diff --git a/doc/LectureNotes/DataFiles/cancer.dot b/doc/LectureNotes/DataFiles/cancer.dot index 398f41587..fb432c33d 100644 --- a/doc/LectureNotes/DataFiles/cancer.dot +++ b/doc/LectureNotes/DataFiles/cancer.dot @@ -10,7 +10,7 @@ edge [fontname="helvetica"] ; 2 -> 3 ; 4 [label="gini = 0.0\nsamples = 239\nvalue = [[239, 0]\n[0, 239]]", fillcolor="#e58139"] ; 3 -> 4 ; -5 [label="mean concavity <= 0.029\ngini = 0.444\nsamples = 3\nvalue = [[2, 1]\n[1, 2]]", fillcolor="#fdf6f0"] ; +5 [label="mean radius <= 12.265\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 ; @@ -22,7 +22,7 @@ edge [fontname="helvetica"] ; 8 -> 9 ; 10 [label="gini = 0.0\nsamples = 3\nvalue = [[0, 3]\n[3, 0]]", fillcolor="#e58139"] ; 8 -> 10 ; -11 [label="worst texture <= 24.785\ngini = 0.278\nsamples = 6\nvalue = [[1, 5]\n[5, 1]]", fillcolor="#f4caac"] ; +11 [label="mean texture <= 16.22\ngini = 0.278\nsamples = 6\nvalue = [[1, 5]\n[5, 1]]", fillcolor="#f4caac"] ; 1 -> 11 ; 12 [label="gini = 0.0\nsamples = 1\nvalue = [[1, 0]\n[0, 1]]", fillcolor="#e58139"] ; 11 -> 12 ; @@ -30,11 +30,11 @@ edge [fontname="helvetica"] ; 11 -> 13 ; 14 [label="worst texture <= 20.645\ngini = 0.202\nsamples = 167\nvalue = [[19, 148]\n[148, 19]]", fillcolor="#f0b68c"] ; 0 -> 14 [labeldistance=2.5, labelangle=-45, headlabel="False"] ; -15 [label="worst perimeter <= 116.8\ngini = 0.375\nsamples = 16\nvalue = [[12, 4]\n[4, 12]]", fillcolor="#f9e3d4"] ; +15 [label="worst radius <= 17.74\ngini = 0.375\nsamples = 16\nvalue = [[12, 4]\n[4, 12]]", fillcolor="#f9e3d4"] ; 14 -> 15 ; 16 [label="gini = 0.0\nsamples = 11\nvalue = [[11, 0]\n[0, 11]]", fillcolor="#e58139"] ; 15 -> 16 ; -17 [label="concavity error <= 0.016\ngini = 0.32\nsamples = 5\nvalue = [[1, 4]\n[4, 1]]", fillcolor="#f6d5bd"] ; +17 [label="symmetry error <= 0.014\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 ; @@ -48,7 +48,7 @@ edge [fontname="helvetica"] ; 21 -> 22 ; 23 [label="gini = 0.0\nsamples = 6\nvalue = [[6, 0]\n[0, 6]]", fillcolor="#e58139"] ; 21 -> 23 ; -24 [label="worst smoothness <= 0.096\ngini = 0.015\nsamples = 136\nvalue = [[1, 135]\n[135, 1]]", fillcolor="#e6853f"] ; +24 [label="mean smoothness <= 0.079\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"] ; 24 -> 25 ; diff --git a/doc/LectureNotes/DataFiles/cancer.png b/doc/LectureNotes/DataFiles/cancer.png index 53ab40093..4ac12d7fa 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 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b/doc/LectureNotes/_build/html/_sources/week39.ipynb @@ -2,7 +2,7 @@ "cells": [ { "cell_type": "markdown", - "id": "97c9bb6c", + "id": "428bf751", "metadata": { "editable": true }, @@ -14,7 +14,7 @@ }, { "cell_type": "markdown", - "id": "ade8d870", + "id": "1a0a75ed", "metadata": { "editable": true }, @@ -27,7 +27,7 @@ }, { "cell_type": "markdown", - "id": "87cd74c3", + "id": "333f3063", "metadata": { "editable": true }, @@ -42,6 +42,8 @@ "\n", " * Work on project 1, in particular resampling methods like cross-validation and bootstrap. **For more discussions of project 1, chapter 5 of Goodfellow et al is a good read, in particular sections 5.1-5.5 and 5.7-5.11**.\n", "\n", + " * [Video on how to write scientific reports recorded during one of the lab sessions](https://youtu.be/tVW1ZDmZnwM)\n", + "\n", "These sections summarize neatly what we have done till now and point to what is coming with respect to deep learning. \n", " * A general guideline can be found at .\n", "\n", @@ -53,7 +55,7 @@ "\n", " * Stochastic Gradient descent with examples and automatic differentiation\n", "\n", - " * [Video of lecture](https://youtu.be/)\n", + " * [Video of lecture](https://youtu.be/bFRVuIJroHs)\n", "\n", " * Whiteboard notes TBA at \n", "\n", @@ -65,12 +67,14 @@ "\n", " * [Video on gradient descent](https://www.youtube.com/watch?v=sDv4f4s2SB8)\n", "\n", - " * [Video on stochastic gradient descent](https://www.youtube.com/watch?v=vMh0zPT0tLI)" + " * [Video on stochastic gradient descent](https://www.youtube.com/watch?v=vMh0zPT0tLI)\n", + "\n", + "" ] }, { "cell_type": "markdown", - "id": "529184e5", + "id": "3ee03ecd", "metadata": { "editable": true }, @@ -91,7 +95,7 @@ }, { "cell_type": "markdown", - "id": "1a65465a", + "id": "148ec577", "metadata": { "editable": true }, @@ -108,7 +112,7 @@ }, { "cell_type": "markdown", - "id": "b67231b3", + "id": "e6e5e661", "metadata": { "editable": true }, @@ -123,7 +127,7 @@ }, { "cell_type": "markdown", - "id": "3f5e03db", + "id": "4a81fe9d", "metadata": { "editable": true }, @@ -133,7 +137,7 @@ }, { "cell_type": "markdown", - "id": "e83141ae", + "id": "ac94af95", "metadata": { "editable": true }, @@ -149,7 +153,7 @@ }, { "cell_type": "markdown", - "id": "cef5864b", + "id": "5bfc3f18", "metadata": { "editable": true }, @@ -161,7 +165,7 @@ }, { "cell_type": "markdown", - "id": "2f59bbb1", + "id": "2437c71a", "metadata": { "editable": true }, @@ -172,7 +176,7 @@ }, { "cell_type": "markdown", - "id": "3869b3c6", + "id": "a5d4163c", "metadata": { "editable": true }, @@ -184,7 +188,7 @@ }, { "cell_type": "markdown", - "id": "e4656e92", + "id": "cca433f5", "metadata": { "editable": true }, @@ -194,7 +198,7 @@ }, { "cell_type": "markdown", - "id": "b73ae554", + "id": "c9ba0136", "metadata": { "editable": true }, @@ -208,7 +212,7 @@ }, { "cell_type": "markdown", - "id": "70a2df05", + "id": "3a6a4c55", "metadata": { "editable": true }, @@ -220,7 +224,7 @@ }, { "cell_type": "markdown", - "id": "2e36f976", + "id": "f71ca135", "metadata": { "editable": true }, @@ -230,7 +234,7 @@ }, { "cell_type": "markdown", - "id": "7c4959c9", + "id": "942a71da", "metadata": { "editable": true }, @@ -242,7 +246,7 @@ }, { "cell_type": "markdown", - "id": "c553379e", + "id": "3d3ddaff", "metadata": { "editable": true }, @@ -254,7 +258,7 @@ }, { "cell_type": "markdown", - "id": "145f9699", + "id": "3dfb626a", "metadata": { "editable": true }, @@ -274,7 +278,7 @@ }, { "cell_type": "markdown", - "id": "9a68f686", + "id": "7a77c9ce", "metadata": { "editable": true }, @@ -290,7 +294,7 @@ }, { "cell_type": "markdown", - "id": "d523ab61", + "id": "d094140b", "metadata": { "editable": true }, @@ -306,7 +310,7 @@ }, { "cell_type": "markdown", - "id": "8205fd3a", + "id": "9c04f928", "metadata": { "editable": true }, @@ -317,7 +321,7 @@ }, { "cell_type": "markdown", - "id": "98d52e46", + "id": "d29b9b63", "metadata": { "editable": true }, @@ -329,7 +333,7 @@ }, { "cell_type": "markdown", - "id": "203f65e8", + "id": "d3e508b5", "metadata": { "editable": true }, @@ -339,7 +343,7 @@ }, { "cell_type": "markdown", - "id": "dd40195b", + "id": "fd5bbc31", "metadata": { "editable": true }, @@ -351,7 +355,7 @@ }, { "cell_type": "markdown", - "id": "e13a7340", + "id": "7e255afd", "metadata": { "editable": true }, @@ -361,7 +365,7 @@ }, { "cell_type": "markdown", - "id": "30c258c9", + "id": "62d43c1c", "metadata": { "editable": true }, @@ -373,7 +377,7 @@ }, { "cell_type": "markdown", - "id": "84f880c9", + "id": "0b23438c", "metadata": { "editable": true }, @@ -395,7 +399,7 @@ }, { "cell_type": "markdown", - "id": "b237f214", + "id": "d7587ee4", "metadata": { "editable": true }, @@ -408,7 +412,7 @@ }, { "cell_type": "markdown", - "id": "b4ea1db2", + "id": "3736c3ae", "metadata": { "editable": true }, @@ -421,7 +425,7 @@ }, { "cell_type": "markdown", - "id": "8b2b6606", + "id": "e9cf6fb6", "metadata": { "editable": true }, @@ -431,7 +435,7 @@ }, { "cell_type": "markdown", - "id": "e9d217a4", + "id": "9c8e6fc9", "metadata": { "editable": true }, @@ -449,7 +453,7 @@ }, { "cell_type": "markdown", - "id": "54f87f7c", + "id": "2059ae9d", "metadata": { "editable": true }, @@ -459,7 +463,7 @@ }, { "cell_type": "markdown", - "id": "c2711fc8", + "id": "64c640e4", "metadata": { "editable": true }, @@ -474,7 +478,7 @@ }, { "cell_type": "markdown", - "id": "e0409f1c", + "id": "647d6bd2", "metadata": { "editable": true }, @@ -484,7 +488,7 @@ }, { "cell_type": "markdown", - "id": "53651890", + "id": "24ff572b", "metadata": { "editable": true }, @@ -498,7 +502,7 @@ }, { "cell_type": "markdown", - "id": "4e70e7ac", + "id": "521c56f5", "metadata": { "editable": true }, @@ -508,7 +512,7 @@ }, { "cell_type": "markdown", - "id": "9336db1d", + "id": "4508944b", "metadata": { "editable": true }, @@ -522,7 +526,7 @@ }, { "cell_type": "markdown", - "id": "2c2ae028", + "id": "69b72003", "metadata": { "editable": true }, @@ -537,7 +541,7 @@ }, { "cell_type": "markdown", - "id": "cdf99885", + "id": "dd11ba71", "metadata": { "editable": true }, @@ -554,7 +558,7 @@ }, { "cell_type": "markdown", - "id": "11bb1b41", + "id": "ccb5e1a6", "metadata": { "editable": true }, @@ -566,7 +570,7 @@ }, { "cell_type": "markdown", - "id": "5d957768", + "id": "6952b928", "metadata": { "editable": true }, @@ -580,7 +584,7 @@ }, { "cell_type": "markdown", - "id": "455b420a", + "id": "f0c5476d", "metadata": { "editable": true }, @@ -595,7 +599,7 @@ }, { "cell_type": "markdown", - "id": "aacb8b05", + "id": "4aeb7465", "metadata": { "editable": true }, @@ -607,7 +611,7 @@ }, { "cell_type": "markdown", - "id": "2bb3385b", + "id": "337ccfd3", "metadata": { "editable": true }, @@ -618,7 +622,7 @@ }, { "cell_type": "markdown", - "id": "21326ef0", + "id": "7540286d", "metadata": { "editable": true }, @@ -646,7 +650,7 @@ }, { "cell_type": "markdown", - "id": "a41b3cc6", + "id": "b7ce5820", "metadata": { "editable": true }, @@ -668,7 +672,7 @@ }, { "cell_type": "markdown", - "id": "a01f11a5", + "id": "0a557da3", "metadata": { "editable": true }, @@ -690,19 +694,28 @@ }, { "cell_type": "markdown", - "id": "076a32fa", + "id": "bdbf8a07", "metadata": { "editable": true }, "source": [ "## Convex function\n", "\n", - "**Convex function**: Let $X \\subset \\mathbb{R}^n$ be a convex set. Assume that the function $f: X \\rightarrow \\mathbb{R}$ is continuous, then $f$ is said to be convex if $$f(tx_1 + (1-t)x_2) \\leq tf(x_1) + (1-t)f(x_2) $$ for all $x_1, x_2 \\in X$ and for all $t \\in [0,1]$. If $\\leq$ is replaced with a strict inequaltiy in the definition, we demand $x_1 \\neq x_2$ and $t\\in(0,1)$ then $f$ is said to be strictly convex. For a single variable function, convexity means that if you draw a straight line connecting $f(x_1)$ and $f(x_2)$, the value of the function on the interval $[x_1,x_2]$ is always below the line as illustrated below." + "**Convex function**: Let $X \\subset \\mathbb{R}^n$ be a convex\n", + "set. Assume that the function $f: X \\rightarrow \\mathbb{R}$ is\n", + "continuous, then $f$ is said to be convex if $f(tx_1 + (1-t)x_2) \\leq tf(x_1) + (1-t)f(x_2)$\n", + "for all $x_1, x_2 \\in X$ and for all $t \\in [0,1]$.\n", + "If $\\leq$ is replaced with a strict inequaltiy in the\n", + "definition, we demand $x_1 \\neq x_2$ and $t\\in(0,1)$ then $f$ is said\n", + "to be strictly convex. For a single variable function, convexity means\n", + "that if you draw a straight line connecting $f(x_1)$ and $f(x_2)$, the\n", + "value of the function on the interval $[x_1,x_2]$ is always below the\n", + "line as illustrated below." ] }, { "cell_type": "markdown", - "id": "73adde3c", + "id": "e02d1dac", "metadata": { "editable": true }, @@ -712,14 +725,16 @@ "In the following we state first and second-order conditions which\n", "ensures convexity of a function $f$. We write $D_f$ to denote the\n", "domain of $f$, i.e the subset of $R^n$ where $f$ is defined. For more\n", - "details and proofs we refer to: [S. Boyd and L. Vandenberghe. Convex Optimization. Cambridge University Press](http://stanford.edu/boyd/cvxbook/, 2004).\n", + "details and proofs we refer to: [S. Boyd and L. Vandenberghe. Convex Optimization. Cambridge University Press](http://stanford.edu/boyd/cvxbook/).\n", "\n", "**First order condition.**\n", "\n", "Suppose $f$ is differentiable (i.e $\\nabla f(x)$ is well defined for\n", "all $x$ in the domain of $f$). Then $f$ is convex if and only if $D_f$\n", - "is a convex set and $$f(y) \\geq f(x) + \\nabla f(x)^T (y-x) $$ holds\n", - "for all $x,y \\in D_f$. This condition means that for a convex function\n", + "is a convex set and $f(y) \\geq f(x) + \\nabla f(x)^T (y-x)$ holds\n", + "for all $x,y \\in D_f$.\n", + "\n", + "This condition means that for a convex function\n", "the first order Taylor expansion (right hand side above) at any point\n", "a global under estimator of the function. To convince yourself you can\n", "make a drawing of $f(x) = x^2+1$ and draw the tangent line to $f(x)$ and\n", @@ -739,7 +754,7 @@ }, { "cell_type": "markdown", - "id": "9f9ab5ff", + "id": "d9a72971", "metadata": { "editable": true }, @@ -767,7 +782,7 @@ }, { "cell_type": "markdown", - "id": "0b2a482b", + "id": "c3e744c2", "metadata": { "editable": true }, @@ -797,7 +812,7 @@ }, { "cell_type": "markdown", - "id": "6566ee55", + "id": "2732cf8b", "metadata": { "editable": true }, @@ -817,7 +832,7 @@ }, { "cell_type": "markdown", - "id": "c2e30cc1", + "id": "1b14ba6d", "metadata": { "editable": true }, @@ -829,7 +844,7 @@ }, { "cell_type": "markdown", - "id": "5012b398", + "id": "5f271cd8", "metadata": { "editable": true }, @@ -839,7 +854,7 @@ }, { "cell_type": "markdown", - "id": "ca65d9a9", + "id": "eb0ce7c8", "metadata": { "editable": true }, @@ -851,7 +866,7 @@ }, { "cell_type": "markdown", - "id": "ec9323dd", + "id": "3482f635", "metadata": { "editable": true }, @@ -863,7 +878,7 @@ }, { "cell_type": "markdown", - "id": "5caf0f7f", + "id": "0c88f8d2", "metadata": { "editable": true }, @@ -875,7 +890,7 @@ }, { "cell_type": "markdown", - "id": "07734ce6", + "id": "fe152e73", "metadata": { "editable": true }, @@ -887,7 +902,7 @@ }, { "cell_type": "markdown", - "id": "468dcb52", + "id": "740c5860", "metadata": { "editable": true }, @@ -898,7 +913,7 @@ }, { "cell_type": "markdown", - "id": "b63a89ae", + "id": "477da242", "metadata": { "editable": true }, @@ -911,7 +926,7 @@ }, { "cell_type": "markdown", - "id": "56a122a3", + "id": "7e2169e6", "metadata": { "editable": true }, @@ -923,7 +938,7 @@ }, { "cell_type": "markdown", - "id": "3256eb29", + "id": "e513c1c5", "metadata": { "editable": true }, @@ -933,7 +948,7 @@ }, { "cell_type": "markdown", - "id": "b6bab858", + "id": "a4a4f67c", "metadata": { "editable": true }, @@ -945,7 +960,7 @@ }, { "cell_type": "markdown", - "id": "7ff39e96", + "id": "96138e83", "metadata": { "editable": true }, @@ -955,7 +970,7 @@ }, { "cell_type": "markdown", - "id": "6678fce9", + "id": "f843d9f8", "metadata": { "editable": true }, @@ -966,7 +981,7 @@ }, { "cell_type": "markdown", - "id": "853eb11f", + "id": "5702234f", "metadata": { "editable": true }, @@ -978,7 +993,7 @@ }, { "cell_type": "markdown", - "id": "7c229917", + "id": "682a415f", "metadata": { "editable": true }, @@ -990,7 +1005,7 @@ }, { "cell_type": "markdown", - "id": "5c8f310a", + "id": "302ac54a", "metadata": { "editable": true }, @@ -1002,7 +1017,7 @@ }, { "cell_type": "markdown", - "id": "f8a8c317", + "id": "dd2cbeb1", "metadata": { "editable": true }, @@ -1013,7 +1028,7 @@ }, { "cell_type": "markdown", - "id": "49b64ed0", + "id": "f280370e", "metadata": { "editable": true }, @@ -1024,7 +1039,7 @@ }, { "cell_type": "markdown", - "id": "857ee939", + "id": "9702a162", "metadata": { "editable": true }, @@ -1036,7 +1051,7 @@ }, { "cell_type": "markdown", - "id": "7e5611fa", + "id": "3a1de3f0", "metadata": { "editable": true }, @@ -1046,7 +1061,7 @@ }, { "cell_type": "markdown", - "id": "384d5aa2", + "id": "4fa494ea", "metadata": { "editable": true }, @@ -1058,7 +1073,7 @@ }, { "cell_type": "markdown", - "id": "0ce677be", + "id": "a4f12308", "metadata": { "editable": true }, @@ -1068,7 +1083,7 @@ }, { "cell_type": "markdown", - "id": "e97f9044", + "id": "df770c35", "metadata": { "editable": true }, @@ -1080,7 +1095,7 @@ }, { "cell_type": "markdown", - "id": "d7fbeb68", + "id": "b1a30174", "metadata": { "editable": true }, @@ -1090,7 +1105,7 @@ }, { "cell_type": "markdown", - "id": "293c09b1", + "id": "6f816a49", "metadata": { "editable": true }, @@ -1102,7 +1117,7 @@ }, { "cell_type": "markdown", - "id": "af88e065", + "id": "4e5cd41c", "metadata": { "editable": true }, @@ -1112,7 +1127,7 @@ }, { "cell_type": "markdown", - "id": "2757e302", + "id": "91f972cb", "metadata": { "editable": true }, @@ -1124,7 +1139,7 @@ }, { "cell_type": "markdown", - "id": "87aab66b", + "id": "97064af3", "metadata": { "editable": true }, @@ -1135,7 +1150,7 @@ { "cell_type": "code", "execution_count": 1, - "id": "a0e20ff7", + "id": "5af74f1d", "metadata": { "collapsed": false, "editable": true @@ -1168,7 +1183,7 @@ }, { "cell_type": "markdown", - "id": "c01b471a", + "id": "db2d4ba3", "metadata": { "editable": true }, @@ -1179,7 +1194,7 @@ { "cell_type": "code", "execution_count": 2, - "id": "5b835c85", + "id": "dbba72eb", "metadata": { "collapsed": false, "editable": true @@ -1193,7 +1208,7 @@ }, { "cell_type": "markdown", - "id": "d6a3c121", + "id": "8290c8f1", "metadata": { "editable": true }, @@ -1204,7 +1219,7 @@ { "cell_type": "code", "execution_count": 3, - "id": "19e1d73c", + "id": "ce55e78a", "metadata": { "collapsed": false, "editable": true @@ -1217,7 +1232,7 @@ }, { "cell_type": "markdown", - "id": "9f7b2dfc", + "id": "0fa3681b", "metadata": { "editable": true }, @@ -1228,7 +1243,7 @@ { "cell_type": "code", "execution_count": 4, - "id": "7d8247e6", + "id": "a5aee074", "metadata": { "collapsed": false, "editable": true @@ -1246,7 +1261,7 @@ }, { "cell_type": "markdown", - "id": "c44006da", + "id": "8f8ed4d2", "metadata": { "editable": true }, @@ -1257,7 +1272,7 @@ { "cell_type": "code", "execution_count": 5, - "id": "bb8a0fd8", + "id": "30855606", "metadata": { "collapsed": false, "editable": true @@ -1272,7 +1287,7 @@ }, { "cell_type": "markdown", - "id": "3d3c98f0", + "id": "fcd5a0c8", "metadata": { "editable": true }, @@ -1282,7 +1297,7 @@ }, { "cell_type": "markdown", - "id": "29e5e792", + "id": "acdc3658", "metadata": { "editable": true }, @@ -1296,7 +1311,7 @@ }, { "cell_type": "markdown", - "id": "2b0e0db3", + "id": "7e07632f", "metadata": { "editable": true }, @@ -1308,7 +1323,7 @@ }, { "cell_type": "markdown", - "id": "401dd643", + "id": "356d5fe1", "metadata": { "editable": true }, @@ -1319,7 +1334,7 @@ }, { "cell_type": "markdown", - "id": "bc29d596", + "id": "2033af61", "metadata": { "editable": true }, @@ -1331,7 +1346,7 @@ }, { "cell_type": "markdown", - "id": "c1b7adb4", + "id": "0a85c783", "metadata": { "editable": true }, @@ -1342,7 +1357,7 @@ }, { "cell_type": "markdown", - "id": "18924232", + "id": "6c19d77d", "metadata": { "editable": true }, @@ -1353,7 +1368,7 @@ }, { "cell_type": "markdown", - "id": "1764ac31", + "id": "f42364fb", "metadata": { "editable": true }, @@ -1365,7 +1380,7 @@ }, { "cell_type": "markdown", - "id": "379d5862", + "id": "c841e7d3", "metadata": { "editable": true }, @@ -1375,7 +1390,7 @@ }, { "cell_type": "markdown", - "id": "9587d8bf", + "id": "297492ba", "metadata": { "editable": true }, @@ -1387,7 +1402,7 @@ }, { "cell_type": "markdown", - "id": "4c3d0bfb", + "id": "4963a2d8", "metadata": { "editable": true }, @@ -1399,7 +1414,7 @@ }, { "cell_type": "markdown", - "id": "4079ca1a", + "id": "4a39d88b", "metadata": { "editable": true }, @@ -1411,7 +1426,7 @@ }, { "cell_type": "markdown", - "id": "e5b487a5", + "id": "83a86148", "metadata": { "editable": true }, @@ -1423,7 +1438,7 @@ }, { "cell_type": "markdown", - "id": "b623d7f7", + "id": "c691f06b", "metadata": { "editable": true }, @@ -1434,7 +1449,7 @@ }, { "cell_type": "markdown", - "id": "8520c560", + "id": "d4df90e8", "metadata": { "editable": true }, @@ -1446,7 +1461,7 @@ }, { "cell_type": "markdown", - "id": "52575016", + "id": "bf3217ad", "metadata": { "editable": true }, @@ -1456,7 +1471,7 @@ }, { "cell_type": "markdown", - "id": "1b8a85bd", + "id": "b43b4b20", "metadata": { "editable": true }, @@ -1468,7 +1483,7 @@ }, { "cell_type": "markdown", - "id": "e53b0f45", + "id": "d82bb554", "metadata": { "editable": true }, @@ -1478,7 +1493,7 @@ }, { "cell_type": "markdown", - "id": "2238e15f", + "id": "dde5ed03", "metadata": { "editable": true }, @@ -1490,7 +1505,7 @@ }, { "cell_type": "markdown", - "id": "f00e8864", + "id": "65ecffe4", "metadata": { "editable": true }, @@ -1510,7 +1525,7 @@ }, { "cell_type": "markdown", - "id": "7a17895d", + "id": "65a1b4c0", "metadata": { "editable": true }, @@ -1522,7 +1537,7 @@ }, { "cell_type": "markdown", - "id": "d4bafcb3", + "id": "41dec06b", "metadata": { "editable": true }, @@ -1532,7 +1547,7 @@ }, { "cell_type": "markdown", - "id": "78a7d2c3", + "id": "7d1da8a1", "metadata": { "editable": true }, @@ -1544,7 +1559,7 @@ }, { "cell_type": "markdown", - "id": "e3192cbf", + "id": "c4850b28", "metadata": { "editable": true }, @@ -1554,7 +1569,7 @@ }, { "cell_type": "markdown", - "id": "0d99ed55", + "id": "ff91db7e", "metadata": { "editable": true }, @@ -1565,7 +1580,7 @@ }, { "cell_type": "markdown", - "id": "b9653ede", + "id": "a8ab3c6e", "metadata": { "editable": true }, @@ -1577,7 +1592,7 @@ }, { "cell_type": "markdown", - "id": "78441105", + "id": "e30ea38e", "metadata": { "editable": true }, @@ -1589,7 +1604,7 @@ }, { "cell_type": "markdown", - "id": "317355d2", + "id": "ede52cdd", "metadata": { "editable": true }, @@ -1601,7 +1616,7 @@ }, { "cell_type": "markdown", - "id": "9bb15157", + "id": "3cdf6ffd", "metadata": { "editable": true }, @@ -1614,7 +1629,7 @@ }, { "cell_type": "markdown", - "id": "ac584971", + "id": "d66ac756", "metadata": { "editable": true }, @@ -1625,7 +1640,7 @@ }, { "cell_type": "markdown", - "id": "911f1dfa", + "id": "59b7a9f5", "metadata": { "editable": true }, @@ -1637,7 +1652,7 @@ }, { "cell_type": "markdown", - "id": "d1472568", + "id": "769980be", "metadata": { "editable": true }, @@ -1653,7 +1668,7 @@ }, { "cell_type": "markdown", - "id": "c79708e8", + "id": "3ae7691f", "metadata": { "editable": true }, @@ -1665,7 +1680,7 @@ }, { "cell_type": "markdown", - "id": "535d3e73", + "id": "9cf530dc", "metadata": { "editable": true }, @@ -1676,7 +1691,7 @@ }, { "cell_type": "markdown", - "id": "ad718f62", + "id": "6398a5d7", "metadata": { "editable": true }, @@ -1688,7 +1703,7 @@ }, { "cell_type": "markdown", - "id": "d0a90fa5", + "id": "b64c8282", "metadata": { "editable": true }, @@ -1698,7 +1713,7 @@ }, { "cell_type": "markdown", - "id": "860e9217", + "id": "4b9bcf21", "metadata": { "editable": true }, @@ -1710,7 +1725,7 @@ }, { "cell_type": "markdown", - "id": "6d8a72c8", + "id": "6d15e4b9", "metadata": { "editable": true }, @@ -1720,7 +1735,7 @@ }, { "cell_type": "markdown", - "id": "746e6fc0", + "id": "592ac2b7", "metadata": { "editable": true }, @@ -1732,7 +1747,7 @@ }, { "cell_type": "markdown", - "id": "f76c0e69", + "id": "c54989ac", "metadata": { "editable": true }, @@ -1742,7 +1757,7 @@ }, { "cell_type": "markdown", - "id": "9aee35ca", + "id": "87adadc2", "metadata": { "editable": true }, @@ -1754,7 +1769,7 @@ }, { "cell_type": "markdown", - "id": "ce9ce258", + "id": "00250e67", "metadata": { "editable": true }, @@ -1778,7 +1793,7 @@ { "cell_type": "code", "execution_count": 6, - "id": "f902a0f2", + "id": "47a98c7c", "metadata": { "collapsed": false, "editable": true @@ -1791,7 +1806,7 @@ }, { "cell_type": "markdown", - "id": "36d883b2", + "id": "e3007f3b", "metadata": { "editable": true }, @@ -1802,7 +1817,7 @@ }, { "cell_type": "markdown", - "id": "cde21ef1", + "id": "c5b7179d", "metadata": { "editable": true }, @@ -1814,7 +1829,7 @@ }, { "cell_type": "markdown", - "id": "f2a021d3", + "id": "8a4d63b4", "metadata": { "editable": true }, @@ -1824,7 +1839,7 @@ }, { "cell_type": "markdown", - "id": "ea0a91e4", + "id": "206c9402", "metadata": { "editable": true }, @@ -1836,7 +1851,7 @@ }, { "cell_type": "markdown", - "id": "854f3ebb", + "id": "939b3b78", "metadata": { "editable": true }, @@ -1850,7 +1865,7 @@ }, { "cell_type": "markdown", - "id": "dd282d2d", + "id": "b867af05", "metadata": { "editable": true }, @@ -1866,7 +1881,7 @@ }, { "cell_type": "markdown", - "id": "fd562029", + "id": "76738c60", "metadata": { "editable": true }, @@ -1876,7 +1891,7 @@ }, { "cell_type": "markdown", - "id": "25369bc3", + "id": "c01273f5", "metadata": { "editable": true }, @@ -1888,7 +1903,7 @@ }, { "cell_type": "markdown", - "id": "a222f3ea", + "id": "5d680787", "metadata": { "editable": true }, @@ -1898,7 +1913,7 @@ }, { "cell_type": "markdown", - "id": "1d5fb6f1", + "id": "2ef9ff3b", "metadata": { "editable": true }, @@ -1910,7 +1925,7 @@ }, { "cell_type": "markdown", - "id": "eab2df73", + "id": "e5a81fba", "metadata": { "editable": true }, @@ -1924,7 +1939,7 @@ }, { "cell_type": "markdown", - "id": "daee1165", + "id": "b7298ace", "metadata": { "editable": true }, @@ -1934,7 +1949,7 @@ }, { "cell_type": "markdown", - "id": "f2c6f5cc", + "id": "64cfb75f", "metadata": { "editable": true }, @@ -1945,7 +1960,7 @@ }, { "cell_type": "markdown", - "id": "ecce0d08", + "id": "99503e16", "metadata": { "editable": true }, @@ -1960,7 +1975,7 @@ }, { "cell_type": "markdown", - "id": "c4308e5f", + "id": "4a567780", "metadata": { "editable": true }, @@ -1970,7 +1985,7 @@ }, { "cell_type": "markdown", - "id": "4ee64b17", + "id": "22c576da", "metadata": { "editable": true }, @@ -1982,7 +1997,7 @@ }, { "cell_type": "markdown", - "id": "57e8db33", + "id": "44a99f62", "metadata": { "editable": true }, @@ -1994,7 +2009,7 @@ }, { "cell_type": "markdown", - "id": "c42e4032", + "id": "7021c749", "metadata": { "editable": true }, @@ -2009,7 +2024,7 @@ }, { "cell_type": "markdown", - "id": "4c430cd3", + "id": "6044e7a8", "metadata": { "editable": true }, @@ -2022,7 +2037,7 @@ { "cell_type": "code", "execution_count": 7, - "id": "9ac6096f", + "id": "72003ff9", "metadata": { "collapsed": false, "editable": true @@ -2079,7 +2094,7 @@ }, { "cell_type": "markdown", - "id": "df783e1d", + "id": "01fdfcaf", "metadata": { "editable": true }, @@ -2090,7 +2105,7 @@ { "cell_type": "code", "execution_count": 8, - "id": "98f08f24", + "id": "d16ddbdc", "metadata": { "collapsed": false, "editable": true @@ -2117,7 +2132,7 @@ }, { "cell_type": "markdown", - "id": "50a5ab0d", + "id": "08aaf479", "metadata": { "editable": true }, @@ -2129,7 +2144,7 @@ }, { "cell_type": "markdown", - "id": "b35293d4", + "id": "0aa5045f", "metadata": { "editable": true }, @@ -2141,7 +2156,7 @@ }, { "cell_type": "markdown", - "id": "fed491ee", + "id": "6474d14b", "metadata": { "editable": true }, @@ -2151,7 +2166,7 @@ }, { "cell_type": "markdown", - "id": "a0b4c94e", + "id": "9335b39d", "metadata": { "editable": true }, @@ -2165,7 +2180,7 @@ }, { "cell_type": "markdown", - "id": "e4f02dce", + "id": "0680a59f", "metadata": { "editable": true }, @@ -2175,7 +2190,7 @@ }, { "cell_type": "markdown", - "id": "b9f297ff", + "id": "de5afdeb", "metadata": { "editable": true }, @@ -2187,7 +2202,7 @@ }, { "cell_type": "markdown", - "id": "4e1caf44", + "id": "0042d7e6", "metadata": { "editable": true }, @@ -2198,7 +2213,7 @@ }, { "cell_type": "markdown", - "id": "a046daea", + "id": "02cf311f", "metadata": { "editable": true }, @@ -2213,7 +2228,7 @@ }, { "cell_type": "markdown", - "id": "02f05574", + "id": "3dbc50e6", "metadata": { "editable": true }, @@ -2227,7 +2242,7 @@ }, { "cell_type": "markdown", - "id": "45484749", + "id": "437e17bc", "metadata": { "editable": true }, @@ -2238,7 +2253,7 @@ { "cell_type": "code", "execution_count": 9, - "id": "9973cd20", + "id": "f11ee927", "metadata": { "collapsed": false, "editable": true @@ -2299,7 +2314,7 @@ }, { "cell_type": "markdown", - "id": "1836d4ef", + "id": "c06cf31f", "metadata": { "editable": true }, @@ -2321,7 +2336,7 @@ }, { "cell_type": "markdown", - "id": "88975d3d", + "id": "870586cd", "metadata": { "editable": true }, @@ -2334,7 +2349,7 @@ { "cell_type": "code", "execution_count": 10, - "id": "56f415e0", + "id": "24517bb5", "metadata": { "collapsed": false, "editable": true @@ -2400,7 +2415,7 @@ }, { "cell_type": "markdown", - "id": "d3343584", + "id": "57647429", "metadata": { "editable": true }, @@ -2411,7 +2426,7 @@ { "cell_type": "code", "execution_count": 11, - "id": "ff1e3778", + "id": "b8365c93", "metadata": { "collapsed": false, "editable": true @@ -2485,7 +2500,7 @@ }, { "cell_type": "markdown", - "id": "c1d70995", + "id": "3c5b105d", "metadata": { "editable": true }, @@ -2497,7 +2512,7 @@ }, { "cell_type": "markdown", - "id": "930a5be8", + "id": "e78e4fcf", "metadata": { "editable": true }, @@ -2518,7 +2533,7 @@ }, { "cell_type": "markdown", - "id": "0a7fb7ef", + "id": "9e856c0b", "metadata": { "editable": true }, @@ -2550,7 +2565,7 @@ }, { "cell_type": "markdown", - "id": "dbff87b0", + "id": "8400c2e5", "metadata": { "editable": true }, @@ -2567,7 +2582,7 @@ }, { "cell_type": "markdown", - "id": "cd292df5", + "id": "d0ceff52", "metadata": { "editable": true }, @@ -2580,7 +2595,7 @@ }, { "cell_type": "markdown", - "id": "1b2ffa4e", + "id": "562ca1d7", "metadata": { "editable": true }, @@ -2593,7 +2608,7 @@ }, { "cell_type": "markdown", - "id": "d0abe4b0", + "id": "ffea7df9", "metadata": { "editable": true }, @@ -2606,7 +2621,7 @@ }, { "cell_type": "markdown", - "id": "65c15c60", + "id": "20f1bd07", "metadata": { "editable": true }, @@ -2620,7 +2635,7 @@ }, { "cell_type": "markdown", - "id": "460354c0", + "id": "4589bb1b", "metadata": { "editable": true }, @@ -2642,7 +2657,7 @@ }, { "cell_type": "markdown", - "id": "c2a5dfcd", + "id": "0df2146b", "metadata": { "editable": true }, @@ -2657,7 +2672,7 @@ }, { "cell_type": "markdown", - "id": "eeeb0fe8", + "id": "890e6746", "metadata": { "editable": true }, @@ -2669,7 +2684,7 @@ }, { "cell_type": "markdown", - "id": "2b49c741", + "id": "b6e42059", "metadata": { "editable": true }, @@ -2682,7 +2697,7 @@ }, { "cell_type": "markdown", - "id": "8ba7b9be", + "id": "9dd3abbf", "metadata": { "editable": true }, @@ -2696,7 +2711,7 @@ }, { "cell_type": "markdown", - "id": "50da33c0", + "id": "97279f92", "metadata": { "editable": true }, @@ -2707,7 +2722,7 @@ { "cell_type": "code", "execution_count": 12, - "id": "31bd6a24", + "id": "cb0c6322", "metadata": { "collapsed": false, "editable": true @@ -2732,7 +2747,7 @@ }, { "cell_type": "markdown", - "id": "0deb8111", + "id": "c0868aae", "metadata": { "editable": true }, @@ -2748,7 +2763,7 @@ }, { "cell_type": "markdown", - "id": "16d54f02", + "id": "1e17bb0f", "metadata": { "editable": true }, @@ -2769,7 +2784,7 @@ }, { "cell_type": "markdown", - "id": "b300d06b", + "id": "f050ca70", "metadata": { "editable": true }, @@ -2789,7 +2804,7 @@ }, { "cell_type": "markdown", - "id": "6bc7778d", + "id": "6a900f78", "metadata": { "editable": true }, @@ -2808,7 +2823,7 @@ { "cell_type": "code", "execution_count": 13, - "id": "a60fe5bd", + "id": "1324db42", "metadata": { "collapsed": false, "editable": true @@ -2843,7 +2858,7 @@ }, { "cell_type": "markdown", - "id": "2192721f", + "id": "0c365408", "metadata": { "editable": true }, @@ -2856,7 +2871,7 @@ { "cell_type": "code", "execution_count": 14, - "id": "e404f2c5", + "id": "79417e81", "metadata": { "collapsed": false, "editable": true @@ -2933,7 +2948,7 @@ }, { "cell_type": "markdown", - "id": "fffdbb91", + "id": "dd16fd67", "metadata": { "editable": true }, @@ -2948,7 +2963,7 @@ }, { "cell_type": "markdown", - "id": "8cce7a0e", + "id": "2bbf7fbd", "metadata": { "editable": true }, @@ -2963,7 +2978,7 @@ }, { "cell_type": "markdown", - "id": "3154c365", + "id": "d4aa4448", "metadata": { "editable": true }, @@ -2975,7 +2990,7 @@ }, { "cell_type": "markdown", - "id": "a2a9ceca", + "id": "fdcd258f", "metadata": { "editable": true }, @@ -2993,7 +3008,7 @@ }, { "cell_type": "markdown", - "id": "3374c700", + "id": "52ec5bfb", "metadata": { "editable": true }, @@ -3012,7 +3027,7 @@ }, { "cell_type": "markdown", - "id": "893d86fe", + "id": "38004062", "metadata": { "editable": true }, @@ -3024,7 +3039,7 @@ }, { "cell_type": "markdown", - "id": "ca2449e8", + "id": "9d07c567", "metadata": { "editable": true }, @@ -3034,7 +3049,7 @@ }, { "cell_type": "markdown", - "id": "3cbd4adb", + "id": "4dbba8bc", "metadata": { "editable": true }, @@ -3050,7 +3065,7 @@ }, { "cell_type": "markdown", - "id": "e3f07cbc", + "id": "dab76529", "metadata": { "editable": true }, @@ -3062,7 +3077,7 @@ }, { "cell_type": "markdown", - "id": "99f2ac0f", + "id": "6e049d15", "metadata": { "editable": true }, @@ -3072,7 +3087,7 @@ }, { "cell_type": "markdown", - "id": "83336244", + "id": "5079f465", "metadata": { "editable": true }, @@ -3084,7 +3099,7 @@ }, { "cell_type": "markdown", - "id": "efd3e708", + "id": "51c2ed45", "metadata": { "editable": true }, @@ -3094,7 +3109,7 @@ }, { "cell_type": "markdown", - "id": "6c24d65c", + "id": "7e8f7b16", "metadata": { "editable": true }, @@ -3106,7 +3121,7 @@ }, { "cell_type": "markdown", - "id": "853d885b", + "id": "ae0505aa", "metadata": { "editable": true }, @@ -3122,7 +3137,7 @@ }, { "cell_type": "markdown", - "id": "5ab54645", + "id": "9e7f520b", "metadata": { "editable": true }, @@ -3134,7 +3149,7 @@ }, { "cell_type": "markdown", - "id": "90f1503b", + "id": "5c0aa1f6", "metadata": { "editable": true }, @@ -3167,7 +3182,7 @@ }, { "cell_type": "markdown", - "id": "d496d988", + "id": "991c2c15", "metadata": { "editable": true }, @@ -3179,7 +3194,7 @@ }, { "cell_type": "markdown", - "id": "258ca1e6", + "id": "c643afb5", "metadata": { "editable": true }, @@ -3197,7 +3212,7 @@ }, { "cell_type": "markdown", - "id": "84278058", + "id": "ac4a060d", "metadata": { "editable": true }, @@ -3207,7 +3222,7 @@ }, { "cell_type": "markdown", - "id": "feaee2f4", + "id": "37584d4d", "metadata": { "editable": true }, @@ -3238,7 +3253,7 @@ }, { "cell_type": "markdown", - "id": "218edcf1", + "id": "0e9c907f", "metadata": { "editable": true }, @@ -3253,7 +3268,7 @@ }, { "cell_type": "markdown", - "id": "18dbc91c", + "id": "cb4567f1", "metadata": { "editable": true }, @@ -3271,7 +3286,7 @@ }, { "cell_type": "markdown", - "id": "0bfcf74a", + "id": "71805d3d", "metadata": { "editable": true }, @@ -3283,7 +3298,7 @@ }, { "cell_type": "markdown", - "id": "5fedd6f0", + "id": "09794996", "metadata": { "editable": true }, @@ -3295,7 +3310,7 @@ }, { "cell_type": "markdown", - "id": "b210b2a4", + "id": "aeb48f66", "metadata": { "editable": true }, @@ -3313,7 +3328,7 @@ }, { "cell_type": "markdown", - "id": "34ffacbb", + "id": "68e08134", "metadata": { "editable": true }, @@ -3342,7 +3357,7 @@ }, { "cell_type": "markdown", - "id": "cd03375d", + "id": "69308397", "metadata": { "editable": true }, @@ -3360,7 +3375,7 @@ }, { "cell_type": "markdown", - "id": "0db5d6e0", + "id": "d23ab794", "metadata": { "editable": true }, @@ -3372,7 +3387,7 @@ }, { "cell_type": "markdown", - "id": "84c709d9", + "id": "c4cef70b", "metadata": { "editable": true }, @@ -3384,7 +3399,7 @@ }, { "cell_type": "markdown", - "id": "e4e47496", + "id": "6aebd1b5", "metadata": { "editable": true }, @@ -3396,7 +3411,7 @@ }, { "cell_type": "markdown", - "id": "164f27df", + "id": "c43fe267", "metadata": { "editable": true }, @@ -3408,7 +3423,7 @@ }, { "cell_type": "markdown", - "id": "591f4833", + "id": "9ae56692", "metadata": { "editable": true }, @@ -3420,7 +3435,7 @@ }, { "cell_type": "markdown", - "id": "e2127e8a", + "id": "784ba00e", "metadata": { "editable": true }, @@ -3437,7 +3452,7 @@ }, { "cell_type": "markdown", - "id": "5cef8b84", + "id": "a187dfb2", "metadata": { "editable": true }, @@ -3456,7 +3471,7 @@ }, { "cell_type": "markdown", - "id": "505c8905", + "id": "832cc99c", "metadata": { "editable": true }, @@ -3468,7 +3483,7 @@ }, { "cell_type": "markdown", - "id": "ca96ddec", + "id": "b1a24342", "metadata": { "editable": true }, @@ -3482,7 +3497,7 @@ }, { "cell_type": "markdown", - "id": "fa011176", + "id": "8a97509f", "metadata": { "editable": true }, @@ -3502,7 +3517,7 @@ }, { "cell_type": "markdown", - "id": "b91c4543", + "id": "631a2aa8", "metadata": { "editable": true }, @@ -3540,7 +3555,7 @@ }, { "cell_type": "markdown", - "id": "f13065e5", + "id": "472b23f2", "metadata": { "editable": true }, @@ -3552,7 +3567,7 @@ }, { "cell_type": "markdown", - "id": "22937f5e", + "id": "1c91c90f", "metadata": { "editable": true }, @@ -3562,7 +3577,7 @@ }, { "cell_type": "markdown", - "id": "e1459fe1", + "id": "a85c6aab", "metadata": { "editable": true }, @@ -3574,7 +3589,7 @@ }, { "cell_type": "markdown", - "id": "441a109a", + "id": "89a0bdbb", "metadata": { "editable": true }, @@ -3585,7 +3600,7 @@ { "cell_type": "code", "execution_count": 15, - "id": "9043abae", + "id": "6fe48a50", "metadata": { "collapsed": false, "editable": true @@ -3630,7 +3645,7 @@ }, { "cell_type": "markdown", - "id": "787d5d78", + "id": "cab7d753", "metadata": { "editable": true }, @@ -3647,7 +3662,7 @@ { "cell_type": "code", "execution_count": 16, - "id": "6a677479", + "id": "ca4d6b32", "metadata": { "collapsed": false, "editable": true @@ -3675,7 +3690,7 @@ }, { "cell_type": "markdown", - "id": "f94a1d32", + "id": "4a748513", "metadata": { "editable": true }, @@ -3690,7 +3705,7 @@ { "cell_type": "code", "execution_count": 17, - "id": "c0eb89fd", + "id": "f235cd43", "metadata": { "collapsed": false, "editable": true @@ -3734,7 +3749,7 @@ }, { "cell_type": "markdown", - "id": "05d7497d", + "id": "5d8df033", "metadata": { "editable": true }, @@ -3744,7 +3759,7 @@ }, { "cell_type": "markdown", - "id": "24e3ca02", + "id": "2f7de144", "metadata": { "editable": true }, @@ -3755,7 +3770,7 @@ { "cell_type": "code", "execution_count": 18, - "id": "a616e696", + "id": "a2ed8fd6", "metadata": { "collapsed": false, "editable": true @@ -3783,7 +3798,7 @@ }, { "cell_type": "markdown", - "id": "f695da56", + "id": "1f633736", "metadata": { "editable": true }, @@ -3798,7 +3813,7 @@ }, { "cell_type": "markdown", - "id": "5ac073ee", + "id": "a73225d4", "metadata": { "editable": true }, @@ -3809,7 +3824,7 @@ { "cell_type": "code", "execution_count": 19, - "id": "efc8906e", + "id": "39059d20", "metadata": { "collapsed": false, "editable": true @@ -3837,7 +3852,7 @@ }, { "cell_type": "markdown", - "id": "7c587e8f", + "id": "d147b69e", "metadata": { "editable": true }, @@ -3848,7 +3863,7 @@ { "cell_type": "code", "execution_count": 20, - "id": "6428be1f", + "id": "90a2d143", "metadata": { "collapsed": false, "editable": true @@ -3873,7 +3888,7 @@ }, { "cell_type": "markdown", - "id": "8e1de777", + "id": "6655faec", "metadata": { "editable": true }, @@ -3884,7 +3899,7 @@ { "cell_type": "code", "execution_count": 21, - "id": "770ff6aa", + "id": "639ea2a9", "metadata": { "collapsed": false, "editable": true @@ -3920,7 +3935,7 @@ { "cell_type": "code", "execution_count": 22, - "id": "b924cc5d", + "id": "a53c5633", "metadata": { "collapsed": false, "editable": true @@ -3940,7 +3955,7 @@ }, { "cell_type": "markdown", - "id": "f0b0e1e9", + "id": "be5e41d4", "metadata": { "editable": true }, @@ -3951,7 +3966,7 @@ { "cell_type": "code", "execution_count": 23, - "id": "1585ab28", + "id": "786e19d0", "metadata": { "collapsed": false, "editable": true @@ -3989,7 +4004,7 @@ }, { "cell_type": "markdown", - "id": "2718df1a", + "id": "114e7e25", "metadata": { "editable": true }, @@ -3999,7 +4014,7 @@ }, { "cell_type": "markdown", - "id": "d8fa5235", + "id": "24c8ffa6", "metadata": { "editable": true }, @@ -4013,7 +4028,7 @@ { "cell_type": "code", "execution_count": 24, - "id": "196a52d6", + "id": "59f521ac", "metadata": { "collapsed": false, "editable": true @@ -4035,7 +4050,7 @@ }, { "cell_type": "markdown", - "id": "9127a2c5", + "id": "686c34bb", "metadata": { "editable": true }, @@ -4045,7 +4060,7 @@ }, { "cell_type": "markdown", - "id": "2b12ed61", + "id": "9b4cc4f3", "metadata": { "editable": true }, @@ -4056,7 +4071,7 @@ { "cell_type": "code", "execution_count": 25, - "id": "8ced55c8", + "id": "dea954af", "metadata": { "collapsed": false, "editable": true @@ -4078,7 +4093,7 @@ }, { "cell_type": "markdown", - "id": "92ebdc2b", + "id": "9732d039", "metadata": { "editable": true }, @@ -4091,7 +4106,7 @@ { "cell_type": "code", "execution_count": 26, - "id": "276f763e", + "id": "f580c6a1", "metadata": { "collapsed": false, "editable": true @@ -4116,7 +4131,7 @@ }, { "cell_type": "markdown", - "id": "7841ad0b", + "id": "d8714004", "metadata": { "editable": true }, @@ -4128,7 +4143,7 @@ { "cell_type": "code", "execution_count": 27, - "id": "10107989", + "id": "e56cbb47", "metadata": { "collapsed": false, "editable": true @@ -4143,7 +4158,7 @@ }, { "cell_type": "markdown", - "id": "4c1139c0", + "id": "fac1a7da", "metadata": { "editable": true }, @@ -4158,7 +4173,7 @@ { "cell_type": "code", "execution_count": 28, - "id": "3022af88", + "id": "c4c5b9c0", "metadata": { "collapsed": false, "editable": true @@ -4218,7 +4233,7 @@ }, { "cell_type": "markdown", - "id": "04d09021", + "id": "b16d7700", "metadata": { "editable": true }, @@ -4229,7 +4244,7 @@ { "cell_type": "code", "execution_count": 29, - "id": "71bf4b6d", + "id": "7453efe5", "metadata": { "collapsed": false, "editable": true @@ -4293,7 +4308,7 @@ }, { "cell_type": "markdown", - "id": "ad417bba", + "id": "0a417277", "metadata": { "editable": true }, @@ -4304,7 +4319,7 @@ { "cell_type": "code", "execution_count": 30, - "id": "c394bcef", + "id": "1a541fef", "metadata": { "collapsed": false, "editable": true @@ -4353,7 +4368,7 @@ }, { "cell_type": "markdown", - "id": "2e4cf4b5", + "id": "9e937e4f", "metadata": { "editable": true }, @@ -4365,7 +4380,7 @@ { "cell_type": "code", "execution_count": 31, - "id": "47411bcf", + "id": "9afef100", "metadata": { "collapsed": false, "editable": true @@ -4449,7 +4464,7 @@ }, { "cell_type": "markdown", - "id": "f8e30af2", + "id": "2a7e982c", "metadata": { "editable": true }, @@ -4460,7 +4475,7 @@ { "cell_type": "code", "execution_count": 32, - "id": "dd594924", + "id": "91311a17", "metadata": { "collapsed": false, "editable": true @@ -4538,7 +4553,7 @@ }, { "cell_type": "markdown", - "id": "75c4c29d", + "id": "516999d8", "metadata": { "editable": true }, @@ -4549,7 +4564,7 @@ { "cell_type": "code", "execution_count": 33, - "id": "4dd14fc5", + "id": "e8292719", "metadata": { "collapsed": false, "editable": true @@ -4608,7 +4623,7 @@ }, { "cell_type": "markdown", - "id": "be4ce0cd", + "id": "bd67f5cb", "metadata": { "editable": true }, @@ -4618,7 +4633,7 @@ }, { "cell_type": "markdown", - "id": "0b739495", + "id": "eb0d5fd0", "metadata": { "editable": true }, @@ -4629,7 +4644,7 @@ { "cell_type": "code", "execution_count": 34, - "id": "ae87789c", + "id": "d2eb93d1", "metadata": { "collapsed": false, "editable": true @@ -4694,7 +4709,7 @@ }, { "cell_type": "markdown", - "id": "76c8872b", + "id": "669b56c2", "metadata": { "editable": true }, @@ -4705,7 +4720,7 @@ { "cell_type": "code", "execution_count": 35, - "id": "e99dbaa4", + "id": "bb3f553d", "metadata": { "collapsed": false, "editable": true @@ -4775,7 +4790,7 @@ }, { "cell_type": "markdown", - "id": "596df570", + "id": "4e5c58ea", "metadata": { "editable": true }, @@ -4786,7 +4801,7 @@ { "cell_type": "code", "execution_count": 36, - "id": "6693f042", + "id": "1fec659e", "metadata": { "collapsed": false, "editable": true @@ -4830,7 +4845,7 @@ }, { "cell_type": "markdown", - "id": "a40ed853", + "id": "ac14943c", "metadata": { "editable": true }, @@ -4849,7 +4864,7 @@ { "cell_type": "code", "execution_count": 37, - "id": "02f88360", + "id": "57b4e540", "metadata": { "collapsed": false, "editable": true diff --git a/doc/LectureNotes/_build/html/_sources/week40.ipynb b/doc/LectureNotes/_build/html/_sources/week40.ipynb new file mode 100644 index 000000000..70ab38246 --- /dev/null +++ b/doc/LectureNotes/_build/html/_sources/week40.ipynb @@ -0,0 +1,3545 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "c410abdb", + "metadata": { + "editable": true + }, + "source": [ + "\n", + "" + ] + }, + { + "cell_type": "markdown", + "id": "ffdc5797", + "metadata": { + "editable": true + }, + "source": [ + "# Week 40: Gradient descent methods (continued) and start Neural networks\n", + "**Morten Hjorth-Jensen**, Department of Physics, University of Oslo, Norway and Department of Physics and Astronomy and Facility for Rare Ion Beams, Michigan State University, USA\n", + "\n", + "Date: **October 2-6, 2023**" + ] + }, + { + "cell_type": "markdown", + "id": "4bab315b", + "metadata": { + "editable": true + }, + "source": [ + "## Plans for week 40\n", + "\n", + "**Material for the active learning sessions on Tuesday and Wednesday.**\n", + "\n", + " * Work on project 1 and discussions on how to structure your report\n", + "\n", + " * No weekly exercises for week 40, project work only\n", + "\n", + " * [Video on how to write scientific reports recorded during one of the lab sessions](https://youtu.be/tVW1ZDmZnwM)\n", + "\n", + " * A general guideline can be found at .\n", + "\n", + " \n", + "\n", + "**Material for the lecture on Thursday October 5, 2023.**\n", + "\n", + " * Stochastic Gradient descent with examples and automatic differentiation\n", + "\n", + " * Neural Networks, setting up the basic steps, from the simple perceptron model to the multi-layer perceptron model. \n", + "\n", + " * Readings and Videos:\n", + "\n", + " * These lecture notes\n", + "\n", + " * For a good discussion on gradient methods, we would like to recommend Goodfellow et al section 4.3-4.5 and sections 8.3-8.6. We will come back to the latter chapter in our discussion of Neural networks as well.\n", + "\n", + " * [Aurelien Geron's chapter 4 on stochastic gradient descent](https://github.com/CompPhysics/MachineLearning/blob/master/doc/Textbooks/TensorflowML.pdf)\n", + "\n", + " * For neural networks we recommend Goodfellow et al chapter 6.\n", + "\n", + " * [Video on gradient descent](https://www.youtube.com/watch?v=sDv4f4s2SB8)\n", + "\n", + " * [Video on stochastic gradient descent](https://www.youtube.com/watch?v=vMh0zPT0tLI)\n", + "\n", + " * [Neural Networks demystified](https://www.youtube.com/watch?v=bxe2T-V8XRs&list=PLiaHhY2iBX9hdHaRr6b7XevZtgZRa1PoU&ab_channel=WelchLabs)\n", + "\n", + " * [Building Neural Networks from scratch](https://www.youtube.com/watch?v=Wo5dMEP_BbI&list=PLQVvvaa0QuDcjD5BAw2DxE6OF2tius3V3&ab_channel=sentdex)" + ] + }, + { + "cell_type": "markdown", + "id": "1ba76140", + "metadata": { + "editable": true + }, + "source": [ + "## Summary from last week, using gradient descent methods, limitations\n", + "\n", + "* **Gradient descent (GD) finds local minima of our function**. Since the GD algorithm is deterministic, if it converges, it will converge to a local minimum of our cost/loss/risk function. Because in ML we are often dealing with extremely rugged landscapes with many local minima, this can lead to poor performance.\n", + "\n", + "* **GD is sensitive to initial conditions**. One consequence of the local nature of GD is that initial conditions matter. Depending on where one starts, one will end up at a different local minima. Therefore, it is very important to think about how one initializes the training process. This is true for GD as well as more complicated variants of GD.\n", + "\n", + "* **Gradients are computationally expensive to calculate for large datasets**. In many cases in statistics and ML, the cost/loss/risk function is a sum of terms, with one term for each data point. For example, in linear regression, $E \\propto \\sum_{i=1}^n (y_i - \\mathbf{w}^T\\cdot\\mathbf{x}_i)^2$; for logistic regression, the square error is replaced by the cross entropy. To calculate the gradient we have to sum over *all* $n$ data points. Doing this at every GD step becomes extremely computationally expensive. An ingenious solution to this, is to calculate the gradients using small subsets of the data called \"mini batches\". This has the added benefit of introducing stochasticity into our algorithm.\n", + "\n", + "* **GD is very sensitive to choices of learning rates**. GD is extremely sensitive to the choice of learning rates. If the learning rate is very small, the training process take an extremely long time. For larger learning rates, GD can diverge and give poor results. Furthermore, depending on what the local landscape looks like, we have to modify the learning rates to ensure convergence. Ideally, we would *adaptively* choose the learning rates to match the landscape.\n", + "\n", + "* **GD treats all directions in parameter space uniformly.** Another major drawback of GD is that unlike Newton's method, the learning rate for GD is the same in all directions in parameter space. For this reason, the maximum learning rate is set by the behavior of the steepest direction and this can significantly slow down training. Ideally, we would like to take large steps in flat directions and small steps in steep directions. Since we are exploring rugged landscapes where curvatures change, this requires us to keep track of not only the gradient but second derivatives. The ideal scenario would be to calculate the Hessian but this proves to be too computationally expensive. \n", + "\n", + "* GD can take exponential time to escape saddle points, even with random initialization. As we mentioned, GD is extremely sensitive to initial condition since it determines the particular local minimum GD would eventually reach. However, even with a good initialization scheme, through the introduction of randomness, GD can still take exponential time to escape saddle points." + ] + }, + { + "cell_type": "markdown", + "id": "a8b56c00", + "metadata": { + "editable": true + }, + "source": [ + "## Overview video on Stochastic Gradient Descent\n", + "\n", + "[What is Stochastic Gradient Descent](https://www.youtube.com/watch?v=vMh0zPT0tLI&ab_channel=StatQuestwithJoshStarmer)" + ] + }, + { + "cell_type": "markdown", + "id": "eba32497", + "metadata": { + "editable": true + }, + "source": [ + "## Batches and mini-batches\n", + "\n", + "In gradient descent we compute the cost function and its gradient for all data points we have.\n", + "\n", + "In large-scale applications such as the [ILSVRC challenge](https://www.image-net.org/challenges/LSVRC/), the\n", + "training data can have on order of millions of examples. Hence, it\n", + "seems wasteful to compute the full cost function over the entire\n", + "training set in order to perform only a single parameter update. A\n", + "very common approach to addressing this challenge is to compute the\n", + "gradient over batches of the training data. For example, a typical batch could contain some thousand examples from\n", + "an entire training set of several millions. This batch is then used to\n", + "perform a parameter update." + ] + }, + { + "cell_type": "markdown", + "id": "55578599", + "metadata": { + "editable": true + }, + "source": [ + "## Stochastic Gradient Descent (SGD)\n", + "\n", + "In stochastic gradient descent, the extreme case is the case where we\n", + "have only one batch, that is we include the whole data set.\n", + "\n", + "This process is called Stochastic Gradient\n", + "Descent (SGD) (or also sometimes on-line gradient descent). This is\n", + "relatively less common to see because in practice due to vectorized\n", + "code optimizations it can be computationally much more efficient to\n", + "evaluate the gradient for 100 examples, than the gradient for one\n", + "example 100 times. Even though SGD technically refers to using a\n", + "single example at a time to evaluate the gradient, you will hear\n", + "people use the term SGD even when referring to mini-batch gradient\n", + "descent (i.e. mentions of MGD for “Minibatch Gradient Descent”, or BGD\n", + "for “Batch gradient descent” are rare to see), where it is usually\n", + "assumed that mini-batches are used. The size of the mini-batch is a\n", + "hyperparameter but it is not very common to cross-validate or bootstrap it. It is\n", + "usually based on memory constraints (if any), or set to some value,\n", + "e.g. 32, 64 or 128. We use powers of 2 in practice because many\n", + "vectorized operation implementations work faster when their inputs are\n", + "sized in powers of 2.\n", + "\n", + "In our notes with SGD we mean stochastic gradient descent with mini-batches." + ] + }, + { + "cell_type": "markdown", + "id": "140607b7", + "metadata": { + "editable": true + }, + "source": [ + "## Stochastic Gradient Descent\n", + "\n", + "Stochastic gradient descent (SGD) and variants thereof address some of\n", + "the shortcomings of the Gradient descent method discussed above.\n", + "\n", + "The underlying idea of SGD comes from the observation that the cost\n", + "function, which we want to minimize, can almost always be written as a\n", + "sum over $n$ data points $\\{\\mathbf{x}_i\\}_{i=1}^n$," + ] + }, + { + "cell_type": "markdown", + "id": "c1a00332", + "metadata": { + "editable": true + }, + "source": [ + "$$\n", + "C(\\mathbf{\\beta}) = \\sum_{i=1}^n c_i(\\mathbf{x}_i,\n", + "\\mathbf{\\beta}).\n", + "$$" + ] + }, + { + "cell_type": "markdown", + "id": "2c04bdee", + "metadata": { + "editable": true + }, + "source": [ + "## Computation of gradients\n", + "\n", + "This in turn means that the gradient can be\n", + "computed as a sum over $i$-gradients" + ] + }, + { + "cell_type": "markdown", + "id": "087684a4", + "metadata": { + "editable": true + }, + "source": [ + "$$\n", + "\\nabla_\\beta C(\\mathbf{\\beta}) = \\sum_i^n \\nabla_\\beta c_i(\\mathbf{x}_i,\n", + "\\mathbf{\\beta}).\n", + "$$" + ] + }, + { + "cell_type": "markdown", + "id": "e0362df4", + "metadata": { + "editable": true + }, + "source": [ + "Stochasticity/randomness is introduced by only taking the\n", + "gradient on a subset of the data called minibatches. If there are $n$\n", + "data points and the size of each minibatch is $M$, there will be $n/M$\n", + "minibatches. We denote these minibatches by $B_k$ where\n", + "$k=1,\\cdots,n/M$." + ] + }, + { + "cell_type": "markdown", + "id": "24051a4e", + "metadata": { + "editable": true + }, + "source": [ + "## SGD example\n", + "As an example, suppose we have $10$ data points $(\\mathbf{x}_1,\\cdots, \\mathbf{x}_{10})$ \n", + "and we choose to have $M=5$ minibathces,\n", + "then each minibatch contains two data points. In particular we have\n", + "$B_1 = (\\mathbf{x}_1,\\mathbf{x}_2), \\cdots, B_5 =\n", + "(\\mathbf{x}_9,\\mathbf{x}_{10})$. Note that if you choose $M=1$ you\n", + "have only a single batch with all data points and on the other extreme,\n", + "you may choose $M=n$ resulting in a minibatch for each datapoint, i.e\n", + "$B_k = \\mathbf{x}_k$.\n", + "\n", + "The idea is now to approximate the gradient by replacing the sum over\n", + "all data points with a sum over the data points in one the minibatches\n", + "picked at random in each gradient descent step" + ] + }, + { + "cell_type": "markdown", + "id": "7723f927", + "metadata": { + "editable": true + }, + "source": [ + "$$\n", + "\\nabla_{\\beta}\n", + "C(\\mathbf{\\beta}) = \\sum_{i=1}^n \\nabla_\\beta c_i(\\mathbf{x}_i,\n", + "\\mathbf{\\beta}) \\rightarrow \\sum_{i \\in B_k}^n \\nabla_\\beta\n", + "c_i(\\mathbf{x}_i, \\mathbf{\\beta}).\n", + "$$" + ] + }, + { + "cell_type": "markdown", + "id": "59221981", + "metadata": { + "editable": true + }, + "source": [ + "## The gradient step\n", + "\n", + "Thus a gradient descent step now looks like" + ] + }, + { + "cell_type": "markdown", + "id": "a7d27b48", + "metadata": { + "editable": true + }, + "source": [ + "$$\n", + "\\beta_{j+1} = \\beta_j - \\gamma_j \\sum_{i \\in B_k}^n \\nabla_\\beta c_i(\\mathbf{x}_i,\n", + "\\mathbf{\\beta})\n", + "$$" + ] + }, + { + "cell_type": "markdown", + "id": "c7595344", + "metadata": { + "editable": true + }, + "source": [ + "where $k$ is picked at random with equal\n", + "probability from $[1,n/M]$. An iteration over the number of\n", + "minibathces (n/M) is commonly referred to as an epoch. Thus it is\n", + "typical to choose a number of epochs and for each epoch iterate over\n", + "the number of minibatches, as exemplified in the code below." + ] + }, + { + "cell_type": "markdown", + "id": "0d7024b5", + "metadata": { + "editable": true + }, + "source": [ + "## Simple example code" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "0f9dc38b", + "metadata": { + "collapsed": false, + "editable": true + }, + "outputs": [], + "source": [ + "import numpy as np \n", + "\n", + "n = 100 #100 datapoints \n", + "M = 5 #size of each minibatch\n", + "m = int(n/M) #number of minibatches\n", + "n_epochs = 10 #number of epochs\n", + "\n", + "j = 0\n", + "for epoch in range(1,n_epochs+1):\n", + " for i in range(m):\n", + " k = np.random.randint(m) #Pick the k-th minibatch at random\n", + " #Compute the gradient using the data in minibatch Bk\n", + " #Compute new suggestion for \n", + " j += 1" + ] + }, + { + "cell_type": "markdown", + "id": "df447303", + "metadata": { + "editable": true + }, + "source": [ + "Taking the gradient only on a subset of the data has two important\n", + "benefits. First, it introduces randomness which decreases the chance\n", + "that our opmization scheme gets stuck in a local minima. Second, if\n", + "the size of the minibatches are small relative to the number of\n", + "datapoints ($M < n$), the computation of the gradient is much\n", + "cheaper since we sum over the datapoints in the $k-th$ minibatch and not\n", + "all $n$ datapoints." + ] + }, + { + "cell_type": "markdown", + "id": "976aef35", + "metadata": { + "editable": true + }, + "source": [ + "## When do we stop?\n", + "\n", + "A natural question is when do we stop the search for a new minimum?\n", + "One possibility is to compute the full gradient after a given number\n", + "of epochs and check if the norm of the gradient is smaller than some\n", + "threshold and stop if true. However, the condition that the gradient\n", + "is zero is valid also for local minima, so this would only tell us\n", + "that we are close to a local/global minimum. However, we could also\n", + "evaluate the cost function at this point, store the result and\n", + "continue the search. If the test kicks in at a later stage we can\n", + "compare the values of the cost function and keep the $\\beta$ that\n", + "gave the lowest value." + ] + }, + { + "cell_type": "markdown", + "id": "0fab1ae1", + "metadata": { + "editable": true + }, + "source": [ + "## Slightly different approach\n", + "\n", + "Another approach is to let the step length $\\gamma_j$ depend on the\n", + "number of epochs in such a way that it becomes very small after a\n", + "reasonable time such that we do not move at all. Such approaches are\n", + "also called scaling. There are many such ways to [scale the learning\n", + "rate](https://towardsdatascience.com/gradient-descent-the-learning-rate-and-the-importance-of-feature-scaling-6c0b416596e1)\n", + "and [discussions here](https://www.jmlr.org/papers/volume23/20-1258/20-1258.pdf). See\n", + "also\n", + "\n", + "for a discussion of different scaling functions for the learning rate." + ] + }, + { + "cell_type": "markdown", + "id": "2db0116b", + "metadata": { + "editable": true + }, + "source": [ + "## Time decay rate\n", + "\n", + "As an example, let $e = 0,1,2,3,\\cdots$ denote the current epoch and let $t_0, t_1 > 0$ be two fixed numbers. Furthermore, let $t = e \\cdot m + i$ where $m$ is the number of minibatches and $i=0,\\cdots,m-1$. Then the function $$\\gamma_j(t; t_0, t_1) = \\frac{t_0}{t+t_1} $$ goes to zero as the number of epochs gets large. I.e. we start with a step length $\\gamma_j (0; t_0, t_1) = t_0/t_1$ which decays in *time* $t$.\n", + "\n", + "In this way we can fix the number of epochs, compute $\\beta$ and\n", + "evaluate the cost function at the end. Repeating the computation will\n", + "give a different result since the scheme is random by design. Then we\n", + "pick the final $\\beta$ that gives the lowest value of the cost\n", + "function." + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "a9ca6f9a", + "metadata": { + "collapsed": false, + "editable": true + }, + "outputs": [], + "source": [ + "import numpy as np \n", + "\n", + "def step_length(t,t0,t1):\n", + " return t0/(t+t1)\n", + "\n", + "n = 100 #100 datapoints \n", + "M = 5 #size of each minibatch\n", + "m = int(n/M) #number of minibatches\n", + "n_epochs = 500 #number of epochs\n", + "t0 = 1.0\n", + "t1 = 10\n", + "\n", + "gamma_j = t0/t1\n", + "j = 0\n", + "for epoch in range(1,n_epochs+1):\n", + " for i in range(m):\n", + " k = np.random.randint(m) #Pick the k-th minibatch at random\n", + " #Compute the gradient using the data in minibatch Bk\n", + " #Compute new suggestion for beta\n", + " t = epoch*m+i\n", + " gamma_j = step_length(t,t0,t1)\n", + " j += 1\n", + "\n", + "print(\"gamma_j after %d epochs: %g\" % (n_epochs,gamma_j))" + ] + }, + { + "cell_type": "markdown", + "id": "fcf9b69b", + "metadata": { + "editable": true + }, + "source": [ + "## Code with a Number of Minibatches which varies\n", + "\n", + "In the code here we vary the number of mini-batches." + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "861b050f", + "metadata": { + "collapsed": false, + "editable": true + }, + "outputs": [], + "source": [ + "%matplotlib inline\n", + "\n", + "# Importing various packages\n", + "from math import exp, sqrt\n", + "from random import random, seed\n", + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "\n", + "n = 100\n", + "x = 2*np.random.rand(n,1)\n", + "y = 4+3*x+np.random.randn(n,1)\n", + "\n", + "X = np.c_[np.ones((n,1)), x]\n", + "XT_X = X.T @ X\n", + "theta_linreg = np.linalg.inv(X.T @ X) @ (X.T @ y)\n", + "print(\"Own inversion\")\n", + "print(theta_linreg)\n", + "# Hessian matrix\n", + "H = (2.0/n)* XT_X\n", + "EigValues, EigVectors = np.linalg.eig(H)\n", + "print(f\"Eigenvalues of Hessian Matrix:{EigValues}\")\n", + "\n", + "theta = np.random.randn(2,1)\n", + "eta = 1.0/np.max(EigValues)\n", + "Niterations = 1000\n", + "\n", + "\n", + "for iter in range(Niterations):\n", + " gradients = 2.0/n*X.T @ ((X @ theta)-y)\n", + " theta -= eta*gradients\n", + "print(\"theta from own gd\")\n", + "print(theta)\n", + "\n", + "xnew = np.array([[0],[2]])\n", + "Xnew = np.c_[np.ones((2,1)), xnew]\n", + "ypredict = Xnew.dot(theta)\n", + "ypredict2 = Xnew.dot(theta_linreg)\n", + "\n", + "n_epochs = 50\n", + "M = 5 #size of each minibatch\n", + "m = int(n/M) #number of minibatches\n", + "t0, t1 = 5, 50\n", + "\n", + "def learning_schedule(t):\n", + " return t0/(t+t1)\n", + "\n", + "theta = np.random.randn(2,1)\n", + "\n", + "for epoch in range(n_epochs):\n", + "# Can you figure out a better way of setting up the contributions to each batch?\n", + " for i in range(m):\n", + " random_index = M*np.random.randint(m)\n", + " xi = X[random_index:random_index+M]\n", + " yi = y[random_index:random_index+M]\n", + " gradients = (2.0/M)* xi.T @ ((xi @ theta)-yi)\n", + " eta = learning_schedule(epoch*m+i)\n", + " theta = theta - eta*gradients\n", + "print(\"theta from own sdg\")\n", + "print(theta)\n", + "\n", + "plt.plot(xnew, ypredict, \"r-\")\n", + "plt.plot(xnew, ypredict2, \"b-\")\n", + "plt.plot(x, y ,'ro')\n", + "plt.axis([0,2.0,0, 15.0])\n", + "plt.xlabel(r'$x$')\n", + "plt.ylabel(r'$y$')\n", + "plt.title(r'Random numbers ')\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "97311aec", + "metadata": { + "editable": true + }, + "source": [ + "## Replace or not\n", + "\n", + "In the above code, we have use replacement in setting up the\n", + "mini-batches. The discussion\n", + "[here](https://sebastianraschka.com/faq/docs/sgd-methods.html) may be\n", + "useful." + ] + }, + { + "cell_type": "markdown", + "id": "423ddc16", + "metadata": { + "editable": true + }, + "source": [ + "## Momentum based GD\n", + "\n", + "The stochastic gradient descent (SGD) is almost always used with a\n", + "*momentum* or inertia term that serves as a memory of the direction we\n", + "are moving in parameter space. This is typically implemented as\n", + "follows" + ] + }, + { + "cell_type": "markdown", + "id": "a4f85670", + "metadata": { + "editable": true + }, + "source": [ + "$$\n", + "\\mathbf{v}_{t}=\\gamma \\mathbf{v}_{t-1}+\\eta_{t}\\nabla_\\theta E(\\boldsymbol{\\theta}_t) \\nonumber\n", + "$$" + ] + }, + { + "cell_type": "markdown", + "id": "f15ea450", + "metadata": { + "editable": true + }, + "source": [ + "\n", + "
\n", + "\n", + "$$\n", + "\\begin{equation} \n", + "\\boldsymbol{\\theta}_{t+1}= \\boldsymbol{\\theta}_t -\\mathbf{v}_{t},\n", + "\\label{_auto1} \\tag{1}\n", + "\\end{equation}\n", + "$$" + ] + }, + { + "cell_type": "markdown", + "id": "233d7b7e", + "metadata": { + "editable": true + }, + "source": [ + "where we have introduced a momentum parameter $\\gamma$, with\n", + "$0\\le\\gamma\\le 1$, and for brevity we dropped the explicit notation to\n", + "indicate the gradient is to be taken over a different mini-batch at\n", + "each step. We call this algorithm gradient descent with momentum\n", + "(GDM). From these equations, it is clear that $\\mathbf{v}_t$ is a\n", + "running average of recently encountered gradients and\n", + "$(1-\\gamma)^{-1}$ sets the characteristic time scale for the memory\n", + "used in the averaging procedure. Consistent with this, when\n", + "$\\gamma=0$, this just reduces down to ordinary SGD as discussed\n", + "earlier. An equivalent way of writing the updates is" + ] + }, + { + "cell_type": "markdown", + "id": "b923e7d5", + "metadata": { + "editable": true + }, + "source": [ + "$$\n", + "\\Delta \\boldsymbol{\\theta}_{t+1} = \\gamma \\Delta \\boldsymbol{\\theta}_t -\\ \\eta_{t}\\nabla_\\theta E(\\boldsymbol{\\theta}_t),\n", + "$$" + ] + }, + { + "cell_type": "markdown", + "id": "60980ded", + "metadata": { + "editable": true + }, + "source": [ + "where we have defined $\\Delta \\boldsymbol{\\theta}_{t}= \\boldsymbol{\\theta}_t-\\boldsymbol{\\theta}_{t-1}$." + ] + }, + { + "cell_type": "markdown", + "id": "cc771e70", + "metadata": { + "editable": true + }, + "source": [ + "## More on momentum based approaches\n", + "\n", + "Let us try to get more intuition from these equations. It is helpful\n", + "to consider a simple physical analogy with a particle of mass $m$\n", + "moving in a viscous medium with drag coefficient $\\mu$ and potential\n", + "$E(\\mathbf{w})$. If we denote the particle's position by $\\mathbf{w}$,\n", + "then its motion is described by" + ] + }, + { + "cell_type": "markdown", + "id": "859f6ffc", + "metadata": { + "editable": true + }, + "source": [ + "$$\n", + "m {d^2 \\mathbf{w} \\over dt^2} + \\mu {d \\mathbf{w} \\over dt }= -\\nabla_w E(\\mathbf{w}).\n", + "$$" + ] + }, + { + "cell_type": "markdown", + "id": "064cc085", + "metadata": { + "editable": true + }, + "source": [ + "We can discretize this equation in the usual way to get" + ] + }, + { + "cell_type": "markdown", + "id": "47d13c3c", + "metadata": { + "editable": true + }, + "source": [ + "$$\n", + "m { \\mathbf{w}_{t+\\Delta t}-2 \\mathbf{w}_{t} +\\mathbf{w}_{t-\\Delta t} \\over (\\Delta t)^2}+\\mu {\\mathbf{w}_{t+\\Delta t}- \\mathbf{w}_{t} \\over \\Delta t} = -\\nabla_w E(\\mathbf{w}).\n", + "$$" + ] + }, + { + "cell_type": "markdown", + "id": "0ca67954", + "metadata": { + "editable": true + }, + "source": [ + "Rearranging this equation, we can rewrite this as" + ] + }, + { + "cell_type": "markdown", + "id": "ea9f63a8", + "metadata": { + "editable": true + }, + "source": [ + "$$\n", + "\\Delta \\mathbf{w}_{t +\\Delta t}= - { (\\Delta t)^2 \\over m +\\mu \\Delta t} \\nabla_w E(\\mathbf{w})+ {m \\over m +\\mu \\Delta t} \\Delta \\mathbf{w}_t.\n", + "$$" + ] + }, + { + "cell_type": "markdown", + "id": "35146ea5", + "metadata": { + "editable": true + }, + "source": [ + "## Momentum parameter\n", + "\n", + "Notice that this equation is identical to previous one if we identify\n", + "the position of the particle, $\\mathbf{w}$, with the parameters\n", + "$\\boldsymbol{\\theta}$. This allows us to identify the momentum\n", + "parameter and learning rate with the mass of the particle and the\n", + "viscous drag as:" + ] + }, + { + "cell_type": "markdown", + "id": "82c87bb1", + "metadata": { + "editable": true + }, + "source": [ + "$$\n", + "\\gamma= {m \\over m +\\mu \\Delta t }, \\qquad \\eta = {(\\Delta t)^2 \\over m +\\mu \\Delta t}.\n", + "$$" + ] + }, + { + "cell_type": "markdown", + "id": "75415fca", + "metadata": { + "editable": true + }, + "source": [ + "Thus, as the name suggests, the momentum parameter is proportional to\n", + "the mass of the particle and effectively provides inertia.\n", + "Furthermore, in the large viscosity/small learning rate limit, our\n", + "memory time scales as $(1-\\gamma)^{-1} \\approx m/(\\mu \\Delta t)$.\n", + "\n", + "Why is momentum useful? SGD momentum helps the gradient descent\n", + "algorithm gain speed in directions with persistent but small gradients\n", + "even in the presence of stochasticity, while suppressing oscillations\n", + "in high-curvature directions. This becomes especially important in\n", + "situations where the landscape is shallow and flat in some directions\n", + "and narrow and steep in others. It has been argued that first-order\n", + "methods (with appropriate initial conditions) can perform comparable\n", + "to more expensive second order methods, especially in the context of\n", + "complex deep learning models.\n", + "\n", + "These beneficial properties of momentum can sometimes become even more\n", + "pronounced by using a slight modification of the classical momentum\n", + "algorithm called Nesterov Accelerated Gradient (NAG).\n", + "\n", + "In the NAG algorithm, rather than calculating the gradient at the\n", + "current parameters, $\\nabla_\\theta E(\\boldsymbol{\\theta}_t)$, one\n", + "calculates the gradient at the expected value of the parameters given\n", + "our current momentum, $\\nabla_\\theta E(\\boldsymbol{\\theta}_t +\\gamma\n", + "\\mathbf{v}_{t-1})$. This yields the NAG update rule" + ] + }, + { + "cell_type": "markdown", + "id": "59892cd6", + "metadata": { + "editable": true + }, + "source": [ + "$$\n", + "\\mathbf{v}_{t}=\\gamma \\mathbf{v}_{t-1}+\\eta_{t}\\nabla_\\theta E(\\boldsymbol{\\theta}_t +\\gamma \\mathbf{v}_{t-1}) \\nonumber\n", + "$$" + ] + }, + { + "cell_type": "markdown", + "id": "a01225ea", + "metadata": { + "editable": true + }, + "source": [ + "\n", + "
\n", + "\n", + "$$\n", + "\\begin{equation} \n", + "\\boldsymbol{\\theta}_{t+1}= \\boldsymbol{\\theta}_t -\\mathbf{v}_{t}.\n", + "\\label{_auto2} \\tag{2}\n", + "\\end{equation}\n", + "$$" + ] + }, + { + "cell_type": "markdown", + "id": "e2c9f57b", + "metadata": { + "editable": true + }, + "source": [ + "One of the major advantages of NAG is that it allows for the use of a larger learning rate than GDM for the same choice of $\\gamma$." + ] + }, + { + "cell_type": "markdown", + "id": "1672a79e", + "metadata": { + "editable": true + }, + "source": [ + "## Second moment of the gradient\n", + "\n", + "In stochastic gradient descent, with and without momentum, we still\n", + "have to specify a schedule for tuning the learning rates $\\eta_t$\n", + "as a function of time. As discussed in the context of Newton's\n", + "method, this presents a number of dilemmas. The learning rate is\n", + "limited by the steepest direction which can change depending on the\n", + "current position in the landscape. To circumvent this problem, ideally\n", + "our algorithm would keep track of curvature and take large steps in\n", + "shallow, flat directions and small steps in steep, narrow directions.\n", + "Second-order methods accomplish this by calculating or approximating\n", + "the Hessian and normalizing the learning rate by the\n", + "curvature. However, this is very computationally expensive for\n", + "extremely large models. Ideally, we would like to be able to\n", + "adaptively change the step size to match the landscape without paying\n", + "the steep computational price of calculating or approximating\n", + "Hessians.\n", + "\n", + "Recently, a number of methods have been introduced that accomplish\n", + "this by tracking not only the gradient, but also the second moment of\n", + "the gradient. These methods include AdaGrad, AdaDelta, Root Mean Squared Propagation (RMS-Prop), and\n", + "[ADAM](https://arxiv.org/abs/1412.6980)." + ] + }, + { + "cell_type": "markdown", + "id": "6d4032f9", + "metadata": { + "editable": true + }, + "source": [ + "## RMS prop\n", + "\n", + "In RMS prop, in addition to keeping a running average of the first\n", + "moment of the gradient, we also keep track of the second moment\n", + "denoted by $\\mathbf{s}_t=\\mathbb{E}[\\mathbf{g}_t^2]$. The update rule\n", + "for RMS prop is given by" + ] + }, + { + "cell_type": "markdown", + "id": "63cde9f3", + "metadata": { + "editable": true + }, + "source": [ + "\n", + "
\n", + "\n", + "$$\n", + "\\begin{equation}\n", + "\\mathbf{g}_t = \\nabla_\\theta E(\\boldsymbol{\\theta}) \n", + "\\label{_auto3} \\tag{3}\n", + "\\end{equation}\n", + "$$" + ] + }, + { + "cell_type": "markdown", + "id": "6f8a52c2", + "metadata": { + "editable": true + }, + "source": [ + "$$\n", + "\\mathbf{s}_t =\\beta \\mathbf{s}_{t-1} +(1-\\beta)\\mathbf{g}_t^2 \\nonumber\n", + "$$" + ] + }, + { + "cell_type": "markdown", + "id": "9edf087d", + "metadata": { + "editable": true + }, + "source": [ + "$$\n", + "\\boldsymbol{\\theta}_{t+1}=\\boldsymbol{\\theta}_t - \\eta_t { \\mathbf{g}_t \\over \\sqrt{\\mathbf{s}_t +\\epsilon}}, \\nonumber\n", + "$$" + ] + }, + { + "cell_type": "markdown", + "id": "7eff676b", + "metadata": { + "editable": true + }, + "source": [ + "where $\\beta$ controls the averaging time of the second moment and is\n", + "typically taken to be about $\\beta=0.9$, $\\eta_t$ is a learning rate\n", + "typically chosen to be $10^{-3}$, and $\\epsilon\\sim 10^{-8} $ is a\n", + "small regularization constant to prevent divergences. Multiplication\n", + "and division by vectors is understood as an element-wise operation. It\n", + "is clear from this formula that the learning rate is reduced in\n", + "directions where the norm of the gradient is consistently large. This\n", + "greatly speeds up the convergence by allowing us to use a larger\n", + "learning rate for flat directions." + ] + }, + { + "cell_type": "markdown", + "id": "3fcb1068", + "metadata": { + "editable": true + }, + "source": [ + "## [ADAM optimizer](https://arxiv.org/abs/1412.6980)\n", + "\n", + "A related algorithm is the ADAM optimizer. In\n", + "[ADAM](https://arxiv.org/abs/1412.6980), we keep a running average of\n", + "both the first and second moment of the gradient and use this\n", + "information to adaptively change the learning rate for different\n", + "parameters. The method isefficient when working with large\n", + "problems involving lots data and/or parameters. It is a combination of the\n", + "gradient descent with momentum algorithm and the RMSprop algorithm\n", + "discussed above.\n", + "\n", + "In addition to keeping a running average of the first and\n", + "second moments of the gradient\n", + "(i.e. $\\mathbf{m}_t=\\mathbb{E}[\\mathbf{g}_t]$ and\n", + "$\\mathbf{s}_t=\\mathbb{E}[\\mathbf{g}^2_t]$, respectively), ADAM\n", + "performs an additional bias correction to account for the fact that we\n", + "are estimating the first two moments of the gradient using a running\n", + "average (denoted by the hats in the update rule below). The update\n", + "rule for ADAM is given by (where multiplication and division are once\n", + "again understood to be element-wise operations below)" + ] + }, + { + "cell_type": "markdown", + "id": "31b034e1", + "metadata": { + "editable": true + }, + "source": [ + "\n", + "
\n", + "\n", + "$$\n", + "\\begin{equation}\n", + "\\mathbf{g}_t = \\nabla_\\theta E(\\boldsymbol{\\theta}) \n", + "\\label{_auto4} \\tag{4}\n", + "\\end{equation}\n", + "$$" + ] + }, + { + "cell_type": "markdown", + "id": "571e9a91", + "metadata": { + "editable": true + }, + "source": [ + "$$\n", + "\\mathbf{m}_t = \\beta_1 \\mathbf{m}_{t-1} + (1-\\beta_1) \\mathbf{g}_t \\nonumber\n", + "$$" + ] + }, + { + "cell_type": "markdown", + "id": "fb5883fa", + "metadata": { + "editable": true + }, + "source": [ + "$$\n", + "\\mathbf{s}_t =\\beta_2 \\mathbf{s}_{t-1} +(1-\\beta_2)\\mathbf{g}_t^2 \\nonumber\n", + "$$" + ] + }, + { + "cell_type": "markdown", + "id": "ebffe7a1", + "metadata": { + "editable": true + }, + "source": [ + "$$\n", + "\\boldsymbol{\\mathbf{m}}_t={\\mathbf{m}_t \\over 1-\\beta_1^t} \\nonumber\n", + "$$" + ] + }, + { + "cell_type": "markdown", + "id": "5a513bd7", + "metadata": { + "editable": true + }, + "source": [ + "$$\n", + "\\boldsymbol{\\mathbf{s}}_t ={\\mathbf{s}_t \\over1-\\beta_2^t} \\nonumber\n", + "$$" + ] + }, + { + "cell_type": "markdown", + "id": "d49bc312", + "metadata": { + "editable": true + }, + "source": [ + "$$\n", + "\\boldsymbol{\\theta}_{t+1}=\\boldsymbol{\\theta}_t - \\eta_t { \\boldsymbol{\\mathbf{m}}_t \\over \\sqrt{\\boldsymbol{\\mathbf{s}}_t} +\\epsilon}, \\nonumber\n", + "$$" + ] + }, + { + "cell_type": "markdown", + "id": "6f4e5040", + "metadata": { + "editable": true + }, + "source": [ + "\n", + "
\n", + "\n", + "$$\n", + "\\begin{equation} \n", + "\\label{_auto5} \\tag{5}\n", + "\\end{equation}\n", + "$$" + ] + }, + { + "cell_type": "markdown", + "id": "4771881e", + "metadata": { + "editable": true + }, + "source": [ + "where $\\beta_1$ and $\\beta_2$ set the memory lifetime of the first and\n", + "second moment and are typically taken to be $0.9$ and $0.99$\n", + "respectively, and $\\eta$ and $\\epsilon$ are identical to RMSprop.\n", + "\n", + "Like in RMSprop, the effective step size of a parameter depends on the\n", + "magnitude of its gradient squared. To understand this better, let us\n", + "rewrite this expression in terms of the variance\n", + "$\\boldsymbol{\\sigma}_t^2 = \\boldsymbol{\\mathbf{s}}_t -\n", + "(\\boldsymbol{\\mathbf{m}}_t)^2$. Consider a single parameter $\\theta_t$. The\n", + "update rule for this parameter is given by" + ] + }, + { + "cell_type": "markdown", + "id": "3a0d438e", + "metadata": { + "editable": true + }, + "source": [ + "$$\n", + "\\Delta \\theta_{t+1}= -\\eta_t { \\boldsymbol{m}_t \\over \\sqrt{\\sigma_t^2 + m_t^2 }+\\epsilon}.\n", + "$$" + ] + }, + { + "cell_type": "markdown", + "id": "6cbb721b", + "metadata": { + "editable": true + }, + "source": [ + "## Algorithms and codes for Adagrad, RMSprop and Adam\n", + "\n", + "The algorithms we have implemented are well described in the text by [Goodfellow, Bengio and Courville, chapter 8](https://www.deeplearningbook.org/contents/optimization.html).\n", + "\n", + "The codes which implement these algorithms are discussed after our presentation of automatic differentiation." + ] + }, + { + "cell_type": "markdown", + "id": "e7d8b851", + "metadata": { + "editable": true + }, + "source": [ + "## Practical tips\n", + "\n", + "* **Randomize the data when making mini-batches**. It is always important to randomly shuffle the data when forming mini-batches. Otherwise, the gradient descent method can fit spurious correlations resulting from the order in which data is presented.\n", + "\n", + "* **Transform your inputs**. Learning becomes difficult when our landscape has a mixture of steep and flat directions. One simple trick for minimizing these situations is to standardize the data by subtracting the mean and normalizing the variance of input variables. Whenever possible, also decorrelate the inputs. To understand why this is helpful, consider the case of linear regression. It is easy to show that for the squared error cost function, the Hessian of the cost function is just the correlation matrix between the inputs. Thus, by standardizing the inputs, we are ensuring that the landscape looks homogeneous in all directions in parameter space. Since most deep networks can be viewed as linear transformations followed by a non-linearity at each layer, we expect this intuition to hold beyond the linear case.\n", + "\n", + "* **Monitor the out-of-sample performance.** Always monitor the performance of your model on a validation set (a small portion of the training data that is held out of the training process to serve as a proxy for the test set. If the validation error starts increasing, then the model is beginning to overfit. Terminate the learning process. This *early stopping* significantly improves performance in many settings.\n", + "\n", + "* **Adaptive optimization methods don't always have good generalization.** Recent studies have shown that adaptive methods such as ADAM, RMSPorp, and AdaGrad tend to have poor generalization compared to SGD or SGD with momentum, particularly in the high-dimensional limit (i.e. the number of parameters exceeds the number of data points). Although it is not clear at this stage why these methods perform so well in training deep neural networks, simpler procedures like properly-tuned SGD may work as well or better in these applications.\n", + "\n", + "Geron's text, see chapter 11, has several interesting discussions." + ] + }, + { + "cell_type": "markdown", + "id": "75afab2b", + "metadata": { + "editable": true + }, + "source": [ + "## Automatic differentiation\n", + "\n", + "[Automatic differentiation (AD)](https://en.wikipedia.org/wiki/Automatic_differentiation), \n", + "also called algorithmic\n", + "differentiation or computational differentiation,is a set of\n", + "techniques to numerically evaluate the derivative of a function\n", + "specified by a computer program. AD exploits the fact that every\n", + "computer program, no matter how complicated, executes a sequence of\n", + "elementary arithmetic operations (addition, subtraction,\n", + "multiplication, division, etc.) and elementary functions (exp, log,\n", + "sin, cos, etc.). By applying the chain rule repeatedly to these\n", + "operations, derivatives of arbitrary order can be computed\n", + "automatically, accurately to working precision, and using at most a\n", + "small constant factor more arithmetic operations than the original\n", + "program.\n", + "\n", + "Automatic differentiation is neither:\n", + "\n", + "* Symbolic differentiation, nor\n", + "\n", + "* Numerical differentiation (the method of finite differences).\n", + "\n", + "Symbolic differentiation can lead to inefficient code and faces the\n", + "difficulty of converting a computer program into a single expression,\n", + "while numerical differentiation can introduce round-off errors in the\n", + "discretization process and cancellation\n", + "\n", + "Python has tools for so-called **automatic differentiation**.\n", + "Consider the following example" + ] + }, + { + "cell_type": "markdown", + "id": "c551bfeb", + "metadata": { + "editable": true + }, + "source": [ + "$$\n", + "f(x) = \\sin\\left(2\\pi x + x^2\\right)\n", + "$$" + ] + }, + { + "cell_type": "markdown", + "id": "f2cbdd82", + "metadata": { + "editable": true + }, + "source": [ + "which has the following derivative" + ] + }, + { + "cell_type": "markdown", + "id": "22e5d8ce", + "metadata": { + "editable": true + }, + "source": [ + "$$\n", + "f'(x) = \\cos\\left(2\\pi x + x^2\\right)\\left(2\\pi + 2x\\right)\n", + "$$" + ] + }, + { + "cell_type": "markdown", + "id": "d2d352b4", + "metadata": { + "editable": true + }, + "source": [ + "Using **autograd** we have" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "19f1b95c", + "metadata": { + "collapsed": false, + "editable": true + }, + "outputs": [], + "source": [ + "import autograd.numpy as np\n", + "\n", + "# To do elementwise differentiation:\n", + "from autograd import elementwise_grad as egrad \n", + "\n", + "# To plot:\n", + "import matplotlib.pyplot as plt \n", + "\n", + "\n", + "def f(x):\n", + " return np.sin(2*np.pi*x + x**2)\n", + "\n", + "def f_grad_analytic(x):\n", + " return np.cos(2*np.pi*x + x**2)*(2*np.pi + 2*x)\n", + "\n", + "# Do the comparison:\n", + "x = np.linspace(0,1,1000)\n", + "\n", + "f_grad = egrad(f)\n", + "\n", + "computed = f_grad(x)\n", + "analytic = f_grad_analytic(x)\n", + "\n", + "plt.title('Derivative computed from Autograd compared with the analytical derivative')\n", + "plt.plot(x,computed,label='autograd')\n", + "plt.plot(x,analytic,label='analytic')\n", + "\n", + "plt.xlabel('x')\n", + "plt.ylabel('y')\n", + "plt.legend()\n", + "\n", + "plt.show()\n", + "\n", + "print(\"The max absolute difference is: %g\"%(np.max(np.abs(computed - analytic))))" + ] + }, + { + "cell_type": "markdown", + "id": "f3a495be", + "metadata": { + "editable": true + }, + "source": [ + "## Using autograd\n", + "\n", + "Here we\n", + "experiment with what kind of functions Autograd is capable\n", + "of finding the gradient of. The following Python functions are just\n", + "meant to illustrate what Autograd can do, but please feel free to\n", + "experiment with other, possibly more complicated, functions as well." + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "5c856602", + "metadata": { + "collapsed": false, + "editable": true + }, + "outputs": [], + "source": [ + "import autograd.numpy as np\n", + "from autograd import grad\n", + "\n", + "def f1(x):\n", + " return x**3 + 1\n", + "\n", + "f1_grad = grad(f1)\n", + "\n", + "# Remember to send in float as argument to the computed gradient from Autograd!\n", + "a = 1.0\n", + "\n", + "# See the evaluated gradient at a using autograd:\n", + "print(\"The gradient of f1 evaluated at a = %g using autograd is: %g\"%(a,f1_grad(a)))\n", + "\n", + "# Compare with the analytical derivative, that is f1'(x) = 3*x**2 \n", + "grad_analytical = 3*a**2\n", + "print(\"The gradient of f1 evaluated at a = %g by finding the analytic expression is: %g\"%(a,grad_analytical))" + ] + }, + { + "cell_type": "markdown", + "id": "b361074b", + "metadata": { + "editable": true + }, + "source": [ + "## Autograd with more complicated functions\n", + "\n", + "To differentiate with respect to two (or more) arguments of a Python\n", + "function, Autograd need to know at which variable the function if\n", + "being differentiated with respect to." + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "a458a151", + "metadata": { + "collapsed": false, + "editable": true + }, + "outputs": [], + "source": [ + "import autograd.numpy as np\n", + "from autograd import grad\n", + "def f2(x1,x2):\n", + " return 3*x1**3 + x2*(x1 - 5) + 1\n", + "\n", + "# By sending the argument 0, Autograd will compute the derivative w.r.t the first variable, in this case x1\n", + "f2_grad_x1 = grad(f2,0)\n", + "\n", + "# ... and differentiate w.r.t x2 by sending 1 as an additional arugment to grad\n", + "f2_grad_x2 = grad(f2,1)\n", + "\n", + "x1 = 1.0\n", + "x2 = 3.0 \n", + "\n", + "print(\"Evaluating at x1 = %g, x2 = %g\"%(x1,x2))\n", + "print(\"-\"*30)\n", + "\n", + "# Compare with the analytical derivatives:\n", + "\n", + "# Derivative of f2 w.r.t x1 is: 9*x1**2 + x2:\n", + "f2_grad_x1_analytical = 9*x1**2 + x2\n", + "\n", + "# Derivative of f2 w.r.t x2 is: x1 - 5:\n", + "f2_grad_x2_analytical = x1 - 5\n", + "\n", + "# See the evaluated derivations:\n", + "print(\"The derivative of f2 w.r.t x1: %g\"%( f2_grad_x1(x1,x2) ))\n", + "print(\"The analytical derivative of f2 w.r.t x1: %g\"%( f2_grad_x1(x1,x2) ))\n", + "\n", + "print()\n", + "\n", + "print(\"The derivative of f2 w.r.t x2: %g\"%( f2_grad_x2(x1,x2) ))\n", + "print(\"The analytical derivative of f2 w.r.t x2: %g\"%( f2_grad_x2(x1,x2) ))" + ] + }, + { + "cell_type": "markdown", + "id": "946c37f1", + "metadata": { + "editable": true + }, + "source": [ + "Note that the grad function will not produce the true gradient of the function. The true gradient of a function with two or more variables will produce a vector, where each element is the function differentiated w.r.t a variable." + ] + }, + { + "cell_type": "markdown", + "id": "a00d38e5", + "metadata": { + "editable": true + }, + "source": [ + "## More complicated functions using the elements of their arguments directly" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "d8c2a448", + "metadata": { + "collapsed": false, + "editable": true + }, + "outputs": [], + "source": [ + "import autograd.numpy as np\n", + "from autograd import grad\n", + "def f3(x): # Assumes x is an array of length 5 or higher\n", + " return 2*x[0] + 3*x[1] + 5*x[2] + 7*x[3] + 11*x[4]**2\n", + "\n", + "f3_grad = grad(f3)\n", + "\n", + "x = np.linspace(0,4,5)\n", + "\n", + "# Print the computed gradient:\n", + "print(\"The computed gradient of f3 is: \", f3_grad(x))\n", + "\n", + "# The analytical gradient is: (2, 3, 5, 7, 22*x[4])\n", + "f3_grad_analytical = np.array([2, 3, 5, 7, 22*x[4]])\n", + "\n", + "# Print the analytical gradient:\n", + "print(\"The analytical gradient of f3 is: \", f3_grad_analytical)" + ] + }, + { + "cell_type": "markdown", + "id": "026d8733", + "metadata": { + "editable": true + }, + "source": [ + "Note that in this case, when sending an array as input argument, the\n", + "output from Autograd is another array. This is the true gradient of\n", + "the function, as opposed to the function in the previous example. By\n", + "using arrays to represent the variables, the output from Autograd\n", + "might be easier to work with, as the output is closer to what one\n", + "could expect form a gradient-evaluting function." + ] + }, + { + "cell_type": "markdown", + "id": "1a4dca11", + "metadata": { + "editable": true + }, + "source": [ + "## Functions using mathematical functions from Numpy" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "c10b664f", + "metadata": { + "collapsed": false, + "editable": true + }, + "outputs": [], + "source": [ + "import autograd.numpy as np\n", + "from autograd import grad\n", + "def f4(x):\n", + " return np.sqrt(1+x**2) + np.exp(x) + np.sin(2*np.pi*x)\n", + "\n", + "f4_grad = grad(f4)\n", + "\n", + "x = 2.7\n", + "\n", + "# Print the computed derivative:\n", + "print(\"The computed derivative of f4 at x = %g is: %g\"%(x,f4_grad(x)))\n", + "\n", + "# The analytical derivative is: x/sqrt(1 + x**2) + exp(x) + cos(2*pi*x)*2*pi\n", + "f4_grad_analytical = x/np.sqrt(1 + x**2) + np.exp(x) + np.cos(2*np.pi*x)*2*np.pi\n", + "\n", + "# Print the analytical gradient:\n", + "print(\"The analytical gradient of f4 at x = %g is: %g\"%(x,f4_grad_analytical))" + ] + }, + { + "cell_type": "markdown", + "id": "07436a71", + "metadata": { + "editable": true + }, + "source": [ + "## More autograd" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "1840a5d2", + "metadata": { + "collapsed": false, + "editable": true + }, + "outputs": [], + "source": [ + "import autograd.numpy as np\n", + "from autograd import grad\n", + "def f5(x):\n", + " if x >= 0:\n", + " return x**2\n", + " else:\n", + " return -3*x + 1\n", + "\n", + "f5_grad = grad(f5)\n", + "\n", + "x = 2.7\n", + "\n", + "# Print the computed derivative:\n", + "print(\"The computed derivative of f5 at x = %g is: %g\"%(x,f5_grad(x)))" + ] + }, + { + "cell_type": "markdown", + "id": "87ee8137", + "metadata": { + "editable": true + }, + "source": [ + "## And with loops" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "id": "f1b25f09", + "metadata": { + "collapsed": false, + "editable": true + }, + "outputs": [], + "source": [ + "import autograd.numpy as np\n", + "from autograd import grad\n", + "def f6_for(x):\n", + " val = 0\n", + " for i in range(10):\n", + " val = val + x**i\n", + " return val\n", + "\n", + "def f6_while(x):\n", + " val = 0\n", + " i = 0\n", + " while i < 10:\n", + " val = val + x**i\n", + " i = i + 1\n", + " return val\n", + "\n", + "f6_for_grad = grad(f6_for)\n", + "f6_while_grad = grad(f6_while)\n", + "\n", + "x = 0.5\n", + "\n", + "# Print the computed derivaties of f6_for and f6_while\n", + "print(\"The computed derivative of f6_for at x = %g is: %g\"%(x,f6_for_grad(x)))\n", + "print(\"The computed derivative of f6_while at x = %g is: %g\"%(x,f6_while_grad(x)))" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "id": "5fa2802b", + "metadata": { + "collapsed": false, + "editable": true + }, + "outputs": [], + "source": [ + "import autograd.numpy as np\n", + "from autograd import grad\n", + "# Both of the functions are implementation of the sum: sum(x**i) for i = 0, ..., 9\n", + "# The analytical derivative is: sum(i*x**(i-1)) \n", + "f6_grad_analytical = 0\n", + "for i in range(10):\n", + " f6_grad_analytical += i*x**(i-1)\n", + "\n", + "print(\"The analytical derivative of f6 at x = %g is: %g\"%(x,f6_grad_analytical))" + ] + }, + { + "cell_type": "markdown", + "id": "eb66fab4", + "metadata": { + "editable": true + }, + "source": [ + "## Using recursion" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "id": "965c8bbb", + "metadata": { + "collapsed": false, + "editable": true + }, + "outputs": [], + "source": [ + "import autograd.numpy as np\n", + "from autograd import grad\n", + "\n", + "def f7(n): # Assume that n is an integer\n", + " if n == 1 or n == 0:\n", + " return 1\n", + " else:\n", + " return n*f7(n-1)\n", + "\n", + "f7_grad = grad(f7)\n", + "\n", + "n = 2.0\n", + "\n", + "print(\"The computed derivative of f7 at n = %d is: %g\"%(n,f7_grad(n)))\n", + "\n", + "# The function f7 is an implementation of the factorial of n.\n", + "# By using the product rule, one can find that the derivative is:\n", + "\n", + "f7_grad_analytical = 0\n", + "for i in range(int(n)-1):\n", + " tmp = 1\n", + " for k in range(int(n)-1):\n", + " if k != i:\n", + " tmp *= (n - k)\n", + " f7_grad_analytical += tmp\n", + "\n", + "print(\"The analytical derivative of f7 at n = %d is: %g\"%(n,f7_grad_analytical))" + ] + }, + { + "cell_type": "markdown", + "id": "2e6e0c8a", + "metadata": { + "editable": true + }, + "source": [ + "Note that if n is equal to zero or one, Autograd will give an error message. This message appears when the output is independent on input." + ] + }, + { + "cell_type": "markdown", + "id": "42adbcc3", + "metadata": { + "editable": true + }, + "source": [ + "## Unsupported functions\n", + "Autograd supports many features. However, there are some functions that is not supported (yet) by Autograd.\n", + "\n", + "Assigning a value to the variable being differentiated with respect to" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "id": "6ca4a5dc", + "metadata": { + "collapsed": false, + "editable": true + }, + "outputs": [], + "source": [ + "import autograd.numpy as np\n", + "from autograd import grad\n", + "def f8(x): # Assume x is an array\n", + " x[2] = 3\n", + " return x*2\n", + "\n", + "f8_grad = grad(f8)\n", + "\n", + "x = 8.4\n", + "\n", + "print(\"The derivative of f8 is:\",f8_grad(x))" + ] + }, + { + "cell_type": "markdown", + "id": "5d816052", + "metadata": { + "editable": true + }, + "source": [ + "Here, Autograd tells us that an 'ArrayBox' does not support item assignment. The item assignment is done when the program tries to assign x[2] to the value 3. However, Autograd has implemented the computation of the derivative such that this assignment is not possible." + ] + }, + { + "cell_type": "markdown", + "id": "73f2e7e4", + "metadata": { + "editable": true + }, + "source": [ + "## The syntax a.dot(b) when finding the dot product" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "id": "2ead27d5", + "metadata": { + "collapsed": false, + "editable": true + }, + "outputs": [], + "source": [ + "import autograd.numpy as np\n", + "from autograd import grad\n", + "def f9(a): # Assume a is an array with 2 elements\n", + " b = np.array([1.0,2.0])\n", + " return a.dot(b)\n", + "\n", + "f9_grad = grad(f9)\n", + "\n", + "x = np.array([1.0,0.0])\n", + "\n", + "print(\"The derivative of f9 is:\",f9_grad(x))" + ] + }, + { + "cell_type": "markdown", + "id": "1edcb932", + "metadata": { + "editable": true + }, + "source": [ + "Here we are told that the 'dot' function does not belong to Autograd's\n", + "version of a Numpy array. To overcome this, an alternative syntax\n", + "which also computed the dot product can be used:" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "id": "05897777", + "metadata": { + "collapsed": false, + "editable": true + }, + "outputs": [], + "source": [ + "import autograd.numpy as np\n", + "from autograd import grad\n", + "def f9_alternative(x): # Assume a is an array with 2 elements\n", + " b = np.array([1.0,2.0])\n", + " return np.dot(x,b) # The same as x_1*b_1 + x_2*b_2\n", + "\n", + "f9_alternative_grad = grad(f9_alternative)\n", + "\n", + "x = np.array([3.0,0.0])\n", + "\n", + "print(\"The gradient of f9 is:\",f9_alternative_grad(x))\n", + "\n", + "# The analytical gradient of the dot product of vectors x and b with two elements (x_1,x_2) and (b_1, b_2) respectively\n", + "# w.r.t x is (b_1, b_2)." + ] + }, + { + "cell_type": "markdown", + "id": "2c899815", + "metadata": { + "editable": true + }, + "source": [ + "## Recommended to avoid\n", + "The documentation recommends to avoid inplace operations such as" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "id": "fd05063c", + "metadata": { + "collapsed": false, + "editable": true + }, + "outputs": [], + "source": [ + "a += b\n", + "a -= b\n", + "a*= b\n", + "a /=b" + ] + }, + { + "cell_type": "markdown", + "id": "94d6f0d9", + "metadata": { + "editable": true + }, + "source": [ + "## Using Autograd with OLS\n", + "\n", + "We conclude the part on optmization by showing how we can make codes\n", + "for linear regression and logistic regression using **autograd**. The\n", + "first example shows results with ordinary leats squares." + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "id": "d002c672", + "metadata": { + "collapsed": false, + "editable": true + }, + "outputs": [], + "source": [ + "# Using Autograd to calculate gradients for OLS\n", + "from random import random, seed\n", + "import numpy as np\n", + "import autograd.numpy as np\n", + "import matplotlib.pyplot as plt\n", + "from autograd import grad\n", + "\n", + "def CostOLS(beta):\n", + " return (1.0/n)*np.sum((y-X @ beta)**2)\n", + "\n", + "n = 100\n", + "x = 2*np.random.rand(n,1)\n", + "y = 4+3*x+np.random.randn(n,1)\n", + "\n", + "X = np.c_[np.ones((n,1)), x]\n", + "XT_X = X.T @ X\n", + "theta_linreg = np.linalg.pinv(XT_X) @ (X.T @ y)\n", + "print(\"Own inversion\")\n", + "print(theta_linreg)\n", + "# Hessian matrix\n", + "H = (2.0/n)* XT_X\n", + "EigValues, EigVectors = np.linalg.eig(H)\n", + "print(f\"Eigenvalues of Hessian Matrix:{EigValues}\")\n", + "\n", + "theta = np.random.randn(2,1)\n", + "eta = 1.0/np.max(EigValues)\n", + "Niterations = 1000\n", + "# define the gradient\n", + "training_gradient = grad(CostOLS)\n", + "\n", + "for iter in range(Niterations):\n", + " gradients = training_gradient(theta)\n", + " theta -= eta*gradients\n", + "print(\"theta from own gd\")\n", + "print(theta)\n", + "\n", + "xnew = np.array([[0],[2]])\n", + "Xnew = np.c_[np.ones((2,1)), xnew]\n", + "ypredict = Xnew.dot(theta)\n", + "ypredict2 = Xnew.dot(theta_linreg)\n", + "\n", + "plt.plot(xnew, ypredict, \"r-\")\n", + "plt.plot(xnew, ypredict2, \"b-\")\n", + "plt.plot(x, y ,'ro')\n", + "plt.axis([0,2.0,0, 15.0])\n", + "plt.xlabel(r'$x$')\n", + "plt.ylabel(r'$y$')\n", + "plt.title(r'Random numbers ')\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "e72279fe", + "metadata": { + "editable": true + }, + "source": [ + "## Same code but now with momentum gradient descent" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "id": "62aa2606", + "metadata": { + "collapsed": false, + "editable": true + }, + "outputs": [], + "source": [ + "# Using Autograd to calculate gradients for OLS\n", + "from random import random, seed\n", + "import numpy as np\n", + "import autograd.numpy as np\n", + "import matplotlib.pyplot as plt\n", + "from autograd import grad\n", + "\n", + "def CostOLS(beta):\n", + " return (1.0/n)*np.sum((y-X @ beta)**2)\n", + "\n", + "n = 100\n", + "x = 2*np.random.rand(n,1)\n", + "y = 4+3*x#+np.random.randn(n,1)\n", + "\n", + "X = np.c_[np.ones((n,1)), x]\n", + "XT_X = X.T @ X\n", + "theta_linreg = np.linalg.pinv(XT_X) @ (X.T @ y)\n", + "print(\"Own inversion\")\n", + "print(theta_linreg)\n", + "# Hessian matrix\n", + "H = (2.0/n)* XT_X\n", + "EigValues, EigVectors = np.linalg.eig(H)\n", + "print(f\"Eigenvalues of Hessian Matrix:{EigValues}\")\n", + "\n", + "theta = np.random.randn(2,1)\n", + "eta = 1.0/np.max(EigValues)\n", + "Niterations = 30\n", + "\n", + "# define the gradient\n", + "training_gradient = grad(CostOLS)\n", + "\n", + "for iter in range(Niterations):\n", + " gradients = training_gradient(theta)\n", + " theta -= eta*gradients\n", + " print(iter,gradients[0],gradients[1])\n", + "print(\"theta from own gd\")\n", + "print(theta)\n", + "\n", + "# Now improve with momentum gradient descent\n", + "change = 0.0\n", + "delta_momentum = 0.3\n", + "for iter in range(Niterations):\n", + " # calculate gradient\n", + " gradients = training_gradient(theta)\n", + " # calculate update\n", + " new_change = eta*gradients+delta_momentum*change\n", + " # take a step\n", + " theta -= new_change\n", + " # save the change\n", + " change = new_change\n", + " print(iter,gradients[0],gradients[1])\n", + "print(\"theta from own gd wth momentum\")\n", + "print(theta)" + ] + }, + { + "cell_type": "markdown", + "id": "bad8e42f", + "metadata": { + "editable": true + }, + "source": [ + "## But noen of these can compete with Newton's method" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "id": "13a572a8", + "metadata": { + "collapsed": false, + "editable": true + }, + "outputs": [], + "source": [ + "# Using Newton's method\n", + "from random import random, seed\n", + "import numpy as np\n", + "import autograd.numpy as np\n", + "import matplotlib.pyplot as plt\n", + "from autograd import grad\n", + "\n", + "def CostOLS(beta):\n", + " return (1.0/n)*np.sum((y-X @ beta)**2)\n", + "\n", + "n = 100\n", + "x = 2*np.random.rand(n,1)\n", + "y = 4+3*x+np.random.randn(n,1)\n", + "\n", + "X = np.c_[np.ones((n,1)), x]\n", + "XT_X = X.T @ X\n", + "beta_linreg = np.linalg.pinv(XT_X) @ (X.T @ y)\n", + "print(\"Own inversion\")\n", + "print(beta_linreg)\n", + "# Hessian matrix\n", + "H = (2.0/n)* XT_X\n", + "# Note that here the Hessian does not depend on the parameters beta\n", + "invH = np.linalg.pinv(H)\n", + "EigValues, EigVectors = np.linalg.eig(H)\n", + "print(f\"Eigenvalues of Hessian Matrix:{EigValues}\")\n", + "\n", + "beta = np.random.randn(2,1)\n", + "Niterations = 5\n", + "\n", + "# define the gradient\n", + "training_gradient = grad(CostOLS)\n", + "\n", + "for iter in range(Niterations):\n", + " gradients = training_gradient(beta)\n", + " beta -= invH @ gradients\n", + " print(iter,gradients[0],gradients[1])\n", + "print(\"beta from own Newton code\")\n", + "print(beta)" + ] + }, + { + "cell_type": "markdown", + "id": "5b2c9e3a", + "metadata": { + "editable": true + }, + "source": [ + "## Including Stochastic Gradient Descent with Autograd\n", + "In this code we include the stochastic gradient descent approach discussed above. Note here that we specify which argument we are taking the derivative with respect to when using **autograd**." + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "id": "830370bf", + "metadata": { + "collapsed": false, + "editable": true + }, + "outputs": [], + "source": [ + "# Using Autograd to calculate gradients using SGD\n", + "# OLS example\n", + "from random import random, seed\n", + "import numpy as np\n", + "import autograd.numpy as np\n", + "import matplotlib.pyplot as plt\n", + "from autograd import grad\n", + "\n", + "# Note change from previous example\n", + "def CostOLS(y,X,theta):\n", + " return np.sum((y-X @ theta)**2)\n", + "\n", + "n = 100\n", + "x = 2*np.random.rand(n,1)\n", + "y = 4+3*x+np.random.randn(n,1)\n", + "\n", + "X = np.c_[np.ones((n,1)), x]\n", + "XT_X = X.T @ X\n", + "theta_linreg = np.linalg.pinv(XT_X) @ (X.T @ y)\n", + "print(\"Own inversion\")\n", + "print(theta_linreg)\n", + "# Hessian matrix\n", + "H = (2.0/n)* XT_X\n", + "EigValues, EigVectors = np.linalg.eig(H)\n", + "print(f\"Eigenvalues of Hessian Matrix:{EigValues}\")\n", + "\n", + "theta = np.random.randn(2,1)\n", + "eta = 1.0/np.max(EigValues)\n", + "Niterations = 1000\n", + "\n", + "# Note that we request the derivative wrt third argument (theta, 2 here)\n", + "training_gradient = grad(CostOLS,2)\n", + "\n", + "for iter in range(Niterations):\n", + " gradients = (1.0/n)*training_gradient(y, X, theta)\n", + " theta -= eta*gradients\n", + "print(\"theta from own gd\")\n", + "print(theta)\n", + "\n", + "xnew = np.array([[0],[2]])\n", + "Xnew = np.c_[np.ones((2,1)), xnew]\n", + "ypredict = Xnew.dot(theta)\n", + "ypredict2 = Xnew.dot(theta_linreg)\n", + "\n", + "plt.plot(xnew, ypredict, \"r-\")\n", + "plt.plot(xnew, ypredict2, \"b-\")\n", + "plt.plot(x, y ,'ro')\n", + "plt.axis([0,2.0,0, 15.0])\n", + "plt.xlabel(r'$x$')\n", + "plt.ylabel(r'$y$')\n", + "plt.title(r'Random numbers ')\n", + "plt.show()\n", + "\n", + "n_epochs = 50\n", + "M = 5 #size of each minibatch\n", + "m = int(n/M) #number of minibatches\n", + "t0, t1 = 5, 50\n", + "def learning_schedule(t):\n", + " return t0/(t+t1)\n", + "\n", + "theta = np.random.randn(2,1)\n", + "\n", + "for epoch in range(n_epochs):\n", + "# Can you figure out a better way of setting up the contributions to each batch?\n", + " for i in range(m):\n", + " random_index = M*np.random.randint(m)\n", + " xi = X[random_index:random_index+M]\n", + " yi = y[random_index:random_index+M]\n", + " gradients = (1.0/M)*training_gradient(yi, xi, theta)\n", + " eta = learning_schedule(epoch*m+i)\n", + " theta = theta - eta*gradients\n", + "print(\"theta from own sdg\")\n", + "print(theta)" + ] + }, + { + "cell_type": "markdown", + "id": "e580483f", + "metadata": { + "editable": true + }, + "source": [ + "## Same code but now with momentum gradient descent" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "id": "68895d3f", + "metadata": { + "collapsed": false, + "editable": true + }, + "outputs": [], + "source": [ + "# Using Autograd to calculate gradients using SGD\n", + "# OLS example\n", + "from random import random, seed\n", + "import numpy as np\n", + "import autograd.numpy as np\n", + "import matplotlib.pyplot as plt\n", + "from autograd import grad\n", + "\n", + "# Note change from previous example\n", + "def CostOLS(y,X,theta):\n", + " return np.sum((y-X @ theta)**2)\n", + "\n", + "n = 100\n", + "x = 2*np.random.rand(n,1)\n", + "y = 4+3*x+np.random.randn(n,1)\n", + "\n", + "X = np.c_[np.ones((n,1)), x]\n", + "XT_X = X.T @ X\n", + "theta_linreg = np.linalg.pinv(XT_X) @ (X.T @ y)\n", + "print(\"Own inversion\")\n", + "print(theta_linreg)\n", + "# Hessian matrix\n", + "H = (2.0/n)* XT_X\n", + "EigValues, EigVectors = np.linalg.eig(H)\n", + "print(f\"Eigenvalues of Hessian Matrix:{EigValues}\")\n", + "\n", + "theta = np.random.randn(2,1)\n", + "eta = 1.0/np.max(EigValues)\n", + "Niterations = 100\n", + "\n", + "# Note that we request the derivative wrt third argument (theta, 2 here)\n", + "training_gradient = grad(CostOLS,2)\n", + "\n", + "for iter in range(Niterations):\n", + " gradients = (1.0/n)*training_gradient(y, X, theta)\n", + " theta -= eta*gradients\n", + "print(\"theta from own gd\")\n", + "print(theta)\n", + "\n", + "\n", + "n_epochs = 50\n", + "M = 5 #size of each minibatch\n", + "m = int(n/M) #number of minibatches\n", + "t0, t1 = 5, 50\n", + "def learning_schedule(t):\n", + " return t0/(t+t1)\n", + "\n", + "theta = np.random.randn(2,1)\n", + "\n", + "change = 0.0\n", + "delta_momentum = 0.3\n", + "\n", + "for epoch in range(n_epochs):\n", + " for i in range(m):\n", + " random_index = M*np.random.randint(m)\n", + " xi = X[random_index:random_index+M]\n", + " yi = y[random_index:random_index+M]\n", + " gradients = (1.0/M)*training_gradient(yi, xi, theta)\n", + " eta = learning_schedule(epoch*m+i)\n", + " # calculate update\n", + " new_change = eta*gradients+delta_momentum*change\n", + " # take a step\n", + " theta -= new_change\n", + " # save the change\n", + " change = new_change\n", + "print(\"theta from own sdg with momentum\")\n", + "print(theta)" + ] + }, + { + "cell_type": "markdown", + "id": "01c29c9e", + "metadata": { + "editable": true + }, + "source": [ + "## Similar (second order function now) problem but now with AdaGrad" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "id": "36a00e5a", + "metadata": { + "collapsed": false, + "editable": true + }, + "outputs": [], + "source": [ + "# Using Autograd to calculate gradients using AdaGrad and Stochastic Gradient descent\n", + "# OLS example\n", + "from random import random, seed\n", + "import numpy as np\n", + "import autograd.numpy as np\n", + "import matplotlib.pyplot as plt\n", + "from autograd import grad\n", + "\n", + "# Note change from previous example\n", + "def CostOLS(y,X,theta):\n", + " return np.sum((y-X @ theta)**2)\n", + "\n", + "n = 1000\n", + "x = np.random.rand(n,1)\n", + "y = 2.0+3*x +4*x*x\n", + "\n", + "X = np.c_[np.ones((n,1)), x, x*x]\n", + "XT_X = X.T @ X\n", + "theta_linreg = np.linalg.pinv(XT_X) @ (X.T @ y)\n", + "print(\"Own inversion\")\n", + "print(theta_linreg)\n", + "\n", + "\n", + "# Note that we request the derivative wrt third argument (theta, 2 here)\n", + "training_gradient = grad(CostOLS,2)\n", + "# Define parameters for Stochastic Gradient Descent\n", + "n_epochs = 50\n", + "M = 5 #size of each minibatch\n", + "m = int(n/M) #number of minibatches\n", + "# Guess for unknown parameters theta\n", + "theta = np.random.randn(3,1)\n", + "\n", + "# Value for learning rate\n", + "eta = 0.01\n", + "# Including AdaGrad parameter to avoid possible division by zero\n", + "delta = 1e-8\n", + "for epoch in range(n_epochs):\n", + " Giter = 0.0\n", + " for i in range(m):\n", + " random_index = M*np.random.randint(m)\n", + " xi = X[random_index:random_index+M]\n", + " yi = y[random_index:random_index+M]\n", + " gradients = (1.0/M)*training_gradient(yi, xi, theta)\n", + " Giter += gradients*gradients\n", + " update = gradients*eta/(delta+np.sqrt(Giter))\n", + " theta -= update\n", + "print(\"theta from own AdaGrad\")\n", + "print(theta)" + ] + }, + { + "cell_type": "markdown", + "id": "18ccca46", + "metadata": { + "editable": true + }, + "source": [ + "Running this code we note an almost perfect agreement with the results from matrix inversion." + ] + }, + { + "cell_type": "markdown", + "id": "e076c773", + "metadata": { + "editable": true + }, + "source": [ + "## RMSprop for adaptive learning rate with Stochastic Gradient Descent" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "id": "cb4ad1d3", + "metadata": { + "collapsed": false, + "editable": true + }, + "outputs": [], + "source": [ + "# Using Autograd to calculate gradients using RMSprop and Stochastic Gradient descent\n", + "# OLS example\n", + "from random import random, seed\n", + "import numpy as np\n", + "import autograd.numpy as np\n", + "import matplotlib.pyplot as plt\n", + "from autograd import grad\n", + "\n", + "# Note change from previous example\n", + "def CostOLS(y,X,theta):\n", + " return np.sum((y-X @ theta)**2)\n", + "\n", + "n = 1000\n", + "x = np.random.rand(n,1)\n", + "y = 2.0+3*x +4*x*x# +np.random.randn(n,1)\n", + "\n", + "X = np.c_[np.ones((n,1)), x, x*x]\n", + "XT_X = X.T @ X\n", + "theta_linreg = np.linalg.pinv(XT_X) @ (X.T @ y)\n", + "print(\"Own inversion\")\n", + "print(theta_linreg)\n", + "\n", + "\n", + "# Note that we request the derivative wrt third argument (theta, 2 here)\n", + "training_gradient = grad(CostOLS,2)\n", + "# Define parameters for Stochastic Gradient Descent\n", + "n_epochs = 50\n", + "M = 5 #size of each minibatch\n", + "m = int(n/M) #number of minibatches\n", + "# Guess for unknown parameters theta\n", + "theta = np.random.randn(3,1)\n", + "\n", + "# Value for learning rate\n", + "eta = 0.01\n", + "# Value for parameter rho\n", + "rho = 0.99\n", + "# Including AdaGrad parameter to avoid possible division by zero\n", + "delta = 1e-8\n", + "for epoch in range(n_epochs):\n", + " Giter = 0.0\n", + " for i in range(m):\n", + " random_index = M*np.random.randint(m)\n", + " xi = X[random_index:random_index+M]\n", + " yi = y[random_index:random_index+M]\n", + " gradients = (1.0/M)*training_gradient(yi, xi, theta)\n", + "\t# Accumulated gradient\n", + "\t# Scaling with rho the new and the previous results\n", + " Giter = (rho*Giter+(1-rho)*gradients*gradients)\n", + "\t# Taking the diagonal only and inverting\n", + " update = gradients*eta/(delta+np.sqrt(Giter))\n", + "\t# Hadamard product\n", + " theta -= update\n", + "print(\"theta from own RMSprop\")\n", + "print(theta)" + ] + }, + { + "cell_type": "markdown", + "id": "2253fa34", + "metadata": { + "editable": true + }, + "source": [ + "## And finally [ADAM](https://arxiv.org/pdf/1412.6980.pdf)" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "id": "92b3454a", + "metadata": { + "collapsed": false, + "editable": true + }, + "outputs": [], + "source": [ + "# Using Autograd to calculate gradients using RMSprop and Stochastic Gradient descent\n", + "# OLS example\n", + "from random import random, seed\n", + "import numpy as np\n", + "import autograd.numpy as np\n", + "import matplotlib.pyplot as plt\n", + "from autograd import grad\n", + "\n", + "# Note change from previous example\n", + "def CostOLS(y,X,theta):\n", + " return np.sum((y-X @ theta)**2)\n", + "\n", + "n = 1000\n", + "x = np.random.rand(n,1)\n", + "y = 2.0+3*x +4*x*x# +np.random.randn(n,1)\n", + "\n", + "X = np.c_[np.ones((n,1)), x, x*x]\n", + "XT_X = X.T @ X\n", + "theta_linreg = np.linalg.pinv(XT_X) @ (X.T @ y)\n", + "print(\"Own inversion\")\n", + "print(theta_linreg)\n", + "\n", + "\n", + "# Note that we request the derivative wrt third argument (theta, 2 here)\n", + "training_gradient = grad(CostOLS,2)\n", + "# Define parameters for Stochastic Gradient Descent\n", + "n_epochs = 50\n", + "M = 5 #size of each minibatch\n", + "m = int(n/M) #number of minibatches\n", + "# Guess for unknown parameters theta\n", + "theta = np.random.randn(3,1)\n", + "\n", + "# Value for learning rate\n", + "eta = 0.01\n", + "# Value for parameters beta1 and beta2, see https://arxiv.org/abs/1412.6980\n", + "beta1 = 0.9\n", + "beta2 = 0.999\n", + "# Including AdaGrad parameter to avoid possible division by zero\n", + "delta = 1e-7\n", + "iter = 0\n", + "for epoch in range(n_epochs):\n", + " first_moment = 0.0\n", + " second_moment = 0.0\n", + " iter += 1\n", + " for i in range(m):\n", + " random_index = M*np.random.randint(m)\n", + " xi = X[random_index:random_index+M]\n", + " yi = y[random_index:random_index+M]\n", + " gradients = (1.0/M)*training_gradient(yi, xi, theta)\n", + " # Computing moments first\n", + " first_moment = beta1*first_moment + (1-beta1)*gradients\n", + " second_moment = beta2*second_moment+(1-beta2)*gradients*gradients\n", + " first_term = first_moment/(1.0-beta1**iter)\n", + " second_term = second_moment/(1.0-beta2**iter)\n", + "\t# Scaling with rho the new and the previous results\n", + " update = eta*first_term/(np.sqrt(second_term)+delta)\n", + " theta -= update\n", + "print(\"theta from own ADAM\")\n", + "print(theta)" + ] + }, + { + "cell_type": "markdown", + "id": "c2025d97", + "metadata": { + "editable": true + }, + "source": [ + "## And Logistic Regression" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "id": "3f6d8746", + "metadata": { + "collapsed": false, + "editable": true + }, + "outputs": [], + "source": [ + "import autograd.numpy as np\n", + "from autograd import grad\n", + "\n", + "def sigmoid(x):\n", + " return 0.5 * (np.tanh(x / 2.) + 1)\n", + "\n", + "def logistic_predictions(weights, inputs):\n", + " # Outputs probability of a label being true according to logistic model.\n", + " return sigmoid(np.dot(inputs, weights))\n", + "\n", + "def training_loss(weights):\n", + " # Training loss is the negative log-likelihood of the training labels.\n", + " preds = logistic_predictions(weights, inputs)\n", + " label_probabilities = preds * targets + (1 - preds) * (1 - targets)\n", + " return -np.sum(np.log(label_probabilities))\n", + "\n", + "# Build a toy dataset.\n", + "inputs = np.array([[0.52, 1.12, 0.77],\n", + " [0.88, -1.08, 0.15],\n", + " [0.52, 0.06, -1.30],\n", + " [0.74, -2.49, 1.39]])\n", + "targets = np.array([True, True, False, True])\n", + "\n", + "# Define a function that returns gradients of training loss using Autograd.\n", + "training_gradient_fun = grad(training_loss)\n", + "\n", + "# Optimize weights using gradient descent.\n", + "weights = np.array([0.0, 0.0, 0.0])\n", + "print(\"Initial loss:\", training_loss(weights))\n", + "for i in range(100):\n", + " weights -= training_gradient_fun(weights) * 0.01\n", + "\n", + "print(\"Trained loss:\", training_loss(weights))" + ] + }, + { + "cell_type": "markdown", + "id": "716627e3", + "metadata": { + "editable": true + }, + "source": [ + "## Introducing [JAX](https://jax.readthedocs.io/en/latest/)\n", + "\n", + "Presently, instead of using **autograd**, we recommend using [JAX](https://jax.readthedocs.io/en/latest/)\n", + "\n", + "**JAX** is Autograd and [XLA (Accelerated Linear Algebra))](https://www.tensorflow.org/xla),\n", + "brought together for high-performance numerical computing and machine learning research.\n", + "It provides composable transformations of Python+NumPy programs: differentiate, vectorize, parallelize, Just-In-Time compile to GPU/TPU, and more.\n", + "\n", + "Here's a simple example on how you can use **JAX** to compute the derivate of the logistic function." + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "id": "5c938af4", + "metadata": { + "collapsed": false, + "editable": true + }, + "outputs": [], + "source": [ + "import jax.numpy as jnp\n", + "from jax import grad, jit, vmap\n", + "\n", + "def sum_logistic(x):\n", + " return jnp.sum(1.0 / (1.0 + jnp.exp(-x)))\n", + "\n", + "x_small = jnp.arange(3.)\n", + "derivative_fn = grad(sum_logistic)\n", + "print(derivative_fn(x_small))" + ] + }, + { + "cell_type": "markdown", + "id": "b087cc5f", + "metadata": { + "editable": true + }, + "source": [ + "## Introduction to Neural networks\n", + "\n", + "Artificial neural networks are computational systems that can learn to\n", + "perform tasks by considering examples, generally without being\n", + "programmed with any task-specific rules. It is supposed to mimic a\n", + "biological system, wherein neurons interact by sending signals in the\n", + "form of mathematical functions between layers. All layers can contain\n", + "an arbitrary number of neurons, and each connection is represented by\n", + "a weight variable." + ] + }, + { + "cell_type": "markdown", + "id": "c040b49e", + "metadata": { + "editable": true + }, + "source": [ + "## Artificial neurons\n", + "\n", + "The field of artificial neural networks has a long history of\n", + "development, and is closely connected with the advancement of computer\n", + "science and computers in general. A model of artificial neurons was\n", + "first developed by McCulloch and Pitts in 1943 to study signal\n", + "processing in the brain and has later been refined by others. The\n", + "general idea is to mimic neural networks in the human brain, which is\n", + "composed of billions of neurons that communicate with each other by\n", + "sending electrical signals. Each neuron accumulates its incoming\n", + "signals, which must exceed an activation threshold to yield an\n", + "output. If the threshold is not overcome, the neuron remains inactive,\n", + "i.e. has zero output.\n", + "\n", + "This behaviour has inspired a simple mathematical model for an artificial neuron." + ] + }, + { + "cell_type": "markdown", + "id": "663a7548", + "metadata": { + "editable": true + }, + "source": [ + "\n", + "
\n", + "\n", + "$$\n", + "\\begin{equation}\n", + " y = f\\left(\\sum_{i=1}^n w_ix_i\\right) = f(u)\n", + "\\label{artificialNeuron} \\tag{6}\n", + "\\end{equation}\n", + "$$" + ] + }, + { + "cell_type": "markdown", + "id": "7d41caae", + "metadata": { + "editable": true + }, + "source": [ + "Here, the output $y$ of the neuron is the value of its activation function, which have as input\n", + "a weighted sum of signals $x_i, \\dots ,x_n$ received by $n$ other neurons.\n", + "\n", + "Conceptually, it is helpful to divide neural networks into four\n", + "categories:\n", + "1. general purpose neural networks for supervised learning,\n", + "\n", + "2. neural networks designed specifically for image processing, the most prominent example of this class being Convolutional Neural Networks (CNNs),\n", + "\n", + "3. neural networks for sequential data such as Recurrent Neural Networks (RNNs), and\n", + "\n", + "4. neural networks for unsupervised learning such as Deep Boltzmann Machines.\n", + "\n", + "In natural science, DNNs and CNNs have already found numerous\n", + "applications. In statistical physics, they have been applied to detect\n", + "phase transitions in 2D Ising and Potts models, lattice gauge\n", + "theories, and different phases of polymers, or solving the\n", + "Navier-Stokes equation in weather forecasting. Deep learning has also\n", + "found interesting applications in quantum physics. Various quantum\n", + "phase transitions can be detected and studied using DNNs and CNNs,\n", + "topological phases, and even non-equilibrium many-body\n", + "localization. Representing quantum states as DNNs quantum state\n", + "tomography are among some of the impressive achievements to reveal the\n", + "potential of DNNs to facilitate the study of quantum systems.\n", + "\n", + "In quantum information theory, it has been shown that one can perform\n", + "gate decompositions with the help of neural. \n", + "\n", + "The applications are not limited to the natural sciences. There is a\n", + "plethora of applications in essentially all disciplines, from the\n", + "humanities to life science and medicine." + ] + }, + { + "cell_type": "markdown", + "id": "8cd5fa1c", + "metadata": { + "editable": true + }, + "source": [ + "## Neural network types\n", + "\n", + "An artificial neural network (ANN), is a computational model that\n", + "consists of layers of connected neurons, or nodes or units. We will\n", + "refer to these interchangeably as units or nodes, and sometimes as\n", + "neurons.\n", + "\n", + "It is supposed to mimic a biological nervous system by letting each\n", + "neuron interact with other neurons by sending signals in the form of\n", + "mathematical functions between layers. A wide variety of different\n", + "ANNs have been developed, but most of them consist of an input layer,\n", + "an output layer and eventual layers in-between, called *hidden\n", + "layers*. All layers can contain an arbitrary number of nodes, and each\n", + "connection between two nodes is associated with a weight variable.\n", + "\n", + "Neural networks (also called neural nets) are neural-inspired\n", + "nonlinear models for supervised learning. As we will see, neural nets\n", + "can be viewed as natural, more powerful extensions of supervised\n", + "learning methods such as linear and logistic regression and soft-max\n", + "methods we discussed earlier." + ] + }, + { + "cell_type": "markdown", + "id": "0b2c6e40", + "metadata": { + "editable": true + }, + "source": [ + "## Feed-forward neural networks\n", + "\n", + "The feed-forward neural network (FFNN) was the first and simplest type\n", + "of ANNs that were devised. In this network, the information moves in\n", + "only one direction: forward through the layers.\n", + "\n", + "Nodes are represented by circles, while the arrows display the\n", + "connections between the nodes, including the direction of information\n", + "flow. Additionally, each arrow corresponds to a weight variable\n", + "(figure to come). We observe that each node in a layer is connected\n", + "to *all* nodes in the subsequent layer, making this a so-called\n", + "*fully-connected* FFNN." + ] + }, + { + "cell_type": "markdown", + "id": "90e946b7", + "metadata": { + "editable": true + }, + "source": [ + "## Convolutional Neural Network\n", + "\n", + "A different variant of FFNNs are *convolutional neural networks*\n", + "(CNNs), which have a connectivity pattern inspired by the animal\n", + "visual cortex. Individual neurons in the visual cortex only respond to\n", + "stimuli from small sub-regions of the visual field, called a receptive\n", + "field. This makes the neurons well-suited to exploit the strong\n", + "spatially local correlation present in natural images. The response of\n", + "each neuron can be approximated mathematically as a convolution\n", + "operation. (figure to come)\n", + "\n", + "Convolutional neural networks emulate the behaviour of neurons in the\n", + "visual cortex by enforcing a *local* connectivity pattern between\n", + "nodes of adjacent layers: Each node in a convolutional layer is\n", + "connected only to a subset of the nodes in the previous layer, in\n", + "contrast to the fully-connected FFNN. Often, CNNs consist of several\n", + "convolutional layers that learn local features of the input, with a\n", + "fully-connected layer at the end, which gathers all the local data and\n", + "produces the outputs. They have wide applications in image and video\n", + "recognition." + ] + }, + { + "cell_type": "markdown", + "id": "1964f9e3", + "metadata": { + "editable": true + }, + "source": [ + "## Recurrent neural networks\n", + "\n", + "So far we have only mentioned ANNs where information flows in one\n", + "direction: forward. *Recurrent neural networks* on the other hand,\n", + "have connections between nodes that form directed *cycles*. This\n", + "creates a form of internal memory which are able to capture\n", + "information on what has been calculated before; the output is\n", + "dependent on the previous computations. Recurrent NNs make use of\n", + "sequential information by performing the same task for every element\n", + "in a sequence, where each element depends on previous elements. An\n", + "example of such information is sentences, making recurrent NNs\n", + "especially well-suited for handwriting and speech recognition." + ] + }, + { + "cell_type": "markdown", + "id": "faf981a1", + "metadata": { + "editable": true + }, + "source": [ + "## Other types of networks\n", + "\n", + "There are many other kinds of ANNs that have been developed. One type\n", + "that is specifically designed for interpolation in multidimensional\n", + "space is the radial basis function (RBF) network. RBFs are typically\n", + "made up of three layers: an input layer, a hidden layer with\n", + "non-linear radial symmetric activation functions and a linear output\n", + "layer (''linear'' here means that each node in the output layer has a\n", + "linear activation function). The layers are normally fully-connected\n", + "and there are no cycles, thus RBFs can be viewed as a type of\n", + "fully-connected FFNN. They are however usually treated as a separate\n", + "type of NN due the unusual activation functions." + ] + }, + { + "cell_type": "markdown", + "id": "3667182a", + "metadata": { + "editable": true + }, + "source": [ + "## Multilayer perceptrons\n", + "\n", + "One uses often so-called fully-connected feed-forward neural networks\n", + "with three or more layers (an input layer, one or more hidden layers\n", + "and an output layer) consisting of neurons that have non-linear\n", + "activation functions.\n", + "\n", + "Such networks are often called *multilayer perceptrons* (MLPs)." + ] + }, + { + "cell_type": "markdown", + "id": "5dd1a89f", + "metadata": { + "editable": true + }, + "source": [ + "## Why multilayer perceptrons?\n", + "\n", + "According to the *Universal approximation theorem*, a feed-forward\n", + "neural network with just a single hidden layer containing a finite\n", + "number of neurons can approximate a continuous multidimensional\n", + "function to arbitrary accuracy, assuming the activation function for\n", + "the hidden layer is a **non-constant, bounded and\n", + "monotonically-increasing continuous function**.\n", + "\n", + "Note that the requirements on the activation function only applies to\n", + "the hidden layer, the output nodes are always assumed to be linear, so\n", + "as to not restrict the range of output values." + ] + }, + { + "cell_type": "markdown", + "id": "da5b8927", + "metadata": { + "editable": true + }, + "source": [ + "## Illustration of a single perceptron model and a multi-perceptron model\n", + "\n", + "\n", + "\n", + "\n", + "

Figure 1: In a) we show a single perceptron model while in b) we dispay a network with two hidden layers, an input layer and an output layer.

\n", + "" + ] + }, + { + "cell_type": "markdown", + "id": "3bba849e", + "metadata": { + "editable": true + }, + "source": [ + "## Examples of XOR, OR and AND gates\n", + "\n", + "Let us first try to fit various gates using standard linear\n", + "regression. The gates we are thinking of are the classical XOR, OR and\n", + "AND gates, well-known elements in computer science. The tables here\n", + "show how we can set up the inputs $x_1$ and $x_2$ in order to yield a\n", + "specific target $y_i$." + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "id": "de11d95e", + "metadata": { + "collapsed": false, + "editable": true + }, + "outputs": [], + "source": [ + "\"\"\"\n", + "Simple code that tests XOR, OR and AND gates with linear regression\n", + "\"\"\"\n", + "\n", + "import numpy as np\n", + "# Design matrix\n", + "X = np.array([ [1, 0, 0], [1, 0, 1], [1, 1, 0],[1, 1, 1]],dtype=np.float64)\n", + "print(f\"The X.TX matrix:{X.T @ X}\")\n", + "Xinv = np.linalg.pinv(X.T @ X)\n", + "print(f\"The invers of X.TX matrix:{Xinv}\")\n", + "\n", + "# The XOR gate \n", + "yXOR = np.array( [ 0, 1 ,1, 0])\n", + "ThetaXOR = Xinv @ X.T @ yXOR\n", + "print(f\"The values of theta for the XOR gate:{ThetaXOR}\")\n", + "print(f\"The linear regression prediction for the XOR gate:{X @ ThetaXOR}\")\n", + "\n", + "\n", + "# The OR gate \n", + "yOR = np.array( [ 0, 1 ,1, 1])\n", + "ThetaOR = Xinv @ X.T @ yOR\n", + "print(f\"The values of theta for the OR gate:{ThetaOR}\")\n", + "print(f\"The linear regression prediction for the OR gate:{X @ ThetaOR}\")\n", + "\n", + "\n", + "# The OR gate \n", + "yAND = np.array( [ 0, 0 ,0, 1])\n", + "ThetaAND = Xinv @ X.T @ yAND\n", + "print(f\"The values of theta for the AND gate:{ThetaAND}\")\n", + "print(f\"The linear regression prediction for the AND gate:{X @ ThetaAND}\")" + ] + }, + { + "cell_type": "markdown", + "id": "b0477050", + "metadata": { + "editable": true + }, + "source": [ + "What is happening here?" + ] + }, + { + "cell_type": "markdown", + "id": "1d72d90e", + "metadata": { + "editable": true + }, + "source": [ + "## Does Logistic Regression do a better Job?" + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "id": "501aa7b5", + "metadata": { + "collapsed": false, + "editable": true + }, + "outputs": [], + "source": [ + "\"\"\"\n", + "Simple code that tests XOR and OR gates with linear regression\n", + "and logistic regression\n", + "\"\"\"\n", + "\n", + "import matplotlib.pyplot as plt\n", + "from sklearn.linear_model import LogisticRegression\n", + "import numpy as np\n", + "\n", + "# Design matrix\n", + "X = np.array([ [1, 0, 0], [1, 0, 1], [1, 1, 0],[1, 1, 1]],dtype=np.float64)\n", + "print(f\"The X.TX matrix:{X.T @ X}\")\n", + "Xinv = np.linalg.pinv(X.T @ X)\n", + "print(f\"The invers of X.TX matrix:{Xinv}\")\n", + "\n", + "# The XOR gate \n", + "yXOR = np.array( [ 0, 1 ,1, 0])\n", + "ThetaXOR = Xinv @ X.T @ yXOR\n", + "print(f\"The values of theta for the XOR gate:{ThetaXOR}\")\n", + "print(f\"The linear regression prediction for the XOR gate:{X @ ThetaXOR}\")\n", + "\n", + "\n", + "# The OR gate \n", + "yOR = np.array( [ 0, 1 ,1, 1])\n", + "ThetaOR = Xinv @ X.T @ yOR\n", + "print(f\"The values of theta for the OR gate:{ThetaOR}\")\n", + "print(f\"The linear regression prediction for the OR gate:{X @ ThetaOR}\")\n", + "\n", + "\n", + "# The OR gate \n", + "yAND = np.array( [ 0, 0 ,0, 1])\n", + "ThetaAND = Xinv @ X.T @ yAND\n", + "print(f\"The values of theta for the AND gate:{ThetaAND}\")\n", + "print(f\"The linear regression prediction for the AND gate:{X @ ThetaAND}\")\n", + "\n", + "# Now we change to logistic regression\n", + "\n", + "\n", + "# Logistic Regression\n", + "logreg = LogisticRegression()\n", + "logreg.fit(X, yOR)\n", + "print(\"Test set accuracy with Logistic Regression for OR gate: {:.2f}\".format(logreg.score(X,yOR)))\n", + "\n", + "logreg.fit(X, yXOR)\n", + "print(\"Test set accuracy with Logistic Regression for XOR gate: {:.2f}\".format(logreg.score(X,yXOR)))\n", + "\n", + "\n", + "logreg.fit(X, yAND)\n", + "print(\"Test set accuracy with Logistic Regression for AND gate: {:.2f}\".format(logreg.score(X,yAND)))" + ] + }, + { + "cell_type": "markdown", + "id": "03908b42", + "metadata": { + "editable": true + }, + "source": [ + "Not exactly impressive, but somewhat better." + ] + }, + { + "cell_type": "markdown", + "id": "91971469", + "metadata": { + "editable": true + }, + "source": [ + "## Adding Neural Networks" + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "id": "f1717531", + "metadata": { + "collapsed": false, + "editable": true + }, + "outputs": [], + "source": [ + "\n", + "# and now neural networks with Scikit-Learn and the XOR\n", + "\n", + "from sklearn.neural_network import MLPClassifier\n", + "from sklearn.datasets import make_classification\n", + "X, yXOR = make_classification(n_samples=100, random_state=1)\n", + "FFNN = MLPClassifier(random_state=1, max_iter=300).fit(X, yXOR)\n", + "FFNN.predict_proba(X)\n", + "print(f\"Test set accuracy with Feed Forward Neural Network for XOR gate:{FFNN.score(X, yXOR)}\")" + ] + }, + { + "cell_type": "markdown", + "id": "05726714", + "metadata": { + "editable": true + }, + "source": [ + "## Mathematical model\n", + "\n", + "The output $y$ is produced via the activation function $f$" + ] + }, + { + "cell_type": "markdown", + "id": "1cf57e1c", + "metadata": { + "editable": true + }, + "source": [ + "$$\n", + "y = f\\left(\\sum_{i=1}^n w_ix_i + b_i\\right) = f(z),\n", + "$$" + ] + }, + { + "cell_type": "markdown", + "id": "57743f5e", + "metadata": { + "editable": true + }, + "source": [ + "This function receives $x_i$ as inputs.\n", + "Here the activation $z=(\\sum_{i=1}^n w_ix_i+b_i)$. \n", + "In an FFNN of such neurons, the *inputs* $x_i$ are the *outputs* of\n", + "the neurons in the preceding layer. Furthermore, an MLP is\n", + "fully-connected, which means that each neuron receives a weighted sum\n", + "of the outputs of *all* neurons in the previous layer." + ] + }, + { + "cell_type": "markdown", + "id": "2d3f8338", + "metadata": { + "editable": true + }, + "source": [ + "## Mathematical model\n", + "\n", + "First, for each node $i$ in the first hidden layer, we calculate a weighted sum $z_i^1$ of the input coordinates $x_j$," + ] + }, + { + "cell_type": "markdown", + "id": "20be0ccb", + "metadata": { + "editable": true + }, + "source": [ + "\n", + "
\n", + "\n", + "$$\n", + "\\begin{equation} z_i^1 = \\sum_{j=1}^{M} w_{ij}^1 x_j + b_i^1\n", + "\\label{_auto6} \\tag{7}\n", + "\\end{equation}\n", + "$$" + ] + }, + { + "cell_type": "markdown", + "id": "d289b4c8", + "metadata": { + "editable": true + }, + "source": [ + "Here $b_i$ is the so-called bias which is normally needed in\n", + "case of zero activation weights or inputs. How to fix the biases and\n", + "the weights will be discussed below. The value of $z_i^1$ is the\n", + "argument to the activation function $f_i$ of each node $i$, The\n", + "variable $M$ stands for all possible inputs to a given node $i$ in the\n", + "first layer. We define the output $y_i^1$ of all neurons in layer 1 as" + ] + }, + { + "cell_type": "markdown", + "id": "498c2494", + "metadata": { + "editable": true + }, + "source": [ + "\n", + "
\n", + "\n", + "$$\n", + "\\begin{equation}\n", + " y_i^1 = f(z_i^1) = f\\left(\\sum_{j=1}^M w_{ij}^1 x_j + b_i^1\\right)\n", + "\\label{outputLayer1} \\tag{8}\n", + "\\end{equation}\n", + "$$" + ] + }, + { + "cell_type": "markdown", + "id": "77995e5d", + "metadata": { + "editable": true + }, + "source": [ + "where we assume that all nodes in the same layer have identical\n", + "activation functions, hence the notation $f$. In general, we could assume in the more general case that different layers have different activation functions.\n", + "In this case we would identify these functions with a superscript $l$ for the $l$-th layer," + ] + }, + { + "cell_type": "markdown", + "id": "ef353d76", + "metadata": { + "editable": true + }, + "source": [ + "\n", + "
\n", + "\n", + "$$\n", + "\\begin{equation}\n", + " y_i^l = f^l(u_i^l) = f^l\\left(\\sum_{j=1}^{N_{l-1}} w_{ij}^l y_j^{l-1} + b_i^l\\right)\n", + "\\label{generalLayer} \\tag{9}\n", + "\\end{equation}\n", + "$$" + ] + }, + { + "cell_type": "markdown", + "id": "0a25d2f4", + "metadata": { + "editable": true + }, + "source": [ + "where $N_l$ is the number of nodes in layer $l$. When the output of\n", + "all the nodes in the first hidden layer are computed, the values of\n", + "the subsequent layer can be calculated and so forth until the output\n", + "is obtained." + ] + }, + { + "cell_type": "markdown", + "id": "d7d29703", + "metadata": { + "editable": true + }, + "source": [ + "## Mathematical model\n", + "\n", + "The output of neuron $i$ in layer 2 is thus," + ] + }, + { + "cell_type": "markdown", + "id": "94eddeb9", + "metadata": { + "editable": true + }, + "source": [ + "\n", + "
\n", + "\n", + "$$\n", + "\\begin{equation}\n", + " y_i^2 = f^2\\left(\\sum_{j=1}^N w_{ij}^2 y_j^1 + b_i^2\\right) \n", + "\\label{_auto7} \\tag{10}\n", + "\\end{equation}\n", + "$$" + ] + }, + { + "cell_type": "markdown", + "id": "f047f4c6", + "metadata": { + "editable": true + }, + "source": [ + "\n", + "
\n", + "\n", + "$$\n", + "\\begin{equation} \n", + " = f^2\\left[\\sum_{j=1}^N w_{ij}^2f^1\\left(\\sum_{k=1}^M w_{jk}^1 x_k + b_j^1\\right) + b_i^2\\right]\n", + "\\label{outputLayer2} \\tag{11}\n", + "\\end{equation}\n", + "$$" + ] + }, + { + "cell_type": "markdown", + "id": "91d4806e", + "metadata": { + "editable": true + }, + "source": [ + "where we have substituted $y_k^1$ with the inputs $x_k$. Finally, the ANN output reads" + ] + }, + { + "cell_type": "markdown", + "id": "7342e125", + "metadata": { + "editable": true + }, + "source": [ + "\n", + "
\n", + "\n", + "$$\n", + "\\begin{equation}\n", + " y_i^3 = f^3\\left(\\sum_{j=1}^N w_{ij}^3 y_j^2 + b_i^3\\right) \n", + "\\label{_auto8} \\tag{12}\n", + "\\end{equation}\n", + "$$" + ] + }, + { + "cell_type": "markdown", + "id": "5068e976", + "metadata": { + "editable": true + }, + "source": [ + "\n", + "
\n", + "\n", + "$$\n", + "\\begin{equation} \n", + " = f_3\\left[\\sum_{j} w_{ij}^3 f^2\\left(\\sum_{k} w_{jk}^2 f^1\\left(\\sum_{m} w_{km}^1 x_m + b_k^1\\right) + b_j^2\\right)\n", + " + b_1^3\\right]\n", + "\\label{_auto9} \\tag{13}\n", + "\\end{equation}\n", + "$$" + ] + }, + { + "cell_type": "markdown", + "id": "b51a241d", + "metadata": { + "editable": true + }, + "source": [ + "## Mathematical model\n", + "\n", + "We can generalize this expression to an MLP with $l$ hidden\n", + "layers. The complete functional form is," + ] + }, + { + "cell_type": "markdown", + "id": "5a7b4915", + "metadata": { + "editable": true + }, + "source": [ + "\n", + "
\n", + "\n", + "$$\n", + "\\begin{equation}\n", + "y^{l+1}_i = f^{l+1}\\left[\\!\\sum_{j=1}^{N_l} w_{ij}^3 f^l\\left(\\sum_{k=1}^{N_{l-1}}w_{jk}^{l-1}\\left(\\dots f^1\\left(\\sum_{n=1}^{N_0} w_{mn}^1 x_n+ b_m^1\\right)\\dots\\right)+b_k^2\\right)+b_1^3\\right] \n", + "\\label{completeNN} \\tag{14}\n", + "\\end{equation}\n", + "$$" + ] + }, + { + "cell_type": "markdown", + "id": "c3215b7f", + "metadata": { + "editable": true + }, + "source": [ + "which illustrates a basic property of MLPs: The only independent\n", + "variables are the input values $x_n$." + ] + }, + { + "cell_type": "markdown", + "id": "f92eedb0", + "metadata": { + "editable": true + }, + "source": [ + "## Mathematical model\n", + "\n", + "This confirms that an MLP, despite its quite convoluted mathematical\n", + "form, is nothing more than an analytic function, specifically a\n", + "mapping of real-valued vectors $\\hat{x} \\in \\mathbb{R}^n \\rightarrow\n", + "\\hat{y} \\in \\mathbb{R}^m$.\n", + "\n", + "Furthermore, the flexibility and universality of an MLP can be\n", + "illustrated by realizing that the expression is essentially a nested\n", + "sum of scaled activation functions of the form" + ] + }, + { + "cell_type": "markdown", + "id": "b658fa6d", + "metadata": { + "editable": true + }, + "source": [ + "\n", + "
\n", + "\n", + "$$\n", + "\\begin{equation}\n", + " f(x) = c_1 f(c_2 x + c_3) + c_4\n", + "\\label{_auto10} \\tag{15}\n", + "\\end{equation}\n", + "$$" + ] + }, + { + "cell_type": "markdown", + "id": "8506281a", + "metadata": { + "editable": true + }, + "source": [ + "where the parameters $c_i$ are weights and biases. By adjusting these\n", + "parameters, the activation functions can be shifted up and down or\n", + "left and right, change slope or be rescaled which is the key to the\n", + "flexibility of a neural network." + ] + }, + { + "cell_type": "markdown", + "id": "0ad3f400", + "metadata": { + "editable": true + }, + "source": [ + "### Matrix-vector notation\n", + "\n", + "We can introduce a more convenient notation for the activations in an A NN. \n", + "\n", + "Additionally, we can represent the biases and activations\n", + "as layer-wise column vectors $\\hat{b}_l$ and $\\hat{y}_l$, so that the $i$-th element of each vector \n", + "is the bias $b_i^l$ and activation $y_i^l$ of node $i$ in layer $l$ respectively. \n", + "\n", + "We have that $\\mathrm{W}_l$ is an $N_{l-1} \\times N_l$ matrix, while $\\hat{b}_l$ and $\\hat{y}_l$ are $N_l \\times 1$ column vectors. \n", + "With this notation, the sum becomes a matrix-vector multiplication, and we can write\n", + "the equation for the activations of hidden layer 2 (assuming three nodes for simplicity) as" + ] + }, + { + "cell_type": "markdown", + "id": "7b431efc", + "metadata": { + "editable": true + }, + "source": [ + "\n", + "
\n", + "\n", + "$$\n", + "\\begin{equation}\n", + " \\hat{y}_2 = f_2(\\mathrm{W}_2 \\hat{y}_{1} + \\hat{b}_{2}) = \n", + " f_2\\left(\\left[\\begin{array}{ccc}\n", + " w^2_{11} &w^2_{12} &w^2_{13} \\\\\n", + " w^2_{21} &w^2_{22} &w^2_{23} \\\\\n", + " w^2_{31} &w^2_{32} &w^2_{33} \\\\\n", + " \\end{array} \\right] \\cdot\n", + " \\left[\\begin{array}{c}\n", + " y^1_1 \\\\\n", + " y^1_2 \\\\\n", + " y^1_3 \\\\\n", + " \\end{array}\\right] + \n", + " \\left[\\begin{array}{c}\n", + " b^2_1 \\\\\n", + " b^2_2 \\\\\n", + " b^2_3 \\\\\n", + " \\end{array}\\right]\\right).\n", + "\\label{_auto11} \\tag{16}\n", + "\\end{equation}\n", + "$$" + ] + }, + { + "cell_type": "markdown", + "id": "d129b057", + "metadata": { + "editable": true + }, + "source": [ + "### Matrix-vector notation and activation\n", + "\n", + "The activation of node $i$ in layer 2 is" + ] + }, + { + "cell_type": "markdown", + "id": "7af14562", + "metadata": { + "editable": true + }, + "source": [ + "\n", + "
\n", + "\n", + "$$\n", + "\\begin{equation}\n", + " y^2_i = f_2\\Bigr(w^2_{i1}y^1_1 + w^2_{i2}y^1_2 + w^2_{i3}y^1_3 + b^2_i\\Bigr) = \n", + " f_2\\left(\\sum_{j=1}^3 w^2_{ij} y_j^1 + b^2_i\\right).\n", + "\\label{_auto12} \\tag{17}\n", + "\\end{equation}\n", + "$$" + ] + }, + { + "cell_type": "markdown", + "id": "0b7e127c", + "metadata": { + "editable": true + }, + "source": [ + "This is not just a convenient and compact notation, but also a useful\n", + "and intuitive way to think about MLPs: The output is calculated by a\n", + "series of matrix-vector multiplications and vector additions that are\n", + "used as input to the activation functions. For each operation\n", + "$\\mathrm{W}_l \\hat{y}_{l-1}$ we move forward one layer." + ] + }, + { + "cell_type": "markdown", + "id": "91266ae3", + "metadata": { + "editable": true + }, + "source": [ + "### Activation functions\n", + "\n", + "A property that characterizes a neural network, other than its\n", + "connectivity, is the choice of activation function(s). As described\n", + "in, the following restrictions are imposed on an activation function\n", + "for a FFNN to fulfill the universal approximation theorem\n", + "\n", + " * Non-constant\n", + "\n", + " * Bounded\n", + "\n", + " * Monotonically-increasing\n", + "\n", + " * Continuous" + ] + }, + { + "cell_type": "markdown", + "id": "54728fbd", + "metadata": { + "editable": true + }, + "source": [ + "### Activation functions, Logistic and Hyperbolic ones\n", + "\n", + "The second requirement excludes all linear functions. Furthermore, in\n", + "a MLP with only linear activation functions, each layer simply\n", + "performs a linear transformation of its inputs.\n", + "\n", + "Regardless of the number of layers, the output of the NN will be\n", + "nothing but a linear function of the inputs. Thus we need to introduce\n", + "some kind of non-linearity to the NN to be able to fit non-linear\n", + "functions Typical examples are the logistic *Sigmoid*" + ] + }, + { + "cell_type": "markdown", + "id": "17b851fb", + "metadata": { + "editable": true + }, + "source": [ + "$$\n", + "f(x) = \\frac{1}{1 + e^{-x}},\n", + "$$" + ] + }, + { + "cell_type": "markdown", + "id": "6e17f015", + "metadata": { + "editable": true + }, + "source": [ + "and the *hyperbolic tangent* function" + ] + }, + { + "cell_type": "markdown", + "id": "574fbcd0", + "metadata": { + "editable": true + }, + "source": [ + "$$\n", + "f(x) = \\tanh(x)\n", + "$$" + ] + }, + { + "cell_type": "markdown", + "id": "daa971d1", + "metadata": { + "editable": true + }, + "source": [ + "### Relevance\n", + "\n", + "The *sigmoid* function are more biologically plausible because the\n", + "output of inactive neurons are zero. Such activation function are\n", + "called *one-sided*. However, it has been shown that the hyperbolic\n", + "tangent performs better than the sigmoid for training MLPs. has\n", + "become the most popular for *deep neural networks*" + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "id": "c12bc7fe", + "metadata": { + "collapsed": false, + "editable": true + }, + "outputs": [], + "source": [ + "\"\"\"The sigmoid function (or the logistic curve) is a \n", + "function that takes any real number, z, and outputs a number (0,1).\n", + "It is useful in neural networks for assigning weights on a relative scale.\n", + "The value z is the weighted sum of parameters involved in the learning algorithm.\"\"\"\n", + "\n", + "import numpy\n", + "import matplotlib.pyplot as plt\n", + "import math as mt\n", + "\n", + "z = numpy.arange(-5, 5, .1)\n", + "sigma_fn = numpy.vectorize(lambda z: 1/(1+numpy.exp(-z)))\n", + "sigma = sigma_fn(z)\n", + "\n", + "fig = plt.figure()\n", + "ax = fig.add_subplot(111)\n", + "ax.plot(z, sigma)\n", + "ax.set_ylim([-0.1, 1.1])\n", + "ax.set_xlim([-5,5])\n", + "ax.grid(True)\n", + "ax.set_xlabel('z')\n", + "ax.set_title('sigmoid function')\n", + "\n", + "plt.show()\n", + "\n", + "\"\"\"Step Function\"\"\"\n", + "z = numpy.arange(-5, 5, .02)\n", + "step_fn = numpy.vectorize(lambda z: 1.0 if z >= 0.0 else 0.0)\n", + "step = step_fn(z)\n", + "\n", + "fig = plt.figure()\n", + "ax = fig.add_subplot(111)\n", + "ax.plot(z, step)\n", + "ax.set_ylim([-0.5, 1.5])\n", + "ax.set_xlim([-5,5])\n", + "ax.grid(True)\n", + "ax.set_xlabel('z')\n", + "ax.set_title('step function')\n", + "\n", + "plt.show()\n", + "\n", + "\"\"\"Sine Function\"\"\"\n", + "z = numpy.arange(-2*mt.pi, 2*mt.pi, 0.1)\n", + "t = numpy.sin(z)\n", + "\n", + "fig = plt.figure()\n", + "ax = fig.add_subplot(111)\n", + "ax.plot(z, t)\n", + "ax.set_ylim([-1.0, 1.0])\n", + "ax.set_xlim([-2*mt.pi,2*mt.pi])\n", + "ax.grid(True)\n", + "ax.set_xlabel('z')\n", + "ax.set_title('sine function')\n", + "\n", + "plt.show()\n", + "\n", + "\"\"\"Plots a graph of the squashing function used by a rectified linear\n", + "unit\"\"\"\n", + "z = numpy.arange(-2, 2, .1)\n", + "zero = numpy.zeros(len(z))\n", + "y = numpy.max([zero, z], axis=0)\n", + "\n", + "fig = plt.figure()\n", + "ax = fig.add_subplot(111)\n", + "ax.plot(z, y)\n", + "ax.set_ylim([-2.0, 2.0])\n", + "ax.set_xlim([-2.0, 2.0])\n", + "ax.grid(True)\n", + "ax.set_xlabel('z')\n", + "ax.set_title('Rectified linear unit')\n", + "\n", + "plt.show()" + ] + } + ], + "metadata": {}, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/doc/LectureNotes/_build/html/_static/basic.css b/doc/LectureNotes/_build/html/_static/basic.css index 9e364ed34..d54be8067 100644 --- a/doc/LectureNotes/_build/html/_static/basic.css +++ b/doc/LectureNotes/_build/html/_static/basic.css @@ -222,7 +222,7 @@ table.modindextable td { /* -- general body styles --------------------------------------------------- */ div.body { - min-width: 360px; + min-width: 450px; max-width: 800px; } @@ -237,6 +237,16 @@ a.headerlink { visibility: hidden; } +a.brackets:before, +span.brackets > a:before{ + content: "["; +} + +a.brackets:after, +span.brackets > a:after { + content: "]"; +} + h1:hover > a.headerlink, h2:hover > a.headerlink, h3:hover > a.headerlink, @@ -324,16 +334,12 @@ aside.sidebar { p.sidebar-title { font-weight: bold; } -nav.contents, -aside.topic, div.admonition, div.topic, blockquote { clear: left; } /* -- topics ---------------------------------------------------------------- */ -nav.contents, -aside.topic, div.topic { border: 1px solid #ccc; @@ -373,9 +379,6 @@ div.body p.centered { div.sidebar > :last-child, aside.sidebar > :last-child, -nav.contents > :last-child, -aside.topic > :last-child, - div.topic > :last-child, div.admonition > :last-child { margin-bottom: 0; @@ -383,9 +386,6 @@ div.admonition > :last-child { div.sidebar::after, aside.sidebar::after, -nav.contents::after, -aside.topic::after, - div.topic::after, div.admonition::after, blockquote::after { @@ -428,6 +428,10 @@ table.docutils td, table.docutils th { border-bottom: 1px solid #aaa; } +table.footnote td, table.footnote th { + border: 0 !important; +} + th { text-align: left; padding-right: 5px; @@ -611,7 +615,6 @@ ul.simple p { margin-bottom: 0; } -/* Docutils 0.17 and older (footnotes & citations) */ dl.footnote > dt, dl.citation > dt { float: left; @@ -629,33 +632,6 @@ dl.citation > dd:after { clear: both; } -/* Docutils 0.18+ (footnotes & citations) */ -aside.footnote > span, -div.citation > span { - float: left; -} -aside.footnote > span:last-of-type, -div.citation > span:last-of-type { - padding-right: 0.5em; -} -aside.footnote > p { - margin-left: 2em; -} -div.citation > p { - margin-left: 4em; -} -aside.footnote > p:last-of-type, -div.citation > p:last-of-type { - margin-bottom: 0em; -} -aside.footnote > p:last-of-type:after, -div.citation > p:last-of-type:after { - content: ""; - clear: both; -} - -/* Footnotes & citations ends */ - dl.field-list { display: grid; grid-template-columns: fit-content(30%) auto; diff --git a/doc/LectureNotes/_build/html/_static/copybutton.css b/doc/LectureNotes/_build/html/_static/copybutton.css index f1916ec7d..40eafe5fc 100644 --- a/doc/LectureNotes/_build/html/_static/copybutton.css +++ b/doc/LectureNotes/_build/html/_static/copybutton.css @@ -35,8 +35,7 @@ div.highlight { position: relative; } -/* Show the copybutton */ -.highlight:hover button.copybtn, button.copybtn.success { +.highlight:hover button.copybtn { opacity: 1; } diff --git a/doc/LectureNotes/_build/html/_static/copybutton.js b/doc/LectureNotes/_build/html/_static/copybutton.js index 2ea7ff3e2..40ac33108 100644 --- a/doc/LectureNotes/_build/html/_static/copybutton.js +++ b/doc/LectureNotes/_build/html/_static/copybutton.js @@ -20,7 +20,7 @@ const messages = { }, 'fr' : { 'copy': 'Copier', - 'copy_to_clipboard': 'Copier dans le presse-papier', + 'copy_to_clipboard': 'Copié dans le presse-papier', 'copy_success': 'Copié !', 'copy_failure': 'Échec de la copie', }, @@ -102,25 +102,18 @@ const clearSelection = () => { } } -// Changes tooltip text for a moment, then changes it back -// We want the timeout of our `success` class to be a bit shorter than the -// tooltip and icon change, so that we can hide the icon before changing back. -var timeoutIcon = 2000; -var timeoutSuccessClass = 1500; - +// Changes tooltip text for two seconds, then changes it back const temporarilyChangeTooltip = (el, oldText, newText) => { el.setAttribute('data-tooltip', newText) el.classList.add('success') - // Remove success a little bit sooner than we change the tooltip - // So that we can use CSS to hide the copybutton first - setTimeout(() => el.classList.remove('success'), timeoutSuccessClass) - setTimeout(() => el.setAttribute('data-tooltip', oldText), timeoutIcon) + setTimeout(() => el.setAttribute('data-tooltip', oldText), 2000) + setTimeout(() => el.classList.remove('success'), 2000) } // Changes the copy button icon for two seconds, then changes it back const temporarilyChangeIcon = (el) => { el.innerHTML = iconCheck; - setTimeout(() => {el.innerHTML = iconCopy}, timeoutIcon) + setTimeout(() => {el.innerHTML = iconCopy}, 2000) } const addCopyButtonToCodeCells = () => { @@ -132,8 +125,7 @@ const addCopyButtonToCodeCells = () => { } // Add copybuttons to all of our code cells - const COPYBUTTON_SELECTOR = 'div.highlight pre'; - const codeCells = document.querySelectorAll(COPYBUTTON_SELECTOR) + const codeCells = document.querySelectorAll('div.highlight pre') codeCells.forEach((codeCell, index) => { const id = codeCellId(index) codeCell.setAttribute('id', id) @@ -149,25 +141,10 @@ function escapeRegExp(string) { return string.replace(/[.*+?^${}()|[\]\\]/g, '\\$&'); // $& means the whole matched string } -/** - * Removes excluded text from a Node. - * - * @param {Node} target Node to filter. - * @param {string} exclude CSS selector of nodes to exclude. - * @returns {DOMString} Text from `target` with text removed. - */ -function filterText(target, exclude) { - const clone = target.cloneNode(true); // clone as to not modify the live DOM - if (exclude) { - // remove excluded nodes - clone.querySelectorAll(exclude).forEach(node => node.remove()); - } - return clone.innerText; -} - // Callback when a copy button is clicked. Will be passed the node that was clicked // should then grab the text and replace pieces of text that shouldn't be used in output function formatCopyText(textContent, copybuttonPromptText, isRegexp = false, onlyCopyPromptLines = true, removePrompts = true, copyEmptyLines = true, lineContinuationChar = "", hereDocDelim = "") { + var regexp; var match; @@ -222,12 +199,7 @@ function formatCopyText(textContent, copybuttonPromptText, isRegexp = false, onl var copyTargetText = (trigger) => { var target = document.querySelector(trigger.attributes['data-clipboard-target'].value); - - // get filtered text - let exclude = '.linenos'; - - let text = filterText(target, exclude); - return formatCopyText(text, '', false, true, true, true, '', '') + return formatCopyText(target.innerText, '', false, true, true, true, '', '') } // Initialize with a callback so we can modify the text before copy diff --git a/doc/LectureNotes/_build/html/_static/copybutton_funcs.js b/doc/LectureNotes/_build/html/_static/copybutton_funcs.js index dbe1aaad7..b9168c556 100644 --- a/doc/LectureNotes/_build/html/_static/copybutton_funcs.js +++ b/doc/LectureNotes/_build/html/_static/copybutton_funcs.js @@ -2,25 +2,10 @@ function escapeRegExp(string) { return string.replace(/[.*+?^${}()|[\]\\]/g, '\\$&'); // $& means the whole matched string } -/** - * Removes excluded text from a Node. - * - * @param {Node} target Node to filter. - * @param {string} exclude CSS selector of nodes to exclude. - * @returns {DOMString} Text from `target` with text removed. - */ -export function filterText(target, exclude) { - const clone = target.cloneNode(true); // clone as to not modify the live DOM - if (exclude) { - // remove excluded nodes - clone.querySelectorAll(exclude).forEach(node => node.remove()); - } - return clone.innerText; -} - // Callback when a copy button is clicked. Will be passed the node that was clicked // should then grab the text and replace pieces of text that shouldn't be used in output export function formatCopyText(textContent, copybuttonPromptText, isRegexp = false, onlyCopyPromptLines = true, removePrompts = true, copyEmptyLines = true, lineContinuationChar = "", hereDocDelim = "") { + var regexp; var match; diff --git a/doc/LectureNotes/_build/html/_static/doctools.js b/doc/LectureNotes/_build/html/_static/doctools.js index c3db08d1c..e509e4834 100644 --- a/doc/LectureNotes/_build/html/_static/doctools.js +++ b/doc/LectureNotes/_build/html/_static/doctools.js @@ -2,263 +2,325 @@ * doctools.js * ~~~~~~~~~~~ * - * Base JavaScript utilities for all Sphinx HTML documentation. + * Sphinx JavaScript utilities for all documentation. * * :copyright: Copyright 2007-2022 by the Sphinx team, see AUTHORS. * :license: BSD, see LICENSE for details. * */ -"use strict"; -const _ready = (callback) => { - if (document.readyState !== "loading") { - callback(); - } else { - document.addEventListener("DOMContentLoaded", callback); +/** + * select a different prefix for underscore + */ +$u = _.noConflict(); + +/** + * make the code below compatible with browsers without + * an installed firebug like debugger +if (!window.console || !console.firebug) { + var names = ["log", "debug", "info", "warn", "error", "assert", "dir", + "dirxml", "group", "groupEnd", "time", "timeEnd", "count", "trace", + "profile", "profileEnd"]; + window.console = {}; + for (var i = 0; i < names.length; ++i) + window.console[names[i]] = function() {}; +} + */ + +/** + * small helper function to urldecode strings + * + * See https://developer.mozilla.org/en-US/docs/Web/JavaScript/Reference/Global_Objects/decodeURIComponent#Decoding_query_parameters_from_a_URL + */ +jQuery.urldecode = function(x) { + if (!x) { + return x } + return decodeURIComponent(x.replace(/\+/g, ' ')); }; /** - * highlight a given string on a node by wrapping it in + * small helper function to urlencode strings + */ +jQuery.urlencode = encodeURIComponent; + +/** + * This function returns the parsed url parameters of the + * current request. Multiple values per key are supported, + * it will always return arrays of strings for the value parts. + */ +jQuery.getQueryParameters = function(s) { + if (typeof s === 'undefined') + s = document.location.search; + var parts = s.substr(s.indexOf('?') + 1).split('&'); + var result = {}; + for (var i = 0; i < parts.length; i++) { + var tmp = parts[i].split('=', 2); + var key = jQuery.urldecode(tmp[0]); + var value = jQuery.urldecode(tmp[1]); + if (key in result) + result[key].push(value); + else + result[key] = [value]; + } + return result; +}; + +/** + * highlight a given string on a jquery object by wrapping it in * span elements with the given class name. */ -const _highlight = (node, addItems, text, className) => { - if (node.nodeType === Node.TEXT_NODE) { - const val = node.nodeValue; - const parent = node.parentNode; - const pos = val.toLowerCase().indexOf(text); - if ( - pos >= 0 && - !parent.classList.contains(className) && - !parent.classList.contains("nohighlight") - ) { - let span; - - const closestNode = parent.closest("body, svg, foreignObject"); - const isInSVG = closestNode && closestNode.matches("svg"); - if (isInSVG) { - span = document.createElementNS("http://www.w3.org/2000/svg", "tspan"); - } else { - span = document.createElement("span"); - span.classList.add(className); - } - - span.appendChild(document.createTextNode(val.substr(pos, text.length))); - parent.insertBefore( - span, - parent.insertBefore( +jQuery.fn.highlightText = function(text, className) { + function highlight(node, addItems) { + if (node.nodeType === 3) { + var val = node.nodeValue; + var pos = val.toLowerCase().indexOf(text); + if (pos >= 0 && + !jQuery(node.parentNode).hasClass(className) && + !jQuery(node.parentNode).hasClass("nohighlight")) { + var span; + var isInSVG = jQuery(node).closest("body, svg, foreignObject").is("svg"); + if (isInSVG) { + span = document.createElementNS("http://www.w3.org/2000/svg", "tspan"); + } else { + span = document.createElement("span"); + span.className = className; + } + span.appendChild(document.createTextNode(val.substr(pos, text.length))); + node.parentNode.insertBefore(span, node.parentNode.insertBefore( document.createTextNode(val.substr(pos + text.length)), - node.nextSibling - ) - ); - node.nodeValue = val.substr(0, pos); - - if (isInSVG) { - const rect = document.createElementNS( - "http://www.w3.org/2000/svg", - "rect" - ); - const bbox = parent.getBBox(); - rect.x.baseVal.value = bbox.x; - rect.y.baseVal.value = bbox.y; - rect.width.baseVal.value = bbox.width; - rect.height.baseVal.value = bbox.height; - rect.setAttribute("class", className); - addItems.push({ parent: parent, target: rect }); + node.nextSibling)); + node.nodeValue = val.substr(0, pos); + if (isInSVG) { + var rect = document.createElementNS("http://www.w3.org/2000/svg", "rect"); + var bbox = node.parentElement.getBBox(); + rect.x.baseVal.value = bbox.x; + rect.y.baseVal.value = bbox.y; + rect.width.baseVal.value = bbox.width; + rect.height.baseVal.value = bbox.height; + rect.setAttribute('class', className); + addItems.push({ + "parent": node.parentNode, + "target": rect}); + } } } - } else if (node.matches && !node.matches("button, select, textarea")) { - node.childNodes.forEach((el) => _highlight(el, addItems, text, className)); + else if (!jQuery(node).is("button, select, textarea")) { + jQuery.each(node.childNodes, function() { + highlight(this, addItems); + }); + } } + var addItems = []; + var result = this.each(function() { + highlight(this, addItems); + }); + for (var i = 0; i < addItems.length; ++i) { + jQuery(addItems[i].parent).before(addItems[i].target); + } + return result; }; -const _highlightText = (thisNode, text, className) => { - let addItems = []; - _highlight(thisNode, addItems, text, className); - addItems.forEach((obj) => - obj.parent.insertAdjacentElement("beforebegin", obj.target) - ); -}; + +/* + * backward compatibility for jQuery.browser + * This will be supported until firefox bug is fixed. + */ +if (!jQuery.browser) { + jQuery.uaMatch = function(ua) { + ua = ua.toLowerCase(); + + var match = /(chrome)[ \/]([\w.]+)/.exec(ua) || + /(webkit)[ \/]([\w.]+)/.exec(ua) || + /(opera)(?:.*version|)[ \/]([\w.]+)/.exec(ua) || + /(msie) ([\w.]+)/.exec(ua) || + ua.indexOf("compatible") < 0 && /(mozilla)(?:.*? rv:([\w.]+)|)/.exec(ua) || + []; + + return { + browser: match[ 1 ] || "", + version: match[ 2 ] || "0" + }; + }; + jQuery.browser = {}; + jQuery.browser[jQuery.uaMatch(navigator.userAgent).browser] = true; +} /** * Small JavaScript module for the documentation. */ -const Documentation = { - init: () => { - Documentation.highlightSearchWords(); - Documentation.initDomainIndexTable(); - Documentation.initOnKeyListeners(); +var Documentation = { + + init : function() { + this.fixFirefoxAnchorBug(); + this.highlightSearchWords(); + this.initIndexTable(); + if (DOCUMENTATION_OPTIONS.NAVIGATION_WITH_KEYS) { + this.initOnKeyListeners(); + } }, /** * i18n support */ - TRANSLATIONS: {}, - PLURAL_EXPR: (n) => (n === 1 ? 0 : 1), - LOCALE: "unknown", + TRANSLATIONS : {}, + PLURAL_EXPR : function(n) { return n === 1 ? 0 : 1; }, + LOCALE : 'unknown', // gettext and ngettext don't access this so that the functions // can safely bound to a different name (_ = Documentation.gettext) - gettext: (string) => { - const translated = Documentation.TRANSLATIONS[string]; - switch (typeof translated) { - case "undefined": - return string; // no translation - case "string": - return translated; // translation exists - default: - return translated[0]; // (singular, plural) translation tuple exists - } + gettext : function(string) { + var translated = Documentation.TRANSLATIONS[string]; + if (typeof translated === 'undefined') + return string; + return (typeof translated === 'string') ? translated : translated[0]; }, - ngettext: (singular, plural, n) => { - const translated = Documentation.TRANSLATIONS[singular]; - if (typeof translated !== "undefined") - return translated[Documentation.PLURAL_EXPR(n)]; - return n === 1 ? singular : plural; + ngettext : function(singular, plural, n) { + var translated = Documentation.TRANSLATIONS[singular]; + if (typeof translated === 'undefined') + return (n == 1) ? singular : plural; + return translated[Documentation.PLURALEXPR(n)]; }, - addTranslations: (catalog) => { - Object.assign(Documentation.TRANSLATIONS, catalog.messages); - Documentation.PLURAL_EXPR = new Function( - "n", - `return (${catalog.plural_expr})` - ); - Documentation.LOCALE = catalog.locale; + addTranslations : function(catalog) { + for (var key in catalog.messages) + this.TRANSLATIONS[key] = catalog.messages[key]; + this.PLURAL_EXPR = new Function('n', 'return +(' + catalog.plural_expr + ')'); + this.LOCALE = catalog.locale; + }, + + /** + * add context elements like header anchor links + */ + addContextElements : function() { + $('div[id] > :header:first').each(function() { + $('\u00B6'). + attr('href', '#' + this.id). + attr('title', _('Permalink to this headline')). + appendTo(this); + }); + $('dt[id]').each(function() { + $('\u00B6'). + attr('href', '#' + this.id). + attr('title', _('Permalink to this definition')). + appendTo(this); + }); + }, + + /** + * workaround a firefox stupidity + * see: https://bugzilla.mozilla.org/show_bug.cgi?id=645075 + */ + fixFirefoxAnchorBug : function() { + if (document.location.hash && $.browser.mozilla) + window.setTimeout(function() { + document.location.href += ''; + }, 10); }, /** * highlight the search words provided in the url in the text */ - highlightSearchWords: () => { - const highlight = - new URLSearchParams(window.location.search).get("highlight") || ""; - const terms = highlight.toLowerCase().split(/\s+/).filter(x => x); - if (terms.length === 0) return; // nothing to do + highlightSearchWords : function() { + var params = $.getQueryParameters(); + var terms = (params.highlight) ? params.highlight[0].split(/\s+/) : []; + if (terms.length) { + var body = $('div.body'); + if (!body.length) { + body = $('body'); + } + window.setTimeout(function() { + $.each(terms, function() { + body.highlightText(this.toLowerCase(), 'highlighted'); + }); + }, 10); + $('

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

') + .appendTo($('#searchbox')); + } + }, - // There should never be more than one element matching "div.body" - const divBody = document.querySelectorAll("div.body"); - const body = divBody.length ? divBody[0] : document.querySelector("body"); - window.setTimeout(() => { - terms.forEach((term) => _highlightText(body, term, "highlighted")); - }, 10); - - const searchBox = document.getElementById("searchbox"); - if (searchBox === null) return; - searchBox.appendChild( - document - .createRange() - .createContextualFragment( - '

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

" - ) - ); + /** + * init the domain index toggle buttons + */ + initIndexTable : function() { + var togglers = $('img.toggler').click(function() { + var src = $(this).attr('src'); + var idnum = $(this).attr('id').substr(7); + $('tr.cg-' + idnum).toggle(); + if (src.substr(-9) === 'minus.png') + $(this).attr('src', src.substr(0, src.length-9) + 'plus.png'); + else + $(this).attr('src', src.substr(0, src.length-8) + 'minus.png'); + }).css('display', ''); + if (DOCUMENTATION_OPTIONS.COLLAPSE_INDEX) { + togglers.click(); + } }, /** * helper function to hide the search marks again */ - hideSearchWords: () => { - document - .querySelectorAll("#searchbox .highlight-link") - .forEach((el) => el.remove()); - document - .querySelectorAll("span.highlighted") - .forEach((el) => el.classList.remove("highlighted")); - const url = new URL(window.location); - url.searchParams.delete("highlight"); - window.history.replaceState({}, "", url); + hideSearchWords : function() { + $('#searchbox .highlight-link').fadeOut(300); + $('span.highlighted').removeClass('highlighted'); + var url = new URL(window.location); + url.searchParams.delete('highlight'); + window.history.replaceState({}, '', url); }, /** - * helper function to focus on search bar + * make the url absolute */ - focusSearchBar: () => { - document.querySelectorAll("input[name=q]")[0]?.focus(); + makeURL : function(relativeURL) { + return DOCUMENTATION_OPTIONS.URL_ROOT + '/' + relativeURL; }, /** - * Initialise the domain index toggle buttons + * get the current relative url */ - initDomainIndexTable: () => { - const toggler = (el) => { - const idNumber = el.id.substr(7); - const toggledRows = document.querySelectorAll(`tr.cg-${idNumber}`); - if (el.src.substr(-9) === "minus.png") { - el.src = `${el.src.substr(0, el.src.length - 9)}plus.png`; - toggledRows.forEach((el) => (el.style.display = "none")); - } else { - el.src = `${el.src.substr(0, el.src.length - 8)}minus.png`; - toggledRows.forEach((el) => (el.style.display = "")); - } - }; - - const togglerElements = document.querySelectorAll("img.toggler"); - togglerElements.forEach((el) => - el.addEventListener("click", (event) => toggler(event.currentTarget)) - ); - togglerElements.forEach((el) => (el.style.display = "")); - if (DOCUMENTATION_OPTIONS.COLLAPSE_INDEX) togglerElements.forEach(toggler); + getCurrentURL : function() { + var path = document.location.pathname; + var parts = path.split(/\//); + $.each(DOCUMENTATION_OPTIONS.URL_ROOT.split(/\//), function() { + if (this === '..') + parts.pop(); + }); + var url = parts.join('/'); + return path.substring(url.lastIndexOf('/') + 1, path.length - 1); }, - initOnKeyListeners: () => { - // only install a listener if it is really needed - if ( - !DOCUMENTATION_OPTIONS.NAVIGATION_WITH_KEYS && - !DOCUMENTATION_OPTIONS.ENABLE_SEARCH_SHORTCUTS - ) - return; - - const blacklistedElements = new Set([ - "TEXTAREA", - "INPUT", - "SELECT", - "BUTTON", - ]); - document.addEventListener("keydown", (event) => { - if (blacklistedElements.has(document.activeElement.tagName)) return; // bail for input elements - if (event.altKey || event.ctrlKey || event.metaKey) return; // bail with special keys - - if (!event.shiftKey) { - switch (event.key) { - case "ArrowLeft": - if (!DOCUMENTATION_OPTIONS.NAVIGATION_WITH_KEYS) break; - - const prevLink = document.querySelector('link[rel="prev"]'); - if (prevLink && prevLink.href) { - window.location.href = prevLink.href; - event.preventDefault(); + initOnKeyListeners: function() { + $(document).keydown(function(event) { + var activeElementType = document.activeElement.tagName; + // don't navigate when in search box, textarea, dropdown or button + if (activeElementType !== 'TEXTAREA' && activeElementType !== 'INPUT' && activeElementType !== 'SELECT' + && activeElementType !== 'BUTTON' && !event.altKey && !event.ctrlKey && !event.metaKey + && !event.shiftKey) { + switch (event.keyCode) { + case 37: // left + var prevHref = $('link[rel="prev"]').prop('href'); + if (prevHref) { + window.location.href = prevHref; + return false; } break; - case "ArrowRight": - if (!DOCUMENTATION_OPTIONS.NAVIGATION_WITH_KEYS) break; - - const nextLink = document.querySelector('link[rel="next"]'); - if (nextLink && nextLink.href) { - window.location.href = nextLink.href; - event.preventDefault(); + case 39: // right + var nextHref = $('link[rel="next"]').prop('href'); + if (nextHref) { + window.location.href = nextHref; + return false; } break; - case "Escape": - if (!DOCUMENTATION_OPTIONS.ENABLE_SEARCH_SHORTCUTS) break; - Documentation.hideSearchWords(); - event.preventDefault(); } } - - // some keyboard layouts may need Shift to get / - switch (event.key) { - case "/": - if (!DOCUMENTATION_OPTIONS.ENABLE_SEARCH_SHORTCUTS) break; - Documentation.focusSearchBar(); - event.preventDefault(); - } }); - }, + } }; // quick alias for translations -const _ = Documentation.gettext; +_ = Documentation.gettext; -_ready(Documentation.init); +$(document).ready(function() { + Documentation.init(); +}); diff --git a/doc/LectureNotes/_build/html/_static/documentation_options.js b/doc/LectureNotes/_build/html/_static/documentation_options.js index 30637825d..93b7c24d6 100644 --- a/doc/LectureNotes/_build/html/_static/documentation_options.js +++ b/doc/LectureNotes/_build/html/_static/documentation_options.js @@ -1,14 +1,12 @@ var DOCUMENTATION_OPTIONS = { URL_ROOT: document.getElementById("documentation_options").getAttribute('data-url_root'), VERSION: '', - LANGUAGE: 'en', + LANGUAGE: 'None', COLLAPSE_INDEX: false, BUILDER: 'html', FILE_SUFFIX: '.html', LINK_SUFFIX: '.html', HAS_SOURCE: true, SOURCELINK_SUFFIX: '', - NAVIGATION_WITH_KEYS: true, - SHOW_SEARCH_SUMMARY: true, - ENABLE_SEARCH_SHORTCUTS: false, + NAVIGATION_WITH_KEYS: true }; \ No newline at end of file diff --git a/doc/LectureNotes/_build/html/_static/jquery.js b/doc/LectureNotes/_build/html/_static/jquery.js index 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}, */ @@ -30,11 +28,9 @@ if (typeof Scorer === "undefined") { // or matches in the last dotted part of the object name objPartialMatch: 6, // Additive scores depending on the priority of the object - objPrio: { - 0: 15, // used to be importantResults - 1: 5, // used to be objectResults - 2: -5, // used to be unimportantResults - }, + objPrio: {0: 15, // used to be importantResults + 1: 5, // used to be objectResults + 2: -5}, // used to be unimportantResults // Used when the priority is not in the mapping. objPrioDefault: 0, @@ -43,455 +39,456 @@ if (typeof Scorer === "undefined") { partialTitle: 7, // query found in terms term: 5, - partialTerm: 2, + partialTerm: 2 }; } -const _removeChildren = (element) => { - while (element && element.lastChild) element.removeChild(element.lastChild); -}; - -/** - * See https://developer.mozilla.org/en-US/docs/Web/JavaScript/Guide/Regular_Expressions#escaping - */ -const _escapeRegExp = (string) => - string.replace(/[.*+\-?^${}()|[\]\\]/g, "\\$&"); // $& means the whole matched string - -const _displayItem = (item, highlightTerms, searchTerms) => { - const docBuilder = DOCUMENTATION_OPTIONS.BUILDER; - const docUrlRoot = DOCUMENTATION_OPTIONS.URL_ROOT; - const docFileSuffix = DOCUMENTATION_OPTIONS.FILE_SUFFIX; - const docLinkSuffix = DOCUMENTATION_OPTIONS.LINK_SUFFIX; - const showSearchSummary = DOCUMENTATION_OPTIONS.SHOW_SEARCH_SUMMARY; - - const [docName, title, anchor, descr] = item; - - let listItem = document.createElement("li"); - let requestUrl; - let linkUrl; - if (docBuilder === "dirhtml") { - // dirhtml builder - let dirname = docName + "/"; - if (dirname.match(/\/index\/$/)) - dirname = dirname.substring(0, dirname.length - 6); - else if (dirname === "index/") dirname = ""; - requestUrl = docUrlRoot + dirname; - linkUrl = requestUrl; - } else { - // normal html builders - requestUrl = docUrlRoot + docName + docFileSuffix; - linkUrl = docName + docLinkSuffix; +if (!splitQuery) { + function splitQuery(query) { + return query.split(/\s+/); } - const params = new URLSearchParams(); - params.set("highlight", [...highlightTerms].join(" ")); - let linkEl = listItem.appendChild(document.createElement("a")); - linkEl.href = linkUrl + "?" + params.toString() + anchor; - linkEl.innerHTML = title; - if (descr) - listItem.appendChild(document.createElement("span")).innerText = - " (" + descr + ")"; - else if (showSearchSummary) - fetch(requestUrl) - .then((responseData) => responseData.text()) - .then((data) => { - if (data) - listItem.appendChild( - Search.makeSearchSummary(data, searchTerms, highlightTerms) - ); - }); - Search.output.appendChild(listItem); -}; -const _finishSearch = (resultCount) => { - Search.stopPulse(); - Search.title.innerText = _("Search Results"); - if (!resultCount) - Search.status.innerText = Documentation.gettext( - "Your search did not match any documents. Please make sure that all words are spelled correctly and that you've selected enough categories." - ); - else - Search.status.innerText = _( - `Search finished, found ${resultCount} page(s) matching the search query.` - ); -}; -const _displayNextItem = ( - results, - resultCount, - highlightTerms, - searchTerms -) => { - // results left, load the summary and display it - // this is intended to be dynamic (don't sub resultsCount) - if (results.length) { - _displayItem(results.pop(), highlightTerms, searchTerms); - setTimeout( - () => _displayNextItem(results, resultCount, highlightTerms, searchTerms), - 5 - ); - } - // search finished, update title and status message - else _finishSearch(resultCount); -}; - -/** - * Default splitQuery function. Can be overridden in ``sphinx.search`` with a - * custom function per language. - * - * The regular expression works by splitting the string on consecutive characters - * that are not Unicode letters, numbers, underscores, or emoji characters. - * This is the same as ``\W+`` in Python, preserving the surrogate pair area. - */ -if (typeof splitQuery === "undefined") { - var splitQuery = (query) => query - .split(/[^\p{Letter}\p{Number}_\p{Emoji_Presentation}]+/gu) - .filter(term => term) // remove remaining empty strings } /** * Search Module */ -const Search = { - _index: null, - _queued_query: null, - _pulse_status: -1, +var Search = { - htmlToText: (htmlString) => { - const htmlElement = document - .createRange() - .createContextualFragment(htmlString); - _removeChildren(htmlElement.querySelectorAll(".headerlink")); - const docContent = htmlElement.querySelector('[role="main"]'); - if (docContent !== undefined) return docContent.textContent; - console.warn( - "Content block not found. Sphinx search tries to obtain it via '[role=main]'. Could you check your theme or template." - ); - return ""; + _index : null, + _queued_query : null, + _pulse_status : -1, + + htmlToText : function(htmlString) { + var virtualDocument = document.implementation.createHTMLDocument('virtual'); + var htmlElement = $(htmlString, virtualDocument); + htmlElement.find('.headerlink').remove(); + docContent = htmlElement.find('[role=main]')[0]; + if(docContent === undefined) { + console.warn("Content block not found. Sphinx search tries to obtain it " + + "via '[role=main]'. Could you check your theme or template."); + return ""; + } + return docContent.textContent || docContent.innerText; }, - init: () => { - const query = new URLSearchParams(window.location.search).get("q"); - document - .querySelectorAll('input[name="q"]') - .forEach((el) => (el.value = query)); - if (query) Search.performSearch(query); + init : function() { + var params = $.getQueryParameters(); + if (params.q) { + var query = params.q[0]; + $('input[name="q"]')[0].value = query; + this.performSearch(query); + } }, - loadIndex: (url) => - (document.body.appendChild(document.createElement("script")).src = url), + loadIndex : function(url) { + $.ajax({type: "GET", url: url, data: null, + dataType: "script", cache: true, + complete: function(jqxhr, textstatus) { + if (textstatus != "success") { + document.getElementById("searchindexloader").src = url; + } + }}); + }, - setIndex: (index) => { - Search._index = index; - if (Search._queued_query !== null) { - const query = Search._queued_query; - Search._queued_query = null; - Search.query(query); + setIndex : function(index) { + var q; + this._index = index; + if ((q = this._queued_query) !== null) { + this._queued_query = null; + Search.query(q); } }, - hasIndex: () => Search._index !== null, + hasIndex : function() { + return this._index !== null; + }, - deferQuery: (query) => (Search._queued_query = query), + deferQuery : function(query) { + this._queued_query = query; + }, - stopPulse: () => (Search._pulse_status = -1), + stopPulse : function() { + this._pulse_status = 0; + }, - startPulse: () => { - if (Search._pulse_status >= 0) return; - - const pulse = () => { + startPulse : function() { + if (this._pulse_status >= 0) + return; + function pulse() { + var i; Search._pulse_status = (Search._pulse_status + 1) % 4; - Search.dots.innerText = ".".repeat(Search._pulse_status); - if (Search._pulse_status >= 0) window.setTimeout(pulse, 500); - }; + var dotString = ''; + for (i = 0; i < Search._pulse_status; i++) + dotString += '.'; + Search.dots.text(dotString); + if (Search._pulse_status > -1) + window.setTimeout(pulse, 500); + } pulse(); }, /** * perform a search for something (or wait until index is loaded) */ - performSearch: (query) => { + performSearch : function(query) { // create the required interface elements - const searchText = document.createElement("h2"); - searchText.textContent = _("Searching"); - const searchSummary = document.createElement("p"); - searchSummary.classList.add("search-summary"); - searchSummary.innerText = ""; - const searchList = document.createElement("ul"); - searchList.classList.add("search"); + this.out = $('#search-results'); + this.title = $('

' + _('Searching') + '

').appendTo(this.out); + this.dots = $('').appendTo(this.title); + this.status = $('

 

').appendTo(this.out); + this.output = $('

At the heart of basically all Machine Learning algorithms we will encounter so-called minimization or optimization algorithms. A large family of such methods are so-called gradient methods.

-
-

3.2.1. A Frequentist approach to data analysis#

+
+

3.2.1. A Frequentist approach to data analysis

When you hear phrases like predictions and estimations and correlations and causations, what do you think of? May be you think of the difference between classifying new data points and generating @@ -580,9 +793,9 @@ where the aim is to make predictions and find correlations. We focus less on for example extracting a probability distribution function (PDF). The PDF can be used in turn to make estimations and find causations such as given \(A\) what is the likelihood of finding \(B\).

-
-
-

3.2.2. What is a good model?#

+ +
+

3.2.2. What is a good model?

In science and engineering we often end up in situations where we want to infer (or learn) a quantitative model \(M\) for a given set of sample points \(\boldsymbol{X} \in [x_1, x_2,\dots x_N]\).

As we will see repeatedly in these lectures, we could try to fit these data points to a model given by a @@ -616,10 +829,10 @@ simplest class of models and increase the complexity of the models only when the simpler models become inadequate. For instance, if we work with a regression problem to fit a set of sample points, one may first try the simplest class of models, namely linear models, followed obviously by more complex models.

How to evaluate which model fits best the data is something we will come back to over and over again in these sets of lectures.

-
- -
-

3.3. Simple linear regression model using scikit-learn#

+ + +
+

3.3. Simple linear regression model using scikit-learn

We start with perhaps our simplest possible example, using Scikit-Learn to perform linear regression analysis on a data set produced by us.

@@ -696,55 +909,7 @@ Thereafter we wish to apply it to data which were not included in the training.
-
---------------------------------------------------------------------------
-ModuleNotFoundError                       Traceback (most recent call last)
-Cell In[1], line 1
-----> 1 get_ipython().run_line_magic('matplotlib', 'inline')
-      3 # Importing various packages
-      4 import numpy as np
-
-File ~/miniforge3/lib/python3.9/site-packages/IPython/core/interactiveshell.py:2432, in InteractiveShell.run_line_magic(self, magic_name, line, _stack_depth)
-   2430     kwargs['local_ns'] = self.get_local_scope(stack_depth)
-   2431 with self.builtin_trap:
--> 2432     result = fn(*args, **kwargs)
-   2434 # The code below prevents the output from being displayed
-   2435 # when using magics with decorator @output_can_be_silenced
-   2436 # when the last Python token in the expression is a ';'.
-   2437 if getattr(fn, magic.MAGIC_OUTPUT_CAN_BE_SILENCED, False):
-
-File ~/miniforge3/lib/python3.9/site-packages/IPython/core/magics/pylab.py:99, in PylabMagics.matplotlib(self, line)
-     97     print("Available matplotlib backends: %s" % backends_list)
-     98 else:
----> 99     gui, backend = self.shell.enable_matplotlib(args.gui.lower() if isinstance(args.gui, str) else args.gui)
-    100     self._show_matplotlib_backend(args.gui, backend)
-
-File ~/miniforge3/lib/python3.9/site-packages/IPython/core/interactiveshell.py:3606, in InteractiveShell.enable_matplotlib(self, gui)
-   3585 def enable_matplotlib(self, gui=None):
-   3586     """Enable interactive matplotlib and inline figure support.
-   3587 
-   3588     This takes the following steps:
-   (...)
-   3604         display figures inline.
-   3605     """
--> 3606     from matplotlib_inline.backend_inline import configure_inline_support
-   3608     from IPython.core import pylabtools as pt
-   3609     gui, backend = pt.find_gui_and_backend(gui, self.pylab_gui_select)
-
-File ~/miniforge3/lib/python3.9/site-packages/matplotlib_inline/__init__.py:1
-----> 1 from . import backend_inline, config  # noqa
-      2 __version__ = "0.1.6"  # noqa
-
-File ~/miniforge3/lib/python3.9/site-packages/matplotlib_inline/backend_inline.py:6
-      1 """A matplotlib backend for publishing figures via display_data"""
-      3 # Copyright (c) IPython Development Team.
-      4 # Distributed under the terms of the BSD 3-Clause License.
-----> 6 import matplotlib
-      7 from matplotlib import colors
-      8 from matplotlib.backends import backend_agg
-
-ModuleNotFoundError: No module named 'matplotlib'
-
-
+_images/chapter1_9_0.png

This example serves several aims. It allows us to demonstrate several @@ -828,6 +993,9 @@ to be dominated by outliers.

+
+_images/chapter1_17_0.png +

Depending on the parameter in front of the normal distribution, we may have a small or larger relative error. Try to play around with @@ -871,6 +1039,19 @@ example of the functionality of Scikit-Learn.

+
+
The intercept alpha: 
+ [2.04292593]
+Coefficient beta : 
+ [[5.00440395]]
+Mean squared error: 0.27
+Variance score: 0.88
+Mean squared log error: 0.01
+Mean absolute error: 0.41
+
+
+_images/chapter1_19_1.png +

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

@@ -964,6 +1145,12 @@ a linear \(x\)-dependence we s +
+_images/chapter1_33_0.png +
0.004999999999999994
+
+
+

Let us now dive into nuclear physics and remind ourselves briefly about some basic features about binding energies. A basic quantity which can be measured for the ground @@ -1025,8 +1212,8 @@ to the experimental data.

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

-
-

3.3.1. Organizing our data#

+
+

3.3.1. Organizing our data

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

@@ -1109,6 +1296,11 @@ data) to actually open the file and simply take a look at it!

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

The data we are interested in are in columns 2, 3, 4 and 11, giving us the number of neutrons, protons, mass numbers and binding energies, @@ -1137,6 +1329,40 @@ covert them into the pandas DataFrame structure.

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

We have now read in the data, grouped them according to the variables we are interested in. We see how easy it is to reorganize the data using pandas. If we @@ -1296,10 +1522,10 @@ functionality.

-
-
-
-

3.4. Linear Regression, basic elements#

+ + +
+

3.4. Linear Regression, basic elements

Video of Lecture.

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

    @@ -1758,8 +1984,8 @@ Since we are not using Scikit-Learn here we can define our own
-
-

3.4.1. The \(\chi^2\) function#

+
+

3.4.1. The \(\chi^2\) function

Normally, the response (dependent or outcome) variable \(y_i\) is the outcome of a numerical experiment or another type of experiment and is thus only an approximation to the true value. It is then always @@ -1877,9 +2103,9 @@ often from both being underdetermined and overdetermined in the unknown coefficients \(\beta_i\). A better approach is to use the Singular Value Decomposition (SVD) method discussed below. Or using Lasso and Ridge regression. See below.

-
-
-

3.4.2. Fitting an Equation of State for Dense Nuclear Matter#

+ +
+

3.4.2. Fitting an Equation of State for Dense Nuclear Matter

Before we continue, let us introduce yet another example. We are going to fit the nuclear equation of state using results from many-body calculations. The equation of state we have made available here, as function of @@ -1971,10 +2197,10 @@ instead of our own matrix inversion implementation.

The above simple polynomial in density \(\rho\) gives an excellent fit to the data.

-
-
-
-

3.5. Splitting our Data in Training and Test data#

+ + +
+

3.5. Splitting our Data in Training and Test data

It is normal in essentially all Machine Learning studies to split the data in a training set and a test set (sometimes also an additional validation set). Scikit-Learn has an own function for this. There @@ -2131,9 +2357,9 @@ but now splitting the data into a training set and a test set.

-
-
-

3.6. The Boston housing data example#

+ +
+

3.6. The Boston housing data example

The Boston housing
data set was originally a part of UCI Machine Learning Repository and has been removed now. The data set is now included in Scikit-Learn’s @@ -2141,7 +2367,7 @@ library. There are 506 samples and 13 feature (predictor) variables in this data set. The objective is to predict the value of prices of the house using the features (predictors) listed here.

The features/predictors are

-
    +

    1. CRIM: Per capita crime rate by town

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

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

      4. @@ -2156,9 +2382,9 @@ the house using the features (predictors) listed here.

    4. LSTAT: Percentage of lower status of the population

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

    -
-
-

3.7. Housing data, the code#

+ +
+

3.7. Housing data, the code

We start by importing the libraries

@@ -2322,9 +2548,9 @@ the house using the features (predictors) listed here.

-
-
-

3.8. Reducing the number of degrees of freedom, overarching view#

+ +
+

3.8. Reducing the number of degrees of freedom, overarching view

Many Machine Learning problems involve thousands or even millions of features for each training instance. Not only does this make training extremely slow, it can also make it much harder to find a good @@ -2435,9 +2661,9 @@ transformation

x_j^{(i)} \rightarrow (b-a)\frac{x_j^{(i)} - \min(x_j)}{\max(x_j) - \min(x_j)} - a \]

where \(\min(x_j)\) and \(\max(x_j)\) return the minimum and maximum value of \(x_j\) over the data set, respectively.

-
-
-

3.9. Testing the Means Squared Error as function of Complexity#

+ +
+

3.9. Testing the Means Squared Error as function of Complexity

Before we proceed with a more detailed analysis of the so-called Bias-Variance tradeoff, we present here an example of the relation between model complexity and the mean squared error for the triaining @@ -2486,12 +2712,12 @@ ourmodel (here in terms of the polynomial degree of the model).

-
-
-

3.10. Exercises#

-
-
-

3.11. Exercise 1: Setting up various Python environments#

+ +
+

3.10. Exercises

+
+
+

3.11. Exercise 1: Setting up various Python environments

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

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

    • @@ -2507,7 +2733,7 @@ on Python.

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

    -
      +

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

      For Tensorflow, we recommend following the instructions in the text of @@ -2517,12 +2743,12 @@ you install the following Python packages via pip as

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

      -
        +

        1. brew install python3

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

        -
          +

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

          If you don’t want to perform these operations separately and venture @@ -2545,9 +2771,9 @@ distribution for scientific and analytic computing distribution and analysis environment, available for free and under a commercial license.

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

          -
-
-

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

+ +
+

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

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

@@ -2558,7 +2784,7 @@ The following simple Python instructions define our +

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

  2. Use thereafter scikit-learn (see again the examples in the regression slides) and compare with your own code. When compairing with scikit_learn, make sure you set the option for the intercept to FALSE, see https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LinearRegression.html. This feature will be explained in more detail during the lectures of week 35 and week 36. You can find more in https://compphysics.github.io/MachineLearning/doc/LectureNotes/_build/html/chapter3.html#more-on-rescaling-data.

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

    4. @@ -2630,9 +2856,9 @@ The code here is an example of where we define our own design matrix and fit par
-
-
-

3.13. Exercise 3: Normalizing our data#

+ +
+

3.13. Exercise 3: Normalizing our data

A much used approach before starting to train the data is to preprocess our data. Normally the data may need a rescaling and/or may be sensitive to extreme values. Scaling the data renders our inputs much more @@ -2750,9 +2976,9 @@ Write a first code which sets up a design matrix \(R2\) factor for the training data and the test data, with and without scaling.

c) Add now a model which allows you to make polynomials up to degree \(15\). Perform a standard OLS fitting of the training data and compute the MSE and \(R2\) for the training and test data and plot both test and training data MSE and \(R2\) as functions of the polynomial degree. Compare what you see with Figure 2.11 of Hastie et al. Comment your results. For which polynomial degree do you find an optimal MSE (smallest value)?

-
-
-

3.14. Exercise 4: Adding Ridge Regression#

+ +
+

3.14. Exercise 4: Adding Ridge Regression

This exercise is a continuation of exercise 2. We will use the same function to generate our data set, still staying with a simple function \(y(x)\) which we want to fit using linear regression, but now extending the @@ -2885,9 +3111,9 @@ parameter \(\lambda\). This pr

-
-
-

3.15. Exercise 5: Analytical exercises#

+ +
+

3.15. Exercise 5: Analytical exercises

In this exercise we derive the expressions for various derivatives of products of vectors and matrices. Such derivatives are central to the optimization of various cost functions. Although we will often use @@ -2967,8 +3193,8 @@ f_i =\sum_{j=0}^{n-1}a_{ij}x_j, \frac{\partial (\boldsymbol{a}^T\boldsymbol{f})}{\partial \boldsymbol{a}}=\boldsymbol{a}^T\boldsymbol{A}+\boldsymbol{f}^T=\boldsymbol{a}^T\left(\boldsymbol{A}+\boldsymbol{A}^T\right), \]

since \(f\) depends on \(a\) and we have used the chain rule for derivatives on the derivative of \(f\) with respect to \(a\).

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

-By Morten Hjorth-Jensen -

- -
- -
- -

- - © Copyright 2022. -
- -

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

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

+
+ + + + + + + -
-
\ No newline at end of file diff --git a/doc/LectureNotes/_build/html/chapter10.html b/doc/LectureNotes/_build/html/chapter10.html index 2760f970e..0ca2b6130 100644 --- a/doc/LectureNotes/_build/html/chapter10.html +++ b/doc/LectureNotes/_build/html/chapter10.html @@ -1,61 +1,50 @@ - - - - + - - + 14. Building a Feed Forward Neural Network — Applied Data Analysis and Machine Learning - - - - - - - - - + + + - - - - - + + + + + + + + - + - + - - - - - - + + + + - - - - + + - - - + + + + + + - - - + + + - - - - -
+ - - - - - - - - - - -
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+ + +
+
probabilities = (n_inputs, n_categories) = (1437, 10)
+probability that image 0 is in category 0,1,2,...,9 = 
+[5.41511965e-04 2.17174962e-03 8.84355903e-03 1.44970586e-03
+ 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,)
+prediction for image 0: 8
+correct label for image 0: 6
+
- -
-

14.2.6. Choose cost function and optimizer#

+
+
+
+
+

14.2.6. Choose cost function and optimizer

To measure how well our neural network is doing we need to introduce a cost function.
We will call the function that gives the error of a single sample output the loss function, and the function that gives the total error of our network across all samples the cost function. @@ -951,9 +1206,9 @@ We define the cost function \(\mathca probability of the correct category \(c'\)
(i.e. the category \(c'\) such that \(y_{ic'} = 1\)). This means that the cross entropy loss only punishes you for how wrong you got the correct label. The probability of category \(c\) is given by the softmax function. The vector \(\hat{\theta}\) represents the parameters of our network, i.e. all the weights and biases.

- -
-

14.2.7. Optimizing the cost function#

+
+
+

14.2.7. Optimizing the cost function

The network is trained by finding the weights and biases that minimize the cost function. One of the most widely used classes of methods is gradient descent and its generalizations. The idea behind gradient descent is simply to adjust the weights in the direction where the gradient of the cost function is large and negative. This ensures we flow toward a local minimum of the cost function.
Each parameter \(\theta\) is iteratively adjusted according to the rule

@@ -972,14 +1227,14 @@ We denote each minibatch \(B_k\)

i.e. instead of averaging the loss over the entire dataset, we average over a minibatch.

This has two important benefits:

-
    +

    1. Introducing stochasticity decreases the chance that the algorithm becomes stuck in a local minima.

    2. It significantly speeds up the calculation, since we do not have to use the entire dataset to calculate the gradient.

    The various optmization methods, with codes and algorithms, are discussed in our lectures on Gradient descent approaches.

    - -
    -

    14.2.8. Regularization#

    +
+
+

14.2.8. Regularization

It is common to add an extra term to the cost function, proportional to the size of the weights. This is equivalent to constraining the size of the weights, so that they do not grow out of control. @@ -1000,9 +1255,9 @@ layer (\(+1\) for the bias), a every parameter. We use the backpropagation algorithm discussed above. This is a clever use of the chain rule that allows us to calculate the gradient efficently.

- -
-

14.2.9. Matrix multiplication#

+
+
+

14.2.9. Matrix multiplication

To more efficently train our network these equations are implemented using matrix operations.
The error in the output layer is calculated simply as, with \(\hat{t}\) being our targets,

@@ -1099,11 +1354,23 @@ the Hadamard product, meaning element-wise multiplication.

+
+
Old accuracy on training data: 0.1440501043841336
+
- - -
-

14.3. Improving performance#

+
/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_18986/953065564.py:4: RuntimeWarning: overflow encountered in exp
+  return 1/(1 + np.exp(-x))
+
+
+
New accuracy on training data: 0.09951287404314545
+
+
+
+
+ + +
+

14.3. Improving performance

As we can see the network does not seem to be learning at all. It seems to be just guessing the label for each image.
In order to obtain a network that does something useful, we will have to do a bit more work.

The choice of hyperparameters such as learning rate and regularization parameter is hugely influential for the performance of the network. Typically a grid-search is performed, wherein we test different hyperparameters separated by orders of magnitude. For example we could test the learning rates \(\eta = 10^{-6}, 10^{-5},...,10^{-1}\) with different regularization parameters \(\lambda = 10^{-6},...,10^{-0}\).

@@ -1218,9 +1485,9 @@ being realizations of this object with different hyperparameters. An implementat
- -
-

14.4. Evaluate model performance on test data#

+ +
+

14.4. Evaluate model performance on test data

To measure the performance of our network we evaluate how well it does it data it has never seen before, i.e. the test data.
We measure the performance of the network using the accuracy score.
The accuracy is as you would expect just the number of images correctly labeled divided by the total number of images. A perfect classifier will have an accuracy score of \(1\).

@@ -1248,10 +1515,15 @@ The accuracy is as you would expect just the number of images correctly labeled
+
+
Accuracy score on test set:  0.9444444444444444
+
-
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-

14.5. Adjust hyperparameters#

+ + + +
+

14.5. Adjust hyperparameters

We now perform a grid search to find the optimal hyperparameters for the network.
Note that we are only using 1 layer with 50 neurons, and human performance is estimated to be around \(98\%\) (\(2\%\) error rate).

@@ -1279,10 +1551,78 @@ Note that we are only using 1 layer with 50 neurons, and human performance is es
+
+
Learning rate  =  1e-05
+Lambda =  1e-05
+Accuracy score on test set:  0.11666666666666667
+
-
-
-

14.6. Visualization#

+
Learning rate  =  1e-05
+Lambda =  0.0001
+Accuracy score on test set:  0.20833333333333334
+
+
+
Learning rate  =  1e-05
+Lambda =  0.001
+Accuracy score on test set:  0.12222222222222222
+
+
+
Learning rate  =  1e-05
+Lambda =  0.01
+Accuracy score on test set:  0.14722222222222223
+
+
+
Learning rate  =  1e-05
+Lambda =  0.1
+Accuracy score on test set:  0.17777777777777778
+
+
+
Learning rate  =  1e-05
+Lambda =  1.0
+Accuracy score on test set:  0.16111111111111112
+
+
+
Learning rate  =  1e-05
+Lambda =  10.0
+Accuracy score on test set:  0.20277777777777778
+
+
+
Learning rate  =  0.0001
+Lambda =  1e-05
+Accuracy score on test set:  0.5305555555555556
+
+
+
---------------------------------------------------------------------------
+KeyboardInterrupt                         Traceback (most recent call last)
+Input In [8], in <cell line: 7>()
+      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)
+
+Input In [6], 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()
+
+Input In [6], 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: 
+
+
+ + + +
+

14.6. Visualization

# visual representation of grid search
@@ -1322,9 +1662,9 @@ Note that we are only using 1 layer with 50 neurons, and human performance is es
-
-
-

14.7. scikit-learn implementation#

+ +
+

14.7. scikit-learn implementation

scikit-learn focuses more on traditional machine learning methods, such as regression, clustering, decision trees, etc. As such, it has only two types of @@ -1357,9 +1697,9 @@ performance overall.

-
-
-

14.8. Visualization#

+ +
+

14.8. Visualization

# optional
@@ -1400,9 +1740,9 @@ performance overall.
-
-
-

14.9. Building neural networks in Tensorflow and Keras#

+ +
+

14.9. Building neural networks in Tensorflow and Keras

Now we want to build on the experience gained from our neural network implementation in NumPy and scikit-learn and use it to construct a neural network in Tensorflow. Once we have constructed a neural network in NumPy and Tensorflow, building one in Keras is really quite trivial, though the performance may suffer.

@@ -1613,9 +1953,9 @@ If you have Anaconda installed you may run the following command

-
-
-

14.10. The Breast Cancer Data, now with Keras#

+ +
+

14.10. The Breast Cancer Data, now with Keras

import tensorflow as tf
@@ -1787,9 +2127,9 @@ If you have Anaconda installed you may run the following command
-
-
-

14.11. Fine-tuning neural network hyperparameters#

+ +
+

14.11. Fine-tuning neural network hyperparameters

The flexibility of neural networks is also one of their main drawbacks: there are many hyperparameters to tweak. Not only can you use any imaginable network topology (how neurons/nodes are interconnected), @@ -1818,9 +2158,9 @@ as large image classification or speech recognition, typically require networks and they need a huge amount of training data. However, you will rarely have to train such networks from scratch: it is much more common to reuse parts of a pretrained state-of-the-art network that performs a similar task.

-
-
-

14.12. Which activation function should I use?#

+ +
+

14.12. Which activation function should I use?

The Back propagation algorithm we derived above works by going from the output layer to the input layer, propagating the error gradient on the way. Once the algorithm has computed the gradient of the cost @@ -1883,9 +2223,9 @@ that other activation functions behave much better in deep neural networks, in particular the ReLU activation function, mostly because it does not saturate for positive values (and also because it is quite fast to compute).

-
-
-

14.13. The RELU function family#

+ +
+

14.13. The RELU function family

The ReLU activation function suffers from a problem known as the dying ReLUs: during training, some neurons effectively die, meaning they stop outputting anything other than 0.

@@ -1920,9 +2260,9 @@ bootstrap to evaluate other activation functions.

  • For classification the softmax activation function is generally a good choice for classification tasks (when the classes are mutually exclusive).

  • For regression tasks, you can simply use no activation function at all.

    • -
    -
    -

    14.14. Batch Normalization#

    + +
    +

    14.14. Batch Normalization

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

    @@ -1933,27 +2273,27 @@ learn the optimal scale and mean of the inputs for each layer. In order to zero-center and normalize the inputs, the algorithm needs to estimate the inputs’ mean and standard deviation. It does so by evaluating the mean and standard deviation of the inputs over the current mini-batch, from this the name batch normalization.

    -
    -
    -

    14.15. Dropout#

    + +
    +

    14.15. Dropout

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

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

    -
    -
    -

    14.16. Gradient Clipping#

    + +
    +

    14.16. Gradient Clipping

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

    This technique is called Gradient Clipping.

    In general however, Batch Normalization is preferred.

    -
    -
    -

    14.17. A top-down perspective on Neural networks#

    + +
    +

    14.17. A top-down perspective on Neural networks

    The first thing we would like to do is divide the data into two or three parts. A training set, a validation or dev (development) set, and a test set. The test set is the data on which we want to make @@ -1985,9 +2325,9 @@ the test data. The difference between the performance of the algorithm on these two validation sets quantifies the train-test mismatch. This can serve as another important diagnostic when using DNNs for supervised learning.

    -
    -
    -

    14.18. Limitations of supervised learning with deep networks#

    + +
    +

    14.18. Limitations of supervised learning with deep networks

    Like all statistical methods, supervised learning using neural networks has important limitations. This is especially important when one seeks to apply these methods, especially to physics problems. Like @@ -2003,8 +2343,8 @@ features).

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

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

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    -By Morten Hjorth-Jensen -

    - -
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    - - © Copyright 2022. -
    - -

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

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    \ No newline at end of file diff --git a/doc/LectureNotes/_build/html/chapter11.html b/doc/LectureNotes/_build/html/chapter11.html index 357146930..99a6d7ba8 100644 --- a/doc/LectureNotes/_build/html/chapter11.html +++ b/doc/LectureNotes/_build/html/chapter11.html @@ -1,61 +1,50 @@ - - - - + - - + 15. Solving Differential Equations with Deep Learning — Applied Data Analysis and Machine Learning - - - - - - - - - + + + - - - - - + + + + + + + + - + - + - - - - - - + + + + - - - - + + - - - + + + + + + - - - + + + - - - - -
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    15.7. Solving the one dimensional Poisson equation

    The Poisson equation for \(g(x)\) in one dimension is

    @@ -1640,9 +1808,23 @@ g(x) = x(1 - x)\exp(x)
    +
    +
    Initial cost: 457.256
+
    -
    -

    15.7.1. Comparing with a numerical scheme#

    +
    /Users/mhjensen/miniforge3/envs/myenv/lib/python3.9/site-packages/numpy/core/fromnumeric.py:3245: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray.
+  return asarray(a).size
+
    +
    +
    Final cost: 0.00310113
+The max absolute difference between the solutions is: 0.000464088
+
    +
    +_images/chapter11_79_3.png +
    +
    +
    +

    15.7.1. Comparing with a numerical scheme

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

    Using Taylor series, the second derivative can be expressed as

    @@ -1659,7 +1841,7 @@ g''(x) = \frac{g(x + \Delta x) - 2g(x) + g(x-\Delta x)}{\Delta x^2} + E_{\Delta g''(x) \approx \frac{g(x + \Delta x) - 2g(x) + g(x-\Delta x)}{\Delta x^2} \end{equation} \]
    -

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

    +

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

    \[\begin{split} \begin{aligned} @@ -1916,11 +2098,27 @@ f(x_{N_x - 2})
    +
    +
    Initial cost: 457.256
+
    - - -
    -

    15.8. Partial Differential Equations#

    +
    /Users/mhjensen/miniforge3/envs/myenv/lib/python3.9/site-packages/numpy/core/fromnumeric.py:3245: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray.
+  return asarray(a).size
+
    +
    +
    Final cost: 0.00310113
+The max absolute difference between the analytical solution and DNN Autograd: 0.000464088
+The max absolute difference between the analytical solution and numerical scheme: 0.00266858
+
    +
    +_images/chapter11_91_3.png +_images/chapter11_91_4.png +
    + + + +
    +

    15.8. Partial Differential Equations

    A partial differential equation (PDE) has a solution here the function is defined by multiple variables. The equation may involve all kinds of combinations of which variables the function is differentiated with @@ -1935,8 +2133,8 @@ respect to.

    \end{equation} \]

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

    -
    -

    15.8.1. Type of problem#

    +
    +

    15.8.1. Type of problem

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

    For instance, the trial solution could be expressed as

    @@ -1949,13 +2147,13 @@ We must have a trial solution \(g_t\)

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

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

    -
    -
    -

    15.8.2. Network requirements#

    + +
    +

    15.8.2. Network requirements

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

    As for the ODE case, the cost function is the mean squared error that the network must try to minimize. The cost function for the network to @@ -1974,10 +2172,10 @@ C\left(\boldsymbol{x}, P\right) = f\left( \left( \boldsymbol{x}, \frac{\partial \[ C\left(X, P \right) = \sum_{i=1}^M f\left( \left( \boldsymbol{x}_i, \frac{\partial g(\boldsymbol{x}_i) }{\partial x_1}, \dots , \frac{\partial g(\boldsymbol{x}_i) }{\partial x_N}, \frac{\partial g(\boldsymbol{x}_i) }{\partial x_1\partial x_2}, \, \dots \, , \frac{\partial^n g(\boldsymbol{x}_i) }{\partial x_N^n} \right) \right)^2. \]

    -
    - -
    -

    15.9. Example: The diffusion equation#

    + + +
    +

    15.9. Example: The diffusion equation

    In one spatial dimension, the equation reads

    \[ @@ -2144,8 +2342,8 @@ mixed derivatives of \(g(x,t)\)
    -
    -

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

    +
    +

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

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

    The analytical solution of our problem is

    @@ -2385,11 +2583,212 @@ Using TensorFlow results in a much better execution time. Try it!

    +
    +
    /Users/mhjensen/miniforge3/envs/myenv/lib/python3.9/site-packages/numpy/core/fromnumeric.py:3245: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray.
+  return asarray(a).size
+
    -
    -
    -
    -

    15.10. Solving the wave equation with Neural Networks#

    +
    Initial cost:  41.05505310046363
+
    +
    +
    ---------------------------------------------------------------------------
+KeyboardInterrupt                         Traceback (most recent call last)
+Input In [9], in <cell line: 129>()
+    140 num_iter = 250
+    141 lmb = 0.01
+--> 143 P = solve_pde_deep_neural_network(x,t, num_hidden_neurons, num_iter, lmb)
+    145 ## Store the results
+    146 g_dnn_ag = np.zeros((Nx, Nt))
+
+Input In [9], in solve_pde_deep_neural_network(x, t, num_neurons, num_iter, lmb)
+    118 # Let the update be done num_iter times
+    119 for i in range(num_iter):
+--> 120     cost_grad =  cost_function_grad(P, x , t)
+    122     for l in range(N_hidden+1):
+    123         P[l] = P[l] - lmb * cost_grad[l]
+
+File ~/miniforge3/envs/myenv/lib/python3.9/site-packages/autograd/wrap_util.py:20, in unary_to_nary.<locals>.nary_operator.<locals>.nary_f(*args, **kwargs)
+     18 else:
+     19     x = tuple(args[i] for i in argnum)
+---> 20 return unary_operator(unary_f, x, *nary_op_args, **nary_op_kwargs)
+
+File ~/miniforge3/envs/myenv/lib/python3.9/site-packages/autograd/differential_operators.py:25, in grad(fun, x)
+     18 @unary_to_nary
+     19 def grad(fun, x):
+     20     """
+     21     Returns a function which computes the gradient of `fun` with respect to
+     22     positional argument number `argnum`. The returned function takes the same
+     23     arguments as `fun`, but returns the gradient instead. The function `fun`
+     24     should be scalar-valued. The gradient has the same type as the argument."""
+---> 25     vjp, ans = _make_vjp(fun, x)
+     26     if not vspace(ans).size == 1:
+     27         raise TypeError("Grad only applies to real scalar-output functions. "
+     28                         "Try jacobian, elementwise_grad or holomorphic_grad.")
+
+File ~/miniforge3/envs/myenv/lib/python3.9/site-packages/autograd/core.py:10, in make_vjp(fun, x)
+      8 def make_vjp(fun, x):
+      9     start_node = VJPNode.new_root()
+---> 10     end_value, end_node =  trace(start_node, fun, x)
+     11     if end_node is None:
+     12         def vjp(g): return vspace(x).zeros()
+
+File ~/miniforge3/envs/myenv/lib/python3.9/site-packages/autograd/tracer.py:10, in trace(start_node, fun, x)
+      8 with trace_stack.new_trace() as t:
+      9     start_box = new_box(x, t, start_node)
+---> 10     end_box = fun(start_box)
+     11     if isbox(end_box) and end_box._trace == start_box._trace:
+     12         return end_box._value, end_box._node
+
+File ~/miniforge3/envs/myenv/lib/python3.9/site-packages/autograd/wrap_util.py:15, in unary_to_nary.<locals>.nary_operator.<locals>.nary_f.<locals>.unary_f(x)
+     13 else:
+     14     subargs = subvals(args, zip(argnum, x))
+---> 15 return fun(*subargs, **kwargs)
+
+Input In [9], in cost_function(P, x, t)
+     78 g_t = g_trial(point,P)
+     79 g_t_jacobian = g_t_jacobian_func(point,P)
+---> 80 g_t_hessian = g_t_hessian_func(point,P)
+     82 g_t_dt = g_t_jacobian[1]
+     83 g_t_d2x = g_t_hessian[0][0]
+
+File ~/miniforge3/envs/myenv/lib/python3.9/site-packages/autograd/wrap_util.py:20, in unary_to_nary.<locals>.nary_operator.<locals>.nary_f(*args, **kwargs)
+     18 else:
+     19     x = tuple(args[i] for i in argnum)
+---> 20 return unary_operator(unary_f, x, *nary_op_args, **nary_op_kwargs)
+
+File ~/miniforge3/envs/myenv/lib/python3.9/site-packages/autograd/differential_operators.py:78, in hessian(fun, x)
+     75 @unary_to_nary
+     76 def hessian(fun, x):
+     77     "Returns a function that computes the exact Hessian."
+---> 78     return jacobian(jacobian(fun))(x)
+
+File ~/miniforge3/envs/myenv/lib/python3.9/site-packages/autograd/wrap_util.py:20, in unary_to_nary.<locals>.nary_operator.<locals>.nary_f(*args, **kwargs)
+     18 else:
+     19     x = tuple(args[i] for i in argnum)
+---> 20 return unary_operator(unary_f, x, *nary_op_args, **nary_op_kwargs)
+
+File ~/miniforge3/envs/myenv/lib/python3.9/site-packages/autograd/differential_operators.py:57, in jacobian(fun, x)
+     47 @unary_to_nary
+     48 def jacobian(fun, x):
+     49     """
+     50     Returns a function which computes the Jacobian of `fun` with respect to
+     51     positional argument number `argnum`, which must be a scalar or array. Unlike
+   (...)
+     55     (out1, out2, ...) then the Jacobian has shape (out1, out2, ..., in1, in2, ...).
+     56     """
+---> 57     vjp, ans = _make_vjp(fun, x)
+     58     ans_vspace = vspace(ans)
+     59     jacobian_shape = ans_vspace.shape + vspace(x).shape
+
+File ~/miniforge3/envs/myenv/lib/python3.9/site-packages/autograd/core.py:10, in make_vjp(fun, x)
+      8 def make_vjp(fun, x):
+      9     start_node = VJPNode.new_root()
+---> 10     end_value, end_node =  trace(start_node, fun, x)
+     11     if end_node is None:
+     12         def vjp(g): return vspace(x).zeros()
+
+File ~/miniforge3/envs/myenv/lib/python3.9/site-packages/autograd/tracer.py:10, in trace(start_node, fun, x)
+      8 with trace_stack.new_trace() as t:
+      9     start_box = new_box(x, t, start_node)
+---> 10     end_box = fun(start_box)
+     11     if isbox(end_box) and end_box._trace == start_box._trace:
+     12         return end_box._value, end_box._node
+
+File ~/miniforge3/envs/myenv/lib/python3.9/site-packages/autograd/wrap_util.py:15, in unary_to_nary.<locals>.nary_operator.<locals>.nary_f.<locals>.unary_f(x)
+     13 else:
+     14     subargs = subvals(args, zip(argnum, x))
+---> 15 return fun(*subargs, **kwargs)
+
+File ~/miniforge3/envs/myenv/lib/python3.9/site-packages/autograd/wrap_util.py:20, in unary_to_nary.<locals>.nary_operator.<locals>.nary_f(*args, **kwargs)
+     18 else:
+     19     x = tuple(args[i] for i in argnum)
+---> 20 return unary_operator(unary_f, x, *nary_op_args, **nary_op_kwargs)
+
+File ~/miniforge3/envs/myenv/lib/python3.9/site-packages/autograd/differential_operators.py:61, in jacobian(fun, x)
+     59 jacobian_shape = ans_vspace.shape + vspace(x).shape
+     60 grads = map(vjp, ans_vspace.standard_basis())
+---> 61 return np.reshape(np.stack(grads), jacobian_shape)
+
+File ~/miniforge3/envs/myenv/lib/python3.9/site-packages/autograd/numpy/numpy_wrapper.py:88, in stack(arrays, axis)
+     83 def stack(arrays, axis=0):
+     84     # this code is basically copied from numpy/core/shape_base.py's stack
+     85     # we need it here because we want to re-implement stack in terms of the
+     86     # primitives defined in this file
+---> 88     arrays = [array(arr) for arr in arrays]
+     89     if not arrays:
+     90         raise ValueError('need at least one array to stack')
+
+File ~/miniforge3/envs/myenv/lib/python3.9/site-packages/autograd/numpy/numpy_wrapper.py:88, in <listcomp>(.0)
+     83 def stack(arrays, axis=0):
+     84     # this code is basically copied from numpy/core/shape_base.py's stack
+     85     # we need it here because we want to re-implement stack in terms of the
+     86     # primitives defined in this file
+---> 88     arrays = [array(arr) for arr in arrays]
+     89     if not arrays:
+     90         raise ValueError('need at least one array to stack')
+
+File ~/miniforge3/envs/myenv/lib/python3.9/site-packages/autograd/core.py:14, in make_vjp.<locals>.vjp(g)
+---> 14 def vjp(g): return backward_pass(g, end_node)
+
+File ~/miniforge3/envs/myenv/lib/python3.9/site-packages/autograd/core.py:21, in backward_pass(g, end_node)
+     19 for node in toposort(end_node):
+     20     outgrad = outgrads.pop(node)
+---> 21     ingrads = node.vjp(outgrad[0])
+     22     for parent, ingrad in zip(node.parents, ingrads):
+     23         outgrads[parent] = add_outgrads(outgrads.get(parent), ingrad)
+
+File ~/miniforge3/envs/myenv/lib/python3.9/site-packages/autograd/core.py:67, in defvjp.<locals>.vjp_argnums.<locals>.<lambda>(g)
+     64         raise NotImplementedError(
+     65             "VJP of {} wrt argnum 0 not defined".format(fun.__name__))
+     66     vjp = vjpfun(ans, *args, **kwargs)
+---> 67     return lambda g: (vjp(g),)
+     68 elif L == 2:
+     69     argnum_0, argnum_1 = argnums
+
+File ~/miniforge3/envs/myenv/lib/python3.9/site-packages/autograd/numpy/numpy_vjps.py:701, in <lambda>(g)
+    699         return A
+    700     return SparseObject(vs, mut_add)
+--> 701 defvjp(func(ArrayBox.__getitem__), lambda ans, A, idx: lambda g: untake(g, idx, vspace(A)))
+    702 defvjp(untake, lambda ans, x, idx, _: lambda g: g[idx])
+    704 def _unpad(array, width):
+
+File ~/miniforge3/envs/myenv/lib/python3.9/site-packages/autograd/tracer.py:44, in primitive.<locals>.f_wrapped(*args, **kwargs)
+     42 parents = tuple(box._node for _     , box in boxed_args)
+     43 argnums = tuple(argnum    for argnum, _   in boxed_args)
+---> 44 ans = f_wrapped(*argvals, **kwargs)
+     45 node = node_constructor(ans, f_wrapped, argvals, kwargs, argnums, parents)
+     46 return new_box(ans, trace, node)
+
+File ~/miniforge3/envs/myenv/lib/python3.9/site-packages/autograd/tracer.py:45, in primitive.<locals>.f_wrapped(*args, **kwargs)
+     43     argnums = tuple(argnum    for argnum, _   in boxed_args)
+     44     ans = f_wrapped(*argvals, **kwargs)
+---> 45     node = node_constructor(ans, f_wrapped, argvals, kwargs, argnums, parents)
+     46     return new_box(ans, trace, node)
+     47 else:
+
+File ~/miniforge3/envs/myenv/lib/python3.9/site-packages/autograd/core.py:36, in VJPNode.__init__(self, value, fun, args, kwargs, parent_argnums, parents)
+     33     fun_name = getattr(fun, '__name__', fun)
+     34     raise NotImplementedError("VJP of {} wrt argnums {} not defined"
+     35                               .format(fun_name, parent_argnums))
+---> 36 self.vjp = vjpmaker(parent_argnums, value, args, kwargs)
+
+File ~/miniforge3/envs/myenv/lib/python3.9/site-packages/autograd/core.py:56, in defvjp.<locals>.vjp_argnums(argnums, ans, args, kwargs)
+     53 argnums = kwargs.get('argnums', count())
+     54 vjps_dict = {argnum : translate_vjp(vjpmaker, fun, argnum)
+     55              for argnum, vjpmaker in zip(argnums, vjpmakers)}
+---> 56 def vjp_argnums(argnums, ans, args, kwargs):
+     57     L = len(argnums)
+     58     # These first two cases are just optimizations
+
+KeyboardInterrupt: 
+
    +
    + + + + +
    +

    15.10. Solving the wave equation with Neural Networks

    The wave equation is

    \[ @@ -2431,7 +2830,7 @@ g(x,0) &= u(x), &x\in[0,1] \\ \end{split}\]

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

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

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

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

    \[ @@ -2674,17 +3073,17 @@ g(x,t) = \sin(\pi x)\cos(\pi t) - \sin(\pi x)\sin(\pi t)
    -
    -
    -

    15.11. Resources on differential equations and deep learning#

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

    -By Morten Hjorth-Jensen -

    - -
    - -
    - -

    - - © Copyright 2022. -
    - -

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

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

    +
    + + + + + + + -
    -
    \ No newline at end of file diff --git a/doc/LectureNotes/_build/html/chapter12.html b/doc/LectureNotes/_build/html/chapter12.html index a4609262f..623a6403e 100644 --- a/doc/LectureNotes/_build/html/chapter12.html +++ b/doc/LectureNotes/_build/html/chapter12.html @@ -1,61 +1,50 @@ - - - - + - - + 16. Convolutional Neural Networks — Applied Data Analysis and Machine Learning - - - - - - - - - + + + - - - - - + + + + + + + + - + - + - - - - - - + + + + - - - - + + - - - + + + + + + - - - + + + - - - - -
    + - - - - - - - - - - -
    -
    -
    -
    - - - Ctrl+K -
    -
    - - - -
    -
    +
    +
    + + +
    +

    16.6. Further Dimensionality Remarks

    In today’s architecture one can train such neural networks, however this is a huge number of parameters for the task at hand. In general, it is a very wasteful and inefficient use of dense matrices as @@ -980,9 +1069,9 @@ other than those that are very far away. Similarly, sounds are repeated in multiple times in the signal. While slightly simplistic, reasoning about such a sound example demonstrates this. The same principles then apply to images and other similar data.

    - -
    -

    16.7. CNNs in more detail, building convolutional neural networks in Tensorflow and Keras#

    +
    +
    +

    16.7. CNNs in more detail, building convolutional neural networks in Tensorflow and Keras

    As discussed above, CNNs are neural networks built from the assumption that the inputs to the network are 2D images. This is important because the number of features or pixels in images grows very fast with the image size, and an enormous number of weights and biases are needed in order to build an accurate network.

    @@ -996,8 +1085,8 @@ dataset of images, we require a 4D matrix or tensor. This tenso \[ (n_{inputs},\, n_{pixels, width},\, n_{pixels, height},\, depth) . \]
    -
    -

    16.7.1. The MNIST dataset again#

    +
    +

    16.7.1. The MNIST dataset again

    The MNIST dataset consists of grayscale images with a pixel size of \(28\times 28\), meaning we require \(28 \times 28 = 724\) weights to each neuron in the first hidden layer.

    @@ -1029,9 +1118,9 @@ further passed through a pooling layer, which reduces the size convolutional layer, e.g. by taking the maximum or average across some small regions, and this serves as input to the next convolutional layer.

    -
    -
    -

    16.7.2. Systematic reduction#

    +
    +
    +

    16.7.2. Systematic reduction

    By systematically reducing the size of the input volume, through convolution and pooling, the network should create representations of small parts of the input, and then from them assemble representations @@ -1040,9 +1129,9 @@ input to a hidden layer, such that each neuron in the final pooling layer is connected to every single neuron in the hidden layer. This then serves as input to the output layer, e.g. a softmax output for classification.

    - -
    -

    16.7.3. Prerequisites: Collect and pre-process data#

    +
    +
    +

    16.7.3. Prerequisites: Collect and pre-process data

    # import necessary packages
@@ -1088,6 +1177,13 @@ classification.
    +
    +
    inputs = (n_inputs, pixel_width, pixel_height, depth) = (1797, 8, 8, 1)
+labels = (n_inputs) = (1797,)
+
    +
    +_images/chapter12_67_1.png +
    @@ -1170,6 +1266,118 @@ classification.

    +
    +
    Metal device set to: Apple M1
+
    +
    +
    /Users/mhjensen/miniforge3/envs/myenv/lib/python3.9/site-packages/keras/optimizer_v2/gradient_descent.py:102: UserWarning: The `lr` argument is deprecated, use `learning_rate` instead.
+  super(SGD, self).__init__(name, **kwargs)
+2023-10-02 06:54:12.770204: W tensorflow/core/platform/profile_utils/cpu_utils.cc:128] Failed to get CPU frequency: 0 Hz
+
    +
    +
    ---------------------------------------------------------------------------
+KeyboardInterrupt                         Traceback (most recent call last)
+Input In [6], in <cell line: 3>()
+      4 for j, lmbd in enumerate(lmbd_vals):
+      5     CNN = create_convolutional_neural_network_keras(input_shape, receptive_field,
+      6                                           n_filters, n_neurons_connected, n_categories,
+      7                                           eta, lmbd)
+----> 8     CNN.fit(X_train, Y_train, epochs=epochs, batch_size=batch_size, verbose=0)
+      9     scores = CNN.evaluate(X_test, Y_test)
+     11     CNN_keras[i][j] = CNN
+
+File ~/miniforge3/envs/myenv/lib/python3.9/site-packages/keras/utils/traceback_utils.py:64, in filter_traceback.<locals>.error_handler(*args, **kwargs)
+     62 filtered_tb = None
+     63 try:
+---> 64   return fn(*args, **kwargs)
+     65 except Exception as e:  # pylint: disable=broad-except
+     66   filtered_tb = _process_traceback_frames(e.__traceback__)
+
+File ~/miniforge3/envs/myenv/lib/python3.9/site-packages/keras/engine/training.py:1384, in Model.fit(self, x, y, batch_size, epochs, verbose, callbacks, validation_split, validation_data, shuffle, class_weight, sample_weight, initial_epoch, steps_per_epoch, validation_steps, validation_batch_size, validation_freq, max_queue_size, workers, use_multiprocessing)
+   1377 with tf.profiler.experimental.Trace(
+   1378     'train',
+   1379     epoch_num=epoch,
+   1380     step_num=step,
+   1381     batch_size=batch_size,
+   1382     _r=1):
+   1383   callbacks.on_train_batch_begin(step)
+-> 1384   tmp_logs = self.train_function(iterator)
+   1385   if data_handler.should_sync:
+   1386     context.async_wait()
+
+File ~/miniforge3/envs/myenv/lib/python3.9/site-packages/tensorflow/python/util/traceback_utils.py:150, in filter_traceback.<locals>.error_handler(*args, **kwargs)
+    148 filtered_tb = None
+    149 try:
+--> 150   return fn(*args, **kwargs)
+    151 except Exception as e:
+    152   filtered_tb = _process_traceback_frames(e.__traceback__)
+
+File ~/miniforge3/envs/myenv/lib/python3.9/site-packages/tensorflow/python/eager/def_function.py:915, in Function.__call__(self, *args, **kwds)
+    912 compiler = "xla" if self._jit_compile else "nonXla"
+    914 with OptionalXlaContext(self._jit_compile):
+--> 915   result = self._call(*args, **kwds)
+    917 new_tracing_count = self.experimental_get_tracing_count()
+    918 without_tracing = (tracing_count == new_tracing_count)
+
+File ~/miniforge3/envs/myenv/lib/python3.9/site-packages/tensorflow/python/eager/def_function.py:947, in Function._call(self, *args, **kwds)
+    944   self._lock.release()
+    945   # In this case we have created variables on the first call, so we run the
+    946   # defunned version which is guaranteed to never create variables.
+--> 947   return self._stateless_fn(*args, **kwds)  # pylint: disable=not-callable
+    948 elif self._stateful_fn is not None:
+    949   # Release the lock early so that multiple threads can perform the call
+    950   # in parallel.
+    951   self._lock.release()
+
+File ~/miniforge3/envs/myenv/lib/python3.9/site-packages/tensorflow/python/eager/function.py:2956, in Function.__call__(self, *args, **kwargs)
+   2953 with self._lock:
+   2954   (graph_function,
+   2955    filtered_flat_args) = self._maybe_define_function(args, kwargs)
+-> 2956 return graph_function._call_flat(
+   2957     filtered_flat_args, captured_inputs=graph_function.captured_inputs)
+
+File ~/miniforge3/envs/myenv/lib/python3.9/site-packages/tensorflow/python/eager/function.py:1853, in ConcreteFunction._call_flat(self, args, captured_inputs, cancellation_manager)
+   1849 possible_gradient_type = gradients_util.PossibleTapeGradientTypes(args)
+   1850 if (possible_gradient_type == gradients_util.POSSIBLE_GRADIENT_TYPES_NONE
+   1851     and executing_eagerly):
+   1852   # No tape is watching; skip to running the function.
+-> 1853   return self._build_call_outputs(self._inference_function.call(
+   1854       ctx, args, cancellation_manager=cancellation_manager))
+   1855 forward_backward = self._select_forward_and_backward_functions(
+   1856     args,
+   1857     possible_gradient_type,
+   1858     executing_eagerly)
+   1859 forward_function, args_with_tangents = forward_backward.forward()
+
+File ~/miniforge3/envs/myenv/lib/python3.9/site-packages/tensorflow/python/eager/function.py:499, in _EagerDefinedFunction.call(self, ctx, args, cancellation_manager)
+    497 with _InterpolateFunctionError(self):
+    498   if cancellation_manager is None:
+--> 499     outputs = execute.execute(
+    500         str(self.signature.name),
+    501         num_outputs=self._num_outputs,
+    502         inputs=args,
+    503         attrs=attrs,
+    504         ctx=ctx)
+    505   else:
+    506     outputs = execute.execute_with_cancellation(
+    507         str(self.signature.name),
+    508         num_outputs=self._num_outputs,
+   (...)
+    511         ctx=ctx,
+    512         cancellation_manager=cancellation_manager)
+
+File ~/miniforge3/envs/myenv/lib/python3.9/site-packages/tensorflow/python/eager/execute.py:54, in quick_execute(op_name, num_outputs, inputs, attrs, ctx, name)
+     52 try:
+     53   ctx.ensure_initialized()
+---> 54   tensors = pywrap_tfe.TFE_Py_Execute(ctx._handle, device_name, op_name,
+     55                                       inputs, attrs, num_outputs)
+     56 except core._NotOkStatusException as e:
+     57   if name is not None:
+
+KeyboardInterrupt: 
+
    +
    +
    @@ -1207,10 +1415,10 @@ classification.

    - - -
    -

    16.8. The CIFAR01 data set#

    +
    + +
    +

    16.8. The CIFAR01 data set

    The CIFAR10 dataset contains 60,000 color images in 10 classes, with 6,000 images in each class. The dataset is divided into 50,000 training images and 10,000 testing images. The classes are mutually @@ -1319,8 +1527,8 @@ history = model.fit(train_images, train_labels, epochs=10,

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

    -By Morten Hjorth-Jensen -

    - -
    - -
    - -

    - - © Copyright 2022. -
    - -

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

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

    +
    + + + + + + + -
    -
    \ No newline at end of file diff --git a/doc/LectureNotes/_build/html/chapter13.html b/doc/LectureNotes/_build/html/chapter13.html index 2117cc6f2..a9f62202b 100644 --- a/doc/LectureNotes/_build/html/chapter13.html +++ b/doc/LectureNotes/_build/html/chapter13.html @@ -1,61 +1,50 @@ - - - - + - - + 17. Recurrent neural networks: Overarching view — Applied Data Analysis and Machine Learning - - - - - - - - - + + + - - - - - + + + + + + + + - + - + - - - - - - + + + + - - - - + + - - - + + + + + + - - - + + + - - - - -
    + - - - - - - - - - - -
    -
    -
    -
    - - - Ctrl+K -
    -
    - - - -
    -
    +
    +
    + + - +
    - - - - - - - -
    - - -
    - -
    -
    - Contents -
    -
    - -
    - - -
    -
    - -
    - -
    - -

    -By Morten Hjorth-Jensen -

    - -
    - -
    - -

    - - © Copyright 2022. -
    - -

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

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

    +
    + + + + + + + -
    -
    \ No newline at end of file diff --git a/doc/LectureNotes/_build/html/chapter2.html b/doc/LectureNotes/_build/html/chapter2.html index 637e3f3cb..67ba10971 100644 --- a/doc/LectureNotes/_build/html/chapter2.html +++ b/doc/LectureNotes/_build/html/chapter2.html @@ -1,61 +1,50 @@ - - - - + - - + 4. Ridge and Lasso Regression — Applied Data Analysis and Machine Learning - - - - - - - - - + + + - - - - - + + + + + + + + - + - + - - - - - - + + + + - - - - + + - - - + + + + + + - - - + + + - - - - -
    + - - - - - - - - - - -
    -
    -
    -
    - - - Ctrl+K -
    -
    - - - -
    -
    +
    +
    + + +
    +

    4.13. Bayes’ Theorem and Ridge and Lasso Regression

    Hitherto we have discussed Ridge and Lasso regression in terms of a linear analysis. This may to many of you feel rather technical and perhaps not that intuitive. The question is whether we can develop a @@ -2341,6 +2761,14 @@ order to another one.

    +
    +
    [ 1.0169643   0.27924636 -1.4087793   1.03308408  0.        ]
+Test MSE OLS
+0.958228616652075
+
    +
    +_images/chapter2_322_1.png +

    How can we understand this?

    Let us write out the values of the coefficients \(\beta_i\) as functions @@ -2402,6 +2830,228 @@ large variance (normally for higher orders in the polynomial).

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

    As an exercise, repeat these calculations with ordinary least squares only with and without noise. Calculate thereafter the variance of the @@ -2410,9 +3060,9 @@ noise. Here we recommend to use \(\si added noise (which follows a normal distribution with mean value zero). Comment your results. If you have a large noise term, do the parameters \(\beta_j\) vary more as function of model complexity? And what about their variance?

    - -
    -

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

    + +
    +

    4.14. Linking Bayes’ Theorem with Ridge and Lasso Regression

    We have seen that Ridge regression suppresses those features which have a small singular value. This corresponds to a feature which exhibits a large variance in the parameters \(\beta_j\). @@ -2498,8 +3148,8 @@ C(\boldsymbol{\beta})=\frac{\vert\vert (\boldsymbol{y}-\boldsymbol{X}\boldsymbol \(\sigma^2=1/(2\lambda)\). Thus, increasing the variance means decreasing \(\lambda\) and shrinking the variance means increasing \(\lambda\). When we increase \(\lambda\), this corresponds to shrinking the role of less important features (small singular values).

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

    -By Morten Hjorth-Jensen -

    - -
    - -
    - -

    - - © Copyright 2022. -
    - -

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

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

    +
    + + + + + + + -
    -
    \ No newline at end of file diff --git a/doc/LectureNotes/_build/html/chapter3.html b/doc/LectureNotes/_build/html/chapter3.html index 9a6d63e7f..1baea9f8b 100644 --- a/doc/LectureNotes/_build/html/chapter3.html +++ b/doc/LectureNotes/_build/html/chapter3.html @@ -1,61 +1,50 @@ - - - - + - - + 5. Resampling Methods — Applied Data Analysis and Machine Learning - - - - - - - - - + + + - - - - - + + + + + + + + - + - + - - - - - - + + + + - - - - + + - - - + + + + + + - - - + + + - - - - -
    + - - - - - - - - - - -
    -
    -
    -
    - - - Ctrl+K -
    -
    - - - -
    -
    +
    +
    + +

    The bias-variance tradeoff summarizes the fundamental tension in machine learning, particularly supervised learning, between the @@ -1243,6 +1442,30 @@ flexible statistical methods have higher variance.

    +
    +
    ============================
+Underfitting vs. Overfitting
+============================
+
+This example demonstrates the problems of underfitting and overfitting and
+how we can use linear regression with polynomial features to approximate
+nonlinear functions. The plot shows the function that we want to approximate,
+which is a part of the cosine function. In addition, the samples from the
+real function and the approximations of different models are displayed. The
+models have polynomial features of different degrees. We can see that a
+linear function (polynomial with degree 1) is not sufficient to fit the
+training samples. This is called **underfitting**. A polynomial of degree 4
+approximates the true function almost perfectly. However, for higher degrees
+the model will **overfit** the training data, i.e. it learns the noise of the
+training data.
+We evaluate quantitatively **overfitting** / **underfitting** by using
+cross-validation. We calculate the mean squared error (MSE) on the validation
+set, the higher, the less likely the model generalizes correctly from the
+training data.
+
    +
    +_images/chapter3_68_1.png +
    @@ -1327,10 +1550,116 @@ flexible statistical methods have higher variance.

    +
    +
    Degree of polynomial:   1
+Mean squared error on training data: 439230.69504801
+Mean squared error on test data: 481979.17861098
+Degree of polynomial:   2
+Mean squared error on training data: 115822.95008046
+Mean squared error on test data: 123711.53703498
+Degree of polynomial:   3
+Mean squared error on training data: 9011.85263220
+Mean squared error on test data: 10913.84780262
+Degree of polynomial:   4
+Mean squared error on training data: 303.47610036
+Mean squared error on test data: 426.30787294
+Degree of polynomial:   5
+Mean squared error on training data: 3.80354994
+Mean squared error on test data: 5.98822371
+Degree of polynomial:   6
+Mean squared error on training data: 3.66204648
+Mean squared error on test data: 8.14812206
+
    - -
    -

    5.5. Cross-validation#

    +
    Degree of polynomial:   7
+Mean squared error on training data: 0.47075725
+Mean squared error on test data: 2.00607783
+Degree of polynomial:   8
+Mean squared error on training data: 0.04912436
+Mean squared error on test data: 0.21596432
+Degree of polynomial:   9
+Mean squared error on training data: 0.02522069
+Mean squared error on test data: 0.08576932
+Degree of polynomial:  10
+Mean squared error on training data: 0.02511518
+Mean squared error on test data: 1.20015436
+Degree of polynomial:  11
+Mean squared error on training data: 0.01640891
+Mean squared error on test data: 1.35533773
+Degree of polynomial:  12
+Mean squared error on training data: 0.00813803
+Mean squared error on test data: 0.17446471
+
    +
    +
    Degree of polynomial:  13
+Mean squared error on training data: 0.00759119
+Mean squared error on test data: 1.08131003
+Degree of polynomial:  14
+Mean squared error on training data: 0.00472199
+Mean squared error on test data: 0.81333804
+Degree of polynomial:  15
+Mean squared error on training data: 0.00410478
+Mean squared error on test data: 92.09172409
+Degree of polynomial:  16
+Mean squared error on training data: 0.00315593
+Mean squared error on test data: 234.38533185
+Degree of polynomial:  17
+Mean squared error on training data: 0.00242999
+Mean squared error on test data: 1271.35771826
+Degree of polynomial:  18
+Mean squared error on training data: 0.00228742
+Mean squared error on test data: 108.27092910
+
    +
    +
    Degree of polynomial:  19
+Mean squared error on training data: 0.00156376
+Mean squared error on test data: 1371.99051150
+Degree of polynomial:  20
+Mean squared error on training data: 0.00137818
+Mean squared error on test data: 1887.86252988
+Degree of polynomial:  21
+Mean squared error on training data: 0.00118508
+Mean squared error on test data: 14859.69908626
+Degree of polynomial:  22
+Mean squared error on training data: 0.00092647
+Mean squared error on test data: 876.51191552
+Degree of polynomial:  23
+Mean squared error on training data: 0.00085889
+Mean squared error on test data: 5594.60815105
+Degree of polynomial:  24
+Mean squared error on training data: 0.00084705
+Mean squared error on test data: 1277.61702282
+
    +
    +
    Degree of polynomial:  25
+Mean squared error on training data: 0.00079129
+Mean squared error on test data: 128664.31650694
+Degree of polynomial:  26
+Mean squared error on training data: 0.00076905
+Mean squared error on test data: 19003.94822514
+Degree of polynomial:  27
+Mean squared error on training data: 0.00068946
+Mean squared error on test data: 2379.66219404
+Degree of polynomial:  28
+Mean squared error on training data: 0.00062595
+Mean squared error on test data: 4082.19983530
+Degree of polynomial:  29
+Mean squared error on training data: 0.00060705
+Mean squared error on test data: 3250.17647619
+
    +
    +
    /var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_19176/626635268.py:73: RuntimeWarning: divide by zero encountered in log10
+  plt.plot(polynomial, np.log10(trainingerror), label='Training Error')
+/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_19176/626635268.py:74: RuntimeWarning: divide by zero encountered in log10
+  plt.plot(polynomial, np.log10(testerror), label='Test Error')
+
    +
    +_images/chapter3_69_6.png +
    + + +
    +

    5.5. Cross-validation

    When the repetitive splitting of the data set is done randomly, samples may accidently end up in a fast majority of the splits in either training or test set. Such samples may have an unbalanced @@ -1370,7 +1699,7 @@ cross-validation (LOOCV).

    \end{align*} \]

    For the various values of \(k\)

    -
      +

      1. shuffle the dataset randomly.

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

      3. For each unique group:

        4. @@ -1379,7 +1708,7 @@ cross-validation (LOOCV).

        b. Take the remaining groups as a training data set

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

        d. Retain the evaluation score and discard the model

        -
          +

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

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

          @@ -1477,6 +1806,9 @@ cross-validation (LOOCV).

          +
          +_images/chapter3_75_0.png +

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

          @@ -1551,11 +1883,18 @@ cross-validation (LOOCV).

          +
          +
          /var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_19176/3817475779.py:63: RuntimeWarning: divide by zero encountered in log10
+  plt.plot(polynomial, np.log10(estimated_mse_sklearn), label='Test Error')
+
          +
          +_images/chapter3_77_1.png +

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

          - -
          -

          5.6. More on Rescaling data#

          + +
          +

          5.6. More on Rescaling data

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

          When you are comparing your own code with for example Scikit-Learn’s library, there are some technicalities to keep in mind. The examples @@ -1616,6 +1955,11 @@ on your eventual new data set before making a prediction. If we translate this

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

          Let us try to understand what this may imply mathematically when we subtract the mean values, also known as zero centering. For @@ -1799,6 +2143,26 @@ Note also that we do not split the data into training and test.

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

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

          @@ -1901,6 +2265,99 @@ intercept.

          +
          +
          Beta values for own Ridge implementation
+[ 1.03032441e+00  6.28336218e-02 -6.24175744e-01  5.21169159e-02
+  2.80847477e-01  2.12552073e-01  8.13220608e-02 -1.69634577e-02
+ -6.50846111e-02 -7.38962192e-02 -5.94226022e-02 -3.50227564e-02
+ -9.80609616e-03  1.08299273e-02  2.41882037e-02  2.93492130e-02
+  2.64742912e-02  1.63249532e-02 -5.01831050e-05 -2.15098090e-02]
+Beta values for Scikit-Learn Ridge implementation
+[ 1.03032441e+00  6.28336218e-02 -6.24175744e-01  5.21169159e-02
+  2.80847477e-01  2.12552073e-01  8.13220608e-02 -1.69634577e-02
+ -6.50846112e-02 -7.38962192e-02 -5.94226022e-02 -3.50227564e-02
+ -9.80609615e-03  1.08299273e-02  2.41882037e-02  2.93492130e-02
+  2.64742912e-02  1.63249532e-02 -5.01831207e-05 -2.15098090e-02]
+MSE values for own Ridge implementation
+4.3632959111950474e-07
+MSE values for Scikit-Learn Ridge implementation
+4.363295916323784e-07
+Beta values for own Ridge implementation
+[ 1.03630548 -0.01963611 -0.37900111 -0.07062318  0.12182967  0.16343471
+  0.13003291  0.07490892  0.02365049 -0.01449782 -0.03814292 -0.04909093
+ -0.05009826 -0.04389027 -0.03279636 -0.01866537 -0.00289724  0.01348565
+  0.02976145  0.04543942]
+Beta values for Scikit-Learn Ridge implementation
+[ 1.03630548 -0.01963611 -0.37900111 -0.07062318  0.12182967  0.16343471
+  0.13003291  0.07490892  0.02365049 -0.01449782 -0.03814292 -0.04909093
+ -0.05009826 -0.04389027 -0.03279636 -0.01866537 -0.00289724  0.01348565
+  0.02976145  0.04543942]
+MSE values for own Ridge implementation
+5.194042826649355e-06
+MSE values for Scikit-Learn Ridge implementation
+5.1940428268204826e-06
+Beta values for own Ridge implementation
+[ 1.04220758 -0.10931453 -0.17641709 -0.06020587  0.02208512  0.05789007
+  0.06491736  0.05785343  0.04537385  0.03196357  0.01969145  0.00934499
+  0.00107405 -0.00526348 -0.00992331 -0.01318643 -0.01531845 -0.01655318
+ -0.01708852 -0.01708781]
+Beta values for Scikit-Learn Ridge implementation
+[ 1.04220758 -0.10931453 -0.17641709 -0.06020587  0.02208512  0.05789007
+  0.06491736  0.05785343  0.04537385  0.03196357  0.01969145  0.00934499
+  0.00107405 -0.00526348 -0.00992331 -0.01318643 -0.01531845 -0.01655318
+ -0.01708852 -0.01708781]
+MSE values for own Ridge implementation
+2.0940821989652176e-05
+MSE values for Scikit-Learn Ridge implementation
+2.094082198961999e-05
+Beta values for own Ridge implementation
+[ 1.01219292 -0.06043581 -0.10391807 -0.05651951 -0.01898855  0.00312361
+  0.01463049  0.01975848  0.02123176  0.02068067  0.01905883  0.01691985
+  0.01458337  0.01223198  0.00996754  0.00784393  0.00588657  0.00410387
+  0.00249435  0.00105081]
+Beta values for Scikit-Learn Ridge implementation
+[ 1.01219292 -0.06043581 -0.10391807 -0.05651951 -0.01898855  0.00312361
+  0.01463049  0.01975848  0.02123176  0.02068067  0.01905883  0.01691985
+  0.01458337  0.01223198  0.00996754  0.00784393  0.00588657  0.00410387
+  0.00249435  0.00105081]
+MSE values for own Ridge implementation
+0.00031535148309577417
+MSE values for Scikit-Learn Ridge implementation
+0.00031535148309580783
+Beta values for own Ridge implementation
+[ 8.38916861e-01  1.31276579e-01  8.97497404e-03 -1.72271878e-02
+ -2.11744554e-02 -1.91492986e-02 -1.57201944e-02 -1.23002365e-02
+ -9.30466214e-03 -6.81048318e-03 -4.78184120e-03 -3.15130074e-03
+ -1.84923989e-03 -8.13661243e-04  7.46984697e-06  6.56636616e-04
+  1.16805821e-03  1.56912044e-03  1.88168312e-03  2.12318726e-03]
+Beta values for Scikit-Learn Ridge implementation
+[ 8.38916861e-01  1.31276579e-01  8.97497404e-03 -1.72271878e-02
+ -2.11744554e-02 -1.91492986e-02 -1.57201944e-02 -1.23002365e-02
+ -9.30466214e-03 -6.81048318e-03 -4.78184120e-03 -3.15130074e-03
+ -1.84923989e-03 -8.13661243e-04  7.46984697e-06  6.56636616e-04
+  1.16805821e-03  1.56912044e-03  1.88168312e-03  2.12318726e-03]
+MSE values for own Ridge implementation
+0.01507238889517717
+MSE values for Scikit-Learn Ridge implementation
+0.0150723888951771
+Beta values for own Ridge implementation
+[0.37396662 0.14174745 0.0764924  0.04892055 0.03447512 0.02586427
+ 0.02024962 0.01633913 0.01347916 0.0113104  0.0096208  0.00827728
+ 0.00719176 0.00630331 0.00556826 0.0049544  0.00443743 0.0039987
+ 0.0036237  0.003301  ]
+Beta values for Scikit-Learn Ridge implementation
+[0.37396662 0.14174745 0.0764924  0.04892055 0.03447512 0.02586427
+ 0.02024962 0.01633913 0.01347916 0.0113104  0.0096208  0.00827728
+ 0.00719176 0.00630331 0.00556826 0.0049544  0.00443743 0.0039987
+ 0.0036237  0.003301  ]
+MSE values for own Ridge implementation
+0.2640931530791004
+MSE values for Scikit-Learn Ridge implementation
+0.26409315307910025
+
          +
          +_images/chapter3_120_1.png +

          The results here agree when we force Scikit-Learn’s Ridge function to include the first column in our design matrix. We see that the results agree very well. Here we have thus explicitely included the intercept column in the design matrix. @@ -1989,6 +2446,121 @@ Let us see how we can change this code by zero centering.

          +
          +
          Beta values for own Ridge implementation
+[ 3.43579948e-02 -5.43330971e-01 -3.10141413e-03  2.47116868e-01
+  2.18613217e-01  1.02054837e-01 -4.25617658e-04 -5.90475506e-02
+ -7.68534263e-02 -6.68929213e-02 -4.24906604e-02 -1.40927184e-02
+  1.11482289e-02  2.88529063e-02  3.67047975e-02  3.38135733e-02
+  2.02198703e-02 -3.46383926e-03 -3.63025821e-02]
+Beta values for Scikit-Learn Ridge implementation
+[ 3.43579948e-02 -5.43330971e-01 -3.10141413e-03  2.47116868e-01
+  2.18613217e-01  1.02054837e-01 -4.25617654e-04 -5.90475506e-02
+ -7.68534263e-02 -6.68929213e-02 -4.24906604e-02 -1.40927184e-02
+  1.11482289e-02  2.88529063e-02  3.67047975e-02  3.38135733e-02
+  2.02198702e-02 -3.46383925e-03 -3.63025821e-02]
+Intercept from own implementation:
+1.0330308045187757
+Intercept from Scikit-Learn Ridge implementation
+1.0330308045183219
+MSE values for own Ridge implementation
+3.139255958997547e-06
+MSE values for Scikit-Learn Ridge implementation
+3.1392559585048734e-06
+Beta values for own Ridge implementation
+[-0.05807125 -0.29822833 -0.08551306  0.08156108  0.13679863  0.12333649
+  0.08251519  0.03815288  0.00111756 -0.02498832 -0.04010697 -0.04566964
+ -0.04355837 -0.03562355 -0.02348765 -0.00848904  0.00831018  0.0260906
+  0.04423486]
+Beta values for Scikit-Learn Ridge implementation
+[-0.05807125 -0.29822833 -0.08551306  0.08156108  0.13679863  0.12333649
+  0.08251519  0.03815288  0.00111756 -0.02498832 -0.04010697 -0.04566964
+ -0.04355837 -0.03562355 -0.02348765 -0.00848904  0.00831018  0.0260906
+  0.04423486]
+Intercept from own implementation:
+1.0411487294305088
+Intercept from Scikit-Learn Ridge implementation
+1.041148729430523
+MSE values for own Ridge implementation
+1.9601304850035702e-05
+MSE values for Scikit-Learn Ridge implementation
+1.960130485007504e-05
+Beta values for own Ridge implementation
+[-0.1416398  -0.14021063 -0.05383795  0.01367553  0.04784395  0.05796251
+  0.05447415  0.044613    0.03267527  0.02098261  0.01066519  0.00217499
+ -0.00440346 -0.00917248 -0.01231917 -0.01405935 -0.0146081  -0.01416528
+ -0.01290947]
+Beta values for Scikit-Learn Ridge implementation
+[-0.1416398  -0.14021063 -0.05383795  0.01367553  0.04784395  0.05796251
+  0.05447415  0.044613    0.03267527  0.02098261  0.01066519  0.00217499
+ -0.00440346 -0.00917248 -0.01231917 -0.01405935 -0.0146081  -0.01416528
+ -0.01290947]
+Intercept from own implementation:
+1.049556996627824
+Intercept from Scikit-Learn Ridge implementation
+1.0495569966278269
+MSE values for own Ridge implementation
+5.4959161509357395e-05
+MSE values for Scikit-Learn Ridge implementation
+5.495916150936645e-05
+Beta values for own Ridge implementation
+[-0.13535942 -0.08593216 -0.03568439 -0.0036367   0.01397146  0.02229529
+  0.02503753  0.0245528   0.02228115  0.01908936  0.01549377  0.01179792
+  0.00817631  0.00472512  0.00149311 -0.00149956 -0.00424967 -0.00676387
+ -0.00905423]
+Beta values for Scikit-Learn Ridge implementation
+[-0.13535942 -0.08593216 -0.03568439 -0.0036367   0.01397146  0.02229529
+  0.02503753  0.0245528   0.02228115  0.01908936  0.01549377  0.01179792
+  0.00817631  0.00472512  0.00149311 -0.00149956 -0.00424967 -0.00676387
+ -0.00905423]
+Intercept from own implementation:
+1.039967668952797
+Intercept from Scikit-Learn Ridge implementation
+1.0399676689527975
+MSE values for own Ridge implementation
+7.571105947979344e-05
+MSE values for Scikit-Learn Ridge implementation
+7.571105947979394e-05
+Beta values for own Ridge implementation
+[-0.05100875 -0.04063602 -0.02723445 -0.01713366 -0.0100706  -0.00517114
+ -0.00174276  0.00068734  0.00243186  0.00369758  0.00462287  0.0053018
+  0.00579953  0.006162    0.00642221  0.00660427  0.00672607  0.0068011
+  0.00683964]
+Beta values for Scikit-Learn Ridge implementation
+[-0.05100875 -0.04063602 -0.02723445 -0.01713366 -0.0100706  -0.00517114
+ -0.00174276  0.00068734  0.00243186  0.00369758  0.00462287  0.0053018
+  0.00579953  0.006162    0.00642221  0.00660427  0.00672607  0.0068011
+  0.00683964]
+Intercept from own implementation:
+0.999955585168597
+Intercept from Scikit-Learn Ridge implementation
+0.999955585168597
+MSE values for own Ridge implementation
+0.0007698473260556343
+MSE values for Scikit-Learn Ridge implementation
+0.0007698473260556325
+Beta values for own Ridge implementation
+[-0.00834567 -0.00803064 -0.00673407 -0.00554552 -0.00458878 -0.0038335
+ -0.00323332 -0.00274989 -0.0023548  -0.00202756 -0.00175331 -0.00152117
+ -0.001323   -0.0011526  -0.00100519 -0.00087697 -0.00076495 -0.00066668
+ -0.00058016]
+Beta values for Scikit-Learn Ridge implementation
+[-0.00834567 -0.00803064 -0.00673407 -0.00554552 -0.00458878 -0.0038335
+ -0.00323332 -0.00274989 -0.0023548  -0.00202756 -0.00175331 -0.00152117
+ -0.001323   -0.0011526  -0.00100519 -0.00087697 -0.00076495 -0.00066668
+ -0.00058016]
+Intercept from own implementation:
+0.9637117593816477
+Intercept from Scikit-Learn Ridge implementation
+0.9637117593816477
+MSE values for own Ridge implementation
+0.0023813163025848865
+MSE values for Scikit-Learn Ridge implementation
+0.002381316302584886
+
          +
          +_images/chapter3_122_1.png +

          We see here, when compared to the code which includes explicitely the intercept column, that our MSE value is actually smaller. This is @@ -1999,9 +2571,9 @@ centered matrix and/or vector that enter the fitting procedure. Note also that the problem with the intercept occurs mainly in these type of polynomial fitting problem.

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

          -
          -
          -

          5.7. More complicated Example: The Ising model#

          + +
          +

          5.7. More complicated Example: The Ising model

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

          @@ -2494,9 +3066,9 @@ testing set that is close to the accuracy of the training set.

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

          -
          -
          -

          5.8. Exercises and Projects#

          + +
          +

          5.8. Exercises and Projects

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

          @@ -2580,8 +3152,8 @@ which polynomial fits the data best.

          -
          -

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

          +
          +

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

          We will generate our own dataset for a function \(\mathrm{FrankeFunction}(x,y)\) with \(x,y \in [0,1]\). The function \(f(x,y)\) is the Franke function. You should explore also the addition @@ -2624,9 +3196,9 @@ is no explicit recipe for how much data should be included as training data and say test data. An accepted rule of thumb is to use approximately \(2/3\) to \(4/5\) of the data as training data.

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

          -
          -
          -

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

          + +
          +

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

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

          With a code which does OLS and includes resampling techniques, we will now discuss the bias-variance trade-off in the context of @@ -2673,9 +3245,9 @@ studying the MSE value as function of the complexity of your model.

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

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

          -
          -
          -

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

          + +
          +

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

          The aim here is to write your own code for another widely popular resampling technique, the so-called cross-validation method. Again, before you start with cross-validation approach, you should scale your @@ -2688,9 +3260,9 @@ from the test folds. You can compare your own code with that from you got from your bootstrap code. Comment your results. Try \(5-10\) folds. You can also compare your own cross-validation code with the one provided by Scikit-Learn.

          -
          -
          -

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

          + +
          +

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

          Write your own code for the Ridge method, either using matrix inversion or the singular value decomposition as done in the previous exercise. Perform the same bootstrap analysis as in the @@ -2699,25 +3271,25 @@ analyze your results with those obtained in exercises 1-3. Study the dependence on \(\lambda\).

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

          -
          -
          -

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

          + +
          +

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

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

          -
          -
          -

          5.8.6. Exercise: Analysis of real data#

          + +
          +

          5.8.6. Exercise: Analysis of real data

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

          -

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

          +

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

          In order to obtain data for a specific region, you need to register as a user (free) at this website and then decide upon which area you want to fetch the digital terrain data from. In order to be able to read @@ -2770,9 +3342,9 @@ model fits the data best.

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

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

          - -
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          - - © Copyright 2022. -
          - -

          - -
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          + + By Morten Hjorth-Jensen
          + + © Copyright 2021.
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          +
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          -
          \ No newline at end of file diff --git a/doc/LectureNotes/_build/html/chapter4.html b/doc/LectureNotes/_build/html/chapter4.html index 630e99757..78e7812bd 100644 --- a/doc/LectureNotes/_build/html/chapter4.html +++ b/doc/LectureNotes/_build/html/chapter4.html @@ -1,61 +1,50 @@ - - - - + - - + 6. Logistic Regression — Applied Data Analysis and Machine Learning - - - - - - - - - + + + - - - - - + + + + + + + + - + - + - - - - - - + + + + - - - - + + - - - + + + + + + - - - + + + - - - - -
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          + +

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

          @@ -939,6 +1080,10 @@ We use Pandas to compute the correlation matrix.

          +
          +_images/chapter4_57_0.png +_images/chapter4_57_1.png +

          In the above example we note two things. In the first plot we display the overlap of benign and malignant tumors as functions of the various @@ -972,7 +1117,7 @@ the classical Principal Component Analysis (PCA) theorem with applications. This will be discussed later this semester (week 43).

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

          -
            +

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

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

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

              4. @@ -1033,9 +1178,35 @@ Based on this we can then define the accuracy score as the sum of correctly pred +
              +
              (426, 30)
+(143, 30)
+Test set accuracy with Logistic Regression: 0.94
+
              +
              +
              /Users/mhjensen/miniforge3/envs/myenv/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:814: ConvergenceWarning: lbfgs failed to converge (status=1):
+STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
+
+Increase the number of iterations (max_iter) or scale the data as shown in:
+    https://scikit-learn.org/stable/modules/preprocessing.html
+Please also refer to the documentation for alternative solver options:
+    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
+  n_iter_i = _check_optimize_result(
+
              +
              +
              Test set accuracy Logistic Regression with scaled data: 0.96
+[1.         1.         1.         1.         1.         1.
+ 1.         1.         0.92857143 0.92857143]
+Test set accuracy with Logistic Regression  and scaled data: 0.96
+
              +
              +_images/chapter4_64_3.png +_images/chapter4_64_4.png +_images/chapter4_64_5.png +
              + + - - - + - - - - - - - - - - -
              - -
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              - Contents -
              -
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              - - - -
              - -
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              -By Morten Hjorth-Jensen -

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              - - © Copyright 2022. -
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              - -
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              +

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

              +
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              \ No newline at end of file diff --git a/doc/LectureNotes/_build/html/chapter5.html b/doc/LectureNotes/_build/html/chapter5.html index a9d12411e..bb667d505 100644 --- a/doc/LectureNotes/_build/html/chapter5.html +++ b/doc/LectureNotes/_build/html/chapter5.html @@ -1,61 +1,50 @@ - - - - + - - + 8. Support Vector Machines, overarching aims — Applied Data Analysis and Machine Learning - - - - - - - - - + + + - - - - - + + + + + + + + - + - + - - - - - - + + + + - - - - + + - - - + + + + + + - - - + + + - - - - -
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              \ No newline at end of file diff --git a/doc/LectureNotes/_build/html/chapter6.html b/doc/LectureNotes/_build/html/chapter6.html index c3f7944ef..cc64ec13a 100644 --- a/doc/LectureNotes/_build/html/chapter6.html +++ b/doc/LectureNotes/_build/html/chapter6.html @@ -1,61 +1,50 @@ - - - - + - - + 9. Decision trees, overarching aims — Applied Data Analysis and Machine Learning - - - - - - - - - + + + - - - - - + + + + + + + + - + - + - - - - - - + + + + - - - - + + - - - + + + + + + - - - + + + - - - - -
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              9.5. Algorithms for Setting up Decision Trees

              Two algorithms stand out in the set up of decision trees:

              -
                +

                1. The CART (Classification And Regression Tree) algorithm for both classification and regression

                2. The ID3 algorithm based on the computation of the information gain for classification

                @@ -939,8 +1221,8 @@ This method doesn’t require the installation of external libraries and is more Scikit-Learn uses the CART algorithm. For classification problems you can use either the gini index or the entropy to split a tree in two branches.

                -
                -

                9.5.1. The CART algorithm for Classification#

                +
                +

                9.5.1. The CART algorithm for Classification

                For classification, the CART algorithm splits the data set in two subsets using a single feature \(k\) and a threshold \(t_k\). This could be for example a threshold set by a number below a certain circumference of a malign tumor.

                How do we find these two quantities? @@ -957,9 +1239,9 @@ and so on, recursively. It stops recursing once it reaches the maximum depth (de \(max\_depth\) hyperparameter), or if it cannot find a split that will reduce impurity. A few other hyperparameters control additional stopping conditions such as the \(min\_samples\_split\), \(min\_samples\_leaf\), \(min\_weight\_fraction\_leaf\), and \(max\_leaf\_nodes\).

                -
                -
                -

                9.5.2. The CART algorithm for Regression#

                +
              +
              +

              9.5.2. The CART algorithm for Regression

              The CART algorithm for regression works is similar to the one for classification except that instead of trying to split the training set in a way that minimizes say the gini or entropy impurity, it now tries to split the training set in a way that minimizes our well-known mean-squared error (MSE). The cost function is now

              @@ -979,9 +1261,9 @@ C(k,t_k) = \frac{m_{\mathrm{left}}}{m}\mathrm{MSE}_{\mathrm{left}}+ \frac{m_{\ma

              the mean value of all observations in a specific node.

              Without any regularization, the regression task for decision trees, just like for classification tasks, is prone to overfitting.

              - -
              -

              9.5.3. Computing the Gini index#

              +
              +
              +

              9.5.3. Computing the Gini index

              The example we will look at is a classical one in many Machine Learning applications. Based on various meteorological features, we have several so-called attributes which decide whether we at the end @@ -1014,9 +1296,9 @@ humidity and weak and strong for wind.

              14 Rain Mild High Strong 0 - -
              -

              9.5.4. Simple Python Code to read in Data and perform Classification#

              +
              +
              +

              9.5.4. Simple Python Code to read in Data and perform Classification

              # Common imports
@@ -1089,6 +1371,20 @@ humidity and weak and strong for wind.
              +
              +
              ---------------------------------------------------------------------------
+FileNotFoundError                         Traceback (most recent call last)
+Input In [6], in <cell line: 37>()
+     34 def save_fig(fig_id):
+     35     plt.savefig(image_path(fig_id) + ".png", format='png')
+---> 37 infile = open(data_path("rideclass.csv"),'r')
+     39 # Read the experimental data with Pandas
+     40 from IPython.display import display
+
+FileNotFoundError: [Errno 2] No such file or directory: 'DataFiles/rideclass.csv'
+
              +
              +

              The above functions (gini, entropy and misclassification error) are important components of the so-called CART algorithm. We will discuss @@ -1161,13 +1457,13 @@ algorithm ID3.

              - - -
              -

              9.6. Entropy and the ID3 algorithm#

              +
              +
              +
              +

              9.6. Entropy and the ID3 algorithm

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

              -
                +

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

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

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

                  4. @@ -1184,8 +1480,8 @@ examples.

                  training examples according to their target classification.

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

                  -
                  -

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

                  +
                  +

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

                  import matplotlib.pyplot as plt
@@ -1233,9 +1529,9 @@ attributes at each step while growing the tree.
                  -
                  -
                  -

                  9.6.2. Another example, the moons again#

                  +
              +
              +

              9.6.2. Another example, the moons again

              from __future__ import division, print_function, unicode_literals
@@ -1432,10 +1728,10 @@ attributes at each step while growing the tree.
              - - -
              -

              9.7. Pros and cons of trees, pros#

              +
              +
              +
              +

              9.7. Pros and cons of trees, pros

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

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

                • @@ -1445,8 +1741,8 @@ attributes at each step while growing the tree.

              • Can model interactions between the different descriptive features

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

              -
              -

              9.7.1. Disadvantages#

              +
              +

              9.7.1. Disadvantages

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

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

                • @@ -1459,9 +1755,9 @@ attributes at each step while growing the tree.

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

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

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              - - © Copyright 2022. -
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              + + By Morten Hjorth-Jensen
              + + © Copyright 2021.
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              \ No newline at end of file diff --git a/doc/LectureNotes/_build/html/chapter7.html b/doc/LectureNotes/_build/html/chapter7.html index 37ec75d61..28c213e50 100644 --- a/doc/LectureNotes/_build/html/chapter7.html +++ b/doc/LectureNotes/_build/html/chapter7.html @@ -1,61 +1,50 @@ - - - - + - - + 10. Ensemble Methods: From a Single Tree to Many Trees and Extreme Boosting, Meet the Jungle of Methods — Applied Data Analysis and Machine Learning - - - - - - - - - + + + - - - - - + + + + + + + + - + - + - - - - - - + + + + - - - - + + - - - + + + + + + - - - + + + - - - - -
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              + + By Morten Hjorth-Jensen
              + + © Copyright 2021.
              +

              +
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              +
              + + -
              -
              \ No newline at end of file diff --git a/doc/LectureNotes/_build/html/chapter8.html b/doc/LectureNotes/_build/html/chapter8.html index 85b37f315..efe2d38c7 100644 --- a/doc/LectureNotes/_build/html/chapter8.html +++ b/doc/LectureNotes/_build/html/chapter8.html @@ -1,61 +1,50 @@ - - - - + - - + 11. Basic ideas of the Principal Component Analysis (PCA) — Applied Data Analysis and Machine Learning - - - - - - - - - + + + - - - - - + + + + + + + + - + - + - - - - - - + + + + - - - - + + - - - + + + + + + - - - + + + - - - - -
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              Depending on the number of points \(n\), we will get results that are close to the covariance values defined above. The plot shows how the data are clustered around a line with slope close to one. Is this expected? Try to change the covariance and the mean values. For example, try to make the variance of the first element much larger than that of the second diagonal element. Try also to shrink the covariance (the non-diagonal elements) and see how the data points are distributed.

              - -
              -

              11.3.3. Diagonalize the sample covariance matrix to obtain the principal components#

              +
              +
              +

              11.3.3. Diagonalize the sample covariance matrix to obtain the principal components

              Now we are ready to solve for the principal components! To do so we diagonalize the sample covariance matrix \(\Sigma\). We can use the function np.linalg.eig to do so. It will return the eigenvalues and @@ -977,12 +1189,27 @@ questions.

              +
              +
              Eigenvalues of Covariance matrix
+5.17615838052499
+0.7506274061293645
+First eigenvector
+[0.84927263 0.52795454]
+Second eigenvector
+[-0.52795454  0.84927263]
+
              +
              +
              Eigenvector of largest eigenvalue
+[0.84927263 0.52795454]
+
              +
              +

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

              - - -
              -

              11.4. Classical PCA Theorem#

              + + +
              +

              11.4. Classical PCA Theorem

              We assume now that we have a design matrix \(\boldsymbol{X}\) which has been centered as discussed above. For the sake of simplicity we skip the overline symbol. The matrix is defined in terms of the various column @@ -1040,9 +1267,9 @@ discussion in chapter 12.2 of Murphy’s text has also a nice link with the Singular Value Decomposition theorem. For categorical data, see chapter 12.4 and discussion therein.

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

              -
              - -
              -

              11.6. PCA and scikit-learn#

              + +
              +

              11.6. PCA and scikit-learn

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

              @@ -1108,6 +1468,20 @@ that it automatically takes care of centering the data):

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

              After fitting the PCA transformer to the dataset, you can access the principal components using the components variable (note that it contains the PCs as horizontal vectors, so, for example, the first @@ -1118,13 +1492,18 @@ principal component is equal to

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

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

              -
              -
              -

              11.7. Back to the Cancer Data#

              + +
              +

              11.7. Back to the Cancer Data

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

              @@ -1160,6 +1539,23 @@ Here we compute performance scores on the training data using logistic regressio
              +
              +
              Train set accuracy from Logistic Regression: 0.95
+Train set accuracy scaled data: 0.99
+Train set accuracy scaled and PCA data: 0.96
+
              +
              +
              /Users/mhjensen/miniforge3/envs/myenv/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:814: ConvergenceWarning: lbfgs failed to converge (status=1):
+STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
+
+Increase the number of iterations (max_iter) or scale the data as shown in:
+    https://scikit-learn.org/stable/modules/preprocessing.html
+Please also refer to the documentation for alternative solver options:
+    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
+  n_iter_i = _check_optimize_result(
+
              +
              +

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

              Instead of arbitrarily choosing the number of dimensions to reduce down to, it is generally preferable to @@ -1189,23 +1585,23 @@ a float between 0.0 and 1.0, indicating the ratio of variance you wish to preser -

              -

              11.7.1. Incremental PCA#

              +
              +

              11.7.1. Incremental PCA

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

              -
              -
              -

              11.7.2. Randomized PCA#

              + +
              +

              11.7.2. Randomized PCA

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

              -
              -
              -

              11.7.3. Kernel PCA#

              + +
              +

              11.7.3. Kernel PCA

              The kernel trick is a mathematical technique that implicitly maps instances into a very high-dimensional space (called the feature space), enabling nonlinear classification and regression with Support Vector Machines. Recall that a linear decision boundary in the high-dimensional feature @@ -1224,10 +1620,10 @@ For example, the following code uses Scikit-Learn’s KernelPCA class to perform

              -
              -
              -
              -

              11.8. Other techniques#

              + + +
              +

              11.8. Other techniques

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

              Here are some of the most popular:

                @@ -1236,8 +1632,8 @@ For example, the following code uses Scikit-Learn’s KernelPCA class to perform

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

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

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              + + By Morten Hjorth-Jensen
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              \ No newline at end of file diff --git a/doc/LectureNotes/_build/html/chapter9.html b/doc/LectureNotes/_build/html/chapter9.html index dd0efd8a0..f8975bba7 100644 --- a/doc/LectureNotes/_build/html/chapter9.html +++ b/doc/LectureNotes/_build/html/chapter9.html @@ -1,61 +1,50 @@ - - - - + - - + 13. Neural networks — Applied Data Analysis and Machine Learning - - - - - - - - - + + + - - - - - + + + + + + + + - + - + - - - - - - + + + + - - - - + + - - - + + + + + + - - - + + + - - - - -
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              -By Morten Hjorth-Jensen -

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              + + By Morten Hjorth-Jensen
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              + + + + + + + -
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              \ No newline at end of file diff --git a/doc/LectureNotes/_build/html/chapteroptimization.html b/doc/LectureNotes/_build/html/chapteroptimization.html index 2ec431c29..39ae43a02 100644 --- a/doc/LectureNotes/_build/html/chapteroptimization.html +++ b/doc/LectureNotes/_build/html/chapteroptimization.html @@ -1,61 +1,50 @@ - - - - + - - + 7. Optimization, the central part of any Machine Learning algortithm — Applied Data Analysis and Machine Learning - - - - - - - - - + + + - - - - - + + + + + + + + - + - + - - - - - - + + + + - - - - + + - - - + + + + + + - - - + + + - - - - -
              + - - - - - - - - - - -
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              - - - Ctrl+K -
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              +
              +
              + + +
              +
              [<matplotlib.lines.Line2D at 0x16c9a8880>]
+
              - -
              -

              7.4. Conjugate gradient method#

              +_images/chapteroptimization_69_1.png +
              +
              +
              +
              +

              7.4. Conjugate gradient method

              In the CG method we define so-called conjugate directions and two vectors \(\boldsymbol{s}\) and \(\boldsymbol{t}\) are said to be @@ -1039,14 +1213,14 @@ This gives the following expression

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

              7.5. Revisiting our Linear Regression Solvers#

              +
              +
              +

              7.5. Revisiting our Linear Regression Solvers

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

              -
                +

                1. An analytical solution.

                2. The gradient can be computed analytically.

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

                  4. @@ -1166,6 +1340,16 @@ when \(||\nabla_\beta C(\beta_k) || \
              +
              +
              [0.31022577 4.45255977]
+[[3.8942133 ]
+ [3.04191629]]
+[[3.8942133 ]
+ [3.04191629]]
+
              +
              +_images/chapteroptimization_123_1.png +

              Alternatively, we can use Scikit-Learn as done here

              @@ -1189,6 +1373,13 @@ when \(||\nabla_\beta C(\beta_k) || \
              +
              +
              [[4.13542726]
+ [2.97804446]]
+[4.07929472] [2.96841776]
+
              +
              +

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

              @@ -1255,10 +1446,19 @@ C_{\text{ridge}}(\beta) = \frac{1}{n}||X\beta -\mathbf{y}||^2 + \lambda ||\beta|
              +
              +
              [[3.9208878 ]
+ [3.21055226]]
+[[3.84859258]
+ [3.26931499]]
+
              - -
              -

              7.6. Using gradient descent methods, limitations#

              +_images/chapteroptimization_132_1.png +
              + + +
              +

              7.6. Using gradient descent methods, limitations

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

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

                • @@ -1267,9 +1467,9 @@ C_{\text{ridge}}(\beta) = \frac{1}{n}||X\beta -\mathbf{y}||^2 + \lambda ||\beta|

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

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

              - -
              -

              7.7. Stochastic Gradient Descent (SGD)#

              +
              +
              +

              7.7. Stochastic Gradient Descent (SGD)

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

              This process is called Stochastic Gradient @@ -1415,10 +1615,15 @@ function.

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

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

              -
              -

              7.7.1. Program for stochastic gradient#

              +
              +

              7.7.1. Program for stochastic gradient

              # Importing various packages
@@ -1493,15 +1698,30 @@ function.
              +
              +
              Own inversion
+[[3.87533278]
+ [2.94854992]]
+Eigenvalues of Hessian Matrix:[0.31803769 4.15962297]
+theta from own gd
+[[3.87533278]
+ [2.94854992]]
+theta from own sdg
+[[3.90803422]
+ [2.93820524]]
+
              +
              +_images/chapteroptimization_148_1.png +

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

              -
              - -
              -

              7.8. Momentum based GD#

              + + +
              +

              7.8. Momentum based GD

              The stochastic gradient descent (SGD) is almost always used with a momentum or inertia term that serves as a memory of the direction we are moving in parameter space. This is typically implemented as @@ -1616,8 +1836,8 @@ Hessians.

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

              -
              -

              7.8.1. RMS prop#

              +
              +

              7.8.1. RMS prop

              In RMS prop, in addition to keeping a running average of the first moment of the gradient, we also keep track of the second moment denoted by \(\mathbf{s}_t=\mathbb{E}[\mathbf{g}_t^2]\). The update rule @@ -1648,9 +1868,9 @@ is clear from this formula that the learning rate is reduced in directions where the norm of the gradient is consistently large. This greatly speeds up the convergence by allowing us to use a larger learning rate for flat directions.

              -
              -
              -

              7.8.2. ADAM optimizer#

              +
              +
              +

              7.8.2. ADAM optimizer

              A related algorithm is the ADAM optimizer. In ADAM, we keep a running average of both the first and second moment of the gradient and use this information to adaptively change the learning rate for different @@ -1713,19 +1933,19 @@ update rule for this parameter is given by

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

              7.9. Practical tips#

              + + +
              +

              7.9. Practical tips

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

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

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

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

              -
              -
              -

              7.10. Automatic differentiation#

              + +
              +

              7.10. Automatic differentiation

              Automatic differentiation (AD), also called algorithmic differentiation or computational differentiation,is a set of @@ -1799,6 +2019,12 @@ f'(x) = \cos\left(2\pi x + x^2\right)\left(2\pi + 2x\right)

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

              Here we experiment with what kind of functions Autograd is capable @@ -1827,6 +2053,12 @@ experiment with other, possibly more complicated, functions as well.

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

              To differentiate with respect to two (or more) arguments of a Python function, Autograd need to know at which variable the function if @@ -1869,6 +2101,17 @@ being differentiated with respect to.

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

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

              @@ -1893,6 +2136,12 @@ being differentiated with respect to.

              +
              +
              The computed gradient of f3 is:  [ 2.  3.  5.  7. 88.]
+The analytical gradient of f3 is:  [ 2.  3.  5.  7. 88.]
+
              +
              +

              Note that in this case, when sending an array as input argument, the output from Autograd is another array. This is the true gradient of @@ -1922,6 +2171,12 @@ could expect form a gradient-evaluting function.

              +
              +
              The computed derivative of f4 at x = 2.7 is: 13.8759
+The analytical gradient of f4 at x = 2.7 is: 13.8759
+
              +
              +
              @@ -1942,6 +2197,11 @@ could expect form a gradient-evaluting function.

              +
              +
              The computed derivative of f5 at x = 2.7 is: 5.4
+
              +
              +
              @@ -1972,6 +2232,12 @@ could expect form a gradient-evaluting function.

              +
              +
              The computed derivative of f6_for at x = 0.5 is: 3.95703
+The computed derivative of f6_while at x = 0.5 is: 3.95703
+
              +
              +
              @@ -1987,6 +2253,11 @@ could expect form a gradient-evaluting function.

              +
              +
              The analytical derivative of f6 at x = 0.5 is: 3.95703
+
              +
              +
              @@ -2020,6 +2291,12 @@ could expect form a gradient-evaluting function.

              +
              +
              The computed derivative of f7 at n = 2 is: 1
+The analytical derivative of f7 at n = 2 is: 1
+
              +
              +

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

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

              @@ -2042,6 +2319,11 @@ could expect form a gradient-evaluting function.

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

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

              @@ -2060,6 +2342,60 @@ could expect form a gradient-evaluting function.

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

              Here we are told that the ‘dot’ function does not belong to Autograd’s version of a Numpy array. To overcome this, an alternative syntax @@ -2095,16 +2431,16 @@ which also computed the dot product can be used:

              -
              -
              -

              7.11. Replace or not#

              + +
              +

              7.11. Replace or not

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

              -
              -
              -

              7.12. Using Autograd#

              + +
              +

              7.12. Using Autograd

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

              @@ -2163,9 +2499,9 @@ first example shows results with ordinary leats squares.

              -
              -
              -

              7.13. Same code but now with momentum gradient descent#

              + +
              +

              7.13. Same code but now with momentum gradient descent

              # Using Autograd to calculate gradients for OLS
@@ -2271,9 +2607,9 @@ However, if we can invert the Hessian matrix, this is the preferred approach, as
              -
              -
              -

              7.14. Including Stochastic Gradient Descent with Autograd#

              + +
              +

              7.14. Including Stochastic Gradient Descent with Autograd

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

              @@ -2428,8 +2764,8 @@ However, if we can invert the Hessian matrix, this is the preferred approach, as
              -
              -

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

              +
              +

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

              # Using Autograd to calculate gradients using AdaGrad and Stochastic Gradient descent
@@ -2555,10 +2891,10 @@ However, if we can invert the Hessian matrix, this is the preferred approach, as
              -
              -
              -
              -

              7.15. Introducing JAX#

              + + +
              +

              7.15. Introducing JAX

              Presently, instead of using autograd, we recommend using JAX

              JAX is Autograd and XLA (Accelerated Linear Algebra)), brought together for high-performance numerical computing and machine learning research. @@ -2579,8 +2915,8 @@ It provides composable transformations of Python+NumPy programs: differentiate,

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

              So what do we have so far? We have ‘picked’ \(k\) centroids at random from our data points. There are other ways of more intelligently choosing their @@ -781,6 +758,11 @@ or a maximum amount of iterations.

              +
              +
              Converged at iteration 5
+
              +
              +

              We now have a simple , un-optimized \(k\)-means clustering implementation. Lets plot the final result

              @@ -802,6 +784,9 @@ clustering implementation. Lets plot the final result

              +
              +_images/clustering_24_0.png +
              @@ -877,8 +862,8 @@ clustering implementation. Lets plot the final result

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"reward": [0, 4, 28], "punish": [0, 1, 28], "anoth": [0, 1, 3, 4, 5, 6, 7, 8, 10, 11, 12, 13, 22, 23, 25, 28, 29, 32, 33], "wai": [0, 1, 2, 3, 4, 5, 6, 7, 8, 10, 11, 12, 13, 14, 17, 22, 25, 28, 29, 30, 32, 33], "categor": [0, 1, 3, 9, 11, 28], "consid": [0, 1, 2, 3, 5, 6, 7, 8, 9, 10, 12, 13, 16, 19, 22, 23, 25, 28, 29, 30, 31, 32, 33], "desir": [0, 2, 4, 5, 13, 14, 28, 29, 32, 33], "output": [0, 1, 3, 4, 5, 6, 7, 8, 9, 10, 12, 13, 14, 16, 22, 23, 25, 28, 29, 30, 31, 32, 33], "classif": [0, 3, 5, 6, 7, 8, 11, 12, 21, 23, 27, 28, 29, 30, 31, 33], "divid": [0, 1, 3, 5, 6, 7, 8, 9, 11, 12, 25, 28, 29, 30, 31, 32], "goal": [0, 7, 9, 28, 32], "assign": [0, 7, 8, 9, 12, 13, 14, 24, 26, 27, 28, 29, 32, 33], "identifi": [0, 1, 7, 9, 11, 12, 13, 14, 28, 29, 32, 33], "digit": [0, 1, 3, 4, 6, 23, 24, 26, 28], "pictur": [0, 28], "ones": [0, 2, 5, 6, 8, 9, 10, 11, 13, 22, 23, 28, 29, 30, 31, 32, 33], "typic": [0, 1, 2, 3, 4, 5, 7, 9, 10, 12, 13, 25, 28, 29, 30, 31, 32, 33], "between": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 11, 12, 13, 14, 17, 23, 25, 28, 29, 30, 31, 32, 33], "refer": [0, 1, 2, 3, 5, 6, 11, 12, 13, 14, 20, 22, 23, 27, 28, 29, 31, 32, 33], "construct": [0, 1, 2, 3, 5, 6, 7, 8, 9, 10, 11, 22, 25, 28, 29, 30, 31, 32], "map": [0, 1, 2, 6, 7, 8, 11, 12, 14, 23, 25, 28, 32], "continu": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 12, 13, 17, 18, 22, 23, 25, 28, 29, 30, 31, 32, 33], "valu": [0, 1, 2, 3, 4, 6, 7, 8, 9, 10, 12, 13, 14, 15, 16, 19, 21, 22, 23, 28, 33], "cluster": [0, 1, 4, 6, 11, 21, 28, 31], "group": [0, 6, 7, 9, 14, 21, 23, 24, 26, 28, 31], "certain": [0, 3, 6, 7, 9, 25, 28, 29, 31, 32], "trait": [0, 28], "beforehand": [0, 25, 28], "cover": [0, 5, 17, 21, 26, 27, 29, 30], "irrespect": [0, 28], "deal": [0, 1, 3, 5, 6, 8, 11, 13, 14, 17, 18, 22, 23, 25, 28, 29, 30, 31, 32, 33], "first": [0, 1, 2, 3, 5, 6, 7, 8, 9, 10, 11, 13, 14, 15, 16, 17, 22, 23, 25, 26, 27, 31], "ingredi": [0, 9, 28], "subdivid": [0, 22, 28], "valid": [0, 1, 4, 7, 9, 10, 13, 21, 25, 28, 29, 33], "difficult": [0, 1, 6, 10, 13, 23, 25, 28, 31, 33], "part": [0, 1, 3, 5, 6, 10, 17, 18, 22, 24, 25, 26, 28, 30, 31], "meaning": [0, 4, 7, 28, 32], "second": [0, 2, 3, 4, 5, 6, 7, 8, 9, 11, 12, 14, 15, 16, 21, 22, 23, 25, 26, 28, 29, 31, 32], "item": [0, 13, 28, 33], "paramet": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 12, 13, 16, 17, 18, 19, 23, 25, 30, 31], "reflect": [0, 1, 4, 5, 23, 25, 28], "lack": [0, 28], "thereof": [0, 6, 13, 28, 31, 33], "As": [0, 1, 2, 3, 4, 5, 6, 8, 10, 12, 13, 16, 22, 23, 25, 28, 29, 30, 31, 32, 33], "similar": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 14, 17, 21, 22, 23, 28, 30, 31, 32], "would": [0, 1, 3, 5, 6, 7, 8, 9, 10, 11, 12, 13, 22, 23, 25, 28, 29, 30, 31, 32, 33], "determin": [0, 2, 3, 4, 5, 6, 8, 9, 10, 11, 12, 13, 16, 22, 25, 28, 29, 30, 31, 32, 33], "so": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 16, 18, 19, 21, 22, 23, 25, 26, 28, 29, 30, 31, 32, 33], "call": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 18, 19, 21, 22, 23, 25, 26, 28, 29, 30, 31, 32, 33], "cost": [0, 2, 3, 5, 6, 7, 8, 9, 12, 13, 16, 17, 19, 23, 28], "loss": [0, 1, 3, 4, 5, 6, 7, 8, 10, 11, 13, 22, 23, 28, 30, 31], "risk": [0, 13, 15, 16, 28, 32, 33], "present": [0, 5, 6, 7, 9, 12, 13, 22, 23, 25, 28, 29, 30, 33], "estim": [0, 1, 5, 6, 7, 10, 11, 13, 21, 25, 28, 29, 32, 33], "reproduc": [0, 5, 6, 9, 12, 15, 16, 21, 23, 25, 28, 29], "suppos": [0, 5, 6, 7, 8, 10, 11, 12, 13, 22, 28, 29, 30, 31, 32, 33], "At": [0, 4, 6, 13, 23, 28, 33], "heart": [0, 7, 28, 32], "encount": [0, 1, 5, 7, 13, 25, 28, 29, 32, 33], "minim": [0, 1, 2, 3, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 17, 18, 23, 29, 30, 31, 33], "optim": [0, 2, 3, 4, 5, 6, 7, 9, 10, 11, 14, 15, 16, 17, 18, 19, 23, 30, 31], "famili": [0, 7, 8, 25, 32], "gradient": [0, 3, 4, 7, 8, 9, 12, 21, 28, 29], "hear": [0, 13, 28, 33], "phrase": [0, 28], "causat": [0, 28], "do": [0, 2, 3, 4, 5, 6, 8, 9, 10, 11, 12, 13, 14, 15, 16, 22, 23, 29, 32], "think": [0, 1, 3, 4, 6, 9, 12, 13, 14, 25, 28, 31, 32, 33], "mai": [0, 1, 2, 3, 5, 6, 7, 8, 9, 11, 12, 13, 18, 21, 22, 23, 25, 29, 30, 31, 32, 33], "classifi": [0, 1, 4, 7, 9, 10, 11, 28], "point": [0, 1, 2, 3, 5, 6, 7, 8, 9, 10, 11, 13, 14, 15, 16, 17, 19, 22, 23, 25, 26, 28, 29, 31, 32, 33], "Or": [0, 1, 6, 23, 28, 32], "perhap": [0, 5, 13, 28, 29, 30, 32, 33], "kind": [0, 2, 3, 4, 8, 12, 13, 14, 28, 29, 33], "symmetr": [0, 5, 8, 11, 12, 13, 22, 28, 29, 33], "statement": [0, 7, 22, 32], "b": [0, 1, 3, 4, 5, 6, 8, 9, 10, 12, 13, 14, 15, 16, 25, 26, 28, 29, 30, 31, 32], "caus": [0, 5, 6, 25, 28, 29, 30, 31], "doe": [0, 1, 2, 3, 4, 5, 6, 8, 10, 11, 12, 13, 16, 22, 23, 25, 28, 31, 32, 33], "necessarili": [0, 4, 11, 25, 28], "sens": [0, 4, 6, 8, 23, 28, 31], "autom": [0, 21, 27, 28], "fashion": [0, 9, 10, 28], "assess": [0, 6, 23, 28, 29, 30, 31], "newli": [0, 28], "primari": [0, 7, 28, 32], "concern": [0, 1, 4, 7, 28, 32], "qualiti": [0, 9, 15, 16, 21, 28, 29], "less": [0, 1, 3, 4, 5, 6, 8, 9, 13, 21, 25, 28, 29, 30, 31, 32, 33], "were": [0, 1, 3, 4, 5, 6, 7, 8, 10, 11, 12, 14, 25, 28, 29, 31, 32], "order": [0, 1, 2, 3, 5, 6, 7, 8, 9, 10, 11, 12, 15, 16, 22, 23, 25, 28, 29, 30, 31, 32], "distribut": [0, 1, 2, 3, 4, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 18, 19, 21, 22, 23, 28, 29, 32, 33], "pdf": [0, 3, 4, 5, 6, 9, 23, 27, 28, 30, 31], "turn": [0, 1, 5, 6, 7, 8, 9, 10, 11, 12, 13, 19, 22, 23, 25, 28, 29, 31, 32, 33], "likelihood": [0, 1, 5, 9, 28, 29, 33], "end": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 16, 22, 23, 25, 26, 28, 29, 30, 31, 32, 33], "situat": [0, 4, 5, 7, 13, 25, 28, 29, 32, 33], "want": [0, 1, 2, 3, 4, 5, 6, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 21, 23, 25, 28, 29, 30, 31, 32, 33], "infer": [0, 1, 4, 6, 27, 28, 31], "quantit": [0, 6, 9, 23, 28, 31], "m": [0, 1, 2, 3, 5, 6, 8, 9, 10, 11, 12, 13, 22, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33], "sampl": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 13, 14, 15, 16, 19, 21, 22, 23, 25, 28, 29, 30, 31, 32, 33], "boldsymbol": [0, 1, 2, 3, 5, 6, 7, 8, 10, 11, 13, 14, 15, 16, 17, 18, 19, 23, 28, 32, 33], "x": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 22, 23, 25, 28, 31, 32, 33], "x_1": [0, 2, 5, 6, 7, 8, 9, 10, 11, 13, 22, 25, 28, 29, 30, 31, 32, 33], "x_2": [0, 2, 5, 6, 7, 8, 9, 10, 11, 13, 22, 25, 28, 29, 31, 32, 33], "dot": [0, 2, 3, 5, 6, 7, 8, 9, 10, 11, 12, 13, 22, 23, 25, 28, 29, 30, 31, 32], "x_n": [0, 2, 3, 6, 8, 11, 12, 13, 22, 25, 28, 31, 32, 33], "repeatedli": [0, 6, 10, 13, 31, 33], "could": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 17, 22, 23, 25, 28, 29, 30, 31, 32, 33], "try": [0, 1, 2, 4, 5, 6, 7, 8, 9, 10, 11, 13, 14, 15, 16, 21, 22, 23, 25, 28, 29, 30, 31, 32, 33], "straight": [0, 6, 8, 13, 28, 29, 31, 32, 33], "line": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 22, 23, 25, 28, 29, 30, 31, 32, 33], "wish": [0, 2, 5, 7, 8, 11, 13, 14, 22, 23, 28, 29, 32, 33], "sophist": [0, 28], "reason": [0, 1, 3, 4, 10, 13, 27, 28, 32, 33], "reveal": [0, 12, 28], "inform": [0, 1, 3, 4, 6, 9, 11, 12, 13, 14, 22, 23, 27, 28, 31, 32, 33], "encod": [0, 3, 5, 9, 11, 14, 17, 28, 29, 30], "mechan": [0, 4, 25, 28], "For": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 21, 22, 23, 24, 25, 27, 28, 29, 30, 31, 32, 33], "instanc": [0, 1, 2, 4, 6, 9, 11, 13, 28, 29, 31, 32, 33], "discov": [0, 28], "interpret": [0, 1, 6, 9, 10, 12, 13, 17, 19, 22, 23, 25], "addit": [0, 2, 3, 5, 6, 7, 8, 9, 10, 12, 13, 15, 17, 21, 22, 23, 25, 26, 27, 28, 29, 30, 31, 32, 33], "simplifi": [0, 6, 9, 15, 21, 23, 28, 29, 30, 31], "represent": [0, 1, 3, 6, 25, 28, 31, 32], "help": [0, 1, 4, 12, 13, 16, 23, 28, 33], "futur": [0, 4, 8, 9, 28], "consider": [0, 1, 5, 13, 28, 29, 30, 31, 32, 33], "correct": [0, 1, 2, 3, 4, 5, 7, 13, 22, 25, 28, 29, 30, 31, 33], "elus": [0, 28], "imposs": [0, 5, 28, 29], "fundament": [0, 6, 21, 28, 31], "difficulti": [0, 1, 13, 28, 32, 33], "equal": [0, 1, 2, 3, 4, 5, 6, 8, 9, 11, 12, 13, 14, 15, 22, 23, 25, 28, 29, 30, 31, 32, 33], "question": [0, 5, 6, 9, 11, 12, 13, 23, 26, 28, 29, 30, 31, 33], "lead": [0, 1, 3, 5, 6, 7, 8, 9, 10, 11, 12, 13, 16, 17, 22, 25, 28, 29, 30, 31, 32, 33], "To": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 22, 25, 30, 31, 32, 33], "pose": [0, 4, 5, 6, 11, 25, 28, 31], "i": [0, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 22, 23, 25, 26, 28, 29, 30, 31, 32, 33], "e": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 18, 19, 25, 26, 28, 29, 30, 31, 32, 33], "guarante": [0, 4, 13, 25, 28, 29, 32, 33], "uniqu": [0, 2, 6, 13, 14, 22, 28, 31, 32, 33], "need": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 17, 22, 25, 29, 30, 31, 32, 33], "impos": [0, 6, 11, 12, 28], "assumpt": [0, 3, 5, 6, 9, 11, 18, 23, 25, 28, 29], "restrict": [0, 3, 9, 12, 28], "just": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 21, 23, 25, 28, 29, 30, 31, 32, 33], "instead": [0, 1, 2, 3, 4, 5, 6, 8, 9, 11, 13, 14, 22, 23, 25, 28, 29, 31, 32, 33], "best": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 12, 13, 23, 26, 28, 29, 31, 32, 33], "among": [0, 3, 5, 9, 10, 12, 22, 28, 29, 30], "computation": [0, 3, 6, 9, 13, 25, 28, 32, 33], "tractabl": [0, 28, 29], "specifi": [0, 3, 5, 6, 7, 9, 11, 13, 14, 23, 25, 28, 30, 31, 32, 33], "strategi": [0, 1, 9, 28], "simplest": [0, 1, 3, 4, 9, 10, 12, 14, 28], "necessari": [0, 1, 3, 4, 8, 14, 28], "solv": [0, 1, 3, 5, 6, 8, 10, 11, 12, 13, 22, 23, 28, 29], "precis": [0, 2, 5, 11, 13, 22, 23, 25, 28, 29, 30, 31, 33], "enough": [0, 6, 13, 28, 31, 32, 33], "contain": [0, 2, 3, 4, 5, 6, 7, 8, 9, 11, 12, 13, 17, 19, 22, 23, 25, 27, 28, 29, 30, 31, 32, 33], "least": [0, 7, 8, 10, 11, 15, 16, 17, 19, 21, 22, 25, 31, 32], "accuraci": [0, 1, 3, 4, 5, 6, 7, 9, 10, 11, 12, 28, 29, 32], "yet": [0, 1, 6, 8, 11, 13, 28, 32, 33], "rel": [0, 4, 6, 7, 9, 12, 13, 25, 28, 29, 31, 32, 33], "onli": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 17, 19, 22, 23, 25, 28, 29, 30, 31, 32, 33], "simpler": [0, 1, 5, 6, 7, 13, 16, 19, 21, 23, 28, 30, 33], "inadequ": [0, 28], "work": [0, 1, 4, 6, 7, 8, 9, 13, 15, 21, 23, 24, 25, 26, 28, 29, 31, 32, 33], "obvious": [0, 4, 5, 6, 22, 28, 30, 31], "evalu": [0, 2, 3, 4, 5, 6, 9, 13, 23, 25, 28, 29, 30, 31, 32, 33], "someth": [0, 1, 3, 4, 7, 9, 11, 23, 25, 28, 29, 32], "come": [0, 1, 3, 4, 5, 12, 13, 14, 15, 28, 29, 30, 31, 33], "back": [0, 3, 4, 5, 6, 8, 9, 10, 15, 22, 25, 28, 29, 30, 32, 33], "over": [0, 1, 3, 4, 5, 6, 9, 10, 12, 13, 22, 23, 28, 29, 30, 31, 32, 33], "again": [0, 1, 4, 5, 6, 7, 8, 10, 11, 12, 13, 15, 16, 23, 25, 28, 29, 30, 31, 33], "perform": [0, 2, 3, 4, 5, 6, 8, 10, 11, 12, 13, 14, 15, 16, 17, 19, 21, 22, 23, 25, 28, 29, 30, 31, 32, 33], "defin": [0, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 22, 23, 25, 29, 30, 31, 32, 33], "y": [0, 1, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 22, 23, 25, 28, 29, 30, 31, 32, 33], "term": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 19, 22, 23, 25, 28, 29, 30, 32, 33], "variabl": [0, 1, 2, 5, 6, 7, 8, 10, 11, 12, 13, 14, 22, 28, 29, 30, 31], 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28, 29, 31, 32, 33], "3605": [0, 1, 2, 3, 4, 6, 7, 8, 9, 12, 13, 14, 22, 23, 25, 28, 29, 31, 32, 33], "matplotlib_inlin": [0, 1, 2, 3, 4, 6, 7, 8, 9, 12, 13, 14, 22, 23, 25, 28, 29, 31, 32, 33], "backend_inlin": [0, 1, 2, 3, 4, 6, 7, 8, 9, 12, 13, 14, 22, 23, 25, 28, 29, 31, 32, 33], "configure_inline_support": [0, 1, 2, 3, 4, 6, 7, 8, 9, 12, 13, 14, 22, 23, 25, 28, 29, 31, 32, 33], "3608": [0, 1, 2, 3, 4, 6, 7, 8, 9, 12, 13, 14, 22, 23, 25, 28, 29, 31, 32, 33], "pylabtool": [0, 1, 2, 3, 4, 6, 7, 8, 9, 12, 13, 14, 22, 23, 25, 28, 29, 31, 32, 33], "pt": [0, 1, 2, 3, 4, 6, 7, 8, 9, 12, 13, 14, 22, 23, 25, 28, 29, 31, 32, 33], "3609": [0, 1, 2, 3, 4, 6, 7, 8, 9, 12, 13, 14, 22, 23, 25, 28, 29, 31, 32, 33], "find_gui_and_backend": [0, 1, 2, 3, 4, 6, 7, 8, 9, 12, 13, 14, 22, 23, 25, 28, 29, 31, 32, 33], "pylab_gui_select": [0, 1, 2, 3, 4, 6, 7, 8, 9, 12, 13, 14, 22, 23, 25, 28, 29, 31, 32, 33], "__init__": [0, 1, 2, 3, 4, 6, 7, 8, 9, 12, 13, 14, 22, 23, 25, 28, 29, 30, 31, 32, 33], 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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","Applied Data Analysis and Machine Learning","2. Linear Algebra, Handling of Arrays and more Python Features","Project 1 on Machine Learning, deadline October 9 (midnight), 2023","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: Statistical interpretation of Linear Regression and Resampling techniques","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 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\ No newline at end of file diff --git a/doc/LectureNotes/_build/html/statistics.html b/doc/LectureNotes/_build/html/statistics.html index 9a92f8b40..668a13d43 100644 --- a/doc/LectureNotes/_build/html/statistics.html +++ b/doc/LectureNotes/_build/html/statistics.html @@ -1,61 +1,50 @@ - - - - + - - + 1. Elements of Probability Theory and Statistical Data Analysis — Applied Data Analysis and Machine Learning - - - - - - - - - + + + - - - - - + + + + + + + + - + - + - - - - - - + + + + - - - - + + - - - + + + + + + - - - + + + - - - - -
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              Since our random numbers, which are typically generated via a linear congruential algorithm, are never fully independent, we can then define an important test which measures the degree of correlation, namely the so-called
              -auto-correlation function defined previously, see again Eq. (9). +auto-correlation function defined previously, see again Eq. (9). We rewrite it here as

              \[ @@ -1498,9 +1587,9 @@ f_d = \frac{1}{nm}\sum_{\alpha=1}^m\sum_{k=1}^{n-d}(x_{\alpha,k}-\langle X_m \ra numbers are not independent. The independence of the random numbers is crucial in the evaluation of other expectation values. If they are not independent, our assumption for approximating \(\sigma_N\) is no longer valid.

              - -
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              1.1.8. Autocorrelation function#

              +
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              1.1.8. Autocorrelation function

              This program computes the autocorrelation function as discussed in the equation on the previous slide for random numbers generated with the normal distribution \(N(0,1)\).

              @@ -1536,12 +1625,18 @@ assumption for approximating \(\sigma
              +
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              -0.0071642501586093735 0.9871776311306221
+
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              +_images/statistics_188_1.png +

              As can be seen from the plot, the first point gives back the variance and a value of one. For the remaining values we notice that there are still non-zero values for the auto-correlation function.

              - - - +
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              -By Morten Hjorth-Jensen -

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              \ No newline at end of file diff --git a/doc/LectureNotes/_build/html/week34.html b/doc/LectureNotes/_build/html/week34.html index 66f183743..93e1b4d9e 100644 --- a/doc/LectureNotes/_build/html/week34.html +++ b/doc/LectureNotes/_build/html/week34.html @@ -1,61 +1,50 @@ - - - - + - - + Week 34: Introduction to the course, Logistics and Practicalities — Applied Data Analysis and Machine Learning - - - - - - - - - + + + - - - - - + + + + + + + + - + - + - - - - - - + + + + - - - - + + - - - + + + + + + - - - + + + - - - - -
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              We can easily append data to this, for example

              @@ -1517,6 +2318,74 @@ Displaying these results, we see that the indices are given by the default numbe
              +
              +
              /var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_19344/1326197715.py:6: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
+  data_pandas=data_pandas.append(pd.DataFrame(new_hobbit, index=['Pippin']))
+
              +
              +
              + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
              First NameLast NamePlace of birthDate of Birth T.A.
              FrodoFrodoBagginsShire2968
              BilboBilboBagginsShire2890
              AragornAragorn IIElessarEriador2931
              SamSamwiseGamgeeShire2980
              PippinPeregrinTookShire2990
              +

              Here are other examples where we use the DataFrame functionality to handle arrays, now with more interesting features for us, namely numbers. We set up a matrix of dimensionality \(10\times 5\) and compute the mean value and standard deviation of each column. Similarly, we can perform mathematial operations like squaring the matrix elements and many other operations.

              @@ -1538,6 +2407,238 @@ of dimensionality \(10\times 5\)
              +
              +
              + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
              01234
              0-1.7497650.3426801.153036-0.2524360.981321
              10.5142190.221180-1.070043-0.1894960.255001
              2-0.4580270.435163-0.5835950.8168470.672721
              3-0.104411-0.5312801.029733-0.438136-1.118318
              41.6189821.541605-0.251879-0.8424360.184519
              50.9370820.7310001.361556-0.3262380.055676
              60.222400-1.443217-0.7563520.8164540.750445
              7-0.4559471.189622-1.690617-1.356399-1.232435
              8-0.544439-0.6681720.007315-0.6129391.299748
              9-1.733096-0.9833100.357508-1.6135791.470714
              +
              0   -0.175300
+1    0.083527
+2   -0.044334
+3   -0.399836
+4    0.331939
+dtype: float64
+0    1.069584
+1    0.965548
+2    1.018232
+3    0.793167
+4    0.918992
+dtype: float64
+
              +
              +
              + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
              01234
              03.0616790.1174301.3294920.0637240.962990
              10.2644210.0489201.1449930.0359090.065026
              20.2097890.1893670.3405830.6672390.452553
              30.0109020.2822591.0603490.1919631.250636
              42.6211022.3765470.0634430.7096980.034047
              50.8781230.5343621.8538350.1064310.003100
              60.0494622.0828750.5720690.6665970.563167
              70.2078881.4152012.8581851.8398181.518895
              80.2964140.4464530.0000540.3756941.689345
              93.0036200.9668990.1278122.6036362.162999
              +

              Thereafter we can select specific columns only and plot final results

              @@ -1563,6 +2664,143 @@ of dimensionality \(10\times 5\)
              +
              +
              + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
              FirstSecondThirdFourthFifth
              0-1.7497650.3426801.153036-0.2524360.981321
              10.5142190.221180-1.070043-0.1894960.255001
              2-0.4580270.435163-0.5835950.8168470.672721
              3-0.104411-0.5312801.029733-0.438136-1.118318
              41.6189821.541605-0.251879-0.8424360.184519
              50.9370820.7310001.361556-0.3262380.055676
              60.222400-1.443217-0.7563520.8164540.750445
              7-0.4559471.189622-1.690617-1.356399-1.232435
              8-0.544439-0.6681720.007315-0.6129391.299748
              9-1.733096-0.9833100.357508-1.6135791.470714
              +
              0.08352721390288316
+<class 'pandas.core.frame.DataFrame'>
+Int64Index: 10 entries, 0 to 9
+Data columns (total 5 columns):
+ #   Column  Non-Null Count  Dtype  
+---  ------  --------------  -----  
+ 0   First   10 non-null     float64
+ 1   Second  10 non-null     float64
+ 2   Third   10 non-null     float64
+ 3   Fourth  10 non-null     float64
+ 4   Fifth   10 non-null     float64
+dtypes: float64(5)
+memory usage: 480.0 bytes
+None
+           First     Second      Third     Fourth      Fifth
+count  10.000000  10.000000  10.000000  10.000000  10.000000
+mean   -0.175300   0.083527  -0.044334  -0.399836   0.331939
+std     1.069584   0.965548   1.018232   0.793167   0.918992
+min    -1.749765  -1.443217  -1.690617  -1.613579  -1.232435
+25%    -0.522836  -0.633949  -0.713163  -0.785061   0.087887
+50%    -0.280179   0.281930  -0.122282  -0.382187   0.463861
+75%     0.441264   0.657041   0.861676  -0.205231   0.923602
+max     1.618982   1.541605   1.361556   0.816847   1.470714
+
              +
              +_images/week34_86_2.png +_images/week34_86_3.png +

              We can produce a \(4\times 4\) matrix

              @@ -1574,6 +2812,19 @@ of dimensionality \(10\times 5\)
              +
              +
              [[ 0  1  2  3]
+ [ 4  5  6  7]
+ [ 8  9 10 11]
+ [12 13 14 15]]
+    0   1   2   3
+0   0   1   2   3
+1   4   5   6   7
+2   8   9  10  11
+3  12  13  14  15
+
              +
              +

              and many other operations.

              The Series class is another important class included in @@ -1582,8 +2833,8 @@ we have just a single column of data. It shares many of the same features as xarray. xarray has much of the same flexibility as pandas, but allows for the extension to higher dimensions than two. We will see examples later of the usage of both pandas and xarray.

              -
              -

              Simple linear regression model using scikit-learn#

              +
              +

              Simple linear regression model using scikit-learn

              We start with perhaps our simplest possible example, using Scikit-Learn to perform linear regression analysis on a data set produced by us.

              What follows is a simple Python code where we have defined a function \(y\) in terms of the variable \(x\). Both are defined as vectors with \(100\) entries. @@ -1646,6 +2897,9 @@ data with a straight line.

              +
              +_images/week34_93_0.png +

              This example serves several aims. It allows us to demonstrate several aspects of data analysis and later machine learning algorithms. The @@ -1727,6 +2981,9 @@ to be dominated by outliers.

              +
              +_images/week34_101_0.png +

              Depending on the parameter in front of the normal distribution, we may have a small or larger relative error. Try to play around with @@ -1770,6 +3027,19 @@ example of the functionality of Scikit-Learn.

              +
              +
              The intercept alpha: 
+ [2.18780801]
+Coefficient beta : 
+ [[4.72228205]]
+Mean squared error: 0.37
+Variance score: 0.83
+Mean squared log error: 0.01
+Mean absolute error: 0.47
+
              +
              +_images/week34_103_1.png +

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

              @@ -1827,9 +3097,9 @@ H_{\delta}(\boldsymbol{a})=\left\{\begin{array}{cc}\frac{1}{2} \boldsymbol{a}^{2

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

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

              - -
              -

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

              + +
              +

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

              Let us now dive into nuclear physics and remind ourselves briefly about some basic features about binding energies. A basic quantity which can be measured for the ground states of nuclei is the atomic mass \(M(N, Z)\) of the neutral atom with @@ -1890,9 +3160,9 @@ to the experimental data.

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

              -
              -
              -

              Organizing our data#

              + +
              +

              Organizing our data

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

              @@ -1975,6 +3245,11 @@ data) to actually open the file and simply take a look at it!

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

              The data we are interested in are in columns 2, 3, 4 and 11, giving us the number of neutrons, protons, mass numbers and binding energies, @@ -2003,6 +3278,40 @@ covert them into the pandas DataFrame structure.

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

              We have now read in the data, grouped them according to the variables we are interested in. We see how easy it is to reorganize the data using pandas. If we @@ -2077,9 +3386,9 @@ Now we can print measures of how our fit is doing, the coefficients from the fit -

              -
              -

              And what about using neural networks?#

              + +
              +

              And what about using neural networks?

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

              @@ -2117,10 +3426,10 @@ functionality.

              -
              - -
              -

              A first summary#

              + + +
              +

              A first summary

              The aim behind these introductory words was to present to you various Python libraries and their functionalities, in particular libraries like numpy, pandas, xarray and matplotlib and other that make our life much easier @@ -2129,9 +3438,9 @@ in handling various data sets and visualizing data.

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

              -
              -
              -

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

              + +
              +

              Why Linear Regression (aka Ordinary Least Squares and family)

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

              • Method of choice for fitting a continuous function!

                • @@ -2146,9 +3455,9 @@ Now it is time to dive more into the details of various methods. We will start w

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

              -
              -
              -

              Regression analysis, overarching aims#

              + +
              +

              Regression analysis, overarching aims

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

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

              @@ -2158,9 +3467,9 @@ The first variable is called the dependent, the outcome

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

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

              -
              -
              -

              Regression analysis, overarching aims II#

              + +
              +

              Regression analysis, overarching aims II

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

              @@ -2176,9 +3485,9 @@ between \(\boldsymbol{X}\) and the linear regression model where \(\boldsymbol{\beta} = [\beta_0, \ldots, \beta_{p-1}]^{T}\) are the regression parameters.

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

              -
              -
              -

              Examples#

              + +
              +

              Examples

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

              There we assumed that we could parametrize the data using a polynomial approximation based on the liquid drop model. @@ -2192,9 +3501,9 @@ This gives \(p=0,1,2,3,4\). Fu \(p\times n\) matrix \(\boldsymbol{X}\).

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

              -
              -
              -

              General linear models and linear algebra#

              + +
              +

              General linear models and linear algebra

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

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

              @@ -2202,9 +3511,9 @@ so-called \(\epsilon_i\) is the error in our approximation.

              -
              -
              -

              Rewriting the fitting procedure as a linear algebra problem#

              + +
              +

              Rewriting the fitting procedure as a linear algebra problem

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

              \[\begin{split} @@ -2216,9 +3525,9 @@ y_2&=\beta_0+\beta_1x_2^1+\beta_2x_2^2+\dots+\beta_{n-1}x_2^{n-1}+\epsilon_2 y_{n-1}&=\beta_0+\beta_1x_{n-1}^1+\beta_2x_{n-1}^2+\dots+\beta_{n-1}x_{n-1}^{n-1}+\epsilon_{n-1}.\\ \end{align*} \end{split}\]
              -
              -
              -

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

              + +
              +

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

              Defining the vectors

              \[ @@ -2252,9 +3561,9 @@ y_{n-1}&=\beta_0+\beta_1x_{n-1}^1+\beta_2x_{n-1}^2+\dots+\beta_{n-1}x_{n-1}^ \boldsymbol{y} = \boldsymbol{X}\boldsymbol{\beta}+\boldsymbol{\epsilon}. \]

              The above design matrix is called a Vandermonde matrix.

              -
              -
              -

              Generalizing the fitting procedure as a linear algebra problem#

              + +
              +

              Generalizing the fitting procedure as a linear algebra problem

              We are obviously not limited to the above polynomial expansions. We could replace the various powers of \(x\) with elements of Fourier series or instead of \(x_i^j\) we could have \(\cos{(j x_i)}\) or \(\sin{(j @@ -2273,9 +3582,9 @@ y_{n-1}&=\beta_0x_{n-1,0}+\beta_1x_{n-1,2}+\beta_2x_{n-1,2}+\dots+\beta_{n-1 \end{align*} \end{split}\]

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

              -
              -
              -

              Generalizing the fitting procedure as a linear algebra problem#

              + +
              +

              Generalizing the fitting procedure as a linear algebra problem

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

              \[\begin{split} @@ -2294,9 +3603,9 @@ x_{n-1,0}& x_{n-1,1} &x_{n-1,2}& \dots & \dots &x_{n-1,n-1}\ \boldsymbol{y} = \boldsymbol{X}\boldsymbol{\beta}+\boldsymbol{\epsilon}. \]

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

              -
              -
              -

              Optimizing our parameters#

              + +
              +

              Optimizing our parameters

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

              \[\begin{split} @@ -2313,9 +3622,9 @@ y_{n-1}&=\beta_0x_{n-1,0}+\beta_1x_{n-1,2}+\beta_2x_{n-1,2}+\dots+\beta_{n-1

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

              -
              -
              -

              Our model for the nuclear binding energies#

              + +
              +

              Our model for the nuclear binding energies

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

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

              @@ -2399,9 +3708,9 @@ our matrix as \(\boldsymbol{X}\in {\m \boldsymbol{\tilde{y}}= \boldsymbol{X}\boldsymbol{\beta}, \]

              throughout these lectures.

              -
              -
              -

              Optimizing our parameters, more details#

              + +
              +

              Optimizing our parameters, more details

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

              \[ @@ -2425,9 +3734,9 @@ the function \(C\) as

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

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

              -
              -
              -

              Interpretations and optimizing our parameters#

              + +
              +

              Interpretations and optimizing our parameters

              The function

              \[ @@ -2468,9 +3777,9 @@ will treat \(y_i\) as our exac \[ \frac{\partial C(\boldsymbol{\beta})}{\partial \boldsymbol{\beta}} = 0 = \boldsymbol{X}^T\left( \boldsymbol{y}-\boldsymbol{X}\boldsymbol{\beta}\right). \]
              -
              -
              -

              Interpretations and optimizing our parameters#

              + +
              +

              Interpretations and optimizing our parameters

              We can rewrite

              \[ @@ -2497,14 +3806,14 @@ regression or support vector machines, exhibit dimensionalities which allow for the usage of direct linear algebra methods such as LU decomposition or Singular Value Decomposition (SVD) for finding the inverse of the matrix \(\boldsymbol{X}^T\boldsymbol{X}\).

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

              -
              -
              -

              Some useful matrix and vector expressions#

              -

              See the handwritten notes at CompPhysics/MachineLearning

              + +
              +

              Some useful matrix and vector expressions

              +

              See the handwritten notes at https://github.com/CompPhysics/MachineLearning/blob/master/doc/HandWrittenNotes/2022/NotesExercise5Week452022.pdf

              These notes will be discussed during one of the lectures.

              -
              -
              -

              Interpretations and optimizing our parameters#

              + +
              +

              Interpretations and optimizing our parameters

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

              \[ @@ -2522,9 +3831,9 @@ allow for the usage of direct linear algebra methods such as LU \]

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

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

              -
              -
              -

              Own code for Ordinary Least Squares#

              + +
              +

              Own code for Ordinary Least Squares

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

              @@ -2565,9 +3874,9 @@ write

              -
              -
              -

              Adding error analysis and training set up#

              + +
              +

              Adding error analysis and training set up

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

              @@ -2608,9 +3917,9 @@ Since we are not using Scikit-Learn here we can define our own
              -
              -
              -

              The \(\chi^2\) function#

              + +
              +

              The \(\chi^2\) function

              Normally, the response (dependent or outcome) variable \(y_i\) is the outcome of a numerical experiment or another type of experiment and is thus only an approximation to the true value. It is then always @@ -2626,9 +3935,9 @@ as

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

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

              -
              -
              -

              The \(\chi^2\) function#

              + +
              +

              The \(\chi^2\) function

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

              \[ @@ -2645,9 +3954,9 @@ as

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

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

              -
              -
              -

              The \(\chi^2\) function#

              + +
              +

              The \(\chi^2\) function

              We can rewrite

              \[ @@ -2663,9 +3972,9 @@ as

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

              The \(\chi^2\) function#

              + +
              +

              The \(\chi^2\) function

              If we then introduce the matrix

              \[ @@ -2686,9 +3995,9 @@ as

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

              The \(\chi^2\) function#

              + +
              +

              The \(\chi^2\) function

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

              \[ @@ -2704,9 +4013,9 @@ y=y(x) \rightarrow y(x_i) \approx \beta_0+\beta_1 x_i. \[ \frac{\partial \chi^2(\boldsymbol{\beta})}{\partial \beta_1} = -\frac{2}{n}\left[ \sum_{i=0}^{n-1}x_i\left(\frac{y_i-\beta_0-\beta_1x_{i}}{\sigma_i^2}\right)\right]=0. \]
              -
              -
              -

              The \(\chi^2\) function#

              + +
              +

              The \(\chi^2\) function

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

              @@ -2742,9 +4051,9 @@ Defining

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

              -
              -
              -

              Fitting an Equation of State for Dense Nuclear Matter#

              + +
              +

              Fitting an Equation of State for Dense Nuclear Matter

              Before we continue, let us introduce yet another example. We are going to fit the nuclear equation of state using results from many-body calculations. The equation of state we have made available here, as function of @@ -2759,9 +4068,9 @@ before, with the same initializations and declarations. We use also instead of our own matrix inversion implementation. Furthermore, we sneak in Ridge regression (to be discussed below) which includes a hyperparameter \(\lambda\), also to be explained below.

              -
              -
              -

              The code#

              + +
              +

              The code

              # Common imports
@@ -2858,9 +4167,9 @@ to the data.
              
 
              We note also that there is a small deviation between the
 standard OLS and the Ridge regression at higher densities. We discuss this in more detail
 below.
              
-
              -
              -

              Splitting our Data in Training and Test data#

              + +
              +

              Splitting our Data in Training and Test data

              It is normal in essentially all Machine Learning studies to split the data in a training set and a test set (sometimes also an additional validation set). Scikit-Learn has an own function for this. There @@ -2941,13 +4250,13 @@ but now splitting the data into a training set and a test set.

              -
              -
              -

              Exercises#

              + +
              +

              Exercises

              Here are three possible exercises for week 34

              -
              -
              -

              Exercise 1: Setting up various Python environments#

              + +
              +

              Exercise 1: Setting up various Python environments

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

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

                • @@ -2963,7 +4272,7 @@ on Python.

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

                -
                  +

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

                  For Tensorflow, we recommend following the instructions in the text of @@ -2973,12 +4282,12 @@ you install the following Python packages via pip as

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

                  -
                    +

                    1. brew install python3

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

                    -
                      +

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

                      If you don’t want to perform these operations separately and venture @@ -3001,9 +4310,9 @@ distribution for scientific and analytic computing distribution and analysis environment, available for free and under a commercial license.

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

                      -
              -
              -

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

              + +
              +

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

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

              @@ -3014,7 +4323,7 @@ The following simple Python instructions define our +

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

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

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

                4. @@ -3037,9 +4346,9 @@ R^2(\boldsymbol{y}, \tilde{\boldsymbol{y}}) = 1 - \frac{\sum_{i=0}^{n - 1} (y_i \]

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

              -
              -
              -

              Exercise 3: Split data in test and training data#

              + +
              +

              Exercise 3: Split data in test and training data

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

              @@ -3064,8 +4373,8 @@ Write thereafter (using either scikit-learn or your matrix inve and perform an ordinary least squares fitting and compute the mean squared error for the training data and the test data. These calculations should apply to a model given by a fifth-order polynomial.

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

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

              -By Morten Hjorth-Jensen -

              - -
              - -
              - -

              - - © Copyright 2022. -
              - -

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

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

              +
              + + + + + + + -
              -
              \ No newline at end of file diff --git a/doc/LectureNotes/_build/html/week35.html b/doc/LectureNotes/_build/html/week35.html index 0590e16d6..cffd2608b 100644 --- a/doc/LectureNotes/_build/html/week35.html +++ b/doc/LectureNotes/_build/html/week35.html @@ -1,61 +1,50 @@ - - - - + - - + Week 35: From Ordinary Linear Regression to Ridge and Lasso Regression — Applied Data Analysis and Machine Learning - - - - - - - - - + + + - - - - - + + + + + + + + - + - + - - - - - - + + + + - - - - + + - - - + + + + + + - - - + + + - - - - -
              + - - - - - - - - - - -
              -
              -
              -
              - - - Ctrl+K -
              -
              - - - -
              -
              +
              +
              + +

              It is now useful to look at the correlation matrix

              @@ -1844,6 +2955,12 @@ the house using the features (predictors) listed here.

              +
              +
              <AxesSubplot:>
+
              +
              +_images/week35_201_1.png +

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

              @@ -1864,6 +2981,9 @@ the house using the features (predictors) listed here.

              +
              +_images/week35_203_0.png +

              Now we start training our model

              @@ -1889,6 +3009,14 @@ the house using the features (predictors) listed here.

              +
              +
              (404, 2)
+(102, 2)
+(404,)
+(102,)
+
              +
              +

              Then we use the linear regression functionality from Scikit-Learn

              @@ -1927,6 +3055,20 @@ the house using the features (predictors) listed here.

              +
              +
              The model performance for training set
+--------------------------------------
+RMSE is 5.637129335071195
+R2 score is 0.6300745149331701
+
+
+The model performance for testing set
+--------------------------------------
+RMSE is 5.137400784702911
+R2 score is 0.6628996975186953
+
              +
              +
              @@ -1937,13 +3079,16 @@ the house using the features (predictors) listed here.

              +
              +_images/week35_210_0.png
              - -
              -

              Material for lecture Thursday, August 31#

              -
              -
              -

              Mathematical Interpretation of Ordinary Least Squares#

              + + +
              +

              Material for lecture Thursday, August 31

              +
              +
              +

              Mathematical Interpretation of Ordinary Least Squares

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

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

              @@ -1968,18 +3113,18 @@ the house using the features (predictors) listed here.

              \]

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

              -
              -
              -

              Residual Error#

              + +
              +

              Residual Error

              We have defined the residual error as

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

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

              -
              -
              -

              Simple case#

              + +
              +

              Simple case

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

              \[ @@ -1996,9 +3141,9 @@ We can then interpret our optimal model -

              The singular value decomposition#

              +
              +
              +

              The singular value decomposition

              The examples we have looked at so far are cases where we normally can invert the matrix \(\boldsymbol{X}^T\boldsymbol{X}\). Using a polynomial expansion where we fit of various functions leads to row vectors of the design matrix which are essentially orthogonal due @@ -2024,9 +3169,9 @@ to the covariance matrix (and thereby the correlation matrix) and in turn the variance of a given quantity. It plays also an important role in the principal component analysis where high-dimensional data can be reduced to the statistically relevant features.

              -
              -
              -

              Linear Regression Problems#

              + +
              +

              Linear Regression Problems

              One of the typical problems we encounter with linear regression, in particular when the matrix \(\boldsymbol{X}\) (our so-called design matrix) is high-dimensional, are problems with near singular or singular matrices. The column vectors of \(\boldsymbol{X}\) @@ -2068,9 +3213,9 @@ that the inverse of the matrix \(\bol \end{split}\]

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

              -
              -
              -

              Fixing the singularity#

              + +
              +

              Fixing the singularity

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

              @@ -2092,9 +3237,9 @@ the regression parameters \(\beta_i\) \boldsymbol{X}^{T} \boldsymbol{X} \rightarrow \boldsymbol{X}^{T} \boldsymbol{X}+\lambda \boldsymbol{I}, \]

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

              -
              -
              -

              Basic math of the SVD#

              + +
              +

              Basic math of the SVD

              From standard linear algebra we know that a square matrix \(\boldsymbol{X}\) can be diagonalized if and only it is a so-called normal matrix, that is if \(\boldsymbol{X}\in {\mathbb{R}}^{n\times n}\) we have \(\boldsymbol{X}\boldsymbol{X}^T=\boldsymbol{X}^T\boldsymbol{X}\) or if \(\boldsymbol{X}\in {\mathbb{C}}^{n\times n}\) we have \(\boldsymbol{X}\boldsymbol{X}^{\dagger}=\boldsymbol{X}^{\dagger}\boldsymbol{X}\). @@ -2124,9 +3269,9 @@ The matrix has then a set of eigenpairs

              \end{split}\]

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

              -
              -
              -

              The SVD, a Fantastic Algorithm#

              + +
              -
              -

              Economy-size SVD#

              + +
              +

              Economy-size SVD

              If we assume that \(n > p\), then our matrix \(\boldsymbol{U}\) has dimension \(n \times n\). The last \(n-p\) columns of \(\boldsymbol{U}\) become however irrelevant in our calculations since they are multiplied with the @@ -2179,9 +3324,9 @@ the decomposition.

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

              -
              -
              -

              Codes for the SVD#

              + +
              +

              Codes for the SVD

              import numpy as np
@@ -2216,6 +3361,25 @@ In general the economy-size SVD leads to less FLOPS and still conserving the des
              +
              +
              [[ 1. -1.]
+ [ 1. -1.]]
+test U
+[[0. 0.]
+ [0. 0.]]
+test VT
+[[0. 0.]
+ [0. 0.]]
+[[-0.70710678 -0.70710678]
+ [-0.70710678  0.70710678]]
+[2.00000000e+00 3.35470445e-17]
+[[-0.70710678  0.70710678]
+ [ 0.70710678  0.70710678]]
+[[-3.33066907e-16  4.44089210e-16]
+ [ 0.00000000e+00  2.22044605e-16]]
+
              +
              +

              The matrix \(\boldsymbol{X}\) has columns that are linearly dependent. The first column is the row-wise sum of the other two columns. The rank of a @@ -2225,9 +3389,9 @@ independent columns, in this case just \(\boldsymbol{X}^T\boldsymbol{X}\) results in the program terminating due to a singular matrix.

              -
              -
              -

              Note about SVD Calculations#

              + +
              +

              Note about SVD Calculations

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

              As you can see from the code, the \(S\) vector must be converted into a @@ -2238,9 +3402,9 @@ matrix.

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

              -
              -
              -

              Mathematics of the SVD and implications#

              + +
              +

              Mathematics of the SVD and implications

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

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

              @@ -2267,9 +3431,9 @@ x_{n-1,0} & x_{n-1,1} & x_{n-1,2}& \dots & \dots x_{n-1,p-1}\\ \sigma_0 > \sigma_1 > \sigma_2 > \dots > \sigma_{p-1} > 0. \]

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

              -
              -
              -

              Example Matrix#

              + +
              +

              Example Matrix

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

              \[\begin{split} @@ -2320,9 +3484,9 @@ x_{n-1,0} & x_{n-1,1} & x_{n-1,2}& \dots & \dots x_{n-1,p-1}\\

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

              -
              -
              -

              Setting up the Matrix to be inverted#

              + +
              +

              Setting up the Matrix to be inverted

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

              \[ @@ -2356,9 +3520,9 @@ vectors of the matrix \(\boldsymbol{U \(p-1\), that is \(\boldsymbol{\tilde{y}}\ne \boldsymbol{y}\). We can thus not use the orthogonality relation for the matrix \(\boldsymbol{U}\). This can already be when we multiply the matrices \(\boldsymbol{\Sigma}^T\boldsymbol{U}^T\).

              -
              -
              -

              Further properties (important for our analyses later)#

              + +
              +

              Further properties (important for our analyses later)

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

              \[ @@ -2399,9 +3563,9 @@ always refer to the number of features in our data set, while the number of rows represents the number of data inputs. Note that in other texts you may find the opposite notation. This has consequences for the definition of for example the covariance matrix and its relation to the SVD.

              -
              -
              -

              Meet the Covariance Matrix#

              + +
              +

              Meet the Covariance Matrix

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

              We have already noted that the matrix \(\boldsymbol{X}^T\boldsymbol{X}\) in ordinary least squares is proportional to the second derivative of the cost @@ -2420,9 +3584,9 @@ function, that is we have

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

              -
              -
              -

              Introducing the Covariance and Correlation functions#

              + +
              +

              Introducing the Covariance and Correlation functions

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

              Suppose we have defined two vectors @@ -2458,9 +3622,9 @@ and covariance. It also partially corrects the bias in the estimation of the population standard deviation. If you use a library like Scikit-Learn or nunmpy’s function to calculate the covariance, this quantity will be computed with a factor \(1/(n-1)\).

              -
              -
              -

              Covariance and Correlation Matrix#

              + +
              +

              Covariance and Correlation Matrix

              The covariance takes values between zero and infinity and may thus lead to problems with loss of numerical precision for particularly large values. It is common to scale the covariance matrix by @@ -2481,9 +3645,9 @@ and \(\boldsymbol{y}\) as

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

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

              -
              -
              -

              Correlation Function and Design/Feature Matrix#

              + +
              +

              Correlation Function and Design/Feature Matrix

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

              @@ -2536,9 +3700,9 @@ covariance matrix for the vectors \(\ \mathrm{corr}[\boldsymbol{x}_{p-1},\boldsymbol{x}_0] & \mathrm{corr}[\boldsymbol{x}_{p-1},\boldsymbol{x}_1] & \mathrm{corr}[\boldsymbol{x}_{p-1},\boldsymbol{x}_{2}] & \dots & \dots & 1\\ \end{bmatrix}, \end{split}\]
              -
              -
              -

              Covariance Matrix Examples#

              + +
              +

              Covariance Matrix Examples

              The Numpy function np.cov calculates the covariance elements using the factor \(1/(n-1)\) instead of \(1/n\) since it assumes we do not have the exact mean values. The following simple function uses the @@ -2571,10 +3735,18 @@ covariance matrix through the np.linalg.eig() function.

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

              Correlation Matrix#

              + + + +
              +

              Correlation Matrix

              The previous example can be converted into the correlation matrix by simply scaling the matrix elements with the variances. We should also subtract the mean values for each column. This leads to the following @@ -2606,14 +3778,22 @@ a more brute force way. Here we scale the mean values for each column of the des

              +
              +
              0.08881497884574564
+1.7086067479626619
+[[1.         0.66080313]
+ [0.66080313 1.        ]]
+
              +
              +

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

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

              -
              -
              -

              Correlation Matrix with Pandas#

              + +
              +

              Correlation Matrix with Pandas

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

              @@ -2634,11 +3814,39 @@ this matrix we easily see that it is a positive definite matrix.

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

              We expand this model to the Franke function discussed above.

              -
              -
              -

              Correlation Matrix with Pandas and the Franke function#

              + +
              +

              Correlation Matrix with Pandas and the Franke function

              # Common imports
@@ -2687,6 +3895,43 @@ this matrix we easily see that it is a positive definite matrix.
              +
              +
                   0         1         2         3         4         5         6         7   \
+0   0.0  0.000000  0.000000  0.000000  0.000000  0.000000  0.000000  0.000000   
+1   0.0  0.078974  0.081276  0.075889  0.077168  0.078540  0.065410  0.066474   
+2   0.0  0.081276  0.084076  0.078336  0.079946  0.081655  0.067707  0.069028   
+3   0.0  0.075889  0.078336  0.077460  0.078986  0.080616  0.069452  0.070737   
+4   0.0  0.077168  0.079946  0.078986  0.080764  0.082653  0.070986  0.072476   
+5   0.0  0.078540  0.081655  0.080616  0.082653  0.084809  0.072621  0.074323   
+6   0.0  0.065410  0.067707  0.069452  0.070986  0.072621  0.064074  0.065378   
+7   0.0  0.066474  0.069028  0.070737  0.072476  0.074323  0.065378  0.066854   
+8   0.0  0.067637  0.070457  0.072132  0.074084  0.076150  0.066787  0.068441   
+9   0.0  0.068906  0.072000  0.073644  0.075816  0.078110  0.068307  0.070146   
+10  0.0  0.055734  0.057835  0.060872  0.062337  0.063894  0.057393  0.058645   
+11  0.0  0.056683  0.058996  0.062016  0.063653  0.065390  0.058552  0.059951   
+12  0.0  0.057722  0.060254  0.063260  0.065077  0.066999  0.059807  0.061359   
+13  0.0  0.058854  0.061614  0.064609  0.066612  0.068727  0.061163  0.062874   
+14  0.0  0.060083  0.063080  0.066066  0.068264  0.070582  0.062624  0.064501   
+
+          8         9         10        11        12        13        14  
+0   0.000000  0.000000  0.000000  0.000000  0.000000  0.000000  0.000000  
+1   0.067637  0.068906  0.055734  0.056683  0.057722  0.058854  0.060083  
+2   0.070457  0.072000  0.057835  0.058996  0.060254  0.061614  0.063080  
+3   0.072132  0.073644  0.060872  0.062016  0.063260  0.064609  0.066066  
+4   0.074084  0.075816  0.062337  0.063653  0.065077  0.066612  0.068264  
+5   0.076150  0.078110  0.063894  0.065390  0.066999  0.068727  0.070582  
+6   0.066787  0.068307  0.057393  0.058552  0.059807  0.061163  0.062624  
+7   0.068441  0.070146  0.058645  0.059951  0.061359  0.062874  0.064501  
+8   0.070213  0.072111  0.059993  0.061452  0.063019  0.064699  0.066500  
+9   0.072111  0.074210  0.061443  0.063061  0.064793  0.066647  0.068629  
+10  0.059993  0.061443  0.052305  0.053417  0.054617  0.055910  0.057300  
+11  0.061452  0.063061  0.053417  0.054655  0.055987  0.057418  0.058952  
+12  0.063019  0.064793  0.054617  0.055987  0.057457  0.059031  0.060716  
+13  0.064699  0.066647  0.055910  0.057418  0.059031  0.060756  0.062599  
+14  0.066500  0.068629  0.057300  0.058952  0.060716  0.062599  0.064606  
+
              +
              +

              We note here that the covariance is zero for the first rows and columns since all matrix elements in the design matrix were set to one @@ -2695,9 +3940,9 @@ columns since all matrix elements in the design matrix were set to one cause problems when we set up the correlation matrix. We can simply drop these elements and construct a correlation matrix without these elements.

              -
              -
              -

              Rewriting the Covariance and/or Correlation Matrix#

              + +
              +

              Rewriting the Covariance and/or Correlation Matrix

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

              \[ @@ -2730,9 +3975,9 @@ x_{01}x_{00}+x_{11}x_{10} & x_{01}^2+x_{11}^2\\ \end{split}\]

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

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

              -
              -
              -

              Linking with the SVD#

              + +
              +

              Linking with the SVD

              We saw earlier that

              \[ @@ -2763,9 +4008,9 @@ x_{01}x_{00}+x_{11}x_{10} & x_{01}^2+x_{11}^2\\ \[ \left(\boldsymbol{X}^T\boldsymbol{X}\right)\boldsymbol{V}=\boldsymbol{V}\tilde{\boldsymbol{\Sigma}}^2. \]
              -
              -
              -

              What does it mean?#

              + +
              +

              What does it mean?

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

              @@ -2794,9 +4039,9 @@ matrix. Every singular value of \(\bo root of an eigenvalue of \(\boldsymbol{X}^T\boldsymbol{X}\). If the matrix \(\boldsymbol{X}\) is self-adjoint, the singular values of \(\boldsymbol{X}\) are equal to the absolute value of the eigenvalues of \(\boldsymbol{X}\).

              -
              -
              -

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

              + +
              +

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

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

              \[ @@ -2824,9 +4069,9 @@ measure how much correlations are contained in the rows of \(\boldsymbol{X}\), the quantity of interest for us are the non-zero singular values and the column vectors of \(\boldsymbol{V}\).

              -
              -
              -

              Ridge and LASSO Regression#

              + +
              +

              Ridge and LASSO Regression

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

              @@ -2872,9 +4117,9 @@ C(\boldsymbol{X},\boldsymbol{\beta})=\frac{1}{n}\vert\vert \boldsymbol{y}-\bolds \[ \vert\vert \boldsymbol{x}\vert\vert_1 = \sum_i \vert x_i\vert. \]
              -
              -
              -

              Deriving the Ridge Regression Equations#

              + +
              +

              Deriving the Ridge Regression Equations

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

              \[ @@ -2924,9 +4169,9 @@ We have already analyzed the OLS solutions in terms of the eigenvectors (the col \tilde{\boldsymbol{y}}_{\mathrm{Ridge}}=\boldsymbol{X}\boldsymbol{\beta}_{\mathrm{Ridge}} = \boldsymbol{U\Sigma V^T}\left(\boldsymbol{V}\boldsymbol{\Sigma}^2\boldsymbol{V}^T+\lambda\boldsymbol{I} \right)^{-1}(\boldsymbol{U\Sigma V^T})^T\boldsymbol{y}=\sum_{j=0}^{p-1}\boldsymbol{u}_j\boldsymbol{u}_j^T\frac{\sigma_j^2}{\sigma_j^2+\lambda}\boldsymbol{y}, \]

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

              -
              -
              -

              Interpreting the Ridge results#

              + +
              +

              Interpreting the Ridge results

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

              \[ @@ -2938,9 +4183,9 @@ orthonormal basis \(\boldsymbol{U}\)< eigenvalues ordered in a descending way, that is \(\sigma_i \geq \sigma_{i+1}\).

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

              -
              -
              -

              More interpretations#

              + +
              +

              More interpretations

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

              \[ @@ -2962,9 +4207,9 @@ infinity.

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

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

              -
              -
              -

              Deriving the Lasso Regression Equations#

              + +
              +

              Deriving the Lasso Regression Equations

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

              \[ @@ -2991,8 +4236,8 @@ C(\boldsymbol{X},\boldsymbol{\beta})=\frac{1}{n}\left\{(\boldsymbol{y}-\boldsymb \boldsymbol{X}^T\boldsymbol{X}\boldsymbol{\beta}+\lambda sgn(\boldsymbol{\beta})=2\boldsymbol{X}^T\boldsymbol{y}. \]

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

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

              - -
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              - - © Copyright 2022. -
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              - -
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              +

              + + By Morten Hjorth-Jensen
              + + © Copyright 2021.
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              +
              + + + + + + + -
              -
              \ No newline at end of file diff --git a/doc/LectureNotes/_build/html/week36.html b/doc/LectureNotes/_build/html/week36.html index 108d88e44..a59e1bf7a 100644 --- a/doc/LectureNotes/_build/html/week36.html +++ b/doc/LectureNotes/_build/html/week36.html @@ -1,61 +1,50 @@ - - - - + - - + Week 36: Statistical interpretation of Linear Regression and Resampling techniques — Applied Data Analysis and Machine Learning - - - - - - - - - + + + - - - - - + + + + + + + + - + - + - - - - - - + + + + - - - - + + - - - + + + + + + - - - + + + - - - - -
              + - - - - - - - - - - -
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              - - - Ctrl+K -
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              + + By Morten Hjorth-Jensen
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              \ No newline at end of file diff --git a/doc/LectureNotes/_build/html/week37.html b/doc/LectureNotes/_build/html/week37.html index 7e955490c..1fe998ab7 100644 --- a/doc/LectureNotes/_build/html/week37.html +++ b/doc/LectureNotes/_build/html/week37.html @@ -1,61 +1,50 @@ - - - - + - - + Week 37: Statistical interpretations and Resampling Methods — Applied Data Analysis and Machine Learning - - - - - - - - - + + + - - - - - + + + + + + + + - + - + - - - - - - + + + + - - - - + + - - - + + + + + + - - - + + + - - - - -
              + - - - - - - - - - - -
              -
              -
              -
              - - - Ctrl+K -
              -
              - - - -
              -
              +
              +
              + + +
              +

              Notes on scaling with examples

              The programs here use both ordinrary least squares (OLS) and Ridge regression with one value only for the hyperparameter \(\lambda\). The first example has no scaling and includes the intercept as well and we @@ -2010,6 +2709,19 @@ we subtract the mean values).

              +
              +
              [1.79934087 0.47179152 5.01549939]
+[1.79909592 0.47176716 5.01550546]
+  
+test MSE of OLS:
+1.139431112903922
+  
+test MSE of Ridge
+1.1395235273363669
+
              +
              +_images/week37_176_1.png +

              In this example we do not include the intercept and we scale the data by subtracting the mean values. This follows the discussion in the lecture material. see also the weekly slides for week 36. @@ -2170,6 +2882,21 @@ and \(\tilde{X}_{ij} = X_{ij} - \frac

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

              Finally, instead of using our own function we repeat the same example using the standardscaler functionality of the library @@ -2238,9 +2965,23 @@ using the standardscaler functionality of the library +

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

              -By Morten Hjorth-Jensen -

              - -
              - -
              - -

              - - © Copyright 2022. -
              - -

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

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

              +
              + + + + + + + -
              -
              \ No newline at end of file diff --git a/doc/LectureNotes/_build/html/week38.html b/doc/LectureNotes/_build/html/week38.html index 6a43181c4..b784da4b8 100644 --- a/doc/LectureNotes/_build/html/week38.html +++ b/doc/LectureNotes/_build/html/week38.html @@ -1,61 +1,50 @@ - - - - + - - + Week 38: Logistic Regression and Optimization — Applied Data Analysis and Machine Learning - - - - - - - - - + + + - - - - - + + + + + + + + - + - + - - - - - - + + + + - - - - + + - - + - - + + + + + + - - - + + + - - - - -
              + - - - - - - - - - - -
              -
              -
              -
              - - - Ctrl+K -
              -
              - - - -
              -
              +
              +
              + + +
              +

              Two parameters

              We assume now that we have two classes with \(y_i\) either \(0\) or \(1\). Furthermore we assume also that we have only two parameters \(\beta\) in our fitting of the Sigmoid function, that is we define probabilities

              \[\begin{split} @@ -1016,9 +1675,9 @@ p(y_i=0|x_i,\boldsymbol{\beta}) &= 1 - p(y_i=1|x_i,\boldsymbol{\beta}), \[ p(y_i=0\vert x_i, \boldsymbol{\beta}) = 1-p(y_i=1\vert x_i, \boldsymbol{\beta}). \]
              - -
              -

              Maximum likelihood#

              +
              +
              +

              Maximum likelihood

              In order to define the total likelihood for all possible outcomes from a
              dataset \(\mathcal{D}=\{(y_i,x_i)\}\), with the binary labels \(y_i\in\{0,1\}\) and where the data points are drawn independently, we use the so-called Maximum Likelihood Estimation (MLE) principle. @@ -1036,9 +1695,9 @@ P(\mathcal{D}|\boldsymbol{\beta})& = \prod_{i=1}^n \left[p(y_i=1|x_i,\boldsy \[ \mathcal{C}(\boldsymbol{\beta}) = \sum_{i=1}^n \left( y_i\log{p(y_i=1|x_i,\boldsymbol{\beta})} + (1-y_i)\log\left[1-p(y_i=1|x_i,\boldsymbol{\beta}))\right]\right). \]

              - -
              -

              The cost function rewritten#

              +
              +
              +

              The cost function rewritten

              Reordering the logarithms, we can rewrite the cost/loss function as

              \[ @@ -1052,9 +1711,9 @@ Since the cost (error) function is just the negative log-likelihood, for logisti \]

              This equation is known in statistics as the cross entropy. Finally, we note that just as in linear regression, in practice we often supplement the cross-entropy with additional regularization terms, usually \(L_1\) and \(L_2\) regularization as we did for Ridge and Lasso regression.

              - -
              -

              Minimizing the cross entropy#

              +
              +
              +

              Minimizing the cross entropy

              The cross entropy is a convex function of the weights \(\boldsymbol{\beta}\) and, therefore, any local minimizer is a global minimizer.

              Minimizing this @@ -1068,9 +1727,9 @@ cost function with respect to the two parameters -

              A more compact expression#

              +
              +
              +

              A more compact expression

              Let us now define a vector \(\boldsymbol{y}\) with \(n\) elements \(y_i\), an \(n\times p\) matrix \(\boldsymbol{X}\) which contains the \(x_i\) values and a vector \(\boldsymbol{p}\) of fitted probabilities \(p(y_i\vert x_i,\boldsymbol{\beta})\). We can rewrite in a more compact form the first @@ -1085,9 +1744,9 @@ derivative of cost function as

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

              Extending to more predictors#

              +
              +
              +

              Extending to more predictors

              Within a binary classification problem, we can easily expand our model to include multiple predictors. Our ratio between likelihoods is then with \(p\) predictors

              \[ @@ -1098,9 +1757,9 @@ derivative of cost function as

              \[ p(\boldsymbol{\beta}\boldsymbol{x})=\frac{ \exp{(\beta_0+\beta_1x_1+\beta_2x_2+\dots+\beta_px_p)}}{1+\exp{(\beta_0+\beta_1x_1+\beta_2x_2+\dots+\beta_px_p)}}. \]
              - -
              -

              Including more classes#

              +
              +
              +

              Including more classes

              Till now we have mainly focused on two classes, the so-called binary system. Suppose we wish to extend to \(K\) classes. Let us for the sake of simplicity assume we have only two predictors. We have then following model

              @@ -1120,9 +1779,9 @@ of simplicity assume we have only two predictors. We have then following model

              and the model is specified in term of \(K-1\) so-called log-odds or logit transformations.

              - -
              -

              More classes#

              +
              +
              +

              More classes

              In our discussion of neural networks we will encounter the above again in terms of a slightly modified function, the so-called Softmax function.

              The softmax function is used in various multiclass classification @@ -1150,12 +1809,12 @@ what we derived earlier is compatible with these equations.

              descent method. Newton’s method and gradient descent methods are discussed in the material on optimization methods.

              - -
              -

              Friday September 23#

              -
              -
              -

              Searching for Optimal Regularization Parameters \(\lambda\)#

              +
              +
              +

              Friday September 23

              +
              +
              +

              Searching for Optimal Regularization Parameters \(\lambda\)

              In project 1, when using Ridge and Lasso regression, we end up searching for the optimal parameter \(\lambda\) which minimizes our selected scores (MSE or \(R2\) values for example). The brute force @@ -1212,13 +1871,16 @@ which results in optimal scores (for example the smallest MSE or an

              +
              +_images/week38_72_0.png +

              Here we have performed a rather data greedy calculation as function of the regularization parameter \(\lambda\). There is no resampling here. The latter can easily be added by employing the function RidgeCV instead of just calling the Ridge function. For RidgeCV we need to pass the array of \(\lambda\) values. By inspecting the figure we can in turn determine which is the optimal regularization parameter. This becomes however less functional in the long run.

              - - - -
              -

              Wisconsin Cancer Data#

              + + + +
              +

              Wisconsin Cancer Data

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

              @@ -1362,10 +2044,27 @@ classification.

              +
              +
              (426, 30)
+(143, 30)
+Test set accuracy with Logistic Regression: 0.94
+
              -
              -
              -

              Using the correlation matrix#

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

              Using the correlation matrix

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

              @@ -1407,10 +2106,14 @@ We use Pandas to compute the correlation matrix.

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

              Discussing the correlation data#

              + + +
              +

              Discussing the correlation data

              In the above example we note two things. In the first plot we display the overlap of benign and malignant tumors as functions of the various features in the Wisconsing breast cancer data set. We see that for @@ -1441,9 +2144,9 @@ matrix.

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

              -
              -
              -

              Other measures in classification studies: Cancer Data again#

              + +
              +

              Other measures in classification studies: Cancer Data again

              import matplotlib.pyplot as plt
@@ -1481,10 +2184,112 @@ applications. This will be discussed later this semester (
+
              (426, 30)
+(143, 30)
+[1.         0.86666667 1.         0.92857143 1.         0.85714286
+ 1.         0.92857143 0.92857143 1.        ]
+Test set accuracy with Logistic Regression: 0.94
+
              
 
              
-
              -
              -

              Optimization, the central part of any Machine Learning algortithm#

              +
              /Users/mhjensen/miniforge3/envs/myenv/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:814: ConvergenceWarning: lbfgs failed to converge (status=1):
+STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
+
+Increase the number of iterations (max_iter) or scale the data as shown in:
+    https://scikit-learn.org/stable/modules/preprocessing.html
+Please also refer to the documentation for alternative solver options:
+    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
+  n_iter_i = _check_optimize_result(
+/Users/mhjensen/miniforge3/envs/myenv/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:814: ConvergenceWarning: lbfgs failed to converge (status=1):
+STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
+
+Increase the number of iterations (max_iter) or scale the data as shown in:
+    https://scikit-learn.org/stable/modules/preprocessing.html
+Please also refer to the documentation for alternative solver options:
+    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
+  n_iter_i = _check_optimize_result(
+/Users/mhjensen/miniforge3/envs/myenv/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:814: ConvergenceWarning: lbfgs failed to converge (status=1):
+STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
+
+Increase the number of iterations (max_iter) or scale the data as shown in:
+    https://scikit-learn.org/stable/modules/preprocessing.html
+Please also refer to the documentation for alternative solver options:
+    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
+  n_iter_i = _check_optimize_result(
+/Users/mhjensen/miniforge3/envs/myenv/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:814: ConvergenceWarning: lbfgs failed to converge (status=1):
+STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
+
+Increase the number of iterations (max_iter) or scale the data as shown in:
+    https://scikit-learn.org/stable/modules/preprocessing.html
+Please also refer to the documentation for alternative solver options:
+    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
+  n_iter_i = _check_optimize_result(
+/Users/mhjensen/miniforge3/envs/myenv/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:814: ConvergenceWarning: lbfgs failed to converge (status=1):
+STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
+
+Increase the number of iterations (max_iter) or scale the data as shown in:
+    https://scikit-learn.org/stable/modules/preprocessing.html
+Please also refer to the documentation for alternative solver options:
+    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
+  n_iter_i = _check_optimize_result(
+/Users/mhjensen/miniforge3/envs/myenv/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:814: ConvergenceWarning: lbfgs failed to converge (status=1):
+STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
+
+Increase the number of iterations (max_iter) or scale the data as shown in:
+    https://scikit-learn.org/stable/modules/preprocessing.html
+Please also refer to the documentation for alternative solver options:
+    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
+  n_iter_i = _check_optimize_result(
+/Users/mhjensen/miniforge3/envs/myenv/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:814: ConvergenceWarning: lbfgs failed to converge (status=1):
+STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
+
+Increase the number of iterations (max_iter) or scale the data as shown in:
+    https://scikit-learn.org/stable/modules/preprocessing.html
+Please also refer to the documentation for alternative solver options:
+    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
+  n_iter_i = _check_optimize_result(
+/Users/mhjensen/miniforge3/envs/myenv/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:814: ConvergenceWarning: lbfgs failed to converge (status=1):
+STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
+
+Increase the number of iterations (max_iter) or scale the data as shown in:
+    https://scikit-learn.org/stable/modules/preprocessing.html
+Please also refer to the documentation for alternative solver options:
+    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
+  n_iter_i = _check_optimize_result(
+/Users/mhjensen/miniforge3/envs/myenv/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:814: ConvergenceWarning: lbfgs failed to converge (status=1):
+STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
+
+Increase the number of iterations (max_iter) or scale the data as shown in:
+    https://scikit-learn.org/stable/modules/preprocessing.html
+Please also refer to the documentation for alternative solver options:
+    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
+  n_iter_i = _check_optimize_result(
+/Users/mhjensen/miniforge3/envs/myenv/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:814: ConvergenceWarning: lbfgs failed to converge (status=1):
+STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
+
+Increase the number of iterations (max_iter) or scale the data as shown in:
+    https://scikit-learn.org/stable/modules/preprocessing.html
+Please also refer to the documentation for alternative solver options:
+    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
+  n_iter_i = _check_optimize_result(
+/Users/mhjensen/miniforge3/envs/myenv/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:814: ConvergenceWarning: lbfgs failed to converge (status=1):
+STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
+
+Increase the number of iterations (max_iter) or scale the data as shown in:
+    https://scikit-learn.org/stable/modules/preprocessing.html
+Please also refer to the documentation for alternative solver options:
+    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
+  n_iter_i = _check_optimize_result(
+
              +
              +_images/week38_89_2.png +_images/week38_89_3.png +_images/week38_89_4.png + + + +
              +

              Optimization, the central part of any Machine Learning algortithm

              Overview Video, why do we care about gradient methods?

              Almost every problem in machine learning and data science starts with a dataset \(X\), a model \(g(\beta)\), which is a function of the @@ -1494,9 +2299,9 @@ us to judge how well the model \(g(\b the cost function. Ideally we would be able to solve for \(\beta\) analytically, however this is not possible in general and we must use some approximative/numerical method to compute the minimum.

              -
              -
              -

              Revisiting our Logistic Regression case#

              + +
              +

              Revisiting our Logistic Regression case

              In our discussion on Logistic Regression we studied the case of two classes, with \(y_i\) either @@ -1511,9 +2316,9 @@ p(y_i=0|x_i,\boldsymbol{\beta}) &= 1 - p(y_i=1|x_i,\boldsymbol{\beta}), \end{align*} \end{split}\]

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

              -
              -
              -

              The equations to solve#

              + +
              +

              The equations to solve

              Our compact equations used a definition of a vector \(\boldsymbol{y}\) with \(n\) elements \(y_i\), an \(n\times p\) matrix \(\boldsymbol{X}\) which contains the \(x_i\) values and a vector \(\boldsymbol{p}\) of fitted probabilities @@ -1530,9 +2335,9 @@ the first derivative of the cost function as

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

              This defines what is called the Hessian matrix.

              -
              -
              -

              Solving using Newton-Raphson’s method#

              + +
              +

              Solving using Newton-Raphson’s method

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

              Our iterative scheme is then given by

              @@ -1546,9 +2351,9 @@ the first derivative of the cost function as

              \]

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

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

              -
              -
              -

              Brief reminder on Newton-Raphson’s method#

              + +
              +

              Brief reminder on Newton-Raphson’s method

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

              Perhaps the most celebrated of all one-dimensional root-finding routines is Newton’s method, also called the Newton-Raphson @@ -1557,9 +2362,9 @@ function \(f\) and its derivat If you can only calculate the derivative numerically and/or your function is not of the smooth type, we normally discourage the use of this method.

              -
              -
              -

              The equations#

              + +
              +

              The equations

              The Newton-Raphson formula consists geometrically of extending the tangent line at a current point until it crosses zero, then setting the next guess to the abscissa of that zero-crossing. The mathematics @@ -1588,9 +2393,9 @@ s\approx x-\frac{f(x)}{f'(x)}. \[ x_{n+1}=x_n-\frac{f(x_n)}{f'(x_n)}. \]

              -
              -
              -

              Simple geometric interpretation#

              + +
              +

              Simple geometric interpretation

              The above is Newton-Raphson’s method. It has a simple geometric interpretation, namely \(x_{n+1}\) is the point where the tangent from \((x_n,f(x_n))\) crosses the \(x\)-axis. Close to the solution, @@ -1602,9 +2407,9 @@ from the true root as to let the search interval include a local maximum or minimum of the function. If an iteration places a trial guess near such a local extremum, so that the first derivative nearly vanishes, then Newton-Raphson may fail totally

              -
              -
              -

              Extending to more than one variable#

              + +
              +

              Extending to more than one variable

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

              @@ -1650,9 +2455,9 @@ is to understand that difficulties may arise in case \({\bf \boldsymbol{J}}\) is nearly singular.

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

              -
              -
              -

              Steepest descent#

              + +
              +

              Steepest descent

              The basic idea of gradient descent is that a function \(F(\mathbf{x})\), \(\mathbf{x} \equiv (x_1,\cdots,x_n)\), decreases fastest if one goes from \(\bf {x}\) in the @@ -1666,9 +2471,9 @@ direction of the negative gradient \(

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

              -
              -
              -

              More on Steepest descent#

              + +
              +

              More on Steepest descent

              The previous observation is the basis of the method of steepest descent, which is also referred to as just gradient descent (GD). One starts with an initial guess \(\mathbf{x}_0\) for a minimum of \(F\) and @@ -1679,9 +2484,9 @@ computes new approximations according to

              \]

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

              -
              -
              -

              The ideal#

              + +
              +

              The ideal

              Ideally the sequence \(\{\mathbf{x}_k \}_{k=0}\) converges to a global minimum of the function \(F\). In general we do not know if we are in a global or local minimum. In the special case when \(F\) is a convex @@ -1697,9 +2502,9 @@ have a very good intial guess. This also implies that the scheme is sensitive to the chosen initial condition.

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

              -
              -
              -

              The sensitiveness of the gradient descent#

              + +
              +

              The sensitiveness of the gradient descent

              The gradient descent method is sensitive to the choice of learning rate \(\gamma_k\). This is due to the fact that we are only guaranteed that \(F(\mathbf{x}_{k+1}) \leq @@ -1710,9 +2515,9 @@ large we can experience erratic behavior.

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

              -
              -
              -

              Convex functions#

              + +
              +

              Convex functions

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

              First we give the definition of a convex set: A set \(C\) in \(\mathbb{R}^n\) is said to be convex if, for all \(x\) and \(y\) in \(C\) and @@ -1722,13 +2527,13 @@ connecting \(x\) and The convex subsets of \(\mathbb{R}\) are the intervals of \(\mathbb{R}\). Examples of convex sets of \(\mathbb{R}^2\) are the regular polygons (triangles, rectangles, pentagons, etc…).

              -
              -
              -

              Convex function#

              + +
              +

              Convex function

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

              -
              -
              -

              Conditions on convex functions#

              + +
              +

              Conditions on convex functions

              In the following we state first and second-order conditions which ensures convexity of a function \(f\). We write \(D_f\) to denote the domain of \(f\), i.e the subset of \(R^n\) where \(f\) is defined. For more @@ -1750,9 +2555,9 @@ Hessian is positive semi-definite for all \(f''(x) \geq 0\). Geometrically this means that \(f\) has nonnegative curvature everywhere.

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

              -
              -
              -

              More on convex functions#

              + +
              +

              More on convex functions

              The next result is of great importance to us and the reason why we are going on about convex functions. In machine learning we frequently have to minimize a loss/cost function in order to find the best @@ -1766,10 +2571,10 @@ is convex the following result provides invaluable information:

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

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

              -
              -
              -

              Some simple problems#

              -
                + +
                +

                Some simple problems

                +

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

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

                @@ -1777,7 +2582,7 @@ is minimal, where \(f\) is con

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

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

                3. -
                  +

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

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

                  @@ -1787,14 +2592,14 @@ is minimal, where \(f\) is con

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

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

                  -
              -
              -

              Revisiting our first homework#

              + +
              +

              Revisiting our first homework

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

              -
                +

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

                2. The gradient can be computed analytically.

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

                  4. @@ -1807,6 +2612,17 @@ desirable properties such as:

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

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

              @@ -1819,9 +2635,9 @@ h_\beta(x) = \boldsymbol{y} = \beta_0 + \beta_1 x, \[ \boldsymbol{y}_i = \beta_0 + \beta_1 x_i. \] -
              -
              -

              Gradient descent example#

              + +
              +

              Gradient descent example

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

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

              @@ -1838,9 +2654,9 @@ X \equiv \begin{bmatrix} C(\beta) = \frac{1}{n}||X\beta-\mathbf{y}||_{2}^{2} = \frac{1}{n}\sum_{i=1}^{100}\left[ (\beta_0 + \beta_1 x_i)^2 - 2 y_i (\beta_0 + \beta_1 x_i) + y_i^2\right] \]

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

              -
              -
              -

              The derivative of the cost/loss function#

              + +
              +

              The derivative of the cost/loss function

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

              \[\begin{split} @@ -1849,9 +2665,9 @@ C(\beta) = \frac{1}{n}||X\beta-\mathbf{y}||_{2}^{2} = \frac{1}{n}\sum_{i=1}^{100 \end{bmatrix} = \frac{2}{n}X^T(X\beta - \mathbf{y}), \end{split}\]

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

              -
              -
              -

              The Hessian matrix#

              + +
              +

              The Hessian matrix

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

              \[\begin{split} @@ -1861,9 +2677,9 @@ C(\beta) = \frac{1}{n}||X\beta-\mathbf{y}||_{2}^{2} = \frac{1}{n}\sum_{i=1}^{100 \end{bmatrix} = \frac{2}{n}X^T X. \end{split}\]

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

              -
              -
              -

              Simple program#

              + +
              +

              Simple program

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

              \[ @@ -1874,9 +2690,9 @@ C(\beta) = \frac{1}{n}||X\beta-\mathbf{y}||_{2}^{2} = \frac{1}{n}\sum_{i=1}^{100 when \(||\nabla_\beta C(\beta_k) || \leq \epsilon = 10^{-8}\). Note that the code below does not include the latter stop criterion.

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

              -
              -
              -

              Gradient Descent Example#

              + +
              +

              Gradient Descent Example

              Here our simple example

              @@ -1929,9 +2745,9 @@ when \(||\nabla_\beta C(\beta_k) || \
              -
              -
              -

              And a corresponding example using scikit-learn#

              + +
              +

              And a corresponding example using scikit-learn

              # Importing various packages
@@ -1954,9 +2770,9 @@ when \(||\nabla_\beta C(\beta_k) || \
              -
              -
              -

              Gradient descent and Ridge#

              + +
              +

              Gradient descent and Ridge

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

              \[ @@ -1974,9 +2790,9 @@ C_{\text{ridge}}(\beta) = \frac{1}{n}||X\beta -\mathbf{y}||^2 + \lambda ||\beta| \[ \beta_{\text{ridge}} = \left(X^T X + n\lambda I_{2 \times 2} \right)^{-1} X^T \mathbf{y}. \]
              -
              -
              -

              The Hessian matrix for Ridge Regression#

              + +
              +

              The Hessian matrix for Ridge Regression

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

              \[\begin{split} @@ -1990,9 +2806,9 @@ minimum. Note that the Ridge cost function is convex being a sum of two convex functions. Therefore, the stationary point is a global minimum of this function.

              -
              -
              -

              Program example for gradient descent with Ridge Regression#

              + +
              +

              Program example for gradient descent with Ridge Regression

              from random import random, seed
@@ -2049,9 +2865,9 @@ minimum of this function.
              -
              -
              -

              Using gradient descent methods, limitations#

              + +
              +

              Using gradient descent methods, limitations

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

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

                • @@ -2060,12 +2876,12 @@ minimum of this function.

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

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

              -
              -
              -

              Challenge yourself the coming weekend#

              + +
              +

              Challenge yourself the coming weekend

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

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

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              - - © Copyright 2022. -
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              + + By Morten Hjorth-Jensen
              + + © Copyright 2021.
              +

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              -
              \ No newline at end of file diff --git a/doc/LectureNotes/_build/html/week39.html b/doc/LectureNotes/_build/html/week39.html index 8861050dc..553fbe981 100644 --- a/doc/LectureNotes/_build/html/week39.html +++ b/doc/LectureNotes/_build/html/week39.html @@ -1,61 +1,50 @@ - - - - + - - + Week 39: Optimization and Gradient Methods — Applied Data Analysis and Machine Learning - - - - - - - - - + + + - - - - - + + + + + + + + - + - + - - - - - - + + + + - - - - + + - - + - - + + + + + + - - - + + + - - - - -
              + - - - - - - - - - - -
              -
              -
              -
              - - - Ctrl+K -
              -
              - - - -
              -
              +
              +
              + +

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

              - -
              -

              Conjugate gradient method#

              +
              +
              +

              Conjugate gradient method

              In the CG method we define so-called conjugate directions and two vectors \(\boldsymbol{s}\) and \(\boldsymbol{t}\) are said to be @@ -1077,18 +1861,18 @@ of our vectors \(\boldsymbol{x}_i\)

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

              - -
              -

              Conjugate gradient method#

              +
              +
              +

              Conjugate gradient method

              An example is given by the eigenvectors of the matrix

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

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

              - -
              -

              Conjugate gradient method#

              +
              +
              +

              Conjugate gradient method

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

              @@ -1102,9 +1886,9 @@ Then the \(\boldsymbol{p}_{i}\) - -
              -

              Conjugate gradient method#

              +
              +
              +

              Conjugate gradient method

              The coefficients are given by

              \[ @@ -1120,9 +1904,9 @@ Then the \(\boldsymbol{p}_{i}\) - -
              -

              Conjugate gradient method and iterations#

              +
              +
              +

              Conjugate gradient method and iterations

              If we choose the conjugate vectors \(\boldsymbol{p}_k\) carefully, then we may not need all of them to obtain a good approximation to the solution \(\boldsymbol{x}\). @@ -1141,9 +1925,9 @@ We can assume without loss of generality that

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

              instead.

              - -
              -

              Conjugate gradient method#

              +
              +
              +

              Conjugate gradient method

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

              \[ @@ -1160,9 +1944,9 @@ which equals

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

              - -
              -

              Conjugate gradient method#

              +
              +
              +

              Conjugate gradient method

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

              \[ @@ -1179,9 +1963,9 @@ This gives the following expression

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

              Conjugate gradient method#

              +
              +
              +

              Conjugate gradient method

              We can also compute the residual iteratively as

              \[ @@ -1202,14 +1986,14 @@ This gives the following expression

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

              Revisiting our first homework#

              +
              +
              +

              Revisiting our first homework

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

              -
                +

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

                2. The gradient can be computed analytically.

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

                  4. @@ -1222,6 +2006,17 @@ desirable properties such as:

              +
              +
              ---------------------------------------------------------------------------
+NameError                                 Traceback (most recent call last)
+Input In [6], in <cell line: 1>()
+----> 1 x = 2*np.random.rand(m,1)
+      2 y = 4+3*x+np.random.randn(m,1)
+
+NameError: name 'm' is not defined
+
              +
              +

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

              @@ -1234,9 +2029,9 @@ h_\beta(x) = \boldsymbol{y} = \beta_0 + \beta_1 x, \[ \boldsymbol{y}_i = \beta_0 + \beta_1 x_i. \]
              - -
              -

              Gradient descent example#

              +
              +
              +

              Gradient descent example

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

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

              @@ -1253,9 +2048,9 @@ X \equiv \begin{bmatrix} C(\beta) = \frac{1}{n}||X\beta-\mathbf{y}||_{2}^{2} = \frac{1}{n}\sum_{i=1}^{100}\left[ (\beta_0 + \beta_1 x_i)^2 - 2 y_i (\beta_0 + \beta_1 x_i) + y_i^2\right] \]

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

              - -
              -

              The derivative of the cost/loss function#

              +
              +
              +

              The derivative of the cost/loss function

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

              \[\begin{split} @@ -1264,9 +2059,9 @@ C(\beta) = \frac{1}{n}||X\beta-\mathbf{y}||_{2}^{2} = \frac{1}{n}\sum_{i=1}^{100 \end{bmatrix} = \frac{2}{n}X^T(X\beta - \mathbf{y}), \end{split}\]

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

              - -
              -

              The Hessian matrix#

              +
              +
              +

              The Hessian matrix

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

              \[\begin{split} @@ -1276,9 +2071,9 @@ C(\beta) = \frac{1}{n}||X\beta-\mathbf{y}||_{2}^{2} = \frac{1}{n}\sum_{i=1}^{100 \end{bmatrix} = \frac{2}{n}X^T X. \end{split}\]

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

              - -
              -

              Simple program#

              +
              +
              +

              Simple program

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

              \[ @@ -1289,9 +2084,9 @@ C(\beta) = \frac{1}{n}||X\beta-\mathbf{y}||_{2}^{2} = \frac{1}{n}\sum_{i=1}^{100 when \(||\nabla_\beta C(\beta_k) || \leq \epsilon = 10^{-8}\). Note that the code below does not include the latter stop criterion.

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

              - -
              -

              Gradient Descent Example#

              +
              +
              +

              Gradient Descent Example

              Here our simple example

              @@ -1344,9 +2139,9 @@ when \(||\nabla_\beta C(\beta_k) || \
              - -
              -

              And a corresponding example using scikit-learn#

              +
              +
              +

              And a corresponding example using scikit-learn

              # Importing various packages
@@ -1369,9 +2164,9 @@ when \(||\nabla_\beta C(\beta_k) || \
              - -
              -

              Gradient descent and Ridge#

              +
              +
              +

              Gradient descent and Ridge

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

              \[ @@ -1389,9 +2184,9 @@ C_{\text{ridge}}(\beta) = \frac{1}{n}||X\beta -\mathbf{y}||^2 + \lambda ||\beta| \[ \beta_{\text{ridge}} = \left(X^T X + n\lambda I_{2 \times 2} \right)^{-1} X^T \mathbf{y}. \]
              - -
              -

              The Hessian matrix for Ridge Regression#

              +
              +
              +

              The Hessian matrix for Ridge Regression

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

              \[\begin{split} @@ -1405,9 +2200,9 @@ minimum. Note that the Ridge cost function is convex being a sum of two convex functions. Therefore, the stationary point is a global minimum of this function.

              - -
              -

              Program example for gradient descent with Ridge Regression#

              +
              +
              +

              Program example for gradient descent with Ridge Regression

              from random import random, seed
@@ -1464,9 +2259,9 @@ minimum of this function.
              - -
              -

              Using gradient descent methods, limitations#

              +
              +
              +

              Using gradient descent methods, limitations

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

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

                • @@ -1475,9 +2270,9 @@ minimum of this function.

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

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

              - -
              -

              Improving gradient descent with momentum#

              +
              +
              +

              Improving gradient descent with momentum

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

              @@ -1540,9 +2335,9 @@ minimum of this function.

              - -
              -

              Same code but now with momentum gradient descent#

              +
              +
              +

              Same code but now with momentum gradient descent

              from numpy import asarray
@@ -1612,13 +2407,13 @@ minimum of this function.
              - -
              -

              Overview video on Stochastic Gradient Descent#

              +
              +
              +

              Overview video on Stochastic Gradient Descent

              What is Stochastic Gradient Descent

              - -
              -

              Batches and mini-batches#

              +
              +
              +

              Batches and mini-batches

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

              In large-scale applications such as the ILSVRC challenge, the training data can have on order of millions of examples. Hence, it @@ -1628,9 +2423,9 @@ very common approach to addressing this challenge is to compute the gradient over batches of the training data. For example, a typical batch could contain some thousand examples from an entire training set of several millions. This batch is then used to perform a parameter update.

              - -
              -

              Stochastic Gradient Descent (SGD)#

              +
              +
              +

              Stochastic Gradient Descent (SGD)

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

              This process is called Stochastic Gradient @@ -1650,9 +2445,9 @@ e.g. 32, 64 or 128. We use powers of 2 in practice because many vectorized operation implementations work faster when their inputs are sized in powers of 2.

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

              - -
              -

              Stochastic Gradient Descent#

              +
              +
              +

              Stochastic Gradient Descent

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

              The underlying idea of SGD comes from the observation that the cost @@ -1663,9 +2458,9 @@ sum over \(n\) data points - -

              -

              Computation of gradients#

              +
              +
              +

              Computation of gradients

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

              @@ -1678,9 +2473,9 @@ gradient on a subset of the data called minibatches. If there are \(M\), there will be \(n/M\) minibatches. We denote these minibatches by \(B_k\) where \(k=1,\cdots,n/M\).

              - -
              -

              SGD example#

              +
              +
              +

              SGD example

              As an example, suppose we have \(10\) data points \((\mathbf{x}_1,\cdots, \mathbf{x}_{10})\) and we choose to have \(M=5\) minibathces, then each minibatch contains two data points. In particular we have @@ -1699,9 +2494,9 @@ C(\mathbf{\beta}) = \sum_{i=1}^n \nabla_\beta c_i(\mathbf{x}_i, \mathbf{\beta}) \rightarrow \sum_{i \in B_k}^n \nabla_\beta c_i(\mathbf{x}_i, \mathbf{\beta}). \]

              - -
              -

              The gradient step#

              +
              +
              +

              The gradient step

              Thus a gradient descent step now looks like

              \[ @@ -1713,9 +2508,9 @@ probability from \([1,n/M]\). minibathces (n/M) is commonly referred to as an epoch. Thus it is typical to choose a number of epochs and for each epoch iterate over the number of minibatches, as exemplified in the code below.

              - -
              -

              Simple example code#

              +
              +
              +

              Simple example code

              import numpy as np 
@@ -1743,9 +2538,9 @@ the size of the minibatches are small relative to the number of
 datapoints (\(M <  n\)), the computation of the gradient is much
 cheaper since we sum over the datapoints in the \(k-th\) minibatch and not
 all \(n\) datapoints.
              
-
-
              
-
              When do we stop?#
              
+
              +
              +

              When do we stop?

              A natural question is when do we stop the search for a new minimum? One possibility is to compute the full gradient after a given number of epochs and check if the norm of the gradient is smaller than some @@ -1756,9 +2551,9 @@ evaluate the cost function at this point, store the result and continue the search. If the test kicks in at a later stage we can compare the values of the cost function and keep the \(\beta\) that gave the lowest value.

              - -
              -

              Slightly different approach#

              +
              +
              +

              Slightly different approach

              Another approach is to let the step length \(\gamma_j\) depend on the number of epochs in such a way that it becomes very small after a reasonable time such that we do not move at all. Such approaches are @@ -1768,9 +2563,9 @@ and https://towardsdatascience.com/learning-rate-schedules-and-adaptive-learning-rate-methods-for-deep-learning-2c8f433990d1 for a discussion of different scaling functions for the learning rate.

              - -
              -

              Time decay rate#

              +
              +
              +

              Time decay rate

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

              In this way we can fix the number of epochs, compute \(\beta\) and evaluate the cost function at the end. Repeating the computation will @@ -1807,9 +2602,9 @@ function.

              - -
              -

              Code with a Number of Minibatches which varies#

              +
              +
              +

              Code with a Number of Minibatches which varies

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

              @@ -1883,16 +2678,16 @@ function.

              - -
              -

              Replace or not#

              +
              +
              +

              Replace or not

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

              - -
              -

              Momentum based GD#

              +
              +
              +

              Momentum based GD

              The stochastic gradient descent (SGD) is almost always used with a momentum or inertia term that serves as a memory of the direction we are moving in parameter space. This is typically implemented as @@ -1925,9 +2720,9 @@ earlier. An equivalent way of writing the updates is

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

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

              - -
              -

              More on momentum based approaches#

              +
              +
              +

              More on momentum based approaches

              Let us try to get more intuition from these equations. It is helpful to consider a simple physical analogy with a particle of mass \(m\) moving in a viscous medium with drag coefficient \(\mu\) and potential @@ -1947,9 +2742,9 @@ m { \mathbf{w}_{t+\Delta t}-2 \mathbf{w}_{t} +\mathbf{w}_{t-\Delta t} \over (\De \[ \Delta \mathbf{w}_{t +\Delta t}= - { (\Delta t)^2 \over m +\mu \Delta t} \nabla_w E(\mathbf{w})+ {m \over m +\mu \Delta t} \Delta \mathbf{w}_t. \]

              - -
              -

              Momentum parameter#

              +
              +
              +

              Momentum parameter

              Notice that this equation is identical to previous one if we identify the position of the particle, \(\mathbf{w}\), with the parameters \(\boldsymbol{\theta}\). This allows us to identify the momentum @@ -1994,9 +2789,9 @@ our current momentum, \(\nabla_\theta \end{equation} \]

              One of the major advantages of NAG is that it allows for the use of a larger learning rate than GDM for the same choice of \(\gamma\).

              - -
              -

              Second moment of the gradient#

              +
              +
              +

              Second moment of the gradient

              In stochastic gradient descent, with and without momentum, we still have to specify a schedule for tuning the learning rates \(\eta_t\) as a function of time. As discussed in the context of Newton’s @@ -2016,9 +2811,9 @@ Hessians.

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

              - -
              -

              RMS prop#

              +
              +
              +

              RMS prop

              In RMS prop, in addition to keeping a running average of the first moment of the gradient, we also keep track of the second moment denoted by \(\mathbf{s}_t=\mathbb{E}[\mathbf{g}_t^2]\). The update rule @@ -2049,9 +2844,9 @@ is clear from this formula that the learning rate is reduced in directions where the norm of the gradient is consistently large. This greatly speeds up the convergence by allowing us to use a larger learning rate for flat directions.

              - -
              -

              ADAM optimizer#

              +
              +
              +

              ADAM optimizer

              A related algorithm is the ADAM optimizer. In ADAM, we keep a running average of both the first and second moment of the gradient and use this @@ -2119,14 +2914,14 @@ update rule for this parameter is given by

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

              Algorithms and codes for Adagrad, RMSprop and Adam#

              +
              +
              +

              Algorithms and codes for Adagrad, RMSprop and Adam

              The algorithms we have implemented are well described in the text by Goodfellow, Bengio and Courville, chapter 8.

              The codes which implement these algorithms are discussed after our presentation of automatic differentiation.

              - -
              -

              Practical tips#

              +
              +
              +

              Practical tips

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

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

                • @@ -2134,9 +2929,9 @@ update rule for this parameter is given by

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

              Geron’s text, see chapter 11, has several interesting discussions.

              - -
              -

              Automatic differentiation#

              +
              +
              +

              Automatic differentiation

              Automatic differentiation (AD), also called algorithmic differentiation or computational differentiation,is a set of @@ -2211,9 +3006,9 @@ f'(x) = \cos\left(2\pi x + x^2\right)\left(2\pi + 2x\right)

              - -
              -

              Using autograd#

              + +
              +

              Using autograd

              Here we experiment with what kind of functions Autograd is capable of finding the gradient of. The following Python functions are just @@ -2242,9 +3037,9 @@ experiment with other, possibly more complicated, functions as well.

              -
              -
              -

              Autograd with more complicated functions#

              + +
              +

              Autograd with more complicated functions

              To differentiate with respect to two (or more) arguments of a Python function, Autograd need to know at which variable the function if being differentiated with respect to.

              @@ -2288,9 +3083,9 @@ being differentiated with respect to.

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

              -
              -
              -

              More complicated functions using the elements of their arguments directly#

              + +
              +

              More complicated functions using the elements of their arguments directly

              import autograd.numpy as np
@@ -2320,9 +3115,9 @@ the function, as opposed to the function in the previous example. By
 using arrays to represent the variables, the output from Autograd
 might be easier to work with, as the output is closer to what one
 could expect form a gradient-evaluting function.
              
-
              -
              -

              Functions using mathematical functions from Numpy#

              + +
              +

              Functions using mathematical functions from Numpy

              import autograd.numpy as np
@@ -2346,9 +3141,9 @@ could expect form a gradient-evaluting function.
              -
              -
              -

              More autograd#

              + +
              +

              More autograd

              import autograd.numpy as np
@@ -2369,9 +3164,9 @@ could expect form a gradient-evaluting function.
              -
              -
              -

              And with loops#

              + +
              +

              And with loops

              import autograd.numpy as np
@@ -2417,9 +3212,9 @@ could expect form a gradient-evaluting function.
              -
              -
              -

              Using recursion#

              + +
              +

              Using recursion

              import autograd.numpy as np
@@ -2454,9 +3249,9 @@ could expect form a gradient-evaluting function.

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

              -
              -
              -

              Unsupported functions#

              + +
              +

              Unsupported functions

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

              Assigning a value to the variable being differentiated with respect to

              @@ -2477,9 +3272,9 @@ could expect form a gradient-evaluting function.

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

              -
              -
              -

              The syntax a.dot(b) when finding the dot product#

              + +
              +

              The syntax a.dot(b) when finding the dot product

              import autograd.numpy as np
@@ -2520,9 +3315,9 @@ which also computed the dot product can be used:
              -
              - -
              -

              Using Autograd with OLS#

              + +
              +

              Using Autograd with OLS

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

              @@ -2595,9 +3390,9 @@ first example shows results with ordinary leats squares.

              -
              -
              -

              Same code but now with momentum gradient descent#

              + +
              +

              Same code but now with momentum gradient descent

              # Using Autograd to calculate gradients for OLS
@@ -2657,9 +3452,9 @@ first example shows results with ordinary leats squares.
              -
              -
              -

              But noen of these can compete with Newton’s method#

              + +
              +

              But noen of these can compete with Newton’s method

              # Using Newton's method
@@ -2704,9 +3499,9 @@ first example shows results with ordinary leats squares.
              -
              -
              -

              Including Stochastic Gradient Descent with Autograd#

              + +
              +

              Including Stochastic Gradient Descent with Autograd

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

              @@ -2787,9 +3582,9 @@ first example shows results with ordinary leats squares.

              -
              -
              -

              Same code but now with momentum gradient descent#

              + +
              +

              Same code but now with momentum gradient descent

              # Using Autograd to calculate gradients using SGD
@@ -2863,9 +3658,9 @@ first example shows results with ordinary leats squares.
              -
              -
              -

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

              + +
              +

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

              # Using Autograd to calculate gradients using AdaGrad and Stochastic Gradient descent
@@ -2921,9 +3716,9 @@ first example shows results with ordinary leats squares.

              Running this code we note an almost perfect agreement with the results from matrix inversion.

              -
              -
              -

              RMSprop for adaptive learning rate with Stochastic Gradient Descent#

              + +
              +

              RMSprop for adaptive learning rate with Stochastic Gradient Descent

              # Using Autograd to calculate gradients using RMSprop  and Stochastic Gradient descent
@@ -2984,9 +3779,9 @@ first example shows results with ordinary leats squares.
              -
              -
              -

              And finally ADAM#

              + +
              +

              And finally ADAM

              # Using Autograd to calculate gradients using RMSprop  and Stochastic Gradient descent
@@ -3052,9 +3847,9 @@ first example shows results with ordinary leats squares.
              -
              -
              -

              And Logistic Regression#

              + +
              +

              And Logistic Regression

              import autograd.numpy as np
@@ -3094,9 +3889,9 @@ first example shows results with ordinary leats squares.
              -
              -
              -

              Introducing JAX#

              + +
              +

              Introducing JAX

              Presently, instead of using autograd, we recommend using JAX

              JAX is Autograd and XLA (Accelerated Linear Algebra)), brought together for high-performance numerical computing and machine learning research. @@ -3117,8 +3912,8 @@ It provides composable transformations of Python+NumPy programs: differentiate,

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              + + By Morten Hjorth-Jensen
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              Week 40: Gradient descent methods (continued) and start Neural networks

              + +
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              + +
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              Contents

              +
              + +
              +
              +
              + +
              + + +
              +

              Week 40: Gradient descent methods (continued) and start Neural networks

              +

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

              +

              Date: October 2-6, 2023

              +
              +

              Plans for week 40

              +

              Material for the active learning sessions on Tuesday and Wednesday.

              + +

              Material for the lecture on Thursday October 5, 2023.

              + +
              +
              +

              Summary from last week, using gradient descent methods, limitations

              +
                +

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

                • +

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

                • +

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

                • +

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

                • +

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

                • +

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

                • +
              +
              +
              +

              Overview video on Stochastic Gradient Descent

              +

              What is Stochastic Gradient Descent

              +
              +
              +

              Batches and mini-batches

              +

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

              +

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

              +
              +
              +

              Stochastic Gradient Descent (SGD)

              +

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

              +

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

              +

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

              +
              +
              +

              Stochastic Gradient Descent

              +

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

              +

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

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

              Computation of gradients

              +

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

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

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

              +
              +
              +

              SGD example

              +

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

              +

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

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

              The gradient step

              +

              Thus a gradient descent step now looks like

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

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

              +
              +
              +

              Simple example code

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

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

              +
              +
              +

              When do we stop?

              +

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

              +
              +
              +

              Slightly different approach

              +

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

              +
              +
              +

              Time decay rate

              +

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

              +

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

              +
              +
              +
              import numpy as np 
+
+def step_length(t,t0,t1):
+    return t0/(t+t1)
+
+n = 100 #100 datapoints 
+M = 5   #size of each minibatch
+m = int(n/M) #number of minibatches
+n_epochs = 500 #number of epochs
+t0 = 1.0
+t1 = 10
+
+gamma_j = t0/t1
+j = 0
+for epoch in range(1,n_epochs+1):
+    for i in range(m):
+        k = np.random.randint(m) #Pick the k-th minibatch at random
+        #Compute the gradient using the data in minibatch Bk
+        #Compute new suggestion for beta
+        t = epoch*m+i
+        gamma_j = step_length(t,t0,t1)
+        j += 1
+
+print("gamma_j after %d epochs: %g" % (n_epochs,gamma_j))
+
              +
              +
              +
              +
              gamma_j after 500 epochs: 9.97108e-05
+
              +
              +
              +
              +
              +
              +

              Code with a Number of Minibatches which varies

              +

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

              +
              +
              +
              %matplotlib inline
+
+# Importing various packages
+from math import exp, sqrt
+from random import random, seed
+import numpy as np
+import matplotlib.pyplot as plt
+
+n = 100
+x = 2*np.random.rand(n,1)
+y = 4+3*x+np.random.randn(n,1)
+
+X = np.c_[np.ones((n,1)), x]
+XT_X = X.T @ X
+theta_linreg = np.linalg.inv(X.T @ X) @ (X.T @ y)
+print("Own inversion")
+print(theta_linreg)
+# Hessian matrix
+H = (2.0/n)* XT_X
+EigValues, EigVectors = np.linalg.eig(H)
+print(f"Eigenvalues of Hessian Matrix:{EigValues}")
+
+theta = np.random.randn(2,1)
+eta = 1.0/np.max(EigValues)
+Niterations = 1000
+
+
+for iter in range(Niterations):
+    gradients = 2.0/n*X.T @ ((X @ theta)-y)
+    theta -= eta*gradients
+print("theta from own gd")
+print(theta)
+
+xnew = np.array([[0],[2]])
+Xnew = np.c_[np.ones((2,1)), xnew]
+ypredict = Xnew.dot(theta)
+ypredict2 = Xnew.dot(theta_linreg)
+
+n_epochs = 50
+M = 5   #size of each minibatch
+m = int(n/M) #number of minibatches
+t0, t1 = 5, 50
+
+def learning_schedule(t):
+    return t0/(t+t1)
+
+theta = np.random.randn(2,1)
+
+for epoch in range(n_epochs):
+# Can you figure out a better way of setting up the contributions to each batch?
+    for i in range(m):
+        random_index = M*np.random.randint(m)
+        xi = X[random_index:random_index+M]
+        yi = y[random_index:random_index+M]
+        gradients = (2.0/M)* xi.T @ ((xi @ theta)-yi)
+        eta = learning_schedule(epoch*m+i)
+        theta = theta - eta*gradients
+print("theta from own sdg")
+print(theta)
+
+plt.plot(xnew, ypredict, "r-")
+plt.plot(xnew, ypredict2, "b-")
+plt.plot(x, y ,'ro')
+plt.axis([0,2.0,0, 15.0])
+plt.xlabel(r'$x$')
+plt.ylabel(r'$y$')
+plt.title(r'Random numbers ')
+plt.show()
+
              +
              +
              +
              +
              Own inversion
+[[4.06481015]
+ [2.84666445]]
+Eigenvalues of Hessian Matrix:[0.28638913 4.44842116]
+theta from own gd
+[[4.06481015]
+ [2.84666445]]
+theta from own sdg
+[[4.02231445]
+ [2.89996783]]
+
              +
              +_images/week40_25_1.png +
              +
              +
              +
              +

              Replace or not

              +

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

              +
              +
              +

              Momentum based GD

              +

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

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

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

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

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

              +
              +
              +

              More on momentum based approaches

              +

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

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

              We can discretize this equation in the usual way to get

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

              Rearranging this equation, we can rewrite this as

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

              Momentum parameter

              +

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

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

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

              +

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

              +

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

              +

              In the NAG algorithm, rather than calculating the gradient at the +current parameters, \(\nabla_\theta E(\boldsymbol{\theta}_t)\), one +calculates the gradient at the expected value of the parameters given +our current momentum, \(\nabla_\theta E(\boldsymbol{\theta}_t +\gamma +\mathbf{v}_{t-1})\). This yields the NAG update rule

              +
              +\[ +\mathbf{v}_{t}=\gamma \mathbf{v}_{t-1}+\eta_{t}\nabla_\theta E(\boldsymbol{\theta}_t +\gamma \mathbf{v}_{t-1}) \nonumber +\]
              + +
              +
              +\[ +\begin{equation} +\boldsymbol{\theta}_{t+1}= \boldsymbol{\theta}_t -\mathbf{v}_{t}. +\label{_auto2} \tag{2} +\end{equation} +\]
              +

              One of the major advantages of NAG is that it allows for the use of a larger learning rate than GDM for the same choice of \(\gamma\).

              +
              +
              +

              Second moment of the gradient

              +

              In stochastic gradient descent, with and without momentum, we still +have to specify a schedule for tuning the learning rates \(\eta_t\) +as a function of time. As discussed in the context of Newton’s +method, this presents a number of dilemmas. The learning rate is +limited by the steepest direction which can change depending on the +current position in the landscape. To circumvent this problem, ideally +our algorithm would keep track of curvature and take large steps in +shallow, flat directions and small steps in steep, narrow directions. +Second-order methods accomplish this by calculating or approximating +the Hessian and normalizing the learning rate by the +curvature. However, this is very computationally expensive for +extremely large models. Ideally, we would like to be able to +adaptively change the step size to match the landscape without paying +the steep computational price of calculating or approximating +Hessians.

              +

              Recently, a number of methods have been introduced that accomplish +this by tracking not only the gradient, but also the second moment of +the gradient. These methods include AdaGrad, AdaDelta, Root Mean Squared Propagation (RMS-Prop), and +ADAM.

              +
              +
              +

              RMS prop

              +

              In RMS prop, in addition to keeping a running average of the first +moment of the gradient, we also keep track of the second moment +denoted by \(\mathbf{s}_t=\mathbb{E}[\mathbf{g}_t^2]\). The update rule +for RMS prop is given by

              + +
              +
              +\[ +\begin{equation} +\mathbf{g}_t = \nabla_\theta E(\boldsymbol{\theta}) +\label{_auto3} \tag{3} +\end{equation} +\]
              +
              +\[ +\mathbf{s}_t =\beta \mathbf{s}_{t-1} +(1-\beta)\mathbf{g}_t^2 \nonumber +\]
              +
              +\[ +\boldsymbol{\theta}_{t+1}=\boldsymbol{\theta}_t - \eta_t { \mathbf{g}_t \over \sqrt{\mathbf{s}_t +\epsilon}}, \nonumber +\]
              +

              where \(\beta\) controls the averaging time of the second moment and is +typically taken to be about \(\beta=0.9\), \(\eta_t\) is a learning rate +typically chosen to be \(10^{-3}\), and \(\epsilon\sim 10^{-8} \) is a +small regularization constant to prevent divergences. Multiplication +and division by vectors is understood as an element-wise operation. It +is clear from this formula that the learning rate is reduced in +directions where the norm of the gradient is consistently large. This +greatly speeds up the convergence by allowing us to use a larger +learning rate for flat directions.

              +
              +
              +

              ADAM optimizer

              +

              A related algorithm is the ADAM optimizer. In +ADAM, we keep a running average of +both the first and second moment of the gradient and use this +information to adaptively change the learning rate for different +parameters. The method isefficient when working with large +problems involving lots data and/or parameters. It is a combination of the +gradient descent with momentum algorithm and the RMSprop algorithm +discussed above.

              +

              In addition to keeping a running average of the first and +second moments of the gradient +(i.e. \(\mathbf{m}_t=\mathbb{E}[\mathbf{g}_t]\) and +\(\mathbf{s}_t=\mathbb{E}[\mathbf{g}^2_t]\), respectively), ADAM +performs an additional bias correction to account for the fact that we +are estimating the first two moments of the gradient using a running +average (denoted by the hats in the update rule below). The update +rule for ADAM is given by (where multiplication and division are once +again understood to be element-wise operations below)

              + +
              +
              +\[ +\begin{equation} +\mathbf{g}_t = \nabla_\theta E(\boldsymbol{\theta}) +\label{_auto4} \tag{4} +\end{equation} +\]
              +
              +\[ +\mathbf{m}_t = \beta_1 \mathbf{m}_{t-1} + (1-\beta_1) \mathbf{g}_t \nonumber +\]
              +
              +\[ +\mathbf{s}_t =\beta_2 \mathbf{s}_{t-1} +(1-\beta_2)\mathbf{g}_t^2 \nonumber +\]
              +
              +\[ +\boldsymbol{\mathbf{m}}_t={\mathbf{m}_t \over 1-\beta_1^t} \nonumber +\]
              +
              +\[ +\boldsymbol{\mathbf{s}}_t ={\mathbf{s}_t \over1-\beta_2^t} \nonumber +\]
              +
              +\[ +\boldsymbol{\theta}_{t+1}=\boldsymbol{\theta}_t - \eta_t { \boldsymbol{\mathbf{m}}_t \over \sqrt{\boldsymbol{\mathbf{s}}_t} +\epsilon}, \nonumber +\]
              + +
              +
              +\[ +\begin{equation} +\label{_auto5} \tag{5} +\end{equation} +\]
              +

              where \(\beta_1\) and \(\beta_2\) set the memory lifetime of the first and +second moment and are typically taken to be \(0.9\) and \(0.99\) +respectively, and \(\eta\) and \(\epsilon\) are identical to RMSprop.

              +

              Like in RMSprop, the effective step size of a parameter depends on the +magnitude of its gradient squared. To understand this better, let us +rewrite this expression in terms of the variance +\(\boldsymbol{\sigma}_t^2 = \boldsymbol{\mathbf{s}}_t - +(\boldsymbol{\mathbf{m}}_t)^2\). Consider a single parameter \(\theta_t\). The +update rule for this parameter is given by

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

              Algorithms and codes for Adagrad, RMSprop and Adam

              +

              The algorithms we have implemented are well described in the text by Goodfellow, Bengio and Courville, chapter 8.

              +

              The codes which implement these algorithms are discussed after our presentation of automatic differentiation.

              +
              +
              +

              Practical tips

              +
                +

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

                • +

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

                • +

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

                • +

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

                • +
              +

              Geron’s text, see chapter 11, has several interesting discussions.

              +
              +
              +

              Automatic differentiation

              +

              Automatic differentiation (AD), +also called algorithmic +differentiation or computational differentiation,is a set of +techniques to numerically evaluate the derivative of a function +specified by a computer program. AD exploits the fact that every +computer program, no matter how complicated, executes a sequence of +elementary arithmetic operations (addition, subtraction, +multiplication, division, etc.) and elementary functions (exp, log, +sin, cos, etc.). By applying the chain rule repeatedly to these +operations, derivatives of arbitrary order can be computed +automatically, accurately to working precision, and using at most a +small constant factor more arithmetic operations than the original +program.

              +

              Automatic differentiation is neither:

              +
                +

              • Symbolic differentiation, nor

                • +

              • Numerical differentiation (the method of finite differences).

                • +
              +

              Symbolic differentiation can lead to inefficient code and faces the +difficulty of converting a computer program into a single expression, +while numerical differentiation can introduce round-off errors in the +discretization process and cancellation

              +

              Python has tools for so-called automatic differentiation. +Consider the following example

              +
              +\[ +f(x) = \sin\left(2\pi x + x^2\right) +\]
              +

              which has the following derivative

              +
              +\[ +f'(x) = \cos\left(2\pi x + x^2\right)\left(2\pi + 2x\right) +\]
              +

              Using autograd we have

              +
              +
              +
              import autograd.numpy as np
+
+# To do elementwise differentiation:
+from autograd import elementwise_grad as egrad 
+
+# To plot:
+import matplotlib.pyplot as plt 
+
+
+def f(x):
+    return np.sin(2*np.pi*x + x**2)
+
+def f_grad_analytic(x):
+    return np.cos(2*np.pi*x + x**2)*(2*np.pi + 2*x)
+
+# Do the comparison:
+x = np.linspace(0,1,1000)
+
+f_grad = egrad(f)
+
+computed = f_grad(x)
+analytic = f_grad_analytic(x)
+
+plt.title('Derivative computed from Autograd compared with the analytical derivative')
+plt.plot(x,computed,label='autograd')
+plt.plot(x,analytic,label='analytic')
+
+plt.xlabel('x')
+plt.ylabel('y')
+plt.legend()
+
+plt.show()
+
+print("The max absolute difference is: %g"%(np.max(np.abs(computed - analytic))))
+
              +
              +
              +
              +_images/week40_68_0.png +
              The max absolute difference is: 1.77636e-15
+
              +
              +
              +
              +
              +
              +

              Using autograd

              +

              Here we +experiment with what kind of functions Autograd is capable +of finding the gradient of. The following Python functions are just +meant to illustrate what Autograd can do, but please feel free to +experiment with other, possibly more complicated, functions as well.

              +
              +
              +
              import autograd.numpy as np
+from autograd import grad
+
+def f1(x):
+    return x**3 + 1
+
+f1_grad = grad(f1)
+
+# Remember to send in float as argument to the computed gradient from Autograd!
+a = 1.0
+
+# See the evaluated gradient at a using autograd:
+print("The gradient of f1 evaluated at a = %g using autograd is: %g"%(a,f1_grad(a)))
+
+# Compare with the analytical derivative, that is f1'(x) = 3*x**2 
+grad_analytical = 3*a**2
+print("The gradient of f1 evaluated at a = %g by finding the analytic expression is: %g"%(a,grad_analytical))
+
              +
              +
              +
              +
              The gradient of f1 evaluated at a = 1 using autograd is: 3
+The gradient of f1 evaluated at a = 1 by finding the analytic expression is: 3
+
              +
              +
              +
              +
              +
              +

              Autograd with more complicated functions

              +

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

              +
              +
              +
              import autograd.numpy as np
+from autograd import grad
+def f2(x1,x2):
+    return 3*x1**3 + x2*(x1 - 5) + 1
+
+# By sending the argument 0, Autograd will compute the derivative w.r.t the first variable, in this case x1
+f2_grad_x1 = grad(f2,0)
+
+# ... and differentiate w.r.t x2 by sending 1 as an additional arugment to grad
+f2_grad_x2 = grad(f2,1)
+
+x1 = 1.0
+x2 = 3.0 
+
+print("Evaluating at x1 = %g, x2 = %g"%(x1,x2))
+print("-"*30)
+
+# Compare with the analytical derivatives:
+
+# Derivative of f2 w.r.t x1 is: 9*x1**2 + x2:
+f2_grad_x1_analytical = 9*x1**2 + x2
+
+# Derivative of f2 w.r.t x2 is: x1 - 5:
+f2_grad_x2_analytical = x1 - 5
+
+# See the evaluated derivations:
+print("The derivative of f2 w.r.t x1: %g"%( f2_grad_x1(x1,x2) ))
+print("The analytical derivative of f2 w.r.t x1: %g"%( f2_grad_x1(x1,x2) ))
+
+print()
+
+print("The derivative of f2 w.r.t x2: %g"%( f2_grad_x2(x1,x2) ))
+print("The analytical derivative of f2 w.r.t x2: %g"%( f2_grad_x2(x1,x2) ))
+
              +
              +
              +
              +
              Evaluating at x1 = 1, x2 = 3
+------------------------------
+The derivative of f2 w.r.t x1: 12
+The analytical derivative of f2 w.r.t x1: 12
+
+The derivative of f2 w.r.t x2: -4
+The analytical derivative of f2 w.r.t x2: -4
+
              +
              +
              +
              +

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

              +
              +
              +

              More complicated functions using the elements of their arguments directly

              +
              +
              +
              import autograd.numpy as np
+from autograd import grad
+def f3(x): # Assumes x is an array of length 5 or higher
+    return 2*x[0] + 3*x[1] + 5*x[2] + 7*x[3] + 11*x[4]**2
+
+f3_grad = grad(f3)
+
+x = np.linspace(0,4,5)
+
+# Print the computed gradient:
+print("The computed gradient of f3 is: ", f3_grad(x))
+
+# The analytical gradient is: (2, 3, 5, 7, 22*x[4])
+f3_grad_analytical = np.array([2, 3, 5, 7, 22*x[4]])
+
+# Print the analytical gradient:
+print("The analytical gradient of f3 is: ", f3_grad_analytical)
+
              +
              +
              +
              +
              The computed gradient of f3 is:  [ 2.  3.  5.  7. 88.]
+The analytical gradient of f3 is:  [ 2.  3.  5.  7. 88.]
+
              +
              +
              +
              +

              Note that in this case, when sending an array as input argument, the +output from Autograd is another array. This is the true gradient of +the function, as opposed to the function in the previous example. By +using arrays to represent the variables, the output from Autograd +might be easier to work with, as the output is closer to what one +could expect form a gradient-evaluting function.

              +
              +
              +

              Functions using mathematical functions from Numpy

              +
              +
              +
              import autograd.numpy as np
+from autograd import grad
+def f4(x):
+    return np.sqrt(1+x**2) + np.exp(x) + np.sin(2*np.pi*x)
+
+f4_grad = grad(f4)
+
+x = 2.7
+
+# Print the computed derivative:
+print("The computed derivative of f4 at x = %g is: %g"%(x,f4_grad(x)))
+
+# The analytical derivative is: x/sqrt(1 + x**2) + exp(x) + cos(2*pi*x)*2*pi
+f4_grad_analytical = x/np.sqrt(1 + x**2) + np.exp(x) + np.cos(2*np.pi*x)*2*np.pi
+
+# Print the analytical gradient:
+print("The analytical gradient of f4 at x = %g is: %g"%(x,f4_grad_analytical))
+
              +
              +
              +
              +
              The computed derivative of f4 at x = 2.7 is: 13.8759
+The analytical gradient of f4 at x = 2.7 is: 13.8759
+
              +
              +
              +
              +
              +
              +

              More autograd

              +
              +
              +
              import autograd.numpy as np
+from autograd import grad
+def f5(x):
+    if x >= 0:
+        return x**2
+    else:
+        return -3*x + 1
+
+f5_grad = grad(f5)
+
+x = 2.7
+
+# Print the computed derivative:
+print("The computed derivative of f5 at x = %g is: %g"%(x,f5_grad(x)))
+
              +
              +
              +
              +
              The computed derivative of f5 at x = 2.7 is: 5.4
+
              +
              +
              +
              +
              +
              +

              And with loops

              +
              +
              +
              import autograd.numpy as np
+from autograd import grad
+def f6_for(x):
+    val = 0
+    for i in range(10):
+        val = val + x**i
+    return val
+
+def f6_while(x):
+    val = 0
+    i = 0
+    while i < 10:
+        val = val + x**i
+        i = i + 1
+    return val
+
+f6_for_grad = grad(f6_for)
+f6_while_grad = grad(f6_while)
+
+x = 0.5
+
+# Print the computed derivaties of f6_for and f6_while
+print("The computed derivative of f6_for at x = %g is: %g"%(x,f6_for_grad(x)))
+print("The computed derivative of f6_while at x = %g is: %g"%(x,f6_while_grad(x)))
+
              +
              +
              +
              +
              The computed derivative of f6_for at x = 0.5 is: 3.95703
+The computed derivative of f6_while at x = 0.5 is: 3.95703
+
              +
              +
              +
              +
              +
              +
              import autograd.numpy as np
+from autograd import grad
+# Both of the functions are implementation of the sum: sum(x**i) for i = 0, ..., 9
+# The analytical derivative is: sum(i*x**(i-1)) 
+f6_grad_analytical = 0
+for i in range(10):
+    f6_grad_analytical += i*x**(i-1)
+
+print("The analytical derivative of f6 at x = %g is: %g"%(x,f6_grad_analytical))
+
              +
              +
              +
              +
              The analytical derivative of f6 at x = 0.5 is: 3.95703
+
              +
              +
              +
              +
              +
              +

              Using recursion

              +
              +
              +
              import autograd.numpy as np
+from autograd import grad
+
+def f7(n): # Assume that n is an integer
+    if n == 1 or n == 0:
+        return 1
+    else:
+        return n*f7(n-1)
+
+f7_grad = grad(f7)
+
+n = 2.0
+
+print("The computed derivative of f7 at n = %d is: %g"%(n,f7_grad(n)))
+
+# The function f7 is an implementation of the factorial of n.
+# By using the product rule, one can find that the derivative is:
+
+f7_grad_analytical = 0
+for i in range(int(n)-1):
+    tmp = 1
+    for k in range(int(n)-1):
+        if k != i:
+            tmp *= (n - k)
+    f7_grad_analytical += tmp
+
+print("The analytical derivative of f7 at n = %d is: %g"%(n,f7_grad_analytical))
+
              +
              +
              +
              +
              The computed derivative of f7 at n = 2 is: 1
+The analytical derivative of f7 at n = 2 is: 1
+
              +
              +
              +
              +

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

              +
              +
              +

              Unsupported functions

              +

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

              +

              Assigning a value to the variable being differentiated with respect to

              +
              +
              +
              import autograd.numpy as np
+from autograd import grad
+def f8(x): # Assume x is an array
+    x[2] = 3
+    return x*2
+
+f8_grad = grad(f8)
+
+x = 8.4
+
+print("The derivative of f8 is:",f8_grad(x))
+
              +
              +
              +
              +
              ---------------------------------------------------------------------------
+TypeError                                 Traceback (most recent call last)
+Input In [13], in <cell line: 11>()
+      7 f8_grad = grad(f8)
+      9 x = 8.4
+---> 11 print("The derivative of f8 is:",f8_grad(x))
+
+File ~/miniforge3/envs/myenv/lib/python3.9/site-packages/autograd/wrap_util.py:20, in unary_to_nary.<locals>.nary_operator.<locals>.nary_f(*args, **kwargs)
+     18 else:
+     19     x = tuple(args[i] for i in argnum)
+---> 20 return unary_operator(unary_f, x, *nary_op_args, **nary_op_kwargs)
+
+File ~/miniforge3/envs/myenv/lib/python3.9/site-packages/autograd/differential_operators.py:25, in grad(fun, x)
+     18 @unary_to_nary
+     19 def grad(fun, x):
+     20     """
+     21     Returns a function which computes the gradient of `fun` with respect to
+     22     positional argument number `argnum`. The returned function takes the same
+     23     arguments as `fun`, but returns the gradient instead. The function `fun`
+     24     should be scalar-valued. The gradient has the same type as the argument."""
+---> 25     vjp, ans = _make_vjp(fun, x)
+     26     if not vspace(ans).size == 1:
+     27         raise TypeError("Grad only applies to real scalar-output functions. "
+     28                         "Try jacobian, elementwise_grad or holomorphic_grad.")
+
+File ~/miniforge3/envs/myenv/lib/python3.9/site-packages/autograd/core.py:10, in make_vjp(fun, x)
+      8 def make_vjp(fun, x):
+      9     start_node = VJPNode.new_root()
+---> 10     end_value, end_node =  trace(start_node, fun, x)
+     11     if end_node is None:
+     12         def vjp(g): return vspace(x).zeros()
+
+File ~/miniforge3/envs/myenv/lib/python3.9/site-packages/autograd/tracer.py:10, in trace(start_node, fun, x)
+      8 with trace_stack.new_trace() as t:
+      9     start_box = new_box(x, t, start_node)
+---> 10     end_box = fun(start_box)
+     11     if isbox(end_box) and end_box._trace == start_box._trace:
+     12         return end_box._value, end_box._node
+
+File ~/miniforge3/envs/myenv/lib/python3.9/site-packages/autograd/wrap_util.py:15, in unary_to_nary.<locals>.nary_operator.<locals>.nary_f.<locals>.unary_f(x)
+     13 else:
+     14     subargs = subvals(args, zip(argnum, x))
+---> 15 return fun(*subargs, **kwargs)
+
+Input In [13], in f8(x)
+      3 def f8(x): # Assume x is an array
+----> 4     x[2] = 3
+      5     return x*2
+
+TypeError: 'ArrayBox' object does not support item assignment
+
              +
              +
              +
              +

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

              +
              +
              +

              The syntax a.dot(b) when finding the dot product

              +
              +
              +
              import autograd.numpy as np
+from autograd import grad
+def f9(a): # Assume a is an array with 2 elements
+    b = np.array([1.0,2.0])
+    return a.dot(b)
+
+f9_grad = grad(f9)
+
+x = np.array([1.0,0.0])
+
+print("The derivative of f9 is:",f9_grad(x))
+
              +
              +
              +
              +

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

              +
              +
              +
              import autograd.numpy as np
+from autograd import grad
+def f9_alternative(x): # Assume a is an array with 2 elements
+    b = np.array([1.0,2.0])
+    return np.dot(x,b) # The same as x_1*b_1 + x_2*b_2
+
+f9_alternative_grad = grad(f9_alternative)
+
+x = np.array([3.0,0.0])
+
+print("The gradient of f9 is:",f9_alternative_grad(x))
+
+# The analytical gradient of the dot product of vectors x and b with two elements (x_1,x_2) and (b_1, b_2) respectively
+# w.r.t x is (b_1, b_2).
+
              +
              +
              +
              +
              + +
              +

              Using Autograd with OLS

              +

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

              +
              +
              +
              # Using Autograd to calculate gradients for OLS
+from random import random, seed
+import numpy as np
+import autograd.numpy as np
+import matplotlib.pyplot as plt
+from autograd import grad
+
+def CostOLS(beta):
+    return (1.0/n)*np.sum((y-X @ beta)**2)
+
+n = 100
+x = 2*np.random.rand(n,1)
+y = 4+3*x+np.random.randn(n,1)
+
+X = np.c_[np.ones((n,1)), x]
+XT_X = X.T @ X
+theta_linreg = np.linalg.pinv(XT_X) @ (X.T @ y)
+print("Own inversion")
+print(theta_linreg)
+# Hessian matrix
+H = (2.0/n)* XT_X
+EigValues, EigVectors = np.linalg.eig(H)
+print(f"Eigenvalues of Hessian Matrix:{EigValues}")
+
+theta = np.random.randn(2,1)
+eta = 1.0/np.max(EigValues)
+Niterations = 1000
+# define the gradient
+training_gradient = grad(CostOLS)
+
+for iter in range(Niterations):
+    gradients = training_gradient(theta)
+    theta -= eta*gradients
+print("theta from own gd")
+print(theta)
+
+xnew = np.array([[0],[2]])
+Xnew = np.c_[np.ones((2,1)), xnew]
+ypredict = Xnew.dot(theta)
+ypredict2 = Xnew.dot(theta_linreg)
+
+plt.plot(xnew, ypredict, "r-")
+plt.plot(xnew, ypredict2, "b-")
+plt.plot(x, y ,'ro')
+plt.axis([0,2.0,0, 15.0])
+plt.xlabel(r'$x$')
+plt.ylabel(r'$y$')
+plt.title(r'Random numbers ')
+plt.show()
+
              +
              +
              +
              +
              +
              +

              Same code but now with momentum gradient descent

              +
              +
              +
              # Using Autograd to calculate gradients for OLS
+from random import random, seed
+import numpy as np
+import autograd.numpy as np
+import matplotlib.pyplot as plt
+from autograd import grad
+
+def CostOLS(beta):
+    return (1.0/n)*np.sum((y-X @ beta)**2)
+
+n = 100
+x = 2*np.random.rand(n,1)
+y = 4+3*x#+np.random.randn(n,1)
+
+X = np.c_[np.ones((n,1)), x]
+XT_X = X.T @ X
+theta_linreg = np.linalg.pinv(XT_X) @ (X.T @ y)
+print("Own inversion")
+print(theta_linreg)
+# Hessian matrix
+H = (2.0/n)* XT_X
+EigValues, EigVectors = np.linalg.eig(H)
+print(f"Eigenvalues of Hessian Matrix:{EigValues}")
+
+theta = np.random.randn(2,1)
+eta = 1.0/np.max(EigValues)
+Niterations = 30
+
+# define the gradient
+training_gradient = grad(CostOLS)
+
+for iter in range(Niterations):
+    gradients = training_gradient(theta)
+    theta -= eta*gradients
+    print(iter,gradients[0],gradients[1])
+print("theta from own gd")
+print(theta)
+
+# Now improve with momentum gradient descent
+change = 0.0
+delta_momentum = 0.3
+for iter in range(Niterations):
+    # calculate gradient
+    gradients = training_gradient(theta)
+    # calculate update
+    new_change = eta*gradients+delta_momentum*change
+    # take a step
+    theta -= new_change
+    # save the change
+    change = new_change
+    print(iter,gradients[0],gradients[1])
+print("theta from own gd wth momentum")
+print(theta)
+
              +
              +
              +
              +
              +
              +

              But noen of these can compete with Newton’s method

              +
              +
              +
              # Using Newton's method
+from random import random, seed
+import numpy as np
+import autograd.numpy as np
+import matplotlib.pyplot as plt
+from autograd import grad
+
+def CostOLS(beta):
+    return (1.0/n)*np.sum((y-X @ beta)**2)
+
+n = 100
+x = 2*np.random.rand(n,1)
+y = 4+3*x+np.random.randn(n,1)
+
+X = np.c_[np.ones((n,1)), x]
+XT_X = X.T @ X
+beta_linreg = np.linalg.pinv(XT_X) @ (X.T @ y)
+print("Own inversion")
+print(beta_linreg)
+# Hessian matrix
+H = (2.0/n)* XT_X
+# Note that here the Hessian does not depend on the parameters beta
+invH = np.linalg.pinv(H)
+EigValues, EigVectors = np.linalg.eig(H)
+print(f"Eigenvalues of Hessian Matrix:{EigValues}")
+
+beta = np.random.randn(2,1)
+Niterations = 5
+
+# define the gradient
+training_gradient = grad(CostOLS)
+
+for iter in range(Niterations):
+    gradients = training_gradient(beta)
+    beta -= invH @ gradients
+    print(iter,gradients[0],gradients[1])
+print("beta from own Newton code")
+print(beta)
+
              +
              +
              +
              +
              +
              +

              Including Stochastic Gradient Descent with Autograd

              +

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

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

              Same code but now with momentum gradient descent

              +
              +
              +
              # Using Autograd to calculate gradients using SGD
+# OLS example
+from random import random, seed
+import numpy as np
+import autograd.numpy as np
+import matplotlib.pyplot as plt
+from autograd import grad
+
+# Note change from previous example
+def CostOLS(y,X,theta):
+    return np.sum((y-X @ theta)**2)
+
+n = 100
+x = 2*np.random.rand(n,1)
+y = 4+3*x+np.random.randn(n,1)
+
+X = np.c_[np.ones((n,1)), x]
+XT_X = X.T @ X
+theta_linreg = np.linalg.pinv(XT_X) @ (X.T @ y)
+print("Own inversion")
+print(theta_linreg)
+# Hessian matrix
+H = (2.0/n)* XT_X
+EigValues, EigVectors = np.linalg.eig(H)
+print(f"Eigenvalues of Hessian Matrix:{EigValues}")
+
+theta = np.random.randn(2,1)
+eta = 1.0/np.max(EigValues)
+Niterations = 100
+
+# Note that we request the derivative wrt third argument (theta, 2 here)
+training_gradient = grad(CostOLS,2)
+
+for iter in range(Niterations):
+    gradients = (1.0/n)*training_gradient(y, X, theta)
+    theta -= eta*gradients
+print("theta from own gd")
+print(theta)
+
+
+n_epochs = 50
+M = 5   #size of each minibatch
+m = int(n/M) #number of minibatches
+t0, t1 = 5, 50
+def learning_schedule(t):
+    return t0/(t+t1)
+
+theta = np.random.randn(2,1)
+
+change = 0.0
+delta_momentum = 0.3
+
+for epoch in range(n_epochs):
+    for i in range(m):
+        random_index = M*np.random.randint(m)
+        xi = X[random_index:random_index+M]
+        yi = y[random_index:random_index+M]
+        gradients = (1.0/M)*training_gradient(yi, xi, theta)
+        eta = learning_schedule(epoch*m+i)
+        # calculate update
+        new_change = eta*gradients+delta_momentum*change
+        # take a step
+        theta -= new_change
+        # save the change
+        change = new_change
+print("theta from own sdg with momentum")
+print(theta)
+
              +
              +
              +
              +
              +
              +

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

              +
              +
              +
              # Using Autograd to calculate gradients using AdaGrad and Stochastic Gradient descent
+# OLS example
+from random import random, seed
+import numpy as np
+import autograd.numpy as np
+import matplotlib.pyplot as plt
+from autograd import grad
+
+# Note change from previous example
+def CostOLS(y,X,theta):
+    return np.sum((y-X @ theta)**2)
+
+n = 1000
+x = np.random.rand(n,1)
+y = 2.0+3*x +4*x*x
+
+X = np.c_[np.ones((n,1)), x, x*x]
+XT_X = X.T @ X
+theta_linreg = np.linalg.pinv(XT_X) @ (X.T @ y)
+print("Own inversion")
+print(theta_linreg)
+
+
+# Note that we request the derivative wrt third argument (theta, 2 here)
+training_gradient = grad(CostOLS,2)
+# Define parameters for Stochastic Gradient Descent
+n_epochs = 50
+M = 5   #size of each minibatch
+m = int(n/M) #number of minibatches
+# Guess for unknown parameters theta
+theta = np.random.randn(3,1)
+
+# Value for learning rate
+eta = 0.01
+# Including AdaGrad parameter to avoid possible division by zero
+delta  = 1e-8
+for epoch in range(n_epochs):
+    Giter = 0.0
+    for i in range(m):
+        random_index = M*np.random.randint(m)
+        xi = X[random_index:random_index+M]
+        yi = y[random_index:random_index+M]
+        gradients = (1.0/M)*training_gradient(yi, xi, theta)
+        Giter += gradients*gradients
+        update = gradients*eta/(delta+np.sqrt(Giter))
+        theta -= update
+print("theta from own AdaGrad")
+print(theta)
+
              +
              +
              +
              +

              Running this code we note an almost perfect agreement with the results from matrix inversion.

              +
              +
              +

              RMSprop for adaptive learning rate with Stochastic Gradient Descent

              +
              +
              +
              # Using Autograd to calculate gradients using RMSprop  and Stochastic Gradient descent
+# OLS example
+from random import random, seed
+import numpy as np
+import autograd.numpy as np
+import matplotlib.pyplot as plt
+from autograd import grad
+
+# Note change from previous example
+def CostOLS(y,X,theta):
+    return np.sum((y-X @ theta)**2)
+
+n = 1000
+x = np.random.rand(n,1)
+y = 2.0+3*x +4*x*x# +np.random.randn(n,1)
+
+X = np.c_[np.ones((n,1)), x, x*x]
+XT_X = X.T @ X
+theta_linreg = np.linalg.pinv(XT_X) @ (X.T @ y)
+print("Own inversion")
+print(theta_linreg)
+
+
+# Note that we request the derivative wrt third argument (theta, 2 here)
+training_gradient = grad(CostOLS,2)
+# Define parameters for Stochastic Gradient Descent
+n_epochs = 50
+M = 5   #size of each minibatch
+m = int(n/M) #number of minibatches
+# Guess for unknown parameters theta
+theta = np.random.randn(3,1)
+
+# Value for learning rate
+eta = 0.01
+# Value for parameter rho
+rho = 0.99
+# Including AdaGrad parameter to avoid possible division by zero
+delta  = 1e-8
+for epoch in range(n_epochs):
+    Giter = 0.0
+    for i in range(m):
+        random_index = M*np.random.randint(m)
+        xi = X[random_index:random_index+M]
+        yi = y[random_index:random_index+M]
+        gradients = (1.0/M)*training_gradient(yi, xi, theta)
+	# Accumulated gradient
+	# Scaling with rho the new and the previous results
+        Giter = (rho*Giter+(1-rho)*gradients*gradients)
+	# Taking the diagonal only and inverting
+        update = gradients*eta/(delta+np.sqrt(Giter))
+	# Hadamard product
+        theta -= update
+print("theta from own RMSprop")
+print(theta)
+
              +
              +
              +
              +
              +
              +

              And finally ADAM

              +
              +
              +
              # Using Autograd to calculate gradients using RMSprop  and Stochastic Gradient descent
+# OLS example
+from random import random, seed
+import numpy as np
+import autograd.numpy as np
+import matplotlib.pyplot as plt
+from autograd import grad
+
+# Note change from previous example
+def CostOLS(y,X,theta):
+    return np.sum((y-X @ theta)**2)
+
+n = 1000
+x = np.random.rand(n,1)
+y = 2.0+3*x +4*x*x# +np.random.randn(n,1)
+
+X = np.c_[np.ones((n,1)), x, x*x]
+XT_X = X.T @ X
+theta_linreg = np.linalg.pinv(XT_X) @ (X.T @ y)
+print("Own inversion")
+print(theta_linreg)
+
+
+# Note that we request the derivative wrt third argument (theta, 2 here)
+training_gradient = grad(CostOLS,2)
+# Define parameters for Stochastic Gradient Descent
+n_epochs = 50
+M = 5   #size of each minibatch
+m = int(n/M) #number of minibatches
+# Guess for unknown parameters theta
+theta = np.random.randn(3,1)
+
+# Value for learning rate
+eta = 0.01
+# Value for parameters beta1 and beta2, see https://arxiv.org/abs/1412.6980
+beta1 = 0.9
+beta2 = 0.999
+# Including AdaGrad parameter to avoid possible division by zero
+delta  = 1e-7
+iter = 0
+for epoch in range(n_epochs):
+    first_moment = 0.0
+    second_moment = 0.0
+    iter += 1
+    for i in range(m):
+        random_index = M*np.random.randint(m)
+        xi = X[random_index:random_index+M]
+        yi = y[random_index:random_index+M]
+        gradients = (1.0/M)*training_gradient(yi, xi, theta)
+        # Computing moments first
+        first_moment = beta1*first_moment + (1-beta1)*gradients
+        second_moment = beta2*second_moment+(1-beta2)*gradients*gradients
+        first_term = first_moment/(1.0-beta1**iter)
+        second_term = second_moment/(1.0-beta2**iter)
+	# Scaling with rho the new and the previous results
+        update = eta*first_term/(np.sqrt(second_term)+delta)
+        theta -= update
+print("theta from own ADAM")
+print(theta)
+
              +
              +
              +
              +
              +
              +

              And Logistic Regression

              +
              +
              +
              import autograd.numpy as np
+from autograd import grad
+
+def sigmoid(x):
+    return 0.5 * (np.tanh(x / 2.) + 1)
+
+def logistic_predictions(weights, inputs):
+    # Outputs probability of a label being true according to logistic model.
+    return sigmoid(np.dot(inputs, weights))
+
+def training_loss(weights):
+    # Training loss is the negative log-likelihood of the training labels.
+    preds = logistic_predictions(weights, inputs)
+    label_probabilities = preds * targets + (1 - preds) * (1 - targets)
+    return -np.sum(np.log(label_probabilities))
+
+# Build a toy dataset.
+inputs = np.array([[0.52, 1.12,  0.77],
+                   [0.88, -1.08, 0.15],
+                   [0.52, 0.06, -1.30],
+                   [0.74, -2.49, 1.39]])
+targets = np.array([True, True, False, True])
+
+# Define a function that returns gradients of training loss using Autograd.
+training_gradient_fun = grad(training_loss)
+
+# Optimize weights using gradient descent.
+weights = np.array([0.0, 0.0, 0.0])
+print("Initial loss:", training_loss(weights))
+for i in range(100):
+    weights -= training_gradient_fun(weights) * 0.01
+
+print("Trained loss:", training_loss(weights))
+
              +
              +
              +
              +
              +
              +

              Introducing JAX

              +

              Presently, instead of using autograd, we recommend using JAX

              +

              JAX is Autograd and XLA (Accelerated Linear Algebra)), +brought together for high-performance numerical computing and machine learning research. +It provides composable transformations of Python+NumPy programs: differentiate, vectorize, parallelize, Just-In-Time compile to GPU/TPU, and more.

              +

              Here’s a simple example on how you can use JAX to compute the derivate of the logistic function.

              +
              +
              +
              import jax.numpy as jnp
+from jax import grad, jit, vmap
+
+def sum_logistic(x):
+  return jnp.sum(1.0 / (1.0 + jnp.exp(-x)))
+
+x_small = jnp.arange(3.)
+derivative_fn = grad(sum_logistic)
+print(derivative_fn(x_small))
+
              +
              +
              +
              +
              +
              +

              Introduction to Neural networks

              +

              Artificial neural networks are computational systems that can learn to +perform tasks by considering examples, generally without being +programmed with any task-specific rules. It is supposed to mimic a +biological system, wherein neurons interact by sending signals in the +form of mathematical functions between layers. All layers can contain +an arbitrary number of neurons, and each connection is represented by +a weight variable.

              +
              +
              +

              Artificial neurons

              +

              The field of artificial neural networks has a long history of +development, and is closely connected with the advancement of computer +science and computers in general. A model of artificial neurons was +first developed by McCulloch and Pitts in 1943 to study signal +processing in the brain and has later been refined by others. The +general idea is to mimic neural networks in the human brain, which is +composed of billions of neurons that communicate with each other by +sending electrical signals. Each neuron accumulates its incoming +signals, which must exceed an activation threshold to yield an +output. If the threshold is not overcome, the neuron remains inactive, +i.e. has zero output.

              +

              This behaviour has inspired a simple mathematical model for an artificial neuron.

              + +
              +
              +\[ +\begin{equation} + y = f\left(\sum_{i=1}^n w_ix_i\right) = f(u) +\label{artificialNeuron} \tag{6} +\end{equation} +\]
              +

              Here, the output \(y\) of the neuron is the value of its activation function, which have as input +a weighted sum of signals \(x_i, \dots ,x_n\) received by \(n\) other neurons.

              +

              Conceptually, it is helpful to divide neural networks into four +categories:

              +
                +

              1. general purpose neural networks for supervised learning,

                2. +

              2. neural networks designed specifically for image processing, the most prominent example of this class being Convolutional Neural Networks (CNNs),

                3. +

              3. neural networks for sequential data such as Recurrent Neural Networks (RNNs), and

                4. +

              4. neural networks for unsupervised learning such as Deep Boltzmann Machines.

                5. +
              +

              In natural science, DNNs and CNNs have already found numerous +applications. In statistical physics, they have been applied to detect +phase transitions in 2D Ising and Potts models, lattice gauge +theories, and different phases of polymers, or solving the +Navier-Stokes equation in weather forecasting. Deep learning has also +found interesting applications in quantum physics. Various quantum +phase transitions can be detected and studied using DNNs and CNNs, +topological phases, and even non-equilibrium many-body +localization. Representing quantum states as DNNs quantum state +tomography are among some of the impressive achievements to reveal the +potential of DNNs to facilitate the study of quantum systems.

              +

              In quantum information theory, it has been shown that one can perform +gate decompositions with the help of neural.

              +

              The applications are not limited to the natural sciences. There is a +plethora of applications in essentially all disciplines, from the +humanities to life science and medicine.

              +
              +
              +

              Neural network types

              +

              An artificial neural network (ANN), is a computational model that +consists of layers of connected neurons, or nodes or units. We will +refer to these interchangeably as units or nodes, and sometimes as +neurons.

              +

              It is supposed to mimic a biological nervous system by letting each +neuron interact with other neurons by sending signals in the form of +mathematical functions between layers. A wide variety of different +ANNs have been developed, but most of them consist of an input layer, +an output layer and eventual layers in-between, called hidden +layers. All layers can contain an arbitrary number of nodes, and each +connection between two nodes is associated with a weight variable.

              +

              Neural networks (also called neural nets) are neural-inspired +nonlinear models for supervised learning. As we will see, neural nets +can be viewed as natural, more powerful extensions of supervised +learning methods such as linear and logistic regression and soft-max +methods we discussed earlier.

              +
              +
              +

              Feed-forward neural networks

              +

              The feed-forward neural network (FFNN) was the first and simplest type +of ANNs that were devised. In this network, the information moves in +only one direction: forward through the layers.

              +

              Nodes are represented by circles, while the arrows display the +connections between the nodes, including the direction of information +flow. Additionally, each arrow corresponds to a weight variable +(figure to come). We observe that each node in a layer is connected +to all nodes in the subsequent layer, making this a so-called +fully-connected FFNN.

              +
              +
              +

              Convolutional Neural Network

              +

              A different variant of FFNNs are convolutional neural networks +(CNNs), which have a connectivity pattern inspired by the animal +visual cortex. Individual neurons in the visual cortex only respond to +stimuli from small sub-regions of the visual field, called a receptive +field. This makes the neurons well-suited to exploit the strong +spatially local correlation present in natural images. The response of +each neuron can be approximated mathematically as a convolution +operation. (figure to come)

              +

              Convolutional neural networks emulate the behaviour of neurons in the +visual cortex by enforcing a local connectivity pattern between +nodes of adjacent layers: Each node in a convolutional layer is +connected only to a subset of the nodes in the previous layer, in +contrast to the fully-connected FFNN. Often, CNNs consist of several +convolutional layers that learn local features of the input, with a +fully-connected layer at the end, which gathers all the local data and +produces the outputs. They have wide applications in image and video +recognition.

              +
              +
              +

              Recurrent neural networks

              +

              So far we have only mentioned ANNs where information flows in one +direction: forward. Recurrent neural networks on the other hand, +have connections between nodes that form directed cycles. This +creates a form of internal memory which are able to capture +information on what has been calculated before; the output is +dependent on the previous computations. Recurrent NNs make use of +sequential information by performing the same task for every element +in a sequence, where each element depends on previous elements. An +example of such information is sentences, making recurrent NNs +especially well-suited for handwriting and speech recognition.

              +
              +
              +

              Other types of networks

              +

              There are many other kinds of ANNs that have been developed. One type +that is specifically designed for interpolation in multidimensional +space is the radial basis function (RBF) network. RBFs are typically +made up of three layers: an input layer, a hidden layer with +non-linear radial symmetric activation functions and a linear output +layer (‘’linear’’ here means that each node in the output layer has a +linear activation function). The layers are normally fully-connected +and there are no cycles, thus RBFs can be viewed as a type of +fully-connected FFNN. They are however usually treated as a separate +type of NN due the unusual activation functions.

              +
              +
              +

              Multilayer perceptrons

              +

              One uses often so-called fully-connected feed-forward neural networks +with three or more layers (an input layer, one or more hidden layers +and an output layer) consisting of neurons that have non-linear +activation functions.

              +

              Such networks are often called multilayer perceptrons (MLPs).

              +
              +
              +

              Why multilayer perceptrons?

              +

              According to the Universal approximation theorem, a feed-forward +neural network with just a single hidden layer containing a finite +number of neurons can approximate a continuous multidimensional +function to arbitrary accuracy, assuming the activation function for +the hidden layer is a non-constant, bounded and +monotonically-increasing continuous function.

              +

              Note that the requirements on the activation function only applies to +the hidden layer, the output nodes are always assumed to be linear, so +as to not restrict the range of output values.

              +
              +
              +

              Illustration of a single perceptron model and a multi-perceptron model

              + + +

              Figure 1: In a) we show a single perceptron model while in b) we dispay a network with two hidden layers, an input layer and an output layer.

              +
              +
              +

              Examples of XOR, OR and AND gates

              +

              Let us first try to fit various gates using standard linear +regression. The gates we are thinking of are the classical XOR, OR and +AND gates, well-known elements in computer science. The tables here +show how we can set up the inputs \(x_1\) and \(x_2\) in order to yield a +specific target \(y_i\).

              +
              +
              +
              """
+Simple code that tests XOR, OR and AND gates with linear regression
+"""
+
+import numpy as np
+# Design matrix
+X = np.array([ [1, 0, 0], [1, 0, 1], [1, 1, 0],[1, 1, 1]],dtype=np.float64)
+print(f"The X.TX  matrix:{X.T @ X}")
+Xinv = np.linalg.pinv(X.T @ X)
+print(f"The invers of X.TX  matrix:{Xinv}")
+
+# The XOR gate 
+yXOR = np.array( [ 0, 1 ,1, 0])
+ThetaXOR  = Xinv @ X.T @ yXOR
+print(f"The values of theta for the XOR gate:{ThetaXOR}")
+print(f"The linear regression prediction  for the XOR gate:{X @ ThetaXOR}")
+
+
+# The OR gate 
+yOR = np.array( [ 0, 1 ,1, 1])
+ThetaOR  = Xinv @ X.T @ yOR
+print(f"The values of theta for the OR gate:{ThetaOR}")
+print(f"The linear regression prediction  for the OR gate:{X @ ThetaOR}")
+
+
+# The OR gate 
+yAND = np.array( [ 0, 0 ,0, 1])
+ThetaAND  = Xinv @ X.T @ yAND
+print(f"The values of theta for the AND gate:{ThetaAND}")
+print(f"The linear regression prediction  for the AND gate:{X @ ThetaAND}")
+
              +
              +
              +
              +

              What is happening here?

              +
              +
              +

              Does Logistic Regression do a better Job?

              +
              +
              +
              """
+Simple code that tests XOR and OR gates with linear regression
+and logistic regression
+"""
+
+import matplotlib.pyplot as plt
+from sklearn.linear_model import LogisticRegression
+import numpy as np
+
+# Design matrix
+X = np.array([ [1, 0, 0], [1, 0, 1], [1, 1, 0],[1, 1, 1]],dtype=np.float64)
+print(f"The X.TX  matrix:{X.T @ X}")
+Xinv = np.linalg.pinv(X.T @ X)
+print(f"The invers of X.TX  matrix:{Xinv}")
+
+# The XOR gate 
+yXOR = np.array( [ 0, 1 ,1, 0])
+ThetaXOR  = Xinv @ X.T @ yXOR
+print(f"The values of theta for the XOR gate:{ThetaXOR}")
+print(f"The linear regression prediction  for the XOR gate:{X @ ThetaXOR}")
+
+
+# The OR gate 
+yOR = np.array( [ 0, 1 ,1, 1])
+ThetaOR  = Xinv @ X.T @ yOR
+print(f"The values of theta for the OR gate:{ThetaOR}")
+print(f"The linear regression prediction  for the OR gate:{X @ ThetaOR}")
+
+
+# The OR gate 
+yAND = np.array( [ 0, 0 ,0, 1])
+ThetaAND  = Xinv @ X.T @ yAND
+print(f"The values of theta for the AND gate:{ThetaAND}")
+print(f"The linear regression prediction  for the AND gate:{X @ ThetaAND}")
+
+# Now we change to logistic regression
+
+
+# Logistic Regression
+logreg = LogisticRegression()
+logreg.fit(X, yOR)
+print("Test set accuracy with Logistic Regression for OR gate: {:.2f}".format(logreg.score(X,yOR)))
+
+logreg.fit(X, yXOR)
+print("Test set accuracy with Logistic Regression for XOR gate: {:.2f}".format(logreg.score(X,yXOR)))
+
+
+logreg.fit(X, yAND)
+print("Test set accuracy with Logistic Regression for AND gate: {:.2f}".format(logreg.score(X,yAND)))
+
              +
              +
              +
              +

              Not exactly impressive, but somewhat better.

              +
              +
              +

              Adding Neural Networks

              +
              +
              +
              # and now neural networks with Scikit-Learn and the XOR
+
+from sklearn.neural_network import MLPClassifier
+from sklearn.datasets import make_classification
+X, yXOR = make_classification(n_samples=100, random_state=1)
+FFNN = MLPClassifier(random_state=1, max_iter=300).fit(X, yXOR)
+FFNN.predict_proba(X)
+print(f"Test set accuracy with Feed Forward Neural Network  for XOR gate:{FFNN.score(X, yXOR)}")
+
              +
              +
              +
              +
              +
              +

              Mathematical model

              +

              The output \(y\) is produced via the activation function \(f\)

              +
              +\[ +y = f\left(\sum_{i=1}^n w_ix_i + b_i\right) = f(z), +\]
              +

              This function receives \(x_i\) as inputs. +Here the activation \(z=(\sum_{i=1}^n w_ix_i+b_i)\). +In an FFNN of such neurons, the inputs \(x_i\) are the outputs of +the neurons in the preceding layer. Furthermore, an MLP is +fully-connected, which means that each neuron receives a weighted sum +of the outputs of all neurons in the previous layer.

              +
              +
              +

              Mathematical model

              +

              First, for each node \(i\) in the first hidden layer, we calculate a weighted sum \(z_i^1\) of the input coordinates \(x_j\),

              + +
              +
              +\[ +\begin{equation} z_i^1 = \sum_{j=1}^{M} w_{ij}^1 x_j + b_i^1 +\label{_auto6} \tag{7} +\end{equation} +\]
              +

              Here \(b_i\) is the so-called bias which is normally needed in +case of zero activation weights or inputs. How to fix the biases and +the weights will be discussed below. The value of \(z_i^1\) is the +argument to the activation function \(f_i\) of each node \(i\), The +variable \(M\) stands for all possible inputs to a given node \(i\) in the +first layer. We define the output \(y_i^1\) of all neurons in layer 1 as

              + +
              +
              +\[ +\begin{equation} + y_i^1 = f(z_i^1) = f\left(\sum_{j=1}^M w_{ij}^1 x_j + b_i^1\right) +\label{outputLayer1} \tag{8} +\end{equation} +\]
              +

              where we assume that all nodes in the same layer have identical +activation functions, hence the notation \(f\). In general, we could assume in the more general case that different layers have different activation functions. +In this case we would identify these functions with a superscript \(l\) for the \(l\)-th layer,

              + +
              +
              +\[ +\begin{equation} + y_i^l = f^l(u_i^l) = f^l\left(\sum_{j=1}^{N_{l-1}} w_{ij}^l y_j^{l-1} + b_i^l\right) +\label{generalLayer} \tag{9} +\end{equation} +\]
              +

              where \(N_l\) is the number of nodes in layer \(l\). When the output of +all the nodes in the first hidden layer are computed, the values of +the subsequent layer can be calculated and so forth until the output +is obtained.

              +
              +
              +

              Mathematical model

              +

              The output of neuron \(i\) in layer 2 is thus,

              + +
              +
              +\[ +\begin{equation} + y_i^2 = f^2\left(\sum_{j=1}^N w_{ij}^2 y_j^1 + b_i^2\right) +\label{_auto7} \tag{10} +\end{equation} +\]
              + +
              +
              +\[ +\begin{equation} + = f^2\left[\sum_{j=1}^N w_{ij}^2f^1\left(\sum_{k=1}^M w_{jk}^1 x_k + b_j^1\right) + b_i^2\right] +\label{outputLayer2} \tag{11} +\end{equation} +\]
              +

              where we have substituted \(y_k^1\) with the inputs \(x_k\). Finally, the ANN output reads

              + +
              +
              +\[ +\begin{equation} + y_i^3 = f^3\left(\sum_{j=1}^N w_{ij}^3 y_j^2 + b_i^3\right) +\label{_auto8} \tag{12} +\end{equation} +\]
              + +
              +
              +\[ +\begin{equation} + = f_3\left[\sum_{j} w_{ij}^3 f^2\left(\sum_{k} w_{jk}^2 f^1\left(\sum_{m} w_{km}^1 x_m + b_k^1\right) + b_j^2\right) + + b_1^3\right] +\label{_auto9} \tag{13} +\end{equation} +\]
              +
              +
              +

              Mathematical model

              +

              We can generalize this expression to an MLP with \(l\) hidden +layers. The complete functional form is,

              + +
              +
              +\[ +\begin{equation} +y^{l+1}_i = f^{l+1}\left[\!\sum_{j=1}^{N_l} w_{ij}^3 f^l\left(\sum_{k=1}^{N_{l-1}}w_{jk}^{l-1}\left(\dots f^1\left(\sum_{n=1}^{N_0} w_{mn}^1 x_n+ b_m^1\right)\dots\right)+b_k^2\right)+b_1^3\right] +\label{completeNN} \tag{14} +\end{equation} +\]
              +

              which illustrates a basic property of MLPs: The only independent +variables are the input values \(x_n\).

              +
              +
              +

              Mathematical model

              +

              This confirms that an MLP, despite its quite convoluted mathematical +form, is nothing more than an analytic function, specifically a +mapping of real-valued vectors \(\hat{x} \in \mathbb{R}^n \rightarrow +\hat{y} \in \mathbb{R}^m\).

              +

              Furthermore, the flexibility and universality of an MLP can be +illustrated by realizing that the expression is essentially a nested +sum of scaled activation functions of the form

              + +
              +
              +\[ +\begin{equation} + f(x) = c_1 f(c_2 x + c_3) + c_4 +\label{_auto10} \tag{15} +\end{equation} +\]
              +

              where the parameters \(c_i\) are weights and biases. By adjusting these +parameters, the activation functions can be shifted up and down or +left and right, change slope or be rescaled which is the key to the +flexibility of a neural network.

              +
              +

              Matrix-vector notation

              +

              We can introduce a more convenient notation for the activations in an A NN.

              +

              Additionally, we can represent the biases and activations +as layer-wise column vectors \(\hat{b}_l\) and \(\hat{y}_l\), so that the \(i\)-th element of each vector +is the bias \(b_i^l\) and activation \(y_i^l\) of node \(i\) in layer \(l\) respectively.

              +

              We have that \(\mathrm{W}_l\) is an \(N_{l-1} \times N_l\) matrix, while \(\hat{b}_l\) and \(\hat{y}_l\) are \(N_l \times 1\) column vectors. +With this notation, the sum becomes a matrix-vector multiplication, and we can write +the equation for the activations of hidden layer 2 (assuming three nodes for simplicity) as

              + +
              +
              +\[\begin{split} +\begin{equation} + \hat{y}_2 = f_2(\mathrm{W}_2 \hat{y}_{1} + \hat{b}_{2}) = + f_2\left(\left[\begin{array}{ccc} + w^2_{11} &w^2_{12} &w^2_{13} \\ + w^2_{21} &w^2_{22} &w^2_{23} \\ + w^2_{31} &w^2_{32} &w^2_{33} \\ + \end{array} \right] \cdot + \left[\begin{array}{c} + y^1_1 \\ + y^1_2 \\ + y^1_3 \\ + \end{array}\right] + + \left[\begin{array}{c} + b^2_1 \\ + b^2_2 \\ + b^2_3 \\ + \end{array}\right]\right). +\label{_auto11} \tag{16} +\end{equation} +\end{split}\]
              +
              +
              +

              Matrix-vector notation and activation

              +

              The activation of node \(i\) in layer 2 is

              + +
              +
              +\[ +\begin{equation} + y^2_i = f_2\Bigr(w^2_{i1}y^1_1 + w^2_{i2}y^1_2 + w^2_{i3}y^1_3 + b^2_i\Bigr) = + f_2\left(\sum_{j=1}^3 w^2_{ij} y_j^1 + b^2_i\right). +\label{_auto12} \tag{17} +\end{equation} +\]
              +

              This is not just a convenient and compact notation, but also a useful +and intuitive way to think about MLPs: The output is calculated by a +series of matrix-vector multiplications and vector additions that are +used as input to the activation functions. For each operation +\(\mathrm{W}_l \hat{y}_{l-1}\) we move forward one layer.

              +
              +
              +

              Activation functions

              +

              A property that characterizes a neural network, other than its +connectivity, is the choice of activation function(s). As described +in, the following restrictions are imposed on an activation function +for a FFNN to fulfill the universal approximation theorem

              +
                +

              • Non-constant

                • +

              • Bounded

                • +

              • Monotonically-increasing

                • +

              • Continuous

                • +
              +
              +
              +

              Activation functions, Logistic and Hyperbolic ones

              +

              The second requirement excludes all linear functions. Furthermore, in +a MLP with only linear activation functions, each layer simply +performs a linear transformation of its inputs.

              +

              Regardless of the number of layers, the output of the NN will be +nothing but a linear function of the inputs. Thus we need to introduce +some kind of non-linearity to the NN to be able to fit non-linear +functions Typical examples are the logistic Sigmoid

              +
              +\[ +f(x) = \frac{1}{1 + e^{-x}}, +\]
              +

              and the hyperbolic tangent function

              +
              +\[ +f(x) = \tanh(x) +\]
              +
              +
              +

              Relevance

              +

              The sigmoid function are more biologically plausible because the +output of inactive neurons are zero. Such activation function are +called one-sided. However, it has been shown that the hyperbolic +tangent performs better than the sigmoid for training MLPs. has +become the most popular for deep neural networks

              +
              +
              +
              """The sigmoid function (or the logistic curve) is a 
+function that takes any real number, z, and outputs a number (0,1).
+It is useful in neural networks for assigning weights on a relative scale.
+The value z is the weighted sum of parameters involved in the learning algorithm."""
+
+import numpy
+import matplotlib.pyplot as plt
+import math as mt
+
+z = numpy.arange(-5, 5, .1)
+sigma_fn = numpy.vectorize(lambda z: 1/(1+numpy.exp(-z)))
+sigma = sigma_fn(z)
+
+fig = plt.figure()
+ax = fig.add_subplot(111)
+ax.plot(z, sigma)
+ax.set_ylim([-0.1, 1.1])
+ax.set_xlim([-5,5])
+ax.grid(True)
+ax.set_xlabel('z')
+ax.set_title('sigmoid function')
+
+plt.show()
+
+"""Step Function"""
+z = numpy.arange(-5, 5, .02)
+step_fn = numpy.vectorize(lambda z: 1.0 if z >= 0.0 else 0.0)
+step = step_fn(z)
+
+fig = plt.figure()
+ax = fig.add_subplot(111)
+ax.plot(z, step)
+ax.set_ylim([-0.5, 1.5])
+ax.set_xlim([-5,5])
+ax.grid(True)
+ax.set_xlabel('z')
+ax.set_title('step function')
+
+plt.show()
+
+"""Sine Function"""
+z = numpy.arange(-2*mt.pi, 2*mt.pi, 0.1)
+t = numpy.sin(z)
+
+fig = plt.figure()
+ax = fig.add_subplot(111)
+ax.plot(z, t)
+ax.set_ylim([-1.0, 1.0])
+ax.set_xlim([-2*mt.pi,2*mt.pi])
+ax.grid(True)
+ax.set_xlabel('z')
+ax.set_title('sine function')
+
+plt.show()
+
+"""Plots a graph of the squashing function used by a rectified linear
+unit"""
+z = numpy.arange(-2, 2, .1)
+zero = numpy.zeros(len(z))
+y = numpy.max([zero, z], axis=0)
+
+fig = plt.figure()
+ax = fig.add_subplot(111)
+ax.plot(z, y)
+ax.set_ylim([-2.0, 2.0])
+ax.set_xlim([-2.0, 2.0])
+ax.grid(True)
+ax.set_xlabel('z')
+ax.set_title('Rectified linear unit')
+
+plt.show()
+
              +
              +
              +
              +
              +
              +
              + + + + +
              + + + +
              + + +
              +

              previous

              +

              Week 39: Optimization and Gradient Methods

              +
              +               + +
              +

              next

              +

              Project 1 on Machine Learning, deadline October 9 (midnight), 2023

              +
              + +               +
              + +
              +
              +
              +

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

              +
              +
              + + +
              +
              + + + + + \ No newline at end of file diff --git a/doc/LectureNotes/_build/jupyter_execute/chapter1.ipynb b/doc/LectureNotes/_build/jupyter_execute/chapter1.ipynb index e339ffdc5..69fd6ba8c 100644 --- a/doc/LectureNotes/_build/jupyter_execute/chapter1.ipynb +++ b/doc/LectureNotes/_build/jupyter_execute/chapter1.ipynb @@ -342,20 +342,18 @@ }, "outputs": [ { - "ename": "ModuleNotFoundError", - "evalue": "No module named 'matplotlib'", - "output_type": "error", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mModuleNotFoundError\u001b[0m Traceback (most recent call last)", - "Cell \u001b[0;32mIn[1], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m 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kwargs[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mlocal_ns\u001b[39m\u001b[38;5;124m'\u001b[39m] \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mget_local_scope(stack_depth)\n\u001b[1;32m 2431\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mbuiltin_trap:\n\u001b[0;32m-> 2432\u001b[0m result \u001b[38;5;241m=\u001b[39m \u001b[43mfn\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 2434\u001b[0m \u001b[38;5;66;03m# The code below prevents the output from being displayed\u001b[39;00m\n\u001b[1;32m 2435\u001b[0m \u001b[38;5;66;03m# when using magics with decorator @output_can_be_silenced\u001b[39;00m\n\u001b[1;32m 2436\u001b[0m \u001b[38;5;66;03m# when the last Python token in the expression is a ';'.\u001b[39;00m\n\u001b[1;32m 2437\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mgetattr\u001b[39m(fn, magic\u001b[38;5;241m.\u001b[39mMAGIC_OUTPUT_CAN_BE_SILENCED, \u001b[38;5;28;01mFalse\u001b[39;00m):\n", - "File \u001b[0;32m~/miniforge3/lib/python3.9/site-packages/IPython/core/magics/pylab.py:99\u001b[0m, in \u001b[0;36mPylabMagics.matplotlib\u001b[0;34m(self, line)\u001b[0m\n\u001b[1;32m 97\u001b[0m \u001b[38;5;28mprint\u001b[39m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mAvailable matplotlib backends: \u001b[39m\u001b[38;5;132;01m%s\u001b[39;00m\u001b[38;5;124m\"\u001b[39m \u001b[38;5;241m%\u001b[39m backends_list)\n\u001b[1;32m 98\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m---> 99\u001b[0m gui, backend \u001b[38;5;241m=\u001b[39m 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noqa\u001b[39;00m\n\u001b[1;32m 2\u001b[0m __version__ \u001b[38;5;241m=\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m0.1.6\u001b[39m\u001b[38;5;124m\"\u001b[39m \u001b[38;5;66;03m# noqa\u001b[39;00m\n", - "File \u001b[0;32m~/miniforge3/lib/python3.9/site-packages/matplotlib_inline/backend_inline.py:6\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[38;5;124;03m\"\"\"A matplotlib backend for publishing figures via display_data\"\"\"\u001b[39;00m\n\u001b[1;32m 3\u001b[0m \u001b[38;5;66;03m# Copyright (c) IPython Development Team.\u001b[39;00m\n\u001b[1;32m 4\u001b[0m \u001b[38;5;66;03m# Distributed under the terms of the BSD 3-Clause License.\u001b[39;00m\n\u001b[0;32m----> 6\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mmatplotlib\u001b[39;00m\n\u001b[1;32m 7\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mmatplotlib\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m colors\n\u001b[1;32m 8\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mmatplotlib\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mbackends\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m backend_agg\n", - "\u001b[0;31mModuleNotFoundError\u001b[0m: No module named 'matplotlib'" - ] + "data": { + "image/png": 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BgwaaOXOmZs2apQ4dOuipp57SXXfdpRNPPDGi5//973+vVq1aaerUqbr++ut14MABNW/eXF27di2tN1O3bl316NFDzz//vDZu3KgjR46oVatWuvPOO3XHHXdIktq3b68TTzxRU6dO1bZt21SnTh2deuqpmj17tsaOHVvh8zdr1kwffvihJk2apEmTJqm4uFjt2rXT1KlTddttt1X7dQXsRuVhwGZuqP4Lqy5Ohw4dNHnyZP35z3+2uzmAZ9FjAwBR+vzzzzV37lz17t1b6enp+uabbzR16lSlp6frmmuusbt5gKcR2ABAlBo0aKDVq1fr2Wef1f79+5WRkaH+/fvrwQcfrHDKN4DEYCgKAAC4hu0F+u677z75fL6Qf+GKlQEAAFTFEUNRp512WshKtWXLdgMAAETKEYFN7dq16aUBAAA15ojAJlC3Iy0tTT169NBf//pXtWvXLuyxJSUlIWXWjx07pn379qlJkyZRl1kHAAD2MMbowIEDysrKiqiwZaRsTx5+66239Msvv+iUU07Rzp079cADD+jrr7/WV199FXaNnPvuuy9uBbsAAEBibd68WdnZ2TH7ebYHNsc7ePCgTj75ZN1xxx1hq18e32NTVFSkVq1aafPmzUpPT09kUwEAQDUVFxcrJyentGRCrDhiKKqsBg0a6IwzzqhwfZi0tLSwK++mp6cT2AAAkGRinUZi+3Tv45WUlGj9+vVhV1sGAACojO2BzYQJE7R8+XJt2LBBH3/8sUaPHq3i4uJKF28DAAAIx/ahqC1btuiyyy7Tnj171KxZM/Xs2VMrV65U69at7W4aAABIMrYHNvPmzbO7CQAAwCVsH4oCAACIFQIbAADgGgQ2AADANQhsAACAaxDYAAAA1yCwAQAArkFgAwAAXIPABgAAuAaBDQAAcA0CGwAA4BoENgAAwDUIbAAAgGsQ2AAAANcgsAEAAK5BYAMAAFyDwAYAALgGgQ0AAHANAhsAAOAaBDYAAMA1CGwAAIBrENgAAADXILABAACuQWADAABcg8AGAAC4BoENAABwDQIbAADgGgQ2AADANQhsAACAaxDYAAAA1yCwAQAArkFgAwAAXIPABgAAuAaBDQAAcA0CGwAA4BoENgAAwDUIbAAAgGsQ2AAAANcgsAEAAK5BYAMAAFyDwAYAALgGgQ0AAHANAhsAAOAaBDYAAMA1CGwAAIBrENgAAADXILABAACuQWADAABcg8AGAAC4BoENAABwDQIbAADgGgQ2AADANQhsAACAaxDYAAAA1yCwAQAArkFgAwAAXIPABgAAuAaBDQAAcA0CGwAA4BoENgAAwDUIbAAAgGsQ2AAAANcgsAEAAK5BYAMAAFzDUYFNXl6efD6fxo8fb3dTAABAEnJMYLNq1Sr9/e9/V+fOne1uCgAASFKOCGx+/vlnXX755frHP/6hRo0a2d0cAACQpBwR2Nx0000aMmSIzj///CqPLSkpUXFxccg/AAAASaptdwPmzZunNWvWaNWqVREdn5eXpylTpsS5VQAAIBnZ2mOzefNmjRs3Ti+88ILq1q0b0WMmTZqkoqKi0n+bN2+OcysBAECy8BljjF1PvmDBAo0cOVIpKSml2/x+v3w+n2rVqqWSkpKQfeEUFxcrIyNDRUVFSk9Pj3eTAQBADMTr/m3rUNTAgQP1xRdfhGy7+uqr1aFDB915551VBjUAAABl2RrYnHDCCTr99NNDtjVo0EBNmjQptx0AAKAqjpgVBQAAEAu2z4o63rJly+xuAgAASFL02AAAANcgsAEAAK5BYAMAAFyDwAYAALgGgQ0AAHANx82KAgAgIfx+qbBQ2r5dysyU+vaVKAyb9AhsAADek58vjRsnbdkS3JadLT3xhDRqlH3tQo0xFAUA8Jb8fGn06NCgRpK2brW25+fb0y7EBIENAMA7/H6rpybc+s+BbePHW8chKRHYAAC8o7CwfE9NWcZImzdbxyEpEdgAALxj+/bYHgfHIbABAHhHZmZsj4PjENgAALyjb19r9pPPF36/zyfl5FjHISkR2AAAvCMlxZrSLZUPbgLfT5tGPZskRmADAPCWUaOkV16RWrYM3Z6dbW2njk1So0AfAMB7Ro2ScnOpPOxCBDYAAG9KSZH697e7FYgxhqIAAIBrENgAAADXILABAACuQY4N4HR+PwmOABAhAhvAyfLzrQX7yq5tk51t1eFgSioAlMNQFOBU+fnS6NHlF+zbutXanp9vT7sAeJvfLy1bJs2da3112EroBDaAE/n9Vk+NMeX3BbaNH++4NxQALpefL7VpIw0YII0ZY31t08ZRH7QIbAAnKiws31NTljHS5s3WcQCQCEnSi0xgAzjR9u2xPQ4AaiKJepEJbAAnysyM7XEAUBNJ1ItMYAM4Ud++1uyn41cfDvD5pJwc6zgAiLck6kUmsAGcKCXFmtItlQ9uAt9Pm0Y9GwCJkUS9yAQ2gFONGiW98orUsmXo9uxsazt1bAAkShL1IlOgD3CyUaOk3FwqDwOoXLwrlAd6kUePtoKYsknEDutFJrABnC4lRerf3+5WAHCqRFUoD/Qih3uuadMc04vsMybc3K3kUVxcrIyMDBUVFSk9Pd3u5gAAkDiB2jLH38oDvSjxGLaOUe9QvO7fBDYAACQjv9+q+lvRNGyfz+pN2bDBEUNEx4vX/ZvkYQAAklES1ZZJJAIbAACSURLVlkkkAhsAAJJREtWWSSQCGwAAklES1ZZJJAIbAACSERXKwyKwAQAgWVGhvBwK9AEAkMyoUB6CwAYAgGRHhfJSDEUBAADXILABAACuQWADAABcg8AGAAC4BoENAABwDQIbAADgGgQ2AADANQhsAACAaxDYAAAA1yCwAQAArkFgAwAAXIPABgAAuAaBDQAAcA0CGwAA4Bq17W4AAABwIb9fKiyUtm+XMjOlvn2llJS4Py2BDQAAiK38fGncOGnLluC27GzpiSekUaPi+tQMRQEAgNjJz5dGjw4NaiRp61Zre35+XJ+ewAYAAMSG32/11BhTfl9g2/jx1nFxQmADAABio7CwfE9NWcZImzdbx8UJgQ0AAIiN7dtje1w1ENgAAIDYyMyM7XHVYHtg89RTT6lz585KT09Xenq6evXqpbfeesvuZgEAgGj17WvNfvL5wu/3+aScHOu4OLE9sMnOztZDDz2k1atXa/Xq1TrvvPOUm5urr776yu6mAQCAaKSkWFO6pfLBTeD7adPiWs/GZ0y41GV7NW7cWI888oiuueaaKo8tLi5WRkaGioqKlJ6enoDWAQCASoWrY5OTYwU1/1fHJl73b0cV6PP7/Xr55Zd18OBB9erVK+wxJSUlKikpKf2+uLg4Uc0DAACRGDVKys31buXhL774Qr169dKhQ4fUsGFDzZ8/X506dQp7bF5enqZMmZLgFgIAgKikpEj9+yf8aR0xFHX48GFt2rRJ+/fv16uvvqpnnnlGy5cvDxvchOuxycnJYSgKAIAkEq+hKEcENsc7//zzdfLJJ+vpp5+u8lhybAAASD7xun/bPisqHGNMSK8MAABAJGzPsfnzn/+siy++WDk5OTpw4IDmzZunZcuWadGiRXY3DQAAJBnbA5udO3fqiiuu0Pbt25WRkaHOnTtr0aJFuuCCC+xuGgAASDK2BzbPPvus3U0AAAAu4cgcGwAAgOogsAEAAK5BYAMAAFzD9hwbAICL+P22lNEHAghsAACxEW7hw+xsa7Xn/1v4EIg3hqIAADWXny+NHh0a1EjS1q3W9vx8e9oFzyGwAQDUjN9v9dSEW6EnsG38eOs4IM4IbAAANVNYWL6npixjpM2breOAOCOwAQDUzPbtsT0OqAECGwBAzWRmxvY4oAYIbAAANdO3rzX7yecLv9/nk3JyrOOAOCOwAQDUTEqKNaVbKh/cBL6fNo16NkgIAhsAQM2NGiW98orUsmXo9uxsazt1bJAgFOgDAMTGqFFSbq4zKw9TEdkzCGwAALGTkiL17293K0JREdlTGIoCALgXFZE9h8AGAOBOVET2JAIbAIA7URHZkwhsAADuREVkTyKwAQC4ExWRPYnABgDgTlRE9iQCGwCAO1ER2ZMIbAAA7kVFZM+hQB8AwN2cXBEZMUdgAwBwPydWREZcMBQFAABcI6rAZvPmzfFqBwAAQI1FFdh06NBB99xzjw4ePBiv9gAAAFRbVIHNkiVL9Pbbb6t9+/aaNWtWvNoEAABQLVEFNr1799bHH3+shx56SPfee6/OPPNMLVu2LE5NAwAAiE61koevvPJKffvttxo2bJiGDBmikSNH6vvvv4912wAAAKJS7VlRxhgNGjRI1113nRYuXKjTTz9dt99+uw4cOBDL9gEAAEQsqjo2M2fO1KpVq7Rq1SqtX79eKSkp6ty5s2666SZ17dpVL774ojp16qT58+erW7du8WozAABAWD5jjIn04JycHPXs2bP0X7du3ZSWlhZyzF//+lfNmTNHX375ZcwbG05xcbEyMjJUVFSk9PT0yB/o91OFEgAAm1T7/l2FqAKbSOzcuVNZWVny+/2x/LEVqtYLk58vjRsnbdkS3JadbS2WxrohAADEXbwCm5hXHm7evLnee++9WP/Y2MnPl0aPDg1qJGnrVmt7fr497QLczu+Xli2T5s61viboww8Ab4l5j02iRRXx+f1Smzblg5oAn8/qudmwgWEpIJboJY09htOR5JKmx8bRCgsrDmokyRhp82brOACxQS9p7OXnWx/SBgyQxoyxvrZpw2sJyGuBzfbtsT0OQOX8fqunJlzHcGDb+PEMS0WDQBGolLcCm8zM2B4HoHL0ksYWgSJQJW8FNn37WuP6Pl/4/T6flJNjHQeg5ugljS0CRXchoT4uvBXYpKRYyYpS+eAm8P20aSTgAbFCL2lsESi6B3lSceOtwEayZmC88orUsmXo9uxsazszNIDYoZc0tggU3YE8qbjy1nTvspgqCSRG4E1cCs0NCQQ7fKCIXKBkxdat4fNsKFnhfJQdKcV071hLSZH695cuu8z66vILCLANvaSxw3B68iNPKu6iWgQTAKpl1CgpNzdxvaRu7pENBIrhCh5Om0ag6HTkScUdgQ2AxAj0ksabF6ocJzpQROyQJxV33s2xAeA+gXye49/WyOeBU5AnVYocGwCoDMXrkAzIk4o7AhsA7kBSJpKB3y81bmwF4U2bhu4joT4myLEB4A4kZSY3Nyd8B4TL/2raVPr9762cKTf+zjagxwaAO5CUmby8UIW3oqJ8e/daQ1P79hHUxAjJwwDcgaTM5OSFhG+K8oVF8jAAVIakzOTjlYRv8r8SisAGgHtQ5Ti5eOWGT/5XQpE8DMBdKF6XPLxywyf/K6EIbAC4T6KqHKNmvHLDD6xyX1X+l5dWuT98WHrnnbj8aIaiAAD2CNzwj8+JCvD5pJyc+N7w/X5p2TJp7lzrazzyecj/shQVWa/z//f/WdPcL7kkLk9DYBNLifgDAQC3sPuGn8hp5l7N/9qyRZoxQxo0SGrWzHqdX3pJOnDA+j4OmO4dK15YeA8A4iHc+2dOTnxXK7drmrnbCxEaI331lbRggVRQIK1eHbq/QwdpxAgpN1fFHTooo1GjmN+/CWxiwQt1GAAgnhJ5w6euTGz5/dKKFVYgs2CB9OOPwX0+n9Srl5XQn5srnXpq6a543b8JbGqKPxAASC7LllnDTlVZupQk9Ir88ou0ZIkVyLz+urRnT3BfWpp0/vlWz8ywYVKLFmF/RLzu38yKqqlo6jDwBwIA9vPKNPNY273bCmIKCqS335Z+/TW4r1EjaehQq1fmwgulhg1ta6btgU1eXp7y8/P19ddfq169eurdu7cefvhhnVqmu8rR+AMBgOTilWnmsfDDD8EhphUrpGPHgvtat7YCmREjpN/+VkpNtauVIWwPbJYvX66bbrpJ55xzjo4ePaq77rpLgwYN0rp169SgQQO7m1c1/kAAILlQV6ZixkiffhpM/v3yy9D9XbuWJv+qS5eKp+rbyHE5Nrt371bz5s21fPlynXvuuVUe75gcGxbeA4DkEZj0IYW+d3tx0sfhw1beUUGB9W/r1uC+lBSpX79g8m/r1jF7Ws/k2BQVFUmSGjduHHZ/SUmJSkpKSr8vLi5OSLsqFKjDMHq09QcR7g/EC4WXACCZBOrKhCvTEc9p5k5RXCy99ZbVM/Pmm9b3AQ0aSBddZPXMDB4sVXA/dipH9dgYY5Sbm6v//Oc/Kqxg0bP77rtPU6ZMKbfdkXVs4l2HAQBQM26vK1PW1q3SwoVWr8x770lHjgT3tWghDR9u9coMHCjVrRv35nhiuvdNN92kN954Qx988IGys7PDHhOuxyYnJ8f+wEby1h8IAITD+6BzGCOtWxdM/l21KnT/qacGk3979JBqJXYxAtcPRf3pT3/SwoUL9f7771cY1EhSWlqa0tLSEtiyKCTbwnu8AQGIJSqw28/vlz76KJj8+/33oft79gwm/3boYEcL4872wMYYoz/96U+aP3++li1bprZt29rdJG/gDQhALFVUgX3rVmu7l5JxE+3XX61ieQUF0muvWfVmAurUsYrl5eZaxfI8MEPX9qGoG2+8UXPmzFFBQUFI7ZqMjAzVq1evysfbPisqGbEEBIBYogJ74u3ZI73xhtUzs3hxaLG8E08MLZZ3wgl2tbJSrs2x8VUwB37WrFm66qqrqnw8gU2UeAOC1zDkGn8sUZAYP/4YnJJdWBhaLC8nJzjEdO65jimWVxnX5tg4KHfZG1gCAl7CkGtieKECux0BsjHSmjXB5N8vvgjd36VLMPm3a1dHFsuzg+2BDRLMC29AgOStnA+7e6XcXoE9kQHykSPS8uXB5N+yz5mSYvXGBIrltWkT2+eOJRuvSQIbr3H7GxAgWW+q48aFrwZujPXJdvx46+aQ7MNSTuiVcvMSBYkIkIuLpUWLgsXy/q9QrSSpfv3QYnlNmtTsuRLB5mvS9hybmiLHJkosAQEvSLacj+p+unXSRAA3LlEQz5zEbdtCi+UdPhzc17x5aLG8CCbSOEYU12S87t+JrcYD+wWWgJDKj8eyBATcIpmGXPPzrZvngAHSmDHW1zZtrO2VqapXSrJ6pfz+GDe4AoElClq2DN2enZ2cQY0UXU5iVYyR1q+X8vKsWjItW0o33GD11Bw+LLVvL/33f1sraG/bJv3jH9bMpsqCGr/fCuLnzrW+JupcV9YeB1yTDEV5kdfXSIH7VXfINdF5ATUZ5nDiRIBRo6xeBrfMQqtpgOz3SytXBvNlvvsudH+PHqHF8qJJ/nXCEOTxHHJNEtgko1i8+brtDQgoqzo5H4m+UdQ0D8ipvVLJVoG9MtUJkH/9VXrnHSuQWbiwfLG8gQODxfKysqrXLqcmxjvlmjRJrqioyEgyRUVFdjclMV591ZjsbGOsS9r6l51tbQcQ9Oqrxvh81r+yfy+BbWX/ZgLHlj2uomNjZenS8s8X7t/SpfF5PKp29Kj1/hru2ghcHzk5xuzcacxzzxkzcqQx9euHHpORYcyYMcb8v/9nTHFx7NpU0fkOtOno0Zo/V7SivCbjdf8mxyaZBKL047v6AlF6VWPygJdEmvNhV15ATT/dBnqlKhq+8Pmsom3JOBPJKarKSTRGSk+3el7GjpXmz5d++cU6LzffbC1zsHu39OKL0n/9V2wqAMcy7yfWHHJNEtgkC4ckZQFJZdQoaeNGa/bTnDnW1w0bQrvp7bpR1LT0AhMBEqOiADnwvvvVV9b7bufO0j33SJ9+Km3aJD35pLVGU6wrADtluCcch1yT5NgkC4ckZQGl7C4KF6mqcj7sulHEovYLEwEqFovr88gR6f33rYD4eD6fVSwvkPybqAWcnV6LzAHXJIFNsnBylJ7skuUG7SROnJFRXXbdKAKfbkePDg5rBETz6ZaJAOXV5Po8cMCagl1QYC0yuX9/cF/9+taikrm50pAhUtOmcWl+pZKhGKLd12RMM3Zs4JnkYRIF44Nk7OjZkWgbT5EmiMYrGTPcNZiTk3yvo1NU5/rcts2Yp5825uKLjalTJ/RxzZoZ84c/GLNwoTG//JL43yecaBLj7XL0qHU/mjPH+hrm7yde928Cm2Rh95uvG7ntBp0ITp6RURN23ygiuAkgAtFcn+vXG5OXZ0zPnuWP+81vjJkwwZjCQueeCycHxBF+YIzX/ZslFZKJG0uW2yWepdLdLNmWKohGuOGLnJzK8wIYxnSWSK/P7Ozyf/vduwdXyu7YMTlWynbi9eeAJRXIsUkmDkjKcg2SsavHzble0eYFuCnPyC0ive62bLFmK513nhXIDB9e/WJ5dnJaMUSHLD5LYJNs7E7Kcgs336DjyekzMmqq7I2isk/DTq386nWRXnf33CNNmGDVoEHsOOQDI4FNMnJalJ6M3H6DjpdkmJERC5X1xuTmOuJTKcr46SdrFtP8+ZUfF7g+J0/m3MSDQz4wEtjAm7xyg461WE1RdrKqemPuu88Rn0o9zRjp88+Di0t+9lnVj3HL9elkDvnASOVheJNDKmQmpUiXKkhGkVT4/tvfIvtZDGPG1pEj0nvvWeenbVvpzDOlKVOsoKZWLatY3mOPST/8IL36qnU9luWG69PpHLKkArOi4G3VmQkDixNnZNRUpLNqIpGMM8Oc5uefpcWLrZ6ZN96Q/vOf4L569YLF8oYOLV8sz43XZzKIYvZuvO7fBDYAb4AImDtXGjOm6uMaN7ZuspUNY1IqoHp27JBee80aYnrnHamkJLivaVNp2DBrJtP551uVgOE8EX5gZLo3EC8kYyMg0rH/ceOsXBu35hkl2jffWIHMggXSypWhr+nJJwfXY+rdm9c1Gdg8e5fABgACIk0qv+su6fTTqSlVXceOSZ98Ekz+/frr0P3nnBMsltepU3IUy0MoGz8wEtgAQEA0s76oKRWdQ4es5N+CAmnhQmvIKSA11cptChTLOz4xHYgCgQ0AlBVNhW+GMSv3n/9Ib75p9cwsWmQlAwekp0uDB1vB4cUXSxkZtjUT7kJgAwDHi3dvjJsT1jdtsgrlPfecVWvm2LHgvqys4BBT//5SnTp2tRIuRmADAOHEqzfGbWtMGSP9+9/BfJm1a8sfc8IJ0t13W8sY1HJJ+TQ3B6dJjuneAJAoUax87GhHj0offBAMZjZurPz4ZPv9quK24NQm1LGpAIENgKTg90tt2lS8HIPT698cPBhaLG/fvuC+unWlCy6wgp2yRfTKcvrvFym3BKcOEK/7t0v6BAHEhd9vVeOdO9f66vfb3aLkFc3Kx06xc6f0zDNWUbwmTaRLLpGef94Kapo0ka66ysqn2bNHuu22ioMayZm/X7QiWXJj/Hj+TmxGjg2A8Ohujy2HrHxcpW+/DRbL++ij0Jt4u3bB5N/evaXaZW4hyfL71UQ0wSmz5WxDYAOgvKpWuKa7PXoOWfm4nGPHpFWrgvky69eH7j/77GDl39NPr7hYnlN/v1jyQvDmAgQ2cAdmKMROVd3tPp/V3Z6by2scjUirGsd55WNJ1vpLS5dawczChaE34tq1rWJ5ublWsbycnMh+ppN+v3jxQvDmAgQ2XueGgIAhk9iiuz0+oqlqHA/794cWyztwILjvhBOsInkjRlhfTzwx+p9v9++XCF4I3lyA5GEvy8+3ZmkMGGCtaDxggPV9fr7dLYtcYMjk+BtxYMgkmX4Xp6C7PX4CVY2PXzIgOzs+w3ubN0v/+7/WjKVmzaTLL5deftkKajIzpT/+UXrrLWn3bumll6TLLqteUBOQ6N8v0QLBm1R+SM4twZsLMN3bq9wwZTHZp8861bJlVpBblaVL6bGprnj1lBojffFFMPl3zZrQ/Z06BZN/u3WLX7E8N/QEVyZcL3FODgugRok6NhUgsKkGtwQE3IDjI3B9VNXd7vTrwyuOHpVWrAgm/27YENzn80l9+ljBTG6u1L69bc10HbcHbwkQr/s3OTZe5JYcCoZM4sMLuRLJ7uBB6e23rUDm9delvXuD+wLF8nJzrfozzZvb1043YwFUxyKw8SK3BATMUIifaFa4RmLs2mUFMQsWSEuWSIcOBfc1biwNHWoNMQ0aJDVoYFcrAdsR2HiRWwICZijEV7xXuHabeAxNfP99cIhpxYrQ67xNGyuQGTHCGm6qzds5IHkhsGEctDy3BAQMmcQf3e2RiVXJgWPHpNWrg8m/69aF7j/rrGCxvDPOqLhYHuBh7g5sqG8SnpsCAoZMYLeaVmk+fDi0WN62bcF9tWtbgWWgWF6rVvH4DQBXce+sKDdMZ443N01ZpGcOdqjuDMOiIqtYXkGB9bVssbyGDUOL5TVqFM/fALAN070rEPaFcct05kQgIACqL5qSA7/5jdUjs2CB9bgjR4L7TzopOCX7vPOktLQ4NRhh8T5oC6Z7R8Mt05kTgRwKoPoinTn4hz+E1peRpI4dg8XyzjknfsXyUDlSFlzHnYGNW6YzA3C2SGcObthg9RT36hVM/j3llLg2DRFgFXtXcmdg45bpzACcrW9fa12krVsrPqZuXevTf26u1KJF4tqGyrGKvWu5s+8zMJ25oqmQPp+VJOv06cwAnGn3bmnWLOmSS6z/V8Tnk158UbruOoIap4kmZQFJxZ09Nm6azgzAGX74IVhfZsUKq+ZMQLNm0q+/Sj//HNyWrDMMvYKUBddyZ2AjUd8EQM0YEyyWV1Agffll6P4zzwwm/3bubAU6zKxJHqQsuJY7p3uXxTQ+AJE6fNiaih0ollc2dyYlRerXzwpkhg+XWre2qZGICVaxtx3TvauL6cwAKlNcLL31lhXMvPmm9X1AgwZWkbzcXGnwYGuxyUTig1n8kLLgWu4PbADgeFu3Wj0yBQXSe++FFstr0cLqkRkxwiqWV7euPW2kvkr8eTFlwQPBMoENAPczxlpQMrBS9qpVoftPPTVYX6ZHD/uL5VFfJXFyc6WMDGsIUrJ6+Pv3d93NXpJngmX359gA8Ca/X/rww+BMph9+CO7z+aSePYPLGHToYFszy2FJmMTxyI1ekiPXT2StqAoQ2AAo9euv0pIlViDz+uuhNWbS0qSBA62emWHDrPWZnCia9afIHwyKdojFgTf6uHFosEzyMACEs2eP9MYbVjCzeLEV3AQ0aiQNGWL1ylx4oXTCCbY1M2LUV4letD0vXqs67LH1EwlsACSfH38MDjF98EFosbxWrYL5Mn37SqmpFf8cJyZSUl8lOtXJR/LYjd5rwTKBDQDnM0ZasyaY/PvFF6H7u3YNFsvr0qXi5VTKcmp+RWBJmKrqq7AkTPV7Xjx2o9d330V2nEuCZQIbAM50+LC0fHmw8m/ZACQlRTr33GCxvDZtovvZTp51RH2VyFW358VLvWL5+dJ991V+jMuCZQIboDJOHKpws+JiadGiYLG8oqLgvgYNpIsusj59DxlS/WJ5yZBf4cX6KtVR3Z4Xr/SKVXatl2WMq4JlAhugIk4dqnCbbdtCi+UdPhzc17x5sFjewIGxKZaX6PyK6gbHo0ZZwRWBdcWq2/OS6F4xuz4gVXWtB0yZ4qr3NAIbIBwnD1UkO2Ok9euDyb+ffBK6/5RTQovlxfoGkMj8ipoGxywJU7ma9LwkqlfMzg9IkV7D7dvHtx2JZmy2fPlyM3ToUJOZmWkkmfnz50f1+KKiIiPJFBUVxaeB8J6jR43JzjbGeqss/8/nMyYnxzoOkTl61JgPPjBmwgRj2rcv/5r27GlMXp4x69fHvy1Ll1Z8bsv+W7q0Zs/z6qvWtRLu+vH5rP2oucDrfPxrHenrfPSoda7nzLG+xvLv2u5rIFHXejXF6/5te4G+t956SytWrNBZZ52lSy65RPPnz9eIESMifny1CvyQN4HKUCAtNn79VXrnHatnZuHC0GJ5deqEFstLZJJmIlZ1dmhBNNcK1yuSk2NvPpITrgGHr2Du2gJ9F198sS6++OLEPSF5E6iK16aCxtLevaHF8n75JbgvI0MaOtQaYrroIvuK5SUiv8JrdVLs5sR8JCdcAx6dYWd7YBOtkpISlZSUlH5fXFwc+YPJm0AkvDQVNBY2bAhOyS4stD4lBuTkBOvLnHtu5cXyEine+RUEx4nntHwkp1wDHpxhl3SBTV5enqZMmRL9A5NhiiecwStTQavLGGnt2mDy77//Hbq/c+dg8u+ZZ0ZWLM8O8fyUT3AMJ10DTuzRiiPbc2zK8vl8VebYhOuxycnJqXqMjrwJRCPQuyeF7771Wu/ekSPS++8HK/9u3hzcV6uW1RsTWCm7bdvIfqabc90cntuABOAaqJJrc2yilZaWprS0tOgf6JRuQSQHD3bflnPggFUsr6DAypvZvz+4r359a1HJESOsYnlNmkT3s92e6+bR3AaUwTVgm6QLbKrNSd2CSA4e676VZP2er71m9cy8+25osbxmzaxiebm50vnnS/XqVe85vJLrRnAcP8nS28c1YAvbh6J+/vlnff/995KkM888U4899pgGDBigxo0bq1WrVlU+PuKuLLoFgfC+/jo4xLRyZei+3/zG6pUZMULq2bPmfxtOmAKbaMlyE04WydjbxzUQVryGomwPbJYtW6YBYXJfxo4dq9mzZ1f5+KheGPImkChOfiM7dswKYALJv99+G7q/e/dg8m/HjrFN/iXXDTVRUW8f7+FJybU5Nv3791fCYqvKugUfe8xaVG/uXOfdiJBcnPiJ8tAha2hpwQJrqGnnzuC+1FSrWF5urjXUlJUVv3bUNNfNyQEj4ouZrYiQ7YFNwoXLm9i9W7r1VmfdiJCcnJQ/sm+flfRbUGAlAR88GNyXkWEl/QaK5cXw01KlapLr5sSAEYnjhIJ3SAq2D0XVVI27sujaRKw4IX/kp5+CQ0zvvx9aLC87O7RYXp068WlDZaqb68bfKebOlcaMqfq4OXOkyy6Lf3tQY64dirIVXZs4Xk2GOuz4RGmM9Nlnwcq/n30Wuv+MM4LBzFln2V8srzpTYPk7hcTMVkTMu4GN3y89+SRdmwiq6VBHomolHTliXZOBmUybNgX31aplBWOBYnnt2tXsueIh2imwDEFAoiI4IubNwCbcDawyFO1zv1jkxsTzE+XPP4cWy/vPf4L76tULLZbXtGn0Pz/RoqkRRHFNSJH39knW7DsSzD3Le4FNRTewytC16W6xGuqI9SfKHTtCi+WVWUpETZuGFsurXz+yn+kkkS5ayBAEAqrq7ZPK57mRYO453koeriq583huLBYWCa9NqY1lbZWa1kr65ptg8u/KlaE/4+STg8XyevVy9zkpi+KaOF6496iCAhLMkwzJw7FQ1Vh9WV5dy8OLU2pjOdQRbf7IsWPSxx8Hg5lvvgndf845wWJ5nTrZn/xrB9bcwfGO7+0jwRxleCuwiWYM3otreTipBksixXqoo6r8kUOHpPfeCxbL27Ej+NjUVOm884LF8lq2jOpXcS3W3EFlSDBHGd4KbCK9MT3+uPSnP3krsvfyJ554zLY4/hPlf/4TWizv55+D+9LTpcGDrZ6Ziy6yiuehPC8uSupWsR7uJsEcZXgrsIn0Bua1oEby9ieeeA11bNoUHGJavjy0WF7LlsEp2f3721MsLxlFmnAM54rHcDcJ5ijDW4ENY/UV8/onnlgMdRgj/fvfwfoya9eG7j/99GCxvLPP9ma+DLwtXsPd1LhBGd6aFRUQ7hNDTo63x+pZddkSbRf50aPW8YHKvxs3BvfVqiX16RNM/j355Hi3HnCueC85UtMZiUi4eM2K8mZgI3lvSnNVmFIbuYMHpcWLrZ6ZN96wFpsMqFdPGjTICmSGDpWaNbOtmYCjJOLDEx9akwrTvWONsfpQDNNVbudOawZTQYG0ZElosbwmTaRhw6yemQsuSM5ieUC8JWK4mwRzyMuBDcpjSm2ob78NJv9+9FFosNeuXXCIqXdvqTZ/SkClEpXgy4dWz/PuUBQq5tVhumPHpFWrgsm/69eH7u/WLZj8e9ppJP8C0WC4G8dhKAqJ46VPPCUlVrG8ggJp4cLQbvData2cgBEjrGJ52dm2NRNIegx3I0EIbOA9+/dLb75p9cy89VZosbwTTrCK5eXmShdfLJ14ok2NBFyI4W4kAIENvGHz5uCU7GXLrGnaAVlZVo/MiBFWT1Vamk2NBDyABF/EGYEN3MkY6Ysvgsm/a9aE7u/UKbhS9tlnWzVnACSGl4a7kXAENnCPo0elFSuCyb8bNgT3+XxWsbzAMgbt29vWTABA/BDYILkdPCi9/bYVyLz2WmixvLp1Q4vlNW9uXzsBAAnhvsDGq1OVvWTXLun1162emSVLpEOHgvuaNLGCmECxvAYN7GolAMAG7gps4rFqLJzhu++Cyb8rVoROFW3bNlgsr0+f+BTLI2AGgKTgnsBm4ULpyitjv2os7HHsmLR6dTD5d9260P1nnx0slnf66fEtlkfADABJwz2Vh7OylL5tW/iDqGiZHEpKrAXwAsXyyp7P2rWtWRSBYnk5OYlpU2DF4OP/TFgxGABqhNW9K1D6wkiq8mWpyaqxiI+iIqtYXkGB9fXAgeC+hg2DxfIGD058sbxACfiyPTVlETADQLWxpEIs1GTV2EiQhxGZLVtCi+UdORLcl5kZLJY3YIC9xfIKCysOaiSrF2fzZus4AmYAcARvBTY1XTW2Ml7Mw4g0kDNG+vLLYL7Mp5+G7u/YMZj8e845zimWF2kgHO+AGQAQMfcENllZ1g2mslVj+/aNz3NXlIfh5sTlqgI5v9+avRQIZn78MXiczyf17h0slnfKKQlvfkQiDYTjGTADAKLinhyb559X+pVXWhvDrRobr+DCi3kYlSXUGmMNIX3xhbRnT3BfWppVV2bECKvOTIsWCW1ytQTO7datlQfMiTy3DHcCcIl45dg4pM8/BoYPt4KXli1Dt2dnx7fHJJo8DDfw+62emnA3+sC2pUutoKZxY2sK/quvWt+/9pp0zTXJEdRIVsDwxBPW/4+fTh74ftq06gUWfr+VXzR3rvXV76/6Mfn5VqA1YIA0Zoz1tU0bazsAQJKbhqIke1aN9VoeRlWBXMDjj0s33xyfYnmJNGqUFRiHG3abNq16AXN18rG8ONwJANWQ5HedMBK9aqwX8jCMCRbLe+65yB7TokXyBzUBsQyYqxOgVNVL5vNJ48dbbWRYCnA3hqOr5J4cmxiP0UXMiXkYsXD4sDVEsmCBVSxv69boHk/NoPKqm4+1bJk17FQVXnPA3Vw2+5YcG6eKZx5GohUVSfPmSZddJjVrJl14ofTUU1ZQ07Ch1aPwr39ZM9AqWsLA57OqAsdrBloyq24+lteGOwGUF+jtPf49JNDbS65dKZeMFSRAZd1/8cjDSJStW60emQULrE/8ZYvltWgRnJJ93nlS3brW9gYNrD+kwCyogGQL5BKtugGKF4Y7AVSM4eioENhEIpLuPzsSl6vDGGtByQULrJyZVatC93foEFxcsnv38MXykjmQs1N1A5S+fa3XtqrhTnrJAHeiCnpUCGyqEk2yZ6ITlyPl90sffhgslvfDD8F9Pp/Uq1ewZ+bUUyP7mckSyDlJdQOUwHAnvWSANzEcHRUCm8okc/ffL79I77xjBTKvvy7t3h3cl5YmnX++1SszbFj168pEG8h5PZu/JgEKvWSAdzEcHRVmRVUm0tkod98tDRxo/416zx4riCkokBYvln79NbivUSNpyBArmLnwQisZOJFcls1fI+Fei5ycyAIUrweHgBe5dPZtvO7fBDaVmTvXqvAaKTtu1D/+GBxi+uAD6dix4L7WrYNDTH37SqmpiWtXWfn50iWXVLz/1Ve9F9y4KUBx0+8COFUgLUJK7LJBcURgUwFH9NgEJOICM0ZasyaY/PvFF6H7u3YNrpTdpUvF07ITxe+3hrr27q34mCZNpJ07uRkmI3rigMSpSW+vAxHYVCCugU1V3X/hxKNL8PBhaflyK5ApKAi9qFNSpH79rEBm+HCrvU7y7rtWPk9V3nnHGs5D8qhsMVQpKT9BAo7noh7SeN2/SR6uTGXJnhWJ1bS74mJp0SKrZ+bNN63ieQENGkgXXWT1zAwebC026VTLlkV+HIFN8kjmxHogmTl19q2DENhUpaLZKFWpzrS7bdusYnkFBVZPx/HF8oYPt24UAwcGi+UBdqCuRtVc9MkaSCYENpEoW7Pl3XelBx6o+jGRTLszRlq/Ppj8+8knoftPOcXqlRkxQurRI3yxPKfr3z+y18urN79kRV2NypF7BNiGHJto1XTand8vrVwZTP797rvQ/T17BpN/O3SIwy+QYCQPuxMLc1asolmA5B4BIUgeroAtq3tHO+3u11+t5NiCAmuoqWyxvDp1rOTa3FyrWJ7TCyxVp3ud6d7u49K6GjVWVSDv1dcFCIPVvZ0kkHfTsmXo9uzsYFCzd6+1EvaoUVLTplZ+zLPPWkHNiSdKl18uvfyyVVTvjTek665zflCTn2/dzAYMsOr7DBhgfV/VqrKjRlnBS7jXi6AmOblpVftYevDBynsnK1rBHUDM0GNTE8f3XmRnByv/FhZa+wNycoJDTOeea1+xvOqKxdRekindx2V1NWrE75eaN5f27av62DlzpMsui3+bAAdjKKoCtgY2xkhr1waTf//979D9XboEV8ru2tX+YnnVFRh2qGgWDN3r3kbAaommoKcXc4+A41DHximOHJHefz+Y/Lt5c3BfSor1pj5ihDX01LatXa2MLab2ojLU1bBEOgOscePyK7gDiBkCm0gcOGAVyysosPJh9u8P7qtf3yqWl5trLTLZpIltzYwbpvYCVYs0R27cOG/2aAEJQmBTke3bQ4vlHT4c3Ne8uTWDacQIq1hevXq2NTMhIn3DdnryMxBPfftaQ7KVLcHSpIl0112JbRfgMQQ2ZX39tTXEtGCB9PHHofvatw8tluelT1xVvWEHcmzoXoeXVbUEi88n/f3v3nrvAGzg7cDG77cCmEC+zLffhu7v0SOY/NuhQ/Im/9ZUZW/YXp7aCxyvoiVYvDpTDLCB92ZFHToUWixv167gvjp1pPPOswKZYcOkrKy4tTspMbUXiAwzxYAqMd27AhG9MPv2WUm/CxZIixdLBw8G92VkWEm/I0ZIF14oJXrKeLLhDRsAEANM947Wxo1Wr0xBgTU9u2yxvOzs0GJ5derY1crkw9ReAICDuSewOb5Y3uefh+7v3DmYL3Pmmd7NlwEAwMUcEdjMmDFDjzzyiLZv367TTjtN06ZNU99oZ9icfnpo7ketWqHF8tq1i2mbASQYw6AAImB7YPPSSy9p/PjxmjFjhvr06aOnn35aF198sdatW6dWrVpF/oO2bLHqyZQtlte0afwaDiBxwiWuZ2dbs/XCJa4TBAGeZXvycI8ePXTWWWfpqaeeKt3WsWNHjRgxQnl5eVU+vjT5aN48pQ8f7v5ieYDXRLsAa7RBEABbxCt5uFbMflI1HD58WJ9++qkGDRoUsn3QoEH68MMPo/thF1/s3aDG77cW4Js71/paNlE6kv2AU/n9VpAS7vNXYNv48cFrOhAEHb+22dat1vb8/Lg2F4D9bB2K2rNnj/x+v1q0aBGyvUWLFtqxY0fYx5SUlKikpKT0+6KiIklW5OdJCxdKd94pbdsW3JaVJT38sJVbVNV+RMfvlz78UNqxQzrpJKl3b4Y44inSBVgXLbLOxZ/+VHkQdMst1grcnDPAdoH7dqwHjmzPsZEk33EzlIwx5bYF5OXlacqUKeW25+TkxKVtSWnbNumKK6q/H0g2Q4dGdtzWrdbq2gAcY+/evcrIyIjZz7M1sGnatKlSUlLK9c7s2rWrXC9OwKRJk3TbbbeVfr9//361bt1amzZtiukLg+gVFxcrJydHmzdvjul4KaqH8+EcnAvn4Fw4R1FRkVq1aqXGMf6wYWtgU6dOHZ199tlasmSJRo4cWbp9yZIlys3NDfuYtLQ0paWllduekZHBReoQ6enpnAsH4Xw4B+fCOTgXzlGrVmzTfW0firrtttt0xRVXqFu3burVq5f+/ve/a9OmTfrjH/9od9MAAECSsT2wufTSS7V3717df//92r59u04//XS9+eabat26td1NAwAAScb2wEaSbrzxRt14443VemxaWpomT54cdngKicW5cBbOh3NwLpyDc+Ec8ToXthfoAwAAiBVbC/QBAADEEoENAABwDQIbAADgGgQ2AADANZIisJkxY4batm2runXr6uyzz1ZhYWGlxy9fvlxnn3226tatq3bt2mnmzJkJaqn7RXMu8vPzdcEFF6hZs2ZKT09Xr169tHjx4gS21t2i/bsIWLFihWrXrq2uXbvGt4EeE+35KCkp0V133aXWrVsrLS1NJ598sv75z38mqLXuFu25ePHFF9WlSxfVr19fmZmZuvrqq7V3794Etda93n//fQ0bNkxZWVny+XxasGBBlY+Jyf3bONy8efNMamqq+cc//mHWrVtnxo0bZxo0aGB++umnsMf/+OOPpn79+mbcuHFm3bp15h//+IdJTU01r7zySoJb7j7Rnotx48aZhx9+2HzyySfm22+/NZMmTTKpqalmzZo1CW65+0R7LgL2799v2rVrZwYNGmS6dOmSmMZ6QHXOx/Dhw02PHj3MkiVLzIYNG8zHH39sVqxYkcBWu1O056KwsNDUqlXLPPHEE+bHH380hYWF5rTTTjMjRoxIcMvd58033zR33XWXefXVV40kM3/+/EqPj9X92/GBTffu3c0f//jHkG0dOnQwEydODHv8HXfcYTp06BCy7frrrzc9e/aMWxu9ItpzEU6nTp3MlClTYt00z6nuubj00kvN3XffbSZPnkxgE0PRno+33nrLZGRkmL179yaieZ4S7bl45JFHTLt27UK2/e1vfzPZ2dlxa6MXRRLYxOr+7eihqMOHD+vTTz/VoEGDQrYPGjRIH374YdjHfPTRR+WOv/DCC7V69WodOXIkbm11u+qci+MdO3ZMBw4ciPmCZ15T3XMxa9Ys/fDDD5o8eXK8m+gp1TkfCxcuVLdu3TR16lS1bNlSp5xyiiZMmKBff/01EU12reqci969e2vLli168803ZYzRzp079corr2jIkCGJaDLKiNX92xGVhyuyZ88e+f3+cit9t2jRotyK4AE7duwIe/zRo0e1Z88eZWZmxq29bladc3G8Rx99VAcPHtTvfve7eDTRM6pzLr777jtNnDhRhYWFql3b0X/2Sac65+PHH3/UBx98oLp162r+/Pnas2ePbrzxRu3bt488mxqozrno3bu3XnzxRV166aU6dOiQjh49quHDh+vJJ59MRJNRRqzu347usQnw+Xwh3xtjym2r6vhw2xG9aM9FwNy5c3XffffppZdeUvPmzePVPE+J9Fz4/X6NGTNGU6ZM0SmnnJKo5nlONH8bx44dk8/n04svvqju3btr8ODBeuyxxzR79mx6bWIgmnOxbt063XLLLbr33nv16aefatGiRdqwYQMLMdskFvdvR390a9q0qVJSUspF2rt27SoX1QWcdNJJYY+vXbu2mjRpEre2ul11zkXASy+9pGuuuUYvv/yyzj///Hg20xOiPRcHDhzQ6tWrtXbtWt18882SrBurMUa1a9fW22+/rfPOOy8hbXej6vxtZGZmqmXLlsrIyCjd1rFjRxljtGXLFrVv3z6ubXar6pyLvLw89enTR//93/8tSercubMaNGigvn376oEHHqCXP4Fidf92dI9NnTp1dPbZZ2vJkiUh25csWaLevXuHfUyvXr3KHf/222+rW7duSk1NjVtb3a4650KyemquuuoqzZkzhzHrGIn2XKSnp+uLL77QZ599Vvrvj3/8o0499VR99tln6tGjR6Ka7krV+dvo06ePtm3bpp9//rl027fffqtatWopOzs7ru11s+qci19++UW1aoXeClNSUiQFewuQGDG7f0eVamyDwNS9Z5991qxbt86MHz/eNGjQwGzcuNEYY8zEiRPNFVdcUXp8YLrYrbfeatatW2eeffZZpnvHSLTnYs6cOaZ27dpm+vTpZvv27aX/9u/fb9ev4BrRnovjMSsqtqI9HwcOHDDZ2dlm9OjR5quvvjLLly837du3N9dee61dv4JrRHsuZs2aZWrXrm1mzJh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\n", + "text/plain": [ + "
              " + ] + }, + "metadata": { + "filenames": { + "image/png": "/Users/mhjensen/Teaching/MachineLearning/doc/LectureNotes/_build/jupyter_execute/chapter1_17_0.png" + } + }, + "output_type": "display_data" + } + ], "source": [ "import numpy as np\n", "import matplotlib.pyplot as plt\n", @@ -564,7 +577,36 @@ "collapsed": false, "editable": true }, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "The intercept alpha: \n", + " [2.04292593]\n", + "Coefficient beta : \n", + " [[5.00440395]]\n", + "Mean squared error: 0.27\n", + "Variance score: 0.88\n", + "Mean squared log error: 0.01\n", + "Mean absolute error: 0.41\n" + ] + }, + { + "data": { + "image/png": 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\n", 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\n", + "text/plain": [ + "
              " + ] + }, + "metadata": { + "filenames": { + "image/png": "/Users/mhjensen/Teaching/MachineLearning/doc/LectureNotes/_build/jupyter_execute/chapter1_33_0.png" + } + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "0.004999999999999994\n" + ] + } + ], "source": [ "import matplotlib.pyplot as plt\n", "import numpy as np\n", @@ -1124,7 +1188,18 @@ "collapsed": false, "editable": true }, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "' \\nThis is taken from the data file of the mass 2016 evaluation. \\nAll files are 3436 lines long with 124 character per line. \\n Headers are 39 lines long. \\n col 1 : Fortran character control: 1 = page feed 0 = line feed \\n format : a1,i3,i5,i5,i5,1x,a3,a4,1x,f13.5,f11.5,f11.3,f9.3,1x,a2,f11.3,f9.3,1x,i3,1x,f12.5,f11.5 \\n These formats are reflected in the pandas widths variable below, see the statement \\n widths=(1,3,5,5,5,1,3,4,1,13,11,11,9,1,2,11,9,1,3,1,12,11,1), \\n Pandas has also a variable header, with length 39 in this case. \\n'" + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "\"\"\" \n", "This is taken from the data file of the mass 2016 evaluation. \n", @@ -1159,7 +1234,21 @@ "collapsed": false, "editable": true }, - "outputs": [], + "outputs": [ + { + "ename": "ValueError", + "evalue": "Length of colspecs must match length of names", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mValueError\u001b[0m Traceback (most recent call last)", + "Input \u001b[0;32mIn [8]\u001b[0m, in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[38;5;66;03m# Read the experimental data with Pandas\u001b[39;00m\n\u001b[0;32m----> 2\u001b[0m Masses \u001b[38;5;241m=\u001b[39m \u001b[43mpd\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mread_fwf\u001b[49m\u001b[43m(\u001b[49m\u001b[43minfile\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43musecols\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m2\u001b[39;49m\u001b[43m,\u001b[49m\u001b[38;5;241;43m3\u001b[39;49m\u001b[43m,\u001b[49m\u001b[38;5;241;43m4\u001b[39;49m\u001b[43m,\u001b[49m\u001b[38;5;241;43m6\u001b[39;49m\u001b[43m,\u001b[49m\u001b[38;5;241;43m11\u001b[39;49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 3\u001b[0m \u001b[43m \u001b[49m\u001b[43mnames\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mN\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mZ\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mA\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mElement\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mEbinding\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 4\u001b[0m \u001b[43m \u001b[49m\u001b[43mwidths\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m1\u001b[39;49m\u001b[43m,\u001b[49m\u001b[38;5;241;43m3\u001b[39;49m\u001b[43m,\u001b[49m\u001b[38;5;241;43m5\u001b[39;49m\u001b[43m,\u001b[49m\u001b[38;5;241;43m5\u001b[39;49m\u001b[43m,\u001b[49m\u001b[38;5;241;43m5\u001b[39;49m\u001b[43m,\u001b[49m\u001b[38;5;241;43m1\u001b[39;49m\u001b[43m,\u001b[49m\u001b[38;5;241;43m3\u001b[39;49m\u001b[43m,\u001b[49m\u001b[38;5;241;43m4\u001b[39;49m\u001b[43m,\u001b[49m\u001b[38;5;241;43m1\u001b[39;49m\u001b[43m,\u001b[49m\u001b[38;5;241;43m13\u001b[39;49m\u001b[43m,\u001b[49m\u001b[38;5;241;43m11\u001b[39;49m\u001b[43m,\u001b[49m\u001b[38;5;241;43m11\u001b[39;49m\u001b[43m,\u001b[49m\u001b[38;5;241;43m9\u001b[39;49m\u001b[43m,\u001b[49m\u001b[38;5;241;43m1\u001b[39;49m\u001b[43m,\u001b[49m\u001b[38;5;241;43m2\u001b[39;49m\u001b[43m,\u001b[49m\u001b[38;5;241;43m11\u001b[39;49m\u001b[43m,\u001b[49m\u001b[38;5;241;43m9\u001b[39;49m\u001b[43m,\u001b[49m\u001b[38;5;241;43m1\u001b[39;49m\u001b[43m,\u001b[49m\u001b[38;5;241;43m3\u001b[39;49m\u001b[43m,\u001b[49m\u001b[38;5;241;43m1\u001b[39;49m\u001b[43m,\u001b[49m\u001b[38;5;241;43m12\u001b[39;49m\u001b[43m,\u001b[49m\u001b[38;5;241;43m11\u001b[39;49m\u001b[43m,\u001b[49m\u001b[38;5;241;43m1\u001b[39;49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 5\u001b[0m \u001b[43m \u001b[49m\u001b[43mheader\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;241;43m39\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[1;32m 6\u001b[0m \u001b[43m \u001b[49m\u001b[43mindex_col\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mFalse\u001b[39;49;00m\u001b[43m)\u001b[49m\n\u001b[1;32m 8\u001b[0m \u001b[38;5;66;03m# Extrapolated values are indicated by '#' in place of the decimal place, so\u001b[39;00m\n\u001b[1;32m 9\u001b[0m \u001b[38;5;66;03m# the Ebinding column won't be numeric. Coerce to float and drop these entries.\u001b[39;00m\n\u001b[1;32m 10\u001b[0m Masses[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mEbinding\u001b[39m\u001b[38;5;124m'\u001b[39m] \u001b[38;5;241m=\u001b[39m pd\u001b[38;5;241m.\u001b[39mto_numeric(Masses[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mEbinding\u001b[39m\u001b[38;5;124m'\u001b[39m], errors\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mcoerce\u001b[39m\u001b[38;5;124m'\u001b[39m)\n", + "File \u001b[0;32m~/miniforge3/envs/myenv/lib/python3.9/site-packages/pandas/util/_decorators.py:311\u001b[0m, in \u001b[0;36mdeprecate_nonkeyword_arguments..decorate..wrapper\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m 305\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mlen\u001b[39m(args) \u001b[38;5;241m>\u001b[39m num_allow_args:\n\u001b[1;32m 306\u001b[0m warnings\u001b[38;5;241m.\u001b[39mwarn(\n\u001b[1;32m 307\u001b[0m msg\u001b[38;5;241m.\u001b[39mformat(arguments\u001b[38;5;241m=\u001b[39marguments),\n\u001b[1;32m 308\u001b[0m \u001b[38;5;167;01mFutureWarning\u001b[39;00m,\n\u001b[1;32m 309\u001b[0m stacklevel\u001b[38;5;241m=\u001b[39mstacklevel,\n\u001b[1;32m 310\u001b[0m )\n\u001b[0;32m--> 311\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mfunc\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n", + "File \u001b[0;32m~/miniforge3/envs/myenv/lib/python3.9/site-packages/pandas/io/parsers/readers.py:871\u001b[0m, in \u001b[0;36mread_fwf\u001b[0;34m(filepath_or_buffer, colspecs, widths, infer_nrows, **kwds)\u001b[0m\n\u001b[1;32m 869\u001b[0m len_index \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mlen\u001b[39m(index_col)\n\u001b[1;32m 870\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mlen\u001b[39m(names) \u001b[38;5;241m+\u001b[39m len_index \u001b[38;5;241m!=\u001b[39m \u001b[38;5;28mlen\u001b[39m(colspecs):\n\u001b[0;32m--> 871\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mLength of colspecs must match length of names\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[1;32m 873\u001b[0m kwds[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mcolspecs\u001b[39m\u001b[38;5;124m\"\u001b[39m] \u001b[38;5;241m=\u001b[39m colspecs\n\u001b[1;32m 874\u001b[0m kwds[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124minfer_nrows\u001b[39m\u001b[38;5;124m\"\u001b[39m] \u001b[38;5;241m=\u001b[39m infer_nrows\n", + "\u001b[0;31mValueError\u001b[0m: Length of colspecs must match length of names" + ] + } + ], "source": [ "# Read the experimental data with Pandas\n", "Masses = pd.read_fwf(infile, usecols=(2,3,4,6,11),\n", @@ -5040,7 +5129,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.9.18" + "version": "3.9.10" } }, "nbformat": 4, diff --git a/doc/LectureNotes/_build/jupyter_execute/chapter10.ipynb b/doc/LectureNotes/_build/jupyter_execute/chapter10.ipynb index 32c9f88d0..e88ed2210 100644 --- a/doc/LectureNotes/_build/jupyter_execute/chapter10.ipynb +++ b/doc/LectureNotes/_build/jupyter_execute/chapter10.ipynb @@ -537,20 +537,27 @@ }, "outputs": [ { - "ename": "ModuleNotFoundError", - "evalue": "No module named 'matplotlib'", - "output_type": "error", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mModuleNotFoundError\u001b[0m Traceback (most recent call last)", - "Cell \u001b[0;32mIn[1], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \u001b[43mget_ipython\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mrun_line_magic\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mmatplotlib\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43minline\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m)\u001b[49m\n\u001b[1;32m 3\u001b[0m \u001b[38;5;66;03m# import necessary packages\u001b[39;00m\n\u001b[1;32m 4\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mnumpy\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m \u001b[38;5;21;01mnp\u001b[39;00m\n", - "File \u001b[0;32m~/miniforge3/lib/python3.9/site-packages/IPython/core/interactiveshell.py:2432\u001b[0m, in \u001b[0;36mInteractiveShell.run_line_magic\u001b[0;34m(self, magic_name, line, _stack_depth)\u001b[0m\n\u001b[1;32m 2430\u001b[0m kwargs[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mlocal_ns\u001b[39m\u001b[38;5;124m'\u001b[39m] \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mget_local_scope(stack_depth)\n\u001b[1;32m 2431\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mbuiltin_trap:\n\u001b[0;32m-> 2432\u001b[0m result \u001b[38;5;241m=\u001b[39m \u001b[43mfn\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 2434\u001b[0m \u001b[38;5;66;03m# The code below prevents the output from being displayed\u001b[39;00m\n\u001b[1;32m 2435\u001b[0m \u001b[38;5;66;03m# when using magics with decorator @output_can_be_silenced\u001b[39;00m\n\u001b[1;32m 2436\u001b[0m \u001b[38;5;66;03m# when the last Python token in the expression is a ';'.\u001b[39;00m\n\u001b[1;32m 2437\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mgetattr\u001b[39m(fn, magic\u001b[38;5;241m.\u001b[39mMAGIC_OUTPUT_CAN_BE_SILENCED, \u001b[38;5;28;01mFalse\u001b[39;00m):\n", - "File \u001b[0;32m~/miniforge3/lib/python3.9/site-packages/IPython/core/magics/pylab.py:99\u001b[0m, in \u001b[0;36mPylabMagics.matplotlib\u001b[0;34m(self, line)\u001b[0m\n\u001b[1;32m 97\u001b[0m \u001b[38;5;28mprint\u001b[39m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mAvailable matplotlib backends: \u001b[39m\u001b[38;5;132;01m%s\u001b[39;00m\u001b[38;5;124m\"\u001b[39m \u001b[38;5;241m%\u001b[39m backends_list)\n\u001b[1;32m 98\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m---> 99\u001b[0m gui, backend \u001b[38;5;241m=\u001b[39m 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3606\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mmatplotlib_inline\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mbackend_inline\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m configure_inline_support\n\u001b[1;32m 3608\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mIPython\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mcore\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m pylabtools \u001b[38;5;28;01mas\u001b[39;00m pt\n\u001b[1;32m 3609\u001b[0m gui, backend \u001b[38;5;241m=\u001b[39m pt\u001b[38;5;241m.\u001b[39mfind_gui_and_backend(gui, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mpylab_gui_select)\n", - "File \u001b[0;32m~/miniforge3/lib/python3.9/site-packages/matplotlib_inline/__init__.py:1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01m.\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m backend_inline, config \u001b[38;5;66;03m# noqa\u001b[39;00m\n\u001b[1;32m 2\u001b[0m __version__ \u001b[38;5;241m=\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m0.1.6\u001b[39m\u001b[38;5;124m\"\u001b[39m \u001b[38;5;66;03m# noqa\u001b[39;00m\n", - "File \u001b[0;32m~/miniforge3/lib/python3.9/site-packages/matplotlib_inline/backend_inline.py:6\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[38;5;124;03m\"\"\"A matplotlib backend for publishing figures via display_data\"\"\"\u001b[39;00m\n\u001b[1;32m 3\u001b[0m \u001b[38;5;66;03m# Copyright (c) IPython Development Team.\u001b[39;00m\n\u001b[1;32m 4\u001b[0m \u001b[38;5;66;03m# Distributed under the terms of the BSD 3-Clause License.\u001b[39;00m\n\u001b[0;32m----> 6\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mmatplotlib\u001b[39;00m\n\u001b[1;32m 7\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mmatplotlib\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m colors\n\u001b[1;32m 8\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mmatplotlib\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mbackends\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m backend_agg\n", - "\u001b[0;31mModuleNotFoundError\u001b[0m: No module named 'matplotlib'" + "name": "stdout", + "output_type": "stream", + "text": [ + "inputs = (n_inputs, pixel_width, pixel_height) = (1797, 8, 8)\n", + "labels = (n_inputs) = (1797,)\n", + "X = (n_inputs, n_features) = (1797, 64)\n" ] + }, + { + "data": { + "image/png": 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+ "text/plain": [ + "
              " + ] + }, + "metadata": { + "filenames": { + "image/png": "/Users/mhjensen/Teaching/MachineLearning/doc/LectureNotes/_build/jupyter_execute/chapter10_39_1.png" + } + }, + "output_type": "display_data" } ], "source": [ @@ -628,7 +635,16 @@ "collapsed": false, "editable": true }, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Number of training images: 1437\n", + "Number of test images: 360\n" + ] + } + ], "source": [ "from sklearn.model_selection import train_test_split\n", "\n", @@ -842,7 +858,24 @@ "collapsed": false, "editable": true }, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "probabilities = (n_inputs, n_categories) = (1437, 10)\n", + "probability that image 0 is in category 0,1,2,...,9 = \n", + "[5.41511965e-04 2.17174962e-03 8.84355903e-03 1.44970586e-03\n", + " 1.10378326e-04 5.08318298e-09 2.03256632e-04 1.92507116e-03\n", + " 9.84443254e-01 3.11507992e-04]\n", + "probabilities sum up to: 1.0\n", + "\n", + "predictions = (n_inputs) = (1437,)\n", + "prediction for image 0: 8\n", + "correct label for image 0: 6\n" + ] + } + ], "source": [ "# setup the feed-forward pass, subscript h = hidden layer\n", "\n", @@ -1032,7 +1065,30 @@ "collapsed": false, "editable": true }, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Old accuracy on training data: 0.1440501043841336\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_18986/953065564.py:4: RuntimeWarning: overflow encountered in exp\n", + " return 1/(1 + np.exp(-x))\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "New accuracy on training data: 0.09951287404314545\n" + ] + } + ], "source": [ "# to categorical turns our integer vector into a onehot representation\n", "from sklearn.metrics import accuracy_score\n", @@ -1265,7 +1321,15 @@ "collapsed": false, "editable": true }, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Accuracy score on test set: 0.9444444444444444\n" + ] + } + ], "source": [ "epochs = 100\n", "batch_size = 100\n", @@ -1306,7 +1370,101 @@ "collapsed": false, "editable": true }, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Learning rate = 1e-05\n", + "Lambda = 1e-05\n", + "Accuracy score on test set: 0.11666666666666667\n", + "\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Learning rate = 1e-05\n", + "Lambda = 0.0001\n", + "Accuracy score on test set: 0.20833333333333334\n", + "\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Learning rate = 1e-05\n", + "Lambda = 0.001\n", + "Accuracy score on test set: 0.12222222222222222\n", + "\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Learning rate = 1e-05\n", + "Lambda = 0.01\n", + "Accuracy score on test set: 0.14722222222222223\n", + "\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Learning rate = 1e-05\n", + "Lambda = 0.1\n", + "Accuracy score on test set: 0.17777777777777778\n", + "\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Learning rate = 1e-05\n", + "Lambda = 1.0\n", + "Accuracy score on test set: 0.16111111111111112\n", + "\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Learning rate = 1e-05\n", + "Lambda = 10.0\n", + "Accuracy score on test set: 0.20277777777777778\n", + "\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Learning rate = 0.0001\n", + "Lambda = 1e-05\n", + "Accuracy score on test set: 0.5305555555555556\n", + "\n" + ] + }, + { + "ename": "KeyboardInterrupt", + "evalue": "", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mKeyboardInterrupt\u001b[0m Traceback (most recent call last)", + "Input \u001b[0;32mIn [8]\u001b[0m, in \u001b[0;36m\u001b[0;34m()\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] 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\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: " + ] + } + ], "source": [ "eta_vals = np.logspace(-5, 1, 7)\n", "lmbd_vals = np.logspace(-5, 1, 7)\n", @@ -2320,7 +2478,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.9.18" + "version": "3.9.10" } }, "nbformat": 4, diff --git a/doc/LectureNotes/_build/jupyter_execute/chapter11.ipynb b/doc/LectureNotes/_build/jupyter_execute/chapter11.ipynb index 502da1094..d0197d594 100644 --- a/doc/LectureNotes/_build/jupyter_execute/chapter11.ipynb +++ b/doc/LectureNotes/_build/jupyter_execute/chapter11.ipynb @@ -782,20 +782,33 @@ }, "outputs": [ { - "ename": "ModuleNotFoundError", - "evalue": "No module named 'matplotlib'", - "output_type": "error", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mModuleNotFoundError\u001b[0m Traceback (most recent call last)", - "Cell \u001b[0;32mIn[1], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \u001b[43mget_ipython\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mrun_line_magic\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mmatplotlib\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43minline\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m)\u001b[49m\n\u001b[1;32m 3\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mautograd\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mnumpy\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m \u001b[38;5;21;01mnp\u001b[39;00m\n\u001b[1;32m 4\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mautograd\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m grad, elementwise_grad\n", - "File \u001b[0;32m~/miniforge3/lib/python3.9/site-packages/IPython/core/interactiveshell.py:2432\u001b[0m, in \u001b[0;36mInteractiveShell.run_line_magic\u001b[0;34m(self, magic_name, line, _stack_depth)\u001b[0m\n\u001b[1;32m 2430\u001b[0m kwargs[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mlocal_ns\u001b[39m\u001b[38;5;124m'\u001b[39m] \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mget_local_scope(stack_depth)\n\u001b[1;32m 2431\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mbuiltin_trap:\n\u001b[0;32m-> 2432\u001b[0m result \u001b[38;5;241m=\u001b[39m \u001b[43mfn\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 2434\u001b[0m \u001b[38;5;66;03m# The code below prevents the output from being displayed\u001b[39;00m\n\u001b[1;32m 2435\u001b[0m \u001b[38;5;66;03m# when using magics with decorator @output_can_be_silenced\u001b[39;00m\n\u001b[1;32m 2436\u001b[0m \u001b[38;5;66;03m# when the last Python token in the expression is a ';'.\u001b[39;00m\n\u001b[1;32m 2437\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mgetattr\u001b[39m(fn, magic\u001b[38;5;241m.\u001b[39mMAGIC_OUTPUT_CAN_BE_SILENCED, \u001b[38;5;28;01mFalse\u001b[39;00m):\n", - "File \u001b[0;32m~/miniforge3/lib/python3.9/site-packages/IPython/core/magics/pylab.py:99\u001b[0m, in \u001b[0;36mPylabMagics.matplotlib\u001b[0;34m(self, line)\u001b[0m\n\u001b[1;32m 97\u001b[0m \u001b[38;5;28mprint\u001b[39m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mAvailable matplotlib backends: \u001b[39m\u001b[38;5;132;01m%s\u001b[39;00m\u001b[38;5;124m\"\u001b[39m \u001b[38;5;241m%\u001b[39m backends_list)\n\u001b[1;32m 98\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m---> 99\u001b[0m gui, backend \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mshell\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43menable_matplotlib\u001b[49m\u001b[43m(\u001b[49m\u001b[43margs\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mgui\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mlower\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mif\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[38;5;28;43misinstance\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43margs\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mgui\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mstr\u001b[39;49m\u001b[43m)\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01melse\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43margs\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mgui\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 100\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_show_matplotlib_backend(args\u001b[38;5;241m.\u001b[39mgui, backend)\n", - "File \u001b[0;32m~/miniforge3/lib/python3.9/site-packages/IPython/core/interactiveshell.py:3606\u001b[0m, in \u001b[0;36mInteractiveShell.enable_matplotlib\u001b[0;34m(self, gui)\u001b[0m\n\u001b[1;32m 3585\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21menable_matplotlib\u001b[39m(\u001b[38;5;28mself\u001b[39m, gui\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mNone\u001b[39;00m):\n\u001b[1;32m 3586\u001b[0m \u001b[38;5;250m \u001b[39m\u001b[38;5;124;03m\"\"\"Enable interactive matplotlib and inline figure support.\u001b[39;00m\n\u001b[1;32m 3587\u001b[0m \n\u001b[1;32m 3588\u001b[0m \u001b[38;5;124;03m This takes the following steps:\u001b[39;00m\n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 3604\u001b[0m \u001b[38;5;124;03m display figures inline.\u001b[39;00m\n\u001b[1;32m 3605\u001b[0m \u001b[38;5;124;03m \"\"\"\u001b[39;00m\n\u001b[0;32m-> 3606\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mmatplotlib_inline\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mbackend_inline\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m configure_inline_support\n\u001b[1;32m 3608\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mIPython\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mcore\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m pylabtools \u001b[38;5;28;01mas\u001b[39;00m pt\n\u001b[1;32m 3609\u001b[0m gui, backend \u001b[38;5;241m=\u001b[39m pt\u001b[38;5;241m.\u001b[39mfind_gui_and_backend(gui, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mpylab_gui_select)\n", - "File \u001b[0;32m~/miniforge3/lib/python3.9/site-packages/matplotlib_inline/__init__.py:1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01m.\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m backend_inline, config \u001b[38;5;66;03m# noqa\u001b[39;00m\n\u001b[1;32m 2\u001b[0m __version__ \u001b[38;5;241m=\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m0.1.6\u001b[39m\u001b[38;5;124m\"\u001b[39m \u001b[38;5;66;03m# noqa\u001b[39;00m\n", - "File \u001b[0;32m~/miniforge3/lib/python3.9/site-packages/matplotlib_inline/backend_inline.py:6\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[38;5;124;03m\"\"\"A matplotlib backend for publishing figures via display_data\"\"\"\u001b[39;00m\n\u001b[1;32m 3\u001b[0m \u001b[38;5;66;03m# Copyright (c) IPython Development Team.\u001b[39;00m\n\u001b[1;32m 4\u001b[0m \u001b[38;5;66;03m# Distributed under the terms of the BSD 3-Clause License.\u001b[39;00m\n\u001b[0;32m----> 6\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mmatplotlib\u001b[39;00m\n\u001b[1;32m 7\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mmatplotlib\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m colors\n\u001b[1;32m 8\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mmatplotlib\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mbackends\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m backend_agg\n", - "\u001b[0;31mModuleNotFoundError\u001b[0m: No module named 'matplotlib'" + "name": "stdout", + "output_type": "stream", + "text": [ + "Initial cost: 367.01\n" ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Final cost: 0.0666807\n", + "Max absolute difference: 0.0437499\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
              " + ] + }, + "metadata": { + "filenames": { + "image/png": "/Users/mhjensen/Teaching/MachineLearning/doc/LectureNotes/_build/jupyter_execute/chapter11_50_2.png" + } + }, + "output_type": "display_data" } ], "source": [ @@ -969,7 +982,44 @@ "collapsed": false, "editable": true }, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Initial cost: 324.246\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/mhjensen/miniforge3/envs/myenv/lib/python3.9/site-packages/numpy/core/fromnumeric.py:3245: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray.\n", + " return asarray(a).size\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Final cost: 0.119936\n" + ] + }, + { + "data": { + "image/png": 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JzvaZmihKSYh74129erWJiYkxYWFh5vfffze5c+c22bJlMydPnjSrVq0yksyQIUMS3PfIkSPG19fXvP322/HT6tevbySZxYsXJ3qsaz+4GGPM/PnzjSQzaNCgBNOnTZuWoKgZc+UF73Q6ze7duxPMG/fG06ZNmwTTe/ToYSSZ1157LcH0hx56yOTMmfO668TlcpmYmBgzYcIE43Q6zfnz5xM9vzVr1iS4T4UKFUzz5s3jf/7444+NJLNw4cLrPs6UKVOMJDNjxowE09etW2ckmREjRtwwY4ECBUzlypWNy+WKnx4WFmby5MljateuHT8tbv1c/QaaXLGxsSYmJsY0btzYtGvX7obz7ty500gyb7zxRoLpcR96r95Zd+vWzQQEBJj//vsvwbxffPGFkRRf5lKyjooWLWosyzKbN29OMG/Tpk1NYGCguXTp0g3zSzJ58+aN/+BjjDEnT540DofD9O/fP35a8+bNTaFCheJ3hHFeeeUV4+PjE7+9pLQoXe91czW3221iYmLMsmXLjCSzZcuW+N+lpChJMtOnT08wvWXLlqZs2bLxP6dk3VesWDHBm0GcMWPGGEnm8OHDxhhjPv30U1OuXDnTtm3b+A8J0dHRxt/f37z77rvGmJRt23HP+cMPP0zyecbtbwYMGGCcTmeCD6zXc/bsWSPJDBs27LrzxG3r3bt3TzB9zZo1RlL8c4nLcfUb8NmzZ42Xl1eCeYwx5rHHHjN58+ZN8MH9ekXp2scdNGiQkWROnDhhjLnywU6S6d27d4L54v6mySlKV4vbJ3788ccmODjYuN3u+N8VLVrU+Pj4JHgtR0REmJw5c5pu3brdcLlxH+5v9GH3jz/+SFQ2blaUevfunWA/Hfeh53q3q8t3Uho1amSyZ88e/0ExKU888YTx9vaO39bjtGjRwvj5+cV/ME2N96u4/2y4+j8pY2NjTbly5UypUqWum/F6+/O49VOyZEkTHR2d4D7lypUz1atXT1QoW7dubfLnzx//Gn388cevm+lmRckYY15++eUk918p+YyQkuUak/r7/ORIjc8X14r7uz777LOmevXqCX53bVHav3+/cTgcZujQofHTIiIiTHBwcILi9vPPPyd6n4pz7T5t+fLlRpJ57733bvLs/9/tvpel9X46uUXJGGMGDx583W382qI0cuTIJN9/Bw4caCSZBQsWJHic5GyfqYlT727gvvvuk6enp7Jly6bWrVsrX758+uOPP5Q3b179/vvvsixLTz31lGJjY+Nv+fLlU9WqVROcQiRJOXLkUKNGjZL1uEuWLJGkRKeCPProo/L399fixYsTTK9Spcp1ByJo3bp1gp/Lly8vSWrVqlWi6efPn09wOsOmTZvUtm1bBQcHy+l0ytPTU507d5bL5dKePXsS3D9fvnyqWbNmolxxpylK0h9//KEyZcqoSZMm13vq+v3335U9e3a1adMmwXqtVq2a8uXLl2i9Xm337t06fvy4OnXqJIfj/zftgIAAPfLII1q9erUuX7583fvfyMiRI3XXXXfJx8dHHh4e8vT01OLFi7Vz584b3i/uNI1rr2l77LHH5OGR8BLB33//XQ0bNlSBAgUSPPcWLVpIkpYtWxY/X0rWUcWKFVW1atUE0zp27KjQ0FBt3Ljxps+9YcOGypYtW/zPefPmVZ48eeL/tpGRkVq8eLHatWsnPz+/BJlatmypyMjIJEfdSo7rvW4OHDigjh07Kl++fPHbZty1Zjf7m1yPZVlq06ZNgmnXbsO3s33Gidv+Fy1aJElauHChmjZtqiZNmmjhwoWSrgxYcOnSpfh5b2XbfuSRR5J8fGOMunXrpo8++kiTJ0/W22+/fdPMOXPmVMmSJTV48GB9+eWX2rRpU6LTauO29Wv3WzVr1lT58uUT7beuFhwcrDZt2ujHH3+MX+6FCxf066+/qnPnzoleK0lp27Ztgp+rVKki6f9PlY57/Tz22GMJ5mvfvn2yli9d2Tc3adJEQUFB8dvdhx9+qHPnzun06dMJ5q1WrZqKFCkS/7OPj4/KlCmTYHu6VeZ/p/qlZBSsuPtc6/XXX9e6desS3W40Auvly5e1bNkyPfbYY8qdO/d151uyZIkaN26swoULJ5jepUsXXb58OdFpo7fzfiVJjRs3Vt68eeN/djqdevzxx7Vv374EpxWlZH/etm1beXp6xv+8b98+7dq1K36ffu3+7sSJE9q9e7ekK6+J62W6HSn9jJBSd2Kfn9qfLyTp559/Vp06dRQQEBD/dx07duxN3xNKlCih1q1ba8SIEfGvk8mTJ+vcuXO3fP34H3/8IUkJTrNOSmq+l6X1fjqtLFmyRP7+/mrfvn2C6XEZr810s+0ztVGUbmDChAlat26dNm3apOPHj2vr1q2qU6eOJOnUqVPx1xt4enomuK1evVpnz55NsKz8+fMn+3HPnTsnDw+PRG9AlmUpX758OnfuXLKXnTNnzgQ/e3l53XB6ZGSkJOnw4cOqW7eujh07pq+++korVqzQunXr4s8/joiISHD/4ODgRI/t7e2dYL4zZ87cdCSlU6dO6eLFi/Ly8kq0Xk+ePJlovV4tbr0ktT4KFCggt9utCxcu3PDxk/Lll1/qpZde0r333qsZM2Zo9erVWrdunR544IFE6+F6mfLly5dguoeHR6J1durUKf3222+JnnfFihUlKf65p3QdXfvYV0+7dltKys3+tufOnVNsbKy++eabRHlatmyZIHtKJfW3DA8PV926dbVmzRp9+umnWrp0qdatW6eZM2dKSrxtJpefn598fHwSTPP29o5/TUi3t33GKVq0qEqWLKlFixbFf1iMK0pHjx7V7t27tWjRIvn6+qp27dqSbm3bvt5+ITo6On5o/LgSfjOWZWnx4sVq3ry5Bg0apLvuuku5c+fWa6+9prCwsGRlvNm21rVrVx07diy+LE6ZMkVRUVHJHjXw2u007oL7q7dTSQk+tEpJvxaTsnbtWjVr1kyS9P3332vlypVat26d3nvvvQSPc708cZlutn3GlauDBw9ed56460muLSA3EvchokCBAgmmFypUSDVq1Eh0u9EIrBcuXJDL5brp/vzcuXPX3R7ifn+1W32/ipOcfV1K9+fX5o+7jrBXr16J9gHdu3eX9P/7u3Pnzt0w061K6WeElErrfX5afL6YOXOmHnvsMRUsWFATJ07UqlWrtG7dOnXt2jXRdpKU119/XXv37o3f/wwfPly1atXSXXfdddP7JuXMmTNyOp03/Fun9nvZndhPp4W418m1//GTJ08eeXh4JMp0q/vWW8WodzdQvnz5+FHvrpUrVy5ZlqUVK1YkOQLOtdNS8j9/wcHBio2N1ZkzZxLsCI0xOnnypO65555bXnZyzZ49W5cuXdLMmTNVtGjR+Om3811DuXPnvunFgnEXYs+fPz/J31/9vwjXinvxnDhxItHvjh8/LofDoRw5cqQg8RUTJ05UgwYN9N133yWYHrfjuZG4TCdPnlTBggXjp8fGxiZ68efKlUtVqlTRZ599luSy4j5cpHQdJXXBd9y05HxAvJkcOXLI6XSqU6dO1/3fs+LFi0tSfBG59gLN672pJrVtL1myRMePH9fSpUsTjFh4s4uYU8PtbJ9Xa9y4sX799VctW7ZMbrdbDRo0ULZs2VSgQAEtXLhQixYtUt26deP3I7eybV9vvxB3QXrz5s3VpEkTzZ8/P1mvi6JFi2rs2LGSrowaOX36dPXt21fR0dEaOXJkgozXfoA+fvy4cuXKdcPlN2/eXAUKFND48ePVvHlzjR8/Xvfee2+qjZAUl+/UqVM3fS0mZerUqfL09NTvv/+eoFDPnj07VfLFadq0qUaPHq3Zs2frnXfeSXKe2bNny8PDI9nfRRIREaFFixapZMmSqTLsd86cOeV0Om+6Pw8ODr7uNivppttESiVnX5fS/fm1r6O4zH369EkwyMjVypYtG/+YN8p0q1L6GSG1pWSfn5S0+HwxceJEFS9eXNOmTUvwN7v2veZ6GjVqpEqVKunbb79VQECANm7cqIkTJ95ynty5c8vlcunkyZPX/U+rtHgvS8v99PXev1OjmK9Zs0bGmAR/u9OnTys2NjbV9xMpxRGlW9S6dWsZY3Ts2LEk/zeucuXKt7zsxo0bS1KiF+mMGTN06dKl+N+npbiN9erCZ4zR999/f8vLbNGihfbs2RN/2kBSWrdurXPnzsnlciW5XuPegJJStmxZFSxYUJMnT05wmsmlS5c0Y8aM+NHCUsqyrETFd+vWrYlOG0lK3AeZSZMmJZg+ffr0RCPZtW7dWtu2bVPJkiWTfO5xRSml62j79u3asmVLgmmTJ09WtmzZbvl/y67m5+enhg0batOmTapSpUqSmeJ2znGjAm3dujXBMq4eBehmkto2JWnUqFG38SySJyXr/kb/w9WkSROdOnVKw4YN03333RdfsBo3bqxZs2Zp3bp1CU5RTe1tu3r16lq2bJmOHj2qBg0aJDpt7GbKlCmj999/X5UrV44/fTPuFMlr91vr1q3Tzp07b7rfivvgNXv2bK1YsULr16+PH+UwNdSrV0/SlVGmrvbLL78kei0mJe7Lx51OZ/y0iIgI/fTTT6mWUZLatWunChUqaMCAAYlOQZKu5F+wYIGee+65ZB2ZcLlceuWVV3Tu3Dn17t07VTL6+vqqfv36+vnnn2945KBx48bxHwavNmHCBPn5+aX6sOSLFy9OMHKky+XStGnTEhTE29mfS1dei6VLl9aWLVuS3AfUqFEj/vXcsGHD62ZKjmuPisa53c8I11tucqVkn5+UtPh8YVmWvLy8EnzQPnny5E1Hvbvaa6+9prlz56pPnz7Kmzdv/Ch3cVKy3uKO1l9byK/NfPVy46TWe1lq76fz5s0rHx+fRO/fSa3jlKyrxo0bKzw8PNF/Ok2YMCH+93biiNItqlOnjl544QU988wzWr9+verVqyd/f3+dOHFCf//9typXrpzo+yqSq2nTpmrevLl69+6t0NBQ1alTR1u3btVHH32k6tWrq1OnTqn8bJLO4OXlpQ4dOujtt99WZGSkvvvuu1s6dS1Ojx49NG3aND344IN65513VLNmTUVERGjZsmVq3bq1GjZsqCeeeEKTJk1Sy5Yt9frrr6tmzZry9PTU0aNH9ddff+nBBx9Uu3btkly+w+HQoEGD9OSTT6p169bq1q2boqKiNHjwYF28eFEDBgy4pdytW7fWJ598oo8++kj169fX7t279fHHH6t48eI3/YBVvnx5PfXUUxo2bJg8PT3VpEkTbdu2TV988YUCAwMTzPvxxx9r4cKFql27tl577TWVLVtWkZGROnTokObNm6eRI0eqUKFCKV5HBQoUUNu2bdW3b1/lz59fEydO1MKFCzVw4MBU+26dr776Svfff7/q1q2rl156ScWKFVNYWJj27dun3377Lb4c33PPPSpbtqx69eql2NhY5ciRQ7NmzdLff/+d7MeqXbu2cuTIoRdffFEfffSRPD09NWnSpERlMC2kZN1XrlxZU6dO1bRp01SiRAn5+PjE/wdKo0aNZFmWFixYoH79+sUvv0mTJnr66afj/x0nLbbt8uXLa8WKFWrSpInq1aunRYsWXfdow9atW/XKK6/o0UcfVenSpeXl5aUlS5Zo69at8Uc9ypYtqxdeeEHffPONHA6HWrRooUOHDumDDz5Q4cKF9cYbb9w0U9euXTVw4EB17NhRvr6+t30tx9UqVqyoDh06aMiQIXI6nWrUqJG2b9+uIUOGKCgoKMG1X0lp1aqVvvzyS3Xs2FEvvPCCzp07py+++CLVv1PH6XRqxowZatq0qWrVqqWePXuqVq1aioqK0m+//abRo0erfv36GjJkSKL7njp1SqtXr5YxRmFhYfFfOLtlyxa98cYbev755xPd5/Dhw0leT5I7d+4bDiv/5Zdf6v7779e9996rd955R6VKldKpU6c0Z84cjRo1StmyZdNHH30Uf+3lhx9+qJw5c2rSpEmaO3euBg0apKCgoNtbWdfIlSuXGjVqpA8++ED+/v4aMWKEdu3alWCI8NvZn8cZNWqUWrRooebNm6tLly4qWLCgzp8/r507d2rjxo36+eefJUnvv/++5syZo0aNGunDDz+Un5+fhg8fnuyhn+P2FwMHDlSLFi3kdDpVpUqV2/6McL3lxp3SmBzJ3ecnJS0+X8R9TUr37t3Vvn17HTlyRJ988ony58+vvXv3JmsZTz31lPr06aPly5fr/fffT7Q+KlWqJEkaPXq0smXLJh8fHxUvXjzJUli3bl116tRJn376qU6dOqXWrVvL29tbmzZtkp+fn1599dVUfy9L6/103HX548aNU8mSJVW1alWtXbtWkydPTjRv3Db21Vdf6emnn5anp6fKli2b5FkXnTt31vDhw/X000/r0KFDqly5sv7++299/vnnatmy5Q2va78j0mSIiAzuesODJ2XcuHHm3nvvNf7+/sbX19eULFnSdO7c2axfvz5+nvr165uKFSsmef+kRr0z5sqIK7179zZFixY1np6eJn/+/Oall14yFy5cSDBf0aJFTatWrRLd/3qjul3vucWNqHL10JC//fabqVq1qvHx8TEFCxY0b731VvxoS9eOTpbU80tquMwLFy6Y119/3RQpUsR4enqaPHnymFatWpldu3bFzxMTE2O++OKL+McOCAgw5cqVM926dTN79+5N9DjXmj17trn33nuNj4+P8ff3N40bNzYrV65M1vpJSlRUlOnVq5cpWLCg8fHxMXfddZeZPXt2ks/vevfv2bOnyZMnj/Hx8TH33XefWbVqVaKRd4wx5syZM+a1114zxYsXN56eniZnzpzm7rvvNu+9954JDw9P8TqK2z5++eUXU7FiRePl5WWKFStmvvzyy5vmNub6I2kllf3gwYOma9eupmDBgsbT09Pkzp3b1K5d23z66acJ5tuzZ49p1qyZCQwMNLlz5zavvvqqmTt3brK3K2OM+eeff0ytWrWMn5+fyZ07t3nuuefMxo0bE43Ik5JR75J6HSZ1/+Su+0OHDplmzZqZbNmyxQ99fLXq1asbSQm2zWPHjhlJiUZRi5OcbTup1/KNnufRo0dNuXLlTLFixa47vOqpU6dMly5dTLly5Yy/v78JCAgwVapUMUOHDk0wrLjL5TIDBw40ZcqUMZ6eniZXrlzmqaeeih+S+uoc13vt1K5d20gyTz75ZJK/13VGvbt2n5bUSIqRkZHmzTffTPRaDAoKSjQyZVLGjRtnypYta7y9vU2JEiVM//79zdixYxON7nS9/fK1oz3dyNmzZ80777xjypUrF7+d1axZ03z77beJRmEzxiQYsc7hcJjAwEBTuXJl88ILL5hVq1Ylmv9mo95db/1fbceOHebRRx81wcHBxsvLyxQpUsR06dIlwYh9//77r2nTpo0JCgoyXl5epmrVqolGzUqN96u4fdWIESNMyZIljaenpylXrlyCIfeNSf7+PG79XO9rFLZs2WIee+wxkydPHuPp6Wny5ctnGjVqZEaOHJlgvpUrV5r77rvPeHt7m3z58pm33nrLjB49Olmj3kVFRZnnnnvO5M6d21iWleA+yf2MkNLlpsU+Pylp8fliwIABplixYsbb29uUL1/efP/990nuw5N6LnG6dOliPDw8zNGjR5P8/bBhw0zx4sWN0+lM8H6TVB6Xy2WGDh1qKlWqZLy8vExQUJCpVauW+e233+LnSc33sjuxnw4JCTHPPfecyZs3r/H39zdt2rQxhw4dSrRfNsaYPn36mAIFChiHw5Hg75rUfvDcuXPmxRdfNPnz5zceHh6maNGipk+fPolG/0zJ9plarP89MIBMqFixYqpUqVLafhkbkIH9888/qlOnjiZNmqSOHTvaHQe3yLIsvfzyy/r222/tjoIMKjo6WsWKFdP999+f5JcHI2vi1DsAQJawcOFCrVq1Snfffbd8fX21ZcsWDRgwQKVLl77uhfkAMrczZ85o9+7dGj9+vE6dOnXdQVSQNVGUAABZQmBgoBYsWKBhw4YpLCxMuXLlUosWLdS/f/9EQ8MDyBrmzp2rZ555Rvnz59eIESNSZZAjZB6cegcAAAAA12B4cAAAAAC4BkUJAAAAAK5BUQIAAACAa2T6wRzcbreOHz+ubNmyJfjGZgAAAABZi/nfl3IXKFDgpl82numL0vHjx1W4cGG7YwAAAABIJ44cOaJChQrdcJ5MX5SyZcsm6crKCAwMtDkNAAAAALuEhoaqcOHC8R3hRjJ9UYo73S4wMJCiBAAAACBZl+QwmAMAAAAAXIOiBAAAAADXoCgBAAAAwDUy/TVKAAAAwM24XC7FxMTYHQO3ydPTU06nM1WWRVECAABAlmWM0cmTJ3Xx4kW7oyCVZM+eXfny5bvt71ClKAEAACDLiitJefLkkZ+f321/uIZ9jDG6fPmyTp8+LUnKnz//bS2PogQAAIAsyeVyxZek4OBgu+MgFfj6+kqSTp8+rTx58tzWaXgM5gAAAIAsKe6aJD8/P5uTIDXF/T1v95ozihIAAACyNE63y1xS6+9JUQIAAACAa1CUAAAAACSpQYMG6tGjx20t49ChQ7IsS5s3b06VTNKVo0azZ89OteUlhcEcAAAAAKSKLl266OLFiwlKTOHChXXixAnlypXLvmC3gKIEAAAAIM04nU7ly5fP7hgpxql3AAAAQAY0f/583X///cqePbuCg4PVunVr7d+/X9L/n+42c+ZMNWzYUH5+fqpatapWrVoVf/9z586pQ4cOKlSokPz8/FS5cmVNmTLluo/38ccfq3Llyomm33333frwww/Vt29f/fjjj/r1119lWZYsy9LSpUuTPPVu+/btatWqlQIDA5UtWzbVrVs3Pvu6devUtGlT5cqVS0FBQapfv742btyYSmst+ShKAAAAwP8YY3Q5OtaWmzEmRVkvXbqkN998U+vWrdPixYvlcDjUrl07ud3u+Hnee+899erVS5s3b1aZMmXUoUMHxcbGSpIiIyN199136/fff9e2bdv0wgsvqFOnTlqzZk2Sj9e1a1ft2LFD69ati5+2detWbdq0SV26dFGvXr302GOP6YEHHtCJEyd04sQJ1a5dO9Fyjh07pnr16snHx0dLlizRhg0b1LVr1/hcYWFhevrpp7VixQqtXr1apUuXVsuWLRUWFpai9XO7OPUOAAAA+J+IGJcqfPinLY+94+Pm8vNK/sfzRx55JMHPY8eOVZ48ebRjxw4FBARIknr16qVWrVpJkvr166eKFStq3759KleunAoWLKhevXrF3//VV1/V/Pnz9fPPP+vee+9N9HiFChVS8+bNNX78eN1zzz2SpPHjx6t+/foqUaKEpCtf+BoVFXXDU+2GDx+uoKAgTZ06VZ6enpKkMmXKxP++UaNGCeYfNWqUcuTIoWXLlql169bJXj+3iyNKAAAAQAa0f/9+dezYUSVKlFBgYKCKFy8uSTp8+HD8PFWqVIn/d/78+SVJp0+fliS5XC599tlnqlKlioKDgxUQEKAFCxYkuP+1nn/+eU2ZMkWRkZGKiYnRpEmT1LVr1xTl3rx5s+rWrRtfkq51+vRpvfjiiypTpoyCgoIUFBSk8PDwG+ZKCxxRAgAAAP7H19OpHR83t+2xU6JNmzYqXLiwvv/+exUoUEBut1uVKlVSdHR0/DxXl5G4L2KNOzVvyJAhGjp0qIYNG6bKlSvL399fPXr0SHD/pB7T29tbs2bNkre3t6KiohId2boZX1/fG/6+S5cuOnPmjIYNG6aiRYvK29tbtWrVumGutEBRAgAAAP7HsqwUnf5ml3Pnzmnnzp0aNWqU6tatK0n6+++/U7SMFStW6MEHH9RTTz0l6UqB2rt3r8qXL3/d+3h4eOjpp5/W+PHj5e3trSeeeEJ+fn7xv/fy8pLL5brh41apUkU//vijYmJikjyqtGLFCo0YMUItW7aUJB05ckRnz55N0XNLDZx6BwAAAGQwOXLkUHBwsEaPHq19+/ZpyZIlevPNN1O0jFKlSmnhwoX6559/tHPnTnXr1k0nT5686f2ee+45LVmyRH/88Uei0+6KFSumrVu3avfu3Tp79qxiYmIS3f+VV15RaGionnjiCa1fv1579+7VTz/9pN27d8fn+umnn7Rz506tWbNGTz755E2PQqUFihIAAACQwTgcDk2dOlUbNmxQpUqV9MYbb2jw4MEpWsYHH3ygu+66S82bN1eDBg2UL18+PfTQQze9X+nSpVW7dm2VLVs20aAPzz//vMqWLasaNWood+7cWrlyZaL7BwcHa8mSJQoPD1f9+vV199136/vvv48/ujRu3DhduHBB1atXV6dOnfTaa68pT548KXpuqcEyKR2HMIMJDQ1VUFCQQkJCFBgYaHccAAAApBORkZE6ePCgihcvLh8fH7vjZBjGGJUrV07dunVL8VGsO+FGf9eUdIP0fwImAAAAgHTh9OnT+umnn3Ts2DE988wzdsdJUxQlAAAAAMmSN29e5cqVS6NHj1aOHDnsjpOmKEoAAAAAkiWTX7WTAIM5AAAAAMA1KEoAAAAAcA2KEgAAAABcw9aitHz5crVp00YFChSQZVmaPXt2gt8bY9S3b18VKFBAvr6+atCggbZv325PWAAAAABZhq1F6dKlS6pataq+/fbbJH8/aNAgffnll/r222+1bt065cuXT02bNlVYWNgdTgoAAAAgK7F11LsWLVqoRYsWSf7OGKNhw4bpvffe08MPPyxJ+vHHH5U3b15NnjxZ3bp1u5NRU01MdJQ8vbztjgEAAADgBtLtNUoHDx7UyZMn1axZs/hp3t7eql+/vv7555/r3i8qKkqhoaEJbulBdFSkVn3fQxc/L6vzp4/ZHQcAAACZUIMGDdSjRw+7Y2QK6bYonTx5UtKVL7W6Wt68eeN/l5T+/fsrKCgo/la4cOE0zZlcnp5eynVyhXLrgvb8OtDuOAAAAABuIN0WpTiWZSX42RiTaNrV+vTpo5CQkPjbkSNH0jpislgOh8LufUOSVPnoNIWeO21zIgAAAADXk26LUr58+SQp0dGj06dPJzrKdDVvb28FBgYmuKUX1Rp31AFHMflbkdr56yC74wAAACADu3Tpkjp37qyAgADlz59fQ4YMSfD7YsWK6fPPP1fXrl2VLVs2FSlSRKNHj47//aFDh2RZlmbOnKmGDRvKz89PVatW1apVq+70U0mX0m1RKl68uPLly6eFCxfGT4uOjtayZctUu3ZtG5PdOofToXM1ekiSKhyepPCQ8/YGAgAAQELGSNGX7LkZk6Kob731lv766y/NmjVLCxYs0NKlS7Vhw4YE8wwZMkQ1atTQpk2b1L17d7300kvatWtXgnnee+899erVS5s3b1aZMmXUoUMHxcbG3vaqzOhsHfUuPDxc+/bti//54MGD2rx5s3LmzKkiRYqoR48e+vzzz1W6dGmVLl1an3/+ufz8/NSxY0cbU9+eu5p31qH1Q1TMfURrZw9Wzaf72x0JAAAAcWIuS58XsOex3z0uefkna9bw8HCNHTtWEyZMUNOmTSVdGSG6UKFCCeZr2bKlunfvLknq3bu3hg4dqqVLl6pcuXLx8/Tq1UutWrWSJPXr108VK1bUvn37EsyTFdl6RGn9+vWqXr26qlevLkl68803Vb16dX344YeSpLfffls9evRQ9+7dVaNGDR07dkwLFixQtmzZ7Ix9W5xOp05VfUWSVObgBEWEXbQ3EAAAADKc/fv3Kzo6WrVq1YqfljNnTpUtWzbBfFWqVIn/t2VZypcvn06fPn3defLnzy9JiebJimw9otSgQQOZGxxitCxLffv2Vd++fe9cqDvgrpbP6sjmYSpsTmjdnC91z5Mf2x0JAAAAkuTpd+XIjl2PnUw3+gydYJGengl+tixLbrf7uvPEDZp27TxZUbq9Rikz8/T01JFKVw6Bltw7XlERYTYnAgAAgCTJsq6c/mbH7QYjO1+rVKlS8vT01OrVq+OnXbhwQXv27EmLtZIlUZRscnfrF3RMeZVTodr261d2xwEAAEAGEhAQoGeffVZvvfWWFi9erG3btqlLly5yOPh4n1pYkzbx9vbRofIvSJKK7hqjmKjLNicCAABARjJ48GDVq1dPbdu2VZMmTXT//ffr7rvvtjtWpmGZ5J7gmEGFhoYqKChIISEh6eo7lSQpIiJCIQMrKZ/OalOl91S9/dt2RwIAAMgyIiMjdfDgQRUvXlw+Pj52x0EqudHfNSXdgCNKNvL19dXeMs9JkgpuHylXdKTNiQAAAABIFCXbVX/wVZ1WDuUx57Rt3ki74wAAAAAQRcl2Af4B2lmiqyQp75bhcsdE25wIAAAAAEUpHaj20Os6Z4KUz5zW9j+/tzsOAAAAkOVRlNKBoMAg/Vu0kyQpeOO3Mq4YmxMBAAAAWRtFKZ2o/NCbumCyqYD7uHYu+sHuOAAAAFmG2+22OwJSUWr9PT1SZSm4bcE5g/VXoY5qeGyUsq39SqbJM7Kc/HkAAADSipeXlxwOh44fP67cuXPLy8tLlmXZHQu3yBij6OhonTlzRg6HQ15eXre1PL5HKR05c+aMvL6tqiDrknbX/VplGz9tdyQAAIBMLTo6WidOnNDly5ftjoJU4ufnp/z58ydZlFLSDThkkY7kzp1bf+V/XA1PjpPvqqFSw06Sg7MjAQAA0oqXl5eKFCmi2NhYuVwuu+PgNjmdTnl4eKTKkUGKUjpT9qG3Ff7dFBWJPah9f09XqXpP2B0JAAAgU7MsS56envL09LQ7CtIRDlekMwXy5dfaPO0lSR5/D5Yy95mRAAAAQLpEUUqHSj/4ti4ZbxWL3qdDq2bZHQcAAADIcihK6VDhQkW0JridJMm9bBBHlQAAAIA7jKKUThVv21uRxlMlonbq8Pq5dscBAAAAshSKUjpVvFgJ/ZOjrSQpZskAjioBAAAAdxBFKR0r1Kq3ooyHSkb8q2NbFtodBwAAAMgyKErpWJnSZbUysKUk6fLC/janAQAAALIOilI6l7flO4o2TpW+tFGnti21Ow4AAACQJVCU0rmK5StqZUAzSVLIn5/ZnAYAAADIGihKGUDO5u8o1jhUJmytzu5eaXccAAAAINOjKGUAVatU099+jSRJ5+Z9bnMaAAAAIPOjKGUQAU16y2UslQ35Wxf2rbc7DgAAAJCpUZQyiLvvukd/+9STJJ2a+4nNaQAAAIDMjaKUQViWJe+Gb8ttLJW7sFQhh7bYHQkAAADItChKGci999bRSq/akqQTv3NUCQAAAEgrFKUMxLIsqd5bkqQyZxcp/NgOmxMBAAAAmRNFKYOpU6eBVnrcK4eMjs751O44AAAAQKZEUcpgHA5L0XV6SZJKnfpDESf32JwIAAAAyHwoShlQ3XpNtNp5lzzk1uFfOaoEAAAApDaKUgbk4XQo/N43JEklT/yuqDMHbU4EAAAAZC4UpQyqXqPWWueoKg+5dGjOZ3bHAQAAADIVilIG5eXh0Pm7X5ckFT8yS9Hnj9icCAAAAMg8KEoZWP1mD2mjVUFeitWhOZ/bHQcAAADINChKGZiPp1Mnq70mSSp66BfFhpywOREAAACQOVCUMrgGD7TXFpWRt6J1cM4Au+MAAAAAmQJFKYPz8/bU4UqvSJIK7Z8id9gZmxMBAAAAGR9FKRNo0KqDtqmkfBWlg78PtDsOAAAAkOFRlDKBbL5e2lfuJUlS/t0TZS6ftzkRAAAAkLFRlDKJ+q07a5cpKj9F6NDvX9gdBwAAAMjQKEqZRI4Ab+0o3U2SlHvnDzIRF+0NBAAAAGRgFKVMpN6DXbXXFFKAuaT//hhmdxwAAAAgw6IoZSK5svlqa4nnJEnB/46RosJsTgQAAABkTBSlTKZ22+d1wORXNhOmw39+bXccAAAAIEOiKGUy+XMEaEORrpKk7JtHSdGXbE4EAAAAZDwUpUzovrbd9J/Jo0B3iI4t/s7uOAAAAECGQ1HKhArnDtKagl0kSf7rh0sxEfYGAgAAADIYilImVaPtSzpmcim767xO/DXa7jgAAABAhkJRyqRK5Mupv/N2kiT5rP1Gio2yOREAAACQcVCUMrHqbV/WSZNDOWLP6PSKcXbHAQAAADIMilImVqZQbi3L1UGS5PxnmOSKsTcQAAAAkEFQlDK5im1e1xkTqOCYkzr7zwS74wAAAAAZAkUpk6tULJ8W53jiyg8rvpRcsfYGAgAAADIAilIWULb1azpvApQr+qjOr51idxwAAAAg3aMoZQHVSxXWwqD2kiTX0sGS22VzIgAAACB9oyhlEcVa9FCI8VPuqP8UsuEXu+MAAAAA6RpFKYuoWa6Y/gh4WJIUtWSA5HbbnAgAAABIvyhKWYRlWSr4QA+FGV/liTigsC2/2h0JAAAASLcoSlnI/ZVKaZ5fW0nS5UX9JWNsTgQAAACkTxSlLMSyLOVu2kOXjLfyXtqtS9vm2R0JAAAASJcoSllMg2rlNde7lSQpbMFnHFUCAAAAkkBRymIcDktBjd9QhPFSvrDtiti1yO5IAAAAQLpDUcqCmtxTWXO9mkuSLs7/lKNKAAAAwDUoSlmQ02HJp8GbijKeyh+yWVF7l9kdCQAAAEhXKEpZVPP7qmmuR2NJ0vk/PrU5DQAAAJC+UJSyKE+nQ1bdNxRtnMp/YZ2iD660OxIAAACQblCUsrCW99+jec5GkqSzczmqBAAAAMShKGVh3h5OxdR6XbHGoQJn/1Hs4XV2RwIAAADSBYpSFte6fm394agnSTo99xOb0wAAAADpA0Upi/P1ciq85utyGUsFTi2T69hmuyMBAAAAtqMoQW0a1dOfVh1J0unfOaoEAAAAUJSgAG8Pnb/rNbmNpfwnFsl9YpvdkQAAAABbUZQgSWrTtJEW6l5J0ql5n9mcBgAAALAXRQmSpCBfT52o+ookKe+RP2TO7LY5EQAAAGAfihLitWnWTItMDTlkdGouR5UAAACQdVGUEC84wFv/VXxZkpT70G8y5/bbnAgAAACwB0UJCbR5oKWWuqvLKbdOz+tvdxwAAADAFhQlJJAn0Ed7yr0oSQreP1O68J/NiQAAAIA7j6KERFq1fFAr3ZXkIZfOzB9gdxwAAADgjqMoIZGC2X31b6krR5Vy7J4uhRy1OREAAABwZ1GUkKQWrR7Wand5eShWZ/8cbHccAAAA4I6iKCFJRYP9tbHY85KkoJ2TpLCTNicCAAAA7hyKEq6rWatHtcFdWp4mRucXDrE7DgAAAHDHUJRwXaXyBmp1oeckSf7/TpDCz9icCAAAALgzKEq4oYatOmiLu4S8TaQuLhlqdxwAAADgjqAo4YYqFAzS8nxdJEk+m8dJl8/bGwgAAAC4AyhKuKn7W3XSDndR+bgjFLL0a7vjAAAAAGmOooSbql40pxbm7ixJ8l4/Woq4aG8gAAAAII1RlJAstVp10W53Ifm4Lyls+Qi74wAAAABpiqKEZKlZIpf+yPmUJMm59jspKszmRAAAAEDaoSgh2e5u8Yz2u/PLzxWq8L9H2h0HAAAASDMUJSTb/WXy6regjpIkx6pvpehLNicCAAAA0gZFCclmWZaqPPCs/nPnkV/sRV3+Z4zdkQAAAIA0QVFCijSsUEAzA56QJLlXfiXFRNicCAAAAEh9FCWkiGVZKtf8OR01uRQQc04Ra8bbHQkAAABIdRQlpFjzykU0w7e9JMm1fKgUG2VzIgAAACB1UZSQYg6HpeJNXtQJk1MB0acVvf4nuyMBAAAAqYqihFvSsnpR/ez9sCQpaukXkivG5kQAAABA6qEo4ZZ4OB0q0KibzpggZYs8oZiNk+2OBAAAAKQaihJuWdsapTTV40FJUsSSwZIr1uZEAAAAQOqgKOGWeXk4FNzgJZ0z2RQYcUSxW3+2OxIAAACQKihKuC0P31dWU51tJUmXFw2Q3C6bEwEAAAC3j6KE2+Lj6VRA3Rd10fgr8NIhubbNsjsSAAAAcNsoSrhtj9apoCmOVpKk8IUDJLfb5kQAAADA7aEo4bb5eXnIo3Z3hRpfBYXtlXvX73ZHAgAAAG4LRQmp4om6lTTVaiFJClvQXzLG5kQAAADAraMoIVVk8/FUbM2XdMl4K+jiDpk98+2OBAAAANwyihJSTYf61TRVzSVJoX9+zlElAAAAZFgUJaSaHP5eCr/7RUUYLwWd3yqzb7HdkQAAAIBbQlFCqnqyUQ1NM40lSaELOKoEAACAjImihFSVK8Bb56q+qCjjqaAzG6RDK+yOBAAAAKQYRQmprmOTe/Wzu6EkKeTPz21OAwAAAKQcRQmpLn+Qr45V6qZo41TQyVXSf6vsjgQAAACkCEUJaaJj09qa6a4v6X/XKgEAAAAZCEUJaaJwTj8dKPeCYo1DgceWS0fX2x0JAAAASDaKEtLME83qapb7fklS2ILPbE4DAAAAJB9FCWmmRO4A7Sz1glzGUrbDS6Tjm+2OBAAAACQLRQlp6rHm9TXHXVuSFL6wv81pAAAAgOShKCFNlcsXqM3FnpXbWAo4OF86uc3uSAAAAMBNUZSQ5to3b6J57pqSpEuLBticBgAAALg5ihLSXOVCQVpdqKskyXff79KZ3TYnAgAAAG6MooQ7ot0DzfSnq4YcMrq8eJDdcQAAAIAboijhjri7aE4tz9dFkuSza6Z0br+9gQAAAIAboCjhjmn1QAstcVWTQ25FLOGoEgAAANIvihLumFolgrUgV2dJkteOn6ULh+wNBAAAAFwHRQl3jGVZav5AGy13VZbTuBT51xC7IwEAAABJoijhjmpQJrfm5nhKkuT572Qp5KjNiQAAAIDEKEq4oyzLUqPmD2mVq4KcJlZRy4baHQkAAABIhKKEO65p+byaFdhRkuTcNEEKO2lzIgAAACAhihLuOIfD0v1NH9Z6dxl5mGhFLx9mdyQAAAAgAYoSbNGqSgH97NdBkmRtGC+Fn7E5EQAAAPD/KEqwhdNh6Z4mj2qzu4Q83ZGKXfmN3ZEAAACAeBQl2ObB6gU1xecJSZJZO1q6fN7mRAAAAMAVFCXYxtPpUJVGj2u7u6g8XRGK/edbuyMBAAAAkihKsFn7GoX1k9fjkiT36lFSxEV7AwEAAACiKMFm3h5OlW3whHa5C8srNlyu1aPsjgQAAABQlGC/J2oW0wSPRyRJrn++lSJDbU4EAACArI6iBNv5ejlVtN6T2u/OL6+YULnXjrE7EgAAALI4ihLShSdrldA455WjSjF/fy1FX7I5EQAAALIyihLShQBvD+Wr85QOufPKO/qC3OvG2h0JAAAAWRhFCelG5zqlNM5qJ0mKWf6VFBNhcyIAAABkVRQlpBtBvp4Kuu8pHTW55B11VmbDj3ZHAgAAQBZFUUK60qVuGY0xD0mSopd9KcVG2RsIAAAAWRJFCelKcIC3fO7ppBMmp7wjTslsmmh3JAAAAGRBFCWkO13rl9MYd1tJUtTSIZIrxuZEAAAAyGooSkh38gT6yNzVWWdMkHwuHZO2TLU7EgAAALIYihLSpWcbVtAYd2tJUtRfgyRXrM2JAAAAkJVQlJAuFczuq8tVntY5k03eYYelbb/YHQkAAABZSLouSrGxsXr//fdVvHhx+fr6qkSJEvr444/ldrvtjoY74LlGlTTW1UqSFLVkkOR22ZwIAAAAWUW6LkoDBw7UyJEj9e2332rnzp0aNGiQBg8erG+++cbuaLgDigb763yFp3XR+Ms7ZL+0fZbdkQAAAJBFpOuitGrVKj344INq1aqVihUrpvbt26tZs2Zav3693dFwhzzbuLLGuVpIkmL+/EiKvmxzIgAAAGQF6boo3X///Vq8eLH27NkjSdqyZYv+/vtvtWzZ8rr3iYqKUmhoaIIbMq7SebPpv7JddcwEyzP8qLTyK7sjAQAAIAtI10Wpd+/e6tChg8qVKydPT09Vr15dPXr0UIcOHa57n/79+ysoKCj+Vrhw4TuYGGnh1eZVNcDVSZLkWvGldP6gzYkAAACQ2aXrojRt2jRNnDhRkydP1saNG/Xjjz/qiy++0I8//njd+/Tp00chISHxtyNHjtzBxEgLpfIEqFCdJ7TSVVFOd7Rc89+1OxIAAAAyOcsYY+wOcT2FCxfWO++8o5dffjl+2qeffqqJEydq165dyVpGaGiogoKCFBISosDAwLSKijR2OTpWz38xST9EvSFPyyU9OUMq3cTuWAAAAMhAUtIN0vURpcuXL8vhSBjR6XQyPHgW5OfloU5tm+sHV3NJUvTvvaTYKJtTAQAAILNK10WpTZs2+uyzzzR37lwdOnRIs2bN0pdffql27drZHQ02aF4xrzYWf0FnTJC8Qg7KrBphdyQAAABkUun61LuwsDB98MEHmjVrlk6fPq0CBQqoQ4cO+vDDD+Xl5ZWsZXDqXeby37lLGj7sUw1yjlCs008er2+QAgvYHQsAAAAZQEq6QbouSqmBopT5DFu4S/ev6KQajj2KqfCIPB8bZ3ckAAAAZACZ5holICkvNiitkX7d5DaWPHfMkA6ttDsSAAAAMhmKEjIcH0+nnmzXVpNdjSRJUXPelFyxNqcCAABAZkJRQobUsGwebSz1si6YAHmf3yWzbozdkQAAAJCJUJSQYfV8qLa+Mk9IkmIWfyaFn7E5EQAAADILihIyrILZfZWvYTdtcxeTV0yoohd8ZHckAAAAZBIUJWRoXeuW0uiAlyRJXlsnSUc32JwIAAAAmQFFCRmal4dDTzzSXjNcdSVJl399Q3K7bU4FAACAjI6ihAyvdslc2limh8KMr/zObJF70092RwIAAEAGR1FCpvD6g/frOz0qSYr+s68UccHeQAAAAMjQKErIFPIE+ihX41e1111QPtHnFbnwU7sjAQAAIAOjKCHT6FynlMZm+9/ADhvHSSe32ZwIAAAAGRVFCZmGh9Oh9o8+qbmumnLIrbBZb0jG2B0LAAAAGRBFCZlKjWI5tbncW4owXsp2aq1cW3+2OxIAAAAyIIoSMp0XH6yvsdbDkqTIee9KUeE2JwIAAEBGQ1FCphMc4K2cTXvqP3ce+Ued0aVF/e2OBAAAgAyGooRM6fFapfVj9isDO3ivGymd3WtzIgAAAGQkFCVkSk6HpYce66olrmryUKwuznyTgR0AAACQbBQlZFpVCmXX5oq9FWU8lP34csXunGt3JAAAAGQQFCVkas+2baqJjraSpMtz3pJiImxOBAAAgIyAooRMLcjPUzmav6PjJqcCI48rbMkQuyMBAAAgA6AoIdN7qGYZTcneTZLkvfor6cJ/NicCAABAekdRQqbncFhq+fhLWuWuIC8TrbMzetkdCQAAAOkcRQlZQvkCQdpc6V3FGodyHV2g6N0L7Y4EAACAdIyihCzjqbYP6GdnS0nSpdk9pdhomxMBAAAgvaIoIcvI5uOpwBYf6IwJVI6I/3Rx6Td2RwIAAEA6RVFCltKyRlnNyPG8JMln5WAp9ITNiQAAAJAeUZSQpViWpSYdemiTu7R8TIRO/PK23ZEAAACQDlGUkOWUyhuoLVXek9tYyn94jqL2/W13JAAAAKQzFCVkSY+1baM5Hk0lSSEzX5dcsTYnAgAAQHpCUUKW5OfloWwt++mi8Veey/t0dtlIuyMBAAAgHaEoIctqdFd5zQnuKknyXdFfJvyMzYkAAACQXlCUkGVZlqX6Hd7WDlNM/iZch3/pY3ckAAAApBMUJWRpRXMHaluV9yRJhQ/9osuH1tmcCAAAAOkBRQlZXtu2j2iBs74cMrrw8+uS2213JAAAANiMooQsz8fTKf82nyvc+Kjgpe06sXyc3ZEAAABgM4oSIKlOtUqan+tpSZLv8k9kIi7YnAgAAAB2oigB/1Or43vabwoou/ui9v/8vt1xAAAAYCOKEvA/BYODtL3qlYEdih2YrLDDW21OBAAAALtQlICrPNC2o5Z71JKH3Do7/TXJGLsjAQAAwAYUJeAqXh4O+bceqEjjqeLhm3R4xUS7IwEAAMAGFCXgGndXq6oluZ6SJPkt7St3ZJjNiQAAAHCnUZSAJNTo+JGOmDzK5T6rnT/3tTsOAAAA7jCKEpCEPME5tKvau5KkMvvH6+LRnTYnAgAAwJ1EUQKuo2GbzlrncZc85dLJqT0Y2AEAACALoSgB1+Hh4ZRPm8GKNk6VC1+t/St/sTsSAAAA7hCKEnADlavW0IrcT0iS/Ja8r9ioyzYnAgAAwJ1AUQJuolrHT3RKOZXffVJbp39qdxwAAADcARQl4CaCcwZrT9XekqTy+77X2WN7bU4EAACAtEZRApKhdtsX9K9HZfla0To6tafdcQAAAJDGKEpAMjidDnm3GaxY41C1sGXa8fevdkcCAABAGqIoAclUpmotrc39sCTJf8l7iomOsjkRAAAA0gpFCUiBih0H6LwCVdR9ROunD7A7DgAAANIIRQlIgaCcubW/Si9JUuW93+nUsUP2BgIAAECaoCgBKXT3g69or0dZBVgROjS1l91xAAAAkAYoSkAKOZxOebT5Qm5j6d6whdq8cr7dkQAAAJDKKErALShetZ42524jSfJf/I6ioqNtTgQAAIDURFECblGZjoMUKn+Vdh/UP9OH2B0HAAAAqYiiBNyigJz5dahKD0lStb3f6tixo/YGAgAAQKqhKAG3ofKDb+g/j+LKYYVr75TedscBAABAKqEoAbfBcnrK2XqwJKle2FytXrnE5kQAAABIDRQl4DYVqtZU24Oby2EZ+S96RxFRMXZHAgAAwG2iKAGpoETHIbosH1U2u7V0+td2xwEAAMBtoigBqcA3uLCOVH5FklRj31c6ePS4zYkAAABwOyhKQCop8+DbOuFRSLmtEO2a+p6MMXZHAgAAwC2iKAGpxPLwlqPVIElS07DZWr5yhc2JAAAAcKsoSkAqylu9lfblbCAPyy2/xe8qPJKBHQAAADIiihKQygp3GKooeeke86/+/Hmk3XEAAABwCyhKQCrzzl1Cxyt1kyTV2jdUe46csjkRAAAAUoqiBKSB4g++p7Me+VTAOqd/p33IwA4AAAAZDEUJSAuevrIe6C9Jah32ixas+MfmQAAAAEgJihKQRoLvbqfDOe6TtxUr378+UEgEAzsAAABkFBQlIK1YlvI98bVi5KF6ZoN++3m83YkAAACQTBQlIA155S2r0xW6SpLu3/+Ftv/HwA4AAAAZAUUJSGMFH/xQIc5gFbNOadO0z+R2M7ADAABAekdRAtKadzap2SeSpIcvTdVvK9bZHAgAAAA3Q1EC7oCgmh11Mvtd8rOi5PPXhzp/KdruSAAAALgBihJwJ1iWcj06TC451FyrNOOXSXYnAgAAwA1QlIA7xKNgVZ0t95Qkqf7+wdp46LTNiQAAAHA9FCXgDsr74McKdwapjOOY1k8fpFiX2+5IAAAASAJFCbiTfHPINP5IkvTEpYn6ZdlGmwMBAAAgKRQl4A7Ldt8zOhtUSYFWhHyWfaLTYZF2RwIAAMA1KErAneZwKMcjwyRJD1lLNfmXX+zNAwAAgEQoSoANnEXu0fkyj0mSGh8crNX7GNgBAAAgPaEoATbJ2fZzRTgCVNlxSKtnDFUMAzsAAACkGxQlwC4BuWUa9pEkPX15giYv3WRzIAAAAMShKAE28qv9oi5mK60cVri8lvfXiZAIuyMBAABAFCXAXk4PBbYbKkl6TIs0/pdfbQ4EAAAAiaIE2M5Roq5CSraV0zJq+t8QLd/NwA4AAAB2oygB6UBQ2wGKdvjqHscerZg5QlGxLrsjAQAAZGkUJSA9CCooU7eXJOn5yPEav3irzYEAAACyNooSkE54131V4f5Flce6KM+Vg3Xk/GW7IwEAAGRZFCUgvfDwlv9DQyRJna35Gj1jns2BAAAAsi6KEpCOWKWbKrxYM3laLjU7PFQLt5+0OxIAAECWRFEC0pmAtoMUa3mprnObls4eq4hoBnYAAAC40yhKQHqTs7hM7dckSS9Fj9OoRdtsDgQAAJD1UJSAdMizfk9F+BVQIeusPFYN04Ez4XZHAgAAyFIoSkB65OUnn9YDJEnPO37TtzMXyRhjcygAAICsg6IEpFNW+baKKFxX3laMWhz9WvP+ZWAHAACAO4WiBKRXliXftkPkspxq6tygRXMmKjwq1u5UAAAAWQJFCUjPcpeVufclSdKr0WM0fOF2mwMBAABkDRQlIJ3zaNBbUT65VcJxUtbqEdp9MszuSAAAAJkeRQlI73wC5d3iM0nSK85ZGjrzLwZ2AAAASGMUJSAjqPKYogrUlJ8VpZYnRmjWpmN2JwIAAMjUKEpARmBZ8m4zRG451Na5Sn/+/rNCImLsTgUAAJBpUZSAjCJ/FZm7n5EkvRE7VsP+ZGAHAACAtEJRAjIQZ+P3FeOdQ+UcR6T1Y7XtWIjdkQAAADIlihKQkfjllGfTjyRJbzh/0eCZf8vtZmAHAACA1EZRAjKauzorJm9VBVqX1fLUaE1bf8TuRAAAAJkORQnIaBxOebYeIkl63GOp5v7xm85firY5FAAAQOZCUQIyosL3yF21oyTpLdcYDf5jh82BAAAAMheKEpBBOZr2U6xnNlV1HJB700RtPHzB7kgAAACZBkUJyKgC8sij0buSpLc9pmrgzNVyMbADAABAqqAoARlZzecVG1xOwVaYWpwdp4mr/7M7EQAAQKZAUQIyMqenPFoPliR1ci7UnAV/6nRYpM2hAAAAMj6KEpDRFa8nd4V2clpGb7vHqf/cnXYnAgAAyPAoSkAm4Gj+qVwevrrXsUuurT9r9YFzdkcCAADI0ChKQGYQVEjOer0kSe95TlL/2WsV43LbHAoAACDjoigBmUXtV+XKXlx5rYtqcX6ixq88aHciAACADIuiBGQWHt5ythwkSerq/EO/LlqqEyERNocCAADImChKQGZSpplM6ebyslzqbcbr09922J0IAAAgQ6IoAZmM1WKA3A4v1XP+q5gdv2n5njN2RwIAAMhwKEpAZpOzhBx1XpMkfej5k/rOXK+QiBibQwEAAGQsFCUgM6r7ptyBBVXIOqsHw6fr7V+2yBhjdyoAAIAMg6IEZEZe/nI0/1yS1N35q07v+FvjVh6yNxMAAEAGQlECMqsKD0oV28nTcukbr280fN46bTx8we5UAAAAGQJFCcisLEtq87VMjuIqZJ3VAOdIvTJxgy5cirY7GQAAQLpHUQIyM59AWY+Ol3F6qZlzg5pfmq03p2+W2831SgAAADdCUQIyuwLVZTX7VJLUx2Oyzu1ZrVHLD9gcCgAAIH2jKAFZQc0XpPJt5GW59K3n1xq1YKPWHDhndyoAAIB0i6IEZAWWJbX9ViZ7ERVxnNFnztF6dfJGnQ2PsjsZAABAukRRArIK3+yy2v8g4/BUK+daNYuYqx5TN8vF9UoAAACJUJSArKTQ3bKa9pMkfejxky7sX69vl+yzORQAAED6Q1ECspr7uktlWsjLitW3nl9pzOLNWrnvrN2pAAAA0hWKEpDVWJb00AgpsJCKO07pM4+xen3KRp0OjbQ7GQAAQLpBUQKyIr+cUvtxMpZTbZ2r1DRyvl6dskmxLrfdyQAAANIFihKQVRW5V1bjDyVJH3lMUMihzRq6aI/NoQAAANIHihKQldV+TSrVVD5WjIZ7fqXxf23XX7tP250KAADAdhQlICtzOKR2o6RsBVTScUKfeI7TG1M36fjFCLuTAQAA2IqiBGR1/sFS+7EylkOPOP9W0+hFemXyRsVwvRIAAMjCKEoApKK1ZTV8V5L0secPCjuyTYPm77I5FAAAgH0oSgCuuL+nVKKhfBWt4Z5faeKKnVqw/aTdqQAAAGxBUQJwhcMhPTxaCsirMo5j6usxQT1/3qIj5y/bnQwAAOCOoygB+H8BeaRHxshYDj3usVSNo5fq5ckbFRXrsjsZAADAHUVRApBQ8Xqy6veWJH3uNVaXju3Q53N32hwKAADgzqIoAUis3ltSsbryU5S+9fxaU1ft1e9bj9udCgAA4I6hKAFIzOGUHhkj+edWeccRfeQxQe/M+FcHz16yOxkAAMAdQVECkLRs+aSHR8vIUkePJWoYs1zdJ21UZAzXKwEAgMyPogTg+ko2klW3pyRpgNcYRZzcrX6/bbc5FAAAQNqjKAG4sQZ9pCK15a9Ifev5jWau3a+ZG4/anQoAACBNUZQA3JjTQ2o/VvILViXHIb3rMUnvzdqmvafC7E4GAACQZihKAG4usIDUbpQk6WmPhWrg+kfdJ23U5ehYm4MBAACkjXRflI4dO6annnpKwcHB8vPzU7Vq1bRhwwa7YwFZT+mmUp3XJUmDvb5X5Jn9en/WNhljbA4GAACQ+tJ1Ubpw4YLq1KkjT09P/fHHH9qxY4eGDBmi7Nmz2x0NyJoafSAVvlcBuqzhnt/o902HNH39EbtTAQAApDoPuwPcyMCBA1W4cGGNHz8+flqxYsXsCwRkdU5P6ZGx0sj7VSXygHp7TNWHv3qrSqHsKp8/0O50AAAAqSZdH1GaM2eOatSooUcffVR58uRR9erV9f3339/wPlFRUQoNDU1wA5CKsheW2o2UJD3r8Yfqudeq+6SNCouMsTkYAABA6knXRenAgQP67rvvVLp0af3555968cUX9dprr2nChAnXvU///v0VFBQUfytcuPAdTAxkEWVbSLVekSQN8Rql6LOH1Gfmv1yvBAAAMg3LpONPNl5eXqpRo4b++eef+Gmvvfaa1q1bp1WrViV5n6ioKEVFRcX/HBoaqsKFCyskJESBgZwaBKSa2Ghp/APSsQ3a5C6tR6M/0EcPVlWnWsXsTgYAAJCk0NBQBQUFJasbpOsjSvnz51eFChUSTCtfvrwOHz583ft4e3srMDAwwQ1AGvDwktqPk7yDVN2xV295TNMnv+/Uv0dD7E4GAABw29J1UapTp452796dYNqePXtUtGhRmxIBSCBHMemh4ZKkbh5zVcdsUPfJGxQSwfVKAAAgY0vXRemNN97Q6tWr9fnnn2vfvn2aPHmyRo8erZdfftnuaADilG8j1ewmSRrm9Z1izx/VWz9v4XolAACQoaXronTPPfdo1qxZmjJliipVqqRPPvlEw4YN05NPPml3NABXa/aJlL+aghSub72+1eIdxzX274N2pwIAALhl6Xowh9SQkgu2ANyG8wekUfWlqFANj22roe4Omv5iLd1VJIfdyQAAACRlosEcAGQgOUtIbb6SJL3sMUe1tVmvTNqoC5eibQ4GAACQchQlAKmn0sNSja6SpK+8Ryo25ITenL5ZbnemPnANAAAyIYoSgNTVvL+Ut7JymBB94/2tlu8+qZHL99udCgAAIEUoSgBSl6eP9OgPkleA7rV26jWPmfriz91ac+Cc3ckAAACSjaIEIPXlKiW1HiZJetVjtu6ztunVKZt0NjzK3lwAAADJRFECkDaqPCrd1VkOGX3rPUIm7JR6TN0sF9crAQCADICiBCDtPDBQylNBOc1Ffe09Qv/sO61vluy1OxUAAMBNUZQApB0vvyvXK3n6qZa1TS87Z+urxXv1996zdicDAAC4IY+U3iEkJESzZs3SihUrdOjQIV2+fFm5c+dW9erV1bx5c9WuXTstcgLIqHKXlVoNkWa/pDc8Z2ptVHm9PtVL816vq7yBPnanAwAASFKyjyidOHFCzz//vPLnz6+PP/5Yly5dUrVq1dS4cWMVKlRIf/31l5o2baoKFSpo2rRpaZkZQEZTraNUtaMccmuEz3Dp0hm9OmWTYl1uu5MBAAAkKdlHlKpWrarOnTtr7dq1qlSpUpLzREREaPbs2fryyy915MgR9erVK9WCAsjgWn0hHdug4LO79ZX3SHU6+Ja+XLhHbz9Qzu5kAAAAiVjGmGQNQXXmzBnlzp072QtO6fxpJTQ0VEFBQQoJCVFgYKDdcYCs7dQO6ftGUmyEBsY8oe9cbTW+yz1qWC6P3ckAAEAWkJJukOxT75JbeuJ6V3ooSQDSmbwVpJaDJEm9PH9WDWuX3pi+WccuRtgcDAAAIKFbGvWuU6dOCg8PTzT90KFDqlev3m2HApCJVe8kVX5MTrn0ne8I6fJ5vTJ5o6JjuV4JAACkH7dUlHbs2KHKlStr5cqV8dN+/PFHVa1aVXnz5k21cAAyIcuSWn8pBZdSbvdZfeUzSpsPn9eg+bvsTgYAABDvlorSmjVr9Pjjj6tRo0Z699139eijj+qVV17R0KFD9csvv6R2RgCZjXe2K9+v5PRWfW3Uc855GvP3Qc3fdtLuZAAAAJJu4XuUJMnDw0MDBgyQt7e3PvnkE3l4eGjZsmWqVatWaucDkFnlqyw90F+a+6be8Zqm9ZFl9dYvHqqQP1BFgv3sTgcAALK4WzqiFBMTo549e2rgwIHq06ePatWqpXbt2mnevHmpnQ9AZlajq1SxnZzGpVG+w2VFXtTLkzcqKtZldzIAAJDF3VJRqlGjhubMmaOlS5fqs88+09KlS/XGG2/o4YcfVvfu3VM7I4DMyrKkNl9LOYorj/u0vvL5Xv8eu6jP5u60OxkAAMjibrkobd68Wffdd58kybIs9e7dW6tXr9by5ctTNSCATM4n8H/XK3mpodbpGed8TVj1n37bctzuZAAAIAtL9hfOJldUVJS8vb1Tc5G3hS+cBTKINaOkP96Wy/JQu8iPtN+zjH579X6VyB1gdzIAAJBJpMkXzl66dClZ88WVpOTODwCSpJovSOXbyGli9b3fcDmjQ9V90kZFxnC9EgAAuPOSXZRKlSqlzz//XMePX/90GGOMFi5cqBYtWujrr79OlYAAsgjLktp+K2Uvoryuk/rSd6x2nQxV3znb7U4GAACyoGQPD7506VK9//776tevn6pVq6YaNWqoQIEC8vHx0YULF7Rjxw6tWrVKnp6e6tOnj1544YW0zA0gM/LNLrX/QRrXXE3cq9XJWV4/rWuqe4rl1CN3F7I7HQAAyEJSfI3S0aNHNX36dK1YsUKHDh1SRESEcuXKperVq6t58+Zq2bKlHI5bGiMiTXCNEpABrRou/fmuYi1PPRjZTwc8SmrOK3VUOm82u5MBAIAMLCXdINUHc0hvKEpABmSMNLWjtHueTnoUUJPwj5UvTx7NeaWO/Lxu6XuyAQAA0r4ovfnmm0kvzLLk4+OjUqVK6cEHH1TOnDlTuuhUR1ECMqjL56WRdaXQo1rgqKMXLnfXw9ULachjVWVZlt3pAABABpTmRalhw4bauHGjXC6XypYtK2OM9u7dK6fTqXLlymn37t2yLEt///23KlSocMtPJDVQlIAM7PAaaXwLybj0XuxzmhTbSAMerqwnahaxOxkAAMiA0mR48Ks9+OCDatKkiY4fP64NGzZo48aNOnbsmJo2baoOHTro2LFjqlevnt54441begIAIEkqcq/U+ENJUj+vn1TOOqwP52zXjuOhNgcDAACZ3S0dUSpYsKAWLlyY6GjR9u3b1axZMx07dkwbN25Us2bNdPbs2VQLeys4ogRkcG63NPkxad9CnfAorMbh/ZQ3V7DmvFJH2Xw87U4HAAAykDQ/ohQSEqLTp08nmn7mzBmFhl75n97s2bMrOjr6VhYPAP/P4ZDajZKyFVD+2CMa4vejDp4N1zsz/lUmH4sGAADY6JZPvevatatmzZqlo0eP6tixY5o1a5aeffZZPfTQQ5KktWvXqkyZMqmZFUBW5R8stR8rWQ61cC/T4x7LNfffE/pp9X92JwMAAJnULZ16Fx4erjfeeEMTJkxQbGysJMnDw0NPP/20hg4dKn9/f23evFmSVK1atdTMm2KcegdkIssHS0s+VazDRy0iPtYhR2HNeKm2qhTKbncyAACQAdyx71EKDw/XgQMHZIxRyZIlFRAQcKuLSjMUJSATcbuliQ9LB/7Scc+iahz2kYJz5NDcV+sqyI/rlQAAwI2l+TVKcQICAlSlShVVrVo1XZYkAJmMwyE9PFoKyKsCMf9psP8kHb0QoV6/bOF6JQAAkKpuqygBwB0XkEd6ZIxkOdTatVjtPVZq4Y5TGvv3QbuTAQCATISiBCDjKV5Pqt9bktTfe5xKWsc04I9d2vDfBZuDAQCAzIKiBCBjqveWVKyuPF0RmpDtOzndUXpl8kadv8TXEgAAgNtHUQKQMTmcV07B88+tgtEH9EXAFJ0IidSb0zfL7eZ6JQAAcHsoSgAyrmz5rgzuIEttYv9UO8/VWrr7jL5btt/uZAAAIIOjKAHI2Eo2kur2lCQN8hqjYtYJDVmwW6sPnLM5GAAAyMgoSgAyvgZ9pCK15em6rElBI+VpovXalE06ExZldzIAAJBBUZQAZHxOD6n9WMkvWAUj92pQ4HSdDotSj2mb5OJ6JQAAcAsoSgAyh8ACUrvRkqQHo+fpIa91WrnvnL5evNfmYAAAICOiKAHIPEo3ker0kCQN9vpeha1T+nrJXq3Ye8beXAAAIMOhKAHIXBq9LxW+V56x4ZqSfZQ8TYx6TN2sU6GRdicDAAAZCEUJQObi9JQeGSv5ZFehiF0aFDRD5y5F69XJmxTrctudDgAAZBAUJQCZT/bCUruRkqSHouaorfdGrT10XkMW7rE5GAAAyCgoSgAyp7ItpFqvSJKGeI1WQZ3Rd0v3a8muUzYHAwAAGQFFCUDm1fgjqeDd8owJ1dTg0fJQrN6cvkXHLkbYnQwAAKRzFCUAmZeHl9R+vOQdpMKXtmtwjtm6eDlGr0zeqOhYrlcCAADXR1ECkLnlKCo9NFyS1C5iplr5bNWmwxc1cP4um4MBAID0jKIEIPMr30aq2U2SNNRrpPLrnMb+fVDzt520ORgAAEivKEoAsoZmn0j5q8kr+qKm5/peTrn01i9bdPjcZbuTAQCAdIiiBCBr8PCWHh0veQeqcPhWDcr5u8IiY9V98gZFxrjsTgcAANIZihKArCNnCant15KkRy5PU0vf7dp2LFSfzd1pczAAAJDeUJQAZC0V20k1npUkDfP6Tnl0QT+t/k+/bTluczAAAJCeUJQAZD3NP5fyVpZX1HnNyDNWTrn0zoytOnAm3O5kAAAgnaAoAch6PH2kR3+QvAJUOHSjBuX6Q5eiXeo+aSPXKwEAAEkUJQBZVa5SUuthkqSHw6eohd9u7ToZpo9+3W5vLgAAkC5QlABkXVUele7qLEtGX3kNVx7roqatP6IZG47anQwAANiMogQga3tgoJSngrwiz+qXvD/IIbfen71Ne06F2Z0MAADYiKIEIGvz8rtyvZKnn4pcXKtBeRYoIsallyZuUMjlGLvTAQAAm1CUACB3WanVEEnSI2ET1SJgv/afuaQuP6zVpahYm8MBAAA7UJQAQJKqdZSqdpRl3Pra6xsV97msTYcv6vkJ6xkJDwCALIiiBABxWn0h5Sorz8un9WvBCQr0kv7Zf06vTN6oGJfb7nQAAOAOoigBQBwv/yvXK3n4KvDYci0qPUM+HtKinafVc/oWudzG7oQAAOAOoSgBwNXyVpAe+V6ynMqzf4YWVpgvD4c0Z8txvT/7XxlDWQIAICugKAHAtcq3kR4aIUkqvGeC/qi8Qg5LmrL2iD6bu5OyBABAFkBRAoCkVH1CavmFJKn07u80q+p6SdKYvw/q68X77EwGAADuAIoSAFxPzeelJn0lSVV3falJ1XZIkoYu2qMxKw7YGAwAAKQ1ihIA3Mj9b0h1e0qS6uz6TKOq7pckfTp3p6auPWxnMgAAkIYoSgBwM40+kGq+IMmo2Z6++qLyUUlSn1n/6rctx+3NBgAA0gRFCQBuxrKkBwb+7wtpXXrkwPv6sMIpGSO9MW2zFu88ZXdCAACQyihKAJAcDofU9hupfBtZrmg9c+Q99ShzXrFuo5cmbdQ/+8/anRAAAKQiihIAJJfTQ3pkrFSysayYy3r91LvqWjJU0bFuPffjem08fMHuhAAAIJVQlAAgJTy8pccnSkVqy4oK1QcX3tOjRS/rcrRLXcat1c4ToXYnBAAAqYCiBAAp5eUndZwq5a8m6/I5Dbz8gR4oFK3QyFh1GrtGB86E250QAADcJooSANwKnyDpqZlS7nJyhJ3Q8Ni+uj9vjM6GR+upMWt07GKE3QkBAMBtoCgBwK3yD5Y6zZZyFJPz4iH94PG5qge7dDwkUk9+v1qnwyLtTggAAG4RRQkAbkdgfqnzr1K2AvI4t1vT/AerTHajQ+cuq/PYtbp4OdruhAAA4BZQlADgduUodqUs+QXL6/RWzcn5tQoHSLtOhunp8esUHhVrd0IAAJBCFCUASA25y0idZkneQfI5vkbz849SHl9py5GLeu7HdYqMcdmdEAAApABFCQBSS/6q0pPTJU8/+R9ZpoVFf1KQt6XVB86r+6SNio51250QAAAkE0UJAFJTkfukJyZJTi8FHfpDi0r9Il9Pacmu03pj+ma53MbuhAAAIBkoSgCQ2ko2ktqPlyyncu+foYXl58vTKc3dekJ9Zm6Vm7IEAEC6R1ECgLRQvrX00HeSLBXaM0HzKy+Xw5Kmrz+qT+bukDGUJQAA0jOKEgCklaqPS62GSJJK7hqpX6utkySNX3lIQxfttTMZAAC4CYoSAKSle56Vmn4sSaq8c6imVd8mSfp68V6NXr7fzmQAAOAGKEoAkNbqvC7V7SVJunfn5xpTbZ8k6fN5uzR5zWE7kwEAgOugKAHAndDofalmN0lSk9399GWVI5Kk92b/q183H7MzGQAASAJFCQDuBMuSHhggVXtSMi612/+B+lY4KWOkN6dv0YLtJ+1OCAAArkJRAoA7xeGQ2nwtVXhQlitaTx9+T2+WPS+X2+iVyZv0996zdicEAAD/Q1ECgDvJ6SE9PEYq1URWbIRePdlHz5UKUbTLrecnrNeG/87bnRAAAIiiBAB3noeX9NhPUtE6sqLC9N759/V4scuKiHGpy/h12n48xO6EAABkeRQlALCDl5/UYapUoLqsy+fUP/x9tSoUpbDIWHUeu1b7TofbnRAAgCyNogQAdvEJlJ6aKeUuL0f4SX0T01f180Xr3KVoPTVmjY6cv2x3QgAAsiyKEgDYyS+n1Hm2lKO4HCH/aZzzc92dy62ToZF6cswanQqNtDshAABZEkUJAOyWLZ/U+VcpWwE5z+3RVL9BKp/D6PD5y3pqzBqdvxRtd0IAALIcihIApAc5il4pS3655Hl6q2ZnH6ai2Yz2ng7X0+PWKiwyxu6EAABkKRQlAEgvcpeROs2SvIPkfWKd/sg3Wnn9LP17LETP/rBeEdEuuxMCAJBlUJQAID3JX0V66hfJ019+R5ZpQZEJyu5jae2h8+o2cYOiYilLAADcCRQlAEhvCteUOkyWnF4KOvSHFpX8WX6elpbvOaMeUzcr1uW2OyEAAJkeRQkA0qMSDaRHf5Qsp3Ltn6lF5efKy2npj20n1XvGv3K7jd0JAQDI1ChKAJBelWsptRslyVKBPRM1v/JSOR2WZmw8qn6/bZcxlCUAANIKRQkA0rMqj0qth0qSSuwapTlV18iypB9X/acvFuy2ORwAAJkXRQkA0rsaz0hNP5EkVdw5TNOqbZMkDf9rv0Ys3WdnMgAAMi2KEgBkBHVek+q9LUmqufNzja+2V5I0aP5u/bTqkI3BAADInChKAJBRNHxXuvelK//c3U9fVT0iSfrg1+2aseGonckAAMh0KEoAkFFYltT8c6n6U5Jxq+3e9/VJpZOSpLd+2aL5207YHBAAgMyDogQAGYnDIbX5WqrwkCx3jJ469J7eKndebiO9OmWTlu05Y3dCAAAyBYoSAGQ0Dqf08PdS6WayYiPU/cS7erF0qGJcRt1+Wq91h87bnRAAgAyPogQAGZGHl/TYBKno/bKiQtX77LvqWPyyImPc6jp+nf49GmJ3QgAAMjSKEgBkVJ6+UsepUsG7ZUWc12dh76tN4SiFRcWq87g12nsqzO6EAABkWBQlAMjIvLNJT/4i5akgK/ykvor+SA3zx+jC5Rg9OWaNDp+7bHdCAAAyJIoSAGR0fjmlTrOknCXkCDmsMdanqpnbrdNhUeo4ZrVOhkTanRAAgAyHogQAmUG2fFLnX6XAgnKe36tJvgNVKadbRy9E6Mkxq3UuPMruhAAAZCgUJQDILLIXkTrPkfxzy/P0v5oR9JWKB0r7z1xS53FrFRIRY3dCAAAyDIoSAGQmuUpdOQ3PJ0jeJ9Zpbt6Ryu9vafvxUHX9YZ0uR8fanRAAgAyBogQAmU2+ytKTMyRPf/kdWa4/C/+gHD6WNvx3QS9M2KDIGJfdCQEASPcoSgCQGRW+R+owRXJ6K/DQn1pUcrr8vSz9ve+sXp2ySTEut90JAQBI1yhKAJBZlagvPfaj5PBQ8P5ZWlzud3l5WFq445Te+nmL3G5jd0IAANItihIAZGZlW0jtRkmylG/PJC2ouEQeDmn25uP64NdtMoayBABAUihKAJDZVW4vtRkmSSq2+3v9Vm2tLEuatOawBvyxi7IEAEASKEoAkBXc3UVq9pkkqfyOYfq5+lZJ0qjlBzT8r302BgMAIH2iKAFAVlH7Fan+O5KkGjsG6Me79kqSvliwR+NXHrQzGQAA6Q5FCQCykgbvSPe9LEmqv7Ofvql6RJLU77cdmr7+iJ3JAABIVyhKAJCVWJbU/DPprs6Scav13vf1eeWTkqR3ZmzV3K0nbA4IAED6QFECgKzGsqTWw6SKD8tyx6jDwffUp8I5uY3UY9om/bX7tN0JAQCwHUUJALIih1N6eLRU5gFZsRF64ei76l4mVDEuoxd/2qDVB87ZnRAAAFtRlAAgq3J6So/+IBWrKys6TG+d6aPOJS4pKtat535cry1HLtqdEAAA21CUACAr8/SVOkyRCt4tK+KC+oW8r4eKRCo8KlZPj1+r3SfD7E4IAIAtKEoAkNV5Z5Oe/EXKU1HWpVP6MuojNS4Qq4uXY/TU2DU6dPaS3QkBALjjKEoAAMkvp9RplpSzpBwhRzRan+jePC6dCYvSk2PW6PjFCLsTAgBwR1GUAABXZMsrdf5VCiwk5/m9mug9UJWCjY5djNBTY9bobHiU3QkBALhjKEoAgP+XvbD09BzJP488z2zTjMChKhUkHTh7SZ3GrlXI5Ri7EwIAcEdQlAAACQWXvHIank92eZ9Yr99yj1ABf0s7T4Sqyw9rdSkq1u6EAACkOYoSACCxfJWkp2ZIXgHyPfq3/iw8XsE+ljYdvqjnJ6xXZIzL7oQAAKQpihIAIGmFakgdpkoePsp2aIEWlpyqAC9L/+w/p1cmb1SMy213QgAA0gxFCQBwfcXrSo9NkBweyrn/Vy0qO0feHpYW7TytntO3yOU2dicEACBNUJQAADdWprn08GhJlvLtnaKFlRbLwyHN2XJc78/+V8ZQlgAAmQ9FCQBwc5Uekdp+LUkqsmuM5lZbK4clTVl7RJ/N3UlZAgBkOhQlAEDy3NVZat5fklR2xzDNqL5VkjTm74P6evE+O5MBAJDqKEoAgOSr1V1q8K4kqfqOAfrprj2SpKGL9mjMigN2JgMAIFVRlAAAKVP/banWK5Kkujs/1nfVDkuSPp27U1PXHrYzGQAAqYaiBABIGcuSmn0q3fW0ZNx6YM8HGlTlpCSpz6x/NXH1fzYHBADg9lGUAAApZ1lS66FSpfay3DF69MC7er/SORkjvT97mz7+bQdDhwMAMjSKEgDg1jicUruRUpkWsmIj9ezhPhp0X7QkadzKg3phwnqFR8XaHBIAgFtDUQIA3Dqnp/ToD1LxerKiw/XYrh76qYWnvD0cWrzrtNp/94+OX4ywOyUAAClGUQIA3B5PH+mJKVKhe6TIi6q7orP+bHpOuQK8tetkmB4cvlJbjly0OyUAAClCUQIA3D7vAOmpmVLpZlJshIr99bKW1FitcnkDdCYsSo+PXqV5/56wOyUAAMmWoYpS//79ZVmWevToYXcUAMC1fAKlDlPjhw4PXD1YvxX4Qc3LBCkyxq3ukzZq+F/7ZAyDPAAA0r8MU5TWrVun0aNHq0qVKnZHAQBcj8MpNf9MavO15PCQ586ZGhn7gV6tGSBJGvznbvX6eauiYl02BwUA4MYyRFEKDw/Xk08+qe+//145cuSwOw4A4GbuflrqNFvyzSHr+Eb1PPiivm3klNNhacbGo+o0Zq3OX4q2OyUAANeVIYrSyy+/rFatWqlJkyY3nTcqKkqhoaEJbgAAGxSvKz23WMpVRgo7rtbrntGcxueVzdtDaw+dV7sRK7X/TLjdKQEASFK6L0pTp07Vxo0b1b9//2TN379/fwUFBcXfChcunMYJAQDXFVxSem6RVLKxFHNZFVe8rL/uXa/COXz037nLajd8pf7Zd9bulAAAJJKui9KRI0f0+uuva+LEifLx8UnWffr06aOQkJD425EjR9I4JQDghnyCpI7TpXtflCTlWjtIi4pN0n1F/BUaGavO49Zq6trDNocEACAhy6Tj4Ydmz56tdu3ayel0xk9zuVyyLEsOh0NRUVEJfpeU0NBQBQUFKSQkRIGBgWkdGQBwI+vGSvPekoxL7oI19KHvu5q4LVKS9Hzd4nqnRXk5HZbNIQEAmVVKukG6LkphYWH677//Ekx75plnVK5cOfXu3VuVKlW66TIoSgCQzhxYJk3vLEVelAkspMklBum91Vd+1aR8Xn31RDX5e3vYmxEAkCmlpBuk61PvsmXLpkqVKiW4+fv7Kzg4OFklCQCQDpWof2WQh+BSskKP6sntz2t6/fPy8nBo0c5TenTkKp0IibA7JQAgi0vXRQkAkEnlKnVlkIfi9aWYS6q55lUtuXeTcvl7aseJUD00fKX+PRpid0oAQBaWrk+9Sw2cegcA6ZgrRpr/jrRujCTpUvnH9Nixx7X9dJR8PZ0a+ng1PVApn80hAQCZRaY59Q4AkMk5PaVWQ6SWX0iWU/47p+vXwEFqXdJDETEuvTRpg0Yu269M/n96AIB0iKIEALBfzeelJ3+WvIPkcXSNvgnvqbeqxcoYacAfu9R7xlZFx7rtTgkAyEIoSgCA9KFU4yvXLeUsISvkiF4+0F1j7jsrhyVNX39Uncet0cXL0XanBABkERQlAED6kbvMlRHxitWVosPVZPPrWnDvVgV4O7X6wHm1G/GPDp69ZHdKAEAWQFECAKQvfjmlTrOku56WZFRq8wD9XX6migZ56ODZS3po+Eqt2n/O7pQAgEyOogQASH+cnlKbr6QHBkiWQ9l3TdPC3ENVt6ClkIgYdR63RtPXH7E7JQAgE6MoAQDSJ8uS7ntJ6jhd8g6U19FV+tHVR8+VjVKMy+jtX7ZqwB+75HYzIh4AIPVRlAAA6VvpptKzC6UcxeS4eEjvnXxdX1Y/LUkauWy/Xpq0QZejY20OCQDIbChKAID0L0856bklUpHasqJC9fCuNzWnxlZ5OS39uf2UHh+1WqdCI+1OCQDIRChKAICMwT9Y6vyrVP0pybhVZdsA/V1xjvL4OfTvsRA9+O1KbTsWYndKAEAmQVECAGQcHl5S22+lZp9JspRnzxQtL/CN7srl1snQSD02apUW7jhld0oAQCZAUQIAZCyWJdV+ReowVfIKkM/RlfrZ8wM9VuyyLke79MJP6/X98gMyhkEeAAC3jqIEAMiYyj4gPbtACioi54WDGnjhTX1Y4ZSMkT6bt1PvzvpXMS633SkBABkURQkAkHHlrSg9v0QqfJ+sqFA9c7CXJlfdKsuSpqw9oi7j1yrkcozdKQEAGRBFCQCQsQXklp6eI1XtIMu4VHv3AK2o+LsCvYxW7jundt+t1H/nLtmdEgCQwVCUAAAZn4e39NB3UpN+kiwV2jdZ/xQeqTKBsTpw5pIeGr5Saw+etzslACADoSgBADIHy5Lu7yE9MUny9FfAsRWa599PD+QP14XLMXpyzGrN2HDU7pQAgAyCogQAyFzKtZKe/VMKLCSPC/v1XcTb6lHyuGJcRj1/3qIv/twtt5sR8QAAN0ZRAgBkPvkqXxnkodA9siIv6vXjvTW6whZJ0rd/7dMrUzYqItplc0gAQHpGUQIAZE7Z8kpP/y5VfkyWcanZgYFaWH6efJxuzfv3pJ4YvUqnwyLtTgkASKcoSgCAzMvTR3p4tNToA0lS6YMTtbroaBX2jdaWoyF66NuV2nki1OaQAID0iKIEAMjcLEuq10t67CfJ00/Zjy/X4uyf6v6coToeEqn23/2jJbtO2Z0SAJDOUJQAAFlDhbbSM39I2QrI68I+TXD3UddCx3Qp2qXnflyvcX8flDEM8gAAuIKiBADIOgpUk174SypwlxyRF/TB+T4aUmqL3Eb6+Pcd+uDXbYpxue1OCQBIByhKAICsJVs+6Zl5UsWHZblj9cjRgfqt9Fw5Lbcmrj6srj+sU0hEjN0pAQA2oygBALIeT1+p/TipwbuSpMpHJml10e+VyzNKK/ae1SPf/aPD5y7bHBIAYCeKEgAga7IsqUFvqf14ycNHuU8u09+5+qt6thDtOx2uh0as1PpD5+1OCQCwCUUJAJC1VXr4yql4Afnkc2GPZni8r8fzHNb5S9Hq+P0azd50zO6EAAAbUJQAACh495VBHvJXlSPinAaEf6C+hTcp2uVWj2mb9eXCPYyIBwBZDEUJAABJCiwgPTNfqvCgLHeMupwZrKnF58kht75evFevTtmkyBiX3SkBAHcIRQkAgDheflL7H6R6b0uS7jsxUSuKjFGQI1K/bz2hDt+v1pmwKHszAgDuCIoSAABXczikRu9Jj4yVnN4qeHqp/skzUOV8LmrT4Yt6aPhK7T4ZZndKAEAaoygBAJCUyu2vDPLgn0f+F3frd98P1Sr7YR27GKFHvvtHS3eftjshACANUZQAALieQjWuDPKQt7I8Is7q25gP1SvvRoVHxarrD+v04z+H7E4IAEgjFCUAAG4kqJDUdb5UrrUsV7ReCflCY/+vvfuMsqow9Db+nDIFBmYE6UUUxYIKKIiKsXeNHQVLLJHY9WpijFGvJbmJb/RGE40l90bRq2JBRcGggoAMXelIUYShOXQYZmCYevb74agcx8YgzJ7y/Nbig2f2sP6stReeh31m7w7DCYIE9w2dy31vf0JFZSLslZKkncxQkiTpx2Q0gYtegKN/A8CJ615kVIdnaEwJz09aytXPT6WopDzkkZKknclQkiRpe0SjcOK9cN4/IZZO53VjmNzmYfZM28DYz9ZywVMTWb6hOOyVkqSdxFCSJKk6uveHK96BrJZkF8xnZJMHOD5rKZ+t3sx5T05g+rKNYS+UJO0EhpIkSdW1x+Hwq9HQ6kDStq7lWe7n+t1nsG5zGf3/ZzJDZ+WHvVCS9BMZSpIk7Yjd9oCr34d9TydSWcrvtjzM422GU15RwS0vz+DvHywkCIKwV0qSdpChJEnSjspoCv1fgj63AHBWwYsMb/sMjSjh0Q8+47ZXZ1JSXhnySEnSjjCUJEn6KaIxOOWPcM6TEE3jgI1jmNDqYdpHN/LWzHwu/dcU1m8uDXulJKmaDCVJknaGQy6FK4ZB491pXjifMTkPcHjmUqYt3ci5T05g4eqisBdKkqrBUJIkaWfpdGTyJg8tDyB96xpejj3AFdnTWb5hK+c/OZFxC9eGvVCStJ0MJUmSdqZme8LVI6DLKUQrS3ig7L/5S4vhFJWWc+XAj3lx8tKwF0qStoOhJEnSzpaZDRe/AkfcCEC/zS8ypNUzxBOl3PPWJ/xh2DwqE94RT5JqM0NJkqRdIRqD0/4MZz0G0TiHFI4mt+XDtGQjz07I41f/N5XNpRVhr5QkfQ9DSZKkXannFfCLt6BRM1oXzSV3tz9wSHwpoxesoe9TE/miYGvYCyVJ38FQkiRpV9vraBgwClrsS6OS1byR8QAXNZ7OglVFnPOPCcxcXhD2QklSFYaSJEk1Yfe9YcAHsPeJRCtLeCjx39y/23DWbS6h3z8n8ca0FQSBP7ckSbWFoSRJUk3JzIFLXoPDrwPgypIXeXn3Z6GihN8MnsV1L05jnQ+nlaRawVCSJKkmxeJw+l/gzEcgEuPILaMY2/KvtIsV8P7c1ZzyaC7vzlkZ9kpJavAMJUmSwnDY1fCLNyFzN9oUzSG3yd1c13waG7aUcv1L0/mPV2ZQUFwW9kpJarAMJUmSwtL5uORNHlofTLx0I3cW/5UP2j5N28gG3p6ZzymP5jJmwZqwV0pSg2QoSZIUphb7wDVj4Ph7IJrGPhvHMb7pXdy42yTWFJVw1XMf87vXZ1NUUh72UklqUCJBPb/FTmFhITk5OWzatIns7Oyw50iS9P1Wz4O3b4T86QAszu7N5WsvZUXQkva7NeKhvt04ap8WIY+UpLqrOm3gFSVJkmqL1l3h6pFw8h8hnknnwo8Ym3UXtzT9kPyCLVz6rync9/YnFJdVhL1Ukuo9ryhJklQbrfscht4EyyYBkJfVgys3XM7SoA177t6Yv17UnZ6dmoc8UpLqFq8oSZJU17XYB64cDqc/DGlZ7LVlJqMb38VtWSNYtn4zfZ+exIPD51NSXhn2UkmqlwwlSZJqq2gUDr8GbpgIex1DrLKE/6h8jjHN/szerOCfuYs56/HxzF5REPZSSap3DCVJkmq7ZnvC5UPhrL9DelM6bZ3HiEZ389vG75C3poDznpzIIyM/o6wiEfZSSao3DCVJkuqCSAR6Xgk3ToZ9TiaaKOfGxCDG5PyR/YI8Hhu1kHOfmMCCVYVhL5WkesFQkiSpLsnpAJcOhvP+CZm70bF0Ie9k3stdmW/w+cr1nPX4eJ788HMqKr26JEk/haEkSVJdE4lA9/5w40ew/8+JBhVcwxuMaXofXRMLeei9T+n79CQWrd0c9lJJqrMMJUmS6qqmraHfi9B3IDRuQfvyJbyVcT/3ZrzC/OVrOOPv43h2fB6JRL1+Eogk7RKGkiRJdVkkAgedDzdOgYP6EiHBLyND+bDJPRxUOZ8/vDOPi/93Mss3FIe9VJLqFENJkqT6IKsF9H0G+g+CJm1oW7GC1zP+wB8zXmB2Xj6n/i2XQVOWUc+fMy9JO42hJElSfbL/mck74/W4jAgBv4i8y4dZd9G9YjZ3DZnDFQM/ZuWmrWGvlKRaz1CSJKm+adQMzn0CLnsDsjvQunIVL6f/if+X/gwzPlvKKY/m8sa0FV5dkqQfYChJklRf7XMS3DAJel0NQP/oKMY0vpOeZVP5zeBZXPPCNNYWlYY8UpJqJ0NJkqT6LDMbfv4IXPEONNuTFol1PJf+EI+kP8VH8xZxyqNjGT5nZdgrJanWMZQkSWoI9joarp8IR9wIRDg/Oo4xje6gd8lEbnhpOje/PIONW8rCXilJtYahJElSQ5GeBaf9Ga4eAS32pXlQwD/TH+WJtMeYOGs+p/wtl1HzV4e9UpJqBUNJkqSGpmNvuHYc/OzXEIlxZmwyoxr9jiO2jOHq5z/mt4NnUVhSHvZKSQqVoSRJUkOUlgkn3Qe/GgWtD2K3oJDH0//B/6Q9wthpczjt0VzGL1wX9kpJCo2hJElSQ9buEPjVGDjuLoimcUpsGqMy76DP5ve57JnJ/Odbn1BcVhH2SkmqcZGgnj9EobCwkJycHDZt2kR2dnbYcyRJqr1Wz4W3b4T8GQCMrezG78sHkLb7Hvz3hd05bM/mIQ+UpJ+mOm3gFSVJkpTU+kC4+gM46QGIZXBsbDYjM+/gZwVD6ffPCfzp3/MoKa8Me6Uk1QivKEmSpG9btzB5dWn5FAAmVXbldxW/Ir3l3vz1wu5077hbuPskaQd4RUmSJP00LbrAVe/CaX+BtMYcGZvHiIzfccz6wfR9ajx/HfEpZRWJsFdK0i7jFSVJkvTDNuTB0JthyTgApiW6cEf5NaS3OYBHLurOAW39/6ukusErSpIkaedpvhdcPhR+/iikN6VndCHDM+7iuDUvct4/xvLEmM+pqPTqkqT6xVCSJEk/LhqFXr+EGybBPieRQTm/S3uF12L/ybARI7ng6Ul8vmZz2CslaacxlCRJ0vbbrSNc+jqc+xRBZg7donkMy7ib41f+i3MfG8O/xi0mkajXn+qX1EAYSpIkqXoiEehxCZEbP4L9ziSNSm6Nv8ng6O95e/i/6f+/k1m2vjjslZL0kxhKkiRpxzRtA/1fgr7PEjTenQOiy3kr/T85fvkTnP33D3hx8lLq+T2jJNVjhpIkSdpxkQgcdEHy6tJBFxCLBFwfH8Yb3MGbb7/J5c9+RH7B1rBXSlK1GUqSJOmny2oBfZ+Ffi8RNGnN3tGVvJ7+AMfnPcK5j47g9WkrvLokqU4xlCRJ0s5zwM+J3DgFelxKNBLwy/h7DA5u5403BvGr/5vKmqKSsBdK0nYxlCRJ0s7VqBmc+yRc+gZBdns6RdfwcvqfOG7hg5z3yHu8Mzs/7IWS9KMMJUmStGt0OYnIDZOTz18CLouP4rXErxn8ynPcNGg6G7eUhTxQkr6foSRJknadzGz4+aNwxTCC3fakfWQ9z6f/hWPn3cf5jwxn5LzVYS+UpO9kKEmSpF1vr2OI3DARjriBgAgXxnN5teI/eO3Fp7l98CwKS8rDXihJ32AoSZKkmpGeBac9SOSX75PYvQutIgX8b/ojHDP7d/R7ZBi5n60Ne6Ekfc1QkiRJNWuPw4leNx5+dhtBJMrZsUm8WHoLrz33d+5+czZbSivCXihJhpIkSQpBWiacdD+RAaNItOzK7pEi/pH+OMfMuI3L/vY2UxavD3uhpAbOUJIkSeFpfyjRa8fCsXeSiMQ5NTaV54pv4rVnHuKPw+ZSUl4Z9kJJDZShJEmSwhVPh+N/T/TasVS27kZOpJi/pj3N0R9dz5V/e5OZywvCXiipATKUJElS7dDmIGLXjIET76Myms5xsVn87+abGfz0H/jv9+ZTVpEIe6GkBsRQkiRJtUcsDkf/mtj146lodxhNI1v5U9oz9JnwS675++vMyy8Me6GkBsJQkiRJtU/L/YgPeB9OfZDKWCZ9YvN4svAm3nzqbv4x6lMqKr26JGnXMpQkSVLtFI3BkTcQu3ESZR2PonGklHti/8fhYy/jlsdf4/M1RWEvlFSPGUqSJKl2a96Z9KveITjzEcpjjTks+hmPbryJNx+/g6dHf+qd8STtEpEgCIKwR+xKhYWF5OTksGnTJrKzs8OeI0mSfoqC5ZQOuZmMpWMAmJfoxLMZl9HntIs595AORKORkAdKqs2q0waGkiRJqluCgGDmS5QP/z3p5cmbO0xLdGFw9pWcfnY/junSgkjEYJL0bYZSCkNJkqR6ast6ysc9Ch/9D2mJUgAmVnZlRJsB9D23Lwe1zwl5oKTaxlBKYShJklTPFa2iZPRDpM18nlhQAcDoyh5M7XwDF59zFh2bNw55oKTawlBKYShJktRAFCxj88gHaTT3FWIkbx/+fqI3iw66hf5nnkrzrPSQB0oKm6GUwlCSJKmBWb+Ignf/SPbnbxElIBFEGM5RFBx+OxecdAyN0mNhL5QUEkMphaEkSVIDtWY+64bdR4vl7wNQEUR5J3YCsePu4PSjDiMe8ykpUkNjKKUwlCRJatgSK2awZti9tFmdC0BpEOfd9FNpftrvOfrQg7xDntSAGEopDCVJkgRQljeRdUPvpd3GjwHYGqQzosnZ7HnOXXTfd++Q10mqCYZSCkNJkiSl2rxgFJveuZf2mz8BoChoRG7zC+l6we/Zq0O7kNdJ2pUMpRSGkiRJ+pYgYP3MYZS8/wfalywEoCDI4qN2l3HIhXfSsnnzkAdK2hUMpRSGkiRJ+l6JBF9Mfg3G/Jn25UsBWBfk8EnnX3JY39vJymoS8kBJO5OhlMJQkiRJPypRyeejnyNr4sO0TawEYDXNyet6PT3PvYW09MyQB0raGQylFIaSJEnaXkFFGXP+/RRtZv6dVsF6APIjrVnb81a6nX4NkVg85IWSfgpDKYWhJEmSqquspJiZb/2Nzgv+SQsKAFgR60jJz+5gn2Mvg6jPYJLqIkMphaEkSZJ21ObNhUwf/BDdlgxkt8hmAJal7038xHto1/s88BlMUp1iKKUwlCRJ0k+1du1aZr3+IIevGkTTyFYAljU6gCanP0Dzg08xmKQ6wlBKYShJkqSdJW/5Mj59488cs/FNGkdKAVje9BCan/1HsrocHfI6ST/GUEphKEmSpJ1t1oJPWT70T5y85d9kRCoAWLF7H1qd80fS9+gV8jpJ38dQSmEoSZKkXSEIAnKnzqRwxIOcVvYBaZFKAFa2PZHWZ/+BaNuDQl4oqSpDKYWhJEmSdqWKygTv5k4imvsQpyXGEosEJIiwvtOZtDzrAWixT9gTJX3JUEphKEmSpJpQXFbBkBGj2f3jRzgtMgmABFE27duXZqffA806hbxQkqGUwlCSJEk1af3mUga/M5wu8x7jxOh0ACqIU9LtMpqcdCdktw15odRwGUopDCVJkhSGpeu38PrbQ+id9xRHxz4BoDySTmWvAWQe9xvIahHyQqnhMZRSGEqSJClMs1cU8NaQVzl97b84LPoZAOWxRkSOuIH4z26GRs1CXig1HIZSCkNJkiSFLQgCPvx0DR8MG0S/oufpFs0DoCwtm/jPbiF6xHWQ0TTklVL9ZyilMJQkSVJtUZkIGDJ9BVPfe4Erywaxf3Q5AGUZzUg79jdEDhsAaY1CXinVX4ZSCkNJkiTVNiXllfzfhEUs+vAFrk28RufoKgDKG7ci7bg74NArIJ4e8kqp/jGUUhhKkiSptiooLuOpMZ9SOPkFboy+QYfIOgAqmnYgfsLvoVt/iMVDXinVH4ZSCkNJkiTVdis2FvP39z8hc84gbooPoXWkAIDKZp2JnXA3HHg+RKPhjpTqAUMphaEkSZLqinn5hTw6fCad8l7h+vhQdo8UAZBoeQDRE+6B/c+ESCTklVLdZSilMJQkSVJdM/Hzdfxt+HQOWz2Ya+PvkB0pBiBoewiRE++BvU80mKQdYCilMJQkSVJdlEgEvDNnJU+9N5Uzit7gl7F3yYqUAhDscSSRE+6BPX8W8kqpbjGUUhhKkiSpLiurSPDSlKW8MGoaF5e9weWxkWREypNf7Hw8nHAPdOgV7kipjjCUUhhKkiSpPigsKeefYxcxbPw0BgRD6B8bTXqkMvnFfU+HE+6GNgeHO1Kq5QylFIaSJEmqT1ZtKuFvH3zGhKnTuTn2JhfEcolFvnw7d+B5cNxd0HLfcEdKtZShlMJQkiRJ9dHC1UX85b1PWbxgBrfG3+Ds2CQAgkiUSLd+cOzvoPleIa+UahdDKYWhJEmS6rOPl2zgz8Pns3X5bH4dH8wpsWkABNE4kUN+Acf8FnLah7xSqh0MpRSGkiRJqu+CIOD9uat46L1PabJ+Nr+JD+bY2Ozk12IZRA67Gn52GzRpFfJSKVyGUgpDSZIkNRTllQle/Xg5f/tgIXttmcXtaa9xeHRB8otpjeHw66DPzdC4ebhDpZAYSikMJUmS1NBsKa3gX+Py+J/czzmkYha3x1+jR3RR8osZ2XDkjXDYAMhqEe5QqYYZSikMJUmS1FCtLSrl8dELGTRlKccxjd/EB3NAdFnyi7F0OOgC6P0raN8z3KFSDTGUUhhKkiSpoctbt4WH31/Au3PyOSP6EdfE/033r64wQTKUel+TvL14PCO8odIuZiilMJQkSZKSZizbyP97dwFT8jbQPfI5l8dHcnZsMmmUJw9o3AJ6XgG9fgk5HcIdK+0ChlIKQ0mSJOmbpi/byMAJS3h3zkpyEgX0i33IFemjaB2sSx4QicL+ZyavMu15NEQioe6VdhZDKYWhJEmS9N1WbSrhhclLGDRlGYXFJZwUncZVaSM5IjJ320Et90/+HFO3/pDRJLyx0k5gKKUwlCRJkn5YSXklb834gmcn5PHZ6s10iazg8tgILkwbT2ZQkjwoIxt6XJK8W16LLuEOlnZQvQmlBx98kDfffJMFCxbQqFEj+vTpw1/+8hf222+/7f49DCVJkqTtEwQBExetZ+CEPEYtWEOToJgLYrkMyPiADon8bQfufULyY3ldToFoLLzBUjXVm1A67bTT6N+/P4cddhgVFRXcfffdzJkzh3nz5pGVlbVdv4ehJEmSVH1L1m3huYlLGDx1OcVl5fws+glXp4/kWKYT4cu3j7t1Sl5hOuQyH2KrOqHehFJVa9eupVWrVowdO5Zjjjlmu77HUJIkSdpxhSXlDJ66gucnLmHZhmI6RNZwefwDLk0bS1aiKHlQPBMOvjD5s0xtu4c7WPoB9TaUPv/8c7p06cKcOXM46KCDtut7DCVJkqSfrjIRMGr+agZOWMKkxevJpJSzYpO4PvMDOlcu3nZgxyOSwXTA2RBPD2+w9B3qZSgFQcA555zDxo0bGTdu3PceV1paSmlp6df/XVhYSMeOHQ0lSZKknWT+ykKem7CEITO/oKyikkMjC7km8wNOZhKxoDJ5UJPW0PMq6HklZLcNda/0lXoZSjfeeCP//ve/GT9+PB06fP8D0O6//34eeOCBb71uKEmSJO1c6zeX8vJHy/i/SUtZU1RKSzZyWdqHXJkxmpyK9cmDovHk1aXe18AeR/hMJoWq3oXSzTffzFtvvUVubi577bXXDx7rFSVJkqSaVVaR4N1PVvLshCXMWl5AnApOi37MjVmjOaA85ZlMrQ9Ofizv4AshvXF4g9Vg1ZtQCoKAm2++mSFDhvDhhx/SpUv179nvzyhJkiTVnOnLNvLs+Dze/WQVlYmArpElXN94NKcH44knvnwmU+ZuyTvlHTYAmv/wP4JLO1O9CaUbbriBQYMG8fbbb3/j2Uk5OTk0atRou34PQ0mSJKnm5Rds5YXJS3n5o2UUFJeTw2YuTc9lQOZompd99UymSPJZTL2vST6bKRoNdbPqv3oTSpHv+QzrwIEDufLKK7fr9zCUJEmSwrO1rJK3Zn7BwAl5fLZ6M1ESHBudxW3ZH9Kt5ONtBzbvDIf9CnpcAo12C22v6rd6E0o7g6EkSZIUviAImPD5egZOyGPUgjUA7BlZyc1Nx3JWYjTpFZuTB6ZlQbeLkj/L1PrAEBerPjKUUhhKkiRJtUveui08P3EJg6cuZ0tZJY0p4eJGk7k2cxStti7admCnn8Hh18B+Z0IsHt5g1RuGUgpDSZIkqXYqLCnntY+X8/ykJSzfsBUIODK2gDuajaXHlglEvnomU3Z76HUVHHolNGkZ5mTVcYZSCkNJkiSpdqtMBIyav5pnJ+QxefEGANqyntuajefsxAdkln75TKZYOhx4XvLmD+17+kwmVZuhlMJQkiRJqjvm5Rfy3MQ83pqZT1lFgnTK6Z81jRsaj6ZN0SfbDmx3SDKYDjwf0jLDG6w6xVBKYShJkiTVPes3lzJoyjJemLyUNUWlAPSM5/H7FuM4tGgM0crkazRqDj2vgF6/hN32CHGx6gJDKYWhJEmSVHeVVSQYPmclAyfkMWvFJgCaU8jtLadwXuX7NCr+8plMkSjsd0bybnl7HevH8vSdDKUUhpIkSVLdFwQB05cV8OyEPN77ZBWViYAYlfTLnsuNWaNpv/GjbQe32C8ZTN37Q0bT8Ear1jGUUhhKkiRJ9Ut+wVZemLyUQVOWsWlrOQDd0ldyT+uJ9Cp4j2j5luSB6U2hx8XJB9m23DfExaotDKUUhpIkSVL9tLWskiEzvmDghDwWrkk+sLZppJjftZ3J+RXDaVy4eNvBnY9L3vxh39MgGgtnsEJnKKUwlCRJkuq3IAgY//k6Bk5YwugFa756lX67L+KmrDF0WDuWSJBIvpyzBxx2NRx6OTRuHtpmhcNQSmEoSZIkNRyL127m+YlLGDxtBcVlyQfWdm20kXvbTqH3hmFESzYmD4xnwkF9kz/L1K5HeINVowylFIaSJElSw7NpazmDpy7nuYlLWLFxKwCNo+Xc1XEu51UMJ2t9yjOZOvROfiyv6zkQTw9psWqCoZTCUJIkSWq4KhMBH8xfzbPj85iSt+HLVwMuarOKm5uMpkP+CCKJ5A0hyGoFPa+EXldBdruwJmsXMpRSGEqSJEkCmJu/iecmLOHtmfmUVSZ/ZumAJsXc134qvde/RXTzquSBkRgccFbyKlOnPj6TqR4xlFIYSpIkSUq1bnMpg6Ys44XJS1lbVApA43iC3++1iPMrhpO1csq2g1sdmPw5pm4XQXpWSIu1sxhKKQwlSZIkfZeyigT/npPPwAlLmL1i09ev9+u4iZuafkiH5cOIlBcnX8zIgUMuS94tr9X+IS3WT2UopTCUJEmS9EOCIGDa0o0MnLCE9+auojKRfHvctVmCezvMoPfaN4kW5G37hnaHQo9L4KALvMV4HWMopTCUJEmStL2+KNjKC5OW8vJHy9i0NXmThybpEe7sks/5ifdpvHQ0JCqSB0fTYL/Tk9G0z0kQSwtxubaHoZTCUJIkSVJ1FZdVMGTGFwycsITP12wGkvd0OKljlGubT6PHhneJr0m5xXhWSzj4wmQ0tTk4pNX6MYZSCkNJkiRJOyoIAsYtXMfACXmM+XTt16/HoxEu6bSJyxtPYu+Vw4kUb/sarQ+GHhfDwRdBk5YhrNb3MZRSGEqSJEnaGb4o2MqwWfkMnZnPvJWFX7/eJC3BjR2XcV50LK1XjiFSWZb8QjQO+5ycjKZ9T4N4RkjL9RVDKYWhJEmSpJ3t8zWbGTorn2Gz8slbt+Xr19tnlvCbtnM4uWw0TdfP2vYNjZrBQX2T0dTuUJ/NFBJDKYWhJEmSpF0lCAI++aKQt2d+wTuzV7KqsOTrr/XKWsutLT6md9FI0otXb/umlvtD94uhWz/IbhvC6obLUEphKEmSJKkmJBIBHy3ZwNBZ+Qyfs5KC4uRd86IkODf7MwY0ncz+BWOJViYfckskCp2PT94AYv8zIa1RiOsbBkMphaEkSZKkmlZemWD8wnW8PfMLRsxbTXFZJQBNKeaqZjPpnzaOdoUpH83LyIEDz4Uel0LH3n40bxcxlFIYSpIkSQrT1rJKRi1YzdCZ+Xz46VrKKhMAdIqs4oZmH3Nm4kOalKzc9g3N905+NK97f9itY0ir6ydDKYWhJEmSpNpi09Zy3p+7iqEz85m4aB2JACIkOCI6n+t2+4g+peNJq9z65dER2Oto6H4JdD0b0rNC3V4fGEopDCVJkiTVRmuKShg+eyVDZ+UzfVkBAI0p4efxj7iqyWQOKJm57eC0rORH87pfDJ2Ogmg0jMl1nqGUwlCSJElSbbd8QzHDZief0bRgVREAHSJruShtAhdnjKdlef62g3fbY9tH85p3Dmlx3WQopTCUJEmSVJd8trqIoTPzGTorn2UbioGAnpHPuCRjPGdGJ5OZ2PbcJvY4MnnXvK7nQqbvdX+MoZTCUJIkSVJdFAQBs1ZsYujMfIbNzmdtUSmZlHJKdCqXZIzn8GA2Eb58Kx9vBAf8PBlNex0L0Vi442spQymFoSRJkqS6rjIRMGXxeobOyufdT1axaWs5rdnAebHxXJw+nk7Bim0HN20H3fslbwLRct/wRtdChlIKQ0mSJEn1SVlFgtzP1jJ0Vj4j561ma3kF3SOLuCA2jvPSJtE02Lzt4Pa9oMfFcNAF0KhZeKNrCUMphaEkSZKk+qq4rIKR81YzbFY+Yz9bS6SyjBOj07kglsvxsVnESD6ziVg67HdG8qN5e58IsXi4w0NiKKUwlCRJktQQFBSX8d4nqxg6K59Ji9eze7CJc2Lj6RsbxwHRZdsOzGoF3S5KRlPrA8MbHAJDKYWhJEmSpIZmdWEJ73z5jKZZywvoGllC31gu58Ym0DxStO3ANt2gx6VwcF/IahHe4BpiKKUwlCRJktSQLV2/hWGz8nl7Zj55awo4LjqLvrFcToxOJy1SCUAQjRPpcmryKlOXUyCeHvLqXcNQSmEoSZIkScnbjS9YVcTQWfkMm5XPlo2rOSs2ib6xXLpF87Yd16g5kYMvTN4Eom0PiETCG72TGUopDCVJkiTpm4IgYPqyAobNyued2StptmURF8RyOT82nlaRgm3HtepKpPvFyZ9patomvME7iaGUwlCSJEmSvl9FZYJJi9czdGY+I+d+QY+yGVwQy+WU6DQyIuUABJEokX1Ogu4XJ++el5YZ8uodYyilMJQkSZKk7VNaUcmHnyaf0fTR/MWcnJjIBbFcekYXfn1MIiOH6MEXJB9o26FXnfponqGUwlCSJEmSqm9zaQUj561i6Mx8ViyczTnRXM6Ljad9ZP3Xx1Q234fYIZdAt/6Q0z7EtdvHUEphKEmSJEk/zYYtZbz7yUqGzVhBbNl4zo/lcnr0YxpHSgEIiFC55zHED7kUDjgL0huHvPi7GUopDCVJkiRp51m5aSvvzFrJyJmf02n1SC6IjeOI6Pyvv14Rz4IDz01GU6c+teqjeYZSCkNJkiRJ2jUWr93MsFkr+XjGNHoWjOCCWC57RNd+/fWtTTqSfuilxA65GJrtGd7QLxlKKQwlSZIkadcKgoB5KwsZOnMFy2eM4titozgjNoWmka0AlEYbwR2LyMjMCnVnddogXkObJEmSJNVTkUiEA9vlcGC7HBKndWXasst4dPpiyua8zakVoylOa8OpIUdSdRlKkiRJknaaaDTCYXs257A9m1NxzqFMWHQL8YqKsGdVm6EkSZIkaZeIx6Icu2/LsGfskGjYAyRJkiSptjGUJEmSJKkKQ0mSJEmSqjCUJEmSJKkKQ0mSJEmSqjCUJEmSJKkKQ0mSJEmSqjCUJEmSJKkKQ0mSJEmSqjCUJEmSJKkKQ0mSJEmSqjCUJEmSJKkKQ0mSJEmSqjCUJEmSJKkKQ0mSJEmSqjCUJEmSJKkKQ0mSJEmSqjCUJEmSJKkKQ0mSJEmSqjCUJEmSJKkKQ0mSJEmSqjCUJEmSJKkKQ0mSJEmSqjCUJEmSJKkKQ0mSJEmSqjCUJEmSJKkKQ0mSJEmSqjCUJEmSJKkKQ0mSJEmSqjCUJEmSJKkKQ0mSJEmSqoiHPWBXC4IAgMLCwpCXSJIkSQrTV03wVSP8kHofSkVFRQB07Ngx5CWSJEmSaoOioiJycnJ+8JhIsD05VYclEgny8/Np2rQpkUgk1C2FhYV07NiR5cuXk52dHeoW1Q2eM6ouzxlVl+eMqstzRtVVm86ZIAgoKiqiXbt2RKM//FNI9f6KUjQapUOHDmHP+Ibs7OzQTxLVLZ4zqi7PGVWX54yqy3NG1VVbzpkfu5L0FW/mIEmSJElVGEqSJEmSVIWhVIMyMjK47777yMjICHuK6gjPGVWX54yqy3NG1eU5o+qqq+dMvb+ZgyRJkiRVl1eUJEmSJKkKQ0mSJEmSqjCUJEmSJKkKQ0mSJEmSqjCUdrInn3ySvfbai8zMTHr27Mm4ceN+8PixY8fSs2dPMjMz6dy5M08//XQNLVVtUZ1z5s033+Tkk0+mZcuWZGdnc+SRR/L+++/X4FrVBtX9e+YrEyZMIB6P06NHj107ULVOdc+Z0tJS7r77bjp16kRGRgZ77703zz77bA2tVW1Q3XPmpZdeonv37jRu3Ji2bdty1VVXsX79+hpaqzDl5uZy1lln0a5dOyKRCG+99daPfk9def9rKO1Er776Krfeeit33303M2bM4Oijj+b0009n2bJl33l8Xl4eZ5xxBkcffTQzZszgrrvu4pZbbuGNN96o4eUKS3XPmdzcXE4++WSGDx/OtGnTOP744znrrLOYMWNGDS9XWKp7znxl06ZNXH755Zx44ok1tFS1xY6cMxdddBGjRo3imWee4dNPP+Xll19m//33r8HVClN1z5nx48dz+eWXc/XVVzN37lwGDx7Mxx9/zIABA2p4ucKwZcsWunfvzj/+8Y/tOr5Ovf8NtNP07t07uO66677x2v777x/ceeed33n8HXfcEey///7feO3aa68NjjjiiF22UbVLdc+Z79K1a9fggQce2NnTVEvt6DnTr1+/4J577gnuu+++oHv37rtwoWqb6p4z7777bpCTkxOsX7++JuapFqruOfPwww8HnTt3/sZrjz32WNChQ4ddtlG1ExAMGTLkB4+pS+9/vaK0k5SVlTFt2jROOeWUb7x+yimnMHHixO/8nkmTJn3r+FNPPZWpU6dSXl6+y7aqdtiRc6aqRCJBUVERzZs33xUTVcvs6DkzcOBAFi1axH333berJ6qW2ZFzZujQofTq1YuHHnqI9u3bs++++3L77bezdevWmpiskO3IOdOnTx9WrFjB8OHDCYKA1atX8/rrr3PmmWfWxGTVMXXp/W887AH1xbp166isrKR169bfeL1169asWrXqO79n1apV33l8RUUF69ato23btrtsr8K3I+dMVX/961/ZsmULF1100a6YqFpmR86ZhQsXcueddzJu3Djicf/Kb2h25JxZvHgx48ePJzMzkyFDhrB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+ "text/plain": [ + "
              " + ] + }, + "metadata": { + "filenames": { + "image/png": "/Users/mhjensen/Teaching/MachineLearning/doc/LectureNotes/_build/jupyter_execute/chapter11_52_3.png" + } + }, + "output_type": "display_data" + } + ], "source": [ "import autograd.numpy as np\n", "from autograd import grad, elementwise_grad\n", @@ -1236,7 +1286,45 @@ "collapsed": false, "editable": true }, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Initial cost: 0.221805\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/mhjensen/miniforge3/envs/myenv/lib/python3.9/site-packages/numpy/core/fromnumeric.py:3245: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray.\n", + " return asarray(a).size\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Final cost: 0.000417932\n", + "The max absolute difference between the solutions is: 0.00424909\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
              " + ] + }, + "metadata": { + "filenames": { + "image/png": "/Users/mhjensen/Teaching/MachineLearning/doc/LectureNotes/_build/jupyter_execute/chapter11_58_3.png" + } + }, + "output_type": "display_data" + } + ], "source": [ "import autograd.numpy as np\n", "from autograd import grad, elementwise_grad\n", @@ -1521,7 +1609,61 @@ "collapsed": false, "editable": true }, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Initial cost: 0.221805\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/mhjensen/miniforge3/envs/myenv/lib/python3.9/site-packages/numpy/core/fromnumeric.py:3245: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray.\n", + " return asarray(a).size\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Final cost: 0.000417932\n", + "The max absolute difference between the solutions is: 0.00424909\n", + "Max absolute difference between Euler method and analytical: 0.011225\n", + "Max absolute difference between deep neural network and analytical: 0.00424909\n" + ] + }, + { + "data": { + "image/png": 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+ "text/plain": [ + "
              " + ] + }, + "metadata": { + "filenames": { + "image/png": "/Users/mhjensen/Teaching/MachineLearning/doc/LectureNotes/_build/jupyter_execute/chapter11_66_4.png" + } + }, + "output_type": "display_data" + } + ], "source": [ "# Assume that all function definitions from the example program using Autograd\n", "# are located here.\n", @@ -1753,7 +1895,45 @@ "collapsed": false, "editable": true }, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Initial cost: 457.256\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/mhjensen/miniforge3/envs/myenv/lib/python3.9/site-packages/numpy/core/fromnumeric.py:3245: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray.\n", + " return asarray(a).size\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Final cost: 0.00310113\n", + "The max absolute difference between the solutions is: 0.000464088\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
              " + ] + }, + "metadata": { + "filenames": { + "image/png": "/Users/mhjensen/Teaching/MachineLearning/doc/LectureNotes/_build/jupyter_execute/chapter11_79_3.png" + } + }, + "output_type": "display_data" + } + ], "source": [ "import autograd.numpy as np\n", "from autograd import grad, elementwise_grad\n", @@ -2096,7 +2276,60 @@ "collapsed": false, "editable": true }, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Initial cost: 457.256\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/mhjensen/miniforge3/envs/myenv/lib/python3.9/site-packages/numpy/core/fromnumeric.py:3245: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray.\n", + " return asarray(a).size\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Final cost: 0.00310113\n", + "The max absolute difference between the analytical solution and DNN Autograd: 0.000464088\n", + "The max absolute difference between the analytical solution and numerical scheme: 0.00266858\n" + ] + }, + { + "data": { + "image/png": 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YYCZPnmy6du1qHMcxX331Va7byc/zs6RQcSriTj4pFy9ebDIzM82xY8fM1KlTTVRUlAkJCTH79u0zixYtMoB5/fXXcyy7a9cuExAQYP79739nT+vQoYMBzK+//prrtm655RYTFBSUY9qMGTMMYF555ZUc0ydMmJCjuBlzYiXi5eVlNmzYkGPekyvF3r1755g+bNgwA5h//vOfOab37dvXREREnPYxcbvdJjMz04wZM8Z4eXmZw4cP57p/S5YsybFMvXr1TLdu3bJ/Hj58uAHMzJkzT3s748ePN4D57rvvckxftmyZAbLfQJ8uY/ny5U2DBg2M2+3Onn7s2DETHR1tWrdunT3t5OPzzTffnPb6Tjr5fLjnnntyTH/llVcMYPbu3WuMMWbnzp3G29s7VwE9duyYiY2NNTfccEP2tHMtTtWrV8/xZiYvWVlZJjMz09x6662mSZMmOX53LsUpJibGJCUlZU/bt2+fcblc5sUXX8ye1q1bN1OhQgWTmJiYY/n77rvP+Pv7Zz8/Tv7d/r5xOKlt27bmiiuuyP65Ro0a5l//+pdxuVxm7ty5xpj/FeGTby4L4rVx8n7ee++95tChQ6Zt27YmLi7OrFq16qyPz2uvvWaAHG9UTvXoo4/m+Xq4++67jeM4OfKcukF+5513DJBjnsOHDxs/Pz/z0EMPZU87XXHKz+vwX//6l3EcJ9ebum7duuWrOJ0qKyvLZGRkmJo1a5oHHngge/rJ11iPHj1yzP/1118bIMebrVPt27fPAKZ///5nvO1+/foZwOzfv98Yc/biZIwxMTExpm7dutk/n3wTlNelevXqZ7z9rVu3Gi8vr1xvNv/uyJEj2W/Q/27nzp3Gz8/P3HjjjdnTbrnlllzrv8zMTBMVFWWA7Dddxhhz6NAh4+XlZR588MHsaSfv/9VXX53jthYsWGAA88ILL+SZ0ePxmMzMTLNjxw4DmO+//z7X4/P000/nWCa/28AjR44Yf3//02Y6W3EyxpigoKA811/9+/c3fn5+ZufOnTmmd+/e3QQGBp7xdXqm6y2MdX5+nFyHd+rUKcfjdXI70KBBgxxlfunSpQYw48ePP+N1Hj9+3AQFBZm33347e/qpxcmYE39rX1/f7NeTMf9bt55cJxtjTM+ePXNsp/7u1HXa0KFDjY+Pj1m3bl1+HoLszBeyLSvs9XR+i5MxxlxyySV5PsfzKk6XXXaZiY6ONseOHcuelpWVZerXr28qVKhgPB5Pjts52/OzJNGhesXEZZddho+PDyEhIfTq1YvY2FimT59OTEwMU6dOxXEcbr75ZrKysrIvsbGxNGrUKNfhLuHh4VxxxRX5ut1Zs2YB5Poy4fXXX09QUBC//vprjukNGzY87cAGvXr1yvFz3bp1AXIdDlW3bl0OHz6c43C9lStXctVVV1G2bFm8vLzw8fFh0KBBuN1uNm7cmGP52NhYWrRokSvX3w8PmD59OrVq1crzi9knTZ06lTJlytC7d+8cj2vjxo2JjY0942FEGzZsYM+ePQwcOBCX638vs+DgYK699loWL15MSkrKaZc/m6uuuirX/YP/Hbr5008/kZWVxaBBg3Jk9/f3p0OHDuc1etHfb9vHxyfX9G+++YY2bdoQHByMt7c3Pj4+fPrpp7kOAToXHTt2JCQkJPvnmJgYoqOjs+9nWloav/76K1dffTWBgYE57muPHj1IS0vLdfhIXjp16sSCBQtITU1lx44dbN68mf79+9O4cWNmzpwJwC+//EKlSpWoWbMmULCvjW3bttGqVSuSkpJYvHgxjRo1Omvmk4ce3nDDDXz99dd5jrI5a9Ys6tWrl+v1MHjwYIwx2fchLzfddBN+fn45Dt8YP3486enpDBky5Kz58vM6nDt3LvXr16devXo55hswYMBZrx8gKyuLESNGUK9ePXx9ffH29sbX15dNmzbl+bw72+vmQhhjAPI8bOZsy5zql19+YdmyZTkuZxvJbebMmbjdbu69997TzrNo0SJSU1NzPWcrVqzIFVdckes56zgOPXr0yP7Z29ubGjVqUK5cOZo0aZI9PSIiIsfr8u9O/Q5u69atqVy5MrNnz86elpCQwF133UXFihWz1x2VK1cGyPPv+PfDZYF8bwMXLVpEWlraaTNdiFmzZtGpUycqVqyYY/rgwYNJSUnJ87Czc3Ex1vkffPABTZs2xd/fP/vv8Ouvv+b5N+jZsydeXl6nzQNw/PhxHnnkEWrUqIG3tzfe3t4EBweTnJx81u3C3XffDZDjcOX//Oc/NGjQgPbt25/1vuRl+vTpdOzYMfu9x+kU5LassNfThSE5OZklS5Zw3XXX5RgkzMvLi4EDBxIfH5/rEOfCXLcWNSpOxcSYMWNYtmwZK1euZM+ePaxevTr7S8j79+/HGENMTAw+Pj45LosXL851nPi5jMx06NAhvL29c33523EcYmNjOXToUL6vOyIiIsfPvr6+Z5yelpYGnDj2t127duzevZu3336befPmsWzZsuzvsKSmpuZYvmzZsrlu28/PL8d8Bw4cOOtoO/v37+fo0aP4+vrmelz37duX63H9u5OPS16PR/ny5fF4PBw5cuSMt38mp97Hk4MznLyPJ4/XvvTSS3NlnzBhwhmzn01e92nixInccMMNxMXF8eWXX7Jo0SKWLVvG0KFDs/+O5+Nsf8tDhw6RlZXFu+++m+t+nnzDl5/72rlzZ9LT05k/fz4zZ84kMjKSJk2a0LlzZ3755RcAfv311xxFuyBfG0uXLmXjxo3069cv36NAtW/fnsmTJ2e/WapQoQL169dn/PjxOTKe7jl48venExERwVVXXcWYMWNwu93AiePZW7RowSWXXHLWfPl5HR46dCjXwAhAntPy8uCDD/LUU0/Rt29fpkyZwpIlS1i2bBmNGjXKtV7IK9Opr5u8REZGEhgYyLZt286YZfv27QQGBuZan51OcnIyhw4dyv5b/F2jRo1o3rx5jkv9+vXPeH0nv3d5pufP2dZLpz4fAgMD8ff3zzHN19c3z/vo6+ub52s9NjY2z2knb8vj8dC1a1cmTpzIv//9b3799VeWLl2a/YFHXn+bU/Pndxt48jZPl+lCXMhrLT8Ke53/xhtvcPfdd9OyZUu+++47Fi9ezLJly7jyyivP+7V044038p///IfbbruNn376iaVLl7Js2TKioqLO+JqDE+uAfv368eGHH+J2u1m9ejXz5s27oMGr8rPdL+htWWGvpwvDkSNHMMacU6bzWbcWVxpVr5ioW7dujpGZ/i4yMhLHcZg3b16eo5udOu1cPhEtW7YsWVlZHDhwIMcbRGMM+/bty/405XyuO78mT55McnIyEydOzPGp4IWcjycqKor4+PgzznPyS44zZszI8/d/3xNyqpMrkb179+b63Z49e3C5XISHh59D4nNzckSub7/99qyfpPr7+5OYmJhr+uk2tHn9jb/88kuqVq3KhAkTcvw+PT39XGKfs/Dw8OxPwU73SXvVqlXPej0tW7YkODiYX375he3bt9OpUyccx6FTp068/vrrLFu2jJ07d+YoTgX52ujXrx+xsbE88cQTeDwennzyybNmBujTpw99+vQhPT2dxYsX8+KLL3LjjTdSpUoVWrVqRdmyZU/7HATOOnLbkCFD+Oabb5g5cyaVKlVi2bJlvP/++/nKlh9ly5bNfsP3d/v27cvX8l9++SWDBg1ixIgROaYfPHiQMmXKFEREvLy86NixIzNmzCA+Pj7PN17x8fGsWLGC7t275/gU/kymTZuG2+3O17mD8uPkczA+Pj7XXo+TzrZeKoyR/PL6W+7bt48aNWoAJwZU+OOPP/j888+55ZZbsufZvHnzaa/z1NdSfreBJ+//6TL9fTCcc3Whr7ULdS7r/Lx8+eWXXH755ble38eOHTuvPImJiUydOpVnnnmGRx99NHt6eno6hw8fztd13H///XzxxRd8//33zJgxgzJlylzQKML52e4XxrasMNfTJz/YSE9Pz/Hcv5APR8PDw3G5XFafz0WZ9jiVAL169cIYw+7du3N9Stm8eXMaNGhw3tfdqVMn4MTK5O++++47kpOTs39fmE6uvP6+UjDGnHXEsTPp3r07GzduPOMu8F69enHo0CHcbneej2vt2rVPu2zt2rWJi4tj3LhxOQ7HSU5O5rvvvsseaa+wdOvWDW9vb7Zs2ZJn9r+X8CpVqrBx48YcG4ZDhw6xcOHCfN+e4zj4+vrm2NDs27fvgkYiyo/AwEA6duzIypUradiwYZ738+SbpTN9Aubj40P79u2ZOXMms2bNokuXLgC0a9cOb29vnnzyyewidVJBvzaefPJJ3nrrLZ5++mkee+yxc1rWz8+PDh068PLLLwNkj6LVqVMn1q1bx++//55j/jFjxuA4Dh07djzj9Xbt2pW4uDhGjRrFqFGj8Pf3z/dhdPnRoUMH1qxZw7p163JM/+qrr/K1vOM4ud4oT5s2rcBPDv7YY49hjOGee+7J3vt2ktvt5u6778YYk++/286dO3n44YcJCwvjzjvvLJCMXbt2xcvL64zFtlWrVgQEBOR6zsbHx2cfalbQxo4dm+PnhQsXsmPHjuzCmNf6HTin0Qbzuw287LLL8Pf3P22m/Dh1r+lJnTp1YtasWblGEhszZgyBgYFnHe75dNebX+eyzs9LXq+l1atXn/chho7jYIzJdZ2ffPJJrtfQ6TRr1ozWrVvz8ssvM3bsWAYPHpzrvJXn8rh1796d2bNnn3EkzcLclhXGevpk2V+9enWO6VOmTMnz9vPzWAUFBdGyZUsmTpyYY36Px8OXX35JhQoVSvW5JrXHqQRo06YNd9xxB0OGDGH58uW0b9+eoKAg9u7dy/z582nQoEH28cLnqkuXLnTr1o1HHnmEpKQk2rRpw+rVq3nmmWdo0qQJAwcOLOB7k3cGX19fBgwYwL///W/S0tJ4//33L+hQt2HDhjFhwgT69OnDo48+SosWLUhNTWXu3Ln06tWLjh070r9/f8aOHUuPHj24//77adGiBT4+PsTHxzN79mz69OnD1Vdfnef1u1wuXnnlFW666SZ69erFnXfeSXp6Oq+++ipHjx7N97DF56tKlSoMHz6cJ554gq1bt3LllVcSHh7O/v37Wbp0KUFBQdnDYQ8cOJAPP/yQm2++mdtvv51Dhw7xyiuvEBoamu/bOznU9j333MN1113Hrl27eP755ylXrhybNm0qrLsJwNtvv03btm1p164dd999N1WqVOHYsWNs3ryZKVOmZJfj6tWrExAQwNixY6lbty7BwcGUL18++9CDTp068dBDDwFk71kKCAigdevW/PzzzzRs2JDo6Ojs2y2M18b9999PcHAwd9xxB8ePH+edd9457Z6qp59+mvj4eDp16kSFChU4evQob7/9Nj4+PtnnA3vggQcYM2YMPXv2ZPjw4VSuXJlp06YxcuRI7r777rNu/Ly8vBg0aBBvvPEGoaGhXHPNNXmemPV8DRs2jM8++4zu3bszfPhwYmJiGDduXPYpAf7+/cC89OrVi88//5w6derQsGFDVqxYwauvvlrgJ71s06YNb731FsOGDaNt27bcd999VKpUKfsEuEuWLOGtt96idevWuZZds2ZN9vdNEhISmDdvHqNGjcLLy4tJkybleQ6sFStW5Pk416tX77SvyypVqvD444/z/PPPk5qayoABAwgLC2PdunUcPHiQ5557jjJlyvDUU0/x+OOPM2jQIAYMGMChQ4d47rnn8Pf355lnnrnwB+sUy5cv57bbbuP6669n165dPPHEE8TFxXHPPfcAUKdOHapXr86jjz6KMYaIiAimTJmS/d3C/MjvNjA8PJyHH36YF154IUemZ599Nt+H6jVo0IA5c+YwZcoUypUrR0hICLVr1+aZZ55h6tSpdOzYkaeffpqIiAjGjh3LtGnTeOWVV876ujnd9ebXuazz89KrVy+ef/55nnnmGTp06MCGDRsYPnw4VatWJSsrK985TgoNDaV9+/a8+uqrREZGUqVKFebOncunn356TnuD77///uxTdZx8zvxdgwYNmDhxIu+//z7NmjXD5XKdtiQOHz6c6dOn0759ex5//HEaNGjA0aNHmTFjBg8++CB16tQp8G1ZYa+ne/ToQUREBLfeeivDhw/H29ubzz//nF27duX5WH311VdMmDCBatWq4e/vf9oP1l988UW6dOlCx44defjhh/H19WXkyJGsWbOG8ePHF8rRRcXGRR+OQs5JfkZmOumzzz4zLVu2NEFBQSYgIMBUr17dDBo0KMcQsR06dDCXXHJJnsvnNaqeMcakpqaaRx55xFSuXNn4+PiYcuXKmbvvvjvHcLLGnBhhpmfPnrmWP92ocae7bydHifn7cKVTpkwxjRo1Mv7+/iYuLs7861//MtOnT881Es/p7t+pI8QZc2KEpfvvv99UqlTJ+Pj4mOjoaNOzZ88cwxhnZmaa1157Lfu2g4ODTZ06dcydd95pNm3alOt2TjV58mTTsmVL4+/vb4KCgkynTp3MggUL8vX45OV0j1leoxKdvP2OHTua0NBQ4+fnZypXrmyuu+4688svv+SYb/To0aZu3brG39/f1KtXz0yYMOG0o+q9+uqreWZ76aWXTJUqVYyfn5+pW7eu+fjjj/Mc8edcRtU7OUz32Zbftm2bGTp0qImLizM+Pj4mKirKtG7dOtfIXePHjzd16tQxPj4+uUYn+uOPPwxgatasmWOZ//u//zNAjhHDTrrQ18bp7uf48eONt7e3GTJkSI5RGf9u6tSppnv37iYuLs74+vqa6Oho06NHj+yh7k/asWOHufHGG03ZsmWNj4+PqV27tnn11VdzXe+pj8dJGzduzB7ZLa9RKE83ql5+X4dr1qwxnTt3Nv7+/iYiIsLceuutZvTo0QYwf/zxR573/aQjR46YW2+91URHR5vAwEDTtm1bM2/evFwjRZ7uNZbXaFJnsmjRInPdddeZmJgY4+3tbaKjo80111xjFi5cmGvek4/LycvJv1GHDh3MiBEjTEJCQq5lzjSq3uke/1ONGTPGXHrppdnrqyZNmuS6f5988olp2LCh8fX1NWFhYaZPnz65RjY83fbgdH/bU5/jJ+//zz//bAYOHGjKlCmTPaLfqevOdevWmS5dupiQkBATHh5urr/+erNz587TjiD2923D3+VnG+jxeMyLL75oKlasaHx9fU3Dhg3NlClTTju66KlWrVpl2rRpYwIDA3ONxPfnn3+a3r17m7CwMOPr62saNWqU7+fW6a63sNb5p0pPTzcPP/ywiYuLM/7+/qZp06Zm8uTJ57QdOPXvFR8fb6699loTHh5uQkJCzJVXXmnWrFmTax1+uvtyMpefn5+58sor88x9+PBhc91115kyZcoYx3FybG/yWqft2rXLDB061MTGxhofHx9Tvnx5c8MNN+QYva8gt2UXYz29dOlS07p1axMUFGTi4uLMM888Yz755JNc6+Xt27ebrl27mpCQEANk/11Ptx6cN2+eueKKK7JfT5dddpmZMmVKjnnO9flZEjjGnGZYHxEREQvuuOMOxo8fz6FDh7IHi5Hi5fPPP2fIkCEsW7bsrIeJiZzOlClTuOqqq5g2bVqOER5FbNGheiIiYs3w4cMpX7481apV4/jx40ydOpVPPvmEJ598UqVJpJRat24dO3bs4KGHHqJx48Z0797ddiQRQMVJREQs8vHx4dVXXyU+Pp6srCxq1qzJG2+8wf333287mohYcs8997BgwQKaNm3K6NGjS/d3aqRI0aF6IiIiIiIiZ6HhyEVERERERM5CxUlEREREROQsVJxERERERETOotQNDuHxeNizZw8hISH6sqGIiIiISClmjOHYsWOUL1/+rCdeL3XFac+ePVSsWNF2DBERERERKSJ27dpFhQoVzjhPqStOISEhwIkHJzQ01HIaERERERGxJSkpiYoVK2Z3hDMpdcXp5OF5oaGhKk4iIiIiIpKvr/BocAgREREREZGzUHESERERERE5CxUnERERERGRsyh133ESERERETkbYwxZWVm43W7bUeQC+fj44OXldcHXo+IkIiIiIvI3GRkZ7N27l5SUFNtRpAA4jkOFChUIDg6+oOtRcRIRERER+S+Px8O2bdvw8vKifPny+Pr65mvENSmajDEcOHCA+Ph4ataseUF7nlScRERERET+KyMjA4/HQ8WKFQkMDLQdRwpAVFQU27dvJzMz84KKkwaHEBERERE5hcult8klRUHtMdQzQkRERERE5CxUnERERERERM5CxUlERERERPLl8ssvZ9iwYRd0Hdu3b8dxHFatWlUgmeDE4XiTJ08usOvLiwaHEBERERGRQjF48GCOHj2ao9RUrFiRvXv3EhkZaS/YeVBxEhERERGRi8bLy4vY2FjbMc6ZDtUTERERETkNYwwpGVlWLsaYc8o6Y8YM2rZtS5kyZShbtiy9evViy5YtwP8Oj5s4cSIdO3YkMDCQRo0asWjRouzlDx06xIABA6hQoQKBgYE0aNCA8ePHn/b2hg8fToMGDXJNb9asGU8//TTPPvsso0eP5vvvv8dxHBzHYc6cOXkeqrd27Vp69uxJaGgoISEhtGvXLjv7smXL6NKlC5GRkYSFhdGhQwd+//33c3psCoL2OImIiIiInEZqppt6T/9k5bbXDe9GoG/+364nJyfz4IMP0qBBA5KTk3n66ae5+uqrcxSUJ554gtdee42aNWvyxBNPMGDAADZv3oy3tzdpaWk0a9aMRx55hNDQUKZNm8bAgQOpVq0aLVu2zHV7Q4cO5bnnnmPZsmVceumlAKxevZqVK1fyzTffEB0dzfr160lKSmLUqFEAREREsGfPnhzXs3v3btq3b8/ll1/OrFmzCA0NZcGCBWRlZQFw7NgxbrnlFt555x0AXn/9dXr06MGmTZsICQk5p8f0Qqg4iYiIiIiUANdee22Onz/99FOio6NZt24dwcHBADz88MP07NkTgOeee45LLrmEzZs3U6dOHeLi4nj44Yezl//HP/7BjBkz+Oabb/IsThUqVKBbt26MGjUquziNGjWKDh06UK1aNQACAgJIT08/46F57733HmFhYXz11Vf4+PgAUKtWrezfX3HFFTnm//DDDwkPD2fu3Ln06tUr34/PhVJxEhERERE5jQAfL9YN72btts/Fli1beOqpp1i8eDEHDx7E4/EAsHPnTurVqwdAw4YNs+cvV64cAAkJCdSpUwe3281LL73EhAkT2L17N+np6aSnpxMUFHTa27z99tsZOnQob7zxBl5eXowdO5bXX3/9nHKvWrWKdu3aZZemUyUkJPD0008za9Ys9u/fj9vtJiUlhZ07d57T7VwoFScRERERkdNwHOecDpezqXfv3lSsWJGPP/6Y8uXL4/F4qF+/PhkZGdnz/L2cOI4DkF2wXn/9dd58803eeustGjRoQFBQEMOGDcuxfF636efnx6RJk/Dz8yM9PT3Xnq+zCQgIOOPvBw8ezIEDB3jrrbeoXLkyfn5+tGrV6oy5CkPxeBaIiIiIiMhpHTp0iPXr1/Phhx/Srl07AObPn39O1zFv3jz69OnDzTffDJwoVJs2baJu3bqnXcbb25tbbrmFUaNG4efnR//+/QkMDMz+va+vL263+4y327BhQ0aPHk1mZmaee53mzZvHyJEj6dGjBwC7du3i4MGD53TfCoJG1RMRERERKebCw8MpW7YsH330EZs3b2bWrFk8+OCD53QdNWrUYObMmSxcuJD169dz5513sm/fvrMud9tttzFr1iymT5/O0KFDc/yuSpUqrF69mg0bNnDw4EEyMzNzLX/fffeRlJRE//79Wb58OZs2beKLL75gw4YN2bm++OIL1q9fz5IlS7jpppvOupeqMKg4iYiIiIgUcy6Xi6+++ooVK1ZQv359HnjgAV599dVzuo6nnnqKpk2b0q1bNy6//HJiY2Pp27fvWZerWbMmrVu3pnbt2rkGkbj99tupXbs2zZs3JyoqigULFuRavmzZssyaNYvjx4/ToUMHmjVrxscff5y99+mzzz7jyJEjNGnShIEDB/LPf/6T6Ojoc7pvBcEx5zpAfDGXlJREWFgYiYmJhIaG2o4jIiIiIkVIWloa27Zto2rVqvj7+9uOUywYY6hTpw533nnnOe/luhjO9Dc9l26g7ziJiIiIiMh5SUhI4IsvvmD37t0MGTLEdpxCpeIkIiIiIiLnJSYmhsjISD766CPCw8NtxylUKk4iIiIiInJeStO3fjQ4hIiIiIiIyFmoOImIiIiIiJyFipOIiIiIiMhZqDiJiIiIiIichYqTiIiIiIjIWWhUPRERESl2MrI8JKemkXz8GCkpx0hLOU56yjGCykRRt1ZtHMexHVFEShgVJxERESl4HjdkpkJmCmSm4ElPIS31OGkpx0hPOU5G2nEyUo+TmZaMOz0Fd3oyJiMFk5ECmam4slJwZaXicqfh7U7Fx52Gr0nD16Tjb9LxJ51wJ4tTzxrjNg6/1HiULgMftXK3RaTkUnESEREpbTweyEr9X7HJSPlvwflf0SEzlcy042SmJp/4Ny0Zd3oy7pPl5r/LOFmpuLJS8XKn4u1Ow8eThq8nDV8yc9ykCwj87+WCnbIzyYNDGn5kOj6EcYyOm19m/oyqtL2yX0HcmogIoOIkIiJS9BgDKYf/VmaSTyk5qXn8LhWTkUxW+ok9OJ70ZDx/K0Qn9uCk4fXfcpMfPv+9XKgU40cqvqTiR6rxI8PlR6bjT4bLH7eXP27vANzeARjvAByfQPANxOUbiJdfEF5+Qfj4B+ETEISvfzD+gSH4BwYTEBRCYGAw3v7BuLz9CHQcMIY/37uJBgen0WjR/ayOrkTDpq0K4B6IiKg4iYiIFC1Hd5H5ZT98Dq4950Udzr3spBkfUvAjFT/SjG+O/6fiRwp+ZDp+ZHmdKDcerwDwCcD4BOL4BuLld6LkePsF/7fgBOMbEERAUDD+ASEEBAYT7O9DkJ83Zf288fdxFd73jxyHS+74jE1vdqFm6moifxjIjshfqFypSuHcnpQOxpz4AMIGn0A4h9fL5ZdfTsOGDfH39+eTTz7B19eXu+66i2effRYAx3H4+OOPmTZtGj/99BNxcXG8/vrrXHXVVYV0B0oWFScREZGi4uBm0j7rhX/KXgDSjQ+p/LfMGD/S/rvXJuUM/0/Fl3T88PgEgHcg/LfguPyC8PIL+O8enGB8A4LxCwgi2N+XID9vgv28CPLzJsjPmzL//TfYz5sgXy+8vYrPILwuX38q3jWRPe+0p7x7D2s/v4Ej/5xJeJkw29GkuMpMgRHl7dz243vAN+icFhk9ejQPPvggS5YsYdGiRQwePJg2bdrQpUsXAJ577jleeeUVXn31Vd59911uuukmduzYQURERGHcgxJFxUlERKQo2LeG9FFX4Z9+iC2ecjwZ+gL+ZSsR7O9zotT4/q3M+HkT5OdFiL83sadMDy7svTrFgH9YFP6DviNpVBcu8WxgwQcDaf7Qd/j5FMSBhyJFW8OGDXnmmWcAqFmzJv/5z3/49ddfs4vT4MGDGTBgAAAjRozg3XffZenSpVx55ZXWMhcXKk4iIiK27VpKxuhr8ctKYq2nMmNqvMnoGzvi61189vQUNRGV67Hrqs8I+H4AbdLm8vOHD9Ll3ndKdaGU8+QTeGLPj63bPkcNGzbM8XO5cuVISEjI8/dBQUGEhITk+L2cnoqTiIiITVtmkzluAL7uVJZ5avFjg7cYcW1rvFx6g3+hKjbtxsaE/6PW4kfpenAMP4+vTtcbh9mOJcWN45zz4XI2+ZyyZ9VxHDweT75/L6enj7JEREQsMeunkvXl9fi4U/nN3YC5l37I09epNBWkWlfezdpqtwLQYcPzzPvle8uJRKS4UnESERGxwLPqKzwTBuFtMpnuvpS1l3/EQ72a6FCyQnDJza/xV3hH/Jws6s+7lz9X/247kogUQypOIiIiF5l7yUe4Jt+JF26+dbfnUPcPubtTPZWmwuJyUfOusWzzq024c4yQiTexa/du26lEpJjRd5xEREQuosw5r+Ez53kARru7Ueaa17muSUXLqUo+L78gYu6cRMJ/OlDFs4eVn/UjdNhPhIUUn++uiJzNnDlzck2bPHly9v+NMbl+f/To0cILVMJoj5OIiMjFYAwZM57KLk3vea6hwoB36KPSdNEERsThddMEkvGniftPVrw/lIxMt+1YIlJMqDiJiIgUNo+H9MnD8F38DgCveG6m6S2v0alerOVgpU/Z6s041P1D3MbhipQZzPzk8Tw/hRcROZWKk4iISGFyZ5L69a34/fE5HuMw3LmTK+/4P1pVL2s7WalVqWVftjR/CoCe+z9gxjcfW04kIsWBipOIiEhhyUwj5cubCPhrIpnGi6e9hzHgrqdoWKGM7WSlXq3eD/FXpf4AXL72CebN/dlyIhEp6lScRERECkP6cZI/v4bAbT+RZnx40v8x7rz339SMCbGdTP6rzi3vsSm0FQFOBrVn3c6f69bajiRFiA7hLDkK6m+p4iQiIlLQUg6T/EkvgnYv4Ljx56mQ53jo3n9QMSLQdjL5Oy9vqt09gXifqkQ7R/H7egDx+xJspxLLfHx8AEhJSbGcRApKRkYGAF5eXhd0PRqOXEREpCAd20/yp70JOrqBIyaYEREv8MTtN1Em0Nd2MsmDV0AYEbdP5sj7HahldrD4kwGEPDCNsCB/29HEEi8vL8qUKUNCwokSHRgYqHOsFWMej4cDBw4QGBiIt/eFVR8VJxERkYJydOeJPU3Hd7DflOGN2Jd5duh1BPlpc1uUBUZXIbX/eNLG9+GyrOXMeP8uOj3wGT5eOjCntIqNPTHi5cnyJMWby+WiUqVKF1yAHVPKDuBMSkoiLCyMxMREQkNDbccREZGS4uAmUj7pRWDaPnZ5ovigyhs8Pagnft4XdmiIXDy75o+j4i93A/B9+Qe46vZntKehlHO73WRmZtqOIRfI19cXlyvvD0LOpRvoIzAREZELtXc1qZ9dRWDmETZ54hhX+x2e638F3tpjUaxUbHsjm/dvosafb9Br91tMn1SNHtcMsh1LLPLy8rrg78VIyaE1uoiIyAUwOxaR/kl3AjKP8KenCpMbf8RTAzqpNBVTNa55mk3lr8LLMbT749/MXzDXdiQRKSK0VhcRETlPZvOvZI7ui5/7OEs8dZjXehQPX90Gl0uHdxVbjkPNoZ+yLbgJIU4qVX8eytoNG22nEpEiQMVJRETkPLjXfo97bD98PWnMcTfir06juOfKpvpOTEng7UvFu75jn3cccc5BPONvJP7AIdupRMQyFScREZFzlLniS5xvBuNtMpnmbsnB3p9zS4d6tmNJAfIOLkvIrZNIckJowCa2fjSQpNR027FExCIVJxERkXOQsfB9fKbciwsP33gux+uGz7iuRTXbsaQQBJWrTca1o8nEm/aZC5j9/jCy3B7bsUTEEhUnERGR/DCGtF9fxvfnRwEYbXpQ7uaPubJBBcvBpDBF1u/EvvYvA9AnaRzfj36NUnYmFxH5LxUnERGRszGGlGmP4z9vBAAjuZ4GQ9+jba1oy8HkYqh4xW1srXMXAL13vMS0Kd9YTiQiNqg4iYiInInHzfHv7iNw+UgAXncNoeOdb9C0coTlYHIxVbvhRbZGd8HXcdNmxQPMX7zYdiQRuchUnERERE7HncmxcUMIXvMlbuMwwvterr3n/6hb7sxnl5cSyOWi6m1j2BlYj3DnOBWmD2bd5h22U4nIRaTiJCIikpfMVJI+v4GQzd+TYbz4v8B/MfQfT1MlMsh2MrHE8Q2k3J2TOOgVTRVnL6ljB7DnUKLtWCJykag4iYiInCr9GEmf9CV01yxSjS8jQp/mvvv+RWyYv+1kYplPWCz+t3xLMgE0M2v588OhHE/LtB1LRC4CFScREZG/SzlM0oc9CN2/mGMmgJciR/DQvfcSEeRrO5kUEcGVGpHS5xPcuOiW8QvTP3hEw5SLlAIqTiIiIicd20fS+10IPbyawyaYN8u/zmN3DSXE38d2Miliopr0Ym+rZwG4/uinTPzyPbuBRKTQqTiJiIgAHNnOsZGdCD22mX0mnA+q/YfHbhuAv4+X7WRSRFXodj/bqw8E4KqtzzH1xx8sJxKRwqTiJCIicmADx9/vQkhqPDs80Yyt9yGPDuyLj5c2k3JmVW56mx1l2+LvZNJyyX0sWLHSdiQRKSTaIoiISKlm9qwk5cOuBGcksNETx7Tmn/HgDV1xuRzb0aQ4cHlR6fbx7PGvTpSTSOQPg1i/Pd52KhEpBCpOIiJSarm3LSD9k54EZh3lD081Frb/gnuuaofjqDRJ/jn+oUTdMZkjrghqOzs5Mnog+44ctx1LRAqYipOIiJRKWRt+xj3mavw9ySz21GVjt7EM7tzMdiwppnwiKuFz81ek4Utr8ztLP7iL5PQs27FEpACpOImISKmTsXoijB+Ar0lntqcJh68ex/Vt6tmOJcVccLWWHO8xEoCr0qfw/YfP4PYYy6lEpKCoOImISKmSunQ03hNvxZsspnla4fT/kh5NqtmOJSVEZIvriW/2CAD9Dr3H1+M+tZxIRAqKipOIiJQayXPfJeDHf+LCw7fmCqJu+YLL61WwHUtKmAq9HmNn5Wvwcgy9Nz3BlJkzbUcSkQKg4iQiIiWfMRz76QWCZj8JwBdOb+rcPooW1aMsB5MSyXGoNPBD4sOaE+yk0Wz+nSxctdZ2KhG5QCpOIiJSshlD4uR/E7LoVQA+8hpA67vfp36FMnZzScnm7Uvcnd+S4FuR8s4hQiYNZMOu/bZTicgFUHESEZGSy+Pm6IS7CPvjIwDe9b2Nnv94k+rRIZaDSWngBIZT5rbJHHNCaOBsYc+oQSQkptiOJSLnScVJRERKpqwMDo8ZSJm/vsJtHF4PHEb/f4wgrkyA7WRSivhG14D+Y8nEm46excz94H5SMjRMuUhxpOIkIiIlT0YKhz+7jojt08gwXrxR5nFu+8eTRIX42U4mpVBI7Q4kdn4dgOtTv+abj1/Co2HKRYodFScRESlZ0pI4/FFvIvbMJdX48nb089x774OEBfjYTialWGTbwexpeB8ANya8wfivx1pOJCLnSsVJRERKjuRDHH6/GxEHl5NkAhhZ8TXuv/NuAn29bScToXzf54mP646P46bn+n8zddZvtiOJyDmwXpxGjhxJ1apV8ff3p1mzZsybNy9fyy1YsABvb28aN25cuAFFRKR4SNrDkZGdiUhcxyETwqga/2HY0EH4elvf1Imc4HJRYfAo9oY0oIyTTP25t7NozUbbqUQkn6xuTSZMmMCwYcN44oknWLlyJe3ataN79+7s3LnzjMslJiYyaNAgOnXqdJGSiohIUWYOb+Poe50IT97KXhPBtw0/5p83X4uXy7EdTSQnnwBi7/iOwz6xVHH24fPNIDbtOWg7lYjkg2OMsfbtxJYtW9K0aVPef//97Gl169alb9++vPjii6ddrn///tSsWRMvLy8mT57MqlWr8n2bSUlJhIWFkZiYSGho6IXEFxGRIsCzbx3Jn/YmJPMg2z0x/NbqEwZe2Q7HUWmSoitj7xqyPupCoEnhR1dHLh32FVGh/rZjiZQ659INrO1xysjIYMWKFXTt2jXH9K5du7Jw4cLTLjdq1Ci2bNnCM888k6/bSU9PJykpKcdFRERKhqxdK0j5+EpCMg/yl6ciy64Yy6Du7VWapMjzLVcf97WjcOOih2c2P33wb9Iy3bZjicgZWCtOBw8exO12ExMTk2N6TEwM+/bty3OZTZs28eijjzJ27Fi8vfP3Rd8XX3yRsLCw7EvFihUvOLuIiNiXvvk3Mj/rRbA7kVWe6mzu+RXXX36p7Vgi+RZS/0oOd3gBgJtTRjP207c0TLlIEWb9G7OnfipojMnzk0K3282NN97Ic889R61atfJ9/Y899hiJiYnZl127dl1wZhERsStt7XScL68lwKSwyHMJh6/9hl4t69uOJXLOojrey966QwC4ae+LjP3uO8uJROR0rI3PGhkZiZeXV669SwkJCbn2QgEcO3aM5cuXs3LlSu6778R5EDweD8YYvL29+fnnn7niiityLefn54efn054KCJSUiSvmIDflLvxxs1s0wz/m7/gilpxtmOJnLdy17/O3g+3Um7/XK5c8wBTYyrQq/1ltmOJyCms7XHy9fWlWbNmzJw5M8f0mTNn0rp161zzh4aG8ueff7Jq1arsy1133UXt2rVZtWoVLVu2vFjRRUTEkqQFnxAw5U68cTOdNkQMnUArlSYp7lxelBs6joTAmkQ5SdT65VaWrNtmO5WInMLqGQEffPBBBg4cSPPmzWnVqhUfffQRO3fu5K677gJOHGa3e/duxowZg8vlon79nIdhREdH4+/vn2u6iIiUPEd+eYPw+c8BMNHVlfq3f0ytcmXshhIpKH7BRN0xicR3O1CLeBZ8fQub7/qBGrFlbCcTkf+yWpz69evHoUOHGD58OHv37qV+/fr8+OOPVK5cGYC9e/ee9ZxOIiJSwhnDoanPUnbFWwCM876atne9R6XIILu5RAqYU6Yi/rd8Q9pn3WnDH0z85E7C7x9N2RANUy5SFFg9j5MNOo+TiEgx4vFw4NsHiVo3CoDP/AbR695Xidb5bqQEO7ZyIkHfD8WF4dOQu7jpnyPw9/GyHUukRCoW53ESERE5I3cWCWNvyy5NHwbfzTX3v67SJCVeSJNrONzqCQAGJ33I559/oGHKRYoAFScRESl6stLZP+pGord8R5Zx8X74v7j5Hy9QJtDXdjKRiyKy68Psr3EDXo5hYPxzfPn9VNuRREo9FScRESlaMlLY/9HVxMT/RLrx5qPYZxhyz2ME+Vn9Wq7IxeU4xAwYyf6yLQly0um86p9MW7DSdiqRUk3FSUREio60RPaP7EFMwgJSjB+fVX6ZO+74p77fIaWTlw8xt03gkH9lyjuHqfTTEJZu2GU7lUippeIkIiJFgjl+gIT/dCHm6EoSTSDjar/DnYNvxdtLmyopxQLCCb9tMsddoTRwbSNp/K1sTUiynUqkVNLWSERErDOJ8Rz6Tyeij2/ggAnl+yYfc+uAfrhcju1oIta5Iqvhc/NXZOJNZ5aw+OP7OZycYTuWSKmj4iQiIla5D27h6H86EZm2g92mLHNaf8Ggvr1wHJUmkZP8qrUhtcc7ANyYOZEJH/4f6Vluy6lEShcVJxERsSZzzxqOv9+Z8Mx9bPPEsrLzV1zf7XLbsUSKpNAWN3Go2TAAbkt8l0/GjKaUnY5TxCoVJxERsSJt+xLSP7mSMPdh1ptKbO39Lb3atbAdS6RIK9vrWRIq98bHcXPzjicYM+UX25FESg0VJxERueiO/zULM7oPwZ5jrDI1OXL9JDpd2sB2LJGiz3GIvvkTDpRpRJiTQofl9zJ9yRrbqURKBRUnERG5qJL++AHfr24gwKSymAaYgZNoXb+G7VgixYePP1G3fcdR33JUce0natpQlm/ZazuVSImn4iQiIhfN4cXjCJw0GF8ymeNcSplbJ9KkRkXbsUSKn+AoQm+dRKoriOauDez74na2HzhuO5VIiabiJCIiF0XCnA8oM+MevHEzw9WBynd9S52K0bZjiRRbrpi6uPqNwY2LXsxj1sf/5miKhikXKSwqTiIiUuj2/fgy0XMewYXhe5/uNPrneKrGlLEdS6TY86vdmeTOLwMwNGMsoz9+nYwsj+VUIiWTipOIiBQeY9j93ePELh0BwNcB19P2n59TrkyQ5WAiJUdo2zs43PB2AO46/DoffDlew5SLFAIVJxERKRzGED/uH8T9+R4AY0OGcuX971M2xN9yMJGSJ6LvyxyM64Sfk8mN2x7li+m/2Y4kUuKoOImISKHYOfM/VNj0BR7jMCbin1z7z9cI9fexHUukZHJ5ETloDIdDahPpJHHZ4ruZvnyD7VQiJYqKk4iIFLiMw/GUXfh/AHxb9g4G3Psc/j5ellOJlHB+wUTcNokkn0hquXYT8sOtrNiWYDuVSImh4iQiIgXLGHZ9eTdBpPInNeky5Dl8vLS5EbkowuIIGvwt6Y4/bV1/sm3Mvew8mGw7lUiJoC2ZiIgUqD2LvqL64d/INF4c7vQa4SEBtiOJlCpecU0w13yMB4frzM9M+fgpElMzbccSKfZUnEREpMC4k48QMPMxAGaED6B92w6WE4mUTv4NruJ4+2cAuDvtMz7+5D0y3RqmXORCqDiJiEiB2Tz2AcLNEbaaOC4dOALHcWxHEim1QjsO43CdG3E5hrsPjuC9cZM0TLnIBVBxEhGRApHwx8/U3jMJgC2XjSC2bJjlRCKlnOMQcf07HIppTZCTTr/ND/PFzCW2U4kUWypOIiJywUxGCmbK/QD8HNiLTt36WE4kIgB4+VB28HiOBlWlnHOYJvPv5OeVW2ynEimWVJxEROSCbfj6KWKy9rDPRFDrptdwuXSInkiREVCGMrdOItm7DA1c23Em3cmqnYdtpxIpdlScRETkghzespwamz8DYGXDp6gSV85yIhHJJaIqfjd/RSY+dHEtY/WoYcQfSbGdSqRYUXESEZHz587i2Nd3442H+b5t6NJ3sO1EInIa3lVa4b7qPwAMMt/zzUcvkJSmYcpF8kvFSUREztuG71+hcvpGEk0QUTe8jbdOdCtSpPk37c+xyx4G4L6UDxj56Scaplwkn7SFExGR85K0dxOVV78JwMLqw6hdo6blRCKSHyHdnuRo9b74OG7uSRjOf76epmHKRfJBxUlERM6dMewfexf+ZLDSqwEd+z9oO5GI5JfjUKb/hxwp24RQJ4Vr1j/I2FkrbKcSKfJUnERE5JxtnvkxNY8vJ8344PR+C39fb9uRRORc+PgTPvRbkvzjqOxKoPbcu5mzdqftVCJFmoqTiIick9TDe4le+BwAc8rfSuPGzS0nEpHzEhRJyNCJpLqCudS1kcNTn7WdSKRIU3ESEZFzsu3LfxDKcTY6VWlz8zO244jIBXCi65DZ820A2qfMZNv+o3YDiRRhKk4iIpJv2xdOpN7hmbiNw9HOrxMSFGg7kohcoNDGfUlylSHSSWLVnEm244gUWSpOIiKSL5kpiQTO/DcAsyNuoEWbTpYTiUiB8PLmQOWeAARvnKgR9kROQ8VJRETyZf3YfxFtDrCLGBoPetl2HBEpQOXbDQKgTdYS/ty2x3IakaJJxUlERM4q/s851I//GoAdrf6PyPBwu4FEpEAFVG1Jgk8cgU46m+d+ZTuOSJGk4iQiImfkyUzHfP8PXI5hflAX2nS9znYkESlojkNyrasBiNn5A1luj+VAIkWPipOIiJzR6q+eoWLWTg6ZUKrd9BaO49iOJCKFoEKHwQC09Kxm6dqNdsOIFEEqTiIicloJW1ZxyeaPAFjT8HHKl69gOZGIFBaf6JrEB9bD2/Gwb8FY23FEihwVJxERyZPxuEn8+h58HDfLfVvQru+dtiOJSCHzNLgegBr7fiQlI8tyGpGiRcVJRETytHrym9RMX8tx409kv3dxeWmTIVLSVWx7E1m4aOhsZuHSJbbjiBQp2gqKiEguR/duo/rq1wBYUeMfVKlex3IiEbkYnJAYdpVpAcDx5RpdT+TvVJxERCQnY4j/8m6CSWWdVx1a9XvEdiIRuYgCmt0IQOMjP3P4eLrlNCJFh4qTiIjksGbm59RPXkSG8cLV5118fX1sRxKRiyi2xbWk4UcVZx+L5/1sO45IkaHiJCIi2Y4fPUD5hc8AsDhuMHUatrCcSEQuOr9g4mM6AmBWf2M5jEjRoeIkIiLZNowZRgSJbHcqcOnNL9iOIyKWlG01EIAWKbPZeSDJchqRokHFSUREAPhr4RSaHZ6KxzgkdX2DgMBA25FExJLwBt1IcoUR5SSxcs4k23FEigQVJxERIS3lGCEzHwZgcdm+NGzVzXIiEbHKy4eEij0ACNwwEWOM5UAi9qk4iYgIq798jDizj/1EcMnAN2zHEZEioFz7WwBonbmIdTv2WU4jYp+Kk4hIKbd19QKa7h4LQHzrFwgLj7CcSESKgqBql3HApzxBTjob5+qcTiIqTiIipVhWZgbm+3/g7XhYHnw5zbreZDuSiBQVjkNSjb4ARG3/AbdHh+tJ6abiJCJSii3/6gWqu7eQSBCVb37XdhwRKWIqXj4YgMs8q1ixboPdMCKWqTiJiJRS8ZvX0HjzSAA2NHyUqNhKlhOJSFHjG1Ob+IA6eDseds8fZzuOiFUqTiIipZDxeDjy9b34O5ms8WvMpX3vsx1JRIood/3rAai690fSMt2W04jYo+IkIlIKLZ38HxpkrCLN+BDR730clzYHIpK3iu1uxo2Lxs4mFi1bZjuOiDXaUoqIlDIH9u6kzuqXAPij5r2Ur1bPciIRKcpcobHsDLsUgKRlOlxPSi8VJxGRUsQYw/Yv/0EYyWzxqk7z/k/ajiQixYB/0wEANDj8M0eT0y2nEbFDxUlEpBRZ8fM4Lk2eQ5Zx4er7Ll7ePrYjiUgxUO6y60jDl2rOXhbP/9V2HBErVJxEREqJo0cOUWHRUwCsjLuRqg3aWE4kIsWGXwi7ojsC4P5jguUwInaoOImIlBJrv3iIWA6xx4ml4cCXbMcRkWIm4rITJ8hukTyb3YePWU4jcvGpOImIlAJ/LJhBq0OTATje9TX8AkLsBhKRYqdsox4kuUKJchL5fc5k23FELjoVJxGREi4lJZnQXx7C5RhWlu1JrVa9bUcSkeLIy4f9FboDELD+O8thRC4+FScRkRJu2RdPUdXEc5gwag1823YcESnGyrW7BYDLMhbx1879ltOIXFwqTiIiJdj6P5bQas/nAOxtM5ygMlF2A4lIsRZcozUHvMsR7KTx128aJEJKFxUnEZESKiMzC88P/8TXcbMmuDWXdL7FdiQRKe4ch8QafQGI2joZj8fYzSNyEak4iYiUUPPHv8Ql7r84TgAVbn4fHMd2JBEpASp2OPEhTAv3Kn7/a7PlNCIXj4qTiEgJtHXLBlpseffE/xs9TJnYKnYDiUiJ4VeuLvH+tfBx3OyeP852HJGLRsVJRKSEcbs9HJpwH8FOGpv9LqFBnwdsRxKREibzkusBqLxnKulZbstpRC4OFScRkRLmt0kfcWnGUjKMN2H9RuK4vGxHEpESpnL7gbhx0ZiNLF7+u+04IheFipOISAmye3c8Df/8PwD+qnk7UdUa2w0kIiWSK6wcO0ObAZC4dKzlNCIXh4qTiEgJYYxhy9gHKOskscu7EvVveNZ2JBEpwXya9geg/qGfSErNsJxGpPCpOImIlBDzfvqW9ik/4zEOrj7v4vL1tx1JREqwuMuuJx1fqjl7WLJglu04IoVOxUlEpAQ4cPgI1RY/DsCaCjcQ1+Byu4FEpMRz/MPYGdUBgMyVOhmulHwqTiIiJcAfXzxCBRI44Iqk7k2v2o4jIqVEmZY3A9D8+Cz2HUm2nEakcKk4iYgUc4vm/0LHw18DkNL5FXwCwywnEpHSIqpxD445IUQ7R1kxd7LtOCKFSsVJRKQYS0pOIeLXh/FyDOvKdqFy62ttRxKR0sTbl70VrgTAd913lsOIFC4VJxGRYmzBF8OpbbaRSAjVBv7HdhwRKYVi294CQKv0BWyOT7CcRqTwqDiJiBRTK1euoOPeTwA42OZp/MvEWk4kIqVRaK22HPCOJdhJY91cDRIhJZeKk4hIMZSWkYWZcj/+TiabgptTvfPttiOJSGnlOByt1geAiC3fY4yxHEikcKg4iYgUQ7+Of4Omnj9Jw5fYmz8Ax7EdSURKsYodThyu19L9O39s2GI5jUjhUHESESlm1m/cRNutbwGws+EDhMTWtBtIREo9/7hLiPeviY/jZuf8cbbjiBQKFScRkWIk0+3hwDfDCHOS2elXk1p9/m07kogIABn1TozqWTF+KhlZHstpRAqeipOISDEyc+JntM+cTxYuQm74ALy8bUcSEQGgcodb8ODQhA0s/f1323FECpyKk4hIMbEtfg/N1rwAwJYaQwiv3txyIhGR//EKK8/2kGYAHFmiw/Wk5FFxEhEpBjwew8axDxPjHGG/d3lq3fCC7UgiIrn4NOkHQN2DMzielmk5jUjBUnESESkGfv5pMt1SpwHguuodHN9Ay4lERHKr0OoGMvChhrObxQvn2I4jUqBUnEREirg9B49Sa/ETAGwsfzVRDbtYTiQikjcnoAzbIzsAkPH7eMtpRAqWipOISBFmjGHZF09SzdnNEVc41W9603YkEZEzCmtxIwDNjv1KQmKy5TQiBUfFSUSkCJs9by7dj574knVa5xfxCgq3nEhE5MximvbmmBNMjHOUFXOn2I4jUmBUnEREiqjDx1KJmvUwvo6bLWU7UK5Vf9uRRETOztuXPXFXAuCz9hvLYUQKjoqTiEgRNfuLETRgE8kEUvGmkeA4tiOJiORLTNtBALRMW8C2vQcspxEpGCpOIiJF0MLfV3Hl/g8BONz6cXwjKlhOJCKSf2VqteOgVzQhTipr5mivk5QMKk4iIkXM8bRMmPoAQU46u4IbUrHzvbYjiYicG5eLQ9X6ABC+eRLGGMuBRC6cipOISBEzffx/aO35nQy8ibzxA3BpVS0ixU/FDoMBaJG1gj83b7eaRaQgaGssIlKErPprM1dsfx2AvQ3vI6D8JZYTiYicn8AK9Yn3q46v42bHb+NsxxG5YCpOIiJFRFqmm4RvH6asc4x9flWpfNUTtiOJiFyQtLrXAVBh1w9kuj2W04hcGBUnEZEiYsrEL+maNRsPDsHXvw/evrYjiYhckCodBuHBoQl/seyPP2zHEbkgKk4iIkXAXzv30XLdCwDsrH4zwTVaWU4kInLhvMMrsD24CQCHF421nEbkwqg4iYhYluX2sH78o1RyEjjkHU2VG160HUlEpMB4NT5x8u46CdNJSc+0nEbk/Kk4iYhY9sP0aVyVMhkAr95vgV+I1TwiIgWpUpt+pONDDSeeJYvm2o4jct5UnERELNqRcJS6y57AyzHsKN+DMo162o4kIlKgnIAy7IhoB0Da719ZTiNy/lScREQsMcaw8IvnqOvs4JgrlEo3vmM7kohIoQhpeRMATRJ/4VBSiuU0IudHxUlExJJpc+ZzTdIXAGRc8TxOcJTlRCIihaNcs94cd4KIdY6w4rcptuOInBcVJxERC/YnphAz99/4OZnEh19G2Ta32I4kIlJ4vP2IL98NANeaby2HETk/Kk4iIheZMYafvniVS1lHGn7E3vQBOI7tWCIihSqm7SAAWqTOY8f+Q5bTiJw7FScRkYts1rLV9D3wAQCJrf6Nd2RVy4lERApfeO0OHPSKItRJZe3sr23HETlnKk4iIhfR0ZQMmP5vQp0U9gXVJabzMNuRREQuDpeLg1WvAiB08ySMMZYDiZwbFScRkYto8viP6GQW48ZFxI0fgpe37UgiIhdNxfYnvs95aeYK1m/ZaTmNyLlRcRIRuUgWrt3KlTtfAyChwV34xjWynEhE5OIKqtSIeN9q+DlZbJ031nYckXOi4iQichGkZGSxf+KjxDpHOOhXkXJXPW07koiIFSl1rgWg/M4puD06XE+KDxUnEZGL4OvvvuZq908ABF73HvgEWE4kImJHlQ6D8ODQ1Kzj99V/2o4jkm8qTiIihWzV1r20Wz8cgD3V+xFYs4PlRCIi9viWrcT2oMYAJCz8wm4YkXOg4iQiUogysjysnfAM1V17SfKKoPx1r9iOJCJindPoBgBq7p9OWqbbchqR/FFxEhEpRF9P+4kb0r4FwOn5GgSUsRtIRKQIqNx2ABl4U8vZxZLFv9mOI5IvKk4iIoVk496jNFjxBD6Om73lOhHS5BrbkUREigRXYDjbItoCkLp8nOU0Ivmj4iQiUgjcHsO8sf9HI9cWUpxAYvu/C45jO5aISJERcumNADQ6+gtHk9MspxE5OxUnEZFCMHHWAvofGw1A5hXP4oTF2Q0kIlLElG/eh+NOEOWcwyybO9V2HJGzUnESESlguw4lEzvvCYKcdPaHNyWsze22I4mIFD0+/uyK7QqA68+vLYcROTsVJxGRAmSMYcrYd2jnrCITH6IGfAgurWpFRPIS02YgAJemzCP+wGHLaUTOTFtzEZECNHXRGvodeg+AYy0fwBVdy3IiEZGiK6JeRw65Igl1Uvhz9re244ickYqTiEgBOXAsHX5+nLLOMQ4G1SCiy79sRxIRKdpcLhKq9AYgZON3GGMsBxI5PRUnEZECMuGrUfTmNzw4lOn3AXj72o4kIlLkVegwGIBLM5ezYfsuu2FEzkDFSUSkAMz6Yyt9418F4HD9oXhXutRyIhGR4iGkcmN2+1bFz8li61yd00mKLhUnEZELlJSWyb7vn6KCc5CjvrFE9h5uO5KISLFyvNaJE4TH7vgBj0eH60nRpOIkInKBvvh2Iv3c0wAIvPpt8Au2nEhEpHipfPktADQ1a1m5Zo3lNCJ5U3ESEbkASzbv44qNz+PlGA5U7YNv3SttRxIRKXb8IyuzNagRAPsXfGk5jUjeVJxERM5TWqab1ROGU9e1i2SvMKKue8N2JBGRYss06AdAjf0/kpbptpxGJDcVJxGR8zR6yi8MyjhxtntX9xchKNJyIhGR4qtquxvJwJta7GT50nm244jkouIkInIe1sQfodGqZ/BzMjkY246AZjfajiQiUqy5gsLZFt4GgOPLxltOI5KbipOIyDnKdHv4ddxrXOZaT7rjT2S/98BxbMcSESn2Av/7IVTDIzNJTEm3nEYkJxUnEZFz9OXMJQxJ/gyAzA5PQHhly4lEREqGCi37cJwgyjuHWPHbVNtxRHJQcRIROQdbDhyn/MKnCXVSOFymPsHt77UdSUSkxHB8AtgZ2xkAs/oby2lEclJxEhHJJ4/HMGns+3RzLSULL8L7fwAuL9uxRERKlKjWNwPQPHkuew8dtRtG5G9UnERE8unrBWsYeOQ/ACRfeh9ObAPLiURESp6o+p045IokzElh9WztdZKiQ8VJRCQf9iam4vrlWWKcoyQGVias6+O2I4mIlEwuL/ZX7glA0IaJlsOI/I+Kk4jIWRhjGD1+LDc4vwAQfN174ONvOZWISMlVsf0tAFyasYzNO+MtpxE5QcVJROQspq/azg17XgXgaL2b8KrWznIiEZGSLaRKU3b7VMHPyWTTnHG244gAKk4iImeU6faw78eXqebax3GfSMr0HmE7kohIyec4JNW6GoDo7d/j8RjLgURUnEREzmjKojVcnzEZAO8eL0FAGat5RERKi6odBwPQxL2W1evX2Q0jgoqTiMhppWe5SZ79BiFOKgeDa+Pf6FrbkURESg3/yCpsDWyIyzHsnf+l7TgiKk4iIqfzw/yVXJc1DYDQHs+CS6tMEZGLyVP/egCq7Z1GRpbHchop7fQuQEQkD2mZbrJ+e4MAJ4MDYQ3wrdvddiQRkVKnaoebycSb2uxg+bIFtuNIKafiJCKSh0lzlnCN+ycAwno+B45jOZGISOnjFRTB1jKtATi2VKPriV0qTiIip0hOz8Jn4Rv4OVkkRDTHt+YVtiOJiJRa/k37A1D/8M8cS023nEZKMxUnEZFTTJo1nz6eWQBE9NbeJhERmypddjXJBBDnHGTFvBm240gppuIkIvI3SWmZhCx5Ax/Hzf6oNnhXbWs7kohIqeb4BrI9pjMAnj++spxGSjMVJxGRv5n082x6md8AiLzqectpREQEILLVQACaHp9LwpEky2mktFJxEhH5r6MpGUSveBMvx7CvXCe8KjazHUlERICYhp057IqgjJPMqtnf2I4jpZSKk4jIf02a/hPdnYUARF/1nOU0IiKSzeXF3kq9AAj86zvLYaS0UnESEQEOHk+n0uq3ANhXoTuucg3sBhIRkRzi2t0CwKXpS9kWv8dyGimNrBenkSNHUrVqVfz9/WnWrBnz5s077bzz58+nTZs2lC1bloCAAOrUqcObb755EdOKSEn1/bQpdHKW48ZFzFXP2o4jIiKnKFOtGbt9KuHnZLJxts7pJBef1eI0YcIEhg0bxhNPPMHKlStp164d3bt3Z+fOnXnOHxQUxH333cdvv/3G+vXrefLJJ3nyySf56KOPLnJyESlJ9ielUWvtOwAcqNIHJ7qO5UQiIpKL45BY42oAIrdNxhhjOZCUNo6x+Kxr2bIlTZs25f3338+eVrduXfr27cuLL76Yr+u45pprCAoK4osvvsjX/ElJSYSFhZGYmEhoaOh55RaRkuXTsV9y66Z7ycILr38sxylbzXYkERHJQ2rCVgJGNsFjHNb2X0SDunVtR5Ji7ly6gbU9ThkZGaxYsYKuXbvmmN61a1cWLlyYr+tYuXIlCxcupEOHDqedJz09naSkpBwXEZGTdh9JocGG/wBwsMb1Kk0iIkVYQHQ1tgY0wOUY9sz70nYcKWWsFaeDBw/idruJiYnJMT0mJoZ9+/adcdkKFSrg5+dH8+bNuffee7nttttOO++LL75IWFhY9qVixYoFkl9ESobpP3xFC9d6MvEhtvdTtuOIiMhZZF1yLQCV9kwj0+2xnEZKE+uDQziOk+NnY0yuaaeaN28ey5cv54MPPuCtt95i/Pjxp533scceIzExMfuya9euAsktIsXfjoPHabblPQAO170JwipYTiQiImdTrcNAsvCiLtv4ffki23GkFPG2dcORkZF4eXnl2ruUkJCQay/UqapWrQpAgwYN2L9/P88++ywDBgzIc14/Pz/8/PwKJrSIlCgzvx/Dba7NpDt+xPR43HYcERHJB++QSDaEtaJ24nwSl46Dlm1sR5JSwtoeJ19fX5o1a8bMmTNzTJ85cyatW7fO9/UYY0hPTy/oeCJSwm3en0SrHR8AkFh/CISc+QMbEREpOnyb9AOg3sGfSE7LsJxGSgurh+o9+OCDfPLJJ3z22WesX7+eBx54gJ07d3LXXXcBJw6zGzRoUPb87733HlOmTGHTpk1s2rSJUaNG8dprr3HzzTfbugsiUkzNmfwpl7h2kOoEEt39EdtxRETkHFRpfS3JBFDBOcCK+TNsx5FSwtqhegD9+vXj0KFDDB8+nL1791K/fn1+/PFHKleuDMDevXtznNPJ4/Hw2GOPsW3bNry9valevTovvfQSd955p627ICLF0PrdR+iw+2NwwfEmdxAQGGE7koiInAPHN4jt0VdwScI0slZNgM5X2Y4kpYDV8zjZoPM4icgn773EbQdeJNkVQtC/1kBAGduRRETkHO39/UfK/TCAIyYY94N/ERkWYjuSFEPF4jxOIiI2/LnzIJ32fwZA2qX3qjSJiBRT5Rp347ATTrhznD9mfWs7jpQCKk4iUqosm/weVV37OeZVhrJX/MN2HBEROV8uL/ZU7AmA31/fWQ4jpYGKk4iUGr9v3UvXQ2MAyGw1DPyC7QYSEZELUr79iUHEmqctZseefWeZW+TCqDiJSKnxx/fvUsE5SKJ3JBEd7rIdR0RELlBE9Rbs9q6Iv5PJhtnjbMeREk7FSURKhSUb4ul5dCwAnnYPg0+A5UQiInLBHIej1fsCELF1MqVszDO5yFScRKTEM8bw19Q3iXaOcsQnlvA2t9qOJCIiBaRKx8EANMlazfqNG+2GkRJNxUlESrwF67bTK2kCAK6Oj4K3r+VEIiJSUIJia7DF/xK8HEP8vC9tx5ESTMVJREo0Ywzbp71OWecYh/wqEtZyoO1IIiJSwDLrXQdAxfipZLk9ltNISaXiJCIl2pw/NnFV8olhan06PwFe3pYTiYhIQavecSCZeFGXraz8fYntOFJCqTiJSInl8Rj2zniNUCeFAwHVCW3Wz3YkEREpBD4hUWwJbQnA0SVjLaeRkkrFSURKrF9XrOOq1O8BCOj6FLi0yhMRKal8GvcHoM6Bn0hNz7KcRkoivYsQkRLJ7TEcmfkqwU4a+4PqENy4r+1IIiJSiKq1vY4U/KnoJLBiwU+240gJpOIkIiXSz0v+4Kr0qQCE9HgGHMdyIhERKUyObxBbo64AIHPleMtppCRScRKREifL7SH115fxdzLZG9qQwHrdbUcSEZGLILzlTQA0SprNkaRky2mkpFFxEpESZ8b8ZfTKPHGYRpmez2lvk4hIKRHX5EqOOGWIcI6zas53tuNICaPiJCIlSkaWB89vr+DruNlT5lICal9hO5KIiFwsXt7Ex/UAwHftN5bDSEmj4iQiJcqPcxfQI2sWAGV7P2c5jYiIXGzl2t0CQNO0xcTvS7CcRkoSFScRKTHSMt34LXgFb8dDfGRb/Kq3sR1JREQusshaLdnjXYEAJ4O/Zo+zHUdKEBUnESkxpv06m27ueQBE9xluOY2IiFjhOByu1geAMpsnY4yxHEhKChUnESkRUjKyCFvyKi7HsCumE74Vm9mOJCIillTuOBiAJlmr2Lhls90wUmKoOIlIiTDtp5/obBbjwSG2j77bJCJSmoWUq8VWv3p4OYadv31pO46UECpOIlLsHUvLJHrF6wDEx3XHp3wDy4lERMS29HrXAhC3awpujw7Xkwun4iQixd6PP06hAytw4yKur77bJCIiUO3ygWThop7Zwh8rl9mOIyWAipOIFGtHUzKo9McbAOyu1BevqJqWE4mISFHgFxbD5pAWABxerMP15MKpOIlIsTZ96re0cv4kE28q9H3GdhwRESlCvBr1A6B2wgzSMrIsp5HiTsVJRIqtQ8fSqLn2bQD2Vr8eV0QVu4FERKRIqd72BlLwp6Kzn5ULf7YdR4o5FScRKbZ+nvoVzZ2/yMCHilc9ZTuOiIgUMS7/YLaWvRyAtN+/shtGij0VJxEplhISU7nkr3cA2F/7ZpywOMuJRESkKAptcSMAjRJnkXgsxXIaKc5UnESkWPr1+9E0dLaQhh8Vej9uO46IiBRRlZr35IhThgjnGKvmTrQdR4oxFScRKXZ2H0mmyZb3ADhwyVCc4GjLiUREpMjy8mZX+e4AeK/9xnIYKc5UnESk2Jk76WPqODtJcQKp0PPftuOIiEgRF9tuIABNUxayN+GA5TRSXKk4iUixsvNAEi13fAjAkUZ34gRGWE4kIiJFXXTt1uzxiiPAyWDd7HG240gxpeIkIsXK/EnvU93Zw3FXCHFXPmg7joiIFAeOw8GqfQAI2zTZbhYptlScRKTY2LLvCG13fwxAUrP7wD/UciIRESkuqnS8BYAmmSvZvHWr5TRSHKk4iUixsXTSu1RyDpDoFU75Lv+wHUdERIqR0Lg6bPWri5dj2D53jO04UgypOIlIsbAhPoEO+0YBkNLyfvANspxIRESKm9Q61wBQfucUPB5jOY0UNypOIlIsrJz0FuWdwxzxjqJcx7ttxxERkWKoesdBZBkX9cxm1qxeYTuOFDMqTiJS5K3ZtpdOB78EIKP1Q+DjbzmRiIgUR/5lYtkccikABxZ+aTmNFDcqTiJS5K37/jWinEQO+pQjpv2ttuOIiEgx5mrUD4CaCT+Skem2nEaKExUnESnSVm7aSZcjXwFg2j8C3r6WE4mISHFWvd0NpOJHJfazctEvtuNIMaLiJCJF2pYprxLuHCfBtxJRrQfajiMiIsWcl38IWyI6AJC6YrzlNFKcqDiJSJG1bP0WuiZ+A4DrisfBy9tyIhERKQmCW9wEQIOjv3IsOcVyGikuVJxEpEgyxhA/9WVCnVT2+VcnskU/25FERKSEqHxpT444YZR1klg1d7LtOFJMqDiJSJG0+M8NdDs+GQC/rk+BS6srEREpGI6XD7vKdQPAteYby2mkuNA7EREpcowxJEx/kUAnnd2BdQlv0td2JBERKWFi2t4CQJPkBSQcPGQ5jRQHKk4iUuTMX7GaK1OmARDU/RlwHMuJRESkpImp24a9XuUIdNJZO1uDRMjZqTiJSJHi8RgSZ76En5PJrpBGlKl/pe1IIiJSEjkOCVWuAiBk40TLYaQ4UHESkSLlt6XL6Zr2EwBlej6nvU0iIlJoKl8+BIDGGSvZtn273TBS5Kk4iUiR4fYY0n59EV/HzY6wFoTU6Wg7koiIlGBlKtZlq29tvB0P2+aOsR1HijgVJxEpMmYvXEiXjFkAlO093HIaEREpDZJrXwNAzI4pGGMsp5GiTMVJRIqELLcH5ryEl2PYFtGO4BqtbEcSEZFSoEbHW8gyLi7xbGTtnyttx5EiTMVJRIqEX+bO4YrMeQDE9NHeJhERuTgCIsqxObg5AAkLv7ScRooyFScRsS4jy4P//JdxOYatUZ0JrNzUdiQRESlFTMPrAai+70cys9yW00hRpeIkItb98utPXO5ZjAeH8n21t0lERC6umu37k4ofldnLqiW/2o4jRZSKk4hYlZbpJmzxKwBsK9cD/7hLLCcSEZHSxjsglM3h7QFIXvaV5TRSVKk4iYhVv/z0A23M72ThokLf52zHERGRUiqo+QAA6h/5heTUNMtppChScRIRa1Iysohe8ToA2ytcjV9MTcuJRESktKrasjdHCSXSSWTV3Em240gRpOIkItb88uM3tDB/koE3la95xnYcEREpxRxvX7bHdjvxw+pv7IaRIknFSUSsOJ6WSaVVbwKws8oN+ERUtpxIRERKu+g2AwFonDyfA4cPW04jRY2Kk4hYMWvKWBqzgXR8qdL3KdtxREREKF+/PXtdsQQ56aybNd52HCliVJxE5KJLTM6gxtq3ANhV4ya8y5S3G0hERATAcdhf+SoAgjbqe06Sk4qTiFx0c374jHpsIxV/qvV5wnYcERGRbJUuHwxA4/QV7Ni1024YKVJUnETkojp8LJV6f70LwJ46g3GFRFlOJCIi8j8RlS9hm29NvB0PW2ePsR1HihAVJxG5qOZN/oiaTjzHnSCqXfWo7TgiIiK5JNW8BoDo7T9gjLGcRooKFScRuWgSEo/TaPPIE/+vfztOYLjlRCIiIrnVuOIW3MbhEs8G/lq32nYcKSJUnETkolk48T2qOPtIckKp2vMh23FERETyFFQ2jk1BzQDYt+ALy2mkqFBxEpGLYs+hRJpv/wiAQ43vwfEPtZxIRETk9DwNrgeg2p5pZGW5LaeRokDFSUQuimUT36aCc5AjrnCqdP+n7TgiIiJnVLPDAFLxpTJ7+GPZXNtxpAhQcRKRQhefcIiW8Z8BkNj8fhzfIMuJREREzswnMIzNZdoBcHzZOMtppChQcRKRQrfiuzeIdY5w0CuKKl3vsR1HREQkXwKaDwDgkkM/k5qWbjmN2KbiJCKFatvu/bTZd+I8GCmXPQTefpYTiYiI5E/1y/pwlBAinURW/faD7ThimYqTiBSqNZNeJdJJYr93eSpdcZvtOCIiIvnmePuyPaYrAJ4/JlhOI7apOIlIodm0I572B8YCkNH23+DlYzmRiIjIuYlsPRCARsfncfjoUbthxCoVJxEpNBsnv0yYk8Jen8pUbD/IdhwREZFzVqHh5exzxRDspLF21njbccQiFScRKRTrt2yn/eFvADAdHwOXl+VEIiIi58Fx2FupNwABf020HEZsUnESkUKx7fsRhDipxPvVoPxl/WzHEREROW8VO9wCQKP0FezevctyGrFFxUlECtzqvzbQMXESAN6dnwSXVjUiIlJ8RVZtyDafGvg4bjbN/sJ2HLFE72ZEpMDtmfoiAU4GOwLqEdu8r+04IiIiFyyx5tUARG77HmOM5TRig4qTiBSolX/+ScdjUwAI6PYMOI7lRCIiIheuesdBuI1DffdfbPzrT9txxAIVJxEpMMYYDk4fgZ+TxdagxkQ36mY7koiISIEIiarExsCmAOydr8P1SiMVJxEpMMtXruTy5J8ACO3xrPY2iYhIieKufx0AlXdPw+32WE4jF5uKk4gUCGMMx376P3wcN5tDWxJ5SUfbkURERApUrctvJA0fqrKbP5fPtR1HLjIVJxEpEEuWLaJD2q8ARPR+znIaERGRgucbVIaNYe0ASFo6znIaudhUnETkghljSP9lBF6OYWOZ9kTUbGU7koiISKHwa9ofgLoHfyYtPcNyGrmYVJxE5IItWDCXDhnzAIjpM9xyGhERkcJTs3VfEgkmyjnK6nk/2I4jF5GKk4hcELfH4JozAoANZTsTVrWJ5UQiIiKFx+Xjx5borgBkrZpgOY1cTCpOInJB5s/9idZZS3DjUL6v9jaJiEjJF9nqZgAaHptHYmKi5TRysag4ich5y3J7CJj/EgAbo3sQUvESy4lEREQKX6VGHdnvRBPspLJmtvY6lRYqTiJy3ub98gMt3CvJxItK12gkPRERKSVcLnZX6gWA3/pvLYeRi0XFSUTOS0amm/AlLwOwqXwfgmJrWk4kIiJy8ZRvc+JwvQZpv5OWlmY5jVwMKk4icl7m/fwtjT3ryMCbatc8azuOiIjIRRVTozHHCMTPyWTb+uW248hFoOIkIucsLSOLmOWvArC54vX4R1a2nEhEROTiclxe7PKvBcCRzUstp5GLQcVJRM7ZvB/HUt9sIg1fql3ztO04IiIiViSXbXDiP3tWWc0hF4eKk4ick9T0TCqtehOALVVvwj+8vOVEIiIidvhVagZA2aR1lpPIxaDiJCLnZP6Uz6jNNpIJoObVT9qOIyIiYk35uq0AqJK1jdTUVMtppLCpOIlIvh1PTaf6mrcB2F7zFnxDIy0nEhERsSeyYm2SCMLPyWLbumW240ghU3ESkXxbOPkDqrGbJIKp3fdR23FERETschx2+dcGNEBEaaDiJCL5kng8hTob3gMgvt5teAeFW04kIiJiX0pkfQBce1fZDSKFTsVJRPJlyaR3qcR+jjph1L7qYdtxREREigT/Ss0BKJu03nISKWwqTiJyVocTj9Fg84cA7G1wN17+IZYTiYiIFA3l6l4GQBX3NpKTky2nkcKk4iQiZ7Vs4luUcw5x0Imgds/7bccREREpMiIr1CKRYHwdN9vXa4CIkkzFSUTOKOHwYZps/wSAg03/icsv0HIiERGRIsRx2BVQB4Cjm1WcSjIVJxE5o1UTXyfaOUqCK5ra3e+xHUdERKTISS3bAADXvpWWk0hhUnESkdPad+AAzXeNBuBoiwdxvP0sJxIRESl6/Cs3AyBSA0SUaCpOInJaf373MhHOMfZ4xVGzy22244iIiBRJ5eu1AqCKewfHk49bTiOFRcVJRPIUv2cPLfeOBSCl9b9wvHwsJxIRESmaypavzhFC8XHcbF+rE+GWVCpOIpKnvya+SKiTwi7vKtToeIvtOCIiIkWX47A7oDYAiVtUnEoqFScRyWXHrp1cduBrADLaPwourSpERETOJDXyxAARXvv+sJxECoveDYlILlsmvUCwk8Z235pUb9ffdhwREZEiL6BKcwAij2mAiJJKxUlEctiydTOtD0088UPHJ8Bx7AYSEREpBuLq/m+AiGPHkiynkcKg4iQiOez6/nn8nUy2+NWjymV9bccREREpFsLLVeUwYXg7Hnas0/ecSiIVJxHJtnHDWlofnQKAb9dntLdJREQkvxyH3YF1AEjcssxyGCkMKk4ikm3flOfxddxsDGxCxWZX2o4jIiJSrKRlDxCxym4QKRQqTiICwLq1q2h97CcAgq58xnIaERGR4ufkABFRxzVAREmk4iQiAByeNhxvx8P64JbENexoO46IiEixU6FeawCquHeSdCzRchopaCpOIsLqlUtonTwLgPBez1lOIyIiUjyVianEQcLxcgw71iy2HUcKmIqTSClnjCHlp+G4HMPasPbE1mllO5KIiEjx5DjsDaoNQOLW5ZbDSEHzPtcFEhMTmTRpEvPmzWP79u2kpKQQFRVFkyZN6NatG61bty6MnCJSSP5Y9huXpc3HYxyiemtvk4iIyIVIi2wIyYvx3r/KdhQpYPne47R3715uv/12ypUrx/Dhw0lOTqZx48Z06tSJChUqMHv2bLp06UK9evWYMGFCYWYWkQJijCHzlxcAWBvRmegaTS0nEhERKd4Cq1wKQPSxvywnkYKW7z1OjRo1YtCgQSxdupT69evnOU9qaiqTJ0/mjTfeYNeuXTz88MMFFlRECt7vC37m0oyluI1D+b7a2yQiInKhKlxyGcyFyp5dJB49QliZcNuRpIDkuzitXbuWqKioM84TEBDAgAEDGDBgAAcOHLjgcCJSeIwxuOaOAGBNVE8aVb7EciIREZHiLyy6EgecCKI4zI51S2jYWudFLCnyfaje2UrTScaYc5pfROxYNvsHmmSuItN4UenqZ23HERERKTH2BtYBIGnrMstJpCCd16h6AwcO5Pjx47mmb9++nfbt219wKBEpXB63h8AFLwGwNrYv4XE1LScSEREpOdKjGgLgu/8Py0mkIJ1XcVq3bh0NGjRgwYIF2dNGjx5No0aNiImJKbBwIlI4lvz6LfXd60jHh2rXPGs7joiISIkSVLU5ANHH11tOIgXpvIrTkiVL6NevH1dccQWPP/44119/Pffddx9vvvkm3377bUFnFJEClJXlJnzxywCsi7ue0JhKlhOJiIiULBUuOXF6nkqe3Rw5cthyGiko53weJwBvb29eeukl/Pz8eP755/H29mbu3Lm0aqUTZ4oUdUt+Gksbz2ZS8aPmNU/ZjiMiIlLihEbGkeCUJZpD7Fy7iPC2PW1HkgJwXnucMjMzeeihh3j55Zd57LHHaNWqFVdffTU//vhjQecTkQKUmZVFzPLXAVhf6UaCy5a3nEhERKRk2htYF4Bj25ZbTiIF5bz2ODVv3pyUlBTmzJnDZZddhjGGV155hWuuuYahQ4cycuTIgs4pIgVgybTPaGu2c5xA6lz7hO04IiIiJVZGdEPYNl8DRJQg57XHqXnz5qxatYrLLrsMAMdxeOSRR1i8eDG//fZbgQYUkYKRmZVF3Kp3ANhY7RYCw3TKABERkcJycoCImOS/LCeRgnJexenTTz8lKCgo1/TGjRuzYsWKCw4lIgVvxcyvqGp2cZxA6l39iO04IiIiJVrF+icGiKhsdnP48CHLaaQg5Ls4JScn52s+Pz+/c5pfRAqfMYbgFe8BsKHCdfiHhFtOJCIiUrKFRJRjv3Pi6I6daxdaTiMFId/FqUaNGowYMYI9e/acdh5jDDNnzqR79+688847BRJQRC7cH4t+pn7WOjKMF9V7/8t2HBERkVJhb9CJASKOa4CIEiHfg0PMmTOHJ598kueee47GjRvTvHlzypcvj7+/P0eOHGHdunUsWrQIHx8fHnvsMe64447CzC0i5yBr3lsArInsTlOdt0lEROSiyIxpCMd/wzdBA0SUBPkuTrVr1+abb74hPj6er7/+mnnz5rFw4UJSU1OJjIykSZMmfPzxx/To0QOX67y+OiUihWDj2t9pmrIIHCjXXXubRERELpbgqpfCFojVABElgmOMMbZDXExJSUmEhYWRmJhIaGio7TgihW7hmzfROnEqfwa1psG/ptuOIyIiUmocO7KfkLdrAXDovk2UjYy2nEhOdS7d4LzO4/Tggw/mOd1xHPz9/alRowZ9+vQhIiLifK5eRArInvjtNDs6AxwIuiLv162IiIgUjpDwGPY60ZQzCexcu4iyHfrYjiQX4LyK08qVK/n9999xu93Url0bYwybNm3Cy8uLOnXqMHLkSB566CHmz59PvXr1CjqziOTTlqmvU97JYqNvPWo17Ww7joiISKmzP7gu5Y4lkLx9Oag4FWvn9WWkPn360LlzZ/bs2cOKFSv4/fff2b17N126dGHAgAHs3r2b9u3b88ADDxR0XhHJp6NHDtNo77cAZLb8BziO5UQiIiKlT2ZMIwD8ElZbTiIX6ryK06uvvsrzzz+f4zjA0NBQnn32WV555RUCAwN5+umndTJcEYv+nPIOoU4K8a446nXsZzuOiIhIqRRS9VJAA0SUBOdVnBITE0lISMg1/cCBAyQlJQFQpkwZMjIyLiydiJyXtLQ0am4dA8CBBnfguLwsJxIRESmdKl7S6sS/7OPAgX2W08iFOO9D9YYOHcqkSZOIj49n9+7dTJo0iVtvvZW+ffsCsHTpUmrVqlWQWUUkn37/8VNiOcQhylC/h86pJiIiYktQmSj2OLEAxK9daDmNXIjzKk4ffvghnTp1on///lSuXJlKlSrRv39/OnXqxAcffABAnTp1+OSTTwo0rIicndvtIXbNhwBsrT4QH79Ay4lERERKt/3BdQFI3q6vsRRn5zWqXnBwMB9//DFvvvkmW7duxRhD9erVCQ4Ozp6ncePGBZVRRM7Bylnf0Nyzg2T8qdd7mO04IiIipV5WbEM4Nht/DRBRrJ1XcTopODiYhg0bFlQWEblAxhj8lv4HgPXlrqZ5mUjLiURERCS0WgvYBOVSNEBEcXZeh+qJSNG0fsUcGmSuJtN4UaXXw7bjiIiICP8bICKOBBL277GcRs6XipNICZI8+w0A/gzvTGRcDctpREREBCAwtCy7XeUAiF+7yHIaOV8qTiIlxI5Nq2l6fB4AUVf+y3IaERER+buE4HoApOxYbjmJnC8VJ5ESYvf01/FyDH8GtKBinUttxxEREZG/yYo5MS6A/wENEFFcqTiJlAAJ++JpemgaAL4dhtkNIyIiIrmEVm8BQPmUDRhjLKeR86HiJFICbJzyBv5OJpu9a1K7ZQ/bcUREROQUleqdGCCiPAdI2Lfbcho5H9aL08iRI6latSr+/v40a9aMefPmnXbeiRMn0qVLF6KioggNDaVVq1b89NNPFzGtSNFz/Fgi9XdPACC5+b3gOJYTiYiIyKkCQsOJd8UBEL9uoeU0cj6sFqcJEyYwbNgwnnjiCVauXEm7du3o3r07O3fuzHP+3377jS5duvDjjz+yYsUKOnbsSO/evVm5cuVFTi5SdKye8h/KcJw9TiwNOg+0HUdEREROIyGkLgCpO1ZYTiLnw2pxeuONN7j11lu57bbbqFu3Lm+99RYVK1bk/fffz3P+t956i3//+99ceuml1KxZkxEjRlCzZk2mTJlykZOLFA2ZmRlU2fg5AHvq3YbL+4LOaS0iIiKFyB3bCICAA39aTiLnw1pxysjIYMWKFXTt2jXH9K5du7JwYf52X3o8Ho4dO0ZERMRp50lPTycpKSnHRaSkWDnjc8qTwBFCqd/zbttxRERE5AzC/jtARFyqBogojqwVp4MHD+J2u4mJickxPSYmhn379uXrOl5//XWSk5O54YYbTjvPiy++SFhYWPalYsWKF5RbpKgwHg8Rq07snd1YeQD+gcGWE4mIiMiZVKp3GR7jEMtB9u/ZZTuOnCPrg0M4p3yR3RiTa1pexo8fz7PPPsuECROIjo4+7XyPPfYYiYmJ2Zddu/QklZJh9bzvqeHeSorxo+5VD9mOIyIiImfhH1yGeK+TA0QsspxGzpW14hQZGYmXl1euvUsJCQm59kKdasKECdx66618/fXXdO7c+Yzz+vn5ERoamuMiUhI4C98BYE3MVYSWPfNrRkRERIqGgyH1AEjbqQEiihtrxcnX15dmzZoxc+bMHNNnzpxJ69atT7vc+PHjGTx4MOPGjaNnz56FHVOkSNq4aj4N038ny7io1PNftuOIiIhIPrnLnRggIvDgastJ5FxZHYLrwQcfZODAgTRv3pxWrVrx0UcfsXPnTu666y7gxGF2u3fvZsyYMcCJ0jRo0CDefvttLrvssuy9VQEBAYSFhVm7HyIXW+KvbwCwOuxymlaubTmNiIiI5Fd49Zbw1/8GiMjPV1SkaLD6Had+/frx1ltvMXz4cBo3bsxvv/3Gjz/+SOXKlQHYu3dvjnM6ffjhh2RlZXHvvfdSrly57Mv9999v6y6IXHS7t22gSdJsAMI7P2w5jYiIiJyLCvVa4DEOMRxm7+7ttuPIOXBMKRsLMSkpibCwMBITE/V9JymWFr13G60OfMMa/6bUf3S27TgiIiJyjnY834DK7p2saP0+zbreaDtOqXYu3cD6qHoikn9HDu6jUcIPADhttKdVRESkODoUemKAiNSdv1tOIudCxUmkGFn/w5sEOuls8apGvTZX2Y4jIiIi58ETe2KAiCANEFGsqDiJFBOpyceps3McAIlN7sZx6eUrIiJSHIXXaAlAXNpGStm3Zoo1vfMSKSb+mPb/7d15dN91ne/x5y972ibpni5J031Jd1pKW+wgCGWpePWOIzPOBfSCyogywOi9jtwzzsyZM56jDlQQcBjBGRlRRtQZwYIUZbVlaWlL971Z2nRJ2jRJ0+zf+0daxkolTZvk81uej3N6DsRUnj18wd/LX/POQwymjqrYMGYtvTl0jiRJOk9FpQtoj2IM5xgHKvaGztE5cjhJCaC9rY2irY8CUD7502RkZQcukiRJ5ys7N4+KjDEAVG1dHbhG58rhJCWA9Ssfpyg6SC0DmHn9F0LnSJKkC1SdPx2ApvK1gUt0rhxOUpyLOjrIW/sQANuKbqDfAL/ZsyRJCW9k54GIATUbA4foXDmcpDi35fXnmNy2g6Yok8nX3x06R5Ik9YDTByKKmrYTdXQErtG5cDhJca7t1fsA2DB0GYMLiwLXSJKknlA8bQFtURpDOc7+it2hc3QOHE5SHNu7+U1mn3yTjijG6Gu/HDpHkiT1kKzc/lRklAAeiEgUDicpjlU//y0A1uctoWjijMA1kiSpJ9UUlALQUv524BKdC4eTFKcOVe5mTu0LAPS/4kuBayRJUk+LjZwDQH8PRCQEh5MUp/Y+8y0yY+1szprJlIsuC50jSZJ62KBJnQciipt30NHugYh453CS4lBdbTUzq34GQNvCLwaukSRJvaF42sW0RukMoY7K8p2hc9QFh5MUh7b8Yjn9Y03sTSth5mV/EjpHkiT1gszsflRkdh6IOOSBiLjncJLiTHNTIxP3PA7AkVmfJS3df0wlSUpWR/OnA9BS4YGIeOcrMinOvLPiEYZSy2EGM+faW0PnSJKkXhQbPQeAAUc9EBHvHE5SHOlob6dw0yMA7J5wM1nZOYGLJElSbxp86kDEmKadHoiIcw4nKY5sfPHHjOnYT13Uj+kfuSN0jiRJ6mXFU+bTEqUzKFZP5b7toXP0PhxOUhzJfuM7AGwa9XHyCwYHrpEkSb0tIzuXisxxABzc5oGIeOZwkuLEjrdWMrV1Cy1RBhM/7De8lSQpVRwr6DwQ0eqBiLjmcJLiRONL9wGwbtDVDB9dErhGkiT1ldMHIvKObgobovflcJLiQOXO9cw58VsAhl/z5cA1kiSpLw2ZtBCAkuYdHoiIYw4nKQ4cfPabALydu5hxU+cGrpEkSX2paMpFtEQZFMROUL5na+gc/QEOJymwowfLmFXzHABZl90VuEaSJPW1jKwcyk8diDi83QMR8crhJAW26+lvkRVrY0tGKdMvuSp0jiRJCuDYwBmAByLimcNJCqix/ijT9j/V+ccX304sFgtcJEmSQkg/dSAi/5gHIuKVw0kKaPMvHiCPRspio5l75Z+FzpEkSYEMmXz6QMRO2j0QEZccTlIgbS1NlOz8NwAOTP8M6enpgYskSVIoRZMvojnKJD/WSMVu33WKRw4nKZCNzz3GcGqoZiBzl30udI4kSQooPTOL8qzxABza9nrgGp2Nw0kKIOroYNCGhwHYNvZ/kZPbL3CRJEkKrXbgdADaKtcFLtHZOJykALa++lPGtpdzIsphxvV3hs6RJElxIH105/dyLPBARFxyOEkBxFbdD8D6wo8xcMiwwDWSJCkeDD11IGJsy07a2toC1+j3OZykPrZ3/ctMa36H1iidccu+FDpHkiTFiaLJc2mKMhkQO0n5rs2hc/R7HE5SHzv+628BsLbgSkaVTAxcI0mS4kVaRiblWRMAOLx9deAa/T6Hk9SHDu7ZxKy6VwEYfKXvNkmSpDMdHzQDgA4PRMQdh5PUhypXfIu0WMS67AVMnrUgdI4kSYoz7x6IqPVARLxxOEl9pO7IfmYeeabzTz5wR9gYSZIUl4ZNOX0gYpcHIuKMw0nqI9uf/ieyY61sS5/MnEuXhc6RJElxaPTE2TRG2fSPNVG2453QOfodDiepDzSdOM7k8icBqJ37F8TS/EdPkiS9V1pGJhWnDkQc2f564Br9Ll+9SX1g8y8fooAGKmIjmXf1jaFzJElSHKsbfOpAxAEPRMQTh5PUyzpaWyja+igAZZM/TWZmZuAiSZIUz9KLTh2IOOb3coonDiepl2164d8ojI5QQz5zrv986BxJkhTnCqcsAmBc6y5aW1sD1+g0h5PUm6KIvLUPAbC56M8YMCAvcJAkSYp3oybMpJFs+sWaKduxPnSOTnE4Sb1ox6r/YlzbHhqjbKZdf1foHEmSlABi6RmUZ00EoHr7G4FrdJrDSepFba99G4C3h36EYYUjA9dIkqREUTd4JuCBiHjicJJ6ScXm1ZSefJu2KI2i674UOkeSJCWQzFMHIgbVeiAiXjicpF5S8/w3AVgz4HLGTpgauEaSJCWS0wcixrbupqWlJXCNwOEk9Yrqyu3MrP0NAHlX3B24RpIkJZqRE2ZwghxyYy2UbV8fOkc4nKReUfb0N0mPRazLuojp8z4QOkeSJCWYWFo65VmTAKjZ+XrgGoHDSepxDccOUXrovwBoW3hH4BpJkpSo6oecOhCxf33YEAEOJ6nHbf/FveTSws608cy77H+EzpEkSQkq69SBiMHHNwUuETicpB7V2tTA+L1PAHB41m2kpfuPmCRJOj+FU08fiNhDc0tz4Br5qk7qQZtXPMwg6tjPcOZd+6nQOZIkKYGNGFdKPbnkxFop2/p26JyU53CSekjU3sbwTd8DYPfET5GTnR24SJIkJbJYWjoV2ZMBqNn5RuAaOZykHrLlxR8yquMgtdEAZn/49tA5kiQpCTQMntH5BwfWB+2Qw0nqGVFE7hsPALBh1CcoGDgwbI8kSUoKWWMuAmDw8c2BS+RwknrA3jXPMb51J01RJpM+fFfoHEmSlCRGTFsMwNi2vTQ1NQWuSW0OJ6kHnHzpXgDeGrSMUaPHBK6RJEnJonDMVOrpR3aslbJta0PnpDSHk3SBDu5YQ+mJN2mPYoy45q9C50iSpCQSS0uj/NSBiKMeiAjK4SRdoEPPfROANf2WMGnqrMA1kiQp2ZwYMrPzD6rWB+1IdQ4n6QLUVu2htGYlANmX+bVNkiSp550+EDHk+JbAJanN4SRdgL3PfJPMWDsbMmYx+5LLQ+dIkqQkNPLUgYiStr00NZ0MXJO6HE7SeWqqq2HK/p8B0Hjx7cRiscBFkiQpGQ0vnsxxBpAda2PvljWhc1KWw0k6T1ufvo9+NLErVsLFH/qT0DmSJClJxdLSqMzpPBBRu8sDEaE4nKTz0N5ykjG7HgfgQOlnyMhID1wkSZKSmQciwnM4Sedhy3OPMCSqpYohzFt2a+gcSZKU5LLHzANgaJ0HIkJxOEndFLW3MXjDPwOwbeyN9O+XG7hIkiQlu1HTFgFQ0raPk42NgWtSk8NJ6qadr/4Ho9v3czzqz8zr7widI0mSUsDQoonUkkdWrJ19W98KnZOSHE5Sd0QRaavvB+Dtwv/J0CFDAgdJkqRUcMaBiJ0eiAjB4SR1Q/mG3zCxeSvNUQYTlv1V6BxJkpRCTgydBUDs4PqwISnK4SR1Q/2vvwXAm/lXM6ZkXOAaSZKUSnJLLgI8EBGKw0k6R0f2bGB6/So6ohiDl94dOkeSJKWYUdMWA1DSXk5jY0PgmtTjcJLO0YFnvwHAWzmLmD5zfuAaSZKUaoaOnsBR8smMtVO2+c3QOSnH4SSdg4Yj5ZQefhaA2AfuDBsjSZJSUyzG/twpABzb5XDqaw4n6RzsevpbZMba2ZheyvxLl4bOkSRJKarx1IGI9IMbApekHoeT1IWWhmNMLP8JALVzP09aWixwkSRJSlX9SuYBMLTeAxF9zeEkdWHbLx9gAI3soYgFV/9p6BxJkpTCRpX+94GIhob6wDWpxeEkvY+orZlR274PwL4pt5CdmRm4SJIkpbIhI8dSw0AyYh2UbfHrnPqSw0l6H9uef4yh0VEORYOY9+HPhs6RJEmp7ncORBzf9UbgmNTicJL+kI4OBrz9EACbij9JQd6AwEGSJEnQNOzUgYhDHojoSw4n6Q/Ys/rnFLeV0xDlMv0jfxk6R5IkCYCcUwcihtVvDVySWhxO0h/Q/up9ALw59H8wYnhh4BpJkqRORdP/+0BEfV1t2JgU4nCSzqJq0ytMatpIS5TOmGvvDp0jSZL0rsEjSqhmEOmxyAMRfcjhJJ3F0ZXfAuD1AVcyceKUwDWSJElnOtCv8/VJ3W6HU19xOEm/51j5FqbVvgJA3hV3Ba6RJEl6r6ZhswFIP/RO4JLU4XCSfk/FL79BWizizcwFzLloYegcSZKk98gd23kgorBhS+CS1OFwkn5H49H9TDn0DABti75ILBYLXCRJkvReRaWLABjTXkld3bHANanB4ST9jl1P30s2rWxKm8KCP1oWOkeSJOmsBhWO4TBDSItFlG32G+H2BYeTdErbyTrG7f0RANWzPktGRnrgIkmSpD+sqn/ngYh6D0T0CYeTdMq2FQ+SxwnKGMkl19wUOkeSJOl9NQ2bBUCGByL6hMNJAqK2Fgo3PwrAzgmfIjcnK3CRJEnS++s/dj4AhQ1bA5ekBoeTBOz8zQ8Y1nGE6qiAudf/RegcSZKkLhVP7zwQUdyxn+PHjgauSX4OJymKyH3rOwCsG3UDQwYWBA6SJEnqWsGwIg7FhpIWiyjf8nronKTncFLKK3/raYpb93Iiymbqh+8MnSNJknTOqvpNBaB+z1uBS5Kfw0kpr+ml+wB4Y9D1FI8eHbhGkiTp3LUM7zwQkXl4Q+CS5OdwUko7sn01kxvfpi1KY8TVd4fOkSRJ6pb+4zwQ0VccTkpph5/7JgCr+32Q0mnTA9dIkiR1T3HpYgDGRAeoPVYduCa5OZyUsuqqdjL16G8AyL7srsA1kiRJ3Zc/dCRVsWEAlG9eHbgmuTmclLLKnv4G6bGINRkXcfElS0LnSJIknZdD/TsPRDR4IKJXOZyUkpqPH2LSgf8EoPHi24nFYmGDJEmSzlPz8NkAZB5+J3BJcnM4KSXtfOY+cmhha2wCC6/4WOgcSZKk8zbg1IGIESe2BS5Jbg4npZyOpgaKd/07AAdKP0tWZnrgIkmSpPM3ZkbngYjiqIpjNUcC1yQvh5NSzo5ffZeCqJ6KqJAFy24OnSNJknRB8gYVciBWCHggojc5nJRa2tsY9M6/ALB17I3k9csNHCRJknThTh+IOLHXAxG9xeGklLLnlScobD/I0WgAsz9ye+gcSZKkHtFa2HkgIssDEb3G4aTUEUWkr74fgDXD/4TCIYMDB0mSJPWM0wciRjZuDVySvBxOShkH1j1HSctOTkZZTFjmN7yVJEnJo/jUgYjR0SFqjhwMXJOcHE5KGQ2/uReAVfnXMmFsSeAaSZKknpM3cBj7YyMAqNjigYje4HBSSji6ey2TG96kPYox5Kq/Cp0jSZLU4w4PmAZA4941gUuSk8NJKeHgs98AYFXOEmbPnBW4RpIkqee1nD4QccQDEb3B4aSk13h4L5Ornwcg/QN3EIvFAhdJkiT1vPzxFwMw6sS2wCXJyeGkpLf36W+QQQdr02ZxyaVXhs6RJEnqFWOmLwJgFIepPlwVuCb5OJyU1Fobahhf8VMAai/6POlpvtskSZKSU/+CIVSmjQKgcvOqwDXJx+GkpLbrl8vJpZntjOXSpX8SOkeSJKlXHR4wFYDGfR6I6GkOJyWtqKWREdt+AMC+KbeQk5URuEiSJKl3tRXOASDbAxE9zuGkpLXrhe8xKKrlQDSUBctuCZ0jSZLU6wpOH4ho9EBET3M4KTl1tJP39sMAbCj+cwbl9w8cJEmS1PuKZywEYCTVHDlYGbgmuTiclJTKV/2EEW0HqI36M/PDXwydI0mS1Cf65Q2mIm00AJVbVgeuSS4OJyWfKKLjteUAvDHkYxSNGBa2R5IkqQ8dHjAN8EBET3M4Kekc3vQbxjZtpTnKZMy1d4XOkSRJ6lPtI2YDkFu9MXBJcnE4KenUrvwWAK8NWMq0SRMD10iSJPWtggkLABjduI0oigLXJA+Hk5LK8bJ3mFy3io4oRt7lvtskSZJSz5jShXREMQqp4UhVReicpOFwUlLZv+IbAKzKWsTF8+YHrpEkSep7uXkDqUgvAmD/Vg9E9BSHk5JGU005kw49C0D7wi8Si8UCF0mSJIVRndd5IOKkByJ6jMNJSWPPM98ikzbWxUpZ/MFrQudIkiQF0z5yDuCBiJ7kcFJSaG88Rsne/wDgyOzbyEz30ZYkSalr4PiLARh9crsHInqIry6VFHateID+nGQXxVx6zZ+FzpEkSQpqzPRLaI9iDOcohw6Uhc5JCg4nJbyotYlhW74PwI4Jn6J/TlbgIkmSpLBy+hdQkV4MwIEtHojoCQ4nJbx9L/0rgzuOcigaxPzrPxs6R5IkKS5U53ceiGgq80BET3A4KbF1dJDz5oMArB31ZwwfmB84SJIkKT50jJwLQG71psAlycHhpIR24K2fM7K1nLool6kfviN0jiRJUtwYOKHzQERR0zaijo7ANYnP4aSE1vzyfQD8duD/YPzokYFrJEmS4seY0s4DEcOopWr/3tA5Cc/hpIR1dNurjGvcSEuUzuhr7gydI0mSFFdy+uVRnlECQNXW1wPXJD6HkxJW9a++CcCruR9i1rRpgWskSZLiT82pAxHNHoi4YA4nJaQT+7cw8dgrAORcdmfYGEmSpDgVnToQ0a/GAxEXyuGkhFTxzDdII+K3GQtYdMni0DmSJElxadDEzgMRxU3bPRBxgRxOSjitx6sYX/U0ACfnf4G0tFjgIkmSpPhUPG0BbVEaQzhOVcXu0DkJzeGkhLN7xf1k0cZGJvGBD304dI4kSVLcys4d4IGIHuJwUkKJWk8yYscTAFRO/TQ5memBiyRJkuJbTX4pAE3lawOXJDaHkxLKvpceZ2BUS1U0mIuvvTl0jiRJUvwb1XkgYkDNxsAhic3hpMQRRWS89V0A1o34BEMLBgQOkiRJin+DJy4AoLjZAxEXwuGkhHFk068pbtlNY5TNxGs/HzpHkiQpIRRPu5jWKJ3B1HOgbGfonITlcFLCOPbr5QD8dsBVTB5bEjZGkiQpQWTl9KM8YywAVdtWh41JYA4nJYTGqh1MrH0NgAF/dHvgGkmSpMRytKDzQERLxduBSxKXw0kJYd+z95FGxOvp87jk4kWhcyRJkhJKbNQcAAbUbAobksAcTop7HY21jC3/GQD1c271G95KkiR106BJnQcixngg4rw5nBT3dj3/XfrRxG5Gs/iqPwmdI0mSlHDGTL2YliidgTSwf9/20DkJyeGk+NbRzsCNjwGwfeyN9M/JDBwkSZKUeDKzcynPHAdA1bbXA9ckJoeT4lrl608xvP0Qx6IBzFn22dA5kiRJCetYwXQAWivWBi5JTA4nxbWW1x4E4M3BH2HUsCGBayRJkhJXbNRcwAMR5yv4cHrooYcYN24cOTk5zJs3j1dfffUPfm5VVRWf/OQnmTJlCmlpadx55519F6o+d2z3W4xv3EBrlM7Iq74QOkeSJCmhDTl1IKKkeQcd7R6I6K6gw+nJJ5/kzjvv5J577mHdunUsWbKEa6+9lvLy8rN+fnNzM8OGDeOee+5h9uzZfVyrvlb1q/sAWJXzAWZOKw1cI0mSlNiKp86jJcqgIHaCyr1bQucknKDD6d577+WWW27h1ltvZdq0aSxfvpzi4mIefvjhs37+2LFj+fa3v81NN91EQUFBH9eqLzXXHmDS4V91/snCvyAW8wS5JEnShcjIyqEsczwAh7Z7IKK7gg2nlpYW1q5dy9KlS8/4+NKlS1m1alWP/XWam5upq6s744fi3+4V95NJG+/EprD4j64OnSNJkpQUagd2/i6e1vK3A5cknmDDqbq6mvb2dgoLC8/4eGFhIQcPHuyxv87Xv/51CgoK3v1RXFzcY//d6h1R60lG7XwCgKqpnyIzPfiX4kmSJCWHUwci8o5tDhySeIK/Iv3934IVRVGP/rasv/7rv+b48ePv/qioqOix/271jj0v/hsDo+MciIaw4LpPhc6RJElKGsMmLwROH4hoD1yTWIINp6FDh5Kenv6ed5cOHz78nnehLkR2djb5+fln/FAciyKy1vwzABtGfYJBef0CB0mSJCWPoslzaY4yyY81UrHbd526I9hwysrKYt68eaxcufKMj69cuZLFixcHqlJoh955geKWPTRG2Uy+5vOhcyRJkpJKRlY2ZVkeiDgfGSH/4nfffTc33ngj8+fPZ9GiRTzyyCOUl5dz2223AZ2/zW7//v384Ac/ePfnrF+/HoCGhgaOHDnC+vXrycrKorTUc9XJ4NhvllMIrM5byodKxoTOkSRJSjq1BdOhejvtlR6I6I6gw+mGG26gpqaGv//7v6eqqooZM2awYsUKSkpKgM5vePv739Np7ty57/7x2rVreeKJJygpKWHfvn19ma5e0FC1ncm1v4UY5H3wi6FzJEmSklLa6Iug+mceiOimWBRFUeiIvlRXV0dBQQHHjx/3653izOZHb2N6xY94I2M+C+55we/dJEmS1AvKtrxByX8spSHKJfdv9pOenh46KZjubIPgV/UkgPbGWsZV/ByAhrmfcTRJkiT1kqLJF9EUZTIgdpKKnRtD5yQMh5Piws7nHqYfTeymiMVXfjx0jiRJUtJKz8ikLGsiAId3eCDiXDmcFF57G4M3PwbAznE3kpsd9EvvJEmSkt7xgdMBPBDRDQ4nBVe++imGtx/maDSA2R/+bOgcSZKkpJde1HlwLd8DEefM4aTgWlc9CMBbQz7KyCGDA9dIkiQlv2GTFwIwtmUXba2tgWsSg8NJQR3d+QYTGt+hNUqnaKknyCVJkvpC0aTZNEbZ9I81UbHLAxHnwuGkoA4+fx8Aq3OXMH3q1MA1kiRJqSEtI5PyrAkAHNnxRuCaxOBwUjBNR/cz6chKANIWfj5wjSRJUmo5PmgGAO2VawOXJAaHk4LZveLbZNLGO7GpLFxyVegcSZKklJJRdBEABbVbApckBoeTgohaTzJ6948AODT902Sk+yhKkiT1peFTLgE8EHGufLWqIHb9+l8ZGNVRFQ1hwTU3h86RJElKOaMnzKIxyqZfrJmyHetD58Q9h5P6XhSRs/afAXhn9A0UDMgNHCRJkpR60jIyKMueBED1Tg9EdMXhpD53YP2vKG7dS2OUzdTrvhA6R5IkKWXVDZoOQEflusAl8c/hpD5X9+L9ALyefzUlRaMD10iSJKWujOJ5AAys3Ry4JP45nNSn6iu3Mfn4KgAGftBveCtJkhRS4ekDEa27aW1tCVwT3xxO6lNlz91HWizizYz5zL3o4tA5kiRJKW3U+JmcIIfcWAtl2/3teu/H4aQ+03biGOMr/xOAxnmfIxaLhQ2SJElKcWnp6ZRlTQagZocHIt6Pw0l9ZsdzD9OPJnZRzMIP/c/QOZIkSQLqB3ceiIj2+47T+3E4qW+0tzF08/cB2DPhRnKyMgIHSZIkCSDz9IGI41sCl8Q3h5P6xN7f/gfDOw5zNBrAnOs+GzpHkiRJp/zugYiW5ubANfHL4aQ+0bH6IQDWDvsYw4cMClwjSZKk00aNn049ueTEWinf/nbonLjlcFKvq96+mgknN9IapVO81BPkkiRJ8SSWlk559qkDETvfDFwTvxxO6nWHVi4H4PXcP2Lq5ClhYyRJkvQeDYNnABAd8B2nP8ThpF51sqaSydUrAci49PbANZIkSTqbrFMHIgZ5IOIPcjipV+1a8W0yaeedtKksuPTK0DmSJEk6ixFTFwIwtnUvzc0nA9fEJ4eTek1HcyPFe34MwJHpt5Ce5je8lSRJikcjxk6jnn5kx1op3+pv1zsbh5N6zc7ffJ+BUR0HoqEsuPbG0DmSJEn6A2JpaZRld34tes2uNwLXxCeHk3pHFNHv7UcA2Fz0p+T1yw0cJEmSpPdz4tSBCA6sD9oRrxxO6hWVbz9Hces+TkTZTL3OoxCSJEnxLquk80DEYA9EnJXDSb2i/uX7AXiz4BqKR48KXCNJkqSuvHsgom0PTScbA9fEH4eTetzxyq1Mq1sFwKAr/Ia3kiRJiWDEmCkcZwBZsXbKt60NnRN3HE7qcWUr7gPgzcyLmT17fuAaSZIknYtYWhrlOZMBOLrrzcA18cfhpB7VeuIYEw/8FwDN8z5HLOYJckmSpETROHgmALED6wKXxB+Hk3rU9hUP0o8mdlPMgg99LHSOJEmSuiFrzKkDEXUeiPh9Dif1mKi9lWFb/w2AfZNuJjszI3CRJEmSumPUtM4DESVt+2g6eSJwTXxxOKnH7HntPyjsOMzRKI/Z130mdI4kSZK6aXjxJGrJIyvWTtmWt0LnxBWHk3pMtPphAN4e/jGGDhoYNkaSJEndFktLoyJnCgDHdnsg4nc5nNQjDm1bxcSmjbRE6Yy9xhPkkiRJiapxyAwAYgfWhw2JMw4n9YgjK5cD8Gb/DzJxwuSwMZIkSTpv2SWd305mSP3WwCXxxeGkC9ZYXcGUmhcAyLr09sA1kiRJuhCjpi0CoKStjJMnGgLXxA+Hky7YrhXfJpN23kmbxvxFV4TOkSRJ0gUYNno8R8knM9bOvq1+ndNpDiddkI7mRkr2/BiAmpm3kpbmN7yVJElKZLG0NCpzpwJQu8vhdJrDSRdk+8pHKaCe/QxjwTX/K3SOJEmSesDpAxHpBzcELokfDiedvyhiwPp/AWBr8Z/SPzcncJAkSZJ6Qu7pAxF1WwKXxA+Hk85b+doVFLeVcSLKZtoyj0JIkiQli1Glpw5EtJfTeKIucE18cDjpvJ14+QEA3hp4HaNHjAxcI0mSpJ4ybNQ4ahhIRqyDss1+nRM4nHSejpZvYVr9agCGfshveCtJkpRUYjEqc6cAULv7rcAx8cHhpPNS8ey9ALyZuYDpMy8KXCNJkqSe1jR0FgDpB9eHDYkTDid1W3N9DZOrfgFA64LPEYt5glySJCnZ5IydB8Cw+q2BS+KDw0ndtv3Zh8ilmV2xMSy4/GOhcyRJktQLRp86EDGmvZwT9ccD14TncFK3RO2tjNj2bwCUT7qZzIz0wEWSJEnqDUNHjuUIg0iPRZRteSN0TnAOJ3XLrld+zPCOIxyN8ph73WdC50iSJKkX7e83FYDjHohwOKl7Ym98F4D1hf+TQQMLAtdIkiSpNzUNnQlAhgciHE46dwe3rGJi0yZaonTGXnNH6BxJkiT1sn5j5wMwrMEDEQ4nnbPDLywH4M3+lzN+/MSwMZIkSep1o6efPhBRSX3dscA1YTmcdE4ajpQzreYFAHKW3B64RpIkSX1hSOEYDjOYtFhE+ebXQ+cE5XDSOdn1y2+TGWvnnfRS5i28PHSOJEmS+siBUwci6vasCVwSlsNJXWpvbmTsvicBqJ11q9/wVpIkKYU0DZsFeCDC4aQubXv+ewyknv0M4+KrbwydI0mSpD7Uf9zFAAxv2Ba4JCyHk95fFJG/4V8A2F7ySXJzsgIHSZIkqS8VlXYeiCju2E/d8aOBa8JxOOl97X3zGYrbymmIcihd5lEISZKkVDNo+GgOMvTUgYjVoXOCcTjpfZ189TsArB10HSOGFwaukSRJUghV/TsPRNSn8IEIh5P+oJqyTZQ2vE5HFGPYlX7DW0mSpFTVfOpAROah9WFDAnI46Q+qePY+ANZmL6B0xtzANZIkSQql/7j5QGofiHA46aya6mqYcvBpANoX/EXgGkmSJIVUXLoYgDHRAY4fqwlcE4bDSWe1fcV3yKWZXbES5n/wI6FzJEmSFNDAYSOpig0DoGLzqsA1YTic9B5Reysjt/8AgMopN5ORkR64SJIkSaEd7D8NgPq9qXkgwuGk99jx0o8YHlVzNMpj7nWfDZ0jSZKkOPDfByI2BC4Jw+Gk90h787sAvDPyjynIzwtcI0mSpHgwYPzFAIw4kZoHIhxOOsP+za8xqXkzLVE6467xBLkkSZI6jSldBEBRVMXxo4cD1/Q9h5POUPPCcgDWDLickrETwsZIkiQpbuQPKeRArBCA8s2rA9f0PYeT3lV3uJxpR38DQL8/+kLgGkmSJMWbg/2nAql5IMLhpHft/uV9ZMba2Zg+ndkLPhg6R5IkSXGmZfhsALIPp96BCIeTAGhramBc2U8AOD7nM8RiscBFkiRJijd54+cDqXkgwuEkALY+/ygDqWc/w5m/9M9D50iSJCkOFc9YDMDo6BC11YcC1/Qth5Mgihi44XsA7Bz7SXKyswIHSZIkKR7lDxzG/tgIAMo3rwpc07ccTmL3609T3F5OQ5TD9GW3h86RJElSHDs4YBoAJ/al1oEIh5Nofu07ALw9eBnDhg0PXCNJkqR41nbqQERWih2IcDiluCN7N1J64g06ohgjrvrL0DmSJEmKc3kTLgZgZOP2wCV9y+GU4iqfuw+AtTmXMLl0duAaSZIkxbvi0kUAjIoOc/TwgcA1fcfhlMJOHq9m6qFnAIgu+YvANZIkSUoEeQOHUBEbBUDFltWBa/qOwymFbVvxHXJpZldsLPMu+0joHEmSJCWIw3mdByIa96bOgQiHU4qK2loYveNxAA5M+xTp6T4KkiRJOjdtw2cBkH3kncAlfcdXyylq64tPMDyqpibKZ+51t4bOkSRJUgLJG78AgJGN2wKX9B2HU4rKfOufAdg06uPkDcgLXCNJkqREMmbGQgBGUk31ocrANX3D4ZSCKja+zKSWLbRE6Uy41hPkkiRJ6p4B+YMpTxsNwP7NqXEgwuGUgo7++n4A1uRdQdGYsWFjJEmSlJAODygF4MS+1DgQ4XBKMbUH91F67EUABlz2xcA1kiRJSlTthZ0HInKrU+NAhMMpxexesZzMWDsbM2Ywc/4fhc6RJElSgsqf2HkgYlTj9sAlfcPhlEJamxqYWP4TABrmfIZYLBa4SJIkSYlqTOlCOqIYhdRQXVUeOqfXOZxSyJbnvkcBDVQynIuWfjJ0jiRJkhJY/7yBVKQXAVC5ZVXgmt7ncEoRUUcHgzd+D4Dd4/4X2VlZgYskSZKU6A7nTQOgcd/awCW9z+GUIna+/jTF7RU0RLnM+PDtoXMkSZKUBDpGzAagX/XGwCW9z+GUIlpfexCAdUOXMWTI0MA1kiRJSgYFE04diDiZ/AciHE4p4NCed5je+AYdUYyRV/kNbyVJktQzSkoX0h7FGM5RjhzYFzqnVzmcUsD+5+4F4O3chUycOitwjSRJkpJF7oB8KtKLAajcsjpwTe9yOCW5E7VHmHb4lwDEFn0+cI0kSZKSzZH8UgCa9q0JXNK7HE5JbvsvHyCXFnaljWXuBz4cOkeSJElJJjp1ICK3JrkPRDicklhHawtFu/4dgIPT/jdp6f7tliRJUs8qmNh5IKLo5Haijo7ANb3HV9JJbMuL/87wqIYaCph73S2hcyRJkpSESkovoT2KMZRaDlftC53TaxxOSSx7zSMAbBn1cfr3HxC4RpIkSckop18e5eklAOzfvCpwTe9xOCWpsg0vMallK81RBhOuuyN0jiRJkpJY9akDEc3lbwcu6T0OpyR17Df3A/B2/ocYVTQ2bIwkSZKSWsfIzgMR/arfCVzSexxOSehY1V5m1L4IQP4Hvxi4RpIkSclu0MRLAChq2pG0ByIcTklo9y+XkxHrYGPGTEov+kDoHEmSJCW5MdMupi1KYwjHObR/T+icXuFwSjItJxuYVPkUAI0XfYZYLBa4SJIkSckup98AyjI6D0Qc2JKcByIcTklm87OPUEAD+xnO3Cs/GTpHkiRJKeJokh+IcDglkaijnaGbHgVgz4QbycrKDFwkSZKkVNExcg4A/Ws2hg3pJQ6nJLJj1S8o7qikIcplxrLbQ+dIkiQphQye1Hkgorhpe1IeiHA4JZHW3z4IwIZhH2bQ4CGBayRJkpRKxkybT2uUziDqqSrfGTqnxzmcksSBneuZcfItOqIYo66+M3SOJEmSUkx2Tn/KMsYCcHDb6rAxvcDhlCQOPL8cgLf7LWbcpBlhYyRJkpSSjhYk74EIh1MSqD92mOmHfwlA+uK/CFwjSZKklHXqQMSAJDwQ4XBKAtt++R1yYy3sTBvH7MXLQudIkiQpRb17IKJ5R9IdiHA4Jbj21hZKdv87AEem/2/S0v1bKkmSpDDGTJ1PS5TOQBo4sG9H6Jwe5avsBLf5hccZHtVQQwFzr701dI4kSZJSWFZOLmWZ4wA4uG1V4Jqe5XBKcLlvPwLA1qJPkNuvX+AaSZIkpbpjBdMBaKlIrgMRDqcEtmfdi0xq3UZLlMGk6+4InSNJkiS9eyAi72hyHYhwOCWw4y/eD8DbBVdSOGpM4BpJkiQJhkzuPBAxpnlnUh2IcDglqOr9u5l5/CUABl3hu02SJEmKD2OmzqMlyiCfE+zfuzV0To9xOCWovSuWkxHrYGPmLKbMuTR0jiRJkgRAZlYO+zLHA3Bo2+rANT3H4ZSAmk7UMXn/zzr/eN5nA9dIkiRJZzpWUAok14EIh1MC2vzcIxTQwH4KmfuhPw2dI0mSJJ0hbdRcAPKObgpc0nMcTgkm6mhn2ObvA7Bv4o1kZGYGLpIkSZLONGTKQqDzQERHe3vgmp7hcEowW1/7T8Z0VNIQ5TJj2e2hcyRJkqT3GDN5Ls1RJvmxRir3bA6d0yMcTgmmY/VDALwz/HoKBg0OXCNJkiS9V0ZW9rsHIg5vfz1wTc9wOCWQyh3rmHFyDR1RjKKr7wydI0mSJP1BtQOnA9CaJAciHE4J5ODzywFY338xYyZODxsjSZIkvY+00Z0HIvKPJceBCIdTgqg7eojpR1YAkLHYr22SJElSfBs6+RIASpp3JcWBCIdTgtj2zAPkxlrYmTaemYuvDZ0jSZIkva/iyXM5GWUxIHaSil0bQ+dcMIdTAmhraWbsnicAODrzFmJp/m2TJElSfMvIzKIsawIAR5LgQISvwBPAphceZzg1VDOQ2df879A5kiRJ0jk5fupARFvlusAlF87hlAD6rXsEgO3FnyAnt1/gGkmSJOncJNOBCIdTnNu55tdMbt1OS5TB5GV3hM6RJEmSztmwKQsBKGnZRXtbW+CaC+NwinMNLz8AwLqBVzFsRHHgGkmSJOncFU+aQ2OUTf9YExW73gmdc0EcTnHsSOUuZta9DMDgK/4ycI0kSZLUPekZGZQnyYEIh1Mc27NiORmxDjZlzWbS7EWhcyRJkqRuqx00A4D2BD8Q4XCKUycb6ph24GcANM//XOAaSZIk6fxkFHUeiCioTewDEQ6nOLXp2X8mnxNUxkYw54obQudIkiRJ52XY5NMHInYn9IEIh1McijraKdz6fQAqJt1IekZG4CJJkiTp/BRNnMWJKId+sWYqdq4PnXPeHE5xaNMrP2dMx37qo1xmLPt86BxJkiTpvKVnZFCWNRFI7AMRDqd49PpDAGwq/Ah5BYMDx0iSJEkXpm7wqQMR+xP3QITDKc6Ub1vLzKa1tEcxxlxzV+gcSZIk6YJlnjoQMfDY5sAl58/hFGcOPr8cgA39L2X0+GlhYyRJkqQeMGzKqQMRrbtpa20JXHN+HE5x5HjNQWbVPAtA9pIvBK6RJEmSekbRhJk0RLnkxlqo2JGYv13P4RRHtj7zADmxVnalT6D0kqtD50iSJEk9Ii09nbLsUwcidrwRuOb8OJziRGtLMxP2PgHA0Zm3EEvzb40kSZKSR/2gzgMRUYIeiPDVeZx45/kfMIyjVDOQ2dd8OnSOJEmS1KMyii8CYGBtYh6IcDjFgygib/0jAOwc86dk5/QLHCRJkiT1rMKpiwAoad1Da0tz4JruczjFge1rf83kth00R5lMXvbF0DmSJElSjxs9rpQ6+pETa6V8e+L9dj2HUxxoeOkBADYMuoohhUWBayRJkqSel5aeTkXWJABqdrweuKb7gg+nhx56iHHjxpGTk8O8efN49dVX3/fzX375ZebNm0dOTg7jx4/nu9/9bh+V9o6D5TuZXf8KAEOvvDNsjCRJktSL6gefOhBxwHecuuXJJ5/kzjvv5J577mHdunUsWbKEa6+9lvLy8rN+/t69e7nuuutYsmQJ69at46tf/Sp33HEHP/3pT/u4vOfse/Y+MmIdbMqew/gZl4TOkSRJknpN5qkDEYOObwlc0n2xKIqiUH/xSy65hIsuuoiHH3743Y9NmzaNj370o3z9619/z+f/3//7f/nFL37B1q1b3/3YbbfdxoYNG1i9evU5/TXr6uooKCjg+PHj5OfnX/gv4gI0NtTS9q1S8jnB+g98lzlX/lnQHkmSJKk37d+zmdE/WExzlEnsq5VkZecE7enONgj2jlNLSwtr165l6dKlZ3x86dKlrFq16qw/Z/Xq1e/5/Kuvvpo1a9bQ2tp61p/T3NxMXV3dGT/ixcZf/jP5nGB/bASzLv9E6BxJkiSpV40aO406+pMda6V829rQOd0SbDhVV1fT3t5OYWHhGR8vLCzk4MGDZ/05Bw8ePOvnt7W1UV1dfdaf8/Wvf52CgoJ3fxQXF/fML6AHZA8Zw670CVROuZm09PTQOZIkSVKviqWlsXPAxbyTM5+ooz10TrdkhA6IxWJn/HkURe/5WFeff7aPn/bXf/3X3H333e/+eV1dXdyMpzlX/hnRFTcwrqMjdIokSZLUJ+Z96b9CJ5yXYMNp6NChpKenv+fdpcOHD7/nXaXTRowYcdbPz8jIYMiQIWf9OdnZ2WRnZ/dMdC+IpaWRnhb8uKEkSZKk9xHsFXtWVhbz5s1j5cqVZ3x85cqVLF68+Kw/Z9GiRe/5/Oeff5758+eTmZnZa62SJEmSUlvQtzruvvtuvve97/HYY4+xdetW7rrrLsrLy7ntttuAzt9md9NNN737+bfddhtlZWXcfffdbN26lccee4xHH32UL33pS6F+CZIkSZJSQNCvcbrhhhuoqanh7//+76mqqmLGjBmsWLGCkpISAKqqqs74nk7jxo1jxYoV3HXXXTz44IOMGjWK+++/nz/+4z8O9UuQJEmSlAKCfh+nEOLp+zhJkiRJCichvo+TJEmSJCUKh5MkSZIkdcHhJEmSJEldcDhJkiRJUhccTpIkSZLUBYeTJEmSJHXB4SRJkiRJXXA4SZIkSVIXHE6SJEmS1AWHkyRJkiR1weEkSZIkSV1wOEmSJElSFxxOkiRJktQFh5MkSZIkdcHhJEmSJEldcDhJkiRJUhccTpIkSZLUBYeTJEmSJHXB4SRJkiRJXXA4SZIkSVIXHE6SJEmS1AWHkyRJkiR1weEkSZIkSV1wOEmSJElSFxxOkiRJktQFh5MkSZIkdcHhJEmSJEl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\n", + "text/plain": [ + "
              " + ] + }, + "metadata": { + "filenames": { + "image/png": "/Users/mhjensen/Teaching/MachineLearning/doc/LectureNotes/_build/jupyter_execute/chapter11_91_4.png" + } + }, + "output_type": "display_data" + } + ], "source": [ "import autograd.numpy as np\n", "from autograd import grad, elementwise_grad\n", @@ -2762,7 +2995,60 @@ "collapsed": false, "editable": true }, - "outputs": [], + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/mhjensen/miniforge3/envs/myenv/lib/python3.9/site-packages/numpy/core/fromnumeric.py:3245: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray.\n", + " return asarray(a).size\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Initial cost: 41.05505310046363\n" + ] + }, + { + "ename": "KeyboardInterrupt", + "evalue": "", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mKeyboardInterrupt\u001b[0m Traceback (most recent call last)", + "Input \u001b[0;32mIn [9]\u001b[0m, in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[1;32m 140\u001b[0m num_iter \u001b[38;5;241m=\u001b[39m \u001b[38;5;241m250\u001b[39m\n\u001b[1;32m 141\u001b[0m lmb \u001b[38;5;241m=\u001b[39m \u001b[38;5;241m0.01\u001b[39m\n\u001b[0;32m--> 143\u001b[0m P \u001b[38;5;241m=\u001b[39m \u001b[43msolve_pde_deep_neural_network\u001b[49m\u001b[43m(\u001b[49m\u001b[43mx\u001b[49m\u001b[43m,\u001b[49m\u001b[43mt\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mnum_hidden_neurons\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mnum_iter\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mlmb\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 145\u001b[0m \u001b[38;5;66;03m## Store the results\u001b[39;00m\n\u001b[1;32m 146\u001b[0m g_dnn_ag \u001b[38;5;241m=\u001b[39m np\u001b[38;5;241m.\u001b[39mzeros((Nx, Nt))\n", + "Input \u001b[0;32mIn [9]\u001b[0m, in \u001b[0;36msolve_pde_deep_neural_network\u001b[0;34m(x, t, num_neurons, num_iter, lmb)\u001b[0m\n\u001b[1;32m 118\u001b[0m \u001b[38;5;66;03m# Let the update be done num_iter times\u001b[39;00m\n\u001b[1;32m 119\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m i \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mrange\u001b[39m(num_iter):\n\u001b[0;32m--> 120\u001b[0m cost_grad \u001b[38;5;241m=\u001b[39m \u001b[43mcost_function_grad\u001b[49m\u001b[43m(\u001b[49m\u001b[43mP\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mx\u001b[49m\u001b[43m \u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mt\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 122\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m l \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mrange\u001b[39m(N_hidden\u001b[38;5;241m+\u001b[39m\u001b[38;5;241m1\u001b[39m):\n\u001b[1;32m 123\u001b[0m P[l] \u001b[38;5;241m=\u001b[39m P[l] \u001b[38;5;241m-\u001b[39m lmb \u001b[38;5;241m*\u001b[39m cost_grad[l]\n", + "File \u001b[0;32m~/miniforge3/envs/myenv/lib/python3.9/site-packages/autograd/wrap_util.py:20\u001b[0m, in \u001b[0;36munary_to_nary..nary_operator..nary_f\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m 18\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m 19\u001b[0m x \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mtuple\u001b[39m(args[i] \u001b[38;5;28;01mfor\u001b[39;00m i \u001b[38;5;129;01min\u001b[39;00m argnum)\n\u001b[0;32m---> 20\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43munary_operator\u001b[49m\u001b[43m(\u001b[49m\u001b[43munary_f\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mx\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mnary_op_args\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mnary_op_kwargs\u001b[49m\u001b[43m)\u001b[49m\n", + "File \u001b[0;32m~/miniforge3/envs/myenv/lib/python3.9/site-packages/autograd/differential_operators.py:25\u001b[0m, in \u001b[0;36mgrad\u001b[0;34m(fun, x)\u001b[0m\n\u001b[1;32m 18\u001b[0m \u001b[38;5;129m@unary_to_nary\u001b[39m\n\u001b[1;32m 19\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mgrad\u001b[39m(fun, x):\n\u001b[1;32m 20\u001b[0m \u001b[38;5;250m \u001b[39m\u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m 21\u001b[0m \u001b[38;5;124;03m Returns a function which computes the gradient of `fun` with respect to\u001b[39;00m\n\u001b[1;32m 22\u001b[0m \u001b[38;5;124;03m positional argument number `argnum`. The returned function takes the same\u001b[39;00m\n\u001b[1;32m 23\u001b[0m \u001b[38;5;124;03m arguments as `fun`, but returns the gradient instead. The function `fun`\u001b[39;00m\n\u001b[1;32m 24\u001b[0m \u001b[38;5;124;03m should be scalar-valued. The gradient has the same type as the argument.\"\"\"\u001b[39;00m\n\u001b[0;32m---> 25\u001b[0m vjp, ans \u001b[38;5;241m=\u001b[39m \u001b[43m_make_vjp\u001b[49m\u001b[43m(\u001b[49m\u001b[43mfun\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mx\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 26\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m vspace(ans)\u001b[38;5;241m.\u001b[39msize \u001b[38;5;241m==\u001b[39m \u001b[38;5;241m1\u001b[39m:\n\u001b[1;32m 27\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mTypeError\u001b[39;00m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mGrad only applies to real scalar-output functions. \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m 28\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mTry jacobian, elementwise_grad or holomorphic_grad.\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n", + "File \u001b[0;32m~/miniforge3/envs/myenv/lib/python3.9/site-packages/autograd/core.py:10\u001b[0m, in \u001b[0;36mmake_vjp\u001b[0;34m(fun, x)\u001b[0m\n\u001b[1;32m 8\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mmake_vjp\u001b[39m(fun, x):\n\u001b[1;32m 9\u001b[0m start_node \u001b[38;5;241m=\u001b[39m VJPNode\u001b[38;5;241m.\u001b[39mnew_root()\n\u001b[0;32m---> 10\u001b[0m end_value, end_node \u001b[38;5;241m=\u001b[39m \u001b[43mtrace\u001b[49m\u001b[43m(\u001b[49m\u001b[43mstart_node\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mfun\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mx\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 11\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m end_node \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m 12\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mvjp\u001b[39m(g): \u001b[38;5;28;01mreturn\u001b[39;00m vspace(x)\u001b[38;5;241m.\u001b[39mzeros()\n", + "File \u001b[0;32m~/miniforge3/envs/myenv/lib/python3.9/site-packages/autograd/tracer.py:10\u001b[0m, in \u001b[0;36mtrace\u001b[0;34m(start_node, fun, x)\u001b[0m\n\u001b[1;32m 8\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m trace_stack\u001b[38;5;241m.\u001b[39mnew_trace() \u001b[38;5;28;01mas\u001b[39;00m t:\n\u001b[1;32m 9\u001b[0m start_box \u001b[38;5;241m=\u001b[39m new_box(x, t, start_node)\n\u001b[0;32m---> 10\u001b[0m end_box \u001b[38;5;241m=\u001b[39m \u001b[43mfun\u001b[49m\u001b[43m(\u001b[49m\u001b[43mstart_box\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 11\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m isbox(end_box) \u001b[38;5;129;01mand\u001b[39;00m end_box\u001b[38;5;241m.\u001b[39m_trace \u001b[38;5;241m==\u001b[39m start_box\u001b[38;5;241m.\u001b[39m_trace:\n\u001b[1;32m 12\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m end_box\u001b[38;5;241m.\u001b[39m_value, end_box\u001b[38;5;241m.\u001b[39m_node\n", + "File \u001b[0;32m~/miniforge3/envs/myenv/lib/python3.9/site-packages/autograd/wrap_util.py:15\u001b[0m, in \u001b[0;36munary_to_nary..nary_operator..nary_f..unary_f\u001b[0;34m(x)\u001b[0m\n\u001b[1;32m 13\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m 14\u001b[0m subargs \u001b[38;5;241m=\u001b[39m subvals(args, \u001b[38;5;28mzip\u001b[39m(argnum, x))\n\u001b[0;32m---> 15\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mfun\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43msubargs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n", + "Input \u001b[0;32mIn [9]\u001b[0m, in \u001b[0;36mcost_function\u001b[0;34m(P, x, t)\u001b[0m\n\u001b[1;32m 78\u001b[0m g_t \u001b[38;5;241m=\u001b[39m g_trial(point,P)\n\u001b[1;32m 79\u001b[0m g_t_jacobian \u001b[38;5;241m=\u001b[39m g_t_jacobian_func(point,P)\n\u001b[0;32m---> 80\u001b[0m g_t_hessian \u001b[38;5;241m=\u001b[39m \u001b[43mg_t_hessian_func\u001b[49m\u001b[43m(\u001b[49m\u001b[43mpoint\u001b[49m\u001b[43m,\u001b[49m\u001b[43mP\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 82\u001b[0m g_t_dt \u001b[38;5;241m=\u001b[39m g_t_jacobian[\u001b[38;5;241m1\u001b[39m]\n\u001b[1;32m 83\u001b[0m g_t_d2x \u001b[38;5;241m=\u001b[39m g_t_hessian[\u001b[38;5;241m0\u001b[39m][\u001b[38;5;241m0\u001b[39m]\n", + "File \u001b[0;32m~/miniforge3/envs/myenv/lib/python3.9/site-packages/autograd/wrap_util.py:20\u001b[0m, in \u001b[0;36munary_to_nary..nary_operator..nary_f\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m 18\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m 19\u001b[0m x \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mtuple\u001b[39m(args[i] \u001b[38;5;28;01mfor\u001b[39;00m i \u001b[38;5;129;01min\u001b[39;00m argnum)\n\u001b[0;32m---> 20\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43munary_operator\u001b[49m\u001b[43m(\u001b[49m\u001b[43munary_f\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mx\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mnary_op_args\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mnary_op_kwargs\u001b[49m\u001b[43m)\u001b[49m\n", + "File \u001b[0;32m~/miniforge3/envs/myenv/lib/python3.9/site-packages/autograd/differential_operators.py:78\u001b[0m, in \u001b[0;36mhessian\u001b[0;34m(fun, x)\u001b[0m\n\u001b[1;32m 75\u001b[0m \u001b[38;5;129m@unary_to_nary\u001b[39m\n\u001b[1;32m 76\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mhessian\u001b[39m(fun, x):\n\u001b[1;32m 77\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mReturns a function that computes the exact Hessian.\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m---> 78\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mjacobian\u001b[49m\u001b[43m(\u001b[49m\u001b[43mjacobian\u001b[49m\u001b[43m(\u001b[49m\u001b[43mfun\u001b[49m\u001b[43m)\u001b[49m\u001b[43m)\u001b[49m\u001b[43m(\u001b[49m\u001b[43mx\u001b[49m\u001b[43m)\u001b[49m\n", + "File \u001b[0;32m~/miniforge3/envs/myenv/lib/python3.9/site-packages/autograd/wrap_util.py:20\u001b[0m, in \u001b[0;36munary_to_nary..nary_operator..nary_f\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m 18\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m 19\u001b[0m x \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mtuple\u001b[39m(args[i] \u001b[38;5;28;01mfor\u001b[39;00m i \u001b[38;5;129;01min\u001b[39;00m argnum)\n\u001b[0;32m---> 20\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43munary_operator\u001b[49m\u001b[43m(\u001b[49m\u001b[43munary_f\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mx\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mnary_op_args\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mnary_op_kwargs\u001b[49m\u001b[43m)\u001b[49m\n", + "File \u001b[0;32m~/miniforge3/envs/myenv/lib/python3.9/site-packages/autograd/differential_operators.py:57\u001b[0m, in \u001b[0;36mjacobian\u001b[0;34m(fun, x)\u001b[0m\n\u001b[1;32m 47\u001b[0m \u001b[38;5;129m@unary_to_nary\u001b[39m\n\u001b[1;32m 48\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mjacobian\u001b[39m(fun, x):\n\u001b[1;32m 49\u001b[0m \u001b[38;5;250m \u001b[39m\u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m 50\u001b[0m \u001b[38;5;124;03m Returns a function which computes the Jacobian of `fun` with respect to\u001b[39;00m\n\u001b[1;32m 51\u001b[0m \u001b[38;5;124;03m positional argument number `argnum`, which must be a scalar or array. Unlike\u001b[39;00m\n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 55\u001b[0m \u001b[38;5;124;03m (out1, out2, ...) then the Jacobian has shape (out1, out2, ..., in1, in2, ...).\u001b[39;00m\n\u001b[1;32m 56\u001b[0m \u001b[38;5;124;03m \"\"\"\u001b[39;00m\n\u001b[0;32m---> 57\u001b[0m vjp, ans \u001b[38;5;241m=\u001b[39m \u001b[43m_make_vjp\u001b[49m\u001b[43m(\u001b[49m\u001b[43mfun\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mx\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 58\u001b[0m ans_vspace \u001b[38;5;241m=\u001b[39m vspace(ans)\n\u001b[1;32m 59\u001b[0m jacobian_shape \u001b[38;5;241m=\u001b[39m ans_vspace\u001b[38;5;241m.\u001b[39mshape \u001b[38;5;241m+\u001b[39m vspace(x)\u001b[38;5;241m.\u001b[39mshape\n", + "File \u001b[0;32m~/miniforge3/envs/myenv/lib/python3.9/site-packages/autograd/core.py:10\u001b[0m, in \u001b[0;36mmake_vjp\u001b[0;34m(fun, x)\u001b[0m\n\u001b[1;32m 8\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mmake_vjp\u001b[39m(fun, x):\n\u001b[1;32m 9\u001b[0m start_node \u001b[38;5;241m=\u001b[39m VJPNode\u001b[38;5;241m.\u001b[39mnew_root()\n\u001b[0;32m---> 10\u001b[0m end_value, end_node \u001b[38;5;241m=\u001b[39m \u001b[43mtrace\u001b[49m\u001b[43m(\u001b[49m\u001b[43mstart_node\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mfun\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mx\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 11\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m end_node \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m 12\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mvjp\u001b[39m(g): \u001b[38;5;28;01mreturn\u001b[39;00m vspace(x)\u001b[38;5;241m.\u001b[39mzeros()\n", + "File \u001b[0;32m~/miniforge3/envs/myenv/lib/python3.9/site-packages/autograd/tracer.py:10\u001b[0m, in \u001b[0;36mtrace\u001b[0;34m(start_node, fun, x)\u001b[0m\n\u001b[1;32m 8\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m trace_stack\u001b[38;5;241m.\u001b[39mnew_trace() \u001b[38;5;28;01mas\u001b[39;00m t:\n\u001b[1;32m 9\u001b[0m start_box \u001b[38;5;241m=\u001b[39m new_box(x, t, start_node)\n\u001b[0;32m---> 10\u001b[0m end_box \u001b[38;5;241m=\u001b[39m \u001b[43mfun\u001b[49m\u001b[43m(\u001b[49m\u001b[43mstart_box\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 11\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m isbox(end_box) \u001b[38;5;129;01mand\u001b[39;00m end_box\u001b[38;5;241m.\u001b[39m_trace \u001b[38;5;241m==\u001b[39m start_box\u001b[38;5;241m.\u001b[39m_trace:\n\u001b[1;32m 12\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m end_box\u001b[38;5;241m.\u001b[39m_value, end_box\u001b[38;5;241m.\u001b[39m_node\n", + "File \u001b[0;32m~/miniforge3/envs/myenv/lib/python3.9/site-packages/autograd/wrap_util.py:15\u001b[0m, in \u001b[0;36munary_to_nary..nary_operator..nary_f..unary_f\u001b[0;34m(x)\u001b[0m\n\u001b[1;32m 13\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m 14\u001b[0m subargs \u001b[38;5;241m=\u001b[39m subvals(args, \u001b[38;5;28mzip\u001b[39m(argnum, x))\n\u001b[0;32m---> 15\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mfun\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43msubargs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n", + "File \u001b[0;32m~/miniforge3/envs/myenv/lib/python3.9/site-packages/autograd/wrap_util.py:20\u001b[0m, in \u001b[0;36munary_to_nary..nary_operator..nary_f\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m 18\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m 19\u001b[0m x \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mtuple\u001b[39m(args[i] \u001b[38;5;28;01mfor\u001b[39;00m i \u001b[38;5;129;01min\u001b[39;00m argnum)\n\u001b[0;32m---> 20\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43munary_operator\u001b[49m\u001b[43m(\u001b[49m\u001b[43munary_f\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mx\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mnary_op_args\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mnary_op_kwargs\u001b[49m\u001b[43m)\u001b[49m\n", + "File \u001b[0;32m~/miniforge3/envs/myenv/lib/python3.9/site-packages/autograd/differential_operators.py:61\u001b[0m, in \u001b[0;36mjacobian\u001b[0;34m(fun, x)\u001b[0m\n\u001b[1;32m 59\u001b[0m jacobian_shape \u001b[38;5;241m=\u001b[39m ans_vspace\u001b[38;5;241m.\u001b[39mshape \u001b[38;5;241m+\u001b[39m vspace(x)\u001b[38;5;241m.\u001b[39mshape\n\u001b[1;32m 60\u001b[0m grads \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mmap\u001b[39m(vjp, ans_vspace\u001b[38;5;241m.\u001b[39mstandard_basis())\n\u001b[0;32m---> 61\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m np\u001b[38;5;241m.\u001b[39mreshape(\u001b[43mnp\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mstack\u001b[49m\u001b[43m(\u001b[49m\u001b[43mgrads\u001b[49m\u001b[43m)\u001b[49m, jacobian_shape)\n", + "File \u001b[0;32m~/miniforge3/envs/myenv/lib/python3.9/site-packages/autograd/numpy/numpy_wrapper.py:88\u001b[0m, in \u001b[0;36mstack\u001b[0;34m(arrays, axis)\u001b[0m\n\u001b[1;32m 83\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mstack\u001b[39m(arrays, axis\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m0\u001b[39m):\n\u001b[1;32m 84\u001b[0m \u001b[38;5;66;03m# this code is basically copied from numpy/core/shape_base.py's stack\u001b[39;00m\n\u001b[1;32m 85\u001b[0m \u001b[38;5;66;03m# we need it here because we want to re-implement stack in terms of the\u001b[39;00m\n\u001b[1;32m 86\u001b[0m \u001b[38;5;66;03m# primitives defined in this file\u001b[39;00m\n\u001b[0;32m---> 88\u001b[0m arrays \u001b[38;5;241m=\u001b[39m [array(arr) \u001b[38;5;28;01mfor\u001b[39;00m arr \u001b[38;5;129;01min\u001b[39;00m arrays]\n\u001b[1;32m 89\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m arrays:\n\u001b[1;32m 90\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mneed at least one array to stack\u001b[39m\u001b[38;5;124m'\u001b[39m)\n", + "File \u001b[0;32m~/miniforge3/envs/myenv/lib/python3.9/site-packages/autograd/numpy/numpy_wrapper.py:88\u001b[0m, in \u001b[0;36m\u001b[0;34m(.0)\u001b[0m\n\u001b[1;32m 83\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mstack\u001b[39m(arrays, axis\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m0\u001b[39m):\n\u001b[1;32m 84\u001b[0m \u001b[38;5;66;03m# this code is basically copied from numpy/core/shape_base.py's stack\u001b[39;00m\n\u001b[1;32m 85\u001b[0m \u001b[38;5;66;03m# we need it here because we want to re-implement stack in terms of the\u001b[39;00m\n\u001b[1;32m 86\u001b[0m \u001b[38;5;66;03m# primitives defined in this file\u001b[39;00m\n\u001b[0;32m---> 88\u001b[0m arrays \u001b[38;5;241m=\u001b[39m [array(arr) \u001b[38;5;28;01mfor\u001b[39;00m arr \u001b[38;5;129;01min\u001b[39;00m arrays]\n\u001b[1;32m 89\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m arrays:\n\u001b[1;32m 90\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mneed at least one array to stack\u001b[39m\u001b[38;5;124m'\u001b[39m)\n", + "File \u001b[0;32m~/miniforge3/envs/myenv/lib/python3.9/site-packages/autograd/core.py:14\u001b[0m, in \u001b[0;36mmake_vjp..vjp\u001b[0;34m(g)\u001b[0m\n\u001b[0;32m---> 14\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mvjp\u001b[39m(g): \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mbackward_pass\u001b[49m\u001b[43m(\u001b[49m\u001b[43mg\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mend_node\u001b[49m\u001b[43m)\u001b[49m\n", + "File \u001b[0;32m~/miniforge3/envs/myenv/lib/python3.9/site-packages/autograd/core.py:21\u001b[0m, in \u001b[0;36mbackward_pass\u001b[0;34m(g, end_node)\u001b[0m\n\u001b[1;32m 19\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m node \u001b[38;5;129;01min\u001b[39;00m toposort(end_node):\n\u001b[1;32m 20\u001b[0m outgrad \u001b[38;5;241m=\u001b[39m outgrads\u001b[38;5;241m.\u001b[39mpop(node)\n\u001b[0;32m---> 21\u001b[0m ingrads \u001b[38;5;241m=\u001b[39m \u001b[43mnode\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mvjp\u001b[49m\u001b[43m(\u001b[49m\u001b[43moutgrad\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;241;43m0\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 22\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m parent, ingrad \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mzip\u001b[39m(node\u001b[38;5;241m.\u001b[39mparents, ingrads):\n\u001b[1;32m 23\u001b[0m outgrads[parent] \u001b[38;5;241m=\u001b[39m add_outgrads(outgrads\u001b[38;5;241m.\u001b[39mget(parent), ingrad)\n", + "File \u001b[0;32m~/miniforge3/envs/myenv/lib/python3.9/site-packages/autograd/core.py:67\u001b[0m, in \u001b[0;36mdefvjp..vjp_argnums..\u001b[0;34m(g)\u001b[0m\n\u001b[1;32m 64\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mNotImplementedError\u001b[39;00m(\n\u001b[1;32m 65\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mVJP of \u001b[39m\u001b[38;5;132;01m{}\u001b[39;00m\u001b[38;5;124m wrt argnum 0 not defined\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;241m.\u001b[39mformat(fun\u001b[38;5;241m.\u001b[39m\u001b[38;5;18m__name__\u001b[39m))\n\u001b[1;32m 66\u001b[0m vjp \u001b[38;5;241m=\u001b[39m vjpfun(ans, \u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n\u001b[0;32m---> 67\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;01mlambda\u001b[39;00m g: (\u001b[43mvjp\u001b[49m\u001b[43m(\u001b[49m\u001b[43mg\u001b[49m\u001b[43m)\u001b[49m,)\n\u001b[1;32m 68\u001b[0m \u001b[38;5;28;01melif\u001b[39;00m L \u001b[38;5;241m==\u001b[39m \u001b[38;5;241m2\u001b[39m:\n\u001b[1;32m 69\u001b[0m argnum_0, argnum_1 \u001b[38;5;241m=\u001b[39m argnums\n", + "File \u001b[0;32m~/miniforge3/envs/myenv/lib/python3.9/site-packages/autograd/numpy/numpy_vjps.py:701\u001b[0m, in \u001b[0;36m\u001b[0;34m(g)\u001b[0m\n\u001b[1;32m 699\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m A\n\u001b[1;32m 700\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m SparseObject(vs, mut_add)\n\u001b[0;32m--> 701\u001b[0m defvjp(func(ArrayBox\u001b[38;5;241m.\u001b[39m\u001b[38;5;21m__getitem__\u001b[39m), \u001b[38;5;28;01mlambda\u001b[39;00m ans, A, idx: \u001b[38;5;28;01mlambda\u001b[39;00m g: \u001b[43muntake\u001b[49m\u001b[43m(\u001b[49m\u001b[43mg\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43midx\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mvspace\u001b[49m\u001b[43m(\u001b[49m\u001b[43mA\u001b[49m\u001b[43m)\u001b[49m\u001b[43m)\u001b[49m)\n\u001b[1;32m 702\u001b[0m defvjp(untake, \u001b[38;5;28;01mlambda\u001b[39;00m ans, x, idx, _: \u001b[38;5;28;01mlambda\u001b[39;00m g: g[idx])\n\u001b[1;32m 704\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21m_unpad\u001b[39m(array, width):\n", + "File \u001b[0;32m~/miniforge3/envs/myenv/lib/python3.9/site-packages/autograd/tracer.py:44\u001b[0m, in \u001b[0;36mprimitive..f_wrapped\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m 42\u001b[0m parents \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mtuple\u001b[39m(box\u001b[38;5;241m.\u001b[39m_node \u001b[38;5;28;01mfor\u001b[39;00m _ , box \u001b[38;5;129;01min\u001b[39;00m boxed_args)\n\u001b[1;32m 43\u001b[0m argnums \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mtuple\u001b[39m(argnum \u001b[38;5;28;01mfor\u001b[39;00m argnum, _ \u001b[38;5;129;01min\u001b[39;00m boxed_args)\n\u001b[0;32m---> 44\u001b[0m ans \u001b[38;5;241m=\u001b[39m \u001b[43mf_wrapped\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margvals\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 45\u001b[0m node \u001b[38;5;241m=\u001b[39m node_constructor(ans, f_wrapped, argvals, kwargs, argnums, parents)\n\u001b[1;32m 46\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m new_box(ans, trace, node)\n", + "File \u001b[0;32m~/miniforge3/envs/myenv/lib/python3.9/site-packages/autograd/tracer.py:45\u001b[0m, in \u001b[0;36mprimitive..f_wrapped\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m 43\u001b[0m argnums \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mtuple\u001b[39m(argnum \u001b[38;5;28;01mfor\u001b[39;00m argnum, _ \u001b[38;5;129;01min\u001b[39;00m boxed_args)\n\u001b[1;32m 44\u001b[0m ans \u001b[38;5;241m=\u001b[39m f_wrapped(\u001b[38;5;241m*\u001b[39margvals, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n\u001b[0;32m---> 45\u001b[0m node \u001b[38;5;241m=\u001b[39m \u001b[43mnode_constructor\u001b[49m\u001b[43m(\u001b[49m\u001b[43mans\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mf_wrapped\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43margvals\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mkwargs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43margnums\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mparents\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 46\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m new_box(ans, trace, node)\n\u001b[1;32m 47\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n", + "File \u001b[0;32m~/miniforge3/envs/myenv/lib/python3.9/site-packages/autograd/core.py:36\u001b[0m, in \u001b[0;36mVJPNode.__init__\u001b[0;34m(self, value, fun, args, kwargs, parent_argnums, parents)\u001b[0m\n\u001b[1;32m 33\u001b[0m fun_name \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mgetattr\u001b[39m(fun, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124m__name__\u001b[39m\u001b[38;5;124m'\u001b[39m, fun)\n\u001b[1;32m 34\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mNotImplementedError\u001b[39;00m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mVJP of \u001b[39m\u001b[38;5;132;01m{}\u001b[39;00m\u001b[38;5;124m wrt argnums \u001b[39m\u001b[38;5;132;01m{}\u001b[39;00m\u001b[38;5;124m not defined\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m 35\u001b[0m \u001b[38;5;241m.\u001b[39mformat(fun_name, parent_argnums))\n\u001b[0;32m---> 36\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mvjp \u001b[38;5;241m=\u001b[39m \u001b[43mvjpmaker\u001b[49m\u001b[43m(\u001b[49m\u001b[43mparent_argnums\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mvalue\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n", + "File \u001b[0;32m~/miniforge3/envs/myenv/lib/python3.9/site-packages/autograd/core.py:56\u001b[0m, in \u001b[0;36mdefvjp..vjp_argnums\u001b[0;34m(argnums, ans, args, kwargs)\u001b[0m\n\u001b[1;32m 53\u001b[0m argnums \u001b[38;5;241m=\u001b[39m kwargs\u001b[38;5;241m.\u001b[39mget(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124margnums\u001b[39m\u001b[38;5;124m'\u001b[39m, count())\n\u001b[1;32m 54\u001b[0m vjps_dict \u001b[38;5;241m=\u001b[39m {argnum : translate_vjp(vjpmaker, fun, argnum)\n\u001b[1;32m 55\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m argnum, vjpmaker \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mzip\u001b[39m(argnums, vjpmakers)}\n\u001b[0;32m---> 56\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mvjp_argnums\u001b[39m(argnums, ans, args, kwargs):\n\u001b[1;32m 57\u001b[0m L \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mlen\u001b[39m(argnums)\n\u001b[1;32m 58\u001b[0m \u001b[38;5;66;03m# These first two cases are just optimizations\u001b[39;00m\n", + "\u001b[0;31mKeyboardInterrupt\u001b[0m: " + ] + } + ], "source": [ "import autograd.numpy as np\n", "from autograd import jacobian,hessian,grad\n", @@ -3397,7 +3683,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.9.18" + "version": "3.9.10" } }, "nbformat": 4, diff --git a/doc/LectureNotes/_build/jupyter_execute/chapter12.ipynb b/doc/LectureNotes/_build/jupyter_execute/chapter12.ipynb index 0300f86fa..a138dc08a 100644 --- a/doc/LectureNotes/_build/jupyter_execute/chapter12.ipynb +++ b/doc/LectureNotes/_build/jupyter_execute/chapter12.ipynb @@ -624,20 +624,18 @@ }, "outputs": [ { - "ename": "ModuleNotFoundError", - "evalue": "No module named 'matplotlib'", - "output_type": "error", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mModuleNotFoundError\u001b[0m Traceback (most recent call last)", - "Cell \u001b[0;32mIn[1], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \u001b[43mget_ipython\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mrun_line_magic\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mmatplotlib\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43minline\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m)\u001b[49m\n\u001b[1;32m 3\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mnumpy\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m \u001b[38;5;21;01mnp\u001b[39;00m\n\u001b[1;32m 4\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mmath\u001b[39;00m\n", - "File \u001b[0;32m~/miniforge3/lib/python3.9/site-packages/IPython/core/interactiveshell.py:2432\u001b[0m, in \u001b[0;36mInteractiveShell.run_line_magic\u001b[0;34m(self, magic_name, line, _stack_depth)\u001b[0m\n\u001b[1;32m 2430\u001b[0m kwargs[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mlocal_ns\u001b[39m\u001b[38;5;124m'\u001b[39m] \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mget_local_scope(stack_depth)\n\u001b[1;32m 2431\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mbuiltin_trap:\n\u001b[0;32m-> 2432\u001b[0m result \u001b[38;5;241m=\u001b[39m \u001b[43mfn\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 2434\u001b[0m \u001b[38;5;66;03m# The code below prevents the output from being displayed\u001b[39;00m\n\u001b[1;32m 2435\u001b[0m \u001b[38;5;66;03m# when using magics with decorator @output_can_be_silenced\u001b[39;00m\n\u001b[1;32m 2436\u001b[0m \u001b[38;5;66;03m# when the last Python token in the expression is a ';'.\u001b[39;00m\n\u001b[1;32m 2437\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mgetattr\u001b[39m(fn, magic\u001b[38;5;241m.\u001b[39mMAGIC_OUTPUT_CAN_BE_SILENCED, \u001b[38;5;28;01mFalse\u001b[39;00m):\n", - "File \u001b[0;32m~/miniforge3/lib/python3.9/site-packages/IPython/core/magics/pylab.py:99\u001b[0m, in \u001b[0;36mPylabMagics.matplotlib\u001b[0;34m(self, line)\u001b[0m\n\u001b[1;32m 97\u001b[0m \u001b[38;5;28mprint\u001b[39m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mAvailable matplotlib backends: \u001b[39m\u001b[38;5;132;01m%s\u001b[39;00m\u001b[38;5;124m\"\u001b[39m \u001b[38;5;241m%\u001b[39m backends_list)\n\u001b[1;32m 98\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m---> 99\u001b[0m gui, backend \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mshell\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43menable_matplotlib\u001b[49m\u001b[43m(\u001b[49m\u001b[43margs\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mgui\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mlower\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mif\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[38;5;28;43misinstance\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43margs\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mgui\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mstr\u001b[39;49m\u001b[43m)\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01melse\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43margs\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mgui\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 100\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_show_matplotlib_backend(args\u001b[38;5;241m.\u001b[39mgui, backend)\n", - "File \u001b[0;32m~/miniforge3/lib/python3.9/site-packages/IPython/core/interactiveshell.py:3606\u001b[0m, in \u001b[0;36mInteractiveShell.enable_matplotlib\u001b[0;34m(self, gui)\u001b[0m\n\u001b[1;32m 3585\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21menable_matplotlib\u001b[39m(\u001b[38;5;28mself\u001b[39m, gui\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mNone\u001b[39;00m):\n\u001b[1;32m 3586\u001b[0m \u001b[38;5;250m \u001b[39m\u001b[38;5;124;03m\"\"\"Enable interactive matplotlib and inline figure support.\u001b[39;00m\n\u001b[1;32m 3587\u001b[0m \n\u001b[1;32m 3588\u001b[0m \u001b[38;5;124;03m This takes the following steps:\u001b[39;00m\n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 3604\u001b[0m \u001b[38;5;124;03m display figures inline.\u001b[39;00m\n\u001b[1;32m 3605\u001b[0m \u001b[38;5;124;03m \"\"\"\u001b[39;00m\n\u001b[0;32m-> 3606\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mmatplotlib_inline\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mbackend_inline\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m configure_inline_support\n\u001b[1;32m 3608\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mIPython\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mcore\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m pylabtools \u001b[38;5;28;01mas\u001b[39;00m pt\n\u001b[1;32m 3609\u001b[0m gui, backend \u001b[38;5;241m=\u001b[39m pt\u001b[38;5;241m.\u001b[39mfind_gui_and_backend(gui, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mpylab_gui_select)\n", - "File \u001b[0;32m~/miniforge3/lib/python3.9/site-packages/matplotlib_inline/__init__.py:1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01m.\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m backend_inline, config \u001b[38;5;66;03m# noqa\u001b[39;00m\n\u001b[1;32m 2\u001b[0m __version__ \u001b[38;5;241m=\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m0.1.6\u001b[39m\u001b[38;5;124m\"\u001b[39m \u001b[38;5;66;03m# noqa\u001b[39;00m\n", - "File \u001b[0;32m~/miniforge3/lib/python3.9/site-packages/matplotlib_inline/backend_inline.py:6\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[38;5;124;03m\"\"\"A matplotlib backend for publishing figures via display_data\"\"\"\u001b[39;00m\n\u001b[1;32m 3\u001b[0m \u001b[38;5;66;03m# Copyright (c) IPython Development Team.\u001b[39;00m\n\u001b[1;32m 4\u001b[0m \u001b[38;5;66;03m# Distributed under the terms of the BSD 3-Clause License.\u001b[39;00m\n\u001b[0;32m----> 6\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mmatplotlib\u001b[39;00m\n\u001b[1;32m 7\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mmatplotlib\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m colors\n\u001b[1;32m 8\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mmatplotlib\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mbackends\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m backend_agg\n", - "\u001b[0;31mModuleNotFoundError\u001b[0m: No module named 'matplotlib'" - ] + "data": { + "image/png": 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Vgp7zQuxUYQenSeQ9L7SBD3OAGQUuXmKMuLlRIp+SyUQpEiVWrO2hIqff1BDOcve5eIkx6sxLZvQUchxdxxw90YpEiXlPM8/TSeIOfBi5Ji6DRoWLlx6A9ragvo9pcfOKxFycZLP3FoUbR6Fo8VLPiIoCFy8xRp5il4Rp7fkHeoUbRyGIezFo+zAkbqdKK8ipRWIUuHiJsQSxpRfww9VSV1iaMyh5lpgbHGlPwEjcTpVYkEu8gU8UuHiJMe7haayRKvHgNIm5zk4sFEGhdoIVPW3gk0a6n6PCxUuMYTvV1JXS/FDibA85SgVucKR90rHELBIl4R5LRz76HxEuXnoA3JSknxgoiexTMKGGURDibCKwduEuG+H2vLh66S4XLzHGbYCSKJ0qdnMj8JQ66myixLmfpdy9RaxE02YTqY+kR4GLlxjjdqq00VPySutQmU/BJJEae+KG3TTePZ3GyDW17YgCFy8xRn1j0GZe0kgdqsReziClGlkkQlczcJv9oQPMKHDxEmPEUarEW85wkVg6xMYeWSSKuTyH2+wP3S8XBS5eYow4SpV4I1Vihyop0zORljOIhSLy4wHwBy8ycg1toiPBxUuMEQ/0kngjVWKHKjFnXtJInSptD5eUEysnzZJ4My+Cth1R4OIlxrJnNbDeGbjRE7SlpxWJEvNpI9gqqCTunhdB97xY4YpavKxcuVIzZszQkCFDlEgk9Pzzzx/39StWrFAikejw9dprrxUzzPiCV/WUtzV3f0C6oadkmjnNjiwSM/c0KdO82cTcOGm5DltZMf/lBw4c0Pjx4/WNb3xD11xzTZf/bvPmzaqoqMj8fMYZZxQjvNij7sXAPTEAiTMOyLlGFYmcUDtFmdFgRBlNRS1epk2bpmnTphX8d4MGDdJpp5128gPqYfB7MSBvbeJsgMQ70EuiPp3BKsZzkbIs5bR1kFznFrRBwLxHwhLJPS/nnXeeqqurdfnll+vll18+7mvb2trU2tqa92X5SJsbJV6xlcbqUHlFYi5SppHLRsC9RRKvrcu9J1iRhy9SxUt1dbUeffRR1dXV6dlnn9WYMWN0+eWXa+XKlcf8mwULFqiysjLzNWzYsBJGHG3UD1fDPSoN3dyInHkRL9nIR6VTV1CaJfEKRe956b6iLhsVasyYMRozZkzm58mTJ2v79u267777dNFFF3X6N/Pnz1dtbW3m59bWVhcwKaC2Eo49SiXdJsTzR2gdqkRdngM+qZhzV7AyHb5Izbx0ZtKkSXrjjTeO+c/Ly8tVUVGR92VJxOUAiTcjQG3oBcuzxJxNpM0kSuCZl9QVk+q8mZfwwiCKfPHS2Nio6urqsMNAonaqtL0Y9IYeBbgXgxRrB7DYaU8q5iLNJkZBUZeN9u/frz/84Q+Zn7ds2aINGzbo9NNP11lnnaX58+drx44deuKJJyRJCxcu1IgRIzR27FgdPHhQTz75pOrq6lRXV1fMMGML26nCZgQS0E0v2YYekmgx72naZ3VJuadzkzKdhRn4eOal24pavKxbt06XXnpp5uf03pTrrrtOS5cuVVNTk7Zt25b55wcPHtS8efO0Y8cO9e/fX2PHjtULL7yg6dOnFzPM+AKOUpNYoydihyp5L0apIJeNqMcs0AY+YQcAVtTi5ZJLLjluI7N06dK8n++44w7dcccdxQypR6F/qjRt9ETqUCXosfWpKynTyCIxdYXd0rhN6O3PebGui/yeF+s+7OgpdeWMnqgnGSevlCJR4p4/InHuZ0nZWVvYXY07pC7ne1quw+biJca4nSpt9JS80jpU5HJG5jtOspFFYurKu6eTKLn2npfuc/ESY9xONYnUqUq8kRNyOQN4TyOLROYedNzTRj7npftcvMQY9W1BezqD2KHmojT0EnM2EVkkYg9eTKLk2ifsdp+Llxijdqq40VOC16FKuctzkERLmV6JdE8jPx4gEyoo0VLOwYucXKfBMh06Fy8xRt3ciBs9pb+h5pmSaDGfoCOOqPF7XiD3tPe8dJ+Llx6A1NBLwo2eIGEeEyl8cq5JsZNi7QwlfNSsZ8S4eIkx7LJR6kp5W2f3YbASHdCGqWLuxQCmGbm3SCIuOef8QEt2yFy89AC09wRxml1idagS70CvXKRUEx+Vzu4tImWadV9IPufl43DxEmPEUarEG6lyHytNXil5lqinGbNmAyTmScYS8UlFn7DbXS5eYizbqbLeFbSRanZzIyzPqSslzxKrAEhDPiqN3ezPKhS9atR9Ll5ijPsUDKsBkmdeSoY4m4h8VDp1BaVZEq9Q9Dkv3efiJcZAbSUasUNN4u15Ic4m0jpUibo8l1uQM7Kdt2wUYhxELl5ijPvEQPIKaX8wcbYH65fykGKnFQBxQHxL+jYpjIuXGMOOnlJXzOgp53tSrmlFosTciwHbQyoJfMwC7FHpXKTZxChw8dID4N4SsGl26hMD2b1FlEwz92Igl42oZxelrshN6KxUh87FS4wRR6kSb8Mu9YkB2n0hMWcTkUUiduYleQWlGlmQR4GLlxijvilwj0pDnxignacjQe9p4MxLGuh2lsSbKZK4g8ywuXiJMeIoVeJ1qrlFFinTtCJRypm9ACWadj9LuZ+UDkq06DMvrFyHzcVLjCFHqcrZdBdyHF0G/WRY2vKcxGzokUVi6kq6nyX2nhdarsPm4iXGiKNUifd0Rt6yESnZwOUMYkOPLBKZTQd05oV5pEXYXLzEGPFAL4k3Us1/VDq0MApGXM4QsKEn3RNpmfceLnZeoUj9EMywuXiJMerUbxqlASI9RdIZSpEoce6JzpDuE1ConULd02EHAOXiJc6Au9hzRx+UNzUlzvaIB3oRC/LsPgwO4t4iCbpsBFwKjQIXLz0AqQGiv4FJ8YNC7YB1TwOLRGiHCgtXEmuWKEpcvMQYeZQqcabZqRt2aR9iJzE7Ve8tKh3ck4pi3tNR4OIlxohPDDCXjagfD5BEybMEfTIDtgFd8hlRpURdogubi5cYyzT0oPdE/sxLaGEUJH/mhYO8nEG6qZGPSqeunCwnZW8LTrJ9wm73uHiJsWxjyXlX5L6BSSPVNNJIFTnzgpxNTF5ZeWZ2qMiCPHWFpTp0Ll5iDPT+7Rzkf4A685JZzgC19MjZROBaBrVDJRbkPuele1y8xBhxI1juui+lrefueQEFmwZs6DmRdgKUZ0nIgjwNlunQuXiJMeLmxvxlI4a8dhKUbOSZGKkrKM3sJ2DCDaNgxJkX4DauSHDxEmfAUWouyugpv3bh5Np7MUoDuGqEnLWVoHteEsSSPHwuXmKM+JZgzrzAl41ALT35nuZkOXfWlpRpf6p0T+LiJcaYo1TinpcsUKqZnSpyNjE9GwDKNLRD9VJoz+HiJcaIbwrio9J5TxuBWnvkckbYAXQDskhMXUG3syTeTJHEHGRGgYuXGCOOUpmH1OUsG4UYR6GynSok0WI29MgiMZ3nkOMoFHvmhZbtcLl4iTHSGziNVGh1hhU+cHNj6kpq6JFFYupKez9mC0VQrqFLdGFz8RJjyAO9cr4HtT+oWNtDhY4Kth1Q7OT7WUKlWrRoo8LFS4xlz2rgVC/EPS8SfEaAk2ZoQQ485wUVbQ7iPe2Zl25x8dIDoN4UpFg7A4qf+FhpGijNzCIR2qGSBg9pxIFPFLh46QFIDRDxUWmJucExAD4GQ+xUiUUitUNF7i8C3tNR4OIlxrIdKuddQTykTmI+Woo+Sh11T/M2RmPPeUldSbkmFVpR4uIlxsgdqgR9YgDVqSavyDxz0pwBSjPyc9Ek+OdI0ZIdMhcvMUb8cLXcRzNRDRByI2kSMc8k7KUM0A2tnLaOk2rkbGIUuHiJsUxjCXpPUB+VBqYauZxB7FQzTxuR8py6crKcxCwUeQOfKHDxEmOkxrIzqOWM1JXUqaaB0oycYifFmpYgTtvmIN3TadBUh8bFS4wRpyOJDb3kTrV0eHsxmHlOIoVOXXJOIw58wuTiJcaQHSpwil1iTVOnZQ9P48TOvKeTUDOJxOW53CcVibkONwwcFy+xxhulZk7IDDeKgiE7VeLhaakrazYR+ARM6srJcrv9cqFFUThi2xEFLl5iDDl6Sl1JIyeJ2qmGHUHhiA098uwRYp5zl41IuUaWiuEravGycuVKzZgxQ0OGDFEikdDzzz9/wr9paGjQhAkT1K9fP40aNUqLFi0qZoixRnxLAA99lcR8YiC7RMfJNvL8EeQ9DTzgMud7L4XGX1GLlwMHDmj8+PF68MEHu/T6LVu2aPr06Zo6daoaGxt11113ae7cuaqrqytmmLHF7lBDDqRALhRLI3NfgG5qZJEI7FAD6LoRse2IgrJi/sunTZumadOmdfn1ixYt0llnnaWFCxdKks455xytW7dO9913n6655ppO/6atrU1tbW2Zn1tbWz9WzHFCfFMQz2mQlHOcOifbyOWM1JWTZWiRmLqCbmf+56KBch0FkdrzsmbNGtXU1OT97oorrtC6det06NChTv9mwYIFqqyszHwNGzasFKEiMEepKaDGR8KFm4dUKJJmL9ojhU7Os8R6P5JijZJIFS/Nzc2qqqrK+11VVZUOHz6sXbt2dfo38+fPV0tLS+Zr+/btpQgVgfgIHmnmIhfxU6UzezGArSfpNiGePsKJNIt0T+QBztpGQVGXjbqjfcWfPcK887dTeXm5ysvLix4XGek9QZxiz4XKdeacFx7URlJgkUh8UjEXaeaIuBQaBZGaeRk8eLCam5vzfrdz506VlZVpwIABIUXFlQA+MZAGansk5cbLyTW7Uw03jkIQi0TiU115h9SFF0bBvOeleyJVvEyePFn19fV5v3vppZc0ceJE9enTJ6SowIgNPXTDLnB7EXQ5g9upukgsLuyG3dSVOMgMU1GLl/3792vDhg3asGGDpOSj0Bs2bNC2bdskJferXHvttZnXz549W1u3blVtba02bdqkJUuWaPHixZo3b14xw4wtZocKfVQauOeF/anS4cZRiMxTXaAiMY3UoebPvHBynfC6UbcUdc/LunXrdOmll2Z+rq2tlSRdd911Wrp0qZqamjKFjCSNHDlSy5cv1+23366HHnpIQ4YM0QMPPHDMx6Tt+JgdavLKaXqSmIViEinXyFGqi8SSyDvmhZRr4GxiFBS1eLnkkkuOu+Fr6dKlHX538cUX69VXXy1iVD1HtkPlvC2wHw+QebILlOvMcgYn18RRKvM8HV6HSmrnchELxSiI1J4XO7lIjWUa8UPsknib7pAzL+jZRE6miR0qdeYljTTwiQIXLzFGaiw7gIVObOyRyxmpK2mUHQDXQslPz0msts9PG3WPi5cYI3aoxNkAibkXgxNpDvA9TUTKc26iSQV5GinVUeDiJcaQHSpwH4YELRSJj/Ai92Ikr6A0M/OMfPifOZsYBS5e4gzYoaabS1KHKrGmqdOyh6dxYkcWiakrqSAnnrCbt2xEzHW4YeC4eIkx5OgJOEqVoJ0qcI2OOZvI24QOfKgrf8NuaFEUjvgEXRS4eIkx5OgpdSWNnCRqp5q8kjJNLBLTSLc0Mc+57Rwq1+CPcQmTi5cYIxb0xA5VYj4xQCwUSUtcacx7On0/c27o/EelOdkmFopR4OIlxhLAxVTqxwOQkVJNvi+IHaoVn1PdPS5eegBQ7YIffZDCJ362URprRsB7XkoBdEvkA87aRoGLlxhD7nnJhEpq6pm5TiMtxaA7VU6aPWtbQsR7OgpcvMQY8U2BbYB4bT3ynBfiKJX4qdLEtkPIvUXsgU+YXLzEGHEjGHNzY84TA6Rco5czOIkmFonEDpW4AV2CFooR4OIl1riP4MHaH/ZnwYByjSzIkUWiz4gqFeKTilHg4iXGkA098BN4pdwjvkMNoyDM5Qxwp8pJM7PtoC45Z74DJTsCXLzEGLNDhTZACWKnyss1sVNNIxWJaaRZW+zAB3xPh8nFS4zR1n4l8NRv6kpqgICrRshRqovE0oA+qIicTYwCFy8xRvy0Uuqmu+yHYHJynXk6A5RrZKeKXDbidajEz5CSBP0A3fC5eIkxUmNJR3xiIFsohhpGQYibG3M+cSfEKAoD3H+OLBIl5hN0UeDiJcaI05HEKXYJ2qmSgm2H1NCD04zKcxptz0sa+T4Jg4uXGENOsaeuuOIldSU19sg9L8h7mleQI/NMnXkBHnAZBS5eegBUhwp/YgCUamRjz5xNTF5BaWbmGXiejpR7wCUp2+Fz8RJjxNGTgKNUid3Yk5p74j1NnE1EnrAL3IAuse6LKHHxEmPIDhU4SpWgnSpy5iWJNJuozFMwnEQDJxKBpXgSse2IAhcvMUZ8U2AflQYjZZp8W6BiJ8XaHix2UlEbJS5eegRO9UIqtDpDmhGgTrNLIt3SyBkB5AeNkoLNkd2wy4w/LC5eYgw58wI9aIr4qHQaKdfopVBQkQgKNYNYJOYith1hcvESY8iGPv0NrAVC7hEAnqmD3EiKuiuScm8JSq6JRaLEHviEycVLjCEbevyGXVCuU1fimjsny9CN0TnBcm5pXjEuMQc+UeDiJcaIb4rsgV6sFoh40BS5U+V0qMwiMW/mJbQoCuOBT8/i4iXGiHteRG2AxKteyMsZpMiZRWL2e0qnSn1SkXhPR4GLl1jjndxIPNBLYj4xQO5UWfc0bxN67iwRJdPcmRfewCcKXLzEGPE9gf14gNQV1KeilzNAac7OJnLSnFcBUO5p4mdISdCDFyPAxUuMIT/WHtoAibgXA9ipEkepxOWMvGUjSLKz7z1OniXo8n4EuHiJsczmxpDjKAT1DYycEQAWiszlOeKyURblPUksxpN47XQUuHiJMdx7OAdplCpB92IAl+iQy3PA09No7z+JubdI8sxLd7l4iTFkh5q64hqg1JWTaVasGcTlubAD+JgouebOvCSRZhOjwMVLjCFP2AWe+ipBzx8B5pq4uZE8wyWxci2x8iwxZxOjwMVLjBGnI7GPSme+4ySbOMvFvKeBRSLxaSPozAtwD3okuHjpAVAjJ+AoVYJ2qsDGnjmbmLyC0sw854W650XAxiMCXLzEGLJDBY5SJV6xlY8TO+2+yEWKnRRre7TNxrBwI8PFSw8Aql1QhVZnSOET97ykEe8TaoFL2fAPCbMDLxt1j4uXGEMe6AWcYpck4swvcs9L5jtOoolFYv4hdQyUONvLLIVS/wdC4uIlxpBPZqS/IbX0YuY6e2w9J9fMpdAkTpbb7XmB5JpYJErKGfhAEh0RLl5iDNnQA08jlaC5Tl1JuSZv2CX1qnmhQpINTLMk5hlRUeDiJcaQDX3qymuAgLkmjlSBo1TiUzDEc16I5+lIzDOiosDFS4whT9iF7nlB5jp1JTX2xFEq8pH0BG/ZiPhZXRLzno4CFy8xxnxTpBsgVgsEC1cSu1PldKjsIlHitB8e+PQsLl7ijLgPg9oAAZ8YoCwH5CIW5MwiMfs9pVPNLjmDEi1eWxcVLl5ijDTSS8Pueck8lc5o6CV2p0rpUJOAe15yl41CjKMQ2IEPcDYxCkpSvDz88MMaOXKk+vXrpwkTJmjVqlXHfO2KFSuUSCQ6fL322mulCDVWkKMn6Ka7NEiaJTFHqqBQM4hFYi7KPR1AqxfkMQsRUPTi5ZlnntFtt92mu+++W42NjZo6daqmTZumbdu2HffvNm/erKampszX6NGjix1q7OStW0PeF5k3MK0BIo6egKlGLs8Bz9OReLOJxEf/JSGX96Og6MXL/fffrxtuuEE33nijzjnnHC1cuFDDhg3TI488cty/GzRokAYPHpz56t27d7FDjR1aY5mLFjlyLwbw6QxahyqxYs2VuS0g4WOLROAxC1FQ1OLl4MGDWr9+vWpqavJ+X1NTo9WrVx/3b8877zxVV1fr8ssv18svv3zM17W1tam1tTXvy5LQTwyw2h/kXgxQqB2QYifF2hlK+MTzdHLR75NSK2rxsmvXLh05ckRVVVV5v6+qqlJzc3Onf1NdXa1HH31UdXV1evbZZzVmzBhdfvnlWrlyZaevX7BggSorKzNfw4YNO+n/H1TIPS+pK23PC3PmJYmUa+LyHHcTOizX9IEPqvUIX1kp/iPtp/GCIDjm1N6YMWM0ZsyYzM+TJ0/W9u3bdd999+miiy7q8Pr58+ertrY283Nra6sLmJS8zycJMY5CIE99FfVDMHm5Jm5upG5Cp+WaWIxLOXlmpDkyijrzMnDgQPXu3bvDLMvOnTs7zMYcz6RJk/TGG290+s/Ky8tVUVGR92UpeTMv4YXRHaQOVeJOVUus2Gn3RS5a7LR402hx0+KNiqIWL3379tWECRNUX1+f9/v6+npNmTKly/+exsZGVVdXn+zwehTM6IkR5jFR8izlfoJ3mFF0D+k+oe7FoD3ZRYmzPdpMUVQUfdmotrZWs2bN0sSJEzV58mQ9+uij2rZtm2bPni0pueyzY8cOPfHEE5KkhQsXasSIERo7dqwOHjyoJ598UnV1daqrqyt2qLGTAM68ZBt61hsa+anSwOUM5JMZ0L0Ygq2EkgYOuYib/aOg6MXLzJkztXv3bn3/+99XU1OTxo0bp+XLl2v48OGSpKamprwzXw4ePKh58+Zpx44d6t+/v8aOHasXXnhB06dPL3aosUNrKyXu00YidqoppFwnshsxMPh7MRjJxj4qDRz4REFJNuzedNNNuummmzr9Z0uXLs37+Y477tAdd9xRgqjij/jJsJQ426M1QLkdEqmpp20ilZgboyXgPZ26wtIs8sAnTP5soxjLP+eF8dYgHlkv8TrV3A6JlGtahypxOyXaTJGLxJ7FxUuMIfe8pBugkOMoFK0Byg2TlWveKNXLGaWBPU8ndaUMfKLCxUuMIc95SV15DRCrU81bNgLlmri5kbqcgetUgRvQJV6RGBUuXmKMeMIu8cMCpdyNpIw858+8cLIN3K8LXs6APSoN/KwuiTfwiQoXLz0E5Y2RbYBYLRDtgN0Aum5E61ClOMy8MAQe+PQoLl5ijLnnJXnFNUC0A70EXTZKXSFpToLueRFsiS4TJizPyHs6Aly8xFjecgDknQFtf7gNvViFInGUyl3OSKJkGjvDBZxNjAIXLzGWN/MCaYKyb2BWE0Rr6HORZgRoy3MSeDYR1qlS9xalUdroqHDxEmPQ97AkXgPEa+iz35NSTVuek8DLGZlwGcnmzrwkr6R7OgpcvMQY8oRd7IfYJUHSzB3lZWZeOPGTYu0Mpu2g7i1KgaQ5Mly8xFj+CbsM1M82op0/kn/CbnhxFAq45YW7bJS6clJNHfjwZhOjwMVLjBHPeaF/iB0RKdfUUbXEKhIlbq5pYdPijQoXLz0Eo3SRhx8lknfMC7DxJN0m9IKckmtKnO3hTjKOCBcvMcbc85JE61B5G3YhgbbDW8qIwVIoJNvYIpF4U0eAi5ceAtMAURv61BWT55zvSbmm7S1KYu7FEGwvRvaprlDDKFhm4BNyHDQuXmKOVtVnP1Wa1gIlL7iGXqxck2JNwxbktHsaWiRml+cgiY4IFy8xB6tdsnHCWiDch6tRnzaCdagSeDkjdcXMJkKLRNrAJypcvMQcby9G8oprf2ANUN5nG4UYR6FoHaqUM6ImJVrEezqJVyTCBj4R4eIl5miNfXbDLq0BSsLkOW/mBZRrWIcqgU9+hUVM/XgAWpEYFS5eYo72xsjueWHB5Tnne1KuiaNU6smvtHs6DZZm3MAnKly8xByxsZeIDRAr4NzNgaRcE5824s68JFE61ezqHCvT1CIxbC5e4g7W2EO3B+A61fxHpTnZpm1Al8jLGbD9coLmGdfaRYOLl5jDnZKZaYBYb2ja6IkSZ3uZ+wIYP+yWzqCkmntPJ6+UgU9UuHiJOVqDSZ15EWx5DjtKBdYu/OUMRraxe4tSV0aWo8PFS8zRPrEUe84LbOZF0CKReKCXC8XSoO4tEmx5LipcvMQcrcFMw41SU1fM5sbUFTdKhXWoErdTwg18qHuLUldK2xEVLl5ijvbGoJ6SSZt5ocTZEatDlVixdo7xP4CdeUnh3yel5eIl5rBPDIQcR6FoM0VptKhpRW0u6iwXjfPcM7h46SEgtQumyDoWSvjUfRhplJlEiVyQJ2Hek5Q426GexRU2Fy8xR9zgKPE61QSspcc+AZO6QtIsibwUyupUsUUibMk5Kly8xB1sg2P24wFYTRDtcUfuU12sZVCJ/IGBSZRcY4vEzHeQREeEi5eYcwNUGrROFfsZUmEH0B3Qexp3OnfmO1aiPfPSPS5eYi67eY3xzsg+whtqGN1G2YvBLRKTV0qHKoGXM1JXSqa59zRr4BMVLl5ijlbVB9D1DFqe03hLGax9GJI71VKhFolplIFPVLh4iTnc6An6FAytU+V2qMkrpUOVwMsZqSulU/U93bO4eIk53OiJemw9rAHyKLV0sCe/wkY+3I3RrIFPVLh4iTnc6Cl1xTX0qSsmz9QPsYMViRL35FdcpwovEkn3dBS4eIk53BuD+qg0dpTKgutQ5UKxVDzw6VlcvMQebNkodcU1QLQDvaDrc7QOVeIWimmUThV78CJs4BMVLl5iLvspvIx3BrRPxZ1kTO1Qs0UtI8+SwMsZsIEPtPEgziZGgYuXmMMdUpd52gjXAkkC5Zm6lAGbSZTAs4mpKyXV9IKcMvCJChcvMUdrMKnvX97oiTobkLxy8sxfzqB0qtSCPI2R5ehw8RJztAYzjdb+0PZiQGfYcctzUs6SLSzZtEKRO/PCm02MAhcvMYfrVFNXWtHFijaLNkqFhZuHFjrtPZhGu0dg4UaGi5cegrZhlwqT57AD+JhI8dPvaUqySbNxnWFHX3ouXmKOu2E35EAKhJvhYq5kiPbov8Tdi0F7UjGNlWXe3qKocPESc7TzR6DbA3BT7PwiEXNHc/dipK6UVGOLxNQVkubIcPHSQ1Aae+xjpbBOFfvp3akrI8tJ1M82EmwjKfXzuhK0ndER4eIl5mjvi2xDz2qCaJ0q9xN4YTd0DtrsHPWehqUZuzwXNhcvMee9GCXiUWpJ0DpUiVwoJq+Y2cTUFVskMtIcGS5eYi4Ba+6hqxm4D1dzh1o6LhRLg3pP0wY+UVGS4uXhhx/WyJEj1a9fP02YMEGrVq067usbGho0YcIE9evXT6NGjdKiRYtKEWYscWdeWC0QLc9puDzjTjImL2ewOlV+kQhJdEQUvXh55plndNttt+nuu+9WY2Ojpk6dqmnTpmnbtm2dvn7Lli2aPn26pk6dqsbGRt11112aO3eu6urqih1qLOFGT9SnYGCdKnWUSiwS6csZlLva93TPUlbs/8D999+vG264QTfeeKMkaeHChfr5z3+uRx55RAsWLOjw+kWLFumss87SwoULJUnnnHOO1q1bp/vuu0/XXHNNscONnfToqWHze9q+54OQozmxP+zcLwk4ekoF/NZ7+/Xsq++EG0wXbN/zoSRentMOHz2KyLMktX54SBK3U1371h59cPBIuMF0we//2CKJWCQm4/1jy4eYe1qSeiUSuvq8M0P77xe1eDl48KDWr1+vO++8M+/3NTU1Wr16dad/s2bNGtXU1OT97oorrtDixYt16NAh9enTJ++ftbW1qa2tLfNza2vrSYo+Hvr2Tk6uPfjyH0KOpDB9erO2Y/UtS8a79q09WvvWnpCj6bo+Zaw8l6fiPXQkUO3//U3I0RSGek8vXf12uIEUqE8Zq3hJ5/k/d7Si7um+Zb3iW7zs2rVLR44cUVVVVd7vq6qq1Nzc3OnfNDc3d/r6w4cPa9euXaqurs77ZwsWLNC99957cgOPkdu/OFo/+vV2HQVNSZ5aXqZrPjs07DAKMmP8EP3njha1fnQ47FC6LCHpKxNYeR5U0U9zL/uUNrzTEnYoBTnr9P4aP7Qy7DAK8u2LP6Xyst46DGo8+vfppa9fMDzsMApy+dmD9OXzztSuAwfDDqUgfXqFWyQWfdlI6nhmRxAExz3Ho7PXd/Z7SZo/f75qa2szP7e2tmrYsGEfJ9xYuXJcta4cV33iF9rHcuZp/fXg1z4bdhg9Qm3NmLBD6BEuHD1QF44eGHYYsfcnp/TV/TM/E3YYOEUtXgYOHKjevXt3mGXZuXNnh9mVtMGDB3f6+rKyMg0YMKDD68vLy1VeXn7ygjYzM7NIK+oibN++fTVhwgTV19fn/b6+vl5Tpkzp9G8mT57c4fUvvfSSJk6c2GG/i5mZmfU8Rd9BVltbq3/913/VkiVLtGnTJt1+++3atm2bZs+eLSm57HPttddmXj979mxt3bpVtbW12rRpk5YsWaLFixdr3rx5xQ7VzMzMAIq+52XmzJnavXu3vv/976upqUnjxo3T8uXLNXx4clNVU1NT3pkvI0eO1PLly3X77bfroYce0pAhQ/TAAw/4MWkzMzOTJCUC0jnbXdDa2qrKykq1tLSooqIi7HDMzMysCwrpv1kHD5iZmVmP5+LFzMzMUFy8mJmZGYqLFzMzM0Nx8WJmZmYoLl7MzMwMxcWLmZmZobh4MTMzMxQXL2ZmZobi4sXMzMxQXLyYmZkZiosXMzMzQ3HxYmZmZiguXszMzAzFxYuZmZmhuHgxMzMzFBcvZmZmhuLixczMzFBcvJiZmRmKixczMzNDcfFiZmZmKC5ezMzMDMXFi5mZmaG4eDEzMzMUFy9mZmaG4uLFzMzMUFy8mJmZGYqLFzMzM0Nx8WJmZmYoLl7MzMwMxcWLmZmZobh4MTMzMxQXL2ZmZobi4sXMzMxQXLyYmZkZiosXMzMzQ3HxYmZmZiguXszMzAzFxYuZmZmhuHgxMzMzFBcvZmZmhuLixczMzFBcvJiZmRmKixczMzNDcfFiZmZmKC5ezMzMDMXFi5mZmaG4eDEzMzMUFy9mZmaG4uLFzMzMUFy8mJmZGUpRi5f3339fs2bNUmVlpSorKzVr1izt3bv3uH9z/fXXK5FI5H1NmjSpmGGamZkZSFkx/+Vf+9rX9M477+jFF1+UJP31X/+1Zs2apZ/+9KfH/bsrr7xSjz/+eObnvn37FjNMMzMzAyla8bJp0ya9+OKLWrt2rS644AJJ0mOPPabJkydr8+bNGjNmzDH/try8XIMHD+7Sf6etrU1tbW2Zn1taWiRJra2tHyN6MzMzK6V0vx0EwYlfHBTJ4sWLg8rKyg6/r6ysDJYsWXLMv7vuuuuCysrK4IwzzghGjx4d3HjjjcG77757zNffc889gSR/+ctf/vKXv/wVg6/t27efsMYo2sxLc3OzBg0a1OH3gwYNUnNz8zH/btq0afrLv/xLDR8+XFu2bNHf/u3f6rLLLtP69etVXl7e4fXz589XbW1t5uejR49qz549GjBggBKJxMn5n0lpbW3VsGHDtH37dlVUVJzUf7dlOc+l41yXhvNcGs5z6RQj10EQaN++fRoyZMgJX1tw8fK9731P995773Ff8+tf/1qSOi0egiA4blExc+bMzPfjxo3TxIkTNXz4cL3wwgv68pe/3OH15eXlHYqa00477bjxfVwVFRV+Y5SA81w6znVpOM+l4TyXzsnOdWVlZZdeV3DxcvPNN+urX/3qcV8zYsQI/fa3v9W7777b4Z+99957qqqq6vJ/r7q6WsOHD9cbb7xRaKhmZmYWQwUXLwMHDtTAgQNP+LrJkyerpaVFv/rVr3T++edLkn75y1+qpaVFU6ZM6fJ/b/fu3dq+fbuqq6sLDdXMzMxiqGjnvJxzzjm68sor9c1vflNr167V2rVr9c1vflN//ud/nvek0dlnn63nnntOkrR//37NmzdPa9as0dtvv60VK1ZoxowZGjhwoP7iL/6iWKF2WXl5ue65555O997YyeM8l45zXRrOc2k4z6UTdq4TQdCVZ5K6Z8+ePZo7d66WLVsmSbrqqqv04IMP5u1JSSQSevzxx3X99dfrww8/1NVXX63Gxkbt3btX1dXVuvTSS/WDH/xAw4YNK1aYZmZmBlLU4sXMzMzsZPNnG5mZmRmKixczMzNDcfFiZmZmKC5ezMzMDMXFSzsPP/ywRo4cqX79+mnChAlatWrVcV/f0NCgCRMmqF+/fho1apQWLVpUokjZCsnzs88+qy9+8Ys644wzVFFRocmTJ+vnP/95CaNlK/SeTvvFL36hsrIyfeYznylugDFRaJ7b2tp09913a/jw4SovL9cnP/lJLVmypETRchWa56eeekrjx4/XJz7xCVVXV+sb3/iGdu/eXaJomVauXKkZM2ZoyJAhSiQSev7550/4NyXvC0/46Uc9yI9+9KOgT58+wWOPPRZs3LgxuPXWW4NTTjkl2Lp1a6evf+utt4JPfOITwa233hps3LgxeOyxx4I+ffoEP/nJT0ocOUuheb711luDv//7vw9+9atfBa+//nowf/78oE+fPsGrr75a4sh5Cs112t69e4NRo0YFNTU1wfjx40sTLFh38nzVVVcFF1xwQVBfXx9s2bIl+OUvfxn84he/KGHUPIXmedWqVUGvXr2Cf/7nfw7eeuutYNWqVcHYsWODq6++usSRsyxfvjy4++67g7q6ukBS8Nxzzx339WH0hS5ecpx//vnB7Nmz83539tlnB3feeWenr7/jjjuCs88+O+933/rWt4JJkyYVLcY4KDTPnTn33HODe++992SHFjvdzfXMmTOD7373u8E999zj4qULCs3zz372s6CysjLYvXt3KcKLjULz/I//+I/BqFGj8n73wAMPBEOHDi1ajHHTleIljL7Qy0YpBw8e1Pr161VTU5P3+5qaGq1evbrTv1mzZk2H119xxRVat26dDh06VLRYybqT5/aOHj2qffv26fTTTy9GiLHR3Vw//vjjevPNN3XPPfcUO8RY6E6ely1bpokTJ+of/uEfdOaZZ+rTn/605s2bpw8//LAUISN1J89TpkzRO++8o+XLlysIAr377rv6yU9+oi996UulCLnHCKMvLPizjeJq165dOnLkSIcPjayqqlJzc3Onf9Pc3Nzp6w8fPqxdu3b585g60Z08t/dP//RPOnDggP7qr/6qGCHGRndy/cYbb+jOO+/UqlWrVFbm5qErupPnt956S6+88or69eun5557Trt27dJNN92kPXv2eN/LMXQ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\n", + "text/plain": [ + "
              " + ] + }, + "metadata": { + "filenames": { + "image/png": "/Users/mhjensen/Teaching/MachineLearning/doc/LectureNotes/_build/jupyter_execute/chapter12_51_0.png" + } + }, + "output_type": "display_data" + } + ], "source": [ "import numpy as np\n", "import math\n", @@ -1208,7 +1221,30 @@ "collapsed": false, "editable": true }, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "inputs = (n_inputs, pixel_width, pixel_height, depth) = (1797, 8, 8, 1)\n", + "labels = (n_inputs) = (1797,)\n" + ] + }, + { + "data": { + "image/png": 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+ "text/plain": [ + "
              " + ] + }, + "metadata": { + "filenames": { + "image/png": "/Users/mhjensen/Teaching/MachineLearning/doc/LectureNotes/_build/jupyter_execute/chapter12_67_1.png" + } + }, + "output_type": "display_data" + } + ], "source": [ "# import necessary packages\n", "import numpy as np\n", @@ -1332,7 +1368,44 @@ "collapsed": false, "editable": true }, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Metal device set to: Apple M1\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/mhjensen/miniforge3/envs/myenv/lib/python3.9/site-packages/keras/optimizer_v2/gradient_descent.py:102: UserWarning: The `lr` argument is deprecated, use `learning_rate` instead.\n", + " super(SGD, self).__init__(name, **kwargs)\n", + "2023-10-02 06:54:12.770204: W tensorflow/core/platform/profile_utils/cpu_utils.cc:128] Failed to get CPU frequency: 0 Hz\n" + ] + }, + { + "ename": "KeyboardInterrupt", + "evalue": "", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mKeyboardInterrupt\u001b[0m Traceback (most recent call last)", + "Input \u001b[0;32mIn [6]\u001b[0m, in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[1;32m 4\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 5\u001b[0m CNN \u001b[38;5;241m=\u001b[39m create_convolutional_neural_network_keras(input_shape, receptive_field,\n\u001b[1;32m 6\u001b[0m n_filters, n_neurons_connected, n_categories,\n\u001b[1;32m 7\u001b[0m eta, lmbd)\n\u001b[0;32m----> 8\u001b[0m \u001b[43mCNN\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mfit\u001b[49m\u001b[43m(\u001b[49m\u001b[43mX_train\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mY_train\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mepochs\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mepochs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mbatch_size\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mbatch_size\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mverbose\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;241;43m0\u001b[39;49m\u001b[43m)\u001b[49m\n\u001b[1;32m 9\u001b[0m scores \u001b[38;5;241m=\u001b[39m CNN\u001b[38;5;241m.\u001b[39mevaluate(X_test, Y_test)\n\u001b[1;32m 11\u001b[0m CNN_keras[i][j] \u001b[38;5;241m=\u001b[39m CNN\n", + "File \u001b[0;32m~/miniforge3/envs/myenv/lib/python3.9/site-packages/keras/utils/traceback_utils.py:64\u001b[0m, in \u001b[0;36mfilter_traceback..error_handler\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m 62\u001b[0m filtered_tb \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[1;32m 63\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m---> 64\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mfn\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 65\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mException\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m e: \u001b[38;5;66;03m# pylint: disable=broad-except\u001b[39;00m\n\u001b[1;32m 66\u001b[0m filtered_tb \u001b[38;5;241m=\u001b[39m _process_traceback_frames(e\u001b[38;5;241m.\u001b[39m__traceback__)\n", + "File \u001b[0;32m~/miniforge3/envs/myenv/lib/python3.9/site-packages/keras/engine/training.py:1384\u001b[0m, in \u001b[0;36mModel.fit\u001b[0;34m(self, x, y, batch_size, epochs, verbose, callbacks, validation_split, validation_data, shuffle, class_weight, sample_weight, initial_epoch, steps_per_epoch, validation_steps, validation_batch_size, validation_freq, max_queue_size, workers, use_multiprocessing)\u001b[0m\n\u001b[1;32m 1377\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m tf\u001b[38;5;241m.\u001b[39mprofiler\u001b[38;5;241m.\u001b[39mexperimental\u001b[38;5;241m.\u001b[39mTrace(\n\u001b[1;32m 1378\u001b[0m \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mtrain\u001b[39m\u001b[38;5;124m'\u001b[39m,\n\u001b[1;32m 1379\u001b[0m epoch_num\u001b[38;5;241m=\u001b[39mepoch,\n\u001b[1;32m 1380\u001b[0m step_num\u001b[38;5;241m=\u001b[39mstep,\n\u001b[1;32m 1381\u001b[0m batch_size\u001b[38;5;241m=\u001b[39mbatch_size,\n\u001b[1;32m 1382\u001b[0m _r\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m1\u001b[39m):\n\u001b[1;32m 1383\u001b[0m callbacks\u001b[38;5;241m.\u001b[39mon_train_batch_begin(step)\n\u001b[0;32m-> 1384\u001b[0m tmp_logs \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mtrain_function\u001b[49m\u001b[43m(\u001b[49m\u001b[43miterator\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 1385\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m data_handler\u001b[38;5;241m.\u001b[39mshould_sync:\n\u001b[1;32m 1386\u001b[0m context\u001b[38;5;241m.\u001b[39masync_wait()\n", + "File \u001b[0;32m~/miniforge3/envs/myenv/lib/python3.9/site-packages/tensorflow/python/util/traceback_utils.py:150\u001b[0m, in \u001b[0;36mfilter_traceback..error_handler\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m 148\u001b[0m filtered_tb \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[1;32m 149\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m--> 150\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mfn\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 151\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mException\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m e:\n\u001b[1;32m 152\u001b[0m filtered_tb \u001b[38;5;241m=\u001b[39m _process_traceback_frames(e\u001b[38;5;241m.\u001b[39m__traceback__)\n", + "File \u001b[0;32m~/miniforge3/envs/myenv/lib/python3.9/site-packages/tensorflow/python/eager/def_function.py:915\u001b[0m, in \u001b[0;36mFunction.__call__\u001b[0;34m(self, *args, **kwds)\u001b[0m\n\u001b[1;32m 912\u001b[0m compiler \u001b[38;5;241m=\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mxla\u001b[39m\u001b[38;5;124m\"\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_jit_compile \u001b[38;5;28;01melse\u001b[39;00m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mnonXla\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m 914\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m OptionalXlaContext(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_jit_compile):\n\u001b[0;32m--> 915\u001b[0m result \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_call\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwds\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 917\u001b[0m new_tracing_count \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mexperimental_get_tracing_count()\n\u001b[1;32m 918\u001b[0m without_tracing \u001b[38;5;241m=\u001b[39m (tracing_count \u001b[38;5;241m==\u001b[39m new_tracing_count)\n", + "File \u001b[0;32m~/miniforge3/envs/myenv/lib/python3.9/site-packages/tensorflow/python/eager/def_function.py:947\u001b[0m, in \u001b[0;36mFunction._call\u001b[0;34m(self, *args, **kwds)\u001b[0m\n\u001b[1;32m 944\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_lock\u001b[38;5;241m.\u001b[39mrelease()\n\u001b[1;32m 945\u001b[0m \u001b[38;5;66;03m# In this case we have created variables on the first call, so we run the\u001b[39;00m\n\u001b[1;32m 946\u001b[0m \u001b[38;5;66;03m# defunned version which is guaranteed to never create variables.\u001b[39;00m\n\u001b[0;32m--> 947\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_stateless_fn\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwds\u001b[49m\u001b[43m)\u001b[49m \u001b[38;5;66;03m# pylint: disable=not-callable\u001b[39;00m\n\u001b[1;32m 948\u001b[0m \u001b[38;5;28;01melif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_stateful_fn \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m 949\u001b[0m \u001b[38;5;66;03m# Release the lock early so that multiple threads can perform the call\u001b[39;00m\n\u001b[1;32m 950\u001b[0m \u001b[38;5;66;03m# in parallel.\u001b[39;00m\n\u001b[1;32m 951\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_lock\u001b[38;5;241m.\u001b[39mrelease()\n", + "File \u001b[0;32m~/miniforge3/envs/myenv/lib/python3.9/site-packages/tensorflow/python/eager/function.py:2956\u001b[0m, in \u001b[0;36mFunction.__call__\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m 2953\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_lock:\n\u001b[1;32m 2954\u001b[0m (graph_function,\n\u001b[1;32m 2955\u001b[0m filtered_flat_args) \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_maybe_define_function(args, kwargs)\n\u001b[0;32m-> 2956\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mgraph_function\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_call_flat\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m 2957\u001b[0m \u001b[43m \u001b[49m\u001b[43mfiltered_flat_args\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mcaptured_inputs\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mgraph_function\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mcaptured_inputs\u001b[49m\u001b[43m)\u001b[49m\n", + "File \u001b[0;32m~/miniforge3/envs/myenv/lib/python3.9/site-packages/tensorflow/python/eager/function.py:1853\u001b[0m, in \u001b[0;36mConcreteFunction._call_flat\u001b[0;34m(self, args, captured_inputs, cancellation_manager)\u001b[0m\n\u001b[1;32m 1849\u001b[0m possible_gradient_type \u001b[38;5;241m=\u001b[39m gradients_util\u001b[38;5;241m.\u001b[39mPossibleTapeGradientTypes(args)\n\u001b[1;32m 1850\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m (possible_gradient_type \u001b[38;5;241m==\u001b[39m gradients_util\u001b[38;5;241m.\u001b[39mPOSSIBLE_GRADIENT_TYPES_NONE\n\u001b[1;32m 1851\u001b[0m \u001b[38;5;129;01mand\u001b[39;00m executing_eagerly):\n\u001b[1;32m 1852\u001b[0m \u001b[38;5;66;03m# No tape is watching; skip to running the function.\u001b[39;00m\n\u001b[0;32m-> 1853\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_build_call_outputs(\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_inference_function\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mcall\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m 1854\u001b[0m \u001b[43m \u001b[49m\u001b[43mctx\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mcancellation_manager\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mcancellation_manager\u001b[49m\u001b[43m)\u001b[49m)\n\u001b[1;32m 1855\u001b[0m forward_backward \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_select_forward_and_backward_functions(\n\u001b[1;32m 1856\u001b[0m args,\n\u001b[1;32m 1857\u001b[0m possible_gradient_type,\n\u001b[1;32m 1858\u001b[0m executing_eagerly)\n\u001b[1;32m 1859\u001b[0m forward_function, args_with_tangents \u001b[38;5;241m=\u001b[39m forward_backward\u001b[38;5;241m.\u001b[39mforward()\n", + "File \u001b[0;32m~/miniforge3/envs/myenv/lib/python3.9/site-packages/tensorflow/python/eager/function.py:499\u001b[0m, in \u001b[0;36m_EagerDefinedFunction.call\u001b[0;34m(self, ctx, args, cancellation_manager)\u001b[0m\n\u001b[1;32m 497\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m _InterpolateFunctionError(\u001b[38;5;28mself\u001b[39m):\n\u001b[1;32m 498\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m cancellation_manager \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[0;32m--> 499\u001b[0m outputs \u001b[38;5;241m=\u001b[39m \u001b[43mexecute\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mexecute\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m 500\u001b[0m \u001b[43m \u001b[49m\u001b[38;5;28;43mstr\u001b[39;49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43msignature\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mname\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 501\u001b[0m \u001b[43m \u001b[49m\u001b[43mnum_outputs\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_num_outputs\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 502\u001b[0m \u001b[43m \u001b[49m\u001b[43minputs\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 503\u001b[0m \u001b[43m \u001b[49m\u001b[43mattrs\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mattrs\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 504\u001b[0m \u001b[43m \u001b[49m\u001b[43mctx\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mctx\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 505\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m 506\u001b[0m outputs \u001b[38;5;241m=\u001b[39m execute\u001b[38;5;241m.\u001b[39mexecute_with_cancellation(\n\u001b[1;32m 507\u001b[0m \u001b[38;5;28mstr\u001b[39m(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39msignature\u001b[38;5;241m.\u001b[39mname),\n\u001b[1;32m 508\u001b[0m num_outputs\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_num_outputs,\n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 511\u001b[0m ctx\u001b[38;5;241m=\u001b[39mctx,\n\u001b[1;32m 512\u001b[0m cancellation_manager\u001b[38;5;241m=\u001b[39mcancellation_manager)\n", + "File \u001b[0;32m~/miniforge3/envs/myenv/lib/python3.9/site-packages/tensorflow/python/eager/execute.py:54\u001b[0m, in \u001b[0;36mquick_execute\u001b[0;34m(op_name, num_outputs, inputs, attrs, ctx, name)\u001b[0m\n\u001b[1;32m 52\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m 53\u001b[0m ctx\u001b[38;5;241m.\u001b[39mensure_initialized()\n\u001b[0;32m---> 54\u001b[0m tensors \u001b[38;5;241m=\u001b[39m \u001b[43mpywrap_tfe\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mTFE_Py_Execute\u001b[49m\u001b[43m(\u001b[49m\u001b[43mctx\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_handle\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mdevice_name\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mop_name\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 55\u001b[0m \u001b[43m \u001b[49m\u001b[43minputs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mattrs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mnum_outputs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 56\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m core\u001b[38;5;241m.\u001b[39m_NotOkStatusException \u001b[38;5;28;01mas\u001b[39;00m e:\n\u001b[1;32m 57\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m name \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n", + "\u001b[0;31mKeyboardInterrupt\u001b[0m: " + ] + } + ], "source": [ "CNN_keras = np.zeros((len(eta_vals), len(lmbd_vals)), dtype=object)\n", " \n", @@ -1599,7 +1672,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.9.18" + "version": "3.9.10" } }, "nbformat": 4, diff --git a/doc/LectureNotes/_build/jupyter_execute/chapter13.ipynb b/doc/LectureNotes/_build/jupyter_execute/chapter13.ipynb index 58b249e1d..1bf5d0cea 100644 --- a/doc/LectureNotes/_build/jupyter_execute/chapter13.ipynb +++ b/doc/LectureNotes/_build/jupyter_execute/chapter13.ipynb @@ -60,19 +60,765 @@ }, "outputs": [ { - "ename": "ModuleNotFoundError", - "evalue": "No module named 'matplotlib'", + "data": { + "image/png": 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              " + ] + }, + "metadata": { + "filenames": { + "image/png": "/Users/mhjensen/Teaching/MachineLearning/doc/LectureNotes/_build/jupyter_execute/chapter13_3_0.png" + } + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Metal device set to: Apple M1\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Model: \"sequential\"\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "_________________________________________________________________\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + " Layer (type) Output Shape Param # \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "=================================================================\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + " simple_rnn (SimpleRNN) (None, 32) 1184 \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + " \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + " dense (Dense) (None, 8) 264 \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + " \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + " dense_1 (Dense) (None, 1) 9 \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + " \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "=================================================================\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Total params: 1,457\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Trainable params: 1,457\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Non-trainable params: 0\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "_________________________________________________________________\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch 1/100\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2023-10-02 06:54:48.552042: W tensorflow/core/platform/profile_utils/cpu_utils.cc:128] Failed to get CPU frequency: 0 Hz\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "50/50 - 4s - loss: 1.7644 - 4s/epoch - 84ms/step\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch 2/100\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "50/50 - 0s - loss: 0.4353 - 499ms/epoch - 10ms/step\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch 3/100\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "50/50 - 0s - loss: 0.4066 - 479ms/epoch - 10ms/step\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch 4/100\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "50/50 - 1s - loss: 0.4033 - 740ms/epoch - 15ms/step\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch 5/100\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "50/50 - 1s - loss: 0.4014 - 596ms/epoch - 12ms/step\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch 6/100\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "50/50 - 1s - loss: 0.3999 - 561ms/epoch - 11ms/step\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch 7/100\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "50/50 - 1s - loss: 0.3996 - 515ms/epoch - 10ms/step\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch 8/100\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "50/50 - 0s - loss: 0.3975 - 495ms/epoch - 10ms/step\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch 9/100\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "50/50 - 0s - loss: 0.3970 - 473ms/epoch - 9ms/step\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch 10/100\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "50/50 - 1s - loss: 0.3944 - 687ms/epoch - 14ms/step\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch 11/100\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "50/50 - 1s - loss: 0.3948 - 500ms/epoch - 10ms/step\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch 12/100\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "50/50 - 1s - loss: 0.3932 - 501ms/epoch - 10ms/step\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch 13/100\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "50/50 - 0s - 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+ ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "50/50 - 1s - loss: 0.3836 - 643ms/epoch - 13ms/step\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch 28/100\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "50/50 - 0s - loss: 0.3834 - 484ms/epoch - 10ms/step\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch 29/100\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "50/50 - 0s - loss: 0.3838 - 483ms/epoch - 10ms/step\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch 30/100\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "50/50 - 0s - loss: 0.3837 - 468ms/epoch - 9ms/step\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch 31/100\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "50/50 - 1s - loss: 0.3821 - 671ms/epoch - 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Traceback (most recent call last)", - "Cell \u001b[0;32mIn[1], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \u001b[43mget_ipython\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mrun_line_magic\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mmatplotlib\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43minline\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m)\u001b[49m\n\u001b[1;32m 3\u001b[0m \u001b[38;5;66;03m# Start importing packages\u001b[39;00m\n\u001b[1;32m 4\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mpandas\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m \u001b[38;5;21;01mpd\u001b[39;00m\n", - "File \u001b[0;32m~/miniforge3/lib/python3.9/site-packages/IPython/core/interactiveshell.py:2432\u001b[0m, in \u001b[0;36mInteractiveShell.run_line_magic\u001b[0;34m(self, magic_name, line, _stack_depth)\u001b[0m\n\u001b[1;32m 2430\u001b[0m kwargs[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mlocal_ns\u001b[39m\u001b[38;5;124m'\u001b[39m] \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mget_local_scope(stack_depth)\n\u001b[1;32m 2431\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mbuiltin_trap:\n\u001b[0;32m-> 2432\u001b[0m result \u001b[38;5;241m=\u001b[39m \u001b[43mfn\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 2434\u001b[0m \u001b[38;5;66;03m# The code below prevents the output from being displayed\u001b[39;00m\n\u001b[1;32m 2435\u001b[0m \u001b[38;5;66;03m# when using magics with decorator @output_can_be_silenced\u001b[39;00m\n\u001b[1;32m 2436\u001b[0m \u001b[38;5;66;03m# when the last Python token in the expression is a ';'.\u001b[39;00m\n\u001b[1;32m 2437\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mgetattr\u001b[39m(fn, magic\u001b[38;5;241m.\u001b[39mMAGIC_OUTPUT_CAN_BE_SILENCED, \u001b[38;5;28;01mFalse\u001b[39;00m):\n", - "File \u001b[0;32m~/miniforge3/lib/python3.9/site-packages/IPython/core/magics/pylab.py:99\u001b[0m, in \u001b[0;36mPylabMagics.matplotlib\u001b[0;34m(self, line)\u001b[0m\n\u001b[1;32m 97\u001b[0m \u001b[38;5;28mprint\u001b[39m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mAvailable matplotlib backends: \u001b[39m\u001b[38;5;132;01m%s\u001b[39;00m\u001b[38;5;124m\"\u001b[39m \u001b[38;5;241m%\u001b[39m backends_list)\n\u001b[1;32m 98\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m---> 99\u001b[0m gui, backend \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mshell\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43menable_matplotlib\u001b[49m\u001b[43m(\u001b[49m\u001b[43margs\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mgui\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mlower\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mif\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[38;5;28;43misinstance\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43margs\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mgui\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mstr\u001b[39;49m\u001b[43m)\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01melse\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43margs\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mgui\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 100\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_show_matplotlib_backend(args\u001b[38;5;241m.\u001b[39mgui, backend)\n", - "File \u001b[0;32m~/miniforge3/lib/python3.9/site-packages/IPython/core/interactiveshell.py:3606\u001b[0m, in \u001b[0;36mInteractiveShell.enable_matplotlib\u001b[0;34m(self, gui)\u001b[0m\n\u001b[1;32m 3585\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21menable_matplotlib\u001b[39m(\u001b[38;5;28mself\u001b[39m, gui\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mNone\u001b[39;00m):\n\u001b[1;32m 3586\u001b[0m \u001b[38;5;250m \u001b[39m\u001b[38;5;124;03m\"\"\"Enable interactive matplotlib and inline figure support.\u001b[39;00m\n\u001b[1;32m 3587\u001b[0m \n\u001b[1;32m 3588\u001b[0m \u001b[38;5;124;03m This takes the following steps:\u001b[39;00m\n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 3604\u001b[0m \u001b[38;5;124;03m display figures inline.\u001b[39;00m\n\u001b[1;32m 3605\u001b[0m \u001b[38;5;124;03m \"\"\"\u001b[39;00m\n\u001b[0;32m-> 3606\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mmatplotlib_inline\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mbackend_inline\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m configure_inline_support\n\u001b[1;32m 3608\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mIPython\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mcore\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m pylabtools \u001b[38;5;28;01mas\u001b[39;00m pt\n\u001b[1;32m 3609\u001b[0m gui, backend \u001b[38;5;241m=\u001b[39m pt\u001b[38;5;241m.\u001b[39mfind_gui_and_backend(gui, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mpylab_gui_select)\n", - "File \u001b[0;32m~/miniforge3/lib/python3.9/site-packages/matplotlib_inline/__init__.py:1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01m.\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m backend_inline, config \u001b[38;5;66;03m# noqa\u001b[39;00m\n\u001b[1;32m 2\u001b[0m __version__ \u001b[38;5;241m=\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m0.1.6\u001b[39m\u001b[38;5;124m\"\u001b[39m \u001b[38;5;66;03m# noqa\u001b[39;00m\n", - "File \u001b[0;32m~/miniforge3/lib/python3.9/site-packages/matplotlib_inline/backend_inline.py:6\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[38;5;124;03m\"\"\"A matplotlib backend for publishing figures via display_data\"\"\"\u001b[39;00m\n\u001b[1;32m 3\u001b[0m \u001b[38;5;66;03m# Copyright (c) IPython Development Team.\u001b[39;00m\n\u001b[1;32m 4\u001b[0m \u001b[38;5;66;03m# Distributed under the terms of the BSD 3-Clause License.\u001b[39;00m\n\u001b[0;32m----> 6\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mmatplotlib\u001b[39;00m\n\u001b[1;32m 7\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mmatplotlib\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m colors\n\u001b[1;32m 8\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mmatplotlib\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mbackends\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m backend_agg\n", - "\u001b[0;31mModuleNotFoundError\u001b[0m: No module named 'matplotlib'" + "\u001b[0;31mKeyboardInterrupt\u001b[0m Traceback (most recent call last)", + "Input \u001b[0;32mIn [1]\u001b[0m, in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[1;32m 55\u001b[0m model\u001b[38;5;241m.\u001b[39mcompile(loss\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mmean_squared_error\u001b[39m\u001b[38;5;124m'\u001b[39m, optimizer\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mrmsprop\u001b[39m\u001b[38;5;124m'\u001b[39m)\n\u001b[1;32m 56\u001b[0m model\u001b[38;5;241m.\u001b[39msummary()\n\u001b[0;32m---> 58\u001b[0m \u001b[43mmodel\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mfit\u001b[49m\u001b[43m(\u001b[49m\u001b[43mtrainX\u001b[49m\u001b[43m,\u001b[49m\u001b[43mtrainY\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mepochs\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;241;43m100\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mbatch_size\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;241;43m16\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mverbose\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;241;43m2\u001b[39;49m\u001b[43m)\u001b[49m\n\u001b[1;32m 59\u001b[0m trainPredict \u001b[38;5;241m=\u001b[39m model\u001b[38;5;241m.\u001b[39mpredict(trainX)\n\u001b[1;32m 60\u001b[0m testPredict\u001b[38;5;241m=\u001b[39m model\u001b[38;5;241m.\u001b[39mpredict(testX)\n", + "File \u001b[0;32m~/miniforge3/envs/myenv/lib/python3.9/site-packages/keras/utils/traceback_utils.py:64\u001b[0m, in \u001b[0;36mfilter_traceback..error_handler\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m 62\u001b[0m filtered_tb \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[1;32m 63\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m---> 64\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mfn\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 65\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mException\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m e: \u001b[38;5;66;03m# pylint: disable=broad-except\u001b[39;00m\n\u001b[1;32m 66\u001b[0m filtered_tb \u001b[38;5;241m=\u001b[39m _process_traceback_frames(e\u001b[38;5;241m.\u001b[39m__traceback__)\n", + "File \u001b[0;32m~/miniforge3/envs/myenv/lib/python3.9/site-packages/keras/engine/training.py:1384\u001b[0m, in \u001b[0;36mModel.fit\u001b[0;34m(self, x, y, batch_size, epochs, verbose, callbacks, validation_split, validation_data, shuffle, class_weight, sample_weight, initial_epoch, steps_per_epoch, validation_steps, validation_batch_size, validation_freq, max_queue_size, workers, use_multiprocessing)\u001b[0m\n\u001b[1;32m 1377\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m tf\u001b[38;5;241m.\u001b[39mprofiler\u001b[38;5;241m.\u001b[39mexperimental\u001b[38;5;241m.\u001b[39mTrace(\n\u001b[1;32m 1378\u001b[0m \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mtrain\u001b[39m\u001b[38;5;124m'\u001b[39m,\n\u001b[1;32m 1379\u001b[0m epoch_num\u001b[38;5;241m=\u001b[39mepoch,\n\u001b[1;32m 1380\u001b[0m step_num\u001b[38;5;241m=\u001b[39mstep,\n\u001b[1;32m 1381\u001b[0m batch_size\u001b[38;5;241m=\u001b[39mbatch_size,\n\u001b[1;32m 1382\u001b[0m _r\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m1\u001b[39m):\n\u001b[1;32m 1383\u001b[0m callbacks\u001b[38;5;241m.\u001b[39mon_train_batch_begin(step)\n\u001b[0;32m-> 1384\u001b[0m tmp_logs \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mtrain_function\u001b[49m\u001b[43m(\u001b[49m\u001b[43miterator\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 1385\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m data_handler\u001b[38;5;241m.\u001b[39mshould_sync:\n\u001b[1;32m 1386\u001b[0m context\u001b[38;5;241m.\u001b[39masync_wait()\n", + "File \u001b[0;32m~/miniforge3/envs/myenv/lib/python3.9/site-packages/tensorflow/python/util/traceback_utils.py:150\u001b[0m, in \u001b[0;36mfilter_traceback..error_handler\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m 148\u001b[0m filtered_tb \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[1;32m 149\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m--> 150\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mfn\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 151\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mException\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m e:\n\u001b[1;32m 152\u001b[0m filtered_tb \u001b[38;5;241m=\u001b[39m _process_traceback_frames(e\u001b[38;5;241m.\u001b[39m__traceback__)\n", + "File \u001b[0;32m~/miniforge3/envs/myenv/lib/python3.9/site-packages/tensorflow/python/eager/def_function.py:915\u001b[0m, in \u001b[0;36mFunction.__call__\u001b[0;34m(self, *args, **kwds)\u001b[0m\n\u001b[1;32m 912\u001b[0m compiler \u001b[38;5;241m=\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mxla\u001b[39m\u001b[38;5;124m\"\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_jit_compile \u001b[38;5;28;01melse\u001b[39;00m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mnonXla\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m 914\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m OptionalXlaContext(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_jit_compile):\n\u001b[0;32m--> 915\u001b[0m result \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_call\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwds\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 917\u001b[0m new_tracing_count \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mexperimental_get_tracing_count()\n\u001b[1;32m 918\u001b[0m without_tracing \u001b[38;5;241m=\u001b[39m (tracing_count \u001b[38;5;241m==\u001b[39m new_tracing_count)\n", + "File \u001b[0;32m~/miniforge3/envs/myenv/lib/python3.9/site-packages/tensorflow/python/eager/def_function.py:947\u001b[0m, in \u001b[0;36mFunction._call\u001b[0;34m(self, *args, **kwds)\u001b[0m\n\u001b[1;32m 944\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_lock\u001b[38;5;241m.\u001b[39mrelease()\n\u001b[1;32m 945\u001b[0m \u001b[38;5;66;03m# In this case we have created variables on the first call, so we run the\u001b[39;00m\n\u001b[1;32m 946\u001b[0m \u001b[38;5;66;03m# defunned version which is guaranteed to never create variables.\u001b[39;00m\n\u001b[0;32m--> 947\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_stateless_fn\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwds\u001b[49m\u001b[43m)\u001b[49m \u001b[38;5;66;03m# pylint: disable=not-callable\u001b[39;00m\n\u001b[1;32m 948\u001b[0m \u001b[38;5;28;01melif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_stateful_fn \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m 949\u001b[0m \u001b[38;5;66;03m# Release the lock early so that multiple threads can perform the call\u001b[39;00m\n\u001b[1;32m 950\u001b[0m \u001b[38;5;66;03m# in parallel.\u001b[39;00m\n\u001b[1;32m 951\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_lock\u001b[38;5;241m.\u001b[39mrelease()\n", + "File \u001b[0;32m~/miniforge3/envs/myenv/lib/python3.9/site-packages/tensorflow/python/eager/function.py:2956\u001b[0m, in \u001b[0;36mFunction.__call__\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m 2953\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_lock:\n\u001b[1;32m 2954\u001b[0m (graph_function,\n\u001b[1;32m 2955\u001b[0m filtered_flat_args) \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_maybe_define_function(args, kwargs)\n\u001b[0;32m-> 2956\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mgraph_function\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_call_flat\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m 2957\u001b[0m \u001b[43m \u001b[49m\u001b[43mfiltered_flat_args\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mcaptured_inputs\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mgraph_function\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mcaptured_inputs\u001b[49m\u001b[43m)\u001b[49m\n", + "File \u001b[0;32m~/miniforge3/envs/myenv/lib/python3.9/site-packages/tensorflow/python/eager/function.py:1853\u001b[0m, in \u001b[0;36mConcreteFunction._call_flat\u001b[0;34m(self, args, captured_inputs, cancellation_manager)\u001b[0m\n\u001b[1;32m 1849\u001b[0m possible_gradient_type \u001b[38;5;241m=\u001b[39m gradients_util\u001b[38;5;241m.\u001b[39mPossibleTapeGradientTypes(args)\n\u001b[1;32m 1850\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m (possible_gradient_type \u001b[38;5;241m==\u001b[39m gradients_util\u001b[38;5;241m.\u001b[39mPOSSIBLE_GRADIENT_TYPES_NONE\n\u001b[1;32m 1851\u001b[0m \u001b[38;5;129;01mand\u001b[39;00m executing_eagerly):\n\u001b[1;32m 1852\u001b[0m \u001b[38;5;66;03m# No tape is watching; skip to running the function.\u001b[39;00m\n\u001b[0;32m-> 1853\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_build_call_outputs(\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_inference_function\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mcall\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m 1854\u001b[0m \u001b[43m \u001b[49m\u001b[43mctx\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mcancellation_manager\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mcancellation_manager\u001b[49m\u001b[43m)\u001b[49m)\n\u001b[1;32m 1855\u001b[0m forward_backward \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_select_forward_and_backward_functions(\n\u001b[1;32m 1856\u001b[0m args,\n\u001b[1;32m 1857\u001b[0m possible_gradient_type,\n\u001b[1;32m 1858\u001b[0m executing_eagerly)\n\u001b[1;32m 1859\u001b[0m forward_function, args_with_tangents \u001b[38;5;241m=\u001b[39m forward_backward\u001b[38;5;241m.\u001b[39mforward()\n", + "File \u001b[0;32m~/miniforge3/envs/myenv/lib/python3.9/site-packages/tensorflow/python/eager/function.py:499\u001b[0m, in \u001b[0;36m_EagerDefinedFunction.call\u001b[0;34m(self, ctx, args, cancellation_manager)\u001b[0m\n\u001b[1;32m 497\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m _InterpolateFunctionError(\u001b[38;5;28mself\u001b[39m):\n\u001b[1;32m 498\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m cancellation_manager \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[0;32m--> 499\u001b[0m outputs \u001b[38;5;241m=\u001b[39m \u001b[43mexecute\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mexecute\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m 500\u001b[0m \u001b[43m \u001b[49m\u001b[38;5;28;43mstr\u001b[39;49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43msignature\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mname\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 501\u001b[0m \u001b[43m \u001b[49m\u001b[43mnum_outputs\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_num_outputs\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 502\u001b[0m \u001b[43m \u001b[49m\u001b[43minputs\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 503\u001b[0m \u001b[43m \u001b[49m\u001b[43mattrs\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mattrs\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 504\u001b[0m \u001b[43m \u001b[49m\u001b[43mctx\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mctx\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 505\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m 506\u001b[0m outputs \u001b[38;5;241m=\u001b[39m execute\u001b[38;5;241m.\u001b[39mexecute_with_cancellation(\n\u001b[1;32m 507\u001b[0m \u001b[38;5;28mstr\u001b[39m(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39msignature\u001b[38;5;241m.\u001b[39mname),\n\u001b[1;32m 508\u001b[0m num_outputs\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_num_outputs,\n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 511\u001b[0m ctx\u001b[38;5;241m=\u001b[39mctx,\n\u001b[1;32m 512\u001b[0m cancellation_manager\u001b[38;5;241m=\u001b[39mcancellation_manager)\n", + "File \u001b[0;32m~/miniforge3/envs/myenv/lib/python3.9/site-packages/tensorflow/python/eager/execute.py:54\u001b[0m, in \u001b[0;36mquick_execute\u001b[0;34m(op_name, num_outputs, inputs, attrs, ctx, name)\u001b[0m\n\u001b[1;32m 52\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m 53\u001b[0m ctx\u001b[38;5;241m.\u001b[39mensure_initialized()\n\u001b[0;32m---> 54\u001b[0m tensors \u001b[38;5;241m=\u001b[39m \u001b[43mpywrap_tfe\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mTFE_Py_Execute\u001b[49m\u001b[43m(\u001b[49m\u001b[43mctx\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_handle\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mdevice_name\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mop_name\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 55\u001b[0m \u001b[43m \u001b[49m\u001b[43minputs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mattrs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mnum_outputs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 56\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m core\u001b[38;5;241m.\u001b[39m_NotOkStatusException \u001b[38;5;28;01mas\u001b[39;00m e:\n\u001b[1;32m 57\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m name \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n", + "\u001b[0;31mKeyboardInterrupt\u001b[0m: " ] } ], @@ -1895,7 +2641,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.9.18" + "version": "3.9.10" } }, "nbformat": 4, diff --git a/doc/LectureNotes/_build/jupyter_execute/chapter13_3_0.png b/doc/LectureNotes/_build/jupyter_execute/chapter13_3_0.png index 2619cfba8..29a15934c 100644 Binary files a/doc/LectureNotes/_build/jupyter_execute/chapter13_3_0.png and 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--git a/doc/LectureNotes/_build/jupyter_execute/chapter1_9_0.png b/doc/LectureNotes/_build/jupyter_execute/chapter1_9_0.png index b454d774d..62cdabb05 100644 Binary files a/doc/LectureNotes/_build/jupyter_execute/chapter1_9_0.png and b/doc/LectureNotes/_build/jupyter_execute/chapter1_9_0.png differ diff --git a/doc/LectureNotes/_build/jupyter_execute/chapter2.ipynb b/doc/LectureNotes/_build/jupyter_execute/chapter2.ipynb index 0ff8d6a7b..e61d72b37 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": [ - "0.015836015068980684\n", - "4.146630977737321\n", - "[[0.88786042 2.4773343 ]\n", - " [2.4773343 7.74767365]]\n" + "-0.033790755027115954\n", + "3.888577549915147\n", + "[[ 1.2697447 3.97644118]\n", + " [ 3.97644118 13.38465596]]\n" ] } ], @@ -1845,10 +1845,10 @@ "name": "stdout", "output_type": "stream", "text": [ - "0.07636331150360104\n", - "1.4231233000637726\n", - "[[1. 0.68477696]\n", - " [0.68477696 1. ]]\n" + "0.08238863600759742\n", + "1.795225339396409\n", + "[[1. 0.64391062]\n", + " [0.64391062 1. ]]\n" ] } ], @@ -1902,14 +1902,33 @@ }, "outputs": [ { - "ename": "ModuleNotFoundError", - "evalue": "No module named 'pandas'", - "output_type": "error", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mModuleNotFoundError\u001b[0m Traceback (most recent call last)", - "Cell \u001b[0;32mIn[7], line 2\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mnumpy\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m \u001b[38;5;21;01mnp\u001b[39;00m\n\u001b[0;32m----> 2\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mpandas\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m \u001b[38;5;21;01mpd\u001b[39;00m\n\u001b[1;32m 3\u001b[0m n \u001b[38;5;241m=\u001b[39m \u001b[38;5;241m10\u001b[39m\n\u001b[1;32m 4\u001b[0m x \u001b[38;5;241m=\u001b[39m np\u001b[38;5;241m.\u001b[39mrandom\u001b[38;5;241m.\u001b[39mnormal(size\u001b[38;5;241m=\u001b[39mn)\n", - "\u001b[0;31mModuleNotFoundError\u001b[0m: No module named 'pandas'" + "name": "stdout", + "output_type": "stream", + "text": [ + "[[ 1.29135778 4.3399612 ]\n", + " [ 0.08815506 -1.48140137]\n", + " [ 0.34149655 1.18571316]\n", + " [-1.00375475 -2.69226802]\n", + " [ 0.42198678 2.56858701]\n", + " [ 0.53278871 2.8969113 ]\n", + " [-1.38020451 -4.26263837]\n", + " [-0.64969451 -2.00778523]\n", + " [-0.32632463 -1.7413913 ]\n", + " [ 0.68419351 1.19431161]]\n", + " 0 1\n", + "0 1.291358 4.339961\n", + "1 0.088155 -1.481401\n", + "2 0.341497 1.185713\n", + "3 -1.003755 -2.692268\n", + "4 0.421987 2.568587\n", + "5 0.532789 2.896911\n", + "6 -1.380205 -4.262638\n", + "7 -0.649695 -2.007785\n", + "8 -0.326325 -1.741391\n", + "9 0.684194 1.194312\n", + " 0 1\n", + "0 1.000000 0.943439\n", + "1 0.943439 1.000000\n" ] } ], @@ -1948,7 +1967,54 @@ "collapsed": false, "editable": true }, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " 0 1 2 3 4 5 6 7 \\\n", + "0 0.0 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 \n", + "1 0.0 0.089710 0.084075 0.088697 0.086518 0.084247 0.079455 0.077756 \n", + "2 0.0 0.084075 0.079226 0.082189 0.080406 0.078545 0.073008 0.071611 \n", + "3 0.0 0.088697 0.082189 0.093408 0.090365 0.087247 0.087250 0.084843 \n", + "4 0.0 0.086518 0.080406 0.090365 0.087603 0.084764 0.083853 0.081680 \n", + "5 0.0 0.084247 0.078545 0.087247 0.084764 0.082205 0.080411 0.078467 \n", + "6 0.0 0.079455 0.073008 0.087250 0.083853 0.080411 0.083988 0.081246 \n", + "7 0.0 0.077756 0.071611 0.084843 0.081680 0.078467 0.081246 0.078707 \n", + "8 0.0 0.076125 0.070275 0.082517 0.079581 0.076592 0.078593 0.076249 \n", + "9 0.0 0.074545 0.068987 0.080256 0.077542 0.074772 0.076012 0.073858 \n", + "10 0.0 0.070597 0.064412 0.079882 0.076354 0.072805 0.078656 0.075758 \n", + "11 0.0 0.068974 0.063055 0.077650 0.074330 0.070986 0.076136 0.073422 \n", + "12 0.0 0.067437 0.061775 0.075523 0.072404 0.069257 0.073728 0.071191 \n", + "13 0.0 0.065982 0.060567 0.073494 0.070569 0.067611 0.071423 0.069055 \n", + "14 0.0 0.064602 0.059427 0.071554 0.068816 0.066042 0.069213 0.067009 \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 0.076125 0.074545 0.070597 0.068974 0.067437 0.065982 0.064602 \n", + "2 0.070275 0.068987 0.064412 0.063055 0.061775 0.060567 0.059427 \n", + "3 0.082517 0.080256 0.079882 0.077650 0.075523 0.073494 0.071554 \n", + "4 0.079581 0.077542 0.076354 0.074330 0.072404 0.070569 0.068816 \n", + "5 0.076592 0.074772 0.072805 0.070986 0.069257 0.067611 0.066042 \n", + "6 0.078593 0.076012 0.078656 0.076136 0.073728 0.071423 0.069213 \n", + "7 0.076249 0.073858 0.075758 0.073422 0.071191 0.069055 0.067009 \n", + "8 0.073980 0.071773 0.072953 0.070795 0.068734 0.066762 0.064874 \n", + "9 0.071773 0.069746 0.070228 0.068241 0.066344 0.064532 0.062797 \n", + "10 0.072953 0.070228 0.074969 0.072310 0.069766 0.067328 0.064987 \n", + "11 0.070795 0.068241 0.072310 0.069822 0.067440 0.065158 0.062967 \n", + "12 0.068734 0.066344 0.069766 0.067440 0.065214 0.063081 0.061034 \n", + "13 0.066762 0.064532 0.067328 0.065158 0.063081 0.061092 0.059182 \n", + "14 0.064874 0.062797 0.064987 0.062967 0.061034 0.059182 0.057406 " + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n" + ] + } + ], "source": [ "# Common imports\n", "import numpy as np\n", @@ -3493,7 +3559,31 @@ "collapsed": false, "editable": true }, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[2. 2.]\n", + "Training MSE for OLS\n", + "3.0\n" + ] + }, + { + "data": { + "image/png": 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IysBihizS4cOHMW3aNCQlJaFMmTKYPXs2+vfvL3daRJSfUlOBdeuAn36SBojLKFQGDJAuF3XpIhUwRfhVVtjxE0AWac2aNXKnQERyefJEGhl3+nT9cBa//KIfKXfwYLkyIwvFYoaIiCzDgwfAvHnA7NnSYwEAaQC6gQOl5w4RGcFihoiI5Pfff9LdSA8eSMulSwMjRwJ9+2Y91gvRC1jMEBGR/Ly8pP4v//wDfPGF1B+GfWEomzjODBER5b9jx4AWLQyf7vzjj9JdSt27s5ChHGExQ0RE+efpU2DMGOnBh7t2GQ5yV7Ro5mffEWUDS18iIsof+/YB/fpJT6YGpNF5v/pK1pSoYGDLDFEuRUZGws3NTe40iCxfcrL0jKSmTaVCxsdHGr139WrpydNEr4nFjAL16dMHKpUKoaGhmbYNGjQIKpUKfTLGYwBw584dDBw4EH5+frCzs4O3tzcCAwNx4MABXUzp0qWhUqkyTd98843RPJo3b46wsDBznlqeyHi/TE250bVrV1zI+AuTiIybPBmYNUt6FEFICHD6tDRiL5GZ8DKTQvn6+iI6OhozZ86Ew//ftvjs2TOsWrUKfn5+BrGdO3fG8+fPERUVhTJlyuC///7D9u3bcf/+fYO4iRMnYsCAAQbrXFxc8vZE8sH3339vUJT5+Phg2bJlaN26dZbxKSkpsLW1feVxHRwcdO89EZkwciSwfTsQHg60ayd3NlQAsWVGoWrXrg0/Pz+sW7dOt27dunXw9fVFrVq1dOsePnyIffv2YerUqWjRogVKlSqF+vXrIzw8HEFBQQbHdHFxgbe3t8Hk9BrPORk9ejQqVKgAR0dHlClTBmPHjsXz589120+cOIEWLVrAxcUFrq6uqFOnDuLi4gAA165dQ/v27VG0aFE4OTnhzTffxObNm3X77t69G/Xr14ednR18fHwwZswYpKamZpmHWq02OCcAcHNz0y1369YNQ4YMwYgRI+Dh4YGWLVsCAGbMmIFq1arByckJvr6+GDRokMGTwF++zPTVV1+hZs2aWLFiBUqXLg21Wo1u3bohKSkp1+8hkSKlpUmPIMh4jrGLi9RfhoUM5REWM8Y8fmx8evYs+7FPn746Npf69u2LZcuW6ZZ//PFHhISEGMQ4OzvD2dkZGzZsQHJycq5fKzdcXFwQGRmJM2fO4Pvvv8eSJUswc+ZM3fYePXqgZMmSOHLkCI4ePYoxY8bAxsYGADB48GAkJydjz549OHXqFKZOnQpnZ2cAwI0bN9C2bVvUq1cPJ06cwIIFC/DDDz9g0qRJuc41KioKRYoUwf79+7Fo0SIAgJWVFWbPno2///4bUVFR2LFjB0aNGmXyOJcuXcKGDRuwadMmbNq0Cbt37zZ5qY6owHnyRHrwY69ewAv/3pHLy7lE2SIKOI1GIwAIjUaTadvTp0/FmTNnxNOnTzPvKP1NkfXUtq1hrKOj8dhmzQxjPTwyx+RQ7969RXBwsEhMTBR2dnbiypUr4urVq8Le3l4kJiaK4OBg0bt3b1382rVrRdGiRYW9vb1o1KiRCA8PFydOnDA4ZqlSpYStra1wcnIymHbu3Gk0j2bNmolhw4ZlO+9p06aJOnXq6JZdXFxEZGRklrHVqlUTX331VZbbPv/8c1GxYkWRnp6uWzdv3jzh7Ows0tLSXpkHALF+/XrdcrNmzUTNmjVfud+aNWuEu7u7bnnZsmVCrVbrlsePHy8cHR2FVqvVrRs5cqRo0KCByeOa/BwSKUliohANG0r/r9nbC7FqldwZkYKZ+v5+GfvMKJiHhweCgoIQFRUFIQSCgoLg4eGRKa5z584ICgrC3r17ceDAAWzduhXTpk3D0qVLDToKjxw50mAZAEqUKJHr/NauXYtZs2bh4sWLePToEVJTU+Hq6qrbPmLECPTv3x8rVqzAu+++iy5duqBs2bIAgE8//RSffPIJtm3bhnfffRedO3dG9erVAQBnz55FQECAQcfdxo0b49GjR/j3338z9RnKjrp162Zat3PnTkyZMgVnzpyBVqtFamoqnj17hsePHxu9/Fa6dGmDfkY+Pj64c+dOjvMhUpyrV4HWrYHz56XxYjZuBBo3ljsrKiR4mcmYR4+MT7/+ahh7547x2C1bDGOvXs0c8xpCQkIQGRmJqKioTJeYXmRvb4+WLVti3LhxiI2NRZ8+fTB+/HiDGA8PD5QrV85gym0H14MHD6Jbt25o06YNNm3ahOPHj+OLL75ASkqKLuarr77C6dOnERQUhB07dqBKlSpYv349AKB///64fPkyevXqhVOnTqFu3bqYM2cOAEAIkekOJPH/1+Zze2fSy8XJtWvX0LZtW1StWhW//vorjh49innz5gGAQb+fl2VcJsugUqmQnp6eq5yIFCM+HggIkAoZPz9g/34WMpSvWMwY4+RkfLK3z37sy8VAVjGvoXXr1khJSUFKSgoCAwOzvV+VKlXw+DX667zK/v37UapUKXzxxReoW7cuypcvj2vXrmWKq1ChAoYPH45t27ahU6dOBn2AfH19ERoainXr1uF///sflixZoss9NjZWV8AAQGxsLFxcXF6rJelFcXFxSE1NxXfffYeGDRuiQoUKuHnzplmOTVSg3LsnPZbg9m2genXgwAHpgZFE+YiXmRTO2toaZ8+e1c2/7N69e+jSpQtCQkJQvXp1uLi4IC4uDtOmTUNwcLBBbFJSEm7fvm2wztHR0eDS0MsSExMRHx9vsM7b2xvlypVDQkICoqOjUa9ePfzxxx+6VhcAePr0KUaOHIn3338f/v7++Pfff3HkyBF07twZABAWFoY2bdqgQoUKePDgAXbs2IHK//8f5KBBgzBr1iwMHToUQ4YMwfnz5zF+/HiMGDECVlbmqc/Lli2L1NRUzJkzB+3bt8f+/fuxcOFCsxybqEBxd5c6+kZFARs2AGq13BlRIcSWmQLA1dXVaMHh7OyMBg0aYObMmWjatCmqVq2KsWPHYsCAAZg7d65B7Lhx4+Dj42MwverunZUrV6JWrVoG08KFCxEcHIzhw4djyJAhqFmzJmJjYzF27FjdftbW1rh37x4++ugjVKhQAR988AHatGmDCRMmAADS0tIwePBgVK5cGa1bt0bFihUxf/58AFI/ns2bN+Pw4cOoUaMGQkND0a9fP3z55Zev8zYaqFmzJmbMmIGpU6eiatWq+PnnnxEREWG24xMVKH36SOPIsJAhmajEi231BZBWq4VarYZGo8n0hf/s2TNcuXIF/v7+sH/50hFRPuHnkBTn4kVg8GBg+XLAy0vubKiAMvX9/TK2zBARUfZduwa8/TawbZtU0BBZABYzRESUPRoN0LYtcP06ULEi8P93+BHJjcUMERG9Wloa0L07cOYMULw48NdfvMREFoPFDBERvdrIkdK4WQ4OwG+/ASVLyp0RkQ6LGQAFvA80WTh+/sjiRUbqn7MUFQVkMWI2kZwK9TgzGaO1PnnyJNcj3RK9ridPngDIPHowkcVo2hSoUgXo1g3o0kXubIgyKdTFjLW1Ndzc3HTPznF0dMz1cPhEOSWEwJMnT3Dnzh24ubllOeghkUUoUwY4dOi1RywnyiuFupgBpNFqAfBhgCQbNzc33eeQyGKkpkrPXMq4pOTsLGs6RKYU+mJGpVLBx8cHnp6eJh8gSJQXbGxs2CJDlmnCBGDKFOCbb6TOv0QWrNAXMxmsra35pUJEBAC7dgGTJwNCSE/BJrJwvJuJiIj07t4FevSQCpmQEKBrV7kzInolFjNERCTJKGBu3gQqVQJmz5Y7I6JsYTFDRESSefOAjRsBW1sgOpp3L5FisJghIiLpeUujRknz06cDNWrImw9RDrADMBERASVKALNmSY8sGDJE7myIckQlCvhY6lqtFmq1GhqNBq6urnKnQ0RERNmQk+9vXmYiIirM7t8HHj2SOwui18JihoioMPvkE+DNN4G9e+XOhCjX2GeGiKiw+v13YM0awNqajysgRWPLDBFRYaTVAoMGSfOffQbUqiVvPkSvgcUMEVFh9PXXwI0bQNmywPjxcmdD9FpYzBARFTb//AN8/700P3s24OAgbz5Er4nFDBFRYTNyJPD8OdC6NdC2rdzZEL02FjNERIVJcjJgZSV1+v3uO7mzITIL3s1ERFSY2NkB69YBFy8C5crJnQ2RWVh0y0xqaiq+/PJL+Pv7w8HBAWXKlMHEiRORnp4ud2pERMrGQoYKEIsuZqZOnYqFCxdi7ty5OHv2LKZNm4Zvv/0Wc+bMkTs1IiJlefhQuhX75k25MyEyO4u+zHTgwAEEBwcjKCgIAFC6dGmsWrUKcXFxMmdGRKQwX38NLFgAxMUBhw4BKpXcGRGZjUW3zDRp0gTbt2/HhQsXAAAnTpzAvn370NZE7/vk5GRotVqDiYioUEtIAObOleYnTmQhQwWORbfMjB49GhqNBpUqVYK1tTXS0tIwefJkdO/e3eg+ERERmDBhQj5mSURk4SZNAlJSgBYtpNuxiQoYi26ZWb16NX766SesXLkSx44dQ1RUFKZPn46oqCij+4SHh0Oj0eim69ev52PGREQW5uJF4Mcfpfmvv5Y3F6I8YtEtMyNHjsSYMWPQrVs3AEC1atVw7do1REREoHfv3lnuY2dnBzs7u/xMk4jIck2YAKSlAW3aAI0by50NUZ6w6JaZJ0+ewMrKMEVra2vemk1ElB1nzgA//yzNs1WGCjCLbplp3749Jk+eDD8/P7z55ps4fvw4ZsyYgZCQELlTIyKyfN7ewKhRwL//AnXqyJ0NUZ5RCSGE3EkYk5SUhLFjx2L9+vW4c+cOihcvju7du2PcuHGwtbXN1jG0Wi3UajU0Gg1cXV3zOGMiIgskBO9gIsXJyfe3RRcz5sBihoiISHly8v1t0X1miIgoFw4eBJo3B/btkzsTonzBYoaIqKCZPBnYvRtYtkzuTIjyBYsZIqKC5NQpYNMmqY/MmDFyZ0OUL1jMEBEVJN98I/18/32gfHl5cyHKJyxmiIgKisuXgehoaT48XN5ciPIRixkiooJi+nQgPR0IDARq1ZI7G6J8w2KGiKgguH1b/wwmtspQIWPRIwATEVE2FSsGLFgA7NgBNG0qdzZE+YqD5hEREZHF4aB5REREVGiwmCEiUrJnz4CAAOkSU0qK3NkQyYLFDBGRkq1aJT2+ICKCD5OkQovFDBGRUgkBzJolzQ8dCtjYyJoOkVxYzBARKdWuXcDJk4CjI9C/v9zZEMmGxQwRkVJltMr06QMULSpnJkSyYjFDRKREFy8CGzdK859+Km8uRDJjMUNEpERz5kh9Ztq2BSpWlDsbIllxBGAiIiXq0gW4dg0YMkTuTIhkx2KGiEiJmjSRJiLiZSYiIiJSNhYzRERKsnkzMGIEcOWK3JkQWQxeZiIiUpIZM4Dt2wF7e2DKFLmzIbIIbJkhIlKKc+ekQsbKCggNlTsbIovBYoaISCkWLpR+tmsH+PnJmwuRBWExQ0SkBI8fA5GR0vygQbKmQmRpWMwQESnBqlWARgOULQu0bCl3NkQWhcUMEZGlEwKYP1+aDw2V+swQkQ7vZiIisnQpKUDz5sDt20DfvnJnQ2RxWN4TEVk6OzvpluyEBMDdXe5siCwOixkiIqUowsZ0oqywmCEismSbNgG7dkn9ZogoSyxmiIgsVXo6EBYGtGgh3c1ERFliMUNEZKl27AAuXQJcXYHgYLmzIbJYLGaIiCzVkiXSz169ACcneXMhsmAsZoiILNGdO8D69dL8gAHy5kJk4VjMEBFZoqgo4PlzoF49oEYNubMhsmgsZoiILI0Q+ktMH38sby5ECsBihojI0ty6JRU0zs5At25yZ0Nk8TgCExGRpSleHLhwAfjnH6mgISKT2DJDRGSJVCqgQgW5syBSBBYzRESW5Px54NkzubMgUhQWM0RElkIIoFMn6TLTvn1yZ0OkGCxmiIgsxYEDwJkzQHIyUK2a3NkQKQaLGSIiS5FxO3bXroBaLW8uRArCYoaIyBJotcCaNdJ8//7y5kKkMCxmiIgsQXQ08OQJUKkSEBAgdzZEisJihojIEvzwg/SzXz/ptmwiyjYWM0REcrt0CTh8GChSBPjoI7mzIVIcjgBMRCS3smWBc+eAQ4cAT0+5syFSHBYzRESWoGJFaSKiHONlJiIiOaWny50BkeKxmCEiklOHDkCXLtJlJiLKFRYzRERyuXYN2LQJWLsWsLGROxsixWIxQ0Qkl8hI6XlMLVpInYCJKFdYzBARySE9HVi2TJrv10/eXIgUjsUMEZEctm+XLjOp1dKTsoko11jMEBHJIWPE3x49AAcHeXMhUjgWM0RE+e3ePWD9emmel5iIXpvFFzM3btxAz5494e7uDkdHR9SsWRNHjx6VOy0iotyzsQGmTgW6dQNq15Y7GyLFs+gRgB88eIDGjRujRYsW2LJlCzw9PXHp0iW4ubnJnRoRUe65ugJhYXJnQVRgWHQxM3XqVPj6+mJZRo9/AKVLl5YvISIiIrI4Fn2Z6ffff0fdunXRpUsXeHp6olatWliyZInJfZKTk6HVag0mIiKL8f330i3Zjx/LnQlRgWHRxczly5exYMEClC9fHn/++SdCQ0Px6aefYvny5Ub3iYiIgFqt1k2+vr75mDERkQlPnwLjxwMhIcDBg3JnQ1RgqIQQQu4kjLG1tUXdunURGxurW/fpp5/iyJEjOHDgQJb7JCcnIzk5Wbes1Wrh6+sLjUYDV1fXPM+ZiMion34CevUC/P2BixcBK4v+e5JIVlqtFmq1Olvf3xb9L8nHxwdVqlQxWFe5cmUkJCQY3cfOzg6urq4GExGRRcgYW6ZvXxYyRGZk0f+aGjdujPPnzxusu3DhAkqVKiVTRkREuXTpErBrF6BSAX36yJ0NUYFi0cXM8OHDcfDgQUyZMgUXL17EypUrsXjxYgwePFju1IiIcubHH6WfgYEA+/IRmZVFFzP16tXD+vXrsWrVKlStWhVff/01Zs2ahR49esidGhFR9qWmSk/IBjjiL1EesOhxZgCgXbt2aNeundxpEBHlXmIiUKUKkJYGvPee3NkQFTgWX8wQESmejw8QEwNotYCtrdzZEBU4Fn2ZiYioQOHdlUR5gsUMEVFe2r8fuHlT7iyICjQWM0REeSU9HejZE/DzA7ZvlzsbogKLxQwRUV7Zvh24ehVwdgYCAuTOhqjAYjFDRJRXMh6M26MH4Ogoby5EBRiLGSKivJCYCGzYIM0PGCBrKkQFHYsZIqK8sHw58Pw5ULcuULOm3NkQFWgsZoiIzE0I/SUmtsoQ5TkWM0RE5nbpEpCQIPWT6dZN7myICjyOAExEZG7lykljy8TFcaA8onzAlhkiorzg5ga8+67cWRAVCixmiIjM6d49uTMgKnRYzBARmUt6OlC/vjRduCB3NkSFBvvMEBGZS0wMcPmy1DpTooTc2RAVGmyZISIylwULpJ+9ewNOTvLmQlSIsJghIjKHf/8FNm6U5gcOlDcXokKGxQwRkTksWSL1mWnWDKhSRe5siAoVFjNERK/r+XP9iL+hofLmQlQIsZghInpdmzcDt24Bb7wBdOokdzZEhQ7vZiIiel1BQcD69YBGA9jayp0NUaHDYoaI6HUVKQJ06CB3FkSFFi8zERG9DiHkzoCo0MtRMTNt2jQ8ffpUt7xnzx4kJyfrlpOSkjBo0CDzZUdEZMkePAAqVAC+/hpISZE7G6JCK0fFTHh4OJKSknTL7dq1w40bN3TLT548waJFi8yXHRGRJVu6FLh4EVi7FrCxkTsbokIrR8WMeKk59eVlIqJCIzUVmDtXmh82DFCp5M2HqBBjnxkiotz47TcgIQHw8AA+/FDubIgKNRYzRES5MXu29PPjjwF7e3lzISrkcnxr9tKlS+Hs7AwASE1NRWRkJDw8PADAoD8NEVGBFR8P7Nkj3ZLNmx6IZJejYsbPzw9LMobsBuDt7Y0VK1ZkiiEiKtC+/176+f77QIkS8uZCRDkrZq5evZpHaRARKUhYGGBlBQwYIHcmRASOAExElHM1agA//CB3FkT0/3LUAfjQoUPYsmWLwbrly5fD398fnp6e+Pjjjw0G0SMiIiLKazkqZr766iucPHlSt3zq1Cn069cP7777LsaMGYONGzciIiLC7EkSEVmEefOAjz4Czp6VOxMiekGOipn4+Hi88847uuXo6Gg0aNAAS5YswYgRIzB79mysWbPG7EkSEcnu+XNg6lRgxQpg7165syGiF+SomHnw4AG8vLx0y7t370br1q11y/Xq1cP169fNlx0RkaVYtQq4fh3w8pJaZ4jIYuSomPHy8sKVK1cAACkpKTh27BgCAgJ025OSkmDD55MQUUGTni61ygDA8OEcJI/IwuSomGndujXGjBmDvXv3Ijw8HI6Ojnjrrbd020+ePImyZcuaPUkiIllt2gScOQO4ugKhoXJnQ0QvydGt2ZMmTUKnTp3QrFkzODs7IzIyEra2trrtP/74I1q1amX2JImIZCME8M030vwnnwBqtbz5EFEmKpGLR19rNBo4OzvD2traYP39+/fh4uJiUZeatFot1Go1NBoNXF1d5U6HiJRm716gaVPAzg64cgXw8ZE7I6JCISff3zlqmQkJCclW3I8//piTwxIRWa5q1YDJk4HHj1nIEFmoHBUzkZGRKFWqFGrVqoVcNOgQESmPmxvw+edyZ0FEJuSomAkNDUV0dDQuX76MkJAQ9OzZE8WKFcur3IiI5CUEoFLJnQURvUKO7maaP38+bt26hdGjR2Pjxo3w9fXFBx98gD///JMtNURUsOzbB9StC2zdKncmRPQKOSpmAMDOzg7du3dHTEwMzpw5gzfffBODBg1CqVKl8OjRo7zIkYgo/40dCxw7BqxfL3cmRPQKOS5mXqRSqaBSqSCEQHp6urlyIiKS144dwK5dgK0t8OWXcmdDRK+Q42ImOTkZq1atQsuWLVGxYkWcOnUKc+fORUJCApydnfMiRyKi/COE1CoDAAMHAr6+8uZDRK+Uow7AgwYNQnR0NPz8/NC3b19ER0fD3d09r3IjIsp/W7cCsbGAgwMQHi53NkSUDTkaNM/Kygp+fn6oVasWVCZ6+K9bt84syZkDB80jomwTAqhXDzh6FPjsM+Dbb+XOiKjQyrNB8z766COTRQwRkaLt3CkVMs7OwKhRcmdDRNmU40HziIgKrBYtgF9+AW7dAt54Q+5siCibclTMEBEVaCoV8P77cmdBRDn0WrdmExEVCPfvAw8eyJ0FEeUSixkiojFjgPLlOUAekULxMhMRFW7x8cDSpdKdTOwnQ6RIbJkhosJLCCAsTPrZtSvQpIncGRFRLrCYIaLCa906YPduwN4emDZN7myIKJdYzBBR4fTkCTBypDQ/ciTg5ydvPkSUayxmiKhwGjcOuHIFKFkSGD1a7myI6DUoqpiJiIiASqVCWFiY3KkQkZIJAaSkSPMLFwJOTvLmQ0SvRTHFzJEjR7B48WJUr15d7lSISOlUKmD2bODMGSAoSO5siOg1KaKYefToEXr06IElS5agaNGicqdDRAVF5cpyZ0BEZqCIYmbw4MEICgrCu++++8rY5ORkaLVag4mICABw8iTQujVw6ZLcmRCRGVl8MRMdHY1jx44hIiIiW/ERERFQq9W6ydfXN48zJCJFSE0FQkKAP/8EvvxS7myIyIwsupi5fv06hg0bhp9++gn29vbZ2ic8PBwajUY3Xb9+PY+zJCJFmDYNOHoUcHMDZsyQOxsiMiOVEELInYQxGzZsQMeOHWFtba1bl5aWBpVKBSsrKyQnJxtsy4pWq4VarYZGo4Grq2tep0xElmjfPqB5cyAtDVi2DOjTR+6MiOgVcvL9bdHPZnrnnXdw6tQpg3V9+/ZFpUqVMHr06FcWMkRESEwEunWTCpkPPwR695Y7IyIyM4suZlxcXFC1alWDdU5OTnB3d8+0nogok/R0oFcv4MYNoGJFYNEi6bZsIipQLLrPDBHRa7l/H/jvP+nZS7/8Ajg7y50REeUBi26ZycquXbvkToGIlMLDAzhwQOr4W62a3NkQUR5hywwRFTzPnunn7e2Bxo3ly4WI8hyLGSIqWJKSgIAA6UGSlnuzJhGZEYsZIio4nj8H3n8fiI+XOvveuSN3RkSUD1jMEFHBIAQwcCCwbRvg6Aj88Qfg5SV3VkSUD1jMEFHBMHGiNCCelRWwZg1Qt67cGRFRPmExQ0TKt2QJ8NVX0vz8+UBQkKzpEFH+YjFDRMr2zz9AaKg0Hx4uXWoiokJFcePMEBEZKF8eWLgQOHMGmDxZ7myISAYsZohIeYQAHj0CXFyk5QED5M2HiGTFy0xEpCxCSJeTGjaUHiJJRIUeixkiUo5Hj4CuXYGpU6XLSlu3yp0REVkAXmYiImW4eBHo2BH4+2/AxgZYsEB6IjYRFXosZojI8m3ZAnz4IfDwIeDtDfz6K9CokdxZEZGFYDFDRJbtt9+kFhkhpGcurV0LFC8ud1ZEZEHYZ4aILFuzZoCPjzR+zM6dLGSIKBMWM0RkWZ49A374Qf/Eazc34MgRaSwZOztZUyMiy8RihogsgxDApk1AzZpA//7A8uX6bWyNISITWMwQkfz27AGaNAHatwfOn5c6+bq7y50VESkEixkiks+RI0CbNlK/mNhYwN4eGDVKGkOmXTu5syMiheDdTEQkn/BwYPt2oEgR6dLS2LG8pEREOcZihojyx5kzwM8/A0OGSHcnAcCwYUCJElIRU66cvPkRkWKxmCGivCGENFrv2rXAL78AZ89K6+3sgHHjpPn27aWJiOg1sJghIvO6dw8YMwbYsQO4fFm/3sYGaNkSqF9fvtyIqEBiMUNEuaPVAvHxwPHjUsfdgQOl9S4uwMqVwJMnUitMYCDQpYvUodfNTc6MiaiAYjFDRNmzZAlw6hRw4QLwzz+GrS5VquiLGVtbYMYMoGRJoGlTqbghIspDLGaICoOUFODxY+mnl5d+/ZYtwLVrQGIicPeu9DMxEbh5E3B2Bg4d0sfOnQucPGl4XF9foFYtoF49qY+MSiWtzyhsiIjyAYuZ13H8uPSsGGM6dgT8/aX5U6eAmBjjse3bA+XLS/PnzgGbNxuPbd1a+ksYAC5dkh7EZ8w77wA1akjzCQlSZ0xjmjYF6taV5m/dAlatMh7bqBHQsKE0n5gIrFhhPLZePeCtt6T5hw+BH380HluzJvD229L848fAokXGY6tWBVq1kuZTUqQv25dlDIlfsaJ+3JL0dGDmzMwxGcqUATp10i/PmAGkpmYdW6oU0K2bfnnmTOnySkacEPqpZEmgXz997LRp0vshhJTTi5O3NzB6tD7288+BGzekPDKm58+lycsLiIzUx773nvR5e/ZMyuXJE33+pUsDV67oY8eNA+LiMr9vAODkZFig9Owp/a4rVJA+q1WqAG+8kfW+RET5SRRwGo1GABAajcb8B589+8WvqszTli362KVLTceuXauPXbXKdOzy5frY3383HbtggT72r79Mx06fro89cMB07MSJ+tiTJ03Hjh6tj7140XTskCH62Fu3TMeGhOhjtVrTsV276mNTU03Htmtn+Hu2szMe+/bbhrFFixqPbdDAMLZkSeOxVasaxlaoYDzW398wtk4d47FvvGEYO2KEEMHBQvTvL0R4uBDffSd9vmJihDh7Voj0dEFEJIecfH+zZeZ1VKwI9OhhfHvGWBoAULas6VhfX/18qVKmYzNaewBpjA5TsRmtPYD0176p2MqV9fMeHqZjq1bVz7u5mY6tWVM/7+wsxWb8tf+yevX08/b2UmuAMQEB+vkiRUzHZrQiAdJr9+pluP3FfF7MF5COm5qadc4ZLWQZevWSWkJePKZKBVhZGf7eAKmV5uFDabu1tRRjZSUtv/jZAYDPPgM0Guk8ixSR4osUkTrYqtWGsUuXSi1VdnaAg4PUwuLkBDg6Sv1ZXvTdd5nPiYhIYVRCvNxuXrBotVqo1WpoNBq4urrKnQ4RERFlQ06+v/lsJiIiIlI0FjNERESkaCxmiIiISNFYzBAREZGisZghIiIiRWMxQ0RERIrGYoaIiIgUjcUMERERKRqLGSIiIlI0FjNERESkaCxmiIiISNFYzBAREZGisZghIiIiRWMxQ0RERIrGYoaIiIgUjcUMERERKRqLGSIiIlI0FjNERESkaCxmiIiISNFYzBAREZGisZghIiIiRWMxQ0RERIrGYoaIiIgUjcUMERERKRqLGSIiIlI0FjNERESkaCxmiIiISNFYzBAREZGiWXQxExERgXr16sHFxQWenp7o0KEDzp8/L3daREREZEEsupjZvXs3Bg8ejIMHDyImJgapqalo1aoVHj9+LHdqREREZCFUQgghdxLZlZiYCE9PT+zevRtNmzbN1j5arRZqtRoajQaurq55nCERERGZQ06+v4vkU05modFoAADFihUzGpOcnIzk5GTdslarzfO8iIiISD4WfZnpRUIIjBgxAk2aNEHVqlWNxkVERECtVusmX1/ffMySiIiI8ptiLjMNHjwYf/zxB/bt24eSJUsajcuqZcbX15eXmYiIiBSkwF1mGjp0KH7//Xfs2bPHZCEDAHZ2drCzs8unzIiIiEhuFl3MCCEwdOhQrF+/Hrt27YK/v7/cKREREZGFsehiZvDgwVi5ciV+++03uLi44Pbt2wAAtVoNBwcHmbMjIiIiS2DRfWZUKlWW65ctW4Y+ffpk6xi8NZuIiEh5CkyfGQuus4iIiMhCKObWbCIiIqKssJghIiIiRWMxQ0RERIrGYoaIiIgUjcUMERERKRqLGSIiIlI0FjNERESkaCxmiIiISNFYzBAREZGisZghIiIiRWMxQ0RERIrGYoaIiIgUjcUMERERKRqLGSIiIlI0FjNERESkaCxmiIiISNFYzBAREZGisZghIiIiRWMxQ0RERIrGYoaIiIgUjcUMERERKRqLGSIiIlI0FjNERESkaCxmiIiISNFYzBAREZGisZghIiIiRWMxQ0RERIrGYoaIiIgUjcUMERERKRqLGSIiIlI0FjNERESkaCxmiIiISNFYzBAREZGisZghIiIiRWMxQ0RERIrGYoaIiIgUjcUMERERKRqLGSIiIlK0InInQERk0r//AsePG64rVw6oXFmaf/wY2LHD+P6lSwPVqknzz54BMTHGY319gZo1pfnUVGDLFuOxxYsDdepI80IAmzYZj/XyAurX1y//8QeQnp51rIcHEBCgX966FXj+POtYNzfgrbf0yzEx0jlmxcUFaN5cv7xzJ/DoUdaxjo7AO+/ol/fsATSarGPt7IBWrfTL+/cD9+9nHWttDbRtq18+eBBITMw6FgDat9fPHzkC3L5tPLZtW+n4AHDsGHDjhvHYwEDA1laaP3kSuHbNeOy77wIODtL86dPA5cvGY5s3l95nADh3DvjnH+Oxb70l/f4AKe7cOeOxjRoB7u7S/JUrwN9/G49t0ADw9JTmExKAEyeMx9atC/j4SPM3bkjvmzG1agElS0rzt29Lv48MlSoB5csb3zc/iAJOo9EIAEKj0cidChFlV3q6EHv2CPH++0JYWwshlQv6afRofezFi5m3vzgNGaKPvXXLdGxIiD5WqzUd27WrPjY11XRsu3aG52dnZzz27bcNY4sVMx7boIFhbMmSxmOrVjWMrVjReGyZMoaxtWsbj/XyMoxt0sR4rLOzYWxgoPFYKyvD2E6dTL/HT5/qY3v2NB17964+duBA07EJCfrYESNMx549q4/98kvTsXFx+tiICNOxe/boY2fPNh27ZYs+dulS07Fr1+pjV60yHbt8uT72998Nt02aJPJCTr6/2TJDRJbn8mWgaVP9crVqUmtBBl9f/bydnfTXqDGlS+vnbWxMx5Ypo5+3sjIdW66c4bKp2IoVDZfr1wdSUrKOzWhxylCnDqDVZh375puGy7VqASVKZB1btqzhco0a+paBl718jKpVpfcuK8WKGS5XqWK8JenF3yEg/UX/8GHWsVYv9YIoX970e6xS6efLljUdW+SFrz5/f9OxGS04AODnZzrW3l4/X7Kk6VgnJ/188eKmYzNaewCplc9UrFqtn3/jDdOxL/7u3N1Nx3p46Ofd3Axjixc3vl8+UQkhhNxJ5CWtVgu1Wg2NRgNXV1e50yGi7OrUSfrPeMgQ/WUiIio0cvL9zZYZIpKfEMC0aUCPHvrr8r/+avjXNhGREbybiYjkN3YsMGYM0KwZ8OSJtI6FDBFlE4sZIpLX1KnA5MnS/P/+l7lfBRHRK/AyExHJQwhg9mypRQaQippBg+TNiYgUicUMEeU/jQYIDQWio6XlL78ERo2SNyciUiwWM0SU/0aPlgoZa2tg0iRpmYgol1jMEFH+mzRJGsV0+nSgYUO5syEihWMHYCLKW2lpwM8/A717S/1kAGkArr17WcgQkVmwmCGivJFRxLz5JtCzJ7B8ueFzkXjrNRGZCS8zEZH5CCFdPlq1SpquXpXWFysGfPaZ4QMUiYjMhMUMEZnP0aNAvXr65YwiZsgQw+fLEBGZEYsZIsqZtDRg/35gxw5g3z7p4Xs//ihtq10bqFBBethgt25Au3aGD9QjIsoDLGaI6NWEAA4flm6nXr0auHVLv83HR9quUklPOj592vCpxEREeYz/4xDRq7VuDWzbpl92cwPatAHeekuaXsRChojyGf/XIaLMrlyRLh9ZW0vLDRtKl5aCg6XLR4GBgK2tvDkSEf0/3ppNRBKtVrqE1KkTUK4c8Pvv+m3DhwP//Sfdat2+PQsZIrIobJkhKqyEAC5dAv76C9iwQerQ+/y5fvvBg0DHjtK8m5scGRIRZYsiWmbmz58Pf39/2Nvbo06dOti7d6/cKREpS1qadOno3Dn9Oq1WuvPok0+AP/+UCplKlaTnJJ06JT3FmohIASy+ZWb16tUICwvD/Pnz0bhxYyxatAht2rTBmTNn4OfnJ3d6RPJLT5fuIsrwyy/SYHU3b0oFzIULUgtMSgrQpIn0GAEAUKuBmjUBR0fp0lFwsFTMEBEpjEqIjIelWKYGDRqgdu3aWLBggW5d5cqV0aFDB0RERLxyf61WC7VaDY1GA1dXV/Mmp9UCDx4Y3+7pCTg4SPOPHgH37hmP9fDQj8fx+DFw967xWHd3wNlZmn/yBEhMNB5brJh+sLKnT4E7d4zHFi0KZLxHycnA7dvGY9Vq/aWHlBTDW3Vf5uoqHRuQ/vq/edN4rIuLlDMgtSb8+680n9XH1NlZet8A6Qv92jXjx3Vykn4fGce6fFm/LePYGT+dnIDixfXbz5+Xji+EfsqId3ICypTRxx4/Lp1jero0paVJU3q6dG4vDij3xx/S5+L5c+k9fHFydwf69tXHjhwpnV9SkrTPo0fAw4fA/ftA2bLAsWP62PLlgYsXM78HdnZA9erAoUP6Rwlk3FJNRGRhcvT9LSxYcnKysLa2FuvWrTNY/+mnn4qmTZtmuc+zZ8+ERqPRTdevXxcAhEajMX+Cs2e/+PWWedqyRR+7dKnp2LVr9bGrVpmOXb5cH/v776ZjFyzQx/71l+nY6dP1sQcOmI6dOFEfe/Kk6djRo/WxFy+ajh0yRB9765bp2JAQfaxWazq2a1d9bGqq6dj27Q1/z7a2xmPfftswtmhR47ENGxrGlihhPLZ6dcPYChWMx/r6GsaGhQnRs6f0vs+dK8S2bUJcuSKdNxGRQmg0mmx/f1v0Zaa7d+8iLS0NXl5eBuu9vLxw20irQUREBCZMmJAf6Um3rdrbG9/+YtO/OWMzbpfN2M9csS+OD6JS5W2ssdYAGxvD5YyWrezEOjpmHadSSa0SL3p5aP0X83n5NYsVk1qqVKrMU0aLUwY/P6nVyspKP1lbSz9fbMEBpOcU3bsnvT+2tvrJxgYoVcowdtQoqWXN2VmaXFykFi93d31LVoaZM7N+H4iICiiLvsx08+ZNlChRArGxsQh44QF1kydPxooVK3Duxc6M/y85ORnJycm6Za1WC19f37y5zERERER5IieXmSy6ZcbDwwPW1taZWmHu3LmTqbUmg52dHexe/iuciIiICiyLvjXb1tYWderUQUxMjMH6mJgYNGrUSKasiIiIyJJYdMsMAIwYMQK9evVC3bp1ERAQgMWLFyMhIQGhoaFyp0ZEREQWwOKLma5du+LevXuYOHEibt26hapVq2Lz5s0o9XIHSSIiIiqULLoDsDnk6TgzRERElCdy8v1t0X1miIiIiF6FxQwREREpGosZIiIiUjQWM0RERKRoLGaIiIhI0VjMEBERkaKxmCEiIiJFYzFDREREisZihoiIiBTN4h9n8LoyBjjWarUyZ0JERETZlfG9nZ0HFRT4YiYpKQkA4OvrK3MmRERElFNJSUlQq9UmYwr8s5nS09Nx8+ZNuLi4QKVSmfXYWq0Wvr6+uH79eoF87hPPT/kK+jny/JSvoJ8jzy/3hBBISkpC8eLFYWVluldMgW+ZsbKyQsmSJfP0NVxdXQvkhzQDz0/5Cvo58vyUr6CfI88vd17VIpOBHYCJiIhI0VjMEBERkaKxmHkNdnZ2GD9+POzs7OROJU/w/JSvoJ8jz0/5Cvo58vzyR4HvAExEREQFG1tmiIiISNFYzBAREZGisZghIiIiRWMxQ0RERIrGYsbMkpOTUbNmTahUKsTHx8udjtm899578PPzg729PXx8fNCrVy/cvHlT7rTM5urVq+jXrx/8/f3h4OCAsmXLYvz48UhJSZE7NbOZPHkyGjVqBEdHR7i5ucmdzmubP38+/P39YW9vjzp16mDv3r1yp2RWe/bsQfv27VG8eHGoVCps2LBB7pTMJiIiAvXq1YOLiws8PT3RoUMHnD9/Xu60zGrBggWoXr26bjC5gIAAbNmyRe608kxERARUKhXCwsJkeX0WM2Y2atQoFC9eXO40zK5FixZYs2YNzp8/j19//RWXLl3C+++/L3daZnPu3Dmkp6dj0aJFOH36NGbOnImFCxfi888/lzs1s0lJSUGXLl3wySefyJ3Ka1u9ejXCwsLwxRdf4Pjx43jrrbfQpk0bJCQkyJ2a2Tx+/Bg1atTA3Llz5U7F7Hbv3o3Bgwfj4MGDiImJQWpqKlq1aoXHjx/LnZrZlCxZEt988w3i4uIQFxeHt99+G8HBwTh9+rTcqZndkSNHsHjxYlSvXl2+JASZzebNm0WlSpXE6dOnBQBx/PhxuVPKM7/99ptQqVQiJSVF7lTyzLRp04S/v7/caZjdsmXLhFqtljuN11K/fn0RGhpqsK5SpUpizJgxMmWUtwCI9evXy51Gnrlz544AIHbv3i13KnmqaNGiYunSpXKnYVZJSUmifPnyIiYmRjRr1kwMGzZMljzYMmMm//33HwYMGIAVK1bA0dFR7nTy1P379/Hzzz+jUaNGsLGxkTudPKPRaFCsWDG506CXpKSk4OjRo2jVqpXB+latWiE2NlamrOh1aDQaACiw/97S0tIQHR2Nx48fIyAgQO50zGrw4MEICgrCu+++K2seLGbMQAiBPn36IDQ0FHXr1pU7nTwzevRoODk5wd3dHQkJCfjtt9/kTinPXLp0CXPmzEFoaKjcqdBL7t69i7S0NHh5eRms9/Lywu3bt2XKinJLCIERI0agSZMmqFq1qtzpmNWpU6fg7OwMOzs7hIaGYv369ahSpYrcaZlNdHQ0jh07hoiICLlTYTFjyldffQWVSmVyiouLw5w5c6DVahEeHi53yjmS3fPLMHLkSBw/fhzbtm2DtbU1PvroIwgLH0A6p+cIADdv3kTr1q3RpUsX9O/fX6bMsyc351dQqFQqg2UhRKZ1ZPmGDBmCkydPYtWqVXKnYnYVK1ZEfHw8Dh48iE8++QS9e/fGmTNn5E7LLK5fv45hw4bhp59+gr29vdzp8HEGpty9exd37941GVO6dGl069YNGzduNPiPNC0tDdbW1ujRoweioqLyOtVcye75ZfVB/ffff+Hr64vY2FiLbjbN6TnevHkTLVq0QIMGDRAZGQkrK8uu93PzO4yMjERYWBgePnyYx9nljZSUFDg6OuKXX35Bx44ddeuHDRuG+Ph47N69W8bs8oZKpcL69evRoUMHuVMxq6FDh2LDhg3Ys2cP/P395U4nz7377rsoW7YsFi1aJHcqr23Dhg3o2LEjrK2tdevS0tKgUqlgZWWF5ORkg215rUi+vZICeXh4wMPD45Vxs2fPxqRJk3TLN2/eRGBgIFavXo0GDRrkZYqvJbvnl5WMGjg5OdmcKZldTs7xxo0baNGiBerUqYNly5ZZfCEDvN7vUKlsbW1Rp04dxMTEGBQzMTExCA4OljEzyi4hBIYOHYr169dj165dhaKQAaTztvT/M7PrnXfewalTpwzW9e3bF5UqVcLo0aPztZABWMyYhZ+fn8Gys7MzAKBs2bIoWbKkHCmZ1eHDh3H48GE0adIERYsWxeXLlzFu3DiULVvWoltlcuLmzZto3rw5/Pz8MH36dCQmJuq2eXt7y5iZ+SQkJOD+/ftISEhAWlqabhykcuXK6T6zSjFixAj06tULdevWRUBAABYvXoyEhIQC1cfp0aNHuHjxom75ypUriI+PR7FixTL9n6M0gwcPxsqVK/Hbb7/BxcVF19dJrVbDwcFB5uzM4/PPP0ebNm3g6+uLpKQkREdHY9euXdi6davcqZmFi4tLpj5OGX0qZen7JMs9VAXclStXCtSt2SdPnhQtWrQQxYoVE3Z2dqJ06dIiNDRU/Pvvv3KnZjbLli0TALKcCorevXtneX47d+6UO7VcmTdvnihVqpSwtbUVtWvXLnC39e7cuTPL31fv3r3lTu21Gfu3tmzZMrlTM5uQkBDd5/ONN94Q77zzjti2bZvcaeUpOW/NZp8ZIiIiUjTL7xRAREREZAKLGSIiIlI0FjNERESkaCxmiIiISNFYzBAREZGisZghIiIiRWMxQ0RERIrGYoaokGrevDnCwsLkTiNL9+7dg6enJ65evQoA2LVrF1QqVZ4/Tyq3rxMZGQk3N7cc7VOvXj2sW7cuR/sQUdZYzBCRWdy6dQsffvghKlasCCsrK6OF0q+//ooqVarAzs4OVapUwfr16zPFREREoH379ihdunTeJi2jsWPHYsyYMUhPT5c7FSLFYzFDRGaRnJyMN954A1988QVq1KiRZcyBAwfQtWtX9OrVCydOnECvXr3wwQcf4NChQ7qYp0+f4ocffkD//v3zK3VZBAUFQaPR4M8//5Q7FSLFYzFDRHjw4AE++ugjFC1aFI6OjmjTpg3++ecfg5glS5bA19cXjo6O6NixI2bMmGFwaaV06dL4/vvv8dFHH0GtVmf5OrNmzULLli0RHh6OSpUqITw8HO+88w5mzZqli9myZQuKFCli8iGm9+7dQ/fu3VGyZEk4OjqiWrVqWLVqlUFM8+bNMXToUISFhaFo0aLw8vLC4sWL8fjxY/Tt2xcuLi4oW7YstmzZkun4+/fvR40aNWBvb48GDRpkejpwZGQk/Pz8dO/FvXv3DLZfunQJwcHB8PLygrOzM+rVq4e//vrLIMba2hpt27bNlDcR5RyLGSJCnz59EBcXh99//x0HDhyAEAJt27bF8+fPAUhf7qGhoRg2bBji4+PRsmVLTJ48Ocevc+DAAbRq1cpgXWBgIGJjY3XLe/bsQd26dU0e59mzZ6hTpw42bdqEv//+Gx9//DF69epl0MIDAFFRUfDw8MDhw4cxdOhQfPLJJ+jSpQsaNWqEY8eOITAwEL169cKTJ08M9hs5ciSmT5+OI0eOwNPTE++9957uvTh06BBCQkIwaNAgxMfHo0WLFpg0aZLB/o8ePULbtm3x119/4fjx4wgMDET79u2RkJBgEFe/fn3s3bs3e28eERkny+MtiUh2GU+4vXDhggAg9u/fr9t29+5d4eDgINasWSOEEKJr164iKCjIYP8ePXoItVpt8tgvs7GxET///LPBup9//lnY2trqloODg0VISIhBTMYTpB88eGD0fNq2bSv+97//GeTQpEkT3XJqaqpwcnISvXr10q27deuWACAOHDhg8DrR0dG6mHv37gkHBwexevVqIYQQ3bt3F61btzZ47a5duxp9LzJUqVJFzJkzx2Ddb7/9JqysrERaWprJfYnINLbMEBVyZ8+eRZEiRdCgQQPdOnd3d1SsWBFnz54FAJw/fx7169c32O/l5exSqVQGy0IIg3VPnz6Fvb29yWOkpaVh8uTJqF69Otzd3eHs7Ixt27ZlavmoXr26bt7a2hru7u6oVq2abp2XlxcA4M6dOwb7vXiJq1ixYgbvxdmzZzNdAnt5+fHjxxg1ahSqVKkCNzc3ODs749y5c5nyc3BwQHp6OpKTk02eLxGZVkTuBIhIXkIIo+szioyXCw5T+5ni7e2N27dvG6y7c+eOrqgAAA8PDzx48MDkcb777jvMnDkTs2bNQrVq1eDk5ISwsDCkpKQYxNnY2Bgsq1Qqg3UZ55SdO4pefC9eZeTIkfjzzz8xffp0lCtXDg4ODnj//fcz5Xf//n04OjrCwcHhlcckIuPYMkNUyFWpUgWpqakG/U3u3buHCxcuoHLlygCASpUq4fDhwwb7xcXF5fi1AgICEBMTY7Bu27ZtaNSokW65Vq1aOHPmjMnj7N27F8HBwejZsydq1KiBMmXKZOqw/DoOHjyom3/w4AEuXLiASpUqAZDerxe3vxyfkV+fPn3QsWNHVKtWDd7e3roxc170999/o3bt2mbLm6iwYjFDVMiVL18ewcHBGDBgAPbt24cTJ06gZ8+eKFGiBIKDgwEAQ4cOxebNmzFjxgz8888/WLRoEbZs2ZKptSY+Ph7x8fF49OgREhMTER8fb1CYDBs2DNu2bcPUqVNx7tw5TJ06FX/99ZfBmDSBgYE4ffq0ydaZcuXKISYmBrGxsTh79iwGDhyYqcXndUycOBHbt2/H33//jT59+sDDwwMdOnQAAHz66afYunUrpk2bhgsXLmDu3LnYunVrpvzWrVuH+Ph4nDhxAh9++GGWrT979+7N1CGaiHKOxQwRYdmyZahTpw7atWuHgIAACCGwefNm3SWZxo0bY+HChZgxYwZq1KiBrVu3Yvjw4Zn6ttSqVQu1atXC0aNHsXLlStSqVQtt27bVbW/UqBGio6OxbNkyVK9eHZGRkVi9erVBf51q1aqhbt26WLNmjdF8x44di9q1ayMwMBDNmzeHt7e3rtgwh2+++QbDhg1DnTp1cOvWLfz++++wtbUFADRs2BBLly7FnDlzULNmTWzbtg1ffvmlwf4zZ85E0aJF0ahRI7Rv3x6BgYGZWmBu3LiB2NhY9O3b12x5ExVWKpGbC99EVOgNGDAA586dy5Nbizdv3ozPPvsMf//9N6ysCubfXCNHjoRGo8HixYvlToVI8dgBmIiyZfr06WjZsiWcnJywZcsWREVFYf78+XnyWm3btsU///yDGzduwNfXN09eQ26enp747LPP5E6DqEBgywwRZcsHH3yAXbt2ISkpCWXKlMHQoUMRGhoqd1pERCxmiIiISNkK5sVoIiIiKjRYzBAREZGisZghIiIiRWMxQ0RERIrGYoaIiIgUjcUMERERKRqLGSIiIlI0FjNERESkaCxmiIiISNH+Dy4xI2xSgakzAAAAAElFTkSuQmCC\n", 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\n", 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}, "outputs": [ { - "ename": "ModuleNotFoundError", - "evalue": "No module named 'matplotlib'", - "output_type": "error", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mModuleNotFoundError\u001b[0m Traceback (most recent call last)", - "Cell \u001b[0;32mIn[2], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \u001b[43mget_ipython\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mrun_line_magic\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mmatplotlib\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43minline\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m)\u001b[49m\n\u001b[1;32m 3\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mnumpy\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m \u001b[38;5;21;01mnp\u001b[39;00m\n\u001b[1;32m 4\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mtime\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m time\n", - "File \u001b[0;32m~/miniforge3/lib/python3.9/site-packages/IPython/core/interactiveshell.py:2432\u001b[0m, in \u001b[0;36mInteractiveShell.run_line_magic\u001b[0;34m(self, magic_name, line, _stack_depth)\u001b[0m\n\u001b[1;32m 2430\u001b[0m kwargs[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mlocal_ns\u001b[39m\u001b[38;5;124m'\u001b[39m] \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mget_local_scope(stack_depth)\n\u001b[1;32m 2431\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mbuiltin_trap:\n\u001b[0;32m-> 2432\u001b[0m result \u001b[38;5;241m=\u001b[39m \u001b[43mfn\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 2434\u001b[0m \u001b[38;5;66;03m# The code below prevents the output from being displayed\u001b[39;00m\n\u001b[1;32m 2435\u001b[0m \u001b[38;5;66;03m# when using magics with decorator @output_can_be_silenced\u001b[39;00m\n\u001b[1;32m 2436\u001b[0m \u001b[38;5;66;03m# when the last Python token in the expression is a ';'.\u001b[39;00m\n\u001b[1;32m 2437\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mgetattr\u001b[39m(fn, magic\u001b[38;5;241m.\u001b[39mMAGIC_OUTPUT_CAN_BE_SILENCED, \u001b[38;5;28;01mFalse\u001b[39;00m):\n", - "File \u001b[0;32m~/miniforge3/lib/python3.9/site-packages/IPython/core/magics/pylab.py:99\u001b[0m, in \u001b[0;36mPylabMagics.matplotlib\u001b[0;34m(self, line)\u001b[0m\n\u001b[1;32m 97\u001b[0m \u001b[38;5;28mprint\u001b[39m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mAvailable matplotlib backends: \u001b[39m\u001b[38;5;132;01m%s\u001b[39;00m\u001b[38;5;124m\"\u001b[39m \u001b[38;5;241m%\u001b[39m backends_list)\n\u001b[1;32m 98\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m---> 99\u001b[0m gui, backend \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mshell\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43menable_matplotlib\u001b[49m\u001b[43m(\u001b[49m\u001b[43margs\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mgui\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mlower\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mif\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[38;5;28;43misinstance\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43margs\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mgui\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mstr\u001b[39;49m\u001b[43m)\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01melse\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43margs\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mgui\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 100\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_show_matplotlib_backend(args\u001b[38;5;241m.\u001b[39mgui, backend)\n", - "File \u001b[0;32m~/miniforge3/lib/python3.9/site-packages/IPython/core/interactiveshell.py:3606\u001b[0m, in \u001b[0;36mInteractiveShell.enable_matplotlib\u001b[0;34m(self, gui)\u001b[0m\n\u001b[1;32m 3585\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21menable_matplotlib\u001b[39m(\u001b[38;5;28mself\u001b[39m, gui\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mNone\u001b[39;00m):\n\u001b[1;32m 3586\u001b[0m \u001b[38;5;250m \u001b[39m\u001b[38;5;124;03m\"\"\"Enable interactive matplotlib and inline figure support.\u001b[39;00m\n\u001b[1;32m 3587\u001b[0m \n\u001b[1;32m 3588\u001b[0m \u001b[38;5;124;03m This takes the following steps:\u001b[39;00m\n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 3604\u001b[0m \u001b[38;5;124;03m display figures inline.\u001b[39;00m\n\u001b[1;32m 3605\u001b[0m \u001b[38;5;124;03m \"\"\"\u001b[39;00m\n\u001b[0;32m-> 3606\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mmatplotlib_inline\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mbackend_inline\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m configure_inline_support\n\u001b[1;32m 3608\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mIPython\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mcore\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m pylabtools \u001b[38;5;28;01mas\u001b[39;00m pt\n\u001b[1;32m 3609\u001b[0m gui, backend \u001b[38;5;241m=\u001b[39m pt\u001b[38;5;241m.\u001b[39mfind_gui_and_backend(gui, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mpylab_gui_select)\n", - "File \u001b[0;32m~/miniforge3/lib/python3.9/site-packages/matplotlib_inline/__init__.py:1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01m.\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m backend_inline, config \u001b[38;5;66;03m# noqa\u001b[39;00m\n\u001b[1;32m 2\u001b[0m __version__ \u001b[38;5;241m=\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m0.1.6\u001b[39m\u001b[38;5;124m\"\u001b[39m \u001b[38;5;66;03m# noqa\u001b[39;00m\n", - "File \u001b[0;32m~/miniforge3/lib/python3.9/site-packages/matplotlib_inline/backend_inline.py:6\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[38;5;124;03m\"\"\"A matplotlib backend for publishing figures via display_data\"\"\"\u001b[39;00m\n\u001b[1;32m 3\u001b[0m \u001b[38;5;66;03m# Copyright (c) IPython Development Team.\u001b[39;00m\n\u001b[1;32m 4\u001b[0m \u001b[38;5;66;03m# Distributed under the terms of the BSD 3-Clause License.\u001b[39;00m\n\u001b[0;32m----> 6\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mmatplotlib\u001b[39;00m\n\u001b[1;32m 7\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mmatplotlib\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m colors\n\u001b[1;32m 8\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mmatplotlib\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mbackends\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m backend_agg\n", - "\u001b[0;31mModuleNotFoundError\u001b[0m: No module named 'matplotlib'" + "name": "stdout", + "output_type": "stream", + "text": [ + "Bootstrap Statistics :\n", + "original bias std. error\n", + " 100.092 14.9578 100.093 0.149299\n" ] } ], @@ -979,7 +972,22 @@ "collapsed": false, "editable": true }, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
              " + ] + }, + "metadata": { + "filenames": { + "image/png": "/Users/mhjensen/Teaching/MachineLearning/doc/LectureNotes/_build/jupyter_execute/chapter3_51_0.png" + } + }, + "output_type": "display_data" + } + ], "source": [ "# the histogram of the bootstrapped data (normalized data if density = True)\n", "n, binsboot, patches = plt.hist(t, 50, density=True, facecolor='red', alpha=0.75)\n", @@ -1164,7 +1172,32 @@ "collapsed": false, "editable": true }, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Error: 0.013121574062587286\n", + "Bias^2: 0.012073649469946107\n", + "Var: 0.0010479245926411787\n", + "0.013121574062587286 >= 0.012073649469946107 + 0.0010479245926411787 = 0.013121574062587286\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
              " + ] + }, + "metadata": { + "filenames": { + "image/png": "/Users/mhjensen/Teaching/MachineLearning/doc/LectureNotes/_build/jupyter_execute/chapter3_65_1.png" + } + }, + "output_type": "display_data" + } + ], "source": [ "import matplotlib.pyplot as plt\n", "import numpy as np\n", @@ -1230,7 +1263,110 @@ "collapsed": false, "editable": true }, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Polynomial degree: 0\n", + "Error: 0.32149601703519115\n", + "Bias^2: 0.3123314713548606\n", + "Var: 0.009164545680330616\n", + "0.32149601703519115 >= 0.3123314713548606 + 0.009164545680330616 = 0.3214960170351912\n", + "Polynomial degree: 1\n", + "Error: 0.08426840630693412\n", + "Bias^2: 0.0796891867672603\n", + "Var: 0.004579219539673834\n", + "0.08426840630693412 >= 0.0796891867672603 + 0.004579219539673834 = 0.08426840630693413\n", + "Polynomial degree: 2\n", + "Error: 0.10398646080125037\n", + "Bias^2: 0.10077114273548984\n", + "Var: 0.0032153180657605116\n", + "0.10398646080125037 >= 0.10077114273548984 + 0.0032153180657605116 = 0.10398646080125036\n", + "Polynomial degree: 3\n", + "Error: 0.06547790180152352\n", + "Bias^2: 0.062082386342319454\n", + "Var: 0.0033955154592040923\n", + "0.06547790180152352 >= 0.062082386342319454 + 0.0033955154592040923 = 0.06547790180152355\n", + "Polynomial degree: 4\n", + "Error: 0.06844519414009445\n", + "Bias^2: 0.06453579006728322\n", + "Var: 0.003909404072811221\n", + "0.06844519414009445 >= 0.06453579006728322 + 0.003909404072811221 = 0.06844519414009444\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Polynomial degree: 5\n", + "Error: 0.05227921801205679\n", + "Bias^2: 0.04818727730430286\n", + "Var: 0.004091940707753925\n", + "0.05227921801205679 >= 0.04818727730430286 + 0.004091940707753925 = 0.05227921801205679\n", + "Polynomial degree: 6\n", + "Error: 0.03781367141738902\n", + "Bias^2: 0.03365768507152769\n", + "Var: 0.0041559863458613296\n", + "0.03781367141738902 >= 0.03365768507152769 + 0.0041559863458613296 = 0.03781367141738902\n", + "Polynomial degree: 7\n", + "Error: 0.027609773491022394\n", + "Bias^2: 0.022999498260366198\n", + "Var: 0.004610275230656182\n", + "0.027609773491022394 >= 0.022999498260366198 + 0.004610275230656182 = 0.02760977349102238\n", + "Polynomial degree: 8\n", + "Error: 0.017355848195593312\n", + "Bias^2: 0.010331721306655165\n", + "Var: 0.007024126888938144\n", + "0.017355848195593312 >= 0.010331721306655165 + 0.007024126888938144 = 0.01735584819559331\n", + "Polynomial degree: 9\n", + "Error: 0.026605727637184558\n", + "Bias^2: 0.010018312644139219\n", + "Var: 0.016587414993045335\n", + "0.026605727637184558 >= 0.010018312644139219 + 0.016587414993045335 = 0.026605727637184554\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Polynomial degree: 10\n", + "Error: 0.021592704588021178\n", + "Bias^2: 0.010516485576646504\n", + "Var: 0.01107621901137467\n", + "0.021592704588021178 >= 0.010516485576646504 + 0.01107621901137467 = 0.021592704588021174\n", + "Polynomial degree: 11\n", + "Error: 0.07160048164232538\n", + "Bias^2: 0.014436800088896381\n", + "Var: 0.05716368155342902\n", + "0.07160048164232538 >= 0.014436800088896381 + 0.05716368155342902 = 0.0716004816423254\n", + "Polynomial degree: 12\n", + "Error: 0.11547777218876518\n", + "Bias^2: 0.016285782696017142\n", + "Var: 0.09919198949274803\n", + "0.11547777218876518 >= 0.016285782696017142 + 0.09919198949274803 = 0.11547777218876518\n", + "Polynomial degree: 13\n", + "Error: 0.2284246870217162\n", + "Bias^2: 0.01975416527168255\n", + "Var: 0.20867052175003364\n", + "0.2284246870217162 >= 0.01975416527168255 + 0.20867052175003364 = 0.2284246870217162\n" + ] + }, + { + "data": { + "image/png": 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              " + ] + }, + "metadata": { + "filenames": { + "image/png": "/Users/mhjensen/Teaching/MachineLearning/doc/LectureNotes/_build/jupyter_execute/chapter3_66_3.png" + } + }, + "output_type": "display_data" + } + ], "source": [ "import matplotlib.pyplot as plt\n", "import numpy as np\n", @@ -1324,7 +1460,49 @@ "collapsed": false, "editable": true }, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "============================\n", + "Underfitting vs. Overfitting\n", + "============================\n", + "\n", + "This example demonstrates the problems of underfitting and overfitting and\n", + "how we can use linear regression with polynomial features to approximate\n", + "nonlinear functions. The plot shows the function that we want to approximate,\n", + "which is a part of the cosine function. In addition, the samples from the\n", + "real function and the approximations of different models are displayed. The\n", + "models have polynomial features of different degrees. We can see that a\n", + "linear function (polynomial with degree 1) is not sufficient to fit the\n", + "training samples. This is called **underfitting**. A polynomial of degree 4\n", + "approximates the true function almost perfectly. However, for higher degrees\n", + "the model will **overfit** the training data, i.e. it learns the noise of the\n", + "training data.\n", + "We evaluate quantitatively **overfitting** / **underfitting** by using\n", + "cross-validation. We calculate the mean squared error (MSE) on the validation\n", + "set, the higher, the less likely the model generalizes correctly from the\n", + "training data.\n", + "\n" + ] + }, + { + "data": { + "image/png": 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data: 426.30787294\n", + "Degree of polynomial: 5\n", + "Mean squared error on training data: 3.80354994\n", + "Mean squared error on test data: 5.98822371\n", + "Degree of polynomial: 6\n", + "Mean squared error on training data: 3.66204648\n", + "Mean squared error on test data: 8.14812206\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Degree of polynomial: 7\n", + "Mean squared error on training data: 0.47075725\n", + "Mean squared error on test data: 2.00607783\n", + "Degree of polynomial: 8\n", + "Mean squared error on training data: 0.04912436\n", + "Mean squared error on test data: 0.21596432\n", + "Degree of polynomial: 9\n", + "Mean squared error on training data: 0.02522069\n", + "Mean squared error on test data: 0.08576932\n", + "Degree of polynomial: 10\n", + "Mean squared error on training data: 0.02511518\n", + "Mean squared error on test data: 1.20015436\n", + "Degree of polynomial: 11\n", + "Mean squared error on training data: 0.01640891\n", + "Mean squared error on test data: 1.35533773\n", + "Degree of polynomial: 12\n", + "Mean squared error on training data: 0.00813803\n", + "Mean squared error on test data: 0.17446471\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Degree of polynomial: 13\n", + "Mean squared error on training data: 0.00759119\n", + "Mean squared error on test data: 1.08131003\n", + "Degree of polynomial: 14\n", + "Mean squared error on training data: 0.00472199\n", + "Mean squared error on test data: 0.81333804\n", + "Degree of polynomial: 15\n", + "Mean squared error on training data: 0.00410478\n", + "Mean squared error on test data: 92.09172409\n", + "Degree of polynomial: 16\n", + "Mean squared error on training data: 0.00315593\n", + "Mean squared error on test data: 234.38533185\n", + "Degree of polynomial: 17\n", + "Mean squared error on training data: 0.00242999\n", + "Mean squared error on test data: 1271.35771826\n", + "Degree of polynomial: 18\n", + "Mean squared error on training data: 0.00228742\n", + "Mean squared error on test data: 108.27092910\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Degree of polynomial: 19\n", + "Mean squared error on training data: 0.00156376\n", + "Mean squared error on test data: 1371.99051150\n", + "Degree of polynomial: 20\n", + "Mean squared error on training data: 0.00137818\n", + "Mean squared error on test data: 1887.86252988\n", + "Degree of polynomial: 21\n", + "Mean squared error on training data: 0.00118508\n", + "Mean squared error on test data: 14859.69908626\n", + "Degree of polynomial: 22\n", + "Mean squared error on training data: 0.00092647\n", + "Mean squared error on test data: 876.51191552\n", + "Degree of polynomial: 23\n", + "Mean squared error on training data: 0.00085889\n", + "Mean squared error on test data: 5594.60815105\n", + "Degree of polynomial: 24\n", + "Mean squared error on training data: 0.00084705\n", + "Mean squared error on test data: 1277.61702282\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Degree of polynomial: 25\n", + "Mean squared error on training data: 0.00079129\n", + "Mean squared error on test data: 128664.31650694\n", + "Degree of polynomial: 26\n", + "Mean squared error on training data: 0.00076905\n", + "Mean squared error on test data: 19003.94822514\n", + "Degree of polynomial: 27\n", + "Mean squared error on training data: 0.00068946\n", + "Mean squared error on test data: 2379.66219404\n", + "Degree of polynomial: 28\n", + "Mean squared error on training data: 0.00062595\n", + "Mean squared error on test data: 4082.19983530\n", + "Degree of polynomial: 29\n", + "Mean squared error on training data: 0.00060705\n", + "Mean squared error on test data: 3250.17647619\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_19176/626635268.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_19176/626635268.py:74: RuntimeWarning: divide by zero encountered in log10\n", + " plt.plot(polynomial, np.log10(testerror), label='Test Error')\n" + ] + }, + { + "data": { + "image/png": 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\n", 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\n", 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              " + ] + }, + "metadata": { + "filenames": { + "image/png": "/Users/mhjensen/Teaching/MachineLearning/doc/LectureNotes/_build/jupyter_execute/chapter3_75_0.png" + } + }, + "output_type": "display_data" + } + ], "source": [ "import numpy as np\n", "import matplotlib.pyplot as plt\n", @@ -1711,7 +2046,30 @@ "collapsed": false, "editable": true }, - "outputs": [], + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_19176/3817475779.py:63: RuntimeWarning: divide by zero encountered in log10\n", + " plt.plot(polynomial, np.log10(estimated_mse_sklearn), label='Test Error')\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
              " + ] + }, + "metadata": { + "filenames": { + "image/png": "/Users/mhjensen/Teaching/MachineLearning/doc/LectureNotes/_build/jupyter_execute/chapter3_77_1.png" + } + }, + "output_type": "display_data" + } + ], "source": [ "# Common imports\n", "import os\n", @@ -1858,7 +2216,18 @@ "collapsed": false, "editable": true }, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "'\\n#Model training, we compute the mean value of y and X\\ny_train_mean = np.mean(y_train)\\nX_train_mean = np.mean(X_train,axis=0)\\nX_train = X_train - X_train_mean\\ny_train = y_train - y_train_mean\\n\\n# The we fit our model with the training data\\ntrained_model = some_model.fit(X_train,y_train)\\n\\n\\n#Model prediction, we need also to transform our data set used for the prediction.\\nX_test = X_test - X_train_mean #Use mean from training data\\ny_pred = trained_model(X_test)\\ny_pred = y_pred + y_train_mean\\n'" + ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "\"\"\"\n", "#Model training, we compute the mean value of y and X\n", @@ -2246,7 +2615,43 @@ "collapsed": false, "editable": true }, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "True beta: [2, 0.5, 3.7]\n", + "Fitted beta: [2.08376632 0.19569961 3.97898392]\n", + "Sklearn fitted beta: [2.08376632 0.19569961 3.97898392]\n", + "MSE with intercept column\n", + "0.00411363461744314\n", + "MSE with intercept column from SKL\n", + "0.004113634617443147\n", + "Manual intercept: 2.083766322923899\n", + "Fitted beta (wiothout intercept): [0.19569961 3.97898392]\n", + "Sklearn intercept: 2.0837663229239043\n", + "Sklearn fitted beta (without intercept): [0.19569961 3.97898392]\n", + "MSE with Manual intercept\n", + "0.00411363461744314\n", + "MSE with Sklearn intercept\n", + "0.004113634617443131\n" + ] + }, + { + "data": { + "image/png": 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              " + ] + }, + "metadata": { + "filenames": { + "image/png": "/Users/mhjensen/Teaching/MachineLearning/doc/LectureNotes/_build/jupyter_execute/chapter3_112_1.png" + } + }, + "output_type": "display_data" + } + ], "source": [ "import numpy as np\n", "import matplotlib.pyplot as plt\n", @@ -2438,7 +2843,116 @@ "collapsed": false, "editable": true }, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Beta values for own Ridge implementation\n", + "[ 1.03032441e+00 6.28336218e-02 -6.24175744e-01 5.21169159e-02\n", + " 2.80847477e-01 2.12552073e-01 8.13220608e-02 -1.69634577e-02\n", + " -6.50846111e-02 -7.38962192e-02 -5.94226022e-02 -3.50227564e-02\n", + " -9.80609616e-03 1.08299273e-02 2.41882037e-02 2.93492130e-02\n", + " 2.64742912e-02 1.63249532e-02 -5.01831050e-05 -2.15098090e-02]\n", + "Beta values for Scikit-Learn Ridge implementation\n", + "[ 1.03032441e+00 6.28336218e-02 -6.24175744e-01 5.21169159e-02\n", + " 2.80847477e-01 2.12552073e-01 8.13220608e-02 -1.69634577e-02\n", + " -6.50846112e-02 -7.38962192e-02 -5.94226022e-02 -3.50227564e-02\n", + " -9.80609615e-03 1.08299273e-02 2.41882037e-02 2.93492130e-02\n", + " 2.64742912e-02 1.63249532e-02 -5.01831207e-05 -2.15098090e-02]\n", + "MSE values for own Ridge implementation\n", + "4.3632959111950474e-07\n", + "MSE values for Scikit-Learn Ridge implementation\n", + "4.363295916323784e-07\n", + "Beta values for own Ridge implementation\n", + "[ 1.03630548 -0.01963611 -0.37900111 -0.07062318 0.12182967 0.16343471\n", + " 0.13003291 0.07490892 0.02365049 -0.01449782 -0.03814292 -0.04909093\n", + " -0.05009826 -0.04389027 -0.03279636 -0.01866537 -0.00289724 0.01348565\n", + " 0.02976145 0.04543942]\n", + "Beta values for Scikit-Learn Ridge implementation\n", + "[ 1.03630548 -0.01963611 -0.37900111 -0.07062318 0.12182967 0.16343471\n", + " 0.13003291 0.07490892 0.02365049 -0.01449782 -0.03814292 -0.04909093\n", + " -0.05009826 -0.04389027 -0.03279636 -0.01866537 -0.00289724 0.01348565\n", + " 0.02976145 0.04543942]\n", + "MSE values for own Ridge implementation\n", + "5.194042826649355e-06\n", + "MSE values for Scikit-Learn Ridge implementation\n", + "5.1940428268204826e-06\n", + "Beta values for own Ridge implementation\n", + "[ 1.04220758 -0.10931453 -0.17641709 -0.06020587 0.02208512 0.05789007\n", + " 0.06491736 0.05785343 0.04537385 0.03196357 0.01969145 0.00934499\n", + " 0.00107405 -0.00526348 -0.00992331 -0.01318643 -0.01531845 -0.01655318\n", + " -0.01708852 -0.01708781]\n", + "Beta values for Scikit-Learn Ridge implementation\n", + "[ 1.04220758 -0.10931453 -0.17641709 -0.06020587 0.02208512 0.05789007\n", + " 0.06491736 0.05785343 0.04537385 0.03196357 0.01969145 0.00934499\n", + " 0.00107405 -0.00526348 -0.00992331 -0.01318643 -0.01531845 -0.01655318\n", + " -0.01708852 -0.01708781]\n", + "MSE values for own Ridge implementation\n", + "2.0940821989652176e-05\n", + "MSE values for Scikit-Learn Ridge implementation\n", + "2.094082198961999e-05\n", + "Beta values for own Ridge implementation\n", + "[ 1.01219292 -0.06043581 -0.10391807 -0.05651951 -0.01898855 0.00312361\n", + " 0.01463049 0.01975848 0.02123176 0.02068067 0.01905883 0.01691985\n", + " 0.01458337 0.01223198 0.00996754 0.00784393 0.00588657 0.00410387\n", + " 0.00249435 0.00105081]\n", + "Beta values for Scikit-Learn Ridge implementation\n", + "[ 1.01219292 -0.06043581 -0.10391807 -0.05651951 -0.01898855 0.00312361\n", + " 0.01463049 0.01975848 0.02123176 0.02068067 0.01905883 0.01691985\n", + " 0.01458337 0.01223198 0.00996754 0.00784393 0.00588657 0.00410387\n", + " 0.00249435 0.00105081]\n", + "MSE values for own Ridge implementation\n", + "0.00031535148309577417\n", + "MSE values for Scikit-Learn Ridge implementation\n", + "0.00031535148309580783\n", + "Beta values for own Ridge implementation\n", + "[ 8.38916861e-01 1.31276579e-01 8.97497404e-03 -1.72271878e-02\n", + " -2.11744554e-02 -1.91492986e-02 -1.57201944e-02 -1.23002365e-02\n", + " -9.30466214e-03 -6.81048318e-03 -4.78184120e-03 -3.15130074e-03\n", + " -1.84923989e-03 -8.13661243e-04 7.46984697e-06 6.56636616e-04\n", + " 1.16805821e-03 1.56912044e-03 1.88168312e-03 2.12318726e-03]\n", + "Beta values for Scikit-Learn Ridge implementation\n", + "[ 8.38916861e-01 1.31276579e-01 8.97497404e-03 -1.72271878e-02\n", + " -2.11744554e-02 -1.91492986e-02 -1.57201944e-02 -1.23002365e-02\n", + " -9.30466214e-03 -6.81048318e-03 -4.78184120e-03 -3.15130074e-03\n", + " -1.84923989e-03 -8.13661243e-04 7.46984697e-06 6.56636616e-04\n", + " 1.16805821e-03 1.56912044e-03 1.88168312e-03 2.12318726e-03]\n", + "MSE values for own Ridge implementation\n", + "0.01507238889517717\n", + "MSE values for Scikit-Learn Ridge implementation\n", + "0.0150723888951771\n", + "Beta values for own Ridge implementation\n", + "[0.37396662 0.14174745 0.0764924 0.04892055 0.03447512 0.02586427\n", + " 0.02024962 0.01633913 0.01347916 0.0113104 0.0096208 0.00827728\n", + " 0.00719176 0.00630331 0.00556826 0.0049544 0.00443743 0.0039987\n", + " 0.0036237 0.003301 ]\n", + "Beta values for Scikit-Learn Ridge implementation\n", + "[0.37396662 0.14174745 0.0764924 0.04892055 0.03447512 0.02586427\n", + " 0.02024962 0.01633913 0.01347916 0.0113104 0.0096208 0.00827728\n", + " 0.00719176 0.00630331 0.00556826 0.0049544 0.00443743 0.0039987\n", + " 0.0036237 0.003301 ]\n", + "MSE values for own Ridge implementation\n", + "0.2640931530791004\n", + "MSE values for Scikit-Learn Ridge implementation\n", + "0.26409315307910025\n" + ] + }, + { + "data": { + "image/png": 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\n", 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              " + ] + }, + "metadata": { + "filenames": { + "image/png": "/Users/mhjensen/Teaching/MachineLearning/doc/LectureNotes/_build/jupyter_execute/chapter3_120_1.png" + } + }, + "output_type": "display_data" + } + ], "source": [ "import numpy as np\n", "import pandas as pd\n", @@ -2528,7 +3042,138 @@ "collapsed": false, "editable": true }, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Beta values for own Ridge implementation\n", + "[ 3.43579948e-02 -5.43330971e-01 -3.10141413e-03 2.47116868e-01\n", + " 2.18613217e-01 1.02054837e-01 -4.25617658e-04 -5.90475506e-02\n", + " -7.68534263e-02 -6.68929213e-02 -4.24906604e-02 -1.40927184e-02\n", + " 1.11482289e-02 2.88529063e-02 3.67047975e-02 3.38135733e-02\n", + " 2.02198703e-02 -3.46383926e-03 -3.63025821e-02]\n", + "Beta values for Scikit-Learn Ridge implementation\n", + "[ 3.43579948e-02 -5.43330971e-01 -3.10141413e-03 2.47116868e-01\n", + " 2.18613217e-01 1.02054837e-01 -4.25617654e-04 -5.90475506e-02\n", + " -7.68534263e-02 -6.68929213e-02 -4.24906604e-02 -1.40927184e-02\n", + " 1.11482289e-02 2.88529063e-02 3.67047975e-02 3.38135733e-02\n", + " 2.02198702e-02 -3.46383925e-03 -3.63025821e-02]\n", + "Intercept from own implementation:\n", + "1.0330308045187757\n", + "Intercept from Scikit-Learn Ridge implementation\n", + "1.0330308045183219\n", + "MSE values for own Ridge implementation\n", + "3.139255958997547e-06\n", + "MSE values for Scikit-Learn Ridge implementation\n", + "3.1392559585048734e-06\n", + "Beta values for own Ridge implementation\n", + "[-0.05807125 -0.29822833 -0.08551306 0.08156108 0.13679863 0.12333649\n", + " 0.08251519 0.03815288 0.00111756 -0.02498832 -0.04010697 -0.04566964\n", + " -0.04355837 -0.03562355 -0.02348765 -0.00848904 0.00831018 0.0260906\n", + " 0.04423486]\n", + "Beta values for Scikit-Learn Ridge implementation\n", + "[-0.05807125 -0.29822833 -0.08551306 0.08156108 0.13679863 0.12333649\n", + " 0.08251519 0.03815288 0.00111756 -0.02498832 -0.04010697 -0.04566964\n", + " -0.04355837 -0.03562355 -0.02348765 -0.00848904 0.00831018 0.0260906\n", + " 0.04423486]\n", + "Intercept from own implementation:\n", + "1.0411487294305088\n", + "Intercept from Scikit-Learn Ridge implementation\n", + "1.041148729430523\n", + "MSE values for own Ridge implementation\n", + "1.9601304850035702e-05\n", + "MSE values for Scikit-Learn Ridge implementation\n", + "1.960130485007504e-05\n", + "Beta values for own Ridge implementation\n", + "[-0.1416398 -0.14021063 -0.05383795 0.01367553 0.04784395 0.05796251\n", + " 0.05447415 0.044613 0.03267527 0.02098261 0.01066519 0.00217499\n", + " -0.00440346 -0.00917248 -0.01231917 -0.01405935 -0.0146081 -0.01416528\n", + " -0.01290947]\n", + "Beta values for Scikit-Learn Ridge implementation\n", + "[-0.1416398 -0.14021063 -0.05383795 0.01367553 0.04784395 0.05796251\n", + " 0.05447415 0.044613 0.03267527 0.02098261 0.01066519 0.00217499\n", + " -0.00440346 -0.00917248 -0.01231917 -0.01405935 -0.0146081 -0.01416528\n", + " -0.01290947]\n", + "Intercept from own implementation:\n", + "1.049556996627824\n", + "Intercept from Scikit-Learn Ridge implementation\n", + "1.0495569966278269\n", + "MSE values for own Ridge implementation\n", + "5.4959161509357395e-05\n", + "MSE values for Scikit-Learn Ridge implementation\n", + "5.495916150936645e-05\n", + "Beta values for own Ridge implementation\n", + "[-0.13535942 -0.08593216 -0.03568439 -0.0036367 0.01397146 0.02229529\n", + " 0.02503753 0.0245528 0.02228115 0.01908936 0.01549377 0.01179792\n", + " 0.00817631 0.00472512 0.00149311 -0.00149956 -0.00424967 -0.00676387\n", + " -0.00905423]\n", + "Beta values for Scikit-Learn Ridge implementation\n", + "[-0.13535942 -0.08593216 -0.03568439 -0.0036367 0.01397146 0.02229529\n", + " 0.02503753 0.0245528 0.02228115 0.01908936 0.01549377 0.01179792\n", + " 0.00817631 0.00472512 0.00149311 -0.00149956 -0.00424967 -0.00676387\n", + " -0.00905423]\n", + "Intercept from own implementation:\n", + "1.039967668952797\n", + "Intercept from Scikit-Learn Ridge implementation\n", + "1.0399676689527975\n", + "MSE values for own Ridge implementation\n", + "7.571105947979344e-05\n", + "MSE values for Scikit-Learn Ridge implementation\n", + "7.571105947979394e-05\n", + "Beta values for own Ridge implementation\n", + "[-0.05100875 -0.04063602 -0.02723445 -0.01713366 -0.0100706 -0.00517114\n", + " -0.00174276 0.00068734 0.00243186 0.00369758 0.00462287 0.0053018\n", + " 0.00579953 0.006162 0.00642221 0.00660427 0.00672607 0.0068011\n", + " 0.00683964]\n", + "Beta values for Scikit-Learn Ridge implementation\n", + "[-0.05100875 -0.04063602 -0.02723445 -0.01713366 -0.0100706 -0.00517114\n", + " -0.00174276 0.00068734 0.00243186 0.00369758 0.00462287 0.0053018\n", + " 0.00579953 0.006162 0.00642221 0.00660427 0.00672607 0.0068011\n", + " 0.00683964]\n", + "Intercept from own implementation:\n", + "0.999955585168597\n", + "Intercept from Scikit-Learn Ridge implementation\n", + "0.999955585168597\n", + "MSE values for own Ridge implementation\n", + "0.0007698473260556343\n", + "MSE values for Scikit-Learn Ridge implementation\n", + "0.0007698473260556325\n", + "Beta values for own Ridge implementation\n", + "[-0.00834567 -0.00803064 -0.00673407 -0.00554552 -0.00458878 -0.0038335\n", + " -0.00323332 -0.00274989 -0.0023548 -0.00202756 -0.00175331 -0.00152117\n", + " -0.001323 -0.0011526 -0.00100519 -0.00087697 -0.00076495 -0.00066668\n", + " -0.00058016]\n", + "Beta values for Scikit-Learn Ridge implementation\n", + "[-0.00834567 -0.00803064 -0.00673407 -0.00554552 -0.00458878 -0.0038335\n", + " -0.00323332 -0.00274989 -0.0023548 -0.00202756 -0.00175331 -0.00152117\n", + " -0.001323 -0.0011526 -0.00100519 -0.00087697 -0.00076495 -0.00066668\n", + " -0.00058016]\n", + "Intercept from own implementation:\n", + "0.9637117593816477\n", + "Intercept from Scikit-Learn Ridge implementation\n", + "0.9637117593816477\n", + "MSE values for own Ridge implementation\n", + "0.0023813163025848865\n", + "MSE values for Scikit-Learn Ridge implementation\n", + "0.002381316302584886\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
              " + ] + }, + "metadata": { + "filenames": { + "image/png": "/Users/mhjensen/Teaching/MachineLearning/doc/LectureNotes/_build/jupyter_execute/chapter3_122_1.png" + } + }, + "output_type": "display_data" + } + ], "source": [ "import numpy as np\n", "import pandas as pd\n", @@ -4155,7 +4800,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.9.18" + "version": "3.9.10" } }, "nbformat": 4, diff --git a/doc/LectureNotes/_build/jupyter_execute/chapter3_51_0.png b/doc/LectureNotes/_build/jupyter_execute/chapter3_51_0.png index dfe225eda..313192a04 100644 Binary files a/doc/LectureNotes/_build/jupyter_execute/chapter3_51_0.png and b/doc/LectureNotes/_build/jupyter_execute/chapter3_51_0.png differ diff --git a/doc/LectureNotes/_build/jupyter_execute/chapter4.ipynb b/doc/LectureNotes/_build/jupyter_execute/chapter4.ipynb index 531d2afc9..e29087a59 100644 --- a/doc/LectureNotes/_build/jupyter_execute/chapter4.ipynb +++ b/doc/LectureNotes/_build/jupyter_execute/chapter4.ipynb @@ -182,20 +182,148 @@ }, "outputs": [ { - "ename": "ModuleNotFoundError", - "evalue": "No module named 'matplotlib'", - "output_type": "error", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mModuleNotFoundError\u001b[0m Traceback (most recent call last)", - "Cell \u001b[0;32mIn[1], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \u001b[43mget_ipython\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mrun_line_magic\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mmatplotlib\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43minline\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m)\u001b[49m\n\u001b[1;32m 3\u001b[0m \u001b[38;5;66;03m# Common imports\u001b[39;00m\n\u001b[1;32m 4\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mos\u001b[39;00m\n", - "File \u001b[0;32m~/miniforge3/lib/python3.9/site-packages/IPython/core/interactiveshell.py:2432\u001b[0m, in \u001b[0;36mInteractiveShell.run_line_magic\u001b[0;34m(self, magic_name, line, _stack_depth)\u001b[0m\n\u001b[1;32m 2430\u001b[0m kwargs[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mlocal_ns\u001b[39m\u001b[38;5;124m'\u001b[39m] \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mget_local_scope(stack_depth)\n\u001b[1;32m 2431\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mbuiltin_trap:\n\u001b[0;32m-> 2432\u001b[0m result \u001b[38;5;241m=\u001b[39m \u001b[43mfn\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 2434\u001b[0m \u001b[38;5;66;03m# The code below prevents the output from being displayed\u001b[39;00m\n\u001b[1;32m 2435\u001b[0m \u001b[38;5;66;03m# when using magics with decorator @output_can_be_silenced\u001b[39;00m\n\u001b[1;32m 2436\u001b[0m \u001b[38;5;66;03m# when the last Python token in the expression is a ';'.\u001b[39;00m\n\u001b[1;32m 2437\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mgetattr\u001b[39m(fn, magic\u001b[38;5;241m.\u001b[39mMAGIC_OUTPUT_CAN_BE_SILENCED, \u001b[38;5;28;01mFalse\u001b[39;00m):\n", - "File \u001b[0;32m~/miniforge3/lib/python3.9/site-packages/IPython/core/magics/pylab.py:99\u001b[0m, in \u001b[0;36mPylabMagics.matplotlib\u001b[0;34m(self, line)\u001b[0m\n\u001b[1;32m 97\u001b[0m \u001b[38;5;28mprint\u001b[39m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mAvailable matplotlib backends: \u001b[39m\u001b[38;5;132;01m%s\u001b[39;00m\u001b[38;5;124m\"\u001b[39m \u001b[38;5;241m%\u001b[39m backends_list)\n\u001b[1;32m 98\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m---> 99\u001b[0m gui, backend \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mshell\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43menable_matplotlib\u001b[49m\u001b[43m(\u001b[49m\u001b[43margs\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mgui\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mlower\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mif\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[38;5;28;43misinstance\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43margs\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mgui\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mstr\u001b[39;49m\u001b[43m)\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01melse\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43margs\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mgui\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 100\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_show_matplotlib_backend(args\u001b[38;5;241m.\u001b[39mgui, backend)\n", - "File \u001b[0;32m~/miniforge3/lib/python3.9/site-packages/IPython/core/interactiveshell.py:3606\u001b[0m, in \u001b[0;36mInteractiveShell.enable_matplotlib\u001b[0;34m(self, gui)\u001b[0m\n\u001b[1;32m 3585\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21menable_matplotlib\u001b[39m(\u001b[38;5;28mself\u001b[39m, gui\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mNone\u001b[39;00m):\n\u001b[1;32m 3586\u001b[0m \u001b[38;5;250m \u001b[39m\u001b[38;5;124;03m\"\"\"Enable interactive matplotlib and inline figure support.\u001b[39;00m\n\u001b[1;32m 3587\u001b[0m \n\u001b[1;32m 3588\u001b[0m \u001b[38;5;124;03m This takes the following steps:\u001b[39;00m\n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 3604\u001b[0m \u001b[38;5;124;03m display figures inline.\u001b[39;00m\n\u001b[1;32m 3605\u001b[0m \u001b[38;5;124;03m \"\"\"\u001b[39;00m\n\u001b[0;32m-> 3606\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mmatplotlib_inline\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mbackend_inline\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m configure_inline_support\n\u001b[1;32m 3608\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mIPython\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mcore\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m pylabtools \u001b[38;5;28;01mas\u001b[39;00m pt\n\u001b[1;32m 3609\u001b[0m gui, backend \u001b[38;5;241m=\u001b[39m pt\u001b[38;5;241m.\u001b[39mfind_gui_and_backend(gui, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mpylab_gui_select)\n", - "File \u001b[0;32m~/miniforge3/lib/python3.9/site-packages/matplotlib_inline/__init__.py:1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01m.\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m backend_inline, config \u001b[38;5;66;03m# noqa\u001b[39;00m\n\u001b[1;32m 2\u001b[0m __version__ \u001b[38;5;241m=\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m0.1.6\u001b[39m\u001b[38;5;124m\"\u001b[39m \u001b[38;5;66;03m# noqa\u001b[39;00m\n", - "File \u001b[0;32m~/miniforge3/lib/python3.9/site-packages/matplotlib_inline/backend_inline.py:6\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[38;5;124;03m\"\"\"A matplotlib backend for publishing figures via display_data\"\"\"\u001b[39;00m\n\u001b[1;32m 3\u001b[0m \u001b[38;5;66;03m# Copyright (c) IPython Development Team.\u001b[39;00m\n\u001b[1;32m 4\u001b[0m \u001b[38;5;66;03m# Distributed under the terms of the BSD 3-Clause License.\u001b[39;00m\n\u001b[0;32m----> 6\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mmatplotlib\u001b[39;00m\n\u001b[1;32m 7\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mmatplotlib\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m colors\n\u001b[1;32m 8\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mmatplotlib\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mbackends\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m backend_agg\n", - 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\n", 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\n", 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\n", + "text/plain": [ + "
              " + ] + }, + "metadata": { + "filenames": { + "image/png": "/Users/mhjensen/Teaching/MachineLearning/doc/LectureNotes/_build/jupyter_execute/chapter4_18_2.png" + } + }, + "output_type": "display_data" + } + ], "source": [ "\"\"\"The sigmoid function (or the logistic curve) is a\n", "function that takes any real number, z, and outputs a number (0,1).\n", @@ -904,7 +1090,32 @@ "collapsed": false, "editable": true }, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "(426, 30)\n", + "(143, 30)\n", + "Test set accuracy with Logistic Regression: 0.94\n", + "Test set accuracy Logistic Regression with scaled data: 0.96\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/mhjensen/miniforge3/envs/myenv/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:814: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n" + ] + } + ], "source": [ "import matplotlib.pyplot as plt\n", "import numpy as np\n", @@ -952,7 +1163,36 @@ "collapsed": false, "editable": true }, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
              " + ] + }, + "metadata": { + "filenames": { + "image/png": "/Users/mhjensen/Teaching/MachineLearning/doc/LectureNotes/_build/jupyter_execute/chapter4_57_1.png" + } + }, + "output_type": "display_data" + } + ], "source": [ "import matplotlib.pyplot as plt\n", "import numpy as np\n", @@ -1094,7 +1334,83 @@ "collapsed": false, "editable": true }, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "(426, 30)\n", + "(143, 30)\n", + "Test set accuracy with Logistic Regression: 0.94\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/mhjensen/miniforge3/envs/myenv/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:814: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Test set accuracy Logistic Regression with scaled data: 0.96\n", + "[1. 1. 1. 1. 1. 1.\n", + " 1. 1. 0.92857143 0.92857143]\n", + "Test set accuracy with Logistic Regression and scaled data: 0.96\n" + ] + }, + { + "data": { + "image/png": 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\n", 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              " + ] + }, + "metadata": { + "filenames": { + "image/png": "/Users/mhjensen/Teaching/MachineLearning/doc/LectureNotes/_build/jupyter_execute/chapter4_64_5.png" + } + }, + "output_type": "display_data" + } + ], "source": [ "import matplotlib.pyplot as plt\n", "import numpy as np\n", @@ -1154,7 +1470,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.9.18" + "version": "3.9.10" } }, "nbformat": 4, diff --git a/doc/LectureNotes/_build/jupyter_execute/chapter4_64_3.png b/doc/LectureNotes/_build/jupyter_execute/chapter4_64_3.png index d77131072..3ee8451f5 100644 Binary files a/doc/LectureNotes/_build/jupyter_execute/chapter4_64_3.png and b/doc/LectureNotes/_build/jupyter_execute/chapter4_64_3.png differ diff --git a/doc/LectureNotes/_build/jupyter_execute/chapter4_64_4.png b/doc/LectureNotes/_build/jupyter_execute/chapter4_64_4.png index d281ad632..d77131072 100644 Binary files a/doc/LectureNotes/_build/jupyter_execute/chapter4_64_4.png and b/doc/LectureNotes/_build/jupyter_execute/chapter4_64_4.png differ diff --git a/doc/LectureNotes/_build/jupyter_execute/chapter4_64_5.png b/doc/LectureNotes/_build/jupyter_execute/chapter4_64_5.png new file mode 100644 index 000000000..d281ad632 Binary files /dev/null and b/doc/LectureNotes/_build/jupyter_execute/chapter4_64_5.png differ diff --git a/doc/LectureNotes/_build/jupyter_execute/chapter5.ipynb b/doc/LectureNotes/_build/jupyter_execute/chapter5.ipynb index 8c3154897..12b1fb151 100644 --- a/doc/LectureNotes/_build/jupyter_execute/chapter5.ipynb +++ b/doc/LectureNotes/_build/jupyter_execute/chapter5.ipynb @@ -54,20 +54,27 @@ }, "outputs": [ { - "ename": "ModuleNotFoundError", - "evalue": "No module named 'matplotlib'", - "output_type": "error", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mModuleNotFoundError\u001b[0m Traceback (most recent call last)", - "Cell \u001b[0;32mIn[1], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \u001b[43mget_ipython\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mrun_line_magic\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mmatplotlib\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43minline\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m)\u001b[49m\n\u001b[1;32m 3\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01msklearn\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m datasets\n\u001b[1;32m 4\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01msklearn\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01msvm\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m SVC, LinearSVC\n", - "File \u001b[0;32m~/miniforge3/lib/python3.9/site-packages/IPython/core/interactiveshell.py:2432\u001b[0m, in \u001b[0;36mInteractiveShell.run_line_magic\u001b[0;34m(self, magic_name, line, _stack_depth)\u001b[0m\n\u001b[1;32m 2430\u001b[0m kwargs[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mlocal_ns\u001b[39m\u001b[38;5;124m'\u001b[39m] \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mget_local_scope(stack_depth)\n\u001b[1;32m 2431\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mbuiltin_trap:\n\u001b[0;32m-> 2432\u001b[0m result \u001b[38;5;241m=\u001b[39m \u001b[43mfn\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 2434\u001b[0m \u001b[38;5;66;03m# The code below prevents the output from being displayed\u001b[39;00m\n\u001b[1;32m 2435\u001b[0m \u001b[38;5;66;03m# when using magics with decorator @output_can_be_silenced\u001b[39;00m\n\u001b[1;32m 2436\u001b[0m \u001b[38;5;66;03m# when the last Python token in the expression is a ';'.\u001b[39;00m\n\u001b[1;32m 2437\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mgetattr\u001b[39m(fn, magic\u001b[38;5;241m.\u001b[39mMAGIC_OUTPUT_CAN_BE_SILENCED, \u001b[38;5;28;01mFalse\u001b[39;00m):\n", - "File \u001b[0;32m~/miniforge3/lib/python3.9/site-packages/IPython/core/magics/pylab.py:99\u001b[0m, in \u001b[0;36mPylabMagics.matplotlib\u001b[0;34m(self, line)\u001b[0m\n\u001b[1;32m 97\u001b[0m \u001b[38;5;28mprint\u001b[39m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mAvailable matplotlib backends: \u001b[39m\u001b[38;5;132;01m%s\u001b[39;00m\u001b[38;5;124m\"\u001b[39m \u001b[38;5;241m%\u001b[39m backends_list)\n\u001b[1;32m 98\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m---> 99\u001b[0m gui, backend \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mshell\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43menable_matplotlib\u001b[49m\u001b[43m(\u001b[49m\u001b[43margs\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mgui\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mlower\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mif\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[38;5;28;43misinstance\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43margs\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mgui\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mstr\u001b[39;49m\u001b[43m)\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01melse\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43margs\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mgui\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 100\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_show_matplotlib_backend(args\u001b[38;5;241m.\u001b[39mgui, backend)\n", - "File \u001b[0;32m~/miniforge3/lib/python3.9/site-packages/IPython/core/interactiveshell.py:3606\u001b[0m, in \u001b[0;36mInteractiveShell.enable_matplotlib\u001b[0;34m(self, gui)\u001b[0m\n\u001b[1;32m 3585\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21menable_matplotlib\u001b[39m(\u001b[38;5;28mself\u001b[39m, gui\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mNone\u001b[39;00m):\n\u001b[1;32m 3586\u001b[0m \u001b[38;5;250m \u001b[39m\u001b[38;5;124;03m\"\"\"Enable interactive matplotlib and inline figure support.\u001b[39;00m\n\u001b[1;32m 3587\u001b[0m \n\u001b[1;32m 3588\u001b[0m \u001b[38;5;124;03m This takes the following steps:\u001b[39;00m\n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 3604\u001b[0m \u001b[38;5;124;03m display figures inline.\u001b[39;00m\n\u001b[1;32m 3605\u001b[0m \u001b[38;5;124;03m \"\"\"\u001b[39;00m\n\u001b[0;32m-> 3606\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mmatplotlib_inline\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mbackend_inline\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m configure_inline_support\n\u001b[1;32m 3608\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mIPython\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mcore\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m pylabtools \u001b[38;5;28;01mas\u001b[39;00m pt\n\u001b[1;32m 3609\u001b[0m gui, backend \u001b[38;5;241m=\u001b[39m pt\u001b[38;5;241m.\u001b[39mfind_gui_and_backend(gui, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mpylab_gui_select)\n", - "File \u001b[0;32m~/miniforge3/lib/python3.9/site-packages/matplotlib_inline/__init__.py:1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01m.\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m backend_inline, config \u001b[38;5;66;03m# noqa\u001b[39;00m\n\u001b[1;32m 2\u001b[0m __version__ \u001b[38;5;241m=\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m0.1.6\u001b[39m\u001b[38;5;124m\"\u001b[39m \u001b[38;5;66;03m# noqa\u001b[39;00m\n", - "File \u001b[0;32m~/miniforge3/lib/python3.9/site-packages/matplotlib_inline/backend_inline.py:6\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[38;5;124;03m\"\"\"A matplotlib backend for publishing figures via display_data\"\"\"\u001b[39;00m\n\u001b[1;32m 3\u001b[0m \u001b[38;5;66;03m# Copyright (c) IPython Development Team.\u001b[39;00m\n\u001b[1;32m 4\u001b[0m \u001b[38;5;66;03m# Distributed under the terms of the BSD 3-Clause License.\u001b[39;00m\n\u001b[0;32m----> 6\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mmatplotlib\u001b[39;00m\n\u001b[1;32m 7\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mmatplotlib\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m colors\n\u001b[1;32m 8\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mmatplotlib\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mbackends\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m backend_agg\n", - "\u001b[0;31mModuleNotFoundError\u001b[0m: No module named 'matplotlib'" + "name": "stdout", + "output_type": "stream", + "text": [ + "LinearSVC: [0.28475098] [[1.05364854 1.09903804]]\n", + "SVC: [0.31896852] [[1.1203284 1.02625193]]\n", + "SGDClassifier(alpha=0.00200): [0.117] [[0.77714169 0.72981762]]\n" ] + }, + { + "data": { + "image/png": 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\n", 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\n", 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\n", + "text/plain": [ + "
              " + ] + }, + "metadata": { + "filenames": { + "image/png": "/Users/mhjensen/Teaching/MachineLearning/doc/LectureNotes/_build/jupyter_execute/chapter5_129_4.png" + } + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Phi(-1.0, -2) = [0.74081822]\n", + "Phi(-1.0, 1) = [0.30119421]\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
              " + ] + }, + "metadata": { + "filenames": { + "image/png": "/Users/mhjensen/Teaching/MachineLearning/doc/LectureNotes/_build/jupyter_execute/chapter5_129_6.png" + } + }, + "output_type": "display_data" + } + ], "source": [ "from __future__ import division, print_function, unicode_literals\n", "\n", @@ -1691,7 +1800,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 4, "metadata": { "collapsed": false, "editable": true @@ -1823,12 +1932,21 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 5, "metadata": { "collapsed": false, "editable": true }, - "outputs": [], + "outputs": [ + { + "ename": "SyntaxError", + "evalue": "invalid character '’' (U+2019) (3974140161.py, line 5)", + "output_type": "error", + "traceback": [ + "\u001b[0;36m Input \u001b[0;32mIn [5]\u001b[0;36m\u001b[0m\n\u001b[0;31m P = matrix(numpy.diag([1,0]), tc=’d’)\u001b[0m\n\u001b[0m ^\u001b[0m\n\u001b[0;31mSyntaxError\u001b[0m\u001b[0;31m:\u001b[0m invalid character '’' (U+2019)\n" + ] + } + ], "source": [ "# Import the necessary packages\n", "import numpy\n", @@ -1889,7 +2007,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.9.18" + "version": "3.9.10" } }, "nbformat": 4, diff --git a/doc/LectureNotes/_build/jupyter_execute/chapter6.ipynb b/doc/LectureNotes/_build/jupyter_execute/chapter6.ipynb index 83fb0823c..97fa5693c 100644 --- a/doc/LectureNotes/_build/jupyter_execute/chapter6.ipynb +++ b/doc/LectureNotes/_build/jupyter_execute/chapter6.ipynb @@ -82,20 +82,42 @@ }, "outputs": [ { - "ename": "ModuleNotFoundError", - "evalue": "No module named 'matplotlib'", - "output_type": "error", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mModuleNotFoundError\u001b[0m Traceback (most recent call last)", - "Cell \u001b[0;32mIn[1], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \u001b[43mget_ipython\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mrun_line_magic\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mmatplotlib\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43minline\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m)\u001b[49m\n\u001b[1;32m 3\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mnumpy\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m \u001b[38;5;21;01mnp\u001b[39;00m\n\u001b[1;32m 4\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mmatplotlib\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mpyplot\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m \u001b[38;5;21;01mplt\u001b[39;00m\n", - "File \u001b[0;32m~/miniforge3/lib/python3.9/site-packages/IPython/core/interactiveshell.py:2432\u001b[0m, in \u001b[0;36mInteractiveShell.run_line_magic\u001b[0;34m(self, magic_name, line, _stack_depth)\u001b[0m\n\u001b[1;32m 2430\u001b[0m kwargs[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mlocal_ns\u001b[39m\u001b[38;5;124m'\u001b[39m] \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mget_local_scope(stack_depth)\n\u001b[1;32m 2431\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mbuiltin_trap:\n\u001b[0;32m-> 2432\u001b[0m result \u001b[38;5;241m=\u001b[39m \u001b[43mfn\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 2434\u001b[0m \u001b[38;5;66;03m# The code below prevents the output from being displayed\u001b[39;00m\n\u001b[1;32m 2435\u001b[0m \u001b[38;5;66;03m# when using magics with decorator @output_can_be_silenced\u001b[39;00m\n\u001b[1;32m 2436\u001b[0m \u001b[38;5;66;03m# when the last Python token in the expression is a ';'.\u001b[39;00m\n\u001b[1;32m 2437\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mgetattr\u001b[39m(fn, magic\u001b[38;5;241m.\u001b[39mMAGIC_OUTPUT_CAN_BE_SILENCED, \u001b[38;5;28;01mFalse\u001b[39;00m):\n", - "File \u001b[0;32m~/miniforge3/lib/python3.9/site-packages/IPython/core/magics/pylab.py:99\u001b[0m, in \u001b[0;36mPylabMagics.matplotlib\u001b[0;34m(self, line)\u001b[0m\n\u001b[1;32m 97\u001b[0m \u001b[38;5;28mprint\u001b[39m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mAvailable matplotlib backends: \u001b[39m\u001b[38;5;132;01m%s\u001b[39;00m\u001b[38;5;124m\"\u001b[39m \u001b[38;5;241m%\u001b[39m backends_list)\n\u001b[1;32m 98\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m---> 99\u001b[0m gui, backend \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mshell\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43menable_matplotlib\u001b[49m\u001b[43m(\u001b[49m\u001b[43margs\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mgui\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mlower\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mif\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[38;5;28;43misinstance\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43margs\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mgui\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mstr\u001b[39;49m\u001b[43m)\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01melse\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43margs\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mgui\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 100\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_show_matplotlib_backend(args\u001b[38;5;241m.\u001b[39mgui, backend)\n", - "File \u001b[0;32m~/miniforge3/lib/python3.9/site-packages/IPython/core/interactiveshell.py:3606\u001b[0m, in \u001b[0;36mInteractiveShell.enable_matplotlib\u001b[0;34m(self, gui)\u001b[0m\n\u001b[1;32m 3585\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21menable_matplotlib\u001b[39m(\u001b[38;5;28mself\u001b[39m, gui\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mNone\u001b[39;00m):\n\u001b[1;32m 3586\u001b[0m \u001b[38;5;250m \u001b[39m\u001b[38;5;124;03m\"\"\"Enable interactive matplotlib and inline figure support.\u001b[39;00m\n\u001b[1;32m 3587\u001b[0m \n\u001b[1;32m 3588\u001b[0m \u001b[38;5;124;03m This takes the following steps:\u001b[39;00m\n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 3604\u001b[0m \u001b[38;5;124;03m display figures inline.\u001b[39;00m\n\u001b[1;32m 3605\u001b[0m \u001b[38;5;124;03m \"\"\"\u001b[39;00m\n\u001b[0;32m-> 3606\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mmatplotlib_inline\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mbackend_inline\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m configure_inline_support\n\u001b[1;32m 3608\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mIPython\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mcore\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m pylabtools \u001b[38;5;28;01mas\u001b[39;00m pt\n\u001b[1;32m 3609\u001b[0m gui, backend \u001b[38;5;241m=\u001b[39m pt\u001b[38;5;241m.\u001b[39mfind_gui_and_backend(gui, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mpylab_gui_select)\n", - "File \u001b[0;32m~/miniforge3/lib/python3.9/site-packages/matplotlib_inline/__init__.py:1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01m.\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m backend_inline, config \u001b[38;5;66;03m# noqa\u001b[39;00m\n\u001b[1;32m 2\u001b[0m __version__ \u001b[38;5;241m=\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m0.1.6\u001b[39m\u001b[38;5;124m\"\u001b[39m \u001b[38;5;66;03m# noqa\u001b[39;00m\n", - "File \u001b[0;32m~/miniforge3/lib/python3.9/site-packages/matplotlib_inline/backend_inline.py:6\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[38;5;124;03m\"\"\"A matplotlib backend for publishing figures via display_data\"\"\"\u001b[39;00m\n\u001b[1;32m 3\u001b[0m \u001b[38;5;66;03m# Copyright (c) IPython Development Team.\u001b[39;00m\n\u001b[1;32m 4\u001b[0m \u001b[38;5;66;03m# Distributed under the terms of the BSD 3-Clause License.\u001b[39;00m\n\u001b[0;32m----> 6\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mmatplotlib\u001b[39;00m\n\u001b[1;32m 7\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mmatplotlib\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m colors\n\u001b[1;32m 8\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mmatplotlib\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mbackends\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m backend_agg\n", - "\u001b[0;31mModuleNotFoundError\u001b[0m: No module named 'matplotlib'" + "name": "stdout", + "output_type": "stream", + "text": [ + "2nd degree coefficients:\n", + "zero power: -1.3439564710454786\n", + "first power: 0.020404272938413143\n", + "second power: 0.0001539814498783133\n" ] + }, + { + "data": { + "image/png": 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\n", 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              " + ] + }, + "metadata": { + "filenames": { + "image/png": "/Users/mhjensen/Teaching/MachineLearning/doc/LectureNotes/_build/jupyter_execute/chapter6_1_2.png" + } + }, + "output_type": "display_data" } ], "source": [ @@ -508,12 +530,122 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 2, "metadata": { "collapsed": false, "editable": true }, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " mean radius mean texture mean perimeter mean area mean smoothness \\\n", + "0 17.99 10.38 122.80 1001.0 0.11840 \n", + "1 20.57 17.77 132.90 1326.0 0.08474 \n", + "2 19.69 21.25 130.00 1203.0 0.10960 \n", + "3 11.42 20.38 77.58 386.1 0.14250 \n", + "4 20.29 14.34 135.10 1297.0 0.10030 \n", + ".. ... ... ... ... ... \n", + "564 21.56 22.39 142.00 1479.0 0.11100 \n", + "565 20.13 28.25 131.20 1261.0 0.09780 \n", + "566 16.60 28.08 108.30 858.1 0.08455 \n", + "567 20.60 29.33 140.10 1265.0 0.11780 \n", + "568 7.76 24.54 47.92 181.0 0.05263 \n", + "\n", + " mean compactness mean concavity mean concave points mean symmetry \\\n", + "0 0.27760 0.30010 0.14710 0.2419 \n", + "1 0.07864 0.08690 0.07017 0.1812 \n", + "2 0.15990 0.19740 0.12790 0.2069 \n", + "3 0.28390 0.24140 0.10520 0.2597 \n", + "4 0.13280 0.19800 0.10430 0.1809 \n", + ".. ... ... ... ... \n", + "564 0.11590 0.24390 0.13890 0.1726 \n", + "565 0.10340 0.14400 0.09791 0.1752 \n", + "566 0.10230 0.09251 0.05302 0.1590 \n", + "567 0.27700 0.35140 0.15200 0.2397 \n", + "568 0.04362 0.00000 0.00000 0.1587 \n", + "\n", + " mean fractal dimension ... worst radius worst texture \\\n", + "0 0.07871 ... 25.380 17.33 \n", + "1 0.05667 ... 24.990 23.41 \n", + "2 0.05999 ... 23.570 25.53 \n", + "3 0.09744 ... 14.910 26.50 \n", + "4 0.05883 ... 22.540 16.67 \n", + ".. ... ... ... ... \n", + "564 0.05623 ... 25.450 26.40 \n", + "565 0.05533 ... 23.690 38.25 \n", + "566 0.05648 ... 18.980 34.12 \n", + "567 0.07016 ... 25.740 39.42 \n", + "568 0.05884 ... 9.456 30.37 \n", + "\n", + " worst perimeter worst area worst smoothness worst compactness \\\n", + "0 184.60 2019.0 0.16220 0.66560 \n", + "1 158.80 1956.0 0.12380 0.18660 \n", + "2 152.50 1709.0 0.14440 0.42450 \n", + "3 98.87 567.7 0.20980 0.86630 \n", + "4 152.20 1575.0 0.13740 0.20500 \n", + ".. ... ... ... ... \n", + "564 166.10 2027.0 0.14100 0.21130 \n", + "565 155.00 1731.0 0.11660 0.19220 \n", + "566 126.70 1124.0 0.11390 0.30940 \n", + "567 184.60 1821.0 0.16500 0.86810 \n", + "568 59.16 268.6 0.08996 0.06444 \n", + "\n", + " worst concavity worst concave points worst symmetry \\\n", + "0 0.7119 0.2654 0.4601 \n", + "1 0.2416 0.1860 0.2750 \n", + "2 0.4504 0.2430 0.3613 \n", + "3 0.6869 0.2575 0.6638 \n", + "4 0.4000 0.1625 0.2364 \n", + ".. ... ... ... \n", + "564 0.4107 0.2216 0.2060 \n", + "565 0.3215 0.1628 0.2572 \n", + "566 0.3403 0.1418 0.2218 \n", + "567 0.9387 0.2650 0.4087 \n", + "568 0.0000 0.0000 0.2871 \n", + "\n", + " worst fractal dimension \n", + "0 0.11890 \n", + "1 0.08902 \n", + "2 0.08758 \n", + "3 0.17300 \n", + "4 0.07678 \n", + ".. ... \n", + "564 0.07115 \n", + "565 0.06637 \n", + "566 0.07820 \n", + "567 0.12400 \n", + "568 0.07039 \n", + "\n", + "[569 rows x 30 columns]\n", + " malignant benign\n", + "0 1 0\n", + "1 1 0\n", + "2 1 0\n", + "3 1 0\n", + "4 1 0\n", + ".. ... ...\n", + "564 1 0\n", + "565 1 0\n", + "566 1 0\n", + "567 1 0\n", + "568 0 1\n", + "\n", + "[569 rows x 2 columns]\n" + ] + }, + { + "data": { + "text/plain": [ + "0" + ] + }, + "execution_count": 2, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "import os\n", "from sklearn.datasets import load_breast_cancer\n", @@ -552,12 +684,23 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 3, "metadata": { "collapsed": false, "editable": true }, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "0" + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + } + ], 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\n", + "text/plain": [ + "
              " + ] + }, + "metadata": { + "filenames": { + "image/png": "/Users/mhjensen/Teaching/MachineLearning/doc/LectureNotes/_build/jupyter_execute/chapter6_24_1.png" + } + }, + "output_type": "display_data" + } + ], "source": [ "from sklearn.datasets import load_iris\n", "from sklearn import tree\n", @@ -622,12 +806,27 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 5, "metadata": { "collapsed": false, "editable": true }, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "|--- petal width (cm) <= 0.80\n", + "| |--- class: 0\n", + "|--- petal width (cm) > 0.80\n", + "| |--- petal width (cm) <= 1.75\n", + "| | |--- class: 1\n", + "| |--- petal width (cm) > 1.75\n", + "| | |--- class: 2\n", + "\n" + ] + } + ], "source": [ "from sklearn.datasets import load_iris\n", "from sklearn.tree import DecisionTreeClassifier\n", @@ -787,12 +986,24 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 6, "metadata": { "collapsed": false, "editable": true }, - "outputs": [], + "outputs": [ + { + "ename": "FileNotFoundError", + "evalue": "[Errno 2] No such file or directory: 'DataFiles/rideclass.csv'", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mFileNotFoundError\u001b[0m Traceback (most recent call last)", + "Input \u001b[0;32mIn [6]\u001b[0m, in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[1;32m 34\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21msave_fig\u001b[39m(fig_id):\n\u001b[1;32m 35\u001b[0m plt\u001b[38;5;241m.\u001b[39msavefig(image_path(fig_id) \u001b[38;5;241m+\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m.png\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;28mformat\u001b[39m\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mpng\u001b[39m\u001b[38;5;124m'\u001b[39m)\n\u001b[0;32m---> 37\u001b[0m infile \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mopen\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43mdata_path\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mrideclass.csv\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mr\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m)\u001b[49m\n\u001b[1;32m 39\u001b[0m \u001b[38;5;66;03m# Read the experimental data with Pandas\u001b[39;00m\n\u001b[1;32m 40\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mIPython\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mdisplay\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m display\n", + "\u001b[0;31mFileNotFoundError\u001b[0m: [Errno 2] No such file or directory: 'DataFiles/rideclass.csv'" + ] + } + ], "source": [ "# Common imports\n", "import numpy as np\n", @@ -1323,7 +1534,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.9.18" + "version": "3.9.10" } }, "nbformat": 4, diff --git a/doc/LectureNotes/_build/jupyter_execute/chapter6_1_1.png b/doc/LectureNotes/_build/jupyter_execute/chapter6_1_1.png index f82e99147..8460c0b0f 100644 Binary files a/doc/LectureNotes/_build/jupyter_execute/chapter6_1_1.png and b/doc/LectureNotes/_build/jupyter_execute/chapter6_1_1.png differ diff --git a/doc/LectureNotes/_build/jupyter_execute/chapter6_1_2.png b/doc/LectureNotes/_build/jupyter_execute/chapter6_1_2.png index 77f794df5..c4c6af9de 100644 Binary files a/doc/LectureNotes/_build/jupyter_execute/chapter6_1_2.png and b/doc/LectureNotes/_build/jupyter_execute/chapter6_1_2.png differ diff --git a/doc/LectureNotes/_build/jupyter_execute/chapter7.ipynb b/doc/LectureNotes/_build/jupyter_execute/chapter7.ipynb index 09663bf62..23bdfcee5 100644 --- a/doc/LectureNotes/_build/jupyter_execute/chapter7.ipynb +++ b/doc/LectureNotes/_build/jupyter_execute/chapter7.ipynb @@ -86,7 +86,7 @@ "traceback": [ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)", - "Cell \u001b[0;32mIn[1], line 2\u001b[0m\n\u001b[1;32m 1\u001b[0m heads_proba \u001b[38;5;241m=\u001b[39m \u001b[38;5;241m0.51\u001b[39m\n\u001b[0;32m----> 2\u001b[0m coin_tosses \u001b[38;5;241m=\u001b[39m (\u001b[43mnp\u001b[49m\u001b[38;5;241m.\u001b[39mrandom\u001b[38;5;241m.\u001b[39mrand(\u001b[38;5;241m10000\u001b[39m, \u001b[38;5;241m10\u001b[39m) \u001b[38;5;241m<\u001b[39m heads_proba)\u001b[38;5;241m.\u001b[39mastype(np\u001b[38;5;241m.\u001b[39mint32)\n\u001b[1;32m 3\u001b[0m cumulative_heads_ratio \u001b[38;5;241m=\u001b[39m np\u001b[38;5;241m.\u001b[39mcumsum(coin_tosses, axis\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m0\u001b[39m) \u001b[38;5;241m/\u001b[39m np\u001b[38;5;241m.\u001b[39marange(\u001b[38;5;241m1\u001b[39m, \u001b[38;5;241m10001\u001b[39m)\u001b[38;5;241m.\u001b[39mreshape(\u001b[38;5;241m-\u001b[39m\u001b[38;5;241m1\u001b[39m, \u001b[38;5;241m1\u001b[39m)\n\u001b[1;32m 4\u001b[0m plt\u001b[38;5;241m.\u001b[39mfigure(figsize\u001b[38;5;241m=\u001b[39m(\u001b[38;5;241m8\u001b[39m,\u001b[38;5;241m3.5\u001b[39m))\n", + "Input \u001b[0;32mIn [1]\u001b[0m, in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[1;32m 1\u001b[0m heads_proba \u001b[38;5;241m=\u001b[39m \u001b[38;5;241m0.51\u001b[39m\n\u001b[0;32m----> 2\u001b[0m coin_tosses \u001b[38;5;241m=\u001b[39m (\u001b[43mnp\u001b[49m\u001b[38;5;241m.\u001b[39mrandom\u001b[38;5;241m.\u001b[39mrand(\u001b[38;5;241m10000\u001b[39m, \u001b[38;5;241m10\u001b[39m) \u001b[38;5;241m<\u001b[39m heads_proba)\u001b[38;5;241m.\u001b[39mastype(np\u001b[38;5;241m.\u001b[39mint32)\n\u001b[1;32m 3\u001b[0m cumulative_heads_ratio \u001b[38;5;241m=\u001b[39m np\u001b[38;5;241m.\u001b[39mcumsum(coin_tosses, axis\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m0\u001b[39m) \u001b[38;5;241m/\u001b[39m np\u001b[38;5;241m.\u001b[39marange(\u001b[38;5;241m1\u001b[39m, \u001b[38;5;241m10001\u001b[39m)\u001b[38;5;241m.\u001b[39mreshape(\u001b[38;5;241m-\u001b[39m\u001b[38;5;241m1\u001b[39m, \u001b[38;5;241m1\u001b[39m)\n\u001b[1;32m 4\u001b[0m plt\u001b[38;5;241m.\u001b[39mfigure(figsize\u001b[38;5;241m=\u001b[39m(\u001b[38;5;241m8\u001b[39m,\u001b[38;5;241m3.5\u001b[39m))\n", "\u001b[0;31mNameError\u001b[0m: name 'np' is not defined" ] } @@ -1597,7 +1597,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.9.18" + "version": "3.9.10" } }, "nbformat": 4, diff --git a/doc/LectureNotes/_build/jupyter_execute/chapter8.ipynb b/doc/LectureNotes/_build/jupyter_execute/chapter8.ipynb index fa0a99fe6..35cbb7873 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": [ - "-0.08860271693417623\n", - "3.7557911271214808\n", - "[[ 1.08361608 3.3060763 ]\n", - " [ 3.3060763 10.96612219]]\n" + "-0.026250840755899812\n", + "3.9783319210079595\n", + "[[ 1.06075426 3.40216748]\n", + " [ 3.40216748 11.8635085 ]]\n" ] } ], @@ -340,10 +340,10 @@ "name": "stdout", "output_type": "stream", "text": [ - "0.07620892676178488\n", - "1.732950730376058\n", - "[[1. 0.64673189]\n", - " [0.64673189 1. ]]\n" + "0.08076969085177746\n", + "1.9295763474254684\n", + "[[1. 0.7135487]\n", + " [0.7135487 1. ]]\n" ] } ], @@ -394,14 +394,33 @@ }, "outputs": [ { - "ename": "ModuleNotFoundError", - "evalue": "No module named 'pandas'", - "output_type": "error", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mModuleNotFoundError\u001b[0m Traceback (most recent call last)", - "Cell \u001b[0;32mIn[3], line 2\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mnumpy\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m \u001b[38;5;21;01mnp\u001b[39;00m\n\u001b[0;32m----> 2\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mpandas\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m \u001b[38;5;21;01mpd\u001b[39;00m\n\u001b[1;32m 3\u001b[0m n \u001b[38;5;241m=\u001b[39m \u001b[38;5;241m10\u001b[39m\n\u001b[1;32m 4\u001b[0m x \u001b[38;5;241m=\u001b[39m np\u001b[38;5;241m.\u001b[39mrandom\u001b[38;5;241m.\u001b[39mnormal(size\u001b[38;5;241m=\u001b[39mn)\n", - "\u001b[0;31mModuleNotFoundError\u001b[0m: No module named 'pandas'" + "name": "stdout", + "output_type": "stream", + "text": [ + "[[ 1.52944573 4.65218729]\n", + " [ 0.12050822 0.97069774]\n", + " [ 0.40413036 1.80861057]\n", + " [-0.07004211 0.30763135]\n", + " [-1.27793476 -4.21460652]\n", + " [-0.14670413 -1.48950243]\n", + " [-0.41506637 -1.52573941]\n", + " [ 1.75627883 5.73410729]\n", + " [-0.47187428 -1.10927588]\n", + " [-1.42874148 -5.13410999]]\n", + " 0 1\n", + "0 1.529446 4.652187\n", + "1 0.120508 0.970698\n", + "2 0.404130 1.808611\n", + "3 -0.070042 0.307631\n", + "4 -1.277935 -4.214607\n", + "5 -0.146704 -1.489502\n", + "6 -0.415066 -1.525739\n", + "7 1.756279 5.734107\n", + "8 -0.471874 -1.109276\n", + "9 -1.428741 -5.134110\n", + " 0 1\n", + "0 1.000000 0.988835\n", + "1 0.988835 1.000000\n" ] } ], @@ -430,12 +449,52 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 4, "metadata": { "collapsed": false, "editable": true }, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " 0 1 2 3 4 5 6 7 \\\n", + "0 0.0 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 \n", + "1 0.0 0.076825 0.078868 0.077705 0.077226 0.076504 0.071258 0.070157 \n", + "2 0.0 0.078868 0.083096 0.081976 0.082506 0.082577 0.076127 0.075513 \n", + "3 0.0 0.077705 0.081976 0.085023 0.085454 0.085391 0.081955 0.081126 \n", + "4 0.0 0.077226 0.082506 0.085454 0.086441 0.086843 0.082760 0.082255 \n", + "5 0.0 0.076504 0.082577 0.085391 0.086843 0.087642 0.083000 0.082781 \n", + "6 0.0 0.071258 0.076127 0.081955 0.082760 0.083000 0.081584 0.080933 \n", + "7 0.0 0.070157 0.075513 0.081126 0.082255 0.082781 0.080933 0.080505 \n", + "8 0.0 0.069033 0.074780 0.080163 0.081570 0.082347 0.080105 0.079878 \n", + "9 0.0 0.067915 0.073984 0.079124 0.080773 0.081772 0.079165 0.079121 \n", + "10 0.0 0.064634 0.069384 0.076833 0.077710 0.078029 0.078187 0.077613 \n", + "11 0.0 0.063407 0.068403 0.075582 0.076662 0.077171 0.076996 0.076587 \n", + "12 0.0 0.062221 0.067419 0.074327 0.075587 0.076266 0.075779 0.075521 \n", + "13 0.0 0.061084 0.066453 0.073088 0.074509 0.075342 0.074560 0.074439 \n", + "14 0.0 0.060001 0.065517 0.071879 0.073445 0.074419 0.073354 0.073362 \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 0.069033 0.067915 0.064634 0.063407 0.062221 0.061084 0.060001 \n", + "2 0.074780 0.073984 0.069384 0.068403 0.067419 0.066453 0.065517 \n", + "3 0.080163 0.079124 0.076833 0.075582 0.074327 0.073088 0.071879 \n", + "4 0.081570 0.080773 0.077710 0.076662 0.075587 0.074509 0.073445 \n", + "5 0.082347 0.081772 0.078029 0.077171 0.076266 0.075342 0.074419 \n", + "6 0.080105 0.079165 0.078187 0.076996 0.075779 0.074560 0.073354 \n", + "7 0.079878 0.079121 0.077613 0.076587 0.075521 0.074439 0.073362 \n", + "8 0.079434 0.078845 0.076857 0.075984 0.075058 0.074108 0.073152 \n", + "9 0.078845 0.078412 0.075980 0.075249 0.074457 0.073630 0.072790 \n", + "10 0.076857 0.075980 0.076105 0.074970 0.073802 0.072624 0.071452 \n", + "11 0.075984 0.075249 0.074970 0.073972 0.072931 0.071872 0.070811 \n", + "12 0.075058 0.074457 0.073802 0.072931 0.072009 0.071062 0.070107 \n", + "13 0.074108 0.073630 0.072624 0.071872 0.071062 0.070220 0.069365 \n", + "14 0.073152 0.072790 0.071452 0.070811 0.070107 0.069365 0.068606 \n" + ] + } + ], "source": [ "# Common imports\n", "import numpy as np\n", @@ -729,7 +788,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 5, "metadata": { "collapsed": false, "editable": true @@ -795,7 +854,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 6, "metadata": { "collapsed": false, "editable": true @@ -846,12 +905,24 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 7, "metadata": { "collapsed": false, "editable": true }, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " 0 1\n", + "0 3.942604 1.984308\n", + "1 1.984308 1.984182\n", + "[[3.94260358 1.98430782]\n", + " [1.98430782 1.98418221]]\n" + ] + } + ], "source": [ "print(df.cov())\n", "print(np.cov(X_centered.T))" @@ -867,12 +938,36 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 8, "metadata": { "collapsed": false, "editable": true }, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Centered covariance using own code\n", + "[[3.94260358 1.98430782]\n", + " [1.98430782 1.98418221]]\n" + ] + }, + { + "data": { + "image/png": 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              " + ] + }, + "metadata": { + "filenames": { + "image/png": "/Users/mhjensen/Teaching/MachineLearning/doc/LectureNotes/_build/jupyter_execute/chapter8_65_1.png" + } + }, + "output_type": "display_data" + } + ], "source": [ "# extract the relevant columns from the centered design matrix of dim n x 2\n", "x = X_centered[:,0]\n", @@ -938,12 +1033,34 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 9, "metadata": { "collapsed": false, "editable": true }, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Eigenvalues of Covariance matrix\n", + "5.17615838052499\n", + "0.7506274061293645\n", + "First eigenvector\n", + "[0.84927263 0.52795454]\n", + "Second eigenvector\n", + "[-0.52795454 0.84927263]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Eigenvector of largest eigenvalue\n", + "[0.84927263 0.52795454]\n" + ] + } + ], "source": [ "# diagonalize and obtain eigenvalues, not necessarily sorted\n", "EigValues, EigVectors = np.linalg.eig(Cov)\n", @@ -1108,12 +1225,170 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 10, "metadata": { "collapsed": false, "editable": true }, - "outputs": [], + "outputs": [ + { + "data": { + "text/html": [ + "
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0.0 0.0 0.0 0.0 0.0\n", + "9 0.0 0.0 0.0 0.0 0.0\n", + "[[-1.5378811 -0.94639099]\n", + " [ 0.86145244 0.89288636]\n", + " [-0.00445655 0.81633628]\n", + " [ 0.07145103 -1.00433417]\n", + " [ 2.03707133 -0.48476997]\n", + " [ 0.72174172 -1.4557763 ]\n", + " [-0.55854694 1.60673226]\n", + " [ 1.6999536 0.43766686]\n", + " [-1.10405456 0.31718909]\n", + " [-2.18673098 -0.17953942]]\n" + ] + } + ], "source": [ "import numpy as np\n", "import pandas as pd\n", @@ -1156,7 +1431,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 11, "metadata": { "collapsed": false, "editable": true @@ -1180,12 +1455,29 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 12, "metadata": { "collapsed": false, "editable": true }, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[[ 1.5378811 -0.94639099]\n", + " [-0.86145244 0.89288636]\n", + " [ 0.00445655 0.81633628]\n", + " [-0.07145103 -1.00433417]\n", + " [-2.03707133 -0.48476997]\n", + " [-0.72174172 -1.4557763 ]\n", + " [ 0.55854694 1.60673226]\n", + " [-1.6999536 0.43766686]\n", + " [ 1.10405456 0.31718909]\n", + " [ 2.18673098 -0.17953942]]\n" + ] + } + ], "source": [ "#thereafter we do a PCA with Scikit-learn\n", "from sklearn.decomposition import PCA\n", @@ -1205,12 +1497,23 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 13, "metadata": { "collapsed": false, "editable": true }, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "array([-0.62373464, -0.5303329 , 0.317367 , 0.01873344, 0.47815203])" + ] + }, + "execution_count": 13, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "pca.components_.T[:, 0]" ] @@ -1230,12 +1533,36 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 14, "metadata": { "collapsed": false, "editable": true }, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Train set accuracy from Logistic Regression: 0.95\n", + "Train set accuracy scaled data: 0.99\n", + "Train set accuracy scaled and PCA data: 0.96\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/mhjensen/miniforge3/envs/myenv/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:814: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n" + ] + } + ], "source": [ "import matplotlib.pyplot as plt\n", "import numpy as np\n", @@ -1284,7 +1611,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 15, "metadata": { "collapsed": false, "editable": true @@ -1308,7 +1635,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 16, "metadata": { "collapsed": false, "editable": true @@ -1355,7 +1682,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 17, "metadata": { "collapsed": false, "editable": true @@ -1398,7 +1725,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.9.18" + "version": "3.9.10" } }, "nbformat": 4, diff --git a/doc/LectureNotes/_build/jupyter_execute/chapter8_65_1.png b/doc/LectureNotes/_build/jupyter_execute/chapter8_65_1.png index eaabe579a..ebd583089 100644 Binary files a/doc/LectureNotes/_build/jupyter_execute/chapter8_65_1.png and b/doc/LectureNotes/_build/jupyter_execute/chapter8_65_1.png differ diff --git a/doc/LectureNotes/_build/jupyter_execute/chapter9.ipynb b/doc/LectureNotes/_build/jupyter_execute/chapter9.ipynb index 3ae8e8703..099f10d42 100644 --- a/doc/LectureNotes/_build/jupyter_execute/chapter9.ipynb +++ b/doc/LectureNotes/_build/jupyter_execute/chapter9.ipynb @@ -577,20 +577,60 @@ }, "outputs": [ { - "ename": "ModuleNotFoundError", - "evalue": "No module named 'matplotlib'", - "output_type": "error", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mModuleNotFoundError\u001b[0m Traceback (most recent call last)", - "Cell \u001b[0;32mIn[1], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \u001b[43mget_ipython\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mrun_line_magic\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mmatplotlib\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43minline\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m)\u001b[49m\n\u001b[1;32m 3\u001b[0m \u001b[38;5;124;03m\"\"\"The sigmoid function (or the logistic curve) is a \u001b[39;00m\n\u001b[1;32m 4\u001b[0m \u001b[38;5;124;03mfunction that takes any real number, z, and outputs a number (0,1).\u001b[39;00m\n\u001b[1;32m 5\u001b[0m \u001b[38;5;124;03mIt is useful in neural networks for assigning weights on a relative scale.\u001b[39;00m\n\u001b[1;32m 6\u001b[0m \u001b[38;5;124;03mThe value z is the weighted sum of parameters involved in the learning algorithm.\"\"\"\u001b[39;00m\n\u001b[1;32m 8\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mnumpy\u001b[39;00m\n", - "File \u001b[0;32m~/miniforge3/lib/python3.9/site-packages/IPython/core/interactiveshell.py:2432\u001b[0m, in \u001b[0;36mInteractiveShell.run_line_magic\u001b[0;34m(self, magic_name, line, _stack_depth)\u001b[0m\n\u001b[1;32m 2430\u001b[0m kwargs[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mlocal_ns\u001b[39m\u001b[38;5;124m'\u001b[39m] \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mget_local_scope(stack_depth)\n\u001b[1;32m 2431\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mbuiltin_trap:\n\u001b[0;32m-> 2432\u001b[0m result \u001b[38;5;241m=\u001b[39m \u001b[43mfn\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 2434\u001b[0m \u001b[38;5;66;03m# The code below prevents the output from being displayed\u001b[39;00m\n\u001b[1;32m 2435\u001b[0m \u001b[38;5;66;03m# when using magics with decorator @output_can_be_silenced\u001b[39;00m\n\u001b[1;32m 2436\u001b[0m \u001b[38;5;66;03m# when the last Python token in the expression is a ';'.\u001b[39;00m\n\u001b[1;32m 2437\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mgetattr\u001b[39m(fn, magic\u001b[38;5;241m.\u001b[39mMAGIC_OUTPUT_CAN_BE_SILENCED, \u001b[38;5;28;01mFalse\u001b[39;00m):\n", - "File \u001b[0;32m~/miniforge3/lib/python3.9/site-packages/IPython/core/magics/pylab.py:99\u001b[0m, in \u001b[0;36mPylabMagics.matplotlib\u001b[0;34m(self, line)\u001b[0m\n\u001b[1;32m 97\u001b[0m \u001b[38;5;28mprint\u001b[39m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mAvailable matplotlib backends: \u001b[39m\u001b[38;5;132;01m%s\u001b[39;00m\u001b[38;5;124m\"\u001b[39m \u001b[38;5;241m%\u001b[39m backends_list)\n\u001b[1;32m 98\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m---> 99\u001b[0m gui, backend \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mshell\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43menable_matplotlib\u001b[49m\u001b[43m(\u001b[49m\u001b[43margs\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mgui\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mlower\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mif\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[38;5;28;43misinstance\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43margs\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mgui\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mstr\u001b[39;49m\u001b[43m)\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01melse\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43margs\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mgui\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 100\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_show_matplotlib_backend(args\u001b[38;5;241m.\u001b[39mgui, backend)\n", - "File \u001b[0;32m~/miniforge3/lib/python3.9/site-packages/IPython/core/interactiveshell.py:3606\u001b[0m, in \u001b[0;36mInteractiveShell.enable_matplotlib\u001b[0;34m(self, gui)\u001b[0m\n\u001b[1;32m 3585\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21menable_matplotlib\u001b[39m(\u001b[38;5;28mself\u001b[39m, gui\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mNone\u001b[39;00m):\n\u001b[1;32m 3586\u001b[0m \u001b[38;5;250m \u001b[39m\u001b[38;5;124;03m\"\"\"Enable interactive matplotlib and inline figure support.\u001b[39;00m\n\u001b[1;32m 3587\u001b[0m \n\u001b[1;32m 3588\u001b[0m \u001b[38;5;124;03m This takes the following steps:\u001b[39;00m\n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 3604\u001b[0m \u001b[38;5;124;03m display figures inline.\u001b[39;00m\n\u001b[1;32m 3605\u001b[0m \u001b[38;5;124;03m \"\"\"\u001b[39;00m\n\u001b[0;32m-> 3606\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mmatplotlib_inline\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mbackend_inline\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m configure_inline_support\n\u001b[1;32m 3608\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mIPython\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mcore\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m pylabtools \u001b[38;5;28;01mas\u001b[39;00m pt\n\u001b[1;32m 3609\u001b[0m gui, backend \u001b[38;5;241m=\u001b[39m pt\u001b[38;5;241m.\u001b[39mfind_gui_and_backend(gui, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mpylab_gui_select)\n", - "File \u001b[0;32m~/miniforge3/lib/python3.9/site-packages/matplotlib_inline/__init__.py:1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01m.\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m backend_inline, config \u001b[38;5;66;03m# noqa\u001b[39;00m\n\u001b[1;32m 2\u001b[0m __version__ \u001b[38;5;241m=\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m0.1.6\u001b[39m\u001b[38;5;124m\"\u001b[39m \u001b[38;5;66;03m# noqa\u001b[39;00m\n", - "File \u001b[0;32m~/miniforge3/lib/python3.9/site-packages/matplotlib_inline/backend_inline.py:6\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[38;5;124;03m\"\"\"A matplotlib backend for publishing figures via display_data\"\"\"\u001b[39;00m\n\u001b[1;32m 3\u001b[0m \u001b[38;5;66;03m# Copyright (c) IPython Development Team.\u001b[39;00m\n\u001b[1;32m 4\u001b[0m \u001b[38;5;66;03m# Distributed under the terms of the BSD 3-Clause License.\u001b[39;00m\n\u001b[0;32m----> 6\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mmatplotlib\u001b[39;00m\n\u001b[1;32m 7\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mmatplotlib\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m colors\n\u001b[1;32m 8\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mmatplotlib\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mbackends\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m backend_agg\n", - "\u001b[0;31mModuleNotFoundError\u001b[0m: No module named 'matplotlib'" - ] + "data": { + "image/png": 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\n", 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\n", 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\n", + "text/plain": [ + "
              " + ] + }, + "metadata": { + "filenames": { + "image/png": "/Users/mhjensen/Teaching/MachineLearning/doc/LectureNotes/_build/jupyter_execute/chapter9_29_3.png" + } + }, + "output_type": "display_data" } ], "source": [ @@ -1305,7 +1345,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.9.18" + "version": "3.9.10" } }, "nbformat": 4, diff --git a/doc/LectureNotes/_build/jupyter_execute/chapteroptimization.ipynb b/doc/LectureNotes/_build/jupyter_execute/chapteroptimization.ipynb index 0ff5de13f..2c12a3933 100644 --- a/doc/LectureNotes/_build/jupyter_execute/chapteroptimization.ipynb +++ b/doc/LectureNotes/_build/jupyter_execute/chapteroptimization.ipynb @@ -921,20 +921,36 @@ }, "outputs": [ { - "ename": "ModuleNotFoundError", - "evalue": "No module named 'matplotlib'", - "output_type": "error", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mModuleNotFoundError\u001b[0m Traceback (most recent call last)", - "Cell \u001b[0;32mIn[1], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \u001b[43mget_ipython\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mrun_line_magic\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mmatplotlib\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43minline\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m)\u001b[49m\n\u001b[1;32m 3\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mnumpy\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m \u001b[38;5;21;01mnp\u001b[39;00m\n\u001b[1;32m 4\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mnumpy\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mlinalg\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m \u001b[38;5;21;01mla\u001b[39;00m\n", - "File \u001b[0;32m~/miniforge3/lib/python3.9/site-packages/IPython/core/interactiveshell.py:2432\u001b[0m, in \u001b[0;36mInteractiveShell.run_line_magic\u001b[0;34m(self, magic_name, line, _stack_depth)\u001b[0m\n\u001b[1;32m 2430\u001b[0m kwargs[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mlocal_ns\u001b[39m\u001b[38;5;124m'\u001b[39m] \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mget_local_scope(stack_depth)\n\u001b[1;32m 2431\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mbuiltin_trap:\n\u001b[0;32m-> 2432\u001b[0m result \u001b[38;5;241m=\u001b[39m \u001b[43mfn\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 2434\u001b[0m \u001b[38;5;66;03m# The code below prevents the output from being displayed\u001b[39;00m\n\u001b[1;32m 2435\u001b[0m \u001b[38;5;66;03m# when using magics with decorator @output_can_be_silenced\u001b[39;00m\n\u001b[1;32m 2436\u001b[0m \u001b[38;5;66;03m# when the last Python token in the expression is a ';'.\u001b[39;00m\n\u001b[1;32m 2437\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mgetattr\u001b[39m(fn, magic\u001b[38;5;241m.\u001b[39mMAGIC_OUTPUT_CAN_BE_SILENCED, \u001b[38;5;28;01mFalse\u001b[39;00m):\n", - "File \u001b[0;32m~/miniforge3/lib/python3.9/site-packages/IPython/core/magics/pylab.py:99\u001b[0m, in \u001b[0;36mPylabMagics.matplotlib\u001b[0;34m(self, line)\u001b[0m\n\u001b[1;32m 97\u001b[0m \u001b[38;5;28mprint\u001b[39m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mAvailable matplotlib backends: \u001b[39m\u001b[38;5;132;01m%s\u001b[39;00m\u001b[38;5;124m\"\u001b[39m \u001b[38;5;241m%\u001b[39m backends_list)\n\u001b[1;32m 98\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m---> 99\u001b[0m gui, backend \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mshell\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43menable_matplotlib\u001b[49m\u001b[43m(\u001b[49m\u001b[43margs\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mgui\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mlower\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mif\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[38;5;28;43misinstance\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43margs\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mgui\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mstr\u001b[39;49m\u001b[43m)\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01melse\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43margs\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mgui\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 100\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_show_matplotlib_backend(args\u001b[38;5;241m.\u001b[39mgui, backend)\n", - "File \u001b[0;32m~/miniforge3/lib/python3.9/site-packages/IPython/core/interactiveshell.py:3606\u001b[0m, in \u001b[0;36mInteractiveShell.enable_matplotlib\u001b[0;34m(self, gui)\u001b[0m\n\u001b[1;32m 3585\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21menable_matplotlib\u001b[39m(\u001b[38;5;28mself\u001b[39m, gui\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mNone\u001b[39;00m):\n\u001b[1;32m 3586\u001b[0m \u001b[38;5;250m \u001b[39m\u001b[38;5;124;03m\"\"\"Enable interactive matplotlib and inline figure support.\u001b[39;00m\n\u001b[1;32m 3587\u001b[0m \n\u001b[1;32m 3588\u001b[0m \u001b[38;5;124;03m This takes the following steps:\u001b[39;00m\n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 3604\u001b[0m \u001b[38;5;124;03m display figures inline.\u001b[39;00m\n\u001b[1;32m 3605\u001b[0m \u001b[38;5;124;03m \"\"\"\u001b[39;00m\n\u001b[0;32m-> 3606\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mmatplotlib_inline\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mbackend_inline\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m configure_inline_support\n\u001b[1;32m 3608\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mIPython\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mcore\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m pylabtools \u001b[38;5;28;01mas\u001b[39;00m pt\n\u001b[1;32m 3609\u001b[0m gui, backend \u001b[38;5;241m=\u001b[39m pt\u001b[38;5;241m.\u001b[39mfind_gui_and_backend(gui, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mpylab_gui_select)\n", - "File \u001b[0;32m~/miniforge3/lib/python3.9/site-packages/matplotlib_inline/__init__.py:1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01m.\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m backend_inline, config \u001b[38;5;66;03m# noqa\u001b[39;00m\n\u001b[1;32m 2\u001b[0m __version__ \u001b[38;5;241m=\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m0.1.6\u001b[39m\u001b[38;5;124m\"\u001b[39m \u001b[38;5;66;03m# noqa\u001b[39;00m\n", - "File \u001b[0;32m~/miniforge3/lib/python3.9/site-packages/matplotlib_inline/backend_inline.py:6\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[38;5;124;03m\"\"\"A matplotlib backend for publishing figures via display_data\"\"\"\u001b[39;00m\n\u001b[1;32m 3\u001b[0m \u001b[38;5;66;03m# Copyright (c) IPython Development Team.\u001b[39;00m\n\u001b[1;32m 4\u001b[0m \u001b[38;5;66;03m# Distributed under the terms of the BSD 3-Clause License.\u001b[39;00m\n\u001b[0;32m----> 6\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mmatplotlib\u001b[39;00m\n\u001b[1;32m 7\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mmatplotlib\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m colors\n\u001b[1;32m 8\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mmatplotlib\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mbackends\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m backend_agg\n", - "\u001b[0;31mModuleNotFoundError\u001b[0m: No module named 'matplotlib'" + "name": "stderr", + "output_type": "stream", + "text": [ + "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_19294/483257001.py:18: MatplotlibDeprecationWarning: Calling gca() with keyword arguments was deprecated in Matplotlib 3.4. Starting two minor releases later, gca() will take no keyword arguments. The gca() function should only be used to get the current axes, or if no axes exist, create new axes with default keyword arguments. To create a new axes with non-default arguments, use plt.axes() or plt.subplot().\n", + " ax = fig.gca(projection=\"3d\")\n" ] + }, + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 1, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", 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\n", + "text/plain": [ + "
              " + ] + }, + "metadata": { + "filenames": { + "image/png": "/Users/mhjensen/Teaching/MachineLearning/doc/LectureNotes/_build/jupyter_execute/chapteroptimization_63_0.png" + } + }, + "output_type": "display_data" + } + ], "source": [ "pt.axis(\"equal\")\n", "pt.contour(xmesh, ymesh, fmesh)\n", @@ -1029,7 +1060,15 @@ "collapsed": false, "editable": true }, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[ 1.33333333 -0.26666667]\n" + ] + } + ], "source": [ "def f1d(alpha):\n", " return f(x + alpha*s)\n", @@ -1058,7 +1097,32 @@ "collapsed": false, "editable": true }, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "[]" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", 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+ "text/plain": [ + "
              " + ] + }, + "metadata": { + "filenames": { + "image/png": "/Users/mhjensen/Teaching/MachineLearning/doc/LectureNotes/_build/jupyter_execute/chapteroptimization_132_1.png" + } + }, + "output_type": "display_data" + } + ], "source": [ "from random import random, seed\n", "import numpy as np\n", @@ -2194,7 +2319,15 @@ "collapsed": false, "editable": true }, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "gamma_j after 500 epochs: 9.97108e-05\n" + ] + } + ], "source": [ "import numpy as np \n", "\n", @@ -2250,7 +2383,38 @@ "collapsed": false, "editable": true }, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Own inversion\n", + "[[3.87533278]\n", + " [2.94854992]]\n", + "Eigenvalues of Hessian Matrix:[0.31803769 4.15962297]\n", + "theta from own gd\n", + "[[3.87533278]\n", + " [2.94854992]]\n", + "theta from own sdg\n", + "[[3.90803422]\n", + " [2.93820524]]\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
              " + ] + }, + "metadata": { + "filenames": { + "image/png": "/Users/mhjensen/Teaching/MachineLearning/doc/LectureNotes/_build/jupyter_execute/chapteroptimization_188_0.png" + } + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "The max absolute difference is: 1.77636e-15\n" + ] + } + ], "source": [ "import autograd.numpy as np\n", "\n", @@ -2996,7 +3182,16 @@ "collapsed": false, "editable": true }, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "The gradient of f1 evaluated at a = 1 using autograd is: 3\n", + "The gradient of f1 evaluated at a = 1 by finding the analytic expression is: 3\n" + ] + } + ], "source": [ "import autograd.numpy as np\n", "from autograd import grad\n", @@ -3037,7 +3232,21 @@ "collapsed": false, "editable": true }, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Evaluating at x1 = 1, x2 = 3\n", + "------------------------------\n", + "The derivative of f2 w.r.t x1: 12\n", + "The analytical derivative of f2 w.r.t x1: 12\n", + "\n", + "The derivative of f2 w.r.t x2: -4\n", + "The analytical derivative of f2 w.r.t x2: -4\n" + ] + } + ], "source": [ "import autograd.numpy as np\n", "from autograd import grad\n", @@ -3092,7 +3301,16 @@ "collapsed": false, "editable": true }, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "The computed gradient of f3 is: [ 2. 3. 5. 7. 88.]\n", + "The analytical gradient of f3 is: [ 2. 3. 5. 7. 88.]\n" + ] + } + ], "source": [ "import autograd.numpy as np\n", "from autograd import grad\n", @@ -3136,7 +3354,16 @@ "collapsed": false, "editable": true }, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "The computed derivative of f4 at x = 2.7 is: 13.8759\n", + "The analytical gradient of f4 at x = 2.7 is: 13.8759\n" + ] + } + ], "source": [ "import autograd.numpy as np\n", "from autograd import grad\n", @@ -3165,7 +3392,15 @@ "collapsed": false, "editable": true }, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "The computed derivative of f5 at x = 2.7 is: 5.4\n" + ] + } + ], "source": [ "import autograd.numpy as np\n", "from autograd import grad\n", @@ -3191,7 +3426,16 @@ "collapsed": false, "editable": true }, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "The computed derivative of f6_for at x = 0.5 is: 3.95703\n", + "The computed derivative of f6_while at x = 0.5 is: 3.95703\n" + ] + } + ], "source": [ "import autograd.numpy as np\n", "from autograd import grad\n", @@ -3227,7 +3471,15 @@ "collapsed": false, "editable": true }, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "The analytical derivative of f6 at x = 0.5 is: 3.95703\n" + ] + } + ], "source": [ "import autograd.numpy as np\n", "from autograd import grad\n", @@ -3248,7 +3500,16 @@ "collapsed": false, "editable": true }, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "The computed derivative of f7 at n = 2 is: 1\n", + "The analytical derivative of f7 at n = 2 is: 1\n" + ] + } + ], "source": [ "import autograd.numpy as np\n", "from autograd import grad\n", @@ -3301,7 +3562,18 @@ "collapsed": false, "editable": true }, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "'\\nimport autograd.numpy as np\\nfrom autograd import grad\\ndef f8(x): # Assume x is an array\\n x[2] = 3\\n return x*2\\n\\nf8_grad = grad(f8)\\n\\nx = 8.4\\n\\nprint(\"The derivative of f8 is:\",f8_grad(x))\\n'" + ] + }, + "execution_count": 22, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "\"\"\"\n", "import autograd.numpy as np\n", @@ -3336,7 +3608,25 @@ "collapsed": false, "editable": true }, - "outputs": [], + "outputs": [ + { + "ename": "AttributeError", + "evalue": "'ArrayBox' object has no attribute 'dot'", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mAttributeError\u001b[0m Traceback (most recent call last)", + "Input \u001b[0;32mIn [23]\u001b[0m, in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[1;32m 7\u001b[0m f9_grad \u001b[38;5;241m=\u001b[39m grad(f9)\n\u001b[1;32m 9\u001b[0m x \u001b[38;5;241m=\u001b[39m np\u001b[38;5;241m.\u001b[39marray([\u001b[38;5;241m1.0\u001b[39m,\u001b[38;5;241m0.0\u001b[39m])\n\u001b[0;32m---> 11\u001b[0m \u001b[38;5;28mprint\u001b[39m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mThe derivative of f9 is:\u001b[39m\u001b[38;5;124m\"\u001b[39m,\u001b[43mf9_grad\u001b[49m\u001b[43m(\u001b[49m\u001b[43mx\u001b[49m\u001b[43m)\u001b[49m)\n", + "File \u001b[0;32m~/miniforge3/envs/myenv/lib/python3.9/site-packages/autograd/wrap_util.py:20\u001b[0m, in \u001b[0;36munary_to_nary..nary_operator..nary_f\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m 18\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m 19\u001b[0m x \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mtuple\u001b[39m(args[i] \u001b[38;5;28;01mfor\u001b[39;00m i \u001b[38;5;129;01min\u001b[39;00m argnum)\n\u001b[0;32m---> 20\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43munary_operator\u001b[49m\u001b[43m(\u001b[49m\u001b[43munary_f\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mx\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mnary_op_args\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mnary_op_kwargs\u001b[49m\u001b[43m)\u001b[49m\n", + "File \u001b[0;32m~/miniforge3/envs/myenv/lib/python3.9/site-packages/autograd/differential_operators.py:25\u001b[0m, in \u001b[0;36mgrad\u001b[0;34m(fun, x)\u001b[0m\n\u001b[1;32m 18\u001b[0m \u001b[38;5;129m@unary_to_nary\u001b[39m\n\u001b[1;32m 19\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mgrad\u001b[39m(fun, x):\n\u001b[1;32m 20\u001b[0m \u001b[38;5;250m \u001b[39m\u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m 21\u001b[0m \u001b[38;5;124;03m Returns a function which computes the gradient of `fun` with respect to\u001b[39;00m\n\u001b[1;32m 22\u001b[0m \u001b[38;5;124;03m positional argument number `argnum`. The returned function takes the same\u001b[39;00m\n\u001b[1;32m 23\u001b[0m \u001b[38;5;124;03m arguments as `fun`, but returns the gradient instead. The function `fun`\u001b[39;00m\n\u001b[1;32m 24\u001b[0m \u001b[38;5;124;03m should be scalar-valued. The gradient has the same type as the argument.\"\"\"\u001b[39;00m\n\u001b[0;32m---> 25\u001b[0m vjp, ans \u001b[38;5;241m=\u001b[39m \u001b[43m_make_vjp\u001b[49m\u001b[43m(\u001b[49m\u001b[43mfun\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mx\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 26\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m vspace(ans)\u001b[38;5;241m.\u001b[39msize \u001b[38;5;241m==\u001b[39m \u001b[38;5;241m1\u001b[39m:\n\u001b[1;32m 27\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mTypeError\u001b[39;00m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mGrad only applies to real scalar-output functions. \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m 28\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mTry jacobian, elementwise_grad or holomorphic_grad.\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n", + "File \u001b[0;32m~/miniforge3/envs/myenv/lib/python3.9/site-packages/autograd/core.py:10\u001b[0m, in \u001b[0;36mmake_vjp\u001b[0;34m(fun, x)\u001b[0m\n\u001b[1;32m 8\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mmake_vjp\u001b[39m(fun, x):\n\u001b[1;32m 9\u001b[0m start_node \u001b[38;5;241m=\u001b[39m VJPNode\u001b[38;5;241m.\u001b[39mnew_root()\n\u001b[0;32m---> 10\u001b[0m end_value, end_node \u001b[38;5;241m=\u001b[39m \u001b[43mtrace\u001b[49m\u001b[43m(\u001b[49m\u001b[43mstart_node\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mfun\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mx\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 11\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m end_node \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m 12\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mvjp\u001b[39m(g): \u001b[38;5;28;01mreturn\u001b[39;00m vspace(x)\u001b[38;5;241m.\u001b[39mzeros()\n", + "File \u001b[0;32m~/miniforge3/envs/myenv/lib/python3.9/site-packages/autograd/tracer.py:10\u001b[0m, in \u001b[0;36mtrace\u001b[0;34m(start_node, fun, x)\u001b[0m\n\u001b[1;32m 8\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m trace_stack\u001b[38;5;241m.\u001b[39mnew_trace() \u001b[38;5;28;01mas\u001b[39;00m t:\n\u001b[1;32m 9\u001b[0m start_box \u001b[38;5;241m=\u001b[39m new_box(x, t, start_node)\n\u001b[0;32m---> 10\u001b[0m end_box \u001b[38;5;241m=\u001b[39m \u001b[43mfun\u001b[49m\u001b[43m(\u001b[49m\u001b[43mstart_box\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 11\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m isbox(end_box) \u001b[38;5;129;01mand\u001b[39;00m end_box\u001b[38;5;241m.\u001b[39m_trace \u001b[38;5;241m==\u001b[39m start_box\u001b[38;5;241m.\u001b[39m_trace:\n\u001b[1;32m 12\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m end_box\u001b[38;5;241m.\u001b[39m_value, end_box\u001b[38;5;241m.\u001b[39m_node\n", + "File \u001b[0;32m~/miniforge3/envs/myenv/lib/python3.9/site-packages/autograd/wrap_util.py:15\u001b[0m, in \u001b[0;36munary_to_nary..nary_operator..nary_f..unary_f\u001b[0;34m(x)\u001b[0m\n\u001b[1;32m 13\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m 14\u001b[0m subargs \u001b[38;5;241m=\u001b[39m subvals(args, \u001b[38;5;28mzip\u001b[39m(argnum, x))\n\u001b[0;32m---> 15\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mfun\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43msubargs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n", + "Input \u001b[0;32mIn [23]\u001b[0m, in \u001b[0;36mf9\u001b[0;34m(a)\u001b[0m\n\u001b[1;32m 3\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mf9\u001b[39m(a): \u001b[38;5;66;03m# Assume a is an array with 2 elements\u001b[39;00m\n\u001b[1;32m 4\u001b[0m b \u001b[38;5;241m=\u001b[39m np\u001b[38;5;241m.\u001b[39marray([\u001b[38;5;241m1.0\u001b[39m,\u001b[38;5;241m2.0\u001b[39m])\n\u001b[0;32m----> 5\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43ma\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mdot\u001b[49m(b)\n", + "\u001b[0;31mAttributeError\u001b[0m: 'ArrayBox' object has no attribute 'dot'" + ] + } + ], "source": [ "import autograd.numpy as np\n", "from autograd import grad\n", @@ -4034,7 +4324,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.9.18" + "version": "3.9.10" } }, "nbformat": 4, diff --git a/doc/LectureNotes/_build/jupyter_execute/chapteroptimization_123_1.png b/doc/LectureNotes/_build/jupyter_execute/chapteroptimization_123_1.png index 1455620df..6cba933bb 100644 Binary files a/doc/LectureNotes/_build/jupyter_execute/chapteroptimization_123_1.png and b/doc/LectureNotes/_build/jupyter_execute/chapteroptimization_123_1.png differ diff --git a/doc/LectureNotes/_build/jupyter_execute/chapteroptimization_132_1.png b/doc/LectureNotes/_build/jupyter_execute/chapteroptimization_132_1.png index ff9bdcf47..11eac25c0 100644 Binary files a/doc/LectureNotes/_build/jupyter_execute/chapteroptimization_132_1.png and b/doc/LectureNotes/_build/jupyter_execute/chapteroptimization_132_1.png differ diff --git a/doc/LectureNotes/_build/jupyter_execute/chapteroptimization_148_1.png b/doc/LectureNotes/_build/jupyter_execute/chapteroptimization_148_1.png index d5d3cef45..cc836188d 100644 Binary files a/doc/LectureNotes/_build/jupyter_execute/chapteroptimization_148_1.png and b/doc/LectureNotes/_build/jupyter_execute/chapteroptimization_148_1.png differ diff --git a/doc/LectureNotes/_build/jupyter_execute/clustering.ipynb b/doc/LectureNotes/_build/jupyter_execute/clustering.ipynb index 57a7b064c..086bd00e3 100644 --- a/doc/LectureNotes/_build/jupyter_execute/clustering.ipynb +++ b/doc/LectureNotes/_build/jupyter_execute/clustering.ipynb @@ -281,24 +281,7 @@ "collapsed": false, "editable": true }, - "outputs": [ - { - "ename": "ModuleNotFoundError", - "evalue": "No module named 'matplotlib'", - "output_type": "error", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mModuleNotFoundError\u001b[0m Traceback (most recent call last)", - "Cell \u001b[0;32mIn[1], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \u001b[43mget_ipython\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mrun_line_magic\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mmatplotlib\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43minline\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m)\u001b[49m\n\u001b[1;32m 3\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mtime\u001b[39;00m\n\u001b[1;32m 4\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mnumpy\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m \u001b[38;5;21;01mnp\u001b[39;00m\n", - "File \u001b[0;32m~/miniforge3/lib/python3.9/site-packages/IPython/core/interactiveshell.py:2432\u001b[0m, in \u001b[0;36mInteractiveShell.run_line_magic\u001b[0;34m(self, magic_name, line, _stack_depth)\u001b[0m\n\u001b[1;32m 2430\u001b[0m kwargs[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mlocal_ns\u001b[39m\u001b[38;5;124m'\u001b[39m] \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mget_local_scope(stack_depth)\n\u001b[1;32m 2431\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mbuiltin_trap:\n\u001b[0;32m-> 2432\u001b[0m result \u001b[38;5;241m=\u001b[39m \u001b[43mfn\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 2434\u001b[0m \u001b[38;5;66;03m# The code below prevents the output from being displayed\u001b[39;00m\n\u001b[1;32m 2435\u001b[0m \u001b[38;5;66;03m# when using magics with decorator @output_can_be_silenced\u001b[39;00m\n\u001b[1;32m 2436\u001b[0m \u001b[38;5;66;03m# when the last Python token in the expression is a ';'.\u001b[39;00m\n\u001b[1;32m 2437\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mgetattr\u001b[39m(fn, magic\u001b[38;5;241m.\u001b[39mMAGIC_OUTPUT_CAN_BE_SILENCED, \u001b[38;5;28;01mFalse\u001b[39;00m):\n", - "File \u001b[0;32m~/miniforge3/lib/python3.9/site-packages/IPython/core/magics/pylab.py:99\u001b[0m, in \u001b[0;36mPylabMagics.matplotlib\u001b[0;34m(self, line)\u001b[0m\n\u001b[1;32m 97\u001b[0m \u001b[38;5;28mprint\u001b[39m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mAvailable matplotlib backends: \u001b[39m\u001b[38;5;132;01m%s\u001b[39;00m\u001b[38;5;124m\"\u001b[39m \u001b[38;5;241m%\u001b[39m backends_list)\n\u001b[1;32m 98\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m---> 99\u001b[0m gui, backend \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mshell\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43menable_matplotlib\u001b[49m\u001b[43m(\u001b[49m\u001b[43margs\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mgui\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mlower\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mif\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[38;5;28;43misinstance\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43margs\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mgui\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mstr\u001b[39;49m\u001b[43m)\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01melse\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43margs\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mgui\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 100\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_show_matplotlib_backend(args\u001b[38;5;241m.\u001b[39mgui, backend)\n", - "File \u001b[0;32m~/miniforge3/lib/python3.9/site-packages/IPython/core/interactiveshell.py:3606\u001b[0m, in \u001b[0;36mInteractiveShell.enable_matplotlib\u001b[0;34m(self, gui)\u001b[0m\n\u001b[1;32m 3585\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21menable_matplotlib\u001b[39m(\u001b[38;5;28mself\u001b[39m, gui\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mNone\u001b[39;00m):\n\u001b[1;32m 3586\u001b[0m \u001b[38;5;250m \u001b[39m\u001b[38;5;124;03m\"\"\"Enable interactive matplotlib and inline figure support.\u001b[39;00m\n\u001b[1;32m 3587\u001b[0m \n\u001b[1;32m 3588\u001b[0m \u001b[38;5;124;03m This takes the following steps:\u001b[39;00m\n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 3604\u001b[0m \u001b[38;5;124;03m display figures inline.\u001b[39;00m\n\u001b[1;32m 3605\u001b[0m \u001b[38;5;124;03m \"\"\"\u001b[39;00m\n\u001b[0;32m-> 3606\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mmatplotlib_inline\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mbackend_inline\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m configure_inline_support\n\u001b[1;32m 3608\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mIPython\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mcore\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m pylabtools \u001b[38;5;28;01mas\u001b[39;00m pt\n\u001b[1;32m 3609\u001b[0m gui, backend \u001b[38;5;241m=\u001b[39m pt\u001b[38;5;241m.\u001b[39mfind_gui_and_backend(gui, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mpylab_gui_select)\n", - "File \u001b[0;32m~/miniforge3/lib/python3.9/site-packages/matplotlib_inline/__init__.py:1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01m.\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m backend_inline, config \u001b[38;5;66;03m# noqa\u001b[39;00m\n\u001b[1;32m 2\u001b[0m __version__ \u001b[38;5;241m=\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m0.1.6\u001b[39m\u001b[38;5;124m\"\u001b[39m \u001b[38;5;66;03m# noqa\u001b[39;00m\n", - "File \u001b[0;32m~/miniforge3/lib/python3.9/site-packages/matplotlib_inline/backend_inline.py:6\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[38;5;124;03m\"\"\"A matplotlib backend for publishing figures via display_data\"\"\"\u001b[39;00m\n\u001b[1;32m 3\u001b[0m \u001b[38;5;66;03m# Copyright (c) IPython Development Team.\u001b[39;00m\n\u001b[1;32m 4\u001b[0m \u001b[38;5;66;03m# Distributed under the terms of the BSD 3-Clause License.\u001b[39;00m\n\u001b[0;32m----> 6\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mmatplotlib\u001b[39;00m\n\u001b[1;32m 7\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mmatplotlib\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m colors\n\u001b[1;32m 8\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mmatplotlib\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mbackends\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m backend_agg\n", - "\u001b[0;31mModuleNotFoundError\u001b[0m: No module named 'matplotlib'" - ] - } - ], + "outputs": [], "source": [ "%matplotlib inline\n", "\n", @@ -332,7 +315,22 @@ "collapsed": false, "editable": true }, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "
              " + ] + }, + "metadata": { + "filenames": { + "image/png": "/Users/mhjensen/Teaching/MachineLearning/doc/LectureNotes/_build/jupyter_execute/clustering_17_0.png" + } + }, + "output_type": "display_data" + } + ], "source": [ "def gaussian_points(dim=2, n_points=1000, mean_vector=np.array([0, 0]),\n", " sample_variance=1):\n", @@ -450,7 +448,22 @@ "collapsed": false, "editable": true }, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
              " + ] + }, + "metadata": { + "filenames": { + "image/png": "/Users/mhjensen/Teaching/MachineLearning/doc/LectureNotes/_build/jupyter_execute/clustering_20_0.png" + } + }, + "output_type": "display_data" + } + ], "source": [ "fig = plt.figure()\n", "ax = fig.add_subplot()\n", @@ -494,7 +507,15 @@ "collapsed": false, "editable": true }, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Converged at iteration 5\n" + ] + } + ], "source": [ "\n", "max_iterations = 100\n", @@ -562,7 +583,22 @@ "collapsed": false, "editable": true }, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": 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a/doc/LectureNotes/_build/jupyter_execute/clustering_20_0.png and b/doc/LectureNotes/_build/jupyter_execute/clustering_20_0.png differ diff --git a/doc/LectureNotes/_build/jupyter_execute/clustering_24_0.png b/doc/LectureNotes/_build/jupyter_execute/clustering_24_0.png index ca76b13fe..14469a056 100644 Binary files a/doc/LectureNotes/_build/jupyter_execute/clustering_24_0.png and b/doc/LectureNotes/_build/jupyter_execute/clustering_24_0.png differ diff --git a/doc/LectureNotes/_build/jupyter_execute/exercisesweek34.ipynb b/doc/LectureNotes/_build/jupyter_execute/exercisesweek34.ipynb index 6bc6c9558..53b974273 100644 --- a/doc/LectureNotes/_build/jupyter_execute/exercisesweek34.ipynb +++ b/doc/LectureNotes/_build/jupyter_execute/exercisesweek34.ipynb @@ -135,7 +135,7 @@ "traceback": [ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)", - "Cell \u001b[0;32mIn[1], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m x \u001b[38;5;241m=\u001b[39m \u001b[43mnp\u001b[49m\u001b[38;5;241m.\u001b[39mrandom\u001b[38;5;241m.\u001b[39mrand(\u001b[38;5;241m100\u001b[39m,\u001b[38;5;241m1\u001b[39m)\n\u001b[1;32m 2\u001b[0m y \u001b[38;5;241m=\u001b[39m \u001b[38;5;241m2.0\u001b[39m\u001b[38;5;241m+\u001b[39m\u001b[38;5;241m5\u001b[39m\u001b[38;5;241m*\u001b[39mx\u001b[38;5;241m*\u001b[39mx\u001b[38;5;241m+\u001b[39m\u001b[38;5;241m0.1\u001b[39m\u001b[38;5;241m*\u001b[39mnp\u001b[38;5;241m.\u001b[39mrandom\u001b[38;5;241m.\u001b[39mrandn(\u001b[38;5;241m100\u001b[39m,\u001b[38;5;241m1\u001b[39m)\n", + "Input \u001b[0;32mIn [1]\u001b[0m, in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0m x \u001b[38;5;241m=\u001b[39m \u001b[43mnp\u001b[49m\u001b[38;5;241m.\u001b[39mrandom\u001b[38;5;241m.\u001b[39mrand(\u001b[38;5;241m100\u001b[39m,\u001b[38;5;241m1\u001b[39m)\n\u001b[1;32m 2\u001b[0m y \u001b[38;5;241m=\u001b[39m \u001b[38;5;241m2.0\u001b[39m\u001b[38;5;241m+\u001b[39m\u001b[38;5;241m5\u001b[39m\u001b[38;5;241m*\u001b[39mx\u001b[38;5;241m*\u001b[39mx\u001b[38;5;241m+\u001b[39m\u001b[38;5;241m0.1\u001b[39m\u001b[38;5;241m*\u001b[39mnp\u001b[38;5;241m.\u001b[39mrandom\u001b[38;5;241m.\u001b[39mrandn(\u001b[38;5;241m100\u001b[39m,\u001b[38;5;241m1\u001b[39m)\n", "\u001b[0;31mNameError\u001b[0m: name 'np' is not defined" ] } @@ -319,7 +319,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.9.18" + "version": "3.9.10" } }, "nbformat": 4, diff --git a/doc/LectureNotes/_build/jupyter_execute/exercisesweek35.ipynb b/doc/LectureNotes/_build/jupyter_execute/exercisesweek35.ipynb index e8be6f76c..0b4eb4f75 100644 --- a/doc/LectureNotes/_build/jupyter_execute/exercisesweek35.ipynb +++ b/doc/LectureNotes/_build/jupyter_execute/exercisesweek35.ipynb @@ -403,7 +403,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.9.18" + "version": "3.9.10" } }, "nbformat": 4, diff --git a/doc/LectureNotes/_build/jupyter_execute/exercisesweek39.txt b/doc/LectureNotes/_build/jupyter_execute/exercisesweek39.txt new file mode 100644 index 000000000..e69de29bb diff --git a/doc/LectureNotes/_build/jupyter_execute/linalg.ipynb b/doc/LectureNotes/_build/jupyter_execute/linalg.ipynb index be4c6b3dd..cf3333aca 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": [ - "[-0.1223405 0.96602098 -0.42045733 2.00897328 0.96178833 -0.30742211\n", - " 0.23867951 -1.84775931 -0.3804507 -0.47953891]\n" + "[ 0.31579721 1.74724767 -0.89609007 2.48464841 -0.36051635 -2.41246325\n", + " 0.28008933 0.45134965 -0.52204004 -0.48145226]\n" ] } ], @@ -662,26 +662,26 @@ "name": "stdout", "output_type": "stream", "text": [ - "[[0.69846205 0.78851232 0.08308983 0.5305063 0.12815515 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0.68302636 0.72968415 0.5859337\n", - " 0.88898063 0.25024282 0.07427349 0.65046291]\n", - " [0.01924293 0.54705652 0.01611258 0.47220243 0.36688196 0.47846644\n", - " 0.17072424 0.48396466 0.7900928 0.28490342]]\n" + "[[0.69873514 0.39457095 0.31927572 0.01704432 0.11837671 0.15047127\n", + " 0.56465688 0.50274255 0.69818111 0.82296251]\n", + " [0.45308692 0.37112277 0.77794957 0.43043913 0.95327702 0.89793609\n", + " 0.29401213 0.08245909 0.1520039 0.59511582]\n", + " [0.98004227 0.101058 0.57572321 0.61394448 0.963198 0.49616116\n", + " 0.83488328 0.05432856 0.12814914 0.03856554]\n", + " [0.283078 0.19166136 0.29275129 0.39300201 0.59004971 0.29199381\n", + " 0.40644745 0.9036573 0.44729805 0.34447052]\n", + " [0.57670824 0.6568551 0.84380376 0.86221134 0.28908491 0.25663096\n", + " 0.62862896 0.1937079 0.15673992 0.44921888]\n", + " [0.25139357 0.11197884 0.26514544 0.11896755 0.13404683 0.21059098\n", + " 0.86012593 0.67890723 0.97948913 0.30567713]\n", + " [0.51845286 0.76010633 0.71640333 0.75282841 0.47447472 0.78882958\n", + " 0.84159521 0.3729492 0.80152684 0.04084872]\n", + " [0.38088413 0.63567272 0.82909728 0.13829298 0.26037366 0.92772833\n", + " 0.74577867 0.42239354 0.20594513 0.72328506]\n", + " [0.38831624 0.44520102 0.17305512 0.0106014 0.94866246 0.84929103\n", + " 0.81753152 0.06664867 0.96750421 0.50462474]\n", + " [0.32584888 0.58948347 0.22729927 0.01919702 0.74280244 0.73448544\n", + " 0.33995567 0.61197218 0.5320148 0.34517495]]\n" ] } ], @@ -800,13 +800,13 @@ "name": "stdout", "output_type": "stream", "text": [ - "-0.0017018997030592974\n", - "3.9531810558360623\n", - "-0.018173592942955955\n", - "[[ 1.06833761 3.33062366 3.10248906]\n", - " [ 3.33062366 11.28977247 10.01231809]\n", - " [ 3.10248906 10.01231809 13.75855891]]\n", - "[23.52658256 0.07563718 2.51444926]\n" + "0.1388976715362099\n", + "4.3703468543933255\n", + "0.08844723450419088\n", + "[[ 1.06638817 3.39483726 3.28607817]\n", + " [ 3.39483726 11.55955126 10.56366546]\n", + " [ 3.28607817 10.56366546 16.19343949]]\n", + "[25.58818643 0.05982961 3.17136288]\n" ] } ], @@ -838,20 +838,32 @@ }, "outputs": [ { - "ename": "ModuleNotFoundError", - "evalue": "No module named 'matplotlib'", - "output_type": "error", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mModuleNotFoundError\u001b[0m Traceback (most recent call last)", - "Cell \u001b[0;32mIn[15], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \u001b[43mget_ipython\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mrun_line_magic\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mmatplotlib\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43minline\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m)\u001b[49m\n\u001b[1;32m 3\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mnumpy\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m \u001b[38;5;21;01mnp\u001b[39;00m\n\u001b[1;32m 4\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mmatplotlib\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mpyplot\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m \u001b[38;5;21;01mplt\u001b[39;00m\n", - "File \u001b[0;32m~/miniforge3/lib/python3.9/site-packages/IPython/core/interactiveshell.py:2432\u001b[0m, in \u001b[0;36mInteractiveShell.run_line_magic\u001b[0;34m(self, magic_name, line, _stack_depth)\u001b[0m\n\u001b[1;32m 2430\u001b[0m kwargs[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mlocal_ns\u001b[39m\u001b[38;5;124m'\u001b[39m] \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mget_local_scope(stack_depth)\n\u001b[1;32m 2431\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mbuiltin_trap:\n\u001b[0;32m-> 2432\u001b[0m result \u001b[38;5;241m=\u001b[39m \u001b[43mfn\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 2434\u001b[0m \u001b[38;5;66;03m# The code below prevents the output from being displayed\u001b[39;00m\n\u001b[1;32m 2435\u001b[0m \u001b[38;5;66;03m# when using magics with decorator @output_can_be_silenced\u001b[39;00m\n\u001b[1;32m 2436\u001b[0m \u001b[38;5;66;03m# when the last Python token in the expression is a ';'.\u001b[39;00m\n\u001b[1;32m 2437\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mgetattr\u001b[39m(fn, magic\u001b[38;5;241m.\u001b[39mMAGIC_OUTPUT_CAN_BE_SILENCED, \u001b[38;5;28;01mFalse\u001b[39;00m):\n", - "File \u001b[0;32m~/miniforge3/lib/python3.9/site-packages/IPython/core/magics/pylab.py:99\u001b[0m, in \u001b[0;36mPylabMagics.matplotlib\u001b[0;34m(self, line)\u001b[0m\n\u001b[1;32m 97\u001b[0m \u001b[38;5;28mprint\u001b[39m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mAvailable matplotlib backends: \u001b[39m\u001b[38;5;132;01m%s\u001b[39;00m\u001b[38;5;124m\"\u001b[39m \u001b[38;5;241m%\u001b[39m backends_list)\n\u001b[1;32m 98\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m---> 99\u001b[0m gui, backend \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mshell\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43menable_matplotlib\u001b[49m\u001b[43m(\u001b[49m\u001b[43margs\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mgui\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mlower\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mif\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[38;5;28;43misinstance\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43margs\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mgui\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mstr\u001b[39;49m\u001b[43m)\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01melse\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43margs\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mgui\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 100\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_show_matplotlib_backend(args\u001b[38;5;241m.\u001b[39mgui, backend)\n", - "File \u001b[0;32m~/miniforge3/lib/python3.9/site-packages/IPython/core/interactiveshell.py:3606\u001b[0m, in \u001b[0;36mInteractiveShell.enable_matplotlib\u001b[0;34m(self, gui)\u001b[0m\n\u001b[1;32m 3585\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21menable_matplotlib\u001b[39m(\u001b[38;5;28mself\u001b[39m, gui\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mNone\u001b[39;00m):\n\u001b[1;32m 3586\u001b[0m \u001b[38;5;250m \u001b[39m\u001b[38;5;124;03m\"\"\"Enable interactive matplotlib and inline figure support.\u001b[39;00m\n\u001b[1;32m 3587\u001b[0m \n\u001b[1;32m 3588\u001b[0m \u001b[38;5;124;03m This takes the following steps:\u001b[39;00m\n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 3604\u001b[0m \u001b[38;5;124;03m display figures inline.\u001b[39;00m\n\u001b[1;32m 3605\u001b[0m \u001b[38;5;124;03m \"\"\"\u001b[39;00m\n\u001b[0;32m-> 3606\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mmatplotlib_inline\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mbackend_inline\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m configure_inline_support\n\u001b[1;32m 3608\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mIPython\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mcore\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m pylabtools \u001b[38;5;28;01mas\u001b[39;00m pt\n\u001b[1;32m 3609\u001b[0m gui, backend \u001b[38;5;241m=\u001b[39m pt\u001b[38;5;241m.\u001b[39mfind_gui_and_backend(gui, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mpylab_gui_select)\n", - "File \u001b[0;32m~/miniforge3/lib/python3.9/site-packages/matplotlib_inline/__init__.py:1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01m.\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m backend_inline, config \u001b[38;5;66;03m# noqa\u001b[39;00m\n\u001b[1;32m 2\u001b[0m __version__ \u001b[38;5;241m=\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m0.1.6\u001b[39m\u001b[38;5;124m\"\u001b[39m \u001b[38;5;66;03m# noqa\u001b[39;00m\n", - "File \u001b[0;32m~/miniforge3/lib/python3.9/site-packages/matplotlib_inline/backend_inline.py:6\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[38;5;124;03m\"\"\"A matplotlib backend for publishing figures via display_data\"\"\"\u001b[39;00m\n\u001b[1;32m 3\u001b[0m \u001b[38;5;66;03m# Copyright (c) IPython Development Team.\u001b[39;00m\n\u001b[1;32m 4\u001b[0m \u001b[38;5;66;03m# Distributed under the terms of the BSD 3-Clause License.\u001b[39;00m\n\u001b[0;32m----> 6\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mmatplotlib\u001b[39;00m\n\u001b[1;32m 7\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mmatplotlib\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m colors\n\u001b[1;32m 8\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mmatplotlib\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mbackends\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m backend_agg\n", - "\u001b[0;31mModuleNotFoundError\u001b[0m: No module named 'matplotlib'" + "name": "stdout", + "output_type": "stream", + "text": [ + "[[1. 0. 0. 0.]\n", + " [0. 1. 0. 0.]\n", + " [0. 0. 1. 0.]\n", + " [0. 0. 0. 1.]]\n", + " (0, 0)\t1.0\n", + " (1, 1)\t1.0\n", + " (2, 2)\t1.0\n", + " (3, 3)\t1.0\n" ] + }, + { + "data": { + "image/png": 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+ "text/plain": [ + "
              " + ] + }, + "metadata": { + "filenames": { + "image/png": "/Users/mhjensen/Teaching/MachineLearning/doc/LectureNotes/_build/jupyter_execute/linalg_44_1.png" + } + }, + "output_type": "display_data" } ], "source": [ @@ -890,7 +902,25 @@ "collapsed": false, "editable": true }, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "The test matrix:[[ 1. 2. 3.]\n", + " [ 4. 5. 6.]\n", + " [ 7. 8. 9.]\n", + " [10. 11. 12.]]\n", + "This is the total mean summed over all elements:6.5\n", + "This is the mean for each column:[[5.5 6.5 7.5]]\n", + "This is the mean value for each row:[[ 2.]\n", + " [ 5.]\n", + " [ 8.]\n", + " [11.]]\n", + "This is the mean value for each row with keepdims false:[ 2. 5. 8. 11.]\n" + ] + } + ], "source": [ "\"\"\"\n", "Simple code that tests various numpy functions\n", @@ -930,7 +960,19 @@ "collapsed": false, "editable": true }, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Flatten the matrix:[ 1. 2. 3. 4. 5. 6. 7. 8. 9. 10. 11. 12.]\n", + "Reshape the matrix to a one-dim array:[ 1. 2. 3. 4. 5. 6. 7. 8. 9. 10. 11. 12.]\n", + "[ 1. 4. 7. 10. 2. 5. 8. 11. 3. 6. 9. 12.]\n", + "[ 1. 4. 7. 10. 2. 5. 8. 11. 3. 6. 9. 12.]\n", + "[ 1. 2. 3. 4. 5. 6. 7. 8. 9. 10. 11. 12.]\n" + ] + } + ], "source": [ "# Ravel return a contiguous flattened array.\n", "print(f\"Flatten the matrix:{np.ravel(a)}\")\n", @@ -2084,7 +2126,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.9.18" + "version": "3.9.10" } }, "nbformat": 4, diff --git a/doc/LectureNotes/_build/jupyter_execute/project1.ipynb b/doc/LectureNotes/_build/jupyter_execute/project1.ipynb index a692327b8..bbd74577f 100644 --- a/doc/LectureNotes/_build/jupyter_execute/project1.ipynb +++ b/doc/LectureNotes/_build/jupyter_execute/project1.ipynb @@ -140,20 +140,26 @@ }, "outputs": [ { - "ename": "ModuleNotFoundError", - "evalue": "No module named 'matplotlib'", - "output_type": "error", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mModuleNotFoundError\u001b[0m Traceback (most recent call last)", - "Cell \u001b[0;32mIn[1], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \u001b[43mget_ipython\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mrun_line_magic\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mmatplotlib\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43minline\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m)\u001b[49m\n\u001b[1;32m 3\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mmpl_toolkits\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mmplot3d\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m Axes3D\n\u001b[1;32m 4\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mmatplotlib\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mpyplot\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m \u001b[38;5;21;01mplt\u001b[39;00m\n", - "File \u001b[0;32m~/miniforge3/lib/python3.9/site-packages/IPython/core/interactiveshell.py:2432\u001b[0m, in \u001b[0;36mInteractiveShell.run_line_magic\u001b[0;34m(self, magic_name, line, _stack_depth)\u001b[0m\n\u001b[1;32m 2430\u001b[0m kwargs[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mlocal_ns\u001b[39m\u001b[38;5;124m'\u001b[39m] \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mget_local_scope(stack_depth)\n\u001b[1;32m 2431\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mbuiltin_trap:\n\u001b[0;32m-> 2432\u001b[0m result \u001b[38;5;241m=\u001b[39m \u001b[43mfn\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 2434\u001b[0m \u001b[38;5;66;03m# The code below prevents the output from being displayed\u001b[39;00m\n\u001b[1;32m 2435\u001b[0m \u001b[38;5;66;03m# when using magics with decorator @output_can_be_silenced\u001b[39;00m\n\u001b[1;32m 2436\u001b[0m \u001b[38;5;66;03m# when the last Python token in the expression is a ';'.\u001b[39;00m\n\u001b[1;32m 2437\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mgetattr\u001b[39m(fn, magic\u001b[38;5;241m.\u001b[39mMAGIC_OUTPUT_CAN_BE_SILENCED, \u001b[38;5;28;01mFalse\u001b[39;00m):\n", - "File \u001b[0;32m~/miniforge3/lib/python3.9/site-packages/IPython/core/magics/pylab.py:99\u001b[0m, in \u001b[0;36mPylabMagics.matplotlib\u001b[0;34m(self, line)\u001b[0m\n\u001b[1;32m 97\u001b[0m \u001b[38;5;28mprint\u001b[39m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mAvailable matplotlib backends: \u001b[39m\u001b[38;5;132;01m%s\u001b[39;00m\u001b[38;5;124m\"\u001b[39m \u001b[38;5;241m%\u001b[39m backends_list)\n\u001b[1;32m 98\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m---> 99\u001b[0m gui, backend \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mshell\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43menable_matplotlib\u001b[49m\u001b[43m(\u001b[49m\u001b[43margs\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mgui\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mlower\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mif\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[38;5;28;43misinstance\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43margs\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mgui\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mstr\u001b[39;49m\u001b[43m)\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01melse\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43margs\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mgui\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 100\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_show_matplotlib_backend(args\u001b[38;5;241m.\u001b[39mgui, backend)\n", - "File \u001b[0;32m~/miniforge3/lib/python3.9/site-packages/IPython/core/interactiveshell.py:3606\u001b[0m, in \u001b[0;36mInteractiveShell.enable_matplotlib\u001b[0;34m(self, gui)\u001b[0m\n\u001b[1;32m 3585\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21menable_matplotlib\u001b[39m(\u001b[38;5;28mself\u001b[39m, gui\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mNone\u001b[39;00m):\n\u001b[1;32m 3586\u001b[0m \u001b[38;5;250m \u001b[39m\u001b[38;5;124;03m\"\"\"Enable interactive matplotlib and inline figure support.\u001b[39;00m\n\u001b[1;32m 3587\u001b[0m \n\u001b[1;32m 3588\u001b[0m \u001b[38;5;124;03m This takes the following steps:\u001b[39;00m\n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 3604\u001b[0m \u001b[38;5;124;03m display figures inline.\u001b[39;00m\n\u001b[1;32m 3605\u001b[0m \u001b[38;5;124;03m \"\"\"\u001b[39;00m\n\u001b[0;32m-> 3606\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mmatplotlib_inline\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mbackend_inline\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m configure_inline_support\n\u001b[1;32m 3608\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mIPython\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mcore\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m pylabtools \u001b[38;5;28;01mas\u001b[39;00m pt\n\u001b[1;32m 3609\u001b[0m gui, backend \u001b[38;5;241m=\u001b[39m pt\u001b[38;5;241m.\u001b[39mfind_gui_and_backend(gui, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mpylab_gui_select)\n", - "File \u001b[0;32m~/miniforge3/lib/python3.9/site-packages/matplotlib_inline/__init__.py:1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01m.\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m backend_inline, config \u001b[38;5;66;03m# noqa\u001b[39;00m\n\u001b[1;32m 2\u001b[0m __version__ \u001b[38;5;241m=\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m0.1.6\u001b[39m\u001b[38;5;124m\"\u001b[39m \u001b[38;5;66;03m# noqa\u001b[39;00m\n", - "File \u001b[0;32m~/miniforge3/lib/python3.9/site-packages/matplotlib_inline/backend_inline.py:6\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[38;5;124;03m\"\"\"A matplotlib backend for publishing figures via display_data\"\"\"\u001b[39;00m\n\u001b[1;32m 3\u001b[0m \u001b[38;5;66;03m# Copyright (c) IPython Development Team.\u001b[39;00m\n\u001b[1;32m 4\u001b[0m \u001b[38;5;66;03m# Distributed under the terms of the BSD 3-Clause License.\u001b[39;00m\n\u001b[0;32m----> 6\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mmatplotlib\u001b[39;00m\n\u001b[1;32m 7\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mmatplotlib\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m colors\n\u001b[1;32m 8\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mmatplotlib\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mbackends\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m backend_agg\n", - "\u001b[0;31mModuleNotFoundError\u001b[0m: No module named 'matplotlib'" + "name": "stderr", + "output_type": "stream", + "text": [ + "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_19329/39730396.py:11: MatplotlibDeprecationWarning: Calling gca() with keyword arguments was deprecated in Matplotlib 3.4. Starting two minor releases later, gca() will take no keyword arguments. The gca() function should only be used to get the current axes, or if no axes exist, create new axes with default keyword arguments. To create a new axes with non-default arguments, use plt.axes() or plt.subplot().\n", + " ax = fig.gca(projection='3d')\n" ] + }, + { + "data": { + "image/png": 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+ "text/plain": [ + "
              " + ] + }, + "metadata": { + "filenames": { + "image/png": "/Users/mhjensen/Teaching/MachineLearning/doc/LectureNotes/_build/jupyter_execute/project1_6_1.png" + } + }, + "output_type": "display_data" } ], "source": [ @@ -726,7 +732,19 @@ "collapsed": false, "editable": true }, - "outputs": [], + "outputs": [ + { + "ename": "NameError", + "evalue": "name 'scipy' is not defined", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)", + "Input \u001b[0;32mIn [2]\u001b[0m, in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \u001b[43mscipy\u001b[49m\u001b[38;5;241m.\u001b[39mmisc\u001b[38;5;241m.\u001b[39mimread\n", + "\u001b[0;31mNameError\u001b[0m: name 'scipy' is not defined" + ] + } + ], "source": [ "scipy.misc.imread" ] @@ -919,7 +937,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.9.18" + "version": "3.9.10" } }, "nbformat": 4, diff --git a/doc/LectureNotes/_build/jupyter_execute/statistics.ipynb b/doc/LectureNotes/_build/jupyter_execute/statistics.ipynb index 7b3c02138..4e6375da4 100644 --- a/doc/LectureNotes/_build/jupyter_execute/statistics.ipynb +++ b/doc/LectureNotes/_build/jupyter_execute/statistics.ipynb @@ -415,20 +415,18 @@ }, "outputs": [ { - "ename": "ModuleNotFoundError", - "evalue": "No module named 'matplotlib'", - "output_type": "error", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mModuleNotFoundError\u001b[0m Traceback (most recent call last)", - "Cell \u001b[0;32mIn[1], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \u001b[43mget_ipython\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mrun_line_magic\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mmatplotlib\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43minline\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m)\u001b[49m\n\u001b[1;32m 3\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mnumpy\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m \u001b[38;5;21;01mnp\u001b[39;00m\n\u001b[1;32m 4\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mmath\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m acos, exp, sqrt\n", - "File \u001b[0;32m~/miniforge3/lib/python3.9/site-packages/IPython/core/interactiveshell.py:2432\u001b[0m, in \u001b[0;36mInteractiveShell.run_line_magic\u001b[0;34m(self, magic_name, line, _stack_depth)\u001b[0m\n\u001b[1;32m 2430\u001b[0m kwargs[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mlocal_ns\u001b[39m\u001b[38;5;124m'\u001b[39m] \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mget_local_scope(stack_depth)\n\u001b[1;32m 2431\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mbuiltin_trap:\n\u001b[0;32m-> 2432\u001b[0m result \u001b[38;5;241m=\u001b[39m \u001b[43mfn\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 2434\u001b[0m \u001b[38;5;66;03m# The code below prevents the output from being displayed\u001b[39;00m\n\u001b[1;32m 2435\u001b[0m \u001b[38;5;66;03m# when using magics with decorator @output_can_be_silenced\u001b[39;00m\n\u001b[1;32m 2436\u001b[0m \u001b[38;5;66;03m# when the last Python token in the expression is a ';'.\u001b[39;00m\n\u001b[1;32m 2437\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mgetattr\u001b[39m(fn, magic\u001b[38;5;241m.\u001b[39mMAGIC_OUTPUT_CAN_BE_SILENCED, \u001b[38;5;28;01mFalse\u001b[39;00m):\n", - "File \u001b[0;32m~/miniforge3/lib/python3.9/site-packages/IPython/core/magics/pylab.py:99\u001b[0m, in \u001b[0;36mPylabMagics.matplotlib\u001b[0;34m(self, line)\u001b[0m\n\u001b[1;32m 97\u001b[0m \u001b[38;5;28mprint\u001b[39m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mAvailable matplotlib backends: \u001b[39m\u001b[38;5;132;01m%s\u001b[39;00m\u001b[38;5;124m\"\u001b[39m \u001b[38;5;241m%\u001b[39m backends_list)\n\u001b[1;32m 98\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m---> 99\u001b[0m gui, backend \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mshell\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43menable_matplotlib\u001b[49m\u001b[43m(\u001b[49m\u001b[43margs\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mgui\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mlower\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mif\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[38;5;28;43misinstance\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43margs\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mgui\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mstr\u001b[39;49m\u001b[43m)\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01melse\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43margs\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mgui\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 100\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_show_matplotlib_backend(args\u001b[38;5;241m.\u001b[39mgui, backend)\n", - "File \u001b[0;32m~/miniforge3/lib/python3.9/site-packages/IPython/core/interactiveshell.py:3606\u001b[0m, in \u001b[0;36mInteractiveShell.enable_matplotlib\u001b[0;34m(self, gui)\u001b[0m\n\u001b[1;32m 3585\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21menable_matplotlib\u001b[39m(\u001b[38;5;28mself\u001b[39m, gui\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mNone\u001b[39;00m):\n\u001b[1;32m 3586\u001b[0m \u001b[38;5;250m \u001b[39m\u001b[38;5;124;03m\"\"\"Enable interactive matplotlib and inline figure support.\u001b[39;00m\n\u001b[1;32m 3587\u001b[0m \n\u001b[1;32m 3588\u001b[0m \u001b[38;5;124;03m This takes the following steps:\u001b[39;00m\n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 3604\u001b[0m \u001b[38;5;124;03m display figures inline.\u001b[39;00m\n\u001b[1;32m 3605\u001b[0m \u001b[38;5;124;03m \"\"\"\u001b[39;00m\n\u001b[0;32m-> 3606\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mmatplotlib_inline\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mbackend_inline\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m configure_inline_support\n\u001b[1;32m 3608\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mIPython\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mcore\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m pylabtools \u001b[38;5;28;01mas\u001b[39;00m pt\n\u001b[1;32m 3609\u001b[0m gui, backend \u001b[38;5;241m=\u001b[39m pt\u001b[38;5;241m.\u001b[39mfind_gui_and_backend(gui, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mpylab_gui_select)\n", - "File \u001b[0;32m~/miniforge3/lib/python3.9/site-packages/matplotlib_inline/__init__.py:1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01m.\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m backend_inline, config \u001b[38;5;66;03m# noqa\u001b[39;00m\n\u001b[1;32m 2\u001b[0m __version__ \u001b[38;5;241m=\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m0.1.6\u001b[39m\u001b[38;5;124m\"\u001b[39m \u001b[38;5;66;03m# noqa\u001b[39;00m\n", - "File \u001b[0;32m~/miniforge3/lib/python3.9/site-packages/matplotlib_inline/backend_inline.py:6\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[38;5;124;03m\"\"\"A matplotlib backend for publishing figures via display_data\"\"\"\u001b[39;00m\n\u001b[1;32m 3\u001b[0m \u001b[38;5;66;03m# Copyright (c) IPython Development Team.\u001b[39;00m\n\u001b[1;32m 4\u001b[0m \u001b[38;5;66;03m# Distributed under the terms of the BSD 3-Clause License.\u001b[39;00m\n\u001b[0;32m----> 6\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mmatplotlib\u001b[39;00m\n\u001b[1;32m 7\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mmatplotlib\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m colors\n\u001b[1;32m 8\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mmatplotlib\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mbackends\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m backend_agg\n", - "\u001b[0;31mModuleNotFoundError\u001b[0m: No module named 'matplotlib'" - ] + "data": { + "image/png": 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7.79675445\n", + " 4.5828247 4.40500157 3.39612983 2.79914677]\n", + " [11.66323494 2.22241171 10.34685874 6.69582036 7.79675445 7.65720414\n", + " 4.50079895 4.32615859 3.33534416 2.7490462 ]\n", + " [ 6.85548858 1.30630294 6.08174081 3.93571082 4.5828247 4.50079895\n", + " 2.64550753 2.54285633 1.96046928 1.61585143]\n", + " [ 6.58948138 1.25561567 5.84575663 3.78299706 4.40500157 4.32615859\n", + " 2.54285633 2.44418822 1.884399 1.55315304]\n", + " [ 5.08030109 0.96804366 4.50691065 2.9165822 3.39612983 3.33534416\n", + " 1.96046928 1.884399 1.45281756 1.19743643]\n", + " [ 4.18726877 0.79787771 3.71467081 2.40389562 2.79914677 2.7490462\n", + " 1.61585143 1.55315304 1.19743643 0.98694705]]\n" + ] + } + ], "source": [ "# Importing various packages\n", "from math import exp, sqrt\n", @@ -1970,7 +1996,23 @@ "collapsed": false, "editable": true }, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "-0.09327269724691106\n", + "3.904648525660773\n", + "-0.17022089147584388\n", + "0.9481513127527335 9.562888614232874 10.71437567912473\n", + "2.845716766413386 2.3597516959642966 7.019140656913589\n", + "[[ 0.94815131 2.84571677 2.3597517 ]\n", + " [ 2.84571677 9.56288861 7.01914066]\n", + " [ 2.3597517 7.01914066 10.71437568]]\n", + "[17.97062694 0.08221578 3.17257288]\n" + ] + } + ], "source": [ "# Importing various packages\n", "from math import exp, sqrt\n", @@ -2593,7 +2635,22 @@ "collapsed": false, "editable": true }, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": 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a/doc/LectureNotes/_build/jupyter_execute/statistics_188_1.png and b/doc/LectureNotes/_build/jupyter_execute/statistics_188_1.png differ diff --git a/doc/LectureNotes/_build/jupyter_execute/week34.ipynb b/doc/LectureNotes/_build/jupyter_execute/week34.ipynb index f91a2fc32..aa00669d5 100644 --- a/doc/LectureNotes/_build/jupyter_execute/week34.ipynb +++ b/doc/LectureNotes/_build/jupyter_execute/week34.ipynb @@ -985,8 +985,8 @@ "name": "stdout", "output_type": "stream", "text": [ - "[ 0.90148776 -0.63784548 0.61907743 -0.95073282 0.3897131 0.88123172\n", - " 0.68407077 1.02936353 -0.85538578 -1.44188631]\n" + "[ 0.5829913 -0.55086461 0.15975618 -0.92729959 -1.95190644 0.60383004\n", + " -1.02308518 -1.48134747 -0.38259375 -1.42172457]\n" ] } ], @@ -1424,36 +1424,26 @@ "name": "stdout", "output_type": "stream", "text": [ - "[[9.01367154e-01 4.99358923e-02 8.62083431e-02 4.87662496e-01\n", - " 4.35268384e-01 4.95778501e-01 8.47259540e-01 8.19492614e-01\n", - " 7.48029002e-01 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\u001b[43mget_ipython\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mrun_line_magic\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mmatplotlib\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43minline\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m)\u001b[49m\n\u001b[1;32m 3\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mnumpy\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m \u001b[38;5;21;01mnp\u001b[39;00m\n\u001b[1;32m 4\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mmatplotlib\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mpyplot\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m \u001b[38;5;21;01mplt\u001b[39;00m\n", - "File \u001b[0;32m~/miniforge3/lib/python3.9/site-packages/IPython/core/interactiveshell.py:2432\u001b[0m, in \u001b[0;36mInteractiveShell.run_line_magic\u001b[0;34m(self, magic_name, line, _stack_depth)\u001b[0m\n\u001b[1;32m 2430\u001b[0m kwargs[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mlocal_ns\u001b[39m\u001b[38;5;124m'\u001b[39m] \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mget_local_scope(stack_depth)\n\u001b[1;32m 2431\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mbuiltin_trap:\n\u001b[0;32m-> 2432\u001b[0m result \u001b[38;5;241m=\u001b[39m \u001b[43mfn\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 2434\u001b[0m \u001b[38;5;66;03m# The code below prevents the output from being displayed\u001b[39;00m\n\u001b[1;32m 2435\u001b[0m \u001b[38;5;66;03m# when using magics with decorator @output_can_be_silenced\u001b[39;00m\n\u001b[1;32m 2436\u001b[0m \u001b[38;5;66;03m# when the last Python token in the expression is a ';'.\u001b[39;00m\n\u001b[1;32m 2437\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mgetattr\u001b[39m(fn, magic\u001b[38;5;241m.\u001b[39mMAGIC_OUTPUT_CAN_BE_SILENCED, \u001b[38;5;28;01mFalse\u001b[39;00m):\n", - "File \u001b[0;32m~/miniforge3/lib/python3.9/site-packages/IPython/core/magics/pylab.py:99\u001b[0m, in \u001b[0;36mPylabMagics.matplotlib\u001b[0;34m(self, line)\u001b[0m\n\u001b[1;32m 97\u001b[0m \u001b[38;5;28mprint\u001b[39m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mAvailable matplotlib backends: \u001b[39m\u001b[38;5;132;01m%s\u001b[39;00m\u001b[38;5;124m\"\u001b[39m \u001b[38;5;241m%\u001b[39m backends_list)\n\u001b[1;32m 98\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m---> 99\u001b[0m gui, backend \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mshell\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43menable_matplotlib\u001b[49m\u001b[43m(\u001b[49m\u001b[43margs\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mgui\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mlower\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mif\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[38;5;28;43misinstance\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43margs\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mgui\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mstr\u001b[39;49m\u001b[43m)\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01melse\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43margs\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mgui\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 100\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_show_matplotlib_backend(args\u001b[38;5;241m.\u001b[39mgui, backend)\n", - "File \u001b[0;32m~/miniforge3/lib/python3.9/site-packages/IPython/core/interactiveshell.py:3606\u001b[0m, in \u001b[0;36mInteractiveShell.enable_matplotlib\u001b[0;34m(self, gui)\u001b[0m\n\u001b[1;32m 3585\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21menable_matplotlib\u001b[39m(\u001b[38;5;28mself\u001b[39m, gui\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mNone\u001b[39;00m):\n\u001b[1;32m 3586\u001b[0m \u001b[38;5;250m \u001b[39m\u001b[38;5;124;03m\"\"\"Enable interactive matplotlib and inline figure support.\u001b[39;00m\n\u001b[1;32m 3587\u001b[0m \n\u001b[1;32m 3588\u001b[0m \u001b[38;5;124;03m This takes the following steps:\u001b[39;00m\n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 3604\u001b[0m \u001b[38;5;124;03m display figures inline.\u001b[39;00m\n\u001b[1;32m 3605\u001b[0m \u001b[38;5;124;03m \"\"\"\u001b[39;00m\n\u001b[0;32m-> 3606\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mmatplotlib_inline\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mbackend_inline\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m configure_inline_support\n\u001b[1;32m 3608\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mIPython\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mcore\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m pylabtools \u001b[38;5;28;01mas\u001b[39;00m pt\n\u001b[1;32m 3609\u001b[0m gui, backend \u001b[38;5;241m=\u001b[39m pt\u001b[38;5;241m.\u001b[39mfind_gui_and_backend(gui, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mpylab_gui_select)\n", - "File \u001b[0;32m~/miniforge3/lib/python3.9/site-packages/matplotlib_inline/__init__.py:1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01m.\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m backend_inline, config \u001b[38;5;66;03m# noqa\u001b[39;00m\n\u001b[1;32m 2\u001b[0m __version__ \u001b[38;5;241m=\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m0.1.6\u001b[39m\u001b[38;5;124m\"\u001b[39m \u001b[38;5;66;03m# noqa\u001b[39;00m\n", - "File \u001b[0;32m~/miniforge3/lib/python3.9/site-packages/matplotlib_inline/backend_inline.py:6\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[38;5;124;03m\"\"\"A matplotlib backend for publishing figures via display_data\"\"\"\u001b[39;00m\n\u001b[1;32m 3\u001b[0m \u001b[38;5;66;03m# Copyright (c) IPython Development Team.\u001b[39;00m\n\u001b[1;32m 4\u001b[0m \u001b[38;5;66;03m# Distributed under the terms of the BSD 3-Clause License.\u001b[39;00m\n\u001b[0;32m----> 6\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mmatplotlib\u001b[39;00m\n\u001b[1;32m 7\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mmatplotlib\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m colors\n\u001b[1;32m 8\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mmatplotlib\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mbackends\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m backend_agg\n", - "\u001b[0;31mModuleNotFoundError\u001b[0m: No module named 'matplotlib'" + "name": "stdout", + "output_type": "stream", + "text": [ + "[[1. 0. 0. 0.]\n", + " [0. 1. 0. 0.]\n", + " [0. 0. 1. 0.]\n", + " [0. 0. 0. 1.]]\n", + " (0, 0)\t1.0\n", + " (1, 1)\t1.0\n", + " (2, 2)\t1.0\n", + " (3, 3)\t1.0\n" ] + }, + { + "data": { + "image/png": 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\n", 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\n", + "text/plain": [ + "
              " + ] + }, + "metadata": { + "filenames": { + "image/png": "/Users/mhjensen/Teaching/MachineLearning/doc/LectureNotes/_build/jupyter_execute/week34_86_3.png" + } + }, + "output_type": "display_data" + } + ], "source": [ "df.columns = ['First', 'Second', 'Third', 'Fourth', 'Fifth']\n", "df.index = np.arange(10)\n", @@ -1856,7 +2571,23 @@ "collapsed": false, "editable": true }, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[[ 0 1 2 3]\n", + " [ 4 5 6 7]\n", + " [ 8 9 10 11]\n", + " [12 13 14 15]]\n", + " 0 1 2 3\n", + "0 0 1 2 3\n", + "1 4 5 6 7\n", + "2 8 9 10 11\n", + "3 12 13 14 15\n" + ] + } + ], "source": [ "b = np.arange(16).reshape((4,4))\n", "print(b)\n", @@ -1959,7 +2690,22 @@ "collapsed": false, "editable": true }, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": 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\n", 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\n", + "text/plain": [ + "
              " + ] + }, + "metadata": { + "filenames": { + "image/png": "/Users/mhjensen/Teaching/MachineLearning/doc/LectureNotes/_build/jupyter_execute/week34_101_0.png" + } + }, + "output_type": "display_data" + } + ], "source": [ "import numpy as np\n", "import matplotlib.pyplot as plt\n", @@ -2162,7 +2923,36 @@ "collapsed": false, "editable": true }, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "The intercept alpha: \n", + " [2.18780801]\n", + "Coefficient beta : \n", + " [[4.72228205]]\n", + "Mean squared error: 0.37\n", + "Variance score: 0.83\n", + "Mean squared log error: 0.01\n", + "Mean absolute error: 0.47\n" + ] + }, + { + "data": { + "image/png": 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              " + ] + }, + "metadata": { + "filenames": { + "image/png": "/Users/mhjensen/Teaching/MachineLearning/doc/LectureNotes/_build/jupyter_execute/week34_103_1.png" + } + }, + "output_type": "display_data" + } + ], "source": [ "import numpy as np \n", "import matplotlib.pyplot as plt \n", @@ -2681,7 +3471,18 @@ "collapsed": false, "editable": true }, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "' \\nThis is taken from the data file of the mass 2016 evaluation. \\nAll files are 3436 lines long with 124 character per line. \\n Headers are 39 lines long. \\n col 1 : Fortran character control: 1 = page feed 0 = line feed \\n format : a1,i3,i5,i5,i5,1x,a3,a4,1x,f13.5,f11.5,f11.3,f9.3,1x,a2,f11.3,f9.3,1x,i3,1x,f12.5,f11.5 \\n These formats are reflected in the pandas widths variable below, see the statement \\n widths=(1,3,5,5,5,1,3,4,1,13,11,11,9,1,2,11,9,1,3,1,12,11,1), \\n Pandas has also a variable header, with length 39 in this case. \\n'" + ] + }, + "execution_count": 29, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "\"\"\" \n", "This is taken from the data file of the mass 2016 evaluation. \n", @@ -2716,7 +3517,21 @@ "collapsed": false, "editable": true }, - "outputs": [], + "outputs": [ + { + "ename": "ValueError", + "evalue": "Length of colspecs must match length of names", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mValueError\u001b[0m Traceback (most recent call last)", + "Input \u001b[0;32mIn [30]\u001b[0m, in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[38;5;66;03m# Read the experimental data with Pandas\u001b[39;00m\n\u001b[0;32m----> 2\u001b[0m Masses \u001b[38;5;241m=\u001b[39m \u001b[43mpd\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mread_fwf\u001b[49m\u001b[43m(\u001b[49m\u001b[43minfile\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43musecols\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m2\u001b[39;49m\u001b[43m,\u001b[49m\u001b[38;5;241;43m3\u001b[39;49m\u001b[43m,\u001b[49m\u001b[38;5;241;43m4\u001b[39;49m\u001b[43m,\u001b[49m\u001b[38;5;241;43m6\u001b[39;49m\u001b[43m,\u001b[49m\u001b[38;5;241;43m11\u001b[39;49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 3\u001b[0m \u001b[43m \u001b[49m\u001b[43mnames\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mN\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mZ\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mA\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mElement\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mEbinding\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 4\u001b[0m \u001b[43m \u001b[49m\u001b[43mwidths\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m1\u001b[39;49m\u001b[43m,\u001b[49m\u001b[38;5;241;43m3\u001b[39;49m\u001b[43m,\u001b[49m\u001b[38;5;241;43m5\u001b[39;49m\u001b[43m,\u001b[49m\u001b[38;5;241;43m5\u001b[39;49m\u001b[43m,\u001b[49m\u001b[38;5;241;43m5\u001b[39;49m\u001b[43m,\u001b[49m\u001b[38;5;241;43m1\u001b[39;49m\u001b[43m,\u001b[49m\u001b[38;5;241;43m3\u001b[39;49m\u001b[43m,\u001b[49m\u001b[38;5;241;43m4\u001b[39;49m\u001b[43m,\u001b[49m\u001b[38;5;241;43m1\u001b[39;49m\u001b[43m,\u001b[49m\u001b[38;5;241;43m13\u001b[39;49m\u001b[43m,\u001b[49m\u001b[38;5;241;43m11\u001b[39;49m\u001b[43m,\u001b[49m\u001b[38;5;241;43m11\u001b[39;49m\u001b[43m,\u001b[49m\u001b[38;5;241;43m9\u001b[39;49m\u001b[43m,\u001b[49m\u001b[38;5;241;43m1\u001b[39;49m\u001b[43m,\u001b[49m\u001b[38;5;241;43m2\u001b[39;49m\u001b[43m,\u001b[49m\u001b[38;5;241;43m11\u001b[39;49m\u001b[43m,\u001b[49m\u001b[38;5;241;43m9\u001b[39;49m\u001b[43m,\u001b[49m\u001b[38;5;241;43m1\u001b[39;49m\u001b[43m,\u001b[49m\u001b[38;5;241;43m3\u001b[39;49m\u001b[43m,\u001b[49m\u001b[38;5;241;43m1\u001b[39;49m\u001b[43m,\u001b[49m\u001b[38;5;241;43m12\u001b[39;49m\u001b[43m,\u001b[49m\u001b[38;5;241;43m11\u001b[39;49m\u001b[43m,\u001b[49m\u001b[38;5;241;43m1\u001b[39;49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 5\u001b[0m \u001b[43m \u001b[49m\u001b[43mheader\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;241;43m39\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[1;32m 6\u001b[0m \u001b[43m \u001b[49m\u001b[43mindex_col\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mFalse\u001b[39;49;00m\u001b[43m)\u001b[49m\n\u001b[1;32m 8\u001b[0m \u001b[38;5;66;03m# Extrapolated values are indicated by '#' in place of the decimal place, so\u001b[39;00m\n\u001b[1;32m 9\u001b[0m \u001b[38;5;66;03m# the Ebinding column won't be numeric. Coerce to float and drop these entries.\u001b[39;00m\n\u001b[1;32m 10\u001b[0m Masses[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mEbinding\u001b[39m\u001b[38;5;124m'\u001b[39m] \u001b[38;5;241m=\u001b[39m pd\u001b[38;5;241m.\u001b[39mto_numeric(Masses[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mEbinding\u001b[39m\u001b[38;5;124m'\u001b[39m], errors\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mcoerce\u001b[39m\u001b[38;5;124m'\u001b[39m)\n", + "File \u001b[0;32m~/miniforge3/envs/myenv/lib/python3.9/site-packages/pandas/util/_decorators.py:311\u001b[0m, in \u001b[0;36mdeprecate_nonkeyword_arguments..decorate..wrapper\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m 305\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mlen\u001b[39m(args) \u001b[38;5;241m>\u001b[39m num_allow_args:\n\u001b[1;32m 306\u001b[0m warnings\u001b[38;5;241m.\u001b[39mwarn(\n\u001b[1;32m 307\u001b[0m msg\u001b[38;5;241m.\u001b[39mformat(arguments\u001b[38;5;241m=\u001b[39marguments),\n\u001b[1;32m 308\u001b[0m \u001b[38;5;167;01mFutureWarning\u001b[39;00m,\n\u001b[1;32m 309\u001b[0m stacklevel\u001b[38;5;241m=\u001b[39mstacklevel,\n\u001b[1;32m 310\u001b[0m )\n\u001b[0;32m--> 311\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mfunc\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n", + "File \u001b[0;32m~/miniforge3/envs/myenv/lib/python3.9/site-packages/pandas/io/parsers/readers.py:871\u001b[0m, in \u001b[0;36mread_fwf\u001b[0;34m(filepath_or_buffer, colspecs, widths, infer_nrows, **kwds)\u001b[0m\n\u001b[1;32m 869\u001b[0m len_index \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mlen\u001b[39m(index_col)\n\u001b[1;32m 870\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mlen\u001b[39m(names) \u001b[38;5;241m+\u001b[39m len_index \u001b[38;5;241m!=\u001b[39m \u001b[38;5;28mlen\u001b[39m(colspecs):\n\u001b[0;32m--> 871\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mLength of colspecs must match length of names\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[1;32m 873\u001b[0m kwds[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mcolspecs\u001b[39m\u001b[38;5;124m\"\u001b[39m] \u001b[38;5;241m=\u001b[39m colspecs\n\u001b[1;32m 874\u001b[0m kwds[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124minfer_nrows\u001b[39m\u001b[38;5;124m\"\u001b[39m] \u001b[38;5;241m=\u001b[39m infer_nrows\n", + "\u001b[0;31mValueError\u001b[0m: Length of colspecs must match length of names" + ] + } + ], "source": [ "# Read the experimental data with Pandas\n", "Masses = pd.read_fwf(infile, usecols=(2,3,4,6,11),\n", @@ -5152,7 +5967,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.9.18" + "version": "3.9.10" } }, "nbformat": 4, diff --git a/doc/LectureNotes/_build/jupyter_execute/week35.ipynb b/doc/LectureNotes/_build/jupyter_execute/week35.ipynb index fa5c6c0ea..a01dbcb4c 100644 --- a/doc/LectureNotes/_build/jupyter_execute/week35.ipynb +++ b/doc/LectureNotes/_build/jupyter_execute/week35.ipynb @@ -1519,7 +1519,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "0.9947950318641428\n" + "0.9963311287748658\n" ] } ], @@ -1550,7 +1550,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "0.010272505059533654\n" + "0.007891914573161948\n" ] } ], @@ -1585,23 +1585,23 @@ "name": "stdout", "output_type": "stream", "text": [ - "[0.01027068 0.05313438 0.04538489 0.01836177 0.02080937 0.01800647\n", - " 0.01875198 0.00467902 0.00753502 0.02005707 0.00232645 0.01531078\n", - " 0.03829802 0.00785147 0.04770861 0.04729106 0.01202626 0.05597075\n", - " 0.00759075 0.01536802 0.02241641 0.00274849 0.0097933 0.01440849\n", - " 0.05803407 0.03087398 0.0131244 0.0291341 0.03955471 0.04581495\n", - " 0.0096887 0.02838502 0.00331854 0.00917575 0.02232857 0.01026639\n", - " 0.01719879 0.04680163 0.03691034 0.05657464 0.01277062 0.02319192\n", - " 0.03410211 0.0114457 0.03498633 0.0345711 0.00908896 0.05115386\n", - " 0.01253218 0.00362237 0.03169296 0.06165423 0.0072295 0.00869224\n", - " 0.03849245 0.05269447 0.01539813 0.04350167 0.05006792 0.00862396\n", - " 0.02270315 0.0141736 0.00537237 0.02931659 0.02817395 0.025761\n", - " 0.05417012 0.01227 0.00370021 0.02342548 0.00361816 0.0400444\n", - " 0.02198873 0.00914991 0.01461902 0.04424899 0.00910613 0.0099676\n", - " 0.02701179 0.03971778 0.04524779 0.05279081 0.01519497 0.00134839\n", - " 0.04233422 0.01660753 0.05817493 0.0167955 0.01299388 0.00265975\n", - " 0.02189559 0.04726103 0.01705804 0.10695767 0.06613465 0.04663936\n", - " 0.00250484 0.02495131 0.01816794 0.01905766]\n" + "[0.00712321 0.05126901 0.00172452 0.00104613 0.01477821 0.06642248\n", + " 0.04547353 0.0207306 0.01552289 0.01492 0.0200568 0.01097423\n", + " 0.02400359 0.04111096 0.02126208 0.00799998 0.00976647 0.0447389\n", + " 0.0006527 0.02468681 0.03543039 0.00627535 0.04259402 0.02635835\n", + " 0.04362755 0.00478655 0.01989299 0.01508632 0.04372783 0.00324969\n", + " 0.01508966 0.08185315 0.01214101 0.01130932 0.00186362 0.02809859\n", + " 0.00480371 0.08481871 0.01881546 0.04314342 0.11022363 0.01288591\n", + " 0.03370315 0.00858536 0.00202679 0.00229911 0.00446979 0.0496375\n", + " 0.00426531 0.02593026 0.03063575 0.03119091 0.05459089 0.01916913\n", + " 0.00183869 0.00015239 0.01347636 0.01267006 0.02702978 0.02944425\n", + " 0.01424197 0.00354492 0.06026294 0.00148709 0.02061026 0.03616508\n", + " 0.03543958 0.02079171 0.00595615 0.01765474 0.01268892 0.00225484\n", + " 0.01135167 0.03894328 0.04426647 0.03717939 0.01502518 0.03195835\n", + " 0.00474485 0.06331463 0.01671556 0.01762067 0.00219624 0.07358383\n", + " 0.01038358 0.0002364 0.04669463 0.00048325 0.00012934 0.01529503\n", + " 0.0093869 0.00952586 0.0154222 0.01463052 0.0296969 0.01372375\n", + " 0.02536494 0.00472549 0.04225015 0.04690007]\n" ] } ], @@ -1652,19 +1652,18 @@ }, "outputs": [ { - "ename": "ModuleNotFoundError", - "evalue": "No module named 'matplotlib'", - "output_type": "error", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mModuleNotFoundError\u001b[0m Traceback (most recent call last)", - "Cell \u001b[0;32mIn[7], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \u001b[43mget_ipython\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mrun_line_magic\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mmatplotlib\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43minline\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m)\u001b[49m\n\u001b[1;32m 3\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mos\u001b[39;00m\n\u001b[1;32m 4\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mnumpy\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m \u001b[38;5;21;01mnp\u001b[39;00m\n", - "File \u001b[0;32m~/miniforge3/lib/python3.9/site-packages/IPython/core/interactiveshell.py:2432\u001b[0m, in \u001b[0;36mInteractiveShell.run_line_magic\u001b[0;34m(self, magic_name, line, _stack_depth)\u001b[0m\n\u001b[1;32m 2430\u001b[0m kwargs[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mlocal_ns\u001b[39m\u001b[38;5;124m'\u001b[39m] \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mget_local_scope(stack_depth)\n\u001b[1;32m 2431\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mbuiltin_trap:\n\u001b[0;32m-> 2432\u001b[0m result \u001b[38;5;241m=\u001b[39m \u001b[43mfn\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 2434\u001b[0m \u001b[38;5;66;03m# The code below prevents the output from being displayed\u001b[39;00m\n\u001b[1;32m 2435\u001b[0m \u001b[38;5;66;03m# when using magics with decorator @output_can_be_silenced\u001b[39;00m\n\u001b[1;32m 2436\u001b[0m \u001b[38;5;66;03m# when the last Python token in the expression is a ';'.\u001b[39;00m\n\u001b[1;32m 2437\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mgetattr\u001b[39m(fn, magic\u001b[38;5;241m.\u001b[39mMAGIC_OUTPUT_CAN_BE_SILENCED, \u001b[38;5;28;01mFalse\u001b[39;00m):\n", - "File \u001b[0;32m~/miniforge3/lib/python3.9/site-packages/IPython/core/magics/pylab.py:99\u001b[0m, in \u001b[0;36mPylabMagics.matplotlib\u001b[0;34m(self, line)\u001b[0m\n\u001b[1;32m 97\u001b[0m \u001b[38;5;28mprint\u001b[39m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mAvailable matplotlib backends: \u001b[39m\u001b[38;5;132;01m%s\u001b[39;00m\u001b[38;5;124m\"\u001b[39m \u001b[38;5;241m%\u001b[39m backends_list)\n\u001b[1;32m 98\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m---> 99\u001b[0m gui, backend \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mshell\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43menable_matplotlib\u001b[49m\u001b[43m(\u001b[49m\u001b[43margs\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mgui\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mlower\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mif\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[38;5;28;43misinstance\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43margs\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mgui\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mstr\u001b[39;49m\u001b[43m)\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01melse\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43margs\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mgui\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 100\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_show_matplotlib_backend(args\u001b[38;5;241m.\u001b[39mgui, backend)\n", - "File \u001b[0;32m~/miniforge3/lib/python3.9/site-packages/IPython/core/interactiveshell.py:3606\u001b[0m, in \u001b[0;36mInteractiveShell.enable_matplotlib\u001b[0;34m(self, gui)\u001b[0m\n\u001b[1;32m 3585\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21menable_matplotlib\u001b[39m(\u001b[38;5;28mself\u001b[39m, gui\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mNone\u001b[39;00m):\n\u001b[1;32m 3586\u001b[0m \u001b[38;5;250m \u001b[39m\u001b[38;5;124;03m\"\"\"Enable interactive matplotlib and inline figure support.\u001b[39;00m\n\u001b[1;32m 3587\u001b[0m \n\u001b[1;32m 3588\u001b[0m \u001b[38;5;124;03m This takes the following steps:\u001b[39;00m\n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 3604\u001b[0m \u001b[38;5;124;03m display figures inline.\u001b[39;00m\n\u001b[1;32m 3605\u001b[0m \u001b[38;5;124;03m \"\"\"\u001b[39;00m\n\u001b[0;32m-> 3606\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mmatplotlib_inline\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mbackend_inline\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m configure_inline_support\n\u001b[1;32m 3608\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mIPython\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mcore\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m pylabtools \u001b[38;5;28;01mas\u001b[39;00m pt\n\u001b[1;32m 3609\u001b[0m gui, backend \u001b[38;5;241m=\u001b[39m pt\u001b[38;5;241m.\u001b[39mfind_gui_and_backend(gui, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mpylab_gui_select)\n", - "File \u001b[0;32m~/miniforge3/lib/python3.9/site-packages/matplotlib_inline/__init__.py:1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01m.\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m backend_inline, config \u001b[38;5;66;03m# noqa\u001b[39;00m\n\u001b[1;32m 2\u001b[0m __version__ \u001b[38;5;241m=\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m0.1.6\u001b[39m\u001b[38;5;124m\"\u001b[39m \u001b[38;5;66;03m# noqa\u001b[39;00m\n", - "File \u001b[0;32m~/miniforge3/lib/python3.9/site-packages/matplotlib_inline/backend_inline.py:6\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[38;5;124;03m\"\"\"A matplotlib backend for publishing figures via display_data\"\"\"\u001b[39;00m\n\u001b[1;32m 3\u001b[0m \u001b[38;5;66;03m# Copyright (c) IPython Development Team.\u001b[39;00m\n\u001b[1;32m 4\u001b[0m \u001b[38;5;66;03m# Distributed under the terms of the BSD 3-Clause License.\u001b[39;00m\n\u001b[0;32m----> 6\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mmatplotlib\u001b[39;00m\n\u001b[1;32m 7\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mmatplotlib\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m colors\n\u001b[1;32m 8\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mmatplotlib\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mbackends\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m backend_agg\n", - 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9ynxfTVFguZCARtB+OCQthk1zYdjFPRBVVy3fnsakuAROniogJMCHV2/qzsB2de+ZoOJu0CO7NOXV73bynzX7+HZbGit3pHPnwCjuv7ytS445IyKV07Nnz1I/2+12nn/+eeLi4jh06BB5eXnk5eUREFB+QOratWvJevGtp7S08q/MDhkyhDlz5pTadvbzMWfXBxAZGVkqdGzbto2IiIiSsAIQHR1NgwYN2LZtW0lgOft9NUV/M1ZEzM3OwLJ5PlwxFWz6Y6soh8Pg9R928cq3OzAMiGkRzOxbY2neoN6F3+zBgut589SYaG7uHcEzi7excsdR3lyxm4UbDvLYyI5c0615nbnyJFKKt7/zSodZn11Nzg4iL730Eq+88gqzZs2iS5cuBAQEMGnSJPLz88sv6awHWC0WCw5H+c+/BQQE0LZt20rVV9Y2wzDKHAfr7O0XCl3VRd+8FdFuGNQLgexU2LMc2g01uyK3kHGqgMnzE/g+yfmvgVv6tGTq2Gh8vWwmV+Y62jYJ5D9/6sV329J49qtE9h87xZT5m/jw5/1MG9uZmIgGZpcoUrsslmq7LeNKVq1axdVXX82tt94KgMPhYOfOnXTq1Mnkys4vOjqa5ORkDhw4UHKVJTExkYyMDFPq1kO3FeHlA12ud67r4dsK2Xo4g7Gvr+b7pDR8vay8eH1Xnru2i8JKGSwWC0Ojw1g2+VIeGdEBfx8bG5NPcs3sH3nk000czdJs0CLurm3btsTHx7NmzRq2bdvGX/7yF1JTU2vks/Ly8khNTS21pKenV3o/Q4cOpWvXrvzxj39kw4YN/PLLL4wfP57LLruszFtKNU2BpaJibna+Jn0FuRnm1uLiFqw/yHWz15B8/BQRIfVYcG9/bugZceE31nG+XjbuG+ycDfra7s1LdYP+90p1gxZxZ0899RQ9evRg+PDhDB48mPDwcK655poa+axvvvmGpk2blloGDhxY6f1YLBY+//xzGjZsyKWXXsrQoUNp3bo1cXFxNVB1BeoxPGTM8MzMTIKDg8nIyCAoqAa6yBoGzO4LR5Ng7L8g9o7q/ww3l1do59nFiXz0czIAgzs0Zta4bjTw9zG5Mve0fv8Jpi3aypZDzoDcunEAT42JZkiHJiZXJlJ9cnNz2bt3L1FRUfj5uecI13Jh5Z3nin5/6wpLRVksv19lSZhbfts66PDJ09z41s989HMyFgtMGtqO927vpbByEWIjG/LFxAH88w9dCa3vw56jOfzp/V+564Nf2avZoEWkjlFgqYyu48BihQM/w/E9ZlfjMn7clc6Y11az6cBJgut5894dvZg0tL16uVQDq9XCjb0i+P7hwdw90Dkb9HdJaQx7ZQUzvt5GtmaDFpE6QoGlMoKaQushzvVN88ytxQUYhsGc5bu57d21HM/Jp3OzIBY/MFC3LGpAkJ83fxsTzTeTLuXS9o0psBu8tWIPQ2YuZ8H6gzgcHnFnV0TkvBRYKqtkqP65cIG+8J4sM7eACR+t54VvknAYcENsCxbc25+IkOobx0DO1bZJff7zp168M74nkY38OZqVx/99sonr5qxh04GTZpcnIlJjFFgqq+No8A2Ck8mQvMbsakyxPTWLq1//kaVbj+BjszLjui788/qu+Hmry3JtOLMb9KMjOuLvYyPhgHM26L9+som0rFyzSxQRqXYKLJXlXQ+ir3au18GHb79IOMQ1b/zI3vQcmgX78cmEftzcu2WZoyFKzfL1snHv4Db88PBgruvunOn6k/UHuXzmCt5euVvdoMWteEiHVTmP6ji/CixVUXxbKPFz5wyfdUCB3cH0L7fy0LwEThfYGdQulMUPDtJIrC4gLMiPl8d1Y8G9/enaIpjsvEKeW5LEiFkr+UGzQYuLKx56/tSpUyZXIjWp+PyePdVAZWgclqowDHi1G5zYB9e+DTHjLvQOt5aWmct9/9vAuv0nALh/SFsmX9kem3oBuRyHw+DT9Qf559Ik0rOdc5Rc3rEJT42JJkqzQYuLSklJ4eTJkzRp0gR/f39dsfUghmFw6tQp0tLSaNCgAU2bNj2nTUW/v6sUWGbPns2LL75ISkoKnTt3ZtasWQwaNKjMtgsXLmTOnDkkJCSQl5dH586dmTZtGsOHDy9p88EHH/CnP/3pnPeePn26wgMJ1WpgAVj+PCyf4ew1NP7zmv88k6zdc4yJH28kPTuPQF8vXh7XjSujw8wuSy4gM7eA177byfs/7qPQYeBts3DnwCgeuLydZoMWl2MYBqmpqZw8edLsUqSGNGjQgPDw8DLDaEW/vyv9N1dcXByTJk1i9uzZDBgwgLfeeouRI0eSmJhIy5Ytz2m/cuVKrrzySp577jkaNGjA+++/z9ixY1m7di3du3cvaRcUFMT27dtLvdelRz2MuckZWPYsh4xDENzc7IqqlWEYvLt6LzO+TsLuMOgYHsicW2P1r3Q3EeTnzZOjoxnXqyXPLk5kxY6jvLViDws3HOLRER25rrtmgxbXYbFYaNq0KU2aNKGgoMDscqSaeXt7Y7NdfKeMSl9h6dOnDz169GDOnDkl2zp16sQ111zDjBkzKrSPzp07M27cOJ5++mnAeYVl0qRJF5Wua/0KC8D7o2D/j3DFVBg0pXY+sxZk5xXy6Keb+WpLCgDXdGvGc9d1wd9H/zJ3R4Zh8H1SGs8uTmTfMed95G4RDZh2VWe66RkkETFZjQzNn5+fz/r16xk2bFip7cOGDWPNmop18XU4HGRlZRESElJqe3Z2NpGRkbRo0YIxY8awcePGcveTl5dHZmZmqaXWFQ/Vv2mu87kWD7ArLZtr3viRr7ak4GW18MzVnXllXDeFFTdmsVi4olMYSydfymMjOxJQ1A36mjd+5GF1gxYRN1GpwJKeno7dbicsrPQzDGFhYRWeJvull14iJyeHG2+8sWRbx44d+eCDD1i0aBFz587Fz8+PAQMGsHPnzvPuZ8aMGQQHB5csEREmzAYcfTV41YP0HXBoQ+1/fjX7eksKV7++ml1p2YQF+RL3l76M79dKD8B5CF8vGxMuK+oG3cN5C/NTdYMWETdRpW7NZ3+BGYZRoS+1uXPnMm3aNOLi4mjS5Pfh2/v27cutt95KTEwMgwYNYv78+bRv357XXnvtvPt6/PHHycjIKFkOHDhQlUO5OH5B0GmMc33Tx7X/+dWk0O7guSXbuPd/G8jJt9MnKoTFDwwiNjLkwm8Wt9MkyI+Xb+zGwvv6E3N2N+gkdYMWEddUqcASGhqKzWY752pKWlraOVddzhYXF8ddd93F/PnzGTp0aPlFWa306tWr3Cssvr6+BAUFlVpMUXxbaMunUJhnTg0X4WhWHre+u5a3Vzonc7zn0tb87+4+NA70NbkyqWk9Wjbks/sG8M/ruxJa35c96Tn86YNfuVOzQYuIC6pUYPHx8SE2Npb4+PhS2+Pj4+nfv/953zd37lzuuOMOPv74Y0aPHn3BzzEMg4SEhDL7a7uc1oMhsBnknoQd35hdTaWs33+CMa+t4uc9xwnwsTH7jz14YlQnvGwaT7CusFot3Ngzgh8evox7Lm2Nl9XC98WzQS/ZRlauemyIiGuo9DfTlClTeOedd3jvvffYtm0bkydPJjk5mQkTJgDOWzXjx48vaT937lzGjx/PSy+9RN++fUlNTSU1NZWMjIySNtOnT2fp0qXs2bOHhIQE7rrrLhISEkr26dKsNuha9DyOm8zgbBgG/1mzj5ve/okjmXm0bVKfL+4fyKgubhAQpUYE+nnzxKhOLJ18KYM7FM0GvXIPl7+0gk/WHdBs0CJiukoHlnHjxjFr1iyeeeYZunXrxsqVK1myZAmRkZGAc8TC5OTkkvZvvfUWhYWFTJw4kaZNm5YsDz30UEmbkydPcs8999CpUyeGDRvGoUOHWLlyJb17966GQ6wFxUP171wGOenm1nIBp/ILmRyXwNRFWymwG4zu0pTPJw6gbZP6ZpcmLqBN4/p88KfevHdHT6JCAzialcdfP93MtXPWsDH5hNnliUgdpqH5q8vbQ+DwBhjxPPS9t/Y/vwL2pecw4aP1JKVmYbNaeHxkR+4aGKVeQFKmvEI77/+4j9e+20lOvh2AP/RowaMjOtAkyIUHdRQRt1Ij47BIOYqvsiS4Zm+h+MQjjH19NUmpWYTW9+Xju/tw96DWCityXmd2g/5DjxYALNhwkCEzl/PWCnWDFpHapSss1eXUcZjZHhwFcO8aCOtc+zWUwe4weCV+B6//sAuAnpENeeOPPQjTv5ClkjYmn2Daoq1sOuh8/iwqNICnxnTi8o6aW0pEqk5XWGqbfwi0L5rQ0UWushzPyeeO938pCSt/GtCKuff0VViRKule1A36xaJu0HvTc7jzg3Xc8f4v7D6abXZ5IuLhFFiqU/Ftoc3zwV5oaimbDpxk7GurWbUznXreNv51Uzemju2Mt7osy0WwWi3ccEY3aG+bheXbjzJi1kqeUzdoEalB+vaqTm2vBP9GkJMGu783pQTDMJj7SzI3vPkTh06eJio0gM8nDuDqbp41m7SYq6Qb9KRLGVLUDfrtlXsYMlPdoEWkZiiwVCcvH+hyg3N909xa//jcAjuPLtjM4wu3kG93MCw6jC/uH0CH8MBar0XqhtaN6/P+Gd2g07PVDVpEaoYeuq1uhxPg7cvA5gsP74B6DWrlYw8cP8WEj9az9XAmVgv8dXhHJlymXkBSe/ILHbz/415ePaMb9HU9mvPYiI7qBi0i56WHbs3SNAaaRIM9D7Z+Visf+cP2NMa8tpqthzMJCfDhw7v6cO/gNgorUqt8vKz85bI2/PDXwVwf6+wGvXDDIYbMXM6bK3aTV2g3uUIRcWcKLNXNYvl9QsQavi3kcBjM+nYHd37wKxmnC4iJaMDiBwYyoG1ojX6uSHmaBPox84YYPruvPzERDcjJt/P810kMf2Ul3ycdMbs8EXFTuiVUE7JS4eVOYDjggQ3QqE21f8TJU/lMjkvgh+1HAbi1b0ueGhONr5et2j9LpKocDoOFGw/xwjdJHM1yzmY+uENjnhoTTZvGmg5CRHRLyFyB4dDmcud6DVxl+e1QBmNfX80P24/i62Vl5g0x/P2aLgor4nKsVgvXx7bg+/+7jL9c9ns36OGvrOQfXyWqG7SIVJgCS00puS00DxzVN4T5J+sO8Ic5azhw/DQRIfVYeF//kucFRFxVoJ83j490doO+vGMTCh0G/161lyEzVzBf3aBFpAIUWGpKx9HgGwwZB2D/6oveXV6hnSc+28JfP91MXqGDyzs2YfH9g+jcLLgaihWpHa0b1+e9O3rx/h29aF3UDfqRTzdz7ewf2aBu0CJSDgWWmuJdDzpf41zfNO+idnXo5GlufPMnPl6bjMUCU65szzvjexLs733xdYqYYEjHJnwz6VKeGNWR+r5ebDqYwXWz1zAlLoEjmblmlyciLkiBpSYVD9Wf+AXk51RpF6t3pjP2tdVsOphBA39v3r+jFw9e0Q6rVV2Wxb35eFm559I2fP/wZdxQ3A164yEun7mcOcvVDVpESlNgqUkRfSCkNeRnw7YvK/VWh8PgjR92Mf69tRzPyeeS5kF8ef9ABndoUkPFipijSaAfL94Qw+cTB9CtqBv0C984u0F/m3gED+nIKCIXSYGlJp05JkslZnDOzC3gLx+t58Wl23EYMK5nBJ9O6E9EiH8NFSpivm4RDVh4b39euiGGxoG+7Dt2irv/u4473v+VXWmaDVqkrlNgqWldxzlf966EjIMXbJ6UmslVr60mPvEIPl5Wnr+uCy9c3xU/b3VZFs9ntVr4Q2wLfnh4cEk36BU7nLNBf/jzfrPLExETKbDUtIaREDkQMC748O0XCYe49o017Dt2iuYN6vHphH7c1Ltl7dQp4kLq+3rx+MhOLJt8WUk36Ge/TGT/sao9CyYi7k+BpTZ0O2Oo/jLux+cXOpi2aCsPzUvgdIGdQe1C+fKBgXRt0aB26xRxMVGhAbx7e08Gtg0l3+7g719tM7skETGJAkttiL4avP3h2C44uK7Ur45k5nLzv3/mgzX7AHjg8rZ88KfehAT4mFCoiOuxWCxMHRuNl9VCfOIRVuw4anZJImICBZba4BsIncY6188Yqv/nPccY/epq1u8/QaCfF++M78n/DeuATV2WRUppFxbI7f1bATD9y63kF1bf6NEi4h4UWGpLcW+h3xZgFOTy75V7+OM7a0nPzqNjeCBf3j+QodFh5tYo4sIeGtqO0Po+7Dmaw3+KrkiKSN2hwFJboi6FoOaQe5J33p3NP5Zsw+4wuLZ7cz67bwCtQgPMrlDEpQX5efPI8I4A/Ou7naRlaURckbpEgaW2WG0cb3stAFGHFuFts/Ds1Z15+cYY6vmoy7JIRVwf24KuLYLJzivkn99sN7scEalFCiy1ZMmWFMavaw3AYNsmFoxvy239WmGx6HkVkYqyWi1Mv6ozAJ+uP8hGTZgoUmcosNSwQruDf3yVyH3/28Bv+eHs8u6AFw66Ho83uzQRt9S9ZUOuL5p7aNqirTgcGrpfpC5QYKlBaVm5/PGdtfx71V4A/nJZa6KG3u38ZcLcct4pIuV5ZESHklmeP11/4RGkRcT9KbDUkHX7jjPm1dWs3Xuc+r5ezPljDx4f2Qlbl+vB5gNHtkDqFrPLFHFLTQL9eOiKdgC88E0SGacLTK5IRGqaAks1MwyDD37cy01v/0xaVh7tmtTni/sHMLJLU2cD/xBoP8K5foGh+kXk/G7v34rWjQM4lpPPq9/tNLscEalhCizV6FR+IZPiEpj2ZSKFDoMxXZvy+cQBtGlcv3TDbrc4XzfPB3th7Rcq4gF8vKxMG+t8APc/a/ax80iWyRWJSE1SYKkme9NzuPaNNXyRcBgvq4Wnx0Tz2s3dCfD1Ordx26HgHwo5abD7u9ovVsRDXNq+MVdGh1HoMJj+ZSJGGXN1iYhnUGCpBsu2pnLVa6vZfiSLxoG+fPznvtw5MOr8XZZt3tD1Rud6wse1V6iIB3pqdDQ+XlZW70pn6dYjZpcjIjVEgeUi2B0G//wmiXs+XE9WXiG9WjXkqwcG0jsq5MJvLh6qf/sSOK2xJESqqmUjf+4Z5Bzj6O9fJZJbYDe5IhGpCQosVXQsO4/b3/uF2ct3A3DXwCg+/nNfmgT5VWwH4V2gSWew58NvC2uwUhHPd9+QNjQN9uPgidO8vXKP2eWISA1QYKmChAMnGfvaalbvSsffx8ZrN3fnqTHReNsq8cdpsUC3oqssmzQmi8jF8Pfx4olRnQCYvXwXh06eNrkiEaluCiyVYBgG/1u7nxvf/InDGbm0Dg3g84kDGBvTrGo77HIjWGxw8FdIV7dMkYsxpmtTekeFkFvg4LmvtpldjohUMwWWCsotsPPXTzfz5Ge/kW93MLxzGF/cP4D2YYFV32lgGLS9wrmuMVlELorFYmHa2M5YLfDVlhTW7E43uyQRqUYKLBWQfOwU181ew6frD2K1wOMjO/LmrbEE+nlf/M6LH77dHAcOx8XvT6QOi24WxB/7RAIwfVEihXb9PyXiKRRYLuCHpDTGvr6axJRMGgX48NFdffjLZW2qb5blDqPALxgyDsC+VdWzT5E6bMqV7Wng7832I1n8b22y2eWISDVRYCnHqfxCHlmwmYzTBXSLaMDiBwfSv21o9X6Itx90vs65rodvRS5awwAfHh7WAYCXlm3nWHaeyRWJSHVQYCmHv48Xr97UnfH9Ion7S1+aBtermQ8qHqo/cRHkZdfMZ4jUITf3bkmnpkFk5hYyc9kOs8sRkWqgwHIB/do04pmrL8HXy1ZzH9KiF4S0gYIc2Lao5j5HpI6wWS1Mv8o5z9C8X5P57VCGyRWJyMVSYHEFFsvvD99qqH6RatE7KoSrYpphGDB10VbNMyTi5hRYXEXMOOfrvlVwUg8KilSHJ0Z1wt/Hxvr9J/gi4bDZ5YjIRVBgcRUNWkKrQc71zXHm1iLiIcKD/Zg4pC0Azy3ZRnZeockViUhVKbC4kuKHbxPmgi5fi1SLuwZGEdnIn7SsPF7/fpfZ5YhIFSmwuJJOV4F3ABzf7RyuX0Qump+3jadGRwPw7uo97E3PMbkiEakKBRZX4lsfoq9yruvhW5Fqc0WnJlzWvjEFdoNnFyeaXY6IVIECi6sp7i20dSEU5Jpbi4iHsFgsPD02Gm+bhe+T0vg+6YjZJYlIJSmwuJpWgyCoBeRmwPYlZlcj4jHaNK7PnQOiAHjmy0TyCu0mVyQilaHA4mqs1t+7OGuofpFqdf/lbWkc6Mu+Y6d4b/U+s8sRkUpQYHFFxbeFdn0HWbp0LVJdAv28eWxERwBe+34nRzJ121XEXSiwuKLQds7h+g07bPnE7GpEPMq13ZvTvWUDTuXbef7rJLPLEZEKqlJgmT17NlFRUfj5+REbG8uqVavO23bhwoVceeWVNG7cmKCgIPr168fSpUvPabdgwQKio6Px9fUlOjqazz77rCqleY7iqyy6LSRSraxF8wxZLPDZxkOs23fc7JJEpAIqHVji4uKYNGkSTz75JBs3bmTQoEGMHDmS5OSyh5NfuXIlV155JUuWLGH9+vUMGTKEsWPHsnHjxpI2P/30E+PGjeO2225j06ZN3Hbbbdx4442sXbu26kfm7i65Dmy+cOQ3SNlsdjUiHqVriwaM6xkBOOcZsjs0UKOIq7MYlZwRrE+fPvTo0YM5c+aUbOvUqRPXXHMNM2bMqNA+OnfuzLhx43j66acBGDduHJmZmXz99dclbUaMGEHDhg2ZO7diVxgyMzMJDg4mIyODoKCgShyRC5t/OyR+Dn3vgxEV+7MVkYpJz85jyMzlZOUW8ty1XbilT0uzSxKpkyr6/V2pKyz5+fmsX7+eYcOGldo+bNgw1qxZU6F9OBwOsrKyCAkJKdn2008/nbPP4cOHl7vPvLw8MjMzSy0ep3io/s3zwV5gbi0iHia0vi+Th7YH4MWlSWSc0v9jIq6sUoElPT0du91OWFhYqe1hYWGkpqZWaB8vvfQSOTk53HjjjSXbUlNTK73PGTNmEBwcXLJERERU4kjcRJvLIaAxnEqHXd+aXY2Ix7mtXyTtw+pz4lQBr3y7w+xyRKQcVXro1mKxlPrZMIxztpVl7ty5TJs2jbi4OJo0aXJR+3z88cfJyMgoWQ4cOFCJI3ATNm/oUhTsNFS/SLXztlmZOrYzAB/+vJ+kVA+8UiviISoVWEJDQ7HZbOdc+UhLSzvnCsnZ4uLiuOuuu5g/fz5Dhw4t9bvw8PBK79PX15egoKBSi0fqVtRbaMc3cEq9GUSq24C2oYy8JBy7w2Daoq1U8rE+EakllQosPj4+xMbGEh8fX2p7fHw8/fv3P+/75s6dyx133MHHH3/M6NGjz/l9v379ztnnsmXLyt1nnRHeBcK6gD3fOb+QiFS7J0Z1wtfLys97jrNkS8Vub4tI7ar0LaEpU6bwzjvv8N5777Ft2zYmT55McnIyEyZMAJy3asaPH1/Sfu7cuYwfP56XXnqJvn37kpqaSmpqKhkZGSVtHnroIZYtW8YLL7xAUlISL7zwAt9++y2TJk26+CP0BMVXWRI0JotITYgI8WfCZW0A+MdXiZzO1zxDIq6m0oFl3LhxzJo1i2eeeYZu3bqxcuVKlixZQmRkJAApKSmlxmR56623KCwsZOLEiTRt2rRkeeihh0ra9O/fn3nz5vH+++/TtWtXPvjgA+Li4ujTp081HKIH6HIDWGxwaB2k7zS7GhGPdO/gNjRvUI/DGbnMWbHb7HJE5CyVHofFVXnkOCxn+nic8zmWgVNg6FSzqxHxSF9vSeHe/23Ax8vKd1MuIyLE3+ySRDxejYzDIiaKucn5ujkOHLpcLVITRlwSTv82jcgvdPD3rxLNLkdEzqDA4i7ajwS/YMg8BHtXml2NiEeyWCxMHdsZm9XC0q1HWLXzqNkliUgRBRZ34e0Hl/zBua4JEUVqTIfwQG7r63wmb/qXiRTYHSZXJCKgwOJeYoqG6t/2JeRlmVuLiAebfGV7QgJ82JWWzX9/2m92OSKCAot7adETGrWFglOQuMjsakQ8VnA9bx4Z3gGAWfE7OJqVZ3JFIqLA4k4sFogpGpNFt4VEatQNPSPo0jyYrLxCXlyaZHY5InWeAou7ibkJsMC+VXBCl6pFaorNamHaVc55huavO0jCgZPmFiRSxymwuJvgFhB1qXN9c5y5tYh4uNjIhlzXozkA0xZtxeHwiGGrRNySAos7OvO2kGeM+yfish4b0ZEAHxsJB06yYMNBs8sRqbMUWNxRp7HgHQDH98CBtWZXI+LRmgT58eAV7QB44ZvtZOYWmFyRSN2kwOKOfOtD9NXO9YSPza1FpA7404AoWocGkJ6dx2vfaT4vETMosLir4hmct34OBadNLUXE0/l4WXlqbDQA7/+4j11p2SZXJFL3KLC4q8iBENwS8jJg+xKzqxHxeEM6NGFopyYUOgymf7kVD5k3VsRtKLC4K6sVYsY51xM0JotIbfjb6Gh8bFZW7UwnPvGI2eWI1CkKLO6suLfQ7u8gK9XcWkTqgFahAdw9KAqAZ79KJLdAM6eL1BYFFnfWqA1E9AHDAZvnm12NSJ0wcUhbwoJ8OXD8NO+s2mN2OSJ1hgKLu4u5yfmqMVlEakWArxdPjOoEwBs/7ObwST30LlIbFFjcXefrwOYLaYmQssnsakTqhKtimtGrVUNOF9iZ8bXmGRKpDQos7q5eA+g4yrmuCRFFaoXF4pxnyGqBLzcd5uc9x8wuScTjKbB4gphbnK9bPgG7RuEUqQ2dmwVzc++WgHOeoUK7w+SKRDybAosnaHM51A+DU8dgZ7zZ1YjUGQ8P60BwPW+SUrOY+0uy2eWIeDQFFk9g84IuNzjXN2mofpHa0jDAh4eHtQdg5rIdnMjJN7kiEc+lwOIpuhXdFtr+DZw6bm4tInXIzb1b0jE8kIzTBcxctt3sckQ8lgKLpwjrDOFdwVEAvy0wuxqROsPLZmXaVZ0B+PiXZH47lGFyRSKeSYHFkxSPfKsZnEVqVd/WjRjTtSmGgeYZEqkhCiyepMsNYPWCwxvgqC5Ni9SmJ0Z1op63jV/3nWDRpsNmlyPicRRYPEn9xtD2Sue6rrKI1KpmDeoxcUgbAGYsSSInr9DkikQ8iwKLp+lWdFto83xwaGI2kdp096DWRITUIzUzlzd+2GV2OSIeRYHF07QfAX4NIOsw7F1hdjUidYqft42nRkcD8M6qvexLzzG5IhHPocDiabx8ocv1zvUEDdUvUtuujA5jULtQ8u0O/v5VotnliHgMBRZPVDxU/7YvITfT3FpE6hiLxcLUsZ3xslr4dlsaP2xPM7skEY+gwOKJmveA0PZQeBoSvzC7GpE6p22T+vxpQCsAnv0ykfxCzTMkcrEUWDyRxQIxNznXNYOziCkevKIdofV92ZOew/s/7jW7HBG3p8DiqbreBFhg/49wYp/Z1YjUOYF+3jw6ogMAr363k7TMXJMrEnFvCiyeKrg5tL7Mub5pnrm1iNRRf+jRgm4RDcjJt/P8N0lmlyPi1hRYPFnxw7eb5oKGChepdVarhelF8wwt3HCI9ftPmFyRiPtSYPFkncaAT33nLaHkn82uRqROiolowI09WwAwbdFW7A7940GkKhRYPJlPAERf41zfpKH6Rczy1+EdCfT1YsuhDD5Zd8DsckTckgKLpyseqn/r51Bw2tRSROqqxoG+PDS0HQD/XLqdjNMFJlck4n4UWDxdy/7QoCXkZULSV2ZXI1Jn3d6/FW2b1Od4Tj6zvt1hdjkibkeBxdNZrUVdnNEMziIm8rZZmTrWOc/Qf3/az44jWSZXJOJeFFjqguJB5Pb8AJkp5tYiUocNateY4Z3DsDsMpi3aiqHeeyIVpsBSFzRqAxF9wXDA5jizqxGp0/42OhofLytrdh/jm99SzS5HxG0osNQVxQ/fakwWEVNFhPgz4dLWAPz9q22czrebXJGIe1BgqSs6XwtefnA0CVISzK5GpE67d3BbmgX7cejkad5audvsckTcggJLXeEXDB1HO9cTNCGiiJnq+dh4crTzAdw5y3dz8MQpkysScX0KLHVJ8VD9Wz6BwnxzaxGp40Z1Cadv6xDyCh3846ttZpcj4vIUWOqS1oOhfjicPg47l5ldjUidZrFYmHZVZ6wW+Pq3VH7clW52SSIuTYGlLrF5QdcbnOubdFtIxGwdw4O4rW8kANO/3EqB3WFyRSKuS4Glrim+LbRjKeQcM7cWEWHKlR1o6O/NjiPZfPTzfrPLEXFZCix1TVg0NI0BRwH89qnZ1YjUecH+3vx1eEcAXo7fwbHsPJMrEnFNCix1UfFVFt0WEnEJ43pF0LlZEFm5hby4dLvZ5Yi4JAWWuqjL9WD1gsMbIS3J7GpE6jyb1cL0qzoDELfuAJsPnjS3IBEXpMBSFwWEQrvhzvVNmhBRxBX0bBXCNd2aYRgwbdFWHA6NSC1ypioFltmzZxMVFYWfnx+xsbGsWrXqvG1TUlK45ZZb6NChA1arlUmTJp3T5oMPPsBisZyz5ObmVqU8qYjiofo3zweHhgYXcQWPj+qEv4+NDckn+TzhkNnliLiUSgeWuLg4Jk2axJNPPsnGjRsZNGgQI0eOJDk5ucz2eXl5NG7cmCeffJKYmJjz7jcoKIiUlJRSi5+fX2XLk4pqNxzqNYSsFOcsziJiurAgPx64vB0AM75OIiu3wOSKRFxHpQPLyy+/zF133cXdd99Np06dmDVrFhEREcyZM6fM9q1ateJf//oX48ePJzg4+Lz7tVgshIeHl1qkBnn5wCXXO9c1VL+Iy7hzYCtaNfLnaFYer3+/y+xyRFxGpQJLfn4+69evZ9iwYaW2Dxs2jDVr1lxUIdnZ2URGRtKiRQvGjBnDxo0by22fl5dHZmZmqUUqqfi2UNJiyM0wtxYRAcDXy8bTY53zDL334152H802uSIR11CpwJKeno7dbicsLKzU9rCwMFJTU6tcRMeOHfnggw9YtGgRc+fOxc/PjwEDBrBz587zvmfGjBkEBweXLBEREVX+/DqrWQ8I7QCFuZD4hdnViEiRyzuGcXnHJhTYDZ75MhHD0AO4IlV66NZisZT62TCMc7ZVRt++fbn11luJiYlh0KBBzJ8/n/bt2/Paa6+d9z2PP/44GRkZJcuBAweq/Pl1lsXy+1UW3RYScSlPjYnG22ZhxY6jfJ+UZnY5IqarVGAJDQ3FZrOdczUlLS3tnKsuF1WU1UqvXr3KvcLi6+tLUFBQqUWqoOs4sFgheQ0c32t2NSJSJCo0gLsGtgbgmcWJ5BaoN5/UbZUKLD4+PsTGxhIfH19qe3x8PP3796+2ogzDICEhgaZNm1bbPuU8gpo5Z3EG2DTP1FJEpLT7L29Lk0Bf9h87xbur9Q8KqdsqfUtoypQpvPPOO7z33nts27aNyZMnk5yczIQJEwDnrZrx48eXek9CQgIJCQlkZ2dz9OhREhISSExMLPn99OnTWbp0KXv27CEhIYG77rqLhISEkn1KDTtzqH6HZosVcRX1fb14fJRznqHXv99FSsZpkysSMY9XZd8wbtw4jh07xjPPPENKSgqXXHIJS5YsITLSOUV6SkrKOWOydO/evWR9/fr1fPzxx0RGRrJv3z4ATp48yT333ENqairBwcF0796dlStX0rt374s4NKmwjqPBJxBO7ofkn6DVALMrEpEi13Rrzkc/J7N+/wme/zqJf93U/cJvEvFAFsNDHj/PzMwkODiYjIwMPc9SFV9MhI0fQfdb4eo3zK5GRM7w26EMxr6+GsOATyb0o1erELNLEqk2Ff3+1lxC4lR8W2jrF5B/ytxaRKSUS5oHc1OvlgBM/WIrds0zJHWQAos4tewHDSIhPwuSvjK7GhE5y8PD2hPk50ViSiZzfyl7KhQRT6bAIk5WK8QUjcmiGZxFXE6j+r5MubI9ADOXbefkqXyTKxKpXQos8ruYm5yve5ZD5mFTSxGRc93aN5IOYYGcPFXAy/E7zC5HpFYpsMjvQqKgZX8wHLA5zuxqROQsXjYrU69yzjP00c/7STysOdSk7lBgkdLOHKrfMzqQiXiU/m1CGd2lKQ4Dpn25VfMMSZ2hwCKlRV8DXn6Qvh0ObzC7GhEpwxOjO+HnbeWXvcdZvDnF7HJEaoUCi5TmFwQdxzjXNSGiiEtq3qAe9w1uC8BzS7ZxKr/Q5IpEap4Ci5yr+LbQb59CYZ65tYhIme65tDUtGtYjJSOXOct3m12OSI1TYJFztR4CgU3h9AnYuczsakSkDH7eNv422vkA7lsr95B8TAM+imdTYJFzWW3Q9Ubnum4Libis4Z3DGNg2lPxCB89+lXjhN4i4MQUWKVvxUP07l0JOurm1iEiZLBYLU8dG42W1EJ94hBU7jppdkkiNUWCRsjXpCM26g6MQtnxqdjUich7twgK5vX8rAKZ/uZX8Qoe5BYnUEAUWOb/iqywaql/EpT00tB2h9X3YczSH//60z+xyRGqEAouc3yV/AKs3pGyCI7o/LuKqgvy8eWR4RwBmfbuTtKxckysSqX4KLHJ+AY2g/XDnuq6yiLi062Nb0LVFMNl5hfzzm+1mlyNS7RRYpHzFMzhvng92DU4l4qqsVgvTr+oMwKfrD7Ix+YTJFYlULwUWKV+7YVAvBLKPOGdxFhGX1b1lQ66PbQHAtEVbcTg0z5B4DgUWKZ+XD3S5wbmu20IiLu+RER2o7+vFpoMZfLr+oNnliFQbBRa5sOKh+pO+gtwMc2sRkXI1CfTjoSvaAfDCN0lknC4wuSKR6qHAIhfWtBs07gSFubD1M7OrEZELuL1/K1o3DuBYTj6vfrfT7HJEqoUCi1yYxfL7VRYN1S/i8ny8rEwb63wA9z9r9rHzSJbJFYlcPAUWqZguN4LFCgd+hmOaGVbE1V3avjFXRodR6DCY/mUihqEHcMW9KbBIxQQ1dc7iDLBpnrm1iEiFPDU6Gh8vK6t3pbN06xGzyxG5KAosUnHdiofqnwcOzVci4upaNvLnnkGtAfj7V4nkFthNrkik6hRYpOI6jgbfIMhIhuQ1ZlcjIhVw35A2NA324+CJ07y9co/Z5YhUmQKLVJx3Peh8jXNdD9+KuAV/Hy+eGNUJgNnLd3Ho5GmTKxKpGgUWqZziGZwTP4f8HFNLEZGKGdO1Kb2jQsgtcPDckm1mlyNSJQosUjkt+0LDKMjPhm2Lza5GRCrAYrEwbWxnrBb4anMKa3anm12SSKUpsEjlWCy/T4ioofpF3EZ0syD+2CcSgOmLEim068F5cS8KLFJ5MeOcr3tWQIbmKhFxF1OubE8Df2+2H8nif2uTzS5HpFIUWKTyGraCyAGAAZvjzK5GRCqoYYAPDw/rAMBLy7ZzPCff5IpEKk6BRaom5oyh+jWCpojbuLl3Szo1DSIzt5AXl243uxyRClNgkaqJvhq86sGxnXBog9nViEgF2awWpl/lnGdo3q/J/HZIM7CLe1BgkarxC4JOY53revhWxK30jgrhqphmGAZMXbRV8wyJW1BgkaornsF5y6dQmGduLSJSKU+M6oS/j431+0/wRcJhs8sRuSAFFqm6qMsgsBnknoQd35hdjYhUQniwHxOHtAXguSXbyM4rNLkikfIpsEjVWW2/d3HWUP0ibueugVFENvInLSuP17/fZXY5IuVSYJGLU9xbaFc8ZB81txYRqRQ/bxtPjY4G4N3Ve9ibruk2xHUpsMjFadwBmvUARyFs+cTsakSkkq7o1ITL2jemwG7w7OJEs8sROS8FFrl43YomRFRvIRG3Y7FYeHpsNN42C98npfF90hGzSxIpkwKLXLxL/gBWb0jdAqm/mV2NiFRSm8b1uXNAFADPLt5GXqHd5IpEzqXAIhfPPwQ6jHCub9LDtyLu6P7L29I40Je96Tm8t3qf2eWInEOBRapHTNFtoc3zwa7ukSLuJtDPm8dGdATgte93ciQz1+SKREpTYJHq0e5K8A+FnDTY/b3Z1YhIFVzbvTndWzbgVL6d579OMrsckVIUWKR62Lyhyw3OdT18K+KWrEXzDFks8NnGQ6zbd9zskkRKKLBI9Ym5yfmatAROnzC3FhGpkq4tGjCuZwQA077cit2heYbENSiwSPVpGgNNosGeB1s/M7saEamih4d3INDPi98OZRL36wGzyxEBFFikOlksv498q6H6RdxWaH1fJg9tD8CLS5PIOFVgckUiCixS3breCBYrHPwFju02uxoRqaLb+kXSPqw+J04V8Mq3O8wuR0SBRapZYDi0ucK5rjFZRNyWt83K1LGdAfjw5/0kpWaaXJHUdQosUv26Fd0W2jQPHA5zaxGRKhvQNpSRl4RjdxhMW7QVw9ADuGIeBRapfh1Gg28wZByA/avNrkZELsITozrh62Xl5z3HWbIl1exypA5TYJHq5+0Hl1zrXNfDtyJuLSLEnwmXtQHgH18lcjpf8wyJOaoUWGbPnk1UVBR+fn7ExsayatWq87ZNSUnhlltuoUOHDlitViZNmlRmuwULFhAdHY2vry/R0dF89pm6xbq14t5CiV9AXra5tYjIRbl3cBuaN6jH4Yxc5qzQw/RijkoHlri4OCZNmsSTTz7Jxo0bGTRoECNHjiQ5ObnM9nl5eTRu3Jgnn3ySmJiYMtv89NNPjBs3jttuu41NmzZx2223ceONN7J27drKlieuIqIPhLSGghzY9qXZ1YjIRfDztvG30Z0AeHPFbg4cP2VyRVIXWYxKPkXVp08fevTowZw5c0q2derUiWuuuYYZM2aU+97BgwfTrVs3Zs2aVWr7uHHjyMzM5Ouvvy7ZNmLECBo2bMjcuRW7pZCZmUlwcDAZGRkEBQVV/ICk5qz4J/zwD4i6DG5fZHY1InIRDMPgj++sZc3uYwzvHMZbt/U0uyTxEBX9/q7UFZb8/HzWr1/PsGHDSm0fNmwYa9asqVqlOK+wnL3P4cOHl7vPvLw8MjMzSy3iYrqOc77uXQkZB82tRUQuisViYerYztisFpZuPcKqnUfNLknqmEoFlvT0dOx2O2FhYaW2h4WFkZpa9afHU1NTK73PGTNmEBwcXLJERERU+fOlhjSMhFaDAMPZxVlE3FqH8EBu6xsJwPQvEymwa9gCqT1VeujWYrGU+tkwjHO21fQ+H3/8cTIyMkqWAwc034VLKn74dtNc0BgOIm5v8pXtCQnwYVdaNv/9ab/Z5UgdUqnAEhoais1mO+fKR1pa2jlXSCojPDy80vv09fUlKCio1CIuKPoq8PaHY7vg4DqzqxGRixRcz5tHhncAYFb8DtKz80yuSOqKSgUWHx8fYmNjiY+PL7U9Pj6e/v37V7mIfv36nbPPZcuWXdQ+xUX4BkKnq5zrmz42txYRqRY39IygS/NgsvIK+ec3SWaXI3VEpW8JTZkyhXfeeYf33nuPbdu2MXnyZJKTk5kwYQLgvFUzfvz4Uu9JSEggISGB7Oxsjh49SkJCAomJiSW/f+ihh1i2bBkvvPACSUlJvPDCC3z77bfnHbNF3EzMTc7X3xZAQa65tYjIRbNZLUy7yjnP0Px1B0k4cNLcgqRO8KrsG8aNG8exY8d45plnSElJ4ZJLLmHJkiVERjofxEpJSTlnTJbu3buXrK9fv56PP/6YyMhI9u3bB0D//v2ZN28ef/vb33jqqado06YNcXFx9OnT5yIOTVxG1KUQ1BwyD8GOr6HztWZXJCIXKTayIdf1aM7CDYeYtmgrC+/tj9V6cc8yipSn0uOwuCqNw+Livp0Oq1+G9iPgljizqxGRapCWmcuQmcvJybcz84YYro9tYXZJ4oZqZBwWkSrrdovzdWc8ZKeZW4uIVIsmQX48eEU7AJ7/OonM3AKTKxJPpsAitSO0HTTvCYYdtnxidjUiUk3+NCCK1qEBpGfn8dp3O80uRzyYAovUnm5FY7JoBmcRj+HjZeWpsdEAvP/jPnalabJTqRkKLFJ7Ol8HNh84sgVSt5hdjYhUkyEdmjC0UxMKHQbTv9yKhzwaKS5GgUVqj38IdBjpXNdVFhGP8rfR0fjYrKzamc632/ScmlQ/BRapXcVD9W+ZD3Y9oCfiKVqFBnD3oCgAnl2cSG6B3eSKxNMosEjtajsU/EMh5yjs+s7sakSkGk0c0pawIF+Sj5/inVV7zC5HPIwCi9Qumzd0vdG5vkm3hUQ8SYCvF0+M6gTAGz/s5vDJ0yZXJJ5EgUVqX/Ftoe1L4PQJc2sRkWp1VUwzerVqyOkCOzO+1jxDUn0UWKT2Ne0KYZeAPR9+W2h2NSJSjSwW5zxDVgt8uekwa/ccM7sk8RAKLGKO4qssui0k4nE6Nwvm5t4tAZi6aCuFdofJFYknUGARc3S9ESw2OPgrpGt0TBFP8/CwDgTX8yYpNYu5vyRf+A0iF6DAIuao38TZYwh0lUXEAzUM8OHhYe0BmLlsBydy8k2uSNydAouYJ+Ym5+umOHDokrGIp7m5d0s6hgeScbqAl+K3m12OuDkFFjFPh1HgFwyZB2HfSrOrEZFq5mWzMu2qzgB8vDaZl5dtZ83udA0qJ1XiZXYBUod5+znnF1r/PmyaB60Hm12RiFSzvq0bcVVMMxZtOsyr3+/i1e934WOzEhMRTJ+oRvSOCiE2siEBvvo6kvJZDA+ZpSozM5Pg4GAyMjIICgoyuxypqAO/wLtXgncAPLwDfOubXZGIVLP8QgcLNhzkp93HWLv3GEcy80r93stq4ZLmwfRpHUKfqBB6tgohyM/bpGqltlX0+1uBRcxlGPBaLBzfDdfMgW63mF2RiNQgwzDYf+wUa/ceY+3e46zdc5xDZ42Ia7VAdLOgkiswvVuF0DDAx6SKpaYpsIj7WPkifP93aDUI7lhsdjUiUssOnjjFL0XhZe3eY+w7duqcNh3DA+kdFVISYhoH+ppQqdQEBRZxHycPwKwugAGTtkCDlmZXJCImOpKZW3T1xXkVZlda9jlt2jQOoHdUI/q2doaY8GA/EyqV6qDAIu7lP2Nh70oY8je47K9mVyMiLiQ9O49f9x5n7d7j/LznGNuPZHH2N1dkI396twqhT+tG9IkKISLE35xipdIUWMS9JHwMn98LIW3ggfVgsZhdkYi4qJOn8vl134mSKzBbD2fgOOubrHmDekW3kJwhplUjfyz6e8UlKbCIe8nLhpntoSAH7lwGLfuYXZGIuIms3ALW7T9R8gzMloMZFJ6VYJoE+joDTNEVmHZN6ivAuAgFFnE/n01wDtMf+ycYO8vsakTETZ3KL2TD/pPOnkh7jpNw4CT5Z03AGBLgU3QLyfkMTMfwQKxWBRgzKLCI+9mzAv57lXP02//b4RxYTkTkIuUW2Ek4cJK1e47zy75jrN9/gtyC0gEmyM+rVC+kzs2C8LJpMPjaoMAi7sfhgH91hYwDcP37cMl1ZlckIh4ov9DBlkMn+XnPcX7Ze5x1+46Tk196uoD6vl7ERjYsGcyuS/MG+HgpwNQEBRZxT989C6tmQrth8MdPzK5GROqAQruDrYczWbv3GL/sdYaYzNzCUm38vK3ERjakd6tG9GkdQreIBvh520yq2LMosIh7St8Fr8eCxQZTtkFgmNkViUgdY3cYJKVmOm8h7T3OL/uOczwnv1QbHy8r3SIaOHshRTWiR2QD/H00H1JVKLCI+3rnSjj4Cwz7O/R/wOxqRKSOczgMdh3NLjWY3dGsc+dD6toimN5RziswPSMbEqj5kCpEgUXc16/vwldToElnuPdHjckiIi7FMAz2puc4pxMoCjGHM3JLtbFa4JLmwSWD2fVuFUKwvwJMWRRYxH2dPgEzO4A9D/6yEprGmF2RiMh5GYbBwROnS12BST5eej4kiwU6hAXSt2gcmN5RITSqr/mQQIHF7HLkYs2/HRI/h773wYgZZlcjIlIpKRmn+WXvcX4uGsxuz9Gcc9q0bVK/ZCTevlEhNAmqm0M5KLCIe9uxFD6+EfxD4f+SwKZLqSLivo5m5RXdQnIOZrf9SNY5bVo18qdP0TMwfVo3onmDeiZUWvsUWMS92Qvh5U6QkwY3z4MOI82uSESk2pzIyeeXfcdLphNITMk8Z0LH5g3q0ad1CH2LBrOL9ND5kBRYxP0tfRJ+eh06XQXjPjS7GhGRGpNxuoD1+4sDzHG2HMrAftZ8SGFBviUj8fZtHUKbxp4xH5ICi7i/1N/gzQFg84H/2w7+IWZXJCJSK3LyClm//0TJYHYJB05SYC/9dR1a34feUSElPZE6hLnnfEgKLOIZ3hwIqVtg1Ezo/WezqxERMUVugZ0NySdKBrPbkHyCvMLS8yE18PemV6uQksHsopsFYXODAKPAIp7hpzdg6RPQPBb+/L3Z1YiIuIS8QjubD2aUdKNev/8Ep86aDynQ14uerRqWDGbXpXkw3i44oaMCi3iG7DR4qSMYdpj4KzRub3ZFIiIup8Du4LdDGSWD2f269zhZeaXnQ6rnbXNO6FjUlTomIhhfL/PnQ1JgEc/x8TjY8Q0MnAJDp5pdjYiIy7M7DLalZJYMZvfLvuOcPFVQqo2Pl5XuEQ1KxoHp3rIh9XxqP8AosIjn2Po5fHI7BDWHSVvAav6/CERE3InDYbAjLct5BaaoK3V6dukJHb1tFrq2aFByBSY2siH1fWt+QkcFFvEchXkwsz3knoTbPoc2Q8yuSETErRmGwe6jOaUGs0vNLD0fks1q4ZJmQfQpmk6gZ6sQgutV/yCeCiziWRZPgXXvQtdxcN3bZlcjIuJRDMPgwPHT/FwUXtbuPcbBE6dLtbFY4N+39WRodFi1fnZFv79r/lqPSHXodoszsGz7EvKywDfQ7IpERDyGxWKhZSN/Wjby58aeEQAcOnmaX0oCzHH2pucQ3cy8CwIKLOIemsdCo3ZwbCckfgHdbzW7IhERj9a8QT2u7d6Ca7u3AJzzITUONG+GadfrkC1SFosFYm5yrifMNbcWEZE6yMywAgos4k5ibgIssH81nNhndjUiIlKLFFjEfQS3gKhLneub55tbi4iI1CoFFnEv3W5xvm6ayzlzsYuIiMdSYBH30mks+NSH43vgwFqzqxERkVqiwCLuxScAoq92rid8bG4tIiJSaxRYxP3E3Ox83foZFJwuv62IiHgEBRZxP5EDILgl5GVC0ldmVyMiIrVAgUXcj9UKMeOc65s0JouISF1QpcAye/ZsoqKi8PPzIzY2llWrVpXbfsWKFcTGxuLn50fr1q158803S/3+gw8+wGKxnLPk5uaeZ49S5xXfFtr9PWSlmluLiIjUuEoHlri4OCZNmsSTTz7Jxo0bGTRoECNHjiQ5ObnM9nv37mXUqFEMGjSIjRs38sQTT/Dggw+yYMGCUu2CgoJISUkptfj5+VXtqMTzNWoDEX3AcGhMFhGROqDSgeXll1/mrrvu4u6776ZTp07MmjWLiIgI5syZU2b7N998k5YtWzJr1iw6derE3XffzZ133snMmTNLtbNYLISHh5daRMpVfJVFY7KIiHi8SgWW/Px81q9fz7Bhw0ptHzZsGGvWrCnzPT/99NM57YcPH866desoKCgo2ZadnU1kZCQtWrRgzJgxbNy4sdxa8vLyyMzMLLVIHdP5WrD5QloipGwyuxoREalBlQos6enp2O12wsLCSm0PCwsjNbXs5whSU1PLbF9YWEh6ejoAHTt25IMPPmDRokXMnTsXPz8/BgwYwM6dO89by4wZMwgODi5ZIiIiKnMo4gnqNYCOo53revhWRMSjVemhW4vFUupnwzDO2Xah9mdu79u3L7feeisxMTEMGjSI+fPn0759e1577bXz7vPxxx8nIyOjZDlw4EBVDkXcXfFQ/Vs+gcJ8c2sREZEa41WZxqGhodhstnOupqSlpZ1zFaVYeHh4me29vLxo1KhRme+xWq306tWr3Cssvr6++PqaO9W1uIDWQ6B+GGQfgV3xv19xERERj1KpKyw+Pj7ExsYSHx9fant8fDz9+/cv8z39+vU7p/2yZcvo2bMn3t7eZb7HMAwSEhJo2rRpZcqTusjmBV1ucK5rqH4REY9V6VtCU6ZM4Z133uG9995j27ZtTJ48meTkZCZMmAA4b9WMHz++pP2ECRPYv38/U6ZMYdu2bbz33nu8++67PPzwwyVtpk+fztKlS9mzZw8JCQncddddJCQklOxTpFzFt4V2LIVTx82tRUREakSlbgkBjBs3jmPHjvHMM8+QkpLCJZdcwpIlS4iMjAQgJSWl1JgsUVFRLFmyhMmTJ/PGG2/QrFkzXn31Vf7whz+UtDl58iT33HMPqampBAcH0717d1auXEnv3r2r4RDF44V1hvCukLoZflsAvf9sdkUiIlLNLIbhGQNYZGZmEhwcTEZGBkFBQWaXI7Xt5znwzWPQrAfc84PZ1YiISAVV9PtbcwmJZ+hyA1i94PAGOLrd7GpERKSaKbCIZwgIhXZFAxTq4VsREY+jwCKeo3io/s1x4LCbW4uIiFQrBRbxHO2HQ72GkJUCe5abXY2IiFQjBRbxHF6+cElR7zMN1S8i4lEUWMSzxBSNybJtMeRqQkwREU+hwCKepXkPCG0Phach8QuzqxERkWqiwCKexWL5/eFb3RYSEfEYCiziebqOAyyw/0c4sc/sakREpBoosIjnCW4OrQc71zfNM7UUERGpHgos4pmKJ0TcNBc8Y/YJEZE6TYFFPFPHMeBT33lLKPkns6sREZGLpMAinsnHH6Kvca5rqH4REbenwCKeq1tRb6Gtn0PBaVNLERGRi6PAIp6rZX9o0BLysyDpK7OrERGRi+BldgEiNcZqdY7JsuIFWD0Lju8Fq61o8QKLrYyfvc6zzVp2m5Kfy9pWvA/rWT8Xt7OY/SckIuI2FFjEs8Xc5AwsR7Y4F1diqUh4sv4eiC7UprYCV3kh7ILhr6IBUYFOREpTYBHPFtIarvu3s6eQw+5cDDs4Cot+LgTDUfrnsrYZ9t/fX9bP52tjOM5fm2EHux3s+bX35+FOLNbSgapeAwgIhYDGENDEuV6/SdHPoUXbGoN/I7DprzYRT6P/q8Xzdb3RuZjBMM4KOYVlBJ/C84SpM38+X5vywtWFAlchOByUDlxnbLtQbZWq/4xX46z18/7ZOYqOrcD5c34WZByowB+6BfxDfg81AY2Lgs0ZoSagMdQvevUJqJZTLSI1S4FFpCZZLM5/7etf/GUzjIoFLkchnD4JOWmQcxSy0yAn3bmeU7SenQanjgGG8/XUMThagRq8A8oJNmdexWkC9Ro6b6+JSK3T36IiYh6L5fdnW6qDww6njp8RbI4WhZqzgk1OuvPnwlwoyIGTOXByfwXqtZ1xW+qMYFN8tebsxduveo5LRBRYRMSDWG3O8FC/8YXbGgbkZ58VbM64cnNmsMk5CqdPOK/+ZB9xLhXhG3RGsDnzas0ZS/FVHb8GetBYpBwKLCJSN1ks4BvoXEJaX7i9veDc21BlBZviqziOAsjLdC7H91x4/1bvMm5DnRlsGlPqgWOb98X/GYi4EQUWEZGKsHlDUFPnciGGAbkZZ9yOKiPYlFzVSYe8DGfAyTrsXCrCr0HFgk39Js55tXT1RtycAouISHWzWJzdsOs1gNB2F25fkAun0s//MHFxsCneZtgh96RzSd9x4f17+Z0n1JTRRdy/UfU9UyRSjRRYRETM5u0HwS2cy4U4HM7nacp8mPjouVdxCnKcDxdnHKhEt/BG53+Y+OyrOj7+F334IhWhwCIi4k6sVgho5FzoeOH2+TlnBJmjZwSbM29XFb2WdAtPdy5Ht114/94B596GKquL+JlXbizWoltUltKvFuu52878nW5r1WkKLCIinswnwLk0bHXhtvZCOH38PA8Tn9VNPDsN7HnOKzgncuDEvpo+kjOcGWYqGn6oZPsz3nfe351vX2VsK699qd9RwbrO3heVbF+BmsvaV9/7oGFkDZzTC1NgERERJ5uX88pI/SYQ1rn8tsXdws/XS+rsqzinT1RjoYbz86H80ZKl+l1yvQKLiIi4kTO7hTdqc+H2DgclQcM4Y/3s1zJ/R9FUDRVtb5zRnnJ+d6F9Ucn2Z/6ujG0V3hcV/Jzz7auSn13yOy58jBXpJVdDFFhERKTmaUoDuUj6L0hERERcngKLiIiIuDwFFhEREXF5CiwiIiLi8hRYRERExOUpsIiIiIjLU2ARERERl6fAIiIiIi5PgUVERERcngKLiIiIuDwFFhEREXF5CiwiIiLi8hRYRERExOV5zGzNRtGU3JmZmSZXIiIiIhVV/L1d/D1+Ph4TWLKysgCIiIgwuRIRERGprKysLIKDg8/7e4txoUjjJhwOB4cPHyYwMBCLxVJt+83MzCQiIoIDBw4QFBRUbft1JZ5+jDo+9+fpx6jjc3+efow1eXyGYZCVlUWzZs2wWs//pIrHXGGxWq20aNGixvYfFBTkkf8RnsnTj1HH5/48/Rh1fO7P04+xpo6vvCsrxfTQrYiIiLg8BRYRERFxeQosF+Dr68vUqVPx9fU1u5Qa4+nHqONzf55+jDo+9+fpx+gKx+cxD92KiIiI59IVFhEREXF5CiwiIiLi8hRYRERExOUpsIiIiIjLU2ABZs+eTVRUFH5+fsTGxrJq1apy269YsYLY2Fj8/Pxo3bo1b775Zi1VWjWVOb7ly5djsVjOWZKSkmqx4opbuXIlY8eOpVmzZlgsFj7//PMLvsfdzl9lj9HdzuGMGTPo1asXgYGBNGnShGuuuYbt27df8H3uch6rcnzudA7nzJlD165dSwYU69evH19//XW573GXc1esssfoTuevLDNmzMBisTBp0qRy29X2eazzgSUuLo5Jkybx5JNPsnHjRgYNGsTIkSNJTk4us/3evXsZNWoUgwYNYuPGjTzxxBM8+OCDLFiwoJYrr5jKHl+x7du3k5KSUrK0a9euliqunJycHGJiYnj99dcr1N7dzh9U/hiLucs5XLFiBRMnTuTnn38mPj6ewsJChg0bRk5Oznnf407nsSrHV8wdzmGLFi14/vnnWbduHevWrePyyy/n6quvZuvWrWW2d6dzV6yyx1jMHc7f2X799VfefvttunbtWm47U86jUcf17t3bmDBhQqltHTt2NB577LEy2z/yyCNGx44dS237y1/+YvTt27fGarw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+ "text/plain": [ + "
              " + ] + }, + "metadata": { + "filenames": { + "image/png": "/Users/mhjensen/Teaching/MachineLearning/doc/LectureNotes/_build/jupyter_execute/week35_146_0.png" + } + }, + "output_type": "display_data" + } + ], "source": [ "import matplotlib.pyplot as plt\n", "import numpy as np\n", @@ -2106,7 +2529,40 @@ "collapsed": false, "editable": true }, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "MSE before scaling: 0.00\n", + "R2 score before scaling 1.00\n", + "Feature min values before scaling:\n", + " [1.00000000e+00 6.97906022e-03 2.43639284e-03 4.87072815e-05\n", + " 1.70037324e-05 5.93601008e-06 3.39931051e-07 1.18670072e-07\n", + " 4.14277718e-08 1.44624525e-08 2.37239927e-09 8.28205578e-10\n", + " 2.89126914e-10 1.00934327e-10 3.52362157e-11 1.65571174e-11\n", + " 5.78009660e-12 2.01783414e-12 7.04426744e-13 2.45915671e-13\n", + " 8.58492636e-14]\n", + "Feature max values before scaling:\n", + " [1. 0.99970894 0.99978365 0.99941797 0.99949266 0.99956735\n", + " 0.99912709 0.99920175 0.99927642 0.9993511 0.99883628 0.99891093\n", + " 0.99898558 0.99906023 0.99913489 0.99854557 0.99862019 0.99869482\n", + " 0.99876945 0.99884409 0.99891873]\n", + "Feature min values after scaling:\n", + " [ 0. -1.71761101 -1.75770568 -1.12330033 -1.12591227 -1.12871842\n", + " -0.88613493 -0.884669 -0.88323026 -0.88182591 -0.75269037 -0.75050135\n", + " -0.74829661 -0.74607851 -0.74384949 -0.6652177 -0.66294408 -0.66064822\n", + " -0.65833132 -0.65599456 -0.6536392 ]\n", + "Feature max values after scaling:\n", + " [0. 1.71737253 1.75576555 2.20916295 2.23971032 2.26995402\n", + " 2.60543038 2.63162342 2.65743689 2.68286725 2.94273542 2.96631321\n", + " 2.98947894 3.01222822 3.034557 3.24159785 3.26297455 3.28391978\n", + " 3.30442964 3.32450054 3.34412923]\n", + "MSE after scaling: 0.00\n", + "R2 score for scaled data: 1.00\n" + ] + } + ], "source": [ "# Common imports\n", "import os\n", @@ -2641,7 +3097,43 @@ "collapsed": false, "editable": true }, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "True beta: [2, 0.5, 3.7]\n", + "Fitted beta: [2.08376632 0.19569961 3.97898392]\n", + "Sklearn fitted beta: [2.08376632 0.19569961 3.97898392]\n", + "MSE with intercept column\n", + "0.00411363461744314\n", + "MSE with intercept column from SKL\n", + "0.004113634617443147\n", + "Manual intercept: 2.083766322923899\n", + "Fitted beta (wiothout intercept): [0.19569961 3.97898392]\n", + "Sklearn intercept: 2.0837663229239043\n", + "Sklearn fitted beta (without intercept): [0.19569961 3.97898392]\n", + "MSE with Manual intercept\n", + "0.00411363461744314\n", + "MSE with Sklearn intercept\n", + "0.004113634617443131\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
              " + ] + }, + "metadata": { + "filenames": { + "image/png": "/Users/mhjensen/Teaching/MachineLearning/doc/LectureNotes/_build/jupyter_execute/week35_181_1.png" + } + }, + "output_type": "display_data" + } + ], "source": [ "import numpy as np\n", "import matplotlib.pyplot as plt\n", @@ -2914,7 +3406,60 @@ "collapsed": false, "editable": true }, - "outputs": [], + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/mhjensen/miniforge3/envs/myenv/lib/python3.9/site-packages/sklearn/utils/deprecation.py:87: FutureWarning: Function load_boston is deprecated; `load_boston` is deprecated in 1.0 and will be removed in 1.2.\n", + "\n", + " The Boston housing prices dataset has an ethical problem. You can refer to\n", + " the documentation of this function for further details.\n", + "\n", + " The scikit-learn maintainers therefore strongly discourage the use of this\n", + " dataset unless the purpose of the code is to study and educate about\n", + " ethical issues in data science and machine learning.\n", + "\n", + " In this special case, you can fetch the dataset from the original\n", + " source::\n", + "\n", + " import pandas as pd\n", + " import numpy as np\n", + "\n", + "\n", + " data_url = \"http://lib.stat.cmu.edu/datasets/boston\"\n", + " raw_df = pd.read_csv(data_url, sep=\"\\s+\", skiprows=22, header=None)\n", + " data = np.hstack([raw_df.values[::2, :], raw_df.values[1::2, :2]])\n", + " target = raw_df.values[1::2, 2]\n", + "\n", + " Alternative datasets include the California housing dataset (i.e.\n", + " :func:`~sklearn.datasets.fetch_california_housing`) and the Ames housing\n", + " dataset. You can load the datasets as follows::\n", + "\n", + " from sklearn.datasets import fetch_california_housing\n", + " housing = fetch_california_housing()\n", + "\n", + " for the California housing dataset and::\n", + "\n", + " from sklearn.datasets import fetch_openml\n", + " housing = fetch_openml(name=\"house_prices\", as_frame=True)\n", + "\n", + " for the Ames housing dataset.\n", + " \n", + " warnings.warn(msg, category=FutureWarning)\n" + ] + }, + { + "data": { + "text/plain": [ + "dict_keys(['data', 'target', 'feature_names', 'DESCR', 'filename', 'data_module'])" + ] + }, + "execution_count": 16, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "from sklearn.datasets import load_boston\n", "\n", @@ -2968,7 +3513,32 @@ "collapsed": false, "editable": true }, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "CRIM 0\n", + "ZN 0\n", + "INDUS 0\n", + "CHAS 0\n", + "NOX 0\n", + "RM 0\n", + "AGE 0\n", + "DIS 0\n", + "RAD 0\n", + "TAX 0\n", + "PTRATIO 0\n", + "B 0\n", + "LSTAT 0\n", + "MEDV 0\n", + "dtype: int64" + ] + }, + "execution_count": 18, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "# check for missing values in all the columns\n", "boston.isnull().sum()" @@ -2992,7 +3562,30 @@ "collapsed": false, "editable": true }, - "outputs": [], + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/mhjensen/miniforge3/envs/myenv/lib/python3.9/site-packages/seaborn/distributions.py:2619: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n", + " warnings.warn(msg, FutureWarning)\n" + ] + }, + { + "data": { + "image/png": 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\n", 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+aTzfD5qDNiM7SBr6/XDcSxbl4Z0HuHm5U963IsvHLOWfzYcBuHk5mN8uraFh+yb8ufgPq9bllzO3KFvEgekfVAOgnldhtHoDK8+H0L2GFyqFzGR7g8HA/H9u0MzblSmtqgLgW6wQeoOBjZfDSMnQoZJLWXk+hJZl3BjesBwAtYsXIiwmiU1XwvI0eFXU/Rj943DS9/8OgD40EGQyFL6t0V7+2+INJ2lhT/RxT9Dfz/niWV6pHob4aNL3/QYGA/p7N5Bo7JH7NCfj6OY8u0C17dUL7e3bxM+cCUD6+fNIZDI0n35K0mbz/Udib4+6TRsSf/6Z5D+Mv5X0y5cBsB88mIRffsGQkIBdr16kXbhA3OTJWXkzbt6k0OrVpNSoQfqlS3lSnzI+ZanZrBbTe0/hcua+c+N8IMtP/kqrnh/w51LLQd+zfWfeoNkm+86X34/I2neadm6Oi6cL49qPIfhSEAD+J/2wL+BAn4l9+bbDWKvWRdnqU/QP7pK6dgEAuhuXQSbHpnlH0v/Znvt5x90Lm5ad0MeZjxBRfvAZhtQUkpdNgAzjMVz/NAr1gInIPL3R3Qk0y2Mt71L7CO+mfLlgU0ZGBlu2bKFjx44mgeszw4cP57fffssaDpwTpVKJVJp7FRUKBc2aNWPfPtPhRIcOHaJs2bJ4enq+fgX+Izdv32Xa/GW0bd2cWRO/ftvFeWWlG1YhPSmVkOPZdxqToxMIPXcD7yZVc8z3+PYDTv+6J+tiHCAjNZ34yGgKFHfJ0zIjVyAvWwXtJdML+oyLJ5CoNcjLVM45r0yGpt8Y0g5tRx953/x9tS2aL75DG+xP8vffWLnguSvaqAoZSak8PJbdFmnRCUSdDaJo05zbwr1xZeJuP8wKXAHibj0k7tZDPDLzebaqgT49g1sbj5rkPT54GccGLLFuReQKZGWqkHH5lElyxqUTSFQa47CrnMhkqD8fTfqR7eijXmgfiQTt9ctkHDftrXm2nbSIm1WKnxPvhlVIS0rl9omrWWnP9pUyTarlmO9xyENOWNhX4iKjcS5eBIC0xBTObzicFbg+8+RuBM7W3J/kCuSVqpF+9rhJcvrpY8Z9p4L5NAf9k8fEjRpA2rG/sxMNBtDpkCgUWUmykqXRJyb854ErQNWG1UlNSsH/+JWstPjoeK6fC8SnSc0c862fs5ZZn08zubh79m+5jfF+ssLGWMfkhOSsbVISU8hIS8e+gINV65Gu1XHxfjRNS5uea5uXcSU5Q8eVB+YX1sGP4rkfl0zX6iVM0j/18WJXv8aoM4PdHzr4MrxROZNtFFIp6bo8nP8lkyP1KIPu1mWTZN3NS0hsVEiLelvMJi3iif5x7kMikcmNwYjBkJVkSElEIleAjfJfF90ihQKbatVIPWF6kyz12DGkGg02Vcz3H4mtLSk7d5J2+rRJuu6+8bglc3dHWqAAUgcH823CwtDHxqLMnJqVF6o39CElKQU/s33nGj5Ncu4wWD9nDTNz2Hee7TMepT1JiE3ICoyeCTx3jXI1y2PrYGu9isjlyLwro/U3/T/U+p0ynndKVcw5r1SGqvtI0o/tQv/IfAqIvEpdMs7+nRW4AujDb5M0sVeeBq7wDrWP8M7Kl8FreHg4sbGxVKtWzeL7RYoUoUqVKshkMovva7Vajh49yo4dO0x6U3PSunXrrKHDz+zdu5cPP/zwjcr/X3FzLcLeP1YwZtiAlwby+Umh0kWJufcI/QsXMNGhURT0yjkYuLjuMKd/3mOSVtDLlSJlPHgU/JKLjn9JWtgNicIG3QvBjS7KeNKRunrkmFfZtifIFKRtz6FnOD2NxPF9SfltLvr/YKjj8xy93UkMe4ThhbZIuBuFQ8mc28KxtDvxdyLN0hNCo7AvabwIdq5YnPg7kbjULseH+6bRPXQV7c8tokzPZtatBCAt5IpEYWMWfOofGeftSl2K5phX+XEPkMlJ22lhXrvBQNqWX9D6nzFJVlSvb/z8B6H/ruAvUbi0u8V95WlYFIVy2VfOrzvEyV92m6QV9HLFpYwHUcHG/6OQU9fY8e0Kk22kchllm/oQZcX9Serqbtx3Hph+pj7i2YW0hRuE2gx0t4MhJRkkEqSFiqDp+wVSV3dS92ePBpB5lcaQmIDdN9Nw2rCHApv2Yfv1JCQFnK1W/px4lPYk6l6UWdtEhkbg5uWeY77oyKfcCwoDQKVRUbleVbqN7s71c4FZ6feCw7h6wo9Ow7vgWaYYdo529JzwOTZqJad2Wben/35cChk6PcWdTS8aPZ2Mf4fFmA9ND34cD4BSLmXYXxeos3g/DZcdZPbhQNIypwxIJBJKFrTD3UGNwWAgLiWdbVfD2X39AZ2rFbdqHZ4ncSyERK7AEGN6Q0MfaxxqLS1g+caMtLAnEhs1yq7foB72I+qB81HUbw/S7OsMrd8/SAoUQV6zJSjVSN1KIvdpju7OVUhNtvi5/5bMzQ2JjQ26cNP9R/fAeO6RWbjBro+MJGHRIrM8ygYNMGRkoAsPR5+YiEGrRfZCB4HEzg6JvT0yt7y7MedR2sPivhMRGoG7V87H6qeRTwkLCgWM+06VelX5bHQPAs8FZqXHR8ehsdOYBUGuxY31dClmvRtz0oKuSOQKs+BT/zjzvFM457rYtO4Gcjnp+8zn/UucXZBo7NBHR6HsNAi7WRuw+34b6gGTkBQobOHTrOtdaZ98xaB/u693TL4cNhwXZ7yAd3R0fKXtd+3axYEDB7L+Tk1Nxd3dnb59+zJo0KCX5n/vvffQaDRZQ4fj4uI4c+YM06ZNIygo6KX53xZHB3scHezfdjFem8pBQ9pzk/efSU9KRWn36sNmFSob2i0YhDY1g3MrD7w8w78g0RgPtIaUFy5QMi9YJCqNxXwyr7IoW3UiadbInOdG6rSWe2T/AzYOGjIstEVGUgqKXNrCxtGWhLvmvV0Ziak42hvzKZ3t0bgWoP6ywVxd8Bdxtx9Sok0d6szqA8DNNYetVAuQaIzz1Q0vXkA+ax+15faRliiDTcuOJM0d9cpzV6VF3FF27I8u7BbaaxfevNCvQO1gS6qF9klLTHntfaXj94PJSE3nzKr9OW7XevxnFPJyZd2ABW9UXkuktplt88K+Y0gx1uvZvpUTVcfP0HTvD0Dq37vRXvPLek/uVRppwcKkHdxN6s4tyDyKo/60Dw4zlhA3sh+kpVqtHi/SONiSkmgesKQkpaB5xbb53W8dCqWC+Oh41kz/3eS9FZN+4dvVk1jw91LAOE3kx6+XcPOSdc9JCWnG372tjenlgMbGGLQlpWvN8sQkG4dCjtpxmdbl3elRoySBkbH8dOYWMcnpzPm4usn2/g9j6LPJuAhYeRcHulXPw+BVadzXDekvtP2zv20stI3GHomtIxKDgYwTWzEkRCMrVg55zVZI7J2Nw4QBfXgw2gsHsGnYCZuGnYxpUWGk7c27qTvSzLU4DMk57T+Wj20vUjZsiKplS5K3bsWQeaM+9Z9/0HzyCdrQUNJOnEBaoAD2X35pXNApD2+I57bvqF9x31nttz5r31k9Pfsm3PHtx2jb/xO+Xj6W3yf/ytOoaGo2rUnTTs0BUKqtWC91DuedtMy2yWEakLSYNzZN25O8+BuTxbOekdgZr32VbXqjC7tpnPNq54jy415ovpxpnPOanmaWz1remfYR3ln5Mnh1djbeNY+NjX2l7Zs2bcrXX3+NXq/H39+fWbNm8d577zFo0CDk8pdX8fmhw23btuXgwYNUq1YNF5d39A7Qf0gikSCRmi56JZFKMDw37Cr7DTDoX+0OkdJOTdffvsK9shd/DFhEfETeriqKJHOQgqVy55QuV6DuO4a0g9vQ3Q3Ou7K9KgttgVRiuUoSCeTSFhKpBAM5taExXWYjR1XQgaP9FnFv30Ugc85r0UJUGd7OqsErzxZWy6l99Dm0T5/RpB/ahj701dpH6uqJ5qvZoM0g+adpOX/fG7C4r0gkFr9DIpFgeMW7qUo7Nd1//Yqilb1YP3AhcTnsK62+/ZT3+rTi2I87uHHw4utXICcva5uX1CPj/GniA68iL10OdbfeyAoVIWGycTXopCVzMKSno7trXKVSe/0qunt3cZjzA8om72fNl/33VTBvG2kOxzGJRILe0u/tBTK5jNl9pyOTy/igz8dM3TKLmb2nEngmgKKlPZi+bQ6PwqOYP2g2yfHJ1GvTgEFzviAtJY2ze0+/9PNf1bM65LQ0ocTCOxmZ9Wvi7ZI1n7VWsYLoDbD0ZDCDo70p4Zy9AKK7o4ZfO9fmcWIqy0/f4rN1p1j3WT0K2ubBUNs3+b2lp5L65wIM0ZEYEo2rpevv38Sg1WJT/xMyzu3GEB2Jonl35BXrkXF2N7p7N5A4FkJRtw3K9sNJ+3MBaPNgER3pG5x7XqBs1AjH8ePJ8Pcn8ZdfstITFiyA9HQcRo9GMnYshpQUkjZtQqJUQqp1bvxY3nekOe47hlfcd2b2nYZMLuPDPh8zfctspveewrUzAdy/Fc7MvtMYPGsoiw/9AMDtq7fYMH8dA6YNIi3Feje0XrqgZw7XBaruI0k/ugP9PcvzqyWZ162GhFhSV8zM+hz9kwhsv/oeRc0mZJzO+Sbk63iX2ydfecVrW+HV5Mvg1dPTk0KFCnHlyhU++OADs/dDQ0OZOnUqY8caJ3bb2tpSvLjxTq6Xlxeurq706dMHmUzG5OcWIsjNBx98wODBg0lMTGTfvn0Wv1d4fY2Gf0LjkR1M0gL3nKOgl/lcZhuNitQE816mFzm4OfPZytEU9HJly9Cl3Dxy5aV5/i1DsvFOtVkPXmaPqyHFfGidqkMfkEpI27ku+wLkGan0Pz+YVR35CVVHtTdJC919DoeS5nPoFBoV6bm0RXpcMjYW7sAqbFWkxxvv2GYkpmLQ67l/xHT13odH/SnapAqqQg6kPol/k6qYefb/L1G/0IuXS/sYVyKWGleKzmqfzJO4hfaRla2KZvAk4yIaC8dieGI+bPrfaDK8Pc1GmO4rAXvOUaikhX3FVkVa/Mv3FUc3Z3r+PoaCJV3Z9MUSgi3sK3Klgg7zB1Hl47oc/2knB+dadzEgQ1LmvvNCD6tErc58P/dV0nVhxjm52utX0SclYjdsLPJyldAGXUMbbD73Sxt0DX1iAjKvUtYoPgAdh3eh88huJmln9pyyODxYpVGZzFXNiU6r4+oJPwACTvqz4O+ltB/akcAzAXzUtw1SiYRpn31HYmyCcZtT/mjsNfSbNpBz+85YvgH4BuyVxrloL/awJqcbh//aKc0vE2wze2Ublixikl7PqzBLTwYT/CjeJHgtYqeiiJ2xR6WSmxNtVxzjr4DwHFcy/jcMz3q9lC8cn2wye3TSLOw32gz0926YJevuBkD9T4yLOaWnIq/cAO25fWSczrwpcv8m+shQ1L2mIK9UD62fdRehA9AnPtt/TM89WfvPc9OdLNF06oTdoEGk+/kRN2ECZGSPMDGkpBA/bx4Jy5YhdXFBFxkJqamoW7cmPcI6q/d3Ht6VLi/sO6f3nML9X+47/pn7ztWT/iz6exkdhnbi2hnj2g3+J/wYVL8/RTyNHRCPwqNo2sk4XSUhNvf/r9eRdd55ceRV5m/PkGrhvPNsJeIDm7LPO89ix8zzjiHV+BvVXr9kEgDrQ4MxJCci9ShptTq8y+0jvLvyZfAqlUrp2LEj69ato1+/fmY9oL/99ht+fn4ULWp57H2dOnXo06cPK1asoGnTpjRs2PCl31mnTh1sbW3566+/uHjxIvPnz7dKXf6/u7ThCDcPm14wl3u/JqUbVsnsPco+MDuXcHnpsytdynnSfc1Y5Cob1vWaS+hZ8wuOvKB/9BCDToe0iOlvTpY5l1L3MMwsj6JmQ6SFXHH8ebfZe44rDpL829z/7FmuADfXH+H+IdO28GxVA/dGVYy9Fc+1hb2XC3G5PHM3PiQC50rmQ//sS7jwxM8YbMTfjUQilSJTyNGnZV8wSTPvKutSrfeImaz2KWx6wpUWMf6tj7DQPjUaIC3kisMPO83ec/h5Pykr55Fx2rhgkNy3Ceo+X6OPekDy4m8xxDyxWtmfubDhMMGHTReZKd+yJt4NK5vtKwWLu/Dodu5DzV3KedJ79TcoVDas7jWHuxb2FaW9ml4rx+Dp483eaes4tcLyY0T+DV3kQww6LTK3ojzf4lI34zxxXXioWR5pEVfkVXxIP3bIZLVO3W3jkFlp4SJIwu1Q1GmI9mYg+nDT9pUoFBjirTd//NCGg1w6bNob7ft+bao2rG7WNq4l3Lh/O+c5wzWb+5IUn8iN89ez0rQZWsKCQvH0LgZAoaJFeBDyICtwfeb6uWvU/bAeDgUdiHtinfp5OGmQSSSEx5pelIZnPiarZEE7szzP5sO+uPCSNvOGj1IuIyldy7GQKCq5OlGsgK1JXgeVgshXuFH5JgyxjzDodUicTOcGSp2MgbY+2jwokxRwQeZZDm3QuezhxWBciInMRZnsnZFIpOge3jb9vqcPMaQkICmY8zznf0P30Hhsk71wvfPsb22Y+bHtGfthw9C0b0/qkSPEzZplErgC2NStiyEhgYxr19CFhgIgcXJCWqQIGTet88iSgxsOcPGw6fQK3/drU83CvuNWwo3w2/dy/KxazX1Jik/i+vnsm1Zm+457IarUr8bxv47yKDx7akvJyqVJiInn8X3rPWZK/yQCg06HpJDp/OBn5yF9pPlxQF6tHtKCLtjP32r2nv2inaSsW4jW75TxNyxXmG2DTGbVx+S8y+0jvLvy5YJNAIMGDaJ48eJ07dqV7du3c+/ePQICAhg/fjxbt25l2rRpZs9lfd7w4cMpUaIE3333HUkvubMPIJfLadGiBYsWLaJWrVpZQ5eFfyfhUSwPA+6avEKOB6C0V1OqUfYqiRpne0rULm+yAvGLHNyc6bFuHAaDgd87TPnPAlfAuIDMzasoatQ3SVbUbIAhKQHdHfN5aEmLJ5I4ZYjJSxd6E13oTRKnDEHrd/a/Kj0AKVGxPL161+T18FgANvZq3Btnr8ardLbHpU45kxWIX/TweACO3u44emdfsD37+1m+Z4/eKdG2jklej5Y+RF8PszjX9o1pM9DdCkDuU88kWVEjs30sDNtOXjaJxOlDTV66sFvowm6ROH0oWn9j+8gr1UL9+Rh0IddJmjMiTwJXMO4rDwLumrxunwhAZa/B28K+cutEzu3j6ObM52u/BYOBnztMthi4SmVSevz2NUWrlOKPL5bmSeAKQEY62sCr2NQxvYlo814j9IkJaG9ZKJuLG3ZfjsWmrmkehY8vANq7IRgyMrAdNAJ1h89Mt6ldH4lSRcZzc2P/rZhH0dwJuG3y8j9+BY29hqqNsud3Ojg7UKF2RZMViF/08YB2DJg5BKks+/SrsddQxqccYTdCAXgYch8Pb0/sHE3PcWVrlCc5PolEK/ZOKOUyfDycOXwr0uRC9dDNSOyVciq5OpnlqeHhjFohY3/QQ5P0oyGPkEslVHF3QiqRMOVAAKsvmK5mHRgZS1xqBmUKW3fV5Cw6Lfr7t5CV9jFJlpWpgSE1CX3kXbMsErsC2DTvjszbdCVVWdlaGNJS0EeFoc8MimUvrFYsKeCCRG2PIS5vjgukp5Ph74/yhZvwqkaN0CckkHHD8nnQrn9/NO3bk7R5M3FTp5oFrgCaNm2wGzzYJM22Y0fQ60k/c8Zs+zcR8yiakIDbJi//435o7DVUM9t3KuF/3C/Hz2oz4BMGzhxstu+U9SlL2A1juzoWdOKLecOoVDf7nOZU2IkGbRpw7uA5q9QpizYDXcg1FFXfM0mWV6uHITkRXZj5DYCUX6aSNG+EyUsXfhtd+G2S5o1Ae+08pKeiC7mOvGpdeG7qm6xMVSRKNboQ6602/E63Tz5iMOjf6utdky97XgHUajXr1q3j999/59dff+Xhw4colUoqVqzI6tWr8fX1zTW/Uqlk2rRp9OzZk4ULFzJhwoSXfucHH3zA5s2b8/0qw//rws4HcffMdTosHsLfszaSHJNI4xHtSY1P5uK6Q1nbFfYuisxGTmSg8c5y68k9sSvsyK5xK1DaqfGonj3kLC0xhce59BRaQ+qu9dh+PRfNkImkn9iPrHRFbFp1JnXLb8Y7oSoNMvfi6B8/xJAQh/6++UXSs+FAutD88SDuR+eCiTx9nQZLh3BpxibSYhKpOuoT0uOTubk2e06qo7c7MhsF0ZltEbrzLJW/bEOzdWO4PNM4zNTn2y7EBIUTttt4Aoo6c4Pwg5epNaU7co2S2KD7lOxUnyK1vPnn84VWr0vang1oRs5GPXACGacOICtVAZuWnUjbuuK59imG/lEEhsQ4iysFP1t4Qx9mnEOJXIGq11eQmkzano3I3IqZbK+PeZJnwSxA6Pkg7pwJpNOioRyYtYHk2ESajuhAanwS59dlt0/h0kWRK+VEZLbPR5N7YVfYke3f/obKXo3nc/tKakIKj28/oHbPlnjVLs/59YeIi3hqsg1A+BXTHqZ/I2XzGuynLsBuzBTSDu1FXq4iqk+6krLmZ+MzKtUaZJ4l0Ec+wBAfhzbQn4yrl9EMGI7E1g7dg3soKldH9Uk3UvfvRH/fWM/Uvzah7tILfWw0GZfPIytRCnXX3qRfOI3WP2+eUfnMjfPXuXYmgOGLv2LtrNUkxiTQaURXkuKT+Htd9nw0D29P5DZyQgONx4M/F//BhLXfMWr5WA6u24/aTs0nQzqgUiv5Y+EGAHb9toMGnzRi0sZpbFu2heSEZGq3qkv9tg1ZPW0FOq11nyfar04pBm05z5jdV2hbyQP/h7GsvnCH4Q3LoVLISEzL4M7TRDycNDhrlGhs5Ax+z5sFx4JwUCpo6u2K/8MYVp0PoVv1EjhrjHNZe9cqya9nb+OoVlC7mPEZrz+fuUWZwva0rZTzCu3/Vsa5PSg7jsTmo4For51C5l4Kec2WZJzYalyYzUaFtKC7cQXilET094PRhQdh06gzGQol+ugIZCWrIK/elIzjf0Ka8bigvXzIuNIwoLt3HYl9QeMzZeOfog3Iu+c9J65dS4Hvv8dx8mRS9u5FUakSmq5dSfzZuP9INBrkJUqgffAAQ1wc8tKl0XTrRkZQEGlHj6KoUMHk87ShoRiSk0neupUC8+dj98UXpJ06hY2PD7bdu5O0fj06Kw0btuT6+UACzlxlxOJRrJ21ioSYBLqM6EZyfBIH1mU/ttDD2xOFjYK7gcYbIFsWb2Li2smMXv4NB9btQ22nof2QDijVKjZl7jshAbe5ceE6A2YMZs3Mlei0ej4b3R2dVs/mRRutXpf0A3+gHjodVZ9vyDj7NzKv8tg0bU/azlWZ5x01UtdiGJ5EYEiMtzgK6Nl1gT48+5ibtms1mmGzUA+cQvqRbUjsnYwLOIUGoQ3I2yDvXWof4d0kMVhr4oxgUcaTOy/fyArOX77K51+O5felc/D1MX/um7XMqDHRKp+jctDw/sTulHu/JhKJhPBLN9k/dR1P72SfMHtvGo+TR2EW1R+BTCHj2xu/I1NYvt8SeuY6q7rOeO1yjGz6es+HlPvUQ9WuF1JXDwwxT0k7soP0A38CxjmRdt98n+twYNux3wOQNGeUxfclBV1wmL/+jYcU7zhoPj/yZWwcNdT8rjue79dAIpXw6MJNLk5ZT3xIdlu03DIeO89CbKszMitN4+5MrSk9cG9YCX2GjofHA7g4eT0pj2KztpEqFVQd+QklO9RD5WxP7K2HXF34F+EHXh5YtPvg9YcPyavXQ9mmB1IXDwyxT0n/ZyfpfxuHZ8nKVMF29HyT4cAv0nw9D4Dk+cYFgWTlqmE7am6O35e2cy1puyw8YseCOX8Xep2qZFE52PLBxO5UaGncV8Iu3WTvtLU8eW5f6btpAgU8CjO//nBkChnfXV+Z475y5+x1VnSdTr8/JuJVu3yO3zu+xKe5lmtU1de7WaSo0wB1tz7Iinqif/qEtL1/kbrD+LB7eaVqOMxYTOLiWaQfyQz81BrUXXph814jpM4F0UdFkHpgF2m7/swe4i6RoGzdFmWrtshci2JIiCPt+GFSNv5uDIpfw0A/p9faHsDWwZZeE/tS6/3aSCUSgi7dYPXU33l4J/v/ZvKm6RT2KMLQ+gOy0irVq0Kn4V0pUcELg15P4JlrbJi3lgfPDQV3L1mUT8f2oFLdykikUu7fCmfHz9s4v//VRmysHv968+KO3Irkp9O3CI1Jooidks7VitOzpvEzLoY/pf/mc0x5vwptngs6d1wLZ+3Fu9yLTaawrZL2VTzp41sKaeZCNnqDgT/977HFP4zw2GQcVAqalnZlaP0yWXNtX8lL5nVaIitdHUXdNkgKuGBIjEXr/w/aS8b9XupRBlXn0aTtX4nueubiVzZqFHU/Rla6OhJbRwyxj8m4cgjdC0GpvHoz5FUbIXEohCEpDn3YddJP/QUpr17GhJ23Xrs+yvr1se3TB7mnJ7onT0jZvp3kzcb9R1GtGs6LFhE3ezap+/dj26cPdr165fhZ0SNGkOHnB4CqaVNse/RA5uaGLiqK5O3bSfnrr9cq2+C7r9+LbutgS5+JffF9vw6SzH1n5dQVJvvO1E0zKOJRhEH1+2elVa5Xhc7Du+GVue9cOxPAuhf2HcdCTvSZ1JdqDaojkWDcZs4aIkJfLSBf3fb1eqrkVepi0/rT7PPOiT1k/GP8P5SVroxm2CzjcODzlhcpVH85C4CUpeNM0qVe5VB+1BNZ8TIY0tPQBpwlbfvvYGENh9z02vH6gyzza/tsCzOf5vO/IO2W9RbZexNK7/devtH/EBG85rH/Knj9r1greM0vXjd4ze/eJHjNr94keM3P3jR4za9eN3jN794keM3PXjd4zdfeIHjNz94keM3P3iR4zc9eN3jN794keM2vRPD6Zt614PXd+UULgiAIgiAIgiAI76x8O+dVEARBEARBEAThf9o7uGjS2yR6XgVBEARBEARBEIR8T/S8CoIgCIIgCIIg5AW9dVeI//9O9LwKgiAIgiAIgiAI+Z4IXgVBEARBEARBEIR8TwwbFgRBEARBEARByAtiwSarEj2vgiAIgiAIgiAIQr4nel4FQRAEQRAEQRDygl70vFqT6HkVBEEQBEEQBEEQ8j0RvAqCIAiCIAiCIAj5nhg2LAiCIAiCIAiCkBfEgk1WJXpeBUEQBEEQBEEQhHxP9LwKgiAIgiAIgiDkBbFgk1WJnldBEARBEARBEAQh3xPBqyAIgiAIgiAIgpDviWHDgiAIgiAIgiAIecBg0L3tIrxTRPCax2bUmPi2i2BV4y9Ne9tFsKq6lXu97SJY1SiJ4W0XwWqa70l720Wwqj0VH7ztIlhVg7Opb7sIVnVxbvm3XQSrGvZdyNsugpCDpwaHt10Eq5qp1L7tIlhV9+3v1qDEVbXi33YRBMGqRPAqCIIgCIIgCIKQF8Sjcqzq3bq9JAiCIAiCIAiCILyTRPAqCIIgCIIgCIIg5Hti2LAgCIIgCIIgCEJeEM95tSrR8yoIgiAIgiAIgiDke6LnVRAEQRAEQRAEIS+IBZusSvS8CoIgCIIgCIIgCPmeCF4FQRAEQRAEQRCEfE8MGxYEQRAEQRAEQcgLet3bLsE7RfS8CoIgCIIgCIIgCPme6HkVBEEQBEEQBEHIC2LBJqsSPa+CIAiCIAiCIAhCvieCV0EQBEEQBEEQBCHfE8OGBUEQBEEQBEEQ8oJeDBu2JhG8/o+wsVXR8ttPKdeyBjZ2KsIv3WL/lLU8vvUg13yuFYrTbExn3KuURCKVEBEQyqE5m4i4FvrfFPwNRUQ9pn3PwSyeNQlfnypvuzgmNLZqhk8aQqNWDbC103D1wjXmT1rC3ZuhueabuXwyLds1M0v/dtBkDu44DICbhyvDJg6mZj0fFAo5F05dZun0n7h3JzwvqgKA3FZFtYmf4tGqBgo7FU8u3uLSxLXEv+S3pSrihM/kz3BtWBmpQk7EsQAuT1pDSmSMxe017s60PjKH4F/3ce37bXlRFeP32KoZNnEwjVrVR2On4erFQBZMXMLdW2G55pu+fBIt25q3z/jBU/h7xxEAChR0YvA3/andqCYOTg7cuxPO2h83cmjnP3lSF0VNX2z79EVerAT6uFhSdu8kZdP6nDMoldj26IOycROkjk5o74SQtHYVGRfPW9xcotFQ4OffSVq7irSD+/OkDs/T2Gr4+rsvada6ERo7DX4XrjJ7wkJCbt7NNd+8n6fzQbsWZulfD5zAvu1/M2PxRNp1/SjH/M1rtCXifuS/Lv/zToVE8cOx69x5kkABjQ0dfbz4vG4ZJBJJjnmO347k5xNB3H4ch6PahmZlizKscQXUNtmn4rtPElj0zzUuhj1BLpPg41mIUc0q41HA1qrlt0Rpq6LTtz2o1tIXla2KkEs32TRlJRG37+eaz9u3PJ983Q3PCiVITU7l0p4zbP9+E6mJKRa3b9q7NS36fcS4+kPzohpZ3rX6qGzV9Pq2N74t66C2UxN8KYgVU37l/q3czw8VfCvy6ejueFXwIjU5ldN7TrFh/jpSnquPs4szPcf3oXojH2QyGTevBLNh/jpu+9+yej1sG9Sg0IieKEt7oouOJ2bTXqJ/3vxKeZUVS1Ni8wLutOxHxoNHJu/ZNa9LoSFdsfHyQPskhrgdR3j682bI0Fq9Di9S26rpPf5zame2TdClG/w2+RfCX9I2z/NtUZvxKyYyvvM4rp0NsLjNR30+pk2/dgyo19daRTcjr+aLultfZJ7F0cfHkn5gJ6l/bcg5g40SdZfeKOo1QerghC40hJTNq9H6mZ53pEWLoe4xEEXFahh0WrTXr5Ky+kf0URF5Vhfh3fROB6/379+nWTPzi9FnfH19KVq0KDt37uSPP/6gcuXKJu9v27aNcePGERwcnNdFfamOS7+gaNWS/D1rE2mJyTQa3p5eG77lh+ZjSIlLspinQLEi9Nk8kYhrd9k55lcMegN1B3zA539O4qcPxvP0Tv48YDyMjGLgyAkkJFqu19s2Y/lkKlYvz5Jpy0lKTKL/V31YvmURnRv1ID42Icd8ZSqVZu+fB9i80jRwC79rvJDS2Kr54Y8F6HQ6Zo/9nvT0dPqN7MUv25bSuXHun/1vvPfjFxSsXhK/6ZvISEim0lftabrlW/Y2HkN6rOU2kMikNF4/BrlGyYVvfkeqkFP12y402fgN+1p8i0Frvix87QUDsHHQ5Ekdnjf9x0lUqF6epdN/Iikhif5f9ebHLQvp0rhX7u1TsTR7/zzIn6v+Mkl/1j5yhZwlG+dj72jHL/N+51HkE5q0bsjMnyajUCjYt/WgVeshr1ARx6kzSTv2D0krV6CoVBnbPv2QSCUkb1hnMY/9199gU8uXpN9+QffgPqqWrXCcPou4r0eSce2qybYSe3scp85E5upm1XLnZt7P06hcvQILpi4jMSGJIV/34/etP9CmQVfiYuNzzFeuojc7t+xj48o/TdLD7twD4KcFv/PHGtN2c3RyYMFvM7lw6jKRD6KsWg+/+08ZvuUM71fwYGijClwJf8qyo9fRG6B/vbIW8xy7FcHIP8/yUeViDG9SkTtP4ll69DoxyWnMblcLgMj4ZHqvPUZxZ3tmta1JqlbPD8euM3jTKbb0a4ZKIbNqPV40YMkISlQtzdbZ60hJSObjEZ0YtfE7JjUfSXJcosU8nhVLMGLNBG6cvMrywfNxcnGm/djPcC1VlEU9p5ttX7ttfTqN70lsVHSe1gXevfp8tfRrvKt6s2bWKpITU+gyvCtTNkxnePOhJOZQH6+KJZm4djJXT/ozd9BsnF2c6T62F0VLeTC1x3cAaOw1zNg6B5Vaycb564i4G0HtVnWYtnkmk7qM55bfTavVQV29PB7LJxG/7wRPFq1BXaMihUf2RCKR8PSnP3LNqyznhecvk5EozC9dNe9Vp+iy8STsPc6j+atQli1B4a96IXd2JGrqcquVPyejlo3Gu2oZVs9cSXJiMl1HfMq0jTP5otngHNvmefZO9gyZ9UWu2zRq15g+E/ryNPKptYptRla2InbfzCD99D+kbFyBvHxlVJ/2A6mU1K2Wzzu2X4xFUc2X5PW/oI94gE3j97H7dhaJ341Ae8MYhEsKFsZ+xlL0D8JJWjQdbGxQd+uL3cT5xH/VB9LT86xO+YJYsMmq3ung1c3NjZMnT5qlHzx4kKlTp9KiRQuuX7+OTqdj3LhxbNu2DRsbm7dQ0tx5+JSmTLPqrO89l1v/+AMQdj6YEScXUatnC44v3W4xX53PW5GRms76PvPJSEkD4O7pQEacXkzt3i3ZO2n1f1WFV6LX69mx7xDzl/32touSo8o1KtKgxXsM/2w0p46cBeDKuavsPPcHnXp/wopFayzmU6qVeHp5sHLJOq5dvm5xmxZtmlKspCedGvXI6sW9feMOuy5sofnHTdi2dqfV61OwRmmKtqjO0e5ziThi/G09PhfMx+cW4d2rBYGLt1vMV+zj2hSoWJw9jccQf9PYQxtzLYwP/plN8TZ1CN12ymT70r2aY1/a3erlf1HlGhWp3+I9RnQfw+kj5wDwO3eV7ec20bFXO35fvNZivmfts3rp+hzbp37zupSt5E2v1gO54R8EwIUTl3D1cKHn0G5WD15te/RGG3KbhDkzAMi4eB6JXI66y2ck/7nZ7GQvdXNH1bgpCYsXkLp7hzGP32UUFSuhatPOJHi1qVsPu6HDkKjVVi1zbqrWrETjFvUZ9OlIThw+DcClc34cvPAXXft04OeFKy3mU6mVFC/pyW9LVnP10jWL24SHPSA8zHSkwOKVc4iLiWfskEkYDAar1uXnE0GUdXFiRpuaANQr5YJWr2flmZv08C1tFmQaDAbm/R1As7JFmfpRDQB8SxRGpzew8eIdUjK0qBVylh+/gcZGwc+f1kOdeYFe1EnDiC1nuR4Rg0+xQlatx/NK+pShSrMaLO49k2tHrwBw60IQs078QJMeLdmzzPJoiRZ9PybhaTzLB3+P7rkerj7zh+JS0p2oOw8BsC/oQLtR3Wj4aXMSY/LmRty7XJ8yPmWp2awW03tP4fI/lwC4cT6Q5Sd/pVXPD/hzqeWey4/7tSX+aTzzBs1G+1x9vvx+BO4li/LwzgOadm6Oi6cL49qPIfiS8djmf9IP+wIO9JnYl287jLVaPQp+8SmpQXeIGD0fgKQTl5DIZTgP6ET0yr8wpFkIYhRynHt8TKHhPTCkWg5yHDu0QPvwMQ+/ng96PcmnryAv6EiB3p8QNfMXsHBD1VrK+pSjVjNfpvaazKV/LgJw/Xwgv5xaQeueH7Jlae5BOcCgGYPRaS33EDsWdOSz0T14/9NWxMfkfJPPGtSde6ELvU3ykpkAaP3OI5HJUH3yKam7LJx3XNyxqdeUpF8WkH7AeI2iDbiMvFwllK3aZQWv6i59MKSkkDBlFKQbr0f1jyKx+2YG8lJls7YThFfxTi/YJJPJKFy4sMnr6dOnzJs3j1atWtGzZ08AXF1dCQ0NZdmyZW+5xJaVbliF9KRUQo5n79zJ0QmEnruBd5OqOeZ7fPsBp3/dkxW4AmSkphMfGU2B4i55WuY3cfP2XabNX0bb1s2ZNfHrt10ci+o29iU5KZmzxy5kpcU+jeXyGX/ea1onx3ze5UsZh2IF5jwE6/CeY/T5aJDJ8OOMjAwAFDaKf194C9waVyEjKZXIY9m/rbToBB6duYFbs5x/W66NqhB/+2FW4AoQf+sB8bce4tasmsm2tsUKU218Vy58nfc3Jeo0rkVyUjLnjl3MSouNjjO2T7NXaZ/bOW6TlJjMtrU7swLXZ8LvhFO0eNF/X/jnKRQoqlQj7eRxk+S048eQajQoKpsPpdc/eUzM0AGkHfk7O9FgwKDTIVFk/34ktnY4fDeNDH8/4saNtm65c1GvcR2Sk5I5ffRcVlrM01gunL5Cg2bv5ZivTAVvZDIZQddeffhioxb1aP5BY+ZOWkRC/Mt7PV5HulbHxXtPaFbWtMe6ebmiJKdruRz+xCxPcFQc92OT6FazpEn6Z76l2T2kJWqFHIPBwOHgCD6pWjwrcAWo6FaAv4e1ztPAFaBiw6qkJqVy/YR/VlpidDw3z12nUhOfHPP9NXc9S/vOMgn0tOnGf8ufGw79wdD2VGhYhR8HzuPq4Ut5UANT71p9qjf0ISUpBb/jV7LS4qPjuX7uGj5NauSYb/2cNcz8fJpJ4Prs38/OKx6lPUmITcgKXJ8JPHeNcjXLY+tgnSHrEoUcTe0qJBw8bZKecOAkMjsN6poVLeaza1SLgl98xtPlf/BovuWbXFIbBfqUVJO5hbroeKQ2CqS2eXuTrnojY9tcOX45Ky0+Op7As9eo2aTmS/PX/7gBVRtUZ9VMy3Xr+EVnqjWozqwBM7hwyPIUEKuQK5BXrEb6uRMmyelnjiFRa5CXt3DeefqY+DEDST9+KDvRYACdDuTZ5x1FnYakH96TFbgC6EKCievfUQSuwmt7p4PXFyUmJjJ8+HBcXV2ZMWNGVnqxYsUYPHgwv/32G1evXs3lE96OQqWLEnPvEXqd6bCD6NAoCnrlPOTv4rrDnP55j0laQS9XipTx4FFw3s2hfFNurkXY+8cKxgwbgEqletvFsaiEd3EehEWg05nexQ0PvU/xUp455itbyRuADj3bsd9/O2fCjvDr9mVUrF4ha5vE+EQCLgUCxiGq3hVKMWXxeGKexubZnEoH76IkhT3C8MJvKzE0CoeSOf+2HLzdibcw7DwhNAr7kq7ZCRIJdRYN4t6uc0Qczft9K6f2uR/6gGIlPXLMV6ZiaQDa92jLPr9tnAo9xC9/LaVi9fJZ21w4cYnZY783ySeTy6jX/D3uBOc+Z/N1ydzckdjYoHtgup/qHhqHMMs8LPzWMjLQ3gzGkJwMEgnSwkWwHfwFMjd3UjJ7YgEMaanE9OtFwrxZ6OPirFru3JQsU4LwsAdmbXMv9D4lShbLMV+5isZ9p3OvTzgWsBe/8JOs2fEzlX0sX+hKJBJGTfqS86cucXD3EetVINP92CQydHqKO9uZpBfLnJN6L9o8WA6KMv4/KxUyvtx8mtpzd9BgwW5mH/AnLbNH6GFcMolpGbg7api5349GC3fjO2cHwzafISIu2er1eJFbaQ+e3IsyO888CovEJZfzTExkNA+CjMO3lRoV5etV5pMx3bh57npWOsCx9QeZ0HgYVw7k4cX3c961+niU9iDKQn0iQiNw98r55tnTyKeEBYUCoNKoqFKvKp+N7kHgucCs9PjoODR2GrMg1bW48VjuUsw6N7sVxdyQ2ihIDzUdJZEeZjyX2ORQj9SAm4Q06W0cVpxDD2rMul3YFHfHuW8HpPa2qKqWpUDvdiQePY/+FYbt/huepT2Iuhdp3jZhEbiXzH3EkWMhJwZOG8Rvk38h5pHl9SL2r9vH4EYDOLv/jNXKbInUxQ2Jwgb9Q9Pzjj7S2F4ydwvnHW0GupBgSDGedySFiqDu8wVSF3fSDhp7YqVFXJHa2qF/HIm633AcV+3AaeNBbMfNRFKoSJ7WKd/Q69/u6x3z/yp4/fbbb4mKimLp0qXY2ZleeAwcOJBy5coxbtw40vPZ2HuVg4Y0CwtFpCelorR79TuKCpUN7RYMQpuawbmVB6xZRKtwdLDHtUjht12MXNk72pNkYS5ucmIytnY5351+FhwpVUrGD5rM+CFTsFHa8PPWxZQuX8ps+8Vr57Lx8Cpq1vfhh1m/8PRx3synsnHQkGHht5WRmIo8l9+WjaMGbYJ5Pm1iCorn8pXt3wq7YkW4MtnyXBlrs3ew+5ftY8P4wVOZMGQqNkoblv+5iNLlS+aYb8R3QylW0oOVSywPR35TElvj8cmQZBq0GJKN/+dSTe49IZpun1FwwxY07TuRenAfGf5+2W9qteju//c3rxwc7ElKMA/CkhOTsLPPuT7lKpUBQKVS8vXACYweNBGl0oaVW3+kTIXSZts3fr8Bpcp48fMiy70Y/1ZCqnE0hK3SdDSERmnslUtMMx/6F5Ns7G346s9zlCrkwLLO7/F53TL85R/KxF3GXrvozG0W/xPIo8RUZrWtxXcfVic4Ko7+60+Skp63i85oHDQmC/g8k5aYgtru1eaqL7zyO1+tn4RSo2LLDNMpFJEhD9Hl4dDNF7179bElJdF8/0lJSkH9itcBq/3WM3nDNFS2alZPX5GVfnz7MfQ6PV8vH4untycaB1satmtE007NAVCqrXMzWZq5n+tfqIc+8zgny6FdtFFPXxqAJp+7ytPftlJkbF/KXNpCiS0L0T2N5eFXc61Q8tzZOtiRbKltEpNf+lsbOvsLgi4Hc3RbzjeoH4Tc/09+a1nnnZQXzjspxv1Ios69Lqr2n+H082ZUH3Uk/Z/9aAP9jPkcnABQdx+ItGBhkhZOI3n5POQlSmM/ZSEo82dnhZB/vdNzXp+3atUqDhw4wLx58/D29jZ7Xy6XM2vWLDp06MDSpUsZNWrUWyilsddAIjVdrVIilViesyUBwyveUVHaqen621e4V/bijwGLiI/I+8Ul/tdJJBKkUtP7O1KJ5baQSCToc2mLDb9s5tCuo1w4mT287PyJS/x1eiOfD+/Bt4Mmm2z/68JV/L5kLY1bN2DC/DEUKOj07wOk1/htSSTkerdOIpFicRqhRMKzN+xLuVFlbCdO9ltMhoVA99+y1D4SqRQs7Sova59ft3B491EunMwe9nXh5GW2nlpPn+E9GD9oilmeYZMG06VvB1YtXcex/eZz6/+VrHayPFfzZft92unTZARcRV6mHJoevZEVLvKfDhG23DY5Hcdyb5s1P2/kwK7DnDuRPRT87IkL7D3zJwNG9ObrARNMtv/0847cCAjm7PELL36UVegzq5DTmsJSC29oM3tkmpZ1Y0TTSgDUKlEYg8HAkqPXGdIwIWsbZ1slCzrURpq5arFnATt6rj7GnmvhdPTxskodLJ5nJFIs7tQSCYZXWGxEJpexrN9spDIZzT//gDFbprK490yCzwRapcy5+f9QH6lUmuO5x6B/+ZxumVzGzL7TkMllfNjnY6Zvmc303lO4diaA+7fCmdl3GoNnDWXxoR8AuH31Fhvmr2PAtEGkpaRap17Pjgk5zEF/lXrkxGXqFzi1b8GTHzaSfMYPhYcrhYZ9hueKadzr9S2G1LSXf8gryPkazfK2udWpScemVPCtyLAWebtK9SuTvKR9XrJ2QPqFU2hvXEVWqizqzr2RFipM4rQxWcOH9bExJM2dmPX5uogHOMz+EZuGLUj/e5f16pEf/Y/2fur1epYtW8aWLVuIj4+nRo0afPfddxQvXtzi9o8fP2bWrFmcOmVc96ROnTqMGzcOV1dXi9u/qf8Xwaufnx/z58/n008/pU2bNjluV7ZsWQYPHswPP/xAixbmj2X4LzQa/gmNR3YwSQvcc46CXuYNb6NRkfoKQYGDmzOfrRxNQS9Xtgxdys0jV16aR4D+X/VmwNefm6Qd2vUPxQqZD51R26pJTMh5deSwkHDCQkx7uxLjE/G/EJDV6/c8v3PGIbaXTl/BwdGeviN6suaHDWZDLl9Hpa8+ofIo09/WvV3nTIf5ZpLbqnINONPjk1DYm9/tl9uqSI9PRiI1DhcO33WOyOMBSGTZgYxEKkEik5oNVX5d/b7qRf9RfUzSDu86irOF4cEva597IeHcs9A+Vy8E4F3BtGfcRmnDpEXf0LJtM9b8sIEfZ/365pXIgSHR2MsgeaGHVaIx/p8bknJfiVsXegcdkBFwFUNiIvZfj0VesRLaQMsLHlnb4FF9GTq6v0nagZ2HKVjK2Wxbja2GhFzaJjTkHqEh90zSEuITuXLhKmUrmN6IdCzggG+9Giyc/uO/KH3u7FXGC7GkF3pCkzN7XO1e6JEF0GTOlWxQ2nRfe6+UC0uOXic4Ko4SBe0B4+JP0ucet1OlqDP2KgXBUdYb4v3R8I60GdHZJO3injO4WJgqoLRVkRL/8mHLOq2O6yeMx60bpwKYcnABHwxp/58Ee+9afToP70qXkd1M0k7vOYW7l/kQVJVGRbKFEQ0v0ml1+J/wA+DqSX8W/b2MDkM7ce2Mcb6h/wk/BtXvTxFP4xDhR+FRNO1kfFpDQqx1ht3qMuefS1/ojZTaGv/W53IcyI3cpSBOnVvx9KfNPHm2KN/5AFICblJyz3IcO7Ygdt3uNy/4c7qM6Ea3kZ+apJ3acxJ3LyezbVW2apJzqJOzS0H6fTeA36etIPZJLFKZFGnmeVIqkyKVSnO9qZcXDMmZ550XelifLez37P2c6O/dRQ9or1/FkJSI7dCxyMpWMg4pBjKunDMJjHW3rqNPTEDuVZr8Nd5ReObHH39k06ZNzJo1CxcXF+bNm0f//v3ZvXu3xQVuR44ciU6nY+VK48inKVOmMGTIELZts+7jEd/54DUmJoYRI0ZkDQl+mYEDB3L48GHGjRtHjx49/oMSmrq04Qg3D5sGl+Xer0nphlWMd/Ge2/GdS7jw+Hbuz+J0KedJ9zVjkatsWNdrLqFnb+RJud9F29bt5MQh04UlGrdqQJ3GvmZt4VnCI9fnvLZs24y4mDjOHb9okq5UKYmNNl6UVqxWHjdPVw7tMh0+dN0viA87tcLJ2fFfDR8OWXeEh3+b/raKtqqJW+MqJj2mAHYlXIi7mfNvKyEkggKVSpil25dw4alfCBr3ghSqUZpCNUrj1bmhyTaVRran0sj27PQdTtJ988VtXtVf63Zx8m/TOUCNWtWnduNaZu3jUaIod2/m/JzXFm2bEhcTz/lc2gfA1t6WRevmULlGRRZN/oENv7zaswlfl+7hQww6LTJ30zlgMndjYK67F2qWR+rqik01H1IPH4KM7EuBjGDjIiyywkXI+6cdGm1Zu51jf5v2Rjdr3Yh6TeqYtU2xEh7cyeU5r63btSA2Oo4zx03nFqpeaBuA+k3rIpfLObDrsBVqYZlnAVtkEgn3Ykwv5O7FGC9SSxZyMMtTLHN+bMYLN2y0OuP/g1Iuw7OALVIJZGjNL1i1Oj1KhfVm+RzfcMhskaHqLX2p2LCqWfsUKe7Kw1yei1q1eU2S45O4dT773KLL0HI/KAz3MjmvA2BN71p9Dm44wMXDpiMHfN+vTbWG1c3q41bCjfDb9178iCy1mvuSFJ/E9fPZQbc2Q0tYUCie3sa55oXcC1GlfjWO/3WUR+HZj5UqWbk0CTHxPL7/yOxz30TGvQgMWh02xUyDcJvixpsMabnUIzdyt8JIpFJSXlgpPv1WGNqYOJSlLfcSvYmDG/Zz8bDpsah2yzpUb+hj3jbF3XJ8zmu1BtWwc7Rj2PzhDJs/3OS9aRtnEBUelafPcrVEH/kQg06H1M30vCN1Nf6tDzc/h0qLuCKv7GNcsOm58472tvG8Iy1UhIxLZ8wWDnxGIpNjSLdOr7hgXenp6fz++++MHj2aRo0aAbBw4UIaNGjA33//zYcffmiyfXx8PBcuXGD58uVUqGBcz2XAgAEMGTKEmJgYChQoYLWyvdNzXg0GA6NHjyYlJYUlS5a80mNwng0fDgsLY8WKFS/d3toSHsXyMOCuySvkeABKezWlGmWv9KZxtqdE7fImKxC/yMHNmR7rxmEwGPi9wxQRuL6mJ1FPueEfbPI6e+wCdva21G3im7WdU0EnfOpWNVmB+EUde7fjm9mjkD+3imhh10JUrVWJS6eNAWX9Fu8x/cdJuBQ1XcCgbpPaPIl6SvQTy4s5vKqUqFiir941eUUeC0BhrzYGsJmUzvYUqVveZAXiF0UcC8DB2x0H7+yTnIN3URy83Yk8FkBKVAwHWk0wewHcXneEA60mkBL17+rzJOopN64Gm7zOZbZPncbPtY+zIz51q3Iut/bp1Y6xs0aatU+VWpW4fNoPMK5evmD1LCpULcf4QVPyLHAFICOdjKtXUdY3DfyVDRuhT0ggI8h8X5a5umE/aqxZHptaxv8L7Z2QvCvvCx5HPSHQP8jkderoOezsbanXJHvV5wIFnaj1XnVOPbcC8Yu69u7ApLljUDzXNkVcC1PdtwoXTpsGLFV8KhLxIIqI+5HWr1QmpVyGT7GCHAl+aHKheijoAfYqBZXczU/QNYoVQq2QsS/QNGg6disCuVRClaLOaGzkVPcsxOHgh6Q/N7/t3N1HpGTo8PG03mrDcY9iCAu4Y/IKPOGP2l5DxUbZq4zbOTtQpnYFkxV7X9Sy/8d0nzEgq9cIQG2voZRPGe7fyPmGkTW9a/WJeRRNSMBtk5f/cT809hqqNaqetZ2DswMValfC/7hfjp/VZsAnDJw52KQ+GnsNZX3KEnbDeNPIsaATX8wbRqW62c+6dyrsRIM2DTh3MOd983UZ0jNIvnAN+5amq4vbv18fXVwCqVff7HmyGWEPMWh1ZqsV23gVRV7AkYz71nvOc3RUNLev3jZ5XTl+BY29huqNslexdnB2oGKdSlw5YXmk24VD5xn10QiT14/jjE+8+HHcMmZ8PtVqZX5lGelor/tjU/uFc0jdRugTE9DeNj/vSIu4YTtkDIraDUzSFdWN5x1d6G1ITUF7IwBF7YYmKxDLK/sgUavRXs9/C6Vam8Gge6uvNxEUFERSUhJ16mSfsx0cHKhQoQIXLphfTymVSjQaDdu3bycxMZHExER27NhBiRIlcHR0fOP/O0ve6Z7Xn3/+mZMnTzJnzhwUCgWPHz82eV8ms/zA97JlyzJkyBAWL178XxTzpcLOB3H3zHU6LB7C37M2khyTSOMR7UmNT+biuuzlyQt7F0VmIycy0HiCbT25J3aFHdk1bgVKOzUe1bOHp6YlpvD4Vu69toK5K2f9uXjqMtOWTWLJ9OXExcQxYNTnJMQlsnXN9qztvMqUwMZGQXDm4z1+W7CKpRu/Z96KGWxeuRUHJwcGjPqc+NgE1i7fBMCfq7fzSfePWbx2Lr8uWEVKcgofdWpN/eZ1GT9kitWfVQnw+FwQUaeuU3fZEPxmbCQ9OpFKo9qTEZfMrbXZvy0H76LIlHJirhl/W/d2nqXisLY0Xj8G/5nG8lf9tiuxN8K5t+scBp2e6KuWe9NSomJyfO/funLuKhdPXWHqsgksm/ETcdHx9B/Vm4S4RLatzV5x18u7OAqlDTeftc/C1SzZMI+5v01j88q/cCzgQP+vjPnW/WR8Rl/H3u2oXqcq29bsIOrhIyr5VDD57pyeD/umkjeswXHOAhwmTiF1/17kFSqi7tSVpN9+hvR0JBoNsuIl0D18gCEujoyr/qRfuYzdF8OR2Nmhu38PRdXqaDp3I2X3TnT3/psL75xcOuvH+VOXmPvjFL6ftozY6DiGju5PfFwim1dnDykqVcYLhY2CoGvGC9nlC1bwy6bFLFo5h40rtuBYwIEhX/cnLjaBlT+uN/mOMuVL59qLay3965Vj4IaTjP7rPO2qFsf/fjSrz95ieNOKqBQyEtMyuPMkAQ8nW5xtlWhs5AxpWJ7vD1/DQaWgWVl3/B9Es/LMTT6tVQpnWyUAwxpXoN/6k3zxxxl61inN06Q0Fh8JpLJ7ARp557xCrjXcOn+DoDPX6LdoOH/OWktSbCIfj+hESnwSx9ZlP37JrbQHcqWc8MBQAHYv+ZMRayYwaPkojq07iMpOTevBn2CjVrFz4cufb5lX3rX6XD8fSMCZq4xYPIq1s1aREJNAlxHdSI5P4sC6fVnbeXh7orBRcDfwDgBbFm9i4trJjF7+DQfW7UNtp6H9kA4o1So2LdwAQEjAbW5cuM6AGYNZM3MlOq2ez0Z3R6fVs3nRRqvW4+nyTXiumoH74nHEbf0bdfXyOPfrwON5KzGkpSO1VWNTuhgZ9yLQveIzTXUx8USv3k7BvsZpMcmnrqBwL0LBLz4l48EjYjfvt2odXnT9fCABp6/y1ZKvWT1zJQkxCXQd+SlJ8UnsX7c3aztPb0/kmW2TEJtAQqzp84FVmdNCHoQ8ICz47RyvU/9ci91332M7ajJpR/YiL1sJZduupKwznndQa5B5lEAf9QBDfBza6/5kBFxG0284qbZ26B6GI69UHVXbrqQd3In+gbE3PWX9L9hPXYTd+Nmk7vwDqZMz6u4D0N68TsbF0y8plfBvNWvWLNf3Dx82H60UGWm8CezmZnruKVKkCBER5k+bUCqVzJgxg6lTp1KzZk0kEgmFCxdm3bp1Zmtg/FsSQ15cEecTPXr04Pz5nJexL1q0KL6+vjx48IC1a00Xw9FqtXTu3JnAwECCg4PfuAyTi3/2xnmfp3LQ8P7E7pR73/iDCL90k/1T1/H0uceV9N40HiePwiyqPwKZQsa3N35HprB8fyL0zHVWdZ1h8b3cjL807Y3r8DrOX77K51+O5felc/D1MX+2mLXUrdzrtfPYO9oxcvKXNG7VAKlUgv+FABZ8t9RkTuvPW5fg5ulKG9/seVi1G9ak31e98S5fCr3ewNmj51k8/UeiHmQPyfIoUZQvvh1I9TpVsbWzJTjgJr8vXsOpI2dfqWyjJDk/ciQnCkcNPpO74/F+TZBKeHLhJpcnryMhJPu31fTP8dh6FmZX7RFZaRp3Z3ym9sS1YSX0GToijwdw+bt1pD6KzfG7uj1cT8D3W7n2/cvnPywk56F9ubF3tGPE5C9o9H79zPa5xsLJy0zmtC7/cxFunq60q901K823YU36jexF6fIlje1z7DxLp/+U1T4/b1tC9To5P/vW171RruXaY/nJLrmyqdcA2559kHl4on/6hJSdf5Hyp7HHV1GlGk7fLyZ+3izSDhovziQaDZruvVDWb4S0YEF0kRGk7tlFyl9/WlyEQ+riSsF1f5h8xqtqfPX1F3FxcLRnzJQRNG3dEKlUypXzV5kzaaHJnNaV236kqKcbLWt9kpVWt6Evg0f1pUyF0uj1ek4dPceCqUuJeGDao7Lz+CaCb9xm9EDTRZxexcW5TV5r+yPBD1l+/Aah0YkUsVfRpUZJetY2zsG9EPaY/utPMuUjH9pWyR6yuN0/jLXnbnEvJonCdio6VC9Bn7plTOa4+t1/yrKj17n2MAaVQkaTMm6MbFYJB9XLRw49b9h3r9/TrnGwpfPEXlRrWQuJRErIpSD+mLaaqDsPs7b5etNkCnoUZlz97EVmyr1XiY+Hd8KzQgkMBgPBZwLZNncDkSGWb5D2mT+UMnUqmHxGXsiv9XlqeLNhkrYOtvSZ2Bff943D74Mu3WDl1BU8vJNdrqmbZlDEowiD6mfPOa9crwqdh3fDq4IXBr2ea2cCWDdvLQ+eGz7tWMiJPpP6Uq1BdSQSjNvMWUNEqPlF6otmKl9vQoJdi7oU+rI7NiU90EY9IXb9bqJ//wsAjW9liq2bQ8TYBcT9dcgsr+MnzXGb8xUhTXqT8cB0OHOBXm1x6vYBCg9XdI+iSTp1mccLVr9yEPzM2NTXv9C2dbSl78R+1H6/LhKJhBsXb/D71F958FzbTP9jFkU8iuQ4HLhSncrM2DyL8Z3Hce2s5ZFPw74fQaU6lV9rSPGqWq83Z1nhWx9V1z7I3D3RRz8hbd920nYZzzvyitWwn7qIpGWzSf8n85yh1qDu1AtFnYZInQuifxRJ2sGdpO3ZanLekZWtiPrTfsi9y2NISyPj/ElSVi9/6Vza5xXYevS16pJfpBz9/a1+/0fT1uf6vqXgdceOHYwZM4YbN26YBJ9jxozh0aNHrFq1ymR7g8HAokWLuHnzJv369UOn07Fw4UISExPZuHGj2VNe/o13OnjND6wVvOYX/1Xw+l95k+A1P3uT4DW/etPgNb96k+A1P3uT4DU/e93gNb97k+BV+G+8afCaX71u8JrfvUnwmp+9bvCan4ng9c2oG3/+8o1ecODAAYYNG4a/vz8qVfbjjIYPH056ejrLly832X737t1MmTKFf/75JytQjYuLo0mTJgwfPpxevax3vf1u7aGCIAiCIAiCIAjCG3s2XPjRI9MRDo8ePbL46JtLly7h5eVl0sPq6OiIl5cXoaGhVi2bCF4FQRAEQRAEQRDygkH/dl9voFy5ctjZ2XHuXPaibfHx8Vy/fp2aNWuabe/m5kZYWBhpadkjS1JSUrh//36Oz4V9UyJ4FQRBEARBEARBEACwsbGhe/fuzJ8/n8OHDxMUFMTIkSNxdXWlRYsW6HQ6Hj9+TGqqcQpRu3btABgxYgRBQUFZ29vY2NC+fXurlk0Er4IgCIIgCIIgCHlBr3+7rzc0bNgwOnbsyIQJE+jWrRsymYwVK1ZgY2NDREQE9evXZ+9e44raRYoUYcOGDRgMBnr16kWfPn1QKBRs3LgRBwfz55//G+/0o3IEQRAEQRAEQRCE1yOTyRg9ejSjR482e8/Dw8PsaSylSpXip59+yvNyiZ5XQRAEQRAEQRAEId8TPa+CIAiCIAiCIAh54Q0XTRIsEz2vgiAIgiAIgiAIQr4nel4FQRAEQRAEQRDywr9YNEkwJ3peBUEQBEEQBEEQhHxPBK+CIAiCIAiCIAhCvieGDQuCIAiCIAiCIOQFsWCTVYmeV0EQBEEQBEEQBCHfEz2vgiAIgiAIgiAIeUEs2GRVoudVEARBEARBEARByPdE8CoIgiAIgiAIgiDke2LYsCAIgiAIgiAIQl4Qw4atSgSveWxk06i3XQSrqlu519suglWdCVj9totgVacqjn3bRbCaY2s6ve0iWNVnAw6+7SJY1cUZNd52EazKZcjmt10Eq4qc3eptF8FqJB7F3nYRrCpt84G3XQSrGnLa6W0Xwao2jnZ720Wwqlazg992Eazm+NsugJAviOBVEARBEARBEAQhL4hH5ViVmPMqCIIgCIIgCIIg5HsieBUEQRAEQRAEQRDyPTFsWBAEQRAEQRAEIS+IBZusSvS8CoIgCIIgCIIgCPme6HkVBEEQBEEQBEHIC2LBJqsSPa+CIAiCIAiCIAhCvieCV0EQBEEQBEEQBCHfE8OGBUEQBEEQBEEQ8oJYsMmqRM+rIAiCIAiCIAiCkO+JnldBEARBEARBEIS8IBZssirR8yoIgiAIgiAIgiDke6LnVRAEQRAEQRAEIS+IOa9WJXpeBUEQBEEQBEEQhHxPBK+CIAiCIAiCIAhCvvdWhg1/8803PHjwgLVr19KjRw8CAwPZvXs37u7uJtstXbqUv/76iyNHjgDQo0cPzp8/n/W+XC6nQIEC1K1bl5EjR5rkf/47XrRt2zbGjRtHcHBwVtrVq1f58ccfuXz5MikpKbi7u9OyZUsGDhyInZ2dtf8LciWvVAtl+97I3ItjSIgj/ehu0vZsfLXMUim2E5ZCWipJc0ZZ3kalwX7aL6RuX0PGqYPWK3gONLZqhk8aQqNWDbC103D1wjXmT1rC3ZuhueabuXwyLds1M0v/dtBkDu44DICbhyvDJg6mZj0fFAo5F05dZun0n7h3JzwvqvLGIqIe077nYBbPmoSvT5W3XZwszk2q4fVNVzRlPMh4Gs/DNQe5t2R7rnlcOjSg2LBPUBV3Ie3BE8J/3EHE+iMm22hKu1NyUg+c3quAIUNH7NkbhExeTWrYozysDZy6HsYPe85yJzKaAnZqOtarxOctaiCRSMy23XHuBt+tP5TjZ039rDltapcH4G5UNIt2nObirfvIZTJ8Srszql19PAo55lldnlHZqun1bW98W9ZBbacm+FIQK6b8yv1buf/GK/hW5NPR3fGq4EVqciqn95xiw/x1pCSmZG3j7OJMz/F9qN7IB5lMxs0rwWyYv47b/resXo9Tdx/xw4mb3HmaQAGNko5Vi/F57VIW2+aZ4yFR/Hz6FrefJOCosqFZGVeGNSiL2sbyqWvekeusv3QXv9EfWr38ltjZ2TJtxjg++rgFdna2nDt3mbGjpxIcdDvHPAHXj1O8uIfF90JDw6lSsZFZ+qDBvRj6ZV8qV2hotbK/ilNhT/nhbAh3Y5JwUtvQsWJRPq9RPNc2OxH6hJ/P3+X200QcVQqalSrMl3VLo1bI/sOSw6ng+/xw8DJ3HsVSwFZFx9rl+LxxZcvHgou3+O7Pkzl+1tRO9WlTwxuAQwGhrDwWQOjjOOxUCnxLuTOidU0K2qvzrC4A8qq1UHXui6xocQzxsaQd3kXajg05Z7BRourYG5s6jZE4OKELCyF122q0/hdMNlPUrIfyk57I3D3Rx0aTfvIgads3gE6bp/VR2ar49Nve1Gzpi8pOza1LQayZsoIHt+7nmq+cbwU6j/6M4hW8SE1O4dye02yZv8HkuAbw4YC2NPu0Jc5uhXjy4DEHVu3h7zX78rJKJv6X9x0Ata2aIRMHUv/9emjs1Fy7eJ0lk34g7FbYK39G/ZbvMXPlNIZ1/Aq/M/5Z6Y4FHBgwrh91mtZGpVERfDWY5dN+4VZgzsfN/3li2LBV5Ys5r0lJSUyYMIHff//9pdu2bt2a8ePHA5CWlkZ4eDgLFy6kS5cubNmyBVdX19f+/lu3btGjRw8+/fRTRowYga2tLUFBQcyaNQt/f3/WrFnz2p/5pmSlK6AZPpWM80dJ27YKWZlKKNv3AYmEtN25nKgyKT/shtyrLNogf4vvS2zt0QybhrTQ6/8/vakZyydTsXp5lkxbTlJiEv2/6sPyLYvo3KgH8bEJOeYrU6k0e/88wOaV20zSw+8aT24aWzU//LEAnU7H7LHfk56eTr+Rvfhl21I6N879s/9LDyOjGDhyAgmJSW+7KCYcapah0pqxPNpxmruzN+FYuxxe47qBVMq9Rdss5in8cR3KLfuC+7/uJfqIH4Va+1J2wWB0qek82mq8+FO6F6T6rukkhzzkxuDFSFU2eH3Tjap/TORC41HoU9PzpD5+dyIY/utu3q/uzdAP63DlzkOW7TmD3mCg//u1zLZvUKEEa0Z2MkkzYGDqpiMkpaZTv0IJACJjEui9cCvFizgxq9f7pGZo+WHPWQb/uIMt33yKKodAylq+Wvo13lW9WTNrFcmJKXQZ3pUpG6YzvPlQEuMSLebxqliSiWsnc/WkP3MHzcbZxZnuY3tRtJQHU3t8B4DGXsOMrXNQqZVsnL+OiLsR1G5Vh2mbZzKpy3hu+d20Wh38HkQzfNtF3i/nztAGZbhyP4ZlJ4KNbVPX22KeY7ejGLn9Ih9V9GB4w3LceZrI0hPBxKSkM/uj6mbbXwp/ysbLd61W5lfx+6rF1KhZhYkT5pAQn8g33w5j9971+NZoSUxMnMU8n3UbhNLGxiTNt7YPs+ZM4PcV5sf4zl3aMmPWtzx8GJUndciJX0QsI/b48763C0PrlMIvIpYfzoZgwEC/ml4W8xy7+5iv9l7lo7JuDHuvFHeik1h2JoSYlAxmvV/pvyt7WBTD1xzm/SpeDG3pw5XQKJYdvGT8vTWtarZ9g3KerBliesPDYICp206RlJpB/bKeABy8epcxG47SsXZZhrb0IToxhR//vkL/X/ez8cuPUSry5lgg866I7dczyDjzD6mbVyAvWxlV577G64Lt6y3m0Qwai6JqLVI2/oo+8j42Dd/HdvQsEqeNRBccAIC8cg00I6eScfYfUjf9gsyzJKou/ZDaO5Gyakme1OWZL5aOolRVbzbOWk1KYgrth3dm/IapjG4+jKQcjmvFK3rxzdpJXDt5lUWD5lDAxZmuY3vgXsqD2T2mZG336be9aNXnQ7Ys2ESI/y2qNfGhz7QB6DK0HNn4d57WC/63951nvvtxAuWrlWX5jF9JTkii91c9WbR5Pj2bfE7CK1xbORRwYNSckRbfm75iKsVLe/LTjF95EvWUz4Z2ZcnWBXzeYgAR4ZHWrorwDsoXwaunpyenTp3ijz/+oEuXLrluq1KpKFy4cNbfHh4eVKpUiY8++ogFCxYwd+7c1/7+bdu2UaxYMcaOHWtSJpVKRb9+/QgKCqJcuXKv/blvQtm2J7p7IaT8OgcA7bULSGQylB90Je3An5CR84W/1LMkyg+7oY99avF9efX3UH86FFR5e4f4eZVrVKRBi/cY/tloTh05C8CVc1fZee4POvX+hBWLLN8YUKqVeHp5sHLJOq5dvm5xmxZtmlKspCedGvXI6sW9feMOuy5sofnHTdi2dmee1OlV6fV6duw7xPxlv73VcuSkxNedSAwMJeiLpQBE/+OHRC6j2JftuP/TbotBptc33Xi86ywhk1YDEHPUH0UBO7xGd8kKXkuM6YwuMQX/TlPRpxg/I/XeIyqtGYt91ZLEnQvKk/r8vP88ZYsWYkbPlgDUq1AcrU7PykOX6NGkulmQ6WyvxvmF3pL1R/24GxnD6pEds95bvvccGpWCn79oh9pGAUDRgg6M+GUP18Oj8ClVNE/qA1DGpyw1m9Vieu8pXP7nEgA3zgey/OSvtOr5AX8u3Wwx38f92hL/NJ55g2ajzcjuQfny+xG4lyzKwzsPaNq5OS6eLoxrP4bgS8Y28T/ph30BB/pM7Mu3HcZa/Ow38fPpW5Qt4sCMD6sBUM+rCFq9npXnQuhRsySqF3oWDAYD845cp1kZN6a2NgYbvsULoTMY2HgplJQMnUlvREq6lu/2XaWwnYqohFSrlTs3vr7VadW6KR3bf87BA0cBOH36AgGBx+jXvzvz5v5gMd9Vf9Pjmb29Hb+vXsL+fUdYtODnrPRChQsycdJX9Pm8G9FPY/KsHjn55fxdyhayZ3qLigDUK14Qrd7AykthdK9WDJXcvM3mn7hFs1JFmNK8AgC+Hs7oDQY2+t83a7O89PMhP8q6OTOji7Gnul5ZD+Pv7ehVejSoiOqFINPZToWzncokbf3JQO4+imP14A+z3vv1iD/1y3ow4ZP3srYrUdiR7j/s5njQfVpULpEn9VF16IUu9DbJP84CMPaeyuSo2nxK2p4tZtcF0iLu2NRtQvKKhaQfMp4HtYFXsC9TCWXLtiRnBq82jVpjePqI5GUzwaBHG3AJiYMTyg86krL2B9Dp8qQ+3j5l8WlWk7m9p+H3z2UAgs5fZ/HJn2jRsxXbl/5pMd8H/doQ/zSehYPmonvuuDbo+2G4lXQn4s5DCnsW4YN+H7Pqu984tHY/ANdPB1DQrRBVGlX/T4LX/+V9B6BijQq817wOY3qM4+wR42hH/3MB/HF2PZ/0asOaxZZvmDzvq5nD0GnNe+89SnpQtXZl5oyaz94/jO0TeOk6O/z/5P2OLVi10Hy05DvBYHjbJXin5Is5rzVr1qRDhw7MmTOHhw8fvnZ+e3t72rdvz8GDB0lPf/1eHYlEwoMHD7h507SnoW7duuzZswcvL8t3yqxOrkBetgraS6bDlzIunkCi1iAvUznnvDIZmn5jSDu0HX2khWE3als0X3yHNtif5O+/sXLBc1a3sS/JScmcPZY9VCn2aSyXz/jzXtM6OebzLl/KOIwxMOfhi4f3HKPPR4NMhh9nZGQAoMgMMt6mm7fvMm3+Mtq2bs6siV+/7eKYkNjIcXqvIk/2nDNJf7z7LHI7NY61zW/WqDwLoyntzpO9L+TZdQa1lyvqkm4AFP6gNhEbjmQFrgAJ/nc4U3VgngWu6Rk6Lt66T7OqpUzSm1crTXJaBpdDXn5ceRKfxA97ztKpfiUqlzCOTDAYDBy+GsIndSpkBa4AFYu58Pf0z/M0cAWo3tCHlKQU/I5fyUqLj47n+rlr+DSpkWO+9XPWMPPzaSaB67N/P9s3PEp7khCbkBW4PhN47hrlapbH1sHWKnVI1+q4GB5NszKmoz2al3EjOUPH5fvRZnmCH8VzPy6Zbj4lTNI/q+HF7gFNzC7kFhy9QUFbJW0rWR6OmxeaNW9IYmIShw+dyEp7+iSaUyfP0/L9xq/8OWPHfUmhQs6MGjnJJP3r0UNo2qwBn3UbzL59h61V7FeSrtNz8UEMTUsVNklvXqoIyRk6rjyMNcsT/CSR+/EpdK1i2gafVi3Grp7v/WcX3+laHRfvRNKsUnGT9OaVSpCcruXy3Zf3YD9JSOaHg5fpVKcslYsZ/w/0egN1SrvTwbeMybbFCxunDtx/Gm+lGrxArkBeoSoZF06YJGecO2a8LihnPg1FH/2YhPEDST/13LQIgwH0OpA/d26UKzCkpZo8g9KQEIdEYYNEpbF6VZ6p0rAaqUkpXD3ul5WWEB3PjXOBVMvluPbHnHXM+3yGSeD67Lgmzzyu1WxZm4x0LUf/MJ0SsvSL71k06PU7N17X//K+84xvo5okJ6Vw4djFrLS46Dj8z/pTp2ntl+Zv2qYxNRvUYPn0X8zes8lsp6TnRqIlJyaTnpaOQwEHK5Re+P8gXwSvAOPGjcPe3j5rSPDrKlOmDCkpKYSGhr523i5duqBQKGjTpg1dunTh+++/5/jx4+h0OkqXLo1SqXyjMr0uaWE3JAobdFGmwacu6oHxfdecL8yUbXuCTEHa9tWWN0hPI3F8X1J+m4s+wfJwtrxQwrs4D8Ii0L1wBzc89D7FS3nmmK9sJeNQwg4927Hffztnwo7w6/ZlVKxeIWubxPhEAi4FAiBXyPGuUIopi8cT8zSWQzv/yYPavB431yLs/WMFY4YNQKVSvTzDf0hd3AWpUkHyC0Fdyl3jkB1NKXezPBpvY6CWHBKRQx43VMWKIHe0JTX8Md6z+lLvxu80DFtPpbVjURYtlBdVAeD+0zgydHqKF3EySS+WeWF57/HLe65+3HsOqVTC0A/rZqU9jI4nMSUdd2cHZm4+SqNvfsX3qx8Z9vMuIqLzfli6R2kPou5FodeZzpeJCI3A3SvnwPlp5FPCgkIBUGlUVKlXlc9G9yDwXGBWenx0HBo7jVmQ6lrcGGS6FHOxSh3uxyUb26aA6fcUy/z7Xoz5cPqgR8ZAQCmX8uXWC9ReuI8GSw4w+9A10rSmx5IzoY/Zdf0BU1tXyXU+mbWVKVuK0NBws2PbnTuhlC79ajc8ixUrysBBPVmy6FfCw033xRW/rad6labs2nnAamV+VffjUsjQGyjuZBrAeDoaRyOExSab5Ql+bNwflDIZw3b5UWf5PzT89RizjwWbtVleuh+dYPy9FTK9EC5W0Pj3vScvP//9+PcVpFIpQ1v6ZKVJpRJGfeRLk4qmQfHha8Y5gKVcC/zbolskLWK8LtBHmF4X6J9dF7hZuC7QZqC7cxNSkkEiQVKwCOqeQ5G6uJN+aFfWZukH/0LqWhTlR12QaGyRlS6PsnVHMq6cxZCUd8c399IePLJwXIsKjcTNy/zc80x05FPCg4z/30qNikr1qtBldHdunAvMSi9ewYuIOw8p51uRGbvns+b2Fhaf+pnm3d/Ps/o8739533mmuHcxIsIi0L3QPg/uPsSjZO43CAsUKsCIGcNY8t0PPH1kfmPyTtBdLhy/RO8RPfAqWwJ7J3uGfjcIlVrFkXxw3Sb8b8g3wau9vT3Tpk3j9OnTbNq06bXzOzgYT0wJCa9/wC1evDg7d+6kT58+xMTE8Msvv9C/f3/q16/P5s2Wh+XlBYnGeDFnSHnh4JZq/DunO6Eyr7IoW3UiZcVc0GZY/nCd1nKPbB6zd7Q3ucP2THJiMrZ2OffslKlYGgClSsn4QZMZP2QKNkobft66mNLlS5ltv3jtXDYeXkXN+j78MOsXnj42P2j+1xwd7HEtUvjlG74Fckfj/73uhUUunv0ts7D4SFaehBfzpGbm0aDIvEAsOfEzlG7OXB+0iOCvfsKuYgmqbfsOqSZvbgQlpKQBYKsynUuoURr/TnzJPNvohGR2nw+ia4MqODxXxujM/4/FO0/zKC6RWb3e57tuTQl+8IT+S7eRkpbD/mYlGgdbUhLNL3ZSklJQ273a8P/VfuuZvGEaKls1q6evyEo/vv0Yep2er5ePxdPbE42DLQ3bNaJpp+YAKNXWueGSkGrsGbF9YTSExsbYm5CYbj60LCbZ2F5fbb9EqUJ2LOtQi89rl+avgHAm7s2ez5+QlsGU/VcZUq8MxZ3/24X1nJwcSIg3n5uXmJCEvcOrlWXw0D6kpaWz/MeVZu/dunkHrYVhd/+FhLRnbWY6vPZZmyWlm19Qx6Qa94VR+65SqqAdSz+uyuc1SrD9+kMmHbI89SMvJGSO+LBVvngsMP7+El+yz0YnprD7cghd65bDQZ378SrsSRyL9l6gfNGC1C+TN73+Elvjb8mQYnoefXadIFHnPkJC2fZTHJf9gbJ1R9KP7kd73S/rPe11P9J2bUL92SAcV+zGftqP6ONjSFo63bqVeIGtg63ZAkvwese1X/zW8O2GKahsVayfvior3aGgA86uzgxdPJJ//jjE7J5TCTjux+czBv0nAez/8r7zjJ2DneXrtqRkbO1y75EfPXckgZeuc3BrzoshLpqwBJVGxeojK9gTuJ2Ofdszd/T3XLv439f1P6PXv93XOyZfzHl9pmHDhnTo0IG5c+fSoEGD18r7LGi1t7cHjCsR63NoML1ej1xuWnUXFxfGjh3L2LFjiYiI4NSpU2zYsIGJEyfi4uJCo0bmK0BanSTzXkJOY+MtpcsVqPuOIe3gNnR3g83f/w9JJBKkUtP7IVKJBIOFckskkhzbB2DDL5s5tOsoF05eyko7f+ISf53eyOfDe/DtoMkm2/+6cBW/L1lL49YNmDB/DAUKOrFyyTs6d8IaMnuoLLUNAHoL6dIc8jzr7NLrkWbOJUt/HMe1PvOzfrMpoZH47J2JS4cGRKzN+aT2pvSZ3yPBcs+b9CU9cltPB6I3GPi0UTWTdK3W+Bt1tlezoO+HSDP/DzwLOdFz4Rb2XAymYz3rLKYhkUiQSE3LKZVKc9x/DJba6AUyuYyZfachk8v4sM/HTN8ym+m9p3DtTAD3b4Uzs+80Bs8ayuJDxvmZt6/eYsP8dQyYNoi0FOvMHc1qmxyaQGohXZt5bGjq7cqIRsYVn2sVK4QBA0uOBzMkOpESznbMO3KdIvYquuewCIq1WDq2SXJoGyQS9K/QNiqVkh49O7F2zRZiY/NoyOkbMvBsf7LMUltmZPbSNClZmOHvGW8+1sqct7f0TAiDY5IoUcA6Q9Fz8/Lf20uOBedvGo8F9Srmut2dR7EM+u0ACrmM+Z81yTo2WF3WdUEO7xtyvzDNuHQabVAA8pJlUXXohbRgEZJmjwFA3fcrbBq3InXbGrTXLiMt4oaqY2/svplD4oxRkJ7274tv4bgmkeZ2XfBqx7X5fWcgk8to1ecjJm2Zwdze07h+5hpyhRyHgo4sHDiHC/uN62xcPx1AQfdCfDKsM4fW5e1Ihv+1fcd4bHuxfaSWL0Nf0j6tOrWkim9lejXrl+M2xUsX48cdS4gIj2Ri/8kkJiTRrE0TxswbRWpKKkd3H3/Tqgj/j+Sr4BWMw4dPnTrFhAkT8PHxeXmGTIGBgajV6qz5qY6OjsTHW74giI2NxdEx+xEX8+bNo379+tStaxwu6ObmRseOHWnTpg0tWrTg2LFj/0nwakg23sWXqF+4s5XZ4/rinVcAVYc+IJWQtnMdvHBxhVT6n95x6f9VbwZ8/blJ2qFd/1CskPnwYLWtmsSEnFffDQsJJyzE9FEgifGJ+F8IyOqVfZ7fuasAXDp9BQdHe/qO6MmaHzaYDekTjLTxxv97+Qt3UWWZd7218ea9fdo4Y5r8hV5Zma0qK48282569JErJjdb4i/dIiM2EbtKeRNk2Gf2kCS90MOanGb8206Vew/KIb/b1C1XzGwBJ43K2FtTr0JxkxN8FS9X7NVKgu8//tdlf6bz8K50GdnNJO30nlO4WxhGp9KoSE4wb6MX6bQ6/E/4AXD1pD+L/l5Gh6GduHbGuGCL/wk/BtXvTxFP4xDhR+FRNO1kfDxVQqzlFT9fl33m/2FSmmkvYnJmD4SdhfnpmsxeiwYli5ikv1eiMEuOBxP8KJ57MUkcCHrI+h710RsM6A2GrAtirV6PVCJ5aaDyqr4ZN4xx44ebpP21bS+Fvc1/z3Z2GuLjXz4CqGmzBjg42LN50w6rlNGa7DP//5PSc2oz80sH28yepYYlTKcH1CtWkKVnQgh+kvifBK/2amOPa1KqaQ9rcmaPq90LozNedCgglLre7mYLOD3vQkgEX609gq1SwU/93qeos/2/LHXOcrouePa3ITn3Vez14cYVuHVBVzEkJ6IZOAZZmUroH0di0/RD0nasJ3VLZs//DX+0IUE4zFuJTePWpB/c/q/L3354ZzqM7GqSdm7PaVy9zB8zptKoSHnF41rACeMIjICTV5n392LaDu3A9TPXSElMQa/X4/fPJZM8/seuULWxDw6FHIl/haHjb+p/bd/pPbIHfUb1Mkn7Z/cxPC0MD9Zo1CTlcN1WyLUQX04ewg9TfyL2SQwymRSpzHhNKpVJkUql6PV6OvXvgEQq4atuY4iPMV6jXzpxGVsHW0bOGMaxPSdyvqn+v+wd7P18m/Jd8Pps+HD//v25f//VhrkmJSWxfft2WrdujUJhvBCqXLkyq1ev5unTpxQsWNBk+4sXL1K5cvbiR6dPn+bmzZtZweszNjY2qFQqs/x5Rf/oIQadDmkR07lsMhfj37qH5s/XUtRsiLSQK44/7zZ7z3HFQZJ/m/ufPMsVYNu6nZw4dNokrXGrBtRp7GvsKXrugORZwiPX57y2bNuMuJg4zh2/aJKuVCmJjTaeeCpWK4+bpyuHdpnOk7juF8SHnVrh5OyYL4YP50epoVEYtDrUXqaL6Dz7O+mm+b6Xcvth1jaJ10It5tFGJ2LQ6ZFaCEikCnmePSbHs5AjMqnEbD7bvcfGv0u6OueYNyomkeAHT+jepJrFz5VKJGRYmHek1emt+miMgxsOcPGw6TMYfd+vTbWG1c32H7cSboTfvpfjZ9Vq7ktSfBLXzwdmlzdDS1hQKJ7exQAo5F6IKvWrcfyvozwKz17EpmTl0iTExPP4vnWeyevppEEmkXAv1vSi59lc15KFzIfYPpsPm/HCnCtt5l1/pVzG38GRpGn1dFxpfqe+5vf7+LiiB9M+MH8syptY+ftG9u8zfZbxRx+3oFnzBmZtU7JkCYJuvPw5ua1aN+Xu3XtcuRJglTJak4ejGplEQnic6dDOZ3+XdDa/kPZ0NAZT6Tm22X8zS8nT2d54LHhhAaVnf5d8YV7886LikgiOiKZ7/Zx7Xff6hTBpy0mKF3Lgx89b4uKYtwG5PuqB8brA1fS6QPrsuuCB+XWBtLAr8oo+pJ/6GzKyg3htiHFxNmnBwmDQI5FK0QZfM/2++6HoE+KQeZSwSvkPbzjI5cOm5/Ga79emcsNqZvuOSwlX7t/O+fnVPs1rkRyfRND57OGlugwt94LC8PA23iSPDI1AKpUiV8jJeG6IuDzzWJ2RR+egZ/7X9p2d6/dw+tBZk7QGrerh26imWfsU9XInNIfnvNZsWAN7J3u+WTCabxaMNnlv0R/ziQiPpEudz3D1cOHe7fCswPUZ/zP+NPmoEU4FnYh58t+vri78b8k3c16f17BhQzp27Mi9e+YXZ6mpqTx+/JjHjx/z8OFDTp48Sf/+/TEYDIwYMSJru6ZNm1KiRAkGDhzI6dOnefDgAVevXmX69OmcOnWKQYMGZW07cuRITp8+zfDhw7lw4QIPHjzgwoULjB49mqSkpJc+vsdqtBnobl5FUaO+SbKiZgMMSQno7piv1Jq0eCKJU4aYvHShN9GF3iRxyhC0fmfN8uSVJ1FPueEfbPI6e+wCdva21G3im7WdU0EnfOpWNVmB+EUde7fjm9mjsk44AIVdC1G1ViUunTauvFq/xXtM/3ESLkVNe2fqNqnNk6inRIsDYI70aRnEnr1BoQ9NVw4s/FEdMmITSbhi/rDwlNBIUkIjKfyx6U2ewh/XJfn2Q9LuP0GXnEpc5udKnrvD7NSgEjJbFXFnb+RJfZQKOT6linLEP8TkZHvI7zb2aiWViue8+NC1e8YFp6pZ6OHUKG2oXsqdw/53SM/IDmDPBYeTkp6Bj4WFrd5UzKNoQgJum7z8j/uhsddQrVH2c00dnB2oULsS/s+t1PmiNgM+YeDMwVl3vsH4XNeyPmUJu2HsiXEs6MQX84ZRqW72jTynwk40aNOAcwfPmX3mm1LKZfh4OnPkZqRp29yMwF4pp5Krk1meGh7OqBUy9gWZLmJ0LCQKuVRCFXcnBtXzZn2Peiav9lWMF7Dre9RjUD3Lz499E5GRj7hyJcDkdfjwCRwc7GneomHWdgULOVOvvi9HjpzM5dOMataqxrmzl1663duglMvwcXficMgj0zYLeWRsMxfzVUFruDuhVsjYf9N0Nd+jdx8b28zVvKctLygVcnxKuHAkMMy07NdCsVfZUMkz54XjroUbR1JUK1HE4vsngsKZuPkEVYsVYdXgD/M8cAUgIwNtkD+KWqZTqRS1G6FPTEB32/yYKi3ihmbgaBS1GprmqWo8D+vCQtBHGoPiF1crlrp5IrV3RP/YOs/bjH0Uw92AEJNXQOZxrcpzxzV7ZwfK165IQC7HtQ8HtOXzmYNMjmtqew3ePmUJuxEKkNXjWvdj0+son+a1CLt+1+JcW2v6X9t3nkY9JfjqTZPXhWMXsbW3xbdx9vPRHZ0dqVqnqskKxM87/fcZ+rcebPKaP3YhAPPHLmRc7wkA3LsdTokyxbF3Mh2tUKlWJRLjE4nPZ1MohPwp3/W8PvNs+PCL9u3bx759+wBQKpW4urrSsGFDFixYgItL9gWqjY0Na9euZcmSJUyYMIFHjx5hZ2dHlSpVWLt2LdWqVcvatmHDhqxdu5Zff/2V4cOHEx8fj6OjI/Xr12fTpk0UKpR3q6S+KHXXemy/notmyETST+xHVroiNq06k7rlN+Oz3FQaZO7F0T9+iCEhDv39u2afYUg1Hpx1oTfN3vuvXTnrz8VTl5m2bBJLpi8nLiaOAaM+JyEuka1rtmdt51WmBDY2CoKvGXssfluwiqUbv2feihlsXrkVBycHBoz6nPjYBNYuNy7o9efq7XzS/WMWr53LrwtWkZKcwkedWlO/eV3GD5nybg49saKwhVupumUiFX79isiNR3CoVRbPoW24M209+tR0ZHZqbMt6kBIaRUZmr0XYgq2UWzKUjOgEnh64SMH3a1Kk7XsE9l+Q9bl3Zm6g2rbJVNnwLeE/7sSmsBMlJ35G/KWbPDlg+cRnDf3fr8nAH7YzeuV+2tUpj//dSFYfuczwNvVQ2chJTEnnTmQ0HoUcTYYH33r4FBu5DM/Cli8Qhn1cl35LtvHFzzvp2bQ6TxNSWLzzFJWLu9Coct7Otbx+PpCAM1cZsXgUa2etIiEmgS4jupEcn8SBdfuytvPw9kRho+Bu4B0AtizexMS1kxm9/BsOrNuH2k5D+yEdUKpVbFq4AYCQgNvcuHCdATMGs2bmSnRaPZ+N7o5Oq2fzoo1WrUf/OqUZuPkco3depl1lT/wfxrD6/B2GNyqHSiEjMS2DO08T8XDS4KxRorGRM6ReGb4/egMHpYJmZVzxfxDDynMhfOpTAufMRbWKOpoOpTweYuwtrmghILa206cucPzYGX5bsZCJE2YTHR3LuPHDiYuL5/ffsp+DWLZcaZRKG5Pnu0qlUsqWLcWfW3ZZ+uh8oV+tEgzafoUx+6/RtoIb/hFxrL4cxvD3SqOSy0hM13InOgkPRzXOahs0NnIG+5ZkwalbOKjkNC1ZBP/IOFZdDqNbFU+c1bkP17Wm/k2rMnDFAUZvOEq7mt74hz1i9fFrDG9VE5VCTmJqOncexeLh7GAyPPhWZIzxWFDQPMBIy9AydespNDYK+jWtwt1HsSbvuzja5lkwm/bXOmy/nY9m+HekH92HvExFlB91IXXjL8brArUGWdHi6KOM1wXa6/5kBF5G3WcYEls79A/DkVeshvLjrqQd2oX+obFjIG3fnyg/Mt6c1wZcRFrIBWWHXugfR5J+xHw0l7UEnb9O4JkAhi4ewYZZa0iMSaDDiC4kxSdx+Lk5qUW9PZDbKAgLNF7n/LV4M9+sncSI5WM4tG4/ajsNbYa0R6lWsnWh8brgxtlALv19nh7f9UWpUREefI8GHRpTpmY5FvSblWd1et7/8r4Dxme6Xj7tx8Sl41g+41fiY+LoM6oXifGJ7Fibfcwq7l0cGxsFtwJvEx8Tb9abqrY1nmfvhYRzJ8jYhn/8soUWHZqz8I95rFu6gaT4JBp+0IDm7ZqybMpydG9hdeX/xEvmpguvR2IQV/h5Kq5P89fOI/eph6pdL6SuHhhinpJ2ZAfpB4wP7ZaVrYrdN9/nOhzYduz3ACTNGWXxfUlBFxzmr3+jIcXN9r/+Ag72jnaMnPwljVs1QCqV4H8hgAXfLTWZ0/rz1iW4ebrSxrdzVlrthjXp91VvvMuXQq83cPboeRZP/5GoB9nDGT1KFOWLbwdSvU5VbO1sCQ64ye+L13DqyKv1OJ8JyOHRQlZ2/vJVPv9yLL8vnYOvj/lz+azlVMWxr7V9oda+lBjTGU0pd9Iio3nw+37u/2S8aHF6rwLV/ppC0LAfiPzjaFYetx7N8RzSBpV7QVLCHnFvyV9E/Wk6dNOhZhm8vu2GQ3Vv9ClpPNl/gZDJayzOpc1J7TWvP8/8iH8Iy/edIzQqhiJOdnRpUJmeTY1z5y/cuk//pX8x5bPmtK1dPivPjM1HOeIfwuEZfXP8XL87ESzbfYZrYVGobOQ0qVySke3qm6xM/DKfDXiz4fu2Drb0mdgX3/frIJFICLp0g5VTV/DwzoOsbaZumkERjyIMqt8/K61yvSp0Ht4NrwpeGPR6rp0JYN28tTy4nT0k3LGQE30m9aVag+pIJBi3mbOGiFDTxyFZsn6i+dzz3By5GcnyUzcJjUmiiJ2SLtVL0LNWSQAu3HtK/z/OMqV1FdpWyp4jvz0gnLUX73AvJpnCdko6VClGn9qlcpzLuvzUTX4+fQu/0R++VtkAXIZvf+08Tk4OzJw9gQ8/aoFUKuXc2Ut8M3Yat29l31Tcs28DxYp7ULlCdi9YocIFuRN6gRHDJvD7ig0v/Z7lP8+lfoM6Jp/xMpGzW71eZSw4EvKIn87fzWqzzpU96Fnd+KiYi/dj6L/9MlOaladN+ewRCDuuP2St3z3uxSZT2FZJ+4pF6VOj+L+afyzxKPb6Zb8WxvJDVwh9HEcRRw1d6pSnZ0Pj4moXQiLo/+t+pnSsT9ua2T30M7af4ci1MA5P6Gr2eedvP2TAbzkv9jOwWTUGt6ie4/vPS9v8+osGKWrWR9WpN1I3T/TRT0j/eztpe7YAIC9fFbtJi0hePpv045mfrdagat8ThW9DpAUKon8USfrhXaTt32qyHoGydQdsmrVBWsQVQ2w0GVcvkvrHbxhe45F6Q047vXZ9bB1s6T6xDzXf/z/27ju8qep/4Pg7SUe6oYtOyip7741skaWADFmyBGQLCF8VBFkiIDJFVJQpyFBkKFM2lA1lzxYoHdC9V/L7I6U0TcooKa38Pq/nyfM0J/fcnNN7c84994xbB4VCwY0z11j91QqC7zwdbfHF+mm4eLkyquHgzLAKDSrRaVQ3fMoXR6vRcuW4PxvmrOVhlnLN3NKcTqO60fC9Jtg52hN06z5/LPid07tPvlDafhrn/tL5ya6g/HYA3v765RfztHWwZfiXQ2nUugEKpYJLpy6zaOpS7t9++n9esHEebt5udKvb0+g+qtarwsJN3zKyyyecP/50lXjvkl4MnjiQag2qoVQqCLgZyG9LN3Do7+ePWDkU9HqfeW0qiWty9xhQU7HqNSNfv9/UpPGax3LTeC3IctN4LcheV+P1dXnZxmtBlpvGa0GW28ZrQfWyjdeCLjeN14LMFI3XgiI3jdeCLDeN14IsN43XgswUjdeCJDeN14LqP9t4XfW/fP1+qz6vZ9TB61Ig57wKIYQQQgghhBBZSeNVCCGEEEIIIUSBV2AXbBJCCCGEEEKI/zSZoWlS0vMqhBBCCCGEEKLAk55XIYQQQgghhMgLGnlUjilJz6sQQgghhBBCiAJPGq9CCCGEEEIIIQo8GTYshBBCCCGEEHlBhg2blPS8CiGEEEIIIYQo8KTnVQghhBBCCCHyglZ6Xk1Jel6FEEIIIYQQQhR40ngVQgghhBBCCFHgybBhIYQQQgghhMgDWo02v5PwRpGeVyGEEEIIIYQQBZ70vAohhBBCCCFEXpBH5ZiU9LwKIYQQQgghhCjwFFqtVgZi56FVnr3yOwkmZf6GnS7u6Sn5nQSTanB5dn4nwWROVRqf30kQ/49cNLPK7ySYlGvqm3OnP1mpyO8kmFS46s3KT7mU1PxOgkmFqMzzOwkmVcM2Ir+TYDJlb+zM7yTkSsKyUfn6/dZDFuTr95uaDBsWQgghhBBCiLwgz3k1KRk2LIQQQgghhBCiwJPGqxBCCCGEEELkBY02f1+5TbZGw8KFC2nUqBFVqlShf//+BAYG5rh9amoq8+bNo1GjRlStWpVevXpx9erVXH9/TqTxKoQQQgghhBAi09KlS1m/fj3Tp09nw4YNKBQKBg0aREqK8fVipkyZwqZNm5g2bRqbN2+mUKFCDBo0iNjYWJOmSxqvQgghhBBCCCEASElJYcWKFYwYMYImTZpQtmxZ5s+fT2hoKHv27DHY/v79+2zatIlZs2bx1ltvUbJkSWbOnImFhQWXLl0yadpkwSYhhBBCCCGEyAv/wee8Xrt2jfj4eOrWrZsZZm9vT/ny5Tl16hRt27bV2/7IkSPY29vTuHFjve33799v8rRJ41UIIYQQQggh3kDNmzd/5uf79u0zCAsJCQHA3d1dL9zV1ZXg4GCD7QMCAvD29mb37t0sX76c0NBQypcvz8SJEylZsuQrpN6QDBsWQgghhBBCiLyg0eTvKxcSExMBsLCw0Au3tLQkOTnZYPu4uDju3bvH0qVL+eSTT/j+++8xMzPjgw8+IDw8PFdpyIn0vAohhBBCCCHEG8hYz+rzqNVqQDf39cnfAMnJyVhZWRlsb25uTmxsLPPnz8/saZ0/fz5NmjThjz/+YODAgblMvSHpeRVCCCGEEEIIATwdLhwWFqYXHhYWhpubm8H2bm5umJmZ6Q0RVqvVeHt78+DBA5OmTRqvQgghhBBCCJEXtNr8feVC2bJlsbW1xc/PLzMsJiaGK1euULNmTYPta9asSVpaGv7+/plhSUlJ3L9/Hx8fn1ylIScybFgIIYQQQgghBKCb69qrVy/mzp2Lo6Mjnp6ezJkzBzc3N1q2bEl6ejoRERHY2dmhVqupWbMm9evXZ8KECXz11VcUKlSIhQsXolKp6Nixo0nTJj2vQgghhBBCCJEX/oMLNgGMHDmSLl268MUXX9CjRw9UKhU///wzFhYWBAcH07BhQ3bu3Jm5/aJFi6hduzbDhw+nS5cuxMXFsWrVKhwdHU3xX8yk0Gpz2Z8sXsgqz175nQSTMn/DThf39JT8ToJJNbg8O7+TYDKnKo3P7ySI/0cumhkuQPFf5pr633uuYE6SlYr8ToJJhaverPyUS0nN7ySYVIjKPL+TYFI1bCPyOwkmU/bGzudvVAAlfDsoX7/f+pMf8/X7TU16XoUQQgghhBBCFHgy57WAMrNRU2NSD4q2roGZrZpHp29yavJqom8+fGY8K9dC1PyyJ+6NK6I0NyP4kD8nJ68mMSRSb7vyg9+hdO9m2Lg7EhcUzrWfd3F95d48y0vVSR/g9XYNzG3VPD59kzOTVhNzM+iZ8dSuhag+pSdujSvp8nLQn7OTVxnk5QlrD0fa7J/N9R//5tK8LXmRFRybVqX4xO5Yl/YiNTyGh6t2c2/hn8+MU6RzI4qOfA+1TxGSgx5zf+lWgtfu1097KQ9KTO5Nofrl0aamE3XiKrenrCQpMCyHveav4NBHdOozlAWzJlO7euX8Tk6mQk2rUnRCD6xKe5MWHkPIql0ELfrjmXGcOzfGa0QnLIu6khL0mKDvtxK2Tn9ZeZeuTfEc2gF1MTdSwiJ5tPEgD77bhDYtPS+z80bl503Ky7OY26ip+3kPirfWlXehp29ydMpqIp9TdmfVavkoUuMS+feT5XmYUkNmNmoqTv4AjzY1MbNVE37qJhcnrSL2xvPL6kpTe+HauBJKCxVhB/258MUqknIoq0sOaE2pIe+wq9aovMhGpjep7gHdudUg49yysFUTfPomh1/y3GqzfBQpcYnsy3ZuWTnZU/fT9/FuXAl1IRui7oRwdtl2bm3zy2FPuefYtAolJ3bDprQXKeExBK3aS+Bz6lG3zg3xGfkuVj5FSAp6zL2l23iYrR5t6P8Dlq6FDOIervgRKY+iTZgDQ2Y2aqpP6pF5rj06fZPTk1YT85xjo3YtRI0pPXFrXBFVxrl2Ots1m2/vZtSe3d8g7s1V+zg58ReT58WmUQ2cR/fBspQ36RExRK7fScQPv79QXMsKpSj2+7fcaTWQ1CD96xfbFvVw/rg7FsW9SHscSfTW/YT/8Dukppk8DwWO5s0atZjfClTPa1paGitXrqRTp05Uq1aNOnXq0K9fP44fP565Te/evZk4caLR+BMnTqR3794G4XFxcVSpUoX69euTkmJ8mOjFixcZMmQItWvXplKlSrRu3Zp58+YRFxdnmsy9pMZLh1G0TU3OztrA0ZHLUDvb0+r3z7AoZJNjHIVKSfM143GqUgK///2C38QVOFctQct1E1CYqTK3q/FFD6pN7Mqt3w6yr89cHuw5S52ZH+Lbs2me5KX+0uF4v1OTCzM3cHzE91g62dNs4/Pz8tbaT3GsUoJTE1dwauIKnKqVoOlvE/XyklWdbz/Cwt46T/IAYF+zNBVXTSD+ZhCX+88ldNMhiv+vB0VHd8oxjkv7upRdPJyIgxe49OE3RB65RJlvh+LauWHmNpYeTlTbNh1zRzuuDl3AjU+XY1PaiyobJqFUW+S47/zyMCSUj0Z/RmxcfH4nRY9dzTKU/XUiCTeDuD7gG8I2HaToxA/wHNU5xzhO7erhu3AEUQfPc73/N0QfvUSpeR/j3KlR5jbuA9viu2A4CTcfcG3AN9yfswGXLk0ovXys5Of/YV6ep8XiYZRoUxO/rzewf5Su7G6//jMsn1HePaFQKmjwVR9KtKn1GlJqqNay4Xi0rcWlGes5Pfx7LJ3tabTpc8yfU1bXX/cphauW4PzEFZz7dAWFq5ak4Yb/GS2rvTs1oNKUnnmZjUxvSt3zROuMc+v41xvYM2oZ1s72vPsS51bjr/pQ0si5pTRX0WHNp3g3qsjJeZvZOeg7Qs/f5u2lIyjTqYFJ8+BQszRVVn1K/M0gLvafR8imw5T8XzeKjX4vxziu7etSfvEwIg5e5OKHc4k8coly3w6mSJZ61MLFAUvXQtyYvJJT73yh90qNzPvruIZLh+H9Tk3Oz9zAsRHLUDvZ0+IFzrWma3XXbKcm/sLJjHOt2W/612yFK/gQdf0B/7T7Uu91ecl2k+fDqlo5vL6fTMqd+wQNn0H01v24jOmD05Buz41rWbY43sunoDA37Bezrl8Nz8WfkxIQxINh04lctwOnwV0p8r/8HU4r/psKTM9rSkoK/fr1Izg4mBEjRlCtWjWSkpLYvHkz/fv3Z9asWbz77ru52veOHTtwcnLi8ePH7Nmzh7Zt2+p9fvPmTXr37s0HH3zA6NGjsbGx4dq1a8yaNYsLFy6watUqE+TwxTnXKIVXi2rs6z2HoP0XAAj1u06nE/Mp07cF/gu2Go3n064OjhV82Np0AtEZd8ojLgfSYf/XFOtQl7tbjmLr7UK5j9pwatKqzJ7WkKNXsPFwwqNJJW6u/dekeXGqUQrPltU40OsbgjPy8sjvOu39vsO3b0suL/jTaLyi7etQuIIPO976lJiMvEReCuSdf7/Gp0NdArYc1du+VN8W2JXyMGnasys27n3iLgdwbfgiACL+PY/CTEXREe/yYNl2NEmGN0aKT+zBo20nuD15pS4PBy5gXtiW4uO7Ebb5iG6/n3YlPS6RC+9/hSZRt4+ke2FUXDUBuyoliPa7lqf5elEajYatf+9l7uKf8jspRnmN7Ur85QBujVgIQNS/51GaqfAa/h7BP2wzenyKTuhB+PYTBHz5qy7OgfOYFbLFe1w3Hm85DEolXp+8T9TB89z4aF5mvLiLt6l2cAEOjSsTfeii5Of/UV6epUj1Uvi0qMbOPnO496+uvAs+eZ2ex+ZToU8Lzi40XnYDOJb1puH0vrhULk5qYvLrSvLT76/hi3vL6hzt+Q2h+84D8NjvGm+fXECJD1ty/bs/jcbzbF+HQhWLsafJeGKv68rq6MuBtDgwG6+Odbm/WVdWWzrbU35CV4r3bkZyRGye5+dNqnsA3KqXoliLamzrM4fAjHPr4cnr9Dk2n0p9WnD6GeeWU1lvGk/vi2sO51ax5tVwqViM39tNJuzCHQAeHLmMnacz1T9ux/VseX4Vxcd1IfZyAFeGLwEg4t8LKMxU+IzoyL1l29EkGc6fLTGxG2Hb/Lg5WXctFpFRj5YY/z6hGfWobcViADzacZKkB49Nlt4X4Zxxrv3baw4PM861ML/rdPSbT+m+LbiUwzVb0fa6a7btb2W5ZrsUSLts51rhCj6En79D+NnbeZ4Xp+EfkHTtDsHj5wIQf/gMCjMVjh+9T8Qvf6BNNtIBZG6GY+/2OI/qjdZIWQ7g0LklaQ8f8XDcXNBoSDh2DjMnBwp/+B6hM5dDPo2UeW20b84aBAVBgel5XbhwIdeuXWPdunW89957FCtWjLJly/L555/TqVMnZs6cSXx87np6Nm/eTMOGDalXrx7r1683+HzLli0ULVqUCRMmULZsWby9vWnZsiVTp07Fz8+Pa9deb+PBs0llUuOTeHjw6bOSkiNiCT1xDc9mVXKM5/FWJaJvPcwsBAGibz4k+uZDvDLieb9dA01KKjd/O6AX99DQxRz8aKFpMwK4v6XLS0i2vIQdv4p785zz4takMjG3HmZePADE3Awi5uZD3JtX1dvWpqgLVT/vzqlxedeoUliYUah+BR7v0B9C9Wj7CcxsrXCoU9YgjtrbBetSHjzemS3OtuNYFXfDqoTuAdAu79QheN3+zIYrQOyFOxyvMrjANFwBbty6y7S5i+nYpgWzJo3L7+ToUViY4VCvAhHZ/tfh24+jsrXCrk45gziWXi5YlfIkfOcJgzhWxd1Rl3DH3MUB88J2ROw+rbdN4o0HpIZHU7il4bPOTOFNys+blJfn8c4ou+8fyvKcu4hYHp64RtGmOZd3AM2+G4JCoeCPDlNIehyT10k1UKRpZdLikwg78LTBnxIey+PjV3HLVuZmjxd782FmwxUg9kYQsTcf6sUrM7Ijrm9V4kT/+YTsOZsXWdDzptQ9TxRtUpmU+CTuGTm3fJ5zbrXIOLc2dZhCopFzKyUukUtr9mc2XJ+IuhuMg08R02QAXVlQuH55Hu04qRcett0PM1srChkpC9TeLtiU8uDRzmxxtvlhnaUetatYjNSouNfecIWn51qwwbl2DY9nnGvuTQyv2WIyrtk8n8RTKChUzovIS4F5lv4nFOZmWNepTOzuY3rhsbuOoLK1xqpmBaPxbJvUwml4T8K/30DYXOPDmJUW5mgSk/RWvk2PiEFpYY7S5s1aLE/kvQLReE1NTWXjxo106dIFNzc3g89HjRrFTz/9hFqtful93759mwsXLtCgQQPefvttTp48ye3b+nevFAoFQUFB3LhxQy+8Xr167Nixg+LFi7/0974KB18P4gLD0Kbr36mJvRuKfUZBbTReKQ9i7oQYhMcGhGJXQvd/dazgQ8ydEIrUKUvbv6fRK+BXOvl9R+k+zU2biQz2vp7EG8lLXMCz82Lv60HMnWCD8Kx5AUChoO53Q7i3zY/gA3nXy2LlUwSlpTkJt/XnryTe1f2/rUsa3nm39vUEIOF2cA5x3FEXdcXMwYak+4/wnTWABldX0DhwLRVXT8DS0zkvspJr7m6u7NzwM5+O/ChXv8W8pM44Pol3sh2fAN3/2qqE4fGxKu0FQFK2OElZ4qTHxKNJTUPt7aq3jcrBBjMHW4NwU3mT8vMm5eV5Cvl6EHPPsLyLCQjF4RnlHcD+0cvY2nkaEdfu52USc2Tn62G8rL4biu0z0m7n60mckbI67m4ItiWfxruzah+7633Cw52nTJfoZ3hT6p4nCudwbkUHhFLoOefW3tHL2NJ5GuE5nFsPjlzmwP9W6IUpzVQUa16N8OsPXi3hWTytR3OuE7OzyaEeTcgWx65iMdKi46m0YixNbv5CkzsrqfjDKCyMzIE1tRyv2QJCsXvWNZuvB7HPuWazL+mGmbUa5xqlaH94Dj0Cf6X94TkU79LQIN6rMi/qjtLCnJQA/TnhKYG6/71FcU+j8ZL8b3C76YeEL9uQYw9q5JptWPh44DigM0o7G9RVylD4w3eJO3ASTXT+TM8T/10FovF6//59oqKiqFq1qtHPXV1dqVy5MiqV8fkmz7Jp0yasra1p3LgxLVq0wMLCgt9++01vm27dumFubk6HDh3o1q0b8+bN49ChQ6Snp1OqVCksLS1zk61cs7C3JjUu0SA8NT4Rc9uc71BZONiQGmskXlwSFna6eJaOdli7Fabh4qHcWn+QvT2/4eGBi9Sd1S9PGrA55iUuCbNn5sWaNCN5SYvT/x+UGfQ2tkVdOTdljWkSnAMzB928lfRseXnyXmVnmJfMOLHZ4yRlxLHG3MkegBKTemLp7siVId9x/ZNl2FYoRtUtX6K0fr3n3rM42Nvh5uqS38kwSmWf0//6GcfnBeJoElMI/+sobv3a4Nq9GSoHG9QlPSj9/Rg0qWl5dnzepPy8SXl5Hkt7a1KMlFsp8YlYPKO8A/Kt0fqEub3x+iMtLhFzI8foaTzrHOLpl/Fxtx6+1kW03pS654lXObdyarQ+S4NJH1CouBunF+U8HPllmTno5gWnvVQ9mhEnNsFoHLOMOLYVfLB0dyLm/G0u9JrNzS9XUah+eWr8mff1qHkO51r2cya7nK7Z0uKSMuMVruADgI2XE2enruVAn3mEX7hD/YVDKPnBW6bJQAalna7c1cTp/6818br3Klvj87rTQsOf2wBN8LtI+E+bcZ0wgNJnNlJs43zSw6N4+Mk3Jkj5f4BGm7+vN0yBmPMaHa1bBc7BweGFtt+2bRu7du0yCE9JSaF69eqZ79PS0ti2bRtNmzbFykpXEDRp0oStW7cyduzYzDAfHx/++usvfv31V/bt28fy5ctZvnw59vb2jB8/nq5du75qFnOmUKDI/gw7pQKjj1NVKJ75sGGFUoEWIxEVoM04eVUWZqid7Dkw8Dvu/a0bbhdy9Ao2ns5UHvUuN1btM4z/oozkRaFUYOxRwgoFz86LQpnz/yDjA7uS7lSe8D5HBi4wWgGYlEKXrxwfi2yscFDmEOfJv0ijQZmxsEHKo2gu9ZubmbfEgBCq75xJkc6NCF6dN6tAv0kyzzuTHJ+M8Iw4tz9djiY5jZLzhlJq/jDSE5IIWvInSitLNAl5MzfxTcrPm5SX7GkxKO8Uxp/fqVAo0L7Cg+JNLod6x+gxek7aFUpljvFe20XTm1b3GDs+r/HcavBFD6r0b82ZxX9xd9cZk+1XocjoL3mpsuBJHIOd6cW5Mvp7NEmpxF0KACDK7xrx1x5Qc/tXuL/fmKCVe14t8Vm+19i5ZuzS63nXbFnPKf3wp2Vf6NEr/NtrDqHHrpCeMR84+KA/amd7qozvwu11B3KZESNfq3z28dG+wu+5yFfDKdSpJY+X/EbC8fOYe7nhPLIn3j9P417fz9Amvf55/uK/q0A0Xh0dHQGIiop6oe2bNWvGuHGGc+7mzp2rt4+DBw/y6NEj3nnnncywd955hz179rBjxw66dOmSGV6kSBEmTJjAhAkTCA4O5ujRo6xbt45JkyZRpEgRmjRpkrvMPUeVMe9RZaz+arUB2/2wL2FvsK25tdrondcnUqITjN6BNbdRkxKju3OWGpeEVqPhQcaiAk88PHABz6aVUTvb53q+VcVP3qPSWP3VQ+9t89MfapXBzEb9zEo/JSbe6N1+s4y8KJS6IVv3t/kRcsgfherpIAKFUoFCpTQYwvMq0mJ0863Nst15VGX8v9NiEgzjROvCzLLlQ2Wjzozz5A50xP5zehVGzJmbpEbFYVvx9Q5Z/6968r/Ofuf+WccnPTrjmNpZk7XafHJ80jPu9GsSkrg9dil3J63A0suF5PthaBKTKdKjOdGBoabOii69b1B+3qS8ZFVz9HvU/ES/7L693Q8H55cvu1+3cmM7UW6cfln9YJsflrkoq1Nj4g3KOF08S1JjDY9tXnjT6p7ao9+jdrZz69Z2P6zz+NxSWZrT/NuPKN2hHmeWbuP47Bd7PMqLelqPvnhZkJZRFhiUH1nqUYCY0zcN4kafuk5qdDy2Gb2XplDpk/eonO2aLXCb8Ws2M5tnH5vUmIQcfjtqUjPylfQ4JnMRqKyC9p7HvXEl1C4OJJnoMUDpMbreU2W26xylje69JjZ3686YFXGiUNe3CV/2O48XrNYFnvQn0f8GJXZ8j0OXlkStMf3KyQVJgbp5+QYoEI1Xb29vnJ2dOXfunF5D84mAgAC++uorJkyYAICNjQ0+PoaFkY2NjV7jdcsW3fPWRo4cabDt+vXrMxuvc+bMyVzQCcDd3Z0uXbrQoUMHWrZsycGDB/Os8Xpj7X4e7D2nF+b9dg08mlQ2uCtnV7wI0c94Pl3M7WAcKxr+X+yKFeHxed1CDDF3Q1AolajMzdAkP13VT2mmOxXSjaz096Jur9nPwz36efF8uybubxnmxbZYEb1FCrKLvR1M4YzVA7PnJfz8baw9nHCuUQrnGqUo3rWx3jYVx3Si4phO/FV7FPEmWrwhKSAUbVo6VsX1L4aevI+/YTgvKPHWw8xtntwNzh4nLSIObboGpYW5QXyluZnRVViFoaTAELRp6aiL6c8vsiqm+18n3jQcNpeYMX9ZXcyN+Et3M8PVT+JkHNPCLWqQFh1H7KnrJN7Q7cfcyR4LDyfi/O+QF96k/LxJecnqyrr9BO7TL++Kta6Bt5Gy275YESKf82zR1+nu6n0EZ1s0yePtmhR5q5JhWV28CLFGyrcnYm8FU6hSMYNw2+JuRJ7L+9VR4c2rey6v209AtnOreOsaFDVybjkUK0KECc4tCzsr2q8cj1uNUhz5ai3nf/z7lfeZXWJAKJqXrEcTMupR62z1qHWWOGb21ri0rU3MmZvEZzu2SnMzUk24uvWtNfsJ2mPkms3IuWb3nHMt5nYwhXO4ZgvPuGZzrVsWGy9n7m46oreNmdoCTVo6KdGme2Rd6r1gtGnpWBTVX4fAwkdXdiffuper/Zq5u6BQKkk8e0UvPOVmIGmR0ViWMt3NBfH/Q4GY86pUKunSpQtbtmwhNNTwbvlPP/3E+fPn8fQ0PlncmIiICA4ePEinTp34888/9V5dunTB39+fy5cvA3Ds2DFWrFhhsA8LCwvUajVOTk65z9xzJIZGEX7xrt7r4UF/LOys8HirUuZ2lo52FKlbVm8F4uweHvLHwdcDB9+nBc+T90/iPXn0TrGOdfXierWqTsSVQKPzNl4mLxEX7+q9Qg76Y25npbuIyJIX13rl9FaBzC74oD/2vh7Y+z495va+ntj7ehBy0J/E0Eh2vf2FwQt0lcuut78gMdT4A+VzQ5OcStSJqzi3raMX7tKuLqlRccSeu2UQJzEghMSAEFza19OP074eCbcekvzgMekJSURn7Fdh8fReUqFGFVHZqIk+cdVkeXiTaZNTiTlxBad39I+PU7t6pEXFEWfk+CQFhJAUEIJTu3oGcRJvBZH84BEARfq0otjkvnrbuH/UDm26hsg9phtSl9WblJ83KS9ZJYRG8ejiXb3Xg0O6stu7ydOyW+1oh0fdsjw4lHN597olhUYRdeGu3iv0oD/mdtYUafq0rLZwssO5XjlCD+Sc9rCD/tj5emBX+mlZbVfaEztfD0Jfw0JG8ObVPfGhUYRdvKv3up9xbhU1cm7df8VzS6FS0vaXsbhWKcE/Hy/Ok4YrPK1HXdvW1gt3bVeH1Kg4YozWo6EkBITg2l6//HBtX4f4Ww9JevAYTWoaZb4egM+Id/W2cW5TE5W1JZHH9BtNr8LYuRacca5lv2ZzrVf2ueeag68H9lmu2ewzrtmerFzs1qgC9b77CNtiWVZ9Vijwblebx2dvoUlJM1netCmpJJy6hF2r+nrhdq0bkh4dS9LFGznEfLbUQN189+yrFVsU98SssAOpD/J2lIx48xSInleAIUOGcPjwYbp3786oUaOoXr060dHRrF+/ni1btjB37lxsbW1feH9bt24lLS2NgQMHUrJkSYPv+uOPP/jtt9+YPn06Y8aMYejQoYwaNYpevXrh4eHBw4cP+f3334mPj6dbt+c/nNmUwvyuE3LsCo0WfcyZGetJjoyjytj3SIlJ4Mbqp3NSHXw9UFmYE3FZt4R6wF8nqDSiA83XfMrZmRsAqP5ZNyKv3Sdwu+4xFaHHr3J/91lqTe2FmbUlUdceUOL9hrjW8uXf/vNNnpdHftcIPXqFeos/5vyM30iJiKPi2E6kRidwM8tcTntfT1SWZpnLwd/76wQVRnbkrbWfcmGm7vFGVT7rTtTV+9zb5oc2XUPExbtGvzMxNDLHz15F4PzNVNk4ifI/fkLIb/uxr1UG72EduDNtLZqkFFS2VtiU8SIxIJTUcN3Q68BvN1N24TBSI2IJ33Uap9Y1ce1Yn8uDvs3c752Z66i6ZQqV133G/aV/YeFSiBKTehJz5gaPd53OKTkim/vfbaLC719SevlYwtbvx65mGTw+7kjg9DWZx8eqtBdJgaGkZRyf+/M34btgOGmRsUTsPoVjq1o4d2zA9SzPDQ3+eScV1k+m2Ff9iNh1CoeGlfAa2ZkHi7aQfC/vKt03KT9vUl6eJdjvOkHHrtB80cecmLGepKg4ao55j+SYBC6veVp2F/b1QGlhTvjlvH/8xYsKP3GNR0cvU2vJMC5NW0dKZBxlx3UmNTqeu1nWQrAr7YnSwozojLL6wdbjlBnZkQbrPuXSdF1ZXfGL7kRfvU/QNj+j3/U6vEl1D8BDv+s8OHaFVos+5ljGuVV7jO664FK2c0tlYc7jlzi3KvVtiWedslxavY+4h+EUqaZ/zRRqwh70gPlbqLbxCyr+OIbg3/7FoVZpfIa159a0dWiSUrPUoyGkhut6TAO+3UL5hR+TGhHHo12ncWldkyId6+M/SHfNoklM4d6Svyg+tgspj6II//cCtuWLUmLc+zzec4bIPL5xFOZ3nZCjV6i/+GPOzVhPckQclce+l3GuPT029r4eqCzNM8+1wL9OUGFkB5qt/ZRzGdds1T7rlnmuAdxYuY9SvZrx1spPuDh3C+mJyZT+sCWFynix9/2ZJs9L+Pfr8f51Bh4L/kf05j1YVSuH48DOPJrzC9rkFJQ2VliUKkrqvWDSI19sill6ZAwRK//EaYBuaH/C0XOYe7jiNPwDUoPCiPr9H5Pno8B5AxdNyk8KbY4r0Lx+CQkJrFixgr///puHDx9iaWlJhQoVGDx4MLVr6+7U9e7dG09PT77++muD+BMnTiQoKIjVq1fTvn17XFxcjPaogu7xO4cOHeLw4cPY2tpy9uxZfvzxRy5cuEBMTAwODg40bNiQESNG4OXlles8rfLslat4Fg7W1PyyF96ta6BQKgg7dYPTU9cSk2W5+FYbP8fW25ktdcdkhll7OFJram88GldEk5rOw0P+nJ6ylsSwqMxtlJbmVBnzHiU6N0DtaEfUzYdcnP8H919gYQbzXJwu5g7WVJ/SC6/WNUGp4PGpG5ydsobYLHlptulzbLxd2FZntF5eqn/VB7eMvIQc8ufsl2tIypKX7Ho8XIv/vM1cmrflhdLmnv5yw3Kd29Sm2KddsS7pQXJIBEEr/uHBMt1cjUL1y1P1j6lcG7mEkA0Hnn5H7xZ4f9wBtYcTiYFh3Fv4B6GbDunt175maYp/1gP7ar5oEpN5/M8pbk9ZZXQO0LM0uDz7pbbPrZNnL9J/xARWLJpN7eqVnx8hF05VGv/ScRzb1MZ7XHesSnqQEhJByC9/8/CHbQDY16tAxS1fcXPUYh79/m9mnCK9W+IxpCOWHk4k3QslaNEfPNp0UG+/zu82xGt0FyyLupL84BEhv/5DyIq86Z14U/NT0PNy0cw0zxq0cLCm/uReFG9VA5QKQk/f4OjUtURnefxKh98/x87LmbX1xxjdR89j83l44ir/frI81+lwTX35OVbmDjZUntoL97drolAqCD91g4uTVxOXpaxutOULrL1d2FVrVGaYlYcjlaf1wbVJJbSp6YQe9Md/8uocy+oaCwbjXL+83j6eJTn74kUvnJ+CWfeEq3KXH0sHaxpmnFsKpYLg0zc4MnUtUVnOrfcyzq1VOZxbfY7NJ+jEVfZlObfe2/QFnkaeVf7EYu9nX8eUS3m56UYubWpR/NP3scmoRx+s2M29LPVojT++5MrIpQRvePpb9+zdgqIft9OVBYFhBCz8k5BNh5/uVKHA68OWeH7YCiufIqRGxhK65Sh35vyO5iWnQ4WoDKfxPI9F5rmmOzaPTt3gzJS1eudai02fY+PtzNY6+tdsNb7qjXvGuRZ8yJ8zX67VO9fsiheh6v+64VKnDOa2asLP3+HC7E08Onn9hdJWwzbipfJi27IeziN6YVHCi7TQx0St3U7Eij906a1diaJrZhM84Vui/zBcTNLhvRa4z/6E200/JDUoTO+zwn07UqjHO5h7uZEeFkH80bM8+nblCzeCAcre2PlSeSko4mf0ydfvt/l8Vb5+v6kVqMbrmyi3jdeCKjeN14LsZRuvBd3rary+DrlpvAqRW6ZqvBYUuWm8FlS5bbwWVLltvBZUL9t4Lehy03gtyF628VqQ/Wcbr9Pzty1g88XreaTX61Ig5rwKIYQQQgghhBDPIo1XIYQQQgghhBAFXoFZsEkIIYQQQggh3iiyYJNJSc+rEEIIIYQQQogCT3pehRBCCCGEECIvaN6cBfQKAul5FUIIIYQQQghR4EnjVQghhBBCCCFEgSfDhoUQQgghhBAiL8iCTSYlPa9CCCGEEEIIIQo86XkVQgghhBBCiLyglQWbTEl6XoUQQgghhBBCFHjSeBVCCCGEEEIIUeDJsGEhhBBCCCGEyAuyYJNJSc+rEEIIIYQQQogCT3pehRBCCCGEECIPaDWyYJMpSc+rEEIIIYQQQogCTxqvQgghhBBCCCEKPBk2nMfefScsv5NgUi12JOd3Ekzq4Kr38zsJJnWq0vj8ToLJ1PKfk99JMKnhNSfkdxJM6rsFtfI7CSbV+P0F+Z0Ek4qZ/15+J8FkFA4O+Z0Ek7o3/Wx+J8GkvlBa5HcSTGrlmEL5nQST6vJtRH4nwWR25ncCcksWbDIp6XkVQgghhBBCCFHgSc+rEEIIIYQQQuQF6Xk1Kel5FUIIIYQQQghR4EnjVQghhBBCCCFEgSfDhoUQQgghhBAiL2jlOa+mJD2vQgghhBBCCCEKPOl5FUIIIYQQQoi8IAs2mZT0vAohhBBCCCGEKPCk8SqEEEIIIYQQosCTYcNCCCGEEEIIkQe0MmzYpKTnVQghhBBCCCFEgSc9r0IIIYQQQgiRF6Tn1aSk51UIIYQQQgghRIEnjVchhBBCCCGEEAWeDBsWQgghhBBCiLyg0eR3Ct4o/6nGa7NmzdBoNGzfvh1bW1u9zyZOnEhQUBCrV68GID09nQ0bNrBlyxZu376NSqWiVKlSdOvWjXfffReFQgHAuXPn6NmzJ4MGDWLMmDF6+zx48CBDhgxhyZIlNGvW7PVkElBVqIn63Q9RuhdFGxdNysEdpPy9/sUiK5XY/G8B2uQkEuaO1/vIrHpDLN/uitLNG21iAmnXzpG86Se0sVGmz0QW1jZWjJw0lCZvN8Ta1pqLpy/z7aSF3L0Z+Mx407+fTKuOzQ3CPx86lT1b9wNQ2KkQQycOok6TmtgXsufenfusXvobe//6N0/ycvRKIEt2nOBOSASFba3o0qAi/VvWyDyfstrqd5Uv1+7NcV9f9WxBhzrlALgbGsF3W49x+uYDzFQqqpfyYOy7DfFydsiTfDxRqGlVik7ogVVpb9LCYwhZtYugRX88M45z58Z4jeiEZVFXUoIeE/T9VsLW7dPbxqVrUzyHdkBdzI2UsEgebTzIg+82oU1Lz8vs5Epw6CM69RnKglmTqV29cn4n57ksbdR0/qw3VVvVRm2j5vaZG/w+9ReCbz14Zjzf2uXoOK4H3uWLkZyQxJkdx9k6bz1JcYmvJd1Hrz9gyT9nuBMWSWEbNV3qlqN/08rGfzunb/Dl74dz3NdXXRvToaYvAJv9rrH2yGWCwmNxK2RL1/pl+aBBBaP7NTVbWxtmfz2Jjh1aY2dny/HjpxkzdjJXr97MMc6tGycoVszb6Gd3797Dt0w9fv5pPn37dM1xHyVK1ebevaBXTn9WRwMes+TELe5GxFPIyoIuFb3oX7PYM/+Ph+8+4oeTd7j1OA4HtTnNS7kyor4vVuYqg23jktPouu44Q+qUpEN5D5Om3Zijt0JYcuAydx7FUNjGki7VS9C/QZln5ufQzWB+OHSFW2HROFhZ0rycJyObVsTKwoygqHjaLvo7x7gdqvjwVYdaeZEVAKwb1sBldB8sShYlPSKaqA07iVj++wvFtaxQCp/187nz9gDSgsL0PrN/rwWO/Tpj7uNB2qMIYrbuI/z73yCPy2q1jRW9P/uQ2q3qoLa14saZa/wy9Sce3Lz/zHjlapenx/heFCtfgqSERI7vOMb6uWtIzFKO2RW244MJfajetAaWVpbc9r/N6pm/EHD5bp7kRelTAfP6HVE6uqNNjCXN/xBpp/7JcXtF4SJY9f3KIFwTEULSqi8z36vK18O8eksUhVzRxkeTfvUEqSd3vJaGkJWNFQM+H0DdVnWxtrXm6pmrLJuyjPvPOT5Z1W1Zl8k/T2ZC1wn4n/DPDHf1cmXAZwOoXK8yZuZmXDh2gV9m/ULQXdOWaeLN9Z9qvAIEBwfz9ddfM3369By3SUtL4+OPP8bf35/hw4fToEED0tPTOXr0KDNnzmTfvn0sWLAAlUpFtWrVGDp0KN9//z2NGjWiZs2aAISEhDBhwgR69+79ehuuJctjPXwqqacOkvTnr5j5VsTy3Q9BoSBl52/PjW/RpjuqYmVIu35BL9ysRiOsh0wi5eB2kv5cidK+EJYd+6Ia9w3x04ZBWmoe5QimL51M+WrlWDR9GfGx8Qz65EOWbpxPt7f6EhMVm2O80hVKsXPTbjb9qt+Yun9Xd5FuZm7Gwt/mYudgy/I5KwgLeUzTNo2ZuWwK5ubm/L15t0nzcf5OMKN+3E7rar4Ma1uXc3cesnjHcTRaLYNaG160NCpfjFVj3tcL06Llq/X7iU9KoWH5YgCERMby4fzN+LgWYlbf1iSlprFkxwmGLt3KxokfoLbIm5+pXc0ylP11Io//Osa92b9hV7scRSd+AEolQQs2G43j1K4evgtHEPzTDqL+PY/j27UpNe9jNEkpPN6ia2y4D2xL8Wn9ebztGAHTVmHuaI/3uG5Yl/fhev9v8iQvufUwJJTBY74gNi4+v5PywgYuHE2xKqXY8vUakmITaDv6fcb89iVTWowhITrOaBzvCsUYueoLrh25yLKhcylUxJH3JvTEraQnC/rkXJaayvmAUEb9uofWVYoz7O0anLsbwuJdp3W/neZVDbZvVNabVcPa64Vp0fLVpiPEJ6fSsKwXAL8fv8rMP47R763K1PX1xP9+GN9uP0liShoDmxnu19TWrl5KrVpVmfi/GcTExjLpi0/Ys+t3KlVpSmRklNE4XboOwNLCUi+sbt0azJs7heU/6m6+zpj5HcuXr9bbxtGxEOt/+4GDB49z//5Dk+bjfHAUo7efp7WvG8PqluJ8cBRLjt9Ci5aBtUoYjXPwziM+2XGedmU9GFnflzsRcSw+dovIxFRmvV1Jb9vopFRGbztPcGySSdOdk/P3HzNqw1FaV/Bm2FsVOHf/MYv/vaQ73xqVMxrn4I2HjPn9GO0q+zCqWSXuPI5h0f5LRMYn83WnOrjYqlnVr6lBvA2nb7Pr8n3eq1o8z/KjrlYOr6VfEvP3IR59twrrGhVwHt0XFEoifnj2jW3LMsXxWjYVhblhPVKod0eKfD6E2H8O82jOz6gK2+M0oheWpYvzcMS0vMoOAKMXjaVUFV/WzFpJQlwC74/qzpfrpjGmxXDicijHilUozherp3DxyAXmDvkaxyKO9JzQB8+SnkzvPSVzu/HL/4dnSS/WfL2SyNAI3h3amakbZjK+zSjC7ocZ3XduKd1LYNnhY9JvnCb52FZUHqUwr98RUJB2yvjNDqWL7uZV0qZ5etde2ix/m1VthsVb3Ui7cYa0I5tRqG0xr9cehbMnKduXmTQPxkxYPIHSVUqzYuYKEuIS+GD0B8z6bRZDmg/J8fhkZVfIjuGzhhuEW9lYMWPtDNLT0ln8+WJSU1LpMbIHszfOfuF9/yfJgk0m9Z9rvHp7e7Nx40Zat25No0aNjG6zbNkyzpw5w5YtW/Dx8ckML1myJLVr16ZLly78/PPPfPTRRwB8/PHHHD16lE8//ZS//voLtVrNmDFj8PT0ZNy4ca8lX09Ytu+F5v5tklboLvLTL58GlQrLNt1I2bMZUlNyjKv0KoFlm+5oosIN99uuJ6kX/Uhas1C3XyA95D62ny/GrHJd0s7m3MvxKirVqEDDlvUZ3etTju33A+C830X+9FtPl77vsmLBaqPxLK0s8S7uxcpFa7l09orRbRq2qEeZir70bTOYqxeuAXDq8BncvIrQZ1gPkzdef/jnJGU8nZnRpxUADcr7kJau4Ze9Z+jdtJpBI9PRzgpHOyu9sLUHznM3JJKVY7pkfvb9Tj+s1eb8MPxdrCzMAfB0smf08h1cuR9K9ZKeJs3HE15juxJ/OYBbI3TnRNS/51GaqfAa/h7BP2xDk2R4rhWd0IPw7ScI+PJXXZwD5zErZIv3uG66xqtSidcn7xN18Dw3PpqXGS/u4m2qHVyAQ+PKRB+6mCf5eRkajYatf+9l7uKf8jspL6VE9dJUbl6DRR/O5NKBcwDcPHWNGYeX8FbvVuxcvMVovBYD2hMXHsOyofNIT03LDP9w7jCKlPAg9I5pG0PZ/bD3HGU8HJnR/S0AGpTxIk2j4Zd/L9K7cUXU2S6sHW2tcLTN9ts5com7YdGsHNYOR1srtFotv/x7gVaVizPqHd3Nozq+HgQ+imH90St53nitW6cGbdu2oH2H3vz9j24kyJEjfty6cYKhQ/oyc9YCo/HOn7+s997Ozpa1a5ayY8de5sxdCsCdO4HcuaM/MmXTxp+IiIiid9/haLWmvRBa7neHMi52TG9dEYAGxZxJ02j55XQAvar5oDbT70nVarXMPXSd5iWLMLVlBQBqezui0cJv5++RmJqe2fv67+0wvjl0ncTU1zfq4odDVynjVogZ79bW5aeUm66sPnad3nVLozY3zM+c3RdoXtYrs/e0dnFX0jVafjt1i8TUNKzMzajs5aQX7/LDCHZdvs+IZhWpVtQ5z/LjPKwnSdfuEDJhLgAJR86AmRlOH71P5K9b0CYbuS4wN6Nwrw44j+xt/HOlEudhPYk/epaHo2dmBiddvkXxHT9gXb8aCcfO5Ul+SlcvQ43mtZj54Vec+/cMANdOXmHJkeW06tOGLYs2Go3XfmBHYsJjmDdkNmlZyrFh80bhUcKTh3eCcC/uQbnaFfj+00X8+7tuRNCNs9f56cwqGndqyqYFG0yaF/O67dA8uk/Krl8A0AReBpUK81pvk3Z2L6QbdgwoXbzRRD9G8+CG8Z0qFJjXbUd64BVSdi7PDNaE3cOqzxTSipZDc++qSfORVdnqZandvDaT+07m9L+nAbh08hK/HP2Fdn3asX7R80cCDpsxjHQjvfeN2jXCs7gng5sPzuzFDbgWwK/HfqVRu0b8vTbn0Q1CPPGfW7CpQ4cO1KtXj0mTJhEXZ3iHRqvVsmbNGt577z29husTZcuWpWPHjqxevRpNxtALlUrFnDlziIqKYvbs2SxdupQbN27w3XffYWFhked5ymRmjqp0ZVLPHtULTj1zGIXaGpVvpRwiAioVVv3Hk7L/TzSh2YYPKhSkXTlL6qGdesFPtlO6upsk+cbUfasWCfEJ+B08nRkWFRHN2eMXqN+8bo7xfMuVRKVScePyrRy3iY9LYMvqvzIbrk/cv3MfTx/TNvhSUtM5ffMBzauU1AtvUbUUCcmpnL39/Iv/xzHxLNlxgvcbVqRSMTdAd77uu3ib9+qWz2y4AlQoWoQ90/vnWcNVYWGGQ70KROz00wsP334cla0VdnUMeycsvVywKuVJ+M4TBnGsirujLuGOuYsD5oXtiNh9Wm+bxBsPSA2PpnDLmqbPTC7cuHWXaXMX07FNC2ZNer03qF5F+cZVSIpP4srhpyMr4iJiuOl3hYpNq+cY749v1rJ4wCy9hmtaiu5vszzq2X8iJS2d07eDaV6xmF54i0rFSUhJ5ezdkOfu43FsAkv+Ocv79cpSqahrZviSAW8zuq3+qAdzlZKU1zA8vVWrJsTFxbN7z8Gn6XwcwaFDJ2jz9ouP1pn0+RhcXJwYMeqzHLdp+04L3u3YhrHjpxAdHfNK6c4uJU3D6QcRNCvpqhfeopQrCanpnAuKMohz/VEsD2IS6V5Ff/jzB1WLsu3DhpkN19jkVMbtvEBNz8Is6VjNpOnOSUpaOqcDH9G8rH7Z2aK8FwkpaZy998ggzvWQKB5ExtOjtn753rOOL9uHt8HKSK+lVqtl5t/nKOFsT686pU2biSwU5uZY1a5M3B7964K4XUdQ2lhjVbOi0Xi2jWvhNKwn4cs28GjuCoPPVc6FUBWyI+5f/Tog5fY90iKisX2rtukykU2VxtVIik/kwqGnjeOYiBiu+F2metOc64i1s1czq/80vYbrk7+flGPmGfVoQmxC5jaJcYmkJqdgV9jepPlAZYbSszTpt/Qb+ek3z6CwUKP0LGU0mtLFG82jnIffKqztUahtSL+jf6NXGxGMNiEWVfFnXAuaQI0mNUiMT+TsobOZYTERMfif8KfmM47PE43bN6Zao2qsmGl43h39+yifvPuJ3vDjJ8fQPMs10BtHo83f1xvmP9d4VSgUzJgxg5iYGGbNmmXw+d27d4mMjKR69Zwv5OrVq0dYWBgPHjxt5Hl7ezNp0iQ2btzIsmXLmDZtGt7exucl5RWlsxsKcwuDxqcmTNcwUhbJuSFj2b43qMxI/stIT6ZWS/LG5aRdOK4XbF6toW7/QQGvlvBnKObrQ1BgMOnp+heTDwKCKFrCK8d4pSvoCv1OvTvy9/ktHA3Yy/I/FlGh2tNG1anDZ/h6wjy9eCozFQ1a1OfOddPObXkQHk1qugYf10J64UVddHNS7z2KfO4+lu70Q6lUMKxtvcywhxExxCWm4OFoz8zfD9Bk4o/U/mQpI3/YRnBEzkOqX5XapwhKS3MSs/W4JQboGhJWJQznpFmV1h2vpGxxkrLESY+JR5Oahtpb/0JY5WCDmYOtQXh+cXdzZeeGn/l05Eeo1er8Ts4Lcy/lxeN7oWjS9ec8hQWGUKR4zjehokIiCLp2DwBLazVlG1Ti3U97cNPvSmZ4XnkQHqv77WSbv13USXchee/R8xtjS3ed1f12WtfIDFMoFJQoUgiPwnZotVqiE5LZ4ned7Wdv0a1+edNmwoiyZX25czfQoGy7dfsuvr7Gh9pm5+PjxbBh/Zj37fc5zmFVKBR8/fUXHDx4jC1bdrxyurN7EJNAqkaLTyFrvXBvB937wCjDIfXXH+vKJkszJSP/OkfdJfto/MO/fH3gGslZbhyozVRs7lWfaa0qUtjq9dwIfhAZrzvfHO30wosW1q2TcS/C8Kb3tdBoACzNVIxYf4Q6s7bQaM5Wvv7nnF5+svr70n0uP4xkfOsqqJR5N7/a3NsNpYU5KQH650fKPV05bFHM+HVBkv8N7jTvS8QP69GmG+ZBExOPNjUNc0/9Mllpb4vK3hZzLzcT5cCQVylvQo2UYyEBwbgXz3k+dERIOPeu6UYkqK3VVGpQhR7je3HF73Jm+L3rgVw8fJ73R3XDu3RRbB1s6fNFfyysLDm6zbQjzBT2zijMzNFG6g9F1kTpbpAoCxcxGk/p4oXC0grLbhOwGr4Yq0HfYN7gPVDqLsm1yQlo09NR2Ov39GNpDWprFPZ518sP4F3Km5B7IQbHJzgwGM8Sz76hXsi5EEOnDeWHKT8QERZh8Hl8TDzXzuo6HMzMzSherjhjvx1LdHg0h7fnzQhA8eb5zw0bBvD09GT8+PFMmTKFt99+W2/4cFRUFACFCxfOMf6TzyIiIihatGhmeKNGjTIvZKtVez13ibNSWOsqV21Sgv4HGe8VVtbZowCgLFYai1ZdiP9m7AvPXVW6emDZZRDpgTdJu3Qq94l+Djt7W+KNzCdMiEvAxtYmx3hPGq+Wags+H/oVDoXt6Tu8J99v+o7+7YZy6+odo/FGfzmMoiW8GN//c9NkIENsYjIANmr9CzBrS937OCNDbLOKiE1g+8lr9GlWDXvrp/PdIjIWmVjw1zEq+Lgyq29rIuMSWLjtOIMWbWHjxA+wsjT93UiVve5/nx6rv1hPekZ6VNmGOwOYvUAcTWIK4X8dxa1fGxKu3yf8bz/MnR0oPq0/mtQ0lNaWBvvNDw72djjY2z1/wwLGyt7a6AJLyXGJqG2Nlw/ZzTu3AnNLc+IiYtg4Y5Wpk2ggNunJb0f/PLbOOK/jjA1pzCIiLpHtZ2/Rp3FF7K2Mnz/nA8Pot3Q7AOU9nenRIO8br4Uc7ImNMWwIxcXFY29vaySGoZHDB5KcnMLCRT/nuE27di0pV9aX0aMn5TqtzxKbrOv1sMnWA29toes9jU8xbPhEJurqmbE7LtCmjDu9q/lwOSyGZSduE5mYwuw2uoXPzFVKihXOuZzPC7HJurTZWGbLT8b7uGTDOjIyQXeOfrLxOG0qeNOnbmkuPYxk2cHLRMQn801nw1FCq07coKq3E7WK5e0NOWVGuauJ078u0MTr3itz+N2nhRlOH8pKm5RM7N+HKNSzA8k37xG39xgqJweKfDYEbVoaCqu8u6lnbW9DYrb8ACTGJ2Jta1j3GLPi/BrMLc2JiYhh1XT9Hr6fJy/ns5WT+XbPIkA3TWTpuIXcOHPN2K5yTaHW/e+1KdnK5JSMud0WRvJibYfCxgGFVkvqkS1oYyNQeZfFrGZrFHaFSflnBaSlkn7jNGZV3kIT/pD02+dRWNth0aQbaNJRmOdtPWpjb0OCseMTl4j1c+qZEV+P4NrZa+zfsp9KdZ/dQzz116lUa1SN9PR0Fn+2mMgX6AQQAv6jjVeA7t27s2vXLiZNmsT27dszwwsVKgRAbGzOvVbR0bq7rFkbuBqNhvHjx+Pp6UlMTAyffvopK1euRKl8jZ3TT1ZBzGk+k7GufzNzrPqNJ2XvFjQB11/oa5Ru3lh/8jWkpZKwbFrO3/eSFAqFwf9LoVSCkd0rFIrMYdvGrPtxI/u2H+DUkafDVk4dOcvmo2vpN6o3nw+ZahBn5OShdBvQmV8XreHgP0dynxEjNBn/IwXG77Arn7Oy6eZjl9FotXzQpKpeeFqa7n/gaGfFtwPaosy4g+/tXIg+8zey4/R1ujQwPizsVSiUuTjXMuIYzLfLyLs2I87tT5ejSU6j5LyhlJo/jPSEJIKW/InSyhJNxkWieD6FQvH0OGWGKY3Pd1Qo0GqfvwKl0kzFkoFfo1KpaNb/HcZv/IqFH87kxvHLz42bW09OpVz/dvyu6347DSvkuI1nYVt+GvwOYTEJfL/7LB8s3MraER1xMnITJjeMlW1KpfFj8byy7Qm1Wk2/ft355Zf1REVF57jdsKH9OHf+Evv2502vxJMs5HQYjIWnZvTINC3pyqgGulWfa3k7otFqWXTsFkPrxr/2RusTmWV1Dvkxdr6lZeSnWRlPRrfQNbxrFXNFq9WycP8lPm4SSzHnpze7zt1/zLWQKOZ3rW/i1BtSKDLOu5yq6VdYeTZkyiJcU1Jxmz4KxcwxaBKSiPh5Iwq1JdpE0yyuZawcUyoVz/jtPP96RGWm4usB01GZqXinX3u+2jiLmR9+xeXj/niW8mL6ltmE3Q9l7pCvSYhJoEGHRgyZPZzkxGRO7DxmknxlpPjZHxsrk5OTSNo8H21kKNo4XWNNE3QTbXoaFg3eJdVvJ9rIEFL2r8UiPRWLlr1RtOqLNjWZ1NO7wcwCbarp6lHjxyeneuZpPW9M8y7NqVi7IkNbDn2h7163YB0bFm+gXut6jJo9CgdHB35f8mIraP/XmHqdgv/v/rON1yfDh9u3b683fNjHxwcXFxdOnjxJq1atjMb18/PDxcUFL6+nw1aXL1/OqVOnWLduHREREQwePJgff/yRwYMH53lentAm6nooFVbZKv0nd/cSDXswdSsRK0nevi5zyElmgapUGlRsqjJVsB46GW1SIgnzJ6B9/Pz5Zi9q4Cd9GTS2n17Yvm0HcDQyPNjKxoq42JxXeL13+z73buvPCYmLiePiKX98y+vPS7KwtGDydxNp1bE5q5asY+msH3OfiRzYZfT4xGfrYU3I6DWyVT/7Tuje87eoV7aowQJO1hm9UQ3K+2Q2XAEqF3fDzsqS6w8M52eZQlq07q5q9h5WVcZd77QYw7uu6dG642VmZ03WqlNlo7tDn54xx0iTkMTtsUu5O2kFll4uJN8PQ5OYTJEezYkODDV1Vt5YbUd1of1o/celnNlxHLsShsODLW3UJBo5Ztlp0tK5elg3j+rqUX++3P0tbT7ulKeNV7uM0Qrxydl/O7oeMFv1s4eT7vW/Sz1fT4MFnLJydbDB1UFXblYq6kKHbzay5eR1oysZ58akL8YwedJYvbBNm7fj62o4PNjGxpro6OcP+W/ZsjH29nas+834IlsAjo6Feeut+nz2+cwct3lVdhk9ktl7WBMy3tsamRP9pJe2cTEXvfAGPs4sOnaL649i863xapfRox+fnKYXnpDx3tbISBbrjPw08tUfKlu/pBsL91/iemiUXuN179UH2KvNaVgq74bWPpEeq+vdz97DqrTRvdfEPv93nxNtQhKhX3xH2MxlmHu4khoUijYxGYdOrUg4aZqF9bqM6kbXMT30wo7vOGp0eLDaWq03VzUn6WnpXDx8HgD/Ixf4ds8iOg3rwuXj/rQb0AGlQsG0nl8Sl/E0A/+jF7C2s2bgtMH4/X3cZA0JbXLGqDiLbL3UT95n75EFSE9Fc9+wBzg9wB8avIvSxYv0yBBITSZl72o4+DsKO0e0MeGQloJZhfpoTXhd8MHoD+g5pqde2OEdh/Esbjg82MrGivgcrtucijjx0Zcf8dO0n4h6HIVSpUSp0l2TKlVKlEqlwU29yyd19c7F4xexdbCl+8jubFq2yWC4shDZ/Wcbr6AbPvzpp5/y5Zdf4u3tjbu7OyqVij59+rB06VK6deuGr6+vXpxr167x559/MnjwYFQq3bCoM2fOsGjRIkaNGkXlyrq7rt27d2fRokXUr1+fSpXydnL8E5qwh2jT01G66BfqSlfde02w4XNRzWs0Qunshv2Svww+s//hHxJ/mUPqsT0AmNVuilW/cWhCg0hY8BnayMcmTf8fa7ZxZI/+vNombzekzlu1UCj077R6FfPk7o2cn/PasmMzoiNjOHlIf+EfS7UlURFPeyls7Gz4bs1sKtWowHdTlrDuBZ9797K8nR1QKRXce6zfQ3Lvke59CTfHHOOGRsZxPegxvZpWNbpfpUJBqpF5VWnpGiyNLBRiCkmBIWjT0lEX028IWWUsJJVo5FluiRmLUqmLuRF/6emcYvWTODd0c7ULt6hBWnQcsaeuk3hDtx9zJ3ssPJyI8zc+3FsYOrxuL/77zuiFVW1Vm/KNqxj8nlx93J75nNfKLWqSGBPPzZNPV6hMT00j6FogHqXzdm6/t5Ndxm9Hf27rvXDd+xLZ5pFnFRoVz/WHEfRqZDj6ID4phQNX7lGpqCtFnZ8uxOLtZI+9lSWhRuZq5taPP61lxw79ZzZ37Pg2rVo2MTgWpUoWf+ZzXp9o+04L7twJ5MzZnBsJrVu/hZmZGZs2b89xm1fl5WCFSqHgfpR+o+F+xg2uEo6GjVDvQrobCSnZLjLTMi5OLc3ybzkNb0dbVAqFwdzWe5G69yVcDBftKeqoG+admmN+9FcnPnQzhKZlPDFX5X0+U+8Fo01Lx7yoflltUVR3XZB8O/dz1m3eqo0mOo7Ec1dIuaXbj8rRATN3F5Ku5LxY4svYu243Z/bp1+O1W9ehSuNqBr8dt2LuPLiV80JGNVvUJj4mjqsnnz6BIC01jcBrAXj76qaAOXu6EnQ7KLPh+sQVv0vUa9sAeyd7oh/nPNLhZWijH6HVpKMolG3ecCHdTR1NeLBBHEXhIqi8ypB2/eTT4cWAwkx3E0+bmHGzonglSEpAE3wbbUTGfqzsUNgVRhNmunUK/l73Nyf3ndQLq9eqHjUa1zA4Pu4+7ty7afy7qzWqhp2DHWPmjmHM3DF6n836bRah90Pp16AfpauUpoh3EYP5rTcu3KB55+Y4ODq8mcOH38BFk/LTf27Bpuy6d+9O/fr1uX//aYE3YMAAGjVqRK9evVi7di2BgYEEBgaydu1a+vbtS506dTIfkxMVFcXYsWOpXr06AwcOzNzHxIkT8fLyYty4cSQk5P7O5ktJSyX9pj9m1RvoBZvXaIQ2Ppb0u4bDghMWTyZu+jC9V3rgTdIDbxI3fRhpF3Qrw5pVrIVV/09Jv32F+NmjTd5wBXgcGs7Vi9f1Xn4HT2FrZ0PdLCsXFnJ0oHq9KvgdzHmubZe+7zJh1hjMsjTeXNycqVyrImePnQd0q0R/u3IW5auU5fMhU/Os4QpgaW5G9ZKe7L9wW68w33v+FnZWllT0Mb4wA8Cle7re7apG7jRbW1pQraQH+y7cISXLoyT8rt8nMSWV6iVzXrziVWiTU4k5cQWnd+rohTu1q0daVBxx5wwvXJICQkgKCMGpXT2DOIm3gkjOuBtcpE8rik3uq7eN+0ft0KZriNyj3xgTOYsOiyTQ/47e68rhC1jZWVO+SZXM7Wwd7fGtU15vBeLsWg1qzwczPsq8Ew6gtrOmRPXSPLia800kU7A0N6N6cTf2XwrQ/+3438XOyoKKRV1yjHvpvu6cqlrM8PelVCqZuukIvx64aBAnOiGZ0h4531B6WcHBoZw5e1HvtWfPQezt7Wjd6q3M7ZydHWncuC579h7MeWcZateuxrHjp5+9Ta1q3L//MMfFnEzB0kxFdc9C7Lsdpn98boVhZ2lGRTcHgzg1PApjZa7inxv6I3cO3HmEmVJBZbdCeZbe57E0U1Hdx5n914L083PlAXZqcyoaOS9q+LhgZa7i70v6DaeD1x/q8pPlETnRiSncj4ijqrdT9t3kCW1KKgmn/bFrpX9dYNu6IenRsSRdfLHpQsYU6v4OLhMG6oUV7vsupGuI//ek8UgvKTIsgjv+t/ReFw6dw9rOmipNnq4tYu9oT/k6FfRWIM6u/Ufv8tHMj/XKMWs7a0pXL0vg1QAAHt5+gJevN7YO+vPOy9QoR0JMPHFRJnyOaHoamqCbqErpr5Gi8q2BNikeTUiAQRSFTSEsmvdEVUp/UVFV6ZpokxMzG6bmlRpj3riL3jbm1ZqDRkv6HX+TZSEiNIKbF2/qvc4eOou1nTU1mjxdIM/e0Z5KdStx7rDx4+O3149R7UbpvRb9TzfneNH/FjG1v26qV+3mtfl04ae4eOiX+zWa1CAiLIKox1Emy5t4c/2ne16fmD59Ou3bP32gvUqlYuHChWzZsoWNGzcyf/58tFotvr6+jBs3ji5duqDImPfy2WefkZCQwJw5c/TmNFlZWTF37ly6d+/OjBkzmDFjxmvJS/KOdViP+RqrwV+QenQXqpLlsWj1Psmbf9Y941VtjcqjKJqwYLRx0UZXCn6y4JMmMOPuv5k56r6fQFICyTt+Q+VeVG97TeTjPGnMApzzu8jpo+f4avEXLJ6xjOiIGAaN/ZDY6Di2rN6auV1xXx/MLS24cUmX5p/mr2Thujl889M0fv/lDxwK2zPoE128Nct0z2nr8uG7VKtbhS2rthL6MIyK1fUXacnp+bC5Nah1TQYv+ZPxv/zDu3XLceFuCCv3n2VUhwaoLcyIS0zhTkgEXs4OesODbz4Mx8JMhbeL4UUgwMj29Ri4cAvDf/iLPs2qER6byIK/jlLJpwhNKuXdg+/vf7eJCr9/SenlYwlbvx+7mmXw+LgjgdPXoElKQWVrhVVpL5ICQ0nL6CW7P38TvguGkxYZS8TuUzi2qoVzxwZcz/JM1+Cfd1Jh/WSKfdWPiF2ncGhYCa+RnXmwaAvJ92TY8Ku4efIq149fYsB3o9g8azXxUXG0G/0+iTHxHFqzJ3M791JemFmacf9yAADbF25i1KovGPz9WA6u2Y3a1oq3h76HpZWav+ab9rmHxgxqXpXBP/7N+DX7ebdWaS4EhrHyoD+j2tRCbW5GXFIKd0Kj8HKy0xsefDMkQvfbcTLsLbOyMOPDtyrz475zFLKxpE4pTwIfR7Nsz1lKuzvSsaavQRxTOnzEjwMHjrFq5SIm/m8G4RGRTJ40lqioGH5Y/nTV93LlfLG0tNB7vqtSqaRcWV82bNhqbNeZKlUsx9WrOTwL0oQG1irBkD/O8OnfF+lY3pMLwVGsPBPAqAa+qM1UxCWncSciDi8HaxytLbC2MGNonZJ8e+QG9pZmNCvlyoXgaH49E0CPKkVxtH6Nj5gzYlDDcgxec4jxm0/wbtViXLgfzsrjNxjVvBJqcxVxyanceRSDV2FbHG0ssbYw4+O3KjBvz0XsrSxoXtaDC/fD+eXYdT6oXQpHm6fTQm6GZYy2MdKDm1civl+P1y8z8fjuM6I370ZdrRyOAzrzaO4KtMkpKG2ssShVlNR7waRHvnivYuTqv/D+eQYu/xtM/P4TWNetgtPg7oQv30DqA9NNKcru6skrXDruz6gFn7B61kriImN5f3R34mPi2bPmn8ztvHy9MbMwI+CybqTPpgUb+GL1l4z9fgK71/yDla0V733cGbWVJRvmrwNg209bafReEyb/No0tizeSEJtAnbfr0bBjY1ZO+9nos0dfRarfTiw7j8binY9Iu3IUlXtJzGq0JPXIH7pnvFqoUTq6o4l+BIlxaIJukH7/OhZN3ifV3BJNZAiq4pUwq9qU1MObIWMocur5/ag7jca8SVfSb19A5V0W89ptSD31N9qYvLlee+LSyUtcOHaB8QvHs2LmCmIiY+g5pifxMfHsXPP0kYvevt6YW5hz5/IdYqNiic3W26221g2ffnD7AQHXAwDYsWYHb3/wNlN/ncq6BetIik+ieZfm1G5em29GfCNzQ8ULUWjlTMlTMYOMz7t9FrNqDbDs0BtlES+0UeGk/PsXKXs2A6AqXRmb8XP1hgNnZz1uDgAJc8fr4pStis3Yb3L8vuS/VpO8zcgjdoxosePlFwqwc7Bl9JThNGndEKVSwYVTl5g/ZbHenNbvN32Hu7cb79bpnhlWu3FNBo7pS6lyJdBotJw4eJJF05cRGqRblv6HLQupVreKwfdlxvdo8ty0Hfzl/ZfKy/4Lt/n+bz8CQiNxLWRLt0aV6NNMdwf11M0HDFr0B1N7tqBjluekzvj9APsv3GbfjAE57vf8nWAWbz/OpcBQ1BZmNK1UgjHvNtRbmfhFnPvw35fa3rFNbbzHdceqpAcpIRGE/PI3D3/YBoB9vQpU3PIVN0ct5tHvT/dbpHdLPIZ0xNLDiaR7oQQt+oNHm/R7mpzfbYjX6C5YFnUl+cEjQn79h5AVL/fw8Vr+c15q+9w6efYi/UdMYMWi2dSuXjnPvmd4zQkm2Y+1vQ3vT+pLlVa1UCqU3DpzjY3TVhKa5RFGn6yfgpOXC583HJYZVrZ+RdqNeh+v8sXQarVcP36ZP79ZR8jt3PXqfbeg1vM3ymL/pQC+332WgEfRuDrY0K1eOfo00U3JOHU7mEE/7GRq10Z0rPn0mZkz/jjKfv9A9k3+wOg+NRotm/yu8fvxq9x/HIO9tSXNKxZjWOsa2L3ko1ns3l/wUtsDFCrkwNw5X9KxQ2uUSiXHjp1i7Pip3LhxO3ObfXs24uPjTanST1esdXFxIjjoIh8Pm8jyH3Muey9e+Bd//6v07PXxS6ctZv57L7X9/tthLDtxm4DIeFxt1XSt7EWf6sUAOP0ggkFbzjC1RQU6lH86GmTrlSBWnw3kXlQCLraWdKrgRb+axYwuivQwJpG2vx4x2MeLUDgYv/H3zPxcC+L7g1cICI/F1c6KbjVL0qee7tw6FRDGoNWHmNqhJh2rFMuM8+f5AFafuMG9iDhc7NR0rlaCfg3K6OVn1+X7TNjixx9DW1HcOXcN2HvTzz5/o2xsW9THeUQvzIt7kRb6mKh124n8RTdf2qp2JYqu+obg/80j5o+9BnHt32uB+6yx3G7el7Qg/ce62LVtgtOQHph7FSH1YRhRv+0gao3hNKRn+SLx5W9W2Njb0HfSAGq1roNSoeDamaus/GoFD+88LY+mrJ+Oi5crwxp+lBlWsUFl3h/VnWLli6PVaLh8/BLr5qwmKMu0CY8SnnwwoTcV61VCoVTy4OZ9tv6whZP/6D+jPCcrx7zcXGZVyaqY122PonARtPFRpF04QNpZ3XFQepVG3WUsybt/Jf1KxrQqCzXmddujKlkVhY0D2uhHpJ7bR/ol/YUmVWVqYV77HRT2zmhjw0m7cJC0Cy9XxwN0+Tbnodg5sXWwZdCkQdRtXRelQsmV01dY/tVygrIcn683fE0RryL0a9DP6D4q1a3E7N9nM6HrBPxPPO0tdvdx58OJH1KxdkWs7ay5fek26xet5/S/zx6JArDz3s7nblMQxQxoma/fb/+z8fbCf5U0XvNYbhqvBVluGq8F2cs2Xgu6l228FmSvq/H6upiq8VpQvGzjtaDLTeO1IHvZxmtBlpvGa0GWm8ZrQZabxmtB9rKN14IuN43Xgkoar7nzpjVe//NzXoUQQgghhBCiINJqtPn6yi2NRsPChQtp1KgRVapUoX///gQGvtg6Gdu2baNMmTI8eJDzYpK5JY1XIYQQQgghhBCZli5dyvr165k+fTobNmxAoVAwaNAgUlJSnhkvKCiIqVOn5lm6pPEqhBBCCCGEEAKAlJQUVqxYwYgRI2jSpAlly5Zl/vz5hIaGsmdPzsOQNRoN48ePp0KFCnmWNmm8CiGEEEIIIURe0Gjz95UL165dIz4+nrp1ny42aG9vT/ny5Tl1KudHXS5btozU1FQGDx6cq+99EW/Eo3KEEEIIIYQQQuhr3rz5Mz/ft2+fQVhIiO5xWe7u7nrhrq6uBAcHG93PxYsXWbFiBZs2bSI0NO8ejSg9r0IIIYQQQgiRFzT5/MqFxMREACws9FcTt7S0JDnZ8MkjCQkJjBs3jnHjxlGsWLHcfekLkp5XIYQQQgghhHgDGetZfR61Wg3o5r4++RsgOTkZKysrg+2nT59OsWLF6N69e+4T+oKk8SqEEEIIIYQQAng6XDgsLIyiRYtmhoeFhVG2bFmD7Tdv3oyFhQXVqlUDID09HYB27drRoUMHvvrqK5OlTRqvQgghhBBCCJEHXuVZq/mlbNmy2Nra4ufnl9l4jYmJ4cqVK/Tq1ctg+927d+u9v3DhAuPHj2f58uWULFnSpGmTxqsQQgghhBBCCEA317VXr17MnTsXR0dHPD09mTNnDm5ubrRs2ZL09HQiIiKws7NDrVbj4+OjF//Jgk8eHh44OTmZNG3SeBVCCCGEEEKIvPAf7HkFGDlyJGlpaXzxxRckJSVRq1Ytfv75ZywsLHjw4AHNmzdn1qxZdOrU6bWmSxqvQgghhBBCCCEyqVQqxo8fz/jx4w0+8/Ly4vr16znGrVOnzjM/fxXyqBwhhBBCCCGEEAWe9LwKIYQQQgghRF7I5bNWhXHS8yqEEEIIIYQQosCTnlchhBBCCCGEyAP/xUflFGTSeM1js/c453cSTGpHhaD8ToJJ9fxo9/M3+g8Zh3l+J8FkhteckN9JMKnFp2fndxJM6vOan+d3EkzqeumK+Z0Ek5o5JyK/k2AyEYTldxJMKkqrzu8kmNRn2jdrTGSl6afyOwkmdWnpu/mdBCFMSoYNCyGEEEIIIYQo8KTnVQghhBBCCCHywps1OCHfSc+rEEIIIYQQQogCT3pehRBCCCGEECIPyIJNpiU9r0IIIYQQQgghCjxpvAohhBBCCCGEKPBk2LAQQgghhBBC5AVZsMmkpOdVCCGEEEIIIUSBJz2vQgghhBBCCJEHtNLzalLS8yqEEEIIIYQQosCTxqsQQgghhBBCiAJPhg0LIYQQQgghRF6QYcMmJT2vQgghhBBCCCEKPOl5FUIIIYQQQog8IAs2mZb0vAohhBBCCCGEKPCk57WAsrBR0+azDyjXqiaWNmrunbnJ9qmreHQr6Jnx3Mv70HJ8N7wql0ChVBDkf5fds9fz8HJA5jY2Tva0HNeVUo0qYV3Ilsd3Qzj8wzb8t58weT7Ma9bGpt8AzIoWQxMdReL2v0hcvzbnCJaW2PTuh+VbTVE6FCLtzm3iV/9K6umTRjdXWFtT+IcVxK/+leTd/5g8/caobazo+9mH1G5VFytbK66fucbPU3/kwc37z4xXvnYFPhjfi+Lli5OUkMSxHUdZN3cNiXGJmds4FnGkz+f9qNakOiqVihvnrrNu7hpuXbiZJ3kp1LQqRSf0wKq0N2nhMYSs2kXQoj+eGce5c2O8RnTCsqgrKUGPCfp+K2Hr9ult49K1KZ5DO6Au5kZKWCSPNh7kwXeb0Kal50k+nsXSRk3nz3pTtVVt1DZqbp+5we9TfyH41oNnxvOtXY6O43rgXb4YyQlJnNlxnK3z1pOU5XgVRMGhj+jUZygLZk2mdvXK+Z0cPZY2atp+1pMKGeVawJmb/DV1JWEvUK61Gd8Nr8olM8u1v2f/RlCWci0rB3dHPtk1hyMrdrLnu815kBOwalATx5F9sShRlPTIaGJ+30HUTxtyjmBmRqEPO2PXoSVmbi6khT4ibse/RP60AdLSDDZX2FjjveV7IpeuIXbrnjzJQ3YWNmrezlbv7HiBesctW73z0P8uu2avJziH41P3w9Y0GPgO8xqOyoNcPGVpo6bTZ72p3KoWahs1d87cYOPUXwh5Tn68yvvQYXwPfCqXQqFUcN//Dn/OXsf9y3czt3EoUphOn/WmfJOqKFVKAs7d5K956wm8cDvP8qO2UfPBZx9Ss1Vt1LZW3DxzjVVTfybo5rPLsrK1y9N1fE98yhcnKSERvx3H2Dh3nV7dA9D2o440/6AVju7OPA56xK5fd7Bn1d8mz4ddk2p4jO+F2tebtPBoHq/9h9AlL/Y7tapUkjJ/fsOVJkNJeRCm95lTj5a4DOiAZdEipAQ95vHqnTxasd3k6TfGxtaaiVNG07LNW9jY2nD21AWmfTaHWzfuPjOenb0t474YTuu2zbC2seb61Vt8O3MJxw+fMrp930Hd6T+0F02qt8uLbOTo6M2HLNl7gTuPoilsbUmXWr70b1wBhUJhsO3Ws7f58o+crye/6lSPDtVK5GVyxRvujet57d27N2XKlNF7VaxYkWbNmjFjxgySkpL0tps+fbrR/SxfvpwyZcowceLE15n8TN0WDqd861rsmr2ejWO+x8bZngG/fY6Vg02OcRyLujJwwyTMrSzYMmE5m8YuQ2WuYtCmL3Eu4Q6AylzFh6smUqpRJfbN38TawfN5cP4W3RePpOp7DU2aB7PyFXD4aibp9+4RPXUSSXt3Y9NvINYf9Moxjt24iajbtSdh/TqiJ39G+sMgHKbPwryi4UW4ws4OhxmzUbm5mzTdz/PJonHUfbsea2avZMGY+Tg4OTB13XRsHWxzjFO8QgkmrZ5CfEw83wz5mjWzV1G/bUPGf//0/LK2s2bG5tlUaVCF3+auYc6QrwkJDGba7zPxrVra5Pmwq1mGsr9OJOFmENcHfEPYpoMUnfgBnqM65xjHqV09fBeOIOrgea73/4boo5coNe9jnDs1ytzGfWBbfBcMJ+HmA64N+Ib7czbg0qUJpZePNXkeXsTAhaOp1roOf8xeyy9jFmHnbM+Y377E+hnHy7tCMUau+oLEmHiWDZ3LH9+so0a7+gxemj95eFEPQ0L5aPRnxMbF53dSjPpg4Qgqtq7Fztm/sX7MUmyd7Rn82xfPLdeGbpiMuZUlmyYs5/ex36MyVzF00xRcShj/7b//zRCs7K3zKhtYVi2P++IppN65R8joacRt24fjyA8p9FGPHOM4TxxC4Y8+IHbrbkJGfEnsln8o1L8rLpNHGGyrtLfD/ftpmHu65VkejOmaUe/snr2eTRn1Tv8XrHcsrCz4Y8JyNo9dhjJbvZNVlY4NaPN5z7zMRqb+C0dRtXVtts5ey8oxi7FztmfUb19i/Yz8OBctwpgNU7GwsmTNhO9ZNXYJKnMVYzd9hWtGftR2VozdNI2yDSqxbe56fhw6j0eBIYzZMJViVUvlWX6GLxpLrbfrsn72ar4fswB7Jwc+X/cVNs8oy3wqFGfi6skkxMTz3ZDZbJi9hrptGzDq+0/1tvvgs750G9+Tfzfs45t+0zm77xT9pn1Esx4tTZoHmxplKfHz5yTdus+dj2YRseUA7uN7UWT4+8+Na1WuGCV/mYTC3LDfxbnX2xSdPZyYvae43W86EZv24/lFf4oM62LS9Ofku+WzaN22GXOmLWLsx5NwcnZkzR8/4FDIPsc4SqWSFRsW06LNW8yeuoBh/cYTExXDz78tpEx5X4PtO3Rpw/++GpOX2TDq/L1HjFp7kOIu9szr0Zi2VYuzeN8Ffjp42ej2jcp4suqj1nqvlYNaUdLVATcHaxqW9njNOSgANPn8esO8kT2vbdq04fPPP898n5CQwJEjR5g1axbp6elMnjwZAHNzc3bt2sXnn39ucPdo586dRu8ovQ7e1X0p27w6Kz/8hhsHzgMQcOoa4w4voE7vlhxY/KfRePX6vU1aUiqr+s0hNTEZgDvHLjP+6ELq9W3Nti9/pUyz6nhUKMbSDl8QdPEOALePXqKQpzONh7Tn/B9HTJYPm94fknb7FrGzZwCQevokCjMzrLr1JGHT75CSore90t0D9VvNiF3wLUnbt+rinD+LeYWKqDu8S+qli5nbWtRrgO2wkSisrEyW3hdRunoZajavxfQPp3L23zMAXD15me+P/Mjbfd5h06LfjcZrP7AjMeExzBnyNWmpT3tZRswbjUcJTx7eCaJZ1xYU8S7C/zp9yvUz1wC4cOQ8doXt6TdpAJ91nmDSvHiN7Ur85QBujVgIQNS/51GaqfAa/h7BP2xDk5RiEKfohB6Ebz9BwJe/6uIcOI9ZIVu8x3Xj8ZbDoFTi9cn7RB08z42P5mXGi7t4m2oHF+DQuDLRhy4a7DevlKhemsrNa7Dow5lcOnAOgJunrjHj8BLe6t2KnYu3GI3XYkB74sJjWDZ0HulZjteHc4dRpIQHoXcevpb0vyiNRsPWv/cyd/FP+Z2UHBWt7ku55tVZ8eFsrmWUa3dPXWPi4YXU692K/YuN9/g37NeG1KRUVvT7JrNcu3XsMv87uoj6fVuzNeNcfKJer5a4lszbiyPHoT1JvnaHsP/NASDx6GkwM6PwgK5Er9yMNjlb2WZvh33XtkTM/5moXzbp4vidB8Bp7CDC569AExkNgHXTejj/byhK69dbtj2pd1Zlq3fG5rLeGXd0IXX7tmZ7xvGxcbKnxdiu1PqgGQmRsXmen+LVfanUvAZLPpzF5Yzf/q1TV/nq8BIa927NPzn89pv2e4fUpBSW9vualIz8XD92ielHl/JW3zb8/uUK6ndthrO3K3M7fcGdszcAuHbEH5vC9nT+og/zukw2eX58q5ehevOafPPhNM7/e1b3nSevsODIMlr2eZs/F20yGu+dgR2ICY9h/pBv9MqyIfNG4l7Cg+A7D3HxduWdge359cuf2LtaN4LpyjF/nNydqdykGvt/M13Pv9vo7iReuUvg6O8AiD14DoW5iiIfdybsx60Gvx0AhbkZLh+2xX1cTzRJyUb3W+TjzkRuO8LDr1cBEHf0IpYlPHDp15bQJcb/N6ZSrWZlmrVqxIDuIziw9ygAp0+c5cCZ7fTq/z5Lvv3ZaLyOXdpQuVp5OjTryfUrutFVfsfOsPPQBho1rZsZ5uRcmE8+G0b3Pp2IjIjK07wY88O//pRxK8yMLg0AaODrQVq6ll8OX6Z3g7Kos91McLRR42ij1gtbe/wadx/FsHJQK4PPhHhZb1zPK4BarcbFxSXz5ePjQ8+ePWnfvj07duzI3K5OnTo8fvyYM2fO6MW/e/cuAQEBlC9f/nUnHQDfxpVJjk/i1uGnF/kJEbEE+F2ldNOqOcZ7dPshh3/ckXkBAZCalEJ0SASOPq4AJMclcnLdvsyG6xOP7wbj6FPEdJkwN8e8clWSjxzSC04+dBCltTXmlQx7UjWPHxE57COS92epKLVatOnpKMzNM4MUNrbYfzmN1Avnif7feNOl+QVUa1ydxPhEzh86lxkWExHDFb9LVG9aI8d4a2evYmb/aXoN1yd/m1vo8uZVypvYqNjMhusTl/0uUbZmOWzsc+4teFkKCzMc6lUgYqefXnj49uOobK2wq1POII6llwtWpTwJ33nCII5VcXfUJdwxd3HAvLAdEbtP622TeOMBqeHRFG5Z02R5eBHlG1chKT6JK4cvZIbFRcRw0+8KFZtWzzHeH9+sZfGAWXoXe2kpur/NLArePb8bt+4ybe5iOrZpwaxJ4/I7OUaVySjXbmQp1+IjYrnjd5WyzyjXwm4HcejH7UbLNadsZZajtyvvTOzBpv/9aPL0ZzI3x6pWZeL36t/oi99zGKWNNeoaFQ2iKO2sifl9B/H/6v92UgJ0w1fNvdwytrPB7btJJJ26SPDgz/IoA8a9Sr1z5Dn1DkCTYR0p1bgS6wbP59q+s3mSh6zKN65KUnwSV/V++7Hc8rtCxabVcowXcvsBe3/cltlwBV1+okLCcck439xKehIfFZfZcH3ipt8VStYsi5UJy+onKjeuSlJ8IhcPnc8Mi42I4arfZao+o+7ZMHsNc/rP0C/LUp+UZbq6p2arOqSmpHFgw169uIuGz+O7Id+YLA8KCzNs61Yk+h/930HUjmOobK2wrW38msu+aQ3cRncnZNFGHs5aZXSbW32mEjTzV70wbWoaCgtzo9ubUqNm9YiPS+Bwlt93RHgUJ4+f4a0WOY9oa92+OSePnc1spAKkJKfQos57/LRkdWbY0NEDaPhWXYb2Hcf+XYeM7SrPpKSlc/puKM3Le+uFt6hQlISUNM4GPnruPh7HJrJk7wXer+1LJW/nvEpqgabV5O/rTfNGNl5zYmlpiVL5NMsuLi7UrFmTf/7Rnyu5c+dO3nrrLWxsTF8BvQiXUh5E3gtDk65/xoUHhuJcPOchsifX7OXIcv35HU7F3ShS2ovQ67o5MbePXmLrZ/p3AZVmKso0q07o9WfP2XwZKncPFBYWpAfp7zP9oS4dKi9vw0ipqaTduI42IQEUCpQurtgMHY7K3YPEjJ5YAG1yEpED+xI7Zxaa6GiTpflFeJXyIvReqMGxCQ4IxqO4Z47xwkPCCbwWAIDaWk3lBlXoOb43l/0uZ4bHRERjbWtt0Eh189Fd2BYparqbC2qfIigtzUnM1oOYGBACgFUJw54rq9JeACRli5OUJU56TDya1DTU3q5626gcbDBzsDUIz2vupbx4bOR4hQWGUOQZv6WokAiCrt0DwNJaTdkGlXj30x7c9LuSGV6QuLu5snPDz3w68iPU6oJ5V9u1lCcRRsu1EFyecSxOrNnLwWzlmnNxN9xKe+mVWQqFgq5zh3BhxwluHLyQfTcmY+7thsLCgtRA/XmTqfd0vwtzHy+DOGlBoTyevpjUAP25ibYtGqBNTc3clyYxmfsdPyLs87mkR8bkUQ6Me1a94/SK9Q7AybX7mP/WJ1zZZXwun6m5lfIk3Mhv/1FgCK7PyM/hNXvYu3ybXphrcXc8SnvzMON8i4uIQW1rZdBIfdK4dc6Dcs6jlBdhRvITGhCCe/GcRxpEhIRz/1ogoCvLKjaoTLfxvbjqdzkz3Kd8cYLvPKRs7QrM2D6XVbc2suDoD7To1dqkebAo6obS0pykO/q/neTAYF36jNQ7AAkXbnK5/iBCF2/Mcd2E5FsPSA3SNaRUDrY4dW+JY6emPF6504Q5MK5U6eLcDwwiPV0/bYF37lOsZNEc45WvWIYbV2/x4eAPOHBmG9dDTrJ1/1pq19e/sbru1000r/0uu3fsz5P0P8uDiDhS0zX4ONnphRd10g1Vv/f4+eXU0v0XUSoVDGteJU/SKP7/+X/ReE1LS+PAgQNs3bqVjh076n3Wpk0bdu3ahVarzQz7+++/adu27etOZiYrexuji8IkxyViafviQ8nM1RZ0mTeU1KQUjv+a82JGbT7viXNxNw4s2ZrjNi9LYaMr2LTxCXrh2gRdvpTWz74xYN2jJ07rNmLd6X2Sdv9N6oXzTz9MSyP9geka2i/D2t6GxLgEg/DE+ESsXvDYrDy/linrpqG2sWLl9Kc3Eg79eRBNuoZx30/A29cba3sbGr/bhGbvtwDA0sp0jRJVxkVXeqz+eZaecd6p7AzzYvYCcTSJKYT/dRS3fm1w7d4MlYMN6pIelP5+DJrUNJTWlibLw4uwsrfO8bektn2xOZHzzq1gzNrJqK3VbJxh/K5/fnOwt8PN1SW/k/FMantrkoz8dpLjkl66XOuWUa4dyVKuNezfBseirmyfvvoZsV+d0k5Xtmmy5UWTUdYpX/C8smnRENv2zYn+bRuamDhdYFqaQQP3dVHb25Bs5LeSkot6p3PG8TmR5fg8vv0QzWtcsM3K3tpgQSKApJf47YMuP33mDSMlKYUDv+oWLzr552E06RoGLR2Du68XVvbW1OrYkHrvvwWARR6Uczb2Nkbz8zJ1z/Lzq/hs3VTUNmrWTv81M9zeyR5HN0eGLRjDvxv28nWfr/A/dJ7+M4aYtAH7pN7RxOVQh+RwXFJDI0iPjnuh77CpWZbK/msp+s1wkm7c49GvO54f6RXZ2dsRF2uYvvi4BGztcp6P7OhUiDYdW9Kt93t8PeU7BvcaQ0J8Ir/+voRyFZ+uc3HnVgBpRhZ1ex1iM6YP2aj1e7CtM3q045JTnxk/Ii6J7efv0L1OaeytLPImkeL/nTey8bpt2zaqVauW+apUqRLTpk1jwIABjBunP6SudevWhIeHZw4dvnHjBsHBwTRp0uS1pFWhUKBUKfVeCoUCsjSms26rfcH+f0tbK/r8Mh7PSsX5fdQSooMjjG739mcfUL/f2xxcupWr2YZ6vhLlk/nChvkA0GqenY/kY8eI+mQEccuWYNm4KQ7TZpkubS/I2LFRKpV6NzqybqvVGM9rViozFTMHTGP6h1O57X+L6Ru/pmK9SgA8uHmfmQOm4e7jzoK9S1jj/xvtBnRg3dw1ACQnJpkub0+Oj5G8AGAsLxlxDPKfMTf8Sf5vf7qcR5sPUXLeUOpcW0WV3XOIPX2duAu30SQYn69kCsZ/S8aPFy/4W1KaqVgy8GsWfTiTwEt3GL/xK0rXq5AHqX+z5HQsjBYHCl6qXOv/y6d4VSrBb6MWZ5ZrLiXcaT2uK5v/9yNJsXm7GvTzfzvPz4tNy4YU+WYCSaf9CZ+/woSpezE51Tuv8lsB3fHpnVHvbHxGvWNqOZ9vr1aPqm2tGPbLRHwqleCXUQuJDA4HIORWEMsGzsalmBuT9nzLvIu/0mxAW/6aq1ttOuUVyzmj+VEaPz4KhQLNC9Y9cwfM4JsPp3HX/zaTN86gfD3dEHczczPsnRxY8fky9q7+hyvH/Pnpf99z4cBZ3hvZ9ZXyopfWnOqQJ3IKfwnJ98O4+f5nBIyYh9LOmjLb52Hm7PDK+31CoVCgUqn0XkqlwmjSddcFOZ9r5hbm2Nvb0u/9YfyzbR8H9h5lYI+RxMXGM3jkhyZL86vQZGRMgfE1YJTPWRtm8+lbaLTwQb2yJk/bf4kMGzatgjd5ywSaNWvGuHHj0Gg0XLhwgVmzZlG/fn2GDBmCmZl+lp2cnKhVqxb//PMPNWvWZOfOnbRs2RILi9dzh6jpqE40H62/uqv/Dj+cSxiuMmlhoyY55vkXZg7ujvRZ8SlOJdxYP3wh1/efM9jGzNKcznOHULl9PQ4t+4vd3zzjEQ+5oI3T3YVUZOthVWQsQqKNf/ZqqOkBd0gHUv0voo2Lw27cBMwqVCTt8iWTpvNZuo7qTrcx+quHHttxFA8jQ7TU1moSYg17lbJLT0vnwuHzAFw8coHv9iym87D3uXTcH4ALh88zpOEgXL11w8/C7ofS7P3mAMRGvdid5xeRFq1La/YeVlXGHfy0GMO8pEfrjpmZnTVZL81UGYsvpGfkX5OQxO2xS7k7aQWWXi4k3w9Dk5hMkR7NiQ4MNVkesms7qgvtR+tfaJ3ZcRw7IyueWtqoSTSSx+w0aelczZgDePWoP1/u/pY2H3fixnHjqywKnRajOtFytP4qnxd3nMA2h2OR9ILlWv8VE3Au4caa4Qu4mlGuKZQKus4bysWdftw87I9S9fSe7JNGQPahlq8iPUb3O8jew6q00b3P3iNrkI8+nXAaO5DEUxcJGTEFUp/dc5EXmo7qRLNs9c6lHX44vWK90/s59U5eeWdUF9qO1l+t9uwr/vYLuzvx8YqJuJZw56fh87m0X3+e7tXDF5nUcDhOXroRD+EPHlE3o+c1/gV7CXPSaVRXOo/prhfmt+MYbsUNG2FqazWJL1j3+GfM//U/cpE5exbQcVhnrhy/RGJcIhqNhvP/6q//ceHgOaq8VR17ZwdiHr/6FJ0nv53sPaxP6p0nn7+KtNAI4kJ1N03iz9+g/MHvcereitDFG1953wAjxn/EqE8H64Xt3LqH4iUdDba1trEiNibncyE+LoHbN+4SEhymF3b21AXKVSxjkvS+Kju17lo4PlsPa0KK7r2t+tlzivdevke9km6ySJMwqTey8WpjY4OPjw8AxYsXx83NjX79+qFSqZgyZYrB9u+88w6LFy/ms88+4++//9ZbqTivnVq3j+vZFq8o16omvo0rGdwJd/IpQthznk1ZpKw3H66ciLnagpV9Z3P3xFWDbSztrOj7y6d4V/dl57Q1HP3Z9HNC0h8+RJuehspDfx6oykM3Hyz9XoBBHKWbGxZVq5O0by+kPl1xMPW6bgEjlYsrr3PgzO51uzi9T39+Vu3WdajauJrBsXEv5s79WznPhazVojbxMfFcOfm00ZOWmkbgtQC8fXVzYpw9nKncsCqH/jhA2P2njbwSlUoRGxnDo2zPtHsVSYEhaNPSURfTv7izKqa7eE008szaxNu6OX3qYm7EX3r67Dr1kzg3dOdm4RY1SIuOI/bUdRJv6PZj7mSPhYcTcf53yCuH1+3Ff5/+xVfVVrUp37iKwfFy9XF75nNeK7eoSWJMPDdPPv39pKemEXQtEI/SRuZrCz1+6/ZzdZ9+46VCq5qUblzZSLnm9txyza1sUQaunIiZ2pyf+37NnSzlWiEPJ3yq+eJTzZeanRvrxWsxqjMtRnVmVsMRRD54bIKcQdr9h2jT0jEvqn8T68n7lNuBOcZ1/t/HOPTsSNw/Bwn935x8abiCrt7JvmhS+VY1KfUK9U7fjHpnVQ71Tl46YuS3X6VVLcoZ+e27+LgR8pz8eJYtyrCVn2GhtmBx35ncPHFF7/PCHk6UbVCZk38eIvzB0wVrilYsQVxkLBEPnr+IzbPsW7ebs/v0R0LVbF2HSo2rGuSnSDE3HtzKeRpN9Ra1SIiJ59rJp3lIT03j3rVAvHx1ZVlIQDBKpRIzczNSszRSzDJWkU01svJ8biRn1DuW2eodSx/d+6TnPCs9J0obKxxa1ib+3HVSAkMyw1MCQ0iPjsPCw3SLBK1ftZl/d+svmtSyTVMaNatncGx8Snhz60bOdV7AnXtYWBp2lJiZm2U+1jG/eTvaoVIquBehv0L4vXBdo7yES8692qHRCVwPiaRX/f/fva7wZvZ+5qc3cthwdnXr1qVfv3789ttvHDpkuFJby5YtiYiIYP369URHR1O/fv3XlrbYsCiC/O/qvW4d9kdtZ41vk6cr8lo72lGsTjluHvbPcV8O7o70X/0ZaLX80HmK0QsIpUpJ75/G4Vm5JBuGL8qThisAqSmkXryIZUP9i0nLxk3QxMaSes0wbSo3d+zGTjCIY1GrNgBpd/Lu4e/GRIZFcNv/lt7rwqHzWNtZU7XJ09Uq7R3tKV+nIheyrAKZXYeP3mPwzKF6vULWdtaUqV6GwKu6hqCDUyGGzxmZOYwYoJBLIRp1aITfbj+Dfb4KbXIqMSeu4PROHb1wp3b1SIuKI+7cLYM4SQEhJAWE4NSunkGcxFtBJGdcsBXp04pik/vqbeP+UTu06Roi9+hfYJpSdFgkgf539F5XDl/Ays6a8k2eLhRh62iPb53yeisQZ9dqUHs+mPGR3vFS21lTonppHlzNuXEidGLCInngf0fvdePwRdR21pTOUq7ZONpRok45bjynXBu0+jO0Wi1LO0/Ra7gCxIRGsrD95wYvAL91+1jY/nNiQiNNljdtSipJZ/yxad5AL9ymZSPSo2NJ9r9uNJ7j6H449OxI1MrNhI6bmW8NV9DVOw/97+q9ntQ7pYzUO7eec3z6ZdQ7y3Ood/JadFgk9/zv6L2uHr6IlZ015fR++3aUqlM+czSFMYXdnRix+gvQwtzOkwwargB2Tg70njOU0vWerixt7+JAzQ4NuLj71RekigqL5K7/bb2Xf0bdUzlL3WPnaE+5OhXwf0bd0/ajjvSfOUSvLLOys8a3ehkCrwYAZPa41muvvzJu9Ra1CLxy1+hc29zQJqcS53eZQm/X1Qsv1LY+adFxxJ+/mUPM5+w3PZ2i3wynyNBOeuHWlUthVtiexCt3c4j58sJCHuN//qrex4MtgAABAABJREFU6/CBE9jZ2dK42dO60dGpELXr1dBbgTi7A3uPUq5iaUr6Fs8MK1TYgRq1q3L6+OsbufAsluYqqvu4sv/Kfb2G+d7L97BTW1DRyynHuJcybhhW9SnY6zGI/543sufVmFGjRrFv3z6+/PJLtm/XXxnR0dGROnXqMG/ePNq2bWswtPh1Czh5jTvHL/P+d8PYNWsdCVFxNBvdmaSYeE6u2Ze5nUspT8wszQi+rLuYbjelL7YuDvz52U+o7azwrvb0YelJsYk8uhVEnT6tKF6nHCfX7iU6OFxvG4D7RhotuZWwbhUOs7/FftJUkv7ZiVn5Cli93534n36AlBQU1taofIqR/jAIbXQ0qRcvkHLuLLbDR6GwtSX9wT3Mq1TDumsPErf/Rfq9/G80XDl5Gf/jFxm9YCyrZ/1KbGQs3Ub3ICEmnl1r/s7czsvXG3MLc+5e1t113bhgPZNWT2H89xPZteZvrGyt6fRxZyyt1Kyfvw6A2/63uHrqCh/NGMqqmb+Qnqah5/hepKdp+P2730yel/vfbaLC719SevlYwtbvx65mGTw+7kjg9DVoklJQ2VphVdqLpMBQ0sJ1Kwren78J3wXDSYuMJWL3KRxb1cK5YwOuZ3mma/DPO6mwfjLFvupHxK5TODSshNfIzjxYtIXke3k3bNiYmyevcv34JQZ8N4rNs1YTHxVHu9HvkxgTz6E1Tx/J5F7KCzNLM+5fDgBg+8JNjFr1BYO/H8vBNbtR21rx9tD3sLRS89d80w6x///i7slr3D5+mR7fDWfnrHXER8XScnQXEmPiObHm6SM6XEt5YmZpzsOMY9FxyofYuTiwOaNcK5qtXAu7FcSDHHr0nzSiTS3yh3W4//Q1ReZ9Tuwfu7CsWp5C/boQMf9ntMkpKGyssShZlNT7wWgio7EoU4JC/buSdOk6cbsOYVlZvyci5fY9g8XtXrfc1jttM+qdrZ/9hKWdFV5Zjk9yRr2TH26dvMqN45fo991I/pi1hvioWNoa+e27ZZxvDzLOt/en9MPepRDrPluO2s6aYtV8M7dNik0g5FYQ9/zvcOvUNXpMH8iWmWvQpKfTYVx3NGnp7FhgmuGp2V07eYXLx/0ZtmA062atIi4yls6juxEfE8++Nbsyt/P09cLMwpzAy7oG2x8Lfmfi6smM/v5T9q75Bytbazp83AlLK0s2z18PwNUTlzmz5yS9vxyApbWa+9fv0ajzW5SuWZZvB5p2vYmQRb9Tat1XFPv+UyI27MWmRllcB7/Hw1mr0CanoLS1Qu3rTUpgCGkRL7bitjYphdBlW3Ab2ZW0yFhij1xAXdwDtzE9SLh8h/CN+56/k1dw6vhZThw5xbfLZjB76gKiIqIZ+elgYmJiWffr02fMlipdHAtLC65k3OD69Yd1dOnRgZ9+W8C3M5cQH5/I8LED0Wq1/Li44CwOOOitigz+dR/jNxzh3eoluHDvMSuPXmFUq2qozc2IS0rlzqNovBxt9YYH3wyNwsJMibej3TP2LsTLU2hznDn/39S7d288PT35+uuvDT47efIkffr0oVevXly/fl1vu40bN/LFF1+watUq6tSp89x9vajPi32Qq3hqexvemdSL8q1qolAoCDxzg53TVvP4TnDmNgPWf0FhLxfmNhyFylzFl1d+QWVuvOF958QVfu4+nYEbJlHcyDM8XzS9o31f7kLEokEjbPr0Q+XljSb8MYl//UHipt8BMK9clULzFhAzZxbJu3WrUiqsrbHu1RfLhk1QOjmRHhJM0o5tJP6xyehiDsoibjit2aC3j5cx+MbLL+RgY29Dv0kDqN26LgqFgmtnrvLLVz/zMMvy/1+tn4GrlytDGg7KDKvUoDJdR/WgePniaDUaLh33Z82c1QRlGcLm4FyIfpMHULVRNRQKdNvMXkVwQDAvYlzKyz3TzrFNbbzHdceqpAcpIRGE/PI3D3/QPSbCvl4FKm75ipujFvPo938z4xTp3RKPIR2x9HAi6V4oQYv+4NGmg3r7dX63IV6ju2BZ1JXkB48I+fUfQlb8zctYaWmagSHW9ja8P6kvVVrVQqlQcuvMNTZOW0lolkf+fLJ+Ck5eLnzecFhmWNn6FWk36n28yhdDq9Vy/fhl/vxmHSG3c3cxvvj07FfOy4s4efYi/UdMYMWi2dSubvg8ZVP5vObLT6+wsreh3aReVGhVE4VCSeCZ62ybtppHWcq1wesnUdjLha8bjkRlrmL6lV9zLNdun7jCD92nGf3sm4Df2PPdJvZ8t/mF0jbY9uUWF7JpXp/Cw3pjUcyLtNBwotdvI3ql7rvUtSrj+cscwj6fS+zWPRQe1gfHoT1z3FdQv/EkndLvDTTzKILP7lWZ+3hZv8Tl3COSkyf1Trks9c7fRuqdQl4uzMuodyY/o965m1HvZNdp7mCK1y3PvIajXihdEbmcMGJlb0OXSX2o0qoWCoWS22eusWnaSsKy5Gf0+i9x8nJhUsPhqMxVfHdldY75uXHiMt91nwqAnbMDXSb1pVyjyqBQcOP4ZbZ+s45HASFG42YVpc1dr7uNvQ29JvWjZus6KBQKbpy5xuqvVhCcpSz7Yv00XLxcGdXw6bzMCg0q0WlUN3zKF0er0XLluD8b5qzlYZa6x9zSnE6jutHwvSbYOdoTdOs+fyz4ndO7Tz43XeNfckykQ+u6uH/SA8sSnqSGhvN45U7CftQ97cC2bkV8f59B4CcLiNhk+GgYxy7N8Pl2FJfrDyIl61QahQLnnq1x7tMGy2LupEXFEf33cR7OXYvmBeYEZ9Ul4eWHL9s72PH5tLG0fOctlEoFZ/wuMH3SPO7eenrTfe3W5Xh5e9CkervMMDd3Vz79ciRNmjfA3NycM37nmTn5W25eN37T7ZtFU6jToKbePp7n0tJ3Xzo/2e2/cp/v918k4HEMrvbWdKtTmj4NdNeSp+6GMmjFXqa+V5eO1Utmxpmx7ST7r9xn34TOOe32pVl1nWyyfb1OoW+9la/fX+TAgXz9flN74xqvBU1uG68F1cs2Xgu63DReC7KXbbwWZKZqvBYUr6vx+rrkpvFakL1s47Wgy03jtaDKbeO1oMpt47WgetnGa0GXm8ZrQWaKxmtBIY3X3HnTGq//b4YNCyGEEEIIIcTr9Ibd38l3b1bXhhBCCCGEEEKIN5I0XoUQQgghhBBCFHgybFgIIYQQQggh8oBWo8jvJLxRpOdVCCGEEEIIIUSBJz2vQgghhBBCCJEHZMEm05KeVyGEEEIIIYQQBZ40XoUQQgghhBBCFHgybFgIIYQQQggh8oBWKws2mZL0vAohhBBCCCGEKPCk51UIIYQQQggh8oAs2GRa0vMqhBBCCCGEEKLAk8arEEIIIYQQQogCT4YNCyGEEEIIIUQe0GpkwSZTkp5XIYQQQgghhBAFnvS8CiGEEEIIIUQe0GrzOwVvFul5FUIIIYQQQghR4EnPax4bWyUov5NgUo1OJOV3Ekzq9Iwa+Z0Ekzo3KTC/k2Ay3y2old9JMKnPa36e30kwqRmnZ+R3EkzKxrNxfifBpMIHV83vJJiM0sE6v5NgUpG7o/M7CSY1J9Q1v5NgUv5f1s3vJJhUn/Fn8jsJJrOxa36nQBQE0ngVQgghhBBCiDwgCzaZlgwbFkIIIYQQQghR4EnPqxBCCCGEEELkAel5NS3peRVCCCGEEEIIUeBJ41UIIYQQQgghRIEnw4aFEEIIIYQQIg/Ic15NS3pehRBCCCGEEEIUeNLzKoQQQgghhBB5QBZsMi3peRVCCCGEEEIIUeBJ41UIIYQQQgghRCaNRsPChQtp1KgRVapUoX///gQGBua4/c2bN/noo4+oU6cO9erVY+TIkTx8+NDk6ZLGqxBCCCGEEELkAa1Wka+v3Fq6dCnr169n+vTpbNiwAYVCwaBBg0hJSTHYNjIykn79+mFjY8OaNWv48ccfiYyMZODAgSQnJ7/Kv8+ANF6FEEIIIYQQQgCQkpLCihUrGDFiBE2aNKFs2bLMnz+f0NBQ9uzZY7D93r17SUxM5Ouvv8bX15eKFSsyZ84cbt++zdmzZ02aNmm8CiGEEEIIIUQe0Gry95Ub165dIz4+nrp162aG2dvbU758eU6dOmWwfb169ViyZAmWlpYGn0VHR+cuETmQ1YaFEEIIIYQQ4g3UvHnzZ36+b98+g7CQkBAA3N3d9cJdXV0JDg422N7LywsvLy+9sB9++AFLS0tq1ar1skl+Jul5FUIIIYQQQggBQGJiIgAWFhZ64ZaWli80h3XVqlWsW7eOTz75BCcnJ5Om7T/d8xoXF0eDBg2wsbHhwIEDBv9ggJMnT7JmzRrOnz9PREQEDg4O1KhRg4EDB1K5cuXM7SZOnMgff/yR43dt2LCBqlWr5kU2DJhXq41VrwGovIuhiY4i+Z+/SNq8NucIFpZY9eiHRcOmKB0KkX73NonrfyX13Em9zQr9ugVlYcMTKLLve2ijIkydjUzWNtaM+3IEzds0wdrWmvOnLvL1F/O5fePuM+PN+WE677zb0iB83OAv+PvPPcxYMIl3u7fLMX6LGh0JfhDyyunP6ujdMJYcvsGd8FgKW1vSpUpR+tcpiUKR84T4Q7dD+eHYTW49jsVBbUHz0m6MbFQGKwvjP785+6+w9sxdzo9va9K0G1OoaVWKTuiBVWlv0sJjCFm1i6BFOf8OAJw7N8ZrRCcsi7qSEvSYoO+3ErZO/66dS9emeA7tgLqYGylhkTzaeJAH321Cm5ael9nh6PUHLPnnDHfCIilso6ZL3XL0b1rZ6PHZevoGX/5+OMd9fdW1MR1q+gKw2e8aa49cJig8FrdCtnStX5YPGlR45nE3FUsbNW0/60mFVjX/j727jm7yehg4/o20SerUKFCjQHF3Hw5jgw0Yv+HuPoZtOAx3HTBgOAwbA7Yx3CleoKUFCkUrUHdL3j9SWkJSbAnteO/nnJ5Db577cG9vHrmOwlJJ8JW7/DFlA+H3nr4xXoFSHrQY9T9cyxVBIpXw9OYD/pq9jad+wQaPty1gz3eH5nJm3Z8cXrTbBDn5MCFhz2nTdQCLZ06kWqVyb4/wEVlZWTJr1nhafdkMa2srzl+4zMjvJnE74G6Oce4EnsfT083gZw+CH1G8eC0A+vTpzPJls/SOWb1mE4MHjzNOBl4hK1kJRcsuSF3c0MTHknb2L1IP73y3yFIpFiPmoUlNIWmpbtqkzq4oWvdAVqwsZKSTfs+PlN9/QRMRZvQ8vEpWtDxmjb9F6uSKJjGW9EuHSTv1e47HSxwLYjFskV64+vlTkpaM0I+gUKEaNJe04ztJv3bSeAnPgaJGVWz790Re2AN1VAwJe/8gbsO2nCPI5Vh3+gaLz5shy+9ERvgLEg8d0cZJT88+zMMN28H9UFQujyY9ndRrN4levJKMZ/o9K0bNj6WS1j90omzTqpn3tTvsnrKBsLfc1wqV8uCLUd/innlfe3zzAftnb+VJ5n2t47wBVG9XP8f4k2sPJurpC2NmxaCzDyNYfiGIB1EJ2KnMaVe6ED0re7zxmXE6+AWrLj7gXkQ8tkozGhVxYkjNoqjMZCZP7+uUliq6/NCdak2ro7RScedKAOun/MKTu4/fGK9ktVJ0GNUZz1JeJCcmcf7gObbP20xSfFLWMdb5rOk4piuVGlRGoVIQdDOITTPWE+z35nfC/zL1v1g0yRgM9ay+jVKpBLRzX1/+GyAlJQWVSpVjPI1Gw+LFi1m5ciX9+vWje/fu7/1/v81/uvJ68OBBHBwcePHiBYcPH6ZlS92X/TVr1rBo0SI6duzI0qVLcXZ2JjQ0lG3bttGxY0fWrFlDzZo1s46vWLEiS5cuNfh/2dnZmTIrWeQlSmP14wxSzxwnafNa5KXKourcG6QSknduNhjHcthYzCpWI2njajJCnqBo0ByrCTOJGz+CdP8bAEhs8yHN50DC2mWkB/jpxNfEGXcs+uvmrppG2YqlWDB1GfFxCQz8vjfrdi+nVd1viYmOzTFeidLF+GPnX2xbv0sn/OH9RwD8vGAdOzbqVrRs7WxY8MsMLp29SuhT474cXX8aybA9l2lWoiCD6npz7UkUy04HotZo6FOzmME4J++FMeL3y3xR2pVh9UpwPyKepacDiUpKZdYXFfWOv/I4gm1XP84N3LpKcUr8OpYXf5zj0extWFcrifvYjiCV8nSx4cqLwxc1KbZkCCG/HCT6+HXsm1ej6PyBqJNTebFHWxEs0Lslhaf15MX+cwRP24iZvQ1u3/8Pi1IeBPacY7L8XA8OY9ivh2lWvjCDmlfm2oNQlh26rC2fRhX0jq9bwo2Ng77UCdOgYequMySkpFGnhHb4y2/nbzNj7zl6fFaOGsUKcfNxOAsOXCQpNZ3eDfXPa2wdlwzBrXwRDs7aSkpcEo2Ht6XftvHMa/w9STEJBuPYuzszYMdEnvoFs2vMajRqNfX6tGTArsksbjmO5/f1X0y/mdMflY2FqbPzXp6FhtFvxHji4g3nM7dt2rScqlUqMO6Hn4iLjWf8+BEcOrSD8hUaEhUVbTBO+/a9MX9tTlCNGpWYN3cya1Zn3+PLly+Nv38gffuN0jk2PPy50fMhLVwCVZ8JpF87TcrBTci8SmHesgtIJKT+89tb45s3/gaZhzfpd2/qhEvsHLEYMQd1+FOSNsxFYmaOomUXLAZOI2HWYEjTX7HSKPlx80bRaQwZt86RcmQ7Uo8SmDX6FiQS0k4abpyTFvAEIGntZEhPy/4g3UAaVZYoO41Bms/Z+Ik3wLxsaRznTSfxyAlifl6HonxZbPr3AomUuF8NN2zbjRiERcumxK3bTKp/AGYlvLHp3RW5S36ifpoHgMzZCac1S0h/+ITICT8hUSiw6d8TpyVzCO3UC1JMUz4A3ZYMxb18Ef6YtZXkuESaD2/H4G0TmNl4JIk53Ncc3PMzdMckHvsFs23MKjRqDQ36tGTYrinMbTmW8PshHFqyh7NbdBeTsbS1ovuK4dy74E/0swiT5eml6yHRDD/oS7Ni+RlUowjXQ6JZfiEIDRp6VylsMM7JB8/57s8bfFG8AENrFeF+ZALLzgcRlZTGzGZlTJ7m1w1fOpKi5YuxeeYGEuMT+WbYt0zaOo0RjQcTHxNvMI5n6cKM3zSZG2d8mdd/Fvb57ek0piuFihRiepfJWceNWj2OQkVc2TxrA1FhkXw1oC1TdsxgVIthhD8O/0g5FN7m5XDh8PBw3N3ds8LDw8MpUaKEwThpaWmMGzeOAwcOMHr0aHr16mWStP2nK6+7d++mTp06hIWFsX37dp3Kq6+vLwsWLOCHH36gS5cuWeEFChSgYsWKJCcnM3/+fHbtyq4YmZmZ4eTk9FHz8DrVt93JeHCPhEU/AWh7T2VyVG07kbzvN3hteWqpS0EUdRqSsHIBKX/vAyD9xlXkJcugaPFVVuVV7qWtXKWdP4X6uWlbvF9VvkoZPmtSh/4dR3D66DkArvhc559Le/m2R1tWLVxvMJ5SpcDDy41flmzgxpVbBo95/PApjx/qttIuXj+bmKhYxgyciEajMWpeVp27S3FnG35qWQGA2oWdSVerWe8TRJcqXihfax3VaDTMPeZPI+8CTG1RHoBqHo5kaDRsuxJMUlqGTotqUmo6k/66gZOVkrC4ZKOm3RDXke1J8Avm3pAlAEQfv45ULsN18NeErNqPOln/xcV9TAciDlwgeNKv2jgnriO3s8Lt+/9pK69SKa7ffUP0yevc6Ts/K178jSAqnlyMbb1yxJy6YZL8rDpyjeIF7fnp288AqF3cVVs+x2/QpV4ZlGa6tzt7KxX2Vrqth1vO3OJBeAwbBn2BvZUKjUbD+uO+NC1XmGGfa+dsVC9WkIfPY9l+1t/klVf3SsUo2agS67rPJuDEdQAeXApg7Okl1OzSlGPLDL+I1+nRgrTkNNb1mENaknZ4z71zfow7u5Ra3ZqxL7P8XqrZuQnORQqaMivvRa1Ws++vI8xb9ktuJyVH1atXouXnjWnVuit//30MgDNnfbgTeJ7+/boyc9YSg/Gu++o2HlpbW7F503IO/nmEefNXZIWXL1eaS5euc/GicVdpNETRvCPqpw9I3rQAgIzbV0Emx7xxO1KP//7GSqa0YGHMm36DOkZ/9I7i805okpNIXDYe0rTfQ3VEGKq+E5C5FSPjvp9eHGMwa9AOdWgwKbuXafNzzxdkcszqfkXa2QO6ldOX+XDxRB0VjjrY/43nlpWognnLHkjMlW88zphsencl7U4QUZNnApBy4RISuQzrrh2I27ZTr5IpsbHGss2XxCxfQ/zmHdo4l68BYDekHzHL16COjsGmb3c0CUm8GPw9msxhgOnPQnCcNx3zksVJva7bGGEsnpWKUbpRJVZ1n4V/5n0t6FIAk04vpU6XpvyTw32tfo/mpCWnsrrHbFIz72t3zt1i8tll1O3WnN2T1hPxKIyIR7rvOL1WfUdidAKbhi8z+nuBIasvPqC4ozXTm5QGoLaHA+lqDeuvPKRzBXeUcv13hXmn79KoiDNTGpcCoJqrPWqNhm2+T/TeFUzNu1JxKjeqyozuU7l2/AoAARf9WX5mNU27tmDPUsMjMr7s3ZrYiFjm959Nelp27/6g+cMo6FWIZ/efUqBwQUpWK83K0Us5/pu2N/DO1UB+ubKRem0asGvxDtNnMBf8m+1qckuJEiWwsrLCx8cnq/IaGxuLv78/nTt3Nhhn9OjRHD58mPnz5+t1KBrTf3bOa1BQEL6+vtSuXZvmzZtz8eJFgoKCsj7ftGkTrq6udOrUyWD8yZMns3bt2o+V3HcjN0NepgKpF07pBKeeO4lEZYG8lP6QOfWL58SM7EvKyVdaGjUayMhAYmaWFSTzKoo6Pu6jVlwBan9Wg8SERM6d8MkKi4qI5tK5a9RtVCvHeN6liiGTyQi4lfPwu9fVb1Kbxp9/xpyJi4iLNdwy+KFS0zO4/DiSRt4uOuGNvQuQmJbB1Sf6L26B4bE8iUmkQyVPnfBOlQtzoG8DvYfRghO3cbBU0LqM7oR3U5CYy7GtWZrIP310wiMOnEdmpcK6ekm9OApXJ1RFCxHx5wW9OKrCBVB6FcDMyRazfNZE/nNZ55ikO09Ii4ghX5Mqxs8MmeUTFEKjMp464Y3LFiYxNY2rD94+fPxFXCLL/77KNzVLUNY9u0dlea/mDG+pu9iAmUxKqomHQAMUr1eOlIRk7pzOrvAnRMZx3+c2JRpUyDFeeNBTTq05kFVxBUhLTiUmNBIHj/w6x9q7OfP52A7sGrfG6On/UHfuPWDavGW0btGYmRO+z+3kGNS0yWfExydw+HD2kNEXLyI5dfoCzZs3fOfz/PjjcJycHBg27MesMIlEQpkyJfC98eaKlFHI5ciKlSXd95xOcPr1s0iUFsiKlM45rlSGsvMIUk/uRx2uP9xTXq4maRcOZ1VcAdSP75EwoZvJKq7I5MgKlybDX/feluF3AYlChcxD/94G2p5XdUjwm8+ttEDR4XsyHviTvOEnIyX4LczMUFQqT9IJ3SkOicdOIbW0QFFB/71AamlJwp79JJ96rUwfaYd8ygtpe1RUn9UlYf+fWRVXgLSAO4R80d5kFVeAEvXKk5KQTMBr97V7Pv6UaqA/IumlsKBnHFtzIKviCtr7WnRoBI6v3ddeKt2wEuWaVWPvtI0kxSYaLxM5SM1Qc/lpFA2L6HaENC7iTGJaBteeRevFCXwRz5PYJL4tp/vs71jenf1da330YcPl61UkOSEJ31PXssJiI2Px9/GjUoOcn+FbZm9iZs9pOhXXl/+WZ06TMjPXvpcmxmWXRVJ8EmkpqVjnszFqPoR/x9zcnM6dOzNv3jyOHj1KQEAAI0aMwMXFhSZNmpCRkcHz589JTtZ2tuzZs4c///yTESNGUK1aNZ4/f5718/IYY/nPVl537dqFhYUF9erVo3Hjxpibm7NtW/b8j8uXL1OjRg2kUsNZtLe3x9bW9mMl951IXQoiMTMn46nunAJ1yBMAZAUNzJNKTyPjXiAkJYJEgtTRGYteg5G6FCQ5sycWQFa4KJr4OKzGTsNu60Hybf8Ly+8nIslnb9I8eXl78vjhUzIydF/0HwU/wdPLPYdY2iHDAO27fc3Jm39y/fEZNu5bRdlKhl+kJBIJIycO4eLZK/xz4JjxMpDpSUwiaRlqPPJZ6oS7Z/7+KEp/mFNAuHZItEIuZcjuS1Rf+Bd1lxxi1pFbpLxW8Tkf/Jz9/k+Z2sLw/ExjU3rkR6owI+n+M53wpGBtJU/lpd8Lp/LWPliTX4uT/EqcjNgE1GnpKN10h9PJbC2R21rphRvLk4g4bfk46l7T7g7ah+Gj5zkPT39pxaGrSKUSBjWrnBUmkUjwym9HwXzWaDQaYhJT2OMTyIGr9/hfrVLGzYQBzkULEfkoHHWG7lr3EQ9DcSpcIIdYcGHzEU6uPqAT5ljYBRdvV8ICs+8vEomE9vP643vwAndO+ho38f9CARdn/tyxltFD++rMtclLSpQoyoMHj/TubUFBwRQr5vVO5/DwcGXQwB4sWPAzjx5lV/68vb2wtLSgerWK+N06RUL8A27dPEnnTm2NmgcAqYMLErmZXuVT/Vx7nUudCuUY17xFB5DLSf1Lf+iqxD4/Egsr1JFhKL7pj9XMrVjN34Oq70Qk+Uw3wklin1+bnwjdofHqCO19SuJo+LqRungiUVqg7DMdi4mbUY1ejVmTjiB9peKQlkLS0hGk7lmOJjHOZHl4lbxQASTm5qQ9eqITnv5EW15yN/3GzoyQUKLnLs6qrL6k+qwumrQ00h49QVbABam1FRkhYdiNGkqBf/ZS6NTfOMz7CVl+0w6Hzl+0EBGPwvTuay8ehuFU2CWHWHB282GOvXZfcypcgALeboQE6s/FlEgktPqhE3cv+OP7l4/e56bwJCaJNLUGDzvdKRhuttpRPg+j9SvQgc+13yWFTMbQ/depsfI49dacZNbJQL13hY/BtagbYQbKJzQ4hAKFcx6hExkawaOAhwAoLZSUrV2eDqM64+/jlxX+KPAhN05f55th/8PN2x0rWyu6ju+JuUrB2f05r0Eh5I6hQ4fSrl07xo8fT4cOHZDJZKxduxZzc3NCQkKoU6cOf/75JwAHDmivzTlz5lCnTh2dn5fHGMt/cthweno6+/fvp0GDBlmThuvXr8++ffsYOXIkKpWKFy9eYG+vWzFbs2YNK1as0Ak7ePAgBQtqL8bLly9TsaJ+q1/x4sXZvn27iXKTTWppBYAmSffmpslc8UtiYakX51XKdp2w6NwHgOTDB0i/dT3rM3nhokgdnEj55wDJf+xE5uqBqmMPbH5aQsyI3pBimmGqNjbWJMTp36wT4xOwss45PyXKeAOgVCr4vt947PLZ0ntIV9bvXkHHlr24439P5/jPmtWliHdhZvw439Dp/rW4ZG3roaW5mU64hbn2xSY+NV0vTlSidijXd79foUXJgnStWphbITH8fO4OkYmpzGlVSXvulDSm/H2DgbW98bC3Mkn6Xyez0f7tM+KSdMIzMhdVkFnrT8aXv0McdVIqEX+cxaVHCxIDHxPxlw9mjrYUntYTdVo6Ugv9/b+MIS5Z2xJvqXytfBTa3+PfMncrMj6JA1fv0bVeGWxUhtN4/WE4PVZob86lCjnSobbpK69KGwuS4/Wvn5T4ZBRW+mWUEzOlOf+bP4C05FTO/Pp3Vnidni2wd3fm195zjZJeY7G1scbWxjq3k/FGtnY2xMbqV2Di4+KxsXm363jw4F6kpKSydJnuKKDy5bSNdO7urowaPYW0tHQ6d2rHunWLMVcoWLdu67/PwEuqzOdO8mvfs5TM547S8PdM6l4M84ZtSFw8VmcBoJckVtqGJEWr7mQ8vKOd82pli+LLblgMmaGd85r69hUr35dEqa00aJJ171OkZuZHYWBet6UtUms71Bo1af9sQR3zAplXWczqtkZq60DKrsy1MDIy0Lww7UJGr5NaZZZPgm4DqSZRW15Sy3ebp65qUBeLFk2I37EHTVw80sxKr+2gPqT6BxA54Sdk+eywGdgbpxULCOvUG42Re0uy0mJjQXJ8kl54cnwSSqt3n3dvpjSnU+Z97dQr97WXSjeuhEvRQuyeZHh6kinEpbx8V9B9vX75rpCQql8ZjUrWDmMf+dcNWni70KWiO37hcfzsc5+opFRmNy9r4lTrsrCxJMnAcycpIQmLd3zurLu+GTOFGbGRsWycvk7ns7UTV/PDhoksOKy9rtRqNSu+X8KdKwH/PvF5lEb93xs2DCCTyRg1ahSjRo3S+8zV1ZXAwMCs39etW6d3jKn8JyuvJ0+e5Pnz53z++edZYZ9//jmHDx/m4MGDtGvXjnz58hEVFaUTr3379jRt2hTQzokdNWoUanV2y1KZMmWYN2+e3v9naBVjk3jZ45bTnIy37DScdvEcsX43kBctgapDd2SOzsRN1n7hEpbMRpOaSsYD7TDcdP8bZDx6gM3s5SgaNMuaL/vvki/R6+mWSCWG55hIJDp/+9dtXLWNQ/uP4nM6e/jphdOX+PP8LvoO7873fcfrHN+xZztu3wzkwin9jZONQZ2Zh5w6RaUGwtMz89ewmAvD62uHqlV1d0SDhiWnAhkYGY+nvRVzj/njbK2kcw4LOZiCRPqW75raQHhmHL3yzPyjaDLjBI1ejTolnSLzB1B04SAyEpN5uvx3pCoF6kTjv6y+mlwJhgtI+pbe7N0+2oW3OtbJeYhkoXxW/NLvc8JjE1n5z1U6LtnHliGtcTBQ0f8QEokku1yywqRgqIgkoHnHnccVViq6rxmJa1kvNvZbQEyIdoi7k1cBmn3fnk0DFpIcp/8iKWQzdG+TSqUG722St9zbXlIqlfTo/i3rf91OdLTuonknTp6jVeuunDhxLmu41eHDJ3FydmDSxJFGrby+daSHoXuE3Ew7XPjEPtSP7hg+r1z7eqGJiyZ57Yys86hfhGD53XzMqjQg7Zx+heNfk7wsp/d4jqYkkrR+GpoXz9DEahf0UQffhow0zBt3IPXkHjTP37wKrsm87V79DnM4VQ3rYT/lB1Ku+RKzXDs9QJK5BkBGZBQRYyZlnSf9yVOc1y7HokVjEvYeyPGc78rQfU0qkRhMtkQiea/7Wp813+Ne1ou1/RYQHaK/EFO9rs144veAO2cNr5thCprM711OV5Whyy0ts4ezgZcTw2oVBaBq5pzXpeeDGBCVgGe+N3defCiD5ZPDe5v23vb275tMLmNWr+nI5DI+7/ElU3fOZEb3qfidv0mhoq5M3zOb8MdhzOs/i8TYRGq3qkv/2YNJSUrhwp/n3np+QfhPVl737NkDaLuzX7d9+3batWtH5cqVuXRJtyJja2ubNVT45ea7r1IqlXh4eJggxe9Gk6Cdp/l6D6sks3f59ZbX12U8vA9oK6bqhHisho5BXqIM6QG3SA/Un1+UHnALdXwcssJFjJF8BozsxaBRfXTCDv1xFIci+kOTLSwtiIvLOT/BQY8IDnqkExYXG8+1SzcoXkp3ZV/bfDZUq12ZhdN1e9WNyTqzRy8hRbeHITGzFdXqtR5ZAIvMlte6XrpDsGp5OrHkVCCB4bE8ikrgUMAztnSpg1qjQa3RZD000tVqpBLJWyteHyI9Rtuq+noPqyyzVTXdwNygjMwVIOXWFrxaBZVZaod1ZmT2sKsTkwkauYIHE9ahcHUi5XE46qQU8ndoRMxD08y5tlZqG5gSXuthTUzRtmhbKd/cAHXk5gNqFiukt4DTq5xtLXG21V6bZd2daDVnJ3suBhpcyfhDNB7WhibD2+mE3Th4ASsv/WGOCkslybFvr3DaFrCn57oxOHq5sHnwYm4f085hkkgltJ8/gBt/+nD39E2ksuyKmUQiQSqT6g0Z+/9s/I8jmDDhO52w3bsP4GxgeLCllSUxMW8fUtqkST1sbKzZtk1/cZrw8BdZi0C96q+/jtK4UT3y53ciLMw4qw5rkrTX9cseyyyKzOdOsv59WvFyJeJD2+Flpf7lbUoqBbU6q+cz3f+KTgVLHRyIJjEeqeu7Da1+Xy/TK1G8di2bv8yPgXmP6Wmo7+vP8cwIvAqNOyB18SAjlyqv6sznpMTytfcCC215qd+yErdVh3bYDulHylVfIkaNhzTtPfFlz23y+Ys65ZN66zbq2DjMihU1SvqbDWtLi9fua9cOXsDZS3/alva+9vZ5qXYFHOi3bjROXgX4dfBi/I7pL2pmYWdF0Zql2T/7DdsJmYB15nM/ITWndwX9127LzF7Zep6OOuG13R1Yej6IwBfxJqu8thv2P9qP6KATdv7gWYPDg5UWSp25qjnJSM/gxunrANw848uCw0tpM6gdfudv8kWvVkglEqZ1mkR8tPY+efOsLxbWFvSe1g+fv85/lEW1PrZPMEu56j9XeY2MjOTkyZO0adOGHj166Hy2YcMGdu3ahZ+fH127dqVTp0789ttvtG/fXu88ISEfd+jPu8gIfYYmIx1ZgUK8uhaitIB2eE/G42C9OFJnF+TlKpF68ojOipAZ97TDL6ROzkgeW2FWox7pd/xQP36oE19iZoYm1jhb5ezc9DsnD5/RCWvUoj61G9TIbFHNvnrdPV25/4Z9Xlt81YToyBjOn9Ldq1apVBAdqZveOg1rIpfLObT//fexeldudhbIJBIeReu+KLyc6+rlqD9M8OV82LTXKgHpmS2XCrmMw4GhpKSrabf+lF78KvP/4svSrkz7vLxR8vCq5IehaNIzUHrqVoxUntr5RkkG9nJLCtLOgVN6upBwK7vslC/j3NHOycrXuDLpMfHEXQok6Y72PGYONpgXdCD+5n2j5wXAzcEamVTCoxe6c1sfRWh/93K2yzFuWHQCgc8i6VxXfzuChORUTvg/oqy7M+6O2YtJuDnYYKNSEBb95hfH9+Gz9Ri3j17TCSvdtAre9crpXT8OHi6E33vy+il0uJRwp/eGsciVZqztNov7F25nfWZX0AGPisXwqFiMKm3r6cRrPKwtjYe1ZWadIUQ9Mf1+iP8Fv6zdwsE/j+iEtW7VjCZN6uuVTZEinty+/faF5j5v0Zj7Dx5y9ar+6tt169bAw70Qm7foblmlUipJT08nKsp425upX4SgycjQmwsqddK+vKpD9e8F8gq1kTrkx3qe/pZa1ov+IGnzQtKvn0WjzkAi12/YQyYz2TY5msgwNBkZSO1deHWAptRBe59SP9e/biSOBZAVLkP6jTNZw6UBMNM2emkSPs78VkPSnz5Fk56B3E137rHcVft72oPgHOPajRyCVfuvSTx8nMgps7IqrgDpT55py93MQPnI5TqLOP0b57Yexe+obuWybNMqlDRwX3P0yE/oW/Z5LVjCnf4bxmKmNOfnbrO4d8HwomYl65dHJpdx/bUFBk3N1VaFTCLhcYxu4+LL373s9SuhbrbahojUHN8VTLc8zZGt/3DlqO4Ci9WaVad8vYp65ePiWYAn93Le57VK42okxMZz+2J2maSnpfMwIBi3Yto1ThwLOfM06GlWxfUlf59b1GxZGxsHG2JemHb7RuG/7z9Xed23bx/p6en07t2bIkV0ewz79+/P3r172bZtG9OnT2fs2LFMmTKFW7du0apVKwoUKEBISAh//PEHu3btolSpUjr7t6alpfH8ueHWbEtLSywsTLwHYloq6X43MK9Rj+S92XNszWvVRx0fR/rd23pRpPkLYDVkDPGpqaSeyn65MqtUDYD0B0Fo0tKw7D+c1LMnSFg0I/uY6nWQKJSkvTI39t94HvaC52G6L7tKlZJ+I3pSu0ENzhw7D0A+Bzuq1qrIqkW/5niub7u3xdnFkVZ1vyUtc7U6ZxcnKlYrx6bVuvOPy1UqTcjTMEKevH1F2Q+lkMuo5GbPsTuhdKvqlTXU7sidEKwVcsq42OnFqexqj8pMxl8Bz6hfNHslxJNBYcilEsoVtKOYkzXfVtLt7d/t+4g9Nx6zpUtt7FSmGbKuSUkj9oI/Dp9X59nK7CHjDl/UJD06nvhr9/TiJAeHkhwcisMXNYk4cF4nTtK9p6Q80V47+bs2xSyfNTe//CHrmAJ9v0CToSbq8BWT5EdhJqdSYReO3QqmW/2y2eVz8wHWKnPKuOe8QMytx9p0V/DUX61SKpUyZdcZvqhUlInt6ujEiUlMwbug8RY8iw2PIjZcd6qDmcqcRkO+xrt+OQJPaBdUsrS3xqt6SY4t/z3Hc9kWsKfPph9QZ2Swou1kwl97IYwNi2LJlz/qxRu6/yd8th7FZ9sxYsOi9D7//yokJIyQEN1RAxYWKsaNG0bTpp9x6NBxABwd7alXtwazctgm51XVqlXk/LnLBj9r2KAO48YN5fyFKwQFBQPaHvE2bVri43OV1FQjVvzS08gIuoVZ+VqkHduTFSyvUBtNYjwZD/WHBSetngqvVUqV3w4GIHn7MtQRYZCaTEaQP/LyNUk5sCFrXqzMuzwShYqMIBOtNpyehvrhbWSlqpN2dn9WsKx0DTRJ8aif6N/bJNb2KFr1gYx00q8ezwqXl6mFJjkR9TPTNLq9k9Q0Uq7fQPVZnaxtbwAsGtZDHRtHqr/heYI2A3tj1f5r4rbuJGbxSr3PNUnJpF6/iapBXWJWrs2q2CqqVERqoSLFSKsNG7qvmavMaTakDSXql+d25lY5lvbWFK1ein+WG94mB7Q9rgM3/UBGhppFbScR9oaKrkeFokQ9iyDq6cdtgFPIZVQqaMfRoHC6VnTPfhYFhWvfFfLrr6hbuaAdKjMZf98Jo37h7GfViQfPte8KLqZbXDQqPJKocN3dEhQqc9oOaU/5+hW5fkLb8GBjb0Op6qXZvczwNjkAX/b9Cht7G0Y2G5o1csfC2gLvSiUIyKzQPgt6QoP2jbGytdLZL7Z45ZIkxiYQH23cnSKET9N/rvK6Z88eatWqpVdxBXBzc6NJkyYcPHiQsWPH0q1bNypWrMjmzZsZNWoUz58/x8rKijJlyjBr1iw+//xz5PLsP8G1a9eoU6eO3nkBRo4cSd++fU2Wr5eSftuI9dQFWI2eQsqRP5GXKI3y629J2rhKu8erygKZmyfq0KdoYmNI9/Ml7cZVLPoOQ2JpRcbTR5iVrYjy6w4k//0H6ifantbkvdtR/a8b6uhI0q5eROZZBNW33Um9dI50X9NUKACuXLjOxbNXmLNiCvOnLSM6MoZBo/oQGxPPbxuyX5SKeBfGzNyMgFvaF6WVC9ayevtiFq2fzba1O7HNZ8PA7/sQEx3H+hW6K1t6lyz6xl5cY+lToyj9fvNh1B9X+aqsG77Pothw8T7D6pdAaSYjPiWN+xHxuNpZYG+hwMJczsDa3sw/cRsbhRmNvF3wfRrFep8gOlbyxD5z8aJCtrqNIqeCtJt0lzZQITamx4t2Ufq3SXivHkn49mNYVylOwYGteTh9M+rkVGRWKlTeriQ/DCM9swfz8cJdFFs8mPSoOCL/uYR906o4tq5N4Ct7uoas/ZPS2yfiObUHkYcuYVunLK5D2/Jk6R5SHplm2DBAn0YV6LfmL0ZtPsZXVb3xfRjOhpM3GdaiKkozOfHJqdwPi8bVwVpnePDd0EjM5TLcHPRfKlTmcrp/Vo41R69hZ6mgetFCPHwRw8+Hr+JdwJ7WVYrpxTGmBxcDCDrvR4dFg/lz5lYSouNoMrwdSbEJXNic3VjlXLQQcoUZz/yCAWg9uTvWTrbs/uEXlNYq3CtmDwFMjksi/N5TnuTQCx4bHpXjZ0K2M2d8OHHiHBt+Xcq4H34iMiKKCRO+Izo6ltVrNmcdV7JEMRQKc539XaVSKSVKFGXHjt8NnnvV6o307t2JvXvWM3XqfBKTkujfvzulSxenSVP9kUT/VuqhHagGTUfZYyxpFw4jK1wS84ZtSPnjV20PqVKF1MUdzYsQNPGxqEMe6p3j5TBh9ePsymHK/g1YDJ2Jqt8UUo/tQWJtp13AKTiA9JumW/019cRulN0noPjfCNKvHkfqXhyz2l+S9s8W7R6vChVSJ1fUkaGQGIc62J+M+7cwb94VzBSoXzxF7l0JeY0WpB7aDAaGTn9Mces247hsLvYzJpG4/y/My5XGqvP/tPNXU1KRWFpgVtiD9CfPUEfHYFasCNZdviXVP4CkoycwL6O7PVDag4doEhKJWfELTisX4LhwJvFbfkNqnw/bwX1IueVP8mnTzT0MuhjA3fN+dFk0mD9mbiExOp7mmfe1s6/c1/Jn3teeZt7X2k7uhrWTHTt+WIPSWoXHa/e1VyuzBUu4E/qW0Smm0ruqJ/1/v8bov2/RulQBfENi2HD1IcNqFUUplxGfms79yARcbVXYq8yxMJczoJoXC87exUYpp6GXM76hMfx69SEdyrlhb6JG7JzcvujPrfM3Gbb4OzbN3EB8VBzfDP+WhNgEDm/OnqfuWswNubmcYD/t+9euxTsYv2kSI1eO4Z/Nf6OyUvH1wLYoVQp2LNTO09//yz7qfl2fidumsWfZThLjEqnevCZ1Wtdjw7S1ZOTC6sofw391waa8SqL5FAeX5yGRreu/dxyzGnVRdeiBrJAb6ogXpPy5l+R9vwEgL1MBm58WE794JqnHMm8iKgtU/+uGea36SO0dUIeFkHxoPyn7d2UPtJdIULRojaJ5a2QuhdDExZBy6ihJ29ZpK8XvqO6F91990MbWmtFThtOwRT2kUinXLt5g9sSFOnNa1+9ZQSG3AjSt+nVWWM161RgwshfepYqiVqs5e8KHBVOXEvJUtwL0x6ntBN6+x6h+uos4vYvLP9V+r+OP3Qll5dk7BEcl4Gyl4H8VPelaVTt369KjCPrsuMCUFuVoXSZ7W6Pfbz5m0+X7PIpKxMlKQdty7vSoXiTHuawrz95h1bm7XB/1/hs8X5ug/1L5JvYtquH2/beoihQkNTSS0PV/8WyVtrfCpmZpyuyZyt1hy3j+W3ZvRP4uTSjYvzWKgg4kPwrj6dK9PN91Uue8jl/VwXV4OxTuzqQ8eU7or38Tuu6v90pbxZ+rv9fxAMduBbPyn6sEP4/B2daS/9UsSdf62pUaLwWF0GfVn0xpX5fWVbyz4vy09yzHbj7k6MSOBs+pVmvY5RPAb+dv8/hFLDYWChqV8WRQs8pYv8dLxaRh195+kAEqG0u+mNCZ0k2rIJFIeXglkP3TNvH8fvbUh37bJ5DP1YlZdYYiM5Mx3f9XZGaG2yaDLviz6ttpBj+bE7yNw4t2cXiR/nDQ1/10+ePsc3nx6g16DhnDuqWzqVZJf09LY7EsVO/tB73Gzs6WuXMm0qpVM6RSKefOX2bUqMncuZNd+T/8z048PFzxLl4zK8zJyYGnT3wZNHgsa16p6L6qaNHCTJ82ltq1q2FtbcXly75MmjyHs2cvGjz+dRH9KrxXXuTlamLeoiPS/K5ooiNIPX2QtOPaXjBZ0bJYDJ2pHQ580fD0DNWQmQAkLR2nEy4tXALFF12ReXijSU0h/eYFUn5fB0nvXiGU2r7/qCdZyaqYN2yPxLEgmthI0nwOkX5OuwCR1LMUql6TSdmznPRrmfcuhQrzhu2RlayKxMoOTVQYaef+JP2K4fxK7JywGLlc9xzvKOqf9+8NVNavg02fbph5uJHx/AXxu/YRv1XbC6aoVB6nlQuJnDqbxIOHsOnbHZteXXM81/MBI0i5qh3JYV62NDYDemJeuiSa5BSST54hesnPaN4yl/ZVc8Pef2sdlY0lX0/oQtnM+9qDK4HsnbaR8Ffua4O3T8Te1YmpdYYgM5Mx139Djve1uxf8Wfbt1Kzfxx2ex7OAR2wY8vZREK+bOfLfj6g5FhTOzxcfZL0rtC/rSteK2lFWl59E0ef3q0xpVJJWJbPnlu7zf8am6494FJ2Ik6WCNqUL0aOyx79e96LbvPefr21pY0m3Cb2o2qw6UomEgCu32TB1Hc/uZ59r8vbpOLk6M6hOdsdOmdrl+GbYt3iWKoxGrcbv/C22zt3E01caEgp6FaLjmC6UqVkWiVTKk7uP2bdqDxf/fvsQ750P//3iornBv8j7v88ZU6mgg7n6/xubqLya2IdUXvOyD6m85mXvW3nN69638pqXfUjlNS/70MprXvWxKq8fy4dUXvOy96285mUfUnnNyz6k8pqXfUjlNS8zRuU1L/mQymte9V+tvN7y+iJX//8y9//9yuF5ielmgQuCIAiCIAiCIAiCkfzn5rwKgiAIgiAIgiD8F2g0Ys6rMYmeV0EQBEEQBEEQBCHPE5VXQRAEQRAEQRAEIc8Tw4YFQRAEQRAEQRBMQCyNa1yi51UQBEEQBEEQBEHI80TPqyAIgiAIgiAIggmoxYJNRiV6XgVBEARBEARBEIQ8T1ReBUEQBEEQBEEQhDxPDBsWBEEQBEEQBEEwAbHPq3GJnldBEARBEARBEAQhzxM9r4IgCIIgCIIgCCYgtsoxLtHzKgiCIAiCIAiCIOR5ovIqCIIgCIIgCIIg5Hli2LAgCIIgCIIgCIIJiH1ejUv0vAqCIAiCIAiCIAh5nuh5NbF+1+1yOwlGdXlOydxOglHlH/hbbifBqObY18rtJBhNvW8W53YSjCrQu0xuJ8GoLAvVy+0kGFXC01O5nQSjali+T24nwWiS1DG5nQSjaqJwy+0kGNUPns9yOwlG1WRWeG4nwai2FxS9frlNbJVjXKLnVRAEQRAEQRAEQcjzROVVEARBEARBEARByPPEsGFBEARBEARBEAQTEAs2GZfoeRUEQRAEQRAEQRDyPNHzKgiCIAiCIAiCYAKa3E7AJ0b0vAqCIAiCIAiCIAh5nqi8CoIgCIIgCIIgCHmeGDYsCIIgCIIgCIJgAmLBJuMSPa+CIAiCIAiCIAhCnid6XgVBEARBEARBEExAI3pejUr0vAqCIAiCIAiCIAh5nqi8CoIgCIIgCIIgCHmeGDYsCIIgCIIgCIJgAurcTsAnRvS8CoIgCIIgCIIgCHme6HkVBEEQBEEQBEEwAQ1iwSZjEpXXPEppqaLLD92p1rQ6SisVd64EsH7KLzy5+/iN8UpWK0WHUZ3xLOVFcmIS5w+eY/u8zSTFJ2UdY53Pmo5julKpQWUUKgVBN4PYNGM9wX4PjJ6Ps0FhLD/pz/0XceSzMKddpcL0rOmNRJLzhXzqXiirTgdw73kMtipzGhUvxNDPSqEyz/66PngRx6Ljt7j88AVymYRKbo6MbFQW13yWRs/D66ysLJn20zi++LIJVlaW+PhcZcyoqQQG3Msxzk3/U3h4uBr8LDj4MeVK19cL7z+gG4OG9KJsqXpGS/u7MrNUUuPHDhRuVhkzKyVhl+9ydvImou4+e+dzNF09jLT4JI5/t9qEKdVnZWXJ7FkTaN2qGdbWVpw/f5kRIydy+/bdHOPcu3MBT083g589ePCIYsVrsvaXhXTr2j7Hc3gVrcajR0//dfpfpapdBfuh3TD3cicjKobY3w4S/cuOnCPI5dh1b4t1qybIXZxID3tO/MHjRP2yA9LT9Q6XWFrgtmclUSs2E7fvsFHTboiVlSWzZo2n1ZeZZXPhMiO/m8TtgJzL5k7g+ZzLJvgRxYvXAqBPn84sXzZL75jVazYxePA442TACELCntOm6wAWz5xItUrlcjs5OlSWKgZO6EfdZrWxsFJx67I/SyYuJ/juw3c+R52mtZi5fhpD2n3H9fO+WeGOLo4MHN+X6p9VRSaX4Xf1Nr/MWcft64GmyAoAFpYqhk0cSP3mdbG0suDGpVvMm7iEB3eC3xhvxsrJNP2qkV74D/0n88++owAUcHVh6IQBVKldCTMzOZfOXmXp9J95dP/Nz+h/w9xSyec/dKRU0yooLJU8vHKXA1M2En7vzfedAqU8aDbqfxQq54VUKuHpzQf8PXs7z/yCs46xc3WkxdiOeNUshdxMTtB5P/6etY0XD0KNn49q1bDq1Qu5hwfq6GgS//iDxK1bc44gl2PRvj2q5s2ROTmR8fw5yUeOkLB1q859Tdm8OZb/+x+yQoVQR0SQdOgQCZs2QUaG0fPwLiwsVQya0I+6zepkXk9+LHqH62nKivE0bt1QL3zigGkc/eO4qZKbRVmzKrb9eyD38kAdFUP8nv3E/bot5whyOdadv8GyZVNk+Z3ICH9B4t9Hif11m075FPx7JzIHe73oT5u3Qx0RZYqsCJ+oT6ry2rBhQ54+zb6Jm5mZ4ejoSMOGDRkyZAj58uUDYOzYsTx9+pRNmzZlHXvmzBlWr16Nn58f6enpuLu78+WXX9KjRw/MzMw+el6GLx1J0fLF2DxzA4nxiXwz7FsmbZ3GiMaDiY+JNxjHs3Rhxm+azI0zvszrPwv7/PZ0GtOVQkUKMb3L5KzjRq0eR6EirmyetYGosEi+GtCWKTtmMKrFMMIfhxstD9efRDBs53malXJlUP1SXHscwbIT/qg10Kd2cYNxTt4NYcSuC3xR1p1hDUpz/0UsS0/4E5WYwqyvqgIQGptI900n8bC3ZmbrKiSnq1l+0p8B28+ys3cjlGYyo+XBkHW/LqZylXJMGD+buNh4xv4wlAN/bqFa5aZERcUYjNOpQ38U5uY6YdWqV2Lm7PGsW6v/0G7/v9b8NPMHnj0LM0ke3qbxskE4V/DiwoztpMYlUXnE13y5/Qd2NBpDSnTCG+NKpBJqTe6CV4uqBO489ZFSnG3LphVUrVqBseN+IjYujgnjv+Pwod8oW74BUVHRBuO0a98LhblCJ6xGjcrMnzeZ1Wu094mfZixi9epNOsfY29uxfdsqTp48z+PH716xfxeKCqUosGwy8X+fJHLJBlSVSmM/tDtIpUSvNvwi4Ti2P9atmhC1agspt+6gKFWUfAO6IC/ozPOJC3WOldpY47JsMmaFXIya7jfZtGk5VatUYNwPPxEXG8/48SM4dGgH5Ss0zLFs2rfvjbni9bKpxLy5k1mzenNWWPnypfH3D6Rvv1E6x4aHPzd6Pj7Us9Aw+o0YT1z8m6+h3DJ5xXhKVijOyp/WkBCXQI/vurLot3l0adCTuOi4t8a3yWfD97NH6IVbWluyfO8iVBZK1sxdz5P7T6nXojZLdy1k6Dff4X8twBTZ4aeVkyldsSRLpq0kIT6BPt/1YOXORbSv34XYN+THu0xR/tx1iN/W79EJf/zgCaCtlCzfsYCMjAxmjZlPamoqvUd0Y/WepbT/7M3n/jc6LBmMa/ki/DVrGylxSTQa3obe235kYeNRJMUY/k7ZuzvTd8cEnvkFs2fMajRqDXX6fE6/XZNY2vIHXtwPwdxSSa9NP6DOyGDf+HWkp6bTcMjX9NkxgUVNRud47g9hVro0dj/9RPLx48SvXYtZ2bJY9e6NRColYfNmg3GsBw9G1awZ8Zs2kR4QgLxYMay6d0eWPz+xc+cCoGrbFpshQ0g+cYK4lSuR2tpi2aMH8iJFiJkwwWjpfx+TV4ynVIUSrPhpNQlxifT4ritLfptPpwY93ng9FStdlL93H2bPr7/rhD9+YNzGUUPMy5XCcf40Eg+fIObn9ZiXL4PtgJ4gkRC33nADQ76RA7Fo2ZTYtZtJ9Q/EvHgxbPp2ReaSn6jp8wCQ2udD5mBP1IIVpN7014mvjo41eb6ET8snVXkF6NmzJz179gQgOTmZO3fuMHfuXC5dusS2bduwsrLSi3Pu3Dn69+/P0KFDmTRpEmZmZly9epWZM2fy4MEDZs6c+VHz4F2pOJUbVWVG96lcO34FgICL/iw/s5qmXVuwZ+lOg/G+7N2a2IhY5vefTXpadmvXoPnDKOhViGf3n1KgcEFKVivNytFLOf6btgX5ztVAfrmykXptGrBr8Rt6dd7TqtMBFM9vx0+tqgBQu0h+0tVq1p+/Q5dqRfUqmRqNhrmHb9KoeCGmflEZgGqeTmSoNWy7fJ+ktHRUZnJWnrqNhbkZqzrWRmWm/QoXsrNg+M4L+IdEUcnd0Wh5eF21ahVp3qIh7dr05J9DJwA4d+4SN/1O0rtPZ+bOWW4w3g1f3Zu1tbUV6zYs4e+/jrFowaqscEcnByZM/I4ePTsQmUstkfkrFcWjcUX+7DqXR8e1PSchFwPpdG4hpbs25uqSfTnGtS/hRp3p3XAqV5i0pJSPleQsNapXpmXLxnzZqgt//X0MgDNnfLh35wID+ndjxszFBuNdv+6n87u1tRVbNq/g4MEjzJ23AoD79x9y/75ui/munb8QGRlNl26D0Wg0Rs2L/YBOpATcJ3yc9uUs6exlkMvJ16s9MRt2o0lJ1TleamONTfuWRC5cS/T6Xdo4PtcBcBjZh4iF61BnNq5YNKiJ47gBSC1URk3zm1SvXomWnzemVeuu/P2ybM76cCfwPP37dWXmrCUG41331S+bzZuWc/DPI8ybvyIrvHy50ly6dJ2LF6+aLhMfSK1Ws++vI8xb9ktuJyVHpSuXolbjGozqMo4Lxy4CcMPnJjsubOHrbq3YuHjLW8/x3YyhpBvo4W/5bXMKuhdgQOsh3LqsvRdePn0FW3tbBk8awMCvhhk3M0DZyqWp26QWwzqN4uyxCwBc87nBHz47+Kb716xdtNFgPIVKgVthV9Yv2cytq/4Gj2nSqiHuXm58U79LVi/uvdv32X9pJ42/bMCeTX8YPT/ulYpRolElfu0+h8AT1wEIvhTAqNOLqdGlCceX/W4wXu0ezUlLTuPXHnOz7slB5/wYfXYJtbo1449Jv1KuZQ0cC7uwsPGorF7c0MBHjDmzhLItq3Nx6zGj5cOyWzfS790jdsYMAFIvXkQik2HRsSMJv/0Gqbr3NYm1NapWrYhftYrEHdr3k9Sr2mvcesAA4lavRhMXh1W3bqRcukTM5MlZcdPu3MFxwwaSKlcm9coVo+XhXZSuXIrajWvyfZdxnD/mA4Cvzw12XthKm26t2bDYcEVdoVTgWrgQm5Ztxe/q7Y+ZZABsencl9U4QkZO0o1iSz19CIpdj060D8Vt3GXzuWLb5kpila4jb/BsAKZeuAWA3rB8xy9agjo7BvHhRAJKOnyYj1HidJP8VauO+Hvy/98kt2GRhYYGTkxNOTk64ubnRqFEj1q1bx5MnT1i7dq3BONu3b6dOnTr07duXIkWK4O7uzldffcWIESPYu3cvsbEft1WofL2KJCck4XvqWlZYbGQs/j5+VGpQJcd4W2ZvYmbPaToV15f/lmcOuTUz1/YiJ8YlZh2TFJ9EWkoq1vlsjJaH1PQMLj96QaPiBXTCG5coRGJqOlcfv9CLExgWw5PoBDpU8dIJ71StKAcGNkVlJkej0XA0MISvy3tkVVwBShfIx+GhLUxacQVo1Lge8fEJHD1yOiss4kUkZ89cpGmzz975PGPGDcHR0Z6RIybqhH8/aiANG9WlU4cB/PXXUWMl+7241S9HWkIyj0/dzApLjozj2YUA3BuUf2Pchov6I5FI2NtqMskvPn5ratOm9YmPT+Cfwyezwl68iOTUqQu0aK4/DCsnE34cgZOTA0OG/ZDjMS0/b8xXrVswctRkYmKMnFczM1RVy5Fw5IxOcMLh00gtLVBWLqMXRWptQexvB0k4fkEnPDVY+zJq5uqSeZwlLosmkHzpBiH9cs6fsTVt8hnx8Qkcfr1sTl+g+XuUzY8/DsfJyYFhw37MCpNIJJQpUwLfG4YrG7ntzr0HTJu3jNYtGjNzwve5nRyDqtWvQmJCEpdOXs4Ki46MwfeCLzUaVn9r/IatPqNq3cqsnK4/TcCjqDuxUbFZFdeXrp+/QdmqZbCy1W9U/rdqflaNxIRELpy8lBUWHRHN1fO+1GpYI8d4xUoWQSaTcccv56HsRw+epMcX/XWGH6elpQHZz1hjK1avHCkJydw9fSMrLCEyjgc+tyneoEKO8cKDnnF6zUGdxsS05FRiQyOx93AG4NbfF1nZZpLO8OOM1JfvDkbMj5kZ5hUqkHz6tE5w8smTSC0sMC+nP4xeYmlJ0h9/kHLunE54xhNtL7isYEGk+fIhtbHRP+bhQ9TR0Shq1jReHt5R9fpVSUxI4uKr37/IGK5f8KXmG66noqW8kMlk3PXLeRqSyZiZoaxcnqTjuuWTdOwUUksLzCuU1YsisbIkYc9+kk7r/u3THmnLR15I+w5o5l0EdWzc/8uKq2B8n1zl1ZCCBQvSpEkTDhw4YPBziURCYGAgoaG6cztat27NwYMHsbCw+BjJzOJa1I2wR2GoM3QX1w4NDqFA4YI5xosMjeBRgLZnSGmhpGzt8nQY1Rl/H7+s8EeBD7lx+jrfDPsfbt7uWNla0XV8T8xVCs7uP53jud/Xk+gE0jLUeNjrvpS4Z85JfRSpP/Q5IEzbK6QwkzHkt3NUn7OPugsOMOuQLynp2jkrz2ISiU9Jo6CtBTP+vk79hQeoNnsfQ387T0hMot45jc27eBGCgx+T8docmvv3gylatPA7ncPdvRD9+ndlyaI1ekNN1/6yhYrlGrL/j0NGS/P7sitWkNhH4Whe+/7FBodh61Ugh1hax4b/zL6204gMMN28rzcpUaIY9x881Cufe0EPKFbMK4dYujw8XBk0qAfzF6zMcQ6rRCJh1qzxnDx5jj17Dv7rdL/OzM0Fibk5aQ91//+0R9rvi5mB+dPpT8N4MX0ZacFPdMKtGtdGk5aWdS51UgqPW/cl/Md5ZER9vAaGEiWK8uDBI72yCQoKfr+yGdiDBQt+1ikbb28vLC0tqF6tIn63TpEQ/4BbN0/SuVNbo+bhQxVwcebPHWsZPbQvSqUyt5NjkEcxd0IehpDx2nX/5MEz3LwMz9d/KZ9jPkb8NJQlk5YTER6p93l0ZAwW1pZ6ldRCntrnWUG3N99XPoRnMQ+ePgzR+749Dn6CRxHDc6gBipcpBkDbrl/xt+/vnH94jDW/L6N0xVJZx8THxnPzinZEgNxMTrFSRZiy+EeiIqI5YqI5ic5FCxL5KFzvvSDiYRiOhXP++/lsPsLp1brvPo6FXcjv7UpYoPZekRybyKOr2sq6zEyGS0l3vpk/gPiIWG4cvKB3zg8lK1AAibk5GY91nw8ZmdO9ZG765aIODSVu0SK9OIq6ddGkpZHx+DHq+Hg06enIXHSnQEisrJBYWyMrYPzv19t4FnPn2cNnBq6np2+8noqV1vZQftWlFX9c28WJB4dYsWcRpSqWMGl6QVvRlJibk/5I9xmS9jizAdRdP90Zz0KJmr2E9Ie6cSwaaMvnZSXW3Lso6rg4HOZMptDxfRQ6eQCHn8YjNTAH9lOkRpKrP5+a/xeVVwBvb28ePXpEQoL+3I3u3bsTERFBo0aN6Nq1K8uWLcPHxwczMzOKFCmCXP5xR1db2FiSFK9fEUtKSMLC6t2G+a27vpmJW6eitFSxcfo6nc/WTlyNwkLJgsNLWX9jC5/3/IKfxyzjzhXjzTuKS9a2QlsqdFttLRTav2V8iv7QsqhEbcvwd7t8KOJow7L2tehZ05u9vsFM2K8d8hOZeczi436Exyczs3VVJrWsSGBYDH22nCEpVf+8xmRnZ0NcrH7FOz4uAWubd+s9GDCoBykpqaxcsV7vs7t37hscdvcxKWwsSI1L0gtPTUjC/C3fv9yqtL5kZ5tD+cQnYPOO5TN0cG9SUlJZstTwSA2AL75oQskSxZgx0/BQ139Laq1Nq/q1+4A6Qfu71OrdGtQsG9fB6stGxGzbj/rl3yU9Xa+C+zHY2tkQG6s/zys+Lv6dy2bw4F6kpKSydJlu2ZQvVxoAd3dXRo2ewldfd+fKlRusW7eYnj07/vvE/0u2Nta4ODvldjLeyNrGigQDc3ETExKxfMv3bdScEfhd8efQ7iMGPz+85yjqjAymrZqIp7cHVjaWNPm6EZ+3bwZoG1uNzdrW2nB+4hOxtMp5YT/vzMqDQqngx/6T+XHgFMwV5qzavZiiJYvoHb940xy2Hf2VKnUqsXzmaiKe61fejUFpY0lKvP59OSU+CcU7vhcAmCnN+Wb+ANKSUzn36996n3dbN5phf82iSK3S/DN3B/HPDa/j8CGkmdO2NIm69zVNkjZfknfsKFDUq4eyaVMSf/8dTXw8pKSQfPw4Fl9/jbJFCyRWVsjc3LCdOFG7YFAuNBhZ2ViRYOA9LjEh6Y3XU7HS2u+YQmnOpIHTmDxoOuYKc5buXECRku/WyPehsp47Ca+VT2Z5SSzfbUFMVYO6WHzemPid+9DEaZ87Zt5FkDk7keofyPMR44letBJF5fI4r1qIJI826Al51yc35zUnNjbaIbHx8fovthUrVmTv3r1s2LCBkydP4uOjnZ/g7OzMpEmTaNy4scnSJZFIkEh1W0WkUonB+XMSiQT1Owycl8llzOo1HZlcxuc9vmTqzpnM6D4Vv/M3KVTUlel7ZhP+OIx5/WeRGJtI7VZ16T97MClJKVz489xbz/8uXiYzp/YeqYEP0jNbKBsWL8DwhtphkVU9ndBoNCw54c/AenFZx9hbKljQtjrSzFWL3fJZ0XXDSQ7eeky7Su/WA/o2EokEqVS3fUcilRqe2/iOZaNUKujS9Rs2bdxJdF5YpMDA9y+nlaAlEgkadd7ZattQ+UhzKB/ttfP2tCuVSnr0+Jb167cTHZ3zS9ugAT24dv0WR48Zb7TCq7LKJKd5tO+QF8smdcg/ewzJl28SsXDdW483JpOVTfdvWf+rftmcOHmOVq27cuLEOZKTkwE4fPgkTs4OTJo4knXr3rCS6f9D2vJ57bqXSg1+3d723Gn+TVPKVytL10a9czwm+O5DxnYfz6g537HpuPa7GOAbyJo56/luxlCSE5M/LCOvpFHv+yZ503M05+/b1tW/cWT/CS6dyZ4jefH0Ffae20bPYV34of9knePXLPyVdUs28VmLuoyfN5p8DnasX6K7sNuH5MfQfTmn/Gg073ZfVlip6LLmOwqVLczmfguJCdGvaB9bsocTK/ZRqmkV2szqg6W9DSdW5LzOwXt5WUY53dfeYd0ARf362P74I2m+vsSvzh6iHrdgAaSmYjNqFJIxY9AkJZGwfTsShQKS/933620MXU9SqcRgfiQS3ng97Vizm2MHTnLlTPa0sctnrrL9zCa6De3ExAHTjJdwQ4mDN5TP279nqob1cJg2jpSrN4h+ZY5/5NS5aFJSSbujHQ6dev0maUHB5F+7BIuWTUjYvf9fJ1/4/+P/TeU1Lk7b4m9owSaAIkWKMHXqVACCg4M5c+YMGzduZNiwYezZs4fixQ2vjvtvtRv2P9qP6KATdv7gWYPDg5UWSp25qjnJSM/gxunrANw848uCw0tpM6gdfudv8kWvVkglEqZ1mkR85mp3N8/6YmFtQe9p/fD567xRFp6xVmp7XBNe6wlNzOxxtVLoz6OxyJyXW7eo7tCfWkXys+SEP4FhMXg6WAPaxZ+kr1SyyhWyx1ppRmCY8VqJx44byrgfdRcS2bvnT5yK6VeOrawsDPYqva5ho7rY2Fjz23YjvQz8S1WGf02V79rohAUd8MHWUX/+s5mF0mCPbG6ZMH4EEyeM1AnbtfsAxZz1W6ctLS2IiXl7+TRpUg8bG2u2btuT4zH29vn47LNa/PDjjPdP9DvKiNX2GL3ewyq11P7+eo/s62y7tsFhZG+SLt0gdMhkyJyP97GM/3EEEyZ8pxO2e/cBnA0MD7a0snyvstm2ba/eZ+HhL7IWgXrVX38dpXGjeuTP70RYWN5ZdTi3dR/RhZ4ju+mEHT9w0uBwRpWFioQ4w6vNOro4MnTyQJZN/ZnoF1HIZFJkMm0FRSaTIpVKsyqKl05doX2NThRw097fQx6HZvW8xv7Lhrw+33Wn7/c9dcKO7D+Ou6P+MFSVpYr4HPID8DDoMQ+DdEeQxMfG43vpZlav7Kuu+2jnoF45dw0bW2t6De/KxuVb9YYrv4+Gw9rQeLjukPebB31w9NJfGdzcUkly7Nvvy7YF7Om2bjSOXi5sHbyEgGPXDB4XfEm7ddH98/6obCxpMOQrTq3arzdc+UOoMzsPXu9hlai0PccaA50Lr7L45hus+vcn9fp1YsaP17mvaZKSiJ07l7hly5Dmz09GaCgkJ6Nq0YLUkJB/nfY36TGiK71eu56OHTiJm1c+vWO111PO+XwU9JhHet+/BG5eukXRUvo9/8b0snxePmdeelle6reskm7VsR12Q/uSctWXFyMn6JTP6ysMA6Te8EMdF495sSLkzfXXjUfs82pc/28qr35+fnh6emL52rCHxMREFi5cSNu2bSlRQjunwNPTE09PT7744gvq1avHmTNnTFZ5PbL1H64cvawTVq1ZdcrXq6jX0uriWYAn93IellmlcTUSYuO5fTH7JpGels7DgGDcirkD4FjImadBT7Mqri/5+9yiZsva2DjYEPPi31cA3fJZIpNIeBSle5N+FKW9RXkZqBy5Z86PTXvtIZmeof0bKOQy3PJZIpVAWrr+gzQ9Q43CzHgj4dev28bff+m+EH/xZRMaNa6rVzZeXp4EvGEf0Zeat2jIgwePuHbt5luP/Rj8tx7j4VHdlxjPZpVxq19O2wr7Sh5tPPMTddf0S/W/qzW/bOHgQd1hiq1bN6dpk/p65VO0SOE37vP6UsvPG3P//kOuXL2R4zHNmn2GXC5n127Dc+iNIf3xMzTpGZi56zZivfw9NSjnfQIdxw3EtlNr4v8+Sdi4uR+94grwy9otHPzztbJp1YwmBsqmSBHPdyqbz1s05v6Dh1w1UDZ169bAw70Qm7fs1glXKZWkp6fnuIXV/1d/bDnIuSO6cxnrNq9NtfpV9MrHtXDBHPelrFqvMtZ21oxbMIpxC3S3KFq0Yx4hj0NpX6MTzgWdqVK3Ev/sOULI4+y1JbzLeRMTFUvok3+3JdiezX9w+ojuqKHPmtelxmfV9PLj5un6xn1em7ZuRExUDD6ndJ/LCqWC6Ejt96h0hZIUcHPhyH7d+a3+1wNo+U1z7Oxt/9Xw4YtbjxJwVHfV7FJNq1CsXlm9/Dh45Cf83punAbiUcKPHhrGYKc1Z3202Dy7ormLrWr4I+VwduXnQRyf8yY0gKrWti4W9tVGGD2c8e4YmIwNZoUI64S9/T3+Y833NeuhQLNq0IfnYMWJmztS7r5nXrIkmLo60W7fICA4GQGJnh9TZmbQ7d/512t/kjy0HOHfkvE5Y3eZ1qG7weir0xn1eG7VqQGxULJdO666O/Or3z1TSn2ifO3JX3fIxc8ssnwc5p9vu+8FY/+9rEg8fJ2LSbJ3ykVhZYtGgLim3buufw0xOxhtGOQmCIf8v5ryGhoZy9OhRvvzyS73PlEolf/zxB9u3b9f7TKVSIZfLcXBwMFnaosIjuX/zns6P76lrWFhbUL5+xazjbOxtKFW9tM4KxK/7su9X9J0xEKksu1gtrC3wrlSCh7eDAXgW9ATXYm56C2cUr1ySxNgE4qPf3PL5rhRyGZXcHTgW+Eznxn0k4CnWSjPKFNRvkazs7ojKTMZffroP4pN3Q5BLJZQrZI+FuZyKbo4cDXxGanp2y7bPg3CS0jKo5Ga81YZDQ8O5du2mzs/Ro6exsbGmcZN6Wcc5ONpTu041jh0784azaVWpWgGfCx93yf43SQyL5vmNBzo/T07dxNxahVv97JUFlfbWFKxRgien8kalGyAkJIwrV2/o/Bw+fBIbG2uaNf0s6zhHR3vq1avB4SMncz5ZpmrVKnLu/OU3H1O1Io8fP8txMSdj0KSmkXzlJpaNauuEWzapS0ZMHCk3Aw3Gsx/eA9tOrYnesJuw72fkSsUVtGVz9eoNnZ/DR05hY2NN09fLpm4Njrxj2Zw/Z7hsGjaowy+/LKRIEc+sMIlEQps2LfHxuUrqa9tv/H8XERZB4I07Oj+XTl7G0tqSap9VzTrOzt6W8jXK66xA/Kqzh8/Tu8UAnZ+5Y7T7Cc8ds5Cx3ccDkM/RjnELRlGpVoWsuPZO+Wj8VUNO/332X+fnRVgEt30DdX4unLyElbUlNRtUy86Pgx2VapbXWYH4de26f8XYWSORv7KavZOLI+WrluHKOe3zt06TWkxfMZH8hZx14tZsUJ0XYRFEvvh325zFhUfz9OYDnZ+7p2+itLagWP3sFXkt7a0pXL0kd0/nfF+2LWBPz00/oNFo+LntZL2KK0CJhhX53+LB2BbUfdfxrl+e2PAoEoy1cnxqKmm+vijq1dMJVtavjzoujrTbhreGserTB4s2bUj47Tdipk41eF+zaNUKqwEDdMIs27UDtZrU8+f1jjemF2ERBNy4o/NzMfN6qv7a9VShRnku5nA9AbTp1prvZw7X+f45ujhStmoZrp33NWk+SE0j5doNVA3q6gSrGtZDHRtHqp/hdVFsB/XC+n9fE7dlFxE/TNcvn7R08o0Zhk23b3XPW782UqWSlKsmzlceoM7ln0/NJ9fzmpiYyPPn2uFhycnJBAYGsmjRIlxdXenRo4fe8VKplO+//57x48ej0Who27Yt9vb2PHr0iHXr1uHi4kLz5s0/ah5uX/Tn1vmbDFv8HZtmbiA+Ko5vhn9LQmwChzdnL7DgWswNubmcYL8HAOxavIPxmyYxcuUY/tn8NyorFV8PbItSpWDHQu2cr/2/7KPu1/WZuG0ae5btJDEukerNa1KndT02TFtLRvqHD3V6XZ/aJei39Qyj9l7kq/Ie+D6JZMOFuwxrWBqlmYz4lDTuv4jD1c4Se0sFFuZyBtYryfyjt7BRmtGoeEF8n0ay/vwdOlYtgr2lAoChn5Wi95YzDN5xnq41ihKRkMLiY36ULZiP+sVMu6rgubOXOHXyPL+sXciE8bOIjIxm3I/DiImJZd0v2fsgFi9RFIXCXGd/V6lUSvHiRdi1M2/P7QjxCeTpOX8aLR3IhZ+2kxwdT5URX5MSm4jf5uzte/IVK4jU3IwIv5xbYz+202d8OHHiHBs3LGXsuJ+IiIxi4oSRREfHsmp19jy0kiWLoVCY6+zvKpVKKVmiGDt2vHlId9kyJbl927Qt+QBRq7ZS4JdZ5J//I3F7D6GoUAq7Hu2IXLgWTUoqEksLzIu4k/Y4BHVUDObFvbDr2Z7kW4HEHzqFopzu6pSpQY/QJJh+Re6cnMksmw2/LmXcDz8RGRHFhAnfER0dy+o12XseliyRWTa+umVTokRRduz43eC5V63eSO/endi7Zz1Tp84nMSmJ/v27U7p0cZo0bW/qrH0SfH1ucvXcdSYuHcfKn9YQExVDz5HdiI+N5/dN2fcsz2IemJmbcdfvHrFRscS+tmK1ylI7BPRR0GPuB2ifTYE37nDj4k1GzhzGiumryUjPoM+YnmSkZ7B+geH9Vv+taxd8uXz2KtOWTWTJ9JXERMXQd2RP4mLi2b3x96zjCnt7Ym5uRuAtbe//Lwt+Zem2+cxd+xO/rd+NjZ0NfUf2JDY6jk0rtY3cuzb8ztedv2TxpjmsWfArSYlJfPFNC+o0rsmPA6cYfc9ngOCLAQSd9+N/iwbx18ytJEbH03h4W5JjE/B55b7sXLQQMoWckMz78peTu2HtZMveH35BYa3CrWL20OeUuCTC7z3FZ/MRqnVsSPd1ozi6ZC+piclUaluPEg0rsn3oMqPmJ37TJvLNn4/t5Mkk/fknZmXKYPHtt8SvWgWpqUgsLJB7epL+9CmamBjkRYti0aEDaQEBpJw4gVmpUjrnSw8ORpOYSOLu3eSbNw+rwYNJOXsW80qVsOzcmYQtW8gw8bBhQ3x9bnD13DUmLf2BFT+tJiYq9q3XE8D6RRtZsGU2M36Zwu71v2NjZ03Pkd2Ii4lj2887TJ7u2HWbcVo+F4eZE0nY/zfm5Uph3aU9MUvXZD13zAp7kP7kGeroGMy8i2Dd9VtS/QNIPHIC8zIldc6X9uAhmoREYjftwLZ3FzIio0g+fwmzol7Y9u1K0ukLpOTBvbmFvE2iMcVdNpc0bNiQp0+ze0MsLCxwcXGhadOm9OzZE1tbWwDGjh3L06dP2bQp+2X2+PHjbNy4EX9/fxISEnB0dKRRo0YMGjQIe/sPX8r7G4/WHxTP0saSbhN6UbWZdlGigCu32TB1Hc/uZ+dv8vbpOLk6M6hO36ywMrXL8c2wb/EsVRiNWo3f+VtsnbuJp68MKyroVYiOY7pQpmZZJFIpT+4+Zt+qPVz8++1L4m+cWvKtx7zqWOAzVp66TXBkPM7WSv5X2Yuu1bVbEVx6+Jw+W84w5YtKtC7nkRXnd9+HbPK5y6OoBJyslLSt6EmPmt46c1yvP4lg2Ql/bj2LQmkmo4F3AUY0KoON0vy90pd/4G/vdTxoVxyeMWs8Lb9oglQqxefCFcaOmca9uw+yjjn411bcPVwpWyq7hdnRyYH7wZcYPnQ869a+fQGZlavmUKduDZ1zvM0c+1rvl5kcmNtaUGtiZwo3rQxSCWGX73B2yhZi7me/BLT67UesXR3ZUmuEwXN0OreQZxduc/w7/X0f38XgsA/bcsLOzpZ5cyfRulUzpFIp585dYuSoKdy5E5R1zNHDO/HwcKOod/Z+j05ODoQ8vcHAQWNZvSbnBVdu+B7n5s3bdOo88L3SFeitvzfr21g2qkW+QV0w93QlPSyCmO37idmgHRqrrFqOQuvnEv7jPOL2HSbfoK7YD+iU47me9hhF8iXdIbfygvnx+Gdj1jneR8m7fm8/6DV2drbMnTORVi/L5vxlRo2azJ0797OOOfzPTjw8XPEunr03o5OTA0+f+DJo8FjWvFLRfVXRooWZPm0stWtXw9raisuXfZk0eQ5nz158p7QlPD313vn5EBev3qDnkDGsWzqbapX097U0lobl+7x3HCtbK4ZMGkDdZrWRSCXcvOTH0ikreByU/fxYsnM+Lm4utK9h+LtWoWZ5lu5awJB233H9lZ6ifI75GDJ5AFUzh1JeO3edVTN/4cmDt49gSFJ/WM+5ta0VIyYP4bPmdZFKJfheusmCSUt15rSu2r2EAm4utKqW3chRvV4Ven/XnWIli6BWa7hw4iKLp68g7Gn2HpWunoUY/EM/KtYoj6WVJYE377Bu8UbOHnv7c7SJIuetet5EaWNJywmdKd1U+zd8eOUOB6Zt4sUr9+U+28eTz9WJOXWGITOTMcV/PTIzw/0U9y/4s+bb6YB2+HGz0d/iWa0ESmsVT2894MSyfQSeuP7WdA33fPbWY16lqFMHyx49kLu5kfHiBUm//07ib9pnsVmFCtgvWkTMrFkk//03lj16YNWtW47nihw+nLTr2jQqGzbEsksXZAUKkBEWRuLvv5O0V3+O/Nu0uWucQYnWtlYMmTSQus1qI828npZMWaEzp3XpzgUUcHOhXY3sVdGr1q1MjxFdKFLSC7Vaw8WTl1gxfTVhzz5sj9TtBd9vvqXqs9rY9O2OmYcrGc9fEL/zD+K27ARAUak8zqsWEDFlDokHDmHTrzu2vbvkeK7wft9pe1YlEqzatcKq7ZfIChVEHRNL4qFjxK7+FU3Ku1/fbpeOvv2gPOhw/v/l6v/fJMz0DR8f0ydVec2LPrTymle9b+U1r/uQymteZqzKa17woZXXvOpDKq952YdUXvOyj1V5/Vg+pPKaV31o5TWv+tDKa171vpXXvM5Ylde84n0rr3nZf7Xy+k/+b99+kAk1DdOfGvlf9mldoYIgCIIgCIIgCMIn6ZOb8yoIgiAIgiAIgpAXfIqLJuUm0fMqCIIgCIIgCIIg5Hmi8ioIgiAIgiAIgiDkeWLYsCAIgiAIgiAIggmIYcPGJXpeBUEQBEEQBEEQhDxP9LwKgiAIgiAIgiCYgIZPZ7uivED0vAqCIAiCIAiCIAh5nqi8CoIgCIIgCIIgCFnUajVLliyhbt26lC9fnp49e/Lw4cMcj4+KimLkyJFUrVqVqlWrMmHCBBITE42eLlF5FQRBEARBEARBMAG1JHd/PtSKFSvYvn0706dPZ8eOHUgkEvr06UNqaqrB44cOHcrjx4/59ddfWbJkCWfPnmXKlCkfnoAciMqrIAiCIAiCIAiCAEBqairr1q1jyJAh1K9fnxIlSrBw4ULCwsI4fPiw3vHXrl3j4sWLzJw5k9KlS1OzZk2mTp3Kvn37CAsLM2raROVVEARBEARBEATBBNRIcvXnQwQEBJCQkECNGjWywmxsbChVqhSXLl3SO/7y5cs4OTlRpEiRrLBq1aohkUi4cuXKB6UhJ2K1YUEQBEEQBEEQhE9Qo0aN3vj50aNH9cJCQ0MBKFCggE64s7MzISEheseHhYXpHWtubo6dnZ3B4/8N0fMqCIIgCIIgCIIgAJCUlARoK6CvUigUpKSkGDz+9WPfdPy/IXpeBUEQBEEQBEEQTECTy/+/oZ7Vt1EqlYB27uvLfwOkpKSgUqkMHm9oIaeUlBQsLCze+/9/E9HzKgiCIAiCIAiCIADZw4XDw8N1wsPDw3FxcdE73sXFRe/Y1NRUoqOjyZ8/v1HTJiqvgiAIgiAIgiAIJqDO5Z8PUaJECaysrPDx8ckKi42Nxd/fnypVqugdX7VqVUJDQ3X2gX0Zt1KlSh+YCsPEsGET2/CjV24nwaiGTgrK7SQYVeis5rmdBKP6+6fo3E6C0cQu/Dq3k2BUM+ZG5nYSjCqiX4XcToJRNSzfJ7eTYFTHfNfkdhKMRv3icW4nwahSV8zN7SQYVbu9+kMI/8sOdf608tNje0ZuJ8FoduZ2Av4fMTc3p3PnzsybNw97e3sKFSrE3LlzcXFxoUmTJmRkZBAZGYm1tTVKpZLy5ctTqVIlRowYweTJk0lMTGTSpEl89dVXoudVEARBEARBEARBMJ2hQ4fSrl07xo8fT4cOHZDJZKxduxZzc3NCQkKoU6cOf/75JwASiYRly5bh6upKt27dGD58OPXq1WPy5MlGT5foeRUEQRAEQRAEQTABteTD9lrNbTKZjFGjRjFq1Ci9z1xdXQkMDNQJc3BwYMmSJSZPl+h5FQRBEARBEARBEPI80fMqCIIgCIIgCIJgArm9Vc6nRvS8CoIgCIIgCIIgCHmeqLwKgiAIgiAIgiAIeZ4YNiwIgiAIgiAIgmACH7rXqmCY6HkVBEEQBEEQBEEQ8jzR8yoIgiAIgiAIgmAC6v/mTjl5luh5FQRBEARBEARBEPI8UXkVBEEQBEEQBEEQ8jwxbFgQBEEQBEEQBMEE1Ihxw8Ykel4FQRAEQRAEQRCEPE/0vAqCIAiCIAiCIJiAJrcT8IkRPa+CIAiCIAiCIAhCnvef63nt0qULFy9e1AkzMzPD2dmZRo0aMXLkSJRKZdZnarWahg0b8uLFC06ePImDg4NO3KVLl7Js2bKs36VSKdbW1pQpU4aBAwdSpUoV02bIgLMPnrP8bCAPIuKxU5nTrrw7PasVQSLJecz86fvhrDp/l3sv4rBVmtOomAtD6nqjMssu4j03HrHlajBPYxJxsVbRvoI7HSp6vvG8xqCwVPLND12o0LQaSkslQVfusH3KekLuPXljvGLVSvL19x1wK+VJcmIyVw6e5/f520mOTzJ4fMPuLWjS+wvG1Rlkimzk6OzDCJZfCOJBVIK2vEoXomdljzeXV/ALVl18wL2IeGyVZjQq4sSQmkVRmck+Ysq15JZKykzsSMEWVZBbKYm4dJcbEzYSd+fpG+Mpne0oO6UzzvXKIjWXEX7yJr7jN5IcGmXw+CK9mlG0/+ccqjrMFNkA4GzwC5ZfuMeDyMyyKONKzypv/o6ffvCcVRfvc+9FZlkUdWZIrWIGyyI+JZ32W8/Tv3oRWpUqaLJ8vMrcUknzHzpSsmkVFJZKHl25y8EpG3l+783l41LKgyaj/odrOS8kUgnPbj7g0OzthPgFGzy+Rvdm1O79OfPrmKZ8ZCUroWjZBamLG5r4WNLO/kXq4Z3vFlkqxWLEPDSpKSQtHaf7kbMritY9kBUrCxnppN/zI+X3X9BEhJkgF7pUlioGTuhH3Wa1sbBSceuyP0smLif47sN3PkedprWYuX4aQ9p9x/Xzvlnhji6ODBzfl+qfVUUml+F39Ta/zFnH7euBpsjKBwsJe06brgNYPHMi1SqVy+3kZDlz9RbLNu/j/uNn5LO15pvm9enVtnmO94LUtDRWbtvPgRM+RMfFU7iQC92+akrLz6obPD4+MYl2w6Yy4Nsvad2olimzAnx614/KUkWfH3tRs1lNLKws8L/sz8rJq3h099E7n6Nm0xpMXjuJUd+M5saFmwaPcSrgyKojP7Pnl71sXrjFWMnXIfOuiHmzDkid3dAkxJJ24RBpJ/a8W2SpFNXAWZCaTNLqiTofySs3wKxea6QOLmjioki/eoLUo7tAnWGCXOhSWqro8kN3qjWtjtJKxZ0rAayf8gtP7j5+Y7yS1UrRYVRnPEt5kZyYxPmD59g+bzNJr7y3WeezpuOYrlRqUBmFSkHQzSA2zVhPsN8DU2dL+ET8J3teW7RowZkzZ7J+Dh48SK9evdi2bRtz5szROfbcuXNER0fj4ODA7t27DZ7PxcUl61zHjx9n7dq1ODs7061bN06fPv0xspTl+tMohv9+GS97K+a1qswXpQqx/Mwd1voE5RjnZFAYw3+/TBEHK5Z8XYUe1bz4w+8JU//Jvpn/dv0h0w7fop6XM4u/qsKXpQux4EQA6y7mfF5j6btkOBWbVWfP7C2sHbEUa0cbRm6bhIWtVY5x3Ep7MnzjeBJjE1g5YB5752ylyhe16L9ipMHjq7euwzc/djVVFnJ0PSSa4Qd98bK3ZF6LcnxR3IXlF4JYeyU4xzgnHzxn+EFfithbsuTL8vSo7MEft0OYeuz2x0v4K6r+PJiCLaty66ftXB68EoWjDXV3/YiZnWWOcSQyKbW2jiZfBS+uj13HtdHryFehCHV2jEMi16/0ubWpTdnJnUyZDW1ZHLiOVz4r5n1eni9KFGD5+XusvZzzA/Hk/ecMP3CdIvZWLGlVkR5VPPnD/xlTj/rrHRuTnMaQP64REpdsymzoab9kMKWaVeWf2dvZNWIllo429Nz2IyrbnMvH3t2Z3jsmYK4yZ++Y1ewe+TNSMxl9dk3C0auA3vHlW9emxY+mKx9p4RKo+kxAHfaYpLUzSLt0DPOWXTBv2v6d4ps3/gaZh7deuMTOEYsRc5BY2ZC0YS7JO5Yjc3HDYuA0MDM3djb0TF4xnvot6rBq5i9MHzqLfI52LPptHtZ21u8U3yafDd/PHqEXbmltyfK9i6hStxJr5q5nfJ8pPA1+ytJdCylVsYSxs/HBnoWG0Xf4D8TFJ+R2UnRcvx3E0J+W4+XmwoJxA/jisxos3fw7a3b+mWOc0XPXsOH3f/jis+os/XEQLepVY+rKzWzef1Tv2Ji4BAZNXcKz8AhTZiPLp3j9jFs2htotarNu1nrmDJuLnaMds7fPxNou5/eCV1nbWTN05pC3HvfdvBFY2uR8r/y3pB7FUXYbizr8Ccmb5pB+9QTmzTpi1qDtO8U3+6wNMrei+uG1W6JsPwRN+BOSN80m9Z/tyCvWR9npe2NnwaDhS0dSvXkNtszeyNIRC7FxsGXS1mlYveG9zbN0YcZvmkxCbALz+s9i6+xN1GpZm5Erx+gcN2r1OKo1rc72+VtYNGQeMpmUKTtm4OzmbOps5Rq1JHd/PjX/uZ5XAKVSiZOTk06Yh4cHt27d4uDBg0ycmN16tXv3bipXroyHhwc7duygd+/eSKW6dXaZTKZzPhcXF2bNmkV0dDSTJ0/m0KFDyOUf50+1+vxdijvbMP3zCgDULuxEulrD+otBdK5cGOVrvUEajYZ5x2/TqJgLU5qXB6CauyNqjYZtVx+SlJaBUi5l/cUgmnoXYFg97YtPdQ9HHkYlsP3aQ3pV179xGotXJW/KNarM4u4zuHXiGgB3LwUw8/RyGnRpysFlhlsnm/T6kriIWFYOmE9GWnpWeI95g8jvVZCw+88AsHaw4auRHajXsTHxUXEmy0dOVl98QHFHa6Y3KQ1AbQ8HbXldeUjnCu4o5QbK6/RdGhVxZkrjUgBUc7XXlpfvE5LSMj5q76t95WIUaFKJs53mEHb0OgAvfAJofnExXt2bELjod4PxCn1ZHbsynhyuP4q4QG0PYIzfQxqfmI1r6xo83n0WAIWjDaXGtKdwl4akRJq2fFb73Ke4kzXTm5UBoLano7YsLgfTuaKH4bI4FUijIvmZkll+1dzsUWtg2/VHOmVxPCicOacCSUozfYv3q9wqFaNEo0ps7D6HOyeuAxB8KYCRpxdTvUsTTiz73WC8mj2ak56cxsYec0lLSgHg/jk/vj+7hBrdmnFg0q8AWDrY0Hhke6p2bEiiCa8fRfOOqJ8+IHnTAgAybl8FmRzzxu1IPf47pKXmGFdasDDmTb9BHROpf97PO6FJTiJx2XhI0+ZTHRGGqu8EZG7FyLjvZ5L8AJSuXIpajWswqss4LhzTjga64XOTHRe28HW3Vmxc/PZenu9mDCU9PV0vvOW3zSnoXoABrYdw67K2IeXy6SvY2tsyeNIABn5lutEL70KtVrPvryPMW/ZLrqYjJz9v30+Jwm7MGNELgDqVypCekcG63X/TtXUTlArditnt+4845nOdIZ2/os83nwNQo0IpVEoFCzfsplWDmthYWQBw3Oc6s9ZsJzE55aPl51O7fkpWKkH1xtUZ33Uil45fAuDWRT82nFvPF12/YNuS7W89x5AZg8hIf/P9+IuuLXEr6maUNOfEvHF71CHBpOxYAkDGnWvasmnQhrTT+yH9DWVTwBPzBm1Rx742Wkkixbxxe9LvXCd5y7ys4IynQViOXIKsWHky7vpiKt6VilO5UVVmdJ/KteNXAAi46M/yM6tp2rUFe5Ya7vH/sndrYiNimd9/NumvvLcNmj+Mgl6FeHb/KQUKF6RktdKsHL2U479pG4buXA3klysbqdemAbsW7zBZvoRPx3+y5zUnCoVCp2IaExPDkSNHqF27Ns2bN+fJkyfv1ZParVs3njx5wvXr102QWn2p6RlcfhJJw6IuOuGNvV1ITMvg2lP9h09geCxPYhL5tqKnTnjHSoXZ3/uzrJfv5W2rMay+bou9mVRKaobauJl4Tel65UlOSMb/dPaNNj4yljs+/pRpUCnHeHvnbGFpr5k6Fdf0VO2/5ebZDQmfD2pDqXrlWNFvLjeOXjFBDnKWmqHm8tMoGhbRbUhpXMRZW17PovXiBL6I50lsEt+Wc9UJ71jenf1da330YcP5G5QjPSGZ8BM3ssJSI+J4cf42Lo0qvDFe3N1nWRVXgLg7T4m7+0wnXvGhrXH+rCwXei4k9PBVU2RBm+Z0tfbaKaLbctu4aGZZPI3WixP4PE5bFuV1X246VnBnf/c6WWURl5LG93/6UqVQPpa3rmiyPBhSrF45UhKSuXc6u3wSI+MI9rmNd4MKOcZ7HvSMM2sOZlVcAdKSU4kJjcTeI/tvVH9Qa4rWK8vWfgsJOGqi8pHLkRUrS7rvOZ3g9OtnkSgtkBUpnXNcqQxl5xGkntyPOlx/mLS8XE3SLhzOevEGUD++R8KEbiatuAJUq1+FxIQkLp28nBUWHRmD7wVfajQ0PNT0VQ1bfUbVupVZOX213mceRd2JjYrNqri+dP38DcpWLfPG3o+P4c69B0ybt4zWLRozc8LH6Ql6V6lpaVy6dYdGNXSv1Sa1KpOYnMJV/7t6ce4/DgGgflXdYc9VyniTlJzCpZvaodqx8YmMmLWSKmWK8/Okj9SA8AleP5XrVyYpIYkrp7Kf2TGRMdy4cJNqDaq+NX79L+tRqW4lfpmxNsdjXNxd6DWuJ4vGLDZKmg2SyZF5lSH91gWd4PSb55EoVMgKl8w5rlSGov0Q0s4eRPNct2wkVrZILKzJuH1ZJ1wT/gRNfAyyEpWNlgVDyterSHJCEr6nrmWFxUbG4u/jR6UGOU+l2zJ7EzN7TtOpuL7898v3NjNzMwAS4xKzjkmKTyItJRXrfDZGzUdeos7ln0/NJ1F5TU9P58SJE+zbt4/WrVtnhR84cIDU1FSaNm1KlSpVcHZ2Zvv2t7fovVS8eHEAAgICjJ5mQ57EJJGWocbDXneIi1vm8M2HUfpDswKfxwKgkEsZuvcSNRb/Tb1l/zDrqB8pma2SEokELwcrCtqo0Gg0xCSlsufGYw74P6V9BQ+T5qlAUVdePApD/VolOfxhKPkL6w9ffCkqNJKnAdq5LwoLJSVrl+Xr0R244+OfFQ5wcss/jP9sKNcOXczpVCbzJCaJNLUGDzsLnXA3WxUAD6MT9eIEPtf2bilkMobuv06Nlcept+Yks04GZpXXx2RdrCAJD8PRvFY+8Q/CsDIwvDQ7XiHi74fohcc/CMWqSHa8+xuP8k/N73j25yXjJdqAJ7GJOZSF9veH0QaunReZZSGXMvSPa9RYfpR6q44z60SATlko5TJ2d67FtKZlyKcy/VDUVzkVLUjUo3C96yfiYRgOb7h+Lm4+wpnVB3TCHAq7kN/blbDA7LnmF7ccZeFn3+F/yHTlI3VwQSI303t5Vj/Xjp6QOhXKMa55iw4gl5P6l34vpsQ+PxILK9SRYSi+6Y/VzK1Yzd+Dqu9EJPmcDJzNuDyKuRPyMISM18rmyYNnuHm55hBLK59jPkb8NJQlk5YTEa7fKBkdGYOFtaVeJbWQp3aedUG3nMv+Yyjg4syfO9YyemhfnTUm8oInoS9IS0/Ho1B+nXD3AtrvxMNn+nM57W21w7xfHwb8OCQcgKfhLwBQKczZu3QKPw3vgZ3Nx2lA+BSvH/diboQ8CtW7rz0LfkYhr5zzA2DnaMeg6QNZOelnIsP0rx3QvvN8v+A7Th04zeUTpmvUljjk15ZNZlm8pI7QPhuljjmvi2DeuD0SmZzUw/rvpJrkBDQZ6UjyvTaMVmUJKiuk9vn14hiTa1E3wgy8t4UGh1CgcM55igyN4FGAdr6/0kJJ2drl6TCqM/4+flnhjwIfcuP0db4Z9j/cvN2xsrWi6/iemKsUnN3/cafpCf9d/8lhw/v37+fQoUNZvycnJ1OwYEF69epF//79s8J3795NhQoVcHXVvkh8/vnnbNq0iZCQEAoUePvD39pa+0CLi/s4w1HjUtIAsDTXLRYLc20PUEKq/vCyqETtkJSR+67SomRBulT2wi80mp/P3yUqMZXZX+q2Pvs+i6LHdm0rYcn8NnSoaNrKq4WNhc5E/ZdS4pNQWVkYiKFv4bV1mCnMiIuMZedPG3U+Cw16lkMs04tL0ZZHzuWlXxmNStaW8ci/btDC24UuFd3xC4/jZ5/7RCWlMrt5WROnWpeZjSVpcfrlkx6fhJm16g3xLIi/H2ogXjJyq+x48fc+Tvl8UFkkZZbFQV9aFC9Al4oe+IXH8vOFIG1ZtND2wpjJpHjmM92cqTdR2liSYuD6SY1PQmGVc/m8zkxpTtv5A0hLTuXCr39nhb/4GNePSvuSr0l+rTEnRZsvidJwPqTuxTBv2IbExWPBwNBaiZUtAIpW3cl4eIekDXORWNmi+LIbFkNmkDBrMKSabmintY0VCQbmeiYmJGL5lnvbqDkj8Lviz6HdR6hQs7ze54f3HOXbft8wbdVEFk9czovQF9RsVIPP2zcDtC+GucnWxhpbm3eb1/uxxSVov2dWKt2/kUXm7/GJ+nPWq5T2xtXFkVlrtqNUmFOmmCeBD56waOMepFIJSZlDhM3M5BR2ddGLb1Kf4PVjaWOl0/P2UlJCEhZvuXaGzx7K7SsBHN1zjHI1DD8vv+79FS7uBZjYY7IxkpsjiTLzuZDy2j365e9Kw3mRuhbFrF5rkn4eDxn6ZUNaKum+ZzGr1QJ12CPS/XyQWNqiaNUL1OlgrjBiLvRZ2FiSFJ9T+bzbc2fd9c2YKcyIjYxl4/R1Op+tnbiaHzZMZMHhpYB2GsKK75dw58rH6SgS/vv+kz2vDRs25Pfff2fPnj1MmjQJGxsbatWqRf/+/bPmpgYGBuLn50eLFi2y4rVs2ZKMjAx+++23d/p/XlZaX1ZiTU2j0e4EldPcaomBT9LU2jgNiuVnWL0SVHV3oHu1IvStUYx/7oQQHBmvc3xBWwvWtK/OjM/LE5+STqfNZ4lIMM4DSiKRIJVJdX4kEiloDOxwJZGg0bx9MINMLmNZ71ks7j6DR7fuM3rnVIrXfMMwqY9Iw1vKy8AHaZktmQ28nBhWqyhVXe3pXsmDvtUK88+9cIIN9K4bjUSCRCbV+UEqybl81DmXj0Sac7mi/vg7mr1MSk6LCr+xLIo4M6x2Maq62dO9sid9q3vxz90w05aFwTQaun4kWfeF1w5+p+sHQGGlosv6URQqW5idw5YTE2K4t8JU3rqauaH8yc20wx1P7EP96I7h82be6zVx0SSvnUFGwDXSL58gaf0spI4FMKvS4N8mPfv/kkiQyaQ6PxKpNIdLQIL6DddA82+aUr5aWeaOWZjjMcF3HzK2+3gKeRZi0/F1/HX7D9r3acuaOesBSDZQARO01G+5GUgNhJuZyfl58nBcHO3pO3EhtToMY/Tc1QzqqB3JpVKatrLwJv/168fQfU1bBvrplkgkaN5w7TRu15gy1cqweNySHI9x9SpEt1FdWTR6scEKslFJMl+hDZUBgKF7tNwMZfshpJ05gPrJvRxPnbJ3FenXTqJoOxCryZuwGDaPjEeBqB/fM2qjgsHykRp+7rzt3vaSTC5jVq/pzOg+lfs3g5i6cyala2obGgoVdeWn3+dkLeo0teNEjv92lP6zB1Pjc9Ov2p1bNLn886n5T/a8Wlpa4uGh7TEsXLgwLi4u9OjRA5lMxuTJkwGyVhaePXu23grEu3btYtCgQW9dhMnPTzvno1SpUkbOgWHWCu1cgNd7WBMze42sFPrptczsWarnpTu8pHZhJ5aeCSQwPBZP++zhTc5WSpyttC3QZQrY0XrtSfbefEzvGv9+0aYvhrWj1XDd1Q8vHzxPfgPDTxWWSpJi3/5gyUjPwD9zvt/tszeZ8s8CPh/YhsDzpp3P9i6sM3v5ciwv8zeUl6ejTnhtdweWng8i8EW8yXr5So5sQ8nvdVdAfLLfB4WXfk+C3FJpsEf2pbTYBOQGembllgrSTP3CYIC14mVZ6PawvrkstGH1PHWHyNX2cGTpuXsEPo/7qD2uDYa1oeFw3fK5ddAHBwPlY26pJCU25/J5ybaAPV3WjcbBy4Xtg5cQeOzaW+MYmyZJ2wggeb0XQqH9/miS9RsJFC27gERC6qHt8HIdg5fv8FIpqNVokrX5T/e/ovPyqA4ORJMYj9TVy2h56D6iCz1HdtMJO37gpMHhwSoLFQlxhhs+HF0cGTp5IMum/kz0i6isijCATCZFKpWizmw0unTqCu1rdKKAm7b8Qx6HZvW8xkbHGi1vnxprS+33LOG1Cn5ikvZ3K0vDPUfuBZz5deYoIqJjiYlLwL2gM6EvolCrNdha5c7IC/jvXz+dhneky3eddcJOHTiNq6Od3rFKC2WO146DiwP9J/Vl9fRfiH4RnVnR0j5PpTJZ1non3y/8ntMHznD19FWksuz+GalUWzF7fSjsv/Lyb59j2eg/C82bddSWzdGdr5SN5GUi4WWjcWoyKbtWkPLHOqT5nFBHhkNaCmZVGqEOMrwt0IdoN+x/tB/RQSfs/MGzBocHKy2U79QgkJGewY3T1wG4ecaXBYeX0mZQO/zO3+SLXq2QSiRM6zSJ+GhtB9HNs75YWFvQe1o/fP46b7jBVhBe8Z+svL6uRo0a9OjRg7Vr19KwYUNq1qzJ/v37qVOnDmPG6C7R/ddff7FixQqOHTtG06ZN33jeLVu24ObmRoUKFUyY+myudhbIJBIevzZX8nHmfD0vB/05Ni/nw76+8FJ65g1QIZeRkJrOyaAwyrjY4f7Ky7ibnSU2SjNC31BJeR+nth7RWzSpYtNqlK5XXq8HydnDhWdv2Oe1fOMqJMYmcPdi9vYxGWnpPAl4SEFv064e+K5cbVXa8orR/fu9/N3LXv+F5+UcTP3y0v5tFHLTDYZ4sOkoIa8tmlSweRXyf1ZW+/B8pXysCucn7k7O5RN3LwS7sp564VaFXYi6Zvrtl16XVRavXzsx2t8NloWd9gUj52vn4w5MubT1qN6iSaWaVqFovbJ614+DR37C37JPcv4SbnTbMBYzpTkbu83mwYXc2YpJ/SIETUYGEkfdRiypk/blSB2qv2+gvEJtpA75sZ6nv72Z9aI/SNq8kPTrZ9GoM5DIzfT/U5nsjSuwvq8/thzk3BHdRVnqNq9NtfpV9MrGtXDBHPd5rVqvMtZ21oxbMIpxC0bpfLZoxzxCHofSvkYnnAs6U6VuJf7Zc4SQx9nD873LeRMTFUvoE9PvYftf5ebihEwq5VHmfNWXHoU8B6CIgfnCySmpHDl/lQoli+Ka3xEHO+3CMf73tOVYsoi7iVOds//69fPn1r/wOaq7JkWtZjWpXL+y3rVT0LNgjvu8VqpbEWs7a0bOG8HIebrbS83ePpPQx2GMbj+akpVKULJSCZp801jnmE7DO9JpeEe61uxG2BPd78aHUkeEosnIQOrgwqvNplIHbVmpwwyUTZmaSO2dsZq+Te8zq5m7SP5tKelXjiMrURlNUgLqhwFZ55FY2iKxdUD99L5R0g9wZOs/XDmquzBUtWbVKV+vol75uHgW4Mm9nPd5rdK4Ggmx8dy+mL3QXHpaOg8DgnErpr2GHAs58zToaVbF9SV/n1vUbFkbGwcbYl7EGCNrecqnuF1NbvokKq8Aw4YN4+jRo0yaNIlhw4YRGRlJjx498PbW3dusYMGCbNq0iW3btmVVXjMyMnj+XPtgU6vVhIWFsWPHDk6fPs2qVav0ttYxFYVcRiVXe47eDaVrlcJZw4WO3AnFWiGnjIudXpzKrvaozGT8HfCM+kWyJ/GfCApHLpVQrqAdUomEKYdu8kWpQkxomj1HxC80mpjkNLydjLPCW0x4FDHhuku+m6sUtBzSltL1y3Mrc6sPK3sbvKuX4s/lhvfdBWja50us7G2Y0nxkVkupytqCIpW8uXspb8yLUMhlVCpox9GgcLpWdM8ur6BwbXnl1/+7Vi5opy2vO2HUL5zd43fiwXNtebnYmiy9yWHRJIdF64TJVApKjPia/A3KEXZMuyK0uYM1jjVLEpDDNjkA4Sdv4tamFtbehYi7o11IxNq7ENbFChKwcK+pspAjhVxGpUKZZVHJI7ss7mWWhYG/a+WC+TLLIpT6Xq+Uxf2XZWH3sZIPQFx4NHHh0Tph5ioFnw35mqL1y3H3hLZ8LOyt8axekpPLf8/xXLYF7Omx6QfUGRmsbjuZ5/f0Vxr9aNLTyAi6hVn5WqQdy94aS16hNprEeDIe6g9rTFo9FV57qVZ+OxiA5O3LUEeEQWoyGUH+yMvXJOXAhqx5fTLv8kgUKjKCjDc6IyIsgogw3cV8lCoF3YZ1ptpnVfE5rn05t7O3pXyN8mxaYnibnLOHz9O7xQCdsOLlvBk1ewRzxyzk1mVtmvM52jFuwShehL7gYuZqxvZO+Wj8VUNO/33WaPn6FCnMzahUuhhHL1yj+9dNs+4Fh89dwdrSgjLFCuvFMZPLmbl6G+2b12dY1zYAZGSo2XbwGO4FnCnqnvMiNSb3H79+IsMi9RZWUqgUdBzagcr1K3P5hPb7bWtvS7kaZdm21PCimhcO+zC45VCdsGJlizJs1lAWj12C/5XbRIRF6h0DsOzgEv7c8hd/bv2LiBwWefog6WlkPPBHXqYGaaf2ZQXLy9ZEkxivHeL7muQNM0CmWzaKNtq1WlL2/Iw6StswZVajGRILa5JWjMs6zqzOF6BRkx5gvEWoosIjiQp/vXzMaTukPeXrV+T6CW2Dqo29DaWql2b3MsPb5AB82fcrbOxtGNlsaNZ7m4W1Bd6VShCQWaF9FvSEBu0bY2VrRXxM9rS24pVLkhibQHx0vMFzC8KrPpnKq0KhYNq0aXTt2pW///4bT09PateurXeclZUV7du3Z926dTx8qG1VDQ0NpU6dOgDI5XIcHR0pX74827Zto1y5cnrnMKXeNYrQf+dFRh+4Rusyrvg+i2bDpfsMq1cCpZmM+JQ07kfE42pngb2FAgtzOQNqFWPByQBsFGY0LOaC77Mofr0YRIeKnthbaOfqdK/qxZoL97BVmVHdXbvH66rzd/F2sqZ1mTevjPlv3L14m4Dzt+i9aBi7Zm4iITqeL4d/Q1JsAic3H846rkBRV+QKOY/9ggE4sGQXwzeOp//KkZzc/A9KKxUtBnyNuUrJHwvzzj5gvat60v/3a4z++xatSxXANySGDVcfMqxWUZRyGfGp6dyPTMDVVoW9ylxbXtW8WHD2LjZKOQ29nPENjeHXqw/pUM4N+4+8mm3EhQCen/Wj6vJB3Jq2ldSoeEp835a0mAQebDyadZy1dyGk5nJibmmvmSf7zlN8aGtqbx3Nrenal40y478l5vZjnu73+ah5eKl3VS/6773C6L9u0LpUIXxDotlwJZhhtYtpyyIlnfuR8bjaWmBvkVkW1Yuw4MwdbBRyGhZ1xjckhl+vBNOhvDv2Fh+3LAwJvhjA/fN+fLNoEIdmbiUxOp6Gw9uSHJvAxc3Z5eNUtBByhZwQP235tJzcDSsnW/b98AsKaxWuFbOnBaTEJX30ymzqoR2oBk1H2WMsaRcOIytcEvOGbUj541dtD49ShdTFHc2LEDTxsahD9HsuXw5zfPWFMGX/BiyGzkTVbwqpx/YgsbbTLkATHED6TdN+D319bnL13HUmLh3Hyp/WEBMVQ8+R3YiPjef3TfuzjvMs5oGZuRl3/e4RGxVLbJTukF9V5jDWR0GPuR/wAIDAG3e4cfEmI2cOY8X01WSkZ9BnTE8y0jNYv0B30TpBX9/2Lek7cSHfz1nFV41r4xtwn1/3/sPwrm1QKsyJT0wi6HEIbi5O2NtaI5NJad/iM7b8cQRnBzsKuxZg+8HjXL8dxOIfB360BuycfGrXzy2fW/ie82Xs0tH88tNaYqPj6DKiE/GxCRzc/GfWce7F3DEzNyPIL4i46DjiXuutU2UuXPYk6AnBAcEA3L2hvxUSaBugcvrs30g7thNl78koO31P2uWjyDxKYFavNal/bdLu8apQIc3vhjoiFBJiUYca6FnOXOBJ/TR71FLa2YOoek/C/MueZPhfRFakLOYN25J6fDeaSNOOvLh90Z9b528ybPF3bJq5gfioOL4Z/i0JsQkc3py94J9rMTfk5nKC/bT3rV2LdzB+0yRGrhzDP5v/RmWl4uuBbVGqFOxYuBWA/b/so+7X9Zm4bRp7lu0kMS6R6s1rUqd1PTZMW/vWvXsFAf6DlddNmzbl+Fm1atXeaVub0aNHM3r0aACGDBnCkCFDjJa+f6uauyPzWlXi53N3+W7fVZytFAyvX4KuVbTzTwLCY+nzmw9TmpWjVWals0sVL2yUZmy6/IC9t57gZKmgf61i9KhWJOu8/WoVw8FSwU7fh2y9GoyN0owm3gUYVMcbhdy0e4uu7DeP9hO60e6HLkgkUoKuBLBq0EISY7PntnSa3hsHVyfG1RkEaOe3LuwyjS+HfUO/5d+h0WgIPO/HL8OX5OoKw6+r5mrPvBZl+fniA747eENbXrWL0jVzFeeA8Dj6/H6VKY1K0qqktvW+S0V3bBRyNl1/xF6/Z9ryquZFj8qmXfk5Jxd6LqLclM6UmdgJiVRCxKU7XOy7hLSY7PKpMKsHFm5OHKqq3dtQnZrOmf/NoNy0rlSc1xtNWgZhJ29yc+ImvW13PpZqbvbMa1meny8E8d2B6zhbKRlepxhdK3kCEPA8lj57rjClcWlalcosi0oe2CjlbLr6kL1+T3GyUtC/ehF6VPHMlTwYsrXfIj6f0JnmP3RCIpHw8ModdgxaQvIr10+r6T2wc3Vifp1hyMxkFG+oXWW89Yzeeud7cMGftd9O/2jpB8i4e4PkdTMxb9ERVZ/xaKIjSNm3nrTj2l56mWtRLIbO1A5nvHj0LWfLpg4OIHHpOBRfdEXVaxya1BTSb14g5fd1hhdLMbIfe09iyKQBDBzfF4lUws1LfkzsP1WnR+G7GUNxcXOhfY1O73Xu8X2mMGTyAL6fPQKJRMK1c9f5sfckwp8ZZ8jjp6x6uRIsGNOfFdv+YPiMlTg72PFd97Z0+0o72up20CN6jZ/PtKHdad1Iu0jMwA5fIpVIWL/nEDHxiZQo7MryiUOoVTH3Fwj8FK+fqX2n029iH3r/2AupVIrfZX9+GjBD59oZ/NMg8rvmp1ut7iZNy7+REXSL5M1zMW/yP5Rdx6KJiSD1z42knf4DAFkhL1T9pmUNB37n8971JXnrAswatcOsWhM00c9J2fcLaef+fHtkI5jXdybdJvSiyw/dkUokBFy5zcJBc0l45bnTe1o/nFydGVSnL6Cduzqty2S+GfYt360YjUatxu/8LZYMX8izIG2D6YunzxnfZiwdx3Sh/6xBSKRSntx9zNx+M7n49wWDafkUfIp7reYmiUbMjDapxNUj3n7Qf8iwGW+eZ/dfs3ik6feD/Jj+/ik6t5NgNM0n2Od2EoxqxtyPu8qvqY1p9WktGvT5buPM/c8rjvmuye0kGI36Rc7z7P6LUlfMze0kGFW7vZ9Wb9nuju++Ddl/QY/tn0757Hy47+0H5UFrXDu//SAT6vNkc67+/8b2n+t5FQRBEARBEARB+C8QPa/G9Z/c51UQBEEQBEEQBEH4/0VUXgVBEARBEARBEIQ8TwwbFgRBEARBEARBMAGN2OfVqETPqyAIgiAIgiAIgpDniZ5XQRAEQRAEQRAEExALNhmX6HkVBEEQBEEQBEEQ8jzR8yoIgiAIgiAIgmACoufVuETPqyAIgiAIgiAIgpDnicqrIAiCIAiCIAiCkOeJYcOCIAiCIAiCIAgmoMntBHxiRM+rIAiCIAiCIAiCkOeJnldBEARBEARBEAQTUEtyOwWfFtHzKgiCIAiCIAiCIOR5ovIqCIIgCIIgCIIg5Hli2LAgCIIgCIIgCIIJiH1ejUtUXk0tPj63UyC8gcTVPbeTYFQp0pjcToLRSGxtczsJRhVJeG4nwaiktha5nQSjSlJ/OtcOgPrF49xOgtFIHd1yOwnC/yMS60/r3vYiIyy3kyAIRiUqr4IgCIIgCIIgCCYgel6NS8x5FQRBEARBEARBEPI8UXkVBEEQBEEQBEEQ8jwxbFgQBEEQBEEQBMEENLmdgE+M6HkVBEEQBEEQBEEQ8jzR8yoIgiAIgiAIgmACaklup+DTInpeBUEQBEEQBEEQhDxPVF4FQRAEQRAEQRCEPE8MGxYEQRAEQRAEQTABsc+rcYmeV0EQBEEQ/o+9s46O8vga8LOWlSiEKMHdIbhrkVKgOMUpWhxaoKW4UyjeUmhpi1uRFmtxh0BxEoIHSwiQEJfV748NCcvuBtsUfnzz9HBOd96Zd+dmduTOvXNHIBAIBIL3HmF5FQgEAoFAIBAIBIIsQFyV41iE5VUgEAgEAoFAIBAIBO89QnkVCAQCgUAgEAgEAsF7j3AbFggEAoFAIBAIBIIswCgchx2KsLwKBAKBQCAQCAQCgeC9R1he30OkeUugqPYpUk8/TMkJ6C8cQn96l938kmy+qHtMtko3RkeQ8vu49M+yAmVRVPkESTYfTElx6ENOoj+1E4yGLJHjGUpnFW1Hd6Fsw0qonFXcPHONdRN/I+LG/UzLFapUjJZffUau4nlJSUrhzI4TbP1+HSkJyTbz1+vehI96fcI3NQZkhRgAHLt6nx92n+XWoxiyOatoU7kon9cphUQiscr757/XGf/HUbvvmtS2Bs3LFwJg76Uwfjt0ibDHsbioFFQq4M/QJhXwdFVnmSzPkDurKDu2IwGNy6NwUfHk3+ucGbuSuOsPMi2n8vYgcEInfGuVQqqQE3HoEmfHrSD54VOb+TX+2WmyfyZXf97F5e83Z4UoHLvxkB8OBnPrcRzZnJW0CczP59WL2GyfZxy+HsGSwyHceBSLu1pJ/WI5GVy3JGonOQ9iEmm60H7fa14mD5OaV8wKUdJROqtoNboLpRtWROWs4taZa2yc+BsPb2TePgHF89B8xGfkKV0QiVTCvUu32DpzDfeCb6fncffJRqvRXSheuyxSmZSwc9f56/t13Llw0+FyyAqWQdGgA1KvAPP4c3oPusNb7eaX5PBHM2SeVbrx8QOSFwyzLqBUox4wC92BjejPHXJcxTNB46xmyLj+1G5cE2cXDRdPX2b2uAXcvhaWablpiyfQ8NP6Vumj+01g95/7APAL8GXw2C+oUD0QhULO6WNnWTjlJ+7eupcVonD07GUWrfqTW/fCyebuStvGtenZurHdvqPV6Vi8dhvbDwYRE59Avpy+dPu0IU3rVLaZPyEpmTZDJvFFh2a0qF8tS2R4WyIiH9Oq6xfMnz6OSoGl33V1LJAVC0TZtAtS31yYEuLQHduFds/GVysslaIZNhuTNpXkhd9YPvIOQNmiB7JCpcCgR38jmNStv2CKiswCKTJQO6vp/W1PqjaqisZFQ8i/ISyesIS71+++8juqNqzChGXjGdF2JBdPXrKZx8svB0v2/sTmX7awau5qR1XfAln+0ijqtEHqlRNTUjz6M/vQHd9mN7/E0w/NF7Os0o1Pwkn+aaTNMk4fdUJRuQmJUzo7rN6ZoXZW029MH2o0qobaRU3wvyEsGv8jd16jfao3rMqUXycxtO2XXDhxEYBRc0bQuF1Du2U6VO5E5INHb13/9w1xVY5j+Z9WXr/++mu2bNmSaZ6rV69iNBqpV68eT5484dChQ3h6elrk6du3L+fPn2fbtm14e3unp+v1erp06YLBYGD16tUoFIoskeN5pH4FULYYiOHqaVKPb0XmXwhFjU9BIjErmrbKeOcCIGXDLDDo09NNOm1GntzFcGr+BYar/6I/sglpjgAUNVoi0big2782S2Xqs2AoecsUZNOMVSTHJ9FsaFu+XDuecQ2GkRSbYLNMrhJ5GbpiDFeOXmTxF7Px8MlOq1Gd8C2Qk3ldp1jlr9yiBm2/7UpMZHSWyXH+TiRDVuyjUel8DGgYyLmwSBbtPoPRZKJ3vTJW+WsWzcWK/k0t0kwmmLT5GIkpOmoUMbfb7ou3GbnmIG0qF2FAw0CiE5L5cc85ev/8N2sHNUOpyNpuWu3HgXiWy8/5KevQxSdRcngr6m0czc46I9HGJNosI5FJqbN6JHKNktNf/4pUIafM6PbUXfs1uz4ajUlvvSFSeU4fnNw0WSbH+XtPGLL+GI1K5GJAnRKcu/eERQcum9unZjGbZQ5dC2fYhuN8UjoPQ+qV4taTOBbuv8zTxFRmtKqMl4uKFT3qWpVb/+9N/gm+R8uy+bJMnmd8vmAIecsUZMuMVaTEJ/Px0DYMWTueyQ2GkRRru31y5PZh2PqJ3Au+zapRizEZTTTo/Qlf/jGJaU1H8uhWBCpXNV/+MRmlWsm22et4FBZB2UaVGLZ+IvM6TCDs/A2HySDNVRhlp1EYLh8nde86pHmKoqjfASQSdIdsj+FSv7wAJC+bAHpdxgO91jqz2hlVp1FIs3lbP8tCpi6eQIlyxVgweTGJCYn0Ht6DxRvn0a52F+Ji4u2WK1yyIDv/+IcNv1lu4ty7bd7Q0zir+WH9HAwGAzNGfY9Wq6XXsG4s3byQdnUyf/ebcP7KTQZP/YHGNSowsHMLzoXcYOGqrRiNRvq0a2qzzMhZP3P434t0+7QhlUsX5cqte0xavIqn8Ql0bmapmMfGJzJ46iLCH0U5tN6OJPxhJH2HjSE+wXafepdI8xVF3Xss+nNHSN2xEln+4jg17QISCdrdG15a3qlBW2R5CqO/bqngSTxyoBn2HcZHD0hePguJwgll0y5o+k8mccZA0Nnoaw7im0WjKFK2CL9MW0ZSfBKdh3Vi5rrp9Knfl/gY2+uC53H1cGXw9EEvzTd89jCc3ZwdUWWbSAMKoWw/HEPISVIP/oE0d2EUdduax7Zjf9ku45MHgOSVUy3HM53Odv7cRZBXbOTwumfG2B9GU7RsEZZM/YWkhCS6DevMnPWz6F6vF/GvMP64ebgyfMZQq/SV81fx16rtVnnH/zSGCycu8ij8saNEEHzA/E8rr99++y1ffvll+ucaNWowevRoPv74Y4t8x48fJyYmBk9PTzZt2kSfPn0snk+bNo1mzZoxevRofv755/Sd5rlz53Ljxg22bNnynyiuAIqqzTA+vof2718BMIYFg0yGolIT9Gf3WC7i0pB65cIY+wTj/Wt23ysvWR1TXDTaXb+AyYTx7hUkGlfkgQ3QHdyQZdbX/IGFKV2/PPO7T+PywXMAXD8dyvQjP1C3S0N2LLJtgfuoZzPio+JY/MX3GHQZCnmP2QPwye9P5K1wAFw93fj0y8+o1bEBCU8du6B7kSV7z1PELztT29cCoHqRAPRGI78dvEiXmiVQvaBkZndRkd1FZZG2+mgwtx/FsvyLpunPft5/gRpFAhjTMsMakdfLnc4/bOdw6H0+KpU3y2TyLF+QnB+V42Dn74jYfwGAx0FXaRY0j0LdPiJ4/lab5XI3q0y2EnnYUWckcdfMFsCnl+/w8YEZ5GlehbDNxyzyF+zWANeC/lkmB8CSw1co4uvB1E8rAVC9oC96g5Hfjl+lS5XCqBQyi/wmk4lZuy9Qv2hAuvW0Uj5vDEYTa0/fIFmnR62QUzrAcrMrODyaf4LvMaheScrlzpGlMuULLESp+uX5oft0gtP6z43TV5h05AdqdWnE33b6T90eH6NL0fJjjxlok1MBuHr8MlOO/Uidbk3YMP5XqrWrR45c3sxuNYZbZ81jR+jRSzhnc6P1mK5832aczXe/CYq6bTA+DCN10yIADDcugEyOouan6I5ttz2u+ebF+PQRxrCQTN8tK1oBp6Y9kDipMs3naEqVL0HNj6oxpNMIju0/CcC5oIv8FbSett1bsmzeCpvllGolufIF8NuCVVw+a1u2j5rXI3f+XLSt3SXdinvjyi22nd5Ig2Z12bzS9qL4Tflp3TaK5svFtGE9AagRWBK9wcCvm/6ma4uPUCmdLPJfuXWX/UHnGdT5U3q3Nc+3VcoWR61SMnf5JprXrYqbi3mj6kDQeWb8vI6klFSH1tlRGI1G/ty1l9mLfnnXVbGLsnFHjA9uk7JyDgCGK2dBJsepQRu0B7ZmqmRK/fPh1LAtxljrjV3lx50wpSSTtGgM6MztY4yKRN1nLLJchTDcCs4SeYoFFqVyg8qM6TqO0wdOA3D5VDDLj//GJ10/Ye2CdS99x6BpAzDY2CR9nk+6NiVXwVwOqbM9FDVbYnx4h9Q/fwLAcOsiSGUoqjVDF7TL9tjmkwdjzCOMd668whcoUTbrgynhKRI3z5fndwDFA4tRtUEVvu76LUH7TwFwMegSa0+spEXXZqxasOal7xg6bTB6vd4qPfxOBOF3IizSJv0ynviYeKYMmo7JJM6G/q+RmprKjBkz+Pvvv0lJSaFmzZqMHz/eylD4PGfPnmXu3LmEhISg0WioVasWI0aMwMPD45W+83/6zKurqyteXl7p/+ylbdq0ifLly1O/fn3Wr1+P0WhpwPf09GTatGkcOXKE1avNbiWHDh1i2bJlTJ06lYCAgP9GIJkcaUBhDNfPWiQbrp1B4qRCmrOQzWJS71wYH7/ElUwmN09wzw0MpuQEJHIFOCnfuur2KFGrDCmJKYQcuZCelhAdx7WgEErWDbRbbst3q1nYc7qF4qrXmv9f7pShJH48oBXFa5Xmx76zuLjvTBZIYEarN/DvrYfUL5nHIr1BybwkafWcvf1yF6sn8Un8sPssbasUoVRu82/TaDRRpaA/rSsVtsibx8sdgPtRcQ6SwDZ+dUqjS0zh4aGMHfnU6HgenbiCX31ra/IzfGuXJu5GeLriChB3/QFx18Pxq1/WIq9zbi/KftuB019l3eJQqzfw753H1C+a0yK9QfEAc/vctd7NvfowhvtPE/msUgGL9E6VC7F9YBPUNizeJpOJabvOkT+HG50rF7Z67miK1ypLSmIKVyz6Tzw3gkIoWbec3XIPb95n78/b0hVXAF2KlpiHUXjl8QHAt0BOEmMS0hXXZ1wPCqFAhaKoHWWtkMmR5SuBISTIItkQfBKJUo0sj22ruNQvL8aIsMzfrdKg/OwrDLdDSFk+1TH1fUWq1qlEUmISJw+dTk+LiYrh7IkLVKtXxW65QsUKIJPJuBZ83W6efTsO0eOTfhbux7o0q4zCybEbqVqdjtOXr1G/iuXv6aNq5UlKSeVsiHU9b90zLz5rV7R0ra1QsjDJKamcvnQVgLiEJIbNWEyFkkX4afwQh9bbUVy7cZvJsxfRokkDpo/96l1Xxxq5HFmhUugvHLdI1p8/hkSlQVaghP2yUhmqzsPQHtqG8ZH1MQN56aroTu5JV1wBjPdukDi2W5YprgDla5cnOTGZM4cz5uzY6FgunrxEpbovP4ZRu1ktAmsG8su0ZXbz+Ob2pec3nzNv1HyH1NkmMjmyPMUwXP3XItlw5bR5bMtdxGYxqW8ejA9fzf3WqcFnmBJi0V84/NbVfVUq1qlAcmIypw9lyBUbHcuFkxepXK/SS8vXbVab8rUCWTL15XN+lfqVqdm4Bj9M/InEuPfP68FRmN7xv6xkwoQJHDt2jIULF7J8+XLu3bvHkCH2x/vbt2/Ts2dPihYtysaNG5k7dy4XL15k8ODBr/yd/9PK66sQGxvL3r17qV69Oo0bN+b+/fscOXLEKl+dOnXo2LEjs2fP5syZM3z99dd06tSJhg3t++Y7Gol7DiRyBaanloqQMcbs/y/N5mOznNQrFxInNcoOX6Me/CPqvrNR1GgF0gxLk/78ASTZvJFXaAhKNVK//MgDG5h3CVOSskwmv4IBPLkbidFguWHw6M5DfPL52S339GE0D0LNg7tSo6JY9VK0HPkZ14JC0tMBDq3ezZg6gzn3z6msESCN+9Hx6AxG8uRws0jP7Wn+fPdJ7Evf8eOec0ilUgY0zFDapVIJX35SibolLJXifZfvAFDAN9vbVj1T3ArlJPHOI0wvtE9CWCRu+e23j1shf+JuRVilx4dF4prfNyNBIqHKvH7c3RZExMGLDqv3i9x/mmhun+yuFum5s7kAcDfa2g0tNNLcZkq5jEHrjlJ5+mZqzvqTGX+fI9XOjv6uy/cIDn/KiEZlkEntn6N1FL4FcxJlo/88vvMQ70z6z5FVe9i71PLMlXc+P/wL5yL8qnmjKyE6DpWL2kpJfabc5sjlGBdcSXYfJHIFxijL34sx6qH5eQ7bckh98yJRaVD1noJm3CrUI5ei+KijxbiGLpXkhcPQbv4BU1LWel68SN5CeXhwJwKDwfK3ci/sPnkK2Lf2FClp3oRs3fVT/r6wlRN39vPz1kWUKFc8PU9CXAKXzpiVB7lCTqHiBZg4/1ueRsWw968DDpXj/sMn6PR68uS0nF9y+5k32O6EW2/MZXc397MX3YDvRZjnqgePngCgVjqxZeFEpg7tgYebi0Pr7Sj8fL3ZuX4ZIwf3QaX6b633r4LU09fcf15QPo2Pzd5HUq+ctooB4NTkM5DL0e6yPuspye6DROOCMToSZdt+uExfg8v3m1H3GYckm5djhXiB3IVyEXH3odW4Fh4WTs789uUB8MjhwYAp/Vk8/iei7RwTkkgkfDVnOIe3H+Hfg1m3qS3x8LY9tj1NG9uy2xnbfHIjUalRdR+P5utfUQ9dhKJue8uxDZDmK4m8VA1Sty21MDxkNXkK5ib8boRV+zwICydX/syNOdlyeDBk6iAWjV9M1EuOCUgkEvqN6cP5Exc4vMN6XS54/4mMjGTr1q2MGTOGChUqULp0aebMmcPp06c5f/68zTJbt27F29ub0aNHkz9/fipUqMD48eMJCgri3r1Xi+nwwSuv27dvR6vV0rBhQypUqIC3tzfr1tl2SRk1ahT+/v507doVX19fRo0a9Z/WVaI0u1mZtCmWD559drIRvEfjisTZHUl2X/QXDpK6eR76S4eRBzbAqVGP9GzGe1fRn/4Hp1pt0QxYgOqzbyApjtSdWesqpXHTkGwjwFJqQjJql1c7/zj33K8MXz0OpUbFxqmWrngPb4a/1HXIEcQnm92ynF9wn9MozVaQhFTbZ1WeEZ2QzPazN+lQtShu6swt3XeexDJv52mK5fSkRuGstfo7uWnQ2WgfXUIKchf7waKc3DXo463L6ROSUTxXrkjvxrjk9ubchFWOqbAd4tP+/s5KS2upJu2zrfZ5mmS2NgzfeIICOdxY9FkNPq9elC3nbjP2z9NW+QFWnLxG2VyeVMz735ytVNvpPykJyahesf8AKFROdP1+ANoULQd/NwegOrX1CEaDkd4/DsOvUABqNw0VW9Sgats6ADhpHOORIVGljWspL8ihNX9+Nu5Z4OyO1NUDSQ5/9Kf+IWXFVPT/7kVRrSnKVv0z8hkMmJ5Yb6L8F7i6u5Jo43xkUkISzi72rdaFSxQEQKlS8m2/CXzbfyJOSieWbJpPwWIFrPLPX/kda/f9ToUagfwwfSlRjx17rj8+0bx56aK2VNw0aZ8TklKsylQoUZgA3xzM+HkdJy9cISEpmTPB15m3YjNSqYTkNBdhhUJOvgBfq/LvE+5urvh6Z62y9laozUq/6cVN5tS0/qOyPU5LcxfCqV4rUlbNAxvumxIXs3ePsnl3JO6eJC+fRcraBUhz5kMzaFqWemQ5u7mQFG+9aZ6cmIzmJePa0JmDuXImlH2b99vN07LXp/jm9mPJpKVvXdfMSB/btC+MbanmPiNR2mgbZzekLh5IPP3Rn9lLyprv0J87gKJyY5TNnzvSplSj/KQX2kObMEU/zCoRbOJip32SEpJe2j5fzhxG8JkQ9mza+9LvqfZRFfIUys3K+VkTSOt9wviO/2UVZ86YN4cqV84I1JcvXz58fHw4fdr2Oqp58+bMnDnTZjDAmJiYV/re/+kzr6/Cpk2bKFu2bLrr78cff8zKlSuJiIjAz89yV0ylUlGrVi1+++03AgMDcXJysvXKrONZQ9rbYTPZ+AlqU0j5Yw6m6IeYEsxRXo33r2HS63Gq0RJd0HZM0Q9RNOiMvER1dCe3Y7h7BYl7DhRVm6NsNYTUP+bYDoLy2tWXIHnBGiWRSG3LI5FgsiXPC8jkMhb1moFUJqPB5x8zcuMk5nefxtUTWefSZAtjmgz2gtZKM4lmC7Dp1DWMJhMdq2fi4gXcehRDv1/+QSGXMbtTXaSOtO7Zah+pxOYZE4kEMNpvH4lEavtnKpGkt7drAT9Kj2rL0V7z0dlQdB3Jm7SPPm1XuV6RnAxtYHZ/rJjXG5PJxIL9l+lfO568OTIsuefuPSH0YQxz22VNpNTX6T+SV+w/ACoXNf1+HkGeUvlZ0nc2TyPMu+EPbzzgp14z6Ti9D2P3mM/S3bl4k79mr6fD5J5okxx0RlHybI/0Nca11CSSf5uM6Uk4pjhzfY1hV8Cgw6nBZ2gPbcb0OPNoy45EIpEglVru9Uol9vqOxOpoyvOsWbqBvdsOcvpohkXo1JEzbDm+ls+HdGF0vwkW+X+e+zu/LlhJnSY1GTN7JNk8Pfhtwcq3kud5nvUde53HVt9RKOT8NGEo4xYsp8+4uQB4ZXNnVO8OjJy9FLUq6xSf/29kFikdsD2/yhVmd+GDf2K8azsWhkRuXv6Z4mNIWTYt/T3GJxE4D/8eRYW66I7//VZ1B9vjmvk3ZWdcM9q3MDZo04CSlUrSp0E/u3kC8uek24iuTO4z1aYC5lDS12x2nttqm9RkkldPxxQVgSnOvBFlvBsKeh1OdduhPfInpqhwnBp2wRQXjT7o7dsgM2zOO3bXBZm3T6M2H1Gqckk+r9/Hbp7n+bR7C65fvsHZo+der9KC16Z+fevo9s+zb9++N3pvZGQk2bJlQ6m0HPO9vb2JiLC9sVyggPUm7c8//4yXlxdFixZ9pe/9oJXXq1evEhwczDffZISGb9q0Kb///jsbNmyw8skOCgpi+fLlVKtWjdWrV1O/fn2qVfvvQvqbnu2kvrhb9ywQSaoNBUCvw3jX+tC/4fYlqNHSHMxJm4K8VE30QbvQHf/TnOH+NYwPw1B3m4i8ZHX059/eFe2TIW1oPrSdRdq/O07gY8P9VOmsIjnu5ROLQW8g5IjZ3fTKsUtM3D2Hj/u3+s+VV1e1eSMjMcXSgpeUZtFzUWW+0bH3UhhVC/lbBXB6ntM3Ixi+cj/OSgU/9WpEzhdcYN+WksNbUurL1hZpd7cFWbr5piF3VmWqcGrjElHYuMZH7qxCG5eERGp2F763LYiHhy8hkWUs/CVSCRKZ1MpV+W1wTbOAJ6ZaWhiS0j67KK3PCWrSzk7XLGQpf7UCvizYf5mrkTEWyuveK/dxUymoUTBrLEkfD2lD06FtLdLO7jiB61v0n2x+nvT/9Wu88/vxy8C5XN5veZ7+ypGLjK0xEM8As+Up6v5jqqRZXhPtRAJ/XUwpZuuk9bimTntuQw69DuMt66svDFfPQoPPkPrmwfAfKq+9h3enz1efW6Tt3XaA3Dms3YPVzmoS4u2f3bpz8x53blq6RiXEJXDh9KV0q+zznA8yj39njp/Dzd2VnkO7suKHNVbuym+Kq7PZkpL4goU1Kdn82cXZtmUvt583v08fQVRMHLHxieT29+bhk6cYjSbcM7E8C14PU3Ja/1G9YPFSPus/1r815bNIxP+sg2ebLs/0E6kUjMZ0Twh9yBkLJcsYdhVTUgLSgPwOqX+noR3pMtzyepfD248QkMPDKq9KoyLRTt/x9PWk3/g+LJ3yCzFPYpDKpEhlZjdbqUyWvrn01dyvOLL9KGePnEX63LwjlUqRyqRWrrBvw7Oxy2psU6osnlug12G8bb1+Mdw4D3XbIfXJDdm8kRevQvKysWkKsiRDUU7f0HSMG3HXYZ3pPryrRdrB7YfJld/DKm9mY1sOX08GTPiCxZOX8PTJU6QyKbI0N2iZ1Nw+z2/quXm4Uq5aWZZOf38DpQng/v37mSq+Q4YMsWnoUyqVpKa+2gb4jBkzOHToEAsWLHjl4LgftPK6adMmAGbOnMl3331n8eyPP/5gwIAByNN2H6Ojo/nqq6+oV68e8+fPp1OnTowaNYq//vqLbNmy9tzhM0wxjzAZDUg8LF2YpB5mF0VjtPUuhiSbD7JcRdGHBmW4F4M5EBNpQZlcsyORSDGEW159YYoKx5Qcj8TTMVFgD6/ZaxU0qVzDSpSoVSbNUpQx2Hrn8SU8k3teyzSoQFJcItdPZSjmBp2e+6F38C+ctdEDbZEruysyqYS7LwRQevY5v7eH3bKRsYlcjYimcw37Vted528ybuNR8uRw48fPG+Lj7vjF381V+wnfY7nDmbNxBfzqlLawmAK45PUh9pp95SD+ZgTZSua1SnfN60PU+Zto/D3JUb4gOcoXJF+7WhZ5Sg5rRclhrfir0hAS7z95O6HSyJXdBZlEYnW29e5T8+f8Xm5WZXJnN7vj6V5YzOjTJlil3PL80eHrD6lbJCcKWdactji6Zi+XXug/ZRpWpJiN/uOVx5eHL7knOWfR3AxYPhonlROLuk3j+knLyLbZ/D0pWr00p7YeJup+RkCr3CXzk/A0nuj7jrmywBQdiclgQJrdl+fVLamneRPA+NhaDkkOP2T5SqK/eNRy005hniRNif/t+dbNq/7iyF7LgDl1GtekSp1KVm2TK29Apve8NmxRn9insQQdtgzyolQpiYk2n8MuUbYYfrl82bvNclMx5HwoTds2xiO7u8Pch3P5eiGTSrkbYXm34t0Ic/sXyGW9eZKSqmXvibOULVaQAJ8ceHqY+1fIDfNZ/WIFcjukbgKzJdRkMFidDZd6medt40PrM2LystWRevrgOnuT1TPXeX+RvGou+vPHzOsNuY3FokzmsGtydq7ZRdA+y5gU1RpVpXzt8lZ9xz+vv917XgNrlsPVw5UvZw/jy9mW9zzPXDedh/ciGdluJMUCi1IssCgftW1gkafT0I50GtqRrlW7EXnfMfeImp6a12zSbD6WY1u2tLHtifUcKsnuhyxvMfSXT6QfnQBAnja2JccjL1kdicIJTb+ZVuWdv12B7sJhtNsc4xK9ffVOTuy1DKZXo1E1Ktpon5x5/blz/Y7N95SvWR5XD1dGfv8VI7+3DHz2/frveHjvIZ9V7ZKeVqluRWRyGYe2/3eBqN4lxqwPkZEpb2pZ9fHxYedO29d0gjm4rVZrPVakpqaiVts/egbmIITjxo1jy5YtjB8//rViDH2wyqtOp2Pbtm3UqFHD6uzqrl27+PHHH9m/fz8NGzbEZDLx9ddfAzBlyhTkcjmzZs2iRYsWjBkzhh9++OG/qbRBj/H+dWQFA9H/uzs9WVa4PKaURIwPb1sVkbhkw6lBZ0wGPYbgjOtJZEUqYkpNxhh5B6RSTEYDspyFMIZdziibzQeJ2hVTrGMUiNhHT4l99NQizUmtpOmg1pSoXYbLB88D4JLdjcKVi7PzB+uJ9RkNezfDJbsbExt/mb5TqnbVUCCwMNdPhzqkvq+DUiEnMK8P+4Pv0K1WyXRXrr2Xw3BVOVEyl/0rUy7fMy8Cy9o5J3kk9B5jNxyhbB4f5nWrj+tLrLhvSnJkDMmRMRZpMrWSkkM/xa9OaSIOmCPaKrO74l21GCHz/7T7rohDl8jTshpuhXISd908QbsVyolbIX+C528lOfIp/zQeY1Wu0d9TuLFqPzdX7Sc58qnV8zdFKZcRmCcH+0Mf0K1q4Yz2CbmPq0pBSf/sVmXK5/FCrZCx6/I9ahfO2MA5dDUcuVRicUVObLKWe9EJfF7NdvRIR2Cv/zQZ1JpitcsQkt5/XClYuTj//GD7mhwwW1wHrRyD0WBkduuxPLxhvYhy9XSny6wviImM5sphc9u7eblToXl1Lu62fVbljdDrMN65gqx4ZXTHMoJIyUpUwZScgPG+9X2yEtfsKJv3BoMe/dkMBU5eshqmlCSM4bccV79X4ElkFE8iLYOPqNQqeg7tRtW6lTi+37z48/D0ILBqGX6db9+tt033T/HyyUHb2l3Qp0VT9/LNQZmKJVn780YAanxUjR6DO3PpbDCRDzIW2lXrVuZJZBTRTxzYd5wUBJYoxL6T5+jesmF639lz/AyuzhpKFrK+y1ghlzN96VraNa7NkK6tADAYjKzdsZ/cft4UzJ2112L9v0Kvw3DzMooy1dDtz+jz8rLVMSUlYLhj7RacvHQSvKCUqjoMBCBl3SKMUZGgTcFwMwR5maqkbl+efi5WVrgMEqUaw03HeDdFR0ZbBVZSqpV0HPwZ5WuX59+D5k0c9+zulK5SirULbcckObkniIFNLaORFipVkCEzBjP/6wWEnLlCVGS0VR6ARTsWsHP1Lnau2UWUI++CN+gw3g1FVrQCupM70pNlxSpiSk7EGH7TqojELRvKjz83j23PRRCWF69iXrNF3EYXHYn+3z0W5eTl6qIIrEfysrEODU4XFRlFlNXYpqTLkE5UrFOBU2lXGblnd6dMldJ2r8k5secEfT8eYJFWpFQhhs8cypxR87h8xvL3VKxcUR6FP7IY3wTvHwqFwqab7zOuXr1KTEwMWq3WwgL76NEjfH3te6klJCQwcOBA/v33X77//nuaNrV9n7g9Pljl9cCBA0RHR9OjRw8KF7a80sLf35+VK1eydu1aGjZsyK+//srhw4f57bff0q2suXPnZvTo0YwZM4b169fTvn37/6TeuqAdKNsMw+mTvugvH0PmXwB5hYbojmwy3xfmpELq6W+OQJycgPH+VQz3QnGq3Q6dQokxOgJZ/tLIy9VDd/gPSDW7rejP7jVHGgYMd0OQuHqa75SNi0J/KeuivF0/dYXQE5fpNW8If0xfSWJMAs2GtiU5LpFDqzIGZ7+CAciVcu4FhwGwfcEfDF0xhn6Lv+TQqt2oXNQ0+aIlTmoVf81dn2X1zYze9crQd9k/jFhzkE8rFOLCnUcsP3yZIY0roFLISUjRcutRDAHZ3Szcg68/fIqTXEYuT2vrX6pOz6RNx9A4KehVrzS3H8VYPPdxd84SK+wzHgeFEnkshKqL+nN+6lq00QmU/LIVutgkrq/MCLjgVignMqWcp2lRkO/+dZISg1tQZ/VILkwzLzbKjO5AzJV73N0WhMlgJPqi9WYLQHLkU7vP3obeNYrRd9VhRmw6yadl83LhXhTLT1xjSP1SqBQyElJ13HocR0A2F7I7K9E4yelfpwTf77mIm9qJ+kX9uXAvit+OX6VjpYJkd844w3H9kdkiZsuCm5XcOHWFaycu02PeYLZMX0ViTDxN0/rP4ef6j2/BnMiVCu6n9Z+2E3rg5uXBmtFLUblqyFsu45qtlPgkHt54wN1Lt7hxOpTPpvRi87RVGA0Gmn/VAaPewI75Gx0qh/bgJlTdx6JsPwz92QNIcxdBUb0Zut2rzeOaUo3UKwBj9ENIiscYFoLh1mWcGncFhRLjkwfICwcir9IE7T+rwIar5H/NuZMX+PfYWSYvGseCKYuJfRpLny8/Jz42gU0rtqbny1c4L05OCq5eNl8588uc31m49ntmLZvKht824ebhRp8vPycuJp6Vi8196Y/lW2nZuRnzV37Hz3N+JzkpmU/aNqFGg6p823+iw+9C7NOuKX3GzeWr75bwaYPqXAi9xe9bdjO0aytUSicSkpK5eS+CXL5eZHd3RSaT0q5JHVb/tRdvTw/yBfixbscBzl+5yfxv+1udDxa8Hdp/1qMeMAVVj6/RndyDLF8xnOq1IvWv380WUpUaqW9uTE8iMCXEYYywto49cxM23svYLErdthzN4Omo+05Eu38zElcPlM27YwgLRX8pyOodjuJy0GUuHL/A1wtH8svUZcTFxNNlWCcS4hLZsSrDypO7UG4UTgpuBt8kPiae+BhLpU2tMc+z92/eJyw0DIDrF21fQRUVGWX32dugPfonqk5fo2w1CP2FQ0gDCqOo2hTdvvVpazY1Ui9/jE8fmce2O1cwhIXg9FEn89gWFYG8YFnklRqi3bsWUpIwpSRZGRVMhcxXWRkjHD93vsjFoEucO36ebxd+zZKpvxD3NI7uw7uQEJfAX6u2p+fLk9Y+N4JvEhcTT9yL7eNsbp+7t+5xO619npGvaD7uXHu164I+BIxZfmHNu6F8+fIYjUbOnDlD1apVAbh16xaRkZFUqFDBZhmtVkvfvn0JDQ3ll19+oUoV+1fL2eODVV43b95M3rx5qV69utUzFxcX2rVrx6+//srFixeZO3cuPXr0SP/DP6Nt27YcOnSI6dOnU7FiRfLnd8wZkMww3gtFu+0nczCl5v0xJcSgO/wH+jPmharUOzeqdiNI/fs3DCHHwWQi9c8fUVRthrz8R0ic3THFPEa7dxWG55RS3eE/MMU/RV6mNvLyH2FKjMV4JwTtsS3pCm5WsbjvbNqN7Uab0V2QSKTcPBPKkgFzSXruTq9OU3rhGeDFNzXMO3dXjl1ibpfJNBvSlr4/DMdkMnH1RDC/DF3Aw5vhWVpfe1Qq6M/3neqxeO85hq3Yh7e7hmFNKtK1VklznR9E0fvnv5nYpgYtKmQoC1EJKXatqRfuPOJx2tnSL5bttnret35ZvvjI/n2ejuBIr7kETuhMuTEdQSrhyelrHOu3EF1sxu+iwvTuOOfyYlvloQAYtXoOdJhO4KSuVPyuJ0adgYeHL3F2/CqHnmV9HSrl8+b7tlVZfCiEYRtO4O2qZliD0nStat68uhLxlN4rDzOxeQValMkLQJcqhXFVObHy5DW2nLuNl6uKL2qXoEd1SwtrVILZJd9N5dg7Nl+FJX2/p83YrrQa3Tm9//wyYC7Jz/WfDmn9Z2yNgcgUMkrVM1/H1HGadeCMayeDmddhIgA/f/E9bcZ2o+O03iCRcO1EMH/2nc3T8MyvOHhdjLeDSV33PU712qHsOAJTXDTaf1ahP25eCEn98qHuOYHUzT+gP3cITCZS1szCqV47FNU/QeLigelpJNq/fkZ/5s3cn7KCET2/ZdiEQQwZ2x+pVMKF05f4pu844p87L/z19OH45fKleSVzPIBTR84wuONX9Brenek/TcRoNHHy4CnmT/mRhDhzuahHUfRqMYCBo/syctownF2cuXrpGkM6jeDY/pMOl6Ny6aLMGdWPH9f+xdBpi/H29GB499Z0+9S84Xnl5l16jvmeyYO706K+OQ5E/8+aIZVI+G3zP8QmJFE0XwA/jBtEtXKZB6UTvD6G6xdJ+XU6Tk06ou49BlNMFKl//obuwBYAZAEF0QyebnYHPvXq/cMYFkrSwm9QftIVdc9vMGlT0V86SerWX20HUnMgk/pMoe+43vT6tidSqZTgf0OY+sU0Ep7rOwOnDsAnwIdu1bpnaV3eBmNYCKl/LMCpViuUbYdhin+Kdu9a9EHmqO5Sv7you3xL6l9L0F88Yh7bNs7FqVYrFFWapI1tj9Du/BX9uYPvVpjnGNd7Iv3H9aPft72RSKVc/jeYif2mWLTP0GmD8Q3wsXAHflWy5cjGrSv/rQeNwPH4+PjQtGlTxowZw7Rp01Cr1YwfP55KlSpRtmxZwKysxsbG4u7ujpOTE0uWLOHMmTN8//33FChQgMePM44oPcvzMiQmR2/hCixImtP7XVfBoQxZEPOuq+BQFswt/66r4FC2DrAOcvO/yqff5X3XVXAoX45x/K7/u2TW669X3mtq//JurtzJKo4dmPKuq+AwpDYCY/0vkzLJ2rX1f5k2W7L+urr/ks19PV+e6X+IT36yvqv5f5UD9/e8PNN7yJi8Hd/p908Js+3u7QiSkpKYNm0a//zzDwC1atVizJgx6Z6sQUFBdO3alRUrVlC5cmUaNWpEWFiYzXc9y/MyPljLq0AgEAgEAoFAIBC8Sz5kK6FGo2HKlClMmWJ7w7Ry5cpcvXo1/fMzJfdtEAdTBAKBQCAQCAQCgUDw3iMsrwKBQCAQCAQCgUCQBbybSCAfLsLyKhAIBAKBQCAQCASC9x6hvAoEAoFAIBAIBAKB4L1HuA0LBAKBQCAQCAQCQRbwod7z+q4QlleBQCAQCAQCgUAgELz3CMurQCAQCAQCgUAgEGQBwu7qWITlVSAQCAQCgUAgEAgE7z1CeRUIBAKBQCAQCAQCwXuPcBsWCAQCgUAgEAgEgixA3PPqWITlVSAQCAQCgUAgEAgE7z3C8ioQCAQCgUAgEAgEWYC4KsexCMurQCAQCAQCgUAgEAjee4TyKhAIBAKBQCAQCASC9x7hNiwQCAQCgUAgEAgEWYBwGnYswvIqEAgEAoFAIBAIBIL3HmF5zWLi/7r+rqvgUKJMbu+6Cg4ldcM/77oKDiVKlvNdV8Fh3J1y9l1XwaHEmFTvugoO5enu2HddBYfykTLXu66CQ9H+OOtdV0FgB9W4Be+6Cg6l1p9j33UVHIr2wv13XQWHkizuaXnniCZwLMLyKhAIBAKBQCAQCASC9x6hvAoEAoFAIBAIBAKB4L1HuA0LBAKBQCAQCAQCQRZgEiGbHIqwvAoEAoFAIBAIBAKB4L1HWF4FAoFAIBAIBAKBIAsQAZsci7C8CgQCgUAgEAgEAoHgvUcorwKBQCAQCAQCgUAgeO8RbsMCgUAgEAgEAoFAkAUYRcAmhyIsrwKBQCAQCAQCgUAgeO8RlleBQCAQCAQCgUAgyAKE3dWxCMurQCAQCAQCgUAgEAjee4TyKhAIBAKBQCAQCASC9x7hNiwQCAQCgUAgEAgEWYAI2ORYhOVVIBAIBAKBQCAQCATvPcLyKhAIBAKBQCAQCARZgPFdV+AD47WU13r16vHgwYP0z1KpFGdnZ4oVK8aQIUM4ceIEixYtyvQd+/bt49SpU3zzzTcW6VKpFFdXV0qVKsWIESMoWrSoVdm5c+fy008/MXr0aLp165aeXqRIkUy/s2XLlsyYMYMuXbqQM2dOZsyYkf7MYDCwfv16Nm/ezM2bN5HJZBQsWJD27dvz6aefIpFIMn13VuBUqRIuPXsiz5MHY0wMSX/9RdKaNfYLyOVo2rVD3bgxMi8vDI8fk7J3L4lr1oBen55N1bgxzu3bI8uZE2NUFMn//EPiypVgMGSpPCpnNd1Gd6dSwyqoXdRcPRPKsok/c//6vUzLFa9Ugo4jOpOveD5SklI4vuMYa2avIjkhOT1Pdp/sdP22B+VqByKTybh27iprZq/ixoXrWSKLvExFVO16IsuZB1NcDKn7tpH6ZyZt46RE1aY7TlXqIHHzwHDnJimbl6O/cNoim6JCdZQtuyLzz4UxJhrt0d2kbl0DBr2dFzsOhbOK6t9+Rr5G5XFyURHx73WOTFjJ0+vhr/yOJkuHoE1IZt/wpRbpak83qoxsS65apVB5OBNz6yFnf9rOjW1BjhYDAE2N8ngN7YpTgdwYomOJWb+T6KUbXqmsskRB8qyby63GPdE/eGTxzK1lA7L3aI0ijz/6x9HE/bmPqMVrQZ+1fQdA5ayi4+juVGhYCZWLmutnQlkxcRkPrt/PtFzRSsVpN6ITeYrnIyUpmaAdx9k4e41F/wFo2qcF9Ts2JLtfDp48eMw/v+9gz4pdDpdDWaUi7v0+R54vD8ansSRu+Yv45WvtF5DLce3UFs3HjZD5eGF49ISkf/aayzw3rsnz5MJ9YF+U5ctg0uvRnrtEzPzFGMIjHC7Dizg5q/h4dEeKN6yA0lnFnTPX2T5xBY9uPMi0nF/xPDQa0Z6cpfMjlUp4cOk2f89cR3hwWHoej4AcNPm6I/mrFkeukHPzRDB/z1jLk9sPs0QWWbFAlE27IPXNhSkhDt2xXWj3bHy1wlIpmmGzMWlTSV74wtzuHYCyRQ9khUqBQY/+RjCpW3/BFBWZBVJk8KHJ8yZERD6mVdcvmD99HJUCS7/r6ljg5Kyi/uiOFG5onnfun7nOnokreXI9877zPK2XDCU1PpntXy2xSFdnc6HuqA4UrFsGhVpJxKXb7Ju2hsjgO44WAwB52UqoO/REFpAHY1wM2t1/kbI183WBul13FNXqIk1bFyRvXI7+/CkApF6+uP+4zm7x1AO7SPpxpqPFsEDjrGbg2C+o3bgGGhc1l/4NZs7YhYRdz/xvOHnxOD5qUc8qfcwXk9j7536r9HY9W/FZn3a0rNzBYXUXfPi8tuX1888/5/PPPwfAZDIRExPDnDlz6NWrF3///TcdOmT8ANu0acPHH3+cnh8ge/bs6f9/9OjR9P83GAzcvn2badOm8fnnn7N37140Gk36c6PRyNatW8mXLx/r1q2zUF6ff8/OnTuZNm2aRZpKpbIpi16vp3///ly6dImBAwdSvXp1DAYDx44dY9q0aezbt4/58+cjk8le98/0xihKlMBj6lRSDhwgYdkyFKVK4dKrFxKplMRVq2yWcR04EHWjRiSsXIk+NBR5oUK4dO+OzMeHuFmzAFC3bo3boEGkHDxI/OLFSN3dce7RA3mBAsSOHZulMg1f+BWFyhRixfTfSUpIpv2QDkxcM4UhDQaQEJtgs0y+EvkZu3ICF49e4Lt+M8juk53Oo7qRs0AAk7qMB0DjqmHqppmo1ErWzl5FxO0IKjeuwuQN0xjX/luun7/mUDlkhUrg/NVUdCcOkLJhGfIipVC16wkSCalbV9sso+k3CkWZiiSv/Rnjw/s41WqE84jpJEwehuHqJQDkpcqjGTYJ3ckDpKxbiixXflTteyF19SD59wUOlcEWjRYNwLtsfo5PW4c2PplKw1ry6brRrKk/itSYxEzLSqQSak7oQoEmFbmy8bDFM6lCRvNVI1G6O3Pq+00kPIymQJOKNP5xEHsUcq5uPuZQOVTlihHw43jidh3m8bwVaMqXIMfQbiCREr3E/kIAQFkkHwE/TUSisB4SPbq0wOfbfsT/fYTHs5Yhy+aG56DOKAvnI3zQZIfKYIuBC7+kQJlCrJ2+nOSEZFoNace3ayYxosFgEu30nzwl8vH1ynFcPnqRef1mks0nOx1GdcG/QAAzukxMz9dxdDca92jKxjnruHnhOmXrBtJjch8MOj371+5xmAxOpUqQY/YUkvYeJPanX1GWKYVbv54gkRL/u+2+4zFsAJqmDYn/dRXakFAURQvj1qsrcl8fnk6dDYDM2wuvnxegv3Of6LFTkSiVuPX7HK8F3/GwU09I1TpMBlt8tmAgAWUKsGvGWlLjk6k/tBW91n7L3AYjSI613Xey5/amz/qxhAeHsXnUUkxGEzV6f0zfP8azsOlontyKwMlZRc+VozEaDPw55lf0Wj31BrWk9/qxzPtopN13vynSfEVR9x6L/twRUnesRJa/OE5Nu4BEgnb3yzd/nBq0RZanMPrrlyzSJR450Az7DuOjByQvn4VE4YSyaRc0/SeTOGMg6LKmfT40ed6E8IeR9B02hvgEx/5WHMWnCwfiXyY/+6evIzUhiZpDWtFpzWiWNBhJykt+3xKphI/Gd6Fo44pceGHeAWizdBieBfw5MGMd8ZFPqfZFM7qsH8vPTb4h9t5jh8ohK1wCl1FT0R4/QPK6ZciLlkL1WS+QSknZbHvN5jxgFIqylUhavRRjxAOc6jTC5evpJEwYij70EsanUcSN7m9VTtn4U5yq1SV1/06HymCLST+Oo3i5ovwwZQmJ8Yn0HN6NHzbO5bM63YiLibdbrlCJguz6Yzd//L7VIv3ebevN1kYtGzB4XH8eP3zi6OoLPnBeW3nVaDR4eXmlf/b29mbixInUqlWL3bt307Vr1/RnMpnMKv/zvJju6+vLuHHj6Ny5MydPnqRevYzdm6NHj/Lw4UN+/PFH+vfvT1BQEJUrV7Z6j6urq8132+Knn37izJkzbN68mTx58qSnFyhQgEqVKtGmTRuWLVtGnz59XvouR+HcrRv6GzeImzYNAO2pU0hkMjQdO5K4YQNoLSdHiasr6ubNSViyhKT1681lzp4FwPWLL4hfuhRTfDwu3bqRevo0sRMmpJfVXbtGjuXLSS5fHu2ZM1kiT+HAIlSoX5Ep3Sdy9oD5O66cCmbx0Z9p3PVj/lhoeyHRrFcL4qLimNVvBnpdhpVl0PdD8c+fk/BbD6jXrgE+uXz4ptVIrp4JBeDC0fO4ZnOjx9iejG49yqGyqFp3wxB2g6QfpwOYracyOarmHUndsdFq4SL19sepal2Sls1Fu/cvc5ngc7gWLomyYQuS0pRXp9pNMEU9ImnRNDAZ0V86g8TNA+XHbUhe+UOWWsZ9AwuSt0E5tnWdxZ0DFwAIP3WVrsfnUqprA/5d8Kfdsp5Fc1FrSje8S+dDl5xq9Txv/XJ4lczLhk/G8ejCLQDuHw3GNWcOAvt/4nDlNceATqSE3uLhKLNik3T0DMjlePZpy9PfN2Oypcgo5GTr3Jwcg7vYfi6VkmNAJxKPnSV86LT05JTgG+TbsQRNtXIkHT/nUDmep1BgEQLrV+C77pM5f8Dcr0NPhTD/6E981LUxWxf+YbPcx72aExcVx9x+32F4rv/0+34wfvn9ibgVjlcubz7u1Yzfx//C3pV/AxBy/BKefjkoXbucQ5VXt15d0V27ydMJ5r6TevI0ErkM166fEb92o5WSKXFzxblVM2J/+JmEVeZxLfVf89/ZY1BfYn/4GWNMLG59umNKTObJwK8wpZp/g/rwCHLMnoJTsSJoz1sqH44kd2AhitYP5Pfu33H14HkAwk6HMuLIfKp0+YgDi7baLFe9R2N0KTp+7zErvd/cPB7MyGMLqNatEX+N/53STauQI58vcxuMSLfiPrx6l1FHF1CqaWVOrbG2YLwNysYdMT64TcrKOQAYrpwFmRynBm3QHtiaqVIm9c+HU8O2GGOjrd/7cSdMKckkLRoDOrOsxqhI1H3GIstVCMOtYIfK8aHK8zoYjUb+3LWX2Yt+eddVsUvOwIIUql+Odd2/42bavHPv1FUGHJ1H+a4fcWzhVrtlvYvmotGk7vjZmXey5/Mld6WibB+5lAsbDgHw4Ox1hp5ZTOlWNTkyf7NDZVG3TVsXLDTPD/rz5jWb6tOOpGy3XrNJffxxqlaPxJ/noN2dti64fBZ5kZIoG32KPvQS6HUYrodYlJPlL4JTtbokr/kFQ2jWjWsAJcsXp8ZHVRnWeRQn9pu9pM4HXWRz0Dpad/uU3+avtFlOqVaSK19OVixcTfDZEJt5ALJ5etB3VE8+7dyM2OjYLJHhfcMkAjY5FIcEbJLLzTqwk5PTW79LqVQCWFk7N2/eTOHChalfvz4BAQGsXZuJu9krYDKZWLVqFS1btrRQXJ9RtGhRWrRowcqVKzEa/yNvdYUCp7JlSTlyxCI55dAhpBoNTqWt3X4kzs4k//UXqcePW6Qb7pt3uWT+/kizZUPq5mad584djDExKKtWdbAgGZSrFUhyYjLnD2cs8OOi4wgJukxg3fJ2y62euYJpn0+2UFyf/b/CSQFAQMFcxMfEpyuuzwgOukzRCsVwdnN2nCByBfLiZdCdtmwbXdAhJGoN8qLWbWOMfkz8t33RHtubkWgygdEAcoXFu02pKWDK+J2Z4mORKJyQqDRkJblrl0abmMLdwxmTYUp0POEnQ8lTt0ymZRvM64dEIuGP5hNIfhJn9VybkMzlVfvTFddnxNyOwD2Pj2MESEOiUKCuVJqEPZYKccI/R5E6a1BXKGmznEutingO6ETUT+t5PPtXq+eyHB7IPFxJOGDp5qy9eRd9dCwudSo5TggblK5VlpTEZC4ePp+eFh8dx5WgYMpm0n/Wz1zFrM+nWiiuz/qPPK3/VGhYGZ1Wz8H1ey3KLhz4PfP6fec4IRQKlIFlSD5o2XeS9h9G6qxBWda670idnUncvI2Uw5Zjlv6u+aiBPKcfAOo6NUnctjNdcQXQhV4j4pN2Waq4AhSqVZrUxBSuH7mYnpYYHc/toCsUqVvWbrlHN8M58vMOi4W3LkVL3MNosufxBuDy36dY3Gq8hfuxQWvZfg5DLkdWqBT6Cy/8rc8fQ6LSICtQwn5ZqQxV52FoD23D+Mja3VNeuiq6k3vSFT0A470bJI7tlnWK3ocmz2ty7cZtJs9eRIsmDZg+9qt3XR2b5K9lnnduPTfvJEXHczfoCgVfMu80n/MFEqmE3z8dT6KNeUeW1j9S4zOOR6QmpKBP1aHO5uIgCdKQK5CXKIs2yHJs057MZF0Q9Zi4UX3RHnlhXWAwgMJ+39b0Horh/h3zRnkWU6VOJZISkzl16N/0tJjoWM6dOE/V+pXtlitYrID5+FbwjUzf321wZyrXrsjXPcdydM8Jh9Vb8P+Ht1ZeIyMjmTRpEhqNhlq1ar3Vu+7du8esWbPw9/enYsWK6ekxMTHs27ePRo0aAfDxxx+zd+9enjx5c1eD27dv8/TpUwIDA+3mqVq1Ko8ePeL+/czPljkKmZ8fEicnDPcsz4Ia0s4Zy3LlsipjfPiQ+HnzrMooa9bEpNNhuHcPY0ICJr0ema+vRR6JiwsSV1dkfn4OliSDgIIBRN6NxGiw3ACICIvAP19Ou+WiHkZxJzQMAJVGRenqZeg0ogvBQcHp6XHRsWhcNFZKqm8es5w+uR2nIEm9/ZAonDBGWP4WjJHmtpH6BVgX0usw3LoGyUkgkSDx9EbddQBSH3+0e7elZ9Pu3oLUNyfKT9oj0TgjK1gMZZM26M6dxJRo3z3HEWQr5E/c3UeYXmif2LBIPPJn/rvYO/QnNreeTFSo7bPL948Gc/AbS4VQKpeRt345oq46tk8pcvkidVKgDbNccGrvms/tOuW1/VtLuXSNW/W7Eb1kHSYbFm5jXCImnR5FTm+LdKmbCzI3FxQBvlZlHIl/wQAe2eg/kWEP8cvnb7dc9MMo7oWazyYpNSpKVi9N+xGduRIUnJ6ep3g+Im6FU7RSCaZun82KGxuZf2wJDTo3cqgM8pzmcU1317LN9ffNbSXPZd13DBEPiZk1P11ZfYa6jnlc0929j8zPF6mrC4aISDxGDMZv9xZyHv4bz9lTkfl4W73T0XgX9Cf67iOrtom6E0mOfPb7TtCqvRxZut0iLUc+X3wKBxCZ1i9S4pK4e9Z8bl+mkOFbLDdtv/+ChKg4Lu446VA5pJ6+SOQKK2XN+Njcd6Re9sdppyafgVyOdpe167ckuw8SjQvG6EiUbfvhMn0NLt9vRt1nHJJsL/eMelM+NHleFz9fb3auX8bIwX3sHpl61+QomJOnNuad6LBIsmfSdwD+Gr6YFW0m8cjOvPP46j1uHblEzSGt8CocgMrdmQZjOqFQOxG8zbGKktQnbV0QblkX48O0NZu/9ZrNvC64mrEuyOGNuvtApL7+pKZZYl9EUaM+8oLFSP5tEfwHxpS8hXITficcwwtz4v2wB+TOb0OmNAqXKAhAqy7N2XF+M0fC9vDTlgWUKFfMIt+WlX/RpnonDu46Yus1HyTGd/zvQ+O13YaXLFnCr7+aF6R6vR6tVkuBAgWYN28e/v72F1O2KFeuXPr/63Q6FAoFNWrUYPr06RbnXbdv345Wq6VJkyYANG3alKVLl7Jp0yb69u37uiIAZoUYIFu2bHbzPHsWHR1N7ty53+h7Xgepi3lX0JSUZJFuSjbvIEo0r2aFU9aqhaphQ5I2bcKUYD4Tl3LgAJqWLdGHhZF65AjSbNlwHTTIHPgkCyc4jZszyQlJVunJicmoXdSv9I7l51ejUCqIi45j+ZRl6emHtx6iRe+WfLV4FL9O+JmoyGgq1KtAvbYNAFCqHSeXxDmtbZItz+KYks2ySdSZW3mVLTqibt8LgNT9O9CHnE9/pg85T+q2dag79UPdqZ857fY1EhdOcVT17dfLTYM2PtkqXZuYjNNL2see0poZ1cd2xCOfLzt6zX3tspkhTdvAML7wWzMmmj9LXWz3Hf2jqEzfa0pJJX7XYTw6NSf1+l0S9h5H5umOz+h+mPR6JA78jdnC2c3ZKsASvF7/WXp+BQqlgvjoOFZP+T093c3Tjey+2Rkwfxib5q0j/OYDqjWrwedTzb/Bvav+cYgM6eNa4gt9J22ckzq/2rimrlsTTZOPSFi/GVN8AtI0pdd9QG+0IaFEj52KLJsHbv174fXjHCI79cKUkuIQGWyhcnMm1UbbpCYko3zFtgFQqJxo+/0X6FK0HP/9b6vn3X4dSaGapTAajGz9dhkJjx3sZqdOa5+UF8bp1LR5R2VbFmnuQjjVa0XS/K8tAmg9Q+LiDoCyeXcMd66Zz4i6uKNs1g3NoGnmM6Jaa7fPt+ZDk+c1cXdzxd3N9V1XI1OUbhq0NvqONjHlpX3HntL6PP+MW06H5SPps8cc1MhkNLLtq6U8OOPYQI4Z6wI7azZ15mObqmUn1J+lrQv2Wa4LLPI1a48+9JLd547Gxc2FRBvrtqSEZJztzKVgPu8KoFQpGfvFJNyzudFlYEd++GMuvT7pz40rZi+sOzfuZk3FBf9veG3ltUOHDnTp0gUwRwj28PBIP2f6umzduhWAx48fs2DBAqKiohg6dCgBAZY78Zs2baJo0aIUKFAAIP3/169fT+/evZFKX9+A7OHhAUB8vH3LVmyseZGQmYLrUJ7JYbLjG28v/TmUtWvj/u236C5cIGFpRuTX+DlzQKvFbcQIJKNGYUpOJnHdOiRKJThogSeRSJBILaMzS6VSTDbqLZFIMBlfLo9MLmNaz8nI5DKa9mjGlI0zmNJ9IpdPXOL+9XtM6zmZL6YPYP7eHwC4cfE6a2avos/kfqQmO3DhKnnWNnaemzLf29KdOY4+9BLy/EVQte6G1NObxBkjAVD3HI5TncakbF6B/vJZpN5+qNp0x+XrmSRM/dJxCyIb7YOdaNrm9nHsfl31MZ9R5vNGnFn0F7f/cewZa8nL2uctZHk4YSHeWh2+U4YgmTYMY1IK0cs2IlEpMTnwN2ar/0ikErv9x/iK/Wd2z6nI5DIa9/iEcRun8l33yYScuIxcIcfN0525fWdy+m+zNS/k+CU8/XPQcnA7hymvPJPpLcY1db1aZJ84mtRzF4j94WeA9OBahuinRI0an/4e/f0HeC/7AU2TBiRu2W73na+DzbaR2G8b00vGg2coXdR0+Xk4OUvlY1XfucRGWJ+z3L9gMwd//JPiDSvQakZvnLO7cfBH+2fRX5eXRtS31T5yhdm99uCfGO/aDownSTtOZIqPIWXZtPT3GJ9E4Dz8exQV6qI7bq2svy0fmjz/87zWuMZbzzueBf3ptnkCsfce8Ue/eaTGJVGieVWazuyFLjmV0J2n3ur9FkgyX7PZkvF5tP8eQ3/lIrICRVC37Y7U04uEqSMt8siKlESevzAJM791SJVfRCKRWK2h7a/byHTeWffzRvZvP8i/R8+mp50+epaNx1bRfUhnxvSb5LiKC/5f89rKq7u7u80zom/Cs/fkyZOHJUuW0LZtW3r27MmWLVvSFcbQ0FBCQkKQSCQUL148vazRaMRkMnHkyBFq1679Rt/t5eXFqVOnaNiwoc08QUFBeHl5WSnTWYUxzUr6ooVVojbvRD6zotpD07YtLv36oT1/ntgxY0CnS39mSk4mbtYs4hctQurjg+HhQ0hJQd2kCdoIx1wp0W5IB9oP+8wi7fiOY/jbcG9UaVQkxVvv7L2IQW/gwpHzAFw8eoF5exbRekBbLp8wn5W5cOQ8/Wr0xjuX2UX40b1I6rWtD0B8TOZ/r9fBlJTWNuoX20aT9jzz6IjGe7cBMIRexJSUgKbvSGSFS2J8/BCnek1J/XM1KRt/M2e+cgH9zVDcZv2GU50maHdvdYgMlYa2pNLwVhZpN7YHocnhZpVXoVHZtMi+CTKlgvpz+lC4eVXO/LiNEzNf7eqa18EQb26fFy2sz6x6xlf4rdnDlJRC5Jh5PJr2Ewp/b3QPIjElp+LeqiFJpy6+/AWvSKsh7Wg9zPK6gKAdx/HN526VV6VRkfyK/efSEXNAlEtHLzJrz3xaDGhNyInLJCckYzQaOX/AciPhwqFzlKkTiFsOd+KevL2Vzxhv7hsSZ0vvhGfjnPEl0VBdPmuD+6C+pJ69QNSIjHHtmeU25cQpi8Wj9vIVjHHxKAoVfOu6P6PekFY0GNraIu3SjiBy5Ld2G3dyVpES9/K+4+6XnW6/jiRHfl/WDFxA6H7bgb/CTl8F4NaJENRuztQd9CmHl2yzcld+U555k1idr1emzTsp1u2jfBa59591GZuuz/QTqRSMRkwp5r+BPuSMRfsYw65iSkpAGpDfIfV/kQ9Nnv91ag5pSa1hln3nyo4gnPNZ9x2FRkXKW847lXs2QSKRsKbTDJLT1gBhx4JRumpoPLk7obtOv9KG2atgSnzJmi0p8zWI8e5tjID+ykVMiQk49x+FrEhJDFcvp+dxqlIbY0IcunOOPS7wjJ7Du9Hry+4Wafu2HSR3fut1r9pZTWK8fZnu3rzH3ZuWlvGEuAQunr5MweKOG4//FxEBmxzLayuvWYVarWb27Nm0adOGSZMmMXeu2a3wjz/+QKFQsGLFClxcMg7bJyYm0qVLF9auXftGyqtMJqNr1678+OOPtG/fnkKFClk8Dw0NZevWrfTt2/c/uyrHEB6OyWBAltPyTM6zz/o79u/Xch08GE2rVqTs30/s9OkWiiuAU9WqmOLj0V2+jCEsDACJhwdSb2901xxzpczuNf/w7z7L+0srNapM2VrlrKwUfnn9uJeJ60jFBpVIjEsk5FRGEAy9Ts+d0DByFTK7cOfwz0HpGmU5vOUgj+5l3LGXv1RB4p/G8fj+I6v3vinGyAeYDAakvpZtI/UxfzY8sG4bqZcv8hKBaI/tsWgP/U1zgCmppxeYjEikUvTPTVYAxvthGONjkQXkdZgMwWv2E7bPcoGcr1F5ctcunbblndE+7nl9iH6N+/bs4eSqptnyEfiWL8jRSas5/7Pj7w8F0N2NwKQ3oMhteV7KKbd54yT15pu7KTnXqYQxNoHkcyFo036zsuzuyP28SAnJPDDF67BvzW7O7vvXIq1Co8qUqlXWqv/45PXl/g377nOBDSqSFJdI6KmMiI8GnZ67oXcIKGQ+s/QwLAKpVIpcIUeXmvH7lKdZNHUpjrn2Q//gASa9AXkuy74jDzB/1t0Os1vW48tBuLRrSdKeA0RPnGHZj+6bx0uJrSAncrlFEKe35dSafYTuO2uRVrxhBQrVKmXVNp55fHh0I/Mz3b5Fc9Fj+dcoVE781m0mt09esXgeUKYA2QJycGmHZaCw+xdvEti6Jprsrg5zHzY+iTD/HXNY9h2pl7nvGB9a/87kZasj9fTBdfYmq2eu8/4iedVc9OePYTIakMhttI9MlmXXynxo8vyvc27Nfm68MO8UblSB/LWs553seX148pI7kl+GW84cRN0MT1dcn3E36ArFmlbG2dPVZqCnN8EYGW57XZD22XjfxrrA2xd5yUBzwKbnfjMZ6wJvnj9pqihfFd2po1l268DWVdusgibVblyDKnUqWo1tAXlzcvua/XVogxb1iHsay6nDlhuiSpXT/5uowoL/BodEG3YURYsWpVevXuzcuZP9+/ej1WrZvn07jRo1IjAwkMKFC6f/K1euHM2aNePQoUOEh4e/0ff17NmTmjVr0rlzZ1avXs2dO3e4c+cOq1evplu3blSuXPk/vSYHrRbdhQsoXwh8papdG2N8PLorV2wWc+ndG02rViRu2EDspElWiiuApnlzXL74wiLNuU0bMBrRnnBMEIOnj6K5eemGxb8Lh8+jcdVQtnbG+Wa37G4Ur1ySC89FUH2R5n1a0nfaF0hlGT9RjauGIoFFuHPFbMV09/Rg4KzBlKxaKj2Ph5cHNZvXJGh3kNU73wqdDn3oBRQVa1okKyrXxpgQj+GGddtIvf3Q9B2BoqJleyrKmCPUGu7cxPjQrBS/GJVQ6pcLqas7xscPHSZCYmQMjy7etvh37/AlnFzV5K6d8TdUZXfFv0pR7h1+u2itEpmUpr99iXeZ/Pzdf1GWKa4AJq2OpH8v4dqwukW6S6MaGGLjSbl49Y3f7dHhY7xG9bJIy9btUzAYSTzgOBe0mEdPuX3ppsW/S2n9p/Rz/cc1uxvFKpfgUib9p2mfFnw+rZ9F/1G7aigUWIQ7V8IA0i2uVZvVsCgb2KAid0Ju2zxr+0ZodaSev4i6juX3aOrVwhgXjzYk1GYxt/69cGnXkvg1G4keM9lqXDMlp6A9fwl13ZoWUTqVFcoh1ahJdWC04fhHMTy4dNvi3/Ujl1C5aihUO6PvOmd3JV/lYlw/Yv+73f2y8/nK0ZhMJn5qPcFKcQUoWq8c7ecPxN3f0yK9cO0yxD166rDFN2AOIHPzMooy1SyS5WWrY0pKwHDHenMzeekkEmcNtfhnuHcDw70bJM4aiv7yKdCmYLgZgrxMVZBn7JPLCpdBolRjuJlF0Xk/NHn+x0l4FEPEpdsW/24dvoTSVU2B5/qOJrsruSsX4/ZbzjtRN8PJUSgnKndLT4+A8oVJiUsi+SV3l78WOi36Kxdwqmw5x5utpfHo7awLnL8YiaLyC2uJss/WBRkbohIXV2R+AVab247kSWQUoRevWvwLOnQaZ1dnqtTJCJzqkd2dclXLEnTotN13te7WghHTh6dvgAJ4+eagdMVSnD1+Pstk+F9ABGxyLO+N5fUZ/fv3559//mHixIkMHTqUp0+f0qlTJ5t5P//8c7Zs2cKGDRsYOnToa3+XTCZjwYIFbN68mY0bNzJ37lxMJhOFChXiq6++ok2bNi8/P+NgElauJNv33+M+YQLJO3eiKFkSTYcOJCxZAlotEo0Ged68ZmtGbCzyggXRfPYZutBQUg8eRPGcazWAPiwMU1ISSZs2kW32bFwGDiT12DGcAgNx7tyZxNWrMTjIbdgWIaeCuXTiIkPnf8nK6b8T/zSe9kM/IykukX9WZSgzAYVyoXBScDvYfKB/4/x1jF05gRGLv+afVbtQu2ho1b81SrWKdXPXAHDz0g2unA6hz9QvWDHtNwx6I51GdMagN7Jh3ttdpWSL1C2rcB49G82Q8WgP7kJeuATKT9qTsnapeQdVrUGWM495NzY+Fn3IBXTBZ1H3GIzE2QVj+D3kJcqibNaB1L3bMIabrXipu/5A+Ul7APSX/kWawwdl624YHz9Eu98xZ/bsER50lfvHQ2i4sD/Hp64jJSaBSsNaoo1L4vKqfen5shXyR+ak4Emw/V3XFynV7SNyVi7K5ZX7SAiPwqdcAYvnkeduOkwOgOjF6wj4bRr+80YTu2k3qnLFyN6zNY9n/4opVYvUWYNTwdzo7kZgePrqu8BPV/5FrmVT8fqmL4n7T6KpUgbPvh2IWroe3X3HbS7YIvRUCMEnLjFg/lDWTF9BwtN4Wg9tT2JcIvueO5Oas1AAcicFd4LNGztb5m/g65XjGLp4JHtX/Y3aRUPz/q1QqpVsmrsOgCsngzmz5xRdxvdEqVFx7+pdarauQ+EKRZnTa7pD5Yj/dRU5Fs0i+7TxJG3bhVPpErh0bm8+v5qqReKsQZEvD/r74RhjYlEUKoBrlw5oQ0JJ3ncQp5KW0Sp1t+9gSkwi9sdf8Fo8hxxzp5OwegPS7NlwH9ib1MshpBw5bqc2jiHsVCg3TwTTft4Adk1fQ1JMAg2GtiYlLpGg5/qOd8GcyJRyItL6TrMJ3XD1cmfL6F9QuqrJVS7DnS41PplHNx4QtGovlTrWo/uvI9i3YAvapBQCW9eiaL1yrBu86KVn6V4X7T/rUQ+YgqrH1+hO7kGWrxhO9VqR+tfv5rFNpUbqmxvTkwhMCXEYI6zHgWdutcZ7GYvv1G3L0QyejrrvRLT7NyNx9TAHPAoLRX/JwRuMH7A8Hxr3ToUSdiKEFvP7s3/6WpKfJlBzaCtS4pI4uyrjCpkchXIic5IT+RrzTtAvuyjZsjqd1o7m2KI/SY1PpmjjipRoUY09k1dh1DvWgpmyaSUuY7/HefgEUvfvNN/X2rwDyavNazbUGmQBec3eW3Fp64LLZ9H0HEKKxgVD+D3kJcuhatGB1D1/YXyQ4SUky212RTfcD3NonV/G+aCLnDl2jgmLxvDD1CXERsfS68vuJMQmsGVlRkTkvIXy4KRUcO2yuY/8OncF89Z8x4xfJvPHb1twy+ZKr+HdiY+NZ/VP6/9TGQQfNhKTo2dBgQWRdeq8dhlljRo49+iBPFcuDE+ekLx1K0kbzOcEFWXLkn3ePGJnzCDl779x7tEDl27d7L4reuhQdOfPA6CqVw/nLl2Q+flhiIwkaetWkrdsea26fXHb+nzky3B2c6bH2J5UalQFiURC6Jkr/DZpGeG3MtyDJq2bineAN/1q9E5PK1W9NO2GfEa+4vkwGY1cPnGJVbNW8uA5lzz3HB70GNeTsjXLIZFgzjNzBRFhr6aQ/1rt9a6iUVSogaptd6R+uTBGP0G7Z2v6vWvyYmVwGTePpMUz0B5OUyzUGlStuqKoVAtpNk+Mjx6i3beN1L83WbhLKZu0xql+c6TevphiotFd/JeU9b9gin89V5tVR+1fA2EPpbuGGuM6k69heSRSCRH/XuPoxNXE3Mr4G7bc8C2uATlYUW2YzXd0PT6XByevsG94RpCwln+MIWflona/d1GuzpnW6yPnzCMB28KlQTVyDOqMIl8A+sgnxKzZztPfzJfSqyuVIveK74j45nvituy1KuvWsgF+07/kZv1u6B9Yupy7Nq2NZ7/PUAT4oAt/RMzaHcSssn2tgT0mJr9ZZGJnN2c6j+1BhUaVkUgkXDsTyspJvxJxK8PjZMy6yXgFeDOkRkb09RLVS9FqSHvyFM+HyWgi5MQl1s9aTfhz/UehVNBqSHtqtKyNa3Y3Hty4x5b5G/h398styt/5vt5vU1W7Bm69u6HIkwvD4yck/PEnCWvMfUcZWAavxXOJnjSTpB3/4NanO249u9p91+MvhpF61nyW16lUCdy++BynEsUwpaSScugoMQt+wvSSs7Qv8kPk6197pHJzpunYzpRoWAGJRMKdM9fYPnklT57rO73XjSFbgBff1RiCTCFjYshvyBS2941vnQzh5w7mKOOeeXxoNLIDeSsVReWq5sHl2xxc9CdXD55/pbqNbv561ll56ao4NemI1CcAU0wU2iM70B0wzw+ygqXQDJ5udp89tc9mefUg84ZH8sJvLNKl+Yqi/KQrsjyFMWlT0V86SerWXyHZgRaw/zF5VOMWvKFUr8epsxf5fNAofl04k0qB1neOOorvyo997TIqNw0NxnamcCNz37l/5hp7Jq0i+rm+03ndt7gHePFDjaE23zHg6DzunLzC9q+WWKRnz+9H3VHtyVu1OBKplCfXH3BiyTau/v2vzfe8SP9Kr+e6rKhUA1W7Hsj8zeuC1L+3krrdvGaTFy+L68R5JP4wA+3BtIBeag3qNt1QVMlYF6Tu+YvUnZbrAkXVOrgMn0DskK7pm91vQpNjL8/zIq7uLgyZMIBajWoglUq4ePoy8yb8YHGm9cc/5uGXy5eWlTNiNVSqVZ7Ph3WjYLH8GI0mgg6dZtGUn4h8YPsY19i5XxNYrazFOzLjZPjB1xfmPaBb3tYvz5SFLA+zPhLxv4xQXrOYN1Fe32feRHl9n3ld5fV9502U1/eVN1Fe32feVHl9X3ld5fV9502U1/eZ11VeBf8d/5Xy+l/xJsrr+8zrKq/vO2+ivL6v/K8qr13ytHp5pixk5Z3N7/T7Hc17deZVIBAIBAKBQCAQCAQCW7x3Z14FAoFAIBAIBAKB4ENAuLg6FmF5FQgEAoFAIBAIBALBe49QXgUCgUAgEAgEAoFA8N4j3IYFAoFAIBAIBAKBIAswCsdhhyIsrwKBQCAQCAQCgUAgeO8RlleBQCAQCAQCgUAgyAJMwvLqUITlVSAQCAQCgUAgEAgE7z1CeRUIBAKBQCAQCAQCwXuPcBsWCAQCgUAgEAgEgizA+K4r8IEhLK8CgUAgEAgEAoFAIHjvEZZXgUAgEAgEAoFAIMgCxFU5jkVYXgUCgUAgEAgEAoFA8N4jlFeBQCAQCAQCgUAgELz3CLdhgUAgEAgEAoFAIMgCxD2vjkVYXgUCgUAgEAgEAoFA8FqkpqYyceJEqlatSrly5Rg8eDBRUVGvXH7x4sUUKVLktb5TWF6zmC9uu73rKjiUaUr9u66CQ+l/3ONdV8Gh9NTq3nUVHMYYqdO7roJDGW36sILlz4r0ftdVcCij84a/6yo4lDZb1O+6CgI71Ppz7LuugkMZeWbyu66CQ+lcfvi7roJD2VHu6buuwv97PqzZ35IJEyZw5swZFi5ciJOTE+PHj2fIkCGsWrXqpWUvXrzIokWLXvs7heVVIBAIBAKBQCAQCASvTGRkJFu3bmXMmDFUqFCB0qVLM2fOHE6fPs358+czLZuUlMSIESOoUKHCa3+vUF4FAoFAIBAIBAKBIAswmUzv9F9WcebMGQAqV66cnpYvXz58fHw4ffp0pmWnTp1K4cKFadGixWt/r3AbFggEAoFAIBAIBIIPkPr162f6fN++fW/03sjISLJly4ZSqbRI9/b2JiIiwm65PXv2cOjQIbZt28aBAwde+3uF8ioQCAQCgUAgEAgEgnTu37+fqeI7ZMgQnJys45MolUpSU1NtlomMjGTs2LF89913ZMuW7Y3qJZRXgUAgEAgEAoFAIMgCjO/4qpw3taz6+Piwc+dOu88PHTqEVqu1Sk9NTUWttg4aaDKZ+Prrr2nSpAm1atV6ozqBUF4FAoFAIBAIBAKBQPAcCoWCAgUK2H1+9epVYmJi0Gq1FhbYR48e4evra5U/PDyc48ePc/bsWbZu3QqAXm++xaRcuXL07duXfv36vbReQnkVCAQCgUAgEAgEgizgQ70qp3z58hiNRs6cOUPVqlUBuHXrFpGRkTajCPv4+LB7926LtN27dzN79my2bt2Ku7v7K32vUF4FAoFAIBAIBAKBQPDK+Pj40LRpU8aMGcO0adNQq9WMHz+eSpUqUbZsWQC0Wi2xsbG4u7vj5OREnjx5LN7h6ekJYJWeGeKqHIFAIBAIBAKBQCAQvBaTJ0+matWqDBw4kJ49e5I/f34WLFiQ/vzcuXPUqFGDc+fOOew7heVVIBAIBAKBQCAQCLIA0zsO2JSVaDQapkyZwpQpU2w+r1y5MlevXrVbvlWrVrRq1eq1vlNYXgUCgUAgEAgEAoFA8N4jLK8CgUAgEAgEAoFAkAW866tyPjSE5VUgEAgEAoFAIBAIBO89QnkVCAQCgUAgEAgEAsF7j3AbFggEAoFAIBAIBIIswGQSbsOORCiv7ykqZzXdRnenUsMqqF3UXD0TyrKJP3P/+r1MyxWvVIKOIzqTr3g+UpJSOL7jGGtmryI5ITk9T3af7HT9tgflagcik8m4du4qa2av4saF6w6Xw7lmeXIM7YqyYC4M0XE8XbeT6CUbXqmsskRB8m6Yw62GvdA9eGTxzKVBVXL074BTvgD0T54S++d+opZsAJ3e4TK8iMpZRcfR3anQsBIqFzXXz4SyYuIyHly/n2m5opWK025EJ/IUz0dKUjJBO46zcfYai7YBaNqnBfU7NiS7Xw6ePHjMP7/vYM+KXVkiS/a6ZSjwdXucCwegjYrjwYq93FmwNdMyvq1rkGfwp6jz+JDy4Al3f9xG+Or9FnlqXFqC0tvDquyRkn3QPo51oATWqJzVdBndnUoNK6NyUXPtTCi/TfzlpX2nWKXifDaiM3mL5yclKZkTO46z7oW+45rNlY6juhJYtzxKtZKbl26yctpvhAXfzhJZXGuXw39EZ1SFcqGPiuXJ6r+J/GHTK5VVlypAka3fEVL7C7T3LfuP52cf4dWzOcrcPmgfPOHJyp08/nV7VohggdJZRYvRnSjVsCJKZxVhZ66xaeJyIm88yLRczuJ5+GREB3KXLoBEKuHepdtsm7mG+8FhAHSc/QWV29S2W35C9YE8ffDEkaLgVKkSLj17Is+TB2NMDEl//UXSmjX2C8jlaNq1Q924MTIvLwyPH5Oydy+Ja9aAPmPcUjVujHP79shy5sQYFUXyP/+QuHIlGAwOrb8t1M5qen/bk6qNqqJx0RDybwiLJyzh7vW7r/yOqg2rMGHZeEa0HcnFk5ds5vHyy8GSvT+x+ZctrJq72lHVt+JDk8fJWUX90R0p3LA8Ti4q7p+5zp6JK3lyPfP+8zytlwwlNT6Z7V8tsUhXZ3Oh7qgOFKxbBoVaScSl2+ybtobI4DuOFuONiYh8TKuuXzB/+jgqBZZ+19WxQOWsovPo7lR8bt75feIvL10XFKtUnPYjOpM3bV1wcsdx1s9enT7vfDF7MHXa1rNbfkC13jx58NihsijKV0LTpSeyXHkxxsWQsvMvUjZm8rtWKtF07IFTrbpI3TzQ375J8trf0Z05ZZmt0SeoWrRB5uuP8XEkKTu2kvLXq81nAsHzCOU1jXr16vHgQcYEoFAoyJkzJ23btqVXr17/eX2GL/yKQmUKsWL67yQlJNN+SAcmrpnCkAYDSIhNsFkmX4n8jF05gYtHL/Bdvxlk98lO51HdyFkggEldxgOgcdUwddNMVGola2evIuJ2BJUbV2HyhmmMa/8t189fc5gM6nLFCFg8jrhdR3gybwXq8iXwGtYViURC1E/rMy2rLJqPXEsnIFFY/0Q11cqRc9G3xO88zKPZv6Mskhev4d2QZ3cnctJih9XfHgMXfkmBMoVYO305yQnJtBrSjm/XTGJEg8Ek2mmbPCXy8fXKcVw+epF5/WaSzSc7HUZ1wb9AADO6TEzP13F0Nxr3aMrGOeu4eeE6ZesG0mNyHww6PfvX7nGoHO4VClNmxUgi/zzOzRnr8ahclALftEcilRA2b4vNMt7NqlB80QDu/byLqP0X8GpSgWJz+mJI0RK56SgATl7uKL09uDZuObH/Wm6I6J7a/vs4kqELv6RgmUKsmr6cpIQk2g7pwPg1kxnWYKDdvpO3RD7GpPWd2Wl9p9OoruQskJMpXSak5xux9BtyFghg1YzlPI2M5tMvWjNx/TRGNBnCo3uPbL77TXEuX5T8y74lZvtRwmetwqVicfxGdAaJlMhFGzMtqy6WlwK/jbXZf3J0bkyuaV8Q+eMm4o6cx7lsYXKO+RypWkXkD384VIYX6bZgMLnLFOCvGWtIiU+i8dA2DFw7lukNviQpNtFmGc/cPgxeP557wWGsHbUEk9FE3d5NGfLHRGY1/ZpHtyL4Z8Fmjq227B/O7i50/3EoN06GEBMe5VA5FCVK4DF1KikHDpCwbBmKUqVw6dULiVRK4qpVNsu4DhyIulEjElauRB8airxQIVy6d0fm40PcrFkAqFu3xm3QIFIOHiR+8WKk7u449+iBvEABYseOdagMtvhm0SiKlC3CL9OWkRSfROdhnZi5bjp96vclPublfdfVw5XB0we9NN/w2cNwdnN2RJUz5UOT59OFA/Evk5/909eRmpBEzSGt6LRmNEsajCTFTv95hkQq4aPxXSjauCIXNh62et5m6TA8C/hzYMY64iOfUu2LZnRZP5afm3xD7D3HKkdvQvjDSPoOG0N8QuZyvisGp807q6evIDkhiTZD2jNuzWSGNxhkd12Qt0Q+Rq8cz6WjF5mTti7omLYumJY272xesIG9q/+2KOfs4cqwH0cQcvIyUeGO3ZSTFyuB69hpaI8cIGnlMuTFS6Hp2guJVELyettjm8vQr1GUr0TS70sxht9HWb8xruOnE/fNMPTBFwFQftwClwHDSd64msRz/6IoUhxNr/5IVGqSN9h+74eE8V1X4ANDKK/P8fnnn/P5558DkJKSwoULFxgzZgxqtZpOnTr9Z/UoHFiECvUrMqX7RM4eOAPAlVPBLD76M427fswfC21bLpv1akFcVByz+s1A/5wFctD3Q/HPn5PwWw+o164BPrl8+KbVSK6eCQXgwtHzuGZzo8fYnoxuPcphcngO7EhK6C0iRswGIPHIGSRyGdn7tCX6ty2YUrXWhRRysndpRo4hXTCl2HgOuLf+CH34Y8K/mg1GI0nHzyH3dCdb95ZETlsK+qyzUBQKLEJg/Qp8130y5w+cBSD0VAjzj/7ER10bs3Wh7cX/x72aExcVx9x+32F4rm36fT8Yv/z+RNwKxyuXNx/3asbv439h70rzZBVy/BKefjkoXbucw5XXfF+1IT44jJCBPwAQfeACErmMPINacPen7RhTdFZl8n/dnkfbgrg+boW5zMELKLK5kH9E23Tl1aVkXgAe7zhFyn3HTqwvo3BgEcrXr8i07pM4l9Z3Qk+F8MPRpTTs2oTNC20rfc/6zvf9Zlr0nQHfD0nvO375/ClWqQSLRy7kwIZ9AFw7e5VfzqygVqu6/DE/8w2Z18V3aAeSQ25zZ+g8AOIPnUOikOHTvzWPfv7TZv+RKOR4dW+K31edMKak2nyvT//WPN12lPAZ5jZMOHYRZX5/vHo0zVLlNW9gIUrUD2RJ9xmEHDwPwM3ToYw/spAaXRqye5HtDZPaPRqjS9GytMdMtMlmma4dv8yEY4uo2a0xm8b/RtTdSKLuRlqU67lkOEkxiawcusjhLlvO3bqhv3GDuGnTANCeOoVEJkPTsSOJGzaA1rJtJK6uqJs3J2HJEpLWm38n2rPm8cP1iy+IX7oUU3w8Lt26kXr6NLETJqSX1V27Ro7ly0kuXx7tmTMOleN5igUWpXKDyozpOo7TB04DcPlUMMuP/8YnXT9h7YJ1L33HoGkDMLxk/P2ka1NyFczlkDpnxocmT87AghSqX4513b/j5oELANw7dZUBR+dRvutHHFu41W5Z76K5aDSpO36l86FLth4XsufzJXelomwfuZQLGw4B8ODsdYaeWUzpVjU5Mn9zlsj0KhiNRv7ctZfZi355Z3V4GYXS5p3p3SdzPn3NFsKio0tolMm80/S5eef5dUH/59YFkXcfEnn3oUW5L5d8TUJMAguHzHX42Kb+rDuGWzdI+H4qALozp5DI5ajbdCJ5i/XYJvX1R1mrHgk/zCF155/mMhfO4lG8JKqmn5KQpryq23Yk9fB+kn5fCoD+wlmkOQNQNWv1/0J5FTgWEbDpOTQaDV5eXnh5eYpBpp4AAJSzSURBVJErVy4++eQTmjVrxqZN/61bQ7lagSQnJnP+8Ln0tLjoOEKCLhNYt7zdcqtnrmDa55MtFt/P/l/hpAAgoGAu4mPi0xXXZwQHXaZohWIO2z2WKORoKpcmfvdxi/T4f44ic9GgrlDCZjmX2hXxHNiJqMXreTT7N5t5pE4KjMkpYMzYyzJExyF1UiB1Vjuk/vYoXassKYnJXDx8Pj0tPjqOK0HBlM2kbdbPXMWsz6daTFDP2kae1jYVGlZGp9VzcP1ei7ILB37PvH7fOVAKkDjJyVatOI93WLr1PNoehNxFjUflYlZlVLm8cC7oz+OdL5TZFoQmny/q/H4AuJbMiy4m4T9XXAHK1CpHSmIyF6z6TjCBdSvYLbd65kqm2+k7cifzHt+zPpQUn5SeJzkhGV2qFtdsbg6VQ+Ikx6VKSWL/PmmRHrPjODIXNS6Vitss51a3PL5DO/Bw4UbCp6+wmedG14k8mPa7RZpJp0eSJl9WUbRWGVITUwg9cjE9LTE6nhtBIRSvW85uucib4ez/eXu64gqgS9ES8zCKHHl8bJYpUS+Q0o0qsWXyCpLjkmzmeWMUCpzKliXlyBGL5JRDh5BqNDiVtnZnlDg7k/zXX6QetxwPDffNLoUyf3+k2bIhdXOzznPnDsaYGJRVqzpWjhcoX7s8yYnJnDmcoSDHRsdy8eQlKtWt+NLytZvVIrBmIL9MW2Y3j29uX3p+8znzRs13SJ0z40OTJ3+t0mgTU7h1OMN1OSk6nrtBVyhYt0ymZZvP+QKJVMLvn44n8Umc1XNZWt9Pjc84IpGakII+VYc6m4uDJHgzrt24zeTZi2jRpAHTx371Tutij2fzzsXn5p34tHkns3XB2pkrmfn5FJvrAoWd8TiwXgUqNa7CisnLSIpzsBVarkBRuiypJywt86lHDyHRaFCUsB7bjE8eEzOkD9oDz22um0zmYw6KDBnix40k6defLAvr9RZ5BIJXRSivL0GtzlplyBYBBQOIvBuJ0WDpaBARFoF/vpx2y0U9jOJOaBgAKo2K0tXL0GlEF4KDgtPT46Jj0bhorJRU3zy+APjktr0YfF0Uuf2QOinQhlmexdHeiQDAyY4cKZeucbNud7NbsZ0d76ertuGUx5/sPVsjdXVGVaYI2bp/SsLBUxjtuOc4Cv+CATyy0TaRYQ/xy+dvt1z0wyjuhZrPDik1KkpWL037EZ25EhScnp6neD4iboVTtFIJpm6fzYobG5l/bAkNOjdyuBzqPD5IlQqSbkZYpCffNu/wagr4WZVxLmRusxfLJL1QxrVkXvSxiZT69UtqX/+N2reWU3LJEJxsnIF1NAEFc9nsOw/DIl7aPnfT2kGlUVGqehk+G9GZkKDg9PS7V+9w8ch52g5pT67CuXFxd6HrmM9xUis5tu2I3Xe/CU65fZEqFaTcsuw/qWn9R5nftixJF64TXK03kYs2YrLTf1Jv3EeXdkZK5u6CZ4ePyN6qLk+W73SgBNb4FMxJlI22eXInEq98vnbLHVu1h/1LLc/jeuXzw69wLiKuWp9jlkgkNB/diesnQ7iwK8gxlX8OmZ8fEicnDPcsv9uQduxElsvaCmd8+JD4efOsyihr1sSk02G4dw9jQgImvR6Zr+XfQuLigsTVFZmfdZ90JLkL5SLi7kOr9gkPCydnfvvzDoBHDg8GTOnP4vE/ER0ZbTOPRCLhqznDObz9CP8ezDoL8jM+NHlyFMzJ07uPML0gT3RYJNnzZf7b+Gv4Yla0mcSjUNvn/h9fvcetI5eoOaQVXoUDULk702BMJxRqJ4K3nXCYDG+Cn683O9cvY+TgPqhUqndaF3vktLNme515R6lRUap6aTqkrQuepT+PRCKh87fdCD5xmaCdjm8XmZ8/EoUTxgeWvxNjRNomW04bHgZ6HYYbVzElJ4FEgtTLG03vgUh9/dMtsQCGe3cwPjZ7x0hcXFE2bIqyXkNSttv2uPnQML3j/z40hNtwJly8eJFt27YxdOjQ//R7NW7OJCdYWwuSE5NRu7yaMr38/GoUSgVx0XEsn5Kxc3x46yFa9G7JV4tH8euEn4mKjKZCvQrUa9sAAKXaMZOD1NWsHBtfkMOYaP4sc9HYLKePfPnZtKSgi0T9sgnvUT3xHtUTgJTgG4QPd6x10hbObs5WAZbg9dpm6fkVKJQK4qPjWD3l9/R0N083svtmZ8D8YWyat47wmw+o1qwGn0/tB8DeVf84RAYAubv5769/QRZD2meZq7Us6WXik2yWkaeVcSmRB6WfJw9W7efekh1oCuck/8h2lN86nqAGX2NMsu3O6ggy6zuaV2yfX8+vSu87K6b8avFs2biljF4+jjl7FgJml7Yfv1rAtRc8Gd4Wmduz/mOnfez0H52dhbYtnCsUpfDmmQAkXbzB4993vElVXxm1m4YUG30nJSEZlR15bKFQOdHp+y/QpWg5/PvfVs9LNAjEt2BONo237bnxtkhdzJYoU5Ll78yUbJZNonk1WZS1aqFq2JCkTZswJZg33VIOHEDTsiX6sDBSjxxBmi0broMGmS0UWbxwd3ZzsfAqeIa572Qu09CZg7lyJpR9m/dTukopm3la9voU39x+jOsxwRHVfSkfmjxKNw1aG/1Hm5iC8iVjmz2l9Xn+GbecDstH0mePeUwwGY1s+2opD844PpDj6+Du5oq7m+s7rcPLcLYz76S8xryz7PzK9Hln5RTbY1f5BhXJWTAXv437+a3qaw+Js52xLenZ2Ja5Z566XSc0XXsDkPLPdnSXzlvlkRcrifts81El/fVQUra9O5d0wf8uQnl9jiVLlvDrr+bFqk6nQ6fTUaZMGT7++OMs+06JRIJEKrFIk0qlNs8xSCQSTMaX76DI5DKm9ZyMTC6jaY9mTNk4gyndJ3L5xCXuX7/HtJ6T+WL6AObvNQ8gNy5eZ83sVfSZ3I/U5BTHyCVNM+rbOY/xKnLYw2fSQDxafcSTH9aSdOI8igBfcgzuRK5lk7nbbTQmO2f9XhdbbSORSuy2jfEV22Z2z6nI5DIa9/iEcRun8l33yYScuIxcIcfN0525fWdyOs1dNOT4JTz9c9BycDuHKq8SSebtgy1Z0tvU6mUWZUKGLsaYoiPhchgAMUGhJIbep8L2Sfi1rcWD5Y45u2u777x9+8zoOQWZXMbHPZoxaeN0pnWfRPCJS+QsGMCUzTN5dC+S2f1mkBSXRPXmNek3cyCpyamc3Hn8pe9/ZdnS5LJ7nskB55xS7z3ietvRKHw98R3+GUW2f8/VZl+if/L20aBtto1EYrPaEokEk+nVwlkoXdT0/vkrcpfKz7K+c4iJsN7sqtW1EfeDb3Pt2OU3qvtLecnY9ipto6xdG/dvv0V34QIJS5emp8fPmQNaLW4jRiAZNQpTcjKJ69YhUSohxTFjM9hvH+vO/fJ5p0GbBpSsVJI+DfrZzROQPyfdRnRlcp+pNhXKt+VDk4fXmnvMiubb4FnQn26bJxB77xF/9JtHalwSJZpXpenMXuiSUwl94ajI/2fsrwtsZn7leee7nlORymV83OMTJmycxozukwg+YTmGNer2MbeDb3Hp2EU7b3pLJJnPOy8bp7Unj6O7fBF5oaKoO3VHmsOb+HEjLPIYIiOIHTUYqacXmk49cJ+3lNhhfTHFPHWMDO8pxg/Q+vkuEcrrc3To0IEuXboAoNfrCQsLY+7cuXTs2JFNmzbh5OTk8O9sN6QD7Yd9ZpF2fMcx/G24mqg0qleaKA16AxeOnAfg4tELzNuziNYD2nL5hPmszIUj5+lXozfeucwuwo/uRVKvbX2AV4rA+CoY4szvkb6wwy11Nn82xr/ZWQ25jyce7RoT9dMGnsxfaU48dYnkS9fIv2Mx7m0+ImaVY678aDWkHa2HdbBIC9pxHN987lZ5VRoVya/YNpeOmINtXDp6kVl75tNiQGtCTlwmOSEZo9GYHvDhGRcOnaNMnUDccrgT5wDFAkCfdlZG/sKusCzts97GOUF9WjTLF62yMmeVRZm4f6136mNPX0UXm4hLiTxvWfMM2gxpT7sX+s6JHcdsumm9Tt+5mNZ3Lh29wJw9C2k1oA3BJy7xSc/mSCUSJncaT0JMvDnPsQtoXDX0mtyXoF0nHBY8w5DWPi9aWJ+1j8EBZ530kdEkpFlqE89fo/ihxXh2aPjSSMavQqMhrWkytI1F2rkdJ/HOb913lM4qUl7hXKqHnyd9fx2JV34/fh84n+D9Z63yaDxcKFi1BNtmrn3zyr8EY5qV9EULqyTtiMkzK6o9NG3b4tKvH9rz54kdMwZ0GYHRTMnJxM2aRfyiRUh9fDA8fAgpKaibNEEbEZHJW1+PTkM70mV4Z4u0w9uPEJDDwyqvSqMi0c547enrSb/xfVg65RdinsQglUmRymQASGUypGmK/ldzv+LI9qOcPXIWqSzjtJJUKkUqk1q5W/5/l6fmkJbUGtbaIu3KjiCcbbjXKzQqUuKtLbKvQ+WeTZBIJKzpNIPktDVA2LFglK4aGk/uTuiu0w7ZMPsQaD2kPW1fWBec3HEMPzvrgjeZd77fs4BPB7SxUF5dPFwpUbUUa2bajmXgCEyJaeu2FyysEk3a2JaY+bxjuHMLAH3wRUyJCbgMHYW8WEn0VzLkMEVHoY82bzrqr4bg8fNqVI2a2o1kLBDYQiivz+Hu7k6ePBmL6wIFCuDu7k6nTp04fvw4derUcfh37l7zD//uO22RVqlRZcrWKpdmkciYMPzy+nHvhv376So2qERiXCIhp4LT0/Q6PXdCw8hVKDcAOfxzULpGWQ5vOcijexnROfOXKkj80zgev3Af5JuiuxuBSW/AKbelIuGUx3w2JzUTOTJD7ueFRCol+WyIRbr2+h30T2NRFnSccrRvzW7O7vvXIq1Co8qUqlXWqm188vpy/4Z916zABhVJiksk9FRGvQ06PXdD7xBQyHyO5GFYBFKpFLlCji41Y0ErT7vuRGcn+vKbkBwWiVFvQP3CYujZ58Rr1nfTJd0IB0CTzzfdqvrs87MycjcNXk0rEXfmOonXLM9rShVydNHxDpNh75rdnHmhfSo1qkwZG33HN69fpu1ToUElEuMSuPJc+1j1nZzePLj5IF1xfUZI0GWqNq2Om6cbsQ7aXEi98xCT3oAyr+VZNmVa/0l5yZ219pA6q3H/qBKJ566ivZMRwVJ75yGG2ASc/HO8eaWf4/iafQTvs1QuSzWsQLFapa3aJkceHx6+5J5X/6K56bf8axQqJ37qNoMbJ0Ns5itWuwwyuYzzO0/afO4IDOHhmAwGZDktz00++6y/Y/9eTNfBg9G0akXK/v3ETp9uobgCOFWtiik+Ht3lyxjCwgCQeHgg9fZGd81x15jtXLOLoH2W1rRqjapSvnZ5q/bxz+tv917UwJrlcPVw5cvZw/hy9jCLZzPXTefhvUhGthtJscCiFAssykdpx1Oe0WloRzoN7UjXqt2IfIu550OT59ya/dzYd84irXCjCuSvVTrN1JohT/a8Pjx5Sf95GW45cxB1MzxdcX3G3aArFGtaGWdPV5uBnv4/sm/NP5x9Yc1WsVFlStuZdx5kMu+UT1sXXMlkXfCMsnXKIZPLOLnDcR4+L2KICMdk0CP1sxzbpH4B5ud3w6zKSH18UZQJJPXAXtBlrFH018xHaaRe3hCmxqlydfRXr2CMyPitGh+GY0qIR5rDOwukEXzICOX1FTG+pVuOPZ4+iubpI8tzakq1kraD2lO2djnOHTQvAN2yu1G8ckk2ZWIVad6nJW7ZXRnWaHD6zq/GVUORwCLpg6O7pwcDZw0m+mFUejRjDy8PajavSdBuxwU3MWl1JJ2+jGvDakQvy4jW7NqoBobYeFIuvtlCTHcnHJPegLpCCRIPZyguTvlyIs/mju5+ZCalX4+YR0+JeWTpyqJUK2k5qC2la5fjQlrbuGZ3o1jlEmxdZP+akaZ9WuCa3Y2vGw1Nbxu1q4ZCgUXSFdrzB87QrF9LqjarYXEtTmCDitwJuW3zrO2bYkzVEXPyCt5NK3H3x23p6d6fVEYXk0DcuRtWZZLDIkkKe4h3s8o82pahHHg3q0zijXBS7j9BqnaiyIyePPrrJCGDfkjPk6NJBWQaJU+P21Y63gTbfceJ1oPaUaZ2Oc5b9J0SmfadZn0+xS27G1++0HcKBxZNb5/wm/ep264BLu4uFvfFFilfjKS4RBIc5LUAYErVkRAUjEfjKjxakhHQwqNpNfSxCSSef7NzaCaDgdzfDSR68wHuff1jerqmdEHk2dxIDrn91nUHiHv0lLgX+o6T2olGg1pRtHYZrqRdleOc3ZWClYuz+wf7QTs8/Dzpv3I0BoORea3HE5nJQj1P2YI8DY/i6YMsjHSt1aK7cAFlrVrp194AqGrXxhgfj+7KFZvFXHr3RtOqFYkbNpDw448282iaN0fi5sbTAQPS05zbtAGjEe0JxwVoiY6MtgpEpFQr6Tj4M8rXLs+/B81jq3t2d0pXKcXahbavlTm5J4iBTQdbpBUqVZAhMwYz/+sFhJy5QlRktFUegEU7FrBz9S52rtlF1Guc1f7/IE/CoxgSHsVYpMnVSmoM+pQCtUtz86DZe0eT3ZXclYtxbNGfNt7y6kTdDKdMu9qo3J0t7osNKF+YlLgkkmPez7tV3wVPHz3lqY11QasX5h3XtHlnSybrgmZ9PsU1uxsjGg2xuy54RsGyhXkS/oQnD7Lwzl2dFv3lizhVq0XK5ow+oqxRG2NCPPpr1mOb1McPlyGjMKVq0R7KuClBUb4SAIbbN8FoxGXISFL37yZx4ez0PLJCRZG6uaO/fTPrZHpPcPSVRv/fEcrrcyQlJfH4sXlgMJlM3L17l2nTpuHt7U3VLL6m4HlCTgVz6cRFhs7/kpXTfyf+aTzth35GUlwi/6zalZ4voFAuFE4KbgebXTU2zl/H2JUTGLH4a/5ZtQu1i4ZW/VujVKtYN3cNADcv3eDK6RD6TP2CFdN+w6A30mlEZwx6IxvmOdbVLmrxOnL9PhX/+d8Qu2kP6nLFyN6rNY9n/YYpVYvUWY1Twdzo7kZgePpqu7qGp3FEL9+KZ0+zS1XSsXMo/L3xHNgR3YNHxGywDuDiSEJPhRB84hID5g9lzfQVJDyNp/XQ9iTGJbLvuTOpOQsFIHdScCfYrAxsmb+Br1eOY+jikexd9TdqFw3N+7dCqVayaa55krhyMpgze07RZXxPlBoV967epWbrOhSuUJQ5vaY7XJawuZspt3EMJX8eRsTaA7hXLEyeAc24MXkNxhQdMhc1zkUCSA57iC7KbG0Mm7OZ4gv6o4tO4PE//+LVqAI+LapxqfdcAIzJWu7+8Bf5vmyD9nEMUQcu4FI8N/m/asuTPWd4+tw1D1nBlVMhXD5xiSHzh7Ny+nISnsbTdmgHEuMS2bMq47cRUCgXcic5YWnt88f89YxZOZ4vF49i96q/Ubuoadm/NSq1kvVpfWfbL39Ss2Vtxq2dzOZFG0mKT6Jy46rUaFGL5ZOXvfQ+yNfl4cINFFwzibyLRxK9fi/O5Yvi3bcl4dNXmPuPixpVoVxo7zxEH/1q/ceUoiXyp834Dm6H/mk88UcvoMrnj++wz0gKvkXUxn0OleF5bp4K5fqJYLrMG8hf01eTFJNA46FtSI5L5NiqjEWPT8GcyJUKHgSHAdB6QjdcvTxYP/pnVK5q8pQrmJ43JT7ZQpn1L5qbhzesvQYcTcLKlWT7/nvcJ0wgeedOFCVLounQgYQlS0CrRaLRIM+bF/2DB5hiY5EXLIjms8/QhYaSevAgiuKWVx3pw8IwJSWRtGkT2WbPxmXgQFKPHcMpMBDnzp1JXL0agwPdhm1xOegyF45f4OuFI/ll6jLiYuLpMqwTCXGJ7FiVEYk6d6HcKJwU3Ay+SXxMPPEveCKoNeZjBPdv3icsLcr99Yu2N1uiIqPsPhPyWHLvVChhJ0JoMb8/+6evJflpAjWHtiIlLomzz/WfHIVyInOSExls3wPgRYJ+2UXJltXptHY0xxb9SWp8MkUbV6REi2rsmbwKYxbenf4hcCVtXTBo/jBWT19OvJ15J2ehABROivR5Z9P89YxeOZ7hi0exJ23N1iJt3tk413I9lrtoHh68ocfN65C0bgVuU+fg8s1EUnfvRF6sBKpWHUj6LW1sU2uQ5c6LIeIBprhY9JcvoLtwFucvhiBxccFw/y6K0uVQt/6MlF1/Ybhn/h0m/7EWdYeumOJi0Z4/gyxnLjQdu6O/dZ3UPbteUiuBwBKJSWwHAFCvXj0ePMhYBEmlUrJly0b58uUZNmwY+fPnf6P3tsrT/I3KObs502NsTyo1qoJEIiH0zBV+m7SM8Oeuzpi0bireAd70q9E7Pa1U9dK0G/IZ+Yrnw2Q0cvnEJVbNWsmD5xZ07jk86DGuJ2VrlkMiwZxn5goiwl6+OJqm1L80z/O4fFSVHIM645Q/AH3kE2JWbyf6V7OVRVOpFLlXzSRi1Bxit+y1KuvesgF+M4dzs253dA8sXbCydWuBx2cfowjwxfAomsRjZ3k8Z/krK8HPmJSqfK38YG6bzmN7UKFRZSQSCdfOhLJy0q9E3ApPzzNm3WS8ArwZUqNvelqJ6qVoNaQ9eYrnw2Q0EXLiEutnrSb8ubZRKBW0GtKeGi1r45rdjQc37rFl/gb+3f1qATN6przeuWyvJhXJN7ItzgX8SX0Yzf1fd3P3J/OZYY9qxSm/ZTwhg38kYv2h9DI5uzQgd/9PUPp7knLnEWELtvLwj+euipFICOj+ETm7N0Sdxwfd03giNx/j1qwNGFN0L1bBLj+p3ixAjbObM93G9qRio8pI0/rO8km/WvSdCeum4BXgzYAafdLTSlYvTdshHcib1neCT1xmzQt9xz9/TjqO6kLJqqWQSKXcv36PP5ds5tTfL3dTHf0GI617oyr4Df8MZf6c6CKjeLJ8J49+NltZXKqUpNCGqdwZPp/oP/Zblc3eph555gwhuFpvtM+7MEok5OjUiBxdm6DM64c+JoHYXScIn70a42sEn/ld+vrXiKndnGk5tgulGlZAIpFy+8xVtkxewaNbGWPPwHXjyB7gxaQag5ApZMwKWY5MYXuf9frJEBZ1mJT++Zs9swkPvcvyQQteu26j8z58eabnUNaogXOPHshz5cLw5AnJW7eStGEDAIqyZfm/9u46vKnrf+D4O0nTNnUcSktxt+LujAHD2WC4M9zG8OE2dBTYgMHYcHeGDtdhxd2hFGlLXdLk90doIE1KW5bSfvf7vPbwPOvJPTfn5No591j6uXN5O20akXv24Ni5M04dOya4r4CBA4m5dAkA+1q1cGzfHlW2bMT6+xO+dSsRW5K/nESHe8k/Pk6uTvT8sTsVv6iIUqnk2rnrLBq/iKcfXDs/rZ9OFo8sdKzUyeI+ilcoxowNPzH06x+4fDrhl1V7n/zFitkrWTlnVbLTmVRpNT/VlOmTnRcAexcH6oxpR/56ZVAoFDw9f5v9E1YS8MH1027tKFw9MrGgykCL++hzfC6PTt9g5/eLTMLT585GzWGtyFmxMAqlktd3nnFq0Q5u7TlncT8f+uH8xE/KT3KdvXCZLv2GscxnOuVKma85ai3tSg9OdhxHF0c6jOlCmXfPnVvnb/LHhKUm5YIf3z13+sV77rT8oFxw7dQV1s5YafLcAZh1wIfHNx7yc79ZyU7bwqLJmwzJtmJVNG07o/LwRPfmNZE7txC5xXBvsylWEtdpPxM6ZypRBwwVc4XGAU2bjthWqo4yQwZ0L/yI3LODyG0b33dxVyiwq98Y+4ZNUWXLjj4kmKhTx4j48zf04Ulv2c+w60jiG6VBNT3qpur3H3pqnYky0wqpvKawT628plXJrbymdZ9SeU3Lklt5Tcs+tfKaVn1K5TUt+5TKa1qW3MprWvcplVfxeXxq5TWt+lyV18/lUyqvaVlyK69pmVReP81/rfKqTHwTIYQQQgghhBAidcmYVyGEEEIIIYRIAXpZ59WqpOVVCCGEEEIIIUSaJy2vQgghhBBCCJECdDK9kFVJy6sQQgghhBBCiDRPKq9CCCGEEEIIIdI86TYshBBCCCGEEClAOg1bl7S8CiGEEEIIIYRI86TlVQghhBBCCCFSgE7aXq1KWl6FEEIIIYQQQqR5UnkVQgghhBBCCJHmSbdhIYQQQgghhEgB0m3YuqTlVQghhBBCCCFEmictr0IIIYQQQgiRAvR6aXm1Jml5FUIIIYQQQgiR5knlVQghhBBCCCFEmifdhoUQQgghhBAiBciETdYlldcU9kcTXWonwarabf1vNdavGZottZNgVVumBqd2Eqzmj0FuqZ0Eqyo26Z/UToJVXRlbIbWTYFV1p71M7SRY1d52mtROgtUonB1SOwlWFe37NLWTYFXtSg9O7SRY1crzs1M7CVbl7FEjtZNgNZGpnQCRJkjlVQghhBBCCCFSgF5aXq3qv9WMJoQQQgghhBDiP0kqr0IIIYQQQggh0jzpNiyEEEIIIYQQKUDWebUuaXkVQgghhBBCCJHmScurEEIIIYQQQqQAWSrHuqTlVQghhBBCCCFEmieVVyGEEEIIIYQQaZ50GxZCCCGEEEKIFCATNlmXtLwKIYQQQgghhEjzpOVVCCGEEEIIIVKATNhkXdLyKoQQQgghhBAizZPKqxBCCCGEEEKINE+6DQshhBBCCCFECtBLt2GrkpZXIYQQQgghhBBpXqq2vNaqVYtmzZrRr18/i58/ePAAHx8fTp06RUhICJkzZ6Z69er06dOHjBkz8vTpU2rXrv3R7+jbt69x/+vXr2fMmDF07NiRkSNHmqTj2bNnCe6jXLlyrFix4hNy+GlUhUph17A9yqye6EODiTnxF9H7NyQtslKJw6CZ6KOjiPAZYfpRZg/smnRGla8YxGrR3r1G1Nbf0L/xT4FcvKdx1NBpVBfKf1EBjZOGm+dv8Nu4xTy58yTJ+yhXtzyjlo5h1DcjuHr6isVtvurciMbdmtKjcldrJT1JTjx6w4LT93gQGIabxpaWRbLTpbQXCoUiwTjHHr5m0dkH3H0Tiqu9mtp5MtGvYl40atVnTLmBjaM9pcZ8i8eXpVE72fPq3B3OjVlB8J3nH41nn9mN0uPakrVaUVRqG/yOXOHcjyuIeBFo3CZf+1qUm97FLO6dPw9ydvjvVs+L0qsI6kpNUKbPhj4iBO2Vo2j/2ZPg9op0WdB0nGAWrgt4QeSfY41/qwpXRF2qLgq3zOjD3hJ74zQxZ3eBTmf1PMTn6OTA8HEDqVu/Bo5Ojlz4x5eJI2dw9/aDj8ZzdnHi+9F9qdewFg6ODty6cZfZUxZw6tg/Frfv2L01XXq1o3qpr1IiGxb9r187ljg4augzpidV61XBwUnD1XPXmPvjAh7eefTReOMXjqZOk1pm4T/2msjB7YdSKrlGqvze2Nb7FmVmT/RhwcSc3kvM4c1Ji6xUouk9DaIjiVj8o8lHNqVroq7WBGWGrOhDAtFeOEz0wY2gi02BXLynyl0cdY2WKDNlRx8egvb8QWJO7khwe0WGbDj0mmEWrnv9nIhff7AYx7ZuW9Tl6xM2qZ3V0p0Qm5Ll0LTuisrDC11wENH7thO5dXXCEWzt0HzTCXWlmihd3Ih9dI+IDX+gvXQWAGWmrLguXJtg9KhDfxG+cLq1s2Fk72hPu5GdKPtFeeydNNw+f5Pl43/j2Z2nH41XqFxhWg1tR87CuYgMj+D0rpOsm7mKiNAIAHrN7E+Nr82vozh9KnXn9bNXVs3Lp/Lzf0XzDr34eeqPlCtVPLWTY8LJyZGpU0fRqNEXODs7cfr0eYYMGcfNm3cSjHPr1gm8vDwtfvbw4WMKFqxiFu7hkY3z5/fj47OUSZPmWC39aY1OlsqxqjTbbfj169d8++23VKtWjSVLlpAuXToePHjAjBkzaN++Pdu2bSNbtmwcP37cGGfZsmXs3r2bjRs3GsMcHByM/79582Zy5crF1q1bGTx4MPb29gBs3LiR2FjDg/TixYv069ePDRs2kC1bNgDUavXnyDIAylwF0XQfg/biMaJ2rUCVuzC2DduDQkH0vvWJxret8zUqr/xo75hW8BRuGXEY9BO6l8+I+GMGCrUtdg3b49B7ImHT+kJMdEpliSHzh5KvRH7+mPI74aHhtB7YholrptC3di9C34YmGt/ZzZneU/t+dJvqTWvQeXRX3rx4Y61kJ8klvyAG7vKlXr4s9KmQh0t+QSw4fQ89erqVyWUxzpEHrxi8+zJfFchG/0p5uB8QxvxT9wiMiGFqvaKfNf0AVRb2IYN3bi5OWktMSATFBjejzoaR7KwxjOigMItxFColNVcNRe1gzz/Df0epVlFyZCtqrRnG7rqj0GsN11O6Il4E3XrK6SFLTOJHvgq2ej6U2XJj17g3sbfPEXVyGyr3vKgrNQEUaP/5y3KcTIYHbeTGWaCNMYbrP/h/m5K1sK3RCu3t82iPb0Jh74S6YiMUGbMTvfNXq+cjvrmLp1LCuwjTx/9MSEgY/Yf2YOWWRdSr3JK3QZZ/R6VSybJ183H3yMr08T/z+lUAnXp8y9I182j2RQduXTctgDRuWZ8REwbh7/f5CnX/hWvHknELR1O4ZEEWTl5MWEg4nQd3YN76WbSt2ZmQoJAE4+Urkpc9m/azeflWk/AnDxJ+sWotSq8C2HccjvbyCaL3rkGVsyC29dqAQkHMoU2JxlfXaI7KMy+x966ahlduiF3jrmgvnyRy9x8oHFywrdsKZdacRK5IuYqR0iMfdq0GE3v9NFGHN6LMkR91za8N+Tmx3XKcLF4ARKyYDNoPnocxMZa3z1EAm7L1rJ52S1T5i+A0bDLRJw8RsXYpNgWLYf9tN1Aqidy80mIcxz7DUJcsR/iqxej8nmFbox5Ow6cSOm4g2ptX0AW+IXhkb7N4dl82xbZSTaL+3p2ieervM4S8JfKxauqfRISG03JAK35cPZHBdfoRlkC5IGeRXIxcMZYrxy8z+7vppMuSnjbD2uOex4Mp7ccBsHneeg6sMn1h6ejmzKCFQ7l++ipvnr9O0Xwl1fMX/vQcNJqQUMvP2NT255/zKVOmBKNGTSE4OJRRowayZ88avL1rExj41mKcb77pgZ2drUlY+fKlmDFjLEuWrLIYZ9Gimbi6ulg9/eK/Lc1WXvfs2YNWq2X69OnGt/DZs2fH3d2d+vXrc+zYMWrXrk2mTJmMcRwcHFCpVCZhce7du8fFixdZsGAB/fr1Y/fu3TRv3hyA9OnTG7dzdXU1hlnaT0qz+7INumcPiFwxG4DYGxdAZYNtnZZEH9r60Uqm0j0Xtl98je5tgPl+G7RFHxlB+PzREBMFgO6NP5oeY1B55iP2/rUUyU+BUgUpW7scEzqO4/yhcwBcP3uNxSeWUr9DQzb4rEt0H99N7kWsVmvxM9cMrrQd2p56bb4kOND6FaLELD77gAIZnZlUtwgAlb0yoNXp+f38I9qVzIG9jWlrkF6vZ+axO9TOk5nxdQoDUM4jPTq9njW+T4mIif2sLUgZS+cle11vDrWbwfO/fQF4eeYWTc7MIX/HOlz9eZvFeDkalSd9ES921hjG29uGwnXA1Ud8dWgaXo0r8HDzCcBQeX1z6T5vLtxL8byoK3yF7tUTovcaWnR1j66BSoW67JdoLxyAWPNCqDKTJ7q3r9E9vW15pwoF6gpfEfvoOtG7FxuDdS8fo+kwDm2OQuge30iR/AB4lylOrS+q0rV1Pw4fMPym505f4PD5nbTr8jULZi+1GK9Jy/oU9y5M41ptjRXVMyfPs/voOqrWrGAMy5AxHYNH9qF1h+YEBgSlWD4s+V+/diwpUrowletU5Pv2Izj19xkAfM9cZsPp1TTv2IQ/frZc0bCzt8MjV3ZWzF/NtQspdz4lxLbON+j8HhK1bh4AsbcvGp47NZsTc2yHaWUuHmW2nNjWbIEuOND0A4US2zrfoL19ichVM43Bsc/u4ThkHqp8JYi945si+VFXbYbuxSOithleLsXevwxKFepKjYg585fJiypjPrJ4oQt6ie5REn5/tR12jXqgDw1E4ZLB2sk3o/m6I7EP7xLuMwUA7aWzKFQq7Ju2IXLneog2PT7KLO7YVqpF2JLZRO8zVNa1Vy9gU6AodvWaor15BbQxxN65bhJPlbsAtpVqErH6N2JvWu7hZA35ShWgdO2yTO00kUuHzgNw4+x15h9fRL0O9dnsY7mnWcNuTQh+E8ys76YTG/O+TNB7Vn+y5XbH7/5z/B+/wP/xC5N4QxYNJzQoFJ8Bc9CncguYTqdj218HmDn/t1RNx8eUL1+KBg1q06RJR/buNfT6OHHiLDdvnqBnzw5Mm+ZjMZ6vr2k50tnZiRUr5rN790FmzfrFbPsePdpToEAe62dA/Oel2TGvCoWCsLAwzpw5YxKeO3dudu3aRYUKFZK1v82bN+Pi4kKNGjUoU6YMa9assWZyrcPGBlW+Ymh9T5oEay+dQGHvgCpPkYTjKlXYtxtE9JEd6F6av6m3KV6RmNP7jRVXAN2Tu4SN6ZhiFVcA7+qliAiL4OLRC8aw4IBgrp2+SpmaZRKNX6VRVUpU9Wb5FMtdTFv2/YaSVb2Z2mMy/xw4a7V0J0V0rI5zzwKplcf0JUedPJkJj4nl4vMgszi3XofyNDiC1sU9TMLblMjBjg6VPnvhO1uN4sSEReJ35H1BJSoghJenbuJeu0TC8aoX4+3d58aKK0Dwnee8vfOc7HHxFArcCnkQePXj3SWtQmWDMnt+Yu9eNAmOvXMeha09yux5LUZTZvJE9yrh7usKBxcU9o6Gwu8H9AF+6MNDUOUq9u/T/hFVa1UkLDScY4dOG8MC3gRx9tR5atQx74IVp16j2pw9ecGkhTU6Kpo65Zvx24L3QyB6DexKlRoV6NXxe/7eezRlMmHBf+HasaR89bKEh0Vw9sj7rtlBAW+5dNqXirXKJxgvb+HcqFQq7ly7+zmSaUplgyp3UbRXT5sEa6+cQmGnQZWrUMJxlSrsvulHzIld6F+ZPncUTq4oHJyJvXHOJFz/8in60LeoCpa2WhZMqGxQeRUi9pbp98be+MeQnxwFLEZTZvVC9+Jxkr7Cts636EPfovX9DNeMjRqbIiWJPnPMJDj69BEUGgdsCpp3N9W9eUXwsJ5EHzvwPlCvh9hY+EhPMofuA4l9+oioXUkcpvSJSlTzJjIsgstH39+vQwKCuX7mGiVrJnxerJm+guldJplUXLXv/l9tazlfpWqVodyXFfhz4lLCg1O/lfP23QdMnDmfJvXrMHXM96mdHIvq1q1OaGgYBw68P79fvw7g2LHT1KtXM8n7GTlyABkzZmDgwNFmn+XKlYPJk0fQq9cwq6Q5rdOn8n//NWm28tqwYUPc3d3p2LEjTZo0YerUqRw4cIDQ0FDy5s2Lo6NjkvcVGxvLtm3bqFOnDjY2NjRs2JDLly9z/fr1xCN/RsoMWVHYqM0qn7pXhrGHykzZE4xrW/9bsLEh+i/zrhmK9FlQODihC/DH7uvvcJq6GqdZm9H0+BFFupRtXfbM64H/4xfoYk3HBvo98sM9t/tH47pmdKPnxO/4bdxiAl8GWtxmz8q/6FW9B6f3nLJampPq6dsIYnR6vNwcTMI9XTUAPAoKN4tz65Wh26CdSkX/HZeo8Mshqi05wrQjt4jSpuwYMEtc87kT+ugl+njHJ+ShP865s300Xsj9F2bhhnhZAXDJkxUbB3syls5Lo2Mz+PbRchodm0GulglXuj6VwiUjChs1+sCXJuG6IEM3WGW6LBbjKTN5oLDTYNdqGJq+89F0/wl15WagNNwa9VHh6GNjzVtX7BzA3gGFS0ar5+VDefPn4smjZ8ZhDXEe3X9Czjw5EoxXuGgBbt+4S6eebTh8fge3Xpxl29+rKFeplMl2q5dvpHa5puzb9XeKpD8h/4Vrx5Kc+XLw/NFzYuNdT08fPMMzt0cCsQxdhgGatm/M9osbOfxgLws3z6Wwd8EUTS+AIkMWw3PnlekYd90bPwCUGRO+T9vW+QaFyobo/eZjJ/WRYehjtSjSZTb9QOMIGieU6S1fk/+Wwi2zIT/v0h9HF2i4XynSW76vKbPkQGGvwb7TWByGL0MzcD7qmq1AafpSRJmrKDbFqhC1Y7GhQpjClFmyoVDbontu+pJN98JQTlC5WxhjqI0h9v4tiAgHhQJFxsxoOvVFmdWdqH2Wu02rq9TGJm8hIn6fn+Jj+bPn9cD/sb9ZueDFQz+y5Ur4fAt48YbHNw0vQ+0c7ClWuTith7bjxplrxvAPKRQK2o3qyLVTVzmz+/OXESzJljUzu9ct5Yf+PYxD19KaAgXy8uDBY7Pnzr17j8iXL3eS9uHl5UHv3p2YM2cRjx/He7GlULBkySw2bdrJ/v1HrJZu8f9Hmq28urm5sXnzZvr27YtOp2P58uX06dOHypUrs2DBgmTt6+jRo7x69YoGDRoAUK9ePdRqNWvXJjxZQarQOAGgj4xXcIsyTESgsNdYjKbMkQ/bWs2JXDkXLHSvVTgZukLbNe6EwjUDEX/MIHLNPJTZc+HQbwrY2lkvD/E4ujgRHmpeEI0IDUfj5GAhxnt9pvXl5oVbHN6c8GQlz+49JTaVCq4hUYbf2tHWtPe9g62hsBMWbZ6uwEhDd7Uhf10mTwYnfBqVoEvpnGy9/pwfD3z+lylqFwdi3k108SFtaARqJ8vnG4CtqyMxIZbiRRrjpStiGEPm6JGBC+NXcbjDLN743qfSvO/I06aGdTLwjsLecC7po+OlKTryXYIt5MXBGYWjK4p0WdH6HiZqy89orxzDxrs2tl90epehGGJvn8OmRA1UhSuBnQOKdFmwq98NdLEo1Cl37QA4uzgTGmI+/issNBwnZ6cE46XP4Eb9JnVp1b4Z08bNpWe7QYSHRbB8/QIKFc1v3O7+3YdoE+iSn5L+C9eOJU4uToRZuN+Fh0Xg+JH7Xb4ihq5zdva2jO09kXF9JmFrZ4vPhtnkKZS0wuKnUti/exEcFe/aifvb3nK6lR55UVdrQuR6H4i1cA7FRKP1PYG6Un1sytQCjSOKjO7YfzsYdNoUe+4keC+IMtwLFHYW7gWOLiid3FBkcEd7/gCRq39Ce/EQ6vJfYte4x/vt7DTYfdWN6COb0AeYv7xLCQrHd+WCCNPzSh/xrlyg+fhz1L5ZW9x+WY99w5ZEH9qD9voly9s1aoX25pUEP7cmRxdHIixcJ5FhETh85LnzoaWXVjB69QTsHe1ZMclyz6zSdcqSPa8nW3wSny/kc3F1cSZr5s8/JC053NxcCLHw3AkNDcXFJeHnzof69OlCVFQ08+cvM/usX7+u5MyZgx9+mPiv0/q/QqfXp+q//5o0O+YVDONP+/XrR79+/Xjz5g2nT59m3bp1zJs3j3Tp0tGmTZsk7WfTpk24ublRsWJFANKlS0fFihXZsWMHP/zwA05OSbsYU9rHZtgELL/ltVEbugsf3obuseVxewobw2HWhwQRuXSKcT+61344Dp6FukxNYk4mPCNrUikUChRK0zwolAqLyVYoFOh1CV9QNVvWonC5IvSv2+dfpyulxHXFSOioWTqcMe/eNNfMnYkBlQytLWXfjdvzOXWPXoFh5EyX9F4FyZLA8bHYo0Sh+Pjbd4XC8vmowDimyP/EdQ61m4H/yevEvqt4+B25gn1GF0oMbcm91Yc/MSMWE/Txj/UW8hIVSeSmOegD/dGHGlr2dc/uoI/VYlu5KTFndqMPfEH036uwjY3Btm57FF90RB8TRcy5fWBji/6Dbvj/OgcKBUql6ftE5Uevn4SPj9pWjYuLE83rtueFn6E1+p/TFzn0z3Z69u/EwB4jE4z7OfzPXTsW06hAGe96UiotXxeGyynh+926JZv4e+cRzh9/343y3PELrD2+go792/JjrxQs5CnenXMJFXAsXTs2auy/6UfM8Z3onibc1TlqyyKIjcGuRW/sv+6LPjqS6CNbUahtIdp6146JuJMnoZ/bUj6jIohYNRX9Gz/0wYY5I3SPb4I2Btua3xB9bBv6N8+x/aI9+uAAtGf+/fMyyRI5PomN4Yw+dwLtjcuo8hRA83UnlBkyETrZdPZkVYGi2OTOT+j0UVZJ8oeSUy5AofjodRJHZaPip66TUdqoaND5K8ZtmMK0ThO4dsp0wrB6HRvw4Np9rpy4nMCehOXnjtLieaVQKNAloVXe3t6OTp1asXz5OoKCTCd3ypcvN+PGDaV1654EByc8gZ0QH5NmK69LlizBw8OD+vXrA5AhQwYaNmxIgwYNaNWqFUeOHElS5TUgIIDDhw8TExND8eLvx4bodDr0ej3btm2jbdu2KZaP5NBHGMZjKOK/6X73plgfaT5ewy5uJuK9a41dHY0lQqUSdDr0kYY3tNrr500egLqHt9CHh6L0sM6b/VYDv+XbQabH5MSu47jncjPb1t5RQ3iI5fEn6bNkoNvYHiybuJSg10EoVUqUKkPelColSqUySTfQlOb8rtUoLNq01SH8XauRk6355eX4rmWpWk7T7qaVc2TA59Q9br0OTbECeLHBzSg+pLlJ2KMdZ3DJbT7Tn42jPdEWWlbjxASHY+Ns/obcxtGemGDDG/XI18HGSaA+9OzAJbJVK4Z9JlciX1metTC59FGG71TYxuuGFfd3/FYYgNgYdE9umgc/vAKVm6LM5EFs4AuIiSL6wAo4sh6Fc3r0wW9AG41NkUron1pvdt5+Q3sw4IeeJmG7t+0nV570Zts6OGoICU54pu6w0HDu3X5grLjGhV34x5dCRS2P+fuc/teuHUs6D+pA1yEdTcL+3nkEz9zpzLbVOGgIs9CSEefxvSc8vmfaLTQ0OIwr/1wlb+EUntAk7rmS4HPHvIUsbibi6IMbPnjuvHvwvHvuABAdSdTGhURtX4YyXSZ0AS8hJgp1mdro7qXMhEBx6TVrYbWzTzA/aGPQPTCf+yH27iWo+Q3KLDkgXWZsClcgYumYd3lVvM+zQvnu2Wr9Fg59mOG8UTiYHh+F5t3xCf/4jP26xw/QAdobl9GHheLYexiqAkWJvfW+omdboTq60GBiLp5OeEefqMWAVnw9qLVJ2OldJ8iWy9VsW3sHe8JDLByfeGK1sVw+dgmAK8d9mbV/Hk37tDSpvDq5OVOkYjFWT//z32XgP27UqIGMHj3IJGzTpl3ky2c+47ujoyNv3yZe4axTpxouLs6sXbvVJFypVPLbb7PZvHkXBw8eQ6VSffCZApVKZdZVWQhL0mzl1dfXl+3bt1O3bl1sbN4nU6FQ4OjoSIYMSZvhb/v27cTExLBgwQJy5Hg/Rkyn09GlSxfWrl2bZiqvutd+hvF1GU3H5CgzGcaA6F6YTyxjU7IyygxZcJ5pvpyB89ztRKycg/bSCfS6WBQ2FiY0UKmstkzOvtV7OHfQdNKk8l9UwLtaKUNL0QcV52xe2RJc57Vk1ZI4uTrRf+YA+s8cYPLZxDWT8X/i/9nXcrXEw1WDSqHgyVvTilHc37nTmxekPV0NBZDoeGN9tO/eNtvZpFxP/rsr/+bZftMJjTy/LI17jeJmLanOObOYTMYUX/A9P9IV9TILd86ZhTeX7gOQuUJBHD0y8mDjcZNtbOxt0WljiX5rvckz9G9fGc5xN9PxdUo3Q/es+OPfwLDGq8qjANpbZ993LwYUNoap/vURhkKhMlcxiAxH53cPfcC7/WicUTinQ/cyaRO8JMXaPzdxaJ/pBDB169ekaq2KZtePV25P7t6+n+C+Ht5/jG28JQsAbNQ2REZGWojxef2vXTuWbF+1k5MHTMfRVf2yCuWrlzE7Xh65sn90ndfajWsSHBjMP8fOm4Tb2dsRFGCdFzwJ0b15gT42FmWGrHxYbFRmMDyHdP4WnjtFK6JMnxmnSeYTHzpN3Ujkeh+05w+hKlgafUQYukc3jftROLqicM2A7lnC5++/oQ98iV4XizJdFtP8pDOMxde9Nr+vKdJnQ5WzENqrp0xfdBnvBSHYFK2MQm2Lw3fmS/w4jvqTGN+jRO9YbPbZv6Xzf244PllN57yI+1v31Py8UmbOik3RUoYJmz54vmvvGV7WKTNkNvlt1KUrEnP2uGFCJys7uHovFw6ari1dtl55ilfzNrtOsubMxrO7CU+gV7pOWcKDw7hx9v0wgdgYLY9vPsIjn+nY35I1vFHZqDi962T83YgPLF26mt27D5qENW78BXXrVjM7PnnyeH10ndc4DRrU4cGDx1y4YNri7enpTvnypShfvhTt2rU0+WzkyIGMHDmQAgUq8ejRx9f6/V/0X5w0KTWleuX10aNHHD1qWmCzs7OjT58+tGnThq5du9K9e3dy5crFy5cv2bt3L5cuXWLkyKR1e9u0aRPe3t7UqVPH7LM2bdrg4+PD+fPnKV06hWY+TA5tDLH3rqIuUYmYv98vDm9TsjL68FBiH5l3C45YPAHiVUrtWxvWRI1cOx/dG3+IjiT23nVsSlQkaucfxnGxqvwlUNhpiL1nndmGA/wDCPA3XabH1t6Ob/q3xrt6KS4cNhTMXNK7UKRCUTbMtzwO5Z8DZxny1UCTsDzF8tJ7al8WjpjPzXOffykJS+xsVJRyd+PgvZd08M5h7PZ94N5LnO1sKJrFvEWztLsbGrWKPbf9qZ7r/biXww9eYaNUUDyr+dtoa4nwDyLCP8gkzEZjS9GBTXGvUYznhwwPGrv0zmSuWJBrCSyTA4buvzmbVcQlnzvBdwwTvbjkc8c1n7txeZ2sVYtQdEATXp27Q+hDf0NEhQLPr8rx+sJddNFWHGsZq0X37A6qvN5oz+8zBqvylUYfGYbuxUOzKApHN2xrt0UfqyX2+vsCjip/GfRREcaKqbpYNdA4EbXufaFV7V0bdHpi71uv9ejli9e8fGG6BqG9RkOfId2oVqsiRw4a0pg+gxvlKpZm4RzLy+QAHD5wgr5DupEnXy7u3XkAgFs6V0qXK8nmtTusluZP9b927Vjy2v8Nr/1N15W209jTaUA7ytcoy+lDhhd5buldKVmhBH/Os7xMDkDzjk3ImCUDbWt2Ns6emjFrRoqVLcr63xJfZ/Vf0cYQ++A6NkUrEHP0/TVvU6wi+vBQdE/MuwVH/jEFVKbPHbvm3wEQtflXdIGG611doR4KB2ciFo4wbqeu8hXodWhvmlbUrSY2Bt3jm6gKliHm9C5jsKpQWUNF+rn5sl0Kl3TYNegCsVqTGYRtClcw3Av8HhAT4I/23H6TeDbeNVGXqkXE0jHow1OoC2RMNNobvtiWr0bU9vdLyxlaS0PQ3jV/HiozZ8Ox1w/oo6OIOf6+YqIuWQ6A2Efvj6nCyRlVNg8it65OkeQHvgw0m3DRTmNH837fUKK6N5cOG1YicE7vQuHyRdgyf2OC+2rUoynO6V0YWm+AcbInjbMD+UoV4OZZ03HveUvm5/Xz17x+9vnWrv5f5Ofnj5+fv0mYg4M9w4f3p27d6uzbdxiAjBnTU7VqBaZPn5/oPsuWLcmpU+fMwp8/96dSpa/Mwk+e3MnSpatZunQ1z5/7m30u0raoqCimTZvGnj17iIyMpGrVqowdO/ajjYyhoaHMmDGDvXv3EhMTQ9myZRk1ahSenhYmoLMg1SuvO3bsYMcO08JUlixZOHr0KBs2bGDhwoWMGDGCwMBAHB0dKVu2LGvXriVfvnyJ7vvq1avcvn2bmTNnWvy8TZs2LFmyhLVr16aNyisQvXcdmj6TsO88nJjT+1HlKoRtreZEbV9ueINqr0GZNQf6137oQ4PR+Zm/dY3rJvxhoSNqxx849J+Kpud4ov/ejMLZDbvGnYh9eBPtlTNm+7CW62evceXkZQbP+54/pvxOSGAIrQe1ISw4jD0r3y+C7pnPExtbNQ+u3SckKISQINOCgL2DoYvUs3vPeHTrMyy9kkTdyubku60X+WHPVZoUzoav31v+uPCIAZXyYm+jIjRay/2AMDxcNaTX2OJga0OvcrmZfeIOLvY21MqdGd8Xb1l+4RHfFvckvca8tSwlvTxzixcnrlNpfm8uTl5LVEAoxYc0I+ZtOHdWvC/0uORzR2WnNi5782j7aYr0b0ytVT9wcYqhQOU9shVBN57weIfhfLr9x0HytqtFjT8Gc3nmZmIjosjfqS5uBTw48PUUq+cl5sxu7FoMxLZBD7TXT6DKlgeb0nWJOb7FsMarrT3K9NnQvX0FEaHont0m9sktbKt/TYzaDl3gC1S5imFTsiYxxzbBu67IMZf+xr75QNTVvyH2ni8qz4Koy9Un5p+/0Aen7IL3/5y6wOnj/zD718lMH/8zQQFv6f+DYazQ6uXvC3l58+fC1s6W61duAbB80WpaftuY39b8zOwpCwgLi6DvkG7o9XqWzE8b3ej+168dS3zPXObCyYuM9RnJwsmLeRsYTJchHQkNDmXrivfPuZz5vFDbqo1L4/w+909mr5rOlN/Gs+n3rbi4OdNlSEdC3oaw5tfE18L+t2L+3oB9t3HYt/2emHMHUXkVRF2tCdF/rTCs8WqnQZnFE92bFxAWbHlJmXcTPOmeva8cxpzYhabbWGwbdSH2+llUeYphW6sF0Yc2oQ9IuUJq9PFt2Lcdjl3zfmh9j6D0yI+6YkNiDq4zrPFqq0GZyR1d4EsID0H36AaxD69jW7ctqO3QvfHDJm9JbMp9QfSBNRAZjj4yHP1b0+tdn8/bkGe/BymWF4DITStwGjMLx8HjiPp7t2G91satiVi1yLDGq8YBlUdOdP7P0Ae/RXvdl5irF3DoOoBIBydinz/Bpqg39k1aE7V/O7pn74+fKodh2FDs04cpmocP3Th7nWunrtDv50GsmvoHIYEhfD2wNWHBYexf+X48cfZ8Hqht1Ty8Zvh9N/28jpErxjL4l2HsX/kXGicHmvRugb3Gjg1zTHsB5CjoxbMEeneJjzt+/CxHjpxk+fJ5jBo1hTdvAhk9ehBBQcEsWfL+JVzBgvmws7M1Wd9VqVRSsGBe1q83n9U6JibGrDU2jp+ff4Kf/Rf8FydNijNu3DjOnz+Pj48Ptra2jB07lgEDBrByZcIvbPv168ezZ8/w8fHB2dmZ8ePH06tXL7Zv3242BtsShT61V2z+jwvpb/6WKTE2xStiW78Nyiwe6IPeEH1sFzGHtgCgylsMh/5TDd2Bzx60GF/TbyoAET4jTMKVuQpi91UHVF750UdHob1ymqityyAi6d03221NdnZwdHWk65hulK9n6P5449wNlk1YwrP777tvTVo3lcwemRPsDly0QjEmr5/KqG9GcPW05dau/rMGUrRCsWR1KV4zNOHlRpLq73sv+fXsAx4GhpHZyY5vinnQwdvQpfbc00C6b73A+NqFaFzo/RIA264/Z8WlxzwOCieTox3Ni2Snc2kvlIlN2pWILVODkx3H1tWBUuPa4VGvNAqlglf/3Ob8uFWE3Hvf1bbOxlE4emZkW/n3Y2Mc3NNTekJ7slUrii4mFr+jVzg/dhWRL4OM2zjnykLJEa3IVL4Aaid73ly6j+/0jbw6eyvRdDX7IfnjF1V5SqKu0AhFuizow4LQ+h5Ge8Gw1qHSIz/2LYcQtW85sdffdfe0tUddoRGqPCVROLqif/uKmIsHib1q2tVZVaAs6nINULhkRB/yBq3vEbS+Cc+CbUmxSf8kvpEFLq7OjJo4hLoNaqBUKjh/xpdJY2bx4O77lzirti3Gw9Od6qXe32+yZsvMD2P7U712ZdRqNefPXGLKj7O5c8tyd82ffMZRvnIZk318zJWxyVtr25K0dO3UnWY+/vlTOLs60W9sb6rWq4xSqeDKP9eYN36hyZhWnw2zyeaZlZYV3s8RULZqaToPak+eQrnR6fScPfIPCyctxv/5S0tfk6i97dyStb2qSHls67ZCmSk7+rdviDm1h5hjhgKoKncRND0nGrsDW6LpMQGAiMU/moTblKiCunZLlOmyoA96Zdjvyd2WdpEghfPHZ9S1mJ8CZbCt1hxFhmzoQwKJObcf7Zm/AFB6FULTfhRR2xehvfxu/VQ7DbbVmqMqUBqFkxv6wJfEnN2D9uLhBL9DXa05ttWaEzapXbLSFu2b/G6R6nJVsP+mMyp3T3QBr4nas5WonYbeSzaFS+I8fi5hC6YRffhd5U/jgKZlR9QVqqFMlwHdyxdE7d9O1O5NJkNE1BVr4DR4HG8HdED3/NOGQXx31i3ZcRxdHOkwpgtl6pVHqVBw6/xN/piwFL/775ds+nHtJDJ5ZKZflfczPhetXJyWA1rhVTgXep2ea6eusHbGSp7dNf1NZx3w4fGNh/zcb1ay07by/Oxkx/kUZy9cpku/YSzzmU65Uubr9VqLs0eNZMdxc3Plp5/G0KhRPZRKBadOnWPo0AncufP++bFv3zq8vDwoUKCyMSxTpgw8eXKRvn1H8ttvCVdePhQZ+ZhJk+YwadKcJG37vyh/pjKp+v23X5m3hFuDv78/NWrUYNGiRVSrVg2ABw8e8OWXX7Ju3TpKlixpFufMmTN07NiRbdu2UaCAYQ6Ou3fv0r17d5YuXUru3InPwyOV1xT2KZXXtOxTKq9pmTUqr2nJp1Re06pPqbymZZ9aeU2rrFF5TUusVXlNK5JbeU3LPqXympZ9SuU1LfuUymta9rkqr5/Lp1Re0yqpvH6alKq87t69myFDhnDp0iXs7N4vf1atWjXat29P9+7dzeLMnDmTo0ePsn275TWnkyLVuw0LIYQQQgghxH9Rak/YVLt27Y9+fvCg5Z6cifH39yddunQmFVeAzJkz4+dnPlEmwMOHD/Hy8mL16tWsWrWK4OBgSpcuzYgRI8iSJUuSvlcqr0IIIYQQQgghjJ4+ffrRiu+AAQOwtTWfb8LOzo6oKMtreYeGhnLt2jUCAwMZP348YGiN7dChA9u3bzerCFsilVchhBBCCCGESAGpPWHTp7asZsmShd27E56j4MiRI0RHmy+3GRUVhUajsRAD1Go1UVFRLFiwAFdXwyoB8+fPp2rVqvz999/Ur18/0XRJ5VUIIYQQQgghhJFarSZPnjwJfn7r1i2CgoKIjo42aYF9+fIlWbNmtRgna9asZMmSxVhxBciYMSNubm48fZq0+QA+76ruQgghhBBCCCH+p5UuXRqdTsf58+/X7b5//z7+/v6UKWN5kqoyZcrw/PlzXr58P5P+y5cvCQwMxMvLK0nfK5VXIYQQQgghhEgB+lT+L6VkyZKFhg0bMnr0aM6cOcPly5cZMmQI5cqVMy6TEx0dzatXr4zdi+vXr0/OnDkZMGAAV65c4fr16wwePJhcuXJRo0aNJH2vVF6FEEIIIYQQQiTLxIkTqVixIn379qVr167kzp2befPmGT+/ePEiVapU4eLFiwDY2tqyfPly3N3d6dSpE+3atSNdunQsX77c4uRPlsiYVyGEEEIIIYRIAXq9LrWTkGIcHByYNGkSkyZNsvh5+fLluXXrlklYpkyZmDVr1id/p7S8CiGEEEIIIYRI86TyKoQQQgghhBAizZNuw0IIIYQQQgiRAnQpOGnS/0fS8iqEEEIIIYQQIs2TllchhBBCCCGESAF6vbS8WpO0vAohhBBCCCGESPOk8iqEEEIIIYQQIs2TbsNCCCGEEEIIkQJkwibrksprCuu47b/VuL28bHBqJ8Gqvpx2K/GN/ocsdrJL7SRYTcvZAamdBKu6urBpaifBqjoMPZ/aSbCqte6K1E6CVXVeG5vaSbCa17H+qZ0Eq4rQpXYKrGuXd2BqJ8GqnD1qpHYSrCrk6eHUToIQViWVVyGEEEIIIYRIATJhk3X9t5oFhRBCCCGEEEL8J0nlVQghhBBCCCFEmifdhoUQQgghhBAiBeik27BVScurEEIIIYQQQog0T1pehRBCCCGEECIF6GWpHKuSllchhBBCCCGEEGmetLwKIYQQQgghRAqQpXKsS1pehRBCCCGEEEKkeVJ5FUIIIYQQQgiR5km3YSGEEEIIIYRIATqZsMmqpOVVCCGEEEIIIUSaJy2vQgghhBBCCJECZMIm65KWVyGEEEIIIYQQaZ5UXoUQQgghhBBCpHnSbTiNsnfU0HFkJ8p9UQGNk4Zb52+ydPwSnt558tF4hcsVoc3QduQqnIvI8EhO7jrB6pkriQiNMG6TPkt6OozqjHf1UqhUKm5fvMXqmSu563vH6vmwKVkOzbddUXl6oQsOInrvdiK3rE44gq0dmladUFeuidLFjdiH94hY/wfaS2dNNlNmz4GmfU/URUqij9WivX6ZiD8WovP3s3oe4tM4aug9pidV6lXGwUnD1XPXmffjAh7deZTkfVT5ohJTfp9I/5aDuXTK1xjums6FHiO6UaFWeewd7Ll1+Ra/TFzMnWt3UyIrOFYtTcaBHbDL60lsQDCBa3cTsGh9kuLaFclLzvWzuf9FN2KevTT5zKlORTL2bo1tLg+0rwN5u+1v3ixaDzHalMiGCY2jhq6julLhiwo4ODlw4/wNfh33K08SuXY+VKFuBX5c+iPDvhnGldNXjOGZPTLTdWRXilcsjo3aBt+Tvvw+9XeePXiWElkxc+LOcxYc8OX+q7ekc7CjZdl8dKlWBIVCYbbttgv3GLvldIL7mtC8Io29c6dkcs3YO2poP7IT5b4oj72Thtvnb/L7+N8Sva8VKleYb4e2I2fh3ESGR3Bq10nWxruvOadzps2wDpSqWRo7jR33rtxjxZTfeXjtQcrkpWJZXL/rjE1uL3SBbwndvIOQ5WsSjmBjg3O7r3Fs+AWqLJmIffma8D0HCV6+BrTvrwv3PRtQZUhvFv3Zly3RvQlMiawY/ZeODxjuBd+N7kGVepXQOGm4du4688cu5NGdx0neR+UvKjJp2QQGfj0E31OXARg2eyhffvNFgnFal2+Lf7x7ojU4OGroO6YX1b+sgoOThivnrjF7jA8PE3n2TPzlR+o2qWUWPrrXBA5s+9ss/Juuzfm2xzc0K9/aamn/kLp0ORzad0XlmRNdcBCRu7cTuWFVwhHs7HBo0xnbaoZygfbBPSLWLCfmvGm5wK7eV9g3aYkqqzu6V/5E7tpK5PZNKZKH+JycHJk6dRSNGn2Bs7MTp0+fZ8iQcdy8mXC56tatE3h5eVr87OHDxxQsWMUs3MMjG+fP78fHZymTJs2xWvqtwc//Fc079OLnqT9SrlTx1E5OmqKTbsNWleqV11q1avHs2TOGDx9O586dzT7/8ccfWbduHX379qVZs2bUrl07wX1VqVKFpUuX8vTpU7Pt7O3t8fT0pGnTpnTq1AkbGxvCw8OpXLkyLVu2ZNSoURb3Wb9+fYoXL8706dP/XUaTabDP9+QrkY8/py4nPDSCVgNaM371JAbU6UPo21CLcXIVyc2YFeO4fNyXn76bRvos6Wk3rCPZ83gwof1YABycHZi8aTr2GjvWzFyJ3wM/yn9ZgYnrp/Bjq1HcuXTbanlQFSiC0/DJRJ88RMSapdgUKoZ9m26gVBK5aaXFOI59h6EuWY7wVYvR+T3DtkY9nEZOJXTsQLQ3DJUIRYZMOE/2QffsCWFzJ4GtLZpvu+I0ZibBgztDdLTV8mDJ2IWjKVSyAL9MXkJ4SBidBndg7vqZdKjZhZCgkETju6RzYcj0QRY/m7R0Al55Pfl18hJe+7+hbZ/WzNs0my51e+D35IVV86HxLoTHLz8S/NcxXs/9E03pImQa1AGFQsGbX9d9NK5dwVx4Lh6HQm1+C3Go5E32+aMI2X2UlzOXY1cgJ5kGd8QmvSv+E36xah4sGTZ/GPlL5GfZlGWEh4bTZmAbpq6Zyne1v0vw2vmQs5szfaf2NQvXOGqYvGoysdpY5o+aT0x0DN/2/5bpG6Yned//xqXHrxiw6gj1iuagT50SXHz0kvkHfdHpoXuNombbVy2QnT971DMJ0+v1TNh2hrCoGKrkd0/R9Foy0GcIeUvkY+XUPwgPDefrAa0Zu3oig+r0TfD3y1kkF6Pf3ddmvruvtR3Wgex5sjOp/TjjdkMXjyB7Hg9WTvuDQP8AmvZqwfh1UxhafwAvn1i3ImFbvDAZZ00kfP9h3v76O7YliuLaqwsoFIT8bvnlXLohvXFo+AXBS1cSff0WtgXy4dKjA6qsWQicNBMAZfp0qDKkJ3D2QqKvXDeJrwsKtmoeLPmvHJ84YxaMpGDJAiya/BvhoeF0HNSO2etm0KlWt6Tdq92cGTxtoFn4ip9Xsn3lTrNtx/46Gt9Tl3n5/JW1smBiwsIfKexdkAWTFhEWEkbXwR1ZsGEO39boSPBH8pOvSF7+2riPjcu3moQ/efDUbNt6zerQ/8fevHrx2trJB8CmUBGcx0wh+tghwlcsxaZwMRw6dEOhVBCxznK5wGngcNSlyxG+fDG650+xq/0lzmOnEjxiENprhhcKdg2a4NRnMBEbVhF28RzqAoVx6NYbhb2GiPWW92tNf/45nzJlSjBq1BSCg0MZNWoge/aswdu7NoGBby3G+eabHtjZ2ZqElS9fihkzxrJkieXK/KJFM3F1dbF6+v+t5y/86TloNCGhYamdFPH/QKpXXgHUajV79uwxq7xqtVr27dtn1qrg4+ODt7e32X5sbW0tbqfX6wkNDeX06dPMmjWL27dv89NPP+Hg4ECDBg3YvXs3w4cPR6VSmcS/fPky9+/fZ8KECVbKadLkL1WAMrXLMqnTeC4cOg/AjbPX+OX4Er7s0ICNPpZbxhp1a0Lwm2BmfDcN7QctXP1mDcQ9d3ae339GrW/qkMUzCyOa/8Ct8zcB8D1+Ced0LnQe05WRLYZZLR+abzoS+/Au4fOmAKC9dBaFSoV9szZE7lhvVslUZnHHtnItwhbPJnrvdkOcKxewKVgUuy+bGiuvmlad0UdEEDJ+CERHAaB7+QKn4ZOxyVPAuF1KKFK6MJXqVOCH9iM4/bfhra/vmSusO72KZh0b8+fPH3l7/M7gKf2J1Zq3QHrk9qBE+WJMHzKT3ev2AHDt/HW2+W6kXsu6LJ+zwqp5ydC3DZE37+M31FBwDjt2HoWNivQ9vibg9y3ooyy8BFDbkL59IzIOaI8+0vJLAtcWddE+f8Xz72eCTkf4yYvYZHAlXadm+E9ZDNpYq+bjQwVLFaRc7XL82PFHzh06B8DVs1f5/cTvfNXhK9b6rE10H30m9yHWQhqrflWV7Lmy07N2T2Mr7sObD1l+cjlVv6rKX6v+sm5m4ll06AoFsqZjcsvKAFTO5442Vs/vx67RvnJB7OO9SEjvaE96R3uTsFWnbvLgVTB/dP/C7LOUlr9UAUrXLsuUThO4+O6+dvPsdRYcX8wXHeqz2WeDxXhx97VZ3003ua/1mTXAeF/LlsudQuWK8MsPPhxafxCA2xdu8dv5P6nWvCYbf/74y5jkcunWgejb9wgYOw2AyFP/oLCxwaXjt4Su3mh27ShdnHFs3oi3PksIWWm4f0f9cxEAtwE9eTt/Cbqgt9gWyAtAxKFjxL5ImQpdQv5LxwegcKlCVKxTgeEdRnHm3b368pkrrDm1giYdGrFy3kd6AL0zcEp/tBbu1c8f+fH8kWkvnwm/jSUkKIRJ/aamyOQsRUsXpkrdigxqN4xTf58B4NKZy2w+s5YWHZvy+8+Wnw92Gjs8c2XnT59VXLtw3eI2AOkyuNFzWFeatmvE2wDLlS1r0Hzbidj7dwmdNRmAmPNnUdjYoGnZlogtFsoFWd2xq1aL0AWzidq9zRDH9wJuhYti37Apoe8qr5qv2xB19G/Cly8GQOt7AWV2D+wbNU/xymv58qVo0KA2TZp0ZO/eQwCcOHGWmzdP0LNnB6ZN87EYz9f3msnfzs5OrFgxn927DzJrlvmL3h492lOgQB7rZ+Bf0Ol0bPvrADPn/5baSUnTZMIm60oTY14rVqyIr68vfn6mD4PTp0/j4OBAtmzZTMJdXV3JlCmT2T9XV1eL22XOnJncuXPTpk0bpk6dyrZt2zh16hQALVu25PXr18a/P7RlyxZy5sxJ2bJlrZzjj/OuVoqIsAguHb1oDAsOCOb6mauUqlk6wXirpv/JlC4TTQoQcf+vtlUD4JHXk5CgEGPFNc61M1cpWKYQji6O1smEjRqbIiWJPnPMJDj61BEUGgdsCpl3KdG9eUXwDz2JPnrgfaBeD7GxYKM2BqkrVCP64C5jxRUg9t4t3nZvmaIVV4By1csQHhbBP0fOGcPeBrzF97QvFWqVTzR+rcY1KFO1NL9MWmz2me27YxT2wZvL8NBwoqOicUln3TetCrUNDuWLE7LvpEl4yN7jqJwc0JQpYjGeU/WyZOjblje/rOPlzN8tbqO0VaOLiASdzhgWGxCM0laN0lFjvUxYULp6aSLCIrhw9IIxLDggmCunr1CmZplE41drVA3vqt4sm7LM7LMTf51gcNPBJt2P419fKSVaG8u5B/7ULmzaxaxOkRyER2u58Cjxlp7XIREsOODL1+XyUcwzY0olNUElqnkTGRaBr9l97RqlPnJsVk1fwdQE7ms2toYKe9zvHx4SbtwmIjSCmKhonK187aBWY1+6BBGHTO9tEX8fRenogG3JYmZRFE6OhG3eQcQx0+st5rGh5csmu+EZp86fB11wyGevuMJ/6Pi8U7ZGGSIs3qsvU75WuUTj12xUndLVSrFocuKF8gq1y1P1yyosGP8rYcEp0/JUoUY5wsMiOPtBfoIC3nLx1CUq1k742ZO3UB7D8KBEhp507N+O8tXLMrzrGI7vNy8PWYWNGnXxkkSdOmoSHHX8CAoHB9RFLJQLXr8iaEAPog/tfx8YVy5Qv7/vhvz4A+HLfjWNrNWabJNS6tatTmhoGAcOvM/X69cBHDt2mnr1aiZ5PyNHDiBjxgwMHDja7LNcuXIwefIIevWyXgODNdy++4CJM+fTpH4dpo75PrWTI/6fSBOV1+LFi+Pu7s6ePXtMwnfv3k39+vUtjuf6VHXr1sXd3Z2dOw1dfry9vcmbNy87duww2S46Oprdu3fTokULq313Unnk9cD/sT+6WJ1JuN9DP9xzZU8w3psXb3h08yEA9g72FK9cgrZD23PtzDVjeHDAWxycHMwqqVm9sgKQJUcWq+RBmSUbCrUtuuemY6V0LwxjA1XuFsZ5aGOIvXcLIsJBoUCRMTOazn1RZnEnap+hJVaZOStKRyd0r16g6TYA1+XbcFuzD8cRU1BkzGyVtH+MV74c+D3yIzbesXn24DkeuT0+GjddxnQMnNyfeWMX8OZlgNnn928+4J+j5+k0sD25CuTE2c2ZPmO/w15jz9/bD1k1H+oc2VDaqol+aDpWM/pda4JtAudZ5JXb3KvZydCtOIEW1MCVO7D1cid91xYonR2xL1GAdJ2aEnr4LLoU7lrrmdeTF49fmF87j/zInjvhawfALaMbvSb2YtG4RQRYOD5hwWHcvGB46WOjtiFXoVwMmT2Et2/ecmznMbPtrelpQCgxsTq8MjibhOfI4ATA49eJdyld+PdllEoFfWqXSJE0JsYjr6fF+9qLh35ky5VwF+aAF294fNMwps/ewZ5ilUvw7dB2XD9zzRj++NYjLh+7xNcDWuGZPwdOrk50GN0FW40dJ3ZY99jYZM+GwtYW7WPTLpcxTwzXkjqH+X0g9vkLAqfPQ/vINI5DzaroY2KMlVjb/HnRhYSQ4adxZD+0jexHdpJh8miUFsbAWtt/5fjE8cqbg+eP/czy8+zhczwTvVe7MWByP+aP/YU3L998dFuFQsF3o3tw6ZQvR3el3H0gZ74cPH/0nNhY0/vu04fPyJHb8rhJgPxFDK35zds3ZtelzRx7uJ9ft8yjiHchk+22rNhOy8ptOfxXyuVBlc3dUC54Fq9c4Gc4/1XZEygX3L2F/l25QJkpMw7d+6LM6m5siQWIffII3St/ABROzth90RC7Wl8QuXNLiuUnToECeXnw4LHZsbl37xH58iVtXgEvLw969+7EnDmLePzY9LmsUChYsmQWmzbtZP/+I1ZLtzVky5qZ3euW8kP/Htjbf97ePOL/rzRReQXD2NIPK6/R0dEcOHCAhg0bWvV7FAoF+fLl48aNG8awFi1asH//fiIjI41hhw8fJjQ0lGbNmln1+5PCwcWRiNBws/CIsAg0Tklrufrj0irGrZ6IvaOGPyYtNYYf3XoEXayO738Zhmc+TxxcHKnWtDq1vq4DgJ3GOjcfhaOhUK2PMM2HPsIwgYdC4/DR+PbN2+K2aD32X7Uk+tAetNcuGeK5uAGgadcTZYZMhM2ZSPgvM7DJmRfn8XPALmVvnk4uTiYto3HCw8JxdPp4nob+NIhr56+zb9OBBLeZO3oe9g72/PH3UnZd20rLrs35aegsrp5LuLvXp1A6G15e6OKdZ7oww9+qBPKi9X+TaAU0/Mxl3vy2iczDupL//AZybphD7Jsgng/+yQop/zhHF0fCLV07oRE4JHJ8+k3rx80LN/l7s/kEJvGNXz6eBXsXULxScZb/tJzAVyk7kU7Iuy7ajvamrQgO71q0QqNiPho/IDSSnZfu07p8flw0th/dNqV87L7mkMT72rJLK/lx9QTsHTX8Ocm0dXzpj4uxc7Bn9n4ffr+8igZdvuLXYfO5Ha+Xyb+ldDbc2+KulTj6cMPfCsek9V7R1KyKQ4M6hG7Yhj7EcE2p8+dBlTkT0ddv8WrQaILm/oJd6RJkXjQHRQoXDP8rxyeOk4uTSUtvnPDQ8ETvBUOmG+7V+z9yr45TqW4FvPLlYEUShoz8G4Znj6X8RHz02ZPvXeXVzt6OMb0m8GPvidja2bJg4xzyFnpfsXp097HF4RLWZCwXhMe/dt6VCxw+fu1ovmlLuuUb0DT9mqgDfxFz5ZLZNjaFipJ+3U6cBvxA7KMHRO7YbJ3Ef4SbmwshIebPxdDQUFxcnJK0jz59uhAVFc38+ea9fvr160rOnDn44YeJ/zqt1ubq4kzWzJlSOxlpng59qv77r0kTY17BUHldunQpfn5+ZMuWjRMnTpAuXToKFy5stm337t3NxqcCzJ49m5o1E++i4ezszMOHD41/N23alNmzZ/P333/ToEEDALZu3UqNGjXIlCllL0qFQoFCadqyrFQqLfaPVygU6HWJn4QqGxVTuk5EZaOiYedGTNowjUmdxnP11BWe3nnClK4T6TW1Dz8fWADA3ct3WD1zJT0mfkdURGQie09qxt69F0mgn39i/f+j/zmB9sZlVHkKoPmmE8qMmQid+IOx+7AuKJCwn8YY9x/r9wyXaQuxrVaX6P07PrbrpGdBoUAZ79golErLWVIo0H3k2Hz59RcUL1eMjrW7JbiNV94cLNw2D78nLxjTfRyhIWHUblyTH2YMITIiksM7jyYYN7kUykSOTxLOs4RkmdAXt+Z1eb1gDeGnLqH2yErG/m3xXDqRxx1Hoo+MSnwnSZCcawfFx/NUu2VtipYrSq+6vZL03at/Xs26+euoWK8iA6YPwDW9K+sXJG2W5k8RN1OhAsu9UJSJ9E7ZdO4uOj20qVjQ6mmzxPKxUSR4X/vYtRNHZaNiWtdJqGxUNOjciAkbpjKl0wSunbpC9rweTNo8nZdP/Jn53TTCg8Op3Lgq303vS1REFKd3n0x0/0kW91sndA/T6yyHf0BTqxoZJo4g6sJlgj4YKxYwYQb6qGhibhu6eEZfukLMvYdkWToPh4Z1CdtkvXvbf+n4WMqP4iP5+di9oF7LuhQrX5QutXsk6bubdmrCnat3uXD8YuIbJ5Hh2WPatpBwuYCPHp+1Szbw987DnDv+fijFP8cvsOHESjoNaMfo7z7jnB7vrp2Env/6RK6d6NMnibl6GZt8BdG07YQyY2ZCfhxqsk2svx9vh/VHmSETDm074zp3MW8H9UQfZJ0XjMk7Ngp0usTvB/b2dnTq1Irly9cRFGQ63jhfvtyMGzeU1q17Ehyc+CRjQvx/kGYqr0WLFsXT09M4cdPu3bv56quvLG47adIkSpQw7/qW1IpmaGgoTk7v34alT5+emjVrsn37dho0aEBAQABHjx7Fx8fyIHtr+mZAa1oN+tYk7OSuE7hb6KZl72Bv8U1yfLHaWHyPXQLg8nFf5u6fT4s+X3P1lGE8qO+xS3xXpTuZPQ1dhF8+8afW14bZmUOCrNOtUx9u2E/8FlaFRmPyeUJ0jx+gA7TXL6MPC8WxzzBUBYoauhQDMRfPmBQeY+9cRxcagk2uvFhrruFOg9rTeUhHk7BDO49Y7HLm4KAhLMTyWKeMWTPSb1xvFkz4laDXgahUSpQqw8NPqVKiVCrR6XR83b0FCqWCwd/+QHCgoRvo+WMXcHRxZNDk/hzZdcxqg/5jgw2/vzLeG3ulo+FvXQJ5SYxNlgy4ffMlb35dz+u4CUTOXiHiym1y7/oF15Z1CYo3S+enajOwDW0HtTUJO7brGNktdHnWOCZ8fDJkyUCPsT34beJvBL0OMhwTC8fnQ9fOGibauHzqMk6uTrTu35qNv24066JoLc72htbSsHgtrOHRhr+d7D8+ruvAtcdUzJP1s03S1HJAK76Jd187teuExe6nybmvXX53X7ty3JfZ+31o3qcl105d4auujVEqFExsO5bQd7OuXjnhi4OzA90m9uTMX6esdu3oQt9dO47x7m0O766dRGbbdGrTErf+PYi64MvrIWMg5v0xjT/DMED05WvoQkKxzZcHa42m/K8dnw6D2tFpcAeTsMM7j+KZ281sW42jhtAE79UZ6DOuF79MXETg60CUKiUqpeFFuUqpMrsXuLg5412pJIunWneymq6DO9JtSCeTsIM7DpPDwrPHcG9L+Hn6+N4THt8z7aYbGhzK5X+ukrdwXmskN8n0Ye+unXgtrAqHd+WCsI+f4bGP7gOgvWYoFzgNHIZNoaJob1x9/x0Bb9AGGLp6a29dx23JKuzrNUxwJuPkGjVqIKNHm64WsGnTLvLly2W2raOjI2/fJl7hrFOnGi4uzqxdu9UkXKlU8ttvs9m8eRcHDx4zabRRKhWoVCqzrsoibZIJm6wrzVRe4X3X4TZt2nDw4EE2bLA8u2GWLFnw8vL6pO/Q6/Vcv36dGjVqmIS3bNmSPn36EBgYyK5du0ifPj3VqlX7pO9Ijn2r93Lu4D8mYeXqladkNW/DG+IPTvhsObPx5G7C69OVrVOOsOAwrp99P4OdNkbLo5sP8cyXA4CM7hkpXqUkR7cc5uUTf+N2uYvlJSQwmFdPrTNRiO7Fc/SxsSizmVYklFkNf+uemK9Lp8ycFZtipQwTNsW8r4Jq7xq6lSkzZibm/Cn0sbEoLEzCoFDZoI+2TqsewPZVuzh5wHSdzKpfVqZc9TJmxyZ7LvcE19orU600zm7ODJ89lOGzTd8Sz103E78nL2hVoS1ZPbLw+O4TY8U1ju8pX2p+VR23DG4EvrbO2+OYx37otbHY5jAtrNp6GSaOifrIefYxNtkyoVAqiYg3q2X0nUdoA99il/fTrltL/lr9F2cPmq7zV/GLipSuVtr82vHKxuME1nb0ruqNs6szg2YOYtBM00LJ1DVT8X/iT+fKnclfIj9ZPLOYjW+97Xub2i1q45reNcW6D3umd0alVPA4wLQg9PiNoTCYO5OrpWgA+L8N59aLQNpV+jytrgAHVu/j/MFzJmHl6pWnhIX7Wtac2Xh6N+F1RMvUKUdYcCg3zr4/p8zua9kz8+zeM2PFKM71M1ep2LAyLhlcePvaOjOoap8+R6+NxcbD9N6m9jT8rX2Q8Jqbbt/3xblVM8L3H+LN2OkmFVeFkyMONasSdfWG+T7UNsQGWSf98N87PjtX7ebUgTMmYVXqVaJsdfN7Qfac7gmuyV26quFe/cOs7/lhlunkM7PW/cSLJy/4tmJ7Y1i5mmVR2ag4YsVeMQBbV+4wmzSp+pdVqFCjrFl+PHJm58HthM+5Ok1qERz4lrNHz5uE29nbpuiswpbE+j1HH6s1LxdkM1TKYx8/NIujzJIVdYlSRB2KVy64/a5ckCkzPNRgW74y2ls30Pm9Hy+qe/EcfWgISivOh7F06Wp27z5oEta48RfUrVvN7NjkyeP10XVe4zRoUIcHDx5z4cJlk3BPT3fKly9F+fKlaNeupclnI0cOZOTIgRQoUIlH8cbSC/Ffl+Yqr4sXL2bjxo14enqSJ4/1pwTfv38/L1++pFGjRibhVapUIX369Bw4cICdO3fSrFkzi12TrS3wZQCB8SaHsdPY8XW/VpSs7s3Fw4auPi7pXShcviib5luu0AM07tEMl/TODKrX39gC5ODsQIFSBYwFC9cMbvSd0Z+AF2+Msxm7ZXKjauOqnNl3JsF9J1tMNNrrvtiWr0bUtvfLINhWrI4uNATt3RtmUZSZs+HY+wf0UVHEHH//cFB7G2aGjH14FyIj0N64grp8NSJW/QZaQ+HPplgpFBoN2uuXzfb7qd74v+GNv+lkHfYaOzoMaEe5GmU5c8hQcXJN70qJCiVYMc/ymKeT+0/Rvb5pd9QCxfPz/fRBzBw2h6vnDC8bHt99QoPWX+Ls5myyBmHRskUJDQ4l2IrrPOqjYwj/5yrOX1QiYOn7Rdyd61Uh9m0IkZc/bb3fmEeGgr2mTBHCjr4vHNvmyo5NOldinvp/JHbyBPgHEOAf79qxt6N1/9aUrl6ac4cN3++S3oViFYqxbr7l5TjOHDjDgK8GmITlLZaXflP74TPChxvnDOdqudrlaNW3FTcv3OTVB+s4lq5emoCXAQS9DrJa3uKzU6so5ZWZv68/oWPlQsZJ7A5ce4yzvS1FPTIkGPfqU8N6jSW9Pt+4JMv3NVta9PuGEtW9uWRyXyvy0ftaox5NcUnvwpB497X8pQpy89197fm9p9T8pg5Ork4m65EWKF2I8OAwQq3UowSA6BiiLl5GU7OqcdkbMHQF1gWHEH3N8hhO1z5dcW7VjJBVGwmaa2G94xgt6YYNIPzAYQLGvV9XXFO9Mkp7e6Iu+FotC/+145PQvbr9gLaUrVGGs4cML4gN9+riCS6Tc2r/KXo26GMSVqBYPgZPH8jsYXO5et50aZNC3gV5+fwl/s+sOzv0a/83vLaQn84D21OhRllOvXv2uKV3xbtiSZYnsEwOQIuOTciYJSPf1uhonAU6U9aMFC9bjHVLNlo13YmKiUZ79TK2laoRufn9smV2Vd6VC25bKBdkyYbTgGHoo6KJPvJ+DLK69LtywYN7oNPhNOAHov7eR5jPTOM2qnwFUbq4on1wz2pZ8PPzx8/P9Dnm4GDP8OH9qVu3Ovv2HQYgY8b0VK1agenT5ye6z7JlS3Lq1Dmz8OfP/alUybwH4smTO1m6dDVLl67m+XPrPVOF+F+RpiqvhQoVwsvLi9mzZ9OzZ88Et3v79i2vXpkvDaFQKMiYMaPZdnHrvJ49e5ZZs2bRtGlTypUznSpfpVLRrFkz1qxZw40bN5g5c2b83X82189e48qpywz8eQgrpi4nJDCEVgO/JTw4jL0r368l6ZHPE7WtmgfXDF1pNvy8ljErxjH0l+HsXfkXGicHmvdugZ3GnrVzDA/re1fucuOf6/SY3Is/p/xOrFZH26HtiNXqWD93jVXzEblxBU5jZ+E4ZBxRf+/GpkBR7Jq0JmLlIsNabhoHVB450fk/Qx/8Fu11X2KuXMCh2wAiHZ2Iff4Em6Le2DdpTdS+7eieGVrOIlYtxnnCXJxGTSNy+zqUbunRtOuB9vZ1Ys5ZcWybBb5nrnDh5CXG+Izgl8lLCA58S+chHQkNDmXbivfj0bzyeWFrq+bOtbsEBwabtaZq3i0Z8/jeE+7ffADAusUbqNuiDnPWzWClz2rCgsOo1qAqdZrWYv74X6w+mcabX9biuXwy7j+P4O2m/Wi8C5G+WwtezfgdfVQ0SkcNtnlzEPPYj9jApFWcYwODCfhjKxm6GmbpDj9xEbV7ZjL0bUPMs5cErd+TyB7+natnr+J70peh84aybMoyggODaTuoLWHBYexeudu4nee7a+f+tfuEBIWYvCwAQ1dJgKf3nvLw1kMAdq3cxZdtvmT88vGs/nk1kWGR1G5Zm3K1y/FTv59SvFtQ9xpF6bn8IEPXHadpqdz4Pn7NHyeuM+ALb+zVNoRGxnD/1Vs80juZdA++4x+ErY0Sz/TOH9l7yrtx9jpXT11hwM+DWTH1D0IDQ/h6YGvCgsPYv/L9eeGRzxMbWxseXjNcFxt/XsfoFWMZ8ssw9q3cg8ZJQ7PeLbDX2LHu3X1tx2/bqNqsOj+umcjm+RsIDwmn/JcVqdKkGn9MXGr1ayd42UoyLZhBhqk/ErZjD7bFC+Pc/hve+ixBHxWNwtEBdS4vtE+fowt6izp/Hpw7tCb6+k3CDxzGtqjpTK8xDx6hDwsneMU6XLu1JzYgkMhT/6DOmxvXHh2IOHaaqLMXEkiNdfyXjg8Y1nS9ePISo3yGs2jybwQHBtNpcHtCg0PZ/sHQBa98OVDbqrl77R7BQSEEx7sXaN5dS4/vP+HBu1n74+QqmItHtz+tl0pyXTpzmfMnLjJu/mgWTF7E24C3dBvSidC3oWxZsd24Xc58Xtjaqbl91TBuetmcP5m7+iem/TaRjb9vwSWdM90GdyLkbQirfrX++rqJCV/7Jy6TZ+M0YjxR+3ZjU6gI9s1bE/67oVyg0DigypGTWL935YKrvsT4XsCx1wAUTk7EPn2Murg3mhbfEvnXdmLf9eKK2LgGTesO6IPfEn3pPKrsnji06YT2/h2i9qfsGtzHj5/lyJGTLF8+j1GjpvDmTSCjRw8iKCiYJUved1cuWDAfdna2Juu7KpVKChbMy/r12832GxMTY9YaG8fPzz/Bz0Tao5Nuw1al0KdyR+xatWrRrFkz+vXrB8DcuXP55ZdfOHjwIB4eHibbNGvWjNq1aye4L1tbW65cucLTp0/NtnNzcyNHjhw0b96cVq1amQ24B3jy5Al169alfPny/PHHH1bJX3Ovxp8Uz9HFkc5julKuXgUUCgU3z9/g9wlLeX7/fZeYCWsnk9kjM99V6W4MK1a5ON8M+JZchXOh1+m4euoKK2es4Nnd991KXDO60fnHrpSs6o1CgWGb6X/i99B0nV1LlpZJXuufulwV7Ft3RuXuiS7gNVF/bSVqh6G1wqZISZwnzCVs/jSiD70rHGkc0HzdEXWFaijTZ0D38gVR+7YTtWuTyRhXVYEiaNp0wyZfIUNL7dnjRPzxS6JjaeNrcjr5retOrk70HduLqvUqo1AquPrPNXzGL+TJvfe/8c8bZpHVMyutKrS1uI+SFUswb+Ns+rcczKVT71tUPPN40HN4N7wre6NUKnh45xFrFq7j6F/Hk5S2xY52yctL3Ypk7NcO29weaP1fE7RqJwHLDEsLOJQrRo6V0/EbNpu3W8xn3XRtVods0wdzr2YnYuK1PKTr2AS3bxug9shK7MsAwk5c4NXsP5JcCQYY/Ilzhzm5OtF9THcq1KuAUqHk+rnrLJ6wmGcfXDvT1k0ji0cWOlfubHEfxSoUY/r66Qz7ZhhXTr9fOzibVzY6De9E0XJFcXB24N7Ve6z1Wcu5Q+ZvzePbNLPCp2XoA39ff8Ivf1/m4etgMrs40Kp8fjpUNlSE/nngT/dlBxjfrAJNSr3vtTJ5x1n+vv6Eg8Osu+xXh6HnE98oHkcXRzqO6UrZeuVRvruv/TFhmcl9bdzaSWTyyEyfKu8nzClauThfD2hNznf3tWunrrI63n3NPXd22gxrT9GKxVAolTy984RtizZzdo9p1/+EzM6cvHuHpkZlXHp0Qu3lQeyr14Ru2E7IKkMLpV2pEmReNJs3438ifOdeXHp2wrVb+wT39bLnYEPLqkKBU8vGOLVohCq7O7q3wYTv/ZvgxcvRRyVvNP/gl0mb5fRDafX4vI5NfMytJU6uTvT+8Tuq1KuEQqnk6rlrLBz3C0/uv0/XnA0zyeqRxaQ78IdKVCzO3A2zGPj1EHxPmVYWfv/7N+7fuM/EPlOSla4I3cdnB0+Is6sTA8b1oVq9KiiVCi7/c5W54xaYjGlduHEu2Tyz0qx8a2NYuWql6TKoI3kL5Uan03PmyD/Mn/Rrgi3GY+YMp1Slkib7+Jhd3skrRtpWrIqmbWdUHp7o3rwmcucWIre8KxcUK4nrtJ8JnTOVqAOGcoFC44CmTUdsK1VHmSEDuhd+RO7ZQeS2je/LBQoFdvUbY9+wKaps2dGHBBN16hgRf/6GPjx5o8WzH3yQrO0B3Nxc+emnMTRqVA+lUsGpU+cYOnQCd+7cN26zb986vLw8KFCgsjEsU6YMPHlykb59R/Lbb0kblxsZ+ZhJk+YwadKcJG0f8vRwsvLyqc5euEyXfsNY5jOdcqXM1+y1BnXGpC09lNY4OZiPif6cQsOTf06nZaleef2v+9TKa1qV3MprWvcplde0LLmV17TsUyuvaZU1Kq9pyadUXtOy5FZe07pPqbymVZ9aeU2rPrXymlYlt/Ka1n1K5TUt+1yV18/hf7Xy6uiQM1W/Pyz8Yap+v7WlmXVehRBCCCGEEEKIhEjlVQghhBBCCCFEmpemJmwSQgghhBBCiP8KmbDJuqTlVQghhBBCCCFEmictr0IIIYQQQgiRAmRuXOuSllchhBBCCCGEEGmeVF6FEEIIIYQQQqR50m1YCCGEEEIIIVKAHuk2bE3S8iqEEEIIIYQQIs2TllchhBBCCCGESAEyYZN1ScurEEIIIYQQQog0TyqvQgghhBBCCCHSPOk2LIQQQgghhBApQLoNW5e0vAohhBBCCCGESPOk5VUIIYQQQgghUoC0u1qXtLwKIYQQQgghhEjzpPIqhBBCCCGEECLNU+hlFLEQQgghhBBCiDROWl6FEEIIIYQQQqR5UnkVQgghhBBCCJHmSeVVCCGEEEIIIUSaJ5VXIYQQQgghhBBpnlRehRBCCCGEEEKkeVJ5FUIIIYQQQgiR5knlVQghhBBCCCFEmieVVyGEEEIIIYQQaZ5UXoUQQgghhBBCpHlSeRVCCCGEEEIIkeZJ5VUIIYQQQgghRJonlVchhBBCCCGEEGmeTWonQIBWq2XVqlVs27aNBw8eYGtrS+HChenRowcVK1YEoH379pw9e9YknlqtJnPmzNSuXZshQ4Zgb28PgI+PD1u2bOHvv/8GYPjw4WzZsoWaNWvy66+/mn3/rl27GDx4MOXKlWPFihUpmtenT59Su3btBD8vV64c2bNnZ/v27axbt45ixYqZfL5582ZGjBjBrVu3rJ624cOH8+zZM1asWEH79u25du0aO3fuxN3d3WS7+L9v/GNjY2NDunTpqFixIoMGDTKJ/+F3xGcpb5cvX2bhwoVcuHCBiIgI3N3d+eKLL+jZsydOTk7Jyl9Sz7Ps2bMzbdq0j/4+HwoNDaVy5co4Ojpy+PBhbG1tzeJaMx+W1KpVC51Ox86dO832Fz/dsbGxrFu3js2bN3Pv3j1UKhV58+alVatWNG3aFIVCAcDFixdp27Yt3bt3Z9CgQSb7PHLkCN999x0LFiygVq1a/zr9iUnq9R+3Xfv27Rk9erTZfhYvXsysWbNo1qyZxWP8OSTlfDl79iwrV67k0qVLBAQE4OrqSunSpenWrRvFixc3bhd3b0vIunXrKFmyZEpkw6hWrVo8e/bM+LdarSZjxozUqlWLfv36kS5dOmNa418/x48fZ/HixVy7dg2tVkuOHDlo1KgRnTt3Rq1Wp2i6P5Sc5wuATqejVq1avH79miNHjpAhQwaTuD4+PsyfP9/4t1KpxNnZmaJFi9K7d2/KlCmTovlJ7LwAuHXrVqL56NmzJ5cuXWLHjh1kzpzZGK7Vamnfvj2xsbGsWrXqo8cq/vmhVCpxdHSkUKFCDBgwgFOnTpn8VpYcPHiQs2fPMmLECJPwuN+1WLFiDB06lIIFC5rFnTNnDr/++isjR46kY8eOxvACBQp89Dvj7hGWnglJvYf+W5aurezZs/P111/TrVs3q3xHUtPRrFkz+vXrZ/HzBw8e4OPjw6lTpwgJCSFz5sxUr16dPn36kDFjxkTLPQB9+/Y17n/9+vWMGTOGjh07MnLkSJN0fPh7xJecMlzcvoYPH07nzp3NPv/xxx9Zt24dffv2pVmzZh9Nf5UqVVi6dKnFfNrb2+Pp6UnTpk3p1KkTNjY2hIeHU7lyZVq2bMmoUaMs7rN+/foUL16c6dOnJyk/4v8XqbymsujoaDp37oyfnx/9+vXD29ubyMhINm3aRJcuXZg6dSpNmzYFDBfzhxd6eHg4x48fZ+rUqcTGxvLjjz8m+D1qtZoTJ04QGhpqVrjfvXu31R42icmWLRvHjx83C9+3bx8TJkygbt26XL9+ndjYWEaMGMHmzZstFm4/h7CwMEaPHs2yZcsS3fbDYxMVFcWTJ0+YM2cOrVq1YsOGDWTNmjXZ33/nzh3at29PmzZtGDhwII6Ojty8eZOpU6fi6+vLn3/+meR9Jec8S65du3aRIUMGXr9+zf79+2nYsGGK5eNj/Pz8mDZtGpMmTUpwG61WS+/evbly5Qp9+/alcuXKxMbGcuLECaZMmcLBgwf5+eefUalUeHt706tXL3755ReqVq1qLHC/ePGCYcOG0b59+89ScY2T1OtfrVazd+9eRo0aZXZdf85rPSGJnS9Llixh7ty5tGnTBh8fHzJnzsyLFy9Ys2YNbdq0YcmSJcaXLQDe3t74+PhY/C43N7eUzIpRly5d6NKlCwCRkZHcvn2bGTNm8M8//7BmzRqLL2hOnjzJd999R//+/Rk7dixqtZoLFy4wdepUHjx4wNSpUz9L2uMk5/ly8uRJgoKCyJAhA5s2baJHjx5m+8uaNSsbN24EDJWdV69esWrVKjp27Mivv/5K1apVUywvo0aNYsiQIca/q1SpwsiRI2nQoIHJdonlY8qUKTRq1IiRI0eyZMkS47UzZ84c7t69y5YtW5L0kuHD80Ov1xMUFMTs2bPp1q0be/bsoXXr1sZtW7ZsSYMGDYzbA6RPn974/x8+P2NjY3nw4AFTpkyhS5cuHDhwAAcHB+PnOp2OrVu3kitXLtauXWtSef1wP7t372bKlCkmYR++rPhQcu6h1hD/2vL19WX06NFoNBratm1rle/4N16/fs23335LtWrVWLJkCenSpePBgwfMmDGD9u3bs23bNrNyz7Jly9i9e7fx+gBMjtvmzZvJlSsXW7duZfDgwcZjsXHjRmJjYwHDy9V+/fqxYcMGsmXLBpDsF15qtZo9e/aYVV61Wi379u0ze1b4+Pjg7e1ttp/4ZbS47fR6PaGhoZw+fZpZs2Zx+/ZtfvrpJxwcHGjQoAG7d+9m+PDhZufK5cuXuX//PhMmTEhWfsT/H1J5TWXz5s3j5s2b7Nq1y6SCM2rUKMLDw5kyZQp169YFDA+TTJkymcT38vLi6tWr7Nq166OV16JFi3Lv3j0OHjxIkyZNjOGhoaEcO3aM0qVLWzlnlqlUKrM83Lx5kxkzZvDll1/SoUMHhg8fTtasWXn48CHz589n8ODBnyVt8Xl6enLixAnWrVtHq1atPrpt/GPj4eFB0aJF+eqrr5g9ezY//fRTsr9/8+bN5MiRg2HDhpmkyd7enm7dunHz5k2Lb9otSc55llybNm2iSpUq+Pv7s3btWrPKiDXz8TGenp5s2LCBevXqJVgw/vXXXzl//jybN2/Gy8vLGJ4nTx7KlStHy5YtWbp0qbEQ27t3b06cOMEPP/zA9u3bsbe3Z9CgQWTPnp3vv//+X6c5OZJ6/ZcvX56TJ09y/vx5kxauBw8e8PDhQwoXLvxZ0x3fx84XX19fZs+ezciRI2nfvr0xPFu2bMYXLrNmzTIp9KnVarPf5XNzcHAwSYOnpyeFChWiYcOGLF26lAEDBpjFWbt2LVWqVDGpMOXIkYPIyEjGjRvHiBEjcHFx+Szph+Q9XzZt2kTp0qXx8vJi3bp1dOvWDaXSdBRS/Ht91qxZmTZtGkFBQYwbN469e/diY5MyRRBnZ2ecnZ3NwuLnL7F8ZMiQgSlTptCzZ09WrVpFu3btOHLkCEuXLmXevHl4eHgkKT3xz4/MmTMzfvx4qlWrxr59++jQoYPxM5VKZbb9h+KHZ82alR9//JF27dpx+vRpkxdqx48f58WLFyxcuJDevXtz5swZypcvb7afuN8qKddRcu+h/5ala+vMmTNs2rQpTVRe9+zZg1arZfr06cbKXvbs2XF3d6d+/focO3aM2rVrm+TBwcHBYlkI4N69e1y8eJEFCxbQr18/du/eTfPmzQHTlxiurq7GsE+9/1WsWJFjx47h5+dnrAADnD59GgcHBzQajcn2rq6uSfquD7fLnDkzuXPnJmPGjPTr149mzZpRsWJFWrZsycaNGzl16hRVqlQxib9lyxZy5sxJ2bJlPylf4r9PxrymopiYGDZs2EDLli0ttswNGDCA3377LcE3oHHs7OzMCg7xqdVqateuzV9//WUSfuDAAQoUKICnp2fyM2AFoaGhDBgwgKxZszJ58mRjeI4cOejVqxe//fYbly9fTpW0lSlThhYtWjB9+nSeP3+e7PjOzs40b96cffv2ER0dnez4CoWCZ8+ecfv2bZPwihUrsmvXLnLlypWk/VjrPLPk3r17+Pr6UrlyZb788kvOnj3LvXv3UiQfiWncuDEVK1ZkzJgxhIaGmn2u1+tZuXIlzZo1Myl0xSlYsCBNmjRhxYoV6HQ6wFCQnDFjBkFBQUyfPp2FCxdy+/Zt5s6dm2o9AuKLf/1nypSJMmXKsGfPHpPtdu/eTY0aNXB0dPzcSTRK7HxZsWIFHh4eCRZKx40bx9KlSz9Xcv8Vd3d36taty86dOy1+rlAouHXrFi9evDAJb9KkCbt27TJpiUlN8c+vt2/fcuDAAeMxfPr0KceOHUvy/jp27MjTp0+5dOlSCqQ26ZKajxo1atCmTRtmzpzJ+fPnGT58OG3btuWLL774V98fV3G3xn3Ezs4OwKwFa/PmzeTPn5/atWvj4eHBmjVr/tX3fMo9NCXEr1SlJoVCQVhYGGfOnDEJz507N7t27aJChQrJ2t/mzZtxcXGhRo0alClT5l8fs48pXrw47u7uFp8V9evXt2ovnbp16+Lu7m68H3p7e5M3b1527Nhhsl10dDS7d++mRYsWVvtu8d8jlddU9OTJE4KCghIck5U5c2aKFy+eYPcbrVbL4cOH2bZtm0lrakLq169v7DocZ/fu3WYtZZ/TyJEj8ff3x8fHx6xrXc+ePSlYsCAjRoz4pMqfNYwYMQJnZ+cEx2UkJn/+/ERERPDw4cNkx23VqhVqtZrGjRvTqlUrZs2axdGjR4mNjSVv3rzGAkti/u159jEbN27EwcGBatWqUadOHWxtbc0ettbKR2IUCgWTJ08mODjYYpfLBw8eEBgYSKlSpRLcR8WKFXn58iVPnz41hnl6ejJmzBg2bNjAr7/+ysSJE1PtZc+HPnb9169fn71796LX641hf/31V6pe65D4+XLu3DkqVKiQ4Mu49OnTG1sc/hfkz5+fx48fExYWZvZZp06dePPmDbVr16ZDhw7Mnz+fM2fOoFaryZMnT4q1SiZVQufXzp07iY6O5osvvqBMmTJkzpyZtWvXJnm/cWMtb968afU0J0dy8jFs2DDc3d3p0KEDWbNmNelF8in8/f2ZMGGC8Vr4N548ecKMGTNwd3c3aakKCgri4MGD1KtXD4AGDRpw4MABXr9+/cnf9an3UGu6fPkyO3bsSLQ31OfSsGFD3N3d6dixI02aNGHq1KkcOHCA0NBQ8ubNm6yXhbGxsWzbto06depgY2NDw4YNuXz5MtevX0+x9NevX9+k8hodHc2BAwes/qxQKBTky5ePGzduGMNatGjB/v37iYyMNIYdPnyY0NBQmjVrZtXvF/8tUnlNRW/fvgVIcmFsx44deHt7G/8VK1aMiRMn0rVr1yR1YaxUqRIODg4cPHjQ+P2nTp2ifv36n56Jf2H58uXs3buXCRMmkC9fPrPPbWxsmDp1Ko8ePUpwTFtKc3Z2ZuLEiZw8eTJZBbQ4cd3+QkJCkh3Xy8uL7du307lzZwIDA1m8eDHdu3enSpUqrF+/Psn7+bfnWdy/+G9ItVotO3bsoGbNmmg0GpydnalevTrbtm0jIiLC6vlIiuzZszN06FA2btxo1ooSFBQEYJxAx5K4zwICAkzCq1atir29Pba2thbH/HwOybn+69Wrx5s3bzh//jwAt2/fxs/Pj+rVq6dG0oGknS+vX7826RoHhjGw8c/FD3tCnDt3zuL5+uE4wtQSd/1b6gng7e3Nli1baNGihfEe16FDB2rWrMmBAwc+d1KTfH5t2rSJkiVL4uHhgVKppEGDBhw5cgQ/P78kfU9cF9VPuSdaU3LyYW9vT7Vq1dBqtZQqVSrZraWLFi0y+V2rVavGnTt3mDt3rtmEgIn58BgVLVqUxo0b4+bmxooVK0xa6+Mq53HP94YNGxITE8OmTZuS9X0f+jf30E/14W9XtGhRvv76azw9Pc3GL6cWNzc3Nm/eTN++fdHpdCxfvpw+ffpQuXJlFixYkKx9HT16lFevXhnzVq9ePdRq9SeVPZKqfv36+Pr6Gs/7EydOkC5dOovDS7p3727xXnvo0KEkfZezs7PJvbBp06ZER0cbJ78E2Lp1KzVq1Ej1oSAibZPKayqKK6TFPRASU6tWLbZu3crmzZsZO3YsLi4uVKpUie+++y5Jb+njdx3et28fJUuWJEuWLJ+ch0916dIlZs6cSZs2bWjcuHGC2xUoUIBevXqxdOnSVOs+XK1aNVq0aMFPP/300Zn+LIkroMUV2GxsbBLsTqXT6cyOY5YsWRg2bBj79u3j8OHDTJ482dgSeOTIkSSl4VPPs/j/4k9OdOTIEZMHLRje7gcHB7Nr1y6r5yOpWrdubbH7cNzkPR8rNMdV9D8snOl0OoYOHUr27Nlxdnbmhx9+SNEucQlJzvWfIUMGypYta3yjvnv3burWrZuqXZ2Tcr6kS5eOwMBAk3jffPON8RwcP3484eHhJr9/0aJFLZ6vc+bM+TwZ+4i4cy2hGbXz5MnDhAkTOHLkCHv37mXMmDFoNBoGDBiQIjOqf0xSzq9bt25x7do1kxeeDRs2JDY2NskvouLfE1NDcvNx5swZ/vjjDypVqsSqVas4efJksr6vdevWxvNy9+7dnDt3jt27d3/Sy6S4/SxZsoRSpUrh7u7OwIEDzcbfbtq0iYIFC5InTx4A4/+vW7fuk+9fn3oP/Tc+/O22bdvGwoULCQ8Pp02bNqnWIys+V1dX+vXrx44dOzh58iSzZ8+mRIkSzJs3j9WrVyd5P5s2bcLNzc04IV3cqgU7duyw+ALMGooWLYqnp6fJs+Krr76yuO2kSZMs3mvjxlEnJv6EoenTp6dmzZps374dMLzwOHr0KC1btvyXuRL/dVJ5TUWenp5kzJiRixcvWvz84cOHdOnSxViIcXR0xMvLi1y5ctG0aVN+/vlnNm7c+NHZVeNr0KCBsevwX3/9lSpvLwMDAxk4cKCxS3Bi0lL3YUvLj3zMtWvX0Gg0xnGdrq6uBAcHW9w2KCjIpHV0xowZnDp1yvh3tmzZaNmyJWvXriVr1qxJrvR96nkW/1/87k+bN28GoH///hQuXJjChQsbW2g+fFNsrXwkVULdh728vMiUKZPZkiAfOnPmDJkyZTIpCC5evJh//vmHqVOnMnHiRM6ePcuSJUusmuakSO7136BBA/bu3YtOp0u1a/1DSTlfSpcuzT///GMSz9XV1XgOWnrRZm9vb/F8/XACktRy7do1cubMaXbthIeHM3nyZJOuszlz5qRdu3asX78elUplcVb2lJSU8yuu1W769OnGYxjXwr1x40a0Wm2i33Pt2jWAVJ04LDn5CAgI4Pvvv6dWrVosWbKEEiVKMGzYMLOXLB/z4Tns6en5ryrucfspU6YMixYtQqFQ0LVrV5P03Lx5k+vXr3Pr1i1j/goXLsz9+/d59uxZssYox//uT7mH/hsf/nZ58uShdu3ajBs3jtu3byf7JUJKWLJkiclcIhkyZKBhw4b88ccflChRIsnPt4CAAA4fPkxQUBDFixc3HrNjx44RHh7Otm3bUioLxq7DUVFRHDx4MMFnRZYsWSzea5MyPl+v13P9+nWKFCliEt6yZUuOHz9OYGAgu3btIn369P+6K73475PKaypSKpW0bNmSzZs34+/vb/b5b7/9xqVLl8iePbvF+BUqVKBz586sWbOGo0ePJuk7K1SogKOjI1u2bOHcuXPG8TCfi16vZ+jQoURERDBv3rwktQR92H04tSZr+bD7cNxbwsSEhYWxdetW6tevb5zCvlixYjx48IA3b96YbX/u3DmTdW1PnjxpcZkeW1tb7O3tzdYkTMi/Pc8sCQgI4MiRIzRv3tzsLWzLli25cuWKsZBqrXwkR/bs2fnhhx/YuHEj586dAwyTmXTo0IGNGzdy584dszg3b95k69attGnTxjj+9/z58/j4+NC/f3+KFy9OjRo1aN26NT4+Ply5csXq6U6OxK7/unXrEhAQwNq1a3n79i2VKlVKhVQaJPV86dChAw8fPkywFS+pXVPTghcvXnDw4EEaNWpk9pm9vT3bt2+32B1Qo9FgY2OTItdFcsQ/v2JiYtixYwdVqlRh27ZtJsewd+/evHz50qT7X0JWrVqFp6dniq+/m5Dk5EOv1zN8+HDA0OpkY2PDjBkzCA0NTfaLzJSg0WiYOXMmr1+/NllWZOPGjajValavXm2SvzVr1qBWqz95EqBPuYempNToAROfr68vCxcuNHtxo1AocHR0TPJ1vH37dmJiYliwYIHZPTJDhgyfpevwxo0b8fT0NLbWW9P+/ft5+fKl2f2wSpUqpE+fngMHDrBz506aNWv2Wc4d8b9NlspJZd999x3Hjh2jdevWDBgwgFKlSvH27VvWrl3L5s2bmTlzZoJdzsAwU+zBgwcZO3YsO3fuTHRyABsbG+rWrcvcuXMpW7as2fiylLZo0SKOHz/O9OnTUavVvHr1yuTzhG5aBQoUoHfv3vz888+fI5kWVatWzTi9e/yKXmRkpDEvMTEx3L9/n4ULF6LX6xk4cKBxu1q1apEzZ0569uzJ4MGD8fLy4s2bN2zfvp0TJ06YrHk6aNAgevXqxYABA2jXrh3u7u48f/6c9evXExYWlqwJK/7teRbftm3b0Gq1dOvWzexB991337FlyxbWrFnDpEmTrJqP5GjdujV79+7l5MmTxla4rl27cuXKFdq1a0f//v2NU/QfP36cefPmUb58eeMSD0FBQQwZMoRSpUrRrVs3436HDx/OmTNn+P7779myZUuqzgob//r/UPr06SlfvjyzZs2iYcOGqToBUHLOl+HDhzN+/HiuXr1K48aNyZYtG35+fmzfvp2NGzdSuHBhk/VbY2JizO4jcRwdHT/L8QkPDzemITIyklu3bjF37lw8PDzM1lAEwwul77//ntGjR6PX62nRogXp06fn8ePHLFu2jKxZs/Lll1+meLoT8+H5NWDAAAICAujcuTP58+c32c7d3Z0VK1awZs0a4yy8cWu7gqGS4e/vz7p16zh27BiLFi1KdIb8lHLo0KEk52PZsmUcPXqU33//3dgNNkeOHIwcOZLRo0cnaRm1lFawYEG6devGL7/8QqNGjahSpQo7d+6kXr16FidWatSoEVu3buX58+fJHm8LybuHWsOH15Zer+fx48dMmTKFzJkzm6z3nNIePXpk9pLQzs6OPn360KZNG7p27Ur37t3JlSsXL1++ZO/evVy6dImRI0cmaf+bNm3C29ubOnXqmH0Wt+b1+fPnU2RZw0KFCuHl5cXs2bPp2bNngtu9ffvW4r1WoVCQMWNGs+3i1nk9e/Yss2bNomnTppQrV84krkqlolmzZqxZs4YbN24wc+ZM62VM/GdJ5TWVaTQaVq5cybJly1iyZAnPnz/Hzs6OIkWK8Mcff5hd6PHZ2dkxceJEOnTowJw5c5L0NrhBgwasX78+VWYePXHiBHq9nh9++MHi59mzZ08wzz169ODAgQPGFr3UMGLECE6cOGEW/tdffxm7DtnZ2ZE1a1aqVavG7NmzTbo62trasmLFCubNm8fo0aN5+fIlTk5OFC9enBUrVpi0RlSrVo0VK1awZMkSBgwYQHBwMK6urlSpUoW1a9eaPCwS82/Ps/g2b95MpUqVLL6h9fT0pG7duuzatYvhw4dbNR/JNWnSJJM3vSqVinnz5rF582Y2bNjAnDlz0Ov15MuXj++//56WLVsalwcYOXIk4eHhzJgxw6SgHdfa0bp1ayZPnmyyxNPnFv/6jy9uhvHUnmU4OedLx44d8fb2ZuXKlQwdOpRXr17h5ORE0aJFmTZtGg0aNDCpiF+8eNFsncA4Q4YMsWpBOiHLli0z9i5wcHAga9asfPHFF3Tp0iXBF4pff/01GTNm5M8//6R79+6EhYWRMWNGateuzU8//fRJS1dZ24fn1549e8iZMyeVK1c2287JyYlvvvmGZcuW8ejRI8DQ8hx3XGxsbMiYMSMlSpRgzZo1FC9e/LPm40ObN29OUj4uX77MnDlz6Ny5s1kl6euvv+bIkSNMnTqVsmXLkjt37s+VfIt69+7N3r17GT9+PAMHDiQwMDDB5aa6dOnCli1bWL9+vcmL1aRKzj3UGj68tpRKJenSpaN06dLMnDnzsy6Zs2PHDrNJC7NkycLRo0fZsGEDCxcuZMSIEQQGBuLo6EjZsmVZu3atxcko47t69Sq3b99OsOLWpk0blixZwtq1a1Ok8gqGZ8Uvv/zy0eEl/fr1sxhua2tr0hPpw+3c3NzIkSMHgwcPTvBFT8uWLVm0aBHly5dPEzP5i7RPof9wLQUhhBBCCCGEECINkjGvQgghhBBCCCHSPKm8CiGEEEIIIYRI86TyKoQQQgghhBAizZPKqxBCCCGEEEKINE8qr0IIIYQQQggh0jypvAohhBBCCCGESPOk8iqEEEIIIYQQIs2TyqsQQgghhBBCiDRPKq9CCCGEEEIIIdI8qbwKIYQQQgghhEjzpPIqhBBCCCGEECLNk8qrEEIIIYQQQog07/8A5Mv/EPjyxVkAAAAASUVORK5CYII=\n", + "text/plain": [ + "
              " + ] + }, + "metadata": { + "filenames": { + "image/png": "/Users/mhjensen/Teaching/MachineLearning/doc/LectureNotes/_build/jupyter_execute/week35_201_1.png" + } + }, + "output_type": "display_data" + } + ], "source": [ "# compute the pair wise correlation for all columns \n", "correlation_matrix = boston.corr().round(2)\n", @@ -3047,7 +3665,22 @@ "collapsed": false, "editable": true }, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "
              " + ] + }, + "metadata": { + "filenames": { + "image/png": "/Users/mhjensen/Teaching/MachineLearning/doc/LectureNotes/_build/jupyter_execute/week35_203_0.png" + } + }, + "output_type": "display_data" + } + ], "source": [ "plt.figure(figsize=(20, 5))\n", "\n", @@ -3106,7 +3739,18 @@ "collapsed": false, "editable": true }, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "(404, 2)\n", + "(102, 2)\n", + "(404,)\n", + "(102,)\n" + ] + } + ], "source": [ "from sklearn.model_selection import train_test_split\n", "\n", @@ -3137,7 +3781,24 @@ "collapsed": false, "editable": true }, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "The model performance for training set\n", + "--------------------------------------\n", + "RMSE is 5.637129335071195\n", + "R2 score is 0.6300745149331701\n", + "\n", + "\n", + "The model performance for testing set\n", + "--------------------------------------\n", + "RMSE is 5.137400784702911\n", + "R2 score is 0.6628996975186953\n" + ] + } + ], "source": [ "from sklearn.linear_model import LinearRegression\n", "from sklearn.metrics import mean_squared_error, r2_score\n", @@ -3180,7 +3841,22 @@ "collapsed": false, "editable": true }, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": 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              " + ] + }, + "metadata": { + "filenames": { + "image/png": "/Users/mhjensen/Teaching/MachineLearning/doc/LectureNotes/_build/jupyter_execute/week35_210_0.png" + } + }, + "output_type": "display_data" + } + ], "source": [ "# plotting the y_test vs y_pred\n", "# ideally should have been a straight line\n", @@ -3847,7 +4523,29 @@ "collapsed": false, "editable": true }, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[[ 1. -1.]\n", + " [ 1. -1.]]\n", + "test U\n", + "[[0. 0.]\n", + " [0. 0.]]\n", + "test VT\n", + "[[0. 0.]\n", + " [0. 0.]]\n", + "[[-0.70710678 -0.70710678]\n", + " [-0.70710678 0.70710678]]\n", + "[2.00000000e+00 3.35470445e-17]\n", + "[[-0.70710678 0.70710678]\n", + " [ 0.70710678 0.70710678]]\n", + "[[-3.33066907e-16 4.44089210e-16]\n", + " [ 0.00000000e+00 2.22044605e-16]]\n" + ] + } + ], "source": [ "import numpy as np\n", "# SVD inversion\n", @@ -4879,7 +5577,18 @@ "collapsed": false, "editable": true }, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "0.10790125813226321\n", + "4.340071371496255\n", + "[[ 1.04193203 3.08165104]\n", + " [ 3.08165104 10.18383522]]\n" + ] + } + ], "source": [ "# Importing various packages\n", "import numpy as np\n", @@ -4917,7 +5626,18 @@ "collapsed": false, "editable": true }, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "0.08881497884574564\n", + "1.7086067479626619\n", + "[[1. 0.66080313]\n", + " [0.66080313 1. ]]\n" + ] + } + ], "source": [ "import numpy as np\n", "n = 100\n", @@ -4976,7 +5696,38 @@ "collapsed": false, "editable": true }, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[[-0.40620066 -2.01265755]\n", + " [ 0.01458611 0.37737221]\n", + " [-1.0895387 -3.65442354]\n", + " [ 0.2338675 1.12044974]\n", + " [ 0.4676059 1.54393936]\n", + " [-0.65891389 -3.16304863]\n", + " [-0.1715252 0.39197698]\n", + " [ 0.71142161 2.95511792]\n", + " [ 0.39214397 0.13069442]\n", + " [ 0.50655336 2.3105791 ]]\n", + " 0 1\n", + "0 -0.406201 -2.012658\n", + "1 0.014586 0.377372\n", + "2 -1.089539 -3.654424\n", + "3 0.233868 1.120450\n", + "4 0.467606 1.543939\n", + "5 -0.658914 -3.163049\n", + "6 -0.171525 0.391977\n", + "7 0.711422 2.955118\n", + "8 0.392144 0.130694\n", + "9 0.506553 2.310579\n", + " 0 1\n", + "0 1.000000 0.952387\n", + "1 0.952387 1.000000\n" + ] + } + ], "source": [ "import numpy as np\n", "import pandas as pd\n", @@ -5022,7 +5773,47 @@ "collapsed": false, "editable": true }, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " 0 1 2 3 4 5 6 7 \\\n", + "0 0.0 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 \n", + "1 0.0 0.078974 0.081276 0.075889 0.077168 0.078540 0.065410 0.066474 \n", + "2 0.0 0.081276 0.084076 0.078336 0.079946 0.081655 0.067707 0.069028 \n", + "3 0.0 0.075889 0.078336 0.077460 0.078986 0.080616 0.069452 0.070737 \n", + "4 0.0 0.077168 0.079946 0.078986 0.080764 0.082653 0.070986 0.072476 \n", + "5 0.0 0.078540 0.081655 0.080616 0.082653 0.084809 0.072621 0.074323 \n", + "6 0.0 0.065410 0.067707 0.069452 0.070986 0.072621 0.064074 0.065378 \n", + "7 0.0 0.066474 0.069028 0.070737 0.072476 0.074323 0.065378 0.066854 \n", + "8 0.0 0.067637 0.070457 0.072132 0.074084 0.076150 0.066787 0.068441 \n", + "9 0.0 0.068906 0.072000 0.073644 0.075816 0.078110 0.068307 0.070146 \n", + "10 0.0 0.055734 0.057835 0.060872 0.062337 0.063894 0.057393 0.058645 \n", + "11 0.0 0.056683 0.058996 0.062016 0.063653 0.065390 0.058552 0.059951 \n", + "12 0.0 0.057722 0.060254 0.063260 0.065077 0.066999 0.059807 0.061359 \n", + "13 0.0 0.058854 0.061614 0.064609 0.066612 0.068727 0.061163 0.062874 \n", + "14 0.0 0.060083 0.063080 0.066066 0.068264 0.070582 0.062624 0.064501 \n", + "\n", + " 8 9 10 11 12 13 14 \n", + "0 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 \n", + "1 0.067637 0.068906 0.055734 0.056683 0.057722 0.058854 0.060083 \n", + "2 0.070457 0.072000 0.057835 0.058996 0.060254 0.061614 0.063080 \n", + "3 0.072132 0.073644 0.060872 0.062016 0.063260 0.064609 0.066066 \n", + "4 0.074084 0.075816 0.062337 0.063653 0.065077 0.066612 0.068264 \n", + "5 0.076150 0.078110 0.063894 0.065390 0.066999 0.068727 0.070582 \n", + "6 0.066787 0.068307 0.057393 0.058552 0.059807 0.061163 0.062624 \n", + "7 0.068441 0.070146 0.058645 0.059951 0.061359 0.062874 0.064501 \n", + "8 0.070213 0.072111 0.059993 0.061452 0.063019 0.064699 0.066500 \n", + "9 0.072111 0.074210 0.061443 0.063061 0.064793 0.066647 0.068629 \n", + "10 0.059993 0.061443 0.052305 0.053417 0.054617 0.055910 0.057300 \n", + "11 0.061452 0.063061 0.053417 0.054655 0.055987 0.057418 0.058952 \n", + "12 0.063019 0.064793 0.054617 0.055987 0.057457 0.059031 0.060716 \n", + "13 0.064699 0.066647 0.055910 0.057418 0.059031 0.060756 0.062599 \n", + "14 0.066500 0.068629 0.057300 0.058952 0.060716 0.062599 0.064606 \n" + ] + } + ], "source": [ "# Common imports\n", "import numpy as np\n", @@ -6106,7 +6897,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.9.18" + "version": "3.9.10" } }, "nbformat": 4, diff --git a/doc/LectureNotes/_build/jupyter_execute/week35_181_2.png b/doc/LectureNotes/_build/jupyter_execute/week35_181_2.png new file mode 100644 index 000000000..a1fd46ceb Binary files /dev/null and b/doc/LectureNotes/_build/jupyter_execute/week35_181_2.png differ diff --git a/doc/LectureNotes/_build/jupyter_execute/week36.ipynb b/doc/LectureNotes/_build/jupyter_execute/week36.ipynb index f0646a41d..49fa8b34f 100644 --- a/doc/LectureNotes/_build/jupyter_execute/week36.ipynb +++ b/doc/LectureNotes/_build/jupyter_execute/week36.ipynb @@ -397,8 +397,8 @@ "traceback": [ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[0;31mAttributeError\u001b[0m Traceback (most recent call last)", - "Cell \u001b[0;32mIn[1], line 3\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mnumpy\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m \u001b[38;5;21;01mnp\u001b[39;00m\n\u001b[1;32m 2\u001b[0m X \u001b[38;5;241m=\u001b[39m np\u001b[38;5;241m.\u001b[39marray( [ [\u001b[38;5;241m1\u001b[39m,\u001b[38;5;241m2\u001b[39m,\u001b[38;5;241m3\u001b[39m],[\u001b[38;5;241m2\u001b[39m,\u001b[38;5;241m4\u001b[39m,\u001b[38;5;241m5\u001b[39m],[\u001b[38;5;241m3\u001b[39m,\u001b[38;5;241m5\u001b[39m,\u001b[38;5;241m6\u001b[39m]])\n\u001b[0;32m----> 3\u001b[0m Xinv \u001b[38;5;241m=\u001b[39m \u001b[43mnp\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mlinlag\u001b[49m\u001b[38;5;241m.\u001b[39mpinv(X)\n", - "File \u001b[0;32m~/miniforge3/lib/python3.9/site-packages/numpy/__init__.py:313\u001b[0m, in \u001b[0;36m__getattr__\u001b[0;34m(attr)\u001b[0m\n\u001b[1;32m 310\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mtesting\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m Tester\n\u001b[1;32m 311\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m Tester\n\u001b[0;32m--> 313\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mAttributeError\u001b[39;00m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mmodule \u001b[39m\u001b[38;5;132;01m{!r}\u001b[39;00m\u001b[38;5;124m has no attribute \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m 314\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;132;01m{!r}\u001b[39;00m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;241m.\u001b[39mformat(\u001b[38;5;18m__name__\u001b[39m, attr))\n", + "Input \u001b[0;32mIn [1]\u001b[0m, in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mnumpy\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m \u001b[38;5;21;01mnp\u001b[39;00m\n\u001b[1;32m 2\u001b[0m X \u001b[38;5;241m=\u001b[39m np\u001b[38;5;241m.\u001b[39marray( [ [\u001b[38;5;241m1\u001b[39m,\u001b[38;5;241m2\u001b[39m,\u001b[38;5;241m3\u001b[39m],[\u001b[38;5;241m2\u001b[39m,\u001b[38;5;241m4\u001b[39m,\u001b[38;5;241m5\u001b[39m],[\u001b[38;5;241m3\u001b[39m,\u001b[38;5;241m5\u001b[39m,\u001b[38;5;241m6\u001b[39m]])\n\u001b[0;32m----> 3\u001b[0m Xinv \u001b[38;5;241m=\u001b[39m \u001b[43mnp\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mlinlag\u001b[49m\u001b[38;5;241m.\u001b[39mpinv(X)\n", + "File \u001b[0;32m~/miniforge3/envs/myenv/lib/python3.9/site-packages/numpy/__init__.py:315\u001b[0m, in \u001b[0;36m__getattr__\u001b[0;34m(attr)\u001b[0m\n\u001b[1;32m 312\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mtesting\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m Tester\n\u001b[1;32m 313\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m Tester\n\u001b[0;32m--> 315\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mAttributeError\u001b[39;00m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mmodule \u001b[39m\u001b[38;5;132;01m{!r}\u001b[39;00m\u001b[38;5;124m has no attribute \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m 316\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;132;01m{!r}\u001b[39;00m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;241m.\u001b[39mformat(\u001b[38;5;18m__name__\u001b[39m, attr))\n", "\u001b[0;31mAttributeError\u001b[0m: module 'numpy' has no attribute 'linlag'" ] } @@ -4260,7 +4260,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.9.18" + "version": "3.9.10" } }, "nbformat": 4, diff --git a/doc/LectureNotes/_build/jupyter_execute/week37.ipynb b/doc/LectureNotes/_build/jupyter_execute/week37.ipynb index 2e79f07db..c6d8f3978 100644 --- a/doc/LectureNotes/_build/jupyter_execute/week37.ipynb +++ b/doc/LectureNotes/_build/jupyter_execute/week37.ipynb @@ -2018,19 +2018,12 @@ }, "outputs": [ { - "ename": "ModuleNotFoundError", - "evalue": "No module named 'matplotlib'", - "output_type": "error", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mModuleNotFoundError\u001b[0m Traceback (most recent call last)", - "Cell \u001b[0;32mIn[1], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \u001b[43mget_ipython\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mrun_line_magic\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mmatplotlib\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43minline\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m)\u001b[49m\n\u001b[1;32m 3\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mnumpy\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m \u001b[38;5;21;01mnp\u001b[39;00m\n\u001b[1;32m 4\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mtime\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m time\n", - "File \u001b[0;32m~/miniforge3/lib/python3.9/site-packages/IPython/core/interactiveshell.py:2432\u001b[0m, in \u001b[0;36mInteractiveShell.run_line_magic\u001b[0;34m(self, magic_name, line, _stack_depth)\u001b[0m\n\u001b[1;32m 2430\u001b[0m kwargs[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mlocal_ns\u001b[39m\u001b[38;5;124m'\u001b[39m] \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mget_local_scope(stack_depth)\n\u001b[1;32m 2431\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mbuiltin_trap:\n\u001b[0;32m-> 2432\u001b[0m result \u001b[38;5;241m=\u001b[39m \u001b[43mfn\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 2434\u001b[0m \u001b[38;5;66;03m# The code below prevents the output from being displayed\u001b[39;00m\n\u001b[1;32m 2435\u001b[0m \u001b[38;5;66;03m# when using magics with decorator @output_can_be_silenced\u001b[39;00m\n\u001b[1;32m 2436\u001b[0m \u001b[38;5;66;03m# when the last Python token in the expression is a ';'.\u001b[39;00m\n\u001b[1;32m 2437\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mgetattr\u001b[39m(fn, magic\u001b[38;5;241m.\u001b[39mMAGIC_OUTPUT_CAN_BE_SILENCED, \u001b[38;5;28;01mFalse\u001b[39;00m):\n", - "File \u001b[0;32m~/miniforge3/lib/python3.9/site-packages/IPython/core/magics/pylab.py:99\u001b[0m, in \u001b[0;36mPylabMagics.matplotlib\u001b[0;34m(self, line)\u001b[0m\n\u001b[1;32m 97\u001b[0m \u001b[38;5;28mprint\u001b[39m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mAvailable matplotlib backends: \u001b[39m\u001b[38;5;132;01m%s\u001b[39;00m\u001b[38;5;124m\"\u001b[39m \u001b[38;5;241m%\u001b[39m backends_list)\n\u001b[1;32m 98\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m---> 99\u001b[0m gui, backend \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mshell\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43menable_matplotlib\u001b[49m\u001b[43m(\u001b[49m\u001b[43margs\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mgui\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mlower\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mif\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[38;5;28;43misinstance\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43margs\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mgui\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mstr\u001b[39;49m\u001b[43m)\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01melse\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43margs\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mgui\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 100\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_show_matplotlib_backend(args\u001b[38;5;241m.\u001b[39mgui, backend)\n", - "File \u001b[0;32m~/miniforge3/lib/python3.9/site-packages/IPython/core/interactiveshell.py:3606\u001b[0m, in \u001b[0;36mInteractiveShell.enable_matplotlib\u001b[0;34m(self, gui)\u001b[0m\n\u001b[1;32m 3585\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21menable_matplotlib\u001b[39m(\u001b[38;5;28mself\u001b[39m, gui\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mNone\u001b[39;00m):\n\u001b[1;32m 3586\u001b[0m \u001b[38;5;250m \u001b[39m\u001b[38;5;124;03m\"\"\"Enable interactive matplotlib and inline figure support.\u001b[39;00m\n\u001b[1;32m 3587\u001b[0m \n\u001b[1;32m 3588\u001b[0m \u001b[38;5;124;03m This takes the following steps:\u001b[39;00m\n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 3604\u001b[0m \u001b[38;5;124;03m display figures inline.\u001b[39;00m\n\u001b[1;32m 3605\u001b[0m \u001b[38;5;124;03m \"\"\"\u001b[39;00m\n\u001b[0;32m-> 3606\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mmatplotlib_inline\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mbackend_inline\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m configure_inline_support\n\u001b[1;32m 3608\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mIPython\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mcore\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m pylabtools \u001b[38;5;28;01mas\u001b[39;00m pt\n\u001b[1;32m 3609\u001b[0m gui, backend \u001b[38;5;241m=\u001b[39m pt\u001b[38;5;241m.\u001b[39mfind_gui_and_backend(gui, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mpylab_gui_select)\n", - "File \u001b[0;32m~/miniforge3/lib/python3.9/site-packages/matplotlib_inline/__init__.py:1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01m.\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m backend_inline, config \u001b[38;5;66;03m# noqa\u001b[39;00m\n\u001b[1;32m 2\u001b[0m __version__ \u001b[38;5;241m=\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m0.1.6\u001b[39m\u001b[38;5;124m\"\u001b[39m \u001b[38;5;66;03m# noqa\u001b[39;00m\n", - "File \u001b[0;32m~/miniforge3/lib/python3.9/site-packages/matplotlib_inline/backend_inline.py:6\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[38;5;124;03m\"\"\"A matplotlib backend for publishing figures via display_data\"\"\"\u001b[39;00m\n\u001b[1;32m 3\u001b[0m \u001b[38;5;66;03m# Copyright (c) IPython Development Team.\u001b[39;00m\n\u001b[1;32m 4\u001b[0m \u001b[38;5;66;03m# Distributed under the terms of the BSD 3-Clause License.\u001b[39;00m\n\u001b[0;32m----> 6\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mmatplotlib\u001b[39;00m\n\u001b[1;32m 7\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mmatplotlib\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m colors\n\u001b[1;32m 8\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mmatplotlib\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mbackends\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m backend_agg\n", - "\u001b[0;31mModuleNotFoundError\u001b[0m: No module named 'matplotlib'" + "name": "stdout", + "output_type": "stream", + "text": [ + "Bootstrap Statistics :\n", + "original bias std. error\n", + " 99.8182 15.0632 99.8197 0.152701\n" ] } ], @@ -2093,7 +2086,22 @@ "collapsed": false, "editable": true }, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
              " + ] + }, + "metadata": { + "filenames": { + "image/png": "/Users/mhjensen/Teaching/MachineLearning/doc/LectureNotes/_build/jupyter_execute/week37_144_0.png" + } + }, + "output_type": "display_data" + } + ], "source": [ "# the histogram of the bootstrapped data (normalized data if density = True)\n", "n, binsboot, patches = plt.hist(t, 50, density=True, facecolor='red', alpha=0.75)\n", @@ -2304,7 +2312,32 @@ "collapsed": false, "editable": true }, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Error: 0.013121574062587286\n", + "Bias^2: 0.012073649469946107\n", + "Var: 0.0010479245926411787\n", + "0.013121574062587286 >= 0.012073649469946107 + 0.0010479245926411787 = 0.013121574062587286\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
              " + ] + }, + "metadata": { + "filenames": { + "image/png": "/Users/mhjensen/Teaching/MachineLearning/doc/LectureNotes/_build/jupyter_execute/week37_160_1.png" + } + }, + "output_type": "display_data" + } + ], "source": [ "import matplotlib.pyplot as plt\n", "import numpy as np\n", @@ -2380,7 +2413,110 @@ "collapsed": false, "editable": true }, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Polynomial degree: 0\n", + "Error: 0.32149601703519115\n", + "Bias^2: 0.3123314713548606\n", + "Var: 0.009164545680330616\n", + "0.32149601703519115 >= 0.3123314713548606 + 0.009164545680330616 = 0.3214960170351912\n", + "Polynomial degree: 1\n", + "Error: 0.08426840630693412\n", + "Bias^2: 0.0796891867672603\n", + "Var: 0.004579219539673834\n", + "0.08426840630693412 >= 0.0796891867672603 + 0.004579219539673834 = 0.08426840630693413\n", + "Polynomial degree: 2\n", + "Error: 0.10398646080125037\n", + "Bias^2: 0.10077114273548984\n", + "Var: 0.0032153180657605116\n", + "0.10398646080125037 >= 0.10077114273548984 + 0.0032153180657605116 = 0.10398646080125036\n", + "Polynomial degree: 3\n", + "Error: 0.06547790180152352\n", + "Bias^2: 0.062082386342319454\n", + "Var: 0.0033955154592040923\n", + "0.06547790180152352 >= 0.062082386342319454 + 0.0033955154592040923 = 0.06547790180152355\n", + "Polynomial degree: 4\n", + "Error: 0.06844519414009445\n", + "Bias^2: 0.06453579006728322\n", + "Var: 0.003909404072811221\n", + "0.06844519414009445 >= 0.06453579006728322 + 0.003909404072811221 = 0.06844519414009444\n", + "Polynomial degree: 5\n", + "Error: 0.05227921801205679\n", + "Bias^2: 0.04818727730430286\n", + "Var: 0.004091940707753925\n", + "0.05227921801205679 >= 0.04818727730430286 + 0.004091940707753925 = 0.05227921801205679\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Polynomial degree: 6\n", + "Error: 0.03781367141738902\n", + "Bias^2: 0.03365768507152769\n", + "Var: 0.0041559863458613296\n", + "0.03781367141738902 >= 0.03365768507152769 + 0.0041559863458613296 = 0.03781367141738902\n", + "Polynomial degree: 7\n", + "Error: 0.027609773491022394\n", + "Bias^2: 0.022999498260366198\n", + "Var: 0.004610275230656182\n", + "0.027609773491022394 >= 0.022999498260366198 + 0.004610275230656182 = 0.02760977349102238\n", + "Polynomial degree: 8\n", + "Error: 0.017355848195593312\n", + "Bias^2: 0.010331721306655165\n", + "Var: 0.007024126888938144\n", + "0.017355848195593312 >= 0.010331721306655165 + 0.007024126888938144 = 0.01735584819559331\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Polynomial degree: 9\n", + "Error: 0.026605727637184558\n", + "Bias^2: 0.010018312644139219\n", + "Var: 0.016587414993045335\n", + "0.026605727637184558 >= 0.010018312644139219 + 0.016587414993045335 = 0.026605727637184554\n", + "Polynomial degree: 10\n", + "Error: 0.021592704588021178\n", + "Bias^2: 0.010516485576646504\n", + "Var: 0.01107621901137467\n", + "0.021592704588021178 >= 0.010516485576646504 + 0.01107621901137467 = 0.021592704588021174\n", + "Polynomial degree: 11\n", + "Error: 0.07160048164232538\n", + "Bias^2: 0.014436800088896381\n", + "Var: 0.05716368155342902\n", + "0.07160048164232538 >= 0.014436800088896381 + 0.05716368155342902 = 0.0716004816423254\n", + "Polynomial degree: 12\n", + "Error: 0.11547777218876518\n", + "Bias^2: 0.016285782696017142\n", + "Var: 0.09919198949274803\n", + "0.11547777218876518 >= 0.016285782696017142 + 0.09919198949274803 = 0.11547777218876518\n", + "Polynomial degree: 13\n", + "Error: 0.2284246870217162\n", + "Bias^2: 0.01975416527168255\n", + "Var: 0.20867052175003364\n", + "0.2284246870217162 >= 0.01975416527168255 + 0.20867052175003364 = 0.2284246870217162\n" + ] + }, + { + "data": { + "image/png": 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\n", 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              " + ] + }, + "metadata": { + "filenames": { + "image/png": "/Users/mhjensen/Teaching/MachineLearning/doc/LectureNotes/_build/jupyter_execute/week37_162_3.png" + } + }, + "output_type": "display_data" + } + ], "source": [ "import matplotlib.pyplot as plt\n", "import numpy as np\n", @@ -2486,7 +2622,49 @@ "collapsed": false, "editable": true }, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "============================\n", + "Underfitting vs. Overfitting\n", + "============================\n", + "\n", + "This example demonstrates the problems of underfitting and overfitting and\n", + "how we can use linear regression with polynomial features to approximate\n", + "nonlinear functions. The plot shows the function that we want to approximate,\n", + "which is a part of the cosine function. In addition, the samples from the\n", + "real function and the approximations of different models are displayed. The\n", + "models have polynomial features of different degrees. We can see that a\n", + "linear function (polynomial with degree 1) is not sufficient to fit the\n", + "training samples. This is called **underfitting**. A polynomial of degree 4\n", + "approximates the true function almost perfectly. However, for higher degrees\n", + "the model will **overfit** the training data, i.e. it learns the noise of the\n", + "training data.\n", + "We evaluate quantitatively **overfitting** / **underfitting** by using\n", + "cross-validation. We calculate the mean squared error (MSE) on the validation\n", + "set, the higher, the less likely the model generalizes correctly from the\n", + "training data.\n", + "\n" + ] + }, + { + "data": { + "image/png": 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\n", 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              " + ] + }, + "metadata": { + "filenames": { + "image/png": "/Users/mhjensen/Teaching/MachineLearning/doc/LectureNotes/_build/jupyter_execute/week37_169_0.png" + } + }, + "output_type": "display_data" + } + ], "source": [ "import numpy as np\n", "import matplotlib.pyplot as plt\n", @@ -2745,7 +2938,149 @@ "collapsed": false, "editable": true }, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Degree of polynomial: 1\n", + "Mean squared error on training data: 446033.51374050\n", + "Mean squared error on test data: 455173.80460179\n", + "Degree of polynomial: 2\n", + "Mean squared error on training data: 114550.54637219\n", + "Mean squared error on test data: 129963.83146596\n", + "Degree of polynomial: 3\n", + "Mean squared error on training data: 9054.61775176\n", + "Mean squared error on test data: 10572.87627342\n", + "Degree of polynomial: 4\n", + "Mean squared error on training data: 302.15313054\n", + "Mean squared error on test data: 433.26292364\n", + "Degree of polynomial: 5\n", + "Mean squared error on training data: 3.64316192\n", + "Mean squared error on test data: 7.23528337\n", + "Degree of polynomial: 6\n", + "Mean squared error on training data: 3.56589683\n", + "Mean squared error on test data: 10.50427787\n", + "Degree of polynomial: 7\n", + "Mean squared error on training data: 0.47313680\n", + "Mean squared error on test data: 1.53738247\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Degree of polynomial: 8\n", + "Mean squared error on training data: 0.04926746\n", + "Mean squared error on test data: 0.14629156\n", + "Degree of polynomial: 9\n", + "Mean squared error on training data: 0.02546675\n", + "Mean squared error on test data: 0.11202337\n", + "Degree of polynomial: 10\n", + "Mean squared error on training data: 0.02424794\n", + "Mean squared error on test data: 0.22467274\n", + "Degree of polynomial: 11\n", + "Mean squared error on training data: 0.01594452\n", + "Mean squared error on test data: 1.07641937\n", + "Degree of polynomial: 12\n", + "Mean squared error on training data: 0.00805074\n", + "Mean squared error on test data: 0.04295757\n", + "Degree of polynomial: 13\n", + "Mean squared error on training data: 0.00781918\n", + "Mean squared error on test data: 0.56965674\n", + "Degree of polynomial: 14\n", + "Mean squared error on training data: 0.00465099\n", + "Mean squared error on test data: 0.28443039\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Degree of polynomial: 15\n", + "Mean squared error on training data: 0.00420072\n", + "Mean squared error on test data: 568.47202442\n", + "Degree of polynomial: 16\n", + "Mean squared error on training data: 0.00325450\n", + "Mean squared error on test data: 48.97690235\n", + "Degree of polynomial: 17\n", + "Mean squared error on training data: 0.00242954\n", + "Mean squared error on test data: 2.52775466\n", + "Degree of polynomial: 18\n", + "Mean squared error on training data: 0.00219194\n", + "Mean squared error on test data: 429.23643365\n", + "Degree of polynomial: 19\n", + "Mean squared error on training data: 0.00154860\n", + "Mean squared error on test data: 238.16356503\n", + "Degree of polynomial: 20\n", + "Mean squared error on training data: 0.00140849\n", + "Mean squared error on test data: 1345.68592431\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Degree of polynomial: 21\n", + "Mean squared error on training data: 0.00119699\n", + "Mean squared error on test data: 1836.21110005\n", + "Degree of polynomial: 22\n", + "Mean squared error on training data: 0.00092904\n", + "Mean squared error on test data: 1182.64316482\n", + "Degree of polynomial: 23\n", + "Mean squared error on training data: 0.00089187\n", + "Mean squared error on test data: 3886.35846425\n", + "Degree of polynomial: 24\n", + "Mean squared error on training data: 0.00083346\n", + "Mean squared error on test data: 1346.92651068\n", + "Degree of polynomial: 25\n", + "Mean squared error on training data: 0.00079910\n", + "Mean squared error on test data: 7697.35412147\n", + "Degree of polynomial: 26\n", + "Mean squared error on training data: 0.00075597\n", + "Mean squared error on test data: 1078.81597834\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Degree of polynomial: 27\n", + "Mean squared error on training data: 0.00068088\n", + "Mean squared error on test data: 3189.20355156\n", + "Degree of polynomial: 28\n", + "Mean squared error on training data: 0.00063364\n", + "Mean squared error on test data: 692.24085321\n", + "Degree of polynomial: 29\n", + "Mean squared error on training data: 0.00063862\n", + "Mean squared error on test data: 3073.63180447\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_19367/626635268.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_19367/626635268.py:74: RuntimeWarning: divide by zero encountered in log10\n", + " plt.plot(polynomial, np.log10(testerror), label='Test Error')\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
              " + ] + }, + "metadata": { + "filenames": { + "image/png": "/Users/mhjensen/Teaching/MachineLearning/doc/LectureNotes/_build/jupyter_execute/week37_171_6.png" + } + }, + "output_type": "display_data" + } + ], "source": [ "# Common imports\n", "import os\n", @@ -2857,7 +3192,30 @@ "collapsed": false, "editable": true }, - "outputs": [], + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_19367/3817475779.py:63: RuntimeWarning: divide by zero encountered in log10\n", + " plt.plot(polynomial, np.log10(estimated_mse_sklearn), label='Test Error')\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
              " + ] + }, + "metadata": { + "filenames": { + "image/png": "/Users/mhjensen/Teaching/MachineLearning/doc/LectureNotes/_build/jupyter_execute/week37_174_1.png" + } + }, + "output_type": "display_data" + } + ], "source": [ "# Common imports\n", "import os\n", @@ -2962,7 +3320,36 @@ "collapsed": false, "editable": true }, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[1.79934087 0.47179152 5.01549939]\n", + "[1.79909592 0.47176716 5.01550546]\n", + " \n", + "test MSE of OLS:\n", + "1.139431112903922\n", + " \n", + "test MSE of Ridge\n", + "1.1395235273363669\n" + ] + }, + { + "data": { + "image/png": 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+ "text/plain": [ + "
              " + ] + }, + "metadata": { + "filenames": { + "image/png": "/Users/mhjensen/Teaching/MachineLearning/doc/LectureNotes/_build/jupyter_execute/week37_176_1.png" + } + }, + "output_type": "display_data" + } + ], "source": [ "import matplotlib.pyplot as plt\n", "import numpy as np\n", @@ -3399,7 +3786,38 @@ "collapsed": false, "editable": true }, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[0.47179152 5.01549939]\n", + "[0.47176783 5.01542292]\n", + "1.7993408651198877\n", + "1.7995707762668065\n", + " \n", + "test MSE of OLS:\n", + "1.1394311129039245\n", + " \n", + "test MSE of Ridge\n", + "1.1395084586525954\n" + ] + }, + { + "data": { + "image/png": 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+ "text/plain": [ + "
              " + ] + }, + "metadata": { + "filenames": { + "image/png": "/Users/mhjensen/Teaching/MachineLearning/doc/LectureNotes/_build/jupyter_execute/week37_208_1.png" + } + }, + "output_type": "display_data" + } + ], "source": [ "\n", "np.random.seed(2018)\n", @@ -3485,7 +3903,19 @@ "collapsed": false, "editable": true }, - "outputs": [], + "outputs": [ + { + "ename": "NameError", + "evalue": "name 'RegRidge' is not defined", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)", + "Input \u001b[0;32mIn [11]\u001b[0m, in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[1;32m 32\u001b[0m ypredictOLS \u001b[38;5;241m=\u001b[39m OLS\u001b[38;5;241m.\u001b[39mpredict(X_test_scaled)\n\u001b[1;32m 33\u001b[0m linear_model\u001b[38;5;241m.\u001b[39mRidge(Lambda)\n\u001b[0;32m---> 34\u001b[0m \u001b[43mRegRidge\u001b[49m\u001b[38;5;241m.\u001b[39mfit(X_train_scaled,y_train_scaled)\n\u001b[1;32m 35\u001b[0m ypredictRidge \u001b[38;5;241m=\u001b[39m RegRidge\u001b[38;5;241m.\u001b[39mpredict(X_test_scaled)\n\u001b[1;32m 36\u001b[0m betaOLS \u001b[38;5;241m=\u001b[39m OLS\u001b[38;5;241m.\u001b[39mcoef_\n", + "\u001b[0;31mNameError\u001b[0m: name 'RegRidge' is not defined" + ] + } + ], "source": [ "from sklearn import linear_model\n", "np.random.seed(2018)\n", @@ -3560,7 +3990,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.9.18" + "version": "3.9.10" } }, "nbformat": 4, diff --git a/doc/LectureNotes/_build/jupyter_execute/week37_144_0.png b/doc/LectureNotes/_build/jupyter_execute/week37_144_0.png index db9cf165e..6adf14c0a 100644 Binary files a/doc/LectureNotes/_build/jupyter_execute/week37_144_0.png and b/doc/LectureNotes/_build/jupyter_execute/week37_144_0.png differ diff --git a/doc/LectureNotes/_build/jupyter_execute/week38.ipynb b/doc/LectureNotes/_build/jupyter_execute/week38.ipynb index edb418296..fd37b75b7 100644 --- a/doc/LectureNotes/_build/jupyter_execute/week38.ipynb +++ b/doc/LectureNotes/_build/jupyter_execute/week38.ipynb @@ -197,20 +197,18 @@ }, "outputs": [ { - "ename": "ModuleNotFoundError", - "evalue": "No module named 'matplotlib'", - "output_type": "error", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mModuleNotFoundError\u001b[0m Traceback (most recent call last)", - "Cell \u001b[0;32mIn[1], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \u001b[43mget_ipython\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mrun_line_magic\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mmatplotlib\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43minline\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m)\u001b[49m\n\u001b[1;32m 3\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mnumpy\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m \u001b[38;5;21;01mnp\u001b[39;00m\n\u001b[1;32m 4\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mmatplotlib\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mpyplot\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m \u001b[38;5;21;01mplt\u001b[39;00m\n", - "File \u001b[0;32m~/miniforge3/lib/python3.9/site-packages/IPython/core/interactiveshell.py:2432\u001b[0m, in \u001b[0;36mInteractiveShell.run_line_magic\u001b[0;34m(self, magic_name, line, _stack_depth)\u001b[0m\n\u001b[1;32m 2430\u001b[0m kwargs[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mlocal_ns\u001b[39m\u001b[38;5;124m'\u001b[39m] \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mget_local_scope(stack_depth)\n\u001b[1;32m 2431\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mbuiltin_trap:\n\u001b[0;32m-> 2432\u001b[0m result \u001b[38;5;241m=\u001b[39m \u001b[43mfn\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 2434\u001b[0m \u001b[38;5;66;03m# The code below prevents the output from being displayed\u001b[39;00m\n\u001b[1;32m 2435\u001b[0m \u001b[38;5;66;03m# when using magics with decorator @output_can_be_silenced\u001b[39;00m\n\u001b[1;32m 2436\u001b[0m \u001b[38;5;66;03m# when the last Python token in the expression is a ';'.\u001b[39;00m\n\u001b[1;32m 2437\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mgetattr\u001b[39m(fn, magic\u001b[38;5;241m.\u001b[39mMAGIC_OUTPUT_CAN_BE_SILENCED, \u001b[38;5;28;01mFalse\u001b[39;00m):\n", - "File \u001b[0;32m~/miniforge3/lib/python3.9/site-packages/IPython/core/magics/pylab.py:99\u001b[0m, in \u001b[0;36mPylabMagics.matplotlib\u001b[0;34m(self, line)\u001b[0m\n\u001b[1;32m 97\u001b[0m \u001b[38;5;28mprint\u001b[39m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mAvailable matplotlib backends: \u001b[39m\u001b[38;5;132;01m%s\u001b[39;00m\u001b[38;5;124m\"\u001b[39m \u001b[38;5;241m%\u001b[39m backends_list)\n\u001b[1;32m 98\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m---> 99\u001b[0m gui, backend \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mshell\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43menable_matplotlib\u001b[49m\u001b[43m(\u001b[49m\u001b[43margs\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mgui\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mlower\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mif\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[38;5;28;43misinstance\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43margs\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mgui\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mstr\u001b[39;49m\u001b[43m)\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01melse\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43margs\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mgui\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 100\u001b[0m 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3606\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mmatplotlib_inline\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mbackend_inline\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m configure_inline_support\n\u001b[1;32m 3608\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mIPython\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mcore\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m pylabtools \u001b[38;5;28;01mas\u001b[39;00m pt\n\u001b[1;32m 3609\u001b[0m gui, backend \u001b[38;5;241m=\u001b[39m pt\u001b[38;5;241m.\u001b[39mfind_gui_and_backend(gui, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mpylab_gui_select)\n", - "File \u001b[0;32m~/miniforge3/lib/python3.9/site-packages/matplotlib_inline/__init__.py:1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01m.\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m backend_inline, config \u001b[38;5;66;03m# noqa\u001b[39;00m\n\u001b[1;32m 2\u001b[0m __version__ \u001b[38;5;241m=\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m0.1.6\u001b[39m\u001b[38;5;124m\"\u001b[39m \u001b[38;5;66;03m# noqa\u001b[39;00m\n", - "File \u001b[0;32m~/miniforge3/lib/python3.9/site-packages/matplotlib_inline/backend_inline.py:6\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[38;5;124;03m\"\"\"A matplotlib backend for publishing figures via display_data\"\"\"\u001b[39;00m\n\u001b[1;32m 3\u001b[0m \u001b[38;5;66;03m# Copyright (c) IPython Development Team.\u001b[39;00m\n\u001b[1;32m 4\u001b[0m \u001b[38;5;66;03m# Distributed under the terms of the BSD 3-Clause License.\u001b[39;00m\n\u001b[0;32m----> 6\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mmatplotlib\u001b[39;00m\n\u001b[1;32m 7\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mmatplotlib\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m colors\n\u001b[1;32m 8\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mmatplotlib\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mbackends\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m backend_agg\n", - "\u001b[0;31mModuleNotFoundError\u001b[0m: No module named 'matplotlib'" - ] + "data": { + "image/png": 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              \n", + "

              100 rows × 4 columns

              \n", + "
              " + ], + "text/plain": [ + " ID Age Agegroup CHD\n", + "0 1 21 1 0\n", + "1 2 23 1 0\n", + "2 3 25 1 1\n", + "3 4 29 1 0\n", + "4 5 21 1 0\n", + ".. ... ... ... ...\n", + "95 96 61 8 1\n", + "96 97 69 8 1\n", + "97 98 65 8 1\n", + "98 99 64 8 1\n", + "99 100 63 8 0\n", + "\n", + "[100 rows x 4 columns]" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
              " + ] + }, + "metadata": { + "filenames": { + "image/png": "/Users/mhjensen/Teaching/MachineLearning/doc/LectureNotes/_build/jupyter_execute/week38_22_1.png" + } + }, + "output_type": "display_data" + } + ], "source": [ "# Common imports\n", "import os\n", @@ -598,7 +741,22 @@ "collapsed": false, "editable": true }, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": 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\n", 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\n", 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\n", + "text/plain": [ + "
              " + ] + }, + "metadata": { + "filenames": { + "image/png": "/Users/mhjensen/Teaching/MachineLearning/doc/LectureNotes/_build/jupyter_execute/week38_32_2.png" + } + }, + "output_type": "display_data" + } + ], "source": [ "\"\"\"The sigmoid function (or the logistic curve) is a\n", "function that takes any real number, z, and outputs a number (0,1).\n", @@ -1273,7 +1474,22 @@ "collapsed": false, "editable": true }, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
              " + ] + }, + "metadata": { + "filenames": { + "image/png": "/Users/mhjensen/Teaching/MachineLearning/doc/LectureNotes/_build/jupyter_execute/week38_72_0.png" + } + }, + "output_type": "display_data" + } + ], "source": [ "import numpy as np\n", "import pandas as pd\n", @@ -1355,7 +1571,21 @@ "collapsed": false, "editable": true }, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "GridSearchCV(estimator=Ridge(),\n", + " param_grid={'alpha': array([1.00000000e-04, 4.64158883e-04, 2.15443469e-03, 1.00000000e-02,\n", + " 4.64158883e-02, 2.15443469e-01, 1.00000000e+00, 4.64158883e+00,\n", + " 2.15443469e+01, 1.00000000e+02])})\n", + "Best estimated lambda-value: 100.0\n", + "MSE score: 1.0892144853354966\n", + "R2 score: -0.0038332550504751595\n" + ] + } + ], "source": [ "import numpy as np\n", "from sklearn.model_selection import train_test_split\n", @@ -1443,7 +1673,19 @@ "collapsed": false, "editable": true }, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "RandomizedSearchCV(estimator=Ridge(), n_iter=100,\n", + " param_distributions={'alpha': })\n", + "Best estimated lambda-value: 0.9849967686928113\n", + "MSE score: 1.0853136633465326\n", + "R2 score: -0.0002382102844775691\n" + ] + } + ], "source": [ "import numpy as np\n", "from sklearn.model_selection import train_test_split\n", @@ -1512,7 +1754,31 @@ "collapsed": false, "editable": true }, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "(426, 30)\n", + "(143, 30)\n", + "Test set accuracy with Logistic Regression: 0.94\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/mhjensen/miniforge3/envs/myenv/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:814: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n" + ] + } + ], "source": [ "import matplotlib.pyplot as plt\n", "import numpy as np\n", @@ -1553,7 +1819,36 @@ "collapsed": false, "editable": true }, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": 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\n", 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+ "text/plain": [ + "
              " + ] + }, + "metadata": { + "filenames": { + "image/png": "/Users/mhjensen/Teaching/MachineLearning/doc/LectureNotes/_build/jupyter_execute/week38_82_1.png" + } + }, + "output_type": "display_data" + } + ], "source": [ "import matplotlib.pyplot as plt\n", "import numpy as np\n", @@ -1682,7 +1977,155 @@ "collapsed": false, "editable": true }, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "(426, 30)\n", + "(143, 30)\n", + "[1. 0.86666667 1. 0.92857143 1. 0.85714286\n", + " 1. 0.92857143 0.92857143 1. ]\n", + "Test set accuracy with Logistic Regression: 0.94\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/mhjensen/miniforge3/envs/myenv/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:814: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "/Users/mhjensen/miniforge3/envs/myenv/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:814: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "/Users/mhjensen/miniforge3/envs/myenv/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:814: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "/Users/mhjensen/miniforge3/envs/myenv/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:814: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "/Users/mhjensen/miniforge3/envs/myenv/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:814: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "/Users/mhjensen/miniforge3/envs/myenv/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:814: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "/Users/mhjensen/miniforge3/envs/myenv/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:814: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "/Users/mhjensen/miniforge3/envs/myenv/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:814: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "/Users/mhjensen/miniforge3/envs/myenv/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:814: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "/Users/mhjensen/miniforge3/envs/myenv/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:814: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n", + "/Users/mhjensen/miniforge3/envs/myenv/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:814: ConvergenceWarning: lbfgs failed to converge (status=1):\n", + "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", + "\n", + "Increase the number of iterations (max_iter) or scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
              " + ] + }, + "metadata": { + "filenames": { + "image/png": "/Users/mhjensen/Teaching/MachineLearning/doc/LectureNotes/_build/jupyter_execute/week38_89_4.png" + } + }, + "output_type": "display_data" + } + ], "source": [ "import matplotlib.pyplot as plt\n", "import numpy as np\n", @@ -2476,7 +2919,19 @@ "collapsed": false, "editable": true }, - "outputs": [], + "outputs": [ + { + "ename": "NameError", + "evalue": "name 'm' is not defined", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)", + "Input \u001b[0;32mIn [13]\u001b[0m, in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0m x \u001b[38;5;241m=\u001b[39m \u001b[38;5;241m2\u001b[39m\u001b[38;5;241m*\u001b[39mnp\u001b[38;5;241m.\u001b[39mrandom\u001b[38;5;241m.\u001b[39mrand(\u001b[43mm\u001b[49m,\u001b[38;5;241m1\u001b[39m)\n\u001b[1;32m 2\u001b[0m y \u001b[38;5;241m=\u001b[39m \u001b[38;5;241m4\u001b[39m\u001b[38;5;241m+\u001b[39m\u001b[38;5;241m3\u001b[39m\u001b[38;5;241m*\u001b[39mx\u001b[38;5;241m+\u001b[39mnp\u001b[38;5;241m.\u001b[39mrandom\u001b[38;5;241m.\u001b[39mrandn(m,\u001b[38;5;241m1\u001b[39m)\n", + "\u001b[0;31mNameError\u001b[0m: name 'm' is not defined" + ] + } + ], "source": [ "x = 2*np.random.rand(m,1)\n", "y = 4+3*x+np.random.randn(m,1)" @@ -3036,7 +3491,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.9.18" + "version": "3.9.10" } }, "nbformat": 4, diff --git a/doc/LectureNotes/_build/jupyter_execute/week38_89_5.png b/doc/LectureNotes/_build/jupyter_execute/week38_89_5.png new file mode 100644 index 000000000..35a493d66 Binary files /dev/null and b/doc/LectureNotes/_build/jupyter_execute/week38_89_5.png differ diff --git a/doc/LectureNotes/_build/jupyter_execute/week38_89_6.png b/doc/LectureNotes/_build/jupyter_execute/week38_89_6.png new file mode 100644 index 000000000..678d1b075 Binary files /dev/null and b/doc/LectureNotes/_build/jupyter_execute/week38_89_6.png differ diff --git a/doc/LectureNotes/_build/jupyter_execute/week39.ipynb b/doc/LectureNotes/_build/jupyter_execute/week39.ipynb index 8cea423cf..129be84cb 100644 --- a/doc/LectureNotes/_build/jupyter_execute/week39.ipynb +++ b/doc/LectureNotes/_build/jupyter_execute/week39.ipynb @@ -2,7 +2,7 @@ "cells": [ { "cell_type": "markdown", - "id": "97c9bb6c", + "id": "428bf751", "metadata": { "editable": true }, @@ -14,7 +14,7 @@ }, { "cell_type": "markdown", - "id": "ade8d870", + "id": "1a0a75ed", "metadata": { "editable": true }, @@ -27,7 +27,7 @@ }, { "cell_type": "markdown", - "id": "87cd74c3", + "id": "333f3063", "metadata": { "editable": true }, @@ -42,6 +42,8 @@ "\n", " * Work on project 1, in particular resampling methods like cross-validation and bootstrap. **For more discussions of project 1, chapter 5 of Goodfellow et al is a good read, in particular sections 5.1-5.5 and 5.7-5.11**.\n", "\n", + " * [Video on how to write scientific reports recorded during one of the lab sessions](https://youtu.be/tVW1ZDmZnwM)\n", + "\n", "These sections summarize neatly what we have done till now and point to what is coming with respect to deep learning. \n", " * A general guideline can be found at .\n", "\n", @@ -53,7 +55,7 @@ "\n", " * Stochastic Gradient descent with examples and automatic differentiation\n", "\n", - " * [Video of lecture](https://youtu.be/)\n", + " * [Video of lecture](https://youtu.be/bFRVuIJroHs)\n", "\n", " * Whiteboard notes TBA at \n", "\n", @@ -65,12 +67,14 @@ "\n", " * [Video on gradient descent](https://www.youtube.com/watch?v=sDv4f4s2SB8)\n", "\n", - " * [Video on stochastic gradient descent](https://www.youtube.com/watch?v=vMh0zPT0tLI)" + " * [Video on stochastic gradient descent](https://www.youtube.com/watch?v=vMh0zPT0tLI)\n", + "\n", + "" ] }, { "cell_type": "markdown", - "id": "529184e5", + "id": "3ee03ecd", "metadata": { "editable": true }, @@ -91,7 +95,7 @@ }, { "cell_type": "markdown", - "id": "1a65465a", + "id": "148ec577", "metadata": { "editable": true }, @@ -108,7 +112,7 @@ }, { "cell_type": "markdown", - "id": "b67231b3", + "id": "e6e5e661", "metadata": { "editable": true }, @@ -123,7 +127,7 @@ }, { "cell_type": "markdown", - "id": "3f5e03db", + "id": "4a81fe9d", "metadata": { "editable": true }, @@ -133,7 +137,7 @@ }, { "cell_type": "markdown", - "id": "e83141ae", + "id": "ac94af95", "metadata": { "editable": true }, @@ -149,7 +153,7 @@ }, { "cell_type": "markdown", - "id": "cef5864b", + "id": "5bfc3f18", "metadata": { "editable": true }, @@ -161,7 +165,7 @@ }, { "cell_type": "markdown", - "id": "2f59bbb1", + "id": "2437c71a", "metadata": { "editable": true }, @@ -172,7 +176,7 @@ }, { "cell_type": "markdown", - "id": "3869b3c6", + "id": "a5d4163c", "metadata": { "editable": true }, @@ -184,7 +188,7 @@ }, { "cell_type": "markdown", - "id": "e4656e92", + "id": "cca433f5", "metadata": { "editable": true }, @@ -194,7 +198,7 @@ }, { "cell_type": "markdown", - "id": "b73ae554", + "id": "c9ba0136", "metadata": { "editable": true }, @@ -208,7 +212,7 @@ }, { "cell_type": "markdown", - "id": "70a2df05", + "id": "3a6a4c55", "metadata": { "editable": true }, @@ -220,7 +224,7 @@ }, { "cell_type": "markdown", - "id": "2e36f976", + "id": "f71ca135", "metadata": { "editable": true }, @@ -230,7 +234,7 @@ }, { "cell_type": "markdown", - "id": "7c4959c9", + "id": "942a71da", "metadata": { "editable": true }, @@ -242,7 +246,7 @@ }, { "cell_type": "markdown", - "id": "c553379e", + "id": "3d3ddaff", "metadata": { "editable": true }, @@ -254,7 +258,7 @@ }, { "cell_type": "markdown", - "id": "145f9699", + "id": "3dfb626a", "metadata": { "editable": true }, @@ -274,7 +278,7 @@ }, { "cell_type": "markdown", - "id": "9a68f686", + "id": "7a77c9ce", "metadata": { "editable": true }, @@ -290,7 +294,7 @@ }, { "cell_type": "markdown", - "id": "d523ab61", + "id": "d094140b", "metadata": { "editable": true }, @@ -306,7 +310,7 @@ }, { "cell_type": "markdown", - "id": "8205fd3a", + "id": "9c04f928", "metadata": { "editable": true }, @@ -317,7 +321,7 @@ }, { "cell_type": "markdown", - "id": "98d52e46", + "id": "d29b9b63", "metadata": { "editable": true }, @@ -329,7 +333,7 @@ }, { "cell_type": "markdown", - "id": "203f65e8", + "id": "d3e508b5", "metadata": { "editable": true }, @@ -339,7 +343,7 @@ }, { "cell_type": "markdown", - "id": "dd40195b", + "id": "fd5bbc31", "metadata": { "editable": true }, @@ -351,7 +355,7 @@ }, { "cell_type": "markdown", - "id": "e13a7340", + "id": "7e255afd", "metadata": { "editable": true }, @@ -361,7 +365,7 @@ }, { "cell_type": "markdown", - "id": "30c258c9", + "id": "62d43c1c", "metadata": { "editable": true }, @@ -373,7 +377,7 @@ }, { "cell_type": "markdown", - "id": "84f880c9", + "id": "0b23438c", "metadata": { "editable": true }, @@ -395,7 +399,7 @@ }, { "cell_type": "markdown", - "id": "b237f214", + "id": "d7587ee4", "metadata": { "editable": true }, @@ -408,7 +412,7 @@ }, { "cell_type": "markdown", - "id": "b4ea1db2", + "id": "3736c3ae", "metadata": { "editable": true }, @@ -421,7 +425,7 @@ }, { "cell_type": "markdown", - "id": "8b2b6606", + "id": "e9cf6fb6", "metadata": { "editable": true }, @@ -431,7 +435,7 @@ }, { "cell_type": "markdown", - "id": "e9d217a4", + "id": "9c8e6fc9", "metadata": { "editable": true }, @@ -449,7 +453,7 @@ }, { "cell_type": "markdown", - "id": "54f87f7c", + "id": "2059ae9d", "metadata": { "editable": true }, @@ -459,7 +463,7 @@ }, { "cell_type": "markdown", - "id": "c2711fc8", + "id": "64c640e4", "metadata": { "editable": true }, @@ -474,7 +478,7 @@ }, { "cell_type": "markdown", - "id": "e0409f1c", + "id": "647d6bd2", "metadata": { "editable": true }, @@ -484,7 +488,7 @@ }, { "cell_type": "markdown", - "id": "53651890", + "id": "24ff572b", "metadata": { "editable": true }, @@ -498,7 +502,7 @@ }, { "cell_type": "markdown", - "id": "4e70e7ac", + "id": "521c56f5", "metadata": { "editable": true }, @@ -508,7 +512,7 @@ }, { "cell_type": "markdown", - "id": "9336db1d", + "id": "4508944b", "metadata": { "editable": true }, @@ -522,7 +526,7 @@ }, { "cell_type": "markdown", - "id": "2c2ae028", + "id": "69b72003", "metadata": { "editable": true }, @@ -537,7 +541,7 @@ }, { "cell_type": "markdown", - "id": "cdf99885", + "id": "dd11ba71", "metadata": { "editable": true }, @@ -554,7 +558,7 @@ }, { "cell_type": "markdown", - "id": "11bb1b41", + "id": "ccb5e1a6", "metadata": { "editable": true }, @@ -566,7 +570,7 @@ }, { "cell_type": "markdown", - "id": "5d957768", + "id": "6952b928", "metadata": { "editable": true }, @@ -580,7 +584,7 @@ }, { "cell_type": "markdown", - "id": "455b420a", + "id": "f0c5476d", "metadata": { "editable": true }, @@ -595,7 +599,7 @@ }, { "cell_type": "markdown", - "id": "aacb8b05", + "id": "4aeb7465", "metadata": { "editable": true }, @@ -607,7 +611,7 @@ }, { "cell_type": "markdown", - "id": "2bb3385b", + "id": "337ccfd3", "metadata": { "editable": true }, @@ -618,7 +622,7 @@ }, { "cell_type": "markdown", - "id": "21326ef0", + "id": "7540286d", "metadata": { "editable": true }, @@ -646,7 +650,7 @@ }, { "cell_type": "markdown", - "id": "a41b3cc6", + "id": "b7ce5820", "metadata": { "editable": true }, @@ -668,7 +672,7 @@ }, { "cell_type": "markdown", - "id": "a01f11a5", + "id": "0a557da3", "metadata": { "editable": true }, @@ -690,19 +694,28 @@ }, { "cell_type": "markdown", - "id": "076a32fa", + "id": "bdbf8a07", "metadata": { "editable": true }, "source": [ "## Convex function\n", "\n", - "**Convex function**: Let $X \\subset \\mathbb{R}^n$ be a convex set. Assume that the function $f: X \\rightarrow \\mathbb{R}$ is continuous, then $f$ is said to be convex if $$f(tx_1 + (1-t)x_2) \\leq tf(x_1) + (1-t)f(x_2) $$ for all $x_1, x_2 \\in X$ and for all $t \\in [0,1]$. If $\\leq$ is replaced with a strict inequaltiy in the definition, we demand $x_1 \\neq x_2$ and $t\\in(0,1)$ then $f$ is said to be strictly convex. For a single variable function, convexity means that if you draw a straight line connecting $f(x_1)$ and $f(x_2)$, the value of the function on the interval $[x_1,x_2]$ is always below the line as illustrated below." + "**Convex function**: Let $X \\subset \\mathbb{R}^n$ be a convex\n", + "set. Assume that the function $f: X \\rightarrow \\mathbb{R}$ is\n", + "continuous, then $f$ is said to be convex if $f(tx_1 + (1-t)x_2) \\leq tf(x_1) + (1-t)f(x_2)$\n", + "for all $x_1, x_2 \\in X$ and for all $t \\in [0,1]$.\n", + "If $\\leq$ is replaced with a strict inequaltiy in the\n", + "definition, we demand $x_1 \\neq x_2$ and $t\\in(0,1)$ then $f$ is said\n", + "to be strictly convex. For a single variable function, convexity means\n", + "that if you draw a straight line connecting $f(x_1)$ and $f(x_2)$, the\n", + "value of the function on the interval $[x_1,x_2]$ is always below the\n", + "line as illustrated below." ] }, { "cell_type": "markdown", - "id": "73adde3c", + "id": "e02d1dac", "metadata": { "editable": true }, @@ -712,14 +725,16 @@ "In the following we state first and second-order conditions which\n", "ensures convexity of a function $f$. We write $D_f$ to denote the\n", "domain of $f$, i.e the subset of $R^n$ where $f$ is defined. For more\n", - "details and proofs we refer to: [S. Boyd and L. Vandenberghe. Convex Optimization. Cambridge University Press](http://stanford.edu/boyd/cvxbook/, 2004).\n", + "details and proofs we refer to: [S. Boyd and L. Vandenberghe. Convex Optimization. Cambridge University Press](http://stanford.edu/boyd/cvxbook/).\n", "\n", "**First order condition.**\n", "\n", "Suppose $f$ is differentiable (i.e $\\nabla f(x)$ is well defined for\n", "all $x$ in the domain of $f$). Then $f$ is convex if and only if $D_f$\n", - "is a convex set and $$f(y) \\geq f(x) + \\nabla f(x)^T (y-x) $$ holds\n", - "for all $x,y \\in D_f$. This condition means that for a convex function\n", + "is a convex set and $f(y) \\geq f(x) + \\nabla f(x)^T (y-x)$ holds\n", + "for all $x,y \\in D_f$.\n", + "\n", + "This condition means that for a convex function\n", "the first order Taylor expansion (right hand side above) at any point\n", "a global under estimator of the function. To convince yourself you can\n", "make a drawing of $f(x) = x^2+1$ and draw the tangent line to $f(x)$ and\n", @@ -739,7 +754,7 @@ }, { "cell_type": "markdown", - "id": "9f9ab5ff", + "id": "d9a72971", "metadata": { "editable": true }, @@ -767,7 +782,7 @@ }, { "cell_type": "markdown", - "id": "0b2a482b", + "id": "c3e744c2", "metadata": { "editable": true }, @@ -797,7 +812,7 @@ }, { "cell_type": "markdown", - "id": "6566ee55", + "id": "2732cf8b", "metadata": { "editable": true }, @@ -817,7 +832,7 @@ }, { "cell_type": "markdown", - "id": "c2e30cc1", + "id": "1b14ba6d", "metadata": { "editable": true }, @@ -829,7 +844,7 @@ }, { "cell_type": "markdown", - "id": "5012b398", + "id": "5f271cd8", "metadata": { "editable": true }, @@ -839,7 +854,7 @@ }, { "cell_type": "markdown", - "id": "ca65d9a9", + "id": "eb0ce7c8", "metadata": { "editable": true }, @@ -851,7 +866,7 @@ }, { "cell_type": "markdown", - "id": "ec9323dd", + "id": "3482f635", "metadata": { "editable": true }, @@ -863,7 +878,7 @@ }, { "cell_type": "markdown", - "id": "5caf0f7f", + "id": "0c88f8d2", "metadata": { "editable": true }, @@ -875,7 +890,7 @@ }, { "cell_type": "markdown", - "id": "07734ce6", + "id": "fe152e73", "metadata": { "editable": true }, @@ -887,7 +902,7 @@ }, { "cell_type": "markdown", - "id": "468dcb52", + "id": "740c5860", "metadata": { "editable": true }, @@ -898,7 +913,7 @@ }, { "cell_type": "markdown", - "id": "b63a89ae", + "id": "477da242", "metadata": { "editable": true }, @@ -911,7 +926,7 @@ }, { "cell_type": "markdown", - "id": "56a122a3", + "id": "7e2169e6", "metadata": { "editable": true }, @@ -923,7 +938,7 @@ }, { "cell_type": "markdown", - "id": "3256eb29", + "id": "e513c1c5", "metadata": { "editable": true }, @@ -933,7 +948,7 @@ }, { "cell_type": "markdown", - "id": "b6bab858", + "id": "a4a4f67c", "metadata": { "editable": true }, @@ -945,7 +960,7 @@ }, { "cell_type": "markdown", - "id": "7ff39e96", + "id": "96138e83", "metadata": { "editable": true }, @@ -955,7 +970,7 @@ }, { "cell_type": "markdown", - "id": "6678fce9", + "id": "f843d9f8", "metadata": { "editable": true }, @@ -966,7 +981,7 @@ }, { "cell_type": "markdown", - "id": "853eb11f", + "id": "5702234f", "metadata": { "editable": true }, @@ -978,7 +993,7 @@ }, { "cell_type": "markdown", - "id": "7c229917", + "id": "682a415f", "metadata": { "editable": true }, @@ -990,7 +1005,7 @@ }, { "cell_type": "markdown", - "id": "5c8f310a", + "id": "302ac54a", "metadata": { "editable": true }, @@ -1002,7 +1017,7 @@ }, { "cell_type": "markdown", - "id": "f8a8c317", + "id": "dd2cbeb1", "metadata": { "editable": true }, @@ -1013,7 +1028,7 @@ }, { "cell_type": "markdown", - "id": "49b64ed0", + "id": "f280370e", "metadata": { "editable": true }, @@ -1024,7 +1039,7 @@ }, { "cell_type": "markdown", - "id": "857ee939", + "id": "9702a162", "metadata": { "editable": true }, @@ -1036,7 +1051,7 @@ }, { "cell_type": "markdown", - "id": "7e5611fa", + "id": "3a1de3f0", "metadata": { "editable": true }, @@ -1046,7 +1061,7 @@ }, { "cell_type": "markdown", - "id": "384d5aa2", + "id": "4fa494ea", "metadata": { "editable": true }, @@ -1058,7 +1073,7 @@ }, { "cell_type": "markdown", - "id": "0ce677be", + "id": "a4f12308", "metadata": { "editable": true }, @@ -1068,7 +1083,7 @@ }, { "cell_type": "markdown", - "id": "e97f9044", + "id": "df770c35", "metadata": { "editable": true }, @@ -1080,7 +1095,7 @@ }, { "cell_type": "markdown", - "id": "d7fbeb68", + "id": "b1a30174", "metadata": { "editable": true }, @@ -1090,7 +1105,7 @@ }, { "cell_type": "markdown", - "id": "293c09b1", + "id": "6f816a49", "metadata": { "editable": true }, @@ -1102,7 +1117,7 @@ }, { "cell_type": "markdown", - "id": "af88e065", + "id": "4e5cd41c", "metadata": { "editable": true }, @@ -1112,7 +1127,7 @@ }, { "cell_type": "markdown", - "id": "2757e302", + "id": "91f972cb", "metadata": { "editable": true }, @@ -1124,7 +1139,7 @@ }, { "cell_type": "markdown", - "id": "87aab66b", + "id": "97064af3", "metadata": { "editable": true }, @@ -1135,27 +1150,43 @@ { "cell_type": "code", "execution_count": 1, - "id": "a0e20ff7", + "id": "5af74f1d", "metadata": { "collapsed": false, "editable": true }, "outputs": [ { - "ename": "ModuleNotFoundError", - "evalue": "No module named 'matplotlib'", - "output_type": "error", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mModuleNotFoundError\u001b[0m Traceback (most recent call last)", - "Cell \u001b[0;32mIn[1], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \u001b[43mget_ipython\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mrun_line_magic\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mmatplotlib\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43minline\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m)\u001b[49m\n\u001b[1;32m 3\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mnumpy\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m \u001b[38;5;21;01mnp\u001b[39;00m\n\u001b[1;32m 4\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mnumpy\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mlinalg\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m \u001b[38;5;21;01mla\u001b[39;00m\n", - "File \u001b[0;32m~/miniforge3/lib/python3.9/site-packages/IPython/core/interactiveshell.py:2432\u001b[0m, in \u001b[0;36mInteractiveShell.run_line_magic\u001b[0;34m(self, magic_name, line, _stack_depth)\u001b[0m\n\u001b[1;32m 2430\u001b[0m kwargs[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mlocal_ns\u001b[39m\u001b[38;5;124m'\u001b[39m] \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mget_local_scope(stack_depth)\n\u001b[1;32m 2431\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mbuiltin_trap:\n\u001b[0;32m-> 2432\u001b[0m result \u001b[38;5;241m=\u001b[39m \u001b[43mfn\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 2434\u001b[0m \u001b[38;5;66;03m# The code below prevents the output from being displayed\u001b[39;00m\n\u001b[1;32m 2435\u001b[0m \u001b[38;5;66;03m# when using magics with decorator @output_can_be_silenced\u001b[39;00m\n\u001b[1;32m 2436\u001b[0m \u001b[38;5;66;03m# when the last Python token in the expression is a ';'.\u001b[39;00m\n\u001b[1;32m 2437\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mgetattr\u001b[39m(fn, magic\u001b[38;5;241m.\u001b[39mMAGIC_OUTPUT_CAN_BE_SILENCED, \u001b[38;5;28;01mFalse\u001b[39;00m):\n", - "File \u001b[0;32m~/miniforge3/lib/python3.9/site-packages/IPython/core/magics/pylab.py:99\u001b[0m, in \u001b[0;36mPylabMagics.matplotlib\u001b[0;34m(self, line)\u001b[0m\n\u001b[1;32m 97\u001b[0m \u001b[38;5;28mprint\u001b[39m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mAvailable matplotlib backends: \u001b[39m\u001b[38;5;132;01m%s\u001b[39;00m\u001b[38;5;124m\"\u001b[39m \u001b[38;5;241m%\u001b[39m backends_list)\n\u001b[1;32m 98\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m---> 99\u001b[0m gui, backend \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mshell\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43menable_matplotlib\u001b[49m\u001b[43m(\u001b[49m\u001b[43margs\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mgui\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mlower\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mif\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[38;5;28;43misinstance\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43margs\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mgui\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mstr\u001b[39;49m\u001b[43m)\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01melse\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43margs\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mgui\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 100\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_show_matplotlib_backend(args\u001b[38;5;241m.\u001b[39mgui, backend)\n", - "File \u001b[0;32m~/miniforge3/lib/python3.9/site-packages/IPython/core/interactiveshell.py:3606\u001b[0m, in \u001b[0;36mInteractiveShell.enable_matplotlib\u001b[0;34m(self, gui)\u001b[0m\n\u001b[1;32m 3585\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21menable_matplotlib\u001b[39m(\u001b[38;5;28mself\u001b[39m, gui\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mNone\u001b[39;00m):\n\u001b[1;32m 3586\u001b[0m \u001b[38;5;250m \u001b[39m\u001b[38;5;124;03m\"\"\"Enable interactive matplotlib and inline figure support.\u001b[39;00m\n\u001b[1;32m 3587\u001b[0m \n\u001b[1;32m 3588\u001b[0m \u001b[38;5;124;03m This takes the following steps:\u001b[39;00m\n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 3604\u001b[0m \u001b[38;5;124;03m display figures inline.\u001b[39;00m\n\u001b[1;32m 3605\u001b[0m \u001b[38;5;124;03m \"\"\"\u001b[39;00m\n\u001b[0;32m-> 3606\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mmatplotlib_inline\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mbackend_inline\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m configure_inline_support\n\u001b[1;32m 3608\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mIPython\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mcore\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m pylabtools \u001b[38;5;28;01mas\u001b[39;00m pt\n\u001b[1;32m 3609\u001b[0m gui, backend \u001b[38;5;241m=\u001b[39m pt\u001b[38;5;241m.\u001b[39mfind_gui_and_backend(gui, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mpylab_gui_select)\n", - "File \u001b[0;32m~/miniforge3/lib/python3.9/site-packages/matplotlib_inline/__init__.py:1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01m.\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m backend_inline, config \u001b[38;5;66;03m# noqa\u001b[39;00m\n\u001b[1;32m 2\u001b[0m __version__ \u001b[38;5;241m=\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m0.1.6\u001b[39m\u001b[38;5;124m\"\u001b[39m \u001b[38;5;66;03m# noqa\u001b[39;00m\n", - "File \u001b[0;32m~/miniforge3/lib/python3.9/site-packages/matplotlib_inline/backend_inline.py:6\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[38;5;124;03m\"\"\"A matplotlib backend for publishing figures via display_data\"\"\"\u001b[39;00m\n\u001b[1;32m 3\u001b[0m \u001b[38;5;66;03m# Copyright (c) IPython Development Team.\u001b[39;00m\n\u001b[1;32m 4\u001b[0m \u001b[38;5;66;03m# Distributed under the terms of the BSD 3-Clause License.\u001b[39;00m\n\u001b[0;32m----> 6\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mmatplotlib\u001b[39;00m\n\u001b[1;32m 7\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mmatplotlib\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m colors\n\u001b[1;32m 8\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mmatplotlib\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mbackends\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m backend_agg\n", - "\u001b[0;31mModuleNotFoundError\u001b[0m: No module named 'matplotlib'" + "name": "stderr", + "output_type": "stream", + "text": [ + "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_19394/3838917029.py:18: MatplotlibDeprecationWarning: Calling gca() with keyword arguments was deprecated in Matplotlib 3.4. Starting two minor releases later, gca() will take no keyword arguments. The gca() function should only be used to get the current axes, or if no axes exist, create new axes with default keyword arguments. To create a new axes with non-default arguments, use plt.axes() or plt.subplot().\n", + " ax = fig.gca(projection=\"3d\")\n" ] + }, + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 1, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", 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vbmB/Vz2tA4bTnhfnH0JxVCrFkclMjUwmLyxelvIWYgQoikLrQD+He5o41NPIoZ4myvpaT1sM0E+tZWpkMjOjU5kVnc7UiGQCtBOri+CeJNvI7p497O7eO2xfnmh9NPOj57Igah7xAfFerNK7JJRMQC/Vv8yWzq3DzqvPCspkQfQ85kTOJlgX7MXqLiyXonCiv539XfXs72pgf3f9aUdlGpWKyWHxQ0dlKUyPTCEhMMxLFQshBhx2SvtaPN3Lkp7G0xaB06rUFEYkMjMqjZnRacyISp1Qy+m7FBfHjSfY0bWTfT0Hhq2EnRmUwb2ZXyIpINGLFXqHhJIJ6Lm659nYsZkYv2jmR81jfvQ84v3jvF3WBdNs7mNHRzW7OmvY01l32nwQvVpDUUQys6LdL2ZTI5MJ0srqi0KMVYqiUGPsYn+3++BiX1fdaeuoaFQqCsITmRebyfzYTKZFpkyY4R6b00ZJ3yF2du3iqOEYGpWaPxb/wSdXw5ZQMgG1DbTR7zCSE5w9ISaSGe1WdnfWsrOjhp0dNTR8ZMZ/oEbHtKgUdwiJSmNKRBJ+MhQjxLilKArNlj72d9Wzr7ue/V31NJh7hz0nUKNjZnQ682MzWRCbRVZI9IR4vTPYDdSa6pgWMdXbpXiFhBIx5iiKwvH+dra1VbG1vYpDPY3Dxp81KhVTI5KZH5vJvNhMpkQkoZsgR0xCiDNrsRjYM3Rwsquz5rRh2oSAMBbHZbMoLpu5sZnSHR2nJJSIMcFot7Kro4Yt7ZVsb68+baGm9OAoFsRmMj82i9nR6RNqbHmicrhcWOyDmAftmO2DWOx2bE4HNocTm8Px4W2n+7bd6cLhcuFUhq5diudjp+vcLzcqFWjUarQqNRq1Cq1ajUaldl+r1fhpNPhptfhpNPhrtfhptB9+rNMRpNMRqNMTrNfhp9FOiCPuiezkXLKT3dN9XXUMnrKwoU6lZkZ0GouGQkp2SIz8n44TEkqE1zSYetjUdoJNrcc50N0wrBvir9EyNyaTRXHZLI7LJjlIlmsebYqi0G+z0WMdoHdg6GIdwGCz0W+zYrTZ6LfZMNis9A/dNg0OuoOI3c6g8/TVb8cDtUpFoE5HkE5PoE5HqJ/fKRf/YbfD/fyJCAggIiCASH/3tV4jXbvRNuCws7erjm3tlWxrrzptqCcpMJyl8ZO4KGESs6LTJ8xclIlIQokYNU7FxeGeJja1nmBT23GqjV3DHs8IjhoKITnMjE6TeSEjQFEUeq0DdFosdJhNdJktdFhMdA5dd5kt9AxYPEHkXDvMflJatXroDV6Ln1b3kW6FBj+NFr1Gg16jcXc7hrobWpXK87FapULF2Y90XSg4XS6crpPdlqEui8uF3eXydGUGnY6hLo3Tc22x2xlw2LHYz7yH0fkK1us9ASU6MJDYoGDPdYznOojowED8tPI7PhLqTN1sbXMHlL0f6aIEafUsjM1macIkFsfl+NSS+OOBhBIxoqxOOzs7aljfUsHmthPDzpTRqtTMiEplaUIuS+MnkRrsmzsNXygnOxtN/QZaTUZajEbaTCZaTUbaTEZajUbazKbz7mAE6XREBgS6OwL+/sO6BWGn3A7R+xHi5+fpMgTp3UMi46Vz4HS5GHA4PJ0e8+Ag5sFBjIM2Tyfo1M6Q0Waj1zpAn9VKz4CFXqsV13m+LEYFBJIQHExCSAjxwSEkBJ+8DiYpNJT44BC0allQ67OwOAbZ3VnLptbjbG47Qdcpc1HUqCiOSmFZwmSWJU6WjuwYIKFEXHBGu5UtbZWsb6lgW3slllN20Q3V+bM4LoeLEiaxKDabUL2/Fysdf2wOB/WGPur7+mgy9tPYb6C530Bjfz9N/QZMg4Of6OtE+gcQHRRETGAgMYFBxAYFER0YRExQEFEBgUQGBBAZEECEf4AczX9CLkWh32alZ2Bg6GKhy2Kh02Kmw2ymy2Ie1pEaPMPmjh+lUalICAkhOSSM5LBQkkPCSAl1384IjyQqIEDmSpwHl6JQ2tvCpjZ3QKkwtA97PC8snksSJ7M8MY8cmYfiFRJKxAVhttvY0Hqc95uPsb2jethuugkBoSxLnMwlCZOZHpUqZ8p8DJei0Go0UtPbQ21fL7V9vZ7bzf39Z9lU/kPRgYEkhoQSHxxM4skj75CQoY9DiQkKGjfdi4lKURT6rFbaTEZaTEMdLeNQR2uou9XS3/+xwSVE70dmRAQZ4RFkDF1nRkSSER5BgG5irYY6EpotfWxqPc76lgr2d9cPG67MCI7isqQCLk8uICc01otV+hYJJeJTszkdbG2v5L2mUja1nhi2q25mcDTLh4448sMT5IjjDFyKQouxn8qebiq7u6ns6eZEdxfVvT3nnN8QovcjLTyclNAwUkJDSQodOnoODSUpJFTejCYIl6LQaTbT2G+gaagTdvJ2g6GPFuPZA6oKSAkNIzsyipyoKCZFRpMdFUV2RKT8fpxFr83CpjZ3QNnRUT0sEE4KjeWq5ClckVxIUlC494r0ARJKxHlxKi72dNaxuvEo61rLMdo/XMo+IziKK5ILuTQpX44sPsJos1HR3Ul5ZycVXZ2Ud3VS2dN91vChU6tJCwt3H/0OHfm6j4ilZS/cTg7l1fT2UtvXQ01vL3VDXbVeq/WMn3MyrEyOjmFydAx5MTHkRceQEjqxd+w9Xya7jc1tJ1jTdIxtHVXDOr/FkSlcmVzIZckFRPkFebHKiUlCifhYiqJQZmjj7YbDrGkqHTZRLD4glCuSC7kyuZC8sHiff2FTFIV2s4nSjnZKOzo8AaSx//RNAcEdPjIiIpkUGXXKUW0UqWHh6GSIRXxK3RaLp/NW1dPt6cb1WAfO+PxgnZ7c6GjyomMoiImlMDaOnKhoGebDvevxupZyVjeVsqez1tOd0qhUzI3J5JqUIpYlTiZQFmu7ICSUiLNqG+hnVeMR3m44QrWx03N/mC6Ay5LzuTJ5CjOiUlH7aBA5NYAcHQohpR3tdFpO36YdICE4xH10OnTJjY4mTcKHGEXdFgsnursoHwrLFV2dVHZ3n3Huil6tITc6msLYOIpi4ySoAB0DRtY0H+O9plKO9DZ77g/U6lmRmMe1qVOZHZ3us6+JF4KEEjGM2THI+pZy3m44wu7OGs9RgV6t4ZKEyVydUsSCuCyfXHzIPDjI0Y52StpaONzWxqH2VjrMpwcQjUpFdmQUhbFx5MfEkhcdw+ToaML9A7xQtRDnZnc6qenrdXf1Ojs41tnB0Y52+m22056rV2vIj4llWkIC0+LiKY5PJDk01Cc7pPWmHt5tPMI7jUeGLdaWEBDK1SlFXJNSRFZojBcrHJ8klAgUReFAdwNv1B/ig+Zjw07hnRmVxjWpRVyamO9Tp+8qikJNbw8HWls41NbKofY2TnR3nbYOhVqlImcogEwZOprMi46RyYRiXFMUhcZ+g6cL6O4EnjmoRAUEMC0+gWnxCRTHJzItPoFAH/r9VxSFQz1N7uHt5mP02z+czzMlIpEb0oq5IqnQp14/PwsJJT6s22bmjfoSXq8rof6UnXdTgyK5NtWd9H1lMSGHy0VZZwd7m5vY19LE/pbmM04WTAgOoXjoBXhqfDyFMXESQIRPUBSFBoOBQ+2t7qDe1kpZZwd2l2vY8zQqFfkxscxMTGJOUjKzk5J9pktoczrY3HaCdxqOsLW90rN1hp9ay4qkPG5Jn8GMqFSf7Cx9UhJKfIyiKOztquPl2gNsaCnHPvRHE6jRcVlyATemFVMcmTLh/2icLhdlXZ3sbKxnd1MTB1qaMdmHLzzmp9EyNS6e4gR3CJkWl0BccLCXKhZi7LE5HJR1dlAyFFIOtrXQYjSe9rzcqGjmJCUzLyWVuUkphPlP/K5Bt83MqsYjvF5XQtUpc/KyQqK5JX0G16ZOJUzvG2HtfEgo8RFGu5W3Gg7z35p91Jq6PfcXRSRxc/p0Lk8unNBbfSuKQm1fLzsbG9jR2MDupkYMtuGdkBC9n+foblZiEgWxcT49qU+IT6PZ2M/+lmb2Njext7mJ6t6eYY+rVSoKY+OYn5zK/JRUZiYm4q+duN1GRVE40tvMa3UHWd1UysDQ8LifWsuVKYXckTmb/PAEL1c5dkgomeAq+zt4qWYf7zQc9swVCdTquTplCremzyQvPN7LFY6cngEL2xvq2dZQz87GelpNpmGPB+v0zElOZl5yKnOSkpkcHYNG9hkR4oLqtJjZ19zMnuZGdjY2nBZS9BoNMxKSWJCSypK0dPJiYifs2StGu5VVjUf5X+0Bjvd/uMR9cWQKd2TOYnlSvk+eRHAqCSUTkMPlYmPrcV6q2cuerjrP/VkhMXwucxbXphQRpPPzXoEjxOFycbi9la31dWypr+Noe9uwFS/1ag3TExKZn5LKgpRUpsTFy2ZnQoyyNpORnY0Nnq5lu3n4wUJ0YCCLU9NZlJbOotQ0IgMm3i6+JyfHvlSzjw+aj3mG0aP9grg5fQa3ZswgLsD33rtAQsmE9Lkt/6KkpxFwTzq7OGEyd2TOYnZ0+oSbK9JpNrOproat9XVsb6w/7eyAydExLE5NY0FKGjMTk2RSqhBjyMmz3HY0NrC9oZ6dTQ3DVjlWAVPi4lmSls5FaRkUxcVPuG5mp9XEq3UHeKX2AB1W93wcjUrFcwvvYkZ0qperG30SSiagp49v5fmqPdycMYNb02eQEBjm7ZIuGEVRKOvsYENtDRvrajjS3jbs8TA/fxamprI4LYNFqWnEB4d4qVIhxPmyORwcbG1hS0MdW+vrqOjqHPZ4VEAAF6VncnFGJgtT0gjxmzgdX7vLyYbWCl6q2UedqZv1lz7gk0M5EkomIItjEK1KjV4zMbactzrs7GhsYGNtDZtqa2j7SLt3SmwcF6VnsGToSEqGZISYGNpNJrY21LGlrpatDXWYBj88Q06nVjMnKYWLMzK5JCOLlLCJc/DVNzhAuI+emSOhRIxJVoedzXV1vFd1nI21NcNaugFaLQtT07g4I4ul6RnEBslpukJMdHank30tzWysrWFDbTX1hr5hjxfGxHJ5ziSuyM4lLTzcKzWKz05CiRgzzhVEEoJDWJaZxcXpmcxNTsFPOzG6QL5MURQcThcDdjs2u4NBhxOH04XT5cLudOFwunC4XDiH7jsnlQqdWo1W8+FFM/SxTqPBT6vFX6/FT6tFrZ5Y86p8VU1vjyeg7G9pxnnKW5IElPFLQonwqkGnk631tbxzouK0IJIUEsoVOZO4InsSRXGyA/FYMuhw0Ge2YrBY6bNYMZgH6LNYMQ3YMNkGMVsHMVptmK2DmKyDmG3ui3XQgdVux2p3YB10nLZs/2jw02rw02nx1+vw12kJ1OsI8tcT7K8nyM/Pfe2vJ8TfjyB/PaEBfoQHBRAe6E9YkD/hgQEE++vl93EM6bZYWFtTxXuVx9nd1HhaQLlyUi5XT5pMYoi8H4x1EkrEqHMpCnubm3jneDlrqiqHLWImQcR7bHYHHQYTXUYzXUYLXf3u626Tme5+C11GM90mC31mKwOD9o//gudBrVKh12qGdTi0p3Q61GoV5/pNcCngcDpxuIY6LENdFofThd3p7sBcSFq1mtBAfyKC/IkOCSIqJIjo0MCh2x9ex4UFExboL7/Ho+hcAWV2YjLX5E7m8uxJRAT45pyNsU5CiRgViqJwrLODd46X8+6J48Mmq8YFBXPVpFyuysmVIDJCXC6Fjn4TzT0GWnqNtPcZaesz0W4w0j503WMaOK+vqVapCAv0JyzQn/Ag93VogL+n6xDsP9R18NN7rk92J/x1Q8MpOi0BOh1ajXpE/9+dLhc2uwOr3YHN7mBg0DH0sR2Lze7p7pisg5it7m6PacDd7ekf6gb1mQcwWKxY7Y7z+t7+Oi1xYcHEhYcQHx5MXFgIceHBJISHkBgZSlJkGAF6OVV9JJwMKKuOV7CnudGzbpFWrWZxajrX5E5mWWa2T20gONZJKBEjqtVo5K3jZbxZXkbVKSs5hvr5cXn2JK6ZNJnZSckTbu0BbxgYtNPQ1Ud9Zy9N3Qaae/pp7jl53Y/d6fzYr+Gv0xITGuQ50o/yHPm7j/4jQwKJCAogLNCfEH8/n5yfYbU7PAGlx2Sh2+i+dBnNnusuo4Vuo/kTB72okECSIsNIigwlOTKM5KgwUqLDSY8JJzokSIL6BdBi7Gd15XHeOV7Bsc4Oz/2BOh2XZeVwQ14Bc5NTJuxqsuOFhBJxwQ3Y7XxQXcUb5cfY0VjvOTrx02i5JCOTa3InsyQtQyarfgoul0Jzj4Hajh7qOvto6OqlrrOX+s4+2vpO3wjtVFq1mviIEJIiQokLdx+tx4eHEO85ig8hNMBP3gAvoEGHg3aDifY+E219RtoNQ9d9Jlp73WHRaLWd82sE+ulIi4kgPTqCtJhw9+2YCDLjIgn0m7j7VY2kqp5uVp2o4J3jFcPO4kkIDuH6yfnckJdPZkSk9wr0YRJKxAWhKAoH21r437FS3qs8jvmUCauzEpO4Ia+AK7InTajFjkaSoii095k40dbFiZZOqtq6qWnvobaj55zDB2GB/qRFh5MSHU5SZBjJkaEkRYWRHBlGbFgwWo10pMYag8Xq6Wi5O1wGmroNNHT10dzTf87JwAkRIWTFRZEVF8WkxGhy4qPJjIvETyeB/5NQFIWStlZeLz/G6srjw1aEnhaXwE35BVw9abK8bo0iCSXiMzENDvJWRRkvHD3Mie4uz/2poWFcn5fP9ZPzSQ0L916B44DLpVDf1UtZUwflTR2UN7svxoEzH0H7aTXuo+XYCM9Rc1pMBGnR4YQHyeS9icTucNLYbaChy90Nc3fFeqnt6KHLaDnj52jUKjJiI8lLiiUvOZa8pFgmJ8UQ7C9vrOdiczjYUFvN6+VlbK2v9UyQDdTpuDY3j89PmUpeTKyXq5z4JJSIT6W8q5MXjx7m7YoyT1fEX6vlypxcbs4vZFZikgwDnIGiKNR39XGkvpWypnbKmzqoaOnEYjv9bBatWk16bAQ58dHkJESTFR9FdnwUSZGhMgdHYLBYqW7rpqq9m6rWbirbuqhs7cJgsZ7x+ekxEZ6gUpgaT0FyHIF+MsHzTDrNZt4+Xs4rx44O29V4RkIid0yZyuXZk2T4eYRIKBGfmM3hYE1VJS8ePcSB1hbP/ZkREdwxZRo35uUT6ufvxQrHnj7zAEcb2jhS38rRhjaONrTRf4YOiL9OS25iDHnJseQPvXFkxkWilxc+cR4URaGj38zx5g6OndJ5O9N8I7VKRU5CNFNS4ylKjWdKWjyZsVE+OXn5bBRFYU9zEy8cOcTamiocQ4v4RfoHcFNBIZ8rLJJO8AUmoUR8rHaTiZdKD/PS0cN0D7jPJtCq1SzPzObzU6YyNzlFuiK4X8DqOnspqW3hYG0zh2pbqO/qO+15flqN+2g1JZ785Fjyk+NIj42Q7ocYMT0miyegHGts50hDGx0G02nPC/bXMyU1nuKMJKZnJDIlNUG6KUM6zCZeOXaUl0uP0Gpy/+xUwNL0TL44rZiFKWnyOngBSCgRZ3WorZV/HzrImqoTniOE+KBgbiss4taCKcQF+/aeM3ank/KmDk8IKaltodd8+img6TERTEmNdx+RpiUwKTEancb3dv8UY0u7wcTR+laO1LdypKGNsqZ2BgaHT6LWqFXkJcUyPTOJ6RlJTEtPJCok0EsVjw0Ol4tNtTW8cPQQ2xrqPffnREbxhanFXD85X9Y9+QwklIhhBp1O1lSdYOWhgxxub/PcPzMxibumFrM8M9tn31AdThflzR3srWpkX1UjB2ubT3sR99NqmJKWQHFGIsXpiRSlJRAWKENaYuxzOF1UtXVxqK6Vg7XNHKxppv0M3ZSsuEhmZacwOzuFWVnJPj25uqa3h/8cLuH18mOeuXWhfn7cVjCFO4uKSZL3n/M2JkPJo48+yhtvvEFFRQUBAQHMnz+f3/72t+Tm5n6iz5dQcv76rAO8ePQIzx8pocNsBkCv1nB17mS+MLWYKbFxXq5w9LlcCidaO9lb1cTeqgYO1DRjsg4Oe05YoD/FGYlMz0iiOCOR/ORYmQciJozW3n4O1DR7uoFVbd3DHlepYFJCDLOyk5mTncKMzGRCAnzvLJ9+m43Xykr5z+ESGvoNgHvOzvLMbO6dPoMZCUlernD8GJOh5LLLLuO2225j1qxZOBwOfvKTn3D06FHKysoICgr62M+XUPLJtZmMPHvwAP8tPcyAw33UHxMYxOeLpnJbYRExgR//855I+ges7Dxez7byOrZX1NFjGn7aZUiAHzMzk5mdnczsnFSy42RioPAdfeYB9lc3sbeqiX3VjaeFFI1axbT0RBbnZbAoL4Ps+CifmmfhdLnYXF/LykMH2dHY4Ll/RkIiX581lyVp6T718/g0xmQo+ajOzk5iY2PZsmULixcvPu1xm82G7ZRFb/r7+0lJSZFQcg4Nhj7+fmAfr5cdY9DlXn48LzqGe4tncuWkXPQ+MkSjKArV7d1sLatlW0UdJbXNOF0f/moH+umYkZnM7KxkZuekkJsYIxNShRjSZTSzv6qJvdWN7K1sPG1id0JEiCegzM5O8ak9fk50d/GvkgO8VVHueY0tiInl/plzuCw7R5azP4txEUqqqqrIycnh6NGjFBYWnvb4Qw89xMMPP3za/RJKTlfZ3c1T+/ew6kSFZ3GgmYlJfH3mHBb7SIp3OF2U1DazsbSaTceqae7pH/Z4ZlwkiyZnsDg/g+L0RHRa3whoQnxWTd0GtpW7A/7eygZsjg/3W/LTapg7KZWLC7NZkp/pMxNm200mni3Zz0tHP+xGZ0VEcv/M2VyTm4dWDnKGGfOhRFEUrr32Wnp7e9m2bdsZnyOdko93oruLv+zdzerK4569aJakpXP/zDnMTkr2am2jYWDQzs7j9WwsrWZLWc2wBab8tBpmZaewKC+DRXnppESFe69QISaIgUE7+6oa2Vpex7byWlp6Pwz/KhUUpydycWE2FxdmkRId7r1CR0nPgIXnDpfw3OESz3L2aWHhfH3WHK6bnC/hZMiYDyVf//rXWb16Ndu3byc5+ZO9ecqckg+dKYysyMrm67PmTvjJq8YBG5uPVbPuSBW7TtQP2zMmPNCfJQWZXFyYzbxJqT7VVhZitCmKQlVbN5uOVbOxtJpjje3DHs+Oj+KSKdksL8phUkL0hO7YGm02Xjx6mH+W7Pes+yTh5ENjOpR84xvf4K233mLr1q1kZGR84s+TUHLmMHJpVg7fnD13Qu/fcDKIfHD4BDuPN2B3ftg+TooMZWlhFhcXZlOcniib0wnhJW29Rk9A2V/d5FkHCdzr+qyYOokVUyd2QLHY7bxw5BDPHNhHj1XCyUljMpQoisI3vvEN3nzzTTZv3kxOTs55fb4vhxJFUfju2jW8fbzcZ8LIuYJIVlwky4tyWOYDR2BCjEcGi5Wt5bWsP1LJ9oo6Bk+Zh+ILAeVM4SQ1NIw/XnYlU+MTvFzd6BuToeRrX/saL730Em+//fawtUnCwsIICPj4hXp8OZQA/GjDWl45dnRChxG7w8nW8lpWHShna1ntaUHk5AtZdny0F6sUQpwPk9XGlrJa1h4+cVpAyYyL5Mrpk7l6Rh4JERPvdf3UcGJx2Nly170+tyQDjNFQcrY0/O9//5u77rrrYz/f10NJi7Efg9U6IcNIRXMHb+8rY/XBimFLumfGRXKpBBEhJoyzBRSVCuZkp3Ld7AIumZKNv25iLVZosds53NbKvJRUb5fiFWMylHxWvh5KJpo+8wCrD1bw1t5jVLR0eu6PCQ3iqumTuXJGHrmJMV6sUAgxkowDNtYfrWLV/jL2VTd57g/213PZtFyum11AUWr8hBze8TUSSsSYpCgK+2uaeW3XUdYdqfQMz+g0GpYWZnLtrALmT0qTyapC+JimbgPv7C/j7X1lw04zzkmI5qa5U7hqxmRCA2S/qfFKQokYU/rMA7y9v4zXdh2lrrPXc39eUizXzS7giuJcn94ATAjh5nIp7K9p4q29x1h7+IRnoTZ/nZZLp03iprlTmJqWIN2TcUZCifC6s3VFAvQ6rpiey81ziyhImdhrqkwUiqJgtgxiNFsxmqz0m6xDt22YLDYGrIMMWO1YrXYGbPYPb1sHGXQ4cdid2B0uHE4nDocLu8OJw+nCccqExzNRq1XotBq0Wg1ajRqtVoNO677WatUE+Onw99MR4K8jwF9/ym0dgQF6QoP9CQnyJyTYn5Agv6Frf3Q6Wc13PDBYrLx7oJzXdh8dth+PdE/GHwklwmsstkHePVDOf3ccHvZCkpcUy03zpnBl8WSC/PVerFCAO2j09Q/Q0W2kq8dEb5+FHoOZXoOFnj4LvQYzPX0W+vot9JusuFxj9qXhvAX46wgPDSAiLIjI8EAiwgKH3Y4KDyImKoSYqGD0E2zC5XikKAqH61p5dfdRPjh03NM9CdDruHpGHrctmEpOgkyEH8sklIhRV9/Zy8s7D/P23jKMVvdyywF6LVcUT+bmeVMoSIn3coW+xW530tppoKXdQEt7Hx1dRjq7TbR399PZbaKz28ig/dydio/S67XujsOp3YcgfwICdO6uxVDHIuCUjoVepz2tw6HVuD/WaNTnbMM7XS4cDndHxeEc6rAMfWx3OLHaHJ4uzYDVjnWoSzNgHcQ8MIjRZMVotg1dWzFbbJzvK1xEWCCx0SHERrpDSmx0CAmxYSTGhZEYF05osL8MJYyik92TV3cdobq9x3P/rKxkbl84jaUFWTInbQySUCJGhaIo7K9uYuXmA2wtr/Xcnxodzm0LpnLtrHxpr44gl0uhvauf2sYu6pt7aG7to7G1l6bWXjq6jR/7BqxSQWRYENFRwUSFBxEeFkhkWCCRw24HEhoSQEiQP3768d01cDpdnrDSa7AMdYXMw657DRa6ekx09JgYHHR87NcMDvQjKSGc5PhwkhMiSEmIID0lirSkSAKkIzhiTr72/HfHITaWVnt2AY8LC+bzi4q5ce4UQgL8vFylOElCiRhRLpfCxtIqnt24z7PfhUoFiyZncPvCacyflIZaLUePF4qiKLR39lPT2EVtYzd1jd2eIDJgtZ/18wL8dSTGhpEQF0Z8TCixUSHERIW4j/yjQoiOCJb5FWehKAr9JisdXUY6uo0fXncbae1wd6C6ekxn/XyVCuJjwshIiSIjJZr0lCj37eQo/PxkT6YLqa3XyP92H+H13UfpMbnXOQry03Pr/CLuXDKd6BDfW6xsrJFQIkaE3elkTclx/rlxHzVDrVN/nZZrZxVw5+Ji0mIivFzh+Odwuqhv6qaytoPK2g5O1HZQWdeByWw74/N1Wg2piRGkJUcNHam7j9iT4sOJCAuUoYURZLPZaW430Nzm7lA1t/VR3+wOjX39A2f8HI1aRWpSJDkZseRkxDIpI46c9BhCQ+Tss8/KZnfw3sEKnttywDO046fVcP2cQu6+aCaJkfK+4S0SSsQFZbU7eHNPKSs3H/CsIRAS4MfnFk7jjoXFRATLC+qn4XIp1Dd3U1bZSlllG8er26hp6DrjXA+tVk1aYuTQEXc0GSlRpCdHkZQQIWPoY1CvwUJtYxd1Td3UDnW2zhVW4qJDmJQZR35OAnnZ8eRlxxMUKMMPn4aiKGwtr+WZ9Xs5Ut8KgFat5orpk7nn4plkxkV5uULfI6FEXBBm6yAv7zzMf7YcpMdkASAqJJAvLJ7OLfOLCPaXF83z0d1rHgog7kt5VRuWgcHTnhcYoCcn/eSRtPs6PTlKhlrGOUVR6O41c6K2nRO1HVQNdcJa2g2nPVelgrSkKPInJZCfk0BBTgIZqdESQM/DyXkn/9iwl10nGgD3z/WSwmzuvWS2LEkwiiSUiM/EbB3kvzsOsXLzAQwWKwCJEaHcvXQm180umHD7UowERVFoau3jcHkTR8qbOFzeTHNb32nP8/fTMjkrnvycBCZnx5ObGUdCbJjMyfEhJrONqroOKqrbKa9yB9bWjv7Tnhfgr6MwN5GpeckU5SVRkJMg81M+odKGNp7duI8NR6s8912Un8lXV8yVcDIKJJSIT+VMYSQ9JoJ7L5nFFdMno9PIkfrZuFwK1fWdlBxr5EhFM0fKm+jpswx7jkoFGSnR5OckeC7pKVFy9CtO09Nnpqyy7ZSuWitmy/CumlarZnJWPEWTk5ian8zUvGSCg6R7eS5VbV08u2Efa0qO4xp6u5NwMvIklIjzYrEN8tL208PIl5fP4fJpufKmeRZtHQb2HKpj/5F6So41njZfQK/TkJedQFFeEkV5SUzJTZI3DfGpuFwKNQ1dwzpvHz37R6NWkZsVz4wpqcyels6U3ES0WjmQOJPajh6eWb+H9w4ODydfu3QeeckTbyd2b5NQIj4Rh9PFG3uO8re1u+k2uo/qJYyc3aDdwaFjTewuqWVPSS31zT3DHg/w11GUl8S0/BSK8pKYnBU/7tf2EGOToii0dhg4XO7uypWUNtL0keHBwAA9M6akMrc4g7nTM4iLltfNjzpTOLm8OJdvXr6A5KgwL1c3cUgoER9re0Udj7+zxXPqXEpUGPdfOk/CyEf0GwfYeaCG7fur2VNSO2xdEI1aRf6kRGZPTWNGURr52fFyZCq8pq2znwNH69l/pIF9h+tO69xNyohl4exsFs3KJjs9Rk4XP0VtRw9Prd3NmpLjAOi1Gj6/qJj7ls2WCf0XgIQScVZVbV08/s5WdhyvByA80J+vrpjLLfOK0MkbKgAt7X1s31fNtr1VHClv8qwWCRAdGcy86RnMmZbBjKJUQoJkxVox9rhcCidq29lTUsfuklqOnWgZtn9RXHQIC2dls3BWNsUFyRKmh5Q3dfDEu1vZU9kIQGRwAF+/bD43zC6Ug7XPQEKJOE230cLfPtjFa7uP4lIUtBo1dyx0HwmEBcoba31TN5t2nWDTrhNU13cOeywrNdpzhJmbFSdHmGLc6TVY2HWghm37qth7qA7bKUvoBwf6sWBWFkvn5TJ7WprPb0J4cp2Tx9/ZSl1nLwDZ8VE8eM0S5uemebm68UlCifAYdDh4YWsJ/9iwF5PVPXt/2ZRsvnPVIlKiw71bnJd5gsjO41Q3dHnu16hVFOUls2h2NgtmZpEUH+69IoW4wGw2O/uONLB9XxU79lfTa/jwLLGgQD0LZ2VLQMG9gvX/dh7hqbW7PScALJyczoPXLJYF2M6ThBIBwNayGn779hYauvoAyE+O5cFrljAzK9m7hXlRU2sv67dXnB5ENGpmFaWxdP4kFs3KlmW/hU9wOl2Unmhh084TbN59YtgZPUGBehbOzObiBbnMmZbus0M8BouVv6/bw3+3H8LhcqFVq7l94TTuXzFXNv37hCSU+LiGrj5++9Zmz869MaFBfOuKBVw9I98nF+XqNVjYuKOCD7aWU1bZ6rlfgogQH3K5lKGAcpxNu4YHlLCQAC5ZkMuKxfkUTErwySHM+s5eHl+1lc3HagD36tYPXLGQa2b65uvq+ZBQ4qNsdgf/2LCXf23cj93pRKtRc+fi6Xxl2RyCfGwbdYfDya6Dtby74Si7D9Z4Jquq1SpmFqVxyYJcCSJCnMXJgLJxx3E27qwYthBgUnw4Vywt5IqlBcREhXixSu/YXlHHb9/a7JlvUpSWwC9uXsakhGgvVzZ2SSjxQQdqmnjof+s9fygLJqfzg2uXkBEb6eXKRldrh4G31x5mzaZjdPeZPfdPzopjxeJ8LlkwmagI2cpciE/K4XRx8GgDH2wtY+ueSs9p8Wq1irnFGVyzfCrzpmeg8aGzU+wOJy9uK+Gpdbux2Oxo1WruvngmX1k2Bz8fnodzNhJKfIjJauP3727j1V1HAYgOCeRH1y9leVGOz7RYXS6FvYfreGNNCbsO1nDytzgiLJDLluRz5SVTSE+WiWlCfFYD1kE2767k3fVHOVze5Lk/ITaUa1dM5cqLpxARFujFCkdXu8HEb97YyMbSasC9+OTDtyxnemaSlysbWySU+IhdJ+r5xf/W0dprBODGuYV8+8pFPnOKb79xgNWbSnnrg8PDNrubNTWN61ZMZcHMLJ+dnCfESGto7uGd9UdYvbEUo8l9dopep2Hp/FxuuKyY/Jx4nzkwWn+kkkfe2EiX0YJKBXcsKuably8gQC8bJoKEkgnPZLXxxKptvLbb3R1Jigzll7euYHZ2ipcrGx2VtR28+t5B1m+vYHBovYXgQD8uX1rA9ZdOIzXJt4ashPAmm83O+h0VvPH+IY5Xt3vuz82K44bLprFsYZ5PbLfQP2Dlsbe38ta+YwCkRYfzy1tXSNcECSUT2ke7I7cvmMYDVy4g0G9iT2R1uRR2HazhlVX7OVja6Lk/Oz2GGy4rZvmiyQT42GReIcYSRVEor2rjjfdL2LjjOIN2J+AeRr3hsmlcd+k0nxja2VZey0OvrqfDYJKuyRAJJRPQmbojv7p1BbMmeHfEarOzZtMx/rf6AI0t7km8GrWKi+blctMVxRTmJvpMi1iI8aKv38K7G47yxppDdHS7D6D0Og2XLsnnlqtmkJEysc9U6R+w8vg7W3lzr3RNQELJhDPocHD9Y897FkHzhe5Id6+Z1947yNtrD9M/NF4dHOjHNcuLuPGKYtnxVIhxwOFwsnl3JS+/s4+KU4Z25hSnc9vVM5lZlDahDyo+2jX53eev4LJpud4ua9RJKJmA/vbBLt7ZXzbhuyO9BjMvvLmXNz847JkvkhgXxi1XzeCKpYUEBkzcICbERKUoCkcqmvnfqgNs3VvpOUMuPyeB+25fyMyi1AkbTk52TXadaOCN793pk6vASiiZgOwOJ3anc8J2R/qNA/z3nf289t5BzzoIhbmJ3H7tLBbOzPKpNRCEmMia2/p4dfVBVq0/4tkYcFpBMvfdtpCp+RN3CwyDxeozZ0Z+lIQSMW6YLTb+9+4BXl61H7PFvWHg5Kw47rt9IbOnpU/YoychfF13r5kX3tzDWx8cxu5wT4qdPS2d+25fQF52gperExeShBIx5g1YB3l9TQkvvbXPM2ckKy2G+25fwIKZWRJGhPAR7V39/Of1Pby74ShOpwuAhbOyuOe2BeSkx3q5OnEhSCgRY5bD6WL1hqP865WdnmXgUxMjuee2+SydlysbWwnho5rb+lj56i4+2FqGa2ivqksX53Pf7QuIjw3zcnXis5BQIsYcRVHYsb+ap57fSn1zD+BemvpLty5g+aI8tDJnRAgB1Dd1889XdrJx53EAdFoNN15RzBdumCMbaI5TEkrEmHLsRCt/+88Wz14ZocH+3HXzPK67dCp62bzK5yiKgtPhwm53nPN5arUanV6DWi2B1RdVVLXxt+e3eBZLDA7y4ws3zuXGy4t9YoXYiURCiRgT2jr7+etzm9m06wQAer2WW66czh3XzyYkyDdnoU8EVssgPV1Gujv66e0xYzRYMPUPYOq3YuofwNg/gKl/ALPRinXAjn3QweCgg0Gbw33b5uB8Xmp0Og06Py06nRa9n/ui02sJCvYnOMSf4NAAgkMDCAkLGLrtT1h4EFGxIURGhxAaESjBZpxSFIXdJbU89fxWahq6AIiLDuFrX1jCxfNzZe7ZOCGhRHiVw+ni1dUH+OfLO7DaHKhUcPnSQu69bQGxUSHeLk+cg8PupKOtj7amXlqbemhr6qWro5+ejn56ukx0d/ZjMdm8XeZ50WjVREQFExUT6gkqcYkRxCdHEJ8cSUJyBMGhMiwwljmdLj7YUsY//rudzh4TADOL0njwK8tJig/3bnHiY0koEV5TUd3G755ay4naDgCK8pL49r2XyCz6MURRFDrbDDRUd1Bf3UFTXZcngHS0GXANnQFxLv4BeqJiQ4iICiYkLJDg0FM6FkPdiqCQAAIC9ej17s7GyQ6H3k+LXq9Fq9Oc80jX6XR5uisnOyzujoudQZsDs9GKyWgd6tJ82KEx9Q9g6LXQ02mkr8f8iboywaEBJKS4A0piShSpWTGkZsaSkhGDn7/v7lky1thsdl56ex/Pv7GHQbsTP72WL906n1uvmiE7go9hEkrEqLMMDPLPV3bw6uqDuFwKIcH+fP0LS7jy4kJpsXqRoddMVXkLdZXt1Nd00FDVQUNNJxbz2bsdej8t8UnuLkJ8UgSxCeFExrg7DZExIUTGhBAY5Dcu/l8ddie93SZ6Oo2eIaeujn7am3tpbeqlramHvh7zWT9frVYRnxxJWlYsaVmxpGbFkJmbQEpGjCzo50WNLb08/sw6DhxtANwbc/7g/hWyvskYJaFEjKpdB2t44pn1tHX2A7Bs4WS+efdSIsODvFyZbzH0mqksa6HyWDOV5S1UlTXT0Wo443M1WjVJadGkZsaQmhlDYkoU8ckRJCRHEhEd7FNzMAYsNtqaemlrdg9ZNdV1ebpIRsPAGT/Hz19HVl4COXlJ5OQnkp2fKEFllCmKwvubj/HnlZvpN1lRqeCmK6Zz3+0LZTuKMUZCiRgVPX1m/vivTWzYUQFAfEwo3/3yMuZNz/RyZROf0+Gk5kQ7x0rqOVZSz/GjjWcNIElp0WTmxpOaGUNadhxpWbEkpkaikzOfzklRFHq7TZ6A0lDdSV1VOzXHWxkYWn34VH7+OrLzEikoTiV/Whr501IJDQ/0QuW+pddg4S8rN/PB1jIAYqND+O59y1gwM8vLlYmTJJSIEaUoCuu3V/CHZzfQb7KiVqu4+crp3HPrAjlCGSEDFhsVR5o8IaTiSOMZ3xiT0qLJyU8kp8B9BJ81OYGgYDnT6UJyOl0013dRWdZCVVkLlWXNVFecOaikZsVSMC2VguI0CqanEZ8UMS6GvcajvYfqeOzv62jtcIfz5YvyeOCeiwmTtU28TkKJGDE9fWYe+/s6tu2tAiAnI5Yf3L+CyVnxXq5sYnE6nJQfaaRkdzUle6qpONJ02gTUoBB/8qemUlCcSt60VLLzEiWAeMnJoFJxtImyknqOlTTQWNt52vNi4sMonptF8ZwsiudmER4V7IVqJy6rzc6/XtnJy6v243IpRIYH8uBXVrBodra3S/NpEkrEiNhTUsuv/vQeff0DaDRq7rppLnfeMEdmvV8gRoOF/dsr2bP1OPt3VGLqHz6fITYhzH3EPXRJy471qbkf401fj5nyww2UHqyn7FA9lcdacAxtPAegUqnInZLM7MWTmLN4Mpm58dJFuUCOnWjl0b+uoa7JvXr0NcuL+NbdS/HzkzOpvEFCibigHA4n//jvDl58ay/g3jjvp9+8XE7zvQB6uozsWF/GtnWllB6o8+z5ARASFuA+qp6bTfGcTOKTI71YqfisrAODHCupp2R3NQd3VVFzvG3Y49FxoSxcVsCiFYXkTU2RwPkZ2QYd/POVHfz37X0oCmSmRvPL715NenKUt0vzORJKxAXT1mHgoSdXU3q8BYDrL5vG//viRbLM82dwahA5ur9u2Doa6dlxzF6Sy5zFuUwuSpGzOSawrvZ+9m47zt6txynZXY3Navc8Fh0bysLlBSy+dAqTi5IloHwG+w7X86s/raanz4K/n5bv3LuMy5cWSFdqFEkoERfEjv3V/PrPazCarAQH+vHDr13KRfMmebusccnQa2bb2lK2rj09iOQWJrNoRSELl+VLN8RH2ax2Du6qYtu6UnZvrhi2au7JgLLksiImFyXLm+mn0N1r5ld/eo/9R+oBuHRJPg9+ZTn+MpwzKiSUiM/E6XTxz1d28p/XdwOQlxPPw9++isS4cO8WNs7Y7Q72bTvB+ndK2Lv1xLD5BJ4gsryA+KQIL1YpxppBm50DO88cUJLTo1l29TQuuXoaMbK8+nlxuRReeHMPz768A5dLISs1mke+fy3JCfL3N9IklIhPra/fwsNPrmbfYfcRxU1XFPP1L1yETieTWT8JRVGoLGth/TslbF5zhP4+i+ex7LxELrq8SIKI+MROBpSta0vZuaHMM8SjUqmYOiuDZdcUs2BZPgGBfl6udPwoOdbIL36/ip4+C8GBfvz0m5ezcJacnTOSJJSIT6WsspWfPv4OHV1G/P20fP/+S1mxKM/bZY0Lhl4z694uYe3bB2mo7vDcHxkTwsVXTmXZ1cWk58R5sUIx3lnMNravP8b6d0o4sq/Wc79/gJ6Fywu4/MaZ5E9LleGdT6Crx8TPnljF0YpmAO68YQ733LYArczhGhFjMpRs3bqVxx57jAMHDtDa2sqbb77Jdddd94k/X0LJyFq1/gi//8cG7A4nyQkR/Ob715CZGuPtssa8qvIW3nh+B9s+KMVudw/P6P20zFuax/Jriimem4VGTpkWF1hbcy8b3j3EhlWHaGno9tyfnhPHtZ+bxyVXTUUv8yXOyeFw8tf/bOHV1QcBmDEllV9992pCZbG1C25MhpI1a9awY8cOpk+fzo033iihZIxwuRSeeWkbL7zpPt138Zwcfvz1ywgOknbw2SiKwsFdVby2cjslu6s99+fkJ3LFTbNYfOkUgkJkETMx8hRFofxwI++/sZ8t7x/1DO9ERAVz7R3zuPKW2YSEypvsuazfXsFvn/qAAaud1MRIHv/pDTJ/7gIbk6Fk2DdSqSSUjAF2u5Pf/PV91m0rB+DuW+bxpVvmS/v3LJwOJ1s+OMprK7d71phQa9QsXlHI9XfOJ7cw2csVCl9m7B9g7ZsHePOFnXS1uzfH9A/Qc/mNM7n+zvnEJoR7t8AxrLq+kwcfeYOObiMRYYE89pMbZJXqC2hChBKbzYbN9uGs8/7+flJSUiSUXCAD1kF+8tg77D1Uh0aj5gdfXcEVFxd6u6wxyelwsnH1YV56ZjOtje4VIv0D9Fx2wwyu+/x8mbQqxhSH3cnm94/w2srt1FW2A6DValhx/XRuvWcxcYny+3omnd1GHvzNG1TVdRLgr+P/fng9M6akerusCWFChJKHHnqIhx9++LT7JZR8dgbjAA8+8gZlla34+2n59YPXMrc4w9tljTlnCiNhEYFcd8d8rrp1NiFhsgOsGLsUReHAzipe/ddWDg9NjJVwcm5mi40f/fYtDpY2otNq+MUDV8raTBfAhAgl0ikZGR3dRr7zy1epa+ohJNifx358A4W5id4ua0w5Wxi56a5FXH3rHPwDZSdkMb6UHqjjhac3cmhPDSDh5Fxsgw5++eRqtuypRK1W8b0vL+ea5UXeLmtcmxCh5KNkTslnV9/Uzbd/9RodXUZiIoN54mc3kZka7e2yxgyn08Wm1Yd58e+bJIyICels4eT2+y4iJj7My9WNHU6ni8efWceq9UcB+PLnFnLnDXNkvt2nJKFEnKa8qpXv/foNDMYBUhIj+MPPbiI+Vl6EwN3m3rvtBP9+ci11Ve4xeAkjYiL7aDjR+2m57o553PylxXK2zhBFUXjmpe08/8YeAG6+cjrfuGsparUEk/M1JkOJyWSiqqoKgOLiYn7/+9+zdOlSIiMjSU39+MlEEko+vdITLXz74VcZsNqZnBXHYz+5kQiZDwFAa1MPf31kFft3VAIQHBrALV9axDW3zZUwIia80gN1rPzzOkoPuldwDosI5N7vXMaya4qlKzDklVX7+fPKzQBcvWwKD35lhQST8zQmQ8nmzZtZunTpafd/8YtfZOXKlR/7+RJKPp3K2g6+8fNXMFlsTC9M4f9+eD2BAfJma7c7eH3ldl56ZjODNgc6nYZr75jHrfcukSNF4VMURWHv1uP88w8f0FDTCcCUmel882fXkpIhCygCrNl8jEf/+j4ul8LNV07nm3cvldB2HsZkKPmsJJScv+a2Pu7/yUv09FmYMjmJ3//sRgL8JZAc3V/Ln3/9jucFeNqcTP7fT64hOV3m1wjf5bA7efP5nbzw9EZsVjtarYabv7SI2+5dgp+/rA67ZlMpj/zlfQDuu30hX7xprpcrGj8klAi6e83c/5OXaGk3kJ0ew19+eZvPr9JqNll59on3WfP6fgDCI4P48oOXs/SKqXLUI8SQtuZe/vbou+zdehyAxNQovvPw9RTOSPduYWPAa+8d5Ml/bgTgwa8s59oVU71c0fggocTHmcw2/t/PX6aqrpPEuDCeeuRzREUEebssrzqws5InH3qLzjYDAFfcPIu7v7VChmqEOANFUdi5sZynHn2Xro5+VCoV19w+l7u/udzn51o989J2/vP6btRqFb/67tUsmSvrmHwcCSU+zDbo4Lu/fo1Dx5qIDA/kqUc+R1J8uLfL8hqzyco/Hl/D+28cACAhJZLvPHw9U2bKYnFCfByz0co/nhj+9/PdX97g010TRVH43dPrWLX+CDqthid+diPTC2Xl13ORUOKjHE4XP3v8HbbtrSIoUM+fH76VSZlx3i7Laz7aHbn2c/PkSE+IT+HUvyXpmrjXMfn5E6vYsqeSwAA9f/mlb7/WfhwJJT7o1PSu12l4/Ke+m96tlkH+/th7nrkj0h0R4rM7U9fkwd/cRP5U33ydsQ06+N6vX6fkWCMRYYE8/Rvf7kqfy6d9/1aPYE1ihL2+poRV64+gVqv4xQNX+mwgaWvq4YE7/+4JJNd+bh5Pvfr/JJAI8RkFhfjzwEPX88jTXyQmPozWxh4evPtZVr28x9uleYWfXsujP7iOnIxYeg0WfvTbtxiwDnq7rAlFQsk4daSi2bO4z9fuXOKzE69KD9TxrTuepq6ynYioYH77zy9x/w+v9NkWsxAjYcb8HJ5+/RssuWwKToeLv/5mFX95ZBUOu9PbpY264CA/HvvJDUSFB1HT0MXvnl7HGB5wGHcklIxDPX1mfvb4OzidLi5ZkMutV8/wdkle8cGbB/jhff/G0GshJz+RP7/8NabOyvR2WUJMSEEh/vzwt7fwpQdWoFKpePeVPfz0a89h7B/wdmmjLjoimF9+92o0GjXrtpXzxpoSb5c0YUgoGWecThe//ON7dPeaSU+O5Af3X+pz6204nS7+8fga/vCLN3E4nCxaUchj/76X6DiZdyTESFKpVNzypcX8/MnP4R+g59CeGh644+801XV5u7RRNzU/ma9/YQkAf3luCxVVbV6uaGKQUDLOPP/mHvYfqcffT8uvvneNzy0fbzZZefhbL/L6f3YAcMdXl/Kj392Cv4/9HITwpnlL8/j9f+4jNiGM5vouHrjjaQ7urvJ2WaPu5iuns3hODnaHk589sQqj2ertksY9CSXjyMHSBv71yk4AvnvfMjJSfGuJ9LamHr5z5zPs3XocvZ+WH/3uVu782iWo1fJrLMRoy8xN4I8v3U/e1BRMRis/vf8/PjcBVqVS8aOvX0pCbCitHQZ++7cPZH7JZySv5uNEr8HMw0+uxuVSuGJpAZcvLfR2SaOq9KB7Qmt9dQeRMSE89u97WXLZFG+XJYRPi4gK5rfPfolLrpqGy/nhBFinw3cmwIYE+fPL716NVqtm8+5KmV/yGUkoGQcUReGRP78/NI8kim/fe4m3SxpVB3dV8aMvr/RMaP3TS/eTW5js7bKEEIDeT8f3Hrlx2ATYR3/wP58KJnnZCXz9CxcB8OfnNlNV1+ndgsYxCSXjwIYdx9ldUotep+GX373ap3b9LTvUwMPfehH7oIM5SybLhFYhxqCTE2B/+vvb0ek0bF93jCcffguXy+Xt0kbNTVcUs3BWFg6Hi989vRaXS4ZxPg0JJWNcv8nKn/7t3qHyCzfOJTPVd+aR1Bxv5Wdf/w82q52ZC3L46e9vkwmtQoxhCy7J54e/uxW1Rs26t0t45rE1PjPHQqVS8d37lhEYoKesspW31x72dknjkoSSMe7vL26jp89CWlIkn7tulrfLGTVNdV38+CsrMRutFBSnDR2Bab1dlhDiYyy4JJ/vPHw9AG+9uIsXn97k5YpGT0xUCF/53EIAnn5xK129Ji9XNP5IKBnDjlY0e9L2g19Zjt5H3pQ7Wvv40Zf/TV+PmazJCfzyL3dKh0SIcWTZNcXc/8MrAXjhqY28+fxOL1c0eq67dBp52fGYLYP86V++E8guFAklY5TD4eSxv68D4MqLC5lWkOLlikZHX7eJH315JZ1tBpLTo3nkqS8SFOLv7bKEEOfp2s/N4wv/zz0p/++Pvcfatw54uaLRodGo+f5XV6BRq9i48zi7DtR4u6RxRULJGFVR005zWx/hoQF8bWjVwInO1D/AT+5/jub6LmITwnj0mbsIjwr2dllCiE/p9vsu4sYvLADgyYfeYvu6Y16uaHTkZMRy81Xu7T827Tru5WrGF5UyhmchfdqtjyeK5rY+WtoNzJqa5u1SRpx1YJAff3UlZSUNREQF8/jKe0lK851JvUJMVIqi8ORDb/HBmwfQajU8/JfPM2N+jrfLGnGWgUF27K9m2cLJPrcVCHz692/plIxhSfHhPhFIAJ55fA1lJQ0Eh/jzyN/vkkAixAShUqn45s+vZdGKQhwOJ49+/390thm8XdaICwzQs3xRnk8Gks9CQonwul2bynnv1X0A/PiJ28icFO/lioQQF5JGo+b7j95ETkESpv4BnvjZ6z61hon45CSUCK/q6TLy5ENvAnDDFxYwfW62lysSQowEnU7L939zE37+Og7tqfGpM3LEJyehRHiNoij84edvYui1kDEpnru+udzbJQkhRlBKRgxffvByAFb+aR01x1u9XJEYaySUCK9Z9coe9m0/gU6v5QeP3oxe7xvrsAjhy664aRZzlkzGbnfy2x++is1q93ZJYgyRUCK8or66g2efeB+Aex5YQXpOnJcrEkKMBpVKxbcfuo7wyCDqqzv415NrvV2SGEMklIhRZ7c7+N2PXmXQ5mD6/Gyu+dxcb5ckhBhF4VHBfOdXNwDw9ku72L+j0ssVibFCQokYdS8+vYnqilZCwwP57q9uRK2WX0MhfM3sRblcfdscAJ742euYjVYvVyTGAnk3EKOqq72fN/6zA4Bv/PQaomJCvFyREMJb7v3OZSSlRdPbZeL1/2z3djliDJBQIkbVi3/fyKDNQUFxGguXF3i7HCGEF/n567j7W+6z7t74z056u2VXXV8noUSMmqa6Lj548yAAd39ruax0KIRgwSX5TCpMwjowyMv/2OLtcoSXSSgRo+b5v23A5XQxe3EuhdPTvV2OEGIMUKlU3P3NFQCs/t9e2pp7vVyR8CYJJWJU1FW1s/WDUgDu+sYyL1cjhBhLiudmMW1OJg6Hk1eelW6JL5NQIkbFf5/ZjKIoLFxeQGZugrfLEUKMMZ+//2IA1r59ULolPkxCiRhxp3ZJ7vjKUi9XI4QYiwqnp1M8NwunwyXdEh8moUSMuDef3+npkmTIDsBCiLO446vug5a1bx+kv8/i5WqEN0goESPKYXeyY0MZgGehJCGEOJPC6elk5sbjdLjYubHM2+UIL5BQIkbUoT3VmPoHiIgKljNuhBAfa/GKQgC2rT3m5UqEN0goESNq6zr3XJIFywrQaOTXTQhxbouGQknJnmoZwvFB8i4hRozD7mTnhnIAFq2Q1VuFEB8vKS2azNx4XE4ZwvFFEkrEiJGhGyHEpyFDOL5LQokYMTJ0I4T4NGQIx3fJO4UYEQ67k10bZehGCHH+ZAjHd0koESOioaYDo2GAoBB/GboRQpy3uRdNBqD0YL2XKxGjSUKJGBH11R0ApGXHytCNEOK8ZU5yb0fRUNPh5UrEaJJ3CzEiPKEkK9bLlQghxqPUrBgAGqo7cblcXq5GjBYJJWJENHhCSZyXKxFCjEeJKVFotRqsA4N0tBq8XY4YJRJKxIior+4EPjzaEUKI86HVaUhOjwZkCMeXSCgRF9ygzU5rYzcgnRIhxKd38qCmvkpCia+QUCIuuKa6LlwuheAQfyKjg71djhBinDo5J+3kHDUx8UkoERdcU10XACmZMahUKi9XI4QYr1Iz3aHk5GuKmPhGJZT87W9/IyMjA39/f2bMmMG2bdtG49sKLxm0OQAIDPL3ciVCiPEsMNgPAPugw8uViNEy4qHklVde4YEHHuAnP/kJJSUlLFq0iMsvv5yGhoaR/tZCCCGEGEdGPJT8/ve/55577uHee+8lLy+PJ598kpSUFJ566qnTnmuz2ejv7x92EUIIIYRvGNFQMjg4yIEDB1ixYsWw+1esWMHOnTtPe/6jjz5KWFiY55KSkjKS5QkhhBBiDBnRUNLV1YXT6SQubvhpoXFxcbS1tZ32/B/96EcYDAbPpbGxcSTLE0IIIcQYoh2Nb/LRMzAURTnjWRl+fn74+fmNRklCCCGEGGNGtFMSHR2NRqM5rSvS0dFxWvdECCGEEL5tREOJXq9nxowZrFu3btj969atY/78+SP5rYUX6f11AJhNVi9XIoQYz8xG92uI3m9UmvpiDBjx/+nvfOc73HnnncycOZN58+bxzDPP0NDQwFe/+tWR/tbCS1JO2a/ibEN1QgjxcU6u5JqSIXto+YoRDyW33nor3d3d/PKXv6S1tZXCwkLee+890tLSRvpbCy9JSo9GrVFjMdno7jASHRfq7ZKEEONQvWe38VgvVyJGy6is6Pq1r32Nuro6bDYbBw4cYPHixaPxbYWX6PVaklKjAKivbvdyNUKI8aqhxr3buIQS3yF734gRkZrpbrc2VHd6uRIhxHhktztornfveZMqocRnSCgRIyIt++TuntIpEUKcv+b6bpwOF4FBfsTEh3m7HDFKJJSIEZGW5T7lW7YcF0J8GieHblJlt3GfIqFEjIiTwzf1VR04HU4vVyOEGG9qKloBGbrxNRJKxIhIzYwhLCIQi9nG4f213i5HCDHO7NpUDkDhjHTvFiJGlYQSMSI0Wg3zL8kHYNvaUi9XI4QYTxpqOqiv7kCr1TB/aZ63yxGjSEKJGDGLVhQCsHNDmQzhCCE+sZMHMsXzsggODfByNWI0SSgRI2bqzAzCIgIx9FpkCEcI8Ylt/cAdShYPHdgI3yGhRIwYjVbD/ItlCEcI8cmdOnQzT4ZufI6EEjGiFl0qQzhCiE9Ohm58m4QSMaJkCEcI8UkpiiJDNz5OQokYURqthgXLCgB456XdXq5GCDGWHdlXS311BzqdDN34KgklYsRd//n5qNUqdm+uoLKsxdvlCCHGqBee2gjAZTfOlKEbHyWhRIy4lIwYLrqiCIAXn97o5WqEEGPR4X01HD1Qh06n4ZYvyU7yvkpCiRgVt993kadbcuJYs7fLEUKMIYqi8PxfNwDuLolswOe7JJSIUZGSEcPSK6YCsPJPa71cjRBiLNm/vZLSg/Xo9Frpkvg4CSVi1Nz5tYvRajUc3FXNoT3V3i5HCDEGuFwuVv55HQDX3D5XuiQ+TkKJGDXxyZFccfMsAP79x3UoiuLlioQQ3rb1g1KqK1oJDPbj1nukS+LrJJSIUXX7ly/Cz1/H8dImNr132NvlCCG8aMBiY+Wf3F2Sm+5aSGh4oJcrEt4moUSMqoioYG4ZOhr662/epaO1z7sFCSG85unfvkdbcy8x8WFc//n53i5HjAESSsSou+2exUyekozZaOWxn7yG0+nydklCiFG2Y0MZH7x5AJVKxYO/uYmAQD9vlyTGAAklYtRptBq+/+jN+AfoObq/jjee2+HtkoQQo6i7o58nH3oTgJvvXkjRzAwvVyTGCgklwisSU6P46g+uAOC5v6yXlV6F8BEul4snfv4GRsMAWZMTuPPrl3i7JDGGSCgRXnPp9TOYf0k+DoeT3/3oVawDg94uSQgxwt55aTcHd1ah99Pyg/+7GZ1O6+2SxBgioUR4jUql4oFfXEdkTAiNtZ08+/sPvF2SEGIE1Z5o459PuhdPvO+7l5OaGevlisRYI6FEeFVoeCDf/dUNALz7yh72bKnwckVCiJFgs9r57Y9exT7oYNaiSVx162xvlyTGIAklwutmzM/hujvmAfB/P/gfx0ubvFyREOJCctid/ObBV6irbCcsIojv/PIGVCqVt8sSY5CEkjGstrGLnQd8Yzn2L337UqbNzmTAMshP73+Ouqp2b5ckhLgAXC4XT/zsdfZsqUDvp+WnT9xGRFSwt8sacWaLjXfWHZElD86ThJIx6mhFM3d/7z/86k9r6DWYvV3OiNPrtfz8j3cweUoyRsMAP/7KSlqberxdlhDiM1AUhb/95l02vXcEjVbNT564nSk+cvrvP/67nd89vZZf/3mNt0sZVySUjFF5OQlkpkRjNFn5y8ot3i5nVAQG+fGrv32B9Ow4ejqN/Oi+f9PV3u/tsoQQn9K//7SOd/+3171A2iM3MWdxrrdLGhXlVa28vqYEgCsvLvRyNeOLhJIxSqtR8/2vrkCtVvHB1jL2Ha73dkmjIiQskEf+fhcJKZG0Nffy46+uxNA78TtFQkw0r/xzC//751YAvvGza7jo8iIvVzQ6HE4Xv3t6HYoCly7JZ2ZRmrdLGlcklIxhk7PjueGyYgCeeGYdNpvdyxWNjqiYEB595m6iY0NpqO7gp1/7D2aT1dtlCSE+odX/28u//+jeaO+eb1/KFTfN8nJFo+e11QeprO0gNNif//fFi7xdzrgjoWSMu+/2BcREBtPU1sd/3tjj7XJGTXxSBL955i5CwwOpPNbMQ998AZvVN0KZEOPZptWH+csjqwC47d4l3Hz3Ii9XNHraOgw8+/J2AL72hSVEhMmux+dLQskYFxToxwP3updhfvGtvZyo8Z2zUlIzY3nk6S8SGOTH0f11PPTNF6RjIsQYtuHdQzz209dRFIWrbp3DF7+xzNsljRqXS+F3f1+H1eZgWn6yzCX5lCSUjAOLZ2ezeE4ODoeLnz+xCrPF5u2SRk1OfhIP//nz+PnrKNldzXfufEbOyhFijHG5XKz88zoe+/FruJwull09ja/96EqfWovkpbf3svdQHXq9lu99ZblP/dsvJAkl44BKpeKH968gLjqEprY+fvf0WhRF8XZZo2bKzAweX3kvUbEh1Fd38MAdT1N6oM7bZQkhAKtlkEe++zIv/8N9luAtX1rEd351A2q177y9HC5v4h8vuYdtvn3PxaQnR3m5ovHLd35rxrnQkAAe/s7VaDRqNuw4zttrD3u7pFGVk5/EH1+8n5yCJAy9Fn5437/54M0D3i5LCJ/W2dbHd+/6Bzs2lKHTafjer2/kSw9c6lOBpK/fwkO/fxenS2HF4jyuumSKt0sa13znN2cCKMxN5Kufd08a+9O/N/nU/BKA6LhQHvvXPSy+tBCHw8kffvEmzzy+RlZMFMILKo408s3PPU11RSthEUH89p/3sOyaYm+XNapcLoVf/2kNnT0mUhMj+d6XZdjms5JQMs7cdvVMFszMYtDu5OdPrMJk9p35JQD+AXp+9Ltb+fz9FwPwxn92yARYIUbZptWHefBL/6S3y0R6Thx/+u9XyZ+W6u2yRt2Lb+1ld0kter2WX33vagID9N4uadyTUDLOqFQqfvL/LvPML/n1n9/D5fKd+SXg/hl8/v6L+fHjt+Hnr2PfthMyAVaIUeByuXjuL+s9u/3OvWgyv//Pl4lLjPB2aaNu3+F6/vFf9zyS79xzCVlpMV6uaGKQUDIOhYYE8MiD16LTati+r5rnfWj9klMtXlHIY//+cALsN29/ij1bj3u7LCEmJEOvmYe/+SL/fWYz4J7Q+vMnP0dgkJ93C/OCtg4DD/3hXVwuhasumcJVy2QeyYUioWScmpwdz3fuc69f8o//bmft1jIvV+QdkwrcE2AnFSZhNAzwi//3PL//xRuY+ge8XZoQE8b29cf4yvV/Ys/W4+j0Wp+c0HqSwTjA9x55HYNxgNysOL49tI6UuDB87zdqArl6WRE3XzkdgEf+8j67S2q9XJF3RMeF8vjK+7j+8/NRqVSsffMgX73hz+zbdsLbpQkxrhl6zTz6/Vf49Xf+S1+PmbSsWP7w/Jd9bkLrSQPWQb7/mzeoa+ohJjKYRx68Fj+91ttlTSgqZQwveNHf309YWBgGg4HQ0FBvlzMmuVwKv/zjatZvr8DfT8uTD91C4aREb5flNaUH6/j9z9+kpaEbgBXXT+fL372c4NAAL1cmxPiyff0x/vLrd+jrMaPWqLnl7kV87qtL0fvom7DD4eQH//cme0rqCAn252+/vo2MlGhvlzVmfdr3bwklE4Dd7v5j2XuojtBgf/7q438s1oFBnvvzet56cReKohAdG8q3fnEdsxZN8nZpQox5hl4zf3v0Xba8fxSAtKxYvvvrG5lUkOTlyrzH5VL49Z/fY+3Wcvz0Wv740C0U5vruwd8nIaHExw1YB/nWQ69SVtlKbFQIT/3mduKifftn9tGuydIrivjyg1cQERXs5cqEGHsURWHDqkP844k1GHot0h0ZoigKf165mf+9ewCNRs3//fA65k3P9HZZY56EEoHBOMDXf/pf6pp6SEuK5K+/vo3wUN/epfKjXZPgEH/ufmAFl9840ycn6QlxJo21nfz51+9wZJ97Xlp6dhzf+dUNPt0dOen5N/bw9xe3AfCzb17BpUvyvVzR+CChRADQ3tXP/T/+Lx3dRnKz4vjjL24h2AdP2fuoE8ea+dMv36aqvAWAyUUpfPPn15I5Kd7LlQnhPTarnZef3cKr/9qGw+HEz1/HHV9dyg13LkCr03i7PK974/0Sfv+PDQB8466LuPXqmV6uaPyQUCI86pq6+X8/e5m+/gEKcxP53Y9vIDTY39tleZ3T6WLVy3v4z1/WYzHbUGvUXHHTLO746lIZ0hE+xeVysfWDUlb+aR1tzb0AzF6cy9d+dBXxSb63ENqZvPXBIR5/Zj0Ad94wh6/cscjLFY0vEkrEMJW1HXzjF69gMtvITI3m9z+7iehIeeMF6Grv5+nfrWb7umOAe+n6G74wn+vvXECInKUjJjBFUTiwo5Ln/rKeyjJ31zA6NpSv/vBKFlySL/u24P4ZPffabp59eQcAN185nW/evVR+NudpTIaSRx55hNWrV3Po0CH0ej19fX3n9fkSSj6b6vpOvvOr1+juNZMQG8rvf3YzKT64HPTZHNlfyz9//wHHS5sACAz24/o75nPdnfMlnIgJ5WQYeeGpjVQcHfp9D/LjprsXcsPnF+AfKHu2gPssmz/9eyOvvVcCwF03zeWe2xZIIPkUxmQo+cUvfkF4eDhNTU3885//lFDiBa0dBr798Ks0tfURERbI4z+9kdzMOG+XNWYoisKO9WW88PRG6irduy5LOBETxZnCiJ+/jitvmc0tX1pMeGSQlyscO+x2J7/56/us21YOwLe+dLFncUpx/sZkKDlp5cqVPPDAAxJKvKTXYOa7v3qdE7UdBAbo+b8fXMf0Kb63o+e5uFwudm4ol3AiJoSzhZGrbpnNTXcvkjlUHzFgHeSnj7/DnpI6NBo1P/nG5axYlOftssa1CRFKbDYbNpvN83F/fz8pKSkSSi4As8XGD//vLUqONaLTanjo21eyZK4sJvZRZwsnV90ym2vvmE9UTIiXKxTi7JxOFzs3lPHqym2cKG0GJIx8nH7jAN9/9E1Kj7fgp9fy6wevkXVILoAJEUoeeughHn744dPul1ByYdgGHTz85Gq27qlErVbxrS9dzI2X++YeFh/nTOFEp9Ow9Kqp3PTFhaRmxnq5QiE+ZLPaWff2QV7/zw5aG3sACSOfREt7Hw8+8gb1zT2EBPvz2I9vkJVaL5BRCyVnCw6n2rdvHzNnfng+t3RKxg6n08UT/1jPO+uOAHDbNTO5//OL0WhkIbEzcblc7NlynNdWbudYSb3n/jlLJnPDnfMpmpUhk+CE1/R0GVnz2j7e+e8eDL1mAELCArj6tjlcc9tcwiWMnFV5VSs/ePRNevosxEaF8PhPbyQz1Xe357jQRi2UdHV10dXVdc7npKen4+//4boYMqdkbFEUheff2MMzL20HYPbUdH7x7SsJC5F5E+dSdriB11ZuZ9fGck7+2SSnR3P5TbO4+MqpcjQqRoXT6aJkdzUfvHmAnRvLcDpcAMQlhnPDFxZw6XUz5Gyaj/Hu+qP8/tn1DNqdZKfH8NiPbyAmSoZmL6QJMXzzURJKRta6beX8398+wDboIC46hEcevJbJ2bLC6cdpquvized3snH1IQYsgwCoNWpmLchh2TXFzLlosk/vFSJGRl1VO+vfKWHj6sP0dBo99+dNTeHaz81j0fICNFpZhfVcbIMO/vDsBt7d4N5scMHMLH7xwJUEBkiIu9DGZChpaGigp6eHd955h8cee4xt29z7B2RnZxMc/PFHlRJKRl51fSc/+d3bNLX1odNq+Pa9l3D1sikyJPEJmE1WNr93hLVvHfSsdQIQHBrAkkunsOyaYiYXJcvPUnxqhl4zm9ccYf07JZ7FzsA9RHPR5UVcfuNMMnMTvFjh+NHaYeAnj73DiZp21GoV992+kDuum41aLX+fI2FMhpK77rqL55577rT7N23axEUXXfSxny+hZHSYzDYe+fMatu2rAuCKiwv57r2X4Oen83Jl40dDTQfrVx1i46pDdHX0e+5PTo/mosuLWLSikLQsmRwrPp7FbGPP5gq2ri1l37YTOBxOADRaNbMWTmLZNcXMXpwr3bjzsLukll8+uZp+k5Xw0AB+8cBVzJqa5u2yJrQxGUo+Kwklo8flUnjxrb3847/bcbkUstJi+OV3riItOcrbpY0rTqeLw/tqWP9OCTvWl2Gz2j2PpWbFsmh5AYtWFJKeLQvYiQ+dDCLb1pWyb3sl9kGH57HsvESWXVPMRZcXyWJn58nhdPHPl3fwwpt7UBTIy47nV9+7hvgYeT8ZaRJKxAWx/0g9Dz+5ml6DBX8/Ld+9bxmXLy30dlnjksVsY+eGMratLeXAzirPES9AamYMi1YUsnBZAek5cTLE44PMRit7tlawbe0x9u8YHkSS0qJZtKKAiy4rIj1HAuyn0dbZz8N/eJejx93DXtddOpVv3r0UvU46TKNBQom4YLp7zfzyj6s5cLQBgMsuyuc79y6TyWCfgal/gN2bK9i2tpSDu6qw2z8MKLGJ4cxZnMucJZMpmpmOXobNJqymui72bD3O3i0VlJbUe86cAXcQWXxpIYtWFJIhQfUz2b6vikf+8j5Gk5WgQD0/uP9SLp6f6+2yfIqEEnFBOZ0unn9zD/96ZScul0JKYgQ/+cblFE6ShYU+K7PRyq7N5Wz7oJSDu6uHHSH7B+iZMjOd6XOzKZ6bRVp2rLw5jWPG/gEO763h0O5qDu6upqWhe9jjKRkxLBwa0pMg8tlZBgZ56oWtvPn+IQAmZ8Xx8HeuJik+3Kt1+SIJJWJEHDrWyMNPrqazx4RareK2q2dyz20L8JNJdheE1TLIob017Nlawd6tx+nuMA57PDQ8kPxpqRQUp1EwPY3svESZ4DhGKYpCR2sfx0rqOXawgWOH6qmv6uDUl1itVkPRrHRmL57M7MWTSEyROVsXysHSBh796we0dhgAuPXqGXz1jsXodHKatDdIKBEjpt84wB//vYkPtpQBkJoYyQ/uX8HU/GQvVzaxKIpC7Yk2Du6qpmR3NaUH64ZNlAXQ6bXkFiZRUJxG/rRUcgqSiIyWRZ+8YXDQQe2JNo4fbXIHkZJ6utr7T3teSkYMxfOyKJ6TRdGsDIKC/c/w1cSnZTLbePrFrbz1wWEA4qJD+OHXLpOza7xMQokYcdv3VfPY02vp7nMvZ33N8iLuv3MxIUHyIjsS7HYHVWWtlB2qH3rTa/AsJX6qqNgQsvOSyMlPJCc/kez8JNk48AIbtNmprWynqqyFyrJmKstaqKtqHzYnBNyn7WZPTiS/eKi7VZwmK/2OoC17KvnDsxvo6jEBcO2KqXztzsUEBfp5uTIhoUSMin6Tlaee38qq9e69c6LCg/jWPRezdN4kGQ8fYYqi0Fzf7TkqrzjaRFNtJy7X6X/CkTEhZE6KJzUrlrShS2pWLIFB8mJ9Lk6ni/bmXuqrO6ivaqe+ptN9Xd1xWgAB9/BaTn4SBdNTKZiWRm5hsizxPgo6uo384dkNbNvrXlspOSGC739lOdOnpHq5MnGShBIxqg4da+R3T6+jocW9I+n8GZl898vLiIuW/6fRNGCxUV3ROnQE30JlectZgwpAbEIYqZnugJKQHElCcgTxyZHEJob7zFwVRVHo6zHT2tRDW1MPrU29NNV10VDdQWNtJ4M2xxk/LzQ8kOz8RCblJ5E91JWKTQiXMD6KnE4Xb609zN9f3IZlYBCNRs3nr5/NF26cK/PcxhgJJWLUDdodPP/GHp5/Yw8Oh4sAfx1f/twibrhsmuw67EUDFhs1x9uGjvY7qK9up6Gmc9h+KR+lUqmIjgslPjmChORIYuLDiIwJISomdOg6hPDIoDG/t4qiKFhMNnq6jHR39NPdaaSn0327rbmXtqZeWpt6Tpurcyq9n5aUjJhhXabM3HgJIF5WXd/J755ey7ETrQAUTErgB/evIDM1xsuViTORUCK8praxi989vY6jFc0A5GbF8e17L5HTh8cYo8FCfXUHDTWdNNZ2et6g25p6sQ4Mfuznq9UqwiKDiIwOISQ0gODQAIJD/YeuAzz3+Qfq0ek16PVa9H46dDoNej8tej8tOp0WzvbGrig4nS7sgw4GBx0M2tzX9kEHdpsDm82B2WTF1D8wdLFiNFjc1/0DGHrN9HQazxk4TlKpVMTEhxKfHEl8cgSJKVGeABKXFCGhegwxW2w899puXnn3AE6ni8AAPV+5YxHXXzpN9q0ZwySUCK9yuRTeWX+Ep5/fisliA2DpvEl85Y5FJCdEeLk6cS6KomDoMdPa1EtbUw8tTT3uLkOHkZ4uIz2d/fR2mc46JDQWBYX4Exnt7vBEDHV64hIjSEhxD1nFJPjOcNV4Zbc7eWfdYf796i76+gcAWDQ7m2/fewmxUTKRe6yTUCLGhJ4+M0+/uI01m0pRFNBo1Fy3Yip33TyXiDDZt2O8cjpdGHrNdHcY6e02ftip8HQt3B+b+gewWu3u7sbJbofN4fn41JVsz0StVqHTa4d1VvR+Ws99gUH+BIf6e7oyIWEBnk5NWHggkTEhREaHyGTTcUxRFDbtOsHfX9xGc1sfACmJEXzjrouYPyPLu8WJT0xCiRhTquo6efqFrewuqQUgMEDPHdfN5parphPgL28YvuqTvNzIvA3fdehYI399fgvllW0ARIYH8qVb5nPVJVPQjvH5TGI4CSViTDpwtIG/Pb+F49XtAERFBPHFm+Zy1SVTZGMsIQQAJ2raefblHew8UANAgL+O26+dxW1Xz5Q9t8YpCSVizHK5FDbsqOCZl7Z7loCOjwnlrpvncdlFBWhlUqEQPqmmoYt/vbKDzbsrAdCoVVy9vIi7b55PVIQM945nEkrEmDdod/DuhqP85/U9nhUYk+PD+dKt87lkwWQ540EIH9HY0su/X93Jum3lKIr7hKzli/K4++b5pCTKxPiJQEKJGDdsNjtvrT3M82/s8cyqT0+O4t7bFrBkbo7MKRBigmrrMLDytd2s2VSKc+hsrovm5vClWxeQmRrt5erEhSShZAKy2R1Y7Q7CAifm3jKWgUFeX1PCi2/txWR2n0aclRbD7dfM5JIFk2V3TyEmiJqGTl5ZdYAPtpbhGFquf/6MTO65bQG5mXFerm7kdPWbiQ71zWEoCSUT0JOrt7Nqfxm/uHkZi/MzvV3OiDGarfxv1QFeefcAlqFFvKIigrjpiulcu7yI0JAAL1cohDhfiqKw91Adr6w6wN7DdZ77Z0xJ5b7bF1KYO3EXV+wxWfjNG5s4UNPEW9//4oQ9sDwXCSUTzKDDwc2/f5GadvfeMtfMzOf71y6Z0L/c/cYB3l53hNfXlHjmnPjptVy+tIBbrppBamKklysUQnwcm83O2m3lvLLqAHVN3YB7/Zklc3K49eqZEzqMAKw9fIJH3thIj2kAjVrFY3deyfKiHG+XNeoklExAA4N2/vL+Tp7fehBFgdjQoAnfNQH3So4bdx7n5VX7qazt8Nw/f0YmN10xnZlFabK8tBBjTEe3kXfWHuattYc9c8UCA/RcfckUbrpyOgmxYV6ucGSd7I58cPgEADkJ0fz6thXkJ0/c4alzkVAygZXUNvOzl9dS39UH+EbXBNzt35Jjjbyy6gA79ld77k9OiOD6S6dy+dJCQoMn9s9AiLFMURQOljbyxpoStu+r8kxejYsO4eYrZ3DVJVMIDvLzcpUj76PdkXsuns1Xl89B58MLvkkomeDO1DX5+c3LWDLBuyYnNbT08MaaQ6zZXIrZ4p534qfXsnxRHjdcNo1JE3iynBBjjcls4/0tx3jz/UPUN/d47p+Wn8z1l01jydxJPrH+ULfRwqNvSnfkTCSU+IiPdk0un5bLD667iKiQQO8WNkosA4Os3VbOm2tKqG7o8txfMCmB6y6dxtJ5k/D303mxQiEmruM17byz7ghrt5YxMLQbc4C/jkuX5HPDZdPITI3xcoWjQ1EU3tlfxmPvbMVgsUp35AwklPiQgUE7f31/F89vPYhLUQgL9Od7Vy/m2ln5PrPGh6IoHKlo5o01h9i8+wROp/s0w6BAPcsX5nHVJVPIzYrzmZ+HECOl32Rl/fZy3l1/lBOnzPFKT47k+suKuWxJPkGBE3+I5qTG7j5++eoGdlc2AJCbGMPDtyynIEW6I6eSUOKDjjW28dD/1lPR0gnA9IxEfnT9UiYnxXq5stHV3Wvm3Q1HeXfDUc8y9gBpSZEsX5zHikV5JMaFe69AIcYZ26CDXQdq+GBrGbsO1njWFtFpNSyek821y6dSXJjiU6F/YNDOPzfu49+b9jPocOKn1XD/pfP4wpLp6DTSHfkoCSU+yu508vyWgzy9bjcDgw7UKhU3z5vCNy5fMOEnwn6Uy6VwsLSBdzccZeveKgYHHZ7HpuQmcsnCyVw0dxLRkcFerFKIscnhcHLgaAMbdx5ny+5KTBab57Hs9BiuvHgKKxbnEeZj6wYpisK6I5U8vmorrb1GAOZkp/Czmy4hLUaWxD8bCSU+rq3XyOOrtnomXIUF+vP/LpvPTXOn+MSEs48yW2xs3VPJB1vLOXC0npO/5SoVFE1OZun8SRJQhM87NYhs21tFv8nqeSw2KoTli/JYsTiPrDTfmCvyUcdbOnnsnS3sqWwEID48hAevWczyItkO4+NIKBEA7K1q5NE3N1HV5l60KDMuku9dvZiFk9N99o+oq8fExp3H2bTzOEePt3juPzWgLJmTQ0xUiBerFGJ02O1ODpaeOYhEhAWyZG4OF8/PZVp+is+uB9TVb+Yv7+/kzb3HcCkKeq2Gu5fO5J6LZxGgl4n0n4SEEuHhcLp4bfdR/vbBLnrN7kWM5k9K47vXLGZSgm9vetXe1c+W3ZVs3Hmc0lMCCsDkrDgWzs5m0axsMlOjfTbEiYnHaLay+2At2/dVsbuk1nNaPZwSROblMjU/2ad367baHTy/5SDPbtyLxeY+u+jSqZN44MqFJEdN7MXfLjQJJeI0/QNWnl2/lxe2HcLudKJWqbhhTiFfv2we0SG+uUnUqdq7+tm86wSbdp3g2IkWTv1LSIgNY+GsLBbNyqYoP9knh8DE+NbWYWD7/mq27a3iUFmT5ww1gMjwQBbPkSBykqIorCk5zpPvbffMGylMieP71y6hOCPJy9WNTxJKxFk1dvXxh9XbWXekEoAgPz33XjKLOxYVSytySHevmZ0H3C/g+482DJskGxLsz+ypacyZlsHs4nSiI2Qeihh77HYnR483s6ekjt0ltVTXdw57PD05koWzslk4O5v87ASfHZr5qJLaZh5/ZytHGtoA97yRB65cyOXTcuVn9BlIKBEf62BNM797ZwvHGtsBiA0L5ivLZnP97EJZ8OcUA9ZB9h6uZ8e+KnYeqPHs43FSdnoMs4rSmFGUxtS8JAL89V6qVPgyRVGoa+rmwNEGDhxp4EBpg2eXbXBvgjclN4mFs7NYODOblEQ5U+RUFc0d/PWDXWw+VgNAgF7HvZfM4s7F0+Vg7QKQUCI+EZdLYXVJBX9Zs5OW3n7AfWRw37LZXD+rQMLJRzidLo5VtrKnpJY9JbVUVLcPe1yrVZOfk8DUvGSK8pKYkpvkE3t9iNHncinUNHRxuLyJI+VNHCprorvXPOw54aEBzJmWwZzpGcyemkZ4qG+s9Hw+Kpo7eGrtbjaWuvfT0qhVXDe7gK9fOo+YUOmCXigSSsR5GXQ4eHXXUf61cR8d/e4XNgknH6/XYGbf4Xr2H23gwJF62ruMwx5Xq1VkpcUwNS+JorxkpuYlExUh83fE+bPbnVRUtw2FkGaOVDRjMtuGPUev1zIlN5GZRWnMKkpjUmacDDmcxUfDiEoFl0+bzFeWzyEzLtLL1U08EkrEp2KzO3h9Tyn/3LB3WDj50tKZXD+nEH+d1ssVjl2KotDc1sehY03uN46KZprb+k57XkJsKPk5CeRlJ5A/KYHcjFj8ZH8ecQpFUWhpN3CsspXyylbKKluprO1g0O4c9rwAfx2FuYkUTU5ial4yBbmJ+Onlb/RcShvaeGb9XjYd+2gYmU1mXJSXq5u4JJSIz+RM4SQyOJAvLJnOrfOLCPaXIYlPoqvH5DmyPVzeRHV9Jx/9C9No1GSlxZCfHU9eTjyTMuJIT45Cp5PulC9QFIXuXjMnats5Xt1OWWUr5VVtp81dAvdwjLvj5g4h2RmxcibYJ6AoCvuqm/jH+r2ePWokjIwuCSXigrDZHbyxp5SVmw945pyEBPhx+4JpfH5RMRHBvrXE9GdlMtuoqG6jbOjot6yylZ4+y2nP02rVZKZEk50Ry6SMWHIyYslJjyUwQCbRjmcul0JTWy+VtR2cqOlwX9e2nzGA6LQasjNiKMhJIH/okhQfLuvlnAeXS2FreQ3/2LCPI/WtgHvOyJXT87jn4lkyTDOKJJSIC8rudLKm5DjPbthHbUcPAAF6LTfOncIXl8wgPlxWP/00FEWhvcvoCSjHq9uprO0Yts/IqRJiw8hIiSI9JYqMlGgykqNIS46UM37GGJdLoa3TQF1TN7WN3dQ2dlHX2E1dUzdWm+O056vVKlITI5mUGesZ2svJiEEvw6WfisPpYu3hEzy7cR+VrV0A6LUabphTyF0XzSApUhY+G20SSsSIcLkUNpZW8Y8Neylrcm9brlWruWJ6Ll9YMoPcRN/cE+NCUhSF1g4DlXWdVNa0c6LWfUTd2WM64/NVKoiPCSM9OZLkhAj3JT6c5IQI4qJD0Mok5RGhKAoG4wDNbQaa23ppbO2lua2PhuYe6pt7GLDaz/h5fnotWWkx7u7XUCcsKzVa5hVdABbbIG/uPcbzWw/S3OPu7Ab56bl1fhF3Lp5OdKhMMvcWCSViRCmKwq4TDfxjw172Vzd57p+ZlcztC6aytDBLtu++wPr6LcOOuk/ePlPr/yS1WkVcdAiJceHERYcQGxVCTFQIsUO3Y6NDCA70kyGBM7DbnXT1mujoMtLRbaSz233d3mWktcNAS7th2DogH6XTakhNjCA9JZqMlKihSzSJ8eEyD+QCq+vs5ZUdh3lr3zFMVvf/SURQAHcsKub2hVMJDfCtHdLHIgklYtSUNrSxcvMB1h+txOly//rEhgVzy7wibpo7hagQWRthJPUaLNQ1dVPf3ENzay9NrX00tvbS0mEYthLt2QT464iJDCEyPJDwsEAiwwKJDA8adjs0xJ/QYH+Cg/zH7RuqoigMWO30m6wYTVZ6DRZ6DRZ6DGZ6+4auDRZ6+yx095rpMZhPm5R8JtGRwSQnhJMcH0FyQjgpCRESPkaB0+Vie0Ud/91+iB3H6z33p0WH84Ul07l6Zr4sejaGSCgRo66tz8iru47y2u6j9Jjckzd1Gg3LirK5ee4UZmYlyxH5KHK5FHr6zDS399HSbqCj68Oj/Y5uIx1dxmE7wn5SgQF6QoL8CAn2JyTIn5BgfwL8dAT46/D31512W6/XotVq0GnV6LQaNBr3tU6rQaNVo+LMvxMKCi6XgsPhwuFwYnc4cTg/vG13uLBa7VhtdizWwVNu27Fa7ZgHbBhNNoxmdwgxmm3D9nv5JHRaDTFRwZ7OUkxkCLFRwSTEhZEYF05CTKgMu4yyrn4zb+07xmu7j3qGaFQqWJyXwe0LpjFvUpqszTIGSSgRXjPocLD2cCX/3XHYM+MdID0mgpvmTuGamfly1s4YYbXZ6eg20tVjcncN+ixDHQQzPX0fdhH6TdZzDlWMJzqthpBgP8JDA4kY6gRFhJ28HUhkWBAR4YHERYcQHhooQXoMcLkUdlc28NruI2wqrcHhcofL0AA/bphTyC3zi0iJCvdukeKcJJSIMaGsqZ3Xdh9l9cEKz9bf0j0ZnxxOFyaz9bTug8lsZcBmZ2CoQ+G+7e5cWKx27HbnGTod7muH49ydC5Va5emqaLVqT8dFq3F/HOCvw3+oMxPgrz/lto5Af/1QN2eoqxPsHoLy02vld26cOFNXBGBqWgI3z5vCiqmTZIhmnJBQIsYUs3WQ90oqeG33Uc9ZO+Ae/71udgFXz8wnLkz2mRDC19mdTraX1/HWvmNsLav1dEVC/P24akYeN82bwqSEaC9XKc6XhBIxZh1rdHdP3iv5sHuiVqmYl5vGdbPyWVqQhZ+szyCET6ls7eLtfWWsOlDumZMG7q7ITXOncOk06YqMZxJKxJhntg6y9vAJ3tp3jIO1LZ77QwP8uLx4MldNn8zU9ARptQsxQXUbLbx/6DirDpRzrPHDHbcjgwO5asZkrp1VIF2RCUJCyQTUaDDQaTEzPSHR26VccPWdvby9v4x39pXRbvhwkbCEiBCWF+Vw6dRJTEmNl4AixDjXY7Kw/mgVaw+fYF9VE66htxytWs3i/Ayum1XAwrz0CbfOUb/NysHWVi5Kz/B2KV4hoWQC+t7aNbxRUcbi1HS+OWfehAwnTpeLPZWNvHugnA2lVZ7hHZCAIsR4dbYgAlCQEseV0ydzRfHkCbmmUb/Nyr9KDvLvQwexOR1s+eK9xAX73vy5T/v+LQP5Y5SiKPhptWjVarY21LG1oW5ChhONWs383DTm56ZhtTvYXl7L2sOVbC6robXXyH+2HOQ/Ww56AsolhdlMTU9Ao5ZFqoQYSzoMJjYfq2btkcrTgkheciyXTp3EiqIcUqLDvVfkCDo1jBgH3XtZTYqKptNi9slQ8mmNWKekrq6OX/3qV2zcuJG2tjYSExP5/Oc/z09+8hP0+k+2mZivd0oAGgx9/G3fHt6oKPPMSp+I4eSjPhpQBgY/7KBEBgdwUX4WF0/JYm5OqkySFcJLatp72FhaxabSao40tA17zBeCCJw9jHxz9jwuy85B7aMd3jE3fPP+++/zyiuvcPvtt5OdnU1paSn33Xcfd955J48//vgn+hoSSj50pnCyKDWNr8+ay+ykZC9XN7Ksdgc7KupYd6SSrWW1GK0f7qgboNexcHIaSwuzWZyXQVig7HkhxEhxulwcbWhjU2k1G0urqevsHfZ4UVoCFxdmTfggAtAzYGHloRKeO1wiYeQMxlwoOZPHHnuMp556ipqamk/0fAklpztTOJmZmMTXZs5hSVr6hJ93YXc62V/dxMahF8WOUybJqlUqpqUnsCgvg0V5GUxKiJ7wPw8hRprBYmXX8Xq2lteyvaKOXvOHG0JqNWrm5KRycWEWSwsyiQmd+MMU7SYT/zi4n/+WHmbA4d5rSsLI6cZFKPnpT3/K+++/z/79+8/4uM1mw2b78Ci4v7+flJQUCSVn0Ggw8PeD+3jtWCmDLicABTGx3D9zjs/8YSiKQllTOxtKq9lUWk1VW/ewx+PDQ1iUl87ivAxmZ6cSKHuWCPGxFEWhqq2breW1bC2v5XBdi2fjTYBgfz2LJmdwcWEWC/PSCfb382K1o6fB0MffD+zj9bJjntfcwphY7p81h0uzfOM193yM+VBSXV3N9OnTeeKJJ7j33nvP+JyHHnqIhx9++LT7JZScXbvJxD9L9vNS6REsdve8i/TwCL40bTo35BUQqPOdN+LmHgPbyuvYVl7LnsoGbA6n5zGtRk1RWgKzs5KZnZPK1LR49FqZiyKEoig0dRvYW9XI3qom9lU30tlvHvacrLhIFg91IKdlJE6403fP5XBbK8+W7GdNVaVn8u6sxCS+Pmsui1LTpBt7FqMWSs4WHE61b98+Zs6c6fm4paWFJUuWsGTJEp599tmzfp50Sj693oEBVh4+yHOHS+gf+hmG+flza0EhdxYVk+RjPz+r3cHeqka2ldeyrbx22D4aAH5aDdPSE5mVncKc7BQKUuN86oVW+LbW3v5hIaS11zjscT+thtk5qUNBJJ2kyDAvVeodg04n71edYOWhEg61f7jJ6OLUdL42a86En8d3IYxaKOnq6qKrq+ucz0lPT8ff3z3hsKWlhaVLlzJnzhxWrlyJ+jxO5ZQ5JefPPDjIq2WlrDx0kIZ+A+Cea7E8M5u7p01nVmKSzyV7RVFoPHkkWNnI3upGuo2WYc/x12kpSkugOCOR6RlJTE1LIMj/k50lJsRY5nIp1HR0c7C2hYM1zZTUttDSOzyka9VqpqTGMzsnhdnZKUxNS/DJs9q6LBZeLj3CC0cP0WF2d4v0ag1X507mS8UzyIuO8XKF48eYHL5pbm5m6dKlzJgxgxdeeAHNeR6JSij59JwuF5vravn34YPsbGzw3J8XHcNd06ZzzaTJ+Pno8IWiKNR29LC3qom9VQ3sq2qiz2Id9hy1SkVuUgwzMpIozkikKC2B+PAQL1UsxCdntTsob2qnpLbFfalrwfCR32+NWkVBchyzst0hZFp6ok/PuTrW0c7KwyWsOlHBoNM97BsTGMTni6ZyW2ERMYFB/7+9+wyO67zvPf7d3hsWZdE7SJBgpyiSlizRYmQpSiQ5Lte+iS07Za40ske6niRKnIzLTDJKMpnrGduxY0/mSk5cZDu2imUrV4wlUbJpiqREECBYQPRetmJ7O+e+2MUCy06KwAK7z2dmZxdnF+BzCGDxe/5POQVu4fqz5kLJ4pBNQ0MD//7v/54XSFwu13V9DRFKbo1+j5vvnjrJ8+fOEMvOFnfo9Xx0Uxf/c8s2Gmz2wjawwCQpE1LeHc70It8dnrxkuAeg0mpiS2M1WxpcbG2sZnNdJUadqKYIhSNJMqNuH71jM/SMTtMzNsOFKXduZd4ig1bN1oZsJbCllq0NohIYT6V4ZeAC3+/t5p3ppWtxbaty8entO7m/rQOtGNK9aWsulDz77LN85jOfuexz1/tPilBya/ljUX7cd5p/7znJVDAzhqwA3t/YxB9t2c7dTc1ip9SsGX+Q7uEp3h2e4uTwJBdm3HkrECBTTWlzOdnS4KKzrpLO2ko6airQl2DZW1h5siwz5VvgzMQcZyfm6BufpXd8hmA0fslryy1GtjZWs7Ollp3NtWysrRBzprLGAn5+cLqH/+w7jTeWWd6sViq5v62Dz2zfyXZXdYFbWBzWXCi5FUQoWRkpSeKNkSG+33uKw6MjueM1Fguf6NrKxzZvEeXKi0TiSc5OztI7OkPP2Ay9YzPM+IOXvE6lVNBS5aSztpJNdZlbe3V5ySybFG6NtCQx5vZzfnKeMxOznJnMBJGFywQQnVrFproqtjRmKnhbG1y47JaSmzt2NYvD2d/rPcWbo8Ms/tGrNpv5RNc2Pra5i0pT8e+xsppEKBFuyqjfzw9Pn+LHZ07jj2XGnTVKJQdb2vjIps3c2dCEWlRPLmsuEKJ3bIa+idls73UWbyh62ddWOyy0u8ppry6n1eWkrcpJc1WZqKqUOFmWmfYFGZz1MDjj4cKMhwszboZmPHlL2hepVUraq8vZlA29XQ0u2qvLRRXkCsYDAX52ro+fnDmdqw5DZjfsP9qynQPNLeL9bYWIUCK8J/FUil9e6Od7vd2cnFlaAldhNPHQho38QedmNoqZ51clyzJzC2HOTsxydmKOMxNznJmcy9t1djmFAmrLbDRWOGiqsNNY7sg+duCyW1AqRU+3WASjccbcPkbm/YzO+xiZ9+Xul18Zezm9Rp0JINmhwU11VbS5nGjUIoBcTTAe55WBfn529gzHpiZyx+3ZeXSf6NpKk91RwBaWBhFKhFvm7PwcPzlzmpfOn8uNuQJsKq/gDzo38+CGTsqNxXfJ8ZUSiMS4MO3mwrSbgRkPA9le8cUrIpbTqVXUl9upc9oytzIbtWVWasts1JbZSnqlxFqUliTmAiEmvAtMegJMegNMeheY9AYYdfsvWYK+nFqppKnSQUtVGW1VTtqrMxW1OqdNzPG6TmlJ4sj4GD8918ergwO5Cf0KYH99Ax/u7OK+tjb0avF7s1pEKBFuuUQ6zeGRYX56ro/Xh4dIZmf0qxQK7mho5KENnRxsacN8nVd9FpbIsownFGF4LtNjztz8jLp9jLn9pNLSVT+/zGygxmHFZbdQZTfjslmosltw2c1U2SxU2EyipH+LyLLMQjTOjD/IrD/IjD/ITCDErD/ErD/ItD/IlG/hmt+zcosxVwlrrHDkKmT15XbxvboJsizTMzfLS+fP8nL/eeYjS7vQtjrK+IPOTTy8YRPVFrGUvxBEKBFWlC8a5ef95/jZuTP0zC5dolyvVnOwuZUHN2zk/Y3NYgndLZBKS0z7Fxid9zOR63UHmPAuMOEJXHa1xcUUCnCYjJRbjJRbTDgtmcdOq4lyi4kyswG70YDdpMdmNGDQqktqYmQqLbEQjeEPxwhEonhDUTyhCJ6FMO5gBHcwc+8JhvEEI8SSqWt+TbVSSbXDkqlmOa3Z6paNhnI7jRV2Mdn5FhnyeXnx/FleOn+O0YA/d9ym0/P7HRv4cOdmtla5SurneS0SoURYNUM+Ly+dP8dL/ecY8S9dutym03N/Wzu/17GRPbV1YgLZClmIxpj0LDDlW2A2EMr24EPMBDI9+dlA6Jq99otp1SrsRj02kwG7UY/FoMOs12LW6zDptZh1Wsx6LSZ95rheo0anUWPQatBp1OiXPdaqVLd8Powsy6QkiXgyRSyRIpZMEUsmiSdTRLMfRxNJQrEEoVicUCxBOJYgFI8TimaOBSIx/JEYgXCMYOzawe5iDpMhU5mymZdVpcy47JkgUmU3i+GWFTIZXOCVC/281H+O03OzueN6tZqDLa081NHJnY1NolO0hohQIqw6WZbpnZvlpfPnePnCudy2zABOg4EPtnXwu20dIqCsMkmS8YYjeIIR3Ath3Nne/mIFwBuM4AlF8Iej+COxGw4w10OhALVShVqlRK1UZu5VSlRKJVfqwMoySLJMKi2RSqdJS/LSvXTr2whgMeiwG/U4TAacFhPlViNOs5HybEXJaTbitBiptJlLctv1QloMIq8M9OdNvlcpFNzZ2MSDHZ38TksrJjF8vCaJUCIUVFqSeHtygpfOn+XVoYHc8mIQAWUtk2WZSDyJPxLNDmXE8IejuWpDKJ4gFE0Qji+vPiSyFYpMpSKWva1EuLkchSKzMkWvWarSLFZqzAYdZp02U93RZ+4teh1GnRabUY/dqMduygxbWQ161Crxs7iWXCmIKIDbaup4oGMDv9vWgVNMtF/zRCgR1oxkOs3RiXF+ceH8JQGlTG/g7qZmPtDcwp0NTVh0Ypy9WKTSErFkkmRKIiWlSadlklI6W/mQSEvSNYOLQqFArVKiUSlRq1SolUpUy6oteo0arVol5gsUCUmW6Zuf47XhQX41PJQ3NLMYRH63vYP72trF5mbrjAglwpp0tYCiVirZU1vHB5pauKe5lUa7vXANFQRhVUSSSY6Mj/La8BCvjQzlDfuKIFI8RCgpQsFkDKVCiUldHGOmyXSad6en+NXwIK+NDDHk8+U93+oo40BTM3c2NrGnpq5kr2IsCMVElmVGAn7eHB3m8OgIvx0fJ55eWs1k0mi4o6GJDzS3cHdTc9Fc4kKWZeZiQaoMpfe3C0QoKUrfOPsG3x04yocat/GJ5ttotpQXukm31LDfl+ktDQ9xfGoibzKjXq1mb109dzU28f7GZppsdlGyF4R1IpxIcHRinMOjw7w5OsLYQiDv+TqrlXuaW/lAUwt7aourAxJLJ/nF+Gl+MHycmUiA1+/732hVxXN+10uEkiL0qbee5bh7NPfx/soW/rBlD3e52lEpimuC3kI8zq/HRjg8OsKboyPMhvO3Zm+w2rizsYn31Teyr64em15foJYKgnCxtCTRNz/HkfEx3hob5cTURG6zRchcT2t3TR3vb2zkQFML7WXOoutkTIR9/HD4BD8dOUkgmdkJW6tU8ewdj7DDWV/g1q0+EUqKkCTL/HZuiO8PHeONmf7clS1rjDY+3rybjzTuxKErvlnosixz3uPmzdFMSLn4DU4BdFVWsb++gffVN7K7pkZsHy0Iq0iWZYb9Pn4zPsaR8TGOTowTiOdfNqHeauOuxibuamxmb119US7dlWSZI3ODfH/oOIcveo/+RPNtfLhxR1G+R18PEUqK3FIKf5dAMvPLr1Wq+GDtJj7WtItdzoai63ksWiwFvzk2wpHxMQZ93rzntSoVu6pr2FfXwJ7aOrZVuYqqHCwIhSbLMuMLAY5NTvD25ARHxkeZDuVXM81aLXtr63lfQ0PRD7l64mGeH+3mJyPvMBZemhv3vspW/mfLbUVZzb5RIpSUiGgqyS8nTvP9oWOcDSxt995qqeB/NO/i9+q2FH0ynwkF+e34OL8ZH+XI+BgzFw31aFUqdriq2VNbx201dexwVRdlL00QVookywx6vbwzPcnbkxMcmxy/JIRolSp21dSwv76B/XUNbKlyFfUeRJIsc8w9wk9G3uXQ5BmScqZ6a1brinbe33shQkmJkWWZ0/4pfjT8Dr+cOE00nbn8uUap4p7qjXy4cTv7KluKPq0vlpF/PTaa68V5ovlXZFUpFHSWV7DdVc0OVw3bXC6a7Y6i7cUJwo1aiMc5NTvNqZkZTs5Mc3JmKm/5PmSW8G+trGJPbT376uvZXV2LQVP8w6aTET8vjJ3i+dFuJiP+3PEtjho+1rSL363rwlgkKyRvJRFKSlgwGePn473858i7edWTSr2F36vfwkMN2+iwVhawhatHlmWGfN5M725qgnemppgMLlzyOrtez7YqF9td1Wyvqmaby4VdbyhAiwVhdaUkiQseNydnpumenaZ7eppBn5eL/xDo1Wq2V1Wzu6aWPbV17KyuwVgCIQQgnIzz6tRZXhw7xdvukdxxs1rHA/VdfKxpF5vs1YVr4DogQokAwBn/ND8bPcnL4725uScAnTYXDzVs44G6Lsr1pbUh0VRwgXenp+iemeHU7DS9c7Mk0ulLXldntdJVUcWWqiq6KqvYUlklgoqwriXTaQa8HnrnZjk9N8vpuTnOuufz9glZVG+1ZUK6q5odrmo2VVSW1AXu0rLEb+eGeGm8h0NTZ4kt+z/aU97Ehxt38Ds1nRjEpPrrIkKJkCeRTnF49gIvjvXw5kx/bvxTpVBwR2UbDzZs5YBrQ0n+giXSac655+memaZ7ZpqTM9N5l0BfbjGobKqopLO8go0VFdSYLWLoR1hzwokE5z1uzrnnOeuep+8qAcSs1V5UKaymvASvJyPLMucXZvn5WA8/n+hlPrY0b6bJ7OThhm38Xv0Wao32wjVynRKhRLgiXzzCLydP89JYDz2+ydxxo0rDPTUbeaBuC/srW9AoS6dXdLGFeIzTc3PZ3uQsvXOzVwwqVp2Ojc4KOisq2FieubU5ysRkWmFVSLLMxEKAfo+bs+55zs5nQshYwH/JEAxkAkhXRRVdlZWZCmCVi0abHWUJB+uxkJdfTJzmFxO9DAbdueM2jYEH6rt4qH4bWxw1ovPxHohQIlyX4aCbF8d7+MV4LxPLJm3ZtQY+WLuJB+q2sMvZUNJvWIsWg0rf/GzujX/Q583beXa5WouVdqeTjjInbWVO2p3lIqwIN20xfFzweOj3uhnwerngcTPg8xJLXVr9AKgwmnIVvU3lFSKALDMXC/LKRB+/nDid1znTKlXc5ergwfqtvN/VjraEO2e3kgglwg2RZZlTvkl+Md7LK5N9eOJLF8WqNlj5YO0m7qvdzFZHregtLBNPpRj0efN6qP0e9yUrfparNptpspfR4nDQbHfQ4iij2e6g1mot6iWUwvXxx6IM+3wM+X2Ze5+XYb+PEb//skMvkPlD2lJWxkZnea5i11leWZJDMFfjjYc5NHWW/5o8w7H5EaRsLUmJgn2VLTxQ18XBmo1YNGKH6FtNhBLhpqUkibfdw/xi/DSHps4SSsVzz1UbbBys2cDB6k52OhvEH9Er8EYjDHi99HvcDHg9XPB66Pd4rhpWtEoVdTYr9VYbtVYb9VYrdRYbdTYbdRYrZQaDCIRFIJ5KMRlcYGJhgfGFABO52wJjAT++i5beLrcYPtrLnHQ4sxW4MicNNrv4XbyC6UiAX02f47+nznHcPZoLIgDby+p4oK6L+2o3l9yE/9UmQolwS8TTKd6cvcD/mzzD6zP9RFKJ3HMOrZEDrg4O1mxkX2ULelXpTZK9Ub5olGG/j2F/tgec7RGP+H2XXQG0nFGjodpswWU2U22xUG22ZD+2UG2x4DKZsep0IrgUUDyVYj4SZjoUZDoYZCYUyjwOZR8Hg8xHwtf8Oi6TmWZHGc0OBy12B83Zqlqd1SbCxzXIssxg0M1r0+c4NHWO0/6pvOc326v5YO0m7q/dTJ3JUaBWlh4RSoRbLpZOcmRuiENTZ3l9uj93kSkAvUrNvooWDlRv4C5XO5V6SwFbuv5IssxUcIGxQIDxhQCTeb3ohUsuSHglWpWKCqOJSpOJCqOJcpOJSqOJCpMJp8GAw2DAaTDi0Buw6fVibsF1iCaTeGNRvNEovmgUbzSCOxJhLhxiPhJhLhzGHQkzFw5fcr2XKzFqNNQtVsOstuwtUyVrtNnFvKMblJTSvOMZ47Xp87wx08/4sq3eFcCOsnp+p6aTe2o2Ui+CSEGIUCKsqJQkccIzyn9PneW16fNMR/M3JNviqOFuVwcfqN7ABmuV6L2/R4sl/8We90woyNSynvhMKHjJjpvXolQocOj1OPQG7AYDNp0Oq06PVafL3pYem7VaTBotRo0Gk1aLSaPBqNGu+X0rJFkmkkwSSSYIJ5NEEpn7UCJBMBFnIZ65BeMxAvHFj2MEYrFcELnSJNIr0SpVuMzmpQqW2bxU1bJYqLVYcOjFUNx7FUhEeWt2gNemz/PruQGCyaVhZo1Sxd6KZg5Wb+RA9QYqxNBMwYlQIqyaxbX9r0/388ZMf95MdoAqvYU7q9q4s6qd/ZUtmDW6ArW0uMVSSdyRCPPhMHORMPPhMPPZ+7lwGF/2j6w3GiWYiF/7C14HjVKJUaNFp1ahU6nRq9XoVCp0ajValRqdWoVWqUKlVKJSKlArlKiUStTK7L1CccU/zrIsI8kyaVkmJUmkJYmUnL2XZFJSmngqTTydIp5OE08t3SfSKaKpFJFk8pacp1apoixbaXIYDJQbjbkKVMWy+0qTCZtOLwLHCpBkmbOBGd6avcBbMwN0eyfy5oeUaY3c7erg7uoO9le2YhJbva8pIpQIBTMXC3J45gJvTPdzZH4wbydEtULJDmc9d1a18f6qdjqsleINvAAS6TT+ZUMSvlg0VzXIVQuWPQ5nKwyLFYdrzX9Za5QKRabKs1jt0WiwLKsEXa5KtDjcVWYwYtJoxM9pAQQSUX4zN8hbswP8enYAdzx/Pk67tZIDrg7udnWwtay26K/ttZ6JUCKsCfF0iuPuEd6cHeCt2QFGQp685yv1FvZXtrC/soV9FS1iBvw6kUyniaYywyCRbEhZXqlYrF7EUilSkrRU6ZAk0rK07NjV324UikwpXqVUZKorivwqy/KKjC7vXo1epc4NNenVahEq1oGklKbHO8mR+UGOzA3R453Mq4YY1Vr2VTTnKq81RlsBWyvcCBFKhDVpPOzjzWz59W33cF4VBWCDtSoXUnY6G8TVNgWhiC2ulDk6P8SRuSGOuUcIL1vhB9BqqeCuqjbudLWz09kgNjNbp0QoKUITkUm8CS+bbZtQKdb/L2Y8neIdzxhH5jK9ouVXNAbQKJR0OWrZXd7AbmcjO50NYj6KIKxjkiwzsDDHcc8oJ9xjnHCP4o7nryyzaw3sq2jJdk5ai6YaMh93MxweZk/ZbYVuSkGIUFKE/u/wsxyefwubxsY+5+3sd+6jwVhfNGVpTzzM0blMj+m388NMRwN5zytR0Gl3sdvZyO7yRnY5G3DoxI6VgrBWpSSJs4FpTrgzIeQdz2je1coBdEo1O5z17K9oYX9VK502V9EsVQ+nIhz3nuCI57ecD/ajUqj42vb/g1lTesPUIpQUof8c/xmvzx8mlFrqWdQZatlfvo99zr2UaYtn/b0sy0xE/Nk3s1FOeEYZW7b3wKJms5MdZfVsd9azs6yeZkt50byhCcJ6409EOeWd4KR3nG7POD2+SaLp/BVQRpWGHc4Gdpc3sMvZyDZHLVqVukAtvvVSUorewGl+4/kt3b5uknJmiFqBgk7rBj7Z+EfUGKoL3MrVJ0JJkUpJKXoCpzniPkK3/9RFP/Ab2eu8nd2OnZjUpgK39NabjS5wwjOWCyoDwflLXmPT6NlWVs8OZx3bHHVscdSKIR9BWAFpWWI46KHHN8FJzzgnveN5V9hdZNXo2eVsYHd5I7udjXTaXUV3BXJJlhgIDXLU8zbHvMcJLus41hpqeJ9zH/vK91KmLStgKwtLhJISkCkNHuc3nt/SH7yQO65SqNhq28Je5x522LejUxXnH2VfPMIp3wTdnnFOeifo8U1cMnFWAbRYytnqqGOLo4atjlo6bFVF96YoCCttNrpAj2+SXt8kPd5JTvunLpmUCtC0WL0sq2Ons4GWIq1eyrLMWGSMo55jvO09hifhzT1n01jZW3Y77yvfR4OxoWiG2N8LEUpKzHx8nqOeYxz1HGMiOpE7rlPq2GHfzl7nHrbYulAri6dMerGklOZ8YJaT3nHe9YzT65tkMuK/5HVapYpN9mo226vZlL21WipEUBGErPlYiD7/FGf805zxz3DaN8lsLHjJ6wwqDZvtNWwvq2OHMxNEynTFV6VdbiY2y1HP2xz1HGM6Np07rlfq2V22k71lt7PJ1lkUixFuJRFKSthEZJKj3rc56nmb+fhSOdWoMrLTsZ3bynbTZd1c1AFlkScepseb6d0t3i6eaAeZoLLBVsUmWyakdNpdtFsrxUUGhaImyzJTkQDnAjOcCUzT55vmTGCa+dil11pSoqDDVskWRy1bs7cWS0VJXCBwJjbLce8JjnmPMxYZzx3XKNRss29jr3MP2+xb0SrFFgZXIkKJgCzLDIWHc+Oc/uTSahajysh2+zZ2l+2ky7q5aId4LibLMqNhL6d9mV5gn3+aM/5pQqlLt11XoqDJ7KTDVkmbtZIOayXt1krqTQ6xc6Sw7gQSUfoX5riw7Na/MJt3zZhFShQ0W8pz1cTF+1LZN0iWZSajk5zwvcs7vnfzgogSJZttm9jrvJ1djh0YVIYCtnT9EKFEyCPJEv3BCxz3nuCE7528gKJVatli62KHfRvb7NuwakrrCr+SLDMR9uUCSp9/mnOBGXyJyGVfr1epaTaX02wpp8VSTqulnBZLBU2msqJaRSCsP7IsMx8LMRicZyjoZijkZijoZjA4f9nqB2T2A2qxVOSFjw22qpIJIIvScpqB0CAnfd286zvJbHwu95wSJZusnexx3sYu+46SXNL7XolQIlyRJEtcCA3wjjfTC3AnlrZ+V6CgzdzKDsd2dti3U613leQkLVmWmY+H6A/M5vUuBxbmiUuXv2qsEgV1JgctlnIaTWU0mstoMjtpNDtxGaxFOdlPKIxQMs5oyMNI2MtoyMNoyMtIyMNQ0H3Zqt+iGqONdkslHbZM1a/DWkWzpbxkd0mNpqOcDvRx0neKU4GevO0WNAo1m22b2eXYyU77dhFE3iMRSoTrsjiD/F1fNyf93YxGxvKer9JVscOxje32bbSb20piHsrVpGWJ8bAv1/scDnpyvdKr/THQKdXUmxw0mp00mBzUmuzUGR3UmxzUGu3oRIVFWEaSZdyxEBMRHxNhP+MRH5NhP2PZEHLxhemWU6KgPhuOM5W8ity9WB6f2Vm1x9/DSf8pzi6cIyUvdTJMKhPb7FvZ4djGFluXGJq5hUQoEW6KJ+6l29/NSf8pziycJS0vXQ3WoDKw2bqJbfYtbLVtwa61F66ha8zysvlIyMNItvc6GvYyEfaRkqWrfn6F3ky9MRNWqg02XAYr1UYbNQYbLoMNi0ZXkhWrYpWU0sxFg0xFA8xEA0xHAkxFA0xFAkyG/UxG/FesyC1y6kzZipyTRnOmMieGES+VklJcCA1wyt/DqUAvU9GpvOerdJW5ynC7pU2smlkhIpQI79ny0mZPoJdgKn9JYKOxga32LWyzbaXF3Cx+ma8gJUlMRfyMZnu5E2E/ExE/E2EfExHfZfd6uJhJrcWVDSuVeguVejMVBkv2ceZWrjeLZc0FJssygWSMuViQ+WiQudiyW/bjmegC87Eg13qjVSkUuAw26ox26kwO6kx26k1lNJnKaDCXYdHoV+Wc1iNvwktvoI8efw+nA2eISUsr7pQoabO0st22lR2OHSU7RL3aRCgRbilJlhgJj+Z6G8Ph4bznjSoDm6yddNm62GLbTLmuvEAtXV9kWcafiObK9FMRP9PRANORhcx9NIA/Eb3ur2fT6HHqzJTpTDh1Jpx6U+6xQ2vEpjVg1xqwaQzYtUb0KrV4Q76KlCSxkIwSSETxZ2+BRARvIoInFsITj+BNhPHEw3hiIbzxMMlrVMUWaZQqqg1Wqg02qo22XIWs3uSgzuTAZbCKkHmdElKC8wv99AZOc3qhj8mLqiFWtZWt9i622rbSZdtUlDter3UilAgrKpAM0Bvo45S/h75AH+F0/koVl76KLbYuuqyb2WDtEGOz70EklWAmmgkp+b3vUK737Y4Fr/uP4XJapQq71ohVo8ei0WPW6LBo9FjUutxjs1qHSa3FoNZiUGmW7pc91ipVaFRqNAplQUNOWpZIpNMkpBTxdIpoOkk0nSCaSmYepxJE00kiqQShVJxgMkYoGc8+jhNKxghmj/sTkcsul70eNo2ByrxKVqayVaGzUG3MBJEynUlMfr5JkiwxEZ2kL3CG3sBp+oP9uUtuQGbCfrOpKVfJbTI1ohTL+AtKhBJh1UiyxHB4JNNLCfQxGBpCYukPpAIFTaYmOi0b2GjdQIelXYSUW0ySZQKJKJ54GG8823OPh/I+Xt7TDySiNxVirkVBpgKgU6nRKtVolSrUShUqhRKVQpG9V6Jc9vhKf5bl7HlJSKRlmbQkkZYXb3ImgEgpEuk0SSlFXEqRXqG3L4tGh11rzFaYDDh0Jpw6I06dGaduqRq1+FhMXL61FkPIuYXznAue59zCecLp/Mm+ZVoHXdmO0GZrp1gts8aIUCIUTCQV4czCOU4v9NEXOMPcsvX+kB9SNlg7aDe3Y1IbC9Ta0iTLMpFUAn8yE1QWEtFMpSAVW6oYJOMEU5lKQmRZhSGWXqw6ZKoQCSl97X+wAJQoMKjzKzp6lQZj9rFZo8Os1l2mQqTHqtFnhrm0BqwaQ0nsWrqWSLLEeGSC88H+K4YQnVJHh6WdLdkh42p9tRiKXMNEKBHWDE/cm3ljyb65XC6k1Bpq2WBpp93SToe5HaeudK+mud6kZYmklCaRThOXUnnVi4SUJimlkbKVjZQsIeXuZVLS1as1SxUVBSqlMlddUWerLYvVGK0qe69U5x6rCzyUJFy/hJRgKDRMf+gC/cELDIQGiabz51IthpCN2Yprk7Gx5LcoWE9EKClCSSmMSqFFqVjf12PJhZSFc5wPXmA2PnvJa8q1Ttot7bSb22gzt1JnrBWrewShSASSAQZCgwyEBukPDjAcHs7bfgBAr9TRZm6j07qxaEJIIh1Aq7IVuhkFIUJJETrr/Q4Dge9RbTpAnekglca96z6gAPgTAS6ELtAfHKA/2M9oZAz5ogWTOqWOFlMzbeZW2syttJpbsJTYdviCsB6lpBTj0QkGQoMMZoPI8guFLrJpbHRkK6UbLO3UGeuKoiMSSowyEf5vJkOHiKXmuL/pVZSK9R2ubsaaDCUPPvgg3d3dzM3N4XA4OHjwIP/4j/9ITU3NdX1+qYeSt6b+F/PRY7mPNUpL0QUUyOyPMhQa5nywn4HQIEPh4UtKuZDZ9KjF3EyzqZkWUzONpgZxlU5BKCBZlpmLzzMcHmY4PMJQaJiRyCgJKX8vnsyQbQ2t5lY6zG20W9qp1FUUzXDb8iASSJzPHVeg4kDd97DrNhawdYWxJkPJV7/6Vfbt20d1dTWTk5P8+Z//OQBHjhy5rs8v9VAiy2k8sW4mQoeYDP+KeHqpt5EJKHdTYzpAlWEfKmXxbKwkyRJT0alcuXcgNMR0bPqS1ylRUmespdnURIupmWZTE7WG2nVf8hWEtUiWZXxJPyPh0bwQcvGEVMhclbzV3JKrdLaYmjEW0eR2WZYJJoeYCr/GZOhXlwSRCsMeas0HqTEdQKdyFLClhbMmQ8nFXnrpJR5++GHi8TgazaW9/Hg8Tjy+tE9AIBCgoaGB8fHxkgwly8lyGm+sh8nw68yEDxNLL11UT6XQ876ab2HXtRewhSsrnAozEhplJJK5jYZHCSQXLnmdSqFiv3Mff9T0iQK0UhCK01BomG8O/CvB1KVXHlYr1NQZa2kyNtJkbqLJ2ECVvqqo9wl5e+YvmY0sda4VqCg37KLadDfVpjtLNogst7CwQH19PX6/H5vt+ufVrFoo8Xq9PPbYY0xOTvLrX//6sq/58pe/zFe+8pXVaI4gCIIgCCtsfHycurq66379ioeSp556im984xtEIhH27t3Lyy+/jNPpvOxrL66USJKE1+tFo9GUXMVkMWWW0jmDOO9SOu9SPGcozfMuxXMGcd5nzpxhw4YNKG9g358bDiXXU804fvw4u3fvBsDtduP1ehkdHeUrX/kKNpuNl19++YYmOJXi3JJSPGcQ511K512K5wyled6leM4gzvtmzvuGZwR+9rOf5eMf//hVX9PU1JR7XF5eTnl5OR0dHXR2dlJfX8/Ro0fZt2/fjf7TgiAIgiAUsRsOJYsh42YsFmWWD9EIgiAIgiDATYSS63Xs2DGOHTvGHXfcgcPhYGhoiC9+8Yu0trbecJVEp9PxpS99CZ1Ot0KtXXtK8ZxBnHcpnXcpnjOU5nmX4jmDOO+bOe8Vm+ja29vLE088walTpwiHw1RXV3Pffffxt3/7t9TW1q7EPykIgiAIwjq2preZFwRBEAShdBTv7jaCIAiCIKwrIpQIgiAIgrAmiFAiCIIgCMKaIEKJIAiCIAhrwroNJfF4nO3bt6NQKOju7i50c1bcgw8+SENDA3q9nurqaj75yU8yNTVV6GatmJGREf7kT/6E5uZmDAYDra2tfOlLXyKRSFz7k9e5v//7v2f//v0YjUbsdnuhm7NivvnNb9Lc3Ixer2fXrl289dZbhW7SinrzzTf5/d//fWpqalAoFLzwwguFbtKKe/rpp7ntttuwWCxUVlby8MMPc/78+Wt/4jr3rW99i61bt2K1WrFarezbt49XXnml0M1aVU8//TQKhYInn3zyhj5v3YaSv/zLv6SmpqbQzVg1Bw4c4Mc//jHnz5/npz/9KYODg3zkIx8pdLNWzLlz55AkiW9/+9v09fXx1a9+lX/913/lC1/4QqGbtuISiQQf/ehHeeyxxwrdlBXzox/9iCeffJK/+Zu/4eTJk9x5553cf//9jI2NFbppKyYcDrNt2za+8Y1vFLopq+bw4cM8/vjjHD16lEOHDpFKpbj33nsJh8OFbtqKqqur4x/+4R84ceIEJ06c4AMf+AAPPfQQfX19hW7aqjh+/Djf+c532Lp1641/srwO/fKXv5Q3btwo9/X1yYB88uTJQjdp1b344ouyQqGQE4lEoZuyav7pn/5Jbm5uLnQzVs0zzzwj22y2QjdjRezZs0d+9NFH845t3LhR/qu/+qsCtWh1AfLzzz9f6Gasurm5ORmQDx8+XOimrDqHwyH/27/9W6GbseKCwaDc3t4uHzp0SL7rrrvkJ5544oY+f91VSmZnZ/mzP/sz/uM//gOj0Vjo5hSE1+vl+9//Pvv370ej0RS6OasmEAhQVlZW6GYI71EikeCdd97h3nvvzTt+7733cuTIkQK1SlgNgUAAoKR+j9PpNM899xzhcLgkrvn2+OOP88ADD3Dw4MGb+vx1FUpkWebTn/40jz76aO4qxKXkqaeewmQy4XQ6GRsb48UXXyx0k1bN4OAgX//613n00UcL3RThPXK73aTTaaqqqvKOV1VVMTMzU6BWCStNlmU+//nPc8cdd9DV1VXo5qy43t5ezGYzOp2ORx99lOeff55NmzYVulkr6rnnnuPdd9/l6aefvumvsSZCyZe//GUUCsVVbydOnODrX/86CwsL/PVf/3Whm3xLXO95L/qLv/gLTp48yauvvopKpeJTn/pU7iKH68WNnjPA1NQU9913Hx/96Ef50z/90wK1/L25mfMudgqFIu9jWZYvOSYUj89+9rP09PTwwx/+sNBNWRUbNmygu7ubo0eP8thjj/HII49w5syZQjdrxYyPj/PEE0/wve99D71ef9NfZ01sM+92u3G73Vd9TVNTEx//+Mf5+c9/nvfGlU6nUalU/OEf/iHf/e53V7qpt9T1nvflvsETExPU19dz5MiRdVUSvNFznpqa4sCBA9x+++08++yzKJVrIkffsJv5Xj/77LM8+eST+P3+FW7d6kokEhiNRn7yk5/woQ99KHf8iSeeoLu7m8OHDxewdatDoVDw/PPP8/DDDxe6Kavic5/7HC+88AJvvvkmzc3NhW5OQRw8eJDW1la+/e1vF7opK+KFF17gQx/6ECqVKncsnU6jUChQKpXE4/G8565kxa4SfCPKy8spLy+/5uu+9rWv8Xd/93e5j6empvjgBz/Ij370I26//faVbOKKuN7zvpzFLBmPx29lk1bcjZzz5OQkBw4cYNeuXTzzzDPrNpDAe/teFxutVsuuXbs4dOhQXig5dOgQDz30UAFbJtxqsizzuc99jueff5433nijZAMJZP4v1tv79Y2455576O3tzTv2mc98ho0bN/LUU09dVyCBNRJKrldDQ0Pex2azGYDW1lbq6uoK0aRVcezYMY4dO8Ydd9yBw+FgaGiIL37xi7S2tq6rKsmNmJqa4u6776ahoYF//ud/Zn5+Pvecy+UqYMtW3tjYGF6vl7GxMdLpdG4fnra2ttzP/Hr3+c9/nk9+8pPs3r2bffv28Z3vfIexsbGinjMUCoUYGBjIfTw8PEx3dzdlZWWXvLcVi8cff5wf/OAHvPjii1gsltycIZvNhsFgKHDrVs4XvvAF7r//furr6wkGgzz33HO88cYb/Nd//Vehm7ZiLBbLJXOFFudA3tAcolu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+ "text/plain": [ + "
              " + ] + }, + "metadata": { + "filenames": { + "image/png": "/Users/mhjensen/Teaching/MachineLearning/doc/LectureNotes/_build/jupyter_execute/week39_82_0.png" + } + }, + "output_type": "display_data" + } + ], "source": [ "pt.axis(\"equal\")\n", "pt.contour(xmesh, ymesh, fmesh)\n", @@ -1210,7 +1256,7 @@ }, { "cell_type": "markdown", - "id": "d6a3c121", + "id": "8290c8f1", "metadata": { "editable": true }, @@ -1221,7 +1267,7 @@ { "cell_type": "code", "execution_count": 3, - "id": "19e1d73c", + "id": "ce55e78a", "metadata": { "collapsed": false, "editable": true @@ -1234,7 +1280,7 @@ }, { "cell_type": "markdown", - "id": "9f7b2dfc", + "id": "0fa3681b", "metadata": { "editable": true }, @@ -1245,12 +1291,20 @@ { "cell_type": "code", "execution_count": 4, - "id": "7d8247e6", + "id": "a5aee074", "metadata": { "collapsed": false, "editable": true }, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[ 0.69230769 -0.38461539]\n" + ] + } + ], "source": [ "def f1d(alpha):\n", " return f(x + alpha*s)\n", @@ -1263,7 +1317,7 @@ }, { "cell_type": "markdown", - "id": "c44006da", + "id": "8f8ed4d2", "metadata": { "editable": true }, @@ -1274,12 +1328,37 @@ { "cell_type": "code", "execution_count": 5, - "id": "bb8a0fd8", + "id": "30855606", "metadata": { "collapsed": false, "editable": true }, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "[]" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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              " + ] + }, + "metadata": { + "filenames": { + "image/png": "/Users/mhjensen/Teaching/MachineLearning/doc/LectureNotes/_build/jupyter_execute/week39_88_1.png" + } + }, + "output_type": "display_data" + } + ], "source": [ "pt.axis(\"equal\")\n", "pt.contour(xmesh, ymesh, fmesh, 50)\n", @@ -1289,7 +1368,7 @@ }, { "cell_type": "markdown", - "id": "3d3c98f0", + "id": "fcd5a0c8", "metadata": { "editable": true }, @@ -1299,7 +1378,7 @@ }, { "cell_type": "markdown", - "id": "29e5e792", + "id": "acdc3658", "metadata": { "editable": true }, @@ -1313,7 +1392,7 @@ }, { "cell_type": "markdown", - "id": "2b0e0db3", + "id": "7e07632f", "metadata": { "editable": true }, @@ -1325,7 +1404,7 @@ }, { "cell_type": "markdown", - "id": "401dd643", + "id": "356d5fe1", "metadata": { "editable": true }, @@ -1336,7 +1415,7 @@ }, { "cell_type": "markdown", - "id": "bc29d596", + "id": "2033af61", "metadata": { "editable": true }, @@ -1348,7 +1427,7 @@ }, { "cell_type": "markdown", - "id": "c1b7adb4", + "id": "0a85c783", "metadata": { "editable": true }, @@ -1359,7 +1438,7 @@ }, { "cell_type": "markdown", - "id": "18924232", + "id": "6c19d77d", "metadata": { "editable": true }, @@ -1370,7 +1449,7 @@ }, { "cell_type": "markdown", - "id": "1764ac31", + "id": "f42364fb", "metadata": { "editable": true }, @@ -1382,7 +1461,7 @@ }, { "cell_type": "markdown", - "id": "379d5862", + "id": "c841e7d3", "metadata": { "editable": true }, @@ -1392,7 +1471,7 @@ }, { "cell_type": "markdown", - "id": "9587d8bf", + "id": "297492ba", "metadata": { "editable": true }, @@ -1404,7 +1483,7 @@ }, { "cell_type": "markdown", - "id": "4c3d0bfb", + "id": "4963a2d8", "metadata": { "editable": true }, @@ -1416,7 +1495,7 @@ }, { "cell_type": "markdown", - "id": "4079ca1a", + "id": "4a39d88b", "metadata": { "editable": true }, @@ -1428,7 +1507,7 @@ }, { "cell_type": "markdown", - "id": "e5b487a5", + "id": "83a86148", "metadata": { "editable": true }, @@ -1440,7 +1519,7 @@ }, { "cell_type": "markdown", - "id": "b623d7f7", + "id": "c691f06b", "metadata": { "editable": true }, @@ -1451,7 +1530,7 @@ }, { "cell_type": "markdown", - "id": "8520c560", + "id": "d4df90e8", "metadata": { "editable": true }, @@ -1463,7 +1542,7 @@ }, { "cell_type": "markdown", - "id": "52575016", + "id": "bf3217ad", "metadata": { "editable": true }, @@ -1473,7 +1552,7 @@ }, { "cell_type": "markdown", - "id": "1b8a85bd", + "id": "b43b4b20", "metadata": { "editable": true }, @@ -1485,7 +1564,7 @@ }, { "cell_type": "markdown", - "id": "e53b0f45", + "id": "d82bb554", "metadata": { "editable": true }, @@ -1495,7 +1574,7 @@ }, { "cell_type": "markdown", - "id": "2238e15f", + "id": "dde5ed03", "metadata": { "editable": true }, @@ -1507,7 +1586,7 @@ }, { "cell_type": "markdown", - "id": "f00e8864", + "id": "65ecffe4", "metadata": { "editable": true }, @@ -1527,7 +1606,7 @@ }, { "cell_type": "markdown", - "id": "7a17895d", + "id": "65a1b4c0", "metadata": { "editable": true }, @@ -1539,7 +1618,7 @@ }, { "cell_type": "markdown", - "id": "d4bafcb3", + "id": "41dec06b", "metadata": { "editable": true }, @@ -1549,7 +1628,7 @@ }, { "cell_type": "markdown", - "id": "78a7d2c3", + "id": "7d1da8a1", "metadata": { "editable": true }, @@ -1561,7 +1640,7 @@ }, { "cell_type": "markdown", - "id": "e3192cbf", + "id": "c4850b28", "metadata": { "editable": true }, @@ -1571,7 +1650,7 @@ }, { "cell_type": "markdown", - "id": "0d99ed55", + "id": "ff91db7e", "metadata": { "editable": true }, @@ -1582,7 +1661,7 @@ }, { "cell_type": "markdown", - "id": "b9653ede", + "id": "a8ab3c6e", "metadata": { "editable": true }, @@ -1594,7 +1673,7 @@ }, { "cell_type": "markdown", - "id": "78441105", + "id": "e30ea38e", "metadata": { "editable": true }, @@ -1606,7 +1685,7 @@ }, { "cell_type": "markdown", - "id": "317355d2", + "id": "ede52cdd", "metadata": { "editable": true }, @@ -1618,7 +1697,7 @@ }, { "cell_type": "markdown", - "id": "9bb15157", + "id": "3cdf6ffd", "metadata": { "editable": true }, @@ -1631,7 +1710,7 @@ }, { "cell_type": "markdown", - "id": "ac584971", + "id": "d66ac756", "metadata": { "editable": true }, @@ -1642,7 +1721,7 @@ }, { "cell_type": "markdown", - "id": "911f1dfa", + "id": "59b7a9f5", "metadata": { "editable": true }, @@ -1654,7 +1733,7 @@ }, { "cell_type": "markdown", - "id": "d1472568", + "id": "769980be", "metadata": { "editable": true }, @@ -1670,7 +1749,7 @@ }, { "cell_type": "markdown", - "id": "c79708e8", + "id": "3ae7691f", "metadata": { "editable": true }, @@ -1682,7 +1761,7 @@ }, { "cell_type": "markdown", - "id": "535d3e73", + "id": "9cf530dc", "metadata": { "editable": true }, @@ -1693,7 +1772,7 @@ }, { "cell_type": "markdown", - "id": "ad718f62", + "id": "6398a5d7", "metadata": { "editable": true }, @@ -1705,7 +1784,7 @@ }, { "cell_type": "markdown", - "id": "d0a90fa5", + "id": "b64c8282", "metadata": { "editable": true }, @@ -1715,7 +1794,7 @@ }, { "cell_type": "markdown", - "id": "860e9217", + "id": "4b9bcf21", "metadata": { "editable": true }, @@ -1727,7 +1806,7 @@ }, { "cell_type": "markdown", - "id": "6d8a72c8", + "id": "6d15e4b9", "metadata": { "editable": true }, @@ -1737,7 +1816,7 @@ }, { "cell_type": "markdown", - "id": "746e6fc0", + "id": "592ac2b7", "metadata": { "editable": true }, @@ -1749,7 +1828,7 @@ }, { "cell_type": "markdown", - "id": "f76c0e69", + "id": "c54989ac", "metadata": { "editable": true }, @@ -1759,7 +1838,7 @@ }, { "cell_type": "markdown", - "id": "9aee35ca", + "id": "87adadc2", "metadata": { "editable": true }, @@ -1771,7 +1850,7 @@ }, { "cell_type": "markdown", - "id": "ce9ce258", + "id": "00250e67", "metadata": { "editable": true }, @@ -1795,12 +1874,24 @@ { "cell_type": "code", "execution_count": 6, - "id": "f902a0f2", + "id": "47a98c7c", "metadata": { "collapsed": false, "editable": true }, - "outputs": [], + "outputs": [ + { + "ename": "NameError", + "evalue": "name 'm' is not defined", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)", + "Input \u001b[0;32mIn [6]\u001b[0m, in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0m x \u001b[38;5;241m=\u001b[39m \u001b[38;5;241m2\u001b[39m\u001b[38;5;241m*\u001b[39mnp\u001b[38;5;241m.\u001b[39mrandom\u001b[38;5;241m.\u001b[39mrand(\u001b[43mm\u001b[49m,\u001b[38;5;241m1\u001b[39m)\n\u001b[1;32m 2\u001b[0m y \u001b[38;5;241m=\u001b[39m \u001b[38;5;241m4\u001b[39m\u001b[38;5;241m+\u001b[39m\u001b[38;5;241m3\u001b[39m\u001b[38;5;241m*\u001b[39mx\u001b[38;5;241m+\u001b[39mnp\u001b[38;5;241m.\u001b[39mrandom\u001b[38;5;241m.\u001b[39mrandn(m,\u001b[38;5;241m1\u001b[39m)\n", + "\u001b[0;31mNameError\u001b[0m: name 'm' is not defined" + ] + } + ], "source": [ "x = 2*np.random.rand(m,1)\n", "y = 4+3*x+np.random.randn(m,1)" @@ -1808,7 +1899,7 @@ }, { "cell_type": "markdown", - "id": "36d883b2", + "id": "e3007f3b", "metadata": { "editable": true }, @@ -1819,7 +1910,7 @@ }, { "cell_type": "markdown", - "id": "cde21ef1", + "id": "c5b7179d", "metadata": { "editable": true }, @@ -1831,7 +1922,7 @@ }, { "cell_type": "markdown", - "id": "f2a021d3", + "id": "8a4d63b4", "metadata": { "editable": true }, @@ -1841,7 +1932,7 @@ }, { "cell_type": "markdown", - "id": "ea0a91e4", + "id": "206c9402", "metadata": { "editable": true }, @@ -1853,7 +1944,7 @@ }, { "cell_type": "markdown", - "id": "854f3ebb", + "id": "939b3b78", "metadata": { "editable": true }, @@ -1867,7 +1958,7 @@ }, { "cell_type": "markdown", - "id": "dd282d2d", + "id": "b867af05", "metadata": { "editable": true }, @@ -1883,7 +1974,7 @@ }, { "cell_type": "markdown", - "id": "fd562029", + "id": "76738c60", "metadata": { "editable": true }, @@ -1893,7 +1984,7 @@ }, { "cell_type": "markdown", - "id": "25369bc3", + "id": "c01273f5", "metadata": { "editable": true }, @@ -1905,7 +1996,7 @@ }, { "cell_type": "markdown", - "id": "a222f3ea", + "id": "5d680787", "metadata": { "editable": true }, @@ -1915,7 +2006,7 @@ }, { "cell_type": "markdown", - "id": "1d5fb6f1", + "id": "2ef9ff3b", "metadata": { "editable": true }, @@ -1927,7 +2018,7 @@ }, { "cell_type": "markdown", - "id": "eab2df73", + "id": "e5a81fba", "metadata": { "editable": true }, @@ -1941,7 +2032,7 @@ }, { "cell_type": "markdown", - "id": "daee1165", + "id": "b7298ace", "metadata": { "editable": true }, @@ -1951,7 +2042,7 @@ }, { "cell_type": "markdown", - "id": "f2c6f5cc", + "id": "64cfb75f", "metadata": { "editable": true }, @@ -1962,7 +2053,7 @@ }, { "cell_type": "markdown", - "id": "ecce0d08", + "id": "99503e16", "metadata": { "editable": true }, @@ -1977,7 +2068,7 @@ }, { "cell_type": "markdown", - "id": "c4308e5f", + "id": "4a567780", "metadata": { "editable": true }, @@ -1987,7 +2078,7 @@ }, { "cell_type": "markdown", - "id": "4ee64b17", + "id": "22c576da", "metadata": { "editable": true }, @@ -1999,7 +2090,7 @@ }, { "cell_type": "markdown", - "id": "57e8db33", + "id": "44a99f62", "metadata": { "editable": true }, @@ -2011,7 +2102,7 @@ }, { "cell_type": "markdown", - "id": "c42e4032", + "id": "7021c749", "metadata": { "editable": true }, @@ -2026,7 +2117,7 @@ }, { "cell_type": "markdown", - "id": "4c430cd3", + "id": "6044e7a8", "metadata": { "editable": true }, @@ -2039,7 +2130,7 @@ { "cell_type": "code", "execution_count": 7, - "id": "9ac6096f", + "id": "72003ff9", "metadata": { "collapsed": false, "editable": true @@ -2096,7 +2187,7 @@ }, { "cell_type": "markdown", - "id": "df783e1d", + "id": "01fdfcaf", "metadata": { "editable": true }, @@ -2107,7 +2198,7 @@ { "cell_type": "code", "execution_count": 8, - "id": "98f08f24", + "id": "d16ddbdc", "metadata": { "collapsed": false, "editable": true @@ -2134,7 +2225,7 @@ }, { "cell_type": "markdown", - "id": "50a5ab0d", + "id": "08aaf479", "metadata": { "editable": true }, @@ -2146,7 +2237,7 @@ }, { "cell_type": "markdown", - "id": "b35293d4", + "id": "0aa5045f", "metadata": { "editable": true }, @@ -2158,7 +2249,7 @@ }, { "cell_type": "markdown", - "id": "fed491ee", + "id": "6474d14b", "metadata": { "editable": true }, @@ -2168,7 +2259,7 @@ }, { "cell_type": "markdown", - "id": "a0b4c94e", + "id": "9335b39d", "metadata": { "editable": true }, @@ -2182,7 +2273,7 @@ }, { "cell_type": "markdown", - "id": "e4f02dce", + "id": "0680a59f", "metadata": { "editable": true }, @@ -2192,7 +2283,7 @@ }, { "cell_type": "markdown", - "id": "b9f297ff", + "id": "de5afdeb", "metadata": { "editable": true }, @@ -2204,7 +2295,7 @@ }, { "cell_type": "markdown", - "id": "4e1caf44", + "id": "0042d7e6", "metadata": { "editable": true }, @@ -2215,7 +2306,7 @@ }, { "cell_type": "markdown", - "id": "a046daea", + "id": "02cf311f", "metadata": { "editable": true }, @@ -2230,7 +2321,7 @@ }, { "cell_type": "markdown", - "id": "02f05574", + "id": "3dbc50e6", "metadata": { "editable": true }, @@ -2244,7 +2335,7 @@ }, { "cell_type": "markdown", - "id": "45484749", + "id": "437e17bc", "metadata": { "editable": true }, @@ -2255,7 +2346,7 @@ { "cell_type": "code", "execution_count": 9, - "id": "9973cd20", + "id": "f11ee927", "metadata": { "collapsed": false, "editable": true @@ -2316,7 +2407,7 @@ }, { "cell_type": "markdown", - "id": "1836d4ef", + "id": "c06cf31f", "metadata": { "editable": true }, @@ -2338,7 +2429,7 @@ }, { "cell_type": "markdown", - "id": "88975d3d", + "id": "870586cd", "metadata": { "editable": true }, @@ -2351,7 +2442,7 @@ { "cell_type": "code", "execution_count": 10, - "id": "56f415e0", + "id": "24517bb5", "metadata": { "collapsed": false, "editable": true @@ -2417,7 +2508,7 @@ }, { "cell_type": "markdown", - "id": "d3343584", + "id": "57647429", "metadata": { "editable": true }, @@ -2428,7 +2519,7 @@ { "cell_type": "code", "execution_count": 11, - "id": "ff1e3778", + "id": "b8365c93", "metadata": { "collapsed": false, "editable": true @@ -2502,7 +2593,7 @@ }, { "cell_type": "markdown", - "id": "c1d70995", + "id": "3c5b105d", "metadata": { "editable": true }, @@ -2514,7 +2605,7 @@ }, { "cell_type": "markdown", - "id": "930a5be8", + "id": "e78e4fcf", "metadata": { "editable": true }, @@ -2535,7 +2626,7 @@ }, { "cell_type": "markdown", - "id": "0a7fb7ef", + "id": "9e856c0b", "metadata": { "editable": true }, @@ -2567,7 +2658,7 @@ }, { "cell_type": "markdown", - "id": "dbff87b0", + "id": "8400c2e5", "metadata": { "editable": true }, @@ -2584,7 +2675,7 @@ }, { "cell_type": "markdown", - "id": "cd292df5", + "id": "d0ceff52", "metadata": { "editable": true }, @@ -2597,7 +2688,7 @@ }, { "cell_type": "markdown", - "id": "1b2ffa4e", + "id": "562ca1d7", "metadata": { "editable": true }, @@ -2610,7 +2701,7 @@ }, { "cell_type": "markdown", - "id": "d0abe4b0", + "id": "ffea7df9", "metadata": { "editable": true }, @@ -2623,7 +2714,7 @@ }, { "cell_type": "markdown", - "id": "65c15c60", + "id": "20f1bd07", "metadata": { "editable": true }, @@ -2637,7 +2728,7 @@ }, { "cell_type": "markdown", - "id": "460354c0", + "id": "4589bb1b", "metadata": { "editable": true }, @@ -2659,7 +2750,7 @@ }, { "cell_type": "markdown", - "id": "c2a5dfcd", + "id": "0df2146b", "metadata": { "editable": true }, @@ -2674,7 +2765,7 @@ }, { "cell_type": "markdown", - "id": "eeeb0fe8", + "id": "890e6746", "metadata": { "editable": true }, @@ -2686,7 +2777,7 @@ }, { "cell_type": "markdown", - "id": "2b49c741", + "id": "b6e42059", "metadata": { "editable": true }, @@ -2699,7 +2790,7 @@ }, { "cell_type": "markdown", - "id": "8ba7b9be", + "id": "9dd3abbf", "metadata": { "editable": true }, @@ -2713,7 +2804,7 @@ }, { "cell_type": "markdown", - "id": "50da33c0", + "id": "97279f92", "metadata": { "editable": true }, @@ -2724,7 +2815,7 @@ { "cell_type": "code", "execution_count": 12, - "id": "31bd6a24", + "id": "cb0c6322", "metadata": { "collapsed": false, "editable": true @@ -2749,7 +2840,7 @@ }, { "cell_type": "markdown", - "id": "0deb8111", + "id": "c0868aae", "metadata": { "editable": true }, @@ -2765,7 +2856,7 @@ }, { "cell_type": "markdown", - "id": "16d54f02", + "id": "1e17bb0f", "metadata": { "editable": true }, @@ -2786,7 +2877,7 @@ }, { "cell_type": "markdown", - "id": "b300d06b", + "id": "f050ca70", "metadata": { "editable": true }, @@ -2806,7 +2897,7 @@ }, { "cell_type": "markdown", - "id": "6bc7778d", + "id": "6a900f78", "metadata": { "editable": true }, @@ -2825,7 +2916,7 @@ { "cell_type": "code", "execution_count": 13, - "id": "a60fe5bd", + "id": "1324db42", "metadata": { "collapsed": false, "editable": true @@ -2860,7 +2951,7 @@ }, { "cell_type": "markdown", - "id": "2192721f", + "id": "0c365408", "metadata": { "editable": true }, @@ -2873,7 +2964,7 @@ { "cell_type": "code", "execution_count": 14, - "id": "e404f2c5", + "id": "79417e81", "metadata": { "collapsed": false, "editable": true @@ -2950,7 +3041,7 @@ }, { "cell_type": "markdown", - "id": "fffdbb91", + "id": "dd16fd67", "metadata": { "editable": true }, @@ -2965,7 +3056,7 @@ }, { "cell_type": "markdown", - "id": "8cce7a0e", + "id": "2bbf7fbd", "metadata": { "editable": true }, @@ -2980,7 +3071,7 @@ }, { "cell_type": "markdown", - "id": "3154c365", + "id": "d4aa4448", "metadata": { "editable": true }, @@ -2992,7 +3083,7 @@ }, { "cell_type": "markdown", - "id": "a2a9ceca", + "id": "fdcd258f", "metadata": { "editable": true }, @@ -3010,7 +3101,7 @@ }, { "cell_type": "markdown", - "id": "3374c700", + "id": "52ec5bfb", "metadata": { "editable": true }, @@ -3029,7 +3120,7 @@ }, { "cell_type": "markdown", - "id": "893d86fe", + "id": "38004062", "metadata": { "editable": true }, @@ -3041,7 +3132,7 @@ }, { "cell_type": "markdown", - "id": "ca2449e8", + "id": "9d07c567", "metadata": { "editable": true }, @@ -3051,7 +3142,7 @@ }, { "cell_type": "markdown", - "id": "3cbd4adb", + "id": "4dbba8bc", "metadata": { "editable": true }, @@ -3067,7 +3158,7 @@ }, { "cell_type": "markdown", - "id": "e3f07cbc", + "id": "dab76529", "metadata": { "editable": true }, @@ -3079,7 +3170,7 @@ }, { "cell_type": "markdown", - "id": "99f2ac0f", + "id": "6e049d15", "metadata": { "editable": true }, @@ -3089,7 +3180,7 @@ }, { "cell_type": "markdown", - "id": "83336244", + "id": "5079f465", "metadata": { "editable": true }, @@ -3101,7 +3192,7 @@ }, { "cell_type": "markdown", - "id": "efd3e708", + "id": "51c2ed45", "metadata": { "editable": true }, @@ -3111,7 +3202,7 @@ }, { "cell_type": "markdown", - "id": "6c24d65c", + "id": "7e8f7b16", "metadata": { "editable": true }, @@ -3123,7 +3214,7 @@ }, { "cell_type": "markdown", - "id": "853d885b", + "id": "ae0505aa", "metadata": { "editable": true }, @@ -3139,7 +3230,7 @@ }, { "cell_type": "markdown", - "id": "5ab54645", + "id": "9e7f520b", "metadata": { "editable": true }, @@ -3151,7 +3242,7 @@ }, { "cell_type": "markdown", - "id": "90f1503b", + "id": "5c0aa1f6", "metadata": { "editable": true }, @@ -3184,7 +3275,7 @@ }, { "cell_type": "markdown", - "id": "d496d988", + "id": "991c2c15", "metadata": { "editable": true }, @@ -3196,7 +3287,7 @@ }, { "cell_type": "markdown", - "id": "258ca1e6", + "id": "c643afb5", "metadata": { "editable": true }, @@ -3214,7 +3305,7 @@ }, { "cell_type": "markdown", - "id": "84278058", + "id": "ac4a060d", "metadata": { "editable": true }, @@ -3224,7 +3315,7 @@ }, { "cell_type": "markdown", - "id": "feaee2f4", + "id": "37584d4d", "metadata": { "editable": true }, @@ -3255,7 +3346,7 @@ }, { "cell_type": "markdown", - "id": "218edcf1", + "id": "0e9c907f", "metadata": { "editable": true }, @@ -3270,7 +3361,7 @@ }, { "cell_type": "markdown", - "id": "18dbc91c", + "id": "cb4567f1", "metadata": { "editable": true }, @@ -3288,7 +3379,7 @@ }, { "cell_type": "markdown", - "id": "0bfcf74a", + "id": "71805d3d", "metadata": { "editable": true }, @@ -3300,7 +3391,7 @@ }, { "cell_type": "markdown", - "id": "5fedd6f0", + "id": "09794996", "metadata": { "editable": true }, @@ -3312,7 +3403,7 @@ }, { "cell_type": "markdown", - "id": "b210b2a4", + "id": "aeb48f66", "metadata": { "editable": true }, @@ -3330,7 +3421,7 @@ }, { "cell_type": "markdown", - "id": "34ffacbb", + "id": "68e08134", "metadata": { "editable": true }, @@ -3359,7 +3450,7 @@ }, { "cell_type": "markdown", - "id": "cd03375d", + "id": "69308397", "metadata": { "editable": true }, @@ -3377,7 +3468,7 @@ }, { "cell_type": "markdown", - "id": "0db5d6e0", + "id": "d23ab794", "metadata": { "editable": true }, @@ -3389,7 +3480,7 @@ }, { "cell_type": "markdown", - "id": "84c709d9", + "id": "c4cef70b", "metadata": { "editable": true }, @@ -3401,7 +3492,7 @@ }, { "cell_type": "markdown", - "id": "e4e47496", + "id": "6aebd1b5", "metadata": { "editable": true }, @@ -3413,7 +3504,7 @@ }, { "cell_type": "markdown", - "id": "164f27df", + "id": "c43fe267", "metadata": { "editable": true }, @@ -3425,7 +3516,7 @@ }, { "cell_type": "markdown", - "id": "591f4833", + "id": "9ae56692", "metadata": { "editable": true }, @@ -3437,7 +3528,7 @@ }, { "cell_type": "markdown", - "id": "e2127e8a", + "id": "784ba00e", "metadata": { "editable": true }, @@ -3454,7 +3545,7 @@ }, { "cell_type": "markdown", - "id": "5cef8b84", + "id": "a187dfb2", "metadata": { "editable": true }, @@ -3473,7 +3564,7 @@ }, { "cell_type": "markdown", - "id": "505c8905", + "id": "832cc99c", "metadata": { "editable": true }, @@ -3485,7 +3576,7 @@ }, { "cell_type": "markdown", - "id": "ca96ddec", + "id": "b1a24342", "metadata": { "editable": true }, @@ -3499,7 +3590,7 @@ }, { "cell_type": "markdown", - "id": "fa011176", + "id": "8a97509f", "metadata": { "editable": true }, @@ -3519,7 +3610,7 @@ }, { "cell_type": "markdown", - "id": "b91c4543", + "id": "631a2aa8", "metadata": { "editable": true }, @@ -3557,7 +3648,7 @@ }, { "cell_type": "markdown", - "id": "f13065e5", + "id": "472b23f2", "metadata": { "editable": true }, @@ -3569,7 +3660,7 @@ }, { "cell_type": "markdown", - "id": "22937f5e", + "id": "1c91c90f", "metadata": { "editable": true }, @@ -3579,7 +3670,7 @@ }, { "cell_type": "markdown", - "id": "e1459fe1", + "id": "a85c6aab", "metadata": { "editable": true }, @@ -3591,7 +3682,7 @@ }, { "cell_type": "markdown", - "id": "441a109a", + "id": "89a0bdbb", "metadata": { "editable": true }, @@ -3602,7 +3693,7 @@ { "cell_type": "code", "execution_count": 15, - "id": "9043abae", + "id": "6fe48a50", "metadata": { "collapsed": false, "editable": true @@ -3647,7 +3738,7 @@ }, { "cell_type": "markdown", - "id": "787d5d78", + "id": "cab7d753", "metadata": { "editable": true }, @@ -3664,7 +3755,7 @@ { "cell_type": "code", "execution_count": 16, - "id": "6a677479", + "id": "ca4d6b32", "metadata": { "collapsed": false, "editable": true @@ -3692,7 +3783,7 @@ }, { "cell_type": "markdown", - "id": "f94a1d32", + "id": "4a748513", "metadata": { "editable": true }, @@ -3707,7 +3798,7 @@ { "cell_type": "code", "execution_count": 17, - "id": "c0eb89fd", + "id": "f235cd43", "metadata": { "collapsed": false, "editable": true @@ -3751,7 +3842,7 @@ }, { "cell_type": "markdown", - "id": "05d7497d", + "id": "5d8df033", "metadata": { "editable": true }, @@ -3761,7 +3852,7 @@ }, { "cell_type": "markdown", - "id": "24e3ca02", + "id": "2f7de144", "metadata": { "editable": true }, @@ -3772,7 +3863,7 @@ { "cell_type": "code", "execution_count": 18, - "id": "a616e696", + "id": "a2ed8fd6", "metadata": { "collapsed": false, "editable": true @@ -3800,7 +3891,7 @@ }, { "cell_type": "markdown", - "id": "f695da56", + "id": "1f633736", "metadata": { "editable": true }, @@ -3815,7 +3906,7 @@ }, { "cell_type": "markdown", - "id": "5ac073ee", + "id": "a73225d4", "metadata": { "editable": true }, @@ -3826,7 +3917,7 @@ { "cell_type": "code", "execution_count": 19, - "id": "efc8906e", + "id": "39059d20", "metadata": { "collapsed": false, "editable": true @@ -3854,7 +3945,7 @@ }, { "cell_type": "markdown", - "id": "7c587e8f", + "id": "d147b69e", "metadata": { "editable": true }, @@ -3865,7 +3956,7 @@ { "cell_type": "code", "execution_count": 20, - "id": "6428be1f", + "id": "90a2d143", "metadata": { "collapsed": false, "editable": true @@ -3890,7 +3981,7 @@ }, { "cell_type": "markdown", - "id": "8e1de777", + "id": "6655faec", "metadata": { "editable": true }, @@ -3901,7 +3992,7 @@ { "cell_type": "code", "execution_count": 21, - "id": "770ff6aa", + "id": "639ea2a9", "metadata": { "collapsed": false, "editable": true @@ -3937,7 +4028,7 @@ { "cell_type": "code", "execution_count": 22, - "id": "b924cc5d", + "id": "a53c5633", "metadata": { "collapsed": false, "editable": true @@ -3957,7 +4048,7 @@ }, { "cell_type": "markdown", - "id": "f0b0e1e9", + "id": "be5e41d4", "metadata": { "editable": true }, @@ -3968,7 +4059,7 @@ { "cell_type": "code", "execution_count": 23, - "id": "1585ab28", + "id": "786e19d0", "metadata": { "collapsed": false, "editable": true @@ -4006,7 +4097,7 @@ }, { "cell_type": "markdown", - "id": "2718df1a", + "id": "114e7e25", "metadata": { "editable": true }, @@ -4016,7 +4107,7 @@ }, { "cell_type": "markdown", - "id": "d8fa5235", + "id": "24c8ffa6", "metadata": { "editable": true }, @@ -4030,7 +4121,7 @@ { "cell_type": "code", "execution_count": 24, - "id": "196a52d6", + "id": "59f521ac", "metadata": { "collapsed": false, "editable": true @@ -4052,7 +4143,7 @@ }, { "cell_type": "markdown", - "id": "9127a2c5", + "id": "686c34bb", "metadata": { "editable": true }, @@ -4062,7 +4153,7 @@ }, { "cell_type": "markdown", - "id": "2b12ed61", + "id": "9b4cc4f3", "metadata": { "editable": true }, @@ -4073,7 +4164,7 @@ { "cell_type": "code", "execution_count": 25, - "id": "8ced55c8", + "id": "dea954af", "metadata": { "collapsed": false, "editable": true @@ -4095,7 +4186,7 @@ }, { "cell_type": "markdown", - "id": "92ebdc2b", + "id": "9732d039", "metadata": { "editable": true }, @@ -4108,7 +4199,7 @@ { "cell_type": "code", "execution_count": 26, - "id": "276f763e", + "id": "f580c6a1", "metadata": { "collapsed": false, "editable": true @@ -4133,7 +4224,7 @@ }, { "cell_type": "markdown", - "id": "7841ad0b", + "id": "d8714004", "metadata": { "editable": true }, @@ -4145,7 +4236,7 @@ { "cell_type": "code", "execution_count": 27, - "id": "10107989", + "id": "e56cbb47", "metadata": { "collapsed": false, "editable": true @@ -4160,7 +4251,7 @@ }, { "cell_type": "markdown", - "id": "4c1139c0", + "id": "fac1a7da", "metadata": { "editable": true }, @@ -4175,7 +4266,7 @@ { "cell_type": "code", "execution_count": 28, - "id": "3022af88", + "id": "c4c5b9c0", "metadata": { "collapsed": false, "editable": true @@ -4235,7 +4326,7 @@ }, { "cell_type": "markdown", - "id": "04d09021", + "id": "b16d7700", "metadata": { "editable": true }, @@ -4246,7 +4337,7 @@ { "cell_type": "code", "execution_count": 29, - "id": "71bf4b6d", + "id": "7453efe5", "metadata": { "collapsed": false, "editable": true @@ -4310,7 +4401,7 @@ }, { "cell_type": "markdown", - "id": "ad417bba", + "id": "0a417277", "metadata": { "editable": true }, @@ -4321,7 +4412,7 @@ { "cell_type": "code", "execution_count": 30, - "id": "c394bcef", + "id": "1a541fef", "metadata": { "collapsed": false, "editable": true @@ -4370,7 +4461,7 @@ }, { "cell_type": "markdown", - "id": "2e4cf4b5", + "id": "9e937e4f", "metadata": { "editable": true }, @@ -4382,7 +4473,7 @@ { "cell_type": "code", "execution_count": 31, - "id": "47411bcf", + "id": "9afef100", "metadata": { "collapsed": false, "editable": true @@ -4466,7 +4557,7 @@ }, { "cell_type": "markdown", - "id": "f8e30af2", + "id": "2a7e982c", "metadata": { "editable": true }, @@ -4477,7 +4568,7 @@ { "cell_type": "code", "execution_count": 32, - "id": "dd594924", + "id": "91311a17", "metadata": { "collapsed": false, "editable": true @@ -4555,7 +4646,7 @@ }, { "cell_type": "markdown", - "id": "75c4c29d", + "id": "516999d8", "metadata": { "editable": true }, @@ -4566,7 +4657,7 @@ { "cell_type": "code", "execution_count": 33, - "id": "4dd14fc5", + "id": "e8292719", "metadata": { "collapsed": false, "editable": true @@ -4625,7 +4716,7 @@ }, { "cell_type": "markdown", - "id": "be4ce0cd", + "id": "bd67f5cb", "metadata": { "editable": true }, @@ -4635,7 +4726,7 @@ }, { "cell_type": "markdown", - "id": "0b739495", + "id": "eb0d5fd0", "metadata": { "editable": true }, @@ -4646,7 +4737,7 @@ { "cell_type": "code", "execution_count": 34, - "id": "ae87789c", + "id": "d2eb93d1", "metadata": { "collapsed": false, "editable": true @@ -4711,7 +4802,7 @@ }, { "cell_type": "markdown", - "id": "76c8872b", + "id": "669b56c2", "metadata": { "editable": true }, @@ -4722,7 +4813,7 @@ { "cell_type": "code", "execution_count": 35, - "id": "e99dbaa4", + "id": "bb3f553d", "metadata": { "collapsed": false, "editable": true @@ -4792,7 +4883,7 @@ }, { "cell_type": "markdown", - "id": "596df570", + "id": "4e5c58ea", "metadata": { "editable": true }, @@ -4803,7 +4894,7 @@ { "cell_type": "code", "execution_count": 36, - "id": "6693f042", + "id": "1fec659e", "metadata": { "collapsed": false, "editable": true @@ -4847,7 +4938,7 @@ }, { "cell_type": "markdown", - "id": "a40ed853", + "id": "ac14943c", "metadata": { "editable": true }, @@ -4866,7 +4957,7 @@ { "cell_type": "code", "execution_count": 37, - "id": "02f88360", + "id": "57b4e540", "metadata": { "collapsed": false, "editable": true @@ -4896,7 +4987,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.9.18" + "version": "3.9.10" } }, "nbformat": 4, diff --git a/doc/LectureNotes/_build/jupyter_execute/week39.py b/doc/LectureNotes/_build/jupyter_execute/week39.py new file mode 100644 index 000000000..d544b23bf --- /dev/null +++ b/doc/LectureNotes/_build/jupyter_execute/week39.py @@ -0,0 +1,2657 @@ +#!/usr/bin/env python +# coding: utf-8 + +# +# + +# # Week 39: Optimization and Gradient Methods +# **Morten Hjorth-Jensen**, Department of Physics, University of Oslo and Department of Physics and Astronomy and Facility for Rare Isotope Beams, Michigan State University +# +# Date: **Week 39** + +# ## Plan for week 39 +# +# **Material for the active learning sessions on Tuesday and Wednesday.** +# +# * Discussions on how to structure your report for the first project +# +# * Exercise for week 39 on how to write the abstract and the introduction of the report and how to include references. +# +# * Work on project 1, in particular resampling methods like cross-validation and bootstrap. **For more discussions of project 1, chapter 5 of Goodfellow et al is a good read, in particular sections 5.1-5.5 and 5.7-5.11**. +# +# * [Video on how to write scientific reports recorded during one of the lab sessions](https://youtu.be/tVW1ZDmZnwM) +# +# These sections summarize neatly what we have done till now and point to what is coming with respect to deep learning. +# * A general guideline can be found at . +# +# +# +# **Material for the lecture on Thursday September 28.** +# +# * Repetition of Logistic regression equations and classification problems and discussion of Gradient methods. Examples on how to implement Logistic Regression and discussion of stochastic gradient descent +# +# * Stochastic Gradient descent with examples and automatic differentiation +# +# * [Video of lecture](https://youtu.be/bFRVuIJroHs) +# +# * Whiteboard notes TBA at +# +# * Readings and Videos: +# +# * These lecture notes +# +# * For a good discussion on gradient methods, we would like to recommend Goodfellow et al section 4.3-4.5 and sections 8.3-8.6. We will come back to the latter chapter in our discussion of Neural networks as well. +# +# * [Video on gradient descent](https://www.youtube.com/watch?v=sDv4f4s2SB8) +# +# * [Video on stochastic gradient descent](https://www.youtube.com/watch?v=vMh0zPT0tLI) +# +# + +# ## Optimization, the central part of any Machine Learning algortithm +# +# The first few slides here are a repetition from last week. +# +# Almost every problem in machine learning and data science starts with +# a dataset $X$, a model $g(\beta)$, which is a function of the +# parameters $\beta$ and a cost function $C(X, g(\beta))$ that allows +# us to judge how well the model $g(\beta)$ explains the observations +# $X$. The model is fit by finding the values of $\beta$ that minimize +# the cost function. Ideally we would be able to solve for $\beta$ +# analytically, however this is not possible in general and we must use +# some approximative/numerical method to compute the minimum. + +# ## Revisiting our Logistic Regression case +# +# In our discussion on Logistic Regression we studied the +# case of +# two classes, with $y_i$ either +# $0$ or $1$. Furthermore we assumed also that we have only two +# parameters $\beta$ in our fitting, that is we +# defined probabilities + +# $$ +# \begin{align*} +# p(y_i=1|x_i,\boldsymbol{\beta}) &= \frac{\exp{(\beta_0+\beta_1x_i)}}{1+\exp{(\beta_0+\beta_1x_i)}},\nonumber\\ +# p(y_i=0|x_i,\boldsymbol{\beta}) &= 1 - p(y_i=1|x_i,\boldsymbol{\beta}), +# \end{align*} +# $$ + +# where $\boldsymbol{\beta}$ are the weights we wish to extract from data, in our case $\beta_0$ and $\beta_1$. + +# ## The equations to solve +# +# Our compact equations used a definition of a vector $\boldsymbol{y}$ with $n$ +# elements $y_i$, an $n\times p$ matrix $\boldsymbol{X}$ which contains the +# $x_i$ values and a vector $\boldsymbol{p}$ of fitted probabilities +# $p(y_i\vert x_i,\boldsymbol{\beta})$. We rewrote in a more compact form +# the first derivative of the cost function as + +# $$ +# \frac{\partial \mathcal{C}(\boldsymbol{\beta})}{\partial \boldsymbol{\beta}} = -\boldsymbol{X}^T\left(\boldsymbol{y}-\boldsymbol{p}\right). +# $$ + +# If we in addition define a diagonal matrix $\boldsymbol{W}$ with elements +# $p(y_i\vert x_i,\boldsymbol{\beta})(1-p(y_i\vert x_i,\boldsymbol{\beta})$, we can obtain a compact expression of the second derivative as + +# $$ +# \frac{\partial^2 \mathcal{C}(\boldsymbol{\beta})}{\partial \boldsymbol{\beta}\partial \boldsymbol{\beta}^T} = \boldsymbol{X}^T\boldsymbol{W}\boldsymbol{X}. +# $$ + +# This defines what is called the Hessian matrix. + +# ## Solving using Newton-Raphson's method +# +# If we can set up these equations, Newton-Raphson's iterative method is normally the method of choice. It requires however that we can compute in an efficient way the matrices that define the first and second derivatives. +# +# Our iterative scheme is then given by + +# $$ +# \boldsymbol{\beta}^{\mathrm{new}} = \boldsymbol{\beta}^{\mathrm{old}}-\left(\frac{\partial^2 \mathcal{C}(\boldsymbol{\beta})}{\partial \boldsymbol{\beta}\partial \boldsymbol{\beta}^T}\right)^{-1}_{\boldsymbol{\beta}^{\mathrm{old}}}\times \left(\frac{\partial \mathcal{C}(\boldsymbol{\beta})}{\partial \boldsymbol{\beta}}\right)_{\boldsymbol{\beta}^{\mathrm{old}}}, +# $$ + +# or in matrix form as + +# $$ +# \boldsymbol{\beta}^{\mathrm{new}} = \boldsymbol{\beta}^{\mathrm{old}}-\left(\boldsymbol{X}^T\boldsymbol{W}\boldsymbol{X} \right)^{-1}\times \left(-\boldsymbol{X}^T(\boldsymbol{y}-\boldsymbol{p}) \right)_{\boldsymbol{\beta}^{\mathrm{old}}}. +# $$ + +# The right-hand side is computed with the old values of $\beta$. +# +# If we can compute these matrices, in particular the Hessian, the above is often the easiest method to implement. + +# ## Brief reminder on Newton-Raphson's method +# +# Let us quickly remind ourselves how we derive the above method. +# +# Perhaps the most celebrated of all one-dimensional root-finding +# routines is Newton's method, also called the Newton-Raphson +# method. This method requires the evaluation of both the +# function $f$ and its derivative $f'$ at arbitrary points. +# If you can only calculate the derivative +# numerically and/or your function is not of the smooth type, we +# normally discourage the use of this method. + +# ## The equations +# +# The Newton-Raphson formula consists geometrically of extending the +# tangent line at a current point until it crosses zero, then setting +# the next guess to the abscissa of that zero-crossing. The mathematics +# behind this method is rather simple. Employing a Taylor expansion for +# $x$ sufficiently close to the solution $s$, we have + +# +#
              +# +# $$ +# f(s)=0=f(x)+(s-x)f'(x)+\frac{(s-x)^2}{2}f''(x) +\dots. +# \label{eq:taylornr} \tag{1} +# $$ + +# For small enough values of the function and for well-behaved +# functions, the terms beyond linear are unimportant, hence we obtain + +# $$ +# f(x)+(s-x)f'(x)\approx 0, +# $$ + +# yielding + +# $$ +# s\approx x-\frac{f(x)}{f'(x)}. +# $$ + +# Having in mind an iterative procedure, it is natural to start iterating with + +# $$ +# x_{n+1}=x_n-\frac{f(x_n)}{f'(x_n)}. +# $$ + +# ## Simple geometric interpretation +# +# The above is Newton-Raphson's method. It has a simple geometric +# interpretation, namely $x_{n+1}$ is the point where the tangent from +# $(x_n,f(x_n))$ crosses the $x$-axis. Close to the solution, +# Newton-Raphson converges fast to the desired result. However, if we +# are far from a root, where the higher-order terms in the series are +# important, the Newton-Raphson formula can give grossly inaccurate +# results. For instance, the initial guess for the root might be so far +# from the true root as to let the search interval include a local +# maximum or minimum of the function. If an iteration places a trial +# guess near such a local extremum, so that the first derivative nearly +# vanishes, then Newton-Raphson may fail totally + +# ## Extending to more than one variable +# +# Newton's method can be generalized to systems of several non-linear equations +# and variables. Consider the case with two equations + +# $$ +# \begin{array}{cc} f_1(x_1,x_2) &=0\\ +# f_2(x_1,x_2) &=0,\end{array} +# $$ + +# which we Taylor expand to obtain + +# $$ +# \begin{array}{cc} 0=f_1(x_1+h_1,x_2+h_2)=&f_1(x_1,x_2)+h_1 +# \partial f_1/\partial x_1+h_2 +# \partial f_1/\partial x_2+\dots\\ +# 0=f_2(x_1+h_1,x_2+h_2)=&f_2(x_1,x_2)+h_1 +# \partial f_2/\partial x_1+h_2 +# \partial f_2/\partial x_2+\dots +# \end{array}. +# $$ + +# Defining the Jacobian matrix ${\bf \boldsymbol{J}}$ we have + +# $$ +# {\bf \boldsymbol{J}}=\left( \begin{array}{cc} +# \partial f_1/\partial x_1 & \partial f_1/\partial x_2 \\ +# \partial f_2/\partial x_1 &\partial f_2/\partial x_2 +# \end{array} \right), +# $$ + +# we can rephrase Newton's method as + +# $$ +# \left(\begin{array}{c} x_1^{n+1} \\ x_2^{n+1} \end{array} \right)= +# \left(\begin{array}{c} x_1^{n} \\ x_2^{n} \end{array} \right)+ +# \left(\begin{array}{c} h_1^{n} \\ h_2^{n} \end{array} \right), +# $$ + +# where we have defined + +# $$ +# \left(\begin{array}{c} h_1^{n} \\ h_2^{n} \end{array} \right)= +# -{\bf \boldsymbol{J}}^{-1} +# \left(\begin{array}{c} f_1(x_1^{n},x_2^{n}) \\ f_2(x_1^{n},x_2^{n}) \end{array} \right). +# $$ + +# We need thus to compute the inverse of the Jacobian matrix and it +# is to understand that difficulties may +# arise in case ${\bf \boldsymbol{J}}$ is nearly singular. +# +# It is rather straightforward to extend the above scheme to systems of +# more than two non-linear equations. In our case, the Jacobian matrix is given by the Hessian that represents the second derivative of cost function. + +# ## Steepest descent +# +# The basic idea of gradient descent is +# that a function $F(\mathbf{x})$, +# $\mathbf{x} \equiv (x_1,\cdots,x_n)$, decreases fastest if one goes from $\bf {x}$ in the +# direction of the negative gradient $-\nabla F(\mathbf{x})$. +# +# It can be shown that if + +# $$ +# \mathbf{x}_{k+1} = \mathbf{x}_k - \gamma_k \nabla F(\mathbf{x}_k), +# $$ + +# with $\gamma_k > 0$. +# +# For $\gamma_k$ small enough, then $F(\mathbf{x}_{k+1}) \leq +# F(\mathbf{x}_k)$. This means that for a sufficiently small $\gamma_k$ +# we are always moving towards smaller function values, i.e a minimum. + +# ## More on Steepest descent +# +# The previous observation is the basis of the method of steepest +# descent, which is also referred to as just gradient descent (GD). One +# starts with an initial guess $\mathbf{x}_0$ for a minimum of $F$ and +# computes new approximations according to + +# $$ +# \mathbf{x}_{k+1} = \mathbf{x}_k - \gamma_k \nabla F(\mathbf{x}_k), \ \ k \geq 0. +# $$ + +# The parameter $\gamma_k$ is often referred to as the step length or +# the learning rate within the context of Machine Learning. + +# ## The ideal +# +# Ideally the sequence $\{\mathbf{x}_k \}_{k=0}$ converges to a global +# minimum of the function $F$. In general we do not know if we are in a +# global or local minimum. In the special case when $F$ is a convex +# function, all local minima are also global minima, so in this case +# gradient descent can converge to the global solution. The advantage of +# this scheme is that it is conceptually simple and straightforward to +# implement. However the method in this form has some severe +# limitations: +# +# In machine learing we are often faced with non-convex high dimensional +# cost functions with many local minima. Since GD is deterministic we +# will get stuck in a local minimum, if the method converges, unless we +# have a very good intial guess. This also implies that the scheme is +# sensitive to the chosen initial condition. +# +# Note that the gradient is a function of $\mathbf{x} = +# (x_1,\cdots,x_n)$ which makes it expensive to compute numerically. + +# ## The sensitiveness of the gradient descent +# +# The gradient descent method +# is sensitive to the choice of learning rate $\gamma_k$. This is due +# to the fact that we are only guaranteed that $F(\mathbf{x}_{k+1}) \leq +# F(\mathbf{x}_k)$ for sufficiently small $\gamma_k$. The problem is to +# determine an optimal learning rate. If the learning rate is chosen too +# small the method will take a long time to converge and if it is too +# large we can experience erratic behavior. +# +# Many of these shortcomings can be alleviated by introducing +# randomness. One such method is that of Stochastic Gradient Descent +# (SGD), see below. + +# ## Convex functions +# +# Ideally we want our cost/loss function to be convex(concave). +# +# First we give the definition of a convex set: A set $C$ in +# $\mathbb{R}^n$ is said to be convex if, for all $x$ and $y$ in $C$ and +# all $t \in (0,1)$ , the point $(1 − t)x + ty$ also belongs to +# C. Geometrically this means that every point on the line segment +# connecting $x$ and $y$ is in $C$ as discussed below. +# +# The convex subsets of $\mathbb{R}$ are the intervals of +# $\mathbb{R}$. Examples of convex sets of $\mathbb{R}^2$ are the +# regular polygons (triangles, rectangles, pentagons, etc...). + +# ## Convex function +# +# **Convex function**: Let $X \subset \mathbb{R}^n$ be a convex +# set. Assume that the function $f: X \rightarrow \mathbb{R}$ is +# continuous, then $f$ is said to be convex if $f(tx_1 + (1-t)x_2) \leq tf(x_1) + (1-t)f(x_2)$ +# for all $x_1, x_2 \in X$ and for all $t \in [0,1]$. +# If $\leq$ is replaced with a strict inequaltiy in the +# definition, we demand $x_1 \neq x_2$ and $t\in(0,1)$ then $f$ is said +# to be strictly convex. For a single variable function, convexity means +# that if you draw a straight line connecting $f(x_1)$ and $f(x_2)$, the +# value of the function on the interval $[x_1,x_2]$ is always below the +# line as illustrated below. + +# ## Conditions on convex functions +# +# In the following we state first and second-order conditions which +# ensures convexity of a function $f$. We write $D_f$ to denote the +# domain of $f$, i.e the subset of $R^n$ where $f$ is defined. For more +# details and proofs we refer to: [S. Boyd and L. Vandenberghe. Convex Optimization. Cambridge University Press](http://stanford.edu/boyd/cvxbook/). +# +# **First order condition.** +# +# Suppose $f$ is differentiable (i.e $\nabla f(x)$ is well defined for +# all $x$ in the domain of $f$). Then $f$ is convex if and only if $D_f$ +# is a convex set and $f(y) \geq f(x) + \nabla f(x)^T (y-x)$ holds +# for all $x,y \in D_f$. +# +# This condition means that for a convex function +# the first order Taylor expansion (right hand side above) at any point +# a global under estimator of the function. To convince yourself you can +# make a drawing of $f(x) = x^2+1$ and draw the tangent line to $f(x)$ and +# note that it is always below the graph. +# +# **Second order condition.** +# +# Assume that $f$ is twice +# differentiable, i.e the Hessian matrix exists at each point in +# $D_f$. Then $f$ is convex if and only if $D_f$ is a convex set and its +# Hessian is positive semi-definite for all $x\in D_f$. For a +# single-variable function this reduces to $f''(x) \geq 0$. Geometrically this means that $f$ has nonnegative curvature +# everywhere. +# +# This condition is particularly useful since it gives us an procedure for determining if the function under consideration is convex, apart from using the definition. + +# ## More on convex functions +# +# The next result is of great importance to us and the reason why we are +# going on about convex functions. In machine learning we frequently +# have to minimize a loss/cost function in order to find the best +# parameters for the model we are considering. +# +# Ideally we want the +# global minimum (for high-dimensional models it is hard to know +# if we have local or global minimum). However, if the cost/loss function +# is convex the following result provides invaluable information: +# +# **Any minimum is global for convex functions.** +# +# Consider the problem of finding $x \in \mathbb{R}^n$ such that $f(x)$ +# is minimal, where $f$ is convex and differentiable. Then, any point +# $x^*$ that satisfies $\nabla f(x^*) = 0$ is a global minimum. +# +# This result means that if we know that the cost/loss function is convex and we are able to find a minimum, we are guaranteed that it is a global minimum. + +# ## Some simple problems +# +# 1. Show that $f(x)=x^2$ is convex for $x \in \mathbb{R}$ using the definition of convexity. Hint: If you re-write the definition, $f$ is convex if the following holds for all $x,y \in D_f$ and any $\lambda \in [0,1]$ $\lambda f(x)+(1-\lambda)f(y)-f(\lambda x + (1-\lambda) y ) \geq 0$. +# +# 2. Using the second order condition show that the following functions are convex on the specified domain. +# +# * $f(x) = e^x$ is convex for $x \in \mathbb{R}$. +# +# * $g(x) = -\ln(x)$ is convex for $x \in (0,\infty)$. +# +# 3. Let $f(x) = x^2$ and $g(x) = e^x$. Show that $f(g(x))$ and $g(f(x))$ is convex for $x \in \mathbb{R}$. Also show that if $f(x)$ is any convex function than $h(x) = e^{f(x)}$ is convex. +# +# 4. A norm is any function that satisfy the following properties +# +# * $f(\alpha x) = |\alpha| f(x)$ for all $\alpha \in \mathbb{R}$. +# +# * $f(x+y) \leq f(x) + f(y)$ +# +# * $f(x) \leq 0$ for all $x \in \mathbb{R}^n$ with equality if and only if $x = 0$ +# +# Using the definition of convexity, try to show that a function satisfying the properties above is convex (the third condition is not needed to show this). + +# ## Standard steepest descent +# +# Before we proceed, we would like to discuss the approach called the +# **standard Steepest descent** (different from the above steepest descent discussion), which again leads to us having to be able +# to compute a matrix. It belongs to the class of Conjugate Gradient methods (CG). +# +# [The success of the CG method](https://www.cs.cmu.edu/~quake-papers/painless-conjugate-gradient.pdf) +# for finding solutions of non-linear problems is based on the theory +# of conjugate gradients for linear systems of equations. It belongs to +# the class of iterative methods for solving problems from linear +# algebra of the type + +# $$ +# \boldsymbol{A}\boldsymbol{x} = \boldsymbol{b}. +# $$ + +# In the iterative process we end up with a problem like + +# $$ +# \boldsymbol{r}= \boldsymbol{b}-\boldsymbol{A}\boldsymbol{x}, +# $$ + +# where $\boldsymbol{r}$ is the so-called residual or error in the iterative process. +# +# When we have found the exact solution, $\boldsymbol{r}=0$. + +# ## Gradient method +# +# The residual is zero when we reach the minimum of the quadratic equation + +# $$ +# P(\boldsymbol{x})=\frac{1}{2}\boldsymbol{x}^T\boldsymbol{A}\boldsymbol{x} - \boldsymbol{x}^T\boldsymbol{b}, +# $$ + +# with the constraint that the matrix $\boldsymbol{A}$ is positive definite and +# symmetric. This defines also the Hessian and we want it to be positive definite. + +# ## Steepest descent method +# +# We denote the initial guess for $\boldsymbol{x}$ as $\boldsymbol{x}_0$. +# We can assume without loss of generality that + +# $$ +# \boldsymbol{x}_0=0, +# $$ + +# or consider the system + +# $$ +# \boldsymbol{A}\boldsymbol{z} = \boldsymbol{b}-\boldsymbol{A}\boldsymbol{x}_0, +# $$ + +# instead. + +# ## Steepest descent method +# One can show that the solution $\boldsymbol{x}$ is also the unique minimizer of the quadratic form + +# $$ +# f(\boldsymbol{x}) = \frac{1}{2}\boldsymbol{x}^T\boldsymbol{A}\boldsymbol{x} - \boldsymbol{x}^T \boldsymbol{x} , \quad \boldsymbol{x}\in\mathbf{R}^n. +# $$ + +# This suggests taking the first basis vector $\boldsymbol{r}_1$ (see below for definition) +# to be the gradient of $f$ at $\boldsymbol{x}=\boldsymbol{x}_0$, +# which equals + +# $$ +# \boldsymbol{A}\boldsymbol{x}_0-\boldsymbol{b}, +# $$ + +# and +# $\boldsymbol{x}_0=0$ it is equal $-\boldsymbol{b}$. + +# ## Final expressions +# We can compute the residual iteratively as + +# $$ +# \boldsymbol{r}_{k+1}=\boldsymbol{b}-\boldsymbol{A}\boldsymbol{x}_{k+1}, +# $$ + +# which equals + +# $$ +# \boldsymbol{b}-\boldsymbol{A}(\boldsymbol{x}_k+\alpha_k\boldsymbol{r}_k), +# $$ + +# or + +# $$ +# (\boldsymbol{b}-\boldsymbol{A}\boldsymbol{x}_k)-\alpha_k\boldsymbol{A}\boldsymbol{r}_k, +# $$ + +# which gives + +# $$ +# \alpha_k = \frac{\boldsymbol{r}_k^T\boldsymbol{r}_k}{\boldsymbol{r}_k^T\boldsymbol{A}\boldsymbol{r}_k} +# $$ + +# leading to the iterative scheme + +# $$ +# \boldsymbol{x}_{k+1}=\boldsymbol{x}_k+\alpha_k\boldsymbol{r}_{k}, +# $$ + +# ## Steepest descent example + +# In[1]: + + +get_ipython().run_line_magic('matplotlib', 'inline') + +import numpy as np +import numpy.linalg as la + +import scipy.optimize as sopt + +import matplotlib.pyplot as pt +from mpl_toolkits.mplot3d import axes3d + +def f(x): + return x[0]**2 + 3.0*x[1]**2 + +def df(x): + return np.array([2*x[0], 6*x[1]]) + +fig = pt.figure() +ax = fig.gca(projection="3d") + +xmesh, ymesh = np.mgrid[-3:3:50j,-3:3:50j] +fmesh = f(np.array([xmesh, ymesh])) +ax.plot_surface(xmesh, ymesh, fmesh) + + +# And then as countor plot + +# In[2]: + + +pt.axis("equal") +pt.contour(xmesh, ymesh, fmesh) +guesses = [np.array([2, 2./5])] + + +# Find guesses + +# In[3]: + + +x = guesses[-1] +s = -df(x) + + +# Run it! + +# In[4]: + + +def f1d(alpha): + return f(x + alpha*s) + +alpha_opt = sopt.golden(f1d) +next_guess = x + alpha_opt * s +guesses.append(next_guess) +print(next_guess) + + +# What happened? + +# In[5]: + + +pt.axis("equal") +pt.contour(xmesh, ymesh, fmesh, 50) +it_array = np.array(guesses) +pt.plot(it_array.T[0], it_array.T[1], "x-") + + +# Note that we did only one iteration here. We can easily add more using our previous guesses. + +# ## Conjugate gradient method +# In the CG method we define so-called conjugate directions and two vectors +# $\boldsymbol{s}$ and $\boldsymbol{t}$ +# are said to be +# conjugate if + +# $$ +# \boldsymbol{s}^T\boldsymbol{A}\boldsymbol{t}= 0. +# $$ + +# The philosophy of the CG method is to perform searches in various conjugate directions +# of our vectors $\boldsymbol{x}_i$ obeying the above criterion, namely + +# $$ +# \boldsymbol{x}_i^T\boldsymbol{A}\boldsymbol{x}_j= 0. +# $$ + +# Two vectors are conjugate if they are orthogonal with respect to +# this inner product. Being conjugate is a symmetric relation: if $\boldsymbol{s}$ is conjugate to $\boldsymbol{t}$, then $\boldsymbol{t}$ is conjugate to $\boldsymbol{s}$. + +# ## Conjugate gradient method +# An example is given by the eigenvectors of the matrix + +# $$ +# \boldsymbol{v}_i^T\boldsymbol{A}\boldsymbol{v}_j= \lambda\boldsymbol{v}_i^T\boldsymbol{v}_j, +# $$ + +# which is zero unless $i=j$. + +# ## Conjugate gradient method +# Assume now that we have a symmetric positive-definite matrix $\boldsymbol{A}$ of size +# $n\times n$. At each iteration $i+1$ we obtain the conjugate direction of a vector + +# $$ +# \boldsymbol{x}_{i+1}=\boldsymbol{x}_{i}+\alpha_i\boldsymbol{p}_{i}. +# $$ + +# We assume that $\boldsymbol{p}_{i}$ is a sequence of $n$ mutually conjugate directions. +# Then the $\boldsymbol{p}_{i}$ form a basis of $R^n$ and we can expand the solution +# $ \boldsymbol{A}\boldsymbol{x} = \boldsymbol{b}$ in this basis, namely + +# $$ +# \boldsymbol{x} = \sum^{n}_{i=1} \alpha_i \boldsymbol{p}_i. +# $$ + +# ## Conjugate gradient method +# The coefficients are given by + +# $$ +# \mathbf{A}\mathbf{x} = \sum^{n}_{i=1} \alpha_i \mathbf{A} \mathbf{p}_i = \mathbf{b}. +# $$ + +# Multiplying with $\boldsymbol{p}_k^T$ from the left gives + +# $$ +# \boldsymbol{p}_k^T \boldsymbol{A}\boldsymbol{x} = \sum^{n}_{i=1} \alpha_i\boldsymbol{p}_k^T \boldsymbol{A}\boldsymbol{p}_i= \boldsymbol{p}_k^T \boldsymbol{b}, +# $$ + +# and we can define the coefficients $\alpha_k$ as + +# $$ +# \alpha_k = \frac{\boldsymbol{p}_k^T \boldsymbol{b}}{\boldsymbol{p}_k^T \boldsymbol{A} \boldsymbol{p}_k} +# $$ + +# ## Conjugate gradient method and iterations +# +# If we choose the conjugate vectors $\boldsymbol{p}_k$ carefully, +# then we may not need all of them to obtain a good approximation to the solution +# $\boldsymbol{x}$. +# We want to regard the conjugate gradient method as an iterative method. +# This will us to solve systems where $n$ is so large that the direct +# method would take too much time. +# +# We denote the initial guess for $\boldsymbol{x}$ as $\boldsymbol{x}_0$. +# We can assume without loss of generality that + +# $$ +# \boldsymbol{x}_0=0, +# $$ + +# or consider the system + +# $$ +# \boldsymbol{A}\boldsymbol{z} = \boldsymbol{b}-\boldsymbol{A}\boldsymbol{x}_0, +# $$ + +# instead. + +# ## Conjugate gradient method +# One can show that the solution $\boldsymbol{x}$ is also the unique minimizer of the quadratic form + +# $$ +# f(\boldsymbol{x}) = \frac{1}{2}\boldsymbol{x}^T\boldsymbol{A}\boldsymbol{x} - \boldsymbol{x}^T \boldsymbol{x} , \quad \boldsymbol{x}\in\mathbf{R}^n. +# $$ + +# This suggests taking the first basis vector $\boldsymbol{p}_1$ +# to be the gradient of $f$ at $\boldsymbol{x}=\boldsymbol{x}_0$, +# which equals + +# $$ +# \boldsymbol{A}\boldsymbol{x}_0-\boldsymbol{b}, +# $$ + +# and +# $\boldsymbol{x}_0=0$ it is equal $-\boldsymbol{b}$. +# The other vectors in the basis will be conjugate to the gradient, +# hence the name conjugate gradient method. + +# ## Conjugate gradient method +# Let $\boldsymbol{r}_k$ be the residual at the $k$-th step: + +# $$ +# \boldsymbol{r}_k=\boldsymbol{b}-\boldsymbol{A}\boldsymbol{x}_k. +# $$ + +# Note that $\boldsymbol{r}_k$ is the negative gradient of $f$ at +# $\boldsymbol{x}=\boldsymbol{x}_k$, +# so the gradient descent method would be to move in the direction $\boldsymbol{r}_k$. +# Here, we insist that the directions $\boldsymbol{p}_k$ are conjugate to each other, +# so we take the direction closest to the gradient $\boldsymbol{r}_k$ +# under the conjugacy constraint. +# This gives the following expression + +# $$ +# \boldsymbol{p}_{k+1}=\boldsymbol{r}_k-\frac{\boldsymbol{p}_k^T \boldsymbol{A}\boldsymbol{r}_k}{\boldsymbol{p}_k^T\boldsymbol{A}\boldsymbol{p}_k} \boldsymbol{p}_k. +# $$ + +# ## Conjugate gradient method +# We can also compute the residual iteratively as + +# $$ +# \boldsymbol{r}_{k+1}=\boldsymbol{b}-\boldsymbol{A}\boldsymbol{x}_{k+1}, +# $$ + +# which equals + +# $$ +# \boldsymbol{b}-\boldsymbol{A}(\boldsymbol{x}_k+\alpha_k\boldsymbol{p}_k), +# $$ + +# or + +# $$ +# (\boldsymbol{b}-\boldsymbol{A}\boldsymbol{x}_k)-\alpha_k\boldsymbol{A}\boldsymbol{p}_k, +# $$ + +# which gives + +# $$ +# \boldsymbol{r}_{k+1}=\boldsymbol{r}_k-\boldsymbol{A}\boldsymbol{p}_{k}, +# $$ + +# ## Revisiting our first homework +# +# We will use linear regression as a case study for the gradient descent +# methods. Linear regression is a great test case for the gradient +# descent methods discussed in the lectures since it has several +# desirable properties such as: +# +# 1. An analytical solution (recall homework set 1). +# +# 2. The gradient can be computed analytically. +# +# 3. The cost function is convex which guarantees that gradient descent converges for small enough learning rates +# +# We revisit an example similar to what we had in the first homework set. We had a function of the type + +# In[6]: + + +x = 2*np.random.rand(m,1) +y = 4+3*x+np.random.randn(m,1) + + +# with $x_i \in [0,1] $ is chosen randomly using a uniform distribution. Additionally we have a stochastic noise chosen according to a normal distribution $\cal {N}(0,1)$. +# The linear regression model is given by + +# $$ +# h_\beta(x) = \boldsymbol{y} = \beta_0 + \beta_1 x, +# $$ + +# such that + +# $$ +# \boldsymbol{y}_i = \beta_0 + \beta_1 x_i. +# $$ + +# ## Gradient descent example +# +# Let $\mathbf{y} = (y_1,\cdots,y_n)^T$, $\mathbf{\boldsymbol{y}} = (\boldsymbol{y}_1,\cdots,\boldsymbol{y}_n)^T$ and $\beta = (\beta_0, \beta_1)^T$ +# +# It is convenient to write $\mathbf{\boldsymbol{y}} = X\beta$ where $X \in \mathbb{R}^{100 \times 2} $ is the design matrix given by (we keep the intercept here) + +# $$ +# X \equiv \begin{bmatrix} +# 1 & x_1 \\ +# \vdots & \vdots \\ +# 1 & x_{100} & \\ +# \end{bmatrix}. +# $$ + +# The cost/loss/risk function is given by ( + +# $$ +# C(\beta) = \frac{1}{n}||X\beta-\mathbf{y}||_{2}^{2} = \frac{1}{n}\sum_{i=1}^{100}\left[ (\beta_0 + \beta_1 x_i)^2 - 2 y_i (\beta_0 + \beta_1 x_i) + y_i^2\right] +# $$ + +# and we want to find $\beta$ such that $C(\beta)$ is minimized. + +# ## The derivative of the cost/loss function +# +# Computing $\partial C(\beta) / \partial \beta_0$ and $\partial C(\beta) / \partial \beta_1$ we can show that the gradient can be written as + +# $$ +# \nabla_{\beta} C(\beta) = \frac{2}{n}\begin{bmatrix} \sum_{i=1}^{100} \left(\beta_0+\beta_1x_i-y_i\right) \\ +# \sum_{i=1}^{100}\left( x_i (\beta_0+\beta_1x_i)-y_ix_i\right) \\ +# \end{bmatrix} = \frac{2}{n}X^T(X\beta - \mathbf{y}), +# $$ + +# where $X$ is the design matrix defined above. + +# ## The Hessian matrix +# The Hessian matrix of $C(\beta)$ is given by + +# $$ +# \boldsymbol{H} \equiv \begin{bmatrix} +# \frac{\partial^2 C(\beta)}{\partial \beta_0^2} & \frac{\partial^2 C(\beta)}{\partial \beta_0 \partial \beta_1} \\ +# \frac{\partial^2 C(\beta)}{\partial \beta_0 \partial \beta_1} & \frac{\partial^2 C(\beta)}{\partial \beta_1^2} & \\ +# \end{bmatrix} = \frac{2}{n}X^T X. +# $$ + +# This result implies that $C(\beta)$ is a convex function since the matrix $X^T X$ always is positive semi-definite. + +# ## Simple program +# +# We can now write a program that minimizes $C(\beta)$ using the gradient descent method with a constant learning rate $\gamma$ according to + +# $$ +# \beta_{k+1} = \beta_k - \gamma \nabla_\beta C(\beta_k), \ k=0,1,\cdots +# $$ + +# We can use the expression we computed for the gradient and let use a +# $\beta_0$ be chosen randomly and let $\gamma = 0.001$. Stop iterating +# when $||\nabla_\beta C(\beta_k) || \leq \epsilon = 10^{-8}$. **Note that the code below does not include the latter stop criterion**. +# +# And finally we can compare our solution for $\beta$ with the analytic result given by +# $\beta= (X^TX)^{-1} X^T \mathbf{y}$. + +# ## Gradient Descent Example +# +# Here our simple example + +# In[7]: + + + +# Importing various packages +from random import random, seed +import numpy as np +import matplotlib.pyplot as plt +from mpl_toolkits.mplot3d import Axes3D +from matplotlib import cm +from matplotlib.ticker import LinearLocator, FormatStrFormatter +import sys + +# the number of datapoints +n = 100 +x = 2*np.random.rand(n,1) +y = 4+3*x+np.random.randn(n,1) + +X = np.c_[np.ones((n,1)), x] +# Hessian matrix +H = (2.0/n)* X.T @ X +# Get the eigenvalues +EigValues, EigVectors = np.linalg.eig(H) +print(f"Eigenvalues of Hessian Matrix:{EigValues}") + +beta_linreg = np.linalg.inv(X.T @ X) @ X.T @ y +print(beta_linreg) +beta = np.random.randn(2,1) + +eta = 1.0/np.max(EigValues) +Niterations = 1000 + +for iter in range(Niterations): + gradient = (2.0/n)*X.T @ (X @ beta-y) + beta -= eta*gradient + +print(beta) +xnew = np.array([[0],[2]]) +xbnew = np.c_[np.ones((2,1)), xnew] +ypredict = xbnew.dot(beta) +ypredict2 = xbnew.dot(beta_linreg) +plt.plot(xnew, ypredict, "r-") +plt.plot(xnew, ypredict2, "b-") +plt.plot(x, y ,'ro') +plt.axis([0,2.0,0, 15.0]) +plt.xlabel(r'$x$') +plt.ylabel(r'$y$') +plt.title(r'Gradient descent example') +plt.show() + + +# ## And a corresponding example using **scikit-learn** + +# In[8]: + + +# Importing various packages +from random import random, seed +import numpy as np +import matplotlib.pyplot as plt +from sklearn.linear_model import SGDRegressor + +n = 100 +x = 2*np.random.rand(n,1) +y = 4+3*x+np.random.randn(n,1) + +X = np.c_[np.ones((n,1)), x] +beta_linreg = np.linalg.inv(X.T @ X) @ (X.T @ y) +print(beta_linreg) +sgdreg = SGDRegressor(max_iter = 50, penalty=None, eta0=0.1) +sgdreg.fit(x,y.ravel()) +print(sgdreg.intercept_, sgdreg.coef_) + + +# ## Gradient descent and Ridge +# +# We have also discussed Ridge regression where the loss function contains a regularized term given by the $L_2$ norm of $\beta$, + +# $$ +# C_{\text{ridge}}(\beta) = \frac{1}{n}||X\beta -\mathbf{y}||^2 + \lambda ||\beta||^2, \ \lambda \geq 0. +# $$ + +# In order to minimize $C_{\text{ridge}}(\beta)$ using GD we adjust the gradient as follows + +# $$ +# \nabla_\beta C_{\text{ridge}}(\beta) = \frac{2}{n}\begin{bmatrix} \sum_{i=1}^{100} \left(\beta_0+\beta_1x_i-y_i\right) \\ +# \sum_{i=1}^{100}\left( x_i (\beta_0+\beta_1x_i)-y_ix_i\right) \\ +# \end{bmatrix} + 2\lambda\begin{bmatrix} \beta_0 \\ \beta_1\end{bmatrix} = 2 (\frac{1}{n}X^T(X\beta - \mathbf{y})+\lambda \beta). +# $$ + +# We can easily extend our program to minimize $C_{\text{ridge}}(\beta)$ using gradient descent and compare with the analytical solution given by + +# $$ +# \beta_{\text{ridge}} = \left(X^T X + n\lambda I_{2 \times 2} \right)^{-1} X^T \mathbf{y}. +# $$ + +# ## The Hessian matrix for Ridge Regression +# The Hessian matrix of Ridge Regression for our simple example is given by + +# $$ +# \boldsymbol{H} \equiv \begin{bmatrix} +# \frac{\partial^2 C(\beta)}{\partial \beta_0^2} & \frac{\partial^2 C(\beta)}{\partial \beta_0 \partial \beta_1} \\ +# \frac{\partial^2 C(\beta)}{\partial \beta_0 \partial \beta_1} & \frac{\partial^2 C(\beta)}{\partial \beta_1^2} & \\ +# \end{bmatrix} = \frac{2}{n}X^T X+2\lambda\boldsymbol{I}. +# $$ + +# This implies that the Hessian matrix is positive definite, hence the stationary point is a +# minimum. +# Note that the Ridge cost function is convex being a sum of two convex +# functions. Therefore, the stationary point is a global +# minimum of this function. + +# ## Program example for gradient descent with Ridge Regression + +# In[9]: + + +from random import random, seed +import numpy as np +import matplotlib.pyplot as plt +from mpl_toolkits.mplot3d import Axes3D +from matplotlib import cm +from matplotlib.ticker import LinearLocator, FormatStrFormatter +import sys + +# the number of datapoints +n = 100 +x = 2*np.random.rand(n,1) +y = 4+3*x+np.random.randn(n,1) + +X = np.c_[np.ones((n,1)), x] +XT_X = X.T @ X + +#Ridge parameter lambda +lmbda = 0.001 +Id = n*lmbda* np.eye(XT_X.shape[0]) + +# Hessian matrix +H = (2.0/n)* XT_X+2*lmbda* np.eye(XT_X.shape[0]) +# Get the eigenvalues +EigValues, EigVectors = np.linalg.eig(H) +print(f"Eigenvalues of Hessian Matrix:{EigValues}") + + +beta_linreg = np.linalg.inv(XT_X+Id) @ X.T @ y +print(beta_linreg) +# Start plain gradient descent +beta = np.random.randn(2,1) + +eta = 1.0/np.max(EigValues) +Niterations = 100 + +for iter in range(Niterations): + gradients = 2.0/n*X.T @ (X @ (beta)-y)+2*lmbda*beta + beta -= eta*gradients + +print(beta) +ypredict = X @ beta +ypredict2 = X @ beta_linreg +plt.plot(x, ypredict, "r-") +plt.plot(x, ypredict2, "b-") +plt.plot(x, y ,'ro') +plt.axis([0,2.0,0, 15.0]) +plt.xlabel(r'$x$') +plt.ylabel(r'$y$') +plt.title(r'Gradient descent example for Ridge') +plt.show() + + +# ## Using gradient descent methods, limitations +# +# * **Gradient descent (GD) finds local minima of our function**. Since the GD algorithm is deterministic, if it converges, it will converge to a local minimum of our cost/loss/risk function. Because in ML we are often dealing with extremely rugged landscapes with many local minima, this can lead to poor performance. +# +# * **GD is sensitive to initial conditions**. One consequence of the local nature of GD is that initial conditions matter. Depending on where one starts, one will end up at a different local minima. Therefore, it is very important to think about how one initializes the training process. This is true for GD as well as more complicated variants of GD. +# +# * **Gradients are computationally expensive to calculate for large datasets**. In many cases in statistics and ML, the cost/loss/risk function is a sum of terms, with one term for each data point. For example, in linear regression, $E \propto \sum_{i=1}^n (y_i - \mathbf{w}^T\cdot\mathbf{x}_i)^2$; for logistic regression, the square error is replaced by the cross entropy. To calculate the gradient we have to sum over *all* $n$ data points. Doing this at every GD step becomes extremely computationally expensive. An ingenious solution to this, is to calculate the gradients using small subsets of the data called "mini batches". This has the added benefit of introducing stochasticity into our algorithm. +# +# * **GD is very sensitive to choices of learning rates**. GD is extremely sensitive to the choice of learning rates. If the learning rate is very small, the training process take an extremely long time. For larger learning rates, GD can diverge and give poor results. Furthermore, depending on what the local landscape looks like, we have to modify the learning rates to ensure convergence. Ideally, we would *adaptively* choose the learning rates to match the landscape. +# +# * **GD treats all directions in parameter space uniformly.** Another major drawback of GD is that unlike Newton's method, the learning rate for GD is the same in all directions in parameter space. For this reason, the maximum learning rate is set by the behavior of the steepest direction and this can significantly slow down training. Ideally, we would like to take large steps in flat directions and small steps in steep directions. Since we are exploring rugged landscapes where curvatures change, this requires us to keep track of not only the gradient but second derivatives. The ideal scenario would be to calculate the Hessian but this proves to be too computationally expensive. +# +# * GD can take exponential time to escape saddle points, even with random initialization. As we mentioned, GD is extremely sensitive to initial condition since it determines the particular local minimum GD would eventually reach. However, even with a good initialization scheme, through the introduction of randomness, GD can still take exponential time to escape saddle points. + +# ## Improving gradient descent with momentum +# +# We discuss here some simple examples where we introduce what is called 'memory'about previous steps, or what is normally called momentum gradient descent. The mathematics is explained below in connection with Stochastic gradient descent. + +# In[10]: + + +from numpy import asarray +from numpy import arange +from numpy.random import rand +from numpy.random import seed +from matplotlib import pyplot + +# objective function +def objective(x): + return x**2.0 + +# derivative of objective function +def derivative(x): + return x * 2.0 + +# gradient descent algorithm +def gradient_descent(objective, derivative, bounds, n_iter, step_size): + # track all solutions + solutions, scores = list(), list() + # generate an initial point + solution = bounds[:, 0] + rand(len(bounds)) * (bounds[:, 1] - bounds[:, 0]) + # run the gradient descent + for i in range(n_iter): + # calculate gradient + gradient = derivative(solution) + # take a step + solution = solution - step_size * gradient + # evaluate candidate point + solution_eval = objective(solution) + # store solution + solutions.append(solution) + scores.append(solution_eval) + # report progress + print('>%d f(%s) = %.5f' % (i, solution, solution_eval)) + return [solutions, scores] + +# seed the pseudo random number generator +seed(4) +# define range for input +bounds = asarray([[-1.0, 1.0]]) +# define the total iterations +n_iter = 30 +# define the step size +step_size = 0.1 +# perform the gradient descent search +solutions, scores = gradient_descent(objective, derivative, bounds, n_iter, step_size) +# sample input range uniformly at 0.1 increments +inputs = arange(bounds[0,0], bounds[0,1]+0.1, 0.1) +# compute targets +results = objective(inputs) +# create a line plot of input vs result +pyplot.plot(inputs, results) +# plot the solutions found +pyplot.plot(solutions, scores, '.-', color='red') +# show the plot +pyplot.show() + + +# ## Same code but now with momentum gradient descent + +# In[11]: + + +from numpy import asarray +from numpy import arange +from numpy.random import rand +from numpy.random import seed +from matplotlib import pyplot + +# objective function +def objective(x): + return x**2.0 + +# derivative of objective function +def derivative(x): + return x * 2.0 + +# gradient descent algorithm +def gradient_descent(objective, derivative, bounds, n_iter, step_size, momentum): + # track all solutions + solutions, scores = list(), list() + # generate an initial point + solution = bounds[:, 0] + rand(len(bounds)) * (bounds[:, 1] - bounds[:, 0]) + # keep track of the change + change = 0.0 + # run the gradient descent + for i in range(n_iter): + # calculate gradient + gradient = derivative(solution) + # calculate update + new_change = step_size * gradient + momentum * change + # take a step + solution = solution - new_change + # save the change + change = new_change + # evaluate candidate point + solution_eval = objective(solution) + # store solution + solutions.append(solution) + scores.append(solution_eval) + # report progress + print('>%d f(%s) = %.5f' % (i, solution, solution_eval)) + return [solutions, scores] + +# seed the pseudo random number generator +seed(4) +# define range for input +bounds = asarray([[-1.0, 1.0]]) +# define the total iterations +n_iter = 30 +# define the step size +step_size = 0.1 +# define momentum +momentum = 0.3 +# perform the gradient descent search with momentum +solutions, scores = gradient_descent(objective, derivative, bounds, n_iter, step_size, momentum) +# sample input range uniformly at 0.1 increments +inputs = arange(bounds[0,0], bounds[0,1]+0.1, 0.1) +# compute targets +results = objective(inputs) +# create a line plot of input vs result +pyplot.plot(inputs, results) +# plot the solutions found +pyplot.plot(solutions, scores, '.-', color='red') +# show the plot +pyplot.show() + + +# ## Overview video on Stochastic Gradient Descent +# +# [What is Stochastic Gradient Descent](https://www.youtube.com/watch?v=vMh0zPT0tLI&ab_channel=StatQuestwithJoshStarmer) + +# ## Batches and mini-batches +# +# In gradient descent we compute the cost function and its gradient for all data points we have. +# +# In large-scale applications such as the [ILSVRC challenge](https://www.image-net.org/challenges/LSVRC/), the +# training data can have on order of millions of examples. Hence, it +# seems wasteful to compute the full cost function over the entire +# training set in order to perform only a single parameter update. A +# very common approach to addressing this challenge is to compute the +# gradient over batches of the training data. For example, a typical batch could contain some thousand examples from +# an entire training set of several millions. This batch is then used to +# perform a parameter update. + +# ## Stochastic Gradient Descent (SGD) +# +# In stochastic gradient descent, the extreme case is the case where we +# have only one batch, that is we include the whole data set. +# +# This process is called Stochastic Gradient +# Descent (SGD) (or also sometimes on-line gradient descent). This is +# relatively less common to see because in practice due to vectorized +# code optimizations it can be computationally much more efficient to +# evaluate the gradient for 100 examples, than the gradient for one +# example 100 times. Even though SGD technically refers to using a +# single example at a time to evaluate the gradient, you will hear +# people use the term SGD even when referring to mini-batch gradient +# descent (i.e. mentions of MGD for “Minibatch Gradient Descent”, or BGD +# for “Batch gradient descent” are rare to see), where it is usually +# assumed that mini-batches are used. The size of the mini-batch is a +# hyperparameter but it is not very common to cross-validate or bootstrap it. It is +# usually based on memory constraints (if any), or set to some value, +# e.g. 32, 64 or 128. We use powers of 2 in practice because many +# vectorized operation implementations work faster when their inputs are +# sized in powers of 2. +# +# In our notes with SGD we mean stochastic gradient descent with mini-batches. + +# ## Stochastic Gradient Descent +# +# Stochastic gradient descent (SGD) and variants thereof address some of +# the shortcomings of the Gradient descent method discussed above. +# +# The underlying idea of SGD comes from the observation that the cost +# function, which we want to minimize, can almost always be written as a +# sum over $n$ data points $\{\mathbf{x}_i\}_{i=1}^n$, + +# $$ +# C(\mathbf{\beta}) = \sum_{i=1}^n c_i(\mathbf{x}_i, +# \mathbf{\beta}). +# $$ + +# ## Computation of gradients +# +# This in turn means that the gradient can be +# computed as a sum over $i$-gradients + +# $$ +# \nabla_\beta C(\mathbf{\beta}) = \sum_i^n \nabla_\beta c_i(\mathbf{x}_i, +# \mathbf{\beta}). +# $$ + +# Stochasticity/randomness is introduced by only taking the +# gradient on a subset of the data called minibatches. If there are $n$ +# data points and the size of each minibatch is $M$, there will be $n/M$ +# minibatches. We denote these minibatches by $B_k$ where +# $k=1,\cdots,n/M$. + +# ## SGD example +# As an example, suppose we have $10$ data points $(\mathbf{x}_1,\cdots, \mathbf{x}_{10})$ +# and we choose to have $M=5$ minibathces, +# then each minibatch contains two data points. In particular we have +# $B_1 = (\mathbf{x}_1,\mathbf{x}_2), \cdots, B_5 = +# (\mathbf{x}_9,\mathbf{x}_{10})$. Note that if you choose $M=1$ you +# have only a single batch with all data points and on the other extreme, +# you may choose $M=n$ resulting in a minibatch for each datapoint, i.e +# $B_k = \mathbf{x}_k$. +# +# The idea is now to approximate the gradient by replacing the sum over +# all data points with a sum over the data points in one the minibatches +# picked at random in each gradient descent step + +# $$ +# \nabla_{\beta} +# C(\mathbf{\beta}) = \sum_{i=1}^n \nabla_\beta c_i(\mathbf{x}_i, +# \mathbf{\beta}) \rightarrow \sum_{i \in B_k}^n \nabla_\beta +# c_i(\mathbf{x}_i, \mathbf{\beta}). +# $$ + +# ## The gradient step +# +# Thus a gradient descent step now looks like + +# $$ +# \beta_{j+1} = \beta_j - \gamma_j \sum_{i \in B_k}^n \nabla_\beta c_i(\mathbf{x}_i, +# \mathbf{\beta}) +# $$ + +# where $k$ is picked at random with equal +# probability from $[1,n/M]$. An iteration over the number of +# minibathces (n/M) is commonly referred to as an epoch. Thus it is +# typical to choose a number of epochs and for each epoch iterate over +# the number of minibatches, as exemplified in the code below. + +# ## Simple example code + +# In[12]: + + +import numpy as np + +n = 100 #100 datapoints +M = 5 #size of each minibatch +m = int(n/M) #number of minibatches +n_epochs = 10 #number of epochs + +j = 0 +for epoch in range(1,n_epochs+1): + for i in range(m): + k = np.random.randint(m) #Pick the k-th minibatch at random + #Compute the gradient using the data in minibatch Bk + #Compute new suggestion for + j += 1 + + +# Taking the gradient only on a subset of the data has two important +# benefits. First, it introduces randomness which decreases the chance +# that our opmization scheme gets stuck in a local minima. Second, if +# the size of the minibatches are small relative to the number of +# datapoints ($M < n$), the computation of the gradient is much +# cheaper since we sum over the datapoints in the $k-th$ minibatch and not +# all $n$ datapoints. + +# ## When do we stop? +# +# A natural question is when do we stop the search for a new minimum? +# One possibility is to compute the full gradient after a given number +# of epochs and check if the norm of the gradient is smaller than some +# threshold and stop if true. However, the condition that the gradient +# is zero is valid also for local minima, so this would only tell us +# that we are close to a local/global minimum. However, we could also +# evaluate the cost function at this point, store the result and +# continue the search. If the test kicks in at a later stage we can +# compare the values of the cost function and keep the $\beta$ that +# gave the lowest value. + +# ## Slightly different approach +# +# Another approach is to let the step length $\gamma_j$ depend on the +# number of epochs in such a way that it becomes very small after a +# reasonable time such that we do not move at all. Such approaches are +# also called scaling. There are many such ways to [scale the learning +# rate](https://towardsdatascience.com/gradient-descent-the-learning-rate-and-the-importance-of-feature-scaling-6c0b416596e1) +# and [discussions here](https://www.jmlr.org/papers/volume23/20-1258/20-1258.pdf). See +# also +# +# for a discussion of different scaling functions for the learning rate. + +# ## Time decay rate +# +# As an example, let $e = 0,1,2,3,\cdots$ denote the current epoch and let $t_0, t_1 > 0$ be two fixed numbers. Furthermore, let $t = e \cdot m + i$ where $m$ is the number of minibatches and $i=0,\cdots,m-1$. Then the function $$\gamma_j(t; t_0, t_1) = \frac{t_0}{t+t_1} $$ goes to zero as the number of epochs gets large. I.e. we start with a step length $\gamma_j (0; t_0, t_1) = t_0/t_1$ which decays in *time* $t$. +# +# In this way we can fix the number of epochs, compute $\beta$ and +# evaluate the cost function at the end. Repeating the computation will +# give a different result since the scheme is random by design. Then we +# pick the final $\beta$ that gives the lowest value of the cost +# function. + +# In[13]: + + +import numpy as np + +def step_length(t,t0,t1): + return t0/(t+t1) + +n = 100 #100 datapoints +M = 5 #size of each minibatch +m = int(n/M) #number of minibatches +n_epochs = 500 #number of epochs +t0 = 1.0 +t1 = 10 + +gamma_j = t0/t1 +j = 0 +for epoch in range(1,n_epochs+1): + for i in range(m): + k = np.random.randint(m) #Pick the k-th minibatch at random + #Compute the gradient using the data in minibatch Bk + #Compute new suggestion for beta + t = epoch*m+i + gamma_j = step_length(t,t0,t1) + j += 1 + +print("gamma_j after %d epochs: %g" % (n_epochs,gamma_j)) + + +# ## Code with a Number of Minibatches which varies +# +# In the code here we vary the number of mini-batches. + +# In[14]: + + +# Importing various packages +from math import exp, sqrt +from random import random, seed +import numpy as np +import matplotlib.pyplot as plt + +n = 100 +x = 2*np.random.rand(n,1) +y = 4+3*x+np.random.randn(n,1) + +X = np.c_[np.ones((n,1)), x] +XT_X = X.T @ X +theta_linreg = np.linalg.inv(X.T @ X) @ (X.T @ y) +print("Own inversion") +print(theta_linreg) +# Hessian matrix +H = (2.0/n)* XT_X +EigValues, EigVectors = np.linalg.eig(H) +print(f"Eigenvalues of Hessian Matrix:{EigValues}") + +theta = np.random.randn(2,1) +eta = 1.0/np.max(EigValues) +Niterations = 1000 + + +for iter in range(Niterations): + gradients = 2.0/n*X.T @ ((X @ theta)-y) + theta -= eta*gradients +print("theta from own gd") +print(theta) + +xnew = np.array([[0],[2]]) +Xnew = np.c_[np.ones((2,1)), xnew] +ypredict = Xnew.dot(theta) +ypredict2 = Xnew.dot(theta_linreg) + +n_epochs = 50 +M = 5 #size of each minibatch +m = int(n/M) #number of minibatches +t0, t1 = 5, 50 + +def learning_schedule(t): + return t0/(t+t1) + +theta = np.random.randn(2,1) + +for epoch in range(n_epochs): +# Can you figure out a better way of setting up the contributions to each batch? + for i in range(m): + random_index = M*np.random.randint(m) + xi = X[random_index:random_index+M] + yi = y[random_index:random_index+M] + gradients = (2.0/M)* xi.T @ ((xi @ theta)-yi) + eta = learning_schedule(epoch*m+i) + theta = theta - eta*gradients +print("theta from own sdg") +print(theta) + +plt.plot(xnew, ypredict, "r-") +plt.plot(xnew, ypredict2, "b-") +plt.plot(x, y ,'ro') +plt.axis([0,2.0,0, 15.0]) +plt.xlabel(r'$x$') +plt.ylabel(r'$y$') +plt.title(r'Random numbers ') +plt.show() + + +# ## Replace or not +# +# In the above code, we have use replacement in setting up the +# mini-batches. The discussion +# [here](https://sebastianraschka.com/faq/docs/sgd-methods.html) may be +# useful. + +# ## Momentum based GD +# +# The stochastic gradient descent (SGD) is almost always used with a +# *momentum* or inertia term that serves as a memory of the direction we +# are moving in parameter space. This is typically implemented as +# follows + +# $$ +# \mathbf{v}_{t}=\gamma \mathbf{v}_{t-1}+\eta_{t}\nabla_\theta E(\boldsymbol{\theta}_t) \nonumber +# $$ + +# +#
              +# +# $$ +# \begin{equation} +# \boldsymbol{\theta}_{t+1}= \boldsymbol{\theta}_t -\mathbf{v}_{t}, +# \label{_auto1} \tag{2} +# \end{equation} +# $$ + +# where we have introduced a momentum parameter $\gamma$, with +# $0\le\gamma\le 1$, and for brevity we dropped the explicit notation to +# indicate the gradient is to be taken over a different mini-batch at +# each step. We call this algorithm gradient descent with momentum +# (GDM). From these equations, it is clear that $\mathbf{v}_t$ is a +# running average of recently encountered gradients and +# $(1-\gamma)^{-1}$ sets the characteristic time scale for the memory +# used in the averaging procedure. Consistent with this, when +# $\gamma=0$, this just reduces down to ordinary SGD as discussed +# earlier. An equivalent way of writing the updates is + +# $$ +# \Delta \boldsymbol{\theta}_{t+1} = \gamma \Delta \boldsymbol{\theta}_t -\ \eta_{t}\nabla_\theta E(\boldsymbol{\theta}_t), +# $$ + +# where we have defined $\Delta \boldsymbol{\theta}_{t}= \boldsymbol{\theta}_t-\boldsymbol{\theta}_{t-1}$. + +# ## More on momentum based approaches +# +# Let us try to get more intuition from these equations. It is helpful +# to consider a simple physical analogy with a particle of mass $m$ +# moving in a viscous medium with drag coefficient $\mu$ and potential +# $E(\mathbf{w})$. If we denote the particle's position by $\mathbf{w}$, +# then its motion is described by + +# $$ +# m {d^2 \mathbf{w} \over dt^2} + \mu {d \mathbf{w} \over dt }= -\nabla_w E(\mathbf{w}). +# $$ + +# We can discretize this equation in the usual way to get + +# $$ +# m { \mathbf{w}_{t+\Delta t}-2 \mathbf{w}_{t} +\mathbf{w}_{t-\Delta t} \over (\Delta t)^2}+\mu {\mathbf{w}_{t+\Delta t}- \mathbf{w}_{t} \over \Delta t} = -\nabla_w E(\mathbf{w}). +# $$ + +# Rearranging this equation, we can rewrite this as + +# $$ +# \Delta \mathbf{w}_{t +\Delta t}= - { (\Delta t)^2 \over m +\mu \Delta t} \nabla_w E(\mathbf{w})+ {m \over m +\mu \Delta t} \Delta \mathbf{w}_t. +# $$ + +# ## Momentum parameter +# +# Notice that this equation is identical to previous one if we identify +# the position of the particle, $\mathbf{w}$, with the parameters +# $\boldsymbol{\theta}$. This allows us to identify the momentum +# parameter and learning rate with the mass of the particle and the +# viscous drag as: + +# $$ +# \gamma= {m \over m +\mu \Delta t }, \qquad \eta = {(\Delta t)^2 \over m +\mu \Delta t}. +# $$ + +# Thus, as the name suggests, the momentum parameter is proportional to +# the mass of the particle and effectively provides inertia. +# Furthermore, in the large viscosity/small learning rate limit, our +# memory time scales as $(1-\gamma)^{-1} \approx m/(\mu \Delta t)$. +# +# Why is momentum useful? SGD momentum helps the gradient descent +# algorithm gain speed in directions with persistent but small gradients +# even in the presence of stochasticity, while suppressing oscillations +# in high-curvature directions. This becomes especially important in +# situations where the landscape is shallow and flat in some directions +# and narrow and steep in others. It has been argued that first-order +# methods (with appropriate initial conditions) can perform comparable +# to more expensive second order methods, especially in the context of +# complex deep learning models. +# +# These beneficial properties of momentum can sometimes become even more +# pronounced by using a slight modification of the classical momentum +# algorithm called Nesterov Accelerated Gradient (NAG). +# +# In the NAG algorithm, rather than calculating the gradient at the +# current parameters, $\nabla_\theta E(\boldsymbol{\theta}_t)$, one +# calculates the gradient at the expected value of the parameters given +# our current momentum, $\nabla_\theta E(\boldsymbol{\theta}_t +\gamma +# \mathbf{v}_{t-1})$. This yields the NAG update rule + +# $$ +# \mathbf{v}_{t}=\gamma \mathbf{v}_{t-1}+\eta_{t}\nabla_\theta E(\boldsymbol{\theta}_t +\gamma \mathbf{v}_{t-1}) \nonumber +# $$ + +# +#
              +# +# $$ +# \begin{equation} +# \boldsymbol{\theta}_{t+1}= \boldsymbol{\theta}_t -\mathbf{v}_{t}. +# \label{_auto2} \tag{3} +# \end{equation} +# $$ + +# One of the major advantages of NAG is that it allows for the use of a larger learning rate than GDM for the same choice of $\gamma$. + +# ## Second moment of the gradient +# +# In stochastic gradient descent, with and without momentum, we still +# have to specify a schedule for tuning the learning rates $\eta_t$ +# as a function of time. As discussed in the context of Newton's +# method, this presents a number of dilemmas. The learning rate is +# limited by the steepest direction which can change depending on the +# current position in the landscape. To circumvent this problem, ideally +# our algorithm would keep track of curvature and take large steps in +# shallow, flat directions and small steps in steep, narrow directions. +# Second-order methods accomplish this by calculating or approximating +# the Hessian and normalizing the learning rate by the +# curvature. However, this is very computationally expensive for +# extremely large models. Ideally, we would like to be able to +# adaptively change the step size to match the landscape without paying +# the steep computational price of calculating or approximating +# Hessians. +# +# Recently, a number of methods have been introduced that accomplish +# this by tracking not only the gradient, but also the second moment of +# the gradient. These methods include AdaGrad, AdaDelta, Root Mean Squared Propagation (RMS-Prop), and +# [ADAM](https://arxiv.org/abs/1412.6980). + +# ## RMS prop +# +# In RMS prop, in addition to keeping a running average of the first +# moment of the gradient, we also keep track of the second moment +# denoted by $\mathbf{s}_t=\mathbb{E}[\mathbf{g}_t^2]$. The update rule +# for RMS prop is given by + +# +#
              +# +# $$ +# \begin{equation} +# \mathbf{g}_t = \nabla_\theta E(\boldsymbol{\theta}) +# \label{_auto3} \tag{4} +# \end{equation} +# $$ + +# $$ +# \mathbf{s}_t =\beta \mathbf{s}_{t-1} +(1-\beta)\mathbf{g}_t^2 \nonumber +# $$ + +# $$ +# \boldsymbol{\theta}_{t+1}=\boldsymbol{\theta}_t - \eta_t { \mathbf{g}_t \over \sqrt{\mathbf{s}_t +\epsilon}}, \nonumber +# $$ + +# where $\beta$ controls the averaging time of the second moment and is +# typically taken to be about $\beta=0.9$, $\eta_t$ is a learning rate +# typically chosen to be $10^{-3}$, and $\epsilon\sim 10^{-8} $ is a +# small regularization constant to prevent divergences. Multiplication +# and division by vectors is understood as an element-wise operation. It +# is clear from this formula that the learning rate is reduced in +# directions where the norm of the gradient is consistently large. This +# greatly speeds up the convergence by allowing us to use a larger +# learning rate for flat directions. + +# ## [ADAM optimizer](https://arxiv.org/abs/1412.6980) +# +# A related algorithm is the ADAM optimizer. In +# [ADAM](https://arxiv.org/abs/1412.6980), we keep a running average of +# both the first and second moment of the gradient and use this +# information to adaptively change the learning rate for different +# parameters. The method isefficient when working with large +# problems involving lots data and/or parameters. It is a combination of the +# gradient descent with momentum algorithm and the RMSprop algorithm +# discussed above. +# +# In addition to keeping a running average of the first and +# second moments of the gradient +# (i.e. $\mathbf{m}_t=\mathbb{E}[\mathbf{g}_t]$ and +# $\mathbf{s}_t=\mathbb{E}[\mathbf{g}^2_t]$, respectively), ADAM +# performs an additional bias correction to account for the fact that we +# are estimating the first two moments of the gradient using a running +# average (denoted by the hats in the update rule below). The update +# rule for ADAM is given by (where multiplication and division are once +# again understood to be element-wise operations below) + +# +#
              +# +# $$ +# \begin{equation} +# \mathbf{g}_t = \nabla_\theta E(\boldsymbol{\theta}) +# \label{_auto4} \tag{5} +# \end{equation} +# $$ + +# $$ +# \mathbf{m}_t = \beta_1 \mathbf{m}_{t-1} + (1-\beta_1) \mathbf{g}_t \nonumber +# $$ + +# $$ +# \mathbf{s}_t =\beta_2 \mathbf{s}_{t-1} +(1-\beta_2)\mathbf{g}_t^2 \nonumber +# $$ + +# $$ +# \boldsymbol{\mathbf{m}}_t={\mathbf{m}_t \over 1-\beta_1^t} \nonumber +# $$ + +# $$ +# \boldsymbol{\mathbf{s}}_t ={\mathbf{s}_t \over1-\beta_2^t} \nonumber +# $$ + +# $$ +# \boldsymbol{\theta}_{t+1}=\boldsymbol{\theta}_t - \eta_t { \boldsymbol{\mathbf{m}}_t \over \sqrt{\boldsymbol{\mathbf{s}}_t} +\epsilon}, \nonumber +# $$ + +# +#
              +# +# $$ +# \begin{equation} +# \label{_auto5} \tag{6} +# \end{equation} +# $$ + +# where $\beta_1$ and $\beta_2$ set the memory lifetime of the first and +# second moment and are typically taken to be $0.9$ and $0.99$ +# respectively, and $\eta$ and $\epsilon$ are identical to RMSprop. +# +# Like in RMSprop, the effective step size of a parameter depends on the +# magnitude of its gradient squared. To understand this better, let us +# rewrite this expression in terms of the variance +# $\boldsymbol{\sigma}_t^2 = \boldsymbol{\mathbf{s}}_t - +# (\boldsymbol{\mathbf{m}}_t)^2$. Consider a single parameter $\theta_t$. The +# update rule for this parameter is given by + +# $$ +# \Delta \theta_{t+1}= -\eta_t { \boldsymbol{m}_t \over \sqrt{\sigma_t^2 + m_t^2 }+\epsilon}. +# $$ + +# ## Algorithms and codes for Adagrad, RMSprop and Adam +# +# The algorithms we have implemented are well described in the text by [Goodfellow, Bengio and Courville, chapter 8](https://www.deeplearningbook.org/contents/optimization.html). +# +# The codes which implement these algorithms are discussed after our presentation of automatic differentiation. + +# ## Practical tips +# +# * **Randomize the data when making mini-batches**. It is always important to randomly shuffle the data when forming mini-batches. Otherwise, the gradient descent method can fit spurious correlations resulting from the order in which data is presented. +# +# * **Transform your inputs**. Learning becomes difficult when our landscape has a mixture of steep and flat directions. One simple trick for minimizing these situations is to standardize the data by subtracting the mean and normalizing the variance of input variables. Whenever possible, also decorrelate the inputs. To understand why this is helpful, consider the case of linear regression. It is easy to show that for the squared error cost function, the Hessian of the cost function is just the correlation matrix between the inputs. Thus, by standardizing the inputs, we are ensuring that the landscape looks homogeneous in all directions in parameter space. Since most deep networks can be viewed as linear transformations followed by a non-linearity at each layer, we expect this intuition to hold beyond the linear case. +# +# * **Monitor the out-of-sample performance.** Always monitor the performance of your model on a validation set (a small portion of the training data that is held out of the training process to serve as a proxy for the test set. If the validation error starts increasing, then the model is beginning to overfit. Terminate the learning process. This *early stopping* significantly improves performance in many settings. +# +# * **Adaptive optimization methods don't always have good generalization.** Recent studies have shown that adaptive methods such as ADAM, RMSPorp, and AdaGrad tend to have poor generalization compared to SGD or SGD with momentum, particularly in the high-dimensional limit (i.e. the number of parameters exceeds the number of data points). Although it is not clear at this stage why these methods perform so well in training deep neural networks, simpler procedures like properly-tuned SGD may work as well or better in these applications. +# +# Geron's text, see chapter 11, has several interesting discussions. + +# ## Automatic differentiation +# +# [Automatic differentiation (AD)](https://en.wikipedia.org/wiki/Automatic_differentiation), +# also called algorithmic +# differentiation or computational differentiation,is a set of +# techniques to numerically evaluate the derivative of a function +# specified by a computer program. AD exploits the fact that every +# computer program, no matter how complicated, executes a sequence of +# elementary arithmetic operations (addition, subtraction, +# multiplication, division, etc.) and elementary functions (exp, log, +# sin, cos, etc.). By applying the chain rule repeatedly to these +# operations, derivatives of arbitrary order can be computed +# automatically, accurately to working precision, and using at most a +# small constant factor more arithmetic operations than the original +# program. +# +# Automatic differentiation is neither: +# +# * Symbolic differentiation, nor +# +# * Numerical differentiation (the method of finite differences). +# +# Symbolic differentiation can lead to inefficient code and faces the +# difficulty of converting a computer program into a single expression, +# while numerical differentiation can introduce round-off errors in the +# discretization process and cancellation +# +# Python has tools for so-called **automatic differentiation**. +# Consider the following example + +# $$ +# f(x) = \sin\left(2\pi x + x^2\right) +# $$ + +# which has the following derivative + +# $$ +# f'(x) = \cos\left(2\pi x + x^2\right)\left(2\pi + 2x\right) +# $$ + +# Using **autograd** we have + +# In[15]: + + +import autograd.numpy as np + +# To do elementwise differentiation: +from autograd import elementwise_grad as egrad + +# To plot: +import matplotlib.pyplot as plt + + +def f(x): + return np.sin(2*np.pi*x + x**2) + +def f_grad_analytic(x): + return np.cos(2*np.pi*x + x**2)*(2*np.pi + 2*x) + +# Do the comparison: +x = np.linspace(0,1,1000) + +f_grad = egrad(f) + +computed = f_grad(x) +analytic = f_grad_analytic(x) + +plt.title('Derivative computed from Autograd compared with the analytical derivative') +plt.plot(x,computed,label='autograd') +plt.plot(x,analytic,label='analytic') + +plt.xlabel('x') +plt.ylabel('y') +plt.legend() + +plt.show() + +print("The max absolute difference is: %g"%(np.max(np.abs(computed - analytic)))) + + +# ## Using autograd +# +# Here we +# experiment with what kind of functions Autograd is capable +# of finding the gradient of. The following Python functions are just +# meant to illustrate what Autograd can do, but please feel free to +# experiment with other, possibly more complicated, functions as well. + +# In[16]: + + +import autograd.numpy as np +from autograd import grad + +def f1(x): + return x**3 + 1 + +f1_grad = grad(f1) + +# Remember to send in float as argument to the computed gradient from Autograd! +a = 1.0 + +# See the evaluated gradient at a using autograd: +print("The gradient of f1 evaluated at a = %g using autograd is: %g"%(a,f1_grad(a))) + +# Compare with the analytical derivative, that is f1'(x) = 3*x**2 +grad_analytical = 3*a**2 +print("The gradient of f1 evaluated at a = %g by finding the analytic expression is: %g"%(a,grad_analytical)) + + +# ## Autograd with more complicated functions +# +# To differentiate with respect to two (or more) arguments of a Python +# function, Autograd need to know at which variable the function if +# being differentiated with respect to. + +# In[17]: + + +import autograd.numpy as np +from autograd import grad +def f2(x1,x2): + return 3*x1**3 + x2*(x1 - 5) + 1 + +# By sending the argument 0, Autograd will compute the derivative w.r.t the first variable, in this case x1 +f2_grad_x1 = grad(f2,0) + +# ... and differentiate w.r.t x2 by sending 1 as an additional arugment to grad +f2_grad_x2 = grad(f2,1) + +x1 = 1.0 +x2 = 3.0 + +print("Evaluating at x1 = %g, x2 = %g"%(x1,x2)) +print("-"*30) + +# Compare with the analytical derivatives: + +# Derivative of f2 w.r.t x1 is: 9*x1**2 + x2: +f2_grad_x1_analytical = 9*x1**2 + x2 + +# Derivative of f2 w.r.t x2 is: x1 - 5: +f2_grad_x2_analytical = x1 - 5 + +# See the evaluated derivations: +print("The derivative of f2 w.r.t x1: %g"%( f2_grad_x1(x1,x2) )) +print("The analytical derivative of f2 w.r.t x1: %g"%( f2_grad_x1(x1,x2) )) + +print() + +print("The derivative of f2 w.r.t x2: %g"%( f2_grad_x2(x1,x2) )) +print("The analytical derivative of f2 w.r.t x2: %g"%( f2_grad_x2(x1,x2) )) + + +# Note that the grad function will not produce the true gradient of the function. The true gradient of a function with two or more variables will produce a vector, where each element is the function differentiated w.r.t a variable. + +# ## More complicated functions using the elements of their arguments directly + +# In[18]: + + +import autograd.numpy as np +from autograd import grad +def f3(x): # Assumes x is an array of length 5 or higher + return 2*x[0] + 3*x[1] + 5*x[2] + 7*x[3] + 11*x[4]**2 + +f3_grad = grad(f3) + +x = np.linspace(0,4,5) + +# Print the computed gradient: +print("The computed gradient of f3 is: ", f3_grad(x)) + +# The analytical gradient is: (2, 3, 5, 7, 22*x[4]) +f3_grad_analytical = np.array([2, 3, 5, 7, 22*x[4]]) + +# Print the analytical gradient: +print("The analytical gradient of f3 is: ", f3_grad_analytical) + + +# Note that in this case, when sending an array as input argument, the +# output from Autograd is another array. This is the true gradient of +# the function, as opposed to the function in the previous example. By +# using arrays to represent the variables, the output from Autograd +# might be easier to work with, as the output is closer to what one +# could expect form a gradient-evaluting function. + +# ## Functions using mathematical functions from Numpy + +# In[19]: + + +import autograd.numpy as np +from autograd import grad +def f4(x): + return np.sqrt(1+x**2) + np.exp(x) + np.sin(2*np.pi*x) + +f4_grad = grad(f4) + +x = 2.7 + +# Print the computed derivative: +print("The computed derivative of f4 at x = %g is: %g"%(x,f4_grad(x))) + +# The analytical derivative is: x/sqrt(1 + x**2) + exp(x) + cos(2*pi*x)*2*pi +f4_grad_analytical = x/np.sqrt(1 + x**2) + np.exp(x) + np.cos(2*np.pi*x)*2*np.pi + +# Print the analytical gradient: +print("The analytical gradient of f4 at x = %g is: %g"%(x,f4_grad_analytical)) + + +# ## More autograd + +# In[20]: + + +import autograd.numpy as np +from autograd import grad +def f5(x): + if x >= 0: + return x**2 + else: + return -3*x + 1 + +f5_grad = grad(f5) + +x = 2.7 + +# Print the computed derivative: +print("The computed derivative of f5 at x = %g is: %g"%(x,f5_grad(x))) + + +# ## And with loops + +# In[21]: + + +import autograd.numpy as np +from autograd import grad +def f6_for(x): + val = 0 + for i in range(10): + val = val + x**i + return val + +def f6_while(x): + val = 0 + i = 0 + while i < 10: + val = val + x**i + i = i + 1 + return val + +f6_for_grad = grad(f6_for) +f6_while_grad = grad(f6_while) + +x = 0.5 + +# Print the computed derivaties of f6_for and f6_while +print("The computed derivative of f6_for at x = %g is: %g"%(x,f6_for_grad(x))) +print("The computed derivative of f6_while at x = %g is: %g"%(x,f6_while_grad(x))) + + +# In[22]: + + +import autograd.numpy as np +from autograd import grad +# Both of the functions are implementation of the sum: sum(x**i) for i = 0, ..., 9 +# The analytical derivative is: sum(i*x**(i-1)) +f6_grad_analytical = 0 +for i in range(10): + f6_grad_analytical += i*x**(i-1) + +print("The analytical derivative of f6 at x = %g is: %g"%(x,f6_grad_analytical)) + + +# ## Using recursion + +# In[23]: + + +import autograd.numpy as np +from autograd import grad + +def f7(n): # Assume that n is an integer + if n == 1 or n == 0: + return 1 + else: + return n*f7(n-1) + +f7_grad = grad(f7) + +n = 2.0 + +print("The computed derivative of f7 at n = %d is: %g"%(n,f7_grad(n))) + +# The function f7 is an implementation of the factorial of n. +# By using the product rule, one can find that the derivative is: + +f7_grad_analytical = 0 +for i in range(int(n)-1): + tmp = 1 + for k in range(int(n)-1): + if k != i: + tmp *= (n - k) + f7_grad_analytical += tmp + +print("The analytical derivative of f7 at n = %d is: %g"%(n,f7_grad_analytical)) + + +# Note that if n is equal to zero or one, Autograd will give an error message. This message appears when the output is independent on input. + +# ## Unsupported functions +# Autograd supports many features. However, there are some functions that is not supported (yet) by Autograd. +# +# Assigning a value to the variable being differentiated with respect to + +# In[24]: + + +import autograd.numpy as np +from autograd import grad +def f8(x): # Assume x is an array + x[2] = 3 + return x*2 + +f8_grad = grad(f8) + +x = 8.4 + +print("The derivative of f8 is:",f8_grad(x)) + + +# Here, Autograd tells us that an 'ArrayBox' does not support item assignment. The item assignment is done when the program tries to assign x[2] to the value 3. However, Autograd has implemented the computation of the derivative such that this assignment is not possible. + +# ## The syntax a.dot(b) when finding the dot product + +# In[25]: + + +import autograd.numpy as np +from autograd import grad +def f9(a): # Assume a is an array with 2 elements + b = np.array([1.0,2.0]) + return a.dot(b) + +f9_grad = grad(f9) + +x = np.array([1.0,0.0]) + +print("The derivative of f9 is:",f9_grad(x)) + + +# Here we are told that the 'dot' function does not belong to Autograd's +# version of a Numpy array. To overcome this, an alternative syntax +# which also computed the dot product can be used: + +# In[26]: + + +import autograd.numpy as np +from autograd import grad +def f9_alternative(x): # Assume a is an array with 2 elements + b = np.array([1.0,2.0]) + return np.dot(x,b) # The same as x_1*b_1 + x_2*b_2 + +f9_alternative_grad = grad(f9_alternative) + +x = np.array([3.0,0.0]) + +print("The gradient of f9 is:",f9_alternative_grad(x)) + +# The analytical gradient of the dot product of vectors x and b with two elements (x_1,x_2) and (b_1, b_2) respectively +# w.r.t x is (b_1, b_2). + + +# ## Recommended to avoid +# The documentation recommends to avoid inplace operations such as + +# In[27]: + + +a += b +a -= b +a*= b +a /=b + + +# ## Using Autograd with OLS +# +# We conclude the part on optmization by showing how we can make codes +# for linear regression and logistic regression using **autograd**. The +# first example shows results with ordinary leats squares. + +# In[28]: + + +# Using Autograd to calculate gradients for OLS +from random import random, seed +import numpy as np +import autograd.numpy as np +import matplotlib.pyplot as plt +from autograd import grad + +def CostOLS(beta): + return (1.0/n)*np.sum((y-X @ beta)**2) + +n = 100 +x = 2*np.random.rand(n,1) +y = 4+3*x+np.random.randn(n,1) + +X = np.c_[np.ones((n,1)), x] +XT_X = X.T @ X +theta_linreg = np.linalg.pinv(XT_X) @ (X.T @ y) +print("Own inversion") +print(theta_linreg) +# Hessian matrix +H = (2.0/n)* XT_X +EigValues, EigVectors = np.linalg.eig(H) +print(f"Eigenvalues of Hessian Matrix:{EigValues}") + +theta = np.random.randn(2,1) +eta = 1.0/np.max(EigValues) +Niterations = 1000 +# define the gradient +training_gradient = grad(CostOLS) + +for iter in range(Niterations): + gradients = training_gradient(theta) + theta -= eta*gradients +print("theta from own gd") +print(theta) + +xnew = np.array([[0],[2]]) +Xnew = np.c_[np.ones((2,1)), xnew] +ypredict = Xnew.dot(theta) +ypredict2 = Xnew.dot(theta_linreg) + +plt.plot(xnew, ypredict, "r-") +plt.plot(xnew, ypredict2, "b-") +plt.plot(x, y ,'ro') +plt.axis([0,2.0,0, 15.0]) +plt.xlabel(r'$x$') +plt.ylabel(r'$y$') +plt.title(r'Random numbers ') +plt.show() + + +# ## Same code but now with momentum gradient descent + +# In[29]: + + +# Using Autograd to calculate gradients for OLS +from random import random, seed +import numpy as np +import autograd.numpy as np +import matplotlib.pyplot as plt +from autograd import grad + +def CostOLS(beta): + return (1.0/n)*np.sum((y-X @ beta)**2) + +n = 100 +x = 2*np.random.rand(n,1) +y = 4+3*x#+np.random.randn(n,1) + +X = np.c_[np.ones((n,1)), x] +XT_X = X.T @ X +theta_linreg = np.linalg.pinv(XT_X) @ (X.T @ y) +print("Own inversion") +print(theta_linreg) +# Hessian matrix +H = (2.0/n)* XT_X +EigValues, EigVectors = np.linalg.eig(H) +print(f"Eigenvalues of Hessian Matrix:{EigValues}") + +theta = np.random.randn(2,1) +eta = 1.0/np.max(EigValues) +Niterations = 30 + +# define the gradient +training_gradient = grad(CostOLS) + +for iter in range(Niterations): + gradients = training_gradient(theta) + theta -= eta*gradients + print(iter,gradients[0],gradients[1]) +print("theta from own gd") +print(theta) + +# Now improve with momentum gradient descent +change = 0.0 +delta_momentum = 0.3 +for iter in range(Niterations): + # calculate gradient + gradients = training_gradient(theta) + # calculate update + new_change = eta*gradients+delta_momentum*change + # take a step + theta -= new_change + # save the change + change = new_change + print(iter,gradients[0],gradients[1]) +print("theta from own gd wth momentum") +print(theta) + + +# ## But noen of these can compete with Newton's method + +# In[30]: + + +# Using Newton's method +from random import random, seed +import numpy as np +import autograd.numpy as np +import matplotlib.pyplot as plt +from autograd import grad + +def CostOLS(beta): + return (1.0/n)*np.sum((y-X @ beta)**2) + +n = 100 +x = 2*np.random.rand(n,1) +y = 4+3*x+np.random.randn(n,1) + +X = np.c_[np.ones((n,1)), x] +XT_X = X.T @ X +beta_linreg = np.linalg.pinv(XT_X) @ (X.T @ y) +print("Own inversion") +print(beta_linreg) +# Hessian matrix +H = (2.0/n)* XT_X +# Note that here the Hessian does not depend on the parameters beta +invH = np.linalg.pinv(H) +EigValues, EigVectors = np.linalg.eig(H) +print(f"Eigenvalues of Hessian Matrix:{EigValues}") + +beta = np.random.randn(2,1) +Niterations = 5 + +# define the gradient +training_gradient = grad(CostOLS) + +for iter in range(Niterations): + gradients = training_gradient(beta) + beta -= invH @ gradients + print(iter,gradients[0],gradients[1]) +print("beta from own Newton code") +print(beta) + + +# ## Including Stochastic Gradient Descent with Autograd +# In this code we include the stochastic gradient descent approach discussed above. Note here that we specify which argument we are taking the derivative with respect to when using **autograd**. + +# In[31]: + + +# Using Autograd to calculate gradients using SGD +# OLS example +from random import random, seed +import numpy as np +import autograd.numpy as np +import matplotlib.pyplot as plt +from autograd import grad + +# Note change from previous example +def CostOLS(y,X,theta): + return np.sum((y-X @ theta)**2) + +n = 100 +x = 2*np.random.rand(n,1) +y = 4+3*x+np.random.randn(n,1) + +X = np.c_[np.ones((n,1)), x] +XT_X = X.T @ X +theta_linreg = np.linalg.pinv(XT_X) @ (X.T @ y) +print("Own inversion") +print(theta_linreg) +# Hessian matrix +H = (2.0/n)* XT_X +EigValues, EigVectors = np.linalg.eig(H) +print(f"Eigenvalues of Hessian Matrix:{EigValues}") + +theta = np.random.randn(2,1) +eta = 1.0/np.max(EigValues) +Niterations = 1000 + +# Note that we request the derivative wrt third argument (theta, 2 here) +training_gradient = grad(CostOLS,2) + +for iter in range(Niterations): + gradients = (1.0/n)*training_gradient(y, X, theta) + theta -= eta*gradients +print("theta from own gd") +print(theta) + +xnew = np.array([[0],[2]]) +Xnew = np.c_[np.ones((2,1)), xnew] +ypredict = Xnew.dot(theta) +ypredict2 = Xnew.dot(theta_linreg) + +plt.plot(xnew, ypredict, "r-") +plt.plot(xnew, ypredict2, "b-") +plt.plot(x, y ,'ro') +plt.axis([0,2.0,0, 15.0]) +plt.xlabel(r'$x$') +plt.ylabel(r'$y$') +plt.title(r'Random numbers ') +plt.show() + +n_epochs = 50 +M = 5 #size of each minibatch +m = int(n/M) #number of minibatches +t0, t1 = 5, 50 +def learning_schedule(t): + return t0/(t+t1) + +theta = np.random.randn(2,1) + +for epoch in range(n_epochs): +# Can you figure out a better way of setting up the contributions to each batch? + for i in range(m): + random_index = M*np.random.randint(m) + xi = X[random_index:random_index+M] + yi = y[random_index:random_index+M] + gradients = (1.0/M)*training_gradient(yi, xi, theta) + eta = learning_schedule(epoch*m+i) + theta = theta - eta*gradients +print("theta from own sdg") +print(theta) + + +# ## Same code but now with momentum gradient descent + +# In[32]: + + +# Using Autograd to calculate gradients using SGD +# OLS example +from random import random, seed +import numpy as np +import autograd.numpy as np +import matplotlib.pyplot as plt +from autograd import grad + +# Note change from previous example +def CostOLS(y,X,theta): + return np.sum((y-X @ theta)**2) + +n = 100 +x = 2*np.random.rand(n,1) +y = 4+3*x+np.random.randn(n,1) + +X = np.c_[np.ones((n,1)), x] +XT_X = X.T @ X +theta_linreg = np.linalg.pinv(XT_X) @ (X.T @ y) +print("Own inversion") +print(theta_linreg) +# Hessian matrix +H = (2.0/n)* XT_X +EigValues, EigVectors = np.linalg.eig(H) +print(f"Eigenvalues of Hessian Matrix:{EigValues}") + +theta = np.random.randn(2,1) +eta = 1.0/np.max(EigValues) +Niterations = 100 + +# Note that we request the derivative wrt third argument (theta, 2 here) +training_gradient = grad(CostOLS,2) + +for iter in range(Niterations): + gradients = (1.0/n)*training_gradient(y, X, theta) + theta -= eta*gradients +print("theta from own gd") +print(theta) + + +n_epochs = 50 +M = 5 #size of each minibatch +m = int(n/M) #number of minibatches +t0, t1 = 5, 50 +def learning_schedule(t): + return t0/(t+t1) + +theta = np.random.randn(2,1) + +change = 0.0 +delta_momentum = 0.3 + +for epoch in range(n_epochs): + for i in range(m): + random_index = M*np.random.randint(m) + xi = X[random_index:random_index+M] + yi = y[random_index:random_index+M] + gradients = (1.0/M)*training_gradient(yi, xi, theta) + eta = learning_schedule(epoch*m+i) + # calculate update + new_change = eta*gradients+delta_momentum*change + # take a step + theta -= new_change + # save the change + change = new_change +print("theta from own sdg with momentum") +print(theta) + + +# ## Similar (second order function now) problem but now with AdaGrad + +# In[33]: + + +# Using Autograd to calculate gradients using AdaGrad and Stochastic Gradient descent +# OLS example +from random import random, seed +import numpy as np +import autograd.numpy as np +import matplotlib.pyplot as plt +from autograd import grad + +# Note change from previous example +def CostOLS(y,X,theta): + return np.sum((y-X @ theta)**2) + +n = 1000 +x = np.random.rand(n,1) +y = 2.0+3*x +4*x*x + +X = np.c_[np.ones((n,1)), x, x*x] +XT_X = X.T @ X +theta_linreg = np.linalg.pinv(XT_X) @ (X.T @ y) +print("Own inversion") +print(theta_linreg) + + +# Note that we request the derivative wrt third argument (theta, 2 here) +training_gradient = grad(CostOLS,2) +# Define parameters for Stochastic Gradient Descent +n_epochs = 50 +M = 5 #size of each minibatch +m = int(n/M) #number of minibatches +# Guess for unknown parameters theta +theta = np.random.randn(3,1) + +# Value for learning rate +eta = 0.01 +# Including AdaGrad parameter to avoid possible division by zero +delta = 1e-8 +for epoch in range(n_epochs): + Giter = 0.0 + for i in range(m): + random_index = M*np.random.randint(m) + xi = X[random_index:random_index+M] + yi = y[random_index:random_index+M] + gradients = (1.0/M)*training_gradient(yi, xi, theta) + Giter += gradients*gradients + update = gradients*eta/(delta+np.sqrt(Giter)) + theta -= update +print("theta from own AdaGrad") +print(theta) + + +# Running this code we note an almost perfect agreement with the results from matrix inversion. + +# ## RMSprop for adaptive learning rate with Stochastic Gradient Descent + +# In[34]: + + +# Using Autograd to calculate gradients using RMSprop and Stochastic Gradient descent +# OLS example +from random import random, seed +import numpy as np +import autograd.numpy as np +import matplotlib.pyplot as plt +from autograd import grad + +# Note change from previous example +def CostOLS(y,X,theta): + return np.sum((y-X @ theta)**2) + +n = 1000 +x = np.random.rand(n,1) +y = 2.0+3*x +4*x*x# +np.random.randn(n,1) + +X = np.c_[np.ones((n,1)), x, x*x] +XT_X = X.T @ X +theta_linreg = np.linalg.pinv(XT_X) @ (X.T @ y) +print("Own inversion") +print(theta_linreg) + + +# Note that we request the derivative wrt third argument (theta, 2 here) +training_gradient = grad(CostOLS,2) +# Define parameters for Stochastic Gradient Descent +n_epochs = 50 +M = 5 #size of each minibatch +m = int(n/M) #number of minibatches +# Guess for unknown parameters theta +theta = np.random.randn(3,1) + +# Value for learning rate +eta = 0.01 +# Value for parameter rho +rho = 0.99 +# Including AdaGrad parameter to avoid possible division by zero +delta = 1e-8 +for epoch in range(n_epochs): + Giter = 0.0 + for i in range(m): + random_index = M*np.random.randint(m) + xi = X[random_index:random_index+M] + yi = y[random_index:random_index+M] + gradients = (1.0/M)*training_gradient(yi, xi, theta) + # Accumulated gradient + # Scaling with rho the new and the previous results + Giter = (rho*Giter+(1-rho)*gradients*gradients) + # Taking the diagonal only and inverting + update = gradients*eta/(delta+np.sqrt(Giter)) + # Hadamard product + theta -= update +print("theta from own RMSprop") +print(theta) + + +# ## And finally [ADAM](https://arxiv.org/pdf/1412.6980.pdf) + +# In[35]: + + +# Using Autograd to calculate gradients using RMSprop and Stochastic Gradient descent +# OLS example +from random import random, seed +import numpy as np +import autograd.numpy as np +import matplotlib.pyplot as plt +from autograd import grad + +# Note change from previous example +def CostOLS(y,X,theta): + return np.sum((y-X @ theta)**2) + +n = 1000 +x = np.random.rand(n,1) +y = 2.0+3*x +4*x*x# +np.random.randn(n,1) + +X = np.c_[np.ones((n,1)), x, x*x] +XT_X = X.T @ X +theta_linreg = np.linalg.pinv(XT_X) @ (X.T @ y) +print("Own inversion") +print(theta_linreg) + + +# Note that we request the derivative wrt third argument (theta, 2 here) +training_gradient = grad(CostOLS,2) +# Define parameters for Stochastic Gradient Descent +n_epochs = 50 +M = 5 #size of each minibatch +m = int(n/M) #number of minibatches +# Guess for unknown parameters theta +theta = np.random.randn(3,1) + +# Value for learning rate +eta = 0.01 +# Value for parameters beta1 and beta2, see https://arxiv.org/abs/1412.6980 +beta1 = 0.9 +beta2 = 0.999 +# Including AdaGrad parameter to avoid possible division by zero +delta = 1e-7 +iter = 0 +for epoch in range(n_epochs): + first_moment = 0.0 + second_moment = 0.0 + iter += 1 + for i in range(m): + random_index = M*np.random.randint(m) + xi = X[random_index:random_index+M] + yi = y[random_index:random_index+M] + gradients = (1.0/M)*training_gradient(yi, xi, theta) + # Computing moments first + first_moment = beta1*first_moment + (1-beta1)*gradients + second_moment = beta2*second_moment+(1-beta2)*gradients*gradients + first_term = first_moment/(1.0-beta1**iter) + second_term = second_moment/(1.0-beta2**iter) + # Scaling with rho the new and the previous results + update = eta*first_term/(np.sqrt(second_term)+delta) + theta -= update +print("theta from own ADAM") +print(theta) + + +# ## And Logistic Regression + +# In[36]: + + +import autograd.numpy as np +from autograd import grad + +def sigmoid(x): + return 0.5 * (np.tanh(x / 2.) + 1) + +def logistic_predictions(weights, inputs): + # Outputs probability of a label being true according to logistic model. + return sigmoid(np.dot(inputs, weights)) + +def training_loss(weights): + # Training loss is the negative log-likelihood of the training labels. + preds = logistic_predictions(weights, inputs) + label_probabilities = preds * targets + (1 - preds) * (1 - targets) + return -np.sum(np.log(label_probabilities)) + +# Build a toy dataset. +inputs = np.array([[0.52, 1.12, 0.77], + [0.88, -1.08, 0.15], + [0.52, 0.06, -1.30], + [0.74, -2.49, 1.39]]) +targets = np.array([True, True, False, True]) + +# Define a function that returns gradients of training loss using Autograd. +training_gradient_fun = grad(training_loss) + +# Optimize weights using gradient descent. +weights = np.array([0.0, 0.0, 0.0]) +print("Initial loss:", training_loss(weights)) +for i in range(100): + weights -= training_gradient_fun(weights) * 0.01 + +print("Trained loss:", training_loss(weights)) + + +# ## Introducing [JAX](https://jax.readthedocs.io/en/latest/) +# +# Presently, instead of using **autograd**, we recommend using [JAX](https://jax.readthedocs.io/en/latest/) +# +# **JAX** is Autograd and [XLA (Accelerated Linear Algebra))](https://www.tensorflow.org/xla), +# brought together for high-performance numerical computing and machine learning research. +# It provides composable transformations of Python+NumPy programs: differentiate, vectorize, parallelize, Just-In-Time compile to GPU/TPU, and more. +# +# Here's a simple example on how you can use **JAX** to compute the derivate of the logistic function. + +# In[37]: + + +import jax.numpy as jnp +from jax import grad, jit, vmap + +def sum_logistic(x): + return jnp.sum(1.0 / (1.0 + jnp.exp(-x))) + +x_small = jnp.arange(3.) +derivative_fn = grad(sum_logistic) +print(derivative_fn(x_small)) + diff --git a/doc/LectureNotes/_build/jupyter_execute/week39_80_2.png b/doc/LectureNotes/_build/jupyter_execute/week39_80_2.png new file mode 100644 index 000000000..acb1fcb54 Binary files /dev/null and b/doc/LectureNotes/_build/jupyter_execute/week39_80_2.png differ diff --git a/doc/LectureNotes/_build/jupyter_execute/week39_82_0.png b/doc/LectureNotes/_build/jupyter_execute/week39_82_0.png new file mode 100644 index 000000000..7ebaf5695 Binary files /dev/null and b/doc/LectureNotes/_build/jupyter_execute/week39_82_0.png differ diff --git a/doc/LectureNotes/_build/jupyter_execute/week39_88_1.png b/doc/LectureNotes/_build/jupyter_execute/week39_88_1.png new file mode 100644 index 000000000..8edc8dbdf Binary files /dev/null and b/doc/LectureNotes/_build/jupyter_execute/week39_88_1.png differ diff --git a/doc/LectureNotes/_build/jupyter_execute/week40.ipynb b/doc/LectureNotes/_build/jupyter_execute/week40.ipynb new file mode 100644 index 000000000..b61608683 --- /dev/null +++ b/doc/LectureNotes/_build/jupyter_execute/week40.ipynb @@ -0,0 +1,3712 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "c410abdb", + "metadata": { + "editable": true + }, + "source": [ + "\n", + "" + ] + }, + { + "cell_type": "markdown", + "id": "ffdc5797", + "metadata": { + "editable": true + }, + "source": [ + "# Week 40: Gradient descent methods (continued) and start Neural networks\n", + "**Morten Hjorth-Jensen**, Department of Physics, University of Oslo, Norway and Department of Physics and Astronomy and Facility for Rare Ion Beams, Michigan State University, USA\n", + "\n", + "Date: **October 2-6, 2023**" + ] + }, + { + "cell_type": "markdown", + "id": "4bab315b", + "metadata": { + "editable": true + }, + "source": [ + "## Plans for week 40\n", + "\n", + "**Material for the active learning sessions on Tuesday and Wednesday.**\n", + "\n", + " * Work on project 1 and discussions on how to structure your report\n", + "\n", + " * No weekly exercises for week 40, project work only\n", + "\n", + " * [Video on how to write scientific reports recorded during one of the lab sessions](https://youtu.be/tVW1ZDmZnwM)\n", + "\n", + " * A general guideline can be found at .\n", + "\n", + " \n", + "\n", + "**Material for the lecture on Thursday October 5, 2023.**\n", + "\n", + " * Stochastic Gradient descent with examples and automatic differentiation\n", + "\n", + " * Neural Networks, setting up the basic steps, from the simple perceptron model to the multi-layer perceptron model. \n", + "\n", + " * Readings and Videos:\n", + "\n", + " * These lecture notes\n", + "\n", + " * For a good discussion on gradient methods, we would like to recommend Goodfellow et al section 4.3-4.5 and sections 8.3-8.6. We will come back to the latter chapter in our discussion of Neural networks as well.\n", + "\n", + " * [Aurelien Geron's chapter 4 on stochastic gradient descent](https://github.com/CompPhysics/MachineLearning/blob/master/doc/Textbooks/TensorflowML.pdf)\n", + "\n", + " * For neural networks we recommend Goodfellow et al chapter 6.\n", + "\n", + " * [Video on gradient descent](https://www.youtube.com/watch?v=sDv4f4s2SB8)\n", + "\n", + " * [Video on stochastic gradient descent](https://www.youtube.com/watch?v=vMh0zPT0tLI)\n", + "\n", + " * [Neural Networks demystified](https://www.youtube.com/watch?v=bxe2T-V8XRs&list=PLiaHhY2iBX9hdHaRr6b7XevZtgZRa1PoU&ab_channel=WelchLabs)\n", + "\n", + " * [Building Neural Networks from scratch](https://www.youtube.com/watch?v=Wo5dMEP_BbI&list=PLQVvvaa0QuDcjD5BAw2DxE6OF2tius3V3&ab_channel=sentdex)" + ] + }, + { + "cell_type": "markdown", + "id": "1ba76140", + "metadata": { + "editable": true + }, + "source": [ + "## Summary from last week, using gradient descent methods, limitations\n", + "\n", + "* **Gradient descent (GD) finds local minima of our function**. Since the GD algorithm is deterministic, if it converges, it will converge to a local minimum of our cost/loss/risk function. Because in ML we are often dealing with extremely rugged landscapes with many local minima, this can lead to poor performance.\n", + "\n", + "* **GD is sensitive to initial conditions**. One consequence of the local nature of GD is that initial conditions matter. Depending on where one starts, one will end up at a different local minima. Therefore, it is very important to think about how one initializes the training process. This is true for GD as well as more complicated variants of GD.\n", + "\n", + "* **Gradients are computationally expensive to calculate for large datasets**. In many cases in statistics and ML, the cost/loss/risk function is a sum of terms, with one term for each data point. For example, in linear regression, $E \\propto \\sum_{i=1}^n (y_i - \\mathbf{w}^T\\cdot\\mathbf{x}_i)^2$; for logistic regression, the square error is replaced by the cross entropy. To calculate the gradient we have to sum over *all* $n$ data points. Doing this at every GD step becomes extremely computationally expensive. An ingenious solution to this, is to calculate the gradients using small subsets of the data called \"mini batches\". This has the added benefit of introducing stochasticity into our algorithm.\n", + "\n", + "* **GD is very sensitive to choices of learning rates**. GD is extremely sensitive to the choice of learning rates. If the learning rate is very small, the training process take an extremely long time. For larger learning rates, GD can diverge and give poor results. Furthermore, depending on what the local landscape looks like, we have to modify the learning rates to ensure convergence. Ideally, we would *adaptively* choose the learning rates to match the landscape.\n", + "\n", + "* **GD treats all directions in parameter space uniformly.** Another major drawback of GD is that unlike Newton's method, the learning rate for GD is the same in all directions in parameter space. For this reason, the maximum learning rate is set by the behavior of the steepest direction and this can significantly slow down training. Ideally, we would like to take large steps in flat directions and small steps in steep directions. Since we are exploring rugged landscapes where curvatures change, this requires us to keep track of not only the gradient but second derivatives. The ideal scenario would be to calculate the Hessian but this proves to be too computationally expensive. \n", + "\n", + "* GD can take exponential time to escape saddle points, even with random initialization. As we mentioned, GD is extremely sensitive to initial condition since it determines the particular local minimum GD would eventually reach. However, even with a good initialization scheme, through the introduction of randomness, GD can still take exponential time to escape saddle points." + ] + }, + { + "cell_type": "markdown", + "id": "a8b56c00", + "metadata": { + "editable": true + }, + "source": [ + "## Overview video on Stochastic Gradient Descent\n", + "\n", + "[What is Stochastic Gradient Descent](https://www.youtube.com/watch?v=vMh0zPT0tLI&ab_channel=StatQuestwithJoshStarmer)" + ] + }, + { + "cell_type": "markdown", + "id": "eba32497", + "metadata": { + "editable": true + }, + "source": [ + "## Batches and mini-batches\n", + "\n", + "In gradient descent we compute the cost function and its gradient for all data points we have.\n", + "\n", + "In large-scale applications such as the [ILSVRC challenge](https://www.image-net.org/challenges/LSVRC/), the\n", + "training data can have on order of millions of examples. Hence, it\n", + "seems wasteful to compute the full cost function over the entire\n", + "training set in order to perform only a single parameter update. A\n", + "very common approach to addressing this challenge is to compute the\n", + "gradient over batches of the training data. For example, a typical batch could contain some thousand examples from\n", + "an entire training set of several millions. This batch is then used to\n", + "perform a parameter update." + ] + }, + { + "cell_type": "markdown", + "id": "55578599", + "metadata": { + "editable": true + }, + "source": [ + "## Stochastic Gradient Descent (SGD)\n", + "\n", + "In stochastic gradient descent, the extreme case is the case where we\n", + "have only one batch, that is we include the whole data set.\n", + "\n", + "This process is called Stochastic Gradient\n", + "Descent (SGD) (or also sometimes on-line gradient descent). This is\n", + "relatively less common to see because in practice due to vectorized\n", + "code optimizations it can be computationally much more efficient to\n", + "evaluate the gradient for 100 examples, than the gradient for one\n", + "example 100 times. Even though SGD technically refers to using a\n", + "single example at a time to evaluate the gradient, you will hear\n", + "people use the term SGD even when referring to mini-batch gradient\n", + "descent (i.e. mentions of MGD for “Minibatch Gradient Descent”, or BGD\n", + "for “Batch gradient descent” are rare to see), where it is usually\n", + "assumed that mini-batches are used. The size of the mini-batch is a\n", + "hyperparameter but it is not very common to cross-validate or bootstrap it. It is\n", + "usually based on memory constraints (if any), or set to some value,\n", + "e.g. 32, 64 or 128. We use powers of 2 in practice because many\n", + "vectorized operation implementations work faster when their inputs are\n", + "sized in powers of 2.\n", + "\n", + "In our notes with SGD we mean stochastic gradient descent with mini-batches." + ] + }, + { + "cell_type": "markdown", + "id": "140607b7", + "metadata": { + "editable": true + }, + "source": [ + "## Stochastic Gradient Descent\n", + "\n", + "Stochastic gradient descent (SGD) and variants thereof address some of\n", + "the shortcomings of the Gradient descent method discussed above.\n", + "\n", + "The underlying idea of SGD comes from the observation that the cost\n", + "function, which we want to minimize, can almost always be written as a\n", + "sum over $n$ data points $\\{\\mathbf{x}_i\\}_{i=1}^n$," + ] + }, + { + "cell_type": "markdown", + "id": "c1a00332", + "metadata": { + "editable": true + }, + "source": [ + "$$\n", + "C(\\mathbf{\\beta}) = \\sum_{i=1}^n c_i(\\mathbf{x}_i,\n", + "\\mathbf{\\beta}).\n", + "$$" + ] + }, + { + "cell_type": "markdown", + "id": "2c04bdee", + "metadata": { + "editable": true + }, + "source": [ + "## Computation of gradients\n", + "\n", + "This in turn means that the gradient can be\n", + "computed as a sum over $i$-gradients" + ] + }, + { + "cell_type": "markdown", + "id": "087684a4", + "metadata": { + "editable": true + }, + "source": [ + "$$\n", + "\\nabla_\\beta C(\\mathbf{\\beta}) = \\sum_i^n \\nabla_\\beta c_i(\\mathbf{x}_i,\n", + "\\mathbf{\\beta}).\n", + "$$" + ] + }, + { + "cell_type": "markdown", + "id": "e0362df4", + "metadata": { + "editable": true + }, + "source": [ + "Stochasticity/randomness is introduced by only taking the\n", + "gradient on a subset of the data called minibatches. If there are $n$\n", + "data points and the size of each minibatch is $M$, there will be $n/M$\n", + "minibatches. We denote these minibatches by $B_k$ where\n", + "$k=1,\\cdots,n/M$." + ] + }, + { + "cell_type": "markdown", + "id": "24051a4e", + "metadata": { + "editable": true + }, + "source": [ + "## SGD example\n", + "As an example, suppose we have $10$ data points $(\\mathbf{x}_1,\\cdots, \\mathbf{x}_{10})$ \n", + "and we choose to have $M=5$ minibathces,\n", + "then each minibatch contains two data points. In particular we have\n", + "$B_1 = (\\mathbf{x}_1,\\mathbf{x}_2), \\cdots, B_5 =\n", + "(\\mathbf{x}_9,\\mathbf{x}_{10})$. Note that if you choose $M=1$ you\n", + "have only a single batch with all data points and on the other extreme,\n", + "you may choose $M=n$ resulting in a minibatch for each datapoint, i.e\n", + "$B_k = \\mathbf{x}_k$.\n", + "\n", + "The idea is now to approximate the gradient by replacing the sum over\n", + "all data points with a sum over the data points in one the minibatches\n", + "picked at random in each gradient descent step" + ] + }, + { + "cell_type": "markdown", + "id": "7723f927", + "metadata": { + "editable": true + }, + "source": [ + "$$\n", + "\\nabla_{\\beta}\n", + "C(\\mathbf{\\beta}) = \\sum_{i=1}^n \\nabla_\\beta c_i(\\mathbf{x}_i,\n", + "\\mathbf{\\beta}) \\rightarrow \\sum_{i \\in B_k}^n \\nabla_\\beta\n", + "c_i(\\mathbf{x}_i, \\mathbf{\\beta}).\n", + "$$" + ] + }, + { + "cell_type": "markdown", + "id": "59221981", + "metadata": { + "editable": true + }, + "source": [ + "## The gradient step\n", + "\n", + "Thus a gradient descent step now looks like" + ] + }, + { + "cell_type": "markdown", + "id": "a7d27b48", + "metadata": { + "editable": true + }, + "source": [ + "$$\n", + "\\beta_{j+1} = \\beta_j - \\gamma_j \\sum_{i \\in B_k}^n \\nabla_\\beta c_i(\\mathbf{x}_i,\n", + "\\mathbf{\\beta})\n", + "$$" + ] + }, + { + "cell_type": "markdown", + "id": "c7595344", + "metadata": { + "editable": true + }, + "source": [ + "where $k$ is picked at random with equal\n", + "probability from $[1,n/M]$. An iteration over the number of\n", + "minibathces (n/M) is commonly referred to as an epoch. Thus it is\n", + "typical to choose a number of epochs and for each epoch iterate over\n", + "the number of minibatches, as exemplified in the code below." + ] + }, + { + "cell_type": "markdown", + "id": "0d7024b5", + "metadata": { + "editable": true + }, + "source": [ + "## Simple example code" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "0f9dc38b", + "metadata": { + "collapsed": false, + "editable": true + }, + "outputs": [], + "source": [ + "import numpy as np \n", + "\n", + "n = 100 #100 datapoints \n", + "M = 5 #size of each minibatch\n", + "m = int(n/M) #number of minibatches\n", + "n_epochs = 10 #number of epochs\n", + "\n", + "j = 0\n", + "for epoch in range(1,n_epochs+1):\n", + " for i in range(m):\n", + " k = np.random.randint(m) #Pick the k-th minibatch at random\n", + " #Compute the gradient using the data in minibatch Bk\n", + " #Compute new suggestion for \n", + " j += 1" + ] + }, + { + "cell_type": "markdown", + "id": "df447303", + "metadata": { + "editable": true + }, + "source": [ + "Taking the gradient only on a subset of the data has two important\n", + "benefits. First, it introduces randomness which decreases the chance\n", + "that our opmization scheme gets stuck in a local minima. Second, if\n", + "the size of the minibatches are small relative to the number of\n", + "datapoints ($M < n$), the computation of the gradient is much\n", + "cheaper since we sum over the datapoints in the $k-th$ minibatch and not\n", + "all $n$ datapoints." + ] + }, + { + "cell_type": "markdown", + "id": "976aef35", + "metadata": { + "editable": true + }, + "source": [ + "## When do we stop?\n", + "\n", + "A natural question is when do we stop the search for a new minimum?\n", + "One possibility is to compute the full gradient after a given number\n", + "of epochs and check if the norm of the gradient is smaller than some\n", + "threshold and stop if true. However, the condition that the gradient\n", + "is zero is valid also for local minima, so this would only tell us\n", + "that we are close to a local/global minimum. However, we could also\n", + "evaluate the cost function at this point, store the result and\n", + "continue the search. If the test kicks in at a later stage we can\n", + "compare the values of the cost function and keep the $\\beta$ that\n", + "gave the lowest value." + ] + }, + { + "cell_type": "markdown", + "id": "0fab1ae1", + "metadata": { + "editable": true + }, + "source": [ + "## Slightly different approach\n", + "\n", + "Another approach is to let the step length $\\gamma_j$ depend on the\n", + "number of epochs in such a way that it becomes very small after a\n", + "reasonable time such that we do not move at all. Such approaches are\n", + "also called scaling. There are many such ways to [scale the learning\n", + "rate](https://towardsdatascience.com/gradient-descent-the-learning-rate-and-the-importance-of-feature-scaling-6c0b416596e1)\n", + "and [discussions here](https://www.jmlr.org/papers/volume23/20-1258/20-1258.pdf). See\n", + "also\n", + "\n", + "for a discussion of different scaling functions for the learning rate." + ] + }, + { + "cell_type": "markdown", + "id": "2db0116b", + "metadata": { + "editable": true + }, + "source": [ + "## Time decay rate\n", + "\n", + "As an example, let $e = 0,1,2,3,\\cdots$ denote the current epoch and let $t_0, t_1 > 0$ be two fixed numbers. Furthermore, let $t = e \\cdot m + i$ where $m$ is the number of minibatches and $i=0,\\cdots,m-1$. Then the function $$\\gamma_j(t; t_0, t_1) = \\frac{t_0}{t+t_1} $$ goes to zero as the number of epochs gets large. I.e. we start with a step length $\\gamma_j (0; t_0, t_1) = t_0/t_1$ which decays in *time* $t$.\n", + "\n", + "In this way we can fix the number of epochs, compute $\\beta$ and\n", + "evaluate the cost function at the end. Repeating the computation will\n", + "give a different result since the scheme is random by design. Then we\n", + "pick the final $\\beta$ that gives the lowest value of the cost\n", + "function." + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "a9ca6f9a", + "metadata": { + "collapsed": false, + "editable": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "gamma_j after 500 epochs: 9.97108e-05\n" + ] + } + ], + "source": [ + "import numpy as np \n", + "\n", + "def step_length(t,t0,t1):\n", + " return t0/(t+t1)\n", + "\n", + "n = 100 #100 datapoints \n", + "M = 5 #size of each minibatch\n", + "m = int(n/M) #number of minibatches\n", + "n_epochs = 500 #number of epochs\n", + "t0 = 1.0\n", + "t1 = 10\n", + "\n", + "gamma_j = t0/t1\n", + "j = 0\n", + "for epoch in range(1,n_epochs+1):\n", + " for i in range(m):\n", + " k = np.random.randint(m) #Pick the k-th minibatch at random\n", + " #Compute the gradient using the data in minibatch Bk\n", + " #Compute new suggestion for beta\n", + " t = epoch*m+i\n", + " gamma_j = step_length(t,t0,t1)\n", + " j += 1\n", + "\n", + "print(\"gamma_j after %d epochs: %g\" % (n_epochs,gamma_j))" + ] + }, + { + "cell_type": "markdown", + "id": "fcf9b69b", + "metadata": { + "editable": true + }, + "source": [ + "## Code with a Number of Minibatches which varies\n", + "\n", + "In the code here we vary the number of mini-batches." + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "861b050f", + "metadata": { + "collapsed": false, + "editable": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Own inversion\n", + "[[4.06481015]\n", + " [2.84666445]]\n", + "Eigenvalues of Hessian Matrix:[0.28638913 4.44842116]\n", + "theta from own gd\n", + "[[4.06481015]\n", + " [2.84666445]]\n", + "theta from own sdg\n", + "[[4.02231445]\n", + " [2.89996783]]\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
              " + ] + }, + "metadata": { + "filenames": { + "image/png": "/Users/mhjensen/Teaching/MachineLearning/doc/LectureNotes/_build/jupyter_execute/week40_25_1.png" + } + }, + "output_type": "display_data" + } + ], + "source": [ + "%matplotlib inline\n", + "\n", + "# Importing various packages\n", + "from math import exp, sqrt\n", + "from random import random, seed\n", + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "\n", + "n = 100\n", + "x = 2*np.random.rand(n,1)\n", + "y = 4+3*x+np.random.randn(n,1)\n", + "\n", + "X = np.c_[np.ones((n,1)), x]\n", + "XT_X = X.T @ X\n", + "theta_linreg = np.linalg.inv(X.T @ X) @ (X.T @ y)\n", + "print(\"Own inversion\")\n", + "print(theta_linreg)\n", + "# Hessian matrix\n", + "H = (2.0/n)* XT_X\n", + "EigValues, EigVectors = np.linalg.eig(H)\n", + "print(f\"Eigenvalues of Hessian Matrix:{EigValues}\")\n", + "\n", + "theta = np.random.randn(2,1)\n", + "eta = 1.0/np.max(EigValues)\n", + "Niterations = 1000\n", + "\n", + "\n", + "for iter in range(Niterations):\n", + " gradients = 2.0/n*X.T @ ((X @ theta)-y)\n", + " theta -= eta*gradients\n", + "print(\"theta from own gd\")\n", + "print(theta)\n", + "\n", + "xnew = np.array([[0],[2]])\n", + "Xnew = np.c_[np.ones((2,1)), xnew]\n", + "ypredict = Xnew.dot(theta)\n", + "ypredict2 = Xnew.dot(theta_linreg)\n", + "\n", + "n_epochs = 50\n", + "M = 5 #size of each minibatch\n", + "m = int(n/M) #number of minibatches\n", + "t0, t1 = 5, 50\n", + "\n", + "def learning_schedule(t):\n", + " return t0/(t+t1)\n", + "\n", + "theta = np.random.randn(2,1)\n", + "\n", + "for epoch in range(n_epochs):\n", + "# Can you figure out a better way of setting up the contributions to each batch?\n", + " for i in range(m):\n", + " random_index = M*np.random.randint(m)\n", + " xi = X[random_index:random_index+M]\n", + " yi = y[random_index:random_index+M]\n", + " gradients = (2.0/M)* xi.T @ ((xi @ theta)-yi)\n", + " eta = learning_schedule(epoch*m+i)\n", + " theta = theta - eta*gradients\n", + "print(\"theta from own sdg\")\n", + "print(theta)\n", + "\n", + "plt.plot(xnew, ypredict, \"r-\")\n", + "plt.plot(xnew, ypredict2, \"b-\")\n", + "plt.plot(x, y ,'ro')\n", + "plt.axis([0,2.0,0, 15.0])\n", + "plt.xlabel(r'$x$')\n", + "plt.ylabel(r'$y$')\n", + "plt.title(r'Random numbers ')\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "97311aec", + "metadata": { + "editable": true + }, + "source": [ + "## Replace or not\n", + "\n", + "In the above code, we have use replacement in setting up the\n", + "mini-batches. The discussion\n", + "[here](https://sebastianraschka.com/faq/docs/sgd-methods.html) may be\n", + "useful." + ] + }, + { + "cell_type": "markdown", + "id": "423ddc16", + "metadata": { + "editable": true + }, + "source": [ + "## Momentum based GD\n", + "\n", + "The stochastic gradient descent (SGD) is almost always used with a\n", + "*momentum* or inertia term that serves as a memory of the direction we\n", + "are moving in parameter space. This is typically implemented as\n", + "follows" + ] + }, + { + "cell_type": "markdown", + "id": "a4f85670", + "metadata": { + "editable": true + }, + "source": [ + "$$\n", + "\\mathbf{v}_{t}=\\gamma \\mathbf{v}_{t-1}+\\eta_{t}\\nabla_\\theta E(\\boldsymbol{\\theta}_t) \\nonumber\n", + "$$" + ] + }, + { + "cell_type": "markdown", + "id": "f15ea450", + "metadata": { + "editable": true + }, + "source": [ + "\n", + "
              \n", + "\n", + "$$\n", + "\\begin{equation} \n", + "\\boldsymbol{\\theta}_{t+1}= \\boldsymbol{\\theta}_t -\\mathbf{v}_{t},\n", + "\\label{_auto1} \\tag{1}\n", + "\\end{equation}\n", + "$$" + ] + }, + { + "cell_type": "markdown", + "id": "233d7b7e", + "metadata": { + "editable": true + }, + "source": [ + "where we have introduced a momentum parameter $\\gamma$, with\n", + "$0\\le\\gamma\\le 1$, and for brevity we dropped the explicit notation to\n", + "indicate the gradient is to be taken over a different mini-batch at\n", + "each step. We call this algorithm gradient descent with momentum\n", + "(GDM). From these equations, it is clear that $\\mathbf{v}_t$ is a\n", + "running average of recently encountered gradients and\n", + "$(1-\\gamma)^{-1}$ sets the characteristic time scale for the memory\n", + "used in the averaging procedure. Consistent with this, when\n", + "$\\gamma=0$, this just reduces down to ordinary SGD as discussed\n", + "earlier. An equivalent way of writing the updates is" + ] + }, + { + "cell_type": "markdown", + "id": "b923e7d5", + "metadata": { + "editable": true + }, + "source": [ + "$$\n", + "\\Delta \\boldsymbol{\\theta}_{t+1} = \\gamma \\Delta \\boldsymbol{\\theta}_t -\\ \\eta_{t}\\nabla_\\theta E(\\boldsymbol{\\theta}_t),\n", + "$$" + ] + }, + { + "cell_type": "markdown", + "id": "60980ded", + "metadata": { + "editable": true + }, + "source": [ + "where we have defined $\\Delta \\boldsymbol{\\theta}_{t}= \\boldsymbol{\\theta}_t-\\boldsymbol{\\theta}_{t-1}$." + ] + }, + { + "cell_type": "markdown", + "id": "cc771e70", + "metadata": { + "editable": true + }, + "source": [ + "## More on momentum based approaches\n", + "\n", + "Let us try to get more intuition from these equations. It is helpful\n", + "to consider a simple physical analogy with a particle of mass $m$\n", + "moving in a viscous medium with drag coefficient $\\mu$ and potential\n", + "$E(\\mathbf{w})$. If we denote the particle's position by $\\mathbf{w}$,\n", + "then its motion is described by" + ] + }, + { + "cell_type": "markdown", + "id": "859f6ffc", + "metadata": { + "editable": true + }, + "source": [ + "$$\n", + "m {d^2 \\mathbf{w} \\over dt^2} + \\mu {d \\mathbf{w} \\over dt }= -\\nabla_w E(\\mathbf{w}).\n", + "$$" + ] + }, + { + "cell_type": "markdown", + "id": "064cc085", + "metadata": { + "editable": true + }, + "source": [ + "We can discretize this equation in the usual way to get" + ] + }, + { + "cell_type": "markdown", + "id": "47d13c3c", + "metadata": { + "editable": true + }, + "source": [ + "$$\n", + "m { \\mathbf{w}_{t+\\Delta t}-2 \\mathbf{w}_{t} +\\mathbf{w}_{t-\\Delta t} \\over (\\Delta t)^2}+\\mu {\\mathbf{w}_{t+\\Delta t}- \\mathbf{w}_{t} \\over \\Delta t} = -\\nabla_w E(\\mathbf{w}).\n", + "$$" + ] + }, + { + "cell_type": "markdown", + "id": "0ca67954", + "metadata": { + "editable": true + }, + "source": [ + "Rearranging this equation, we can rewrite this as" + ] + }, + { + "cell_type": "markdown", + "id": "ea9f63a8", + "metadata": { + "editable": true + }, + "source": [ + "$$\n", + "\\Delta \\mathbf{w}_{t +\\Delta t}= - { (\\Delta t)^2 \\over m +\\mu \\Delta t} \\nabla_w E(\\mathbf{w})+ {m \\over m +\\mu \\Delta t} \\Delta \\mathbf{w}_t.\n", + "$$" + ] + }, + { + "cell_type": "markdown", + "id": "35146ea5", + "metadata": { + "editable": true + }, + "source": [ + "## Momentum parameter\n", + "\n", + "Notice that this equation is identical to previous one if we identify\n", + "the position of the particle, $\\mathbf{w}$, with the parameters\n", + "$\\boldsymbol{\\theta}$. This allows us to identify the momentum\n", + "parameter and learning rate with the mass of the particle and the\n", + "viscous drag as:" + ] + }, + { + "cell_type": "markdown", + "id": "82c87bb1", + "metadata": { + "editable": true + }, + "source": [ + "$$\n", + "\\gamma= {m \\over m +\\mu \\Delta t }, \\qquad \\eta = {(\\Delta t)^2 \\over m +\\mu \\Delta t}.\n", + "$$" + ] + }, + { + "cell_type": "markdown", + "id": "75415fca", + "metadata": { + "editable": true + }, + "source": [ + "Thus, as the name suggests, the momentum parameter is proportional to\n", + "the mass of the particle and effectively provides inertia.\n", + "Furthermore, in the large viscosity/small learning rate limit, our\n", + "memory time scales as $(1-\\gamma)^{-1} \\approx m/(\\mu \\Delta t)$.\n", + "\n", + "Why is momentum useful? SGD momentum helps the gradient descent\n", + "algorithm gain speed in directions with persistent but small gradients\n", + "even in the presence of stochasticity, while suppressing oscillations\n", + "in high-curvature directions. This becomes especially important in\n", + "situations where the landscape is shallow and flat in some directions\n", + "and narrow and steep in others. It has been argued that first-order\n", + "methods (with appropriate initial conditions) can perform comparable\n", + "to more expensive second order methods, especially in the context of\n", + "complex deep learning models.\n", + "\n", + "These beneficial properties of momentum can sometimes become even more\n", + "pronounced by using a slight modification of the classical momentum\n", + "algorithm called Nesterov Accelerated Gradient (NAG).\n", + "\n", + "In the NAG algorithm, rather than calculating the gradient at the\n", + "current parameters, $\\nabla_\\theta E(\\boldsymbol{\\theta}_t)$, one\n", + "calculates the gradient at the expected value of the parameters given\n", + "our current momentum, $\\nabla_\\theta E(\\boldsymbol{\\theta}_t +\\gamma\n", + "\\mathbf{v}_{t-1})$. This yields the NAG update rule" + ] + }, + { + "cell_type": "markdown", + "id": "59892cd6", + "metadata": { + "editable": true + }, + "source": [ + "$$\n", + "\\mathbf{v}_{t}=\\gamma \\mathbf{v}_{t-1}+\\eta_{t}\\nabla_\\theta E(\\boldsymbol{\\theta}_t +\\gamma \\mathbf{v}_{t-1}) \\nonumber\n", + "$$" + ] + }, + { + "cell_type": "markdown", + "id": "a01225ea", + "metadata": { + "editable": true + }, + "source": [ + "\n", + "
              \n", + "\n", + "$$\n", + "\\begin{equation} \n", + "\\boldsymbol{\\theta}_{t+1}= \\boldsymbol{\\theta}_t -\\mathbf{v}_{t}.\n", + "\\label{_auto2} \\tag{2}\n", + "\\end{equation}\n", + "$$" + ] + }, + { + "cell_type": "markdown", + "id": "e2c9f57b", + "metadata": { + "editable": true + }, + "source": [ + "One of the major advantages of NAG is that it allows for the use of a larger learning rate than GDM for the same choice of $\\gamma$." + ] + }, + { + "cell_type": "markdown", + "id": "1672a79e", + "metadata": { + "editable": true + }, + "source": [ + "## Second moment of the gradient\n", + "\n", + "In stochastic gradient descent, with and without momentum, we still\n", + "have to specify a schedule for tuning the learning rates $\\eta_t$\n", + "as a function of time. As discussed in the context of Newton's\n", + "method, this presents a number of dilemmas. The learning rate is\n", + "limited by the steepest direction which can change depending on the\n", + "current position in the landscape. To circumvent this problem, ideally\n", + "our algorithm would keep track of curvature and take large steps in\n", + "shallow, flat directions and small steps in steep, narrow directions.\n", + "Second-order methods accomplish this by calculating or approximating\n", + "the Hessian and normalizing the learning rate by the\n", + "curvature. However, this is very computationally expensive for\n", + "extremely large models. Ideally, we would like to be able to\n", + "adaptively change the step size to match the landscape without paying\n", + "the steep computational price of calculating or approximating\n", + "Hessians.\n", + "\n", + "Recently, a number of methods have been introduced that accomplish\n", + "this by tracking not only the gradient, but also the second moment of\n", + "the gradient. These methods include AdaGrad, AdaDelta, Root Mean Squared Propagation (RMS-Prop), and\n", + "[ADAM](https://arxiv.org/abs/1412.6980)." + ] + }, + { + "cell_type": "markdown", + "id": "6d4032f9", + "metadata": { + "editable": true + }, + "source": [ + "## RMS prop\n", + "\n", + "In RMS prop, in addition to keeping a running average of the first\n", + "moment of the gradient, we also keep track of the second moment\n", + "denoted by $\\mathbf{s}_t=\\mathbb{E}[\\mathbf{g}_t^2]$. The update rule\n", + "for RMS prop is given by" + ] + }, + { + "cell_type": "markdown", + "id": "63cde9f3", + "metadata": { + "editable": true + }, + "source": [ + "\n", + "
              \n", + "\n", + "$$\n", + "\\begin{equation}\n", + "\\mathbf{g}_t = \\nabla_\\theta E(\\boldsymbol{\\theta}) \n", + "\\label{_auto3} \\tag{3}\n", + "\\end{equation}\n", + "$$" + ] + }, + { + "cell_type": "markdown", + "id": "6f8a52c2", + "metadata": { + "editable": true + }, + "source": [ + "$$\n", + "\\mathbf{s}_t =\\beta \\mathbf{s}_{t-1} +(1-\\beta)\\mathbf{g}_t^2 \\nonumber\n", + "$$" + ] + }, + { + "cell_type": "markdown", + "id": "9edf087d", + "metadata": { + "editable": true + }, + "source": [ + "$$\n", + "\\boldsymbol{\\theta}_{t+1}=\\boldsymbol{\\theta}_t - \\eta_t { \\mathbf{g}_t \\over \\sqrt{\\mathbf{s}_t +\\epsilon}}, \\nonumber\n", + "$$" + ] + }, + { + "cell_type": "markdown", + "id": "7eff676b", + "metadata": { + "editable": true + }, + "source": [ + "where $\\beta$ controls the averaging time of the second moment and is\n", + "typically taken to be about $\\beta=0.9$, $\\eta_t$ is a learning rate\n", + "typically chosen to be $10^{-3}$, and $\\epsilon\\sim 10^{-8} $ is a\n", + "small regularization constant to prevent divergences. Multiplication\n", + "and division by vectors is understood as an element-wise operation. It\n", + "is clear from this formula that the learning rate is reduced in\n", + "directions where the norm of the gradient is consistently large. This\n", + "greatly speeds up the convergence by allowing us to use a larger\n", + "learning rate for flat directions." + ] + }, + { + "cell_type": "markdown", + "id": "3fcb1068", + "metadata": { + "editable": true + }, + "source": [ + "## [ADAM optimizer](https://arxiv.org/abs/1412.6980)\n", + "\n", + "A related algorithm is the ADAM optimizer. In\n", + "[ADAM](https://arxiv.org/abs/1412.6980), we keep a running average of\n", + "both the first and second moment of the gradient and use this\n", + "information to adaptively change the learning rate for different\n", + "parameters. The method isefficient when working with large\n", + "problems involving lots data and/or parameters. It is a combination of the\n", + "gradient descent with momentum algorithm and the RMSprop algorithm\n", + "discussed above.\n", + "\n", + "In addition to keeping a running average of the first and\n", + "second moments of the gradient\n", + "(i.e. $\\mathbf{m}_t=\\mathbb{E}[\\mathbf{g}_t]$ and\n", + "$\\mathbf{s}_t=\\mathbb{E}[\\mathbf{g}^2_t]$, respectively), ADAM\n", + "performs an additional bias correction to account for the fact that we\n", + "are estimating the first two moments of the gradient using a running\n", + "average (denoted by the hats in the update rule below). The update\n", + "rule for ADAM is given by (where multiplication and division are once\n", + "again understood to be element-wise operations below)" + ] + }, + { + "cell_type": "markdown", + "id": "31b034e1", + "metadata": { + "editable": true + }, + "source": [ + "\n", + "
              \n", + "\n", + "$$\n", + "\\begin{equation}\n", + "\\mathbf{g}_t = \\nabla_\\theta E(\\boldsymbol{\\theta}) \n", + "\\label{_auto4} \\tag{4}\n", + "\\end{equation}\n", + "$$" + ] + }, + { + "cell_type": "markdown", + "id": "571e9a91", + "metadata": { + "editable": true + }, + "source": [ + "$$\n", + "\\mathbf{m}_t = \\beta_1 \\mathbf{m}_{t-1} + (1-\\beta_1) \\mathbf{g}_t \\nonumber\n", + "$$" + ] + }, + { + "cell_type": "markdown", + "id": "fb5883fa", + "metadata": { + "editable": true + }, + "source": [ + "$$\n", + "\\mathbf{s}_t =\\beta_2 \\mathbf{s}_{t-1} +(1-\\beta_2)\\mathbf{g}_t^2 \\nonumber\n", + "$$" + ] + }, + { + "cell_type": "markdown", + "id": "ebffe7a1", + "metadata": { + "editable": true + }, + "source": [ + "$$\n", + "\\boldsymbol{\\mathbf{m}}_t={\\mathbf{m}_t \\over 1-\\beta_1^t} \\nonumber\n", + "$$" + ] + }, + { + "cell_type": "markdown", + "id": "5a513bd7", + "metadata": { + "editable": true + }, + "source": [ + "$$\n", + "\\boldsymbol{\\mathbf{s}}_t ={\\mathbf{s}_t \\over1-\\beta_2^t} \\nonumber\n", + "$$" + ] + }, + { + "cell_type": "markdown", + "id": "d49bc312", + "metadata": { + "editable": true + }, + "source": [ + "$$\n", + "\\boldsymbol{\\theta}_{t+1}=\\boldsymbol{\\theta}_t - \\eta_t { \\boldsymbol{\\mathbf{m}}_t \\over \\sqrt{\\boldsymbol{\\mathbf{s}}_t} +\\epsilon}, \\nonumber\n", + "$$" + ] + }, + { + "cell_type": "markdown", + "id": "6f4e5040", + "metadata": { + "editable": true + }, + "source": [ + "\n", + "
              \n", + "\n", + "$$\n", + "\\begin{equation} \n", + "\\label{_auto5} \\tag{5}\n", + "\\end{equation}\n", + "$$" + ] + }, + { + "cell_type": "markdown", + "id": "4771881e", + "metadata": { + "editable": true + }, + "source": [ + "where $\\beta_1$ and $\\beta_2$ set the memory lifetime of the first and\n", + "second moment and are typically taken to be $0.9$ and $0.99$\n", + "respectively, and $\\eta$ and $\\epsilon$ are identical to RMSprop.\n", + "\n", + "Like in RMSprop, the effective step size of a parameter depends on the\n", + "magnitude of its gradient squared. To understand this better, let us\n", + "rewrite this expression in terms of the variance\n", + "$\\boldsymbol{\\sigma}_t^2 = \\boldsymbol{\\mathbf{s}}_t -\n", + "(\\boldsymbol{\\mathbf{m}}_t)^2$. Consider a single parameter $\\theta_t$. The\n", + "update rule for this parameter is given by" + ] + }, + { + "cell_type": "markdown", + "id": "3a0d438e", + "metadata": { + "editable": true + }, + "source": [ + "$$\n", + "\\Delta \\theta_{t+1}= -\\eta_t { \\boldsymbol{m}_t \\over \\sqrt{\\sigma_t^2 + m_t^2 }+\\epsilon}.\n", + "$$" + ] + }, + { + "cell_type": "markdown", + "id": "6cbb721b", + "metadata": { + "editable": true + }, + "source": [ + "## Algorithms and codes for Adagrad, RMSprop and Adam\n", + "\n", + "The algorithms we have implemented are well described in the text by [Goodfellow, Bengio and Courville, chapter 8](https://www.deeplearningbook.org/contents/optimization.html).\n", + "\n", + "The codes which implement these algorithms are discussed after our presentation of automatic differentiation." + ] + }, + { + "cell_type": "markdown", + "id": "e7d8b851", + "metadata": { + "editable": true + }, + "source": [ + "## Practical tips\n", + "\n", + "* **Randomize the data when making mini-batches**. It is always important to randomly shuffle the data when forming mini-batches. Otherwise, the gradient descent method can fit spurious correlations resulting from the order in which data is presented.\n", + "\n", + "* **Transform your inputs**. Learning becomes difficult when our landscape has a mixture of steep and flat directions. One simple trick for minimizing these situations is to standardize the data by subtracting the mean and normalizing the variance of input variables. Whenever possible, also decorrelate the inputs. To understand why this is helpful, consider the case of linear regression. It is easy to show that for the squared error cost function, the Hessian of the cost function is just the correlation matrix between the inputs. Thus, by standardizing the inputs, we are ensuring that the landscape looks homogeneous in all directions in parameter space. Since most deep networks can be viewed as linear transformations followed by a non-linearity at each layer, we expect this intuition to hold beyond the linear case.\n", + "\n", + "* **Monitor the out-of-sample performance.** Always monitor the performance of your model on a validation set (a small portion of the training data that is held out of the training process to serve as a proxy for the test set. If the validation error starts increasing, then the model is beginning to overfit. Terminate the learning process. This *early stopping* significantly improves performance in many settings.\n", + "\n", + "* **Adaptive optimization methods don't always have good generalization.** Recent studies have shown that adaptive methods such as ADAM, RMSPorp, and AdaGrad tend to have poor generalization compared to SGD or SGD with momentum, particularly in the high-dimensional limit (i.e. the number of parameters exceeds the number of data points). Although it is not clear at this stage why these methods perform so well in training deep neural networks, simpler procedures like properly-tuned SGD may work as well or better in these applications.\n", + "\n", + "Geron's text, see chapter 11, has several interesting discussions." + ] + }, + { + "cell_type": "markdown", + "id": "75afab2b", + "metadata": { + "editable": true + }, + "source": [ + "## Automatic differentiation\n", + "\n", + "[Automatic differentiation (AD)](https://en.wikipedia.org/wiki/Automatic_differentiation), \n", + "also called algorithmic\n", + "differentiation or computational differentiation,is a set of\n", + "techniques to numerically evaluate the derivative of a function\n", + "specified by a computer program. AD exploits the fact that every\n", + "computer program, no matter how complicated, executes a sequence of\n", + "elementary arithmetic operations (addition, subtraction,\n", + "multiplication, division, etc.) and elementary functions (exp, log,\n", + "sin, cos, etc.). By applying the chain rule repeatedly to these\n", + "operations, derivatives of arbitrary order can be computed\n", + "automatically, accurately to working precision, and using at most a\n", + "small constant factor more arithmetic operations than the original\n", + "program.\n", + "\n", + "Automatic differentiation is neither:\n", + "\n", + "* Symbolic differentiation, nor\n", + "\n", + "* Numerical differentiation (the method of finite differences).\n", + "\n", + "Symbolic differentiation can lead to inefficient code and faces the\n", + "difficulty of converting a computer program into a single expression,\n", + "while numerical differentiation can introduce round-off errors in the\n", + "discretization process and cancellation\n", + "\n", + "Python has tools for so-called **automatic differentiation**.\n", + "Consider the following example" + ] + }, + { + "cell_type": "markdown", + "id": "c551bfeb", + "metadata": { + "editable": true + }, + "source": [ + "$$\n", + "f(x) = \\sin\\left(2\\pi x + x^2\\right)\n", + "$$" + ] + }, + { + "cell_type": "markdown", + "id": "f2cbdd82", + "metadata": { + "editable": true + }, + "source": [ + "which has the following derivative" + ] + }, + { + "cell_type": "markdown", + "id": "22e5d8ce", + "metadata": { + "editable": true + }, + "source": [ + "$$\n", + "f'(x) = \\cos\\left(2\\pi x + x^2\\right)\\left(2\\pi + 2x\\right)\n", + "$$" + ] + }, + { + "cell_type": "markdown", + "id": "d2d352b4", + "metadata": { + "editable": true + }, + "source": [ + "Using **autograd** we have" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "19f1b95c", + "metadata": { + "collapsed": false, + "editable": true + }, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
              " + ] + }, + "metadata": { + "filenames": { + "image/png": "/Users/mhjensen/Teaching/MachineLearning/doc/LectureNotes/_build/jupyter_execute/week40_68_0.png" + } + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "The max absolute difference is: 1.77636e-15\n" + ] + } + ], + "source": [ + "import autograd.numpy as np\n", + "\n", + "# To do elementwise differentiation:\n", + "from autograd import elementwise_grad as egrad \n", + "\n", + "# To plot:\n", + "import matplotlib.pyplot as plt \n", + "\n", + "\n", + "def f(x):\n", + " return np.sin(2*np.pi*x + x**2)\n", + "\n", + "def f_grad_analytic(x):\n", + " return np.cos(2*np.pi*x + x**2)*(2*np.pi + 2*x)\n", + "\n", + "# Do the comparison:\n", + "x = np.linspace(0,1,1000)\n", + "\n", + "f_grad = egrad(f)\n", + "\n", + "computed = f_grad(x)\n", + "analytic = f_grad_analytic(x)\n", + "\n", + "plt.title('Derivative computed from Autograd compared with the analytical derivative')\n", + "plt.plot(x,computed,label='autograd')\n", + "plt.plot(x,analytic,label='analytic')\n", + "\n", + "plt.xlabel('x')\n", + "plt.ylabel('y')\n", + "plt.legend()\n", + "\n", + "plt.show()\n", + "\n", + "print(\"The max absolute difference is: %g\"%(np.max(np.abs(computed - analytic))))" + ] + }, + { + "cell_type": "markdown", + "id": "f3a495be", + "metadata": { + "editable": true + }, + "source": [ + "## Using autograd\n", + "\n", + "Here we\n", + "experiment with what kind of functions Autograd is capable\n", + "of finding the gradient of. The following Python functions are just\n", + "meant to illustrate what Autograd can do, but please feel free to\n", + "experiment with other, possibly more complicated, functions as well." + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "5c856602", + "metadata": { + "collapsed": false, + "editable": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "The gradient of f1 evaluated at a = 1 using autograd is: 3\n", + "The gradient of f1 evaluated at a = 1 by finding the analytic expression is: 3\n" + ] + } + ], + "source": [ + "import autograd.numpy as np\n", + "from autograd import grad\n", + "\n", + "def f1(x):\n", + " return x**3 + 1\n", + "\n", + "f1_grad = grad(f1)\n", + "\n", + "# Remember to send in float as argument to the computed gradient from Autograd!\n", + "a = 1.0\n", + "\n", + "# See the evaluated gradient at a using autograd:\n", + "print(\"The gradient of f1 evaluated at a = %g using autograd is: %g\"%(a,f1_grad(a)))\n", + "\n", + "# Compare with the analytical derivative, that is f1'(x) = 3*x**2 \n", + "grad_analytical = 3*a**2\n", + "print(\"The gradient of f1 evaluated at a = %g by finding the analytic expression is: %g\"%(a,grad_analytical))" + ] + }, + { + "cell_type": "markdown", + "id": "b361074b", + "metadata": { + "editable": true + }, + "source": [ + "## Autograd with more complicated functions\n", + "\n", + "To differentiate with respect to two (or more) arguments of a Python\n", + "function, Autograd need to know at which variable the function if\n", + "being differentiated with respect to." + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "a458a151", + "metadata": { + "collapsed": false, + "editable": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Evaluating at x1 = 1, x2 = 3\n", + "------------------------------\n", + "The derivative of f2 w.r.t x1: 12\n", + "The analytical derivative of f2 w.r.t x1: 12\n", + "\n", + "The derivative of f2 w.r.t x2: -4\n", + "The analytical derivative of f2 w.r.t x2: -4\n" + ] + } + ], + "source": [ + "import autograd.numpy as np\n", + "from autograd import grad\n", + "def f2(x1,x2):\n", + " return 3*x1**3 + x2*(x1 - 5) + 1\n", + "\n", + "# By sending the argument 0, Autograd will compute the derivative w.r.t the first variable, in this case x1\n", + "f2_grad_x1 = grad(f2,0)\n", + "\n", + "# ... and differentiate w.r.t x2 by sending 1 as an additional arugment to grad\n", + "f2_grad_x2 = grad(f2,1)\n", + "\n", + "x1 = 1.0\n", + "x2 = 3.0 \n", + "\n", + "print(\"Evaluating at x1 = %g, x2 = %g\"%(x1,x2))\n", + "print(\"-\"*30)\n", + "\n", + "# Compare with the analytical derivatives:\n", + "\n", + "# Derivative of f2 w.r.t x1 is: 9*x1**2 + x2:\n", + "f2_grad_x1_analytical = 9*x1**2 + x2\n", + "\n", + "# Derivative of f2 w.r.t x2 is: x1 - 5:\n", + "f2_grad_x2_analytical = x1 - 5\n", + "\n", + "# See the evaluated derivations:\n", + "print(\"The derivative of f2 w.r.t x1: %g\"%( f2_grad_x1(x1,x2) ))\n", + "print(\"The analytical derivative of f2 w.r.t x1: %g\"%( f2_grad_x1(x1,x2) ))\n", + "\n", + "print()\n", + "\n", + "print(\"The derivative of f2 w.r.t x2: %g\"%( f2_grad_x2(x1,x2) ))\n", + "print(\"The analytical derivative of f2 w.r.t x2: %g\"%( f2_grad_x2(x1,x2) ))" + ] + }, + { + "cell_type": "markdown", + "id": "946c37f1", + "metadata": { + "editable": true + }, + "source": [ + "Note that the grad function will not produce the true gradient of the function. The true gradient of a function with two or more variables will produce a vector, where each element is the function differentiated w.r.t a variable." + ] + }, + { + "cell_type": "markdown", + "id": "a00d38e5", + "metadata": { + "editable": true + }, + "source": [ + "## More complicated functions using the elements of their arguments directly" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "d8c2a448", + "metadata": { + "collapsed": false, + "editable": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "The computed gradient of f3 is: [ 2. 3. 5. 7. 88.]\n", + "The analytical gradient of f3 is: [ 2. 3. 5. 7. 88.]\n" + ] + } + ], + "source": [ + "import autograd.numpy as np\n", + "from autograd import grad\n", + "def f3(x): # Assumes x is an array of length 5 or higher\n", + " return 2*x[0] + 3*x[1] + 5*x[2] + 7*x[3] + 11*x[4]**2\n", + "\n", + "f3_grad = grad(f3)\n", + "\n", + "x = np.linspace(0,4,5)\n", + "\n", + "# Print the computed gradient:\n", + "print(\"The computed gradient of f3 is: \", f3_grad(x))\n", + "\n", + "# The analytical gradient is: (2, 3, 5, 7, 22*x[4])\n", + "f3_grad_analytical = np.array([2, 3, 5, 7, 22*x[4]])\n", + "\n", + "# Print the analytical gradient:\n", + "print(\"The analytical gradient of f3 is: \", f3_grad_analytical)" + ] + }, + { + "cell_type": "markdown", + "id": "026d8733", + "metadata": { + "editable": true + }, + "source": [ + "Note that in this case, when sending an array as input argument, the\n", + "output from Autograd is another array. This is the true gradient of\n", + "the function, as opposed to the function in the previous example. By\n", + "using arrays to represent the variables, the output from Autograd\n", + "might be easier to work with, as the output is closer to what one\n", + "could expect form a gradient-evaluting function." + ] + }, + { + "cell_type": "markdown", + "id": "1a4dca11", + "metadata": { + "editable": true + }, + "source": [ + "## Functions using mathematical functions from Numpy" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "c10b664f", + "metadata": { + "collapsed": false, + "editable": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "The computed derivative of f4 at x = 2.7 is: 13.8759\n", + "The analytical gradient of f4 at x = 2.7 is: 13.8759\n" + ] + } + ], + "source": [ + "import autograd.numpy as np\n", + "from autograd import grad\n", + "def f4(x):\n", + " return np.sqrt(1+x**2) + np.exp(x) + np.sin(2*np.pi*x)\n", + "\n", + "f4_grad = grad(f4)\n", + "\n", + "x = 2.7\n", + "\n", + "# Print the computed derivative:\n", + "print(\"The computed derivative of f4 at x = %g is: %g\"%(x,f4_grad(x)))\n", + "\n", + "# The analytical derivative is: x/sqrt(1 + x**2) + exp(x) + cos(2*pi*x)*2*pi\n", + "f4_grad_analytical = x/np.sqrt(1 + x**2) + np.exp(x) + np.cos(2*np.pi*x)*2*np.pi\n", + "\n", + "# Print the analytical gradient:\n", + "print(\"The analytical gradient of f4 at x = %g is: %g\"%(x,f4_grad_analytical))" + ] + }, + { + "cell_type": "markdown", + "id": "07436a71", + "metadata": { + "editable": true + }, + "source": [ + "## More autograd" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "1840a5d2", + "metadata": { + "collapsed": false, + "editable": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "The computed derivative of f5 at x = 2.7 is: 5.4\n" + ] + } + ], + "source": [ + "import autograd.numpy as np\n", + "from autograd import grad\n", + "def f5(x):\n", + " if x >= 0:\n", + " return x**2\n", + " else:\n", + " return -3*x + 1\n", + "\n", + "f5_grad = grad(f5)\n", + "\n", + "x = 2.7\n", + "\n", + "# Print the computed derivative:\n", + "print(\"The computed derivative of f5 at x = %g is: %g\"%(x,f5_grad(x)))" + ] + }, + { + "cell_type": "markdown", + "id": "87ee8137", + "metadata": { + "editable": true + }, + "source": [ + "## And with loops" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "id": "f1b25f09", + "metadata": { + "collapsed": false, + "editable": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "The computed derivative of f6_for at x = 0.5 is: 3.95703\n", + "The computed derivative of f6_while at x = 0.5 is: 3.95703\n" + ] + } + ], + "source": [ + "import autograd.numpy as np\n", + "from autograd import grad\n", + "def f6_for(x):\n", + " val = 0\n", + " for i in range(10):\n", + " val = val + x**i\n", + " return val\n", + "\n", + "def f6_while(x):\n", + " val = 0\n", + " i = 0\n", + " while i < 10:\n", + " val = val + x**i\n", + " i = i + 1\n", + " return val\n", + "\n", + "f6_for_grad = grad(f6_for)\n", + "f6_while_grad = grad(f6_while)\n", + "\n", + "x = 0.5\n", + "\n", + "# Print the computed derivaties of f6_for and f6_while\n", + "print(\"The computed derivative of f6_for at x = %g is: %g\"%(x,f6_for_grad(x)))\n", + "print(\"The computed derivative of f6_while at x = %g is: %g\"%(x,f6_while_grad(x)))" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "id": "5fa2802b", + "metadata": { + "collapsed": false, + "editable": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "The analytical derivative of f6 at x = 0.5 is: 3.95703\n" + ] + } + ], + "source": [ + "import autograd.numpy as np\n", + "from autograd import grad\n", + "# Both of the functions are implementation of the sum: sum(x**i) for i = 0, ..., 9\n", + "# The analytical derivative is: sum(i*x**(i-1)) \n", + "f6_grad_analytical = 0\n", + "for i in range(10):\n", + " f6_grad_analytical += i*x**(i-1)\n", + "\n", + "print(\"The analytical derivative of f6 at x = %g is: %g\"%(x,f6_grad_analytical))" + ] + }, + { + "cell_type": "markdown", + "id": "eb66fab4", + "metadata": { + "editable": true + }, + "source": [ + "## Using recursion" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "id": "965c8bbb", + "metadata": { + "collapsed": false, + "editable": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "The computed derivative of f7 at n = 2 is: 1\n", + "The analytical derivative of f7 at n = 2 is: 1\n" + ] + } + ], + "source": [ + "import autograd.numpy as np\n", + "from autograd import grad\n", + "\n", + "def f7(n): # Assume that n is an integer\n", + " if n == 1 or n == 0:\n", + " return 1\n", + " else:\n", + " return n*f7(n-1)\n", + "\n", + "f7_grad = grad(f7)\n", + "\n", + "n = 2.0\n", + "\n", + "print(\"The computed derivative of f7 at n = %d is: %g\"%(n,f7_grad(n)))\n", + "\n", + "# The function f7 is an implementation of the factorial of n.\n", + "# By using the product rule, one can find that the derivative is:\n", + "\n", + "f7_grad_analytical = 0\n", + "for i in range(int(n)-1):\n", + " tmp = 1\n", + " for k in range(int(n)-1):\n", + " if k != i:\n", + " tmp *= (n - k)\n", + " f7_grad_analytical += tmp\n", + "\n", + "print(\"The analytical derivative of f7 at n = %d is: %g\"%(n,f7_grad_analytical))" + ] + }, + { + "cell_type": "markdown", + "id": "2e6e0c8a", + "metadata": { + "editable": true + }, + "source": [ + "Note that if n is equal to zero or one, Autograd will give an error message. This message appears when the output is independent on input." + ] + }, + { + "cell_type": "markdown", + "id": "42adbcc3", + "metadata": { + "editable": true + }, + "source": [ + "## Unsupported functions\n", + "Autograd supports many features. However, there are some functions that is not supported (yet) by Autograd.\n", + "\n", + "Assigning a value to the variable being differentiated with respect to" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "id": "6ca4a5dc", + "metadata": { + "collapsed": false, + "editable": true + }, + "outputs": [ + { + "ename": "TypeError", + "evalue": "'ArrayBox' object does not support item assignment", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mTypeError\u001b[0m Traceback (most recent call last)", + "Input \u001b[0;32mIn [13]\u001b[0m, in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[1;32m 7\u001b[0m f8_grad \u001b[38;5;241m=\u001b[39m grad(f8)\n\u001b[1;32m 9\u001b[0m x \u001b[38;5;241m=\u001b[39m \u001b[38;5;241m8.4\u001b[39m\n\u001b[0;32m---> 11\u001b[0m \u001b[38;5;28mprint\u001b[39m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mThe derivative of f8 is:\u001b[39m\u001b[38;5;124m\"\u001b[39m,\u001b[43mf8_grad\u001b[49m\u001b[43m(\u001b[49m\u001b[43mx\u001b[49m\u001b[43m)\u001b[49m)\n", + "File \u001b[0;32m~/miniforge3/envs/myenv/lib/python3.9/site-packages/autograd/wrap_util.py:20\u001b[0m, in \u001b[0;36munary_to_nary..nary_operator..nary_f\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m 18\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m 19\u001b[0m x \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mtuple\u001b[39m(args[i] \u001b[38;5;28;01mfor\u001b[39;00m i \u001b[38;5;129;01min\u001b[39;00m argnum)\n\u001b[0;32m---> 20\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43munary_operator\u001b[49m\u001b[43m(\u001b[49m\u001b[43munary_f\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mx\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mnary_op_args\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mnary_op_kwargs\u001b[49m\u001b[43m)\u001b[49m\n", + "File \u001b[0;32m~/miniforge3/envs/myenv/lib/python3.9/site-packages/autograd/differential_operators.py:25\u001b[0m, in \u001b[0;36mgrad\u001b[0;34m(fun, x)\u001b[0m\n\u001b[1;32m 18\u001b[0m \u001b[38;5;129m@unary_to_nary\u001b[39m\n\u001b[1;32m 19\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mgrad\u001b[39m(fun, x):\n\u001b[1;32m 20\u001b[0m \u001b[38;5;250m \u001b[39m\u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m 21\u001b[0m \u001b[38;5;124;03m Returns a function which computes the gradient of `fun` with respect to\u001b[39;00m\n\u001b[1;32m 22\u001b[0m \u001b[38;5;124;03m positional argument number `argnum`. The returned function takes the same\u001b[39;00m\n\u001b[1;32m 23\u001b[0m \u001b[38;5;124;03m arguments as `fun`, but returns the gradient instead. The function `fun`\u001b[39;00m\n\u001b[1;32m 24\u001b[0m \u001b[38;5;124;03m should be scalar-valued. The gradient has the same type as the argument.\"\"\"\u001b[39;00m\n\u001b[0;32m---> 25\u001b[0m vjp, ans \u001b[38;5;241m=\u001b[39m \u001b[43m_make_vjp\u001b[49m\u001b[43m(\u001b[49m\u001b[43mfun\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mx\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 26\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m vspace(ans)\u001b[38;5;241m.\u001b[39msize \u001b[38;5;241m==\u001b[39m \u001b[38;5;241m1\u001b[39m:\n\u001b[1;32m 27\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mTypeError\u001b[39;00m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mGrad only applies to real scalar-output functions. \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m 28\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mTry jacobian, elementwise_grad or holomorphic_grad.\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n", + "File \u001b[0;32m~/miniforge3/envs/myenv/lib/python3.9/site-packages/autograd/core.py:10\u001b[0m, in \u001b[0;36mmake_vjp\u001b[0;34m(fun, x)\u001b[0m\n\u001b[1;32m 8\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mmake_vjp\u001b[39m(fun, x):\n\u001b[1;32m 9\u001b[0m start_node \u001b[38;5;241m=\u001b[39m VJPNode\u001b[38;5;241m.\u001b[39mnew_root()\n\u001b[0;32m---> 10\u001b[0m end_value, end_node \u001b[38;5;241m=\u001b[39m \u001b[43mtrace\u001b[49m\u001b[43m(\u001b[49m\u001b[43mstart_node\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mfun\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mx\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 11\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m end_node \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m 12\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mvjp\u001b[39m(g): \u001b[38;5;28;01mreturn\u001b[39;00m vspace(x)\u001b[38;5;241m.\u001b[39mzeros()\n", + "File \u001b[0;32m~/miniforge3/envs/myenv/lib/python3.9/site-packages/autograd/tracer.py:10\u001b[0m, in \u001b[0;36mtrace\u001b[0;34m(start_node, fun, x)\u001b[0m\n\u001b[1;32m 8\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m trace_stack\u001b[38;5;241m.\u001b[39mnew_trace() \u001b[38;5;28;01mas\u001b[39;00m t:\n\u001b[1;32m 9\u001b[0m start_box \u001b[38;5;241m=\u001b[39m new_box(x, t, start_node)\n\u001b[0;32m---> 10\u001b[0m end_box \u001b[38;5;241m=\u001b[39m \u001b[43mfun\u001b[49m\u001b[43m(\u001b[49m\u001b[43mstart_box\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 11\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m isbox(end_box) \u001b[38;5;129;01mand\u001b[39;00m end_box\u001b[38;5;241m.\u001b[39m_trace \u001b[38;5;241m==\u001b[39m start_box\u001b[38;5;241m.\u001b[39m_trace:\n\u001b[1;32m 12\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m end_box\u001b[38;5;241m.\u001b[39m_value, end_box\u001b[38;5;241m.\u001b[39m_node\n", + "File \u001b[0;32m~/miniforge3/envs/myenv/lib/python3.9/site-packages/autograd/wrap_util.py:15\u001b[0m, in \u001b[0;36munary_to_nary..nary_operator..nary_f..unary_f\u001b[0;34m(x)\u001b[0m\n\u001b[1;32m 13\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m 14\u001b[0m subargs \u001b[38;5;241m=\u001b[39m subvals(args, \u001b[38;5;28mzip\u001b[39m(argnum, x))\n\u001b[0;32m---> 15\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mfun\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43msubargs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n", + "Input \u001b[0;32mIn [13]\u001b[0m, in \u001b[0;36mf8\u001b[0;34m(x)\u001b[0m\n\u001b[1;32m 3\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mf8\u001b[39m(x): \u001b[38;5;66;03m# Assume x is an array\u001b[39;00m\n\u001b[0;32m----> 4\u001b[0m x[\u001b[38;5;241m2\u001b[39m] \u001b[38;5;241m=\u001b[39m \u001b[38;5;241m3\u001b[39m\n\u001b[1;32m 5\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m x\u001b[38;5;241m*\u001b[39m\u001b[38;5;241m2\u001b[39m\n", + "\u001b[0;31mTypeError\u001b[0m: 'ArrayBox' object does not support item assignment" + ] + } + ], + "source": [ + "import autograd.numpy as np\n", + "from autograd import grad\n", + "def f8(x): # Assume x is an array\n", + " x[2] = 3\n", + " return x*2\n", + "\n", + "f8_grad = grad(f8)\n", + "\n", + "x = 8.4\n", + "\n", + "print(\"The derivative of f8 is:\",f8_grad(x))" + ] + }, + { + "cell_type": "markdown", + "id": "5d816052", + "metadata": { + "editable": true + }, + "source": [ + "Here, Autograd tells us that an 'ArrayBox' does not support item assignment. The item assignment is done when the program tries to assign x[2] to the value 3. However, Autograd has implemented the computation of the derivative such that this assignment is not possible." + ] + }, + { + "cell_type": "markdown", + "id": "73f2e7e4", + "metadata": { + "editable": true + }, + "source": [ + "## The syntax a.dot(b) when finding the dot product" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "id": "2ead27d5", + "metadata": { + "collapsed": false, + "editable": true + }, + "outputs": [], + "source": [ + "import autograd.numpy as np\n", + "from autograd import grad\n", + "def f9(a): # Assume a is an array with 2 elements\n", + " b = np.array([1.0,2.0])\n", + " return a.dot(b)\n", + "\n", + "f9_grad = grad(f9)\n", + "\n", + "x = np.array([1.0,0.0])\n", + "\n", + "print(\"The derivative of f9 is:\",f9_grad(x))" + ] + }, + { + "cell_type": "markdown", + "id": "1edcb932", + "metadata": { + "editable": true + }, + "source": [ + "Here we are told that the 'dot' function does not belong to Autograd's\n", + "version of a Numpy array. To overcome this, an alternative syntax\n", + "which also computed the dot product can be used:" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "id": "05897777", + "metadata": { + "collapsed": false, + "editable": true + }, + "outputs": [], + "source": [ + "import autograd.numpy as np\n", + "from autograd import grad\n", + "def f9_alternative(x): # Assume a is an array with 2 elements\n", + " b = np.array([1.0,2.0])\n", + " return np.dot(x,b) # The same as x_1*b_1 + x_2*b_2\n", + "\n", + "f9_alternative_grad = grad(f9_alternative)\n", + "\n", + "x = np.array([3.0,0.0])\n", + "\n", + "print(\"The gradient of f9 is:\",f9_alternative_grad(x))\n", + "\n", + "# The analytical gradient of the dot product of vectors x and b with two elements (x_1,x_2) and (b_1, b_2) respectively\n", + "# w.r.t x is (b_1, b_2)." + ] + }, + { + "cell_type": "markdown", + "id": "2c899815", + "metadata": { + "editable": true + }, + "source": [ + "## Recommended to avoid\n", + "The documentation recommends to avoid inplace operations such as" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "id": "fd05063c", + "metadata": { + "collapsed": false, + "editable": true + }, + "outputs": [], + "source": [ + "a += b\n", + "a -= b\n", + "a*= b\n", + "a /=b" + ] + }, + { + "cell_type": "markdown", + "id": "94d6f0d9", + "metadata": { + "editable": true + }, + "source": [ + "## Using Autograd with OLS\n", + "\n", + "We conclude the part on optmization by showing how we can make codes\n", + "for linear regression and logistic regression using **autograd**. The\n", + "first example shows results with ordinary leats squares." + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "id": "d002c672", + "metadata": { + "collapsed": false, + "editable": true + }, + "outputs": [], + "source": [ + "# Using Autograd to calculate gradients for OLS\n", + "from random import random, seed\n", + "import numpy as np\n", + "import autograd.numpy as np\n", + "import matplotlib.pyplot as plt\n", + "from autograd import grad\n", + "\n", + "def CostOLS(beta):\n", + " return (1.0/n)*np.sum((y-X @ beta)**2)\n", + "\n", + "n = 100\n", + "x = 2*np.random.rand(n,1)\n", + "y = 4+3*x+np.random.randn(n,1)\n", + "\n", + "X = np.c_[np.ones((n,1)), x]\n", + "XT_X = X.T @ X\n", + "theta_linreg = np.linalg.pinv(XT_X) @ (X.T @ y)\n", + "print(\"Own inversion\")\n", + "print(theta_linreg)\n", + "# Hessian matrix\n", + "H = (2.0/n)* XT_X\n", + "EigValues, EigVectors = np.linalg.eig(H)\n", + "print(f\"Eigenvalues of Hessian Matrix:{EigValues}\")\n", + "\n", + "theta = np.random.randn(2,1)\n", + "eta = 1.0/np.max(EigValues)\n", + "Niterations = 1000\n", + "# define the gradient\n", + "training_gradient = grad(CostOLS)\n", + "\n", + "for iter in range(Niterations):\n", + " gradients = training_gradient(theta)\n", + " theta -= eta*gradients\n", + "print(\"theta from own gd\")\n", + "print(theta)\n", + "\n", + "xnew = np.array([[0],[2]])\n", + "Xnew = np.c_[np.ones((2,1)), xnew]\n", + "ypredict = Xnew.dot(theta)\n", + "ypredict2 = Xnew.dot(theta_linreg)\n", + "\n", + "plt.plot(xnew, ypredict, \"r-\")\n", + "plt.plot(xnew, ypredict2, \"b-\")\n", + "plt.plot(x, y ,'ro')\n", + "plt.axis([0,2.0,0, 15.0])\n", + "plt.xlabel(r'$x$')\n", + "plt.ylabel(r'$y$')\n", + "plt.title(r'Random numbers ')\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "e72279fe", + "metadata": { + "editable": true + }, + "source": [ + "## Same code but now with momentum gradient descent" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "id": "62aa2606", + "metadata": { + "collapsed": false, + "editable": true + }, + "outputs": [], + "source": [ + "# Using Autograd to calculate gradients for OLS\n", + "from random import random, seed\n", + "import numpy as np\n", + "import autograd.numpy as np\n", + "import matplotlib.pyplot as plt\n", + "from autograd import grad\n", + "\n", + "def CostOLS(beta):\n", + " return (1.0/n)*np.sum((y-X @ beta)**2)\n", + "\n", + "n = 100\n", + "x = 2*np.random.rand(n,1)\n", + "y = 4+3*x#+np.random.randn(n,1)\n", + "\n", + "X = np.c_[np.ones((n,1)), x]\n", + "XT_X = X.T @ X\n", + "theta_linreg = np.linalg.pinv(XT_X) @ (X.T @ y)\n", + "print(\"Own inversion\")\n", + "print(theta_linreg)\n", + "# Hessian matrix\n", + "H = (2.0/n)* XT_X\n", + "EigValues, EigVectors = np.linalg.eig(H)\n", + "print(f\"Eigenvalues of Hessian Matrix:{EigValues}\")\n", + "\n", + "theta = np.random.randn(2,1)\n", + "eta = 1.0/np.max(EigValues)\n", + "Niterations = 30\n", + "\n", + "# define the gradient\n", + "training_gradient = grad(CostOLS)\n", + "\n", + "for iter in range(Niterations):\n", + " gradients = training_gradient(theta)\n", + " theta -= eta*gradients\n", + " print(iter,gradients[0],gradients[1])\n", + "print(\"theta from own gd\")\n", + "print(theta)\n", + "\n", + "# Now improve with momentum gradient descent\n", + "change = 0.0\n", + "delta_momentum = 0.3\n", + "for iter in range(Niterations):\n", + " # calculate gradient\n", + " gradients = training_gradient(theta)\n", + " # calculate update\n", + " new_change = eta*gradients+delta_momentum*change\n", + " # take a step\n", + " theta -= new_change\n", + " # save the change\n", + " change = new_change\n", + " print(iter,gradients[0],gradients[1])\n", + "print(\"theta from own gd wth momentum\")\n", + "print(theta)" + ] + }, + { + "cell_type": "markdown", + "id": "bad8e42f", + "metadata": { + "editable": true + }, + "source": [ + "## But noen of these can compete with Newton's method" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "id": "13a572a8", + "metadata": { + "collapsed": false, + "editable": true + }, + "outputs": [], + "source": [ + "# Using Newton's method\n", + "from random import random, seed\n", + "import numpy as np\n", + "import autograd.numpy as np\n", + "import matplotlib.pyplot as plt\n", + "from autograd import grad\n", + "\n", + "def CostOLS(beta):\n", + " return (1.0/n)*np.sum((y-X @ beta)**2)\n", + "\n", + "n = 100\n", + "x = 2*np.random.rand(n,1)\n", + "y = 4+3*x+np.random.randn(n,1)\n", + "\n", + "X = np.c_[np.ones((n,1)), x]\n", + "XT_X = X.T @ X\n", + "beta_linreg = np.linalg.pinv(XT_X) @ (X.T @ y)\n", + "print(\"Own inversion\")\n", + "print(beta_linreg)\n", + "# Hessian matrix\n", + "H = (2.0/n)* XT_X\n", + "# Note that here the Hessian does not depend on the parameters beta\n", + "invH = np.linalg.pinv(H)\n", + "EigValues, EigVectors = np.linalg.eig(H)\n", + "print(f\"Eigenvalues of Hessian Matrix:{EigValues}\")\n", + "\n", + "beta = np.random.randn(2,1)\n", + "Niterations = 5\n", + "\n", + "# define the gradient\n", + "training_gradient = grad(CostOLS)\n", + "\n", + "for iter in range(Niterations):\n", + " gradients = training_gradient(beta)\n", + " beta -= invH @ gradients\n", + " print(iter,gradients[0],gradients[1])\n", + "print(\"beta from own Newton code\")\n", + "print(beta)" + ] + }, + { + "cell_type": "markdown", + "id": "5b2c9e3a", + "metadata": { + "editable": true + }, + "source": [ + "## Including Stochastic Gradient Descent with Autograd\n", + "In this code we include the stochastic gradient descent approach discussed above. Note here that we specify which argument we are taking the derivative with respect to when using **autograd**." + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "id": "830370bf", + "metadata": { + "collapsed": false, + "editable": true + }, + "outputs": [], + "source": [ + "# Using Autograd to calculate gradients using SGD\n", + "# OLS example\n", + "from random import random, seed\n", + "import numpy as np\n", + "import autograd.numpy as np\n", + "import matplotlib.pyplot as plt\n", + "from autograd import grad\n", + "\n", + "# Note change from previous example\n", + "def CostOLS(y,X,theta):\n", + " return np.sum((y-X @ theta)**2)\n", + "\n", + "n = 100\n", + "x = 2*np.random.rand(n,1)\n", + "y = 4+3*x+np.random.randn(n,1)\n", + "\n", + "X = np.c_[np.ones((n,1)), x]\n", + "XT_X = X.T @ X\n", + "theta_linreg = np.linalg.pinv(XT_X) @ (X.T @ y)\n", + "print(\"Own inversion\")\n", + "print(theta_linreg)\n", + "# Hessian matrix\n", + "H = (2.0/n)* XT_X\n", + "EigValues, EigVectors = np.linalg.eig(H)\n", + "print(f\"Eigenvalues of Hessian Matrix:{EigValues}\")\n", + "\n", + "theta = np.random.randn(2,1)\n", + "eta = 1.0/np.max(EigValues)\n", + "Niterations = 1000\n", + "\n", + "# Note that we request the derivative wrt third argument (theta, 2 here)\n", + "training_gradient = grad(CostOLS,2)\n", + "\n", + "for iter in range(Niterations):\n", + " gradients = (1.0/n)*training_gradient(y, X, theta)\n", + " theta -= eta*gradients\n", + "print(\"theta from own gd\")\n", + "print(theta)\n", + "\n", + "xnew = np.array([[0],[2]])\n", + "Xnew = np.c_[np.ones((2,1)), xnew]\n", + "ypredict = Xnew.dot(theta)\n", + "ypredict2 = Xnew.dot(theta_linreg)\n", + "\n", + "plt.plot(xnew, ypredict, \"r-\")\n", + "plt.plot(xnew, ypredict2, \"b-\")\n", + "plt.plot(x, y ,'ro')\n", + "plt.axis([0,2.0,0, 15.0])\n", + "plt.xlabel(r'$x$')\n", + "plt.ylabel(r'$y$')\n", + "plt.title(r'Random numbers ')\n", + "plt.show()\n", + "\n", + "n_epochs = 50\n", + "M = 5 #size of each minibatch\n", + "m = int(n/M) #number of minibatches\n", + "t0, t1 = 5, 50\n", + "def learning_schedule(t):\n", + " return t0/(t+t1)\n", + "\n", + "theta = np.random.randn(2,1)\n", + "\n", + "for epoch in range(n_epochs):\n", + "# Can you figure out a better way of setting up the contributions to each batch?\n", + " for i in range(m):\n", + " random_index = M*np.random.randint(m)\n", + " xi = X[random_index:random_index+M]\n", + " yi = y[random_index:random_index+M]\n", + " gradients = (1.0/M)*training_gradient(yi, xi, theta)\n", + " eta = learning_schedule(epoch*m+i)\n", + " theta = theta - eta*gradients\n", + "print(\"theta from own sdg\")\n", + "print(theta)" + ] + }, + { + "cell_type": "markdown", + "id": "e580483f", + "metadata": { + "editable": true + }, + "source": [ + "## Same code but now with momentum gradient descent" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "id": "68895d3f", + "metadata": { + "collapsed": false, + "editable": true + }, + "outputs": [], + "source": [ + "# Using Autograd to calculate gradients using SGD\n", + "# OLS example\n", + "from random import random, seed\n", + "import numpy as np\n", + "import autograd.numpy as np\n", + "import matplotlib.pyplot as plt\n", + "from autograd import grad\n", + "\n", + "# Note change from previous example\n", + "def CostOLS(y,X,theta):\n", + " return np.sum((y-X @ theta)**2)\n", + "\n", + "n = 100\n", + "x = 2*np.random.rand(n,1)\n", + "y = 4+3*x+np.random.randn(n,1)\n", + "\n", + "X = np.c_[np.ones((n,1)), x]\n", + "XT_X = X.T @ X\n", + "theta_linreg = np.linalg.pinv(XT_X) @ (X.T @ y)\n", + "print(\"Own inversion\")\n", + "print(theta_linreg)\n", + "# Hessian matrix\n", + "H = (2.0/n)* XT_X\n", + "EigValues, EigVectors = np.linalg.eig(H)\n", + "print(f\"Eigenvalues of Hessian Matrix:{EigValues}\")\n", + "\n", + "theta = np.random.randn(2,1)\n", + "eta = 1.0/np.max(EigValues)\n", + "Niterations = 100\n", + "\n", + "# Note that we request the derivative wrt third argument (theta, 2 here)\n", + "training_gradient = grad(CostOLS,2)\n", + "\n", + "for iter in range(Niterations):\n", + " gradients = (1.0/n)*training_gradient(y, X, theta)\n", + " theta -= eta*gradients\n", + "print(\"theta from own gd\")\n", + "print(theta)\n", + "\n", + "\n", + "n_epochs = 50\n", + "M = 5 #size of each minibatch\n", + "m = int(n/M) #number of minibatches\n", + "t0, t1 = 5, 50\n", + "def learning_schedule(t):\n", + " return t0/(t+t1)\n", + "\n", + "theta = np.random.randn(2,1)\n", + "\n", + "change = 0.0\n", + "delta_momentum = 0.3\n", + "\n", + "for epoch in range(n_epochs):\n", + " for i in range(m):\n", + " random_index = M*np.random.randint(m)\n", + " xi = X[random_index:random_index+M]\n", + " yi = y[random_index:random_index+M]\n", + " gradients = (1.0/M)*training_gradient(yi, xi, theta)\n", + " eta = learning_schedule(epoch*m+i)\n", + " # calculate update\n", + " new_change = eta*gradients+delta_momentum*change\n", + " # take a step\n", + " theta -= new_change\n", + " # save the change\n", + " change = new_change\n", + "print(\"theta from own sdg with momentum\")\n", + "print(theta)" + ] + }, + { + "cell_type": "markdown", + "id": "01c29c9e", + "metadata": { + "editable": true + }, + "source": [ + "## Similar (second order function now) problem but now with AdaGrad" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "id": "36a00e5a", + "metadata": { + "collapsed": false, + "editable": true + }, + "outputs": [], + "source": [ + "# Using Autograd to calculate gradients using AdaGrad and Stochastic Gradient descent\n", + "# OLS example\n", + "from random import random, seed\n", + "import numpy as np\n", + "import autograd.numpy as np\n", + "import matplotlib.pyplot as plt\n", + "from autograd import grad\n", + "\n", + "# Note change from previous example\n", + "def CostOLS(y,X,theta):\n", + " return np.sum((y-X @ theta)**2)\n", + "\n", + "n = 1000\n", + "x = np.random.rand(n,1)\n", + "y = 2.0+3*x +4*x*x\n", + "\n", + "X = np.c_[np.ones((n,1)), x, x*x]\n", + "XT_X = X.T @ X\n", + "theta_linreg = np.linalg.pinv(XT_X) @ (X.T @ y)\n", + "print(\"Own inversion\")\n", + "print(theta_linreg)\n", + "\n", + "\n", + "# Note that we request the derivative wrt third argument (theta, 2 here)\n", + "training_gradient = grad(CostOLS,2)\n", + "# Define parameters for Stochastic Gradient Descent\n", + "n_epochs = 50\n", + "M = 5 #size of each minibatch\n", + "m = int(n/M) #number of minibatches\n", + "# Guess for unknown parameters theta\n", + "theta = np.random.randn(3,1)\n", + "\n", + "# Value for learning rate\n", + "eta = 0.01\n", + "# Including AdaGrad parameter to avoid possible division by zero\n", + "delta = 1e-8\n", + "for epoch in range(n_epochs):\n", + " Giter = 0.0\n", + " for i in range(m):\n", + " random_index = M*np.random.randint(m)\n", + " xi = X[random_index:random_index+M]\n", + " yi = y[random_index:random_index+M]\n", + " gradients = (1.0/M)*training_gradient(yi, xi, theta)\n", + " Giter += gradients*gradients\n", + " update = gradients*eta/(delta+np.sqrt(Giter))\n", + " theta -= update\n", + "print(\"theta from own AdaGrad\")\n", + "print(theta)" + ] + }, + { + "cell_type": "markdown", + "id": "18ccca46", + "metadata": { + "editable": true + }, + "source": [ + "Running this code we note an almost perfect agreement with the results from matrix inversion." + ] + }, + { + "cell_type": "markdown", + "id": "e076c773", + "metadata": { + "editable": true + }, + "source": [ + "## RMSprop for adaptive learning rate with Stochastic Gradient Descent" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "id": "cb4ad1d3", + "metadata": { + "collapsed": false, + "editable": true + }, + "outputs": [], + "source": [ + "# Using Autograd to calculate gradients using RMSprop and Stochastic Gradient descent\n", + "# OLS example\n", + "from random import random, seed\n", + "import numpy as np\n", + "import autograd.numpy as np\n", + "import matplotlib.pyplot as plt\n", + "from autograd import grad\n", + "\n", + "# Note change from previous example\n", + "def CostOLS(y,X,theta):\n", + " return np.sum((y-X @ theta)**2)\n", + "\n", + "n = 1000\n", + "x = np.random.rand(n,1)\n", + "y = 2.0+3*x +4*x*x# +np.random.randn(n,1)\n", + "\n", + "X = np.c_[np.ones((n,1)), x, x*x]\n", + "XT_X = X.T @ X\n", + "theta_linreg = np.linalg.pinv(XT_X) @ (X.T @ y)\n", + "print(\"Own inversion\")\n", + "print(theta_linreg)\n", + "\n", + "\n", + "# Note that we request the derivative wrt third argument (theta, 2 here)\n", + "training_gradient = grad(CostOLS,2)\n", + "# Define parameters for Stochastic Gradient Descent\n", + "n_epochs = 50\n", + "M = 5 #size of each minibatch\n", + "m = int(n/M) #number of minibatches\n", + "# Guess for unknown parameters theta\n", + "theta = np.random.randn(3,1)\n", + "\n", + "# Value for learning rate\n", + "eta = 0.01\n", + "# Value for parameter rho\n", + "rho = 0.99\n", + "# Including AdaGrad parameter to avoid possible division by zero\n", + "delta = 1e-8\n", + "for epoch in range(n_epochs):\n", + " Giter = 0.0\n", + " for i in range(m):\n", + " random_index = M*np.random.randint(m)\n", + " xi = X[random_index:random_index+M]\n", + " yi = y[random_index:random_index+M]\n", + " gradients = (1.0/M)*training_gradient(yi, xi, theta)\n", + "\t# Accumulated gradient\n", + "\t# Scaling with rho the new and the previous results\n", + " Giter = (rho*Giter+(1-rho)*gradients*gradients)\n", + "\t# Taking the diagonal only and inverting\n", + " update = gradients*eta/(delta+np.sqrt(Giter))\n", + "\t# Hadamard product\n", + " theta -= update\n", + "print(\"theta from own RMSprop\")\n", + "print(theta)" + ] + }, + { + "cell_type": "markdown", + "id": "2253fa34", + "metadata": { + "editable": true + }, + "source": [ + "## And finally [ADAM](https://arxiv.org/pdf/1412.6980.pdf)" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "id": "92b3454a", + "metadata": { + "collapsed": false, + "editable": true + }, + "outputs": [], + "source": [ + "# Using Autograd to calculate gradients using RMSprop and Stochastic Gradient descent\n", + "# OLS example\n", + "from random import random, seed\n", + "import numpy as np\n", + "import autograd.numpy as np\n", + "import matplotlib.pyplot as plt\n", + "from autograd import grad\n", + "\n", + "# Note change from previous example\n", + "def CostOLS(y,X,theta):\n", + " return np.sum((y-X @ theta)**2)\n", + "\n", + "n = 1000\n", + "x = np.random.rand(n,1)\n", + "y = 2.0+3*x +4*x*x# +np.random.randn(n,1)\n", + "\n", + "X = np.c_[np.ones((n,1)), x, x*x]\n", + "XT_X = X.T @ X\n", + "theta_linreg = np.linalg.pinv(XT_X) @ (X.T @ y)\n", + "print(\"Own inversion\")\n", + "print(theta_linreg)\n", + "\n", + "\n", + "# Note that we request the derivative wrt third argument (theta, 2 here)\n", + "training_gradient = grad(CostOLS,2)\n", + "# Define parameters for Stochastic Gradient Descent\n", + "n_epochs = 50\n", + "M = 5 #size of each minibatch\n", + "m = int(n/M) #number of minibatches\n", + "# Guess for unknown parameters theta\n", + "theta = np.random.randn(3,1)\n", + "\n", + "# Value for learning rate\n", + "eta = 0.01\n", + "# Value for parameters beta1 and beta2, see https://arxiv.org/abs/1412.6980\n", + "beta1 = 0.9\n", + "beta2 = 0.999\n", + "# Including AdaGrad parameter to avoid possible division by zero\n", + "delta = 1e-7\n", + "iter = 0\n", + "for epoch in range(n_epochs):\n", + " first_moment = 0.0\n", + " second_moment = 0.0\n", + " iter += 1\n", + " for i in range(m):\n", + " random_index = M*np.random.randint(m)\n", + " xi = X[random_index:random_index+M]\n", + " yi = y[random_index:random_index+M]\n", + " gradients = (1.0/M)*training_gradient(yi, xi, theta)\n", + " # Computing moments first\n", + " first_moment = beta1*first_moment + (1-beta1)*gradients\n", + " second_moment = beta2*second_moment+(1-beta2)*gradients*gradients\n", + " first_term = first_moment/(1.0-beta1**iter)\n", + " second_term = second_moment/(1.0-beta2**iter)\n", + "\t# Scaling with rho the new and the previous results\n", + " update = eta*first_term/(np.sqrt(second_term)+delta)\n", + " theta -= update\n", + "print(\"theta from own ADAM\")\n", + "print(theta)" + ] + }, + { + "cell_type": "markdown", + "id": "c2025d97", + "metadata": { + "editable": true + }, + "source": [ + "## And Logistic Regression" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "id": "3f6d8746", + "metadata": { + "collapsed": false, + "editable": true + }, + "outputs": [], + "source": [ + "import autograd.numpy as np\n", + "from autograd import grad\n", + "\n", + "def sigmoid(x):\n", + " return 0.5 * (np.tanh(x / 2.) + 1)\n", + "\n", + "def logistic_predictions(weights, inputs):\n", + " # Outputs probability of a label being true according to logistic model.\n", + " return sigmoid(np.dot(inputs, weights))\n", + "\n", + "def training_loss(weights):\n", + " # Training loss is the negative log-likelihood of the training labels.\n", + " preds = logistic_predictions(weights, inputs)\n", + " label_probabilities = preds * targets + (1 - preds) * (1 - targets)\n", + " return -np.sum(np.log(label_probabilities))\n", + "\n", + "# Build a toy dataset.\n", + "inputs = np.array([[0.52, 1.12, 0.77],\n", + " [0.88, -1.08, 0.15],\n", + " [0.52, 0.06, -1.30],\n", + " [0.74, -2.49, 1.39]])\n", + "targets = np.array([True, True, False, True])\n", + "\n", + "# Define a function that returns gradients of training loss using Autograd.\n", + "training_gradient_fun = grad(training_loss)\n", + "\n", + "# Optimize weights using gradient descent.\n", + "weights = np.array([0.0, 0.0, 0.0])\n", + "print(\"Initial loss:\", training_loss(weights))\n", + "for i in range(100):\n", + " weights -= training_gradient_fun(weights) * 0.01\n", + "\n", + "print(\"Trained loss:\", training_loss(weights))" + ] + }, + { + "cell_type": "markdown", + "id": "716627e3", + "metadata": { + "editable": true + }, + "source": [ + "## Introducing [JAX](https://jax.readthedocs.io/en/latest/)\n", + "\n", + "Presently, instead of using **autograd**, we recommend using [JAX](https://jax.readthedocs.io/en/latest/)\n", + "\n", + "**JAX** is Autograd and [XLA (Accelerated Linear Algebra))](https://www.tensorflow.org/xla),\n", + "brought together for high-performance numerical computing and machine learning research.\n", + "It provides composable transformations of Python+NumPy programs: differentiate, vectorize, parallelize, Just-In-Time compile to GPU/TPU, and more.\n", + "\n", + "Here's a simple example on how you can use **JAX** to compute the derivate of the logistic function." + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "id": "5c938af4", + "metadata": { + "collapsed": false, + "editable": true + }, + "outputs": [], + "source": [ + "import jax.numpy as jnp\n", + "from jax import grad, jit, vmap\n", + "\n", + "def sum_logistic(x):\n", + " return jnp.sum(1.0 / (1.0 + jnp.exp(-x)))\n", + "\n", + "x_small = jnp.arange(3.)\n", + "derivative_fn = grad(sum_logistic)\n", + "print(derivative_fn(x_small))" + ] + }, + { + "cell_type": "markdown", + "id": "b087cc5f", + "metadata": { + "editable": true + }, + "source": [ + "## Introduction to Neural networks\n", + "\n", + "Artificial neural networks are computational systems that can learn to\n", + "perform tasks by considering examples, generally without being\n", + "programmed with any task-specific rules. It is supposed to mimic a\n", + "biological system, wherein neurons interact by sending signals in the\n", + "form of mathematical functions between layers. All layers can contain\n", + "an arbitrary number of neurons, and each connection is represented by\n", + "a weight variable." + ] + }, + { + "cell_type": "markdown", + "id": "c040b49e", + "metadata": { + "editable": true + }, + "source": [ + "## Artificial neurons\n", + "\n", + "The field of artificial neural networks has a long history of\n", + "development, and is closely connected with the advancement of computer\n", + "science and computers in general. A model of artificial neurons was\n", + "first developed by McCulloch and Pitts in 1943 to study signal\n", + "processing in the brain and has later been refined by others. The\n", + "general idea is to mimic neural networks in the human brain, which is\n", + "composed of billions of neurons that communicate with each other by\n", + "sending electrical signals. Each neuron accumulates its incoming\n", + "signals, which must exceed an activation threshold to yield an\n", + "output. If the threshold is not overcome, the neuron remains inactive,\n", + "i.e. has zero output.\n", + "\n", + "This behaviour has inspired a simple mathematical model for an artificial neuron." + ] + }, + { + "cell_type": "markdown", + "id": "663a7548", + "metadata": { + "editable": true + }, + "source": [ + "\n", + "
              \n", + "\n", + "$$\n", + "\\begin{equation}\n", + " y = f\\left(\\sum_{i=1}^n w_ix_i\\right) = f(u)\n", + "\\label{artificialNeuron} \\tag{6}\n", + "\\end{equation}\n", + "$$" + ] + }, + { + "cell_type": "markdown", + "id": "7d41caae", + "metadata": { + "editable": true + }, + "source": [ + "Here, the output $y$ of the neuron is the value of its activation function, which have as input\n", + "a weighted sum of signals $x_i, \\dots ,x_n$ received by $n$ other neurons.\n", + "\n", + "Conceptually, it is helpful to divide neural networks into four\n", + "categories:\n", + "1. general purpose neural networks for supervised learning,\n", + "\n", + "2. neural networks designed specifically for image processing, the most prominent example of this class being Convolutional Neural Networks (CNNs),\n", + "\n", + "3. neural networks for sequential data such as Recurrent Neural Networks (RNNs), and\n", + "\n", + "4. neural networks for unsupervised learning such as Deep Boltzmann Machines.\n", + "\n", + "In natural science, DNNs and CNNs have already found numerous\n", + "applications. In statistical physics, they have been applied to detect\n", + "phase transitions in 2D Ising and Potts models, lattice gauge\n", + "theories, and different phases of polymers, or solving the\n", + "Navier-Stokes equation in weather forecasting. Deep learning has also\n", + "found interesting applications in quantum physics. Various quantum\n", + "phase transitions can be detected and studied using DNNs and CNNs,\n", + "topological phases, and even non-equilibrium many-body\n", + "localization. Representing quantum states as DNNs quantum state\n", + "tomography are among some of the impressive achievements to reveal the\n", + "potential of DNNs to facilitate the study of quantum systems.\n", + "\n", + "In quantum information theory, it has been shown that one can perform\n", + "gate decompositions with the help of neural. \n", + "\n", + "The applications are not limited to the natural sciences. There is a\n", + "plethora of applications in essentially all disciplines, from the\n", + "humanities to life science and medicine." + ] + }, + { + "cell_type": "markdown", + "id": "8cd5fa1c", + "metadata": { + "editable": true + }, + "source": [ + "## Neural network types\n", + "\n", + "An artificial neural network (ANN), is a computational model that\n", + "consists of layers of connected neurons, or nodes or units. We will\n", + "refer to these interchangeably as units or nodes, and sometimes as\n", + "neurons.\n", + "\n", + "It is supposed to mimic a biological nervous system by letting each\n", + "neuron interact with other neurons by sending signals in the form of\n", + "mathematical functions between layers. A wide variety of different\n", + "ANNs have been developed, but most of them consist of an input layer,\n", + "an output layer and eventual layers in-between, called *hidden\n", + "layers*. All layers can contain an arbitrary number of nodes, and each\n", + "connection between two nodes is associated with a weight variable.\n", + "\n", + "Neural networks (also called neural nets) are neural-inspired\n", + "nonlinear models for supervised learning. As we will see, neural nets\n", + "can be viewed as natural, more powerful extensions of supervised\n", + "learning methods such as linear and logistic regression and soft-max\n", + "methods we discussed earlier." + ] + }, + { + "cell_type": "markdown", + "id": "0b2c6e40", + "metadata": { + "editable": true + }, + "source": [ + "## Feed-forward neural networks\n", + "\n", + "The feed-forward neural network (FFNN) was the first and simplest type\n", + "of ANNs that were devised. In this network, the information moves in\n", + "only one direction: forward through the layers.\n", + "\n", + "Nodes are represented by circles, while the arrows display the\n", + "connections between the nodes, including the direction of information\n", + "flow. Additionally, each arrow corresponds to a weight variable\n", + "(figure to come). We observe that each node in a layer is connected\n", + "to *all* nodes in the subsequent layer, making this a so-called\n", + "*fully-connected* FFNN." + ] + }, + { + "cell_type": "markdown", + "id": "90e946b7", + "metadata": { + "editable": true + }, + "source": [ + "## Convolutional Neural Network\n", + "\n", + "A different variant of FFNNs are *convolutional neural networks*\n", + "(CNNs), which have a connectivity pattern inspired by the animal\n", + "visual cortex. Individual neurons in the visual cortex only respond to\n", + "stimuli from small sub-regions of the visual field, called a receptive\n", + "field. This makes the neurons well-suited to exploit the strong\n", + "spatially local correlation present in natural images. The response of\n", + "each neuron can be approximated mathematically as a convolution\n", + "operation. (figure to come)\n", + "\n", + "Convolutional neural networks emulate the behaviour of neurons in the\n", + "visual cortex by enforcing a *local* connectivity pattern between\n", + "nodes of adjacent layers: Each node in a convolutional layer is\n", + "connected only to a subset of the nodes in the previous layer, in\n", + "contrast to the fully-connected FFNN. Often, CNNs consist of several\n", + "convolutional layers that learn local features of the input, with a\n", + "fully-connected layer at the end, which gathers all the local data and\n", + "produces the outputs. They have wide applications in image and video\n", + "recognition." + ] + }, + { + "cell_type": "markdown", + "id": "1964f9e3", + "metadata": { + "editable": true + }, + "source": [ + "## Recurrent neural networks\n", + "\n", + "So far we have only mentioned ANNs where information flows in one\n", + "direction: forward. *Recurrent neural networks* on the other hand,\n", + "have connections between nodes that form directed *cycles*. This\n", + "creates a form of internal memory which are able to capture\n", + "information on what has been calculated before; the output is\n", + "dependent on the previous computations. Recurrent NNs make use of\n", + "sequential information by performing the same task for every element\n", + "in a sequence, where each element depends on previous elements. An\n", + "example of such information is sentences, making recurrent NNs\n", + "especially well-suited for handwriting and speech recognition." + ] + }, + { + "cell_type": "markdown", + "id": "faf981a1", + "metadata": { + "editable": true + }, + "source": [ + "## Other types of networks\n", + "\n", + "There are many other kinds of ANNs that have been developed. One type\n", + "that is specifically designed for interpolation in multidimensional\n", + "space is the radial basis function (RBF) network. RBFs are typically\n", + "made up of three layers: an input layer, a hidden layer with\n", + "non-linear radial symmetric activation functions and a linear output\n", + "layer (''linear'' here means that each node in the output layer has a\n", + "linear activation function). The layers are normally fully-connected\n", + "and there are no cycles, thus RBFs can be viewed as a type of\n", + "fully-connected FFNN. They are however usually treated as a separate\n", + "type of NN due the unusual activation functions." + ] + }, + { + "cell_type": "markdown", + "id": "3667182a", + "metadata": { + "editable": true + }, + "source": [ + "## Multilayer perceptrons\n", + "\n", + "One uses often so-called fully-connected feed-forward neural networks\n", + "with three or more layers (an input layer, one or more hidden layers\n", + "and an output layer) consisting of neurons that have non-linear\n", + "activation functions.\n", + "\n", + "Such networks are often called *multilayer perceptrons* (MLPs)." + ] + }, + { + "cell_type": "markdown", + "id": "5dd1a89f", + "metadata": { + "editable": true + }, + "source": [ + "## Why multilayer perceptrons?\n", + "\n", + "According to the *Universal approximation theorem*, a feed-forward\n", + "neural network with just a single hidden layer containing a finite\n", + "number of neurons can approximate a continuous multidimensional\n", + "function to arbitrary accuracy, assuming the activation function for\n", + "the hidden layer is a **non-constant, bounded and\n", + "monotonically-increasing continuous function**.\n", + "\n", + "Note that the requirements on the activation function only applies to\n", + "the hidden layer, the output nodes are always assumed to be linear, so\n", + "as to not restrict the range of output values." + ] + }, + { + "cell_type": "markdown", + "id": "da5b8927", + "metadata": { + "editable": true + }, + "source": [ + "## Illustration of a single perceptron model and a multi-perceptron model\n", + "\n", + "\n", + "\n", + "\n", + "

              Figure 1: In a) we show a single perceptron model while in b) we dispay a network with two hidden layers, an input layer and an output layer.

              \n", + "" + ] + }, + { + "cell_type": "markdown", + "id": "3bba849e", + "metadata": { + "editable": true + }, + "source": [ + "## Examples of XOR, OR and AND gates\n", + "\n", + "Let us first try to fit various gates using standard linear\n", + "regression. The gates we are thinking of are the classical XOR, OR and\n", + "AND gates, well-known elements in computer science. The tables here\n", + "show how we can set up the inputs $x_1$ and $x_2$ in order to yield a\n", + "specific target $y_i$." + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "id": "de11d95e", + "metadata": { + "collapsed": false, + "editable": true + }, + "outputs": [], + "source": [ + "\"\"\"\n", + "Simple code that tests XOR, OR and AND gates with linear regression\n", + "\"\"\"\n", + "\n", + "import numpy as np\n", + "# Design matrix\n", + "X = np.array([ [1, 0, 0], [1, 0, 1], [1, 1, 0],[1, 1, 1]],dtype=np.float64)\n", + "print(f\"The X.TX matrix:{X.T @ X}\")\n", + "Xinv = np.linalg.pinv(X.T @ X)\n", + "print(f\"The invers of X.TX matrix:{Xinv}\")\n", + "\n", + "# The XOR gate \n", + "yXOR = np.array( [ 0, 1 ,1, 0])\n", + "ThetaXOR = Xinv @ X.T @ yXOR\n", + "print(f\"The values of theta for the XOR gate:{ThetaXOR}\")\n", + "print(f\"The linear regression prediction for the XOR gate:{X @ ThetaXOR}\")\n", + "\n", + "\n", + "# The OR gate \n", + "yOR = np.array( [ 0, 1 ,1, 1])\n", + "ThetaOR = Xinv @ X.T @ yOR\n", + "print(f\"The values of theta for the OR gate:{ThetaOR}\")\n", + "print(f\"The linear regression prediction for the OR gate:{X @ ThetaOR}\")\n", + "\n", + "\n", + "# The OR gate \n", + "yAND = np.array( [ 0, 0 ,0, 1])\n", + "ThetaAND = Xinv @ X.T @ yAND\n", + "print(f\"The values of theta for the AND gate:{ThetaAND}\")\n", + "print(f\"The linear regression prediction for the AND gate:{X @ ThetaAND}\")" + ] + }, + { + "cell_type": "markdown", + "id": "b0477050", + "metadata": { + "editable": true + }, + "source": [ + "What is happening here?" + ] + }, + { + "cell_type": "markdown", + "id": "1d72d90e", + "metadata": { + "editable": true + }, + "source": [ + "## Does Logistic Regression do a better Job?" + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "id": "501aa7b5", + "metadata": { + "collapsed": false, + "editable": true + }, + "outputs": [], + "source": [ + "\"\"\"\n", + "Simple code that tests XOR and OR gates with linear regression\n", + "and logistic regression\n", + "\"\"\"\n", + "\n", + "import matplotlib.pyplot as plt\n", + "from sklearn.linear_model import LogisticRegression\n", + "import numpy as np\n", + "\n", + "# Design matrix\n", + "X = np.array([ [1, 0, 0], [1, 0, 1], [1, 1, 0],[1, 1, 1]],dtype=np.float64)\n", + "print(f\"The X.TX matrix:{X.T @ X}\")\n", + "Xinv = np.linalg.pinv(X.T @ X)\n", + "print(f\"The invers of X.TX matrix:{Xinv}\")\n", + "\n", + "# The XOR gate \n", + "yXOR = np.array( [ 0, 1 ,1, 0])\n", + "ThetaXOR = Xinv @ X.T @ yXOR\n", + "print(f\"The values of theta for the XOR gate:{ThetaXOR}\")\n", + "print(f\"The linear regression prediction for the XOR gate:{X @ ThetaXOR}\")\n", + "\n", + "\n", + "# The OR gate \n", + "yOR = np.array( [ 0, 1 ,1, 1])\n", + "ThetaOR = Xinv @ X.T @ yOR\n", + "print(f\"The values of theta for the OR gate:{ThetaOR}\")\n", + "print(f\"The linear regression prediction for the OR gate:{X @ ThetaOR}\")\n", + "\n", + "\n", + "# The OR gate \n", + "yAND = np.array( [ 0, 0 ,0, 1])\n", + "ThetaAND = Xinv @ X.T @ yAND\n", + "print(f\"The values of theta for the AND gate:{ThetaAND}\")\n", + "print(f\"The linear regression prediction for the AND gate:{X @ ThetaAND}\")\n", + "\n", + "# Now we change to logistic regression\n", + "\n", + "\n", + "# Logistic Regression\n", + "logreg = LogisticRegression()\n", + "logreg.fit(X, yOR)\n", + "print(\"Test set accuracy with Logistic Regression for OR gate: {:.2f}\".format(logreg.score(X,yOR)))\n", + "\n", + "logreg.fit(X, yXOR)\n", + "print(\"Test set accuracy with Logistic Regression for XOR gate: {:.2f}\".format(logreg.score(X,yXOR)))\n", + "\n", + "\n", + "logreg.fit(X, yAND)\n", + "print(\"Test set accuracy with Logistic Regression for AND gate: {:.2f}\".format(logreg.score(X,yAND)))" + ] + }, + { + "cell_type": "markdown", + "id": "03908b42", + "metadata": { + "editable": true + }, + "source": [ + "Not exactly impressive, but somewhat better." + ] + }, + { + "cell_type": "markdown", + "id": "91971469", + "metadata": { + "editable": true + }, + "source": [ + "## Adding Neural Networks" + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "id": "f1717531", + "metadata": { + "collapsed": false, + "editable": true + }, + "outputs": [], + "source": [ + "\n", + "# and now neural networks with Scikit-Learn and the XOR\n", + "\n", + "from sklearn.neural_network import MLPClassifier\n", + "from sklearn.datasets import make_classification\n", + "X, yXOR = make_classification(n_samples=100, random_state=1)\n", + "FFNN = MLPClassifier(random_state=1, max_iter=300).fit(X, yXOR)\n", + "FFNN.predict_proba(X)\n", + "print(f\"Test set accuracy with Feed Forward Neural Network for XOR gate:{FFNN.score(X, yXOR)}\")" + ] + }, + { + "cell_type": "markdown", + "id": "05726714", + "metadata": { + "editable": true + }, + "source": [ + "## Mathematical model\n", + "\n", + "The output $y$ is produced via the activation function $f$" + ] + }, + { + "cell_type": "markdown", + "id": "1cf57e1c", + "metadata": { + "editable": true + }, + "source": [ + "$$\n", + "y = f\\left(\\sum_{i=1}^n w_ix_i + b_i\\right) = f(z),\n", + "$$" + ] + }, + { + "cell_type": "markdown", + "id": "57743f5e", + "metadata": { + "editable": true + }, + "source": [ + "This function receives $x_i$ as inputs.\n", + "Here the activation $z=(\\sum_{i=1}^n w_ix_i+b_i)$. \n", + "In an FFNN of such neurons, the *inputs* $x_i$ are the *outputs* of\n", + "the neurons in the preceding layer. Furthermore, an MLP is\n", + "fully-connected, which means that each neuron receives a weighted sum\n", + "of the outputs of *all* neurons in the previous layer." + ] + }, + { + "cell_type": "markdown", + "id": "2d3f8338", + "metadata": { + "editable": true + }, + "source": [ + "## Mathematical model\n", + "\n", + "First, for each node $i$ in the first hidden layer, we calculate a weighted sum $z_i^1$ of the input coordinates $x_j$," + ] + }, + { + "cell_type": "markdown", + "id": "20be0ccb", + "metadata": { + "editable": true + }, + "source": [ + "\n", + "
              \n", + "\n", + "$$\n", + "\\begin{equation} z_i^1 = \\sum_{j=1}^{M} w_{ij}^1 x_j + b_i^1\n", + "\\label{_auto6} \\tag{7}\n", + "\\end{equation}\n", + "$$" + ] + }, + { + "cell_type": "markdown", + "id": "d289b4c8", + "metadata": { + "editable": true + }, + "source": [ + "Here $b_i$ is the so-called bias which is normally needed in\n", + "case of zero activation weights or inputs. How to fix the biases and\n", + "the weights will be discussed below. The value of $z_i^1$ is the\n", + "argument to the activation function $f_i$ of each node $i$, The\n", + "variable $M$ stands for all possible inputs to a given node $i$ in the\n", + "first layer. We define the output $y_i^1$ of all neurons in layer 1 as" + ] + }, + { + "cell_type": "markdown", + "id": "498c2494", + "metadata": { + "editable": true + }, + "source": [ + "\n", + "
              \n", + "\n", + "$$\n", + "\\begin{equation}\n", + " y_i^1 = f(z_i^1) = f\\left(\\sum_{j=1}^M w_{ij}^1 x_j + b_i^1\\right)\n", + "\\label{outputLayer1} \\tag{8}\n", + "\\end{equation}\n", + "$$" + ] + }, + { + "cell_type": "markdown", + "id": "77995e5d", + "metadata": { + "editable": true + }, + "source": [ + "where we assume that all nodes in the same layer have identical\n", + "activation functions, hence the notation $f$. In general, we could assume in the more general case that different layers have different activation functions.\n", + "In this case we would identify these functions with a superscript $l$ for the $l$-th layer," + ] + }, + { + "cell_type": "markdown", + "id": "ef353d76", + "metadata": { + "editable": true + }, + "source": [ + "\n", + "
              \n", + "\n", + "$$\n", + "\\begin{equation}\n", + " y_i^l = f^l(u_i^l) = f^l\\left(\\sum_{j=1}^{N_{l-1}} w_{ij}^l y_j^{l-1} + b_i^l\\right)\n", + "\\label{generalLayer} \\tag{9}\n", + "\\end{equation}\n", + "$$" + ] + }, + { + "cell_type": "markdown", + "id": "0a25d2f4", + "metadata": { + "editable": true + }, + "source": [ + "where $N_l$ is the number of nodes in layer $l$. When the output of\n", + "all the nodes in the first hidden layer are computed, the values of\n", + "the subsequent layer can be calculated and so forth until the output\n", + "is obtained." + ] + }, + { + "cell_type": "markdown", + "id": "d7d29703", + "metadata": { + "editable": true + }, + "source": [ + "## Mathematical model\n", + "\n", + "The output of neuron $i$ in layer 2 is thus," + ] + }, + { + "cell_type": "markdown", + "id": "94eddeb9", + "metadata": { + "editable": true + }, + "source": [ + "\n", + "
              \n", + "\n", + "$$\n", + "\\begin{equation}\n", + " y_i^2 = f^2\\left(\\sum_{j=1}^N w_{ij}^2 y_j^1 + b_i^2\\right) \n", + "\\label{_auto7} \\tag{10}\n", + "\\end{equation}\n", + "$$" + ] + }, + { + "cell_type": "markdown", + "id": "f047f4c6", + "metadata": { + "editable": true + }, + "source": [ + "\n", + "
              \n", + "\n", + "$$\n", + "\\begin{equation} \n", + " = f^2\\left[\\sum_{j=1}^N w_{ij}^2f^1\\left(\\sum_{k=1}^M w_{jk}^1 x_k + b_j^1\\right) + b_i^2\\right]\n", + "\\label{outputLayer2} \\tag{11}\n", + "\\end{equation}\n", + "$$" + ] + }, + { + "cell_type": "markdown", + "id": "91d4806e", + "metadata": { + "editable": true + }, + "source": [ + "where we have substituted $y_k^1$ with the inputs $x_k$. Finally, the ANN output reads" + ] + }, + { + "cell_type": "markdown", + "id": "7342e125", + "metadata": { + "editable": true + }, + "source": [ + "\n", + "
              \n", + "\n", + "$$\n", + "\\begin{equation}\n", + " y_i^3 = f^3\\left(\\sum_{j=1}^N w_{ij}^3 y_j^2 + b_i^3\\right) \n", + "\\label{_auto8} \\tag{12}\n", + "\\end{equation}\n", + "$$" + ] + }, + { + "cell_type": "markdown", + "id": "5068e976", + "metadata": { + "editable": true + }, + "source": [ + "\n", + "
              \n", + "\n", + "$$\n", + "\\begin{equation} \n", + " = f_3\\left[\\sum_{j} w_{ij}^3 f^2\\left(\\sum_{k} w_{jk}^2 f^1\\left(\\sum_{m} w_{km}^1 x_m + b_k^1\\right) + b_j^2\\right)\n", + " + b_1^3\\right]\n", + "\\label{_auto9} \\tag{13}\n", + "\\end{equation}\n", + "$$" + ] + }, + { + "cell_type": "markdown", + "id": "b51a241d", + "metadata": { + "editable": true + }, + "source": [ + "## Mathematical model\n", + "\n", + "We can generalize this expression to an MLP with $l$ hidden\n", + "layers. The complete functional form is," + ] + }, + { + "cell_type": "markdown", + "id": "5a7b4915", + "metadata": { + "editable": true + }, + "source": [ + "\n", + "
              \n", + "\n", + "$$\n", + "\\begin{equation}\n", + "y^{l+1}_i = f^{l+1}\\left[\\!\\sum_{j=1}^{N_l} w_{ij}^3 f^l\\left(\\sum_{k=1}^{N_{l-1}}w_{jk}^{l-1}\\left(\\dots f^1\\left(\\sum_{n=1}^{N_0} w_{mn}^1 x_n+ b_m^1\\right)\\dots\\right)+b_k^2\\right)+b_1^3\\right] \n", + "\\label{completeNN} \\tag{14}\n", + "\\end{equation}\n", + "$$" + ] + }, + { + "cell_type": "markdown", + "id": "c3215b7f", + "metadata": { + "editable": true + }, + "source": [ + "which illustrates a basic property of MLPs: The only independent\n", + "variables are the input values $x_n$." + ] + }, + { + "cell_type": "markdown", + "id": "f92eedb0", + "metadata": { + "editable": true + }, + "source": [ + "## Mathematical model\n", + "\n", + "This confirms that an MLP, despite its quite convoluted mathematical\n", + "form, is nothing more than an analytic function, specifically a\n", + "mapping of real-valued vectors $\\hat{x} \\in \\mathbb{R}^n \\rightarrow\n", + "\\hat{y} \\in \\mathbb{R}^m$.\n", + "\n", + "Furthermore, the flexibility and universality of an MLP can be\n", + "illustrated by realizing that the expression is essentially a nested\n", + "sum of scaled activation functions of the form" + ] + }, + { + "cell_type": "markdown", + "id": "b658fa6d", + "metadata": { + "editable": true + }, + "source": [ + "\n", + "
              \n", + "\n", + "$$\n", + "\\begin{equation}\n", + " f(x) = c_1 f(c_2 x + c_3) + c_4\n", + "\\label{_auto10} \\tag{15}\n", + "\\end{equation}\n", + "$$" + ] + }, + { + "cell_type": "markdown", + "id": "8506281a", + "metadata": { + "editable": true + }, + "source": [ + "where the parameters $c_i$ are weights and biases. By adjusting these\n", + "parameters, the activation functions can be shifted up and down or\n", + "left and right, change slope or be rescaled which is the key to the\n", + "flexibility of a neural network." + ] + }, + { + "cell_type": "markdown", + "id": "0ad3f400", + "metadata": { + "editable": true + }, + "source": [ + "### Matrix-vector notation\n", + "\n", + "We can introduce a more convenient notation for the activations in an A NN. \n", + "\n", + "Additionally, we can represent the biases and activations\n", + "as layer-wise column vectors $\\hat{b}_l$ and $\\hat{y}_l$, so that the $i$-th element of each vector \n", + "is the bias $b_i^l$ and activation $y_i^l$ of node $i$ in layer $l$ respectively. \n", + "\n", + "We have that $\\mathrm{W}_l$ is an $N_{l-1} \\times N_l$ matrix, while $\\hat{b}_l$ and $\\hat{y}_l$ are $N_l \\times 1$ column vectors. \n", + "With this notation, the sum becomes a matrix-vector multiplication, and we can write\n", + "the equation for the activations of hidden layer 2 (assuming three nodes for simplicity) as" + ] + }, + { + "cell_type": "markdown", + "id": "7b431efc", + "metadata": { + "editable": true + }, + "source": [ + "\n", + "
              \n", + "\n", + "$$\n", + "\\begin{equation}\n", + " \\hat{y}_2 = f_2(\\mathrm{W}_2 \\hat{y}_{1} + \\hat{b}_{2}) = \n", + " f_2\\left(\\left[\\begin{array}{ccc}\n", + " w^2_{11} &w^2_{12} &w^2_{13} \\\\\n", + " w^2_{21} &w^2_{22} &w^2_{23} \\\\\n", + " w^2_{31} &w^2_{32} &w^2_{33} \\\\\n", + " \\end{array} \\right] \\cdot\n", + " \\left[\\begin{array}{c}\n", + " y^1_1 \\\\\n", + " y^1_2 \\\\\n", + " y^1_3 \\\\\n", + " \\end{array}\\right] + \n", + " \\left[\\begin{array}{c}\n", + " b^2_1 \\\\\n", + " b^2_2 \\\\\n", + " b^2_3 \\\\\n", + " \\end{array}\\right]\\right).\n", + "\\label{_auto11} \\tag{16}\n", + "\\end{equation}\n", + "$$" + ] + }, + { + "cell_type": "markdown", + "id": "d129b057", + "metadata": { + "editable": true + }, + "source": [ + "### Matrix-vector notation and activation\n", + "\n", + "The activation of node $i$ in layer 2 is" + ] + }, + { + "cell_type": "markdown", + "id": "7af14562", + "metadata": { + "editable": true + }, + "source": [ + "\n", + "
              \n", + "\n", + "$$\n", + "\\begin{equation}\n", + " y^2_i = f_2\\Bigr(w^2_{i1}y^1_1 + w^2_{i2}y^1_2 + w^2_{i3}y^1_3 + b^2_i\\Bigr) = \n", + " f_2\\left(\\sum_{j=1}^3 w^2_{ij} y_j^1 + b^2_i\\right).\n", + "\\label{_auto12} \\tag{17}\n", + "\\end{equation}\n", + "$$" + ] + }, + { + "cell_type": "markdown", + "id": "0b7e127c", + "metadata": { + "editable": true + }, + "source": [ + "This is not just a convenient and compact notation, but also a useful\n", + "and intuitive way to think about MLPs: The output is calculated by a\n", + "series of matrix-vector multiplications and vector additions that are\n", + "used as input to the activation functions. For each operation\n", + "$\\mathrm{W}_l \\hat{y}_{l-1}$ we move forward one layer." + ] + }, + { + "cell_type": "markdown", + "id": "91266ae3", + "metadata": { + "editable": true + }, + "source": [ + "### Activation functions\n", + "\n", + "A property that characterizes a neural network, other than its\n", + "connectivity, is the choice of activation function(s). As described\n", + "in, the following restrictions are imposed on an activation function\n", + "for a FFNN to fulfill the universal approximation theorem\n", + "\n", + " * Non-constant\n", + "\n", + " * Bounded\n", + "\n", + " * Monotonically-increasing\n", + "\n", + " * Continuous" + ] + }, + { + "cell_type": "markdown", + "id": "54728fbd", + "metadata": { + "editable": true + }, + "source": [ + "### Activation functions, Logistic and Hyperbolic ones\n", + "\n", + "The second requirement excludes all linear functions. Furthermore, in\n", + "a MLP with only linear activation functions, each layer simply\n", + "performs a linear transformation of its inputs.\n", + "\n", + "Regardless of the number of layers, the output of the NN will be\n", + "nothing but a linear function of the inputs. Thus we need to introduce\n", + "some kind of non-linearity to the NN to be able to fit non-linear\n", + "functions Typical examples are the logistic *Sigmoid*" + ] + }, + { + "cell_type": "markdown", + "id": "17b851fb", + "metadata": { + "editable": true + }, + "source": [ + "$$\n", + "f(x) = \\frac{1}{1 + e^{-x}},\n", + "$$" + ] + }, + { + "cell_type": "markdown", + "id": "6e17f015", + "metadata": { + "editable": true + }, + "source": [ + "and the *hyperbolic tangent* function" + ] + }, + { + "cell_type": "markdown", + "id": "574fbcd0", + "metadata": { + "editable": true + }, + "source": [ + "$$\n", + "f(x) = \\tanh(x)\n", + "$$" + ] + }, + { + "cell_type": "markdown", + "id": "daa971d1", + "metadata": { + "editable": true + }, + "source": [ + "### Relevance\n", + "\n", + "The *sigmoid* function are more biologically plausible because the\n", + "output of inactive neurons are zero. Such activation function are\n", + "called *one-sided*. However, it has been shown that the hyperbolic\n", + "tangent performs better than the sigmoid for training MLPs. has\n", + "become the most popular for *deep neural networks*" + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "id": "c12bc7fe", + "metadata": { + "collapsed": false, + "editable": true + }, + "outputs": [], + "source": [ + "\"\"\"The sigmoid function (or the logistic curve) is a \n", + "function that takes any real number, z, and outputs a number (0,1).\n", + "It is useful in neural networks for assigning weights on a relative scale.\n", + "The value z is the weighted sum of parameters involved in the learning algorithm.\"\"\"\n", + "\n", + "import numpy\n", + "import matplotlib.pyplot as plt\n", + "import math as mt\n", + "\n", + "z = numpy.arange(-5, 5, .1)\n", + "sigma_fn = numpy.vectorize(lambda z: 1/(1+numpy.exp(-z)))\n", + "sigma = sigma_fn(z)\n", + "\n", + "fig = plt.figure()\n", + "ax = fig.add_subplot(111)\n", + "ax.plot(z, sigma)\n", + "ax.set_ylim([-0.1, 1.1])\n", + "ax.set_xlim([-5,5])\n", + "ax.grid(True)\n", + "ax.set_xlabel('z')\n", + "ax.set_title('sigmoid function')\n", + "\n", + "plt.show()\n", + "\n", + "\"\"\"Step Function\"\"\"\n", + "z = numpy.arange(-5, 5, .02)\n", + "step_fn = numpy.vectorize(lambda z: 1.0 if z >= 0.0 else 0.0)\n", + "step = step_fn(z)\n", + "\n", + "fig = plt.figure()\n", + "ax = fig.add_subplot(111)\n", + "ax.plot(z, step)\n", + "ax.set_ylim([-0.5, 1.5])\n", + "ax.set_xlim([-5,5])\n", + "ax.grid(True)\n", + "ax.set_xlabel('z')\n", + "ax.set_title('step function')\n", + "\n", + "plt.show()\n", + "\n", + "\"\"\"Sine Function\"\"\"\n", + "z = numpy.arange(-2*mt.pi, 2*mt.pi, 0.1)\n", + "t = numpy.sin(z)\n", + "\n", + "fig = plt.figure()\n", + "ax = fig.add_subplot(111)\n", + "ax.plot(z, t)\n", + "ax.set_ylim([-1.0, 1.0])\n", + "ax.set_xlim([-2*mt.pi,2*mt.pi])\n", + "ax.grid(True)\n", + "ax.set_xlabel('z')\n", + "ax.set_title('sine function')\n", + "\n", + "plt.show()\n", + "\n", + "\"\"\"Plots a graph of the squashing function used by a rectified linear\n", + "unit\"\"\"\n", + "z = numpy.arange(-2, 2, .1)\n", + "zero = numpy.zeros(len(z))\n", + "y = numpy.max([zero, z], axis=0)\n", + "\n", + "fig = plt.figure()\n", + "ax = fig.add_subplot(111)\n", + "ax.plot(z, y)\n", + "ax.set_ylim([-2.0, 2.0])\n", + "ax.set_xlim([-2.0, 2.0])\n", + "ax.grid(True)\n", + "ax.set_xlabel('z')\n", + "ax.set_title('Rectified linear unit')\n", + "\n", + "plt.show()" + ] + } + ], + "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": "3.9.10" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} \ No newline at end of file diff --git a/doc/LectureNotes/_build/jupyter_execute/week40.py b/doc/LectureNotes/_build/jupyter_execute/week40.py new file mode 100644 index 000000000..14ae8fee8 --- /dev/null +++ b/doc/LectureNotes/_build/jupyter_execute/week40.py @@ -0,0 +1,2202 @@ +#!/usr/bin/env python +# coding: utf-8 + +# +# + +# # Week 40: Gradient descent methods (continued) and start Neural networks +# **Morten Hjorth-Jensen**, Department of Physics, University of Oslo, Norway and Department of Physics and Astronomy and Facility for Rare Ion Beams, Michigan State University, USA +# +# Date: **October 2-6, 2023** + +# ## Plans for week 40 +# +# **Material for the active learning sessions on Tuesday and Wednesday.** +# +# * Work on project 1 and discussions on how to structure your report +# +# * No weekly exercises for week 40, project work only +# +# * [Video on how to write scientific reports recorded during one of the lab sessions](https://youtu.be/tVW1ZDmZnwM) +# +# * A general guideline can be found at . +# +# +# +# **Material for the lecture on Thursday October 5, 2023.** +# +# * Stochastic Gradient descent with examples and automatic differentiation +# +# * Neural Networks, setting up the basic steps, from the simple perceptron model to the multi-layer perceptron model. +# +# * Readings and Videos: +# +# * These lecture notes +# +# * For a good discussion on gradient methods, we would like to recommend Goodfellow et al section 4.3-4.5 and sections 8.3-8.6. We will come back to the latter chapter in our discussion of Neural networks as well. +# +# * [Aurelien Geron's chapter 4 on stochastic gradient descent](https://github.com/CompPhysics/MachineLearning/blob/master/doc/Textbooks/TensorflowML.pdf) +# +# * For neural networks we recommend Goodfellow et al chapter 6. +# +# * [Video on gradient descent](https://www.youtube.com/watch?v=sDv4f4s2SB8) +# +# * [Video on stochastic gradient descent](https://www.youtube.com/watch?v=vMh0zPT0tLI) +# +# * [Neural Networks demystified](https://www.youtube.com/watch?v=bxe2T-V8XRs&list=PLiaHhY2iBX9hdHaRr6b7XevZtgZRa1PoU&ab_channel=WelchLabs) +# +# * [Building Neural Networks from scratch](https://www.youtube.com/watch?v=Wo5dMEP_BbI&list=PLQVvvaa0QuDcjD5BAw2DxE6OF2tius3V3&ab_channel=sentdex) + +# ## Summary from last week, using gradient descent methods, limitations +# +# * **Gradient descent (GD) finds local minima of our function**. Since the GD algorithm is deterministic, if it converges, it will converge to a local minimum of our cost/loss/risk function. Because in ML we are often dealing with extremely rugged landscapes with many local minima, this can lead to poor performance. +# +# * **GD is sensitive to initial conditions**. One consequence of the local nature of GD is that initial conditions matter. Depending on where one starts, one will end up at a different local minima. Therefore, it is very important to think about how one initializes the training process. This is true for GD as well as more complicated variants of GD. +# +# * **Gradients are computationally expensive to calculate for large datasets**. In many cases in statistics and ML, the cost/loss/risk function is a sum of terms, with one term for each data point. For example, in linear regression, $E \propto \sum_{i=1}^n (y_i - \mathbf{w}^T\cdot\mathbf{x}_i)^2$; for logistic regression, the square error is replaced by the cross entropy. To calculate the gradient we have to sum over *all* $n$ data points. Doing this at every GD step becomes extremely computationally expensive. An ingenious solution to this, is to calculate the gradients using small subsets of the data called "mini batches". This has the added benefit of introducing stochasticity into our algorithm. +# +# * **GD is very sensitive to choices of learning rates**. GD is extremely sensitive to the choice of learning rates. If the learning rate is very small, the training process take an extremely long time. For larger learning rates, GD can diverge and give poor results. Furthermore, depending on what the local landscape looks like, we have to modify the learning rates to ensure convergence. Ideally, we would *adaptively* choose the learning rates to match the landscape. +# +# * **GD treats all directions in parameter space uniformly.** Another major drawback of GD is that unlike Newton's method, the learning rate for GD is the same in all directions in parameter space. For this reason, the maximum learning rate is set by the behavior of the steepest direction and this can significantly slow down training. Ideally, we would like to take large steps in flat directions and small steps in steep directions. Since we are exploring rugged landscapes where curvatures change, this requires us to keep track of not only the gradient but second derivatives. The ideal scenario would be to calculate the Hessian but this proves to be too computationally expensive. +# +# * GD can take exponential time to escape saddle points, even with random initialization. As we mentioned, GD is extremely sensitive to initial condition since it determines the particular local minimum GD would eventually reach. However, even with a good initialization scheme, through the introduction of randomness, GD can still take exponential time to escape saddle points. + +# ## Overview video on Stochastic Gradient Descent +# +# [What is Stochastic Gradient Descent](https://www.youtube.com/watch?v=vMh0zPT0tLI&ab_channel=StatQuestwithJoshStarmer) + +# ## Batches and mini-batches +# +# In gradient descent we compute the cost function and its gradient for all data points we have. +# +# In large-scale applications such as the [ILSVRC challenge](https://www.image-net.org/challenges/LSVRC/), the +# training data can have on order of millions of examples. Hence, it +# seems wasteful to compute the full cost function over the entire +# training set in order to perform only a single parameter update. A +# very common approach to addressing this challenge is to compute the +# gradient over batches of the training data. For example, a typical batch could contain some thousand examples from +# an entire training set of several millions. This batch is then used to +# perform a parameter update. + +# ## Stochastic Gradient Descent (SGD) +# +# In stochastic gradient descent, the extreme case is the case where we +# have only one batch, that is we include the whole data set. +# +# This process is called Stochastic Gradient +# Descent (SGD) (or also sometimes on-line gradient descent). This is +# relatively less common to see because in practice due to vectorized +# code optimizations it can be computationally much more efficient to +# evaluate the gradient for 100 examples, than the gradient for one +# example 100 times. Even though SGD technically refers to using a +# single example at a time to evaluate the gradient, you will hear +# people use the term SGD even when referring to mini-batch gradient +# descent (i.e. mentions of MGD for “Minibatch Gradient Descent”, or BGD +# for “Batch gradient descent” are rare to see), where it is usually +# assumed that mini-batches are used. The size of the mini-batch is a +# hyperparameter but it is not very common to cross-validate or bootstrap it. It is +# usually based on memory constraints (if any), or set to some value, +# e.g. 32, 64 or 128. We use powers of 2 in practice because many +# vectorized operation implementations work faster when their inputs are +# sized in powers of 2. +# +# In our notes with SGD we mean stochastic gradient descent with mini-batches. + +# ## Stochastic Gradient Descent +# +# Stochastic gradient descent (SGD) and variants thereof address some of +# the shortcomings of the Gradient descent method discussed above. +# +# The underlying idea of SGD comes from the observation that the cost +# function, which we want to minimize, can almost always be written as a +# sum over $n$ data points $\{\mathbf{x}_i\}_{i=1}^n$, + +# $$ +# C(\mathbf{\beta}) = \sum_{i=1}^n c_i(\mathbf{x}_i, +# \mathbf{\beta}). +# $$ + +# ## Computation of gradients +# +# This in turn means that the gradient can be +# computed as a sum over $i$-gradients + +# $$ +# \nabla_\beta C(\mathbf{\beta}) = \sum_i^n \nabla_\beta c_i(\mathbf{x}_i, +# \mathbf{\beta}). +# $$ + +# Stochasticity/randomness is introduced by only taking the +# gradient on a subset of the data called minibatches. If there are $n$ +# data points and the size of each minibatch is $M$, there will be $n/M$ +# minibatches. We denote these minibatches by $B_k$ where +# $k=1,\cdots,n/M$. + +# ## SGD example +# As an example, suppose we have $10$ data points $(\mathbf{x}_1,\cdots, \mathbf{x}_{10})$ +# and we choose to have $M=5$ minibathces, +# then each minibatch contains two data points. In particular we have +# $B_1 = (\mathbf{x}_1,\mathbf{x}_2), \cdots, B_5 = +# (\mathbf{x}_9,\mathbf{x}_{10})$. Note that if you choose $M=1$ you +# have only a single batch with all data points and on the other extreme, +# you may choose $M=n$ resulting in a minibatch for each datapoint, i.e +# $B_k = \mathbf{x}_k$. +# +# The idea is now to approximate the gradient by replacing the sum over +# all data points with a sum over the data points in one the minibatches +# picked at random in each gradient descent step + +# $$ +# \nabla_{\beta} +# C(\mathbf{\beta}) = \sum_{i=1}^n \nabla_\beta c_i(\mathbf{x}_i, +# \mathbf{\beta}) \rightarrow \sum_{i \in B_k}^n \nabla_\beta +# c_i(\mathbf{x}_i, \mathbf{\beta}). +# $$ + +# ## The gradient step +# +# Thus a gradient descent step now looks like + +# $$ +# \beta_{j+1} = \beta_j - \gamma_j \sum_{i \in B_k}^n \nabla_\beta c_i(\mathbf{x}_i, +# \mathbf{\beta}) +# $$ + +# where $k$ is picked at random with equal +# probability from $[1,n/M]$. An iteration over the number of +# minibathces (n/M) is commonly referred to as an epoch. Thus it is +# typical to choose a number of epochs and for each epoch iterate over +# the number of minibatches, as exemplified in the code below. + +# ## Simple example code + +# In[1]: + + +import numpy as np + +n = 100 #100 datapoints +M = 5 #size of each minibatch +m = int(n/M) #number of minibatches +n_epochs = 10 #number of epochs + +j = 0 +for epoch in range(1,n_epochs+1): + for i in range(m): + k = np.random.randint(m) #Pick the k-th minibatch at random + #Compute the gradient using the data in minibatch Bk + #Compute new suggestion for + j += 1 + + +# Taking the gradient only on a subset of the data has two important +# benefits. First, it introduces randomness which decreases the chance +# that our opmization scheme gets stuck in a local minima. Second, if +# the size of the minibatches are small relative to the number of +# datapoints ($M < n$), the computation of the gradient is much +# cheaper since we sum over the datapoints in the $k-th$ minibatch and not +# all $n$ datapoints. + +# ## When do we stop? +# +# A natural question is when do we stop the search for a new minimum? +# One possibility is to compute the full gradient after a given number +# of epochs and check if the norm of the gradient is smaller than some +# threshold and stop if true. However, the condition that the gradient +# is zero is valid also for local minima, so this would only tell us +# that we are close to a local/global minimum. However, we could also +# evaluate the cost function at this point, store the result and +# continue the search. If the test kicks in at a later stage we can +# compare the values of the cost function and keep the $\beta$ that +# gave the lowest value. + +# ## Slightly different approach +# +# Another approach is to let the step length $\gamma_j$ depend on the +# number of epochs in such a way that it becomes very small after a +# reasonable time such that we do not move at all. Such approaches are +# also called scaling. There are many such ways to [scale the learning +# rate](https://towardsdatascience.com/gradient-descent-the-learning-rate-and-the-importance-of-feature-scaling-6c0b416596e1) +# and [discussions here](https://www.jmlr.org/papers/volume23/20-1258/20-1258.pdf). See +# also +# +# for a discussion of different scaling functions for the learning rate. + +# ## Time decay rate +# +# As an example, let $e = 0,1,2,3,\cdots$ denote the current epoch and let $t_0, t_1 > 0$ be two fixed numbers. Furthermore, let $t = e \cdot m + i$ where $m$ is the number of minibatches and $i=0,\cdots,m-1$. Then the function $$\gamma_j(t; t_0, t_1) = \frac{t_0}{t+t_1} $$ goes to zero as the number of epochs gets large. I.e. we start with a step length $\gamma_j (0; t_0, t_1) = t_0/t_1$ which decays in *time* $t$. +# +# In this way we can fix the number of epochs, compute $\beta$ and +# evaluate the cost function at the end. Repeating the computation will +# give a different result since the scheme is random by design. Then we +# pick the final $\beta$ that gives the lowest value of the cost +# function. + +# In[2]: + + +import numpy as np + +def step_length(t,t0,t1): + return t0/(t+t1) + +n = 100 #100 datapoints +M = 5 #size of each minibatch +m = int(n/M) #number of minibatches +n_epochs = 500 #number of epochs +t0 = 1.0 +t1 = 10 + +gamma_j = t0/t1 +j = 0 +for epoch in range(1,n_epochs+1): + for i in range(m): + k = np.random.randint(m) #Pick the k-th minibatch at random + #Compute the gradient using the data in minibatch Bk + #Compute new suggestion for beta + t = epoch*m+i + gamma_j = step_length(t,t0,t1) + j += 1 + +print("gamma_j after %d epochs: %g" % (n_epochs,gamma_j)) + + +# ## Code with a Number of Minibatches which varies +# +# In the code here we vary the number of mini-batches. + +# In[3]: + + +get_ipython().run_line_magic('matplotlib', 'inline') + +# Importing various packages +from math import exp, sqrt +from random import random, seed +import numpy as np +import matplotlib.pyplot as plt + +n = 100 +x = 2*np.random.rand(n,1) +y = 4+3*x+np.random.randn(n,1) + +X = np.c_[np.ones((n,1)), x] +XT_X = X.T @ X +theta_linreg = np.linalg.inv(X.T @ X) @ (X.T @ y) +print("Own inversion") +print(theta_linreg) +# Hessian matrix +H = (2.0/n)* XT_X +EigValues, EigVectors = np.linalg.eig(H) +print(f"Eigenvalues of Hessian Matrix:{EigValues}") + +theta = np.random.randn(2,1) +eta = 1.0/np.max(EigValues) +Niterations = 1000 + + +for iter in range(Niterations): + gradients = 2.0/n*X.T @ ((X @ theta)-y) + theta -= eta*gradients +print("theta from own gd") +print(theta) + +xnew = np.array([[0],[2]]) +Xnew = np.c_[np.ones((2,1)), xnew] +ypredict = Xnew.dot(theta) +ypredict2 = Xnew.dot(theta_linreg) + +n_epochs = 50 +M = 5 #size of each minibatch +m = int(n/M) #number of minibatches +t0, t1 = 5, 50 + +def learning_schedule(t): + return t0/(t+t1) + +theta = np.random.randn(2,1) + +for epoch in range(n_epochs): +# Can you figure out a better way of setting up the contributions to each batch? + for i in range(m): + random_index = M*np.random.randint(m) + xi = X[random_index:random_index+M] + yi = y[random_index:random_index+M] + gradients = (2.0/M)* xi.T @ ((xi @ theta)-yi) + eta = learning_schedule(epoch*m+i) + theta = theta - eta*gradients +print("theta from own sdg") +print(theta) + +plt.plot(xnew, ypredict, "r-") +plt.plot(xnew, ypredict2, "b-") +plt.plot(x, y ,'ro') +plt.axis([0,2.0,0, 15.0]) +plt.xlabel(r'$x$') +plt.ylabel(r'$y$') +plt.title(r'Random numbers ') +plt.show() + + +# ## Replace or not +# +# In the above code, we have use replacement in setting up the +# mini-batches. The discussion +# [here](https://sebastianraschka.com/faq/docs/sgd-methods.html) may be +# useful. + +# ## Momentum based GD +# +# The stochastic gradient descent (SGD) is almost always used with a +# *momentum* or inertia term that serves as a memory of the direction we +# are moving in parameter space. This is typically implemented as +# follows + +# $$ +# \mathbf{v}_{t}=\gamma \mathbf{v}_{t-1}+\eta_{t}\nabla_\theta E(\boldsymbol{\theta}_t) \nonumber +# $$ + +# +#
              +# +# $$ +# \begin{equation} +# \boldsymbol{\theta}_{t+1}= \boldsymbol{\theta}_t -\mathbf{v}_{t}, +# \label{_auto1} \tag{1} +# \end{equation} +# $$ + +# where we have introduced a momentum parameter $\gamma$, with +# $0\le\gamma\le 1$, and for brevity we dropped the explicit notation to +# indicate the gradient is to be taken over a different mini-batch at +# each step. We call this algorithm gradient descent with momentum +# (GDM). From these equations, it is clear that $\mathbf{v}_t$ is a +# running average of recently encountered gradients and +# $(1-\gamma)^{-1}$ sets the characteristic time scale for the memory +# used in the averaging procedure. Consistent with this, when +# $\gamma=0$, this just reduces down to ordinary SGD as discussed +# earlier. An equivalent way of writing the updates is + +# $$ +# \Delta \boldsymbol{\theta}_{t+1} = \gamma \Delta \boldsymbol{\theta}_t -\ \eta_{t}\nabla_\theta E(\boldsymbol{\theta}_t), +# $$ + +# where we have defined $\Delta \boldsymbol{\theta}_{t}= \boldsymbol{\theta}_t-\boldsymbol{\theta}_{t-1}$. + +# ## More on momentum based approaches +# +# Let us try to get more intuition from these equations. It is helpful +# to consider a simple physical analogy with a particle of mass $m$ +# moving in a viscous medium with drag coefficient $\mu$ and potential +# $E(\mathbf{w})$. If we denote the particle's position by $\mathbf{w}$, +# then its motion is described by + +# $$ +# m {d^2 \mathbf{w} \over dt^2} + \mu {d \mathbf{w} \over dt }= -\nabla_w E(\mathbf{w}). +# $$ + +# We can discretize this equation in the usual way to get + +# $$ +# m { \mathbf{w}_{t+\Delta t}-2 \mathbf{w}_{t} +\mathbf{w}_{t-\Delta t} \over (\Delta t)^2}+\mu {\mathbf{w}_{t+\Delta t}- \mathbf{w}_{t} \over \Delta t} = -\nabla_w E(\mathbf{w}). +# $$ + +# Rearranging this equation, we can rewrite this as + +# $$ +# \Delta \mathbf{w}_{t +\Delta t}= - { (\Delta t)^2 \over m +\mu \Delta t} \nabla_w E(\mathbf{w})+ {m \over m +\mu \Delta t} \Delta \mathbf{w}_t. +# $$ + +# ## Momentum parameter +# +# Notice that this equation is identical to previous one if we identify +# the position of the particle, $\mathbf{w}$, with the parameters +# $\boldsymbol{\theta}$. This allows us to identify the momentum +# parameter and learning rate with the mass of the particle and the +# viscous drag as: + +# $$ +# \gamma= {m \over m +\mu \Delta t }, \qquad \eta = {(\Delta t)^2 \over m +\mu \Delta t}. +# $$ + +# Thus, as the name suggests, the momentum parameter is proportional to +# the mass of the particle and effectively provides inertia. +# Furthermore, in the large viscosity/small learning rate limit, our +# memory time scales as $(1-\gamma)^{-1} \approx m/(\mu \Delta t)$. +# +# Why is momentum useful? SGD momentum helps the gradient descent +# algorithm gain speed in directions with persistent but small gradients +# even in the presence of stochasticity, while suppressing oscillations +# in high-curvature directions. This becomes especially important in +# situations where the landscape is shallow and flat in some directions +# and narrow and steep in others. It has been argued that first-order +# methods (with appropriate initial conditions) can perform comparable +# to more expensive second order methods, especially in the context of +# complex deep learning models. +# +# These beneficial properties of momentum can sometimes become even more +# pronounced by using a slight modification of the classical momentum +# algorithm called Nesterov Accelerated Gradient (NAG). +# +# In the NAG algorithm, rather than calculating the gradient at the +# current parameters, $\nabla_\theta E(\boldsymbol{\theta}_t)$, one +# calculates the gradient at the expected value of the parameters given +# our current momentum, $\nabla_\theta E(\boldsymbol{\theta}_t +\gamma +# \mathbf{v}_{t-1})$. This yields the NAG update rule + +# $$ +# \mathbf{v}_{t}=\gamma \mathbf{v}_{t-1}+\eta_{t}\nabla_\theta E(\boldsymbol{\theta}_t +\gamma \mathbf{v}_{t-1}) \nonumber +# $$ + +# +#
              +# +# $$ +# \begin{equation} +# \boldsymbol{\theta}_{t+1}= \boldsymbol{\theta}_t -\mathbf{v}_{t}. +# \label{_auto2} \tag{2} +# \end{equation} +# $$ + +# One of the major advantages of NAG is that it allows for the use of a larger learning rate than GDM for the same choice of $\gamma$. + +# ## Second moment of the gradient +# +# In stochastic gradient descent, with and without momentum, we still +# have to specify a schedule for tuning the learning rates $\eta_t$ +# as a function of time. As discussed in the context of Newton's +# method, this presents a number of dilemmas. The learning rate is +# limited by the steepest direction which can change depending on the +# current position in the landscape. To circumvent this problem, ideally +# our algorithm would keep track of curvature and take large steps in +# shallow, flat directions and small steps in steep, narrow directions. +# Second-order methods accomplish this by calculating or approximating +# the Hessian and normalizing the learning rate by the +# curvature. However, this is very computationally expensive for +# extremely large models. Ideally, we would like to be able to +# adaptively change the step size to match the landscape without paying +# the steep computational price of calculating or approximating +# Hessians. +# +# Recently, a number of methods have been introduced that accomplish +# this by tracking not only the gradient, but also the second moment of +# the gradient. These methods include AdaGrad, AdaDelta, Root Mean Squared Propagation (RMS-Prop), and +# [ADAM](https://arxiv.org/abs/1412.6980). + +# ## RMS prop +# +# In RMS prop, in addition to keeping a running average of the first +# moment of the gradient, we also keep track of the second moment +# denoted by $\mathbf{s}_t=\mathbb{E}[\mathbf{g}_t^2]$. The update rule +# for RMS prop is given by + +# +#
              +# +# $$ +# \begin{equation} +# \mathbf{g}_t = \nabla_\theta E(\boldsymbol{\theta}) +# \label{_auto3} \tag{3} +# \end{equation} +# $$ + +# $$ +# \mathbf{s}_t =\beta \mathbf{s}_{t-1} +(1-\beta)\mathbf{g}_t^2 \nonumber +# $$ + +# $$ +# \boldsymbol{\theta}_{t+1}=\boldsymbol{\theta}_t - \eta_t { \mathbf{g}_t \over \sqrt{\mathbf{s}_t +\epsilon}}, \nonumber +# $$ + +# where $\beta$ controls the averaging time of the second moment and is +# typically taken to be about $\beta=0.9$, $\eta_t$ is a learning rate +# typically chosen to be $10^{-3}$, and $\epsilon\sim 10^{-8} $ is a +# small regularization constant to prevent divergences. Multiplication +# and division by vectors is understood as an element-wise operation. It +# is clear from this formula that the learning rate is reduced in +# directions where the norm of the gradient is consistently large. This +# greatly speeds up the convergence by allowing us to use a larger +# learning rate for flat directions. + +# ## [ADAM optimizer](https://arxiv.org/abs/1412.6980) +# +# A related algorithm is the ADAM optimizer. In +# [ADAM](https://arxiv.org/abs/1412.6980), we keep a running average of +# both the first and second moment of the gradient and use this +# information to adaptively change the learning rate for different +# parameters. The method isefficient when working with large +# problems involving lots data and/or parameters. It is a combination of the +# gradient descent with momentum algorithm and the RMSprop algorithm +# discussed above. +# +# In addition to keeping a running average of the first and +# second moments of the gradient +# (i.e. $\mathbf{m}_t=\mathbb{E}[\mathbf{g}_t]$ and +# $\mathbf{s}_t=\mathbb{E}[\mathbf{g}^2_t]$, respectively), ADAM +# performs an additional bias correction to account for the fact that we +# are estimating the first two moments of the gradient using a running +# average (denoted by the hats in the update rule below). The update +# rule for ADAM is given by (where multiplication and division are once +# again understood to be element-wise operations below) + +# +#
              +# +# $$ +# \begin{equation} +# \mathbf{g}_t = \nabla_\theta E(\boldsymbol{\theta}) +# \label{_auto4} \tag{4} +# \end{equation} +# $$ + +# $$ +# \mathbf{m}_t = \beta_1 \mathbf{m}_{t-1} + (1-\beta_1) \mathbf{g}_t \nonumber +# $$ + +# $$ +# \mathbf{s}_t =\beta_2 \mathbf{s}_{t-1} +(1-\beta_2)\mathbf{g}_t^2 \nonumber +# $$ + +# $$ +# \boldsymbol{\mathbf{m}}_t={\mathbf{m}_t \over 1-\beta_1^t} \nonumber +# $$ + +# $$ +# \boldsymbol{\mathbf{s}}_t ={\mathbf{s}_t \over1-\beta_2^t} \nonumber +# $$ + +# $$ +# \boldsymbol{\theta}_{t+1}=\boldsymbol{\theta}_t - \eta_t { \boldsymbol{\mathbf{m}}_t \over \sqrt{\boldsymbol{\mathbf{s}}_t} +\epsilon}, \nonumber +# $$ + +# +#
              +# +# $$ +# \begin{equation} +# \label{_auto5} \tag{5} +# \end{equation} +# $$ + +# where $\beta_1$ and $\beta_2$ set the memory lifetime of the first and +# second moment and are typically taken to be $0.9$ and $0.99$ +# respectively, and $\eta$ and $\epsilon$ are identical to RMSprop. +# +# Like in RMSprop, the effective step size of a parameter depends on the +# magnitude of its gradient squared. To understand this better, let us +# rewrite this expression in terms of the variance +# $\boldsymbol{\sigma}_t^2 = \boldsymbol{\mathbf{s}}_t - +# (\boldsymbol{\mathbf{m}}_t)^2$. Consider a single parameter $\theta_t$. The +# update rule for this parameter is given by + +# $$ +# \Delta \theta_{t+1}= -\eta_t { \boldsymbol{m}_t \over \sqrt{\sigma_t^2 + m_t^2 }+\epsilon}. +# $$ + +# ## Algorithms and codes for Adagrad, RMSprop and Adam +# +# The algorithms we have implemented are well described in the text by [Goodfellow, Bengio and Courville, chapter 8](https://www.deeplearningbook.org/contents/optimization.html). +# +# The codes which implement these algorithms are discussed after our presentation of automatic differentiation. + +# ## Practical tips +# +# * **Randomize the data when making mini-batches**. It is always important to randomly shuffle the data when forming mini-batches. Otherwise, the gradient descent method can fit spurious correlations resulting from the order in which data is presented. +# +# * **Transform your inputs**. Learning becomes difficult when our landscape has a mixture of steep and flat directions. One simple trick for minimizing these situations is to standardize the data by subtracting the mean and normalizing the variance of input variables. Whenever possible, also decorrelate the inputs. To understand why this is helpful, consider the case of linear regression. It is easy to show that for the squared error cost function, the Hessian of the cost function is just the correlation matrix between the inputs. Thus, by standardizing the inputs, we are ensuring that the landscape looks homogeneous in all directions in parameter space. Since most deep networks can be viewed as linear transformations followed by a non-linearity at each layer, we expect this intuition to hold beyond the linear case. +# +# * **Monitor the out-of-sample performance.** Always monitor the performance of your model on a validation set (a small portion of the training data that is held out of the training process to serve as a proxy for the test set. If the validation error starts increasing, then the model is beginning to overfit. Terminate the learning process. This *early stopping* significantly improves performance in many settings. +# +# * **Adaptive optimization methods don't always have good generalization.** Recent studies have shown that adaptive methods such as ADAM, RMSPorp, and AdaGrad tend to have poor generalization compared to SGD or SGD with momentum, particularly in the high-dimensional limit (i.e. the number of parameters exceeds the number of data points). Although it is not clear at this stage why these methods perform so well in training deep neural networks, simpler procedures like properly-tuned SGD may work as well or better in these applications. +# +# Geron's text, see chapter 11, has several interesting discussions. + +# ## Automatic differentiation +# +# [Automatic differentiation (AD)](https://en.wikipedia.org/wiki/Automatic_differentiation), +# also called algorithmic +# differentiation or computational differentiation,is a set of +# techniques to numerically evaluate the derivative of a function +# specified by a computer program. AD exploits the fact that every +# computer program, no matter how complicated, executes a sequence of +# elementary arithmetic operations (addition, subtraction, +# multiplication, division, etc.) and elementary functions (exp, log, +# sin, cos, etc.). By applying the chain rule repeatedly to these +# operations, derivatives of arbitrary order can be computed +# automatically, accurately to working precision, and using at most a +# small constant factor more arithmetic operations than the original +# program. +# +# Automatic differentiation is neither: +# +# * Symbolic differentiation, nor +# +# * Numerical differentiation (the method of finite differences). +# +# Symbolic differentiation can lead to inefficient code and faces the +# difficulty of converting a computer program into a single expression, +# while numerical differentiation can introduce round-off errors in the +# discretization process and cancellation +# +# Python has tools for so-called **automatic differentiation**. +# Consider the following example + +# $$ +# f(x) = \sin\left(2\pi x + x^2\right) +# $$ + +# which has the following derivative + +# $$ +# f'(x) = \cos\left(2\pi x + x^2\right)\left(2\pi + 2x\right) +# $$ + +# Using **autograd** we have + +# In[4]: + + +import autograd.numpy as np + +# To do elementwise differentiation: +from autograd import elementwise_grad as egrad + +# To plot: +import matplotlib.pyplot as plt + + +def f(x): + return np.sin(2*np.pi*x + x**2) + +def f_grad_analytic(x): + return np.cos(2*np.pi*x + x**2)*(2*np.pi + 2*x) + +# Do the comparison: +x = np.linspace(0,1,1000) + +f_grad = egrad(f) + +computed = f_grad(x) +analytic = f_grad_analytic(x) + +plt.title('Derivative computed from Autograd compared with the analytical derivative') +plt.plot(x,computed,label='autograd') +plt.plot(x,analytic,label='analytic') + +plt.xlabel('x') +plt.ylabel('y') +plt.legend() + +plt.show() + +print("The max absolute difference is: %g"%(np.max(np.abs(computed - analytic)))) + + +# ## Using autograd +# +# Here we +# experiment with what kind of functions Autograd is capable +# of finding the gradient of. The following Python functions are just +# meant to illustrate what Autograd can do, but please feel free to +# experiment with other, possibly more complicated, functions as well. + +# In[5]: + + +import autograd.numpy as np +from autograd import grad + +def f1(x): + return x**3 + 1 + +f1_grad = grad(f1) + +# Remember to send in float as argument to the computed gradient from Autograd! +a = 1.0 + +# See the evaluated gradient at a using autograd: +print("The gradient of f1 evaluated at a = %g using autograd is: %g"%(a,f1_grad(a))) + +# Compare with the analytical derivative, that is f1'(x) = 3*x**2 +grad_analytical = 3*a**2 +print("The gradient of f1 evaluated at a = %g by finding the analytic expression is: %g"%(a,grad_analytical)) + + +# ## Autograd with more complicated functions +# +# To differentiate with respect to two (or more) arguments of a Python +# function, Autograd need to know at which variable the function if +# being differentiated with respect to. + +# In[6]: + + +import autograd.numpy as np +from autograd import grad +def f2(x1,x2): + return 3*x1**3 + x2*(x1 - 5) + 1 + +# By sending the argument 0, Autograd will compute the derivative w.r.t the first variable, in this case x1 +f2_grad_x1 = grad(f2,0) + +# ... and differentiate w.r.t x2 by sending 1 as an additional arugment to grad +f2_grad_x2 = grad(f2,1) + +x1 = 1.0 +x2 = 3.0 + +print("Evaluating at x1 = %g, x2 = %g"%(x1,x2)) +print("-"*30) + +# Compare with the analytical derivatives: + +# Derivative of f2 w.r.t x1 is: 9*x1**2 + x2: +f2_grad_x1_analytical = 9*x1**2 + x2 + +# Derivative of f2 w.r.t x2 is: x1 - 5: +f2_grad_x2_analytical = x1 - 5 + +# See the evaluated derivations: +print("The derivative of f2 w.r.t x1: %g"%( f2_grad_x1(x1,x2) )) +print("The analytical derivative of f2 w.r.t x1: %g"%( f2_grad_x1(x1,x2) )) + +print() + +print("The derivative of f2 w.r.t x2: %g"%( f2_grad_x2(x1,x2) )) +print("The analytical derivative of f2 w.r.t x2: %g"%( f2_grad_x2(x1,x2) )) + + +# Note that the grad function will not produce the true gradient of the function. The true gradient of a function with two or more variables will produce a vector, where each element is the function differentiated w.r.t a variable. + +# ## More complicated functions using the elements of their arguments directly + +# In[7]: + + +import autograd.numpy as np +from autograd import grad +def f3(x): # Assumes x is an array of length 5 or higher + return 2*x[0] + 3*x[1] + 5*x[2] + 7*x[3] + 11*x[4]**2 + +f3_grad = grad(f3) + +x = np.linspace(0,4,5) + +# Print the computed gradient: +print("The computed gradient of f3 is: ", f3_grad(x)) + +# The analytical gradient is: (2, 3, 5, 7, 22*x[4]) +f3_grad_analytical = np.array([2, 3, 5, 7, 22*x[4]]) + +# Print the analytical gradient: +print("The analytical gradient of f3 is: ", f3_grad_analytical) + + +# Note that in this case, when sending an array as input argument, the +# output from Autograd is another array. This is the true gradient of +# the function, as opposed to the function in the previous example. By +# using arrays to represent the variables, the output from Autograd +# might be easier to work with, as the output is closer to what one +# could expect form a gradient-evaluting function. + +# ## Functions using mathematical functions from Numpy + +# In[8]: + + +import autograd.numpy as np +from autograd import grad +def f4(x): + return np.sqrt(1+x**2) + np.exp(x) + np.sin(2*np.pi*x) + +f4_grad = grad(f4) + +x = 2.7 + +# Print the computed derivative: +print("The computed derivative of f4 at x = %g is: %g"%(x,f4_grad(x))) + +# The analytical derivative is: x/sqrt(1 + x**2) + exp(x) + cos(2*pi*x)*2*pi +f4_grad_analytical = x/np.sqrt(1 + x**2) + np.exp(x) + np.cos(2*np.pi*x)*2*np.pi + +# Print the analytical gradient: +print("The analytical gradient of f4 at x = %g is: %g"%(x,f4_grad_analytical)) + + +# ## More autograd + +# In[9]: + + +import autograd.numpy as np +from autograd import grad +def f5(x): + if x >= 0: + return x**2 + else: + return -3*x + 1 + +f5_grad = grad(f5) + +x = 2.7 + +# Print the computed derivative: +print("The computed derivative of f5 at x = %g is: %g"%(x,f5_grad(x))) + + +# ## And with loops + +# In[10]: + + +import autograd.numpy as np +from autograd import grad +def f6_for(x): + val = 0 + for i in range(10): + val = val + x**i + return val + +def f6_while(x): + val = 0 + i = 0 + while i < 10: + val = val + x**i + i = i + 1 + return val + +f6_for_grad = grad(f6_for) +f6_while_grad = grad(f6_while) + +x = 0.5 + +# Print the computed derivaties of f6_for and f6_while +print("The computed derivative of f6_for at x = %g is: %g"%(x,f6_for_grad(x))) +print("The computed derivative of f6_while at x = %g is: %g"%(x,f6_while_grad(x))) + + +# In[11]: + + +import autograd.numpy as np +from autograd import grad +# Both of the functions are implementation of the sum: sum(x**i) for i = 0, ..., 9 +# The analytical derivative is: sum(i*x**(i-1)) +f6_grad_analytical = 0 +for i in range(10): + f6_grad_analytical += i*x**(i-1) + +print("The analytical derivative of f6 at x = %g is: %g"%(x,f6_grad_analytical)) + + +# ## Using recursion + +# In[12]: + + +import autograd.numpy as np +from autograd import grad + +def f7(n): # Assume that n is an integer + if n == 1 or n == 0: + return 1 + else: + return n*f7(n-1) + +f7_grad = grad(f7) + +n = 2.0 + +print("The computed derivative of f7 at n = %d is: %g"%(n,f7_grad(n))) + +# The function f7 is an implementation of the factorial of n. +# By using the product rule, one can find that the derivative is: + +f7_grad_analytical = 0 +for i in range(int(n)-1): + tmp = 1 + for k in range(int(n)-1): + if k != i: + tmp *= (n - k) + f7_grad_analytical += tmp + +print("The analytical derivative of f7 at n = %d is: %g"%(n,f7_grad_analytical)) + + +# Note that if n is equal to zero or one, Autograd will give an error message. This message appears when the output is independent on input. + +# ## Unsupported functions +# Autograd supports many features. However, there are some functions that is not supported (yet) by Autograd. +# +# Assigning a value to the variable being differentiated with respect to + +# In[13]: + + +import autograd.numpy as np +from autograd import grad +def f8(x): # Assume x is an array + x[2] = 3 + return x*2 + +f8_grad = grad(f8) + +x = 8.4 + +print("The derivative of f8 is:",f8_grad(x)) + + +# Here, Autograd tells us that an 'ArrayBox' does not support item assignment. The item assignment is done when the program tries to assign x[2] to the value 3. However, Autograd has implemented the computation of the derivative such that this assignment is not possible. + +# ## The syntax a.dot(b) when finding the dot product + +# In[14]: + + +import autograd.numpy as np +from autograd import grad +def f9(a): # Assume a is an array with 2 elements + b = np.array([1.0,2.0]) + return a.dot(b) + +f9_grad = grad(f9) + +x = np.array([1.0,0.0]) + +print("The derivative of f9 is:",f9_grad(x)) + + +# Here we are told that the 'dot' function does not belong to Autograd's +# version of a Numpy array. To overcome this, an alternative syntax +# which also computed the dot product can be used: + +# In[15]: + + +import autograd.numpy as np +from autograd import grad +def f9_alternative(x): # Assume a is an array with 2 elements + b = np.array([1.0,2.0]) + return np.dot(x,b) # The same as x_1*b_1 + x_2*b_2 + +f9_alternative_grad = grad(f9_alternative) + +x = np.array([3.0,0.0]) + +print("The gradient of f9 is:",f9_alternative_grad(x)) + +# The analytical gradient of the dot product of vectors x and b with two elements (x_1,x_2) and (b_1, b_2) respectively +# w.r.t x is (b_1, b_2). + + +# ## Recommended to avoid +# The documentation recommends to avoid inplace operations such as + +# In[16]: + + +a += b +a -= b +a*= b +a /=b + + +# ## Using Autograd with OLS +# +# We conclude the part on optmization by showing how we can make codes +# for linear regression and logistic regression using **autograd**. The +# first example shows results with ordinary leats squares. + +# In[17]: + + +# Using Autograd to calculate gradients for OLS +from random import random, seed +import numpy as np +import autograd.numpy as np +import matplotlib.pyplot as plt +from autograd import grad + +def CostOLS(beta): + return (1.0/n)*np.sum((y-X @ beta)**2) + +n = 100 +x = 2*np.random.rand(n,1) +y = 4+3*x+np.random.randn(n,1) + +X = np.c_[np.ones((n,1)), x] +XT_X = X.T @ X +theta_linreg = np.linalg.pinv(XT_X) @ (X.T @ y) +print("Own inversion") +print(theta_linreg) +# Hessian matrix +H = (2.0/n)* XT_X +EigValues, EigVectors = np.linalg.eig(H) +print(f"Eigenvalues of Hessian Matrix:{EigValues}") + +theta = np.random.randn(2,1) +eta = 1.0/np.max(EigValues) +Niterations = 1000 +# define the gradient +training_gradient = grad(CostOLS) + +for iter in range(Niterations): + gradients = training_gradient(theta) + theta -= eta*gradients +print("theta from own gd") +print(theta) + +xnew = np.array([[0],[2]]) +Xnew = np.c_[np.ones((2,1)), xnew] +ypredict = Xnew.dot(theta) +ypredict2 = Xnew.dot(theta_linreg) + +plt.plot(xnew, ypredict, "r-") +plt.plot(xnew, ypredict2, "b-") +plt.plot(x, y ,'ro') +plt.axis([0,2.0,0, 15.0]) +plt.xlabel(r'$x$') +plt.ylabel(r'$y$') +plt.title(r'Random numbers ') +plt.show() + + +# ## Same code but now with momentum gradient descent + +# In[18]: + + +# Using Autograd to calculate gradients for OLS +from random import random, seed +import numpy as np +import autograd.numpy as np +import matplotlib.pyplot as plt +from autograd import grad + +def CostOLS(beta): + return (1.0/n)*np.sum((y-X @ beta)**2) + +n = 100 +x = 2*np.random.rand(n,1) +y = 4+3*x#+np.random.randn(n,1) + +X = np.c_[np.ones((n,1)), x] +XT_X = X.T @ X +theta_linreg = np.linalg.pinv(XT_X) @ (X.T @ y) +print("Own inversion") +print(theta_linreg) +# Hessian matrix +H = (2.0/n)* XT_X +EigValues, EigVectors = np.linalg.eig(H) +print(f"Eigenvalues of Hessian Matrix:{EigValues}") + +theta = np.random.randn(2,1) +eta = 1.0/np.max(EigValues) +Niterations = 30 + +# define the gradient +training_gradient = grad(CostOLS) + +for iter in range(Niterations): + gradients = training_gradient(theta) + theta -= eta*gradients + print(iter,gradients[0],gradients[1]) +print("theta from own gd") +print(theta) + +# Now improve with momentum gradient descent +change = 0.0 +delta_momentum = 0.3 +for iter in range(Niterations): + # calculate gradient + gradients = training_gradient(theta) + # calculate update + new_change = eta*gradients+delta_momentum*change + # take a step + theta -= new_change + # save the change + change = new_change + print(iter,gradients[0],gradients[1]) +print("theta from own gd wth momentum") +print(theta) + + +# ## But noen of these can compete with Newton's method + +# In[19]: + + +# Using Newton's method +from random import random, seed +import numpy as np +import autograd.numpy as np +import matplotlib.pyplot as plt +from autograd import grad + +def CostOLS(beta): + return (1.0/n)*np.sum((y-X @ beta)**2) + +n = 100 +x = 2*np.random.rand(n,1) +y = 4+3*x+np.random.randn(n,1) + +X = np.c_[np.ones((n,1)), x] +XT_X = X.T @ X +beta_linreg = np.linalg.pinv(XT_X) @ (X.T @ y) +print("Own inversion") +print(beta_linreg) +# Hessian matrix +H = (2.0/n)* XT_X +# Note that here the Hessian does not depend on the parameters beta +invH = np.linalg.pinv(H) +EigValues, EigVectors = np.linalg.eig(H) +print(f"Eigenvalues of Hessian Matrix:{EigValues}") + +beta = np.random.randn(2,1) +Niterations = 5 + +# define the gradient +training_gradient = grad(CostOLS) + +for iter in range(Niterations): + gradients = training_gradient(beta) + beta -= invH @ gradients + print(iter,gradients[0],gradients[1]) +print("beta from own Newton code") +print(beta) + + +# ## Including Stochastic Gradient Descent with Autograd +# In this code we include the stochastic gradient descent approach discussed above. Note here that we specify which argument we are taking the derivative with respect to when using **autograd**. + +# In[20]: + + +# Using Autograd to calculate gradients using SGD +# OLS example +from random import random, seed +import numpy as np +import autograd.numpy as np +import matplotlib.pyplot as plt +from autograd import grad + +# Note change from previous example +def CostOLS(y,X,theta): + return np.sum((y-X @ theta)**2) + +n = 100 +x = 2*np.random.rand(n,1) +y = 4+3*x+np.random.randn(n,1) + +X = np.c_[np.ones((n,1)), x] +XT_X = X.T @ X +theta_linreg = np.linalg.pinv(XT_X) @ (X.T @ y) +print("Own inversion") +print(theta_linreg) +# Hessian matrix +H = (2.0/n)* XT_X +EigValues, EigVectors = np.linalg.eig(H) +print(f"Eigenvalues of Hessian Matrix:{EigValues}") + +theta = np.random.randn(2,1) +eta = 1.0/np.max(EigValues) +Niterations = 1000 + +# Note that we request the derivative wrt third argument (theta, 2 here) +training_gradient = grad(CostOLS,2) + +for iter in range(Niterations): + gradients = (1.0/n)*training_gradient(y, X, theta) + theta -= eta*gradients +print("theta from own gd") +print(theta) + +xnew = np.array([[0],[2]]) +Xnew = np.c_[np.ones((2,1)), xnew] +ypredict = Xnew.dot(theta) +ypredict2 = Xnew.dot(theta_linreg) + +plt.plot(xnew, ypredict, "r-") +plt.plot(xnew, ypredict2, "b-") +plt.plot(x, y ,'ro') +plt.axis([0,2.0,0, 15.0]) +plt.xlabel(r'$x$') +plt.ylabel(r'$y$') +plt.title(r'Random numbers ') +plt.show() + +n_epochs = 50 +M = 5 #size of each minibatch +m = int(n/M) #number of minibatches +t0, t1 = 5, 50 +def learning_schedule(t): + return t0/(t+t1) + +theta = np.random.randn(2,1) + +for epoch in range(n_epochs): +# Can you figure out a better way of setting up the contributions to each batch? + for i in range(m): + random_index = M*np.random.randint(m) + xi = X[random_index:random_index+M] + yi = y[random_index:random_index+M] + gradients = (1.0/M)*training_gradient(yi, xi, theta) + eta = learning_schedule(epoch*m+i) + theta = theta - eta*gradients +print("theta from own sdg") +print(theta) + + +# ## Same code but now with momentum gradient descent + +# In[21]: + + +# Using Autograd to calculate gradients using SGD +# OLS example +from random import random, seed +import numpy as np +import autograd.numpy as np +import matplotlib.pyplot as plt +from autograd import grad + +# Note change from previous example +def CostOLS(y,X,theta): + return np.sum((y-X @ theta)**2) + +n = 100 +x = 2*np.random.rand(n,1) +y = 4+3*x+np.random.randn(n,1) + +X = np.c_[np.ones((n,1)), x] +XT_X = X.T @ X +theta_linreg = np.linalg.pinv(XT_X) @ (X.T @ y) +print("Own inversion") +print(theta_linreg) +# Hessian matrix +H = (2.0/n)* XT_X +EigValues, EigVectors = np.linalg.eig(H) +print(f"Eigenvalues of Hessian Matrix:{EigValues}") + +theta = np.random.randn(2,1) +eta = 1.0/np.max(EigValues) +Niterations = 100 + +# Note that we request the derivative wrt third argument (theta, 2 here) +training_gradient = grad(CostOLS,2) + +for iter in range(Niterations): + gradients = (1.0/n)*training_gradient(y, X, theta) + theta -= eta*gradients +print("theta from own gd") +print(theta) + + +n_epochs = 50 +M = 5 #size of each minibatch +m = int(n/M) #number of minibatches +t0, t1 = 5, 50 +def learning_schedule(t): + return t0/(t+t1) + +theta = np.random.randn(2,1) + +change = 0.0 +delta_momentum = 0.3 + +for epoch in range(n_epochs): + for i in range(m): + random_index = M*np.random.randint(m) + xi = X[random_index:random_index+M] + yi = y[random_index:random_index+M] + gradients = (1.0/M)*training_gradient(yi, xi, theta) + eta = learning_schedule(epoch*m+i) + # calculate update + new_change = eta*gradients+delta_momentum*change + # take a step + theta -= new_change + # save the change + change = new_change +print("theta from own sdg with momentum") +print(theta) + + +# ## Similar (second order function now) problem but now with AdaGrad + +# In[22]: + + +# Using Autograd to calculate gradients using AdaGrad and Stochastic Gradient descent +# OLS example +from random import random, seed +import numpy as np +import autograd.numpy as np +import matplotlib.pyplot as plt +from autograd import grad + +# Note change from previous example +def CostOLS(y,X,theta): + return np.sum((y-X @ theta)**2) + +n = 1000 +x = np.random.rand(n,1) +y = 2.0+3*x +4*x*x + +X = np.c_[np.ones((n,1)), x, x*x] +XT_X = X.T @ X +theta_linreg = np.linalg.pinv(XT_X) @ (X.T @ y) +print("Own inversion") +print(theta_linreg) + + +# Note that we request the derivative wrt third argument (theta, 2 here) +training_gradient = grad(CostOLS,2) +# Define parameters for Stochastic Gradient Descent +n_epochs = 50 +M = 5 #size of each minibatch +m = int(n/M) #number of minibatches +# Guess for unknown parameters theta +theta = np.random.randn(3,1) + +# Value for learning rate +eta = 0.01 +# Including AdaGrad parameter to avoid possible division by zero +delta = 1e-8 +for epoch in range(n_epochs): + Giter = 0.0 + for i in range(m): + random_index = M*np.random.randint(m) + xi = X[random_index:random_index+M] + yi = y[random_index:random_index+M] + gradients = (1.0/M)*training_gradient(yi, xi, theta) + Giter += gradients*gradients + update = gradients*eta/(delta+np.sqrt(Giter)) + theta -= update +print("theta from own AdaGrad") +print(theta) + + +# Running this code we note an almost perfect agreement with the results from matrix inversion. + +# ## RMSprop for adaptive learning rate with Stochastic Gradient Descent + +# In[23]: + + +# Using Autograd to calculate gradients using RMSprop and Stochastic Gradient descent +# OLS example +from random import random, seed +import numpy as np +import autograd.numpy as np +import matplotlib.pyplot as plt +from autograd import grad + +# Note change from previous example +def CostOLS(y,X,theta): + return np.sum((y-X @ theta)**2) + +n = 1000 +x = np.random.rand(n,1) +y = 2.0+3*x +4*x*x# +np.random.randn(n,1) + +X = np.c_[np.ones((n,1)), x, x*x] +XT_X = X.T @ X +theta_linreg = np.linalg.pinv(XT_X) @ (X.T @ y) +print("Own inversion") +print(theta_linreg) + + +# Note that we request the derivative wrt third argument (theta, 2 here) +training_gradient = grad(CostOLS,2) +# Define parameters for Stochastic Gradient Descent +n_epochs = 50 +M = 5 #size of each minibatch +m = int(n/M) #number of minibatches +# Guess for unknown parameters theta +theta = np.random.randn(3,1) + +# Value for learning rate +eta = 0.01 +# Value for parameter rho +rho = 0.99 +# Including AdaGrad parameter to avoid possible division by zero +delta = 1e-8 +for epoch in range(n_epochs): + Giter = 0.0 + for i in range(m): + random_index = M*np.random.randint(m) + xi = X[random_index:random_index+M] + yi = y[random_index:random_index+M] + gradients = (1.0/M)*training_gradient(yi, xi, theta) + # Accumulated gradient + # Scaling with rho the new and the previous results + Giter = (rho*Giter+(1-rho)*gradients*gradients) + # Taking the diagonal only and inverting + update = gradients*eta/(delta+np.sqrt(Giter)) + # Hadamard product + theta -= update +print("theta from own RMSprop") +print(theta) + + +# ## And finally [ADAM](https://arxiv.org/pdf/1412.6980.pdf) + +# In[24]: + + +# Using Autograd to calculate gradients using RMSprop and Stochastic Gradient descent +# OLS example +from random import random, seed +import numpy as np +import autograd.numpy as np +import matplotlib.pyplot as plt +from autograd import grad + +# Note change from previous example +def CostOLS(y,X,theta): + return np.sum((y-X @ theta)**2) + +n = 1000 +x = np.random.rand(n,1) +y = 2.0+3*x +4*x*x# +np.random.randn(n,1) + +X = np.c_[np.ones((n,1)), x, x*x] +XT_X = X.T @ X +theta_linreg = np.linalg.pinv(XT_X) @ (X.T @ y) +print("Own inversion") +print(theta_linreg) + + +# Note that we request the derivative wrt third argument (theta, 2 here) +training_gradient = grad(CostOLS,2) +# Define parameters for Stochastic Gradient Descent +n_epochs = 50 +M = 5 #size of each minibatch +m = int(n/M) #number of minibatches +# Guess for unknown parameters theta +theta = np.random.randn(3,1) + +# Value for learning rate +eta = 0.01 +# Value for parameters beta1 and beta2, see https://arxiv.org/abs/1412.6980 +beta1 = 0.9 +beta2 = 0.999 +# Including AdaGrad parameter to avoid possible division by zero +delta = 1e-7 +iter = 0 +for epoch in range(n_epochs): + first_moment = 0.0 + second_moment = 0.0 + iter += 1 + for i in range(m): + random_index = M*np.random.randint(m) + xi = X[random_index:random_index+M] + yi = y[random_index:random_index+M] + gradients = (1.0/M)*training_gradient(yi, xi, theta) + # Computing moments first + first_moment = beta1*first_moment + (1-beta1)*gradients + second_moment = beta2*second_moment+(1-beta2)*gradients*gradients + first_term = first_moment/(1.0-beta1**iter) + second_term = second_moment/(1.0-beta2**iter) + # Scaling with rho the new and the previous results + update = eta*first_term/(np.sqrt(second_term)+delta) + theta -= update +print("theta from own ADAM") +print(theta) + + +# ## And Logistic Regression + +# In[25]: + + +import autograd.numpy as np +from autograd import grad + +def sigmoid(x): + return 0.5 * (np.tanh(x / 2.) + 1) + +def logistic_predictions(weights, inputs): + # Outputs probability of a label being true according to logistic model. + return sigmoid(np.dot(inputs, weights)) + +def training_loss(weights): + # Training loss is the negative log-likelihood of the training labels. + preds = logistic_predictions(weights, inputs) + label_probabilities = preds * targets + (1 - preds) * (1 - targets) + return -np.sum(np.log(label_probabilities)) + +# Build a toy dataset. +inputs = np.array([[0.52, 1.12, 0.77], + [0.88, -1.08, 0.15], + [0.52, 0.06, -1.30], + [0.74, -2.49, 1.39]]) +targets = np.array([True, True, False, True]) + +# Define a function that returns gradients of training loss using Autograd. +training_gradient_fun = grad(training_loss) + +# Optimize weights using gradient descent. +weights = np.array([0.0, 0.0, 0.0]) +print("Initial loss:", training_loss(weights)) +for i in range(100): + weights -= training_gradient_fun(weights) * 0.01 + +print("Trained loss:", training_loss(weights)) + + +# ## Introducing [JAX](https://jax.readthedocs.io/en/latest/) +# +# Presently, instead of using **autograd**, we recommend using [JAX](https://jax.readthedocs.io/en/latest/) +# +# **JAX** is Autograd and [XLA (Accelerated Linear Algebra))](https://www.tensorflow.org/xla), +# brought together for high-performance numerical computing and machine learning research. +# It provides composable transformations of Python+NumPy programs: differentiate, vectorize, parallelize, Just-In-Time compile to GPU/TPU, and more. +# +# Here's a simple example on how you can use **JAX** to compute the derivate of the logistic function. + +# In[26]: + + +import jax.numpy as jnp +from jax import grad, jit, vmap + +def sum_logistic(x): + return jnp.sum(1.0 / (1.0 + jnp.exp(-x))) + +x_small = jnp.arange(3.) +derivative_fn = grad(sum_logistic) +print(derivative_fn(x_small)) + + +# ## Introduction to Neural networks +# +# Artificial neural networks are computational systems that can learn to +# perform tasks by considering examples, generally without being +# programmed with any task-specific rules. It is supposed to mimic a +# biological system, wherein neurons interact by sending signals in the +# form of mathematical functions between layers. All layers can contain +# an arbitrary number of neurons, and each connection is represented by +# a weight variable. + +# ## Artificial neurons +# +# The field of artificial neural networks has a long history of +# development, and is closely connected with the advancement of computer +# science and computers in general. A model of artificial neurons was +# first developed by McCulloch and Pitts in 1943 to study signal +# processing in the brain and has later been refined by others. The +# general idea is to mimic neural networks in the human brain, which is +# composed of billions of neurons that communicate with each other by +# sending electrical signals. Each neuron accumulates its incoming +# signals, which must exceed an activation threshold to yield an +# output. If the threshold is not overcome, the neuron remains inactive, +# i.e. has zero output. +# +# This behaviour has inspired a simple mathematical model for an artificial neuron. + +# +#
              +# +# $$ +# \begin{equation} +# y = f\left(\sum_{i=1}^n w_ix_i\right) = f(u) +# \label{artificialNeuron} \tag{6} +# \end{equation} +# $$ + +# Here, the output $y$ of the neuron is the value of its activation function, which have as input +# a weighted sum of signals $x_i, \dots ,x_n$ received by $n$ other neurons. +# +# Conceptually, it is helpful to divide neural networks into four +# categories: +# 1. general purpose neural networks for supervised learning, +# +# 2. neural networks designed specifically for image processing, the most prominent example of this class being Convolutional Neural Networks (CNNs), +# +# 3. neural networks for sequential data such as Recurrent Neural Networks (RNNs), and +# +# 4. neural networks for unsupervised learning such as Deep Boltzmann Machines. +# +# In natural science, DNNs and CNNs have already found numerous +# applications. In statistical physics, they have been applied to detect +# phase transitions in 2D Ising and Potts models, lattice gauge +# theories, and different phases of polymers, or solving the +# Navier-Stokes equation in weather forecasting. Deep learning has also +# found interesting applications in quantum physics. Various quantum +# phase transitions can be detected and studied using DNNs and CNNs, +# topological phases, and even non-equilibrium many-body +# localization. Representing quantum states as DNNs quantum state +# tomography are among some of the impressive achievements to reveal the +# potential of DNNs to facilitate the study of quantum systems. +# +# In quantum information theory, it has been shown that one can perform +# gate decompositions with the help of neural. +# +# The applications are not limited to the natural sciences. There is a +# plethora of applications in essentially all disciplines, from the +# humanities to life science and medicine. + +# ## Neural network types +# +# An artificial neural network (ANN), is a computational model that +# consists of layers of connected neurons, or nodes or units. We will +# refer to these interchangeably as units or nodes, and sometimes as +# neurons. +# +# It is supposed to mimic a biological nervous system by letting each +# neuron interact with other neurons by sending signals in the form of +# mathematical functions between layers. A wide variety of different +# ANNs have been developed, but most of them consist of an input layer, +# an output layer and eventual layers in-between, called *hidden +# layers*. All layers can contain an arbitrary number of nodes, and each +# connection between two nodes is associated with a weight variable. +# +# Neural networks (also called neural nets) are neural-inspired +# nonlinear models for supervised learning. As we will see, neural nets +# can be viewed as natural, more powerful extensions of supervised +# learning methods such as linear and logistic regression and soft-max +# methods we discussed earlier. + +# ## Feed-forward neural networks +# +# The feed-forward neural network (FFNN) was the first and simplest type +# of ANNs that were devised. In this network, the information moves in +# only one direction: forward through the layers. +# +# Nodes are represented by circles, while the arrows display the +# connections between the nodes, including the direction of information +# flow. Additionally, each arrow corresponds to a weight variable +# (figure to come). We observe that each node in a layer is connected +# to *all* nodes in the subsequent layer, making this a so-called +# *fully-connected* FFNN. + +# ## Convolutional Neural Network +# +# A different variant of FFNNs are *convolutional neural networks* +# (CNNs), which have a connectivity pattern inspired by the animal +# visual cortex. Individual neurons in the visual cortex only respond to +# stimuli from small sub-regions of the visual field, called a receptive +# field. This makes the neurons well-suited to exploit the strong +# spatially local correlation present in natural images. The response of +# each neuron can be approximated mathematically as a convolution +# operation. (figure to come) +# +# Convolutional neural networks emulate the behaviour of neurons in the +# visual cortex by enforcing a *local* connectivity pattern between +# nodes of adjacent layers: Each node in a convolutional layer is +# connected only to a subset of the nodes in the previous layer, in +# contrast to the fully-connected FFNN. Often, CNNs consist of several +# convolutional layers that learn local features of the input, with a +# fully-connected layer at the end, which gathers all the local data and +# produces the outputs. They have wide applications in image and video +# recognition. + +# ## Recurrent neural networks +# +# So far we have only mentioned ANNs where information flows in one +# direction: forward. *Recurrent neural networks* on the other hand, +# have connections between nodes that form directed *cycles*. This +# creates a form of internal memory which are able to capture +# information on what has been calculated before; the output is +# dependent on the previous computations. Recurrent NNs make use of +# sequential information by performing the same task for every element +# in a sequence, where each element depends on previous elements. An +# example of such information is sentences, making recurrent NNs +# especially well-suited for handwriting and speech recognition. + +# ## Other types of networks +# +# There are many other kinds of ANNs that have been developed. One type +# that is specifically designed for interpolation in multidimensional +# space is the radial basis function (RBF) network. RBFs are typically +# made up of three layers: an input layer, a hidden layer with +# non-linear radial symmetric activation functions and a linear output +# layer (''linear'' here means that each node in the output layer has a +# linear activation function). The layers are normally fully-connected +# and there are no cycles, thus RBFs can be viewed as a type of +# fully-connected FFNN. They are however usually treated as a separate +# type of NN due the unusual activation functions. + +# ## Multilayer perceptrons +# +# One uses often so-called fully-connected feed-forward neural networks +# with three or more layers (an input layer, one or more hidden layers +# and an output layer) consisting of neurons that have non-linear +# activation functions. +# +# Such networks are often called *multilayer perceptrons* (MLPs). + +# ## Why multilayer perceptrons? +# +# According to the *Universal approximation theorem*, a feed-forward +# neural network with just a single hidden layer containing a finite +# number of neurons can approximate a continuous multidimensional +# function to arbitrary accuracy, assuming the activation function for +# the hidden layer is a **non-constant, bounded and +# monotonically-increasing continuous function**. +# +# Note that the requirements on the activation function only applies to +# the hidden layer, the output nodes are always assumed to be linear, so +# as to not restrict the range of output values. + +# ## Illustration of a single perceptron model and a multi-perceptron model +# +# +# +# +#

              Figure 1: In a) we show a single perceptron model while in b) we dispay a network with two hidden layers, an input layer and an output layer.

              +# + +# ## Examples of XOR, OR and AND gates +# +# Let us first try to fit various gates using standard linear +# regression. The gates we are thinking of are the classical XOR, OR and +# AND gates, well-known elements in computer science. The tables here +# show how we can set up the inputs $x_1$ and $x_2$ in order to yield a +# specific target $y_i$. + +# In[27]: + + +""" +Simple code that tests XOR, OR and AND gates with linear regression +""" + +import numpy as np +# Design matrix +X = np.array([ [1, 0, 0], [1, 0, 1], [1, 1, 0],[1, 1, 1]],dtype=np.float64) +print(f"The X.TX matrix:{X.T @ X}") +Xinv = np.linalg.pinv(X.T @ X) +print(f"The invers of X.TX matrix:{Xinv}") + +# The XOR gate +yXOR = np.array( [ 0, 1 ,1, 0]) +ThetaXOR = Xinv @ X.T @ yXOR +print(f"The values of theta for the XOR gate:{ThetaXOR}") +print(f"The linear regression prediction for the XOR gate:{X @ ThetaXOR}") + + +# The OR gate +yOR = np.array( [ 0, 1 ,1, 1]) +ThetaOR = Xinv @ X.T @ yOR +print(f"The values of theta for the OR gate:{ThetaOR}") +print(f"The linear regression prediction for the OR gate:{X @ ThetaOR}") + + +# The OR gate +yAND = np.array( [ 0, 0 ,0, 1]) +ThetaAND = Xinv @ X.T @ yAND +print(f"The values of theta for the AND gate:{ThetaAND}") +print(f"The linear regression prediction for the AND gate:{X @ ThetaAND}") + + +# What is happening here? + +# ## Does Logistic Regression do a better Job? + +# In[28]: + + +""" +Simple code that tests XOR and OR gates with linear regression +and logistic regression +""" + +import matplotlib.pyplot as plt +from sklearn.linear_model import LogisticRegression +import numpy as np + +# Design matrix +X = np.array([ [1, 0, 0], [1, 0, 1], [1, 1, 0],[1, 1, 1]],dtype=np.float64) +print(f"The X.TX matrix:{X.T @ X}") +Xinv = np.linalg.pinv(X.T @ X) +print(f"The invers of X.TX matrix:{Xinv}") + +# The XOR gate +yXOR = np.array( [ 0, 1 ,1, 0]) +ThetaXOR = Xinv @ X.T @ yXOR +print(f"The values of theta for the XOR gate:{ThetaXOR}") +print(f"The linear regression prediction for the XOR gate:{X @ ThetaXOR}") + + +# The OR gate +yOR = np.array( [ 0, 1 ,1, 1]) +ThetaOR = Xinv @ X.T @ yOR +print(f"The values of theta for the OR gate:{ThetaOR}") +print(f"The linear regression prediction for the OR gate:{X @ ThetaOR}") + + +# The OR gate +yAND = np.array( [ 0, 0 ,0, 1]) +ThetaAND = Xinv @ X.T @ yAND +print(f"The values of theta for the AND gate:{ThetaAND}") +print(f"The linear regression prediction for the AND gate:{X @ ThetaAND}") + +# Now we change to logistic regression + + +# Logistic Regression +logreg = LogisticRegression() +logreg.fit(X, yOR) +print("Test set accuracy with Logistic Regression for OR gate: {:.2f}".format(logreg.score(X,yOR))) + +logreg.fit(X, yXOR) +print("Test set accuracy with Logistic Regression for XOR gate: {:.2f}".format(logreg.score(X,yXOR))) + + +logreg.fit(X, yAND) +print("Test set accuracy with Logistic Regression for AND gate: {:.2f}".format(logreg.score(X,yAND))) + + +# Not exactly impressive, but somewhat better. + +# ## Adding Neural Networks + +# In[29]: + + + +# and now neural networks with Scikit-Learn and the XOR + +from sklearn.neural_network import MLPClassifier +from sklearn.datasets import make_classification +X, yXOR = make_classification(n_samples=100, random_state=1) +FFNN = MLPClassifier(random_state=1, max_iter=300).fit(X, yXOR) +FFNN.predict_proba(X) +print(f"Test set accuracy with Feed Forward Neural Network for XOR gate:{FFNN.score(X, yXOR)}") + + +# ## Mathematical model +# +# The output $y$ is produced via the activation function $f$ + +# $$ +# y = f\left(\sum_{i=1}^n w_ix_i + b_i\right) = f(z), +# $$ + +# This function receives $x_i$ as inputs. +# Here the activation $z=(\sum_{i=1}^n w_ix_i+b_i)$. +# In an FFNN of such neurons, the *inputs* $x_i$ are the *outputs* of +# the neurons in the preceding layer. Furthermore, an MLP is +# fully-connected, which means that each neuron receives a weighted sum +# of the outputs of *all* neurons in the previous layer. + +# ## Mathematical model +# +# First, for each node $i$ in the first hidden layer, we calculate a weighted sum $z_i^1$ of the input coordinates $x_j$, + +# +#
              +# +# $$ +# \begin{equation} z_i^1 = \sum_{j=1}^{M} w_{ij}^1 x_j + b_i^1 +# \label{_auto6} \tag{7} +# \end{equation} +# $$ + +# Here $b_i$ is the so-called bias which is normally needed in +# case of zero activation weights or inputs. How to fix the biases and +# the weights will be discussed below. The value of $z_i^1$ is the +# argument to the activation function $f_i$ of each node $i$, The +# variable $M$ stands for all possible inputs to a given node $i$ in the +# first layer. We define the output $y_i^1$ of all neurons in layer 1 as + +# +#
              +# +# $$ +# \begin{equation} +# y_i^1 = f(z_i^1) = f\left(\sum_{j=1}^M w_{ij}^1 x_j + b_i^1\right) +# \label{outputLayer1} \tag{8} +# \end{equation} +# $$ + +# where we assume that all nodes in the same layer have identical +# activation functions, hence the notation $f$. In general, we could assume in the more general case that different layers have different activation functions. +# In this case we would identify these functions with a superscript $l$ for the $l$-th layer, + +# +#
              +# +# $$ +# \begin{equation} +# y_i^l = f^l(u_i^l) = f^l\left(\sum_{j=1}^{N_{l-1}} w_{ij}^l y_j^{l-1} + b_i^l\right) +# \label{generalLayer} \tag{9} +# \end{equation} +# $$ + +# where $N_l$ is the number of nodes in layer $l$. When the output of +# all the nodes in the first hidden layer are computed, the values of +# the subsequent layer can be calculated and so forth until the output +# is obtained. + +# ## Mathematical model +# +# The output of neuron $i$ in layer 2 is thus, + +# +#
              +# +# $$ +# \begin{equation} +# y_i^2 = f^2\left(\sum_{j=1}^N w_{ij}^2 y_j^1 + b_i^2\right) +# \label{_auto7} \tag{10} +# \end{equation} +# $$ + +# +#
              +# +# $$ +# \begin{equation} +# = f^2\left[\sum_{j=1}^N w_{ij}^2f^1\left(\sum_{k=1}^M w_{jk}^1 x_k + b_j^1\right) + b_i^2\right] +# \label{outputLayer2} \tag{11} +# \end{equation} +# $$ + +# where we have substituted $y_k^1$ with the inputs $x_k$. Finally, the ANN output reads + +# +#
              +# +# $$ +# \begin{equation} +# y_i^3 = f^3\left(\sum_{j=1}^N w_{ij}^3 y_j^2 + b_i^3\right) +# \label{_auto8} \tag{12} +# \end{equation} +# $$ + +# +#
              +# +# $$ +# \begin{equation} +# = f_3\left[\sum_{j} w_{ij}^3 f^2\left(\sum_{k} w_{jk}^2 f^1\left(\sum_{m} w_{km}^1 x_m + b_k^1\right) + b_j^2\right) +# + b_1^3\right] +# \label{_auto9} \tag{13} +# \end{equation} +# $$ + +# ## Mathematical model +# +# We can generalize this expression to an MLP with $l$ hidden +# layers. The complete functional form is, + +# +#
              +# +# $$ +# \begin{equation} +# y^{l+1}_i = f^{l+1}\left[\!\sum_{j=1}^{N_l} w_{ij}^3 f^l\left(\sum_{k=1}^{N_{l-1}}w_{jk}^{l-1}\left(\dots f^1\left(\sum_{n=1}^{N_0} w_{mn}^1 x_n+ b_m^1\right)\dots\right)+b_k^2\right)+b_1^3\right] +# \label{completeNN} \tag{14} +# \end{equation} +# $$ + +# which illustrates a basic property of MLPs: The only independent +# variables are the input values $x_n$. + +# ## Mathematical model +# +# This confirms that an MLP, despite its quite convoluted mathematical +# form, is nothing more than an analytic function, specifically a +# mapping of real-valued vectors $\hat{x} \in \mathbb{R}^n \rightarrow +# \hat{y} \in \mathbb{R}^m$. +# +# Furthermore, the flexibility and universality of an MLP can be +# illustrated by realizing that the expression is essentially a nested +# sum of scaled activation functions of the form + +# +#
              +# +# $$ +# \begin{equation} +# f(x) = c_1 f(c_2 x + c_3) + c_4 +# \label{_auto10} \tag{15} +# \end{equation} +# $$ + +# where the parameters $c_i$ are weights and biases. By adjusting these +# parameters, the activation functions can be shifted up and down or +# left and right, change slope or be rescaled which is the key to the +# flexibility of a neural network. + +# ### Matrix-vector notation +# +# We can introduce a more convenient notation for the activations in an A NN. +# +# Additionally, we can represent the biases and activations +# as layer-wise column vectors $\hat{b}_l$ and $\hat{y}_l$, so that the $i$-th element of each vector +# is the bias $b_i^l$ and activation $y_i^l$ of node $i$ in layer $l$ respectively. +# +# We have that $\mathrm{W}_l$ is an $N_{l-1} \times N_l$ matrix, while $\hat{b}_l$ and $\hat{y}_l$ are $N_l \times 1$ column vectors. +# With this notation, the sum becomes a matrix-vector multiplication, and we can write +# the equation for the activations of hidden layer 2 (assuming three nodes for simplicity) as + +# +#
              +# +# $$ +# \begin{equation} +# \hat{y}_2 = f_2(\mathrm{W}_2 \hat{y}_{1} + \hat{b}_{2}) = +# f_2\left(\left[\begin{array}{ccc} +# w^2_{11} &w^2_{12} &w^2_{13} \\ +# w^2_{21} &w^2_{22} &w^2_{23} \\ +# w^2_{31} &w^2_{32} &w^2_{33} \\ +# \end{array} \right] \cdot +# \left[\begin{array}{c} +# y^1_1 \\ +# y^1_2 \\ +# y^1_3 \\ +# \end{array}\right] + +# \left[\begin{array}{c} +# b^2_1 \\ +# b^2_2 \\ +# b^2_3 \\ +# \end{array}\right]\right). +# \label{_auto11} \tag{16} +# \end{equation} +# $$ + +# ### Matrix-vector notation and activation +# +# The activation of node $i$ in layer 2 is + +# +#
              +# +# $$ +# \begin{equation} +# y^2_i = f_2\Bigr(w^2_{i1}y^1_1 + w^2_{i2}y^1_2 + w^2_{i3}y^1_3 + b^2_i\Bigr) = +# f_2\left(\sum_{j=1}^3 w^2_{ij} y_j^1 + b^2_i\right). +# \label{_auto12} \tag{17} +# \end{equation} +# $$ + +# This is not just a convenient and compact notation, but also a useful +# and intuitive way to think about MLPs: The output is calculated by a +# series of matrix-vector multiplications and vector additions that are +# used as input to the activation functions. For each operation +# $\mathrm{W}_l \hat{y}_{l-1}$ we move forward one layer. + +# ### Activation functions +# +# A property that characterizes a neural network, other than its +# connectivity, is the choice of activation function(s). As described +# in, the following restrictions are imposed on an activation function +# for a FFNN to fulfill the universal approximation theorem +# +# * Non-constant +# +# * Bounded +# +# * Monotonically-increasing +# +# * Continuous + +# ### Activation functions, Logistic and Hyperbolic ones +# +# The second requirement excludes all linear functions. Furthermore, in +# a MLP with only linear activation functions, each layer simply +# performs a linear transformation of its inputs. +# +# Regardless of the number of layers, the output of the NN will be +# nothing but a linear function of the inputs. Thus we need to introduce +# some kind of non-linearity to the NN to be able to fit non-linear +# functions Typical examples are the logistic *Sigmoid* + +# $$ +# f(x) = \frac{1}{1 + e^{-x}}, +# $$ + +# and the *hyperbolic tangent* function + +# $$ +# f(x) = \tanh(x) +# $$ + +# ### Relevance +# +# The *sigmoid* function are more biologically plausible because the +# output of inactive neurons are zero. Such activation function are +# called *one-sided*. However, it has been shown that the hyperbolic +# tangent performs better than the sigmoid for training MLPs. has +# become the most popular for *deep neural networks* + +# In[30]: + + +"""The sigmoid function (or the logistic curve) is a +function that takes any real number, z, and outputs a number (0,1). +It is useful in neural networks for assigning weights on a relative scale. +The value z is the weighted sum of parameters involved in the learning algorithm.""" + +import numpy +import matplotlib.pyplot as plt +import math as mt + +z = numpy.arange(-5, 5, .1) +sigma_fn = numpy.vectorize(lambda z: 1/(1+numpy.exp(-z))) +sigma = sigma_fn(z) + +fig = plt.figure() +ax = fig.add_subplot(111) +ax.plot(z, sigma) +ax.set_ylim([-0.1, 1.1]) +ax.set_xlim([-5,5]) +ax.grid(True) +ax.set_xlabel('z') +ax.set_title('sigmoid function') + +plt.show() + +"""Step Function""" +z = numpy.arange(-5, 5, .02) +step_fn = numpy.vectorize(lambda z: 1.0 if z >= 0.0 else 0.0) +step = step_fn(z) + +fig = plt.figure() +ax = fig.add_subplot(111) +ax.plot(z, step) +ax.set_ylim([-0.5, 1.5]) +ax.set_xlim([-5,5]) +ax.grid(True) +ax.set_xlabel('z') +ax.set_title('step function') + +plt.show() + +"""Sine Function""" +z = numpy.arange(-2*mt.pi, 2*mt.pi, 0.1) +t = numpy.sin(z) + +fig = plt.figure() +ax = fig.add_subplot(111) +ax.plot(z, t) +ax.set_ylim([-1.0, 1.0]) +ax.set_xlim([-2*mt.pi,2*mt.pi]) +ax.grid(True) +ax.set_xlabel('z') +ax.set_title('sine function') + +plt.show() + +"""Plots a graph of the squashing function used by a rectified linear +unit""" +z = numpy.arange(-2, 2, .1) +zero = numpy.zeros(len(z)) +y = numpy.max([zero, z], axis=0) + +fig = plt.figure() +ax = fig.add_subplot(111) +ax.plot(z, y) +ax.set_ylim([-2.0, 2.0]) +ax.set_xlim([-2.0, 2.0]) +ax.grid(True) +ax.set_xlabel('z') +ax.set_title('Rectified linear unit') + +plt.show() + diff --git a/doc/LectureNotes/_build/jupyter_execute/week40_25_1.png b/doc/LectureNotes/_build/jupyter_execute/week40_25_1.png new file mode 100644 index 000000000..464798a94 Binary files /dev/null and b/doc/LectureNotes/_build/jupyter_execute/week40_25_1.png differ diff --git a/doc/LectureNotes/_build/jupyter_execute/week40_68_0.png b/doc/LectureNotes/_build/jupyter_execute/week40_68_0.png new file mode 100644 index 000000000..c8dbe4ea0 Binary files /dev/null and b/doc/LectureNotes/_build/jupyter_execute/week40_68_0.png differ diff --git a/doc/LectureNotes/_toc.yml b/doc/LectureNotes/_toc.yml index 5172dc12e..9e713b957 100644 --- a/doc/LectureNotes/_toc.yml +++ b/doc/LectureNotes/_toc.yml @@ -53,6 +53,7 @@ parts: - file: week38.ipynb - file: exercisesweek39.ipynb - file: week39.ipynb + - file: week40.ipynb - caption: Projects numbered: false chapters: diff --git a/doc/LectureNotes/gaussian.pdf b/doc/LectureNotes/gaussian.pdf index eb998dcbb..5b59305c3 100644 Binary files a/doc/LectureNotes/gaussian.pdf and b/doc/LectureNotes/gaussian.pdf differ diff --git a/doc/LectureNotes/week39.ipynb b/doc/LectureNotes/week39.ipynb index f8ed9613e..ea2c34ebe 100644 --- a/doc/LectureNotes/week39.ipynb +++ b/doc/LectureNotes/week39.ipynb @@ -2,7 +2,7 @@ "cells": [ { "cell_type": "markdown", - "id": "97c9bb6c", + "id": "428bf751", "metadata": { "editable": true }, @@ -14,7 +14,7 @@ }, { "cell_type": "markdown", - "id": "ade8d870", + "id": "1a0a75ed", "metadata": { "editable": true }, @@ -27,7 +27,7 @@ }, { "cell_type": "markdown", - "id": "87cd74c3", + "id": "333f3063", "metadata": { "editable": true }, @@ -42,6 +42,8 @@ "\n", " * Work on project 1, in particular resampling methods like cross-validation and bootstrap. **For more discussions of project 1, chapter 5 of Goodfellow et al is a good read, in particular sections 5.1-5.5 and 5.7-5.11**.\n", "\n", + " * [Video on how to write scientific reports recorded during one of the lab sessions](https://youtu.be/tVW1ZDmZnwM)\n", + "\n", "These sections summarize neatly what we have done till now and point to what is coming with respect to deep learning. \n", " * A general guideline can be found at .\n", "\n", @@ -53,7 +55,7 @@ "\n", " * Stochastic Gradient descent with examples and automatic differentiation\n", "\n", - " * [Video of lecture](https://youtu.be/)\n", + " * [Video of lecture](https://youtu.be/bFRVuIJroHs)\n", "\n", " * Whiteboard notes TBA at \n", "\n", @@ -65,12 +67,14 @@ "\n", " * [Video on gradient descent](https://www.youtube.com/watch?v=sDv4f4s2SB8)\n", "\n", - " * [Video on stochastic gradient descent](https://www.youtube.com/watch?v=vMh0zPT0tLI)" + " * [Video on stochastic gradient descent](https://www.youtube.com/watch?v=vMh0zPT0tLI)\n", + "\n", + "" ] }, { "cell_type": "markdown", - "id": "529184e5", + "id": "3ee03ecd", "metadata": { "editable": true }, @@ -91,7 +95,7 @@ }, { "cell_type": "markdown", - "id": "1a65465a", + "id": "148ec577", "metadata": { "editable": true }, @@ -108,7 +112,7 @@ }, { "cell_type": "markdown", - "id": "b67231b3", + "id": "e6e5e661", "metadata": { "editable": true }, @@ -123,7 +127,7 @@ }, { "cell_type": "markdown", - "id": "3f5e03db", + "id": "4a81fe9d", "metadata": { "editable": true }, @@ -133,7 +137,7 @@ }, { "cell_type": "markdown", - "id": "e83141ae", + "id": "ac94af95", "metadata": { "editable": true }, @@ -149,7 +153,7 @@ }, { "cell_type": "markdown", - "id": "cef5864b", + "id": "5bfc3f18", "metadata": { "editable": true }, @@ -161,7 +165,7 @@ }, { "cell_type": "markdown", - "id": "2f59bbb1", + "id": "2437c71a", "metadata": { "editable": true }, @@ -172,7 +176,7 @@ }, { "cell_type": "markdown", - "id": "3869b3c6", + "id": "a5d4163c", "metadata": { "editable": true }, @@ -184,7 +188,7 @@ }, { "cell_type": "markdown", - "id": "e4656e92", + "id": "cca433f5", "metadata": { "editable": true }, @@ -194,7 +198,7 @@ }, { "cell_type": "markdown", - "id": "b73ae554", + "id": "c9ba0136", "metadata": { "editable": true }, @@ -208,7 +212,7 @@ }, { "cell_type": "markdown", - "id": "70a2df05", + "id": "3a6a4c55", "metadata": { "editable": true }, @@ -220,7 +224,7 @@ }, { "cell_type": "markdown", - "id": "2e36f976", + "id": "f71ca135", "metadata": { "editable": true }, @@ -230,7 +234,7 @@ }, { "cell_type": "markdown", - "id": "7c4959c9", + "id": "942a71da", "metadata": { "editable": true }, @@ -242,7 +246,7 @@ }, { "cell_type": "markdown", - "id": "c553379e", + "id": "3d3ddaff", "metadata": { "editable": true }, @@ -254,7 +258,7 @@ }, { "cell_type": "markdown", - "id": "145f9699", + "id": "3dfb626a", "metadata": { "editable": true }, @@ -274,7 +278,7 @@ }, { "cell_type": "markdown", - "id": "9a68f686", + "id": "7a77c9ce", "metadata": { "editable": true }, @@ -290,7 +294,7 @@ }, { "cell_type": "markdown", - "id": "d523ab61", + "id": "d094140b", "metadata": { "editable": true }, @@ -306,7 +310,7 @@ }, { "cell_type": "markdown", - "id": "8205fd3a", + "id": "9c04f928", "metadata": { "editable": true }, @@ -317,7 +321,7 @@ }, { "cell_type": "markdown", - "id": "98d52e46", + "id": "d29b9b63", "metadata": { "editable": true }, @@ -329,7 +333,7 @@ }, { "cell_type": "markdown", - "id": "203f65e8", + "id": "d3e508b5", "metadata": { "editable": true }, @@ -339,7 +343,7 @@ }, { "cell_type": "markdown", - "id": "dd40195b", + "id": "fd5bbc31", "metadata": { "editable": true }, @@ -351,7 +355,7 @@ }, { "cell_type": "markdown", - "id": "e13a7340", + "id": "7e255afd", "metadata": { "editable": true }, @@ -361,7 +365,7 @@ }, { "cell_type": "markdown", - "id": "30c258c9", + "id": "62d43c1c", "metadata": { "editable": true }, @@ -373,7 +377,7 @@ }, { "cell_type": "markdown", - "id": "84f880c9", + "id": "0b23438c", "metadata": { "editable": true }, @@ -395,7 +399,7 @@ }, { "cell_type": "markdown", - "id": "b237f214", + "id": "d7587ee4", "metadata": { "editable": true }, @@ -408,7 +412,7 @@ }, { "cell_type": "markdown", - "id": "b4ea1db2", + "id": "3736c3ae", "metadata": { "editable": true }, @@ -421,7 +425,7 @@ }, { "cell_type": "markdown", - "id": "8b2b6606", + "id": "e9cf6fb6", "metadata": { "editable": true }, @@ -431,7 +435,7 @@ }, { "cell_type": "markdown", - "id": "e9d217a4", + "id": "9c8e6fc9", "metadata": { "editable": true }, @@ -449,7 +453,7 @@ }, { "cell_type": "markdown", - "id": "54f87f7c", + "id": "2059ae9d", "metadata": { "editable": true }, @@ -459,7 +463,7 @@ }, { "cell_type": "markdown", - "id": "c2711fc8", + "id": "64c640e4", "metadata": { "editable": true }, @@ -474,7 +478,7 @@ }, { "cell_type": "markdown", - "id": "e0409f1c", + "id": "647d6bd2", "metadata": { "editable": true }, @@ -484,7 +488,7 @@ }, { "cell_type": "markdown", - "id": "53651890", + "id": "24ff572b", "metadata": { "editable": true }, @@ -498,7 +502,7 @@ }, { "cell_type": "markdown", - "id": "4e70e7ac", + "id": "521c56f5", "metadata": { "editable": true }, @@ -508,7 +512,7 @@ }, { "cell_type": "markdown", - "id": "9336db1d", + "id": "4508944b", "metadata": { "editable": true }, @@ -522,7 +526,7 @@ }, { "cell_type": "markdown", - "id": "2c2ae028", + "id": "69b72003", "metadata": { "editable": true }, @@ -537,7 +541,7 @@ }, { "cell_type": "markdown", - "id": "cdf99885", + "id": "dd11ba71", "metadata": { "editable": true }, @@ -554,7 +558,7 @@ }, { "cell_type": "markdown", - "id": "11bb1b41", + "id": "ccb5e1a6", "metadata": { "editable": true }, @@ -566,7 +570,7 @@ }, { "cell_type": "markdown", - "id": "5d957768", + "id": "6952b928", "metadata": { "editable": true }, @@ -580,7 +584,7 @@ }, { "cell_type": "markdown", - "id": "455b420a", + "id": "f0c5476d", "metadata": { "editable": true }, @@ -595,7 +599,7 @@ }, { "cell_type": "markdown", - "id": "aacb8b05", + "id": "4aeb7465", "metadata": { "editable": true }, @@ -607,7 +611,7 @@ }, { "cell_type": "markdown", - "id": "2bb3385b", + "id": "337ccfd3", "metadata": { "editable": true }, @@ -618,7 +622,7 @@ }, { "cell_type": "markdown", - "id": "21326ef0", + "id": "7540286d", "metadata": { "editable": true }, @@ -646,7 +650,7 @@ }, { "cell_type": "markdown", - "id": "a41b3cc6", + "id": "b7ce5820", "metadata": { "editable": true }, @@ -668,7 +672,7 @@ }, { "cell_type": "markdown", - "id": "a01f11a5", + "id": "0a557da3", "metadata": { "editable": true }, @@ -690,19 +694,28 @@ }, { "cell_type": "markdown", - "id": "076a32fa", + "id": "bdbf8a07", "metadata": { "editable": true }, "source": [ "## Convex function\n", "\n", - "**Convex function**: Let $X \\subset \\mathbb{R}^n$ be a convex set. Assume that the function $f: X \\rightarrow \\mathbb{R}$ is continuous, then $f$ is said to be convex if $$f(tx_1 + (1-t)x_2) \\leq tf(x_1) + (1-t)f(x_2) $$ for all $x_1, x_2 \\in X$ and for all $t \\in [0,1]$. If $\\leq$ is replaced with a strict inequaltiy in the definition, we demand $x_1 \\neq x_2$ and $t\\in(0,1)$ then $f$ is said to be strictly convex. For a single variable function, convexity means that if you draw a straight line connecting $f(x_1)$ and $f(x_2)$, the value of the function on the interval $[x_1,x_2]$ is always below the line as illustrated below." + "**Convex function**: Let $X \\subset \\mathbb{R}^n$ be a convex\n", + "set. Assume that the function $f: X \\rightarrow \\mathbb{R}$ is\n", + "continuous, then $f$ is said to be convex if $f(tx_1 + (1-t)x_2) \\leq tf(x_1) + (1-t)f(x_2)$\n", + "for all $x_1, x_2 \\in X$ and for all $t \\in [0,1]$.\n", + "If $\\leq$ is replaced with a strict inequaltiy in the\n", + "definition, we demand $x_1 \\neq x_2$ and $t\\in(0,1)$ then $f$ is said\n", + "to be strictly convex. For a single variable function, convexity means\n", + "that if you draw a straight line connecting $f(x_1)$ and $f(x_2)$, the\n", + "value of the function on the interval $[x_1,x_2]$ is always below the\n", + "line as illustrated below." ] }, { "cell_type": "markdown", - "id": "73adde3c", + "id": "e02d1dac", "metadata": { "editable": true }, @@ -712,14 +725,16 @@ "In the following we state first and second-order conditions which\n", "ensures convexity of a function $f$. We write $D_f$ to denote the\n", "domain of $f$, i.e the subset of $R^n$ where $f$ is defined. For more\n", - "details and proofs we refer to: [S. Boyd and L. Vandenberghe. Convex Optimization. Cambridge University Press](http://stanford.edu/boyd/cvxbook/, 2004).\n", + "details and proofs we refer to: [S. Boyd and L. Vandenberghe. Convex Optimization. Cambridge University Press](http://stanford.edu/boyd/cvxbook/).\n", "\n", "**First order condition.**\n", "\n", "Suppose $f$ is differentiable (i.e $\\nabla f(x)$ is well defined for\n", "all $x$ in the domain of $f$). Then $f$ is convex if and only if $D_f$\n", - "is a convex set and $$f(y) \\geq f(x) + \\nabla f(x)^T (y-x) $$ holds\n", - "for all $x,y \\in D_f$. This condition means that for a convex function\n", + "is a convex set and $f(y) \\geq f(x) + \\nabla f(x)^T (y-x)$ holds\n", + "for all $x,y \\in D_f$.\n", + "\n", + "This condition means that for a convex function\n", "the first order Taylor expansion (right hand side above) at any point\n", "a global under estimator of the function. To convince yourself you can\n", "make a drawing of $f(x) = x^2+1$ and draw the tangent line to $f(x)$ and\n", @@ -739,7 +754,7 @@ }, { "cell_type": "markdown", - "id": "9f9ab5ff", + "id": "d9a72971", "metadata": { "editable": true }, @@ -767,7 +782,7 @@ }, { "cell_type": "markdown", - "id": "0b2a482b", + "id": "c3e744c2", "metadata": { "editable": true }, @@ -797,7 +812,7 @@ }, { "cell_type": "markdown", - "id": "6566ee55", + "id": "2732cf8b", "metadata": { "editable": true }, @@ -817,7 +832,7 @@ }, { "cell_type": "markdown", - "id": "c2e30cc1", + "id": "1b14ba6d", "metadata": { "editable": true }, @@ -829,7 +844,7 @@ }, { "cell_type": "markdown", - "id": "5012b398", + "id": "5f271cd8", "metadata": { "editable": true }, @@ -839,7 +854,7 @@ }, { "cell_type": "markdown", - "id": "ca65d9a9", + "id": "eb0ce7c8", "metadata": { "editable": true }, @@ -851,7 +866,7 @@ }, { "cell_type": "markdown", - "id": "ec9323dd", + "id": "3482f635", "metadata": { "editable": true }, @@ -863,7 +878,7 @@ }, { "cell_type": "markdown", - "id": "5caf0f7f", + "id": "0c88f8d2", "metadata": { "editable": true }, @@ -875,7 +890,7 @@ }, { "cell_type": "markdown", - "id": "07734ce6", + "id": "fe152e73", "metadata": { "editable": true }, @@ -887,7 +902,7 @@ }, { "cell_type": "markdown", - "id": "468dcb52", + "id": "740c5860", "metadata": { "editable": true }, @@ -898,7 +913,7 @@ }, { "cell_type": "markdown", - "id": "b63a89ae", + "id": "477da242", "metadata": { "editable": true }, @@ -911,7 +926,7 @@ }, { "cell_type": "markdown", - "id": "56a122a3", + "id": "7e2169e6", "metadata": { "editable": true }, @@ -923,7 +938,7 @@ }, { "cell_type": "markdown", - "id": "3256eb29", + "id": "e513c1c5", "metadata": { "editable": true }, @@ -933,7 +948,7 @@ }, { "cell_type": "markdown", - "id": "b6bab858", + "id": "a4a4f67c", "metadata": { "editable": true }, @@ -945,7 +960,7 @@ }, { "cell_type": "markdown", - "id": "7ff39e96", + "id": "96138e83", "metadata": { "editable": true }, @@ -955,7 +970,7 @@ }, { "cell_type": "markdown", - "id": "6678fce9", + "id": "f843d9f8", "metadata": { "editable": true }, @@ -966,7 +981,7 @@ }, { "cell_type": "markdown", - "id": "853eb11f", + "id": "5702234f", "metadata": { "editable": true }, @@ -978,7 +993,7 @@ }, { "cell_type": "markdown", - "id": "7c229917", + "id": "682a415f", "metadata": { "editable": true }, @@ -990,7 +1005,7 @@ }, { "cell_type": "markdown", - "id": "5c8f310a", + "id": "302ac54a", "metadata": { "editable": true }, @@ -1002,7 +1017,7 @@ }, { "cell_type": "markdown", - "id": "f8a8c317", + "id": "dd2cbeb1", "metadata": { "editable": true }, @@ -1013,7 +1028,7 @@ }, { "cell_type": "markdown", - "id": "49b64ed0", + "id": "f280370e", "metadata": { "editable": true }, @@ -1024,7 +1039,7 @@ }, { "cell_type": "markdown", - "id": "857ee939", + "id": "9702a162", "metadata": { "editable": true }, @@ -1036,7 +1051,7 @@ }, { "cell_type": "markdown", - "id": "7e5611fa", + "id": "3a1de3f0", "metadata": { "editable": true }, @@ -1046,7 +1061,7 @@ }, { "cell_type": "markdown", - "id": "384d5aa2", + "id": "4fa494ea", "metadata": { "editable": true }, @@ -1058,7 +1073,7 @@ }, { "cell_type": "markdown", - "id": "0ce677be", + "id": "a4f12308", "metadata": { "editable": true }, @@ -1068,7 +1083,7 @@ }, { "cell_type": "markdown", - "id": "e97f9044", + "id": "df770c35", "metadata": { "editable": true }, @@ -1080,7 +1095,7 @@ }, { "cell_type": "markdown", - "id": "d7fbeb68", + "id": "b1a30174", "metadata": { "editable": true }, @@ -1090,7 +1105,7 @@ }, { "cell_type": "markdown", - "id": "293c09b1", + "id": "6f816a49", "metadata": { "editable": true }, @@ -1102,7 +1117,7 @@ }, { "cell_type": "markdown", - "id": "af88e065", + "id": "4e5cd41c", "metadata": { "editable": true }, @@ -1112,7 +1127,7 @@ }, { "cell_type": "markdown", - "id": "2757e302", + "id": "91f972cb", "metadata": { "editable": true }, @@ -1124,7 +1139,7 @@ }, { "cell_type": "markdown", - "id": "87aab66b", + "id": "97064af3", "metadata": { "editable": true }, @@ -1135,7 +1150,7 @@ { "cell_type": "code", "execution_count": 1, - "id": "a0e20ff7", + "id": "5af74f1d", "metadata": { "collapsed": false, "editable": true @@ -1168,7 +1183,7 @@ }, { "cell_type": "markdown", - "id": "c01b471a", + "id": "db2d4ba3", "metadata": { "editable": true }, @@ -1179,7 +1194,7 @@ { "cell_type": "code", "execution_count": 2, - "id": "5b835c85", + "id": "dbba72eb", "metadata": { "collapsed": false, "editable": true @@ -1193,7 +1208,7 @@ }, { "cell_type": "markdown", - "id": "d6a3c121", + "id": "8290c8f1", "metadata": { "editable": true }, @@ -1204,7 +1219,7 @@ { "cell_type": "code", "execution_count": 3, - "id": "19e1d73c", + "id": "ce55e78a", "metadata": { "collapsed": false, "editable": true @@ -1217,7 +1232,7 @@ }, { "cell_type": "markdown", - "id": "9f7b2dfc", + "id": "0fa3681b", "metadata": { "editable": true }, @@ -1228,7 +1243,7 @@ { "cell_type": "code", "execution_count": 4, - "id": "7d8247e6", + "id": "a5aee074", "metadata": { "collapsed": false, "editable": true @@ -1246,7 +1261,7 @@ }, { "cell_type": "markdown", - "id": "c44006da", + "id": "8f8ed4d2", "metadata": { "editable": true }, @@ -1257,7 +1272,7 @@ { "cell_type": "code", "execution_count": 5, - "id": "bb8a0fd8", + "id": "30855606", "metadata": { "collapsed": false, "editable": true @@ -1272,7 +1287,7 @@ }, { "cell_type": "markdown", - "id": "3d3c98f0", + "id": "fcd5a0c8", "metadata": { "editable": true }, @@ -1282,7 +1297,7 @@ }, { "cell_type": "markdown", - "id": "29e5e792", + "id": "acdc3658", "metadata": { "editable": true }, @@ -1296,7 +1311,7 @@ }, { "cell_type": "markdown", - "id": "2b0e0db3", + "id": "7e07632f", "metadata": { "editable": true }, @@ -1308,7 +1323,7 @@ }, { "cell_type": "markdown", - "id": "401dd643", + "id": "356d5fe1", "metadata": { "editable": true }, @@ -1319,7 +1334,7 @@ }, { "cell_type": "markdown", - "id": "bc29d596", + "id": "2033af61", "metadata": { "editable": true }, @@ -1331,7 +1346,7 @@ }, { "cell_type": "markdown", - "id": "c1b7adb4", + "id": "0a85c783", "metadata": { "editable": true }, @@ -1342,7 +1357,7 @@ }, { "cell_type": "markdown", - "id": "18924232", + "id": "6c19d77d", "metadata": { "editable": true }, @@ -1353,7 +1368,7 @@ }, { "cell_type": "markdown", - "id": "1764ac31", + "id": "f42364fb", "metadata": { "editable": true }, @@ -1365,7 +1380,7 @@ }, { "cell_type": "markdown", - "id": "379d5862", + "id": "c841e7d3", "metadata": { "editable": true }, @@ -1375,7 +1390,7 @@ }, { "cell_type": "markdown", - "id": "9587d8bf", + "id": "297492ba", "metadata": { "editable": true }, @@ -1387,7 +1402,7 @@ }, { "cell_type": "markdown", - "id": "4c3d0bfb", + "id": "4963a2d8", "metadata": { "editable": true }, @@ -1399,7 +1414,7 @@ }, { "cell_type": "markdown", - "id": "4079ca1a", + "id": "4a39d88b", "metadata": { "editable": true }, @@ -1411,7 +1426,7 @@ }, { "cell_type": "markdown", - "id": "e5b487a5", + "id": "83a86148", "metadata": { "editable": true }, @@ -1423,7 +1438,7 @@ }, { "cell_type": "markdown", - "id": "b623d7f7", + "id": "c691f06b", "metadata": { "editable": true }, @@ -1434,7 +1449,7 @@ }, { "cell_type": "markdown", - "id": "8520c560", + "id": "d4df90e8", "metadata": { "editable": true }, @@ -1446,7 +1461,7 @@ }, { "cell_type": "markdown", - "id": "52575016", + "id": "bf3217ad", "metadata": { "editable": true }, @@ -1456,7 +1471,7 @@ }, { "cell_type": "markdown", - "id": "1b8a85bd", + "id": "b43b4b20", "metadata": { "editable": true }, @@ -1468,7 +1483,7 @@ }, { "cell_type": "markdown", - "id": "e53b0f45", + "id": "d82bb554", "metadata": { "editable": true }, @@ -1478,7 +1493,7 @@ }, { "cell_type": "markdown", - "id": "2238e15f", + "id": "dde5ed03", "metadata": { "editable": true }, @@ -1490,7 +1505,7 @@ }, { "cell_type": "markdown", - "id": "f00e8864", + "id": "65ecffe4", "metadata": { "editable": true }, @@ -1510,7 +1525,7 @@ }, { "cell_type": "markdown", - "id": "7a17895d", + "id": "65a1b4c0", "metadata": { "editable": true }, @@ -1522,7 +1537,7 @@ }, { "cell_type": "markdown", - "id": "d4bafcb3", + "id": "41dec06b", "metadata": { "editable": true }, @@ -1532,7 +1547,7 @@ }, { "cell_type": "markdown", - "id": "78a7d2c3", + "id": "7d1da8a1", "metadata": { "editable": true }, @@ -1544,7 +1559,7 @@ }, { "cell_type": "markdown", - "id": "e3192cbf", + "id": "c4850b28", "metadata": { "editable": true }, @@ -1554,7 +1569,7 @@ }, { "cell_type": "markdown", - "id": "0d99ed55", + "id": "ff91db7e", "metadata": { "editable": true }, @@ -1565,7 +1580,7 @@ }, { "cell_type": "markdown", - "id": "b9653ede", + "id": "a8ab3c6e", "metadata": { "editable": true }, @@ -1577,7 +1592,7 @@ }, { "cell_type": "markdown", - "id": "78441105", + "id": "e30ea38e", "metadata": { "editable": true }, @@ -1589,7 +1604,7 @@ }, { "cell_type": "markdown", - "id": "317355d2", + "id": "ede52cdd", "metadata": { "editable": true }, @@ -1601,7 +1616,7 @@ }, { "cell_type": "markdown", - "id": "9bb15157", + "id": "3cdf6ffd", "metadata": { "editable": true }, @@ -1614,7 +1629,7 @@ }, { "cell_type": "markdown", - "id": "ac584971", + "id": "d66ac756", "metadata": { "editable": true }, @@ -1625,7 +1640,7 @@ }, { "cell_type": "markdown", - "id": "911f1dfa", + "id": "59b7a9f5", "metadata": { "editable": true }, @@ -1637,7 +1652,7 @@ }, { "cell_type": "markdown", - "id": "d1472568", + "id": "769980be", "metadata": { "editable": true }, @@ -1653,7 +1668,7 @@ }, { "cell_type": "markdown", - "id": "c79708e8", + "id": "3ae7691f", "metadata": { "editable": true }, @@ -1665,7 +1680,7 @@ }, { "cell_type": "markdown", - "id": "535d3e73", + "id": "9cf530dc", "metadata": { "editable": true }, @@ -1676,7 +1691,7 @@ }, { "cell_type": "markdown", - "id": "ad718f62", + "id": "6398a5d7", "metadata": { "editable": true }, @@ -1688,7 +1703,7 @@ }, { "cell_type": "markdown", - "id": "d0a90fa5", + "id": "b64c8282", "metadata": { "editable": true }, @@ -1698,7 +1713,7 @@ }, { "cell_type": "markdown", - "id": "860e9217", + "id": "4b9bcf21", "metadata": { "editable": true }, @@ -1710,7 +1725,7 @@ }, { "cell_type": "markdown", - "id": "6d8a72c8", + "id": "6d15e4b9", "metadata": { "editable": true }, @@ -1720,7 +1735,7 @@ }, { "cell_type": "markdown", - "id": "746e6fc0", + "id": "592ac2b7", "metadata": { "editable": true }, @@ -1732,7 +1747,7 @@ }, { "cell_type": "markdown", - "id": "f76c0e69", + "id": "c54989ac", "metadata": { "editable": true }, @@ -1742,7 +1757,7 @@ }, { "cell_type": "markdown", - "id": "9aee35ca", + "id": "87adadc2", "metadata": { "editable": true }, @@ -1754,7 +1769,7 @@ }, { "cell_type": "markdown", - "id": "ce9ce258", + "id": "00250e67", "metadata": { "editable": true }, @@ -1778,7 +1793,7 @@ { "cell_type": "code", "execution_count": 6, - "id": "f902a0f2", + "id": "47a98c7c", "metadata": { "collapsed": false, "editable": true @@ -1791,7 +1806,7 @@ }, { "cell_type": "markdown", - "id": "36d883b2", + "id": "e3007f3b", "metadata": { "editable": true }, @@ -1802,7 +1817,7 @@ }, { "cell_type": "markdown", - "id": "cde21ef1", + "id": "c5b7179d", "metadata": { "editable": true }, @@ -1814,7 +1829,7 @@ }, { "cell_type": "markdown", - "id": "f2a021d3", + "id": "8a4d63b4", "metadata": { "editable": true }, @@ -1824,7 +1839,7 @@ }, { "cell_type": "markdown", - "id": "ea0a91e4", + "id": "206c9402", "metadata": { "editable": true }, @@ -1836,7 +1851,7 @@ }, { "cell_type": "markdown", - "id": "854f3ebb", + "id": "939b3b78", "metadata": { "editable": true }, @@ -1850,7 +1865,7 @@ }, { "cell_type": "markdown", - "id": "dd282d2d", + "id": "b867af05", "metadata": { "editable": true }, @@ -1866,7 +1881,7 @@ }, { "cell_type": "markdown", - "id": "fd562029", + "id": "76738c60", "metadata": { "editable": true }, @@ -1876,7 +1891,7 @@ }, { "cell_type": "markdown", - "id": "25369bc3", + "id": "c01273f5", "metadata": { "editable": true }, @@ -1888,7 +1903,7 @@ }, { "cell_type": "markdown", - "id": "a222f3ea", + "id": "5d680787", "metadata": { "editable": true }, @@ -1898,7 +1913,7 @@ }, { "cell_type": "markdown", - "id": "1d5fb6f1", + "id": "2ef9ff3b", "metadata": { "editable": true }, @@ -1910,7 +1925,7 @@ }, { "cell_type": "markdown", - "id": "eab2df73", + "id": "e5a81fba", "metadata": { "editable": true }, @@ -1924,7 +1939,7 @@ }, { "cell_type": "markdown", - "id": "daee1165", + "id": "b7298ace", "metadata": { "editable": true }, @@ -1934,7 +1949,7 @@ }, { "cell_type": "markdown", - "id": "f2c6f5cc", + "id": "64cfb75f", "metadata": { "editable": true }, @@ -1945,7 +1960,7 @@ }, { "cell_type": "markdown", - "id": "ecce0d08", + "id": "99503e16", "metadata": { "editable": true }, @@ -1960,7 +1975,7 @@ }, { "cell_type": "markdown", - "id": "c4308e5f", + "id": "4a567780", "metadata": { "editable": true }, @@ -1970,7 +1985,7 @@ }, { "cell_type": "markdown", - "id": "4ee64b17", + "id": "22c576da", "metadata": { "editable": true }, @@ -1982,7 +1997,7 @@ }, { "cell_type": "markdown", - "id": "57e8db33", + "id": "44a99f62", "metadata": { "editable": true }, @@ -1994,7 +2009,7 @@ }, { "cell_type": "markdown", - "id": "c42e4032", + "id": "7021c749", "metadata": { "editable": true }, @@ -2009,7 +2024,7 @@ }, { "cell_type": "markdown", - "id": "4c430cd3", + "id": "6044e7a8", "metadata": { "editable": true }, @@ -2022,7 +2037,7 @@ { "cell_type": "code", "execution_count": 7, - "id": "9ac6096f", + "id": "72003ff9", "metadata": { "collapsed": false, "editable": true @@ -2079,7 +2094,7 @@ }, { "cell_type": "markdown", - "id": "df783e1d", + "id": "01fdfcaf", "metadata": { "editable": true }, @@ -2090,7 +2105,7 @@ { "cell_type": "code", "execution_count": 8, - "id": "98f08f24", + "id": "d16ddbdc", "metadata": { "collapsed": false, "editable": true @@ -2117,7 +2132,7 @@ }, { "cell_type": "markdown", - "id": "50a5ab0d", + "id": "08aaf479", "metadata": { "editable": true }, @@ -2129,7 +2144,7 @@ }, { "cell_type": "markdown", - "id": "b35293d4", + "id": "0aa5045f", "metadata": { "editable": true }, @@ -2141,7 +2156,7 @@ }, { "cell_type": "markdown", - "id": "fed491ee", + "id": "6474d14b", "metadata": { "editable": true }, @@ -2151,7 +2166,7 @@ }, { "cell_type": "markdown", - "id": "a0b4c94e", + "id": "9335b39d", "metadata": { "editable": true }, @@ -2165,7 +2180,7 @@ }, { "cell_type": "markdown", - "id": "e4f02dce", + "id": "0680a59f", "metadata": { "editable": true }, @@ -2175,7 +2190,7 @@ }, { "cell_type": "markdown", - "id": "b9f297ff", + "id": "de5afdeb", "metadata": { "editable": true }, @@ -2187,7 +2202,7 @@ }, { "cell_type": "markdown", - "id": "4e1caf44", + "id": "0042d7e6", "metadata": { "editable": true }, @@ -2198,7 +2213,7 @@ }, { "cell_type": "markdown", - "id": "a046daea", + "id": "02cf311f", "metadata": { "editable": true }, @@ -2213,7 +2228,7 @@ }, { "cell_type": "markdown", - "id": "02f05574", + "id": "3dbc50e6", "metadata": { "editable": true }, @@ -2227,7 +2242,7 @@ }, { "cell_type": "markdown", - "id": "45484749", + "id": "437e17bc", "metadata": { "editable": true }, @@ -2238,7 +2253,7 @@ { "cell_type": "code", "execution_count": 9, - "id": "9973cd20", + "id": "f11ee927", "metadata": { "collapsed": false, "editable": true @@ -2299,7 +2314,7 @@ }, { "cell_type": "markdown", - "id": "1836d4ef", + "id": "c06cf31f", "metadata": { "editable": true }, @@ -2321,7 +2336,7 @@ }, { "cell_type": "markdown", - "id": "88975d3d", + "id": "870586cd", "metadata": { "editable": true }, @@ -2334,7 +2349,7 @@ { "cell_type": "code", "execution_count": 10, - "id": "56f415e0", + "id": "24517bb5", "metadata": { "collapsed": false, "editable": true @@ -2400,7 +2415,7 @@ }, { "cell_type": "markdown", - "id": "d3343584", + "id": "57647429", "metadata": { "editable": true }, @@ -2411,7 +2426,7 @@ { "cell_type": "code", "execution_count": 11, - "id": "ff1e3778", + "id": "b8365c93", "metadata": { "collapsed": false, "editable": true @@ -2485,7 +2500,7 @@ }, { "cell_type": "markdown", - "id": "c1d70995", + "id": "3c5b105d", "metadata": { "editable": true }, @@ -2497,7 +2512,7 @@ }, { "cell_type": "markdown", - "id": "930a5be8", + "id": "e78e4fcf", "metadata": { "editable": true }, @@ -2518,7 +2533,7 @@ }, { "cell_type": "markdown", - "id": "0a7fb7ef", + "id": "9e856c0b", "metadata": { "editable": true }, @@ -2550,7 +2565,7 @@ }, { "cell_type": "markdown", - "id": "dbff87b0", + "id": "8400c2e5", "metadata": { "editable": true }, @@ -2567,7 +2582,7 @@ }, { "cell_type": "markdown", - "id": "cd292df5", + "id": "d0ceff52", "metadata": { "editable": true }, @@ -2580,7 +2595,7 @@ }, { "cell_type": "markdown", - "id": "1b2ffa4e", + "id": "562ca1d7", "metadata": { "editable": true }, @@ -2593,7 +2608,7 @@ }, { "cell_type": "markdown", - "id": "d0abe4b0", + "id": "ffea7df9", "metadata": { "editable": true }, @@ -2606,7 +2621,7 @@ }, { "cell_type": "markdown", - "id": "65c15c60", + "id": "20f1bd07", "metadata": { "editable": true }, @@ -2620,7 +2635,7 @@ }, { "cell_type": "markdown", - "id": "460354c0", + "id": "4589bb1b", "metadata": { "editable": true }, @@ -2642,7 +2657,7 @@ }, { "cell_type": "markdown", - "id": "c2a5dfcd", + "id": "0df2146b", "metadata": { "editable": true }, @@ -2657,7 +2672,7 @@ }, { "cell_type": "markdown", - "id": "eeeb0fe8", + "id": "890e6746", "metadata": { "editable": true }, @@ -2669,7 +2684,7 @@ }, { "cell_type": "markdown", - "id": "2b49c741", + "id": "b6e42059", "metadata": { "editable": true }, @@ -2682,7 +2697,7 @@ }, { "cell_type": "markdown", - "id": "8ba7b9be", + "id": "9dd3abbf", "metadata": { "editable": true }, @@ -2696,7 +2711,7 @@ }, { "cell_type": "markdown", - "id": "50da33c0", + "id": "97279f92", "metadata": { "editable": true }, @@ -2707,7 +2722,7 @@ { "cell_type": "code", "execution_count": 12, - "id": "31bd6a24", + "id": "cb0c6322", "metadata": { "collapsed": false, "editable": true @@ -2732,7 +2747,7 @@ }, { "cell_type": "markdown", - "id": "0deb8111", + "id": "c0868aae", "metadata": { "editable": true }, @@ -2748,7 +2763,7 @@ }, { "cell_type": "markdown", - "id": "16d54f02", + "id": "1e17bb0f", "metadata": { "editable": true }, @@ -2769,7 +2784,7 @@ }, { "cell_type": "markdown", - "id": "b300d06b", + "id": "f050ca70", "metadata": { "editable": true }, @@ -2789,7 +2804,7 @@ }, { "cell_type": "markdown", - "id": "6bc7778d", + "id": "6a900f78", "metadata": { "editable": true }, @@ -2808,7 +2823,7 @@ { "cell_type": "code", "execution_count": 13, - "id": "a60fe5bd", + "id": "1324db42", "metadata": { "collapsed": false, "editable": true @@ -2843,7 +2858,7 @@ }, { "cell_type": "markdown", - "id": "2192721f", + "id": "0c365408", "metadata": { "editable": true }, @@ -2856,7 +2871,7 @@ { "cell_type": "code", "execution_count": 14, - "id": "e404f2c5", + "id": "79417e81", "metadata": { "collapsed": false, "editable": true @@ -2933,7 +2948,7 @@ }, { "cell_type": "markdown", - "id": "fffdbb91", + "id": "dd16fd67", "metadata": { "editable": true }, @@ -2948,7 +2963,7 @@ }, { "cell_type": "markdown", - "id": "8cce7a0e", + "id": "2bbf7fbd", "metadata": { "editable": true }, @@ -2963,7 +2978,7 @@ }, { "cell_type": "markdown", - "id": "3154c365", + "id": "d4aa4448", "metadata": { "editable": true }, @@ -2975,7 +2990,7 @@ }, { "cell_type": "markdown", - "id": "a2a9ceca", + "id": "fdcd258f", "metadata": { "editable": true }, @@ -2993,7 +3008,7 @@ }, { "cell_type": "markdown", - "id": "3374c700", + "id": "52ec5bfb", "metadata": { "editable": true }, @@ -3012,7 +3027,7 @@ }, { "cell_type": "markdown", - "id": "893d86fe", + "id": "38004062", "metadata": { "editable": true }, @@ -3024,7 +3039,7 @@ }, { "cell_type": "markdown", - "id": "ca2449e8", + "id": "9d07c567", "metadata": { "editable": true }, @@ -3034,7 +3049,7 @@ }, { "cell_type": "markdown", - "id": "3cbd4adb", + "id": "4dbba8bc", "metadata": { "editable": true }, @@ -3050,7 +3065,7 @@ }, { "cell_type": "markdown", - "id": "e3f07cbc", + "id": "dab76529", "metadata": { "editable": true }, @@ -3062,7 +3077,7 @@ }, { "cell_type": "markdown", - "id": "99f2ac0f", + "id": "6e049d15", "metadata": { "editable": true }, @@ -3072,7 +3087,7 @@ }, { "cell_type": "markdown", - "id": "83336244", + "id": "5079f465", "metadata": { "editable": true }, @@ -3084,7 +3099,7 @@ }, { "cell_type": "markdown", - "id": "efd3e708", + "id": "51c2ed45", "metadata": { "editable": true }, @@ -3094,7 +3109,7 @@ }, { "cell_type": "markdown", - "id": "6c24d65c", + "id": "7e8f7b16", "metadata": { "editable": true }, @@ -3106,7 +3121,7 @@ }, { "cell_type": "markdown", - "id": "853d885b", + "id": "ae0505aa", "metadata": { "editable": true }, @@ -3122,7 +3137,7 @@ }, { "cell_type": "markdown", - "id": "5ab54645", + "id": "9e7f520b", "metadata": { "editable": true }, @@ -3134,7 +3149,7 @@ }, { "cell_type": "markdown", - "id": "90f1503b", + "id": "5c0aa1f6", "metadata": { "editable": true }, @@ -3167,7 +3182,7 @@ }, { "cell_type": "markdown", - "id": "d496d988", + "id": "991c2c15", "metadata": { "editable": true }, @@ -3179,7 +3194,7 @@ }, { "cell_type": "markdown", - "id": "258ca1e6", + "id": "c643afb5", "metadata": { "editable": true }, @@ -3197,7 +3212,7 @@ }, { "cell_type": "markdown", - "id": "84278058", + "id": "ac4a060d", "metadata": { "editable": true }, @@ -3207,7 +3222,7 @@ }, { "cell_type": "markdown", - "id": "feaee2f4", + "id": "37584d4d", "metadata": { "editable": true }, @@ -3238,7 +3253,7 @@ }, { "cell_type": "markdown", - "id": "218edcf1", + "id": "0e9c907f", "metadata": { "editable": true }, @@ -3253,7 +3268,7 @@ }, { "cell_type": "markdown", - "id": "18dbc91c", + "id": "cb4567f1", "metadata": { "editable": true }, @@ -3271,7 +3286,7 @@ }, { "cell_type": "markdown", - "id": "0bfcf74a", + "id": "71805d3d", "metadata": { "editable": true }, @@ -3283,7 +3298,7 @@ }, { "cell_type": "markdown", - "id": "5fedd6f0", + "id": "09794996", "metadata": { "editable": true }, @@ -3295,7 +3310,7 @@ }, { "cell_type": "markdown", - "id": "b210b2a4", + "id": "aeb48f66", "metadata": { "editable": true }, @@ -3313,7 +3328,7 @@ }, { "cell_type": "markdown", - "id": "34ffacbb", + "id": "68e08134", "metadata": { "editable": true }, @@ -3342,7 +3357,7 @@ }, { "cell_type": "markdown", - "id": "cd03375d", + "id": "69308397", "metadata": { "editable": true }, @@ -3360,7 +3375,7 @@ }, { "cell_type": "markdown", - "id": "0db5d6e0", + "id": "d23ab794", "metadata": { "editable": true }, @@ -3372,7 +3387,7 @@ }, { "cell_type": "markdown", - "id": "84c709d9", + "id": "c4cef70b", "metadata": { "editable": true }, @@ -3384,7 +3399,7 @@ }, { "cell_type": "markdown", - "id": "e4e47496", + "id": "6aebd1b5", "metadata": { "editable": true }, @@ -3396,7 +3411,7 @@ }, { "cell_type": "markdown", - "id": "164f27df", + "id": "c43fe267", "metadata": { "editable": true }, @@ -3408,7 +3423,7 @@ }, { "cell_type": "markdown", - "id": "591f4833", + "id": "9ae56692", "metadata": { "editable": true }, @@ -3420,7 +3435,7 @@ }, { "cell_type": "markdown", - "id": "e2127e8a", + "id": "784ba00e", "metadata": { "editable": true }, @@ -3437,7 +3452,7 @@ }, { "cell_type": "markdown", - "id": "5cef8b84", + "id": "a187dfb2", "metadata": { "editable": true }, @@ -3456,7 +3471,7 @@ }, { "cell_type": "markdown", - "id": "505c8905", + "id": "832cc99c", "metadata": { "editable": true }, @@ -3468,7 +3483,7 @@ }, { "cell_type": "markdown", - "id": "ca96ddec", + "id": "b1a24342", "metadata": { "editable": true }, @@ -3482,7 +3497,7 @@ }, { "cell_type": "markdown", - "id": "fa011176", + "id": "8a97509f", "metadata": { "editable": true }, @@ -3502,7 +3517,7 @@ }, { "cell_type": "markdown", - "id": "b91c4543", + "id": "631a2aa8", "metadata": { "editable": true }, @@ -3540,7 +3555,7 @@ }, { "cell_type": "markdown", - "id": "f13065e5", + "id": "472b23f2", "metadata": { "editable": true }, @@ -3552,7 +3567,7 @@ }, { "cell_type": "markdown", - "id": "22937f5e", + "id": "1c91c90f", "metadata": { "editable": true }, @@ -3562,7 +3577,7 @@ }, { "cell_type": "markdown", - "id": "e1459fe1", + "id": "a85c6aab", "metadata": { "editable": true }, @@ -3574,7 +3589,7 @@ }, { "cell_type": "markdown", - "id": "441a109a", + "id": "89a0bdbb", "metadata": { "editable": true }, @@ -3585,7 +3600,7 @@ { "cell_type": "code", "execution_count": 15, - "id": "9043abae", + "id": "6fe48a50", "metadata": { "collapsed": false, "editable": true @@ -3630,7 +3645,7 @@ }, { "cell_type": "markdown", - "id": "787d5d78", + "id": "cab7d753", "metadata": { "editable": true }, @@ -3647,7 +3662,7 @@ { "cell_type": "code", "execution_count": 16, - "id": "6a677479", + "id": "ca4d6b32", "metadata": { "collapsed": false, "editable": true @@ -3675,7 +3690,7 @@ }, { "cell_type": "markdown", - "id": "f94a1d32", + "id": "4a748513", "metadata": { "editable": true }, @@ -3690,7 +3705,7 @@ { "cell_type": "code", "execution_count": 17, - "id": "c0eb89fd", + "id": "f235cd43", "metadata": { "collapsed": false, "editable": true @@ -3734,7 +3749,7 @@ }, { "cell_type": "markdown", - "id": "05d7497d", + "id": "5d8df033", "metadata": { "editable": true }, @@ -3744,7 +3759,7 @@ }, { "cell_type": "markdown", - "id": "24e3ca02", + "id": "2f7de144", "metadata": { "editable": true }, @@ -3755,7 +3770,7 @@ { "cell_type": "code", "execution_count": 18, - "id": "a616e696", + "id": "a2ed8fd6", "metadata": { "collapsed": false, "editable": true @@ -3783,7 +3798,7 @@ }, { "cell_type": "markdown", - "id": "f695da56", + "id": "1f633736", "metadata": { "editable": true }, @@ -3798,7 +3813,7 @@ }, { "cell_type": "markdown", - "id": "5ac073ee", + "id": "a73225d4", "metadata": { "editable": true }, @@ -3809,7 +3824,7 @@ { "cell_type": "code", "execution_count": 19, - "id": "efc8906e", + "id": "39059d20", "metadata": { "collapsed": false, "editable": true @@ -3837,7 +3852,7 @@ }, { "cell_type": "markdown", - "id": "7c587e8f", + "id": "d147b69e", "metadata": { "editable": true }, @@ -3848,7 +3863,7 @@ { "cell_type": "code", "execution_count": 20, - "id": "6428be1f", + "id": "90a2d143", "metadata": { "collapsed": false, "editable": true @@ -3873,7 +3888,7 @@ }, { "cell_type": "markdown", - "id": "8e1de777", + "id": "6655faec", "metadata": { "editable": true }, @@ -3884,7 +3899,7 @@ { "cell_type": "code", "execution_count": 21, - "id": "770ff6aa", + "id": "639ea2a9", "metadata": { "collapsed": false, "editable": true @@ -3920,7 +3935,7 @@ { "cell_type": "code", "execution_count": 22, - "id": "b924cc5d", + "id": "a53c5633", "metadata": { "collapsed": false, "editable": true @@ -3940,7 +3955,7 @@ }, { "cell_type": "markdown", - "id": "f0b0e1e9", + "id": "be5e41d4", "metadata": { "editable": true }, @@ -3951,7 +3966,7 @@ { "cell_type": "code", "execution_count": 23, - "id": "1585ab28", + "id": "786e19d0", "metadata": { "collapsed": false, "editable": true @@ -3989,7 +4004,7 @@ }, { "cell_type": "markdown", - "id": "2718df1a", + "id": "114e7e25", "metadata": { "editable": true }, @@ -3999,7 +4014,7 @@ }, { "cell_type": "markdown", - "id": "d8fa5235", + "id": "24c8ffa6", "metadata": { "editable": true }, @@ -4013,7 +4028,7 @@ { "cell_type": "code", "execution_count": 24, - "id": "196a52d6", + "id": "59f521ac", "metadata": { "collapsed": false, "editable": true @@ -4035,7 +4050,7 @@ }, { "cell_type": "markdown", - "id": "9127a2c5", + "id": "686c34bb", "metadata": { "editable": true }, @@ -4045,7 +4060,7 @@ }, { "cell_type": "markdown", - "id": "2b12ed61", + "id": "9b4cc4f3", "metadata": { "editable": true }, @@ -4056,7 +4071,7 @@ { "cell_type": "code", "execution_count": 25, - "id": "8ced55c8", + "id": "dea954af", "metadata": { "collapsed": false, "editable": true @@ -4078,7 +4093,7 @@ }, { "cell_type": "markdown", - "id": "92ebdc2b", + "id": "9732d039", "metadata": { "editable": true }, @@ -4091,7 +4106,7 @@ { "cell_type": "code", "execution_count": 26, - "id": "276f763e", + "id": "f580c6a1", "metadata": { "collapsed": false, "editable": true @@ -4116,7 +4131,7 @@ }, { "cell_type": "markdown", - "id": "7841ad0b", + "id": "d8714004", "metadata": { "editable": true }, @@ -4128,7 +4143,7 @@ { "cell_type": "code", "execution_count": 27, - "id": "10107989", + "id": "e56cbb47", "metadata": { "collapsed": false, "editable": true @@ -4143,7 +4158,7 @@ }, { "cell_type": "markdown", - "id": "4c1139c0", + "id": "fac1a7da", "metadata": { "editable": true }, @@ -4158,7 +4173,7 @@ { "cell_type": "code", "execution_count": 28, - "id": "3022af88", + "id": "c4c5b9c0", "metadata": { "collapsed": false, "editable": true @@ -4218,7 +4233,7 @@ }, { "cell_type": "markdown", - "id": "04d09021", + "id": "b16d7700", "metadata": { "editable": true }, @@ -4229,7 +4244,7 @@ { "cell_type": "code", "execution_count": 29, - "id": "71bf4b6d", + "id": "7453efe5", "metadata": { "collapsed": false, "editable": true @@ -4293,7 +4308,7 @@ }, { "cell_type": "markdown", - "id": "ad417bba", + "id": "0a417277", "metadata": { "editable": true }, @@ -4304,7 +4319,7 @@ { "cell_type": "code", "execution_count": 30, - "id": "c394bcef", + "id": "1a541fef", "metadata": { "collapsed": false, "editable": true @@ -4353,7 +4368,7 @@ }, { "cell_type": "markdown", - "id": "2e4cf4b5", + "id": "9e937e4f", "metadata": { "editable": true }, @@ -4365,7 +4380,7 @@ { "cell_type": "code", "execution_count": 31, - "id": "47411bcf", + "id": "9afef100", "metadata": { "collapsed": false, "editable": true @@ -4449,7 +4464,7 @@ }, { "cell_type": "markdown", - "id": "f8e30af2", + "id": "2a7e982c", "metadata": { "editable": true }, @@ -4460,7 +4475,7 @@ { "cell_type": "code", "execution_count": 32, - "id": "dd594924", + "id": "91311a17", "metadata": { "collapsed": false, "editable": true @@ -4538,7 +4553,7 @@ }, { "cell_type": "markdown", - "id": "75c4c29d", + "id": "516999d8", "metadata": { "editable": true }, @@ -4549,7 +4564,7 @@ { "cell_type": "code", "execution_count": 33, - "id": "4dd14fc5", + "id": "e8292719", "metadata": { "collapsed": false, "editable": true @@ -4608,7 +4623,7 @@ }, { "cell_type": "markdown", - "id": "be4ce0cd", + "id": "bd67f5cb", "metadata": { "editable": true }, @@ -4618,7 +4633,7 @@ }, { "cell_type": "markdown", - "id": "0b739495", + "id": "eb0d5fd0", "metadata": { "editable": true }, @@ -4629,7 +4644,7 @@ { "cell_type": "code", "execution_count": 34, - "id": "ae87789c", + "id": "d2eb93d1", "metadata": { "collapsed": false, "editable": true @@ -4694,7 +4709,7 @@ }, { "cell_type": "markdown", - "id": "76c8872b", + "id": "669b56c2", "metadata": { "editable": true }, @@ -4705,7 +4720,7 @@ { "cell_type": "code", "execution_count": 35, - "id": "e99dbaa4", + "id": "bb3f553d", "metadata": { "collapsed": false, "editable": true @@ -4775,7 +4790,7 @@ }, { "cell_type": "markdown", - "id": "596df570", + "id": "4e5c58ea", "metadata": { "editable": true }, @@ -4786,7 +4801,7 @@ { "cell_type": "code", "execution_count": 36, - "id": "6693f042", + "id": "1fec659e", "metadata": { "collapsed": false, "editable": true @@ -4830,7 +4845,7 @@ }, { "cell_type": "markdown", - "id": "a40ed853", + "id": "ac14943c", "metadata": { "editable": true }, @@ -4849,7 +4864,7 @@ { "cell_type": "code", "execution_count": 37, - "id": "02f88360", + "id": "57b4e540", "metadata": { "collapsed": false, "editable": true diff --git a/doc/LectureNotes/week40.ipynb b/doc/LectureNotes/week40.ipynb new file mode 100644 index 000000000..70ab38246 --- /dev/null +++ b/doc/LectureNotes/week40.ipynb @@ -0,0 +1,3545 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "c410abdb", + "metadata": { + "editable": true + }, + "source": [ + "\n", + "" + ] + }, + { + "cell_type": "markdown", + "id": "ffdc5797", + "metadata": { + "editable": true + }, + "source": [ + "# Week 40: Gradient descent methods (continued) and start Neural networks\n", + "**Morten Hjorth-Jensen**, Department of Physics, University of Oslo, Norway and Department of Physics and Astronomy and Facility for Rare Ion Beams, Michigan State University, USA\n", + "\n", + "Date: **October 2-6, 2023**" + ] + }, + { + "cell_type": "markdown", + "id": "4bab315b", + "metadata": { + "editable": true + }, + "source": [ + "## Plans for week 40\n", + "\n", + "**Material for the active learning sessions on Tuesday and Wednesday.**\n", + "\n", + " * Work on project 1 and discussions on how to structure your report\n", + "\n", + " * No weekly exercises for week 40, project work only\n", + "\n", + " * [Video on how to write scientific reports recorded during one of the lab sessions](https://youtu.be/tVW1ZDmZnwM)\n", + "\n", + " * A general guideline can be found at .\n", + "\n", + " \n", + "\n", + "**Material for the lecture on Thursday October 5, 2023.**\n", + "\n", + " * Stochastic Gradient descent with examples and automatic differentiation\n", + "\n", + " * Neural Networks, setting up the basic steps, from the simple perceptron model to the multi-layer perceptron model. \n", + "\n", + " * Readings and Videos:\n", + "\n", + " * These lecture notes\n", + "\n", + " * For a good discussion on gradient methods, we would like to recommend Goodfellow et al section 4.3-4.5 and sections 8.3-8.6. We will come back to the latter chapter in our discussion of Neural networks as well.\n", + "\n", + " * [Aurelien Geron's chapter 4 on stochastic gradient descent](https://github.com/CompPhysics/MachineLearning/blob/master/doc/Textbooks/TensorflowML.pdf)\n", + "\n", + " * For neural networks we recommend Goodfellow et al chapter 6.\n", + "\n", + " * [Video on gradient descent](https://www.youtube.com/watch?v=sDv4f4s2SB8)\n", + "\n", + " * [Video on stochastic gradient descent](https://www.youtube.com/watch?v=vMh0zPT0tLI)\n", + "\n", + " * [Neural Networks demystified](https://www.youtube.com/watch?v=bxe2T-V8XRs&list=PLiaHhY2iBX9hdHaRr6b7XevZtgZRa1PoU&ab_channel=WelchLabs)\n", + "\n", + " * [Building Neural Networks from scratch](https://www.youtube.com/watch?v=Wo5dMEP_BbI&list=PLQVvvaa0QuDcjD5BAw2DxE6OF2tius3V3&ab_channel=sentdex)" + ] + }, + { + "cell_type": "markdown", + "id": "1ba76140", + "metadata": { + "editable": true + }, + "source": [ + "## Summary from last week, using gradient descent methods, limitations\n", + "\n", + "* **Gradient descent (GD) finds local minima of our function**. Since the GD algorithm is deterministic, if it converges, it will converge to a local minimum of our cost/loss/risk function. Because in ML we are often dealing with extremely rugged landscapes with many local minima, this can lead to poor performance.\n", + "\n", + "* **GD is sensitive to initial conditions**. One consequence of the local nature of GD is that initial conditions matter. Depending on where one starts, one will end up at a different local minima. Therefore, it is very important to think about how one initializes the training process. This is true for GD as well as more complicated variants of GD.\n", + "\n", + "* **Gradients are computationally expensive to calculate for large datasets**. In many cases in statistics and ML, the cost/loss/risk function is a sum of terms, with one term for each data point. For example, in linear regression, $E \\propto \\sum_{i=1}^n (y_i - \\mathbf{w}^T\\cdot\\mathbf{x}_i)^2$; for logistic regression, the square error is replaced by the cross entropy. To calculate the gradient we have to sum over *all* $n$ data points. Doing this at every GD step becomes extremely computationally expensive. An ingenious solution to this, is to calculate the gradients using small subsets of the data called \"mini batches\". This has the added benefit of introducing stochasticity into our algorithm.\n", + "\n", + "* **GD is very sensitive to choices of learning rates**. GD is extremely sensitive to the choice of learning rates. If the learning rate is very small, the training process take an extremely long time. For larger learning rates, GD can diverge and give poor results. Furthermore, depending on what the local landscape looks like, we have to modify the learning rates to ensure convergence. Ideally, we would *adaptively* choose the learning rates to match the landscape.\n", + "\n", + "* **GD treats all directions in parameter space uniformly.** Another major drawback of GD is that unlike Newton's method, the learning rate for GD is the same in all directions in parameter space. For this reason, the maximum learning rate is set by the behavior of the steepest direction and this can significantly slow down training. Ideally, we would like to take large steps in flat directions and small steps in steep directions. Since we are exploring rugged landscapes where curvatures change, this requires us to keep track of not only the gradient but second derivatives. The ideal scenario would be to calculate the Hessian but this proves to be too computationally expensive. \n", + "\n", + "* GD can take exponential time to escape saddle points, even with random initialization. As we mentioned, GD is extremely sensitive to initial condition since it determines the particular local minimum GD would eventually reach. However, even with a good initialization scheme, through the introduction of randomness, GD can still take exponential time to escape saddle points." + ] + }, + { + "cell_type": "markdown", + "id": "a8b56c00", + "metadata": { + "editable": true + }, + "source": [ + "## Overview video on Stochastic Gradient Descent\n", + "\n", + "[What is Stochastic Gradient Descent](https://www.youtube.com/watch?v=vMh0zPT0tLI&ab_channel=StatQuestwithJoshStarmer)" + ] + }, + { + "cell_type": "markdown", + "id": "eba32497", + "metadata": { + "editable": true + }, + "source": [ + "## Batches and mini-batches\n", + "\n", + "In gradient descent we compute the cost function and its gradient for all data points we have.\n", + "\n", + "In large-scale applications such as the [ILSVRC challenge](https://www.image-net.org/challenges/LSVRC/), the\n", + "training data can have on order of millions of examples. Hence, it\n", + "seems wasteful to compute the full cost function over the entire\n", + "training set in order to perform only a single parameter update. A\n", + "very common approach to addressing this challenge is to compute the\n", + "gradient over batches of the training data. For example, a typical batch could contain some thousand examples from\n", + "an entire training set of several millions. This batch is then used to\n", + "perform a parameter update." + ] + }, + { + "cell_type": "markdown", + "id": "55578599", + "metadata": { + "editable": true + }, + "source": [ + "## Stochastic Gradient Descent (SGD)\n", + "\n", + "In stochastic gradient descent, the extreme case is the case where we\n", + "have only one batch, that is we include the whole data set.\n", + "\n", + "This process is called Stochastic Gradient\n", + "Descent (SGD) (or also sometimes on-line gradient descent). This is\n", + "relatively less common to see because in practice due to vectorized\n", + "code optimizations it can be computationally much more efficient to\n", + "evaluate the gradient for 100 examples, than the gradient for one\n", + "example 100 times. Even though SGD technically refers to using a\n", + "single example at a time to evaluate the gradient, you will hear\n", + "people use the term SGD even when referring to mini-batch gradient\n", + "descent (i.e. mentions of MGD for “Minibatch Gradient Descent”, or BGD\n", + "for “Batch gradient descent” are rare to see), where it is usually\n", + "assumed that mini-batches are used. The size of the mini-batch is a\n", + "hyperparameter but it is not very common to cross-validate or bootstrap it. It is\n", + "usually based on memory constraints (if any), or set to some value,\n", + "e.g. 32, 64 or 128. We use powers of 2 in practice because many\n", + "vectorized operation implementations work faster when their inputs are\n", + "sized in powers of 2.\n", + "\n", + "In our notes with SGD we mean stochastic gradient descent with mini-batches." + ] + }, + { + "cell_type": "markdown", + "id": "140607b7", + "metadata": { + "editable": true + }, + "source": [ + "## Stochastic Gradient Descent\n", + "\n", + "Stochastic gradient descent (SGD) and variants thereof address some of\n", + "the shortcomings of the Gradient descent method discussed above.\n", + "\n", + "The underlying idea of SGD comes from the observation that the cost\n", + "function, which we want to minimize, can almost always be written as a\n", + "sum over $n$ data points $\\{\\mathbf{x}_i\\}_{i=1}^n$," + ] + }, + { + "cell_type": "markdown", + "id": "c1a00332", + "metadata": { + "editable": true + }, + "source": [ + "$$\n", + "C(\\mathbf{\\beta}) = \\sum_{i=1}^n c_i(\\mathbf{x}_i,\n", + "\\mathbf{\\beta}).\n", + "$$" + ] + }, + { + "cell_type": "markdown", + "id": "2c04bdee", + "metadata": { + "editable": true + }, + "source": [ + "## Computation of gradients\n", + "\n", + "This in turn means that the gradient can be\n", + "computed as a sum over $i$-gradients" + ] + }, + { + "cell_type": "markdown", + "id": "087684a4", + "metadata": { + "editable": true + }, + "source": [ + "$$\n", + "\\nabla_\\beta C(\\mathbf{\\beta}) = \\sum_i^n \\nabla_\\beta c_i(\\mathbf{x}_i,\n", + "\\mathbf{\\beta}).\n", + "$$" + ] + }, + { + "cell_type": "markdown", + "id": "e0362df4", + "metadata": { + "editable": true + }, + "source": [ + "Stochasticity/randomness is introduced by only taking the\n", + "gradient on a subset of the data called minibatches. If there are $n$\n", + "data points and the size of each minibatch is $M$, there will be $n/M$\n", + "minibatches. We denote these minibatches by $B_k$ where\n", + "$k=1,\\cdots,n/M$." + ] + }, + { + "cell_type": "markdown", + "id": "24051a4e", + "metadata": { + "editable": true + }, + "source": [ + "## SGD example\n", + "As an example, suppose we have $10$ data points $(\\mathbf{x}_1,\\cdots, \\mathbf{x}_{10})$ \n", + "and we choose to have $M=5$ minibathces,\n", + "then each minibatch contains two data points. In particular we have\n", + "$B_1 = (\\mathbf{x}_1,\\mathbf{x}_2), \\cdots, B_5 =\n", + "(\\mathbf{x}_9,\\mathbf{x}_{10})$. Note that if you choose $M=1$ you\n", + "have only a single batch with all data points and on the other extreme,\n", + "you may choose $M=n$ resulting in a minibatch for each datapoint, i.e\n", + "$B_k = \\mathbf{x}_k$.\n", + "\n", + "The idea is now to approximate the gradient by replacing the sum over\n", + "all data points with a sum over the data points in one the minibatches\n", + "picked at random in each gradient descent step" + ] + }, + { + "cell_type": "markdown", + "id": "7723f927", + "metadata": { + "editable": true + }, + "source": [ + "$$\n", + "\\nabla_{\\beta}\n", + "C(\\mathbf{\\beta}) = \\sum_{i=1}^n \\nabla_\\beta c_i(\\mathbf{x}_i,\n", + "\\mathbf{\\beta}) \\rightarrow \\sum_{i \\in B_k}^n \\nabla_\\beta\n", + "c_i(\\mathbf{x}_i, \\mathbf{\\beta}).\n", + "$$" + ] + }, + { + "cell_type": "markdown", + "id": "59221981", + "metadata": { + "editable": true + }, + "source": [ + "## The gradient step\n", + "\n", + "Thus a gradient descent step now looks like" + ] + }, + { + "cell_type": "markdown", + "id": "a7d27b48", + "metadata": { + "editable": true + }, + "source": [ + "$$\n", + "\\beta_{j+1} = \\beta_j - \\gamma_j \\sum_{i \\in B_k}^n \\nabla_\\beta c_i(\\mathbf{x}_i,\n", + "\\mathbf{\\beta})\n", + "$$" + ] + }, + { + "cell_type": "markdown", + "id": "c7595344", + "metadata": { + "editable": true + }, + "source": [ + "where $k$ is picked at random with equal\n", + "probability from $[1,n/M]$. An iteration over the number of\n", + "minibathces (n/M) is commonly referred to as an epoch. Thus it is\n", + "typical to choose a number of epochs and for each epoch iterate over\n", + "the number of minibatches, as exemplified in the code below." + ] + }, + { + "cell_type": "markdown", + "id": "0d7024b5", + "metadata": { + "editable": true + }, + "source": [ + "## Simple example code" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "0f9dc38b", + "metadata": { + "collapsed": false, + "editable": true + }, + "outputs": [], + "source": [ + "import numpy as np \n", + "\n", + "n = 100 #100 datapoints \n", + "M = 5 #size of each minibatch\n", + "m = int(n/M) #number of minibatches\n", + "n_epochs = 10 #number of epochs\n", + "\n", + "j = 0\n", + "for epoch in range(1,n_epochs+1):\n", + " for i in range(m):\n", + " k = np.random.randint(m) #Pick the k-th minibatch at random\n", + " #Compute the gradient using the data in minibatch Bk\n", + " #Compute new suggestion for \n", + " j += 1" + ] + }, + { + "cell_type": "markdown", + "id": "df447303", + "metadata": { + "editable": true + }, + "source": [ + "Taking the gradient only on a subset of the data has two important\n", + "benefits. First, it introduces randomness which decreases the chance\n", + "that our opmization scheme gets stuck in a local minima. Second, if\n", + "the size of the minibatches are small relative to the number of\n", + "datapoints ($M < n$), the computation of the gradient is much\n", + "cheaper since we sum over the datapoints in the $k-th$ minibatch and not\n", + "all $n$ datapoints." + ] + }, + { + "cell_type": "markdown", + "id": "976aef35", + "metadata": { + "editable": true + }, + "source": [ + "## When do we stop?\n", + "\n", + "A natural question is when do we stop the search for a new minimum?\n", + "One possibility is to compute the full gradient after a given number\n", + "of epochs and check if the norm of the gradient is smaller than some\n", + "threshold and stop if true. However, the condition that the gradient\n", + "is zero is valid also for local minima, so this would only tell us\n", + "that we are close to a local/global minimum. However, we could also\n", + "evaluate the cost function at this point, store the result and\n", + "continue the search. If the test kicks in at a later stage we can\n", + "compare the values of the cost function and keep the $\\beta$ that\n", + "gave the lowest value." + ] + }, + { + "cell_type": "markdown", + "id": "0fab1ae1", + "metadata": { + "editable": true + }, + "source": [ + "## Slightly different approach\n", + "\n", + "Another approach is to let the step length $\\gamma_j$ depend on the\n", + "number of epochs in such a way that it becomes very small after a\n", + "reasonable time such that we do not move at all. Such approaches are\n", + "also called scaling. There are many such ways to [scale the learning\n", + "rate](https://towardsdatascience.com/gradient-descent-the-learning-rate-and-the-importance-of-feature-scaling-6c0b416596e1)\n", + "and [discussions here](https://www.jmlr.org/papers/volume23/20-1258/20-1258.pdf). See\n", + "also\n", + "\n", + "for a discussion of different scaling functions for the learning rate." + ] + }, + { + "cell_type": "markdown", + "id": "2db0116b", + "metadata": { + "editable": true + }, + "source": [ + "## Time decay rate\n", + "\n", + "As an example, let $e = 0,1,2,3,\\cdots$ denote the current epoch and let $t_0, t_1 > 0$ be two fixed numbers. Furthermore, let $t = e \\cdot m + i$ where $m$ is the number of minibatches and $i=0,\\cdots,m-1$. Then the function $$\\gamma_j(t; t_0, t_1) = \\frac{t_0}{t+t_1} $$ goes to zero as the number of epochs gets large. I.e. we start with a step length $\\gamma_j (0; t_0, t_1) = t_0/t_1$ which decays in *time* $t$.\n", + "\n", + "In this way we can fix the number of epochs, compute $\\beta$ and\n", + "evaluate the cost function at the end. Repeating the computation will\n", + "give a different result since the scheme is random by design. Then we\n", + "pick the final $\\beta$ that gives the lowest value of the cost\n", + "function." + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "a9ca6f9a", + "metadata": { + "collapsed": false, + "editable": true + }, + "outputs": [], + "source": [ + "import numpy as np \n", + "\n", + "def step_length(t,t0,t1):\n", + " return t0/(t+t1)\n", + "\n", + "n = 100 #100 datapoints \n", + "M = 5 #size of each minibatch\n", + "m = int(n/M) #number of minibatches\n", + "n_epochs = 500 #number of epochs\n", + "t0 = 1.0\n", + "t1 = 10\n", + "\n", + "gamma_j = t0/t1\n", + "j = 0\n", + "for epoch in range(1,n_epochs+1):\n", + " for i in range(m):\n", + " k = np.random.randint(m) #Pick the k-th minibatch at random\n", + " #Compute the gradient using the data in minibatch Bk\n", + " #Compute new suggestion for beta\n", + " t = epoch*m+i\n", + " gamma_j = step_length(t,t0,t1)\n", + " j += 1\n", + "\n", + "print(\"gamma_j after %d epochs: %g\" % (n_epochs,gamma_j))" + ] + }, + { + "cell_type": "markdown", + "id": "fcf9b69b", + "metadata": { + "editable": true + }, + "source": [ + "## Code with a Number of Minibatches which varies\n", + "\n", + "In the code here we vary the number of mini-batches." + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "861b050f", + "metadata": { + "collapsed": false, + "editable": true + }, + "outputs": [], + "source": [ + "%matplotlib inline\n", + "\n", + "# Importing various packages\n", + "from math import exp, sqrt\n", + "from random import random, seed\n", + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "\n", + "n = 100\n", + "x = 2*np.random.rand(n,1)\n", + "y = 4+3*x+np.random.randn(n,1)\n", + "\n", + "X = np.c_[np.ones((n,1)), x]\n", + "XT_X = X.T @ X\n", + "theta_linreg = np.linalg.inv(X.T @ X) @ (X.T @ y)\n", + "print(\"Own inversion\")\n", + "print(theta_linreg)\n", + "# Hessian matrix\n", + "H = (2.0/n)* XT_X\n", + "EigValues, EigVectors = np.linalg.eig(H)\n", + "print(f\"Eigenvalues of Hessian Matrix:{EigValues}\")\n", + "\n", + "theta = np.random.randn(2,1)\n", + "eta = 1.0/np.max(EigValues)\n", + "Niterations = 1000\n", + "\n", + "\n", + "for iter in range(Niterations):\n", + " gradients = 2.0/n*X.T @ ((X @ theta)-y)\n", + " theta -= eta*gradients\n", + "print(\"theta from own gd\")\n", + "print(theta)\n", + "\n", + "xnew = np.array([[0],[2]])\n", + "Xnew = np.c_[np.ones((2,1)), xnew]\n", + "ypredict = Xnew.dot(theta)\n", + "ypredict2 = Xnew.dot(theta_linreg)\n", + "\n", + "n_epochs = 50\n", + "M = 5 #size of each minibatch\n", + "m = int(n/M) #number of minibatches\n", + "t0, t1 = 5, 50\n", + "\n", + "def learning_schedule(t):\n", + " return t0/(t+t1)\n", + "\n", + "theta = np.random.randn(2,1)\n", + "\n", + "for epoch in range(n_epochs):\n", + "# Can you figure out a better way of setting up the contributions to each batch?\n", + " for i in range(m):\n", + " random_index = M*np.random.randint(m)\n", + " xi = X[random_index:random_index+M]\n", + " yi = y[random_index:random_index+M]\n", + " gradients = (2.0/M)* xi.T @ ((xi @ theta)-yi)\n", + " eta = learning_schedule(epoch*m+i)\n", + " theta = theta - eta*gradients\n", + "print(\"theta from own sdg\")\n", + "print(theta)\n", + "\n", + "plt.plot(xnew, ypredict, \"r-\")\n", + "plt.plot(xnew, ypredict2, \"b-\")\n", + "plt.plot(x, y ,'ro')\n", + "plt.axis([0,2.0,0, 15.0])\n", + "plt.xlabel(r'$x$')\n", + "plt.ylabel(r'$y$')\n", + "plt.title(r'Random numbers ')\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "97311aec", + "metadata": { + "editable": true + }, + "source": [ + "## Replace or not\n", + "\n", + "In the above code, we have use replacement in setting up the\n", + "mini-batches. The discussion\n", + "[here](https://sebastianraschka.com/faq/docs/sgd-methods.html) may be\n", + "useful." + ] + }, + { + "cell_type": "markdown", + "id": "423ddc16", + "metadata": { + "editable": true + }, + "source": [ + "## Momentum based GD\n", + "\n", + "The stochastic gradient descent (SGD) is almost always used with a\n", + "*momentum* or inertia term that serves as a memory of the direction we\n", + "are moving in parameter space. This is typically implemented as\n", + "follows" + ] + }, + { + "cell_type": "markdown", + "id": "a4f85670", + "metadata": { + "editable": true + }, + "source": [ + "$$\n", + "\\mathbf{v}_{t}=\\gamma \\mathbf{v}_{t-1}+\\eta_{t}\\nabla_\\theta E(\\boldsymbol{\\theta}_t) \\nonumber\n", + "$$" + ] + }, + { + "cell_type": "markdown", + "id": "f15ea450", + "metadata": { + "editable": true + }, + "source": [ + "\n", + "
              \n", + "\n", + "$$\n", + "\\begin{equation} \n", + "\\boldsymbol{\\theta}_{t+1}= \\boldsymbol{\\theta}_t -\\mathbf{v}_{t},\n", + "\\label{_auto1} \\tag{1}\n", + "\\end{equation}\n", + "$$" + ] + }, + { + "cell_type": "markdown", + "id": "233d7b7e", + "metadata": { + "editable": true + }, + "source": [ + "where we have introduced a momentum parameter $\\gamma$, with\n", + "$0\\le\\gamma\\le 1$, and for brevity we dropped the explicit notation to\n", + "indicate the gradient is to be taken over a different mini-batch at\n", + "each step. We call this algorithm gradient descent with momentum\n", + "(GDM). From these equations, it is clear that $\\mathbf{v}_t$ is a\n", + "running average of recently encountered gradients and\n", + "$(1-\\gamma)^{-1}$ sets the characteristic time scale for the memory\n", + "used in the averaging procedure. Consistent with this, when\n", + "$\\gamma=0$, this just reduces down to ordinary SGD as discussed\n", + "earlier. An equivalent way of writing the updates is" + ] + }, + { + "cell_type": "markdown", + "id": "b923e7d5", + "metadata": { + "editable": true + }, + "source": [ + "$$\n", + "\\Delta \\boldsymbol{\\theta}_{t+1} = \\gamma \\Delta \\boldsymbol{\\theta}_t -\\ \\eta_{t}\\nabla_\\theta E(\\boldsymbol{\\theta}_t),\n", + "$$" + ] + }, + { + "cell_type": "markdown", + "id": "60980ded", + "metadata": { + "editable": true + }, + "source": [ + "where we have defined $\\Delta \\boldsymbol{\\theta}_{t}= \\boldsymbol{\\theta}_t-\\boldsymbol{\\theta}_{t-1}$." + ] + }, + { + "cell_type": "markdown", + "id": "cc771e70", + "metadata": { + "editable": true + }, + "source": [ + "## More on momentum based approaches\n", + "\n", + "Let us try to get more intuition from these equations. It is helpful\n", + "to consider a simple physical analogy with a particle of mass $m$\n", + "moving in a viscous medium with drag coefficient $\\mu$ and potential\n", + "$E(\\mathbf{w})$. If we denote the particle's position by $\\mathbf{w}$,\n", + "then its motion is described by" + ] + }, + { + "cell_type": "markdown", + "id": "859f6ffc", + "metadata": { + "editable": true + }, + "source": [ + "$$\n", + "m {d^2 \\mathbf{w} \\over dt^2} + \\mu {d \\mathbf{w} \\over dt }= -\\nabla_w E(\\mathbf{w}).\n", + "$$" + ] + }, + { + "cell_type": "markdown", + "id": "064cc085", + "metadata": { + "editable": true + }, + "source": [ + "We can discretize this equation in the usual way to get" + ] + }, + { + "cell_type": "markdown", + "id": "47d13c3c", + "metadata": { + "editable": true + }, + "source": [ + "$$\n", + "m { \\mathbf{w}_{t+\\Delta t}-2 \\mathbf{w}_{t} +\\mathbf{w}_{t-\\Delta t} \\over (\\Delta t)^2}+\\mu {\\mathbf{w}_{t+\\Delta t}- \\mathbf{w}_{t} \\over \\Delta t} = -\\nabla_w E(\\mathbf{w}).\n", + "$$" + ] + }, + { + "cell_type": "markdown", + "id": "0ca67954", + "metadata": { + "editable": true + }, + "source": [ + "Rearranging this equation, we can rewrite this as" + ] + }, + { + "cell_type": "markdown", + "id": "ea9f63a8", + "metadata": { + "editable": true + }, + "source": [ + "$$\n", + "\\Delta \\mathbf{w}_{t +\\Delta t}= - { (\\Delta t)^2 \\over m +\\mu \\Delta t} \\nabla_w E(\\mathbf{w})+ {m \\over m +\\mu \\Delta t} \\Delta \\mathbf{w}_t.\n", + "$$" + ] + }, + { + "cell_type": "markdown", + "id": "35146ea5", + "metadata": { + "editable": true + }, + "source": [ + "## Momentum parameter\n", + "\n", + "Notice that this equation is identical to previous one if we identify\n", + "the position of the particle, $\\mathbf{w}$, with the parameters\n", + "$\\boldsymbol{\\theta}$. This allows us to identify the momentum\n", + "parameter and learning rate with the mass of the particle and the\n", + "viscous drag as:" + ] + }, + { + "cell_type": "markdown", + "id": "82c87bb1", + "metadata": { + "editable": true + }, + "source": [ + "$$\n", + "\\gamma= {m \\over m +\\mu \\Delta t }, \\qquad \\eta = {(\\Delta t)^2 \\over m +\\mu \\Delta t}.\n", + "$$" + ] + }, + { + "cell_type": "markdown", + "id": "75415fca", + "metadata": { + "editable": true + }, + "source": [ + "Thus, as the name suggests, the momentum parameter is proportional to\n", + "the mass of the particle and effectively provides inertia.\n", + "Furthermore, in the large viscosity/small learning rate limit, our\n", + "memory time scales as $(1-\\gamma)^{-1} \\approx m/(\\mu \\Delta t)$.\n", + "\n", + "Why is momentum useful? SGD momentum helps the gradient descent\n", + "algorithm gain speed in directions with persistent but small gradients\n", + "even in the presence of stochasticity, while suppressing oscillations\n", + "in high-curvature directions. This becomes especially important in\n", + "situations where the landscape is shallow and flat in some directions\n", + "and narrow and steep in others. It has been argued that first-order\n", + "methods (with appropriate initial conditions) can perform comparable\n", + "to more expensive second order methods, especially in the context of\n", + "complex deep learning models.\n", + "\n", + "These beneficial properties of momentum can sometimes become even more\n", + "pronounced by using a slight modification of the classical momentum\n", + "algorithm called Nesterov Accelerated Gradient (NAG).\n", + "\n", + "In the NAG algorithm, rather than calculating the gradient at the\n", + "current parameters, $\\nabla_\\theta E(\\boldsymbol{\\theta}_t)$, one\n", + "calculates the gradient at the expected value of the parameters given\n", + "our current momentum, $\\nabla_\\theta E(\\boldsymbol{\\theta}_t +\\gamma\n", + "\\mathbf{v}_{t-1})$. This yields the NAG update rule" + ] + }, + { + "cell_type": "markdown", + "id": "59892cd6", + "metadata": { + "editable": true + }, + "source": [ + "$$\n", + "\\mathbf{v}_{t}=\\gamma \\mathbf{v}_{t-1}+\\eta_{t}\\nabla_\\theta E(\\boldsymbol{\\theta}_t +\\gamma \\mathbf{v}_{t-1}) \\nonumber\n", + "$$" + ] + }, + { + "cell_type": "markdown", + "id": "a01225ea", + "metadata": { + "editable": true + }, + "source": [ + "\n", + "
              \n", + "\n", + "$$\n", + "\\begin{equation} \n", + "\\boldsymbol{\\theta}_{t+1}= \\boldsymbol{\\theta}_t -\\mathbf{v}_{t}.\n", + "\\label{_auto2} \\tag{2}\n", + "\\end{equation}\n", + "$$" + ] + }, + { + "cell_type": "markdown", + "id": "e2c9f57b", + "metadata": { + "editable": true + }, + "source": [ + "One of the major advantages of NAG is that it allows for the use of a larger learning rate than GDM for the same choice of $\\gamma$." + ] + }, + { + "cell_type": "markdown", + "id": "1672a79e", + "metadata": { + "editable": true + }, + "source": [ + "## Second moment of the gradient\n", + "\n", + "In stochastic gradient descent, with and without momentum, we still\n", + "have to specify a schedule for tuning the learning rates $\\eta_t$\n", + "as a function of time. As discussed in the context of Newton's\n", + "method, this presents a number of dilemmas. The learning rate is\n", + "limited by the steepest direction which can change depending on the\n", + "current position in the landscape. To circumvent this problem, ideally\n", + "our algorithm would keep track of curvature and take large steps in\n", + "shallow, flat directions and small steps in steep, narrow directions.\n", + "Second-order methods accomplish this by calculating or approximating\n", + "the Hessian and normalizing the learning rate by the\n", + "curvature. However, this is very computationally expensive for\n", + "extremely large models. Ideally, we would like to be able to\n", + "adaptively change the step size to match the landscape without paying\n", + "the steep computational price of calculating or approximating\n", + "Hessians.\n", + "\n", + "Recently, a number of methods have been introduced that accomplish\n", + "this by tracking not only the gradient, but also the second moment of\n", + "the gradient. These methods include AdaGrad, AdaDelta, Root Mean Squared Propagation (RMS-Prop), and\n", + "[ADAM](https://arxiv.org/abs/1412.6980)." + ] + }, + { + "cell_type": "markdown", + "id": "6d4032f9", + "metadata": { + "editable": true + }, + "source": [ + "## RMS prop\n", + "\n", + "In RMS prop, in addition to keeping a running average of the first\n", + "moment of the gradient, we also keep track of the second moment\n", + "denoted by $\\mathbf{s}_t=\\mathbb{E}[\\mathbf{g}_t^2]$. The update rule\n", + "for RMS prop is given by" + ] + }, + { + "cell_type": "markdown", + "id": "63cde9f3", + "metadata": { + "editable": true + }, + "source": [ + "\n", + "
              \n", + "\n", + "$$\n", + "\\begin{equation}\n", + "\\mathbf{g}_t = \\nabla_\\theta E(\\boldsymbol{\\theta}) \n", + "\\label{_auto3} \\tag{3}\n", + "\\end{equation}\n", + "$$" + ] + }, + { + "cell_type": "markdown", + "id": "6f8a52c2", + "metadata": { + "editable": true + }, + "source": [ + "$$\n", + "\\mathbf{s}_t =\\beta \\mathbf{s}_{t-1} +(1-\\beta)\\mathbf{g}_t^2 \\nonumber\n", + "$$" + ] + }, + { + "cell_type": "markdown", + "id": "9edf087d", + "metadata": { + "editable": true + }, + "source": [ + "$$\n", + "\\boldsymbol{\\theta}_{t+1}=\\boldsymbol{\\theta}_t - \\eta_t { \\mathbf{g}_t \\over \\sqrt{\\mathbf{s}_t +\\epsilon}}, \\nonumber\n", + "$$" + ] + }, + { + "cell_type": "markdown", + "id": "7eff676b", + "metadata": { + "editable": true + }, + "source": [ + "where $\\beta$ controls the averaging time of the second moment and is\n", + "typically taken to be about $\\beta=0.9$, $\\eta_t$ is a learning rate\n", + "typically chosen to be $10^{-3}$, and $\\epsilon\\sim 10^{-8} $ is a\n", + "small regularization constant to prevent divergences. Multiplication\n", + "and division by vectors is understood as an element-wise operation. It\n", + "is clear from this formula that the learning rate is reduced in\n", + "directions where the norm of the gradient is consistently large. This\n", + "greatly speeds up the convergence by allowing us to use a larger\n", + "learning rate for flat directions." + ] + }, + { + "cell_type": "markdown", + "id": "3fcb1068", + "metadata": { + "editable": true + }, + "source": [ + "## [ADAM optimizer](https://arxiv.org/abs/1412.6980)\n", + "\n", + "A related algorithm is the ADAM optimizer. In\n", + "[ADAM](https://arxiv.org/abs/1412.6980), we keep a running average of\n", + "both the first and second moment of the gradient and use this\n", + "information to adaptively change the learning rate for different\n", + "parameters. The method isefficient when working with large\n", + "problems involving lots data and/or parameters. It is a combination of the\n", + "gradient descent with momentum algorithm and the RMSprop algorithm\n", + "discussed above.\n", + "\n", + "In addition to keeping a running average of the first and\n", + "second moments of the gradient\n", + "(i.e. $\\mathbf{m}_t=\\mathbb{E}[\\mathbf{g}_t]$ and\n", + "$\\mathbf{s}_t=\\mathbb{E}[\\mathbf{g}^2_t]$, respectively), ADAM\n", + "performs an additional bias correction to account for the fact that we\n", + "are estimating the first two moments of the gradient using a running\n", + "average (denoted by the hats in the update rule below). The update\n", + "rule for ADAM is given by (where multiplication and division are once\n", + "again understood to be element-wise operations below)" + ] + }, + { + "cell_type": "markdown", + "id": "31b034e1", + "metadata": { + "editable": true + }, + "source": [ + "\n", + "
              \n", + "\n", + "$$\n", + "\\begin{equation}\n", + "\\mathbf{g}_t = \\nabla_\\theta E(\\boldsymbol{\\theta}) \n", + "\\label{_auto4} \\tag{4}\n", + "\\end{equation}\n", + "$$" + ] + }, + { + "cell_type": "markdown", + "id": "571e9a91", + "metadata": { + "editable": true + }, + "source": [ + "$$\n", + "\\mathbf{m}_t = \\beta_1 \\mathbf{m}_{t-1} + (1-\\beta_1) \\mathbf{g}_t \\nonumber\n", + "$$" + ] + }, + { + "cell_type": "markdown", + "id": "fb5883fa", + "metadata": { + "editable": true + }, + "source": [ + "$$\n", + "\\mathbf{s}_t =\\beta_2 \\mathbf{s}_{t-1} +(1-\\beta_2)\\mathbf{g}_t^2 \\nonumber\n", + "$$" + ] + }, + { + "cell_type": "markdown", + "id": "ebffe7a1", + "metadata": { + "editable": true + }, + "source": [ + "$$\n", + "\\boldsymbol{\\mathbf{m}}_t={\\mathbf{m}_t \\over 1-\\beta_1^t} \\nonumber\n", + "$$" + ] + }, + { + "cell_type": "markdown", + "id": "5a513bd7", + "metadata": { + "editable": true + }, + "source": [ + "$$\n", + "\\boldsymbol{\\mathbf{s}}_t ={\\mathbf{s}_t \\over1-\\beta_2^t} \\nonumber\n", + "$$" + ] + }, + { + "cell_type": "markdown", + "id": "d49bc312", + "metadata": { + "editable": true + }, + "source": [ + "$$\n", + "\\boldsymbol{\\theta}_{t+1}=\\boldsymbol{\\theta}_t - \\eta_t { \\boldsymbol{\\mathbf{m}}_t \\over \\sqrt{\\boldsymbol{\\mathbf{s}}_t} +\\epsilon}, \\nonumber\n", + "$$" + ] + }, + { + "cell_type": "markdown", + "id": "6f4e5040", + "metadata": { + "editable": true + }, + "source": [ + "\n", + "
              \n", + "\n", + "$$\n", + "\\begin{equation} \n", + "\\label{_auto5} \\tag{5}\n", + "\\end{equation}\n", + "$$" + ] + }, + { + "cell_type": "markdown", + "id": "4771881e", + "metadata": { + "editable": true + }, + "source": [ + "where $\\beta_1$ and $\\beta_2$ set the memory lifetime of the first and\n", + "second moment and are typically taken to be $0.9$ and $0.99$\n", + "respectively, and $\\eta$ and $\\epsilon$ are identical to RMSprop.\n", + "\n", + "Like in RMSprop, the effective step size of a parameter depends on the\n", + "magnitude of its gradient squared. To understand this better, let us\n", + "rewrite this expression in terms of the variance\n", + "$\\boldsymbol{\\sigma}_t^2 = \\boldsymbol{\\mathbf{s}}_t -\n", + "(\\boldsymbol{\\mathbf{m}}_t)^2$. Consider a single parameter $\\theta_t$. The\n", + "update rule for this parameter is given by" + ] + }, + { + "cell_type": "markdown", + "id": "3a0d438e", + "metadata": { + "editable": true + }, + "source": [ + "$$\n", + "\\Delta \\theta_{t+1}= -\\eta_t { \\boldsymbol{m}_t \\over \\sqrt{\\sigma_t^2 + m_t^2 }+\\epsilon}.\n", + "$$" + ] + }, + { + "cell_type": "markdown", + "id": "6cbb721b", + "metadata": { + "editable": true + }, + "source": [ + "## Algorithms and codes for Adagrad, RMSprop and Adam\n", + "\n", + "The algorithms we have implemented are well described in the text by [Goodfellow, Bengio and Courville, chapter 8](https://www.deeplearningbook.org/contents/optimization.html).\n", + "\n", + "The codes which implement these algorithms are discussed after our presentation of automatic differentiation." + ] + }, + { + "cell_type": "markdown", + "id": "e7d8b851", + "metadata": { + "editable": true + }, + "source": [ + "## Practical tips\n", + "\n", + "* **Randomize the data when making mini-batches**. It is always important to randomly shuffle the data when forming mini-batches. Otherwise, the gradient descent method can fit spurious correlations resulting from the order in which data is presented.\n", + "\n", + "* **Transform your inputs**. Learning becomes difficult when our landscape has a mixture of steep and flat directions. One simple trick for minimizing these situations is to standardize the data by subtracting the mean and normalizing the variance of input variables. Whenever possible, also decorrelate the inputs. To understand why this is helpful, consider the case of linear regression. It is easy to show that for the squared error cost function, the Hessian of the cost function is just the correlation matrix between the inputs. Thus, by standardizing the inputs, we are ensuring that the landscape looks homogeneous in all directions in parameter space. Since most deep networks can be viewed as linear transformations followed by a non-linearity at each layer, we expect this intuition to hold beyond the linear case.\n", + "\n", + "* **Monitor the out-of-sample performance.** Always monitor the performance of your model on a validation set (a small portion of the training data that is held out of the training process to serve as a proxy for the test set. If the validation error starts increasing, then the model is beginning to overfit. Terminate the learning process. This *early stopping* significantly improves performance in many settings.\n", + "\n", + "* **Adaptive optimization methods don't always have good generalization.** Recent studies have shown that adaptive methods such as ADAM, RMSPorp, and AdaGrad tend to have poor generalization compared to SGD or SGD with momentum, particularly in the high-dimensional limit (i.e. the number of parameters exceeds the number of data points). Although it is not clear at this stage why these methods perform so well in training deep neural networks, simpler procedures like properly-tuned SGD may work as well or better in these applications.\n", + "\n", + "Geron's text, see chapter 11, has several interesting discussions." + ] + }, + { + "cell_type": "markdown", + "id": "75afab2b", + "metadata": { + "editable": true + }, + "source": [ + "## Automatic differentiation\n", + "\n", + "[Automatic differentiation (AD)](https://en.wikipedia.org/wiki/Automatic_differentiation), \n", + "also called algorithmic\n", + "differentiation or computational differentiation,is a set of\n", + "techniques to numerically evaluate the derivative of a function\n", + "specified by a computer program. AD exploits the fact that every\n", + "computer program, no matter how complicated, executes a sequence of\n", + "elementary arithmetic operations (addition, subtraction,\n", + "multiplication, division, etc.) and elementary functions (exp, log,\n", + "sin, cos, etc.). By applying the chain rule repeatedly to these\n", + "operations, derivatives of arbitrary order can be computed\n", + "automatically, accurately to working precision, and using at most a\n", + "small constant factor more arithmetic operations than the original\n", + "program.\n", + "\n", + "Automatic differentiation is neither:\n", + "\n", + "* Symbolic differentiation, nor\n", + "\n", + "* Numerical differentiation (the method of finite differences).\n", + "\n", + "Symbolic differentiation can lead to inefficient code and faces the\n", + "difficulty of converting a computer program into a single expression,\n", + "while numerical differentiation can introduce round-off errors in the\n", + "discretization process and cancellation\n", + "\n", + "Python has tools for so-called **automatic differentiation**.\n", + "Consider the following example" + ] + }, + { + "cell_type": "markdown", + "id": "c551bfeb", + "metadata": { + "editable": true + }, + "source": [ + "$$\n", + "f(x) = \\sin\\left(2\\pi x + x^2\\right)\n", + "$$" + ] + }, + { + "cell_type": "markdown", + "id": "f2cbdd82", + "metadata": { + "editable": true + }, + "source": [ + "which has the following derivative" + ] + }, + { + "cell_type": "markdown", + "id": "22e5d8ce", + "metadata": { + "editable": true + }, + "source": [ + "$$\n", + "f'(x) = \\cos\\left(2\\pi x + x^2\\right)\\left(2\\pi + 2x\\right)\n", + "$$" + ] + }, + { + "cell_type": "markdown", + "id": "d2d352b4", + "metadata": { + "editable": true + }, + "source": [ + "Using **autograd** we have" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "19f1b95c", + "metadata": { + "collapsed": false, + "editable": true + }, + "outputs": [], + "source": [ + "import autograd.numpy as np\n", + "\n", + "# To do elementwise differentiation:\n", + "from autograd import elementwise_grad as egrad \n", + "\n", + "# To plot:\n", + "import matplotlib.pyplot as plt \n", + "\n", + "\n", + "def f(x):\n", + " return np.sin(2*np.pi*x + x**2)\n", + "\n", + "def f_grad_analytic(x):\n", + " return np.cos(2*np.pi*x + x**2)*(2*np.pi + 2*x)\n", + "\n", + "# Do the comparison:\n", + "x = np.linspace(0,1,1000)\n", + "\n", + "f_grad = egrad(f)\n", + "\n", + "computed = f_grad(x)\n", + "analytic = f_grad_analytic(x)\n", + "\n", + "plt.title('Derivative computed from Autograd compared with the analytical derivative')\n", + "plt.plot(x,computed,label='autograd')\n", + "plt.plot(x,analytic,label='analytic')\n", + "\n", + "plt.xlabel('x')\n", + "plt.ylabel('y')\n", + "plt.legend()\n", + "\n", + "plt.show()\n", + "\n", + "print(\"The max absolute difference is: %g\"%(np.max(np.abs(computed - analytic))))" + ] + }, + { + "cell_type": "markdown", + "id": "f3a495be", + "metadata": { + "editable": true + }, + "source": [ + "## Using autograd\n", + "\n", + "Here we\n", + "experiment with what kind of functions Autograd is capable\n", + "of finding the gradient of. The following Python functions are just\n", + "meant to illustrate what Autograd can do, but please feel free to\n", + "experiment with other, possibly more complicated, functions as well." + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "5c856602", + "metadata": { + "collapsed": false, + "editable": true + }, + "outputs": [], + "source": [ + "import autograd.numpy as np\n", + "from autograd import grad\n", + "\n", + "def f1(x):\n", + " return x**3 + 1\n", + "\n", + "f1_grad = grad(f1)\n", + "\n", + "# Remember to send in float as argument to the computed gradient from Autograd!\n", + "a = 1.0\n", + "\n", + "# See the evaluated gradient at a using autograd:\n", + "print(\"The gradient of f1 evaluated at a = %g using autograd is: %g\"%(a,f1_grad(a)))\n", + "\n", + "# Compare with the analytical derivative, that is f1'(x) = 3*x**2 \n", + "grad_analytical = 3*a**2\n", + "print(\"The gradient of f1 evaluated at a = %g by finding the analytic expression is: %g\"%(a,grad_analytical))" + ] + }, + { + "cell_type": "markdown", + "id": "b361074b", + "metadata": { + "editable": true + }, + "source": [ + "## Autograd with more complicated functions\n", + "\n", + "To differentiate with respect to two (or more) arguments of a Python\n", + "function, Autograd need to know at which variable the function if\n", + "being differentiated with respect to." + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "a458a151", + "metadata": { + "collapsed": false, + "editable": true + }, + "outputs": [], + "source": [ + "import autograd.numpy as np\n", + "from autograd import grad\n", + "def f2(x1,x2):\n", + " return 3*x1**3 + x2*(x1 - 5) + 1\n", + "\n", + "# By sending the argument 0, Autograd will compute the derivative w.r.t the first variable, in this case x1\n", + "f2_grad_x1 = grad(f2,0)\n", + "\n", + "# ... and differentiate w.r.t x2 by sending 1 as an additional arugment to grad\n", + "f2_grad_x2 = grad(f2,1)\n", + "\n", + "x1 = 1.0\n", + "x2 = 3.0 \n", + "\n", + "print(\"Evaluating at x1 = %g, x2 = %g\"%(x1,x2))\n", + "print(\"-\"*30)\n", + "\n", + "# Compare with the analytical derivatives:\n", + "\n", + "# Derivative of f2 w.r.t x1 is: 9*x1**2 + x2:\n", + "f2_grad_x1_analytical = 9*x1**2 + x2\n", + "\n", + "# Derivative of f2 w.r.t x2 is: x1 - 5:\n", + "f2_grad_x2_analytical = x1 - 5\n", + "\n", + "# See the evaluated derivations:\n", + "print(\"The derivative of f2 w.r.t x1: %g\"%( f2_grad_x1(x1,x2) ))\n", + "print(\"The analytical derivative of f2 w.r.t x1: %g\"%( f2_grad_x1(x1,x2) ))\n", + "\n", + "print()\n", + "\n", + "print(\"The derivative of f2 w.r.t x2: %g\"%( f2_grad_x2(x1,x2) ))\n", + "print(\"The analytical derivative of f2 w.r.t x2: %g\"%( f2_grad_x2(x1,x2) ))" + ] + }, + { + "cell_type": "markdown", + "id": "946c37f1", + "metadata": { + "editable": true + }, + "source": [ + "Note that the grad function will not produce the true gradient of the function. The true gradient of a function with two or more variables will produce a vector, where each element is the function differentiated w.r.t a variable." + ] + }, + { + "cell_type": "markdown", + "id": "a00d38e5", + "metadata": { + "editable": true + }, + "source": [ + "## More complicated functions using the elements of their arguments directly" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "d8c2a448", + "metadata": { + "collapsed": false, + "editable": true + }, + "outputs": [], + "source": [ + "import autograd.numpy as np\n", + "from autograd import grad\n", + "def f3(x): # Assumes x is an array of length 5 or higher\n", + " return 2*x[0] + 3*x[1] + 5*x[2] + 7*x[3] + 11*x[4]**2\n", + "\n", + "f3_grad = grad(f3)\n", + "\n", + "x = np.linspace(0,4,5)\n", + "\n", + "# Print the computed gradient:\n", + "print(\"The computed gradient of f3 is: \", f3_grad(x))\n", + "\n", + "# The analytical gradient is: (2, 3, 5, 7, 22*x[4])\n", + "f3_grad_analytical = np.array([2, 3, 5, 7, 22*x[4]])\n", + "\n", + "# Print the analytical gradient:\n", + "print(\"The analytical gradient of f3 is: \", f3_grad_analytical)" + ] + }, + { + "cell_type": "markdown", + "id": "026d8733", + "metadata": { + "editable": true + }, + "source": [ + "Note that in this case, when sending an array as input argument, the\n", + "output from Autograd is another array. This is the true gradient of\n", + "the function, as opposed to the function in the previous example. By\n", + "using arrays to represent the variables, the output from Autograd\n", + "might be easier to work with, as the output is closer to what one\n", + "could expect form a gradient-evaluting function." + ] + }, + { + "cell_type": "markdown", + "id": "1a4dca11", + "metadata": { + "editable": true + }, + "source": [ + "## Functions using mathematical functions from Numpy" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "c10b664f", + "metadata": { + "collapsed": false, + "editable": true + }, + "outputs": [], + "source": [ + "import autograd.numpy as np\n", + "from autograd import grad\n", + "def f4(x):\n", + " return np.sqrt(1+x**2) + np.exp(x) + np.sin(2*np.pi*x)\n", + "\n", + "f4_grad = grad(f4)\n", + "\n", + "x = 2.7\n", + "\n", + "# Print the computed derivative:\n", + "print(\"The computed derivative of f4 at x = %g is: %g\"%(x,f4_grad(x)))\n", + "\n", + "# The analytical derivative is: x/sqrt(1 + x**2) + exp(x) + cos(2*pi*x)*2*pi\n", + "f4_grad_analytical = x/np.sqrt(1 + x**2) + np.exp(x) + np.cos(2*np.pi*x)*2*np.pi\n", + "\n", + "# Print the analytical gradient:\n", + "print(\"The analytical gradient of f4 at x = %g is: %g\"%(x,f4_grad_analytical))" + ] + }, + { + "cell_type": "markdown", + "id": "07436a71", + "metadata": { + "editable": true + }, + "source": [ + "## More autograd" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "1840a5d2", + "metadata": { + "collapsed": false, + "editable": true + }, + "outputs": [], + "source": [ + "import autograd.numpy as np\n", + "from autograd import grad\n", + "def f5(x):\n", + " if x >= 0:\n", + " return x**2\n", + " else:\n", + " return -3*x + 1\n", + "\n", + "f5_grad = grad(f5)\n", + "\n", + "x = 2.7\n", + "\n", + "# Print the computed derivative:\n", + "print(\"The computed derivative of f5 at x = %g is: %g\"%(x,f5_grad(x)))" + ] + }, + { + "cell_type": "markdown", + "id": "87ee8137", + "metadata": { + "editable": true + }, + "source": [ + "## And with loops" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "id": "f1b25f09", + "metadata": { + "collapsed": false, + "editable": true + }, + "outputs": [], + "source": [ + "import autograd.numpy as np\n", + "from autograd import grad\n", + "def f6_for(x):\n", + " val = 0\n", + " for i in range(10):\n", + " val = val + x**i\n", + " return val\n", + "\n", + "def f6_while(x):\n", + " val = 0\n", + " i = 0\n", + " while i < 10:\n", + " val = val + x**i\n", + " i = i + 1\n", + " return val\n", + "\n", + "f6_for_grad = grad(f6_for)\n", + "f6_while_grad = grad(f6_while)\n", + "\n", + "x = 0.5\n", + "\n", + "# Print the computed derivaties of f6_for and f6_while\n", + "print(\"The computed derivative of f6_for at x = %g is: %g\"%(x,f6_for_grad(x)))\n", + "print(\"The computed derivative of f6_while at x = %g is: %g\"%(x,f6_while_grad(x)))" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "id": "5fa2802b", + "metadata": { + "collapsed": false, + "editable": true + }, + "outputs": [], + "source": [ + "import autograd.numpy as np\n", + "from autograd import grad\n", + "# Both of the functions are implementation of the sum: sum(x**i) for i = 0, ..., 9\n", + "# The analytical derivative is: sum(i*x**(i-1)) \n", + "f6_grad_analytical = 0\n", + "for i in range(10):\n", + " f6_grad_analytical += i*x**(i-1)\n", + "\n", + "print(\"The analytical derivative of f6 at x = %g is: %g\"%(x,f6_grad_analytical))" + ] + }, + { + "cell_type": "markdown", + "id": "eb66fab4", + "metadata": { + "editable": true + }, + "source": [ + "## Using recursion" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "id": "965c8bbb", + "metadata": { + "collapsed": false, + "editable": true + }, + "outputs": [], + "source": [ + "import autograd.numpy as np\n", + "from autograd import grad\n", + "\n", + "def f7(n): # Assume that n is an integer\n", + " if n == 1 or n == 0:\n", + " return 1\n", + " else:\n", + " return n*f7(n-1)\n", + "\n", + "f7_grad = grad(f7)\n", + "\n", + "n = 2.0\n", + "\n", + "print(\"The computed derivative of f7 at n = %d is: %g\"%(n,f7_grad(n)))\n", + "\n", + "# The function f7 is an implementation of the factorial of n.\n", + "# By using the product rule, one can find that the derivative is:\n", + "\n", + "f7_grad_analytical = 0\n", + "for i in range(int(n)-1):\n", + " tmp = 1\n", + " for k in range(int(n)-1):\n", + " if k != i:\n", + " tmp *= (n - k)\n", + " f7_grad_analytical += tmp\n", + "\n", + "print(\"The analytical derivative of f7 at n = %d is: %g\"%(n,f7_grad_analytical))" + ] + }, + { + "cell_type": "markdown", + "id": "2e6e0c8a", + "metadata": { + "editable": true + }, + "source": [ + "Note that if n is equal to zero or one, Autograd will give an error message. This message appears when the output is independent on input." + ] + }, + { + "cell_type": "markdown", + "id": "42adbcc3", + "metadata": { + "editable": true + }, + "source": [ + "## Unsupported functions\n", + "Autograd supports many features. However, there are some functions that is not supported (yet) by Autograd.\n", + "\n", + "Assigning a value to the variable being differentiated with respect to" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "id": "6ca4a5dc", + "metadata": { + "collapsed": false, + "editable": true + }, + "outputs": [], + "source": [ + "import autograd.numpy as np\n", + "from autograd import grad\n", + "def f8(x): # Assume x is an array\n", + " x[2] = 3\n", + " return x*2\n", + "\n", + "f8_grad = grad(f8)\n", + "\n", + "x = 8.4\n", + "\n", + "print(\"The derivative of f8 is:\",f8_grad(x))" + ] + }, + { + "cell_type": "markdown", + "id": "5d816052", + "metadata": { + "editable": true + }, + "source": [ + "Here, Autograd tells us that an 'ArrayBox' does not support item assignment. The item assignment is done when the program tries to assign x[2] to the value 3. However, Autograd has implemented the computation of the derivative such that this assignment is not possible." + ] + }, + { + "cell_type": "markdown", + "id": "73f2e7e4", + "metadata": { + "editable": true + }, + "source": [ + "## The syntax a.dot(b) when finding the dot product" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "id": "2ead27d5", + "metadata": { + "collapsed": false, + "editable": true + }, + "outputs": [], + "source": [ + "import autograd.numpy as np\n", + "from autograd import grad\n", + "def f9(a): # Assume a is an array with 2 elements\n", + " b = np.array([1.0,2.0])\n", + " return a.dot(b)\n", + "\n", + "f9_grad = grad(f9)\n", + "\n", + "x = np.array([1.0,0.0])\n", + "\n", + "print(\"The derivative of f9 is:\",f9_grad(x))" + ] + }, + { + "cell_type": "markdown", + "id": "1edcb932", + "metadata": { + "editable": true + }, + "source": [ + "Here we are told that the 'dot' function does not belong to Autograd's\n", + "version of a Numpy array. To overcome this, an alternative syntax\n", + "which also computed the dot product can be used:" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "id": "05897777", + "metadata": { + "collapsed": false, + "editable": true + }, + "outputs": [], + "source": [ + "import autograd.numpy as np\n", + "from autograd import grad\n", + "def f9_alternative(x): # Assume a is an array with 2 elements\n", + " b = np.array([1.0,2.0])\n", + " return np.dot(x,b) # The same as x_1*b_1 + x_2*b_2\n", + "\n", + "f9_alternative_grad = grad(f9_alternative)\n", + "\n", + "x = np.array([3.0,0.0])\n", + "\n", + "print(\"The gradient of f9 is:\",f9_alternative_grad(x))\n", + "\n", + "# The analytical gradient of the dot product of vectors x and b with two elements (x_1,x_2) and (b_1, b_2) respectively\n", + "# w.r.t x is (b_1, b_2)." + ] + }, + { + "cell_type": "markdown", + "id": "2c899815", + "metadata": { + "editable": true + }, + "source": [ + "## Recommended to avoid\n", + "The documentation recommends to avoid inplace operations such as" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "id": "fd05063c", + "metadata": { + "collapsed": false, + "editable": true + }, + "outputs": [], + "source": [ + "a += b\n", + "a -= b\n", + "a*= b\n", + "a /=b" + ] + }, + { + "cell_type": "markdown", + "id": "94d6f0d9", + "metadata": { + "editable": true + }, + "source": [ + "## Using Autograd with OLS\n", + "\n", + "We conclude the part on optmization by showing how we can make codes\n", + "for linear regression and logistic regression using **autograd**. The\n", + "first example shows results with ordinary leats squares." + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "id": "d002c672", + "metadata": { + "collapsed": false, + "editable": true + }, + "outputs": [], + "source": [ + "# Using Autograd to calculate gradients for OLS\n", + "from random import random, seed\n", + "import numpy as np\n", + "import autograd.numpy as np\n", + "import matplotlib.pyplot as plt\n", + "from autograd import grad\n", + "\n", + "def CostOLS(beta):\n", + " return (1.0/n)*np.sum((y-X @ beta)**2)\n", + "\n", + "n = 100\n", + "x = 2*np.random.rand(n,1)\n", + "y = 4+3*x+np.random.randn(n,1)\n", + "\n", + "X = np.c_[np.ones((n,1)), x]\n", + "XT_X = X.T @ X\n", + "theta_linreg = np.linalg.pinv(XT_X) @ (X.T @ y)\n", + "print(\"Own inversion\")\n", + "print(theta_linreg)\n", + "# Hessian matrix\n", + "H = (2.0/n)* XT_X\n", + "EigValues, EigVectors = np.linalg.eig(H)\n", + "print(f\"Eigenvalues of Hessian Matrix:{EigValues}\")\n", + "\n", + "theta = np.random.randn(2,1)\n", + "eta = 1.0/np.max(EigValues)\n", + "Niterations = 1000\n", + "# define the gradient\n", + "training_gradient = grad(CostOLS)\n", + "\n", + "for iter in range(Niterations):\n", + " gradients = training_gradient(theta)\n", + " theta -= eta*gradients\n", + "print(\"theta from own gd\")\n", + "print(theta)\n", + "\n", + "xnew = np.array([[0],[2]])\n", + "Xnew = np.c_[np.ones((2,1)), xnew]\n", + "ypredict = Xnew.dot(theta)\n", + "ypredict2 = Xnew.dot(theta_linreg)\n", + "\n", + "plt.plot(xnew, ypredict, \"r-\")\n", + "plt.plot(xnew, ypredict2, \"b-\")\n", + "plt.plot(x, y ,'ro')\n", + "plt.axis([0,2.0,0, 15.0])\n", + "plt.xlabel(r'$x$')\n", + "plt.ylabel(r'$y$')\n", + "plt.title(r'Random numbers ')\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "e72279fe", + "metadata": { + "editable": true + }, + "source": [ + "## Same code but now with momentum gradient descent" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "id": "62aa2606", + "metadata": { + "collapsed": false, + "editable": true + }, + "outputs": [], + "source": [ + "# Using Autograd to calculate gradients for OLS\n", + "from random import random, seed\n", + "import numpy as np\n", + "import autograd.numpy as np\n", + "import matplotlib.pyplot as plt\n", + "from autograd import grad\n", + "\n", + "def CostOLS(beta):\n", + " return (1.0/n)*np.sum((y-X @ beta)**2)\n", + "\n", + "n = 100\n", + "x = 2*np.random.rand(n,1)\n", + "y = 4+3*x#+np.random.randn(n,1)\n", + "\n", + "X = np.c_[np.ones((n,1)), x]\n", + "XT_X = X.T @ X\n", + "theta_linreg = np.linalg.pinv(XT_X) @ (X.T @ y)\n", + "print(\"Own inversion\")\n", + "print(theta_linreg)\n", + "# Hessian matrix\n", + "H = (2.0/n)* XT_X\n", + "EigValues, EigVectors = np.linalg.eig(H)\n", + "print(f\"Eigenvalues of Hessian Matrix:{EigValues}\")\n", + "\n", + "theta = np.random.randn(2,1)\n", + "eta = 1.0/np.max(EigValues)\n", + "Niterations = 30\n", + "\n", + "# define the gradient\n", + "training_gradient = grad(CostOLS)\n", + "\n", + "for iter in range(Niterations):\n", + " gradients = training_gradient(theta)\n", + " theta -= eta*gradients\n", + " print(iter,gradients[0],gradients[1])\n", + "print(\"theta from own gd\")\n", + "print(theta)\n", + "\n", + "# Now improve with momentum gradient descent\n", + "change = 0.0\n", + "delta_momentum = 0.3\n", + "for iter in range(Niterations):\n", + " # calculate gradient\n", + " gradients = training_gradient(theta)\n", + " # calculate update\n", + " new_change = eta*gradients+delta_momentum*change\n", + " # take a step\n", + " theta -= new_change\n", + " # save the change\n", + " change = new_change\n", + " print(iter,gradients[0],gradients[1])\n", + "print(\"theta from own gd wth momentum\")\n", + "print(theta)" + ] + }, + { + "cell_type": "markdown", + "id": "bad8e42f", + "metadata": { + "editable": true + }, + "source": [ + "## But noen of these can compete with Newton's method" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "id": "13a572a8", + "metadata": { + "collapsed": false, + "editable": true + }, + "outputs": [], + "source": [ + "# Using Newton's method\n", + "from random import random, seed\n", + "import numpy as np\n", + "import autograd.numpy as np\n", + "import matplotlib.pyplot as plt\n", + "from autograd import grad\n", + "\n", + "def CostOLS(beta):\n", + " return (1.0/n)*np.sum((y-X @ beta)**2)\n", + "\n", + "n = 100\n", + "x = 2*np.random.rand(n,1)\n", + "y = 4+3*x+np.random.randn(n,1)\n", + "\n", + "X = np.c_[np.ones((n,1)), x]\n", + "XT_X = X.T @ X\n", + "beta_linreg = np.linalg.pinv(XT_X) @ (X.T @ y)\n", + "print(\"Own inversion\")\n", + "print(beta_linreg)\n", + "# Hessian matrix\n", + "H = (2.0/n)* XT_X\n", + "# Note that here the Hessian does not depend on the parameters beta\n", + "invH = np.linalg.pinv(H)\n", + "EigValues, EigVectors = np.linalg.eig(H)\n", + "print(f\"Eigenvalues of Hessian Matrix:{EigValues}\")\n", + "\n", + "beta = np.random.randn(2,1)\n", + "Niterations = 5\n", + "\n", + "# define the gradient\n", + "training_gradient = grad(CostOLS)\n", + "\n", + "for iter in range(Niterations):\n", + " gradients = training_gradient(beta)\n", + " beta -= invH @ gradients\n", + " print(iter,gradients[0],gradients[1])\n", + "print(\"beta from own Newton code\")\n", + "print(beta)" + ] + }, + { + "cell_type": "markdown", + "id": "5b2c9e3a", + "metadata": { + "editable": true + }, + "source": [ + "## Including Stochastic Gradient Descent with Autograd\n", + "In this code we include the stochastic gradient descent approach discussed above. Note here that we specify which argument we are taking the derivative with respect to when using **autograd**." + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "id": "830370bf", + "metadata": { + "collapsed": false, + "editable": true + }, + "outputs": [], + "source": [ + "# Using Autograd to calculate gradients using SGD\n", + "# OLS example\n", + "from random import random, seed\n", + "import numpy as np\n", + "import autograd.numpy as np\n", + "import matplotlib.pyplot as plt\n", + "from autograd import grad\n", + "\n", + "# Note change from previous example\n", + "def CostOLS(y,X,theta):\n", + " return np.sum((y-X @ theta)**2)\n", + "\n", + "n = 100\n", + "x = 2*np.random.rand(n,1)\n", + "y = 4+3*x+np.random.randn(n,1)\n", + "\n", + "X = np.c_[np.ones((n,1)), x]\n", + "XT_X = X.T @ X\n", + "theta_linreg = np.linalg.pinv(XT_X) @ (X.T @ y)\n", + "print(\"Own inversion\")\n", + "print(theta_linreg)\n", + "# Hessian matrix\n", + "H = (2.0/n)* XT_X\n", + "EigValues, EigVectors = np.linalg.eig(H)\n", + "print(f\"Eigenvalues of Hessian Matrix:{EigValues}\")\n", + "\n", + "theta = np.random.randn(2,1)\n", + "eta = 1.0/np.max(EigValues)\n", + "Niterations = 1000\n", + "\n", + "# Note that we request the derivative wrt third argument (theta, 2 here)\n", + "training_gradient = grad(CostOLS,2)\n", + "\n", + "for iter in range(Niterations):\n", + " gradients = (1.0/n)*training_gradient(y, X, theta)\n", + " theta -= eta*gradients\n", + "print(\"theta from own gd\")\n", + "print(theta)\n", + "\n", + "xnew = np.array([[0],[2]])\n", + "Xnew = np.c_[np.ones((2,1)), xnew]\n", + "ypredict = Xnew.dot(theta)\n", + "ypredict2 = Xnew.dot(theta_linreg)\n", + "\n", + "plt.plot(xnew, ypredict, \"r-\")\n", + "plt.plot(xnew, ypredict2, \"b-\")\n", + "plt.plot(x, y ,'ro')\n", + "plt.axis([0,2.0,0, 15.0])\n", + "plt.xlabel(r'$x$')\n", + "plt.ylabel(r'$y$')\n", + "plt.title(r'Random numbers ')\n", + "plt.show()\n", + "\n", + "n_epochs = 50\n", + "M = 5 #size of each minibatch\n", + "m = int(n/M) #number of minibatches\n", + "t0, t1 = 5, 50\n", + "def learning_schedule(t):\n", + " return t0/(t+t1)\n", + "\n", + "theta = np.random.randn(2,1)\n", + "\n", + "for epoch in range(n_epochs):\n", + "# Can you figure out a better way of setting up the contributions to each batch?\n", + " for i in range(m):\n", + " random_index = M*np.random.randint(m)\n", + " xi = X[random_index:random_index+M]\n", + " yi = y[random_index:random_index+M]\n", + " gradients = (1.0/M)*training_gradient(yi, xi, theta)\n", + " eta = learning_schedule(epoch*m+i)\n", + " theta = theta - eta*gradients\n", + "print(\"theta from own sdg\")\n", + "print(theta)" + ] + }, + { + "cell_type": "markdown", + "id": "e580483f", + "metadata": { + "editable": true + }, + "source": [ + "## Same code but now with momentum gradient descent" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "id": "68895d3f", + "metadata": { + "collapsed": false, + "editable": true + }, + "outputs": [], + "source": [ + "# Using Autograd to calculate gradients using SGD\n", + "# OLS example\n", + "from random import random, seed\n", + "import numpy as np\n", + "import autograd.numpy as np\n", + "import matplotlib.pyplot as plt\n", + "from autograd import grad\n", + "\n", + "# Note change from previous example\n", + "def CostOLS(y,X,theta):\n", + " return np.sum((y-X @ theta)**2)\n", + "\n", + "n = 100\n", + "x = 2*np.random.rand(n,1)\n", + "y = 4+3*x+np.random.randn(n,1)\n", + "\n", + "X = np.c_[np.ones((n,1)), x]\n", + "XT_X = X.T @ X\n", + "theta_linreg = np.linalg.pinv(XT_X) @ (X.T @ y)\n", + "print(\"Own inversion\")\n", + "print(theta_linreg)\n", + "# Hessian matrix\n", + "H = (2.0/n)* XT_X\n", + "EigValues, EigVectors = np.linalg.eig(H)\n", + "print(f\"Eigenvalues of Hessian Matrix:{EigValues}\")\n", + "\n", + "theta = np.random.randn(2,1)\n", + "eta = 1.0/np.max(EigValues)\n", + "Niterations = 100\n", + "\n", + "# Note that we request the derivative wrt third argument (theta, 2 here)\n", + "training_gradient = grad(CostOLS,2)\n", + "\n", + "for iter in range(Niterations):\n", + " gradients = (1.0/n)*training_gradient(y, X, theta)\n", + " theta -= eta*gradients\n", + "print(\"theta from own gd\")\n", + "print(theta)\n", + "\n", + "\n", + "n_epochs = 50\n", + "M = 5 #size of each minibatch\n", + "m = int(n/M) #number of minibatches\n", + "t0, t1 = 5, 50\n", + "def learning_schedule(t):\n", + " return t0/(t+t1)\n", + "\n", + "theta = np.random.randn(2,1)\n", + "\n", + "change = 0.0\n", + "delta_momentum = 0.3\n", + "\n", + "for epoch in range(n_epochs):\n", + " for i in range(m):\n", + " random_index = M*np.random.randint(m)\n", + " xi = X[random_index:random_index+M]\n", + " yi = y[random_index:random_index+M]\n", + " gradients = (1.0/M)*training_gradient(yi, xi, theta)\n", + " eta = learning_schedule(epoch*m+i)\n", + " # calculate update\n", + " new_change = eta*gradients+delta_momentum*change\n", + " # take a step\n", + " theta -= new_change\n", + " # save the change\n", + " change = new_change\n", + "print(\"theta from own sdg with momentum\")\n", + "print(theta)" + ] + }, + { + "cell_type": "markdown", + "id": "01c29c9e", + "metadata": { + "editable": true + }, + "source": [ + "## Similar (second order function now) problem but now with AdaGrad" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "id": "36a00e5a", + "metadata": { + "collapsed": false, + "editable": true + }, + "outputs": [], + "source": [ + "# Using Autograd to calculate gradients using AdaGrad and Stochastic Gradient descent\n", + "# OLS example\n", + "from random import random, seed\n", + "import numpy as np\n", + "import autograd.numpy as np\n", + "import matplotlib.pyplot as plt\n", + "from autograd import grad\n", + "\n", + "# Note change from previous example\n", + "def CostOLS(y,X,theta):\n", + " return np.sum((y-X @ theta)**2)\n", + "\n", + "n = 1000\n", + "x = np.random.rand(n,1)\n", + "y = 2.0+3*x +4*x*x\n", + "\n", + "X = np.c_[np.ones((n,1)), x, x*x]\n", + "XT_X = X.T @ X\n", + "theta_linreg = np.linalg.pinv(XT_X) @ (X.T @ y)\n", + "print(\"Own inversion\")\n", + "print(theta_linreg)\n", + "\n", + "\n", + "# Note that we request the derivative wrt third argument (theta, 2 here)\n", + "training_gradient = grad(CostOLS,2)\n", + "# Define parameters for Stochastic Gradient Descent\n", + "n_epochs = 50\n", + "M = 5 #size of each minibatch\n", + "m = int(n/M) #number of minibatches\n", + "# Guess for unknown parameters theta\n", + "theta = np.random.randn(3,1)\n", + "\n", + "# Value for learning rate\n", + "eta = 0.01\n", + "# Including AdaGrad parameter to avoid possible division by zero\n", + "delta = 1e-8\n", + "for epoch in range(n_epochs):\n", + " Giter = 0.0\n", + " for i in range(m):\n", + " random_index = M*np.random.randint(m)\n", + " xi = X[random_index:random_index+M]\n", + " yi = y[random_index:random_index+M]\n", + " gradients = (1.0/M)*training_gradient(yi, xi, theta)\n", + " Giter += gradients*gradients\n", + " update = gradients*eta/(delta+np.sqrt(Giter))\n", + " theta -= update\n", + "print(\"theta from own AdaGrad\")\n", + "print(theta)" + ] + }, + { + "cell_type": "markdown", + "id": "18ccca46", + "metadata": { + "editable": true + }, + "source": [ + "Running this code we note an almost perfect agreement with the results from matrix inversion." + ] + }, + { + "cell_type": "markdown", + "id": "e076c773", + "metadata": { + "editable": true + }, + "source": [ + "## RMSprop for adaptive learning rate with Stochastic Gradient Descent" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "id": "cb4ad1d3", + "metadata": { + "collapsed": false, + "editable": true + }, + "outputs": [], + "source": [ + "# Using Autograd to calculate gradients using RMSprop and Stochastic Gradient descent\n", + "# OLS example\n", + "from random import random, seed\n", + "import numpy as np\n", + "import autograd.numpy as np\n", + "import matplotlib.pyplot as plt\n", + "from autograd import grad\n", + "\n", + "# Note change from previous example\n", + "def CostOLS(y,X,theta):\n", + " return np.sum((y-X @ theta)**2)\n", + "\n", + "n = 1000\n", + "x = np.random.rand(n,1)\n", + "y = 2.0+3*x +4*x*x# +np.random.randn(n,1)\n", + "\n", + "X = np.c_[np.ones((n,1)), x, x*x]\n", + "XT_X = X.T @ X\n", + "theta_linreg = np.linalg.pinv(XT_X) @ (X.T @ y)\n", + "print(\"Own inversion\")\n", + "print(theta_linreg)\n", + "\n", + "\n", + "# Note that we request the derivative wrt third argument (theta, 2 here)\n", + "training_gradient = grad(CostOLS,2)\n", + "# Define parameters for Stochastic Gradient Descent\n", + "n_epochs = 50\n", + "M = 5 #size of each minibatch\n", + "m = int(n/M) #number of minibatches\n", + "# Guess for unknown parameters theta\n", + "theta = np.random.randn(3,1)\n", + "\n", + "# Value for learning rate\n", + "eta = 0.01\n", + "# Value for parameter rho\n", + "rho = 0.99\n", + "# Including AdaGrad parameter to avoid possible division by zero\n", + "delta = 1e-8\n", + "for epoch in range(n_epochs):\n", + " Giter = 0.0\n", + " for i in range(m):\n", + " random_index = M*np.random.randint(m)\n", + " xi = X[random_index:random_index+M]\n", + " yi = y[random_index:random_index+M]\n", + " gradients = (1.0/M)*training_gradient(yi, xi, theta)\n", + "\t# Accumulated gradient\n", + "\t# Scaling with rho the new and the previous results\n", + " Giter = (rho*Giter+(1-rho)*gradients*gradients)\n", + "\t# Taking the diagonal only and inverting\n", + " update = gradients*eta/(delta+np.sqrt(Giter))\n", + "\t# Hadamard product\n", + " theta -= update\n", + "print(\"theta from own RMSprop\")\n", + "print(theta)" + ] + }, + { + "cell_type": "markdown", + "id": "2253fa34", + "metadata": { + "editable": true + }, + "source": [ + "## And finally [ADAM](https://arxiv.org/pdf/1412.6980.pdf)" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "id": "92b3454a", + "metadata": { + "collapsed": false, + "editable": true + }, + "outputs": [], + "source": [ + "# Using Autograd to calculate gradients using RMSprop and Stochastic Gradient descent\n", + "# OLS example\n", + "from random import random, seed\n", + "import numpy as np\n", + "import autograd.numpy as np\n", + "import matplotlib.pyplot as plt\n", + "from autograd import grad\n", + "\n", + "# Note change from previous example\n", + "def CostOLS(y,X,theta):\n", + " return np.sum((y-X @ theta)**2)\n", + "\n", + "n = 1000\n", + "x = np.random.rand(n,1)\n", + "y = 2.0+3*x +4*x*x# +np.random.randn(n,1)\n", + "\n", + "X = np.c_[np.ones((n,1)), x, x*x]\n", + "XT_X = X.T @ X\n", + "theta_linreg = np.linalg.pinv(XT_X) @ (X.T @ y)\n", + "print(\"Own inversion\")\n", + "print(theta_linreg)\n", + "\n", + "\n", + "# Note that we request the derivative wrt third argument (theta, 2 here)\n", + "training_gradient = grad(CostOLS,2)\n", + "# Define parameters for Stochastic Gradient Descent\n", + "n_epochs = 50\n", + "M = 5 #size of each minibatch\n", + "m = int(n/M) #number of minibatches\n", + "# Guess for unknown parameters theta\n", + "theta = np.random.randn(3,1)\n", + "\n", + "# Value for learning rate\n", + "eta = 0.01\n", + "# Value for parameters beta1 and beta2, see https://arxiv.org/abs/1412.6980\n", + "beta1 = 0.9\n", + "beta2 = 0.999\n", + "# Including AdaGrad parameter to avoid possible division by zero\n", + "delta = 1e-7\n", + "iter = 0\n", + "for epoch in range(n_epochs):\n", + " first_moment = 0.0\n", + " second_moment = 0.0\n", + " iter += 1\n", + " for i in range(m):\n", + " random_index = M*np.random.randint(m)\n", + " xi = X[random_index:random_index+M]\n", + " yi = y[random_index:random_index+M]\n", + " gradients = (1.0/M)*training_gradient(yi, xi, theta)\n", + " # Computing moments first\n", + " first_moment = beta1*first_moment + (1-beta1)*gradients\n", + " second_moment = beta2*second_moment+(1-beta2)*gradients*gradients\n", + " first_term = first_moment/(1.0-beta1**iter)\n", + " second_term = second_moment/(1.0-beta2**iter)\n", + "\t# Scaling with rho the new and the previous results\n", + " update = eta*first_term/(np.sqrt(second_term)+delta)\n", + " theta -= update\n", + "print(\"theta from own ADAM\")\n", + "print(theta)" + ] + }, + { + "cell_type": "markdown", + "id": "c2025d97", + "metadata": { + "editable": true + }, + "source": [ + "## And Logistic Regression" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "id": "3f6d8746", + "metadata": { + "collapsed": false, + "editable": true + }, + "outputs": [], + "source": [ + "import autograd.numpy as np\n", + "from autograd import grad\n", + "\n", + "def sigmoid(x):\n", + " return 0.5 * (np.tanh(x / 2.) + 1)\n", + "\n", + "def logistic_predictions(weights, inputs):\n", + " # Outputs probability of a label being true according to logistic model.\n", + " return sigmoid(np.dot(inputs, weights))\n", + "\n", + "def training_loss(weights):\n", + " # Training loss is the negative log-likelihood of the training labels.\n", + " preds = logistic_predictions(weights, inputs)\n", + " label_probabilities = preds * targets + (1 - preds) * (1 - targets)\n", + " return -np.sum(np.log(label_probabilities))\n", + "\n", + "# Build a toy dataset.\n", + "inputs = np.array([[0.52, 1.12, 0.77],\n", + " [0.88, -1.08, 0.15],\n", + " [0.52, 0.06, -1.30],\n", + " [0.74, -2.49, 1.39]])\n", + "targets = np.array([True, True, False, True])\n", + "\n", + "# Define a function that returns gradients of training loss using Autograd.\n", + "training_gradient_fun = grad(training_loss)\n", + "\n", + "# Optimize weights using gradient descent.\n", + "weights = np.array([0.0, 0.0, 0.0])\n", + "print(\"Initial loss:\", training_loss(weights))\n", + "for i in range(100):\n", + " weights -= training_gradient_fun(weights) * 0.01\n", + "\n", + "print(\"Trained loss:\", training_loss(weights))" + ] + }, + { + "cell_type": "markdown", + "id": "716627e3", + "metadata": { + "editable": true + }, + "source": [ + "## Introducing [JAX](https://jax.readthedocs.io/en/latest/)\n", + "\n", + "Presently, instead of using **autograd**, we recommend using [JAX](https://jax.readthedocs.io/en/latest/)\n", + "\n", + "**JAX** is Autograd and [XLA (Accelerated Linear Algebra))](https://www.tensorflow.org/xla),\n", + "brought together for high-performance numerical computing and machine learning research.\n", + "It provides composable transformations of Python+NumPy programs: differentiate, vectorize, parallelize, Just-In-Time compile to GPU/TPU, and more.\n", + "\n", + "Here's a simple example on how you can use **JAX** to compute the derivate of the logistic function." + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "id": "5c938af4", + "metadata": { + "collapsed": false, + "editable": true + }, + "outputs": [], + "source": [ + "import jax.numpy as jnp\n", + "from jax import grad, jit, vmap\n", + "\n", + "def sum_logistic(x):\n", + " return jnp.sum(1.0 / (1.0 + jnp.exp(-x)))\n", + "\n", + "x_small = jnp.arange(3.)\n", + "derivative_fn = grad(sum_logistic)\n", + "print(derivative_fn(x_small))" + ] + }, + { + "cell_type": "markdown", + "id": "b087cc5f", + "metadata": { + "editable": true + }, + "source": [ + "## Introduction to Neural networks\n", + "\n", + "Artificial neural networks are computational systems that can learn to\n", + "perform tasks by considering examples, generally without being\n", + "programmed with any task-specific rules. It is supposed to mimic a\n", + "biological system, wherein neurons interact by sending signals in the\n", + "form of mathematical functions between layers. All layers can contain\n", + "an arbitrary number of neurons, and each connection is represented by\n", + "a weight variable." + ] + }, + { + "cell_type": "markdown", + "id": "c040b49e", + "metadata": { + "editable": true + }, + "source": [ + "## Artificial neurons\n", + "\n", + "The field of artificial neural networks has a long history of\n", + "development, and is closely connected with the advancement of computer\n", + "science and computers in general. A model of artificial neurons was\n", + "first developed by McCulloch and Pitts in 1943 to study signal\n", + "processing in the brain and has later been refined by others. The\n", + "general idea is to mimic neural networks in the human brain, which is\n", + "composed of billions of neurons that communicate with each other by\n", + "sending electrical signals. Each neuron accumulates its incoming\n", + "signals, which must exceed an activation threshold to yield an\n", + "output. If the threshold is not overcome, the neuron remains inactive,\n", + "i.e. has zero output.\n", + "\n", + "This behaviour has inspired a simple mathematical model for an artificial neuron." + ] + }, + { + "cell_type": "markdown", + "id": "663a7548", + "metadata": { + "editable": true + }, + "source": [ + "\n", + "
              \n", + "\n", + "$$\n", + "\\begin{equation}\n", + " y = f\\left(\\sum_{i=1}^n w_ix_i\\right) = f(u)\n", + "\\label{artificialNeuron} \\tag{6}\n", + "\\end{equation}\n", + "$$" + ] + }, + { + "cell_type": "markdown", + "id": "7d41caae", + "metadata": { + "editable": true + }, + "source": [ + "Here, the output $y$ of the neuron is the value of its activation function, which have as input\n", + "a weighted sum of signals $x_i, \\dots ,x_n$ received by $n$ other neurons.\n", + "\n", + "Conceptually, it is helpful to divide neural networks into four\n", + "categories:\n", + "1. general purpose neural networks for supervised learning,\n", + "\n", + "2. neural networks designed specifically for image processing, the most prominent example of this class being Convolutional Neural Networks (CNNs),\n", + "\n", + "3. neural networks for sequential data such as Recurrent Neural Networks (RNNs), and\n", + "\n", + "4. neural networks for unsupervised learning such as Deep Boltzmann Machines.\n", + "\n", + "In natural science, DNNs and CNNs have already found numerous\n", + "applications. In statistical physics, they have been applied to detect\n", + "phase transitions in 2D Ising and Potts models, lattice gauge\n", + "theories, and different phases of polymers, or solving the\n", + "Navier-Stokes equation in weather forecasting. Deep learning has also\n", + "found interesting applications in quantum physics. Various quantum\n", + "phase transitions can be detected and studied using DNNs and CNNs,\n", + "topological phases, and even non-equilibrium many-body\n", + "localization. Representing quantum states as DNNs quantum state\n", + "tomography are among some of the impressive achievements to reveal the\n", + "potential of DNNs to facilitate the study of quantum systems.\n", + "\n", + "In quantum information theory, it has been shown that one can perform\n", + "gate decompositions with the help of neural. \n", + "\n", + "The applications are not limited to the natural sciences. There is a\n", + "plethora of applications in essentially all disciplines, from the\n", + "humanities to life science and medicine." + ] + }, + { + "cell_type": "markdown", + "id": "8cd5fa1c", + "metadata": { + "editable": true + }, + "source": [ + "## Neural network types\n", + "\n", + "An artificial neural network (ANN), is a computational model that\n", + "consists of layers of connected neurons, or nodes or units. We will\n", + "refer to these interchangeably as units or nodes, and sometimes as\n", + "neurons.\n", + "\n", + "It is supposed to mimic a biological nervous system by letting each\n", + "neuron interact with other neurons by sending signals in the form of\n", + "mathematical functions between layers. A wide variety of different\n", + "ANNs have been developed, but most of them consist of an input layer,\n", + "an output layer and eventual layers in-between, called *hidden\n", + "layers*. All layers can contain an arbitrary number of nodes, and each\n", + "connection between two nodes is associated with a weight variable.\n", + "\n", + "Neural networks (also called neural nets) are neural-inspired\n", + "nonlinear models for supervised learning. As we will see, neural nets\n", + "can be viewed as natural, more powerful extensions of supervised\n", + "learning methods such as linear and logistic regression and soft-max\n", + "methods we discussed earlier." + ] + }, + { + "cell_type": "markdown", + "id": "0b2c6e40", + "metadata": { + "editable": true + }, + "source": [ + "## Feed-forward neural networks\n", + "\n", + "The feed-forward neural network (FFNN) was the first and simplest type\n", + "of ANNs that were devised. In this network, the information moves in\n", + "only one direction: forward through the layers.\n", + "\n", + "Nodes are represented by circles, while the arrows display the\n", + "connections between the nodes, including the direction of information\n", + "flow. Additionally, each arrow corresponds to a weight variable\n", + "(figure to come). We observe that each node in a layer is connected\n", + "to *all* nodes in the subsequent layer, making this a so-called\n", + "*fully-connected* FFNN." + ] + }, + { + "cell_type": "markdown", + "id": "90e946b7", + "metadata": { + "editable": true + }, + "source": [ + "## Convolutional Neural Network\n", + "\n", + "A different variant of FFNNs are *convolutional neural networks*\n", + "(CNNs), which have a connectivity pattern inspired by the animal\n", + "visual cortex. Individual neurons in the visual cortex only respond to\n", + "stimuli from small sub-regions of the visual field, called a receptive\n", + "field. This makes the neurons well-suited to exploit the strong\n", + "spatially local correlation present in natural images. The response of\n", + "each neuron can be approximated mathematically as a convolution\n", + "operation. (figure to come)\n", + "\n", + "Convolutional neural networks emulate the behaviour of neurons in the\n", + "visual cortex by enforcing a *local* connectivity pattern between\n", + "nodes of adjacent layers: Each node in a convolutional layer is\n", + "connected only to a subset of the nodes in the previous layer, in\n", + "contrast to the fully-connected FFNN. Often, CNNs consist of several\n", + "convolutional layers that learn local features of the input, with a\n", + "fully-connected layer at the end, which gathers all the local data and\n", + "produces the outputs. They have wide applications in image and video\n", + "recognition." + ] + }, + { + "cell_type": "markdown", + "id": "1964f9e3", + "metadata": { + "editable": true + }, + "source": [ + "## Recurrent neural networks\n", + "\n", + "So far we have only mentioned ANNs where information flows in one\n", + "direction: forward. *Recurrent neural networks* on the other hand,\n", + "have connections between nodes that form directed *cycles*. This\n", + "creates a form of internal memory which are able to capture\n", + "information on what has been calculated before; the output is\n", + "dependent on the previous computations. Recurrent NNs make use of\n", + "sequential information by performing the same task for every element\n", + "in a sequence, where each element depends on previous elements. An\n", + "example of such information is sentences, making recurrent NNs\n", + "especially well-suited for handwriting and speech recognition." + ] + }, + { + "cell_type": "markdown", + "id": "faf981a1", + "metadata": { + "editable": true + }, + "source": [ + "## Other types of networks\n", + "\n", + "There are many other kinds of ANNs that have been developed. One type\n", + "that is specifically designed for interpolation in multidimensional\n", + "space is the radial basis function (RBF) network. RBFs are typically\n", + "made up of three layers: an input layer, a hidden layer with\n", + "non-linear radial symmetric activation functions and a linear output\n", + "layer (''linear'' here means that each node in the output layer has a\n", + "linear activation function). The layers are normally fully-connected\n", + "and there are no cycles, thus RBFs can be viewed as a type of\n", + "fully-connected FFNN. They are however usually treated as a separate\n", + "type of NN due the unusual activation functions." + ] + }, + { + "cell_type": "markdown", + "id": "3667182a", + "metadata": { + "editable": true + }, + "source": [ + "## Multilayer perceptrons\n", + "\n", + "One uses often so-called fully-connected feed-forward neural networks\n", + "with three or more layers (an input layer, one or more hidden layers\n", + "and an output layer) consisting of neurons that have non-linear\n", + "activation functions.\n", + "\n", + "Such networks are often called *multilayer perceptrons* (MLPs)." + ] + }, + { + "cell_type": "markdown", + "id": "5dd1a89f", + "metadata": { + "editable": true + }, + "source": [ + "## Why multilayer perceptrons?\n", + "\n", + "According to the *Universal approximation theorem*, a feed-forward\n", + "neural network with just a single hidden layer containing a finite\n", + "number of neurons can approximate a continuous multidimensional\n", + "function to arbitrary accuracy, assuming the activation function for\n", + "the hidden layer is a **non-constant, bounded and\n", + "monotonically-increasing continuous function**.\n", + "\n", + "Note that the requirements on the activation function only applies to\n", + "the hidden layer, the output nodes are always assumed to be linear, so\n", + "as to not restrict the range of output values." + ] + }, + { + "cell_type": "markdown", + "id": "da5b8927", + "metadata": { + "editable": true + }, + "source": [ + "## Illustration of a single perceptron model and a multi-perceptron model\n", + "\n", + "\n", + "\n", + "\n", + "

              Figure 1: In a) we show a single perceptron model while in b) we dispay a network with two hidden layers, an input layer and an output layer.

              \n", + "" + ] + }, + { + "cell_type": "markdown", + "id": "3bba849e", + "metadata": { + "editable": true + }, + "source": [ + "## Examples of XOR, OR and AND gates\n", + "\n", + "Let us first try to fit various gates using standard linear\n", + "regression. The gates we are thinking of are the classical XOR, OR and\n", + "AND gates, well-known elements in computer science. The tables here\n", + "show how we can set up the inputs $x_1$ and $x_2$ in order to yield a\n", + "specific target $y_i$." + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "id": "de11d95e", + "metadata": { + "collapsed": false, + "editable": true + }, + "outputs": [], + "source": [ + "\"\"\"\n", + "Simple code that tests XOR, OR and AND gates with linear regression\n", + "\"\"\"\n", + "\n", + "import numpy as np\n", + "# Design matrix\n", + "X = np.array([ [1, 0, 0], [1, 0, 1], [1, 1, 0],[1, 1, 1]],dtype=np.float64)\n", + "print(f\"The X.TX matrix:{X.T @ X}\")\n", + "Xinv = np.linalg.pinv(X.T @ X)\n", + "print(f\"The invers of X.TX matrix:{Xinv}\")\n", + "\n", + "# The XOR gate \n", + "yXOR = np.array( [ 0, 1 ,1, 0])\n", + "ThetaXOR = Xinv @ X.T @ yXOR\n", + "print(f\"The values of theta for the XOR gate:{ThetaXOR}\")\n", + "print(f\"The linear regression prediction for the XOR gate:{X @ ThetaXOR}\")\n", + "\n", + "\n", + "# The OR gate \n", + "yOR = np.array( [ 0, 1 ,1, 1])\n", + "ThetaOR = Xinv @ X.T @ yOR\n", + "print(f\"The values of theta for the OR gate:{ThetaOR}\")\n", + "print(f\"The linear regression prediction for the OR gate:{X @ ThetaOR}\")\n", + "\n", + "\n", + "# The OR gate \n", + "yAND = np.array( [ 0, 0 ,0, 1])\n", + "ThetaAND = Xinv @ X.T @ yAND\n", + "print(f\"The values of theta for the AND gate:{ThetaAND}\")\n", + "print(f\"The linear regression prediction for the AND gate:{X @ ThetaAND}\")" + ] + }, + { + "cell_type": "markdown", + "id": "b0477050", + "metadata": { + "editable": true + }, + "source": [ + "What is happening here?" + ] + }, + { + "cell_type": "markdown", + "id": "1d72d90e", + "metadata": { + "editable": true + }, + "source": [ + "## Does Logistic Regression do a better Job?" + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "id": "501aa7b5", + "metadata": { + "collapsed": false, + "editable": true + }, + "outputs": [], + "source": [ + "\"\"\"\n", + "Simple code that tests XOR and OR gates with linear regression\n", + "and logistic regression\n", + "\"\"\"\n", + "\n", + "import matplotlib.pyplot as plt\n", + "from sklearn.linear_model import LogisticRegression\n", + "import numpy as np\n", + "\n", + "# Design matrix\n", + "X = np.array([ [1, 0, 0], [1, 0, 1], [1, 1, 0],[1, 1, 1]],dtype=np.float64)\n", + "print(f\"The X.TX matrix:{X.T @ X}\")\n", + "Xinv = np.linalg.pinv(X.T @ X)\n", + "print(f\"The invers of X.TX matrix:{Xinv}\")\n", + "\n", + "# The XOR gate \n", + "yXOR = np.array( [ 0, 1 ,1, 0])\n", + "ThetaXOR = Xinv @ X.T @ yXOR\n", + "print(f\"The values of theta for the XOR gate:{ThetaXOR}\")\n", + "print(f\"The linear regression prediction for the XOR gate:{X @ ThetaXOR}\")\n", + "\n", + "\n", + "# The OR gate \n", + "yOR = np.array( [ 0, 1 ,1, 1])\n", + "ThetaOR = Xinv @ X.T @ yOR\n", + "print(f\"The values of theta for the OR gate:{ThetaOR}\")\n", + "print(f\"The linear regression prediction for the OR gate:{X @ ThetaOR}\")\n", + "\n", + "\n", + "# The OR gate \n", + "yAND = np.array( [ 0, 0 ,0, 1])\n", + "ThetaAND = Xinv @ X.T @ yAND\n", + "print(f\"The values of theta for the AND gate:{ThetaAND}\")\n", + "print(f\"The linear regression prediction for the AND gate:{X @ ThetaAND}\")\n", + "\n", + "# Now we change to logistic regression\n", + "\n", + "\n", + "# Logistic Regression\n", + "logreg = LogisticRegression()\n", + "logreg.fit(X, yOR)\n", + "print(\"Test set accuracy with Logistic Regression for OR gate: {:.2f}\".format(logreg.score(X,yOR)))\n", + "\n", + "logreg.fit(X, yXOR)\n", + "print(\"Test set accuracy with Logistic Regression for XOR gate: {:.2f}\".format(logreg.score(X,yXOR)))\n", + "\n", + "\n", + "logreg.fit(X, yAND)\n", + "print(\"Test set accuracy with Logistic Regression for AND gate: {:.2f}\".format(logreg.score(X,yAND)))" + ] + }, + { + "cell_type": "markdown", + "id": "03908b42", + "metadata": { + "editable": true + }, + "source": [ + "Not exactly impressive, but somewhat better." + ] + }, + { + "cell_type": "markdown", + "id": "91971469", + "metadata": { + "editable": true + }, + "source": [ + "## Adding Neural Networks" + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "id": "f1717531", + "metadata": { + "collapsed": false, + "editable": true + }, + "outputs": [], + "source": [ + "\n", + "# and now neural networks with Scikit-Learn and the XOR\n", + "\n", + "from sklearn.neural_network import MLPClassifier\n", + "from sklearn.datasets import make_classification\n", + "X, yXOR = make_classification(n_samples=100, random_state=1)\n", + "FFNN = MLPClassifier(random_state=1, max_iter=300).fit(X, yXOR)\n", + "FFNN.predict_proba(X)\n", + "print(f\"Test set accuracy with Feed Forward Neural Network for XOR gate:{FFNN.score(X, yXOR)}\")" + ] + }, + { + "cell_type": "markdown", + "id": "05726714", + "metadata": { + "editable": true + }, + "source": [ + "## Mathematical model\n", + "\n", + "The output $y$ is produced via the activation function $f$" + ] + }, + { + "cell_type": "markdown", + "id": "1cf57e1c", + "metadata": { + "editable": true + }, + "source": [ + "$$\n", + "y = f\\left(\\sum_{i=1}^n w_ix_i + b_i\\right) = f(z),\n", + "$$" + ] + }, + { + "cell_type": "markdown", + "id": "57743f5e", + "metadata": { + "editable": true + }, + "source": [ + "This function receives $x_i$ as inputs.\n", + "Here the activation $z=(\\sum_{i=1}^n w_ix_i+b_i)$. \n", + "In an FFNN of such neurons, the *inputs* $x_i$ are the *outputs* of\n", + "the neurons in the preceding layer. Furthermore, an MLP is\n", + "fully-connected, which means that each neuron receives a weighted sum\n", + "of the outputs of *all* neurons in the previous layer." + ] + }, + { + "cell_type": "markdown", + "id": "2d3f8338", + "metadata": { + "editable": true + }, + "source": [ + "## Mathematical model\n", + "\n", + "First, for each node $i$ in the first hidden layer, we calculate a weighted sum $z_i^1$ of the input coordinates $x_j$," + ] + }, + { + "cell_type": "markdown", + "id": "20be0ccb", + "metadata": { + "editable": true + }, + "source": [ + "\n", + "
              \n", + "\n", + "$$\n", + "\\begin{equation} z_i^1 = \\sum_{j=1}^{M} w_{ij}^1 x_j + b_i^1\n", + "\\label{_auto6} \\tag{7}\n", + "\\end{equation}\n", + "$$" + ] + }, + { + "cell_type": "markdown", + "id": "d289b4c8", + "metadata": { + "editable": true + }, + "source": [ + "Here $b_i$ is the so-called bias which is normally needed in\n", + "case of zero activation weights or inputs. How to fix the biases and\n", + "the weights will be discussed below. The value of $z_i^1$ is the\n", + "argument to the activation function $f_i$ of each node $i$, The\n", + "variable $M$ stands for all possible inputs to a given node $i$ in the\n", + "first layer. We define the output $y_i^1$ of all neurons in layer 1 as" + ] + }, + { + "cell_type": "markdown", + "id": "498c2494", + "metadata": { + "editable": true + }, + "source": [ + "\n", + "
              \n", + "\n", + "$$\n", + "\\begin{equation}\n", + " y_i^1 = f(z_i^1) = f\\left(\\sum_{j=1}^M w_{ij}^1 x_j + b_i^1\\right)\n", + "\\label{outputLayer1} \\tag{8}\n", + "\\end{equation}\n", + "$$" + ] + }, + { + "cell_type": "markdown", + "id": "77995e5d", + "metadata": { + "editable": true + }, + "source": [ + "where we assume that all nodes in the same layer have identical\n", + "activation functions, hence the notation $f$. In general, we could assume in the more general case that different layers have different activation functions.\n", + "In this case we would identify these functions with a superscript $l$ for the $l$-th layer," + ] + }, + { + "cell_type": "markdown", + "id": "ef353d76", + "metadata": { + "editable": true + }, + "source": [ + "\n", + "
              \n", + "\n", + "$$\n", + "\\begin{equation}\n", + " y_i^l = f^l(u_i^l) = f^l\\left(\\sum_{j=1}^{N_{l-1}} w_{ij}^l y_j^{l-1} + b_i^l\\right)\n", + "\\label{generalLayer} \\tag{9}\n", + "\\end{equation}\n", + "$$" + ] + }, + { + "cell_type": "markdown", + "id": "0a25d2f4", + "metadata": { + "editable": true + }, + "source": [ + "where $N_l$ is the number of nodes in layer $l$. When the output of\n", + "all the nodes in the first hidden layer are computed, the values of\n", + "the subsequent layer can be calculated and so forth until the output\n", + "is obtained." + ] + }, + { + "cell_type": "markdown", + "id": "d7d29703", + "metadata": { + "editable": true + }, + "source": [ + "## Mathematical model\n", + "\n", + "The output of neuron $i$ in layer 2 is thus," + ] + }, + { + "cell_type": "markdown", + "id": "94eddeb9", + "metadata": { + "editable": true + }, + "source": [ + "\n", + "
              \n", + "\n", + "$$\n", + "\\begin{equation}\n", + " y_i^2 = f^2\\left(\\sum_{j=1}^N w_{ij}^2 y_j^1 + b_i^2\\right) \n", + "\\label{_auto7} \\tag{10}\n", + "\\end{equation}\n", + "$$" + ] + }, + { + "cell_type": "markdown", + "id": "f047f4c6", + "metadata": { + "editable": true + }, + "source": [ + "\n", + "
              \n", + "\n", + "$$\n", + "\\begin{equation} \n", + " = f^2\\left[\\sum_{j=1}^N w_{ij}^2f^1\\left(\\sum_{k=1}^M w_{jk}^1 x_k + b_j^1\\right) + b_i^2\\right]\n", + "\\label{outputLayer2} \\tag{11}\n", + "\\end{equation}\n", + "$$" + ] + }, + { + "cell_type": "markdown", + "id": "91d4806e", + "metadata": { + "editable": true + }, + "source": [ + "where we have substituted $y_k^1$ with the inputs $x_k$. Finally, the ANN output reads" + ] + }, + { + "cell_type": "markdown", + "id": "7342e125", + "metadata": { + "editable": true + }, + "source": [ + "\n", + "
              \n", + "\n", + "$$\n", + "\\begin{equation}\n", + " y_i^3 = f^3\\left(\\sum_{j=1}^N w_{ij}^3 y_j^2 + b_i^3\\right) \n", + "\\label{_auto8} \\tag{12}\n", + "\\end{equation}\n", + "$$" + ] + }, + { + "cell_type": "markdown", + "id": "5068e976", + "metadata": { + "editable": true + }, + "source": [ + "\n", + "
              \n", + "\n", + "$$\n", + "\\begin{equation} \n", + " = f_3\\left[\\sum_{j} w_{ij}^3 f^2\\left(\\sum_{k} w_{jk}^2 f^1\\left(\\sum_{m} w_{km}^1 x_m + b_k^1\\right) + b_j^2\\right)\n", + " + b_1^3\\right]\n", + "\\label{_auto9} \\tag{13}\n", + "\\end{equation}\n", + "$$" + ] + }, + { + "cell_type": "markdown", + "id": "b51a241d", + "metadata": { + "editable": true + }, + "source": [ + "## Mathematical model\n", + "\n", + "We can generalize this expression to an MLP with $l$ hidden\n", + "layers. The complete functional form is," + ] + }, + { + "cell_type": "markdown", + "id": "5a7b4915", + "metadata": { + "editable": true + }, + "source": [ + "\n", + "
              \n", + "\n", + "$$\n", + "\\begin{equation}\n", + "y^{l+1}_i = f^{l+1}\\left[\\!\\sum_{j=1}^{N_l} w_{ij}^3 f^l\\left(\\sum_{k=1}^{N_{l-1}}w_{jk}^{l-1}\\left(\\dots f^1\\left(\\sum_{n=1}^{N_0} w_{mn}^1 x_n+ b_m^1\\right)\\dots\\right)+b_k^2\\right)+b_1^3\\right] \n", + "\\label{completeNN} \\tag{14}\n", + "\\end{equation}\n", + "$$" + ] + }, + { + "cell_type": "markdown", + "id": "c3215b7f", + "metadata": { + "editable": true + }, + "source": [ + "which illustrates a basic property of MLPs: The only independent\n", + "variables are the input values $x_n$." + ] + }, + { + "cell_type": "markdown", + "id": "f92eedb0", + "metadata": { + "editable": true + }, + "source": [ + "## Mathematical model\n", + "\n", + "This confirms that an MLP, despite its quite convoluted mathematical\n", + "form, is nothing more than an analytic function, specifically a\n", + "mapping of real-valued vectors $\\hat{x} \\in \\mathbb{R}^n \\rightarrow\n", + "\\hat{y} \\in \\mathbb{R}^m$.\n", + "\n", + "Furthermore, the flexibility and universality of an MLP can be\n", + "illustrated by realizing that the expression is essentially a nested\n", + "sum of scaled activation functions of the form" + ] + }, + { + "cell_type": "markdown", + "id": "b658fa6d", + "metadata": { + "editable": true + }, + "source": [ + "\n", + "
              \n", + "\n", + "$$\n", + "\\begin{equation}\n", + " f(x) = c_1 f(c_2 x + c_3) + c_4\n", + "\\label{_auto10} \\tag{15}\n", + "\\end{equation}\n", + "$$" + ] + }, + { + "cell_type": "markdown", + "id": "8506281a", + "metadata": { + "editable": true + }, + "source": [ + "where the parameters $c_i$ are weights and biases. By adjusting these\n", + "parameters, the activation functions can be shifted up and down or\n", + "left and right, change slope or be rescaled which is the key to the\n", + "flexibility of a neural network." + ] + }, + { + "cell_type": "markdown", + "id": "0ad3f400", + "metadata": { + "editable": true + }, + "source": [ + "### Matrix-vector notation\n", + "\n", + "We can introduce a more convenient notation for the activations in an A NN. \n", + "\n", + "Additionally, we can represent the biases and activations\n", + "as layer-wise column vectors $\\hat{b}_l$ and $\\hat{y}_l$, so that the $i$-th element of each vector \n", + "is the bias $b_i^l$ and activation $y_i^l$ of node $i$ in layer $l$ respectively. \n", + "\n", + "We have that $\\mathrm{W}_l$ is an $N_{l-1} \\times N_l$ matrix, while $\\hat{b}_l$ and $\\hat{y}_l$ are $N_l \\times 1$ column vectors. \n", + "With this notation, the sum becomes a matrix-vector multiplication, and we can write\n", + "the equation for the activations of hidden layer 2 (assuming three nodes for simplicity) as" + ] + }, + { + "cell_type": "markdown", + "id": "7b431efc", + "metadata": { + "editable": true + }, + "source": [ + "\n", + "
              \n", + "\n", + "$$\n", + "\\begin{equation}\n", + " \\hat{y}_2 = f_2(\\mathrm{W}_2 \\hat{y}_{1} + \\hat{b}_{2}) = \n", + " f_2\\left(\\left[\\begin{array}{ccc}\n", + " w^2_{11} &w^2_{12} &w^2_{13} \\\\\n", + " w^2_{21} &w^2_{22} &w^2_{23} \\\\\n", + " w^2_{31} &w^2_{32} &w^2_{33} \\\\\n", + " \\end{array} \\right] \\cdot\n", + " \\left[\\begin{array}{c}\n", + " y^1_1 \\\\\n", + " y^1_2 \\\\\n", + " y^1_3 \\\\\n", + " \\end{array}\\right] + \n", + " \\left[\\begin{array}{c}\n", + " b^2_1 \\\\\n", + " b^2_2 \\\\\n", + " b^2_3 \\\\\n", + " \\end{array}\\right]\\right).\n", + "\\label{_auto11} \\tag{16}\n", + "\\end{equation}\n", + "$$" + ] + }, + { + "cell_type": "markdown", + "id": "d129b057", + "metadata": { + "editable": true + }, + "source": [ + "### Matrix-vector notation and activation\n", + "\n", + "The activation of node $i$ in layer 2 is" + ] + }, + { + "cell_type": "markdown", + "id": "7af14562", + "metadata": { + "editable": true + }, + "source": [ + "\n", + "
              \n", + "\n", + "$$\n", + "\\begin{equation}\n", + " y^2_i = f_2\\Bigr(w^2_{i1}y^1_1 + w^2_{i2}y^1_2 + w^2_{i3}y^1_3 + b^2_i\\Bigr) = \n", + " f_2\\left(\\sum_{j=1}^3 w^2_{ij} y_j^1 + b^2_i\\right).\n", + "\\label{_auto12} \\tag{17}\n", + "\\end{equation}\n", + "$$" + ] + }, + { + "cell_type": "markdown", + "id": "0b7e127c", + "metadata": { + "editable": true + }, + "source": [ + "This is not just a convenient and compact notation, but also a useful\n", + "and intuitive way to think about MLPs: The output is calculated by a\n", + "series of matrix-vector multiplications and vector additions that are\n", + "used as input to the activation functions. For each operation\n", + "$\\mathrm{W}_l \\hat{y}_{l-1}$ we move forward one layer." + ] + }, + { + "cell_type": "markdown", + "id": "91266ae3", + "metadata": { + "editable": true + }, + "source": [ + "### Activation functions\n", + "\n", + "A property that characterizes a neural network, other than its\n", + "connectivity, is the choice of activation function(s). As described\n", + "in, the following restrictions are imposed on an activation function\n", + "for a FFNN to fulfill the universal approximation theorem\n", + "\n", + " * Non-constant\n", + "\n", + " * Bounded\n", + "\n", + " * Monotonically-increasing\n", + "\n", + " * Continuous" + ] + }, + { + "cell_type": "markdown", + "id": "54728fbd", + "metadata": { + "editable": true + }, + "source": [ + "### Activation functions, Logistic and Hyperbolic ones\n", + "\n", + "The second requirement excludes all linear functions. Furthermore, in\n", + "a MLP with only linear activation functions, each layer simply\n", + "performs a linear transformation of its inputs.\n", + "\n", + "Regardless of the number of layers, the output of the NN will be\n", + "nothing but a linear function of the inputs. Thus we need to introduce\n", + "some kind of non-linearity to the NN to be able to fit non-linear\n", + "functions Typical examples are the logistic *Sigmoid*" + ] + }, + { + "cell_type": "markdown", + "id": "17b851fb", + "metadata": { + "editable": true + }, + "source": [ + "$$\n", + "f(x) = \\frac{1}{1 + e^{-x}},\n", + "$$" + ] + }, + { + "cell_type": "markdown", + "id": "6e17f015", + "metadata": { + "editable": true + }, + "source": [ + "and the *hyperbolic tangent* function" + ] + }, + { + "cell_type": "markdown", + "id": "574fbcd0", + "metadata": { + "editable": true + }, + "source": [ + "$$\n", + "f(x) = \\tanh(x)\n", + "$$" + ] + }, + { + "cell_type": "markdown", + "id": "daa971d1", + "metadata": { + "editable": true + }, + "source": [ + "### Relevance\n", + "\n", + "The *sigmoid* function are more biologically plausible because the\n", + "output of inactive neurons are zero. Such activation function are\n", + "called *one-sided*. However, it has been shown that the hyperbolic\n", + "tangent performs better than the sigmoid for training MLPs. has\n", + "become the most popular for *deep neural networks*" + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "id": "c12bc7fe", + "metadata": { + "collapsed": false, + "editable": true + }, + "outputs": [], + "source": [ + "\"\"\"The sigmoid function (or the logistic curve) is a \n", + "function that takes any real number, z, and outputs a number (0,1).\n", + "It is useful in neural networks for assigning weights on a relative scale.\n", + "The value z is the weighted sum of parameters involved in the learning algorithm.\"\"\"\n", + "\n", + "import numpy\n", + "import matplotlib.pyplot as plt\n", + "import math as mt\n", + "\n", + "z = numpy.arange(-5, 5, .1)\n", + "sigma_fn = numpy.vectorize(lambda z: 1/(1+numpy.exp(-z)))\n", + "sigma = sigma_fn(z)\n", + "\n", + "fig = plt.figure()\n", + "ax = fig.add_subplot(111)\n", + "ax.plot(z, sigma)\n", + "ax.set_ylim([-0.1, 1.1])\n", + "ax.set_xlim([-5,5])\n", + "ax.grid(True)\n", + "ax.set_xlabel('z')\n", + "ax.set_title('sigmoid function')\n", + "\n", + "plt.show()\n", + "\n", + "\"\"\"Step Function\"\"\"\n", + "z = numpy.arange(-5, 5, .02)\n", + "step_fn = numpy.vectorize(lambda z: 1.0 if z >= 0.0 else 0.0)\n", + "step = step_fn(z)\n", + "\n", + "fig = plt.figure()\n", + "ax = fig.add_subplot(111)\n", + "ax.plot(z, step)\n", + "ax.set_ylim([-0.5, 1.5])\n", + "ax.set_xlim([-5,5])\n", + "ax.grid(True)\n", + "ax.set_xlabel('z')\n", + "ax.set_title('step function')\n", + "\n", + "plt.show()\n", + "\n", + "\"\"\"Sine Function\"\"\"\n", + "z = numpy.arange(-2*mt.pi, 2*mt.pi, 0.1)\n", + "t = numpy.sin(z)\n", + "\n", + "fig = plt.figure()\n", + "ax = fig.add_subplot(111)\n", + "ax.plot(z, t)\n", + "ax.set_ylim([-1.0, 1.0])\n", + "ax.set_xlim([-2*mt.pi,2*mt.pi])\n", + "ax.grid(True)\n", + "ax.set_xlabel('z')\n", + "ax.set_title('sine function')\n", + "\n", + "plt.show()\n", + "\n", + "\"\"\"Plots a graph of the squashing function used by a rectified linear\n", + "unit\"\"\"\n", + "z = numpy.arange(-2, 2, .1)\n", + "zero = numpy.zeros(len(z))\n", + "y = numpy.max([zero, z], axis=0)\n", + "\n", + "fig = plt.figure()\n", + "ax = fig.add_subplot(111)\n", + "ax.plot(z, y)\n", + "ax.set_ylim([-2.0, 2.0])\n", + "ax.set_xlim([-2.0, 2.0])\n", + "ax.grid(True)\n", + "ax.set_xlabel('z')\n", + "ax.set_title('Rectified linear unit')\n", + "\n", + "plt.show()" + ] + } + ], + "metadata": {}, + "nbformat": 4, + "nbformat_minor": 5 +}