diff --git a/doc/LectureNotes/DataFiles/cancer.dot b/doc/LectureNotes/DataFiles/cancer.dot index 4b5537038..850904527 100644 --- a/doc/LectureNotes/DataFiles/cancer.dot +++ b/doc/LectureNotes/DataFiles/cancer.dot @@ -6,11 +6,11 @@ edge [fontname="helvetica"] ; 0 -> 1 [labeldistance=2.5, labelangle=45, headlabel="True"] ; 2 [label="worst concave points <= 0.135\ngini = 0.031\nsamples = 253\nvalue = [[249, 4]\n[4, 249]]", fillcolor="#e78946"] ; 1 -> 2 ; -3 [label="area error <= 48.975\ngini = 0.008\nsamples = 242\nvalue = [[241, 1]\n[1, 241]]", fillcolor="#e5833c"] ; +3 [label="radius error <= 0.643\ngini = 0.008\nsamples = 242\nvalue = [[241, 1]\n[1, 241]]", fillcolor="#e5833c"] ; 2 -> 3 ; 4 [label="gini = 0.0\nsamples = 239\nvalue = [[239, 0]\n[0, 239]]", fillcolor="#e58139"] ; 3 -> 4 ; -5 [label="radius error <= 0.688\ngini = 0.444\nsamples = 3\nvalue = [[2, 1]\n[1, 2]]", fillcolor="#fdf6f0"] ; +5 [label="worst area <= 566.55\ngini = 0.444\nsamples = 3\nvalue = [[2, 1]\n[1, 2]]", fillcolor="#fdf6f0"] ; 3 -> 5 ; 6 [label="gini = 0.0\nsamples = 1\nvalue = [[0, 1]\n[1, 0]]", fillcolor="#e58139"] ; 5 -> 6 ; @@ -34,7 +34,7 @@ edge [fontname="helvetica"] ; 14 -> 15 ; 16 [label="gini = 0.0\nsamples = 11\nvalue = [[11, 0]\n[0, 11]]", fillcolor="#e58139"] ; 15 -> 16 ; -17 [label="area error <= 23.16\ngini = 0.32\nsamples = 5\nvalue = [[1, 4]\n[4, 1]]", fillcolor="#f6d5bd"] ; +17 [label="radius error <= 0.251\ngini = 0.32\nsamples = 5\nvalue = [[1, 4]\n[4, 1]]", fillcolor="#f6d5bd"] ; 15 -> 17 ; 18 [label="gini = 0.0\nsamples = 1\nvalue = [[1, 0]\n[0, 1]]", fillcolor="#e58139"] ; 17 -> 18 ; @@ -48,10 +48,10 @@ edge [fontname="helvetica"] ; 21 -> 22 ; 23 [label="gini = 0.0\nsamples = 6\nvalue = [[6, 0]\n[0, 6]]", fillcolor="#e58139"] ; 21 -> 23 ; -24 [label="mean smoothness <= 0.079\ngini = 0.015\nsamples = 136\nvalue = [[1, 135]\n[135, 1]]", fillcolor="#e6853f"] ; +24 [label="fractal dimension error <= 0.013\ngini = 0.015\nsamples = 136\nvalue = [[1, 135]\n[135, 1]]", fillcolor="#e6853f"] ; 20 -> 24 ; -25 [label="gini = 0.0\nsamples = 1\nvalue = [[1, 0]\n[0, 1]]", fillcolor="#e58139"] ; +25 [label="gini = 0.0\nsamples = 135\nvalue = [[0, 135]\n[135, 0]]", fillcolor="#e58139"] ; 24 -> 25 ; -26 [label="gini = 0.0\nsamples = 135\nvalue = [[0, 135]\n[135, 0]]", fillcolor="#e58139"] ; +26 [label="gini = 0.0\nsamples = 1\nvalue = [[1, 0]\n[0, 1]]", fillcolor="#e58139"] ; 24 -> 26 ; } \ No newline at end of file diff --git a/doc/LectureNotes/DataFiles/cancer.png b/doc/LectureNotes/DataFiles/cancer.png index fa5789786..cbf1fc251 100644 Binary files a/doc/LectureNotes/DataFiles/cancer.png and b/doc/LectureNotes/DataFiles/cancer.png differ diff --git a/doc/LectureNotes/_build/.doctrees/chapter1.doctree b/doc/LectureNotes/_build/.doctrees/chapter1.doctree index d90b7dccd..3c144649c 100644 Binary files a/doc/LectureNotes/_build/.doctrees/chapter1.doctree and b/doc/LectureNotes/_build/.doctrees/chapter1.doctree differ diff --git 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"cells": [ + { + "cell_type": "markdown", + "id": "30071d16", + "metadata": { + "editable": true + }, + "source": [ + "\n", + "" + ] + }, + { + "cell_type": "markdown", + "id": "268eaea0", + "metadata": { + "editable": true + }, + "source": [ + "# Exercises week 48\n", + "**November 25-29, 2024**\n", + "\n", + "Date: **Deadline is Friday November 29 at midnight**" + ] + }, + { + "cell_type": "markdown", + "id": "1e04cbee", + "metadata": { + "editable": true + }, + "source": [ + "# Overarching aims of the exercises this week\n", + "\n", + "The exercise this week is a simple course survey and feedback. This\n", + "is important for us in order to improve our teaching material, the\n", + "active learning format and anything else related to a succesful\n", + "mastering of central machine learning methods and their applications." + ] + }, + { + "cell_type": "markdown", + "id": "78363328", + "metadata": { + "editable": true + }, + "source": [ + "### Why did you choose this course?" + ] + }, + { + "cell_type": "markdown", + "id": "d4950ede", + "metadata": { + "editable": true + }, + "source": [ + "### What was your programming knowledge before you started?\n", + "\n", + "And do you feel this course added to your programming competences and skills?" + ] + }, + { + "cell_type": "markdown", + "id": "95e23f0c", + "metadata": { + "editable": true + }, + "source": [ + "### How do you judge your own level of knowledge on machine learning before and after this course?\n", + "\n", + "Here you can discuss your level of skill/knowledge at the start of course\n", + "and at the end of the course and how these matched the level of\n", + "skill/knowledge needed to complete the projects." + ] + }, + { + "cell_type": "markdown", + "id": "db819721", + "metadata": { + "editable": true + }, + "source": [ + "### Did the projects and the teaching material allow you to deepen your insights about Machine Learning methods?\n", + "\n", + "Feel free to comment here." + ] + }, + { + "cell_type": "markdown", + "id": "61d153a4", + "metadata": { + "editable": true + }, + "source": [ + "### Project based teaching and active learning\n", + "\n", + "This is a project based course and we as teachers would like to keep\n", + "it as it is since we see very clearly that people who attend this\n", + "course have a very good learning outcome. Project based courses are\n", + "however demanding (and expensive seen from the university admin) when\n", + "it comes to proper feedback and evaluations. Feel free to discuss\n", + "whether you found a project-based useful. Feel also free to comment upon things we can improve upon or\n", + "alternative ways to assess whether the learning outcomes have been\n", + "achieved. Would you for example prefer a standard 4 hours written exam\n", + "be something you would prefer? Or other alternatives to projects? We\n", + "would very much value your thoughts here since projects are an\n", + "essential part of this course." + ] + }, + { + "cell_type": "markdown", + "id": "ee1af3ff", + "metadata": { + "editable": true + }, + "source": [ + "### Usefulness of the weekly exercises\n", + "\n", + "Did the weekly exercises help in getting started with the projects?\n", + "How relevant where they for solving the projects? Feel free to\n", + "elaborate" + ] + }, + { + "cell_type": "markdown", + "id": "46a35899", + "metadata": { + "editable": true + }, + "source": [ + "### Lab sessions and lectures\n", + "\n", + "Was there a good link between lectures and lab sessions?\n", + "Feel\n", + "free to comment." + ] + }, + { + "cell_type": "markdown", + "id": "f6524f03", + "metadata": { + "editable": true + }, + "source": [ + "### How would you improve this course?\n", + "\n", + "Are there topics which are missing, topics which could have been\n", + "omitted and/or discussed in more depth? Feel free to add your comments\n", + "here such as how to improve to teaching material and more." + ] + }, + { + "cell_type": "markdown", + "id": "ab89b772", + "metadata": { + "editable": true + }, + "source": [ + "## Then some basic questions" + ] + }, + { + "cell_type": "markdown", + "id": "9811e1b4", + "metadata": { + "editable": true + }, + "source": [ + "### Which is your preferred information chanel, Canvas, Discord, mail or other?" + ] + }, + { + "cell_type": "markdown", + "id": "6a3741fa", + "metadata": { + "editable": true + }, + "source": [ + "### Was the weekly update with plans etc useful?" + ] + }, + { + "cell_type": "markdown", + "id": "a51df0b9", + "metadata": { + "editable": true + }, + "source": [ + "### Was it easy to access the course material?" + ] + }, + { + "cell_type": "markdown", + "id": "762d5153", + "metadata": { + "editable": true + }, + "source": [ + "### Which resources and tools did you use? Jupyter-notebooks, GitHub, the various textbooks we have recommended, etc etc" + ] + }, + { + "cell_type": "markdown", + "id": "2bf11aae", + "metadata": { + "editable": true + }, + "source": [ + "### If you did not attend the lectures or the lab sessions, which resources did you use?" + ] + }, + { + "cell_type": "markdown", + "id": "3f92f5ff", + "metadata": { + "editable": true + }, + "source": [ + "### Any other topics, impressions, ideas etc you would like to share with us?" + ] + } + ], + "metadata": {}, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/doc/LectureNotes/_build/html/_sources/week48.ipynb b/doc/LectureNotes/_build/html/_sources/week48.ipynb new file mode 100644 index 000000000..c0298d422 --- /dev/null +++ b/doc/LectureNotes/_build/html/_sources/week48.ipynb @@ -0,0 +1,2818 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "d7c532d5", + "metadata": { + "editable": true + }, + "source": [ + "\n", + "" + ] + }, + { + "cell_type": "markdown", + "id": "d27ab2b4", + "metadata": { + "editable": true + }, + "source": [ + "# Week 48: Gradient boosting and summary of course\n", + "**Morten Hjorth-Jensen**, Department of Physics and Center for Computing in Science Education, University of Oslo, Norway\n", + "\n", + "Date: **Nov 24, 2024**\n", + "\n", + "Copyright 1999-2024, Morten Hjorth-Jensen. Released under CC Attribution-NonCommercial 4.0 license" + ] + }, + { + "cell_type": "markdown", + "id": "9f55b05a", + "metadata": { + "editable": true + }, + "source": [ + "## Overview of week 48" + ] + }, + { + "cell_type": "markdown", + "id": "e72578f8", + "metadata": { + "editable": true + }, + "source": [ + "## Lecture Monday, November 25\n", + "**Plans for the lecture Monday 25 November, with video suggestions etc.**\n", + "\n", + "1. Boosting and gradient boosting and ensemble models\n", + "\n", + "2. Summary of course\n", + "\n", + "3. Readings and Videos:\n", + "\n", + "a. These lecture notes at \n", + "\n", + "b. See also lecture notes from week 47 at . The lecture on Monday starts with a repetition on AdaBoost before we move over to gradient boosting with examples\n", + "\n", + "\n", + "\n", + "c. Video on Decision trees \n", + "\n", + "d. Video on boosting methods \n", + "\n", + "e. Video on AdaBoost \n", + "\n", + "f. Video on Gradient boost, part 1, parts 2-4 follow thereafter \n", + "\n", + "g. Decision Trees: Rashcka et al chapter 3 pages 86-98, and chapter 7 on Ensemble methods, Voting and Bagging and Gradient Boosting. See also lecture from STK-IN4300, lecture 7 at ." + ] + }, + { + "cell_type": "markdown", + "id": "51ee8fb3", + "metadata": { + "editable": true + }, + "source": [ + "## Lab sessions\n", + "**Lab sessions on Tuesday and Wednesday.**\n", + "\n", + " * Work and Discussion of project 3\n", + "\n", + " * Last weekly exercise\n", + "\n", + " * Lab sessions at usual times.\n", + "\n", + " * For the week of December 2-6, lab sessions start at 10am and end at 4pm, room FØ434, Tuesday and Wednesday" + ] + }, + { + "cell_type": "markdown", + "id": "5b4ed2be", + "metadata": { + "editable": true + }, + "source": [ + "## Random Forest Algorithm, reminder from last week\n", + "\n", + "The algorithm described here can be applied to both classification and regression problems.\n", + "\n", + "We will grow of forest of say $B$ trees.\n", + "* For $b=1:B$\n", + "\n", + "a. Draw a bootstrap sample from the training data organized in our $\\boldsymbol{X}$ matrix.\n", + "\n", + "b. We grow then a random forest tree $T_b$ based on the bootstrapped data by repeating the steps outlined till we reach the maximum node size is reached\n", + "\n", + "1. we select $m \\le p$ variables at random from the $p$ predictors/features\n", + "\n", + "2. pick the best split point among the $m$ features using for example the CART algorithm and create a new node\n", + "\n", + "3. split the node into daughter nodes\n", + "\n", + "Finally we output then the ensemble of trees $\\{T_b\\}_1^{B}$ and make predictions for either a regression type of problem or a classification type of problem." + ] + }, + { + "cell_type": "markdown", + "id": "3e76e282", + "metadata": { + "editable": true + }, + "source": [ + "## Random Forests Compared with other Methods on the Cancer Data" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "acba62b3", + "metadata": { + "collapsed": false, + "editable": true + }, + "outputs": [], + "source": [ + "%matplotlib inline\n", + "\n", + "import matplotlib.pyplot as plt\n", + "import numpy as np\n", + "from sklearn.model_selection import train_test_split \n", + "from sklearn.datasets import load_breast_cancer\n", + "from sklearn.svm import SVC\n", + "from sklearn.linear_model import LogisticRegression\n", + "from sklearn.tree import DecisionTreeClassifier\n", + "from sklearn.ensemble import BaggingClassifier\n", + "\n", + "# Load the data\n", + "cancer = load_breast_cancer()\n", + "\n", + "X_train, X_test, y_train, y_test = train_test_split(cancer.data,cancer.target,random_state=0)\n", + "print(X_train.shape)\n", + "print(X_test.shape)\n", + "#define methods\n", + "# Logistic Regression\n", + "logreg = LogisticRegression(solver='lbfgs')\n", + "# Support vector machine\n", + "svm = SVC(gamma='auto', C=100)\n", + "# Decision Trees\n", + "deep_tree_clf = DecisionTreeClassifier(max_depth=None)\n", + "#Scale the data\n", + "from sklearn.preprocessing import StandardScaler\n", + "scaler = StandardScaler()\n", + "scaler.fit(X_train)\n", + "X_train_scaled = scaler.transform(X_train)\n", + "X_test_scaled = scaler.transform(X_test)\n", + "# Logistic Regression\n", + "logreg.fit(X_train_scaled, y_train)\n", + "print(\"Test set accuracy Logistic Regression with scaled data: {:.2f}\".format(logreg.score(X_test_scaled,y_test)))\n", + "# Support Vector Machine\n", + "svm.fit(X_train_scaled, y_train)\n", + "print(\"Test set accuracy SVM with scaled data: {:.2f}\".format(logreg.score(X_test_scaled,y_test)))\n", + "# Decision Trees\n", + "deep_tree_clf.fit(X_train_scaled, y_train)\n", + "print(\"Test set accuracy with Decision Trees and scaled data: {:.2f}\".format(deep_tree_clf.score(X_test_scaled,y_test)))\n", + "\n", + "\n", + "from sklearn.ensemble import RandomForestClassifier\n", + "from sklearn.preprocessing import LabelEncoder\n", + "from sklearn.model_selection import cross_validate\n", + "# Data set not specificied\n", + "#Instantiate the model with 500 trees and entropy as splitting criteria\n", + "Random_Forest_model = RandomForestClassifier(n_estimators=500,criterion=\"entropy\")\n", + "Random_Forest_model.fit(X_train_scaled, y_train)\n", + "#Cross validation\n", + "accuracy = cross_validate(Random_Forest_model,X_test_scaled,y_test,cv=10)['test_score']\n", + "print(accuracy)\n", + "print(\"Test set accuracy with Random Forests and scaled data: {:.2f}\".format(Random_Forest_model.score(X_test_scaled,y_test)))\n", + "\n", + "\n", + "import scikitplot as skplt\n", + "y_pred = Random_Forest_model.predict(X_test_scaled)\n", + "skplt.metrics.plot_confusion_matrix(y_test, y_pred, normalize=True)\n", + "plt.show()\n", + "y_probas = Random_Forest_model.predict_proba(X_test_scaled)\n", + "skplt.metrics.plot_roc(y_test, y_probas)\n", + "plt.show()\n", + "skplt.metrics.plot_cumulative_gain(y_test, y_probas)\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "c5cbe0e8", + "metadata": { + "editable": true + }, + "source": [ + "Recall that the cumulative gains curve shows the percentage of the\n", + "overall number of cases in a given category *gained* by targeting a\n", + "percentage of the total number of cases.\n", + "\n", + "Similarly, the receiver operating characteristic curve, or ROC curve,\n", + "displays the diagnostic ability of a binary classifier system as its\n", + "discrimination threshold is varied. It plots the true positive rate against the false positive rate." + ] + }, + { + "cell_type": "markdown", + "id": "19bd77f2", + "metadata": { + "editable": true + }, + "source": [ + "## Compare Bagging on Trees with Random Forests" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "84f1af2e", + "metadata": { + "collapsed": false, + "editable": true + }, + "outputs": [], + "source": [ + "bag_clf = BaggingClassifier(\n", + " DecisionTreeClassifier(splitter=\"random\", max_leaf_nodes=16, random_state=42),\n", + " n_estimators=500, max_samples=1.0, bootstrap=True, n_jobs=-1, random_state=42)" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "6c01d900", + "metadata": { + "collapsed": false, + "editable": true + }, + "outputs": [], + "source": [ + "bag_clf.fit(X_train, y_train)\n", + "y_pred = bag_clf.predict(X_test)\n", + "from sklearn.ensemble import RandomForestClassifier\n", + "rnd_clf = RandomForestClassifier(n_estimators=500, max_leaf_nodes=16, n_jobs=-1, random_state=42)\n", + "rnd_clf.fit(X_train, y_train)\n", + "y_pred_rf = rnd_clf.predict(X_test)\n", + "np.sum(y_pred == y_pred_rf) / len(y_pred)" + ] + }, + { + "cell_type": "markdown", + "id": "896072f6", + "metadata": { + "editable": true + }, + "source": [ + "## Boosting, a Bird's Eye View\n", + "\n", + "The basic idea is to combine weak classifiers in order to create a good\n", + "classifier. With a weak classifier we often intend a classifier which\n", + "produces results which are only slightly better than we would get by\n", + "random guesses.\n", + "\n", + "This is done by applying in an iterative way a weak (or a standard\n", + "classifier like decision trees) to modify the data. In each iteration\n", + "we emphasize those observations which are misclassified by weighting\n", + "them with a factor." + ] + }, + { + "cell_type": "markdown", + "id": "babc9c9c", + "metadata": { + "editable": true + }, + "source": [ + "## What is boosting? Additive Modelling/Iterative Fitting\n", + "\n", + "Boosting is a way of fitting an additive expansion in a set of\n", + "elementary basis functions like for example some simple polynomials.\n", + "Assume for example that we have a function" + ] + }, + { + "cell_type": "markdown", + "id": "2ed28584", + "metadata": { + "editable": true + }, + "source": [ + "$$\n", + "f_M(x) = \\sum_{i=1}^M \\beta_m b(x;\\gamma_m),\n", + "$$" + ] + }, + { + "cell_type": "markdown", + "id": "5b5b9631", + "metadata": { + "editable": true + }, + "source": [ + "where $\\beta_m$ are the expansion parameters to be determined in a\n", + "minimization process and $b(x;\\gamma_m)$ are some simple functions of\n", + "the multivariable parameter $x$ which is characterized by the\n", + "parameters $\\gamma_m$.\n", + "\n", + "As an example, consider the Sigmoid function we used in logistic\n", + "regression. In that case, we can translate the function\n", + "$b(x;\\gamma_m)$ into the Sigmoid function" + ] + }, + { + "cell_type": "markdown", + "id": "7b6c43ec", + "metadata": { + "editable": true + }, + "source": [ + "$$\n", + "\\sigma(t) = \\frac{1}{1+\\exp{(-t)}},\n", + "$$" + ] + }, + { + "cell_type": "markdown", + "id": "3962039f", + "metadata": { + "editable": true + }, + "source": [ + "where $t=\\gamma_0+\\gamma_1 x$ and the parameters $\\gamma_0$ and\n", + "$\\gamma_1$ were determined by the Logistic Regression fitting\n", + "algorithm.\n", + "\n", + "As another example, consider the cost function we defined for linear regression" + ] + }, + { + "cell_type": "markdown", + "id": "ee5f259c", + "metadata": { + "editable": true + }, + "source": [ + "$$\n", + "C(\\boldsymbol{y},\\boldsymbol{f}) = \\frac{1}{n} \\sum_{i=0}^{n-1}(y_i-f(x_i))^2.\n", + "$$" + ] + }, + { + "cell_type": "markdown", + "id": "079d8ee3", + "metadata": { + "editable": true + }, + "source": [ + "In this case the function $f(x)$ was replaced by the design matrix\n", + "$\\boldsymbol{X}$ and the unknown linear regression parameters $\\boldsymbol{\\beta}$,\n", + "that is $\\boldsymbol{f}=\\boldsymbol{X}\\boldsymbol{\\beta}$. In linear regression we can \n", + "simply invert a matrix and obtain the parameters $\\beta$ by" + ] + }, + { + "cell_type": "markdown", + "id": "11f821a2", + "metadata": { + "editable": true + }, + "source": [ + "$$\n", + "\\boldsymbol{\\beta}=\\left(\\boldsymbol{X}^T\\boldsymbol{X}\\right)^{-1}\\boldsymbol{X}^T\\boldsymbol{y}.\n", + "$$" + ] + }, + { + "cell_type": "markdown", + "id": "60990073", + "metadata": { + "editable": true + }, + "source": [ + "In iterative fitting or additive modeling, we minimize the cost function with respect to the parameters $\\beta_m$ and $\\gamma_m$." + ] + }, + { + "cell_type": "markdown", + "id": "4d7195ab", + "metadata": { + "editable": true + }, + "source": [ + "## Iterative Fitting, Regression and Squared-error Cost Function\n", + "\n", + "The way we proceed is as follows (here we specialize to the squared-error cost function)\n", + "\n", + "1. Establish a cost function, here ${\\cal C}(\\boldsymbol{y},\\boldsymbol{f}) = \\frac{1}{n} \\sum_{i=0}^{n-1}(y_i-f_M(x_i))^2$ with $f_M(x) = \\sum_{i=1}^M \\beta_m b(x;\\gamma_m)$.\n", + "\n", + "2. Initialize with a guess $f_0(x)$. It could be one or even zero or some random numbers.\n", + "\n", + "3. For $m=1:M$\n", + "\n", + "a. minimize $\\sum_{i=0}^{n-1}(y_i-f_{m-1}(x_i)-\\beta b(x;\\gamma))^2$ wrt $\\gamma$ and $\\beta$\n", + "\n", + "b. This gives the optimal values $\\beta_m$ and $\\gamma_m$\n", + "\n", + "c. Determine then the new values $f_m(x)=f_{m-1}(x) +\\beta_m b(x;\\gamma_m)$\n", + "\n", + "We could use any of the algorithms we have discussed till now. If we\n", + "use trees, $\\gamma$ parameterizes the split variables and split points\n", + "at the internal nodes, and the predictions at the terminal nodes." + ] + }, + { + "cell_type": "markdown", + "id": "4bee726a", + "metadata": { + "editable": true + }, + "source": [ + "## Squared-Error Example and Iterative Fitting\n", + "\n", + "To better understand what happens, let us develop the steps for the iterative fitting using the above squared error function.\n", + "\n", + "For simplicity we assume also that our functions $b(x;\\gamma)=1+\\gamma x$. \n", + "\n", + "This means that for every iteration $m$, we need to optimize" + ] + }, + { + "cell_type": "markdown", + "id": "1190b9a0", + "metadata": { + "editable": true + }, + "source": [ + "$$\n", + "(\\beta_m,\\gamma_m) = \\mathrm{argmin}_{\\beta,\\lambda}\\hspace{0.1cm} \\sum_{i=0}^{n-1}(y_i-f_{m-1}(x_i)-\\beta b(x;\\gamma))^2=\\sum_{i=0}^{n-1}(y_i-f_{m-1}(x_i)-\\beta(1+\\gamma x_i))^2.\n", + "$$" + ] + }, + { + "cell_type": "markdown", + "id": "2702211b", + "metadata": { + "editable": true + }, + "source": [ + "We start our iteration by simply setting $f_0(x)=0$. \n", + "Taking the derivatives with respect to $\\beta$ and $\\gamma$ we obtain" + ] + }, + { + "cell_type": "markdown", + "id": "1b1d9984", + "metadata": { + "editable": true + }, + "source": [ + "$$\n", + "\\frac{\\partial {\\cal C}}{\\partial \\beta} = -2\\sum_{i}(1+\\gamma x_i)(y_i-\\beta(1+\\gamma x_i))=0,\n", + "$$" + ] + }, + { + "cell_type": "markdown", + "id": "cdf7e56a", + "metadata": { + "editable": true + }, + "source": [ + "and" + ] + }, + { + "cell_type": "markdown", + "id": "ac6f3b86", + "metadata": { + "editable": true + }, + "source": [ + "$$\n", + "\\frac{\\partial {\\cal C}}{\\partial \\gamma} =-2\\sum_{i}\\beta x_i(y_i-\\beta(1+\\gamma x_i))=0.\n", + "$$" + ] + }, + { + "cell_type": "markdown", + "id": "faf6fb5f", + "metadata": { + "editable": true + }, + "source": [ + "We can then rewrite these equations as (defining $\\boldsymbol{w}=\\boldsymbol{e}+\\gamma \\boldsymbol{x})$ with $\\boldsymbol{e}$ being the unit vector)" + ] + }, + { + "cell_type": "markdown", + "id": "969f86ed", + "metadata": { + "editable": true + }, + "source": [ + "$$\n", + "\\gamma \\boldsymbol{w}^T(\\boldsymbol{y}-\\beta\\gamma \\boldsymbol{w})=0,\n", + "$$" + ] + }, + { + "cell_type": "markdown", + "id": "a98a541c", + "metadata": { + "editable": true + }, + "source": [ + "which gives us $\\beta = \\boldsymbol{w}^T\\boldsymbol{y}/(\\boldsymbol{w}^T\\boldsymbol{w})$. Similarly we have" + ] + }, + { + "cell_type": "markdown", + "id": "d8be5438", + "metadata": { + "editable": true + }, + "source": [ + "$$\n", + "\\beta\\gamma \\boldsymbol{x}^T(\\boldsymbol{y}-\\beta(1+\\gamma \\boldsymbol{x}))=0,\n", + "$$" + ] + }, + { + "cell_type": "markdown", + "id": "932f43c5", + "metadata": { + "editable": true + }, + "source": [ + "which leads to $\\gamma =(\\boldsymbol{x}^T\\boldsymbol{y}-\\beta\\boldsymbol{x}^T\\boldsymbol{e})/(\\beta\\boldsymbol{x}^T\\boldsymbol{x})$. Inserting\n", + "for $\\beta$ gives us an equation for $\\gamma$. This is a non-linear equation in the unknown $\\gamma$ and has to be solved numerically. \n", + "\n", + "The solution to these two equations gives us in turn $\\beta_1$ and $\\gamma_1$ leading to the new expression for $f_1(x)$ as\n", + "$f_1(x) = \\beta_1(1+\\gamma_1x)$. Doing this $M$ times results in our final estimate for the function $f$." + ] + }, + { + "cell_type": "markdown", + "id": "24630eb9", + "metadata": { + "editable": true + }, + "source": [ + "## Iterative Fitting, Classification and AdaBoost\n", + "\n", + "Let us consider a binary classification problem with two outcomes $y_i \\in \\{-1,1\\}$ and $i=0,1,2,\\dots,n-1$ as our set of\n", + "observations. We define a classification function $G(x)$ which produces a prediction taking one or the other of the two values \n", + "$\\{-1,1\\}$.\n", + "\n", + "The error rate of the training sample is then" + ] + }, + { + "cell_type": "markdown", + "id": "9a7fa4b6", + "metadata": { + "editable": true + }, + "source": [ + "$$\n", + "\\mathrm{\\overline{err}}=\\frac{1}{n} \\sum_{i=0}^{n-1} I(y_i\\ne G(x_i)).\n", + "$$" + ] + }, + { + "cell_type": "markdown", + "id": "7ead62b2", + "metadata": { + "editable": true + }, + "source": [ + "The iterative procedure starts with defining a weak classifier whose\n", + "error rate is barely better than random guessing. The iterative\n", + "procedure in boosting is to sequentially apply a weak\n", + "classification algorithm to repeatedly modified versions of the data\n", + "producing a sequence of weak classifiers $G_m(x)$.\n", + "\n", + "Here we will express our function $f(x)$ in terms of $G(x)$. That is" + ] + }, + { + "cell_type": "markdown", + "id": "91639c49", + "metadata": { + "editable": true + }, + "source": [ + "$$\n", + "f_M(x) = \\sum_{i=1}^M \\beta_m b(x;\\gamma_m),\n", + "$$" + ] + }, + { + "cell_type": "markdown", + "id": "e43865c2", + "metadata": { + "editable": true + }, + "source": [ + "will be a function of" + ] + }, + { + "cell_type": "markdown", + "id": "1884c219", + "metadata": { + "editable": true + }, + "source": [ + "$$\n", + "G_M(x) = \\mathrm{sign} \\sum_{i=1}^M \\alpha_m G_m(x).\n", + "$$" + ] + }, + { + "cell_type": "markdown", + "id": "bbb1cdbb", + "metadata": { + "editable": true + }, + "source": [ + "## Adaptive Boosting, AdaBoost\n", + "\n", + "In our iterative procedure we define thus" + ] + }, + { + "cell_type": "markdown", + "id": "7e3590f6", + "metadata": { + "editable": true + }, + "source": [ + "$$\n", + "f_m(x) = f_{m-1}(x)+\\beta_mG_m(x).\n", + "$$" + ] + }, + { + "cell_type": "markdown", + "id": "2eb8c86b", + "metadata": { + "editable": true + }, + "source": [ + "The simplest possible cost function which leads (also simple from a computational point of view) to the AdaBoost algorithm is the\n", + "exponential cost/loss function defined as" + ] + }, + { + "cell_type": "markdown", + "id": "e477d15a", + "metadata": { + "editable": true + }, + "source": [ + "$$\n", + "C(\\boldsymbol{y},\\boldsymbol{f}) = \\sum_{i=0}^{n-1}\\exp{(-y_i(f_{m-1}(x_i)+\\beta G(x_i))}.\n", + "$$" + ] + }, + { + "cell_type": "markdown", + "id": "b6f01e59", + "metadata": { + "editable": true + }, + "source": [ + "We optimize $\\beta$ and $G$ for each value of $m=1:M$ as we did in the regression case.\n", + "This is normally done in two steps. Let us however first rewrite the cost function as" + ] + }, + { + "cell_type": "markdown", + "id": "71c85471", + "metadata": { + "editable": true + }, + "source": [ + "$$\n", + "C(\\boldsymbol{y},\\boldsymbol{f}) = \\sum_{i=0}^{n-1}w_i^{m}\\exp{(-y_i\\beta G(x_i))},\n", + "$$" + ] + }, + { + "cell_type": "markdown", + "id": "2d5be340", + "metadata": { + "editable": true + }, + "source": [ + "where we have defined $w_i^m= \\exp{(-y_if_{m-1}(x_i))}$." + ] + }, + { + "cell_type": "markdown", + "id": "e426517c", + "metadata": { + "editable": true + }, + "source": [ + "## Building up AdaBoost\n", + "\n", + "First, for any $\\beta > 0$, we optimize $G$ by setting" + ] + }, + { + "cell_type": "markdown", + "id": "c0c7e993", + "metadata": { + "editable": true + }, + "source": [ + "$$\n", + "G_m(x) = \\mathrm{sign} \\sum_{i=0}^{n-1} w_i^m I(y_i \\ne G_(x_i)),\n", + "$$" + ] + }, + { + "cell_type": "markdown", + "id": "7457a096", + "metadata": { + "editable": true + }, + "source": [ + "which is the classifier that minimizes the weighted error rate in predicting $y$.\n", + "\n", + "We can do this by rewriting" + ] + }, + { + "cell_type": "markdown", + "id": "424d5bc9", + "metadata": { + "editable": true + }, + "source": [ + "$$\n", + "\\exp{-(\\beta)}\\sum_{y_i=G(x_i)}w_i^m+\\exp{(\\beta)}\\sum_{y_i\\ne G(x_i)}w_i^m,\n", + "$$" + ] + }, + { + "cell_type": "markdown", + "id": "d1fa658b", + "metadata": { + "editable": true + }, + "source": [ + "which can be rewritten as" + ] + }, + { + "cell_type": "markdown", + "id": "f8fc6b15", + "metadata": { + "editable": true + }, + "source": [ + "$$\n", + "(\\exp{(\\beta)}-\\exp{-(\\beta)})\\sum_{i=0}^{n-1}w_i^mI(y_i\\ne G(x_i))+\\exp{(-\\beta)}\\sum_{i=0}^{n-1}w_i^m=0,\n", + "$$" + ] + }, + { + "cell_type": "markdown", + "id": "9ca82f54", + "metadata": { + "editable": true + }, + "source": [ + "which leads to" + ] + }, + { + "cell_type": "markdown", + "id": "6c028686", + "metadata": { + "editable": true + }, + "source": [ + "$$\n", + "\\beta_m = \\frac{1}{2}\\log{\\frac{1-\\mathrm{\\overline{err}}}{\\mathrm{\\overline{err}}}},\n", + "$$" + ] + }, + { + "cell_type": "markdown", + "id": "4007a0ef", + "metadata": { + "editable": true + }, + "source": [ + "where we have redefined the error as" + ] + }, + { + "cell_type": "markdown", + "id": "ba456347", + "metadata": { + "editable": true + }, + "source": [ + "$$\n", + "\\mathrm{\\overline{err}}_m=\\frac{1}{n}\\frac{\\sum_{i=0}^{n-1}w_i^mI(y_i\\ne G(x_i)}{\\sum_{i=0}^{n-1}w_i^m},\n", + "$$" + ] + }, + { + "cell_type": "markdown", + "id": "c8df4ef3", + "metadata": { + "editable": true + }, + "source": [ + "which leads to an update of" + ] + }, + { + "cell_type": "markdown", + "id": "da46f9d9", + "metadata": { + "editable": true + }, + "source": [ + "$$\n", + "f_m(x) = f_{m-1}(x) +\\beta_m G_m(x).\n", + "$$" + ] + }, + { + "cell_type": "markdown", + "id": "5c60f30d", + "metadata": { + "editable": true + }, + "source": [ + "This leads to the new weights" + ] + }, + { + "cell_type": "markdown", + "id": "a5bc4dfe", + "metadata": { + "editable": true + }, + "source": [ + "$$\n", + "w_i^{m+1} = w_i^m \\exp{(-y_i\\beta_m G_m(x_i))}\n", + "$$" + ] + }, + { + "cell_type": "markdown", + "id": "3987cc1e", + "metadata": { + "editable": true + }, + "source": [ + "## Adaptive boosting: AdaBoost, Basic Algorithm\n", + "\n", + "The algorithm here is rather straightforward. Assume that our weak\n", + "classifier is a decision tree and we consider a binary set of outputs\n", + "with $y_i \\in \\{-1,1\\}$ and $i=0,1,2,\\dots,n-1$ as our set of\n", + "observations. Our design matrix is given in terms of the\n", + "feature/predictor vectors\n", + "$\\boldsymbol{X}=[\\boldsymbol{x}_0\\boldsymbol{x}_1\\dots\\boldsymbol{x}_{p-1}]$. Finally, we define also a\n", + "classifier determined by our data via a function $G(x)$. This function tells us how well we are able to classify our outputs/targets $\\boldsymbol{y}$. \n", + "\n", + "We have already defined the misclassification error $\\mathrm{err}$ as" + ] + }, + { + "cell_type": "markdown", + "id": "b35df09e", + "metadata": { + "editable": true + }, + "source": [ + "$$\n", + "\\mathrm{err}=\\frac{1}{n}\\sum_{i=0}^{n-1}I(y_i\\ne G(x_i)),\n", + "$$" + ] + }, + { + "cell_type": "markdown", + "id": "5b6a209c", + "metadata": { + "editable": true + }, + "source": [ + "where the function $I()$ is one if we misclassify and zero if we classify correctly." + ] + }, + { + "cell_type": "markdown", + "id": "d619a097", + "metadata": { + "editable": true + }, + "source": [ + "## Basic Steps of AdaBoost\n", + "\n", + "With the above definitions we are now ready to set up the algorithm for AdaBoost.\n", + "The basic idea is to set up weights which will be used to scale the correctly classified and the misclassified cases.\n", + "1. We start by initializing all weights to $w_i = 1/n$, with $i=0,1,2,\\dots n-1$. It is easy to see that we must have $\\sum_{i=0}^{n-1}w_i = 1$.\n", + "\n", + "2. We rewrite the misclassification error as" + ] + }, + { + "cell_type": "markdown", + "id": "b7311abb", + "metadata": { + "editable": true + }, + "source": [ + "$$\n", + "\\mathrm{\\overline{err}}_m=\\frac{\\sum_{i=0}^{n-1}w_i^m I(y_i\\ne G(x_i))}{\\sum_{i=0}^{n-1}w_i},\n", + "$$" + ] + }, + { + "cell_type": "markdown", + "id": "4bfb4209", + "metadata": { + "editable": true + }, + "source": [ + "1. Then we start looping over all attempts at classifying, namely we start an iterative process for $m=1:M$, where $M$ is the final number of classifications. Our given classifier could for example be a plain decision tree.\n", + "\n", + "a. Fit then a given classifier to the training set using the weights $w_i$.\n", + "\n", + "b. Compute then $\\mathrm{err}$ and figure out which events are classified properly and which are classified wrongly.\n", + "\n", + "c. Define a quantity $\\alpha_{m} = \\log{(1-\\mathrm{\\overline{err}}_m)/\\mathrm{\\overline{err}}_m}$\n", + "\n", + "d. Set the new weights to $w_i = w_i\\times \\exp{(\\alpha_m I(y_i\\ne G(x_i)}$.\n", + "\n", + "5. Compute the new classifier $G(x)= \\sum_{i=0}^{n-1}\\alpha_m I(y_i\\ne G(x_i)$.\n", + "\n", + "For the iterations with $m \\le 2$ the weights are modified\n", + "individually at each steps. The observations which were misclassified\n", + "at iteration $m-1$ have a weight which is larger than those which were\n", + "classified properly. As this proceeds, the observations which were\n", + "difficult to classifiy correctly are given a larger influence. Each\n", + "new classification step $m$ is then forced to concentrate on those\n", + "observations that are missed in the previous iterations." + ] + }, + { + "cell_type": "markdown", + "id": "4cf4c1f4", + "metadata": { + "editable": true + }, + "source": [ + "## AdaBoost Examples\n", + "\n", + "Using **Scikit-Learn** it is easy to apply the adaptive boosting algorithm, as done here." + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "453dff54", + "metadata": { + "collapsed": false, + "editable": true + }, + "outputs": [], + "source": [ + "from sklearn.ensemble import AdaBoostClassifier\n", + "\n", + "ada_clf = AdaBoostClassifier(\n", + " DecisionTreeClassifier(max_depth=2), n_estimators=200,\n", + " algorithm=\"SAMME.R\", learning_rate=0.01, random_state=42)\n", + "ada_clf.fit(X_train, y_train)\n", + "y_pred = ada_clf.predict(X_test)\n", + "skplt.metrics.plot_confusion_matrix(y_test, y_pred, normalize=True)\n", + "plt.show()\n", + "y_probas = ada_clf.predict_proba(X_test)\n", + "skplt.metrics.plot_roc(y_test, y_probas)\n", + "plt.show()\n", + "skplt.metrics.plot_cumulative_gain(y_test, y_probas)\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "d6470164", + "metadata": { + "editable": true + }, + "source": [ + "## Making an ADAboost code yourself" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "b6e01886", + "metadata": { + "collapsed": false, + "editable": true + }, + "outputs": [], + "source": [ + "import numpy as np\n", + "\n", + "class DecisionStump:\n", + " def fit(self, X, y, weights):\n", + " m, n = X.shape\n", + " self.alpha = 0\n", + " self.threshold = None\n", + " self.polarity = 1\n", + "\n", + " min_error = float('inf')\n", + "\n", + " for feature in range(n):\n", + " feature_values = np.unique(X[:, feature])\n", + "\n", + " for threshold in feature_values:\n", + " for polarity in [1, -1]:\n", + " predictions = np.ones(m)\n", + " predictions[X[:, feature] < threshold] = -1\n", + " predictions *= polarity\n", + "\n", + " error = sum(weights[predictions != y])\n", + "\n", + " if error < min_error:\n", + " min_error = error\n", + " self.alpha = 0.5 * np.log((1 - error) / (error + 1e-10))\n", + " self.threshold = threshold\n", + " self.feature_index = feature\n", + " self.polarity = polarity\n", + "\n", + " def predict(self, X):\n", + " m = X.shape[0]\n", + " predictions = np.ones(m)\n", + " if self.polarity == 1:\n", + " predictions[X[:, self.feature_index] < self.threshold] = -1\n", + " else:\n", + " predictions[X[:, self.feature_index] >= self.threshold] = -1\n", + " return predictions\n", + "\n", + "class AdaBoost:\n", + " def fit(self, X, y, n_estimators):\n", + " m = X.shape[0]\n", + " self.alphas = []\n", + " self.models = []\n", + "\n", + " weights = np.ones(m) / m\n", + "\n", + " for _ in range(n_estimators):\n", + " stump = DecisionStump()\n", + " stump.fit(X, y, weights)\n", + " predictions = stump.predict(X)\n", + "\n", + " error = sum(weights[predictions != y])\n", + " if error == 0:\n", + " break\n", + "\n", + " self.models.append(stump)\n", + " self.alphas.append(stump.alpha)\n", + "\n", + " weights *= np.exp(-stump.alpha * y * predictions)\n", + " weights /= np.sum(weights)\n", + "\n", + " def predict(self, X):\n", + " final_predictions = np.zeros(X.shape[0])\n", + " for alpha, model in zip(self.alphas, self.models):\n", + " final_predictions += alpha * model.predict(X)\n", + " return np.sign(final_predictions)\n", + "\n", + "# Example dataset (X, y)\n", + "X = np.array([[1], [2], [3], [4], [5], [6], [7], [8], [9], [10]])\n", + "y = np.array([-1, -1, -1, -1, 1, 1, 1, 1, 1, 1]) # Labels must be -1 or 1\n", + "\n", + "# Train AdaBoost\n", + "ada = AdaBoost()\n", + "ada.fit(X, y, n_estimators=10)\n", + "\n", + "# Predictions\n", + "predictions = ada.predict(X)\n", + "print(\"Predictions:\", predictions)" + ] + }, + { + "cell_type": "markdown", + "id": "3cede4c9", + "metadata": { + "editable": true + }, + "source": [ + "## Gradient boosting: Basics with Steepest Descent/Functional Gradient Descent\n", + "\n", + "Gradient boosting is again a similar technique to Adaptive boosting,\n", + "it combines so-called weak classifiers or regressors into a strong\n", + "method via a series of iterations.\n", + "\n", + "In order to understand the method, let us illustrate its basics by\n", + "bringing back the essential steps in linear regression, where our cost\n", + "function was the least squares function." + ] + }, + { + "cell_type": "markdown", + "id": "94e3b59f", + "metadata": { + "editable": true + }, + "source": [ + "## The Squared-Error again! Steepest Descent\n", + "\n", + "We start again with our cost function ${\\cal C}(\\boldsymbol{y}m\\boldsymbol{f})=\\sum_{i=0}^{n-1}{\\cal L}(y_i, f(x_i))$ where we want to minimize\n", + "This means that for every iteration, we need to optimize" + ] + }, + { + "cell_type": "markdown", + "id": "2f000f62", + "metadata": { + "editable": true + }, + "source": [ + "$$\n", + "(\\hat{\\boldsymbol{f}}) = \\mathrm{argmin}_{\\boldsymbol{f}}\\hspace{0.1cm} \\sum_{i=0}^{n-1}(y_i-f(x_i))^2.\n", + "$$" + ] + }, + { + "cell_type": "markdown", + "id": "014da5e9", + "metadata": { + "editable": true + }, + "source": [ + "We define a real function $h_m(x)$ that defines our final function $f_M(x)$ as" + ] + }, + { + "cell_type": "markdown", + "id": "022aee06", + "metadata": { + "editable": true + }, + "source": [ + "$$\n", + "f_M(x) = \\sum_{m=0}^M h_m(x).\n", + "$$" + ] + }, + { + "cell_type": "markdown", + "id": "d0d3e174", + "metadata": { + "editable": true + }, + "source": [ + "In the steepest decent approach we approximate $h_m(x) = -\\rho_m g_m(x)$, where $\\rho_m$ is a scalar and $g_m(x)$ the gradient defined as" + ] + }, + { + "cell_type": "markdown", + "id": "8af32042", + "metadata": { + "editable": true + }, + "source": [ + "$$\n", + "g_m(x_i) = \\left[ \\frac{\\partial {\\cal L}(y_i, f(x_i))}{\\partial f(x_i)}\\right]_{f(x_i)=f_{m-1}(x_i)}.\n", + "$$" + ] + }, + { + "cell_type": "markdown", + "id": "4a384d95", + "metadata": { + "editable": true + }, + "source": [ + "With the new gradient we can update $f_m(x) = f_{m-1}(x) -\\rho_m g_m(x)$. Using the above squared-error function we see that\n", + "the gradient is $g_m(x_i) = -2(y_i-f(x_i))$.\n", + "\n", + "Choosing $f_0(x)=0$ we obtain $g_m(x) = -2y_i$ and inserting this into the minimization problem for the cost function we have" + ] + }, + { + "cell_type": "markdown", + "id": "bd5b9bb9", + "metadata": { + "editable": true + }, + "source": [ + "$$\n", + "(\\rho_1) = \\mathrm{argmin}_{\\rho}\\hspace{0.1cm} \\sum_{i=0}^{n-1}(y_i+2\\rho y_i)^2.\n", + "$$" + ] + }, + { + "cell_type": "markdown", + "id": "560b3106", + "metadata": { + "editable": true + }, + "source": [ + "## Steepest Descent Example\n", + "\n", + "Optimizing with respect to $\\rho$ we obtain (taking the derivative) that $\\rho_1 = -1/2$. We have then that" + ] + }, + { + "cell_type": "markdown", + "id": "4742d327", + "metadata": { + "editable": true + }, + "source": [ + "$$\n", + "f_1(x) = f_{0}(x) -\\rho_1 g_1(x)=-y_i.\n", + "$$" + ] + }, + { + "cell_type": "markdown", + "id": "d160a37f", + "metadata": { + "editable": true + }, + "source": [ + "We can then proceed and compute" + ] + }, + { + "cell_type": "markdown", + "id": "f3cde07e", + "metadata": { + "editable": true + }, + "source": [ + "$$\n", + "g_2(x_i) = \\left[ \\frac{\\partial {\\cal L}(y_i, f(x_i))}{\\partial f(x_i)}\\right]_{f(x_i)=f_{1}(x_i)=y_i}=-4y_i,\n", + "$$" + ] + }, + { + "cell_type": "markdown", + "id": "cf95ad5e", + "metadata": { + "editable": true + }, + "source": [ + "and find a new value for $\\rho_2=-1/2$ and continue till we have reached $m=M$. We can modify the steepest descent method, or steepest boosting, by introducing what is called **gradient boosting**." + ] + }, + { + "cell_type": "markdown", + "id": "bb614b9d", + "metadata": { + "editable": true + }, + "source": [ + "## Gradient Boosting, algorithm\n", + "\n", + "Steepest descent is however not much used, since it only optimizes $f$ at a fixed set of $n$ points,\n", + "so we do not learn a function that can generalize. However, we can modify the algorithm by\n", + "fitting a weak learner to approximate the negative gradient signal. \n", + "\n", + "Suppose we have a cost function $C(f)=\\sum_{i=0}^{n-1}L(y_i, f(x_i))$ where $y_i$ is our target and $f(x_i)$ the function which is meant to model $y_i$. The above cost function could be our standard squared-error function" + ] + }, + { + "cell_type": "markdown", + "id": "19e3e8fa", + "metadata": { + "editable": true + }, + "source": [ + "$$\n", + "C(\\boldsymbol{y},\\boldsymbol{f})=\\sum_{i=0}^{n-1}(y_i-f(x_i))^2.\n", + "$$" + ] + }, + { + "cell_type": "markdown", + "id": "9330b34b", + "metadata": { + "editable": true + }, + "source": [ + "The way we proceed in an iterative fashion is to\n", + "1. Initialize our estimate $f_0(x)$.\n", + "\n", + "2. For $m=1:M$, we\n", + "\n", + "a. compute the negative gradient vector $\\boldsymbol{u}_m = -\\partial C(\\boldsymbol{y},\\boldsymbol{f})/\\partial \\boldsymbol{f}(x)$ at $f(x) = f_{m-1}(x)$;\n", + "\n", + "b. fit the so-called base-learner to the negative gradient $h_m(u_m,x)$;\n", + "\n", + "c. update the estimate $f_m(x) = f_{m-1}(x)+h_m(u_m,x)$;\n", + "\n", + "4. The final estimate is then $f_M(x) = \\sum_{m=1}^M h_m(u_m,x)$." + ] + }, + { + "cell_type": "markdown", + "id": "e648539a", + "metadata": { + "editable": true + }, + "source": [ + "## Gradient Boosting, Examples of Regression" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "d12810d3", + "metadata": { + "collapsed": false, + "editable": true + }, + "outputs": [], + "source": [ + "import matplotlib.pyplot as plt\n", + "import numpy as np\n", + "from sklearn.model_selection import train_test_split\n", + "from sklearn.ensemble import GradientBoostingRegressor\n", + "import scikitplot as skplt\n", + "from sklearn.metrics import mean_squared_error\n", + "\n", + "n = 100\n", + "maxdegree = 6\n", + "\n", + "# Make data set.\n", + "x = np.linspace(-3, 3, n).reshape(-1, 1)\n", + "y = np.exp(-x**2) + 1.5 * np.exp(-(x-2)**2)+ np.random.normal(0, 0.1, x.shape)\n", + "\n", + "error = np.zeros(maxdegree)\n", + "bias = np.zeros(maxdegree)\n", + "variance = np.zeros(maxdegree)\n", + "polydegree = np.zeros(maxdegree)\n", + "X_train, X_test, y_train, y_test = train_test_split(x, y, test_size=0.2)\n", + "\n", + "for degree in range(1,maxdegree):\n", + " model = GradientBoostingRegressor(max_depth=degree, n_estimators=100, learning_rate=1.0) \n", + " model.fit(X_train,y_train)\n", + " y_pred = model.predict(X_test)\n", + " polydegree[degree] = degree\n", + " error[degree] = np.mean( np.mean((y_test - y_pred)**2) )\n", + " bias[degree] = np.mean( (y_test - np.mean(y_pred))**2 )\n", + " variance[degree] = np.mean( np.var(y_pred) )\n", + " print('Max depth:', degree)\n", + " print('Error:', error[degree])\n", + " print('Bias^2:', bias[degree])\n", + " print('Var:', variance[degree])\n", + " print('{} >= {} + {} = {}'.format(error[degree], bias[degree], variance[degree], bias[degree]+variance[degree]))\n", + "\n", + "plt.xlim(1,maxdegree-1)\n", + "plt.plot(polydegree, error, label='Error')\n", + "plt.plot(polydegree, bias, label='bias')\n", + "plt.plot(polydegree, variance, label='Variance')\n", + "plt.legend()\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "f3e95eca", + "metadata": { + "editable": true + }, + "source": [ + "## Gradient Boosting, Classification Example" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "d6426469", + "metadata": { + "collapsed": false, + "editable": true + }, + "outputs": [], + "source": [ + "import matplotlib.pyplot as plt\n", + "import numpy as np\n", + "from sklearn.model_selection import train_test_split \n", + "from sklearn.datasets import load_breast_cancer\n", + "import scikitplot as skplt\n", + "from sklearn.ensemble import GradientBoostingClassifier\n", + "from sklearn.model_selection import cross_validate\n", + "\n", + "# Load the data\n", + "cancer = load_breast_cancer()\n", + "\n", + "X_train, X_test, y_train, y_test = train_test_split(cancer.data,cancer.target,random_state=0)\n", + "print(X_train.shape)\n", + "print(X_test.shape)\n", + "#now scale the data\n", + "from sklearn.preprocessing import StandardScaler\n", + "scaler = StandardScaler()\n", + "scaler.fit(X_train)\n", + "X_train_scaled = scaler.transform(X_train)\n", + "X_test_scaled = scaler.transform(X_test)\n", + "\n", + "gd_clf = GradientBoostingClassifier(max_depth=3, n_estimators=100, learning_rate=1.0) \n", + "gd_clf.fit(X_train_scaled, y_train)\n", + "#Cross validation\n", + "accuracy = cross_validate(gd_clf,X_test_scaled,y_test,cv=10)['test_score']\n", + "print(accuracy)\n", + "print(\"Test set accuracy with Gradient boosting and scaled data: {:.2f}\".format(gd_clf.score(X_test_scaled,y_test)))\n", + "\n", + "import scikitplot as skplt\n", + "y_pred = gd_clf.predict(X_test_scaled)\n", + "skplt.metrics.plot_confusion_matrix(y_test, y_pred, normalize=True)\n", + "plt.show()\n", + "y_probas = gd_clf.predict_proba(X_test_scaled)\n", + "skplt.metrics.plot_roc(y_test, y_probas)\n", + "plt.show()\n", + "skplt.metrics.plot_cumulative_gain(y_test, y_probas)\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "8de56415", + "metadata": { + "editable": true + }, + "source": [ + "## XGBoost: Extreme Gradient Boosting\n", + "\n", + "[XGBoost](https://github.com/dmlc/xgboost) or Extreme Gradient\n", + "Boosting, is an optimized distributed gradient boosting library\n", + "designed to be highly efficient, flexible and portable. It implements\n", + "machine learning algorithms under the Gradient Boosting\n", + "framework. XGBoost provides a parallel tree boosting that solve many\n", + "data science problems in a fast and accurate way. See the [article by Chen and Guestrin](https://arxiv.org/abs/1603.02754).\n", + "\n", + "The authors design and build a highly scalable end-to-end tree\n", + "boosting system. It has a theoretically justified weighted quantile\n", + "sketch for efficient proposal calculation. It introduces a novel sparsity-aware algorithm for parallel tree learning and an effective cache-aware block structure for out-of-core tree learning.\n", + "\n", + "It is now the algorithm which wins essentially all ML competitions!!!" + ] + }, + { + "cell_type": "markdown", + "id": "dfc59b18", + "metadata": { + "editable": true + }, + "source": [ + "## Xgboost on the Cancer Data\n", + "\n", + "As you will see from the confusion matrix below, XGBoots does an excellent job on the Wisconsin cancer data and outperforms essentially all agorithms we have discussed till now." + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "79e4cc00", + "metadata": { + "collapsed": false, + "editable": true + }, + "outputs": [], + "source": [ + "\n", + "import matplotlib.pyplot as plt\n", + "import numpy as np\n", + "from sklearn.model_selection import train_test_split \n", + "from sklearn.datasets import load_breast_cancer\n", + "from sklearn.preprocessing import LabelEncoder\n", + "from sklearn.model_selection import cross_validate\n", + "import scikitplot as skplt\n", + "import xgboost as xgb\n", + "# Load the data\n", + "cancer = load_breast_cancer()\n", + "\n", + "X_train, X_test, y_train, y_test = train_test_split(cancer.data,cancer.target,random_state=0)\n", + "print(X_train.shape)\n", + "print(X_test.shape)\n", + "#now scale the data\n", + "from sklearn.preprocessing import StandardScaler\n", + "scaler = StandardScaler()\n", + "scaler.fit(X_train)\n", + "X_train_scaled = scaler.transform(X_train)\n", + "X_test_scaled = scaler.transform(X_test)\n", + "\n", + "xg_clf = xgb.XGBClassifier()\n", + "xg_clf.fit(X_train_scaled,y_train)\n", + "\n", + "y_test = xg_clf.predict(X_test_scaled)\n", + "\n", + "print(\"Test set accuracy with Gradient Boosting and scaled data: {:.2f}\".format(xg_clf.score(X_test_scaled,y_test)))\n", + "\n", + "import scikitplot as skplt\n", + "y_pred = xg_clf.predict(X_test_scaled)\n", + "skplt.metrics.plot_confusion_matrix(y_test, y_pred, normalize=True)\n", + "plt.show()\n", + "y_probas = xg_clf.predict_proba(X_test_scaled)\n", + "skplt.metrics.plot_roc(y_test, y_probas)\n", + "plt.show()\n", + "skplt.metrics.plot_cumulative_gain(y_test, y_probas)\n", + "plt.show()\n", + "\n", + "\n", + "xgb.plot_tree(xg_clf,num_trees=0)\n", + "plt.rcParams['figure.figsize'] = [50, 10]\n", + "plt.show()\n", + "\n", + "xgb.plot_importance(xg_clf)\n", + "plt.rcParams['figure.figsize'] = [5, 5]\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "c4700b19", + "metadata": { + "editable": true + }, + "source": [ + "## Gradient boosting, making our own code for a regression case" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "eb0594c5", + "metadata": { + "collapsed": false, + "editable": true + }, + "outputs": [], + "source": [ + "import numpy as np\n", + "class DecisionTreeRegressor:\n", + " def __init__(self, max_depth=3):\n", + " self.max_depth = max_depth\n", + " self.tree = None\n", + " def fit(self, X, y):\n", + " self.tree = self._grow_tree(X, y)\n", + " def _grow_tree(self, X, y, depth=0):\n", + " n_samples, n_features = X.shape\n", + " if depth < self.max_depth:\n", + " best_feature, best_threshold = self._best_split(X, y)\n", + " if best_feature is not None:\n", + " left_indices = X[:, best_feature] < best_threshold\n", + " right_indices = X[:, best_feature] >= best_threshold\n", + " left_child = self._grow_tree(X[left_indices], y[left_indices], depth + 1)\n", + " right_child = self._grow_tree(X[right_indices], y[right_indices], depth + 1)\n", + " return (best_feature, best_threshold, left_child, right_child)\n", + " return np.mean(y)\n", + " def _best_split(self, X, y):\n", + " best_mse = float('inf')\n", + " best_feature, best_threshold = None, None\n", + " n_samples, n_features = X.shape\n", + " \n", + " for feature in range(n_features):\n", + " thresholds = np.unique(X[:, feature])\n", + " for threshold in thresholds:\n", + " left_indices = X[:, feature] < threshold\n", + " right_indices = X[:, feature] >= threshold\n", + " if len(y[left_indices]) > 0 and len(y[right_indices]) > 0:\n", + " left_mse = np.mean((y[left_indices] - np.mean(y[left_indices])) ** 2)\n", + " right_mse = np.mean((y[right_indices] - np.mean(y[right_indices])) ** 2)\n", + " mse = (len(y[left_indices]) * left_mse + len(y[right_indices]) * right_mse) / n_samples\n", + " \n", + " if mse < best_mse:\n", + " best_mse = mse\n", + " best_feature = feature\n", + " best_threshold = threshold\n", + " return best_feature, best_threshold\n", + " def predict(self, X):\n", + " return np.array([self._predict_sample(sample, self.tree) for sample in X])\n", + " def _predict_sample(self, sample, node):\n", + " if isinstance(node, tuple):\n", + " feature, threshold, left_child, right_child = node\n", + " if sample[feature] < threshold:\n", + " return self._predict_sample(sample, left_child)\n", + " else:\n", + " return self._predict_sample(sample, right_child)\n", + " return node\n", + "class GradientBoostingRegressor:\n", + " def __init__(self, n_estimators=100, learning_rate=0.1, max_depth=3):\n", + " self.n_estimators = n_estimators\n", + " self.learning_rate = learning_rate\n", + " self.max_depth = max_depth\n", + " self.models = []\n", + " def fit(self, X, y):\n", + " y_pred = np.zeros(y.shape)\n", + " for _ in range(self.n_estimators):\n", + " residuals = y - y_pred\n", + " model = DecisionTreeRegressor(max_depth=self.max_depth)\n", + " model.fit(X, residuals)\n", + " y_pred += self.learning_rate * model.predict(X)\n", + " self.models.append(model)\n", + " def predict(self, X):\n", + " y_pred = np.zeros(X.shape[0])\n", + " for model in self.models:\n", + " y_pred += self.learning_rate * model.predict(X)\n", + " return y_pred\n", + "# Example usage\n", + "if __name__ == \"__main__\":\n", + " # Sample data\n", + " X = np.array([[1], [2], [3], [4], [5]])\n", + " y = np.array([1.5, 1.7, 3.5, 3.7, 5.0])\n", + " model = GradientBoostingRegressor(n_estimators=100, learning_rate=0.1, max_depth=2)\n", + " model.fit(X, y)\n", + " predictions = model.predict(X)\n", + " print(\"Predictions:\", predictions)" + ] + }, + { + "cell_type": "markdown", + "id": "bbf154c4", + "metadata": { + "editable": true + }, + "source": [ + "## Summary of course" + ] + }, + { + "cell_type": "markdown", + "id": "84d512fc", + "metadata": { + "editable": true + }, + "source": [ + "## What? Me worry? No final exam in this course!\n", + "\n", + "\n", + "\n", + "

Figure 1:

\n", + "" + ] + }, + { + "cell_type": "markdown", + "id": "71b8eb59", + "metadata": { + "editable": true + }, + "source": [ + "## Topics we have covered this year\n", + "\n", + "The course has two central parts\n", + "\n", + "1. Statistical analysis and optimization of data\n", + "\n", + "2. Machine learning" + ] + }, + { + "cell_type": "markdown", + "id": "473329b7", + "metadata": { + "editable": true + }, + "source": [ + "## Statistical analysis and optimization of data\n", + "\n", + "The following topics have been discussed:\n", + "1. Basic concepts, expectation values, variance, covariance, correlation functions and errors;\n", + "\n", + "2. Simpler models, binomial distribution, the Poisson distribution, simple and multivariate normal distributions;\n", + "\n", + "3. Central elements from linear algebra, matrix inversion and SVD\n", + "\n", + "4. Gradient methods for data optimization\n", + "\n", + "5. Estimation of errors using cross-validation, bootstrapping and jackknife methods;\n", + "\n", + "6. Practical optimization using Singular-value decomposition and least squares for parameterizing data.\n", + "\n", + "7. Not discussed: Principal Component Analysis to reduce the number of features." + ] + }, + { + "cell_type": "markdown", + "id": "6c94f44a", + "metadata": { + "editable": true + }, + "source": [ + "## Machine learning\n", + "\n", + "* Linear methods for regression and classification:\n", + "\n", + "a. Ordinary Least Squares\n", + "\n", + "b. Ridge regression\n", + "\n", + "c. Lasso regression\n", + "\n", + "d. Logistic regression\n", + "\n", + "* Neural networks and deep learning:\n", + "\n", + "a. Feed Forward Neural Networks\n", + "\n", + "b. Convolutional Neural Networks\n", + "\n", + "c. Recurrent Neural Networks\n", + "\n", + "* Decisions trees and ensemble methods:\n", + "\n", + "a. Decision trees\n", + "\n", + "b. Bagging and voting\n", + "\n", + "c. Random forests\n", + "\n", + "d. Boosting and gradient boosting\n", + "\n", + "* Not discussed this year: Support vector machines\n", + "\n", + "a. Binary classification and multiclass classification\n", + "\n", + "b. Kernel methods\n", + "\n", + "c. Regression" + ] + }, + { + "cell_type": "markdown", + "id": "d9942ded", + "metadata": { + "editable": true + }, + "source": [ + "## Learning outcomes and overarching aims of this course\n", + "\n", + "The course introduces a variety of central algorithms and methods\n", + "essential for studies of data analysis and machine learning. The\n", + "course is project based and through the various projects, normally\n", + "three, you will be exposed to fundamental research problems\n", + "in these fields, with the aim to reproduce state of the art scientific\n", + "results. The students will learn to develop and structure large codes\n", + "for studying these systems, get acquainted with computing facilities\n", + "and learn to handle large scientific projects. A good scientific and\n", + "ethical conduct is emphasized throughout the course. \n", + "\n", + "* Understand linear methods for regression and classification;\n", + "\n", + "* Learn about neural network;\n", + "\n", + "* Learn about bagging, boosting and trees\n", + "\n", + "\n", + "* Learn about basic data analysis;\n", + "\n", + "* Be capable of extending the acquired knowledge to other systems and cases;\n", + "\n", + "* Have an understanding of central algorithms used in data analysis and machine learning;\n", + "\n", + "* Work on numerical projects to illustrate the theory. The projects play a central role." + ] + }, + { + "cell_type": "markdown", + "id": "7818c700", + "metadata": { + "editable": true + }, + "source": [ + "## Perspective on Machine Learning\n", + "\n", + "1. Rapidly emerging application area\n", + "\n", + "2. Experiment AND theory are evolving in many many fields. \n", + "\n", + "3. Requires education/retraining for more widespread adoption\n", + "\n", + "4. A lot of “word-of-mouth” development methods\n", + "\n", + "Huge amounts of data sets require automation, classical analysis tools often inadequate. \n", + "High energy physics hit this wall in the 90’s.\n", + "In 2009 single top quark production was determined via [Boosted decision trees, Bayesian\n", + "Neural Networks, etc.](https://arxiv.org/pdf/0903.0850.pdf)" + ] + }, + { + "cell_type": "markdown", + "id": "cc946602", + "metadata": { + "editable": true + }, + "source": [ + "## Machine Learning Research\n", + "\n", + "Where to find recent results:\n", + "1. Conference proceedings, arXiv and blog posts!\n", + "\n", + "2. **NIPS**: [Neural Information Processing Systems](https://papers.nips.cc)\n", + "\n", + "3. **ICLR**: [International Conference on Learning Representations](https://openreview.net/group?id=ICLR.cc/2018/Conference#accepted-oral-papers)\n", + "\n", + "4. **ICML**: International Conference on Machine Learning\n", + "\n", + "5. [Journal of Machine Learning Research](http://www.jmlr.org/papers/v19/) \n", + "\n", + "6. [Follow ML on ArXiv](https://arxiv.org/list/cs.LG/recent)" + ] + }, + { + "cell_type": "markdown", + "id": "c2b75185", + "metadata": { + "editable": true + }, + "source": [ + "## Starting your Machine Learning Project\n", + "\n", + "1. Identify problem type: classification, regression\n", + "\n", + "2. Consider your data carefully\n", + "\n", + "3. Choose a simple model that fits 1 and 2\n", + "\n", + "4. Consider your data carefully again! Think of data representation more carefully.\n", + "\n", + "5. Based on your results, feedback loop to earliest possible point" + ] + }, + { + "cell_type": "markdown", + "id": "4600986a", + "metadata": { + "editable": true + }, + "source": [ + "## Choose a Model and Algorithm\n", + "\n", + "* Supervised?\n", + "\n", + "* Start with the simplest model that fits your problem\n", + "\n", + "* Start with minimal processing of data" + ] + }, + { + "cell_type": "markdown", + "id": "cfb572d5", + "metadata": { + "editable": true + }, + "source": [ + "## Preparing Your Data\n", + "\n", + "* Shuffle your data\n", + "\n", + "* Mean center your data\n", + "\n", + " * Why?\n", + "\n", + "* Normalize the variance\n", + "\n", + " * Why?\n", + "\n", + "* **Whitening**\n", + "\n", + " * Decorrelates data\n", + "\n", + " * Can be hit or miss\n", + "\n", + " * When to do train/test split?" + ] + }, + { + "cell_type": "markdown", + "id": "3e38f862", + "metadata": { + "editable": true + }, + "source": [ + "## Which activation and weights to choose in neural networks\n", + "\n", + "* RELU? ELU? GELU? etc\n", + "\n", + "* Sigmoid or Tanh?\n", + "\n", + "* Set all weights to 0? Terrible idea\n", + "\n", + "* Set all weights to random values? Small random values" + ] + }, + { + "cell_type": "markdown", + "id": "76abe122", + "metadata": { + "editable": true + }, + "source": [ + "## Optimization Methods and Hyperparameters\n", + "* Stochastic gradient descent\n", + "\n", + " * Stochastic gradient descent + momentum\n", + "\n", + "* State-of-the-art approaches:\n", + "\n", + "a. RMSProp\n", + "\n", + "b. Adam\n", + "\n", + "c. and more\n", + "\n", + "Which regularization and hyperparameters? $L_1$ or $L_2$, soft\n", + "classifiers, depths of trees and many other. Need to explore a large\n", + "set of hyperparameters and regularization methods." + ] + }, + { + "cell_type": "markdown", + "id": "0b246db8", + "metadata": { + "editable": true + }, + "source": [ + "## Resampling\n", + "\n", + "When do we resample?\n", + "\n", + "1. [Bootstrap](https://www.cambridge.org/core/books/bootstrap-methods-and-their-application/ED2FD043579F27952363566DC09CBD6A)\n", + "\n", + "2. [Cross-validation](https://www.youtube.com/watch?v=fSytzGwwBVw&ab_channel=StatQuestwithJoshStarmer)\n", + "\n", + "3. Jackknife and many other" + ] + }, + { + "cell_type": "markdown", + "id": "9a6555fc", + "metadata": { + "editable": true + }, + "source": [ + "## Other courses on Data science and Machine Learning at UiO\n", + "\n", + "1. [FYS5429 – Advanced machine learning and data analysis for the physical sciences](https://www.uio.no/studier/emner/matnat/fys/FYS5429/index-eng.html)\n", + "\n", + "2. [IN3050/IN4050 Introduction to Artificial Intelligence and Machine Learning](https://www.uio.no/studier/emner/matnat/ifi/IN3050/index-eng.html). Introductory course in machine learning and AI\n", + "\n", + "3. [STK-INF3000/4000 Selected Topics in Data Science](http://www.uio.no/studier/emner/matnat/math/STK-INF3000/index-eng.html). The course provides insight into selected contemporary relevant topics within Data Science. \n", + "\n", + "4. [IN4080 Natural Language Processing](https://www.uio.no/studier/emner/matnat/ifi/IN4080/index.html). Probabilistic and machine learning techniques applied to natural language processing. \n", + "\n", + "5. [STK-IN4300 – Statistical learning methods in Data Science](https://www.uio.no/studier/emner/matnat/math/STK-IN4300/index-eng.html). An advanced introduction to statistical and machine learning. For students with a good mathematics and statistics background.\n", + "\n", + "6. [IN-STK5000 Responsible Data Science](https://www.uio.no/studier/emner/matnat/ifi/IN-STK5000/index-eng.html). Methods for adaptive collection and processing of data based on machine learning techniques. \n", + "\n", + "7. [IN4310 – Machine Learning for Image Analysis](https://www.uio.no/studier/emner/matnat/ifi/IN4310/index.html). An introduction to deep learning with particular emphasis on applications within Image analysis, but useful for other application areas too.\n", + "\n", + "8. [IN5310 – Advanced Deep Learning for Image Analysis](https://www.uio.no/studier/emner/matnat/ifi/IN5310/index.html)\n", + "\n", + "9. [IN5490 – Advanced Topics in Artificial Intelligence for Intelligent Systems](https://www.uio.no/studier/emner/matnat/ifi/IN5490/index.html)\n", + "\n", + "10. [TEK5040 – Deep learning for autonomous systems](https://www.uio.no/studier/emner/matnat/its/TEK5040/). The course addresses advanced algorithms and architectures for deep learning with neural networks. The course provides an introduction to how deep-learning techniques can be used in the construction of key parts of advanced autonomous systems that exist in physical environments and cyber environments." + ] + }, + { + "cell_type": "markdown", + "id": "c2561e72", + "metadata": { + "editable": true + }, + "source": [ + "## Additional courses of interest\n", + "\n", + "1. [STK4051 Computational Statistics](https://www.uio.no/studier/emner/matnat/math/STK4051/index-eng.html)\n", + "\n", + "2. [STK4021 Applied Bayesian Analysis and Numerical Methods](https://www.uio.no/studier/emner/matnat/math/STK4021/index-eng.html)" + ] + }, + { + "cell_type": "markdown", + "id": "796637c8", + "metadata": { + "editable": true + }, + "source": [ + "## What's the future like?\n", + "\n", + "Based on multi-layer nonlinear neural networks, deep learning can\n", + "learn directly from raw data, automatically extract and abstract\n", + "features from layer to layer, and then achieve the goal of regression,\n", + "classification, or ranking. Deep learning has made breakthroughs in\n", + "computer vision, speech processing and natural language, and reached\n", + "or even surpassed human level. The success of deep learning is mainly\n", + "due to the three factors: big data, big model, and big computing.\n", + "\n", + "In the past few decades, many different architectures of deep neural\n", + "networks have been proposed, such as\n", + "1. Convolutional neural networks, which are mostly used in image and video data processing, and have also been applied to sequential data such as text processing;\n", + "\n", + "2. Recurrent neural networks, which can process sequential data of variable length and have been widely used in natural language understanding and speech processing;\n", + "\n", + "3. Encoder-decoder framework, which is mostly used for image or sequence generation, such as machine translation, text summarization, and image captioning." + ] + }, + { + "cell_type": "markdown", + "id": "6afdb59e", + "metadata": { + "editable": true + }, + "source": [ + "## Types of Machine Learning, a repetition\n", + "\n", + "The approaches to machine learning are many, but are often split into two main categories. \n", + "In *supervised learning* we know the answer to a problem,\n", + "and let the computer deduce the logic behind it. On the other hand, *unsupervised learning*\n", + "is a method for finding patterns and relationship in data sets without any prior knowledge of the system.\n", + "Some authours also operate with a third category, namely *reinforcement learning*. This is a paradigm \n", + "of learning inspired by behavioural psychology, where learning is achieved by trial-and-error, \n", + "solely from rewards and punishment.\n", + "\n", + "Another way to categorize machine learning tasks is to consider the desired output of a system.\n", + "Some of the most common tasks are:\n", + "\n", + " * Classification: Outputs are divided into two or more classes. The goal is to produce a model that assigns inputs into one of these classes. An example is to identify digits based on pictures of hand-written ones. Classification is typically supervised learning.\n", + "\n", + " * Regression: Finding a functional relationship between an input data set and a reference data set. The goal is to construct a function that maps input data to continuous output values.\n", + "\n", + " * Clustering: Data are divided into groups with certain common traits, without knowing the different groups beforehand. It is thus a form of unsupervised learning.\n", + "\n", + " * Other unsupervised learning algortihms like **Boltzmann machines**" + ] + }, + { + "cell_type": "markdown", + "id": "95097424", + "metadata": { + "editable": true + }, + "source": [ + "## Why Boltzmann machines?\n", + "\n", + "What is known as restricted Boltzmann Machines (RMB) have received a lot of attention lately. \n", + "One of the major reasons is that they can be stacked layer-wise to build deep neural networks that capture complicated statistics.\n", + "\n", + "The original RBMs had just one visible layer and a hidden layer, but recently so-called Gaussian-binary RBMs have gained quite some popularity in imaging since they are capable of modeling continuous data that are common to natural images. \n", + "\n", + "Furthermore, they have been used to solve complicated [quantum mechanical many-particle problems or classical statistical physics problems like the Ising and Potts classes of models](https://journals.aps.org/rmp/abstract/10.1103/RevModPhys.91.045002)." + ] + }, + { + "cell_type": "markdown", + "id": "31a33cea", + "metadata": { + "editable": true + }, + "source": [ + "## Boltzmann Machines\n", + "\n", + "Why use a generative model rather than the more well known discriminative deep neural networks (DNN)? \n", + "\n", + "* Discriminitave methods have several limitations: They are mainly supervised learning methods, thus requiring labeled data. And there are tasks they cannot accomplish, like drawing new examples from an unknown probability distribution.\n", + "\n", + "* A generative model can learn to represent and sample from a probability distribution. The core idea is to learn a parametric model of the probability distribution from which the training data was drawn. As an example\n", + "\n", + "a. A model for images could learn to draw new examples of cats and dogs, given a training dataset of images of cats and dogs.\n", + "\n", + "b. Generate a sample of an ordered or disordered phase, having been given samples of such phases.\n", + "\n", + "c. Model the trial function for [Monte Carlo calculations](https://journals.aps.org/rmp/abstract/10.1103/RevModPhys.91.045002)." + ] + }, + { + "cell_type": "markdown", + "id": "d6583875", + "metadata": { + "editable": true + }, + "source": [ + "## Some similarities and differences from DNNs\n", + "\n", + "1. Both use gradient-descent based learning procedures for minimizing cost functions\n", + "\n", + "2. Energy based models don't use backpropagation and automatic differentiation for computing gradients, instead turning to Markov Chain Monte Carlo methods.\n", + "\n", + "3. DNNs often have several hidden layers. A restricted Boltzmann machine has only one hidden layer, however several RBMs can be stacked to make up Deep Belief Networks, of which they constitute the building blocks.\n", + "\n", + "History: The RBM was developed by amongst others [Geoffrey Hinton](https://en.wikipedia.org/wiki/Geoffrey_Hinton), called by some the \"Godfather of Deep Learning\", working with the University of Toronto and Google." + ] + }, + { + "cell_type": "markdown", + "id": "285bbf5d", + "metadata": { + "editable": true + }, + "source": [ + "## Boltzmann machines (BM)\n", + "\n", + "A BM is what we would call an undirected probabilistic graphical model\n", + "with stochastic continuous or discrete units.\n", + "\n", + "It is interpreted as a stochastic recurrent neural network where the\n", + "state of each unit(neurons/nodes) depends on the units it is connected\n", + "to. The weights in the network represent thus the strength of the\n", + "interaction between various units/nodes.\n", + "\n", + "It turns into a Hopfield network if we choose deterministic rather\n", + "than stochastic units. In contrast to a Hopfield network, a BM is a\n", + "so-called generative model. It allows us to generate new samples from\n", + "the learned distribution." + ] + }, + { + "cell_type": "markdown", + "id": "5b51dec9", + "metadata": { + "editable": true + }, + "source": [ + "## A standard BM setup\n", + "\n", + "A standard BM network is divided into a set of observable and visible units $\\hat{x}$ and a set of unknown hidden units/nodes $\\hat{h}$.\n", + "\n", + "Additionally there can be bias nodes for the hidden and visible layers. These biases are normally set to $1$.\n", + "\n", + "BMs are stackable, meaning they cwe can train a BM which serves as input to another BM. We can construct deep networks for learning complex PDFs. The layers can be trained one after another, a feature which makes them popular in deep learning\n", + "\n", + "However, they are often hard to train. This leads to the introduction of so-called restricted BMs, or RBMS.\n", + "Here we take away all lateral connections between nodes in the visible layer as well as connections between nodes in the hidden layer. The network is illustrated in the figure below." + ] + }, + { + "cell_type": "markdown", + "id": "4cb37b8f", + "metadata": { + "editable": true + }, + "source": [ + "## The structure of the RBM network\n", + "\n", + "\n", + "\n", + "\n", + "

Figure 1:

\n", + "" + ] + }, + { + "cell_type": "markdown", + "id": "45fe33d7", + "metadata": { + "editable": true + }, + "source": [ + "## The network\n", + "\n", + "**The network layers**:\n", + "1. A function $\\mathbf{x}$ that represents the visible layer, a vector of $M$ elements (nodes). This layer represents both what the RBM might be given as training input, and what we want it to be able to reconstruct. This might for example be given by the pixels of an image or coefficients representing speech, or the coordinates of a quantum mechanical state function.\n", + "\n", + "2. The function $\\mathbf{h}$ represents the hidden, or latent, layer. A vector of $N$ elements (nodes). Also called \"feature detectors\"." + ] + }, + { + "cell_type": "markdown", + "id": "3bbbaa88", + "metadata": { + "editable": true + }, + "source": [ + "## Goals\n", + "\n", + "The goal of the hidden layer is to increase the model's expressive\n", + "power. We encode complex interactions between visible variables by\n", + "introducing additional, hidden variables that interact with visible\n", + "degrees of freedom in a simple manner, yet still reproduce the complex\n", + "correlations between visible degrees in the data once marginalized\n", + "over (integrated out).\n", + "\n", + "**The network parameters, to be optimized/learned**:\n", + "1. $\\mathbf{a}$ represents the visible bias, a vector of same length as $\\mathbf{x}$.\n", + "\n", + "2. $\\mathbf{b}$ represents the hidden bias, a vector of same lenght as $\\mathbf{h}$.\n", + "\n", + "3. $W$ represents the interaction weights, a matrix of size $M\\times N$." + ] + }, + { + "cell_type": "markdown", + "id": "d6a9f606", + "metadata": { + "editable": true + }, + "source": [ + "## Joint distribution\n", + "\n", + "The restricted Boltzmann machine is described by a Boltzmann distribution" + ] + }, + { + "cell_type": "markdown", + "id": "81aaff6e", + "metadata": { + "editable": true + }, + "source": [ + "\n", + "
\n", + "\n", + "$$\n", + "\\begin{equation}\n", + "\tP_{rbm}(\\mathbf{x},\\mathbf{h}) = \\frac{1}{Z} e^{-\\frac{1}{T_0}E(\\mathbf{x},\\mathbf{h})},\n", + "\\label{_auto1} \\tag{1}\n", + "\\end{equation}\n", + "$$" + ] + }, + { + "cell_type": "markdown", + "id": "9f856eb3", + "metadata": { + "editable": true + }, + "source": [ + "where $Z$ is the normalization constant or partition function, defined as" + ] + }, + { + "cell_type": "markdown", + "id": "f15429d1", + "metadata": { + "editable": true + }, + "source": [ + "\n", + "
\n", + "\n", + "$$\n", + "\\begin{equation}\n", + "\tZ = \\int \\int e^{-\\frac{1}{T_0}E(\\mathbf{x},\\mathbf{h})} d\\mathbf{x} d\\mathbf{h}.\n", + "\\label{_auto2} \\tag{2}\n", + "\\end{equation}\n", + "$$" + ] + }, + { + "cell_type": "markdown", + "id": "e8a8f8e7", + "metadata": { + "editable": true + }, + "source": [ + "It is common to ignore $T_0$ by setting it to one." + ] + }, + { + "cell_type": "markdown", + "id": "4fb17989", + "metadata": { + "editable": true + }, + "source": [ + "## Network Elements, the energy function\n", + "\n", + "The function $E(\\mathbf{x},\\mathbf{h})$ gives the **energy** of a\n", + "configuration (pair of vectors) $(\\mathbf{x}, \\mathbf{h})$. The lower\n", + "the energy of a configuration, the higher the probability of it. This\n", + "function also depends on the parameters $\\mathbf{a}$, $\\mathbf{b}$ and\n", + "$W$. Thus, when we adjust them during the learning procedure, we are\n", + "adjusting the energy function to best fit our problem.\n", + "\n", + "An expression for the energy function is" + ] + }, + { + "cell_type": "markdown", + "id": "0bed5ddd", + "metadata": { + "editable": true + }, + "source": [ + "$$\n", + "E(\\hat{x},\\hat{h}) = -\\sum_{ia}^{NA}b_i^a \\alpha_i^a(x_i)-\\sum_{jd}^{MD}c_j^d \\beta_j^d(h_j)-\\sum_{ijad}^{NAMD}b_i^a \\alpha_i^a(x_i)c_j^d \\beta_j^d(h_j)w_{ij}^{ad}.\n", + "$$" + ] + }, + { + "cell_type": "markdown", + "id": "f8e64229", + "metadata": { + "editable": true + }, + "source": [ + "Here $\\beta_j^d(h_j)$ and $\\alpha_i^a(x_j)$ are so-called transfer functions that map a given input value to a desired feature value. The labels $a$ and $d$ denote that there can be multiple transfer functions per variable. The first sum depends only on the visible units. The second on the hidden ones. **Note** that there is no connection between nodes in a layer.\n", + "\n", + "The quantities $b$ and $c$ can be interpreted as the visible and hidden biases, respectively.\n", + "\n", + "The connection between the nodes in the two layers is given by the weights $w_{ij}$." + ] + }, + { + "cell_type": "markdown", + "id": "f8f02179", + "metadata": { + "editable": true + }, + "source": [ + "## Defining different types of RBMs\n", + "There are different variants of RBMs, and the differences lie in the types of visible and hidden units we choose as well as in the implementation of the energy function $E(\\mathbf{x},\\mathbf{h})$. \n", + "\n", + "**Binary-Binary RBM:**\n", + "\n", + "RBMs were first developed using binary units in both the visible and hidden layer. The corresponding energy function is defined as follows:" + ] + }, + { + "cell_type": "markdown", + "id": "19008689", + "metadata": { + "editable": true + }, + "source": [ + "\n", + "
\n", + "\n", + "$$\n", + "\\begin{equation}\n", + "\tE(\\mathbf{x}, \\mathbf{h}) = - \\sum_i^M x_i a_i- \\sum_j^N b_j h_j - \\sum_{i,j}^{M,N} x_i w_{ij} h_j,\n", + "\\label{_auto3} \\tag{3}\n", + "\\end{equation}\n", + "$$" + ] + }, + { + "cell_type": "markdown", + "id": "a3d31b3a", + "metadata": { + "editable": true + }, + "source": [ + "where the binary values taken on by the nodes are most commonly 0 and 1.\n", + "\n", + "**Gaussian-Binary RBM:**\n", + "\n", + "Another varient is the RBM where the visible units are Gaussian while the hidden units remain binary:" + ] + }, + { + "cell_type": "markdown", + "id": "79d29641", + "metadata": { + "editable": true + }, + "source": [ + "\n", + "
\n", + "\n", + "$$\n", + "\\begin{equation}\n", + "\tE(\\mathbf{x}, \\mathbf{h}) = \\sum_i^M \\frac{(x_i - a_i)^2}{2\\sigma_i^2} - \\sum_j^N b_j h_j - \\sum_{i,j}^{M,N} \\frac{x_i w_{ij} h_j}{\\sigma_i^2}. \n", + "\\label{_auto4} \\tag{4}\n", + "\\end{equation}\n", + "$$" + ] + }, + { + "cell_type": "markdown", + "id": "4d19d779", + "metadata": { + "editable": true + }, + "source": [ + "## More about RBMs\n", + "1. Useful when we model continuous data (i.e., we wish $\\mathbf{x}$ to be continuous)\n", + "\n", + "2. Requires a smaller learning rate, since there's no upper bound to the value a component might take in the reconstruction\n", + "\n", + "Other types of units include:\n", + "1. Softmax and multinomial units\n", + "\n", + "2. Gaussian visible and hidden units\n", + "\n", + "3. Binomial units\n", + "\n", + "4. Rectified linear units\n", + "\n", + "To read more, see [Lectures on Boltzmann machines in Physics](https://github.com/CompPhysics/ComputationalPhysics2/blob/gh-pages/doc/pub/notebook2/ipynb/notebook2.ipynb)." + ] + }, + { + "cell_type": "markdown", + "id": "b8fa06ec", + "metadata": { + "editable": true + }, + "source": [ + "## Autoencoders: Overarching view\n", + "\n", + "Autoencoders are artificial neural networks capable of learning\n", + "efficient representations of the input data (these representations are called codings) without\n", + "any supervision (i.e., the training set is unlabeled). These codings\n", + "typically have a much lower dimensionality than the input data, making\n", + "autoencoders useful for dimensionality reduction. \n", + "\n", + "More importantly, autoencoders act as powerful feature detectors, and\n", + "they can be used for unsupervised pretraining of deep neural networks.\n", + "\n", + "Lastly, they are capable of randomly generating new data that looks\n", + "very similar to the training data; this is called a generative\n", + "model. For example, you could train an autoencoder on pictures of\n", + "faces, and it would then be able to generate new faces. Surprisingly,\n", + "autoencoders work by simply learning to copy their inputs to their\n", + "outputs. This may sound like a trivial task, but we will see that\n", + "constraining the network in various ways can make it rather\n", + "difficult. For example, you can limit the size of the internal\n", + "representation, or you can add noise to the inputs and train the\n", + "network to recover the original inputs. These constraints prevent the\n", + "autoencoder from trivially copying the inputs directly to the outputs,\n", + "which forces it to learn efficient ways of representing the data. In\n", + "short, the codings are byproducts of the autoencoder’s attempt to\n", + "learn the identity function under some constraints.\n", + "\n", + "[Video on autoencoders](https://www.coursera.org/lecture/building-deep-learning-models-with-tensorflow/autoencoders-1U4L3)\n", + "\n", + "See also A. Geron's textbook, chapter 15." + ] + }, + { + "cell_type": "markdown", + "id": "262fc510", + "metadata": { + "editable": true + }, + "source": [ + "## Bayesian Machine Learning\n", + "\n", + "This is an important topic if we aim at extracting a probability\n", + "distribution. This gives us also a confidence interval and error\n", + "estimates.\n", + "\n", + "Bayesian machine learning allows us to encode our prior beliefs about\n", + "what those models should look like, independent of what the data tells\n", + "us. This is especially useful when we don’t have a ton of data to\n", + "confidently learn our model.\n", + "\n", + "[Video on Bayesian deep learning](https://www.youtube.com/watch?v=E1qhGw8QxqY&ab_channel=AndrewGordonWilson)\n", + "\n", + "See also the [slides here](https://github.com/CompPhysics/MachineLearning/blob/master/doc/Articles/lec03.pdf)." + ] + }, + { + "cell_type": "markdown", + "id": "36608f14", + "metadata": { + "editable": true + }, + "source": [ + "## Reinforcement Learning\n", + "\n", + "Reinforcement Learning (RL) is one of the most exciting fields of\n", + "Machine Learning today, and also one of the oldest. It has been around\n", + "since the 1950s, producing many interesting applications over the\n", + "years.\n", + "\n", + "It studies\n", + "how agents take actions based on trial and error, so as to maximize\n", + "some notion of cumulative reward in a dynamic system or\n", + "environment. Due to its generality, the problem has also been studied\n", + "in many other disciplines, such as game theory, control theory,\n", + "operations research, information theory, multi-agent systems, swarm\n", + "intelligence, statistics, and genetic algorithms.\n", + "\n", + "In March 2016, AlphaGo, a computer program that plays the board game\n", + "Go, beat Lee Sedol in a five-game match. This was the first time a\n", + "computer Go program had beaten a 9-dan (highest rank) professional\n", + "without handicaps. AlphaGo is based on deep convolutional neural\n", + "networks and reinforcement learning. AlphaGo’s victory was a major\n", + "milestone in artificial intelligence and it has also made\n", + "reinforcement learning a hot research area in the field of machine\n", + "learning.\n", + "\n", + "[Lecture on Reinforcement Learning](https://www.youtube.com/watch?v=FgzM3zpZ55o&ab_channel=stanfordonline).\n", + "\n", + "See also A. Geron's textbook, chapter 16." + ] + }, + { + "cell_type": "markdown", + "id": "a2b4652f", + "metadata": { + "editable": true + }, + "source": [ + "## Transfer learning\n", + "\n", + "The goal of transfer learning is to transfer the model or knowledge\n", + "obtained from a source task to the target task, in order to resolve\n", + "the issues of insufficient training data in the target task. The\n", + "rationality of doing so lies in that usually the source and target\n", + "tasks have inter-correlations, and therefore either the features,\n", + "samples, or models in the source task might provide useful information\n", + "for us to better solve the target task. Transfer learning is a hot\n", + "research topic in recent years, with many problems still waiting to be studied.\n", + "\n", + "[Lecture on transfer learning](https://www.ias.edu/video/machinelearning/2020/0331-SamoryKpotufe)." + ] + }, + { + "cell_type": "markdown", + "id": "8cef8c03", + "metadata": { + "editable": true + }, + "source": [ + "## Adversarial learning\n", + "\n", + "The conventional deep generative model has a potential problem: the\n", + "model tends to generate extreme instances to maximize the\n", + "probabilistic likelihood, which will hurt its performance. Adversarial\n", + "learning utilizes the adversarial behaviors (e.g., generating\n", + "adversarial instances or training an adversarial model) to enhance the\n", + "robustness of the model and improve the quality of the generated\n", + "data. In recent years, one of the most promising unsupervised learning\n", + "technologies, generative adversarial networks (GAN), has already been\n", + "successfully applied to image, speech, and text.\n", + "\n", + "[Lecture on adversial learning](https://www.youtube.com/watch?v=CIfsB_EYsVI&ab_channel=StanfordUniversitySchoolofEngineering)." + ] + }, + { + "cell_type": "markdown", + "id": "df85a3e6", + "metadata": { + "editable": true + }, + "source": [ + "## Dual learning\n", + "\n", + "Dual learning is a new learning paradigm, the basic idea of which is\n", + "to use the primal-dual structure between machine learning tasks to\n", + "obtain effective feedback/regularization, and guide and strengthen the\n", + "learning process, thus reducing the requirement of large-scale labeled\n", + "data for deep learning. The idea of dual learning has been applied to\n", + "many problems in machine learning, including machine translation,\n", + "image style conversion, question answering and generation, image\n", + "classification and generation, text classification and generation,\n", + "image-to-text, and text-to-image." + ] + }, + { + "cell_type": "markdown", + "id": "f01aaa3c", + "metadata": { + "editable": true + }, + "source": [ + "## Distributed machine learning\n", + "\n", + "Distributed computation will speed up machine learning algorithms,\n", + "significantly improve their efficiency, and thus enlarge their\n", + "application. When distributed meets machine learning, more than just\n", + "implementing the machine learning algorithms in parallel is required." + ] + }, + { + "cell_type": "markdown", + "id": "0005330c", + "metadata": { + "editable": true + }, + "source": [ + "## Meta learning\n", + "\n", + "Meta learning is an emerging research direction in machine\n", + "learning. Roughly speaking, meta learning concerns learning how to\n", + "learn, and focuses on the understanding and adaptation of the learning\n", + "itself, instead of just completing a specific learning task. That is,\n", + "a meta learner needs to be able to evaluate its own learning methods\n", + "and adjust its own learning methods according to specific learning\n", + "tasks." + ] + }, + { + "cell_type": "markdown", + "id": "efcfaccf", + "metadata": { + "editable": true + }, + "source": [ + "## The Challenges Facing Machine Learning\n", + "\n", + "While there has been much progress in machine learning, there are also challenges.\n", + "\n", + "For example, the mainstream machine learning technologies are\n", + "black-box approaches, making us concerned about their potential\n", + "risks. To tackle this challenge, we may want to make machine learning\n", + "more explainable and controllable. As another example, the\n", + "computational complexity of machine learning algorithms is usually\n", + "very high and we may want to invent lightweight algorithms or\n", + "implementations. Furthermore, in many domains such as physics,\n", + "chemistry, biology, and social sciences, people usually seek elegantly\n", + "simple equations (e.g., the Schrödinger equation) to uncover the\n", + "underlying laws behind various phenomena. In the field of machine\n", + "learning, can we reveal simple laws instead of designing more complex\n", + "models for data fitting? Although there are many challenges, we are\n", + "still very optimistic about the future of machine learning. As we look\n", + "forward to the future, here are what we think the research hotspots in\n", + "the next ten years will be.\n", + "\n", + "See the article on [Discovery of Physics From Data: Universal Laws and Discrepancies](https://www.frontiersin.org/articles/10.3389/frai.2020.00025/full)" + ] + }, + { + "cell_type": "markdown", + "id": "f7b109c9", + "metadata": { + "editable": true + }, + "source": [ + "## Explainable machine learning\n", + "\n", + "Machine learning, especially deep learning, evolves rapidly. The\n", + "ability gap between machine and human on many complex cognitive tasks\n", + "becomes narrower and narrower. However, we are still in the very early\n", + "stage in terms of explaining why those effective models work and how\n", + "they work.\n", + "\n", + "**What is missing: the gap between correlation and causation**. Standard Machine Learning is based on what e have called a frequentist approach. \n", + "\n", + "Most\n", + "machine learning techniques, especially the statistical ones, depend\n", + "highly on correlations in data sets to make predictions and analyses. In\n", + "contrast, rational humans tend to reply on clear and trustworthy\n", + "causality relations obtained via logical reasoning on real and clear\n", + "facts. It is one of the core goals of explainable machine learning to\n", + "transition from solving problems by data correlation to solving\n", + "problems by logical reasoning.\n", + "\n", + "**Bayesian Machine Learning is one of the exciting research directions in this field**." + ] + }, + { + "cell_type": "markdown", + "id": "e9142380", + "metadata": { + "editable": true + }, + "source": [ + "## Quantum machine learning\n", + "\n", + "Quantum machine learning is an emerging interdisciplinary research\n", + "area at the intersection of quantum computing and machine learning.\n", + "\n", + "Quantum computers use effects such as quantum coherence and quantum\n", + "entanglement to process information, which is fundamentally different\n", + "from classical computers. Quantum algorithms have surpassed the best\n", + "classical algorithms in several problems (e.g., searching for an\n", + "unsorted database, inverting a sparse matrix), which we call quantum\n", + "acceleration.\n", + "\n", + "When quantum computing meets machine learning, it can be a mutually\n", + "beneficial and reinforcing process, as it allows us to take advantage\n", + "of quantum computing to improve the performance of classical machine\n", + "learning algorithms. In addition, we can also use the machine learning\n", + "algorithms (on classic computers) to analyze and improve quantum\n", + "computing systems.\n", + "\n", + "[Lecture on Quantum ML](https://www.youtube.com/watch?v=Xh9pUu3-WxM&ab_channel=InstituteforPure%26AppliedMathematics%28IPAM%29).\n", + "\n", + "[Read interview with Maria Schuld on her work on Quantum Machine Learning](https://physics.aps.org/articles/v13/179?utm_campaign=weekly&utm_medium=email&utm_source=emailalert). See also [her recent textbook](https://www.springer.com/gp/book/9783319964232)." + ] + }, + { + "cell_type": "markdown", + "id": "cb638c92", + "metadata": { + "editable": true + }, + "source": [ + "## Quantum machine learning algorithms based on linear algebra\n", + "\n", + "Many quantum machine learning algorithms are based on variants of\n", + "quantum algorithms for solving linear equations, which can efficiently\n", + "solve N-variable linear equations with complexity of O(log2 N) under\n", + "certain conditions. The quantum matrix inversion algorithm can\n", + "accelerate many machine learning methods, such as least square linear\n", + "regression, least square version of support vector machine, Gaussian\n", + "process, and more. The training of these algorithms can be simplified\n", + "to solve linear equations. The key bottleneck of this type of quantum\n", + "machine learning algorithms is data input—that is, how to initialize\n", + "the quantum system with the entire data set. Although efficient\n", + "data-input algorithms exist for certain situations, how to efficiently\n", + "input data into a quantum system is as yet unknown for most cases." + ] + }, + { + "cell_type": "markdown", + "id": "676fd56e", + "metadata": { + "editable": true + }, + "source": [ + "## Quantum reinforcement learning\n", + "\n", + "In quantum reinforcement learning, a quantum agent interacts with the\n", + "classical environment to obtain rewards from the environment, so as to\n", + "adjust and improve its behavioral strategies. In some cases, it\n", + "achieves quantum acceleration by the quantum processing capabilities\n", + "of the agent or the possibility of exploring the environment through\n", + "quantum superposition. Such algorithms have been proposed in\n", + "superconducting circuits and systems of trapped ions." + ] + }, + { + "cell_type": "markdown", + "id": "ac1d93d3", + "metadata": { + "editable": true + }, + "source": [ + "## Quantum deep learning\n", + "\n", + "Dedicated quantum information processors, such as quantum annealers\n", + "and programmable photonic circuits, are well suited for building deep\n", + "quantum networks. The simplest deep quantum network is the Boltzmann\n", + "machine. The classical Boltzmann machine consists of bits with tunable\n", + "interactions and is trained by adjusting the interaction of these bits\n", + "so that the distribution of its expression conforms to the statistics\n", + "of the data. To quantize the Boltzmann machine, the neural network can\n", + "simply be represented as a set of interacting quantum spins that\n", + "correspond to an adjustable Ising model. Then, by initializing the\n", + "input neurons in the Boltzmann machine to a fixed state and allowing\n", + "the system to heat up, we can read out the output qubits to get the\n", + "result." + ] + }, + { + "cell_type": "markdown", + "id": "c5047fcb", + "metadata": { + "editable": true + }, + "source": [ + "## Social machine learning\n", + "\n", + "Machine learning aims to imitate how humans\n", + "learn. While we have developed successful machine learning algorithms,\n", + "until now we have ignored one important fact: humans are social. Each\n", + "of us is one part of the total society and it is difficult for us to\n", + "live, learn, and improve ourselves, alone and isolated. Therefore, we\n", + "should design machines with social properties. Can we let machines\n", + "evolve by imitating human society so as to achieve more effective,\n", + "intelligent, interpretable “social machine learning”?\n", + "\n", + "And much more." + ] + }, + { + "cell_type": "markdown", + "id": "bdd66000", + "metadata": { + "editable": true + }, + "source": [ + "## The last words?\n", + "\n", + "Early computer scientist Alan Kay said, **The best way to predict the\n", + "future is to create it**. Therefore, all machine learning\n", + "practitioners, whether scholars or engineers, professors or students,\n", + "need to work together to advance these important research\n", + "topics. Together, we will not just predict the future, but create it." + ] + }, + { + "cell_type": "markdown", + "id": "7fb56aeb", + "metadata": { + "editable": true + }, + "source": [ + "## Best wishes to you all and thanks so much for your heroic efforts this semester\n", + "\n", + "\n", + "\n", + "\n", + "

Figure 1:

\n", + "" + ] + } + ], + "metadata": {}, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/doc/LectureNotes/_build/html/additionweek42.html b/doc/LectureNotes/_build/html/additionweek42.html index e81a40a00..8b6416a7b 100644 --- a/doc/LectureNotes/_build/html/additionweek42.html +++ b/doc/LectureNotes/_build/html/additionweek42.html @@ -257,6 +257,9 @@
  • Week 47: From Decision Trees to Ensemble Methods, Random Forests and Boosting Methods
  • Exercise week 47
    • +
  • Week 48: Gradient boosting and summary of course
    • +
  • Exercises week 48
    • +

    Projects

    Projects

      @@ -730,7 +733,7 @@ Thereafter we wish to apply it to data which were not included in the training.
      -_images/7208bf883e220945a23bf4a64d7c474c22032a9479d0230f3f7627fde6c870e3.png +_images/01d44d8b3777894a56df96cdebd9411c2a9b9484e40698be0bb84652c7d0f7cf.png

      This example serves several aims. It allows us to demonstrate several @@ -815,7 +818,7 @@ to be dominated by outliers.

      -_images/daf635918e102d6af1572d0f6fcb2bcd9c93425cd41e91dc85022a813e3b1da5.png +_images/326bc2bdd25593d0947bbe11be557f7c976fd425b872153c8c8317ebbb51c5ae.png

      Depending on the parameter in front of the normal distribution, we may @@ -862,16 +865,16 @@ example of the functionality of Scikit-Learn.

      The intercept alpha: 
- [1.97386121]
+ [2.00949975]
 Coefficient beta : 
- [[5.12574106]]
-Mean squared error: 0.20
-Variance score: 0.92
-Mean squared log error: 0.01
-Mean absolute error: 0.36
+ [[5.07244691]]
+Mean squared error: 0.30
+Variance score: 0.88
+Mean squared log error: 0.02
+Mean absolute error: 0.42
      -_images/1168b639886622ca16f51897f200d03b2de325bfc3f6688b4b75ff4473160f59.png +_images/b39b8ed8a4aed5f4a94d2137c267e12d64d23f66d1c50c3e3ad1ae8cd8b9ff5e.png

      The function coef gives us the parameter \(\beta\) of our fit while intercept yields @@ -967,8 +970,8 @@ a linear \(x\)-dependence we s

      -_images/2e42f972e6bd064ce2062209501a6f469d0ebd090cf62a0159f888f2deaa3de7.png -
      0.004999999999999993
+_images/99bd4e87bbb54a5fb018ef916c872e3158711414d535c467a50d947ace53c954.png
+
      0.005000000000000009
      diff --git a/doc/LectureNotes/_build/html/chapter10.html b/doc/LectureNotes/_build/html/chapter10.html index d253ae521..76cd1eb3a 100644 --- a/doc/LectureNotes/_build/html/chapter10.html +++ b/doc/LectureNotes/_build/html/chapter10.html @@ -257,6 +257,9 @@
    • Week 47: From Decision Trees to Ensemble Methods, Random Forests and Boosting Methods
    • Exercise week 47
      • +
    • Week 48: Gradient boosting and summary of course
      • +
    • Exercises week 48
      • +

    Projects

      @@ -935,8 +938,9 @@ probability that image 0 is in category 0,1,2,...,9 = 1.10378326e-04 5.08318298e-09 2.03256632e-04 1.92507116e-03 9.84443254e-01 3.11507992e-04] probabilities sum up to: 1.0 - -predictions = (n_inputs) = (1437,) + + +
      predictions = (n_inputs) = (1437,)
 prediction for image 0: 8
 correct label for image 0: 6
      @@ -1114,7 +1118,7 @@ the Hadamard product, meaning element-wise multiplication.

      Old accuracy on training data: 0.1440501043841336
      -
      /var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_58742/953065564.py:4: RuntimeWarning: overflow encountered in exp
+
      /var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_23025/953065564.py:4: RuntimeWarning: overflow encountered in exp
   return 1/(1 + np.exp(-x))
 
      
 
      
@@ -1448,7 +1452,7 @@ Lambda =  10.0
 Accuracy score on test set:  0.19166666666666668
      -
      /var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_58742/953065564.py:4: RuntimeWarning: overflow encountered in exp
+
      /var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_23025/953065564.py:4: RuntimeWarning: overflow encountered in exp
   return 1/(1 + np.exp(-x))
 
      
 
      
@@ -1457,7 +1461,7 @@ Lambda =  1e-05
 Accuracy score on test set:  0.10555555555555556
      -
      /var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_58742/953065564.py:4: RuntimeWarning: overflow encountered in exp
+
      /var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_23025/953065564.py:4: RuntimeWarning: overflow encountered in exp
   return 1/(1 + np.exp(-x))
 
      
 
      
@@ -1466,10 +1470,211 @@ Lambda =  0.0001
 Accuracy score on test set:  0.08611111111111111
      -
      /var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_58742/953065564.py:4: RuntimeWarning: overflow encountered in exp
+
      /var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_23025/953065564.py:4: RuntimeWarning: overflow encountered in exp
   return 1/(1 + np.exp(-x))
 
      
 
      
+
      Learning rate  =  0.1
+Lambda =  0.001
+Accuracy score on test set:  0.10555555555555556
+
      
+
      
+
      /var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_23025/953065564.py:4: RuntimeWarning: overflow encountered in exp
+  return 1/(1 + np.exp(-x))
+
      
+
      
+
      Learning rate  =  0.1
+Lambda =  0.01
+Accuracy score on test set:  0.08888888888888889
+
      
+
      
+
      /var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_23025/953065564.py:4: RuntimeWarning: overflow encountered in exp
+  return 1/(1 + np.exp(-x))
+
      
+
      
+
      Learning rate  =  0.1
+Lambda =  0.1
+Accuracy score on test set:  0.08611111111111111
+
      
+
      
+
      /var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_23025/953065564.py:4: RuntimeWarning: overflow encountered in exp
+  return 1/(1 + np.exp(-x))
+
      
+
      
+
      Learning rate  =  0.1
+Lambda =  1.0
+Accuracy score on test set:  0.08888888888888889
+
      
+
      
+
      /var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_23025/953065564.py:4: RuntimeWarning: overflow encountered in exp
+  return 1/(1 + np.exp(-x))
+
      
+
      
+
      Learning rate  =  0.1
+Lambda =  10.0
+Accuracy score on test set:  0.09166666666666666
+
      
+
      
+
      /var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_23025/953065564.py:4: RuntimeWarning: overflow encountered in exp
+  return 1/(1 + np.exp(-x))
+/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_23025/1630775253.py:43: RuntimeWarning: overflow encountered in exp
+  exp_term = np.exp(self.z_o)
+/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_23025/1630775253.py:44: RuntimeWarning: invalid value encountered in divide
+  self.probabilities = exp_term / np.sum(exp_term, axis=1, keepdims=True)
+
      
+
      
+
      Learning rate  =  1.0
+Lambda =  1e-05
+Accuracy score on test set:  0.07777777777777778
+
      
+
      
+
      /var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_23025/953065564.py:4: RuntimeWarning: overflow encountered in exp
+  return 1/(1 + np.exp(-x))
+/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_23025/1630775253.py:43: RuntimeWarning: overflow encountered in exp
+  exp_term = np.exp(self.z_o)
+/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_23025/1630775253.py:44: RuntimeWarning: invalid value encountered in divide
+  self.probabilities = exp_term / np.sum(exp_term, axis=1, keepdims=True)
+
      
+
      
+
      Learning rate  =  1.0
+Lambda =  0.0001
+Accuracy score on test set:  0.07777777777777778
+
      
+
      
+
      /var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_23025/953065564.py:4: RuntimeWarning: overflow encountered in exp
+  return 1/(1 + np.exp(-x))
+/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_23025/1630775253.py:43: RuntimeWarning: overflow encountered in exp
+  exp_term = np.exp(self.z_o)
+/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_23025/1630775253.py:44: RuntimeWarning: invalid value encountered in divide
+  self.probabilities = exp_term / np.sum(exp_term, axis=1, keepdims=True)
+
      
+
      
+
      Learning rate  =  1.0
+Lambda =  0.001
+Accuracy score on test set:  0.07777777777777778
+
      
+
      
+
      /var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_23025/953065564.py:4: RuntimeWarning: overflow encountered in exp
+  return 1/(1 + np.exp(-x))
+/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_23025/1630775253.py:43: RuntimeWarning: overflow encountered in exp
+  exp_term = np.exp(self.z_o)
+/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_23025/1630775253.py:44: RuntimeWarning: invalid value encountered in divide
+  self.probabilities = exp_term / np.sum(exp_term, axis=1, keepdims=True)
+
      
+
      
+
      Learning rate  =  1.0
+Lambda =  0.01
+Accuracy score on test set:  0.07777777777777778
+
      
+
      
+
      /var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_23025/953065564.py:4: RuntimeWarning: overflow encountered in exp
+  return 1/(1 + np.exp(-x))
+/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_23025/1630775253.py:43: RuntimeWarning: overflow encountered in exp
+  exp_term = np.exp(self.z_o)
+/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_23025/1630775253.py:44: RuntimeWarning: invalid value encountered in divide
+  self.probabilities = exp_term / np.sum(exp_term, axis=1, keepdims=True)
+
      
+
      
+
      Learning rate  =  1.0
+Lambda =  0.1
+Accuracy score on test set:  0.07777777777777778
+
      
+
      
+
      /var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_23025/953065564.py:4: RuntimeWarning: overflow encountered in exp
+  return 1/(1 + np.exp(-x))
+
      
+
      
+
      Learning rate  =  1.0
+Lambda =  1.0
+Accuracy score on test set:  0.10555555555555556
+
      
+
      
+
      /var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_23025/953065564.py:4: RuntimeWarning: overflow encountered in exp
+  return 1/(1 + np.exp(-x))
+/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_23025/1630775253.py:43: RuntimeWarning: overflow encountered in exp
+  exp_term = np.exp(self.z_o)
+/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_23025/1630775253.py:44: RuntimeWarning: invalid value encountered in divide
+  self.probabilities = exp_term / np.sum(exp_term, axis=1, keepdims=True)
+
      
+
      
+
      Learning rate  =  1.0
+Lambda =  10.0
+Accuracy score on test set:  0.07777777777777778
+
      
+
      
+
      /var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_23025/953065564.py:4: RuntimeWarning: overflow encountered in exp
+  return 1/(1 + np.exp(-x))
+/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_23025/1630775253.py:43: RuntimeWarning: overflow encountered in exp
+  exp_term = np.exp(self.z_o)
+/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_23025/1630775253.py:44: RuntimeWarning: invalid value encountered in divide
+  self.probabilities = exp_term / np.sum(exp_term, axis=1, keepdims=True)
+
      
+
      
+
      Learning rate  =  10.0
+Lambda =  1e-05
+Accuracy score on test set:  0.07777777777777778
+
      
+
      
+
      /var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_23025/953065564.py:4: RuntimeWarning: overflow encountered in exp
+  return 1/(1 + np.exp(-x))
+/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_23025/1630775253.py:43: RuntimeWarning: overflow encountered in exp
+  exp_term = np.exp(self.z_o)
+/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_23025/1630775253.py:44: RuntimeWarning: invalid value encountered in divide
+  self.probabilities = exp_term / np.sum(exp_term, axis=1, keepdims=True)
+
      
+
      
+
      Learning rate  =  10.0
+Lambda =  0.0001
+Accuracy score on test set:  0.07777777777777778
+
      
+
      
+
      /var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_23025/953065564.py:4: RuntimeWarning: overflow encountered in exp
+  return 1/(1 + np.exp(-x))
+/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_23025/1630775253.py:43: RuntimeWarning: overflow encountered in exp
+  exp_term = np.exp(self.z_o)
+/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_23025/1630775253.py:44: RuntimeWarning: invalid value encountered in divide
+  self.probabilities = exp_term / np.sum(exp_term, axis=1, keepdims=True)
+
      
+
      
+
      Learning rate  =  10.0
+Lambda =  0.001
+Accuracy score on test set:  0.07777777777777778
+
      
+
      
+
      /var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_23025/953065564.py:4: RuntimeWarning: overflow encountered in exp
+  return 1/(1 + np.exp(-x))
+/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_23025/1630775253.py:43: RuntimeWarning: overflow encountered in exp
+  exp_term = np.exp(self.z_o)
+/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_23025/1630775253.py:44: RuntimeWarning: invalid value encountered in divide
+  self.probabilities = exp_term / np.sum(exp_term, axis=1, keepdims=True)
+
      
+
      
+
      Learning rate  =  10.0
+Lambda =  0.01
+Accuracy score on test set:  0.07777777777777778
+
      
+
      
+
      /var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_23025/953065564.py:4: RuntimeWarning: overflow encountered in exp
+  return 1/(1 + np.exp(-x))
+/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_23025/1630775253.py:43: RuntimeWarning: overflow encountered in exp
+  exp_term = np.exp(self.z_o)
+/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_23025/1630775253.py:44: RuntimeWarning: invalid value encountered in divide
+  self.probabilities = exp_term / np.sum(exp_term, axis=1, keepdims=True)
+
      
+
      
+
      Learning rate  =  10.0
+Lambda =  0.1
+Accuracy score on test set:  0.07777777777777778
+
      
+
      
+
      /var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_23025/953065564.py:4: RuntimeWarning: overflow encountered in exp
+  return 1/(1 + np.exp(-x))
+/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_23025/1630775253.py:43: RuntimeWarning: overflow encountered in exp
+  exp_term = np.exp(self.z_o)
+/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_23025/1630775253.py:44: RuntimeWarning: invalid value encountered in divide
+  self.probabilities = exp_term / np.sum(exp_term, axis=1, keepdims=True)
+
      
+
      
 
      ---------------------------------------------------------------------------
 KeyboardInterrupt                         Traceback (most recent call last)
 Cell In[8], line 11
@@ -1480,18 +1685,17 @@ Accuracy score on test set:  0.08611111111111111
      13     DNN_numpy[i][j] = dnn
      15     test_predict = dnn.predict(X_test)
 
-Cell In[6], line 98, in NeuralNetwork.train(self)
-     95 self.X_data = self.X_data_full[chosen_datapoints]
+Cell In[6], line 99, in NeuralNetwork.train(self)
      96 self.Y_data = self.Y_data_full[chosen_datapoints]
----> 98 self.feed_forward()
-     99 self.backpropagation()
+     98 self.feed_forward()
+---> 99 self.backpropagation()
 
-Cell In[6], line 38, in NeuralNetwork.feed_forward(self)
-     36 def feed_forward(self):
-     37     # feed-forward for training
----> 38     self.z_h = np.matmul(self.X_data, self.hidden_weights) + self.hidden_bias
-     39     self.a_h = sigmoid(self.z_h)
-     41     self.z_o = np.matmul(self.a_h, self.output_weights) + self.output_bias
+Cell In[6], line 64, in NeuralNetwork.backpropagation(self)
+     61 self.output_weights_gradient = np.matmul(self.a_h.T, error_output)
+     62 self.output_bias_gradient = np.sum(error_output, axis=0)
+---> 64 self.hidden_weights_gradient = np.matmul(self.X_data.T, error_hidden)
+     65 self.hidden_bias_gradient = np.sum(error_hidden, axis=0)
+     67 if self.lmbd > 0.0:
 
 KeyboardInterrupt: 
 
      
diff --git a/doc/LectureNotes/_build/html/chapter11.html b/doc/LectureNotes/_build/html/chapter11.html
index f2ad28f93..52d8c5212 100644
--- a/doc/LectureNotes/_build/html/chapter11.html
+++ b/doc/LectureNotes/_build/html/chapter11.html
@@ -257,6 +257,9 @@
 
    • Week 47: From Decision Trees to Ensemble Methods, Random Forests and Boosting Methods
      • 
 
    • Exercise week 47
      • 
 
+
    • Week 48: Gradient boosting  and summary of course
      • 
+
    • Exercises week 48
      • 
+

    Projects

    Projects

    Projects

      @@ -558,7 +561,7 @@ systems such as automatic translation and speech-to-text.

      -_images/29361dbec77d5254822f2cf57bdddb701f65d5a7a34bf4d781aba818766c3cbf.png +_images/d213fd49b73afa7db6fe63b54689d9b6c4aff1adac65c06e412ebb9574c86d39.png 
      /Users/mhjensen/miniforge3/envs/myenv/lib/python3.9/site-packages/keras/src/layers/rnn/rnn.py:204: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead.
   super().__init__(**kwargs)
      @@ -584,360 +587,402 @@ systems such as automatic translation and speech-to-text.

      Epoch 1/100
      -
      50/50 - 2s - 41ms/step - loss: 0.5362
+
      50/50 - 2s - 48ms/step - loss: 0.5174
 
      
 
      
 
      Epoch 2/100
 
      
 
      
-
      50/50 - 0s - 8ms/step - loss: 0.4106
+
      50/50 - 0s - 7ms/step - loss: 0.4188
 
      
 
      
 
      Epoch 3/100
 
      
 
      
-
      50/50 - 0s - 8ms/step - loss: 0.4015
+
      50/50 - 0s - 7ms/step - loss: 0.4065
 
      
 
      
 
      Epoch 4/100
 
      
 
      
-
      50/50 - 0s - 8ms/step - loss: 0.3978
+
      50/50 - 0s - 8ms/step - loss: 0.4041
 
      
 
      
 
      Epoch 5/100
 
      
 
      
-
      50/50 - 0s - 7ms/step - loss: 0.3953
+
      50/50 - 0s - 8ms/step - loss: 0.4032
 
      
 
      
 
      Epoch 6/100
 
      
 
      
-
      50/50 - 0s - 7ms/step - loss: 0.3941
+
      50/50 - 0s - 7ms/step - loss: 0.4008
 
      
 
      
 
      Epoch 7/100
 
      
 
      
-
      50/50 - 0s - 7ms/step - loss: 0.3899
+
      50/50 - 0s - 7ms/step - loss: 0.4000
 
      
 
      
 
      Epoch 8/100
 
      
 
      
-
      50/50 - 0s - 7ms/step - loss: 0.3917
+
      50/50 - 0s - 7ms/step - loss: 0.3989
 
      
 
      
 
      Epoch 9/100
 
      
 
      
-
      50/50 - 0s - 8ms/step - loss: 0.3893
+
      50/50 - 0s - 7ms/step - loss: 0.3963
 
      
 
      
 
      Epoch 10/100
 
      
 
      
-
      50/50 - 0s - 7ms/step - loss: 0.3903
+
      50/50 - 0s - 7ms/step - loss: 0.3964
 
      
 
      
 
      Epoch 11/100
 
      
 
      
-
      50/50 - 0s - 7ms/step - loss: 0.3860
+
      50/50 - 0s - 7ms/step - loss: 0.3957
 
      
 
      
 
      Epoch 12/100
 
      
 
      
-
      50/50 - 0s - 7ms/step - loss: 0.3843
+
      50/50 - 0s - 7ms/step - loss: 0.3965
 
      
 
      
 
      Epoch 13/100
 
      
 
      
-
      50/50 - 1s - 11ms/step - loss: 0.3874
+
      50/50 - 0s - 7ms/step - loss: 0.3965
 
      
 
      
 
      Epoch 14/100
 
      
 
      
-
      50/50 - 0s - 8ms/step - loss: 0.3868
+
      50/50 - 0s - 7ms/step - loss: 0.3953
 
      
 
      
 
      Epoch 15/100
 
      
 
      
-
      50/50 - 0s - 8ms/step - loss: 0.3842
+
      50/50 - 0s - 7ms/step - loss: 0.3943
 
      
 
      
 
      Epoch 16/100
 
      
 
      
-
      50/50 - 0s - 8ms/step - loss: 0.3855
+
      50/50 - 0s - 7ms/step - loss: 0.3938
 
      
 
      
 
      Epoch 17/100
 
      
 
      
-
      50/50 - 1s - 13ms/step - loss: 0.3861
+
      50/50 - 0s - 7ms/step - loss: 0.3920
 
      
 
      
 
      Epoch 18/100
 
      
 
      
-
      50/50 - 0s - 10ms/step - loss: 0.3809
+
      50/50 - 0s - 7ms/step - loss: 0.3935
 
      
 
      
 
      Epoch 19/100
 
      
 
      
-
      50/50 - 0s - 7ms/step - loss: 0.3829
+
      50/50 - 0s - 7ms/step - loss: 0.3949
 
      
 
      
 
      Epoch 20/100
 
      
 
      
-
      50/50 - 0s - 7ms/step - loss: 0.3809
+
      50/50 - 0s - 7ms/step - loss: 0.3922
 
      
 
      
 
      Epoch 21/100
 
      
 
      
-
      50/50 - 0s - 7ms/step - loss: 0.3824
+
      50/50 - 0s - 9ms/step - loss: 0.3927
 
      
 
      
 
      Epoch 22/100
 
      
 
      
-
      50/50 - 0s - 7ms/step - loss: 0.3804
+
      50/50 - 0s - 9ms/step - loss: 0.3914
 
      
 
      
 
      Epoch 23/100
 
      
 
      
-
      50/50 - 0s - 7ms/step - loss: 0.3797
+
      50/50 - 0s - 7ms/step - loss: 0.3924
 
      
 
      
 
      Epoch 24/100
 
      
 
      
-
      50/50 - 0s - 7ms/step - loss: 0.3807
+
      50/50 - 0s - 8ms/step - loss: 0.3885
 
      
 
      
 
      Epoch 25/100
 
      
 
      
-
      50/50 - 0s - 7ms/step - loss: 0.3796
+
      50/50 - 0s - 7ms/step - loss: 0.3896
 
      
 
      
 
      Epoch 26/100
 
      
 
      
-
      50/50 - 2s - 31ms/step - loss: 0.3783
+
      50/50 - 0s - 7ms/step - loss: 0.3901
 
      
 
      
 
      Epoch 27/100
 
      
 
      
-
      50/50 - 0s - 9ms/step - loss: 0.3775
+
      50/50 - 0s - 7ms/step - loss: 0.3885
 
      
 
      
 
      Epoch 28/100
 
      
 
      
-
      50/50 - 0s - 8ms/step - loss: 0.3789
+
      50/50 - 0s - 7ms/step - loss: 0.3905
 
      
 
      
 
      Epoch 29/100
 
      
 
      
-
      50/50 - 0s - 8ms/step - loss: 0.3774
+
      50/50 - 0s - 7ms/step - loss: 0.3884
 
      
 
      
 
      Epoch 30/100
 
      
 
      
-
      50/50 - 0s - 9ms/step - loss: 0.3759
+
      50/50 - 0s - 7ms/step - loss: 0.3904
 
      
 
      
 
      Epoch 31/100
 
      
 
      
-
      50/50 - 0s - 7ms/step - loss: 0.3768
+
      50/50 - 0s - 7ms/step - loss: 0.3889
 
      
 
      
 
      Epoch 32/100
 
      
 
      
-
      50/50 - 0s - 7ms/step - loss: 0.3741
+
      50/50 - 0s - 7ms/step - loss: 0.3874
 
      
 
      
 
      Epoch 33/100
 
      
 
      
-
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+
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      ---------------------------------------------------------------------------
 KeyboardInterrupt                         Traceback (most recent call last)
 Cell In[1], line 58
diff --git a/doc/LectureNotes/_build/html/chapter2.html b/doc/LectureNotes/_build/html/chapter2.html
index 1ac08d55e..5264f92ca 100644
--- a/doc/LectureNotes/_build/html/chapter2.html
+++ b/doc/LectureNotes/_build/html/chapter2.html
@@ -257,6 +257,9 @@
 
    • Week 47: From Decision Trees to Ensemble Methods, Random Forests and Boosting Methods
      • 
 
    • Exercise week 47
      • 
 
+
    • Week 48: Gradient boosting  and summary of course
      • 
+
    • Exercises week 48
      • 
+

    Projects

      @@ -1185,10 +1188,10 @@ covariance matrix through the np.linalg.eig() function.

      -
      -0.07163028969289174
-3.7656278764040367
-[[0.7647107  2.29986727]
- [2.29986727 7.88107866]]
+
      0.00239782448350092
+3.945643027765946
+[[1.04582521 3.00214849]
+ [3.00214849 9.76163974]]
      @@ -1225,10 +1228,10 @@ a more brute force way. Here we scale the mean values for each column of the des
      -
      0.08212703190343323
-2.425866065899094
-[[1.         0.65333306]
- [0.65333306 1.        ]]
+
      0.07878080847813602
+2.1500369042116563
+[[1.         0.63295205]
+ [0.63295205 1.        ]]
      @@ -1258,30 +1261,30 @@ this matrix we easily see that it is a positive definite matrix.

      -
      [[-0.68481734 -2.74241828]
- [-1.03710525 -3.82202137]
- [ 0.71505805  2.54840587]
- [ 0.4645853   0.98764188]
- [-1.95392781 -4.51089358]
- [ 0.79256149  3.37757489]
- [-0.18745641  0.4457749 ]
- [ 2.53950612  7.87543978]
- [ 0.25354074 -0.28008123]
- [-0.90194489 -3.87942287]]
+
      [[-0.05862476  0.45533295]
+ [ 0.84689611  4.01381445]
+ [ 0.21778772  0.62032544]
+ [-1.3117471  -2.69182187]
+ [-0.78512935 -3.44198948]
+ [-0.12030798 -0.93994208]
+ [ 0.60559053  2.2457079 ]
+ [ 1.8170149   4.51652722]
+ [-0.44215901 -3.25646952]
+ [-0.76932106 -1.52148501]]
           0         1
-0 -0.684817 -2.742418
-1 -1.037105 -3.822021
-2  0.715058  2.548406
-3  0.464585  0.987642
-4 -1.953928 -4.510894
-5  0.792561  3.377575
-6 -0.187456  0.445775
-7  2.539506  7.875440
-8  0.253541 -0.280081
-9 -0.901945 -3.879423
-          0         1
-0  1.000000  0.972082
-1  0.972082  1.000000
+0 -0.058625  0.455333
+1  0.846896  4.013814
+2  0.217788  0.620325
+3 -1.311747 -2.691822
+4 -0.785129 -3.441989
+5 -0.120308 -0.939942
+6  0.605591  2.245708
+7  1.817015  4.516527
+8 -0.442159 -3.256470
+9 -0.769321 -1.521485
+         0        1
+0  1.00000  0.92403
+1  0.92403  1.00000
      @@ -1338,37 +1341,37 @@ 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  0.070253  0.070611  0.070163  0.068562  0.067004  0.062136  0.060528   
-2   0.0  0.070611  0.071502  0.070808  0.069529  0.068256  0.062916  0.061526   
-3   0.0  0.070163  0.070808  0.074767  0.073368  0.071996  0.069069  0.067536   
-4   0.0  0.068562  0.069529  0.073368  0.072239  0.071113  0.068033  0.066707   
-5   0.0  0.067004  0.068256  0.071996  0.071113  0.070210  0.067007  0.065872   
-6   0.0  0.062136  0.062916  0.069069  0.068033  0.067007  0.065727  0.064490   
-7   0.0  0.060528  0.061526  0.067536  0.066707  0.065872  0.064490  0.063421   
-8   0.0  0.059023  0.060216  0.066097  0.065457  0.064796  0.063327  0.062411   
-9   0.0  0.057616  0.058984  0.064749  0.064281  0.063778  0.062233  0.061458   
-10  0.0  0.054096  0.054970  0.061945  0.061234  0.060520  0.060261  0.059314   
-11  0.0  0.052750  0.053781  0.060606  0.060054  0.059486  0.059137  0.058322   
-12  0.0  0.051499  0.052672  0.059359  0.058952  0.058518  0.058088  0.057394   
-13  0.0  0.050336  0.051637  0.058198  0.057923  0.057612  0.057109  0.056526   
-14  0.0  0.049255  0.050672  0.057116  0.056962  0.056764  0.056195  0.055714   
+1   0.0  0.081622  0.081406  0.078296  0.079396  0.080527  0.068235  0.069327   
+2   0.0  0.081406  0.081919  0.078612  0.080089  0.081603  0.068822  0.070146   
+3   0.0  0.078296  0.078612  0.080411  0.081717  0.083038  0.073368  0.074578   
+4   0.0  0.079396  0.080089  0.081717  0.083265  0.084831  0.074620  0.075994   
+5   0.0  0.080527  0.081603  0.083038  0.084831  0.086645  0.075870  0.077411   
+6   0.0  0.068235  0.068822  0.073368  0.074620  0.075870  0.069194  0.070300   
+7   0.0  0.069327  0.070146  0.074578  0.075994  0.077411  0.070300  0.071523   
+8   0.0  0.070483  0.071536  0.075839  0.077423  0.079011  0.071441  0.072784   
+9   0.0  0.071702  0.072994  0.077153  0.078908  0.080672  0.072619  0.074087   
+10  0.0  0.059187  0.059821  0.065770  0.066863  0.067945  0.063580  0.064519   
+11  0.0  0.060114  0.060903  0.066767  0.067977  0.069178  0.064472  0.065498   
+12  0.0  0.061096  0.062042  0.067812  0.069142  0.070466  0.065402  0.066517   
+13  0.0  0.062134  0.063240  0.068908  0.070362  0.071812  0.066371  0.067578   
+14  0.0  0.063230  0.064502  0.070057  0.071639  0.073220  0.067381  0.068684   
 
           8         9         10        11        12        13        14  
 0   0.000000  0.000000  0.000000  0.000000  0.000000  0.000000  0.000000  
-1   0.059023  0.057616  0.054096  0.052750  0.051499  0.050336  0.049255  
-2   0.060216  0.058984  0.054970  0.053781  0.052672  0.051637  0.050672  
-3   0.066097  0.064749  0.061945  0.060606  0.059359  0.058198  0.057116  
-4   0.065457  0.064281  0.061234  0.060054  0.058952  0.057923  0.056962  
-5   0.064796  0.063778  0.060520  0.059486  0.058518  0.057612  0.056764  
-6   0.063327  0.062233  0.060261  0.059137  0.058088  0.057109  0.056195  
-7   0.062411  0.061458  0.059314  0.058322  0.057394  0.056526  0.055714  
-8   0.061541  0.060718  0.058420  0.057549  0.056732  0.055967  0.055250  
-9   0.060718  0.060014  0.057576  0.056817  0.056103  0.055433  0.054805  
-10  0.058420  0.057576  0.056198  0.055300  0.054459  0.053671  0.052934  
-11  0.057549  0.056817  0.055300  0.054507  0.053763  0.053066  0.052412  
-12  0.056732  0.056103  0.054459  0.053763  0.053109  0.052495  0.051918  
-13  0.055967  0.055433  0.053671  0.053066  0.052495  0.051957  0.051452  
-14  0.055250  0.054805  0.052934  0.052412  0.051918  0.051452  0.051015  
+1   0.070483  0.071702  0.059187  0.060114  0.061096  0.062134  0.063230  
+2   0.071536  0.072994  0.059821  0.060903  0.062042  0.063240  0.064502  
+3   0.075839  0.077153  0.065770  0.066767  0.067812  0.068908  0.070057  
+4   0.077423  0.078908  0.066863  0.067977  0.069142  0.070362  0.071639  
+5   0.079011  0.080672  0.067945  0.069178  0.070466  0.071812  0.073220  
+6   0.071441  0.072619  0.063580  0.064472  0.065402  0.066371  0.067381  
+7   0.072784  0.074087  0.064519  0.065498  0.066517  0.067578  0.068684  
+8   0.074170  0.075601  0.065481  0.066550  0.067660  0.068817  0.070021  
+9   0.075601  0.077163  0.066469  0.067629  0.068835  0.070089  0.071396  
+10  0.065481  0.066469  0.059543  0.060291  0.061066  0.061870  0.062705  
+11  0.066550  0.067629  0.060291  0.061104  0.061946  0.062820  0.063728  
+12  0.067660  0.068835  0.061066  0.061946  0.062859  0.063805  0.064788  
+13  0.068817  0.070089  0.061870  0.062820  0.063805  0.064827  0.065887  
+14  0.070021  0.071396  0.062705  0.063728  0.064788  0.065887  0.067030
      diff --git a/doc/LectureNotes/_build/html/chapter3.html b/doc/LectureNotes/_build/html/chapter3.html index cd57cea9d..3ba609b3e 100644 --- a/doc/LectureNotes/_build/html/chapter3.html +++ b/doc/LectureNotes/_build/html/chapter3.html @@ -257,6 +257,9 @@
    • Week 47: From Decision Trees to Ensemble Methods, Random Forests and Boosting Methods
    • Exercise week 47
      • +
    • Week 48: Gradient boosting and summary of course
      • +
    • Exercises week 48
      • +

    Projects

      @@ -726,10 +729,10 @@ number \(i\) is left out. Usin
      -
      Runtime: 0.177848 sec
+
      Runtime: 0.154011 sec
 Jackknife Statistics :
 original           bias      std. error
- 99.9735        99.9635        0.149504
+ 100.105        100.095         0.14726
      @@ -948,7 +951,7 @@ theorem.

      Bootstrap Statistics :
 original           bias      std. error
- 99.9629  14.9658        99.9626        0.148488
+ 100.182  15.0891        100.184         0.15218
      @@ -970,7 +973,7 @@ original bias std. error
      -_images/01ac462cedf743599925f73cc6294e533c9fdc93068eb00daba5834da82ae1f3.png +_images/1f1e999736d47a2811569158c3eb924dab62b0f520b0f854e48f2e2dbb32e047.png
      @@ -1155,9 +1158,7 @@ Error: 0.08426840630693411 Bias^2: 0.0796891867672603 Var: 0.004579219539673834 0.08426840630693411 >= 0.0796891867672603 + 0.004579219539673834 = 0.08426840630693413 - - -
      Polynomial degree: 2
+Polynomial degree: 2
 Error: 0.10398646080125035
 Bias^2: 0.1007711427354898
 Var: 0.0032153180657605116
@@ -1189,9 +1190,7 @@ Error: 0.02760977349102253
 Bias^2: 0.022999498260366312
 Var: 0.004610275230656212
 0.02760977349102253 >= 0.022999498260366312 + 0.004610275230656212 = 0.027609773491022525
-
      -
      -
      Polynomial degree: 8
+Polynomial degree: 8
 Error: 0.017355848195593347
 Bias^2: 0.010331721306655127
 Var: 0.007024126888938232
@@ -1206,14 +1205,14 @@ Error: 0.021592704588025025
 Bias^2: 0.010516485576645508
 Var: 0.011076219011379514
 0.021592704588025025 >= 0.010516485576645508 + 0.011076219011379514 = 0.021592704588025022
-Polynomial degree: 11
+
      +
      +
      Polynomial degree: 11
 Error: 0.07160048164233104
 Bias^2: 0.014436800088904942
 Var: 0.05716368155342608
 0.07160048164233104 >= 0.014436800088904942 + 0.05716368155342608 = 0.07160048164233102
-
      -
      -
      Polynomial degree: 12
+Polynomial degree: 12
 Error: 0.11547777218872497
 Bias^2: 0.01628578269596628
 Var: 0.09919198949275869
@@ -1540,9 +1539,9 @@ Mean squared error on training data: 0.00060704
 Mean squared error on test data: 3262.26814548
      -
      /var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_58812/626635268.py:73: RuntimeWarning: divide by zero encountered in log10
+
      /var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_23110/626635268.py:73: RuntimeWarning: divide by zero encountered in log10
   plt.plot(polynomial, np.log10(trainingerror), label='Training Error')
-/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_58812/626635268.py:74: RuntimeWarning: divide by zero encountered in log10
+/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_23110/626635268.py:74: RuntimeWarning: divide by zero encountered in log10
   plt.plot(polynomial, np.log10(testerror), label='Test Error')
 
      
 
      
@@ -1776,7 +1775,7 @@ cross-validation (LOOCV).
      -
      /var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_58812/3817475779.py:63: RuntimeWarning: divide by zero encountered in log10
+
      /var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_23110/3817475779.py:63: RuntimeWarning: divide by zero encountered in log10
   plt.plot(polynomial, np.log10(estimated_mse_sklearn), label='Test Error')
 
      
 
      
@@ -2665,7 +2664,7 @@ linear system as an equation would reduce this down to
      -
      /var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_58812/4162706317.py:7: UserWarning: set_ticklabels() should only be used with a fixed number of ticks, i.e. after set_ticks() or using a FixedLocator.
+
      /var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_23110/4162706317.py:7: UserWarning: set_ticklabels() should only be used with a fixed number of ticks, i.e. after set_ticks() or using a FixedLocator.
   cb.ax.set_yticklabels(cb.ax.get_yticklabels(), fontsize=18)
 
      
 
      
@@ -2809,7 +2808,7 @@ with the form utilized in linear regression, viz.
      -
      /var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_58812/3777801602.py:7: UserWarning: set_ticklabels() should only be used with a fixed number of ticks, i.e. after set_ticks() or using a FixedLocator.
+
      /var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_23110/3777801602.py:7: UserWarning: set_ticklabels() should only be used with a fixed number of ticks, i.e. after set_ticks() or using a FixedLocator.
   cb.ax.set_yticklabels(cb.ax.get_yticklabels(), fontsize=18)
 
      
 
      
@@ -2849,7 +2848,7 @@ cost function is given by
      -
      /var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_58812/438060758.py:10: UserWarning: set_ticklabels() should only be used with a fixed number of ticks, i.e. after set_ticks() or using a FixedLocator.
+
      /var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_23110/438060758.py:10: UserWarning: set_ticklabels() should only be used with a fixed number of ticks, i.e. after set_ticks() or using a FixedLocator.
   cb.ax.set_yticklabels(cb.ax.get_yticklabels(), fontsize=18)
 
      
 
      
@@ -2884,7 +2883,7 @@ cost function is given by
      -
      /var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_58812/3544313922.py:9: UserWarning: set_ticklabels() should only be used with a fixed number of ticks, i.e. after set_ticks() or using a FixedLocator.
+
      /var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_23110/3544313922.py:9: UserWarning: set_ticklabels() should only be used with a fixed number of ticks, i.e. after set_ticks() or using a FixedLocator.
   cb.ax.set_yticklabels(cb.ax.get_yticklabels(), fontsize=18)
 
      
 
      
@@ -2937,43 +2936,43 @@ constant as opposed to ridge and OLS. We get a sparse solution with
      -
        0%|                                                                                                 | 0/10 [00:00<?, ?it/s]
+
        0%|                                                                                                                                              | 0/10 [00:00<?, ?it/s]
 
      
 
      
 
      /Users/mhjensen/miniforge3/envs/myenv/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.924e+00, tolerance: 1.797e+00
   model = cd_fast.enet_coordinate_descent(
 
- 10%|████████▉                                                                                | 1/10 [00:00<00:07,  1.22it/s]
+ 10%|█████████████▍                                                                                                                        | 1/10 [00:01<00:11,  1.33s/it]
 
      
 
      
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       30%|████████████████████████████████████████▏                                                                                             | 3/10 [00:03<00:07,  1.04s/it]
 
      
 
      
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+
      100%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 10/10 [00:09<00:00,  1.09it/s]
 
      
 
      
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+
      100%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 10/10 [00:09<00:00,  1.09it/s]
 
      
 
      
 
      
diff --git a/doc/LectureNotes/_build/html/chapter4.html b/doc/LectureNotes/_build/html/chapter4.html
index 9bbf23ee6..b59ea01be 100644
--- a/doc/LectureNotes/_build/html/chapter4.html
+++ b/doc/LectureNotes/_build/html/chapter4.html
@@ -257,6 +257,9 @@
 
    • Week 47: From Decision Trees to Ensemble Methods, Random Forests and Boosting Methods
      • 
 
    • Exercise week 47
      • 
 
+
    • Week 48: Gradient boosting  and summary of course
      • 
+
    • Exercises week 48
      • 
+

    Projects

    Projects

    Projects

      @@ -621,13 +624,13 @@ predicting the target features of query instances is as follows:

      2nd degree coefficients:
-zero power:  -3.6801072677808095
-first power:  0.14054303349959596
-second power:  -0.0002999281168222194
+zero power:  -1.399819665759832
+first power:  -0.04120453230477482
+second power:  0.0007267213049602343
      -_images/928e4d75f6fb1b7a9e9c65e0db52145f075c9aed9b2e97bdef41ed3d957ace12.png -_images/deb9b3ea985ab0b0e0d38d4431927a533e89aac53bf320412eff2b3ca55387f7.png +_images/b27afd9b9c2cb0727463c018ad254fd8be50dfbc3e2597029b6cb3b42667fdb5.png +_images/71a39e681516930bc9bdd50d3be601c8226c85a4b39549563a05614380c05e0c.png
      @@ -1486,10 +1489,10 @@ attributes at each step while growing the tree.

      (426, 30)
 (143, 30)
+Test set accuracy with Logistic Regression: 0.94
      -
      Test set accuracy with Logistic Regression: 0.94
-Test set accuracy with SVM: 0.63
+
      Test set accuracy with SVM: 0.63
 Test set accuracy with Decision Trees: 0.90
 Test set accuracy Logistic Regression with scaled data: 0.96
 Test set accuracy SVM with scaled data: 0.96
diff --git a/doc/LectureNotes/_build/html/chapter7.html b/doc/LectureNotes/_build/html/chapter7.html
index 7c31bda98..a85c4a12c 100644
--- a/doc/LectureNotes/_build/html/chapter7.html
+++ b/doc/LectureNotes/_build/html/chapter7.html
@@ -257,6 +257,9 @@
 
    • Week 47: From Decision Trees to Ensemble Methods, Random Forests and Boosting Methods
      • 
 
    • Exercise week 47
      • 
 
+
    • Week 48: Gradient boosting  and summary of course
      • 
+
    • Exercises week 48
      • 
+

    Projects

    Projects

      @@ -617,10 +620,10 @@ covariance matrix through the np.linalg.eig() function.

      -
      -0.08913527419249101
-3.7127415072708665
-[[ 1.13025431  3.39215451]
- [ 3.39215451 11.15061293]]
+
      -0.02745698767039481
+3.9527157086177156
+[[0.80249705 2.35440603]
+ [2.35440603 7.83541057]]
      @@ -660,10 +663,10 @@ a more brute force way. Here we scale the mean values for each column of the des
      -
      0.09335279187105122
-2.2108106787032815
-[[1.         0.66771869]
- [0.66771869 1.        ]]
+
      0.07408022521552643
+1.9519435372439522
+[[1.         0.56323995]
+ [0.56323995 1.        ]]
      @@ -692,30 +695,30 @@ this matrix we easily see that it is a positive definite matrix.

      -
      [[ 0.35582913  0.96196912]
- [ 0.20039135  1.07902642]
- [-0.47708415 -1.39752266]
- [-0.0118395   0.55664957]
- [-0.18990703  1.30646904]
- [ 0.22370063 -0.76173777]
- [-0.25925579 -1.7132548 ]
- [-0.56061802 -1.9921751 ]
- [-1.14025916 -4.44655399]
- [ 1.85904253  6.40713017]]
+
      [[ 0.53340467  1.72530188]
+ [-0.56167889 -3.2544002 ]
+ [ 1.85988539  5.87145508]
+ [-1.23561281 -4.57354138]
+ [ 0.52739566  0.93489572]
+ [ 0.89775088  2.87036267]
+ [-0.71834014 -0.65667853]
+ [ 0.38699758  1.30537358]
+ [-1.48772565 -3.9664681 ]
+ [-0.20207669 -0.25630072]]
           0         1
-0  0.355829  0.961969
-1  0.200391  1.079026
-2 -0.477084 -1.397523
-3 -0.011839  0.556650
-4 -0.189907  1.306469
-5  0.223701 -0.761738
-6 -0.259256 -1.713255
-7 -0.560618 -1.992175
-8 -1.140259 -4.446554
-9  1.859043  6.407130
+0  0.533405  1.725302
+1 -0.561679 -3.254400
+2  1.859885  5.871455
+3 -1.235613 -4.573541
+4  0.527396  0.934896
+5  0.897751  2.870363
+6 -0.718340 -0.656679
+7  0.386998  1.305374
+8 -1.487726 -3.966468
+9 -0.202077 -0.256301
           0         1
-0  1.000000  0.946278
-1  0.946278  1.000000
+0  1.000000  0.966262
+1  0.966262  1.000000
      @@ -772,37 +775,37 @@ 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  0.075894  0.073179  0.073456  0.072746  0.071970  0.064485  0.064123   
-2   0.0  0.073179  0.070833  0.070597  0.070031  0.069409  0.061930  0.061649   
-3   0.0  0.073456  0.070597  0.076555  0.075647  0.074674  0.070295  0.069812   
-4   0.0  0.072746  0.070031  0.075647  0.074821  0.073930  0.069398  0.068968   
-5   0.0  0.071970  0.069409  0.074674  0.073930  0.073126  0.068445  0.068069   
-6   0.0  0.064485  0.061930  0.070295  0.069398  0.068445  0.066551  0.066050   
-7   0.0  0.064123  0.061649  0.069812  0.068968  0.068069  0.066050  0.065589   
-8   0.0  0.063763  0.061372  0.069330  0.068539  0.067694  0.065550  0.065128   
-9   0.0  0.063403  0.061097  0.068844  0.068107  0.067318  0.065047  0.064664   
-10  0.0  0.055942  0.053732  0.062913  0.062085  0.061210  0.060946  0.060463   
-11  0.0  0.055680  0.053525  0.062569  0.061780  0.060945  0.060582  0.060130   
-12  0.0  0.055429  0.053330  0.062237  0.061487  0.060692  0.060229  0.059808   
-13  0.0  0.055190  0.053147  0.061915  0.061205  0.060449  0.059887  0.059497   
-14  0.0  0.054960  0.052974  0.061603  0.060933  0.060217  0.059554  0.059195   
+1   0.0  0.073438  0.077471  0.075458  0.076186  0.076594  0.069080  0.068939   
+2   0.0  0.077471  0.083242  0.080919  0.082408  0.083412  0.074516  0.074729   
+3   0.0  0.075458  0.080919  0.083069  0.084410  0.085279  0.079308  0.079408   
+4   0.0  0.076186  0.082408  0.084410  0.086139  0.087327  0.080792  0.081105   
+5   0.0  0.076594  0.083412  0.085279  0.087327  0.088786  0.081792  0.082290   
+6   0.0  0.069080  0.074516  0.079308  0.080792  0.081792  0.077847  0.078070   
+7   0.0  0.068939  0.074729  0.079408  0.081105  0.082290  0.078070  0.078429   
+8   0.0  0.068716  0.074791  0.079363  0.081242  0.082589  0.078134  0.078618   
+9   0.0  0.068441  0.074753  0.079219  0.081258  0.082750  0.078091  0.078687   
+10  0.0  0.061939  0.066921  0.073159  0.074603  0.075599  0.073240  0.073520   
+11  0.0  0.061580  0.066741  0.072883  0.074456  0.075571  0.073054  0.073431   
+12  0.0  0.061213  0.066524  0.072574  0.074264  0.075487  0.072829  0.073296   
+13  0.0  0.060849  0.066290  0.072252  0.074047  0.075371  0.072584  0.073135   
+14  0.0  0.060497  0.066053  0.071930  0.073822  0.075239  0.072334  0.072965   
 
           8         9         10        11        12        13        14  
 0   0.000000  0.000000  0.000000  0.000000  0.000000  0.000000  0.000000  
-1   0.063763  0.063403  0.055942  0.055680  0.055429  0.055190  0.054960  
-2   0.061372  0.061097  0.053732  0.053525  0.053330  0.053147  0.052974  
-3   0.069330  0.068844  0.062913  0.062569  0.062237  0.061915  0.061603  
-4   0.068539  0.068107  0.062085  0.061780  0.061487  0.061205  0.060933  
-5   0.067694  0.067318  0.061210  0.060945  0.060692  0.060449  0.060217  
-6   0.065550  0.065047  0.060946  0.060582  0.060229  0.059887  0.059554  
-7   0.065128  0.064664  0.060463  0.060130  0.059808  0.059497  0.059195  
-8   0.064706  0.064282  0.059981  0.059679  0.059388  0.059108  0.058837  
-9   0.064282  0.063898  0.059497  0.059226  0.058967  0.058718  0.058478  
-10  0.059981  0.059497  0.056837  0.056475  0.056124  0.055783  0.055452  
-11  0.059679  0.059226  0.056475  0.056139  0.055814  0.055499  0.055194  
-12  0.059388  0.058967  0.056124  0.055814  0.055515  0.055226  0.054947  
-13  0.059108  0.058718  0.055783  0.055499  0.055226  0.054963  0.054709  
-14  0.058837  0.058478  0.055452  0.055194  0.054947  0.054709  0.054481  
+1   0.068716  0.068441  0.061939  0.061580  0.061213  0.060849  0.060497  
+2   0.074791  0.074753  0.066921  0.066741  0.066524  0.066290  0.066053  
+3   0.079363  0.079219  0.073159  0.072883  0.072574  0.072252  0.071930  
+4   0.081242  0.081258  0.074603  0.074456  0.074264  0.074047  0.073822  
+5   0.082589  0.082750  0.075599  0.075571  0.075487  0.075371  0.075239  
+6   0.078134  0.078091  0.073240  0.073054  0.072829  0.072584  0.072334  
+7   0.078618  0.078687  0.073520  0.073431  0.073296  0.073135  0.072965  
+8   0.078919  0.079094  0.073650  0.073652  0.073602  0.073523  0.073430  
+9   0.079094  0.079368  0.073677  0.073765  0.073796  0.073795  0.073777  
+10  0.073650  0.073677  0.069911  0.069801  0.069653  0.069484  0.069308  
+11  0.073652  0.073765  0.069801  0.069768  0.069692  0.069593  0.069484  
+12  0.073602  0.073796  0.069653  0.069692  0.069686  0.069654  0.069611  
+13  0.073523  0.073795  0.069484  0.069593  0.069654  0.069688  0.069709  
+14  0.073430  0.073777  0.069308  0.069484  0.069611  0.069709  0.069791
      @@ -991,10 +994,10 @@ We can write our own code or simply use either the functionaly of numpy< 
                0         1
-0  3.946263  1.971035
-1  1.971035  1.988524
-[[3.94626291 1.97103474]
- [1.97103474 1.98852413]]
+0  4.066103  2.050297
+1  2.050297  2.019626
+[[4.06610301 2.0502966 ]
+ [2.0502966  2.01962561]]
      @@ -1021,11 +1024,11 @@ Our own code here is not very elegant and asks for obvious improvements. It is t 
      Centered covariance using own code
-[[3.94626291 1.97103474]
- [1.97103474 1.98852413]]
+[[4.06610301 2.0502966 ]
+ [2.0502966  2.01962561]]
      -_images/6ca0e6a8c6122c37cbb752b49917fd1e67a1de46298e8c2247a990d083fdb3c7.png +_images/6221ee55bb26dba5cef5540ef9967df02eb119abda11c5c5903f7762c8c9f763.png

      Depending on the number of points \(n\), we will get results that are close to the covariance values defined above. @@ -1082,16 +1085,16 @@ questions.

      Eigenvalues of Covariance matrix
-5.168112312789667
-0.7666747242371026
+5.3343122336302145
+0.7514163800957537
 First eigenvector
-[0.84993979 0.52687982]
+[0.85045481 0.5260481 ]
 Second eigenvector
-[-0.52687982  0.84993979]
+[-0.5260481   0.85045481]
 
      Eigenvector of largest eigenvalue
-[0.84993979 0.52687982]
+[0.85045481 0.5260481 ]
      diff --git a/doc/LectureNotes/_build/html/chapter9.html b/doc/LectureNotes/_build/html/chapter9.html index 2fc3c06e5..c8c75d55e 100644 --- a/doc/LectureNotes/_build/html/chapter9.html +++ b/doc/LectureNotes/_build/html/chapter9.html @@ -257,6 +257,9 @@
    • Week 47: From Decision Trees to Ensemble Methods, Random Forests and Boosting Methods
    • Exercise week 47
      • +
    • Week 48: Gradient boosting and summary of course
      • +
    • Exercises week 48
      • +

    Projects

    Projects

    Projects

    Projects

    Projects

    Projects

    Projects

    Projects

    Projects

    Projects

      @@ -599,18 +602,18 @@ regression.

      Own inversion
-[[4.0010275 ]
- [3.10085547]]
-Eigenvalues of Hessian Matrix:[0.32462943 4.22813513]
+[[4.41622083]
+ [2.74692745]]
+Eigenvalues of Hessian Matrix:[0.26594878 4.45190996]
 theta from own gd
-[[4.0010275 ]
- [3.10085547]]
+[[4.41622083]
+ [2.74692745]]
 theta from own sdg
-[[4.08051993]
- [3.13522576]]
+[[4.38222303]
+ [2.74411787]]
      -_images/28af7ec857f6b0c73278974d8a0f02963d4300b7ede7129fc2e95b8d68a8492b.png +_images/74585d2c5d042ce0364bf21b5a8e9bc5b5504011eaa4a30c445a4415a4c1df98.png

      In the above code, we have use replacement in setting up the @@ -729,17 +732,17 @@ first example shows results with ordinary leats squares.

      Own inversion
-[[3.8679219 ]
- [3.12497024]]
-Eigenvalues of Hessian Matrix:[0.31887646 4.68712364]
+[[4.32147704]
+ [2.78166226]]
+Eigenvalues of Hessian Matrix:[0.32306518 4.38577204]
 
      theta from own gd
-[[3.8679219 ]
- [3.12497024]]
+[[4.32147704]
+ [2.78166226]]
      -_images/634d0baa356c54adef6974350a5d0f31fb981ef2c1199e2bf8abc3dbbd76d38b.png +_images/c61bca31ee6787e0e5d1512e4bf365143d789096f02da0a858ff7e7c3834630c.png
      @@ -807,73 +810,73 @@ Eigenvalues of Hessian Matrix:[0.31887646 4.68712364] 
      Own inversion
 [[4.]
  [3.]]
-Eigenvalues of Hessian Matrix:[0.3011187  4.49781862]
-0 [-10.84111032] [-12.09572749]
-1 [-0.48072858] [0.39646149]
-2 [-0.4485449] [0.3699193]
-3 [-0.41851584] [0.34515405]
-4 [-0.39049716] [0.32204677]
-5 [-0.36435426] [0.30048647]
-6 [-0.33996158] [0.28036959]
-7 [-0.31720192] [0.26159948]
-8 [-0.29596598] [0.24408599]
-9 [-0.27615173] [0.22774499]
-10 [-0.257664] [0.21249799]
-11 [-0.24041398] [0.19827173]
-12 [-0.22431882] [0.18499789]
-13 [-0.20930118] [0.1726127]
-14 [-0.19528895] [0.16105668]
-15 [-0.1822148] [0.1502743]
-16 [-0.17001593] [0.14021378]
-17 [-0.15863375] [0.13082678]
-18 [-0.14801358] [0.12206823]
-19 [-0.13810441] [0.11389604]
-20 [-0.12885864] [0.10627096]
-21 [-0.12023184] [0.09915636]
-22 [-0.1121826] [0.09251807]
-23 [-0.10467223] [0.08632419]
-24 [-0.09766466] [0.08054499]
-25 [-0.09112623] [0.07515268]
-26 [-0.08502554] [0.07012138]
-27 [-0.07933327] [0.06542692]
-28 [-0.07402209] [0.06104673]
-29 [-0.06906648] [0.05695979]
+Eigenvalues of Hessian Matrix:[0.26136301 4.38259367]
+0 [-12.42926102] [-13.7797646]
+1 [-0.35774662] [0.30560116]
+2 [-0.33641182] [0.28737615]
+3 [-0.31634936] [0.27023801]
+4 [-0.29748336] [0.25412193]
+5 [-0.27974246] [0.23896696]
+6 [-0.26305957] [0.22471578]
+7 [-0.24737159] [0.2113145]
+8 [-0.23261919] [0.19871242]
+9 [-0.21874657] [0.18686188]
+10 [-0.20570127] [0.17571808]
+11 [-0.19343394] [0.16523885]
+12 [-0.1818982] [0.15538456]
+13 [-0.17105041] [0.14611795]
+14 [-0.16084954] [0.13740398]
+15 [-0.15125702] [0.12920967]
+16 [-0.14223657] [0.12150404]
+17 [-0.13375406] [0.11425795]
+18 [-0.12577742] [0.107444]
+19 [-0.11827649] [0.1010364]
+20 [-0.11122288] [0.09501094]
+21 [-0.10458992] [0.08934481]
+22 [-0.09835253] [0.08401658]
+23 [-0.09248712] [0.07900612]
+24 [-0.08697151] [0.07429446]
+25 [-0.08178482] [0.06986379]
+26 [-0.07690745] [0.06569735]
+27 [-0.07232095] [0.06177939]
+28 [-0.06800798] [0.05809507]
+29 [-0.06395221] [0.05463048]
 theta from own gd
-[[3.78598925]
- [3.17649673]]
-0 [-0.06444264] [0.05314647]
-1 [-0.06012835] [0.04958843]
-2 [-0.05480861] [0.04520119]
-3 [-0.04954337] [0.0408589]
-4 [-0.04464699] [0.0368208]
-5 [-0.04018906] [0.0331443]
-6 [-0.03616111] [0.02982242]
-7 [-0.03253183] [0.02682931]
-8 [-0.02926511] [0.02413522]
-9 [-0.02632586] [0.02171119]
-10 [-0.02368163] [0.01953047]
-11 [-0.02130293] [0.01756873]
-12 [-0.01916314] [0.01580402]
-13 [-0.01723827] [0.01421657]
-14 [-0.01550675] [0.01278856]
-15 [-0.01394915] [0.011504]
-16 [-0.01254801] [0.01034846]
-17 [-0.0112876] [0.009309]
-18 [-0.0101538] [0.00837394]
-19 [-0.00913389] [0.00753281]
-20 [-0.00821642] [0.00677616]
-21 [-0.00739111] [0.00609552]
-22 [-0.0066487] [0.00548325]
-23 [-0.00598086] [0.00493247]
-24 [-0.0053801] [0.00443702]
-25 [-0.00483969] [0.00399134]
-26 [-0.00435356] [0.00359042]
-27 [-0.00391626] [0.00322978]
-28 [-0.00352289] [0.00290536]
-29 [-0.00316902] [0.00261353]
+[[3.76990501]
+ [3.19655615]]
+0 [-0.06013832] [0.05137251]
+1 [-0.05655187] [0.04830882]
+2 [-0.05210338] [0.04450874]
+3 [-0.04766156] [0.04071437]
+4 [-0.04348664] [0.03714799]
+5 [-0.03964077] [0.0338627]
+6 [-0.03612297] [0.03085765]
+7 [-0.03291338] [0.02811589]
+8 [-0.02998766] [0.02561663]
+9 [-0.02732158] [0.02333916]
+10 [-0.02489239] [0.02126405]
+11 [-0.02267913] [0.0193734]
+12 [-0.02066265] [0.01765085]
+13 [-0.01882546] [0.01608144]
+14 [-0.01715161] [0.01465158]
+15 [-0.01562659] [0.01334885]
+16 [-0.01423717] [0.01216195]
+17 [-0.01297129] [0.01108058]
+18 [-0.01181796] [0.01009536]
+19 [-0.01076718] [0.00919775]
+20 [-0.00980983] [0.00837994]
+21 [-0.0089376] [0.00763484]
+22 [-0.00814292] [0.006956]
+23 [-0.0074189] [0.00633751]
+24 [-0.00675926] [0.00577402]
+25 [-0.00615826] [0.00526063]
+26 [-0.00561071] [0.00479289]
+27 [-0.00511184] [0.00436673]
+28 [-0.00465732] [0.00397847]
+29 [-0.00424322] [0.00362473]
 theta from own gd wth momentum
-[[3.99053294]
- [3.00780757]]
+[[3.98520854]
+ [3.01263545]]
      @@ -926,17 +929,17 @@ theta from own gd wth momentum 
      Own inversion
-[[4.1739262 ]
- [2.85239235]]
-Eigenvalues of Hessian Matrix:[0.27968902 4.49424386]
-0 [-13.16403338] [-14.97007223]
-1 [7.61543606e-16] [2.78435018e-15]
-2 [-4.23272528e-16] [-4.70203152e-16]
-3 [-4.23272528e-16] [-4.70203152e-16]
-4 [-4.23272528e-16] [-4.70203152e-16]
+[[3.96856022]
+ [2.98116672]]
+Eigenvalues of Hessian Matrix:[0.3407072  4.46918198]
+0 [-14.47784776] [-17.95282048]
+1 [2.49106291e-15] [-6.96409595e-15]
+2 [8.32667268e-17] [8.72456212e-17]
+3 [8.32667268e-17] [8.72456212e-17]
+4 [8.32667268e-17] [8.72456212e-17]
 beta from own Newton code
-[[4.1739262 ]
- [2.85239235]]
+[[3.96856022]
+ [2.98116672]]
      @@ -1025,18 +1028,18 @@ beta from own Newton code 
      Own inversion
-[[4.50455587]
- [2.69978846]]
-Eigenvalues of Hessian Matrix:[0.35172802 4.35427615]
+[[4.20603792]
+ [2.82345148]]
+Eigenvalues of Hessian Matrix:[0.26589296 4.18756348]
 theta from own gd
-[[4.50455587]
- [2.69978846]]
+[[4.20603792]
+ [2.82345148]]
      -_images/c8d1a6036144492783b21bafb5df5462fba5688ed1014961e0a3ecd4909a9caf.png +_images/5cfc76ca7e820abfb8e15070adf2fa59d6dcfb6d702ef48441625e67b326e24c.png 
      theta from own sdg
-[[4.51550231]
- [2.6728048 ]]
+[[4.20773884]
+ [2.81706996]]
      @@ -1118,15 +1121,15 @@ theta from own gd 
      Own inversion
-[[3.8301107 ]
- [3.31610195]]
-Eigenvalues of Hessian Matrix:[0.31047897 4.32701817]
+[[3.79928328]
+ [3.09772988]]
+Eigenvalues of Hessian Matrix:[0.33500183 3.88706629]
 theta from own gd
-[[3.82958153]
- [3.31655284]]
+[[3.79937084]
+ [3.09764763]]
 theta from own sdg with momentum
-[[3.82977678]
- [3.25067525]]
+[[3.77947186]
+ [3.08314421]]
      @@ -1201,9 +1204,9 @@ theta from own sdg with momentum 
      theta from own AdaGrad
-[[1.99978312]
- [3.00126504]
- [3.99880408]]
+[[2.00000205]
+ [2.99998905]
+ [4.00001059]]
      @@ -1285,9 +1288,9 @@ theta from own sdg with momentum 
      theta from own RMSprop
-[[1.99827523]
- [2.99976195]
- [3.99585312]]
+[[1.99957289]
+ [3.00371103]
+ [3.99595959]]
      @@ -1373,9 +1376,9 @@ theta from own sdg with momentum 
      theta from own ADAM
-[[2.00000058]
- [2.99998203]
- [4.00001412]]
+[[2.00001509]
+ [2.99992841]
+ [4.00006811]]
      @@ -1448,7 +1451,7 @@ It provides composable transformations of Python+NumPy programs: differentiate, return asarray(x, dtype=self.dtype) -
      [<matplotlib.lines.Line2D at 0x11d60e820>]
+
      [<matplotlib.lines.Line2D at 0x139592670>]
 
      
 
      
 _images/02e94753795fba95a52acc4488a9ab82172ba372cbb7c1cc79a3e88c3ba03d69.png
@@ -1483,7 +1486,7 @@ It provides composable transformations of Python+NumPy programs: differentiate,
      -
      <matplotlib.collections.PathCollection at 0x107a83760>
+
      <matplotlib.collections.PathCollection at 0x1394faca0>
 
      
 
      
 _images/60f34a975702165b9275820e0dda6259f453a92c5ad587b37191bb47a9c67ae9.png
diff --git a/doc/LectureNotes/_build/html/exercisesweek42.html b/doc/LectureNotes/_build/html/exercisesweek42.html
index b103c7ae7..6285c55da 100644
--- a/doc/LectureNotes/_build/html/exercisesweek42.html
+++ b/doc/LectureNotes/_build/html/exercisesweek42.html
@@ -257,6 +257,9 @@
 
    • Week 47: From Decision Trees to Ensemble Methods, Random Forests and Boosting Methods
      • 
 
    • Exercise week 47
      • 
 
+
    • Week 48: Gradient boosting  and summary of course
      • 
+
    • Exercises week 48
      • 
+

    Projects

    Projects

    Projects

    Projects

    Projects

    Projects

      @@ -573,8 +576,8 @@ matrices and vectors.

      -
      [-0.61405495 -0.32387761  1.12215626 -0.4519346   0.45982886  0.30950817
- -0.99310295  0.38292897  1.44400587 -0.28739213]
+
      [ 1.32917488  0.42655046 -1.04006029 -0.11860214 -0.68913413  0.23546487
+  0.91092242  0.83425723  0.05541192  0.19079267]
      @@ -795,26 +798,26 @@ as (recall that we user lowercase letters for vectors and uppercase letters for
      -
      [[0.56237933 0.1025416  0.22316615 0.85503611 0.99057991 0.94485713
-  0.03845685 0.74210311 0.91828517 0.29234676]
- [0.20570617 0.55663962 0.14395076 0.63318471 0.88506797 0.79793482
-  0.21572768 0.70817694 0.98132273 0.47451039]
- [0.10238488 0.08006058 0.26302678 0.0532921  0.89460147 0.11961481
-  0.90199101 0.17545974 0.89611233 0.76803496]
- [0.02942421 0.99685226 0.12021078 0.72362232 0.02251816 0.67824444
-  0.70638884 0.32589372 0.39505738 0.5378907 ]
- [0.88579143 0.45698195 0.61143771 0.59118363 0.92409815 0.68270325
-  0.58619632 0.77509651 0.93481453 0.87482389]
- [0.40760778 0.62136996 0.7761515  0.76287014 0.81606967 0.17622628
-  0.65211459 0.55721129 0.20955256 0.20757376]
- [0.59910949 0.22876398 0.59992922 0.0586431  0.71238022 0.59293896
-  0.58318087 0.50099904 0.15835637 0.1221625 ]
- [0.3510257  0.5364078  0.15260744 0.21019486 0.53797608 0.92789992
-  0.91110946 0.33914178 0.20822698 0.62281444]
- [0.88111434 0.05117772 0.30607722 0.34939146 0.30612395 0.06384183
-  0.23829156 0.11534663 0.44474604 0.49985968]
- [0.17578147 0.63422131 0.86986521 0.53418362 0.30679815 0.33175971
-  0.83105371 0.97275788 0.37250759 0.05652557]]
+
      [[0.76374594 0.87699656 0.93752045 0.58920669 0.11762693 0.41835272
+  0.14130145 0.22405983 0.63569519 0.57023469]
+ [0.51205632 0.0815808  0.83997706 0.62319185 0.9918084  0.82551589
+  0.23523437 0.5917984  0.26977294 0.22313112]
+ [0.78875244 0.33827328 0.15734346 0.76351169 0.88571475 0.8744876
+  0.8179527  0.96419308 0.28859292 0.21034124]
+ [0.56604693 0.0014122  0.69927222 0.76825307 0.81056827 0.18710226
+  0.96298663 0.51905411 0.75661166 0.19343497]
+ [0.80954821 0.27400301 0.19608145 0.90678518 0.52612126 0.57201534
+  0.9065678  0.72410901 0.77533887 0.73086608]
+ [0.0583118  0.57305201 0.99949947 0.6534883  0.87204797 0.81128625
+  0.39326284 0.67711227 0.61020809 0.31082354]
+ [0.13856233 0.04308921 0.87608062 0.27598375 0.37282031 0.5906656
+  0.05406637 0.09201716 0.39585666 0.16639711]
+ [0.93928366 0.33845772 0.1675106  0.86044604 0.2131759  0.13239107
+  0.06461985 0.25020484 0.93644206 0.93511011]
+ [0.52539786 0.72253483 0.80209029 0.8317496  0.06071723 0.60162183
+  0.41902867 0.01471716 0.75263199 0.47058817]
+ [0.61513099 0.99809525 0.36655653 0.36519275 0.38100512 0.84195998
+  0.06668051 0.41250502 0.54002942 0.66316435]]
      @@ -874,13 +877,13 @@ covariance matrix through the np.linalg.eig() function.

      -
      -0.07252404965482757
-3.80181024148491
--0.6317631821295671
-[[ 0.91103528  2.76447289  2.63577152]
- [ 2.76447289  9.56058946  7.79271587]
- [ 2.63577152  7.79271587 15.61196303]]
-[21.62109277  0.08978688  4.37270812]
+
      0.10396409632756674
+4.152389775605299
+0.1319830915583838
+[[ 1.1503634   3.6073857   3.74515461]
+ [ 3.6073857  12.39943598 12.04015097]
+ [ 3.74515461 12.04015097 18.79739603]]
+[29.01518302  0.08664758  3.24536482]
      diff --git a/doc/LectureNotes/_build/html/objects.inv b/doc/LectureNotes/_build/html/objects.inv index b9ea58210..6606f2a25 100644 Binary files a/doc/LectureNotes/_build/html/objects.inv and b/doc/LectureNotes/_build/html/objects.inv differ diff --git a/doc/LectureNotes/_build/html/project1.html b/doc/LectureNotes/_build/html/project1.html index 90bf3ae34..e47b4b7c8 100644 --- a/doc/LectureNotes/_build/html/project1.html +++ b/doc/LectureNotes/_build/html/project1.html @@ -66,7 +66,7 @@ - + @@ -257,6 +257,9 @@
    • Week 47: From Decision Trees to Ensemble Methods, Random Forests and Boosting Methods
    • Exercise week 47
      • +
    • Week 48: Gradient boosting and summary of course
      • +
    • Exercises week 48
      • +

    Projects

    Projects

    Projects

    Projects

    Projects

      diff --git a/doc/LectureNotes/_build/html/searchindex.js b/doc/LectureNotes/_build/html/searchindex.js index 60d7b5686..123007877 100644 --- a/doc/LectureNotes/_build/html/searchindex.js +++ b/doc/LectureNotes/_build/html/searchindex.js @@ -1 +1 @@ -Search.setIndex({"alltitles": {"3D volumes of neurons": [[45, "d-volumes-of-neurons"], [46, "d-volumes-of-neurons"]], "A Classification Tree": [[10, "a-classification-tree"], [47, "a-classification-tree"], [48, "a-classification-tree"]], "A Frequentist approach to data analysis": [[1, "a-frequentist-approach-to-data-analysis"], [35, "a-frequentist-approach-to-data-analysis"]], "A better approach": [[9, "a-better-approach"]], "A deep CNN model (From Raschka et al)": [[45, "a-deep-cnn-model-from-raschka-et-al"], [46, "a-deep-cnn-model-from-raschka-et-al"]], "A first summary": [[35, "a-first-summary"]], "A more advanced example": [[22, "a-more-advanced-example"], [41, "a-more-advanced-example"]], "A more compact expression": [[0, "a-more-compact-expression"], [39, "a-more-compact-expression"]], "A more efficient way of coding the above Convolution": [[45, "a-more-efficient-way-of-coding-the-above-convolution"]], "A new Cost Function": [[37, "a-new-cost-function"], [38, "a-new-cost-function"]], "A possible code using Scikit-Learn": [[47, "a-possible-code-using-scikit-learn"], [48, "a-possible-code-using-scikit-learn"]], "A possible implementation of a neural network": [[44, "a-possible-implementation-of-a-neural-network"]], "A quick Reminder on Lagrangian Multipliers": [[9, "a-quick-reminder-on-lagrangian-multipliers"]], "A simple example": [[5, "a-simple-example"], [46, "a-simple-example"]], "A soft classifier": [[9, "a-soft-classifier"]], "A top-down approach, recursive binary splitting": [[47, "a-top-down-approach-recursive-binary-splitting"], [48, "a-top-down-approach-recursive-binary-splitting"]], "A top-down perspective on Neural networks": [[2, "a-top-down-perspective-on-neural-networks"], [42, "a-top-down-perspective-on-neural-networks"], [43, "a-top-down-perspective-on-neural-networks"]], "A typical Decision Tree with its pertinent Jargon, Classification Problem": [[47, "a-typical-decision-tree-with-its-pertinent-jargon-classification-problem"]], "A warm-up example": [[22, "a-warm-up-example"], [41, "a-warm-up-example"]], "A way to Read the Bias-Variance Tradeoff": [[38, "a-way-to-read-the-bias-variance-tradeoff"]], "ADAM algorithm, taken from Goodfellow et al": [[22, "adam-algorithm-taken-from-goodfellow-et-al"], [41, "adam-algorithm-taken-from-goodfellow-et-al"]], "ADAM optimizer": [[14, "adam-optimizer"], [40, "adam-optimizer"], [41, "adam-optimizer"]], "Activation functions": [[13, "activation-functions"], [41, "activation-functions"], [42, "activation-functions"], [43, "activation-functions"], [43, "id3"], [44, "activation-functions"], [44, "id3"], [45, "activation-functions"], [46, "activation-functions"]], "Activation functions, Logistic and Hyperbolic ones": [[41, "activation-functions-logistic-and-hyperbolic-ones"], [42, "activation-functions-logistic-and-hyperbolic-ones"], [43, "activation-functions-logistic-and-hyperbolic-ones"]], "Activation functions, examples": [[44, "activation-functions-examples"]], "AdaBoost Examples": [[47, "adaboost-examples"], [48, "adaboost-examples"]], "AdaGrad algorithm, taken from Goodfellow et al": [[22, "adagrad-algorithm-taken-from-goodfellow-et-al"], [41, "adagrad-algorithm-taken-from-goodfellow-et-al"]], "Adaptive Boosting, AdaBoost": [[47, "adaptive-boosting-adaboost"], [48, "adaptive-boosting-adaboost"]], "Adaptive boosting: AdaBoost, Basic Algorithm": [[11, "adaptive-boosting-adaboost-basic-algorithm"], [47, "adaptive-boosting-adaboost-basic-algorithm"], [48, "adaptive-boosting-adaboost-basic-algorithm"]], "Add Dense layers on top": [[45, "add-dense-layers-on-top"], [46, "add-dense-layers-on-top"]], "Adding Neural Networks": [[41, "adding-neural-networks"], [42, "adding-neural-networks"]], "Adding a hidden layer": [[42, "adding-a-hidden-layer"], [43, "adding-a-hidden-layer"]], "Adding error analysis and training set up": [[35, "adding-error-analysis-and-training-set-up"], [36, "adding-error-analysis-and-training-set-up"]], "Additional Remarks": [[45, "additional-remarks"], [46, "additional-remarks"]], "Adjust hyperparameters": [[2, "adjust-hyperparameters"], [43, "adjust-hyperparameters"], [44, "adjust-hyperparameters"]], "Algorithms and codes for Adagrad, RMSprop and Adam": [[22, "algorithms-and-codes-for-adagrad-rmsprop-and-adam"], [40, "algorithms-and-codes-for-adagrad-rmsprop-and-adam"], [41, "algorithms-and-codes-for-adagrad-rmsprop-and-adam"]], "Algorithms for Setting up Decision Trees": [[10, "algorithms-for-setting-up-decision-trees"], [47, "algorithms-for-setting-up-decision-trees"], [48, "algorithms-for-setting-up-decision-trees"]], "Alternative differential equations": [[30, "alternative-differential-equations"]], "An Overview of Ensemble Methods": [[11, "an-overview-of-ensemble-methods"], [47, "an-overview-of-ensemble-methods"], [48, "an-overview-of-ensemble-methods"]], "An extrapolation example": [[5, "an-extrapolation-example"]], "An optimization/minimization problem": [[35, "an-optimization-minimization-problem"]], "Analyzing the last results": [[42, "analyzing-the-last-results"], [43, "analyzing-the-last-results"]], "And with loops": [[40, "and-with-loops"], [41, "and-with-loops"]], "And Logistic Regression": [[40, "and-logistic-regression"], [41, "and-logistic-regression"]], "And a corresponding example using scikit-learn": [[39, "and-a-corresponding-example-using-scikit-learn"], [40, "and-a-corresponding-example-using-scikit-learn"]], "And finally \\boldsymbol{X}\\boldsymbol{X}^T": [[36, "and-finally-boldsymbol-x-boldsymbol-x-t"], [37, "and-finally-boldsymbol-x-boldsymbol-x-t"]], "And finally ADAM": [[22, "and-finally-adam"], [40, "and-finally-adam"], [41, "and-finally-adam"]], "And what about using neural networks?": [[35, "and-what-about-using-neural-networks"]], "Another Example from Scikit-Learn\u2019s Repository": [[38, "another-example-from-scikit-learn-s-repository"]], "Another Example, now with a polynomial fit": [[37, "another-example-now-with-a-polynomial-fit"]], "Another example, the moons again": [[10, "another-example-the-moons-again"]], "Applied Data Analysis and Machine Learning": [[26, null]], "Artificial neurons": [[41, "artificial-neurons"], [42, "artificial-neurons"]], "Assumptions made": [[37, "assumptions-made"], [38, "assumptions-made"]], "Autocorrelation function": [[32, "autocorrelation-function"]], "Autograd with more complicated functions": [[40, "autograd-with-more-complicated-functions"], [41, "autograd-with-more-complicated-functions"]], "Automatic differentiation": [[14, "automatic-differentiation"], [40, "automatic-differentiation"], [41, "automatic-differentiation"], [42, "automatic-differentiation"]], "Automatic differentiation through examples": [[42, "automatic-differentiation-through-examples"]], "Back propagation": [[44, "back-propagation"]], "Back propagation and automatic differentiation": [[44, "back-propagation-and-automatic-differentiation"]], "Back propagation in time in equations": [[46, "back-propagation-in-time-in-equations"]], "Back propagation in time through figures, part 1": [[46, "back-propagation-in-time-through-figures-part-1"]], "Back propagation in time, part 2": [[46, "back-propagation-in-time-part-2"]], "Back propagation in time, part 3": [[46, "back-propagation-in-time-part-3"]], "Back propagation in time, part 4": [[46, "back-propagation-in-time-part-4"]], "Back to the Cancer Data": [[12, "back-to-the-cancer-data"]], "Background literature": [[28, "background-literature"], [29, "background-literature"]], "Backpropagation in the convolutional layer": [[45, "backpropagation-in-the-convolutional-layer"], [46, "backpropagation-in-the-convolutional-layer"]], "Backpropagation through time": [[46, "backpropagation-through-time"]], "Bagging": [[11, "bagging"], [47, "bagging"], [48, "bagging"]], "Bagging Examples": [[11, "bagging-examples"]], "Basic Matrix Features": [[27, "basic-matrix-features"]], "Basic Steps of AdaBoost": [[47, "basic-steps-of-adaboost"], [48, "basic-steps-of-adaboost"]], "Basic ideas of the Principal Component Analysis (PCA)": [[12, null]], "Basic layout": [[46, "basic-layout"]], "Basic layout, Figures from Sebastian Rashcka et al, Machine learning with Sickit-Learn and PyTorch": [[46, "basic-layout-figures-from-sebastian-rashcka-et-al-machine-learning-with-sickit-learn-and-pytorch"]], "Basic math of the SVD": [[6, "basic-math-of-the-svd"], [36, "basic-math-of-the-svd"]], "Basics": [[8, "basics"], [39, "basics"]], "Basics of a tree": [[10, "basics-of-a-tree"], [47, "basics-of-a-tree"]], "Basics of an NN": [[42, "basics-of-an-nn"]], "Batch Normalization": [[2, "batch-normalization"], [42, "batch-normalization"], [43, "batch-normalization"]], "Batches and mini-batches": [[40, "batches-and-mini-batches"], [41, "batches-and-mini-batches"]], "Bayes\u2019 Theorem": [[37, "bayes-theorem"], [38, "bayes-theorem"]], "Bayes\u2019 Theorem and Ridge and Lasso Regression": [[6, "bayes-theorem-and-ridge-and-lasso-regression"], [37, "bayes-theorem-and-ridge-and-lasso-regression"], [38, "bayes-theorem-and-ridge-and-lasso-regression"]], "Boosting, a Bird\u2019s Eye View": [[11, "boosting-a-bird-s-eye-view"], [47, "boosting-a-bird-s-eye-view"], [48, "boosting-a-bird-s-eye-view"]], "Bootstrap": [[7, "bootstrap"]], "Brief reminder on Newton-Raphson\u2019s method": [[39, "brief-reminder-on-newton-raphson-s-method"], [40, "brief-reminder-on-newton-raphson-s-method"]], "Bringing it together": [[42, "bringing-it-together"], [43, "bringing-it-together"]], "Bringing it together, first back propagation equation": [[13, "bringing-it-together-first-back-propagation-equation"]], "Building a Feed Forward Neural Network": [[2, null]], "Building a neural network code": [[43, "building-a-neural-network-code"], [44, "building-a-neural-network-code"]], "Building a tree, regression": [[10, "building-a-tree-regression"], [47, "building-a-tree-regression"], [48, "building-a-tree-regression"]], "Building convolutional neural networks in Tensorflow and Keras": [[45, "building-convolutional-neural-networks-in-tensorflow-and-keras"], [46, "building-convolutional-neural-networks-in-tensorflow-and-keras"]], "Building neural networks in Tensorflow and Keras": [[2, "building-neural-networks-in-tensorflow-and-keras"], [43, "building-neural-networks-in-tensorflow-and-keras"], [44, "building-neural-networks-in-tensorflow-and-keras"]], "Building our own CNN code": [[45, "building-our-own-cnn-code"], [46, "building-our-own-cnn-code"]], "Building up AdaBoost": [[47, "building-up-adaboost"], [48, "building-up-adaboost"]], "But noen of these can compete with Newton\u2019s method": [[22, "but-noen-of-these-can-compete-with-newton-s-method"]], "But none of these can compete with Newton\u2019s method": [[40, "but-none-of-these-can-compete-with-newton-s-method"], [41, "but-none-of-these-can-compete-with-newton-s-method"]], "CNNs in brief": [[45, "cnns-in-brief"], [46, "cnns-in-brief"]], "CNNs in more detail, building convolutional neural networks in Tensorflow and Keras": [[4, "cnns-in-more-detail-building-convolutional-neural-networks-in-tensorflow-and-keras"]], "CNNs in more detail, simple example": [[45, "cnns-in-more-detail-simple-example"]], "Cancer Data again now with Decision Trees and other Methods": [[10, "cancer-data-again-now-with-decision-trees-and-other-methods"]], "Chain rule": [[42, "chain-rule"]], "Chain rule again": [[46, "chain-rule-again"]], "Chain rule, forward and reverse modes": [[42, "chain-rule-forward-and-reverse-modes"]], "Challenge yourself the coming weekend": [[39, "challenge-yourself-the-coming-weekend"]], "Choose cost function and optimizer": [[2, "choose-cost-function-and-optimizer"], [43, "choose-cost-function-and-optimizer"], [44, "choose-cost-function-and-optimizer"]], "Class of functions we can approximate": [[42, "class-of-functions-we-can-approximate"]], "Classical PCA Theorem": [[12, "classical-pca-theorem"]], "Classification and Regression, from linear and logistic regression to neural networks": [[29, "classification-and-regression-from-linear-and-logistic-regression-to-neural-networks"]], "Classification problems": [[39, "classification-problems"]], "Classification tree, how to split nodes": [[47, "classification-tree-how-to-split-nodes"], [48, "classification-tree-how-to-split-nodes"]], "Clustering and Unsupervised Learning": [[15, null]], "Code Example for Cross-validation and k-fold Cross-validation": [[38, "code-example-for-cross-validation-and-k-fold-cross-validation"], [39, "code-example-for-cross-validation-and-k-fold-cross-validation"]], "Code example": [[42, "code-example"], [43, "code-example"]], "Code example for the Bootstrap method": [[38, "code-example-for-the-bootstrap-method"]], "Code examples from week 39 and 40": [[22, "code-examples-from-week-39-and-40"]], "Code for SVD and Inversion of Matrices": [[6, "code-for-svd-and-inversion-of-matrices"], [37, "code-for-svd-and-inversion-of-matrices"]], "Code with a Number of Minibatches which varies": [[40, "code-with-a-number-of-minibatches-which-varies"], [41, "code-with-a-number-of-minibatches-which-varies"]], "Code with a Number of Minibatches which varies, analytical gradient": [[22, "code-with-a-number-of-minibatches-which-varies-analytical-gradient"]], "Codes and Approaches": [[15, "codes-and-approaches"]], "Codes for the SVD": [[6, "codes-for-the-svd"], [36, "codes-for-the-svd"]], "Collect and pre-process data": [[2, "collect-and-pre-process-data"], [43, "collect-and-pre-process-data"], [43, "id2"], [44, "collect-and-pre-process-data"], [44, "id2"]], "Communication channels": [[35, "communication-channels"]], "Commutative process": [[45, "commutative-process"]], "Compact expressions": [[42, "compact-expressions"], [43, "compact-expressions"]], "Compare Bagging on Trees with Random Forests": [[11, "compare-bagging-on-trees-with-random-forests"], [47, "compare-bagging-on-trees-with-random-forests"], [48, "compare-bagging-on-trees-with-random-forests"]], "Comparing with a numerical scheme": [[3, "comparing-with-a-numerical-scheme"], [44, "comparing-with-a-numerical-scheme"]], "Comparison with OLS": [[37, "comparison-with-ols"]], "Compile and train the model": [[45, "compile-and-train-the-model"], [46, "compile-and-train-the-model"]], "Completing the list": [[42, "completing-the-list"], [43, "completing-the-list"]], "Computation of gradients": [[40, "computation-of-gradients"], [41, "computation-of-gradients"]], "Computing a Tree using the Gini Index": [[47, "computing-a-tree-using-the-gini-index"], [48, "computing-a-tree-using-the-gini-index"]], "Computing the Gini Factor": [[47, "computing-the-gini-factor"], [48, "computing-the-gini-factor"]], "Computing the Gini index": [[10, "computing-the-gini-index"]], "Computing the various Gini Indices": [[47, "computing-the-various-gini-indices"], [48, "computing-the-various-gini-indices"]], "Computing the various Gini Indices, Hours slept": [[47, "computing-the-various-gini-indices-hours-slept"]], "Computing the various Gini Indices, Hours studied": [[47, "computing-the-various-gini-indices-hours-studied"]], "Conditional Probability": [[37, "conditional-probability"], [38, "conditional-probability"]], "Conditions on convex functions": [[39, "conditions-on-convex-functions"], [40, "conditions-on-convex-functions"]], "Confidence Intervals": [[38, "confidence-intervals"]], "Conjugate gradient method": [[14, "conjugate-gradient-method"], [40, "conjugate-gradient-method"], [40, "id2"], [40, "id3"], [40, "id4"], [40, "id5"], [40, "id6"], [40, "id7"]], "Conjugate gradient method and iterations": [[40, "conjugate-gradient-method-and-iterations"]], "Convex function": [[39, "convex-function"], [40, "convex-function"]], "Convex functions": [[14, "convex-functions"], [39, "convex-functions"], [40, "convex-functions"]], "Convolution": [[45, "convolution"], [46, "convolution"]], "Convolution Examples: Polynomial multiplication": [[4, "convolution-examples-polynomial-multiplication"], [45, "convolution-examples-polynomial-multiplication"]], "Convolution Examples: Principle of Superposition and Periodic Forces (Fourier Transforms)": [[4, "convolution-examples-principle-of-superposition-and-periodic-forces-fourier-transforms"]], "Convolution in the Fourier domain": [[45, "convolution-in-the-fourier-domain"], [46, "convolution-in-the-fourier-domain"]], "Convolution using separable kernels": [[45, "convolution-using-separable-kernels"], [46, "convolution-using-separable-kernels"]], "Convolution2DLayer: convolution in a hidden layer": [[45, "convolution2dlayer-convolution-in-a-hidden-layer"], [46, "convolution2dlayer-convolution-in-a-hidden-layer"]], "Convolutional Neural Network": [[13, "convolutional-neural-network"], [41, "convolutional-neural-network"], [42, "convolutional-neural-network"]], "Convolutional Neural Networks": [[4, null]], "Convolutional Neural Networks (recognizing images)": [[45, "convolutional-neural-networks-recognizing-images"], [46, "convolutional-neural-networks-recognizing-images"]], "Correlation Function and Design/Feature Matrix": [[36, "correlation-function-and-design-feature-matrix"]], "Correlation Matrix": [[12, "correlation-matrix"], [36, "correlation-matrix"]], "Correlation Matrix with Pandas": [[36, "correlation-matrix-with-pandas"]], "Correlation Matrix with Pandas and the Franke function": [[36, "correlation-matrix-with-pandas-and-the-franke-function"]], "Cost complexity pruning": [[47, "cost-complexity-pruning"], [48, "cost-complexity-pruning"]], "Cost functions": [[43, "cost-functions"], [44, "cost-functions"], [45, "cost-functions"], [46, "cost-functions"]], "Counting the number of floating point operations": [[42, "counting-the-number-of-floating-point-operations"]], "Course Format": [[35, "course-format"]], "Covariance Matrix Examples": [[36, "covariance-matrix-examples"]], "Covariance and Correlation Matrix": [[36, "covariance-and-correlation-matrix"]], "Cross correlation": [[45, "cross-correlation"]], "Cross-validation": [[7, "cross-validation"]], "Cross-validation in brief": [[38, "cross-validation-in-brief"], [39, "cross-validation-in-brief"]], "Deadlines for projects (tentative)": [[35, "deadlines-for-projects-tentative"]], "Decision trees and Regression": [[47, "decision-trees-and-regression"]], "Decision trees, overarching aims": [[10, null], [47, "decision-trees-overarching-aims"]], "Deep learning methods": [[35, "deep-learning-methods"]], "Define model and architecture": [[2, "define-model-and-architecture"], [43, "define-model-and-architecture"], [44, "define-model-and-architecture"]], "Defining intermediate operations": [[42, "defining-intermediate-operations"]], "Defining the cost function": [[2, "defining-the-cost-function"], [43, "defining-the-cost-function"], [44, "defining-the-cost-function"]], "Defining the data sets to analyze yourself": [[30, "defining-the-data-sets-to-analyze-yourself"]], "Defining the problem": [[44, "defining-the-problem"]], "Definitions": [[42, "definitions"], [43, "definitions"]], "Demonstration": [[45, "demonstration"], [46, "demonstration"]], "Derivative of the cost function": [[42, "derivative-of-the-cost-function"], [43, "derivative-of-the-cost-function"]], "Derivatives and the chain rule": [[13, "derivatives-and-the-chain-rule"], [42, "derivatives-and-the-chain-rule"], [43, "derivatives-and-the-chain-rule"]], "Derivatives in terms of z_j^L": [[42, "derivatives-in-terms-of-z-j-l"], [43, "derivatives-in-terms-of-z-j-l"]], "Derivatives of the hidden layer": [[42, "derivatives-of-the-hidden-layer"], [43, "derivatives-of-the-hidden-layer"]], "Derivatives, example 1": [[36, "derivatives-example-1"]], "Deriving OLS from a probability distribution": [[6, "deriving-ols-from-a-probability-distribution"], [37, "deriving-ols-from-a-probability-distribution"], [38, "deriving-ols-from-a-probability-distribution"]], "Deriving the Lasso Regression Equations": [[36, "deriving-the-lasso-regression-equations"], [37, "deriving-the-lasso-regression-equations"]], "Deriving the Ridge Regression Equations": [[36, "deriving-the-ridge-regression-equations"], [37, "deriving-the-ridge-regression-equations"]], "Deriving the back propagation code for a multilayer perceptron model": [[13, "deriving-the-back-propagation-code-for-a-multilayer-perceptron-model"]], "Description of two-dimensional function": [[28, "description-of-two-dimensional-function"]], "Developing a code for doing neural networks with back propagation": [[2, "developing-a-code-for-doing-neural-networks-with-back-propagation"], [43, "developing-a-code-for-doing-neural-networks-with-back-propagation"], [44, "developing-a-code-for-doing-neural-networks-with-back-propagation"]], "Diagonalize the sample covariance matrix to obtain the principal components": [[12, "diagonalize-the-sample-covariance-matrix-to-obtain-the-principal-components"]], "Different kernels and Mercer\u2019s theorem": [[9, "different-kernels-and-mercer-s-theorem"]], "Differential equations": [[46, "differential-equations"]], "Disadvantages": [[10, "disadvantages"], [47, "disadvantages"], [48, "disadvantages"]], "Discussing the correlation data": [[0, "discussing-the-correlation-data"], [39, "discussing-the-correlation-data"]], "Does Logistic Regression do a better Job?": [[41, "does-logistic-regression-do-a-better-job"], [42, "does-logistic-regression-do-a-better-job"]], "Doing it correctly": [[37, "doing-it-correctly"], [38, "doing-it-correctly"]], "Domains and probabilities": [[32, "domains-and-probabilities"]], "Dropout": [[2, "dropout"], [42, "dropout"], [43, "dropout"]], "ELU function": [[42, "elu-function"], [43, "elu-function"], [44, "elu-function"]], "Economy-size SVD": [[36, "economy-size-svd"]], "Efficient Polynomial Multiplication": [[45, "efficient-polynomial-multiplication"]], "Elements of Probability Theory and Statistical Data Analysis": [[32, null]], "Ensemble Methods: From a Single Tree to Many Trees and Extreme Boosting, Meet the Jungle of Methods": [[11, null], [47, "ensemble-methods-from-a-single-tree-to-many-trees-and-extreme-boosting-meet-the-jungle-of-methods"], [48, "ensemble-methods-from-a-single-tree-to-many-trees-and-extreme-boosting-meet-the-jungle-of-methods"]], "Entropy and the ID3 algorithm": [[10, "entropy-and-the-id3-algorithm"]], "Essential elements of ML": [[35, "essential-elements-of-ml"]], "Evaluate model performance on test data": [[2, "evaluate-model-performance-on-test-data"], [43, "evaluate-model-performance-on-test-data"], [44, "evaluate-model-performance-on-test-data"]], "Example 2": [[36, "example-2"]], "Example 3": [[36, "example-3"]], "Example 4": [[36, "example-4"]], "Example Matrix": [[36, "example-matrix"]], "Example code for Bias-Variance tradeoff": [[38, "example-code-for-bias-variance-tradeoff"]], "Example of Usage of Bayes\u2019 theorem": [[37, "example-of-usage-of-bayes-theorem"], [38, "example-of-usage-of-bayes-theorem"]], "Example of own Standard scaling": [[36, "example-of-own-standard-scaling"]], "Example relevant for the exercises": [[36, "example-relevant-for-the-exercises"]], "Example: Exponential decay": [[3, "example-exponential-decay"], [44, "example-exponential-decay"]], "Example: Population growth": [[3, "example-population-growth"], [44, "example-population-growth"]], "Example: Solving the one dimensional Poisson equation": [[44, "example-solving-the-one-dimensional-poisson-equation"]], "Example: Solving the wave equation with Neural Networks": [[44, "example-solving-the-wave-equation-with-neural-networks"]], "Example: The diffusion equation": [[3, "example-the-diffusion-equation"], [44, "example-the-diffusion-equation"]], "Example: binary classification problem": [[2, "example-binary-classification-problem"], [43, "example-binary-classification-problem"], [44, "example-binary-classification-problem"]], "Examples": [[35, "examples"]], "Examples of CNN setups": [[45, "examples-of-cnn-setups"]], "Examples of XOR, OR and AND gates": [[41, "examples-of-xor-or-and-and-gates"], [42, "examples-of-xor-or-and-and-gates"]], "Examples of likelihood functions used in logistic regression and neural networks": [[8, "examples-of-likelihood-functions-used-in-logistic-regression-and-neural-networks"]], "Examples of likelihood functions used in logistic regression and nueral networks": [[0, "examples-of-likelihood-functions-used-in-logistic-regression-and-nueral-networks"], [39, "examples-of-likelihood-functions-used-in-logistic-regression-and-nueral-networks"]], "Exercise 1": [[23, "exercise-1"]], "Exercise 1 - Understand the feed forward pass": [[24, "exercise-1-understand-the-feed-forward-pass"]], "Exercise 1: Analytical exercises": [[17, "exercise-1-analytical-exercises"], [18, "exercise-1-analytical-exercises"]], "Exercise 1: Expectation values for ordinary least squares expressions": [[19, "exercise-1-expectation-values-for-ordinary-least-squares-expressions"]], "Exercise 1: Including more data": [[42, "exercise-1-including-more-data"]], "Exercise 1: Linear and logistic regression methods": [[25, "exercise-1-linear-and-logistic-regression-methods"]], "Exercise 1: Setting up various Python environments": [[1, "exercise-1-setting-up-various-python-environments"], [16, "exercise-1-setting-up-various-python-environments"], [35, "exercise-1-setting-up-various-python-environments"]], "Exercise 2": [[23, "exercise-2"]], "Exercise 2 - Gradient with one layer using autograd": [[24, "exercise-2-gradient-with-one-layer-using-autograd"]], "Exercise 2: Adding Ridge Regression": [[18, "exercise-2-adding-ridge-regression"]], "Exercise 2: Deep learning": [[25, "exercise-2-deep-learning"]], "Exercise 2: Expectation values for Ridge regression": [[19, "exercise-2-expectation-values-for-ridge-regression"]], "Exercise 2: Extended program": [[42, "exercise-2-extended-program"]], "Exercise 2: making your own data and exploring scikit-learn": [[1, "exercise-2-making-your-own-data-and-exploring-scikit-learn"], [16, "exercise-2-making-your-own-data-and-exploring-scikit-learn"], [17, "exercise-2-making-your-own-data-and-exploring-scikit-learn"], [35, "exercise-2-making-your-own-data-and-exploring-scikit-learn"]], "Exercise 3": [[23, "exercise-3"]], "Exercise 3 - Gradient with one layer writing backpropagation by hand": [[24, "exercise-3-gradient-with-one-layer-writing-backpropagation-by-hand"]], "Exercise 3: Decision trees and ensemble methods": [[25, "exercise-3-decision-trees-and-ensemble-methods"]], "Exercise 3: Normalizing our data": [[1, "exercise-3-normalizing-our-data"]], "Exercise 3: Split data in test and training data": [[16, "exercise-3-split-data-in-test-and-training-data"], [17, "exercise-3-split-data-in-test-and-training-data"], [35, "exercise-3-split-data-in-test-and-training-data"]], "Exercise 4 - Custom activation for each layer": [[23, "exercise-4-custom-activation-for-each-layer"]], "Exercise 4 - Gradient with two layers writing backpropagation by hand": [[24, "exercise-4-gradient-with-two-layers-writing-backpropagation-by-hand"]], "Exercise 4: Adding Ridge Regression": [[1, "exercise-4-adding-ridge-regression"]], "Exercise 4: Optimization part": [[25, "exercise-4-optimization-part"]], "Exercise 5 - Gradient with any number of layers writing backpropagation by hand": [[24, "exercise-5-gradient-with-any-number-of-layers-writing-backpropagation-by-hand"]], "Exercise 5 - Processing multiple inputs at once": [[23, "exercise-5-processing-multiple-inputs-at-once"]], "Exercise 5: Analysis of results": [[25, "exercise-5-analysis-of-results"]], "Exercise 5: Analytical exercises": [[1, "exercise-5-analytical-exercises"]], "Exercise 6 - Batched inputs": [[24, "exercise-6-batched-inputs"]], "Exercise 6 - Predicting on real data": [[23, "exercise-6-predicting-on-real-data"]], "Exercise 7 - Training": [[24, "exercise-7-training"]], "Exercise 7 - Training on real data (Optional)": [[23, "exercise-7-training-on-real-data-optional"]], "Exercise 8 (Optional) - Object orientation": [[24, "exercise-8-optional-object-orientation"]], "Exercise week 47": [[25, null]], "Exercise: Cross-validation as resampling techniques, adding more complexity": [[7, "exercise-cross-validation-as-resampling-techniques-adding-more-complexity"]], "Exercise: Analysis of real data": [[7, "exercise-analysis-of-real-data"]], "Exercise: Bias-variance trade-off and resampling techniques": [[7, "exercise-bias-variance-trade-off-and-resampling-techniques"]], "Exercise: Lasso Regression on the Franke function with resampling": [[7, "exercise-lasso-regression-on-the-franke-function-with-resampling"]], "Exercise: Ordinary Least Square (OLS) on the Franke function": [[7, "exercise-ordinary-least-square-ols-on-the-franke-function"]], "Exercise: Ridge Regression on the Franke function with resampling": [[7, "exercise-ridge-regression-on-the-franke-function-with-resampling"]], "Exercises": [[1, "exercises"], [16, "exercises"], [35, "exercises"]], "Exercises Week 42: Logistic Regression and Optimization, reminders from week 38 and week 40": [[0, null]], "Exercises and Projects": [[7, "exercises-and-projects"]], "Exercises and lab session week 43": [[44, "exercises-and-lab-session-week-43"]], "Exercises week 34": [[16, null]], "Exercises week 35": [[17, null]], "Exercises week 36": [[18, null]], "Exercises week 37": [[19, null]], "Exercises week 38": [[20, null]], "Exercises week 39": [[21, null]], "Exercises week 41": [[22, null]], "Exercises week 42": [[23, null]], "Exercises week 43": [[24, null]], "Expectation value and variance": [[37, "expectation-value-and-variance"], [38, "expectation-value-and-variance"]], "Expectation value and variance for \\boldsymbol{\\beta}": [[37, "expectation-value-and-variance-for-boldsymbol-beta"], [38, "expectation-value-and-variance-for-boldsymbol-beta"]], "Expectation values": [[32, "expectation-values"]], "Explicit derivatives": [[42, "explicit-derivatives"], [43, "explicit-derivatives"]], "Exploding gradients": [[42, "exploding-gradients"], [43, "exploding-gradients"]], "Extending to more predictors": [[0, "extending-to-more-predictors"], [39, "extending-to-more-predictors"]], "Extending to more than one variable": [[39, "extending-to-more-than-one-variable"], [40, "extending-to-more-than-one-variable"]], "Extremely useful tools, strongly recommended": [[35, "extremely-useful-tools-strongly-recommended"]], "Feed-forward neural networks": [[13, "feed-forward-neural-networks"], [41, "feed-forward-neural-networks"], [42, "feed-forward-neural-networks"]], "Feed-forward pass": [[2, "feed-forward-pass"], [43, "feed-forward-pass"], [44, "feed-forward-pass"]], "Feedback connections": [[46, "feedback-connections"]], "Final back propagating equation": [[13, "final-back-propagating-equation"], [42, "final-back-propagating-equation"], [43, "final-back-propagating-equation"]], "Final derivatives": [[42, "final-derivatives"]], "Final expression": [[42, "final-expression"], [43, "final-expression"]], "Final expressions": [[40, "final-expressions"]], "Final expressions for the biases of the hidden layer": [[42, "final-expressions-for-the-biases-of-the-hidden-layer"], [43, "final-expressions-for-the-biases-of-the-hidden-layer"]], "Final part": [[45, "final-part"], [46, "final-part"]], "Final regressor code": [[47, "final-regressor-code"], [48, "final-regressor-code"]], "Final technicalities I": [[44, "final-technicalities-i"]], "Final technicalities II": [[44, "final-technicalities-ii"]], "Final technicalities III": [[44, "final-technicalities-iii"]], "Final technicalities IV": [[44, "final-technicalities-iv"]], "Final visualization": [[45, "final-visualization"], [46, "final-visualization"]], "Finally, evaluate the model": [[45, "finally-evaluate-the-model"], [46, "finally-evaluate-the-model"]], "Finding the Limit": [[38, "finding-the-limit"]], "Finding the number of parameters": [[45, "finding-the-number-of-parameters"]], "Fine-tuning neural network hyperparameters": [[2, "fine-tuning-neural-network-hyperparameters"], [42, "fine-tuning-neural-network-hyperparameters"], [43, "fine-tuning-neural-network-hyperparameters"]], "First network example, simple percepetron with one input": [[42, "first-network-example-simple-percepetron-with-one-input"], [43, "first-network-example-simple-percepetron-with-one-input"]], "Fitting an Equation of State for Dense Nuclear Matter": [[1, "fitting-an-equation-of-state-for-dense-nuclear-matter"], [35, "fitting-an-equation-of-state-for-dense-nuclear-matter"]], "Fixing the singularity": [[36, "fixing-the-singularity"]], "Flattening Layer": [[45, "flattening-layer"], [46, "flattening-layer"]], "For exercise sessions: Why Linear Regression (aka Ordinary Least Squares and family), repeat from last week": [[36, "for-exercise-sessions-why-linear-regression-aka-ordinary-least-squares-and-family-repeat-from-last-week"]], "Forget and input": [[46, "forget-and-input"]], "Format for electronic delivery of report and programs": [[28, "format-for-electronic-delivery-of-report-and-programs"], [29, "format-for-electronic-delivery-of-report-and-programs"], [30, "format-for-electronic-delivery-of-report-and-programs"]], "Forward and reverse modes": [[42, "forward-and-reverse-modes"]], "Four effective ways to learn an RNN and preparing for next week": [[46, "four-effective-ways-to-learn-an-rnn-and-preparing-for-next-week"]], "Fourier series and Toeplitz matrices": [[45, "fourier-series-and-toeplitz-matrices"]], "Frequently used scaling functions": [[36, "frequently-used-scaling-functions"]], "From FFNNs and CNNs to recurrent neural networks (RNNs)": [[46, "from-ffnns-and-cnns-to-recurrent-neural-networks-rnns"]], "From OLS to Ridge and Lasso": [[37, "from-ols-to-ridge-and-lasso"]], "From one to many layers, the universal approximation theorem": [[13, "from-one-to-many-layers-the-universal-approximation-theorem"]], "Full object-oriented implementation": [[43, "full-object-oriented-implementation"], [44, "full-object-oriented-implementation"]], "Fully Connected Layers": [[45, "fully-connected-layers"], [46, "fully-connected-layers"]], "Functionality in Scikit-Learn": [[36, "functionality-in-scikit-learn"]], "Functions using mathematical functions from Numpy": [[40, "functions-using-mathematical-functions-from-numpy"], [41, "functions-using-mathematical-functions-from-numpy"]], "Further Dimensionality Remarks": [[4, "further-dimensionality-remarks"]], "Further Manipulations": [[36, "further-manipulations"]], "Further example: Computing the Gini index": [[47, "further-example-computing-the-gini-index"], [48, "further-example-computing-the-gini-index"]], "Further properties (important for our analyses later)": [[6, "further-properties-important-for-our-analyses-later"], [36, "further-properties-important-for-our-analyses-later"]], "Further remarks": [[45, "further-remarks"]], "Further simplification": [[45, "further-simplification"]], "Gating mechanism: Long Short Term Memory (LSTM)": [[46, "gating-mechanism-long-short-term-memory-lstm"]], "Gaussian Elimination": [[27, "gaussian-elimination"]], "General Features": [[10, "general-features"], [47, "general-features"]], "General linear models and linear algebra": [[35, "general-linear-models-and-linear-algebra"]], "Generalizing the above one-dimensional case": [[45, "generalizing-the-above-one-dimensional-case"]], "Generalizing the fitting procedure as a linear algebra problem": [[35, "generalizing-the-fitting-procedure-as-a-linear-algebra-problem"], [35, "id1"]], "Generative Adversarial Networks": [[5, "generative-adversarial-networks"]], "Generative Models": [[5, "generative-models"]], "Geometric Interpretation and link with Singular Value Decomposition": [[12, "geometric-interpretation-and-link-with-singular-value-decomposition"]], "Getting serious, the back propagation equations for a neural network": [[42, "getting-serious-the-back-propagation-equations-for-a-neural-network"]], "Getting started with Jax, note the way we import numpy": [[22, "getting-started-with-jax-note-the-way-we-import-numpy"], [41, "getting-started-with-jax-note-the-way-we-import-numpy"]], "Gradient Boosting, Classification Example": [[11, "gradient-boosting-classification-example"], [48, "gradient-boosting-classification-example"]], "Gradient Boosting, Examples of Regression": [[11, "gradient-boosting-examples-of-regression"], [48, "gradient-boosting-examples-of-regression"]], "Gradient Boosting, algorithm": [[48, "gradient-boosting-algorithm"]], "Gradient Clipping": [[2, "gradient-clipping"], [42, "gradient-clipping"], [43, "gradient-clipping"]], "Gradient Descent Example": [[39, "id1"], [40, "id8"]], "Gradient boosting, making our own code for a regression case": [[48, "gradient-boosting-making-our-own-code-for-a-regression-case"]], "Gradient boosting: Basics with Steepest Descent/Functional Gradient Descent": [[11, "gradient-boosting-basics-with-steepest-descent-functional-gradient-descent"], [48, "gradient-boosting-basics-with-steepest-descent-functional-gradient-descent"]], "Gradient descent": [[3, "gradient-descent"], [44, "gradient-descent"]], "Gradient descent and Logistic regression": [[0, "gradient-descent-and-logistic-regression"], [41, "gradient-descent-and-logistic-regression"]], "Gradient descent and Ridge": [[39, "gradient-descent-and-ridge"], [40, "gradient-descent-and-ridge"]], "Gradient descent example": [[39, "gradient-descent-example"], [40, "gradient-descent-example"]], "Gradient expressions": [[42, "gradient-expressions"], [43, "gradient-expressions"]], "Gradient method": [[40, "gradient-method"]], "Gradients of loss functions": [[46, "gradients-of-loss-functions"]], "Grading": [[33, "grading"], [35, "grading"]], "Grid Search": [[39, "grid-search"]], "Growing a classification tree": [[47, "growing-a-classification-tree"], [48, "growing-a-classification-tree"]], "Hidden layers": [[42, "hidden-layers"], [43, "hidden-layers"]], "Homogeneous data": [[42, "homogeneous-data"], [43, "homogeneous-data"]], "Housing data, the code": [[1, "housing-data-the-code"]], "How do we set it up?": [[47, "how-do-we-set-it-up"]], "How to do image compression before the era of deep learning": [[45, "how-to-do-image-compression-before-the-era-of-deep-learning"]], "How to set up the cross-validation for Ridge and/or Lasso": [[39, "how-to-set-up-the-cross-validation-for-ridge-and-or-lasso"]], "Hyperplanes and all that": [[9, "hyperplanes-and-all-that"]], "Identifying Terms": [[38, "identifying-terms"]], "Illustration of a single perceptron model and a multi-perceptron model": [[41, "illustration-of-a-single-perceptron-model-and-a-multi-perceptron-model"], [42, "illustration-of-a-single-perceptron-model-and-a-multi-perceptron-model"]], "Implementing a memory cell in a neural network": [[46, "implementing-a-memory-cell-in-a-neural-network"]], "Important Matrix and vector handling packages": [[27, "important-matrix-and-vector-handling-packages"]], "Important observations": [[42, "important-observations"], [43, "important-observations"]], "Important technicalities: More on Rescaling data": [[37, "important-technicalities-more-on-rescaling-data"]], "Importing Keras and Tensorflow": [[45, "importing-keras-and-tensorflow"], [46, "importing-keras-and-tensorflow"]], "Improving gradient descent with momentum": [[40, "improving-gradient-descent-with-momentum"]], "Improving performance": [[2, "improving-performance"], [43, "improving-performance"], [44, "improving-performance"]], "In general not this simple": [[42, "in-general-not-this-simple"]], "In summary": [[33, "in-summary"]], "Including Stochastic Gradient Descent with Autograd": [[14, "including-stochastic-gradient-descent-with-autograd"], [22, "including-stochastic-gradient-descent-with-autograd"], [40, "including-stochastic-gradient-descent-with-autograd"], [41, "including-stochastic-gradient-descent-with-autograd"]], "Including more classes": [[0, "including-more-classes"], [39, "including-more-classes"]], "Incremental PCA": [[12, "incremental-pca"]], "Independent and Identically Distributed (iid)": [[37, "independent-and-identically-distributed-iid"]], "Independent and Identically Distrubuted (iid)": [[38, "independent-and-identically-distrubuted-iid"]], "Input gate": [[46, "input-gate"]], "Inputs to the activation function": [[42, "inputs-to-the-activation-function"], [43, "inputs-to-the-activation-function"]], "Insights from the paper by Glorot and Bengio": [[42, "insights-from-the-paper-by-glorot-and-bengio"], [43, "insights-from-the-paper-by-glorot-and-bengio"]], "Installing R, C++, cython or Julia": [[35, "installing-r-c-cython-or-julia"]], "Installing R, C++, cython, Numba etc": [[35, "installing-r-c-cython-numba-etc"]], "Instructor information": [[33, "instructor-information"]], "Interpretations and optimizing our parameters": [[35, "interpretations-and-optimizing-our-parameters"], [35, "id2"], [35, "id3"], [36, "interpretations-and-optimizing-our-parameters"], [36, "id1"], [36, "id2"]], "Interpretations of Bayes\u2019 Theorem": [[37, "interpretations-of-bayes-theorem"], [38, "interpretations-of-bayes-theorem"]], "Interpreting the Ridge results": [[36, "interpreting-the-ridge-results"], [37, "interpreting-the-ridge-results"]], "Introducing JAX": [[14, "introducing-jax"], [22, "introducing-jax"], [40, "introducing-jax"], [41, "introducing-jax"]], "Introducing the Covariance and Correlation functions": [[12, "introducing-the-covariance-and-correlation-functions"], [36, "introducing-the-covariance-and-correlation-functions"]], "Introduction": [[1, "introduction"], [7, "introduction"], [26, "introduction"], [27, "introduction"]], "Introduction to Neural networks": [[41, "introduction-to-neural-networks"], [42, "introduction-to-neural-networks"]], "Introduction to numerical projects": [[28, "introduction-to-numerical-projects"], [29, "introduction-to-numerical-projects"], [30, "introduction-to-numerical-projects"]], "Inverse of Rectangular Matrix": [[37, "inverse-of-rectangular-matrix"]], "Is the Logistic activation function (Sigmoid) our choice?": [[42, "is-the-logistic-activation-function-sigmoid-our-choice"], [43, "is-the-logistic-activation-function-sigmoid-our-choice"]], "Iterative Fitting, Classification and AdaBoost": [[11, "iterative-fitting-classification-and-adaboost"], [47, "iterative-fitting-classification-and-adaboost"], [48, "iterative-fitting-classification-and-adaboost"]], "Iterative Fitting, Regression and Squared-error Cost Function": [[11, "iterative-fitting-regression-and-squared-error-cost-function"], [47, "iterative-fitting-regression-and-squared-error-cost-function"], [48, "iterative-fitting-regression-and-squared-error-cost-function"]], "Kernel PCA": [[12, "kernel-pca"]], "Kernels and non-linearity": [[9, "kernels-and-non-linearity"]], "Key Idea": [[45, "key-idea"], [46, "key-idea"]], "LSTM details": [[46, "lstm-details"]], "LU Decomposition, the inverse of a matrix": [[27, "lu-decomposition-the-inverse-of-a-matrix"]], "Lab sessions on Tuesday and Wednesday": [[45, "lab-sessions-on-tuesday-and-wednesday"]], "Lab session: Material from last week and relevant for the first project": [[39, "lab-session-material-from-last-week-and-relevant-for-the-first-project"]], "Lab sessions Tuesday and Wednesday": [[41, "lab-sessions-tuesday-and-wednesday"]], "Lab sessions week 39": [[40, "lab-sessions-week-39"]], "Lasso Regression": [[37, "lasso-regression"]], "Lasso and Bayes": [[37, "lasso-and-bayes"], [38, "lasso-and-bayes"]], "Lasso case": [[37, "lasso-case"]], "Layers": [[2, "layers"], [43, "layers"], [44, "layers"], [45, "layers"], [46, "layers"]], "Layers of a CNN": [[45, "layers-of-a-cnn"], [46, "layers-of-a-cnn"]], "Layers used to build CNNs": [[4, "layers-used-to-build-cnns"], [45, "layers-used-to-build-cnns"], [46, "layers-used-to-build-cnns"]], "Layout of a neural network with three hidden layers": [[42, "layout-of-a-neural-network-with-three-hidden-layers"]], "Layout of a neural network with three hidden layers (last later = l=L=4, first layer l=0)": [[43, "layout-of-a-neural-network-with-three-hidden-layers-last-later-l-l-4-first-layer-l-0"]], "Layout of a simple neural network with no hidden layer": [[42, "layout-of-a-simple-neural-network-with-no-hidden-layer"], [43, "layout-of-a-simple-neural-network-with-no-hidden-layer"]], "Layout of a simple neural network with one hidden layer": [[42, "layout-of-a-simple-neural-network-with-one-hidden-layer"], [43, "layout-of-a-simple-neural-network-with-one-hidden-layer"]], "Layout of a simple neural network with two input nodes, one hidden layer and one output node": [[42, "layout-of-a-simple-neural-network-with-two-input-nodes-one-hidden-layer-and-one-output-node"]], "Layout of a simple neural network with two input nodes, one hidden layer with two hidden noeds and one output node": [[43, "layout-of-a-simple-neural-network-with-two-input-nodes-one-hidden-layer-with-two-hidden-noeds-and-one-output-node"]], "Layout of input to first hidden layer l=1 from input layer l=0": [[43, "layout-of-input-to-first-hidden-layer-l-1-from-input-layer-l-0"]], "Learning outcomes": [[26, "learning-outcomes"], [35, "learning-outcomes"]], "Learning rate methods": [[43, "learning-rate-methods"], [44, "learning-rate-methods"]], "Lecture Monday October 21": [[44, "lecture-monday-october-21"]], "Lecture Monday October 7": [[42, "lecture-monday-october-7"]], "Lecture Monday September 23": [[40, "lecture-monday-september-23"]], "Lecture Monday September 23, Optimization, the central part of any Machine Learning algortithm": [[40, "lecture-monday-september-23-optimization-the-central-part-of-any-machine-learning-algortithm"]], "Lecture Monday September 30, 2024": [[41, "lecture-monday-september-30-2024"]], "Lecture October 14, 2024": [[43, "lecture-october-14-2024"]], "Lectures and ComputerLab": [[35, "lectures-and-computerlab"]], "Limitations of NNs": [[42, "limitations-of-nns"], [43, "limitations-of-nns"]], "Limitations of supervised learning with deep networks": [[2, "limitations-of-supervised-learning-with-deep-networks"], [42, "limitations-of-supervised-learning-with-deep-networks"], [43, "limitations-of-supervised-learning-with-deep-networks"]], "Linear Algebra, Handling of Arrays and more Python Features": [[27, null]], "Linear Regression": [[1, null]], "Linear Regression Problems": [[36, "linear-regression-problems"]], "Linear Regression and the SVD": [[37, "linear-regression-and-the-svd"]], "Linear Regression code, Intercept handling first": [[36, "linear-regression-code-intercept-handling-first"]], "Linear Regression, basic elements": [[1, "linear-regression-basic-elements"]], "Linear classifier": [[39, "linear-classifier"]], "Linking Bayes\u2019 Theorem with Ridge and Lasso Regression": [[6, "linking-bayes-theorem-with-ridge-and-lasso-regression"]], "Linking the regression analysis with a statistical interpretation": [[6, "linking-the-regression-analysis-with-a-statistical-interpretation"], [37, "linking-the-regression-analysis-with-a-statistical-interpretation"], [38, "linking-the-regression-analysis-with-a-statistical-interpretation"]], "Linking with RNNs": [[46, "linking-with-rnns"]], "Linking with the SVD": [[6, "linking-with-the-svd"], [36, "linking-with-the-svd"]], "Links to relevant courses at the University of Oslo": [[34, "links-to-relevant-courses-at-the-university-of-oslo"]], "List of contents:": [[45, "list-of-contents"], [46, "list-of-contents"]], "Logistic Regression": [[8, null], [8, "id1"], [39, "logistic-regression"]], "Logistic function as the root of problems": [[42, "logistic-function-as-the-root-of-problems"], [43, "logistic-function-as-the-root-of-problems"]], "MNIST and GANs": [[5, "mnist-and-gans"]], "Machine Learning": [[35, "machine-learning"]], "Machine learning": [[26, "machine-learning"]], "Main textbooks": [[35, "main-textbooks"]], "Making a tree": [[10, "making-a-tree"], [47, "making-a-tree"], [48, "making-a-tree"]], "Making an ADAboost code yourself": [[48, "making-an-adaboost-code-yourself"]], "Making your own Bootstrap: Changing the Level of the Decision Tree": [[11, "making-your-own-bootstrap-changing-the-level-of-the-decision-tree"], [47, "making-your-own-bootstrap-changing-the-level-of-the-decision-tree"], [48, "making-your-own-bootstrap-changing-the-level-of-the-decision-tree"]], "Making your own test-train splitting": [[36, "making-your-own-test-train-splitting"]], "Marginal Probability": [[37, "marginal-probability"], [38, "marginal-probability"]], "Material for Lecture Monday November 4": [[46, "material-for-lecture-monday-november-4"]], "Material for Lecture Monday October 28": [[45, "material-for-lecture-monday-october-28"]], "Material for lecture Monday September 16": [[39, "material-for-lecture-monday-september-16"]], "Material for lecture Monday September 2": [[37, "material-for-lecture-monday-september-2"]], "Material for lecture Monday September 9": [[38, "material-for-lecture-monday-september-9"]], "Material for lecture Monday, August 26": [[36, "material-for-lecture-monday-august-26"]], "Material for the active learning sessions Tuesday and Wednesday": [[37, "material-for-the-active-learning-sessions-tuesday-and-wednesday"]], "Material for the active learning sessions on Tuesday and Wednesday": [[42, "material-for-the-active-learning-sessions-on-tuesday-and-wednesday"], [43, "material-for-the-active-learning-sessions-on-tuesday-and-wednesday"]], "Material for the lab sessions": [[38, "material-for-the-lab-sessions"]], "Material for the lab sessions, additional ways to present classification results and other practicalities": [[46, "material-for-the-lab-sessions-additional-ways-to-present-classification-results-and-other-practicalities"]], "Material for the lecture on Monday October 7, 2024": [[42, "material-for-the-lecture-on-monday-october-7-2024"]], "Mathematical Interpretation of Ordinary Least Squares": [[6, "mathematical-interpretation-of-ordinary-least-squares"], [36, "mathematical-interpretation-of-ordinary-least-squares"]], "Mathematical model": [[41, "mathematical-model"], [41, "id2"], [41, "id3"], [41, "id4"], [41, "id5"]], "Mathematical optimization of convex functions": [[9, "mathematical-optimization-of-convex-functions"]], "Mathematical setup": [[46, "mathematical-setup"]], "Mathematics of CNNs": [[4, "mathematics-of-cnns"], [45, "mathematics-of-cnns"]], "Mathematics of deep learning": [[42, "mathematics-of-deep-learning"], [43, "mathematics-of-deep-learning"], [44, "mathematics-of-deep-learning"]], "Mathematics of deep learning and neural networks": [[42, "mathematics-of-deep-learning-and-neural-networks"]], "Mathematics of the SVD and implications": [[6, "mathematics-of-the-svd-and-implications"], [36, "mathematics-of-the-svd-and-implications"]], "Matrices in Python": [[35, "matrices-in-python"]], "Matrix multiplication": [[2, "matrix-multiplication"], [43, "matrix-multiplication"], [44, "matrix-multiplication"]], "Matrix multiplications": [[43, "matrix-multiplications"], [44, "matrix-multiplications"]], "Matrix-vector notation": [[41, "matrix-vector-notation"]], "Matrix-vector notation and activation": [[13, "matrix-vector-notation-and-activation"], [41, "matrix-vector-notation-and-activation"]], "Maximum Likelihood Estimation (MLE)": [[37, "maximum-likelihood-estimation-mle"], [38, "maximum-likelihood-estimation-mle"]], "Maximum likelihood": [[39, "maximum-likelihood"]], "Meet the covariance!": [[32, "meet-the-covariance"]], "Meet the Covariance Matrix": [[6, "meet-the-covariance-matrix"], [36, "meet-the-covariance-matrix"]], "Meet the Hessian Matrix": [[36, "meet-the-hessian-matrix"]], "Meet the Pandas": [[35, "meet-the-pandas"]], "Memory considerations": [[45, "memory-considerations"]], "Min-Max Scaling": [[36, "min-max-scaling"]], "Minimization process": [[44, "minimization-process"]], "Minimizing the cost function using gradient descent and automatic differentiation": [[44, "minimizing-the-cost-function-using-gradient-descent-and-automatic-differentiation"]], "Minimizing the cross entropy": [[0, "minimizing-the-cross-entropy"], [39, "minimizing-the-cross-entropy"]], "Minor rewrite": [[46, "minor-rewrite"]], "Momentum based GD": [[14, "momentum-based-gd"], [22, "momentum-based-gd"], [40, "momentum-based-gd"], [41, "momentum-based-gd"]], "Momentum parameter": [[40, "momentum-parameter"], [41, "momentum-parameter"]], "More LSTM details": [[46, "more-lstm-details"]], "More autograd": [[40, "more-autograd"], [41, "more-autograd"]], "More bagging": [[47, "more-bagging"], [48, "more-bagging"]], "More basic Statistics and Bayes\u2019 theorem": [[37, "more-basic-statistics-and-bayes-theorem"], [38, "more-basic-statistics-and-bayes-theorem"]], "More classes": [[0, "more-classes"], [39, "more-classes"]], "More complicated Example: The Ising model": [[7, "more-complicated-example-the-ising-model"]], "More complicated function": [[42, "more-complicated-function"]], "More complicated functions using the elements of their arguments directly": [[40, "more-complicated-functions-using-the-elements-of-their-arguments-directly"], [41, "more-complicated-functions-using-the-elements-of-their-arguments-directly"]], "More considerations": [[42, "more-considerations"], [43, "more-considerations"]], "More details": [[44, "more-details"], [44, "id6"]], "More examples on bootstrap and cross-validation and errors": [[38, "more-examples-on-bootstrap-and-cross-validation-and-errors"]], "More interpretations": [[36, "more-interpretations"], [37, "more-interpretations"]], "More limitations": [[42, "more-limitations"], [43, "more-limitations"]], "More on Dimensionalities": [[4, "more-on-dimensionalities"], [45, "more-on-dimensionalities"]], "More on Rescaling data": [[7, "more-on-rescaling-data"]], "More on Steepest descent": [[39, "more-on-steepest-descent"], [40, "more-on-steepest-descent"]], "More on activation functions, output layers": [[42, "more-on-activation-functions-output-layers"], [43, "more-on-activation-functions-output-layers"], [44, "more-on-activation-functions-output-layers"]], "More on convex functions": [[39, "more-on-convex-functions"], [40, "more-on-convex-functions"]], "More on momentum based approaches": [[40, "more-on-momentum-based-approaches"], [41, "more-on-momentum-based-approaches"]], "More on the general approximation theorem": [[42, "more-on-the-general-approximation-theorem"]], "More preprocessing": [[36, "more-preprocessing"]], "More preprocessing examples, two-dimensional example, the Franke function": [[36, "more-preprocessing-examples-two-dimensional-example-the-franke-function"]], "More technicalities": [[44, "more-technicalities"]], "More thinking": [[36, "more-thinking"]], "More top-down perspectives": [[42, "more-top-down-perspectives"], [43, "more-top-down-perspectives"]], "Multiclass classification": [[43, "multiclass-classification"], [44, "multiclass-classification"]], "Multilayer perceptrons": [[13, "multilayer-perceptrons"], [41, "multilayer-perceptrons"], [42, "multilayer-perceptrons"]], "Multivariable functions": [[42, "multivariable-functions"]], "Network requirements": [[3, "network-requirements"], [44, "network-requirements"]], "Neural Networks vs CNNs": [[4, "neural-networks-vs-cnns"], [45, "neural-networks-vs-cnns"], [46, "neural-networks-vs-cnns"]], "Neural network types": [[41, "neural-network-types"], [42, "neural-network-types"]], "Neural networks": [[13, null]], "New expression for the derivative": [[42, "new-expression-for-the-derivative"]], "New image (or volume)": [[45, "new-image-or-volume"]], "New vector": [[45, "new-vector"]], "Note about SVD Calculations": [[36, "note-about-svd-calculations"]], "Note on Scikit-Learn": [[37, "note-on-scikit-learn"]], "Numerical experiments and the covariance, central limit theorem": [[32, "numerical-experiments-and-the-covariance-central-limit-theorem"]], "Numpy and arrays": [[27, "numpy-and-arrays"], [35, "numpy-and-arrays"]], "Numpy examples and Important Matrix and vector handling packages": [[35, "numpy-examples-and-important-matrix-and-vector-handling-packages"]], "Optimization and Deep learning": [[39, "optimization-and-deep-learning"]], "Optimization, the central part of any Machine Learning algortithm": [[14, null], [39, "optimization-the-central-part-of-any-machine-learning-algortithm"]], "Optimized Convolution2DLayer": [[45, "optimized-convolution2dlayer"], [46, "optimized-convolution2dlayer"]], "Optimizing our parameters": [[35, "optimizing-our-parameters"]], "Optimizing our parameters, more details": [[35, "optimizing-our-parameters-more-details"]], "Optimizing the cost function": [[2, "optimizing-the-cost-function"], [43, "optimizing-the-cost-function"], [44, "optimizing-the-cost-function"]], "Optimizing the parameters": [[42, "optimizing-the-parameters"], [43, "optimizing-the-parameters"]], "Ordinary Differential Equations first": [[44, "ordinary-differential-equations-first"]], "Organizing our data": [[1, "organizing-our-data"], [35, "organizing-our-data"]], "Other Matrix and Vector Operations": [[27, "other-matrix-and-vector-operations"]], "Other Types of Recurrent Neural Networks": [[5, "other-types-of-recurrent-neural-networks"]], "Other courses on Data science and Machine Learning at UiO": [[35, "other-courses-on-data-science-and-machine-learning-at-uio"]], "Other courses on Data science and Machine Learning at UiO, contn": [[35, "other-courses-on-data-science-and-machine-learning-at-uio-contn"]], "Other ingredients of a neural network": [[42, "other-ingredients-of-a-neural-network"]], "Other measures in classification studies: Cancer Data again": [[0, "other-measures-in-classification-studies-cancer-data-again"], [39, "other-measures-in-classification-studies-cancer-data-again"]], "Other parameters": [[42, "other-parameters"]], "Other popular texts": [[35, "other-popular-texts"]], "Other techniques": [[12, "other-techniques"]], "Other types of networks": [[13, "other-types-of-networks"], [41, "other-types-of-networks"], [42, "other-types-of-networks"]], "Other useful relations": [[36, "other-useful-relations"]], "Other ways of visualizing the trees": [[10, "other-ways-of-visualizing-the-trees"], [47, "other-ways-of-visualizing-the-trees"], [48, "other-ways-of-visualizing-the-trees"]], "Our model for the nuclear binding energies": [[35, "our-model-for-the-nuclear-binding-energies"]], "Output gate": [[46, "output-gate"]], "Output layer": [[42, "output-layer"], [43, "output-layer"]], "Overarching aims of the exercises this week": [[18, "overarching-aims-of-the-exercises-this-week"], [19, "overarching-aims-of-the-exercises-this-week"], [20, "overarching-aims-of-the-exercises-this-week"], [21, "overarching-aims-of-the-exercises-this-week"], [22, "overarching-aims-of-the-exercises-this-week"], [23, "overarching-aims-of-the-exercises-this-week"], [24, "overarching-aims-of-the-exercises-this-week"], [25, "overarching-aims-of-the-exercises-this-week"]], "Overarching view of a neural network": [[42, "overarching-view-of-a-neural-network"]], "Overview of first week": [[35, "overview-of-first-week"]], "Overview video on Stochastic Gradient Descent": [[40, "overview-video-on-stochastic-gradient-descent"], [41, "overview-video-on-stochastic-gradient-descent"]], "Own code for Ordinary Least Squares": [[35, "own-code-for-ordinary-least-squares"], [36, "own-code-for-ordinary-least-squares"]], "PCA and scikit-learn": [[12, "pca-and-scikit-learn"]], "Padding": [[45, "padding"]], "Pandas AI": [[35, "pandas-ai"]], "Parameters of neural networks": [[42, "parameters-of-neural-networks"]], "Parameters to train, common settings": [[45, "parameters-to-train-common-settings"]], "Part a)": [[30, "part-a"]], "Part a) : Ordinary Least Square (OLS) on the Franke function": [[28, "part-a-ordinary-least-square-ols-on-the-franke-function"]], "Part a), setting up the problem": [[30, "part-a-setting-up-the-problem"]], "Part a): Write your own Stochastic Gradient Descent code, first step": [[29, "part-a-write-your-own-stochastic-gradient-descent-code-first-step"]], "Part b)": [[30, "part-b"], [30, "id1"]], "Part b): Adding Ridge regression for the Franke function": [[28, "part-b-adding-ridge-regression-for-the-franke-function"]], "Part b): Writing your own Neural Network code": [[29, "part-b-writing-your-own-neural-network-code"]], "Part c)": [[30, "part-c"]], "Part c) Neural networks": [[30, "part-c-neural-networks"]], "Part c): Adding Lasso for the Franke function": [[28, "part-c-adding-lasso-for-the-franke-function"]], "Part c): Testing different activation functions": [[29, "part-c-testing-different-activation-functions"]], "Part d)": [[30, "part-d"]], "Part d) Neural network complexity": [[30, "part-d-neural-network-complexity"]], "Part d): Classification analysis using neural networks": [[29, "part-d-classification-analysis-using-neural-networks"]], "Part d): Paper and pencil part": [[28, "part-d-paper-and-pencil-part"]], "Part e)": [[30, "part-e"], [30, "id2"]], "Part e): Bias-variance trade-off and resampling techniques": [[28, "part-e-bias-variance-trade-off-and-resampling-techniques"]], "Part e): Write your Logistic Regression code, final step": [[29, "part-e-write-your-logistic-regression-code-final-step"]], "Part f) Critical evaluation of the various algorithms": [[29, "part-f-critical-evaluation-of-the-various-algorithms"]], "Part f): Cross-validation as resampling techniques, adding more complexity": [[28, "part-f-cross-validation-as-resampling-techniques-adding-more-complexity"]], "Part g): Analysis of real data": [[28, "part-g-analysis-of-real-data"]], "Partial Differential Equations": [[3, "partial-differential-equations"], [44, "partial-differential-equations"]], "Paths for project 3": [[30, "paths-for-project-3"]], "Plan for week 39, September 23-27, 2024": [[40, "plan-for-week-39-september-23-27-2024"]], "Plan for week 41, October 7-11": [[42, "plan-for-week-41-october-7-11"]], "Plan for week 44": [[45, "plan-for-week-44"]], "Plan for week 46": [[47, "plan-for-week-46"]], "Plan for week 47": [[48, "plan-for-week-47"]], "Plans for the lab sessions": [[39, "plans-for-the-lab-sessions"]], "Plans for week 35": [[36, "plans-for-week-35"]], "Plans for week 36": [[37, "plans-for-week-36"]], "Plans for week 37, lab sessions": [[38, "plans-for-week-37-lab-sessions"]], "Plans for week 37, lecture Monday": [[38, "plans-for-week-37-lecture-monday"]], "Plans for week 38, lecture Monday September 16": [[39, "plans-for-week-38-lecture-monday-september-16"]], "Plans for week 40": [[41, "plans-for-week-40"]], "Plans for week 43": [[44, "plans-for-week-43"]], "Plans for week 45": [[46, "plans-for-week-45"]], "Plotting the Histogram": [[38, "plotting-the-histogram"]], "Plotting the mean value for each group": [[39, "plotting-the-mean-value-for-each-group"]], "Pooling": [[45, "pooling"]], "Pooling Layer": [[45, "pooling-layer"], [46, "pooling-layer"]], "Pooling arithmetic": [[45, "pooling-arithmetic"]], "Pooling types (From Raschka et al)": [[45, "pooling-types-from-raschka-et-al"]], "Practical tips": [[14, "practical-tips"], [22, "practical-tips"], [40, "practical-tips"], [41, "practical-tips"]], "Practicalities": [[33, "practicalities"]], "Preamble: Note on writing reports, using reference material, AI and other tools": [[28, "preamble-note-on-writing-reports-using-reference-material-ai-and-other-tools"]], "Predicting New Points With A Trained Recurrent Neural Network": [[5, "predicting-new-points-with-a-trained-recurrent-neural-network"]], "Preprocessing our data": [[36, "preprocessing-our-data"]], "Prerequisites": [[35, "prerequisites"]], "Prerequisites and background": [[26, "prerequisites-and-background"]], "Prerequisites: Collect and pre-process data": [[4, "prerequisites-collect-and-pre-process-data"], [45, "prerequisites-collect-and-pre-process-data"], [46, "prerequisites-collect-and-pre-process-data"]], "Printing out as text": [[47, "printing-out-as-text"], [48, "printing-out-as-text"]], "Probability Distribution Functions": [[32, "probability-distribution-functions"]], "Program example for gradient descent with Ridge Regression": [[39, "program-example-for-gradient-descent-with-ridge-regression"], [40, "program-example-for-gradient-descent-with-ridge-regression"]], "Program for stochastic gradient": [[14, "program-for-stochastic-gradient"]], "Project 1 on Machine Learning, deadline October 7 (midnight), 2024": [[28, null]], "Project 2 on Machine Learning, deadline November 4 (Midnight)": [[29, null]], "Project 3 on Machine Learning, deadline December 9 (midnight), 2024": [[30, null]], "Properties of PDFs": [[32, "properties-of-pdfs"]], "Pros and cons of trees, pros": [[10, "pros-and-cons-of-trees-pros"], [47, "pros-and-cons-of-trees-pros"], [48, "pros-and-cons-of-trees-pros"]], "Pruning the tree": [[47, "pruning-the-tree"], [48, "pruning-the-tree"]], "Python installers": [[26, "python-installers"], [35, "python-installers"]], "RMS prop": [[14, "rms-prop"], [40, "rms-prop"], [41, "rms-prop"]], "RMSProp algorithm, taken from Goodfellow et al": [[22, "rmsprop-algorithm-taken-from-goodfellow-et-al"], [41, "rmsprop-algorithm-taken-from-goodfellow-et-al"]], "RMSprop for adaptive learning rate with Stochastic Gradient Descent": [[22, "rmsprop-for-adaptive-learning-rate-with-stochastic-gradient-descent"], [40, "rmsprop-for-adaptive-learning-rate-with-stochastic-gradient-descent"], [41, "rmsprop-for-adaptive-learning-rate-with-stochastic-gradient-descent"]], "RNNs": [[46, "rnns"]], "RNNs in more detail": [[46, "rnns-in-more-detail"]], "RNNs in more detail, part 2": [[46, "rnns-in-more-detail-part-2"]], "RNNs in more detail, part 3": [[46, "rnns-in-more-detail-part-3"]], "RNNs in more detail, part 4": [[46, "rnns-in-more-detail-part-4"]], "RNNs in more detail, part 5": [[46, "rnns-in-more-detail-part-5"]], "RNNs in more detail, part 6": [[46, "rnns-in-more-detail-part-6"]], "RNNs in more detail, part 7": [[46, "rnns-in-more-detail-part-7"]], "Random Forest Algorithm": [[47, "random-forest-algorithm"], [48, "random-forest-algorithm"]], "Random Forests Compared with other Methods on the Cancer Data": [[47, "random-forests-compared-with-other-methods-on-the-cancer-data"], [48, "random-forests-compared-with-other-methods-on-the-cancer-data"]], "Random Numbers": [[32, "random-numbers"]], "Random forests": [[11, "random-forests"], [47, "random-forests"], [48, "random-forests"]], "Randomized Grid Search": [[39, "randomized-grid-search"]], "Randomized PCA": [[12, "randomized-pca"]], "Reading material": [[35, "reading-material"]], "Reading recommendations": [[42, "reading-recommendations"], [43, "reading-recommendations"], [44, "reading-recommendations"]], "Reading recommendations:": [[36, "reading-recommendations"]], "Reading suggestions week 34": [[35, "reading-suggestions-week-34"]], "Recurrent neural networks": [[13, "recurrent-neural-networks"], [41, "recurrent-neural-networks"], [42, "recurrent-neural-networks"]], "Recurrent neural networks (RNNs): Overarching view": [[46, "recurrent-neural-networks-rnns-overarching-view"]], "Recurrent neural networks: Overarching view": [[5, null]], "Reducing the number of degrees of freedom, overarching view": [[1, "reducing-the-number-of-degrees-of-freedom-overarching-view"], [36, "reducing-the-number-of-degrees-of-freedom-overarching-view"]], "Reducing the number of operations": [[42, "reducing-the-number-of-operations"]], "Reformulating the problem": [[3, "reformulating-the-problem"], [44, "reformulating-the-problem"]], "Regression Case": [[11, "regression-case"], [48, "regression-case"]], "Regression analysis and resampling methods": [[28, "regression-analysis-and-resampling-methods"]], "Regression analysis, overarching aims": [[35, "regression-analysis-overarching-aims"]], "Regression analysis, overarching aims II": [[35, "regression-analysis-overarching-aims-ii"]], "Regression trees": [[47, "regression-trees"], [48, "regression-trees"]], "Regular NNs don\u2019t scale well to full images": [[45, "regular-nns-dont-scale-well-to-full-images"], [46, "regular-nns-dont-scale-well-to-full-images"]], "Regularization": [[2, "regularization"], [43, "regularization"], [44, "regularization"]], "Relevance": [[41, "relevance"], [42, "relevance"], [43, "relevance"]], "Remarks on the speed": [[45, "remarks-on-the-speed"], [46, "remarks-on-the-speed"]], "Reminder on Statistics": [[7, "reminder-on-statistics"]], "Reminder on books with hands-on material and codes": [[42, "reminder-on-books-with-hands-on-material-and-codes"], [43, "reminder-on-books-with-hands-on-material-and-codes"], [44, "reminder-on-books-with-hands-on-material-and-codes"]], "Reminder on the chain rule and gradients": [[42, "reminder-on-the-chain-rule-and-gradients"]], "Replace or not": [[14, "replace-or-not"], [40, "replace-or-not"], [41, "replace-or-not"]], "Required Technologies": [[26, "required-technologies"]], "Resampling Methods": [[7, null]], "Resampling approaches can be computationally expensive": [[38, "resampling-approaches-can-be-computationally-expensive"]], "Resampling methods": [[7, "id1"], [38, "resampling-methods"], [38, "id2"]], "Resampling methods: Bootstrap": [[38, "resampling-methods-bootstrap"]], "Resampling methods: Bootstrap approach": [[38, "resampling-methods-bootstrap-approach"]], "Resampling methods: Bootstrap background": [[38, "resampling-methods-bootstrap-background"]], "Resampling methods: Bootstrap steps": [[38, "resampling-methods-bootstrap-steps"]], "Resampling methods: More Bootstrap background": [[38, "resampling-methods-more-bootstrap-background"]], "Residual Error": [[36, "residual-error"]], "Resources on differential equations and deep learning": [[3, "resources-on-differential-equations-and-deep-learning"], [44, "resources-on-differential-equations-and-deep-learning"]], "Revisiting our Linear Regression Solvers": [[14, "revisiting-our-linear-regression-solvers"]], "Revisiting our Logistic Regression case": [[39, "revisiting-our-logistic-regression-case"], [40, "revisiting-our-logistic-regression-case"]], "Revisiting our first homework": [[39, "revisiting-our-first-homework"], [40, "revisiting-our-first-homework"]], "Rewriting as dot products": [[45, "rewriting-as-dot-products"]], "Rewriting the Covariance and/or Correlation Matrix": [[36, "rewriting-the-covariance-and-or-correlation-matrix"]], "Rewriting the \\delta-function": [[38, "rewriting-the-delta-function"]], "Rewriting the fitting procedure as a linear algebra problem": [[35, "rewriting-the-fitting-procedure-as-a-linear-algebra-problem"]], "Rewriting the fitting procedure as a linear algebra problem, more details": [[35, "rewriting-the-fitting-procedure-as-a-linear-algebra-problem-more-details"]], "Ridge Regression": [[37, "ridge-regression"]], "Ridge and Bayes": [[37, "ridge-and-bayes"], [38, "ridge-and-bayes"]], "Ridge and LASSO Regression": [[36, "ridge-and-lasso-regression"], [37, "ridge-and-lasso-regression"]], "Ridge and Lasso Regression": [[6, null], [6, "id1"]], "Running with Keras": [[45, "running-with-keras"], [46, "running-with-keras"]], "SGD example": [[40, "sgd-example"], [41, "sgd-example"]], "SVD analysis": [[37, "svd-analysis"]], "Same code but now with momentum gradient descent": [[14, "same-code-but-now-with-momentum-gradient-descent"], [22, "same-code-but-now-with-momentum-gradient-descent"], [22, "id1"], [40, "same-code-but-now-with-momentum-gradient-descent"], [40, "id9"], [40, "id10"], [41, "same-code-but-now-with-momentum-gradient-descent"], [41, "id1"]], "Schedule first week": [[35, "schedule-first-week"]], "Schedulers": [[45, "schedulers"], [46, "schedulers"]], "Schematic Regression Procedure": [[10, "schematic-regression-procedure"], [47, "schematic-regression-procedure"], [48, "schematic-regression-procedure"]], "Searching for Optimal Regularization Parameters \\lambda": [[39, "searching-for-optimal-regularization-parameters-lambda"]], "Second moment of the gradient": [[40, "second-moment-of-the-gradient"], [41, "second-moment-of-the-gradient"]], "Sequential data only?": [[46, "sequential-data-only"]], "Set up the model": [[45, "set-up-the-model"], [46, "set-up-the-model"]], "Setting it up": [[45, "setting-it-up"], [46, "setting-it-up"]], "Setting up a Multi-layer perceptron model for classification": [[43, "setting-up-a-multi-layer-perceptron-model-for-classification"], [44, "setting-up-a-multi-layer-perceptron-model-for-classification"]], "Setting up the Back propagation algorithm": [[13, "setting-up-the-back-propagation-algorithm"]], "Setting up the Back propagation algorithm, part 3": [[42, "setting-up-the-back-propagation-algorithm-part-3"], [43, "setting-up-the-back-propagation-algorithm-part-3"], [44, "setting-up-the-back-propagation-algorithm-part-3"]], "Setting up the Matrix to be inverted": [[36, "setting-up-the-matrix-to-be-inverted"]], "Setting up the back propagation algorithm": [[42, "setting-up-the-back-propagation-algorithm"]], "Setting up the back propagation algorithm and algorithm for a feed forward NN, initalizations": [[43, "setting-up-the-back-propagation-algorithm-and-algorithm-for-a-feed-forward-nn-initalizations"], [44, "setting-up-the-back-propagation-algorithm-and-algorithm-for-a-feed-forward-nn-initalizations"]], "Setting up the back propagation algorithm, part 1": [[43, "setting-up-the-back-propagation-algorithm-part-1"], [44, "setting-up-the-back-propagation-algorithm-part-1"]], "Setting up the back propagation algorithm, part 2": [[42, "setting-up-the-back-propagation-algorithm-part-2"], [43, "setting-up-the-back-propagation-algorithm-part-2"], [44, "setting-up-the-back-propagation-algorithm-part-2"]], "Setting up the code": [[44, "setting-up-the-code"]], "Setting up the equations for a neural network": [[42, "setting-up-the-equations-for-a-neural-network"], [43, "setting-up-the-equations-for-a-neural-network"]], "Setting up the network using Autograd": [[44, "setting-up-the-network-using-autograd"]], "Setting up the network using Autograd; The full program": [[3, "setting-up-the-network-using-autograd-the-full-program"], [44, "setting-up-the-network-using-autograd-the-full-program"]], "Setting up the network using Autograd; The trial solution": [[44, "setting-up-the-network-using-autograd-the-trial-solution"]], "Setting up the problem": [[44, "setting-up-the-problem"]], "Setup of Network": [[44, "setup-of-network"]], "Similar (second order function now) problem but now with AdaGrad": [[14, "similar-second-order-function-now-problem-but-now-with-adagrad"], [22, "similar-second-order-function-now-problem-but-now-with-adagrad"], [40, "similar-second-order-function-now-problem-but-now-with-adagrad"], [41, "similar-second-order-function-now-problem-but-now-with-adagrad"]], "Simple Python Code to read in Data and perform Classification": [[10, "simple-python-code-to-read-in-data-and-perform-classification"], [47, "simple-python-code-to-read-in-data-and-perform-classification"], [48, "simple-python-code-to-read-in-data-and-perform-classification"]], "Simple Voting Example, head or tail": [[47, "simple-voting-example-head-or-tail"], [48, "simple-voting-example-head-or-tail"]], "Simple case": [[36, "simple-case"]], "Simple code for solving the above problem": [[37, "simple-code-for-solving-the-above-problem"]], "Simple example": [[39, "simple-example"], [42, "simple-example"]], "Simple 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"simpler-examples-first-and-automatic-differentiation"]], "Slightly different approach": [[40, "slightly-different-approach"], [41, "slightly-different-approach"]], "Smarter way of evaluating the above function": [[42, "smarter-way-of-evaluating-the-above-function"]], "Software and needed installations": [[28, "software-and-needed-installations"], [30, "software-and-needed-installations"], [35, "software-and-needed-installations"]], "Solving Differential Equations with Deep Learning": [[3, null]], "Solving differential equations with Deep Learning": [[44, "solving-differential-equations-with-deep-learning"]], "Solving differential equations with RNNs": [[46, "solving-differential-equations-with-rnns"]], "Solving partial differential equations with neural networks": [[30, "solving-partial-differential-equations-with-neural-networks"]], "Solving the equation using Autograd": [[44, "solving-the-equation-using-autograd"]], "Solving the one dimensional Poisson equation": [[3, "solving-the-one-dimensional-poisson-equation"]], "Solving the wave equation - the full program using Autograd": [[44, "solving-the-wave-equation-the-full-program-using-autograd"]], "Solving the wave equation with Neural Networks": [[3, "solving-the-wave-equation-with-neural-networks"]], "Solving using Newton-Raphson\u2019s method": [[39, "solving-using-newton-raphson-s-method"], [40, "solving-using-newton-raphson-s-method"]], "Some famous Matrices": [[27, "some-famous-matrices"]], "Some parallels from real analysis": [[42, "some-parallels-from-real-analysis"]], "Some selected properties": [[39, "some-selected-properties"]], "Some simple problems": [[14, "some-simple-problems"], [39, "some-simple-problems"], [40, "some-simple-problems"]], "Some useful matrix and vector expressions": [[36, "some-useful-matrix-and-vector-expressions"]], "Splitting our Data in Training and Test data": [[1, "splitting-our-data-in-training-and-test-data"], [35, 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methods, limitations": [[41, "summary-from-last-week-using-gradient-descent-methods-limitations"]], "Summary of RNNs": [[46, "summary-of-rnns"]], "Summary of a typical RNN": [[46, "summary-of-a-typical-rnn"]], "Summing up": [[38, "summing-up"]], "Support Vector Machines, overarching aims": [[9, null]], "Systematic reduction": [[4, "systematic-reduction"], [45, "systematic-reduction"], [46, "systematic-reduction"]], "Teachers": [[35, "teachers"]], "Teachers and Grading": [[33, null]], "Teaching Assistants Fall semester 2023": [[33, "teaching-assistants-fall-semester-2023"]], "Teaching schedule with links to material": [[31, null]], "Technicalities": [[44, "technicalities"]], "Tensorflow": [[43, "tensorflow"], [44, "tensorflow"]], "Test Function for what happens with OLS, Ridge and Lasso": [[37, "test-function-for-what-happens-with-ols-ridge-and-lasso"]], "Testing the Means Squared Error as function of Complexity": [[1, "testing-the-means-squared-error-as-function-of-complexity"], [36, 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"the-cart-algorithm-for-regression"]], "The CIFAR01 data set": [[4, "the-cifar01-data-set"], [45, "the-cifar01-data-set"], [46, "the-cifar01-data-set"]], "The Central Limit Theorem": [[38, "the-central-limit-theorem"]], "The Convolutional Neural Network (CNN)": [[45, "the-convolutional-neural-network-cnn"], [46, "the-convolutional-neural-network-cnn"]], "The Hessian matrix": [[39, "the-hessian-matrix"], [40, "the-hessian-matrix"]], "The Hessian matrix for Ridge Regression": [[39, "the-hessian-matrix-for-ridge-regression"], [40, "the-hessian-matrix-for-ridge-regression"]], "The Jacobian": [[36, "the-jacobian"]], "The MNIST dataset again": [[4, "the-mnist-dataset-again"], [45, "the-mnist-dataset-again"], [46, "the-mnist-dataset-again"]], "The Neural Network": [[43, "the-neural-network"], [44, "the-neural-network"]], "The OLS case": [[37, "the-ols-case"]], "The RELU function family": [[2, "the-relu-function-family"], [42, "the-relu-function-family"], [43, "the-relu-function-family"], [44, "the-relu-function-family"]], "The Ridge case": [[37, "the-ridge-case"]], "The SVD example": [[45, "the-svd-example"]], "The SVD, a Fantastic Algorithm": [[36, "the-svd-a-fantastic-algorithm"]], "The Softmax function": [[2, "the-softmax-function"], [43, "the-softmax-function"], [44, "the-softmax-function"]], "The Squared-Error again! Steepest Descent": [[48, "the-squared-error-again-steepest-descent"]], "The Table": [[47, "the-table"], [48, "the-table"]], "The \\chi^2 function": [[1, "the-chi-2-function"], [35, "the-chi-2-function"], [35, "id4"], [35, "id5"], [35, "id6"], [35, "id7"], [35, "id8"]], "The analytical solution": [[44, "the-analytical-solution"]], "The approximation theorem in words": [[42, "the-approximation-theorem-in-words"]], "The backward pass is linear": [[46, "the-backward-pass-is-linear"]], "The basic structure of your project": [[30, "the-basic-structure-of-your-project"]], "The bias-variance tradeoff": [[7, "the-bias-variance-tradeoff"], [38, "the-bias-variance-tradeoff"]], "The code": [[35, "the-code"]], "The code for solving the ODE": [[3, "the-code-for-solving-the-ode"], [44, "the-code-for-solving-the-ode"]], "The complete code with a simple data set": [[36, "the-complete-code-with-a-simple-data-set"]], "The convolution stage": [[45, "the-convolution-stage"]], "The cost function": [[0, "the-cost-function"]], "The cost function rewritten": [[39, "the-cost-function-rewritten"]], "The cost/loss function": [[36, "the-cost-loss-function"]], "The course has two central parts": [[26, "the-course-has-two-central-parts"]], "The derivative of the Logistic funtion": [[42, "the-derivative-of-the-logistic-funtion"], [43, "the-derivative-of-the-logistic-funtion"]], "The derivative of the cost/loss function": [[39, "the-derivative-of-the-cost-loss-function"], [40, "the-derivative-of-the-cost-loss-function"]], "The derivatives": [[42, "the-derivatives"], [43, "the-derivatives"]], "The equations": [[39, "the-equations"], [40, "the-equations"]], "The equations for ordinary least squares": [[36, "the-equations-for-ordinary-least-squares"]], "The equations to solve": [[39, "the-equations-to-solve"], [40, "the-equations-to-solve"]], "The first Case": [[37, "the-first-case"]], "The forget gate": [[46, "the-forget-gate"]], "The function to solve for": [[44, "the-function-to-solve-for"]], "The gradient step": [[40, "the-gradient-step"], [41, "the-gradient-step"]], "The ideal": [[39, "the-ideal"], [40, "the-ideal"]], "The logistic function": [[0, "the-logistic-function"], [8, "the-logistic-function"], [39, "the-logistic-function"]], "The mean squared error and its derivative": [[36, "the-mean-squared-error-and-its-derivative"]], "The moons example": [[9, "the-moons-example"]], "The multilayer perceptron (MLP)": [[13, "the-multilayer-perceptron-mlp"]], "The network with one input layer, specified number of hidden layers, and one output layer": [[3, "the-network-with-one-input-layer-specified-number-of-hidden-layers-and-one-output-layer"], [44, "the-network-with-one-input-layer-specified-number-of-hidden-layers-and-one-output-layer"]], "The optimization problem": [[42, "the-optimization-problem"]], "The ouput layer": [[42, "the-ouput-layer"], [43, "the-ouput-layer"]], "The problem of exploding or vanishing gradients": [[46, "the-problem-of-exploding-or-vanishing-gradients"]], "The problem to solve for": [[44, "the-problem-to-solve-for"]], "The program using Autograd": [[44, "the-program-using-autograd"]], "The same example but now with cross-validation": [[38, "the-same-example-but-now-with-cross-validation"]], "The sensitiveness of the gradient descent": [[39, "the-sensitiveness-of-the-gradient-descent"], [40, "the-sensitiveness-of-the-gradient-descent"]], "The singular value decomposition": [[6, "the-singular-value-decomposition"], [36, "the-singular-value-decomposition"]], "The specific equation to solve for": [[44, "the-specific-equation-to-solve-for"]], "The syntax a.dot(b) when finding the dot product": [[40, "the-syntax-a-dot-b-when-finding-the-dot-product"]], "The training": [[42, "the-training"], [43, "the-training"]], "The trial solution": [[44, "the-trial-solution"], [44, "id4"], [44, "id5"], [44, "id7"]], "The two-dimensional case": [[9, "the-two-dimensional-case"]], "Time decay rate": [[40, "time-decay-rate"], [41, "time-decay-rate"]], "To our real data: nuclear binding energies. 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"zero": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 18, 19, 20, 22, 23, 24, 27, 28, 32, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48], "zeros_lik": 5, "zeroth": [36, 45], "zfill": 5, "zip": [5, 7, 23, 24, 48], "zm_h": [1, 35], "zn": 1, "zone": 1, "zoom": 35, "zx": [27, 35], "zy": [27, 35], "zz": [27, 35], "\u00f8yvind": [7, 36, 37], "\u03b4": [43, 44]}, "titles": ["Exercises Week 42: Logistic Regression and Optimization, reminders from week 38 and week 40", "3. Linear Regression", "14. Building a Feed Forward Neural Network", "15. Solving Differential Equations with Deep Learning", "16. Convolutional Neural Networks", "17. Recurrent neural networks: Overarching view", "4. Ridge and Lasso Regression", "5. Resampling Methods", "6. Logistic Regression", "8. Support Vector Machines, overarching aims", "9. Decision trees, overarching aims", "10. Ensemble Methods: From a Single Tree to Many Trees and Extreme Boosting, Meet the Jungle of Methods", "11. Basic ideas of the Principal Component Analysis (PCA)", "13. Neural networks", "7. Optimization, the central part of any Machine Learning algortithm", "12. Clustering and Unsupervised Learning", "Exercises week 34", "Exercises week 35", "Exercises week 36", "Exercises week 37", "Exercises week 38", "Exercises week 39", "Exercises week 41", "Exercises week 42", "Exercises week 43", "Exercise week 47", "Applied Data Analysis and Machine Learning", "2. Linear Algebra, Handling of Arrays and more Python Features", "Project 1 on Machine Learning, deadline October 7 (midnight), 2024", "Project 2 on Machine Learning, deadline November 4 (Midnight)", "Project 3 on Machine Learning, deadline December 9 (midnight), 2024", "Teaching schedule with links to material", "1. Elements of Probability Theory and Statistical Data Analysis", "Teachers and Grading", "Textbooks", "Week 34: Introduction to the course, Logistics and Practicalities", "Week 35: From Ordinary Linear Regression to Ridge and Lasso Regression", "Week 36: Linear Regression and Statistical interpretations", "Week 37: Statistical interpretations and Resampling Methods", "Week 38: Logistic Regression and Optimization", "Week 39: Optimization and Gradient Methods", "Week 40: Gradient descent methods (continued) and start Neural networks", "Week 41 Neural networks and constructing a neural network code", "Week 42 Constructing a Neural Network code with examples", "Week 43: Deep Learning: Constructing a Neural Network code and solving differential equations", "Week 44, Convolutional Neural Networks (CNN)", "Week 45, Convolutional Neural Networks (CCNs) and Recurrent Neural Networks (RNNs)", "Week 46: Decision Trees, Ensemble methods and Random Forests", "Week 47: From Decision Trees to Ensemble 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"d-volumes-of-neurons"], [47, "d-volumes-of-neurons"]], "A Classification Tree": [[10, "a-classification-tree"], [48, "a-classification-tree"], [49, "a-classification-tree"]], "A Frequentist approach to data analysis": [[1, "a-frequentist-approach-to-data-analysis"], [36, "a-frequentist-approach-to-data-analysis"]], "A better approach": [[9, "a-better-approach"]], "A deep CNN model (From Raschka et al)": [[46, "a-deep-cnn-model-from-raschka-et-al"], [47, "a-deep-cnn-model-from-raschka-et-al"]], "A first summary": [[36, "a-first-summary"]], "A more advanced example": [[22, "a-more-advanced-example"], [42, "a-more-advanced-example"]], "A more compact expression": [[0, "a-more-compact-expression"], [40, "a-more-compact-expression"]], "A more efficient way of coding the above Convolution": [[46, "a-more-efficient-way-of-coding-the-above-convolution"]], "A new Cost Function": [[38, "a-new-cost-function"], [39, "a-new-cost-function"]], "A possible code using Scikit-Learn": [[48, "a-possible-code-using-scikit-learn"], [49, "a-possible-code-using-scikit-learn"]], "A possible implementation of a neural network": [[45, "a-possible-implementation-of-a-neural-network"]], "A quick Reminder on Lagrangian Multipliers": [[9, "a-quick-reminder-on-lagrangian-multipliers"]], "A simple example": [[5, "a-simple-example"], [47, "a-simple-example"]], "A soft classifier": [[9, "a-soft-classifier"]], "A standard BM setup": [[50, "a-standard-bm-setup"]], "A top-down approach, recursive binary splitting": [[48, "a-top-down-approach-recursive-binary-splitting"], [49, "a-top-down-approach-recursive-binary-splitting"]], "A top-down perspective on Neural networks": [[2, "a-top-down-perspective-on-neural-networks"], [43, "a-top-down-perspective-on-neural-networks"], [44, "a-top-down-perspective-on-neural-networks"]], "A typical Decision Tree with its pertinent Jargon, Classification Problem": [[48, "a-typical-decision-tree-with-its-pertinent-jargon-classification-problem"]], "A warm-up example": [[22, "a-warm-up-example"], [42, "a-warm-up-example"]], "A way to Read the Bias-Variance Tradeoff": [[39, "a-way-to-read-the-bias-variance-tradeoff"]], "ADAM algorithm, taken from Goodfellow et al": [[22, "adam-algorithm-taken-from-goodfellow-et-al"], [42, "adam-algorithm-taken-from-goodfellow-et-al"]], "ADAM optimizer": [[14, "adam-optimizer"], [41, "adam-optimizer"], [42, "adam-optimizer"]], "Activation functions": [[13, "activation-functions"], [42, "activation-functions"], [43, "activation-functions"], [44, "activation-functions"], [44, "id3"], [45, "activation-functions"], [45, "id3"], [46, "activation-functions"], [47, "activation-functions"]], "Activation functions, Logistic and Hyperbolic ones": [[42, "activation-functions-logistic-and-hyperbolic-ones"], [43, "activation-functions-logistic-and-hyperbolic-ones"], [44, "activation-functions-logistic-and-hyperbolic-ones"]], "Activation functions, examples": [[45, "activation-functions-examples"]], "AdaBoost Examples": [[48, "adaboost-examples"], [49, "adaboost-examples"], [50, "adaboost-examples"]], "AdaGrad algorithm, taken from Goodfellow et al": [[22, "adagrad-algorithm-taken-from-goodfellow-et-al"], [42, "adagrad-algorithm-taken-from-goodfellow-et-al"]], "Adaptive Boosting, AdaBoost": [[48, "adaptive-boosting-adaboost"], [49, "adaptive-boosting-adaboost"], [50, "adaptive-boosting-adaboost"]], "Adaptive boosting: AdaBoost, Basic Algorithm": [[11, "adaptive-boosting-adaboost-basic-algorithm"], [48, "adaptive-boosting-adaboost-basic-algorithm"], [49, "adaptive-boosting-adaboost-basic-algorithm"], [50, "adaptive-boosting-adaboost-basic-algorithm"]], "Add Dense layers on top": [[46, "add-dense-layers-on-top"], [47, "add-dense-layers-on-top"]], "Adding Neural Networks": [[42, "adding-neural-networks"], [43, "adding-neural-networks"]], "Adding a hidden layer": [[43, "adding-a-hidden-layer"], [44, "adding-a-hidden-layer"]], "Adding error analysis and training set up": [[36, "adding-error-analysis-and-training-set-up"], [37, "adding-error-analysis-and-training-set-up"]], "Additional Remarks": [[46, "additional-remarks"], [47, "additional-remarks"]], "Additional courses of interest": [[50, "additional-courses-of-interest"]], "Adjust hyperparameters": [[2, "adjust-hyperparameters"], [44, "adjust-hyperparameters"], [45, "adjust-hyperparameters"]], "Adversarial learning": [[50, "adversarial-learning"]], "Algorithms and codes for Adagrad, RMSprop and Adam": [[22, "algorithms-and-codes-for-adagrad-rmsprop-and-adam"], [41, "algorithms-and-codes-for-adagrad-rmsprop-and-adam"], [42, "algorithms-and-codes-for-adagrad-rmsprop-and-adam"]], "Algorithms for Setting up Decision Trees": [[10, "algorithms-for-setting-up-decision-trees"], [48, "algorithms-for-setting-up-decision-trees"], [49, "algorithms-for-setting-up-decision-trees"]], "Alternative differential equations": [[31, "alternative-differential-equations"]], "An Overview of Ensemble Methods": [[11, "an-overview-of-ensemble-methods"], [48, "an-overview-of-ensemble-methods"], [49, "an-overview-of-ensemble-methods"]], "An extrapolation example": [[5, "an-extrapolation-example"]], "An optimization/minimization problem": [[36, "an-optimization-minimization-problem"]], "Analyzing the last results": [[43, "analyzing-the-last-results"], [44, "analyzing-the-last-results"]], "And with loops": [[41, "and-with-loops"], [42, "and-with-loops"]], "And Logistic Regression": [[41, "and-logistic-regression"], [42, "and-logistic-regression"]], "And a corresponding example using scikit-learn": [[40, "and-a-corresponding-example-using-scikit-learn"], [41, "and-a-corresponding-example-using-scikit-learn"]], "And finally \\boldsymbol{X}\\boldsymbol{X}^T": [[37, "and-finally-boldsymbol-x-boldsymbol-x-t"], [38, "and-finally-boldsymbol-x-boldsymbol-x-t"]], "And finally ADAM": [[22, "and-finally-adam"], [41, "and-finally-adam"], [42, "and-finally-adam"]], "And what about using neural networks?": [[36, "and-what-about-using-neural-networks"]], "Another Example from Scikit-Learn\u2019s Repository": [[39, "another-example-from-scikit-learn-s-repository"]], "Another Example, now with a polynomial fit": [[38, "another-example-now-with-a-polynomial-fit"]], "Another example, the moons again": [[10, "another-example-the-moons-again"]], "Any other topics, impressions, ideas etc you would like to share with us?": [[26, "any-other-topics-impressions-ideas-etc-you-would-like-to-share-with-us"]], "Applied Data Analysis and Machine Learning": [[27, null]], "Artificial neurons": [[42, "artificial-neurons"], [43, "artificial-neurons"]], "Assumptions made": [[38, "assumptions-made"], [39, "assumptions-made"]], "Autocorrelation function": [[33, "autocorrelation-function"]], "Autoencoders: Overarching view": [[50, "autoencoders-overarching-view"]], "Autograd with more complicated functions": [[41, "autograd-with-more-complicated-functions"], [42, "autograd-with-more-complicated-functions"]], "Automatic differentiation": [[14, "automatic-differentiation"], [41, "automatic-differentiation"], [42, "automatic-differentiation"], [43, "automatic-differentiation"]], "Automatic differentiation through examples": [[43, "automatic-differentiation-through-examples"]], "Back propagation": [[45, "back-propagation"]], "Back propagation and automatic differentiation": [[45, "back-propagation-and-automatic-differentiation"]], "Back propagation in time in equations": [[47, "back-propagation-in-time-in-equations"]], "Back propagation in time through figures, part 1": [[47, "back-propagation-in-time-through-figures-part-1"]], "Back propagation in time, part 2": [[47, "back-propagation-in-time-part-2"]], "Back propagation in time, part 3": [[47, "back-propagation-in-time-part-3"]], "Back propagation in time, part 4": [[47, "back-propagation-in-time-part-4"]], "Back to the Cancer Data": [[12, "back-to-the-cancer-data"]], "Background literature": [[29, "background-literature"], [30, "background-literature"]], "Backpropagation in the convolutional layer": [[46, "backpropagation-in-the-convolutional-layer"], [47, "backpropagation-in-the-convolutional-layer"]], "Backpropagation through time": [[47, "backpropagation-through-time"]], "Bagging": [[11, "bagging"], [48, "bagging"], [49, "bagging"]], "Bagging Examples": [[11, "bagging-examples"]], "Basic Matrix Features": [[28, "basic-matrix-features"]], "Basic Steps of AdaBoost": [[48, "basic-steps-of-adaboost"], [49, "basic-steps-of-adaboost"], [50, "basic-steps-of-adaboost"]], "Basic ideas of the Principal Component Analysis (PCA)": [[12, null]], "Basic layout": [[47, "basic-layout"]], "Basic layout, Figures from Sebastian Rashcka et al, Machine learning with Sickit-Learn and PyTorch": [[47, "basic-layout-figures-from-sebastian-rashcka-et-al-machine-learning-with-sickit-learn-and-pytorch"]], "Basic math of the SVD": [[6, "basic-math-of-the-svd"], [37, "basic-math-of-the-svd"]], "Basics": [[8, "basics"], [40, "basics"]], "Basics of a tree": [[10, "basics-of-a-tree"], [48, "basics-of-a-tree"]], "Basics of an NN": [[43, "basics-of-an-nn"]], "Batch Normalization": [[2, "batch-normalization"], [43, "batch-normalization"], [44, "batch-normalization"]], "Batches and mini-batches": [[41, "batches-and-mini-batches"], [42, "batches-and-mini-batches"]], "Bayesian Machine Learning": [[50, "bayesian-machine-learning"]], "Bayes\u2019 Theorem": [[38, "bayes-theorem"], [39, "bayes-theorem"]], "Bayes\u2019 Theorem and Ridge and Lasso Regression": [[6, "bayes-theorem-and-ridge-and-lasso-regression"], [38, "bayes-theorem-and-ridge-and-lasso-regression"], [39, "bayes-theorem-and-ridge-and-lasso-regression"]], "Best wishes to you all and thanks so much for your heroic efforts this semester": [[50, "best-wishes-to-you-all-and-thanks-so-much-for-your-heroic-efforts-this-semester"]], "Boltzmann Machines": [[50, "boltzmann-machines"]], "Boltzmann machines (BM)": [[50, "boltzmann-machines-bm"]], "Boosting, a Bird\u2019s Eye View": [[11, "boosting-a-bird-s-eye-view"], [48, "boosting-a-bird-s-eye-view"], [49, "boosting-a-bird-s-eye-view"], [50, "boosting-a-bird-s-eye-view"]], "Bootstrap": [[7, "bootstrap"]], "Brief reminder on Newton-Raphson\u2019s method": [[40, "brief-reminder-on-newton-raphson-s-method"], [41, "brief-reminder-on-newton-raphson-s-method"]], "Bringing it together": [[43, "bringing-it-together"], [44, "bringing-it-together"]], "Bringing it together, first back propagation equation": [[13, "bringing-it-together-first-back-propagation-equation"]], "Building a Feed Forward Neural Network": [[2, null]], "Building a neural network code": [[44, "building-a-neural-network-code"], [45, "building-a-neural-network-code"]], "Building a tree, regression": [[10, "building-a-tree-regression"], [48, "building-a-tree-regression"], [49, "building-a-tree-regression"]], "Building convolutional neural networks in Tensorflow and Keras": [[46, "building-convolutional-neural-networks-in-tensorflow-and-keras"], [47, "building-convolutional-neural-networks-in-tensorflow-and-keras"]], "Building neural networks in Tensorflow and Keras": [[2, "building-neural-networks-in-tensorflow-and-keras"], [44, "building-neural-networks-in-tensorflow-and-keras"], [45, "building-neural-networks-in-tensorflow-and-keras"]], "Building our own CNN code": [[46, "building-our-own-cnn-code"], [47, "building-our-own-cnn-code"]], "Building up AdaBoost": [[48, "building-up-adaboost"], [49, "building-up-adaboost"], [50, "building-up-adaboost"]], "But noen of these can compete with Newton\u2019s method": [[22, "but-noen-of-these-can-compete-with-newton-s-method"]], "But none of these can compete with Newton\u2019s method": [[41, "but-none-of-these-can-compete-with-newton-s-method"], [42, "but-none-of-these-can-compete-with-newton-s-method"]], "CNNs in brief": [[46, "cnns-in-brief"], [47, "cnns-in-brief"]], "CNNs in more detail, building convolutional neural networks in Tensorflow and Keras": [[4, "cnns-in-more-detail-building-convolutional-neural-networks-in-tensorflow-and-keras"]], "CNNs in more detail, simple example": [[46, "cnns-in-more-detail-simple-example"]], "Cancer Data again now with Decision Trees and other Methods": [[10, "cancer-data-again-now-with-decision-trees-and-other-methods"]], "Chain rule": [[43, "chain-rule"]], "Chain rule again": [[47, "chain-rule-again"]], "Chain rule, forward and reverse modes": [[43, "chain-rule-forward-and-reverse-modes"]], "Challenge yourself the coming weekend": [[40, "challenge-yourself-the-coming-weekend"]], "Choose a Model and Algorithm": [[50, "choose-a-model-and-algorithm"]], "Choose cost function and optimizer": [[2, "choose-cost-function-and-optimizer"], [44, "choose-cost-function-and-optimizer"], [45, "choose-cost-function-and-optimizer"]], "Class of functions we can approximate": [[43, "class-of-functions-we-can-approximate"]], "Classical PCA Theorem": [[12, "classical-pca-theorem"]], "Classification and Regression, from linear and logistic regression to neural networks": [[30, "classification-and-regression-from-linear-and-logistic-regression-to-neural-networks"]], "Classification problems": [[40, "classification-problems"]], "Classification tree, how to split nodes": [[48, "classification-tree-how-to-split-nodes"], [49, "classification-tree-how-to-split-nodes"]], "Clustering and Unsupervised Learning": [[15, null]], "Code Example for Cross-validation and k-fold Cross-validation": [[39, "code-example-for-cross-validation-and-k-fold-cross-validation"], [40, "code-example-for-cross-validation-and-k-fold-cross-validation"]], "Code example": [[43, "code-example"], [44, "code-example"]], "Code example for the Bootstrap method": [[39, "code-example-for-the-bootstrap-method"]], "Code examples from week 39 and 40": [[22, "code-examples-from-week-39-and-40"]], "Code for SVD and Inversion of Matrices": [[6, "code-for-svd-and-inversion-of-matrices"], [38, "code-for-svd-and-inversion-of-matrices"]], "Code with a Number of Minibatches which varies": [[41, "code-with-a-number-of-minibatches-which-varies"], [42, "code-with-a-number-of-minibatches-which-varies"]], "Code with a Number of Minibatches which varies, analytical gradient": [[22, "code-with-a-number-of-minibatches-which-varies-analytical-gradient"]], "Codes and Approaches": [[15, "codes-and-approaches"]], "Codes for the SVD": [[6, "codes-for-the-svd"], [37, "codes-for-the-svd"]], "Collect and pre-process data": [[2, "collect-and-pre-process-data"], [44, "collect-and-pre-process-data"], [44, "id2"], [45, "collect-and-pre-process-data"], [45, "id2"]], "Communication channels": [[36, "communication-channels"]], "Commutative process": [[46, "commutative-process"]], "Compact expressions": [[43, "compact-expressions"], [44, "compact-expressions"]], "Compare Bagging on Trees with Random Forests": [[11, "compare-bagging-on-trees-with-random-forests"], [48, "compare-bagging-on-trees-with-random-forests"], [49, "compare-bagging-on-trees-with-random-forests"], [50, "compare-bagging-on-trees-with-random-forests"]], "Comparing with a numerical scheme": [[3, "comparing-with-a-numerical-scheme"], [45, "comparing-with-a-numerical-scheme"]], "Comparison with OLS": [[38, "comparison-with-ols"]], "Compile and train the model": [[46, "compile-and-train-the-model"], [47, "compile-and-train-the-model"]], "Completing the list": [[43, "completing-the-list"], [44, "completing-the-list"]], "Computation of gradients": [[41, "computation-of-gradients"], [42, "computation-of-gradients"]], "Computing a Tree using the Gini Index": [[48, "computing-a-tree-using-the-gini-index"], [49, "computing-a-tree-using-the-gini-index"]], "Computing the Gini Factor": [[48, "computing-the-gini-factor"], [49, "computing-the-gini-factor"]], "Computing the Gini index": [[10, "computing-the-gini-index"]], "Computing the various Gini Indices": [[48, "computing-the-various-gini-indices"], [49, "computing-the-various-gini-indices"]], "Computing the various Gini Indices, Hours slept": [[48, "computing-the-various-gini-indices-hours-slept"]], "Computing the various Gini Indices, Hours studied": [[48, "computing-the-various-gini-indices-hours-studied"]], "Conditional Probability": [[38, "conditional-probability"], [39, "conditional-probability"]], "Conditions on convex functions": [[40, "conditions-on-convex-functions"], [41, "conditions-on-convex-functions"]], "Confidence Intervals": [[39, "confidence-intervals"]], "Conjugate gradient method": [[14, "conjugate-gradient-method"], [41, "conjugate-gradient-method"], [41, "id2"], [41, "id3"], [41, "id4"], [41, "id5"], [41, "id6"], [41, "id7"]], "Conjugate gradient method and iterations": [[41, "conjugate-gradient-method-and-iterations"]], "Convex function": [[40, "convex-function"], [41, "convex-function"]], "Convex functions": [[14, "convex-functions"], [40, "convex-functions"], [41, "convex-functions"]], "Convolution": [[46, "convolution"], [47, "convolution"]], "Convolution Examples: Polynomial multiplication": [[4, "convolution-examples-polynomial-multiplication"], [46, "convolution-examples-polynomial-multiplication"]], "Convolution Examples: Principle of Superposition and Periodic Forces (Fourier Transforms)": [[4, "convolution-examples-principle-of-superposition-and-periodic-forces-fourier-transforms"]], "Convolution in the Fourier domain": [[46, "convolution-in-the-fourier-domain"], [47, "convolution-in-the-fourier-domain"]], "Convolution using separable kernels": [[46, "convolution-using-separable-kernels"], [47, "convolution-using-separable-kernels"]], "Convolution2DLayer: convolution in a hidden layer": [[46, "convolution2dlayer-convolution-in-a-hidden-layer"], [47, "convolution2dlayer-convolution-in-a-hidden-layer"]], "Convolutional Neural Network": [[13, "convolutional-neural-network"], [42, "convolutional-neural-network"], [43, "convolutional-neural-network"]], "Convolutional Neural Networks": [[4, null]], "Convolutional Neural Networks (recognizing images)": [[46, "convolutional-neural-networks-recognizing-images"], [47, "convolutional-neural-networks-recognizing-images"]], "Correlation Function and Design/Feature Matrix": [[37, "correlation-function-and-design-feature-matrix"]], "Correlation Matrix": [[12, "correlation-matrix"], [37, "correlation-matrix"]], "Correlation Matrix with Pandas": [[37, "correlation-matrix-with-pandas"]], "Correlation Matrix with Pandas and the Franke function": [[37, "correlation-matrix-with-pandas-and-the-franke-function"]], "Cost complexity pruning": [[48, "cost-complexity-pruning"], [49, "cost-complexity-pruning"]], "Cost functions": [[44, "cost-functions"], [45, "cost-functions"], [46, "cost-functions"], [47, "cost-functions"]], "Counting the number of floating point operations": [[43, "counting-the-number-of-floating-point-operations"]], "Course Format": [[36, "course-format"]], "Covariance Matrix Examples": [[37, "covariance-matrix-examples"]], "Covariance and Correlation Matrix": [[37, "covariance-and-correlation-matrix"]], "Cross correlation": [[46, "cross-correlation"]], "Cross-validation": [[7, "cross-validation"]], "Cross-validation in brief": [[39, "cross-validation-in-brief"], [40, "cross-validation-in-brief"]], "Deadlines for projects (tentative)": [[36, "deadlines-for-projects-tentative"]], "Decision trees and Regression": [[48, "decision-trees-and-regression"]], "Decision trees, overarching aims": [[10, null], [48, "decision-trees-overarching-aims"]], "Deep learning methods": [[36, "deep-learning-methods"]], "Define model and architecture": [[2, "define-model-and-architecture"], [44, "define-model-and-architecture"], [45, "define-model-and-architecture"]], "Defining different types of RBMs": [[50, "defining-different-types-of-rbms"]], "Defining intermediate operations": [[43, "defining-intermediate-operations"]], "Defining the cost function": [[2, "defining-the-cost-function"], [44, "defining-the-cost-function"], [45, "defining-the-cost-function"]], "Defining the data sets to analyze yourself": [[31, "defining-the-data-sets-to-analyze-yourself"]], "Defining the problem": [[45, "defining-the-problem"]], "Definitions": [[43, "definitions"], [44, "definitions"]], "Demonstration": [[46, "demonstration"], [47, "demonstration"]], "Derivative of the cost function": [[43, "derivative-of-the-cost-function"], [44, "derivative-of-the-cost-function"]], "Derivatives and the chain rule": [[13, "derivatives-and-the-chain-rule"], [43, "derivatives-and-the-chain-rule"], [44, "derivatives-and-the-chain-rule"]], "Derivatives in terms of z_j^L": [[43, "derivatives-in-terms-of-z-j-l"], [44, "derivatives-in-terms-of-z-j-l"]], "Derivatives of the hidden layer": [[43, "derivatives-of-the-hidden-layer"], [44, "derivatives-of-the-hidden-layer"]], "Derivatives, example 1": [[37, "derivatives-example-1"]], "Deriving OLS from a probability distribution": [[6, "deriving-ols-from-a-probability-distribution"], [38, "deriving-ols-from-a-probability-distribution"], [39, "deriving-ols-from-a-probability-distribution"]], "Deriving the Lasso Regression Equations": [[37, "deriving-the-lasso-regression-equations"], [38, "deriving-the-lasso-regression-equations"]], "Deriving the Ridge Regression Equations": [[37, "deriving-the-ridge-regression-equations"], [38, "deriving-the-ridge-regression-equations"]], "Deriving the back propagation code for a multilayer perceptron model": [[13, "deriving-the-back-propagation-code-for-a-multilayer-perceptron-model"]], "Description of two-dimensional function": [[29, "description-of-two-dimensional-function"]], "Developing a code for doing neural networks with back propagation": [[2, "developing-a-code-for-doing-neural-networks-with-back-propagation"], [44, "developing-a-code-for-doing-neural-networks-with-back-propagation"], [45, "developing-a-code-for-doing-neural-networks-with-back-propagation"]], "Diagonalize the sample covariance matrix to obtain the principal components": [[12, "diagonalize-the-sample-covariance-matrix-to-obtain-the-principal-components"]], "Did the projects and the teaching material allow you to deepen your insights about Machine Learning methods?": [[26, "did-the-projects-and-the-teaching-material-allow-you-to-deepen-your-insights-about-machine-learning-methods"]], "Different kernels and Mercer\u2019s theorem": [[9, "different-kernels-and-mercer-s-theorem"]], "Differential equations": [[47, "differential-equations"]], "Disadvantages": [[10, "disadvantages"], [48, "disadvantages"], [49, "disadvantages"]], "Discussing the correlation data": [[0, "discussing-the-correlation-data"], [40, "discussing-the-correlation-data"]], "Distributed machine learning": [[50, "distributed-machine-learning"]], "Does Logistic Regression do a better Job?": [[42, "does-logistic-regression-do-a-better-job"], [43, "does-logistic-regression-do-a-better-job"]], "Doing it correctly": [[38, "doing-it-correctly"], [39, "doing-it-correctly"]], "Domains and probabilities": [[33, "domains-and-probabilities"]], "Dropout": [[2, "dropout"], [43, "dropout"], [44, "dropout"]], "Dual learning": [[50, "dual-learning"]], "ELU function": [[43, "elu-function"], [44, "elu-function"], [45, "elu-function"]], "Economy-size SVD": [[37, "economy-size-svd"]], "Efficient Polynomial Multiplication": [[46, "efficient-polynomial-multiplication"]], "Elements of Probability Theory and Statistical Data Analysis": [[33, null]], "Ensemble Methods: From a Single Tree to Many Trees and Extreme Boosting, Meet the Jungle of Methods": [[11, null], [48, "ensemble-methods-from-a-single-tree-to-many-trees-and-extreme-boosting-meet-the-jungle-of-methods"], [49, "ensemble-methods-from-a-single-tree-to-many-trees-and-extreme-boosting-meet-the-jungle-of-methods"]], "Entropy and the ID3 algorithm": [[10, "entropy-and-the-id3-algorithm"]], "Essential elements of ML": [[36, "essential-elements-of-ml"]], "Evaluate model performance on test data": [[2, "evaluate-model-performance-on-test-data"], [44, "evaluate-model-performance-on-test-data"], [45, "evaluate-model-performance-on-test-data"]], "Example 2": [[37, "example-2"]], "Example 3": [[37, "example-3"]], "Example 4": [[37, "example-4"]], "Example Matrix": [[37, "example-matrix"]], "Example code for Bias-Variance tradeoff": [[39, "example-code-for-bias-variance-tradeoff"]], "Example of Usage of Bayes\u2019 theorem": [[38, "example-of-usage-of-bayes-theorem"], [39, "example-of-usage-of-bayes-theorem"]], "Example of own Standard scaling": [[37, "example-of-own-standard-scaling"]], "Example relevant for the exercises": [[37, "example-relevant-for-the-exercises"]], "Example: Exponential decay": [[3, "example-exponential-decay"], [45, "example-exponential-decay"]], "Example: Population growth": [[3, "example-population-growth"], [45, "example-population-growth"]], "Example: Solving the one dimensional Poisson equation": [[45, "example-solving-the-one-dimensional-poisson-equation"]], "Example: Solving the wave equation with Neural Networks": [[45, "example-solving-the-wave-equation-with-neural-networks"]], "Example: The diffusion equation": [[3, "example-the-diffusion-equation"], [45, "example-the-diffusion-equation"]], "Example: binary classification problem": [[2, "example-binary-classification-problem"], [44, "example-binary-classification-problem"], [45, "example-binary-classification-problem"]], "Examples": [[36, "examples"]], "Examples of CNN setups": [[46, "examples-of-cnn-setups"]], "Examples of XOR, OR and AND gates": [[42, "examples-of-xor-or-and-and-gates"], [43, "examples-of-xor-or-and-and-gates"]], "Examples of likelihood functions used in logistic regression and neural networks": [[8, "examples-of-likelihood-functions-used-in-logistic-regression-and-neural-networks"]], "Examples of likelihood functions used in logistic regression and nueral networks": [[0, "examples-of-likelihood-functions-used-in-logistic-regression-and-nueral-networks"], [40, "examples-of-likelihood-functions-used-in-logistic-regression-and-nueral-networks"]], "Exercise 1": [[23, "exercise-1"]], "Exercise 1 - Understand the feed forward pass": [[24, "exercise-1-understand-the-feed-forward-pass"]], "Exercise 1: Analytical exercises": [[17, "exercise-1-analytical-exercises"], [18, "exercise-1-analytical-exercises"]], "Exercise 1: Expectation values for ordinary least squares expressions": [[19, "exercise-1-expectation-values-for-ordinary-least-squares-expressions"]], "Exercise 1: Including more data": [[43, "exercise-1-including-more-data"]], "Exercise 1: Linear and logistic regression methods": [[25, "exercise-1-linear-and-logistic-regression-methods"]], "Exercise 1: Setting up various Python environments": [[1, "exercise-1-setting-up-various-python-environments"], [16, "exercise-1-setting-up-various-python-environments"], [36, "exercise-1-setting-up-various-python-environments"]], "Exercise 2": [[23, "exercise-2"]], "Exercise 2 - Gradient with one layer using autograd": [[24, "exercise-2-gradient-with-one-layer-using-autograd"]], "Exercise 2: Adding Ridge Regression": [[18, "exercise-2-adding-ridge-regression"]], "Exercise 2: Deep learning": [[25, "exercise-2-deep-learning"]], "Exercise 2: Expectation values for Ridge regression": [[19, "exercise-2-expectation-values-for-ridge-regression"]], "Exercise 2: Extended program": [[43, "exercise-2-extended-program"]], "Exercise 2: making your own data and exploring scikit-learn": [[1, "exercise-2-making-your-own-data-and-exploring-scikit-learn"], [16, "exercise-2-making-your-own-data-and-exploring-scikit-learn"], [17, "exercise-2-making-your-own-data-and-exploring-scikit-learn"], [36, "exercise-2-making-your-own-data-and-exploring-scikit-learn"]], "Exercise 3": [[23, "exercise-3"]], "Exercise 3 - Gradient with one layer writing backpropagation by hand": [[24, "exercise-3-gradient-with-one-layer-writing-backpropagation-by-hand"]], "Exercise 3: Decision trees and ensemble methods": [[25, "exercise-3-decision-trees-and-ensemble-methods"]], "Exercise 3: Normalizing our data": [[1, "exercise-3-normalizing-our-data"]], "Exercise 3: Split data in test and training data": [[16, "exercise-3-split-data-in-test-and-training-data"], [17, "exercise-3-split-data-in-test-and-training-data"], [36, "exercise-3-split-data-in-test-and-training-data"]], "Exercise 4 - Custom activation for each layer": [[23, "exercise-4-custom-activation-for-each-layer"]], "Exercise 4 - Gradient with two layers writing backpropagation by hand": [[24, "exercise-4-gradient-with-two-layers-writing-backpropagation-by-hand"]], "Exercise 4: Adding Ridge Regression": [[1, "exercise-4-adding-ridge-regression"]], "Exercise 4: Optimization part": [[25, "exercise-4-optimization-part"]], "Exercise 5 - Gradient with any number of layers writing backpropagation by hand": [[24, "exercise-5-gradient-with-any-number-of-layers-writing-backpropagation-by-hand"]], "Exercise 5 - Processing multiple inputs at once": [[23, "exercise-5-processing-multiple-inputs-at-once"]], "Exercise 5: Analysis of results": [[25, "exercise-5-analysis-of-results"]], "Exercise 5: Analytical exercises": [[1, "exercise-5-analytical-exercises"]], "Exercise 6 - Batched inputs": [[24, "exercise-6-batched-inputs"]], "Exercise 6 - Predicting on real data": [[23, "exercise-6-predicting-on-real-data"]], "Exercise 7 - Training": [[24, "exercise-7-training"]], "Exercise 7 - Training on real data (Optional)": [[23, "exercise-7-training-on-real-data-optional"]], "Exercise 8 (Optional) - Object orientation": [[24, "exercise-8-optional-object-orientation"]], "Exercise week 47": [[25, null]], "Exercise: Cross-validation as resampling techniques, adding more complexity": [[7, "exercise-cross-validation-as-resampling-techniques-adding-more-complexity"]], "Exercise: Analysis of real data": [[7, "exercise-analysis-of-real-data"]], "Exercise: Bias-variance trade-off and resampling techniques": [[7, "exercise-bias-variance-trade-off-and-resampling-techniques"]], "Exercise: Lasso Regression on the Franke function with resampling": [[7, "exercise-lasso-regression-on-the-franke-function-with-resampling"]], "Exercise: Ordinary Least Square (OLS) on the Franke function": [[7, "exercise-ordinary-least-square-ols-on-the-franke-function"]], "Exercise: Ridge Regression on the Franke function with resampling": [[7, "exercise-ridge-regression-on-the-franke-function-with-resampling"]], "Exercises": [[1, "exercises"], [16, "exercises"], [36, "exercises"]], "Exercises Week 42: Logistic Regression and Optimization, reminders from week 38 and week 40": [[0, null]], "Exercises and Projects": [[7, "exercises-and-projects"]], "Exercises and lab session week 43": [[45, "exercises-and-lab-session-week-43"]], "Exercises week 34": [[16, null]], "Exercises week 35": [[17, null]], "Exercises week 36": [[18, null]], "Exercises week 37": [[19, null]], "Exercises week 38": [[20, null]], "Exercises week 39": [[21, null]], "Exercises week 41": [[22, null]], "Exercises week 42": [[23, null]], "Exercises week 43": [[24, null]], "Exercises week 48": [[26, null]], "Expectation value and variance": [[38, "expectation-value-and-variance"], [39, "expectation-value-and-variance"]], "Expectation value and variance for \\boldsymbol{\\beta}": [[38, "expectation-value-and-variance-for-boldsymbol-beta"], [39, "expectation-value-and-variance-for-boldsymbol-beta"]], "Expectation values": [[33, "expectation-values"]], "Explainable machine learning": [[50, "explainable-machine-learning"]], "Explicit derivatives": [[43, "explicit-derivatives"], [44, "explicit-derivatives"]], "Exploding gradients": [[43, "exploding-gradients"], [44, "exploding-gradients"]], "Extending to more predictors": [[0, "extending-to-more-predictors"], [40, "extending-to-more-predictors"]], "Extending to more than one variable": [[40, "extending-to-more-than-one-variable"], [41, "extending-to-more-than-one-variable"]], "Extremely useful tools, strongly recommended": [[36, "extremely-useful-tools-strongly-recommended"]], "Feed-forward neural networks": [[13, "feed-forward-neural-networks"], [42, "feed-forward-neural-networks"], [43, "feed-forward-neural-networks"]], "Feed-forward pass": [[2, "feed-forward-pass"], [44, "feed-forward-pass"], [45, "feed-forward-pass"]], "Feedback connections": [[47, "feedback-connections"]], "Final back propagating equation": [[13, "final-back-propagating-equation"], [43, "final-back-propagating-equation"], [44, "final-back-propagating-equation"]], "Final derivatives": [[43, "final-derivatives"]], "Final expression": [[43, "final-expression"], [44, "final-expression"]], "Final expressions": [[41, "final-expressions"]], "Final expressions for the biases of the hidden layer": [[43, "final-expressions-for-the-biases-of-the-hidden-layer"], [44, "final-expressions-for-the-biases-of-the-hidden-layer"]], "Final part": [[46, "final-part"], [47, "final-part"]], "Final regressor code": [[48, "final-regressor-code"], [49, "final-regressor-code"]], "Final technicalities I": [[45, "final-technicalities-i"]], "Final technicalities II": [[45, "final-technicalities-ii"]], "Final technicalities III": [[45, "final-technicalities-iii"]], "Final technicalities IV": [[45, "final-technicalities-iv"]], "Final visualization": [[46, "final-visualization"], [47, "final-visualization"]], "Finally, evaluate the model": [[46, "finally-evaluate-the-model"], [47, "finally-evaluate-the-model"]], "Finding the Limit": [[39, "finding-the-limit"]], "Finding the number of parameters": [[46, "finding-the-number-of-parameters"]], "Fine-tuning neural network hyperparameters": [[2, "fine-tuning-neural-network-hyperparameters"], [43, "fine-tuning-neural-network-hyperparameters"], [44, "fine-tuning-neural-network-hyperparameters"]], "First network example, simple percepetron with one input": [[43, "first-network-example-simple-percepetron-with-one-input"], [44, "first-network-example-simple-percepetron-with-one-input"]], "Fitting an Equation of State for Dense Nuclear Matter": [[1, "fitting-an-equation-of-state-for-dense-nuclear-matter"], [36, "fitting-an-equation-of-state-for-dense-nuclear-matter"]], "Fixing the singularity": [[37, "fixing-the-singularity"]], "Flattening Layer": [[46, "flattening-layer"], [47, "flattening-layer"]], "For exercise sessions: Why Linear Regression (aka Ordinary Least Squares and family), repeat from last week": [[37, "for-exercise-sessions-why-linear-regression-aka-ordinary-least-squares-and-family-repeat-from-last-week"]], "Forget and input": [[47, "forget-and-input"]], "Format for electronic delivery of report and programs": [[29, "format-for-electronic-delivery-of-report-and-programs"], [30, "format-for-electronic-delivery-of-report-and-programs"], [31, "format-for-electronic-delivery-of-report-and-programs"]], "Forward and reverse modes": [[43, "forward-and-reverse-modes"]], "Four effective ways to learn an RNN and preparing for next week": [[47, "four-effective-ways-to-learn-an-rnn-and-preparing-for-next-week"]], "Fourier series and Toeplitz matrices": [[46, "fourier-series-and-toeplitz-matrices"]], "Frequently used scaling functions": [[37, "frequently-used-scaling-functions"]], "From FFNNs and CNNs to recurrent neural networks (RNNs)": [[47, "from-ffnns-and-cnns-to-recurrent-neural-networks-rnns"]], "From OLS to Ridge and Lasso": [[38, "from-ols-to-ridge-and-lasso"]], "From one to many layers, the universal approximation theorem": [[13, "from-one-to-many-layers-the-universal-approximation-theorem"]], "Full object-oriented implementation": [[44, "full-object-oriented-implementation"], [45, "full-object-oriented-implementation"]], "Fully Connected Layers": [[46, "fully-connected-layers"], [47, "fully-connected-layers"]], "Functionality in Scikit-Learn": [[37, "functionality-in-scikit-learn"]], "Functions using mathematical functions from Numpy": [[41, "functions-using-mathematical-functions-from-numpy"], [42, "functions-using-mathematical-functions-from-numpy"]], "Further Dimensionality Remarks": [[4, "further-dimensionality-remarks"]], "Further Manipulations": [[37, "further-manipulations"]], "Further example: Computing the Gini index": [[48, "further-example-computing-the-gini-index"], [49, "further-example-computing-the-gini-index"]], "Further properties (important for our analyses later)": [[6, "further-properties-important-for-our-analyses-later"], [37, "further-properties-important-for-our-analyses-later"]], "Further remarks": [[46, "further-remarks"]], "Further simplification": [[46, "further-simplification"]], "Gating mechanism: Long Short Term Memory (LSTM)": [[47, "gating-mechanism-long-short-term-memory-lstm"]], "Gaussian Elimination": [[28, "gaussian-elimination"]], "General Features": [[10, "general-features"], [48, "general-features"]], "General linear models and linear algebra": [[36, "general-linear-models-and-linear-algebra"]], "Generalizing the above one-dimensional case": [[46, "generalizing-the-above-one-dimensional-case"]], "Generalizing the fitting procedure as a linear algebra problem": [[36, "generalizing-the-fitting-procedure-as-a-linear-algebra-problem"], [36, "id1"]], "Generative Adversarial Networks": [[5, "generative-adversarial-networks"]], "Generative Models": [[5, "generative-models"]], "Geometric Interpretation and link with Singular Value Decomposition": [[12, "geometric-interpretation-and-link-with-singular-value-decomposition"]], "Getting serious, the back propagation equations for a neural network": [[43, "getting-serious-the-back-propagation-equations-for-a-neural-network"]], "Getting started with Jax, note the way we import numpy": [[22, "getting-started-with-jax-note-the-way-we-import-numpy"], [42, "getting-started-with-jax-note-the-way-we-import-numpy"]], "Goals": [[50, "goals"]], "Gradient Boosting, Classification Example": [[11, "gradient-boosting-classification-example"], [49, "gradient-boosting-classification-example"], [50, "gradient-boosting-classification-example"]], "Gradient Boosting, Examples of Regression": [[11, "gradient-boosting-examples-of-regression"], [49, "gradient-boosting-examples-of-regression"], [50, "gradient-boosting-examples-of-regression"]], "Gradient Boosting, algorithm": [[49, "gradient-boosting-algorithm"], [50, "gradient-boosting-algorithm"]], "Gradient Clipping": [[2, "gradient-clipping"], [43, "gradient-clipping"], [44, "gradient-clipping"]], "Gradient Descent Example": [[40, "id1"], [41, "id8"]], "Gradient boosting, making our own code for a regression case": [[49, "gradient-boosting-making-our-own-code-for-a-regression-case"], [50, "gradient-boosting-making-our-own-code-for-a-regression-case"]], "Gradient boosting: Basics with Steepest Descent/Functional Gradient Descent": [[11, "gradient-boosting-basics-with-steepest-descent-functional-gradient-descent"], [49, "gradient-boosting-basics-with-steepest-descent-functional-gradient-descent"], [50, "gradient-boosting-basics-with-steepest-descent-functional-gradient-descent"]], "Gradient descent": [[3, "gradient-descent"], [45, "gradient-descent"]], "Gradient descent and Logistic regression": [[0, "gradient-descent-and-logistic-regression"], [42, "gradient-descent-and-logistic-regression"]], "Gradient descent and Ridge": [[40, "gradient-descent-and-ridge"], [41, "gradient-descent-and-ridge"]], "Gradient descent example": [[40, "gradient-descent-example"], [41, "gradient-descent-example"]], "Gradient expressions": [[43, "gradient-expressions"], [44, "gradient-expressions"]], "Gradient method": [[41, "gradient-method"]], "Gradients of loss functions": [[47, "gradients-of-loss-functions"]], "Grading": [[34, "grading"], [36, "grading"]], "Grid Search": [[40, "grid-search"]], "Growing a classification tree": [[48, "growing-a-classification-tree"], [49, "growing-a-classification-tree"]], "Hidden layers": [[43, "hidden-layers"], [44, "hidden-layers"]], "Homogeneous data": [[43, "homogeneous-data"], [44, "homogeneous-data"]], "Housing data, the code": [[1, "housing-data-the-code"]], "How do we set it up?": [[48, "how-do-we-set-it-up"]], "How do you judge your own level of knowledge on machine learning before and after this course?": [[26, "how-do-you-judge-your-own-level-of-knowledge-on-machine-learning-before-and-after-this-course"]], "How to do image compression before the era of deep learning": [[46, "how-to-do-image-compression-before-the-era-of-deep-learning"]], "How to set up the cross-validation for Ridge and/or Lasso": [[40, "how-to-set-up-the-cross-validation-for-ridge-and-or-lasso"]], "How would you improve this course?": [[26, "how-would-you-improve-this-course"]], "Hyperplanes and all that": [[9, "hyperplanes-and-all-that"]], "Identifying Terms": [[39, "identifying-terms"]], "If you did not attend the lectures or the lab sessions, which resources did you use?": [[26, "if-you-did-not-attend-the-lectures-or-the-lab-sessions-which-resources-did-you-use"]], "Illustration of a single perceptron model and a multi-perceptron model": [[42, "illustration-of-a-single-perceptron-model-and-a-multi-perceptron-model"], [43, "illustration-of-a-single-perceptron-model-and-a-multi-perceptron-model"]], "Implementing a memory cell in a neural network": [[47, "implementing-a-memory-cell-in-a-neural-network"]], "Important Matrix and vector handling packages": [[28, "important-matrix-and-vector-handling-packages"]], "Important observations": [[43, "important-observations"], [44, "important-observations"]], "Important technicalities: More on Rescaling data": [[38, "important-technicalities-more-on-rescaling-data"]], "Importing Keras and Tensorflow": [[46, "importing-keras-and-tensorflow"], [47, "importing-keras-and-tensorflow"]], "Improving gradient descent with momentum": [[41, "improving-gradient-descent-with-momentum"]], "Improving performance": [[2, "improving-performance"], [44, "improving-performance"], [45, "improving-performance"]], "In general not this simple": [[43, "in-general-not-this-simple"]], "In summary": [[34, "in-summary"]], "Including Stochastic Gradient Descent with Autograd": [[14, "including-stochastic-gradient-descent-with-autograd"], [22, "including-stochastic-gradient-descent-with-autograd"], [41, "including-stochastic-gradient-descent-with-autograd"], [42, "including-stochastic-gradient-descent-with-autograd"]], "Including more classes": [[0, "including-more-classes"], [40, "including-more-classes"]], "Incremental PCA": [[12, "incremental-pca"]], "Independent and Identically Distributed (iid)": [[38, "independent-and-identically-distributed-iid"]], "Independent and Identically Distrubuted (iid)": [[39, "independent-and-identically-distrubuted-iid"]], "Input gate": [[47, "input-gate"]], "Inputs to the activation function": [[43, "inputs-to-the-activation-function"], [44, "inputs-to-the-activation-function"]], "Insights from the paper by Glorot and Bengio": [[43, "insights-from-the-paper-by-glorot-and-bengio"], [44, "insights-from-the-paper-by-glorot-and-bengio"]], "Installing R, C++, cython or Julia": [[36, "installing-r-c-cython-or-julia"]], "Installing R, C++, cython, Numba etc": [[36, "installing-r-c-cython-numba-etc"]], "Instructor information": [[34, "instructor-information"]], "Interpretations and optimizing our parameters": [[36, "interpretations-and-optimizing-our-parameters"], [36, "id2"], [36, "id3"], [37, "interpretations-and-optimizing-our-parameters"], [37, "id1"], [37, "id2"]], "Interpretations of Bayes\u2019 Theorem": [[38, "interpretations-of-bayes-theorem"], [39, "interpretations-of-bayes-theorem"]], "Interpreting the Ridge results": [[37, "interpreting-the-ridge-results"], [38, "interpreting-the-ridge-results"]], "Introducing JAX": [[14, "introducing-jax"], [22, "introducing-jax"], [41, "introducing-jax"], [42, "introducing-jax"]], "Introducing the Covariance and Correlation functions": [[12, "introducing-the-covariance-and-correlation-functions"], [37, "introducing-the-covariance-and-correlation-functions"]], "Introduction": [[1, "introduction"], [7, "introduction"], [27, "introduction"], [28, "introduction"]], "Introduction to Neural networks": [[42, "introduction-to-neural-networks"], [43, "introduction-to-neural-networks"]], "Introduction to numerical projects": [[29, "introduction-to-numerical-projects"], [30, "introduction-to-numerical-projects"], [31, "introduction-to-numerical-projects"]], "Inverse of Rectangular Matrix": [[38, "inverse-of-rectangular-matrix"]], "Is the Logistic activation function (Sigmoid) our choice?": [[43, "is-the-logistic-activation-function-sigmoid-our-choice"], [44, "is-the-logistic-activation-function-sigmoid-our-choice"]], "Iterative Fitting, Classification and AdaBoost": [[11, "iterative-fitting-classification-and-adaboost"], [48, "iterative-fitting-classification-and-adaboost"], [49, "iterative-fitting-classification-and-adaboost"], [50, "iterative-fitting-classification-and-adaboost"]], "Iterative Fitting, Regression and Squared-error Cost Function": [[11, "iterative-fitting-regression-and-squared-error-cost-function"], [48, "iterative-fitting-regression-and-squared-error-cost-function"], [49, "iterative-fitting-regression-and-squared-error-cost-function"], [50, "iterative-fitting-regression-and-squared-error-cost-function"]], "Joint distribution": [[50, "joint-distribution"]], "Kernel PCA": [[12, "kernel-pca"]], "Kernels and non-linearity": [[9, "kernels-and-non-linearity"]], "Key Idea": [[46, "key-idea"], [47, "key-idea"]], "LSTM details": [[47, "lstm-details"]], "LU Decomposition, the inverse of a matrix": [[28, "lu-decomposition-the-inverse-of-a-matrix"]], "Lab sessions on Tuesday and Wednesday": [[46, "lab-sessions-on-tuesday-and-wednesday"]], "Lab session: Material from last week and relevant for the first project": [[40, "lab-session-material-from-last-week-and-relevant-for-the-first-project"]], "Lab sessions": [[50, "lab-sessions"]], "Lab sessions Tuesday and Wednesday": [[42, "lab-sessions-tuesday-and-wednesday"]], "Lab sessions and lectures": [[26, "lab-sessions-and-lectures"]], "Lab sessions week 39": [[41, "lab-sessions-week-39"]], "Lasso Regression": [[38, "lasso-regression"]], "Lasso and Bayes": [[38, "lasso-and-bayes"], [39, "lasso-and-bayes"]], "Lasso case": [[38, "lasso-case"]], "Layers": [[2, "layers"], [44, "layers"], [45, "layers"], [46, "layers"], [47, "layers"]], "Layers of a CNN": [[46, "layers-of-a-cnn"], [47, "layers-of-a-cnn"]], "Layers used to build CNNs": [[4, "layers-used-to-build-cnns"], [46, "layers-used-to-build-cnns"], [47, "layers-used-to-build-cnns"]], "Layout of a neural network with three hidden layers": [[43, "layout-of-a-neural-network-with-three-hidden-layers"]], "Layout of a neural network with three hidden layers (last later = l=L=4, first layer l=0)": [[44, "layout-of-a-neural-network-with-three-hidden-layers-last-later-l-l-4-first-layer-l-0"]], "Layout of a simple neural network with no hidden layer": [[43, "layout-of-a-simple-neural-network-with-no-hidden-layer"], [44, "layout-of-a-simple-neural-network-with-no-hidden-layer"]], "Layout of a simple neural network with one hidden layer": [[43, "layout-of-a-simple-neural-network-with-one-hidden-layer"], [44, "layout-of-a-simple-neural-network-with-one-hidden-layer"]], "Layout of a simple neural network with two input nodes, one hidden layer and one output node": [[43, "layout-of-a-simple-neural-network-with-two-input-nodes-one-hidden-layer-and-one-output-node"]], "Layout of a simple neural network with two input nodes, one hidden layer with two hidden noeds and one output node": [[44, "layout-of-a-simple-neural-network-with-two-input-nodes-one-hidden-layer-with-two-hidden-noeds-and-one-output-node"]], "Layout of input to first hidden layer l=1 from input layer l=0": [[44, "layout-of-input-to-first-hidden-layer-l-1-from-input-layer-l-0"]], "Learning outcomes": [[27, "learning-outcomes"], [36, "learning-outcomes"]], "Learning outcomes and overarching aims of this course": [[50, "learning-outcomes-and-overarching-aims-of-this-course"]], "Learning rate methods": [[44, "learning-rate-methods"], [45, "learning-rate-methods"]], "Lecture Monday October 21": [[45, "lecture-monday-october-21"]], "Lecture Monday October 7": [[43, "lecture-monday-october-7"]], "Lecture Monday September 23": [[41, "lecture-monday-september-23"]], "Lecture Monday September 23, Optimization, the central part of any Machine Learning algortithm": [[41, "lecture-monday-september-23-optimization-the-central-part-of-any-machine-learning-algortithm"]], "Lecture Monday September 30, 2024": [[42, "lecture-monday-september-30-2024"]], "Lecture Monday, November 25": [[50, "lecture-monday-november-25"]], "Lecture October 14, 2024": [[44, "lecture-october-14-2024"]], "Lectures and ComputerLab": [[36, "lectures-and-computerlab"]], "Limitations of NNs": [[43, "limitations-of-nns"], [44, "limitations-of-nns"]], "Limitations of supervised learning with deep networks": [[2, "limitations-of-supervised-learning-with-deep-networks"], [43, "limitations-of-supervised-learning-with-deep-networks"], [44, "limitations-of-supervised-learning-with-deep-networks"]], "Linear Algebra, Handling of Arrays and more Python Features": [[28, null]], "Linear Regression": [[1, null]], "Linear Regression Problems": [[37, "linear-regression-problems"]], "Linear Regression and the SVD": [[38, "linear-regression-and-the-svd"]], "Linear Regression code, Intercept handling first": [[37, "linear-regression-code-intercept-handling-first"]], "Linear Regression, basic elements": [[1, "linear-regression-basic-elements"]], "Linear classifier": [[40, "linear-classifier"]], "Linking Bayes\u2019 Theorem with Ridge and Lasso Regression": [[6, "linking-bayes-theorem-with-ridge-and-lasso-regression"]], "Linking the regression analysis with a statistical interpretation": [[6, "linking-the-regression-analysis-with-a-statistical-interpretation"], [38, "linking-the-regression-analysis-with-a-statistical-interpretation"], [39, "linking-the-regression-analysis-with-a-statistical-interpretation"]], "Linking with RNNs": [[47, "linking-with-rnns"]], "Linking with the SVD": [[6, "linking-with-the-svd"], [37, "linking-with-the-svd"]], "Links to relevant courses at the University of Oslo": [[35, "links-to-relevant-courses-at-the-university-of-oslo"]], "List of contents:": [[46, "list-of-contents"], [47, "list-of-contents"]], "Logistic Regression": [[8, null], [8, "id1"], [40, "logistic-regression"]], "Logistic function as the root of problems": [[43, "logistic-function-as-the-root-of-problems"], [44, "logistic-function-as-the-root-of-problems"]], "MNIST and GANs": [[5, "mnist-and-gans"]], "Machine Learning": [[36, "machine-learning"]], "Machine Learning Research": [[50, "machine-learning-research"]], "Machine learning": [[27, "machine-learning"], [50, "machine-learning"]], "Main textbooks": [[36, "main-textbooks"]], "Making a tree": [[10, "making-a-tree"], [48, "making-a-tree"], [49, "making-a-tree"]], "Making an ADAboost code yourself": [[49, "making-an-adaboost-code-yourself"], [50, "making-an-adaboost-code-yourself"]], "Making your own Bootstrap: Changing the Level of the Decision Tree": [[11, "making-your-own-bootstrap-changing-the-level-of-the-decision-tree"], [48, "making-your-own-bootstrap-changing-the-level-of-the-decision-tree"], [49, "making-your-own-bootstrap-changing-the-level-of-the-decision-tree"]], "Making your own test-train splitting": [[37, "making-your-own-test-train-splitting"]], "Marginal Probability": [[38, "marginal-probability"], [39, "marginal-probability"]], "Material for Lecture Monday November 4": [[47, "material-for-lecture-monday-november-4"]], "Material for Lecture Monday October 28": [[46, "material-for-lecture-monday-october-28"]], "Material for lecture Monday September 16": [[40, "material-for-lecture-monday-september-16"]], "Material for lecture Monday September 2": [[38, "material-for-lecture-monday-september-2"]], "Material for lecture Monday September 9": [[39, "material-for-lecture-monday-september-9"]], "Material for lecture Monday, August 26": [[37, "material-for-lecture-monday-august-26"]], "Material for the active learning sessions Tuesday and Wednesday": [[38, "material-for-the-active-learning-sessions-tuesday-and-wednesday"]], "Material for the active learning sessions on Tuesday and Wednesday": [[43, "material-for-the-active-learning-sessions-on-tuesday-and-wednesday"], [44, "material-for-the-active-learning-sessions-on-tuesday-and-wednesday"]], "Material for the lab sessions": [[39, "material-for-the-lab-sessions"]], "Material for the lab sessions, additional ways to present classification results and other practicalities": [[47, "material-for-the-lab-sessions-additional-ways-to-present-classification-results-and-other-practicalities"]], "Material for the lecture on Monday October 7, 2024": [[43, "material-for-the-lecture-on-monday-october-7-2024"]], "Mathematical Interpretation of Ordinary Least Squares": [[6, "mathematical-interpretation-of-ordinary-least-squares"], [37, "mathematical-interpretation-of-ordinary-least-squares"]], "Mathematical model": [[42, "mathematical-model"], [42, "id2"], [42, "id3"], [42, "id4"], [42, "id5"]], "Mathematical optimization of convex functions": [[9, "mathematical-optimization-of-convex-functions"]], "Mathematical setup": [[47, "mathematical-setup"]], "Mathematics of CNNs": [[4, "mathematics-of-cnns"], [46, "mathematics-of-cnns"]], "Mathematics of deep learning": [[43, "mathematics-of-deep-learning"], [44, "mathematics-of-deep-learning"], [45, "mathematics-of-deep-learning"]], "Mathematics of deep learning and neural networks": [[43, "mathematics-of-deep-learning-and-neural-networks"]], "Mathematics of the SVD and implications": [[6, "mathematics-of-the-svd-and-implications"], [37, "mathematics-of-the-svd-and-implications"]], "Matrices in Python": [[36, "matrices-in-python"]], "Matrix multiplication": [[2, "matrix-multiplication"], [44, "matrix-multiplication"], [45, "matrix-multiplication"]], "Matrix multiplications": [[44, "matrix-multiplications"], [45, "matrix-multiplications"]], "Matrix-vector notation": [[42, "matrix-vector-notation"]], "Matrix-vector notation and activation": [[13, "matrix-vector-notation-and-activation"], [42, "matrix-vector-notation-and-activation"]], "Maximum Likelihood Estimation (MLE)": [[38, "maximum-likelihood-estimation-mle"], [39, "maximum-likelihood-estimation-mle"]], "Maximum likelihood": [[40, "maximum-likelihood"]], "Meet the covariance!": [[33, "meet-the-covariance"]], "Meet the Covariance Matrix": [[6, "meet-the-covariance-matrix"], [37, "meet-the-covariance-matrix"]], "Meet the Hessian Matrix": [[37, "meet-the-hessian-matrix"]], "Meet the Pandas": [[36, "meet-the-pandas"]], "Memory considerations": [[46, "memory-considerations"]], "Meta learning": [[50, "meta-learning"]], "Min-Max Scaling": [[37, "min-max-scaling"]], "Minimization process": [[45, "minimization-process"]], "Minimizing the cost function using gradient descent and automatic differentiation": [[45, "minimizing-the-cost-function-using-gradient-descent-and-automatic-differentiation"]], "Minimizing the cross entropy": [[0, "minimizing-the-cross-entropy"], [40, "minimizing-the-cross-entropy"]], "Minor rewrite": [[47, "minor-rewrite"]], "Momentum based GD": [[14, "momentum-based-gd"], [22, "momentum-based-gd"], [41, "momentum-based-gd"], [42, "momentum-based-gd"]], "Momentum parameter": [[41, "momentum-parameter"], [42, "momentum-parameter"]], "More LSTM details": [[47, "more-lstm-details"]], "More about RBMs": [[50, "more-about-rbms"]], "More autograd": [[41, "more-autograd"], [42, "more-autograd"]], "More bagging": [[48, "more-bagging"], [49, "more-bagging"]], "More basic Statistics and Bayes\u2019 theorem": [[38, "more-basic-statistics-and-bayes-theorem"], [39, "more-basic-statistics-and-bayes-theorem"]], "More classes": [[0, "more-classes"], [40, "more-classes"]], "More complicated Example: The Ising model": [[7, "more-complicated-example-the-ising-model"]], "More complicated function": [[43, "more-complicated-function"]], "More complicated functions using the elements of their arguments directly": [[41, "more-complicated-functions-using-the-elements-of-their-arguments-directly"], [42, "more-complicated-functions-using-the-elements-of-their-arguments-directly"]], "More considerations": [[43, "more-considerations"], [44, "more-considerations"]], "More details": [[45, "more-details"], [45, "id6"]], "More examples on bootstrap and cross-validation and errors": [[39, "more-examples-on-bootstrap-and-cross-validation-and-errors"]], "More interpretations": [[37, "more-interpretations"], [38, "more-interpretations"]], "More limitations": [[43, "more-limitations"], [44, "more-limitations"]], "More on Dimensionalities": [[4, "more-on-dimensionalities"], [46, "more-on-dimensionalities"]], "More on Rescaling data": [[7, "more-on-rescaling-data"]], "More on Steepest descent": [[40, "more-on-steepest-descent"], [41, "more-on-steepest-descent"]], "More on activation functions, output layers": [[43, "more-on-activation-functions-output-layers"], [44, "more-on-activation-functions-output-layers"], [45, "more-on-activation-functions-output-layers"]], "More on convex functions": [[40, "more-on-convex-functions"], [41, "more-on-convex-functions"]], "More on momentum based approaches": [[41, "more-on-momentum-based-approaches"], [42, "more-on-momentum-based-approaches"]], "More on the general approximation theorem": [[43, "more-on-the-general-approximation-theorem"]], "More preprocessing": [[37, "more-preprocessing"]], "More preprocessing examples, two-dimensional example, the Franke function": [[37, "more-preprocessing-examples-two-dimensional-example-the-franke-function"]], "More technicalities": [[45, "more-technicalities"]], "More thinking": [[37, "more-thinking"]], "More top-down perspectives": [[43, "more-top-down-perspectives"], [44, "more-top-down-perspectives"]], "Multiclass classification": [[44, "multiclass-classification"], [45, "multiclass-classification"]], "Multilayer perceptrons": [[13, "multilayer-perceptrons"], [42, "multilayer-perceptrons"], [43, "multilayer-perceptrons"]], "Multivariable functions": [[43, "multivariable-functions"]], "Network Elements, the energy function": [[50, "network-elements-the-energy-function"]], "Network requirements": [[3, "network-requirements"], [45, "network-requirements"]], "Neural Networks vs CNNs": [[4, "neural-networks-vs-cnns"], [46, "neural-networks-vs-cnns"], [47, "neural-networks-vs-cnns"]], "Neural network types": [[42, "neural-network-types"], [43, "neural-network-types"]], "Neural networks": [[13, null]], "New expression for the derivative": [[43, "new-expression-for-the-derivative"]], "New image (or volume)": [[46, "new-image-or-volume"]], "New vector": [[46, "new-vector"]], "Note about SVD Calculations": [[37, "note-about-svd-calculations"]], "Note on Scikit-Learn": [[38, "note-on-scikit-learn"]], "Numerical experiments and the covariance, central limit theorem": [[33, "numerical-experiments-and-the-covariance-central-limit-theorem"]], "Numpy and arrays": [[28, "numpy-and-arrays"], [36, "numpy-and-arrays"]], "Numpy examples and Important Matrix and vector handling packages": [[36, "numpy-examples-and-important-matrix-and-vector-handling-packages"]], "Optimization Methods and Hyperparameters": [[50, "optimization-methods-and-hyperparameters"]], "Optimization and Deep learning": [[40, "optimization-and-deep-learning"]], "Optimization, the central part of any Machine Learning algortithm": [[14, null], [40, "optimization-the-central-part-of-any-machine-learning-algortithm"]], "Optimized Convolution2DLayer": [[46, "optimized-convolution2dlayer"], [47, "optimized-convolution2dlayer"]], "Optimizing our parameters": [[36, "optimizing-our-parameters"]], "Optimizing our parameters, more details": [[36, "optimizing-our-parameters-more-details"]], "Optimizing the cost function": [[2, "optimizing-the-cost-function"], [44, "optimizing-the-cost-function"], [45, "optimizing-the-cost-function"]], "Optimizing the parameters": [[43, "optimizing-the-parameters"], [44, "optimizing-the-parameters"]], "Ordinary Differential Equations first": [[45, "ordinary-differential-equations-first"]], "Organizing our data": [[1, "organizing-our-data"], [36, "organizing-our-data"]], "Other Matrix and Vector Operations": [[28, "other-matrix-and-vector-operations"]], "Other Types of Recurrent Neural Networks": [[5, "other-types-of-recurrent-neural-networks"]], "Other courses on Data science and Machine Learning at UiO": [[36, "other-courses-on-data-science-and-machine-learning-at-uio"], [50, "other-courses-on-data-science-and-machine-learning-at-uio"]], "Other courses on Data science and Machine Learning at UiO, contn": [[36, "other-courses-on-data-science-and-machine-learning-at-uio-contn"]], "Other ingredients of a neural network": [[43, "other-ingredients-of-a-neural-network"]], "Other measures in classification studies: Cancer Data again": [[0, "other-measures-in-classification-studies-cancer-data-again"], [40, "other-measures-in-classification-studies-cancer-data-again"]], "Other parameters": [[43, "other-parameters"]], "Other popular texts": [[36, "other-popular-texts"]], "Other techniques": [[12, "other-techniques"]], "Other types of networks": [[13, "other-types-of-networks"], [42, "other-types-of-networks"], [43, "other-types-of-networks"]], "Other useful relations": [[37, "other-useful-relations"]], "Other ways of visualizing the trees": [[10, "other-ways-of-visualizing-the-trees"], [48, "other-ways-of-visualizing-the-trees"], [49, "other-ways-of-visualizing-the-trees"]], "Our model for the nuclear binding energies": [[36, "our-model-for-the-nuclear-binding-energies"]], "Output gate": [[47, "output-gate"]], "Output layer": [[43, "output-layer"], [44, "output-layer"]], "Overarching aims of the exercises this week": [[18, "overarching-aims-of-the-exercises-this-week"], [19, "overarching-aims-of-the-exercises-this-week"], [20, "overarching-aims-of-the-exercises-this-week"], [21, "overarching-aims-of-the-exercises-this-week"], [22, "overarching-aims-of-the-exercises-this-week"], [23, "overarching-aims-of-the-exercises-this-week"], [24, "overarching-aims-of-the-exercises-this-week"], [25, "overarching-aims-of-the-exercises-this-week"], [26, "overarching-aims-of-the-exercises-this-week"]], "Overarching view of a neural network": [[43, "overarching-view-of-a-neural-network"]], "Overview of first week": [[36, "overview-of-first-week"]], "Overview of week 48": [[50, "overview-of-week-48"]], "Overview video on Stochastic Gradient Descent": [[41, "overview-video-on-stochastic-gradient-descent"], [42, "overview-video-on-stochastic-gradient-descent"]], "Own code for Ordinary Least Squares": [[36, "own-code-for-ordinary-least-squares"], [37, "own-code-for-ordinary-least-squares"]], "PCA and scikit-learn": [[12, "pca-and-scikit-learn"]], "Padding": [[46, "padding"]], "Pandas AI": [[36, "pandas-ai"]], "Parameters of neural networks": [[43, "parameters-of-neural-networks"]], "Parameters to train, common settings": [[46, "parameters-to-train-common-settings"]], "Part a)": [[31, "part-a"]], "Part a) : Ordinary Least Square (OLS) on the Franke function": [[29, "part-a-ordinary-least-square-ols-on-the-franke-function"]], "Part a), setting up the problem": [[31, "part-a-setting-up-the-problem"]], "Part a): Write your own Stochastic Gradient Descent code, first step": [[30, "part-a-write-your-own-stochastic-gradient-descent-code-first-step"]], "Part b)": [[31, "part-b"], [31, "id1"]], "Part b): Adding Ridge regression for the Franke function": [[29, "part-b-adding-ridge-regression-for-the-franke-function"]], "Part b): Writing your own Neural Network code": [[30, "part-b-writing-your-own-neural-network-code"]], "Part c)": [[31, "part-c"]], "Part c) Neural networks": [[31, "part-c-neural-networks"]], "Part c): Adding Lasso for the Franke function": [[29, "part-c-adding-lasso-for-the-franke-function"]], "Part c): Testing different activation functions": [[30, "part-c-testing-different-activation-functions"]], "Part d)": [[31, "part-d"]], "Part d) Neural network complexity": [[31, "part-d-neural-network-complexity"]], "Part d): Classification analysis using neural networks": [[30, "part-d-classification-analysis-using-neural-networks"]], "Part d): Paper and pencil part": [[29, "part-d-paper-and-pencil-part"]], "Part e)": [[31, "part-e"], [31, "id2"]], "Part e): Bias-variance trade-off and resampling techniques": [[29, "part-e-bias-variance-trade-off-and-resampling-techniques"]], "Part e): Write your Logistic Regression code, final step": [[30, "part-e-write-your-logistic-regression-code-final-step"]], "Part f) Critical evaluation of the various algorithms": [[30, "part-f-critical-evaluation-of-the-various-algorithms"]], "Part f): Cross-validation as resampling techniques, adding more complexity": [[29, "part-f-cross-validation-as-resampling-techniques-adding-more-complexity"]], "Part g): Analysis of real data": [[29, "part-g-analysis-of-real-data"]], "Partial Differential Equations": [[3, "partial-differential-equations"], [45, "partial-differential-equations"]], "Paths for project 3": [[31, "paths-for-project-3"]], "Perspective on Machine Learning": [[50, "perspective-on-machine-learning"]], "Plan for week 39, September 23-27, 2024": [[41, "plan-for-week-39-september-23-27-2024"]], "Plan for week 41, October 7-11": [[43, "plan-for-week-41-october-7-11"]], "Plan for week 44": [[46, "plan-for-week-44"]], "Plan for week 46": [[48, "plan-for-week-46"]], "Plan for week 47": [[49, "plan-for-week-47"]], "Plans for the lab sessions": [[40, "plans-for-the-lab-sessions"]], "Plans for week 35": [[37, "plans-for-week-35"]], "Plans for week 36": [[38, "plans-for-week-36"]], "Plans for week 37, lab sessions": [[39, "plans-for-week-37-lab-sessions"]], "Plans for week 37, lecture Monday": [[39, "plans-for-week-37-lecture-monday"]], "Plans for week 38, lecture Monday September 16": [[40, "plans-for-week-38-lecture-monday-september-16"]], "Plans for week 40": [[42, "plans-for-week-40"]], "Plans for week 43": [[45, "plans-for-week-43"]], "Plans for week 45": [[47, "plans-for-week-45"]], "Plotting the Histogram": [[39, "plotting-the-histogram"]], "Plotting the mean value for each group": [[40, "plotting-the-mean-value-for-each-group"]], "Pooling": [[46, "pooling"]], "Pooling Layer": [[46, "pooling-layer"], [47, "pooling-layer"]], "Pooling arithmetic": [[46, "pooling-arithmetic"]], "Pooling types (From Raschka et al)": [[46, "pooling-types-from-raschka-et-al"]], "Practical tips": [[14, "practical-tips"], [22, "practical-tips"], [41, "practical-tips"], [42, "practical-tips"]], "Practicalities": [[34, "practicalities"]], "Preamble: Note on writing reports, using reference material, AI and other tools": [[29, "preamble-note-on-writing-reports-using-reference-material-ai-and-other-tools"]], "Predicting New Points With A Trained Recurrent Neural Network": [[5, "predicting-new-points-with-a-trained-recurrent-neural-network"]], "Preparing Your Data": [[50, "preparing-your-data"]], "Preprocessing our data": [[37, "preprocessing-our-data"]], "Prerequisites": [[36, "prerequisites"]], "Prerequisites and background": [[27, "prerequisites-and-background"]], "Prerequisites: Collect and pre-process data": [[4, "prerequisites-collect-and-pre-process-data"], [46, "prerequisites-collect-and-pre-process-data"], [47, "prerequisites-collect-and-pre-process-data"]], "Printing out as text": [[48, "printing-out-as-text"], [49, "printing-out-as-text"]], "Probability Distribution Functions": [[33, "probability-distribution-functions"]], "Program example for gradient descent with Ridge Regression": [[40, "program-example-for-gradient-descent-with-ridge-regression"], [41, "program-example-for-gradient-descent-with-ridge-regression"]], "Program for stochastic gradient": [[14, "program-for-stochastic-gradient"]], "Project 1 on Machine Learning, deadline October 7 (midnight), 2024": [[29, null]], "Project 2 on Machine Learning, deadline November 4 (Midnight)": [[30, null]], "Project 3 on Machine Learning, deadline December 9 (midnight), 2024": [[31, null]], "Project based teaching and active learning": [[26, "project-based-teaching-and-active-learning"]], "Properties of PDFs": [[33, "properties-of-pdfs"]], "Pros and cons of trees, pros": [[10, "pros-and-cons-of-trees-pros"], [48, "pros-and-cons-of-trees-pros"], [49, "pros-and-cons-of-trees-pros"]], "Pruning the tree": [[48, "pruning-the-tree"], [49, "pruning-the-tree"]], "Python installers": [[27, "python-installers"], [36, "python-installers"]], "Quantum deep learning": [[50, "quantum-deep-learning"]], "Quantum machine learning": [[50, "quantum-machine-learning"]], "Quantum machine learning algorithms based on linear algebra": [[50, "quantum-machine-learning-algorithms-based-on-linear-algebra"]], "Quantum reinforcement learning": [[50, "quantum-reinforcement-learning"]], "RMS prop": [[14, "rms-prop"], [41, "rms-prop"], [42, "rms-prop"]], "RMSProp algorithm, taken from Goodfellow et al": [[22, "rmsprop-algorithm-taken-from-goodfellow-et-al"], [42, "rmsprop-algorithm-taken-from-goodfellow-et-al"]], "RMSprop for adaptive learning rate with Stochastic Gradient Descent": [[22, "rmsprop-for-adaptive-learning-rate-with-stochastic-gradient-descent"], [41, "rmsprop-for-adaptive-learning-rate-with-stochastic-gradient-descent"], [42, "rmsprop-for-adaptive-learning-rate-with-stochastic-gradient-descent"]], "RNNs": [[47, "rnns"]], "RNNs in more detail": [[47, "rnns-in-more-detail"]], "RNNs in more detail, part 2": [[47, "rnns-in-more-detail-part-2"]], "RNNs in more detail, part 3": [[47, "rnns-in-more-detail-part-3"]], "RNNs in more detail, part 4": [[47, "rnns-in-more-detail-part-4"]], "RNNs in more detail, part 5": [[47, "rnns-in-more-detail-part-5"]], "RNNs in more detail, part 6": [[47, "rnns-in-more-detail-part-6"]], "RNNs in more detail, part 7": [[47, "rnns-in-more-detail-part-7"]], "Random Forest Algorithm": [[48, "random-forest-algorithm"], [49, "random-forest-algorithm"]], "Random Forest Algorithm, reminder from last week": [[50, "random-forest-algorithm-reminder-from-last-week"]], "Random Forests Compared with other Methods on the Cancer Data": [[48, "random-forests-compared-with-other-methods-on-the-cancer-data"], [49, "random-forests-compared-with-other-methods-on-the-cancer-data"], [50, "random-forests-compared-with-other-methods-on-the-cancer-data"]], "Random Numbers": [[33, "random-numbers"]], "Random forests": [[11, "random-forests"], [48, "random-forests"], [49, "random-forests"]], "Randomized Grid Search": [[40, "randomized-grid-search"]], "Randomized PCA": [[12, "randomized-pca"]], "Reading material": [[36, "reading-material"]], "Reading recommendations": [[43, "reading-recommendations"], [44, "reading-recommendations"], [45, "reading-recommendations"]], "Reading recommendations:": [[37, "reading-recommendations"]], "Reading suggestions week 34": [[36, "reading-suggestions-week-34"]], "Recurrent neural networks": [[13, "recurrent-neural-networks"], [42, "recurrent-neural-networks"], [43, "recurrent-neural-networks"]], "Recurrent neural networks (RNNs): Overarching view": [[47, "recurrent-neural-networks-rnns-overarching-view"]], "Recurrent neural networks: Overarching view": [[5, null]], "Reducing the number of degrees of freedom, overarching view": [[1, "reducing-the-number-of-degrees-of-freedom-overarching-view"], [37, "reducing-the-number-of-degrees-of-freedom-overarching-view"]], "Reducing the number of operations": [[43, "reducing-the-number-of-operations"]], "Reformulating the problem": [[3, "reformulating-the-problem"], [45, "reformulating-the-problem"]], "Regression Case": [[11, "regression-case"], [49, "regression-case"]], "Regression analysis and resampling methods": [[29, "regression-analysis-and-resampling-methods"]], "Regression analysis, overarching aims": [[36, "regression-analysis-overarching-aims"]], "Regression analysis, overarching aims II": [[36, "regression-analysis-overarching-aims-ii"]], "Regression trees": [[48, "regression-trees"], [49, "regression-trees"]], "Regular NNs don\u2019t scale well to full images": [[46, "regular-nns-dont-scale-well-to-full-images"], [47, "regular-nns-dont-scale-well-to-full-images"]], "Regularization": [[2, "regularization"], [44, "regularization"], [45, "regularization"]], "Reinforcement Learning": [[50, "reinforcement-learning"]], "Relevance": [[42, "relevance"], [43, "relevance"], [44, "relevance"]], "Remarks on the speed": [[46, "remarks-on-the-speed"], [47, "remarks-on-the-speed"]], "Reminder on Statistics": [[7, "reminder-on-statistics"]], "Reminder on books with hands-on material and codes": [[43, "reminder-on-books-with-hands-on-material-and-codes"], [44, "reminder-on-books-with-hands-on-material-and-codes"], [45, "reminder-on-books-with-hands-on-material-and-codes"]], "Reminder on the chain rule and gradients": [[43, "reminder-on-the-chain-rule-and-gradients"]], "Replace or not": [[14, "replace-or-not"], [41, "replace-or-not"], [42, "replace-or-not"]], "Required Technologies": [[27, "required-technologies"]], "Resampling": [[50, "resampling"]], "Resampling Methods": [[7, null]], "Resampling approaches can be computationally expensive": [[39, "resampling-approaches-can-be-computationally-expensive"]], "Resampling methods": [[7, "id1"], [39, "resampling-methods"], [39, "id2"]], "Resampling methods: Bootstrap": [[39, "resampling-methods-bootstrap"]], "Resampling methods: Bootstrap approach": [[39, "resampling-methods-bootstrap-approach"]], "Resampling methods: Bootstrap background": [[39, "resampling-methods-bootstrap-background"]], "Resampling methods: Bootstrap steps": [[39, "resampling-methods-bootstrap-steps"]], "Resampling methods: More Bootstrap background": [[39, "resampling-methods-more-bootstrap-background"]], "Residual Error": [[37, "residual-error"]], "Resources on differential equations and deep learning": [[3, "resources-on-differential-equations-and-deep-learning"], [45, "resources-on-differential-equations-and-deep-learning"]], "Revisiting our Linear Regression Solvers": [[14, "revisiting-our-linear-regression-solvers"]], "Revisiting our Logistic Regression case": [[40, "revisiting-our-logistic-regression-case"], [41, "revisiting-our-logistic-regression-case"]], "Revisiting our first homework": [[40, "revisiting-our-first-homework"], [41, "revisiting-our-first-homework"]], "Rewriting as dot products": [[46, "rewriting-as-dot-products"]], "Rewriting the Covariance and/or Correlation Matrix": [[37, "rewriting-the-covariance-and-or-correlation-matrix"]], "Rewriting the \\delta-function": [[39, "rewriting-the-delta-function"]], "Rewriting the fitting procedure as a linear algebra problem": [[36, "rewriting-the-fitting-procedure-as-a-linear-algebra-problem"]], "Rewriting the fitting procedure as a linear algebra problem, more details": [[36, "rewriting-the-fitting-procedure-as-a-linear-algebra-problem-more-details"]], "Ridge Regression": [[38, "ridge-regression"]], "Ridge and Bayes": [[38, "ridge-and-bayes"], [39, "ridge-and-bayes"]], "Ridge and LASSO Regression": [[37, "ridge-and-lasso-regression"], [38, "ridge-and-lasso-regression"]], "Ridge and Lasso Regression": [[6, null], [6, "id1"]], "Running with Keras": [[46, "running-with-keras"], [47, "running-with-keras"]], "SGD example": [[41, "sgd-example"], [42, "sgd-example"]], "SVD analysis": [[38, "svd-analysis"]], "Same code but now with momentum gradient descent": [[14, "same-code-but-now-with-momentum-gradient-descent"], [22, "same-code-but-now-with-momentum-gradient-descent"], [22, "id1"], [41, "same-code-but-now-with-momentum-gradient-descent"], [41, "id9"], [41, "id10"], [42, "same-code-but-now-with-momentum-gradient-descent"], [42, "id1"]], "Schedule first week": [[36, "schedule-first-week"]], "Schedulers": [[46, "schedulers"], [47, "schedulers"]], "Schematic Regression Procedure": [[10, "schematic-regression-procedure"], [48, "schematic-regression-procedure"], [49, "schematic-regression-procedure"]], "Searching for Optimal Regularization Parameters \\lambda": [[40, "searching-for-optimal-regularization-parameters-lambda"]], "Second moment of the gradient": [[41, "second-moment-of-the-gradient"], [42, "second-moment-of-the-gradient"]], "Sequential data only?": [[47, "sequential-data-only"]], "Set up the model": [[46, "set-up-the-model"], [47, "set-up-the-model"]], "Setting it up": [[46, "setting-it-up"], [47, "setting-it-up"]], "Setting up a Multi-layer perceptron model for classification": [[44, "setting-up-a-multi-layer-perceptron-model-for-classification"], [45, "setting-up-a-multi-layer-perceptron-model-for-classification"]], "Setting up the Back propagation algorithm": [[13, "setting-up-the-back-propagation-algorithm"]], "Setting up the Back propagation algorithm, part 3": [[43, "setting-up-the-back-propagation-algorithm-part-3"], [44, "setting-up-the-back-propagation-algorithm-part-3"], [45, "setting-up-the-back-propagation-algorithm-part-3"]], "Setting up the Matrix to be inverted": [[37, "setting-up-the-matrix-to-be-inverted"]], "Setting up the back propagation algorithm": [[43, "setting-up-the-back-propagation-algorithm"]], "Setting up the back propagation algorithm and algorithm for a feed forward NN, initalizations": [[44, "setting-up-the-back-propagation-algorithm-and-algorithm-for-a-feed-forward-nn-initalizations"], [45, "setting-up-the-back-propagation-algorithm-and-algorithm-for-a-feed-forward-nn-initalizations"]], "Setting up the back propagation algorithm, part 1": [[44, "setting-up-the-back-propagation-algorithm-part-1"], [45, "setting-up-the-back-propagation-algorithm-part-1"]], "Setting up the back propagation algorithm, part 2": [[43, "setting-up-the-back-propagation-algorithm-part-2"], [44, "setting-up-the-back-propagation-algorithm-part-2"], [45, "setting-up-the-back-propagation-algorithm-part-2"]], "Setting up the code": [[45, "setting-up-the-code"]], "Setting up the equations for a neural network": [[43, "setting-up-the-equations-for-a-neural-network"], [44, "setting-up-the-equations-for-a-neural-network"]], "Setting up the network using Autograd": [[45, "setting-up-the-network-using-autograd"]], "Setting up the network using Autograd; The full program": [[3, "setting-up-the-network-using-autograd-the-full-program"], [45, "setting-up-the-network-using-autograd-the-full-program"]], "Setting up the network using Autograd; The trial solution": [[45, "setting-up-the-network-using-autograd-the-trial-solution"]], "Setting up the problem": [[45, "setting-up-the-problem"]], "Setup of Network": [[45, "setup-of-network"]], "Similar (second order function now) problem but now with AdaGrad": [[14, "similar-second-order-function-now-problem-but-now-with-adagrad"], [22, "similar-second-order-function-now-problem-but-now-with-adagrad"], [41, "similar-second-order-function-now-problem-but-now-with-adagrad"], [42, "similar-second-order-function-now-problem-but-now-with-adagrad"]], "Simple Python Code to read in Data and perform Classification": [[10, "simple-python-code-to-read-in-data-and-perform-classification"], [48, "simple-python-code-to-read-in-data-and-perform-classification"], [49, "simple-python-code-to-read-in-data-and-perform-classification"]], "Simple Voting Example, head or tail": [[48, "simple-voting-example-head-or-tail"], [49, "simple-voting-example-head-or-tail"]], "Simple case": [[37, "simple-case"]], "Simple code for solving the above problem": [[38, "simple-code-for-solving-the-above-problem"]], "Simple example": [[40, "simple-example"], [43, "simple-example"]], "Simple example code": [[41, "simple-example-code"], [42, "simple-example-code"]], "Simple example to illustrate Ordinary Least Squares, Ridge and Lasso Regression": [[38, "simple-example-to-illustrate-ordinary-least-squares-ridge-and-lasso-regression"]], "Simple geometric interpretation": [[40, "simple-geometric-interpretation"], [41, "simple-geometric-interpretation"]], "Simple implementation of GD for OLS, Ridge and Lasso": [[42, "simple-implementation-of-gd-for-ols-ridge-and-lasso"]], "Simple linear regression model using scikit-learn": [[1, "simple-linear-regression-model-using-scikit-learn"], [36, "simple-linear-regression-model-using-scikit-learn"]], "Simple neural network and the back propagation equations": [[43, "simple-neural-network-and-the-back-propagation-equations"], [44, "simple-neural-network-and-the-back-propagation-equations"]], "Simple program": [[40, "simple-program"], [41, "simple-program"]], "Simpler examples first, and automatic differentiation": [[43, "simpler-examples-first-and-automatic-differentiation"]], "Slightly different approach": [[41, "slightly-different-approach"], [42, "slightly-different-approach"]], "Smarter way of evaluating the above function": [[43, "smarter-way-of-evaluating-the-above-function"]], "Social machine learning": [[50, "social-machine-learning"]], "Software and needed installations": [[29, "software-and-needed-installations"], [31, "software-and-needed-installations"], [36, "software-and-needed-installations"]], "Solving Differential Equations with Deep Learning": [[3, null]], "Solving differential equations with Deep Learning": [[45, "solving-differential-equations-with-deep-learning"]], "Solving differential equations with RNNs": [[47, "solving-differential-equations-with-rnns"]], "Solving partial differential equations with neural networks": [[31, "solving-partial-differential-equations-with-neural-networks"]], "Solving the equation using Autograd": [[45, "solving-the-equation-using-autograd"]], "Solving the one dimensional Poisson equation": [[3, "solving-the-one-dimensional-poisson-equation"]], "Solving the wave equation - the full program using Autograd": [[45, "solving-the-wave-equation-the-full-program-using-autograd"]], "Solving the wave equation with Neural Networks": [[3, "solving-the-wave-equation-with-neural-networks"]], "Solving using Newton-Raphson\u2019s method": [[40, "solving-using-newton-raphson-s-method"], [41, "solving-using-newton-raphson-s-method"]], "Some famous Matrices": [[28, "some-famous-matrices"]], "Some parallels from real analysis": [[43, "some-parallels-from-real-analysis"]], "Some selected properties": [[40, "some-selected-properties"]], "Some similarities and differences from DNNs": [[50, "some-similarities-and-differences-from-dnns"]], "Some simple problems": [[14, "some-simple-problems"], [40, "some-simple-problems"], [41, "some-simple-problems"]], "Some useful matrix and vector expressions": [[37, "some-useful-matrix-and-vector-expressions"]], "Splitting our Data in Training and Test data": [[1, "splitting-our-data-in-training-and-test-data"], [36, "splitting-our-data-in-training-and-test-data"], [37, "splitting-our-data-in-training-and-test-data"]], "Squared-Error Example and Iterative Fitting": [[48, "squared-error-example-and-iterative-fitting"], [49, "squared-error-example-and-iterative-fitting"], [50, "squared-error-example-and-iterative-fitting"]], "Standard Approach based on the Normal Distribution": [[39, "standard-approach-based-on-the-normal-distribution"]], "Standard imports first": [[48, "standard-imports-first"], [49, "standard-imports-first"]], "Standard steepest descent": [[14, "standard-steepest-descent"], [41, "standard-steepest-descent"]], "Starting your Machine Learning Project": [[50, "starting-your-machine-learning-project"]], "Statistical analysis": [[39, "statistical-analysis"]], "Statistical analysis and optimization of data": [[27, "statistical-analysis-and-optimization-of-data"], [36, "statistical-analysis-and-optimization-of-data"], [50, "statistical-analysis-and-optimization-of-data"]], "Steepest Descent Example": [[49, "steepest-descent-example"], [50, "steepest-descent-example"]], "Steepest descent": [[14, "steepest-descent"], [40, "steepest-descent"], [41, "steepest-descent"]], "Steepest descent method": [[41, "steepest-descent-method"], [41, "id1"]], "Steepest descent example": [[41, "steepest-descent-example"]], "Still thinking": [[37, "still-thinking"]], "Stochastic Gradient Descent": [[41, "stochastic-gradient-descent"], [42, "stochastic-gradient-descent"]], "Stochastic Gradient Descent (SGD)": [[14, "stochastic-gradient-descent-sgd"], [41, "stochastic-gradient-descent-sgd"], [42, "stochastic-gradient-descent-sgd"]], "Stochastic variables and the main concepts, the discrete case": [[33, "stochastic-variables-and-the-main-concepts-the-discrete-case"]], "Strong correlations": [[46, "strong-correlations"], [47, "strong-correlations"]], "Suggested reading and videos": [[40, "suggested-reading-and-videos"]], "Suggested readings and videos": [[42, "suggested-readings-and-videos"]], "Summarizing: Performing a general discrete convolution (From Raschka et al)": [[46, "summarizing-performing-a-general-discrete-convolution-from-raschka-et-al"]], "Summary from last week, using gradient descent methods, limitations": [[42, "summary-from-last-week-using-gradient-descent-methods-limitations"]], "Summary of RNNs": [[47, "summary-of-rnns"]], "Summary of a typical RNN": [[47, "summary-of-a-typical-rnn"]], "Summary of course": [[50, "summary-of-course"]], "Summing up": [[39, "summing-up"]], "Support Vector Machines, overarching aims": [[9, null]], "Systematic reduction": [[4, "systematic-reduction"], [46, "systematic-reduction"], [47, "systematic-reduction"]], "Teachers": [[36, "teachers"]], "Teachers and Grading": [[34, null]], "Teaching Assistants Fall semester 2023": [[34, "teaching-assistants-fall-semester-2023"]], "Teaching schedule with links to material": [[32, null]], "Technicalities": [[45, "technicalities"]], "Tensorflow": [[44, "tensorflow"], [45, "tensorflow"]], "Test Function for what happens with OLS, Ridge and Lasso": [[38, "test-function-for-what-happens-with-ols-ridge-and-lasso"]], "Testing the Means Squared Error as function of Complexity": [[1, "testing-the-means-squared-error-as-function-of-complexity"], [37, "testing-the-means-squared-error-as-function-of-complexity"]], "Testing the XOR gate and other gates": [[44, "testing-the-xor-gate-and-other-gates"], [45, "testing-the-xor-gate-and-other-gates"]], "Textbooks": [[35, null]], "The back propagation equations for a neural network": [[44, "the-back-propagation-equations-for-a-neural-network"]], "The Algorithm before theorem": [[12, "the-algorithm-before-theorem"]], "The Boston housing data example": [[1, "the-boston-housing-data-example"]], "The Breast Cancer Data, now with Keras": [[2, "the-breast-cancer-data-now-with-keras"], [44, "the-breast-cancer-data-now-with-keras"], [45, "the-breast-cancer-data-now-with-keras"]], "The CART algorithm for Classification": [[10, "the-cart-algorithm-for-classification"], [48, "the-cart-algorithm-for-classification"], [49, "the-cart-algorithm-for-classification"]], "The CART algorithm for Regression": [[10, "the-cart-algorithm-for-regression"], [48, "the-cart-algorithm-for-regression"], [49, "the-cart-algorithm-for-regression"]], "The CIFAR01 data set": [[4, "the-cifar01-data-set"], [46, "the-cifar01-data-set"], [47, "the-cifar01-data-set"]], "The Central Limit Theorem": [[39, "the-central-limit-theorem"]], "The Challenges Facing Machine Learning": [[50, "the-challenges-facing-machine-learning"]], "The Convolutional Neural Network (CNN)": [[46, "the-convolutional-neural-network-cnn"], [47, "the-convolutional-neural-network-cnn"]], "The Hessian matrix": [[40, "the-hessian-matrix"], [41, "the-hessian-matrix"]], "The Hessian matrix for Ridge Regression": [[40, "the-hessian-matrix-for-ridge-regression"], [41, "the-hessian-matrix-for-ridge-regression"]], "The Jacobian": [[37, "the-jacobian"]], "The MNIST dataset again": [[4, "the-mnist-dataset-again"], [46, "the-mnist-dataset-again"], [47, "the-mnist-dataset-again"]], "The Neural Network": [[44, "the-neural-network"], [45, "the-neural-network"]], "The OLS case": [[38, "the-ols-case"]], "The RELU function family": [[2, "the-relu-function-family"], [43, "the-relu-function-family"], [44, "the-relu-function-family"], [45, "the-relu-function-family"]], "The Ridge case": [[38, "the-ridge-case"]], "The SVD example": [[46, "the-svd-example"]], "The SVD, a Fantastic Algorithm": [[37, "the-svd-a-fantastic-algorithm"]], "The Softmax function": [[2, "the-softmax-function"], [44, "the-softmax-function"], [45, "the-softmax-function"]], "The Squared-Error again! Steepest Descent": [[49, "the-squared-error-again-steepest-descent"], [50, "the-squared-error-again-steepest-descent"]], "The Table": [[48, "the-table"], [49, "the-table"]], "The \\chi^2 function": [[1, "the-chi-2-function"], [36, "the-chi-2-function"], [36, "id4"], [36, "id5"], [36, "id6"], [36, "id7"], [36, "id8"]], "The analytical solution": [[45, "the-analytical-solution"]], "The approximation theorem in words": [[43, "the-approximation-theorem-in-words"]], "The backward pass is linear": [[47, "the-backward-pass-is-linear"]], "The basic structure of your project": [[31, "the-basic-structure-of-your-project"]], "The bias-variance tradeoff": [[7, "the-bias-variance-tradeoff"], [39, "the-bias-variance-tradeoff"]], "The code": [[36, "the-code"]], "The code for solving the ODE": [[3, "the-code-for-solving-the-ode"], [45, "the-code-for-solving-the-ode"]], "The complete code with a simple data set": [[37, "the-complete-code-with-a-simple-data-set"]], "The convolution stage": [[46, "the-convolution-stage"]], "The cost function": [[0, "the-cost-function"]], "The cost function rewritten": [[40, "the-cost-function-rewritten"]], "The cost/loss function": [[37, "the-cost-loss-function"]], "The course has two central parts": [[27, "the-course-has-two-central-parts"]], "The derivative of the Logistic funtion": [[43, "the-derivative-of-the-logistic-funtion"], [44, "the-derivative-of-the-logistic-funtion"]], "The derivative of the cost/loss function": [[40, "the-derivative-of-the-cost-loss-function"], [41, "the-derivative-of-the-cost-loss-function"]], "The derivatives": [[43, "the-derivatives"], [44, "the-derivatives"]], "The equations": [[40, "the-equations"], [41, "the-equations"]], "The equations for ordinary least squares": [[37, "the-equations-for-ordinary-least-squares"]], "The equations to solve": [[40, "the-equations-to-solve"], [41, "the-equations-to-solve"]], "The first Case": [[38, "the-first-case"]], "The forget gate": [[47, "the-forget-gate"]], "The function to solve for": [[45, "the-function-to-solve-for"]], "The gradient step": [[41, "the-gradient-step"], [42, "the-gradient-step"]], "The ideal": [[40, "the-ideal"], [41, "the-ideal"]], "The last words?": [[50, "the-last-words"]], "The logistic function": [[0, "the-logistic-function"], [8, "the-logistic-function"], [40, "the-logistic-function"]], "The mean squared error and its derivative": [[37, "the-mean-squared-error-and-its-derivative"]], "The moons example": [[9, "the-moons-example"]], "The multilayer perceptron (MLP)": [[13, "the-multilayer-perceptron-mlp"]], "The network": [[50, "the-network"]], "The network with one input layer, specified number of hidden layers, and one output layer": [[3, "the-network-with-one-input-layer-specified-number-of-hidden-layers-and-one-output-layer"], [45, "the-network-with-one-input-layer-specified-number-of-hidden-layers-and-one-output-layer"]], "The optimization problem": [[43, "the-optimization-problem"]], "The ouput layer": [[43, "the-ouput-layer"], [44, "the-ouput-layer"]], "The problem of exploding or vanishing gradients": [[47, "the-problem-of-exploding-or-vanishing-gradients"]], "The problem to solve for": [[45, "the-problem-to-solve-for"]], "The program using Autograd": [[45, "the-program-using-autograd"]], "The same example but now with cross-validation": [[39, "the-same-example-but-now-with-cross-validation"]], "The sensitiveness of the gradient descent": [[40, "the-sensitiveness-of-the-gradient-descent"], [41, "the-sensitiveness-of-the-gradient-descent"]], "The singular value decomposition": [[6, "the-singular-value-decomposition"], [37, "the-singular-value-decomposition"]], "The specific equation to solve for": [[45, "the-specific-equation-to-solve-for"]], "The structure of the RBM network": [[50, "the-structure-of-the-rbm-network"]], "The syntax a.dot(b) when finding the dot product": [[41, "the-syntax-a-dot-b-when-finding-the-dot-product"]], "The training": [[43, "the-training"], [44, "the-training"]], "The trial solution": [[45, "the-trial-solution"], [45, "id4"], [45, "id5"], [45, "id7"]], "The two-dimensional case": [[9, "the-two-dimensional-case"]], "Then some basic questions": [[26, "then-some-basic-questions"]], "Time decay rate": [[41, "time-decay-rate"], [42, "time-decay-rate"]], "To our real data: nuclear binding energies. Brief reminder on masses and binding energies": [[36, "to-our-real-data-nuclear-binding-energies-brief-reminder-on-masses-and-binding-energies"]], "To think about, first part": [[37, "to-think-about-first-part"]], "Toeplitz matrices": [[46, "toeplitz-matrices"]], "Topics covered in this course: Statistical analysis and optimization of data": [[36, "topics-covered-in-this-course-statistical-analysis-and-optimization-of-data"]], "Topics we have covered this year": [[50, "topics-we-have-covered-this-year"]], "Tossing coins": [[48, "tossing-coins"], [49, "tossing-coins"]], "Towards the PCA theorem": [[12, "towards-the-pca-theorem"]], "Train and test datasets": [[2, "train-and-test-datasets"], [44, "train-and-test-datasets"], [45, "train-and-test-datasets"]], "Transfer learning": [[50, "transfer-learning"]], "Transforming images": [[46, "transforming-images"]], "Two first-order differential equations": [[47, "two-first-order-differential-equations"]], "Two parameters": [[0, "two-parameters"], [40, "two-parameters"]], "Two-dimensional Objects": [[4, "two-dimensional-objects"]], "Two-dimensional objects": [[46, "two-dimensional-objects"]], "Type of problem": [[3, "type-of-problem"], [45, "type-of-problem"]], "Types of Machine Learning": [[36, "types-of-machine-learning"]], "Types of Machine Learning, a repetition": [[50, "types-of-machine-learning-a-repetition"]], "Understanding what happens": [[39, "understanding-what-happens"]], "Universal approximation theorem": [[43, "universal-approximation-theorem"]], "Unsupported functions": [[41, "unsupported-functions"]], "Updating the gradients": [[43, "updating-the-gradients"], [44, "updating-the-gradients"], [45, "updating-the-gradients"]], "Usage of CNN code": [[46, "usage-of-cnn-code"], [47, "usage-of-cnn-code"]], "Usage of activation functions": [[46, "usage-of-activation-functions"], [47, "usage-of-activation-functions"]], "Usage of cost functions": [[46, "usage-of-cost-functions"], [47, 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Linear Regression", "14. Building a Feed Forward Neural Network", "15. Solving Differential Equations with Deep Learning", "16. Convolutional Neural Networks", "17. Recurrent neural networks: Overarching view", "4. Ridge and Lasso Regression", "5. Resampling Methods", "6. Logistic Regression", "8. Support Vector Machines, overarching aims", "9. Decision trees, overarching aims", "10. Ensemble Methods: From a Single Tree to Many Trees and Extreme Boosting, Meet the Jungle of Methods", "11. Basic ideas of the Principal Component Analysis (PCA)", "13. Neural networks", "7. Optimization, the central part of any Machine Learning algortithm", "12. Clustering and Unsupervised Learning", "Exercises week 34", "Exercises week 35", "Exercises week 36", "Exercises week 37", "Exercises week 38", "Exercises week 39", "Exercises week 41", "Exercises week 42", "Exercises week 43", "Exercise week 47", "Exercises week 48", "Applied Data Analysis and Machine Learning", "2. Linear Algebra, Handling of Arrays and more Python Features", "Project 1 on Machine Learning, deadline October 7 (midnight), 2024", "Project 2 on Machine Learning, deadline November 4 (Midnight)", "Project 3 on Machine Learning, deadline December 9 (midnight), 2024", "Teaching schedule with links to material", "1. Elements of Probability Theory and Statistical Data Analysis", "Teachers and Grading", "Textbooks", "Week 34: Introduction to the course, Logistics and Practicalities", "Week 35: From Ordinary Linear Regression to Ridge and Lasso Regression", "Week 36: Linear Regression and Statistical interpretations", "Week 37: Statistical interpretations and Resampling Methods", "Week 38: Logistic Regression and Optimization", "Week 39: Optimization and Gradient Methods", "Week 40: Gradient descent methods (continued) and start Neural networks", "Week 41 Neural networks and constructing a neural network code", "Week 42 Constructing a Neural Network code with examples", "Week 43: Deep Learning: Constructing a Neural Network code and solving differential equations", "Week 44, Convolutional Neural Networks (CNN)", "Week 45, Convolutional Neural Networks (CCNs) and Recurrent Neural Networks (RNNs)", "Week 46: Decision Trees, Ensemble methods and Random Forests", "Week 47: From Decision Trees to Ensemble 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"which": [2, 22, 26, 41, 42, 43, 44, 45, 50], "why": [26, 36, 37, 39, 42, 43, 45, 46, 47, 48, 49, 50], "wisconsin": [0, 8, 40], "wish": 50, "word": [43, 50], "worri": 50, "would": 26, "wrap": [37, 39], "write": [5, 12, 24, 29, 30, 38, 44], "x": [37, 38], "xgboost": [11, 49, 50], "xor": [42, 43, 44, 45], "year": 50, "yet": 38, "you": [26, 50], "your": [1, 11, 16, 17, 26, 30, 31, 36, 37, 48, 49, 50], "yourself": [31, 40, 49, 50], "z_j": [43, 44]}}) \ No newline at end of file diff --git a/doc/LectureNotes/_build/html/statistics.html b/doc/LectureNotes/_build/html/statistics.html index 6985b4413..64c4d0498 100644 --- a/doc/LectureNotes/_build/html/statistics.html +++ b/doc/LectureNotes/_build/html/statistics.html @@ -257,6 +257,9 @@
    • Week 47: From Decision Trees to Ensemble Methods, Random Forests and Boosting Methods
    • Exercise week 47
      • +
    • Week 48: Gradient boosting and summary of course
      • +
    • Exercises week 48
      • +

    Projects

      @@ -938,27 +941,27 @@ uncorrelated.

      -
      1.4472072453812375
-[[19.67682459 13.19317931 14.24528007  8.94643492 16.06820064 19.95552222
-  14.31817138  9.32718019 17.21017449 24.16728032]
- [13.19317931  8.8459385   9.55136503  5.99852479 10.77362108 13.3800442
-   9.60023816  6.25381195 11.53930691 16.20399985]
- [14.24528007  9.55136503 10.31304636  6.476882   11.63277222 14.44704666
-  10.36581693  6.75252724 12.45951828 17.49620093]
- [ 8.94643492  5.99852479  6.476882    4.06766333  7.30570681  9.07314999
-   6.51002339  4.24077626  7.82492649 10.98810428]
- [16.06820064 10.77362108 11.63277222  7.30570681 13.12137895 16.29578663
-  11.69229565  7.61662544 14.05392092 19.73513091]
- [19.95552222 13.3800442  14.44704666  9.07314999 16.29578663 20.23816725
-  14.52097038  9.45928804 17.4539351  24.50957964]
- [14.31817138  9.60023816 10.36581693  6.51002339 11.69229565 14.52097038
-  10.41885751  6.78707908 12.52327207 17.58572679]
- [ 9.32718019  6.25381195  6.75252724  4.24077626  7.61662544  9.45928804
-   6.78707908  4.42125659  8.15794224 11.45573958]
- [17.21017449 11.53930691 12.45951828  7.82492649 14.05392092 17.4539351
-  12.52327207  8.15794224 15.05273905 21.13771505]
- [24.16728032 16.20399985 17.49620093 10.98810428 19.73513091 24.50957964
-  17.58572679 11.45573958 21.13771505 29.68250468]]
+
      1.6630365942333707
+[[ 2.9789621   5.29596775  9.61069934  8.94881093  7.29305818  5.06734969
+   8.35612808  9.50699066  6.58518977  5.96699567]
+ [ 5.29596775  9.41511622 17.08580103 15.90910271 12.96552275  9.00868142
+  14.85543733 16.90142882 11.70708167 10.60806265]
+ [ 9.61069934 17.08580103 31.00594727 28.87056912 23.52879528 16.34823563
+  26.95846133 30.67136331 21.2450769  19.25066495]
+ [ 8.94881093 15.90910271 28.87056912 26.88225436 21.90836824 15.2223334
+  25.10183335 28.55902794 19.78192944 17.92487255]
+ [ 7.29305818 12.96552275 23.52879528 21.90836824 17.85477485 12.40582286
+  20.45736943 23.27489698 16.12178015 14.608325  ]
+ [ 5.06734969  9.00868142 16.34823563 15.2223334  12.40582286  8.61979174
+  14.21415299 16.17182246 11.20170657 10.15013036]
+ [ 8.35612808 14.85543733 26.95846133 25.10183335 20.45736943 14.21415299
+  23.43933023 26.66755363 18.4717654  16.73770203]
+ [ 9.50699066 16.90142882 30.67136331 28.55902794 23.27489698 16.17182246
+  26.66755363 30.34038983 21.01582211 19.04293178]
+ [ 6.58518977 11.70708167 21.2450769  19.78192944 16.12178015 11.20170657
+  18.4717654  21.01582211 14.55699091 13.19043259]
+ [ 5.96699567 10.60806265 19.25066495 17.92487255 14.608325   10.15013036
+  16.73770203 19.04293178 13.19043259 11.95216188]]
      @@ -1226,15 +1229,15 @@ more practically oriented methods like the blocking technique.

      -
      -0.14555142287587747
-3.471032560330219
--0.36388945426885777
-0.8471097389658617 8.314152725401152 16.209550199940885
-2.467818446250011 2.622158030566299 8.055384838739426
-[[ 0.84710974  2.46781845  2.62215803]
- [ 2.46781845  8.31415273  8.05538484]
- [ 2.62215803  8.05538484 16.2095502 ]]
-[21.82751857  0.10066746  3.44262663]
+
      0.02067725499485532
+4.1599466577177715
+-0.010435324202206745
+1.0573975468113945 11.60638779409259 34.06300749480313
+3.3628139153668575 3.9755893793908617 13.085147027563798
+[[ 1.05739755  3.36281392  3.97558938]
+ [ 3.36281392 11.60638779 13.08514703]
+ [ 3.97558938 13.08514703 34.06300749]]
+[40.7125057   0.07536391  5.93892323]
      @@ -1502,7 +1505,7 @@ these limiting values.

      -_images/2d32a16373526bea9bc6f4e8fe926f0ed4c4e23d38a4de33454551f6599a83cf.png +_images/46a1fad1ccb7c31a75875feb55a5327e9f0ae2b02295543e814d1b2fc23ab459.png

      Since our random numbers, which are typically generated via a linear congruential algorithm, @@ -1564,10 +1567,10 @@ assumption for approximating \(\sigma

      -
      0.028592327979280928 1.0458514144974842
+
      0.0065879787737229524 1.0071299074549438
 
      
 
      
-_images/8dd8c0fbf4c42ac2d98c5a66035e455e4d40a3c6c7114c3d05daba0ad8fdf4ba.png
+_images/467927f86cd8a5304cb2f39dbf480015e72d87541abf390f2a0eb8672b465f1e.png

      As can be seen from the plot, the first point gives back the variance and a value of one. diff --git a/doc/LectureNotes/_build/html/teachers.html b/doc/LectureNotes/_build/html/teachers.html index 4df14c2a0..bf7eb6da0 100644 --- a/doc/LectureNotes/_build/html/teachers.html +++ b/doc/LectureNotes/_build/html/teachers.html @@ -255,6 +255,9 @@

    • Week 47: From Decision Trees to Ensemble Methods, Random Forests and Boosting Methods
    • Exercise week 47
      • +
    • Week 48: Gradient boosting and summary of course
      • +
    • Exercises week 48
      • +

    Projects

    Projects

    Projects

      @@ -1005,8 +1008,8 @@ developed in the 1970s, namely EISPACK and LINPACK. We describe them shortly he
      -
      [ 0.17506058 -1.14302491  0.63485898  1.1235978   0.38873383 -0.17169218
-  0.88719525 -0.01015838 -0.27342837 -1.80652926]
+
      [ 2.05896588  0.43642532 -0.21096409  0.40779804 -1.13963886 -0.53677418
+  0.83572684  0.58831595  0.30882611 -2.07060477]
      @@ -1231,26 +1234,36 @@ lowercase letters for vectors and uppercase letters for matrices)

      -
      [[0.3418169  0.08757698 0.45405198 0.04340609 0.58148701 0.14855269
-  0.07347117 0.79453819 0.78257332 0.91637778]
- [0.81517637 0.35341707 0.55717092 0.03395449 0.79320614 0.22562101
-  0.65971268 0.64276252 0.49192086 0.91375988]
- [0.10740566 0.77091954 0.10656055 0.36547141 0.83190714 0.25533245
-  0.40632604 0.48183592 0.22261204 0.16509611]
- [0.47804768 0.92388196 0.85585927 0.38675536 0.30896123 0.22930735
-  0.78408961 0.30789787 0.58654863 0.5228503 ]
- [0.35826417 0.13768194 0.41982096 0.32226801 0.39507682 0.76931267
-  0.97369602 0.12082909 0.97147296 0.30115792]
- [0.72544872 0.9789659  0.98184665 0.98596221 0.68564602 0.80794793
-  0.66307293 0.83836165 0.98251519 0.63387948]
- [0.21352978 0.27418384 0.91865691 0.36851668 0.0525856  0.7259156
-  0.83058696 0.99696669 0.83207908 0.32633042]
- [0.10500719 0.64631695 0.2220673  0.5920376  0.85070792 0.84332209
-  0.73691262 0.06028861 0.62436053 0.00758035]
- [0.13963148 0.11686929 0.34936681 0.14068659 0.59525417 0.83232065
-  0.32968695 0.3371494  0.85006441 0.96828809]
- [0.88555932 0.25272278 0.26684239 0.38777146 0.73658394 0.81886022
-  0.01409764 0.23749007 0.26368616 0.10068772]]
+
      [[4.62790954e-01 3.05355909e-01 4.36307837e-01 5.75663043e-01
+  8.47455900e-01 8.88156953e-01 3.41214900e-01 4.80217592e-01
+  3.51294625e-01 4.55894547e-01]
+ [9.36928932e-01 1.34614286e-01 1.68706337e-01 5.12168283e-01
+  1.82057187e-01 5.04384702e-01 3.90807328e-01 7.21325310e-01
+  6.82139912e-04 7.75656839e-01]
+ [7.39975586e-01 7.28371709e-02 4.47008569e-01 1.48689896e-01
+  8.77801149e-01 8.77111122e-01 6.14302755e-01 3.05347541e-01
+  5.22304438e-01 1.19480182e-01]
+ [3.75927527e-01 1.79526117e-01 1.10245050e-01 2.14621443e-02
+  2.18149821e-01 6.60754644e-01 7.14889592e-01 2.00365236e-01
+  1.87081411e-01 8.98532247e-01]
+ [9.76246245e-01 7.27600064e-02 6.40428736e-01 2.44633057e-01
+  2.13315320e-01 9.04923596e-01 1.01278843e-01 1.60040487e-02
+  9.57338433e-01 4.54609970e-01]
+ [9.70716394e-01 8.19560587e-01 8.00696646e-01 8.50286966e-01
+  6.01413231e-01 5.35401167e-01 5.09318595e-01 7.50871033e-01
+  1.77320278e-01 1.41563755e-01]
+ [5.58999323e-01 8.27823274e-02 2.45162718e-01 9.35805174e-01
+  3.97967922e-01 6.41998971e-01 2.85999165e-01 9.42305845e-01
+  6.51639460e-01 2.40037335e-01]
+ [2.07463601e-01 6.36551310e-01 3.74118364e-01 9.48754846e-01
+  6.84043753e-01 8.69630846e-01 2.90701292e-01 3.73794006e-01
+  9.56898405e-01 7.53969725e-01]
+ [8.69421667e-01 9.16907984e-01 2.60498453e-01 1.05394015e-01
+  8.28209931e-01 2.18273724e-01 5.70725653e-01 5.90412777e-01
+  3.15706255e-01 1.00031589e-01]
+ [3.13129360e-02 9.74280470e-01 1.56927535e-01 2.05412497e-01
+  3.29614274e-01 1.32235211e-01 9.66580589e-01 6.25369588e-01
+  5.91506268e-01 3.63969272e-01]]
      @@ -1305,13 +1318,13 @@ covariance matrix through the np.linalg.eig() function.

      -
      0.12144818928795939
-4.458165594343705
-0.07083732341182088
-[[ 1.01630108  3.10680954  3.23143243]
- [ 3.10680954 10.41130925  9.90126156]
- [ 3.23143243  9.90126156 15.84271739]]
-[24.2481237   0.07228932  2.94991469]
+
      -0.022796599742659046
+4.026047728952776
+-0.08626455960524361
+[[ 1.07037904  3.23910863  3.46675556]
+ [ 3.23910863 10.63080397 10.39964939]
+ [ 3.46675556 10.39964939 18.1421739 ]]
+[26.31853116  0.06730967  3.45751608]
      @@ -1536,7 +1549,7 @@ Name: Aragorn, dtype: object 
      ---------------------------------------------------------------------------
 AttributeError                            Traceback (most recent call last)
-/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_58960/1326197715.py in ?()
+/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_23270/1326197715.py in ?()
 ----> 6 new_hobbit = {'First Name': ["Peregrin"],
       7               'Last Name': ["Took"],
       8               'Place of birth': ["Shire"],
diff --git a/doc/LectureNotes/_build/html/week35.html b/doc/LectureNotes/_build/html/week35.html
index 312abccdb..0dd7b3a9d 100644
--- a/doc/LectureNotes/_build/html/week35.html
+++ b/doc/LectureNotes/_build/html/week35.html
@@ -257,6 +257,9 @@
 
    • Week 47: From Decision Trees to Ensemble Methods, Random Forests and Boosting Methods
      • 
 
    • Exercise week 47
      • 
 
+
    • Week 48: Gradient boosting  and summary of course
      • 
+
    • Exercises week 48
      • 
+

    Projects

      @@ -1021,7 +1024,7 @@ Since we are not using Scikit-Learn here we can define our own
      -
      0.9952260624201319
+
      0.9954482269282812
      @@ -1038,7 +1041,7 @@ Since we are not using Scikit-Learn here we can define our own
      -
      0.01138349118139106
+
      0.008936538168866639
      @@ -1053,23 +1056,23 @@ Since we are not using Scikit-Learn here we can define our own
      -
      [0.03126071 0.00179226 0.05225439 0.00372999 0.02734884 0.05333172
- 0.02020016 0.01521008 0.00965948 0.0056274  0.01055702 0.01870768
- 0.07847027 0.00766418 0.00385358 0.01374419 0.04181664 0.00933893
- 0.00312107 0.01294475 0.07413571 0.11552511 0.01637903 0.02176175
- 0.00854766 0.04706879 0.05041282 0.00575302 0.02289663 0.01263793
- 0.03325052 0.01806972 0.0066441  0.05997838 0.02861775 0.04882654
- 0.08480358 0.03542954 0.01143948 0.00619982 0.00278866 0.00995497
- 0.02001832 0.03635157 0.00732822 0.01114894 0.07709391 0.01125655
- 0.00816671 0.01114243 0.00550993 0.06590956 0.04805237 0.04394445
- 0.00825943 0.00935925 0.023291   0.05290093 0.01249864 0.02700809
- 0.00271844 0.01806574 0.01238543 0.03213764 0.02680834 0.02269712
- 0.02734783 0.00461485 0.0352873  0.01616311 0.00968169 0.02333957
- 0.00912667 0.03871805 0.01637128 0.02097868 0.00230726 0.08493636
- 0.05203452 0.0498517  0.01619572 0.02339214 0.03510055 0.04566385
- 0.0024319  0.02956756 0.05981231 0.00394204 0.02607172 0.02627472
- 0.02413198 0.00518272 0.02283931 0.01528494 0.01208287 0.02055319
- 0.01180608 0.01603956 0.02803521 0.07787677]
+
      [0.04030775 0.05734435 0.01740914 0.02044029 0.02486199 0.03573222
+ 0.00752453 0.0573522  0.00102185 0.04459142 0.03221764 0.00342095
+ 0.01898739 0.03160225 0.00450564 0.01979508 0.00152143 0.00857666
+ 0.00762566 0.00703236 0.00490815 0.02171406 0.04265231 0.03063862
+ 0.0273619  0.00668013 0.03013331 0.00788426 0.01275493 0.01698978
+ 0.02715714 0.0164838  0.00424614 0.06575617 0.03937194 0.0459393
+ 0.03321665 0.00857944 0.01458459 0.03839052 0.04106931 0.01962178
+ 0.01758251 0.06598382 0.01262347 0.05373197 0.00508233 0.02902908
+ 0.01284702 0.02266783 0.05481819 0.02857842 0.00817663 0.00478164
+ 0.02844986 0.03430817 0.00845377 0.01544721 0.00867529 0.01881047
+ 0.02900428 0.00525951 0.00516358 0.05597496 0.03021621 0.00458845
+ 0.0134627  0.00244413 0.00194759 0.04527404 0.02602746 0.00545794
+ 0.00747748 0.01109822 0.08180185 0.02539315 0.02321685 0.02859203
+ 0.01136476 0.00341608 0.00341404 0.00651846 0.05339963 0.01042549
+ 0.02637872 0.00639212 0.02177328 0.02384275 0.00133169 0.00699813
+ 0.0205469  0.04806452 0.01573671 0.05430636 0.01207681 0.0160536
+ 0.01157049 0.08333949 0.01405686 0.00212278]
      @@ -1138,15 +1141,15 @@ but now splitting the data into a training set and a test set.

      -
      [ 2.06741397 -0.19826431  5.13801381  0.12625265 -0.15070691]
+
      [ 2.04491327 -0.39593882  6.26462211 -1.4899045   0.59718501]
 Training R2
-0.9959102280411898
+0.9973808396916977
 Training MSE
-0.007840268112476161
+0.005799563292953798
 Test R2
-0.9923171995387728
+0.9950846163116835
 Test MSE
-0.016855069963547485
+0.011719944818178452
      @@ -2095,7 +2098,9 @@ MSE with intercept column 0.00411363461744314 MSE with intercept column from SKL 0.004113634617443147 -Manual intercept: 2.083766322923899 +
      +
      +
      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]
diff --git a/doc/LectureNotes/_build/html/week36.html b/doc/LectureNotes/_build/html/week36.html
index 8a8f0a36b..3a7c38468 100644
--- a/doc/LectureNotes/_build/html/week36.html
+++ b/doc/LectureNotes/_build/html/week36.html
@@ -257,6 +257,9 @@
 
    • Week 47: From Decision Trees to Ensemble Methods, Random Forests and Boosting Methods
      • 
 
    • Exercise week 47
      • 
 
+
    • Week 48: Gradient boosting  and summary of course
      • 
+
    • Exercises week 48
      • 
+

    Projects

    Projects

      @@ -1166,7 +1169,7 @@ theorem.

      Bootstrap Statistics :
 original           bias      std. error
- 100.106  15.2126        100.109        0.153146
+ 100.088  15.1355        100.087        0.151546
      @@ -1190,7 +1193,7 @@ original bias std. error
      -_images/3c2235cc56c92d97d90ee28891636edca95a10ee0aaa7903396bc4ca0722bd51.png +_images/9369a99fab3ad63476a60dda7c34258ee45c7d9980558aabda81b593e0a3d307.png
      @@ -1386,9 +1389,7 @@ Error: 0.08426840630693411 Bias^2: 0.0796891867672603 Var: 0.004579219539673834 0.08426840630693411 >= 0.0796891867672603 + 0.004579219539673834 = 0.08426840630693413 - - -
      Polynomial degree: 2
+Polynomial degree: 2
 Error: 0.10398646080125035
 Bias^2: 0.1007711427354898
 Var: 0.0032153180657605116
@@ -1403,9 +1404,7 @@ Error: 0.06844519414009445
 Bias^2: 0.06453579006728324
 Var: 0.003909404072811226
 0.06844519414009445 >= 0.06453579006728324 + 0.003909404072811226 = 0.06844519414009446
-
      -
      -
      Polynomial degree: 5
+Polynomial degree: 5
 Error: 0.05227921801205686
 Bias^2: 0.0481872773043029
 Var: 0.004091940707753939
@@ -1420,9 +1419,7 @@ Error: 0.02760977349102253
 Bias^2: 0.022999498260366312
 Var: 0.004610275230656212
 0.02760977349102253 >= 0.022999498260366312 + 0.004610275230656212 = 0.027609773491022525
-
      -
      -
      Polynomial degree: 8
+Polynomial degree: 8
 Error: 0.017355848195593347
 Bias^2: 0.010331721306655127
 Var: 0.007024126888938232
@@ -1432,9 +1429,7 @@ Error: 0.02660572763718093
 Bias^2: 0.010018312644137363
 Var: 0.016587414993043573
 0.02660572763718093 >= 0.010018312644137363 + 0.016587414993043573 = 0.026605727637180936
-
      -
      -
      Polynomial degree: 10
+Polynomial degree: 10
 Error: 0.021592704588025025
 Bias^2: 0.010516485576645508
 Var: 0.011076219011379514
@@ -1449,9 +1444,7 @@ Error: 0.11547777218872497
 Bias^2: 0.01628578269596628
 Var: 0.09919198949275869
 0.11547777218872497 >= 0.01628578269596628 + 0.09919198949275869 = 0.11547777218872497
-
      -
      -
      Polynomial degree: 13
+Polynomial degree: 13
 Error: 0.22842468702219465
 Bias^2: 0.01975416527185249
 Var: 0.20867052175034223
@@ -1826,12 +1819,12 @@ Mean squared error on test data: 1.07641937
 Degree of polynomial:  12
 Mean squared error on training data: 0.00805074
 Mean squared error on test data: 0.04295757
-
      -
      -
      Degree of polynomial:  13
+Degree of polynomial:  13
 Mean squared error on training data: 0.00781918
 Mean squared error on test data: 0.56965674
-Degree of polynomial:  14
+
      +
      +
      Degree of polynomial:  14
 Mean squared error on training data: 0.00465099
 Mean squared error on test data: 0.28443039
 Degree of polynomial:  15
@@ -1840,45 +1833,43 @@ Mean squared error on test data: 568.47051432
 Degree of polynomial:  16
 Mean squared error on training data: 0.00325450
 Mean squared error on test data: 48.97630233
-
      -
      -
      Degree of polynomial:  17
+Degree of polynomial:  17
 Mean squared error on training data: 0.00242954
 Mean squared error on test data: 2.52780600
-Degree of polynomial:  18
+
      +
      +
      Degree of polynomial:  18
 Mean squared error on training data: 0.00219195
 Mean squared error on test data: 429.25695398
 Degree of polynomial:  19
 Mean squared error on training data: 0.00154853
 Mean squared error on test data: 239.97065359
-
      -
      -
      Degree of polynomial:  20
+Degree of polynomial:  20
 Mean squared error on training data: 0.00140846
 Mean squared error on test data: 1350.24493666
 Degree of polynomial:  21
 Mean squared error on training data: 0.00119688
 Mean squared error on test data: 1840.50530832
-Degree of polynomial:  22
+
      +
      +
      Degree of polynomial:  22
 Mean squared error on training data: 0.00092898
 Mean squared error on test data: 1184.60929685
 Degree of polynomial:  23
 Mean squared error on training data: 0.00089193
 Mean squared error on test data: 3892.17483760
-
      -
      -
      Degree of polynomial:  24
+Degree of polynomial:  24
 Mean squared error on training data: 0.00083355
 Mean squared error on test data: 1332.46736215
 Degree of polynomial:  25
 Mean squared error on training data: 0.00079904
 Mean squared error on test data: 7577.76690383
-Degree of polynomial:  26
-Mean squared error on training data: 0.00075590
-Mean squared error on test data: 1079.36895644
      -
      Degree of polynomial:  27
+
      Degree of polynomial:  26
+Mean squared error on training data: 0.00075590
+Mean squared error on test data: 1079.36895644
+Degree of polynomial:  27
 Mean squared error on training data: 0.00068091
 Mean squared error on test data: 3207.25343155
 Degree of polynomial:  28
@@ -1889,9 +1880,9 @@ Mean squared error on training data: 0.00063866
 Mean squared error on test data: 3099.60342978
 
      
 
      
-
      /var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_58983/626635268.py:73: RuntimeWarning: divide by zero encountered in log10
+
      /var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_23293/626635268.py:73: RuntimeWarning: divide by zero encountered in log10
   plt.plot(polynomial, np.log10(trainingerror), label='Training Error')
-/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_58983/626635268.py:74: RuntimeWarning: divide by zero encountered in log10
+/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_23293/626635268.py:74: RuntimeWarning: divide by zero encountered in log10
   plt.plot(polynomial, np.log10(testerror), label='Test Error')
 
      
 
      
@@ -1976,7 +1967,7 @@ Mean squared error on test data: 3099.60342978
 
      
 
      
 
      
-
      /var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_58983/3817475779.py:63: RuntimeWarning: divide by zero encountered in log10
+
      /var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_23293/3817475779.py:63: RuntimeWarning: divide by zero encountered in log10
   plt.plot(polynomial, np.log10(estimated_mse_sklearn), label='Test Error')
 
      
 
      
diff --git a/doc/LectureNotes/_build/html/week38.html b/doc/LectureNotes/_build/html/week38.html
index 49970b23b..c0a815d8d 100644
--- a/doc/LectureNotes/_build/html/week38.html
+++ b/doc/LectureNotes/_build/html/week38.html
@@ -257,6 +257,9 @@
 
    • Week 47: From Decision Trees to Ensemble Methods, Random Forests and Boosting Methods
      • 
 
    • Exercise week 47
      • 
 
+
    • Week 48: Gradient boosting  and summary of course
      • 
+
    • Exercises week 48
      • 
+

    Projects

    Projects

      @@ -1010,7 +1013,7 @@ which equals

      -
      <mpl_toolkits.mplot3d.art3d.Poly3DCollection at 0x11ff98dc0>
+
      <mpl_toolkits.mplot3d.art3d.Poly3DCollection at 0x126ab3dc0>
 
      
 
      
 _images/6e7ee017368b6108792de2f8f1bc208a3f62fb66ba3ace91d0bb16b8c8313ed3.png
@@ -1068,7 +1071,7 @@ which equals
      -
      [<matplotlib.lines.Line2D at 0x126e8a9d0>]
+
      [<matplotlib.lines.Line2D at 0x126fb8b20>]
 
      
 
      
 _images/87c2ad9509e698434c22f92f294f3ea8a669bfd9e5cce9fc96ab15f997da69ab.png
@@ -1362,14 +1365,14 @@ when \(||\nabla_\beta C(\beta_k) || \
      -
      Eigenvalues of Hessian Matrix:[0.31668291 4.15666977]
-[[3.74118981]
- [3.28649035]]
-[[3.74118981]
- [3.28649035]]
+
      Eigenvalues of Hessian Matrix:[0.34526171 4.42891508]
+[[3.73401546]
+ [3.2913872 ]]
+[[3.73401546]
+ [3.2913872 ]]
 
      
 
      
-_images/ec89b6bb83d134561d17bfc7f7dc31aa6db94db6ece70faf26265ae81d65a613.png
+_images/3430c2e5bb18ad9f457af6ed6748c3fa16381ffd0271ca7c0c248e53b04e0fe6.png
      @@ -1397,9 +1400,9 @@ when \(||\nabla_\beta C(\beta_k) || \
      -
      [[4.23463496]
- [2.74399625]]
-[4.20387361] [2.73436201]
+
      [[3.96383084]
+ [2.94004512]]
+[3.99618085] [2.9822844]
      @@ -1499,14 +1502,14 @@ minimum of this function.

      -
      Eigenvalues of Hessian Matrix:[0.30988581 4.80658849]
-[[4.14058224]
- [2.99719628]]
-[[4.14080023]
- [2.99702696]]
+
      Eigenvalues of Hessian Matrix:[0.31443711 4.41185301]
+[[4.01380409]
+ [2.87614787]]
+[[4.0124208 ]
+ [2.87730544]]
 
      
 
      
-_images/b2b6d80c00707d89a55ad71288bf1803be1c0b2d7ecc12a02a5917e520a9cd6d.png
+_images/b4424ef99a3917f39ceb54b5132d60d97fff9ed86b004003694a9733a9d7e3c2.png
      @@ -1617,7 +1620,7 @@ minimum of this function.

      >29 f([0.00115631]) = 0.00000
      -
      /var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_58998/394505933.py:33: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)
+
      /var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_23327/394505933.py:33: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)
   print('>%d f(%s) = %.5f' % (i, solution, solution_eval))
 
      
 
      
@@ -1728,7 +1731,7 @@ minimum of this function.
      
 >29 f([6.17748881e-07]) = 0.00000
      -
      /var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_58998/476849792.py:39: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)
+
      /var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_23327/476849792.py:39: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)
   print('>%d f(%s) = %.5f' % (i, solution, solution_eval))
 
      
 
      
@@ -2790,8 +2793,12 @@ first example shows results with ordinary leats squares.
      
 [[3.94499279]
  [3.03306538]]
 Eigenvalues of Hessian Matrix:[0.31248425 4.44418124]
-theta from own gd
-[[3.94499279]
+
      +
      +
      theta from own gd
+
      +
      +
      [[3.94499279]
  [3.03306538]]
      @@ -3084,16 +3091,16 @@ beta from own Newton code [[4.0586484] [3.0718316]] Eigenvalues of Hessian Matrix:[0.29860173 3.8931686 ] -theta from own gd + + +
      theta from own gd
 [[4.0586484]
  [3.0718316]]
      _images/df9f7915879c2371a6245aa34d566f5914b9f11786649103613f16a241fdf046.png 
      theta from own sdg
-
      -
      -
      [[4.02496085]
+[[4.02496085]
  [3.12081773]]
      @@ -3182,7 +3189,9 @@ Eigenvalues of Hessian Matrix:[0.27470622 4.24106503] theta from own gd [[3.95906059] [3.02296298]] -theta from own sdg with momentum + + +
      theta from own sdg with momentum
 [[3.95611042]
  [2.99475306]]
      diff --git a/doc/LectureNotes/_build/html/week40.html b/doc/LectureNotes/_build/html/week40.html index 1efcd205d..1e32e3884 100644 --- a/doc/LectureNotes/_build/html/week40.html +++ b/doc/LectureNotes/_build/html/week40.html @@ -257,6 +257,9 @@
    • Week 47: From Decision Trees to Ensemble Methods, Random Forests and Boosting Methods
    • Exercise week 47
      • +
    • Week 48: Gradient boosting and summary of course
      • +
    • Exercises week 48
      • +

    Projects

      @@ -631,17 +634,17 @@ We summarize some of these here for the methods we hvae studied in project one, 
      Parameters for OLS using gradient descent
-[[3.633198  ]
- [4.1182509 ]
- [4.44504789]]
+[[4.18426686]
+ [2.51331698]
+ [5.22403374]]
 Parameters for Ridge using gradient descent
-[[3.81542861]
- [3.39782637]
- [4.80980326]]
+[[4.13677307]
+ [2.53440516]
+ [5.21680493]]
 Parameters for Lasso using gradient descent
-[[3.47633954]
- [4.56277628]
- [4.22765626]]
+[[3.40725426]
+ [4.31893412]
+ [4.43233965]]
      @@ -693,11 +696,11 @@ Parameters for Lasso using gradient descent [[4.] [3.] [5.]] -0 [-25.50604212] [-33.67380616] -1 [-1.07718279e-13] [-1.67721558e-13] -2 [-9.23705556e-16] [-1.2228689e-15] -3 [8.70414851e-16] [1.13386647e-15] -4 [-1.86517468e-15] [-2.57274984e-15] +0 [-21.31398212] [-28.05552428] +1 [-3.03046477e-14] [-1.33496431e-14] +2 [-7.99360578e-16] [-9.75256689e-16] +3 [1.77635684e-16] [2.91496906e-16] +4 [1.77635684e-16] [2.91496906e-16] beta from own Newton code [[4.] [3.] @@ -1039,18 +1042,18 @@ function.

      Own inversion
-[[4.06629035]
- [2.90434687]]
-Eigenvalues of Hessian Matrix:[0.28385837 4.07166128]
+[[3.93518583]
+ [3.05586004]]
+Eigenvalues of Hessian Matrix:[0.31020049 4.44279756]
 theta from own gd
-[[4.06629035]
- [2.90434687]]
+[[3.93518583]
+ [3.05586004]]
 theta from own sdg
-[[4.04098389]
- [2.9248133 ]]
+[[3.92681541]
+ [3.07007326]]
      -_images/2de542effe405ced9195c07c5d2061f8911ca88584548f85cb6d28df1f83d693.png +_images/81f5882581a53e21f2c20be0ef5d63c90956ddfe5d34db4fb8f83012ab7b8c4a.png
      @@ -1761,15 +1764,17 @@ first example shows results with ordinary leats squares.

      Own inversion
-[[4.05658312]
- [2.97504928]]
-Eigenvalues of Hessian Matrix:[0.31549215 4.38384294]
-theta from own gd
-[[4.05658312]
- [2.97504928]]
      -_images/1bf8fa67622cbeaf3f9c469a206d89f5936b2fade360d11439ef4a3e43cf6f31.png +
      [[3.95215017]
+ [2.98138287]]
+Eigenvalues of Hessian Matrix:[0.28889272 3.80178359]
+theta from own gd
+[[3.95215017]
+ [2.98138287]]
+
      +
      +_images/5a1776c87da1ba203336178287b47b42ec33ec260ba18e3e2bb9d9f8ed8c8134.png
      @@ -1837,73 +1842,73 @@ theta from own gd 
      Own inversion
 [[4.]
  [3.]]
-Eigenvalues of Hessian Matrix:[0.32421852 4.40457549]
-0 [-8.91626016] [-10.12979569]
-1 [-0.25100049] [0.20953861]
-2 [-0.23252447] [0.19411458]
-3 [-0.21540847] [0.17982591]
-4 [-0.19955236] [0.16658901]
-5 [-0.18486342] [0.15432648]
-6 [-0.17125572] [0.14296659]
-7 [-0.15864967] [0.13244289]
-8 [-0.14697155] [0.12269384]
-9 [-0.13615305] [0.11366241]
-10 [-0.1261309] [0.10529578]
-11 [-0.11684647] [0.09754501]
-12 [-0.10824546] [0.09036477]
-13 [-0.10027757] [0.08371307]
-14 [-0.09289619] [0.07755099]
-15 [-0.08605815] [0.07184251]
-16 [-0.07972345] [0.06655422]
-17 [-0.07385505] [0.06165519]
-18 [-0.06841862] [0.05711679]
-19 [-0.06338236] [0.05291245]
-20 [-0.05871681] [0.04901759]
-21 [-0.0543947] [0.04540943]
-22 [-0.05039073] [0.04206687]
-23 [-0.0466815] [0.03897035]
-24 [-0.0432453] [0.03610176]
-25 [-0.04006203] [0.03344433]
-26 [-0.03711309] [0.03098251]
-27 [-0.03438121] [0.0287019]
-28 [-0.03185043] [0.02658917]
-29 [-0.02950594] [0.02463195]
+Eigenvalues of Hessian Matrix:[0.29823315 4.72747492]
+0 [-17.30620124] [-21.78309726]
+1 [-0.05759628] [0.04549505]
+2 [-0.05396282] [0.04262499]
+3 [-0.05055857] [0.03993599]
+4 [-0.04736908] [0.03741662]
+5 [-0.0443808] [0.03505619]
+6 [-0.04158103] [0.03284467]
+7 [-0.03895789] [0.03077266]
+8 [-0.03650022] [0.02883136]
+9 [-0.03419761] [0.02701253]
+10 [-0.03204025] [0.02530845]
+11 [-0.03001898] [0.02371186]
+12 [-0.02812523] [0.022216]
+13 [-0.02635095] [0.0208145]
+14 [-0.0246886] [0.01950141]
+15 [-0.02313112] [0.01827117]
+16 [-0.02167189] [0.01711853]
+17 [-0.02030472] [0.0160386]
+18 [-0.01902379] [0.01502681]
+19 [-0.01782367] [0.01407884]
+20 [-0.01669927] [0.01319068]
+21 [-0.01564579] [0.01235854]
+22 [-0.01465878] [0.0115789]
+23 [-0.01373403] [0.01084844]
+24 [-0.01286761] [0.01016407]
+25 [-0.01205586] [0.00952287]
+26 [-0.01129531] [0.00892212]
+27 [-0.01058275] [0.00835927]
+28 [-0.00991513] [0.00783192]
+29 [-0.00928964] [0.00733784]
 theta from own gd
-[[3.9156926 ]
- [3.07038096]]
-0 [-0.02733402] [0.02281881]
-1 [-0.02532198] [0.02113913]
-2 [-0.02285443] [0.01907918]
-3 [-0.02043186] [0.01705679]
-4 [-0.01820111] [0.01519453]
-5 [-0.01619211] [0.01351739]
-6 [-0.01439752] [0.01201924]
-7 [-0.01279935] [0.01068507]
-8 [-0.01137774] [0.00949829]
-9 [-0.01011375] [0.0084431]
-10 [-0.00899009] [0.00750504]
-11 [-0.00799123] [0.00667119]
-12 [-0.00710334] [0.00592997]
-13 [-0.0063141] [0.0052711]
-14 [-0.00561255] [0.00468544]
-15 [-0.00498895] [0.00416484]
-16 [-0.00443464] [0.0037021]
-17 [-0.00394191] [0.00329076]
-18 [-0.00350393] [0.00292513]
-19 [-0.00311462] [0.00260012]
-20 [-0.00276855] [0.00231123]
-21 [-0.00246094] [0.00205443]
-22 [-0.00218751] [0.00182617]
-23 [-0.00194446] [0.00162326]
-24 [-0.00172842] [0.0014429]
-25 [-0.00153637] [0.00128259]
-26 [-0.00136567] [0.00114008]
-27 [-0.00121393] [0.00101341]
-28 [-0.00107905] [0.00090081]
-29 [-0.00095916] [0.00080072]
+[[3.97081612]
+ [3.02305221]]
+0 [-0.0087036] [0.00687493]
+1 [-0.00815453] [0.00644123]
+2 [-0.00747538] [0.00590477]
+3 [-0.00680005] [0.00537133]
+4 [-0.00616847] [0.00487245]
+5 [-0.00558986] [0.0044154]
+6 [-0.00506364] [0.00399975]
+7 [-0.00458633] [0.00362272]
+8 [-0.00415381] [0.00328108]
+9 [-0.00376201] [0.0029716]
+10 [-0.00340714] [0.00269129]
+11 [-0.00308574] [0.00243742]
+12 [-0.00279466] [0.00220749]
+13 [-0.00253103] [0.00199925]
+14 [-0.00229228] [0.00181066]
+15 [-0.00207604] [0.00163985]
+16 [-0.0018802] [0.00148516]
+17 [-0.00170284] [0.00134506]
+18 [-0.0015422] [0.00121818]
+19 [-0.00139672] [0.00110327]
+20 [-0.00126497] [0.00099919]
+21 [-0.00114564] [0.00090494]
+22 [-0.00103757] [0.00081957]
+23 [-0.00093969] [0.00074226]
+24 [-0.00085105] [0.00067224]
+25 [-0.00077077] [0.00060883]
+26 [-0.00069806] [0.00055139]
+27 [-0.00063221] [0.00049938]
+28 [-0.00057257] [0.00045227]
+29 [-0.00051856] [0.00040961]
 theta from own gd wth momentum
-[[3.99737032]
- [3.00219529]]
+[[3.99842525]
+ [3.00124389]]
      @@ -1992,22 +1997,20 @@ theta from own gd wth momentum 
      Own inversion
-[[3.80284827]
- [3.14558948]]
-Eigenvalues of Hessian Matrix:[0.33477228 4.32758606]
+[[3.93505095]
+ [3.03255202]]
+Eigenvalues of Hessian Matrix:[0.35715186 4.00152151]
 
      theta from own gd
+[[3.93505095]
+ [3.03255202]]
      -
      [[3.80284827]
- [3.14558948]]
-
      -
      -_images/0fe401714ec88e67e8834af30a476bb19ae5e8a9677eb38e4161007bae099f84.png +_images/327366ea5051007f61df82fc528a36028c298264f7cb3d1565bb97cd4c20255c.png 
      theta from own sdg
-[[3.8286293 ]
- [3.19901555]]
+[[3.93535203]
+ [3.02339886]]
      @@ -2089,19 +2092,15 @@ Eigenvalues of Hessian Matrix:[0.33477228 4.32758606] 
      Own inversion
-[[4.14809298]
- [2.84983419]]
-Eigenvalues of Hessian Matrix:[0.35213219 4.34352956]
+[[4.00794132]
+ [3.02667095]]
+Eigenvalues of Hessian Matrix:[0.29709966 4.68284888]
 theta from own gd
-[[4.14764332]
- [2.85021125]]
-
      -
      -
      theta from own sdg with momentum
-
      -
      -
      [[4.15364228]
- [2.8846002 ]]
+[[4.0068889 ]
+ [3.02750941]]
+theta from own sdg with momentum
+[[4.01544241]
+ [3.0176573 ]]
      @@ -2170,9 +2169,9 @@ theta from own gd 
      theta from own AdaGrad
-[[2.00000539]
- [2.99996887]
- [4.0000263 ]]
+[[1.99856653]
+ [3.01053812]
+ [3.99013096]]
      @@ -2248,9 +2247,9 @@ theta from own gd 
      theta from own RMSprop
-[[2.00051557]
- [2.99775568]
- [4.00224621]]
+[[2.00002452]
+ [2.99973034]
+ [4.00026406]]
      @@ -2330,9 +2329,9 @@ theta from own gd 
      theta from own ADAM
-[[1.99997454]
- [3.00018485]
- [3.99980356]]
+[[1.99997633]
+ [3.00007035]
+ [3.99994513]]
      @@ -2453,7 +2452,7 @@ It provides composable transformations of Python+NumPy programs: differentiate, return asarray(x, dtype=self.dtype) -
      [<matplotlib.lines.Line2D at 0x1109cc4f0>]
+
      [<matplotlib.lines.Line2D at 0x1323c4070>]
 
      
 
      
 _images/02e94753795fba95a52acc4488a9ab82172ba372cbb7c1cc79a3e88c3ba03d69.png
@@ -2488,7 +2487,7 @@ It provides composable transformations of Python+NumPy programs: differentiate,
      -
      <matplotlib.collections.PathCollection at 0x110957c40>
+
      <matplotlib.collections.PathCollection at 0x133fa12e0>
 
      
 
      
 _images/60f34a975702165b9275820e0dda6259f453a92c5ad587b37191bb47a9c67ae9.png
diff --git a/doc/LectureNotes/_build/html/week41.html b/doc/LectureNotes/_build/html/week41.html
index 2682e5f49..d7ccb38fb 100644
--- a/doc/LectureNotes/_build/html/week41.html
+++ b/doc/LectureNotes/_build/html/week41.html
@@ -257,6 +257,9 @@
 
    • Week 47: From Decision Trees to Ensemble Methods, Random Forests and Boosting Methods
      • 
 
    • Exercise week 47
      • 
 
+
    • Week 48: Gradient boosting  and summary of course
      • 
+
    • Exercises week 48
      • 
+

    Projects

    Projects

      @@ -2379,7 +2382,7 @@ the Hadamard product, meaning element-wise multiplication.

      Old accuracy on training data: 0.1440501043841336
      -
      /var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_59019/953065564.py:4: RuntimeWarning: overflow encountered in exp
+
      /var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_23356/953065564.py:4: RuntimeWarning: overflow encountered in exp
   return 1/(1 + np.exp(-x))
 
      
 
      
@@ -2715,7 +2718,7 @@ Lambda =  10.0
 Accuracy score on test set:  0.19166666666666668
      -
      /var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_59019/953065564.py:4: RuntimeWarning: overflow encountered in exp
+
      /var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_23356/953065564.py:4: RuntimeWarning: overflow encountered in exp
   return 1/(1 + np.exp(-x))
 
      
 
      
@@ -2724,7 +2727,7 @@ Lambda =  1e-05
 Accuracy score on test set:  0.10555555555555556
      -
      /var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_59019/953065564.py:4: RuntimeWarning: overflow encountered in exp
+
      /var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_23356/953065564.py:4: RuntimeWarning: overflow encountered in exp
   return 1/(1 + np.exp(-x))
 
      
 
      
@@ -2733,7 +2736,7 @@ Lambda =  0.0001
 Accuracy score on test set:  0.08611111111111111
      -
      /var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_59019/953065564.py:4: RuntimeWarning: overflow encountered in exp
+
      /var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_23356/953065564.py:4: RuntimeWarning: overflow encountered in exp
   return 1/(1 + np.exp(-x))
 
      
 
      
@@ -2742,7 +2745,7 @@ Lambda =  0.001
 Accuracy score on test set:  0.10555555555555556
      -
      /var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_59019/953065564.py:4: RuntimeWarning: overflow encountered in exp
+
      /var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_23356/953065564.py:4: RuntimeWarning: overflow encountered in exp
   return 1/(1 + np.exp(-x))
 
      
 
      
@@ -2751,7 +2754,7 @@ Lambda =  0.01
 Accuracy score on test set:  0.08888888888888889
      -
      /var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_59019/953065564.py:4: RuntimeWarning: overflow encountered in exp
+
      /var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_23356/953065564.py:4: RuntimeWarning: overflow encountered in exp
   return 1/(1 + np.exp(-x))
 
      
 
      
@@ -2760,7 +2763,7 @@ Lambda =  0.1
 Accuracy score on test set:  0.08611111111111111
      -
      /var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_59019/953065564.py:4: RuntimeWarning: overflow encountered in exp
+
      /var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_23356/953065564.py:4: RuntimeWarning: overflow encountered in exp
   return 1/(1 + np.exp(-x))
 
      
 
      
@@ -2769,7 +2772,7 @@ Lambda =  1.0
 Accuracy score on test set:  0.08888888888888889
      -
      /var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_59019/953065564.py:4: RuntimeWarning: overflow encountered in exp
+
      /var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_23356/953065564.py:4: RuntimeWarning: overflow encountered in exp
   return 1/(1 + np.exp(-x))
 
      
 
      
@@ -2778,11 +2781,11 @@ Lambda =  10.0
 Accuracy score on test set:  0.09166666666666666
      -
      /var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_59019/953065564.py:4: RuntimeWarning: overflow encountered in exp
+
      /var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_23356/953065564.py:4: RuntimeWarning: overflow encountered in exp
   return 1/(1 + np.exp(-x))
-/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_59019/1630775253.py:43: RuntimeWarning: overflow encountered in exp
+/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_23356/1630775253.py:43: RuntimeWarning: overflow encountered in exp
   exp_term = np.exp(self.z_o)
-/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_59019/1630775253.py:44: RuntimeWarning: invalid value encountered in divide
+/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_23356/1630775253.py:44: RuntimeWarning: invalid value encountered in divide
   self.probabilities = exp_term / np.sum(exp_term, axis=1, keepdims=True)
 
      
 
      
@@ -2791,11 +2794,11 @@ Lambda =  1e-05
 Accuracy score on test set:  0.07777777777777778
      -
      /var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_59019/953065564.py:4: RuntimeWarning: overflow encountered in exp
+
      /var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_23356/953065564.py:4: RuntimeWarning: overflow encountered in exp
   return 1/(1 + np.exp(-x))
-/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_59019/1630775253.py:43: RuntimeWarning: overflow encountered in exp
+/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_23356/1630775253.py:43: RuntimeWarning: overflow encountered in exp
   exp_term = np.exp(self.z_o)
-/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_59019/1630775253.py:44: RuntimeWarning: invalid value encountered in divide
+/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_23356/1630775253.py:44: RuntimeWarning: invalid value encountered in divide
   self.probabilities = exp_term / np.sum(exp_term, axis=1, keepdims=True)
 
      
 
      
@@ -2804,11 +2807,11 @@ Lambda =  0.0001
 Accuracy score on test set:  0.07777777777777778
      -
      /var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_59019/953065564.py:4: RuntimeWarning: overflow encountered in exp
+
      /var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_23356/953065564.py:4: RuntimeWarning: overflow encountered in exp
   return 1/(1 + np.exp(-x))
-/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_59019/1630775253.py:43: RuntimeWarning: overflow encountered in exp
+/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_23356/1630775253.py:43: RuntimeWarning: overflow encountered in exp
   exp_term = np.exp(self.z_o)
-/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_59019/1630775253.py:44: RuntimeWarning: invalid value encountered in divide
+/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_23356/1630775253.py:44: RuntimeWarning: invalid value encountered in divide
   self.probabilities = exp_term / np.sum(exp_term, axis=1, keepdims=True)
 
      
 
      
@@ -2817,11 +2820,11 @@ Lambda =  0.001
 Accuracy score on test set:  0.07777777777777778
      -
      /var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_59019/953065564.py:4: RuntimeWarning: overflow encountered in exp
+
      /var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_23356/953065564.py:4: RuntimeWarning: overflow encountered in exp
   return 1/(1 + np.exp(-x))
-/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_59019/1630775253.py:43: RuntimeWarning: overflow encountered in exp
+/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_23356/1630775253.py:43: RuntimeWarning: overflow encountered in exp
   exp_term = np.exp(self.z_o)
-/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_59019/1630775253.py:44: RuntimeWarning: invalid value encountered in divide
+/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_23356/1630775253.py:44: RuntimeWarning: invalid value encountered in divide
   self.probabilities = exp_term / np.sum(exp_term, axis=1, keepdims=True)
 
      
 
      
@@ -2830,11 +2833,11 @@ Lambda =  0.01
 Accuracy score on test set:  0.07777777777777778
      -
      /var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_59019/953065564.py:4: RuntimeWarning: overflow encountered in exp
+
      /var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_23356/953065564.py:4: RuntimeWarning: overflow encountered in exp
   return 1/(1 + np.exp(-x))
-/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_59019/1630775253.py:43: RuntimeWarning: overflow encountered in exp
+/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_23356/1630775253.py:43: RuntimeWarning: overflow encountered in exp
   exp_term = np.exp(self.z_o)
-/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_59019/1630775253.py:44: RuntimeWarning: invalid value encountered in divide
+/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_23356/1630775253.py:44: RuntimeWarning: invalid value encountered in divide
   self.probabilities = exp_term / np.sum(exp_term, axis=1, keepdims=True)
 
      
 
      
@@ -2843,7 +2846,7 @@ Lambda =  0.1
 Accuracy score on test set:  0.07777777777777778
      -
      /var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_59019/953065564.py:4: RuntimeWarning: overflow encountered in exp
+
      /var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_23356/953065564.py:4: RuntimeWarning: overflow encountered in exp
   return 1/(1 + np.exp(-x))
 
      
 
      
@@ -2852,11 +2855,11 @@ Lambda =  1.0
 Accuracy score on test set:  0.10555555555555556
      -
      /var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_59019/953065564.py:4: RuntimeWarning: overflow encountered in exp
+
      /var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_23356/953065564.py:4: RuntimeWarning: overflow encountered in exp
   return 1/(1 + np.exp(-x))
-/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_59019/1630775253.py:43: RuntimeWarning: overflow encountered in exp
+/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_23356/1630775253.py:43: RuntimeWarning: overflow encountered in exp
   exp_term = np.exp(self.z_o)
-/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_59019/1630775253.py:44: RuntimeWarning: invalid value encountered in divide
+/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_23356/1630775253.py:44: RuntimeWarning: invalid value encountered in divide
   self.probabilities = exp_term / np.sum(exp_term, axis=1, keepdims=True)
 
      
 
      
@@ -2865,11 +2868,11 @@ Lambda =  10.0
 Accuracy score on test set:  0.07777777777777778
      -
      /var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_59019/953065564.py:4: RuntimeWarning: overflow encountered in exp
+
      /var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_23356/953065564.py:4: RuntimeWarning: overflow encountered in exp
   return 1/(1 + np.exp(-x))
-/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_59019/1630775253.py:43: RuntimeWarning: overflow encountered in exp
+/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_23356/1630775253.py:43: RuntimeWarning: overflow encountered in exp
   exp_term = np.exp(self.z_o)
-/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_59019/1630775253.py:44: RuntimeWarning: invalid value encountered in divide
+/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_23356/1630775253.py:44: RuntimeWarning: invalid value encountered in divide
   self.probabilities = exp_term / np.sum(exp_term, axis=1, keepdims=True)
 
      
 
      
@@ -2878,11 +2881,11 @@ Lambda =  1e-05
 Accuracy score on test set:  0.07777777777777778
      -
      /var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_59019/953065564.py:4: RuntimeWarning: overflow encountered in exp
+
      /var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_23356/953065564.py:4: RuntimeWarning: overflow encountered in exp
   return 1/(1 + np.exp(-x))
-/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_59019/1630775253.py:43: RuntimeWarning: overflow encountered in exp
+/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_23356/1630775253.py:43: RuntimeWarning: overflow encountered in exp
   exp_term = np.exp(self.z_o)
-/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_59019/1630775253.py:44: RuntimeWarning: invalid value encountered in divide
+/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_23356/1630775253.py:44: RuntimeWarning: invalid value encountered in divide
   self.probabilities = exp_term / np.sum(exp_term, axis=1, keepdims=True)
 
      
 
      
@@ -2891,11 +2894,11 @@ Lambda =  0.0001
 Accuracy score on test set:  0.07777777777777778
      -
      /var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_59019/953065564.py:4: RuntimeWarning: overflow encountered in exp
+
      /var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_23356/953065564.py:4: RuntimeWarning: overflow encountered in exp
   return 1/(1 + np.exp(-x))
-/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_59019/1630775253.py:43: RuntimeWarning: overflow encountered in exp
+/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_23356/1630775253.py:43: RuntimeWarning: overflow encountered in exp
   exp_term = np.exp(self.z_o)
-/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_59019/1630775253.py:44: RuntimeWarning: invalid value encountered in divide
+/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_23356/1630775253.py:44: RuntimeWarning: invalid value encountered in divide
   self.probabilities = exp_term / np.sum(exp_term, axis=1, keepdims=True)
 
      
 
      
@@ -2904,11 +2907,11 @@ Lambda =  0.001
 Accuracy score on test set:  0.07777777777777778
      -
      /var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_59019/953065564.py:4: RuntimeWarning: overflow encountered in exp
+
      /var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_23356/953065564.py:4: RuntimeWarning: overflow encountered in exp
   return 1/(1 + np.exp(-x))
-/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_59019/1630775253.py:43: RuntimeWarning: overflow encountered in exp
+/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_23356/1630775253.py:43: RuntimeWarning: overflow encountered in exp
   exp_term = np.exp(self.z_o)
-/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_59019/1630775253.py:44: RuntimeWarning: invalid value encountered in divide
+/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_23356/1630775253.py:44: RuntimeWarning: invalid value encountered in divide
   self.probabilities = exp_term / np.sum(exp_term, axis=1, keepdims=True)
 
      
 
      
@@ -2917,11 +2920,11 @@ Lambda =  0.01
 Accuracy score on test set:  0.07777777777777778
      -
      /var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_59019/953065564.py:4: RuntimeWarning: overflow encountered in exp
+
      /var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_23356/953065564.py:4: RuntimeWarning: overflow encountered in exp
   return 1/(1 + np.exp(-x))
-/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_59019/1630775253.py:43: RuntimeWarning: overflow encountered in exp
+/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_23356/1630775253.py:43: RuntimeWarning: overflow encountered in exp
   exp_term = np.exp(self.z_o)
-/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_59019/1630775253.py:44: RuntimeWarning: invalid value encountered in divide
+/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_23356/1630775253.py:44: RuntimeWarning: invalid value encountered in divide
   self.probabilities = exp_term / np.sum(exp_term, axis=1, keepdims=True)
 
      
 
      
@@ -2930,11 +2933,11 @@ Lambda =  0.1
 Accuracy score on test set:  0.07777777777777778
      -
      /var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_59019/953065564.py:4: RuntimeWarning: overflow encountered in exp
+
      /var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_23356/953065564.py:4: RuntimeWarning: overflow encountered in exp
   return 1/(1 + np.exp(-x))
-/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_59019/1630775253.py:43: RuntimeWarning: overflow encountered in exp
+/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_23356/1630775253.py:43: RuntimeWarning: overflow encountered in exp
   exp_term = np.exp(self.z_o)
-/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_59019/1630775253.py:44: RuntimeWarning: invalid value encountered in divide
+/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_23356/1630775253.py:44: RuntimeWarning: invalid value encountered in divide
   self.probabilities = exp_term / np.sum(exp_term, axis=1, keepdims=True)
 
      
 
      
@@ -2943,11 +2946,11 @@ Lambda =  1.0
 Accuracy score on test set:  0.07777777777777778
      -
      /var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_59019/953065564.py:4: RuntimeWarning: overflow encountered in exp
+
      /var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_23356/953065564.py:4: RuntimeWarning: overflow encountered in exp
   return 1/(1 + np.exp(-x))
-/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_59019/1630775253.py:43: RuntimeWarning: overflow encountered in exp
+/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_23356/1630775253.py:43: RuntimeWarning: overflow encountered in exp
   exp_term = np.exp(self.z_o)
-/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_59019/1630775253.py:44: RuntimeWarning: invalid value encountered in divide
+/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_23356/1630775253.py:44: RuntimeWarning: invalid value encountered in divide
   self.probabilities = exp_term / np.sum(exp_term, axis=1, keepdims=True)
 
      
 
      
@@ -3000,15 +3003,15 @@ Accuracy score on test set:  0.07777777777777778
      -
      /var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_59019/953065564.py:4: RuntimeWarning: overflow encountered in exp
+
      /var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_23356/953065564.py:4: RuntimeWarning: overflow encountered in exp
   return 1/(1 + np.exp(-x))
-/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_59019/953065564.py:4: RuntimeWarning: overflow encountered in exp
+/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_23356/953065564.py:4: RuntimeWarning: overflow encountered in exp
   return 1/(1 + np.exp(-x))
-/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_59019/953065564.py:4: RuntimeWarning: overflow encountered in exp
+/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_23356/953065564.py:4: RuntimeWarning: overflow encountered in exp
   return 1/(1 + np.exp(-x))
-/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_59019/953065564.py:4: RuntimeWarning: overflow encountered in exp
+/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_23356/953065564.py:4: RuntimeWarning: overflow encountered in exp
   return 1/(1 + np.exp(-x))
-/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_59019/953065564.py:4: RuntimeWarning: overflow encountered in exp
+/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_23356/953065564.py:4: RuntimeWarning: overflow encountered in exp
   return 1/(1 + np.exp(-x))
 
      
 
      
@@ -3159,218 +3162,31 @@ Lambda =  0.1
 Accuracy score on test set:  0.8888888888888888
      -
      /Users/mhjensen/miniforge3/envs/myenv/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.
-  warnings.warn(
-
      -
      Learning rate  =  0.0001
 Lambda =  1.0
-Accuracy score on test set:  0.8805555555555555
-
      -
      -
      /Users/mhjensen/miniforge3/envs/myenv/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.
-  warnings.warn(
-
      -
      -
      Learning rate  =  0.0001
-Lambda =  10.0
-Accuracy score on test set:  0.8944444444444445
-
      -
      -
      /Users/mhjensen/miniforge3/envs/myenv/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.
-  warnings.warn(
-
      -
      -
      Learning rate  =  0.001
-Lambda =  1e-05
-Accuracy score on test set:  0.975
-
      -
      -
      /Users/mhjensen/miniforge3/envs/myenv/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.
-  warnings.warn(
-
      -
      -
      Learning rate  =  0.001
-Lambda =  0.0001
-Accuracy score on test set:  0.9777777777777777
-
      -
      -
      /Users/mhjensen/miniforge3/envs/myenv/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.
-  warnings.warn(
-
      -
      -
      Learning rate  =  0.001
-Lambda =  0.001
-Accuracy score on test set:  0.9805555555555555
-
      -
      -
      /Users/mhjensen/miniforge3/envs/myenv/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.
-  warnings.warn(
-
      -
      -
      Learning rate  =  0.001
-Lambda =  0.01
-Accuracy score on test set:  0.9861111111111112
-
      -
      -
      /Users/mhjensen/miniforge3/envs/myenv/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.
-  warnings.warn(
-
      -
      -
      Learning rate  =  0.001
-Lambda =  0.1
-Accuracy score on test set:  0.9805555555555555
-
      -
      -
      /Users/mhjensen/miniforge3/envs/myenv/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.
-  warnings.warn(
-
      -
      -
      Learning rate  =  0.001
-Lambda =  1.0
-Accuracy score on test set:  0.9777777777777777
-
      -
      -
      /Users/mhjensen/miniforge3/envs/myenv/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.
-  warnings.warn(
-
      -
      -
      Learning rate  =  0.001
-Lambda =  10.0
-Accuracy score on test set:  0.9444444444444444
-
      -
      -
      /Users/mhjensen/miniforge3/envs/myenv/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.
-  warnings.warn(
-
      -
      -
      Learning rate  =  0.01
-Lambda =  1e-05
-Accuracy score on test set:  0.9861111111111112
-
      -
      -
      Learning rate  =  0.01
-Lambda =  0.0001
-Accuracy score on test set:  0.9888888888888889
-
      -
      -
      Learning rate  =  0.01
-Lambda =  0.001
-Accuracy score on test set:  0.9888888888888889
-
      -
      -
      Learning rate  =  0.01
-Lambda =  0.01
-Accuracy score on test set:  0.9861111111111112
-
      -
      -
      Learning rate  =  0.01
-Lambda =  0.1
-Accuracy score on test set:  0.9888888888888889
-
      -
      -
      Learning rate  =  0.01
-Lambda =  1.0
-Accuracy score on test set:  0.9722222222222222
-
      -
      -
      Learning rate  =  0.01
-Lambda =  10.0
-Accuracy score on test set:  0.9527777777777777
-
      -
      -
      Learning rate  =  0.1
-Lambda =  1e-05
-Accuracy score on test set:  0.9027777777777778
-
-Learning rate  =  0.1
-Lambda =  0.0001
 Accuracy score on test set:  0.8583333333333333
      -
      Learning rate  =  0.1
-Lambda =  0.001
-Accuracy score on test set:  0.8722222222222222
+
      /Users/mhjensen/miniforge3/envs/myenv/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:698: UserWarning: Training interrupted by user.
+  warnings.warn("Training interrupted by user.")
 
      
 
      
-
      Learning rate  =  0.1
-Lambda =  0.01
-Accuracy score on test set:  0.9055555555555556
-
-Learning rate  =  0.1
-Lambda =  0.1
-Accuracy score on test set:  0.8805555555555555
+
      /Users/mhjensen/miniforge3/envs/myenv/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.
+  warnings.warn(
 
      
 
      
-
      Learning rate  =  0.1
-Lambda =  1.0
-Accuracy score on test set:  0.8722222222222222
-
-Learning rate  =  0.1
+
      Learning rate  =  0.0001
 Lambda =  10.0
-Accuracy score on test set:  0.8666666666666667
+Accuracy score on test set:  0.8361111111111111
 
      
 
      
-
      Learning rate  =  1.0
+
      /Users/mhjensen/miniforge3/envs/myenv/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.
+  warnings.warn(
+
      
+
      
+
      Learning rate  =  0.001
 Lambda =  1e-05
-Accuracy score on test set:  0.08611111111111111
-
-Learning rate  =  1.0
-Lambda =  0.0001
-Accuracy score on test set:  0.10555555555555556
-
      
-
      
-
      Learning rate  =  1.0
-Lambda =  0.001
-Accuracy score on test set:  0.10555555555555556
-
-Learning rate  =  1.0
-Lambda =  0.01
-Accuracy score on test set:  0.17777777777777778
-
-Learning rate  =  1.0
-Lambda =  0.1
-Accuracy score on test set:  0.08333333333333333
-
      
-
      
-
      Learning rate  =  1.0
-Lambda =  1.0
-Accuracy score on test set:  0.08888888888888889
-
-Learning rate  =  1.0
-Lambda =  10.0
-Accuracy score on test set:  0.09444444444444444
-
      
-
      
-
      Learning rate  =  10.0
-Lambda =  1e-05
-Accuracy score on test set:  0.17222222222222222
-
-Learning rate  =  10.0
-Lambda =  0.0001
-Accuracy score on test set:  0.11666666666666667
-
-Learning rate  =  10.0
-Lambda =  0.001
-Accuracy score on test set:  0.10555555555555556
-
-Learning rate  =  10.0
-Lambda =  0.01
-Accuracy score on test set:  0.1388888888888889
-
      
-
      
-
      Learning rate  =  10.0
-Lambda =  0.1
-Accuracy score on test set:  0.11388888888888889
-
      
-
      
-
      Learning rate  =  10.0
-Lambda =  1.0
-Accuracy score on test set:  0.10555555555555556
-
-Learning rate  =  10.0
-Lambda =  10.0
-Accuracy score on test set:  0.09444444444444444
+Accuracy score on test set:  0.9777777777777777
 
      
 
      
 
      
@@ -3417,10 +3233,6 @@ Accuracy score on test set:  0.09444444444444444
 
      
 
      
 
      
-
      
-_images/511c09db890499149b1572167479e7c928860b0aaed18526447cb972a68957dd.png
-_images/d189a2d67f3b4e9b555480356aed0829649f8bb2c6cfe1d6fbd861f8f33d0c50.png
-
      
 
      
 
 
      
@@ -3459,14 +3271,6 @@ how simple solving a machine learning problem can be.
      -
      -
        Cell In[14], line 1
-    pip3 install tensorflow
-         ^
-SyntaxError: invalid syntax
-
      -
      -

      and/or if you use anaconda, just write (or install from the graphical user interface) (current release of CPU-only TensorFlow)

      diff --git a/doc/LectureNotes/_build/html/week43.html b/doc/LectureNotes/_build/html/week43.html index 479bdb87d..c0791c021 100644 --- a/doc/LectureNotes/_build/html/week43.html +++ b/doc/LectureNotes/_build/html/week43.html @@ -257,6 +257,9 @@
    • Week 47: From Decision Trees to Ensemble Methods, Random Forests and Boosting Methods
    • Exercise week 47
      • +
    • Week 48: Gradient boosting and summary of course
      • +
    • Exercises week 48
      • +

    Projects

      @@ -1403,7 +1406,7 @@ the Hadamard product, meaning element-wise multiplication.

      Old accuracy on training data: 0.1440501043841336
      -
      /var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_59025/953065564.py:4: RuntimeWarning: overflow encountered in exp
+
      /var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_23401/953065564.py:4: RuntimeWarning: overflow encountered in exp
   return 1/(1 + np.exp(-x))
 
      
 
      
@@ -1739,7 +1742,7 @@ Lambda =  10.0
 Accuracy score on test set:  0.19166666666666668
      -
      /var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_59025/953065564.py:4: RuntimeWarning: overflow encountered in exp
+
      /var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_23401/953065564.py:4: RuntimeWarning: overflow encountered in exp
   return 1/(1 + np.exp(-x))
 
      
 
      
@@ -1748,7 +1751,7 @@ Lambda =  1e-05
 Accuracy score on test set:  0.10555555555555556
      -
      /var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_59025/953065564.py:4: RuntimeWarning: overflow encountered in exp
+
      /var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_23401/953065564.py:4: RuntimeWarning: overflow encountered in exp
   return 1/(1 + np.exp(-x))
 
      
 
      
@@ -1757,7 +1760,7 @@ Lambda =  0.0001
 Accuracy score on test set:  0.08611111111111111
      -
      /var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_59025/953065564.py:4: RuntimeWarning: overflow encountered in exp
+
      /var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_23401/953065564.py:4: RuntimeWarning: overflow encountered in exp
   return 1/(1 + np.exp(-x))
 
      
 
      
@@ -1766,7 +1769,7 @@ Lambda =  0.001
 Accuracy score on test set:  0.10555555555555556
      -
      /var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_59025/953065564.py:4: RuntimeWarning: overflow encountered in exp
+
      /var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_23401/953065564.py:4: RuntimeWarning: overflow encountered in exp
   return 1/(1 + np.exp(-x))
 
      
 
      
@@ -1775,7 +1778,7 @@ Lambda =  0.01
 Accuracy score on test set:  0.08888888888888889
      -
      /var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_59025/953065564.py:4: RuntimeWarning: overflow encountered in exp
+
      /var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_23401/953065564.py:4: RuntimeWarning: overflow encountered in exp
   return 1/(1 + np.exp(-x))
 
      
 
      
@@ -1784,7 +1787,7 @@ Lambda =  0.1
 Accuracy score on test set:  0.08611111111111111
      -
      /var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_59025/953065564.py:4: RuntimeWarning: overflow encountered in exp
+
      /var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_23401/953065564.py:4: RuntimeWarning: overflow encountered in exp
   return 1/(1 + np.exp(-x))
 
      
 
      
@@ -1793,7 +1796,7 @@ Lambda =  1.0
 Accuracy score on test set:  0.08888888888888889
      -
      /var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_59025/953065564.py:4: RuntimeWarning: overflow encountered in exp
+
      /var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_23401/953065564.py:4: RuntimeWarning: overflow encountered in exp
   return 1/(1 + np.exp(-x))
 
      
 
      
@@ -1802,11 +1805,11 @@ Lambda =  10.0
 Accuracy score on test set:  0.09166666666666666
      -
      /var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_59025/953065564.py:4: RuntimeWarning: overflow encountered in exp
+
      /var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_23401/953065564.py:4: RuntimeWarning: overflow encountered in exp
   return 1/(1 + np.exp(-x))
-/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_59025/1630775253.py:43: RuntimeWarning: overflow encountered in exp
+/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_23401/1630775253.py:43: RuntimeWarning: overflow encountered in exp
   exp_term = np.exp(self.z_o)
-/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_59025/1630775253.py:44: RuntimeWarning: invalid value encountered in divide
+/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_23401/1630775253.py:44: RuntimeWarning: invalid value encountered in divide
   self.probabilities = exp_term / np.sum(exp_term, axis=1, keepdims=True)
 
      
 
      
@@ -1815,11 +1818,11 @@ Lambda =  1e-05
 Accuracy score on test set:  0.07777777777777778
      -
      /var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_59025/953065564.py:4: RuntimeWarning: overflow encountered in exp
+
      /var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_23401/953065564.py:4: RuntimeWarning: overflow encountered in exp
   return 1/(1 + np.exp(-x))
-/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_59025/1630775253.py:43: RuntimeWarning: overflow encountered in exp
+/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_23401/1630775253.py:43: RuntimeWarning: overflow encountered in exp
   exp_term = np.exp(self.z_o)
-/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_59025/1630775253.py:44: RuntimeWarning: invalid value encountered in divide
+/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_23401/1630775253.py:44: RuntimeWarning: invalid value encountered in divide
   self.probabilities = exp_term / np.sum(exp_term, axis=1, keepdims=True)
 
      
 
      
@@ -1828,11 +1831,11 @@ Lambda =  0.0001
 Accuracy score on test set:  0.07777777777777778
      -
      /var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_59025/953065564.py:4: RuntimeWarning: overflow encountered in exp
+
      /var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_23401/953065564.py:4: RuntimeWarning: overflow encountered in exp
   return 1/(1 + np.exp(-x))
-/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_59025/1630775253.py:43: RuntimeWarning: overflow encountered in exp
+/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_23401/1630775253.py:43: RuntimeWarning: overflow encountered in exp
   exp_term = np.exp(self.z_o)
-/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_59025/1630775253.py:44: RuntimeWarning: invalid value encountered in divide
+/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_23401/1630775253.py:44: RuntimeWarning: invalid value encountered in divide
   self.probabilities = exp_term / np.sum(exp_term, axis=1, keepdims=True)
 
      
 
      
@@ -1841,11 +1844,11 @@ Lambda =  0.001
 Accuracy score on test set:  0.07777777777777778
      -
      /var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_59025/953065564.py:4: RuntimeWarning: overflow encountered in exp
+
      /var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_23401/953065564.py:4: RuntimeWarning: overflow encountered in exp
   return 1/(1 + np.exp(-x))
-/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_59025/1630775253.py:43: RuntimeWarning: overflow encountered in exp
+/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_23401/1630775253.py:43: RuntimeWarning: overflow encountered in exp
   exp_term = np.exp(self.z_o)
-/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_59025/1630775253.py:44: RuntimeWarning: invalid value encountered in divide
+/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_23401/1630775253.py:44: RuntimeWarning: invalid value encountered in divide
   self.probabilities = exp_term / np.sum(exp_term, axis=1, keepdims=True)
 
      
 
      
@@ -1854,11 +1857,11 @@ Lambda =  0.01
 Accuracy score on test set:  0.07777777777777778
      -
      /var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_59025/953065564.py:4: RuntimeWarning: overflow encountered in exp
+
      /var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_23401/953065564.py:4: RuntimeWarning: overflow encountered in exp
   return 1/(1 + np.exp(-x))
-/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_59025/1630775253.py:43: RuntimeWarning: overflow encountered in exp
+/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_23401/1630775253.py:43: RuntimeWarning: overflow encountered in exp
   exp_term = np.exp(self.z_o)
-/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_59025/1630775253.py:44: RuntimeWarning: invalid value encountered in divide
+/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_23401/1630775253.py:44: RuntimeWarning: invalid value encountered in divide
   self.probabilities = exp_term / np.sum(exp_term, axis=1, keepdims=True)
 
      
 
      
@@ -1867,7 +1870,7 @@ Lambda =  0.1
 Accuracy score on test set:  0.07777777777777778
      -
      /var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_59025/953065564.py:4: RuntimeWarning: overflow encountered in exp
+
      /var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_23401/953065564.py:4: RuntimeWarning: overflow encountered in exp
   return 1/(1 + np.exp(-x))
 
      
 
      
@@ -1876,11 +1879,11 @@ Lambda =  1.0
 Accuracy score on test set:  0.10555555555555556
      -
      /var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_59025/953065564.py:4: RuntimeWarning: overflow encountered in exp
+
      /var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_23401/953065564.py:4: RuntimeWarning: overflow encountered in exp
   return 1/(1 + np.exp(-x))
-/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_59025/1630775253.py:43: RuntimeWarning: overflow encountered in exp
+/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_23401/1630775253.py:43: RuntimeWarning: overflow encountered in exp
   exp_term = np.exp(self.z_o)
-/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_59025/1630775253.py:44: RuntimeWarning: invalid value encountered in divide
+/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_23401/1630775253.py:44: RuntimeWarning: invalid value encountered in divide
   self.probabilities = exp_term / np.sum(exp_term, axis=1, keepdims=True)
 
      
 
      
@@ -1889,11 +1892,11 @@ Lambda =  10.0
 Accuracy score on test set:  0.07777777777777778
      -
      /var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_59025/953065564.py:4: RuntimeWarning: overflow encountered in exp
+
      /var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_23401/953065564.py:4: RuntimeWarning: overflow encountered in exp
   return 1/(1 + np.exp(-x))
-/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_59025/1630775253.py:43: RuntimeWarning: overflow encountered in exp
+/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_23401/1630775253.py:43: RuntimeWarning: overflow encountered in exp
   exp_term = np.exp(self.z_o)
-/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_59025/1630775253.py:44: RuntimeWarning: invalid value encountered in divide
+/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_23401/1630775253.py:44: RuntimeWarning: invalid value encountered in divide
   self.probabilities = exp_term / np.sum(exp_term, axis=1, keepdims=True)
 
      
 
      
@@ -1902,11 +1905,11 @@ Lambda =  1e-05
 Accuracy score on test set:  0.07777777777777778
      -
      /var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_59025/953065564.py:4: RuntimeWarning: overflow encountered in exp
+
      /var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_23401/953065564.py:4: RuntimeWarning: overflow encountered in exp
   return 1/(1 + np.exp(-x))
-/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_59025/1630775253.py:43: RuntimeWarning: overflow encountered in exp
+/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_23401/1630775253.py:43: RuntimeWarning: overflow encountered in exp
   exp_term = np.exp(self.z_o)
-/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_59025/1630775253.py:44: RuntimeWarning: invalid value encountered in divide
+/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_23401/1630775253.py:44: RuntimeWarning: invalid value encountered in divide
   self.probabilities = exp_term / np.sum(exp_term, axis=1, keepdims=True)
 
      
 
      
@@ -1915,11 +1918,11 @@ Lambda =  0.0001
 Accuracy score on test set:  0.07777777777777778
      -
      /var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_59025/953065564.py:4: RuntimeWarning: overflow encountered in exp
+
      /var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_23401/953065564.py:4: RuntimeWarning: overflow encountered in exp
   return 1/(1 + np.exp(-x))
-/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_59025/1630775253.py:43: RuntimeWarning: overflow encountered in exp
+/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_23401/1630775253.py:43: RuntimeWarning: overflow encountered in exp
   exp_term = np.exp(self.z_o)
-/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_59025/1630775253.py:44: RuntimeWarning: invalid value encountered in divide
+/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_23401/1630775253.py:44: RuntimeWarning: invalid value encountered in divide
   self.probabilities = exp_term / np.sum(exp_term, axis=1, keepdims=True)
 
      
 
      
@@ -1928,11 +1931,11 @@ Lambda =  0.001
 Accuracy score on test set:  0.07777777777777778
      -
      /var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_59025/953065564.py:4: RuntimeWarning: overflow encountered in exp
+
      /var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_23401/953065564.py:4: RuntimeWarning: overflow encountered in exp
   return 1/(1 + np.exp(-x))
-/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_59025/1630775253.py:43: RuntimeWarning: overflow encountered in exp
+/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_23401/1630775253.py:43: RuntimeWarning: overflow encountered in exp
   exp_term = np.exp(self.z_o)
-/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_59025/1630775253.py:44: RuntimeWarning: invalid value encountered in divide
+/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_23401/1630775253.py:44: RuntimeWarning: invalid value encountered in divide
   self.probabilities = exp_term / np.sum(exp_term, axis=1, keepdims=True)
 
      
 
      
@@ -1941,38 +1944,43 @@ Lambda =  0.01
 Accuracy score on test set:  0.07777777777777778
      -
      /var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_59025/953065564.py:4: RuntimeWarning: overflow encountered in exp
+
      /var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_23401/953065564.py:4: RuntimeWarning: overflow encountered in exp
   return 1/(1 + np.exp(-x))
-/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_59025/1630775253.py:43: RuntimeWarning: overflow encountered in exp
+/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_23401/1630775253.py:43: RuntimeWarning: overflow encountered in exp
   exp_term = np.exp(self.z_o)
-/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_59025/1630775253.py:44: RuntimeWarning: invalid value encountered in divide
+/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_23401/1630775253.py:44: RuntimeWarning: invalid value encountered in divide
   self.probabilities = exp_term / np.sum(exp_term, axis=1, keepdims=True)
 
      
 
      
-
      ---------------------------------------------------------------------------
-KeyboardInterrupt                         Traceback (most recent call last)
-Cell In[8], line 11
-      8 for j, lmbd in enumerate(lmbd_vals):
-      9     dnn = NeuralNetwork(X_train, Y_train_onehot, eta=eta, lmbd=lmbd, epochs=epochs, batch_size=batch_size,
-     10                         n_hidden_neurons=n_hidden_neurons, n_categories=n_categories)
----> 11     dnn.train()
-     13     DNN_numpy[i][j] = dnn
-     15     test_predict = dnn.predict(X_test)
-
-Cell In[6], line 98, in NeuralNetwork.train(self)
-     95 self.X_data = self.X_data_full[chosen_datapoints]
-     96 self.Y_data = self.Y_data_full[chosen_datapoints]
----> 98 self.feed_forward()
-     99 self.backpropagation()
-
-Cell In[6], line 38, in NeuralNetwork.feed_forward(self)
-     36 def feed_forward(self):
-     37     # feed-forward for training
----> 38     self.z_h = np.matmul(self.X_data, self.hidden_weights) + self.hidden_bias
-     39     self.a_h = sigmoid(self.z_h)
-     41     self.z_o = np.matmul(self.a_h, self.output_weights) + self.output_bias
-
-KeyboardInterrupt: 
+
      Learning rate  =  10.0
+Lambda =  0.1
+Accuracy score on test set:  0.07777777777777778
+
      
+
      
+
      /var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_23401/953065564.py:4: RuntimeWarning: overflow encountered in exp
+  return 1/(1 + np.exp(-x))
+/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_23401/1630775253.py:43: RuntimeWarning: overflow encountered in exp
+  exp_term = np.exp(self.z_o)
+/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_23401/1630775253.py:44: RuntimeWarning: invalid value encountered in divide
+  self.probabilities = exp_term / np.sum(exp_term, axis=1, keepdims=True)
+
      
+
      
+
      Learning rate  =  10.0
+Lambda =  1.0
+Accuracy score on test set:  0.07777777777777778
+
      
+
      
+
      /var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_23401/953065564.py:4: RuntimeWarning: overflow encountered in exp
+  return 1/(1 + np.exp(-x))
+/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_23401/1630775253.py:43: RuntimeWarning: overflow encountered in exp
+  exp_term = np.exp(self.z_o)
+/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_23401/1630775253.py:44: RuntimeWarning: invalid value encountered in divide
+  self.probabilities = exp_term / np.sum(exp_term, axis=1, keepdims=True)
+
      
+
      
+
      Learning rate  =  10.0
+Lambda =  10.0
+Accuracy score on test set:  0.07777777777777778
 
      
 
      
 
      
@@ -2018,6 +2026,22 @@ Accuracy score on test set:  0.07777777777777778
      +
      +
      /var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_23401/953065564.py:4: RuntimeWarning: overflow encountered in exp
+  return 1/(1 + np.exp(-x))
+/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_23401/953065564.py:4: RuntimeWarning: overflow encountered in exp
+  return 1/(1 + np.exp(-x))
+/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_23401/953065564.py:4: RuntimeWarning: overflow encountered in exp
+  return 1/(1 + np.exp(-x))
+/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_23401/953065564.py:4: RuntimeWarning: overflow encountered in exp
+  return 1/(1 + np.exp(-x))
+/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_23401/953065564.py:4: RuntimeWarning: overflow encountered in exp
+  return 1/(1 + np.exp(-x))
+
      +
      +_images/e7be2ceafe348b5d5f0e390d3915e62a6f18c62d65f8255a2252346c88a1a2bf.png +_images/4021319392c5e4b499c0bad3ee2a1e5eda71857601b96f9cf9ff5d7d32397a0c.png +
      @@ -2053,6 +2077,329 @@ performance overall.

      +
      +
      /Users/mhjensen/miniforge3/envs/myenv/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.
+  warnings.warn(
+
      +
      +
      Learning rate  =  1e-05
+Lambda =  1e-05
+Accuracy score on test set:  0.18333333333333332
+
      +
      +
      /Users/mhjensen/miniforge3/envs/myenv/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.
+  warnings.warn(
+
      +
      +
      Learning rate  =  1e-05
+Lambda =  0.0001
+Accuracy score on test set:  0.18611111111111112
+
      +
      +
      /Users/mhjensen/miniforge3/envs/myenv/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.
+  warnings.warn(
+
      +
      +
      Learning rate  =  1e-05
+Lambda =  0.001
+Accuracy score on test set:  0.13055555555555556
+
      +
      +
      /Users/mhjensen/miniforge3/envs/myenv/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.
+  warnings.warn(
+
      +
      +
      Learning rate  =  1e-05
+Lambda =  0.01
+Accuracy score on test set:  0.24444444444444444
+
      +
      +
      /Users/mhjensen/miniforge3/envs/myenv/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.
+  warnings.warn(
+
      +
      +
      Learning rate  =  1e-05
+Lambda =  0.1
+Accuracy score on test set:  0.23333333333333334
+
      +
      +
      /Users/mhjensen/miniforge3/envs/myenv/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.
+  warnings.warn(
+
      +
      +
      Learning rate  =  1e-05
+Lambda =  1.0
+Accuracy score on test set:  0.12777777777777777
+
      +
      +
      /Users/mhjensen/miniforge3/envs/myenv/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.
+  warnings.warn(
+
      +
      +
      Learning rate  =  1e-05
+Lambda =  10.0
+Accuracy score on test set:  0.1527777777777778
+
      +
      +
      /Users/mhjensen/miniforge3/envs/myenv/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.
+  warnings.warn(
+
      +
      +
      Learning rate  =  0.0001
+Lambda =  1e-05
+Accuracy score on test set:  0.9111111111111111
+
      +
      +
      /Users/mhjensen/miniforge3/envs/myenv/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.
+  warnings.warn(
+
      +
      +
      Learning rate  =  0.0001
+Lambda =  0.0001
+Accuracy score on test set:  0.8888888888888888
+
      +
      +
      /Users/mhjensen/miniforge3/envs/myenv/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.
+  warnings.warn(
+
      +
      +
      Learning rate  =  0.0001
+Lambda =  0.001
+Accuracy score on test set:  0.8722222222222222
+
      +
      +
      /Users/mhjensen/miniforge3/envs/myenv/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.
+  warnings.warn(
+
      +
      +
      Learning rate  =  0.0001
+Lambda =  0.01
+Accuracy score on test set:  0.8305555555555556
+
      +
      +
      /Users/mhjensen/miniforge3/envs/myenv/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.
+  warnings.warn(
+
      +
      +
      Learning rate  =  0.0001
+Lambda =  0.1
+Accuracy score on test set:  0.8888888888888888
+
      +
      +
      /Users/mhjensen/miniforge3/envs/myenv/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.
+  warnings.warn(
+
      +
      +
      Learning rate  =  0.0001
+Lambda =  1.0
+Accuracy score on test set:  0.8805555555555555
+
      +
      +
      /Users/mhjensen/miniforge3/envs/myenv/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.
+  warnings.warn(
+
      +
      +
      Learning rate  =  0.0001
+Lambda =  10.0
+Accuracy score on test set:  0.8944444444444445
+
      +
      +
      /Users/mhjensen/miniforge3/envs/myenv/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.
+  warnings.warn(
+
      +
      +
      Learning rate  =  0.001
+Lambda =  1e-05
+Accuracy score on test set:  0.975
+
      +
      +
      /Users/mhjensen/miniforge3/envs/myenv/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.
+  warnings.warn(
+
      +
      +
      Learning rate  =  0.001
+Lambda =  0.0001
+Accuracy score on test set:  0.9777777777777777
+
      +
      +
      /Users/mhjensen/miniforge3/envs/myenv/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.
+  warnings.warn(
+
      +
      +
      Learning rate  =  0.001
+Lambda =  0.001
+Accuracy score on test set:  0.9805555555555555
+
      +
      +
      /Users/mhjensen/miniforge3/envs/myenv/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.
+  warnings.warn(
+
      +
      +
      Learning rate  =  0.001
+Lambda =  0.01
+Accuracy score on test set:  0.9861111111111112
+
      +
      +
      /Users/mhjensen/miniforge3/envs/myenv/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.
+  warnings.warn(
+
      +
      +
      Learning rate  =  0.001
+Lambda =  0.1
+Accuracy score on test set:  0.9805555555555555
+
      +
      +
      /Users/mhjensen/miniforge3/envs/myenv/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.
+  warnings.warn(
+
      +
      +
      Learning rate  =  0.001
+Lambda =  1.0
+Accuracy score on test set:  0.9777777777777777
+
      +
      +
      /Users/mhjensen/miniforge3/envs/myenv/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.
+  warnings.warn(
+
      +
      +
      Learning rate  =  0.001
+Lambda =  10.0
+Accuracy score on test set:  0.9444444444444444
+
      +
      +
      /Users/mhjensen/miniforge3/envs/myenv/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.
+  warnings.warn(
+
      +
      +
      Learning rate  =  0.01
+Lambda =  1e-05
+Accuracy score on test set:  0.9861111111111112
+
      +
      +
      Learning rate  =  0.01
+Lambda =  0.0001
+Accuracy score on test set:  0.9888888888888889
+
      +
      +
      Learning rate  =  0.01
+Lambda =  0.001
+Accuracy score on test set:  0.9888888888888889
+
      +
      +
      Learning rate  =  0.01
+Lambda =  0.01
+Accuracy score on test set:  0.9861111111111112
+
      +
      +
      Learning rate  =  0.01
+Lambda =  0.1
+Accuracy score on test set:  0.9888888888888889
+
      +
      +
      Learning rate  =  0.01
+Lambda =  1.0
+Accuracy score on test set:  0.9722222222222222
+
+Learning rate  =  0.01
+Lambda =  10.0
+Accuracy score on test set:  0.9527777777777777
+
      +
      +
      Learning rate  =  0.1
+Lambda =  1e-05
+Accuracy score on test set:  0.9027777777777778
+
+Learning rate  =  0.1
+Lambda =  0.0001
+Accuracy score on test set:  0.8583333333333333
+
      +
      +
      Learning rate  =  0.1
+Lambda =  0.001
+Accuracy score on test set:  0.8722222222222222
+
      +
      +
      Learning rate  =  0.1
+Lambda =  0.01
+Accuracy score on test set:  0.9055555555555556
+
+Learning rate  =  0.1
+Lambda =  0.1
+Accuracy score on test set:  0.8805555555555555
+
      +
      +
      Learning rate  =  0.1
+Lambda =  1.0
+Accuracy score on test set:  0.8722222222222222
+
      +
      +
      Learning rate  =  0.1
+Lambda =  10.0
+Accuracy score on test set:  0.8666666666666667
+
+Learning rate  =  1.0
+Lambda =  1e-05
+Accuracy score on test set:  0.08611111111111111
+
      +
      +
      Learning rate  =  1.0
+Lambda =  0.0001
+Accuracy score on test set:  0.10555555555555556
+
+Learning rate  =  1.0
+Lambda =  0.001
+Accuracy score on test set:  0.10555555555555556
+
      +
      +
      Learning rate  =  1.0
+Lambda =  0.01
+Accuracy score on test set:  0.17777777777777778
+
+Learning rate  =  1.0
+Lambda =  0.1
+Accuracy score on test set:  0.08333333333333333
+
+Learning rate  =  1.0
+Lambda =  1.0
+Accuracy score on test set:  0.08888888888888889
+
      +
      +
      Learning rate  =  1.0
+Lambda =  10.0
+Accuracy score on test set:  0.09444444444444444
+
+Learning rate  =  10.0
+Lambda =  1e-05
+Accuracy score on test set:  0.17222222222222222
+
      +
      +
      Learning rate  =  10.0
+Lambda =  0.0001
+Accuracy score on test set:  0.11666666666666667
+
+Learning rate  =  10.0
+Lambda =  0.001
+Accuracy score on test set:  0.10555555555555556
+
+Learning rate  =  10.0
+Lambda =  0.01
+Accuracy score on test set:  0.1388888888888889
+
+Learning rate  =  10.0
+Lambda =  0.1
+Accuracy score on test set:  0.11388888888888889
+
      +
      +
      Learning rate  =  10.0
+Lambda =  1.0
+Accuracy score on test set:  0.10555555555555556
+
+Learning rate  =  10.0
+Lambda =  10.0
+Accuracy score on test set:  0.09444444444444444
+
      +
      +
      @@ -2096,6 +2443,10 @@ performance overall.

      +
      +_images/511c09db890499149b1572167479e7c928860b0aaed18526447cb972a68957dd.png +_images/d189a2d67f3b4e9b555480356aed0829649f8bb2c6cfe1d6fbd861f8f33d0c50.png +
      @@ -2134,6 +2485,14 @@ how simple solving a machine learning problem can be.

      +
      +
        Cell In[12], line 1
+    pip3 install tensorflow
+         ^
+SyntaxError: invalid syntax
+
      +
      +

      and/or if you use anaconda, just write (or install from the graphical user interface) (current release of CPU-only TensorFlow)

      diff --git a/doc/LectureNotes/_build/html/week44.html b/doc/LectureNotes/_build/html/week44.html index 371cb2f57..f3ea96e35 100644 --- a/doc/LectureNotes/_build/html/week44.html +++ b/doc/LectureNotes/_build/html/week44.html @@ -257,6 +257,9 @@
    • Week 47: From Decision Trees to Ensemble Methods, Random Forests and Boosting Methods
    • Exercise week 47
      • +
    • Week 48: Gradient boosting and summary of course
      • +
    • Exercises week 48
      • +

    Projects

      @@ -1574,40 +1577,24 @@ labels = (n_inputs) = (1797,) super().__init__(activity_regularizer=activity_regularizer, **kwargs) -
       1/12 ━━━━━━━━━━━━━━━━━━━━ 1s 131ms/step - accuracy: 0.0938 - loss: 3.2865
+
       1/12 ━━━━━━━━━━━━━━━━━━━━ 1s 176ms/step - accuracy: 0.0625 - loss: 2.4493
 
      
 
      
 
      
- 9/12 ━━━━━━━━━━━━━━━━━━━━ 0s 7ms/step - accuracy: 0.1090 - loss: 3.1259  
+ 8/12 ━━━━━━━━━━━━━━━━━━━━ 0s 8ms/step - accuracy: 0.1149 - loss: 2.4557  
 
      
 
      
 
      
-12/12 ━━━━━━━━━━━━━━━━━━━━ 0s 9ms/step - accuracy: 0.1140 - loss: 3.1040
+12/12 ━━━━━━━━━━━━━━━━━━━━ 0s 31ms/step - accuracy: 0.1176 - loss: 2.4545
+
      
+
      
+
      
+12/12 ━━━━━━━━━━━━━━━━━━━━ 1s 32ms/step - accuracy: 0.1184 - loss: 2.4540
 
      
 
      
 
      Learning rate =  1e-05
 Lambda =  1e-05
-Test accuracy: 0.125
-
      
-
      
-
       1/12 ━━━━━━━━━━━━━━━━━━━━ 1s 133ms/step - accuracy: 0.2812 - loss: 2.6529
-
      
-
      
-
      
- 8/12 ━━━━━━━━━━━━━━━━━━━━ 0s 7ms/step - accuracy: 0.2350 - loss: 2.5504  
-
      
-
      
-
      
-12/12 ━━━━━━━━━━━━━━━━━━━━ 0s 9ms/step - accuracy: 0.2247 - loss: 2.5413
-
      
-
      
-
      
-12/12 ━━━━━━━━━━━━━━━━━━━━ 0s 10ms/step - accuracy: 0.2230 - loss: 2.5413
-
      
-
      
-
      Learning rate =  1e-05
-Lambda =  0.0001
-Test accuracy: 0.203
+Test accuracy: 0.128
 
      
 
      
 
      ---------------------------------------------------------------------------
diff --git a/doc/LectureNotes/_build/html/week45.html b/doc/LectureNotes/_build/html/week45.html
index 8f9cd60a9..515cb5726 100644
--- a/doc/LectureNotes/_build/html/week45.html
+++ b/doc/LectureNotes/_build/html/week45.html
@@ -257,6 +257,9 @@
 
    • Week 47: From Decision Trees to Ensemble Methods, Random Forests and Boosting Methods
      • 
 
    • Exercise week 47
      • 
 
+
    • Week 48: Gradient boosting  and summary of course
      • 
+
    • Exercises week 48
      • 
+

    Projects

      @@ -941,40 +944,36 @@ labels = (n_inputs) = (1797,) super().__init__(activity_regularizer=activity_regularizer, **kwargs) -
       1/12 ━━━━━━━━━━━━━━━━━━━━ 2s 214ms/step - accuracy: 0.1250 - loss: 3.1816
+
       1/12 ━━━━━━━━━━━━━━━━━━━━ 2s 203ms/step - accuracy: 0.2812 - loss: 2.7253
 
      
 
      
 
      
- 8/12 ━━━━━━━━━━━━━━━━━━━━ 0s 7ms/step - accuracy: 0.1078 - loss: 3.0683  
+ 9/12 ━━━━━━━━━━━━━━━━━━━━ 0s 7ms/step - accuracy: 0.1957 - loss: 2.8906  
 
      
 
      
 
      
-12/12 ━━━━━━━━━━━━━━━━━━━━ 0s 10ms/step - accuracy: 0.1005 - loss: 3.0830
-
      
-
      
-
      
-12/12 ━━━━━━━━━━━━━━━━━━━━ 0s 10ms/step - accuracy: 0.0998 - loss: 3.0851
+12/12 ━━━━━━━━━━━━━━━━━━━━ 0s 9ms/step - accuracy: 0.1826 - loss: 2.9261
 
      
 
      
 
      Learning rate =  1e-05
 Lambda =  1e-05
-Test accuracy: 0.092
+Test accuracy: 0.150
 
      
 
      
-
       1/12 ━━━━━━━━━━━━━━━━━━━━ 1s 128ms/step - accuracy: 0.0312 - loss: 3.1505
+
       1/12 ━━━━━━━━━━━━━━━━━━━━ 1s 126ms/step - accuracy: 0.0938 - loss: 2.8780
 
      
 
      
 
      
- 8/12 ━━━━━━━━━━━━━━━━━━━━ 0s 7ms/step - accuracy: 0.0554 - loss: 3.1156  
+ 9/12 ━━━━━━━━━━━━━━━━━━━━ 0s 7ms/step - accuracy: 0.1082 - loss: 2.8294  
 
      
 
      
 
      
-12/12 ━━━━━━━━━━━━━━━━━━━━ 0s 9ms/step - accuracy: 0.0560 - loss: 3.1184
+12/12 ━━━━━━━━━━━━━━━━━━━━ 0s 9ms/step - accuracy: 0.1134 - loss: 2.8359
 
      
 
      
 
      Learning rate =  1e-05
 Lambda =  0.0001
-Test accuracy: 0.058
+Test accuracy: 0.125
 
      
 
      
 
      ---------------------------------------------------------------------------
diff --git a/doc/LectureNotes/_build/html/week46.html b/doc/LectureNotes/_build/html/week46.html
index 7bd5a23de..5f1ed3788 100644
--- a/doc/LectureNotes/_build/html/week46.html
+++ b/doc/LectureNotes/_build/html/week46.html
@@ -257,6 +257,9 @@
 
    • Week 47: From Decision Trees to Ensemble Methods, Random Forests and Boosting Methods
      • 
 
    • Exercise week 47
      • 
 
+
    • Week 48: Gradient boosting  and summary of course
      • 
+
    • Exercises week 48
      • 
+

    Projects

      @@ -693,13 +696,13 @@ predicting the target features of query instances is as follows:

      2nd degree coefficients:
-zero power:  1.391131752817723
-first power:  0.023929544236763082
-second power:  -0.00011233101388608391
+zero power:  -9.690066070734556
+first power:  0.2579633987876525
+second power:  -0.0008519856133366335
      -_images/54da4dcf618f83f729a4226ed97b89f907d17d25d6f1e00ebabeaabefc39c127.png -_images/eb8a1d5930cd9fc5960f605b1df68a8d022a1f3d3f06c78dd6848a2fc9de92be.png +_images/8e8566a48272bbcd741d192ea8f10c53172ad3cba8f805ceb38c6695c7dd5c2d.png +_images/eb601133698cb5198be6ed45531c33666c9fa7198b8b8a8e59f3cf4e95ddc8eb.png
      @@ -2184,7 +2187,9 @@ SVC 0.896 
      LogisticRegression 0.864
-RandomForestClassifier 0.896
+
      +
      +
      RandomForestClassifier 0.896
 SVC 0.896
      @@ -2302,7 +2307,9 @@ Error: 0.06380941468319971 Bias^2: 0.05160313473529168 Var: 0.01220627994790804 0.06380941468319971 >= 0.05160313473529168 + 0.01220627994790804 = 0.06380941468319971 -Polynomial degree: 2 +
      + +
      Polynomial degree: 2
 Error: 0.043464037468677004
 Bias^2: 0.02659851591375224
 Var: 0.01686552155492476
@@ -2312,9 +2319,7 @@ Error: 0.020716391693769383
 Bias^2: 0.01159033914386312
 Var: 0.00912605254990626
 0.020716391693769383 >= 0.01159033914386312 + 0.00912605254990626 = 0.02071639169376938
-
      -
      -
      Polynomial degree: 4
+Polynomial degree: 4
 Error: 0.02063627410934057
 Bias^2: 0.0117496656370668
 Var: 0.008886608472273775
@@ -2324,7 +2329,9 @@ Error: 0.02087627881701288
 Bias^2: 0.01349183949256158
 Var: 0.007384439324451296
 0.02087627881701288 >= 0.01349183949256158 + 0.007384439324451296 = 0.020876278817012876
-Polynomial degree: 6
+
      +
      +
      Polynomial degree: 6
 Error: 0.02069601123831537
 Bias^2: 0.013918526350129823
 Var: 0.0067774848881855445
@@ -2463,9 +2470,7 @@ this setting.
      
 
      (426, 30)
 (143, 30)
 Test set accuracy Logistic Regression with scaled data: 0.96
-
      
-
      
-
      Test set accuracy SVM with scaled data: 0.96
+Test set accuracy SVM with scaled data: 0.96
 Test set accuracy with Decision Trees and scaled data: 0.87
 
      
 
      
diff --git a/doc/LectureNotes/_build/html/week47.html b/doc/LectureNotes/_build/html/week47.html
index 1871ef282..8ecd398b5 100644
--- a/doc/LectureNotes/_build/html/week47.html
+++ b/doc/LectureNotes/_build/html/week47.html
@@ -257,6 +257,9 @@
 
    • Week 47: From Decision Trees to Ensemble Methods, Random Forests and Boosting Methods
      • 
 
    • Exercise week 47
      • 
 
+
    • Week 48: Gradient boosting  and summary of course
      • 
+
    • Exercises week 48
      • 
+

    Projects

      @@ -2015,9 +2018,7 @@ SVC 0.896 
      RandomForestClassifier 0.896
 SVC 0.896
-
      -
      -
      VotingClassifier 0.92
+VotingClassifier 0.92
      @@ -2141,14 +2142,14 @@ Error: 0.020716391693769383 Bias^2: 0.01159033914386312 Var: 0.00912605254990626 0.020716391693769383 >= 0.01159033914386312 + 0.00912605254990626 = 0.02071639169376938 - - -
      Polynomial degree: 4
+Polynomial degree: 4
 Error: 0.02063627410934057
 Bias^2: 0.0117496656370668
 Var: 0.008886608472273775
 0.02063627410934057 >= 0.0117496656370668 + 0.008886608472273775 = 0.020636274109340574
-Polynomial degree: 5
+
      +
      +
      Polynomial degree: 5
 Error: 0.02087627881701288
 Bias^2: 0.01349183949256158
 Var: 0.007384439324451296
@@ -2985,7 +2986,7 @@ sketch for efficient proposal calculation. It introduces a novel sparsity-aware
      -
      [15:08:02] WARNING: /var/folders/nz/j6p8yfhx1mv_0grj5xl4650h0000gp/T/abs_21wtzqx5vy/croot/xgboost-split_1675457780668/work/src/learner.cc:767: 
+
      [08:11:29] WARNING: /var/folders/nz/j6p8yfhx1mv_0grj5xl4650h0000gp/T/abs_21wtzqx5vy/croot/xgboost-split_1675457780668/work/src/learner.cc:767: 
 Parameters: { "colsaobjective" } are not used.
 
      
 
      
@@ -3058,17 +3059,17 @@ Parameters: { "colsaobjective" } are not used.
     278 if ret != 0:
 --> 279     raise XGBoostError(py_str(_LIB.XGBGetLastError()))
 
-XGBoostError: [15:08:02] /var/folders/nz/j6p8yfhx1mv_0grj5xl4650h0000gp/T/abs_21wtzqx5vy/croot/xgboost-split_1675457780668/work/src/tree/updater_colmaker.cc:177: Check failed: param_.max_depth > 0 (0 vs. 0) : exact tree method doesn't support unlimited depth.
+XGBoostError: [08:11:29] /var/folders/nz/j6p8yfhx1mv_0grj5xl4650h0000gp/T/abs_21wtzqx5vy/croot/xgboost-split_1675457780668/work/src/tree/updater_colmaker.cc:177: Check failed: param_.max_depth > 0 (0 vs. 0) : exact tree method doesn't support unlimited depth.
 Stack trace:
-  [bt] (0) 1   libxgboost.dylib                    0x0000000156871b90 dmlc::LogMessageFatal::~LogMessageFatal() + 124
-  [bt] (1) 2   libxgboost.dylib                    0x0000000156a67f1c xgboost::tree::ColMaker::Builder::Update(std::__1::vector<xgboost::detail::GradientPairInternal<float>, std::__1::allocator<xgboost::detail::GradientPairInternal<float>>> const&, xgboost::DMatrix*, xgboost::RegTree*) + 1092
-  [bt] (2) 3   libxgboost.dylib                    0x0000000156a66d88 xgboost::tree::ColMaker::Update(xgboost::HostDeviceVector<xgboost::detail::GradientPairInternal<float>>*, xgboost::DMatrix*, xgboost::common::Span<xgboost::HostDeviceVector<int>, 18446744073709551615ul>, std::__1::vector<xgboost::RegTree*, std::__1::allocator<xgboost::RegTree*>> const&) + 412
-  [bt] (3) 4   libxgboost.dylib                    0x000000015696274c xgboost::gbm::GBTree::BoostNewTrees(xgboost::HostDeviceVector<xgboost::detail::GradientPairInternal<float>>*, xgboost::DMatrix*, int, std::__1::vector<std::__1::unique_ptr<xgboost::RegTree, std::__1::default_delete<xgboost::RegTree>>, std::__1::allocator<std::__1::unique_ptr<xgboost::RegTree, std::__1::default_delete<xgboost::RegTree>>>>*) + 1944
-  [bt] (4) 5   libxgboost.dylib                    0x000000015695c7bc xgboost::gbm::GBTree::DoBoost(xgboost::DMatrix*, xgboost::HostDeviceVector<xgboost::detail::GradientPairInternal<float>>*, xgboost::PredictionCacheEntry*, xgboost::ObjFunction const*) + 472
-  [bt] (5) 6   libxgboost.dylib                    0x000000015697b35c xgboost::LearnerImpl::UpdateOneIter(int, std::__1::shared_ptr<xgboost::DMatrix>) + 708
-  [bt] (6) 7   libxgboost.dylib                    0x000000015688b908 XGBoosterUpdateOneIter + 160
-  [bt] (7) 8   libffi.8.dylib                      0x000000010142404c ffi_call_SYSV + 76
-  [bt] (8) 9   libffi.8.dylib                      0x0000000101421834 ffi_call_int + 1404
+  [bt] (0) 1   libxgboost.dylib                    0x0000000162809b90 dmlc::LogMessageFatal::~LogMessageFatal() + 124
+  [bt] (1) 2   libxgboost.dylib                    0x00000001629fff1c xgboost::tree::ColMaker::Builder::Update(std::__1::vector<xgboost::detail::GradientPairInternal<float>, std::__1::allocator<xgboost::detail::GradientPairInternal<float>>> const&, xgboost::DMatrix*, xgboost::RegTree*) + 1092
+  [bt] (2) 3   libxgboost.dylib                    0x00000001629fed88 xgboost::tree::ColMaker::Update(xgboost::HostDeviceVector<xgboost::detail::GradientPairInternal<float>>*, xgboost::DMatrix*, xgboost::common::Span<xgboost::HostDeviceVector<int>, 18446744073709551615ul>, std::__1::vector<xgboost::RegTree*, std::__1::allocator<xgboost::RegTree*>> const&) + 412
+  [bt] (3) 4   libxgboost.dylib                    0x00000001628fa74c xgboost::gbm::GBTree::BoostNewTrees(xgboost::HostDeviceVector<xgboost::detail::GradientPairInternal<float>>*, xgboost::DMatrix*, int, std::__1::vector<std::__1::unique_ptr<xgboost::RegTree, std::__1::default_delete<xgboost::RegTree>>, std::__1::allocator<std::__1::unique_ptr<xgboost::RegTree, std::__1::default_delete<xgboost::RegTree>>>>*) + 1944
+  [bt] (4) 5   libxgboost.dylib                    0x00000001628f47bc xgboost::gbm::GBTree::DoBoost(xgboost::DMatrix*, xgboost::HostDeviceVector<xgboost::detail::GradientPairInternal<float>>*, xgboost::PredictionCacheEntry*, xgboost::ObjFunction const*) + 472
+  [bt] (5) 6   libxgboost.dylib                    0x000000016291335c xgboost::LearnerImpl::UpdateOneIter(int, std::__1::shared_ptr<xgboost::DMatrix>) + 708
+  [bt] (6) 7   libxgboost.dylib                    0x0000000162823908 XGBoosterUpdateOneIter + 160
+  [bt] (7) 8   libffi.8.dylib                      0x00000001053bc04c ffi_call_SYSV + 76
+  [bt] (8) 9   libffi.8.dylib                      0x00000001053b9834 ffi_call_int + 1404
      diff --git a/doc/LectureNotes/_build/html/week48.html b/doc/LectureNotes/_build/html/week48.html new file mode 100644 index 000000000..dc97ce0ba --- /dev/null +++ b/doc/LectureNotes/_build/html/week48.html @@ -0,0 +1,2280 @@ + + + + + + + + + + + Week 48: Gradient boosting and summary of course — Applied Data Analysis and Machine Learning + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
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      Week 48: Gradient boosting and summary of course

      + +
      +
      + +
      +

      Contents

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

      Week 48: Gradient boosting and summary of course#

      +

      Morten Hjorth-Jensen, Department of Physics and Center for Computing in Science Education, University of Oslo, Norway

      +

      Date: Nov 24, 2024

      +

      Copyright 1999-2024, Morten Hjorth-Jensen. Released under CC Attribution-NonCommercial 4.0 license

      +
      +

      Overview of week 48#

      +
      +
      +

      Lecture Monday, November 25#

      +

      Plans for the lecture Monday 25 November, with video suggestions etc.

      +
        +

      1. Boosting and gradient boosting and ensemble models

        2. +

      2. Summary of course

        3. +

      3. Readings and Videos:

        4. +
      +

      a. These lecture notes at CompPhysics/MachineLearning

      +

      b. See also lecture notes from week 47 at CompPhysics/MachineLearning. The lecture on Monday starts with a repetition on AdaBoost before we move over to gradient boosting with examples

      + + +

      c. Video on Decision trees https://www.youtube.com/watch?v=RmajweUFKvM&ab_channel=Simplilearn

      +

      d. Video on boosting methods https://www.youtube.com/watch?v=wPqtzj5VZus&ab_channel=H2O.ai

      +

      e. Video on AdaBoost https://www.youtube.com/watch?v=LsK-xG1cLYA

      +

      f. Video on Gradient boost, part 1, parts 2-4 follow thereafter https://www.youtube.com/watch?v=3CC4N4z3GJc

      +

      g. Decision Trees: Rashcka et al chapter 3 pages 86-98, and chapter 7 on Ensemble methods, Voting and Bagging and Gradient Boosting. See also lecture from STK-IN4300, lecture 7 at https://www.uio.no/studier/emner/matnat/math/STK-IN4300/h20/slides/lecture_7.pdf.

      +
      +
      +

      Lab sessions#

      +

      Lab sessions on Tuesday and Wednesday.

      +
        +

      • Work and Discussion of project 3

        • +

      • Last weekly exercise

        • +

      • Lab sessions at usual times.

        • +

      • For the week of December 2-6, lab sessions start at 10am and end at 4pm, room FØ434, Tuesday and Wednesday

        • +
      +
      +
      +

      Random Forest Algorithm, reminder from last week#

      +

      The algorithm described here can be applied to both classification and regression problems.

      +

      We will grow of forest of say \(B\) trees.

      +
        +

      • For \(b=1:B\)

        • +
      +

      a. Draw a bootstrap sample from the training data organized in our \(\boldsymbol{X}\) matrix.

      +

      b. We grow then a random forest tree \(T_b\) based on the bootstrapped data by repeating the steps outlined till we reach the maximum node size is reached

      +
        +

      1. we select \(m \le p\) variables at random from the \(p\) predictors/features

        2. +

      2. pick the best split point among the \(m\) features using for example the CART algorithm and create a new node

        3. +

      3. split the node into daughter nodes

        4. +
      +

      Finally we output then the ensemble of trees \(\{T_b\}_1^{B}\) and make predictions for either a regression type of problem or a classification type of problem.

      +
      +
      +

      Random Forests Compared with other Methods on the Cancer Data#

      +
      +
      +
      %matplotlib inline
+
+import matplotlib.pyplot as plt
+import numpy as np
+from sklearn.model_selection import  train_test_split 
+from sklearn.datasets import load_breast_cancer
+from sklearn.svm import SVC
+from sklearn.linear_model import LogisticRegression
+from sklearn.tree import DecisionTreeClassifier
+from sklearn.ensemble import BaggingClassifier
+
+# Load the data
+cancer = load_breast_cancer()
+
+X_train, X_test, y_train, y_test = train_test_split(cancer.data,cancer.target,random_state=0)
+print(X_train.shape)
+print(X_test.shape)
+#define methods
+# Logistic Regression
+logreg = LogisticRegression(solver='lbfgs')
+# Support vector machine
+svm = SVC(gamma='auto', C=100)
+# Decision Trees
+deep_tree_clf = DecisionTreeClassifier(max_depth=None)
+#Scale the data
+from sklearn.preprocessing import StandardScaler
+scaler = StandardScaler()
+scaler.fit(X_train)
+X_train_scaled = scaler.transform(X_train)
+X_test_scaled = scaler.transform(X_test)
+# Logistic Regression
+logreg.fit(X_train_scaled, y_train)
+print("Test set accuracy Logistic Regression with scaled data: {:.2f}".format(logreg.score(X_test_scaled,y_test)))
+# Support Vector Machine
+svm.fit(X_train_scaled, y_train)
+print("Test set accuracy SVM with scaled data: {:.2f}".format(logreg.score(X_test_scaled,y_test)))
+# Decision Trees
+deep_tree_clf.fit(X_train_scaled, y_train)
+print("Test set accuracy with Decision Trees and scaled data: {:.2f}".format(deep_tree_clf.score(X_test_scaled,y_test)))
+
+
+from sklearn.ensemble import RandomForestClassifier
+from sklearn.preprocessing import LabelEncoder
+from sklearn.model_selection import cross_validate
+# Data set not specificied
+#Instantiate the model with 500 trees and entropy as splitting criteria
+Random_Forest_model = RandomForestClassifier(n_estimators=500,criterion="entropy")
+Random_Forest_model.fit(X_train_scaled, y_train)
+#Cross validation
+accuracy = cross_validate(Random_Forest_model,X_test_scaled,y_test,cv=10)['test_score']
+print(accuracy)
+print("Test set accuracy with Random Forests and scaled data: {:.2f}".format(Random_Forest_model.score(X_test_scaled,y_test)))
+
+
+import scikitplot as skplt
+y_pred = Random_Forest_model.predict(X_test_scaled)
+skplt.metrics.plot_confusion_matrix(y_test, y_pred, normalize=True)
+plt.show()
+y_probas = Random_Forest_model.predict_proba(X_test_scaled)
+skplt.metrics.plot_roc(y_test, y_probas)
+plt.show()
+skplt.metrics.plot_cumulative_gain(y_test, y_probas)
+plt.show()
+
      +
      +
      +
      +
      (426, 30)
+(143, 30)
+Test set accuracy Logistic Regression with scaled data: 0.96
+Test set accuracy SVM with scaled data: 0.96
+Test set accuracy with Decision Trees and scaled data: 0.91
+
      +
      +
      [1.         0.8        0.93333333 1.         1.         0.92857143
+ 1.         0.92857143 0.92857143 0.92857143]
+Test set accuracy with Random Forests and scaled data: 0.98
+
      +
      +_images/936367d3bdcae10aafd2cc903d30ce54287b55ddddaa7af46f455a620a3745cd.png +_images/be8d5df8bb940da757ef8fd6eac65ebe0352641500f781f4284c949ce274e1ee.png +_images/8f696a60652d0003039dd9a563eb80367f1d574ca15b61c7a3f9757b19083d26.png +
      +
      +

      Recall that the cumulative gains curve shows the percentage of the +overall number of cases in a given category gained by targeting a +percentage of the total number of cases.

      +

      Similarly, the receiver operating characteristic curve, or ROC curve, +displays the diagnostic ability of a binary classifier system as its +discrimination threshold is varied. It plots the true positive rate against the false positive rate.

      +
      +
      +

      Compare Bagging on Trees with Random Forests#

      +
      +
      +
      bag_clf = BaggingClassifier(
+    DecisionTreeClassifier(splitter="random", max_leaf_nodes=16, random_state=42),
+    n_estimators=500, max_samples=1.0, bootstrap=True, n_jobs=-1, random_state=42)
+
      +
      +
      +
      +
      +
      +
      bag_clf.fit(X_train, y_train)
+y_pred = bag_clf.predict(X_test)
+from sklearn.ensemble import RandomForestClassifier
+rnd_clf = RandomForestClassifier(n_estimators=500, max_leaf_nodes=16, n_jobs=-1, random_state=42)
+rnd_clf.fit(X_train, y_train)
+y_pred_rf = rnd_clf.predict(X_test)
+np.sum(y_pred == y_pred_rf) / len(y_pred)
+
      +
      +
      +
      +
      0.9790209790209791
+
      +
      +
      +
      +
      +
      +

      Boosting, a Bird’s Eye View#

      +

      The basic idea is to combine weak classifiers in order to create a good +classifier. With a weak classifier we often intend a classifier which +produces results which are only slightly better than we would get by +random guesses.

      +

      This is done by applying in an iterative way a weak (or a standard +classifier like decision trees) to modify the data. In each iteration +we emphasize those observations which are misclassified by weighting +them with a factor.

      +
      +
      +

      What is boosting? Additive Modelling/Iterative Fitting#

      +

      Boosting is a way of fitting an additive expansion in a set of +elementary basis functions like for example some simple polynomials. +Assume for example that we have a function

      +
      +\[ +f_M(x) = \sum_{i=1}^M \beta_m b(x;\gamma_m), +\]
      +

      where \(\beta_m\) are the expansion parameters to be determined in a +minimization process and \(b(x;\gamma_m)\) are some simple functions of +the multivariable parameter \(x\) which is characterized by the +parameters \(\gamma_m\).

      +

      As an example, consider the Sigmoid function we used in logistic +regression. In that case, we can translate the function +\(b(x;\gamma_m)\) into the Sigmoid function

      +
      +\[ +\sigma(t) = \frac{1}{1+\exp{(-t)}}, +\]
      +

      where \(t=\gamma_0+\gamma_1 x\) and the parameters \(\gamma_0\) and +\(\gamma_1\) were determined by the Logistic Regression fitting +algorithm.

      +

      As another example, consider the cost function we defined for linear regression

      +
      +\[ +C(\boldsymbol{y},\boldsymbol{f}) = \frac{1}{n} \sum_{i=0}^{n-1}(y_i-f(x_i))^2. +\]
      +

      In this case the function \(f(x)\) was replaced by the design matrix +\(\boldsymbol{X}\) and the unknown linear regression parameters \(\boldsymbol{\beta}\), +that is \(\boldsymbol{f}=\boldsymbol{X}\boldsymbol{\beta}\). In linear regression we can +simply invert a matrix and obtain the parameters \(\beta\) by

      +
      +\[ +\boldsymbol{\beta}=\left(\boldsymbol{X}^T\boldsymbol{X}\right)^{-1}\boldsymbol{X}^T\boldsymbol{y}. +\]
      +

      In iterative fitting or additive modeling, we minimize the cost function with respect to the parameters \(\beta_m\) and \(\gamma_m\).

      +
      +
      +

      Iterative Fitting, Regression and Squared-error Cost Function#

      +

      The way we proceed is as follows (here we specialize to the squared-error cost function)

      +
        +

      1. Establish a cost function, here \({\cal C}(\boldsymbol{y},\boldsymbol{f}) = \frac{1}{n} \sum_{i=0}^{n-1}(y_i-f_M(x_i))^2\) with \(f_M(x) = \sum_{i=1}^M \beta_m b(x;\gamma_m)\).

        2. +

      2. Initialize with a guess \(f_0(x)\). It could be one or even zero or some random numbers.

        3. +

      3. For \(m=1:M\)

        4. +
      +

      a. minimize \(\sum_{i=0}^{n-1}(y_i-f_{m-1}(x_i)-\beta b(x;\gamma))^2\) wrt \(\gamma\) and \(\beta\)

      +

      b. This gives the optimal values \(\beta_m\) and \(\gamma_m\)

      +

      c. Determine then the new values \(f_m(x)=f_{m-1}(x) +\beta_m b(x;\gamma_m)\)

      +

      We could use any of the algorithms we have discussed till now. If we +use trees, \(\gamma\) parameterizes the split variables and split points +at the internal nodes, and the predictions at the terminal nodes.

      +
      +
      +

      Squared-Error Example and Iterative Fitting#

      +

      To better understand what happens, let us develop the steps for the iterative fitting using the above squared error function.

      +

      For simplicity we assume also that our functions \(b(x;\gamma)=1+\gamma x\).

      +

      This means that for every iteration \(m\), we need to optimize

      +
      +\[ +(\beta_m,\gamma_m) = \mathrm{argmin}_{\beta,\lambda}\hspace{0.1cm} \sum_{i=0}^{n-1}(y_i-f_{m-1}(x_i)-\beta b(x;\gamma))^2=\sum_{i=0}^{n-1}(y_i-f_{m-1}(x_i)-\beta(1+\gamma x_i))^2. +\]
      +

      We start our iteration by simply setting \(f_0(x)=0\). +Taking the derivatives with respect to \(\beta\) and \(\gamma\) we obtain

      +
      +\[ +\frac{\partial {\cal C}}{\partial \beta} = -2\sum_{i}(1+\gamma x_i)(y_i-\beta(1+\gamma x_i))=0, +\]
      +

      and

      +
      +\[ +\frac{\partial {\cal C}}{\partial \gamma} =-2\sum_{i}\beta x_i(y_i-\beta(1+\gamma x_i))=0. +\]
      +

      We can then rewrite these equations as (defining \(\boldsymbol{w}=\boldsymbol{e}+\gamma \boldsymbol{x})\) with \(\boldsymbol{e}\) being the unit vector)

      +
      +\[ +\gamma \boldsymbol{w}^T(\boldsymbol{y}-\beta\gamma \boldsymbol{w})=0, +\]
      +

      which gives us \(\beta = \boldsymbol{w}^T\boldsymbol{y}/(\boldsymbol{w}^T\boldsymbol{w})\). Similarly we have

      +
      +\[ +\beta\gamma \boldsymbol{x}^T(\boldsymbol{y}-\beta(1+\gamma \boldsymbol{x}))=0, +\]
      +

      which leads to \(\gamma =(\boldsymbol{x}^T\boldsymbol{y}-\beta\boldsymbol{x}^T\boldsymbol{e})/(\beta\boldsymbol{x}^T\boldsymbol{x})\). Inserting +for \(\beta\) gives us an equation for \(\gamma\). This is a non-linear equation in the unknown \(\gamma\) and has to be solved numerically.

      +

      The solution to these two equations gives us in turn \(\beta_1\) and \(\gamma_1\) leading to the new expression for \(f_1(x)\) as +\(f_1(x) = \beta_1(1+\gamma_1x)\). Doing this \(M\) times results in our final estimate for the function \(f\).

      +
      +
      +

      Iterative Fitting, Classification and AdaBoost#

      +

      Let us consider a binary classification problem with two outcomes \(y_i \in \{-1,1\}\) and \(i=0,1,2,\dots,n-1\) as our set of +observations. We define a classification function \(G(x)\) which produces a prediction taking one or the other of the two values +\(\{-1,1\}\).

      +

      The error rate of the training sample is then

      +
      +\[ +\mathrm{\overline{err}}=\frac{1}{n} \sum_{i=0}^{n-1} I(y_i\ne G(x_i)). +\]
      +

      The iterative procedure starts with defining a weak classifier whose +error rate is barely better than random guessing. The iterative +procedure in boosting is to sequentially apply a weak +classification algorithm to repeatedly modified versions of the data +producing a sequence of weak classifiers \(G_m(x)\).

      +

      Here we will express our function \(f(x)\) in terms of \(G(x)\). That is

      +
      +\[ +f_M(x) = \sum_{i=1}^M \beta_m b(x;\gamma_m), +\]
      +

      will be a function of

      +
      +\[ +G_M(x) = \mathrm{sign} \sum_{i=1}^M \alpha_m G_m(x). +\]
      +
      +
      +

      Adaptive Boosting, AdaBoost#

      +

      In our iterative procedure we define thus

      +
      +\[ +f_m(x) = f_{m-1}(x)+\beta_mG_m(x). +\]
      +

      The simplest possible cost function which leads (also simple from a computational point of view) to the AdaBoost algorithm is the +exponential cost/loss function defined as

      +
      +\[ +C(\boldsymbol{y},\boldsymbol{f}) = \sum_{i=0}^{n-1}\exp{(-y_i(f_{m-1}(x_i)+\beta G(x_i))}. +\]
      +

      We optimize \(\beta\) and \(G\) for each value of \(m=1:M\) as we did in the regression case. +This is normally done in two steps. Let us however first rewrite the cost function as

      +
      +\[ +C(\boldsymbol{y},\boldsymbol{f}) = \sum_{i=0}^{n-1}w_i^{m}\exp{(-y_i\beta G(x_i))}, +\]
      +

      where we have defined \(w_i^m= \exp{(-y_if_{m-1}(x_i))}\).

      +
      +
      +

      Building up AdaBoost#

      +

      First, for any \(\beta > 0\), we optimize \(G\) by setting

      +
      +\[ +G_m(x) = \mathrm{sign} \sum_{i=0}^{n-1} w_i^m I(y_i \ne G_(x_i)), +\]
      +

      which is the classifier that minimizes the weighted error rate in predicting \(y\).

      +

      We can do this by rewriting

      +
      +\[ +\exp{-(\beta)}\sum_{y_i=G(x_i)}w_i^m+\exp{(\beta)}\sum_{y_i\ne G(x_i)}w_i^m, +\]
      +

      which can be rewritten as

      +
      +\[ +(\exp{(\beta)}-\exp{-(\beta)})\sum_{i=0}^{n-1}w_i^mI(y_i\ne G(x_i))+\exp{(-\beta)}\sum_{i=0}^{n-1}w_i^m=0, +\]
      +

      which leads to

      +
      +\[ +\beta_m = \frac{1}{2}\log{\frac{1-\mathrm{\overline{err}}}{\mathrm{\overline{err}}}}, +\]
      +

      where we have redefined the error as

      +
      +\[ +\mathrm{\overline{err}}_m=\frac{1}{n}\frac{\sum_{i=0}^{n-1}w_i^mI(y_i\ne G(x_i)}{\sum_{i=0}^{n-1}w_i^m}, +\]
      +

      which leads to an update of

      +
      +\[ +f_m(x) = f_{m-1}(x) +\beta_m G_m(x). +\]
      +

      This leads to the new weights

      +
      +\[ +w_i^{m+1} = w_i^m \exp{(-y_i\beta_m G_m(x_i))} +\]
      +
      +
      +

      Adaptive boosting: AdaBoost, Basic Algorithm#

      +

      The algorithm here is rather straightforward. Assume that our weak +classifier is a decision tree and we consider a binary set of outputs +with \(y_i \in \{-1,1\}\) and \(i=0,1,2,\dots,n-1\) as our set of +observations. Our design matrix is given in terms of the +feature/predictor vectors +\(\boldsymbol{X}=[\boldsymbol{x}_0\boldsymbol{x}_1\dots\boldsymbol{x}_{p-1}]\). Finally, we define also a +classifier determined by our data via a function \(G(x)\). This function tells us how well we are able to classify our outputs/targets \(\boldsymbol{y}\).

      +

      We have already defined the misclassification error \(\mathrm{err}\) as

      +
      +\[ +\mathrm{err}=\frac{1}{n}\sum_{i=0}^{n-1}I(y_i\ne G(x_i)), +\]
      +

      where the function \(I()\) is one if we misclassify and zero if we classify correctly.

      +
      +
      +

      Basic Steps of AdaBoost#

      +

      With the above definitions we are now ready to set up the algorithm for AdaBoost. +The basic idea is to set up weights which will be used to scale the correctly classified and the misclassified cases.

      +
        +

      1. We start by initializing all weights to \(w_i = 1/n\), with \(i=0,1,2,\dots n-1\). It is easy to see that we must have \(\sum_{i=0}^{n-1}w_i = 1\).

        2. +

      2. We rewrite the misclassification error as

        3. +
      +
      +\[ +\mathrm{\overline{err}}_m=\frac{\sum_{i=0}^{n-1}w_i^m I(y_i\ne G(x_i))}{\sum_{i=0}^{n-1}w_i}, +\]
      +
        +

      1. Then we start looping over all attempts at classifying, namely we start an iterative process for \(m=1:M\), where \(M\) is the final number of classifications. Our given classifier could for example be a plain decision tree.

        2. +
      +

      a. Fit then a given classifier to the training set using the weights \(w_i\).

      +

      b. Compute then \(\mathrm{err}\) and figure out which events are classified properly and which are classified wrongly.

      +

      c. Define a quantity \(\alpha_{m} = \log{(1-\mathrm{\overline{err}}_m)/\mathrm{\overline{err}}_m}\)

      +

      d. Set the new weights to \(w_i = w_i\times \exp{(\alpha_m I(y_i\ne G(x_i)}\).

      +
        +

      1. Compute the new classifier \(G(x)= \sum_{i=0}^{n-1}\alpha_m I(y_i\ne G(x_i)\).

        2. +
      +

      For the iterations with \(m \le 2\) the weights are modified +individually at each steps. The observations which were misclassified +at iteration \(m-1\) have a weight which is larger than those which were +classified properly. As this proceeds, the observations which were +difficult to classifiy correctly are given a larger influence. Each +new classification step \(m\) is then forced to concentrate on those +observations that are missed in the previous iterations.

      +
      +
      +

      AdaBoost Examples#

      +

      Using Scikit-Learn it is easy to apply the adaptive boosting algorithm, as done here.

      +
      +
      +
      from sklearn.ensemble import AdaBoostClassifier
+
+ada_clf = AdaBoostClassifier(
+    DecisionTreeClassifier(max_depth=2), n_estimators=200,
+    algorithm="SAMME.R", learning_rate=0.01, random_state=42)
+ada_clf.fit(X_train, y_train)
+y_pred = ada_clf.predict(X_test)
+skplt.metrics.plot_confusion_matrix(y_test, y_pred, normalize=True)
+plt.show()
+y_probas = ada_clf.predict_proba(X_test)
+skplt.metrics.plot_roc(y_test, y_probas)
+plt.show()
+skplt.metrics.plot_cumulative_gain(y_test, y_probas)
+plt.show()
+
      +
      +
      +
      +_images/4b551662b71f36e771bedba88b8176e1bd7b16262de80f1445234d9175a77e42.png +_images/42c69c0d24892802c490b4669010a3c7927840c483799216381719a88df1dcea.png +_images/94ace5b7042b960e9356e09b25e6338703d1e93b5c6e886432ee07b71c47404b.png +
      +
      +
      +
      +

      Making an ADAboost code yourself#

      +
      +
      +
      import numpy as np
+
+class DecisionStump:
+    def fit(self, X, y, weights):
+        m, n = X.shape
+        self.alpha = 0
+        self.threshold = None
+        self.polarity = 1
+
+        min_error = float('inf')
+
+        for feature in range(n):
+            feature_values = np.unique(X[:, feature])
+
+            for threshold in feature_values:
+                for polarity in [1, -1]:
+                    predictions = np.ones(m)
+                    predictions[X[:, feature] < threshold] = -1
+                    predictions *= polarity
+
+                    error = sum(weights[predictions != y])
+
+                    if error < min_error:
+                        min_error = error
+                        self.alpha = 0.5 * np.log((1 - error) / (error + 1e-10))
+                        self.threshold = threshold
+                        self.feature_index = feature
+                        self.polarity = polarity
+
+    def predict(self, X):
+        m = X.shape[0]
+        predictions = np.ones(m)
+        if self.polarity == 1:
+            predictions[X[:, self.feature_index] < self.threshold] = -1
+        else:
+            predictions[X[:, self.feature_index] >= self.threshold] = -1
+        return predictions
+
+class AdaBoost:
+    def fit(self, X, y, n_estimators):
+        m = X.shape[0]
+        self.alphas = []
+        self.models = []
+
+        weights = np.ones(m) / m
+
+        for _ in range(n_estimators):
+            stump = DecisionStump()
+            stump.fit(X, y, weights)
+            predictions = stump.predict(X)
+
+            error = sum(weights[predictions != y])
+            if error == 0:
+                break
+
+            self.models.append(stump)
+            self.alphas.append(stump.alpha)
+
+            weights *= np.exp(-stump.alpha * y * predictions)
+            weights /= np.sum(weights)
+
+    def predict(self, X):
+        final_predictions = np.zeros(X.shape[0])
+        for alpha, model in zip(self.alphas, self.models):
+            final_predictions += alpha * model.predict(X)
+        return np.sign(final_predictions)
+
+# Example dataset (X, y)
+X = np.array([[1], [2], [3], [4], [5], [6], [7], [8], [9], [10]])
+y = np.array([-1, -1, -1, -1, 1, 1, 1, 1, 1, 1])  # Labels must be -1 or 1
+
+# Train AdaBoost
+ada = AdaBoost()
+ada.fit(X, y, n_estimators=10)
+
+# Predictions
+predictions = ada.predict(X)
+print("Predictions:", predictions)
+
      +
      +
      +
      +
      Predictions: [0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]
+
      +
      +
      +
      +
      +
      +

      Gradient boosting: Basics with Steepest Descent/Functional Gradient Descent#

      +

      Gradient boosting is again a similar technique to Adaptive boosting, +it combines so-called weak classifiers or regressors into a strong +method via a series of iterations.

      +

      In order to understand the method, let us illustrate its basics by +bringing back the essential steps in linear regression, where our cost +function was the least squares function.

      +
      +
      +

      The Squared-Error again! Steepest Descent#

      +

      We start again with our cost function \({\cal C}(\boldsymbol{y}m\boldsymbol{f})=\sum_{i=0}^{n-1}{\cal L}(y_i, f(x_i))\) where we want to minimize +This means that for every iteration, we need to optimize

      +
      +\[ +(\hat{\boldsymbol{f}}) = \mathrm{argmin}_{\boldsymbol{f}}\hspace{0.1cm} \sum_{i=0}^{n-1}(y_i-f(x_i))^2. +\]
      +

      We define a real function \(h_m(x)\) that defines our final function \(f_M(x)\) as

      +
      +\[ +f_M(x) = \sum_{m=0}^M h_m(x). +\]
      +

      In the steepest decent approach we approximate \(h_m(x) = -\rho_m g_m(x)\), where \(\rho_m\) is a scalar and \(g_m(x)\) the gradient defined as

      +
      +\[ +g_m(x_i) = \left[ \frac{\partial {\cal L}(y_i, f(x_i))}{\partial f(x_i)}\right]_{f(x_i)=f_{m-1}(x_i)}. +\]
      +

      With the new gradient we can update \(f_m(x) = f_{m-1}(x) -\rho_m g_m(x)\). Using the above squared-error function we see that +the gradient is \(g_m(x_i) = -2(y_i-f(x_i))\).

      +

      Choosing \(f_0(x)=0\) we obtain \(g_m(x) = -2y_i\) and inserting this into the minimization problem for the cost function we have

      +
      +\[ +(\rho_1) = \mathrm{argmin}_{\rho}\hspace{0.1cm} \sum_{i=0}^{n-1}(y_i+2\rho y_i)^2. +\]
      +
      +
      +

      Steepest Descent Example#

      +

      Optimizing with respect to \(\rho\) we obtain (taking the derivative) that \(\rho_1 = -1/2\). We have then that

      +
      +\[ +f_1(x) = f_{0}(x) -\rho_1 g_1(x)=-y_i. +\]
      +

      We can then proceed and compute

      +
      +\[ +g_2(x_i) = \left[ \frac{\partial {\cal L}(y_i, f(x_i))}{\partial f(x_i)}\right]_{f(x_i)=f_{1}(x_i)=y_i}=-4y_i, +\]
      +

      and find a new value for \(\rho_2=-1/2\) and continue till we have reached \(m=M\). We can modify the steepest descent method, or steepest boosting, by introducing what is called gradient boosting.

      +
      +
      +

      Gradient Boosting, algorithm#

      +

      Steepest descent is however not much used, since it only optimizes \(f\) at a fixed set of \(n\) points, +so we do not learn a function that can generalize. However, we can modify the algorithm by +fitting a weak learner to approximate the negative gradient signal.

      +

      Suppose we have a cost function \(C(f)=\sum_{i=0}^{n-1}L(y_i, f(x_i))\) where \(y_i\) is our target and \(f(x_i)\) the function which is meant to model \(y_i\). The above cost function could be our standard squared-error function

      +
      +\[ +C(\boldsymbol{y},\boldsymbol{f})=\sum_{i=0}^{n-1}(y_i-f(x_i))^2. +\]
      +

      The way we proceed in an iterative fashion is to

      +
        +

      1. Initialize our estimate \(f_0(x)\).

        2. +

      2. For \(m=1:M\), we

        3. +
      +

      a. compute the negative gradient vector \(\boldsymbol{u}_m = -\partial C(\boldsymbol{y},\boldsymbol{f})/\partial \boldsymbol{f}(x)\) at \(f(x) = f_{m-1}(x)\);

      +

      b. fit the so-called base-learner to the negative gradient \(h_m(u_m,x)\);

      +

      c. update the estimate \(f_m(x) = f_{m-1}(x)+h_m(u_m,x)\);

      +
        +

      1. The final estimate is then \(f_M(x) = \sum_{m=1}^M h_m(u_m,x)\).

        2. +
      +
      +
      +

      Gradient Boosting, Examples of Regression#

      +
      +
      +
      import matplotlib.pyplot as plt
+import numpy as np
+from sklearn.model_selection import train_test_split
+from sklearn.ensemble import GradientBoostingRegressor
+import scikitplot as skplt
+from sklearn.metrics import mean_squared_error
+
+n = 100
+maxdegree = 6
+
+# Make data set.
+x = np.linspace(-3, 3, n).reshape(-1, 1)
+y = np.exp(-x**2) + 1.5 * np.exp(-(x-2)**2)+ np.random.normal(0, 0.1, x.shape)
+
+error = np.zeros(maxdegree)
+bias = np.zeros(maxdegree)
+variance = np.zeros(maxdegree)
+polydegree = np.zeros(maxdegree)
+X_train, X_test, y_train, y_test = train_test_split(x, y, test_size=0.2)
+
+for degree in range(1,maxdegree):
+    model = GradientBoostingRegressor(max_depth=degree, n_estimators=100, learning_rate=1.0)  
+    model.fit(X_train,y_train)
+    y_pred = model.predict(X_test)
+    polydegree[degree] = degree
+    error[degree] = np.mean( np.mean((y_test - y_pred)**2) )
+    bias[degree] = np.mean( (y_test - np.mean(y_pred))**2 )
+    variance[degree] = np.mean( np.var(y_pred) )
+    print('Max depth:', degree)
+    print('Error:', error[degree])
+    print('Bias^2:', bias[degree])
+    print('Var:', variance[degree])
+    print('{} >= {} + {} = {}'.format(error[degree], bias[degree], variance[degree], bias[degree]+variance[degree]))
+
+plt.xlim(1,maxdegree-1)
+plt.plot(polydegree, error, label='Error')
+plt.plot(polydegree, bias, label='bias')
+plt.plot(polydegree, variance, label='Variance')
+plt.legend()
+plt.show()
+
      +
      +
      +
      +
      Max depth: 1
+Error: 0.4203129333425336
+Bias^2: 0.21226966048908316
+Var: 0.20804327285345042
+0.4203129333425336 >= 0.21226966048908316 + 0.20804327285345042 = 0.4203129333425336
+Max depth: 2
+Error: 0.40767639731018696
+Bias^2: 0.21200998139721822
+Var: 0.19566641591296877
+0.40767639731018696 >= 0.21200998139721822 + 0.19566641591296877 = 0.407676397310187
+Max depth: 3
+Error: 0.4076774836661818
+Bias^2: 0.2120099429256955
+Var: 0.19566754074048626
+0.4076774836661818 >= 0.2120099429256955 + 0.19566754074048626 = 0.40767748366618173
+Max depth: 4
+Error: 0.4076774836661818
+Bias^2: 0.2120099429256955
+Var: 0.19566754074048626
+0.4076774836661818 >= 0.2120099429256955 + 0.19566754074048626 = 0.40767748366618173
+Max depth: 5
+Error: 0.4076774836661816
+Bias^2: 0.2120099429256955
+Var: 0.1956675407404862
+0.4076774836661816 >= 0.2120099429256955 + 0.1956675407404862 = 0.40767748366618173
+
      +
      +
      /Users/mhjensen/miniforge3/envs/myenv/lib/python3.9/site-packages/sklearn/ensemble/_gb.py:424: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().
+  y = column_or_1d(y, warn=True)
+/Users/mhjensen/miniforge3/envs/myenv/lib/python3.9/site-packages/sklearn/ensemble/_gb.py:424: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().
+  y = column_or_1d(y, warn=True)
+/Users/mhjensen/miniforge3/envs/myenv/lib/python3.9/site-packages/sklearn/ensemble/_gb.py:424: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().
+  y = column_or_1d(y, warn=True)
+/Users/mhjensen/miniforge3/envs/myenv/lib/python3.9/site-packages/sklearn/ensemble/_gb.py:424: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().
+  y = column_or_1d(y, warn=True)
+/Users/mhjensen/miniforge3/envs/myenv/lib/python3.9/site-packages/sklearn/ensemble/_gb.py:424: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().
+  y = column_or_1d(y, warn=True)
+
      +
      +_images/82abcfe355d89bb3d7dd4eb6e9ffe48d1f3f3511015d80687fbe0db1b8fd5999.png +
      +
      +
      +
      +

      Gradient Boosting, Classification Example#

      +
      +
      +
      import matplotlib.pyplot as plt
+import numpy as np
+from sklearn.model_selection import  train_test_split 
+from sklearn.datasets import load_breast_cancer
+import scikitplot as skplt
+from sklearn.ensemble import GradientBoostingClassifier
+from sklearn.model_selection import cross_validate
+
+# Load the data
+cancer = load_breast_cancer()
+
+X_train, X_test, y_train, y_test = train_test_split(cancer.data,cancer.target,random_state=0)
+print(X_train.shape)
+print(X_test.shape)
+#now scale the data
+from sklearn.preprocessing import StandardScaler
+scaler = StandardScaler()
+scaler.fit(X_train)
+X_train_scaled = scaler.transform(X_train)
+X_test_scaled = scaler.transform(X_test)
+
+gd_clf = GradientBoostingClassifier(max_depth=3, n_estimators=100, learning_rate=1.0)  
+gd_clf.fit(X_train_scaled, y_train)
+#Cross validation
+accuracy = cross_validate(gd_clf,X_test_scaled,y_test,cv=10)['test_score']
+print(accuracy)
+print("Test set accuracy with Gradient boosting and scaled data: {:.2f}".format(gd_clf.score(X_test_scaled,y_test)))
+
+import scikitplot as skplt
+y_pred = gd_clf.predict(X_test_scaled)
+skplt.metrics.plot_confusion_matrix(y_test, y_pred, normalize=True)
+plt.show()
+y_probas = gd_clf.predict_proba(X_test_scaled)
+skplt.metrics.plot_roc(y_test, y_probas)
+plt.show()
+skplt.metrics.plot_cumulative_gain(y_test, y_probas)
+plt.show()
+
      +
      +
      +
      +
      (426, 30)
+(143, 30)
+
      +
      +
      [0.93333333 0.93333333 0.93333333 0.92857143 1.         0.92857143
+ 1.         0.92857143 0.85714286 0.92857143]
+Test set accuracy with Gradient boosting and scaled data: 0.99
+
      +
      +_images/e0c9e5cfc32bfe482c04b2091d5d98aa80212c0eef5e22892d16f73e93be8afc.png +_images/796ded1719cb864b630c37e7692028ee18f0df7fb5a0ed2de15817b0c2f36c0c.png +_images/45972a93ed8e1f6ed66fe9c322a65b549b39dd2c80e16d5081151b2bc713b669.png +
      +
      +
      +
      +

      XGBoost: Extreme Gradient Boosting#

      +

      XGBoost or Extreme Gradient +Boosting, is an optimized distributed gradient boosting library +designed to be highly efficient, flexible and portable. It implements +machine learning algorithms under the Gradient Boosting +framework. XGBoost provides a parallel tree boosting that solve many +data science problems in a fast and accurate way. See the article by Chen and Guestrin.

      +

      The authors design and build a highly scalable end-to-end tree +boosting system. It has a theoretically justified weighted quantile +sketch for efficient proposal calculation. It introduces a novel sparsity-aware algorithm for parallel tree learning and an effective cache-aware block structure for out-of-core tree learning.

      +

      It is now the algorithm which wins essentially all ML competitions!!!

      +
      +
      +

      Xgboost on the Cancer Data#

      +

      As you will see from the confusion matrix below, XGBoots does an excellent job on the Wisconsin cancer data and outperforms essentially all agorithms we have discussed till now.

      +
      +
      +
      import matplotlib.pyplot as plt
+import numpy as np
+from sklearn.model_selection import  train_test_split 
+from sklearn.datasets import load_breast_cancer
+from sklearn.preprocessing import LabelEncoder
+from sklearn.model_selection import cross_validate
+import scikitplot as skplt
+import xgboost as xgb
+# Load the data
+cancer = load_breast_cancer()
+
+X_train, X_test, y_train, y_test = train_test_split(cancer.data,cancer.target,random_state=0)
+print(X_train.shape)
+print(X_test.shape)
+#now scale the data
+from sklearn.preprocessing import StandardScaler
+scaler = StandardScaler()
+scaler.fit(X_train)
+X_train_scaled = scaler.transform(X_train)
+X_test_scaled = scaler.transform(X_test)
+
+xg_clf = xgb.XGBClassifier()
+xg_clf.fit(X_train_scaled,y_train)
+
+y_test = xg_clf.predict(X_test_scaled)
+
+print("Test set accuracy with Gradient Boosting and scaled data: {:.2f}".format(xg_clf.score(X_test_scaled,y_test)))
+
+import scikitplot as skplt
+y_pred = xg_clf.predict(X_test_scaled)
+skplt.metrics.plot_confusion_matrix(y_test, y_pred, normalize=True)
+plt.show()
+y_probas = xg_clf.predict_proba(X_test_scaled)
+skplt.metrics.plot_roc(y_test, y_probas)
+plt.show()
+skplt.metrics.plot_cumulative_gain(y_test, y_probas)
+plt.show()
+
+
+xgb.plot_tree(xg_clf,num_trees=0)
+plt.rcParams['figure.figsize'] = [50, 10]
+plt.show()
+
+xgb.plot_importance(xg_clf)
+plt.rcParams['figure.figsize'] = [5, 5]
+plt.show()
+
      +
      +
      +
      +
      (426, 30)
+(143, 30)
+Test set accuracy with Gradient Boosting and scaled data: 1.00
+
      +
      +_images/bae16b30a452ef5c69ddef05f0de3bbba23676920f8affa40a5a964be4504dd8.png +_images/429975495ea036cdaa180dafa8c2225aba5cd58c9c9a67fe5998acd764007772.png +_images/1baae63cbdd15ce7abeb765e3d3adddddca04d8f36b9292d03ad703844b287d4.png +_images/3a39cedd7df8be523efd25c1ba51760790f33378dd9e7a9d1eff178bc4b629e1.png +_images/a9306da9994b47ad9b17c6ed3244dbd079e10a74b85153831b07c9570b8677c3.png +
      +
      +
      +
      +

      Gradient boosting, making our own code for a regression case#

      +
      +
      +
      import numpy as np
+class DecisionTreeRegressor:
+    def __init__(self, max_depth=3):
+        self.max_depth = max_depth
+        self.tree = None
+    def fit(self, X, y):
+        self.tree = self._grow_tree(X, y)
+    def _grow_tree(self, X, y, depth=0):
+        n_samples, n_features = X.shape
+        if depth < self.max_depth:
+            best_feature, best_threshold = self._best_split(X, y)
+            if best_feature is not None:
+                left_indices = X[:, best_feature] < best_threshold
+                right_indices = X[:, best_feature] >= best_threshold
+                left_child = self._grow_tree(X[left_indices], y[left_indices], depth + 1)
+                right_child = self._grow_tree(X[right_indices], y[right_indices], depth + 1)
+                return (best_feature, best_threshold, left_child, right_child)
+        return np.mean(y)
+    def _best_split(self, X, y):
+        best_mse = float('inf')
+        best_feature, best_threshold = None, None
+        n_samples, n_features = X.shape
+        
+        for feature in range(n_features):
+            thresholds = np.unique(X[:, feature])
+            for threshold in thresholds:
+                left_indices = X[:, feature] < threshold
+                right_indices = X[:, feature] >= threshold
+                if len(y[left_indices]) > 0 and len(y[right_indices]) > 0:
+                    left_mse = np.mean((y[left_indices] - np.mean(y[left_indices])) ** 2)
+                    right_mse = np.mean((y[right_indices] - np.mean(y[right_indices])) ** 2)
+                    mse = (len(y[left_indices]) * left_mse + len(y[right_indices]) * right_mse) / n_samples
+                    
+                    if mse < best_mse:
+                        best_mse = mse
+                        best_feature = feature
+                        best_threshold = threshold
+        return best_feature, best_threshold
+    def predict(self, X):
+        return np.array([self._predict_sample(sample, self.tree) for sample in X])
+    def _predict_sample(self, sample, node):
+        if isinstance(node, tuple):
+            feature, threshold, left_child, right_child = node
+            if sample[feature] < threshold:
+                return self._predict_sample(sample, left_child)
+            else:
+                return self._predict_sample(sample, right_child)
+        return node
+class GradientBoostingRegressor:
+    def __init__(self, n_estimators=100, learning_rate=0.1, max_depth=3):
+        self.n_estimators = n_estimators
+        self.learning_rate = learning_rate
+        self.max_depth = max_depth
+        self.models = []
+    def fit(self, X, y):
+        y_pred = np.zeros(y.shape)
+        for _ in range(self.n_estimators):
+            residuals = y - y_pred
+            model = DecisionTreeRegressor(max_depth=self.max_depth)
+            model.fit(X, residuals)
+            y_pred += self.learning_rate * model.predict(X)
+            self.models.append(model)
+    def predict(self, X):
+        y_pred = np.zeros(X.shape[0])
+        for model in self.models:
+            y_pred += self.learning_rate * model.predict(X)
+        return y_pred
+# Example usage
+if __name__ == "__main__":
+    # Sample data
+    X = np.array([[1], [2], [3], [4], [5]])
+    y = np.array([1.5, 1.7, 3.5, 3.7, 5.0])
+    model = GradientBoostingRegressor(n_estimators=100, learning_rate=0.1, max_depth=2)
+    model.fit(X, y)
+    predictions = model.predict(X)
+    print("Predictions:", predictions)
+
      +
      +
      +
      +
      Predictions: [1.49999399 1.69995637 3.49991627 3.6998795  4.99984484]
+
      +
      +
      +
      +
      +
      +

      Summary of course#

      +
      +
      +

      What? Me worry? No final exam in this course!#

      + + +

      Figure 1:

      +
      +
      +

      Topics we have covered this year#

      +

      The course has two central parts

      +
        +

      1. Statistical analysis and optimization of data

        2. +

      2. Machine learning

        3. +
      +
      +
      +

      Statistical analysis and optimization of data#

      +

      The following topics have been discussed:

      +
        +

      1. Basic concepts, expectation values, variance, covariance, correlation functions and errors;

        2. +

      2. Simpler models, binomial distribution, the Poisson distribution, simple and multivariate normal distributions;

        3. +

      3. Central elements from linear algebra, matrix inversion and SVD

        4. +

      4. Gradient methods for data optimization

        5. +

      5. Estimation of errors using cross-validation, bootstrapping and jackknife methods;

        6. +

      6. Practical optimization using Singular-value decomposition and least squares for parameterizing data.

        7. +

      7. Not discussed: Principal Component Analysis to reduce the number of features.

        8. +
      +
      +
      +

      Machine learning#

      +
        +

      • Linear methods for regression and classification:

        • +
      +

      a. Ordinary Least Squares

      +

      b. Ridge regression

      +

      c. Lasso regression

      +

      d. Logistic regression

      +
        +

      • Neural networks and deep learning:

        • +
      +

      a. Feed Forward Neural Networks

      +

      b. Convolutional Neural Networks

      +

      c. Recurrent Neural Networks

      +
        +

      • Decisions trees and ensemble methods:

        • +
      +

      a. Decision trees

      +

      b. Bagging and voting

      +

      c. Random forests

      +

      d. Boosting and gradient boosting

      +
        +

      • Not discussed this year: Support vector machines

        • +
      +

      a. Binary classification and multiclass classification

      +

      b. Kernel methods

      +

      c. Regression

      +
      +
      +

      Learning outcomes and overarching aims of this course#

      +

      The course introduces a variety of central algorithms and methods +essential for studies of data analysis and machine learning. The +course is project based and through the various projects, normally +three, you will be exposed to fundamental research problems +in these fields, with the aim to reproduce state of the art scientific +results. The students will learn to develop and structure large codes +for studying these systems, get acquainted with computing facilities +and learn to handle large scientific projects. A good scientific and +ethical conduct is emphasized throughout the course.

      +
        +

      • Understand linear methods for regression and classification;

        • +

      • Learn about neural network;

        • +

      • Learn about bagging, boosting and trees

        • +
      + +
        +

      • Learn about basic data analysis;

        • +

      • Be capable of extending the acquired knowledge to other systems and cases;

        • +

      • Have an understanding of central algorithms used in data analysis and machine learning;

        • +

      • Work on numerical projects to illustrate the theory. The projects play a central role.

        • +
      +
      +
      +

      Perspective on Machine Learning#

      +
        +

      1. Rapidly emerging application area

        2. +

      2. Experiment AND theory are evolving in many many fields.

        3. +

      3. Requires education/retraining for more widespread adoption

        4. +

      4. A lot of “word-of-mouth” development methods

        5. +
      +

      Huge amounts of data sets require automation, classical analysis tools often inadequate. +High energy physics hit this wall in the 90’s. +In 2009 single top quark production was determined via Boosted decision trees, Bayesian +Neural Networks, etc.

      +
      +
      +

      Machine Learning Research#

      +

      Where to find recent results:

      +
        +

      1. Conference proceedings, arXiv and blog posts!

        2. +

      2. NIPS: Neural Information Processing Systems

        3. +

      3. ICLR: International Conference on Learning Representations

        4. +

      4. ICML: International Conference on Machine Learning

        5. +

      5. Journal of Machine Learning Research

        6. +

      6. Follow ML on ArXiv

        7. +
      +
      +
      +

      Starting your Machine Learning Project#

      +
        +

      1. Identify problem type: classification, regression

        2. +

      2. Consider your data carefully

        3. +

      3. Choose a simple model that fits 1 and 2

        4. +

      4. Consider your data carefully again! Think of data representation more carefully.

        5. +

      5. Based on your results, feedback loop to earliest possible point

        6. +
      +
      +
      +

      Choose a Model and Algorithm#

      +
        +

      • Supervised?

        • +

      • Start with the simplest model that fits your problem

        • +

      • Start with minimal processing of data

        • +
      +
      +
      +

      Preparing Your Data#

      +
        +

      • Shuffle your data

        • +

      • Mean center your data

        +
          +

        • Why?

          • +
        +
        • +

      • Normalize the variance

        +
          +

        • Why?

          • +
        +
        • +

      • Whitening

        +
          +

        • Decorrelates data

          • +

        • Can be hit or miss

          • +
        +
        • +

      • When to do train/test split?

        • +
      +
      +
      +

      Which activation and weights to choose in neural networks#

      +
        +

      • RELU? ELU? GELU? etc

        • +

      • Sigmoid or Tanh?

        • +

      • Set all weights to 0? Terrible idea

        • +

      • Set all weights to random values? Small random values

        • +
      +
      +
      +

      Optimization Methods and Hyperparameters#

      +
        +

      • Stochastic gradient descent

        • +

      • Stochastic gradient descent + momentum

        • +

      • State-of-the-art approaches:

        • +
      +

      a. RMSProp

      +

      b. Adam

      +

      c. and more

      +

      Which regularization and hyperparameters? \(L_1\) or \(L_2\), soft +classifiers, depths of trees and many other. Need to explore a large +set of hyperparameters and regularization methods.

      +
      +
      +

      Resampling#

      +

      When do we resample?

      +
        +

      1. Bootstrap

        2. +

      2. Cross-validation

        3. +

      3. Jackknife and many other

        4. +
      +
      +
      +

      Other courses on Data science and Machine Learning at UiO#

      +
        +

      1. FYS5429 – Advanced machine learning and data analysis for the physical sciences

        2. +

      2. IN3050/IN4050 Introduction to Artificial Intelligence and Machine Learning. Introductory course in machine learning and AI

        3. +

      3. STK-INF3000/4000 Selected Topics in Data Science. The course provides insight into selected contemporary relevant topics within Data Science.

        4. +

      4. IN4080 Natural Language Processing. Probabilistic and machine learning techniques applied to natural language processing.

        5. +

      5. STK-IN4300 – Statistical learning methods in Data Science. An advanced introduction to statistical and machine learning. For students with a good mathematics and statistics background.

        6. +

      6. IN-STK5000 Responsible Data Science. Methods for adaptive collection and processing of data based on machine learning techniques.

        7. +

      7. IN4310 – Machine Learning for Image Analysis. An introduction to deep learning with particular emphasis on applications within Image analysis, but useful for other application areas too.

        8. +

      8. IN5310 – Advanced Deep Learning for Image Analysis

        9. +

      9. IN5490 – Advanced Topics in Artificial Intelligence for Intelligent Systems

        10. +

      10. TEK5040 – Deep learning for autonomous systems. The course addresses advanced algorithms and architectures for deep learning with neural networks. The course provides an introduction to how deep-learning techniques can be used in the construction of key parts of advanced autonomous systems that exist in physical environments and cyber environments.

        11. +
      +
      +
      +

      Additional courses of interest#

      +
        +

      1. STK4051 Computational Statistics

        2. +

      2. STK4021 Applied Bayesian Analysis and Numerical Methods

        3. +
      +
      +
      +

      What’s the future like?#

      +

      Based on multi-layer nonlinear neural networks, deep learning can +learn directly from raw data, automatically extract and abstract +features from layer to layer, and then achieve the goal of regression, +classification, or ranking. Deep learning has made breakthroughs in +computer vision, speech processing and natural language, and reached +or even surpassed human level. The success of deep learning is mainly +due to the three factors: big data, big model, and big computing.

      +

      In the past few decades, many different architectures of deep neural +networks have been proposed, such as

      +
        +

      1. Convolutional neural networks, which are mostly used in image and video data processing, and have also been applied to sequential data such as text processing;

        2. +

      2. Recurrent neural networks, which can process sequential data of variable length and have been widely used in natural language understanding and speech processing;

        3. +

      3. Encoder-decoder framework, which is mostly used for image or sequence generation, such as machine translation, text summarization, and image captioning.

        4. +
      +
      +
      +

      Types of Machine Learning, a repetition#

      +

      The approaches to machine learning are many, but are often split into two main categories. +In supervised learning we know the answer to a problem, +and let the computer deduce the logic behind it. On the other hand, unsupervised learning +is a method for finding patterns and relationship in data sets without any prior knowledge of the system. +Some authours also operate with a third category, namely reinforcement learning. This is a paradigm +of learning inspired by behavioural psychology, where learning is achieved by trial-and-error, +solely from rewards and punishment.

      +

      Another way to categorize machine learning tasks is to consider the desired output of a system. +Some of the most common tasks are:

      +
        +

      • Classification: Outputs are divided into two or more classes. The goal is to produce a model that assigns inputs into one of these classes. An example is to identify digits based on pictures of hand-written ones. Classification is typically supervised learning.

        • +

      • Regression: Finding a functional relationship between an input data set and a reference data set. The goal is to construct a function that maps input data to continuous output values.

        • +

      • Clustering: Data are divided into groups with certain common traits, without knowing the different groups beforehand. It is thus a form of unsupervised learning.

        • +

      • Other unsupervised learning algortihms like Boltzmann machines

        • +
      +
      +
      +

      Why Boltzmann machines?#

      +

      What is known as restricted Boltzmann Machines (RMB) have received a lot of attention lately. +One of the major reasons is that they can be stacked layer-wise to build deep neural networks that capture complicated statistics.

      +

      The original RBMs had just one visible layer and a hidden layer, but recently so-called Gaussian-binary RBMs have gained quite some popularity in imaging since they are capable of modeling continuous data that are common to natural images.

      +

      Furthermore, they have been used to solve complicated quantum mechanical many-particle problems or classical statistical physics problems like the Ising and Potts classes of models.

      +
      +
      +

      Boltzmann Machines#

      +

      Why use a generative model rather than the more well known discriminative deep neural networks (DNN)?

      +
        +

      • Discriminitave methods have several limitations: They are mainly supervised learning methods, thus requiring labeled data. And there are tasks they cannot accomplish, like drawing new examples from an unknown probability distribution.

        • +

      • A generative model can learn to represent and sample from a probability distribution. The core idea is to learn a parametric model of the probability distribution from which the training data was drawn. As an example

        • +
      +

      a. A model for images could learn to draw new examples of cats and dogs, given a training dataset of images of cats and dogs.

      +

      b. Generate a sample of an ordered or disordered phase, having been given samples of such phases.

      +

      c. Model the trial function for Monte Carlo calculations.

      +
      +
      +

      Some similarities and differences from DNNs#

      +
        +

      1. Both use gradient-descent based learning procedures for minimizing cost functions

        2. +

      2. Energy based models don’t use backpropagation and automatic differentiation for computing gradients, instead turning to Markov Chain Monte Carlo methods.

        3. +

      3. DNNs often have several hidden layers. A restricted Boltzmann machine has only one hidden layer, however several RBMs can be stacked to make up Deep Belief Networks, of which they constitute the building blocks.

        4. +
      +

      History: The RBM was developed by amongst others Geoffrey Hinton, called by some the “Godfather of Deep Learning”, working with the University of Toronto and Google.

      +
      +
      +

      Boltzmann machines (BM)#

      +

      A BM is what we would call an undirected probabilistic graphical model +with stochastic continuous or discrete units.

      +

      It is interpreted as a stochastic recurrent neural network where the +state of each unit(neurons/nodes) depends on the units it is connected +to. The weights in the network represent thus the strength of the +interaction between various units/nodes.

      +

      It turns into a Hopfield network if we choose deterministic rather +than stochastic units. In contrast to a Hopfield network, a BM is a +so-called generative model. It allows us to generate new samples from +the learned distribution.

      +
      +
      +

      A standard BM setup#

      +

      A standard BM network is divided into a set of observable and visible units \(\hat{x}\) and a set of unknown hidden units/nodes \(\hat{h}\).

      +

      Additionally there can be bias nodes for the hidden and visible layers. These biases are normally set to \(1\).

      +

      BMs are stackable, meaning they cwe can train a BM which serves as input to another BM. We can construct deep networks for learning complex PDFs. The layers can be trained one after another, a feature which makes them popular in deep learning

      +

      However, they are often hard to train. This leads to the introduction of so-called restricted BMs, or RBMS. +Here we take away all lateral connections between nodes in the visible layer as well as connections between nodes in the hidden layer. The network is illustrated in the figure below.

      +
      +
      +

      The structure of the RBM network#

      + + +

      Figure 1:

      +
      +
      +

      The network#

      +

      The network layers:

      +
        +

      1. A function \(\mathbf{x}\) that represents the visible layer, a vector of \(M\) elements (nodes). This layer represents both what the RBM might be given as training input, and what we want it to be able to reconstruct. This might for example be given by the pixels of an image or coefficients representing speech, or the coordinates of a quantum mechanical state function.

        2. +

      2. The function \(\mathbf{h}\) represents the hidden, or latent, layer. A vector of \(N\) elements (nodes). Also called “feature detectors”.

        3. +
      +
      +
      +

      Goals#

      +

      The goal of the hidden layer is to increase the model’s expressive +power. We encode complex interactions between visible variables by +introducing additional, hidden variables that interact with visible +degrees of freedom in a simple manner, yet still reproduce the complex +correlations between visible degrees in the data once marginalized +over (integrated out).

      +

      The network parameters, to be optimized/learned:

      +
        +

      1. \(\mathbf{a}\) represents the visible bias, a vector of same length as \(\mathbf{x}\).

        2. +

      2. \(\mathbf{b}\) represents the hidden bias, a vector of same lenght as \(\mathbf{h}\).

        3. +

      3. \(W\) represents the interaction weights, a matrix of size \(M\times N\).

        4. +
      +
      +
      +

      Joint distribution#

      +

      The restricted Boltzmann machine is described by a Boltzmann distribution

      + +
      +
      +\[ +\begin{equation} + P_{rbm}(\mathbf{x},\mathbf{h}) = \frac{1}{Z} e^{-\frac{1}{T_0}E(\mathbf{x},\mathbf{h})}, +\label{_auto1} \tag{1} +\end{equation} +\]
      +

      where \(Z\) is the normalization constant or partition function, defined as

      + +
      +
      +\[ +\begin{equation} + Z = \int \int e^{-\frac{1}{T_0}E(\mathbf{x},\mathbf{h})} d\mathbf{x} d\mathbf{h}. +\label{_auto2} \tag{2} +\end{equation} +\]
      +

      It is common to ignore \(T_0\) by setting it to one.

      +
      +
      +

      Network Elements, the energy function#

      +

      The function \(E(\mathbf{x},\mathbf{h})\) gives the energy of a +configuration (pair of vectors) \((\mathbf{x}, \mathbf{h})\). The lower +the energy of a configuration, the higher the probability of it. This +function also depends on the parameters \(\mathbf{a}\), \(\mathbf{b}\) and +\(W\). Thus, when we adjust them during the learning procedure, we are +adjusting the energy function to best fit our problem.

      +

      An expression for the energy function is

      +
      +\[ +E(\hat{x},\hat{h}) = -\sum_{ia}^{NA}b_i^a \alpha_i^a(x_i)-\sum_{jd}^{MD}c_j^d \beta_j^d(h_j)-\sum_{ijad}^{NAMD}b_i^a \alpha_i^a(x_i)c_j^d \beta_j^d(h_j)w_{ij}^{ad}. +\]
      +

      Here \(\beta_j^d(h_j)\) and \(\alpha_i^a(x_j)\) are so-called transfer functions that map a given input value to a desired feature value. The labels \(a\) and \(d\) denote that there can be multiple transfer functions per variable. The first sum depends only on the visible units. The second on the hidden ones. Note that there is no connection between nodes in a layer.

      +

      The quantities \(b\) and \(c\) can be interpreted as the visible and hidden biases, respectively.

      +

      The connection between the nodes in the two layers is given by the weights \(w_{ij}\).

      +
      +
      +

      Defining different types of RBMs#

      +

      There are different variants of RBMs, and the differences lie in the types of visible and hidden units we choose as well as in the implementation of the energy function \(E(\mathbf{x},\mathbf{h})\).

      +

      Binary-Binary RBM:

      +

      RBMs were first developed using binary units in both the visible and hidden layer. The corresponding energy function is defined as follows:

      + +
      +
      +\[ +\begin{equation} + E(\mathbf{x}, \mathbf{h}) = - \sum_i^M x_i a_i- \sum_j^N b_j h_j - \sum_{i,j}^{M,N} x_i w_{ij} h_j, +\label{_auto3} \tag{3} +\end{equation} +\]
      +

      where the binary values taken on by the nodes are most commonly 0 and 1.

      +

      Gaussian-Binary RBM:

      +

      Another varient is the RBM where the visible units are Gaussian while the hidden units remain binary:

      + +
      +
      +\[ +\begin{equation} + E(\mathbf{x}, \mathbf{h}) = \sum_i^M \frac{(x_i - a_i)^2}{2\sigma_i^2} - \sum_j^N b_j h_j - \sum_{i,j}^{M,N} \frac{x_i w_{ij} h_j}{\sigma_i^2}. +\label{_auto4} \tag{4} +\end{equation} +\]
      +
      +
      +

      More about RBMs#

      +
        +

      1. Useful when we model continuous data (i.e., we wish \(\mathbf{x}\) to be continuous)

        2. +

      2. Requires a smaller learning rate, since there’s no upper bound to the value a component might take in the reconstruction

        3. +
      +

      Other types of units include:

      +
        +

      1. Softmax and multinomial units

        2. +

      2. Gaussian visible and hidden units

        3. +

      3. Binomial units

        4. +

      4. Rectified linear units

        5. +
      +

      To read more, see Lectures on Boltzmann machines in Physics.

      +
      +
      +

      Autoencoders: Overarching view#

      +

      Autoencoders are artificial neural networks capable of learning +efficient representations of the input data (these representations are called codings) without +any supervision (i.e., the training set is unlabeled). These codings +typically have a much lower dimensionality than the input data, making +autoencoders useful for dimensionality reduction.

      +

      More importantly, autoencoders act as powerful feature detectors, and +they can be used for unsupervised pretraining of deep neural networks.

      +

      Lastly, they are capable of randomly generating new data that looks +very similar to the training data; this is called a generative +model. For example, you could train an autoencoder on pictures of +faces, and it would then be able to generate new faces. Surprisingly, +autoencoders work by simply learning to copy their inputs to their +outputs. This may sound like a trivial task, but we will see that +constraining the network in various ways can make it rather +difficult. For example, you can limit the size of the internal +representation, or you can add noise to the inputs and train the +network to recover the original inputs. These constraints prevent the +autoencoder from trivially copying the inputs directly to the outputs, +which forces it to learn efficient ways of representing the data. In +short, the codings are byproducts of the autoencoder’s attempt to +learn the identity function under some constraints.

      +

      Video on autoencoders

      +

      See also A. Geron’s textbook, chapter 15.

      +
      +
      +

      Bayesian Machine Learning#

      +

      This is an important topic if we aim at extracting a probability +distribution. This gives us also a confidence interval and error +estimates.

      +

      Bayesian machine learning allows us to encode our prior beliefs about +what those models should look like, independent of what the data tells +us. This is especially useful when we don’t have a ton of data to +confidently learn our model.

      +

      Video on Bayesian deep learning

      +

      See also the slides here.

      +
      +
      +

      Reinforcement Learning#

      +

      Reinforcement Learning (RL) is one of the most exciting fields of +Machine Learning today, and also one of the oldest. It has been around +since the 1950s, producing many interesting applications over the +years.

      +

      It studies +how agents take actions based on trial and error, so as to maximize +some notion of cumulative reward in a dynamic system or +environment. Due to its generality, the problem has also been studied +in many other disciplines, such as game theory, control theory, +operations research, information theory, multi-agent systems, swarm +intelligence, statistics, and genetic algorithms.

      +

      In March 2016, AlphaGo, a computer program that plays the board game +Go, beat Lee Sedol in a five-game match. This was the first time a +computer Go program had beaten a 9-dan (highest rank) professional +without handicaps. AlphaGo is based on deep convolutional neural +networks and reinforcement learning. AlphaGo’s victory was a major +milestone in artificial intelligence and it has also made +reinforcement learning a hot research area in the field of machine +learning.

      +

      Lecture on Reinforcement Learning.

      +

      See also A. Geron’s textbook, chapter 16.

      +
      +
      +

      Transfer learning#

      +

      The goal of transfer learning is to transfer the model or knowledge +obtained from a source task to the target task, in order to resolve +the issues of insufficient training data in the target task. The +rationality of doing so lies in that usually the source and target +tasks have inter-correlations, and therefore either the features, +samples, or models in the source task might provide useful information +for us to better solve the target task. Transfer learning is a hot +research topic in recent years, with many problems still waiting to be studied.

      +

      Lecture on transfer learning.

      +
      +
      +

      Adversarial learning#

      +

      The conventional deep generative model has a potential problem: the +model tends to generate extreme instances to maximize the +probabilistic likelihood, which will hurt its performance. Adversarial +learning utilizes the adversarial behaviors (e.g., generating +adversarial instances or training an adversarial model) to enhance the +robustness of the model and improve the quality of the generated +data. In recent years, one of the most promising unsupervised learning +technologies, generative adversarial networks (GAN), has already been +successfully applied to image, speech, and text.

      +

      Lecture on adversial learning.

      +
      +
      +

      Dual learning#

      +

      Dual learning is a new learning paradigm, the basic idea of which is +to use the primal-dual structure between machine learning tasks to +obtain effective feedback/regularization, and guide and strengthen the +learning process, thus reducing the requirement of large-scale labeled +data for deep learning. The idea of dual learning has been applied to +many problems in machine learning, including machine translation, +image style conversion, question answering and generation, image +classification and generation, text classification and generation, +image-to-text, and text-to-image.

      +
      +
      +

      Distributed machine learning#

      +

      Distributed computation will speed up machine learning algorithms, +significantly improve their efficiency, and thus enlarge their +application. When distributed meets machine learning, more than just +implementing the machine learning algorithms in parallel is required.

      +
      +
      +

      Meta learning#

      +

      Meta learning is an emerging research direction in machine +learning. Roughly speaking, meta learning concerns learning how to +learn, and focuses on the understanding and adaptation of the learning +itself, instead of just completing a specific learning task. That is, +a meta learner needs to be able to evaluate its own learning methods +and adjust its own learning methods according to specific learning +tasks.

      +
      +
      +

      The Challenges Facing Machine Learning#

      +

      While there has been much progress in machine learning, there are also challenges.

      +

      For example, the mainstream machine learning technologies are +black-box approaches, making us concerned about their potential +risks. To tackle this challenge, we may want to make machine learning +more explainable and controllable. As another example, the +computational complexity of machine learning algorithms is usually +very high and we may want to invent lightweight algorithms or +implementations. Furthermore, in many domains such as physics, +chemistry, biology, and social sciences, people usually seek elegantly +simple equations (e.g., the Schrödinger equation) to uncover the +underlying laws behind various phenomena. In the field of machine +learning, can we reveal simple laws instead of designing more complex +models for data fitting? Although there are many challenges, we are +still very optimistic about the future of machine learning. As we look +forward to the future, here are what we think the research hotspots in +the next ten years will be.

      +

      See the article on Discovery of Physics From Data: Universal Laws and Discrepancies

      +
      +
      +

      Explainable machine learning#

      +

      Machine learning, especially deep learning, evolves rapidly. The +ability gap between machine and human on many complex cognitive tasks +becomes narrower and narrower. However, we are still in the very early +stage in terms of explaining why those effective models work and how +they work.

      +

      What is missing: the gap between correlation and causation. Standard Machine Learning is based on what e have called a frequentist approach.

      +

      Most +machine learning techniques, especially the statistical ones, depend +highly on correlations in data sets to make predictions and analyses. In +contrast, rational humans tend to reply on clear and trustworthy +causality relations obtained via logical reasoning on real and clear +facts. It is one of the core goals of explainable machine learning to +transition from solving problems by data correlation to solving +problems by logical reasoning.

      +

      Bayesian Machine Learning is one of the exciting research directions in this field.

      +
      +
      +

      Quantum machine learning#

      +

      Quantum machine learning is an emerging interdisciplinary research +area at the intersection of quantum computing and machine learning.

      +

      Quantum computers use effects such as quantum coherence and quantum +entanglement to process information, which is fundamentally different +from classical computers. Quantum algorithms have surpassed the best +classical algorithms in several problems (e.g., searching for an +unsorted database, inverting a sparse matrix), which we call quantum +acceleration.

      +

      When quantum computing meets machine learning, it can be a mutually +beneficial and reinforcing process, as it allows us to take advantage +of quantum computing to improve the performance of classical machine +learning algorithms. In addition, we can also use the machine learning +algorithms (on classic computers) to analyze and improve quantum +computing systems.

      +

      Lecture on Quantum ML.

      +

      Read interview with Maria Schuld on her work on Quantum Machine Learning. See also her recent textbook.

      +
      +
      +

      Quantum machine learning algorithms based on linear algebra#

      +

      Many quantum machine learning algorithms are based on variants of +quantum algorithms for solving linear equations, which can efficiently +solve N-variable linear equations with complexity of O(log2 N) under +certain conditions. The quantum matrix inversion algorithm can +accelerate many machine learning methods, such as least square linear +regression, least square version of support vector machine, Gaussian +process, and more. The training of these algorithms can be simplified +to solve linear equations. The key bottleneck of this type of quantum +machine learning algorithms is data input—that is, how to initialize +the quantum system with the entire data set. Although efficient +data-input algorithms exist for certain situations, how to efficiently +input data into a quantum system is as yet unknown for most cases.

      +
      +
      +

      Quantum reinforcement learning#

      +

      In quantum reinforcement learning, a quantum agent interacts with the +classical environment to obtain rewards from the environment, so as to +adjust and improve its behavioral strategies. In some cases, it +achieves quantum acceleration by the quantum processing capabilities +of the agent or the possibility of exploring the environment through +quantum superposition. Such algorithms have been proposed in +superconducting circuits and systems of trapped ions.

      +
      +
      +

      Quantum deep learning#

      +

      Dedicated quantum information processors, such as quantum annealers +and programmable photonic circuits, are well suited for building deep +quantum networks. The simplest deep quantum network is the Boltzmann +machine. The classical Boltzmann machine consists of bits with tunable +interactions and is trained by adjusting the interaction of these bits +so that the distribution of its expression conforms to the statistics +of the data. To quantize the Boltzmann machine, the neural network can +simply be represented as a set of interacting quantum spins that +correspond to an adjustable Ising model. Then, by initializing the +input neurons in the Boltzmann machine to a fixed state and allowing +the system to heat up, we can read out the output qubits to get the +result.

      +
      +
      +

      Social machine learning#

      +

      Machine learning aims to imitate how humans +learn. While we have developed successful machine learning algorithms, +until now we have ignored one important fact: humans are social. Each +of us is one part of the total society and it is difficult for us to +live, learn, and improve ourselves, alone and isolated. Therefore, we +should design machines with social properties. Can we let machines +evolve by imitating human society so as to achieve more effective, +intelligent, interpretable “social machine learning”?

      +

      And much more.

      +
      +
      +

      The last words?#

      +

      Early computer scientist Alan Kay said, The best way to predict the +future is to create it. Therefore, all machine learning +practitioners, whether scholars or engineers, professors or students, +need to work together to advance these important research +topics. Together, we will not just predict the future, but create it.

      +
      +
      +

      Best wishes to you all and thanks so much for your heroic efforts this semester#

      + + +

      Figure 1:

      +
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      + + + + +
      + + +
      +
      + Contents +
      +
      + +
      + + +
      +
      + +
      + +
      + +

      +By Morten Hjorth-Jensen +

      + +
      + +
      + + +

      + + © Copyright 2023. +
      + +

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KisKaNWuwbds2/P3vf3f7/qNGjcKgQYPwzDPP4PDhw+jWrRtWr16NJUuWYNq0aU6JwkSmo0dXLCIjWbFihbjzzjtFx44dRVRUlAgLCxNt27YVDzzwgMjPz3fa1l1374pdph3djE+ePOm0fMKECaJ+/fpljx3dqefMmSP+/ve/i8TERBEeHi7S09PF7t27Xb5mRYsWLRL9+/cX9evXF/Xr1xcdO3YUkydPFvv376/yczvK7u7f0aNH3Xb3Lv8ZqivfvHnzRGpqqoiMjBTR0dHiqquuEo8//rj49ddfy7bZuHGj6N27t4iMjBRxcXFlXe4BiOzs7LLtMjIyqu3+7KpMFY+DEEIUFBSI2NhYp+73O3fuFOPGjRONGzcW4eHhIikpSdx4441i7dq1Ts9du3atuPrqq0VYWJhISUkR77//vnjkkUdERESE03YA3HbFzs/PF5MnTxaJiYmibt26okWLFmLw4MFi3rx5Zdu8++67YsCAAWXlSUlJEY899pgoKCgQQghRVFQkHnvsMdGtWzcRHR0t6tevL7p16ybeeustp/eq2N1bCCHOnTsnHnroIREXFyfq1q0r2rVrJ+bMmePU3b2qz1Dxt0BkFBYhmP1FpJfDhw+jdevWmDNnjke1K2RcVqsVe/fudZkvRUSBwxwbIiIvXbp0yenxgQMHsHz5cgwcOFCfAhFRGebYEBF5qU2bNrjjjjvQpk0b/PLLL3j77bcRFhZWZU4UEQUGAxsiIi9dd911WLhwIU6cOIHw8HD06dMHL774otvBEokocHTPsXn22WfLBppy6NChQ6Vh1ImIiIiqY4gam86dO2PNmjVlj+vUMUSxiIiIyGQMEUHUqVMHLVq00LsYREREZHKGCGwOHDiAuLg4REREoE+fPpg1a5bLSdcAoKioyGlEzdLSUpw5cwaNGzd2ml2ZiIiIjEsIgXPnziEuLg4hIb7rpK17js2KFStw/vx5dOjQAcePH8eMGTOQl5eHH374odLMs4DrnBwiIiIyp6NHjyIhIcFnr6d7YFPR2bNnkZSUhFdffRWTJk2qtL5ijU1BQQFatWqFo0ePIiYmJpBFJSIiohoqLCxEYmIizp49i9jYWJ+9riGaospr0KAB2rdv73am3PDwcISHh1daHhMTw8CGiIjIZHydRmK4kYfPnz+Pn3/+GS1bttS7KERERGQyugc2jz76KL7++mscPnwYmzZtwtixYxEaGopbbrlF76IRERGRyejeFHXs2DHccsstOH36NJo2bYr+/ftj8+bNaNq0qd5FIyIiIpPRPbD5+OOP9S4CERERKUL3pigiIiIiX2FgQ0RERMpgYENERETKYGBDREREymBgQ0RERMpgYENERETKYGBDREREymBgQ0RERMpgYENERETKYGBDREREymBgQ0RERMpgYENERETKYGBDREREymBgQ0RERMpgYENERETKYGBDREREymBgQ0RERMpgYENERETKYGBDREREymBgQ0RERMpgYENERETKYGBDREREymBgQ0RERMpgYENERETKYGBDREREymBgQ0RERMpgYENERETKYGBDREREymBgQ0RERMpgYENERETKYGBDREREymBgQ0RERMpgYENERETKYGBDREREymBgQ0RERMpgYENERETKYGBDREREymBgQ0RERMpgYENERETKYGBDREREymBgQ0RERMpgYENERETKYGBDREREymBgQ0RERMpgYENERETKYGBDREREymBgQ0RERMpgYENERETKYGBDREREymBgQ0RERMpgYENERETKYGBDREREymBgQ0RERMpgYENERETKYGBDREREymBgQ0RERMpgYENERETKYGBDREREymBgQ0RERMpgYENERETKYGBDREREymBgQ0RERMpgYENERETKMFRgM3v2bFgsFkybNk3vohAREZEJGSaw2bZtG95991107dpV76IQERGRSRkisDl//jxuvfVWvPfee2jYsKHexSEiIiKTMkRgM3nyZIwcORJDhgypdtuioiIUFhY6/SMiIiICgDp6F+Djjz/Gjh07sG3bNo+2nzVrFmbMmOHnUhEREZEZ6Vpjc/ToUTz44IP46KOPEBER4dFznnrqKRQUFJT9O3r0qJ9LSURERGZhEUIIvd7cZrNh7NixCA0NLVtmt9thsVgQEhKCoqIip3WuFBYWIjY2FgUFBYiJifF3kYmIiMgH/HX91rUpavDgwdizZ4/TsokTJ6Jjx4544oknqg1qiIiIiMrTNbCJjo5Gly5dnJbVr18fjRs3rrSciIiIqDqG6BVFRERE5Au694qqKCcnR+8iEBERkUmxxoaIiIiUwcCGiIiIlMHAhoiIiJTBwIaIiIiUwcCGiIiIlMHAhoiIiJTBwIaIiIiUwcCGiIiIlMHAhoiIiJTBwIaIiIiUwcCGiIiIlMHAhoiIiJTBwIaIiIiUwcCGiIiIlMHAhoiIiJTBwIaIiIiUwcCGiIiIlMHAhoiIiJTBwIaIiIiUwcCGiIiIlFFH7wIQEREpwW4H1q8Hjh8HWrYE0tOB0FC9SxV0GNgQERHVVlYW8OCDwLFj2rKEBOCNN4Bx4/QrVxBiUxQREVFtZGUBmZnOQQ0A5OXJ5VlZ+pQrSDGwISIiqim7XdbUCFF5nWPZtGlyO29fNycHWLhQ/vX2+UGMgQ0REVFNrV9fuaamPCGAo0fldp7KygKSk4FBg4Dx4+Xf5GTW/HiIgQ0REVFNHT/u2+3YrFVrDGyIiIhqqmVL323nr2atIMPAhoiIvMccECk9XfZ+slhcr7dYgMREuV11fNmsFcTHh4ENERF5hzkgmtBQ2aUbqBzcOB6//rpn49n4qlkryI8PAxsiIvIcc0AqGzcO+PxzID7eeXlCglzu6Tg2vmjW4vGBRQhXjXnmUVhYiNjYWBQUFCAmJkbv4hARqctul3f+7ppLLBZ5Mc/NDc4Rd2s78rBj/+bluc6zqW7/muz4+Ov6zRobIiLyjD+6NqskNBQYOBC45Rb519vgobbNWjw+ABjYEBGRp3zdtZkqq02zFo8PAM4VRUREnvJl12Zyb9w4YMwY75u1eHwAMMeGiIg8VdscEPIvkx0f5tgQEZG+fNm1mXyPxwcAAxsiIvKGr7o2k3/w+LApioiIaqC2XZvJv0xwfPx1/WbyMBERec/RtZmMKYiPD5uiiIiISBkMbIiIiEgZDGyIiIhIGQxsiIiISBkMbIiIiEgZDGyIiIhIGQxsiIiISBkMbIiIiEgZDGyIiIhIGQxsiIiISBmcUoGIiKg8E8yzZBo67EsGNkRERA5ZWcCDDwLHjmnLEhKAN94IipmxfUqnfcmmKCIiIkBeiDMznS/EAJCXJ5dnZelTLjPScV9ahBDCb68eAP6a9pyIiIKI3Q4kJ1e+EDtYLLK2ITeXzVLV8XBfFu7ejdhGjXx+/WaNDRER0fr17i/EACAEcPSo3I6q5um+3LTJL2/PwIaIiOj4cd9uF8w83UcnTvjl7RnYEBERtWzp2+2Cmaf7qEULv7w9AxsiIqL0dJlDY7G4Xm+xAImJcjuqmqf7sm9fv7w9AxsiIqLQUNkNGah8QXY8fv11Jg57Qud9ycCGiIgIkGOrfP45EB/vvDwhQS7nODae03Ffsrs3ERFReRx52Heq2Jf+un5z5GEiIqLyQkOBgQP1LoUadNiXbIoiIiIiZTCwISIiImXoHti8/fbb6Nq1K2JiYhATE4M+ffpgxYoVeheLiIiITEj3wCYhIQGzZ8/G9u3b8d133+Haa6/FmDFjsHfvXr2LRkRERCZjyF5RjRo1wpw5czBp0qRqt2WvKCIiIvMJil5Rdrsdn332GS5cuIA+ffq43KaoqAhFRUVljwsLCwNVPCIiIjI43ZuiAGDPnj2IiopCeHg47r33XixevBidOnVyue2sWbMQGxtb9i8xMTHApSUiIiKjMkRTVHFxMY4cOYKCggJ8/vnneP/99/H111+7DG5c1dgkJiayKYqIiMhE/NUUZYjApqIhQ4YgJSUF7777brXbMseGiIjIfPx1/TZEU1RFpaWlTrUyRERERJ7QPXn4qaeewogRI9CqVSucO3cOCxYsQE5ODlatWqV30YiIKBhwbiil6B7Y/Pbbb7j99ttx/PhxxMbGomvXrli1ahWGDh2qd9GIiEh1WVnAgw8Cx45pyxISgDfe4GzeJmXIHBtvMMeGiIhqJCsLyMwEKl4GLRb59/PPGdz4UVDl2BAREfmV3S5ralzd2zuWTZsmtyNTYWBDRETBZ/165+anioQAjh6V25GpMLAhIqLgc/y4b7cjw9A9eZiIiIKMEXohtWzp2+3IMFhjQ0REgZOVBSQnA4MGAePHy7/JyXJ5IKWny95PjkThiiwWIDFRbkemwsCGiIgCw9ELqWJuS16eXB7I4CY0VHbpBioHN47Hr7/O8WxMiIENkZHZ7UBODrBwofzLHhpkVkbshTRunOzSHR/vvDwhgV29TYw5NkRGxYHDSCXe9EIaODBgxcK4ccCYMfrn/JDPMLAhMiJ3A4c5qux5N0lmY+ReSKGhgQ2myK/YFEVkNEassieqLfZCogBhYENkNBw4jFTEXkgUIAxsiIzGyFX2RDXFXkgUIAxsiIyGVfakKvZCogDg7N5ERmO3ywHL8vJc59lYLPJCkJvLu1syJyOMPGxEQbZf/HX9Zq8oIqNxVNlnZsogpnxwwyp7UgF7IVXG4R18hk1RREbEKnui4GGkEZkVwKYoIiMLsqppIuVU9xt2ND276wmpcNMzm6KIghGr7InMy5PmJaOOyGxibIoiIiLyNU+blzi8g88xsCEiIvIlb0YP5/AOPsfAhoiIyJe8aV7iiMw+x8CGiIjIl7xpXuKIzD7HwIaIiMiXvG1e4vAOPsXu3kRERL5U09HDg2x4B3b3JiIi8jV/BBM1HT3c0+EdgiwA8habooiIKDhlZcmalUGDgPHj5d/kZN+M9Ouv5iV/llkRXjVFHT16FImJif4sj9fYFEVERF5zjDNT8RLoqFHxVW6LL2tXAlXmAPHX9durwKZ+/fp45JFH8OSTT6JevXo+K0RtMLAhIiKvmHEaAzOWuRr+un571RT11VdfYdWqVWjXrh3mz5/vs0IQEREFjDfjzAAyqMjJARYulH/t9kCU0pm3ZQ5iXgU2ffv2xZYtWzBr1iz89a9/RWpqKtZzJxIRkZl4M86MUXJaOPWCx2qUPHz77bdj//79GDlyJEaMGIHMzEzk5ub6umxERESSL2tNPB1n5sABz+Z7CgROveCxWvWKGjZsGP785z9j8eLF6NSpEx5//HGcP3/eV2UjIiLyfa2JJ9MYJCQA8+Z5Nt9TIHDqBY95Fdi88847mDRpErp27YrY2FgMHjwY69evx7333os33ngD3333HTp16oTvvvvOX+UlIqJg4uks2d7wZBqDu+6S7+FOoHNaOPWCx7zqFZWYmIi0tDT07t0bvXv3RmpqKiIjI522efHFF7FgwQL88MMPPi+sK+wVRUSkKH/3BMrKkrNwl3/9xEQZIBQVydqh6ixYANxyi/fvXVNVldlEXb0Bg3T39kR+fj7i4uJgD1D1HAMbIiJF5eTIZqfqZGd7NmKvK+7GmQnEe9eUIiMPm2ZKhWbNmmHdunW+flkiIgo2gegJ5G4aA0dOS3XzPemR0+Lp1AtByudTKlgsFmRkZPj6ZYmIKNjo2ROIOS2mxbmiiIjImPTuCeSv+Z7Irzi7NxERGVNNZ8n2pXHjgDFjlMhpCRYMbIiIyLgctSYVewIlJASuJxBzWkyFgQ0RERkba018T5GeVa4wsCEiIuNjrYnvuBoLJyFBNvspkDfEwKYmFI50iYic8HynFsdIzhW7sDtGclYgKZq9orxllJleiYj8jec7tdjtsqbGKPNf+QkDG2/4Y84SIiIj4vlOPevXu5+eAgj8/Fd+wsDGU0ES6RIR8XynqECM5GwADGw8FSSRLhERz3eK0nMk5wBi8rCngiTSJSLi+c7Eqkr2NvL8Vz7EGhtPBUmkSyZkt8uZiBculH/ZPEC1xfOdOVWX7B0k818xsPGU3nOWELli1l4rDMaMjec78/E02TsI5r+yCOGqPso8CgsLERsbi4KCAsTExPj3zRxfHMD1nCWKfCnIJNyNR2H076Pig4Mpg+c787Db5Q2Nu7woRxNTbq5WG2OA8Yn8df1mYOMtVyflxMTAzVlCBNTsRGYEZg3GghXPd+aQkyNra6uTnW2o0ZsZ2LgR8MAGMESkS0HOjCcyswZjwU7V851Kn2vhQtkUXZ0FC4BbbvF/eTzkr+s3e0XVBOcsIb2ZsdeKN12I+fsyDhXPd6o1hzLZ2wmTh4nMyIwnMjMGY6QeFUdUZrK3EwY2RGZkpBOZpz2czBiMkVpUHVHZbN24S0uBrVuBGTP88vIMbIi8YZRuykY5kXnT3dxIwRgFJ5VHVDZ6N+7iYmD1auD+++XvPC0NePVVv7wVAxsiTxltzBi9T2TeVukbJRij4KV6c+i4ccDhw7LTwIIF8m9urn5BTWEh8MknMmG5aVNg+HDg7beBX38FoqOBsWP98rbsFUXkCSN3U9ajd0dtejixC7H5qNKDyIy9Cc3m11+BpUsBmw1Ytw64ckVb16IFMGYMYLUCgwahsKiI3b1dYWBDfsduypXV9gKhyoUyGKjUg8jxW65urqRg+i37wr59MpCx2YAtW5zXdewoAxmrFbjmGiBEayhid28ivbCbcmW1rdJXsQuxitzVVDqaG42Qu+ENR3NoZqYMYlyNqMzm0OqVlsoAxhHM/Pe/zut795aBzJgxMrAJMAY2RNVRvV2+JtjDSX3V9SCyWGQPojFjzBUIOHLTXNVCsTnUvcuXZdPSkiXyX36+ti4sDBg8WAYzo0bp/rtnYENUHV7EK3P0cKquSt9VDyc2Q5mDyjWV48bJgIzfw6qdPQssXy5rZVasAM6f19bFxgIjR8r9eN11gIFSQXQPbGbNmoWsrCzs27cPkZGR6Nu3L1566SV06NBB76IRSbW5iKuqplX6KuVrqE71mko2h7p27JhWK5OdDZSUaOvi47Xk34wMWVNjQLp39/76668xefJkbN68GV999RWuXLmCYcOG4cKFC3oXjUhiN2XXvO1uruKIrypjTWVwEALYuxeYOVMm9yYmAlOmAF99JYOazp2BZ56RA+odOQK8+SYwdKhhgxrAgL2iTp48iWbNmuHrr7/GgAEDqt2evaIoYNhN2TVPmpbYs8x82INIXXY78O23solpyRLg4EFtncUC9O2rJf+2a+e3YgRNr6iCggIAQKNGjVyuLyoqQlFRUdnjwsLCgJSLiO3ybnhSpR/ofA3m8dQeexCp5dIlYO1aGcwsXQqcPKmtCw+XtTBWK3DDDUDz5nqV0icMFdiUlpZi2rRp6NevH7p06eJym1mzZmGGn+aXIKoW2+VrJpD5Gszj8R32IDK3M2eAL7+UwczKlcDFi9q6Bg1kEGO1yhGBo6J0KqTvGaop6r777sOKFSuwYcMGJCQkuNzGVY1NYmIim6KIjCxQI74aeYRoM2MNmHkcOSKbl2w24OuvneezS0zUBstLTwfq1tWpkJK/mqIME9hMmTIFS5YswTfffIPWrVt7/Dzm2BCZQCDyNZjHQ8FICGDPHm2wvJ07ndd37aoFM927u5+EVgfK5tgIIfDAAw9g8eLFyMnJ8SqoISKTCES+hsrjrpBrwVqTVFICbNyoBTOHD2vrQkKA/v215N82bfQpo450D2wmT56MBQsWYMmSJYiOjsaJEycAALGxsYiMjNS5dETkM/7O11B93BVyFmy5VBcvyi7YNhvwxRfA6dPauogImSfjSP5t0sR372vC4FH3piiLm2qxDz/8EHfccUe1z2dTFJHJ+OtEyZmbg0ew5FKdOgUsWyaDmdWrZc8mh8aNteTfoUOB+vV9//5+Dh6Vz7GpKQY2RASA464EC9VzqXJztfFl1q+XE046JCdr+TL9+gF1/NjoEoDgUdkcGyIin+C4K8FBtVwqIYBdu7R8me+/d15/9dVavkzXroFJ/jX5BKgMbIhIHRx3RX0q5FJduSIDL0fNzJEj2rrQUGDAAC2YSUoKfPlMHjwysCEitXCEaLWZdQ6rCxeAVatkMLNsGfD779q6evXkDNlWq5wx283I+wFj8uCRgY0KTJi1TuRXHCFaXenpsgauulyq9PTAlsvVefj0admDyWYD1qwBLl/Wtm/SBBg9WgYzQ4YARuoFbNbg8f8wsDG7YOvySETBzYi5VK7Ow2FhQHGx83Zt2gBjx8pgpk8f496AGjV49FCI3gWgWnBkrVdsC83Lk8uzsvQpFxGRPzlyqeLjnZcnJAS+q/eiRcAf/1j5POwIatq0AZ5/Xo4OfPAg8MorcgA9owY1gBY8ApWTlU2QiM/u3malepdHIqLq6NUMX1ws52FavBiYN895PqbyzH4edlUTlZjos0R8jmPjRtAGNhyMjIgocM6dkzNk22xyxuyCAs+fa+bzsB+DR45jQ85MnrVORGR4J04AS5fKYGbtWuecmWbNgM6dZdBSHTOfh02YiM/AxqxMnrVORGRI+/fLsWVsNmDzZufk2XbtZPLvmDFAWpqsyfAksOF5OKAY2JiVybPWiYgMobQU2LZNG/l33z7n9b16adMYdOzonEzL87AhMbAxKyN2eSQiDceXMq6iIlnTYrPJpqbyTUV16wLXXisDmdGjgbg4969jhvNwEH4PGdiYGYePJzKmrCxg6lR5J+8QHw/84x/8XeqloABYsUIGM8uXy2Rgh+hoOeLvmDHAiBFAbKznr2vk83CQjnPGXlEqCMKInMiwsrLkuCbuLFqk9EXFUPLytOTf7Gw5R5NDy5YykLFaZXJseHjt3sto5+EAzM5dW+zu7QYDGyIyDLsdaN5cDqXvTuPGQH4+bz78QQiZI+PIl9m61Xn9H/6g5cv07AmEKDpGrUnGOWN3byIio8vJqTqoAeT6nBxg8OBAlEh9paWy95IjmDlwQFtnsQC9e2szZXfooFMhA8zks3PXFgMbIiJfycnxfDsGNjV3+TKwbp2W/Jufr60LC5OTSlqtwKhRQIsWepVSP0E+zhkDGyIiMr7ff5dJvzabTAK+cEFbFxsrk3+tVmDoUGDXLnnR3rcPaNo0+Jr9PB03p1kzGWQbJS/IRxjYEBH5ysCBwAsveLYdVe/oUTlY3pIl8gJcUqKti4/X8mUGDJA1NVlZwFVXBV0voEo8GV+nUSNgwgTnnnuK7CsmDxMR+QqTh2tHCOCHH7SRf7dvd17fpYuWL5Oa6jxYngl6AQWUY38AlcfXcXfZD/C+Yq8oNxjYEHnIaN1RVcXu3t6x24FNm7Tk30OHtHUWC9CvnxbMtG3r/jVM0Aso4NyNY3PpkvvgO4D7ioGNGwxsiDxQ3UBdDHp8K0gHRvPYpUvAV1/JmpmlS4FTp7R14eHAsGEymLnhBpkHUp2cHGDQoOq3M/Ms2zVV8bdtt8vk6uoEYF+xuzcR1Yy7Kvq8PLn80UeBhQt5EfalceNkDUMggkWzBKWnTwNffilrZVatAi5e1NY1bCiDGKtVBjVRUd69dpD3AqpSxdm5Fy707Hkm3lcMbIhUZrfLmgNXFbOOZXPmVF7nCHqCLS+htgIdZBi9ZujwYS3595tv5P5xaNVKS/7t31/O0VRTnvYC4izbQbGv2BRFpDJPq+hdCda8hJoKdJBhxGRZIYDvv9fyZXbtcl7ftasWzHTv7pz8WxuOHJvqZtnmd9lQ+8pf129Fx5MmIgC1q04uPzopVc0RZFRMXnXUfGVl+fb9PKmJmzbNuYbEX0pKZAA9bRrQurUMWJ59VgY1ISFARgbw2msyKXj3bmDGDODqq30X1ADaLNtA5dc1yizbRhEE+4qBDZHKfFGdbOK29oDQI8jwZsh8f7h4UdbI3HGH7N4+aJC8WP7yCxAZKWtkPvxQdmsvH/T4k2OW7fh45+UJCWxSrUjxfcUcGyKVVTdQlydM3NYeEHrMy6NHsuzJk8CyZTKgWb1aTmvg0LixnL7AMfJvvXq+e19vBDJp2+wU3lcMbIhU5qh2zsysPDBXVQN1OdYnJMiTHbmnR5ARqATQQ4e0wfI2bJATTjq0bq3ly/TtC9QxyOWkYi8gck/RfWWQbyIR+Y2j2rliYmujRkBREXD+fOXnKNLWHhB69DLxZMj8mgSlQgA7d2rJv3v2OK/v0UPe5VutcuoCX+bJEPkIe0UZmVnGpyBzKP99OnAAmD7d/baNGwPz5pm+rT0g9OplUtWQ+YDnuRJXrsiu2DabrJ05elRbFxoqk3+tVmD0aCApyVelJ+IAfUHH6ONTkPk4qp0dF+KqREbKO3OqXnXNfYB/ar7c1cQlJMj3q+o8cf68HCTPZpN5M2fPauvq1QNGjJDHf+RIWbNHZCKssTEiI45PQerg8PP+4epmJDGx+iCjtjyt2c3PB774QgYza9bIZkiHpk1ljYzVCgweLANbIj9jjU2wqK7rqMUiu06OGcNmKaoZDj/vH3r1MqkqAfTAAa2JadMm5/NKSgowdqwMZnr35vmElMHAxmj06DpKwSUIhlTXjd69TEpLge3bteTfH390Xt+zp9aTqVMnJv+SkhjYGA3vpsnf/NWjhvRRXCybF202OVN2Xp62rk4d2ezoSP5NSNCpkESBw8DGaHg3Tf6mV7Ir+U5hIbBypQxmvvxSPnaIipLJv1YrcP31QIMGOhWSSB8MbIyGd9MUCLXpUUP6OH5c1sjYbMC6dbKmxqF5c218mWuvBcLD9Solke4Y2BiNke6mOY6O2hQeUl0Z+/ZpI/9u3uy8rn17mfw7ZgyQliYnnCQiBjaGZIS7aY6jExz0Tnb1BZUC8NJSYOtWLfl3/37n9WlpWvJvx46BLx+RCXAcGyPT64TNcXTILFQIwIuKZNPSkiXy34kT2rq6deW4MlarnGQyLk63YhL5mr+u3wxsyJljVFp3Xc79NTw8qSUQQbmZA/CCAmD5clkrs2IFcO6cti4mRib9Wq0yCZjnNVIUB+ijwDDTODoqNUGoJBC1KGYcyDIvT6uVyc6WczQ5xMVpyb8DBwJhYXqVksj0GNiQM7OMo6NCE4SK3NWi5OXJ5b6qRTFDAC6EHCDPkfy7bZvz+k6dtGCmZ08m/xL5CAMbcmaGcXQCdfEk7wSyFsWoAbjdLnsvOZJ/Dx7U1lksQJ8+MpAZM0b2aiIin2NgQ86MPo6OGZsggkUga1GMFIBfviwnlbTZ5CSTv/2mrQsPB4YM0ZJ/mzf3f3mIghwDG3JmpHF0XDFDE0SwCmQtit4B+O+/yxF/bTY5AvCFC9q6Bg2AkSNlMDN8OBAd7Z8yEJFLDGyoMiOMo+OOUZsgKLC1KHoE4EeOaPkyX38taw8dEhK08WUGDJDdtIlIFwxsVOCP3kFGHZXWSE0Q5CzQtSj+DsCFAH74QcuX2bHDef1VV2nJvz161H6mbPbyI/IJjmNjdsHWO8gxzk51F0+Os6MPR2I34LoWxR+J3b4MCOx2YONGLZjJzdXWhYQA/fppyb8pKT4o/P8Jtt8xEThAn1tBHdiYeYCy2tDj4kmec3WRTkzUvxnTnYsXga++ks1MX3wBnDqlrYuIAIYNk8HMDTcATZv6/v2D9XdMQY+BjRtBG9gE+wjBZrt4BhujN6ucPg0sWyZrZVatAi5d0tY1bCh7MFmtMqipX99/5Qj23zEFNQY2bgRtYJOTAwwaVP122dnq9g4y+sWTjCU3Vxv595tv5ISTDklJWvJv//5AnQClH/J3TEGMUyqQM/YOUmNmavIfIYDdu7V8md27ndd366YFM9261T75tyb4OybyOQY2ZsXeQUSVlZTIWjybTdbM/PKLti4kRHbFdiT/JifrVMhy+Dsm8jkGNp4yWrOHr7rWGu1zEXnrwgWZJ7NkicybOXNGWxcZKQfJs1rloHlNmuhWTJf0HmiQSEEMbDxhxK6YvhigzIifi8gTJ0/KHkw2m+zRdPmytq5xY2D0aBnMDBkC1KunVymr5+nvGJD5OLwBIaoWk4erY/SumDXtHWT0z0VU0c8/a/kymzY5J/+2bg2MHSubmPr2DVzyr69U9TsGeANCSmKvKDf8GtiYpSumt81JZvlcFNyEkKP9OoKZH35wXt+jh5b826WLPsm/vuTqd7xkCW9ASFkMbNzwa2CjaldMVT8Xmd+VK3IeJkfyb/ng29ELzmqVTU2tWulUyADhDQgpjt299aBqV0xVPxeZ07lzMvnXZpMzZp89q62rXx8YMUI2MY0cKQfPCxacyd577AxBYGBTNVW7Yqr6ucg8TpzQkn/XrAGKi7V1zZppyb+DB8tpDYIRb0C8w84Q9H8Y2FRF1a6Yqn4uMrb//ldrYvr2W+fvXtu2WvJv7968ywZ4A+INd50h8vLkcuYiBZUQvQvwzTffYNSoUYiLi4PFYoHNZtO7SBpHV0ygcmKip12qjUjVz0XGUloKbN0KPP000KkT0KED8MQTskeTEMA11wAzZwJ798qg5+WX5ezZ/N5JjhsQd0nRjhsQux1YuFDmztntAS2iIdjtsqbG1U2aY9m0acG5b4KU7oHNhQsX0K1bN7z55pt6F8W1ceNktB8f77w8IcHcdwGqfi5V2O3yQmW2C1ZxscyXue8+2V05LQ2YNQv46SfZBXvYMOCtt2RzQfmgx+w9mvyhuhsQIeTknUOGAOPHyw4Bycmy9iKYeJOLREHBUL2iLBYLFi9eDKvV6vFzAjYJpqpJaap+LjPzZa5AII5vYSGwYoVsZlq+XD52iI6Wyb9Wq/zboIFv3zsYuPo+NG4sZyivKBi7gS9cKAO76ixYANxyi//LQx5jr6j/U1RUhKKiorLHheVPov6k6oSLqn4us/JlroA/kyl//RVYulQGM+vWyW7aDi1ayFwZq1XWIoSH1+69gt24cXJ/OgLUZs2ACRNcbyuEDG6mTZPPqS6IVeHGhrlIvmf274UwEABi8eLFVW4zffp0AaDSv4KCgsAUkshfSkqESEgQQl6eKv+zWIRITJTbVWfRIrm9q9ewWOR6b5SWCvHjj0K8+KIQaWmVX7djRyGeeEKIb78Vwm6v2ecnz2Rnu/+OlP+XnV316yxaVPn7lpDg/XdDb47fjavvu7e/Gwro96KgoMAv12/dc2y89dRTT6GgoKDs39GjR/UuEpFv+CpXwFfJlKWlsvfSE08AHTvKXJinnwa2bJHre/cGZs+W+TM//ST/37u3nEWb/McX3cAdNYMVv2+OmkEz5emwM4TvKPK9MF1TVHh4OMJZtU0q8tW4JbUZ2O3yZdm0ZLPJpqb8fG1dWJgcV8ZqBUaNYtW+Xmrb9FJd4OtNU5ZRODpDuGp6rW7ePJIU+l6YLrAhUpavcgW8DZDOnpVJvzabTAI+f17bJiZGjvhrtQLXXScfk75qOw6VqiMaV8xFMmNuiJ4U+l7oHticP38eBw8eLHucm5uLXbt2oVGjRmil+lwwROX5auBETwOkzZuBDz6Q3clLSrTlcXHa5JIZGbKmhozD0fSSmal1+3bwpOlF5RGN2Rmi5hT6Xuge2Hz33XcYVG5CxocffhgAMGHCBMyfP1+nUhHpoLYXLIfqAiSHf/xD+3+nTlowk5rKPBmjq03TC3sR+Z7ZexEBSn0vDDWOTU0EbBwbokBx1U07MdG7XAF33cbL69dPBjJjxgDt2tWmxKSXmlxQHbOGV1czyFnDPaPKHFU6fC/8df1mYENkRDW9A7x0SU4quWQJ8NlnzoPlAXJCyYkTgenTgebN/VN2Mj5H4Au4rhkMpgH+asPdDYRZ92OAvxcMbNxgYENB78wZ4MsvZfLvypXAxYvauthYOSdTly5yOoNhw3gXTpIvagaDmaOGw13CrVlrvgL4vWBg4wYDG8Wp0HbtD7/8ImtlbDbgm2+cx6RJTARGjwbatAGaNpWPud/IFf6+quduH+XkyJG1q5Odbb6E5gB9LzilAgUfVdqufUEIYM8eGcjYbMDOnc7ru3bVpjHIzZXjTQRyv/ECaU7sRVS1qs5B5ab2qZIJehFVYvLvBQMbMiZfzplkViUlwMaNWjBz+LC2LiQE6N9fS/5t00Yuz8oC/t//C+x+YwBKKqruHPTss569jgl6EamGTVFkPKq2XXvi4kVg9WrZzPTFF84zOEdEAMOHy0DmhhtkM1N5euw31ZIniQDPfkvx8fJ7/+uv7F1WQ/66fnOwCvItu122PS9cKP9WNx+RK76aM8ksTp0C5s+XtS9NmgBjx8rHp08DjRrJmZwXL5bb2WyyV1PFoAYI/H7z1ZxUREbjyW/p2DHg7rvlY85RZShsiiLf8VWThEIjYLp16JCW/Lthg5xw0iE5WWti6t8fqOPhzzTQ+02hIdiDDnOiqubpb6RdO85RZUAMbMg3fJkTo9AImGWEkAm/jmDm+++d13fvro3827Vr5TtATwR6vwVDAKoi1XKi/BGkefNbGjiQc1QZDHNsqPZ8nduhysioV67Ik53NJgOaI0e0daGhwIABMpAZPVp+3toK9H5TuburqlTLifJXkKbKOcjg/Hb9FiZXUFAgAIiCggK9ixK8srOFkD//qv9lZ3v+mosWCWGxyH/lX8OxbNEif32a2jl3TojPPxfittuEaNjQuez16gkxdqwQ//63EKdO+ef9A7nfSkqESEio/F7l3zMxUW5H+nMcL3e/T7MdL8d33dXn8MV33aznIBPx1/WbycNUe/5oknBM8hcf77w8IcF4d5W//Qb861/AqFEy+TczE/jPf4Dff5eP77wTWLpUJv9mZQG33w40buyfsgRyvzkm7QSYPGkGKiXlByJx3WjnIF90zAgSzLGh2vNXbse4ccZtuz54UGti2rjR+QTbpo2WL9O3b+DLG8j9VptZpimwVMqJClTiulHOQarlRfkZAxuqvfR0+SOrrj06Pd371zbKCJhCANu3a4Pl7d3rvD41VQtmOneuWfKvLwVyvxnl5E9VUykpP5BBmt7nIA5W6jUGNlR7jiaJzEx5QXc1K6wZmySKi4Gvv9ZqZvLytHV16siTnSP5NzFRp0IahN4nf6qeP29AAk2lIK0q1TW5WSyyyW3MGPOdX/2IgQ35hipNEufOyRmybTY5Y3ZBgbaufn1gxAgZzFx/PdCwoV6lJPKeSjcgKgVpVeFYUTXCwIZ8x6xNEidOyORemw1Yu1bW1Dg0a6ZNLnnttXJaAyKzUuUGRKUgrSoq5UUFEAMb8i2zNEns3681MW3e7HxibNdOy5dJSzP/yZGoPLPegFSkSpBWlWBpcvMxDtBHwaG0FNi2TUv+3bfPeX2vXlow07Gj/sm/ROQZlaeHUHygQH9dv1ljQ+oqKpKj3tpssqmpfHVt3bqyacmR/BsXp1cpiag2zFJLXBPB0uTmYwxsSC0FBcDy5bKJaflymQzsEB0tk36tVpkEHBurWzGJiDwSDE1uPsbAhswvL09L/s3OlnM0ObRsqSX/DhwIhIfrVEgiohpSJS8qQBjYkPkIAfz0k5b8u3Wr8/o//EELZq65BgjhzCFEZHIqN7n5GAMbMge7HdiyRUv+PXBAW2exAL17y0BmzBigQwffvi/vkoiITIOBDRnX5ctyXBmbDfjiCyA/X1sXFgYMGSIDmVGj/NPdkfOzEBGZDgMbMpbff5dJvzYbsGIFcOGCti42Fhg5UtbMXHedTAb2F87PQkRkShzHhvzH02aco0dlrozNJudmKinR1sXHa+PLDBgga2oCUe7kZPdDmZt87AgiIiPgODZkLlU144wdC/zwgxbMbN/u/NzOnbVgJjU18IPlcX4WIiLTYmBDvueuGefYMeCPfwSaN3fOl7FYgH79tOTftm0DWtxKOD8LEZFpqRXYsAeL/ux2WVNTVQtnfr5sUho2TAYzo0bJySaNgvOzEBGZljqBzdKlwFNPsQeL3pYtq7oZx8Fmk6P/GlF6uvzuVDc/S3p64MtGRERVUmfksttuq3xBdfRgycpy/zy7HcjJARYulH/tdn+WUk2HD8sActAgmT/jibNn/Vmi2nHMzwJUzu/h/CxERIamTmDjiuNue9o01wFLVpbs/TJoEDB+vPybnFx1IERyv+7aBcyYAVx9NdC6tdzHOTlVN0GVZ/RmHMf8LPHxzssTEtjVm4jIwNTp7g2gys5i2dnOPVjcJbg67sh58XJWUgJs2KCN/PvLL9q6kBDZLGO1AjfcIAPE6ppxzNJVmnlbRER+we7etVW+B0tVCa5CyIvvtGmyh04wX8QuXABWr5aBzLJlwJkz2rrISGD4cLmPbrgBaNJEW/fGGzJotFic97EZm3E4PwsRkakET2BTvumD45S4d/KkDGJsNhnUXL6srWvcWPZgslqBoUOBevVcv4ajGcfVODavv86aMCIi8hv1AxtXPVg4TomzQ4e0JqaNG4HSUm1dcrJMCB4zRo41U8fDr8y4cfI5bMYhIqIAUiuw8bTpI9jHKREC2LlTC2b27HFef/XV2si/V11V85F/2YxDREQBpk5g85//uB7HxlXTR9++8qJbVdfu0FC5nSquXAG++UYGMkuWyKY2h9BQICNDBjKjRwNJSXqVkoiIqFbUCWxGjwZuucWzpo9Nm6ofr8Zul9uZucbh/Hlg5UoZyCxb5jx2TL16coZsq1XOmN2okV6lJCIi8hl1AhvA86YPlXNs8vOBL76QNTNr1gBFRdq6pk1lAGi1AoMHy55NREREClErsPGUajk2Bw5oTUybNjnnGaWkaPkyffowedcfONYNEZFhBGdgY/a5gEpLge++k4GMzQb8+KPz+p49tWCmU6eaJ/9S9bKyXHdr5xxlRES6CM7AxjEXkJkGkSsullMWOGpmfv1VW1enjhztd8wY2dSUmKhXKYOLu9GrHXOUcfRqIqKAU2dKhZoMyezqbjsx0TiDyBUWAitWyEDmyy/lY4eoKDk7ttUKXH890KCBXqUMTna7HOPH3UCPZps6gogowDilgj8YcRC548eBpUtlzczatbKbtkPz5rK8VqusoYmI0KuUxNGriYgMKbgDG8AYg8jt26cNlrdli/O69u21fJm0NDnhJHnHH8m9KvesIyIyMQY2eigtBbZu1YKZ/fud16elacFMx46BL59K/JXcq1rPOiIiRQR3jk0gFRUB69bJQGbpUuDECW1d3brAtddqI//GxelVSrW4S+51JIjXJrnXkWNTXc865tgQEbnkr+s3Axt/OntWJv/abMDy5XIkYIeYGJn0a7XKEYBjY3UqpKL8ldxbvlnrwAFg+nT3PevYK4qIyC0mD5vFsWNa8m92NlBSoq2Li9OSfwcOBMLCdCpkEPBHcq+rZq3GjeXf06e1Ze7mKCMidXBgTsNiYFNbQsgB8hzjy2zb5ry+UyctmOnZk8m/geLr5F53zVpnzsi/M2YA7drxBEcUDDgwp6ExsKkJux3YvFlL/j14UFtnscipC6xWGdC0b69TIYOcL5N77XZ5EnPVaiuEPObvv898GqJgwIE5DY85Np66fFlOKmmzyUkmf/tNWxcWBgwdKgOZUaOAFi38Vw7yjC+Te3Ny5LhB1cnO1n/ogEBiVTwFGw7M6VPMsdHDmTNyxN8lS4CVK4ELF7R1sbHADTfImpnhw4HoaN2KSS74ctoMT5ur1q4Nnos8q+IpGHFgTlNgYFPRkSPa5JJffy0jdIeEBK2JKSNDdtMm4xo3TlYLu7oAe5Pc62mz1gsvOL+Hqhd5VsVTsOLAnKbApighgD17tOTfHTuc13fpog2W16NHYGbKZhW/b9V2f1bXrOWKql2+WRVPwYzN0j7FcWzcqNGOKSkBNm3Skn9zc7V1FgvQv79WM5OS4odSV4FV/MbkqKUAvAtuVLvI88ROwYwDc/oUc2xq6+JF4KuvtOTf8uOORETI5F+rVebNNGvm+ev6snaFVfzG5a5ZqyoqtrezKp6CmS9z98hv1A5sTp8Gli2TwcyqVcClS9q6hg1lDyarFRg2DKhf3/vX92XtiiddiqdNk7VI/NHoo+Js8D/+6JxX445KF3nOkUXBzle5e0w58Bv1mqJyc7Xk3/Xr5YSTDklJ2mB56elAnVrEdb6eh4hV/OYTjMeMVfFEUm0CE6YcAGBTVPVmzZLzMu3e7by8Wzct+bdbN98k//qjdoVV/OaTni5PRtVd5NPTA182f2FVPJEUGlqzGxamHPidYcb3f/PNN5GcnIyIiAikpaVh69at3r3A7NkyqAkJkV+2114DDh0Cdu0Cnn0W6N7ddz2avBnLwFOs4jcfx0UeqPzdUvki76iKj493Xp6QwJMyUVWquykG5E1x+WFGyGuGCGw++eQTPPzww5g+fTp27NiBbt26Yfjw4fit/Oi+1Rk5Epg/H8jPl1X/06YBrVv7p8D+qF1x3P27C74sFiAxUa27fz3Z7bIpaeFC+bemJ5JgvciPGwccPix/awsWyL+5uep+XiJf8MdNMVViiKaoV199FXfddRcmTpwIAHjnnXfw5Zdf4oMPPsCTTz7p2YssWAD4c0qF8vxRu8Iq/sDxdft2xaTiYEkErGlVPFGwYspBQOheY1NcXIzt27djyJAhZctCQkIwZMgQfPvttzqWrAr+ql0x0t2/r2o0jMbRvl3xrsnRvp2VVbPXdVzkb7lF/lU9qCEi7zHlICB0r7E5deoU7HY7mjdv7rS8efPm2LdvX6Xti4qKUFRUVPa4oKAAgMyuDqhZs4DbbnO9TgjgxRed55by1JAhwPffywEET5yQE2r27SsvlIH6jEuXAk88Afz6q7YsLg546SVg9OjAlMEf7HbggQeqbt+eOlX2dGJgQkS+1q2bPJeWP7dWFB8vtwv0NU0Hjuu2rztn6x7YeGvWrFmYMWNGpeWJiYk6lKYK7oIes/r1V/U+kyt5eUCjRnqXgoiCVRCeg06fPo3Y2FifvZ7ugU2TJk0QGhqK/Px8p+X5+flo0aJFpe2feuopPPzww2WPz549i6SkJBw5csSnO4a8V1hYiMTERBw9etSnYxJQzfB4GAePhXHwWBhHQUEBWrVqhUY+DuR0D2zCwsKQmpqKtWvXwmq1AgBKS0uxdu1aTJkypdL24eHhCA8Pr7Q8NjaWX1KDiImJ4bEwEB4P4+CxMA4eC+MICfFtuq/ugQ0APPzww5gwYQJ69uyJXr164fXXX8eFCxfKekkRERERecIQgc1NN92EkydP4m9/+xtOnDiB7t27Y+XKlZUSiomIiIiqYojABgCmTJnisumpOuHh4Zg+fbrL5ikKLB4LY+HxMA4eC+PgsTAOfx0L00+CSUREROSg+wB9RERERL7CwIaIiIiUwcCGiIiIlMHAhoiIiJRhisDmzTffRHJyMiIiIpCWloatW7dWuf1nn32Gjh07IiIiAldddRWWL18eoJKqz5tj8d577yE9PR0NGzZEw4YNMWTIkGqPHXnO29+Fw8cffwyLxVI2ICb5hrfH4+zZs5g8eTJatmyJ8PBwtG/fnucqH/H2WLz++uvo0KEDIiMjkZiYiIceegiXL18OUGnV9c0332DUqFGIi4uDxWKBzWar9jk5OTno0aMHwsPD0bZtW8yfP9/7NxYG9/HHH4uwsDDxwQcfiL1794q77rpLNGjQQOTn57vcfuPGjSI0NFS8/PLL4scffxR/+ctfRN26dcWePXsCXHL1eHssxo8fL958802xc+dO8dNPP4k77rhDxMbGimPHjgW45Orx9lg45Obmivj4eJGeni7GjBkTmMIGAW+PR1FRkejZs6e4/vrrxYYNG0Rubq7IyckRu3btCnDJ1ePtsfjoo49EeHi4+Oijj0Rubq5YtWqVaNmypXjooYcCXHL1LF++XDzzzDMiKytLABCLFy+ucvtDhw6JevXqiYcfflj8+OOPYu7cuSI0NFSsXLnSq/c1fGDTq1cvMXny5LLHdrtdxMXFiVmzZrnc/sYbbxQjR450WpaWlibuuecev5YzGHh7LCoqKSkR0dHR4t///re/ihg0anIsSkpKRN++fcX7778vJkyYwMDGh7w9Hm+//bZo06aNKC4uDlQRg4a3x2Ly5Mni2muvdVr28MMPi379+vm1nMHGk8Dm8ccfF507d3ZadtNNN4nhw4d79V6GbooqLi7G9u3bMWTIkLJlISEhGDJkCL799luXz/n222+dtgeA4cOHu92ePFOTY1HRxYsXceXKFZ9PeBZsanosnnvuOTRr1gyTJk0KRDGDRk2Ox9KlS9GnTx9MnjwZzZs3R5cuXfDiiy/CbrcHqthKqsmx6Nu3L7Zv317WXHXo0CEsX74c119/fUDKTBpfXb8NM/KwK6dOnYLdbq80tULz5s2xb98+l885ceKEy+1PnDjht3IGg5oci4qeeOIJxMXFVfrikndqciw2bNiAf/3rX9i1a1cAShhcanI8Dh06hHXr1uHWW2/F8uXLcfDgQdx///24cuUKpk+fHohiK6kmx2L8+PE4deoU+vfvDyEESkpKcO+99+Lpp58ORJGpHHfX78LCQly6dAmRkZEevY6ha2xIHbNnz8bHH3+MxYsXIyIiQu/iBJVz587htttuw3vvvYcmTZroXRwCUFpaimbNmmHevHlITU3FTTfdhGeeeQbvvPOO3kULOjk5OXjxxRfx1ltvYceOHcjKysKXX36J559/Xu+iUQ0ZusamSZMmCA0NRX5+vtPy/Px8tGjRwuVzWrRo4dX25JmaHAuHV155BbNnz8aaNWvQtWtXfxYzKHh7LH7++WccPnwYo0aNKltWWloKAKhTpw7279+PlJQU/xZaYTX5bbRs2RJ169ZFaGho2bI//OEPOHHiBIqLixEWFubXMquqJsfir3/9K2677Tb8+c9/BgBcddVVuHDhAu6++24888wzCAnh/X+guLt+x8TEeFxbAxi8xiYsLAypqalYu3Zt2bLS0lKsXbsWffr0cfmcPn36OG0PAF999ZXb7ckzNTkWAPDyyy/j+eefx8qVK9GzZ89AFFV53h6Ljh07Ys+ePdi1a1fZv9GjR2PQoEHYtWsXEhMTA1l85dTkt9GvXz8cPHiwLMAEgP/+979o2bIlg5paqMmxuHjxYqXgxRFwCk6lGFA+u357l9cceB9//LEIDw8X8+fPFz/++KO4++67RYMGDcSJEyeEEELcdttt4sknnyzbfuPGjaJOnTrilVdeET/99JOYPn06u3v7iLfHYvbs2SIsLEx8/vnn4vjx42X/zp07p9dHUIa3x6Ii9oryLW+Px5EjR0R0dLSYMmWK2L9/v1i2bJlo1qyZeOGFF/T6CMrw9lhMnz5dREdHi4ULF4pDhw6J1atXi5SUFHHjjTfq9RGUce7cObFz506xc+dOAUC8+uqrYufOneKXX34RQgjx5JNPittuu61se0d378cee0z89NNP4s0331Szu7cQQsydO1e0atVKhIWFiV69eonNmzeXrcvIyBATJkxw2v7TTz8V7du3F2FhYaJz587iyy+/DHCJ1eXNsUhKShIAKv2bPn164AuuIG9/F+UxsPE9b4/Hpk2bRFpamggPDxdt2rQRM2fOFCUlJQEutZq8ORZXrlwRzz77rEhJSREREREiMTFR3H///eL3338PfMEVk52d7fIa4Nj/EyZMEBkZGZWe0717dxEWFibatGkjPvzwQ6/f1yIE69qIiIhIDYbOsSEiIiLyBgMbIiIiUgYDGyIiIlIGAxsiIiJSBgMbIiIiUgYDGyIiIlIGAxsiIiJSBgMbIiIiUgYDGyIiIlIGAxsiIiJSBgMbIjKchQsXIjIyEsePHy9bNnHiRHTt2hUFBQU6loyIjI5zRRGR4Qgh0L17dwwYMABz587F9OnT8cEHH2Dz5s2Ij4/Xu3hEZGB19C4AEVFFFosFM2fORGZmJlq0aIG5c+di/fr1DGqIqFqssSEiw+rRowf27t2L1atXIyMjQ+/iEJEJMMeGiAxp5cqV2LdvH+x2O5o3b653cYjIJFhjQ0SGs2PHDgwcOBDvvvsu5s+fj5iYGHz22Wd6F4uITIA5NkRkKIcPH8bIkSPx9NNP45ZbbkGbNm3Qp08f7NixAz169NC7eERkcKyxISLDOHPmDPr27YuBAwfinXfeKVs+cuRI2O12rFy5UsfSEZEZMLAhIiIiZTB5mIiIiJTBwIaIiIiUwcCGiIiIlMHAhoiIiJTBwIaIiIiUwcCGiIiIlMHAhoiIiJTBwIaIiIiUwcCGiIiIlMHAhoiIiJTBwIaIiIiUwcCGiIiIlPH/AdJWRzFOmB0nAAAAAElFTkSuQmCC", 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", 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      " ] @@ -511,7 +511,7 @@ "outputs": [ { "data": { - "image/png": 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", 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", 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      " ] @@ -575,18 +575,18 @@ "output_type": "stream", "text": [ "The intercept alpha: \n", - " [1.97386121]\n", + " [2.00949975]\n", "Coefficient beta : \n", - " [[5.12574106]]\n", - "Mean squared error: 0.20\n", - "Variance score: 0.92\n", - "Mean squared log error: 0.01\n", - "Mean absolute error: 0.36\n" + " [[5.07244691]]\n", + "Mean squared error: 0.30\n", + "Variance score: 0.88\n", + "Mean squared log error: 0.02\n", + "Mean absolute error: 0.42\n" ] }, { "data": { - "image/png": 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", + "image/png": 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", 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      " ] @@ -810,7 +810,7 @@ "outputs": [ { "data": { - "image/png": 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", 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", "text/plain": [ "
      " ] @@ -822,7 +822,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "0.004999999999999993\n" + "0.005000000000000009\n" ] } ], diff --git a/doc/LectureNotes/_build/jupyter_execute/chapter10.ipynb b/doc/LectureNotes/_build/jupyter_execute/chapter10.ipynb index 52166eb53..915ab522d 100644 --- a/doc/LectureNotes/_build/jupyter_execute/chapter10.ipynb +++ b/doc/LectureNotes/_build/jupyter_execute/chapter10.ipynb @@ -865,7 +865,13 @@ " 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", + "\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ "predictions = (n_inputs) = (1437,)\n", "prediction for image 0: 8\n", "correct label for image 0: 6\n" @@ -1073,7 +1079,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_58742/953065564.py:4: RuntimeWarning: overflow encountered in exp\n", + "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_23025/953065564.py:4: RuntimeWarning: overflow encountered in exp\n", " return 1/(1 + np.exp(-x))\n" ] }, @@ -1651,7 +1657,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_58742/953065564.py:4: RuntimeWarning: overflow encountered in exp\n", + "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_23025/953065564.py:4: RuntimeWarning: overflow encountered in exp\n", " return 1/(1 + np.exp(-x))\n" ] }, @@ -1669,7 +1675,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_58742/953065564.py:4: RuntimeWarning: overflow encountered in exp\n", + "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_23025/953065564.py:4: RuntimeWarning: overflow encountered in exp\n", " return 1/(1 + np.exp(-x))\n" ] }, @@ -1687,10 +1693,364 @@ "name": "stderr", "output_type": "stream", "text": [ - "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_58742/953065564.py:4: RuntimeWarning: overflow encountered in exp\n", + "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_23025/953065564.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_23025/953065564.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_23025/953065564.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_23025/953065564.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_23025/953065564.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_23025/953065564.py:4: RuntimeWarning: overflow encountered in exp\n", + " return 1/(1 + np.exp(-x))\n", + "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_23025/1630775253.py:43: RuntimeWarning: overflow encountered in exp\n", + " exp_term = np.exp(self.z_o)\n", + "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_23025/1630775253.py:44: RuntimeWarning: invalid value encountered in 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_23025/953065564.py:4: RuntimeWarning: overflow encountered in exp\n", + " return 1/(1 + np.exp(-x))\n", + "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_23025/1630775253.py:43: RuntimeWarning: overflow encountered in exp\n", + " exp_term = np.exp(self.z_o)\n", + "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_23025/1630775253.py:44: RuntimeWarning: invalid value encountered in 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_23025/953065564.py:4: RuntimeWarning: overflow encountered in exp\n", + " return 1/(1 + np.exp(-x))\n", + "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_23025/1630775253.py:43: RuntimeWarning: overflow encountered in exp\n", + " exp_term = np.exp(self.z_o)\n", + "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_23025/1630775253.py:44: RuntimeWarning: invalid value encountered in 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_23025/953065564.py:4: RuntimeWarning: overflow encountered in exp\n", + " return 1/(1 + np.exp(-x))\n", + "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_23025/1630775253.py:43: RuntimeWarning: overflow encountered in exp\n", + " exp_term = np.exp(self.z_o)\n", + "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_23025/1630775253.py:44: RuntimeWarning: invalid value encountered in 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_23025/953065564.py:4: RuntimeWarning: overflow encountered in exp\n", + " return 1/(1 + np.exp(-x))\n", + "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_23025/1630775253.py:43: RuntimeWarning: overflow encountered in exp\n", + " exp_term = np.exp(self.z_o)\n", + "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_23025/1630775253.py:44: RuntimeWarning: invalid value encountered in 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_23025/953065564.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_23025/953065564.py:4: RuntimeWarning: overflow encountered in exp\n", + " return 1/(1 + np.exp(-x))\n", + "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_23025/1630775253.py:43: RuntimeWarning: overflow encountered in exp\n", + " exp_term = np.exp(self.z_o)\n", + "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_23025/1630775253.py:44: RuntimeWarning: invalid value encountered in 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_23025/953065564.py:4: RuntimeWarning: overflow encountered in exp\n", + " return 1/(1 + np.exp(-x))\n", + "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_23025/1630775253.py:43: RuntimeWarning: overflow encountered in exp\n", + " exp_term = np.exp(self.z_o)\n", + "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_23025/1630775253.py:44: RuntimeWarning: invalid value encountered in 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_23025/953065564.py:4: RuntimeWarning: overflow encountered in exp\n", + " return 1/(1 + np.exp(-x))\n", + "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_23025/1630775253.py:43: RuntimeWarning: overflow encountered in exp\n", + " exp_term = np.exp(self.z_o)\n", + "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_23025/1630775253.py:44: RuntimeWarning: invalid value encountered in 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_23025/953065564.py:4: RuntimeWarning: overflow encountered in exp\n", + " return 1/(1 + np.exp(-x))\n", + "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_23025/1630775253.py:43: RuntimeWarning: overflow encountered in exp\n", + " exp_term = np.exp(self.z_o)\n", + "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_23025/1630775253.py:44: RuntimeWarning: invalid value encountered in 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_23025/953065564.py:4: RuntimeWarning: overflow encountered in exp\n", + " return 1/(1 + np.exp(-x))\n", + "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_23025/1630775253.py:43: RuntimeWarning: overflow encountered in exp\n", + " exp_term = np.exp(self.z_o)\n", + "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_23025/1630775253.py:44: RuntimeWarning: invalid value encountered in 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_23025/953065564.py:4: RuntimeWarning: overflow encountered in exp\n", + " return 1/(1 + np.exp(-x))\n", + "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_23025/1630775253.py:43: RuntimeWarning: overflow encountered in exp\n", + " exp_term = np.exp(self.z_o)\n", + "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_23025/1630775253.py:44: RuntimeWarning: invalid value encountered in 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_23025/953065564.py:4: RuntimeWarning: overflow encountered in exp\n", + " return 1/(1 + np.exp(-x))\n", + "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_23025/1630775253.py:43: RuntimeWarning: overflow encountered in exp\n", + " exp_term = np.exp(self.z_o)\n", + "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_23025/1630775253.py:44: RuntimeWarning: invalid value encountered in divide\n", + " self.probabilities = exp_term / np.sum(exp_term, axis=1, keepdims=True)\n" + ] + }, { "ename": "KeyboardInterrupt", "evalue": "", @@ -1699,8 +2059,8 @@ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[0;31mKeyboardInterrupt\u001b[0m Traceback (most recent call last)", "Cell \u001b[0;32mIn[8], line 11\u001b[0m\n\u001b[1;32m 8\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m j, lmbd \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28menumerate\u001b[39m(lmbd_vals):\n\u001b[1;32m 9\u001b[0m dnn \u001b[38;5;241m=\u001b[39m NeuralNetwork(X_train, Y_train_onehot, eta\u001b[38;5;241m=\u001b[39meta, lmbd\u001b[38;5;241m=\u001b[39mlmbd, epochs\u001b[38;5;241m=\u001b[39mepochs, batch_size\u001b[38;5;241m=\u001b[39mbatch_size,\n\u001b[1;32m 10\u001b[0m n_hidden_neurons\u001b[38;5;241m=\u001b[39mn_hidden_neurons, n_categories\u001b[38;5;241m=\u001b[39mn_categories)\n\u001b[0;32m---> 11\u001b[0m \u001b[43mdnn\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mtrain\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 13\u001b[0m DNN_numpy[i][j] \u001b[38;5;241m=\u001b[39m dnn\n\u001b[1;32m 15\u001b[0m test_predict \u001b[38;5;241m=\u001b[39m dnn\u001b[38;5;241m.\u001b[39mpredict(X_test)\n", - "Cell \u001b[0;32mIn[6], line 98\u001b[0m, in \u001b[0;36mNeuralNetwork.train\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 95\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mX_data \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mX_data_full[chosen_datapoints]\n\u001b[1;32m 96\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mY_data \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mY_data_full[chosen_datapoints]\n\u001b[0;32m---> 98\u001b[0m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mfeed_forward\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 99\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mbackpropagation()\n", - "Cell \u001b[0;32mIn[6], line 38\u001b[0m, in \u001b[0;36mNeuralNetwork.feed_forward\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 36\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mfeed_forward\u001b[39m(\u001b[38;5;28mself\u001b[39m):\n\u001b[1;32m 37\u001b[0m \u001b[38;5;66;03m# feed-forward for training\u001b[39;00m\n\u001b[0;32m---> 38\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mz_h \u001b[38;5;241m=\u001b[39m \u001b[43mnp\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mmatmul\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mX_data\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mhidden_weights\u001b[49m\u001b[43m)\u001b[49m \u001b[38;5;241m+\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mhidden_bias\n\u001b[1;32m 39\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39ma_h \u001b[38;5;241m=\u001b[39m sigmoid(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mz_h)\n\u001b[1;32m 41\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mz_o \u001b[38;5;241m=\u001b[39m np\u001b[38;5;241m.\u001b[39mmatmul(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39ma_h, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39moutput_weights) \u001b[38;5;241m+\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39moutput_bias\n", + "Cell \u001b[0;32mIn[6], line 99\u001b[0m, in \u001b[0;36mNeuralNetwork.train\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 96\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mY_data \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mY_data_full[chosen_datapoints]\n\u001b[1;32m 98\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mfeed_forward()\n\u001b[0;32m---> 99\u001b[0m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mbackpropagation\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n", + "Cell \u001b[0;32mIn[6], line 64\u001b[0m, in \u001b[0;36mNeuralNetwork.backpropagation\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 61\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39moutput_weights_gradient \u001b[38;5;241m=\u001b[39m np\u001b[38;5;241m.\u001b[39mmatmul(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39ma_h\u001b[38;5;241m.\u001b[39mT, error_output)\n\u001b[1;32m 62\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39moutput_bias_gradient \u001b[38;5;241m=\u001b[39m np\u001b[38;5;241m.\u001b[39msum(error_output, axis\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m0\u001b[39m)\n\u001b[0;32m---> 64\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mhidden_weights_gradient \u001b[38;5;241m=\u001b[39m \u001b[43mnp\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mmatmul\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mX_data\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mT\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43merror_hidden\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 65\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mhidden_bias_gradient \u001b[38;5;241m=\u001b[39m np\u001b[38;5;241m.\u001b[39msum(error_hidden, axis\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m0\u001b[39m)\n\u001b[1;32m 67\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mlmbd \u001b[38;5;241m>\u001b[39m \u001b[38;5;241m0.0\u001b[39m:\n", "\u001b[0;31mKeyboardInterrupt\u001b[0m: " ] } diff --git a/doc/LectureNotes/_build/jupyter_execute/chapter13.ipynb b/doc/LectureNotes/_build/jupyter_execute/chapter13.ipynb index e7e0e02fa..fb7c0b2d9 100644 --- a/doc/LectureNotes/_build/jupyter_execute/chapter13.ipynb +++ b/doc/LectureNotes/_build/jupyter_execute/chapter13.ipynb @@ -61,7 +61,7 @@ "outputs": [ { "data": { - "image/png": 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", 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", 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      " ] @@ -169,7 +169,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "50/50 - 2s - 41ms/step - loss: 0.5362\n" + "50/50 - 2s - 48ms/step - loss: 0.5174\n" ] }, { @@ -183,7 +183,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "50/50 - 0s - 8ms/step - loss: 0.4106\n" + "50/50 - 0s - 7ms/step - loss: 0.4188\n" ] }, { @@ -197,7 +197,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "50/50 - 0s - 8ms/step - loss: 0.4015\n" + "50/50 - 0s - 7ms/step - loss: 0.4065\n" ] }, { @@ -211,7 +211,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "50/50 - 0s - 8ms/step - loss: 0.3978\n" + "50/50 - 0s - 8ms/step - loss: 0.4041\n" ] }, { @@ -225,7 +225,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "50/50 - 0s - 7ms/step - loss: 0.3953\n" + "50/50 - 0s - 8ms/step - loss: 0.4032\n" ] }, { @@ -239,7 +239,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "50/50 - 0s - 7ms/step - loss: 0.3941\n" + "50/50 - 0s - 7ms/step - loss: 0.4008\n" ] }, { @@ -253,7 +253,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "50/50 - 0s - 7ms/step - loss: 0.3899\n" + "50/50 - 0s - 7ms/step - loss: 0.4000\n" ] }, { @@ -267,7 +267,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "50/50 - 0s - 7ms/step - loss: 0.3917\n" + "50/50 - 0s - 7ms/step - loss: 0.3989\n" ] }, { @@ -281,7 +281,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "50/50 - 0s - 8ms/step - loss: 0.3893\n" + "50/50 - 0s - 7ms/step - loss: 0.3963\n" ] }, { @@ -295,7 +295,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "50/50 - 0s - 7ms/step - loss: 0.3903\n" + "50/50 - 0s - 7ms/step - loss: 0.3964\n" ] }, { @@ -309,7 +309,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "50/50 - 0s - 7ms/step - loss: 0.3860\n" + "50/50 - 0s - 7ms/step - loss: 0.3957\n" ] }, { @@ -323,7 +323,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "50/50 - 0s - 7ms/step - loss: 0.3843\n" + "50/50 - 0s - 7ms/step - loss: 0.3965\n" ] }, { @@ -337,7 +337,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "50/50 - 1s - 11ms/step - loss: 0.3874\n" + "50/50 - 0s - 7ms/step - loss: 0.3965\n" ] }, { @@ -351,7 +351,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "50/50 - 0s - 8ms/step - loss: 0.3868\n" + "50/50 - 0s - 7ms/step - loss: 0.3953\n" ] }, { @@ -365,7 +365,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "50/50 - 0s - 8ms/step - loss: 0.3842\n" + "50/50 - 0s - 7ms/step - loss: 0.3943\n" ] }, { @@ -379,7 +379,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "50/50 - 0s - 8ms/step - loss: 0.3855\n" + "50/50 - 0s - 7ms/step - loss: 0.3938\n" ] }, { @@ -393,7 +393,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "50/50 - 1s - 13ms/step - loss: 0.3861\n" + "50/50 - 0s - 7ms/step - loss: 0.3920\n" ] }, { @@ -407,7 +407,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "50/50 - 0s - 10ms/step - loss: 0.3809\n" + "50/50 - 0s - 7ms/step - loss: 0.3935\n" ] }, { @@ -421,7 +421,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "50/50 - 0s - 7ms/step - loss: 0.3829\n" + "50/50 - 0s - 7ms/step - loss: 0.3949\n" ] }, { @@ -435,7 +435,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "50/50 - 0s - 7ms/step - loss: 0.3809\n" + "50/50 - 0s - 7ms/step - loss: 0.3922\n" ] }, { @@ -449,7 +449,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "50/50 - 0s - 7ms/step - loss: 0.3824\n" + "50/50 - 0s - 9ms/step - loss: 0.3927\n" ] }, { @@ -463,7 +463,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "50/50 - 0s - 7ms/step - loss: 0.3804\n" + "50/50 - 0s - 9ms/step - loss: 0.3914\n" ] }, { @@ -477,7 +477,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "50/50 - 0s - 7ms/step - loss: 0.3797\n" + "50/50 - 0s - 7ms/step - loss: 0.3924\n" ] }, { @@ -491,7 +491,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "50/50 - 0s - 7ms/step - loss: 0.3807\n" + "50/50 - 0s - 8ms/step - loss: 0.3885\n" ] }, { @@ -505,7 +505,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "50/50 - 0s - 7ms/step - loss: 0.3796\n" + "50/50 - 0s - 7ms/step - loss: 0.3896\n" ] }, { @@ -519,7 +519,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "50/50 - 2s - 31ms/step - loss: 0.3783\n" + "50/50 - 0s - 7ms/step - loss: 0.3901\n" ] }, { @@ -533,7 +533,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "50/50 - 0s - 9ms/step - loss: 0.3775\n" + "50/50 - 0s - 7ms/step - loss: 0.3885\n" ] }, { @@ -547,7 +547,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "50/50 - 0s - 8ms/step - loss: 0.3789\n" + "50/50 - 0s - 7ms/step - loss: 0.3905\n" ] }, { @@ -561,7 +561,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "50/50 - 0s - 8ms/step - loss: 0.3774\n" + "50/50 - 0s - 7ms/step - loss: 0.3884\n" ] }, { @@ -575,7 +575,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "50/50 - 0s - 9ms/step - loss: 0.3759\n" + "50/50 - 0s - 7ms/step - loss: 0.3904\n" ] }, { @@ -589,7 +589,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "50/50 - 0s - 7ms/step - loss: 0.3768\n" + "50/50 - 0s - 7ms/step - loss: 0.3889\n" ] }, { @@ -603,7 +603,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "50/50 - 0s - 7ms/step - loss: 0.3741\n" + "50/50 - 0s - 7ms/step - loss: 0.3874\n" ] }, { @@ -617,7 +617,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "50/50 - 0s - 7ms/step - loss: 0.3755\n" + "50/50 - 0s - 7ms/step - loss: 0.3899\n" ] }, { @@ -631,7 +631,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "50/50 - 0s - 8ms/step - loss: 0.3733\n" + "50/50 - 0s - 7ms/step - loss: 0.3886\n" ] }, { @@ -645,7 +645,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "50/50 - 1s - 22ms/step - loss: 0.3736\n" + "50/50 - 0s - 7ms/step - loss: 0.3886\n" ] }, { @@ -659,7 +659,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "50/50 - 1s - 10ms/step - loss: 0.3721\n" + "50/50 - 0s - 7ms/step - loss: 0.3862\n" ] }, { @@ -673,7 +673,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "50/50 - 0s - 10ms/step - loss: 0.3736\n" + "50/50 - 0s - 7ms/step - loss: 0.3873\n" ] }, { @@ -687,7 +687,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "50/50 - 0s - 8ms/step - loss: 0.3728\n" + "50/50 - 0s - 7ms/step - loss: 0.3875\n" ] }, { @@ -701,7 +701,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "50/50 - 0s - 7ms/step - loss: 0.3719\n" + "50/50 - 0s - 7ms/step - loss: 0.3857\n" ] }, { @@ -715,7 +715,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "50/50 - 1s - 11ms/step - loss: 0.3718\n" + "50/50 - 0s - 9ms/step - loss: 0.3860\n" ] }, { @@ -729,7 +729,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "50/50 - 0s - 7ms/step - loss: 0.3704\n" + "50/50 - 0s - 8ms/step - loss: 0.3860\n" ] }, { @@ -743,7 +743,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "50/50 - 0s - 8ms/step - loss: 0.3730\n" + "50/50 - 0s - 7ms/step - loss: 0.3850\n" ] }, { @@ -757,7 +757,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "50/50 - 0s - 7ms/step - loss: 0.3704\n" + "50/50 - 0s - 7ms/step - loss: 0.3863\n" ] }, { @@ -771,7 +771,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "50/50 - 0s - 7ms/step - loss: 0.3698\n" + "50/50 - 0s - 7ms/step - loss: 0.3836\n" ] }, { @@ -785,7 +785,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "50/50 - 0s - 7ms/step - loss: 0.3712\n" + "50/50 - 0s - 7ms/step - loss: 0.3844\n" ] }, { @@ -799,7 +799,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "50/50 - 0s - 7ms/step - loss: 0.3691\n" + "50/50 - 0s - 7ms/step - loss: 0.3841\n" ] }, { @@ -813,7 +813,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "50/50 - 0s - 7ms/step - loss: 0.3672\n" + "50/50 - 0s - 7ms/step - loss: 0.3828\n" ] }, { @@ -827,7 +827,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "50/50 - 0s - 8ms/step - loss: 0.3657\n" + "50/50 - 0s - 7ms/step - loss: 0.3843\n" ] }, { @@ -841,7 +841,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "50/50 - 0s - 7ms/step - loss: 0.3678\n" + "50/50 - 0s - 7ms/step - loss: 0.3837\n" ] }, { @@ -855,7 +855,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "50/50 - 0s - 7ms/step - loss: 0.3634\n" + "50/50 - 0s - 7ms/step - loss: 0.3850\n" ] }, { @@ -869,7 +869,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "50/50 - 0s - 7ms/step - loss: 0.3667\n" + "50/50 - 0s - 7ms/step - loss: 0.3847\n" ] }, { @@ -883,7 +883,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "50/50 - 0s - 7ms/step - loss: 0.3685\n" + "50/50 - 0s - 7ms/step - loss: 0.3809\n" ] }, { @@ -897,7 +897,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "50/50 - 0s - 7ms/step - loss: 0.3669\n" + "50/50 - 0s - 7ms/step - loss: 0.3835\n" ] }, { @@ -911,7 +911,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "50/50 - 0s - 7ms/step - loss: 0.3654\n" + "50/50 - 0s - 7ms/step - loss: 0.3830\n" ] }, { @@ -925,7 +925,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "50/50 - 0s - 7ms/step - loss: 0.3635\n" + "50/50 - 0s - 7ms/step - loss: 0.3813\n" ] }, { @@ -939,7 +939,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "50/50 - 0s - 7ms/step - loss: 0.3643\n" + "50/50 - 0s - 7ms/step - loss: 0.3826\n" ] }, { @@ -953,7 +953,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "50/50 - 0s - 7ms/step - loss: 0.3649\n" + "50/50 - 0s - 7ms/step - loss: 0.3835\n" ] }, { @@ -967,7 +967,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "50/50 - 0s - 7ms/step - loss: 0.3617\n" + "50/50 - 0s - 8ms/step - loss: 0.3823\n" ] }, { @@ -981,7 +981,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "50/50 - 0s - 7ms/step - loss: 0.3648\n" + "50/50 - 0s - 7ms/step - loss: 0.3820\n" ] }, { @@ -991,6 +991,104 @@ "Epoch 60/100\n" ] }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "50/50 - 0s - 7ms/step - loss: 0.3814\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch 61/100\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "50/50 - 0s - 7ms/step - loss: 0.3825\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch 62/100\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "50/50 - 0s - 7ms/step - loss: 0.3817\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch 63/100\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "50/50 - 0s - 7ms/step - loss: 0.3815\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch 64/100\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "50/50 - 0s - 7ms/step - loss: 0.3796\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch 65/100\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "50/50 - 0s - 7ms/step - loss: 0.3802\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch 66/100\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "50/50 - 0s - 7ms/step - loss: 0.3805\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch 67/100\n" + ] + }, { "ename": "KeyboardInterrupt", "evalue": "", diff --git a/doc/LectureNotes/_build/jupyter_execute/chapter2.ipynb b/doc/LectureNotes/_build/jupyter_execute/chapter2.ipynb index 73a0ee52f..b2da3c556 100644 --- a/doc/LectureNotes/_build/jupyter_execute/chapter2.ipynb +++ b/doc/LectureNotes/_build/jupyter_execute/chapter2.ipynb @@ -1798,10 +1798,10 @@ "name": "stdout", "output_type": "stream", "text": [ - "-0.07163028969289174\n", - "3.7656278764040367\n", - "[[0.7647107 2.29986727]\n", - " [2.29986727 7.88107866]]\n" + "0.00239782448350092\n", + "3.945643027765946\n", + "[[1.04582521 3.00214849]\n", + " [3.00214849 9.76163974]]\n" ] } ], @@ -1845,10 +1845,10 @@ "name": "stdout", "output_type": "stream", "text": [ - "0.08212703190343323\n", - "2.425866065899094\n", - "[[1. 0.65333306]\n", - " [0.65333306 1. ]]\n" + "0.07878080847813602\n", + "2.1500369042116563\n", + "[[1. 0.63295205]\n", + " [0.63295205 1. ]]\n" ] } ], @@ -1905,30 +1905,30 @@ "name": "stdout", "output_type": "stream", "text": [ - "[[-0.68481734 -2.74241828]\n", - " [-1.03710525 -3.82202137]\n", - " [ 0.71505805 2.54840587]\n", - " [ 0.4645853 0.98764188]\n", - " [-1.95392781 -4.51089358]\n", - " [ 0.79256149 3.37757489]\n", - " [-0.18745641 0.4457749 ]\n", - " [ 2.53950612 7.87543978]\n", - " [ 0.25354074 -0.28008123]\n", - " [-0.90194489 -3.87942287]]\n", + "[[-0.05862476 0.45533295]\n", + " [ 0.84689611 4.01381445]\n", + " [ 0.21778772 0.62032544]\n", + " [-1.3117471 -2.69182187]\n", + " [-0.78512935 -3.44198948]\n", + " [-0.12030798 -0.93994208]\n", + " [ 0.60559053 2.2457079 ]\n", + " [ 1.8170149 4.51652722]\n", + " [-0.44215901 -3.25646952]\n", + " [-0.76932106 -1.52148501]]\n", 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"2 0.0 0.081406 0.081919 0.078612 0.080089 0.081603 0.068822 0.070146 \n", + "3 0.0 0.078296 0.078612 0.080411 0.081717 0.083038 0.073368 0.074578 \n", + "4 0.0 0.079396 0.080089 0.081717 0.083265 0.084831 0.074620 0.075994 \n", + "5 0.0 0.080527 0.081603 0.083038 0.084831 0.086645 0.075870 0.077411 \n", + "6 0.0 0.068235 0.068822 0.073368 0.074620 0.075870 0.069194 0.070300 \n", + "7 0.0 0.069327 0.070146 0.074578 0.075994 0.077411 0.070300 0.071523 \n", + "8 0.0 0.070483 0.071536 0.075839 0.077423 0.079011 0.071441 0.072784 \n", + "9 0.0 0.071702 0.072994 0.077153 0.078908 0.080672 0.072619 0.074087 \n", + "10 0.0 0.059187 0.059821 0.065770 0.066863 0.067945 0.063580 0.064519 \n", + "11 0.0 0.060114 0.060903 0.066767 0.067977 0.069178 0.064472 0.065498 \n", + "12 0.0 0.061096 0.062042 0.067812 0.069142 0.070466 0.065402 0.066517 \n", + "13 0.0 0.062134 0.063240 0.068908 0.070362 0.071812 0.066371 0.067578 \n", + "14 0.0 0.063230 0.064502 0.070057 0.071639 0.073220 0.067381 0.068684 \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 0.059023 0.057616 0.054096 0.052750 0.051499 0.050336 0.049255 \n", - "2 0.060216 0.058984 0.054970 0.053781 0.052672 0.051637 0.050672 \n", - "3 0.066097 0.064749 0.061945 0.060606 0.059359 0.058198 0.057116 \n", - "4 0.065457 0.064281 0.061234 0.060054 0.058952 0.057923 0.056962 \n", - "5 0.064796 0.063778 0.060520 0.059486 0.058518 0.057612 0.056764 \n", - "6 0.063327 0.062233 0.060261 0.059137 0.058088 0.057109 0.056195 \n", - "7 0.062411 0.061458 0.059314 0.058322 0.057394 0.056526 0.055714 \n", - "8 0.061541 0.060718 0.058420 0.057549 0.056732 0.055967 0.055250 \n", - "9 0.060718 0.060014 0.057576 0.056817 0.056103 0.055433 0.054805 \n", - "10 0.058420 0.057576 0.056198 0.055300 0.054459 0.053671 0.052934 \n", - "11 0.057549 0.056817 0.055300 0.054507 0.053763 0.053066 0.052412 \n", - "12 0.056732 0.056103 0.054459 0.053763 0.053109 0.052495 0.051918 \n", - "13 0.055967 0.055433 0.053671 0.053066 0.052495 0.051957 0.051452 \n", - "14 0.055250 0.054805 0.052934 0.052412 0.051918 0.051452 0.051015 \n" + "1 0.070483 0.071702 0.059187 0.060114 0.061096 0.062134 0.063230 \n", + "2 0.071536 0.072994 0.059821 0.060903 0.062042 0.063240 0.064502 \n", + "3 0.075839 0.077153 0.065770 0.066767 0.067812 0.068908 0.070057 \n", + "4 0.077423 0.078908 0.066863 0.067977 0.069142 0.070362 0.071639 \n", + "5 0.079011 0.080672 0.067945 0.069178 0.070466 0.071812 0.073220 \n", + "6 0.071441 0.072619 0.063580 0.064472 0.065402 0.066371 0.067381 \n", + "7 0.072784 0.074087 0.064519 0.065498 0.066517 0.067578 0.068684 \n", + "8 0.074170 0.075601 0.065481 0.066550 0.067660 0.068817 0.070021 \n", + "9 0.075601 0.077163 0.066469 0.067629 0.068835 0.070089 0.071396 \n", + "10 0.065481 0.066469 0.059543 0.060291 0.061066 0.061870 0.062705 \n", + "11 0.066550 0.067629 0.060291 0.061104 0.061946 0.062820 0.063728 \n", + "12 0.067660 0.068835 0.061066 0.061946 0.062859 0.063805 0.064788 \n", + "13 0.068817 0.070089 0.061870 0.062820 0.063805 0.064827 0.065887 \n", + "14 0.070021 0.071396 0.062705 0.063728 0.064788 0.065887 0.067030 \n" ] } ], diff --git a/doc/LectureNotes/_build/jupyter_execute/chapter3.ipynb b/doc/LectureNotes/_build/jupyter_execute/chapter3.ipynb index 68ccea2da..4c46e274b 100644 --- a/doc/LectureNotes/_build/jupyter_execute/chapter3.ipynb +++ b/doc/LectureNotes/_build/jupyter_execute/chapter3.ipynb @@ -489,10 +489,10 @@ "name": "stdout", "output_type": "stream", "text": [ - "Runtime: 0.177848 sec\n", + "Runtime: 0.154011 sec\n", "Jackknife Statistics :\n", "original bias std. error\n", - " 99.9735 99.9635 0.149504\n" + " 100.105 100.095 0.14726\n" ] } ], @@ -917,7 +917,7 @@ "text": [ "Bootstrap Statistics :\n", "original bias std. error\n", - " 99.9629 14.9658 99.9626 0.148488\n" + " 100.182 15.0891 100.184 0.15218\n" ] } ], @@ -975,7 +975,7 @@ "outputs": [ { "data": { - "image/png": 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", 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bAWebEoS4J4qOYnLGesahoNIZ+/eLTkJEurDcEJFOkZHAYLejVnmhzLvp7pyC5spr2LZNdBIi0oXlhohqKC4G/vgDCHVPEB3FJMlkwBjPeGzdqp2bRESmheWGiGrYvx9Qq1lu7mSU55+4eBE4xwPJiEwOyw0R1RAVBTRrBnRyTBUdxWQNck2Era12rIjItLDcEFENkZFAaKh29wvp5mx7E0FBLDdEpojlhoiqycoCjh8HwsJEJzF9oaFAdDRQWSk6CRHdjuWGiKrZu1f7ccgQsTnMwZAhQH4+cOSI6CREdDuWGyKqJjIS6NIF8PUVncT09e4NODtz1xSRqbEVHYCIBAkJqbFIkoCoP3/GQ01igZCvBYQyLwoFEBysLTdz5ohOQ0S3CN1ys2TJEtx3331wcXGBt7c3xo8fj+Tk5Ls+7tdff0WnTp1gb2+Prl27YseOHUZIS2T5zpb6I63Mh4eA10NoKHDgAFBSIjoJEd0itNzs27cP4eHh+PPPPxEZGQm1Wo2hQ4eiuLi41sfExcVh0qRJeOaZZ3D06FGMHz8e48ePx4kTJ4yYnMgyReX1hEKmxkDXY6KjmI3QUKC8XHvSQyIyDUJ3S+3cubPa/bVr18Lb2xsJCQkYOHCgzsd8/vnnGD58OF5//XUAwKJFixAZGYmvvvoKK1eurLF+WVkZysrKqu4XFhYCANRqNdRqcaeVv/XaIjOYIo6LbgYZFzu7Got2F/RCX7fTUDpqoEbNr5satUJR7aPxA6jRvj3g62uLXbs0CA7WiMnxL/w50o3jopu5jEt98skkyXROHp6SkoL27dsjKSkJXbp00blOixYtMHv2bLzyyitVy+bPn4/Nmzfj2LGaf20uWLAA7777bo3l69evh6Ojo96yE5m7ykoZnnhiBMaNS8HEiWdFxzErS5cGIi3NBZ99tk90FCKLVVJSgsmTJ6OgoAAqleqO65rMhGKNRoNXXnkF/fv3r7XYAEBmZiZ8fHyqLfPx8UFmZqbO9efMmYPZs2dX3S8sLIS/vz+GDh1618ExJLVajcjISISFhUEh6q9OE8Rx0c0g4zJ6dLW7B/M7oaRkLGacX4k+X5/Rz2sYmFqhQOT06QhbtQoKEX91RkQAALKzZZg+3QZ9+oyEp6fxY/wbf45047joZi7jcmvPS12YTLkJDw/HiRMncODAAb0+r1KphFKprLFcoVCYxD+iqeQwNRwX3fQ6LuXl1e7GXOsGlc0N9LU/Adty09i9UlcKtRqKf70f47yw9t9i2DDtkWYHDijw0EPGj1Eb/hzpxnHRzdTHpT7ZTOI8NzNnzkRERASio6PRvHnzO67r6+uLrKysasuysrLgy5NyEDVKZF4vhLglwlZuXsXGFDRvDnTsyPPdEJkKoeVGkiTMnDkTmzZtwt69e9G6deu7PiYoKAh79uyptiwyMhJBQUGGiklk8W5U2CO+sDPCPHgIeEOFhrLcEJkKoeUmPDwcP/74I9avXw8XFxdkZmYiMzMTpaWlVes8+eSTmHPb2bFefvll7Ny5E59++inOnDmDBQsW4PDhw5g5c6aIt0BkEfYXdINaUvD8No0QGgqcPw9cvCg6CREJLTcrVqxAQUEBgoOD4efnV3XbsGFD1TqpqanIyMiout+vXz+sX78e33zzDQICArBx40Zs3rz5jpOQiejOIvN6obnyGjo4pImOYraCgwG5HPjXhmUiEkDohOK6HIUeExNTY9nDDz+Mhx9+2ACJiKxTVF4gwtwTIJOJTmK+3NyA++7T7pqaNk10GiLrZhITiolInMwydyQVt+UuKT0IDdVuudFwTjaRUCw3RFZuT35PAMAQ9yOCk5i/0FAgOxs4flx0EiLrxnJDZOWi8gLRzek8fOzyREcxe0FBgIMD590QiWYyJ/EjIuOTJCAytxcmekeLjmKeQkKq3VUCGGj/IaIWS3g14k3twmiOLZGxccsNkRVLLvHH1fImCHM/LDqKxRjifgSx+d1QpjHdM70SWTqWGyIrFpXXEwqZGgPckkRHsRih7gko0Tjgz8LOoqMQWS2WGyIrFpnXC/1UJ+Fkc1N0FIsR4HwenrYFiMoLFB2FyGqx3BBZqQqNHNH53RHmwV1S+iSXSRjifgRReT1FRyGyWiw3RFbqUFEnFFU6IZSHgOtdqHsC/irshIIKJ9FRiKwSyw2RlYrM6wVXmxvo6ZwsOorFCXVPgAY2iMnvLjoKkVViuSGyUlF5gRjsfgS2cp5OV99aO2Sijf1VzrshEoTlhsgKFRUB8YX3cpeUAYW6H8EelhsiIVhuiKxQbCxQIdny/DYGFOqegNMlrXD1qugkRNaH5YbICkVFAf7KLLRz4P+8hhLidhQyaHgpBiIBWG6IrFBMDDDY7ShkMtFJLJeXXSG6O6cgKkp0EiLrw3JDZGVyc4Fjx4Bgt0TRUSxeqPsRREVpr+FFRMbDckNkZWJjtf/ZstwY3hD3I8jIAJJ5tD2RUbHcEFmZmBigVSuglUOW6CgWr78qCba22jEnIuNhuSGyMjExQHCw6BTWwdn2Ju67D4iOFp2EyLqw3BBZkZwc7XybkBDRSaxHcLC2UHLeDZHxsNwQWZHYWO3HQYPE5rAmwcHAtWvAmTOikxBZD5YbIisSEwO0bg20bCk6ifXo3x+cd0NkZLaiAxCRAdSy3yn60GqEuCQDIR8bOZD1cnICevfWzrt54QXRaYisA7fcEFmJ7HIVkorb8hBwATjvhsi4WG6IrMS+gu4AeH4bEYKDgevXgdOnRSchsg4sN0RWIiY/AG3tr8Lf/rroKFanXz9AoeC8GyJjYbkhshIx+d251UaQ2+fdEJHhsdwQWYHr5a44UdwGIe6JoqNYLc67ITIelhsiK7AvPwAAMMg1UWwQKxYcDGRnA6dOiU5CZPlYboisQEx+d7RzuILm9tmio1gtzrshMh6e54bICkTn90AI59uI8fc5hxwB9HH8HNEL8hC+cUH1dTgZh0ivuOWGyMJdK3fDqZJWnExsAoLdErGvIAAaSSY6CpFFY7khsnAx+d0B8Pw2piDY7Riy1W44VczrXxAZEssNkYWLye+ODg6paKrMER3F6gWpTkIhU1cVTiIyDJYbIgsXkx+AYLdjomMQAEebMvRVnUJ0fg/RUYgsGssNkQXLLHPHac63MSnBbsewL5/zbogMieWGyILxelKmJ9gtETkVrjhZ3Ep0FCKLxXJDZMFi8gPQ0SEVfspc0VHob0Gqk7CTlXPeDZEBsdwQWbDovB4IcT8qOgbdxsGmHH1VpxHNckNkMCw3RBYqo8wDyaUtOJnYBAW7JXLeDZEBsdwQWSheT8p0BbslIrfCFSeKW4uOQmSRWG6ILFR0fg/c43gJvso80VHoX/qqTnHeDZEBsdwQWSie38Z0OdiUI0h1ivNuiAykQeUmmhd5IzJp6WWeOFvagoeAmzDOuyEynAaVm+HDh6Nt27Z47733kJaWpu9MRNRIvJ6U6Qt2S0RehQpJxW1ERyGyOA0qN1evXsXMmTOxceNGtGnTBsOGDcMvv/yC8vJyfecjogaIye+Ozo6X4G2XLzoK1aKv6hSUnHdDZBANKjdeXl6YNWsWEhMTcfDgQXTo0AEzZsxA06ZN8dJLL+HYMe7nJxIpOr87t9qYOHsbNYJcTyI6r7voKEQWp9ETigMDAzFnzhzMnDkTN27cwJo1a9CzZ08MGDAAJ0+e1EdGIqqHK1eAlNLmCHHjyftMXbBbImILukGjEZ2EyLI0uNyo1Wps3LgRI0eORMuWLbFr1y589dVXyMrKQkpKClq2bImHH35Yn1mJqA727dN+HMQjpUxesNsx5FWocPy46CRElsW2IQ968cUX8dNPP0GSJDzxxBP46KOP0KVLl6qvOzk54ZNPPkHTpk31FpSI6iY6GrjX8SKa2BWIjkJ30cfl73k3MXbo3l10GiLL0aAtN6dOncKXX36J9PR0LFu2rFqxucXLy4uHjBMJEBMDXk/KTFTNu+GvSiK9alC5mT9/Ph5++GEolcpqyysqKhAbGwsAsLW1xaBBgxqfkIjqLC0NOH8eCOFkYrMR4paI2FigslJ0EiLL0aByExISgtzc3BrLCwoKEBIS0uhQRNQwMTHajwNdOd/GXAS7JSI/H5x3Q6RHDSo3kiRBJqt5Vs2cnBw4OTk1OhQRNUx0NNCtG+BlVyg6CtVRH9Vp2NuDu6aI9KheE4onTJgAAJDJZHjqqaeq7ZaqrKzE8ePH0a9fP/0mJKI6i44Gxo4FwK0AZkMpV6NfP+2/3ezZotMQWYZ6lRtXV1cA2i03Li4ucHBwqPqanZ0d+vbti+nTp+s3IRHVyaVL2ltICFhuzExICPDxx0BFBWDboGNYieh29fox+u677wAArVq1wmuvvdboXVCxsbH4+OOPkZCQgIyMDGzatAnjx4+vdf2YmBidc3oyMjLg6+vbqCxE5i4mBpDJgIEDAXwuOg3VR0gIMG8ekJgI9OolOg2R+WvQ3wjz58/Xy4sXFxcjICAATz/9dNUur7pITk6GSqWquu/t7a2XPETmLCYGCAgAPDxEJ6H6uu+tMDjKtyJ64vfo1WKD7pU4KYeozupcbgIDA7Fnzx64u7ujR48eOicU33LkyJE6PeeIESMwYsSIukao4u3tDTc3tzqtW1ZWhrKysqr7hYXaiZZqtRpqtbrer60vt15bZAZTxHHR7W7jIklAdLQtxo/XQK3WAHZ2xownjFqhqPbRXMkA9HM/hb2FgXjFbpPulerxM8GfI904LrqZy7jUJ1+dy824ceOqJhDfadeRMXTv3h1lZWXo0qULFixYgP79+9e67pIlS/Duu+/WWL579244OjoaMmadREZGio5gkjguutU2LllZjkhNDYOz82Hs2JEJzJhh5GRiRVrAXD/fjQ7YuLEDtj0XDhsbqeYKO3bU+zn5c6Qbx0U3Ux+XkpKSOq8rkyRJx0+R8clksrvOuUlOTkZMTAx69eqFsrIyrF69GuvWrcPBgwcRGBio8zG6ttz4+/sjOzu72q4tY1Or1YiMjERYWBgUZv5Xpz5xXHS727isXSvDc8/ZIDOzAu7uAEaPNn5IAdQKBSKnT0fYqlVQmPhfnXfzZ/49GPjXMhzo/RJ6uyXXXCEios7PxZ8j3TguupnLuBQWFsLLywsFBQV3/f/brObld+zYER07dqy6369fP5w/fx5Lly7FunXrdD5GqVTWOJMyACgUCpP4RzSVHKaG46JbbeOyfz/Qowfg7f3318rLjZxMLIVaDYWZv+e+9ifgJC/F/utd0N8xqeYKDfh54M+RbhwX3Ux9XOqTrc7lxt3d/Y7zbG6n6+zFhtK7d28cOHDAaK9HZGq0822ARx4RnYQaQyGvxAC344jJ7443W/4kOg6RWatzuVm2bJkBYzRcYmIi/Pz8RMcgEubCBeDKlb/Pb0NmLdgtEYsuPQm1xgYKOS82RdRQdS43U6ZM0fuL37hxAykpKVX3L168iMTERHh4eKBFixaYM2cOrl69ih9++AGAtmC1bt0a9957L27evInVq1dj79692L17t96zEZmL6GhALgcGDBCdhBorxC0Rb2qew6GiTujnelJ0HCKzVedyU1hYWDWB59bh1LWp60Tdw4cPVzsp3+y/zz0+ZcoUrF27FhkZGUhNTa36enl5OV599VVcvXoVjo6O6NatG6KionixTrJqMTFAYCDw9wnEyYwFOp+Fi00xYvK7s9wQNUK95txkZGRUnWNG1/ybWxfUrKys2+bU4OBg3OlgrbVr11a7/8Ybb+CNN96oa2Qii3drvs1jj4lOQvpgK9dgoOtxROd3x1st/ys6DpHZqnO52bt3Lzz+PvVpNM+USWQSUlKA9HQgOFh0EtKXYLdEvHNpKso0Cijl5n14O5EodS43gwYN0vk5EYkTHQ3Y2AD33y86CelLiHsiSi/Y41BhR9zvdkJ0HCKz1ODz3OTl5eHbb7/F6dOnAQCdO3fG1KlTq7buEJHhxcQAPXsCAs9HSXrW3TkFrjY3EJ3fg+WGqIHkDXlQbGwsWrVqhS+++AJ5eXnIy8vDF198gdatWyM2NlbfGYlIh1vzbTif3rLYyDQY6HYM0fndRUchMlsN2nITHh6OiRMnYsWKFbCxsQEAVFZWYsaMGQgPD0dSko6zaxKRXp09C2Rmcr6NJQpxS8RbF6fhZqUC9jacd0NUXw3acpOSkoJXX321qtgAgI2NDWbPnl3tvDVEZDicb2O5QtyO4qZGiYNFnUVHITJLDSo3gYGBVXNtbnf69GkEBAQ0OhQR3V1MDHDffYCzs+gkpG/dnC/A3bYQ0XndRUchMkt13i11/Pjxqs9feuklvPzyy0hJSUHfvn0BAH/++SeWL1+ODz74QP8piagaSdKWm6efFp2EDEEukzDI7Rhi8rsD+F50HCKzU+dy0717d8hksmon3dN1Qr3Jkydj4sSJ+klHRDWFhOBMcQtkZX2P4O2vA/GHRSciAwhxS8Tr559DaaUdHGzM+4rnRMZW53Jz8eJFQ+YgonqIzu8BW1kF+rvyUGFLFeyWiHLJDvGF92Kw+1HRcYjMSp3LTcuWLQ2Zg4jqISa/O3q7nIGTzU3RUchAujhdhKdtAWLyu7PcENVTg0/iBwCnTp1CamoqysurbzIdO3Zso0IRUe0kCYjJD8CzfhGio5AByWUSgt0Seb4bogZoULm5cOECHnjgASQlJVWbh3PrYpp1vXAmEdXfqZJWuK52R7BbougoZGAh7omYlTIDJZVKOIoOQ2RGGnQo+Msvv4zWrVvj2rVrcHR0xMmTJxEbG4tevXohJiZGzxGJ6HbRed2hkKnRz/Wk6ChkYMFuiVBLCvxR0EV0FCKz0qByEx8fj4ULF8LLywtyuRxyuRz3338/lixZgpdeeknfGYnoNjH53dFHdRqONmWio5CBdXa8BG9F7t+HhBNRXTWo3FRWVsLFxQUA4OXlhfT0dADaScfJycn6S0dE1Wg02nITwl1SVkEmA4J5nSmiemtQuenSpQuOHTsGAOjTpw8++ugj/PHHH1i4cCHatGmj14BE9I8TJ4CcCleEuPHoGWsR7JaIQ0WdcOOG6CRE5qNB5Wbu3LnQaDQAgIULF+LixYsYMGAAduzYgS+++EKvAYnoH7GxctjJytFXdUp0FDKSELejqJBs8ccfopMQmY8GHS01bNiwqs/btWuHM2fOIDc3F+7u7lVHTBGR/kVHyxCkOsUz1lqRjo5p8LXLQXS0J2771UtEd9CgLTe3S0tLQ1paGjw8PFhsiAyoslKGfftkCPPg5RasiXbeTSKio0UnITIfDSo3FRUVmDdvHlxdXdGqVSu0atUKrq6umDt3LtRqtb4zEhGAc+fcUFgoQ6j7EdFRyMhC3BKRkAAUFopOQmQeGrRb6sUXX8Rvv/2Gjz76CEFBQQC0h4cvWLAAOTk5WLFihV5DEhFw7FgTuLpK6OnMIxKtzRD3I6is1F4JnieAJ7q7BpWb9evX4+eff8aIESOqlnXr1g3+/v6YNGkSyw2RARw/3gSDBkmwLdSIjkJG1tYhHa1bA1FRLDdEddGg3VJKpRKtWrWqsbx169aws7NrbCYi+pcbN4DkZA+Ehkqio5AgoaFAZKToFETmoUHlZubMmVi0aBHKyv45Q2pZWRnef/99zJw5U2/hiEhr/34ZKirkGDyYW22sVVgYcOYMcOWK6CREpq/Ou6UmTJhQ7X5UVBSaN2+OgIAAAMCxY8dQXl6OIUOG6DchEWHvXhm8vErQvr1CdBQSZPBg7ZFTUVHAU0+JTkNk2upcblxdXavdf/DBB6vd9/f3108iIqphzx45AgKuQyZrKjoKCeLpCQQGandNsdwQ3Vmdy813331nyBxEVIvMTODECRmGDr0OgOXGmoWFAWvWAJKk3YpDRLo16iR+169fx4EDB3DgwAFcv35dX5mI6DZ79mg/duuWLTYICRcWBly7BiQliU5CZNoaVG6Ki4vx9NNPw8/PDwMHDsTAgQPRtGlTPPPMMygpKdF3RiKrFhUFdO0qwc2t7O4rk0Xr1w+wt+dRU0R306ByM3v2bOzbtw/btm1Dfn4+8vPzsWXLFuzbtw+vvvqqvjMSWS1J0pabIUN4lBRpi83AgdrvCSKqXYPKzf/+9z98++23GDFiBFQqFVQqFUaOHIlVq1Zh48aN+s5IZLXOntUe+jt4MM9vQ1qhocC+fUAZN+QR1apB5aakpAQ+Pj41lnt7e3O3FJEeRUYCCgUwYADLDWmFhQGlpUBcnOgkRKarQeUmKCgI8+fPx82bN6uWlZaW4t1336261hQRNV5UlHaehZOT6CRkKrp1A5o04a4pojtp0LWlli1bhuHDh9c4iZ+9vT127dql14BE1qqiAoiOBl5/XXQSMiVyOTBkiHar3vvvi05DZJoaVG66du2Kc+fO4b///S/OnDkDAJg0aRIee+wxODg46DUgkbU6fBgoLNTOsSBCSEjVp2EZI7Ah+TXk3j8eHoqif9aJjhYQjMj01LvcqNVqdOrUCREREZg+fbohMhERtLsdXF2BXr20R00R3RLqngAJckTn98CDTWJFxyEyOfWec6NQKKrNtSEiw4iM1P6xbtug7atkyVrYX0MHh1RE5vYUHYXIJDVoQnF4eDg+/PBDVFRU6DsPEQG4cQOIj+cuKapdmHsCovJYboh0adDfhIcOHcKePXuwe/dudO3aFU7/OpTjt99+00s4Imu1fz+gVrPcUO1C3ROwPP0BXCz1RWuHTNFxiExKg8qNm5tbjauCE5H+REUBzZsDHTqITkKmKsQ9ETaoRGReLzzrECE6DpFJqVe50Wg0+Pjjj3H27FmUl5dj8ODBWLBgAY+QItKzqCjtydp45WeqjattMXqrTiMqryeebcpyQ3S7es25ef/99/HWW2/B2dkZzZo1wxdffIHw8HBDZSOySllZwPHj3CVFdxfqfgR78nqgUmrQ9Ekii1Wvn4gffvgBX3/9NXbt2oXNmzdj27Zt+O9//wuNhhf1I9KLkBDsCXkPADBk+QTt4VIhIcDo0dqv3/pIBCDM/TByK1xxtKid6ChEJqVe5SY1NRUjR46suh8aGgqZTIb09HS9ByOyVlF5PdHV6Tx87PJERyET11d1Cs42JTxqiuhf6lVuKioqYG9vX22ZQqGAWq3WaygiayVJQFReIMLcE0RHITOgkFdikOsxROb1Eh2FyKTUa0KxJEl46qmnoFQqq5bdvHkTzz//fLXDwXkoOFHDnC31R1qZD0JZbqiOwjwS8Mb5Z1FSqYSj6DBEJqJe5WbKlCk1lj3++ON6C0Nk7aLyekIhU2OA63HRUchMhLkfRrk0EwcKumKo6DBEJqJe5ea7774zVA4ignaXVJDqFJxteYkTqpt7HC+jqd11ROb1ZLkh+huPHyQyERUVQHReD+6SonqRybSHhHNSMdE/WG6ITERCAlBQ6Yww98Oio5CZCXVPQOKN9rh2TXQSItPAckNkIiIjAZXNDfRySRYdhczMra19e/YIDkJkIlhuiEzE778DQ9yPwFbOk2JS/fgpc9HF6QKiokQnITINLDdEJiA7G4iPB0Z7/ik6CpmpUPcEREZqz5VEZO1YbohMwM6d2v+URnqw3FDDDHU/jLQ04PRp0UmIxGO5ITIBERFAr16Ar5KXXKCGCXZLhIMDsH276CRE4gktN7GxsRgzZgyaNm0KmUyGzZs33/UxMTExCAwMhFKpRLt27bB27VqD5yQyJLVau+WG18SkxnCwKUdoqLYoE1k7oeWmuLgYAQEBWL58eZ3Wv3jxIkaNGoWQkBAkJibilVdewbRp07Br1y4DJyUynLg4oKAAGDVKdBIyd6NHA3/8AeTmik5CJFa9zlCsbyNGjMCIESPqvP7KlSvRunVrfPrppwCAe+65BwcOHMDSpUsxbNgwnY8pKytDWVlZ1f3CwkIAgFqtFnrBz1uvzYuOVmeN47J1qxy+vnJ07VoBtZ2dznXUCkW1j6TFcalu6FA1KisV2LFDAw8P6/o5qgtr/P1SF+YyLvXJJ5Mk05hbL5PJsGnTJowfP77WdQYOHIjAwEAsW7asatl3332HV155BQUFBTofs2DBArz77rs1lq9fvx6OjrzMHIk3c+ZgdOyYixdfTBQdhSzA7NmD0KzZDbz6Ks90TZalpKQEkydPRkFBAVQq1R3XFbrlpr4yMzPh4+NTbZmPjw8KCwtRWloKBweHGo+ZM2cOZs+eXXW/sLAQ/v7+GDp06F0Hx5DUajUiIyMRFhYGBf/qrGJt43LhAnDligKffeaAkSOb1jrxRq1QIHL6dIStWgWFif91ZUwcl3+JiMChQ3IsX+6KysojGD481Cp+jurK2n6/1JW5jMutPS91YVblpiGUSiWUSmWN5QqFwiT+EU0lh6mxlnHZvRtQKIDhw22hUAAoL7/j+gq1Goq7rGONOC5/Uygwbhzw/vvA6dMeGDPGOn6O6stafr/Ul6mPS32ymdWh4L6+vsjKyqq2LCsrCyqVSudWGyJTFxEBBAcDLi6ik5Cl6NkT8PWVcPiwz91XJrJQZrXlJigoCDt27Ki2LDIyEkFBQYISEdVDSEi1uzcq7BHzxxZ81PYbIOR/gkKRpZHLgREjJERG+oqOQiSM0C03N27cQGJiIhITEwFoD/VOTExEamoqAO18mSeffLJq/eeffx4XLlzAG2+8gTNnzuDrr7/GL7/8glmzZomIT9QoUXk9US7ZYbRnvOgoZGFGjtTgyhUXpKSITkIkhtByc/jwYfTo0QM9evQAAMyePRs9evTAO++8AwDIyMioKjoA0Lp1a2zfvh2RkZEICAjAp59+itWrV9d6GDiRKYvICUJHh1S0dUgXHYUszJAhEmxtK7Fjh1nNPCDSG6G7pYKDg3GnI9F1nX04ODgYR48eNWAqIsPTSDLsyO2Dyd57REchC+TsDHTrlo0dO5rg1VdFpyEyPtZ6IgGO3miPjHIv7pIig+nVKwuxsTLU4+hZIovBckMkwPacvnC1uYH+ridERyEL1bNnJioqZODVacgasdwQCRCR0xfDPA5BIa8UHYUslI9PKe69V+KFNMkqsdwQGVlWuTsOFd2DUZ5/io5CFm7UKA127AAq2aHJyrDcEBnZjpw+kEGDER4HRUchCzdqlITsbOCvv0QnITIulhsiI9ue0xd9VKfRxE73xV6J9KV3bwmensC2baKTEBkXyw2REZVrbLE7rxePkiKjsLEBRo4E592Q1WG5ITKi/QXdUFTphFEenG9DxjFmDJCUBFy+LDoJkfGw3BAZUUROXzSzu44A5/Oio5CVGDoUsLUFtm8XnYTIeFhuiIwoIicIozz/hEwmOglZpJAQ7W30aO390aPhOj4EA52PYNu8gzUu3kpkqVhuiIzkbElzpJQ253wbMrrRnvHYm9cDNyrsRUchMgqWGyIjicgJglJWjsHuvDYaGdcYzziUS3bYk99TdBQio2C5ITKS7Tl9Mdj9CJxsboqOQlamnWM6OjqkYlt2kOgoREbBckNkBAUFQGxBN4zy5In7SIzRnvHYntsXGo3oJESGx3JDZASRkUCFZItRHpxvQ2KM9oxHZrknjhwRnYTI8FhuiIxgyxbgXseLaOWQJToKWan+rifgZlvEE/qRVWC5ITKwmze15eZh7xjRUciKKeSVGO7xFy/FQFaB5YbIwH7/HSgqAiY2iRYdhazcaM8/ceQIcOWK6CREhsVyQ2RgGzYAAQFAJ6c00VHIyo32jIedHfDLL6KTEBkWyw2RARUXa6/IPHGi6CREgKttMUaOBH7+WXQSIsNiuSEyoIgIoKSE5YZMx6RJwKFDQEqK6CREhsNyQ2RAGzYA990HtGkjOgmR1ujRgJOT9nuTyFKx3BAZSGEhsGMHt9qQaXF0BMaNA376SXQSIsNhuSEykC1bgLIy4JFHRCchqu7RR4GTJ4ETJ0QnITIMlhsiA9mwAejfH/D3F52EqLqhQwE3N04sJsvFckNkALm5wO7d3CVFpkmpBB58ULtrSpJEpyHSP5YbIgPYtAmoqAAeekh0EiLdHn0UuHABOHxYdBIi/WO5ITKADRuA4GDAz090EiLdQkIAHx9OLCbLZCs6AJGluXYN2LsXWL5cdBIiHUJCAAA2AB62fREblg/AJ0cmQi67bf9UNC8VQuaNW26I9Ox//9N+fPBBsTmI7maS916klzfBgYKuoqMQ6RXLDZGebdgAhIYCXl6ikxDdWV/VKbRQZuKna4NFRyHSK5YbIj1KTwdiY3mUFJkHuUzCRO8YbLw+CGqNjeg4RHrDckOkR7/+CtjaAuPHi05CVDeTvPcgW+2GPXmBoqMQ6Q3LDZEebdgADB8OuLuLTkJUN92dU9DBIRU/c9cUWRCWGyI9uXwZiI/nLikyLzKZdmLxpuz7cbNSIToOkV6w3BDpyS+/APb2wNixopMQ1c+j3ntRWOmM33P7iI5CpBcsN0R6smEDMGoU4OIiOglR/XRySkN353PcNUUWg+WGSA9SUoCEBO6SIvP1qPdebMsJwo0Ke9FRiBqN5YZIDzZsAJyctFtuiMzRxCbRKNXYY2tOf9FRiBqN5YZIDzZs0M61cXQUnYSoYVo5ZCFIdQI/XwsRHYWo0VhuiBrp1CkgKYm7pMj8PeodjZ25vZGbKzoJUeOw3BA10oYNgEqlPb8NkTl7pEk0KiU5Nm0SnYSocVhuiBqhogJYswZ45BFAqRSdhqhxfJV5CHY7hp9+Ep2EqHFYbogaYft24MoV4IUXRCch0o9J3nsQHa29ThqRuWK5IWqEFSuA3r2BQF6WhyzEw977YG8PrF4tOglRw9mKDkBkrs6fB3btAr77DkAIjzAhy+BqW4zHHwf+8x9gzhxAwSsykBnilhuiBvrPf7QXyORRUmRpwsO1u6W2bBGdhKhhWG6IGuDmTe1E4qeeAhwcRKch0q9u3YD77we+/lp0EqKGYbkhaoCNG4GcHOD550UnITKMGTOA6GjteZyIzA3LDVEDrFgBDBkCdOggOgmRYTz4IODjw603ZJ5Ybojq6fhxIC6Oh3+TZbOzA6ZPB374ASgqEp2GqH5YbojqacUKwM9Pey0pIkv27LNAcTHw44+ikxDVDw8FJ7qb2w7zLqpwwI/xGzG7+a9QDF0rLhOREfj7A+PGAcuXa+eXyWSiExHVDbfcENXDj1lhKK1UYrpfhOgoREYRHg6cPAns3y86CVHdccsNUR1JErAifSzGeMWhuX226DhEhnPb1srBEtDR4Xssf+g8Bt678J91oqMFBCOqG265IaqjuMIuSCpuixeabhUdhchoZDJgRrMt+C17ADLKPETHIaoTkyg3y5cvR6tWrWBvb48+ffrgr7/+qnXdtWvXQiaTVbvZ29sbMS1ZqxVXx6Kt/VWEuieIjkJkVFN8d0EpU+ObjNGioxDVifBys2HDBsyePRvz58/HkSNHEBAQgGHDhuHatWu1PkalUiEjI6PqdvnyZSMmJmt0vdwVv14fhOebboVcJomOQ2RUrrbFeNwnEt+kj4ZaYyM6DtFdCS83n332GaZPn46pU6eic+fOWLlyJRwdHbFmzZpaHyOTyeDr61t18/HxMWJiskbfZY6ADMBUv52ioxAJMaPZFqSXN8GW7P6ioxDdldAJxeXl5UhISMCcOXOqlsnlcoSGhiI+Pr7Wx924cQMtW7aERqNBYGAgFi9ejHvvvVfnumVlZSgrK6u6X1hYCABQq9VQq9V6eif1d+u1RWYwRaY4LhqFEv/JGIOHfGOhcroJNeyMnkH996WZ1bxEczUcF90MMS73eFxBf7cT+CpjAsY1/xMwoZ/RujLF3y+mwFzGpT75ZJIkCdvGnp6ejmbNmiEuLg5BQUFVy9944w3s27cPBw8erPGY+Ph4nDt3Dt26dUNBQQE++eQTxMbG4uTJk2jevHmN9RcsWIB33323xvL169fD0dFRv2+ILNKRI95YuDAIH3wQi06d8kTHIRJm//5m+PTTXvjyy73w9+dpi8m4SkpKMHnyZBQUFEClUt1xXbMrN/+mVqtxzz33YNKkSVi0aFGNr+vacuPv74/s7Oy7Do4hqdVqREZGIiwsDAr+1VnFFMdlQrPDSL3pjUN9Zwg7iZlaoUDk9OkIW7UKChP/68qYOC66GWpcyjW2aBu7DhN8DuDz8yP09rzGYoq/X0yBuYxLYWEhvLy86lRuhO6W8vLygo2NDbKysqotz8rKgq+vb52eQ6FQoEePHkhJSdH5daVSCaVSqfNxpvCPaCo5TI2pjEtqKrDjem983eFz2KnLRceBQq2Golx8DlPDcdFN3+OiQDmm+0Xg8ysP4sObCri46O2pjcpUfr+YGlMfl/pkEzqh2M7ODj179sSePXuqlmk0GuzZs6falpw7qaysRFJSEvz8/AwVk6zYN98ATjY38Zh3pOgoRCbhOb9tKKm05/WmyKQJP1pq9uzZWLVqFb7//nucPn0aL7zwAoqLizF16lQAwJNPPlltwvHChQuxe/duXLhwAUeOHMHjjz+Oy5cvY9q0aaLeAlmo3Fzgyy+BaX474Gx7U3QcIpPQ3D4b47z+wPLl2rN2E5ki4ZdfmDhxIq5fv4533nkHmZmZ6N69O3bu3Fl1eHdqairk8n86WF5eHqZPn47MzEy4u7ujZ8+eiIuLQ+fOnUW9BbJQn34KVFQAb7ZYLzoKkUkJb7YZQ44NxM6dwAjzm3pDVkB4uQGAmTNnYubMmTq/FhMTU+3+0qVLsXTpUiOkImt2/Trw+efAiy8C3gfzRcchMikhbkdx//3A228Dw4YBcuH7AIiq47ckkQ4ffqj9hf3666KTEJkemQxYvBg4ehT47TfRaYhqYrkh+peMDGD5cmDWLMDTU3QaItM0YIB2q828eUBlpeg0RNWx3BD9y5IlgL29ttwQUe3efx84cwY8copMDssN0W3S0oD//Ad47TXAzU10GiLT1rMn8OCDwIIFAE8zRKaE5YboNu+9B7i4AC+9JDoJkXlYuBC4fBlYvVp0EqJ/sNwQ/e3CBWDNGuDNN2G2Z14lMrbOnYEnngAWLQJKSkSnIdJiuSH626JFgJcXMGOG6CRE5mX+fCA7G/jqK9FJiLRYbogAnD0L/PADMGcOwIvFE9VPmzbA9OnaUygUFIhOQ8RyQwRAOyGyaVPg2WdFJyEyT3PnandLffaZ6CREJnKGYiJhQkJw4kYr/Hz4W3zdfhnsR2wTnYjILDVtCsycqS03L76o3cVLJAq33JDVW3DpKbS0z8LTfr+LjkJk1v7v/7RnL/7gA9FJyNqx3JBVSyxqi/9lD8I7LX+AnbxCdBwis+blBcyerZ1YfPWq6DRkzVhuyKq9c2kq2juk4Qmf3aKjEFmE2bMBJyftOaOIRGG5Iat18CCwLac/5rf6AbZyjeg4RBZBpdIedbh6tfbcUUQisNyQVdJogFdfBTo7XsKj3ntFxyGyKOHhQJMm2qMQiURguSGr9OWXwB9/ACs6LIWNjFttiPTJwUF7tfAffwQOHxadhqwRDwUnq5OSot1s/uKLwMCk46LjEJmnkJA7fnmaxgare0RhyhQgIQGwtzdSLiJwyw1ZGY0GeOYZwM8PWLJEdBoiy6WQV+L777V/TLzzjug0ZG1YbsiqfP01EBsLfPut9ogOIjKcLl20Vw3/5BMgLk50GrImLDdkNS5c0J5kLDwcCA4WnYbIOrz2GtCnDzBlClBcLDoNWQuWG7IKt3ZHeXvz7KlExmRjA3z/vfakfnPmiE5D1oLlhqzCypVATIx2d5Szs+g0RNalQwftHxVffgns5ZkXyAhYbsjiXbwIvPEG8PzzwODBotMQWaeZM7W7g59+GigsFJ2GLB0PBSfzdJfDUAEA0dFVu6M8PYGPPjJ8LCLSTS4H1qwBunXTnkBz1SrRiciSccsNWbRvvgGio7W7o1xcRKchsm6tWwOffaa9NMPvv4tOQ5aMW27IYl3q+yheP7wGz/rtQej7nwHvi05EZGV0bGGdJgG/eXyAaePb4MR9T8P9wDYBwcjSccsNWSRJAqYlvw532yJ83Hal6DhE9DeZDFjd8ROUaOzxUsqLouOQhWK5IYu0Mn0s9uT3xOqOn0BlWyI6DhHdppkyG1+0+xI/Zg3Fb7+JTkOWiOWGLE5kbk+8lPIiXmi6BUM9eNU+IlP0uE8kxnvtx/PPa8+BQ6RPLDdkUY4WtcOEkwsR5n4Yn7f7UnQcIqqFTAas7LAU9vbA8OFAXp7oRGRJWG7IYlwq9cHIpA/QyTEVv3R+Fwp5pehIRHQHPnZ52L0byMgAxowBSrgHmfSE5YYsQo5aheHHP4KT/Ca2d50DZ9uboiMRUR106gRs3w4cPQpMnAio1aITkSVguSGzV1KpxJik95Fb4YKd3d6At12+6EhEVA99+gC//Qbs3AlMn6492pGoMVhuyKxVaOSYdGoejt1oi4iub6GdY7roSETUAMOGaS+w+f33wP/9n+g0ZO54Ej8yW5IEzDz3Mrbn9MWWrnPRW3VGdCQiaoTJk4HsbODllwFvb+C110QnInPFckNma3HqY/hPxlis7vgxRnn+KToOEenBSy8BWVnA669rC86TT4pOROaI5YbM0tqMYZh7cRrebfUdnvHbIToOEenRe+8B165pryDu6QmMGiU6EZkblhsyO9u3ay+tMN0vAvNa/iA6DhE1ho7rT8kArNDIke2+AA+PvQ9RAa+h35GvjJ+NzBYnFJPZkCRg2TJg3DhgtGc8vm6/FDKZ6FREZAi2cg1+umcRerucweikxdi/X3QiMicsN2QWbt4Epk4FZs0CXnkF2HjvfNjKNaJjEZEB2duosaXrXAQ4n8fgwcAXX/Awcaob7pYik3f1KjBhAnDsGLBuHfD44wBCWGyIrIGrbTEiu72G/7vwHF5++REcfD8Kqzp+AkebsporR0cbPyCZJG65IZMWFwf06gWkpwMHDvxdbIjIqtjKNfi03Qr83HkhNmf3R9CR5Thf2lR0LDJhLDdkslavBoKDgXbtgMOHtSWHiKzXRO9oHAycgRKNEr0SVmJHTh/RkchEsdyQyamokOGltr9j+nTgmSZbsEceBp9HQ7RHVdy6EZFV6uJ8CYcCn8cA1+MYnbQYCy89CY3EIwuoOs65IZNy7Rowf34/nL3iipUdPsNzTbeJjkREJsZNUYzNXebh/cuPY/6lp3CoqCPWdVoMN9HByGRwyw2ZBLUa+OorICDAFlevOmN3r/9jsSGiWsllEua1WoeIrm/hQEFX3HdkJSIjRaciU8FyQ0JJEhARAXTtqj3t+tixEpYujUF/95OioxGRGRjpeRCHez4PH0Uehg7VXoDz2DHRqUg0lhsS5vhxYOhQYMwYoFkz4OhR4D//qYS7u45DPImIatHWIR37e7yE334DLl0CevQAnnoKSEsTnYxEYbkho8vMBKZP1/4CSksDtm0DoqKAgADRyYjIXMlkwAMPACdOAMuXA7//DrRvD7z5JpCfLzodGRvLDRlNaSmweLH2F85vv2kvpZCUBIweDV5GgYj0QqEAXngBSEkB3ngD+PJLoG1b7e+bMm4Utho8WooMLjER+O474L//BQoLgRdfBObOBdzdRScjIoty22kiXAAsBPB8gCcWXHoKr84agS/eysaAh9shIABo1UpUSDIGlhvSn9t+seSoVfhvVii+yxyOxBvt4a3IxVTf3Xi+3Ta0PZIOTKjlOezsgBkzjJOXiCxeU2UOvun4KV5pvhHvpk3BTz8NxLofZAh1P4Qpvrsx3usAnGxuVn8QL+Ng9lhuSG8qNHLszrsP32UOx9bsftBAjtGe8Xi31VqM8DgIhbxSdEQislKdnS7jx24f4JcnS3Bj8Vn8eHUIHj/9NpxtSvBQk32Y4rMLA92OQy7jlTktAcsNNUpuLhAbC8TEAL/++TPSy5ugi9MFfNBmFR7ziYK3Xb7oiEREVZydK/BI8514znsrzpc2xY9ZofghcyjWZo5AC2UmHveJwrBYoHdvwN5edFpqKJYbqpfs7H/KzL592sO5AaBlS2C81x+Y6vs7erqc5QRhIjJ5bR3SMb/VD3in5Q+IK+yC7zOH4ev0cVg8CLCTlaO36gwGuh7HANfj6Od6Eirbkn8ezF1XJo3lhmp14waQnAycPg38+X+bsK8gACeK2wAAWtunI9jtGGZ3OoZBrolo5ZAlOC0RUcPIZEB/1xPo73oCKzosxfEbbbC/oBti87thdcZILE59HHJUorvzeQxwPY4Bbknokgy0aaM9OotMj0mUm+XLl+Pjjz9GZmYmAgIC8OWXX6J37961rv/rr79i3rx5uHTpEtq3b48PP/wQI0eONGJiyyFJQEYGcOZMzdvtJ8Bqa98bwW6JeMP/ZwxyO4YW9tfEhSYiMhAbmQY9XFLQwyUFLzX/DZIEnCttjtj8bthf0A1bcvrj86sPAZ0AW1ttwenYEejQQfvx1uc+PjzFhUjCy82GDRswe/ZsrFy5En369MGyZcswbNgwJCcnw9vbu8b6cXFxmDRpEpYsWYLRo0dj/fr1GD9+PI4cOYIuXboIeAemR6PRbnUpKACuX9eWl8zM6h9v//zm3wcK2Moq0N7hCjo5puJxxzR06pSKexwvo6NjWvXNsUREVkImAzo4XkEHxyuY1nQHACCjzANnSlogucRfe/vDH5sj/XHxpi80sAEAqGxuoFkHZ/j4AL6+2tu/P/fx0Z4Sw96eRUjfhJebzz77DNOnT8fUqVMBACtXrsT27duxZs0avPnmmzXW//zzzzF8+HC8/vrrAIBFixYhMjISX331FVauXGnU7P9WUABUjBqHSkkOjSRDJWyqfa6RZKiU5KhY8wNKSoBz59wQHy+DJAEVFdqLR6rV2s/LyrQnvbt585+Pt39eWvpPgSks1H689XlRkXaLzL81aaL9ofLz0/5lMXCg9vNWq+fiHsfLaG2fwSOaiIjuwk+ZCz9lLkLcE6stL9MocL60Kc6WNMfZUn9kFHkgK8cDmYnuOFbugaxyd+RWuNZ4PltbQKXSfXNxARwdtQXoTjeFQnuzta39o42N9iaXa2+3Pq+sBAoLFcjL056NQy7Xli2ZrPbPgZqf3/5RNKHlpry8HAkJCZgzZ07VMrlcjtDQUMTHx+t8THx8PGbPnl1t2bBhw7B582ad65eVlaHsttNSFhQUAAByc3OhVqsb+Q6q69HDFmlp6+6+YtfCW48AcOctIjaogL28HEp5ORxs1FDKy/++XwFn2xI425TC37YYXWxL4GxbAlWTUrj4lcDFtgQqmxJ4KQrRRJkPb7s8KOQa7ZNWAEj7+wYAf28g06YSe9JqNYCSkhLkAFDIeQLtWzguunFcdOO46GbwcZFXwsclDT4uaRgA3f+HlWlscb3cDdllrshSu6OwwgFFFY7aW6UDbuQ4ojDLAUWVjrhaqf1aadN2KC//54/cW3/8SpI+m8T9AEr/vjXekiWVmD5do5fnuqWoqAgAIOn66/3fJIGuXr0qAZDi4uKqLX/99del3r1763yMQqGQ1q9fX23Z8uXLJW9vb53rz58/XwLAG2+88cYbb7xZwC0tLe2u/UL4bilDmzNnTrUtPRqNBrm5ufD09IRM4PazwsJC+Pv7Iy0tDSqVSlgOU8Nx0Y3johvHRTeOi24cF93MZVwkSUJRURGaNm1613WFlhsvLy/Y2NggK6v6YcRZWVnw9fXV+RhfX996ra9UKqFUKqstc3Nza3hoPVOpVCb9zSQKx0U3jotuHBfdOC66cVx0M4dxcXV1rdN6QnfG2tnZoWfPntizZ0/VMo1Ggz179iAoKEjnY4KCgqqtDwCRkZG1rk9ERETWRfhuqdmzZ2PKlCno1asXevfujWXLlqG4uLjq6Kknn3wSzZo1w5IlSwAAL7/8MgYNGoRPP/0Uo0aNws8//4zDhw/jm2++Efk2iIiIyEQILzcTJ07E9evX8c477yAzMxPdu3fHzp074ePjAwBITU2F/LZZ7f369cP69esxd+5cvPXWW2jfvj02b95sdue4USqVmD9/fo1dZtaO46Ibx0U3jotuHBfdOC66WeK4yCSpLsdUEREREZkHngCBiIiILArLDREREVkUlhsiIiKyKCw3REREZFFYbvSkqKgIr7zyClq2bAkHBwf069cPhw4dqvp6VlYWnnrqKTRt2hSOjo4YPnw4zp07d9fnzc/PR3h4OPz8/KBUKtGhQwfs2LHDkG9Frww1LsuWLUPHjh3h4OAAf39/zJo1CzdvXd7cxMTGxmLMmDFo2rQpZDJZjeugSZKEd955B35+fnBwcEBoaGiNMcjNzcVjjz0GlUoFNzc3PPPMM7hx48YdX/fmzZsIDw+Hp6cnnJ2d8eCDD9Y4AaZIIsYlNzcXL774YtX3TosWLfDSSy9VXXPOFIj6frn9+UeMGKHztUUSOS7x8fEYPHgwnJycoFKpMHDgQJSW6ucaTI0lalwyMzPxxBNPwNfXF05OTggMDMT//vc/fb+9BmO50ZNp06YhMjIS69atQ1JSEoYOHYrQ0FBcvXoVkiRh/PjxuHDhArZs2YKjR4+iZcuWCA0NRXFxca3PWV5ejrCwMFy6dAkbN25EcnIyVq1ahWbNmhnxnTWOIcZl/fr1ePPNNzF//nycPn0a3377LTZs2IC33nrLiO+s7oqLixEQEIDly5fr/PpHH32EL774AitXrsTBgwfh5OSEYcOGVStrjz32GE6ePInIyEhEREQgNjYWzz777B1fd9asWdi2bRt+/fVX7Nu3D+np6ZgwYYJe31tjiBiX9PR0pKen45NPPsGJEyewdu1a7Ny5E88884ze319Difp+uWXZsmVCL01TG1HjEh8fj+HDh2Po0KH466+/cOjQIcycObPaKUpEEjUuTz75JJKTk7F161YkJSVhwoQJeOSRR3D06FG9vr8Gu+vVp+iuSkpKJBsbGykiIqLa8sDAQOntt9+WkpOTJQDSiRMnqr5WWVkpNWnSRFq1alWtz7tixQqpTZs2Unl5ucGyG5KhxiU8PFwaPHhwtWWzZ8+W+vfvr983YAAApE2bNlXd12g0kq+vr/Txxx9XLcvPz5eUSqX0008/SZIkSadOnZIASIcOHapa5/fff5dkMpl09epVna+Tn58vKRQK6ddff61advr0aQmAFB8fr+d31XjGGhddfvnlF8nOzk5Sq9WNfyN6ZuxxOXr0qNSsWTMpIyOjxmubEmOOS58+faS5c+fq/00YgDHHxcnJSfrhhx+qLfPw8Ljj725jMo3qaeYqKipQWVkJe3v7assdHBxw4MABlJWVAUC1r8vlciiVShw4cKDW5926dSuCgoIQHh4OHx8fdOnSBYsXL0ZlZaVh3oieGWpc+vXrh4SEBPz1118AgAsXLmDHjh0YOXKkAd6FYV28eBGZmZkIDQ2tWubq6oo+ffogPj4egPYvRzc3N/Tq1atqndDQUMjlchw8eFDn8yYkJECtVld73k6dOqFFixZVz2vKDDUuuhQUFEClUsHWVvg5Te/KkONSUlKCyZMnY/ny5bVeq89UGWpcrl27hoMHD8Lb2xv9+vWDj48PBg0adMffT6bEkN8v/fr1w4YNG5CbmwuNRoOff/4ZN2/eRHBwsMHeT32w3OiBi4sLgoKCsGjRIqSnp6OyshI//vgj4uPjkZGRUfWfypw5c5CXl4fy8nJ8+OGHuHLlCjIyMmp93gsXLmDjxo2orKzEjh07MG/ePHz66ad47733jPjuGs5Q4zJ58mQsXLgQ999/PxQKBdq2bYvg4GCT3S11J5mZmQBQdUbuW3x8fKq+lpmZCW9v72pft7W1hYeHR9U6up7Xzs6uxkVib39eU2aocfm37OxsLFq0qM67bEQz5LjMmjUL/fr1w7hx4/Sc2vAMNS4XLlwAACxYsADTp0/Hzp07ERgYiCFDhtRpbqBohvx++eWXX6BWq+Hp6QmlUonnnnsOmzZtQrt27fT8LhqG5UZP1q1bB0mS0KxZMyiVSnzxxReYNGkS5HI5FAoFfvvtN5w9exYeHh5wdHREdHQ0RowYccf9thqNBt7e3vjmm2/Qs2dPTJw4EW+//TZWrlxpxHfWOIYYl5iYGCxevBhff/01jhw5gt9++w3bt2/HokWLjPjOyNwVFhZi1KhR6Ny5MxYsWCA6jlBbt27F3r17sWzZMtFRTIpGowEAPPfcc5g6dSp69OiBpUuXomPHjlizZo3gdGLNmzcP+fn5iIqKwuHDhzF79mw88sgjSEpKEh0NAMuN3rRt2xb79u3DjRs3kJaWhr/++gtqtRpt2rQBAPTs2ROJiYnIz89HRkYGdu7ciZycnKqv6+Ln54cOHTrAxsamatk999yDzMxMlJeXG/w96YMhxmXevHl44oknMG3aNHTt2hUPPPAAFi9ejCVLllT9MjIXtzb///sopqysrKqv+fr64tq1a9W+XlFRgdzc3Fp3H/j6+qK8vBz5+fm1Pq8pM9S43FJUVIThw4fDxcUFmzZtgkKh0GN6wzHUuOzduxfnz5+Hm5sbbG1tq3bRPfjggyazm+FODDUufn5+AIDOnTtXW37PPfcgNTVVL9kNyVDjcv78eXz11VdYs2YNhgwZgoCAAMyfPx+9evWqdWKzsbHc6JmTkxP8/PyQl5eHXbt21djE6+rqiiZNmuDcuXM4fPjwHTcB9+/fHykpKdX+wz579iz8/PxgZ2dnsPdgCPocl5KSkhpbdm4VQMnMLpXWunVr+Pr6Ys+ePVXLCgsLcfDgQQQFBQEAgoKCkJ+fj4SEhKp19u7dC41Ggz59+uh83p49e0KhUFR73uTkZKSmplY9rykz1Ljcep6hQ4fCzs4OW7durTEnzJQZalzefPNNHD9+HImJiVU3AFi6dCm+++47w70hPTHUuLRq1QpNmzZFcnJyteVnz55Fy5YtDfBO9MtQ41JSUgIAOn8Pm8wfmIInNFuMnTt3Sr///rt04cIFaffu3VJAQIDUp0+fqiOdfvnlFyk6Olo6f/68tHnzZqlly5bShAkTqj3HE088Ib355ptV91NTUyUXFxdp5syZUnJyshQRESF5e3tL7733nlHfW2MYYlzmz58vubi4SD/99FPV87Zt21Z65JFHjPre6qqoqEg6evSodPToUQmA9Nlnn0lHjx6VLl++LEmSJH3wwQeSm5ubtGXLFun48ePSuHHjpNatW0ulpaVVzzF8+HCpR48e0sGDB6UDBw5I7du3lyZNmlT19StXrkgdO3aUDh48WLXs+eefl1q0aCHt3btXOnz4sBQUFCQFBQUZ743fhYhxKSgokPr06SN17dpVSklJkTIyMqpuFRUVxh2AWoj6fvk3mNjRUqLGZenSpZJKpZJ+/fVX6dy5c9LcuXMle3t7KSUlxXhv/g5EjEt5ebnUrl07acCAAdLBgwellJQU6ZNPPpFkMpm0fft24w5ALVhu9GTDhg1SmzZtJDs7O8nX11cKDw+X8vPzq77++eefS82bN5cUCoXUokULae7cuVJZWVm15xg0aJA0ZcqUasvi4uKkPn36SEqlUmrTpo30/vvvm8wv4bowxLio1WppwYIFUtu2bSV7e3vJ399fmjFjhpSXl2ekd1U/0dHREoAat1vvSaPRSPPmzZN8fHwkpVIpDRkyREpOTq72HDk5OdKkSZMkZ2dnSaVSSVOnTpWKioqqvn7x4kUJgBQdHV21rLS0VJoxY4bk7u4uOTo6Sg888ICUkZFhjLdcJyLGpbbXBCBdvHjRSO/8zkR9v/ybqZUbkeOyZMkSqXnz5pKjo6MUFBQk7d+/39Bvt85EjcvZs2elCRMmSN7e3pKjo6PUrVu3GoeGiySTJDPbjk9ERER0B5xzQ0RERBaF5YaIiIgsCssNERERWRSWGyIiIrIoLDdERERkUVhuiIiIyKKw3BAREZFFYbkhIiIii8JyQ0RERBaF5YaIiIgsCssNERERWRSWGyIye9evX4evry8WL15ctSwuLg52dnbYs2ePwGREJAIvnElEFmHHjh0YP3484uLi0LFjR3Tv3h3jxo3DZ599JjoaERkZyw0RWYzw8HBERUWhV69eSEpKwqFDh6BUKkXHIiIjY7khIotRWlqKLl26IC0tDQkJCejatavoSEQkAOfcEJHFOH/+PNLT06HRaHDp0iXRcYhIEG65ISKLUF5ejt69e6N79+7o2LEjli1bhqSkJHh7e4uORkRGxnJDRBbh9ddfx8aNG3Hs2DE4Oztj0KBBcHV1RUREhOhoRGRk3C1FRGYvJiYGy5Ytw7p166BSqSCXy7Fu3Trs378fK1asEB2PiIyMW26IiIjIonDLDREREVkUlhsiIiKyKCw3REREZFFYboiIiMiisNwQERGRRWG5ISIiIovCckNEREQWheWGiIiILArLDREREVkUlhsiIiKyKCw3REREZFH+H1melt48cNPpAAAAAElFTkSuQmCC", "text/plain": [ "
      " ] @@ -1269,13 +1269,7 @@ "Error: 0.08426840630693411\n", "Bias^2: 0.0796891867672603\n", "Var: 0.004579219539673834\n", - "0.08426840630693411 >= 0.0796891867672603 + 0.004579219539673834 = 0.08426840630693413\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ + "0.08426840630693411 >= 0.0796891867672603 + 0.004579219539673834 = 0.08426840630693413\n", "Polynomial degree: 2\n", "Error: 0.10398646080125035\n", "Bias^2: 0.1007711427354898\n", @@ -1311,13 +1305,7 @@ "Error: 0.02760977349102253\n", "Bias^2: 0.022999498260366312\n", "Var: 0.004610275230656212\n", - "0.02760977349102253 >= 0.022999498260366312 + 0.004610275230656212 = 0.027609773491022525\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ + "0.02760977349102253 >= 0.022999498260366312 + 0.004610275230656212 = 0.027609773491022525\n", "Polynomial degree: 8\n", "Error: 0.017355848195593347\n", "Bias^2: 0.010331721306655127\n", @@ -1332,18 +1320,18 @@ "Error: 0.021592704588025025\n", "Bias^2: 0.010516485576645508\n", "Var: 0.011076219011379514\n", - "0.021592704588025025 >= 0.010516485576645508 + 0.011076219011379514 = 0.021592704588025022\n", - "Polynomial degree: 11\n", - "Error: 0.07160048164233104\n", - "Bias^2: 0.014436800088904942\n", - "Var: 0.05716368155342608\n", - "0.07160048164233104 >= 0.014436800088904942 + 0.05716368155342608 = 0.07160048164233102\n" + "0.021592704588025025 >= 0.010516485576645508 + 0.011076219011379514 = 0.021592704588025022\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ + "Polynomial degree: 11\n", + "Error: 0.07160048164233104\n", + "Bias^2: 0.014436800088904942\n", + "Var: 0.05716368155342608\n", + "0.07160048164233104 >= 0.014436800088904942 + 0.05716368155342608 = 0.07160048164233102\n", "Polynomial degree: 12\n", "Error: 0.11547777218872497\n", "Bias^2: 0.01628578269596628\n", @@ -1721,9 +1709,9 @@ "name": "stderr", "output_type": "stream", "text": [ - "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_58812/626635268.py:73: RuntimeWarning: divide by zero encountered in log10\n", + "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_23110/626635268.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_58812/626635268.py:74: RuntimeWarning: divide by zero encountered in log10\n", + "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_23110/626635268.py:74: RuntimeWarning: divide by zero encountered in log10\n", " plt.plot(polynomial, np.log10(testerror), label='Test Error')\n" ] }, @@ -2057,7 +2045,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_58812/3817475779.py:63: RuntimeWarning: divide by zero encountered in log10\n", + "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_23110/3817475779.py:63: RuntimeWarning: divide by zero encountered in log10\n", " plt.plot(polynomial, np.log10(estimated_mse_sklearn), label='Test Error')\n" ] }, @@ -3715,7 +3703,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_58812/4162706317.py:7: UserWarning: set_ticklabels() should only be used with a fixed number of ticks, i.e. after set_ticks() or using a FixedLocator.\n", + "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_23110/4162706317.py:7: UserWarning: set_ticklabels() should only be used with a fixed number of ticks, i.e. after set_ticks() or using a FixedLocator.\n", " cb.ax.set_yticklabels(cb.ax.get_yticklabels(), fontsize=18)\n" ] }, @@ -4021,7 +4009,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_58812/3777801602.py:7: UserWarning: set_ticklabels() should only be used with a fixed number of ticks, i.e. after set_ticks() or using a FixedLocator.\n", + "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_23110/3777801602.py:7: UserWarning: set_ticklabels() should only be used with a fixed number of ticks, i.e. after set_ticks() or using a FixedLocator.\n", " cb.ax.set_yticklabels(cb.ax.get_yticklabels(), fontsize=18)\n" ] }, @@ -4094,7 +4082,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_58812/438060758.py:10: UserWarning: set_ticklabels() should only be used with a fixed number of ticks, i.e. after set_ticks() or using a FixedLocator.\n", + "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_23110/438060758.py:10: UserWarning: set_ticklabels() should only be used with a fixed number of ticks, i.e. after set_ticks() or using a FixedLocator.\n", " cb.ax.set_yticklabels(cb.ax.get_yticklabels(), fontsize=18)\n" ] }, @@ -4175,7 +4163,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_58812/3544313922.py:9: UserWarning: set_ticklabels() should only be used with a fixed number of ticks, i.e. after set_ticks() or using a FixedLocator.\n", + "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_23110/3544313922.py:9: UserWarning: set_ticklabels() should only be used with a fixed number of ticks, i.e. after set_ticks() or using a FixedLocator.\n", " cb.ax.set_yticklabels(cb.ax.get_yticklabels(), fontsize=18)\n" ] }, @@ -4232,7 +4220,7 @@ "output_type": "stream", "text": [ "\r", - " 0%| | 0/10 [00:00" ] @@ -103,7 +103,7 @@ }, { "data": { - "image/png": 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"text/plain": [ "
      " ] @@ -1318,14 +1318,14 @@ "output_type": "stream", "text": [ "(426, 30)\n", - "(143, 30)\n" + "(143, 30)\n", + "Test set accuracy with Logistic Regression: 0.94\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "Test set accuracy with Logistic Regression: 0.94\n", "Test set accuracy with SVM: 0.63\n", "Test set accuracy with Decision Trees: 0.90\n", "Test set accuracy Logistic Regression with scaled data: 0.96\n", diff --git a/doc/LectureNotes/_build/jupyter_execute/chapter8.ipynb b/doc/LectureNotes/_build/jupyter_execute/chapter8.ipynb index ce63c3a03..8dfc5d096 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": [ - "-0.08913527419249101\n", - "3.7127415072708665\n", - "[[ 1.13025431 3.39215451]\n", - " [ 3.39215451 11.15061293]]\n" + "-0.02745698767039481\n", + "3.9527157086177156\n", + "[[0.80249705 2.35440603]\n", + " [2.35440603 7.83541057]]\n" ] } ], @@ -340,10 +340,10 @@ "name": "stdout", "output_type": "stream", "text": [ - "0.09335279187105122\n", - "2.2108106787032815\n", - "[[1. 0.66771869]\n", - " [0.66771869 1. ]]\n" + "0.07408022521552643\n", + "1.9519435372439522\n", + "[[1. 0.56323995]\n", + " [0.56323995 1. ]]\n" ] } ], @@ -397,30 +397,30 @@ "name": "stdout", "output_type": "stream", "text": [ - "[[ 0.35582913 0.96196912]\n", - " [ 0.20039135 1.07902642]\n", - " [-0.47708415 -1.39752266]\n", - " [-0.0118395 0.55664957]\n", - " [-0.18990703 1.30646904]\n", - " [ 0.22370063 -0.76173777]\n", - " [-0.25925579 -1.7132548 ]\n", - " [-0.56061802 -1.9921751 ]\n", - " [-1.14025916 -4.44655399]\n", - " [ 1.85904253 6.40713017]]\n", + "[[ 0.53340467 1.72530188]\n", + " [-0.56167889 -3.2544002 ]\n", + " [ 1.85988539 5.87145508]\n", + " [-1.23561281 -4.57354138]\n", + " [ 0.52739566 0.93489572]\n", + " [ 0.89775088 2.87036267]\n", + " [-0.71834014 -0.65667853]\n", + " [ 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0.069692 0.069593 0.069484 \n", + "12 0.073602 0.073796 0.069653 0.069692 0.069686 0.069654 0.069611 \n", + "13 0.073523 0.073795 0.069484 0.069593 0.069654 0.069688 0.069709 \n", + "14 0.073430 0.073777 0.069308 0.069484 0.069611 0.069709 0.069791 \n" ] } ], @@ -916,10 +916,10 @@ "output_type": "stream", "text": [ " 0 1\n", - "0 3.946263 1.971035\n", - "1 1.971035 1.988524\n", - "[[3.94626291 1.97103474]\n", - " [1.97103474 1.98852413]]\n" + "0 4.066103 2.050297\n", + "1 2.050297 2.019626\n", + "[[4.06610301 2.0502966 ]\n", + " [2.0502966 2.01962561]]\n" ] } ], @@ -949,13 +949,13 @@ "output_type": "stream", "text": [ "Centered covariance using own code\n", - "[[3.94626291 1.97103474]\n", - " [1.97103474 1.98852413]]\n" + "[[4.06610301 2.0502966 ]\n", + " [2.0502966 2.01962561]]\n" ] }, { "data": { - "image/png": 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", + "image/png": 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", "text/plain": [ "
      " ] @@ -1040,12 +1040,12 @@ "output_type": "stream", "text": [ "Eigenvalues of Covariance matrix\n", - "5.168112312789667\n", - "0.7666747242371026\n", + "5.3343122336302145\n", + "0.7514163800957537\n", "First eigenvector\n", - "[0.84993979 0.52687982]\n", + "[0.85045481 0.5260481 ]\n", "Second eigenvector\n", - "[-0.52687982 0.84993979]\n" + "[-0.5260481 0.85045481]\n" ] }, { @@ -1053,7 +1053,7 @@ "output_type": "stream", "text": [ "Eigenvector of largest eigenvalue\n", - "[0.84993979 0.52687982]\n" + "[0.85045481 0.5260481 ]\n" ] } ], diff --git a/doc/LectureNotes/_build/jupyter_execute/d213fd49b73afa7db6fe63b54689d9b6c4aff1adac65c06e412ebb9574c86d39.png b/doc/LectureNotes/_build/jupyter_execute/d213fd49b73afa7db6fe63b54689d9b6c4aff1adac65c06e412ebb9574c86d39.png new file mode 100644 index 000000000..24afd36e2 Binary files /dev/null and b/doc/LectureNotes/_build/jupyter_execute/d213fd49b73afa7db6fe63b54689d9b6c4aff1adac65c06e412ebb9574c86d39.png differ diff --git a/doc/LectureNotes/_build/jupyter_execute/e0c9e5cfc32bfe482c04b2091d5d98aa80212c0eef5e22892d16f73e93be8afc.png b/doc/LectureNotes/_build/jupyter_execute/e0c9e5cfc32bfe482c04b2091d5d98aa80212c0eef5e22892d16f73e93be8afc.png new file mode 100644 index 000000000..3f43b901f Binary files /dev/null and b/doc/LectureNotes/_build/jupyter_execute/e0c9e5cfc32bfe482c04b2091d5d98aa80212c0eef5e22892d16f73e93be8afc.png differ diff --git a/doc/LectureNotes/_build/jupyter_execute/eb601133698cb5198be6ed45531c33666c9fa7198b8b8a8e59f3cf4e95ddc8eb.png b/doc/LectureNotes/_build/jupyter_execute/eb601133698cb5198be6ed45531c33666c9fa7198b8b8a8e59f3cf4e95ddc8eb.png new file mode 100644 index 000000000..f3f2dbe7c Binary files /dev/null and b/doc/LectureNotes/_build/jupyter_execute/eb601133698cb5198be6ed45531c33666c9fa7198b8b8a8e59f3cf4e95ddc8eb.png differ diff --git a/doc/LectureNotes/_build/jupyter_execute/exercisesweek41.ipynb b/doc/LectureNotes/_build/jupyter_execute/exercisesweek41.ipynb index 4825677a7..a14ce1364 100644 --- a/doc/LectureNotes/_build/jupyter_execute/exercisesweek41.ipynb +++ b/doc/LectureNotes/_build/jupyter_execute/exercisesweek41.ipynb @@ -103,20 +103,20 @@ "output_type": "stream", "text": [ "Own inversion\n", - "[[4.0010275 ]\n", - " [3.10085547]]\n", - "Eigenvalues of Hessian Matrix:[0.32462943 4.22813513]\n", + "[[4.41622083]\n", + " [2.74692745]]\n", + "Eigenvalues of Hessian Matrix:[0.26594878 4.45190996]\n", "theta from own gd\n", - "[[4.0010275 ]\n", - " [3.10085547]]\n", + "[[4.41622083]\n", + " [2.74692745]]\n", "theta from own sdg\n", - "[[4.08051993]\n", - " [3.13522576]]\n" + "[[4.38222303]\n", + " [2.74411787]]\n" ] }, { "data": { - "image/png": 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", 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", "text/plain": [ "
      " ] @@ -334,9 +334,9 @@ "output_type": "stream", "text": [ "Own inversion\n", - "[[3.8679219 ]\n", - " [3.12497024]]\n", - "Eigenvalues of Hessian Matrix:[0.31887646 4.68712364]\n" + "[[4.32147704]\n", + " [2.78166226]]\n", + "Eigenvalues of Hessian Matrix:[0.32306518 4.38577204]\n" ] }, { @@ -344,13 +344,13 @@ "output_type": "stream", "text": [ "theta from own gd\n", - "[[3.8679219 ]\n", - " [3.12497024]]\n" + "[[4.32147704]\n", + " [2.78166226]]\n" ] }, { "data": { - "image/png": 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", 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", 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      " ] @@ -432,73 +432,73 @@ "Own inversion\n", "[[4.]\n", " [3.]]\n", - "Eigenvalues of Hessian Matrix:[0.3011187 4.49781862]\n", - "0 [-10.84111032] [-12.09572749]\n", - "1 [-0.48072858] [0.39646149]\n", - "2 [-0.4485449] [0.3699193]\n", - "3 [-0.41851584] [0.34515405]\n", - "4 [-0.39049716] [0.32204677]\n", - "5 [-0.36435426] [0.30048647]\n", - "6 [-0.33996158] [0.28036959]\n", - "7 [-0.31720192] [0.26159948]\n", - "8 [-0.29596598] [0.24408599]\n", - "9 [-0.27615173] [0.22774499]\n", - "10 [-0.257664] [0.21249799]\n", - "11 [-0.24041398] [0.19827173]\n", - "12 [-0.22431882] [0.18499789]\n", - "13 [-0.20930118] [0.1726127]\n", - "14 [-0.19528895] [0.16105668]\n", - "15 [-0.1822148] [0.1502743]\n", - "16 [-0.17001593] [0.14021378]\n", - "17 [-0.15863375] [0.13082678]\n", - "18 [-0.14801358] [0.12206823]\n", - "19 [-0.13810441] [0.11389604]\n", - "20 [-0.12885864] [0.10627096]\n", - "21 [-0.12023184] [0.09915636]\n", - "22 [-0.1121826] [0.09251807]\n", - "23 [-0.10467223] [0.08632419]\n", - "24 [-0.09766466] [0.08054499]\n", - "25 [-0.09112623] [0.07515268]\n", - "26 [-0.08502554] [0.07012138]\n", - "27 [-0.07933327] [0.06542692]\n", - "28 [-0.07402209] [0.06104673]\n", - "29 [-0.06906648] [0.05695979]\n", + "Eigenvalues of Hessian Matrix:[0.26136301 4.38259367]\n", + "0 [-12.42926102] [-13.7797646]\n", + "1 [-0.35774662] [0.30560116]\n", + "2 [-0.33641182] [0.28737615]\n", + "3 [-0.31634936] [0.27023801]\n", + "4 [-0.29748336] [0.25412193]\n", + "5 [-0.27974246] [0.23896696]\n", + "6 [-0.26305957] [0.22471578]\n", + "7 [-0.24737159] [0.2113145]\n", + "8 [-0.23261919] [0.19871242]\n", + "9 [-0.21874657] [0.18686188]\n", + "10 [-0.20570127] [0.17571808]\n", + "11 [-0.19343394] [0.16523885]\n", + "12 [-0.1818982] [0.15538456]\n", + "13 [-0.17105041] [0.14611795]\n", + "14 [-0.16084954] [0.13740398]\n", + "15 [-0.15125702] [0.12920967]\n", + "16 [-0.14223657] [0.12150404]\n", + "17 [-0.13375406] [0.11425795]\n", + "18 [-0.12577742] [0.107444]\n", + "19 [-0.11827649] [0.1010364]\n", + "20 [-0.11122288] [0.09501094]\n", + "21 [-0.10458992] [0.08934481]\n", + "22 [-0.09835253] [0.08401658]\n", + "23 [-0.09248712] [0.07900612]\n", + "24 [-0.08697151] [0.07429446]\n", + "25 [-0.08178482] [0.06986379]\n", + "26 [-0.07690745] [0.06569735]\n", + "27 [-0.07232095] [0.06177939]\n", + "28 [-0.06800798] [0.05809507]\n", + "29 [-0.06395221] [0.05463048]\n", "theta from own gd\n", - "[[3.78598925]\n", - " [3.17649673]]\n", - "0 [-0.06444264] [0.05314647]\n", - "1 [-0.06012835] [0.04958843]\n", - "2 [-0.05480861] [0.04520119]\n", - "3 [-0.04954337] [0.0408589]\n", - "4 [-0.04464699] [0.0368208]\n", - "5 [-0.04018906] [0.0331443]\n", - "6 [-0.03616111] [0.02982242]\n", - "7 [-0.03253183] [0.02682931]\n", - "8 [-0.02926511] [0.02413522]\n", - "9 [-0.02632586] [0.02171119]\n", - "10 [-0.02368163] [0.01953047]\n", - "11 [-0.02130293] [0.01756873]\n", - "12 [-0.01916314] [0.01580402]\n", - "13 [-0.01723827] [0.01421657]\n", - "14 [-0.01550675] [0.01278856]\n", - "15 [-0.01394915] [0.011504]\n", - "16 [-0.01254801] [0.01034846]\n", - "17 [-0.0112876] [0.009309]\n", - "18 [-0.0101538] [0.00837394]\n", - "19 [-0.00913389] [0.00753281]\n", - "20 [-0.00821642] [0.00677616]\n", - "21 [-0.00739111] [0.00609552]\n", - "22 [-0.0066487] [0.00548325]\n", - "23 [-0.00598086] [0.00493247]\n", - "24 [-0.0053801] [0.00443702]\n", - "25 [-0.00483969] [0.00399134]\n", - "26 [-0.00435356] [0.00359042]\n", - "27 [-0.00391626] [0.00322978]\n", - "28 [-0.00352289] [0.00290536]\n", - "29 [-0.00316902] [0.00261353]\n", + "[[3.76990501]\n", + " [3.19655615]]\n", + "0 [-0.06013832] [0.05137251]\n", + "1 [-0.05655187] [0.04830882]\n", + "2 [-0.05210338] [0.04450874]\n", + "3 [-0.04766156] [0.04071437]\n", + "4 [-0.04348664] [0.03714799]\n", + "5 [-0.03964077] [0.0338627]\n", + "6 [-0.03612297] [0.03085765]\n", + "7 [-0.03291338] [0.02811589]\n", + "8 [-0.02998766] [0.02561663]\n", + "9 [-0.02732158] [0.02333916]\n", + "10 [-0.02489239] [0.02126405]\n", + "11 [-0.02267913] [0.0193734]\n", + "12 [-0.02066265] [0.01765085]\n", + "13 [-0.01882546] [0.01608144]\n", + "14 [-0.01715161] [0.01465158]\n", + "15 [-0.01562659] [0.01334885]\n", + "16 [-0.01423717] [0.01216195]\n", + "17 [-0.01297129] [0.01108058]\n", + "18 [-0.01181796] [0.01009536]\n", + "19 [-0.01076718] [0.00919775]\n", + "20 [-0.00980983] [0.00837994]\n", + "21 [-0.0089376] [0.00763484]\n", + "22 [-0.00814292] [0.006956]\n", + "23 [-0.0074189] [0.00633751]\n", + "24 [-0.00675926] [0.00577402]\n", + "25 [-0.00615826] [0.00526063]\n", + "26 [-0.00561071] [0.00479289]\n", + "27 [-0.00511184] [0.00436673]\n", + "28 [-0.00465732] [0.00397847]\n", + "29 [-0.00424322] [0.00362473]\n", "theta from own gd wth momentum\n", - "[[3.99053294]\n", - " [3.00780757]]\n" + "[[3.98520854]\n", + " [3.01263545]]\n" ] } ], @@ -577,17 +577,17 @@ "output_type": "stream", "text": [ "Own inversion\n", - "[[4.1739262 ]\n", - " [2.85239235]]\n", - "Eigenvalues of Hessian Matrix:[0.27968902 4.49424386]\n", - "0 [-13.16403338] [-14.97007223]\n", - "1 [7.61543606e-16] [2.78435018e-15]\n", - "2 [-4.23272528e-16] [-4.70203152e-16]\n", - "3 [-4.23272528e-16] [-4.70203152e-16]\n", - "4 [-4.23272528e-16] [-4.70203152e-16]\n", + "[[3.96856022]\n", + " [2.98116672]]\n", + "Eigenvalues of Hessian Matrix:[0.3407072 4.46918198]\n", + "0 [-14.47784776] [-17.95282048]\n", + "1 [2.49106291e-15] [-6.96409595e-15]\n", + "2 [8.32667268e-17] [8.72456212e-17]\n", + "3 [8.32667268e-17] [8.72456212e-17]\n", + "4 [8.32667268e-17] [8.72456212e-17]\n", "beta from own Newton code\n", - "[[4.1739262 ]\n", - " [2.85239235]]\n" + "[[3.96856022]\n", + " [2.98116672]]\n" ] } ], @@ -652,17 +652,17 @@ "output_type": "stream", "text": [ "Own inversion\n", - "[[4.50455587]\n", - " [2.69978846]]\n", - "Eigenvalues of Hessian Matrix:[0.35172802 4.35427615]\n", + "[[4.20603792]\n", + " [2.82345148]]\n", + "Eigenvalues of Hessian Matrix:[0.26589296 4.18756348]\n", "theta from own gd\n", - "[[4.50455587]\n", - " [2.69978846]]\n" + "[[4.20603792]\n", + " [2.82345148]]\n" ] }, { "data": { - "image/png": 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", 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", 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      " ] @@ -675,8 +675,8 @@ "output_type": "stream", "text": [ "theta from own sdg\n", - "[[4.51550231]\n", - " [2.6728048 ]]\n" + "[[4.20773884]\n", + " [2.81706996]]\n" ] } ], @@ -775,15 +775,15 @@ "output_type": "stream", "text": [ "Own inversion\n", - "[[3.8301107 ]\n", - " [3.31610195]]\n", - "Eigenvalues of Hessian Matrix:[0.31047897 4.32701817]\n", + "[[3.79928328]\n", + " [3.09772988]]\n", + "Eigenvalues of Hessian Matrix:[0.33500183 3.88706629]\n", "theta from own gd\n", - "[[3.82958153]\n", - " [3.31655284]]\n", + "[[3.79937084]\n", + " [3.09764763]]\n", "theta from own sdg with momentum\n", - "[[3.82977678]\n", - " [3.25067525]]\n" + "[[3.77947186]\n", + " [3.08314421]]\n" ] } ], @@ -900,9 +900,9 @@ "output_type": "stream", "text": [ "theta from own AdaGrad\n", - "[[1.99978312]\n", - " [3.00126504]\n", - " [3.99880408]]\n" + "[[2.00000205]\n", + " [2.99998905]\n", + " [4.00001059]]\n" ] } ], @@ -1008,9 +1008,9 @@ "output_type": "stream", "text": [ "theta from own RMSprop\n", - "[[1.99827523]\n", - " [2.99976195]\n", - " [3.99585312]]\n" + "[[1.99957289]\n", + " [3.00371103]\n", + " [3.99595959]]\n" ] } ], @@ -1114,9 +1114,9 @@ "output_type": "stream", "text": [ "theta from own ADAM\n", - "[[2.00000058]\n", - " [2.99998203]\n", - " [4.00001412]]\n" + "[[2.00001509]\n", + " [2.99992841]\n", + " [4.00006811]]\n" ] } ], @@ -1244,7 +1244,7 @@ { "data": { "text/plain": [ - "[]" + "[]" ] }, "execution_count": 11, @@ -1316,7 +1316,7 @@ { "data": { "text/plain": [ - "" + "" ] }, "execution_count": 12, diff --git a/doc/LectureNotes/_build/jupyter_execute/exercisesweek48.ipynb b/doc/LectureNotes/_build/jupyter_execute/exercisesweek48.ipynb new file mode 100644 index 000000000..175bcd5a1 --- /dev/null +++ b/doc/LectureNotes/_build/jupyter_execute/exercisesweek48.ipynb @@ -0,0 +1,229 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "30071d16", + "metadata": { + "editable": true + }, + "source": [ + "\n", + "" + ] + }, + { + "cell_type": "markdown", + "id": "268eaea0", + "metadata": { + "editable": true + }, + "source": [ + "# Exercises week 48\n", + "**November 25-29, 2024**\n", + "\n", + "Date: **Deadline is Friday November 29 at midnight**" + ] + }, + { + "cell_type": "markdown", + "id": "1e04cbee", + "metadata": { + "editable": true + }, + "source": [ + "# Overarching aims of the exercises this week\n", + "\n", + "The exercise this week is a simple course survey and feedback. This\n", + "is important for us in order to improve our teaching material, the\n", + "active learning format and anything else related to a succesful\n", + "mastering of central machine learning methods and their applications." + ] + }, + { + "cell_type": "markdown", + "id": "78363328", + "metadata": { + "editable": true + }, + "source": [ + "### Why did you choose this course?" + ] + }, + { + "cell_type": "markdown", + "id": "d4950ede", + "metadata": { + "editable": true + }, + "source": [ + "### What was your programming knowledge before you started?\n", + "\n", + "And do you feel this course added to your programming competences and skills?" + ] + }, + { + "cell_type": "markdown", + "id": "95e23f0c", + "metadata": { + "editable": true + }, + "source": [ + "### How do you judge your own level of knowledge on machine learning before and after this course?\n", + "\n", + "Here you can discuss your level of skill/knowledge at the start of course\n", + "and at the end of the course and how these matched the level of\n", + "skill/knowledge needed to complete the projects." + ] + }, + { + "cell_type": "markdown", + "id": "db819721", + "metadata": { + "editable": true + }, + "source": [ + "### Did the projects and the teaching material allow you to deepen your insights about Machine Learning methods?\n", + "\n", + "Feel free to comment here." + ] + }, + { + "cell_type": "markdown", + "id": "61d153a4", + "metadata": { + "editable": true + }, + "source": [ + "### Project based teaching and active learning\n", + "\n", + "This is a project based course and we as teachers would like to keep\n", + "it as it is since we see very clearly that people who attend this\n", + "course have a very good learning outcome. Project based courses are\n", + "however demanding (and expensive seen from the university admin) when\n", + "it comes to proper feedback and evaluations. Feel free to discuss\n", + "whether you found a project-based useful. Feel also free to comment upon things we can improve upon or\n", + "alternative ways to assess whether the learning outcomes have been\n", + "achieved. Would you for example prefer a standard 4 hours written exam\n", + "be something you would prefer? Or other alternatives to projects? We\n", + "would very much value your thoughts here since projects are an\n", + "essential part of this course." + ] + }, + { + "cell_type": "markdown", + "id": "ee1af3ff", + "metadata": { + "editable": true + }, + "source": [ + "### Usefulness of the weekly exercises\n", + "\n", + "Did the weekly exercises help in getting started with the projects?\n", + "How relevant where they for solving the projects? Feel free to\n", + "elaborate" + ] + }, + { + "cell_type": "markdown", + "id": "46a35899", + "metadata": { + "editable": true + }, + "source": [ + "### Lab sessions and lectures\n", + "\n", + "Was there a good link between lectures and lab sessions?\n", + "Feel\n", + "free to comment." + ] + }, + { + "cell_type": "markdown", + "id": "f6524f03", + "metadata": { + "editable": true + }, + "source": [ + "### How would you improve this course?\n", + "\n", + "Are there topics which are missing, topics which could have been\n", + "omitted and/or discussed in more depth? Feel free to add your comments\n", + "here such as how to improve to teaching material and more." + ] + }, + { + "cell_type": "markdown", + "id": "ab89b772", + "metadata": { + "editable": true + }, + "source": [ + "## Then some basic questions" + ] + }, + { + "cell_type": "markdown", + "id": "9811e1b4", + "metadata": { + "editable": true + }, + "source": [ + "### Which is your preferred information chanel, Canvas, Discord, mail or other?" + ] + }, + { + "cell_type": "markdown", + "id": "6a3741fa", + "metadata": { + "editable": true + }, + "source": [ + "### Was the weekly update with plans etc useful?" + ] + }, + { + "cell_type": "markdown", + "id": "a51df0b9", + "metadata": { + "editable": true + }, + "source": [ + "### Was it easy to access the course material?" + ] + }, + { + "cell_type": "markdown", + "id": "762d5153", + "metadata": { + "editable": true + }, + "source": [ + "### Which resources and tools did you use? Jupyter-notebooks, GitHub, the various textbooks we have recommended, etc etc" + ] + }, + { + "cell_type": "markdown", + "id": "2bf11aae", + "metadata": { + "editable": true + }, + "source": [ + "### If you did not attend the lectures or the lab sessions, which resources did you use?" + ] + }, + { + "cell_type": "markdown", + "id": "3f92f5ff", + "metadata": { + "editable": true + }, + "source": [ + "### Any other topics, impressions, ideas etc you would like to share with us?" + ] + } + ], + "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 972542b0e..8f876f46e 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": [ - "[-0.61405495 -0.32387761 1.12215626 -0.4519346 0.45982886 0.30950817\n", - " -0.99310295 0.38292897 1.44400587 -0.28739213]\n" + "[ 1.32917488 0.42655046 -1.04006029 -0.11860214 -0.68913413 0.23546487\n", + " 0.91092242 0.83425723 0.05541192 0.19079267]\n" ] } ], @@ -662,26 +662,26 @@ "name": "stdout", "output_type": "stream", "text": [ - "[[0.56237933 0.1025416 0.22316615 0.85503611 0.99057991 0.94485713\n", - " 0.03845685 0.74210311 0.91828517 0.29234676]\n", - " [0.20570617 0.55663962 0.14395076 0.63318471 0.88506797 0.79793482\n", - " 0.21572768 0.70817694 0.98132273 0.47451039]\n", - " [0.10238488 0.08006058 0.26302678 0.0532921 0.89460147 0.11961481\n", - " 0.90199101 0.17545974 0.89611233 0.76803496]\n", - " [0.02942421 0.99685226 0.12021078 0.72362232 0.02251816 0.67824444\n", - " 0.70638884 0.32589372 0.39505738 0.5378907 ]\n", - " [0.88579143 0.45698195 0.61143771 0.59118363 0.92409815 0.68270325\n", - " 0.58619632 0.77509651 0.93481453 0.87482389]\n", - " [0.40760778 0.62136996 0.7761515 0.76287014 0.81606967 0.17622628\n", - " 0.65211459 0.55721129 0.20955256 0.20757376]\n", - " [0.59910949 0.22876398 0.59992922 0.0586431 0.71238022 0.59293896\n", - " 0.58318087 0.50099904 0.15835637 0.1221625 ]\n", - " [0.3510257 0.5364078 0.15260744 0.21019486 0.53797608 0.92789992\n", - " 0.91110946 0.33914178 0.20822698 0.62281444]\n", - " [0.88111434 0.05117772 0.30607722 0.34939146 0.30612395 0.06384183\n", - " 0.23829156 0.11534663 0.44474604 0.49985968]\n", - " [0.17578147 0.63422131 0.86986521 0.53418362 0.30679815 0.33175971\n", - " 0.83105371 0.97275788 0.37250759 0.05652557]]\n" + "[[0.76374594 0.87699656 0.93752045 0.58920669 0.11762693 0.41835272\n", + " 0.14130145 0.22405983 0.63569519 0.57023469]\n", + " [0.51205632 0.0815808 0.83997706 0.62319185 0.9918084 0.82551589\n", + " 0.23523437 0.5917984 0.26977294 0.22313112]\n", + " [0.78875244 0.33827328 0.15734346 0.76351169 0.88571475 0.8744876\n", + " 0.8179527 0.96419308 0.28859292 0.21034124]\n", + " [0.56604693 0.0014122 0.69927222 0.76825307 0.81056827 0.18710226\n", + " 0.96298663 0.51905411 0.75661166 0.19343497]\n", + " [0.80954821 0.27400301 0.19608145 0.90678518 0.52612126 0.57201534\n", + " 0.9065678 0.72410901 0.77533887 0.73086608]\n", + " [0.0583118 0.57305201 0.99949947 0.6534883 0.87204797 0.81128625\n", + " 0.39326284 0.67711227 0.61020809 0.31082354]\n", + " [0.13856233 0.04308921 0.87608062 0.27598375 0.37282031 0.5906656\n", + " 0.05406637 0.09201716 0.39585666 0.16639711]\n", + " [0.93928366 0.33845772 0.1675106 0.86044604 0.2131759 0.13239107\n", + " 0.06461985 0.25020484 0.93644206 0.93511011]\n", + " [0.52539786 0.72253483 0.80209029 0.8317496 0.06071723 0.60162183\n", + " 0.41902867 0.01471716 0.75263199 0.47058817]\n", + " [0.61513099 0.99809525 0.36655653 0.36519275 0.38100512 0.84195998\n", + " 0.06668051 0.41250502 0.54002942 0.66316435]]\n" ] } ], @@ -800,13 +800,13 @@ "name": "stdout", "output_type": "stream", "text": [ - "-0.07252404965482757\n", - "3.80181024148491\n", - "-0.6317631821295671\n", - "[[ 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", 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", 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      " ] @@ -2756,12 +2756,12 @@ "name": "stdout", "output_type": "stream", "text": [ - "0.028592327979280928 1.0458514144974842\n" + "0.0065879787737229524 1.0071299074549438\n" ] }, { "data": { - "image/png": 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", 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", 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      " ] diff --git a/doc/LectureNotes/_build/jupyter_execute/week34.ipynb b/doc/LectureNotes/_build/jupyter_execute/week34.ipynb index 83538e59c..924989de6 100644 --- a/doc/LectureNotes/_build/jupyter_execute/week34.ipynb +++ b/doc/LectureNotes/_build/jupyter_execute/week34.ipynb @@ -874,8 +874,8 @@ "name": "stdout", "output_type": "stream", "text": [ - "[ 0.17506058 -1.14302491 0.63485898 1.1235978 0.38873383 -0.17169218\n", - " 0.88719525 -0.01015838 -0.27342837 -1.80652926]\n" + "[ 2.05896588 0.43642532 -0.21096409 0.40779804 -1.13963886 -0.53677418\n", + " 0.83572684 0.58831595 0.30882611 -2.07060477]\n" ] } ], @@ -1313,26 +1313,36 @@ "name": "stdout", "output_type": "stream", "text": [ - "[[0.3418169 0.08757698 0.45405198 0.04340609 0.58148701 0.14855269\n", - " 0.07347117 0.79453819 0.78257332 0.91637778]\n", - " [0.81517637 0.35341707 0.55717092 0.03395449 0.79320614 0.22562101\n", - " 0.65971268 0.64276252 0.49192086 0.91375988]\n", - " [0.10740566 0.77091954 0.10656055 0.36547141 0.83190714 0.25533245\n", - " 0.40632604 0.48183592 0.22261204 0.16509611]\n", - " [0.47804768 0.92388196 0.85585927 0.38675536 0.30896123 0.22930735\n", - " 0.78408961 0.30789787 0.58654863 0.5228503 ]\n", - " [0.35826417 0.13768194 0.41982096 0.32226801 0.39507682 0.76931267\n", - " 0.97369602 0.12082909 0.97147296 0.30115792]\n", - " [0.72544872 0.9789659 0.98184665 0.98596221 0.68564602 0.80794793\n", - " 0.66307293 0.83836165 0.98251519 0.63387948]\n", - " [0.21352978 0.27418384 0.91865691 0.36851668 0.0525856 0.7259156\n", - " 0.83058696 0.99696669 0.83207908 0.32633042]\n", - " [0.10500719 0.64631695 0.2220673 0.5920376 0.85070792 0.84332209\n", - " 0.73691262 0.06028861 0.62436053 0.00758035]\n", - " [0.13963148 0.11686929 0.34936681 0.14068659 0.59525417 0.83232065\n", - " 0.32968695 0.3371494 0.85006441 0.96828809]\n", - " [0.88555932 0.25272278 0.26684239 0.38777146 0.73658394 0.81886022\n", - " 0.01409764 0.23749007 0.26368616 0.10068772]]\n" + "[[4.62790954e-01 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1.77320278e-01 1.41563755e-01]\n", + " [5.58999323e-01 8.27823274e-02 2.45162718e-01 9.35805174e-01\n", + " 3.97967922e-01 6.41998971e-01 2.85999165e-01 9.42305845e-01\n", + " 6.51639460e-01 2.40037335e-01]\n", + " [2.07463601e-01 6.36551310e-01 3.74118364e-01 9.48754846e-01\n", + " 6.84043753e-01 8.69630846e-01 2.90701292e-01 3.73794006e-01\n", + " 9.56898405e-01 7.53969725e-01]\n", + " [8.69421667e-01 9.16907984e-01 2.60498453e-01 1.05394015e-01\n", + " 8.28209931e-01 2.18273724e-01 5.70725653e-01 5.90412777e-01\n", + " 3.15706255e-01 1.00031589e-01]\n", + " [3.13129360e-02 9.74280470e-01 1.56927535e-01 2.05412497e-01\n", + " 3.29614274e-01 1.32235211e-01 9.66580589e-01 6.25369588e-01\n", + " 5.91506268e-01 3.63969272e-01]]\n" ] } ], @@ -1446,13 +1456,13 @@ "name": "stdout", "output_type": "stream", "text": [ - "0.12144818928795939\n", - "4.458165594343705\n", - "0.07083732341182088\n", - "[[ 1.01630108 3.10680954 3.23143243]\n", - " [ 3.10680954 10.41130925 9.90126156]\n", - " [ 3.23143243 9.90126156 15.84271739]]\n", - "[24.2481237 0.07228932 2.94991469]\n" + "-0.022796599742659046\n", + "4.026047728952776\n", + "-0.08626455960524361\n", + "[[ 1.07037904 3.23910863 3.46675556]\n", + " [ 3.23910863 10.63080397 10.39964939]\n", + " [ 3.46675556 10.39964939 18.1421739 ]]\n", + "[26.31853116 0.06730967 3.45751608]\n" ] } ], @@ -1804,7 +1814,7 @@ "traceback": [ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[0;31mAttributeError\u001b[0m Traceback (most recent call last)", - "\u001b[0;32m/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_58960/1326197715.py\u001b[0m in \u001b[0;36m?\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 6\u001b[0;31m new_hobbit = {'First Name': [\"Peregrin\"],\n\u001b[0m\u001b[1;32m 7\u001b[0m \u001b[0;34m'Last Name'\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m\"Took\"\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 8\u001b[0m \u001b[0;34m'Place of birth'\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m\"Shire\"\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 9\u001b[0m \u001b[0;34m'Date of Birth T.A.'\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;36m2990\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;32m/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_23270/1326197715.py\u001b[0m in \u001b[0;36m?\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 6\u001b[0;31m new_hobbit = {'First Name': [\"Peregrin\"],\n\u001b[0m\u001b[1;32m 7\u001b[0m \u001b[0;34m'Last Name'\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m\"Took\"\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 8\u001b[0m \u001b[0;34m'Place of birth'\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m\"Shire\"\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 9\u001b[0m \u001b[0;34m'Date of Birth T.A.'\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;36m2990\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", "\u001b[0;32m~/miniforge3/envs/myenv/lib/python3.9/site-packages/pandas/core/generic.py\u001b[0m in \u001b[0;36m?\u001b[0;34m(self, name)\u001b[0m\n\u001b[1;32m 6200\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0mname\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_accessors\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 6201\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_info_axis\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_can_hold_identifiers_and_holds_name\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mname\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 6202\u001b[0m ):\n\u001b[1;32m 6203\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mname\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 6204\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mobject\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__getattribute__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mname\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m", "\u001b[0;31mAttributeError\u001b[0m: 'DataFrame' object has no attribute 'append'" ] diff --git a/doc/LectureNotes/_build/jupyter_execute/week35.ipynb b/doc/LectureNotes/_build/jupyter_execute/week35.ipynb index 3337e84cb..f3402fbe5 100644 --- a/doc/LectureNotes/_build/jupyter_execute/week35.ipynb +++ b/doc/LectureNotes/_build/jupyter_execute/week35.ipynb @@ -1533,7 +1533,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "0.9952260624201319\n" + "0.9954482269282812\n" ] } ], @@ -1564,7 +1564,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "0.01138349118139106\n" + "0.008936538168866639\n" ] } ], @@ -1599,23 +1599,23 @@ "name": "stdout", "output_type": "stream", "text": [ - "[0.03126071 0.00179226 0.05225439 0.00372999 0.02734884 0.05333172\n", - " 0.02020016 0.01521008 0.00965948 0.0056274 0.01055702 0.01870768\n", - " 0.07847027 0.00766418 0.00385358 0.01374419 0.04181664 0.00933893\n", - " 0.00312107 0.01294475 0.07413571 0.11552511 0.01637903 0.02176175\n", - " 0.00854766 0.04706879 0.05041282 0.00575302 0.02289663 0.01263793\n", - " 0.03325052 0.01806972 0.0066441 0.05997838 0.02861775 0.04882654\n", - " 0.08480358 0.03542954 0.01143948 0.00619982 0.00278866 0.00995497\n", - " 0.02001832 0.03635157 0.00732822 0.01114894 0.07709391 0.01125655\n", - " 0.00816671 0.01114243 0.00550993 0.06590956 0.04805237 0.04394445\n", - " 0.00825943 0.00935925 0.023291 0.05290093 0.01249864 0.02700809\n", - " 0.00271844 0.01806574 0.01238543 0.03213764 0.02680834 0.02269712\n", - " 0.02734783 0.00461485 0.0352873 0.01616311 0.00968169 0.02333957\n", - " 0.00912667 0.03871805 0.01637128 0.02097868 0.00230726 0.08493636\n", - " 0.05203452 0.0498517 0.01619572 0.02339214 0.03510055 0.04566385\n", - " 0.0024319 0.02956756 0.05981231 0.00394204 0.02607172 0.02627472\n", - " 0.02413198 0.00518272 0.02283931 0.01528494 0.01208287 0.02055319\n", - " 0.01180608 0.01603956 0.02803521 0.07787677]\n" + "[0.04030775 0.05734435 0.01740914 0.02044029 0.02486199 0.03573222\n", + " 0.00752453 0.0573522 0.00102185 0.04459142 0.03221764 0.00342095\n", + " 0.01898739 0.03160225 0.00450564 0.01979508 0.00152143 0.00857666\n", + " 0.00762566 0.00703236 0.00490815 0.02171406 0.04265231 0.03063862\n", + " 0.0273619 0.00668013 0.03013331 0.00788426 0.01275493 0.01698978\n", + " 0.02715714 0.0164838 0.00424614 0.06575617 0.03937194 0.0459393\n", + " 0.03321665 0.00857944 0.01458459 0.03839052 0.04106931 0.01962178\n", + " 0.01758251 0.06598382 0.01262347 0.05373197 0.00508233 0.02902908\n", + " 0.01284702 0.02266783 0.05481819 0.02857842 0.00817663 0.00478164\n", + " 0.02844986 0.03430817 0.00845377 0.01544721 0.00867529 0.01881047\n", + " 0.02900428 0.00525951 0.00516358 0.05597496 0.03021621 0.00458845\n", + " 0.0134627 0.00244413 0.00194759 0.04527404 0.02602746 0.00545794\n", + " 0.00747748 0.01109822 0.08180185 0.02539315 0.02321685 0.02859203\n", + " 0.01136476 0.00341608 0.00341404 0.00651846 0.05339963 0.01042549\n", + " 0.02637872 0.00639212 0.02177328 0.02384275 0.00133169 0.00699813\n", + " 0.0205469 0.04806452 0.01573671 0.05430636 0.01207681 0.0160536\n", + " 0.01157049 0.08333949 0.01405686 0.00212278]\n" ] } ], @@ -1669,15 +1669,15 @@ "name": "stdout", "output_type": "stream", "text": [ - "[ 2.06741397 -0.19826431 5.13801381 0.12625265 -0.15070691]\n", + "[ 2.04491327 -0.39593882 6.26462211 -1.4899045 0.59718501]\n", "Training R2\n", - "0.9959102280411898\n", + "0.9973808396916977\n", "Training MSE\n", - "0.007840268112476161\n", + "0.005799563292953798\n", "Test R2\n", - "0.9923171995387728\n", + "0.9950846163116835\n", "Test MSE\n", - "0.016855069963547485\n" + "0.011719944818178452\n" ] } ], @@ -3118,7 +3118,13 @@ "MSE with intercept column\n", "0.00411363461744314\n", "MSE with intercept column from SKL\n", - "0.004113634617443147\n", + "0.004113634617443147\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ "Manual intercept: 2.083766322923899\n", "Fitted beta (wiothout intercept): [0.19569961 3.97898392]\n", "Sklearn intercept: 2.0837663229239043\n", diff --git a/doc/LectureNotes/_build/jupyter_execute/week37.ipynb b/doc/LectureNotes/_build/jupyter_execute/week37.ipynb index 222a8a2b6..fd068f15c 100644 --- a/doc/LectureNotes/_build/jupyter_execute/week37.ipynb +++ b/doc/LectureNotes/_build/jupyter_execute/week37.ipynb @@ -1671,7 +1671,7 @@ "text": [ "Bootstrap Statistics :\n", "original bias std. error\n", - " 100.106 15.2126 100.109 0.153146\n" + " 100.088 15.1355 100.087 0.151546\n" ] } ], @@ -1737,7 +1737,7 @@ "outputs": [ { "data": { - "image/png": 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", + "image/png": 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", "text/plain": [ "
      " ] @@ -2067,13 +2067,7 @@ "Error: 0.08426840630693411\n", "Bias^2: 0.0796891867672603\n", "Var: 0.004579219539673834\n", - "0.08426840630693411 >= 0.0796891867672603 + 0.004579219539673834 = 0.08426840630693413\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ + "0.08426840630693411 >= 0.0796891867672603 + 0.004579219539673834 = 0.08426840630693413\n", "Polynomial degree: 2\n", "Error: 0.10398646080125035\n", "Bias^2: 0.1007711427354898\n", @@ -2088,13 +2082,7 @@ "Error: 0.06844519414009445\n", "Bias^2: 0.06453579006728324\n", "Var: 0.003909404072811226\n", - "0.06844519414009445 >= 0.06453579006728324 + 0.003909404072811226 = 0.06844519414009446\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ + "0.06844519414009445 >= 0.06453579006728324 + 0.003909404072811226 = 0.06844519414009446\n", "Polynomial degree: 5\n", "Error: 0.05227921801205686\n", "Bias^2: 0.0481872773043029\n", @@ -2109,13 +2097,7 @@ "Error: 0.02760977349102253\n", "Bias^2: 0.022999498260366312\n", "Var: 0.004610275230656212\n", - "0.02760977349102253 >= 0.022999498260366312 + 0.004610275230656212 = 0.027609773491022525\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ + "0.02760977349102253 >= 0.022999498260366312 + 0.004610275230656212 = 0.027609773491022525\n", "Polynomial degree: 8\n", "Error: 0.017355848195593347\n", "Bias^2: 0.010331721306655127\n", @@ -2125,13 +2107,7 @@ "Error: 0.02660572763718093\n", "Bias^2: 0.010018312644137363\n", "Var: 0.016587414993043573\n", - "0.02660572763718093 >= 0.010018312644137363 + 0.016587414993043573 = 0.026605727637180936\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ + "0.02660572763718093 >= 0.010018312644137363 + 0.016587414993043573 = 0.026605727637180936\n", "Polynomial degree: 10\n", "Error: 0.021592704588025025\n", "Bias^2: 0.010516485576645508\n", @@ -2146,13 +2122,7 @@ "Error: 0.11547777218872497\n", "Bias^2: 0.01628578269596628\n", "Var: 0.09919198949275869\n", - "0.11547777218872497 >= 0.01628578269596628 + 0.09919198949275869 = 0.11547777218872497\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ + "0.11547777218872497 >= 0.01628578269596628 + 0.09919198949275869 = 0.11547777218872497\n", "Polynomial degree: 13\n", "Error: 0.22842468702219465\n", "Bias^2: 0.01975416527185249\n", @@ -2605,16 +2575,16 @@ "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" + "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" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "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", @@ -2623,61 +2593,55 @@ "Mean squared error on test data: 568.47051432\n", "Degree of polynomial: 16\n", "Mean squared error on training data: 0.00325450\n", - "Mean squared error on test data: 48.97630233\n" + "Mean squared error on test data: 48.97630233\n", + "Degree of polynomial: 17\n", + "Mean squared error on training data: 0.00242954\n", + "Mean squared error on test data: 2.52780600\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "Degree of polynomial: 17\n", - "Mean squared error on training data: 0.00242954\n", - "Mean squared error on test data: 2.52780600\n", "Degree of polynomial: 18\n", "Mean squared error on training data: 0.00219195\n", "Mean squared error on test data: 429.25695398\n", "Degree of polynomial: 19\n", "Mean squared error on training data: 0.00154853\n", - "Mean squared error on test data: 239.97065359\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ + "Mean squared error on test data: 239.97065359\n", "Degree of polynomial: 20\n", "Mean squared error on training data: 0.00140846\n", "Mean squared error on test data: 1350.24493666\n", "Degree of polynomial: 21\n", "Mean squared error on training data: 0.00119688\n", - "Mean squared error on test data: 1840.50530832\n", + "Mean squared error on test data: 1840.50530832\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ "Degree of polynomial: 22\n", "Mean squared error on training data: 0.00092898\n", "Mean squared error on test data: 1184.60929685\n", "Degree of polynomial: 23\n", "Mean squared error on training data: 0.00089193\n", - "Mean squared error on test data: 3892.17483760\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ + "Mean squared error on test data: 3892.17483760\n", "Degree of polynomial: 24\n", "Mean squared error on training data: 0.00083355\n", "Mean squared error on test data: 1332.46736215\n", "Degree of polynomial: 25\n", "Mean squared error on training data: 0.00079904\n", - "Mean squared error on test data: 7577.76690383\n", - "Degree of polynomial: 26\n", - "Mean squared error on training data: 0.00075590\n", - "Mean squared error on test data: 1079.36895644\n" + "Mean squared error on test data: 7577.76690383\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ + "Degree of polynomial: 26\n", + "Mean squared error on training data: 0.00075590\n", + "Mean squared error on test data: 1079.36895644\n", "Degree of polynomial: 27\n", "Mean squared error on training data: 0.00068091\n", "Mean squared error on test data: 3207.25343155\n", @@ -2693,9 +2657,9 @@ "name": "stderr", "output_type": "stream", "text": [ - "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_58983/626635268.py:73: RuntimeWarning: divide by zero encountered in log10\n", + "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_23293/626635268.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_58983/626635268.py:74: RuntimeWarning: divide by zero encountered in log10\n", + "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_23293/626635268.py:74: RuntimeWarning: divide by zero encountered in log10\n", " plt.plot(polynomial, np.log10(testerror), label='Test Error')\n" ] }, @@ -2826,7 +2790,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_58983/3817475779.py:63: RuntimeWarning: divide by zero encountered in log10\n", + "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_23293/3817475779.py:63: RuntimeWarning: divide by zero encountered in log10\n", " plt.plot(polynomial, np.log10(estimated_mse_sklearn), label='Test Error')\n" ] }, diff --git a/doc/LectureNotes/_build/jupyter_execute/week39.ipynb b/doc/LectureNotes/_build/jupyter_execute/week39.ipynb index c4be9e12b..c4f91dec9 100644 --- a/doc/LectureNotes/_build/jupyter_execute/week39.ipynb +++ b/doc/LectureNotes/_build/jupyter_execute/week39.ipynb @@ -1176,7 +1176,7 @@ { "data": { "text/plain": [ - "" + "" ] }, "execution_count": 1, @@ -1338,7 +1338,7 @@ { "data": { "text/plain": [ - "[]" + "[]" ] }, "execution_count": 5, @@ -2126,16 +2126,16 @@ "name": "stdout", "output_type": "stream", "text": [ - "Eigenvalues of Hessian Matrix:[0.31668291 4.15666977]\n", - "[[3.74118981]\n", - " [3.28649035]]\n", - "[[3.74118981]\n", - " [3.28649035]]\n" + "Eigenvalues of Hessian Matrix:[0.34526171 4.42891508]\n", + "[[3.73401546]\n", + " [3.2913872 ]]\n", + "[[3.73401546]\n", + " [3.2913872 ]]\n" ] }, { "data": { - "image/png": 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biqeeekr8+uuvAoBYvny5UVm1Wi06deokAgICRExMjMjIyBDjx4+3OLReCCEKCgrElClThFKpFHXq1BGhoaFi0KBB4qOPPjKU0Q+tX716tdFr9cPKK9enuLhYjB07VjRs2NDk8P6qbt68KZ577jnRpEkTERQUJIYNGyZyc3PtruuHH34o7rvvPtGkSRMREBAg2rZtK2bNmiU0Go3Rsc6ePSsee+wx0axZMxEQECDatGkjpkyZYjR1wPXr18XcuXNFu3bthL+/v2jatKno27evWLRokSgrKzO6Bm+88Ua1c6t6DuXl5eLZZ58VzZo1EwqFwqph9tu2bROJiYkiJCREBAYGirZt24oJEyaIn3/+WQghxIwZM4Svr6/Yt2+f0et+/vln4efnJyZNmmTYtn79ehEXFycCAwNFVFSUWLhwofj0008FAJGTk2Mo16pVK5GUlGTyfKZMmWK0zdT56z/Xp06dEkOGDBH16tUTLVq0EPPmzRNarbbGaySEdfeZyBMohHByxiQREUnShAkTkJ6ebrFlkkjqmDNEREREssZgiIiIiGSNwRARERHJGnOGiIiISNbYMkRERESyxmCIiIiIZE0Wky5WVFTg3LlzaNCggU3T6BMREZH7CCFw/fp1hIeHV1v70ZFkEQydO3eu2grNREREJA25ublWzfpvL1kEQ/qp+3NzcxEcHOzm2hAREZE1ioqKoFQqDc9xZ5FFMKTvGgsODmYwREREJDHOTnFhAjURERHJGoMhIiIikjUGQ0RERCRrDIaIiIhI1hgMERERkawxGCIiIiJZYzBEREREssZgiIiIiGSNwRARERHJGoMhIiIikjUGQ0RERCRrDIaIiIhI1hgMERERkawxGCIiIiJZYzBEREREssZgiIiIiGSNwRARERHJmtuDoR07dmDYsGEIDw+HQqHA2rVrzZb929/+BoVCgcWLF7usfkREROTd3B4MlZSUoGvXrli6dGmN5dasWYO9e/ciPDzcRTUjIiIiOfBzdwWGDh2KoUOH1lgmPz8fzz77LL7//nskJSW5qGZEREQkB25vGbKkoqICjz76KGbNmoXY2Fh3V4eIiIi8jNtbhixZuHAh/Pz88Nxzz1n9mtLSUpSWlhp+LioqckbViIiIyAt4dMvQgQMHsGTJEqxYsQIKhcLq16WmpiIkJMTwpVQqnVhLIiIikjKPDoZ27tyJixcvIjIyEn5+fvDz88PZs2fx/PPPIyoqyuzr5s6dC41GY/jKzc11XaWJiIhIUjy6m+zRRx9FQkKC0bbExEQ8+uijmDhxotnXBQQEICAgwNnVIyIiIi/g9mCouLgYJ0+eNPyck5ODQ4cOoXHjxoiMjESTJk2MytepUwehoaGIjo52dVWJiIjIC7k9GPr5558xcOBAw88zZ84EAIwfPx4rVqxwU62IiIhILtweDA0YMABCCKvLnzlzxnmVISIiItnx6ARqIiIiImdjMERERESyxmCIiIiIZI3BEBEREckagyEiIiKSNQZDREREJGsMhoiIiEjWGAwRERGRrDEYIiIiIlljMERERESyxmCIiIiIZI3BEBEREckagyEiIiKSNQZDREREJGsMhoiIiEjWGAwRERGRrDEYIiIiIlljMERERESyxmCIiIiIZI3BEBEREckagyEiIiKSNQZDREREJGsMhoiIiEjWGAwRERGRrDEYIiIiIlljMERERESyxmCIiIiIZI3BEBEREckagyEiIiKSNQZDREREJGsMhoiIiEjWGAwRERGRrDEYIiIiIlljMERERESyxmCIiIiIZI3BEBEREckagyEiIiKSNQZDREREJGsMhoiIiEjW3B4M7dixA8OGDUN4eDgUCgXWrl1r2Hf79m3Mnj0bXbp0QVBQEMLDw/HYY4/h3Llz7qswERER2U6rBTIzgZUrdd+1WnfXyMDtwVBJSQm6du2KpUuXVtt348YNHDx4EP/6179w8OBBZGRk4Pjx43jooYfcUFMiIiKyS0YGEBUFDBwIjB2r+x4VpdvuARRCCOHuSugpFAqsWbMGI0aMMFtm//796NWrF86ePYvIyEirjltUVISQkBBoNBoEBwc7qLZERERkUUYGoFIBVcMNhUL3PT0dSE42+VJXPb/d3jJkK41GA4VCgYYNG5otU1paiqKiIqMvIiIicjGtFpg2rXogBNzZNn2627vMJBUM3bp1C7Nnz8aYMWNqjBBTU1MREhJi+FIqlS6sJREREQEAdu4E8vLM7xcCyM3VlXMjyQRDt2/fxsiRIyGEwLJly2osO3fuXGg0GsNXbm6ui2pJREREBufPO7ack/i59d2tpA+Ezp49ix9++MFiv2FAQAACAgJcVDsiIiIyKSzMseWcxONbhvSB0IkTJ7BlyxY0adLE3VUiIiIia8THAxERd5Klq1IoAKVSV86N3N4yVFxcjJMnTxp+zsnJwaFDh9C4cWOEhYVBpVLh4MGD2LBhA7RaLS5cuAAAaNy4Mfz9/d1VbSIiIrLE1xdYskQ3mkyhME6k1gdIixfryrmR24fWZ2ZmYuDAgdW2jx8/Hi+//DJat25t8nXbtm3DgAEDrHoPDq0nIiJyo4wM3aiyysnUSqUuEDIzrB5w3fPb7cGQKzAYIiIicjOtVjdq7Px5XY5QfLzFFiFXPb/d3k1GREREMuDrC1jZo+NqHp9ATURERORMDIaIiIhI1hgMERERkawxZ4iIiIjcx47EakdjMERERETuYWrIfUSEbm6iGobcOxq7yYiIiMj1MjJ0kzFWXcg1P1+3PSPDZVVhMERERESupdXqWoRMTXWo3zZ9uq6cC7CbjIiIiJyral6QVlu9RagyIYDcXGD3bpdUj8EQEREROY+pvKDGja177Z/rkTobgyEiIiJyDn1eUNXusCtXrHt9aKjj62QCgyEiIiJyvJrygixRKHSjyvr2dXy9TGACNRERETnezp015wWZo1Dovi9e7LL5hhgMERERkeOdP29duar5QxERQHq6S+cZYjcZEREROV5YmHXlvvpK1wLEGaiJiIjIq8TH61p58vNN5w3p84IGDHB58FMVu8mIiIgcSasFMjOBlSt13100caDH8fXVLasB3MkD0nNDXlBNGAwRERE5SkYGEBUFDBwIjB2r+x4V5dKlJTxKcrIu/6dlS+PtbsgLqolCCHvGvElLUVERQkJCoNFoEBwc7O7qEBGRNzI3p46+FcSDHv4uZ+fK9K56fjMYIiIiqi2tVtcCZG4ouT4/JifHI7qFpMJVz292kxEREdWWpTl19Gtt7dzpujqR1RgMERER1Za1c+pYW45cisEQERFRbVk7p4615cilGAwRERHVln5OnapDyPUUCkCp1JUjj8NgiIiIqLYkNKcOVcdgiIiIyBEkMqcOVcflOIiIiBwlORkYPtyuOXXIfRgMEREROZKvr269LZIMdpMRERGRrDEYIiIiIlljMERERESyxmCIiIiIZI3BEBEREckaR5MREZHjabUcXk61Unj8MlbN3++S92IwRETk6aQWWGRkANOmGa/iHhGhm6HZFRMPSu16kUFB9iWsee0o0jfWR+bVOGjR1yXvy2CIiMiTuTuwsFVGBqBSAUIYb8/P12139kzMUrtehPyfzyNjwe9I3xKCnZo4CNxn2Ncl8HccvuX8OiiEqPqJ9T5FRUUICQmBRqNBcHCwu6tDRGQdc4GFfq0rT1viQasFoqKMA5HKFApdYJKT45yWmtpcL7YmudSZXXlQLzwJdWYT7CnuYrSvV1A2Uu4rRMoLrdHs7kYueX4zGCIi8kTuDizskZkJDBxoudy2bY6fobk214utSS5xYvMZqN88A/XO5vj5RozRvn4NsqAadBXJL7ZDZJ87a7u56vnt9tFkO3bswLBhwxAeHg6FQoG1a9ca7RdC4KWXXkJYWBjq1q2LhIQEnDhxwj2VJSJylZ07zT/YAV3rR26urpynOH/eseVsYe/10rcmVX2tvlsvI8PxdZWRI+tP4pVBmeha9zg6DInC3O8H4OcbMfCBFgMa/oL3/rod+QcuYFdRHKav6W8UCLmS23OGSkpK0LVrVzz++ONINhGBv/7663jnnXfw2WefoXXr1vjXv/6FxMREHDlyBIGBgW6oMRGRC7gzsLBXWJhjy1lSuWvryBHrXlP5emm1uhYhUx0kQuhak6ZP1y286imtbx5OVAhkpf8O9Xvnkb5PiaNl7QC0AwD4ohx/afwrVENLMGJuJzSP7e7eylbi9mBo6NChGDp0qMl9QggsXrwY//znPzF8+HAAwOeff44WLVpg7dq1GD16tCurSkTkOq4OLBwhPl7XvZSfbzrA0HdVxcfX/r1MdW1Zo/L1sqU1iQuvmiUqBA58eRTp71+E+mAUTt6OBhANAKiDMgxpfggpSaV4aG4smrS/y72VNcPtwVBNcnJycOHCBSQkJBi2hYSEoHfv3tizZ4/ZYKi0tBSlpaWGn4uKipxeVyLycFJLkHVlYOEovr66PBuVSle/yvXWJzEvXlz7624uUbompq6XFFvfPERFeQX2ffob0j+6DPWhdjirjQGgywMKxE3cH/YrVCO0eHBOZ4RE9nJvZa3g0cHQhQsXAAAtWrQw2t6iRQvDPlNSU1Mxf/58p9aNiCREigmyrgosHC05WTdqy9T1Xry49te7pq4tc8xdLym2vrmRtkyLHz/MRvr/XUNGdgfkV9wZBVYPJUiKyIJKJfDA7DjUD73HjTW1nUcHQ/aaO3cuZs6cafi5qKgISqXSjTUiIrdx97w3teHswMJZkpN1eTbOaImz1LVlirnrJcXWNxcrv1WOzHeyoP6sGBlHO+Gi6GrY1wBFGNbqMFSjfJE4Kw71mvZxY01rx6ODodDQUABAQUEBwipF5gUFBejWrZvZ1wUEBCAgIMDZ1SMiT+cNCbLODCycydfXOXk21nZZ/fOfQExMzddLqq1vTlZWXIatb/0K9X9vYe2JGFwWPQz7GiquYUTbw0gZE4DBL3RFQHA/N9bUcTw6GGrdujVCQ0OxdetWQ/BTVFSEffv2YdKkSe6tHBF5Pm9JkHVWYCFF1nZZDRpk3TWTauubg926dgub3vgV6atuY/3pLtCgp2FfU0UhHo4+gpRH6mHgtDj41/e+ljK3B0PFxcU4efKk4eecnBwcOnQIjRs3RmRkJKZPn47//Oc/aN++vWFofXh4OEaMGOG+ShORNDBB1vs4o2tLqq1vtVRysQQb38hC+lcCG/7ogmL0NuwL9SlAcswxqCYGI35yF/gF3lfDkaTP7cHQzz//jIGVZizV5/qMHz8eK1aswIsvvoiSkhI8/fTTuHbtGu69915s3LiRcwwRkWVMkPU+zurakknr2/Vz17Eh9TDUaxT4Nr8rbuJOnk+E7zmkdDkB1VON0OfJWPj693dPJSuP/HTRqhFcjoOIvJd+iQZLrQietKQFWcfUCEGlUlZdW9a6dlaD9a8ehvrrOvj+QleU4k5jQmu/P6DqfhopzzRFz/Ex8PFz88IUVe5rEYAQgGuTOQKDISIZ048mA0y3InjyaDKqmdTmjnKhwuOXsS71CNK/CcTWwq64DX/Dvg51cqDqeRYpk1ug+5iOUPgo3FjTSkyM/GQw5EAMhohkjq0IJAMF2Zew5rWjSN9YH5lX46CtlAkTG3ACqnvyoZrWErHD23lOAATogtrMTGDkSODKFaNdDIYciMEQEbEVwQvwHlaT//N5ZCz4HelbQrBTEwdRaf317nWPIqVfAVJmRKLjA23cWMsaWFhWxVXBkNsTqImIXEImCbJeS4qziDvJmV15UC88CXVmE+wp7gLgzgCAXkHZSLmvECkvtEbbv3QC0Mlt9bTInmVVnIQtQ0RE5NnMPTRllPd1YvMZqN88A/XO5vj5RozRvn4NsqAadBXJL7ZDZJ+WbqqhjfSDGyzMJs5uMgdiMEREJFGWHppSHhFoodvvyPqTUC/JQ/ruMGTdijZs94EW9zXMgmpwER6eE43wHqHuqH3tZGYClabVMYfdZERERN4yi3hVJrr9REQEzo58EZ/u74L0fUocLWsHoB0AwBfl+EvjX6EaWoIRczuheWx3N1XcQTxsolMGQ0REciHFBGQpzyJu7nqb6fYTefmIfGsaspGOoxiAOijDkOaHkJJUiofmxqJJ+7vcdCJO4GETnTIYIiKSA6kmIEt1FnFz1/vttyFmzACEQNXB7T4QqIACH/pMguqppkj6e1eERPZyabVdxtKyKgDQpAnw6ae6pVKcjDlDRETeTsoJyFKcRdxcyw8Uhv9atG2btLr97GHFhKhFCQkueX67ed5tIiJyKq1W10JhKpDQb5s+XVfOE+nXIgPuPCT1arMWmbPUcL0VELC69cETu/0cLTlZF4i3rDICLiLC5QE6gyEiIm9mSwKyp/Kgh6Yltzf9UOP1tvqh62ndfs6SnAycOaNrCUtL033PyXH5PWXOEBGRN5NyAnJlycm63BEPTAC/de0WNr3xK9JX3Uad03/gk9ocTN/tFx/vqOp5Pg+YEJXBEBGRN5NqArIpHvDQ1Cu5WILvFmYhY3U5ruZeR0NocB5h8EGU9QdRKEznynhSt59MMBgiIveR4lBvqbE0ascTWyI89HNRlFeEbxZmQ71GgW/zu+J+nMcSTIMSd7rFSkOaQ/g2geLqlZqv95tvAjNnVh9tJtXFgz30nlmLwRARuYdUh3pLjT4BWaWyvSXCHQ84D/tcXM25hq9Ts5G+vg42FXRFKfoCAB5GBtKhgqJKSnRA0aU717im652crPuy5/p6WuDhYffMLkIGNBqNACA0Go27q0JEQgihVguhUAihe1Tc+VIodF9qtbtr6H3UaiEiIoyvt1Jp/lqbKh8R4dx789VX1T8TbvhcXDpWKP5v/A5xf9OfRB2UGlWlQ53T4h99tojSxi1Eham66uvbpIlt19ta7rgvlurjxN9lVz2/Oc8QEbmWN6815emsbVFwx7xE6enA6NHmh/g7+XNxIesi1qQeg/r7+si8GgdtpY6TzgEnkHJPPlTTWiJ2eDsodmy3al0tbNmiq6ujWnAs3ZevvgKaNnVdi5ELfpdd9fxmMERErmXlAo2ymHTOE7kjWM3IAFJSrCvrwM9F3v7zyFjwO9RbQ7BTEwdRaeB797pHobq3ACkzIhE9tI3xC1euBMaOtfwGaWnAmDEOqatVq7z7+hoHk87uqnLB77Krnt/MGSIi1/KWod7eytULo+onKbRWLT8XZ3blQb3wJNIzm2BvcRcAd0bR9QrKhqp/IVJmtUGbAZ0AdNLVLzPTuLXFHSP0LN0XoHqrWn6+riXJWXMxedHvMoMhInItbxrq7Y1c/YCz5iFfmR2fixObz0D95hmk72yOAzdiAEQAABSoQN8G2VANuorkF9shsk9n4xfWsL6Yy0fo2XO9hdDVZfp03RxNju4y86LfZQZDRORaUhzqbQ1PG+FjL1c/4Gx5yCuVVn8ujqw/CfWSPKTvDkPWrWjgz/l/fKBF/4ZZSBlchIfnRCO8R5zpA5jLz8nPB0aOBF54AVi0yHVzBdl7vR3dkleZN/0uOzU920NwNBmRh9GPQKk6CkWqo8k8bYRPbZSX6+puaoSQ/h4plbpyjrBtm+n3MfVVw/Ws0FaIQ/87Jv557zbRyf+k0ct8cVsMabJffDhuuyjIvmj9NahptJhSqRv95owRYzXVydx9sfT15ZeOqcO2bUKkpem+l5c7/XfZVc9vBkNE5B62DvX2VN44TYArg1VrHvK+vrrAo4oKbYXY/9lvYnbvbaJdnRyjl9RBqUhqvk8sf2KnuHzyim11sjZA0wcEVQMEZzF3X6z5atasdvetpoDfib/LDIYciMEQkYdy5YPEGaxtQZDaeQnh2mDV0kN+9WpDUe1trfjxgywx865topVvrlGxQNwQI8L2iC8n7RLXzl6zvz5padYFGGlpDjh5G5m6L76+lutam0DWmoDfSb/LnGfIgTi0nshFvCVvxlrePk2AK++nqWRlpRJYvBjaB4dj17LDUH+qgTq7A85V3MmfqYcSJEVkQaUSeGB2HOqH1q99XTz9vla9L5cuAaNG6UKUmtgzLYKb5wXj0HoikhZvmJLfVl40tNgkVy6MWmVVem2T5th2MBjp/7yJNaoruCi6GYo2QBEeijqMlJG+SJwVh3pN+zi2Lp6eGGzqvvj6As88AxQWmn+dELYnU7t6qgU3YTBERLVX08gbZ85z4m5eNLTYE5Td1GLrjiCkfxmBdSdjcFk0MexrpLiK4W2zoRoXiISZcQgI7ue8itRmPTd3SU4Gbt4EHnnEcllbgnNvD/j/xGCIiGpHP2meqb+ghXDuPCfu5uktCBJw69otbHrjV6Svuo31p7tAg56GfU0VhXg4+ghUjwVh4LQ41KnnwuuYnKwL4k21dnrqyvItW1pXzpbgXCYBP3OGiKh2PD2/wtn0rWKA6RYEb20Vq4WSiyX4bmEW1OkCG/7ogmI0MOwL9SlAcswxqCYGI35yF/gFuvlvdinlwenzeywF5/bkDDnymDZgzhARSYNMmtHNkloLgqWHu5Me/kV5RfhmYTbUaxT4Nr8rbuJOnk+E7zmo4k4g5clG6Pt0Z/j4taj1+zmMK/OmassZ3XtS7DK0h1PHqnkIDq0nciJb5mTxZlKYJsDS5JAOnjzyyumr4rOndophLfaKANw0Omxrv7NiVs9tYu//HRba21oHniQ5ZVoEN80LxqH1DsRuMiIncnMzuqzUptXGXJK7/q97/fIS5vZb2d1XePwy1qUeQfo3gdha2BW34W/Y16FODlQ9z0I1NRTdRkVD4aOwru5kO2e08Lmhy9BVz28GQ0RUe8ybcb7aTF1gaa4YQPdQq7rquZ6FgPZC1kWsST0G9ff1kXk1DtpKGRidA04g5Z58qKa1ROzwdgyAyCYMhhyIwRCRC9QwaR4DoVqy1KpjKdi0NsndkkpJ8Hn7zyNjwe9Qbw3BTk0cBHwMxbrXPQrVvQVImRGJ6KFtav++JFtMoCYiaakyaZ7Hj7yRCkdMXeCg5PVLmdn4/E0gPbMJ9hZ3AXBnOHWvoGyo+hciZVYbtBnQCUAnh7wnkSswGCIix5HSyBs9Tx867YgZgB00B8xf53fGdujeQ4EK9G2QDdWgq0h+sR0i+3R2yHtIkqd/hsgiBkNEJF9SWELEEVMXWJocEtA9vCsqTO6vgAJ5iMCP6IuBDX9ByuAiPDwnGuE94qyrmzeTwmeILPKxXMS9tFot/vWvf6F169aoW7cu2rZti1deeQUySHUiImfS5+FUbXXRLyGSkeGeelXliBmA9XPFAHfyjPQUCgiFAhcfmAAhBCpgvL8CCiggcCz+aeT/egU/rNFgysPnEF50zHzCtVxI5TNEljl14L4DvPrqq6JJkyZiw4YNIicnR6xevVrUr19fLFmyxOpjcJ4hkh0pzHnjTuXl1edMqfylUOjmUPGE66avq0JR+7qamCvmqn8zMdl3mQCEeBhq8QeM95eHtdS9zsFzEEmelD5DEuaq57fHd5Pt3r0bw4cPR1JSEgAgKioKK1euxE8//eTmmhF5KDbbWyallbgdOANwxUMjsLegDQ4tzsSxE37IEp2xsyweFfBFIG5ChIVj50P/xUMDi1C/4joQFgbf+Hhg3Tp5LsRbEyl9hsgijw+G+vbti48++gi///47OnTogF9//RW7du3CW2+95e6qEXkeua4ebyupLSFSiyU/tGVa7Fp2GOpPNVBnd8C5im4AugEA6qEEKRE/QaUSeGB2HOqH3mPiADJeiLcmUvsMUY08PhiaM2cOioqK0LFjR/j6+kKr1eLVV1/FuHHjzL6mtLQUpaWlhp+LiopcUVUi9/L2h5YjR+xIcSVuG6YuKL9Vjsx3spC+ohhrjnXCRdHNsK8BivBQ1GGkjPRF4qw41Gvap9rrjdjTAiKH0VVS/AyReU7thHOAlStXioiICLFy5UqRlZUlPv/8c9G4cWOxYsUKs6+ZN2+eAFDtizlD5NW8eY0wR+erODIPx0OUXi8V387/STzefodooig0Op1GiitiQrsdYsO8n8QtzS3bDpyWZt3nKi1NV14uuUVe+BnyRK7KGfL4YCgiIkK89957RtteeeUVER0dbfY1t27dEhqNxvCVm5vLYIi8n60PLalQq00/cBQK3Ze9D1n9caseu7bHdaGbV2+KdX/fKx5ts1OE4JrRaTRVXBJPddwuvn/tZ1FWUmb/m9gSZDvrXnkqL/gMeTomUP/pxo0b8PExngHA19cXFRUVZl8TEBCAgIAAZ1eNyLN4Y7O9M7v+apGH404lF0vw3cIsqNMFNvzRBcXobdgX6lOA5JhjUE0MRvzkLvALvK/2b2hpjiL9umV9+wJt23pvNy1Qvftv+HBJfoaoOo8PhoYNG4ZXX30VkZGRiI2NxS+//IK33noLjz/+uLurRuRZrH1oxce7vm72cvaIHYksIVKUV4RvFmZDvUaBb/O74ibu5PlE+J6DKu4EUp5shL5Pd4aPXwvHvrm1o9l27/bu0VU1jdI8c8bjP0NkgVPbnRygqKhITJs2TURGRorAwEDRpk0b8Y9//EOUlpZafQzOM0Sy4W3N9t7a9WeFK6evis+e2imGtdgrAnDT6HRb+50Vs3puE3v/77DQ3ta6pkKmcoGUyjufKW++V3Lr/vMgrnp+c9V6Im/jqavH2zPCyNrV1iutpi5lhccvY13qEaR/E4ithV1xG/6GfR3q5EDV8yxUU0PRbVQ0FD6KGo7kJDXdQ2+9V1otEBVlvtVL3+Kak8PWICdw1fObwRCRN/K0oc32TgSpfxBZ6vqT8IPoQtZFrEk9BvX39ZF5NQ7aStkLnQNOIOWefKimtUTs8HbuCYCs5a33yluDPIlw1fPb43OGiMgO7lg93lwAVpuJIB04+7Inydt/HhkLfod6awh2auIgcCfRuXvdo1DdW4CUGZGIHtoeQHv3VdQWXnqvOLmiPNgUDOXm5kKpVDqrLkQkVeZaft56C5g5s3YjjCQ66quqM7vyoF54EumZTbC3uAuAO6P6egVlQ9W/ECmz2qDNgE4AOrmtnrUi1XtVU0uqN47SpGps6iYLCgrC888/jzlz5qBevXrOrJdDsZuMyInMtfxUbR2oiTVdDJ7W9WeFE5vPQP3mGaTvbI4DN2IM2xWoQN8G2VANuorkF9shsk9LN9bSCaR0ryx14Xpr959EeGTO0O7duzFjxgzk5eXh1VdfxYQJE5xWMUdiMETkJJaSS62VlgaMGeOQKrnbkfUnoV6Sh/TdYci6FW3Y7gMt+jfMQsrgIjw8JxrhPULdWEsCUHMgD9zpwtWXA0x3/3HNP6fxyGBI7/PPP8c//vEPNG/eHIsXL0a8h89bwmCIyEmsTS61RMLJp6JCICv9d6S/ex7qn5Q4WtbWsM8X5RjU5BBS7r+BEXM7oXlsMzfWlIzYOkrMU0dpejmPDoYA3czQCxYswFtvvYX7778fb7zxBlq3bu3o+jkEgyEiJ1m5Ehg71v7XS7SLQVQIHPjyKNLfvwj1wSicvB1l2FcHZRjS/BBUw8rw0NxYNG7byH0VJfPsGSUmpe4/LyGJ0WRDhgxBUVER3n33XXzzzTd49tln8dJLL6F+/fqOqh8ReTJbkkYlPsKoorwCez/5DeqPL0N9qB3OamMA6PKAAnET94f9CtUILR6c0xkhkb3cW1myzJ5RYu4YpUkuYVMw9MEHH2D//v3Yv38/jh49Ch8fH3Tu3Bl/+9vf0LVrV6xatQoxMTHIyMjA3Xff7aw6E5GnsLQECAA0bgw8+yzwySfSGmEEQFumxa5lh6H+VAN1dgecq+hi2FcPJUiKyIJKJfDA7DjUD73HjTUlm3GUGFViUzeZUqlE7969cc899+Cee+7BXXfdhbp16xqVee2115CWlobs7GyHV9Ze7CYjI2zqdixzyaVVtWwJPP000L69R1/38lvlyHwnC+krirHmWCdcFHfyfBqgCA9FHUbKSF8kzopDvabSGVVLVXCUmCR4fM6QOQUFBQgPD4dWq3XkYWuFwRAZ2DsTMtXM1HWtytTIGw8JTMuKy7D1rV+R/uUtrDsZg8uiiWFfI8VVDG+bDdW4QCTMjENAcIDL60dOwlFiHk+ywZAQAjt27ED//v0dedhaYTBEAKwfRkv20Wp1SakjRwJXrpguU/mv7XXr3BqY3rp2C5ve+BXpq25j/eku0CDEsK+pohAPRx+B6rEgDJwWhzr16ji9PuQmHCXm0SQbDHkiBkPExRZdxNoROvPnAy+/7PLAtORiCb5bmAV1usCGP7qgGA0M+0J9CpAccwyqicGIn9wFfoFeuFqRtS1xHtJi5zJyO18JkcRoMiLJ2Lmz5i4cIYDcXF05jhaxn7UjdJYsqd0SHTYoyivCNwuzoV6jwLf5XXETfQz7InzPQRV3AilPNkLfpzvDx69Frd/PY1nbRSzHrmSOEpM9BkMkD1xs0TWsHXljrhsNcEhgejXnGr5OzUb6+jrYVNAVpehr2Nfa7w+oup9GyjNN0XN8DHz8wu16D0mxdrHc2iyqSyRhDIZIHjiM1jUsDbVXKIBGjWoOhvRsDEwLj1/GutQjSP8mEFsLu+I27gWgWwbjEd+VeLDNEfQY1R7tXhoLRZ1Im44taVqtrqXHUkvcgw9aV85BLXZEnsTH3RUgcgn9Q1qfk1KVQqFLmvTwpWVcTp8UvXKl7rulUaK+vrruFKD6tdb/PG2ade9tRWB6Iesilo3ZgYTGBxHaMQRPfhaPjYU9cRv+6BxwAqti/o2bTSLwhXYsRp34D9r/ZzwUbVrrWkDkwtou4vfft74rmcjLMBgiebDmIS2RmZBdJiNDl3Q+cKBuyY2BA3U/WwokkpN13Sktq6zEHhGh2/6Pf9QqMM3bfx7vpGxH/4aHEN61KSavug9br/aAFn7oXvcoXh2ciWPfnsbhtMMYdfRl+F++YHwAfZePXAIia1vYTp1y7PGIJITdZCQf+oe0qeRQDqM1HlFz4gQwb171MtbmjiQn67pTzI3QWbJEdxwrl+g4sysP6oUnkZ7ZBHuLuwC402rUKygbqv6FSJnVBm0GdALQ6c/Rg/3Z5QNY3/Xbtq3lMrYcj0hCOLSe5IfDaKuzZtJEPUdNQ2BhfpcTm89A/eYZpO9sjgM3Yu68PSrQt0E2VIOuIvnFdojs07L6se1ZhNNbWTvT8smTuoCIMzKTB+HQeiJn4TBaY+ZGEJnjqGkITLQeHbkaBvV755E+7jiybkUDiAKgS4Lu3zALKYOL8PCcaIT3iKv52Bw9eIe+i9hSS5y/v80tdkTegsEQkZzVNNLIEgcEEkLhg6yLYUh/H1D/pMTRsrYAogEAvijHoCaHkHL/DYyY2wnNY7tbf2Bru3KOHNG1Inn75IPWdhGzK5lkit1kRHJmbXeSKXZ2MYkKgQNfHkX6+xehPhiFk7ejDPvqoAxDmh+CalgZHpobi8ZtG9lXN0tdQ1XJZfJBzkBNEsPlOByIwRCRGStX6kaK2cKO3JGK8grs/eQ3qD++DPWhdjirjTDsC8RN3B/2K1QjtHhwTmeERIbUcCQbmFuE05Sqy4BwHTsij8BgyIEYDBGZYWvLkD6XRD8Sq4YWA22ZFruWHYb6Uw3U2R1wruJO11U9lCApIgsqlcADs+NQP7R+7c7DHHsSw/WJxFJex44tO+QlGAw5EIMhIjNs7U7y9TWeeLFKt1H5rXJkvpOF9BXFWHOsEy6KZoaiDVCEh6IOI2WkLxJnxaFe03oOPhkz9IHB1q3Af/5jufzbbwMzZlgu56kj0RzRvcdgijwER5MRkfNZM9Lo5ZeBq1d1CbRVZ6DOz4dQqfDLyAVYerAP1p2MwWXRw7C7keIqhrfNhmpcIBJmxiEguJ/TT6ka/ehBOUw+6Ii1xbwxV4rIAs5ATSR31swYnZ5u+rVCQAig6f/ew4oTfXFZNEFTRSGe6rgD3792AAXF9bH8RDySXu6JgOAA559LTbx98kFLa5ABuu7NmpZU0QdTVbsI5TZrN8kOu8mIPIk7uyfMvbeVeUWLo95G12f7I35yF/gFemCjs7dPPljbiSb110fKuVLkdVz1/GbLEJGnsHctMEfRdyeNGaP77uuLorwi7E7dbtXLp7/WAgNndvfMQAiwfn06/eSDlsp5WkBQ24kmrV3QlQu1khdiMETkCTyoe+JqzjV8/vQuPBS6D82V/vj7pv7WvdDTuo1MsdQlWHXyQUvltFpdi8zKlbrvNXVBOZu1199cOc7aTTLGbjIid/OA7onC45exLvUI0r8JxNbCrrgNf8O+jn4nsdenL4LLCqGAhLqNauKIyQc9LdHY2m5Ac/eJ67mRB+LQegdiMEQezU0PoQtZF7Em9RjU39dH5tU4aCsNLu0ccAIp9+RDNa0lYoe3g2LtGtMTGMp1EkJPnZTR3EST1tSrtsEUkRMwZ4jIWTypawNwafdE3v7zeCdlO/o3PITwrk0xedV92Hq1B7TwQ/e6R/Hq4Ewc+/Y0Dt9qj5czB6Dzw+2h8FFY320kB44YteUstblP1uZUMRAiL8SWIZIXT+vaAJzeMnRmVx7UC08iPbMJ9hZ3MdrXKygbqv6FSJnVBm0GRFo+GCfjk0Z3Um3uk6nfEaWSC7WSW7CbzIEYDBEAz+3acEL3xInNZ6B+8wzSdzbHgRsxdw6FCvRtkA3VoKtIfrEdIvu0rOEoZJK167mlpelG5kkRg17yEJyBmsiRLHVtKBR31tty9T/61swCbUX3xJH1J6Fekof03WHIuhUNIAoA4AMt+jfMQsrgIjw8JxrhPeJqX2c5PyxrO2pLCvTTLBDJBFuGSB6k0LVhY/eEqBDISv8d6e+eh/onJY6W3Zk52RflGNTkEFLuv4ERczuheWyzaq93aD3d3dXoSkw0JnIZtgwROZIU5lBJTta1TNXQ4iIqBA58eRTp71+E+mAUTt6OBhANAKiDMgxpfgiqYWV4aG4sGre92/F1dMTaV1LnoJY8IvIckmgZys/Px+zZs/Hdd9/hxo0baNeuHZYvX46777buH3u2DJHVLUNbtugeYh7U/VNRXoG9n/wG9ceXoT7UDme1EYZ9gbiJ+8N+hWqEFg/O6YyQyBDnVcQD5kPyKEw0JnI6JlD/6erVq+jevTsGDhyISZMmoVmzZjhx4gTatm2LtlYuqMhgiKzq2mjcGAgM1JXRc1P3j7ZMi13LDkP9qQbq7A44V3En/6QeSpAUkQWVSuCB2XGoH1rfNZWSQlejq8k5d4rIBdhN9qeFCxdCqVRi+fLlhm2tW7d2Y41Ikix1bQgBXL5c/XUu7P4pv1WOzHeykL6iGGuOdcJF0c2wrwGK8FDUYaSM9EXirDjUa9rHqXUxSQpdja7GRGMir+DxLUMxMTFITExEXl4etm/fjpYtW2Ly5Ml46qmnzL6mtLQUpaWlhp+LioqgVCrZMlSVHP+qNZf8e/Om6WAIcGr3T1lxGba+9SvSv7yFdSdjcFk0MexrpLiK4W2zoRoXiISZcQgIDnDoe9uMLUOWyfF3isiJ2E32p8DAQADAzJkz8de//hX79+/HtGnT8MEHH2D8+PEmX/Pyyy9j/vz51bbX6mJ6yz9y+vNYtw748kugsPDOPrmMCKp6L7VaICHB8uv0D/lafhZuXbuFTW/8ivRVt7H+dBdocCfPp6miEA9HH4HqsSAMnBaHOvXq2HGCTsJRVDWT+yg7IidwWZqL8HB16tQRffr0Mdr27LPPinvuucfsa27duiU0Go3hKzc3VwAQGo3Gvkqo1UJERAihewToviIidNulxNR5VP5SKHRfUjuv2kpLM39NKn+lpdn9WSguKBarZ+4WoyN/FPVRZPTyUJ8LYnLnTPHDmwfF7Zu3XXTSdlKr73xO+Nm5Q39d+DtF5FAajaZ2z28reXwwFBkZKZ544gmjbe+//74IDw+3+hi1upje8o+cufMwdV5KpRDl5e6usets22ZdMDR/vk2fBU2uRqRN/VGktNwt6qLEOH7yzRfTu2eKnUt/FdrbWvect71MBYRKpXR+FxytvNzyHxly+50ichBXBUMe3002duxY5ObmYufOnYZtM2bMwL59+7B7926rjmF3M5u3DCW2dB6myCnvw5ruH/3ClxY+C1d/+AVfv34U6evrYFNBV5Qi0FCktd8fUHU/jZRnmqLn+Bj4+El4nWRv6TZ2BOZSETkNR5P9acaMGejbty9ee+01jBw5Ej/99BM++ugjfPTRR85/8507aw4ghAByc3XlPPkfOUvnYYqURgTV9sFszSR6Tz0FzJtn/hh/fhb+2v4gtmKwYXOHOjlQ9TwL1dRQdBsVDYWPFYuhSgFHUd3BUXZEkufxwVDPnj2xZs0azJ07F//+97/RunVrLF68GOPGjXP+m3vLP3L21E8q6yrZm7RaNYAaPlw3fN7UsRYvBiqNTqxJMxSic8AJpNyTD9W0logd3g4KH04F4dXksFYZkZfz+GAIAB588EE8+OCDrn9jKf0jV1PriC3103f9xcc7p56OZO/SEDUFUGfOmL6OmZlWVWnhAoHI2e0BtLf3rEhq4uN1nx9Lo+yk8DtFJFMenzPkCLXOGfL0ocSWWkcsnUdlCoU01peyN5/LXACl7w6rcu5nduVBvfAkMrY1xKqSYWiJfPjAgz8L5B76zxVguptVCr9TRB7IVTlDEs7gdAF9Lglw5x81PU9ZkFH/j3DVoEDfOpKRUfN5VKZUSucfbVvyufS0Wl3QaCog1G+bPh0nNp7CgvszcXfQEbSOj8ALGwZgd0k3TMfbUADVQyFP+SyQ+yQn63539In2ehER0vmdIpIxtgxZw1MXZLS1dcTUeTRrBowbp8uZkdKIoJUrgbFjLZdLSwPGjNH9v5WjfgZgG7ZjAADAB1r0b5iFlMFFeHhONMLP7HbdZ8FTR2x5ar08Aa8NkUNxNJknSU7WBQue9o+craPd3H0ejnxQ2JPPZWUieUvkYUiTn5Fy/w2MmNsJzWO739nZw0XX0NmzGdt7LzjLcs04yo5Impw6i5GHcNWkTS5ny8zJ7uboWbz1E92Zm0jSxER3FVt/sOp6ab5c76CTtpOzJ/q09154ywSkRCQZrnp+M2dIyqQy2s2avCZbWZnPVSEU2P3hYTx/dybaDm6DXESgAmbyphQKQKlE8OgHbK+Po1iZ1wSt1r7j23svnF0vIiI3Ys6QlElhtJuzZ/E20W0jIiJwZPB0fPDz3cj4rQPOVdwJBkcjDWnQzVFlFBJ5yqgfZ85mXJt7wVmWicgNOJqMLJPCaDdr85refde+VoXkZODMGZR/txmHx7yKtyIXIzxvPzovfx7vHe6PcxVhaIAijIv6ERkv7sUnl0ZAoVZDERFhfBxPGfXjzIk+7RmB54p6ERG5GROopU4/pNfczMlSebjPmAG8+aZNibhlxWXY8uavUP/3Ftae7IErIsGwr5HiKoa3zYZqXCASZsYhILjfnRc6MpG8aiJy377A7t32H9eZXZ+1CWik0iVLRGQHdpN5C08d0mtt9wpgVVfVzSs3sWlRFtT/u431p7tAgxDDvqaKQjwcfQSqx4IwcFoc6tSrU8vKW2BqZJWvr3ELl60jrZzZ9Vmbri4pdMkSkddx1fObwZCjeWpQ4i62zH4NmHyollwswXcLs5C+WuCb3C4oRgND8VCfAiTHHINqYjDiJ3eBX6CLGjvNzWRdlT25SM6azbi2AQ1nWSYiF3PZ89upY9U8hMuG1jt6+Li30A/JNjcM3sRX8VcbRNrUH0Vy+B5RFyXGl9Q3X0zvnil2Lv1VaG9rXX8++mH9Vp6LqWH+Fpn6LCmVjhlWb+peWDs83ln1Ki8XYts23TQQ27bZdq2IyGu56vnNliFHsXHNK9kx1aVUg0fxGb7EY4afW/v9AVX300h5pil6jo+Bj58bc/9t6fqrzNaRVs5qZaztjOr21svc6ziRIxGZwW4yB3L6xXT28HFvodXqRo3NmGGx6ABsw/k6raDqeRaqqaHoNioaCp8a1lVzJWuXAqmq8tIg7ubq7lxzAc+YMcCiRfwjgohMYjDkQIaLuWEDgouKHP+PP+dgsZ5WC21LJXwKzpuc+rACClwPaIrcL3cgNtmDAqDKXNUy5C2sza+qin9EEMke5xlyhgcf1P1FP3CgriXHnpmPTeEcLBbl7T+Pd1K2477G2RhZ8A4EFNVmghZQwEcBhKR9gM6qjp4ZCAG6QDoiovrcTub8ObM14uOdWy9PVNPM1ZbUNO8REZEDySsYqqw2S0FUxTlYTDqzKw9vDstEnwaHoewVhmkZ/bGzqCsyoMLcgLdQHNjUqLxC6SETH1pS02SXVXnK5JfuYmmiR2vI+I8IInIN+U66KITuQTV9um4Cvto8qPQtBZaGLNemZUAiQ/ZPbD6D9EVnoN7VHAduxADQzfSsQAX6BR9Gyl+uIfnFdojsMx3QPiuJcwJQ/foPH256sktT8wx5wuSX7uKIQEZmf0QQkevJNxgCjJvha5PLoW8pUKl0gY+pOVhq0zLg4aNtjqw/ifTFeVDvCUPWrWgAUQAAH2jRv2EWVIlFeHhOR4R162r8Ql/f2ufQuCJIrOn6nznj2BmovU1tAhlH/BFBRGQNpw7c9xCGeQrMzQOTluaYNzI1B0uzZkKsXl27Y5qan8faeWGcoEJbIQ7975j4573bREf/k0bV8kOZGNJkv/jo0e2iIPuicyviinmdPPD6S4p+TiZLc0zZO+8REXk1zjPkQIZsdAAmc9EdOcpn9Wpg8mSgsPDONntbcTxoyL6oEPj5i6NQL7uI9AOtcaq8lWGfP0oxuPmvUA0rw0NzY9G4bSOn1gWAa+Z18qDrL2mWZq5+4QXddAX2zntERF6LQ+sdyGww5OiHmaMf0G4esl9RXoG9n/wG9ceXoT7UDme1d1Z6D8RNDA37FSkjtHhwTmeERIbUcCQHc1WQwikTHMfSRI8SyYkjItdyVTAk35whR4/yqWkIsb3J2m4Ysq8t02LXssNI/0SDjN864FxFF8O+IBQjSZmFlBTggdlxqB96j8Pe1yaWRig5KheMUyY4TnKy7rNvLuBxRP4YEZGd5BsMOXqUjzMe0C4asn/7xm1sf+8w0lcUY82xTrgouhn2NUARHoo6DNVoPyTOikPdxn1r9V5WsdRK4KoghVMmOBYDHiLyUPIKhjZsAJwxAzXgnAd0375As2bApUum99ditE1ZcRm2vPkr1P+9hbUnY3FF9DDsa6S4iuFts6EaF4iEmXEICO5n8/HtZs3IOXuCFHu6YVwxZYK3Y/cXEUmBU9OzPYRLstG3bbNuBfNt26w7nqmRUrUcbXPj8g2xdu5e8WibnSIE14wHvSkuiqc6bhffv/azKCsps+sS1Jq1I7csjVCqukp8bUad1XaVdzlzxWg/IvJqrhpNxmDIUWx9QNfEXFBQ+UuptOqhUlxQLFbP3C1GKX8U9VFkdIhQnwtiSpdM8cObB8Xtm7cdcBGqKC/XBX9pabrvNZ27/vrVFPxVDXCsCVIcMTTe1EPdyusvW5ySgIgcgMGQA7nqYjqkFcFSUKCfu6i01OwhNLkakTb1R5EcvkfURYnxM9w3T0zvnil2vf+r0N7WOvDkq7C1VcCeljVLQYqtAVZNbAns5M6R152IZM1Vz2955Qw5W3Ky6SUazCVrm8qnsGYtp0uXdLMcV0pGvZpzDV+nZiN9fR18X9ANZbiT6NzG7yxSuudANakZeo6PgcKnZe3PtSbmphjQrwdnaooBe3KuLI1QcmRSO5N/reeq0X5ERA7CYMjRLD2g9cwlCnfrZt37nD+PwuOXsfa1I1B/G4gthd1QjnsNuzvUyYGq51mopoai26hoKHxa1f7crGHvFAP2jtyqKUjh0Hj34HUnIolhMOQMlloRzLWc5OVZvcL3809fx5LiEGhxZyRT54ATUPXJR8pzLRE7vB0UPq3tqHwt2dsq4IyRWxwa7x687kQkMQyGXK2mlhMrVADIgxKLi59ABXzRve5RqO4tQMqMSEQPbQ+gvUOrazN7WwWcsdhtfDzQpAlw+bL5Mk2acGi8o3FKAiKSGB93V0B2rMkJsmBpwEwseGAnTm37AwdvdMLfNw1A9NA2DqpgLdWmVUCfc9WySk5TRIRj1hoj19AHtsCdQFbP0TO/ExE5gLzWJnPy2iZWWbkSGDvW7pcXjXoSwas+dmCFHEy/bpilVoGa1g1z1ER9XFvMvSytR0ZEZAHXJvNWtcyTCP7bOAdVxEkc0d3lqJFbTOR1L2sHExARuRm7yVxM9LsXZY1DYa45zmwznUKh+6taCnkWntLdxURe99MHtmPG6L4zECIiD8RuMhcQFQI/f3EU6mUXkX6gNeLKDyAdKgCAT6XwRwBQmDqAvkVFankz7l6XyhFddkRE5DbsJpO4ivIK7P3kN6g/vgz1oXY4q40BEAMAyEdzvNHwNUy9/RaCSu4swqpQKoHRo3V5RdZM2ugKtQlo3D1RoTNGqBERkdeRXMvQggULMHfuXEybNg2LFy+26jUOjyzNBAjaMi12LTuM9E80yPitA85V3Ol+CUIxkpRZSEkBHpgdh/qh9c0HGu5uUdGzZgV5KWAiLxGRJLmqZUhSwdD+/fsxcuRIBAcHY+DAge4Jhkw8WG+FNMeK+s9i3rlncFE0M2xvgCI8FHUYqtF+SJwVh7qN69buvV3J3MSQ7LIjIiIXYTBURXFxMXr06IH3338f//nPf9CtWzfXB0N/BghCCKPcnoo/f1IhHZmKgRjeNhuqcYFImBmHgOAA+9/PXfS5NubmQ2KuDRERuYCrgiHJjCabMmUKkpKSkJCQYLFsaWkpioqKjL5q6+alYtycMKlaIATokqAVEPhvyGQUaOpi+Yl4JL3cU5qBEGDbkhpEREQSJ4kE6lWrVuHgwYPYv3+/VeVTU1Mxf/78Wr9vycUSfLcwC+mrBTS51/AdLpotqwBQV1MAHNhbPWlYal00nJ+HiIhkxONbhnJzczFt2jT897//RWBgoFWvmTt3LjQajeErNzfX6vcryivCymd3I6XlXjRrocBf3+qD/+X2RUNorDtA1QAhI0PX5TRwoG7m6YEDdT9nZFhdJ5fj/DxERCQjHp8ztHbtWjz88MPwrdSSotVqoVAo4OPjg9LSUqN9pljqc7yacw1fp2YjfX0dfF/QDWW4073Vxu8sUrrnYOLAHHR6/XHLFa68tINUk5A5Pw8REXkAJlD/6fr16zh79qzRtokTJ6Jjx46YPXs2OnfubPEYpi5m4fHLWPvaEai/DcSWwm4oRx1D+Q51cqDqeRaqqaHoNioaCh+F7QGC1JOQ9YEcYHp+Hk8N5IiIyGtw0sU/NWjQoFrAExQUhCZNmlgVCFVWkH0J/333ENTf10fm1ThocWdpi84BJ6Dqk4+U51oidng7KHxaG7/Y1gn8bElCdsfEhJbymPRLapiaZ4jz8xARkRfx+GDIkTr0awKgreHn7nWPQnVvAVJmRCJ6aHsA7Ws+gC0BgquTkG1J0rZ2MkUutElERDLg8d1kjqBvZgM06BX0B1T9C5Eyqw3aDIi074DWBB6ZmbpkaUsq5xjZy5aZoqWax0RERLLDnCEH0l/M7M1HEZvQ0foX1mZIvKuSkG0JbqSex0RERLLCSRedQNkr3PrCtR0Sr88xAu4EJnqOWiRUq9W1CJkKtvTbpk/XlQM4mSIREZEJsgqGrKZvbakaOOTn67ZbGxDpc4xatjTeHhHhmO4oW4MbTqZIRERUjawSqK1iqbVFodC1tgwfbl2rjjOTkG0NbjiZIhERUTUMhqpyxpB4X1/nDJ+3NbiJj9e1SlnKY4qPr76PiIjIS7GbrCopdSXpg5uqOUl6CgWgVN4JblyRx0RERCQxDIaqklJXkj3BjbPzmIiIiCRGVkPrrRqaJ8V1uUzNM6RU1jxTdG2mDSAiInIBzjPkQDZfTCmuy8XghoiIvAzXJnMnKa7L5awkbSIiIi/HYMgcrstFREQkCwyGasLWFiIiIq8n32CIOTZEREQEuQZDtqzyTkRERF5NfvMMOWrdMW+g1QKZmcDKlbrv+gVdiYiIZERewZCtq7x7s4wM3XxKAwcCY8fqvkdFySsYJCIigtyCod27bVvl3VuxdYyIiMhAXsHQhQvWlfOEdcecha1jRERERuQVDIWGWlfOE9Ydc5adO9k6RkREVIm8gqG+fW1b5d0bWdvq5c2tY0RERJXIKxiyZ5V3b2Ntq5c3t44RERFVIq9gCLiz7ljLlsbbIyI8cwFWR4uPZ+sYERFRJfKcdFHO647pW8dUKl3gUzmRWi6tY0RERJXIMxgC5L3umL51zNQs3IsXe3/rGBERUSXyDYbkTs6tY0RERJUwGJIzObeOERER/Ul+CdRERERElTAYIiIiIlljMERERESyxmCIiIiIZI3BEBEREckagyEiIiKSNQZDREREJGsMhoiIiEjWGAwRERGRrDEYIiIiIlljMERERESyxmCIiIiIZM3jg6HU1FT07NkTDRo0QPPmzTFixAgcP37c3dUiIiIiL+HxwdD27dsxZcoU7N27F5s3b8bt27cxZMgQlJSUuLtqRERE5AUUQgjh7krY4tKlS2jevDm2b9+O++67z6rXFBUVISQkBBqNBsHBwU6uIRERETmCq57ffk47spNoNBoAQOPGjc2WKS0tRWlpqeHnoqIip9eLiIiIpMnju8kqq6iowPTp09GvXz907tzZbLnU1FSEhIQYvpRKpQtrSURERFIiqW6ySZMm4bvvvsOuXbsQERFhtpypliGlUsluMiIiIglhN1kVU6dOxYYNG7Bjx44aAyEACAgIQEBAgItqRkRERFLm8cGQEALPPvss1qxZg8zMTLRu3drdVSIiIiIv4vHB0JQpU5CWloZ169ahQYMGuHDhAgAgJCQEdevWdXPtiIiISOo8PmdIoVCY3L58+XJMmDDBqmNwaD0REZH0MGfoTx4eqxEREZHESWpoPREREZGjMRgiIiIiWWMwRERERLLGYIiIiIhkjcEQERERyRqDISIiIpI1BkNEREQkawyGiIiISNY8ftJFp9BqgZ07gfPngbAwID4e8PV1d62IiIjIDeQXDGVkANOmAXl5d7ZFRABLlgDJye6rFxEREbmFvLrJ1q8HVCrjQAgA8vN12zMy3FMvIiIicht5BUOzZwOm1jrTb5s+XdeFRkRERLIhr2Do3Dnz+4QAcnN1uUREREQkG/IKhqxx/ry7a0BEREQuxGCoqrAwd9eAiIiIXEhewVB4OKBQmN6nUABKpW6YPREREcmGvIKhhQt136sGRPqfFy/mfENEREQyI69g6KGHgPR0oGVL4+0REbrtnGeIiIhIduQ36WJyMjB8OGegJiIiIgByDIYAXeAzYIC7a0FEREQeQF7dZERERERVMBgiIiIiWWMwRERERLLGYIiIiIhkjcEQERERyRqDISIiIpI1BkNEREQkawyGiIiISNYYDBEREZGsMRgiIiIiWWMwRERERLLGYIiIiIhkjcEQERERyRqDISIiIpI1BkNEREQkawyGiIiISNYYDBEREZGsMRgiIiIiWZNMMLR06VJERUUhMDAQvXv3xk8//eTuKhEREZEXkEQw9L///Q8zZ87EvHnzcPDgQXTt2hWJiYm4ePGiu6tGREREEieJYOitt97CU089hYkTJyImJgYffPAB6tWrh08//dTdVSMiIiKJ8/hgqKysDAcOHEBCQoJhm4+PDxISErBnzx431oyIiIi8gZ+7K2BJYWEhtFotWrRoYbS9RYsWOHbsmMnXlJaWorS01PCzRqMBABQVFTmvokRERORQ+ue2EMKp7+PxwZA9UlNTMX/+/GrblUqlG2pDREREtXH58mWEhIQ47fgeHww1bdoUvr6+KCgoMNpeUFCA0NBQk6+ZO3cuZs6cafj52rVraNWqFf744w+nXkxPU1RUBKVSidzcXAQHB7u7Oi7D8+Z5ywHPm+ctBxqNBpGRkWjcuLFT38fjgyF/f3/cdddd2Lp1K0aMGAEAqKiowNatWzF16lSTrwkICEBAQEC17SEhIbL6EOkFBwfzvGWE5y0vPG95ket5+/g4N8XZ44MhAJg5cybGjx+Pu+++G7169cLixYtRUlKCiRMnurtqREREJHGSCIZGjRqFS5cu4aWXXsKFCxfQrVs3bNy4sVpSNREREZGtJBEMAcDUqVPNdotZEhAQgHnz5pnsOvNmPG+etxzwvHnecsDzdu55K4Szx6sREREReTCPn3SRiIiIyJkYDBEREZGsMRgiIiIiWWMwRERERLImyWBo6dKliIqKQmBgIHr37o2ffvqpxvKrV69Gx44dERgYiC5duuDbb7812i+EwEsvvYSwsDDUrVsXCQkJOHHihDNPwS62nPfHH3+M+Ph4NGrUCI0aNUJCQkK18hMmTIBCoTD6uv/++519Gjaz5bxXrFhR7ZwCAwONynjj/R4wYEC181YoFEhKSjKUkcL93rFjB4YNG4bw8HAoFAqsXbvW4msyMzPRo0cPBAQEoF27dlixYkW1Mrb+m+Fqtp53RkYGBg8ejGbNmiE4OBh9+vTB999/b1Tm5Zdfrna/O3bs6MSzsJ2t552ZmWnyc37hwgWjct52v0397ioUCsTGxhrKSOF+p6amomfPnmjQoAGaN2+OESNG4Pjx4xZf54pnuOSCof/973+YOXMm5s2bh4MHD6Jr165ITEzExYsXTZbfvXs3xowZgyeeeAK//PILRowYgREjRiA7O9tQ5vXXX8c777yDDz74APv27UNQUBASExNx69YtV52WRbaed2ZmJsaMGYNt27Zhz549UCqVGDJkCPLz843K3X///Th//rzha+XKla44HavZet6AbobWyud09uxZo/3eeL8zMjKMzjk7Oxu+vr7461//alTO0+93SUkJunbtiqVLl1pVPicnB0lJSRg4cCAOHTqE6dOn48knnzQKDOz5DLmaree9Y8cODB48GN9++y0OHDiAgQMHYtiwYfjll1+MysXGxhrd7127djmj+naz9bz1jh8/bnRezZs3N+zzxvu9ZMkSo/PNzc1F48aNq/1+e/r93r59O6ZMmYK9e/di8+bNuH37NoYMGYKSkhKzr3HZM1xITK9evcSUKVMMP2u1WhEeHi5SU1NNlh85cqRISkoy2ta7d2/xzDPPCCGEqKioEKGhoeKNN94w7L927ZoICAgQK1eudMIZ2MfW866qvLxcNGjQQHz22WeGbePHjxfDhw93dFUdytbzXr58uQgJCTF7PLnc77fffls0aNBAFBcXG7ZJ4X5XBkCsWbOmxjIvvviiiI2NNdo2atQokZiYaPi5ttfS1aw5b1NiYmLE/PnzDT/PmzdPdO3a1XEVczJrznvbtm0CgLh69arZMnK432vWrBEKhUKcOXPGsE1q91sIIS5evCgAiO3bt5st46pnuKRahsrKynDgwAEkJCQYtvn4+CAhIQF79uwx+Zo9e/YYlQeAxMREQ/mcnBxcuHDBqExISAh69+5t9piuZs95V3Xjxg3cvn272mJ3mZmZaN68OaKjozFp0iRcvnzZoXWvDXvPu7i4GK1atYJSqcTw4cPx22+/GfbJ5X5/8sknGD16NIKCgoy2e/L9toel329HXEspqKiowPXr16v9fp84cQLh4eFo06YNxo0bhz/++MNNNXSsbt26ISwsDIMHD8aPP/5o2C6X+/3JJ58gISEBrVq1Mtoutfut0WgAoMZFWF31DJdUMFRYWAitVlttGY4WLVpU6zPWu3DhQo3l9d9tOaar2XPeVc2ePRvh4eFGH5j7778fn3/+ObZu3YqFCxdi+/btGDp0KLRarUPrby97zjs6Ohqffvop1q1bhy+//BIVFRXo27cv8vLyAMjjfv/000/Izs7Gk08+abTd0++3Pcz9fhcVFeHmzZsO+d2RgkWLFqG4uBgjR440bOvduzdWrFiBjRs3YtmyZcjJyUF8fDyuX7/uxprWTlhYGD744AOo1Wqo1WoolUoMGDAABw8eBOCYfys93blz5/Ddd99V+/2W2v2uqKjA9OnT0a9fP3Tu3NlsOVc9wyWzHAfZb8GCBVi1ahUyMzONkolHjx5t+P8uXbogLi4Obdu2RWZmJgYNGuSOqtZanz590KdPH8PPffv2RadOnfDhhx/ilVdecWPNXOeTTz5Bly5d0KtXL6Pt3ni/CUhLS8P8+fOxbt06o9yZoUOHGv4/Li4OvXv3RqtWrfDVV1/hiSeecEdVay06OhrR0dGGn/v27YtTp07h7bffxhdffOHGmrnOZ599hoYNG2LEiBFG26V2v6dMmYLs7GyPyWuSVMtQ06ZN4evri4KCAqPtBQUFCA0NNfma0NDQGsvrv9tyTFez57z1Fi1ahAULFmDTpk2Ii4ursWybNm3QtGlTnDx5stZ1doTanLdenTp10L17d8M5efv9LikpwapVq6z6x8/T7rc9zP1+BwcHo27dug75DHmyVatW4cknn8RXX31VrSuhqoYNG6JDhw6Svt+m9OrVy3BO3n6/hRD49NNP8eijj8Lf37/Gsp58v6dOnYoNGzZg27ZtiIiIqLGsq57hkgqG/P39cdddd2Hr1q2GbRUVFdi6datRa0Blffr0MSoPAJs3bzaUb926NUJDQ43KFBUVYd++fWaP6Wr2nDegy7B/5ZVXsHHjRtx9990W3ycvLw+XL19GWFiYQ+pdW/aed2VarRaHDx82nJM3329ANwS1tLQUjzzyiMX38bT7bQ9Lv9+O+Ax5qpUrV2LixIlYuXKl0RQK5hQXF+PUqVOSvt+mHDp0yHBO3ny/Ad1orJMnT1r1x44n3m8hBKZOnYo1a9bghx9+QOvWrS2+xmXPcJtSvz3AqlWrREBAgFixYoU4cuSIePrpp0XDhg3FhQsXhBBCPProo2LOnDmG8j/++KPw8/MTixYtEkePHhXz5s0TderUEYcPHzaUWbBggWjYsKFYt26dyMrKEsOHDxetW7cWN2/edPn5mWPreS9YsED4+/uL9PR0cf78ecPX9evXhRBCXL9+Xbzwwgtiz549IicnR2zZskX06NFDtG/fXty6dcst52iKrec9f/588f3334tTp06JAwcOiNGjR4vAwEDx22+/Gcp44/3Wu/fee8WoUaOqbZfK/b5+/br45ZdfxC+//CIAiLfeekv88ssv4uzZs0IIIebMmSMeffRRQ/nTp0+LevXqiVmzZomjR4+KpUuXCl9fX7Fx40ZDGUvX0hPYet7//e9/hZ+fn1i6dKnR7/e1a9cMZZ5//nmRmZkpcnJyxI8//igSEhJE06ZNxcWLF11+fubYet5vv/22WLt2rThx4oQ4fPiwmDZtmvDx8RFbtmwxlPHG+633yCOPiN69e5s8phTu96RJk0RISIjIzMw0+tzeuHHDUMZdz3DJBUNCCPHuu++KyMhI4e/vL3r16iX27t1r2Ne/f38xfvx4o/JfffWV6NChg/D39xexsbHim2++MdpfUVEh/vWvf4kWLVqIgIAAMWjQIHH8+HFXnIpNbDnvVq1aCQDVvubNmyeEEOLGjRtiyJAholmzZqJOnTqiVatW4qmnnvKofzD0bDnv6dOnG8q2aNFCPPDAA+LgwYNGx/PG+y2EEMeOHRMAxKZNm6odSyr3Wz90uuqX/lzHjx8v+vfvX+013bp1E/7+/qJNmzZi+fLl1Y5b07X0BLaed//+/WssL4RuioGwsDDh7+8vWrZsKUaNGiVOnjzp2hOzwNbzXrhwoWjbtq0IDAwUjRs3FgMGDBA//PBDteN62/0WQjdcvG7duuKjjz4yeUwp3G9T5wzA6HfWXc9wxZ8VJCIiIpIlSeUMERERETkagyEiIiKSNQZDREREJGsMhoiIiEjWGAwRERGRrDEYIiIiIlljMERERESyxmCIiIiIZI3BEBEREckagyEiIiKSNQZDRCRJK1euRN26dXH+/HnDtokTJyIuLg4ajcaNNSMiqeHaZEQkSUIIdOvWDffddx/effddzJs3D59++in27t2Lli1burt6RCQhfu6uABGRPRQKBV599VWoVCqEhobi3Xffxc6dOxkIEZHN2DJERJLWo0cP/Pbbb9i0aRP69+/v7uoQkQQxZ4iIJGvjxo04duwYtFotWrRo4e7qEJFEsWWIiCTp4MGDGDBgAD788EOsWLECwcHBWL16tburRUQSxJwhIpKcM2fOICkpCX//+98xZswYtGnTBn369MHBgwfRo0cPd1ePiCSGLUNEJClXrlxB3759MWDAAHzwwQeG7UlJSdBqtdi4caMba0dEUsRgiIiIiGSNCdREREQkawyGiIiISNYYDBEREZGsMRgiIiIiWWMwRERERLLGYIiIiIhkjcEQERERyRqDISIiIpI1BkNEREQkawyGiIiISNYYDBEREZGsMRgiIiIiWft/oM8QQnDJ+F8AAAAASUVORK5CYII=", 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", 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      " ] @@ -2216,9 +2216,9 @@ "name": "stdout", "output_type": "stream", "text": [ - "[[4.23463496]\n", - " [2.74399625]]\n", - "[4.20387361] [2.73436201]\n" + "[[3.96383084]\n", + " [2.94004512]]\n", + "[3.99618085] [2.9822844]\n" ] } ], @@ -2374,16 +2374,16 @@ "name": "stdout", "output_type": "stream", "text": [ - "Eigenvalues of Hessian Matrix:[0.30988581 4.80658849]\n", - "[[4.14058224]\n", - " [2.99719628]]\n", - "[[4.14080023]\n", - " [2.99702696]]\n" + "Eigenvalues of Hessian Matrix:[0.31443711 4.41185301]\n", + "[[4.01380409]\n", + " [2.87614787]]\n", + "[[4.0124208 ]\n", + " [2.87730544]]\n" ] }, { "data": { - "image/png": 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", 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", "text/plain": [ "
      " ] @@ -2528,7 +2528,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_58998/394505933.py:33: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n", + "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_23327/394505933.py:33: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n", " print('>%d f(%s) = %.5f' % (i, solution, solution_eval))\n" ] }, @@ -2660,7 +2660,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_58998/476849792.py:39: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n", + "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_23327/476849792.py:39: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n", " print('>%d f(%s) = %.5f' % (i, solution, solution_eval))\n" ] }, @@ -4538,8 +4538,21 @@ "Own inversion\n", "[[3.94499279]\n", " [3.03306538]]\n", - "Eigenvalues of Hessian Matrix:[0.31248425 4.44418124]\n", - "theta from own gd\n", + "Eigenvalues of Hessian Matrix:[0.31248425 4.44418124]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "theta from own gd" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", "[[3.94499279]\n", " [3.03306538]]\n" ] @@ -4865,7 +4878,13 @@ "Own inversion\n", "[[4.0586484]\n", " [3.0718316]]\n", - "Eigenvalues of Hessian Matrix:[0.29860173 3.8931686 ]\n", + "Eigenvalues of Hessian Matrix:[0.29860173 3.8931686 ]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ "theta from own gd\n", "[[4.0586484]\n", " [3.0718316]]\n" @@ -4885,14 +4904,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "theta from own sdg" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", + "theta from own sdg\n", "[[4.02496085]\n", " [3.12081773]]\n" ] @@ -5003,7 +5015,13 @@ "Eigenvalues of Hessian Matrix:[0.27470622 4.24106503]\n", "theta from own gd\n", "[[3.95906059]\n", - " [3.02296298]]\n", + " [3.02296298]]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ "theta from own sdg with momentum\n", "[[3.95611042]\n", " [2.99475306]]\n" diff --git a/doc/LectureNotes/_build/jupyter_execute/week40.ipynb b/doc/LectureNotes/_build/jupyter_execute/week40.ipynb index f1357fa3b..d3048cd6c 100644 --- a/doc/LectureNotes/_build/jupyter_execute/week40.ipynb +++ b/doc/LectureNotes/_build/jupyter_execute/week40.ipynb @@ -145,17 +145,17 @@ "output_type": "stream", "text": [ "Parameters for OLS using gradient descent\n", - "[[3.633198 ]\n", - " [4.1182509 ]\n", - " [4.44504789]]\n", + "[[4.18426686]\n", + " [2.51331698]\n", + " [5.22403374]]\n", "Parameters for Ridge using gradient descent\n", - "[[3.81542861]\n", - " [3.39782637]\n", - " [4.80980326]]\n", + "[[4.13677307]\n", + " [2.53440516]\n", + " [5.21680493]]\n", "Parameters for Lasso using gradient descent\n", - "[[3.47633954]\n", - " [4.56277628]\n", - " [4.22765626]]\n" + "[[3.40725426]\n", + " [4.31893412]\n", + " [4.43233965]]\n" ] } ], @@ -234,11 +234,11 @@ "[[4.]\n", " [3.]\n", " [5.]]\n", - "0 [-25.50604212] [-33.67380616]\n", - "1 [-1.07718279e-13] [-1.67721558e-13]\n", - "2 [-9.23705556e-16] [-1.2228689e-15]\n", - "3 [8.70414851e-16] [1.13386647e-15]\n", - "4 [-1.86517468e-15] [-2.57274984e-15]\n", + "0 [-21.31398212] [-28.05552428]\n", + "1 [-3.03046477e-14] [-1.33496431e-14]\n", + "2 [-7.99360578e-16] [-9.75256689e-16]\n", + "3 [1.77635684e-16] [2.91496906e-16]\n", + "4 [1.77635684e-16] [2.91496906e-16]\n", "beta from own Newton code\n", "[[4.]\n", " [3.]\n", @@ -746,20 +746,20 @@ "output_type": "stream", "text": [ "Own inversion\n", - "[[4.06629035]\n", - " [2.90434687]]\n", - "Eigenvalues of Hessian Matrix:[0.28385837 4.07166128]\n", + "[[3.93518583]\n", + " [3.05586004]]\n", + "Eigenvalues of Hessian Matrix:[0.31020049 4.44279756]\n", "theta from own gd\n", - "[[4.06629035]\n", - " [2.90434687]]\n", + "[[3.93518583]\n", + " [3.05586004]]\n", "theta from own sdg\n", - "[[4.04098389]\n", - " [2.9248133 ]]\n" + "[[3.92681541]\n", + " [3.07007326]]\n" ] }, { "data": { - "image/png": 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", + "image/png": 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GiIiISNUYDBEREZGqMRgiIiIiVWMwRERERKrGYIiIiIhUjcEQERERqRqDISIiIlI1BkNERESkagyGiIiISNUYDBEREZGqMRgiIiIiVWMwRERERKrGYIiIiIhUjcEQERERqRqDISIiIlI1BkNERESkagyGiIiISNUYDBEREZGqMRgiIiIiVWMwRERERKrGYIiIiIhUze/B0Pr165GZmYn4+HhIkoRly5aZ9129ehXPPPMM2rdvjzp16iA+Ph4PPPAA/vzzT/8VmIiIiAKK34Oh8vJydOzYEXPnzq227+LFi9i2bRuee+45bNu2DTk5Odi3bx+0Wq0fSkpERESBSBJCCH8XwkSSJCxduhTDhg2zeczWrVvRvXt3HDlyBM2aNXPqvCUlJYiKikJxcTEiIyM9VFoiIiLyJl/dv0O8dmYvKS4uhiRJqF+/vs1jKioqUFFRYf69pKTEByUjIiIiJfJ7N5krLl++jGeeeQajRo2yGyFmZWUhKirK/NBoND4sJRERESmJYoKhq1evYuTIkRBC4L333rN77LRp01BcXGx+FBYW+qiUREREpDSK6CYzBUJHjhzBunXrHPYbhoWFISwszEelIyIiIiWTfTBkCoQOHDiAvLw8NGzY0N9FIiIiogDi92CorKwMBw8eNP9eUFCA7du3Izo6GnFxcRgxYgS2bduGFStWQK/X4+TJkwCA6OhohIaG+qvYREREFCD8PrQ+Pz8f6enp1baPGTMGL7zwApKSkqw+Ly8vD2lpaU5dg0PriYiIlEc1Q+vT0tJgLx6T0TRIREREFIAUM5qMiIiIyBsYDBEREZGqMRgiIiIiVWMwRERERKrGYIiIiIhUjcEQERERqZrfh9YTERGRj+n1wIYNwIkTQFwc0KcPEBzs71L5DYMhIiIiNcnJAZ58Ejh27Nq2hARgzhxg+HD/lcuP2E1GRESkFjk5wIgRloEQABw/btyek2P9eXo9kJ8PLFpk/KnXe7ukPsVgiIiISA30emOLkLWVHUzbJk+uHujk5ACJiUB6OjB6tPFnYqLtwEmBGAwRERGpwYYN1VuEqhICKCw0HmfibkuSwjAYIiIiUoMTJ1w7zt2WJAViMERERKQGcXGuHedOS5JCMRgiIiJSgz59jKPGJMn6fkkCNBrjcYDrLUkKxmCIiIhIDYKDjcPngeoBken32bOvzTfkakuSgjEYIiIiUovhw4HFi4GmTS23JyQYt1edZ8jVliQF46SLREREajJ8ODB0qOMZqE0tSSNGGAOfqonU1lqSFIzBEBERkdoEBwNpaY6PM7UkWZuxevbsgJmxmsEQERER2eZsS5KCMRgiIiIi+5xtSVIoJlATERGRqjEYIiIiIlVjNxkRERHVjF6v6JwiBkNERETkvpwc66PN5sxRzGgzdpMRERGRewJkVXsGQ0REROS6AFrVnsEQERERuS6AVrVnzhARESmHwhN1HVJS/QJoVXsGQ0REgUhJN1VnBUCirl1Kq18ArWrPbjIiokCTkwMkJgLp6cDo0cafiYmKSWa1KkASdW1SYv0CaFV7BkNERIFEiTdVRwIoUdcqpdbPtKo9UD0gUtiq9gyGiIgChVJvqo4EUKKuVc7W74UXgPx8eb1/plXtmza13J6QYNwux+49KxgMEREFikANGgIoUdcqZ8v90kvy7PIcPhw4fBhYswb417+MjwULjCvdKwSDISKiQBGoQUMAJepa5Wq55djluXw5MHasMWB76SUgI0N+QZsdDIaIiAJFoAYNSknU1euN3ViLFrnWneWofteTW5dnAOSpMRgiIgoUSgkaXKWERF1rI/iaNAFefNFxwGKvfrbIpcszQPLUGAwREQUKJQQN7pJzoq6tlpFz54Dp04GYGMetI7bq54i/uzwDJE+NwRARUSCRc9BQU6ZE3bw8IDvb+LOgwL91stcyYnL2rHPdRVXr969/OXd9f3d5Bkiemt+DofXr1yMzMxPx8fGQJAnLli2z2C+EwPPPP4+4uDhEREQgIyMDBw4c8E9hiYjkwl5+ihyDhpoy1ffrr42/jxwJpKX5v5XLUcuIiRDOdRcFBxvr9cILyujyDJA8Nb8HQ+Xl5ejYsSPmzp1rdf9rr72Gt956C/PmzcOWLVtQp04dDBgwAJcvX/ZxSYmIZMKZGaZNN9VRo+QRNNSEnGfUdqXFw5XuoqpdnrbIocszUPLUhIwAEEuXLjX/bjAYRGxsrHj99dfN2y5cuCDCwsLEokWLnD5vcXGxACCKi4s9WVwiIt9bskQISRLC2NZw7SFJxseSJf4uoWfJvb55edXLZu+Rne3a+Z9+WojgYMtzBAcbt8uF6T26/n1y8z0y6A3i189/F9P75okO4b/45P7t95YhewoKCnDy5ElkZGSYt0VFRaFHjx7YtGmTzedVVFSgpKTE4kFEpHgBMnLHaUqor6llxFmudBfl5ABvvFG9fgaDcbscWsYAj+SpXb5wGd+/uBUTUtdDU+skutzXFjN+TMOOy629U+bryDoYOnnyJAAgJibGYntMTIx5nzVZWVmIiooyPzQajVfLSUTkEwEycsdpSqivM91ZgOvdRUoIBKtyI0/t9J4zWDj+JwyP34xGDSpx+/RumPf7LThuiENtlOOOuM2Ye+/PPil+iE+u4mPTpk3DlClTzL+XlJQwICIi5QuQkTtO83R99Xpj4HTihLGFpk8fz+TcDB8OLFkCPPKIceTY9dyZ1sCVQDAtzdUSe4cpT80GYRDY+90f0M0thG5jNDaVtoNAb/P++KAT0LY5AO3dEUh/oj3C69+EkpISTPzC+0WXdTAUGxsLACgqKkJclabFoqIidOrUyebzwsLCEBYW5u3iERH5VoCM3HGaJ+ubk2NsaakaYCQkGFt1PDHKbvhw41pcL79sPOe5c5bXmT3btesESOBbebkSP83bBd1nF6DbkYRDlckAks37b4zYA233ImSOj0Hn0W0gBfnnsyvrYCgpKQmxsbFYu3atOfgpKSnBli1bMGHCBP8WjojI10z5KcePW+8+kSTjfrmP3HGWp+prmhTx+nOYlovw1PxLwcHA888D//xnzVugFBz4Fh8txsqZu6FbLvD90RScF53M+0JRgVsb7UBmv4vIfKoVND3aAmjrt7Ka+D0YKisrw8GDB82/FxQUYPv27YiOjkazZs0wefJkvPTSS2jVqhWSkpLw3HPPIT4+HsOGDfNfoYmI/MGUnzJihDEQqHpzV/oM09Z4or6Ocm8kyZh7M3So5143B91FTlFY4FuwvhC5c/5Abn5d5J/rgEr0Mu9rKJ3F4KQ90A4Pxm1PtUO9+G5+LKkNXh2r5oS8vDwBoNpjzJgxQgjj8PrnnntOxMTEiLCwMNGvXz+xb98+l67BofVEFFCWLBEiIcFyGLNG4/9h5t5Sk/o6O/Q9L8/btXDdN99YL6sMphXQX9WLzR/uFP+8OU+0D99XrYhtQg+Kp7vliQ1z/ycqKyrdvo6v7t+SEPbmEA8MJSUliIqKQnFxMSIjI/1dHCKimvNWMrBcuVvfRYuMEzU6kp1tnKBSLqzlOJloNK7nIHnAxTMXsXbOLui+uYzcA21QZGhi3hcEPfpE7URmnwvInNQcNwxI8sg1fXX/9ns3GRERucETXTFK4m59lZh7YyvHyWTmTJ8FQid3nMKKmfugWxWK1UUdcBndzfvqoQQDE3ZDO8SAQVPaomGrTj4pkzcwGCIiosDlKPcGABo2NLY86fX+b11ztPCrJAFTpxqDIS+UVRgEduYcQO78P6Hb1Ai/lLcDcK0FqFnwMWhTDkE7ui76TmqP0Lo9PV4Gf2AwREREgcteErbJ2bNARoZnh9q7yw/zC10pu4L17+6C7otS6HYn44j+BgA3mPd3q7Mb2ptOQ/tYPNoPbwUpyIUZtxWCwRAREQU203IRtnJwTDw91N4dzs4btHZtjfLEzh06j+9n/g7dCgkrC1NRgs7mfeG4hIwmO6EdcBmDJ9+A+M6pbl1DSZhATURE6qDXA/n5wMiRlpMiVmUasl5Q4J8us/x8ID3duWNdbMk6uPYIdG8VQPdjffxU3A76Ku0hTaTTyGy1F9oRoch4qj1qN6p97Yl+TNZnAjUREZEnBQcbH7YCIcD/y1w4k+Nk4qAlS39Fj80f7YZu4Tnotmuw90oygObm/e3CDkDb5TgyxzVC97EpCAqxMmeRt2fulgkGQ0REpB5yX+bCmRwnEyuTRpadLMMPb+6CLqcS3/7RBmdEB/PhIbiKvg12QpteiswnWyDpllYAWtk+v6dn7pbxdBAMhoiISD2UMNTe2RwnwNyStbz/O5i3sxfWnemAK7jJvLu+dAG3N9uNTK2EgVNTUb95Zzsnq8LTM3fLvIWJOUNERKQeej2QmOh4mQt/5QxVpdcDL7wAvPSSw0NHIRtfwjhpZIuQI9C2PwztfZHo/Vg71Kpdy/VrO5u7lJfnuDvRVguTaUkVOy1Mvrp/B3ntzERERHJj6oYCrt2MTeS2vltwMNCvn1OHNoy4iKwB+di9/CAOVjTDrG19kT7lRvcCIcBz3YmOWpgAYwuTXu9S8TyNwRAREamLqRuqaVPL7QkJ/h1Wf53Te87g088knAlqAgMkq8cIAPrYeLxTOhbPrkxDirYlpCDrx7rEU92Jrsyb5EfMGSIiIvUZPtyY7yKjhF5hENj73R/IfbcQup+i8XNpOwj0xR14D4sxAgZICEKVFhZJggQgeO7bni+3o1Ftpu7EPlZGoFUl94T1vzAYIiIidZLB+m6Vlyuxcf4u6D67AN2ORBy8mgwg2by/U8RetO8WjYJOs9Ei5/XqCcjeWrDV3qg2V7oTlZCwDiZQExFRIPHV8O0aXKf4aDFWzdoN3TKB746k4LxoYN5XC1dwa8Md0GaUY8iTLdGsZ5WuPH8MTbc2CkyjcT4Iq2HCuq/u3wyGiIgoMPhq+LYb1zn80zHkzj4EXV5d5J/rgEpcS2xuKJ3F4KQ90A4Pxm1PtUO9+HqeK6sn1DQIM40mA6y3MMlgNBmDISIiZ8h4wjhCjYZve+M6hkoD/vvZHug+Og3dr/HYefkGi8Nbh/4Bbcej0I6NRs/xqQgODfDPkpstTAyGPIjBEBHViMwnjFM9U1eMrVFLnpo7yMF1hCThUr0mmBy7CLkHU3DSEGPeFwQ9ekfthLbPBWROao4bBiS5Xw5XyiunAN6N8nBtMiIiOfD0kgTkea4M365JwrSD60hCoHZJEfaXSDiJGNRDCQYm7EbmYD1un5qChq06uX9tV8kxgJdBwrotDIaIiGzx9JIEzlxPTt/klcJXw7edfP4YzTr8Y0I99J3YDmGRPWt2TXcwgHcZJ10kIrLFlxPG5eQYu2DS04HRo40/ExON271BrzcuubBokfGnn2cArhEvD9++UnYFa17bhrefK3Lq+HGf3orbpnVBWGSYW9erEYXM+Cw3DIaIiGzxVYuD6Zv89YGX6Zu8pwMiXwde3maaIPD65TVMJMmYrOtogsAqzh06jy/+thH3NPsZjetdQv9nOmPyocdRiASbs0G7cx2PU8iMz3LDYIiIAosnWzx8MWGcr7/J+zrw8gUPrTd2cO0RzBqWj/QGv6FJy3q4772b8VVhL5QgCk2k0xjb6mecGD7ReEpb15k50xho+KvFTSEzPsuOUIHi4mIBQBQXF/u7KETkTUuWCJGQIIQxjDA+EhKM291RWWl8viRZntP0kCQhNBrjce7Ky7N+7usfeXnuX+P6+ti6hifq40/W3n+Nxub7X1lRKTbO2yGe6ZEn2oYerPZypIbtF9N65olNH+wU+qt6x9d5+mnPfv7c4cvPkw/46v7NofVEFBi8Nc9MDSaMc8qiRcauKkeys4FRo9y/DmBsqUhPd3xcXp5sR/045CAJvexkGVbP2gVdzlWsONQWZ0Qj874QXMUtDXZCm1aKzCeS0CKtmfPXOXMGGDnS+/McOVLDGZ/lhkPriYic5c1RX6YVzq0NU/bEulC+XLtJDV0oVoZvH9t6AitmHYBuTQTWnW6PCtxk3heFYtzefBe0WgkDp6aifvPOrl/HFID4atSho3J5Yk0xlWEwRETK5+15Zry5wrmnVgd3hkIWzawpYRDY/tU+6N4/Cd2WGGy71BbAtTq1CDkCbfvD0N4Xid6PtUOt2jfX7IK+mufIWd4O4AMQgyEiUj5ftHh4a8I4X36T92Xg5WMVJRXIe2sndF9eRO7eljimbwOgDQBAggE31d0N7c1nof1bAtoOSYYU1NxzF5dji5s3A/gAxGCIiJRP6S0evvomH2BdKGf2ncW3b+yB7rsQ/PBnKsrQ1byvNspxW9xOaAdexeCpbdAktb33CiLXz5+MZ3yWGyZQE5HyBUrSqK9moHZz0Ux/EwaBfSsLoHvnKHI3NsDPJe1gwLXXJy7oJLSt9yNzZARufaIdIqIjfFOwQPn8yRAXavUgBkNEKuDtUV+BRiFLf1RersTG+bug++wCcnc0x4GrlgucdorYC223k8h8qAk6j26DoBA/TZ/n7udPIe+DvzAY8iAGQ0QqodAWD7JUcqwEK2fuQu5yA749nIrzooF5Xy1cwa0NdyDz1nJkPtUSzXo29WNJr+Pq50+Oi6nKDIMhD2IwRKQi/KatSEc2HkPu7EPQrauL/HPtcRWh5n3R0jkMSfodmcOCcdtTqYhMsPP/uL/ff2ev7615sQIMgyEPYjBERCQvhkoDfv1iL3QfnoLuv3HYcbm1xf7WoX8gs8NRaMc0QM/xqQgJd2K8j1JaWkw5RraG4zPHyIyTLhIRUUC5dO4S1s7eCd03l5G7vzVOGlIApAAAgqDHzZG7oO1zHpkTm6H1oBYAWjh/clstLaY11+TU0iK3eYmIwRAREXlP0a7TWPHGXuhWhWL1yfa4hO7mfXVRioFNd0E7RI/bp6agYauO7l3EmzOQe4Mc5yVSOQZDRETkMcIgsHv5QejeOw7dz43wS3kKBK5N4qgJPg5tykFoR9VF34ntEBbZs+YXVVpLi1znJVIxBkNESuDvpFAiO65evIr1c3dC90UpdLtb4HBlKwCtzPu71v4d2ptOQftoHDqMuAFSkIdHgCmtpSWAZwJXKgZDRHKnlKRQUpXzBRfw/Ru7ocuVsLIwFcW4tsBpGC4jo8kOaPtfxpApNyC+87XcIK9QWkuL0mYC9/SXMTl+uRMyV1lZKf71r3+JxMREER4eLlq0aCFefPFFYTAYnD5HcXGxACCKi4u9WFIiL1iyRAhJEsL43+W1hyQZH0uW+LuEpCIH1x4Wbw7NE+n1t4lgXLX4SDaWTokHW60Xy6ZtFmVFZb4tWGWlEAkJ1v9WTI/oaCHWrDEeKxdLlhjLXbWcGo28/q6tlTEhwf0yung+X92/ZR8Mvfzyy6Jhw4ZixYoVoqCgQHzzzTeibt26Ys6cOU6fg8EQKZLpP3hb/7lLkvE/Tjn9504BpbKiUmyct0M8e1OeSAk7UO0jmBq2X0zrmSd+nr9DVFb4+XNo+uJgLyCq6Y3cGyorhcjLEyI72/hTTn/Pnv4y5sb5fHX/lv08Q0OGDEFMTAw++ugj87Y777wTERER+Pzzz506B+cZIkXKzwfS0x0fl5cnj6RQCgjlp8qxetYu6JZcwYqDbXBaNDbvC0Yl+jbYgcy+pch8PBHJt3pw5XdPsNalfD1OaugcT8+F5Ob5fHX/9tMiLs7r1asX1q5di/379wMA/ve//+Gnn37CoEGD/FwyIi9TWlIoKdbx/57A/HvXY3CTrWgYE4w7XumBBQf64LRojCgUY1Tzjcie9DNO/1GGtec6Y/LSvvILhABjcHP4MLBmDRAdbf0Y0/f/yZONN2iyzpURev44n4fJPoH62WefRUlJCdq0aYPg4GDo9Xq8/PLLuPfee20+p6KiAhUVFebfS0pKfFFUIs9SWlIoKYYwCGz/ah9yPzgJ3ZYm+PViCoBrn6OkkKPQtiuA9r5I9JnQDrVq3+y/wroqONj4OHfO9jFyG2ovR57+MibzL3eyD4a+/vprfPHFF8jOzkZqaiq2b9+OyZMnIz4+HmPGjLH6nKysLMyYMcPHJSXyMA6/JQ+qKKlA/ts7ofuyHLl7WqJQ3wZAGwCABAN61N0Nba+z0P4tASmZyZCCmvm3wDUh8xuvLF0/wqtJE+ee5+kvbX76cif7nCGNRoNnn30WEydONG976aWX8Pnnn2Pv3r1Wn2OtZUij0TBniJTHtMQAYH34LfMeyI4z+87iu5l7oPsuGKuOt0MZ6pn3ReAibovdCe3AKxg8tQ1i2jW2cyaFYb6da2xN33HpkrGFzd6XMVdzhhx9ufNTzpDsW4YuXryIoCDL1Kbg4GAYDAabzwkLC0NYWJi3i0bkfcOHGwMea/9RzZ7NQIiq2ff9H9C9cxS6nxrg55J2MKC3eV9c0Elk3rAf2rsjcOsT7RAR3cOPJfUitqo6z96abqZtnpgLSeZzK8k+GMrMzMTLL7+MZs2aITU1Fb/99hvefPNNPPjgg/4uGpFvDB9uXFNJbpOUkSxUXq7Ezx/shu6T88jd0Rz7r1oucNoxfB+03U5AO74JOo9ug6CQWP8V1ldkfuOVDWfWdIuOBsLDjcGRibtfxmT85U723WSlpaV47rnnsHTpUpw6dQrx8fEYNWoUnn/+eYSGhjp1Dg6tJ6JAUnKsBKve3A3dMj2+O5yCc+LayKlauIL0hjugvbUcQ55MRvObE/xYUj+z1v2j0fj9xisbznYnrlljDBz9MAO1r+7fsg+GPIHBEFGAM/3nevw4cPo00Lgx0LSpe/9py3GpAABHNh5D7uxD0K2ri/xz7XEV174MRkvnMDjpd2iHBeO2p1IRmcD/58xk+n7KwqJFwOjRjo/LzgZGjfJ+eaxgzhARkTPsTbTn6hpu3lgHzs2bsaHSgF+/2Avdh6eQ+984/O9yawDXWnla1SrA0I5HkPlAA/R6OBUh4b1tn0xufBmgBAczSdoWmY/w8iW2DBGRctlK/qxKkpwbdWfrXDUZuedicHXp3CWsnb0Tud9cRu7+G3DCcC2/Jwh63By5C9o+55E5sRlaD2pR7flu82Vw4qmAky0+NefmCC9f8tn925W1O44ePerZxUB8hGuTEQUgR2u3Xb/4pb01n7yxDpyT6zCd3HlKfDR2vRgau1lEoNzi0LooESOa/iw+fXSDOL33TA1fMDvl9ORCnI6u5Ym1rnxZ5kBna003mSwGLcuFWmvXri2ee+45UV5e7q3yeAWDIaIAlJfnXCBkeuTl1fxc9s5RlYPgygBJXAhrLHrV+U1I0FvGbcHHxMT2+WLlS1vF5eLLNX+d7PH0Qpz2eCrg9GWZ1cJacKnRyOK19NX926WcodWrV+Opp57CRx99hJdffhljx471QlsVkcywOV6eXJ092N7xnp6x2ME6TBIEoipOo1bFBQgEoWvt35HZ4xS0j8ah4103QApq6tx1asKZYdVPPglERQGnTjn/2bf19+LK2lS2cnycKfPkycapKPg36jxO3+FaAnWvXr2wZcsWfPrpp/jnP/+Jt99+G7Nnz0YfTlxFgcobCbXkGa4mddo73tOJpE4GTf/qtQ5fzGmNpl1TAKQ4d25PcSY4OXYMyMi4ts3RZ9/e30uVVQHssvfaeSKgIutUnmju1qr1DzzwAPbt24fBgwdj0KBBGDFiBAoKCjxdNiL/MiXUXv+f7/Hjxu05Ob4ri15vnBNk0SLjT662fW2WYVOCsz0ajf3Zhh2dS5IcnwPAoXVHMPuOH/HUw2WOywQg4+Vb0bSrn0bquLMul73PvqO/lwMHnLuGvYCTa46Rt7jbv1ZeXi42bNggnnzySREUFCTCw8PF008/LUpLSz3ZjecRzBkil3kjodZdTBa1zVb+yPXvlTOvlRuJpJUVleLn+TvEszfliZSwA+anBKFSHEWC0MNG2Xz5+bFlzRrXcq7sld2Zv5eEBOPD1vvlzGvi6dwukj1ZJlC/99574sEHHxTt27cXISEhIjQ0VHTu3Fn87W9/E/Pnzxfp6elCo9GIrVu3equ8bmEwRC6Ty3+6TBZ1zFqw6G4SqBOJpGVFZWLps5vFuFbrRRPplMWhwbgq0utvE7OG5YsTL8yT7yidJUuEaNrUvWDI2mff2b+XGTNq9pqYgq6aBFSkKL66f7s0z5BGo0GPHj1w00034aabbkKXLl0QERFhccx//vMfZGdnY9euXR5twaoJzjNELpPDzKymOUBs5UjIYA4Q2bA3AzXgWmKolQTgP/93Grkz9yN3TTjWnOqACoSbD49CMQY12w2tFhg4JQUNkupfO5ccl4NwZm4mZ1T97Lvy9xIWVrPXxFR+wLIONZkPimRLsctxFBUVIT4+HnoZ5TQwGCKXObtmT16e95IO5VAGpXMzAV4YBP73zX7o5p9A7pYm+O9Fy+TmpJCj0LYrgPa+SPSZ0A61ateyXQY5jUZ0FGC7Ys0aoF8/479d/azW9DWRY5BJXqHYYEgIgfXr16Nv376ePG2NMBgil8lhZlY5tE75irs3R3vPc3FG6YqSCuS/vRO6L8uRu6clCvXXhrdLMKBH3d3Q9jqLzMeaInVoS0hBTiRuy42zQUujRsDZs/Zbj6oGlf74e5FTkEleI8sZqJWKOUPkFn/PzCqXvCVvs5a/0rSp49fXXmK5kwnwp3cXiU8e3iBGNP1Z1EWJxSERKBdDYzeLD8esFyf+V+Sb18LbsrOd+0xNnmz9s2/v78CXfy+VlcbPfXa28SdzhAKWLBOolYrBELnNnzOzqiFZdMkS+zdlW6+zo8TyGTOcuumnY43FprigE+KRNj+K3Oe2iItnL/r2tfAFVwJsZ5Ksr/8M+uLvhaMrlacGwassE6iVit1kVCP+bI4P5GRRvR6IiTF2x9jSsCFQVGT5ejuTWB4dbf+8fxmFbOwJ74zMriegHd8EXe5tg6AQt6ZfUwZXu7PWrrWcdNGWqnlr3vx78cZiuuRdNZy4lt1kHsSWIVI0uawb5OmuCWfnuVmzxvJ5rq5JZudx4q2va1YHJXKlO8vZbrXsbO+XW05zf5FzPDA1iK/u3wH8FYgoQAwfDhw+bPz2nZ1t/FlQ4NtvwDk5xhaF9HRjUnd6uvH3mszCnZ/v3HHvvGM567aTswvbbfL+a0bp2L+psBVh+HBjC0rT69Y/S0io3rLi6WVKasKVpTjI/xytIwcY15GTychzl9YmIyI/8ee6Qba6JkzLLHi7a2LZMuPD1LTu5I3X7lgvIYzDsNU6+sjZhTlNy5Q46lbzxfqUXIpDWRS2jhxbhoh8Qalri3nz252r/wEePw4xYgR+nbsZ50Iaw2A/3LFvxgzmlpgC7FGjjD+tBYbBwcYAFKi+bpvpd18FlXJqpSLHFBa8Mhgi8jZvdDH5ije7JtLSjAnSzhICQgCNF7+LRyrnAoD7AVGrVu49T41c6VbzJg8tpks+orDglcEQkTc5Wslb7gGRN7/dBQcD77/v0lOCINAMheideBz/G/UKpPh4ywMaN3buRL78D1iprYJVySFvTU6tVOSYwoJXBkNE3qKwBEKrvP3tbvhwYMkSiPimjo+tYvJ/YnBj9t8hHT1ieYM+dkxe/wEruVXwes50q3mbXFqpyDGFBa+cZ4jIWwJhbTEvLrNw4Ugxvn9jN3JzBVYeaYsO2IFbsRbP4yXHT7b3msllbibOieM9XIpDOWq4jpyv7t8cTUbkLTXpYpLLf/amb3cjRhhv4taCCxe+3f2RfxS5bxVAl18P68+3RyV6mff9LqUiORm4VPQ+wstOQ3J39JKp9cDaRG++WsjTUaugJBlbBYcO5U3cHf4cXUmucXbkop8xGCLyFne7mGo4Y6vH1SC4MFQasOXj3chdeBa6bU2xu6IVgGbm/SlhB6G98RgyxzZEj3EpCA5NA3Leq3nw5e//gBU2rJjIqxQQvLKbjMhbHHUxAcbm4qpdTLa6VgBjMODPrhUnW6vKT5Vjzexd0C2+ghUH2+CUuJbUHIxK3FJ/JzL7FiPz8SS07Nfc+rVq2LTud4sWGXOEHMnONubgEJFV7CYjUrqqXUy23HPPtYDCXtcKYNzuz64VO9/u/tx2Eive3A/d6nCsPdUel9HDvC8SxRik2Q1tpsCg/0tFg6QbHV/L3y07NaWwYcVEasdgiMhb9HrjgqGDBwMrVlg/5o03gJtuMt78HXWtALLpWhEGgR2L90M3/wR0m5vgvxdTAMSa9yeGFEKb+ge099ZDnwntEFq3l+2T2SKXpnV38rfkNHMzETnEYIjIG6x189hiau05fty5czt7nIdVlFTgx7m7oFtUBt3vLVGobw2gNQBAggE96u5GZs+z0E5oitShLSEFafxSTpvcCWrczd/ycOI5EXkXgyEiT7OX93O9qom0p087d35nj/OAswfO4buZv0O3IhirjqeiFF3M+yJwEf1jd0I74AoGT2mN2A7trZ9EDiPj3AlqarommxxGtRGRU5hATeRJpqRpZ1qEqsrOBgwG4L77HB/7+efAvfe6VTxn7F9VAN3bR5D7U338VNweBlwLXGKDipB5wz5o7wpHv8ntEREdYf9kchgZ5858P47eR1fmV5JDMEikUEygJlIiZ/J+rHElkfb62XdrqPJyJTZ9uBu5n56H7n/NsO9KCwBJ5v0dwvdB2/UEtOOboMu9bRAUEuPcif292j3g/nw/nhwaL5fcJ2cxeCMVYjBE5EmurtF1fSJtQoL9m7CHlpIo/bMUq2buQu4yPb4taIuzoqN5Xy1cQVr0TmhvLUPm5GQ0v/labpDT5DLpoLtBjcJW3PYYObTkEfkBgyEiT3KlhcdaIm3VofgeTro9uuk4cmcdRO66Osg72x5X0NO8r4F0HoMTdyNzaBAGPJWKqGZd7JzJCXKZdNDdoEaNQ+Pl0JJH5CcMhog8ydGQ6qqsJdJ6MOnWUGnAtuy9yP3oFHRbY7H9UhsA17rYWtY6jKEdDkM7pgF6PZyKkPDeTp/bIbm0rLgb1KhtaLxcWvKI/ITBEJEn2RtSbWK6qdjKxajBhIOXL1zGujk7ofvqEnL3tcKfhhQAKQCAIOjRK3IXtL3PI3NiM7QemAQpKLFG1bVJLi0r7gY1ahsaL5eWPCI/YTDkT0xUDEy2WndcWU7ChaTbU7tP49uZe6FbWQs/nGiPi+hm3lcHZRjYdCcyB+lx+5Q2aNy2o50zeZBcWlZqEtSoaWi8XFryiPyEQ+v9hYmKgc/VYNfJ44VBYM+KQ9C9ewy6jQ2xuSwVAkHm/QnBfyKzzUFo76mNtEntEF4/3Bu1c8yUgwJYD0J8mYNSk7XO1PClJT8fSE93fFxeHluGyKd8df9mMOQP7sx7QoHNQXB89eJV/DRvF3SflyB3ZyIOVVoucNql9u/I7H4K2kdi0enu1pCCJB9XwAY5LbiqhqDGXY4WFXZlXiUiD2IwVMXx48fxzDPP4Pvvv8fFixfRsmVLLFiwAF27dnXq+bIKhjw5mRt5ny9uoDaCY/FXt87sxi/jxTN/wwVR37wvDJdxa+Od0GZcwpCnWiGhm4xHNTEIUQY5teQR/YXB0F/Onz+PG2+8Eenp6ZgwYQIaN26MAwcOIDk5GcnJyU6dwysvprv/wbM5Wjl80ZXpIDg2QMIxJCAJBWgoncPg5L3QDq+F/k+1Q93Yup4pA5GJnFryiMAZqM1effVVaDQaLFiwwLwtKSnJzjN8oCY3SSYqKoOP5lwx5P+IIDujeIIg0AyF+N9Tn6DtK2MQHBogQ7lJnmowkpFIyYIcH+JfOp0OXbt2xV133YUmTZrgxhtvxAcffGD3ORUVFSgpKbF4eIzpJnn9Dcx0k8zJsf98uQw5JtsczbkCGIfH6/Vunb78VDmW/2MLxrfegL/13+/Uc9p1i0BwKG9I5AOmkYyjRhl/MhAiFZB9MPTHH3/gvffeQ6tWrbBq1SpMmDABTzzxBD755BObz8nKykJUVJT5odFoPFMYT9wkTUOOJRsJrpLksSUXyE2uzLnipBPbi/DBA+uRGfMLGsUEYVhWD3y0vw/2ijbOnUBuwbFeb+zyXbTI+NPNwJCISA5knzMUGhqKrl274ueffzZve+KJJ7B161Zs2rTJ6nMqKipQUVFh/r2kpAQajabmfY6eyvdhoqK8LVoEjB7t+LjsbOO3ZyuEQWBnzgHo5v0J3ebG2FqearE/MaQQ2tQ/oB1VG7e+fQekP/9UzigeTgtBRD7CnKG/xMXFISUlxWJb27ZtsWTJEpvPCQsLQ1hYmOcL46l8HzVN5qZEbnZlXim7gvy3dyJ3URl0vyfjqP4GADeY9/eoswvaXmeQ+Wg82t3RClLQXy2Wrd6Sz0zHjgYGcP0qIgpAsg+Gbr75Zuzbt89i2/79+9G8eXMbz/AiT+b7MFFRvlyYPfnsgXP4/s090K0IwspjqSjFtQVOI3AR/WN3IvO2KxgytTViO7Szfj25BMeOWny4fhURBSjZd5Nt3boVvXr1wowZMzBy5Ej88ssvePjhh/H+++/j3nvvdeocHmtm48Rk6mGnK1MIQNflBbx5UIufitvDgGvvdWxQEYa02gftXeHo92Q71G5U2/lr+nM+HmcmAo2O5rQQRORTnGeoihUrVmDatGk4cOAAkpKSMGXKFDz88MNOP9+jLybzfdTDSkvJn4jDJLyDpbj2HncI34fMLiegfagxut7fFkEhsh+XYMnZiUCzsoD77nN8Pju5VERErmDOUBVDhgzBkCFD/F0MI292aXCmXtko/bMUP2yKQ26tz3EeF1AbF3ECcdiAPgiGHv2jf0VmehkyJycjsXdrAK39XWT3OTt67vRp584nt5FvREQOKCIYkh1v5PtwhI7fFW75E7mzDiB3bW2sO9MBV9DTvK+BdB63N/8di4ZtwYCnUhHVrIudMymMswMDGjeWx0r0REQexmDIXaaJyTyBI3T8QhgEtmXvhe6DIuRujcFvl9oCiDfvb1nrMLQdDkN7f33c/Gg7hITf7L/CepOzLTlNmxqDc7mMfCMi8hBF5AzVlKwWar0eF271qcsXLmPdnJ3QfXUJK/a1wnHDtUBAggG96u2Ctvc5aCc1Q+uBSfJZ/d2bXB0YwPWriMhHmDOkFq7MdswROm45tfs0vp25F7kra+GHE+1Qjm7mfXVQhgHxu6C9vRK3T2mDxm07+LGkfhIc7FqLD6eFIKIAw2DI37hwq8cJg8CeFYeQ+94x6DZGY1NpOwhcy2NpGnQC2rYHoL2nNtImtUN4/Zv8WFqZcHVggCe7iYmI/IzBkL9x4VaPuHrxKja+vxu6zy5AtyMJhypbAmhp3t85Yg+0PYqgfSQWne5uDSnIyuup9tF8bPEhIpVizpC/cSJHtxUfLcbKmbuhWy7w3dFUXBD1zftCUYF+jXdAm3EJQ55qhYRuDoJJjuYjIpId5gyphav5GoHIhRaZgvWFyJ3zB3R59fDj+faoRC/zvkbSGQxJ3oPMO2rhtintUDe2m9VzVMPRfEREqsaWIblQ6wgdBy0yhkoDtn7yO3Qfn4Hu16bYVdHK4ultQw8hs1MhtGOjcdNDqQgOdTFo5Gg+IiLZ4nIcHqSIYAhQX86KjRYZ8VcL2Xtx/8aLRY+gyNDEvC8YlehTfycybylG5qREtOqfaPv8zrye+flcb4uISKbYTaZG3hihI9cAy84K6JIQMEDCkBPv43FMQySKMUizG5lDBAZNTUF08o2Oz+9sDhBH86mTXP8uiMgvGAwFMn8lBTtxoxE/rodkZ36lIAg0QyF+eeQjtJ85FqF1e9k8thpXcoA4mk99mCxPRNdR2PLa5DRTQHB9wGEKCHJyvHfdxERj19Po0cafiYlATg6ulF3B6ld+xRMdf8STGbudOl2XtHoIrRvq/PXttDiZt02ebDwOMAZqCQnXktWvJ0nG3C2utxUY/PV3QUSyxmAoELkaEHiKjRuNOHYM4s478WC9r3DbtC54e0df7BDtnDunvRYZvd6Y87NokfGnqUXK2Rm9gWuj+YDqAZFaRvOphb/+LohI9hgMBSJXAwJXWAtATNtt5QABEJDwH/wTcdIJjG+9HlP/EQZDfFP3W2RstUAtX+5cParmAJlmX27a1PKYhAQOqw8k3vy7ICJFY85QIPJWUrCNXAv9zFn4/editHciB+jY6j0I6nercWOXt9ybX8leTtDs2c7V5foWJ86+HPiYLE9ENjAYCkTeSAq2EYAYjh2DdPdIrMaTaO/EaYJOFV37xdX1sADnujqCgwGDwf6M3tZanNQ0mk+NmCxPRDZwnqFA5OklPv46nzh2DNY6tQyQcBYN0RhnHJ/L2nw9jgKGqvuLioCnnnJ8HcB2i5Ovur44akleuPQNkeL46v7NnKFA5KGkYGEQ2PbFHizs8jZgIxACjF1gjXEGaNzYvRwgU4vMqFHGn1XLdX1ukLOB0OTJ/s0B4qgl+WGyPBHZwGDIU2wlFvuLm0nBly9cxvcvbsWE1PXQ1DqJLve1xar/xTh3zXvvNf701I3GVkDhjKFDgcOHjS1R2dnGnwUFvgmEOGpJvpgsT0RWsJvME+TcHeJEzsrpPWfw7cy90H0Xgh9OtEM56pr31UEZpjT8FC+enej4Wnl5wLlznlljzdGaYbbY6+rwVf4Ol/iQP+ZyESkCl+NQCrmveG4lKVgYBPZ+9wd0cwuh2xiNTaXtINDbvL9p0AlktjkA7d0RSH+iPcLrPQokZjnOtTDdUDwxKsvRMGhr7LVA+TJg5agl+fNGsjwRKRaDoZpw1B0iScbukKFD/f6ts/JyJX6atwu6zy4gd2ciDl5NBpBs3n9jxB5ouxdB+3AMbhzVBlLQdSNq5sxxfhi8J2407gQKtkah+Tpg5aglIiJFYTdZTci8O6T4aDFWztyNXJ3Ad0dScF40MO8LRQVubbQD2oxLGDK5JTQ94h2f0FrrijtdYM5w9rWdNQuIibHdAuWou80bI4g4aomIyCPYTaYEMuwOOfzTMeTOPgRdXl3kn+uASlxb4LShdBZDWuyBdngI+k9ORb34bq6d3JmJCT2Vi2FaM8xRQPH44/bP78qsw54KWE2jltyZUJKIiHyOwVBNyKA7xFBpwNZPfkfugjPQ/RqPnZdvAJBg3t8m9BC0nQqhHRuNmx5KRXBob9snc4a9LjBP5uV4KqDwV8DqzoSSRETkF+wmqwk/dYdcPHMRa+fsgu6by8g90AZFhibmfcGoRO+oXdD2vYDMSYlo1T/RY9e1y1ZejsnXXwN33eXeeWvSNefvrkyOWiIicpuvuskYDNWUKQgAvDrb8ckdp7Bi5j7oVoVidVEHXEaEeV89lGCQZhe0QwQGTU1BdHIDO2fyAmeGwQcHA19+ee21cvX87gYUzN8hIlIsBkMeZH4xV6xA5MCB7t307N2QvZBYLAwCu5YegG7en9BtaoRfyttZ7G8efAza1EPIHFUXfSe1R2jdULeu4xHOtr4AwJIlvu8i8lHASkREnsVgyIPMLyaASHdyWJzJhfFAd8iVsitY/+4u6L4ohW53Mo7oEyz2d6+zC5k3nYH2sXi0H94KUpCtBTJ8bNEi41IZztBo/NMK48uRcERE5BEMhjzIIhhytTXAVi6Mh1oVzh06j+9n/g7dCgkrC1NRgijzvnBcQv+YHci8rQJDprRGXCcby2L4Oy/FlZYhwH8zL/v7dSIiIpcwGPIgi2AIcD5PxEtz1BxcewS6twqQuz4KGy60h77KoL6YoFPIbLUXmXeGIeOp9qjdqLb9k8lhKRBXl87IzjYuykpERGQH5xnyJmfnlvHQHDX6K3ps/mg3dAvPIXe7BnuuJANojiDo0Qcb0KPWNrS6IQjtn0xHt3HtERRyi3P1kMtSIKZh8Hfe6dzxnHmZiIhkRJ3BkImjuWVqMEdN2cky/PDmLuQuvYoVh9rijOhg3heCq/hnnTmYangN9S6dBq4C2A3gxQSgoZMtOnJbCmT4cOPw+VGjbK/GXnUNMyIiIplQdzDkqIXCxUkVj209gRWzDkC3JgJrT3fAFdxkPqS+dAG3N9sN7VAJQ1IOoc6Ev1cPZI4dM7auODMnjz9mVnbkrruMAY+1snPmZSIikil1BkPOtlA4WBJCSBKuNojBKy/osfyXPdh2qS2AawFUcsgRaDsUQHt/fdz8SCpq1b75r/yau21PTggYW1ckyf6cPDJcCgSAscxLlnDmZSIiUgz1BUOutFDYWRJCwPjrPefmYumP/YynhgE96+2C9uZz0E7UoM3tLSAFNbc8p6MWHcAYMN11l/05eTy9FIizI62cOc6ZNczkiiPOiIjUR6hAcXGxACCKASE0GiGWLHHp+RfmLBBldRoLYYx/hADEEWjEHVgiaqNM3BG3SSx4aIMo2nXK8cmysy3OY/eh0QhRWWn9PJWVQiQkCCFJ1p8rSfafX9WSJcZzVX1+QkL118nZ45Qq0OtHRKQw5vt3cbFXr6O4YCgrK0sAEE8++aTTzzG/mCtWOBUcGPQGsefbQ+LVQXni5nr/ExL0IgiVoi/yxD3IFndKi8WEtuvEdzN+EZfOX3KtAnl5zgdDgPF4W5YsMQY91wdEpm3O3MRN57AWTFU9h7PHKVWg14+ISIF8FQwpap6hrVu3YuTIkYiMjER6ejpmz57t1POcmaeg8nIlNs7fBd1nF6DbkYiDVxMt9t8YsQfa7kXIHB+DzqPbuD/7s6fn5KnJzMrOzqN08CCQnOzx+ZZkw0vzSRERUc1wnqHrlJWV4d5778UHH3yAl156ySPnLD5ajFWzdkO3TGDl4dZohwuIwwk0BXAMTZDWaDcy+11E5lOtoOnRFkDbml/U03Py1CQ/x9kRae++K7+Ra54kx5F5RETkM4oJhiZOnIjBgwcjIyPDYTBUUVGBiooK8+8lJSXmfx/+6RhyZx9Cbl5d5J9rj6vohTuQg99wDzS4dkM0xMUj6J23vTP6ydNz8gQHu3eTdnak2aFDnj2f3Mh1ZB4REfmEIoKhL7/8Etu2bcPWrVudOj4rKwszZsyotr1XkwPYXdEFwLUFUB8PeRdzKidWOzbo5AnvzuIshzl5nB1plpzs2fPJjadH5hERkaLIPmeosLAQXbt2xerVq9Ghg3EW57S0NHTq1MlmzpC1liGNRgOgGEGogz5RO5HZ5wIyJyTghkfT/Zsr4s/V1E25MjbmUaqWM+ToOKXm1Dj7Oii1fkRECsWFWv+ybNky3HHHHQiuchPS6/WQJAlBQUGoqKiw2GeN6cV8/8FVGP5sVzRsFW3c4exq695eZd2fc9uY1jcDLAMBU+uUqWXM2eOUKtDrR0SkQL4KhoK8dmYP6devH3bu3Int27ebH127dsW9996L7du3OwyEqrp71k3XAiFAPrkippyfUaOMP33Z+jB8uPFG37Sp5faEBMsAwNnjlCrQ60dERDbJPmeoXr16aNeuncW2OnXqoGHDhtW2u8yXuSJyntnY2RFpSp5Z2hmBXj8iIrJK9sGQVzlYe8xjq6xbywtKSDAOsZdLi4OzI9LcHbmmFIFePyIiqkb2OUOeYLfP0du5IqbzX/8yMxeFiIjILuYM+Yo3c0X0emOLkLV407Rt8mTbcw0RERGR16m7m8zEW7kinNmYiIhI9hgMmVjLFalp0rNcRqsRERGRTQyGbPFE0jNnNiYiIpI95gxZY0p6vr6L6/hx4/acHOfOYxqtZkqWvp4kGWebruloNSIiInIbg6HreTLp2bRCPVA9IPLV+mNERERkF4Oh67mS9OwMzmxMREQka8wZup43kp45szEREZFsMRi6nreSnjmzMRERkSyxm+x6THomIiJSFQZD12PSMxERkaowGLKGSc9ERESqwZwhW5j0TEREpAoMhuxh0jMREVHAYzcZERERqRqDISIiIlI1BkNERESkaswZIvfp9UwwJyIixWMwRO7JyTEuaFt1HbeEBOMcTZx6gIiIFITdZHKg1wP5+cCiRcafer2/S2RfTg4wYkT1BW2PHzduz8nxT7mIiIjcwGDI33JygMREID0dGD3a+DMxUb4BhV5vbBESovo+07bJk+Uf0BEREf2FwZA/KbGFZcOG6uWtSgigsNB4HBERkQIwGPIXpbawnDjh2eOIiIj8jMGQvyi1hSUuzrPHERER+RmDIX9RagtLnz7GUWOSZH2/JAEajfE4IiIiBWAw5C9KbWEJDjYOnweqB0Sm32fP5nxDRESkGAyG/EXJLSzDhwOLFwNNm1puT0gwbuc8Q0REpCCcdNFfTC0sI0YYA5+qidRKaGEZPhwYOpQzUBMRkeIxGPInUwuLtZmcZ8+WfwtLcDCQlubvUhAREdUIgyF/YwsLERGRXzEYkgO2sBAREfkNE6iJiIhI1RgMERERkaoxGCIiIiJVYzBEREREqsZgiIiIiFSNwRARERGpGoMhIiIiUjXZB0NZWVno1q0b6tWrhyZNmmDYsGHYt2+fv4tFREREAUL2wdCPP/6IiRMnYvPmzVi9ejWuXr2K2267DeXl5f4uGhEREQUASYiqK4TK3+nTp9GkSRP8+OOPuOWWW5x6TklJCaKiolBcXIzIyEgvl5CIiIg8wVf3b8Utx1FcXAwAiI6OtnlMRUUFKioqzL+XlJR4vVxERESkTLLvJqvKYDBg8uTJuPnmm9GuXTubx2VlZSEqKsr80Gg0PiwlERERKYmiuskmTJiA77//Hj/99BMSEhJsHmetZUij0bCbjIiISEHYTXadSZMmYcWKFVi/fr3dQAgAwsLCEBYW5qOSERERkZLJPhgSQuDxxx/H0qVLkZ+fj6SkJH8XiYiIiAKI7IOhiRMnIjs7G8uXL0e9evVw8uRJAEBUVBQiIiL8XDoiIiJSOtnnDEmSZHX7ggULMHbsWKfOwaH1REREysOcob/IPFYjIiIihVPU0HoiIiIiT2MwRERERKrGYIiIiIhUjcEQERERqRqDISIiIlI1BkNERESkagyGiIiISNUYDBEREZGqMRgiIiIiVWMwRERERKrGYIiIiIhUjcEQERERqRqDISIiIlI1BkNERESkagyGiIiISNUYDBEREZGqMRgiIiIiVWMwRERERKrGYIiIiIhUjcEQERERqRqDISIiIlI1BkNERESkagyGiIiISNUYDBEREZGqMRgiIiIiVWMwRERERKrGYIiIiIhUjcEQERERqRqDISIiIlI1BkNERESkagyGiIiISNUYDBEREZGqMRgiIiIiVWMwRERERKrGYIiIiIhUjcEQERERqRqDISIiIlI1BkNERESkaooJhubOnYvExESEh4ejR48e+OWXX/xdJCIiIgoAigiGvvrqK0yZMgXTp0/Htm3b0LFjRwwYMACnTp3yd9GIiIhI4RQRDL355pt4+OGHMW7cOKSkpGDevHmoXbs2Pv74Y38XjYiIiBRO9sHQlStX8OuvvyIjI8O8LSgoCBkZGdi0aZMfS0ZERESBIMTfBXDkzJkz0Ov1iImJsdgeExODvXv3Wn1ORUUFKioqzL8XFxcDAEpKSrxXUCIiIvIo031bCOHV68g+GHJHVlYWZsyYUW27RqPxQ2mIiIioJs6ePYuoqCivnV/2wVCjRo0QHByMoqIii+1FRUWIjY21+pxp06ZhypQp5t8vXLiA5s2b4+jRo159MeWmpKQEGo0GhYWFiIyM9HdxfIb1Zr3VgPVmvdWguLgYzZo1Q3R0tFevI/tgKDQ0FF26dMHatWsxbNgwAIDBYMDatWsxadIkq88JCwtDWFhYte1RUVGq+hCZREZGst4qwnqrC+utLmqtd1CQd1OcZR8MAcCUKVMwZswYdO3aFd27d8fs2bNRXl6OcePG+btoREREpHCKCIbuvvtunD59Gs8//zxOnjyJTp06YeXKldWSqomIiIhcpYhgCAAmTZpks1vMkbCwMEyfPt1q11kgY71ZbzVgvVlvNWC9vVtvSXh7vBoRERGRjMl+0kUiIiIib2IwRERERKrGYIiIiIhUjcEQERERqZoig6G5c+ciMTER4eHh6NGjB3755Re7x3/zzTdo06YNwsPD0b59e3z33XcW+4UQeP755xEXF4eIiAhkZGTgwIED3qyCW1yp9wcffIA+ffqgQYMGaNCgATIyMqodP3bsWEiSZPEYOHCgt6vhMlfqvXDhwmp1Cg8PtzgmEN/vtLS0avWWJAmDBw82H6OE93v9+vXIzMxEfHw8JEnCsmXLHD4nPz8fnTt3RlhYGFq2bImFCxdWO8bV/zN8zdV65+TkoH///mjcuDEiIyPRs2dPrFq1yuKYF154odr73aZNGy/WwnWu1js/P9/q5/zkyZMWxwXa+23tb1eSJKSmppqPUcL7nZWVhW7duqFevXpo0qQJhg0bhn379jl8ni/u4YoLhr766itMmTIF06dPx7Zt29CxY0cMGDAAp06dsnr8zz//jFGjRuGhhx7Cb7/9hmHDhmHYsGHYtWuX+ZjXXnsNb731FubNm4ctW7agTp06GDBgAC5fvuyrajnkar3z8/MxatQo5OXlYdOmTdBoNLjttttw/Phxi+MGDhyIEydOmB+LFi3yRXWc5mq9AeMMrVXrdOTIEYv9gfh+5+TkWNR5165dCA4Oxl133WVxnNzf7/LycnTs2BFz58516viCggIMHjwY6enp2L59OyZPnozx48dbBAbufIZ8zdV6r1+/Hv3798d3332HX3/9Fenp6cjMzMRvv/1mcVxqaqrF+/3TTz95o/huc7XeJvv27bOoV5MmTcz7AvH9njNnjkV9CwsLER0dXe3vW+7v948//oiJEydi8+bNWL16Na5evYrbbrsN5eXlNp/js3u4UJju3buLiRMnmn/X6/UiPj5eZGVlWT1+5MiRYvDgwRbbevToIR599FEhhBAGg0HExsaK119/3bz/woULIiwsTCxatMgLNXCPq/W+XmVlpahXr5745JNPzNvGjBkjhg4d6umiepSr9V6wYIGIioqyeT61vN+zZs0S9erVE2VlZeZtSni/qwIgli5daveYv//97yI1NdVi29133y0GDBhg/r2mr6WvOVNva1JSUsSMGTPMv0+fPl107NjRcwXzMmfqnZeXJwCI8+fP2zxGDe/30qVLhSRJ4vDhw+ZtSnu/hRDi1KlTAoD48ccfbR7jq3u4olqGrly5gl9//RUZGRnmbUFBQcjIyMCmTZusPmfTpk0WxwPAgAEDzMcXFBTg5MmTFsdERUWhR48eNs/pa+7U+3oXL17E1atXqy12l5+fjyZNmqB169aYMGECzp4969Gy14S79S4rK0Pz5s2h0WgwdOhQ7N6927xPLe/3Rx99hHvuuQd16tSx2C7n99sdjv6+PfFaKoHBYEBpaWm1v+8DBw4gPj4eLVq0wL333oujR4/6qYSe1alTJ8TFxaF///7YuHGjebta3u+PPvoIGRkZaN68ucV2pb3fxcXFAGB3EVZf3cMVFQydOXMGer2+2jIcMTEx1fqMTU6ePGn3eNNPV87pa+7U+3rPPPMM4uPjLT4wAwcOxKeffoq1a9fi1VdfxY8//ohBgwZBr9d7tPzucqferVu3xscff4zly5fj888/h8FgQK9evXDs2DEA6ni/f/nlF+zatQvjx4+32C7399sdtv6+S0pKcOnSJY/87SjBG2+8gbKyMowcOdK8rUePHli4cCFWrlyJ9957DwUFBejTpw9KS0v9WNKaiYuLw7x587BkyRIsWbIEGo0GaWlp2LZtGwDP/F8pd3/++Se+//77an/fSnu/DQYDJk+ejJtvvhnt2rWzeZyv7uGKWY6D3PfKK6/gyy+/RH5+vkUy8T333GP+d/v27dGhQwckJycjPz8f/fr180dRa6xnz57o2bOn+fdevXqhbdu2mD9/Pv7973/7sWS+89FHH6F9+/bo3r27xfZAfL8JyM7OxowZM7B8+XKL3JlBgwaZ/92hQwf06NEDzZs3x9dff42HHnrIH0WtsdatW6N169bm33v16oVDhw5h1qxZ+Oyzz/xYMt/55JNPUL9+fQwbNsxiu9Le74kTJ2LXrl2yyWtSVMtQo0aNEBwcjKKiIovtRUVFiI2Ntfqc2NhYu8ebfrpyTl9zp94mb7zxBl555RX88MMP6NChg91jW7RogUaNGuHgwYM1LrMn1KTeJrVq1cKNN95orlOgv9/l5eX48ssvnfrPT27vtzts/X1HRkYiIiLCI58hOfvyyy8xfvx4fP3119W6Eq5Xv3593HDDDYp+v63p3r27uU6B/n4LIfDxxx/j/vvvR2hoqN1j5fx+T5o0CStWrEBeXh4SEhLsHuure7iigqHQ0FB06dIFa9euNW8zGAxYu3atRWtAVT179rQ4HgBWr15tPj4pKQmxsbEWx5SUlGDLli02z+lr7tQbMGbY//vf/8bKlSvRtWtXh9c5duwYzp49i7i4OI+Uu6bcrXdVer0eO3fuNNcpkN9vwDgEtaKiAvfdd5/D68jt/XaHo79vT3yG5GrRokUYN24cFi1aZDGFgi1lZWU4dOiQot9va7Zv326uUyC/34BxNNbBgwed+rIjx/dbCIFJkyZh6dKlWLduHZKSkhw+x2f3cJdSv2Xgyy+/FGFhYWLhwoXi999/F4888oioX7++OHnypBBCiPvvv188++yz5uM3btwoQkJCxBtvvCH27Nkjpk+fLmrVqiV27txpPuaVV14R9evXF8uXLxc7duwQQ4cOFUlJSeLSpUs+r58trtb7lVdeEaGhoWLx4sXixIkT5kdpaakQQojS0lLxf//3f2LTpk2ioKBArFmzRnTu3Fm0atVKXL582S91tMbVes+YMUOsWrVKHDp0SPz666/innvuEeHh4WL37t3mYwLx/Tbp3bu3uPvuu6ttV8r7XVpaKn777Tfx22+/CQDizTffFL/99ps4cuSIEEKIZ599Vtx///3m4//44w9Ru3Zt8fTTT4s9e/aIuXPniuDgYLFy5UrzMY5eSzlwtd5ffPGFCAkJEXPnzrX4+75w4YL5mKlTp4r8/HxRUFAgNm7cKDIyMkSjRo3EqVOnfF4/W1yt96xZs8SyZcvEgQMHxM6dO8WTTz4pgoKCxJo1a8zHBOL7bXLfffeJHj16WD2nEt7vCRMmiKioKJGfn2/xub148aL5GH/dwxUXDAkhxNtvvy2aNWsmQkNDRffu3cXmzZvN+/r27SvGjBljcfzXX38tbrjhBhEaGipSU1PFt99+a7HfYDCI5557TsTExIiwsDDRr18/sW/fPl9UxSWu1Lt58+YCQLXH9OnThRBCXLx4Udx2222icePGolatWqJ58+bi4YcfltV/GCau1Hvy5MnmY2NiYsTtt98utm3bZnG+QHy/hRBi7969AoD44Ycfqp1LKe+3aej09Q9TXceMGSP69u1b7TmdOnUSoaGhokWLFmLBggXVzmvvtZQDV+vdt29fu8cLYZxiIC4uToSGhoqmTZuKu+++Wxw8eNC3FXPA1Xq/+uqrIjk5WYSHh4vo6GiRlpYm1q1bV+28gfZ+C2EcLh4RESHef/99q+dUwvttrc4ALP5m/XUPl/4qIBEREZEqKSpniIiIiMjTGAwRERGRqjEYIiIiIlVjMERERESqxmCIiIiIVI3BEBEREakagyEiIiJSNQZDREREpGoMhoiIiEjVGAwRERGRqjEYIiJFWrRoESIiInDixAnztnHjxqFDhw4oLi72Y8mISGm4NhkRKZIQAp06dcItt9yCt99+G9OnT8fHH3+MzZs3o2nTpv4uHhEpSIi/C0BE5A5JkvDyyy9jxIgRiI2Nxdtvv40NGzYwECIil7FliIgUrXPnzti9ezd++OEH9O3b19/FISIFYs4QESnWypUrsXfvXuj1esTExPi7OESkUGwZIiJF2rZtG9LS0jB//nwsXLgQkZGR+Oabb/xdLCJSIOYMEZHiHD58GIMHD8Y//vEPjBo1Ci1atEDPnj2xbds2dO7c2d/FIyKFYcsQESnKuXPn0KtXL6SlpWHevHnm7YMHD4Zer8fKlSv9WDoiUiIGQ0RERKRqTKAmIiIiVWMwRERERKrGYIiIiIhUjcEQERERqRqDISIiIlI1BkNERESkagyGiIiISNUYDBEREZGqMRgiIiIiVWMwRERERKrGYIiIiIhUjcEQERERqdr/AxTHyxDAT5MkAAAAAElFTkSuQmCC", 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      " ] @@ -2073,18 +2073,25 @@ "name": "stdout", "output_type": "stream", "text": [ - "Own inversion\n", - "[[4.05658312]\n", - " [2.97504928]]\n", - "Eigenvalues of Hessian Matrix:[0.31549215 4.38384294]\n", + "Own inversion" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "[[3.95215017]\n", + " [2.98138287]]\n", + "Eigenvalues of Hessian Matrix:[0.28889272 3.80178359]\n", "theta from own gd\n", - "[[4.05658312]\n", - " [2.97504928]]\n" + "[[3.95215017]\n", + " [2.98138287]]\n" ] }, { "data": { - "image/png": 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QERERyRqDISIiIpI1BkNEREQkawyGiIiISNYYDBEREZGsMRgiIiIiWWMwRERERLLGYIiIiIhkjcEQERERyRqDISIiIpI1BkNEREQkawyGiIiISNYYDBEREZGsMRgiIiIiWWMwRERERLLGYIiIiIhkjcEQERERyRqDISIiIpI1BkNEREQkawyGiIiISNYYDBEREZGsOT0YysjIwKBBgxAaGgqFQoH169fr15WVleH1119Hu3btUK9ePYSGhuLvf/87Ll686LwCExERkVtxejBUXFyMDh06YNGiRVXWlZSU4ODBg3jrrbdw8OBBpKSk4OTJk/jb3/7mhJISERGRO1IIIYSzC6GjUCiwbt06DBkyxOQ2Bw4cQNeuXXHu3Dk0b97couMWFhYiICAABQUF8Pf3t1FpiYiIyJ4c9fz2stuR7aSgoAAKhQINGzY0uU1paSlKS0v1vxcWFjqgZEREROSKnN5MZo07d+7g9ddfx6hRo6qNEJOTkxEQEKD/iYiIcGApiYiIyJW4TDBUVlaG4cOHQwiBzz//vNptZ8yYgYKCAv1PTk6Og0pJRERErsYlmsl0gdC5c+fwyy+/mG039PHxgY+Pj4NKR0RERHpqNbBzJ3DpEhASAvTqBXh6OrtU1ZJ8MKQLhLKyspCWlobGjRs7u0hERERkTEoKMHkycOHCvWXh4cCCBcDQoc4rlxlOD4Zu3bqF06dP63/Pzs7G4cOHERgYiJCQEAwbNgwHDx6ESqWCWq1GXl4eACAwMBDe3t7OKjYRERFVlJICDBsGVO6knpurXb5mjWQDIqd3rU9PT0fv3r2rLB8zZgzeeecdREVFGd0vLS0NCQkJFp2DXeuJiIjsSK0GWrQwrBGqSKHQ1hBlZ1vVZCabrvUJCQmoLh6T0DBIREREZMzOnaYDIUBbW5STo93OwooMR3KZ3mREREQkUZcu2XY7B2MwRERERLUTEmLb7RyMwRARERHVTq9e2pwghcL4eoUCiIjQbidBDIaIiIiodjw9td3ngaoBke73+fMtT55Wq4H0dGD1aluVsFoMhoiIiKj2hg7Vdp8PCzNcHh5uXbf6lBRtz7TevYFx42xeTGOc3rXeEdi1noiIyEFqMwJ1pbGKCgEEAO7ftZ6IiIjciKdnzbrPq9Xa0audUEfDZjIiIiJyPnNjFdkRgyEiIiJyPieOQcRgiIiIiJzPiWMQMWeIiIhIKmqTfOzqdGMV5eY6PG+INUNERERSULFL+ejR2n9btNAul4PqxiqyMwZDREREzqbrUl45gTg3V7tcLgGRqbGK7IzjDBERETmTWq2tATLVk0qh0DYfZWfLp8nsr+bCwjNnEDBunN2f36wZIiIiciZzXcqFAHJytNvJhW6soiefdMjpmEBNRETyILXkZF151q61bHsndj13dwyGiIjI/aWkaEc3rlgDEx6uTdi1dM4se5fHHCd2PXd3bCYjIiL3JrXkZFPlMUWhACIitDVZZBcMhoiIyH1VN9+VbtmUKdrtnF0eY3RdzOfPl0/ytBMwGCIiIvclteRka+ffCg/XdjV3RlOejDBniIiI3JelSceOSk629DwTJwJPPOH8JG+ZYDBERETuy9KkY0clJ1t6niee0HYtJ4dgMxkREbkv3XxXpqZ3cHRystTKQwAYDBERkTurbr4rZyQnS608BIDBEBERuTtT8105KzlZauUhzk1GREQyIdURqKVSHgly1PObCdREROQYzn746+a7kgqplUfGGAwREZH9SW06DKIKmDNERET2JbXpMORMrQbS04EVK7T/OmrkbYljzRAREdmPuekwFArtdBiDBzNfxt5cpXauYnOqg/J8GQwREZH9WDMdBvNn7EdXO1c5KNXVzkmlF5uxgM0B2ExGRET2I7XpMGzNFZqdpDZZrSmmmlMdgMEQERHZj9Smw7CllBSgRQugd29g9Gjtvy1aSC8HSmqT1RpTXcDmAAyGiIjIftx1+glXSgq3d+2cLWrHzAVsdsZgiIiI7Mcdp59wlWYnHXvWztmqdszJzaQMhoiIyL7cbfoJV2h2qshetXO2rB1zcjMpgyEiIrK/oUOBs2eBtDRg+XLtv9nZrhcIAa6XFG6P2jlb146ZC9jsjMEQERE5hm76iVGjtP+6UtNYRa6YFG7r2jlb145VF7A5gNODoYyMDAwaNAihoaFQKBRYv369wXohBN5++22EhITAz88PiYmJyMrKck5hiYiIXDUp3Ja1c/aoHTMVsDmA04Oh4uJidOjQAYsWLTK6fu7cufjkk0+wePFi7Nu3D/Xq1UO/fv1w584dB5eUiIgIrp0UbqvaOXvVjlUO2FQqq4tWEwohnNSp3wiFQoF169ZhyJAhALS1QqGhoZg2bRr++c9/AgAKCgoQFBSEZcuWYeTIkRYdt7CwEAEBASgoKIC/g4b2JiIiN2dstOSICG0g5Iq5UNZQq7W9xnJzjecNKRTa2rPs7FoFhY56fju9Zqg62dnZyMvLQ2Jion5ZQEAAunXrhj179pjcr7S0FIWFhQY/RERENuVOSeHWsmPtmPquGnu+OIL/65mOh5o5Ji1G0nOT5eXlAQCCgoIMlgcFBenXGZOcnIxZs2bZtWxERET6Zic50U2kWloKvPMO8MUX2hoinfBwq2vHCs4XYMu8o0j9SYON2W1wVbT7a41jKjMkHQzV1IwZMzB16lT974WFhYiIiHBiiYiIiNyAsabB8HBg1iygVSttjlCvXmZrhIRG4NSWbKg+O4/UXwOw82Y8ytFDvz4ABegfcRSPJhbhhaX2uph7JB0MBQcHAwAuX76MkApJWJcvX0bHjh1N7ufj4wMfHx97F4+IiEg+dIMsVs4Rys3V1hCtWVNtLdndW3eR8VkmVCuKkHo0EqfLWgJoqV/fxvsMlO1zoHy6IXqMj0Oduj1QWFjIYCgqKgrBwcHYvn27PvgpLCzEvn378NJLLzm3cERERHJhbpBFhUI7yOLgwQa1Qpczr2Djxyeg2lIH2y7GoggP6Nd5oxSPBGZCmXALSRNaIPrRaADR9r8WI5weDN26dQunT5/W/56dnY3Dhw8jMDAQzZs3x5QpU/Dee++hVatWiIqKwltvvYXQ0FB9jzMiIiKn0eXPXLpkcRORS7JwkEWxIwOHLgVD9dVlqPY3xYHiOABN9ZsFe1zGwOhTUD5eB4mT49AgtJP9y24BpwdDv/32G3r37q3/XZfrM2bMGCxbtgyvvfYaiouL8Y9//AM3b95Ez549sXnzZvj6+jqryERERKbzZxYscL8eZRYOnjgh8QQ+F70BtNUv61z3GJK65EP5XDM8MLoNPLyCTB/ASSQ1zpC9cJwhIiKyKVP5M7pu5a44AW110tO1M9KbkYA0/IbOeCwkE0mPlWHglNYI6Vjz4MdRz28GQ0RERNbQDThoqtnIRgMOSkl5cSnU4ZHwvnkZxiYh0UCBgjpNcWDmf/HIKx3g42+bTkwcdJGIiEiKbD1JqURdy7qOH17ehdEtdqFZgxKMuvkZBBTQVAqHBBRQKIBGKz/HY//qarNAyJGcnjNERETkUmwxSakEE6+FRuDohtNI/SIXqt2NsLswHho8pF+/Q5GAT5u+i/ElC1D31hX9ckWE9YMsSg2DISIiImvUdpJSCSVe37l5B2mfHEHqmhKojkXjnLoVgFb69e18T0F5/0UoxzRGt7Gx8PT+F6B+w3QgJ8EgzxLMGSIiIrJGbSYplUDide5vl5A6Pwup233wc148SlBPv84Xt/Fo00wo+9xG0qRoNO8eZvmB7RDkMYHahhgMERGRTemCGsAwsKkuqHFS4rWmXIMD3xyDatlVpP4ehEO32xqsD/O4BGVMFpTDfPHopHjUbVLX+pPYKchjMGRDDIaIiMjmjNWERESYzp+xsHs60tJqPflr4YVCbJ13FKoNamz6Mwb54t7Ahwpo8GD9o0h68BqU40PQflhrKDyM9RGzkKVB3unTwO7dVjWhOer5zZwhIiKimhg6VDv9hKU5MrZIvK7GqS3ZSP38HFQZ/si40Q7l6K5f5//XxKdJ/TUY8GobNG3brpojWcnS3nXh4cCVe4nXUhqgksEQERFRTXl6Wl6LU9vE60ru3rqLnZ9nInVlIVRHIpFVFgUgSr8+xvtPKNudR9LoAPR8MR516vYwfbDasDR4qxgIAdqcq2HDJDFAJYMhIiIiR+jVS1sbYi7xulcvk4fIP3oFG+edROpmT2zJjTOY+LQO7uKRwCNQPqKd+PS+PoazwtuNpUFeZdVM8OpoDIaIiIgcwdNT2yw0bJg2CDCWeD1/vkFQIDQCh388CdWXeUjd1wT7i2Mh0FO/vpniCpLuOwHlkDpInBQL/3AnTHxqLsirTsUBKmuZJ1UbDIaIiIgcZehQbbOQsS7ofyVeF+cXY/uCTKSmlCL1VCvkatoAaKPf9AG/41B2uQzlc83Q6ak28PAyXZPkENUFeZaqYZ6UrbA3GRER2YaLDrjnFJXu1VlFFFIXnUVqel38cqUdSuGr37QuitE3OBPKx+5i4ORWCH0g2IkFr4ax3nVNm1bNFTLGRA869iYjIiLXIaFRlV1BeZnA3mOBUH0LpB4OQ2ZpJIBI/foWXjlQtv0TyhH18MiEePg27Oa8wlrKWO+6Hj2A6Oha5Uk5AmuGiIioduw5qrIb1TZdP3MDW+Yfh+q/ApvPt8V1Eahf54ly9PA/CuVDN6B8MRxtldHmx/5xlXtTkwEq/8JBF22IwRARkZ3Yc1RlF69tEhqB46ozUC2+ANWuRthdGAd1hQaZRoobGND8GJSDFOg3pS0CoxtZfnBXuzfWDlD5FwZDNsRgiIjITuw1qrIE5vCqiTs372DHokyofiyG6nhLnC2PgAfU6IWdCMEl+NZRI/j+ECSNaYoHn4uFl28NslVc9N7UpCaLOUNERCR99hhVWa3W1iIY+64uobFpdC4ezMPGBVlQbfXGtrx4lKCzft1wrMRCj8loqsnXLigDcDEcCF4A+La3/mQudm8MWDNApYMxGCIiopqz8ajKACyf3sFJY9NoyjX47bvjSF12BaoDQTh4uy2Aez28Qj0uQdk6C+Nid6Pzujeh0FQKXGoz8rLE742rYjBERETmmWrisMGoylXYeQ6vmii8UIhtC45Ctb4cG8+0Qb6I069TQIOu9Y5B+eBVKMeHoMOTraEQzYAWT9m+BkeC98YdMBgiIqLqmUvWtXJUZbPsUdtUA6e3n4Nq4VmkZtTHjuvtUFZh4tMGKES/sKNQDlBjwJQYNIuLN9w53U41OBK5NzYlgV5xDIaIiMg0U8m6lZt6zIyqbBV71DZZoKykDL8uzoRqeQFSjzTHybstUXHsn1Z1sqFsdw7K0QHo+UIcvOt3N30we9XgOOne2I1EesWxNxkRERlnbbd5W37Dr8XYNNa4cvwqNs07AdUmT2y5EItCBOjXeaEMDzc6AuXDhUh6KRKt+0VVc6RK7NXLDnDYvbE7C3rFFSYmsmu9rTAYIiKqAXs+0C1Rw7FpqiM0An+sOQXVfy5Btbcx9t2Kg4CHfn1TxRUMbHkSysGe6Ds5FgHNA6o5WjV0gaS5GpyajL8EmJ764rPP7gVKUmZhoF34v/8hIDCQXeuJiMhJnJ2sa2x6hxrUNpVcLcEvn2RCteYOUk/dhwvqGAAx+vX3+x1HUqfLUD7bBF3GxMLDq6fpg1mqBjPUW2XoUG1A8fLLwNWr2mVXrgCvvgp4eEi/ZsjSXnG7dzukOAyGiIjIOCkk69ZwbJrze3KR+skZqLb74Zcr8biDrvp1fihB3+AjSOpTioGTWyG8S1sAbW1XZh1b51JVlJICjBhhPpdLqiwNoPPy7FuOv7CZjIiIjLN3U48Nqe+qsfero0j97jo2/94ED9/9GdE4gzOIxiK8jDDPfChjzyBpWF0kTIyHX6CfAwtn495S9pwCxVEsbIItVKkQoFQyZ8gWGAwREdWQhJN1b2TfxJZ5x5Cq0mDT2ba4JhpjNl7DNHwML6j12wkPD2DqVCg++MAp5bQ5Z+dy2YKFgbajcoY8zG9CRESypWvqCQszXB4e7vBASDfx6QdJ6UhoeBhNW9bHqE974PvsnrgmGmM+JuE1fADPCoEQACg0Gig+/BB47TWHldWunJ3LZQu6nCrgXmCtY4ucKiuxZoiIiMxz0sB4pYWl2olPV95C6rEW+LM80mB9rM9pKDtcgHJUPfSc1h0KjdrEkaAtb0kJ4O1t51LbmTvUDOmY6THIWettiMEQkRUkMBosydulw5excf4pqLbWwbZL8ShGff06b5Sid+MjUPYuRtIrLRH1cIR2xfz52p5U5sybp50Gw5W5UC6XRar5m8NZ64nI8SQyGizJi6Zcg4PLT0D1dT5UB5rh95JYAEH69SEeeUhqdQrKoT7oMykO9YM7Vz3ImTOWnczS7RylJl8+7N1t39EkMJs9gyFybazFsB1Lp10gsoGii0X4ecFRqNaVIfVMDC5rYgHE6td3rZeJpK5XoXw+CPePagOFR7DpgwFAdLRlJ7Z0O0eozZeP6rrtf/QREBgIrFjBv4sWYjMZuS7WYtiOO3TVJck788s5pC46C1V6faRfb4cy3MvdqY8i9AvLRNJj5RgwJQbB7ZtZd/C7d4G6dbXvZVOklDNkwVQUFv0dq/yF8OpVbXOhm/xdZM6QDTEYckO2+kNCWu6UkEmSUVZShl1fHEXqDzeh+iMCJ+4a1spEe53DoHZnkTSyAR5+OR7e9WsZpLz2GlBd9/np04G5c51fo2yvLx9u+HeROUNEpqjV2hohY3G8ENoP/pQp2mH8WYthGXfoqkuScPXkNWyadxypGz2wOScOBeioX+eFMvRqmAnlwwVIerE5WveLgsIj0vTBrDV3rvbfjz82rCHy9ASmTtWul0KNsqVTUezcafrLR+WArkcP/l2sBQZD5Hps8YeEDElh2gVySUIjcCQlC6ovLiJ1byD2FMVD4N7cXk0UVzEw6gSUgz3x2JRYBDS/374FmjsXeO897YSlZ85oc4ReflnbNCaVvLjafvkwNUnrlSumj8W/i9ViMESuh7UYtterl/bbsbmuur16Ob5sJDm3r9/WTny6+jZST0YjR90aQGv9+g6+J6HsdEk78enf28LT2wYTn1rD27tq93kp1SjX5suHqYCuukCoIv5dNEryI1Cr1Wq89dZbiIqKgp+fH6Kjo/Huu+9CBqlOZAprMWxPYqPBkvTk7LuIxaMzoAzaj8aNBZSzumDxsYeRow6DH0qgbLYfi0dn4Pzeizh8Owbv/ZqAB8fFw9NbIu8Za2qU7U335aPyZ01HodAOPFj5y0d1AZ2l+HfRKMnXDM2ZMweff/45vvnmG8TFxeG3337D2LFjERAQgEmTJjm7eOQMrMWwD3vOsF2ZsxNYySz1XTX2LzsG1TfXkHowBP+7EwMgVL8+wjMXyjZnoHzSD71fiYdfYFfTB5MCKdUo13ScIHMBXXX4d7Fakg+Gdu/ejcGDByMpKQkA0KJFC6xYsQL79+93csnIadxtwDEpGTpU20xgz0BFCgmsZNTNcwXYOv8oVD9psCm7Da6Kdvp1HlDjwQZHoex+Hcp/hCL+8VZQeIRVczSJkVqNck2+fNQ0UOPfRfOExL3//vsiMjJSnDx5UgghxOHDh0WzZs3E999/b/ExCgoKBABRUFBgr2KSM6xdK0R4uBDacEj7ExGhXU7StHatEAqF4WsGaJcpFHztHEyj1ogTG8+ID5VpIqHhQeGFuwYvSwBuihERu8R3L/4qrpy46uzi1k55ufbvhbH3n+49GBGh3c7R5UpLE2L5cu2/1Z0/Lc142Sv/NGniNn8XHfX8lvw4QxqNBm+++Sbmzp0LT09PqNVqvP/++5gxY4bJfUpLS1FaWqr/vbCwEBERERxnyB2xucV1yGVgR4m/J0sLS5HxWSZSf7wFVWYLnKk08Wlb7zNQdshB0lMN0WN8HOrUreOkkhpR23urSz4GjNcoS30cHkvnJDt9Gti9W7LvQWs4bJxAu4ZaNrBixQoRHh4uVqxYIf744w/x7bffisDAQLFs2TKT+8ycOVMAqPLDmiEiJ7L0W+28eZZ9S5YiY7WV4eFO/1Z+6X+XxVfPZoihoXtEfRQaFM8bd8RjjQ+IT55IF2fSzjm1nNWy1b119RplXe1q5RouN61ddVTNkOSDofDwcLFw4UKDZe+++66IiYkxuc+dO3dEQUGB/icnJ4fBEJGzLV9uWTAksUDCYhJqAlSXqcVv3x0T7ySkiS71MqsUKdgjTzzfOkOse2OvKLpU5LBy1Zit7601TVNS5OoBnRUcFQxJPoG6pKQEHh6GIwB4enpCo9GY3MfHxwc+Pj72LhoRWaMmiamuMkmsBMawuZV3SzvxacpdbDzdCpc0bQG01a/vXPcYlF3ztROfjoyBh1eQ6YNJiT3urQRmSa8VR3R0kBnJB0ODBg3C+++/j+bNmyMuLg6HDh3Cxx9/jOeee87ZRSMia5gbEsEYV5lGwEmjov+Zfh6pC7OhSq+H9GvtcBfd9Ovqowh9Q45C2a8MAya3RkhHw1nhXQZHnDfO1QM6iZF8MPTpp5/irbfewssvv4z8/HyEhobihRdewNtvv+3sohGRNaobEqE6UnnYVZe866AxbMrvlGP3f45C9d0NqP4XgeN3owE0169v6XUOyrizUI6sj4dfjoeP/4O1Op9TVL7PubmW7bdhA4MDqjHJ9yazBc5aT25N4r2XqjA2zpAlli8HRo2yT5nMMTc2Uno60Lu3+eOkpVn9wL6WdR2b5x2HKlWBzTmxuCka6td5ohw9AzKh7HkTypebI6Z/FBQeJkY1dgU1mXOrorVrpd2cSlZz1PObwRC5LlcLAuzBVQcwrPjaXb4MvPqq+X1qEEjYhKm5oCp2xx482LIuzxYMGyA0Akc3nIZqSS5UuwOxpygOGtzbp7HiGga0OA7lIA/0mxqHhpEBVQ9i7LMBSPvzUt19tvQxFRHh+kMzkAF2rbchDrrohiTahdmhJNR7qVakOhhexbKZ6u1WsWy16PJccq1EbJy1X7wcny6ae+ZUOU173xNiRvc0sWvxH6K81Mx9MPbZaNxY+yPVz4u5+2zNT1qas6+GbEiSXevPnz9vr3LYFYMhN+MuQUBtWPOQdgVSHTvF0rGRdA9gK7o8XzhwUSx5aocYFLRX+KHYYBdflIikZvvEZyN3iHO7L1heXlOfDVPvEXP31lFd0C29z5b8LF9unzKSU0iya32bNm0wbdo0vPHGG6hbt649KqqIqieBLsyS4Co9bCo21zRrpl2Wn1+1mcbUPE1NmgCLFjmvyc/axOhqujyr76px4NvjSP3mKlS/B+Pw7TYA7g03EO55EcqY00h6whePTopH3SZWTnxq7Yzm5j4vjmyCteXkqJyVnWrCmshp165domvXriI0NFQsXbrUTvGZ7bFmyI1Y+03dXVk6gKEzvyUbqyUx10yzerUQTZtKpzmnlu+3m+duilWv7hZjoneKpop8w4oZqEX3+n+I9/umicM/nhAatcYxZbWk/I6ufbVmzi0pNqeS3UiymUznm2++EeHh4eKBBx4QGRkZti6TzTEYciOuEAQ4gtSDQkuaayo/WGvyALZ3M87q1UJ4elZ/DZUewCc3/yk++luaeLTR70YnPh0esUt8+8JOkX/sim3L+v33tmlackYTrKV5Y6tWSbM5lexG0sGQEEIUFxeLt956S9SrV0888cQT4s8//7RluWyKwZAbkXoQ4Cg1STp2VP6HNcmwunKWllr/ALZ3Er2FAd3d738UP8/9XUy5P13cVye7yiZtvE+LaZ3SRNq8Q+Ju8V3blM2YefNqHgxV/Lw46zNmad6YjKaiIBcJhnbu3CkmT54sPDw8hK+vr5g+fbooKpLePDcMhtyIlHseOZo1SceO7H1Xk+YaSx/kFROV7dmMY0FAp1Z4iA8avScaoMBgVR2Uir6Bv4n5j6eLrJ/P1vJmWqEmNUPGPi/OrH21NNBx9bnFyGKSTKBevHgxDhw4gAMHDuD48ePw8PBAfHw8XnzxRXTo0AErV65EbGwsUlJS0LlzZ1unNxFVP4qxbtyX+fPdO3lax1TScXi49h7oklxNjd9ir3m/apIMe+aM5cd2RBK9uQR1AB5CA9WNh1AEfwR55GNg9Ekoh9RB3ylxaBDaqWbnrY2wMOu2N/V5sTQB2R6JypbOucWpKMjGrBp0MSIiAt26dcODDz6IBx98EJ06dYKfn5/BNv/+97+xfPlyZGZm2rywNcVBF92QsZ4uERGGQYBcVDf4pFqtHQzQ1IPdisEALWbpaMwVzZtn+cCLgGXHnzcPCAqq2QCDK1YAo0eb3Wx1m/9DizdGodNTbeDh5WF2e7sy91pXZurzojuODQaQJKotlx2B+vLlywgNDYVarbblYWuFwZCbssUI1O4+irUdp4kwydzDtCLdg/X0aSA62rIH8KpVFgUqBizsEn721wtI/eQMzm05jrmFL5k/rrNGxTZFVwsIVK01FQKYNQto1cr8e7264wC2r00kMsFlR6DWaDQiPT3d1oetFeYMkVFyGMXaWfkfpvKZzCXGWpIDVZOcJBO5RGW3y0TGp4fF693SRJzPKf3mHigX5xEu1HDB3LTp06v2gPP01C63BhOVSQIkn0DtShgMURVyGcXamb3vzI0zZOzBaskD2FwSvZlk4Wsnr4gfXv5VjI78VTRSXDeMGVAmHg44JOYOTBPn31goNOaCM6kl8lbXA64m72upXR/JjqOe35yoleTHGXk0zuLs/A9LR6A2tY+p7Uw141jgUfyMNPTR/x6ouI4BkcehHKRAv1dj0SiqoeF5TOWmAdKaJFdO72uSDZfNGZIiBkNkwBl5NM7krvkfxgIVC4zCcmT6dIby/lwo/x6IbmNj4eVbTcdaY8HZhg3mZ7J39D2V2/uaZMFRz28nd38gcgJr55tydbou+JW7XjdqBLzzjrYrsysaOhQ4e1b7cF++HAVTZ1m027xPvHDkTisk70nAQy+1rz4QAu514x4+XPv7ypXAiy+a7toPaLv2O7oTidze10Q2xGCI5MeZ46g4iy5wmDULCAzULrt+HZg5U9u0kpLizNLViKZcg/3fnsDbbwMPPN8RgR//CzkIhwYK4zsoFEBEBIJfrkGNTUqK9j717g08/TRw5YrpbYW4N0muI8nxfU1kI1YNukjkFnr10uZOmMuj6dXL8WWzpw0btDVBjhp80Q4KLxRi24KjUK0vx8YzbZAv4vTrFNBgke9UJN+ZCgEFFDDSJFiTATlNDVppjiNqYCrnZIWFARcvyut9TWQDDIZIfuQ4irUjRm22k6xtZ5H62TmodjRAxo14lKG7fp0/CtAv/BiS+qsxYEoMmsW9CqREmh+V21LV3Tdz7F0DYyxnqnHje6+nHN7XRDbCBGqSLzmNYm1tcq09BqO08Jh3b93Fr0uOInVFAVR/ROJUWZTB+tZ1sqFsfw5JowLQ84U4eNf3rvG5zKrJaNqO6LVlqrZKFwQ1bgxcu3Zvubu+r8ntOer5zZohki9L50FyB9Yk1xoLEmvbZdzMMfOPXsGm+SeRutkTWy7EohD36zfzQhkeaXQEykeKkPRyJFr1jQIQVfUcFdlq7iprm7ocUQNjSS2fnx/w88/mhzEgIgAMhoxz9yka6B65TPhoaZNNVpbt84pM1GKIC7nAE09ghu/HmHtnMgR66tc1VVxBUvQJKId4oe/kOPiHP2DdOW3F2qaumjbHWcPcJLJCaNd7egKjRtmvHERuhM1kldnjWzGRNezVRGVu8EVd13tbDtpnZiBADRS4gHBEIRsd/E5B2fkylGObovMzbZ0/8Slg2TxrTZtqJ4UNC3PMFycLJ5HF8uUMhsjlcZwhZ9B9g638h1v3rdgFux+Ti6nYhXv0aO2/tuj6rksaB+415ejofh8/3nyNg5VdxvM+S6n2mB4QaI4c5C1ej4MlbfH/MhLQdWycNAIhwPx9UyiAxYuBp57S1jA6ogaZXeiJbE4if3EkwFw7POCcgdRIPuwdjJsafDE8XLu8VSvLjlNNHo36rhq7Pv8DM7qno51vFl6dVG7RIZv637Xs3M5g7r45usZYNzRE5eBM56/xlNiFnshyzBnSsaQdXvetWA45JuRYjur6Xl3SeHq6ZceoVONwI/smtsw7BtV/BTada4vror1+XVOcr9ExJUdKyfZyHBrCGZg7KisMhnQ4lD05kyODcVNJ4xYORike6okTqjNQfZ4D1a6G2FUQDzV66DdrpLiB/s2PQZkE9J/cAejjJgNcSinZXldbZavxlMgQc0dlh8GQDtvhyZnsGYxb+g23mhoH8dfvX3mMx7/rXkR2eTSAaP36OJ8sJHXIhfKZRug+Lg5evg/dOy5rMexDSrVV7sTUGE4uNFI7WY+9yXQs6W1j74HUSL7sNeN4Tb7hGtknB2GYjE+wDtp9vFGKR5v8gaSEEiS90hJRD0dYXw4OBEhSY6b3I58Djueo3mQMhirSfSMAjH+Dldo3ArZpuw97BOPVjVIMVHk/a8o1+P2HE1B9nY9NBxqj7u1rCMElXEIIdqIXgj3ykdQ6C0mP+6DPpDjUD65v/TXy/WobvJf2Ya8vJVRjHIHaGVypHZ5t2rUjtYeJrZNiLUzILuryKLZ9egKq9WXYeCYGlzWxAGK1p4UGXeodg7LbVcwbl4WOI2Kg8KhFM7GUcm5Mkdr7whh+9u2HuaPyJWSgoKBAABAFBQWW7VBeLkRamhDLl2v/LS+3Z/Gst3atEAqFENrH2r0fhUL7s3ats0sobWvXChEebnjvwsOlcd+MlS0iwvqypaVVfX8Y+emDrQaLGqBAPBG2Wyx9fqfIO5Jvl0uULCm/L3T42bcvCz83Ii3N2SWVDauf3zXEZjJXwzbt2rGy6cgpbFE7YeEoxaOwHL/V6Q5l/DkoRzVAr5fijU986u5c5X3Bz759MXdUcjgCNRlnTRdsMuQqA2vqmpNGjarxqMYFZXUt2m7uHCDrbgvMO/gI+kx/QJ6BkKu8L/jZtz9LRmpn70e3xGDI1bBNu+bc+GEiNAJ/rDmFfz+Wjh4NjqDxmIHIQTg0qH6U4ohpwx1bUClylfcFP/uOIbURx8khmEDtajgeUs252cOk5GoJ0hYehWr1bahO3ocL6tYAWuvXz/eejg/vToaAAgrUICHbFZKJbcFV3hf87DsOx3CSHQZDrsbCUYJdYkRfR3ODh0nOvotIXXAaqu2+2J7fDnfQRb/ODyVIDDqCpD6lSJrSCuFdJgEp4TXrHSmnHkuu8r7gZ9+xXKH3I9kME6hdQeVv6FevAsP/at5whfGQpMIFkyPVd9XY99URHFuUjqxTauwr64Sd6AUNtOVr7nkByrZnkDSsLnq/Eg+/QD8jB7GyhscVkoltyZXeF642FhpRLTns+W3XvmoS4aiueXZhqrvv9Om26YItN7quyZW7J0uoa/KNszfFykm7xDMtd4q/Y5k4D8PX+bIiSKztOEv8seak0Kg1tj15eXnV91Xl+xQRIb3hJmrLBd4XerYafoHIBbBrfQW5ubl4/fXXsWnTJpSUlOC+++7D0qVL0blzZ4v2d9maIXPf0H/8EWjalG3a1pLY1BBCI3ByczZUn51H6q4A7LzZDmp44XGkYA2GARCGPR3sWQsg5xF4Jfa+qJZc8rlI9jgdx19u3LiB+++/H71798ZLL72Epk2bIisrC9HR0YiOjjZ/ALhoMMQxRezLyQ+T0sJSZHyWCdXKW0g92gJnyiMN1sfVOYVf0RMBZVeM9wez1+tv4fhEWL5c2/Xf3TDIIJIUTsfxlzlz5iAiIgJLly7VL4uKinJiiRzEmu6+7vYN3RGckByZ90c+Ns47CdWWOth2KQ630Em/zhulSGh8BMqEYiRNjEJLXAR6XzF9MHu9/q6STGwvTJolkiXJB0M//fQT+vXrhyeffBI7duxAWFgYXn75ZYwfP97kPqWlpSgtLdX/XlhY6Iii2pardPd1BBf9tq4p1+Dg8hNIXZoP1f5m+K0kFkAz/fpgj8tQtjqJpMd9kDg5DvWDKzT7vjrPspPY+vVnjyUikiHJB0N//vknPv/8c0ydOhVvvvkmDhw4gEmTJsHb2xtjxowxuk9ycjJmzZrl4JLamNy/oeu4WBfvW3m38POCo1Cl3EXq6dbIqzDxKQB0qXcUyq5XkPRcEO4fGQMPr6CqB1GrgR9+sOyEtn79bT1hLBGRC5B8zpC3tzc6d+6M3bt365dNmjQJBw4cwJ49e4zuY6xmKCIiwjVzhlyhu6+9uEgX7z/TzyN1YTZU6fWQfq0d7sJHv64+ivBY6FEkPVaGga/GILh9s2qO9BdLk5h1yfP2eP1dKZmYiNwWc4b+EhISgtjYWINlbdu2xdq1a03u4+PjAx8fH5PrXYLcv6Gbmy9KodDOFzV4sMPvQVlJGXb/5yhSf7gJ1f8icPxuNIDm+vUtvc5hUPxZJI2oj4dfjoeP/4PWncDSpq+nnrLftdtqBF4XbeIkInmRfDD00EMP4eTJkwbLTp06hcjISBN7uBHdHDk1GUHY1Uksgfxa1nVs+vg4UjcqsDknFjdFR/06T5SjV8MjUPYsQNJLzRHTPwoKj1q8Py1t+ho8uObnsERtk4lNNXF+/DGHhCAiSZF8MPTqq6+iR48e+Pe//43hw4dj//79+OKLL/DFF184u2iOIdc5cpycQC40ApnrsqD64iJS9wRiT1EcNHhIv76x4hoGRh2H8m8eeGxKHBpG3m+7k7tDErOpJs4LF+6Nnq4j4RwwIpIHyecMAYBKpcKMGTOQlZWFqKgoTJ06tdreZJW55DhDcueEwf9uX7+NtE8zoVp9G6knWuK8OtxgfXvfk1A+cAnKMY3R9dlYeHrXMiCtrgnJ0dMu2LI5y9wYWZVJLAeMiKSDgy7aEIMhF2RpAvnp08Du3TV+iF84cAmp87Og+tkX2/PjcRt19et8cRt9mh2Bss8dDHwlGs27h9ngwv5iSS85RyUx27rHnqWBbEVy6BBARFZjMGRDDIZclLnakX/+UztishUPcfVdNQ58exyqZVeh+j0E/7sTY7A+wjMXSTFnoHzSD70nxqFuk7pGj1Mr1vSSs3cCsj167Fk6irUx7jjNBxHVGIMhG2Iw5MJM1Y6MHAl8+KFFD/GC8wXYOv8YVBvU2JjdBldFk3ubQ4PuDTKR9OB1KP8RinZDW0HhYXQCDNuQ0jQr9ipLTWqGdNx1mg+5YS9CshF2rScCjCeQ9+gBREdX2+2+7MWJWLgsAP/dGYidN+NRju76TQJQgP4RR5E0UIP+k9ugadv2jrseKfWSs1dZzCWAV8fdBxGVAxcbKJUIYDBErqByF+/0dLMP8TpXLmHDfz2xA9peXm28z0DZPgdJTzXEQ/+IQ526PYzva+9vtJb2ftONo2XPb9T26rFX3RhZprhCDzkyz1Sza26udjmT5EmiPJxdACKrWfhwfqzeLiwYugOnt5/D8dJofHAgAQlTOqJO3TrGd0hJ0TYb9e6tzXnp3Vv7e0qKzYpucc3HwoX2OX9NylKT2hrdGFlhFiSdy2EQUTkwN1AqoB0oVa12aLGILMGcISlie7tRQiNwaMUJHPlwC8YcftX8DtYk4zpq6g9zveQqs2e3c0dM+VL5vXz1KvDqq5zmwx05YTgMcn9MoLYhlwqG2N5uoDi/GD/Pz4QqpRQbs1rhoiYEHlDjLFogDLnwgA0e4o5OajbVS84UeyZVO3o8I4DBvruytBchk+TJCo56frOZTEp0D6bKD2Vde3tNm0vUau23thUrtP9KvJo6OyMHC5/cgf5NfkPjIE8MSe6GL08+jIuaENTDLfwt5ABOJrygfV4rKvX8qq7JxdR9sCaR2BasaUKyx/ktKUt4uP3yO3Q5YKNGaf9lIOQe7NnsSmRvQgYKCgoEAFFQUODsophWXi5EeLgQ2kdf1R+FQoiICO121li7tupxw8O1yyWi7HaZyPj0sHita5qI9cmqculRXufEK+3TxZZ//ybuFNy5t6Oxa4uIMH5t1d2H5ctN3/eKP8uX2/bCy8uFSEsTYuJE55zfWFmWL9f+a+37jEj3N0yhsO3fMJI1Rz2/2ZtMKuzRzVnCPTuun7mBzR8fgyoV2Hw+FjdEB/06T5SjZ0Amkh66CeVLEWgzsCUUHs2rHsTSedvM3Yd33rGs0Lb+Rluxl9zChY4/v6myENVEdb0ImSRPEsecIamwdXu7lAb3gzb5+eiG00j9Iheq3Y2wuzAeGtw7b6DiOga2OIYkpQf6vRqLRlENbXNiS+5DWJj2D/fFi/ZLJLakjPZMZCZyFEdNI0OywEEX5cbW7e0SGNzvzs07SF+YCdWqYqiOReOcuhWAVvr17XxPQXn/RSQ9E4gHn4+Dp3dP2xfCkvtw4QIwa5a2hsgZ32j5jZrciaU1tkQSwmBIKsyN2mvtoHT2GlDPjNzfLmHjJ1lQbfPBz3nxKEFn/Tof3EGfpkeg7HMbAye2RORDrQG0tun5q7D0+lq10jYbGuvJ54hvtLpEZmedn8iW2OxKLobBkFRYUjvw0UeWf9tyUM8OTbkGB745htRvrkL1WxAO3W4L4N4xwzwuQRmThaQnfPHoK3Go16xLrc5nNWvuQ0KCc7/R8hs1EZFTMGdIaqqbmNSaGdrtmIdSeKEQW+cdhWqDGpv+jEG+aHrvsNCgW/2jUD54DUnjQtDhydb2nfjUHObjEBG5LA66aEMuFQwBxkftHT7c+tGRbTigXta2s1AtOgtVhj8ybrRDOe5NaeGPAvQLPwblADUGvNoGTds2qeZITuCMgQWJiKjWGAzZkMsFQxXVtldYDXt23L11F78uOQrV8gKojkQiqyzKYH2M959IanceytEB6PlivOn5vqSCPVyIiFwOgyEbculgyBbz/Vg4/UH+0SvYNP8kVJs8sSU3DkW4d6/q4C4eCTyCpIdvIenlSLTq26JGl+NUnAaCiMilsGs9admiV5iJnh1CI3D4x5NI/SoPqr1NsL84FgL3urc3U1xB0n0nkDTYC30nx8E/vJOVhXciU4EPe7gQEVElDIakzsa9worzi7F9QSZSU0qReqoVcjVtALTRr3/A7ziUXS4j6dmm6PxMW3h4WdiVX0o42S0REVmBzWRSZ4PeUOd2XUDqp2eg+qUufrnSDqXw1a+ri2L0Dc6E8rG7GDi5FUIfCLbThTiIqak3mCxNRORymDNkQy4dDAFW94Yqv1OOvV8fQ+p316E6FIbM0laoqIVXDpRt/4RyRD08MiEevg194TKqy/uR2BQkRERUO8wZonssGJ34RvZNbP74GFJVApvOtcV10V6/mQfUeMg/E8qHbiDphXDEDoqGwiPC8ddRW+aavyQwBQkREbkeBkOuotLoxCIoGMcLQqH6zyWkPncYuwrioUYP/eaNFDcwoPkxKAcp0G9KWwRGd6jm4C7A3Mzza9YApaWWHcvGU5AQEZFrYzDkQu4UlWHHrvpQ/RiK1ONROFcehl64hFAcR0/cxHXvYAy8Pw/Kvwfiwedi4eX7kLOLbBtqtbZGyFiLrhDa5q8pU4ClSy07Xi2nICEiIvfCYEjiLh7Mw8YFWVBt9cbPeXEo/mvi08eRggz0QDhy723cLBx4bQEwNME5hbUXS5u/ANtOdktERLLAYEhiNOUa/PbdcaQuuwLVgSAcvN0WwL0eXqEel/B/IV/hxdy3qu5cscnInXpMWdqslZ9vfrLb+fOZPE1ERAYYDElA4YVCbFtwFKkbyrHxTAwua+L06xTQoGu9Y0jqdhXKccHoOOw+KFouMX6gik1Ggwe7z0Pf2pnnzSSbExERVcRgyElObz8H1cKzSM2ojx3X26EM3fXrGqAQ/cKOaic+nRKDZnHx93ZMT3e9HlO1nQbj6lXt9mq18fWVm78qJZtz6g0iIqoOgyFbMfPALyspw6+LM5G6vACqI81x8m5LAJH69a3qZEPZ7px24tMX4uBdv7uRk8A203M4Um1Hg05JAYYPN54DVFHl5i9OvUFERBZiMGQLJh74BdPfxYbM+5C62QObc+JQiPv1q71QhocbHYHy4UIkvRSJ1v2iAERVPXZlNp6ew64s6Q5fXUBUXS8yHU9PYMUKy5u/OFkrERFVwhGoa8vEA18DbcLuMKzBOmgf1E0VVzCw5UkoB3ui7+RYBDQPsP58NpiewyFsMRp0ejrQu7f5c6WlWVYLxDnLiIhcCkegtge1WvuAtVWtgFoNzSuToBDir9DnHg8IaKDAZ5iA+IcCkDQ2CF3GxMLDq6fRQ1nM09M1ekzZYjRoWzYJ1raWioiI3JaHswvgUPHx2pqG0aO1/7ZooX1IWun8nlx8PioDbzT+Dzwu5lYJhHQ8IBCMPPy/9zzR7fl4eHjZ6HbrpucICzNcHh4unYe6LQIZWzUJmhu0EdD2wDOVoE1ERG5NXjVDFy8a/m5hrYD6rhr7lh6D6ptrUB0KxZE7rQGEYWTFAQ+rY49kZqn3mLJFINOrl20GUeScZUREVA15BUOVVTMuz81zBdjy8VGo/qvBprNtcU2006/zgBo9/DMxKOYycMCC89grmVnKPaZsEcjYqknQ1XrgERGRQ8k7GAL0tQJiRwZOlDRH6uIcqH5tiF8rTXzaUHETA5ofRdJAoP+rbdG4VQdAHQ+0+IjTPxhjq0BG1yRYm0EUXakHHhEROZy8epMBMJWLPsVjARZoJhksi/U5DWWHC0h6uhF6jI+Dl2+l2FGtBt5/H5g5s+oBdQ98qeTwOIuxHlwREdaPBl2bLvGu0gOPiIgMOKo3mcsFQ7Nnz8aMGTMwefJkzJ8/36J9LAmGEpCGPeiO3o2PQNm7GEmvtETUwxGmD2rsIV9RTR747koKY/voepMBxmup5B60EhFJELvWG3HgwAEsWbIE7du3t9kxNVDgpldTvDrNG6opZagf3Nn8Tqa6aevMmgX861+sZdCRQm6TLZrbiIjILblM1/pbt27hqaeewn/+8x80atTIJscUUEChAAJ//ByDZ/dA/eD65ncyNyqyQgF8+aVNykc2NnQocPasdpDG5cu1/2ZnMxAiIpI5lwmGJkyYgKSkJCQmJprdtrS0FIWFhQY/xijCw6CwtnnEmm7aJD26WqpRo7T/svaOiEj2XKKZbOXKlTh48CAOHLCkHzuQnJyMWbNmmd9w2TKgTx/rClPTbtpSyJshIiKiKiRfM5STk4PJkyfjhx9+gK+vr0X7zJgxAwUFBfqfnJwc4xvm51tfoJp0005J0fZmssHo10RERGRbku9Ntn79ejz++OPwrFCLolaroVAo4OHhgdLSUoN1xpjsTWbpBJ8VmZuAFND2JNN10zaVbM1eTERERNVyVG8yydcM9enTB0eOHMHhw4f1P507d8ZTTz2Fw4cPmw2EjFIotAFLTQZD9PTU5ptUZ+RI7XacE4uIiEjyJJ8z1KBBA8THxxssq1evHho3blxluUVqO7O7Wg2sWFH9NitXAsnJnBOLiIjIBUi+Zsjmajuzu7kAB7gX4HBOLCIiIsmTfM2QMenp6TXbUaUC+vevXS8uawIca5Kt2duMiIjIKVwyGKoxWwQY1gQ4ls7cfuVK1aTs8HDg44+Bpk0ZIBEREdmRvIIhW7A0wNEFLuZmbh85EhgxouqxLlwAhg83XBYerj0ee58RERHZjPxyhmpLF+AA9wIaHWPJ2bo5scLCDLcNDwdWrdImY1s6ukFurjaw4vhERERENsNgqCaqC3CMJWebmhOrSRPzydgVsTs+ERGRzbGZrKaGDgUGD7Y86dnYzO016UXG7vhEREQ2xWCoNowFONawNBnbGHbHJyIisgk2kzmTLhm7cu6RJWoTSBEREZEegyFnqi4Z25TaTCVCREREVTAYcjZTydjG1HYqESIiIqqCwZAUGOtttnq1tgmtotpOJUJERERVyCuBevVqIDpamiM5G0vGfvxx6UzRwelCiIjITSmEsHTEP9dVWFiIgIAAFADwBziSs7VSUoDJk6tOF8J7SEREdqR/fhcUwN/f327nkWczGUdytlxKivZeVR4ckveQiIjchDxrhoB7c4hlZ7O5xxS1uuoEshXxHhIRkR2xZsjeKo7kTMbt3Fn9dCH2uodqNZCerp23LT2dU48QEZFdySuB2hiO5GyapffGlveQ+UlERORg8q0Z0uFIzqZZem9sdQ+Zn0RERE7AnCHmu5imyxnKzdU2iVVmy3vI/CQiIqqEOUP2xJGcLVPddCG2vofOyk8iIiLZk2cwxJGcLWdquhBb30Nn5CcRERFBbgnUX34p3RGopWzoUGDwYPuOQO3o/CQiIqK/yCtnyM5tjlQLjsxPIiIil8CcIZIXR+YnERERVcBgiKTDUflJREREFcgrZ4ikzxH5SURERBUwGCLp8fQEEhKcXQoiIpIJNpMRERGRrDEYIiIiIlljMERERESyxmCIiIiIZI3BEBEREckagyEiIiKSNQZDREREJGsMhoiIiEjWGAwRERGRrDEYIiIiIlljMERERESyxmCIiIiIZE3ywVBycjK6dOmCBg0aoFmzZhgyZAhOnjzp7GIRERGRm5B8MLRjxw5MmDABe/fuxbZt21BWVobHHnsMxcXFzi4aERERuQGFEEI4uxDWuHLlCpo1a4YdO3bg4YcftmifwsJCBAQEoKCgAP7+/nYuIREREdmCo57fXnY7sp0UFBQAAAIDA01uU1paitLSUv3vhYWFdi8XERERuSbJN5NVpNFoMGXKFDz00EOIj483uV1ycjICAgL0PxEREQ4sJREREbkSl2ome+mll7Bp0yb8+uuvCA8PN7mdsZqhiIgINpMRERG5EDaTVTJx4kSoVCpkZGRUGwgBgI+PD3x8fBxUMiIiInJlkg+GhBB45ZVXsG7dOqSnpyMqKsrZRSIiIiI3IvlgaMKECVi+fDk2bNiABg0aIC8vDwAQEBAAPz8/J5eOiIiIXJ3kc4YUCoXR5UuXLsWzzz5r0THYtZ6IiMj1MGfoLxKP1YiIiMjFuVTXeiIiIiJbYzBEREREssZgiIiIiGSNwRARERHJGoMhIiIikjUGQ0RERCRrDIaIiIhI1hgMERERkawxGCIiIiJZYzBEREREssZgiIiIiGSNwRARERHJGoMhIiIikjUGQ0RERCRrDIaIiIhI1hgMERERkawxGCIiIiJZYzBEREREssZgiIiIiGSNwRARERHJGoMhIiIikjUGQ0RERCRrDIaIiIhI1hgMERERkawxGCIiIiJZYzBEREREssZgiIiIiGSNwRARERHJGoMhIiIikjUGQ0RERCRrDIaIiIhI1hgMERERkawxGCIiIiJZYzBEREREssZgiIiIiGSNwRARERHJGoMhIiIikjUGQ0RERCRrLhMMLVq0CC1atICvry+6deuG/fv3O7tIRERE5AZcIhj68ccfMXXqVMycORMHDx5Ehw4d0K9fP+Tn5zu7aEREROTiXCIY+vjjjzF+/HiMHTsWsbGxWLx4MerWrYuvv/7a2UUjIiIiFyf5YOju3bv4/fffkZiYqF/m4eGBxMRE7Nmzx4klIyIiInfg5ewCmHP16lWo1WoEBQUZLA8KCsKJEyeM7lNaWorS0lL97wUFBQCAwsJC+xWUiIiIbEr33BZC2PU8kg+GaiI5ORmzZs2qsjwiIsIJpSEiIqLauHbtGgICAux2fMkHQ02aNIGnpycuX75ssPzy5csIDg42us+MGTMwdepU/e83b95EZGQkzp8/b9ebKTWFhYWIiIhATk4O/P39nV0ch+F187rlgNfN65aDgoICNG/eHIGBgXY9j+SDIW9vb3Tq1Anbt2/HkCFDAAAajQbbt2/HxIkTje7j4+MDHx+fKssDAgJk9SbS8ff353XLCK9bXnjd8iLX6/bwsG+Ks+SDIQCYOnUqxowZg86dO6Nr166YP38+iouLMXbsWGcXjYiIiFycSwRDI0aMwJUrV/D2228jLy8PHTt2xObNm6skVRMRERFZyyWCIQCYOHGiyWYxc3x8fDBz5kyjTWfujNfN65YDXjevWw543fa9boWwd381IiIiIgmT/KCLRERERPbEYIiIiIhkjcEQERERyRqDISIiIpI1lwyGFi1ahBYtWsDX1xfdunXD/v37q91+9erVaNOmDXx9fdGuXTts3LjRYL0QAm+//TZCQkLg5+eHxMREZGVl2fMSasSa6/7Pf/6DXr16oVGjRmjUqBESExOrbP/ss89CoVAY/PTv39/el2E1a6572bJlVa7J19fXYBt3fL0TEhKqXLdCoUBSUpJ+G1d4vTMyMjBo0CCEhoZCoVBg/fr1ZvdJT0/HAw88AB8fH9x3331YtmxZlW2s/ZvhaNZed0pKCvr27YumTZvC398f3bt3x5YtWwy2eeedd6q83m3atLHjVVjP2utOT083+j7Py8sz2M7dXm9jn12FQoG4uDj9Nq7weicnJ6NLly5o0KABmjVrhiFDhuDkyZNm93PEM9zlgqEff/wRU6dOxcyZM3Hw4EF06NAB/fr1Q35+vtHtd+/ejVGjRuH555/HoUOHMGTIEAwZMgSZmZn6bebOnYtPPvkEixcvxr59+1CvXj3069cPd+7ccdRlmWXtdaenp2PUqFFIS0vDnj17EBERgcceewy5ubkG2/Xv3x+XLl3S/6xYscIRl2Mxa68b0I7QWvGazp07Z7DeHV/vlJQUg2vOzMyEp6cnnnzySYPtpP56FxcXo0OHDli0aJFF22dnZyMpKQm9e/fG4cOHMWXKFIwbN84gMKjJe8jRrL3ujIwM9O3bFxs3bsTvv/+O3r17Y9CgQTh06JDBdnFxcQav96+//mqP4teYtdetc/LkSYPratasmX6dO77eCxYsMLjenJwcBAYGVvl8S/313rFjByZMmIC9e/di27ZtKCsrw2OPPYbi4mKT+zjsGS5cTNeuXcWECRP0v6vVahEaGiqSk5ONbj98+HCRlJRksKxbt27ihRdeEEIIodFoRHBwsPjggw/062/evCl8fHzEihUr7HAFNWPtdVdWXl4uGjRoIL755hv9sjFjxojBgwfbuqg2Ze11L126VAQEBJg8nlxe73nz5okGDRqIW7du6Ze5wutdEQCxbt26ard57bXXRFxcnMGyESNGiH79+ul/r+29dDRLrtuY2NhYMWvWLP3vM2fOFB06dLBdwezMkutOS0sTAMSNGzdMbiOH13vdunVCoVCIs2fP6pe52usthBD5+fkCgNixY4fJbRz1DHepmqG7d+/i999/R2Jion6Zh4cHEhMTsWfPHqP77Nmzx2B7AOjXr59+++zsbOTl5RlsExAQgG7dupk8pqPV5LorKykpQVlZWZXJ7tLT09GsWTPExMTgpZdewrVr12xa9tqo6XXfunULkZGRiIiIwODBg3H06FH9Orm83l999RVGjhyJevXqGSyX8utdE+Y+37a4l65Ao9GgqKioyuc7KysLoaGhaNmyJZ566imcP3/eSSW0rY4dOyIkJAR9+/bFrl279Mvl8np/9dVXSExMRGRkpMFyV3u9CwoKAKDaSVgd9Qx3qWDo6tWrUKvVVabhCAoKqtJmrJOXl1ft9rp/rTmmo9Xkuit7/fXXERoaavCG6d+/P7799lts374dc+bMwY4dOzBgwACo1Wqblr+manLdMTEx+Prrr7FhwwZ8//330Gg06NGjBy5cuABAHq/3/v37kZmZiXHjxhksl/rrXROmPt+FhYW4ffu2TT47ruDDDz/ErVu3MHz4cP2ybt26YdmyZdi8eTM+//xzZGdno1evXigqKnJiSWsnJCQEixcvxtq1a7F27VpEREQgISEBBw8eBGCbv5VSd/HiRWzatKnK59vVXm+NRoMpU6bgoYceQnx8vMntHPUMd5npOKjmZs+ejZUrVyI9Pd0gmXjkyJH6/7dr1w7t27dHdHQ00tPT0adPH2cUtda6d++O7t2763/v0aMH2rZtiyVLluDdd991Yskc56uvvkK7du3QtWtXg+Xu+HoTsHz5csyaNQsbNmwwyJ0ZMGCA/v/t27dHt27dEBkZiVWrVuH55593RlFrLSYmBjExMfrfe/TogTNnzmDevHn47rvvnFgyx/nmm2/QsGFDDBkyxGC5q73eEyZMQGZmpmTymlyqZqhJkybw9PTE5cuXDZZfvnwZwcHBRvcJDg6udnvdv9Yc09Fqct06H374IWbPno2tW7eiffv21W7bsmVLNGnSBKdPn651mW2hNtetU6dOHdx///36a3L317u4uBgrV6606I+f1F7vmjD1+fb394efn59N3kNStnLlSowbNw6rVq2q0pRQWcOGDdG6dWuXfr2N6dq1q/6a3P31FkLg66+/xjPPPANvb+9qt5Xy6z1x4kSoVCqkpaUhPDy82m0d9Qx3qWDI29sbnTp1wvbt2/XLNBoNtm/fblAbUFH37t0NtgeAbdu26bePiopCcHCwwTaFhYXYt2+fyWM6Wk2uG9Bm2L/77rvYvHkzOnfubPY8Fy5cwLVr1xASEmKTctdWTa+7IrVajSNHjuivyZ1fb0DbBbW0tBRPP/202fNI7fWuCXOfb1u8h6RqxYoVGDt2LFasWGEwhIIpt27dwpkzZ1z69Tbm8OHD+mty59cb0PbGOn36tEVfdqT4egshMHHiRKxbtw6//PILoqKizO7jsGe4VanfErBy5Urh4+Mjli1bJo4dOyb+8Y9/iIYNG4q8vDwhhBDPPPOMeOONN/Tb79q1S3h5eYkPP/xQHD9+XMycOVPUqVNHHDlyRL/N7NmzRcOGDcWGDRvEH3/8IQYPHiyioqLE7du3HX59plh73bNnzxbe3t5izZo14tKlS/qfoqIiIYQQRUVF4p///KfYs2ePyM7OFj///LN44IEHRKtWrcSdO3ecco3GWHvds2bNElu2bBFnzpwRv//+uxg5cqTw9fUVR48e1W/jjq+3Ts+ePcWIESOqLHeV17uoqEgcOnRIHDp0SAAQH3/8sTh06JA4d+6cEEKIN954QzzzzDP67f/8809Rt25dMX36dHH8+HGxaNEi4enpKTZv3qzfxty9lAJrr/uHH34QXl5eYtGiRQaf75s3b+q3mTZtmkhPTxfZ2dli165dIjExUTRp0kTk5+c7/PpMsfa6582bJ9avXy+ysrLEkSNHxOTJk4WHh4f4+eef9du44+ut8/TTT4tu3boZPaYrvN4vvfSSCAgIEOnp6Qbv25KSEv02znqGu1wwJIQQn376qWjevLnw9vYWXbt2FXv37tWve+SRR8SYMWMMtl+1apVo3bq18Pb2FnFxcSI1NdVgvUajEW+99ZYICgoSPj4+ok+fPuLkyZOOuBSrWHPdkZGRAkCVn5kzZwohhCgpKRGPPfaYaNq0qahTp46IjIwU48ePl9QfDB1rrnvKlCn6bYOCgsTAgQPFwYMHDY7njq+3EEKcOHFCABBbt26tcixXeb11Xacr/+iudcyYMeKRRx6psk/Hjh2Ft7e3aNmypVi6dGmV41Z3L6XA2ut+5JFHqt1eCO0QAyEhIcLb21uEhYWJESNGiNOnTzv2wsyw9rrnzJkjoqOjha+vrwgMDBQJCQnil19+qXJcd3u9hdB2F/fz8xNffPGF0WO6wutt7JoBGHxmnfUMV/xVQCIiIiJZcqmcISIiIiJbYzBEREREssZgiIiIiGSNwRARERHJGoMhIiIikjUGQ0RERCRrDIaIiIhI1hgMERERkawxGCIiIiJZYzBEREREssZgiIhc0ooVK+Dn54dLly7pl40dOxbt27dHQUGBE0tGRK6Gc5MRkUsSQqBjx454+OGH8emnn2LmzJn4+uuvsXfvXoSFhTm7eETkQrycXQAioppQKBR4//33MWzYMAQHB+PTTz/Fzp07GQgRkdVYM0RELu2BBx7A0aNHsXXrVjzyyCPOLg4RuSDmDBGRy9q8eTNOnDgBtVqNoKAgZxeHiFwUa4aIyCUdPHgQCQkJWLJkCZYtWwZ/f3+sXr3a2cUiIhfEnCEicjlnz55FUlIS3nzzTYwaNQotW7ZE9+7dcfDgQTzwwAPOLh4RuRjWDBGRS7l+/Tp69OiBhIQELF68WL88KSkJarUamzdvdmLpiMgVMRgiIiIiWWMCNREREckagyEiIiKSNQZDREREJGsMhoiIiEjWGAwRERGRrDEYIiIiIlljMERERESyxmCIiIiIZI3BEBEREckagyEiIiKSNQZDREREJGsMhoiIiEjW/j9pv5Nqppd6iwAAAABJRU5ErkJggg==", + "image/png": 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", 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      " ] @@ -2171,73 +2178,73 @@ "Own inversion\n", "[[4.]\n", " [3.]]\n", - "Eigenvalues of Hessian Matrix:[0.32421852 4.40457549]\n", - "0 [-8.91626016] [-10.12979569]\n", - "1 [-0.25100049] [0.20953861]\n", - "2 [-0.23252447] [0.19411458]\n", - "3 [-0.21540847] [0.17982591]\n", - "4 [-0.19955236] [0.16658901]\n", - "5 [-0.18486342] [0.15432648]\n", - "6 [-0.17125572] [0.14296659]\n", - "7 [-0.15864967] [0.13244289]\n", - "8 [-0.14697155] [0.12269384]\n", - "9 [-0.13615305] [0.11366241]\n", - "10 [-0.1261309] [0.10529578]\n", - "11 [-0.11684647] [0.09754501]\n", - "12 [-0.10824546] [0.09036477]\n", - "13 [-0.10027757] [0.08371307]\n", - "14 [-0.09289619] [0.07755099]\n", - "15 [-0.08605815] [0.07184251]\n", - "16 [-0.07972345] [0.06655422]\n", - "17 [-0.07385505] [0.06165519]\n", - "18 [-0.06841862] [0.05711679]\n", - "19 [-0.06338236] [0.05291245]\n", - "20 [-0.05871681] [0.04901759]\n", - "21 [-0.0543947] [0.04540943]\n", - "22 [-0.05039073] [0.04206687]\n", - "23 [-0.0466815] [0.03897035]\n", - "24 [-0.0432453] [0.03610176]\n", - "25 [-0.04006203] [0.03344433]\n", - "26 [-0.03711309] [0.03098251]\n", - "27 [-0.03438121] [0.0287019]\n", - "28 [-0.03185043] [0.02658917]\n", - "29 [-0.02950594] [0.02463195]\n", + "Eigenvalues of Hessian Matrix:[0.29823315 4.72747492]\n", + "0 [-17.30620124] [-21.78309726]\n", + "1 [-0.05759628] [0.04549505]\n", + "2 [-0.05396282] [0.04262499]\n", + "3 [-0.05055857] [0.03993599]\n", + "4 [-0.04736908] [0.03741662]\n", + "5 [-0.0443808] [0.03505619]\n", + "6 [-0.04158103] [0.03284467]\n", + "7 [-0.03895789] [0.03077266]\n", + "8 [-0.03650022] [0.02883136]\n", + "9 [-0.03419761] [0.02701253]\n", + "10 [-0.03204025] [0.02530845]\n", + "11 [-0.03001898] [0.02371186]\n", + "12 [-0.02812523] [0.022216]\n", + "13 [-0.02635095] [0.0208145]\n", + "14 [-0.0246886] [0.01950141]\n", + "15 [-0.02313112] [0.01827117]\n", + "16 [-0.02167189] [0.01711853]\n", + "17 [-0.02030472] [0.0160386]\n", + "18 [-0.01902379] [0.01502681]\n", + "19 [-0.01782367] [0.01407884]\n", + "20 [-0.01669927] [0.01319068]\n", + "21 [-0.01564579] [0.01235854]\n", + "22 [-0.01465878] [0.0115789]\n", + "23 [-0.01373403] [0.01084844]\n", + "24 [-0.01286761] [0.01016407]\n", + "25 [-0.01205586] [0.00952287]\n", + "26 [-0.01129531] [0.00892212]\n", + "27 [-0.01058275] [0.00835927]\n", + "28 [-0.00991513] [0.00783192]\n", + "29 [-0.00928964] [0.00733784]\n", "theta from own gd\n", - "[[3.9156926 ]\n", - " [3.07038096]]\n", - "0 [-0.02733402] [0.02281881]\n", - "1 [-0.02532198] [0.02113913]\n", - "2 [-0.02285443] [0.01907918]\n", - "3 [-0.02043186] [0.01705679]\n", - "4 [-0.01820111] [0.01519453]\n", - "5 [-0.01619211] [0.01351739]\n", - "6 [-0.01439752] [0.01201924]\n", - "7 [-0.01279935] [0.01068507]\n", - "8 [-0.01137774] [0.00949829]\n", - "9 [-0.01011375] [0.0084431]\n", - "10 [-0.00899009] [0.00750504]\n", - "11 [-0.00799123] [0.00667119]\n", - "12 [-0.00710334] [0.00592997]\n", - "13 [-0.0063141] [0.0052711]\n", - "14 [-0.00561255] [0.00468544]\n", - "15 [-0.00498895] [0.00416484]\n", - "16 [-0.00443464] [0.0037021]\n", - "17 [-0.00394191] [0.00329076]\n", - "18 [-0.00350393] [0.00292513]\n", - "19 [-0.00311462] [0.00260012]\n", - "20 [-0.00276855] [0.00231123]\n", - "21 [-0.00246094] [0.00205443]\n", - "22 [-0.00218751] [0.00182617]\n", - "23 [-0.00194446] [0.00162326]\n", - "24 [-0.00172842] [0.0014429]\n", - "25 [-0.00153637] [0.00128259]\n", - "26 [-0.00136567] [0.00114008]\n", - "27 [-0.00121393] [0.00101341]\n", - "28 [-0.00107905] [0.00090081]\n", - "29 [-0.00095916] [0.00080072]\n", + "[[3.97081612]\n", + " [3.02305221]]\n", + "0 [-0.0087036] [0.00687493]\n", + "1 [-0.00815453] [0.00644123]\n", + "2 [-0.00747538] [0.00590477]\n", + "3 [-0.00680005] [0.00537133]\n", + "4 [-0.00616847] [0.00487245]\n", + "5 [-0.00558986] [0.0044154]\n", + "6 [-0.00506364] [0.00399975]\n", + "7 [-0.00458633] [0.00362272]\n", + "8 [-0.00415381] [0.00328108]\n", + "9 [-0.00376201] [0.0029716]\n", + "10 [-0.00340714] [0.00269129]\n", + "11 [-0.00308574] [0.00243742]\n", + "12 [-0.00279466] [0.00220749]\n", + "13 [-0.00253103] [0.00199925]\n", + "14 [-0.00229228] [0.00181066]\n", + "15 [-0.00207604] [0.00163985]\n", + "16 [-0.0018802] [0.00148516]\n", + "17 [-0.00170284] [0.00134506]\n", + "18 [-0.0015422] [0.00121818]\n", + "19 [-0.00139672] [0.00110327]\n", + "20 [-0.00126497] [0.00099919]\n", + "21 [-0.00114564] [0.00090494]\n", + "22 [-0.00103757] [0.00081957]\n", + "23 [-0.00093969] [0.00074226]\n", + "24 [-0.00085105] [0.00067224]\n", + "25 [-0.00077077] [0.00060883]\n", + "26 [-0.00069806] [0.00055139]\n", + "27 [-0.00063221] [0.00049938]\n", + "28 [-0.00057257] [0.00045227]\n", + "29 [-0.00051856] [0.00040961]\n", "theta from own gd wth momentum\n", - "[[3.99737032]\n", - " [3.00219529]]\n" + "[[3.99842525]\n", + " [3.00124389]]\n" ] } ], @@ -2322,30 +2329,23 @@ "output_type": "stream", "text": [ "Own inversion\n", - "[[3.80284827]\n", - " [3.14558948]]\n", - "Eigenvalues of Hessian Matrix:[0.33477228 4.32758606]\n" + "[[3.93505095]\n", + " [3.03255202]]\n", + "Eigenvalues of Hessian Matrix:[0.35715186 4.00152151]\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "theta from own gd" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "[[3.80284827]\n", - " [3.14558948]]\n" + "theta from own gd\n", + "[[3.93505095]\n", + " [3.03255202]]\n" ] }, { "data": { - "image/png": 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", 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", 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      " ] @@ -2358,8 +2358,8 @@ "output_type": "stream", "text": [ "theta from own sdg\n", - "[[3.8286293 ]\n", - " [3.19901555]]\n" + "[[3.93535203]\n", + " [3.02339886]]\n" ] } ], @@ -2463,28 +2463,15 @@ "output_type": "stream", "text": [ "Own inversion\n", - "[[4.14809298]\n", - " [2.84983419]]\n", - "Eigenvalues of Hessian Matrix:[0.35213219 4.34352956]\n", + "[[4.00794132]\n", + " [3.02667095]]\n", + "Eigenvalues of Hessian Matrix:[0.29709966 4.68284888]\n", "theta from own gd\n", - "[[4.14764332]\n", - " [2.85021125]]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "theta from own sdg with momentum" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "[[4.15364228]\n", - " [2.8846002 ]]\n" + "[[4.0068889 ]\n", + " [3.02750941]]\n", + "theta from own sdg with momentum\n", + "[[4.01544241]\n", + " [3.0176573 ]]\n" ] } ], @@ -2592,9 +2579,9 @@ "output_type": "stream", "text": [ "theta from own AdaGrad\n", - "[[2.00000539]\n", - " [2.99996887]\n", - " [4.0000263 ]]\n" + "[[1.99856653]\n", + " [3.01053812]\n", + " [3.99013096]]\n" ] } ], @@ -2693,9 +2680,9 @@ "output_type": "stream", "text": [ "theta from own RMSprop\n", - "[[2.00051557]\n", - " [2.99775568]\n", - " [4.00224621]]\n" + "[[2.00002452]\n", + " [2.99973034]\n", + " [4.00026406]]\n" ] } ], @@ -2790,9 +2777,9 @@ "output_type": "stream", "text": [ "theta from own ADAM\n", - "[[1.99997454]\n", - " [3.00018485]\n", - " [3.99980356]]\n" + "[[1.99997633]\n", + " [3.00007035]\n", + " [3.99994513]]\n" ] } ], @@ -2996,7 +2983,7 @@ { "data": { "text/plain": [ - "[]" + "[]" ] }, "execution_count": 25, @@ -3073,7 +3060,7 @@ { "data": { "text/plain": [ - "" + "" ] }, "execution_count": 26, diff --git a/doc/LectureNotes/_build/jupyter_execute/week42.ipynb b/doc/LectureNotes/_build/jupyter_execute/week42.ipynb index 5e81d31b7..df03142e9 100644 --- a/doc/LectureNotes/_build/jupyter_execute/week42.ipynb +++ b/doc/LectureNotes/_build/jupyter_execute/week42.ipynb @@ -3970,7 +3970,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_59019/953065564.py:4: RuntimeWarning: overflow encountered in exp\n", + "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_23356/953065564.py:4: RuntimeWarning: overflow encountered in exp\n", " return 1/(1 + np.exp(-x))\n" ] }, @@ -4558,7 +4558,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_59019/953065564.py:4: RuntimeWarning: overflow encountered in exp\n", + "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_23356/953065564.py:4: RuntimeWarning: overflow encountered in exp\n", " return 1/(1 + np.exp(-x))\n" ] }, @@ -4576,7 +4576,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_59019/953065564.py:4: RuntimeWarning: overflow encountered in exp\n", + "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_23356/953065564.py:4: RuntimeWarning: overflow encountered in exp\n", " return 1/(1 + np.exp(-x))\n" ] }, @@ -4594,7 +4594,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_59019/953065564.py:4: RuntimeWarning: overflow encountered in exp\n", + "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_23356/953065564.py:4: RuntimeWarning: overflow encountered in exp\n", " return 1/(1 + np.exp(-x))\n" ] }, @@ -4612,7 +4612,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_59019/953065564.py:4: RuntimeWarning: overflow encountered in exp\n", + "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_23356/953065564.py:4: RuntimeWarning: overflow encountered in exp\n", " return 1/(1 + np.exp(-x))\n" ] }, @@ -4630,7 +4630,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_59019/953065564.py:4: RuntimeWarning: overflow encountered in exp\n", + "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_23356/953065564.py:4: RuntimeWarning: overflow encountered in exp\n", " return 1/(1 + np.exp(-x))\n" ] }, @@ -4648,7 +4648,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_59019/953065564.py:4: RuntimeWarning: overflow encountered in exp\n", + "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_23356/953065564.py:4: RuntimeWarning: overflow encountered in exp\n", " return 1/(1 + np.exp(-x))\n" ] }, @@ -4666,7 +4666,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_59019/953065564.py:4: RuntimeWarning: overflow encountered in exp\n", + "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_23356/953065564.py:4: RuntimeWarning: overflow encountered in exp\n", " return 1/(1 + np.exp(-x))\n" ] }, @@ -4684,11 +4684,11 @@ "name": "stderr", "output_type": "stream", "text": [ - "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_59019/953065564.py:4: RuntimeWarning: overflow encountered in exp\n", + "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_23356/953065564.py:4: RuntimeWarning: overflow encountered in exp\n", " return 1/(1 + np.exp(-x))\n", - "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_59019/1630775253.py:43: RuntimeWarning: overflow encountered in exp\n", + "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_23356/1630775253.py:43: RuntimeWarning: overflow encountered in exp\n", " exp_term = np.exp(self.z_o)\n", - "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_59019/1630775253.py:44: RuntimeWarning: invalid value encountered in divide\n", + "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_23356/1630775253.py:44: RuntimeWarning: invalid value encountered in divide\n", " self.probabilities = exp_term / np.sum(exp_term, axis=1, keepdims=True)\n" ] }, @@ -4706,11 +4706,11 @@ "name": "stderr", "output_type": "stream", "text": [ - "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_59019/953065564.py:4: RuntimeWarning: overflow encountered in exp\n", + "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_23356/953065564.py:4: RuntimeWarning: overflow encountered in exp\n", " return 1/(1 + np.exp(-x))\n", - "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_59019/1630775253.py:43: RuntimeWarning: overflow encountered in exp\n", + "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_23356/1630775253.py:43: RuntimeWarning: overflow encountered in exp\n", " exp_term = np.exp(self.z_o)\n", - "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_59019/1630775253.py:44: RuntimeWarning: invalid value encountered in divide\n", + "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_23356/1630775253.py:44: RuntimeWarning: invalid value encountered in divide\n", " self.probabilities = exp_term / np.sum(exp_term, axis=1, keepdims=True)\n" ] }, @@ -4728,11 +4728,11 @@ "name": "stderr", "output_type": "stream", "text": [ - "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_59019/953065564.py:4: RuntimeWarning: overflow encountered in exp\n", + "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_23356/953065564.py:4: RuntimeWarning: overflow encountered in exp\n", " return 1/(1 + np.exp(-x))\n", - "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_59019/1630775253.py:43: RuntimeWarning: overflow encountered in exp\n", + "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_23356/1630775253.py:43: RuntimeWarning: overflow encountered in exp\n", " exp_term = np.exp(self.z_o)\n", - "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_59019/1630775253.py:44: RuntimeWarning: invalid value encountered in divide\n", + "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_23356/1630775253.py:44: RuntimeWarning: invalid value encountered in divide\n", " self.probabilities = exp_term / np.sum(exp_term, axis=1, keepdims=True)\n" ] }, @@ -4750,11 +4750,11 @@ "name": "stderr", "output_type": "stream", "text": [ - "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_59019/953065564.py:4: RuntimeWarning: overflow encountered in exp\n", + "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_23356/953065564.py:4: RuntimeWarning: overflow encountered in exp\n", " return 1/(1 + np.exp(-x))\n", - "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_59019/1630775253.py:43: RuntimeWarning: overflow encountered in exp\n", + "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_23356/1630775253.py:43: RuntimeWarning: overflow encountered in exp\n", " exp_term = np.exp(self.z_o)\n", - "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_59019/1630775253.py:44: RuntimeWarning: invalid value encountered in divide\n", + "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_23356/1630775253.py:44: RuntimeWarning: invalid value encountered in divide\n", " self.probabilities = exp_term / np.sum(exp_term, axis=1, keepdims=True)\n" ] }, @@ -4772,11 +4772,11 @@ "name": "stderr", "output_type": "stream", "text": [ - "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_59019/953065564.py:4: RuntimeWarning: overflow encountered in exp\n", + "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_23356/953065564.py:4: RuntimeWarning: overflow encountered in exp\n", " return 1/(1 + np.exp(-x))\n", - "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_59019/1630775253.py:43: RuntimeWarning: overflow encountered in exp\n", + "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_23356/1630775253.py:43: RuntimeWarning: overflow encountered in exp\n", " exp_term = np.exp(self.z_o)\n", - "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_59019/1630775253.py:44: RuntimeWarning: invalid value encountered in divide\n", + "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_23356/1630775253.py:44: RuntimeWarning: invalid value encountered in divide\n", " self.probabilities = exp_term / np.sum(exp_term, axis=1, keepdims=True)\n" ] }, @@ -4794,7 +4794,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_59019/953065564.py:4: RuntimeWarning: overflow encountered in exp\n", + "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_23356/953065564.py:4: RuntimeWarning: overflow encountered in exp\n", " return 1/(1 + np.exp(-x))\n" ] }, @@ -4812,11 +4812,11 @@ "name": "stderr", "output_type": "stream", "text": [ - "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_59019/953065564.py:4: RuntimeWarning: overflow encountered in exp\n", + "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_23356/953065564.py:4: RuntimeWarning: overflow encountered in exp\n", " return 1/(1 + np.exp(-x))\n", - "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_59019/1630775253.py:43: RuntimeWarning: overflow encountered in exp\n", + "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_23356/1630775253.py:43: RuntimeWarning: overflow encountered in exp\n", " exp_term = np.exp(self.z_o)\n", - "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_59019/1630775253.py:44: RuntimeWarning: invalid value encountered in divide\n", + "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_23356/1630775253.py:44: RuntimeWarning: invalid value encountered in divide\n", " self.probabilities = exp_term / np.sum(exp_term, axis=1, keepdims=True)\n" ] }, @@ -4834,11 +4834,11 @@ "name": "stderr", "output_type": "stream", "text": [ - "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_59019/953065564.py:4: RuntimeWarning: overflow encountered in exp\n", + "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_23356/953065564.py:4: RuntimeWarning: overflow encountered in exp\n", " return 1/(1 + np.exp(-x))\n", - "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_59019/1630775253.py:43: RuntimeWarning: overflow encountered in exp\n", + "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_23356/1630775253.py:43: RuntimeWarning: overflow encountered in exp\n", " exp_term = np.exp(self.z_o)\n", - "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_59019/1630775253.py:44: RuntimeWarning: invalid value encountered in divide\n", + "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_23356/1630775253.py:44: RuntimeWarning: invalid value encountered in divide\n", " self.probabilities = exp_term / np.sum(exp_term, axis=1, keepdims=True)\n" ] }, @@ -4856,11 +4856,11 @@ "name": "stderr", "output_type": "stream", "text": [ - "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_59019/953065564.py:4: RuntimeWarning: overflow encountered in exp\n", + "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_23356/953065564.py:4: RuntimeWarning: overflow encountered in exp\n", " return 1/(1 + np.exp(-x))\n", - "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_59019/1630775253.py:43: RuntimeWarning: overflow encountered in exp\n", + "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_23356/1630775253.py:43: RuntimeWarning: overflow encountered in exp\n", " exp_term = np.exp(self.z_o)\n", - "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_59019/1630775253.py:44: RuntimeWarning: invalid value encountered in divide\n", + "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_23356/1630775253.py:44: RuntimeWarning: invalid value encountered in divide\n", " self.probabilities = exp_term / np.sum(exp_term, axis=1, keepdims=True)\n" ] }, @@ -4878,11 +4878,11 @@ "name": "stderr", "output_type": "stream", "text": [ - "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_59019/953065564.py:4: RuntimeWarning: overflow encountered in exp\n", + "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_23356/953065564.py:4: RuntimeWarning: overflow encountered in exp\n", " return 1/(1 + np.exp(-x))\n", - "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_59019/1630775253.py:43: RuntimeWarning: overflow encountered in exp\n", + "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_23356/1630775253.py:43: RuntimeWarning: overflow encountered in exp\n", " exp_term = np.exp(self.z_o)\n", - "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_59019/1630775253.py:44: RuntimeWarning: invalid value encountered in divide\n", + "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_23356/1630775253.py:44: RuntimeWarning: invalid value encountered in divide\n", " self.probabilities = exp_term / np.sum(exp_term, axis=1, keepdims=True)\n" ] }, @@ -4900,11 +4900,11 @@ "name": "stderr", "output_type": "stream", "text": [ - "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_59019/953065564.py:4: RuntimeWarning: overflow encountered in exp\n", + "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_23356/953065564.py:4: RuntimeWarning: overflow encountered in exp\n", " return 1/(1 + np.exp(-x))\n", - "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_59019/1630775253.py:43: RuntimeWarning: overflow encountered in exp\n", + "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_23356/1630775253.py:43: RuntimeWarning: overflow encountered in exp\n", " exp_term = np.exp(self.z_o)\n", - "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_59019/1630775253.py:44: RuntimeWarning: invalid value encountered in divide\n", + "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_23356/1630775253.py:44: RuntimeWarning: invalid value encountered in divide\n", " self.probabilities = exp_term / np.sum(exp_term, axis=1, keepdims=True)\n" ] }, @@ -4922,11 +4922,11 @@ "name": "stderr", "output_type": "stream", "text": [ - "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_59019/953065564.py:4: RuntimeWarning: overflow encountered in exp\n", + "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_23356/953065564.py:4: RuntimeWarning: overflow encountered in exp\n", " return 1/(1 + np.exp(-x))\n", - "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_59019/1630775253.py:43: RuntimeWarning: overflow encountered in exp\n", + "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_23356/1630775253.py:43: RuntimeWarning: overflow encountered in exp\n", " exp_term = np.exp(self.z_o)\n", - "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_59019/1630775253.py:44: RuntimeWarning: invalid value encountered in divide\n", + "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_23356/1630775253.py:44: RuntimeWarning: invalid value encountered in divide\n", " self.probabilities = exp_term / np.sum(exp_term, axis=1, keepdims=True)\n" ] }, @@ -4944,11 +4944,11 @@ "name": "stderr", "output_type": "stream", "text": [ - "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_59019/953065564.py:4: RuntimeWarning: overflow encountered in exp\n", + "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_23356/953065564.py:4: RuntimeWarning: overflow encountered in exp\n", " return 1/(1 + np.exp(-x))\n", - "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_59019/1630775253.py:43: RuntimeWarning: overflow encountered in exp\n", + "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_23356/1630775253.py:43: RuntimeWarning: overflow encountered in exp\n", " exp_term = np.exp(self.z_o)\n", - "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_59019/1630775253.py:44: RuntimeWarning: invalid value encountered in divide\n", + "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_23356/1630775253.py:44: RuntimeWarning: invalid value encountered in divide\n", " self.probabilities = exp_term / np.sum(exp_term, axis=1, keepdims=True)\n" ] }, @@ -4966,11 +4966,11 @@ "name": "stderr", "output_type": "stream", "text": [ - "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_59019/953065564.py:4: RuntimeWarning: overflow encountered in exp\n", + "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_23356/953065564.py:4: RuntimeWarning: overflow encountered in exp\n", " return 1/(1 + np.exp(-x))\n", - "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_59019/1630775253.py:43: RuntimeWarning: overflow encountered in exp\n", + "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_23356/1630775253.py:43: RuntimeWarning: overflow encountered in exp\n", " exp_term = np.exp(self.z_o)\n", - "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_59019/1630775253.py:44: RuntimeWarning: invalid value encountered in divide\n", + "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_23356/1630775253.py:44: RuntimeWarning: invalid value encountered in divide\n", " self.probabilities = exp_term / np.sum(exp_term, axis=1, keepdims=True)\n" ] }, @@ -5031,15 +5031,15 @@ "name": "stderr", "output_type": "stream", "text": [ - "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_59019/953065564.py:4: RuntimeWarning: overflow encountered in exp\n", + "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_23356/953065564.py:4: RuntimeWarning: overflow encountered in exp\n", " return 1/(1 + np.exp(-x))\n", - "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_59019/953065564.py:4: RuntimeWarning: overflow encountered in exp\n", + "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_23356/953065564.py:4: RuntimeWarning: overflow encountered in exp\n", " return 1/(1 + np.exp(-x))\n", - "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_59019/953065564.py:4: RuntimeWarning: overflow encountered in exp\n", + "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_23356/953065564.py:4: RuntimeWarning: overflow encountered in exp\n", " return 1/(1 + np.exp(-x))\n", - "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_59019/953065564.py:4: RuntimeWarning: overflow encountered in exp\n", + "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_23356/953065564.py:4: RuntimeWarning: overflow encountered in exp\n", " return 1/(1 + np.exp(-x))\n", - "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_59019/953065564.py:4: RuntimeWarning: overflow encountered in exp\n", + "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_23356/953065564.py:4: RuntimeWarning: overflow encountered in exp\n", " return 1/(1 + np.exp(-x))\n" ] }, @@ -5347,387 +5347,57 @@ "\n" ] }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/Users/mhjensen/miniforge3/envs/myenv/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: 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:691: 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:691: 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:691: 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:691: 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:691: 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:691: 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:691: 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:691: 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:691: 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" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "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" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Learning rate = 0.1\n", - "Lambda = 1e-05\n", - "Accuracy score on test set: 0.9027777777777778\n", - "\n", - "Learning rate = 0.1\n", - "Lambda = 0.0001\n", "Accuracy score on test set: 0.8583333333333333\n", "\n" ] }, { - "name": "stdout", + "name": "stderr", "output_type": "stream", "text": [ - "Learning rate = 0.1\n", - "Lambda = 0.001\n", - "Accuracy score on test set: 0.8722222222222222\n", - "\n" + "/Users/mhjensen/miniforge3/envs/myenv/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:698: UserWarning: Training interrupted by user.\n", + " warnings.warn(\"Training interrupted by user.\")\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/mhjensen/miniforge3/envs/myenv/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: 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 = 0.1\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" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Learning rate = 0.1\n", - "Lambda = 1.0\n", - "Accuracy score on test set: 0.8722222222222222\n", - "\n", - "Learning rate = 0.1\n", + "Learning rate = 0.0001\n", "Lambda = 10.0\n", - "Accuracy score on test set: 0.8666666666666667\n", + "Accuracy score on test set: 0.8361111111111111\n", "\n" ] }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/mhjensen/miniforge3/envs/myenv/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: 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 = 1.0\n", + "Learning rate = 0.001\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" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Learning rate = 10.0\n", - "Lambda = 1e-05\n", - "Accuracy score on test set: 0.17222222222222222\n", - "\n", - "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" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "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", + "Accuracy score on test set: 0.9777777777777777\n", "\n" ] } @@ -5769,28 +5439,7 @@ "collapsed": false, "editable": true }, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
      " - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "# optional\n", "# visual representation of grid search\n", @@ -5888,16 +5537,7 @@ "collapsed": false, "editable": true }, - "outputs": [ - { - "ename": "SyntaxError", - "evalue": "invalid syntax (2357089093.py, line 1)", - "output_type": "error", - "traceback": [ - "\u001b[0;36m Cell \u001b[0;32mIn[14], line 1\u001b[0;36m\u001b[0m\n\u001b[0;31m pip3 install tensorflow\u001b[0m\n\u001b[0m ^\u001b[0m\n\u001b[0;31mSyntaxError\u001b[0m\u001b[0;31m:\u001b[0m invalid syntax\n" - ] - } - ], + "outputs": [], "source": [ "pip3 install tensorflow" ] diff --git a/doc/LectureNotes/_build/jupyter_execute/week43.ipynb b/doc/LectureNotes/_build/jupyter_execute/week43.ipynb index 585504cf2..64f6b682b 100644 --- a/doc/LectureNotes/_build/jupyter_execute/week43.ipynb +++ b/doc/LectureNotes/_build/jupyter_execute/week43.ipynb @@ -1588,7 +1588,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_59025/953065564.py:4: RuntimeWarning: overflow encountered in exp\n", + "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_23401/953065564.py:4: RuntimeWarning: overflow encountered in exp\n", " return 1/(1 + np.exp(-x))\n" ] }, @@ -2176,7 +2176,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_59025/953065564.py:4: RuntimeWarning: overflow encountered in exp\n", + "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_23401/953065564.py:4: RuntimeWarning: overflow encountered in exp\n", " return 1/(1 + np.exp(-x))\n" ] }, @@ -2194,7 +2194,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_59025/953065564.py:4: RuntimeWarning: overflow encountered in exp\n", + "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_23401/953065564.py:4: RuntimeWarning: overflow encountered in exp\n", " return 1/(1 + np.exp(-x))\n" ] }, @@ -2212,7 +2212,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_59025/953065564.py:4: RuntimeWarning: overflow encountered in exp\n", + "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_23401/953065564.py:4: RuntimeWarning: overflow encountered in exp\n", " return 1/(1 + np.exp(-x))\n" ] }, @@ -2230,7 +2230,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_59025/953065564.py:4: RuntimeWarning: overflow encountered in exp\n", + "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_23401/953065564.py:4: RuntimeWarning: overflow encountered in exp\n", " return 1/(1 + np.exp(-x))\n" ] }, @@ -2248,7 +2248,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_59025/953065564.py:4: RuntimeWarning: overflow encountered in exp\n", + "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_23401/953065564.py:4: RuntimeWarning: overflow encountered in exp\n", " return 1/(1 + np.exp(-x))\n" ] }, @@ -2266,7 +2266,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_59025/953065564.py:4: RuntimeWarning: overflow encountered in exp\n", + "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_23401/953065564.py:4: RuntimeWarning: overflow encountered in exp\n", " return 1/(1 + np.exp(-x))\n" ] }, @@ -2284,7 +2284,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_59025/953065564.py:4: RuntimeWarning: overflow encountered in exp\n", + "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_23401/953065564.py:4: RuntimeWarning: overflow encountered in exp\n", " return 1/(1 + np.exp(-x))\n" ] }, @@ -2302,11 +2302,11 @@ "name": "stderr", "output_type": "stream", "text": [ - "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_59025/953065564.py:4: RuntimeWarning: overflow encountered in exp\n", + "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_23401/953065564.py:4: RuntimeWarning: overflow encountered in exp\n", " return 1/(1 + np.exp(-x))\n", - "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_59025/1630775253.py:43: RuntimeWarning: overflow encountered in exp\n", + "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_23401/1630775253.py:43: RuntimeWarning: overflow encountered in exp\n", " exp_term = np.exp(self.z_o)\n", - "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_59025/1630775253.py:44: RuntimeWarning: invalid value encountered in divide\n", + "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_23401/1630775253.py:44: RuntimeWarning: invalid value encountered in divide\n", " self.probabilities = exp_term / np.sum(exp_term, axis=1, keepdims=True)\n" ] }, @@ -2324,11 +2324,11 @@ "name": "stderr", "output_type": "stream", "text": [ - "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_59025/953065564.py:4: RuntimeWarning: overflow encountered in exp\n", + "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_23401/953065564.py:4: RuntimeWarning: overflow encountered in exp\n", " return 1/(1 + np.exp(-x))\n", - "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_59025/1630775253.py:43: RuntimeWarning: overflow encountered in exp\n", + "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_23401/1630775253.py:43: RuntimeWarning: overflow encountered in exp\n", " exp_term = np.exp(self.z_o)\n", - "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_59025/1630775253.py:44: RuntimeWarning: invalid value encountered in divide\n", + "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_23401/1630775253.py:44: RuntimeWarning: invalid value encountered in divide\n", " self.probabilities = exp_term / np.sum(exp_term, axis=1, keepdims=True)\n" ] }, @@ -2346,11 +2346,11 @@ "name": "stderr", "output_type": "stream", "text": [ - "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_59025/953065564.py:4: RuntimeWarning: overflow encountered in exp\n", + "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_23401/953065564.py:4: RuntimeWarning: overflow encountered in exp\n", " return 1/(1 + np.exp(-x))\n", - "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_59025/1630775253.py:43: RuntimeWarning: overflow encountered in exp\n", + "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_23401/1630775253.py:43: RuntimeWarning: overflow encountered in exp\n", " exp_term = np.exp(self.z_o)\n", - "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_59025/1630775253.py:44: RuntimeWarning: invalid value encountered in divide\n", + "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_23401/1630775253.py:44: RuntimeWarning: invalid value encountered in divide\n", " self.probabilities = exp_term / np.sum(exp_term, axis=1, keepdims=True)\n" ] }, @@ -2368,11 +2368,11 @@ "name": "stderr", "output_type": "stream", "text": [ - "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_59025/953065564.py:4: RuntimeWarning: overflow encountered in exp\n", + "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_23401/953065564.py:4: RuntimeWarning: overflow encountered in exp\n", " return 1/(1 + np.exp(-x))\n", - "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_59025/1630775253.py:43: RuntimeWarning: overflow encountered in exp\n", + "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_23401/1630775253.py:43: RuntimeWarning: overflow encountered in exp\n", " exp_term = np.exp(self.z_o)\n", - "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_59025/1630775253.py:44: RuntimeWarning: invalid value encountered in divide\n", + "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_23401/1630775253.py:44: RuntimeWarning: invalid value encountered in divide\n", " self.probabilities = exp_term / np.sum(exp_term, axis=1, keepdims=True)\n" ] }, @@ -2390,11 +2390,11 @@ "name": "stderr", "output_type": "stream", "text": [ - "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_59025/953065564.py:4: RuntimeWarning: overflow encountered in exp\n", + "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_23401/953065564.py:4: RuntimeWarning: overflow encountered in exp\n", " return 1/(1 + np.exp(-x))\n", - "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_59025/1630775253.py:43: RuntimeWarning: overflow encountered in exp\n", + "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_23401/1630775253.py:43: RuntimeWarning: overflow encountered in exp\n", " exp_term = np.exp(self.z_o)\n", - "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_59025/1630775253.py:44: RuntimeWarning: invalid value encountered in divide\n", + "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_23401/1630775253.py:44: RuntimeWarning: invalid value encountered in divide\n", " self.probabilities = exp_term / np.sum(exp_term, axis=1, keepdims=True)\n" ] }, @@ -2412,7 +2412,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_59025/953065564.py:4: RuntimeWarning: overflow encountered in exp\n", + "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_23401/953065564.py:4: RuntimeWarning: overflow encountered in exp\n", " return 1/(1 + np.exp(-x))\n" ] }, @@ -2430,11 +2430,11 @@ "name": "stderr", "output_type": "stream", "text": [ - "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_59025/953065564.py:4: RuntimeWarning: overflow encountered in exp\n", + "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_23401/953065564.py:4: RuntimeWarning: overflow encountered in exp\n", " return 1/(1 + np.exp(-x))\n", - "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_59025/1630775253.py:43: RuntimeWarning: overflow encountered in exp\n", + "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_23401/1630775253.py:43: RuntimeWarning: overflow encountered in exp\n", " exp_term = np.exp(self.z_o)\n", - "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_59025/1630775253.py:44: RuntimeWarning: invalid value encountered in divide\n", + "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_23401/1630775253.py:44: RuntimeWarning: invalid value encountered in divide\n", " self.probabilities = exp_term / np.sum(exp_term, axis=1, keepdims=True)\n" ] }, @@ -2452,11 +2452,11 @@ "name": "stderr", "output_type": "stream", "text": [ - "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_59025/953065564.py:4: RuntimeWarning: overflow encountered in exp\n", + "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_23401/953065564.py:4: RuntimeWarning: overflow encountered in exp\n", " return 1/(1 + np.exp(-x))\n", - "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_59025/1630775253.py:43: RuntimeWarning: overflow encountered in exp\n", + "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_23401/1630775253.py:43: RuntimeWarning: overflow encountered in exp\n", " exp_term = np.exp(self.z_o)\n", - "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_59025/1630775253.py:44: RuntimeWarning: invalid value encountered in divide\n", + "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_23401/1630775253.py:44: RuntimeWarning: invalid value encountered in divide\n", " self.probabilities = exp_term / np.sum(exp_term, axis=1, keepdims=True)\n" ] }, @@ -2474,11 +2474,11 @@ "name": "stderr", "output_type": "stream", "text": [ - "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_59025/953065564.py:4: RuntimeWarning: overflow encountered in exp\n", + "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_23401/953065564.py:4: RuntimeWarning: overflow encountered in exp\n", " return 1/(1 + np.exp(-x))\n", - "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_59025/1630775253.py:43: RuntimeWarning: overflow encountered in exp\n", + "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_23401/1630775253.py:43: RuntimeWarning: overflow encountered in exp\n", " exp_term = np.exp(self.z_o)\n", - "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_59025/1630775253.py:44: RuntimeWarning: invalid value encountered in divide\n", + "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_23401/1630775253.py:44: RuntimeWarning: invalid value encountered in divide\n", " self.probabilities = exp_term / np.sum(exp_term, axis=1, keepdims=True)\n" ] }, @@ -2496,11 +2496,11 @@ "name": "stderr", "output_type": "stream", "text": [ - "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_59025/953065564.py:4: RuntimeWarning: overflow encountered in exp\n", + "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_23401/953065564.py:4: RuntimeWarning: overflow encountered in exp\n", " return 1/(1 + np.exp(-x))\n", - "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_59025/1630775253.py:43: RuntimeWarning: overflow encountered in exp\n", + "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_23401/1630775253.py:43: RuntimeWarning: overflow encountered in exp\n", " exp_term = np.exp(self.z_o)\n", - "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_59025/1630775253.py:44: RuntimeWarning: invalid value encountered in divide\n", + "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_23401/1630775253.py:44: RuntimeWarning: invalid value encountered in divide\n", " self.probabilities = exp_term / np.sum(exp_term, axis=1, keepdims=True)\n" ] }, @@ -2518,11 +2518,11 @@ "name": "stderr", "output_type": "stream", "text": [ - "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_59025/953065564.py:4: RuntimeWarning: overflow encountered in exp\n", + "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_23401/953065564.py:4: RuntimeWarning: overflow encountered in exp\n", " return 1/(1 + np.exp(-x))\n", - "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_59025/1630775253.py:43: RuntimeWarning: overflow encountered in exp\n", + "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_23401/1630775253.py:43: RuntimeWarning: overflow encountered in exp\n", " exp_term = np.exp(self.z_o)\n", - "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_59025/1630775253.py:44: RuntimeWarning: invalid value encountered in divide\n", + "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_23401/1630775253.py:44: RuntimeWarning: invalid value encountered in divide\n", " self.probabilities = exp_term / np.sum(exp_term, axis=1, keepdims=True)\n" ] }, @@ -2540,25 +2540,66 @@ "name": "stderr", "output_type": "stream", "text": [ - "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_59025/953065564.py:4: RuntimeWarning: overflow encountered in exp\n", + "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_23401/953065564.py:4: RuntimeWarning: overflow encountered in exp\n", " return 1/(1 + np.exp(-x))\n", - "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_59025/1630775253.py:43: RuntimeWarning: overflow encountered in exp\n", + "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_23401/1630775253.py:43: RuntimeWarning: overflow encountered in exp\n", " exp_term = np.exp(self.z_o)\n", - "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_59025/1630775253.py:44: RuntimeWarning: invalid value encountered in divide\n", + "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_23401/1630775253.py:44: RuntimeWarning: invalid value encountered in divide\n", " self.probabilities = exp_term / np.sum(exp_term, axis=1, keepdims=True)\n" ] }, { - "ename": "KeyboardInterrupt", - "evalue": "", - "output_type": "error", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mKeyboardInterrupt\u001b[0m Traceback (most recent call last)", - "Cell \u001b[0;32mIn[8], line 11\u001b[0m\n\u001b[1;32m 8\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m j, lmbd \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28menumerate\u001b[39m(lmbd_vals):\n\u001b[1;32m 9\u001b[0m dnn \u001b[38;5;241m=\u001b[39m NeuralNetwork(X_train, Y_train_onehot, eta\u001b[38;5;241m=\u001b[39meta, lmbd\u001b[38;5;241m=\u001b[39mlmbd, epochs\u001b[38;5;241m=\u001b[39mepochs, batch_size\u001b[38;5;241m=\u001b[39mbatch_size,\n\u001b[1;32m 10\u001b[0m n_hidden_neurons\u001b[38;5;241m=\u001b[39mn_hidden_neurons, n_categories\u001b[38;5;241m=\u001b[39mn_categories)\n\u001b[0;32m---> 11\u001b[0m \u001b[43mdnn\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mtrain\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 13\u001b[0m DNN_numpy[i][j] \u001b[38;5;241m=\u001b[39m dnn\n\u001b[1;32m 15\u001b[0m test_predict \u001b[38;5;241m=\u001b[39m dnn\u001b[38;5;241m.\u001b[39mpredict(X_test)\n", - "Cell \u001b[0;32mIn[6], line 98\u001b[0m, in \u001b[0;36mNeuralNetwork.train\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 95\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mX_data \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mX_data_full[chosen_datapoints]\n\u001b[1;32m 96\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mY_data \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mY_data_full[chosen_datapoints]\n\u001b[0;32m---> 98\u001b[0m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mfeed_forward\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 99\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mbackpropagation()\n", - "Cell \u001b[0;32mIn[6], line 38\u001b[0m, in \u001b[0;36mNeuralNetwork.feed_forward\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 36\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mfeed_forward\u001b[39m(\u001b[38;5;28mself\u001b[39m):\n\u001b[1;32m 37\u001b[0m \u001b[38;5;66;03m# feed-forward for training\u001b[39;00m\n\u001b[0;32m---> 38\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mz_h \u001b[38;5;241m=\u001b[39m \u001b[43mnp\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mmatmul\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mX_data\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mhidden_weights\u001b[49m\u001b[43m)\u001b[49m \u001b[38;5;241m+\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mhidden_bias\n\u001b[1;32m 39\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39ma_h \u001b[38;5;241m=\u001b[39m sigmoid(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mz_h)\n\u001b[1;32m 41\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mz_o \u001b[38;5;241m=\u001b[39m np\u001b[38;5;241m.\u001b[39mmatmul(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39ma_h, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39moutput_weights) \u001b[38;5;241m+\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39moutput_bias\n", - "\u001b[0;31mKeyboardInterrupt\u001b[0m: " + "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_23401/953065564.py:4: RuntimeWarning: overflow encountered in exp\n", + " return 1/(1 + np.exp(-x))\n", + "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_23401/1630775253.py:43: RuntimeWarning: overflow encountered in exp\n", + " exp_term = np.exp(self.z_o)\n", + "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_23401/1630775253.py:44: RuntimeWarning: invalid value encountered in 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_23401/953065564.py:4: RuntimeWarning: overflow encountered in exp\n", + " return 1/(1 + np.exp(-x))\n", + "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_23401/1630775253.py:43: RuntimeWarning: overflow encountered in exp\n", + " exp_term = np.exp(self.z_o)\n", + "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_23401/1630775253.py:44: RuntimeWarning: invalid value encountered in 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" ] } ], @@ -2603,7 +2644,44 @@ "collapsed": false, "editable": true }, - "outputs": [], + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_23401/953065564.py:4: RuntimeWarning: overflow encountered in exp\n", + " return 1/(1 + np.exp(-x))\n", + "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_23401/953065564.py:4: RuntimeWarning: overflow encountered in exp\n", + " return 1/(1 + np.exp(-x))\n", + "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_23401/953065564.py:4: RuntimeWarning: overflow encountered in exp\n", + " return 1/(1 + np.exp(-x))\n", + "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_23401/953065564.py:4: RuntimeWarning: overflow encountered in exp\n", + " return 1/(1 + np.exp(-x))\n", + "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_23401/953065564.py:4: RuntimeWarning: overflow encountered in exp\n", + " return 1/(1 + np.exp(-x))\n" + ] + }, + { + "data": { + "image/png": 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", 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UybLpa1n4zep7Wrd0laKMWTWU5+uk0ocfq8WTrz5s68MRSX34k0XO14cbl6fXm+0oVi6A8EvXWT57Awsnr72ndUtXKszXSwfzfNNPCTt31e69OVs+JH+hPCnWebraO0Rei8qIqj8wajYoQ6/XWlOsjO04sfz7v1k4Y8M9rVu6QiG+nv8Sz7cdRdiFcLv36resQJcXWlCkZH6uXb7BmqU7+XHKOhITzJmwF9lXzUbl6DW4DcXKFiT88g2Wz93Ewmnr7mnd0hUD+XrhAJ5v9QVh56/ZvVekVAGef6sdVeuWxpxoYe/WE0wZvoyQs1fT+DTnUrNeaXq/1JLipQsQfiWKZT9tZcGcTWku7+pqolP3BrRqX40CBXNzKTSStb/t4YcZG0hMdK4+ez+y66xZLVu2vOP7a9asSbU8JCQEgEKFCtmV+/v7J793e3lYWBhmsxmTyQTA+fPnAbhy5UqK5dOiQMSBgmqX4qPvX2X9z9uYNXwxleuW5fkPO2EyGflxzG93XLd4hUA+nj8AF9eUf7KmHYP539R+/DJjHTM//Zm8/rnpOfRxvljyBq80/4SEuPTdOPSgCqpcmI9Hd2Xd6n3MnriWStWK8fyrD2E0Gflx1sZU13nx9Ta0fKQq301bz5EDFyhXIZBufZviH5CH0cOW2i3r7ePBhyOfIaBwXkfsTrYUVKskH817hfWLtzNr+BIq1y3D8x90wmQy8ePYu/ThoEA+/v5VXFxNKd5r2iGY/03pyy8z1zHz08Xk9fel5/8e54ufX+eVlsOcpw/XKM6HM/qyfvlOZo9cQaXgUjw39FGMLkZ+mpD6l8dNxcsF8NHMfqm2r29eL/IXysPUYUvYv+2E3Xs3ImMydB+yu6CqRfnw256s/3Uvs8etplLNEjz3ehuMJiM/TV1/x3WLlynIR9/2SrWNa9Qvw7tjurH+173M+Golxcv403vQw+TO68W3ny7LrN3JdoKqF+PDyc+yfsVuZn+9kkq1SvDcW4/Y+vCkOwfUxcsW5KMpz6XavvkL5WbUDy9z7uQlPh/4He6ebvQa9DCfzuzLi4+MIt5JjhFpCapchI+/7sq6VfuZ/e0fVKpejOcHJH3/zUw9yH7xjba0bFeN76au48j+85SrWJhu/ZK+/z5Z4uA9kKwWE2P7LnBzc7Mrd3d3JyIiIsXybdu2ZcKECQwfPpzBgwcTHR3NsGHDcHFxISEh4Z63q0DEgXq8/RjH955hxIvTAdixZj8mVxNPD36ExZPWEB+b8g/n4mrisX4t6Pn248THpf6HfWbQI2xdtYdxr89NLjt3LIQxq4dS9+GqbFj6T+bsUDbTo18zjh++yIgPFgOwffNxXFxMPNO7MYvnb0nxReWT25O2HWoxbdzvLJhru2q0a9tJAJ5/tRXTx/9ORHg0APWalOPF19uSK5f9f1Bn0+Otxzi+9ywjXkrqw38k9eFBbVk8+Q59uG8Ler71WNp9eGBbtq7ey7g3vksuO3cslDGr/kfd1lXZsMw5+nD3wW05vv88Iwfa2mHHukO4uJp45uWHWDJtfart5+Jq4rHejenxets0T8ZKVSoMwKaVe7h4+t6vVOVE3V9pyfGDFxn59k8A7NhwFBcXI8/0a8aSOZtSbUMXVxOPdatPj1daER+fehs/1KEWly5GMOKtH7FYrOzcfIw8+b3p2KsRk7/4BXMqI6w5UffXWnP8wAVGvjEfgB3rD9uOwy+2YMnMv9Ju354N6fHaw2keI7oPaE3U9ViG9pxMXNJxJvTsVT6Y1JuyVYqyf/vJzNupB0CPF5pz/HAII95fBMD2zcds7f5sYxZ//3fq338dazFt7O8smGO7UJf8/TfgIaaPW538/ScPlrRGPO7Gw8MDsN0rcvPfAHFxcXh6eqZYvkSJEowZM4b333+f7777jly5cvHqq69y7NgxfHx87nm7ukfEQVzdXKjSsByblu+0K9+wdAdePp5Uqlcm1fWCH6pCtzcfZf7oFUz/aGGK9w0GA//8eYAVs+yv5J09kjTEVsI/g/Yge3N1NVG1Vgk2/nnIrvyvPw7g5e1O5WrFUqyTy8udXxZt5++/DtuVnz11GSB55MPL2533v3yavf+cYuiAuSk+x1kk9+FfbuvDy5L6cN00+nCrKnQb0p75X//K9I8XpXjfYDDwz7oDrJh9Wx8+erMPF8igPcjeXN1MVK1Xhk0r99iVb/hlF7l8PKhUp2Sq6wU3r0i3gW34YfzvTB+e+pX30hULE3091umDEFdXE1XrlGLT7/vtyjes2kcubw8q1SyR6nrBTcrR7aUW/DDlT6aPSn3kz83dhdiYeCz/mrbmeng0rm4ueHq5Z9g+ZGeubiaq1i3NptX77Mo3/LbH1r610+jDTYPo9upD/PDtGqaPWJHqMg0frsKqBduSgxCAo/vO0b3hMKcPQpK//9YetCv/a81+vLw9qFw9je+/hdv5e739d+bZU5cACCjivCP/98qMIVv+3K+bKVlhYWF25WFhYRQsWDDVdVq0aMGGDRtYt24dmzdv5qmnnuLy5csULVr0nrerQMRBAkrkx83dlfPHQ+3KL5yw/cGLlAlIdb0jO0/Rq9rbzP9qRapX1KxWK1Pe+4m/f7W/X6FBu+oAnD50IQNqn/0FFM6Lm5sL58/Yn2hdSModLlI8X4p1Qi+EM/6LFZy77eSsQbPyJCSYkz8rLjaBfk9PYORHS4h04itEAcXT6sO2L6479uEa/2P+6BWYU8k7tlqtTHl/Qco+/Eh1AE4fdpI+XCw/ru4unE9qz5sunLYFxkVKpX5R4cjuM/Rq8DHzx69O86p7qYqFuR4ezTsTn2XBvuEsOvgFb3/Ti7z+vhm7E9lcQFE/XN1cOH/qtuNE0v/1IiXzp7rekb3n6fXQCOZP+hOzOfU2Xv793wQWz0enZxvh5eNBUNWiPNGjIVvXHeJGhHOkvwUUzWdr35O39+Gk9i2V+kWFI3vP0qvpZ8z/9o9U+3DBInnx9vUk7MI1XvqwAz9u/4gl+z/j/Ym9yR+QO+N35AGT/P13Oq3vv5T9OvRCOOM//yWV778KJCQkpvgsyfmCgoLw9vZmy5YtyWWRkZEcOHCA4ODgFMtv376dHj16kJiYiL+/P25ubqxatQpPT09q1qx5z9vNNqlZiYmJrFq1im3btnHx4kXi4+Px9PSkYMGCBAcH07p16+SbYR5EXr65AIi+HmtXHn3D9jqXT8phL4ArF8PTva1CJQrQ5+MnObbnDNtW7033+g8iL2/bFcfoqDi78uho2+tc93hFskGzIFq1q87SH7dyI+lvlZhoSXGwdkZevrY+mnYf9kixDsCVkPB0b6tQiQL0+ahzUh/ed/cVcgCvpPa72Z43Rd9I6sPeabRvaMrc3duVqlSYfAG5+XXeJhZPX0exMgXpPrgtX/74Cq+0HUlcTMo54nMir6Q2jI66rY2jbPufyzv148SVsMi7fvauv4+zYNpf9BnyCH2GPALAsQPn+WLID/+lyg+UW334tuNw1N368J3bN7efNwDPDXmEw3vO8vnA78idz5tn32jL53P78/Kjo4mLufec9JzmVr++/fsvqV/f6/df8yBata9m9/0nzsPNzY3u3bszcuRI/Pz8KFy4MCNGjCAgIIDWrVtjNpu5evUqPj4+eHh4UKpUKQ4fPswXX3xBz549OXz4MMOGDaN///54e3vf83azRSBy7tw5nn/+eUJDQ6lYsSL+/v7kzp2buLg4Dh06xKJFixg3bhxTp04lMDAwq6t7XwzGOw+XWS0Zkz9cpGwAny0ciCXRzKe9J2K1/sen2zwgjHdpX8s9tEPD5kG8/Ukn9u86w9Rx9zYLlDO5ex/OmL5WpEwAny0YiMVs4dPnJjlNH75b+95LH07LmLd+wJJo5sge2xNy9289wekjIYxa9BqtOgXzy9zUJ3PIae7axv+hD7/yweO07liLed/+wa6/j1OwcF66v9ySYZOf5X/PTbNLKcqpDIbMaV/XpJvXw6/cYNhLs5OPCRdPX2b0gldp8VhNfv1hy50+IkfLmO+/Crz9adL33xh9/90LizV7zpr1XwwYMIDExETeffddYmNjCQ4OZtq0abi6unLu3DlatmzJ8OHD6dixI35+fkycOJHPP/+c9u3bU6BAAV555RV69+6drm1mi0Dk448/pkiRIixYsCDVG1wiIyMZNGgQH3/8MRMnTsyCGv530Ukz03jedkXo5lXkqAyYuaZqw3K8N/slYqJiefPxUVw8denuK+UQUUlX4Dxz2V/58Uq6EnT7FbrbdehSj76vPcSef07x0Rs/kBCvqQtvd6sP27dxch++nkF9eOaLxETFOV8fTroCefvVy5tXkaMj7/8K5aF/TqUoO7D9JDciYihZ8cG8uHM/om6O3uW6vY2TjhPX73ycSEs+f1/aPhnMD5PXMWfc7wDs3XaSI3vPMWnZQFp3rMWyeX//h5o/GJLbN0Ufvtm+99eHb17p37bukN2FiUO7znAjMobSFQvf1+fmFDfb3dPLfjKVe/7+61qfvgNbs2fHKT56/XsS0piQQXI+k8nEkCFDGDJkSIr3ihQpwuHD9vfU1qxZkx9//PE/bTNbBCLbtm1j/vz5ad5l7+vry5AhQ+jWrZuDa5ZxLpwMw5xoJvC2HNnApLzvs0cu/qfPb9axDq9PeJZzR0N496kx95XS9SC7cO4q5kQLgUX87Mpvvj5zMu0T2hdfb8MTz9Rl7W97GfnR4lSfHyJw4dSlpD5sf69CYMmM6sPBvD4+qQ8/Pfa+UroeZBdPX8acaKZQCft87sCk12eOhaa22l3l8vGgUdtqHN51mtNHbs0FbzAYcHEzEXHlxv1X+gFz8cxVWxvfds9YYDHb6zMnwlJb7a4KFMqN0WjkwM7TduVnjocRcS2K4mVSv9Ezp7l4+krq7Zt0j8KZ4/fXhy+euYLFYsHVLeUpi8lkJC6NmbacxYVz12zH5iK3tXvRe/j+G9KWJ56px9rf9jDyg8V6fog4XLa4Wd3Hx4fQ0DsfoC5cuGA3ndiDJiEukb2bjtKwvf0NPI0ercWNiGgOp3LF8l4Ft6rMkInPcXDrcV5/5AunC0IAEuLN7N15mobNg+zKG7WoyI3rsRzefz7V9Z59uSVPPFOXhd9t5vP3FikIuYOEuET2bj5Kw3Y17MobPVozY/rwhOc4uO04r7f70umCEEhq3y3Hadimml15o0eqcSMihiO7Tqex5l0+Nz6RFz/pxNMvt7Irr/dQZTw83diz+dh91/lBkxCfyN7tp2jYqpJdeaOHKnEjMoYje8/d1+feDHAq1SphV164RH5y5/Xi4jnneOBeQnwie7edpOHDVezKGz1cxda+u8/e1+fGRsezb9tJGraugqvbrXtFq9cvg6eXO/u3OfesWQnxibbvvxYV7Mpt338xaX//vdKKJ56px8K5m/j8nYUKQtIpq2fHyuhZs7JKthgR6dy5M2+//TavvfYa9erVo1ChQri5uREfH09oaChbt25l5MiRdO7cOaur+p98P+oXhv88iHdm9GfldxupWKc0nV9tzfSPFhEXE08uHw+KlQ/k4smwe75K6eruwsAxvYi+Ecv3X/1CsfL2aRaXL1zj8oVraayds8ybvp7Pv+nJO8M7s3LZLipWLUrnHg2YPv534uISyeXlRrGSBbh47hoR4dGUKleQp3o25PD+86z/fT9Ble2H98+cvJR8E6vYfP/VLwxfOIh3pvVn5byNVAwuRedXWjP9k59tfdjbg2LlC3Hx1KX09eGveyb14RUUK2//VNfLF65x2UmC6/njVvPZvBcZ+m1vVv2whQq1StCpf3NmfL6cuNgEcnm7U6xsABdPXybi6r09DT0hLpGfJqyhx+ttuXb5BtvWHqBE+UJ0H9SGTSv3snvT0Uzeq+xl/qS1fDbtOYaO7sKqRTuoUL0YnZ5rzIyvVtra2MudYqX9uXj2KhH3+MT5iGtRLJ6zic7PNgZg56Zj+AfmodtLLQg9f43fftqWmbuUrcz/5nc+m92PoeO6s+qnbVSoWYJOfZsyY8Svt/pwmYJcPHPlnvswwIyRv/Lldy/w8dTnWTh1HXny+/Dcm49waNdp/l6z/+4fkMPNm7aezyf05J0vnmLlkn+oWK0YnXs2YPq432/165IFuHjuatL3XwBP9WrI4X3nWL96P0GVi9h9nu377/5SFUXSI1sEIq+++ipGo5Evv/yS6OiU06N6eXnRrVs3XnvttSyoXcbZ/dchhvWaSI+3H+P9OS9x5WI4Uz9YwKJvbDeGlalajC+XDWHUyzNY/f2me/rMinXKkK9QHgCGLxqc4v25Xyxl7hfO8VTf3dtP8clbP9KjXzM+GPE0Vy5dZ+rY1Sz8bjMAZcoXYsSk3oz8aDGrl++mUfMKGI0GylcqzJgZfVJ83pD+M9nzz/1dhc6pdv91mGG9J9Ljrcd4f/aLtj784UIWTUjqw9WK8eWSNxj1ygxWz998T59ZsU5p8gXkAWD4wkEp3p/75TLmfukkfXjTUT7tP4Pug9vy/pTnuRwazrRPl7Joyp8AlK5clC9/fIVRg+fx+4Kt9/y5349dRcSVG7Tv1Yh2PRpw/Vo0K77bxNyvUn8mRk62e8sJPn1tHt1facn747pzOTSSaSN/Y1HS06dLVwzky1l9GTV0Ab8vvvcHaU4d8SuXQyJ45Om6dOrdiKuXrvPPxqPMGrM6+f4fZ7D77+N8+vIcur/Wmvcn9uZySATTvviFRdNszwkqXakwX373IqPe/IHfF22/5889tPM0b3WbSK/X2/DONz2Ji0lg8+/7mDp8+X+aZCCn2L3tJJ+8+QM9+jfng1FduBIWydQxq1mY9LDeMkGFGDH5WUZ++DOrl+2iUYsKGI1GylcuwphZfVN83pB+M9iz45SD90KckcGajaakSUhI4ODBg4SGhhITE4OHhwcBAQEEBQWleOT8f9HGL+V/Osk41tJF7r6Q/CeGk6kPtUvGMOTKldVVyPm81caZLt65753IbJbc6sOZbeWOj7K6CmnadqZEVlchVcHFTmV1FdIlW4yI3OTq6krVqlWzuhoiIiIiIpLJssXN6iIiIiIi4lyy1YiIiIiIiEh2lxMfaJgVNCIiIiIiIiIOp0BEREREREQcTqlZIiIiIiLp8CA+PDA70oiIiIiIiIg4nAIRERERERFxOKVmiYiIiIikg9mqa/kZQa0oIiIiIiIOp0BEREREREQcTqlZIiIiIiLpYNG1/AyhVhQREREREYdTICIiIiIiIg6n1CwRERERkXTQAw0zhkZERERERETE4RSIiIiIiIiIwyk1S0REREQkHfRAw4yhVhQREREREYdTICIiIiIiIg6n1CwRERERkXSwaNasDKERERERERERcTgFIiIiIiIi4nBKzRIRERERSQezruVnCLWiiIiIiIg4nAIRERERERFxOKVmiYiIiIikgx5omDHUiiIiIiIi4nAKRERERERExOGUmiUiIiIikg4WXcvPEGpFERERERFxOAUiIiIiIiLicErNEhERERFJB7PVkNVVyBE0IiIiIiIiIg6nQERERERERBxOgYiIiIiIiDic7hEREREREUkHs67lZwi1ooiIiIiIOJwCERERERERcTilZomIiIiIpIPFqmv5GUGtKCIiIiIiDqdAREREREREHE6pWSIiIiIi6aBZszKGWlFERERERBxOgYiIiIiIiDicUrNERERERNLBbDVkdRVyBI2IiIiIiIiIwykQERERERERh1NqloiIiIhIOlh0LT9DOGUgcuiToKyuQo5mzBuX1VXI8azWklldhRzNxc2c1VXI8RJDPLO6CjmexdOS1VXI0QY2XJ3VVRB54CmcExERERERh3PKERERERERkftltupafkZQK4qIiIiIiMMpEBEREREREYdTapaIiIiISDpY0AMNM4JGRERERERExOEUiIiIiIiIiMMpNUtEREREJB00a1bGUCuKiIiIiIjDKRARERERERGHU2qWiIiIiEg6mHUtP0OoFUVERERExOEUiIiIiIiIiMMpNUtEREREJB0sVj3QMCNoRERERERERBxOgYiIiIiIiDicUrNERERERNJBs2ZlDLWiiIiIiIg4nAIRERERERFxOKVmiYiIiIikg8Wqa/kZQa0oIiIiIuLkLBYLY8eOpXHjxlSvXp2+ffty9uzZNJe/cuUKr7/+OvXq1aNu3boMGjSI0NDQdG1TgYiIiIiIiJObMGEC8+bN45NPPmH+/PlYLBb69OlDfHx8qssPHDiQCxcuMGPGDGbMmMGFCxd4+eWX07VNBSIiIiIiIulgxpAtf+5XfHw806dPZ8CAATRr1oygoCBGjx5NSEgIq1atSrF8ZGQkW7dupW/fvlSoUIGKFSvSr18/9u7dS3h4+D1vV4GIiIiIiIgTO3ToEFFRUdSvXz+5zNfXl4oVK7Jt27YUy3t4eODl5cXixYu5ceMGN27cYMmSJZQsWRJfX9973q5uVhcRERERcWIhISEAFCpUyK7c398/+b1/c3Nz4/PPP+f999+ndu3aGAwG/P39mTt3LkbjvY9zKBAREREREUmH7DprVsuWLe/4/po1a1Itj4mJAWwBxr+5u7sTERGRYnmr1crBgwepUaMGffr0wWw2M3r0aF566SW+//57vL2976m+CkRERERERJyYh4cHYLtX5Oa/AeLi4vD09Eyx/K+//srcuXNZu3ZtctAxceJEmjdvzoIFC+jdu/c9bVeBiIiIiIhIDpDWiMfd3EzJCgsLo1ixYsnlYWFhlC9fPsXy27dvp2TJknYjH7lz56ZkyZKcPn36nrebPceVRERERESyqayeHSujZ80KCgrC29ubLVu2JJdFRkZy4MABgoODUywfEBDA6dOniYuLSy6Ljo7m3LlzlChR4p63q0BERERERMSJubm50b17d0aOHMmaNWs4dOgQgwYNIiAggNatW2M2m7l06RKxsbEAPPHEE4DtWSKHDh3i0KFDDB48GHd3dzp27HjP21UgIiIiIiLi5AYMGEDnzp1599136dKlCyaTiWnTpuHq6srFixdp1KgRK1asAGyzac2bNw+r1UqvXr149tlncXV1Zd68efj4+NzzNg1Wq9WaWTuUXZX4ZlRWVyFHM+aNu/tC8p9Yrfc//Cp35+Jmzuoq5HiJISlvfpSMZfG0ZHUVcrSBDVdndRVyvEEVUj5IL7v4dH/7rK5Cqt6ptDyrq5AuGhERERERERGHUyAiIiIiIiIOp+l7RURERETSwZxNH2j4oFErioiIiIiIwykQERERERERh1NqloiIiIhIOlj+w8MD5RaNiIiIiIiIiMMpEBEREREREYdTapaIiIiISDpo1qyMoVYUERERERGHUyAiIiIiIiIOp9QsEREREZF0sFg1a1ZG0IiIiIiIiIg4nAIRERERERFxOKVmiYiIiIikg1nX8jOEAhEHa1y0OG/UbUQ5v3xcjolm9t5dTNm1Pc3li+fOw7ruz6coP3zlMg/PnwWAAehTvTZdK1WlkLc3J8OvMXHnNpYcOZRZu5FtNQooyRvVmlI2dwEux0Yx58gOph7akubyxb3zsvaxF1OUHw6/RNsVUwBb+z4fVJeuZWtQKJcvZ2+EM+foDuYc2ZFZu5GtNQ4oyevVm1Iud/7kNp5ycGuayxf3zsufj7+Qovxw+CXa/DIVSOrDFerQtWxNCuXysbXxkX+Y7YRt3KhgKQZVbkZZ3wJcjoviu2PbmXbk7zuu0yygDK9Uakz53P6Ex8Ww8vwhRu1dS4w5IcWyXi5uLGvdj/H717Po9J7M2o1srXHR4rxeP+k4HB3NnL27mLLzzsfhP3umfhxuM+/WcbhL5ar0qFKdor65uRITzeoTx/l6yyZuJMRn1q5kW40Ll2BI7UaUy5ufyzFRzD6wi8l7t6W5fHHfPKx/qm+K8sNXL9F60czk1zUKFOKt4CZUKxBAVGIC686e5LOt67gSG50Zu5Ftnd0Zw9bvwrl2JgHPPEYqPeJDtcd9MRhSv2/BYraye3EkB3+/QfRVM7kLuVCjc27KNPJKdfn4GAs/vXaBWk/nIaild2buijg5BSIOVKNgIaa168DyY4f5autGahcqzP8aNMHFaOTbf1I/kauYvwAAXRb/SGxiYnJ5TOKtE4zBdRvSv0Ywo7duYndYCM2Ll2TMQ+2wWmHpUecJRqrnC2Rq06f45cwBvtqzntoFivJ2jRa4GI1MPLA51XUq5i0IQLc139m1aey/TuDeqdmK54Lq8N3Rf1h59jDFvfMyqGoTinrl4bOdazJ3p7KZ6vkCmdrsSX45c5Cvdq8n2L8Ib9dogclgZOKB1E+WK+b1B6Dr7/Pu0MYteb5CHeYe+YdVZ49QzCcPg6s2oah3bj7954/M3alspLpfYSY1epoVZw/w9b4/qZ2/GG9WbYnJYGTy4U2prtOiUFkmNHySxaf2MnLPWsr45uf1Ks3xc8/F4C2L7Zb1dfVgYsOnKOqVJ/N3JpuqXrAQUx/twC9HD/PV3xsJDizM2w2bYDIambjjzsfhrj//SEzCreNw7L/6c/9adXi9XkMm/7ONTefOUDJPXgbXbUj5fPnpsWRB5u5UNlOjQCGmt+7I8hOHGLVjA8EFi/C/Ok0xGQx8uyf1Nq7kZztOdPnlB7vjRIz5VntXzleQ+e2eZsOF0/T7fQkFc3nxVnATSubOS8dl8zJ3p7KR0MNx/PppGKUbehHcNQ8hB2P5e1Y4VjPU6JQ71XW2zw9n58JIaj2Vm4AK7pzcEsPvIy9jNEKpBvbBSNwNM799donrYWZH7I44OQUiDjSoTgMOXA5j8O+/ArDuzClcjUZeqlWH6bv/Ie5fB9ybKub358L162w+fzbVz/RwceG5ajWZseef5GBm07kzVClQkN5VazhVIDKwahMOXAvh9c3LAFh/8QSuRiMvVmrAjMPbUm3fCnkLciEqks2hp1P9zLzunvQsV5v5x3by3rbfANjASS5GRzKpSWfmH9/FicgrmbdT2cygqo05cC2UwZtutbGLwchLlRsw4/D21Puw393buFf52sw/tov3tq20FYbAxejrTG7Sie+P7eJE5NVM26fsZEClJhy8FsKQrUsA+Cv0BC5GIy9WaMiso1uJs6Rs36HVH+K3cwd5e7vtb/L3pVOYDAZ6lq2Dh8mF2KS/SYtCZXmvxsN4ubg5boeyoUF1G3DgUhiDV9uOw+vPnMLFaOSl2nWYsSuN43CBpOPwudSPwwbghZrBfL9vDyM2bwBg49kzXIuNZXyb9lTxL8jesNBM26fsZlCthuy/EsqgdSsAWHfO1sYvV6/H9P1ptHE+fy7ciGTTxTNpfu7QOk3ZfyWMPqt+xppUdiMhng/qtaCod27O3ojIjN3JdrZ9H07+km60HJQfgGI1PbEkwj8LIqjS3gcX95QpQ4d+j6JMYy9qP5MHgCLVPLl8PI59K67bBSKntkazYcpVEmKtKT5D7GnWrIyhBDcHcTOaqFu4CCtPHLMrX3H8CD5u7gQXKpzqehXzF+DA5bA0PzfebKbTwu+Zelt6V7zZjLvJeeJMN6OJuv7FWHXuiF35r2cO4ePqTnCBIqmuVzGvPwfD0z5BKOnjh4vRyJrz9n+3zaGnMRmNNC1U6r9X/gHhZjRRt2AxVp49bFf+65nDd2zjCnkLcvDaPbTxuaN25ZtDbrZx6f9e+QeAm9FE3QLFWXXevn1/O3cQb1d3aucvmmKdinkKUtzbjzlH7f//zzq2jZa/fpMchPi4ujOh4ZNsvXSG5/76PvN2IptzM5qoWyTlcfjXY0nH4cDUj8MV8hfg4B2Ow95u7vx8+ABLjhy0Kz9xzRZAF8ud579V/AHiZjRRr1BRVp6y//+84mRSGwek8V2Xz58DV9Nu4zzuHtQrVJQ5B3fx71Pk304dpf78SU4ThJgTrFzYF0vJernsyks1yEVCjJWLB+PSXM8tl/2Js7uPidjrluTXcTcsrPz8EoGVPWj3gX/GV14kFc5zpprFiubOjbvJhRPh9ld2T0WEA1Aqb142nEt5xbhifn9OR4SzsGMXKhfwJzI+jgUH9zNq60YSLRYsViuHrlxOXj6/Zy6erFCZRkWLM/TP1Zm6T9lJUe88uJtcOHnblfNT168BUNI3HxtCTqVYr0Legpy+fo2fHupJZb8AIuNjWXhiD1/tWU+i1cK1uBgACnvZD3cX98mTvF1nkdzG129r4xu2Ni6VRhtXzOvP6evXWNC6R3IbLzixl69231sbF3OSNi7qlQc3kwunbtiPsJ1Oat+SPvnYGHbS7r0KeQIAiLMkMrnh09QvWIJYcyKLT+1hxN4/iLfYUitiExNo+9tETt64SuFcqaduOIObx+GTdzoOn03lOFzAn9Ph4SzonHQcjotjwaH9fPW37Th8PT6Oj9avTbHeQ6XKAHD0X8fonK6Yb1IbR16zKz+V9Lp0bj82nE+ljfP5cyryGose7UqlfAVt33VH9zFq+wYSrRYq+BXAZDRyJSaaMc3a0apYGQwGWyDy4eY1RManfgKe00SGJGJJhNyBrnbluQvZTucizidQtLpnivWqPOrD7p8jKR6ci4Agd05ti+bsPzHU7ZEneRkXdwNPjwskT2FXIkNTjlqJZAYFIg7i6+YOwI14+5sWo5Jee7ulTJfI6+FJIW8fXIxGhm9az/nrkTQoUowXagZTyMeHgatX2C3/WNkgxrZuB8CaU8f5+fDBFJ+ZU/m4JrVvgv2XUVSi7bWPi3uKdfK6e1Ioly8uBiOf7/qD81GRNChYgv4V61HIy5dBm5Zy8vpVtoWdZWCVxoRE29KLinrn4bM6bYkzJ5LLxTXF5+ZUt9r4tj6c1Oberqn04X+38c61nI+KoEFAUhvnuq2NqzbmYvT1W21c19bGnk7Sxj6uHkAq7Zt4s31T9mE/d9tV0QkNnmTZmX1MO/I3Vf0CGVCpCfk8vJLvEUmwWjh5wznS2+7E578chw1GPt+0nvORkTQoWoz+tYIp5O3DoFUrUqwDUL1gAC/WCub3E8c5ctV50jdvHieu397GSf06zeOElw8mg5HhW9dx/kYEDQOL80K1OgR6+fDan7/g52Hr6yOatOHPcyfp9/vPlPDNy1vBTSjmk5vOy51jpC8+2jaCcfvohqunMen91FOqqj7mS+jhOFZ8fGvUKailF9U73LowYXI1kKewcxxvM4JFSUUZQoGIg6Q1k8VN1lSOHdEJCXRfsoBTEdc4dz0SgC0XzhFvNjOkXiPGbf+b49dunVzsCr3IU4vmE5S/AK/XacjsRzvy9OIfM3Q/sivjXdrXQsoGjk5MoOcf8zh5/Rrno2zD+lvDzhBvSeSNas0Yv28jxyOv8PKGRQwLbsPEJp0BiIiP4fOda3mtSmNiEp3nqtFd2ziVThydmECPNd/btfGWsLPEm828Ub1pchu/9NciPq3TlklNOwEQEZfUxlUb293UnpPd7RiRWvu6Gk0ArD5/iBF7bTf1b7l0GgMGhlRtwdj96zmlACTZ3ftwyrLohAR6LF7AyfBrnL/tOPxG/UaM32Z/HAaoVSiQae07cDYykiFrfsuw+j8I7vc40e3XH23fdTeS2jjkHPEWM0NqN2bcrs24mWx9fe/lUN76y3Yv2cYLZ4iMj2N8i0dpXLgEf50/lbE7kw1ZUztZ+BdDKufG5gQrS/4XQvQ1M01e9CNPYVdCDsXxz08RuHhepVEfv0yqrcjdKZxzkOvxqV819k66Qnc9LuWwcpw5kQ3nTicHITetPX0CgIr5CtiVn4mMYOvF88zeu4uPNqylbuGi1Enj3pOc5nrSVXmv29s3+epcGu0bcir5BPmmtUn3g1RImlHrcmwUL/y1kGo/jaL18snUXTSWBSd2U8DDi4j4mAzfl+wquY1d0mjjhHtv4z8u3GxjWx7y5dho+q9fSNUfv+KhZZOps2gcP53YQwEPL8LjYjN8X7Kj6wm2/UzRvi43R6JStsPN0ai1F+3vefgr5DgAFZNSt8Tm5nEgxXHibsfhs6eTg5Cb/jhlOw5XyG9/HG5Xtjxzn+jMhRuRdPv5J8JjnaP/3hR5t++6VKYyjjMnsuH86eQg5KY/ziS1sZ9/8ijWmjPH7ZZZd86Wrlgpn3Pc0+CWy3balhBjH5DcGilJeVp3YlM0V04l0HJwfio+7ENgZQ9qds5NvV552bf8OldPO9/00pJ9ZJsRkR49etz1iuBNs2fPzuTaZLwzEeEkWiwUz5PHrrxE0k2Mx66lHLovkTsPDYoUY/nRw3b5rx5JN6FfiY3Bz8OTZsVLsu7MSa7E3Dop3nfJdnNwQS/nmP/79PVrtvb1yWtXfvP1sciUOdolfPJSv2AJlp8+YHcS7ZGUCnQ1aV769sUrciziEofCLyUvV8UvAJPRyL6rIZmyP9nRzTYucVsb33x9LCKVPuyTl/oFi7P89EH7Njbd3sYVOBZxOdU23n/NOdr4zI2kPux9Wx/2tl2tPHY9ZfveHO1wSxoZucnFaDsZcZbRpHt1Ouk4XOK2m8dLJB2X0zoO1086Dv/7goaHi+04fPVfx92+NWrzdsMm/H3uLC+sWJIiPckZnLme1Ma+eezKb74+Fp5KG/vmoUFgcZafOGT/XXezjWOjCYuOAsDdlFZfd47Rad8AVwxGiLho/3874qJt//MUSZladf2S7b2AIPv0zsBKttdXzybgV9y5Z9O7H2bNmpUhss2ISKNGjdi+fTtXrlyhcOHCd/x5EMWZzWy9cI42pcralbctXZbIuFh2haU82fLP5cVnzR7ikTLl7Mrbly1PZFwc+8JC8XBx4atWbXm6QhW7ZRoXLQHAwSuXMnZHsql4i5mtYWd4uGh5u/K2RYOIjI9l95ULKdYp4OHNp3Xa8kixCnbl7YtV4Hp8LHuvXgTg5UoNebFiA7tlnguqQ2R8LFvC0p5qMqdJq43bFC2fZhv7e3rzWd22tCsWZFfevngFIuNj2ZsUyL1SuSEvVkq9jf8OdY42jreY2Xb5DK2L2LfVw0VsfXjP1fMp1tl26QxRifG0L1bJrrxlYDkSLGZ2XUm5jjOLN5vZev4cD5e2Pw63SToO7w5N5Tjs5cVnLR6iXdnUj8M3p+XtUqkqQxs15Zejh+m9dKFTBiGQ9F0XcpY2Jezbq22JckTExbIr7GKKdfxzeTO8UWseKWl/bGlfqjyR8XHsvRzK0fArnL0ezqOl7Y/XDxWzTQiwLeRcBu9J9uTiZqBQJXdO/h1tl6Z1cnM0brkM+JdL5R6cpODk4gH7Eb+QQ7bXvgWzzTVpcULZpvf1798fb29vRo0axaRJkyhSJPWpQB9k47b/zXePP8k3D7fnp4P7qBkQSL8awXyx+S9iExPxdnWjrF8+TkeEczU2hm0Xz7Ph7GneadgUDxcXjl69QovipehdtSbDNvxJZHwckfFx/HBgLwOC65FgsbD/chh1ChXmxZp1mH9gL8euOU9++Df7NzKnRVfGN+rAT8d3U7NAEfpWqMeXu9YSa07E28WNMrnzc+ZGOFfjotl+6SwbQ04ytGZLPEwuHIu4TPPCZehVPphP//k9+cr8rMPbGFanLYcjLvHPpfO0L16Rx0tU5t2tv6aajpSTjd+3ibktu/BNoyf48cQeauUvQr+KqbXxNa7GxbAt7CwbLp5kaK2WeLi4cjTiMs0DS9O7fG2G7VjzrzbezrA6bTgSfokdl8/xaPGKPFGyEu9s/c2p2njCgQ3MatqNsfU6suDUbmrmK0Kf8vUZufePW+3rW8DWvvHRRJsTGLNvHUOrP0REfCyrzh+iZr4i9A1qwKyjW7ka71xPm74X47f/zdwnnuSbNu358eA+agUE0q9mMF9uunUcLuOXjzM3j8MXbMfhoY2a4mFy4ei1KzQvXore1Woy7K8/uR4fR/5cuXivcTPORkQwe88uKhUoaLfNm5/lLMbt/JvvHnmKCS0e48cje6lVMJD+Vevw+bb1tn7s6kbZPPk4fT2pjUPOseH8ad6t28z2XXftCi2KluLZSrX45O+1yaMkn21ZxzctH2N8i0f5/tAeyubNx5BajVlx8jD7r6Q99W9OU+vJ3Cz7IIzVIy4T1NKbkENx7FocSd0eeXB1NxIfbeHa2QR8A1zwzG2ieLAn/uXcWPP1ZYKfyUOeIq6EHYljx08RlKjjiX/ZlBNhiDiKwXq3O58c7IUXXsDNzY2xY8dm2jZKfDMq0z77bh4uWYaBdRpQKm9eQm/cYPa+XUzdtQOAeoFFmN/had5Y8xsLDu0HbHm2r9WpT5tSZfHP5cXpyHCm7fqHHw7uTf5MV6ORfjWC6RRUkcI+vly8cZ3v9+9l8s5tqdyinfmMebPuxLF1kXIMrNKEkr5+hMZcZ86RHUw7ZHvQY13/YnzfqjtDNi9j4Ulb+3m7uDGgSmMeLloef09vTl+/xvTDW/nx+G67z+1dPpie5Wrh7+nNicirTDn4N8tOH3D4/t1kzcIh4dZFyjGoamNbG0dfZ86Rf5j6rzae/1A33ti8nIUnbrXxa1Ub2bfxoW38kKKNa9OrfG38Pb04EXmVyQe2ZFkbu7hl3ROFHwosz4BKTSjlk4/QmOvMPb6d6Ue2AFCnQHG+a9aDt7YuZdHpPcnrdCpRjefK1aWEtx+hsTf44cQ/TD60KdX//4Vz5ebPdq+m+AxHSwxJOcWoo7QuVYZBdRtQMuk4PGfvLqbutB2H6xYuwvyOT/PG6t9Y+O/jcN36PFyqLP5eXpyOCGf6rn/44YCtjz9ZoTJftno4ze39+7McyeJpuftCmeTh4mUZVKshpXLnJTTqBrMP7mTKXtvzbuoVKsoP7Z7h9XUrWHD0VhsPrNmANiXKUsDTmzPXw5m2bzvzD++1+9wWRUvxWo0GBPkVICIulsXHDzBy+4bkqaodaWDDrJsi/+Tf0Wz7Ppzw8wl45XOhclsfqj3hC8D5vbEsey+UZq/mI6ilLT07PtrC1rnhnNgcTdwNM74FXSnX3Iuqj/lick35fRIZmsi8/uftPiMrDKqwKsu2fTev7eyS1VVI1ZgaD9YMctkuEAkLC2P//v00b94807aRlYGIM8jKQMRZZGUg4gyyMhBxFlkZiDiLrAxEnEFWBiLOQoFI+j1ogUi2Sc26yd/fH39/55j9QkRERETEWWW7QEREREREJDuzWLPNfE8PNLWiiIiIiIg4nAIRERERERFxOKVmiYiIiIikgxlNGpMRNCIiIiIiIiIOp0BEREREREQcTqlZIiIiIiLpYNHzvDKERkRERERERMThFIiIiIiIiIjDKTVLRERERCQd9EDDjKFWFBERERERh1MgIiIiIiIiDqfULBERERGRdLDogYYZQiMiIiIiIiLicApERERERETE4ZSaJSIiIiKSDmY90DBDaEREREREREQcToGIiIiIiIg4nFKzRERERETSQQ80zBhqRRERERERcTgFIiIiIiIi4nBKzRIRERERSQeLZs3KEBoRERERERERh1MgIiIiIiIiDqfULBERERGRdLCg1KyMoBERERERERFxOAUiIiIiIiLicErNEhERERFJB82alTE0IiIiIiIiIg6nQERERERERBxOqVkiIiIiIulgsepafkZQK4qIiIiIiMMpEBEREREREYdTapaIiIiISDpo1qyMoRERERERERFxOAUiIiIiIiLicErNEhERERFJBwtKzcoIGhERERERERGHUyAiIiIiIiIOp9QsEREREZF00KxZGUMjIiIiIiIi4nAKRERERERExOGUmiUiIiIikg5KzcoYGhERERERERGHUyAiIiIiIiIOp9QsEREREZF0UGpWxtCIiIiIiIiIOJwCERERERERJ2exWBg7diyNGzemevXq9O3bl7Nnz6a67Lhx4yhfvnyqP//73//ueZsGq9VqzagdeFAsOVE9q6sg8p/4GW9kdRVytARMWV2FHM9s1XWwzNbS05zVVcjRrliisroKOV6BwPNZXYU0tVs/IKurkKpfmoy973XHjx/P3Llz+fzzzwkICGDEiBGcO3eOZcuW4ebmZrdsVFQU0dHRdmUzZszg+++/Z/78+ZQvX/6etqlvAhERERERJxYfH8/06dMZMGAAzZo1IygoiNGjRxMSEsKqVatSLO/l5UWBAgWSfy5dusTs2bN5//337zkIAQUiIiIiIiJO7dChQ0RFRVG/fv3kMl9fXypWrMi2bdvuuv7HH39M7dq16dChQ7q2q1mzRERERETSwUL2nDWrZcuWd3x/zZo1qZaHhIQAUKhQIbtyf3//5PfSsnbtWnbu3MnixYvvvaJJNCIiIiIiIuLEYmJiAFLcC+Lu7k5cXNwd150xYwbNmzenQoUK6d6uRkRERERERHKAtEY87sbDwwOw3Sty898AcXFxeHp6prnehQsX2LJlC5MnT76v7SoQERERERFJh5z2QMObKVlhYWEUK1YsuTwsLOyON5///vvv+Pn50bBhw/varlKzREREREScWFBQEN7e3mzZsiW5LDIykgMHDhAcHJzmetu3b6dOnTq4uNzf2IZGREREREREnJibmxvdu3dn5MiR+Pn5UbhwYUaMGEFAQACtW7fGbDZz9epVfHx87FK3Dhw4QKdOne57uwpERERERETSIaelZgEMGDCAxMRE3n33XWJjYwkODmbatGm4urpy7tw5WrZsyfDhw+nYsWPyOpcuXSJPnjz3vU09WV3kAaQnq2cuPVk98+nJ6plPT1bPXHqyeubLzk9Wf+jPQVldhVStbjY6q6uQLvomEBERERERh1NqloiIiIhIOuTE1KysoBERERERERFxOAUiIiIiIiLicErNEhERERFJB6VmZQyNiIiIiIiIiMMpEBEREREREYdTapaIiIiISDpYlZqVITQiIiIiIiIiDqdAREREREREHE6pWSIiIiIi6WBBqVkZQSMiIiIiIiLicApERERERETE4ZSaJSIiIiKSDnqgYcbQiIiIiIiIiDicAhEREREREXE4pWaJiIiIiKSDHmiYMTQiIiIiIiIiDqdAREREREREHE6pWSIiIiIi6aBZszKGRkRERERERMThFIiIiIiIiIjDKTVLRERERCQdNGtWxtCIiIiIiIiIOJwCERERERERcTilZomIiIiIpINmzcoYGhERERERERGHUyAiIiIiIiIOp9QsEREREZF0sFqzugY5g0ZERERERETE4RSIiIiIiIiIwyk1S0REREQkHSxo1qyMoBERERERERFxOAUiIiIiIiLicErNEhERERFJB6seaJghNCIiIiIiIiIOp0BEREREREQcTqlZDnZkh5nfZiUQesaKdx4DDdqbaNLJBYMh9SE+s9nK+oWJbF1pJvKKlfyFDTR/yoXqTe3/dJ92jyHiSsr1P5jvgVdu5xk+VPtmvv074OeZRi6cBt880PxRK607W0mjiTGbYeUCAxt+MxB+BQoWhrZPW6nTzP5pUDs3wbLvjISeA9+8UL+VlUeetuLimvn7lJ0c2AFLZ5Lcvk0fhVaduWP7/r4ANv4GEVfAvzA8/DTUbmZ7/0oIvNsr7e3Vfwh6vpHBO5HNHdxhZdlMKxfP2Nq48aMGWnbijseJNQtg80orEVegQGFo/bSBWk3tlw85a2XxVCvH9oLRCGWqQse+BvIXcq5jxMZt8PUUOHYK8uWFrh3g2afT7sPx8TB+JixbBdcioFRxeL4LtGtpv1zoJRg5Ef7aComJUKUCDHkBKpbL7D3KfrZuMzF5mhsnTxnxy2ulwxMJdHkq4Y5tPH2WG6tWuxAeYaBEcQtdn0mgVYtEu+U2bTYxfZYbp04byZ3byiMPJ9KzezyuTnYcvhcWpWZlCAUiDnT6oIUZH8ZTrYmJh3uaOLnfworpiVgs0Pyp1P+Xr56byNofE2nV1YUSFY3s32xm3ucJmEwGqjQyARAVYftybPe8CyUqmezW9/DO9N3KNtS+me/4QRj7vpHgplae6Gnl6H4DC6YZMFvgkadTf8zs0jkGVvxg4NFuVspWsvLPRgOThxsxmczUamxbZv8OmPCx7XM7PWflwmkDi2YYuBEBXV92nsfXnjgIE96HWk3h0Z5wbD/8PA0sFltwkZrlc2DlD/BINyhTCXZthGnDwWiCmo3B1w+GfJ1yvXXLYMc6aNAmU3cp2zl50MrED6zUbALtexk4vs/KkmlWLGYDrdNo4xVzrKz6Edp2NVC6EuzeZGXGcCtGE9RoZDsZuXbJyleDrRQsAr3fMhAfB8tnWRk/1MrQieDm7hwnLbv2w4tvQ5vmMOB52LHXFjyYzdC3W+rrDP4I1m2GZ5+BejVh/xF470u4Fg7dO9mWiYqGHgPAzRU+eh3c3GDibHj+DVgyA/zzOWwXs9y+A0beHOpBy+aJ9Hkunj17TXw7yQ2zGXp0TUh1nQ8+8WDTZhNdnk6gVk0zh48Y+XyEO+HhBjp3tK2zdZuJt9/1oE3rRPr3jefMGSMTp7hx+YqBt96Ic+QuihNRIOJAq+cmEFjawDND3AAoX9uEJRH+mJ9Io8ddcE3li2rbqkRqNDPxUDfbiXTZGibOHY1l47LE5BPlCycsAFRuYCJfoPNm26l9M9/SOUaKlYY+b9qCg8rBVsyJsGK+gVZPWHFzT7nOhpUG6ja38lh32zoValg5fczKH0uN1Gpsa9uNqwz4+ds+12iCSrWsRIbD6kUGnupvxcVJjlTL50DR0vDsm7bXlYLBkgi/zYfmT5Bq+25eCcHNoX132+ugGnDmGKxbagtEXN2gVAX7dU4ftQUhj/eGMpUzc4+yn1/mWClSGnq9afu/XLG2AbPZwqofrDR7IvWAYfMq2wjTI91t75WvYeDMUQvrl1qTA5EVc6x4esGrww24edjK8gXApA+tnDnqPO08fgZUKAtfvmt73biubfRi0lzo0Rk8buvDB47Amg0GXutj5YUetrIGtSGXB3w1GR5rDb4+MOsnCI+E5bNvBR2Vy0PnfrBtJ7Rr5bh9zGrTZ7hRtoyF94bagoN6dcwkJsKc79x4qlMC7re18ZGjRv7a4ELf5+Po1d0WdATXMuPpARMnu/Fw6wR8vGHOPFfKl7Mw9K245GXCIwzMnuPKgJfj8PR06G6Kk3DusyoHSoy3cnyPhcoN7K+oV2lkIi4GTu63pLEeuOeyL8vlayA68tZV4gvHLbh7gp+TDf//m9o38yXEw+E9UKOh/QhFrcZWYqMNHN2X9nqet7Wxtw/cuP6vZRIMuLvbruL/e5nEBAOx0Rm0A9lcQjwc3QPVG9qX12gMsdFw/A7t63Fb+3r5QNT11Je3WmH+eChUDFp2/O/1fpAkxNvSpqo1sP+/XKORrZ8d35/6eomptbEvREXa/m21Wtm1Eeq3vhWEABQvZ+CzeUbKVHaOY0d8PGzdBa0a25c/3BSiog3s2JNynROnbb+bN7Avr1MDomMMbN1le71qHbRuaj/yUSAfrFvoXEFIfDzs3G2iSWP7lKrmTROJjjawZ68pxTqnTttO9RrWN9uV16huJibWwM5dtnXeHhLHe/+LtVvG1cWKxQqJ9qsKtmNpdvx50CgQcZArIbYrx/kL2zd5vkDbF9Slc6n3nkZPuLBjjZnD283ERln5549Ejmy3ULPlrYPNhRNWcvkYmPNpPO93iuHdDjHMHR5P5NUHsEfeJ7Vv5rsUYgsMCha232//QNvv0HOpn2w91MHKpt8N7NsGMVHw9x8G9m2H+i1vfU6LRy2EXoCVPxmIvmFLAft9sYEqdax4+2baLmUrl0MgMcF2j8e/FUhu39TXa9EBtvwO+5Pad+sfcGA71GmZ+vLb18GpQ/DkC/aBnzO4cpc2DkujjZt3MLB1DRzYbiUmysq2P6wc3A51Wtr6/JVQW9v7+cMP4y282dnCwEctTPrQwrVLznOcOHvBdlGheFH78mJFbL9PnU25Tt48tt8XQlJ+FsC5C5CQCMdPQcmiMGYaNO4AVVpAr9fg6MmM3IPs78JFAwkJBooVsb+4Vriw7fWZsylP6/LktvXBkFD7Y/SFC4ak37Z1CgdaKVbMtmxUFKxbb+L7H91o1SIRHydLQxbHyTYJD6dOnWLZsmVERETQpEkTmjRpYvf+jRs3+PTTTxk+fHgW1fC/iY2y/b79qtrNq/Fx0al/WTXu4MKZQxamvRefXBbc2kSzzrfuebhwwkLEFSt125po/IQLoWesrJqbwMQ3LQwc7253hS6nUvtmvpikNr59dONmm8ekMXLxUEcrxw8a+PrdW2e9jR620ObJW3+ToOrQ5kkrP0018tNUW1mxMlb6vp36SFZOFJNGH75b+7bsaLu3ZPy7t8oaPAytn0x9+dU/QelKUK7af6vvgyitNr55nEhr9K15Bzh5ECa8e6vP1m8NrZ60/d+/EWErWzzdSony8Oz/DFwPh6UzrIx9y8rbE8DdCY4T15Pa1/v20aOklJ4bUSnXCa4ORQOtDBsLHh5QJQgOHYNRE8FgsBIdC5HXIdFsYNZPVooGwidvQnwCjJsOPV+DJdPBP3+m7lq2cSPK1o9yedmX50pq86hU2rhGdTOBgRa+HuuOh3scFYLMHDtu5NvJ7hgMVmLtB0G4fMXAE51tGwgMtNCvT3zKDxXJINkiENmxYwfPP/88/v7+GAwGvvvuO1q3bs2IESNwc7Pl+8fGxrJ48eIHNhCx3mW8zJDK2FRivJVvh8Rx/aqVjq+6UqCIgdMHLaz5PhE3z3gef8HWNp1fc8VoNFC0vO1DSlaGgOIGJrwRz47fzdRvny3+zJlK7Zv5rHeJCVJr44R4+OJ1I+FXoccACwFFrRw/YGD5PAPuntDlRdvfbe5YAxtWGWjf1UJQdStXQg0snWvg63eMvP65BXePTNihbOZu7WtMo31HvQ4RV6HrAChYFE4cgF/ngbsnPPWi/fLH98PZY/DCBxlX7wfJ3dIWUptxKCHeyug3rERehWdeNSS38crvrbh7Wuj8ohFz0v3Bvnmhz3sGjEbbBxUIhFGDrGz7Axo9ksE7kw3drX1T68NurjBlBLzzBTw3OKnd8ll5ZwAM+hA8PSDhX/dfT/4SvJJOuiuXhzbd4LtFMKhfxuxDdnc/x2FXV/jqyxiGf+nBwDdsUWG+fBYGvhrH+x95pDi+urtbGTMqhohImD7Tnf4veTJtUgwFCjjP6N690AMNM0a2OIMaNWoUnTp14r333gNg5cqVDB06lJdeeomJEyfikgPuVPXwsnXYuNuuuN187ZErZYfeu9HMxRNW+n7mRtkatqvJpaua8PAysPibBOq2sRBQwkjxCinzK0pUMuHhBRdOOscVZbVv5vNMugIXG2NfntZICcA/GwycPWFg8HAzFWvayspXtd3U+914I03amsnlDet/NfDIM1ae6HXzi85KyfJW3u9nYuNKAy0ez/lfgDfbN+629k1rtA9g5wY4dwIGDIcKSe1brqrts+aPh4ZtoXAJ++VzeUPlOhle/QfCzT6aoo2TjhOet11lBtssZOdPwCufGQiqaTuOlE1q4x+/sdKgrTV5RKVibZKDEICSFQx4elk5d9wK5PyTFu+k9ou67Th8I9r+/dsVLwJzx8GVa1bCI2yvL4bZTvRy+1iTA4861W8FIQCBBaF0cTh4NEN3I1vzSmrD6NvaOPrmaJRX6sfKIoWtfDMmhmvXDEREQpEiVkJDDVitBnx97Nfx8YZaNW03hVQIiuGprrlYvsKFZ3ulPiOXyH+RLe4ROXz4MD179kx+/fDDDzNlyhR27NjBW2+9lYU1yzj5ChkwGuHyRfsT18sXbAcA/2Ipv6SuhdneK1HR/s9UqrLtdchpiy1feWUiIafsP9disd0z4e0kz7hQ+2Y+/0AwGq2EXbDf57CkXO5CxVJ+AV4Js/0uU8m+vFwV27LnTxm4mnTCUaaS/fqBxcHb18r50xlT/+yuQKDtivHN9rzpUnL7plznalL7lr6tfctUsf2+eMq+fO8WqNYATA/+tZ37kj+pjS+l0cYFi6Zc52qo7XeptNr4NBQoZBtNSUzlPM1sts1c5gyKBYLJZOXMefvym69LF0+5TmwcLF0F5y7anjlSugS4uNhm0wLbM0J8vMEvj5X4VNo3IZEUs0TlZIULWzAZrZw/b/+9dS7pdfHiKY/DcXGwcrULFy4ayJvXSoniVlxMttm0AMqVtWA2w5q1LsllNxUKsOLrA5evZIvTRcmBskXP8vb25soV+6fF1axZkxEjRvDrr78+sOlY/+bqZqBkFSP7Nprt0oj2bTDj4QVFy6X8U/gXsZWd3Gd/EnzqgO11vgAjLq6w+NsE/vjBfgaNA39bSIiD0lWzxZ8406l9M5+rG5SrAv9sNNilYPyzwXbVt2T5lOsEFLUtePuMWsf2J6VgFLImBzhH997+cDi4EWmgQKEM3Y1sy9XNdnK7a6N9isvODbar7yVSbV/b72O3te+JpNmf8v+r7aIiIex8yqDFmbi6GZLa2Gp3nNi1wXrXNr591rLkNg4Ad08DpSvD7o22VK6bDu+0Eh+L08ya5e4OtavC6vX2fXjVOvDxtlKlQsp1XF1g2Bj4cdmtssREmLsIihW2Uq6UraxJPdi8w/ZskZtOnrHdAF+raqbsTrbk7gbVqplZ95eLXRv/ud4Fby8rFYNSTm/l4gKjx7izdPmtex8TzbBwkRuFAy2ULmXBZIKJU9yYONk+aj58xEhEpIHSpTRt1u2sVkO2/HnQZIvrYk2bNuWjjz7io48+olKlSrgmPcKzVatWDB06lGHDhnHx4sUsruV/1/IZF6YMjWfuZ/EEt3bh9EEL6xYm0vZZF9w8DMRGWQk9YyVfIQPeeQxUrGekWHkD34+Ip3V3VwoUNXD2sO0ehor1jMn3LDR70oXVcxPxyRtPULCJi6es/D43gUr1jZSp7jzT4qh9M1+7rha+etvIxE+NNHrYwvEDBlYuMNDxOSvuHrY0rQtnwL8Q+OSB6vWgVJCVqV8YeayHlUJFrZw4bOCXeQaq17sVvLTqYGXlAtsBtGJNK1fCDCybayBfQStN2ub8tKyb2naFsW/D1E+h/sO2exFWL4AnngO3pPa9eMZ2Bd4nD1StByWCYMYX0L6H7aT51GHbPSJV69mfWJ8/Zfud2siKM3m4i4Hx/7My/VMr9R6GkwdsT01/7Dnb1LsxUVZCztiCOJ88BqoktfGsL62062EbNTl1CH773kqVelC8vK3fPvasgbFvWfn2fSstO8H1a7BkupUSQVClXhbvtAO90BOeGwyDPoCOj8DO/TB9PgzuZ7vf40aU7YnrxQqDXx4wmaDL4zB7ARQsYJsZa97PsHMfjBt2676Sl3rBmg22Bxi+1Mt238jXUyGgADzZPiv32PF6dU9g4BsevPeRB+3aJrBvv4nvf3Dlhb7xeHjYblg/edpI4UALefPY2rjD4wn8uNCVAvmtFCtmYdFiV/buM/LZsNjkNn6uVzyffu7ByNHuNGuSyIWLBqbNdKNUSTPt2ibesU4i98tgvdtdvg4QERHBoEGD2Lx5M5MmTUoxY9a8efP47LPPMJvNHDx48D9vb8mJ6v/5M+7Xvo1mVs1N4NI5K7nzG6jf3kTTTrbA6/geM5Peiuepwa7UfsgWI8ZGWfltVgJ7N5qJuQ5+AQZqtTLRuIMLLq62L0CLxcqWFWY2LU/kykUrXj5Qo7kLD3VP/SF+OZmztK+f8UaWbBfgn42wZI6R0HOQJx80f9TKw51th5FDu2Hkmyaefd1Cw9a2spgo+HmmgR0bDERdt51E129lpXVHKy5JF+isVvj9ZwPrfjFwORRy+0GlmlY69Lbik8fx+5hA1gWYuzbaHmwYeg5y54Nmj0Krzrb3juyG0W9Cz9dtszaBrX2XzrSNnERdt51A12tlm03L5dYFUHasg6mfwQdTICAbBCNma9aNJu7eaOWXOVbCztvauMmjBlp2sv1fPrLbNtNV98EG6rW2lcVEWVk2y8quDRCd1MZ1Whpo0ZHk4wTAiQNWls20cuqw7eGTVetDh74GcnlnzXGipWfWXMVevd72YMOTZ6FgfujaAZ5Nemr91p3Qa6CBz9620qGtrSwhEb6ZCUtXQsR1CCpjCzYaBtt/7rFTttm0tu6ynVw3qAVvvwIB/g7cuX+5YklliioHWfeXiekz3Thz1kj+/FY6PpFAl6dsuWv/7DIxYJAnQ9+K5ZE2tgAiMRGmz3Ljt1UuXI80UKaMhWd7xlMn2L6PrF1nYu48N06fMeLpaaVJIzP9+8bh6+PwXQSgQOD5uy+URaoszZ6zfux97KOsrkK6ZItA5KYzZ86QN29efHxS9viTJ0+yatUq+vfv/5+3k5WBiEhGyMpAxBlkZSDiLLIyEHEWWRWIOIusDEScRXYORCot+TCrq5Cq/Y9/mNVVSJdskZp1U7FiaV+mK1myZIYEISIiIiIikvV0SUpERERERBwuW42IiIiIiIhkd9nnxoYHm0ZERERERETE4RSIiIiIiIiIwyk1S0REREQkHR7EhwdmRxoRERERERERh1MgIiIiIiIiDqfULBERERGRdFBqVsbQiIiIiIiIiDicAhEREREREXE4pWaJiIiIiKSDnmeYMTQiIiIiIiIiDqdAREREREREHE6pWSIiIiIi6aBZszKGRkRERERERMThFIiIiIiIiIjDKTVLRERERCQ9NG1WhtCIiIiIiIiIOJwCERERERERcTilZomIiIiIpINmzcoYGhERERERERGHUyAiIiIiIiIOp9QsEREREZF0sGrWrAyhEREREREREXE4BSIiIiIiIuJwCkRERERERNLBajVky5//wmKxMHbsWBo3bkz16tXp27cvZ8+eTXP5hIQERo0albx89+7dOXjwYLq2qUBERERERMTJTZgwgXnz5vHJJ58wf/58LBYLffr0IT4+PtXlP/zwQxYtWsRnn33GwoUL8fPzo2/fvly/fv2et6lARERERETEicXHxzN9+nQGDBhAs2bNCAoKYvTo0YSEhLBq1aoUy589e5aFCxfy6aef0rhxY0qXLs2wYcNwc3Nj375997xdzZolIiIiIpIeOeyBhocOHSIqKor69esnl/n6+lKxYkW2bdtG+/bt7ZbfuHEjPj4+NGnSxG75P/74I13bVSAiIiIiIpIDtGzZ8o7vr1mzJtXykJAQAAoVKmRX7u/vn/zev508eZKiRYuyatUqJk+eTGhoKBUrVuTtt9+mdOnS91xfpWaJiIiIiDixmJgYANzc3OzK3d3diYuLS7H8jRs3OH36NBMmTGDw4MF8++23uLi40LVrV65cuXLP29WIiIiIiIhIOmTXBxqmNeJxNx4eHoDtXpGb/waIi4vD09MzxfIuLi7cuHGD0aNHJ4+AjB49mqZNm/Lzzz/Tp0+fe9quRkRERERERJzYzZSssLAwu/KwsDAKFiyYYvmAgABcXFzs0rA8PDwoWrQo586du+ftKhAREREREXFiQUFBeHt7s2XLluSyyMhIDhw4QHBwcIrlg4ODSUxMZO/evcllsbGxnD17luLFi9/zdpWaJSIiIiKSHtk0Net+ubm50b17d0aOHImfnx+FCxdmxIgRBAQE0Lp1a8xmM1evXsXHxwcPDw9q165NgwYNeOutt/j444/JkycPY8eOxWQy8fjjj9/zdjUiIiIiIiLi5AYMGEDnzp1599136dKlCyaTiWnTpuHq6srFixdp1KgRK1asSF5+3Lhx1KlTh1deeYXOnTtz48YNZs+ejZ+f3z1v02C1ZtfbbTLPkhPVs7oKIv+Jn/FGVlchR0vAlNVVyPHMVl0Hy2wtPc1ZXYUc7YolKqurkOMVCDyf1VVIU6l5n2V1FVJ1ouvQrK5Cuig1S0REREQkHaw57IGGWUWXpERERERExOEUiIiIiIiIiMMpNUtEREREJD2c7g7rzKERERERERERcTgFIiIiIiIi4nBKzRIRERERSQfNmpUxNCIiIiIiIiIOp0BEREREREQcTqlZIiIiIiLpoVmzMoRGRERERERExOGcckSkjWdkVlchR4u0xmV1FXI8X4N7VlchR7thjc3qKuR4eY25sroKOd6boTWyugo52r6e5bK6Cjneb7uzugaS2ZwyEBERERERuX+aNSsjKDVLREREREQcToGIiIiIiIg4nFKzRERERETSQ7NmZQiNiIiIiIiIiMMpEBEREREREYdTapaIiIiISHooNStDaEREREREREQcToGIiIiIiIg4nFKzRERERETSw6oHGmYEjYiIiIiIiIjDKRARERERERGHU2qWiIiIiEg6WDVrVobQiIiIiIiIiDicAhEREREREXE4pWaJiIiIiKSHUrMyhEZERERERETE4RSIiIiIiIiIwyk1S0REREQkPfRAwwyhEREREREREXE4BSIiIiIiIuJwSs0SEREREUkHg2bNyhAaEREREREREYdTICIiIiIiIg6n1CwRERERkfRQalaG0IiIiIiIiIg4nAIRERERERFxOKVmiYiIiIikhx5omCE0IiIiIiIiIg6nQERERERERBxOqVkiIiIiIumhWbMyhEZERERERETE4RSIiIiIiIiIwyk1S0REREQkPZSalSE0IiIiIiIiIg6nQERERERERBxOqVkiIiIiIumh1KwMoRERERERERFxOAUiIiIiIiLicErNEhERERFJD6shq2uQI2hEREREREREHE6BiIiIiIiIOJxSs0RERERE0sGgWbMyhEZERERERETE4RSIiIiIiIiIwyk1S0REREQkPZSalSE0IiIiIiIiIg6nEREH27TNwLipRo6fMuCXF555wkKvpy0Y0piOOj4evp1pZPlqI+ERULI4PPuMmbYt7UPxXq+Y2LkvZVz5/cREKgU5T9i+dZuJydPcOHnKiF9eKx2eSKDLUwl3bN/ps9xYtdqF8AgDJYpb6PpMAq1aJNott2mziemz3Dh12kju3FYeeTiRnt3jcXV1wE5lM+rDmWvLNhOTprkm9+FOTyTQ5anEO7bvtFmurPxXH+72TAIPtTDbLbdxs4lps1yT+3C7hxPp1T3BKfvwxm3w9RQ4dgry5YWuHeDZp7ljG4+fCctWwbUIKFUcnu8C7VraLxd6CUZOhL+2QmIiVKkAQ16AiuUye4+yl7DdkRyaf5HrZ2Nxz+1KyYfzU+rRAhhSaeCzf15h14SzaX5W9ZeKka+iF2teOZjmMkWb+VH9pWIZUvcHRc36pen9SiuKlfYn/GoUy+ZvYeHsjfe0bumgQoyZ25/nH/ua0Avhdu899FgNOvVqSGBRP65cus7qpTuZP3U9FrMlE/ZCRIGIQ+3eb+CV/5lo09zKy8+Z2bnXwOhJRsxmeL5b6v/J3/zYxPrNBno9baFuTSsHjhj4YISJaxEWuna0rWO1wpETBno+ZeahpvYnbCWLO88J3L4DRt4c6kHL5on0eS6ePXtNfDvJDbMZenRNSHWdDz7xYNNmE12eTqBWTTOHjxj5fIQ74eEGOne0rbN1m4m33/WgTetE+veN58wZIxOnuHH5ioG33ohz5C5mOfXhzLXvgJEhQ91p2dxMv+fi2L3XyDeT3Eg0G+iZRh9+/xN3Nm420fXpBGrXNHPoiInhI9wJD4/nyY62gHrLNhNvvetO29aJvNg3ntP/6sNvvxHvyF3Mcrv2w4tvQ5vmMOB52LHXFjyYzdC3W+rrDP4I1m2GZ5+BejVh/xF470u4Fg7dO9mWiYqGHgPAzRU+eh3c3GDibHj+DVgyA/zzOWwXs9S1I1Fs/fwkgQ3yEPR0Ia4eiuLAdxewWKyUfaJgiuX9a+am0TCPFOW7J50lIdqMf01fXDyNNBpWNsUyJ1de5sKmcIq28MuUfcmugqoU4aNx3Vm/ch+zvllD5RrFeX5Qa0wuRn6c/tcd1y1exp+Px3XHxdWU4r3Hu9bjxbfa8deqfUz9aiW583rR86UWlCoXwLDX52fW7oiTUyDiQN/ONBJU1spn79iuVDaqayXRDFO/M9KtswUPd/vlDx6FPzYYeeV5M/162E7Y6tW24ukBX0820v4hC74+cPY8REUbaFTXSrVKznPSdrvpM9woW8bCe0NtwUG9OmYSE2HOd2481SkB99va98hRI39tcKHv83H06m47yQuuZcbTAyZOduPh1gn4eMOcea6UL2dh6FtxycuERxiYPceVAS/H4enp0N3MUurDmWvqDFfKlbHwwW19ePZ3rjydSh8+fNTI+g0u9Hs+nt7JfdiCp4eVbye70aZ1ol0ffuet+ORlwiMMzJrjymsvxztVHx4/AyqUhS/ftb1uXNc2ejFpLvToTIo+fOAIrNlg4LU+Vl7oYStrUBtyecBXk+Gx1uDrA7N+gvBIWD77VtBRuTx07gfbdkK7Vo7bx6x0+KcQcpf0pOarxQHwr+6LJdHKsZ9DKfVIAUxu9qOe7r4uuPvan4qcWHGJ6+diaTSsbPJ7ect52S0TfiKaC5vCqdClEPmCvDNxj7KfHi+14Pihi4x4ZyEAOzYdw+Rq4unnm7D4u83ExyWmWMfFxcRjXevS86WWqb5vNBro1r85OzYf49MhPySXHzt0gUkLX6VGvdLs/Pt45u2UOK103SNy/fp1vvnmG1544QUGDhzIZ599xoIFC9i7dy+xsbGZVcccIT4etu0y0LKR/UnWQ02tREUb2Lk35ZD1idO2smYN7K80B9ewEBNrYPsu2/uHjtl+B5Vx3hO4+HjYudtEk8b2B9jmTROJjjawZ2/Kqz+nTtu6f8P69iksNaqbiYk1sHOXbZ23h8Tx3v/s+7erixWLFRLtV83R1Icz160+bN+pmjc1Ex1tYPfelIfr00nt26i+fb+vmdSH/0nqw/8bEsf7/7MfvXN1wSn78NZd0KqxffnDTW2B8I49Kdc5cdr2u3kD+/I6NSA6xsDWXbbXq9ZB66b2Ix8F8sG6hc4ThJgTLFzZf4OAOrntygPr5SExxsLVQ1F3/Yy48AQO/3CREq3zk7esV6rLWK1W9k47h08Rd0q1L5AhdX9QuLqaqFK7JJv+sE9V27B6P17eHlSqUTzV9YIbl6Nb/+bMn7qe6V+vSvF+nnze+ObJxdb1h+3KTx8LI/xqFHUaO1l+oThMukZEhgwZwrp168ifPz958uRh8+bNREREYDAYMBgMFC1alHLlylG+fHnKlStHuXLlKF68eKp5oc7m3EVISDBQvKj9iVaxwrbXp84YqF/b/r28ScfyC6EGypW+9d7ZC4akzzQAVg4fM5DL08qob02s22QgOgbq1LQy5GUzJZ0kbfbCRQMJCQaKFbE/4S1c2Pb6zFkjwbXtz7jy5La1aUiogTKl//VZSe174YIRMFM48FbbR0XB9h0mvv/RjVYtbFebnYX6cOZKqw8X+VcfrlPb/r3cSe0bEmqkTOlb/fv8BVvQcrMv396Ht+0wMe9HVx5qYXaqPnz2Qhp9uIjt96mz0DDYfp28eWy/L4RA+X8dJ85esP0+dwESEuH4KXj0IRgzDRYsh/AIqFkF3h0IZUtmxt5kP9Gh8VgSrXgXsh9WyhXgBsCNC7EUqOpzx884/GMIGCDomYA0l7mwKZzwo9HU/6A0BqNznV8EFPHDzc2F86ev2JVfOGN7XaRE/lRHLo7sP0evtl9xIzKGhx6rkeL9qOuxJCaY8S+Ux67c28cDH18PChVxrvS3e6EHGmaMdAUimzdv5pFHHmHUqFHJZaGhoRw5coTDhw9z5MgRjhw5wp9//klCQgIGgwEPDw927tx518+Oi4vj6NGjlClTBg8PDw4ePMjcuXMJDQ2lbNmy9OrVi4CAtA9M2d31G7aDpXcu+/JcSSkRN6JTrhNc3UqRQCufjzHh4W6mcpCVw8cNfD3JhMFgJSbpIv3hYwaiYwz4+lgYPczCxRCYOMtE7wEu/DQ1Ef/8mbhj2cSNKFv75rrtAlqupPaOSuVCXI3qZgIDLXw91h0P9zgqBJk5dtzIt5PdMRis3D7Id/mKgSc62zYQGGihXx/nyq1XH85cN/uwl5f9t9utPpzyhKtmUh/+aqxbUh+2cPS4kQmT3ZL6sP06l68YeKyz7QMDAy30d7Y+nHQcuL0Pe93sw6kcJ4KrQ9FAK8PGgocHVAmCQ8dg1EQwGKxEx0LkdUg0G5j1k5WigfDJmxCfAOOmQ8/XYMl0nKIPJ0bbgmEXT/sR6JuvE6PvfMNzXEQCZ9ddpfSj/rh6pX16cnxpGHnLe5G/0p2DmpzIy9sW5EXfsP+Cio62/V/O5eWeYh2AK2HX7/i5cbEJrF+1j0efqcvp42FsWnOAPH7evPDWI5jNFtw9nXBWC3GIdAUiPj4+1KlTx66sYMGCFCxYkMaNb411m81mTp48mRyc3M2JEyfo3bs3YWFhBAYGMmzYMF566SUKFy5MmTJl+P3331m0aBHz5s2jdOnSd/287Mh6l8g5tYs6rq4w8ctE3v/SRL/XbX+qAvmsvPWqmSEfmZJzmV/tY6Z3Fwu1qyVtpCpUr5zI471c+G6hkUH9c/5sF9a77KIhlSREV1f46ssYhn/pwcA3bGci+fJZGPhqHO9/5IH7bfdPurtbGTMqhohImD7Tnf4veTJtUgwFCjjHZRH14cx1v3346y9j+fRLdwYk9eH8+SwMfDWe9z5yx93D/o/m7m5l7KgYIiMNTJ3pRt+XPJgxKVZ9OIkxlTZ2c4UpI+CdL+C5wbZOXiCflXcGwKAPwdMDEv41j8DkL8ErKdCpXB7adIPvFsGgfhmzD9nZ3dr3bsngZ9ZcxWqBko+knW519XAUESdjCB7iJMNMt7nbCJD1rn+EtI39ZCkJ8YkM/OBxBn/UgdiYeH6c8ReeudyIi019sgyR/ypdgUibNm3YvXs3Tz/99B2XM5lMlClThjJlytCuXbu7fu4XX3xB9erVeemll5g5cyYvvvgi7dq149NPP8VgMJCYmMhbb73F8OHDmTp1anqqnG14J13ljIqxL49KuorsnUZ6RLEiMHOsmSvXzEREQrHCEBIGVquB3L62zyxfBm5/sk6RQChVzHal2Rl4JY2ERN92VT765hVQr9QPzkUKW/lmTAzXrhmIiIQiRayEhhqwWg34+tiv4+MNtWrarvhVCIrhqa65WL7ChWd7OccBWn04c90cCYmOtt/fe+nD346J5eo1iIw03NaH7Zf18YbaNW0RT4WgWDp39WTZCheec5o+bPsdddtx4uZonnfqtyRQvAjMHQdXrlkJj7C9vnizD/tYkwOPOtVvBSEAgQWhdHHbpA3OwDWXLdJIjLVPg02MMSe9n/JevX+7sCWcAtV8Uty8/m8X/w7H1cuEfw3f/1jbB1P0Ddu9Xp63jXzcHAmJun7/9+vGxsQz+sPFfPvFCgoG5iH0QjixMfG06VCLC2ev3n+lcyqrc3w3ZbZ03azeqVMnjhw5wsqVKzO0Elu3bmXgwIEEBQXx5ptvEhcXR/fu3ZPvLXFxcaF///7s2LEjQ7frSEUDwWS0cua8fce9+bpUsZQnGbFxsHyVgXMXbXPdlyoOLi5w4IhtnQplrSQmwpLfDOzen/I/RGw8+OXJ+H3JjgoXtmAyWjl/3r5Ln0t6XTyVKWDj4mDlahcuXDSQN6+VEsWtuJhss2kBlCtrwWyGNWtdkstuKhRgxdcHLl9xnmeCqg9nrsKFrZiM1uQ+e9PN1yWKpxwyiYuD31abuHDR9kyXm334cFJ/LZ/ch03JZTfd6sPO82VaLBBMJitnztuX33xdOpX7fGPjYOkqkvtw6RI3+7Dt/YrlbAGeXx4r8anEcwmJpJjtLKfKVdAdgxGiQuxT/qJCbCfP3oXTboiYq/FEnowhsH6eO24j9J9IAoJzY3Rxnn77bxfOXsWcaCawqP09G4HFbK/Pnrx0359dp0k5KlYvRmxMPKePhxEbE09uPy/yF/Tl2MEL/6neImlJ11lUhw4dOHDgAAMHDqR3797MmjWL7du3c/36nXMP78bDw4OYGNtlVj8/P5566incbztyR0ZG4uPz4OaDurtDzWpW1qw32A1f/77egI+XlcoVUp7EubrA8DEmFi679WdKTITvfzZSNNBK2VK2L8SJs0x89a39n/LAEduUqME1cn5KC4C7G1SrZmbdXy527fvnehe8vaxUDEo5NZCLC4we487S5bdyXxPNsHCRG4UDLZQuZcFkgolT3Jg42c1u3cNHjEREGihdynmmHFIfzly2Pmzhz79Mdu27dr0pqQ+nbAcXF/hqjDtLlt+6gpxohgWLXO368LdT3Ph2sn2O960+7BztC7Y+XLsqrF5vn0a0ah34eFupUiHlOq4uMGwM/LjsVlliIsxdZJuooVwpW1mTerB5h+3ZIjedPGO7Ab5W1UzZnWzH5GbEr4I3IVvC7VKELv4dgUsuI3nKpDHkBIQftQ1L+ZVPe5n4G4lEXYzDLyjtZXK6hPhE9v5zmoYtK9qVN2pViRuRMRzedz6NNe+u3ZN16Dv4YbuyDt3qYzFbU8ymJZJR0pWaNXz48OQb0g8fPszff/+dPGoREBBA+fLlKV++PIMGDUpXJRo1asQnn3zCsGHDKFOmDB9//HHyexaLhc2bN/PJJ5/QqtWDPQdivx4W+r1u4o0PTTzxiIXd+wzMnG/ktX4WPD1sN0oeP2WgaGErfnnAZIKnnrDw3QIjBQtAiaJW5i82smuvga+HmZPzmV/sbebd4S4M/QwebW3hQghMmGGifBl47GHnyP0G6NU9gYFvePDeRx60a5vAvv0mvv/BlRf6xuPhYbth/eRpI4UDLeTNY2vfDo8n8ONCVwrkt1KsmIVFi13Zu8/IZ8Nik9v3uV7xfPq5ByNHu9OsSSIXLhqYNtONUiXNtGubcj72nEx9OHP17h7Pa2948O5H7rRvm8je/Ubm/eDKi30T0uzDHR9P4IeFrvgn9eGFSX3482Fxye37fK8EPvncnRGjrTRvksj5i0amzXSlVEkL7Z2sD7/QE54bDIM+gI6PwM79MH0+DO5Hch8+dsqWQnizD3d5HGYvgIIFoGRRmPcz7NwH44bduq/kpV6wZoPtAYYv9bLdN/L1VAgoAE+2z8o9dqxynQqy+ZPj7Bh9iqLN83HtcBTHl4VRoWshXNyNJESbuXEullwB7nYpWJFnYjG6GvAKSHvUJPKMLe3Iu0jKByA6k++n/MnwSb15Z8TTrFz8DxWrF6Nzr4ZMH7OauNgEcnm5U6xUAS6eu0rEtVRmEUnDknmb+Wxib/q90Za/1x2iRp1SPNOnKT9MX8/Fc9cycY8eUM7z1ZSpDNb/cGfT1atXOXz4cPJN6YcPH+bEiRP3NEvW7Z/z0ksvUaRIEUaOHGn33ooVKxg8eDBNmzZl1KhReKeViJ4OcRdL/efPuF9r/jIwYYaJU2dts6g884SFXk/brkhu22ng+UEufPJWIo+3tf1ZEhJh4kwjy1YZiYi0PWehfy8LDYLt/2wr1xqY8b2RE2cMeHpAy8YWXutrIXcWpNFGWrPuaePr/jIxfaYbZ84ayZ/fSscnEujylC1f4p9dJgYM8mToW7E80sZ28pWYCNNnufHbKheuRxooU8bCsz3jqRNsP9Kxdp2JufPcOH3GiKenlSaNzPTvG5ciB99RfA1Zl+vhDH34hjXrZpNa95eJqTPdOHPWQIGkPtz1KVt//WeXkVcGefLOW3G0+1cfnjbLld9WuRAZaaBsGQvP9kyg7m19+I91JubOc+XUGSOentC0USIv9I3Psj6c15jr7gtlktXrbQ82PHkWCuaHrh3g2aRbH7fuhF4DDXz2tpUObW1lCYnwzUxYuhIirkNQGVuwcftUv8dO2WbT2rrLFsA0qAVvvwIB/g7cuX95MzTlNK2OcHFrOId/DCHqQhwefq6UeDg/pR+1NcLl/dfZ/NFxqr9UlKLNbj10Zc/Us4RsiaD1lMppfu6FTdfY8fVpmo0Owqdw1gcj+3pm3bM1GrSoQI8XW1C4RH6uhEWy7IctLJq9CYCqtUvw5bTnGfXeIlYvTXk+9tBjNXj9k470ajuK0Avhdu81a1OFLv2aUTAwD2EXI1j+4xaWfr/FEbuUqt92f5Jl276bUl9/ldVVSNWJgYOzugrp8p8CkdRYrdb7fm5IZGQkvr72Zx3Xrl3j8uXLlC1bNiOqB2RtIOIMsjIQcRZZGYg4g6wMRJxFVgYiziKrAhFnkZWBiLNQIJJ+D1ogkq7UrHvxXx5eeHsQApA3b17y5s37X6okIiIiIpJxlJqVIZxnyh8REREREck2FIiIiIiIiIjDZXhqloiIiIhITmZQalaG0IiIiIiIiIiTs1gsjB07lsaNG1O9enX69u3L2bNn01x+6dKlyY/u+PfPuXPn7nmbGhEREREREXFyEyZMYN68eXz++ecEBAQwYsQI+vTpw7Jly3Bzc0ux/OHDh6lTpw5ffWU/g5ifn989b1MjIiIiIiIi6WHNpj/3KT4+nunTpzNgwACaNWtGUFAQo0ePJiQkhFWrVqW6zpEjRyhfvjwFChSw+zGZTPe8XQUiIiIiIiJO7NChQ0RFRVG/fv3kMl9fXypWrMi2bdtSXefw4cOULl36P21XqVkiIiIiIjlAy5Yt7/j+mjVrUi0PCQkBoFChQnbl/v7+ye/9W0REBKGhoWzfvp158+Zx7do1qlatypAhQyhZsuQ911cjIiIiIiIi6ZHVKVgZnJoVExMDkOJeEHd3d+Li4lIsf/ToUVszWK0MHz6cr7/+mri4OLp27crly5fvebsaERERERERyQHSGvG4Gw8PD8B2r8jNfwPExcXh6emZYvnatWuzefNm8ubNi8FgAGD8+PE0a9aMRYsW0a9fv3varkZERERERESc2M2UrLCwMLvysLAwChYsmOo6fn5+yUEIgKenJ0WKFCE0NPSet6tAREREREQkHQzW7Plzv4KCgvD29mbLli3JZZGRkRw4cIDg4OAUy//www/UrVuX6Ojo5LIbN25w6tQpypQpc8/bVSAiIiIiIuLE3Nzc6N69OyNHjmTNmjUcOnSIQYMGERAQQOvWrTGbzVy6dInY2FgAmjRpgsVi4c033+To0aPs3buXV199FT8/Pzp27HjP21UgIiIiIiLi5AYMGEDnzp1599136dKlCyaTiWnTpuHq6srFixdp1KgRK1asAGypXDNnziQ6OpouXbrQu3dvfHx8mD17Nu7u7ve8TYPVav0PAzkPpriLpbK6CjlapDXl7AqSsXwN9/6fXNLvhjU+q6uQ4+U15srqKuR4b4bWyOoq5Gj7epbL6irkeL/t/iSrq5CmMl+OzuoqpOrYm4OyugrpohERERERERFxOAUiIiIiIiLicHqOiIiIiIhIejjdjQ2ZQyMiIiIiIiLicApERERERETE4ZSaJSIiIiKSDv/l4YFyi0ZERERERETE4RSIiIiIiIiIwyk1S0REREQkPZSalSE0IiIiIiIiIg6nQERERERERBxOqVkiIiIiIumgWbMyhkZERERERETE4RSIiIiIiIiIwyk1S0REREQkPZSalSE0IiIiIiIiIg6nQERERERERBxOqVkiIiIiIumh1KwMoRERERERERFxOAUiIiIiIiLicErNEhERERFJBz3QMGNoRERERERERBxOgYiIiIiIiDicAhEREREREXE4BSIiIiIiIuJwCkRERERERMThNGuWiIiIiEh6aNasDKERERERERERcTgFIiIiIiIi4nBKzRIRERERSQc90DBjaEREREREREQcToGIiIiIiIg4nFKzRERERETSQ6lZGcIpA5HHyzfN6irkaJaYmKyuQo5n9PTM6irkaJbYuKyuQs5ntWR1DXI+g5IeMpPBeCKrqyDywNNRSkREREREHM4pR0RERERERO6bUrMyhEZERERERETE4RSIiIiIiIiIwyk1S0REREQkHfRAw4yhEREREREREXE4BSIiIiIiIuJwSs0SEREREUkPpWZlCI2IiIiIiIiIwykQERERERERh1NqloiIiIhIOmjWrIyhEREREREREXE4BSIiIiIiIuJwSs0SEREREUkPpWZlCI2IiIiIiIiIwykQERERERERh1NqloiIiIhIeig1K0NoRERERERERBxOgYiIiIiIiDicUrNERERERNJBDzTMGBoRERERERERh1MgIiIiIiIiDqfULBERERGR9FBqVobQiIiIiIiIiDicAhEREREREXE4pWaJiIiIiKSHUrMyhEZERERERETE4RSIiIiIiIiIwyk1S0REREQkHfRAw4yhEREREREREXE4BSIiIiIiIuJwSs0SEREREUkPpWZlCI2IiIiIiIiIwykQERERERERh1NqloiIiIhIOmjWrIyhEREREREREXE4BSIiIiIiIuJwSs0SEREREUkPpWZlCI2IiIiIiIiIwykQERERERERh1MgIiIiIiIiDqdAREREREQkPazZ9Oc/sFgsjB07lsaNG1O9enX69u3L2bNn72ndpUuXUr58ec6dO5eubSoQERERERFxchMmTGDevHl88sknzJ8/H4vFQp8+fYiPj7/jeufPn+fjjz++r20qEBERERERcWLx8fFMnz6dAQMG0KxZM4KCghg9ejQhISGsWrUqzfUsFgtDhgyhUqVK97VdBSIiIiIiIulgyKY/9+vQoUNERUVRv3795DJfX18qVqzItm3b0lxv4sSJJCQk0L9///varp4jIiIiIiLixEJCQgAoVKiQXbm/v3/ye7fbs2cP06dPZ8GCBYSGht7XdhWIiIiIiIjkAC1btrzj+2vWrEm1PCYmBgA3Nze7cnd3dyIiIlIsHx0dzRtvvMEbb7xBiRIl7jsQUWqWg9VsUYmxf77P4pCJzNzzJZ1ebXPP65auWozlV6ZQsFi++3o/J6rVqirjNn/K0ohZzD4yls6D2t91nWZPN2DyrhEsi5zN1D2jeKhHkxTLNHisNt9s+Ywl12Yy4+DXdH+3Ey6uJrtlvHLn4tVxzzH/7ESWXJvJ1399TPVm95cj+SBRH85YtVpVYdymT1gaPoPZh7+m86B2d12n2dP1mbzrS5ZFzGTqnhE81KNximUaPFabb/7+lCVXpzPjwFd0f7djij5coW5Zvlz1DkvDZ/DjuW8ZPLkfefx9M2zfsgMdIzKf+nDmqtmqCmM3fMySq9OZdWg0nQc+ctd1mj1Vn8n/fMHSazOYsutLWnVP2b4P9WjCpB2fsyx8BjMPjqbb0A4YTfanhYGlCvLO3FeZd2I8i0KnMOqP96nePOf14fuS1bNjZfCsWR4eHgApbkyPi4vD09MzxfLDhg2jZMmSPPPMM/e/UTQi4lBBwaX46MeBrF+0lVnDFlG5fjme/+RJTC5Gfhy94o7rFq9QmI9/GoiLa+p/sru9nxMF1S3Dx0veZN1Pm5n1wY9UbliePp93xeRi5IcRS1Ndp1GHOrw9+xUWj/uNbSvn0ODxYIZMf4mEuAT+/HEzADVbVuH9nwaz7sfNTBv6PcUrFeG5YV3wze/DhIEzATAaDXy6/G38i+Zn6v++41poBB1ebcuwZW/xaoN3Obn3jKOawaHUhzNWUJ0yfLx4iK0Pf/iTrQ8P75LUh5eluk6jDsG8PetlWx9etYcGj9VmyLQXSYhL/Fcfrsz7Pw5k3U9/M+2d7ylesQjPDXsG33w+TBg0C4DytUsz4vd3OXvoPCOen0h8TDydB7VjzPqPebHO/4iOjHFYO2QWHSMyn/pw5gqqU4aPF73BugV/M/ujBVRqWJ7nP+uC0cXEjyPTaN8ngnlr5kssHr+S7at229p36gskxCWw7qe/AXji5Yd5cVRP1i/cwpT/fU+eAj70eL8zpaoU45MuYwDw8fNmxOp3ibx6g4lD5hAdGUPb55rz2fK3eavNp+z965DD2kHuXVojHndzMyUrLCyMYsWKJZeHhYVRvnz5FMsvXLgQNzc3atSoAYDZbAagffv2vPDCC7zwwgv3tF3n+cbPBnoM7cD/27v7+Jrr/4/jz9mFzcVcZRtzzZiJEDHMRb6pb5SISq5zEfq2IuQ6RbmahJD6ob7hS+VaLlNJyEWKtOZyudpsGMbGNtv5/TE7dWwujs75nLOzx/12O7dbe5/zPp/Xee3dsdd5vz6fc+zACU3p+4kk6edvDsrdw13Pv9FGK+dsVur1tGxzPDzd9fTL/1K3ke2UmmL9/a6s25iOOvbrn5rcY5Ykae+m/XL39NALw57Ripnrc8xnz3Ev6Ievdumjwf+VJP28+YAKFyuk7mOfM/8D2Kp7c8WfvKBJ3T9URoZJ+7b8pmJ+RdT+9daaO/hzpd9IV4tOTVT14Uoa8Mhw/Xkw8xrbB374Q3P3TdbD/6rlMn9k3Io1bFvdxnTIXMM950iS9m46IHdPd73wZlutmLnhNmv4ef2wbJc+GrJQ0s01XLygur/V8W9ruNnNNTzr5ho+qGL+RdT+tSc1d8hCpd9IV6fhbZV8OVlDHntXVy8lSZJ+/e53zfstQs8NfkqfjvnCoCzYD+8R9scatq+uo5/VsV//1JSXbuZ38wF5eLjrhaFttfLDnPPb453ntG3Zbs0dejO/3/yWuYbf6qitX/6kfPnc1HlEO/38zW96t/MM87wjv/ypj/dNUt2WD2rfloN6rEuYipT0VXjYGF2IuShJ2rflN83ZM0EdB7ahEHExwcHBKlSokHbt2mUuRBITExUZGakuXbpke/ytV9Lav3+/hgwZoo8//lhVq1a95+PSmmUQTy8P1WxSTTvW7LMY/3HVXhX09VGN0KAc59VvVUudh7XVkqlrNX/Ml1bf76o8vTxUq1mItq/cbTG+bflPKuhbQA82Ds42x798SZWtVlo7VmWfExhUSqWrBEiSvLw9dT3pujIy/trjTLxwVV75PVWgcObWZVj7Bjrwwx/mPzAkKS0lTS/VGKivpq212et0Jqxh28pcw9W1fZXl1Ui2Ldt9cw1n/wTKv/wDKlu1tHas2ms5Z/luBQYFWK7h5JQ7ruFywYE6uOOQ+Q84SUq5lqqoPcfU4N+1bfUyHYb3CPtjDduXp5eHajWtru2rb8nVit0q6Otzx/xuX33L72TFLgVWCVDpyv4q6l9EviUKa9e6XywecyLytC6dS9QjT9SWJJ0/k6Dl09eZixBJysgw6czRsypVyc9GrzL3cjM55+1+eXl5qUuXLoqIiNCWLVsUFRWlgQMHKiAgQK1atVJ6errOnTun69evS5LKly9vcfP395cklS5dWkWLFr3n41KIGCSgQkl55ffUmaOWJ/PEHM/8uUxQqZym6fC+aHWvOURLItYq/Ua61fe7qoBKfvLK76nTR2ItxmNu5rdMtdLZ5pQLDpSk7HOOZc4pWzVzzuo5mxQYVEodBrZRwSIFFNygitqF/1u71u3TlYuZ/+BVfqi8TkSeVrvwf+u/R2ZqXfJCffjTuzn+ceMqWMO29dcatrwaSdZ6LFP1Dmv48K1rOPM5ylbN/B2snrNZgVUC1GFg68w1/EgVtXv1Ce1a94t5DV8+f0X+5UpmO0apSn4KqJj7/8jgPcL+WMP2FVAxM79nsq3HzFzl9J5btlpmfs/c5ndStmppJV1K1o20G/Iv94DFYwoVLaDCxQqac/fDsl2aP3pptsfUCquuE5Fn/sErg7MKDw9Xhw4dNGrUKHXq1Enu7u6aN2+ePD09FRsbqyZNmmjduju3YVvL6Vuz+vbtq/Hjx8vPL3e/qRQsknmiT/IVy57V5CuZlWXWJzy3uhB76Y7Pe7f7XVXBIgUkKVsPcFZ+CxTOfmKV+Xdwy5xrWXN8M+//9buD+jJitfpO7qK+kzO3I4/8Eq0JXWea5xQp6aumzzbQlYtJ+uTNhbqenKIXhrbVhPUjFN7Ydfq//441bFsFfe+yhn1zWsMFLB6T5VrW78C8hn/Xl1PXqO+kzuo7qbOkm2u424fmORs//V6D5vZVv4iu+mLqGpkyTGr/2r9Vvnqg3F3gPB3eI+yPNWxft8tV8i25spxzuzX815yUa6na+tVPeqr/Y/rzj9PasWqPipYsov5Tuyr9Rrq8C+bPMR43Nze9PruPCvj66Mv3cz4/Bbmbu7u7hgwZoiFDhmS7r0yZMjp06NBt5zZo0OCO99+OU/yfunLlytvet2vXLq1du1bFixeXJD3zzDPGBGVjbm533nwyZfyD/bQ8KF++u+UzI9uY2z3OCZ/VS4/3aKFF7y7XL98elH+Fkuo6uoPe+3q43mw1XinXUuXp5aGCRQvq1UajdP5MgiTp4I9R+uzQdD0/pK0mdpt5p0PlSqxh28qX785fPZXzGr7bnMzfQfiHL+nxHs216L0VmWu4fEl1Hf1s5kmmj7+rlGup2rDgexX0LaBub3VQ+/B/KyMjQ9uW79a6//tWrbo3u/8X5iR4j7A/1rB93S2/GTm8595t3Wfc/J3M+M98paXc0MA5vfXG3L66npyiLyLWyKewj1KSU7PNc/dw1+D/e1lh7R/Rh699qkN7j1vxSlwU/+TZhFMUIm+//ba558xkyv6bnTx5sqTMajy3FiLJicmSJJ9Clp8aZ32ikeQCV/cwUtLlzHze+qlmwTvkM2uOzy1zzL+Dy9dUonQxPdm7pZZMXKnPxt480fEH6dCeo/q/A1P1eM8WWj17o5KvXNPJqBjzHxiSdO3qdUXuPKwqtSvY5DU6G9awbSUlZq1hy3wW/Nt6zDbHvIZv+R0U9jHfn7mGH9WSSav02disc27+0KG9x/R/+6fo8R7NtXpO5kmGy6av08pZG1W6sr8SL1zR5fNXNGR+f11JuGqz1+kovEfYH2vYvsy5KpTzGk6+eX+Oc27N7y2/k+tJKZrW7xPNeeO/8i/3gOJOntf1pBQ90bO5ufXLfLwiBTRm6euqGVZds17/VGvmbv7nLw64ySnOEVm+fLlCQkLUsGFDbd26VVFRUeabj4+PNm/erKioKP3xxx+ODvW+xUTHK/1GukrfcoJX1s+nDsU4IqxcK+ZYXGY+K/tbjGed6Hjyj+z9q6cPZ+Y425zKN+dEnZFfuQeUL18+/b7jsMVjTv5xRpfPJ6p8SBlJ0pmjZ+WVP3sd7+7prpTr2T9NcgWsYduKOXYznzfXX5a/r8dbZfXVZ5tTxd88x6/sndbwFfMaDqpbUY2fqa/0G+k6dShGl89fyRyvU0FHf/3zn79AB+M9wv5Yw/YVczw+5zV88+eTObzn/pXfnOecOpT5O2nw7zoKCa2q60kpOvHHGV1PSlGRkr56ILC4jv7yp3neA4HF9cHWsQppGKQJXWdq9UcUIbAtpyhEKlasqKVLl6pmzZpq27atzU+EcQZpKTf02/bDavz0wxbjTZ6up6uXknXo52gHRZY7paWk6bdtf6hxu0csxpu0a6Crl5J0aM/RbHNijsUp9nicwto3zDbn9OFYxZ04p5ijZ5V+I10PNrE8obRM1VIq8oCvzt48MXvP+l9V6aEKKhv818mYhYsXUo1G1XTwR9e8pCFr2LYy13CUGj9T32K8Sfv6N9fwsWxzMtdwvMLa37ruH9HpI7GKO3FeMcfutIYL62x0vCTpoWYhGvbZK+Y+dCnzuxsq1CirHbdcpSc34j3C/ljD9pWWkqbffoxS47a35PeZR26f3+Nxio2OV1i7BtnmZOVXklr3aak+E160eEz7V59QRnqGdq3PvJpWgcI+mrh+uEqUKqbhrSdq23LLq8nleY7+4kIbf6GhozhFa5YkeXh4aNCgQQoLC9Obb76pb7/9Vm+99Zajw7Kp/01ZowmrB2vkZwO0ceE2hTSoog6vPaH5b32llGupKlDYW+WCSyv2+DldvnDF0eE6vcXvrdDEjSM16n+va+On3ysktKo6vtFG80b872Y+fVQuJFCxx+LMn5QtfHe5hszrrysJV7Rzzc8Kfbqemj8XqndfzPwCp8vnr2j5jPXq+Ebmty/v++aA/MqXVNdRz+rsn+e0bt63kqQVM9erVfdmGr/qTS0Ys1TXk1L04oh2MplM+up917g0Z05Yw7a1eMIKTdwwQqP+99pfa3hQG80bueSvNVw9ULHHb13D/XTlwlXtXPuzQp+qp+YdQ83fB2Bew4Myv9163ze/ya/cA+o6qr3FGt6y+Ee9MPRpjVocri/fXyu/sg/o5SlddHD7IW1Z9KNjEmJjvEfYH2vYvhZPXKmJ64Zr5KJwbfxsq0JCg9RhUGvNH7X0tvld9N4KDf7kZSUmXNFPa/cp9KmH1axjQ73X5a/zklbO3qgJa4fp5cld9NPX+1S7RQ29MLStlk5ZrdjjmYVe19HPqmzV0vrvuGW6kZau4EeqmOenpaTp2P4TxiYDLsnNlNNJGQ6WmJiot99+W3v37tWFCxe0fv16lS1b1mbP/4RvT5s9l7UatamrriOeUWBQgC7EXNSaT77V8g83SpJqNammyeuGaWq//9PmxduzzX3sxcZ646Pe6v7gYMWdvGD1/UbJuGbcuQKN29ZX17c6qEzV0rpwJkGr52zSsg++liTVahqiiC1jNKXXHG3+71bznNZ9WqrDwDYqWbaEYo/Ha8nkVdqyaJvF87YL/7fa9PmX/Cv6KSH2kvZ9c0ALRi8xv9FLmVvWvSe8qHqP15anl4cObo/Sx0MX6kTkabu/7nw+2a+WYpQ8sYavpxh2rMZt66nrmA4qU7WULpy5qNUfbdKyDzJ3hWs1ra6Ib0ZrSq+PtPnzH8xzWvd+9OYaLq7Y6Hgtmbw62x9e7V59Qm36/kv+FUreXMO/acGYpRZrOKhORb08pYuq1KmgpEvJ2rZitz4b+6WuXb1u/xduyn4isz3k1fcISdJdLjBhK3l1Dd/txHtbafR0PXUd/WxmfmMuas1Hm7Vs+l/5nbJplCL6zLXI75O9H1WH11urZJniio0+p6VTVmvLYsv8Nn8uVC8Oe0b+FUoq/uR5rZn7jfncG0n6/Mh0+ZW1vMRvlrMnzql7tddt/2JvsfH6Irsf4349FD7N0SHkaP+MgY4OwSpOWYhkWblypZYvX66IiAibXr7XkYVIXmBkIZJXObIQyQuMLETyLIMKkTzNoEIkrzKqEMnLnLkQqf2qcxYiv87MXYWI07Rm5eSZZ57JtVfJAgAAAHB7fFwCAAAAwHBOvSMCAAAAOB2nPbEhd2FHBAAAAIDhKEQAAAAAGI7WLAAAAMAKbrRm2QQ7IgAAAAAMRyECAAAAwHC0ZgEAAADWoDXLJtgRAQAAAGA4ChEAAAAAhqM1CwAAALACV82yDXZEAAAAABiOQgQAAACA4WjNAgAAAKxBa5ZNsCMCAAAAwHAUIgAAAAAMR2sWAAAAYA1as2yCHREAAAAAhqMQAQAAAGA4WrMAAAAAK/CFhrbBjggAAAAAw1GIAAAAADAcrVkAAACANWjNsgl2RAAAAAAYjkIEAAAAgOFozQIAAACs4GaiN8sW2BEBAAAAYDgKEQAAAACGozULAAAAsAadWTbBjggAAAAAw1GIAAAAADAcrVkAAACAFdxozbIJdkQAAAAAGI5CBAAAAIDhaM0CAAAArEFrlk2wIwIAAADAcBQiAAAAAAxHaxYAAABgBa6aZRvsiAAAAAAwHIUIAAAAAMPRmgUAAABYg9Ysm2BHBAAAAIDhKEQAAAAAGI7WLAAAAMAKXDXLNtgRAQAAAGA4ChEAAAAAhqM1CwAAALAGrVk2wY4IAAAAAMPlyR2R9KtXHR0C8I+whgHcjXshH0eH4NJ4Hwb+uTxZiAAAAAD3i6tm2QatWQAAAAAMRyECAAAAwHC0ZgEAAADWMNGbZQvsiAAAAAAwHIUIAAAAAMPRmgUAAABYgatm2QY7IgAAAAAMRyECAAAAwHC0ZgEAAADWoDXLJtgRAQAAAGA4ChEAAAAAhqM1CwAAALCCW4ajI3AN7IgAAAAAMByFCAAAAADD0ZoFAAAAWIOrZtkEOyIAAAAADEchAgAAAMBwtGYBAAAAVnCjNcsm2BEBAAAA8riMjAzNmDFDYWFhql27tvr06aNTp07d9vG///67unfvrjp16qhhw4YaM2aMrly5YtUxKUQAAACAPG727NlavHixxo0bpyVLligjI0O9e/dWampqtseeP39ePXv2VGBgoJYvX67Zs2fr559/1rBhw6w6JoUIAAAAYA2TyTlv9yk1NVXz589XeHi4mjdvruDgYE2bNk1nz57Vpk2bsj3+zJkzatKkid555x1VrFhRdevW1XPPPaft27dbdVwKEQAAACAPi4qKUlJSkkJDQ81jvr6+CgkJ0Z49e7I9/qGHHtL7778vD4/M082PHTumVatWqXHjxlYdl5PVAQAAABfQsmXLO96/ZcuWHMfPnj0rSSpVqpTFuJ+fn/m+23n88cf1559/KjAwUB9++KEV0bIjAgAAAFjFzeSct/t17do1SZKXl5fFeP78+ZWSknLHuREREfr8889VokQJdevWTUlJSfd8XHZEAAAAABdwux2Pu/H29paUea5I1n9LUkpKinx8fO44t2bNmpKkDz/8UM2aNdPmzZv1zDPP3NNx2REBAAAA8rCslqz4+HiL8fj4ePn7+2d7/PHjx/X9999bjPn7+6to0aKKi4u75+NSiAAAAADWMDnp7T4FBwerUKFC2rVrl3ksMTFRkZGRql+/frbH79ixQ+Hh4UpMTDSPnTx5UhcvXlTlypXv+bgUIgAAAEAe5uXlpS5duigiIkJbtmxRVFSUBg4cqICAALVq1Urp6ek6d+6crl+/Lklq06aNihYtqiFDhujIkSPau3evwsPDVatWLbVo0eKej0shAgAAAORx4eHh6tChg0aNGqVOnTrJ3d1d8+bNk6enp2JjY9WkSROtW7dOklS0aFF99tlnkqROnTrplVdeUUhIiObNmyd3d/d7PqabyfQPvv0kl3osX0dHhwAAgF25Fyrk6BBcWvrVq44OweVtzvjS0SHcVli7CEeHkKNtKwY7OgSrsCMCAAAAwHAUIgAAAAAMx/eIAAAAANbIe2c22AU7IgAAAAAMRyECAAAAwHC0ZgEAAABWcKMzyybYEQEAAABgOAoRAAAAAIajNQsAAACwBq1ZNsGOCAAAAADDUYgAAAAAMBytWQAAAIAVuGqWbbAjAgAAAMBwFCIAAAAADEdrFgAAAGCNDHqzbIEdEQAAAACGY0fEjh5+rJZ6ju+k8jXK6mLcZa2evUFfTV1zxzktXmisF0c+q1KV/BX3Z7yWTFqpzf/dKknyL19SC6Nn33buxgXfKaJX5v0VHiynPpO6KLhBkNJS0vTzpv365M2FuhR/2XYv0AmQY/siv/ZHju2L/DpG3UdrqMeYZ1UuuLQuxSdqzSffatnMDfc0t3Ktcpr+3Wj1qjNMcScvWH2/q2ENw5VRiNhJ9QZBGrdmuLYu3aFPxyzVg02C1WdSF7l7uGvppJU5zmnSvoGGLQzXihnrtHfDr2r0zCMa+ul/lJaSpu+X7lBC7EWFh47INu/pAU+o2fONtH7+t5Kkon5FNGXLWzp36rwies6Sl4+Xek/srPfWjdCrDUco/Ua6PV+6YcixfZFf+yPH9kV+HSO4fiW9/cXr+mH5bn02frkeDK2qXuM6yt0jn76Ytu6Oc8tXD9Q7X74uD8+c/zy52/2uhjXsxOjMsom88X+yA3Qb+5yO/hKtSd1nSpL2bvxVHp7u6jS8nVZMX6fU66nZ5rz07ov64cuf9NGgzzLnbNqvwsUKqcc7L+j7pTuUlnpDf+w6YjEnqG4lNXu+keaPXKzft0dJkhq1ra+iJX0VHjpCscfjJElJl5I0YcMo1WhUTQd+iLTnSzcMObYv8mt/5Ni+yK9jdB3RTscOnNCUvp9Ikn7+5qDcPdz1/BtttHLOZqVeT8s2x8PTXU+//C91G9lOqSnW3++qWMNwdZwjYgeeXh6q1byGtq/cbTH+w1c/qaBvAT3YJDjbHP/yJVW2WmltX7nLYnzbsp0KDCqlwCoBOR7r1Q976WTkaS2f9rV5zMvbU5KUnHjNPJZ44YokybdEoft7UU6GHNsX+bU/cmxf5NcxPL08VLNJNe1Ys89i/MdVe1XQ10c1QoNynFe/VS11HtZWS6au1fwxX1p9vytiDSMvoBCxg1KV/OWV31NnDsdYjMccPStJKlutdLY55aoHSpJOH461GD9zc06ZHOY0f76RqjesqtkDFygjI8M8vvWLnboQk6D/zOyl4gFFFVDBT30md9WFmATt++a3f/binAQ5ti/ya3/k2L7Ir2MEVCiZmfejcRbjMTc/US8TVCrHeYf3Rat7zSFaErE2x5afu93viljDzs3N5Jy33MYpWrNWrlypJ598Ul5eXuaxn376SfPnz9fZs2cVFBSkAQMGqHLlyg6M8t4VLFJAkpT0t08RJCn5SubPBXx9bjsn+ZY5165cvzmnQLY5HQe31cEfo3Rgq+X26MW4S5re/xON+N/rav58I0lSYsJVDXl0rDmG3I4c2xf5tT9ybF/k1zEKFsnM662vMTkrh4W9c5x3IfbSHZ/3bve7ItYw8gKn2BEZPny4rly5Yv5527Zt6tmzp0wmk5o0aaL4+Hi1b99e+/btu8OzOA+3fG53vD8jh2tPu+W786/C9LdPKSQpJLSqqj5cSV9ErMr22Badmuit5UO0c/VeDXt8nMa0naQ/D57UxI2jcvwEJTcix/ZFfu2PHNsX+XUMN7e75TAXfmTrIKxh5AVOUYiYTJb/M82ZM0c9evTQJ598oqFDh+rzzz/XCy+8oIiICAdFaJ2ky8mSpAKFLT+tKHjzk4is+3Oa43PLp0VZn3jcOiesQ6gSE65q97pfsj1Xt7c6KnLHIb334gf6efMB7VyzV8OfeFcp11LVY1yn+3xVzoUc2xf5tT9ybF/k1zGSE2/msNBtcpjIJ+n3ijXs5Ewm57zlMk5RiNzqxIkTeuqppyzGnn/+eUVG5o4rNMQci1P6jXSVvuWksKyfT/5xOtuc04cye0BvPZHsrzlnLMYbtq6rHat259gr61e+pCJ3HrIYS72eqsN7j6tCjTJWvhrnRI7ti/zaHzm2L/LrGDHR8Zl5r+RnMZ7186lDMTlNQw5Yw8gLnKIQcXOz3H6sWLGirl69ajGWkJCgwoULGxnWfUtLSdOBH/5Qk3YNLMbDnm2gq5eSdGj30WxzYo6dVezxOIU9G2o5p31DnT4co7gT58xjhYsVUpmqpfX79kO3Po0k6VTUGdVoZHk1Dc/8ngqqW1Gxx+Pv92U5FXJsX+TX/sixfZFfx0hLuaHfth9W46cfthhv8nQ9Xb2UrEM/RzsostyHNYy8wClOVjeZTGrZsqUqVKigypUry8PDQxMnTtSSJUvk5eWlPXv26J133lHTpk0dHeo9W/zuMk3aPFqjlw7ShgXfKqRRNXUc/LTmDV+klGupKlDYR+VDyijmWJwun0+UJC0c95WGLHhFiQlXtHP1XjVqW1/Nn2+k8S9Ms3juijXLSZJORGb/NESSPhuzVGNXDNHopYO0fv638szvoWdfb6MSgcU1ofN0+75wA5Fj+yK/9keO7Yv8Osb/pqzRhNWDNfKzAdq4cJtCGlRRh9ee0Py3vrqZd2+VCy6t2OPndPnClbs/YR7GGnZeufEKVc7IKXZEtm7dqhkzZqhNmzbKly+fLl68qCNHjig9PXOrsF+/fvLx8dEbb7zh4Ejv3a/fHdQ7HaaqTLXSGrtiqFq+GKZPhn6uL6asliRVqVtRM3a+pwat65rnbPrse33Qb64e/lctvb1iiGo1DdGkbjO19YsdFs9d1L+IJOnqRctdoyw71+zVyNYTVKJ0MY1dPkQDP+6na1ev6z+PDMv2JUa5GTm2L/Jrf+TYvsivY+z/4Q+N7zJLZYICNGbxq2rRsaH+b9QX+mr6eklSlYfK64Mto/XI47UcHKnzYw3D1bmZbj1T3Emkp6fL3d1dknT06FFVrlw5WwvX/XosX0ebPA8AAM7KvRBfOmdP6Vdz/gMetrM5w3m/vLLF45McHUKOvtv4pqNDsIpTtGblJKsIkaQqVao4MBIAAADgb5zyY/zcxylaswAAAADkLRQiAAAAAAzntK1ZAAAAgDNyc85TrHMddkQAAAAAGI5CBAAAAIDhaM0CAAAArJHh6ABcAzsiAAAAAAxHIQIAAADAcLRmAQAAAFbgqlm2wY4IAAAAAMNRiAAAAAAwHK1ZAAAAgDXozLIJdkQAAAAAGI5CBAAAAIDhaM0CAAAArMFVs2yCHREAAAAAhqMQAQAAAGA4WrMAAAAAK7jRmWUT7IgAAAAAMByFCAAAAADD0ZoFAAAAWIOrZtkEOyIAAAAADEchAgAAAMBwtGYBAAAAVnDLcHQEroEdEQAAAACGoxABAAAAYDhaswAAAABrcNUsm2BHBAAAAIDhKEQAAAAAGI7WLAAAAMAadGbZBDsiAAAAAAxHIQIAAADAcLRmAQAAAFZw46pZNsGOCAAAAADDUYgAAAAAMBytWQAAAIA1aM2yCXZEAAAAABiOQgQAAACA4WjNAgAAAKyR4egAXAM7IgAAAAAMRyECAAAAwHC0ZgEAAABW4AsNbYMdEQAAAACGoxABAAAAYDhaswAAAABr0JplE+yIAAAAADAchQgAAACQx2VkZGjGjBkKCwtT7dq11adPH506deq2jz9y5Ij69u2rBg0aKDQ0VOHh4YqJibHqmBQiAAAAgDVMJue8/QOzZ8/W4sWLNW7cOC1ZskQZGRnq3bu3UlNTsz324sWL6tmzp7y9vfX555/rk08+UUJCgnr37q2UlJR7PibniAAA4ILSr151dAgAconU1FTNnz9fgwcPVvPmzSVJ06ZNU1hYmDZt2qQ2bdpYPP6bb75RcnKyJk+eLG9vb0nSlClT1Lx5c+3bt0+hoaH3dFx2RAAAAIA8LCoqSklJSRYFhK+vr0JCQrRnz55sjw8NDdXs2bPNRYgk5cuXWVYkJibe83HZEQEAAACskeHoAHLWsmXLO96/ZcuWHMfPnj0rSSpVqpTFuJ+fn/m+vytTpozKlCljMfbxxx/L29tb9evXv+d42REBAAAA8rBr165Jkry8vCzG8+fPf0/nfHz++edauHChBg8erOLFi9/zcdkRAQAAAFzA7XY87iarxSo1NdWi3SolJUU+Pj63nWcymTR9+nTNmTNH/fv3V9euXa06LoUIAAAAYAU3F/tCw6yWrPj4eJUrV848Hh8fr2rVquU4Jy0tTcOHD9fatWs1fPhw9ejRw+rj0poFAAAA5GHBwcEqVKiQdu3aZR5LTExUZGTkbc/5GDp0qDZs2KCpU6feVxEisSMCAAAA5GleXl7q0qWLIiIiVLx4cQUGBmrKlCkKCAhQq1atlJ6eroSEBBUuXFje3t5avny51q1bp6FDh+qRRx7RuXPnzM+V9Zh7wY4IAAAAYA1Hf3GhHb7QMDw8XB06dNCoUaPUqVMnubu7a968efL09FRsbKyaNGmidevWSZLWrl0rSZo8ebKaNGlicct6zL1wM5lcrMntHjyWr6OjQwAAAMAdbM740tEh3NYTD412dAg52rB/nKNDsAo7IgAAAAAMxzkiAAAAgDXyXkORXbAjAgAAAMBwFCIAAAAADEdrFgAAAGANWrNsgh0RAAAAAIajEAEAAABgOFqzAAAAAGtkODoA18COCAAAAADDUYgAAAAAMBytWQAAAIAV3Lhqlk2wIwIAAADAcBQiAAAAAAxHaxYAAABgDVqzbIIdEQAAAACGoxABAAAAYDhaswAAAABrZNCaZQvsiAAAAAAwHIUIAAAAAMPRmgUAAABYg6tm2QQ7IgAAAAAMRyECAAAAwHC0ZgEAAADWoDXLJtgRAQAAAGA4ChEAAAAAhqM1CwAAALAGrVk2wY4IAAAAAMNRiAAAAAAwHK1ZAAAAgDUyaM2yBXZEAAAAABiOQgQAAACA4WjNAgAAAKxhynB0BC6BHREAAAAAhqMQAQAAAGA4WrMAAAAAa/CFhjbBjggAAAAAw1GIAAAAADAcrVkAAACANfhCQ5ugELGjhx+rpZ7jO6l8jbK6GHdZq2dv0FdT19xxTosXGuvFkc+qVCV/xf0ZryWTVmrzf7dKkvzLl9TC6Nm3nbtxwXeK6JV5f4UHy6nPpC4KbhCktJQ0/bxpvz55c6EuxV+23Qt0AuTYvsiv/ZFj+yK/9keO7Yv8wpVRiNhJ9QZBGrdmuLYu3aFPxyzVg02C1WdSF7l7uGvppJU5zmnSvoGGLQzXihnrtHfDr2r0zCMa+ul/lJaSpu+X7lBC7EWFh47INu/pAU+o2fONtH7+t5Kkon5FNGXLWzp36rwies6Sl4+Xek/srPfWjdCrDUco/Ua6PV+6YcixfZFf+yPH9kV+7Y8c2xf5haujELGTbmOf09FfojWp+0xJ0t6Nv8rD012dhrfTiunrlHo9Nducl959UT98+ZM+GvRZ5pxN+1W4WCH1eOcFfb90h9JSb+iPXUcs5gTVraRmzzfS/JGL9fv2KElSo7b1VbSkr8JDRyj2eJwkKelSkiZsGKUajarpwA+R9nzphiHH9kV+7Y8c2xf5tT9ybF/k14lx1Syb4GR1O/D08lCt5jW0feVui/EfvvpJBX0L6MEmwdnm+JcvqbLVSmv7yl0W49uW7VRgUCkFVgnI8VivfthLJyNPa/m0r81jXt6ekqTkxGvmscQLVyRJviUK3d+LcjLk2L7Ir/2RY/siv/ZHju2L/CIvoBCxg1KV/OWV31NnDsdYjMccPStJKlutdLY55aoHSpJOH461GD9zc06ZHOY0f76RqjesqtkDFygjI8M8vvWLnboQk6D/zOyl4gFFFVDBT30md9WFmATt++a3f/binAQ5ti/ya3/k2L7Ir/2RY/siv8gLnKY1a//+/dq1a5f69u0rSfrpp5/06aef6vTp0ypXrpxeeukl1atXz8FR3puCRQpIkpL+9imCJCVfyfy5gK/Pbeck3zLn2pXrN+cUyDan4+C2OvhjlA5stdwevRh3SdP7f6IR/3tdzZ9vJElKTLiqIY+ONceQ25Fj+yK/9keO7Yv82h85ti/y6+RozbIJp9gR2bBhgzp16qTduzO3H7/77jv17NlTJpNJzZo1U1pamrp3767vvvvOwZHeG7d8bne8PyOHS7655bvzr8L0t08pJCkktKqqPlxJX0SsyvbYFp2a6K3lQ7Rz9V4Ne3ycxrSdpD8PntTEjaNy/AQlNyLH9kV+7Y8c2xf5tT9ybF/kF3mBU+yIfPjhhwoPD1e/fv0kSXPmzFG/fv302muvmR8zZ84czZgxQy1atHBUmPcs6XKyJKlAYctPKwre/CQi6/6c5vgU9rYYz/rE49Y5YR1ClZhwVbvX/ZLtubq91VGROw7pvRc/MI/9vPmA5kVOU49xnTTuualWviLnQ47ti/zaHzm2L/Jrf+TYvsgv8gKn2BE5efKkWrdubf759OnTevzxxy0e06ZNGx07dszo0O5LzLE4pd9IV+lbTgrL+vnkH6ezzTl9KLMH9NYTyf6ac8ZivGHrutqxaneOl8/zK19SkTsPWYylXk/V4b3HVaFGGStfjXMix/ZFfu2PHNsX+bU/cmxf5NfJmUzOectlnKIQKVu2rLZv327+uXr16oqKirJ4zIEDB+Tv7290aPclLSVNB374Q03aNbAYD3u2ga5eStKh3UezzYk5dlaxx+MU9myo5Zz2DXX6cIziTpwzjxUuVkhlqpbW79sP3fo0kqRTUWdUo5Hl1TQ883sqqG5FxR6Pv9+X5VTIsX2RX/sjx/ZFfu2PHNsX+UVe4BStWX369NGoUaN0+vRptWnTRgMGDNCwYcOUkpKioKAg7d+/X7NmzdJ//vMfR4d6zxa/u0yTNo/W6KWDtGHBtwppVE0dBz+tecMXKeVaqgoU9lH5kDKKORany+cTJUkLx32lIQteUWLCFe1cvVeN2tZX8+cbafwL0yyeu2LNcpKkE5HZPw2RpM/GLNXYFUM0eukgrZ//rTzze+jZ19uoRGBxTeg83b4v3EDk2L7Ir/2RY/siv/ZHju2L/MLVuZlMzrGPs2rVKs2YMUNnzpyRm5ub/h5WwYIF1bt3b/Xv398mx3osX0ebPM/dNH7mEXUb+5zKVCutC2cStHr2Bn31/lpJUq1mIZr63dua0nOWNn32vXlO677/Usc3nlbJsiUUezxeSyau0DcLf7B43qYdQzV66SC9VP01nTpkeVm/LPUer60uo55VlbqVlHzlmg7tPqr5Ixcr+reTdnu9jkCO7Yv82h85ti/ya3/k2L7ycn43Z3xpyHHux79LveLoEHK0PnaWo0OwitMUIlmio6MVHR2tq1evysPDQwEBAapRo4by589vs2MYVYgAAADg/lCIWC+3FSJO0Zr1dxUrVlTFihUdHQYAAAAAO3K6QgQAAABwas7VUJRrOcVVswAAAADkLRQiAAAAAAxHaxYAAABgDVqzbIIdEQAAAACGoxABAAAAYDhaswAAAABrZNCaZQvsiAAAAAAwHIUIAAAAAMPRmgUAAABYwWTKcHQILoEdEQAAAACGoxABAAAAYDhaswAAAABrcNUsm2BHBAAAAIDhKEQAAAAAGI7WLAAAAMAaJlqzbIEdEQAAAACGoxABAAAAYDhaswAAAABrZPCFhrbAjggAAAAAw1GIAAAAADAcrVkAAACANbhqlk2wIwIAAADAcBQiAAAAQB6XkZGhGTNmKCwsTLVr11afPn106tSpe5rXu3dvzZw50+pjUogAAAAAVjBlZDjl7Z+YPXu2Fi9erHHjxmnJkiXmAiM1NfW2c1JTUzVixAht27btvo5JIQIAAADkYampqZo/f77Cw8PVvHlzBQcHa9q0aTp79qw2bdqU45x9+/apffv22rt3r3x9fe/ruBQiAAAAQB4WFRWlpKQkhYaGmsd8fX0VEhKiPXv25Dhn69atCgsL08qVK1W4cOH7Oi5XzQIAAACs4WJXzTp79qwkqVSpUhbjfn5+5vtuNXDgwH98XAoRAAAAwAW0bNnyjvdv2bIlx/Fr165Jkry8vCzG8+fPr8uXL9smuBzQmgUAAADkYd7e3pKU7cT0lJQU+fj42O247IgAAAAA1shwztas2+143E1WS1Z8fLzKlStnHo+Pj1e1atVsEltO2BEBAAAA8rDg4GAVKlRIu3btMo8lJiYqMjJS9evXt9tx2REBAAAA8jAvLy916dJFERERKl68uAIDAzVlyhQFBASoVatWSk9PV0JCggoXLmxu47IFChEAAADAGqZ/9uWBzig8PFw3btzQqFGjdP36ddWvX1/z5s2Tp6enTp8+rZYtW2rChAlq3769zY7pZjK52PXH7sFj+To6OgQAAADcweaMLx0dwm097t3Z0SHkaOP1RY4OwSqcIwIAAADAcLRmAQAAAFYwOelVs3IbdkQAAAAAGI5CBAAAAIDhaM0CAAAArOGCV81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+ "text/plain": [ + "
      " + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "# visual representation of grid search\n", "# uses seaborn heatmap, you can also do this with matplotlib imshow\n", @@ -2670,7 +2748,602 @@ "collapsed": false, "editable": true }, - "outputs": [], + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/mhjensen/miniforge3/envs/myenv/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: 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:691: 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:691: 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:691: 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:691: 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:691: 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:691: 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:691: 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:691: 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:691: 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:691: 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:691: 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:691: 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:691: 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:691: 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:691: 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:691: 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:691: 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:691: 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:691: 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:691: 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:691: 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" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Learning rate = 0.01\n", + "Lambda = 1.0\n", + "Accuracy score on test set: 0.9722222222222222\n", + "\n", + "Learning rate = 0.01\n", + "Lambda = 10.0\n", + "Accuracy score on test set: 0.9527777777777777\n", + "\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Learning rate = 0.1\n", + "Lambda = 1e-05\n", + "Accuracy score on test set: 0.9027777777777778\n", + "\n", + "Learning rate = 0.1\n", + "Lambda = 0.0001\n", + "Accuracy score on test set: 0.8583333333333333\n", + "\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "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" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "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" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Learning rate = 1.0\n", + "Lambda = 0.0001\n", + "Accuracy score on test set: 0.10555555555555556\n", + "\n", + "Learning rate = 1.0\n", + "Lambda = 0.001\n", + "Accuracy score on test set: 0.10555555555555556\n", + "\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "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", + "Learning rate = 1.0\n", + "Lambda = 1.0\n", + "Accuracy score on test set: 0.08888888888888889\n", + "\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "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" + ] + } + ], "source": [ "from sklearn.neural_network import MLPClassifier\n", "# store models for later use\n", @@ -2708,7 +3381,28 @@ "collapsed": false, "editable": true }, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
      " + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "# optional\n", "# visual representation of grid search\n", @@ -2806,7 +3500,16 @@ "collapsed": false, "editable": true }, - "outputs": [], + "outputs": [ + { + "ename": "SyntaxError", + "evalue": "invalid syntax (2357089093.py, line 1)", + "output_type": "error", + "traceback": [ + "\u001b[0;36m Cell \u001b[0;32mIn[12], line 1\u001b[0;36m\u001b[0m\n\u001b[0;31m pip3 install tensorflow\u001b[0m\n\u001b[0m ^\u001b[0m\n\u001b[0;31mSyntaxError\u001b[0m\u001b[0;31m:\u001b[0m invalid syntax\n" + ] + } + ], "source": [ "pip3 install tensorflow" ] diff --git a/doc/LectureNotes/_build/jupyter_execute/week44.ipynb b/doc/LectureNotes/_build/jupyter_execute/week44.ipynb index 883834bcf..ec4957341 100644 --- a/doc/LectureNotes/_build/jupyter_execute/week44.ipynb +++ b/doc/LectureNotes/_build/jupyter_execute/week44.ipynb @@ -2238,7 +2238,7 @@ "output_type": "stream", "text": [ "\r", - "\u001b[1m 1/12\u001b[0m \u001b[32m━\u001b[0m\u001b[37m━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[1m1s\u001b[0m 131ms/step - accuracy: 0.0938 - loss: 3.2865" + "\u001b[1m 1/12\u001b[0m \u001b[32m━\u001b[0m\u001b[37m━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[1m1s\u001b[0m 176ms/step - accuracy: 0.0625 - loss: 2.4493" ] }, { @@ -2246,7 +2246,7 @@ "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", - "\u001b[1m 9/12\u001b[0m \u001b[32m━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━━━━━\u001b[0m \u001b[1m0s\u001b[0m 7ms/step - accuracy: 0.1090 - loss: 3.1259 " + "\u001b[1m 8/12\u001b[0m \u001b[32m━━━━━━━━━━━━━\u001b[0m\u001b[37m━━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 8ms/step - accuracy: 0.1149 - loss: 2.4557 " ] }, { @@ -2254,7 +2254,15 @@ "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", - "\u001b[1m12/12\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 9ms/step - accuracy: 0.1140 - loss: 3.1040\n" + "\u001b[1m12/12\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 31ms/step - accuracy: 0.1176 - loss: 2.4545" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", + "\u001b[1m12/12\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 32ms/step - accuracy: 0.1184 - loss: 2.4540\n" ] }, { @@ -2263,49 +2271,7 @@ "text": [ "Learning rate = 1e-05\n", "Lambda = 1e-05\n", - "Test accuracy: 0.125\n", - "\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\r", - "\u001b[1m 1/12\u001b[0m \u001b[32m━\u001b[0m\u001b[37m━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[1m1s\u001b[0m 133ms/step - accuracy: 0.2812 - loss: 2.6529" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", - "\u001b[1m 8/12\u001b[0m \u001b[32m━━━━━━━━━━━━━\u001b[0m\u001b[37m━━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 7ms/step - accuracy: 0.2350 - loss: 2.5504 " - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", - "\u001b[1m12/12\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 9ms/step - accuracy: 0.2247 - loss: 2.5413" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", - "\u001b[1m12/12\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 10ms/step - accuracy: 0.2230 - loss: 2.5413\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Learning rate = 1e-05\n", - "Lambda = 0.0001\n", - "Test accuracy: 0.203\n", + "Test accuracy: 0.128\n", "\n" ] }, diff --git a/doc/LectureNotes/_build/jupyter_execute/week45.ipynb b/doc/LectureNotes/_build/jupyter_execute/week45.ipynb index 82d86bf78..d312eceb7 100644 --- a/doc/LectureNotes/_build/jupyter_execute/week45.ipynb +++ b/doc/LectureNotes/_build/jupyter_execute/week45.ipynb @@ -662,7 +662,7 @@ "output_type": "stream", "text": [ "\r", - "\u001b[1m 1/12\u001b[0m \u001b[32m━\u001b[0m\u001b[37m━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[1m2s\u001b[0m 214ms/step - accuracy: 0.1250 - loss: 3.1816" + "\u001b[1m 1/12\u001b[0m \u001b[32m━\u001b[0m\u001b[37m━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[1m2s\u001b[0m 203ms/step - accuracy: 0.2812 - loss: 2.7253" ] }, { @@ -670,7 +670,7 @@ "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", - "\u001b[1m 8/12\u001b[0m \u001b[32m━━━━━━━━━━━━━\u001b[0m\u001b[37m━━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 7ms/step - accuracy: 0.1078 - loss: 3.0683 " + "\u001b[1m 9/12\u001b[0m \u001b[32m━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━━━━━\u001b[0m \u001b[1m0s\u001b[0m 7ms/step - accuracy: 0.1957 - loss: 2.8906 " ] }, { @@ -678,15 +678,7 @@ "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", - "\u001b[1m12/12\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 10ms/step - accuracy: 0.1005 - loss: 3.0830" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", - "\u001b[1m12/12\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 10ms/step - accuracy: 0.0998 - loss: 3.0851\n" + "\u001b[1m12/12\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 9ms/step - accuracy: 0.1826 - loss: 2.9261\n" ] }, { @@ -695,7 +687,7 @@ "text": [ "Learning rate = 1e-05\n", "Lambda = 1e-05\n", - "Test accuracy: 0.092\n", + "Test accuracy: 0.150\n", "\n" ] }, @@ -704,7 +696,7 @@ "output_type": "stream", "text": [ "\r", - "\u001b[1m 1/12\u001b[0m \u001b[32m━\u001b[0m\u001b[37m━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[1m1s\u001b[0m 128ms/step - accuracy: 0.0312 - loss: 3.1505" + "\u001b[1m 1/12\u001b[0m \u001b[32m━\u001b[0m\u001b[37m━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[1m1s\u001b[0m 126ms/step - accuracy: 0.0938 - loss: 2.8780" ] }, { @@ -712,7 +704,7 @@ "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", - "\u001b[1m 8/12\u001b[0m \u001b[32m━━━━━━━━━━━━━\u001b[0m\u001b[37m━━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 7ms/step - accuracy: 0.0554 - loss: 3.1156 " + "\u001b[1m 9/12\u001b[0m \u001b[32m━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━━━━━\u001b[0m \u001b[1m0s\u001b[0m 7ms/step - accuracy: 0.1082 - loss: 2.8294 " ] }, { @@ -720,7 +712,7 @@ "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", - "\u001b[1m12/12\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 9ms/step - accuracy: 0.0560 - loss: 3.1184\n" + "\u001b[1m12/12\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 9ms/step - accuracy: 0.1134 - loss: 2.8359\n" ] }, { @@ -729,7 +721,7 @@ "text": [ "Learning rate = 1e-05\n", "Lambda = 0.0001\n", - "Test accuracy: 0.058\n", + "Test accuracy: 0.125\n", "\n" ] }, diff --git a/doc/LectureNotes/_build/jupyter_execute/week46.ipynb b/doc/LectureNotes/_build/jupyter_execute/week46.ipynb index 60cc17fbf..17c76e132 100644 --- a/doc/LectureNotes/_build/jupyter_execute/week46.ipynb +++ b/doc/LectureNotes/_build/jupyter_execute/week46.ipynb @@ -170,14 +170,14 @@ "output_type": "stream", "text": [ "2nd degree coefficients:\n", - "zero power: 1.391131752817723\n", - "first power: 0.023929544236763082\n", - "second power: -0.00011233101388608391\n" + "zero power: -9.690066070734556\n", + "first power: 0.2579633987876525\n", + "second power: -0.0008519856133366335\n" ] }, { "data": { - "image/png": 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", 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", 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", + "image/png": 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", "text/plain": [ "
      " ] @@ -2397,7 +2397,13 @@ "name": "stdout", "output_type": "stream", "text": [ - "LogisticRegression 0.864\n", + "LogisticRegression 0.864\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ "RandomForestClassifier 0.896\n", "SVC 0.896\n" ] @@ -2494,7 +2500,13 @@ "Error: 0.06380941468319971\n", "Bias^2: 0.05160313473529168\n", "Var: 0.01220627994790804\n", - "0.06380941468319971 >= 0.05160313473529168 + 0.01220627994790804 = 0.06380941468319971\n", + "0.06380941468319971 >= 0.05160313473529168 + 0.01220627994790804 = 0.06380941468319971\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ "Polynomial degree: 2\n", "Error: 0.043464037468677004\n", "Bias^2: 0.02659851591375224\n", @@ -2504,13 +2516,7 @@ "Error: 0.020716391693769383\n", "Bias^2: 0.01159033914386312\n", "Var: 0.00912605254990626\n", - "0.020716391693769383 >= 0.01159033914386312 + 0.00912605254990626 = 0.02071639169376938\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ + "0.020716391693769383 >= 0.01159033914386312 + 0.00912605254990626 = 0.02071639169376938\n", "Polynomial degree: 4\n", "Error: 0.02063627410934057\n", "Bias^2: 0.0117496656370668\n", @@ -2520,7 +2526,13 @@ "Error: 0.02087627881701288\n", "Bias^2: 0.01349183949256158\n", "Var: 0.007384439324451296\n", - "0.02087627881701288 >= 0.01349183949256158 + 0.007384439324451296 = 0.020876278817012876\n", + "0.02087627881701288 >= 0.01349183949256158 + 0.007384439324451296 = 0.020876278817012876\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ "Polynomial degree: 6\n", "Error: 0.02069601123831537\n", "Bias^2: 0.013918526350129823\n", @@ -2704,13 +2716,7 @@ "text": [ "(426, 30)\n", "(143, 30)\n", - "Test set accuracy Logistic Regression with scaled data: 0.96\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ + "Test set accuracy Logistic Regression with scaled data: 0.96\n", "Test set accuracy SVM with scaled data: 0.96\n", "Test set accuracy with Decision Trees and scaled data: 0.87\n" ] diff --git a/doc/LectureNotes/_build/jupyter_execute/week47.ipynb b/doc/LectureNotes/_build/jupyter_execute/week47.ipynb index a530aacad..5be210680 100644 --- a/doc/LectureNotes/_build/jupyter_execute/week47.ipynb +++ b/doc/LectureNotes/_build/jupyter_execute/week47.ipynb @@ -2333,13 +2333,7 @@ "output_type": "stream", "text": [ "RandomForestClassifier 0.896\n", - "SVC 0.896\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ + "SVC 0.896\n", "VotingClassifier 0.92\n" ] } @@ -2447,18 +2441,18 @@ "Error: 0.020716391693769383\n", "Bias^2: 0.01159033914386312\n", "Var: 0.00912605254990626\n", - "0.020716391693769383 >= 0.01159033914386312 + 0.00912605254990626 = 0.02071639169376938\n" + "0.020716391693769383 >= 0.01159033914386312 + 0.00912605254990626 = 0.02071639169376938\n", + "Polynomial degree: 4\n", + "Error: 0.02063627410934057\n", + "Bias^2: 0.0117496656370668\n", + "Var: 0.008886608472273775\n", + "0.02063627410934057 >= 0.0117496656370668 + 0.008886608472273775 = 0.020636274109340574\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "Polynomial degree: 4\n", - "Error: 0.02063627410934057\n", - "Bias^2: 0.0117496656370668\n", - "Var: 0.008886608472273775\n", - "0.02063627410934057 >= 0.0117496656370668 + 0.008886608472273775 = 0.020636274109340574\n", "Polynomial degree: 5\n", "Error: 0.02087627881701288\n", "Bias^2: 0.01349183949256158\n", @@ -4226,14 +4220,14 @@ "name": "stdout", "output_type": "stream", "text": [ - "[15:08:02] WARNING: /var/folders/nz/j6p8yfhx1mv_0grj5xl4650h0000gp/T/abs_21wtzqx5vy/croot/xgboost-split_1675457780668/work/src/learner.cc:767: \n", + "[08:11:29] WARNING: /var/folders/nz/j6p8yfhx1mv_0grj5xl4650h0000gp/T/abs_21wtzqx5vy/croot/xgboost-split_1675457780668/work/src/learner.cc:767: \n", "Parameters: { \"colsaobjective\" } are not used.\n", "\n" ] }, { "ename": "XGBoostError", - "evalue": "[15:08:02] /var/folders/nz/j6p8yfhx1mv_0grj5xl4650h0000gp/T/abs_21wtzqx5vy/croot/xgboost-split_1675457780668/work/src/tree/updater_colmaker.cc:177: Check failed: param_.max_depth > 0 (0 vs. 0) : exact tree method doesn't support unlimited depth.\nStack trace:\n [bt] (0) 1 libxgboost.dylib 0x0000000156871b90 dmlc::LogMessageFatal::~LogMessageFatal() + 124\n [bt] (1) 2 libxgboost.dylib 0x0000000156a67f1c xgboost::tree::ColMaker::Builder::Update(std::__1::vector, std::__1::allocator>> const&, xgboost::DMatrix*, xgboost::RegTree*) + 1092\n [bt] (2) 3 libxgboost.dylib 0x0000000156a66d88 xgboost::tree::ColMaker::Update(xgboost::HostDeviceVector>*, xgboost::DMatrix*, xgboost::common::Span, 18446744073709551615ul>, std::__1::vector> const&) + 412\n [bt] (3) 4 libxgboost.dylib 0x000000015696274c xgboost::gbm::GBTree::BoostNewTrees(xgboost::HostDeviceVector>*, xgboost::DMatrix*, int, std::__1::vector>, std::__1::allocator>>>*) + 1944\n [bt] (4) 5 libxgboost.dylib 0x000000015695c7bc xgboost::gbm::GBTree::DoBoost(xgboost::DMatrix*, xgboost::HostDeviceVector>*, xgboost::PredictionCacheEntry*, xgboost::ObjFunction const*) + 472\n [bt] (5) 6 libxgboost.dylib 0x000000015697b35c xgboost::LearnerImpl::UpdateOneIter(int, std::__1::shared_ptr) + 708\n [bt] (6) 7 libxgboost.dylib 0x000000015688b908 XGBoosterUpdateOneIter + 160\n [bt] (7) 8 libffi.8.dylib 0x000000010142404c ffi_call_SYSV + 76\n [bt] (8) 9 libffi.8.dylib 0x0000000101421834 ffi_call_int + 1404\n\n", + "evalue": "[08:11:29] /var/folders/nz/j6p8yfhx1mv_0grj5xl4650h0000gp/T/abs_21wtzqx5vy/croot/xgboost-split_1675457780668/work/src/tree/updater_colmaker.cc:177: Check failed: param_.max_depth > 0 (0 vs. 0) : exact tree method doesn't support unlimited depth.\nStack trace:\n [bt] (0) 1 libxgboost.dylib 0x0000000162809b90 dmlc::LogMessageFatal::~LogMessageFatal() + 124\n [bt] (1) 2 libxgboost.dylib 0x00000001629fff1c xgboost::tree::ColMaker::Builder::Update(std::__1::vector, std::__1::allocator>> const&, xgboost::DMatrix*, xgboost::RegTree*) + 1092\n [bt] (2) 3 libxgboost.dylib 0x00000001629fed88 xgboost::tree::ColMaker::Update(xgboost::HostDeviceVector>*, xgboost::DMatrix*, xgboost::common::Span, 18446744073709551615ul>, std::__1::vector> const&) + 412\n [bt] (3) 4 libxgboost.dylib 0x00000001628fa74c xgboost::gbm::GBTree::BoostNewTrees(xgboost::HostDeviceVector>*, xgboost::DMatrix*, int, std::__1::vector>, std::__1::allocator>>>*) + 1944\n [bt] (4) 5 libxgboost.dylib 0x00000001628f47bc xgboost::gbm::GBTree::DoBoost(xgboost::DMatrix*, xgboost::HostDeviceVector>*, xgboost::PredictionCacheEntry*, xgboost::ObjFunction const*) + 472\n [bt] (5) 6 libxgboost.dylib 0x000000016291335c xgboost::LearnerImpl::UpdateOneIter(int, std::__1::shared_ptr) + 708\n [bt] (6) 7 libxgboost.dylib 0x0000000162823908 XGBoosterUpdateOneIter + 160\n [bt] (7) 8 libffi.8.dylib 0x00000001053bc04c ffi_call_SYSV + 76\n [bt] (8) 9 libffi.8.dylib 0x00000001053b9834 ffi_call_int + 1404\n\n", "output_type": "error", "traceback": [ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", @@ -4245,7 +4239,7 @@ "File \u001b[0;32m~/miniforge3/envs/myenv/lib/python3.9/site-packages/xgboost/training.py:185\u001b[0m, in \u001b[0;36mtrain\u001b[0;34m(params, dtrain, num_boost_round, evals, obj, feval, maximize, early_stopping_rounds, evals_result, verbose_eval, xgb_model, callbacks, custom_metric)\u001b[0m\n\u001b[1;32m 183\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m cb_container\u001b[38;5;241m.\u001b[39mbefore_iteration(bst, i, dtrain, evals):\n\u001b[1;32m 184\u001b[0m \u001b[38;5;28;01mbreak\u001b[39;00m\n\u001b[0;32m--> 185\u001b[0m \u001b[43mbst\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mupdate\u001b[49m\u001b[43m(\u001b[49m\u001b[43mdtrain\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mi\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mobj\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 186\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m cb_container\u001b[38;5;241m.\u001b[39mafter_iteration(bst, i, dtrain, evals):\n\u001b[1;32m 187\u001b[0m \u001b[38;5;28;01mbreak\u001b[39;00m\n", "File \u001b[0;32m~/miniforge3/envs/myenv/lib/python3.9/site-packages/xgboost/core.py:1918\u001b[0m, in \u001b[0;36mBooster.update\u001b[0;34m(self, dtrain, iteration, fobj)\u001b[0m\n\u001b[1;32m 1915\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_validate_dmatrix_features(dtrain)\n\u001b[1;32m 1917\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m fobj \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[0;32m-> 1918\u001b[0m \u001b[43m_check_call\u001b[49m\u001b[43m(\u001b[49m\u001b[43m_LIB\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mXGBoosterUpdateOneIter\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mhandle\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 1919\u001b[0m \u001b[43m \u001b[49m\u001b[43mctypes\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mc_int\u001b[49m\u001b[43m(\u001b[49m\u001b[43miteration\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 1920\u001b[0m \u001b[43m \u001b[49m\u001b[43mdtrain\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mhandle\u001b[49m\u001b[43m)\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 1921\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m 1922\u001b[0m pred \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mpredict(dtrain, output_margin\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mTrue\u001b[39;00m, training\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mTrue\u001b[39;00m)\n", "File \u001b[0;32m~/miniforge3/envs/myenv/lib/python3.9/site-packages/xgboost/core.py:279\u001b[0m, in \u001b[0;36m_check_call\u001b[0;34m(ret)\u001b[0m\n\u001b[1;32m 268\u001b[0m \u001b[38;5;250m\u001b[39m\u001b[38;5;124;03m\"\"\"Check the return value of C API call\u001b[39;00m\n\u001b[1;32m 269\u001b[0m \n\u001b[1;32m 270\u001b[0m \u001b[38;5;124;03mThis function will raise exception when error occurs.\u001b[39;00m\n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 276\u001b[0m \u001b[38;5;124;03m return value from API calls\u001b[39;00m\n\u001b[1;32m 277\u001b[0m \u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m 278\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m ret \u001b[38;5;241m!=\u001b[39m \u001b[38;5;241m0\u001b[39m:\n\u001b[0;32m--> 279\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m XGBoostError(py_str(_LIB\u001b[38;5;241m.\u001b[39mXGBGetLastError()))\n", - "\u001b[0;31mXGBoostError\u001b[0m: [15:08:02] /var/folders/nz/j6p8yfhx1mv_0grj5xl4650h0000gp/T/abs_21wtzqx5vy/croot/xgboost-split_1675457780668/work/src/tree/updater_colmaker.cc:177: Check failed: param_.max_depth > 0 (0 vs. 0) : exact tree method doesn't support unlimited depth.\nStack trace:\n [bt] (0) 1 libxgboost.dylib 0x0000000156871b90 dmlc::LogMessageFatal::~LogMessageFatal() + 124\n [bt] (1) 2 libxgboost.dylib 0x0000000156a67f1c xgboost::tree::ColMaker::Builder::Update(std::__1::vector, std::__1::allocator>> const&, xgboost::DMatrix*, xgboost::RegTree*) + 1092\n [bt] (2) 3 libxgboost.dylib 0x0000000156a66d88 xgboost::tree::ColMaker::Update(xgboost::HostDeviceVector>*, xgboost::DMatrix*, xgboost::common::Span, 18446744073709551615ul>, std::__1::vector> const&) + 412\n [bt] (3) 4 libxgboost.dylib 0x000000015696274c xgboost::gbm::GBTree::BoostNewTrees(xgboost::HostDeviceVector>*, xgboost::DMatrix*, int, std::__1::vector>, std::__1::allocator>>>*) + 1944\n [bt] (4) 5 libxgboost.dylib 0x000000015695c7bc xgboost::gbm::GBTree::DoBoost(xgboost::DMatrix*, xgboost::HostDeviceVector>*, xgboost::PredictionCacheEntry*, xgboost::ObjFunction const*) + 472\n [bt] (5) 6 libxgboost.dylib 0x000000015697b35c xgboost::LearnerImpl::UpdateOneIter(int, std::__1::shared_ptr) + 708\n [bt] (6) 7 libxgboost.dylib 0x000000015688b908 XGBoosterUpdateOneIter + 160\n [bt] (7) 8 libffi.8.dylib 0x000000010142404c ffi_call_SYSV + 76\n [bt] (8) 9 libffi.8.dylib 0x0000000101421834 ffi_call_int + 1404\n\n" + "\u001b[0;31mXGBoostError\u001b[0m: [08:11:29] /var/folders/nz/j6p8yfhx1mv_0grj5xl4650h0000gp/T/abs_21wtzqx5vy/croot/xgboost-split_1675457780668/work/src/tree/updater_colmaker.cc:177: Check failed: param_.max_depth > 0 (0 vs. 0) : exact tree method doesn't support unlimited depth.\nStack trace:\n [bt] (0) 1 libxgboost.dylib 0x0000000162809b90 dmlc::LogMessageFatal::~LogMessageFatal() + 124\n [bt] (1) 2 libxgboost.dylib 0x00000001629fff1c xgboost::tree::ColMaker::Builder::Update(std::__1::vector, std::__1::allocator>> const&, xgboost::DMatrix*, xgboost::RegTree*) + 1092\n [bt] (2) 3 libxgboost.dylib 0x00000001629fed88 xgboost::tree::ColMaker::Update(xgboost::HostDeviceVector>*, xgboost::DMatrix*, xgboost::common::Span, 18446744073709551615ul>, std::__1::vector> const&) + 412\n [bt] (3) 4 libxgboost.dylib 0x00000001628fa74c xgboost::gbm::GBTree::BoostNewTrees(xgboost::HostDeviceVector>*, xgboost::DMatrix*, int, std::__1::vector>, std::__1::allocator>>>*) + 1944\n [bt] (4) 5 libxgboost.dylib 0x00000001628f47bc xgboost::gbm::GBTree::DoBoost(xgboost::DMatrix*, xgboost::HostDeviceVector>*, xgboost::PredictionCacheEntry*, xgboost::ObjFunction const*) + 472\n [bt] (5) 6 libxgboost.dylib 0x000000016291335c xgboost::LearnerImpl::UpdateOneIter(int, std::__1::shared_ptr) + 708\n [bt] (6) 7 libxgboost.dylib 0x0000000162823908 XGBoosterUpdateOneIter + 160\n [bt] (7) 8 libffi.8.dylib 0x00000001053bc04c ffi_call_SYSV + 76\n [bt] (8) 9 libffi.8.dylib 0x00000001053b9834 ffi_call_int + 1404\n\n" ] } ], diff --git a/doc/LectureNotes/_build/jupyter_execute/week48.ipynb b/doc/LectureNotes/_build/jupyter_execute/week48.ipynb new file mode 100644 index 000000000..5b915bbfe --- /dev/null +++ b/doc/LectureNotes/_build/jupyter_execute/week48.ipynb @@ -0,0 +1,3106 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "d7c532d5", + "metadata": { + "editable": true + }, + "source": [ + "\n", + "" + ] + }, + { + "cell_type": "markdown", + "id": "d27ab2b4", + "metadata": { + "editable": true + }, + "source": [ + "# Week 48: Gradient boosting and summary of course\n", + "**Morten Hjorth-Jensen**, Department of Physics and Center for Computing in Science Education, University of Oslo, Norway\n", + "\n", + "Date: **Nov 24, 2024**\n", + "\n", + "Copyright 1999-2024, Morten Hjorth-Jensen. Released under CC Attribution-NonCommercial 4.0 license" + ] + }, + { + "cell_type": "markdown", + "id": "9f55b05a", + "metadata": { + "editable": true + }, + "source": [ + "## Overview of week 48" + ] + }, + { + "cell_type": "markdown", + "id": "e72578f8", + "metadata": { + "editable": true + }, + "source": [ + "## Lecture Monday, November 25\n", + "**Plans for the lecture Monday 25 November, with video suggestions etc.**\n", + "\n", + "1. Boosting and gradient boosting and ensemble models\n", + "\n", + "2. Summary of course\n", + "\n", + "3. Readings and Videos:\n", + "\n", + "a. These lecture notes at \n", + "\n", + "b. See also lecture notes from week 47 at . The lecture on Monday starts with a repetition on AdaBoost before we move over to gradient boosting with examples\n", + "\n", + "\n", + "\n", + "c. Video on Decision trees \n", + "\n", + "d. Video on boosting methods \n", + "\n", + "e. Video on AdaBoost \n", + "\n", + "f. Video on Gradient boost, part 1, parts 2-4 follow thereafter \n", + "\n", + "g. Decision Trees: Rashcka et al chapter 3 pages 86-98, and chapter 7 on Ensemble methods, Voting and Bagging and Gradient Boosting. See also lecture from STK-IN4300, lecture 7 at ." + ] + }, + { + "cell_type": "markdown", + "id": "51ee8fb3", + "metadata": { + "editable": true + }, + "source": [ + "## Lab sessions\n", + "**Lab sessions on Tuesday and Wednesday.**\n", + "\n", + " * Work and Discussion of project 3\n", + "\n", + " * Last weekly exercise\n", + "\n", + " * Lab sessions at usual times.\n", + "\n", + " * For the week of December 2-6, lab sessions start at 10am and end at 4pm, room FØ434, Tuesday and Wednesday" + ] + }, + { + "cell_type": "markdown", + "id": "5b4ed2be", + "metadata": { + "editable": true + }, + "source": [ + "## Random Forest Algorithm, reminder from last week\n", + "\n", + "The algorithm described here can be applied to both classification and regression problems.\n", + "\n", + "We will grow of forest of say $B$ trees.\n", + "* For $b=1:B$\n", + "\n", + "a. Draw a bootstrap sample from the training data organized in our $\\boldsymbol{X}$ matrix.\n", + "\n", + "b. We grow then a random forest tree $T_b$ based on the bootstrapped data by repeating the steps outlined till we reach the maximum node size is reached\n", + "\n", + "1. we select $m \\le p$ variables at random from the $p$ predictors/features\n", + "\n", + "2. pick the best split point among the $m$ features using for example the CART algorithm and create a new node\n", + "\n", + "3. split the node into daughter nodes\n", + "\n", + "Finally we output then the ensemble of trees $\\{T_b\\}_1^{B}$ and make predictions for either a regression type of problem or a classification type of problem." + ] + }, + { + "cell_type": "markdown", + "id": "3e76e282", + "metadata": { + "editable": true + }, + "source": [ + "## Random Forests Compared with other Methods on the Cancer Data" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "acba62b3", + "metadata": { + "collapsed": false, + "editable": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "(426, 30)\n", + "(143, 30)\n", + "Test set accuracy Logistic Regression with scaled data: 0.96\n", + "Test set accuracy SVM with scaled data: 0.96\n", + "Test set accuracy with Decision Trees and scaled data: 0.91\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[1. 0.8 0.93333333 1. 1. 0.92857143\n", + " 1. 0.92857143 0.92857143 0.92857143]\n", + "Test set accuracy with Random Forests and scaled data: 0.98\n" + ] + }, + { + "data": { + "image/png": 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", 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", 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", + "text/plain": [ + "
      " + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "%matplotlib inline\n", + "\n", + "import matplotlib.pyplot as plt\n", + "import numpy as np\n", + "from sklearn.model_selection import train_test_split \n", + "from sklearn.datasets import load_breast_cancer\n", + "from sklearn.svm import SVC\n", + "from sklearn.linear_model import LogisticRegression\n", + "from sklearn.tree import DecisionTreeClassifier\n", + "from sklearn.ensemble import BaggingClassifier\n", + "\n", + "# Load the data\n", + "cancer = load_breast_cancer()\n", + "\n", + "X_train, X_test, y_train, y_test = train_test_split(cancer.data,cancer.target,random_state=0)\n", + "print(X_train.shape)\n", + "print(X_test.shape)\n", + "#define methods\n", + "# Logistic Regression\n", + "logreg = LogisticRegression(solver='lbfgs')\n", + "# Support vector machine\n", + "svm = SVC(gamma='auto', C=100)\n", + "# Decision Trees\n", + "deep_tree_clf = DecisionTreeClassifier(max_depth=None)\n", + "#Scale the data\n", + "from sklearn.preprocessing import StandardScaler\n", + "scaler = StandardScaler()\n", + "scaler.fit(X_train)\n", + "X_train_scaled = scaler.transform(X_train)\n", + "X_test_scaled = scaler.transform(X_test)\n", + "# Logistic Regression\n", + "logreg.fit(X_train_scaled, y_train)\n", + "print(\"Test set accuracy Logistic Regression with scaled data: {:.2f}\".format(logreg.score(X_test_scaled,y_test)))\n", + "# Support Vector Machine\n", + "svm.fit(X_train_scaled, y_train)\n", + "print(\"Test set accuracy SVM with scaled data: {:.2f}\".format(logreg.score(X_test_scaled,y_test)))\n", + "# Decision Trees\n", + "deep_tree_clf.fit(X_train_scaled, y_train)\n", + "print(\"Test set accuracy with Decision Trees and scaled data: {:.2f}\".format(deep_tree_clf.score(X_test_scaled,y_test)))\n", + "\n", + "\n", + "from sklearn.ensemble import RandomForestClassifier\n", + "from sklearn.preprocessing import LabelEncoder\n", + "from sklearn.model_selection import cross_validate\n", + "# Data set not specificied\n", + "#Instantiate the model with 500 trees and entropy as splitting criteria\n", + "Random_Forest_model = RandomForestClassifier(n_estimators=500,criterion=\"entropy\")\n", + "Random_Forest_model.fit(X_train_scaled, y_train)\n", + "#Cross validation\n", + "accuracy = cross_validate(Random_Forest_model,X_test_scaled,y_test,cv=10)['test_score']\n", + "print(accuracy)\n", + "print(\"Test set accuracy with Random Forests and scaled data: {:.2f}\".format(Random_Forest_model.score(X_test_scaled,y_test)))\n", + "\n", + "\n", + "import scikitplot as skplt\n", + "y_pred = Random_Forest_model.predict(X_test_scaled)\n", + "skplt.metrics.plot_confusion_matrix(y_test, y_pred, normalize=True)\n", + "plt.show()\n", + "y_probas = Random_Forest_model.predict_proba(X_test_scaled)\n", + "skplt.metrics.plot_roc(y_test, y_probas)\n", + "plt.show()\n", + "skplt.metrics.plot_cumulative_gain(y_test, y_probas)\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "c5cbe0e8", + "metadata": { + "editable": true + }, + "source": [ + "Recall that the cumulative gains curve shows the percentage of the\n", + "overall number of cases in a given category *gained* by targeting a\n", + "percentage of the total number of cases.\n", + "\n", + "Similarly, the receiver operating characteristic curve, or ROC curve,\n", + "displays the diagnostic ability of a binary classifier system as its\n", + "discrimination threshold is varied. It plots the true positive rate against the false positive rate." + ] + }, + { + "cell_type": "markdown", + "id": "19bd77f2", + "metadata": { + "editable": true + }, + "source": [ + "## Compare Bagging on Trees with Random Forests" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "84f1af2e", + "metadata": { + "collapsed": false, + "editable": true + }, + "outputs": [], + "source": [ + "bag_clf = BaggingClassifier(\n", + " DecisionTreeClassifier(splitter=\"random\", max_leaf_nodes=16, random_state=42),\n", + " n_estimators=500, max_samples=1.0, bootstrap=True, n_jobs=-1, random_state=42)" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "6c01d900", + "metadata": { + "collapsed": false, + "editable": true + }, + "outputs": [ + { + "data": { + "text/plain": [ + "0.9790209790209791" + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "bag_clf.fit(X_train, y_train)\n", + "y_pred = bag_clf.predict(X_test)\n", + "from sklearn.ensemble import RandomForestClassifier\n", + "rnd_clf = RandomForestClassifier(n_estimators=500, max_leaf_nodes=16, n_jobs=-1, random_state=42)\n", + "rnd_clf.fit(X_train, y_train)\n", + "y_pred_rf = rnd_clf.predict(X_test)\n", + "np.sum(y_pred == y_pred_rf) / len(y_pred)" + ] + }, + { + "cell_type": "markdown", + "id": "896072f6", + "metadata": { + "editable": true + }, + "source": [ + "## Boosting, a Bird's Eye View\n", + "\n", + "The basic idea is to combine weak classifiers in order to create a good\n", + "classifier. With a weak classifier we often intend a classifier which\n", + "produces results which are only slightly better than we would get by\n", + "random guesses.\n", + "\n", + "This is done by applying in an iterative way a weak (or a standard\n", + "classifier like decision trees) to modify the data. In each iteration\n", + "we emphasize those observations which are misclassified by weighting\n", + "them with a factor." + ] + }, + { + "cell_type": "markdown", + "id": "babc9c9c", + "metadata": { + "editable": true + }, + "source": [ + "## What is boosting? Additive Modelling/Iterative Fitting\n", + "\n", + "Boosting is a way of fitting an additive expansion in a set of\n", + "elementary basis functions like for example some simple polynomials.\n", + "Assume for example that we have a function" + ] + }, + { + "cell_type": "markdown", + "id": "2ed28584", + "metadata": { + "editable": true + }, + "source": [ + "$$\n", + "f_M(x) = \\sum_{i=1}^M \\beta_m b(x;\\gamma_m),\n", + "$$" + ] + }, + { + "cell_type": "markdown", + "id": "5b5b9631", + "metadata": { + "editable": true + }, + "source": [ + "where $\\beta_m$ are the expansion parameters to be determined in a\n", + "minimization process and $b(x;\\gamma_m)$ are some simple functions of\n", + "the multivariable parameter $x$ which is characterized by the\n", + "parameters $\\gamma_m$.\n", + "\n", + "As an example, consider the Sigmoid function we used in logistic\n", + "regression. In that case, we can translate the function\n", + "$b(x;\\gamma_m)$ into the Sigmoid function" + ] + }, + { + "cell_type": "markdown", + "id": "7b6c43ec", + "metadata": { + "editable": true + }, + "source": [ + "$$\n", + "\\sigma(t) = \\frac{1}{1+\\exp{(-t)}},\n", + "$$" + ] + }, + { + "cell_type": "markdown", + "id": "3962039f", + "metadata": { + "editable": true + }, + "source": [ + "where $t=\\gamma_0+\\gamma_1 x$ and the parameters $\\gamma_0$ and\n", + "$\\gamma_1$ were determined by the Logistic Regression fitting\n", + "algorithm.\n", + "\n", + "As another example, consider the cost function we defined for linear regression" + ] + }, + { + "cell_type": "markdown", + "id": "ee5f259c", + "metadata": { + "editable": true + }, + "source": [ + "$$\n", + "C(\\boldsymbol{y},\\boldsymbol{f}) = \\frac{1}{n} \\sum_{i=0}^{n-1}(y_i-f(x_i))^2.\n", + "$$" + ] + }, + { + "cell_type": "markdown", + "id": "079d8ee3", + "metadata": { + "editable": true + }, + "source": [ + "In this case the function $f(x)$ was replaced by the design matrix\n", + "$\\boldsymbol{X}$ and the unknown linear regression parameters $\\boldsymbol{\\beta}$,\n", + "that is $\\boldsymbol{f}=\\boldsymbol{X}\\boldsymbol{\\beta}$. In linear regression we can \n", + "simply invert a matrix and obtain the parameters $\\beta$ by" + ] + }, + { + "cell_type": "markdown", + "id": "11f821a2", + "metadata": { + "editable": true + }, + "source": [ + "$$\n", + "\\boldsymbol{\\beta}=\\left(\\boldsymbol{X}^T\\boldsymbol{X}\\right)^{-1}\\boldsymbol{X}^T\\boldsymbol{y}.\n", + "$$" + ] + }, + { + "cell_type": "markdown", + "id": "60990073", + "metadata": { + "editable": true + }, + "source": [ + "In iterative fitting or additive modeling, we minimize the cost function with respect to the parameters $\\beta_m$ and $\\gamma_m$." + ] + }, + { + "cell_type": "markdown", + "id": "4d7195ab", + "metadata": { + "editable": true + }, + "source": [ + "## Iterative Fitting, Regression and Squared-error Cost Function\n", + "\n", + "The way we proceed is as follows (here we specialize to the squared-error cost function)\n", + "\n", + "1. Establish a cost function, here ${\\cal C}(\\boldsymbol{y},\\boldsymbol{f}) = \\frac{1}{n} \\sum_{i=0}^{n-1}(y_i-f_M(x_i))^2$ with $f_M(x) = \\sum_{i=1}^M \\beta_m b(x;\\gamma_m)$.\n", + "\n", + "2. Initialize with a guess $f_0(x)$. It could be one or even zero or some random numbers.\n", + "\n", + "3. For $m=1:M$\n", + "\n", + "a. minimize $\\sum_{i=0}^{n-1}(y_i-f_{m-1}(x_i)-\\beta b(x;\\gamma))^2$ wrt $\\gamma$ and $\\beta$\n", + "\n", + "b. This gives the optimal values $\\beta_m$ and $\\gamma_m$\n", + "\n", + "c. Determine then the new values $f_m(x)=f_{m-1}(x) +\\beta_m b(x;\\gamma_m)$\n", + "\n", + "We could use any of the algorithms we have discussed till now. If we\n", + "use trees, $\\gamma$ parameterizes the split variables and split points\n", + "at the internal nodes, and the predictions at the terminal nodes." + ] + }, + { + "cell_type": "markdown", + "id": "4bee726a", + "metadata": { + "editable": true + }, + "source": [ + "## Squared-Error Example and Iterative Fitting\n", + "\n", + "To better understand what happens, let us develop the steps for the iterative fitting using the above squared error function.\n", + "\n", + "For simplicity we assume also that our functions $b(x;\\gamma)=1+\\gamma x$. \n", + "\n", + "This means that for every iteration $m$, we need to optimize" + ] + }, + { + "cell_type": "markdown", + "id": "1190b9a0", + "metadata": { + "editable": true + }, + "source": [ + "$$\n", + "(\\beta_m,\\gamma_m) = \\mathrm{argmin}_{\\beta,\\lambda}\\hspace{0.1cm} \\sum_{i=0}^{n-1}(y_i-f_{m-1}(x_i)-\\beta b(x;\\gamma))^2=\\sum_{i=0}^{n-1}(y_i-f_{m-1}(x_i)-\\beta(1+\\gamma x_i))^2.\n", + "$$" + ] + }, + { + "cell_type": "markdown", + "id": "2702211b", + "metadata": { + "editable": true + }, + "source": [ + "We start our iteration by simply setting $f_0(x)=0$. \n", + "Taking the derivatives with respect to $\\beta$ and $\\gamma$ we obtain" + ] + }, + { + "cell_type": "markdown", + "id": "1b1d9984", + "metadata": { + "editable": true + }, + "source": [ + "$$\n", + "\\frac{\\partial {\\cal C}}{\\partial \\beta} = -2\\sum_{i}(1+\\gamma x_i)(y_i-\\beta(1+\\gamma x_i))=0,\n", + "$$" + ] + }, + { + "cell_type": "markdown", + "id": "cdf7e56a", + "metadata": { + "editable": true + }, + "source": [ + "and" + ] + }, + { + "cell_type": "markdown", + "id": "ac6f3b86", + "metadata": { + "editable": true + }, + "source": [ + "$$\n", + "\\frac{\\partial {\\cal C}}{\\partial \\gamma} =-2\\sum_{i}\\beta x_i(y_i-\\beta(1+\\gamma x_i))=0.\n", + "$$" + ] + }, + { + "cell_type": "markdown", + "id": "faf6fb5f", + "metadata": { + "editable": true + }, + "source": [ + "We can then rewrite these equations as (defining $\\boldsymbol{w}=\\boldsymbol{e}+\\gamma \\boldsymbol{x})$ with $\\boldsymbol{e}$ being the unit vector)" + ] + }, + { + "cell_type": "markdown", + "id": "969f86ed", + "metadata": { + "editable": true + }, + "source": [ + "$$\n", + "\\gamma \\boldsymbol{w}^T(\\boldsymbol{y}-\\beta\\gamma \\boldsymbol{w})=0,\n", + "$$" + ] + }, + { + "cell_type": "markdown", + "id": "a98a541c", + "metadata": { + "editable": true + }, + "source": [ + "which gives us $\\beta = \\boldsymbol{w}^T\\boldsymbol{y}/(\\boldsymbol{w}^T\\boldsymbol{w})$. Similarly we have" + ] + }, + { + "cell_type": "markdown", + "id": "d8be5438", + "metadata": { + "editable": true + }, + "source": [ + "$$\n", + "\\beta\\gamma \\boldsymbol{x}^T(\\boldsymbol{y}-\\beta(1+\\gamma \\boldsymbol{x}))=0,\n", + "$$" + ] + }, + { + "cell_type": "markdown", + "id": "932f43c5", + "metadata": { + "editable": true + }, + "source": [ + "which leads to $\\gamma =(\\boldsymbol{x}^T\\boldsymbol{y}-\\beta\\boldsymbol{x}^T\\boldsymbol{e})/(\\beta\\boldsymbol{x}^T\\boldsymbol{x})$. Inserting\n", + "for $\\beta$ gives us an equation for $\\gamma$. This is a non-linear equation in the unknown $\\gamma$ and has to be solved numerically. \n", + "\n", + "The solution to these two equations gives us in turn $\\beta_1$ and $\\gamma_1$ leading to the new expression for $f_1(x)$ as\n", + "$f_1(x) = \\beta_1(1+\\gamma_1x)$. Doing this $M$ times results in our final estimate for the function $f$." + ] + }, + { + "cell_type": "markdown", + "id": "24630eb9", + "metadata": { + "editable": true + }, + "source": [ + "## Iterative Fitting, Classification and AdaBoost\n", + "\n", + "Let us consider a binary classification problem with two outcomes $y_i \\in \\{-1,1\\}$ and $i=0,1,2,\\dots,n-1$ as our set of\n", + "observations. We define a classification function $G(x)$ which produces a prediction taking one or the other of the two values \n", + "$\\{-1,1\\}$.\n", + "\n", + "The error rate of the training sample is then" + ] + }, + { + "cell_type": "markdown", + "id": "9a7fa4b6", + "metadata": { + "editable": true + }, + "source": [ + "$$\n", + "\\mathrm{\\overline{err}}=\\frac{1}{n} \\sum_{i=0}^{n-1} I(y_i\\ne G(x_i)).\n", + "$$" + ] + }, + { + "cell_type": "markdown", + "id": "7ead62b2", + "metadata": { + "editable": true + }, + "source": [ + "The iterative procedure starts with defining a weak classifier whose\n", + "error rate is barely better than random guessing. The iterative\n", + "procedure in boosting is to sequentially apply a weak\n", + "classification algorithm to repeatedly modified versions of the data\n", + "producing a sequence of weak classifiers $G_m(x)$.\n", + "\n", + "Here we will express our function $f(x)$ in terms of $G(x)$. That is" + ] + }, + { + "cell_type": "markdown", + "id": "91639c49", + "metadata": { + "editable": true + }, + "source": [ + "$$\n", + "f_M(x) = \\sum_{i=1}^M \\beta_m b(x;\\gamma_m),\n", + "$$" + ] + }, + { + "cell_type": "markdown", + "id": "e43865c2", + "metadata": { + "editable": true + }, + "source": [ + "will be a function of" + ] + }, + { + "cell_type": "markdown", + "id": "1884c219", + "metadata": { + "editable": true + }, + "source": [ + "$$\n", + "G_M(x) = \\mathrm{sign} \\sum_{i=1}^M \\alpha_m G_m(x).\n", + "$$" + ] + }, + { + "cell_type": "markdown", + "id": "bbb1cdbb", + "metadata": { + "editable": true + }, + "source": [ + "## Adaptive Boosting, AdaBoost\n", + "\n", + "In our iterative procedure we define thus" + ] + }, + { + "cell_type": "markdown", + "id": "7e3590f6", + "metadata": { + "editable": true + }, + "source": [ + "$$\n", + "f_m(x) = f_{m-1}(x)+\\beta_mG_m(x).\n", + "$$" + ] + }, + { + "cell_type": "markdown", + "id": "2eb8c86b", + "metadata": { + "editable": true + }, + "source": [ + "The simplest possible cost function which leads (also simple from a computational point of view) to the AdaBoost algorithm is the\n", + "exponential cost/loss function defined as" + ] + }, + { + "cell_type": "markdown", + "id": "e477d15a", + "metadata": { + "editable": true + }, + "source": [ + "$$\n", + "C(\\boldsymbol{y},\\boldsymbol{f}) = \\sum_{i=0}^{n-1}\\exp{(-y_i(f_{m-1}(x_i)+\\beta G(x_i))}.\n", + "$$" + ] + }, + { + "cell_type": "markdown", + "id": "b6f01e59", + "metadata": { + "editable": true + }, + "source": [ + "We optimize $\\beta$ and $G$ for each value of $m=1:M$ as we did in the regression case.\n", + "This is normally done in two steps. Let us however first rewrite the cost function as" + ] + }, + { + "cell_type": "markdown", + "id": "71c85471", + "metadata": { + "editable": true + }, + "source": [ + "$$\n", + "C(\\boldsymbol{y},\\boldsymbol{f}) = \\sum_{i=0}^{n-1}w_i^{m}\\exp{(-y_i\\beta G(x_i))},\n", + "$$" + ] + }, + { + "cell_type": "markdown", + "id": "2d5be340", + "metadata": { + "editable": true + }, + "source": [ + "where we have defined $w_i^m= \\exp{(-y_if_{m-1}(x_i))}$." + ] + }, + { + "cell_type": "markdown", + "id": "e426517c", + "metadata": { + "editable": true + }, + "source": [ + "## Building up AdaBoost\n", + "\n", + "First, for any $\\beta > 0$, we optimize $G$ by setting" + ] + }, + { + "cell_type": "markdown", + "id": "c0c7e993", + "metadata": { + "editable": true + }, + "source": [ + "$$\n", + "G_m(x) = \\mathrm{sign} \\sum_{i=0}^{n-1} w_i^m I(y_i \\ne G_(x_i)),\n", + "$$" + ] + }, + { + "cell_type": "markdown", + "id": "7457a096", + "metadata": { + "editable": true + }, + "source": [ + "which is the classifier that minimizes the weighted error rate in predicting $y$.\n", + "\n", + "We can do this by rewriting" + ] + }, + { + "cell_type": "markdown", + "id": "424d5bc9", + "metadata": { + "editable": true + }, + "source": [ + "$$\n", + "\\exp{-(\\beta)}\\sum_{y_i=G(x_i)}w_i^m+\\exp{(\\beta)}\\sum_{y_i\\ne G(x_i)}w_i^m,\n", + "$$" + ] + }, + { + "cell_type": "markdown", + "id": "d1fa658b", + "metadata": { + "editable": true + }, + "source": [ + "which can be rewritten as" + ] + }, + { + "cell_type": "markdown", + "id": "f8fc6b15", + "metadata": { + "editable": true + }, + "source": [ + "$$\n", + "(\\exp{(\\beta)}-\\exp{-(\\beta)})\\sum_{i=0}^{n-1}w_i^mI(y_i\\ne G(x_i))+\\exp{(-\\beta)}\\sum_{i=0}^{n-1}w_i^m=0,\n", + "$$" + ] + }, + { + "cell_type": "markdown", + "id": "9ca82f54", + "metadata": { + "editable": true + }, + "source": [ + "which leads to" + ] + }, + { + "cell_type": "markdown", + "id": "6c028686", + "metadata": { + "editable": true + }, + "source": [ + "$$\n", + "\\beta_m = \\frac{1}{2}\\log{\\frac{1-\\mathrm{\\overline{err}}}{\\mathrm{\\overline{err}}}},\n", + "$$" + ] + }, + { + "cell_type": "markdown", + "id": "4007a0ef", + "metadata": { + "editable": true + }, + "source": [ + "where we have redefined the error as" + ] + }, + { + "cell_type": "markdown", + "id": "ba456347", + "metadata": { + "editable": true + }, + "source": [ + "$$\n", + "\\mathrm{\\overline{err}}_m=\\frac{1}{n}\\frac{\\sum_{i=0}^{n-1}w_i^mI(y_i\\ne G(x_i)}{\\sum_{i=0}^{n-1}w_i^m},\n", + "$$" + ] + }, + { + "cell_type": "markdown", + "id": "c8df4ef3", + "metadata": { + "editable": true + }, + "source": [ + "which leads to an update of" + ] + }, + { + "cell_type": "markdown", + "id": "da46f9d9", + "metadata": { + "editable": true + }, + "source": [ + "$$\n", + "f_m(x) = f_{m-1}(x) +\\beta_m G_m(x).\n", + "$$" + ] + }, + { + "cell_type": "markdown", + "id": "5c60f30d", + "metadata": { + "editable": true + }, + "source": [ + "This leads to the new weights" + ] + }, + { + "cell_type": "markdown", + "id": "a5bc4dfe", + "metadata": { + "editable": true + }, + "source": [ + "$$\n", + "w_i^{m+1} = w_i^m \\exp{(-y_i\\beta_m G_m(x_i))}\n", + "$$" + ] + }, + { + "cell_type": "markdown", + "id": "3987cc1e", + "metadata": { + "editable": true + }, + "source": [ + "## Adaptive boosting: AdaBoost, Basic Algorithm\n", + "\n", + "The algorithm here is rather straightforward. Assume that our weak\n", + "classifier is a decision tree and we consider a binary set of outputs\n", + "with $y_i \\in \\{-1,1\\}$ and $i=0,1,2,\\dots,n-1$ as our set of\n", + "observations. Our design matrix is given in terms of the\n", + "feature/predictor vectors\n", + "$\\boldsymbol{X}=[\\boldsymbol{x}_0\\boldsymbol{x}_1\\dots\\boldsymbol{x}_{p-1}]$. Finally, we define also a\n", + "classifier determined by our data via a function $G(x)$. This function tells us how well we are able to classify our outputs/targets $\\boldsymbol{y}$. \n", + "\n", + "We have already defined the misclassification error $\\mathrm{err}$ as" + ] + }, + { + "cell_type": "markdown", + "id": "b35df09e", + "metadata": { + "editable": true + }, + "source": [ + "$$\n", + "\\mathrm{err}=\\frac{1}{n}\\sum_{i=0}^{n-1}I(y_i\\ne G(x_i)),\n", + "$$" + ] + }, + { + "cell_type": "markdown", + "id": "5b6a209c", + "metadata": { + "editable": true + }, + "source": [ + "where the function $I()$ is one if we misclassify and zero if we classify correctly." + ] + }, + { + "cell_type": "markdown", + "id": "d619a097", + "metadata": { + "editable": true + }, + "source": [ + "## Basic Steps of AdaBoost\n", + "\n", + "With the above definitions we are now ready to set up the algorithm for AdaBoost.\n", + "The basic idea is to set up weights which will be used to scale the correctly classified and the misclassified cases.\n", + "1. We start by initializing all weights to $w_i = 1/n$, with $i=0,1,2,\\dots n-1$. It is easy to see that we must have $\\sum_{i=0}^{n-1}w_i = 1$.\n", + "\n", + "2. We rewrite the misclassification error as" + ] + }, + { + "cell_type": "markdown", + "id": "b7311abb", + "metadata": { + "editable": true + }, + "source": [ + "$$\n", + "\\mathrm{\\overline{err}}_m=\\frac{\\sum_{i=0}^{n-1}w_i^m I(y_i\\ne G(x_i))}{\\sum_{i=0}^{n-1}w_i},\n", + "$$" + ] + }, + { + "cell_type": "markdown", + "id": "4bfb4209", + "metadata": { + "editable": true + }, + "source": [ + "1. Then we start looping over all attempts at classifying, namely we start an iterative process for $m=1:M$, where $M$ is the final number of classifications. Our given classifier could for example be a plain decision tree.\n", + "\n", + "a. Fit then a given classifier to the training set using the weights $w_i$.\n", + "\n", + "b. Compute then $\\mathrm{err}$ and figure out which events are classified properly and which are classified wrongly.\n", + "\n", + "c. Define a quantity $\\alpha_{m} = \\log{(1-\\mathrm{\\overline{err}}_m)/\\mathrm{\\overline{err}}_m}$\n", + "\n", + "d. Set the new weights to $w_i = w_i\\times \\exp{(\\alpha_m I(y_i\\ne G(x_i)}$.\n", + "\n", + "5. Compute the new classifier $G(x)= \\sum_{i=0}^{n-1}\\alpha_m I(y_i\\ne G(x_i)$.\n", + "\n", + "For the iterations with $m \\le 2$ the weights are modified\n", + "individually at each steps. The observations which were misclassified\n", + "at iteration $m-1$ have a weight which is larger than those which were\n", + "classified properly. As this proceeds, the observations which were\n", + "difficult to classifiy correctly are given a larger influence. Each\n", + "new classification step $m$ is then forced to concentrate on those\n", + "observations that are missed in the previous iterations." + ] + }, + { + "cell_type": "markdown", + "id": "4cf4c1f4", + "metadata": { + "editable": true + }, + "source": [ + "## AdaBoost Examples\n", + "\n", + "Using **Scikit-Learn** it is easy to apply the adaptive boosting algorithm, as done here." + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "453dff54", + "metadata": { + "collapsed": false, + "editable": true + }, + "outputs": [ + { + "data": { + "image/png": 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zzXrsscdUtWpVHTx4UMuWLdNtt92md955R9LFS/juu+8+tWzZUk888YROnjypSZMm6cYbb9SZM2euGs9LL72kX375RW+++aZWr16tTp06KTQ0VCkpKVq8eLE2btxou/PcoEGD9NFHH+mee+5R3759ValSJc2ZM0f79+/XwoUL8408OKNnz5565pln9NBDD6lt27bavn27vvrqq3xVbmRkpFq3bq0mTZqoUqVK2rRpkxYsWKA+ffoUuu3S/ExFRUUpKirqin3q16+vOnXqaMCAATp8+LACAgK0cOHCAs/pN2nSRJLUt29fxcXFycvLS4899liR4+rXr59OnDihb775Rl5eXrr77rvVs2dPjRkzRh06dLhqzECxlNp8fLjUpZe7XS4+Pt6QZHe5m2EYxvnz541Ro0YZtWrVMsqUKWOEhYUZgwcPNs6dO2fXLzw8vMBLn/Iuv/r0008diqWgy4+WLFliNG7c2PDz8zMiIiKM1157zZg5c6Yhydi/f7+t39Uud8vbZ0HL5ZenrV692oiLizMCAwMNPz8/o06dOkaPHj2MTZs22fVbuHCh0aBBA8PX19eIjIw0PvvsMyM+Pv6ql7tdasGCBUa7du2MSpUqGd7e3ka1atWMRx991FizZo1dv7179xqdOnUygoKCDD8/P6NZs2bG0qVL88Vd0M+7oMusCrvcLScnxxg4cKBRpUoVo1y5ckZcXJyxZ8+efJe7jRkzxmjWrJkRFBRklC1b1qhfv77xyiuv2F3WePnlbobh/Gfq8v/PhdFfl7tdSUE/g19//dWIjY01/P39jSpVqhi9evUytm/fnu/nd+HCBeP55583qlatalgsFttx5v2sx40bl29/l/9/+Pzzzw1JxptvvmnXLyMjwwgPDzeioqLsfp6Aq1gMw4kZNwAA4G+Fc+wAAHgQEjsAAB6ExA4AgAchsQMA4EFI7AAAeBASOwAAHsStb1CTm5urI0eOqEKFCi69BSQA4NowDEOnT59W9erVXXozpsudO3dO2dnZTm/Hx8dHfn5+Loio5Lh1Yj9y5Ei+J4gBANzPoUOHdN1115XIts+dO6eyFSpLF846va3Q0FDt37//b53c3Tqx5z3v2qdhgixePlfpDbinA9/kf4gP4ClOn87QDbVr2v6el4Ts7Gzpwln5RsZLzuSKnGyl/DpH2dnZJPaSkjf8bvHykcXLt5SjAUpG3vPdAU92TU6nevs5VQQaFveYlubWiR0AAIdZJDnzBcJNpnKR2AEA5mCxXlycWd8NuEeUAADAIVTsAABzsFicHIp3j7F4EjsAwBwYigcAAO6Gih0AYA4MxQMA4EmcHIp3k0Fu94gSAAA4hIodAGAODMUDAOBBmBUPAADcDRU7AMAcGIoHAMCDmGQonsQOADAHk1Ts7vH1AwAAOISKHQBgDgzFAwDgQSwWJxM7Q/EAAOAao2IHAJiD1XJxcWZ9N0BiBwCYg0nOsbtHlAAAwCFU7AAAczDJdewkdgCAOTAUDwAA3A0VOwDAHBiKBwDAg5hkKJ7EDgAwB5NU7O7x9QMAADiEih0AYA4MxQMA4EEYigcAAO6Gih0AYBJODsW7SS1MYgcAmAND8QAAwN1QsQMAzMFicXJWvHtU7CR2AIA5mORyN/eIEgAAOISKHQBgDiaZPEdiBwCYg0mG4knsAABzMEnF7h5fPwAAgEOo2AEA5sBQPAAAHoSheAAA4G6o2AEApmCxWGQxQcVOYgcAmIJZEjtD8QAAeBAqdgCAOVj+WpxZ3w2Q2AEApsBQPAAAcDtU7AAAUzBLxU5iBwCYAokdAAAPYpbEzjl2AAA8CBU7AMAcuNwNAADPwVA8AABwO1TsAABTuPjUVmcqdtfFUpJI7AAAU7DIyaF4N8nsDMUDAOBBqNgBAKZglslzJHYAgDmY5HI3huIBAPAgJHYAgDn8NRRf3KW4Q/GTJ09WRESE/Pz8FBMTo40bN16x/4QJE1SvXj2VLVtWYWFh6t+/v86dO+fw/kjsAABTcCapF/f8/Pz585WYmKikpCRt2bJFUVFRiouL07Fjxwrs/+GHH2rQoEFKSkrSjh079P7772v+/PkaMmSIw/sksQMATKE0Evv48ePVq1cvJSQkKDIyUlOnTlW5cuU0c+bMAvuvXbtWt912m7p06aKIiAi1a9dOnTt3vmqVfykSOwAAJSA7O1ubN29WbGysrc1qtSo2Nlbr1q0rcJ0WLVpo8+bNtkS+b98+LV++XPfee6/D+2VWPADAHFw0Kz4jI8Ou2dfXV76+vvm6p6WlKScnRyEhIXbtISEh2rlzZ4G76NKli9LS0tSyZUsZhqELFy7omWeeYSgeAIDLuWooPiwsTIGBgbYlOTnZZTGuWbNGY8eO1bvvvqstW7bos88+07JlyzR69GiHt0HFDgBAERw6dEgBAQG21wVV65JUpUoVeXl5KTU11a49NTVVoaGhBa4zfPhwdevWTT179pQkNWrUSJmZmXrqqac0dOhQWa1Xr8ep2AEApuCqij0gIMBuKSyx+/j4qEmTJlq1apWtLTc3V6tWrVLz5s0LXOfs2bP5kreXl5ckyTAMh46Tih0AYArO3lK2OOsmJiYqPj5eTZs2VbNmzTRhwgRlZmYqISFBktS9e3fVqFHDNpzfvn17jR8/XjfddJNiYmK0Z88eDR8+XO3bt7cl+KshsQMAUEIeffRRHT9+XCNGjFBKSoqio6O1YsUK24S6gwcP2lXow4YNk8Vi0bBhw3T48GFVrVpV7du31yuvvOLwPi2Go7X931BGRoYCAwPlG/W0LF4FD4UA7u7EugmlHQJQYjIyMlStapDS09Ptzlu7eh+BgYEK6TFPVp9yxd5ObvZZpc7uVqKxugIVOwDAHHgIDAAAcDdU7AAAUyiNyXOlgcQOADAFEjsAAB7ELImdc+wAAHgQKnYAgDmYZFY8iR0AYAoMxQMAALdDYje5px9uqZ1fjNAfa9/Qd3P6q+mNNQvt6+1t1eBecfrl8+H6Y+0b2vDRP9W2eX27PlarRSOevVc7lozQyR/H6ZfPh2tQz3YlfRhAod6bMlkNbqilSgFl1arlrdr0fxuv2P+zhZ/qpkYNVCmgrG65ubFWfLnc9t758+c1bMhA3XJzY1Wt6K86ETXU84l4HT1ypKQPAy7gqofA/N39LRL75MmTFRERIT8/P8XExGjjxiv/4sE1OrW9Sa8lPqBXpn2l5l3H6afdR7TknWdVtaJ/gf1HPnufej7YQomvL9RNDydrxsIfNf+NJxVVr4atz4vxserV6Tb1f32Bojsla9jEJUrsfpeee+yOa3VYgM2CT+dr0D9f1OChI/Tjhs1q1KixOvzjbh07dqzA/uvXrVWPbl3UvccTWrthi9rf30GPPfyAfvnlP5IuPnlr29atGjRkmH5cv1kfzV+o/+7epYcf6nAtDwvFZJGTid1NTrKXemKfP3++EhMTlZSUpC1btigqKkpxcXGF/uLBdfo+3lqzFq3VvC82aOf+VD0/9hP9eS5b8R1uLbB/l/tu0eszV+qrH3/VgcMnNH3Bj/rqxx3q9/idtj63RtXS0jX/0YofftXBoye1aNV2rVq/S01vDL9WhwXYTHr7LSU80VPd4xPUoEGkJk6eqrLlymnunJkF9n/3nYlq2+5u9X/xJdVv0EAjRo5W9E03671335EkBQYGaumXX+uhTo/ohnr11CzmVo2fMElbt2zWoYMHr+WhAYUq9cQ+fvx49erVSwkJCYqMjNTUqVNVrlw5zZxZ8C8eXKOMt5duqh+mbzfutrUZhqFvN+5Ws0YRBa7jU8Zb57Iv2LX9mXVeLaJr2V6v375fbZpdr7o1q0qSGl1fXc2ja+vrtb+6/iCAK8jOztbWLZvV5s5YW5vValWbO2O1cf36AtfZsGGd2tx5l11bbNt22rCh4P6SlJ6eLovFosCgIJfEjZJjlqH4Up0Vn52drc2bN2vw4MG2NqvVqtjYWK1bt64UI/N8VYLKy9vbS8dOnLZrP3bitOpFBBe4zjfrd6pv19b6Ycte7fs9TW2a3aAOdzaW1yWPHHxj9jcK8PfT9oVDlJNryMtqUdK7y/Txl5tL9HiAy51IS1NOTo6C/3o8Zp7g4GDt3rWzwHVSU1IK6B+i1NSUAvufO3dOw4cO0sOPdv5bP+0Lf+Fyt5KX9tcvXshlv0ghISHauTP/L15WVpaysrJsrzMyMko8RvzPgHEL9e7wx7R94RAZhqF9v6dp7pINir8/xtanU9toPXZ3E/UYOle/7ktR4xtqaNyLD+ro8XR9sPT/SjF6wLXOnz+vbl0elWEYenvSu6UdDmDjVtexJycna9SoUaUdhkdIO5WpCxdyFFy5gl17cOUKSkk7Xeg6j7z4vnx9vFU5sLyOHE/XmOfba//hE7Y+Y/t10Buzv9GnX2+VJP2y56hqVquklxLakthxTVWuUkVeXl46lppq137s2DGFhIQWuE5IaGgB/VPz9c9L6gcP/qblX62iWncTXMd+DVT56xcv9bJfpNTUVIWG5v/FGzx4sNLT023LoUOHrlWoHuf8hRxt3XlIbW65wdZmsVjU5pYbtPHnA1dcNyv7go4cT5e3t1Ud74rS0n//x/ZeWT8f5RqGXf+c3FxZ3eQXAp7Dx8dHN93cRGtWr7K15ebmas3qVWp2a8ETRGNimmvN6m/t2r5d9Y1iYv7XPy+p79nzXy39cqUqV65cMgcAl+Mc+zXg4+OjJk2aaNWqVerYsaOki794q1atUp8+ffL19/X1la+v7zWO0nNN/NcaTR/VVZt3HNSm/xxUny6tVK6sj+Yu2SBJmjGqq44cT9eId5ZKkm5pGK7qVQO1ffdh1agaqKFP3yOrxaLxc/73h3P59//RwCfa6VDKH/p1b4qi61+nvl3baO7nhU8+AkrK8/3666kne+imJk3VtGkzTZ40QWczM9Wte4IkqecT8apevbpeHpMsSXquT1/FxbbW22+9qbvvuU8LPv1YWzZv0qR335N0Mal3fexhbdu2RQsWfaGcnBylpFw8/16pUiX5+PiUzoHCIRbLxcWZ9d1BqQ/FJyYmKj4+Xk2bNlWzZs00YcIEZWZmKiEhobRD83gLVm5VlYr+GvHMvQqpHKCfdv+uDs9P1bGTF4fiw0Ir2lXfvj7eSnruPtWqUVln/szSVz/8qieHz1P6mT9tfRJfX6ikZ+/V24MeVtWK/jqalqH3F/6osdO/uubHB3R6+FGlHT+uMS8nKTUlRY2jorX4iy9t83p+P3RQ1ksmf97avIVmzf1ALycN18gRQ1Wn7vX6+NNFuvHGhpKkI4cPa9nSJZKk5rfcZLevL7/+Vne0an1tDgy4AothXDZuWgreeecdjRs3TikpKYqOjtbEiRMVExNz1fUyMjIUGBgo36inZfGikodnOrFuQmmHAJSYjIwMVasapPT09BKbq5CXK2o/v0BW3/LF3k5uVqb2TepUorG6QqlX7JLUp0+fAofeAQBwGSeH4t3lcrdSv0ENAABwnb9FxQ4AQEkzy+VuJHYAgCmYZVY8Q/EAAHgQKnYAgClYrRZZrcUvuw0n1r2WSOwAAFNgKB4AALgdKnYAgCkwKx4AAA9ilqF4EjsAwBTMUrFzjh0AAA9CxQ4AMAWzVOwkdgCAKZjlHDtD8QAAeBAqdgCAKVjk5FC8mzy3lcQOADAFhuIBAIDboWIHAJgCs+IBAPAgDMUDAAC3Q8UOADAFhuIBAPAgZhmKJ7EDAEzBLBU759gBAPAgVOwAAHNwcijeTW48R2IHAJgDQ/EAAMDtULEDAEyBWfEAAHgQhuIBAIDboWIHAJgCQ/EAAHgQhuIBAIDboWIHAJiCWSp2EjsAwBQ4xw4AgAcxS8XOOXYAADwIFTsAwBQYigcAwIMwFA8AANwOFTsAwBQscnIo3mWRlCwSOwDAFKwWi6xOZHZn1r2WGIoHAMCDULEDAEzBLLPiqdgBAKaQNyvemaU4Jk+erIiICPn5+SkmJkYbN268Yv9Tp06pd+/eqlatmnx9fXXDDTdo+fLlDu+Pih0AYApWy8XFmfWLav78+UpMTNTUqVMVExOjCRMmKC4uTrt27VJwcHC+/tnZ2Wrbtq2Cg4O1YMEC1ahRQ7/99puCgoIc3ieJHQCAEjJ+/Hj16tVLCQkJkqSpU6dq2bJlmjlzpgYNGpSv/8yZM3Xy5EmtXbtWZcqUkSRFREQUaZ8MxQMAzMHi3HB83vVuGRkZdktWVlaBu8vOztbmzZsVGxtra7NarYqNjdW6desKXGfJkiVq3ry5evfurZCQEDVs2FBjx45VTk6Ow4dJYgcAmELe5DlnFkkKCwtTYGCgbUlOTi5wf2lpacrJyVFISIhde0hIiFJSUgpcZ9++fVqwYIFycnK0fPlyDR8+XG+++abGjBnj8HEyFA8AQBEcOnRIAQEBtte+vr4u23Zubq6Cg4M1bdo0eXl5qUmTJjp8+LDGjRunpKQkh7ZBYgcAmILlr3/OrC9JAQEBdom9MFWqVJGXl5dSU1Pt2lNTUxUaGlrgOtWqVVOZMmXk5eVla2vQoIFSUlKUnZ0tHx+fq+6XoXgAgCnkzYp3ZikKHx8fNWnSRKtWrbK15ebmatWqVWrevHmB69x2223as2ePcnNzbW27d+9WtWrVHErqEokdAIASk5iYqOnTp2vOnDnasWOHnn32WWVmZtpmyXfv3l2DBw+29X/22Wd18uRJ9evXT7t379ayZcs0duxY9e7d2+F9MhQPADCF0nhs66OPPqrjx49rxIgRSklJUXR0tFasWGGbUHfw4EFZrf+rscPCwvTVV1+pf//+aty4sWrUqKF+/fpp4MCBDu/TocS+ZMkShzd4//33O9wXAIBrpbRuKdunTx/16dOnwPfWrFmTr6158+Zav3598XYmBxN7x44dHdqYxWIp0rV2AADAtRxK7JeexAcAwB2Z5bGtTp1jP3funPz8/FwVCwAAJYanuxUiJydHo0ePVo0aNeTv7699+/ZJkoYPH67333/f5QECAOAKpfV0t2utyIn9lVde0ezZs/X666/bXVPXsGFDzZgxw6XBAQCAoilyYp87d66mTZumrl272t0ZJyoqSjt37nRpcAAAuIqr7hX/d1fkc+yHDx9W3bp187Xn5ubq/PnzLgkKAABXM8vkuSJX7JGRkfr+++/ztS9YsEA33XSTS4ICAADFU+SKfcSIEYqPj9fhw4eVm5urzz77TLt27dLcuXO1dOnSkogRAACnWSQnHgHj3LrXUpEr9g4dOuiLL77QN998o/Lly2vEiBHasWOHvvjiC7Vt27YkYgQAwGlmmRVfrOvYb7/9dq1cudLVsQAAACcV+wY1mzZt0o4dOyRdPO/epEkTlwUFAICrFefRq5ev7w6KnNh///13de7cWT/++KOCgoIkSadOnVKLFi308ccf67rrrnN1jAAAOK00nu5WGop8jr1nz546f/68duzYoZMnT+rkyZPasWOHcnNz1bNnz5KIEQAAOKjIFfu///1vrV27VvXq1bO11atXT5MmTdLtt9/u0uAAAHAlNym6nVLkxB4WFlbgjWhycnJUvXp1lwQFAICrMRRfiHHjxun555/Xpk2bbG2bNm1Sv3799MYbb7g0OAAAXCVv8pwziztwqGKvWLGi3TeVzMxMxcTEyNv74uoXLlyQt7e3nnjiCXXs2LFEAgUAAFfnUGKfMGFCCYcBAEDJMstQvEOJPT4+vqTjAACgRJnllrLFvkGNJJ07d07Z2dl2bQEBAU4FBAAAiq/IiT0zM1MDBw7UJ598ohMnTuR7PycnxyWBAQDgSjy2tRD//Oc/9e2332rKlCny9fXVjBkzNGrUKFWvXl1z584tiRgBAHCaxeL84g6KXLF/8cUXmjt3rlq3bq2EhATdfvvtqlu3rsLDw/XBBx+oa9euJREnAABwQJEr9pMnT6p27dqSLp5PP3nypCSpZcuW+u6771wbHQAALmKWx7YWObHXrl1b+/fvlyTVr19fn3zyiaSLlXzeQ2EAAPi7MctQfJETe0JCgrZv3y5JGjRokCZPniw/Pz/1799fL730kssDBAAAjivyOfb+/fvb/js2NlY7d+7U5s2bVbduXTVu3NilwQEA4CpmmRXv1HXskhQeHq7w8HBXxAIAQIlxdjjdTfK6Y4l94sSJDm+wb9++xQ4GAICSwi1lL/HWW285tDGLxUJiBwCgFDmU2PNmwf9dHfz2dW5lC49V8ZY+pR0CUGKMnOyrd3IRq4oxY/yy9d2B0+fYAQBwB2YZineXLyAAAMABVOwAAFOwWCQrs+IBAPAMVicTuzPrXksMxQMA4EGKldi///57Pf7442revLkOHz4sSZo3b55++OEHlwYHAICr8BCYQixcuFBxcXEqW7astm7dqqysLElSenq6xo4d6/IAAQBwhbyheGcWd1DkxD5mzBhNnTpV06dPV5kyZWztt912m7Zs2eLS4AAAQNEUefLcrl27dMcdd+RrDwwM1KlTp1wREwAALmeWe8UXuWIPDQ3Vnj178rX/8MMPql27tkuCAgDA1fKe7ubM4g6KnNh79eqlfv36acOGDbJYLDpy5Ig++OADDRgwQM8++2xJxAgAgNOsLljcQZGH4gcNGqTc3FzdddddOnv2rO644w75+vpqwIABev7550siRgAA4KAiJ3aLxaKhQ4fqpZde0p49e3TmzBlFRkbK39+/JOIDAMAlzHKOvdh3nvPx8VFkZKQrYwEAoMRY5dx5cqvcI7MXObG3adPmihfpf/vtt04FBAAAiq/IiT06Otru9fnz57Vt2zb95z//UXx8vKviAgDApRiKL8Rbb71VYPvIkSN15swZpwMCAKAk8BCYInr88cc1c+ZMV20OAAAUg8se27pu3Tr5+fm5anMAALjUxeexF7/s9tih+AcffNDutWEYOnr0qDZt2qThw4e7LDAAAFyJc+yFCAwMtHtttVpVr149vfzyy2rXrp3LAgMAAEVXpMSek5OjhIQENWrUSBUrViypmAAAcDkmzxXAy8tL7dq14yluAAC3Y3HBP3dQ5FnxDRs21L59+0oiFgAASkxexe7M4g6KnNjHjBmjAQMGaOnSpTp69KgyMjLsFgAAUHocPsf+8ssv68UXX9S9994rSbr//vvtbi1rGIYsFotycnJcHyUAAE4yyzl2hxP7qFGj9Mwzz2j16tUlGQ8AACXCYrFc8VknjqzvDhxO7IZhSJJatWpVYsEAAADnFOlyN3f5tgIAwOUYii/ADTfccNXkfvLkSacCAgCgJHDnuQKMGjUq353nAADA30eREvtjjz2m4ODgkooFAIASY7VYnHoIjDPrXksOX8fO+XUAgDsrrRvUTJ48WREREfLz81NMTIw2btzo0Hoff/yxLBaLOnbsWKT9OZzY82bFAwAAx8yfP1+JiYlKSkrSli1bFBUVpbi4OB07duyK6x04cEADBgzQ7bffXuR9OpzYc3NzGYYHALgvy/8m0BVnKc6t4sePH69evXopISFBkZGRmjp1qsqVK6eZM2cWuk5OTo66du2qUaNGqXbt2kXeZ5FvKQsAgDuyyuL0UhTZ2dnavHmzYmNj/xeD1arY2FitW7eu0PVefvllBQcH68knnyzWcRb5eewAALgjV13udvlzUXx9feXr65uvf1pamnJychQSEmLXHhISop07dxa4jx9++EHvv/++tm3bVuw4qdgBACiCsLAwBQYG2pbk5GSXbPf06dPq1q2bpk+fripVqhR7O1TsAABTcNWd5w4dOqSAgABbe0HVuiRVqVJFXl5eSk1NtWtPTU1VaGhovv579+7VgQMH1L59e1tbbm6uJMnb21u7du1SnTp1rhoniR0AYAquuo49ICDALrEXxsfHR02aNNGqVatsl6zl5uZq1apV6tOnT77+9evX188//2zXNmzYMJ0+fVpvv/22wsLCHIqTxA4AQAlJTExUfHy8mjZtqmbNmmnChAnKzMxUQkKCJKl79+6qUaOGkpOT5efnp4YNG9qtHxQUJEn52q+ExA4AMIXSuFf8o48+quPHj2vEiBFKSUlRdHS0VqxYYZtQd/DgQVmtrp3uRmIHAJiCVU4OxRfnQnZJffr0KXDoXZLWrFlzxXVnz55d5P0xKx4AAA9CxQ4AMAUe2woAgAexyrlhancZ4naXOAEAgAOo2AEApmCxWJx6BLm7PL6cxA4AMIViPqDNbn13QGIHAJiCq+4893fHOXYAADwIFTsAwDTco+Z2DokdAGAKZrmOnaF4AAA8CBU7AMAUuNwNAAAPwp3nAACA26FiBwCYAkPxAAB4ELPceY6heAAAPAgVOwDAFBiKBwDAg5hlVjyJHQBgCmap2N3lCwgAAHAAFTsAwBTMMiuexA4AMAUeAgMAANwOFTsAwBSsssjqxIC6M+teSyR2AIApMBQPAADcDhU7AMAULH/9c2Z9d0BiBwCYAkPxAADA7VCxAwBMweLkrHiG4gEA+Bsxy1A8iR0AYApmSeycYwcAwINQsQMATIHL3QAA8CBWy8XFmfXdAUPxAAB4ECp2AIApMBQPAIAHYVY8AABwO1TsAABTsMi54XQ3KdhJ7AAAc2BWPAAAcDskdpOb+u5k1asboSB/P93eIkb/t3HjFfsvXPCpohrWV5C/n5pGN9KKL5fbvb940Wf6xz3tVCOkssqWsWj7tm0lGD1wdU8/cod2LhulP9a/pe/mDlDTG8ML7evtbdXgp+7WL0uS9Mf6t7Rh/iC1bdHAro9/OV+NG/CQdi1/WSfXjdfq2YlqElmzpA8DLmBxwT93UKqJ/bvvvlP79u1VvXp1WSwWLV68uDTDMZ1PP5mvgS8lauiwJK3buEWNG0fp/vvidOzYsQL7r1u7VvGPd1Z8wpNa/39b1b5DRz3yUEf98p//2PqczcxUi9taaszY167VYQCF6tTuZr324gN65b0v1bzLa/pp92Etebe3qlb0L7D/yOfaq+dDLZX4+qe66aExmrHgB81/s5ei6l1n6zNlRBfdeWt9PTFsjpo+MlbfrNupZVOfV/WqgdfqsFBMebPinVncQakm9szMTEVFRWny5MmlGYZpTZwwXglP9lL3HglqEBmpSe9OVdly5TRn9swC+09+5221i7tbiS++pPoNGihp1GhF33Szpr77jq1Pl8e7aciwEbrzrthrdRhAofo+fqdmfbZW85as1859KXr+lY/157lsxXdsXmD/Lv9optff/1pf/fCrDhw+oemf/qCvfvxV/brdKUny8y2jjndFa+iExfpxy17tO5SmV95brr2HjqvXw7dfy0NDMVhcsLiDUk3s99xzj8aMGaMHHnigNMMwpezsbG3dstkuAVutVt15Z6w2rl9X4Dob1q9TmzvtE3bbdnHaUEh/oDSV8fbSTQ3C9O2GXbY2wzD07YZdata4VoHr+JTx1rns83Ztf57LVoub6kiSvL2s8vb2ytfnXNZ5Wx+gtLnVOfasrCxlZGTYLSietLQ05eTkKDg4xK49OCREKSkpBa6TmpKi4JDL+geHKDW14P5AaapS0V/e3l46dvK0XfuxExkKrRxQ4DrfrNuhvo/fqTo1q8pisejOmPrqcGe0Qqtc7H/mbJbWb9+nwb3uUbWqgbJaLXrs3lsU07iWrQ/+vqyyyGpxYnGTmt2tEntycrICAwNtS1hYWGmHBMCDDBi3QHsPHtP2z4YrY+MEvTXoYc1dsl65uYatzxPD5spikfZ9/YrSN0xQ786t9MmKTXZ98PdklqF4t7qOffDgwUpMTLS9zsjIILkXU5UqVeTl5aVjx1Lt2o+lpio0NLTAdUJCQ3Us9bL+x1IVElJwf6A0pf1xRhcu5Ci4UgW79uDKAUo5UfBoX9ofZ/RI4nT5+nircmB5HTmerjF9O2j/4RO2Pvt/T1O7nm+rnJ+PAvz9lJKWoXmvJmj/4bQSPR7AUW5Vsfv6+iogIMBuQfH4+PjoppubaPW3q2xtubm5Wr16lZrdWvDEophbm2vN6lV2bau+WamYQvoDpen8hRxt3XFIbWLq2dosFovaNLtBG3/af8V1s7Iv6MjxdHl7W9XxrmgtXfNTvj5nz2UrJS1DQRXKKrZFAy1d87PLjwEuZpKS3a0qdrhW3xcS1euJeDVp0lRNb2mmdyZO0NnMTHWPT5AkPdmju6rXqKHRryRLknr36ad2d7XShLfe1D333KdPP/lYWzZv0uQp02zbPHnypA4dPKijR49IknbvvjhxKSQ0tNCRAKCkTPzXt5r+cjdt/vWgNv3ngPp0aaNyZX019/P1kqQZo7vpyLF0jZi0RJJ0S8NwVQ8O0vZdv6tGcJCGPn2vrFaLxs/+xrbN2OYNZLFIuw8cU52wqhrbv6N270/V3CVMIv274+lu18CZM2e0Z88e2+v9+/dr27ZtqlSpkmrW5IYPJe3hRx5V2vHjennUCKWmpKhxVLQ+X7pCIX9NkDt06KCs1v8N6jRv0UKz532oUUnDlDRsiOpef70+WbhYNzZsaOuz7Isleqpngu11966PSZKGDk/SsBEjr82BAX9Z8PUWVanorxHP3qeQyhX0067D6tB7sm1CXVhoJbtz476+ZZTU+x+qVaOKzpzN0lc//qInh89V+pk/bX0C/f308vP3q0ZIkE6mn9Xnq7YpafIXunAh95ofH1AQi2EYpTbjY82aNWrTpk2+9vj4eM2ePfuq62dkZCgwMFCpJ9IZlofHqnhLn9IOASgxRk62sn6ervT0kvs7npcrVm07KP8Kxd/HmdMZuiu6ZonG6gqlWrG3bt1apfi9AgBgIs6eJnePgXg3mzwHAACujMlzAABzMEnJTmIHAJgCs+IBAPAgzj6hjae7AQCAa46KHQBgCiY5xU5iBwCYhEkyO0PxAAB4ECp2AIApMCseAAAPwqx4AADgtMmTJysiIkJ+fn6KiYnRxo0bC+07ffp03X777apYsaIqVqyo2NjYK/YvCIkdAGAKpfE49vnz5ysxMVFJSUnasmWLoqKiFBcXp2PHjhXYf82aNercubNWr16tdevWKSwsTO3atdPhw4cd3ieJHQBgDqWQ2cePH69evXopISFBkZGRmjp1qsqVK6eZM2cW2P+DDz7Qc889p+joaNWvX18zZsxQbm6uVq1a5fA+SewAABRBRkaG3ZKVlVVgv+zsbG3evFmxsbG2NqvVqtjYWK1bt86hfZ09e1bnz59XpUqVHI6PxA4AMAWLC/5JUlhYmAIDA21LcnJygftLS0tTTk6OQkJC7NpDQkKUkpLiUMwDBw5U9erV7b4cXA2z4gEApuCqWfGHDh1SQECArd3X19fJyAr26quv6uOPP9aaNWvk5+fn8HokdgCAKbjqxnMBAQF2ib0wVapUkZeXl1JTU+3aU1NTFRoaesV133jjDb366qv65ptv1Lhx4yLFyVA8AAAlwMfHR02aNLGb+JY3Ea558+aFrvf6669r9OjRWrFihZo2bVrk/VKxAwDMoRTuFZ+YmKj4+Hg1bdpUzZo104QJE5SZmamEhARJUvfu3VWjRg3befrXXntNI0aM0IcffqiIiAjbuXh/f3/5+/s7tE8SOwDAFErjlrKPPvqojh8/rhEjRiglJUXR0dFasWKFbULdwYMHZbX+b/B8ypQpys7OVqdOney2k5SUpJEjRzq0TxI7AAAlqE+fPurTp0+B761Zs8bu9YEDB5zeH4kdAGAKZrlXPIkdAGAKJnkcO7PiAQDwJFTsAABzMEnJTmIHAJhCacyKLw0MxQMA4EGo2AEApsCseAAAPIhJTrGT2AEAJmGSzM45dgAAPAgVOwDAFMwyK57EDgAwBycnz7lJXmcoHgAAT0LFDgAwBZPMnSOxAwBMwiSZnaF4AAA8CBU7AMAUmBUPAIAHMcstZRmKBwDAg1CxAwBMwSRz50jsAACTMElmJ7EDAEzBLJPnOMcOAIAHoWIHAJiCRU7OindZJCWLxA4AMAWTnGJnKB4AAE9CxQ4AMAWz3KCGxA4AMAlzDMYzFA8AgAehYgcAmAJD8QAAeBBzDMQzFA8AgEehYgcAmAJD8QAAeBCz3CuexA4AMAeTnGTnHDsAAB6Eih0AYAomKdhJ7AAAczDL5DmG4gEA8CBU7AAAU2BWPAAAnsQkJ9kZigcAwINQsQMATMEkBTuJHQBgDsyKBwAAboeKHQBgEs7NineXwXgSOwDAFBiKBwAAbofEDgCAB2EoHgBgCmYZiiexAwBMwSy3lGUoHgAAD0LFDgAwBYbiAQDwIGa5pSxD8QAAeBAqdgCAOZikZCexAwBMgVnxAADA7VCxAwBMgVnxAAB4EJOcYiexAwBMwiSZnXPsAAB4ECp2AIApmGVWPIkdAGAKTJ5zA4ZhSJJOZ2SUciRAyTFysks7BKDE5H2+8/6el6QMJ3OFs+tfK26d2E+fPi1JqlsrrJQjAQA44/Tp0woMDCyRbfv4+Cg0NFTXuyBXhIaGysfHxwVRlRyLcS2+JpWQ3NxcHTlyRBUqVJDFXcZI3FxGRobCwsJ06NAhBQQElHY4gEvx+b72DMPQ6dOnVb16dVmtJTef+9y5c8rOdn70y8fHR35+fi6IqOS4dcVutVp13XXXlXYYphQQEMAfPngsPt/XVklV6pfy8/P72ydkV+FyNwAAPAiJHQAAD0JiR5H4+voqKSlJvr6+pR0K4HJ8vuEJ3HryHAAAsEfFDgCAByGxAwDgQUjsAAB4EBI7AAAehMQOh02ePFkRERHy8/NTTEyMNm7cWNohAS7x3XffqX379qpevbosFosWL15c2iEBxUZih0Pmz5+vxMREJSUlacuWLYqKilJcXJyOHTtW2qEBTsvMzFRUVJQmT55c2qEATuNyNzgkJiZGt9xyi9555x1JF+/THxYWpueff16DBg0q5egA17FYLFq0aJE6duxY2qEAxULFjqvKzs7W5s2bFRsba2uzWq2KjY3VunXrSjEyAMDlSOy4qrS0NOXk5CgkJMSuPSQkRCkpKaUUFQCgICR2AAA8CIkdV1WlShV5eXkpNTXVrj01NVWhoaGlFBUAoCAkdlyVj4+PmjRpolWrVtnacnNztWrVKjVv3rwUIwMAXM67tAOAe0hMTFR8fLyaNm2qZs2aacKECcrMzFRCQkJphwY47cyZM9qzZ4/t9f79+7Vt2zZVqlRJNWvWLMXIgKLjcjc47J133tG4ceOUkpKi6OhoTZw4UTExMaUdFuC0NWvWqE2bNvna4+PjNXv27GsfEOAEEjsAAB6Ec+wAAHgQEjsAAB6ExA4AgAchsQMA4EFI7AAAeBASOwAAHoTEDgCAByGxA07q0aOH3bO7W7durRdeeOGax7FmzRpZLBadOnWq0D4Wi0WLFy92eJsjR45UdHS0U3EdOHBAFotF27Ztc2o7ABxDYodH6tGjhywWiywWi3x8fFS3bl29/PLLunDhQonv+7PPPtPo0aMd6utIMgaAouBe8fBYd999t2bNmqWsrCwtX75cvXv3VpkyZTR48OB8fbOzs+Xj4+OS/VaqVMkl2wGA4qBih8fy9fVVaGiowsPD9eyzzyo2NlZLliyR9L/h81deeUXVq1dXvXr1JEmHDh3SI488oqCgIFWqVEkdOnTQgQMHbNvMyclRYmKigoKCVLlyZf3zn//U5XdlvnwoPisrSwMHDlRYWJh8fX1Vt25dvf/++zpw4IDt/uQVK1aUxWJRjx49JF18el5ycrJq1aqlsmXLKioqSgsWLLDbz/Lly3XDDTeobNmyatOmjV2cjho4cKBuuOEGlStXTrVr19bw4cN1/vz5fP3ee+89hYWFqVy5cnrkkUeUnp5u9/6MGTPUoEED+fn5qX79+nr33XeLHAsA1yCxwzTKli2r7Oxs2+tVq1Zp165dWrlypZYuXarz588rLi5OFSpU0Pfff68ff/xR/v7+uvvuu23rvfnmm5o9e7ZmzpypH374QSdPntSiRYuuuN/u3bvro48+0sSJE7Vjxw6999578vf3V1hYmBYuXChJ2rVrl44ePaq3335bkpScnKy5c+dq6tSp+uWXX9S/f389/vjj+ve//y3p4heQBx98UO3bt9e2bdvUs2dPDRo0qMg/kwoVKmj27Nn69ddf9fbbb2v69Ol666237Prs2bNHn3zyib744gutWLFCW7du1XPPPWd7/4MPPtCIESP0yiuvaMeOHRo7dqyGDx+uOXPmFDkeAC5gAB4oPj7e6NChg2EYhpGbm2usXLnS8PX1NQYMGGB7PyQkxMjKyrKtM2/ePKNevXpGbm6urS0rK8soW7as8dVXXxmGYRjVqlUzXn/9ddv758+fN6677jrbvgzDMFq1amX069fPMAzD2LVrlyHJWLlyZYFxrl692pBk/PHHH7a2c+fOGeXKlTPWrl1r1/fJJ580OnfubBiGYQwePNiIjIy0e3/gwIH5tnU5ScaiRYsKfX/cuHFGkyZNbK+TkpIMLy8v4/fff7e1ffnll4bVajWOHj1qGIZh1KlTx/jwww/ttjN69GijefPmhmEYxv79+w1JxtatWwvdLwDX4Rw7PNbSpUvl7++v8+fPKzc3V126dNHIkSNt7zdq1MjuvPr27du1Z88eVahQwW47586d0969e5Wenq6jR4/aParW29tbTZs2zTccn2fbtm3y8vJSq1atHI57z549Onv2rNq2bWvXnp2drZtuukmStGPHjnyPzG3evLnD+8gzf/58TZw4UXv37tWZM2d04cIFBQQE2PWpWbOmatSoYbef3Nxc7dq1SxUqVNDevXv15JNPqlevXrY+Fy5cUGBgYJHjAeA8Ejs8Vps2bTRlyhT5+PioevXq8va2/7iXL1/e7vWZM2fUpEkTffDBB/m2VbVq1WLFULZs2SKvc+bMGUnSsmXL7BKqdHHegKusW7dOXbt21ahRoxQXF6fAwEB9/PHHevPNN4sc6/Tp0/N90fDy8nJZrAAcR2KHxypfvrzq1q3rcP+bb75Z8+fPV3BwcL6qNU+1atW0YcMG3XHHHZIuVqabN2/WzTffXGD/Ro0aKTc3V//+978VGxub7/28EYOcnBxbW2RkpHx9fXXw4MFCK/0GDRrYJgLmWb9+/dUP8hJr165VeHi4hg4damv77bff8vU7ePCgjhw5ourVq9v2Y7VaVa9ePYWEhKh69erat2+funbtWqT9AygZTJ4D/tK1a1dVqVJFHTp00Pfff6/9+/drzZo16tu3r37//XdJUr9+/fTqq69q8eLF2rlzp5577rkrXoMeERGh+Ph4PfHEE1q8eLFtm5988okkKTw8XBaLRUuXLtXx48d15swZVahQQQMGDFD//v01Z84c7d27V1u2bNGkSZNsE9KeeeYZ/fe//9VLL72kXbt26cMPP9Ts2bOLdLzXX3+9Dh48qI8//lh79+7VxIkTC5wI6Ofnp/j4eG3fvl3ff/+9+vbtq0ceeUShoaGSpFGjRik5OVkTJ07U7t279fPPP2vWrFkaP358keIB4BokduAv5cqV03fffaeaNWvqwQcfVIMGDfTkk0/q3Llztgr+xRdfVLdu3RQfH6/mzZurQoUKeuCBB6643SlTpqhTp0567rnnVL9+ffXq1UuZmZmSpBo1amjUqFEaNGiQQkJC1KdPH0nS6NGjNXz4cCUnJ6tBgwa6++67tWzZMtWqVUvSxfPeCxcu1OLFixUVFaWpU6dq7NixRTre+++/X/3791efPn0UHR2ttWvXavjw4fn61a1bVw8++KDuvfdetWvXTo0bN7a7nK1nz56aMWOGZs2apUaNGqlVq1aaPXu2LVYA15bFKGzWDwAAcDtU7AAAeBASOwAAHoTEDgCAByGxAwDgQUjsAAB4EBI7AAAehMQOAIAHIbEDAOBBSOwAAHgQEjsAAB6ExA4AgAchsQMA4EH+H1TnuQFt+zRqAAAAAElFTkSuQmCC", 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", 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", + "text/plain": [ + "
      " + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "from sklearn.ensemble import AdaBoostClassifier\n", + "\n", + "ada_clf = AdaBoostClassifier(\n", + " DecisionTreeClassifier(max_depth=2), n_estimators=200,\n", + " algorithm=\"SAMME.R\", learning_rate=0.01, random_state=42)\n", + "ada_clf.fit(X_train, y_train)\n", + "y_pred = ada_clf.predict(X_test)\n", + "skplt.metrics.plot_confusion_matrix(y_test, y_pred, normalize=True)\n", + "plt.show()\n", + "y_probas = ada_clf.predict_proba(X_test)\n", + "skplt.metrics.plot_roc(y_test, y_probas)\n", + "plt.show()\n", + "skplt.metrics.plot_cumulative_gain(y_test, y_probas)\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "d6470164", + "metadata": { + "editable": true + }, + "source": [ + "## Making an ADAboost code yourself" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "b6e01886", + "metadata": { + "collapsed": false, + "editable": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Predictions: [0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]\n" + ] + } + ], + "source": [ + "import numpy as np\n", + "\n", + "class DecisionStump:\n", + " def fit(self, X, y, weights):\n", + " m, n = X.shape\n", + " self.alpha = 0\n", + " self.threshold = None\n", + " self.polarity = 1\n", + "\n", + " min_error = float('inf')\n", + "\n", + " for feature in range(n):\n", + " feature_values = np.unique(X[:, feature])\n", + "\n", + " for threshold in feature_values:\n", + " for polarity in [1, -1]:\n", + " predictions = np.ones(m)\n", + " predictions[X[:, feature] < threshold] = -1\n", + " predictions *= polarity\n", + "\n", + " error = sum(weights[predictions != y])\n", + "\n", + " if error < min_error:\n", + " min_error = error\n", + " self.alpha = 0.5 * np.log((1 - error) / (error + 1e-10))\n", + " self.threshold = threshold\n", + " self.feature_index = feature\n", + " self.polarity = polarity\n", + "\n", + " def predict(self, X):\n", + " m = X.shape[0]\n", + " predictions = np.ones(m)\n", + " if self.polarity == 1:\n", + " predictions[X[:, self.feature_index] < self.threshold] = -1\n", + " else:\n", + " predictions[X[:, self.feature_index] >= self.threshold] = -1\n", + " return predictions\n", + "\n", + "class AdaBoost:\n", + " def fit(self, X, y, n_estimators):\n", + " m = X.shape[0]\n", + " self.alphas = []\n", + " self.models = []\n", + "\n", + " weights = np.ones(m) / m\n", + "\n", + " for _ in range(n_estimators):\n", + " stump = DecisionStump()\n", + " stump.fit(X, y, weights)\n", + " predictions = stump.predict(X)\n", + "\n", + " error = sum(weights[predictions != y])\n", + " if error == 0:\n", + " break\n", + "\n", + " self.models.append(stump)\n", + " self.alphas.append(stump.alpha)\n", + "\n", + " weights *= np.exp(-stump.alpha * y * predictions)\n", + " weights /= np.sum(weights)\n", + "\n", + " def predict(self, X):\n", + " final_predictions = np.zeros(X.shape[0])\n", + " for alpha, model in zip(self.alphas, self.models):\n", + " final_predictions += alpha * model.predict(X)\n", + " return np.sign(final_predictions)\n", + "\n", + "# Example dataset (X, y)\n", + "X = np.array([[1], [2], [3], [4], [5], [6], [7], [8], [9], [10]])\n", + "y = np.array([-1, -1, -1, -1, 1, 1, 1, 1, 1, 1]) # Labels must be -1 or 1\n", + "\n", + "# Train AdaBoost\n", + "ada = AdaBoost()\n", + "ada.fit(X, y, n_estimators=10)\n", + "\n", + "# Predictions\n", + "predictions = ada.predict(X)\n", + "print(\"Predictions:\", predictions)" + ] + }, + { + "cell_type": "markdown", + "id": "3cede4c9", + "metadata": { + "editable": true + }, + "source": [ + "## Gradient boosting: Basics with Steepest Descent/Functional Gradient Descent\n", + "\n", + "Gradient boosting is again a similar technique to Adaptive boosting,\n", + "it combines so-called weak classifiers or regressors into a strong\n", + "method via a series of iterations.\n", + "\n", + "In order to understand the method, let us illustrate its basics by\n", + "bringing back the essential steps in linear regression, where our cost\n", + "function was the least squares function." + ] + }, + { + "cell_type": "markdown", + "id": "94e3b59f", + "metadata": { + "editable": true + }, + "source": [ + "## The Squared-Error again! Steepest Descent\n", + "\n", + "We start again with our cost function ${\\cal C}(\\boldsymbol{y}m\\boldsymbol{f})=\\sum_{i=0}^{n-1}{\\cal L}(y_i, f(x_i))$ where we want to minimize\n", + "This means that for every iteration, we need to optimize" + ] + }, + { + "cell_type": "markdown", + "id": "2f000f62", + "metadata": { + "editable": true + }, + "source": [ + "$$\n", + "(\\hat{\\boldsymbol{f}}) = \\mathrm{argmin}_{\\boldsymbol{f}}\\hspace{0.1cm} \\sum_{i=0}^{n-1}(y_i-f(x_i))^2.\n", + "$$" + ] + }, + { + "cell_type": "markdown", + "id": "014da5e9", + "metadata": { + "editable": true + }, + "source": [ + "We define a real function $h_m(x)$ that defines our final function $f_M(x)$ as" + ] + }, + { + "cell_type": "markdown", + "id": "022aee06", + "metadata": { + "editable": true + }, + "source": [ + "$$\n", + "f_M(x) = \\sum_{m=0}^M h_m(x).\n", + "$$" + ] + }, + { + "cell_type": "markdown", + "id": "d0d3e174", + "metadata": { + "editable": true + }, + "source": [ + "In the steepest decent approach we approximate $h_m(x) = -\\rho_m g_m(x)$, where $\\rho_m$ is a scalar and $g_m(x)$ the gradient defined as" + ] + }, + { + "cell_type": "markdown", + "id": "8af32042", + "metadata": { + "editable": true + }, + "source": [ + "$$\n", + "g_m(x_i) = \\left[ \\frac{\\partial {\\cal L}(y_i, f(x_i))}{\\partial f(x_i)}\\right]_{f(x_i)=f_{m-1}(x_i)}.\n", + "$$" + ] + }, + { + "cell_type": "markdown", + "id": "4a384d95", + "metadata": { + "editable": true + }, + "source": [ + "With the new gradient we can update $f_m(x) = f_{m-1}(x) -\\rho_m g_m(x)$. Using the above squared-error function we see that\n", + "the gradient is $g_m(x_i) = -2(y_i-f(x_i))$.\n", + "\n", + "Choosing $f_0(x)=0$ we obtain $g_m(x) = -2y_i$ and inserting this into the minimization problem for the cost function we have" + ] + }, + { + "cell_type": "markdown", + "id": "bd5b9bb9", + "metadata": { + "editable": true + }, + "source": [ + "$$\n", + "(\\rho_1) = \\mathrm{argmin}_{\\rho}\\hspace{0.1cm} \\sum_{i=0}^{n-1}(y_i+2\\rho y_i)^2.\n", + "$$" + ] + }, + { + "cell_type": "markdown", + "id": "560b3106", + "metadata": { + "editable": true + }, + "source": [ + "## Steepest Descent Example\n", + "\n", + "Optimizing with respect to $\\rho$ we obtain (taking the derivative) that $\\rho_1 = -1/2$. We have then that" + ] + }, + { + "cell_type": "markdown", + "id": "4742d327", + "metadata": { + "editable": true + }, + "source": [ + "$$\n", + "f_1(x) = f_{0}(x) -\\rho_1 g_1(x)=-y_i.\n", + "$$" + ] + }, + { + "cell_type": "markdown", + "id": "d160a37f", + "metadata": { + "editable": true + }, + "source": [ + "We can then proceed and compute" + ] + }, + { + "cell_type": "markdown", + "id": "f3cde07e", + "metadata": { + "editable": true + }, + "source": [ + "$$\n", + "g_2(x_i) = \\left[ \\frac{\\partial {\\cal L}(y_i, f(x_i))}{\\partial f(x_i)}\\right]_{f(x_i)=f_{1}(x_i)=y_i}=-4y_i,\n", + "$$" + ] + }, + { + "cell_type": "markdown", + "id": "cf95ad5e", + "metadata": { + "editable": true + }, + "source": [ + "and find a new value for $\\rho_2=-1/2$ and continue till we have reached $m=M$. We can modify the steepest descent method, or steepest boosting, by introducing what is called **gradient boosting**." + ] + }, + { + "cell_type": "markdown", + "id": "bb614b9d", + "metadata": { + "editable": true + }, + "source": [ + "## Gradient Boosting, algorithm\n", + "\n", + "Steepest descent is however not much used, since it only optimizes $f$ at a fixed set of $n$ points,\n", + "so we do not learn a function that can generalize. However, we can modify the algorithm by\n", + "fitting a weak learner to approximate the negative gradient signal. \n", + "\n", + "Suppose we have a cost function $C(f)=\\sum_{i=0}^{n-1}L(y_i, f(x_i))$ where $y_i$ is our target and $f(x_i)$ the function which is meant to model $y_i$. The above cost function could be our standard squared-error function" + ] + }, + { + "cell_type": "markdown", + "id": "19e3e8fa", + "metadata": { + "editable": true + }, + "source": [ + "$$\n", + "C(\\boldsymbol{y},\\boldsymbol{f})=\\sum_{i=0}^{n-1}(y_i-f(x_i))^2.\n", + "$$" + ] + }, + { + "cell_type": "markdown", + "id": "9330b34b", + "metadata": { + "editable": true + }, + "source": [ + "The way we proceed in an iterative fashion is to\n", + "1. Initialize our estimate $f_0(x)$.\n", + "\n", + "2. For $m=1:M$, we\n", + "\n", + "a. compute the negative gradient vector $\\boldsymbol{u}_m = -\\partial C(\\boldsymbol{y},\\boldsymbol{f})/\\partial \\boldsymbol{f}(x)$ at $f(x) = f_{m-1}(x)$;\n", + "\n", + "b. fit the so-called base-learner to the negative gradient $h_m(u_m,x)$;\n", + "\n", + "c. update the estimate $f_m(x) = f_{m-1}(x)+h_m(u_m,x)$;\n", + "\n", + "4. The final estimate is then $f_M(x) = \\sum_{m=1}^M h_m(u_m,x)$." + ] + }, + { + "cell_type": "markdown", + "id": "e648539a", + "metadata": { + "editable": true + }, + "source": [ + "## Gradient Boosting, Examples of Regression" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "d12810d3", + "metadata": { + "collapsed": false, + "editable": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Max depth: 1\n", + "Error: 0.4203129333425336\n", + "Bias^2: 0.21226966048908316\n", + "Var: 0.20804327285345042\n", + "0.4203129333425336 >= 0.21226966048908316 + 0.20804327285345042 = 0.4203129333425336\n", + "Max depth: 2\n", + "Error: 0.40767639731018696\n", + "Bias^2: 0.21200998139721822\n", + "Var: 0.19566641591296877\n", + "0.40767639731018696 >= 0.21200998139721822 + 0.19566641591296877 = 0.407676397310187\n", + "Max depth: 3\n", + "Error: 0.4076774836661818\n", + "Bias^2: 0.2120099429256955\n", + "Var: 0.19566754074048626\n", + "0.4076774836661818 >= 0.2120099429256955 + 0.19566754074048626 = 0.40767748366618173\n", + "Max depth: 4\n", + "Error: 0.4076774836661818\n", + "Bias^2: 0.2120099429256955\n", + "Var: 0.19566754074048626\n", + "0.4076774836661818 >= 0.2120099429256955 + 0.19566754074048626 = 0.40767748366618173\n", + "Max depth: 5\n", + "Error: 0.4076774836661816\n", + "Bias^2: 0.2120099429256955\n", + "Var: 0.1956675407404862\n", + "0.4076774836661816 >= 0.2120099429256955 + 0.1956675407404862 = 0.40767748366618173\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/mhjensen/miniforge3/envs/myenv/lib/python3.9/site-packages/sklearn/ensemble/_gb.py:424: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n", + " y = column_or_1d(y, warn=True)\n", + "/Users/mhjensen/miniforge3/envs/myenv/lib/python3.9/site-packages/sklearn/ensemble/_gb.py:424: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n", + " y = column_or_1d(y, warn=True)\n", + "/Users/mhjensen/miniforge3/envs/myenv/lib/python3.9/site-packages/sklearn/ensemble/_gb.py:424: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n", + " y = column_or_1d(y, warn=True)\n", + "/Users/mhjensen/miniforge3/envs/myenv/lib/python3.9/site-packages/sklearn/ensemble/_gb.py:424: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n", + " y = column_or_1d(y, warn=True)\n", + "/Users/mhjensen/miniforge3/envs/myenv/lib/python3.9/site-packages/sklearn/ensemble/_gb.py:424: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n", + " y = column_or_1d(y, warn=True)\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
      " + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import matplotlib.pyplot as plt\n", + "import numpy as np\n", + "from sklearn.model_selection import train_test_split\n", + "from sklearn.ensemble import GradientBoostingRegressor\n", + "import scikitplot as skplt\n", + "from sklearn.metrics import mean_squared_error\n", + "\n", + "n = 100\n", + "maxdegree = 6\n", + "\n", + "# Make data set.\n", + "x = np.linspace(-3, 3, n).reshape(-1, 1)\n", + "y = np.exp(-x**2) + 1.5 * np.exp(-(x-2)**2)+ np.random.normal(0, 0.1, x.shape)\n", + "\n", + "error = np.zeros(maxdegree)\n", + "bias = np.zeros(maxdegree)\n", + "variance = np.zeros(maxdegree)\n", + "polydegree = np.zeros(maxdegree)\n", + "X_train, X_test, y_train, y_test = train_test_split(x, y, test_size=0.2)\n", + "\n", + "for degree in range(1,maxdegree):\n", + " model = GradientBoostingRegressor(max_depth=degree, n_estimators=100, learning_rate=1.0) \n", + " model.fit(X_train,y_train)\n", + " y_pred = model.predict(X_test)\n", + " polydegree[degree] = degree\n", + " error[degree] = np.mean( np.mean((y_test - y_pred)**2) )\n", + " bias[degree] = np.mean( (y_test - np.mean(y_pred))**2 )\n", + " variance[degree] = np.mean( np.var(y_pred) )\n", + " print('Max depth:', degree)\n", + " print('Error:', error[degree])\n", + " print('Bias^2:', bias[degree])\n", + " print('Var:', variance[degree])\n", + " print('{} >= {} + {} = {}'.format(error[degree], bias[degree], variance[degree], bias[degree]+variance[degree]))\n", + "\n", + "plt.xlim(1,maxdegree-1)\n", + "plt.plot(polydegree, error, label='Error')\n", + "plt.plot(polydegree, bias, label='bias')\n", + "plt.plot(polydegree, variance, label='Variance')\n", + "plt.legend()\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "f3e95eca", + "metadata": { + "editable": true + }, + "source": [ + "## Gradient Boosting, Classification Example" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "d6426469", + "metadata": { + "collapsed": false, + "editable": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "(426, 30)\n", + "(143, 30)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[0.93333333 0.93333333 0.93333333 0.92857143 1. 0.92857143\n", + " 1. 0.92857143 0.85714286 0.92857143]\n", + "Test set accuracy with Gradient boosting and scaled data: 0.99\n" + ] + }, + { + "data": { + "image/png": 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", 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", 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", + "text/plain": [ + "
      " + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import matplotlib.pyplot as plt\n", + "import numpy as np\n", + "from sklearn.model_selection import train_test_split \n", + "from sklearn.datasets import load_breast_cancer\n", + "import scikitplot as skplt\n", + "from sklearn.ensemble import GradientBoostingClassifier\n", + "from sklearn.model_selection import cross_validate\n", + "\n", + "# Load the data\n", + "cancer = load_breast_cancer()\n", + "\n", + "X_train, X_test, y_train, y_test = train_test_split(cancer.data,cancer.target,random_state=0)\n", + "print(X_train.shape)\n", + "print(X_test.shape)\n", + "#now scale the data\n", + "from sklearn.preprocessing import StandardScaler\n", + "scaler = StandardScaler()\n", + "scaler.fit(X_train)\n", + "X_train_scaled = scaler.transform(X_train)\n", + "X_test_scaled = scaler.transform(X_test)\n", + "\n", + "gd_clf = GradientBoostingClassifier(max_depth=3, n_estimators=100, learning_rate=1.0) \n", + "gd_clf.fit(X_train_scaled, y_train)\n", + "#Cross validation\n", + "accuracy = cross_validate(gd_clf,X_test_scaled,y_test,cv=10)['test_score']\n", + "print(accuracy)\n", + "print(\"Test set accuracy with Gradient boosting and scaled data: {:.2f}\".format(gd_clf.score(X_test_scaled,y_test)))\n", + "\n", + "import scikitplot as skplt\n", + "y_pred = gd_clf.predict(X_test_scaled)\n", + "skplt.metrics.plot_confusion_matrix(y_test, y_pred, normalize=True)\n", + "plt.show()\n", + "y_probas = gd_clf.predict_proba(X_test_scaled)\n", + "skplt.metrics.plot_roc(y_test, y_probas)\n", + "plt.show()\n", + "skplt.metrics.plot_cumulative_gain(y_test, y_probas)\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "8de56415", + "metadata": { + "editable": true + }, + "source": [ + "## XGBoost: Extreme Gradient Boosting\n", + "\n", + "[XGBoost](https://github.com/dmlc/xgboost) or Extreme Gradient\n", + "Boosting, is an optimized distributed gradient boosting library\n", + "designed to be highly efficient, flexible and portable. It implements\n", + "machine learning algorithms under the Gradient Boosting\n", + "framework. XGBoost provides a parallel tree boosting that solve many\n", + "data science problems in a fast and accurate way. See the [article by Chen and Guestrin](https://arxiv.org/abs/1603.02754).\n", + "\n", + "The authors design and build a highly scalable end-to-end tree\n", + "boosting system. It has a theoretically justified weighted quantile\n", + "sketch for efficient proposal calculation. It introduces a novel sparsity-aware algorithm for parallel tree learning and an effective cache-aware block structure for out-of-core tree learning.\n", + "\n", + "It is now the algorithm which wins essentially all ML competitions!!!" + ] + }, + { + "cell_type": "markdown", + "id": "dfc59b18", + "metadata": { + "editable": true + }, + "source": [ + "## Xgboost on the Cancer Data\n", + "\n", + "As you will see from the confusion matrix below, XGBoots does an excellent job on the Wisconsin cancer data and outperforms essentially all agorithms we have discussed till now." + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "79e4cc00", + "metadata": { + "collapsed": false, + "editable": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "(426, 30)\n", + "(143, 30)\n", + "Test set accuracy with Gradient Boosting and scaled data: 1.00\n" + ] + }, + { + "data": { + "image/png": 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", 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", 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", 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", 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+ "text/plain": [ + "
      " + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "\n", + "import matplotlib.pyplot as plt\n", + "import numpy as np\n", + "from sklearn.model_selection import train_test_split \n", + "from sklearn.datasets import load_breast_cancer\n", + "from sklearn.preprocessing import LabelEncoder\n", + "from sklearn.model_selection import cross_validate\n", + "import scikitplot as skplt\n", + "import xgboost as xgb\n", + "# Load the data\n", + "cancer = load_breast_cancer()\n", + "\n", + "X_train, X_test, y_train, y_test = train_test_split(cancer.data,cancer.target,random_state=0)\n", + "print(X_train.shape)\n", + "print(X_test.shape)\n", + "#now scale the data\n", + "from sklearn.preprocessing import StandardScaler\n", + "scaler = StandardScaler()\n", + "scaler.fit(X_train)\n", + "X_train_scaled = scaler.transform(X_train)\n", + "X_test_scaled = scaler.transform(X_test)\n", + "\n", + "xg_clf = xgb.XGBClassifier()\n", + "xg_clf.fit(X_train_scaled,y_train)\n", + "\n", + "y_test = xg_clf.predict(X_test_scaled)\n", + "\n", + "print(\"Test set accuracy with Gradient Boosting and scaled data: {:.2f}\".format(xg_clf.score(X_test_scaled,y_test)))\n", + "\n", + "import scikitplot as skplt\n", + "y_pred = xg_clf.predict(X_test_scaled)\n", + "skplt.metrics.plot_confusion_matrix(y_test, y_pred, normalize=True)\n", + "plt.show()\n", + "y_probas = xg_clf.predict_proba(X_test_scaled)\n", + "skplt.metrics.plot_roc(y_test, y_probas)\n", + "plt.show()\n", + "skplt.metrics.plot_cumulative_gain(y_test, y_probas)\n", + "plt.show()\n", + "\n", + "\n", + "xgb.plot_tree(xg_clf,num_trees=0)\n", + "plt.rcParams['figure.figsize'] = [50, 10]\n", + "plt.show()\n", + "\n", + "xgb.plot_importance(xg_clf)\n", + "plt.rcParams['figure.figsize'] = [5, 5]\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "c4700b19", + "metadata": { + "editable": true + }, + "source": [ + "## Gradient boosting, making our own code for a regression case" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "eb0594c5", + "metadata": { + "collapsed": false, + "editable": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Predictions: [1.49999399 1.69995637 3.49991627 3.6998795 4.99984484]\n" + ] + } + ], + "source": [ + "import numpy as np\n", + "class DecisionTreeRegressor:\n", + " def __init__(self, max_depth=3):\n", + " self.max_depth = max_depth\n", + " self.tree = None\n", + " def fit(self, X, y):\n", + " self.tree = self._grow_tree(X, y)\n", + " def _grow_tree(self, X, y, depth=0):\n", + " n_samples, n_features = X.shape\n", + " if depth < self.max_depth:\n", + " best_feature, best_threshold = self._best_split(X, y)\n", + " if best_feature is not None:\n", + " left_indices = X[:, best_feature] < best_threshold\n", + " right_indices = X[:, best_feature] >= best_threshold\n", + " left_child = self._grow_tree(X[left_indices], y[left_indices], depth + 1)\n", + " right_child = self._grow_tree(X[right_indices], y[right_indices], depth + 1)\n", + " return (best_feature, best_threshold, left_child, right_child)\n", + " return np.mean(y)\n", + " def _best_split(self, X, y):\n", + " best_mse = float('inf')\n", + " best_feature, best_threshold = None, None\n", + " n_samples, n_features = X.shape\n", + " \n", + " for feature in range(n_features):\n", + " thresholds = np.unique(X[:, feature])\n", + " for threshold in thresholds:\n", + " left_indices = X[:, feature] < threshold\n", + " right_indices = X[:, feature] >= threshold\n", + " if len(y[left_indices]) > 0 and len(y[right_indices]) > 0:\n", + " left_mse = np.mean((y[left_indices] - np.mean(y[left_indices])) ** 2)\n", + " right_mse = np.mean((y[right_indices] - np.mean(y[right_indices])) ** 2)\n", + " mse = (len(y[left_indices]) * left_mse + len(y[right_indices]) * right_mse) / n_samples\n", + " \n", + " if mse < best_mse:\n", + " best_mse = mse\n", + " best_feature = feature\n", + " best_threshold = threshold\n", + " return best_feature, best_threshold\n", + " def predict(self, X):\n", + " return np.array([self._predict_sample(sample, self.tree) for sample in X])\n", + " def _predict_sample(self, sample, node):\n", + " if isinstance(node, tuple):\n", + " feature, threshold, left_child, right_child = node\n", + " if sample[feature] < threshold:\n", + " return self._predict_sample(sample, left_child)\n", + " else:\n", + " return self._predict_sample(sample, right_child)\n", + " return node\n", + "class GradientBoostingRegressor:\n", + " def __init__(self, n_estimators=100, learning_rate=0.1, max_depth=3):\n", + " self.n_estimators = n_estimators\n", + " self.learning_rate = learning_rate\n", + " self.max_depth = max_depth\n", + " self.models = []\n", + " def fit(self, X, y):\n", + " y_pred = np.zeros(y.shape)\n", + " for _ in range(self.n_estimators):\n", + " residuals = y - y_pred\n", + " model = DecisionTreeRegressor(max_depth=self.max_depth)\n", + " model.fit(X, residuals)\n", + " y_pred += self.learning_rate * model.predict(X)\n", + " self.models.append(model)\n", + " def predict(self, X):\n", + " y_pred = np.zeros(X.shape[0])\n", + " for model in self.models:\n", + " y_pred += self.learning_rate * model.predict(X)\n", + " return y_pred\n", + "# Example usage\n", + "if __name__ == \"__main__\":\n", + " # Sample data\n", + " X = np.array([[1], [2], [3], [4], [5]])\n", + " y = np.array([1.5, 1.7, 3.5, 3.7, 5.0])\n", + " model = GradientBoostingRegressor(n_estimators=100, learning_rate=0.1, max_depth=2)\n", + " model.fit(X, y)\n", + " predictions = model.predict(X)\n", + " print(\"Predictions:\", predictions)" + ] + }, + { + "cell_type": "markdown", + "id": "bbf154c4", + "metadata": { + "editable": true + }, + "source": [ + "## Summary of course" + ] + }, + { + "cell_type": "markdown", + "id": "84d512fc", + "metadata": { + "editable": true + }, + "source": [ + "## What? Me worry? No final exam in this course!\n", + "\n", + "\n", + "\n", + "

      Figure 1:

      \n", + "" + ] + }, + { + "cell_type": "markdown", + "id": "71b8eb59", + "metadata": { + "editable": true + }, + "source": [ + "## Topics we have covered this year\n", + "\n", + "The course has two central parts\n", + "\n", + "1. Statistical analysis and optimization of data\n", + "\n", + "2. Machine learning" + ] + }, + { + "cell_type": "markdown", + "id": "473329b7", + "metadata": { + "editable": true + }, + "source": [ + "## Statistical analysis and optimization of data\n", + "\n", + "The following topics have been discussed:\n", + "1. Basic concepts, expectation values, variance, covariance, correlation functions and errors;\n", + "\n", + "2. Simpler models, binomial distribution, the Poisson distribution, simple and multivariate normal distributions;\n", + "\n", + "3. Central elements from linear algebra, matrix inversion and SVD\n", + "\n", + "4. Gradient methods for data optimization\n", + "\n", + "5. Estimation of errors using cross-validation, bootstrapping and jackknife methods;\n", + "\n", + "6. Practical optimization using Singular-value decomposition and least squares for parameterizing data.\n", + "\n", + "7. Not discussed: Principal Component Analysis to reduce the number of features." + ] + }, + { + "cell_type": "markdown", + "id": "6c94f44a", + "metadata": { + "editable": true + }, + "source": [ + "## Machine learning\n", + "\n", + "* Linear methods for regression and classification:\n", + "\n", + "a. Ordinary Least Squares\n", + "\n", + "b. Ridge regression\n", + "\n", + "c. Lasso regression\n", + "\n", + "d. Logistic regression\n", + "\n", + "* Neural networks and deep learning:\n", + "\n", + "a. Feed Forward Neural Networks\n", + "\n", + "b. Convolutional Neural Networks\n", + "\n", + "c. Recurrent Neural Networks\n", + "\n", + "* Decisions trees and ensemble methods:\n", + "\n", + "a. Decision trees\n", + "\n", + "b. Bagging and voting\n", + "\n", + "c. Random forests\n", + "\n", + "d. Boosting and gradient boosting\n", + "\n", + "* Not discussed this year: Support vector machines\n", + "\n", + "a. Binary classification and multiclass classification\n", + "\n", + "b. Kernel methods\n", + "\n", + "c. Regression" + ] + }, + { + "cell_type": "markdown", + "id": "d9942ded", + "metadata": { + "editable": true + }, + "source": [ + "## Learning outcomes and overarching aims of this course\n", + "\n", + "The course introduces a variety of central algorithms and methods\n", + "essential for studies of data analysis and machine learning. The\n", + "course is project based and through the various projects, normally\n", + "three, you will be exposed to fundamental research problems\n", + "in these fields, with the aim to reproduce state of the art scientific\n", + "results. The students will learn to develop and structure large codes\n", + "for studying these systems, get acquainted with computing facilities\n", + "and learn to handle large scientific projects. A good scientific and\n", + "ethical conduct is emphasized throughout the course. \n", + "\n", + "* Understand linear methods for regression and classification;\n", + "\n", + "* Learn about neural network;\n", + "\n", + "* Learn about bagging, boosting and trees\n", + "\n", + "\n", + "* Learn about basic data analysis;\n", + "\n", + "* Be capable of extending the acquired knowledge to other systems and cases;\n", + "\n", + "* Have an understanding of central algorithms used in data analysis and machine learning;\n", + "\n", + "* Work on numerical projects to illustrate the theory. The projects play a central role." + ] + }, + { + "cell_type": "markdown", + "id": "7818c700", + "metadata": { + "editable": true + }, + "source": [ + "## Perspective on Machine Learning\n", + "\n", + "1. Rapidly emerging application area\n", + "\n", + "2. Experiment AND theory are evolving in many many fields. \n", + "\n", + "3. Requires education/retraining for more widespread adoption\n", + "\n", + "4. A lot of “word-of-mouth” development methods\n", + "\n", + "Huge amounts of data sets require automation, classical analysis tools often inadequate. \n", + "High energy physics hit this wall in the 90’s.\n", + "In 2009 single top quark production was determined via [Boosted decision trees, Bayesian\n", + "Neural Networks, etc.](https://arxiv.org/pdf/0903.0850.pdf)" + ] + }, + { + "cell_type": "markdown", + "id": "cc946602", + "metadata": { + "editable": true + }, + "source": [ + "## Machine Learning Research\n", + "\n", + "Where to find recent results:\n", + "1. Conference proceedings, arXiv and blog posts!\n", + "\n", + "2. **NIPS**: [Neural Information Processing Systems](https://papers.nips.cc)\n", + "\n", + "3. **ICLR**: [International Conference on Learning Representations](https://openreview.net/group?id=ICLR.cc/2018/Conference#accepted-oral-papers)\n", + "\n", + "4. **ICML**: International Conference on Machine Learning\n", + "\n", + "5. [Journal of Machine Learning Research](http://www.jmlr.org/papers/v19/) \n", + "\n", + "6. [Follow ML on ArXiv](https://arxiv.org/list/cs.LG/recent)" + ] + }, + { + "cell_type": "markdown", + "id": "c2b75185", + "metadata": { + "editable": true + }, + "source": [ + "## Starting your Machine Learning Project\n", + "\n", + "1. Identify problem type: classification, regression\n", + "\n", + "2. Consider your data carefully\n", + "\n", + "3. Choose a simple model that fits 1 and 2\n", + "\n", + "4. Consider your data carefully again! Think of data representation more carefully.\n", + "\n", + "5. Based on your results, feedback loop to earliest possible point" + ] + }, + { + "cell_type": "markdown", + "id": "4600986a", + "metadata": { + "editable": true + }, + "source": [ + "## Choose a Model and Algorithm\n", + "\n", + "* Supervised?\n", + "\n", + "* Start with the simplest model that fits your problem\n", + "\n", + "* Start with minimal processing of data" + ] + }, + { + "cell_type": "markdown", + "id": "cfb572d5", + "metadata": { + "editable": true + }, + "source": [ + "## Preparing Your Data\n", + "\n", + "* Shuffle your data\n", + "\n", + "* Mean center your data\n", + "\n", + " * Why?\n", + "\n", + "* Normalize the variance\n", + "\n", + " * Why?\n", + "\n", + "* **Whitening**\n", + "\n", + " * Decorrelates data\n", + "\n", + " * Can be hit or miss\n", + "\n", + " * When to do train/test split?" + ] + }, + { + "cell_type": "markdown", + "id": "3e38f862", + "metadata": { + "editable": true + }, + "source": [ + "## Which activation and weights to choose in neural networks\n", + "\n", + "* RELU? ELU? GELU? etc\n", + "\n", + "* Sigmoid or Tanh?\n", + "\n", + "* Set all weights to 0? Terrible idea\n", + "\n", + "* Set all weights to random values? Small random values" + ] + }, + { + "cell_type": "markdown", + "id": "76abe122", + "metadata": { + "editable": true + }, + "source": [ + "## Optimization Methods and Hyperparameters\n", + "* Stochastic gradient descent\n", + "\n", + " * Stochastic gradient descent + momentum\n", + "\n", + "* State-of-the-art approaches:\n", + "\n", + "a. RMSProp\n", + "\n", + "b. Adam\n", + "\n", + "c. and more\n", + "\n", + "Which regularization and hyperparameters? $L_1$ or $L_2$, soft\n", + "classifiers, depths of trees and many other. Need to explore a large\n", + "set of hyperparameters and regularization methods." + ] + }, + { + "cell_type": "markdown", + "id": "0b246db8", + "metadata": { + "editable": true + }, + "source": [ + "## Resampling\n", + "\n", + "When do we resample?\n", + "\n", + "1. [Bootstrap](https://www.cambridge.org/core/books/bootstrap-methods-and-their-application/ED2FD043579F27952363566DC09CBD6A)\n", + "\n", + "2. [Cross-validation](https://www.youtube.com/watch?v=fSytzGwwBVw&ab_channel=StatQuestwithJoshStarmer)\n", + "\n", + "3. Jackknife and many other" + ] + }, + { + "cell_type": "markdown", + "id": "9a6555fc", + "metadata": { + "editable": true + }, + "source": [ + "## Other courses on Data science and Machine Learning at UiO\n", + "\n", + "1. [FYS5429 – Advanced machine learning and data analysis for the physical sciences](https://www.uio.no/studier/emner/matnat/fys/FYS5429/index-eng.html)\n", + "\n", + "2. [IN3050/IN4050 Introduction to Artificial Intelligence and Machine Learning](https://www.uio.no/studier/emner/matnat/ifi/IN3050/index-eng.html). Introductory course in machine learning and AI\n", + "\n", + "3. [STK-INF3000/4000 Selected Topics in Data Science](http://www.uio.no/studier/emner/matnat/math/STK-INF3000/index-eng.html). The course provides insight into selected contemporary relevant topics within Data Science. \n", + "\n", + "4. [IN4080 Natural Language Processing](https://www.uio.no/studier/emner/matnat/ifi/IN4080/index.html). Probabilistic and machine learning techniques applied to natural language processing. \n", + "\n", + "5. [STK-IN4300 – Statistical learning methods in Data Science](https://www.uio.no/studier/emner/matnat/math/STK-IN4300/index-eng.html). An advanced introduction to statistical and machine learning. For students with a good mathematics and statistics background.\n", + "\n", + "6. [IN-STK5000 Responsible Data Science](https://www.uio.no/studier/emner/matnat/ifi/IN-STK5000/index-eng.html). Methods for adaptive collection and processing of data based on machine learning techniques. \n", + "\n", + "7. [IN4310 – Machine Learning for Image Analysis](https://www.uio.no/studier/emner/matnat/ifi/IN4310/index.html). An introduction to deep learning with particular emphasis on applications within Image analysis, but useful for other application areas too.\n", + "\n", + "8. [IN5310 – Advanced Deep Learning for Image Analysis](https://www.uio.no/studier/emner/matnat/ifi/IN5310/index.html)\n", + "\n", + "9. [IN5490 – Advanced Topics in Artificial Intelligence for Intelligent Systems](https://www.uio.no/studier/emner/matnat/ifi/IN5490/index.html)\n", + "\n", + "10. [TEK5040 – Deep learning for autonomous systems](https://www.uio.no/studier/emner/matnat/its/TEK5040/). The course addresses advanced algorithms and architectures for deep learning with neural networks. The course provides an introduction to how deep-learning techniques can be used in the construction of key parts of advanced autonomous systems that exist in physical environments and cyber environments." + ] + }, + { + "cell_type": "markdown", + "id": "c2561e72", + "metadata": { + "editable": true + }, + "source": [ + "## Additional courses of interest\n", + "\n", + "1. [STK4051 Computational Statistics](https://www.uio.no/studier/emner/matnat/math/STK4051/index-eng.html)\n", + "\n", + "2. [STK4021 Applied Bayesian Analysis and Numerical Methods](https://www.uio.no/studier/emner/matnat/math/STK4021/index-eng.html)" + ] + }, + { + "cell_type": "markdown", + "id": "796637c8", + "metadata": { + "editable": true + }, + "source": [ + "## What's the future like?\n", + "\n", + "Based on multi-layer nonlinear neural networks, deep learning can\n", + "learn directly from raw data, automatically extract and abstract\n", + "features from layer to layer, and then achieve the goal of regression,\n", + "classification, or ranking. Deep learning has made breakthroughs in\n", + "computer vision, speech processing and natural language, and reached\n", + "or even surpassed human level. The success of deep learning is mainly\n", + "due to the three factors: big data, big model, and big computing.\n", + "\n", + "In the past few decades, many different architectures of deep neural\n", + "networks have been proposed, such as\n", + "1. Convolutional neural networks, which are mostly used in image and video data processing, and have also been applied to sequential data such as text processing;\n", + "\n", + "2. Recurrent neural networks, which can process sequential data of variable length and have been widely used in natural language understanding and speech processing;\n", + "\n", + "3. Encoder-decoder framework, which is mostly used for image or sequence generation, such as machine translation, text summarization, and image captioning." + ] + }, + { + "cell_type": "markdown", + "id": "6afdb59e", + "metadata": { + "editable": true + }, + "source": [ + "## Types of Machine Learning, a repetition\n", + "\n", + "The approaches to machine learning are many, but are often split into two main categories. \n", + "In *supervised learning* we know the answer to a problem,\n", + "and let the computer deduce the logic behind it. On the other hand, *unsupervised learning*\n", + "is a method for finding patterns and relationship in data sets without any prior knowledge of the system.\n", + "Some authours also operate with a third category, namely *reinforcement learning*. This is a paradigm \n", + "of learning inspired by behavioural psychology, where learning is achieved by trial-and-error, \n", + "solely from rewards and punishment.\n", + "\n", + "Another way to categorize machine learning tasks is to consider the desired output of a system.\n", + "Some of the most common tasks are:\n", + "\n", + " * Classification: Outputs are divided into two or more classes. The goal is to produce a model that assigns inputs into one of these classes. An example is to identify digits based on pictures of hand-written ones. Classification is typically supervised learning.\n", + "\n", + " * Regression: Finding a functional relationship between an input data set and a reference data set. The goal is to construct a function that maps input data to continuous output values.\n", + "\n", + " * Clustering: Data are divided into groups with certain common traits, without knowing the different groups beforehand. It is thus a form of unsupervised learning.\n", + "\n", + " * Other unsupervised learning algortihms like **Boltzmann machines**" + ] + }, + { + "cell_type": "markdown", + "id": "95097424", + "metadata": { + "editable": true + }, + "source": [ + "## Why Boltzmann machines?\n", + "\n", + "What is known as restricted Boltzmann Machines (RMB) have received a lot of attention lately. \n", + "One of the major reasons is that they can be stacked layer-wise to build deep neural networks that capture complicated statistics.\n", + "\n", + "The original RBMs had just one visible layer and a hidden layer, but recently so-called Gaussian-binary RBMs have gained quite some popularity in imaging since they are capable of modeling continuous data that are common to natural images. \n", + "\n", + "Furthermore, they have been used to solve complicated [quantum mechanical many-particle problems or classical statistical physics problems like the Ising and Potts classes of models](https://journals.aps.org/rmp/abstract/10.1103/RevModPhys.91.045002)." + ] + }, + { + "cell_type": "markdown", + "id": "31a33cea", + "metadata": { + "editable": true + }, + "source": [ + "## Boltzmann Machines\n", + "\n", + "Why use a generative model rather than the more well known discriminative deep neural networks (DNN)? \n", + "\n", + "* Discriminitave methods have several limitations: They are mainly supervised learning methods, thus requiring labeled data. And there are tasks they cannot accomplish, like drawing new examples from an unknown probability distribution.\n", + "\n", + "* A generative model can learn to represent and sample from a probability distribution. The core idea is to learn a parametric model of the probability distribution from which the training data was drawn. As an example\n", + "\n", + "a. A model for images could learn to draw new examples of cats and dogs, given a training dataset of images of cats and dogs.\n", + "\n", + "b. Generate a sample of an ordered or disordered phase, having been given samples of such phases.\n", + "\n", + "c. Model the trial function for [Monte Carlo calculations](https://journals.aps.org/rmp/abstract/10.1103/RevModPhys.91.045002)." + ] + }, + { + "cell_type": "markdown", + "id": "d6583875", + "metadata": { + "editable": true + }, + "source": [ + "## Some similarities and differences from DNNs\n", + "\n", + "1. Both use gradient-descent based learning procedures for minimizing cost functions\n", + "\n", + "2. Energy based models don't use backpropagation and automatic differentiation for computing gradients, instead turning to Markov Chain Monte Carlo methods.\n", + "\n", + "3. DNNs often have several hidden layers. A restricted Boltzmann machine has only one hidden layer, however several RBMs can be stacked to make up Deep Belief Networks, of which they constitute the building blocks.\n", + "\n", + "History: The RBM was developed by amongst others [Geoffrey Hinton](https://en.wikipedia.org/wiki/Geoffrey_Hinton), called by some the \"Godfather of Deep Learning\", working with the University of Toronto and Google." + ] + }, + { + "cell_type": "markdown", + "id": "285bbf5d", + "metadata": { + "editable": true + }, + "source": [ + "## Boltzmann machines (BM)\n", + "\n", + "A BM is what we would call an undirected probabilistic graphical model\n", + "with stochastic continuous or discrete units.\n", + "\n", + "It is interpreted as a stochastic recurrent neural network where the\n", + "state of each unit(neurons/nodes) depends on the units it is connected\n", + "to. The weights in the network represent thus the strength of the\n", + "interaction between various units/nodes.\n", + "\n", + "It turns into a Hopfield network if we choose deterministic rather\n", + "than stochastic units. In contrast to a Hopfield network, a BM is a\n", + "so-called generative model. It allows us to generate new samples from\n", + "the learned distribution." + ] + }, + { + "cell_type": "markdown", + "id": "5b51dec9", + "metadata": { + "editable": true + }, + "source": [ + "## A standard BM setup\n", + "\n", + "A standard BM network is divided into a set of observable and visible units $\\hat{x}$ and a set of unknown hidden units/nodes $\\hat{h}$.\n", + "\n", + "Additionally there can be bias nodes for the hidden and visible layers. These biases are normally set to $1$.\n", + "\n", + "BMs are stackable, meaning they cwe can train a BM which serves as input to another BM. We can construct deep networks for learning complex PDFs. The layers can be trained one after another, a feature which makes them popular in deep learning\n", + "\n", + "However, they are often hard to train. This leads to the introduction of so-called restricted BMs, or RBMS.\n", + "Here we take away all lateral connections between nodes in the visible layer as well as connections between nodes in the hidden layer. The network is illustrated in the figure below." + ] + }, + { + "cell_type": "markdown", + "id": "4cb37b8f", + "metadata": { + "editable": true + }, + "source": [ + "## The structure of the RBM network\n", + "\n", + "\n", + "\n", + "\n", + "

      Figure 1:

      \n", + "" + ] + }, + { + "cell_type": "markdown", + "id": "45fe33d7", + "metadata": { + "editable": true + }, + "source": [ + "## The network\n", + "\n", + "**The network layers**:\n", + "1. A function $\\mathbf{x}$ that represents the visible layer, a vector of $M$ elements (nodes). This layer represents both what the RBM might be given as training input, and what we want it to be able to reconstruct. This might for example be given by the pixels of an image or coefficients representing speech, or the coordinates of a quantum mechanical state function.\n", + "\n", + "2. The function $\\mathbf{h}$ represents the hidden, or latent, layer. A vector of $N$ elements (nodes). Also called \"feature detectors\"." + ] + }, + { + "cell_type": "markdown", + "id": "3bbbaa88", + "metadata": { + "editable": true + }, + "source": [ + "## Goals\n", + "\n", + "The goal of the hidden layer is to increase the model's expressive\n", + "power. We encode complex interactions between visible variables by\n", + "introducing additional, hidden variables that interact with visible\n", + "degrees of freedom in a simple manner, yet still reproduce the complex\n", + "correlations between visible degrees in the data once marginalized\n", + "over (integrated out).\n", + "\n", + "**The network parameters, to be optimized/learned**:\n", + "1. $\\mathbf{a}$ represents the visible bias, a vector of same length as $\\mathbf{x}$.\n", + "\n", + "2. $\\mathbf{b}$ represents the hidden bias, a vector of same lenght as $\\mathbf{h}$.\n", + "\n", + "3. $W$ represents the interaction weights, a matrix of size $M\\times N$." + ] + }, + { + "cell_type": "markdown", + "id": "d6a9f606", + "metadata": { + "editable": true + }, + "source": [ + "## Joint distribution\n", + "\n", + "The restricted Boltzmann machine is described by a Boltzmann distribution" + ] + }, + { + "cell_type": "markdown", + "id": "81aaff6e", + "metadata": { + "editable": true + }, + "source": [ + "\n", + "
      \n", + "\n", + "$$\n", + "\\begin{equation}\n", + "\tP_{rbm}(\\mathbf{x},\\mathbf{h}) = \\frac{1}{Z} e^{-\\frac{1}{T_0}E(\\mathbf{x},\\mathbf{h})},\n", + "\\label{_auto1} \\tag{1}\n", + "\\end{equation}\n", + "$$" + ] + }, + { + "cell_type": "markdown", + "id": "9f856eb3", + "metadata": { + "editable": true + }, + "source": [ + "where $Z$ is the normalization constant or partition function, defined as" + ] + }, + { + "cell_type": "markdown", + "id": "f15429d1", + "metadata": { + "editable": true + }, + "source": [ + "\n", + "
      \n", + "\n", + "$$\n", + "\\begin{equation}\n", + "\tZ = \\int \\int e^{-\\frac{1}{T_0}E(\\mathbf{x},\\mathbf{h})} d\\mathbf{x} d\\mathbf{h}.\n", + "\\label{_auto2} \\tag{2}\n", + "\\end{equation}\n", + "$$" + ] + }, + { + "cell_type": "markdown", + "id": "e8a8f8e7", + "metadata": { + "editable": true + }, + "source": [ + "It is common to ignore $T_0$ by setting it to one." + ] + }, + { + "cell_type": "markdown", + "id": "4fb17989", + "metadata": { + "editable": true + }, + "source": [ + "## Network Elements, the energy function\n", + "\n", + "The function $E(\\mathbf{x},\\mathbf{h})$ gives the **energy** of a\n", + "configuration (pair of vectors) $(\\mathbf{x}, \\mathbf{h})$. The lower\n", + "the energy of a configuration, the higher the probability of it. This\n", + "function also depends on the parameters $\\mathbf{a}$, $\\mathbf{b}$ and\n", + "$W$. Thus, when we adjust them during the learning procedure, we are\n", + "adjusting the energy function to best fit our problem.\n", + "\n", + "An expression for the energy function is" + ] + }, + { + "cell_type": "markdown", + "id": "0bed5ddd", + "metadata": { + "editable": true + }, + "source": [ + "$$\n", + "E(\\hat{x},\\hat{h}) = -\\sum_{ia}^{NA}b_i^a \\alpha_i^a(x_i)-\\sum_{jd}^{MD}c_j^d \\beta_j^d(h_j)-\\sum_{ijad}^{NAMD}b_i^a \\alpha_i^a(x_i)c_j^d \\beta_j^d(h_j)w_{ij}^{ad}.\n", + "$$" + ] + }, + { + "cell_type": "markdown", + "id": "f8e64229", + "metadata": { + "editable": true + }, + "source": [ + "Here $\\beta_j^d(h_j)$ and $\\alpha_i^a(x_j)$ are so-called transfer functions that map a given input value to a desired feature value. The labels $a$ and $d$ denote that there can be multiple transfer functions per variable. The first sum depends only on the visible units. The second on the hidden ones. **Note** that there is no connection between nodes in a layer.\n", + "\n", + "The quantities $b$ and $c$ can be interpreted as the visible and hidden biases, respectively.\n", + "\n", + "The connection between the nodes in the two layers is given by the weights $w_{ij}$." + ] + }, + { + "cell_type": "markdown", + "id": "f8f02179", + "metadata": { + "editable": true + }, + "source": [ + "## Defining different types of RBMs\n", + "There are different variants of RBMs, and the differences lie in the types of visible and hidden units we choose as well as in the implementation of the energy function $E(\\mathbf{x},\\mathbf{h})$. \n", + "\n", + "**Binary-Binary RBM:**\n", + "\n", + "RBMs were first developed using binary units in both the visible and hidden layer. The corresponding energy function is defined as follows:" + ] + }, + { + "cell_type": "markdown", + "id": "19008689", + "metadata": { + "editable": true + }, + "source": [ + "\n", + "
      \n", + "\n", + "$$\n", + "\\begin{equation}\n", + "\tE(\\mathbf{x}, \\mathbf{h}) = - \\sum_i^M x_i a_i- \\sum_j^N b_j h_j - \\sum_{i,j}^{M,N} x_i w_{ij} h_j,\n", + "\\label{_auto3} \\tag{3}\n", + "\\end{equation}\n", + "$$" + ] + }, + { + "cell_type": "markdown", + "id": "a3d31b3a", + "metadata": { + "editable": true + }, + "source": [ + "where the binary values taken on by the nodes are most commonly 0 and 1.\n", + "\n", + "**Gaussian-Binary RBM:**\n", + "\n", + "Another varient is the RBM where the visible units are Gaussian while the hidden units remain binary:" + ] + }, + { + "cell_type": "markdown", + "id": "79d29641", + "metadata": { + "editable": true + }, + "source": [ + "\n", + "
      \n", + "\n", + "$$\n", + "\\begin{equation}\n", + "\tE(\\mathbf{x}, \\mathbf{h}) = \\sum_i^M \\frac{(x_i - a_i)^2}{2\\sigma_i^2} - \\sum_j^N b_j h_j - \\sum_{i,j}^{M,N} \\frac{x_i w_{ij} h_j}{\\sigma_i^2}. \n", + "\\label{_auto4} \\tag{4}\n", + "\\end{equation}\n", + "$$" + ] + }, + { + "cell_type": "markdown", + "id": "4d19d779", + "metadata": { + "editable": true + }, + "source": [ + "## More about RBMs\n", + "1. Useful when we model continuous data (i.e., we wish $\\mathbf{x}$ to be continuous)\n", + "\n", + "2. Requires a smaller learning rate, since there's no upper bound to the value a component might take in the reconstruction\n", + "\n", + "Other types of units include:\n", + "1. Softmax and multinomial units\n", + "\n", + "2. Gaussian visible and hidden units\n", + "\n", + "3. Binomial units\n", + "\n", + "4. Rectified linear units\n", + "\n", + "To read more, see [Lectures on Boltzmann machines in Physics](https://github.com/CompPhysics/ComputationalPhysics2/blob/gh-pages/doc/pub/notebook2/ipynb/notebook2.ipynb)." + ] + }, + { + "cell_type": "markdown", + "id": "b8fa06ec", + "metadata": { + "editable": true + }, + "source": [ + "## Autoencoders: Overarching view\n", + "\n", + "Autoencoders are artificial neural networks capable of learning\n", + "efficient representations of the input data (these representations are called codings) without\n", + "any supervision (i.e., the training set is unlabeled). These codings\n", + "typically have a much lower dimensionality than the input data, making\n", + "autoencoders useful for dimensionality reduction. \n", + "\n", + "More importantly, autoencoders act as powerful feature detectors, and\n", + "they can be used for unsupervised pretraining of deep neural networks.\n", + "\n", + "Lastly, they are capable of randomly generating new data that looks\n", + "very similar to the training data; this is called a generative\n", + "model. For example, you could train an autoencoder on pictures of\n", + "faces, and it would then be able to generate new faces. Surprisingly,\n", + "autoencoders work by simply learning to copy their inputs to their\n", + "outputs. This may sound like a trivial task, but we will see that\n", + "constraining the network in various ways can make it rather\n", + "difficult. For example, you can limit the size of the internal\n", + "representation, or you can add noise to the inputs and train the\n", + "network to recover the original inputs. These constraints prevent the\n", + "autoencoder from trivially copying the inputs directly to the outputs,\n", + "which forces it to learn efficient ways of representing the data. In\n", + "short, the codings are byproducts of the autoencoder’s attempt to\n", + "learn the identity function under some constraints.\n", + "\n", + "[Video on autoencoders](https://www.coursera.org/lecture/building-deep-learning-models-with-tensorflow/autoencoders-1U4L3)\n", + "\n", + "See also A. Geron's textbook, chapter 15." + ] + }, + { + "cell_type": "markdown", + "id": "262fc510", + "metadata": { + "editable": true + }, + "source": [ + "## Bayesian Machine Learning\n", + "\n", + "This is an important topic if we aim at extracting a probability\n", + "distribution. This gives us also a confidence interval and error\n", + "estimates.\n", + "\n", + "Bayesian machine learning allows us to encode our prior beliefs about\n", + "what those models should look like, independent of what the data tells\n", + "us. This is especially useful when we don’t have a ton of data to\n", + "confidently learn our model.\n", + "\n", + "[Video on Bayesian deep learning](https://www.youtube.com/watch?v=E1qhGw8QxqY&ab_channel=AndrewGordonWilson)\n", + "\n", + "See also the [slides here](https://github.com/CompPhysics/MachineLearning/blob/master/doc/Articles/lec03.pdf)." + ] + }, + { + "cell_type": "markdown", + "id": "36608f14", + "metadata": { + "editable": true + }, + "source": [ + "## Reinforcement Learning\n", + "\n", + "Reinforcement Learning (RL) is one of the most exciting fields of\n", + "Machine Learning today, and also one of the oldest. It has been around\n", + "since the 1950s, producing many interesting applications over the\n", + "years.\n", + "\n", + "It studies\n", + "how agents take actions based on trial and error, so as to maximize\n", + "some notion of cumulative reward in a dynamic system or\n", + "environment. Due to its generality, the problem has also been studied\n", + "in many other disciplines, such as game theory, control theory,\n", + "operations research, information theory, multi-agent systems, swarm\n", + "intelligence, statistics, and genetic algorithms.\n", + "\n", + "In March 2016, AlphaGo, a computer program that plays the board game\n", + "Go, beat Lee Sedol in a five-game match. This was the first time a\n", + "computer Go program had beaten a 9-dan (highest rank) professional\n", + "without handicaps. AlphaGo is based on deep convolutional neural\n", + "networks and reinforcement learning. AlphaGo’s victory was a major\n", + "milestone in artificial intelligence and it has also made\n", + "reinforcement learning a hot research area in the field of machine\n", + "learning.\n", + "\n", + "[Lecture on Reinforcement Learning](https://www.youtube.com/watch?v=FgzM3zpZ55o&ab_channel=stanfordonline).\n", + "\n", + "See also A. Geron's textbook, chapter 16." + ] + }, + { + "cell_type": "markdown", + "id": "a2b4652f", + "metadata": { + "editable": true + }, + "source": [ + "## Transfer learning\n", + "\n", + "The goal of transfer learning is to transfer the model or knowledge\n", + "obtained from a source task to the target task, in order to resolve\n", + "the issues of insufficient training data in the target task. The\n", + "rationality of doing so lies in that usually the source and target\n", + "tasks have inter-correlations, and therefore either the features,\n", + "samples, or models in the source task might provide useful information\n", + "for us to better solve the target task. Transfer learning is a hot\n", + "research topic in recent years, with many problems still waiting to be studied.\n", + "\n", + "[Lecture on transfer learning](https://www.ias.edu/video/machinelearning/2020/0331-SamoryKpotufe)." + ] + }, + { + "cell_type": "markdown", + "id": "8cef8c03", + "metadata": { + "editable": true + }, + "source": [ + "## Adversarial learning\n", + "\n", + "The conventional deep generative model has a potential problem: the\n", + "model tends to generate extreme instances to maximize the\n", + "probabilistic likelihood, which will hurt its performance. Adversarial\n", + "learning utilizes the adversarial behaviors (e.g., generating\n", + "adversarial instances or training an adversarial model) to enhance the\n", + "robustness of the model and improve the quality of the generated\n", + "data. In recent years, one of the most promising unsupervised learning\n", + "technologies, generative adversarial networks (GAN), has already been\n", + "successfully applied to image, speech, and text.\n", + "\n", + "[Lecture on adversial learning](https://www.youtube.com/watch?v=CIfsB_EYsVI&ab_channel=StanfordUniversitySchoolofEngineering)." + ] + }, + { + "cell_type": "markdown", + "id": "df85a3e6", + "metadata": { + "editable": true + }, + "source": [ + "## Dual learning\n", + "\n", + "Dual learning is a new learning paradigm, the basic idea of which is\n", + "to use the primal-dual structure between machine learning tasks to\n", + "obtain effective feedback/regularization, and guide and strengthen the\n", + "learning process, thus reducing the requirement of large-scale labeled\n", + "data for deep learning. The idea of dual learning has been applied to\n", + "many problems in machine learning, including machine translation,\n", + "image style conversion, question answering and generation, image\n", + "classification and generation, text classification and generation,\n", + "image-to-text, and text-to-image." + ] + }, + { + "cell_type": "markdown", + "id": "f01aaa3c", + "metadata": { + "editable": true + }, + "source": [ + "## Distributed machine learning\n", + "\n", + "Distributed computation will speed up machine learning algorithms,\n", + "significantly improve their efficiency, and thus enlarge their\n", + "application. When distributed meets machine learning, more than just\n", + "implementing the machine learning algorithms in parallel is required." + ] + }, + { + "cell_type": "markdown", + "id": "0005330c", + "metadata": { + "editable": true + }, + "source": [ + "## Meta learning\n", + "\n", + "Meta learning is an emerging research direction in machine\n", + "learning. Roughly speaking, meta learning concerns learning how to\n", + "learn, and focuses on the understanding and adaptation of the learning\n", + "itself, instead of just completing a specific learning task. That is,\n", + "a meta learner needs to be able to evaluate its own learning methods\n", + "and adjust its own learning methods according to specific learning\n", + "tasks." + ] + }, + { + "cell_type": "markdown", + "id": "efcfaccf", + "metadata": { + "editable": true + }, + "source": [ + "## The Challenges Facing Machine Learning\n", + "\n", + "While there has been much progress in machine learning, there are also challenges.\n", + "\n", + "For example, the mainstream machine learning technologies are\n", + "black-box approaches, making us concerned about their potential\n", + "risks. To tackle this challenge, we may want to make machine learning\n", + "more explainable and controllable. As another example, the\n", + "computational complexity of machine learning algorithms is usually\n", + "very high and we may want to invent lightweight algorithms or\n", + "implementations. Furthermore, in many domains such as physics,\n", + "chemistry, biology, and social sciences, people usually seek elegantly\n", + "simple equations (e.g., the Schrödinger equation) to uncover the\n", + "underlying laws behind various phenomena. In the field of machine\n", + "learning, can we reveal simple laws instead of designing more complex\n", + "models for data fitting? Although there are many challenges, we are\n", + "still very optimistic about the future of machine learning. As we look\n", + "forward to the future, here are what we think the research hotspots in\n", + "the next ten years will be.\n", + "\n", + "See the article on [Discovery of Physics From Data: Universal Laws and Discrepancies](https://www.frontiersin.org/articles/10.3389/frai.2020.00025/full)" + ] + }, + { + "cell_type": "markdown", + "id": "f7b109c9", + "metadata": { + "editable": true + }, + "source": [ + "## Explainable machine learning\n", + "\n", + "Machine learning, especially deep learning, evolves rapidly. The\n", + "ability gap between machine and human on many complex cognitive tasks\n", + "becomes narrower and narrower. However, we are still in the very early\n", + "stage in terms of explaining why those effective models work and how\n", + "they work.\n", + "\n", + "**What is missing: the gap between correlation and causation**. Standard Machine Learning is based on what e have called a frequentist approach. \n", + "\n", + "Most\n", + "machine learning techniques, especially the statistical ones, depend\n", + "highly on correlations in data sets to make predictions and analyses. In\n", + "contrast, rational humans tend to reply on clear and trustworthy\n", + "causality relations obtained via logical reasoning on real and clear\n", + "facts. It is one of the core goals of explainable machine learning to\n", + "transition from solving problems by data correlation to solving\n", + "problems by logical reasoning.\n", + "\n", + "**Bayesian Machine Learning is one of the exciting research directions in this field**." + ] + }, + { + "cell_type": "markdown", + "id": "e9142380", + "metadata": { + "editable": true + }, + "source": [ + "## Quantum machine learning\n", + "\n", + "Quantum machine learning is an emerging interdisciplinary research\n", + "area at the intersection of quantum computing and machine learning.\n", + "\n", + "Quantum computers use effects such as quantum coherence and quantum\n", + "entanglement to process information, which is fundamentally different\n", + "from classical computers. Quantum algorithms have surpassed the best\n", + "classical algorithms in several problems (e.g., searching for an\n", + "unsorted database, inverting a sparse matrix), which we call quantum\n", + "acceleration.\n", + "\n", + "When quantum computing meets machine learning, it can be a mutually\n", + "beneficial and reinforcing process, as it allows us to take advantage\n", + "of quantum computing to improve the performance of classical machine\n", + "learning algorithms. In addition, we can also use the machine learning\n", + "algorithms (on classic computers) to analyze and improve quantum\n", + "computing systems.\n", + "\n", + "[Lecture on Quantum ML](https://www.youtube.com/watch?v=Xh9pUu3-WxM&ab_channel=InstituteforPure%26AppliedMathematics%28IPAM%29).\n", + "\n", + "[Read interview with Maria Schuld on her work on Quantum Machine Learning](https://physics.aps.org/articles/v13/179?utm_campaign=weekly&utm_medium=email&utm_source=emailalert). See also [her recent textbook](https://www.springer.com/gp/book/9783319964232)." + ] + }, + { + "cell_type": "markdown", + "id": "cb638c92", + "metadata": { + "editable": true + }, + "source": [ + "## Quantum machine learning algorithms based on linear algebra\n", + "\n", + "Many quantum machine learning algorithms are based on variants of\n", + "quantum algorithms for solving linear equations, which can efficiently\n", + "solve N-variable linear equations with complexity of O(log2 N) under\n", + "certain conditions. The quantum matrix inversion algorithm can\n", + "accelerate many machine learning methods, such as least square linear\n", + "regression, least square version of support vector machine, Gaussian\n", + "process, and more. The training of these algorithms can be simplified\n", + "to solve linear equations. The key bottleneck of this type of quantum\n", + "machine learning algorithms is data input—that is, how to initialize\n", + "the quantum system with the entire data set. Although efficient\n", + "data-input algorithms exist for certain situations, how to efficiently\n", + "input data into a quantum system is as yet unknown for most cases." + ] + }, + { + "cell_type": "markdown", + "id": "676fd56e", + "metadata": { + "editable": true + }, + "source": [ + "## Quantum reinforcement learning\n", + "\n", + "In quantum reinforcement learning, a quantum agent interacts with the\n", + "classical environment to obtain rewards from the environment, so as to\n", + "adjust and improve its behavioral strategies. In some cases, it\n", + "achieves quantum acceleration by the quantum processing capabilities\n", + "of the agent or the possibility of exploring the environment through\n", + "quantum superposition. Such algorithms have been proposed in\n", + "superconducting circuits and systems of trapped ions." + ] + }, + { + "cell_type": "markdown", + "id": "ac1d93d3", + "metadata": { + "editable": true + }, + "source": [ + "## Quantum deep learning\n", + "\n", + "Dedicated quantum information processors, such as quantum annealers\n", + "and programmable photonic circuits, are well suited for building deep\n", + "quantum networks. The simplest deep quantum network is the Boltzmann\n", + "machine. The classical Boltzmann machine consists of bits with tunable\n", + "interactions and is trained by adjusting the interaction of these bits\n", + "so that the distribution of its expression conforms to the statistics\n", + "of the data. To quantize the Boltzmann machine, the neural network can\n", + "simply be represented as a set of interacting quantum spins that\n", + "correspond to an adjustable Ising model. Then, by initializing the\n", + "input neurons in the Boltzmann machine to a fixed state and allowing\n", + "the system to heat up, we can read out the output qubits to get the\n", + "result." + ] + }, + { + "cell_type": "markdown", + "id": "c5047fcb", + "metadata": { + "editable": true + }, + "source": [ + "## Social machine learning\n", + "\n", + "Machine learning aims to imitate how humans\n", + "learn. While we have developed successful machine learning algorithms,\n", + "until now we have ignored one important fact: humans are social. Each\n", + "of us is one part of the total society and it is difficult for us to\n", + "live, learn, and improve ourselves, alone and isolated. Therefore, we\n", + "should design machines with social properties. Can we let machines\n", + "evolve by imitating human society so as to achieve more effective,\n", + "intelligent, interpretable “social machine learning”?\n", + "\n", + "And much more." + ] + }, + { + "cell_type": "markdown", + "id": "bdd66000", + "metadata": { + "editable": true + }, + "source": [ + "## The last words?\n", + "\n", + "Early computer scientist Alan Kay said, **The best way to predict the\n", + "future is to create it**. Therefore, all machine learning\n", + "practitioners, whether scholars or engineers, professors or students,\n", + "need to work together to advance these important research\n", + "topics. Together, we will not just predict the future, but create it." + ] + }, + { + "cell_type": "markdown", + "id": "7fb56aeb", + "metadata": { + "editable": true + }, + "source": [ + "## Best wishes to you all and thanks so much for your heroic efforts this semester\n", + "\n", + "\n", + "\n", + "\n", + "

      Figure 1:

      \n", + "" + ] + } + ], + "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": "3.9.15" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} \ No newline at end of file diff --git a/doc/LectureNotes/exercisesweek48.ipynb b/doc/LectureNotes/exercisesweek48.ipynb new file mode 100644 index 000000000..96805589d --- /dev/null +++ b/doc/LectureNotes/exercisesweek48.ipynb @@ -0,0 +1,229 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "30071d16", + "metadata": { + "editable": true + }, + "source": [ + "\n", + "" + ] + }, + { + "cell_type": "markdown", + "id": "268eaea0", + "metadata": { + "editable": true + }, + "source": [ + "# Exercises week 48\n", + "**November 25-29, 2024**\n", + "\n", + "Date: **Deadline is Friday November 29 at midnight**" + ] + }, + { + "cell_type": "markdown", + "id": "1e04cbee", + "metadata": { + "editable": true + }, + "source": [ + "# Overarching aims of the exercises this week\n", + "\n", + "The exercise this week is a simple course survey and feedback. This\n", + "is important for us in order to improve our teaching material, the\n", + "active learning format and anything else related to a succesful\n", + "mastering of central machine learning methods and their applications." + ] + }, + { + "cell_type": "markdown", + "id": "78363328", + "metadata": { + "editable": true + }, + "source": [ + "### Why did you choose this course?" + ] + }, + { + "cell_type": "markdown", + "id": "d4950ede", + "metadata": { + "editable": true + }, + "source": [ + "### What was your programming knowledge before you started?\n", + "\n", + "And do you feel this course added to your programming competences and skills?" + ] + }, + { + "cell_type": "markdown", + "id": "95e23f0c", + "metadata": { + "editable": true + }, + "source": [ + "### How do you judge your own level of knowledge on machine learning before and after this course?\n", + "\n", + "Here you can discuss your level of skill/knowledge at the start of course\n", + "and at the end of the course and how these matched the level of\n", + "skill/knowledge needed to complete the projects." + ] + }, + { + "cell_type": "markdown", + "id": "db819721", + "metadata": { + "editable": true + }, + "source": [ + "### Did the projects and the teaching material allow you to deepen your insights about Machine Learning methods?\n", + "\n", + "Feel free to comment here." + ] + }, + { + "cell_type": "markdown", + "id": "61d153a4", + "metadata": { + "editable": true + }, + "source": [ + "### Project based teaching and active learning\n", + "\n", + "This is a project based course and we as teachers would like to keep\n", + "it as it is since we see very clearly that people who attend this\n", + "course have a very good learning outcome. Project based courses are\n", + "however demanding (and expensive seen from the university admin) when\n", + "it comes to proper feedback and evaluations. Feel free to discuss\n", + "whether you found a project-based useful. Feel also free to comment upon things we can improve upon or\n", + "alternative ways to assess whether the learning outcomes have been\n", + "achieved. Would you for example prefer a standard 4 hours written exam\n", + "be something you would prefer? Or other alternatives to projects? We\n", + "would very much value your thoughts here since projects are an\n", + "essential part of this course." + ] + }, + { + "cell_type": "markdown", + "id": "ee1af3ff", + "metadata": { + "editable": true + }, + "source": [ + "### Usefulness of the weekly exercises\n", + "\n", + "Did the weekly exercises help in getting started with the projects?\n", + "How relevant where they for solving the projects? Feel free to\n", + "elaborate" + ] + }, + { + "cell_type": "markdown", + "id": "46a35899", + "metadata": { + "editable": true + }, + "source": [ + "### Lab sessions and lectures\n", + "\n", + "Was there a good link between lectures and lab sessions?\n", + "Feel\n", + "free to comment." + ] + }, + { + "cell_type": "markdown", + "id": "f6524f03", + "metadata": { + "editable": true + }, + "source": [ + "### How would you improve this course?\n", + "\n", + "Are there topics which are missing, topics which could have been\n", + "omitted and/or discussed in more depth? Feel free to add your comments\n", + "here such as how to improve to teaching material and more." + ] + }, + { + "cell_type": "markdown", + "id": "ab89b772", + "metadata": { + "editable": true + }, + "source": [ + "## Then some basic questions" + ] + }, + { + "cell_type": "markdown", + "id": "9811e1b4", + "metadata": { + "editable": true + }, + "source": [ + "### Which is your preferred information chanel, Canvas, Discord, mail or other?" + ] + }, + { + "cell_type": "markdown", + "id": "6a3741fa", + "metadata": { + "editable": true + }, + "source": [ + "### Was the weekly update with plans etc useful?" + ] + }, + { + "cell_type": "markdown", + "id": "a51df0b9", + "metadata": { + "editable": true + }, + "source": [ + "### Was it easy to access the course material?" + ] + }, + { + "cell_type": "markdown", + "id": "762d5153", + "metadata": { + "editable": true + }, + "source": [ + "### Which resources and tools did you use? Jupyter-notebooks, GitHub, the various textbooks we have recommended, etc etc" + ] + }, + { + "cell_type": "markdown", + "id": "2bf11aae", + "metadata": { + "editable": true + }, + "source": [ + "### If you did not attend the lectures or the lab sessions, which resources did you use?" + ] + }, + { + "cell_type": "markdown", + "id": "3f92f5ff", + "metadata": { + "editable": true + }, + "source": [ + "### Any other topics, impressions, ideas etc you would like to share with us?" + ] + } + ], + "metadata": {}, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/doc/LectureNotes/gaussian.pdf b/doc/LectureNotes/gaussian.pdf index b384d88d2..133ec69fa 100644 Binary files a/doc/LectureNotes/gaussian.pdf and b/doc/LectureNotes/gaussian.pdf differ diff --git a/doc/LectureNotes/week48.ipynb b/doc/LectureNotes/week48.ipynb index 486829beb..c0298d422 100644 --- a/doc/LectureNotes/week48.ipynb +++ b/doc/LectureNotes/week48.ipynb @@ -2,7 +2,7 @@ "cells": [ { "cell_type": "markdown", - "id": "3b64c66e", + "id": "d7c532d5", "metadata": { "editable": true }, @@ -14,7 +14,7 @@ }, { "cell_type": "markdown", - "id": "45ba1703", + "id": "d27ab2b4", "metadata": { "editable": true }, @@ -22,14 +22,14 @@ "# Week 48: Gradient boosting and summary of course\n", "**Morten Hjorth-Jensen**, Department of Physics and Center for Computing in Science Education, University of Oslo, Norway\n", "\n", - "Date: **Nov 23, 2024**\n", + "Date: **Nov 24, 2024**\n", "\n", "Copyright 1999-2024, Morten Hjorth-Jensen. Released under CC Attribution-NonCommercial 4.0 license" ] }, { "cell_type": "markdown", - "id": "7258309e", + "id": "9f55b05a", "metadata": { "editable": true }, @@ -39,25 +39,12 @@ }, { "cell_type": "markdown", - "id": "8a4ef0a3", + "id": "e72578f8", "metadata": { "editable": true }, "source": [ - "## Plan for week 47\n", - "\n", - "**Lab sessions on Tuesday and Wednesday.**\n", - "\n", - " * Work and Discussion of project 3\n", - "\n", - " * Last weekly exercise\n", - "\n", - " * Lab sessions at usual times.\n", - "\n", - " * For the week of December 2-6, lab sessions start at 10am and end at 4pm, room FØ434, Tuesday and Wednesday\n", - "\n", - " \n", - "\n", + "## Lecture Monday, November 25\n", "**Plans for the lecture Monday 25 November, with video suggestions etc.**\n", "\n", "1. Boosting and gradient boosting and ensemble models\n", @@ -85,7 +72,26 @@ }, { "cell_type": "markdown", - "id": "e01d37bc", + "id": "51ee8fb3", + "metadata": { + "editable": true + }, + "source": [ + "## Lab sessions\n", + "**Lab sessions on Tuesday and Wednesday.**\n", + "\n", + " * Work and Discussion of project 3\n", + "\n", + " * Last weekly exercise\n", + "\n", + " * Lab sessions at usual times.\n", + "\n", + " * For the week of December 2-6, lab sessions start at 10am and end at 4pm, room FØ434, Tuesday and Wednesday" + ] + }, + { + "cell_type": "markdown", + "id": "5b4ed2be", "metadata": { "editable": true }, @@ -112,7 +118,7 @@ }, { "cell_type": "markdown", - "id": "1160253d", + "id": "3e76e282", "metadata": { "editable": true }, @@ -123,7 +129,7 @@ { "cell_type": "code", "execution_count": 1, - "id": "ac77382f", + "id": "acba62b3", "metadata": { "collapsed": false, "editable": true @@ -197,7 +203,7 @@ }, { "cell_type": "markdown", - "id": "254c4495", + "id": "c5cbe0e8", "metadata": { "editable": true }, @@ -213,7 +219,7 @@ }, { "cell_type": "markdown", - "id": "5633e7a9", + "id": "19bd77f2", "metadata": { "editable": true }, @@ -224,7 +230,7 @@ { "cell_type": "code", "execution_count": 2, - "id": "250bca6d", + "id": "84f1af2e", "metadata": { "collapsed": false, "editable": true @@ -239,7 +245,7 @@ { "cell_type": "code", "execution_count": 3, - "id": "a8506102", + "id": "6c01d900", "metadata": { "collapsed": false, "editable": true @@ -257,7 +263,7 @@ }, { "cell_type": "markdown", - "id": "8713c888", + "id": "896072f6", "metadata": { "editable": true }, @@ -277,7 +283,7 @@ }, { "cell_type": "markdown", - "id": "efc5e7e7", + "id": "babc9c9c", "metadata": { "editable": true }, @@ -291,7 +297,7 @@ }, { "cell_type": "markdown", - "id": "ee68d9b3", + "id": "2ed28584", "metadata": { "editable": true }, @@ -303,7 +309,7 @@ }, { "cell_type": "markdown", - "id": "6efcd942", + "id": "5b5b9631", "metadata": { "editable": true }, @@ -320,7 +326,7 @@ }, { "cell_type": "markdown", - "id": "8b2a4e15", + "id": "7b6c43ec", "metadata": { "editable": true }, @@ -332,7 +338,7 @@ }, { "cell_type": "markdown", - "id": "77d4f75d", + "id": "3962039f", "metadata": { "editable": true }, @@ -346,7 +352,7 @@ }, { "cell_type": "markdown", - "id": "9d0680d2", + "id": "ee5f259c", "metadata": { "editable": true }, @@ -358,7 +364,7 @@ }, { "cell_type": "markdown", - "id": "a5f51b6a", + "id": "079d8ee3", "metadata": { "editable": true }, @@ -371,7 +377,7 @@ }, { "cell_type": "markdown", - "id": "c6eafc82", + "id": "11f821a2", "metadata": { "editable": true }, @@ -383,7 +389,7 @@ }, { "cell_type": "markdown", - "id": "d42a3420", + "id": "60990073", "metadata": { "editable": true }, @@ -393,7 +399,7 @@ }, { "cell_type": "markdown", - "id": "ecac5a19", + "id": "4d7195ab", "metadata": { "editable": true }, @@ -421,7 +427,7 @@ }, { "cell_type": "markdown", - "id": "632d7379", + "id": "4bee726a", "metadata": { "editable": true }, @@ -437,7 +443,7 @@ }, { "cell_type": "markdown", - "id": "3a2dc7cd", + "id": "1190b9a0", "metadata": { "editable": true }, @@ -449,7 +455,7 @@ }, { "cell_type": "markdown", - "id": "d1dae1f7", + "id": "2702211b", "metadata": { "editable": true }, @@ -460,7 +466,7 @@ }, { "cell_type": "markdown", - "id": "db8e52c8", + "id": "1b1d9984", "metadata": { "editable": true }, @@ -472,7 +478,7 @@ }, { "cell_type": "markdown", - "id": "b71e4562", + "id": "cdf7e56a", "metadata": { "editable": true }, @@ -482,7 +488,7 @@ }, { "cell_type": "markdown", - "id": "8b287320", + "id": "ac6f3b86", "metadata": { "editable": true }, @@ -494,7 +500,7 @@ }, { "cell_type": "markdown", - "id": "444b31c7", + "id": "faf6fb5f", "metadata": { "editable": true }, @@ -504,7 +510,7 @@ }, { "cell_type": "markdown", - "id": "975f549b", + "id": "969f86ed", "metadata": { "editable": true }, @@ -516,7 +522,7 @@ }, { "cell_type": "markdown", - "id": "49707e43", + "id": "a98a541c", "metadata": { "editable": true }, @@ -526,7 +532,7 @@ }, { "cell_type": "markdown", - "id": "30fff982", + "id": "d8be5438", "metadata": { "editable": true }, @@ -538,7 +544,7 @@ }, { "cell_type": "markdown", - "id": "8936f422", + "id": "932f43c5", "metadata": { "editable": true }, @@ -552,7 +558,7 @@ }, { "cell_type": "markdown", - "id": "373a4a0c", + "id": "24630eb9", "metadata": { "editable": true }, @@ -568,7 +574,7 @@ }, { "cell_type": "markdown", - "id": "c412ab0e", + "id": "9a7fa4b6", "metadata": { "editable": true }, @@ -580,7 +586,7 @@ }, { "cell_type": "markdown", - "id": "0018a744", + "id": "7ead62b2", "metadata": { "editable": true }, @@ -596,7 +602,7 @@ }, { "cell_type": "markdown", - "id": "d092eb4b", + "id": "91639c49", "metadata": { "editable": true }, @@ -608,7 +614,7 @@ }, { "cell_type": "markdown", - "id": "17820826", + "id": "e43865c2", "metadata": { "editable": true }, @@ -618,7 +624,7 @@ }, { "cell_type": "markdown", - "id": "f4910537", + "id": "1884c219", "metadata": { "editable": true }, @@ -630,7 +636,7 @@ }, { "cell_type": "markdown", - "id": "bfae7a07", + "id": "bbb1cdbb", "metadata": { "editable": true }, @@ -642,7 +648,7 @@ }, { "cell_type": "markdown", - "id": "f00bee3c", + "id": "7e3590f6", "metadata": { "editable": true }, @@ -654,7 +660,7 @@ }, { "cell_type": "markdown", - "id": "bf267f5b", + "id": "2eb8c86b", "metadata": { "editable": true }, @@ -665,7 +671,7 @@ }, { "cell_type": "markdown", - "id": "a6eb9909", + "id": "e477d15a", "metadata": { "editable": true }, @@ -677,7 +683,7 @@ }, { "cell_type": "markdown", - "id": "2bbceaad", + "id": "b6f01e59", "metadata": { "editable": true }, @@ -688,7 +694,7 @@ }, { "cell_type": "markdown", - "id": "9ff79322", + "id": "71c85471", "metadata": { "editable": true }, @@ -700,7 +706,7 @@ }, { "cell_type": "markdown", - "id": "6c4299e2", + "id": "2d5be340", "metadata": { "editable": true }, @@ -710,7 +716,7 @@ }, { "cell_type": "markdown", - "id": "068e2930", + "id": "e426517c", "metadata": { "editable": true }, @@ -722,7 +728,7 @@ }, { "cell_type": "markdown", - "id": "53eb174c", + "id": "c0c7e993", "metadata": { "editable": true }, @@ -734,7 +740,7 @@ }, { "cell_type": "markdown", - "id": "3baeddb4", + "id": "7457a096", "metadata": { "editable": true }, @@ -746,7 +752,7 @@ }, { "cell_type": "markdown", - "id": "87178662", + "id": "424d5bc9", "metadata": { "editable": true }, @@ -758,7 +764,7 @@ }, { "cell_type": "markdown", - "id": "c48316bd", + "id": "d1fa658b", "metadata": { "editable": true }, @@ -768,7 +774,7 @@ }, { "cell_type": "markdown", - "id": "6ccbebe4", + "id": "f8fc6b15", "metadata": { "editable": true }, @@ -780,7 +786,7 @@ }, { "cell_type": "markdown", - "id": "526ec37c", + "id": "9ca82f54", "metadata": { "editable": true }, @@ -790,7 +796,7 @@ }, { "cell_type": "markdown", - "id": "84e556c0", + "id": "6c028686", "metadata": { "editable": true }, @@ -802,7 +808,7 @@ }, { "cell_type": "markdown", - "id": "931a10f7", + "id": "4007a0ef", "metadata": { "editable": true }, @@ -812,7 +818,7 @@ }, { "cell_type": "markdown", - "id": "45b79d8b", + "id": "ba456347", "metadata": { "editable": true }, @@ -824,7 +830,7 @@ }, { "cell_type": "markdown", - "id": "e30b9dee", + "id": "c8df4ef3", "metadata": { "editable": true }, @@ -834,7 +840,7 @@ }, { "cell_type": "markdown", - "id": "c8d42197", + "id": "da46f9d9", "metadata": { "editable": true }, @@ -846,7 +852,7 @@ }, { "cell_type": "markdown", - "id": "56d6acfd", + "id": "5c60f30d", "metadata": { "editable": true }, @@ -856,7 +862,7 @@ }, { "cell_type": "markdown", - "id": "df837af9", + "id": "a5bc4dfe", "metadata": { "editable": true }, @@ -868,7 +874,7 @@ }, { "cell_type": "markdown", - "id": "1fafddb8", + "id": "3987cc1e", "metadata": { "editable": true }, @@ -888,7 +894,7 @@ }, { "cell_type": "markdown", - "id": "8a898c02", + "id": "b35df09e", "metadata": { "editable": true }, @@ -900,7 +906,7 @@ }, { "cell_type": "markdown", - "id": "03435f7c", + "id": "5b6a209c", "metadata": { "editable": true }, @@ -910,7 +916,7 @@ }, { "cell_type": "markdown", - "id": "ac433aeb", + "id": "d619a097", "metadata": { "editable": true }, @@ -926,7 +932,7 @@ }, { "cell_type": "markdown", - "id": "37e79583", + "id": "b7311abb", "metadata": { "editable": true }, @@ -938,7 +944,7 @@ }, { "cell_type": "markdown", - "id": "6eff57bb", + "id": "4bfb4209", "metadata": { "editable": true }, @@ -966,7 +972,7 @@ }, { "cell_type": "markdown", - "id": "143fddc5", + "id": "4cf4c1f4", "metadata": { "editable": true }, @@ -979,7 +985,7 @@ { "cell_type": "code", "execution_count": 4, - "id": "627dcde1", + "id": "453dff54", "metadata": { "collapsed": false, "editable": true @@ -1004,7 +1010,7 @@ }, { "cell_type": "markdown", - "id": "0586f2f5", + "id": "d6470164", "metadata": { "editable": true }, @@ -1015,7 +1021,7 @@ { "cell_type": "code", "execution_count": 5, - "id": "57f72ee8", + "id": "b6e01886", "metadata": { "collapsed": false, "editable": true @@ -1104,7 +1110,7 @@ }, { "cell_type": "markdown", - "id": "98bcb1fd", + "id": "3cede4c9", "metadata": { "editable": true }, @@ -1122,7 +1128,7 @@ }, { "cell_type": "markdown", - "id": "adfad730", + "id": "94e3b59f", "metadata": { "editable": true }, @@ -1135,7 +1141,7 @@ }, { "cell_type": "markdown", - "id": "32e3c207", + "id": "2f000f62", "metadata": { "editable": true }, @@ -1147,7 +1153,7 @@ }, { "cell_type": "markdown", - "id": "460df33e", + "id": "014da5e9", "metadata": { "editable": true }, @@ -1157,7 +1163,7 @@ }, { "cell_type": "markdown", - "id": "ec1e9c33", + "id": "022aee06", "metadata": { "editable": true }, @@ -1169,7 +1175,7 @@ }, { "cell_type": "markdown", - "id": "5f517f3c", + "id": "d0d3e174", "metadata": { "editable": true }, @@ -1179,7 +1185,7 @@ }, { "cell_type": "markdown", - "id": "8352e0e4", + "id": "8af32042", "metadata": { "editable": true }, @@ -1191,7 +1197,7 @@ }, { "cell_type": "markdown", - "id": "b158a9ff", + "id": "4a384d95", "metadata": { "editable": true }, @@ -1204,7 +1210,7 @@ }, { "cell_type": "markdown", - "id": "17fb6c35", + "id": "bd5b9bb9", "metadata": { "editable": true }, @@ -1216,7 +1222,7 @@ }, { "cell_type": "markdown", - "id": "64c802de", + "id": "560b3106", "metadata": { "editable": true }, @@ -1228,7 +1234,7 @@ }, { "cell_type": "markdown", - "id": "f4b660f8", + "id": "4742d327", "metadata": { "editable": true }, @@ -1240,7 +1246,7 @@ }, { "cell_type": "markdown", - "id": "d592d84b", + "id": "d160a37f", "metadata": { "editable": true }, @@ -1250,7 +1256,7 @@ }, { "cell_type": "markdown", - "id": "265f57f6", + "id": "f3cde07e", "metadata": { "editable": true }, @@ -1262,7 +1268,7 @@ }, { "cell_type": "markdown", - "id": "149a5a32", + "id": "cf95ad5e", "metadata": { "editable": true }, @@ -1272,7 +1278,7 @@ }, { "cell_type": "markdown", - "id": "fb347cb0", + "id": "bb614b9d", "metadata": { "editable": true }, @@ -1288,7 +1294,7 @@ }, { "cell_type": "markdown", - "id": "66b696bb", + "id": "19e3e8fa", "metadata": { "editable": true }, @@ -1300,7 +1306,7 @@ }, { "cell_type": "markdown", - "id": "6c776311", + "id": "9330b34b", "metadata": { "editable": true }, @@ -1321,7 +1327,7 @@ }, { "cell_type": "markdown", - "id": "b7cecfb0", + "id": "e648539a", "metadata": { "editable": true }, @@ -1332,7 +1338,7 @@ { "cell_type": "code", "execution_count": 6, - "id": "7b1eb376", + "id": "d12810d3", "metadata": { "collapsed": false, "editable": true @@ -1383,7 +1389,7 @@ }, { "cell_type": "markdown", - "id": "9d97b3d7", + "id": "f3e95eca", "metadata": { "editable": true }, @@ -1394,7 +1400,7 @@ { "cell_type": "code", "execution_count": 7, - "id": "7e853c55", + "id": "d6426469", "metadata": { "collapsed": false, "editable": true @@ -1442,7 +1448,7 @@ }, { "cell_type": "markdown", - "id": "b458d799", + "id": "8de56415", "metadata": { "editable": true }, @@ -1465,7 +1471,7 @@ }, { "cell_type": "markdown", - "id": "48d1de5e", + "id": "dfc59b18", "metadata": { "editable": true }, @@ -1478,7 +1484,7 @@ { "cell_type": "code", "execution_count": 8, - "id": "4059e0f6", + "id": "79e4cc00", "metadata": { "collapsed": false, "editable": true @@ -1536,7 +1542,7 @@ }, { "cell_type": "markdown", - "id": "da6ac07a", + "id": "c4700b19", "metadata": { "editable": true }, @@ -1547,7 +1553,7 @@ { "cell_type": "code", "execution_count": 9, - "id": "8ab70463", + "id": "eb0594c5", "metadata": { "collapsed": false, "editable": true @@ -1634,7 +1640,7 @@ }, { "cell_type": "markdown", - "id": "d3761d54", + "id": "bbf154c4", "metadata": { "editable": true }, @@ -1644,7 +1650,7 @@ }, { "cell_type": "markdown", - "id": "780df2c0", + "id": "84d512fc", "metadata": { "editable": true }, @@ -1659,7 +1665,7 @@ }, { "cell_type": "markdown", - "id": "8ead86e5", + "id": "71b8eb59", "metadata": { "editable": true }, @@ -1675,7 +1681,7 @@ }, { "cell_type": "markdown", - "id": "31266704", + "id": "473329b7", "metadata": { "editable": true }, @@ -1700,7 +1706,7 @@ }, { "cell_type": "markdown", - "id": "2021a43b", + "id": "6c94f44a", "metadata": { "editable": true }, @@ -1746,7 +1752,7 @@ }, { "cell_type": "markdown", - "id": "170e1be1", + "id": "d9942ded", "metadata": { "editable": true }, @@ -1781,7 +1787,7 @@ }, { "cell_type": "markdown", - "id": "07f2b380", + "id": "7818c700", "metadata": { "editable": true }, @@ -1804,7 +1810,7 @@ }, { "cell_type": "markdown", - "id": "4408cc2b", + "id": "cc946602", "metadata": { "editable": true }, @@ -1827,7 +1833,7 @@ }, { "cell_type": "markdown", - "id": "13833ad1", + "id": "c2b75185", "metadata": { "editable": true }, @@ -1847,7 +1853,7 @@ }, { "cell_type": "markdown", - "id": "690b357b", + "id": "4600986a", "metadata": { "editable": true }, @@ -1863,7 +1869,7 @@ }, { "cell_type": "markdown", - "id": "a382da4c", + "id": "cfb572d5", "metadata": { "editable": true }, @@ -1891,7 +1897,7 @@ }, { "cell_type": "markdown", - "id": "aef19b0c", + "id": "3e38f862", "metadata": { "editable": true }, @@ -1909,7 +1915,7 @@ }, { "cell_type": "markdown", - "id": "f301c68e", + "id": "76abe122", "metadata": { "editable": true }, @@ -1934,7 +1940,7 @@ }, { "cell_type": "markdown", - "id": "7f82dc43", + "id": "0b246db8", "metadata": { "editable": true }, @@ -1952,7 +1958,7 @@ }, { "cell_type": "markdown", - "id": "7a1117b1", + "id": "9a6555fc", "metadata": { "editable": true }, @@ -1982,7 +1988,7 @@ }, { "cell_type": "markdown", - "id": "90fe2084", + "id": "c2561e72", "metadata": { "editable": true }, @@ -1996,7 +2002,7 @@ }, { "cell_type": "markdown", - "id": "bc8ad0a3", + "id": "796637c8", "metadata": { "editable": true }, @@ -2022,7 +2028,7 @@ }, { "cell_type": "markdown", - "id": "bea1957b", + "id": "6afdb59e", "metadata": { "editable": true }, @@ -2051,7 +2057,7 @@ }, { "cell_type": "markdown", - "id": "c1347ce6", + "id": "95097424", "metadata": { "editable": true }, @@ -2068,7 +2074,7 @@ }, { "cell_type": "markdown", - "id": "5bcb69f0", + "id": "31a33cea", "metadata": { "editable": true }, @@ -2090,7 +2096,7 @@ }, { "cell_type": "markdown", - "id": "39597386", + "id": "d6583875", "metadata": { "editable": true }, @@ -2108,7 +2114,7 @@ }, { "cell_type": "markdown", - "id": "24cb4d77", + "id": "285bbf5d", "metadata": { "editable": true }, @@ -2131,7 +2137,7 @@ }, { "cell_type": "markdown", - "id": "abe0e029", + "id": "5b51dec9", "metadata": { "editable": true }, @@ -2150,7 +2156,7 @@ }, { "cell_type": "markdown", - "id": "be9ea72c", + "id": "4cb37b8f", "metadata": { "editable": true }, @@ -2166,7 +2172,7 @@ }, { "cell_type": "markdown", - "id": "a85f3d50", + "id": "45fe33d7", "metadata": { "editable": true }, @@ -2181,7 +2187,7 @@ }, { "cell_type": "markdown", - "id": "3b68f9bc", + "id": "3bbbaa88", "metadata": { "editable": true }, @@ -2205,7 +2211,7 @@ }, { "cell_type": "markdown", - "id": "eeb46a9a", + "id": "d6a9f606", "metadata": { "editable": true }, @@ -2217,7 +2223,7 @@ }, { "cell_type": "markdown", - "id": "911fc5be", + "id": "81aaff6e", "metadata": { "editable": true }, @@ -2235,7 +2241,7 @@ }, { "cell_type": "markdown", - "id": "44398f76", + "id": "9f856eb3", "metadata": { "editable": true }, @@ -2245,7 +2251,7 @@ }, { "cell_type": "markdown", - "id": "d22991a2", + "id": "f15429d1", "metadata": { "editable": true }, @@ -2263,7 +2269,7 @@ }, { "cell_type": "markdown", - "id": "c645e455", + "id": "e8a8f8e7", "metadata": { "editable": true }, @@ -2273,7 +2279,7 @@ }, { "cell_type": "markdown", - "id": "99a56dc1", + "id": "4fb17989", "metadata": { "editable": true }, @@ -2292,7 +2298,7 @@ }, { "cell_type": "markdown", - "id": "9a39b491", + "id": "0bed5ddd", "metadata": { "editable": true }, @@ -2304,7 +2310,7 @@ }, { "cell_type": "markdown", - "id": "286d0ab5", + "id": "f8e64229", "metadata": { "editable": true }, @@ -2318,7 +2324,7 @@ }, { "cell_type": "markdown", - "id": "e21e58ac", + "id": "f8f02179", "metadata": { "editable": true }, @@ -2333,7 +2339,7 @@ }, { "cell_type": "markdown", - "id": "d9e99856", + "id": "19008689", "metadata": { "editable": true }, @@ -2351,7 +2357,7 @@ }, { "cell_type": "markdown", - "id": "da27eff3", + "id": "a3d31b3a", "metadata": { "editable": true }, @@ -2365,7 +2371,7 @@ }, { "cell_type": "markdown", - "id": "ad466922", + "id": "79d29641", "metadata": { "editable": true }, @@ -2383,7 +2389,7 @@ }, { "cell_type": "markdown", - "id": "e3e1a9ee", + "id": "4d19d779", "metadata": { "editable": true }, @@ -2407,7 +2413,7 @@ }, { "cell_type": "markdown", - "id": "8a463ba1", + "id": "b8fa06ec", "metadata": { "editable": true }, @@ -2445,7 +2451,7 @@ }, { "cell_type": "markdown", - "id": "8b88bdc9", + "id": "262fc510", "metadata": { "editable": true }, @@ -2468,7 +2474,7 @@ }, { "cell_type": "markdown", - "id": "dcf0e541", + "id": "36608f14", "metadata": { "editable": true }, @@ -2504,7 +2510,7 @@ }, { "cell_type": "markdown", - "id": "9d1c6243", + "id": "a2b4652f", "metadata": { "editable": true }, @@ -2525,7 +2531,7 @@ }, { "cell_type": "markdown", - "id": "b06e1c4c", + "id": "8cef8c03", "metadata": { "editable": true }, @@ -2547,7 +2553,7 @@ }, { "cell_type": "markdown", - "id": "8bd5d6c4", + "id": "df85a3e6", "metadata": { "editable": true }, @@ -2567,7 +2573,7 @@ }, { "cell_type": "markdown", - "id": "40c7ca7e", + "id": "f01aaa3c", "metadata": { "editable": true }, @@ -2582,7 +2588,7 @@ }, { "cell_type": "markdown", - "id": "bc167c7d", + "id": "0005330c", "metadata": { "editable": true }, @@ -2600,7 +2606,7 @@ }, { "cell_type": "markdown", - "id": "06e830c9", + "id": "efcfaccf", "metadata": { "editable": true }, @@ -2630,7 +2636,7 @@ }, { "cell_type": "markdown", - "id": "51b7b00d", + "id": "f7b109c9", "metadata": { "editable": true }, @@ -2659,7 +2665,7 @@ }, { "cell_type": "markdown", - "id": "265badfe", + "id": "e9142380", "metadata": { "editable": true }, @@ -2690,7 +2696,7 @@ }, { "cell_type": "markdown", - "id": "28cda775", + "id": "cb638c92", "metadata": { "editable": true }, @@ -2713,7 +2719,7 @@ }, { "cell_type": "markdown", - "id": "eab731bc", + "id": "676fd56e", "metadata": { "editable": true }, @@ -2731,7 +2737,7 @@ }, { "cell_type": "markdown", - "id": "105015b2", + "id": "ac1d93d3", "metadata": { "editable": true }, @@ -2754,7 +2760,7 @@ }, { "cell_type": "markdown", - "id": "ddbe105b", + "id": "c5047fcb", "metadata": { "editable": true }, @@ -2775,7 +2781,7 @@ }, { "cell_type": "markdown", - "id": "3e53243c", + "id": "bdd66000", "metadata": { "editable": true }, @@ -2791,7 +2797,7 @@ }, { "cell_type": "markdown", - "id": "011485c3", + "id": "7fb56aeb", "metadata": { "editable": true }, diff --git a/doc/src/week48/exercisesweek48.do.txt b/doc/src/week48/exercisesweek48.do.txt index d5498df0d..9e5b4e87d 100644 --- a/doc/src/week48/exercisesweek48.do.txt +++ b/doc/src/week48/exercisesweek48.do.txt @@ -1,4 +1,4 @@ -TITLE: Exercise week 48 +TITLE: Exercises week 48 AUTHOR: November 25-29, 2024 DATE: Deadline is Friday November 29 at midnight @@ -18,11 +18,11 @@ And do you feel this course added to your programming competences and skills? === How do you judge your own level of knowledge on machine learning before and after this course? === -Here you can discuss your level of skill/knowledge at start of course +Here you can discuss your level of skill/knowledge at the start of course and at the end of the course and how these matched the level of skill/knowledge needed to complete the projects. -=== Did the projects and the teaching material allow you to deepen your insights about Machine Learning? === +=== Did the projects and the teaching material allow you to deepen your insights about Machine Learning methods? === Feel free to comment here. @@ -34,8 +34,7 @@ it as it is since we see very clearly that people who attend this course have a very good learning outcome. Project based courses are however demanding (and expensive seen from the university admin) when it comes to proper feedback and evaluations. Feel free to discuss -whether you found a project-based and active learning approach -useful. Feel also free to comment upon things we can improve upon or +whether you found a project-based useful. Feel also free to comment upon things we can improve upon or alternative ways to assess whether the learning outcomes have been achieved. Would you for example prefer a standard 4 hours written exam be something you would prefer? Or other alternatives to projects? We @@ -48,12 +47,10 @@ Did the weekly exercises help in getting started with the projects? How relevant where they for solving the projects? Feel free to elaborate -=== Active learning/lab sessions and lectures === +=== Lab sessions and lectures === -Was there a good link between lectures and active learning sessions? -Would you prefer an active learning environment only with no lectures -or would you prefer a more lecture based format with lab sessions only -(that is no discussion at the beginning of the lab sessions)? Feel +Was there a good link between lectures and lab sessions? +Feel free to comment. @@ -76,7 +73,7 @@ here such as how to improve to teaching material and more. === Which resources and tools did you use? Jupyter-notebooks, GitHub, the various textbooks we have recommended, etc etc === -=== If you did not attend the lectures or the active learning/lab sessions, which resources did you use? === +=== If you did not attend the lectures or the lab sessions, which resources did you use? === === Any other topics, impressions, ideas etc you would like to share with us? ===