + "\n", + "# Note that we request the derivative wrt third argument (theta, 2 here)\n", + "training_gradient = grad(CostOLS,2)\n", + "# Define parameters for Stochastic Gradient Descent\n", + "n_epochs = 50\n", + "M = 5 #size of each minibatch\n", + "m = int(n/M) #number of minibatches\n", + "# Guess for unknown parameters theta\n", + "theta = np.random.randn(3,1)\n", + "\n", + "# Value for learning rate\n", + "eta = 0.01\n", + "# Value for parameters beta1 and beta2, see https://arxiv.org/abs/1412.6980\n", + "beta1 = 0.9\n", + "beta2 = 0.999\n", + "# Including AdaGrad parameter to avoid possible division by zero\n", + "delta = 1e-7\n", + "iter = 0\n", + "for epoch in range(n_epochs):\n", + " first_moment = 0.0\n", + " second_moment = 0.0\n", + " iter += 1\n", + " for i in range(m):\n", + " random_index = M*np.random.randint(m)\n", + " xi = X[random_index:random_index+M]\n", + " yi = y[random_index:random_index+M]\n", + " gradients = (1.0/M)*training_gradient(yi, xi, theta)\n", + " # Computing moments first\n", + " first_moment = beta1*first_moment + (1-beta1)*gradients\n", + " second_moment = beta2*second_moment+(1-beta2)*gradients*gradients\n", + " first_term = first_moment/(1.0-beta1**iter)\n", + " second_term = second_moment/(1.0-beta2**iter)\n", + "\t# Scaling with rho the new and the previous results\n", + " update = eta*first_term/(np.sqrt(second_term)+delta)\n", + " theta -= update\n", + "print(\"theta from own ADAM\")\n", + "print(theta)" + ] + }, + { + "cell_type": "markdown", + "id": "596df570", + "metadata": { + "editable": true + }, + "source": [ + "## And Logistic Regression" + ] + }, + { + "cell_type": "code", + "execution_count": 36, + "id": "6693f042", + "metadata": { + "collapsed": false, + "editable": true + }, + "outputs": [], + "source": [ + "import autograd.numpy as np\n", + "from autograd import grad\n", + "\n", + "def sigmoid(x):\n", + " return 0.5 * (np.tanh(x / 2.) + 1)\n", + "\n", + "def logistic_predictions(weights, inputs):\n", + " # Outputs probability of a label being true according to logistic model.\n", + " return sigmoid(np.dot(inputs, weights))\n", + "\n", + "def training_loss(weights):\n", + " # Training loss is the negative log-likelihood of the training labels.\n", + " preds = logistic_predictions(weights, inputs)\n", + " label_probabilities = preds * targets + (1 - preds) * (1 - targets)\n", + " return -np.sum(np.log(label_probabilities))\n", + "\n", + "# Build a toy dataset.\n", + "inputs = np.array([[0.52, 1.12, 0.77],\n", + " [0.88, -1.08, 0.15],\n", + " [0.52, 0.06, -1.30],\n", + " [0.74, -2.49, 1.39]])\n", + "targets = np.array([True, True, False, True])\n", + "\n", + "# Define a function that returns gradients of training loss using Autograd.\n", + "training_gradient_fun = grad(training_loss)\n", + "\n", + "# Optimize weights using gradient descent.\n", + "weights = np.array([0.0, 0.0, 0.0])\n", + "print(\"Initial loss:\", training_loss(weights))\n", + "for i in range(100):\n", + " weights -= training_gradient_fun(weights) * 0.01\n", + "\n", + "print(\"Trained loss:\", training_loss(weights))" + ] + }, + { + "cell_type": "markdown", + "id": "a40ed853", + "metadata": { + "editable": true + }, + "source": [ + "## Introducing [JAX](https://jax.readthedocs.io/en/latest/)\n", + "\n", + "Presently, instead of using **autograd**, we recommend using [JAX](https://jax.readthedocs.io/en/latest/)\n", + "\n", + "**JAX** is Autograd and [XLA (Accelerated Linear Algebra))](https://www.tensorflow.org/xla),\n", + "brought together for high-performance numerical computing and machine learning research.\n", + "It provides composable transformations of Python+NumPy programs: differentiate, vectorize, parallelize, Just-In-Time compile to GPU/TPU, and more.\n", + "\n", + "Here's a simple example on how you can use **JAX** to compute the derivate of the logistic function." + ] + }, + { + "cell_type": "code", + "execution_count": 37, + "id": "02f88360", + "metadata": { + "collapsed": false, + "editable": true + }, + "outputs": [], + "source": [ + "import jax.numpy as jnp\n", + "from jax import grad, jit, vmap\n", + "\n", + "def sum_logistic(x):\n", + " return jnp.sum(1.0 / (1.0 + jnp.exp(-x)))\n", + "\n", + "x_small = jnp.arange(3.)\n", + "derivative_fn = grad(sum_logistic)\n", + "print(derivative_fn(x_small))" + ] + } + ], + "metadata": { + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.18" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} \ No newline at end of file diff --git a/doc/LectureNotes/_toc.yml b/doc/LectureNotes/_toc.yml index 802377ef9..5172dc12e 100644 --- a/doc/LectureNotes/_toc.yml +++ b/doc/LectureNotes/_toc.yml @@ -51,6 +51,8 @@ parts: - file: week37.ipynb - file: exercisesweek38.ipynb - file: week38.ipynb + - file: exercisesweek39.ipynb + - file: week39.ipynb - caption: Projects numbered: false chapters: