diff --git a/doc/LectureNotes/DataFiles/cancer.dot b/doc/LectureNotes/DataFiles/cancer.dot index 44799ecb3..75fc2296d 100644 --- a/doc/LectureNotes/DataFiles/cancer.dot +++ b/doc/LectureNotes/DataFiles/cancer.dot @@ -6,11 +6,11 @@ edge [fontname="helvetica"] ; 0 -> 1 [labeldistance=2.5, labelangle=45, headlabel="True"] ; 2 [label="worst concave points <= 0.135\ngini = 0.031\nsamples = 253\nvalue = [[249, 4]\n[4, 249]]", fillcolor="#e78946"] ; 1 -> 2 ; -3 [label="radius error <= 0.643\ngini = 0.008\nsamples = 242\nvalue = [[241, 1]\n[1, 241]]", fillcolor="#e5833c"] ; +3 [label="area error <= 48.975\ngini = 0.008\nsamples = 242\nvalue = [[241, 1]\n[1, 241]]", fillcolor="#e5833c"] ; 2 -> 3 ; 4 [label="gini = 0.0\nsamples = 239\nvalue = [[239, 0]\n[0, 239]]", fillcolor="#e58139"] ; 3 -> 4 ; -5 [label="mean perimeter <= 78.51\ngini = 0.444\nsamples = 3\nvalue = [[2, 1]\n[1, 2]]", fillcolor="#fdf6f0"] ; +5 [label="mean texture <= 18.935\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 ; @@ -34,7 +34,7 @@ edge [fontname="helvetica"] ; 14 -> 15 ; 16 [label="gini = 0.0\nsamples = 11\nvalue = [[11, 0]\n[0, 11]]", fillcolor="#e58139"] ; 15 -> 16 ; -17 [label="worst concavity <= 0.212\ngini = 0.32\nsamples = 5\nvalue = [[1, 4]\n[4, 1]]", fillcolor="#f6d5bd"] ; +17 [label="worst concave points <= 0.104\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 ; @@ -42,13 +42,13 @@ edge [fontname="helvetica"] ; 17 -> 19 ; 20 [label="mean concave points <= 0.049\ngini = 0.088\nsamples = 151\nvalue = [[7, 144]\n[144, 7]]", fillcolor="#ea985d"] ; 14 -> 20 ; -21 [label="concave points error <= 0.01\ngini = 0.48\nsamples = 15\nvalue = [[6, 9]\n[9, 6]]", fillcolor="#ffffff"] ; +21 [label="compactness error <= 0.016\ngini = 0.48\nsamples = 15\nvalue = [[6, 9]\n[9, 6]]", fillcolor="#ffffff"] ; 20 -> 21 ; 22 [label="gini = 0.0\nsamples = 9\nvalue = [[0, 9]\n[9, 0]]", fillcolor="#e58139"] ; 21 -> 22 ; 23 [label="gini = 0.0\nsamples = 6\nvalue = [[6, 0]\n[0, 6]]", fillcolor="#e58139"] ; 21 -> 23 ; -24 [label="mean smoothness <= 0.079\ngini = 0.015\nsamples = 136\nvalue = [[1, 135]\n[135, 1]]", fillcolor="#e6853f"] ; +24 [label="worst smoothness <= 0.096\ngini = 0.015\nsamples = 136\nvalue = [[1, 135]\n[135, 1]]", 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b/doc/LectureNotes/_build/html/_sources/exercisesweek41.ipynb index d776eda31..f57ebb43b 100644 --- a/doc/LectureNotes/_build/html/_sources/exercisesweek41.ipynb +++ b/doc/LectureNotes/_build/html/_sources/exercisesweek41.ipynb @@ -3,9 +3,7 @@ { "cell_type": "markdown", "id": "b18bcd06", - "metadata": { - "editable": true - }, + "metadata": {}, "source": [ "\n", @@ -15,9 +13,7 @@ { "cell_type": "markdown", "id": "7542d6aa", - "metadata": { - "editable": true - }, + "metadata": {}, "source": [ "# Exercises week 41\n", "**October 4-11, 2024**\n", @@ -28,9 +24,7 @@ { "cell_type": "markdown", "id": "80943a15", - "metadata": { - "editable": true - }, + "metadata": {}, "source": [ "# Overarching aims of the exercises this week\n", "\n", @@ -83,9 +77,7 @@ { "cell_type": "markdown", "id": "2095d197", - "metadata": { - "editable": true - }, + "metadata": {}, "source": [ "# Code examples from week 39 and 40" ] @@ -93,9 +85,7 @@ { "cell_type": "markdown", "id": "f428decb", - "metadata": { - "editable": true - }, + "metadata": {}, "source": [ "## Code with a Number of Minibatches which varies, analytical gradient\n", "\n", @@ -106,10 +96,7 @@ "cell_type": "code", "execution_count": 1, "id": "ba38d454", - "metadata": { - "collapsed": false, - "editable": true - }, + "metadata": {}, "outputs": [], "source": [ "%matplotlib inline\n", @@ -185,9 +172,7 @@ { "cell_type": "markdown", "id": "de04b41a", - "metadata": { - "editable": true - }, + "metadata": {}, "source": [ "In the above code, we have use replacement in setting up the\n", "mini-batches. The discussion\n", @@ -198,9 +183,7 @@ { "cell_type": "markdown", "id": "77fc1cca", - "metadata": { - "editable": true - }, + "metadata": {}, "source": [ "## Momentum based GD\n", "\n", @@ -213,9 +196,7 @@ { "cell_type": "markdown", "id": "441d1f36", - "metadata": { - "editable": true - }, + "metadata": {}, "source": [ "$$\n", "\\mathbf{v}_{t}=\\gamma \\mathbf{v}_{t-1}+\\eta_{t}\\nabla_\\theta E(\\boldsymbol{\\theta}_t) \\nonumber\n", @@ -225,9 +206,7 @@ { "cell_type": "markdown", "id": "47434945", - "metadata": { - "editable": true - }, + "metadata": {}, "source": [ "\n", "
\n", @@ -243,9 +222,7 @@ { "cell_type": "markdown", "id": "f3ea5060", - "metadata": { - "editable": true - }, + "metadata": {}, "source": [ "where we have introduced a momentum parameter $\\gamma$, with\n", "$0\\le\\gamma\\le 1$, and for brevity we dropped the explicit notation to\n", @@ -262,9 +239,7 @@ { "cell_type": "markdown", "id": "923628c8", - "metadata": { - "editable": true - }, + "metadata": {}, "source": [ "$$\n", "\\Delta \\boldsymbol{\\theta}_{t+1} = \\gamma \\Delta \\boldsymbol{\\theta}_t -\\ \\eta_{t}\\nabla_\\theta E(\\boldsymbol{\\theta}_t),\n", @@ -274,9 +249,7 @@ { "cell_type": "markdown", "id": "5c94031c", - "metadata": { - "editable": true - }, + "metadata": {}, "source": [ "where we have defined $\\Delta \\boldsymbol{\\theta}_{t}= \\boldsymbol{\\theta}_t-\\boldsymbol{\\theta}_{t-1}$." ] @@ -284,9 +257,7 @@ { "cell_type": "markdown", "id": "f3f0e9c9", - "metadata": { - "editable": true - }, + "metadata": {}, "source": [ "## Algorithms and codes for Adagrad, RMSprop and Adam\n", "\n", @@ -298,9 +269,7 @@ { "cell_type": "markdown", "id": "92253eff", - "metadata": { - "editable": true - }, + "metadata": {}, "source": [ "## Practical tips\n", "\n", @@ -318,9 +287,7 @@ { "cell_type": "markdown", "id": "08209015", - "metadata": { - "editable": true - }, + "metadata": {}, "source": [ "## Using Automatic differentation with OLS\n", "\n", @@ -333,10 +300,7 @@ "cell_type": "code", "execution_count": 2, "id": "f1f7d4aa", - "metadata": { - "collapsed": false, - "editable": true - }, + "metadata": {}, "outputs": [], "source": [ "# Using Autograd to calculate gradients for OLS\n", @@ -393,9 +357,7 @@ { "cell_type": "markdown", "id": "1bc83f33", - "metadata": { - "editable": true - }, + "metadata": {}, "source": [ "## Same code but now with momentum gradient descent" ] @@ -404,10 +366,7 @@ "cell_type": "code", "execution_count": 3, "id": "dc2a3f65", - "metadata": { - "collapsed": false, - "editable": true - }, + "metadata": {}, "outputs": [], "source": [ "# Using Autograd to calculate gradients for OLS\n", @@ -468,9 +427,7 @@ { "cell_type": "markdown", "id": "0ef007d0", - "metadata": { - "editable": true - }, + "metadata": {}, "source": [ "## But noen of these can compete with Newton's method" ] @@ -479,10 +436,7 @@ "cell_type": "code", "execution_count": 4, "id": "0e498aa4", - "metadata": { - "collapsed": false, - "editable": true - }, + "metadata": {}, "outputs": [], "source": [ "# Using Newton's method\n", @@ -528,9 +482,7 @@ { "cell_type": "markdown", "id": "40292cf3", - "metadata": { - "editable": true - }, + "metadata": {}, "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**." @@ -540,10 +492,7 @@ "cell_type": "code", "execution_count": 5, "id": "fa819b9d", - "metadata": { - "collapsed": false, - "editable": true - }, + "metadata": {}, "outputs": [], "source": [ "# Using Autograd to calculate gradients using SGD\n", @@ -624,9 +573,7 @@ { "cell_type": "markdown", "id": "2ca466b4", - "metadata": { - "editable": true - }, + "metadata": {}, "source": [ "## Same code but now with momentum gradient descent" ] @@ -635,10 +582,7 @@ "cell_type": "code", "execution_count": 6, "id": "0d44a49c", - "metadata": { - "collapsed": false, - "editable": true - }, + "metadata": {}, "outputs": [], "source": [ "# Using Autograd to calculate gradients using SGD\n", @@ -713,9 +657,7 @@ { "cell_type": "markdown", "id": "b82627f6", - "metadata": { - "editable": true - }, + "metadata": {}, "source": [ "## AdaGrad algorithm, taken from [Goodfellow et al](https://www.deeplearningbook.org/contents/optimization.html)\n", "\n", @@ -729,9 +671,7 @@ { "cell_type": "markdown", "id": "00d3aff0", - "metadata": { - "editable": true - }, + "metadata": {}, "source": [ "## Similar (second order function now) problem but now with AdaGrad" ] @@ -740,10 +680,7 @@ "cell_type": "code", "execution_count": 7, "id": "6b85aacc", - "metadata": { - "collapsed": false, - "editable": true - }, + "metadata": {}, "outputs": [], "source": [ "# Using Autograd to calculate gradients using AdaGrad and Stochastic Gradient descent\n", @@ -799,9 +736,7 @@ { "cell_type": "markdown", "id": "d8ddde38", - "metadata": { - "editable": true - }, + "metadata": {}, "source": [ "Running this code we note an almost perfect agreement with the results from matrix inversion." ] @@ -809,9 +744,7 @@ { "cell_type": "markdown", "id": "ff15b503", - "metadata": { - "editable": true - }, + "metadata": {}, "source": [ "## RMSProp algorithm, taken from [Goodfellow et al](https://www.deeplearningbook.org/contents/optimization.html)\n", "\n", @@ -825,9 +758,7 @@ { "cell_type": "markdown", "id": "66f96d12", - "metadata": { - "editable": true - }, + "metadata": {}, "source": [ "## RMSprop for adaptive learning rate with Stochastic Gradient Descent" ] @@ -836,10 +767,7 @@ "cell_type": "code", "execution_count": 8, "id": "888f1b4e", - "metadata": { - "collapsed": false, - "editable": true - }, + "metadata": {}, "outputs": [], "source": [ "# Using Autograd to calculate gradients using RMSprop and Stochastic Gradient descent\n", @@ -901,9 +829,7 @@ { "cell_type": "markdown", "id": "2e0860f7", - "metadata": { - "editable": true - }, + "metadata": {}, "source": [ "## ADAM algorithm, taken from [Goodfellow et al](https://www.deeplearningbook.org/contents/optimization.html)\n", "\n", @@ -917,9 +843,7 @@ { "cell_type": "markdown", "id": "ab4a9859", - "metadata": { - "editable": true - }, + "metadata": {}, "source": [ "## And finally [ADAM](https://arxiv.org/pdf/1412.6980.pdf)" ] @@ -928,10 +852,7 @@ "cell_type": "code", "execution_count": 9, "id": "ccdd4d77", - "metadata": { - "collapsed": false, - "editable": true - }, + "metadata": {}, "outputs": [], "source": [ "# Using Autograd to calculate gradients using RMSprop and Stochastic Gradient descent\n", @@ -998,9 +919,7 @@ { "cell_type": "markdown", "id": "25ac988c", - "metadata": { - "editable": true - }, + "metadata": {}, "source": [ "## Introducing [JAX](https://jax.readthedocs.io/en/latest/)\n", "\n", @@ -1014,9 +933,7 @@ { "cell_type": "markdown", "id": "37d556d0", - "metadata": { - "editable": true - }, + "metadata": {}, "source": [ "### Getting started with Jax, note the way we import numpy" ] @@ -1025,10 +942,7 @@ "cell_type": "code", "execution_count": 10, "id": "5b81d6e4", - "metadata": { - "collapsed": false, - "editable": true - }, + "metadata": {}, "outputs": [], "source": [ "import jax\n", @@ -1042,9 +956,7 @@ { "cell_type": "markdown", "id": "c42db672", - "metadata": { - "editable": true - }, + "metadata": {}, "source": [ "### A warm-up example" ] @@ -1053,10 +965,7 @@ "cell_type": "code", "execution_count": 11, "id": "98eb2f26", - "metadata": { - "collapsed": false, - "editable": true - }, + "metadata": {}, "outputs": [], "source": [ "def function(x):\n", @@ -1098,9 +1007,7 @@ { "cell_type": "markdown", "id": "8a5f19b5", - "metadata": { - "editable": true - }, + "metadata": {}, "source": [ "### A more advanced example" ] @@ -1109,10 +1016,7 @@ "cell_type": "code", "execution_count": 12, "id": "d8f5eb38", - "metadata": { - "collapsed": false, - "editable": true - }, + "metadata": {}, "outputs": [], "source": [ "backend = np\n", @@ -1138,7 +1042,25 @@ ] } ], - "metadata": {}, + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.18" + } + }, "nbformat": 4, "nbformat_minor": 5 } diff --git a/doc/LectureNotes/_build/html/_sources/project2.ipynb b/doc/LectureNotes/_build/html/_sources/project2.ipynb index c1411ffd6..9a6c5c33c 100644 --- a/doc/LectureNotes/_build/html/_sources/project2.ipynb +++ b/doc/LectureNotes/_build/html/_sources/project2.ipynb @@ -3,9 +3,7 @@ { "cell_type": "markdown", "id": "5b2f9dda", - "metadata": { - "editable": true - }, + "metadata": {}, "source": [ "\n", @@ -15,9 +13,7 @@ { "cell_type": "markdown", "id": "cacbd604", - "metadata": { - "editable": true - }, + "metadata": {}, "source": [ "# Project 2 on Machine Learning, deadline November 4 (Midnight)\n", "**[Data Analysis and Machine Learning FYS-STK3155/FYS4155](http://www.uio.no/studier/emner/matnat/fys/FYS3155/index-eng.html)**, Department of Physics, University of Oslo, Norway\n", @@ -30,9 +26,7 @@ { "cell_type": "markdown", "id": "acb32119", - "metadata": { - "editable": true - }, + "metadata": {}, "source": [ "## Classification and Regression, from linear and logistic regression to neural networks\n", "\n", @@ -76,9 +70,7 @@ { "cell_type": "markdown", "id": "027202f0", - "metadata": { - "editable": true - }, + "metadata": {}, "source": [ "### Part a): Write your own Stochastic Gradient Descent code, first step\n", "\n", @@ -132,9 +124,7 @@ { "cell_type": "markdown", "id": "9388fa74", - "metadata": { - "editable": true - }, + "metadata": {}, "source": [ "### Part b): Writing your own Neural Network code\n", "\n", @@ -170,9 +160,7 @@ { "cell_type": "markdown", "id": "49666354", - "metadata": { - "editable": true - }, + "metadata": {}, "source": [ "### Part c): Testing different activation functions\n", "\n", @@ -182,9 +170,7 @@ { "cell_type": "markdown", "id": "79aacf29", - "metadata": { - "editable": true - }, + "metadata": {}, "source": [ "### Part d): Classification analysis using neural networks\n", "\n", @@ -208,9 +194,7 @@ { "cell_type": "markdown", "id": "42e22900", - "metadata": { - "editable": true - }, + "metadata": {}, "source": [ "$$\n", "\\text{Accuracy} = \\frac{\\sum_{i=1}^n I(t_i = y_i)}{n} ,\n", @@ -220,9 +204,7 @@ { "cell_type": "markdown", "id": "82ae763d", - "metadata": { - "editable": true - }, + "metadata": {}, "source": [ "where $I$ is the indicator function, $1$ if $t_i = y_i$ and $0$\n", "otherwise if we have a binary classification problem. Here $t_i$\n", @@ -240,9 +222,7 @@ { "cell_type": "markdown", "id": "1d6b84d1", - "metadata": { - "editable": true - }, + "metadata": {}, "source": [ "### Part e): Write your Logistic Regression code, final step\n", "\n", @@ -262,9 +242,7 @@ { "cell_type": "markdown", "id": "0bce8832", - "metadata": { - "editable": true - }, + "metadata": {}, "source": [ "### Part f) Critical evaluation of the various algorithms\n", "\n", @@ -278,9 +256,7 @@ { "cell_type": "markdown", "id": "51b1b29b", - "metadata": { - "editable": true - }, + "metadata": {}, "source": [ "## Background literature\n", "\n", @@ -294,9 +270,7 @@ { "cell_type": "markdown", "id": "7e4ffbbd", - "metadata": { - "editable": true - }, + "metadata": {}, "source": [ "## Introduction to numerical projects\n", "\n", @@ -325,9 +299,7 @@ { "cell_type": "markdown", "id": "56112b03", - "metadata": { - "editable": true - }, + "metadata": {}, "source": [ "## Format for electronic delivery of report and programs\n", "\n", @@ -345,7 +317,25 @@ ] } ], - "metadata": {}, + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.18" + } + }, "nbformat": 4, "nbformat_minor": 5 } diff --git a/doc/LectureNotes/_build/html/chapter1.html b/doc/LectureNotes/_build/html/chapter1.html index e329f00b2..82b934576 100644 --- a/doc/LectureNotes/_build/html/chapter1.html +++ b/doc/LectureNotes/_build/html/chapter1.html @@ -308,6 +308,21 @@ const thebe_selector_output = ".output, .cell_output" Week 39: Optimization and Gradient Methods +
  • + + Week 40: Gradient descent methods (continued) and start Neural networks + +
    • +
  • + + Exercises week 41 + +
    • +
  • + + Week 41 Neural networks and constructing a neural network code + +

    • @@ -320,6 +335,11 @@ const thebe_selector_output = ".output, .cell_output" Project 1 on Machine Learning, deadline October 7 (midnight), 2024 +

  • + + Project 2 on Machine Learning, deadline November 4 (Midnight) + +
    • @@ -1036,13 +1056,13 @@ example of the functionality of Scikit-Learn.

    The intercept alpha: 
- [1.92622842]
+ [1.94485679]
 Coefficient beta : 
- [[5.21332621]]
-Mean squared error: 0.22
-Variance score: 0.90
+ [[5.13059483]]
+Mean squared error: 0.32
+Variance score: 0.87
 Mean squared log error: 0.01
-Mean absolute error: 0.38
+Mean absolute error: 0.45
    _images/chapter1_19_1.png @@ -1142,7 +1162,7 @@ a linear \(x\)-dependence we s
    _images/chapter1_33_0.png -
    0.005
+
    0.004999999999999996
    diff --git a/doc/LectureNotes/_build/html/chapter10.html b/doc/LectureNotes/_build/html/chapter10.html index 534c7c871..2f2d9e60e 100644 --- a/doc/LectureNotes/_build/html/chapter10.html +++ b/doc/LectureNotes/_build/html/chapter10.html @@ -308,6 +308,21 @@ const thebe_selector_output = ".output, .cell_output" Week 39: Optimization and Gradient Methods +
  • + + Week 40: Gradient descent methods (continued) and start Neural networks + +
    • +
  • + + Exercises week 41 + +
    • +
  • + + Week 41 Neural networks and constructing a neural network code + +

    • @@ -320,6 +335,11 @@ const thebe_selector_output = ".output, .cell_output" Project 1 on Machine Learning, deadline October 7 (midnight), 2024 +

  • + + Project 2 on Machine Learning, deadline November 4 (Midnight) + +
    • @@ -1353,7 +1373,7 @@ the Hadamard product, meaning element-wise multiplication.

    Old accuracy on training data: 0.1440501043841336
    -
    /var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_95354/953065564.py:4: RuntimeWarning: overflow encountered in exp
+
    /var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_22411/953065564.py:4: RuntimeWarning: overflow encountered in exp
   return 1/(1 + np.exp(-x))
 
    
 
    
@@ -1687,7 +1707,7 @@ Lambda =  10.0
 Accuracy score on test set:  0.19166666666666668
    -
    /var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_95354/953065564.py:4: RuntimeWarning: overflow encountered in exp
+
    /var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_22411/953065564.py:4: RuntimeWarning: overflow encountered in exp
   return 1/(1 + np.exp(-x))
 
    
 
    
@@ -1696,7 +1716,7 @@ Lambda =  1e-05
 Accuracy score on test set:  0.10555555555555556
    -
    /var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_95354/953065564.py:4: RuntimeWarning: overflow encountered in exp
+
    /var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_22411/953065564.py:4: RuntimeWarning: overflow encountered in exp
   return 1/(1 + np.exp(-x))
 
    
 
    
@@ -1705,7 +1725,7 @@ Lambda =  0.0001
 Accuracy score on test set:  0.08611111111111111
    -
    /var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_95354/953065564.py:4: RuntimeWarning: overflow encountered in exp
+
    /var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_22411/953065564.py:4: RuntimeWarning: overflow encountered in exp
   return 1/(1 + np.exp(-x))
 
    
 
    
@@ -1714,7 +1734,7 @@ Lambda =  0.001
 Accuracy score on test set:  0.10555555555555556
    -
    /var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_95354/953065564.py:4: RuntimeWarning: overflow encountered in exp
+
    /var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_22411/953065564.py:4: RuntimeWarning: overflow encountered in exp
   return 1/(1 + np.exp(-x))
 
    
 
    
@@ -1723,7 +1743,7 @@ Lambda =  0.01
 Accuracy score on test set:  0.08888888888888889
    -
    /var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_95354/953065564.py:4: RuntimeWarning: overflow encountered in exp
+
    /var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_22411/953065564.py:4: RuntimeWarning: overflow encountered in exp
   return 1/(1 + np.exp(-x))
 
    
 
    
@@ -1732,7 +1752,7 @@ Lambda =  0.1
 Accuracy score on test set:  0.08611111111111111
    -
    /var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_95354/953065564.py:4: RuntimeWarning: overflow encountered in exp
+
    /var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_22411/953065564.py:4: RuntimeWarning: overflow encountered in exp
   return 1/(1 + np.exp(-x))
 
    
 
    
@@ -1741,7 +1761,7 @@ Lambda =  1.0
 Accuracy score on test set:  0.08888888888888889
    -
    /var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_95354/953065564.py:4: RuntimeWarning: overflow encountered in exp
+
    /var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_22411/953065564.py:4: RuntimeWarning: overflow encountered in exp
   return 1/(1 + np.exp(-x))
 
    
 
    
@@ -1750,11 +1770,11 @@ Lambda =  10.0
 Accuracy score on test set:  0.09166666666666666
    -
    /var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_95354/953065564.py:4: RuntimeWarning: overflow encountered in exp
+
    /var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_22411/953065564.py:4: RuntimeWarning: overflow encountered in exp
   return 1/(1 + np.exp(-x))
-/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_95354/1630775253.py:43: RuntimeWarning: overflow encountered in exp
+/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_22411/1630775253.py:43: RuntimeWarning: overflow encountered in exp
   exp_term = np.exp(self.z_o)
-/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_95354/1630775253.py:44: RuntimeWarning: invalid value encountered in true_divide
+/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_22411/1630775253.py:44: RuntimeWarning: invalid value encountered in true_divide
   self.probabilities = exp_term / np.sum(exp_term, axis=1, keepdims=True)
 
    
 
    
@@ -1763,11 +1783,11 @@ Lambda =  1e-05
 Accuracy score on test set:  0.07777777777777778
    -
    /var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_95354/953065564.py:4: RuntimeWarning: overflow encountered in exp
+
    /var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_22411/953065564.py:4: RuntimeWarning: overflow encountered in exp
   return 1/(1 + np.exp(-x))
-/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_95354/1630775253.py:43: RuntimeWarning: overflow encountered in exp
+/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_22411/1630775253.py:43: RuntimeWarning: overflow encountered in exp
   exp_term = np.exp(self.z_o)
-/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_95354/1630775253.py:44: RuntimeWarning: invalid value encountered in true_divide
+/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_22411/1630775253.py:44: RuntimeWarning: invalid value encountered in true_divide
   self.probabilities = exp_term / np.sum(exp_term, axis=1, keepdims=True)
 
    
 
    
@@ -1776,11 +1796,11 @@ Lambda =  0.0001
 Accuracy score on test set:  0.07777777777777778
    -
    /var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_95354/953065564.py:4: RuntimeWarning: overflow encountered in exp
+
    /var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_22411/953065564.py:4: RuntimeWarning: overflow encountered in exp
   return 1/(1 + np.exp(-x))
-/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_95354/1630775253.py:43: RuntimeWarning: overflow encountered in exp
+/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_22411/1630775253.py:43: RuntimeWarning: overflow encountered in exp
   exp_term = np.exp(self.z_o)
-/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_95354/1630775253.py:44: RuntimeWarning: invalid value encountered in true_divide
+/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_22411/1630775253.py:44: RuntimeWarning: invalid value encountered in true_divide
   self.probabilities = exp_term / np.sum(exp_term, axis=1, keepdims=True)
 
    
 
    
@@ -1789,11 +1809,11 @@ Lambda =  0.001
 Accuracy score on test set:  0.07777777777777778
    -
    /var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_95354/953065564.py:4: RuntimeWarning: overflow encountered in exp
+
    /var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_22411/953065564.py:4: RuntimeWarning: overflow encountered in exp
   return 1/(1 + np.exp(-x))
-/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_95354/1630775253.py:43: RuntimeWarning: overflow encountered in exp
+/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_22411/1630775253.py:43: RuntimeWarning: overflow encountered in exp
   exp_term = np.exp(self.z_o)
-/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_95354/1630775253.py:44: RuntimeWarning: invalid value encountered in true_divide
+/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_22411/1630775253.py:44: RuntimeWarning: invalid value encountered in true_divide
   self.probabilities = exp_term / np.sum(exp_term, axis=1, keepdims=True)
 
    
 
    
@@ -1802,11 +1822,11 @@ Lambda =  0.01
 Accuracy score on test set:  0.07777777777777778
    -
    /var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_95354/953065564.py:4: RuntimeWarning: overflow encountered in exp
+
    /var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_22411/953065564.py:4: RuntimeWarning: overflow encountered in exp
   return 1/(1 + np.exp(-x))
-/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_95354/1630775253.py:43: RuntimeWarning: overflow encountered in exp
+/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_22411/1630775253.py:43: RuntimeWarning: overflow encountered in exp
   exp_term = np.exp(self.z_o)
-/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_95354/1630775253.py:44: RuntimeWarning: invalid value encountered in true_divide
+/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_22411/1630775253.py:44: RuntimeWarning: invalid value encountered in true_divide
   self.probabilities = exp_term / np.sum(exp_term, axis=1, keepdims=True)
 
    
 
    
@@ -1815,7 +1835,7 @@ Lambda =  0.1
 Accuracy score on test set:  0.07777777777777778
    -
    /var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_95354/953065564.py:4: RuntimeWarning: overflow encountered in exp
+
    /var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_22411/953065564.py:4: RuntimeWarning: overflow encountered in exp
   return 1/(1 + np.exp(-x))
 
    
 
    
@@ -1824,11 +1844,11 @@ Lambda =  1.0
 Accuracy score on test set:  0.10555555555555556
    -
    /var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_95354/953065564.py:4: RuntimeWarning: overflow encountered in exp
+
    /var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_22411/953065564.py:4: RuntimeWarning: overflow encountered in exp
   return 1/(1 + np.exp(-x))
-/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_95354/1630775253.py:43: RuntimeWarning: overflow encountered in exp
+/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_22411/1630775253.py:43: RuntimeWarning: overflow encountered in exp
   exp_term = np.exp(self.z_o)
-/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_95354/1630775253.py:44: RuntimeWarning: invalid value encountered in true_divide
+/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_22411/1630775253.py:44: RuntimeWarning: invalid value encountered in true_divide
   self.probabilities = exp_term / np.sum(exp_term, axis=1, keepdims=True)
 
    
 
    
@@ -1837,11 +1857,11 @@ Lambda =  10.0
 Accuracy score on test set:  0.07777777777777778
    -
    /var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_95354/953065564.py:4: RuntimeWarning: overflow encountered in exp
+
    /var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_22411/953065564.py:4: RuntimeWarning: overflow encountered in exp
   return 1/(1 + np.exp(-x))
-/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_95354/1630775253.py:43: RuntimeWarning: overflow encountered in exp
+/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_22411/1630775253.py:43: RuntimeWarning: overflow encountered in exp
   exp_term = np.exp(self.z_o)
-/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_95354/1630775253.py:44: RuntimeWarning: invalid value encountered in true_divide
+/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_22411/1630775253.py:44: RuntimeWarning: invalid value encountered in true_divide
   self.probabilities = exp_term / np.sum(exp_term, axis=1, keepdims=True)
 
    
 
    
@@ -1850,37 +1870,82 @@ Lambda =  1e-05
 Accuracy score on test set:  0.07777777777777778
    -
    /var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_95354/953065564.py:4: RuntimeWarning: overflow encountered in exp
+
    /var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_22411/953065564.py:4: RuntimeWarning: overflow encountered in exp
   return 1/(1 + np.exp(-x))
-/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_95354/1630775253.py:43: RuntimeWarning: overflow encountered in exp
+/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_22411/1630775253.py:43: RuntimeWarning: overflow encountered in exp
   exp_term = np.exp(self.z_o)
-/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_95354/1630775253.py:44: RuntimeWarning: invalid value encountered in true_divide
+/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_22411/1630775253.py:44: RuntimeWarning: invalid value encountered in true_divide
   self.probabilities = exp_term / np.sum(exp_term, axis=1, keepdims=True)
 
    
 
    
-
    ---------------------------------------------------------------------------
-KeyboardInterrupt                         Traceback (most recent call last)
-Cell In[8], line 11
-      8 for j, lmbd in enumerate(lmbd_vals):
-      9     dnn = NeuralNetwork(X_train, Y_train_onehot, eta=eta, lmbd=lmbd, epochs=epochs, batch_size=batch_size,
-     10                         n_hidden_neurons=n_hidden_neurons, n_categories=n_categories)
----> 11     dnn.train()
-     13     DNN_numpy[i][j] = dnn
-     15     test_predict = dnn.predict(X_test)
-
-Cell In[6], line 99, in NeuralNetwork.train(self)
-     96 self.Y_data = self.Y_data_full[chosen_datapoints]
-     98 self.feed_forward()
----> 99 self.backpropagation()
-
-Cell In[6], line 64, in NeuralNetwork.backpropagation(self)
-     61 self.output_weights_gradient = np.matmul(self.a_h.T, error_output)
-     62 self.output_bias_gradient = np.sum(error_output, axis=0)
----> 64 self.hidden_weights_gradient = np.matmul(self.X_data.T, error_hidden)
-     65 self.hidden_bias_gradient = np.sum(error_hidden, axis=0)
-     67 if self.lmbd > 0.0:
-
-KeyboardInterrupt: 
+
    Learning rate  =  10.0
+Lambda =  0.0001
+Accuracy score on test set:  0.07777777777777778
+
    
+
    
+
    /var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_22411/953065564.py:4: RuntimeWarning: overflow encountered in exp
+  return 1/(1 + np.exp(-x))
+/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_22411/1630775253.py:43: RuntimeWarning: overflow encountered in exp
+  exp_term = np.exp(self.z_o)
+/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_22411/1630775253.py:44: RuntimeWarning: invalid value encountered in true_divide
+  self.probabilities = exp_term / np.sum(exp_term, axis=1, keepdims=True)
+
    
+
    
+
    Learning rate  =  10.0
+Lambda =  0.001
+Accuracy score on test set:  0.07777777777777778
+
    
+
    
+
    /var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_22411/953065564.py:4: RuntimeWarning: overflow encountered in exp
+  return 1/(1 + np.exp(-x))
+/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_22411/1630775253.py:43: RuntimeWarning: overflow encountered in exp
+  exp_term = np.exp(self.z_o)
+/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_22411/1630775253.py:44: RuntimeWarning: invalid value encountered in true_divide
+  self.probabilities = exp_term / np.sum(exp_term, axis=1, keepdims=True)
+
    
+
    
+
    Learning rate  =  10.0
+Lambda =  0.01
+Accuracy score on test set:  0.07777777777777778
+
    
+
    
+
    /var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_22411/953065564.py:4: RuntimeWarning: overflow encountered in exp
+  return 1/(1 + np.exp(-x))
+/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_22411/1630775253.py:43: RuntimeWarning: overflow encountered in exp
+  exp_term = np.exp(self.z_o)
+/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_22411/1630775253.py:44: RuntimeWarning: invalid value encountered in true_divide
+  self.probabilities = exp_term / np.sum(exp_term, axis=1, keepdims=True)
+
    
+
    
+
    Learning rate  =  10.0
+Lambda =  0.1
+Accuracy score on test set:  0.07777777777777778
+
    
+
    
+
    /var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_22411/953065564.py:4: RuntimeWarning: overflow encountered in exp
+  return 1/(1 + np.exp(-x))
+/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_22411/1630775253.py:43: RuntimeWarning: overflow encountered in exp
+  exp_term = np.exp(self.z_o)
+/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_22411/1630775253.py:44: RuntimeWarning: invalid value encountered in true_divide
+  self.probabilities = exp_term / np.sum(exp_term, axis=1, keepdims=True)
+
    
+
    
+
    Learning rate  =  10.0
+Lambda =  1.0
+Accuracy score on test set:  0.07777777777777778
+
    
+
    
+
    /var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_22411/953065564.py:4: RuntimeWarning: overflow encountered in exp
+  return 1/(1 + np.exp(-x))
+/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_22411/1630775253.py:43: RuntimeWarning: overflow encountered in exp
+  exp_term = np.exp(self.z_o)
+/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_22411/1630775253.py:44: RuntimeWarning: invalid value encountered in true_divide
+  self.probabilities = exp_term / np.sum(exp_term, axis=1, keepdims=True)
+
    
+
    
+
    Learning rate  =  10.0
+Lambda =  10.0
+Accuracy score on test set:  0.07777777777777778
 
    
 
    
 
    
@@ -1926,6 +1991,22 @@ Accuracy score on test set:  0.07777777777777778
    +
    +
    /var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_22411/953065564.py:4: RuntimeWarning: overflow encountered in exp
+  return 1/(1 + np.exp(-x))
+/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_22411/953065564.py:4: RuntimeWarning: overflow encountered in exp
+  return 1/(1 + np.exp(-x))
+/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_22411/953065564.py:4: RuntimeWarning: overflow encountered in exp
+  return 1/(1 + np.exp(-x))
+/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_22411/953065564.py:4: RuntimeWarning: overflow encountered in exp
+  return 1/(1 + np.exp(-x))
+/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_22411/953065564.py:4: RuntimeWarning: overflow encountered in exp
+  return 1/(1 + np.exp(-x))
+
    +
    +_images/chapter10_59_1.png +_images/chapter10_59_2.png +
    @@ -1961,6 +2042,333 @@ performance overall.

    +
    +
    /Users/mhjensen/miniforge3/envs/myenv/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.
+  warnings.warn(
+
    +
    +
    Learning rate  =  1e-05
+Lambda =  1e-05
+Accuracy score on test set:  0.18333333333333332
+
    +
    +
    /Users/mhjensen/miniforge3/envs/myenv/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.
+  warnings.warn(
+
    +
    +
    Learning rate  =  1e-05
+Lambda =  0.0001
+Accuracy score on test set:  0.18611111111111112
+
    +
    +
    /Users/mhjensen/miniforge3/envs/myenv/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.
+  warnings.warn(
+
    +
    +
    Learning rate  =  1e-05
+Lambda =  0.001
+Accuracy score on test set:  0.13055555555555556
+
    +
    +
    /Users/mhjensen/miniforge3/envs/myenv/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.
+  warnings.warn(
+
    +
    +
    Learning rate  =  1e-05
+Lambda =  0.01
+Accuracy score on test set:  0.24444444444444444
+
    +
    +
    /Users/mhjensen/miniforge3/envs/myenv/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.
+  warnings.warn(
+
    +
    +
    Learning rate  =  1e-05
+Lambda =  0.1
+Accuracy score on test set:  0.23333333333333334
+
    +
    +
    /Users/mhjensen/miniforge3/envs/myenv/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.
+  warnings.warn(
+
    +
    +
    Learning rate  =  1e-05
+Lambda =  1.0
+Accuracy score on test set:  0.12777777777777777
+
    +
    +
    /Users/mhjensen/miniforge3/envs/myenv/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.
+  warnings.warn(
+
    +
    +
    Learning rate  =  1e-05
+Lambda =  10.0
+Accuracy score on test set:  0.1527777777777778
+
    +
    +
    /Users/mhjensen/miniforge3/envs/myenv/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.
+  warnings.warn(
+
    +
    +
    Learning rate  =  0.0001
+Lambda =  1e-05
+Accuracy score on test set:  0.9111111111111111
+
    +
    +
    /Users/mhjensen/miniforge3/envs/myenv/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.
+  warnings.warn(
+
    +
    +
    Learning rate  =  0.0001
+Lambda =  0.0001
+Accuracy score on test set:  0.8888888888888888
+
    +
    +
    /Users/mhjensen/miniforge3/envs/myenv/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.
+  warnings.warn(
+
    +
    +
    Learning rate  =  0.0001
+Lambda =  0.001
+Accuracy score on test set:  0.8722222222222222
+
    +
    +
    /Users/mhjensen/miniforge3/envs/myenv/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.
+  warnings.warn(
+
    +
    +
    Learning rate  =  0.0001
+Lambda =  0.01
+Accuracy score on test set:  0.8305555555555556
+
    +
    +
    /Users/mhjensen/miniforge3/envs/myenv/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.
+  warnings.warn(
+
    +
    +
    Learning rate  =  0.0001
+Lambda =  0.1
+Accuracy score on test set:  0.8888888888888888
+
    +
    +
    /Users/mhjensen/miniforge3/envs/myenv/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.
+  warnings.warn(
+
    +
    +
    Learning rate  =  0.0001
+Lambda =  1.0
+Accuracy score on test set:  0.8805555555555555
+
    +
    +
    /Users/mhjensen/miniforge3/envs/myenv/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.
+  warnings.warn(
+
    +
    +
    Learning rate  =  0.0001
+Lambda =  10.0
+Accuracy score on test set:  0.8944444444444445
+
    +
    +
    /Users/mhjensen/miniforge3/envs/myenv/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.
+  warnings.warn(
+
    +
    +
    Learning rate  =  0.001
+Lambda =  1e-05
+Accuracy score on test set:  0.975
+
    +
    +
    /Users/mhjensen/miniforge3/envs/myenv/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.
+  warnings.warn(
+
    +
    +
    Learning rate  =  0.001
+Lambda =  0.0001
+Accuracy score on test set:  0.9777777777777777
+
    +
    +
    /Users/mhjensen/miniforge3/envs/myenv/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.
+  warnings.warn(
+
    +
    +
    Learning rate  =  0.001
+Lambda =  0.001
+Accuracy score on test set:  0.9805555555555555
+
    +
    +
    /Users/mhjensen/miniforge3/envs/myenv/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.
+  warnings.warn(
+
    +
    +
    Learning rate  =  0.001
+Lambda =  0.01
+Accuracy score on test set:  0.9861111111111112
+
    +
    +
    /Users/mhjensen/miniforge3/envs/myenv/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.
+  warnings.warn(
+
    +
    +
    Learning rate  =  0.001
+Lambda =  0.1
+Accuracy score on test set:  0.9805555555555555
+
    +
    +
    /Users/mhjensen/miniforge3/envs/myenv/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.
+  warnings.warn(
+
    +
    +
    Learning rate  =  0.001
+Lambda =  1.0
+Accuracy score on test set:  0.9777777777777777
+
    +
    +
    /Users/mhjensen/miniforge3/envs/myenv/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.
+  warnings.warn(
+
    +
    +
    Learning rate  =  0.001
+Lambda =  10.0
+Accuracy score on test set:  0.9444444444444444
+
    +
    +
    /Users/mhjensen/miniforge3/envs/myenv/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.
+  warnings.warn(
+
    +
    +
    Learning rate  =  0.01
+Lambda =  1e-05
+Accuracy score on test set:  0.9861111111111112
+
    +
    +
    Learning rate  =  0.01
+Lambda =  0.0001
+Accuracy score on test set:  0.9888888888888889
+
    +
    +
    Learning rate  =  0.01
+Lambda =  0.001
+Accuracy score on test set:  0.9888888888888889
+
    +
    +
    Learning rate  =  0.01
+Lambda =  0.01
+Accuracy score on test set:  0.9861111111111112
+
    +
    +
    Learning rate  =  0.01
+Lambda =  0.1
+Accuracy score on test set:  0.9888888888888889
+
    +
    +
    Learning rate  =  0.01
+Lambda =  1.0
+Accuracy score on test set:  0.9722222222222222
+
+Learning rate  =  0.01
+Lambda =  10.0
+Accuracy score on test set:  0.9527777777777777
+
    +
    +
    Learning rate  =  0.1
+Lambda =  1e-05
+Accuracy score on test set:  0.9027777777777778
+
    +
    +
    Learning rate  =  0.1
+Lambda =  0.0001
+Accuracy score on test set:  0.8583333333333333
+
    +
    +
    Learning rate  =  0.1
+Lambda =  0.001
+Accuracy score on test set:  0.8722222222222222
+
    +
    +
    Learning rate  =  0.1
+Lambda =  0.01
+Accuracy score on test set:  0.9055555555555556
+
    +
    +
    Learning rate  =  0.1
+Lambda =  0.1
+Accuracy score on test set:  0.8805555555555555
+
    +
    +
    Learning rate  =  0.1
+Lambda =  1.0
+Accuracy score on test set:  0.8722222222222222
+
    +
    +
    Learning rate  =  0.1
+Lambda =  10.0
+Accuracy score on test set:  0.8666666666666667
+
    +
    +
    Learning rate  =  1.0
+Lambda =  1e-05
+Accuracy score on test set:  0.08611111111111111
+
+Learning rate  =  1.0
+Lambda =  0.0001
+Accuracy score on test set:  0.10555555555555556
+
    +
    +
    Learning rate  =  1.0
+Lambda =  0.001
+Accuracy score on test set:  0.10555555555555556
+
+Learning rate  =  1.0
+Lambda =  0.01
+Accuracy score on test set:  0.17777777777777778
+
    +
    +
    Learning rate  =  1.0
+Lambda =  0.1
+Accuracy score on test set:  0.08333333333333333
+
+Learning rate  =  1.0
+Lambda =  1.0
+Accuracy score on test set:  0.08888888888888889
+
    +
    +
    Learning rate  =  1.0
+Lambda =  10.0
+Accuracy score on test set:  0.09444444444444444
+
+Learning rate  =  10.0
+Lambda =  1e-05
+Accuracy score on test set:  0.17222222222222222
+
    +
    +
    Learning rate  =  10.0
+Lambda =  0.0001
+Accuracy score on test set:  0.11666666666666667
+
+Learning rate  =  10.0
+Lambda =  0.001
+Accuracy score on test set:  0.10555555555555556
+
    +
    +
    Learning rate  =  10.0
+Lambda =  0.01
+Accuracy score on test set:  0.1388888888888889
+
+Learning rate  =  10.0
+Lambda =  0.1
+Accuracy score on test set:  0.11388888888888889
+
    +
    +
    Learning rate  =  10.0
+Lambda =  1.0
+Accuracy score on test set:  0.10555555555555556
+
+Learning rate  =  10.0
+Lambda =  10.0
+Accuracy score on test set:  0.09444444444444444
+
    +
    +
    @@ -2004,6 +2412,10 @@ performance overall.

    +
    +_images/chapter10_63_0.png +_images/chapter10_63_1.png +
    @@ -2042,6 +2454,14 @@ and/or if you use anaconda, just write (or install from the gra
    +
    +
      Cell In[12], line 1
+    conda create -n tf tensorflow
+          ^
+SyntaxError: invalid syntax
+
    +
    +

    To install the current release of GPU TensorFlow

    diff --git a/doc/LectureNotes/_build/html/chapter11.html b/doc/LectureNotes/_build/html/chapter11.html index e400bbe53..bb831fd2d 100644 --- a/doc/LectureNotes/_build/html/chapter11.html +++ b/doc/LectureNotes/_build/html/chapter11.html @@ -308,6 +308,21 @@ const thebe_selector_output = ".output, .cell_output" Week 39: Optimization and Gradient Methods +
  • + + Week 40: Gradient descent methods (continued) and start Neural networks + +
    • +
  • + + Exercises week 41 + +
    • +
  • + + Week 41 Neural networks and constructing a neural network code + +

    • @@ -320,6 +335,11 @@ const thebe_selector_output = ".output, .cell_output" Project 1 on Machine Learning, deadline October 7 (midnight), 2024 +

  • + + Project 2 on Machine Learning, deadline November 4 (Midnight) + +
    • @@ -2587,11 +2607,83 @@ Using TensorFlow results in a much better execution time. Try it!

    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:32, in grad(fun, x) - 29 if not vspace(ans).size == 1: - 30 raise TypeError("Grad only applies to real scalar-output functions. " - 31 "Try jacobian, elementwise_grad or holomorphic_grad.") ----> 32 return vjp(vspace(ans).ones()) +File ~/miniforge3/envs/myenv/lib/python3.9/site-packages/autograd/differential_operators.py:28, in grad(fun, x) + 21 @unary_to_nary + 22 def grad(fun, x): + 23 """ + 24 Returns a function which computes the gradient of `fun` with respect to + 25 positional argument number `argnum`. The returned function takes the same + 26 arguments as `fun`, but returns the gradient instead. The function `fun` + 27 should be scalar-valued. The gradient has the same type as the argument.""" +---> 28 vjp, ans = _make_vjp(fun, x) + 29 if not vspace(ans).size == 1: + 30 raise TypeError("Grad only applies to real scalar-output functions. " + 31 "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) + +Cell In[9], line 80, 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:81, in hessian(fun, x) + 78 @unary_to_nary + 79 def hessian(fun, x): + 80 "Returns a function that computes the exact Hessian." +---> 81 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:64, in jacobian(fun, x) + 62 jacobian_shape = ans_vspace.shape + vspace(x).shape + 63 grads = map(vjp, ans_vspace.standard_basis()) +---> 64 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) @@ -2603,44 +2695,44 @@ Using TensorFlow results in a much better execution time. Try it!

    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/core.py:78, in defvjp.<locals>.vjp_argnums.<locals>.<lambda>(g) + 76 vjp_0 = vjp_0_fun(ans, *args, **kwargs) + 77 vjp_1 = vjp_1_fun(ans, *args, **kwargs) +---> 78 return lambda g: (vjp_0(g), vjp_1(g)) + 79 else: + 80 vjps = [vjps_dict[argnum](ans, *args, **kwargs) for argnum in argnums] File ~/miniforge3/envs/myenv/lib/python3.9/site-packages/autograd/numpy/numpy_vjps.py:660, in unbroadcast_f.<locals>.<lambda>(g) 658 def unbroadcast_f(target, f): 659 target_meta = anp.metadata(target) --> 660 return lambda g: unbroadcast(f(g), target_meta) -File ~/miniforge3/envs/myenv/lib/python3.9/site-packages/autograd/numpy/numpy_vjps.py:653, in unbroadcast(x, target_meta, broadcast_idx) - 651 for axis, size in enumerate(target_shape): - 652 if size == 1: ---> 653 x = anp.sum(x, axis=axis, keepdims=True) - 654 if anp.iscomplexobj(x) and not target_iscomplex: - 655 x = anp.real(x) +File ~/miniforge3/envs/myenv/lib/python3.9/site-packages/autograd/numpy/numpy_vjps.py:34, in <lambda>(g) + 30 # ----- Binary ufuncs ----- + 32 defvjp(anp.add, lambda ans, x, y : unbroadcast_f(x, lambda g: g), + 33 lambda ans, x, y : unbroadcast_f(y, lambda g: g)) +---> 34 defvjp(anp.multiply, lambda ans, x, y : unbroadcast_f(x, lambda g: y * g), + 35 lambda ans, x, y : unbroadcast_f(y, lambda g: x * g)) + 36 defvjp(anp.subtract, lambda ans, x, y : unbroadcast_f(x, lambda g: g), + 37 lambda ans, x, y : unbroadcast_f(y, lambda g: -g)) + 38 defvjp(anp.divide, lambda ans, x, y : unbroadcast_f(x, lambda g: g / y), + 39 lambda ans, x, y : unbroadcast_f(y, lambda g: - g * x / y**2)) + +File ~/miniforge3/envs/myenv/lib/python3.9/site-packages/autograd/numpy/numpy_boxes.py:27, in ArrayBox.__mul__(self, other) +---> 27 def __mul__(self, other): return anp.multiply(self, other) + +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:48, in primitive.<locals>.f_wrapped(*args, **kwargs) 46 return new_box(ans, trace, node) 47 else: ---> 48 return f_raw(*args, **kwargs) -File <__array_function__ internals>:180, in sum(*args, **kwargs) - -File ~/miniforge3/envs/myenv/lib/python3.9/site-packages/numpy/core/fromnumeric.py:2296, in sum(a, axis, dtype, out, keepdims, initial, where) - 2293 return out - 2294 return res --> 2296 return _wrapreduction(a, np.add, 'sum', axis, dtype, out, keepdims=keepdims, - 2297 initial=initial, where=where) - -File ~/miniforge3/envs/myenv/lib/python3.9/site-packages/numpy/core/fromnumeric.py:86, in _wrapreduction(obj, ufunc, method, axis, dtype, out, **kwargs) - 83 else: - 84 return reduction(axis=axis, out=out, **passkwargs) ----> 86 return ufunc.reduce(obj, axis, dtype, out, **passkwargs) - KeyboardInterrupt: diff --git a/doc/LectureNotes/_build/html/chapter12.html b/doc/LectureNotes/_build/html/chapter12.html index 828286c85..29a64c88f 100644 --- a/doc/LectureNotes/_build/html/chapter12.html +++ b/doc/LectureNotes/_build/html/chapter12.html @@ -308,6 +308,21 @@ const thebe_selector_output = ".output, .cell_output" Week 39: Optimization and Gradient Methods +
  • + + Week 40: Gradient descent methods (continued) and start Neural networks + +
    • +
  • + + Exercises week 41 + +
    • +
  • + + Week 41 Neural networks and constructing a neural network code + +

    • @@ -320,6 +335,11 @@ const thebe_selector_output = ".output, .cell_output" Project 1 on Machine Learning, deadline October 7 (midnight), 2024 +

  • + + Project 2 on Machine Learning, deadline November 4 (Midnight) + +
    • diff --git a/doc/LectureNotes/_build/html/chapter13.html b/doc/LectureNotes/_build/html/chapter13.html index b81629b6a..3828cc5ae 100644 --- a/doc/LectureNotes/_build/html/chapter13.html +++ b/doc/LectureNotes/_build/html/chapter13.html @@ -308,6 +308,21 @@ const thebe_selector_output = ".output, .cell_output" Week 39: Optimization and Gradient Methods +
  • + + Week 40: Gradient descent methods (continued) and start Neural networks + +
    • +
  • + + Exercises week 41 + +
    • +
  • + + Week 41 Neural networks and constructing a neural network code + +

    • @@ -320,6 +335,11 @@ const thebe_selector_output = ".output, .cell_output" Project 1 on Machine Learning, deadline October 7 (midnight), 2024 +

  • + + Project 2 on Machine Learning, deadline November 4 (Midnight) + +
    • diff --git a/doc/LectureNotes/_build/html/chapter2.html b/doc/LectureNotes/_build/html/chapter2.html index f9c60ad7d..cbd7b7d74 100644 --- a/doc/LectureNotes/_build/html/chapter2.html +++ b/doc/LectureNotes/_build/html/chapter2.html @@ -308,6 +308,21 @@ const thebe_selector_output = ".output, .cell_output" Week 39: Optimization and Gradient Methods +
  • + + Week 40: Gradient descent methods (continued) and start Neural networks + +
    • +
  • + + Exercises week 41 + +
    • +
  • + + Week 41 Neural networks and constructing a neural network code + +

    • @@ -320,6 +335,11 @@ const thebe_selector_output = ".output, .cell_output" Project 1 on Machine Learning, deadline October 7 (midnight), 2024 +

  • + + Project 2 on Machine Learning, deadline November 4 (Midnight) + +
    • @@ -1285,10 +1305,10 @@ covariance matrix through the np.linalg.eig() function.

    -
    -0.024792624800382218
-3.9225384545636204
-[[0.94623184 2.84401886]
- [2.84401886 9.4477214 ]]
+
    -0.009134699065945493
+4.0244965108017645
+[[0.85613835 2.50655379]
+ [2.50655379 8.3404509 ]]
    @@ -1325,10 +1345,10 @@ a more brute force way. Here we scale the mean values for each column of the des
    -
    0.08536248571780691
-1.8207274870895702
-[[1.         0.74900488]
- [0.74900488 1.        ]]
+
    0.07971187802560528
+1.800782161095708
+[[1.         0.59411814]
+ [0.59411814 1.        ]]
    @@ -1358,32 +1378,30 @@ this matrix we easily see that it is a positive definite matrix.

    -
    [[ 1.10115017  1.66431407]
- [ 0.12043521  1.32305911]
- [-1.30023144 -3.36154104]
- [-0.25200841 -1.11277166]
- [-1.55102329 -4.20158083]
- [ 0.72770687  0.97206657]
- [ 0.76533281  2.10747579]
- [-0.20666447  0.79623487]
- [-0.63919355 -2.48490503]
- [ 1.23449607  4.29764815]]
-
    -
    -
              0         1
-0  1.101150  1.664314
-1  0.120435  1.323059
-2 -1.300231 -3.361541
-3 -0.252008 -1.112772
-4 -1.551023 -4.201581
-5  0.727707  0.972067
-6  0.765333  2.107476
-7 -0.206664  0.796235
-8 -0.639194 -2.484905
-9  1.234496  4.297648
-        0       1
-0  1.0000  0.9434
-1  0.9434  1.0000
+
    [[ 0.81395716  1.89155934]
+ [-1.34726166 -4.13453411]
+ [-0.46229544 -2.34061974]
+ [ 0.24429334  1.4051634 ]
+ [ 0.41971814  1.6405671 ]
+ [ 2.02456235  5.03973227]
+ [-1.97311824 -4.72521196]
+ [ 0.10738656  0.24578123]
+ [-0.52702419 -2.34023682]
+ [ 0.69978197  3.31779928]]
+          0         1
+0  0.813957  1.891559
+1 -1.347262 -4.134534
+2 -0.462295 -2.340620
+3  0.244293  1.405163
+4  0.419718  1.640567
+5  2.024562  5.039732
+6 -1.973118 -4.725212
+7  0.107387  0.245781
+8 -0.527024 -2.340237
+9  0.699782  3.317799
+          0         1
+0  1.000000  0.969413
+1  0.969413  1.000000
    @@ -1440,37 +1458,37 @@ this matrix we easily see that it is a positive definite matrix.

         0         1         2         3         4         5         6         7   \
 0   0.0  0.000000  0.000000  0.000000  0.000000  0.000000  0.000000  0.000000   
-1   0.0  0.083179  0.086483  0.081217  0.083548  0.086239  0.072037  0.073514   
-2   0.0  0.086483  0.091362  0.082533  0.085853  0.089626  0.071638  0.073800   
-3   0.0  0.081217  0.082533  0.084963  0.086068  0.087429  0.079030  0.079670   
-4   0.0  0.083548  0.085853  0.086068  0.087871  0.089996  0.078945  0.080094   
-5   0.0  0.086239  0.089626  0.087429  0.089996  0.092956  0.079002  0.080706   
-6   0.0  0.072037  0.071638  0.079030  0.078945  0.079002  0.076071  0.075876   
-7   0.0  0.073514  0.073800  0.079670  0.080094  0.080706  0.075876  0.076066   
-8   0.0  0.075289  0.076321  0.080549  0.081527  0.082742  0.075842  0.076448   
-9   0.0  0.077391  0.079240  0.081686  0.083268  0.085145  0.075978  0.077035   
-10  0.0  0.063786  0.062216  0.072380  0.071437  0.070552  0.071436  0.070622   
-11  0.0  0.064606  0.063536  0.072591  0.072031  0.071560  0.071049  0.070531   
-12  0.0  0.065654  0.065121  0.072997  0.072849  0.072826  0.070805  0.070605   
-13  0.0  0.066948  0.067000  0.073612  0.073911  0.074374  0.070712  0.070855   
-14  0.0  0.068512  0.069203  0.074454  0.075239  0.076232  0.070780  0.071294   
+1   0.0  0.092746  0.090302  0.090561  0.088058  0.085463  0.081530  0.078898   
+2   0.0  0.090302  0.088694  0.089052  0.086797  0.084423  0.080286  0.077782   
+3   0.0  0.090561  0.089052  0.095106  0.092533  0.089858  0.089404  0.086489   
+4   0.0  0.088058  0.086797  0.092533  0.090114  0.087585  0.086913  0.084127   
+5   0.0  0.085463  0.084423  0.089858  0.087585  0.085197  0.084340  0.081681   
+6   0.0  0.081530  0.080286  0.089404  0.086913  0.084340  0.086471  0.083596   
+7   0.0  0.078898  0.077782  0.086489  0.084127  0.081681  0.083596  0.080849   
+8   0.0  0.076334  0.075334  0.083645  0.081405  0.079080  0.080793  0.078170   
+9   0.0  0.073841  0.072946  0.080875  0.078753  0.076543  0.078067  0.075562   
+10  0.0  0.072973  0.071789  0.082361  0.079982  0.077541  0.081296  0.078544   
+11  0.0  0.070485  0.069391  0.079518  0.077254  0.074928  0.078454  0.075823   
+12  0.0  0.068088  0.067077  0.076777  0.074622  0.072404  0.075712  0.073198   
+13  0.0  0.065778  0.064845  0.074134  0.072083  0.069970  0.073071  0.070667   
+14  0.0  0.063554  0.062693  0.071588  0.069637  0.067623  0.070527  0.068229   
 
           8         9         10        11        12        13        14  
 0   0.000000  0.000000  0.000000  0.000000  0.000000  0.000000  0.000000  
-1   0.075289  0.077391  0.063786  0.064606  0.065654  0.066948  0.068512  
-2   0.076321  0.079240  0.062216  0.063536  0.065121  0.067000  0.069203  
-3   0.080549  0.081686  0.072380  0.072591  0.072997  0.073612  0.074454  
-4   0.081527  0.083268  0.071437  0.072031  0.072849  0.073911  0.075239  
-5   0.082742  0.085145  0.070552  0.071560  0.072826  0.074374  0.076232  
-6   0.075842  0.075978  0.071436  0.071049  0.070805  0.070712  0.070780  
-7   0.076448  0.077035  0.070622  0.070531  0.070605  0.070855  0.071294  
-8   0.077280  0.078359  0.069907  0.070135  0.070552  0.071173  0.072015  
-9   0.078359  0.079976  0.069293  0.069866  0.070655  0.071680  0.072961  
-10  0.069907  0.069293  0.068353  0.067515  0.066778  0.066145  0.065619  
-11  0.070135  0.069866  0.067515  0.066912  0.066425  0.066059  0.065821  
-12  0.070552  0.070655  0.066778  0.066425  0.066205  0.066127  0.066199  
-13  0.071173  0.071680  0.066145  0.066059  0.066127  0.066358  0.066766  
-14  0.072015  0.072961  0.065619  0.065821  0.066199  0.066766  0.067539  
+1   0.076334  0.073841  0.072973  0.070485  0.068088  0.065778  0.063554  
+2   0.075334  0.072946  0.071789  0.069391  0.067077  0.064845  0.062693  
+3   0.083645  0.080875  0.082361  0.079518  0.076777  0.074134  0.071588  
+4   0.081405  0.078753  0.079982  0.077254  0.074622  0.072083  0.069637  
+5   0.079080  0.076543  0.077541  0.074928  0.072404  0.069970  0.067623  
+6   0.080793  0.078067  0.081296  0.078454  0.075712  0.073071  0.070527  
+7   0.078170  0.075562  0.078544  0.075823  0.073198  0.070667  0.068229  
+8   0.075609  0.073115  0.075864  0.073260  0.070747  0.068324  0.065989  
+9   0.073115  0.070730  0.073260  0.070769  0.068364  0.066044  0.063809  
+10  0.075864  0.073260  0.077608  0.074866  0.072223  0.069676  0.067224  
+11  0.073260  0.070769  0.074866  0.072241  0.069711  0.067273  0.064924  
+12  0.070747  0.068364  0.072223  0.069711  0.067288  0.064954  0.062705  
+13  0.068324  0.066044  0.069676  0.067273  0.064954  0.062719  0.060565  
+14  0.065989  0.063809  0.067224  0.064924  0.062705  0.060565  0.058503
    diff --git a/doc/LectureNotes/_build/html/chapter3.html b/doc/LectureNotes/_build/html/chapter3.html index ef10eb74a..67d3871ce 100644 --- a/doc/LectureNotes/_build/html/chapter3.html +++ b/doc/LectureNotes/_build/html/chapter3.html @@ -308,6 +308,21 @@ const thebe_selector_output = ".output, .cell_output" Week 39: Optimization and Gradient Methods +
  • + + Week 40: Gradient descent methods (continued) and start Neural networks + +
    • +
  • + + Exercises week 41 + +
    • +
  • + + Week 41 Neural networks and constructing a neural network code + +

    • @@ -320,6 +335,11 @@ const thebe_selector_output = ".output, .cell_output" Project 1 on Machine Learning, deadline October 7 (midnight), 2024 +

  • + + Project 2 on Machine Learning, deadline November 4 (Midnight) + +
    • @@ -839,10 +859,10 @@ number \(i\) is left out. Usin
    -
    Runtime: 0.147545 sec
+
    Runtime: 0.146141 sec
 Jackknife Statistics :
 original           bias      std. error
-  99.977         99.967        0.152494
+ 100.139        100.129        0.148776
    @@ -1061,7 +1081,7 @@ theorem.

    Bootstrap Statistics :
 original           bias      std. error
- 100.041  14.8133        100.041        0.149266
+  99.989  15.1792        99.9878        0.152149
    @@ -1278,9 +1298,7 @@ Error: 0.06547790180152355 Bias^2: 0.06208238634231949 Var: 0.0033955154592040936 0.06547790180152355 >= 0.06208238634231949 + 0.0033955154592040936 = 0.06547790180152359 -
    -
    -
    Polynomial degree: 4
+Polynomial degree: 4
 Error: 0.06844519414009445
 Bias^2: 0.06453579006728324
 Var: 0.003909404072811226
@@ -1307,21 +1325,19 @@ Error: 0.017355848195593347
 Bias^2: 0.010331721306655127
 Var: 0.007024126888938232
 0.017355848195593347 >= 0.010331721306655127 + 0.007024126888938232 = 0.01735584819559336
-
    -
    -
    Polynomial degree: 9
+Polynomial degree: 9
 Error: 0.02660572763718093
 Bias^2: 0.010018312644137363
 Var: 0.016587414993043573
 0.02660572763718093 >= 0.010018312644137363 + 0.016587414993043573 = 0.026605727637180936
-Polynomial degree: 10
+
    +
    +
    Polynomial degree: 10
 Error: 0.021592704588025025
 Bias^2: 0.010516485576645508
 Var: 0.011076219011379514
 0.021592704588025025 >= 0.010516485576645508 + 0.011076219011379514 = 0.021592704588025022
-
    -
    -
    Polynomial degree: 11
+Polynomial degree: 11
 Error: 0.07160048164233104
 Bias^2: 0.014436800088904942
 Var: 0.05716368155342608
@@ -1331,14 +1347,16 @@ Error: 0.11547777218872497
 Bias^2: 0.01628578269596628
 Var: 0.09919198949275869
 0.11547777218872497 >= 0.01628578269596628 + 0.09919198949275869 = 0.11547777218872497
-Polynomial degree: 13
+
    +
    +
    Polynomial degree: 13
 Error: 0.22842468702219465
 Bias^2: 0.01975416527185249
 Var: 0.20867052175034223
 0.22842468702219465 >= 0.01975416527185249 + 0.20867052175034223 = 0.2284246870221947
    -_images/chapter3_66_5.png +_images/chapter3_66_4.png

    The bias-variance tradeoff summarizes the fundamental tension in @@ -1653,9 +1671,9 @@ Mean squared error on training data: 0.00060704 Mean squared error on test data: 3262.26814548 -

    /var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_95419/626635268.py:73: RuntimeWarning: divide by zero encountered in log10
+
    /var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_22458/626635268.py:73: RuntimeWarning: divide by zero encountered in log10
   plt.plot(polynomial, np.log10(trainingerror), label='Training Error')
-/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_95419/626635268.py:74: RuntimeWarning: divide by zero encountered in log10
+/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_22458/626635268.py:74: RuntimeWarning: divide by zero encountered in log10
   plt.plot(polynomial, np.log10(testerror), label='Test Error')
 
    
 
    
@@ -1889,7 +1907,7 @@ cross-validation (LOOCV).
    -
    /var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_95419/3817475779.py:63: RuntimeWarning: divide by zero encountered in log10
+
    /var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_22458/3817475779.py:63: RuntimeWarning: divide by zero encountered in log10
   plt.plot(polynomial, np.log10(estimated_mse_sklearn), label='Test Error')
 
    
 
    
@@ -2778,7 +2796,7 @@ linear system as an equation would reduce this down to
    -
    /var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_95419/4162706317.py:7: UserWarning: set_ticklabels() should only be used with a fixed number of ticks, i.e. after set_ticks() or using a FixedLocator.
+
    /var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_22458/4162706317.py:7: UserWarning: set_ticklabels() should only be used with a fixed number of ticks, i.e. after set_ticks() or using a FixedLocator.
   cb.ax.set_yticklabels(cb.ax.get_yticklabels(), fontsize=18)
 
    
 
    
@@ -2922,7 +2940,7 @@ with the form utilized in linear regression, viz.
    -
    /var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_95419/3777801602.py:7: UserWarning: set_ticklabels() should only be used with a fixed number of ticks, i.e. after set_ticks() or using a FixedLocator.
+
    /var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_22458/3777801602.py:7: UserWarning: set_ticklabels() should only be used with a fixed number of ticks, i.e. after set_ticks() or using a FixedLocator.
   cb.ax.set_yticklabels(cb.ax.get_yticklabels(), fontsize=18)
 
    
 
    
@@ -2962,7 +2980,7 @@ cost function is given by
    -
    /var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_95419/438060758.py:10: UserWarning: set_ticklabels() should only be used with a fixed number of ticks, i.e. after set_ticks() or using a FixedLocator.
+
    /var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_22458/438060758.py:10: UserWarning: set_ticklabels() should only be used with a fixed number of ticks, i.e. after set_ticks() or using a FixedLocator.
   cb.ax.set_yticklabels(cb.ax.get_yticklabels(), fontsize=18)
 
    
 
    
@@ -2997,7 +3015,7 @@ cost function is given by
    -
    /var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_95419/3544313922.py:9: UserWarning: set_ticklabels() should only be used with a fixed number of ticks, i.e. after set_ticks() or using a FixedLocator.
+
    /var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_22458/3544313922.py:9: UserWarning: set_ticklabels() should only be used with a fixed number of ticks, i.e. after set_ticks() or using a FixedLocator.
   cb.ax.set_yticklabels(cb.ax.get_yticklabels(), fontsize=18)
 
    
 
    
@@ -3050,43 +3068,43 @@ constant as opposed to ridge and OLS. We get a sparse solution with
    -
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+
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    /Users/mhjensen/miniforge3/envs/myenv/lib/python3.9/site-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.924e+00, tolerance: 1.797e+00
   model = cd_fast.enet_coordinate_descent(
 
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diff --git a/doc/LectureNotes/_build/html/chapter4.html b/doc/LectureNotes/_build/html/chapter4.html
index 27126646e..3e81bb26c 100644
--- a/doc/LectureNotes/_build/html/chapter4.html
+++ b/doc/LectureNotes/_build/html/chapter4.html
@@ -308,6 +308,21 @@ const thebe_selector_output = ".output, .cell_output"
    Week 39: Optimization and  Gradient Methods
   
  
+ 
  • 
+  
+   Week 40: Gradient descent methods (continued) and start Neural networks
+  
+ 
    • 
+ 
  • 
+  
+   Exercises week 41
+  
+ 
    • 
+ 
  • 
+  
+   Week 41 Neural networks and constructing a neural network code
+  
+ 
    • 
 
 
    
  
@@ -320,6 +335,11 @@ const thebe_selector_output = ".output, .cell_output"
    Project 1 on Machine Learning, deadline October 7 (midnight), 2024
   
  
+ 
  • 
+  
+   Project 2 on Machine Learning, deadline November 4 (Midnight)
+  
+ 
    • 
 
 
     
    
diff --git a/doc/LectureNotes/_build/html/chapter5.html b/doc/LectureNotes/_build/html/chapter5.html
index 1a1304365..394c0c69a 100644
--- a/doc/LectureNotes/_build/html/chapter5.html
+++ b/doc/LectureNotes/_build/html/chapter5.html
@@ -308,6 +308,21 @@ const thebe_selector_output = ".output, .cell_output"
    Week 39: Optimization and  Gradient Methods
   
  
+ 
  • 
+  
+   Week 40: Gradient descent methods (continued) and start Neural networks
+  
+ 
    • 
+ 
  • 
+  
+   Exercises week 41
+  
+ 
    • 
+ 
  • 
+  
+   Week 41 Neural networks and constructing a neural network code
+  
+ 
    • 
 
 
    
  
@@ -320,6 +335,11 @@ const thebe_selector_output = ".output, .cell_output"
    Project 1 on Machine Learning, deadline October 7 (midnight), 2024
   
  
+ 
  • 
+  
+   Project 2 on Machine Learning, deadline November 4 (Midnight)
+  
+ 
    • 
 
 
     
    
diff --git a/doc/LectureNotes/_build/html/chapter6.html b/doc/LectureNotes/_build/html/chapter6.html
index c60fd098e..9c904f401 100644
--- a/doc/LectureNotes/_build/html/chapter6.html
+++ b/doc/LectureNotes/_build/html/chapter6.html
@@ -308,6 +308,21 @@ const thebe_selector_output = ".output, .cell_output"
    Week 39: Optimization and  Gradient Methods
   
  
+ 
  • 
+  
+   Week 40: Gradient descent methods (continued) and start Neural networks
+  
+ 
    • 
+ 
  • 
+  
+   Exercises week 41
+  
+ 
    • 
+ 
  • 
+  
+   Week 41 Neural networks and constructing a neural network code
+  
+ 
    • 
 
 
    
  
@@ -320,6 +335,11 @@ const thebe_selector_output = ".output, .cell_output"
    Project 1 on Machine Learning, deadline October 7 (midnight), 2024
   
  
+ 
  • 
+  
+   Project 2 on Machine Learning, deadline November 4 (Midnight)
+  
+ 
    • 
 
 
     
    
@@ -767,9 +787,9 @@ predicting the target features of query instances is as follows:
    
 
    
 
    
 
    2nd degree coefficients:
-zero power:  -1.5105332296929628
-first power:  0.08398399377155011
-second power:  -0.0003701342170783489
+zero power:  -5.030164788184997
+first power:  0.001268544905013411
+second power:  -5.234332597247826e-05
 
    
 
    
 _images/chapter6_1_1.png
@@ -1632,10 +1652,10 @@ attributes at each step while growing the tree.
    
 
    
 
    (426, 30)
 (143, 30)
-Test set accuracy with Logistic Regression: 0.94
 
    
 
    
-
    Test set accuracy with SVM: 0.63
+
    Test set accuracy with Logistic Regression: 0.94
+Test set accuracy with SVM: 0.63
 Test set accuracy with Decision Trees: 0.90
 Test set accuracy Logistic Regression with scaled data: 0.96
 Test set accuracy SVM with scaled data: 0.96
diff --git a/doc/LectureNotes/_build/html/chapter7.html b/doc/LectureNotes/_build/html/chapter7.html
index 76d26aa2e..1e6480c23 100644
--- a/doc/LectureNotes/_build/html/chapter7.html
+++ b/doc/LectureNotes/_build/html/chapter7.html
@@ -308,6 +308,21 @@ const thebe_selector_output = ".output, .cell_output"
    Week 39: Optimization and  Gradient Methods
   
  
+ 
  • 
+  
+   Week 40: Gradient descent methods (continued) and start Neural networks
+  
+ 
    • 
+ 
  • 
+  
+   Exercises week 41
+  
+ 
    • 
+ 
  • 
+  
+   Week 41 Neural networks and constructing a neural network code
+  
+ 
    • 
 
 
    
  
@@ -320,6 +335,11 @@ const thebe_selector_output = ".output, .cell_output"
    Project 1 on Machine Learning, deadline October 7 (midnight), 2024
   
  
+ 
  • 
+  
+   Project 2 on Machine Learning, deadline November 4 (Midnight)
+  
+ 
    • 
 
 
     
    
diff --git a/doc/LectureNotes/_build/html/chapter8.html b/doc/LectureNotes/_build/html/chapter8.html
index 972245b7f..d85525649 100644
--- a/doc/LectureNotes/_build/html/chapter8.html
+++ b/doc/LectureNotes/_build/html/chapter8.html
@@ -308,6 +308,21 @@ const thebe_selector_output = ".output, .cell_output"
    Week 39: Optimization and  Gradient Methods
   
  
+ 
  • 
+  
+   Week 40: Gradient descent methods (continued) and start Neural networks
+  
+ 
    • 
+ 
  • 
+  
+   Exercises week 41
+  
+ 
    • 
+ 
  • 
+  
+   Week 41 Neural networks and constructing a neural network code
+  
+ 
    • 
 
 
    
  
@@ -320,6 +335,11 @@ const thebe_selector_output = ".output, .cell_output"
    Project 1 on Machine Learning, deadline October 7 (midnight), 2024
   
  
+ 
  • 
+  
+   Project 2 on Machine Learning, deadline November 4 (Midnight)
+  
+ 
    • 
 
 
     
    
@@ -721,10 +741,10 @@ covariance matrix through the np.linalg.eig() function.
    
 
    
 
    
 
    
-
    0.13035147135400782
-4.25879315330607
-[[0.86867512 2.59009792]
- [2.59009792 8.82533209]]
+
    -0.012423940191689783
+4.101008878523571
+[[0.89527291 2.65532045]
+ [2.65532045 8.81987609]]
 
    
 
    
 
    
@@ -764,10 +784,10 @@ a more brute force way. Here we scale the mean values for each column of the des
 
    
 
    
 
    
-
    0.08690184845323
-1.521422502348998
-[[1.         0.69768266]
- [0.69768266 1.        ]]
+
    0.08705631913312815
+1.7026908764394864
+[[1.         0.65870313]
+ [0.65870313 1.        ]]
 
    
 
    
 
    
@@ -796,30 +816,30 @@ this matrix we easily see that it is a positive definite matrix.
    
 
    
 
    
 
    
-
    [[ 1.14550854  1.96870431]
- [ 0.79787194  3.11438414]
- [-0.18497496 -1.31315504]
- [-1.52706754 -4.97482498]
- [-1.30190897 -3.11113486]
- [-0.08421808 -1.70928399]
- [ 0.11992194 -0.07776381]
- [-0.90717653 -2.20404927]
- [ 1.05201041  5.38762019]
- [ 0.89003324  2.9195033 ]]
+
    [[-1.7755649  -4.56778296]
+ [-0.81015037 -2.80072356]
+ [ 0.73628249  1.95206335]
+ [ 0.97366347  1.61130099]
+ [ 0.7271324   1.97965627]
+ [ 0.36881837  0.56037913]
+ [-1.33163086 -2.59391196]
+ [-0.68953877 -1.58298728]
+ [ 0.19982428 -1.08010965]
+ [ 1.60116388  6.52211567]]
           0         1
-0  1.145509  1.968704
-1  0.797872  3.114384
-2 -0.184975 -1.313155
-3 -1.527068 -4.974825
-4 -1.301909 -3.111135
-5 -0.084218 -1.709284
-6  0.119922 -0.077764
-7 -0.907177 -2.204049
-8  1.052010  5.387620
-9  0.890033  2.919503
-          0         1
-0  1.000000  0.937057
-1  0.937057  1.000000
+0 -1.775565 -4.567783
+1 -0.810150 -2.800724
+2  0.736282  1.952063
+3  0.973663  1.611301
+4  0.727132  1.979656
+5  0.368818  0.560379
+6 -1.331631 -2.593912
+7 -0.689539 -1.582987
+8  0.199824 -1.080110
+9  1.601164  6.522116
+         0        1
+0  1.00000  0.94335
+1  0.94335  1.00000
 
    
 
    
 
    
@@ -876,37 +896,37 @@ this matrix we easily see that it is a positive definite matrix.
    
 
    
 
         0         1         2         3         4         5         6         7   \
 0   0.0  0.000000  0.000000  0.000000  0.000000  0.000000  0.000000  0.000000   
-1   0.0  0.080345  0.078573  0.079174  0.077839  0.076679  0.070275  0.069320   
-2   0.0  0.078573  0.078146  0.079202  0.078688  0.078268  0.071580  0.071186   
-3   0.0  0.079174  0.079202  0.083342  0.083016  0.082784  0.076947  0.076648   
-4   0.0  0.077839  0.078688  0.083016  0.083260  0.083547  0.077485  0.077601   
-5   0.0  0.076679  0.078268  0.082784  0.083547  0.084312  0.078044  0.078541   
-6   0.0  0.070275  0.071580  0.076947  0.077485  0.078044  0.072947  0.073258   
-7   0.0  0.069320  0.071186  0.076648  0.077601  0.078541  0.073258  0.073882   
-8   0.0  0.068539  0.070918  0.076479  0.077813  0.079107  0.073647  0.074561   
-9   0.0  0.067922  0.070772  0.076434  0.078123  0.079747  0.074115  0.075302   
-10  0.0  0.061319  0.063395  0.068977  0.070098  0.071191  0.066686  0.067431   
-11  0.0  0.060726  0.063206  0.068843  0.070274  0.071656  0.066988  0.067974   
-12  0.0  0.060275  0.063130  0.068827  0.070547  0.072200  0.067374  0.068587   
-13  0.0  0.059956  0.063161  0.068924  0.070916  0.072824  0.067843  0.069270   
-14  0.0  0.059761  0.063294  0.069129  0.071379  0.073528  0.068394  0.070024   
+1   0.0  0.088650  0.084209  0.092689  0.091653  0.090476  0.085764  0.085378   
+2   0.0  0.084209  0.080559  0.088599  0.087876  0.087020  0.082478  0.082249   
+3   0.0  0.092689  0.088599  0.102209  0.101292  0.100209  0.097667  0.097380   
+4   0.0  0.091653  0.087876  0.101292  0.100523  0.099588  0.097017  0.096811   
+5   0.0  0.090476  0.087020  0.100209  0.099588  0.098803  0.096203  0.096078   
+6   0.0  0.085764  0.082478  0.097667  0.097017  0.096203  0.095425  0.095278   
+7   0.0  0.085378  0.082249  0.097380  0.096811  0.096078  0.095278  0.095178   
+8   0.0  0.084976  0.082002  0.097060  0.096570  0.095915  0.095090  0.095037   
+9   0.0  0.084550  0.081730  0.096700  0.096287  0.095710  0.094857  0.094849   
+10  0.0  0.077672  0.075070  0.090429  0.090002  0.089421  0.089826  0.089786   
+11  0.0  0.077490  0.074976  0.090319  0.089940  0.089408  0.089800  0.089790   
+12  0.0  0.077310  0.074882  0.090204  0.089872  0.089386  0.089763  0.089783   
+13  0.0  0.077131  0.074787  0.090081  0.089795  0.089354  0.089714  0.089763   
+14  0.0  0.076951  0.074688  0.089949  0.089708  0.089311  0.089653  0.089730   
 
           8         9         10        11        12        13        14  
 0   0.000000  0.000000  0.000000  0.000000  0.000000  0.000000  0.000000  
-1   0.068539  0.067922  0.061319  0.060726  0.060275  0.059956  0.059761  
-2   0.070918  0.070772  0.063395  0.063206  0.063130  0.063161  0.063294  
-3   0.076479  0.076434  0.068977  0.068843  0.068827  0.068924  0.069129  
-4   0.077813  0.078123  0.070098  0.070274  0.070547  0.070916  0.071379  
-5   0.079107  0.079747  0.071191  0.071656  0.072200  0.072824  0.073528  
-6   0.073647  0.074115  0.066686  0.066988  0.067374  0.067843  0.068394  
-7   0.074561  0.075302  0.067431  0.067974  0.068587  0.069270  0.070024  
-8   0.075513  0.076508  0.068215  0.068985  0.069811  0.070696  0.071643  
-9   0.076508  0.077745  0.069045  0.070027  0.071055  0.072132  0.073262  
-10  0.068215  0.069045  0.061898  0.062520  0.063197  0.063933  0.064728  
-11  0.068985  0.070027  0.062520  0.063332  0.064189  0.065096  0.066054  
-12  0.069811  0.071055  0.063197  0.064189  0.065217  0.066286  0.067400  
-13  0.070696  0.072132  0.063933  0.065096  0.066286  0.067511  0.068775  
-14  0.071643  0.073262  0.064728  0.066054  0.067400  0.068775  0.070183  
+1   0.084976  0.084550  0.077672  0.077490  0.077310  0.077131  0.076951  
+2   0.082002  0.081730  0.075070  0.074976  0.074882  0.074787  0.074688  
+3   0.097060  0.096700  0.090429  0.090319  0.090204  0.090081  0.089949  
+4   0.096570  0.096287  0.090002  0.089940  0.089872  0.089795  0.089708  
+5   0.095915  0.095710  0.089421  0.089408  0.089386  0.089354  0.089311  
+6   0.095090  0.094857  0.089826  0.089800  0.089763  0.089714  0.089653  
+7   0.095037  0.094849  0.089786  0.089790  0.089783  0.089763  0.089730  
+8   0.094941  0.094797  0.089704  0.089738  0.089760  0.089769  0.089763  
+9   0.094797  0.094697  0.089576  0.089639  0.089690  0.089726  0.089748  
+10  0.089704  0.089576  0.085655  0.085690  0.085712  0.085722  0.085716  
+11  0.089738  0.089639  0.085690  0.085747  0.085790  0.085819  0.085833  
+12  0.089760  0.089690  0.085712  0.085790  0.085853  0.085902  0.085935  
+13  0.089769  0.089726  0.085722  0.085819  0.085902  0.085970  0.086021  
+14  0.089763  0.089748  0.085716  0.085833  0.085935  0.086021  0.086092  
 
    
 
    
 
    
@@ -1095,10 +1115,10 @@ We can write our own code or simply use either the functionaly of numpy<
 
    
 
    
 
              0         1
-0  4.032196  2.034476
-1  2.034476  1.997746
-[[4.0321956  2.03447649]
- [2.03447649 1.99774602]]
+0  3.914672  1.954823
+1  1.954823  1.963858
+[[3.91467223 1.95482298]
+ [1.95482298 1.96385798]]
 
    
 
    
 
    
@@ -1125,8 +1145,8 @@ Our own code here is not very elegant and asks for obvious improvements. It is t
 
    
 
    
 
    Centered covariance using own code
-[[4.0321956  2.03447649]
- [2.03447649 1.99774602]]
+[[3.91467223 1.95482298]
+ [1.95482298 1.96385798]]
 
    
 
    
 _images/chapter8_65_1.png
@@ -1186,16 +1206,16 @@ questions.
    
 
    
 
    
 
    Eigenvalues of Covariance matrix
-5.2895786617507
-0.7403629637766833
+5.123928000581825
+0.7546022135629743
 First eigenvector
-[0.85064942 0.52573336]
+[0.85043503 0.5260801 ]
 Second eigenvector
-[-0.52573336  0.85064942]
+[-0.5260801   0.85043503]
 
    
 
    
 
    Eigenvector of largest eigenvalue
-[-0.85064942 -0.52573336]
+[0.85043503 0.5260801 ]
 
    
 
    
 
    
diff --git a/doc/LectureNotes/_build/html/chapter9.html b/doc/LectureNotes/_build/html/chapter9.html
index 312fa1bbb..82abdae5b 100644
--- a/doc/LectureNotes/_build/html/chapter9.html
+++ b/doc/LectureNotes/_build/html/chapter9.html
@@ -308,6 +308,21 @@ const thebe_selector_output = ".output, .cell_output"
    Week 39: Optimization and  Gradient Methods
   
  
+ 
  • 
+  
+   Week 40: Gradient descent methods (continued) and start Neural networks
+  
+ 
    • 
+ 
  • 
+  
+   Exercises week 41
+  
+ 
    • 
+ 
  • 
+  
+   Week 41 Neural networks and constructing a neural network code
+  
+ 
    • 
 
 
    
  
@@ -320,6 +335,11 @@ const thebe_selector_output = ".output, .cell_output"
    Project 1 on Machine Learning, deadline October 7 (midnight), 2024
   
  
+ 
  • 
+  
+   Project 2 on Machine Learning, deadline November 4 (Midnight)
+  
+ 
    • 
 
 
     
    
diff --git a/doc/LectureNotes/_build/html/chapteroptimization.html b/doc/LectureNotes/_build/html/chapteroptimization.html
index a7b1cb2c9..637c870f3 100644
--- a/doc/LectureNotes/_build/html/chapteroptimization.html
+++ b/doc/LectureNotes/_build/html/chapteroptimization.html
@@ -308,6 +308,21 @@ const thebe_selector_output = ".output, .cell_output"
    Week 39: Optimization and  Gradient Methods
   
  
+ 
  • 
+  
+   Week 40: Gradient descent methods (continued) and start Neural networks
+  
+ 
    • 
+ 
  • 
+  
+   Exercises week 41
+  
+ 
    • 
+ 
  • 
+  
+   Week 41 Neural networks and constructing a neural network code
+  
+ 
    • 
 
 
    
  
@@ -320,6 +335,11 @@ const thebe_selector_output = ".output, .cell_output"
    Project 1 on Machine Learning, deadline October 7 (midnight), 2024
   
  
+ 
  • 
+  
+   Project 2 on Machine Learning, deadline November 4 (Midnight)
+  
+ 
    • 
 
 
     
    
diff --git a/doc/LectureNotes/_build/html/clustering.html b/doc/LectureNotes/_build/html/clustering.html
index 239eb6e28..ee79e09c1 100644
--- a/doc/LectureNotes/_build/html/clustering.html
+++ b/doc/LectureNotes/_build/html/clustering.html
@@ -308,6 +308,21 @@ const thebe_selector_output = ".output, .cell_output"
    Week 39: Optimization and  Gradient Methods
   
  
+ 
  • 
+  
+   Week 40: Gradient descent methods (continued) and start Neural networks
+  
+ 
    • 
+ 
  • 
+  
+   Exercises week 41
+  
+ 
    • 
+ 
  • 
+  
+   Week 41 Neural networks and constructing a neural network code
+  
+ 
    • 
 
 
    
  
@@ -320,6 +335,11 @@ const thebe_selector_output = ".output, .cell_output"
    Project 1 on Machine Learning, deadline October 7 (midnight), 2024
   
  
+ 
  • 
+  
+   Project 2 on Machine Learning, deadline November 4 (Midnight)
+  
+ 
    • 
 
 
     
    
diff --git a/doc/LectureNotes/_build/html/exercisesweek34.html b/doc/LectureNotes/_build/html/exercisesweek34.html
index 13f0341d1..ea7980312 100644
--- a/doc/LectureNotes/_build/html/exercisesweek34.html
+++ b/doc/LectureNotes/_build/html/exercisesweek34.html
@@ -308,6 +308,21 @@ const thebe_selector_output = ".output, .cell_output"
    Week 39: Optimization and  Gradient Methods
   
  
+ 
  • 
+  
+   Week 40: Gradient descent methods (continued) and start Neural networks
+  
+ 
    • 
+ 
  • 
+  
+   Exercises week 41
+  
+ 
    • 
+ 
  • 
+  
+   Week 41 Neural networks and constructing a neural network code
+  
+ 
    • 
 
 
    
  
@@ -320,6 +335,11 @@ const thebe_selector_output = ".output, .cell_output"
    Project 1 on Machine Learning, deadline October 7 (midnight), 2024
   
  
+ 
  • 
+  
+   Project 2 on Machine Learning, deadline November 4 (Midnight)
+  
+ 
    • 
 
 
     
    
diff --git a/doc/LectureNotes/_build/html/exercisesweek35.html b/doc/LectureNotes/_build/html/exercisesweek35.html
index 8e8d805c3..2f0422528 100644
--- a/doc/LectureNotes/_build/html/exercisesweek35.html
+++ b/doc/LectureNotes/_build/html/exercisesweek35.html
@@ -308,6 +308,21 @@ const thebe_selector_output = ".output, .cell_output"
    Week 39: Optimization and  Gradient Methods
   
  
+ 
  • 
+  
+   Week 40: Gradient descent methods (continued) and start Neural networks
+  
+ 
    • 
+ 
  • 
+  
+   Exercises week 41
+  
+ 
    • 
+ 
  • 
+  
+   Week 41 Neural networks and constructing a neural network code
+  
+ 
    • 
 
 
    
  
@@ -320,6 +335,11 @@ const thebe_selector_output = ".output, .cell_output"
    Project 1 on Machine Learning, deadline October 7 (midnight), 2024
   
  
+ 
  • 
+  
+   Project 2 on Machine Learning, deadline November 4 (Midnight)
+  
+ 
    • 
 
 
     
    
diff --git a/doc/LectureNotes/_build/html/exercisesweek36.html b/doc/LectureNotes/_build/html/exercisesweek36.html
index 763e4b698..b2187bd49 100644
--- a/doc/LectureNotes/_build/html/exercisesweek36.html
+++ b/doc/LectureNotes/_build/html/exercisesweek36.html
@@ -308,6 +308,21 @@ const thebe_selector_output = ".output, .cell_output"
    Week 39: Optimization and  Gradient Methods
   
  
+ 
  • 
+  
+   Week 40: Gradient descent methods (continued) and start Neural networks
+  
+ 
    • 
+ 
  • 
+  
+   Exercises week 41
+  
+ 
    • 
+ 
  • 
+  
+   Week 41 Neural networks and constructing a neural network code
+  
+ 
    • 
 
 
    
  
@@ -320,6 +335,11 @@ const thebe_selector_output = ".output, .cell_output"
    Project 1 on Machine Learning, deadline October 7 (midnight), 2024
   
  
+ 
  • 
+  
+   Project 2 on Machine Learning, deadline November 4 (Midnight)
+  
+ 
    • 
 
 
     
    
diff --git a/doc/LectureNotes/_build/html/exercisesweek37.html b/doc/LectureNotes/_build/html/exercisesweek37.html
index 2e89bd614..f6a46f580 100644
--- a/doc/LectureNotes/_build/html/exercisesweek37.html
+++ b/doc/LectureNotes/_build/html/exercisesweek37.html
@@ -308,6 +308,21 @@ const thebe_selector_output = ".output, .cell_output"
    Week 39: Optimization and  Gradient Methods
   
  
+ 
  • 
+  
+   Week 40: Gradient descent methods (continued) and start Neural networks
+  
+ 
    • 
+ 
  • 
+  
+   Exercises week 41
+  
+ 
    • 
+ 
  • 
+  
+   Week 41 Neural networks and constructing a neural network code
+  
+ 
    • 
 
 
    
  
@@ -320,6 +335,11 @@ const thebe_selector_output = ".output, .cell_output"
    Project 1 on Machine Learning, deadline October 7 (midnight), 2024
   
  
+ 
  • 
+  
+   Project 2 on Machine Learning, deadline November 4 (Midnight)
+  
+ 
    • 
 
 
     
    
diff --git a/doc/LectureNotes/_build/html/exercisesweek38.html b/doc/LectureNotes/_build/html/exercisesweek38.html
index 01a909575..8bea48d90 100644
--- a/doc/LectureNotes/_build/html/exercisesweek38.html
+++ b/doc/LectureNotes/_build/html/exercisesweek38.html
@@ -308,6 +308,21 @@ const thebe_selector_output = ".output, .cell_output"
    Week 39: Optimization and  Gradient Methods
   
  
+ 
  • 
+  
+   Week 40: Gradient descent methods (continued) and start Neural networks
+  
+ 
    • 
+ 
  • 
+  
+   Exercises week 41
+  
+ 
    • 
+ 
  • 
+  
+   Week 41 Neural networks and constructing a neural network code
+  
+ 
    • 
 
 
    
  
@@ -320,6 +335,11 @@ const thebe_selector_output = ".output, .cell_output"
    Project 1 on Machine Learning, deadline October 7 (midnight), 2024
   
  
+ 
  • 
+  
+   Project 2 on Machine Learning, deadline November 4 (Midnight)
+  
+ 
    • 
 
 
     
    
diff --git a/doc/LectureNotes/_build/html/exercisesweek39.html b/doc/LectureNotes/_build/html/exercisesweek39.html
index 476b0dc7d..74bb4cb9b 100644
--- a/doc/LectureNotes/_build/html/exercisesweek39.html
+++ b/doc/LectureNotes/_build/html/exercisesweek39.html
@@ -306,6 +306,21 @@ const thebe_selector_output = ".output, .cell_output"
    Week 39: Optimization and  Gradient Methods
   
  
+ 
  • 
+  
+   Week 40: Gradient descent methods (continued) and start Neural networks
+  
+ 
    • 
+ 
  • 
+  
+   Exercises week 41
+  
+ 
    • 
+ 
  • 
+  
+   Week 41 Neural networks and constructing a neural network code
+  
+ 
    • 
 
 
    
  
@@ -318,6 +333,11 @@ const thebe_selector_output = ".output, .cell_output"
    Project 1 on Machine Learning, deadline October 7 (midnight), 2024
   
  
+ 
  • 
+  
+   Project 2 on Machine Learning, deadline November 4 (Midnight)
+  
+ 
    • 
 
 
     
    
diff --git a/doc/LectureNotes/_build/html/exercisesweek41.html b/doc/LectureNotes/_build/html/exercisesweek41.html
index f8b8ed3f0..666c1b83d 100644
--- a/doc/LectureNotes/_build/html/exercisesweek41.html
+++ b/doc/LectureNotes/_build/html/exercisesweek41.html
@@ -794,15 +794,15 @@ regression.
    
 
    
 
    
 
    Own inversion
-[[4.0846409]
- [2.8352834]]
-Eigenvalues of Hessian Matrix:[0.33241952 4.39292847]
+[[3.96581896]
+ [3.03624103]]
+Eigenvalues of Hessian Matrix:[0.26120167 4.71129677]
 theta from own gd
-[[4.0846409]
- [2.8352834]]
+[[3.96581896]
+ [3.03624103]]
 theta from own sdg
-[[4.03188133]
- [2.82130663]]
+[[3.99729015]
+ [3.02646648]]
 
    
 
    
 _images/exercisesweek41_5_1.png
@@ -924,14 +924,14 @@ first example shows results with ordinary leats squares.
    
 
    
 
    
 
    Own inversion
-[[3.89543945]
- [3.14433067]]
-Eigenvalues of Hessian Matrix:[0.30069427 4.55916434]
+[[4.03656288]
+ [2.95497813]]
+Eigenvalues of Hessian Matrix:[0.29560433 4.58257727]
 
    
 
    
 
    theta from own gd
-[[3.89543945]
- [3.14433067]]
+[[4.03656288]
+ [2.95497813]]
 
    
 
    
 _images/exercisesweek41_16_2.png
@@ -1002,73 +1002,73 @@ Eigenvalues of Hessian Matrix:[0.30069427 4.55916434]
 
    Own inversion
 [[4.]
  [3.]]
-Eigenvalues of Hessian Matrix:[0.30043041 4.28093636]
-0 [-13.544421] [-15.11052859]
-1 [-0.26692247] [0.23040825]
-2 [-0.2481902] [0.2142385]
-3 [-0.23077255] [0.19920353]
-4 [-0.21457724] [0.18522369]
-5 [-0.19951849] [0.17222494]
-6 [-0.18551655] [0.16013842]
-7 [-0.17249725] [0.14890012]
-8 [-0.16039162] [0.13845051]
-9 [-0.14913555] [0.12873424]
-10 [-0.13866942] [0.11969984]
-11 [-0.12893778] [0.11129947]
-12 [-0.1198891] [0.10348862]
-13 [-0.11147544] [0.09622593]
-14 [-0.10365224] [0.08947292]
-15 [-0.09637807] [0.08319383]
-16 [-0.08961438] [0.0773554]
-17 [-0.08332537] [0.0719267]
-18 [-0.0774777] [0.06687898]
-19 [-0.07204042] [0.0621855]
-20 [-0.06698472] [0.0578214]
-21 [-0.06228382] [0.05376358]
-22 [-0.05791283] [0.04999052]
-23 [-0.05384858] [0.04648225]
-24 [-0.05006956] [0.04322019]
-25 [-0.04655574] [0.04018705]
-26 [-0.04328852] [0.03736678]
-27 [-0.04025059] [0.03474443]
-28 [-0.03742586] [0.03230611]
-29 [-0.03479936] [0.03003891]
+Eigenvalues of Hessian Matrix:[0.27750534 4.13217667]
+0 [-10.24816468] [-11.05638246]
+1 [-0.1602625] [0.14404536]
+2 [-0.14949972] [0.13437168]
+3 [-0.13945974] [0.12534765]
+4 [-0.13009402] [0.11692966]
+5 [-0.12135727] [0.10907699]
+6 [-0.11320726] [0.10175169]
+7 [-0.10560458] [0.09491833]
+8 [-0.09851247] [0.08854388]
+9 [-0.09189665] [0.08259753]
+10 [-0.08572513] [0.07705051]
+11 [-0.07996807] [0.07187601]
+12 [-0.07459764] [0.06704902]
+13 [-0.06958788] [0.0625462]
+14 [-0.06491455] [0.05834577]
+15 [-0.06055507] [0.05442743]
+16 [-0.05648836] [0.05077224]
+17 [-0.05269476] [0.04736252]
+18 [-0.04915593] [0.04418179]
+19 [-0.04585476] [0.04121466]
+20 [-0.04277528] [0.0384468]
+21 [-0.03990261] [0.03586482]
+22 [-0.03722287] [0.03345624]
+23 [-0.03472308] [0.03120942]
+24 [-0.03239118] [0.02911348]
+25 [-0.03021588] [0.0271583]
+26 [-0.02818667] [0.02533443]
+27 [-0.02629373] [0.02363304]
+28 [-0.02452792] [0.02204591]
+29 [-0.02288069] [0.02056537]
 theta from own gd
-[[3.89229722]
- [3.09296935]]
-0 [-0.03235719] [0.02793082]
-1 [-0.03008641] [0.02597067]
-2 [-0.02729375] [0.02356004]
-3 [-0.02454051] [0.02118344]
-4 [-0.02199232] [0.01898383]
-5 [-0.01968447] [0.01699169]
-6 [-0.01761069] [0.01520159]
-7 [-0.01575266] [0.01359774]
-8 [-0.01408975] [0.01216231]
-9 [-0.01260207] [0.01087815]
-10 [-0.01127138] [0.00972948]
-11 [-0.01008116] [0.00870208]
-12 [-0.00901661] [0.00778316]
-13 [-0.00806447] [0.00696127]
-14 [-0.00721287] [0.00622617]
-15 [-0.00645121] [0.0055687]
-16 [-0.00576997] [0.00498065]
-17 [-0.00516067] [0.0044547]
-18 [-0.00461571] [0.00398429]
-19 [-0.0041283] [0.00356356]
-20 [-0.00369236] [0.00318725]
-21 [-0.00330245] [0.00285068]
-22 [-0.00295372] [0.00254966]
-23 [-0.00264181] [0.00228042]
-24 [-0.00236284] [0.00203961]
-25 [-0.00211332] [0.00182423]
-26 [-0.00189016] [0.00163159]
-27 [-0.00169056] [0.0014593]
-28 [-0.00151204] [0.0013052]
-29 [-0.00135237] [0.00116737]
+[[3.92308585]
+ [3.06913112]]
+0 [-0.02134409] [0.01918426]
+1 [-0.01991068] [0.01789589]
+2 [-0.01814351] [0.01630755]
+3 [-0.01639489] [0.01473588]
+4 [-0.01476927] [0.01327475]
+5 [-0.01328972] [0.01194492]
+6 [-0.01195336] [0.01074379]
+7 [-0.0107497] [0.00966192]
+8 [-0.00966668] [0.0086885]
+9 [-0.00869259] [0.00781297]
+10 [-0.00781659] [0.00702562]
+11 [-0.00702885] [0.00631759]
+12 [-0.00632049] [0.00568091]
+13 [-0.00568352] [0.00510839]
+14 [-0.00511073] [0.00459357]
+15 [-0.00459568] [0.00413064]
+16 [-0.00413253] [0.00371435]
+17 [-0.00371605] [0.00334002]
+18 [-0.00334155] [0.00300342]
+19 [-0.00300479] [0.00270073]
+20 [-0.00270197] [0.00242856]
+21 [-0.00242967] [0.00218381]
+22 [-0.00218481] [0.00196372]
+23 [-0.00196462] [0.00176582]
+24 [-0.00176663] [0.00158786]
+25 [-0.00158859] [0.00142784]
+26 [-0.00142849] [0.00128394]
+27 [-0.00128453] [0.00115455]
+28 [-0.00115508] [0.00103819]
+29 [-0.00103867] [0.00093356]
 theta from own gd wth momentum
-[[3.9959739 ]
- [3.00347534]]
+[[3.99663433]
+ [3.00302509]]
 
    
 
    
 
    
@@ -1121,17 +1121,17 @@ theta from own gd wth momentum
 
    
 
    
 
    Own inversion
-[[3.96414331]
- [3.22481166]]
-Eigenvalues of Hessian Matrix:[0.26674792 4.38123077]
-0 [-15.60151468] [-17.64192473]
-1 [-1.66342634e-14] [-1.21941484e-14]
-2 [-6.41847686e-17] [1.63371136e-16]
-3 [-6.76542156e-17] [-4.78340902e-17]
-4 [-6.76542156e-17] [-4.78340902e-17]
+[[3.81924504]
+ [3.09224244]]
+Eigenvalues of Hessian Matrix:[0.31096156 4.57735536]
+0 [-15.77829399] [-18.53684024]
+1 [-3.15242624e-15] [-1.65243388e-16]
+2 [3.85975973e-17] [5.35468713e-17]
+3 [3.85975973e-17] [5.35468713e-17]
+4 [3.85975973e-17] [5.35468713e-17]
 beta from own Newton code
-[[3.96414331]
- [3.22481166]]
+[[3.81924504]
+ [3.09224244]]
 
    
 
    
 
    
@@ -1220,20 +1220,20 @@ beta from own Newton code
 
    
 
    
 
    Own inversion
-[[3.84668873]
- [3.21776599]]
-Eigenvalues of Hessian Matrix:[0.28766899 4.2084894 ]
+[[3.84019294]
+ [3.22756518]]
+Eigenvalues of Hessian Matrix:[0.2729933  4.56108455]
 
    
 
    
 
    theta from own gd
-[[3.84668873]
- [3.21776599]]
+[[3.84019294]
+ [3.22756518]]
 
    
 
    
 _images/exercisesweek41_22_2.png
 
    theta from own sdg
-[[3.86757762]
- [3.17214797]]
+[[3.85298956]
+ [3.28358004]]
 
    
 
    
 
    
@@ -1315,15 +1315,15 @@ Eigenvalues of Hessian Matrix:[0.28766899 4.2084894 ]
 
    
 
    
 
    Own inversion
-[[3.90140097]
- [3.0552878 ]]
-Eigenvalues of Hessian Matrix:[0.32683063 4.01722503]
+[[4.1356995 ]
+ [3.09209032]]
+Eigenvalues of Hessian Matrix:[0.31362428 4.21949761]
 theta from own gd
-[[3.90137221]
- [3.055314  ]]
+[[4.13531906]
+ [3.09242193]]
 theta from own sdg with momentum
-[[3.83824223]
- [3.10556487]]
+[[4.25003261]
+ [3.05169046]]
 
    
 
    
 
    
@@ -1398,9 +1398,9 @@ theta from own sdg with momentum
 
    
 
    
 
    theta from own AdaGrad
-[[2.00003645]
- [2.99977853]
- [4.00021916]]
+[[2.00006114]
+ [2.99959609]
+ [4.00040218]]
 
    
 
    
 
    
@@ -1482,9 +1482,9 @@ theta from own sdg with momentum
 
    
 
    
 
    theta from own RMSprop
-[[1.99985591]
- [3.00049636]
- [3.99943837]]
+[[1.99952851]
+ [3.00380258]
+ [3.995522  ]]
    @@ -1570,9 +1570,9 @@ theta from own sdg with momentum 
    theta from own ADAM
-[[2.00008335]
- [2.99969576]
- [4.00033679]]
+[[1.99984836]
+ [3.00097416]
+ [3.99895448]]
    @@ -1645,7 +1645,7 @@ It provides composable transformations of Python+NumPy programs: differentiate, return asarray(x, dtype=self.dtype)
    -
    [<matplotlib.lines.Line2D at 0x126431130>]
+
    [<matplotlib.lines.Line2D at 0x145270160>]
 
    
 
    
 _images/exercisesweek41_39_2.png
@@ -1680,7 +1680,7 @@ It provides composable transformations of Python+NumPy programs: differentiate,
    -
    <matplotlib.collections.PathCollection at 0x117edb760>
+
    <matplotlib.collections.PathCollection at 0x1452d6eb0>
 
    
 
    
 _images/exercisesweek41_41_1.png
diff --git a/doc/LectureNotes/_build/html/linalg.html b/doc/LectureNotes/_build/html/linalg.html
index 3dd26d471..682be95ec 100644
--- a/doc/LectureNotes/_build/html/linalg.html
+++ b/doc/LectureNotes/_build/html/linalg.html
@@ -308,6 +308,21 @@ const thebe_selector_output = ".output, .cell_output"
    Week 39: Optimization and  Gradient Methods
   
  
+ 
  • 
+  
+   Week 40: Gradient descent methods (continued) and start Neural networks
+  
+ 
    • 
+ 
  • 
+  
+   Exercises week 41
+  
+ 
    • 
+ 
  • 
+  
+   Week 41 Neural networks and constructing a neural network code
+  
+ 
    • 
 
 
    
  
@@ -320,6 +335,11 @@ const thebe_selector_output = ".output, .cell_output"
    Project 1 on Machine Learning, deadline October 7 (midnight), 2024
   
  
+ 
  • 
+  
+   Project 2 on Machine Learning, deadline November 4 (Midnight)
+  
+ 
    •
    @@ -623,8 +643,8 @@ matrices and vectors.

    -
    [ 0.69465386  1.75956617 -0.23303727 -0.53125507  1.34598722 -1.09928714
-  1.37013105  0.79898903 -0.23663482  0.99427512]
+
    [ 0.03563709  0.57852915  0.83220985  1.27108866 -0.3587467  -0.38713573
+ -0.09584387  0.5223261   1.7663967   0.94027059]
    @@ -845,26 +865,26 @@ as (recall that we user lowercase letters for vectors and uppercase letters for
    -
    [[0.81947005 0.93619369 0.37449168 0.91933003 0.76855921 0.41820116
-  0.96853248 0.60375434 0.96015381 0.30269539]
- [0.46086448 0.16777789 0.9930742  0.10837392 0.69089532 0.94221383
-  0.53629564 0.50327198 0.33734605 0.04757138]
- [0.51069279 0.12363332 0.79171202 0.16791183 0.62617788 0.9288904
-  0.85112594 0.86519139 0.61192712 0.90842732]
- [0.82551764 0.67524588 0.02175561 0.1118933  0.42575338 0.45731379
-  0.61069681 0.40184681 0.18702469 0.71838601]
- [0.68655456 0.11747908 0.28253033 0.4591127  0.68072161 0.59372982
-  0.95343966 0.24780663 0.98740373 0.06808421]
- [0.99512017 0.14828178 0.02354386 0.90860768 0.891715   0.39039235
-  0.48151166 0.43563433 0.52657934 0.73176319]
- [0.77030637 0.00676256 0.37454707 0.5076963  0.51937727 0.46065811
-  0.65917558 0.72962885 0.99370678 0.92341148]
- [0.16292908 0.17214545 0.44995924 0.20367355 0.64885265 0.34225662
-  0.4215795  0.27933134 0.02552966 0.62908496]
- [0.8084934  0.51364117 0.4937346  0.05296475 0.69247718 0.56783103
-  0.85276538 0.52635761 0.96461948 0.67374815]
- [0.02137508 0.03177331 0.78186404 0.33096549 0.8423144  0.07745579
-  0.4619526  0.61414743 0.38460453 0.51928402]]
+
    [[0.79010601 0.87637891 0.68824222 0.4636751  0.50100007 0.22715479
+  0.17865868 0.90903158 0.5736973  0.96961052]
+ [0.57293306 0.96660465 0.65525178 0.52480767 0.70159137 0.30894285
+  0.11675128 0.60321647 0.68281739 0.64952115]
+ [0.21833102 0.74761815 0.52789388 0.27530242 0.69463406 0.89961861
+  0.91864509 0.41794469 0.27403356 0.11416086]
+ [0.77596142 0.20224027 0.68830164 0.50895251 0.83741078 0.71957514
+  0.78945959 0.94466211 0.06443054 0.29474356]
+ [0.39914635 0.22706777 0.23499891 0.9794096  0.33435637 0.28614301
+  0.21641173 0.16925937 0.79086674 0.41259788]
+ [0.70408202 0.57833531 0.01817739 0.64689773 0.71380438 0.69311221
+  0.09930135 0.90168941 0.47308061 0.445128  ]
+ [0.10100211 0.60575887 0.69824402 0.06423317 0.24582593 0.97235642
+  0.21181534 0.72033728 0.77014839 0.13298019]
+ [0.25816519 0.81826799 0.19336703 0.34098895 0.10688434 0.34134773
+  0.21635399 0.57016227 0.69925648 0.01418766]
+ [0.80374623 0.58202531 0.71460518 0.66363129 0.02553865 0.7204561
+  0.34704885 0.52927353 0.02631244 0.02944974]
+ [0.57080764 0.04516434 0.15388662 0.99458998 0.2765068  0.05148401
+  0.82259916 0.05648118 0.14249052 0.96164155]]
    @@ -924,13 +944,15 @@ covariance matrix through the np.linalg.eig() function.

    -
    0.07218473624441492
-4.346000154268618
-0.09048717285916912
-[[ 1.0974119   3.2131067   3.36880327]
- [ 3.2131067  10.51564682  9.75138064]
- [ 3.36880327  9.75138064 17.26527089]]
-[25.09487358  0.09070064  3.6927554 ]
+
    0.22370461988004753
+4.592766658048914
+
    
+
    
+
    0.7163835161938888
+[[ 1.11935351  3.19945294  3.19001247]
+ [ 3.19945294 10.52074297  8.75737136]
+ [ 3.19001247  8.75737136 16.29078236]]
+[23.51532469  0.10801706  4.3075371 ]
    diff --git a/doc/LectureNotes/_build/html/schedule.html b/doc/LectureNotes/_build/html/schedule.html index c3792f453..853e47b42 100644 --- a/doc/LectureNotes/_build/html/schedule.html +++ b/doc/LectureNotes/_build/html/schedule.html @@ -306,6 +306,21 @@ const thebe_selector_output = ".output, .cell_output" Week 39: Optimization and Gradient Methods +
  • + + Week 40: Gradient descent methods (continued) and start Neural networks + +
    • +
  • + + Exercises week 41 + +
    • +
  • + + Week 41 Neural networks and constructing a neural network code + +

    • @@ -318,6 +333,11 @@ const thebe_selector_output = ".output, .cell_output" Project 1 on Machine Learning, deadline October 7 (midnight), 2024 +

  • + + Project 2 on Machine Learning, deadline November 4 (Midnight) + +
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Linear Regression","14. Building a Feed Forward Neural Network","15. Solving Differential Equations with Deep Learning","16. Convolutional Neural Networks","17. Recurrent neural networks: Overarching view","4. Ridge and Lasso Regression","5. Resampling Methods","6. Logistic Regression","8. Support Vector Machines, overarching aims","9. Decision trees, overarching aims","10. Ensemble Methods: From a Single Tree to Many Trees and Extreme Boosting, Meet the Jungle of Methods","11. Basic ideas of the Principal Component Analysis (PCA)","13. Neural networks","7. Optimization, the central part of any Machine Learning algortithm","12. Clustering and Unsupervised Learning","Exercises week 34","Exercises week 35","Exercises week 36","Exercises week 37","Exercises week 38","Exercises week 39","Exercises week 41","Applied Data Analysis and Machine Learning","2. Linear Algebra, Handling of Arrays and more Python Features","Project 1 on Machine Learning, deadline October 7 (midnight), 2024","Project 2 on Machine Learning, deadline November 4 (Midnight)","Teaching schedule with links to material","1. Elements of Probability Theory and Statistical Data Analysis","Teachers and Grading","Textbooks","Week 34: Introduction to the course, Logistics and Practicalities","Week 35: From Ordinary Linear Regression to Ridge and Lasso Regression","Week 36: Linear Regression and Statistical interpretations","Week 37: Statistical interpretations and Resampling Methods","Week 38: Logistic Regression and Optimization","Week 39: Optimization and Gradient Methods","Week 40: Gradient descent methods (continued) and start Neural networks","Week 41 Neural networks and constructing a neural network 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Linear Regression","14. Building a Feed Forward Neural Network","15. Solving Differential Equations with Deep Learning","16. Convolutional Neural Networks","17. Recurrent neural networks: Overarching view","4. Ridge and Lasso Regression","5. Resampling Methods","6. Logistic Regression","8. Support Vector Machines, overarching aims","9. Decision trees, overarching aims","10. Ensemble Methods: From a Single Tree to Many Trees and Extreme Boosting, Meet the Jungle of Methods","11. Basic ideas of the Principal Component Analysis (PCA)","13. Neural networks","7. Optimization, the central part of any Machine Learning algortithm","12. Clustering and Unsupervised Learning","Exercises week 34","Exercises week 35","Exercises week 36","Exercises week 37","Exercises week 38","Exercises week 39","Exercises week 41","Applied Data Analysis and Machine Learning","2. Linear Algebra, Handling of Arrays and more Python Features","Project 1 on Machine Learning, deadline October 7 (midnight), 2024","Project 2 on Machine Learning, deadline November 4 (Midnight)","Teaching schedule with links to material","1. Elements of Probability Theory and Statistical Data Analysis","Teachers and Grading","Textbooks","Week 34: Introduction to the course, Logistics and Practicalities","Week 35: From Ordinary Linear Regression to Ridge and Lasso Regression","Week 36: Linear Regression and Statistical interpretations","Week 37: Statistical interpretations and Resampling Methods","Week 38: Logistic Regression and Optimization","Week 39: Optimization and Gradient Methods","Week 40: Gradient descent methods (continued) and start Neural networks","Week 41 Neural networks and constructing a neural network 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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 571a90694..c89677b53 100644 --- a/doc/LectureNotes/_build/html/statistics.html +++ b/doc/LectureNotes/_build/html/statistics.html @@ -308,6 +308,21 @@ const thebe_selector_output = ".output, .cell_output" Week 39: Optimization and Gradient Methods +
  • + + Week 40: Gradient descent methods (continued) and start Neural networks + +
    • +
  • + + Exercises week 41 + +
    • +
  • + + Week 41 Neural networks and constructing a neural network code + +

    • @@ -320,6 +335,11 @@ const thebe_selector_output = ".output, .cell_output" Project 1 on Machine Learning, deadline October 7 (midnight), 2024 +

  • + + Project 2 on Machine Learning, deadline November 4 (Midnight) + +
    • @@ -995,27 +1015,27 @@ uncorrelated.

    -
    3.613893902124586
-[[18.33217312 13.09829902  1.98271562 18.53534008 -0.88860399 18.12957454
-   7.18043393  1.59698459 17.87186187 10.81979166]
- [13.09829902  9.35870701  1.41664613 13.24346138 -0.63490568 12.95354276
-   5.13040489  1.14104212 12.76940759  7.73071831]
- [ 1.98271562  1.41664613  0.21444055  2.00468914 -0.09610694  1.96080358
-   0.77659961  0.17272182  1.93293067  1.17021424]
- [18.53534008 13.24346138  2.00468914 18.74075864 -0.89845198 18.33049619
-   7.26001134  1.61468323 18.0699274  10.93970238]
- [-0.88860399 -0.63490568 -0.09610694 -0.89845198  0.04307275 -0.87878356
-  -0.3480527  -0.07740964 -0.86629161 -0.52446101]
- [18.12957454 12.95354276  1.96080358 18.33049619 -0.87878356 17.92921498
-   7.10107914  1.57933547 17.67435043 10.7002164 ]
- [ 7.18043393  5.13040489  0.77659961  7.26001134 -0.3480527   7.10107914
-   2.81246697  0.62551462  7.000137    4.23794815]
- [ 1.59698459  1.14104212  0.17272182  1.61468323 -0.07740964  1.57933547
-   0.62551462  0.13911934  1.55688514  0.94255277]
- [17.87186187 12.76940759  1.93293067 18.0699274  -0.86629161 17.67435043
-   7.000137    1.55688514 17.42310878 10.54811237]
- [10.81979166  7.73071831  1.17021424 10.93970238 -0.52446101 10.7002164
-   4.23794815  0.94255277 10.54811237  6.38592549]]
+
    4.177786562639708
+[[ 8.02409835 11.56990537 11.96799937  6.11006373  8.01793972  4.09014451
+  12.65476419 -4.31938579 13.04431557 14.12208052]
+ [11.56990537 16.68258595 17.25659561  8.81006889 11.56102529  5.89755794
+  18.2468382  -6.22809974 18.80853029 20.36255393]
+ [11.96799937 17.25659561 17.85035562  9.11320323 11.95881375  6.10047943
+  18.8746702  -6.44239442 19.45568883 21.06318287]
+ [ 6.11006373  8.81006889  9.11320323  4.65259487  6.10537416  3.11449867
+   9.63615006 -3.2890577   9.93277949 10.75345893]
+ [ 8.01793972 11.56102529 11.95881375  6.10537416  8.01178583  4.08700526
+  12.64505146 -4.31607059 13.03430385 14.1112416 ]
+ [ 4.09014451  5.89755794  6.10047943  3.11449867  4.08700526  2.08487999
+   6.45054585 -2.20173175  6.64911288  7.19848482]
+ [12.65476419 18.2468382  18.8746702   9.63615006 12.64505146  6.45054585
+  19.95776346 -6.81208109 20.57212292 22.27186049]
+ [-4.31938579 -6.22809974 -6.44239442 -3.2890577  -4.31607059 -2.20173175
+  -6.81208109  2.32513272 -7.02177725 -7.60193996]
+ [13.04431557 18.80853029 19.45568883  9.93277949 13.03430385  6.64911288
+  20.57212292 -7.02177725 21.20539421 22.95745476]
+ [14.12208052 20.36255393 21.06318287 10.75345893 14.1112416   7.19848482
+  22.27186049 -7.60193996 22.95745476 24.85427641]]
    @@ -1283,15 +1303,15 @@ more practically oriented methods like the blocking technique.

    -
    0.0973900819831327
-4.559791959661649
-0.6333349473431832
-1.1064601390492845 11.271260217275557 17.072290639572326
-3.390334838933238 3.4951301940723654 11.033233504714921
-[[ 1.10646014  3.39033484  3.49513019]
- [ 3.39033484 11.27126022 11.0332335 ]
- [ 3.49513019 11.0332335  17.07229064]]
-[26.5020612  0.075885   2.8720648]
+
    0.061772005077293704
+4.355734685106391
+-0.03692132794542241
+1.0765856870687196 10.882972378790114 11.598549916355722
+3.274394546800107 2.662153330760193 7.985725003240627
+[[ 1.07658569  3.27439455  2.66215333]
+ [ 3.27439455 10.88297238  7.985725  ]
+ [ 2.66215333  7.985725   11.59854992]]
+[20.15422927  0.07415258  3.32972613]
    @@ -1621,7 +1641,7 @@ assumption for approximating \(\sigma
    -
    -0.02214409916811925 1.073576975500551
+
    -0.0370757046153366 1.001106660107757
 
    
 
    
 _images/statistics_188_1.png
diff --git a/doc/LectureNotes/_build/html/teachers.html b/doc/LectureNotes/_build/html/teachers.html
index 4dd51b860..106e7a10b 100644
--- a/doc/LectureNotes/_build/html/teachers.html
+++ b/doc/LectureNotes/_build/html/teachers.html
@@ -306,6 +306,21 @@ const thebe_selector_output = ".output, .cell_output"
    Week 39: Optimization and  Gradient Methods
   
  
+ 
  • 
+  
+   Week 40: Gradient descent methods (continued) and start Neural networks
+  
+ 
    • 
+ 
  • 
+  
+   Exercises week 41
+  
+ 
    • 
+ 
  • 
+  
+   Week 41 Neural networks and constructing a neural network code
+  
+ 
    • 
 
 
    
  
@@ -318,6 +333,11 @@ const thebe_selector_output = ".output, .cell_output"
    Project 1 on Machine Learning, deadline October 7 (midnight), 2024
   
  
+ 
  • 
+  
+   Project 2 on Machine Learning, deadline November 4 (Midnight)
+  
+ 
    •
    diff --git a/doc/LectureNotes/_build/html/textbooks.html b/doc/LectureNotes/_build/html/textbooks.html index 10e4103c0..0a4269c6a 100644 --- a/doc/LectureNotes/_build/html/textbooks.html +++ b/doc/LectureNotes/_build/html/textbooks.html @@ -306,6 +306,21 @@ const thebe_selector_output = ".output, .cell_output" Week 39: Optimization and Gradient Methods +
  • + + Week 40: Gradient descent methods (continued) and start Neural networks + +
    • +
  • + + Exercises week 41 + +
    • +
  • + + Week 41 Neural networks and constructing a neural network code + +

    • @@ -318,6 +333,11 @@ const thebe_selector_output = ".output, .cell_output" Project 1 on Machine Learning, deadline October 7 (midnight), 2024 +

  • + + Project 2 on Machine Learning, deadline November 4 (Midnight) + +
    • diff --git a/doc/LectureNotes/_build/html/week34.html b/doc/LectureNotes/_build/html/week34.html index 09b6c1ace..a12b13183 100644 --- a/doc/LectureNotes/_build/html/week34.html +++ b/doc/LectureNotes/_build/html/week34.html @@ -308,6 +308,21 @@ const thebe_selector_output = ".output, .cell_output" Week 39: Optimization and Gradient Methods +
  • + + Week 40: Gradient descent methods (continued) and start Neural networks + +
    • +
  • + + Exercises week 41 + +
    • +
  • + + Week 41 Neural networks and constructing a neural network code + +

    • @@ -320,6 +335,11 @@ const thebe_selector_output = ".output, .cell_output" Project 1 on Machine Learning, deadline October 7 (midnight), 2024 +

  • + + Project 2 on Machine Learning, deadline November 4 (Midnight) + +
    • @@ -1683,8 +1703,8 @@ developed in the 1970s, namely EISPACK and LINPACK. We describe them shortly he
    -
    [-0.60588173  1.59884169 -1.56922102 -1.32068736 -0.21631786  0.37600869
- -0.14841322 -0.83655756  0.88809826 -0.85625134]
+
    [ 1.51262599  0.63980912 -1.25680702  0.97680846 -1.33095972 -0.41396339
+ -0.81478187 -0.6087346   2.11164003 -1.21061589]
    @@ -1909,26 +1929,26 @@ lowercase letters for vectors and uppercase letters for matrices)

    -
    [[0.32641434 0.35731585 0.64796751 0.41146753 0.67561021 0.53312817
-  0.3910042  0.88157423 0.4576811  0.48004784]
- [0.70469324 0.28496213 0.84862355 0.6988905  0.95890587 0.19849513
-  0.89058005 0.51546825 0.88289263 0.06926192]
- [0.38198866 0.33000573 0.80740939 0.54935794 0.38934047 0.87740526
-  0.45053025 0.27231287 0.7070883  0.7989356 ]
- [0.4198932  0.3727791  0.95400323 0.86459987 0.2666905  0.13564988
-  0.97498674 0.9450635  0.6383903  0.57803254]
- [0.13896095 0.13663125 0.68826552 0.13729154 0.91672129 0.08266769
-  0.88639567 0.16407038 0.36353321 0.81007381]
- [0.31849289 0.68735473 0.1767857  0.42873361 0.44454123 0.21333766
-  0.94285762 0.72710494 0.37153115 0.21070843]
- [0.11930916 0.28021598 0.69566966 0.98770503 0.88653291 0.82161167
-  0.90114639 0.7127128  0.97486336 0.26152075]
- [0.55386681 0.37919989 0.57468142 0.35980374 0.7150195  0.70499955
-  0.9647801  0.63142399 0.97512176 0.97570392]
- [0.24613581 0.62573269 0.41487642 0.42095725 0.51447004 0.41869784
-  0.34483955 0.55582742 0.85711016 0.17739525]
- [0.89533642 0.03382942 0.918785   0.79718864 0.64361375 0.4843772
-  0.33532886 0.13164176 0.63209435 0.39279291]]
+
    [[0.1169204  0.51615779 0.40961688 0.169299   0.08009874 0.67925887
+  0.8475889  0.92080432 0.07712724 0.2863391 ]
+ [0.36161658 0.84155431 0.70135856 0.1576057  0.04686491 0.67511113
+  0.29593305 0.22946401 0.78385675 0.20527785]
+ [0.66575697 0.37717637 0.52407775 0.55094784 0.68989446 0.30013135
+  0.39991048 0.20300793 0.25294371 0.91433102]
+ [0.05080769 0.92665802 0.77039278 0.13455019 0.89692576 0.09621323
+  0.48511333 0.8529175  0.32738537 0.12206812]
+ [0.98108401 0.73397147 0.62288579 0.66003032 0.18712313 0.63307537
+  0.2032806  0.17418673 0.06061276 0.92991181]
+ [0.53480404 0.69484973 0.09821823 0.93019783 0.34478594 0.18646225
+  0.11861803 0.25646067 0.55225408 0.84907109]
+ [0.50352245 0.92678221 0.27037635 0.9833205  0.84985833 0.82844656
+  0.34112554 0.9306628  0.89155606 0.24149532]
+ [0.37137157 0.65751456 0.63693246 0.25068519 0.75674251 0.43724406
+  0.34131583 0.74180248 0.63801791 0.76426396]
+ [0.2311959  0.77594586 0.52606333 0.54222783 0.86434639 0.72364915
+  0.4008393  0.68827947 0.56408898 0.68640031]
+ [0.90137794 0.02599188 0.40848657 0.94114646 0.67199457 0.02124568
+  0.32717946 0.59030403 0.59188296 0.81707832]]
    @@ -1983,13 +2003,13 @@ covariance matrix through the np.linalg.eig() function.

    -
    -0.050315327923114654
-3.7539148284817965
--0.09246293455466892
-[[ 0.90894281  2.8691787   2.58589719]
- [ 2.8691787  10.23481471  8.16535252]
- [ 2.58589719  8.16535252 12.44536331]]
-[20.33742926  0.08458591  3.16710567]
+
    -0.04372067685794603
+3.777112373675558
+-0.028188673105960467
+[[ 1.0680225   3.36841864  2.42592679]
+ [ 3.36841864 11.49312535  7.50233673]
+ [ 2.42592679  7.50233673  7.94272259]]
+[18.418656    0.05992237  2.02529207]
    @@ -2214,7 +2234,7 @@ Name: Aragorn, dtype: object 
    ---------------------------------------------------------------------------
 AttributeError                            Traceback (most recent call last)
-/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_95519/1326197715.py in ?()
+/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_22556/1326197715.py in ?()
 ----> 6 new_hobbit = {'First Name': ["Peregrin"],
       7               'Last Name': ["Took"],
       8               'Place of birth': ["Shire"],
diff --git a/doc/LectureNotes/_build/html/week35.html b/doc/LectureNotes/_build/html/week35.html
index 4f021bb89..86e75508b 100644
--- a/doc/LectureNotes/_build/html/week35.html
+++ b/doc/LectureNotes/_build/html/week35.html
@@ -308,6 +308,21 @@ const thebe_selector_output = ".output, .cell_output"
    Week 39: Optimization and  Gradient Methods
   
  
+ 
  • 
+  
+   Week 40: Gradient descent methods (continued) and start Neural networks
+  
+ 
    • 
+ 
  • 
+  
+   Exercises week 41
+  
+ 
    • 
+ 
  • 
+  
+   Week 41 Neural networks and constructing a neural network code
+  
+ 
    • 
 
 
    
  
@@ -320,6 +335,11 @@ const thebe_selector_output = ".output, .cell_output"
    Project 1 on Machine Learning, deadline October 7 (midnight), 2024
   
  
+ 
  • 
+  
+   Project 2 on Machine Learning, deadline November 4 (Midnight)
+  
+ 
    •
    @@ -1651,7 +1671,7 @@ Since we are not using Scikit-Learn here we can define our own
    -
    0.9952505213910134
+
    0.9960887274532307
    @@ -1668,7 +1688,7 @@ Since we are not using Scikit-Learn here we can define our own
    -
    0.008753288788081405
+
    0.010148621093080332
    @@ -1683,31 +1703,23 @@ Since we are not using Scikit-Learn here we can define our own
    -
    [8.90304177e-02 3.21655059e-02 1.15924557e-02 1.83823317e-02
- 8.19559737e-03 1.66018725e-02 1.79135536e-03 1.03313259e-01
- 7.69182892e-04 1.29306584e-02 1.21565007e-02 2.83463634e-03
- 3.03538673e-02 2.37415625e-02 1.56790195e-02 9.03400724e-03
- 1.35613536e-02 5.35506347e-02 1.01123792e-02 4.10604579e-02
- 2.63898112e-02 1.94766419e-02 3.81425129e-02 3.27482829e-02
- 5.12829994e-03 6.02901273e-03 8.26321660e-02 4.04504728e-02
- 2.20601797e-02 4.62113349e-03 9.03476611e-04 4.87494456e-02
- 3.82060913e-03 2.53729411e-02 2.38612299e-02 1.59355752e-02
- 3.60160003e-03 1.65738717e-02 2.98947674e-02 5.18501900e-03
- 9.36303682e-03 4.81218742e-02 1.49392067e-02 4.88551766e-03
- 2.17643975e-02 2.20608548e-04 1.90135464e-02 2.74291603e-02
- 1.23344210e-02 6.03309191e-03 1.57252451e-02 9.02612988e-03
- 3.32084559e-02 3.76692036e-03 2.87169607e-02 4.85551266e-02
- 1.48826894e-02 6.41842093e-04 1.89017198e-02 3.49584063e-02
- 1.77652198e-02 6.38298234e-03 1.05034088e-03 1.99753321e-02
- 5.52031552e-03 8.22217237e-03 6.86192682e-02 8.40354798e-03
- 1.29491144e-02 7.44658658e-03 1.00731392e-02 9.52284329e-02
- 1.51437058e-02 2.00002585e-05 2.37700967e-02 1.95166920e-02
- 4.82376174e-02 3.73986200e-02 4.84707251e-02 8.76887316e-02
- 2.74724414e-02 5.14825560e-03 1.26254957e-02 2.81042619e-02
- 2.11265643e-02 2.52301447e-03 3.13819592e-02 2.93900569e-02
- 3.65720152e-02 1.02850506e-02 4.85945208e-02 2.79870689e-02
- 3.12846660e-02 6.17869861e-02 9.09590269e-03 1.11715109e-02
- 3.62863106e-02 1.21277816e-02 9.05665429e-03 4.85293303e-02]
+
    [0.01006224 0.02667406 0.0272743  0.00100202 0.01480927 0.00410183
+ 0.02088307 0.02506901 0.00463124 0.00672505 0.0304378  0.00076521
+ 0.05364579 0.01232366 0.00969623 0.00677215 0.02239876 0.03858916
+ 0.00923474 0.00848739 0.01701429 0.07172932 0.08858739 0.04575775
+ 0.02988432 0.00988356 0.01546126 0.00033415 0.03201922 0.01425384
+ 0.00575681 0.01882218 0.05853984 0.00528252 0.03254614 0.03092729
+ 0.01094916 0.05696574 0.03412067 0.02444266 0.05749111 0.0689303
+ 0.00736737 0.02104639 0.00274212 0.00588836 0.05043958 0.01456865
+ 0.01813363 0.06019833 0.01078008 0.01059242 0.02369044 0.02692965
+ 0.00657601 0.01218403 0.03745816 0.05363722 0.00561212 0.03820746
+ 0.00988992 0.00774376 0.03426412 0.01323341 0.02387182 0.01151556
+ 0.01097287 0.07292363 0.02846506 0.04186033 0.00836649 0.00340452
+ 0.06200455 0.03246707 0.02987496 0.00355323 0.01740381 0.01196506
+ 0.02635861 0.07487128 0.08472879 0.0073544  0.01150437 0.00571884
+ 0.02025574 0.0014028  0.01512884 0.02146636 0.05097344 0.0284405
+ 0.06151386 0.00737863 0.04452918 0.03906948 0.01163942 0.07468007
+ 0.01647074 0.0096667  0.00369201 0.0168171 ]
    @@ -1776,15 +1788,15 @@ but now splitting the data into a training set and a test set.

    -
    [ 2.05054319 -0.48521055  6.95338273 -2.63619709  1.0524497 ]
+
    [ 1.97802245  0.57331229  2.49761526  3.47609206 -1.5187643 ]
 Training R2
-0.9959308805732706
+0.995702810640425
 Training MSE
-0.009211602191395454
+0.007370297974992432
 Test R2
-0.9955318336036834
+0.9950019477819025
 Test MSE
-0.011818646101922625
+0.009880124918446542
    @@ -2448,9 +2460,7 @@ the aims is to reproduce Figure 2.11 of 
    MSE before scaling: 0.00
 R2 score before scaling 1.00
-
    -
    -
    Feature min values before scaling:
+Feature min values before scaling:
  [1.00000000e+00 6.97906022e-03 2.43639284e-03 4.87072815e-05
  1.70037324e-05 5.93601008e-06 3.39931051e-07 1.18670072e-07
  4.14277718e-08 1.44624525e-08 2.37239927e-09 8.28205578e-10
diff --git a/doc/LectureNotes/_build/html/week36.html b/doc/LectureNotes/_build/html/week36.html
index 6eaf8709a..d0517fa57 100644
--- a/doc/LectureNotes/_build/html/week36.html
+++ b/doc/LectureNotes/_build/html/week36.html
@@ -308,6 +308,21 @@ const thebe_selector_output = ".output, .cell_output"
    Week 39: Optimization and  Gradient Methods
   
  
+ 
  • 
+  
+   Week 40: Gradient descent methods (continued) and start Neural networks
+  
+ 
    • 
+ 
  • 
+  
+   Exercises week 41
+  
+ 
    • 
+ 
  • 
+  
+   Week 41 Neural networks and constructing a neural network code
+  
+ 
    • 
 
 
    
  
@@ -320,6 +335,11 @@ const thebe_selector_output = ".output, .cell_output"
    Project 1 on Machine Learning, deadline October 7 (midnight), 2024
   
  
+ 
  • 
+  
+   Project 2 on Machine Learning, deadline November 4 (Midnight)
+  
+ 
    •
    diff --git a/doc/LectureNotes/_build/html/week37.html b/doc/LectureNotes/_build/html/week37.html index 066137474..d2fea156e 100644 --- a/doc/LectureNotes/_build/html/week37.html +++ b/doc/LectureNotes/_build/html/week37.html @@ -308,6 +308,21 @@ const thebe_selector_output = ".output, .cell_output" Week 39: Optimization and Gradient Methods +
  • + + Week 40: Gradient descent methods (continued) and start Neural networks + +
    • +
  • + + Exercises week 41 + +
    • +
  • + + Week 41 Neural networks and constructing a neural network code + +

    • @@ -320,6 +335,11 @@ const thebe_selector_output = ".output, .cell_output" Project 1 on Machine Learning, deadline October 7 (midnight), 2024 +

  • + + Project 2 on Machine Learning, deadline November 4 (Midnight) + +
    • @@ -1639,7 +1659,7 @@ theorem.

    Bootstrap Statistics :
 original           bias      std. error
- 99.8485  15.0562        99.8488        0.149353
+ 100.257   14.853        100.257        0.149111
    @@ -1859,9 +1879,7 @@ Error: 0.08426840630693411 Bias^2: 0.0796891867672603 Var: 0.004579219539673834 0.08426840630693411 >= 0.0796891867672603 + 0.004579219539673834 = 0.08426840630693413 -
    -
    -
    Polynomial degree: 2
+Polynomial degree: 2
 Error: 0.10398646080125035
 Bias^2: 0.1007711427354898
 Var: 0.0032153180657605116
@@ -1871,7 +1889,9 @@ Error: 0.06547790180152355
 Bias^2: 0.06208238634231949
 Var: 0.0033955154592040936
 0.06547790180152355 >= 0.06208238634231949 + 0.0033955154592040936 = 0.06547790180152359
-Polynomial degree: 4
+
    +
    +
    Polynomial degree: 4
 Error: 0.06844519414009445
 Bias^2: 0.06453579006728324
 Var: 0.003909404072811226
@@ -1881,9 +1901,7 @@ Error: 0.05227921801205686
 Bias^2: 0.0481872773043029
 Var: 0.004091940707753939
 0.05227921801205686 >= 0.0481872773043029 + 0.004091940707753939 = 0.052279218012056844
-
    -
    -
    Polynomial degree: 6
+Polynomial degree: 6
 Error: 0.037813671417389005
 Bias^2: 0.033657685071527665
 Var: 0.00415598634586135
@@ -1893,19 +1911,21 @@ Error: 0.02760977349102253
 Bias^2: 0.022999498260366312
 Var: 0.004610275230656212
 0.02760977349102253 >= 0.022999498260366312 + 0.004610275230656212 = 0.027609773491022525
-
    -
    -
    Polynomial degree: 8
+Polynomial degree: 8
 Error: 0.017355848195593347
 Bias^2: 0.010331721306655127
 Var: 0.007024126888938232
 0.017355848195593347 >= 0.010331721306655127 + 0.007024126888938232 = 0.01735584819559336
-Polynomial degree: 9
+
    +
    +
    Polynomial degree: 9
 Error: 0.02660572763718093
 Bias^2: 0.010018312644137363
 Var: 0.016587414993043573
 0.02660572763718093 >= 0.010018312644137363 + 0.016587414993043573 = 0.026605727637180936
-Polynomial degree: 10
+
    +
    +
    Polynomial degree: 10
 Error: 0.021592704588025025
 Bias^2: 0.010516485576645508
 Var: 0.011076219011379514
@@ -1915,9 +1935,7 @@ Error: 0.07160048164233104
 Bias^2: 0.014436800088904942
 Var: 0.05716368155342608
 0.07160048164233104 >= 0.014436800088904942 + 0.05716368155342608 = 0.07160048164233102
-
    -
    -
    Polynomial degree: 12
+Polynomial degree: 12
 Error: 0.11547777218872497
 Bias^2: 0.01628578269596628
 Var: 0.09919198949275869
@@ -1929,7 +1947,7 @@ Var: 0.20867052175034223
 0.22842468702219465 >= 0.01975416527185249 + 0.20867052175034223 = 0.2284246870221947
    -_images/week37_139_5.png +_images/week37_139_4.png
    @@ -2336,33 +2354,33 @@ Mean squared error on test data: 1184.60929685 Degree of polynomial: 23 Mean squared error on training data: 0.00089193 Mean squared error on test data: 3892.17483760 -Degree of polynomial: 24 -Mean squared error on training data: 0.00083355 -Mean squared error on test data: 1332.46736215 -
    Degree of polynomial:  25
+
    Degree of polynomial:  24
+Mean squared error on training data: 0.00083355
+Mean squared error on test data: 1332.46736215
+Degree of polynomial:  25
 Mean squared error on training data: 0.00079904
 Mean squared error on test data: 7577.76690383
 Degree of polynomial:  26
 Mean squared error on training data: 0.00075590
 Mean squared error on test data: 1079.36895644
-Degree of polynomial:  27
+
    
+
    
+
    Degree of polynomial:  27
 Mean squared error on training data: 0.00068091
 Mean squared error on test data: 3207.25343155
 Degree of polynomial:  28
 Mean squared error on training data: 0.00063362
 Mean squared error on test data: 674.79633065
-
    
-
    
-
    Degree of polynomial:  29
+Degree of polynomial:  29
 Mean squared error on training data: 0.00063866
 Mean squared error on test data: 3099.60342978
 
    
 
    
-
    /var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_95542/626635268.py:73: RuntimeWarning: divide by zero encountered in log10
+
    /var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_22575/626635268.py:73: RuntimeWarning: divide by zero encountered in log10
   plt.plot(polynomial, np.log10(trainingerror), label='Training Error')
-/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_95542/626635268.py:74: RuntimeWarning: divide by zero encountered in log10
+/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_22575/626635268.py:74: RuntimeWarning: divide by zero encountered in log10
   plt.plot(polynomial, np.log10(testerror), label='Test Error')
 
    
 
    
@@ -2447,7 +2465,7 @@ Mean squared error on test data: 3099.60342978
 
    
 
    
 
    
-
    /var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_95542/3817475779.py:63: RuntimeWarning: divide by zero encountered in log10
+
    /var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_22575/3817475779.py:63: RuntimeWarning: divide by zero encountered in log10
   plt.plot(polynomial, np.log10(estimated_mse_sklearn), label='Test Error')
 
    
 
    
diff --git a/doc/LectureNotes/_build/html/week38.html b/doc/LectureNotes/_build/html/week38.html
index 27cbb28c9..7b34097ca 100644
--- a/doc/LectureNotes/_build/html/week38.html
+++ b/doc/LectureNotes/_build/html/week38.html
@@ -308,6 +308,21 @@ const thebe_selector_output = ".output, .cell_output"
    Week 39: Optimization and  Gradient Methods
   
  
+ 
  • 
+  
+   Week 40: Gradient descent methods (continued) and start Neural networks
+  
+ 
    • 
+ 
  • 
+  
+   Exercises week 41
+  
+ 
    • 
+ 
  • 
+  
+   Week 41 Neural networks and constructing a neural network code
+  
+ 
    • 
 
 
    
  
@@ -320,6 +335,11 @@ const thebe_selector_output = ".output, .cell_output"
    Project 1 on Machine Learning, deadline October 7 (midnight), 2024
   
  
+ 
  • 
+  
+   Project 2 on Machine Learning, deadline November 4 (Midnight)
+  
+ 
    • 
 
 
     
    
diff --git a/doc/LectureNotes/_build/html/week39.html b/doc/LectureNotes/_build/html/week39.html
index 20147b173..1aa26087c 100644
--- a/doc/LectureNotes/_build/html/week39.html
+++ b/doc/LectureNotes/_build/html/week39.html
@@ -55,7 +55,7 @@ const thebe_selector_output = ".output, .cell_output"
     
     
     
-    
+    
     
     
     
@@ -308,6 +308,21 @@ const thebe_selector_output = ".output, .cell_output"
    Week 39: Optimization and  Gradient Methods
   
  
+ 
  • 
+  
+   Week 40: Gradient descent methods (continued) and start Neural networks
+  
+ 
    • 
+ 
  • 
+  
+   Exercises week 41
+  
+ 
    • 
+ 
  • 
+  
+   Week 41 Neural networks and constructing a neural network code
+  
+ 
    • 
 
 
    
  
@@ -320,6 +335,11 @@ const thebe_selector_output = ".output, .cell_output"
    Project 1 on Machine Learning, deadline October 7 (midnight), 2024
   
  
+ 
  • 
+  
+   Project 2 on Machine Learning, deadline November 4 (Midnight)
+  
+ 
    • 
 
 
     
    
@@ -1782,7 +1802,7 @@ which equals
    -
    <mpl_toolkits.mplot3d.art3d.Poly3DCollection at 0x11f13fb20>
+
    <mpl_toolkits.mplot3d.art3d.Poly3DCollection at 0x12aaa0b20>
 
    
 
    
 _images/week39_82_1.png
@@ -1840,7 +1860,7 @@ which equals
    -
    [<matplotlib.lines.Line2D at 0x12e13a850>]
+
    [<matplotlib.lines.Line2D at 0x12b14fa90>]
 
    
 
    
 _images/week39_90_1.png
@@ -2134,11 +2154,11 @@ when \(||\nabla_\beta C(\beta_k) || \
    -
    Eigenvalues of Hessian Matrix:[0.24602146 5.15830902]
-[[3.94388948]
- [3.14880915]]
-[[3.94388948]
- [3.14880915]]
+
    Eigenvalues of Hessian Matrix:[0.29950088 4.27458376]
+[[3.66841959]
+ [3.26280614]]
+[[3.66841959]
+ [3.26280614]]
 
    
 
    
 _images/week39_153_1.png
@@ -2169,9 +2189,9 @@ when \(||\nabla_\beta C(\beta_k) || \
    -
    [[4.1567286 ]
- [2.83641435]]
-[4.12466453] [2.80907609]
+
    [[4.06267057]
+ [2.86868711]]
+[4.05285677] [2.86799623]
    @@ -2271,11 +2291,11 @@ minimum of this function.

    -
    Eigenvalues of Hessian Matrix:[0.29902518 4.33006628]
-[[3.73252708]
- [3.18093549]]
-[[3.73265885]
- [3.1808228 ]]
+
    Eigenvalues of Hessian Matrix:[0.24719968 4.31175179]
+[[4.07235641]
+ [3.05476114]]
+[[4.06908518]
+ [3.05761244]]
 
    
 
    
 _images/week39_166_1.png
@@ -3851,13 +3871,11 @@ Eigenvalues of Hessian Matrix:[0.29860173 3.8931686 ]
 
    theta from own gd
-
    -
    -
    [[4.0586484]
+[[4.0586484]
  [3.0718316]]
    -_images/week39_269_3.png +_images/week39_269_2.png 
    theta from own sdg
 [[4.02496085]
  [3.12081773]]
@@ -4299,10 +4317,10 @@ It provides composable transformations of Python+NumPy programs: differentiate,
             
    Exercises week 39
    - +

    next

    -

    Project 1 on Machine Learning, deadline October 7 (midnight), 2024

    +

    Week 40: Gradient descent methods (continued) and start Neural networks

     diff --git a/doc/LectureNotes/_build/html/week40.html b/doc/LectureNotes/_build/html/week40.html index ff86d72d9..488a94283 100644 --- a/doc/LectureNotes/_build/html/week40.html +++ b/doc/LectureNotes/_build/html/week40.html @@ -55,6 +55,7 @@ const thebe_selector_output = ".output, .cell_output" + @@ -312,6 +313,33 @@ const thebe_selector_output = ".output, .cell_output" Week 40: Gradient descent methods (continued) and start Neural networks +
  • + + Exercises week 41 + +
    • +
  • + + Week 41 Neural networks and constructing a neural network code + +
    • + +

    + + Projects + +

    +
    @@ -1276,17 +1304,17 @@ We summarize some of these here for the methods we hvae studied in project one, 
    Parameters for OLS using gradient descent
-[[4.14721582]
- [2.54760497]
- [5.2212296 ]]
+[[4.26604611]
+ [2.29234681]
+ [5.33329216]]
 Parameters for Ridge using gradient descent
-[[3.75424998]
- [3.49608088]
- [4.78010668]]
+[[3.6161104 ]
+ [3.78762558]
+ [4.65410649]]
 Parameters for Lasso using gradient descent
-[[3.62716271]
- [3.82146046]
- [4.64516328]]
+[[4.24335376]
+ [2.36219301]
+ [5.29669411]]
    @@ -1338,11 +1366,11 @@ Parameters for Lasso using gradient descent [[4.] [3.] [5.]] -0 [-25.77106886] [-35.02189606] -1 [3.49285045e-13] [4.71505132e-13] -2 [1.24344979e-16] [3.86639832e-16] -3 [9.05941988e-16] [1.48786826e-15] -4 [-7.99360578e-16] [-1.35823372e-15] +0 [-26.28886314] [-34.64721597] +1 [-1.83231208e-13] [-1.80848093e-13] +2 [6.92779167e-16] [1.22835717e-15] +3 [-1.3500312e-15] [-1.8110093e-15] +4 [7.46069873e-16] [9.30442002e-16] beta from own Newton code [[4.] [3.] @@ -1603,9 +1631,6 @@ function.

    gamma_j after 500 epochs: 9.97108e-05
    -
    
-
    -
    @@ -1687,15 +1712,15 @@ function.

    Own inversion
-[[4.16180248]
- [2.8250103 ]]
-Eigenvalues of Hessian Matrix:[0.26646852 4.68110474]
+[[4.36014743]
+ [2.76030841]]
+Eigenvalues of Hessian Matrix:[0.36278226 3.73204357]
 theta from own gd
-[[4.16180248]
- [2.8250103 ]]
+[[4.36014743]
+ [2.76030841]]
 theta from own sdg
-[[4.16257872]
- [2.80576215]]
+[[4.34582863]
+ [2.81684805]]
    _images/week40_34_1.png @@ -2409,12 +2434,12 @@ first example shows results with ordinary leats squares.

    Own inversion
-[[3.98246764]
- [3.04018043]]
-Eigenvalues of Hessian Matrix:[0.31541884 4.4735968 ]
+[[3.85068028]
+ [3.09808149]]
+Eigenvalues of Hessian Matrix:[0.31897935 3.95770298]
 theta from own gd
-[[3.98246764]
- [3.04018043]]
+[[3.85068028]
+ [3.09808149]]
    _images/week40_100_1.png @@ -2483,75 +2508,77 @@ theta from own gd 
    Own inversion
-[[4.]
+
    +
    +
    [[4.]
  [3.]]
-Eigenvalues of Hessian Matrix:[0.32799141 4.41229845]
-0 [-16.27698328] [-18.5653087]
-1 [-0.44867801] [0.37354121]
-2 [-0.41532521] [0.34577375]
-3 [-0.38445171] [0.32007041]
-4 [-0.35587321] [0.29627774]
-5 [-0.32941912] [0.27425372]
-6 [-0.30493151] [0.25386687]
-7 [-0.2822642] [0.23499549]
-8 [-0.26128189] [0.21752693]
-9 [-0.24185931] [0.20135691]
-10 [-0.22388052] [0.1863889]
-11 [-0.2072382] [0.17253354]
-12 [-0.191833] [0.15970814]
-13 [-0.17757295] [0.14783612]
-14 [-0.16437294] [0.13684661]
-15 [-0.15215415] [0.12667402]
-16 [-0.14084366] [0.11725761]
-17 [-0.13037395] [0.10854118]
-18 [-0.12068251] [0.1004727]
-19 [-0.11171148] [0.09300398]
-20 [-0.10340733] [0.08609047]
-21 [-0.09572047] [0.07969087]
-22 [-0.08860502] [0.07376699]
-23 [-0.0820185] [0.06828347]
-24 [-0.0759216] [0.06320757]
-25 [-0.07027791] [0.05850899]
-26 [-0.06505375] [0.05415968]
-27 [-0.06021793] [0.05013368]
-28 [-0.05574159] [0.04640695]
-29 [-0.051598] [0.04295726]
+Eigenvalues of Hessian Matrix:[0.35713539 3.88632765]
+0 [-9.01615836] [-9.6932681]
+1 [0.01454469] [-0.01357366]
+2 [0.0132081] [-0.0123263]
+3 [0.01199434] [-0.01119357]
+4 [0.01089212] [-0.01016493]
+5 [0.00989118] [-0.00923082]
+6 [0.00898223] [-0.00838255]
+7 [0.0081568] [-0.00761224]
+8 [0.00740723] [-0.00691271]
+9 [0.00672654] [-0.00627746]
+10 [0.0061084] [-0.00570059]
+11 [0.00554707] [-0.00517673]
+12 [0.00503732] [-0.00470102]
+13 [0.00457441] [-0.00426902]
+14 [0.00415405] [-0.00387671]
+15 [0.00377231] [-0.00352046]
+16 [0.00342565] [-0.00319695]
+17 [0.00311085] [-0.00290316]
+18 [0.00282498] [-0.00263638]
+19 [0.00256537] [-0.0023941]
+20 [0.00232963] [-0.0021741]
+21 [0.00211555] [-0.00197431]
+22 [0.00192114] [-0.00179288]
+23 [0.00174459] [-0.00162812]
+24 [0.00158427] [-0.0014785]
+25 [0.00143869] [-0.00134264]
+26 [0.00130648] [-0.00121925]
+27 [0.00118642] [-0.00110721]
+28 [0.00107739] [-0.00100546]
+29 [0.00097839] [-0.00091307]
 theta from own gd
-[[3.85437905]
- [3.12123488]]
-0 [-0.04776242] [0.039764]
-1 [-0.04421197] [0.03680811]
-2 [-0.0398603] [0.03318519]
-3 [-0.03559176] [0.02963147]
-4 [-0.03166546] [0.02636268]
-5 [-0.02813369] [0.02342235]
-6 [-0.02498282] [0.02079913]
-7 [-0.02218045] [0.01846605]
-8 [-0.01969093] [0.01639344]
-9 [-0.01748034] [0.01455304]
-10 [-0.01551775] [0.0129191]
-11 [-0.01377545] [0.01146857]
-12 [-0.01222875] [0.01018089]
-13 [-0.01085571] [0.00903778]
-14 [-0.00963683] [0.00802302]
-15 [-0.0085548] [0.00712219]
-16 [-0.00759427] [0.00632251]
-17 [-0.00674158] [0.00561262]
-18 [-0.00598464] [0.00498243]
-19 [-0.00531268] [0.004423]
-20 [-0.00471617] [0.00392639]
-21 [-0.00418664] [0.00348553]
-22 [-0.00371656] [0.00309418]
-23 [-0.00329927] [0.00274676]
-24 [-0.00292882] [0.00243835]
-25 [-0.00259997] [0.00216458]
-26 [-0.00230805] [0.00192154]
-27 [-0.0020489] [0.00170579]
-28 [-0.00181885] [0.00151426]
-29 [-0.00161463] [0.00134424]
+[[4.00248779]
+ [2.9976783 ]]
+0 [0.00088848] [-0.00082916]
+1 [0.00080683] [-0.00075296]
+2 [0.00070819] [-0.00066091]
+3 [0.00061352] [-0.00057256]
+4 [0.00052874] [-0.00049344]
+5 [0.00045472] [-0.00042436]
+6 [0.00039072] [-0.00036464]
+7 [0.00033562] [-0.00031321]
+8 [0.00028825] [-0.000269]
+9 [0.00024755] [-0.00023102]
+10 [0.00021259] [-0.0001984]
+11 [0.00018256] [-0.00017038]
+12 [0.00015678] [-0.00014631]
+13 [0.00013464] [-0.00012565]
+14 [0.00011562] [-0.0001079]
+15 [9.92929225e-05] [-9.26639089e-05]
+16 [8.52694246e-05] [-7.95766504e-05]
+17 [7.32265127e-05] [-6.83377498e-05]
+18 [6.28844641e-05] [-5.86861591e-05]
+19 [5.40030606e-05] [-5.03976976e-05]
+20 [4.63760101e-05] [-4.32798457e-05]
+21 [3.98261559e-05] [-3.71672742e-05]
+22 [3.42013616e-05] [-3.19180036e-05]
+23 [2.93709777e-05] [-2.74101067e-05]
+24 [2.52228066e-05] [-2.35388766e-05]
+25 [2.1660497e-05] [-2.02143946e-05]
+26 [1.86013055e-05] [-1.73594414e-05]
+27 [1.59741748e-05] [-1.49077038e-05]
+28 [1.37180834e-05] [-1.2802234e-05]
+29 [1.17806281e-05] [-1.09941275e-05]
 theta from own gd wth momentum
-[[3.99562995]
- [3.00363823]]
+[[4.00002833]
+ [2.99997356]]
    @@ -2640,18 +2667,20 @@ theta from own gd wth momentum 
    Own inversion
-[[4.10426556]
- [2.93942872]]
-Eigenvalues of Hessian Matrix:[0.31367041 4.07517385]
-theta from own gd
-[[4.10426556]
- [2.93942872]]
+[[3.91650453]
+ [2.94495682]]
+Eigenvalues of Hessian Matrix:[0.34862407 4.10453899]
    -_images/week40_104_1.png +
    theta from own gd
+[[3.91650453]
+ [2.94495682]]
+
    +
    +_images/week40_104_2.png 
    theta from own sdg
-[[4.08587209]
- [2.96301764]]
+[[3.8901199 ]
+ [2.92458892]]
    @@ -2733,17 +2762,15 @@ theta from own gd 
    Own inversion
-[[4.33528448]
- [2.81889188]]
-Eigenvalues of Hessian Matrix:[0.30916402 4.51611732]
+[[4.05785974]
+ [2.95842106]]
+Eigenvalues of Hessian Matrix:[0.29678339 4.37215356]
 theta from own gd
-[[4.33477019]
- [2.81931348]]
-
    -
    -
    theta from own sdg with momentum
-[[4.40800396]
- [2.78459458]]
+[[4.05738629]
+ [2.95882223]]
+theta from own sdg with momentum
+[[4.07489511]
+ [2.90281987]]
    @@ -2812,9 +2839,9 @@ theta from own gd 
    theta from own AdaGrad
-[[2.00003828]
- [2.99979896]
- [4.00019258]]
+[[1.99994537]
+ [3.00034209]
+ [3.99966798]]
    @@ -2890,9 +2917,9 @@ theta from own gd 
    theta from own RMSprop
-[[2.01129731]
- [3.01249445]
- [4.00885858]]
+[[1.99975636]
+ [3.00348281]
+ [3.99607299]]
    @@ -2972,9 +2999,9 @@ theta from own gd 
    theta from own ADAM
-[[2.00001716]
- [2.99992107]
- [4.00007706]]
+[[2.00002276]
+ [2.99985884]
+ [4.00009004]]
    @@ -3095,7 +3122,7 @@ It provides composable transformations of Python+NumPy programs: differentiate, return asarray(x, dtype=self.dtype) -
    [<matplotlib.lines.Line2D at 0x11753f700>]
+
    [<matplotlib.lines.Line2D at 0x11bcee130>]
 
    
 
    
 _images/week40_120_2.png
@@ -3130,7 +3157,7 @@ It provides composable transformations of Python+NumPy programs: differentiate,
    -
    <matplotlib.collections.PathCollection at 0x11bc3a1f0>
+
    <matplotlib.collections.PathCollection at 0x11bdd4640>
 
    
 
    
 _images/week40_122_1.png
@@ -3810,6 +3837,13 @@ become the most popular for deep neural networks
    
             
    Week 39: Optimization and  Gradient Methods
    + +
    +

    next

    +

    Exercises week 41

    +
    + +
    diff --git a/doc/LectureNotes/_build/html/week41.html b/doc/LectureNotes/_build/html/week41.html index 27f8d1715..3d9a50040 100644 --- a/doc/LectureNotes/_build/html/week41.html +++ b/doc/LectureNotes/_build/html/week41.html @@ -55,6 +55,7 @@ const thebe_selector_output = ".output, .cell_output" + @@ -323,6 +324,23 @@ const thebe_selector_output = ".output, .cell_output" +

    + + Projects + +

    + @@ -3592,6 +3610,13 @@ features).

    Exercises week 41

    + +
    +

    next

    +

    Project 1 on Machine Learning, deadline October 7 (midnight), 2024

    +
    + +     diff --git a/doc/LectureNotes/_build/jupyter_execute/chapter1.ipynb b/doc/LectureNotes/_build/jupyter_execute/chapter1.ipynb index bf4b324e7..7759019af 100644 --- a/doc/LectureNotes/_build/jupyter_execute/chapter1.ipynb +++ b/doc/LectureNotes/_build/jupyter_execute/chapter1.ipynb @@ -343,7 +343,7 @@ "outputs": [ { "data": { - "image/png": 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", + "image/png": 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", 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    " ] @@ -515,7 +515,7 @@ "outputs": [ { "data": { - "image/png": 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", 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", 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    " ] @@ -583,18 +583,18 @@ "output_type": "stream", "text": [ "The intercept alpha: \n", - " [1.92622842]\n", + " [1.94485679]\n", "Coefficient beta : \n", - " [[5.21332621]]\n", - "Mean squared error: 0.22\n", - "Variance score: 0.90\n", + " [[5.13059483]]\n", + "Mean squared error: 0.32\n", + "Variance score: 0.87\n", "Mean squared log error: 0.01\n", - "Mean absolute error: 0.38\n" + "Mean absolute error: 0.45\n" ] }, { "data": { - "image/png": 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8eLEqKytT3ldcXGzb3bh9+/YEGwAA7Nx9t2Q1ZLWbz3joITPcpGMY0v/5P9JLL3177ZxzpGY8f50uI/F8u7dl7ty56tatm04++WSvhwIAQLiUl0tPPintv3/89WhUWrjQ/Pt0qqulFi3iQ80rr0gzZzo71mbyxYxNfX295s6dqwkTJqhlS18MCQCAcCkvl8aMkZYtkzZskHr0kIYPz2z56dprpVtu+fbPbdpIO3ZINisoXvNFili8eLE+++wz/fSnP/V6KAAAhFdRkXTMMZnfv3GjGYAau/tu6dJLHR2Wk3wRbEaPHi0f1TADAIDzzpMeeST+2saNUvfu3ownQ74INgAAwCdiMcmuLCQgExC+KR4GAAAee+SRxFDzwAOBCTUSMzYAAECS7LZd794ttWqV/7E0AzM2AAAUsmXLEkPNGWeYszQBCzUSMzYAgKCJxXLbsoxEdrM069ZJDrY4yDeCDQAgOCorpcmTpfXrv71WVibNmZPZIXMw7dwpdeiQeD1AtTTJsBQFAAiGykqzLUDjUCNJNTXm9TQtefBfkUhiqLn77lCEGslnTTBzUVtbq9LSUu3cuZNeUQAQVrGY1Lt3YqixRCLmzE11tf+XpbxcSrNbeqqvt7/uMree38zYAAD8b9my5KFGMmcb1q0z7/OzykozoI0aJY0fb37u3dv92aaLLrIPL4bhSahxEzU2AAD/27DB2fu8YC2lNV0osZbSnnzy2zohJ2d17ILLhx9K3/lObq/nc8zYAAD8r2m/oubel2+xmFn0bFf9YV2bMsW8z6lZnX/+M/ksTUhDjUSwAQAEwfDhZg1NsmWTSESKRs37/CjTpbTf/taZAulIRBo8OP7ab38bmgLhVAg2AAD/Kyoyt3RLieHG+vPs2f4tHM50iWzOnMxmdZLZvTv5LM1112U2hoAj2AAAgqG83KxD2X//+OtlZfH1KX6U6RLZ9u3J/y5dgXTfvlJxsf3XFRCKhwEAwVFeLo0Zk5/t0k4W8FpLaTU19kEjEpE6dkwdbCx2sz92szS1tdK++2Y/1oBjxgYAECxFRdIxx0jjxpmf3Qg1Tm/LzmQpbfLkzF5r0yapokKqqpLmzUu+9FSAoUbigD4AAOIl25ZtBYjmLHvZtYSIRs36oDFjzPCUbFZHMgNSqhqb55+XTj45t7HlmVvPb4INAACWfJxwnGqJywpVUva1MQF7nHPyMAAAbsvHCcepltKSFUinC1HRaOqZnAJCsAEAwOKHE47Ly6W1a6UlS6T586U770wfWoLQTiJP2BUFAIDF6ROOc91ZZc3qSNKIEZl9Lz+3k8gjgg0AoLA1Dh/duqXfll1WltkJx3aFwmVl5u6oTIuPs2lQ6dd2EnnGUhQAoHA13dZ93HHS11/bd73O5oRjqwg419YIVVWZhxq/t5PIM4INAKAwJQsf1iF5nTrFX8/0hONsGl7aiUTMoNXYqaea14PYTiLPWIoCABSedOEjEpFKSqTFi6XNm7Orj8lmZ5VVRyNJe/ZIrVvb3y8lX9qaPdvf7STyjGADACg8mYSP9evNIDNuXHavncvOqmTLTo2DVz7bSQQYwQYAUHjc3Nad7c4qu1CzZYvUpUvi9ca7pWCLGhsAQOFxelt3Y1bDy2SzMFax76JFyfs82YUaZIRgAwAoPJmGj1x2GmXS8HLdOumWW+L/7t57A9cWwY8INgCAwpNJ+GjOTqNkrRG6d09esPyLX+T2vRCHYAMAKEzJwkem27ozef3GrREkaePGxPuYpXEU3b0BAIUt17YH2bBb8tq9W2rVytnvEyBuPb/ZFQUAKGxu7jTKZBs3HMVSFAAAbrALNf/3/xJqXMaMDQAATrrvPumSSxKvOx1o8rGEFkAEGwAAnJKvpScnOoc3FqKQxFIUAMA7sZjZybqiwvycrDGk39XVJT9sz41Q05zO4Xav17jD+ahR5p+zfR2fINgAALwRlgdqJCK1aZN43Y1amuZ2Dm/K6ZDkAwQbAED+heWBajdLs2aNewXC2XQOT8fpkOQTBBsAKDReL/+E4YE6cGDypadDD3Xv+zrZvNPJkOQjFA8DQCFxuug0F9k8UJ06X8bJ4li7QNOvn/Tuu+6PycnmnW52OPcQMzYAUCj8svyT7weqU7U8772XfJYm21CT65icbN7pZodzDxFsAKAQ+Gn5J58PVKfCXCRizso0lUstTXPG5GTzTjc7nHuIYAMAhcBP9RT5eqA6FebsxvnVV7mFGifG5FTzTrc7nHuEYAMAhcBP9RT5eqA2N8xFIsmXnkpKvBmTpWnn8CVLpOrq7Ouk3O5w7gGKhwGgEPitnsJ6oNoVMs+e7cwDtTlhzi7Q3HqrdPXVidezKQJ2MmA61byzvFwaMyY0Jw8TbACgEFjLPzU19ssgkYj59/msp3D7gZpLmLv/fmnixMR7ki07ZbvLzG8B0+Jmh/M8ixhGsNuM1tbWqrS0VDt37lT79u29Hg4A+JdVtCrFP6it2YmALj0kFYuZO43ShbnqavPBnm2fJ+v32fTvU/0+sx1TiLn1/KbGBgAKRQjrKVKyanmSBRPDMJe96uuz7/OUaxFwSAt2/YRgAwCFxKmi07A480ypdevE6+kWM5pTBFxoATPPqLEBgEITonqKlKxZlWy88YZ0xBHp72tuEXDICnb9hGADAPAHJ9seSOlnVZrKpuTUiSLgQgmYecZSFADAe061PWgs01mVjh2zP2wvpKf2hgHBBgDgLbd6WGU6q5LL61ME7FsEGwCAd9zsYTV0aPpgUVRk3pcLioB9iWADAPCOmz2s/vGP9IEoFjPvyxW7zHzHF8GmpqZG5557rjp37qy2bdtq4MCBevPNN70eFgDAbW71sIpEzDodN167KasIeNw48zPLT57yfFfUF198oWHDhmnUqFH629/+pm7duunjjz9Whw4dvB4aAMBtbrQYSFbQ68Rrw/c8Dza33nqrotGo5s6d23Ctd+/e3g0IAJA/Tvawuv126aqr7F/DL/2x4DrPl6KeffZZDRo0SGeddZa6deum73//+3rwwQeT3l9XV6fa2tq4DwBAQDm1uygSsQ81Cxc2/7URKJ4Hm08++UT33Xef+vbtq0WLFmnixIm67LLL9Je//MX2/lmzZqm0tLThIxqN5nnEAABHNWd30d69qfs8sXOp4Hje3bt169YaNGiQ/tGoKv2yyy7TypUrtXz58oT76+rqVFdX1/Dn2tpaRaNRunsDQNBle/JwNt24nT7VOFd+GYcPuNXd2/Mamx49eqhfv35x1w499FAttKYPmyguLlZxcXE+hgYAyKdsWgzYhZq//lU68cTmv7ZbKivNM3sab28vKzOX4pg5coznS1HDhg3T+++/H3ftgw8+UK9evTwaEQDAt7p0Sb70lCzU+IFbpysjgefB5vLLL9eKFSs0c+ZMffTRR5o/f74eeOABTZo0yeuhAQD8JBKRtm1LvO5tRUV6bp6ujASeB5sjjzxSTz31lCoqKtS/f3/ddNNNmj17tn784x97PTQAQFOxmFRVJVVUmJ/z8TB+//3UBcJ+5+bpykjgeY2NJJ1yyik65ZRTvB4GACAVL2pEsikQ9iu3TleGLc9nbAAAAeBFjYhdqNmyJVihRnLndGUk5fl27+Zya7sYAOC/YjGpd+/kyynWCb7V1c5sXQ7DLE1j1u8v3enKTv3+AsKt5zczNgCA1PJZI2IXao49NrihRnLudGVkhGADAEgtHzUiM2cmLxBevDj31/ULTkDOG18UDwMAfMztGpGwLT0lU14ujRnDycMuI9gAAFJzsgN3Y/X19g/1+vrkYSfo/HACcsixFAUASM2NGpFIxP5+wwhvqEFeEGwAAOk5WSNiF1zuuy98S0/wBEtRAIDMNLdGpFBqaeApgg0AIHO51ojkI9TEYsEozA3KOAOKpSgAgHvy1eepstI8BG/UKGn8ePNz797+65odlHEGGMEGAOCOSEQ65JDE604vPXnR7iEXQRlnwBFsAADOs5ulqa52PtTEYmZjTrvXta5NmZKfLuSpBGWcIUCwAQA4JxJJvvTUu7fz3y+f7R6aIyjjDAGCDQDAGXaBpkULd3c95aPdgxOCMs4QINgAAJrnt79NPkvj9tKK2+0enBKUcYYA270BIGj8tF3Y67Np3Gr3YHHqd+32ONGAGRsA+RWLSVVVUkWF+Zliyez4ZbtwstYH9fX5PXDPjXYPFid/126OE3EINgDyxy8P5aDyy3bhSMSsnWnKqz5PTrZ7sLjxu3ZjnEgQMYxgn2VdW1ur0tJS7dy5U+3bt/d6OACSsR4UTf+TYz0I+Q97arGYGQKT7ayxljKqq939f/12weWmm6SpU937nplyatnI7d+1n5YSPeTW85tgA8B9fnkoB1lVlTnDlc6SJbm1PEjH61qafPL6d10g3Hp+sxQFwH2c4REvlzojL7cLF1KokdiaHXAEGwDu40HxrVzrjLzYLvzJJ/np8+Q3bM0ONIINAPfxoDA1pyDV2i6cbPYkEpGiUee2C0ci0kEHJV4Pc6Cx5Pt3DUcRbAC4jwdF83sF5XO7sN37tHp1YYQaia3ZAUewAeA+HhTO1Bm5vV04VZ+n/v2b99pBw9bswOLkYQD5YT0oJk+Of8CXlZmhJuwPCqfqjMrLpTFjnN8uXGgFwplw63cNVxFsAORPIT8onKwzKipybpvxbbdJV1+deL2QA01jTv6ukRcEGwD5VagPCj/2CmKWBiFEsAGApjI5GTbb02OtOqOxY81A0Tg85LvOyDDsWyLs3VsYs2cINYqHAaCxTM6ZyfUsGj8UpKbq82QX3mhYioChpQIAWDLpZyU1v+eVV72C7JaeLrlEuueexOuVlfaF3nPm5B7A6JGERugVlQTBBoAjMulntf/+ZqCpqUl+jx97XhUXS7t3J15P9p9/NxqWuhGUEGj0igIAN2Vyzsz69clDjXWP33peRSLZhZp0BwkahjRxovToo5kvTzXnxGUgSwQbAJCc7VPlh55XNTW59XlKF/AkacsW6dxzM6stau6Jy0CWCDYAIDnbp8rrnlfWklhTmVQeZBvK0s260NkdeUawAQAps35WZWVmnY2fe17Zje2f/8z8bJpsQ1m6WZdsT1xmJxaaiWADAFJm/azmzJHuuiv1PV71vErV5+nIIzN/nXQBz06qWZdsTlzOdRs90AjBBgAsmZwz44ezaJpy8gThVAEvHbvZmUw7u2/ZQoExHMF2bwBoyo2Th93wwAPSxRcnXnfiP+t227PTWbLEvl2GtSuq6dissPP449Lll6feau/HbfRoFs6xSYJgA8D33AhB+ejzZI27psYMHlu3pu5zlSp42AWlaNRcuuvUyVx2SidZcEIgufX8plcUALjJjYPp7EJNXZ3UunVur5dM44alJSXN63OVqrN7RUVm4/HDNnr4HjU2AOAWpw+mS1Ug7HSoacqJ2iIrKI0bZ362glA2BcZAGixFAYAbMmnRkE3diF2gKS+XFi5s1jCz5saymvW7qqnJfakLgcNSFAAESTYH06WqG8lHLU02Gi9POfmac+Y0b6kL+C+WogDADdkeTGfHb6HGTX7cRo9AYsYGANzQnLqRTZuk/fZLvB7GQNNYqgJjIEMEGwBwg3UwXbq6kabtFwpplsaOG0tdKCgsRQGAGzJp0dC0bsQu1Lz4ovOhhn5MCDGCDQC4JdO6kVTbuE84wdkx0Y8JIcd2bwBwW6ot0vlcerLO1Wn62tYYKNJFHtFSIQmCDYBAevBB6aKLEq+79Z9kp8/VAZqJc2wAICy8KBB26lwdwOeosQGAfLILNbW17u96cuJcHSAACDYAkA/JCoQlqV8/94t36ceEAkGwAVC48rXtOVmgseTaFDMb1rk6ycYSiUjRaOK5OkDAeB5spk+frkgkEvexn92JmwDgpHxse041S9OYtQw1ZYp74SqXc3WAAPI82EjSYYcdpg0bNjR8rF692ushAQgza9tz02JaJ2dOMgk0jTUu3nUL/ZhQAHyxK6ply5bM0gDIj1hMmjzZvljXMMxAMmWK2bMol9mL7dulzp0Tr8+fb84MpeN28S79mBByvgg2H374oXr27Kni4mINHjxYM2fOVJ8+fbweFoAwynTbc1WV+bDP5uGfaht3VVVm48tH8S79mBBini9FDR48WH/5y1+0aNEiPfjgg9q4caOGDh2qbdu22d5fV1en2trauA8AyFimMyJnn51d/Y1dqHn00W9nhijeBfLC82Bz4okn6swzz9T3vvc9HXfccXrhhRckSX/+859t7581a5ZKS0sbPqLRaD6HCyDoMp0R2b49/s/J6m9S9XlqvPSUa/EuDSuBrHgebJpq166dvve97+nDDz+0/ftrr71WO3fubPhYt25dnkcIINDSzZwkY7dzKdsThLMt3qVhJZA13wWburo6vffee+qR5P9VFRcXq3379nEfAJCxVDMn6Vj1N9dem3yWJt0JwuXl0tq10pIlZkHxkiVmfya7UOP2zi0ghDxvgnnVVVfp1FNP1QEHHKDNmzfr5ptv1tKlS7V69Wr16tUr7dfTBBNATiorzd1RjYNDJJJ7awMn/1NKw0oUALee357P2Kxfv17jxo3TwQcfrPLycrVu3VorVqzIKNQAQM6azpzceWdu4eSLL5zv85RNw0oAcTzf7r1gwQKvhwCgUDXe9lxRkf3XuzXhTcNKIGeez9gAgC9ke36Mm6v4NKwEckawAcKKbcLZyXS31N697oaaTMbCmTdAUgQbIIzYJpy9xrulkpkxI/9joWElkBWCDRA2bBPO3fHHp56NmTYtfwGRhpVATjzf7t1cbPcGGmGbcO4yPdPGui9f4SIWo2ElQsmt5zfBBgiTqipz2SmdJUvcbYLo94dx0/HZ/c46dJB27LD/egIi0GxuPb893+4NwEF+2CZsd/BdWZlZM9J4hsOr8GM3vqaWLEkdEBufI0OXbMBXqLEBwsTrbcKZ1vd4VdycbHyNGYY/AiKAnBBsgDDxcptwLGbOhNitbjduIPnEE94UN6can/Tt7yYW8z4gAsgZwQYIEy+3CWfaBuCSS9KHHzfO3MmmTQHnyACBRbABwsarbcKZLsts3Zr879zsgZRJUbVk/hycIwMEFsXDQBiVl0tjxuS3ONfJZRkna1cy3cZtsX4OKyDaFULPnu3NOTJ+320G+ADbvQE4wzpDp6bGfqkpEpG6dJG2bEn/Wk5tR88m1CTbwu2XMJHpbjMgINx6frMUBcAZmSzf3HtvfmpXolH777FwoXk9m+UlqwP4uHHmZ69CDadJAxkh2ABwTrr6nrFj3a9diUTsi4QNI/n4OnaUpk83l+/8JtPdZjQ5BSQRbAA4rbxcWrvWXE6aP9/8XF397XKJW8XNX39tP0tjGPGhwBrfjBlSp07mte3b89sHKhvZ7OYCQPEwABdYyzfJOF3cnGxpK1kJ4TPPmDM0Tf/eWtrxU5NJDgsEskKwAeCNdOEnU3ah5oYbzBkZu8JfKfXSTiRiLu2MGeOPHUccFghkhWADBJFfdup4Kd0sTbJdRD//eeZLO37oA2UdFphqt1lZGYcFAv9FjQ0QNF71WfKTZKFmyRKpokK68UbpzDPtdxFNm5bZ9/DL0g6HBQJZIdgAQVLo235feCH5Nu6ysm/DXrLwks2xXX5a2vHqNGkggDigDwgK6wC8ZMsoyQ6YC4tkszQLF5qhzqn/lPn598gSJELErec3NTZAUGSz7dcPtSFOsgs1n3wiHXCAGfZyDTWRSPzX+n1px6mCayDEWIoCgqIQt/3anRIsmWHkwAPTh71UZsxgaQcIIWZsgKAotG2/mZxNk2uI69xZuv5684OlHSBUCDZAUBTKtt8jjpDeeivxut3PnGuI27bNPKSvvJylHSBkWIoCgqIQtv1GIpmHGunbsJdNF2/r+9BfCQglgg0QJGHd9rtnT2Z9nppKFfZSob8SEFosRQFB07jPUk2NtGWL1LWr2dAxFgvejE22fZ6assJe01OGMxGmQmsAkgg2QDAVFZkdqa+5JrFlwJw5wZm5sQs1kyZJf/hDdq/TtKnmpk3S5Zen/7qwFFoDaMABfUAQWScQN/3X1woKfl+Wau4sTTrWYYbpCq39eAgfUCDcen5TYwMETSyWuju15O/CWLdDjVQYhdYAbBFsgKDJ5gRiP3n99dwKhHMV1kJrAClRYwMETRBPIM7HLI2dprU3HMIHhB7BBgiaoJ1AbBdq1qyRDj00P9+f/kpAQSHYAEETlBOIvZqlAVDQqLEBgiYIhbGEGgAeIdgAQeTXwtgTT8xvgTAANMFSFBBUfiuMZZYGgA8QbIAg80NhbH29fZiqr8++OSUANFNWwWbdunWKRqNujQWAF2Kx7GZ9Gt8/frz9PX6Ypcn25wIQClnV2BxyyCH6zW9+oy+//NKt8QDIp8pKs/XAqFFmSBk1yvxzZWVm9zf1P//jj1CT7c8FIDSyCjYvv/yyXnrpJfXt21dz5851a0wA8sHqN9X0FOOaGvN60xCQ7H7LwoVSRYU7Y81Gtj8XgFDJqQnmX/7yF11//fXq0qWL7rzzTh3j4Ro/TTCBHFhNIpOFlKZNIrO93ytBGScAfzXB/MlPfqIPPvhAp556qk4++WSdccYZ+uijjxwbFACXZdtvau7cYPSnCmofLQCOyfkcG8MwNHr0aF100UV69tln1b9/f1155ZXatWuXk+MD4IZs+k1FItLPf+7s67oliH20ADgqq11Rf/zjH7Vy5UqtXLlS7733noqKijRgwABNmjRJAwcO1KOPPqp+/frpqaee0qBBg9waM4DmyrSPVLJdT819XbcErY8WAMdlVWMTjUZ19NFHN3wMGjRIxcXFcffMnDlT8+fP1zvvvOP4YO1QYwPkwKpFSdZvKplIJHV/Kq9rV9L9XH4ZJwDXnt85FQ+nsmnTJvXs2VOxWMzJl02KYAPkyNo9JGUWbhYutL/fOoTPy1YOjSX7uZo7Ts7FARzlq+LhVLp166ZXX33V6ZcFkE4sJlVVmVuuq6rMP6eSrN9UU1afp1z6U2U7Jie40UeLc3GAwHB8xibfmLEBZD5gJ0+O3xFUVmZ2AU/3II/FpJZJyu3s/vOQ6cxFc8bkBKdmWKwZoKa/C7/NVAEBE5ilqHwj2KDgNefBaxhSC5uJ28WLpc2bcw8EYQkDnIsDuIZgkwTBBgWtOQ/eZA0qy8qaN8sSpjBQVWUuO6WzZIn3zUiBgAlMjQ2APMr1QDq7UHPEEeb15rYiCNMheZyLAwQOwQYIsmwfvJGIfajZu1fatMm+psa6NmVKZsW/YQoDnIsDBA7BBgiybB68yZaeDMPZWZYwhYHhw81ls2S/u0hEikbN+wD4gq+CzaxZsxSJRDRlyhSvhwIEQyYP3v32s68TsbZxS87OsoQpDBQVmfVFUuLPY/159mz/1woBBcQ3wWblypV64IEHNGDAAK+HAr/w4gyUoEn34DUMaePGxK9ruuTk5CxL2MKAG+fiAHCNL4LNf/7zH/34xz/Wgw8+qI4dO3o9HPgBB6JlLtmD165eZtky++tOz7KELQyUl0tr15q7n+bPNz9XVwfv5wAKgC+2e0+YMEGdOnXSnXfeqWOOOUYDBw7U7NmzM/patnuHUFjOQMk360C6ZNuT0/2r7kYrAtoQAEjCred3Vt293bBgwQK99dZbWrlyZUb319XVqa6uruHPtbW1bg0NXojFzNNqk+3OiUTM3TljxvCAbKqoKPdQI307y2J3WvDs2bmFyaIizncBkFeeLkWtW7dOkydP1iOPPKI2bdpk9DWzZs1SaWlpw0c0GnV5lJCUv3qXMJ2Bkk/XX2+/jNS4QDgTLLkACDhPl6KefvppnXHGGSpq9P+8Y7GYIpGIWrRoobq6uri/k+xnbKLRKEtRbspnz5+KCrOmJp3586Vx45z93kGVahs3APhUKJeijj32WK1evTru2gUXXKBDDjlEv/71rxNCjSQVFxeruLg4X0NEsnoX6zRap+tdwnQGSj7YhZrdu6VWrfI/FgDwAU+Dzb777qv+/fvHXWvXrp06d+6ccB0e8KLexdqdU1Nj/32tPkNBOAPFTczSAIAtX2z3hk95Ue8StjNQ3GAXaoYPJ9QAgHywK6qpqqoqr4cAi1c9f9zYnRMGzNIAQFq+CzbwES/rXcrLzSWuIJ2B4uaZLYQaAMgIwQbJeV3vEqQzUNzaObZ+vXnib1MEGgCwRY0NkqPeJTPWzrGm9UjWzrFc20BYbQyaWrKEvlkAkATBBqmFreeP09LtHJPMnWPZBpFkS08SfbMAIAVf9IpqDnpF5Qk9f+xVVSVvY9DYkiWZLaulCjR29xEuAQRUKA/oQ4AEqd4ln5zcOZZpqJHomwUASbAUBUi598JyYufY3Xfbh5quXVO/Jn2zACABMzZAc3Y0NXfnWLJZmkhE2rIls/E7fY4QAAQYMzbwVr66hifT3B1Nzdk5Zhdq/vMfMwhlU/pG3ywAaECwgXcqK83dPaNGmR29873bx6kdTdnuHItE7EONYUgrV6ZuY9H0daJR+mYBQCMEG3jDrbNfsuFkL6zycmntWnP30/z55ufqavtQ09RRR30bpLJdVuIcIQCIQ40N8s+LruF2nO6FlWrnWEmJ9M03ideb/g4yXVbq2lX64x/Z6g0ATTBjg/zzomu4nXz1wopEMgs10rfFyKm2fnftav7+CDUAkIBgg/zzqmt4U+lCRHNrWLZuTV5Lk6w4OF0xciRiztS0bp3bmAAg5Ag2yD8vu4Y35mYvrEjE/hyaTHY70cYCAHJGSwXkXyxm7n5Kd/ZLdXV+CmPtzrGJRs1Qk0uIsJuleeYZ6bTTsnsd2lgACDG3nt8EG3jD2hUlxYcbr3ogOREiki1pBftfMQBwBb2iEC7Wcovdib+5zpQ0R3N7YRFqAMAXCDbwTnm5uaU7yMstjz4qnXtu4nUCDQB4gmADbwW5azizNImoCwLgMXZFAbmwCzW7dhV2qPG6RQYAiGADZCdVn6d99sn/ePzCDy0yAEAEGyBzdoFmwIDCnqWRnGsmCgAOINgAycRiUlWV1KtX8lmaf/0r78PyHb+0yAAAEWwAe43rRT77LPHvC32WpjG/tMgAALErCkhk1YskOxX5ySfzPyY/80uLDAAQJw8D8WIxqWWKvJ/vdg9B4LcWGQACwa3nN0tRQGOpQo1EvYgdN5uJAkCWCDaAlHwbdzLUi8SjIzkAn6DGBsgm0FioF0kUhhYZAAKPYIPkwn48/uLF0vHHJ14vK0tfLzJ8uPvjC6Igt8gAEAoEG7/zKlxUVtp33p4zJxzLCqn6PFm7oiKR+HBDvQgA+B41Nn7mVe+dbI7Htw6xq6gwPwfhdFm7UPPFF9+GGOpFACCw2O7tV8nOUrEeym49YK2tu8lOkm28dfeZZ4I1q5NtN+6wL8UBgIfcen4TbPwom3Dh9IO2qsqcGUpnxgxp+vT8B69c2YWaQYOklSvzPxYAAOfYFBQve+9kuo15zpxgND0888zkfZ4INQAQOhQP+5GXvXcy3ca8fXvyv2scvLzcIZPt0pPTWMoCgLxjxsaPvOy9M3y4ucyVLBREIlLnzpm9lleH2H3zTfJZmnyFGq8KvwGgwBFs/CiTcBGNunOWSibH4192WWav5cUhdpGIVFKSeD2fpWTZ7CoDADiKYONHXvfeSbfd+frrvQteqdiNp6LCnVCTbJt7LGbuFAtC/REAhBC7ovzM7pC8aNQMNfnYcZSqRsSalZDsD7HL566ofNfSpDq8sFOnzHaVLVnCCb0ACppbz2+Kh/3M6947qY7Ht2Z17B7w+Qpekjehxu58IWuZafLkzF6HJpoA4ApmbNA8Xu38Wb5cGjo08bqb/zhncr5Qly7Sli3pX4sZGwAFjhkb+JMXTQ+92sadyflCW7ZIXbtKW7fSRBMAPEDxcNDEYtIrr0i/+Y358corhVWIahdqNm/Oz66nTJePfvxj87MXhd8AUOAINkFSWSl17y4dd5x0883mx3HHmdfCvoU4Ekl+Nk3XrvkZQ6bb18eMoYkmAHiEGpugqKw02wOksnBhOB+adoHmoIOkjz7K7zisGpuamtTLTFYPr2zqjzilGECBoQlmEgURbGIxqVcv84GaSlmZtHZt8B6IyR7qkyZJ996beL+X/8i6sc091fbxMAZVABDBJqmCCDaZdtyWgrfbJtlDPVmR7pIlzs1q5DpL4uT5Qsm2j/u1SzoAOIRdUYUsmzNPgnQ+SrKHul2oWbjQDBONA15zZjWaM0vi1PlC6U4pjkTMU4rHjAneLBwAeITi4SDIpueSF/2ZcpHqod7UwoXO9l5yopeTtc193Djzcy7BI5Pt41aXdABARgg2QTB8eOIOGztBOh8l3UPdsnixs72X/NTLKdPZtSDNwgGAxwg2QVBUJN11V/r75swJzpJFpg/rqipnZzX8NEuS6exat27ujgMAQoRgExTl5eaSTOfOiX/XuXPwtno7vWTm9OxHPmZJhg9P3SXdMmFC+M8pAgCHEGyCpLxc2rTJXJ6ZOtX8WLzYvBakUPP+++l3eUUi5k6jTHd4ZRqUnL6vOYqKzFk2KXW4+fzz3GqJAKAAsd0b+ZVudqLxPU8+ae4IyuZQvHSyPWQvH+x2aPlhXADgIree38zYeCkWM2tIKirMz2Hv+WQXah5+2HxgN9a49UCqWY1cei85/XpOKC+X5s1LfQ87pAAgI54Hm/vuu08DBgxQ+/bt1b59ew0ZMkR/+9vfvB6W+yorzZmDUaOk8ePNz717h3O5IVWfpwsuME9LXrJEmj/f/FxdHb+0Vl7ubO8lp1/PCZs3Z3YfO6QAICXPD+grKyvTLbfcou985zuSpD//+c8aM2aMVq1apcMOO8zj0bkk2cF01jkqYTpt1i7QDB8uvfZadq/j1KF4br1ec/mp9gcAAsyXNTadOnXSbbfdpgsvvDDtvYGrsbFqPJLVU4SlluLee81eT001/ceNPkkmP9b+AICLCqLGJhaLacGCBfryyy81ZMgQ23vq6upUW1sb9xEofjpHxS2RSOahxskThYPMj7U/ABBAvgg2q1ev1j777KPi4mJNnDhRTz31lPr162d776xZs1RaWtrwEY1G8zzaZvLTOSpOq6+3X3qqr08MNX46Adgv/Fj7AwAB44ulqN27d+uzzz7Tjh07tHDhQj300ENaunSpbbipq6tTXV1dw59ra2sVjUaDsxSVaafuoHXpTraNO9k/XmH9PTgh167jABAgoe7u3bp164bi4UGDBmnlypWaM2eO7r///oR7i4uLVVxcnO8hOsc6bTZdLUVQej5J9qFmwQLpnHOSf02YZ66ay2qwCQDImi+WopoyDCNuViZUwlRLMXRo8m3cqUKNxC4gAIArPJ+xue6663TiiScqGo1q165dWrBggaqqqvTiiy96PTT3WLUUdruBZs8ORi1FtktPTYVx5goA4DnPg82mTZt03nnnacOGDSotLdWAAQP04osv6vjjj/d6aO7y2zkqmaqpSTwpWMo80FismauxY80Q0/jrgzZzBQDwDV8UDzdH4M6xCbLmztLYsTvHJhoNzswVACAnoS4ehoPc2lFjF2o2bpS6d2/e6wZ15goA4EsEmzBx4xRfN2ZpmmIXEADAIb7cFYUcuHGKr12oOeMMZ0MNAAAOItiEgdOn+D73XPJt3IXU5gAAEDgsRYVBNv2nrCWfZLU4+Vh6AgDAJQSbMMj2FN9ktTh24ShZ/ycAAHyIYBMG2Zzia9XiNJ2BsQs1zNIAAAKGYBMGmZ7iO3SodNBB6QPLc89Jp5yS/O9p0ggA8CmKh8Mg0/5T//hH6locyz77JP+7ykqpd2+zM/f48ebn3r0pKgYA+ALBJiys/lP77x9/vazMvF5e3vyO2m5sKQcAwEEsRUnhWVpJd4rvvvtm9jp2NTvptpRHIuaW8jFjgvm7AwCEAsHGjdN6vZTsFN9Mdjal6qidy5ZyAADyrLCXogplacUu1KSqxbGbcWnuMhYAAHlQuMHG6dN6/WjgQPtQs3Bh6locO9lsKQcAwCOFuxQV9qUVu0Dz619Lt9xi/u9sO2pnuqXcbhkLAIA8KdxgE9allbfflr7//cTrTcNIth21rS3lY8eaIabx66VbxgIAIE8KdykqjEsrkUhmocYSi0lVVVJFhfk53bJbJlvKAQDwUMQwgn1ufm1trUpLS7Vz5061b98+8y+MxcyD5dItrVRX+38WwjCkFjYZNRazvy41bzdYWLbHAwA8k/PzO43CXYoKy9JKy5b2My2p8mqyflHWbrB0sy/ZLmMBAJAnhbsUJQV/aSUSSQw1S5emDjWFsBsMAFCwCnfGxpLutF4/evpp6YwzEq9nsqoYxN1gLH0BADJEsJGCtbRit437mGOkJUsy+/qg7QYL28nQAABXEWyCYvduqbg48Xq2td9e7QbLZdalslI688zE6+vXm9cXLiTcAADiFHaNTVD06eNMqJG+PWgvWe+oSESKRp09aK+y0tyBNmqUNH68+bl379QtK2Ix6aKLUr/uRRdRCwQAiEOw8btIxNxy3tiOHbmFGunb3WDWazf9XoYh/exn0uOPZ3a2TTq59uOqqpK2bUv92tu2mfcBAPBfBBu/evZZ+1kVw5BKS5v32sl2g3XqJHXuLE2blvnMSirN2YGVaWAh2AAAGiHY+FEkYu7Uauz113OfpbFTXi6tXWsWHc+fL82YIW3fnjhL0pxO59nswAIAwAEEGz+prU0+SzNsmPPfz9oNdvbZ0oMPOn+2TXN2YGW6Sy0ou9kAAHlBsPGLU05JXGK6805nZ2mScWtmpTk7sI45xlwWS6VzZ4INACAOwcYPIhHphRfir736qtS9uzMFvOm4dbZNc3ZgFRVJDzyQ+vUfeICD+gAAcQg2Xlq2LPGhf8ABZhj40Y+cKeDNhFtn26TbgSWl7sdVXm6eVWPX8oIzbAAANgq3u7fX2rWTvvoq/tqf/mRutW76llghwK3+VZl0Ot9/f2nePGnz5uzbGtidHhyNmqEmk5+HlgoAEDpuPb8JNvm2ZYvUrVvi9b17zXCRrNYlEjFnKqqr3XmoW+fNSImdzg3DrGdpvGMq27YGhBMAQCNuPb9Zisqnn/88MdS88YYZHLzeGp3qbBup+dvArR1Y48aZnwk1AAAX0CsqH+rr7R/kjWdG/NCcsmmn827dpAkT7O81DHM2Z8oU82sIKgAAH2DGxm2PP5740P/DHxJrWbxqTtlU45mVoiJzZiYZDtgDAPgMMzZustvmXFcntW6deN3aGp2qgLeszNnmlOn4YRYJAIAsMGPjhg8/TAw1J5xgBha7UCM1f2u0G/wyiwQAQIYINk4bPlz67nfjr1VXSy++mP5rkxXwlpW5t9U7leYcsOeGWMw8sLCiIj8HFwIAAoelKKd8/bXUtm3i9Wx30zct4PVya7Q1izR27Lfbvi35nkWyOwsn2y3nAIDQY8bGCbffnhhqnnkm9z5Pftoa7YdZJOuMnabb4ZvTeRwAEEoc0Ndcdss09fXJl2/c5tZBeF4dsGediuzVwYUAAFdwQJ/fLF+eGF4uu+zb8128UFlphoBRo5zvM+XVLJLXBxcCAAKFGptcdOwo7dgRf237dvO6V6zlmqYTcNZyjRfFx05gyzkAIAvM2GRj2zZzNqZxqOne3QwTXoaaWMwsrLVbVbSuTZkSzF1EbDkHAGSBYJOpSZOkLl3ir/3v/0obN3oznsbCvFzjty3nAABfYykqnUz6PHktzMs1ftpyDgDwPWZsUqmsTHxgzp7tr1AjhX+5xg9bzgEAgcB272Tslj6++UYqLnbue2Qj1XZra0t0uj5TQd8S7dWWcwCA49junS8ff5wYakaNMgODV6Em3TZuP/aZcoOfDi4EAPgSwaaxY4+VvvOd+Gsffyy9+qo345EyP3WX5RoAAFiKkiTt3Su1apV43etfTS6n7rJcAwAIAJai3LJiRWKoWbjQ+1Aj5baNm+UaAEABK+zt3mefLT3xRPy1WExq4ZO8F+Zt3AAAuMAnT/A8q6kxl3Eah5rnnzdnQPwSaqTwb+MGAMBhPnqK58ltt5l1KY19+aV08snejCcVTt0FACArngebWbNm6cgjj9S+++6rbt266fTTT9f777/v/Df6+mszCFx99bfXZs40Z2natnX++zmhULZxAwDgEM+DzdKlSzVp0iStWLFCL7/8svbu3avRo0fryy+/dO6b/O1vieHls8+ka6917nu4hW3cAABkzHfbvbds2aJu3bpp6dKlGjFiRNr7U24XMwxp6FBz55Pl9NOlp55ydtD5wDZuAECIuLXd23e7onbu3ClJ6tSpk+3f19XVqa6uruHPtbW19i/0/vvSIYfEX/v7382gE0TWNm4AAJCU50tRjRmGoSuuuEI//OEP1b9/f9t7Zs2apdLS0oaPaDSaeNNVV8WHmi5dpN27gxtqAABARny1FDVp0iS98MILev3111XWdOfSf9nN2ESjUXMqq75e6tgx/gsefli64AI3hw0AALIU+qWoX/7yl3r22Wf12muvJQ01klRcXKxiu2aUjz0mXXRR/LWtW6XOnR0eKQAA8CvPl6IMw9Cll16qyspKvfrqqzrwwANze6HGoWbSJLNwmFADAEBB8XzGZtKkSZo/f76eeeYZ7bvvvtq4caMkqbS0VCUlJdm/4DvvSIcd5vAoAQBAEHheYxNJcqru3Llzdf7556f9+oY1utNOU/spU6QRI9gGDQCAz7lVY+N5sGmuhl+MpPaSeXDdnDkcXAcAgI+5FWw8r7FxXE2NNHasVFnp9UgAAECehS/YWBNQU6aYp/UCAICCEb5gI5nhZt06swUBAAAoGOEMNpYNG7weAQAAyKNwB5sePbweAQAAyCPPz7FxRSRi7o4aPtzrkQAAgDwK34yNdS7O7NmcZwMAQIEJX7ApK5OefJJzbAAAKEDhWYp66CHpoIPM5SdmagAAKEjhCTZnnSU5eHIhAAAInvAtRQEAgIJFsAEAAKFBsAEAAKFBsAEAAKFBsAEAAKFBsAEAAKFBsAEAAKFBsAEAAKFBsAEAAKFBsAEAAKFBsAEAAKFBsAEAAKFBsAEAAKFBsAEAAKFBsAEAAKFBsAEAAKHR0usBNJdhGJKk2tpaj0cCAAAyZT23ree4UwIfbLZt2yZJikajHo8EAABka9u2bSotLXXs9QIfbDp16iRJ+uyzzxz9xSB7tbW1ikajWrdundq3b+/1cAoe74d/8F74B++Ff+zcuVMHHHBAw3PcKYEPNi1amGVCpaWl/EPqE+3bt+e98BHeD//gvfAP3gv/sJ7jjr2eo68GAADgIYINAAAIjcAHm+LiYk2bNk3FxcVeD6Xg8V74C++Hf/Be+AfvhX+49V5EDKf3WQEAAHgk8DM2AAAAFoINAAAIDYINAAAIDYINAAAIjUAEm3vvvVcHHnig2rRpoyOOOELLli1Lef/SpUt1xBFHqE2bNurTp4/++Mc/5mmk4ZfNe1FZWanjjz9eXbt2Vfv27TVkyBAtWrQoj6MNt2z/vbD8/e9/V8uWLTVw4EB3B1hgsn0/6urqdP3116tXr14qLi7WQQcdpIcffjhPow23bN+LRx99VIcffrjatm2rHj166IILLmho14Pcvfbaazr11FPVs2dPRSIRPf3002m/xpHnt+FzCxYsMFq1amU8+OCDxpo1a4zJkycb7dq1Mz799FPb+z/55BOjbdu2xuTJk401a9YYDz74oNGqVSvjySefzPPIwyfb92Ly5MnGrbfeavzzn/80PvjgA+Paa681WrVqZbz11lt5Hnn4ZPteWHbs2GH06dPHGD16tHH44YfnZ7AFIJf347TTTjMGDx5svPzyy0Z1dbXxv//7v8bf//73PI46nLJ9L5YtW2a0aNHCmDNnjvHJJ58Yy5YtMw477DDj9NNPz/PIw+evf/2rcf311xsLFy40JBlPPfVUyvuden77PtgcddRRxsSJE+OuHXLIIcY111xje//VV19tHHLIIXHXLr74YuPoo492bYyFItv3wk6/fv2MGTNmOD20gpPre3HOOecYU6dONaZNm0awcVC278ff/vY3o7S01Ni2bVs+hldQsn0vbrvtNqNPnz5x1+666y6jrKzMtTEWokyCjVPPb18vRe3evVtvvvmmRo8eHXd99OjR+sc//mH7NcuXL0+4/4QTTtAbb7yhPXv2uDbWsMvlvWiqvr5eu3btcrzhWaHJ9b2YO3euPv74Y02bNs3tIRaUXN6PZ599VoMGDdLvfvc77b///vrud7+rq666Sl9//XU+hhxaubwXQ4cO1fr16/XXv/5VhmFo06ZNevLJJ3XyySfnY8hoxKnnt6+bYG7dulWxWEzdu3ePu969e3dt3LjR9ms2btxoe//evXu1detW9ejRw7Xxhlku70VTt99+u7788kudffbZbgyxYOTyXnz44Ye65pprtGzZMrVs6et/7QMnl/fjk08+0euvv642bdroqaee0tatW3XJJZdo+/bt1Nk0Qy7vxdChQ/Xoo4/qnHPO0TfffKO9e/fqtNNO0913352PIaMRp57fvp6xsUQikbg/G4aRcC3d/XbXkb1s3wtLRUWFpk+frscee0zdunVza3gFJdP3IhaLafz48ZoxY4a++93v5mt4BSebfzfq6+sViUT06KOP6qijjtJJJ52kO+64Q/PmzWPWxgHZvBdr1qzRZZddphtuuEFvvvmmXnzxRVVXV2vixIn5GCqacOL57ev/69alSxcVFRUlJO3NmzcnpDrLfvvtZ3t/y5Yt1blzZ9fGGna5vBeWxx57TBdeeKGeeOIJHXfccW4OsyBk+17s2rVLb7zxhlatWqVLL71UkvlgNQxDLVu21EsvvaQf/ehHeRl7GOXy70aPHj20//77q7S0tOHaoYceKsMwtH79evXt29fVMYdVLu/FrFmzNGzYMP3qV7+SJA0YMEDt2rXT8OHDdfPNNzPLn0dOPb99PWPTunVrHXHEEXr55Zfjrr/88ssaOnSo7dcMGTIk4f6XXnpJgwYNUqtWrVwba9jl8l5I5kzN+eefr/nz57Nm7ZBs34v27dtr9erVevvttxs+Jk6cqIMPPlhvv/22Bg8enK+hh1Iu/24MGzZMn3/+uf7zn/80XPvggw/UokULlZWVuTreMMvlvfjqq6/UokX8o7CoqEjSt7MFyA/Hnt9ZlRp7wNq696c//clYs2aNMWXKFKNdu3bG2rVrDcMwjGuuucY477zzGu63totdfvnlxpo1a4w//elPbPd2SLbvxfz5842WLVsa99xzj7Fhw4aGjx07dnj1I4RGtu9FU+yKcla278euXbuMsrIyY+zYsca7775rLF261Ojbt6/xs5/9zKsfITSyfS/mzp1rtGzZ0rj33nuNjz/+2Hj99deNQYMGGUcddZRXP0Jo7Nq1y1i1apWxatUqQ5Jxxx13GKtWrWrYeu/W89v3wcYwDOOee+4xevXqZbRu3dr4wQ9+YCxdurTh7yZMmGCMHDky7v6qqirj+9//vtG6dWujd+/exn333ZfnEYdXNu/FyJEjDUkJHxMmTMj/wEMo238vGiPYOC/b9+O9994zjjvuOKOkpMQoKyszrrjiCuOrr77K86jDKdv34q677jL69etnlJSUGD169DB+/OMfG+vXr8/zqMNnyZIlKZ8Bbj2/I4bBXBsAAAgHX9fYAAAAZINgAwAAQoNgAwAAQoNgAwAAQoNgAwAAQoNgAwAAQoNgAwAAQoNgAwAAQoNgAwAAQoNgAwAAQoNgA8B3Kioq1KZNG9XU1DRc+9nPfqYBAwZo586dHo4MgN/RKwqA7xiGoYEDB2r48OH6wx/+oBkzZuihhx7SihUrtP/++3s9PAA+1tLrAQBAU5FIRL/97W81duxY9ezZU3PmzNGyZcsINQDSYsYGgG/94Ac/0LvvvquXXnpJI0eO9Ho4AAKAGhsAvrRo0SL9+9//ViwWU/fu3b0eDoCAYMYGgO+89dZbOuaYY3TPPfdowYIFatu2rZ544gmvhwUgAKixAeAra9eu1cknn6xrrrlG5513nvr166cjjzxSb775po444givhwfA55ixAeAb27dv17BhwzRixAjdf//9DdfHjBmjuro6vfjiix6ODkAQEGwAAEBoUDwMAABCg2ADAABCg2ADAABCg2ADAABCg2ADAABCg2ADAABCg2ADAABCg2ADAABCg2ADAABCg2ADAABCg2ADAABCg2ADAABC4/8DGICQWXkEZWsAAAAASUVORK5CYII=", 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", 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    " ] @@ -822,7 +822,7 @@ "outputs": [ { "data": { - "image/png": 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", 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ll14KQFhYGDfddBP33nsv7du3JyIigvvuu49+/foxduxYx70zERERaZzwfua0TuUgxcU7HdsdoGzcuJExY8ZU/F6eGzJ16lReeeUVtm3bxltvvUVGRgZxcXGMGTOGRYsWERISUvGcf/7zn/j6+nLllVeSn5/PH/7wBxYsWFBn+XURERFxosQZZs6JxWoGKW6w03Gz6qC4iuqgCOhvLSLiUCnrnL7TsT11UFrHZoEiIiJSPzfb6bh1bBYoIiIiHkUBihupvNGin58fMTExjBs3jjfeeAObzdbo6yxYsIDw8HDnNVRERMTJFKC4mfKNFg8ePMjnn3/OmDFjuOuuu7jooosoKSlxdfNERERahAIUNxMQEEBsbCwdOnRg0KBBPPTQQ3z88cd8/vnnLFiwAIDnnnuOfv360aZNGxISErjtttvIyckBzH2QbrjhBjIzMytGY2bOnAnAO++8w5AhQwgJCSE2NpZrrrmmyj5JIiIi7kIBSn3cZF+C8847j/79+/PRRx8B4OPjw9y5c9m+fTtvvvkmX3/9NX/9618BGDFiBM8//zyhoaEkJSWRlJTEfffdB0BRURFPPvkkP//8M0uXLuXAgQNMmzbNJe9JRESkPlrFUxc325egV69e/PLLLwBMnz694njXrl158skn+fOf/8y8efPw9/cnLCwMi8VCbGxslWvceOONFT9369aNuXPnMnToUHJycmjbtm2LvA8REZHG0AhKXdxsX4LKmymuXr2acePG0aFDB0JCQrj++utJS0sjNze33mts2bKFSZMm0blzZ0JCQhg9ejQAhw8fdnbzRURE7KIApS5uti/Brl276Nq1K4cOHeKCCy4gMTGRDz/8kE2bNvHyyy8DUFxcXOfzc3NzGT9+PG3btuWdd95hw4YNLFmyBDCnfkRERNyJpnjq4kb7Enz99dds27aNu+++m40bN1JSUsKzzz6Lj48ZX37wwQdVzvf396e0tGpw9euvv5KamsozzzxDQkICYG5bICIi4o40glKXxBmApWw/AlpsX4LCwkKSk5M5duwYmzdv5umnn2bSpElcdNFFXH/99Zx22mmUlJTw4osvsn//ft5++21effXVKtfo0qULOTk5rFq1itTUVPLy8ujUqRP+/v4Vz1u2bBlPPvmkU9+LiIhIUylAqUvUSDMhNnYcBHUw78d+4/B9Car74osviIuLo0uXLpx//vmsXr2auXPn8vHHH2O1WhkwYADPPfccf//730lMTGThwoXMnj27yjVGjBjBrbfeylVXXUVUVBRz5swhKiqKBQsWsHjxYvr06cMzzzzD//3f/zn1vYiIiDSVNgsUj6W/tYiIZ7Fns0CNoIiIiIjbUYAiIiIibkcBioiIiLgdBSgiIiLidhSgiIiIiNvx2gDFAxcniZ30NxYR8V5eF6D4+fkBkJeX5+KWiLOV/43L/+YiIuI9vK7UvdVqJTw8nBMnTgAQHBxcscmeeAfDMMjLy+PEiROEh4djtVpd3SQREXEwrwtQAGJjYwEqghTxTuHh4RV/axER8S5eGaBYLBbi4uKIjo6ud4df8Vx+fn4aORER8WJeGaCUs1qt6sREREQ8kNclyYqIiIjnU4AiIiIibkcBioiIiLgdBSgiIiLidhSgiIiIiNtRgCIiIiJuRwGKiIiIuB0FKCIiIuJ2FKCIiIiI21GAIiIi4ggp62D1RFjS0bxPWefqFnk0ry51LyIi0iJS1sHK0YABRikUJEPyShi7BqJGurhxnkkjKCIiIs21fRYVwQmU3Rtlx6UpFKCIiIg0V8a2U8FJOaPUPC5NogBFRESkucL7gcVa9ZjFah6XJrE7QFm7di0XX3wx8fHxWCwWli5dWvFYcXExf/vb3+jXrx9t2rQhPj6e66+/nuPHj1e5xujRo7FYLFVuV199dbPfjIiIiEskzgAsp4IUi9X8PfERV7bKo9kdoOTm5tK/f39eeumlGo/l5eWxefNmHnnkETZv3sxHH33E7t27ueSSS2qce/PNN5OUlFRxe+2115r2DkRERFwtaqSZEBs7DoI6mPdjv4GoEa5umceyexXPxIkTmThxYq2PhYWFsWLFiirHXnzxRYYOHcrhw4fp1KlTxfHg4GBiY2PtfXkRERH3FDUSxnzu6lZ4DafnoGRmZmKxWAgPD69yfOHChURGRtK3b1/uu+8+srOz67xGYWEhWVlZVW4iIiIeQzVS7ObUOigFBQU88MADXHPNNYSGhlYcv/baa+natSuxsbFs376dBx98kJ9//rnG6Eu52bNn8/jjjzuzqSIiIs5hb42UlHXm8uSMbWaSbeKMVllLxWIYhtHkJ1ssLFmyhMmTJ9d4rLi4mCuuuILDhw+zZs2aKgFKdZs2bWLIkCFs2rSJQYMG1Xi8sLCQwsLCit+zsrJISEggMzOz3uuKiIi43OqJkLyi6jJki9XMU6k+JVQ9mClPtm1OwTc3CniysrIICwtrVP/tlCme4uJirrzySg4cOMCKFSsabMSgQYPw8/Njz549tT4eEBBAaGholZuIiEiLa8pUjT01Uhxd8K084EleAfnHzPuVoz1iisnhAUp5cLJnzx5WrlxJ+/btG3zOjh07KC4uJi4uztHNERERcYymdvb21EhxdME3D65wa3cOSk5ODnv37q34/cCBA2zdupWIiAji4+O5/PLL2bx5M59++imlpaUkJycDEBERgb+/P/v27WPhwoVccMEFREZGsnPnTu69914GDhzIyJGtb45NREQ8RG2dvcVqHq9v9U7iDDPnxGKtOm1TW42U8H5mjkr16aCmFnzz4Aq3do+gbNy4kYEDBzJw4EAA7rnnHgYOHMijjz7K0aNHWbZsGUePHmXAgAHExcVV3L7//nsA/P39WbVqFRMmTKBnz57ceeedjB8/npUrV2K1Wut7aREREddpamdvT40URxd88+AKt81KknUVe5JsREREHMKeZNfmqJHU+kjTC77VmXTrmiJy9vTfClBEREQaw806+0ZzZMDTTPb0306tgyIiIuI1yqdqmtPZu2LJr4dWuFWAIiIi0ljN6eztLdjWyjm91L2IiIjg0Ut+XUEBioiISEvw4CW/rqAARUREpCV48JJfV1CAIiIiUpmzdh52dI0TL6cARUREpJwz966xp2CbaBWPiIhIhaaWs28sD13y6woaQRERESmnRFa3oQBFRESknBJZ3YYCFBERkXJKZHUbClBERETKKZHVbShJVkREpDIlsmKzGfj4WFzaBo2giIiISIX8olLOf2Etr6zZR2FJacNPcBIFKCIiIlLhjXUH2P17Du/8eMil7VCAIiIiIgCczC3i1TX7ALhvQg8CfK0NPMN5FKCIiIgIAC9+vYfswhL6xIUyqX8Hl7ZFAYqIiIhwOC2vYlrnwQt6KUlWREREXG/Ol79SXGpwTvdIzuke5ermKEARERFp7TYdSufTX5KwWODBib1d3RxAAYqIiEirZhgGsz7bCcAVgzvSJz7UxS0yKUARERFpxT7blsSWwxkE+Vm5d3xPSFkHqyfCko7mfco6l7RLlWRFRERaqcKSUv7+xa8A3DKqGzGFm2DlaMAwd3EuSIbklWb5/6iRLdo2jaCIiIi0Um9+f5AjJ/OJCQ3gf8/tBttnURGcQNm9UXa8ZSlAERERaYVO5hbx4td7Abh3fE+C/X0hY9up4KScUWoeb2EKUEREROzlJnkazfHCyt1kF5hF2S4b1NE8GN4PLNWqx1qs5vEWpgBFRETEHinrzDyN5BWQf8y8Xznao4KUfSk5LPzpMAAzLuyNtbwoW+IMwHIqSLFYzd8TH2nxNipAERERsYcb5Wk01ezlv1JiM/hDr2hGnB556oGokWZCbOw4COpg3o/9BqJGtHgbtYpHRETEHm6Up9EU3+9LZeWu37H6WHjwglqKskWNhDGft3zDqtEIioiIiD3cKE/DXjabwVOf7QLgmqGdOD26rYtbVDcFKCIiIvZwozwNe/1381F2HM8iJMCX6WO7u7o59VKAIiIiYg83ytOwR3ZBMXO++A2Av5x3Ou3bBri4RfVTDoqIiIi93CRPo1Yp68yE3Yxt5rRT4gyIGsmLX+8lNaeQrpFtuGFkV1e3skEKUERERLxF+RLoaqXq9w9axfx1OQA8elEf/H3dfwLF/VsoIiIijVPHEugnP9lGcanBmJ5RjOkV7coWNpoCFBEREW9RyxLo1ZkDWZ3aBT+rhUcu6uOihtlPAYqIiIi3qLYEusjmy5NJNwNww8iudIty32XF1SlAERER8RbVlkAvSJvE/sIORAZbuOO8013bNjvZHaCsXbuWiy++mPj4eCwWC0uXLq3yuGEYzJw5k/j4eIKCghg9ejQ7duyock5hYSF33HEHkZGRtGnThksuuYSjR482642IiIi0epWWQJ+w9mbuiWsB+OsF/QgJ9HNt2+xkd4CSm5tL//79eemll2p9fM6cOTz33HO89NJLbNiwgdjYWMaNG0d2dnbFOdOnT2fJkiW8//77fPfdd+Tk5HDRRRdRWlpa6zVFRESkkcqWQP/D5z1ySv3p3zGMy8t3K/YgFsMwjCY/2WJhyZIlTJ48GTBHT+Lj45k+fTp/+9vfAHO0JCYmhr///e/ccsstZGZmEhUVxdtvv81VV10FwPHjx0lISGD58uVMmDChwdfNysoiLCyMzMxMQkNDm9p8ERERr/TzkQwmvWzurvzRbSMY1Kmdi1tksqf/dmgOyoEDB0hOTmb8+PEVxwICAhg1ahTff/89AJs2baK4uLjKOfHx8SQmJlacU11hYSFZWVlVbiIiIlKTzWYw8xMztWLKwA5uE5zYy6EBSnJyMgAxMTFVjsfExFQ8lpycjL+/P+3atavznOpmz55NWFhYxS0hIcGRzRYREfEa/918lC2HMwj2t/K3ib1c3Zwmc8oqHovFUuV3wzBqHKuuvnMefPBBMjMzK25HjhxxWFtFRES8RUZeEc98/isAd4/tQUxooItb1HQODVBiY2MBaoyEnDhxomJUJTY2lqKiItLT0+s8p7qAgABCQ0Or3ERERKSqOV/+xsncInrEtGXayC6ubk6zODRA6dq1K7GxsaxYsaLiWFFREd988w0jRpi7PA4ePBg/P78q5yQlJbF9+/aKc0RERMQ+W49k8N76wwA8OSkRP6tnlzqze7PAnJwc9u7dW/H7gQMH2Lp1KxEREXTq1Inp06fz9NNP0717d7p3787TTz9NcHAw11xzDQBhYWHcdNNN3HvvvbRv356IiAjuu+8++vXrx9ixYx33zkRERFqJUpvBjKXbMAyYMqgDw7q1d3WTms3uAGXjxo2MGTOm4vd77rkHgKlTp7JgwQL++te/kp+fz2233UZ6ejrDhg3jq6++IiQkpOI5//znP/H19eXKK68kPz+fP/zhDyxYsACr1Vrj9URERKR+7/50iO3HsggJ9OXBib1d3RyHaFYdFFdRHRQRERFTSnYh5z27huyCEp6c1Jfrzuri6ibVyWV1UERERMQJUtbB6omwpKN5n7Ku4qHZn+8iu6CEfh3CuGZYZxc20rHsnuIRERGRFpSyDlaOBgwwSqEgGZJXwtg1/JTdi482H8NigScnJ2L1qb+khyfRCIqIiIg72z6LiuAEyu4Nin95mkc+3g7AH4d2YkBCuKta6BQKUERERNxZxrZTwUk5o5T5v0Wz+/ccItr489cJPR37mp98AhkZjr2mnRSgiIiIuLPwfmCpusr1aHEszx+7BIAHJvYiPNjfca+3ciVceikMHw5paY67rp0UoIiIiLizxBmApSJIMbDy6NFbyCv1Z2iXCC4f1NFxr7V7N1xxBZSWwtChEBHhuGvbSQGKiIiIO4saCWPXQOw4COrAZ5Y/83X2mfhbfXh6SiI+jkqMTU+Hiy82p3aGD4d//Qsa2EfPmbSKR0RExN1FjYQxn5OZV8zM574BCrltzGmcHh3S4FMbpaQErrzSHEFJSIClSyHQtRsNagRFRETEQzzzxS5Scwo5Pbotfx59muMufPfdZu5JcDAsWwZ1bN7bkhSgiIiIeICf9qfx3vojAMye0o8AXwdtD/Pqq/DSS+bP77wDAwY45rrNpABFRETEzRWWlPLgkm0AXDOsE2d2cVDy6tdfw1/+Yv781FPm6h03oQBFRETEzc1bvY/9KblEhQTwt/N7Oeaie/fC5ZebK3auvRYefNAx13UQBSgiIiJubM/v2cxbsxeAxy/pS1iQX/MvWr5iJz0dhg2Df//bpSt2aqMARURExE3ZbAYPfrSN4lKDsb2jmZgY2/yLFhXBZZfBr79Cx45usWKnNgpQRERE3NR7Gw6z8VA6bfytPDEpEUtzRzkMA/73f2H1aggJgc8+g1gHBD1OoABFRETEDR3LyGf28l8BuHd8T+LDg5p/0Vmz4M03wWqFxYvhjDOaf00nUYAiIiLiZgzDnNrJKSxhUKdwpo7o0vyLLlwIjz5q/jxvHkyY0PxrOpECFBERETezeNNR1u5Owd/Xh39c0R9rc8vZr10LN95o/nz//eY0j5tTgCIiIuJGkjMLePLTnQDcO64Hp0W1bd4Fd+8265uUJ8c+84wDWul8ClBERETchGEYPLRkG9kFJfRPCOdP53Rr3gVTU+GCC+DkSXM58dtvg49ndP2e0UoREZFWYMmWY3z96wn8rT783+VnNG9qp6AAJk2Cffuga1dzj50gByTathAFKCIiIm7gRFYBM5ftAOCusd3pHtOMnYptNpg2Db7/HsLDzeXE0dEOaWdLUYAiIiLiYoZh8PDS7WQVlNCvQxi3nNvMqZ1HHoFFi8DPDz76CHr3dkxDW5ACFBERERdb9vNxVuz8HT+rhX9ccQa+1mZ0z6++Ck8/bf78+uswZoxjGtnCFKCIiIi4UEp2YcXUzl/GdKdXbGjTL7ZkCdx+u/nzzJkwdWrzG+giClBERERcpHzVTnpeMb3jQrltzGlNv9h338Ef/2jmn/zv/54qyuahFKCIiIi4yOJNRyumdp69oj9+TZ3a2bHD3J24sBAuuQReftntdie2lwIUERERFzhyMo8nPjELst09rgd94ps4tXP0KJx/PmRkwIgR8N574OvruIa6iAIUERGRFmazGdy3+GdyCksY0rkdt5zbxKmd9HQzODl61Fyp88knEBzs2Ma6iAIUERGRFvaf7w7w04GTBPtbefbKJu61k59vFmLbsQPi4+GLLyAiwvGNdREFKCIiIi3ot+Rs/vHlbwDMuLAPndu3sf8ipaVw7bXw7bcQFmYGJ506ObilrqUARUREpIUUldiYvmgrRaU2zusVzR+HJth/EcOAv/zFXFLs7w8ffwz9+jm+sS6mAEVERKQuKetg9URY0tG8T1nXrMu9sGo3u5KyaBfsxzOX9cPSlJU2Tz1lFmOzWGDhQhg1qlltcleen+YrIiLiDCnrYOVowACjFAqSIXkljF0DUSPtvtymQyd5Zc0+AJ6+tB/RIYH2t+nVV80y9gBz58Lll9t/DQ+hERQREZHabJ9FRXACZfdG2XH75BaWcM8HP2MzYMrADkzsF2d/e957D267zfz54YfNaR4vpgBFRESkNhnbTgUn5YxS87idZi7bwaG0POLCAnnskr72t+Wzz+D66838k9tvhyeftP8aHkYBioiISG3C+4HFWvWYxWoet8MnPx9n8aajWCzw3JUDCAvys68d33xjTuWUlMD//I85tePhVWIbQwGKiIhIbRJnAJZTQYrFav6e+EijL3HkZB4PfWSOuNw++nTOOq29fW3YuNEsYV9QYJawf+MN8GkdXXfreJciIiL2ihppJsTGjoOgDub92G8gakSjnl5SauOu97eQXVjCwE7h3DW2u32vv3OnWSU2OxvGjIFFi8DPztEXD+bwAKVLly5YLJYat9vLtn+eNm1ajceGDx/u6GaIiIg0X9RIGPM5XHrUvG9kcAIwd9UeNh/OICTAl7lXD7RvI8CDB2H8eEhLgzPPNGudBDZh1Y8Hc/gy4w0bNlBaeiqpaPv27YwbN44rrrii4tj555/P/PnzK3739/d3dDNERERc5qf9aby0ei8Asy5NJCHCjv1xkpNh7Fg4dgz69oXPP4eQEPOxlHXmKqKMbWYuTOKMJi159gQOD1CioqKq/P7MM89w2mmnMapSIZmAgABiY2Md/dIiIuJqragDrUtGXhHTF23FZsBlgzoyaUCHxj85Pd0cOdm3D7p2ha++gvZleSsOrsvi7pyag1JUVMQ777zDjTfeWKVa3po1a4iOjqZHjx7cfPPNnDhxot7rFBYWkpWVVeUmIiJuprwDTV4B+cfM+5Wjm119td7Xc2CVV0cwDIMHPtxGUmYBXdoH8/gkO5YU5+TABRfAtm0QFwcrV5qbAJZzYF0WT+DUAGXp0qVkZGQwbdq0imMTJ05k4cKFfP311zz77LNs2LCB8847j8LCwjqvM3v2bMLCwipuCQlN2LtAREScqyU70JYOhhrpvfVH+GJHMn5WC3P/OJC2AY2cqMjNhQsvhB9/NHck/uor6Nat6jkOrMviCSyGYRjOuviECRPw9/fnk08+qfOcpKQkOnfuzPvvv8+UKVNqPaewsLBKAJOVlUVCQgKZmZmEhoY6vN0iItIESzqawUJ1QR3MJFNHWj3RDEoqd9gWq7nSZsznjn2tRvotOZtJL39HQbGNByf24pZRpzXuifn55lLiVasgNNQcOTnzzJrnueF7tldWVhZhYWGN6r+dthfPoUOHWLlyJR999FG958XFxdG5c2f27NlT5zkBAQEEBAQ4uokiIuJI4f3MvIjqHaidhc0axc1GE3ILS7ht4SYKim2c0z2Sm8/p1vCTwKxvcumlZnDSti18+WXtwQmY+TzJK83P1ChtUl0WT+K0KZ758+cTHR3NhRdeWO95aWlpHDlyhLi4JuxLICIi7sMBhc0arbFVXlsgT8UwDB5aso19KbnEhgby/FUD8PFpRKXXoiKzQuyXX0JwsLlap76yG82sy+JpnDKCYrPZmD9/PlOnTsXX99RL5OTkMHPmTC677DLi4uI4ePAgDz30EJGRkVx66aXOaIqIiLSU8g60yiqeR5rWgTa0GqgxowkttOrlvfVH+Hjrcaw+Fl68ZiDt2zZixL+4GK66ytxjJzAQPv0Uzj674eeV12VpBZwSoKxcuZLDhw9z4403VjlutVrZtm0bb731FhkZGcTFxTFmzBgWLVpESPkabxER8VyO6EAbE1g0JhiqLWnXYjWPO6iT334sk5mf7ADgrxN6cmaXiIafVFIC114LS5dCQIBZhG3MGIe0x5s4NUnWWexJshEREQ/jqGRQJyftZhUUc8mL33EwLY8/9Irm9euHNDy1U1pq7kr87rtm2fqlS82lxa2EPf239uIRERH34qgEWAftRlwbs97JLxxMy6NDeBDPXtm/4eDEZoObbjKDE19fWLy4VQUn9lKAIiIizmdPsqq9gUVd13Zi0u5bPxxi+Taz3slL1wwkPLiBLVtsNrj1VnjzTbBa4f33YdKkZrfDmzltmbGIiAhgf7KqPctpG7q2o5J2K/n5SAazPtsJwIMTezOwU7v6n2CzwS23wL//DT4+8PbbcNllzWpDa6ARFBERcS57K8zas5y2oWs3Yzfi2pzMLeK2hZspLjU4v28sN4zsUv8TSkvNaZ3y4GTBAvjjH5vVhtZCIygiIuJcTckpaexqoBYs2FZSauPO97ZwLCOfLu2DmXPFGVX2mav5hBK44QZ45x1zWufttxWc2EEjKCIiUpMjC5w5MVnVqdeu5h9f/cZ3e1MJ9rfy2nVDCA30q/vkkhK47jozOPH1hffeU3BiJwUoIiJSlaM34nNmhdkWql776S/Hee2b/QDMufwMesbWU7uruBiuvtpMhPXzgw8+gCuucGh7WgMFKCIiUpWjdyV2Zon2Fij//ltyNn/97y8A3HJuNy46I77uk4uK4Mor4cMPwd/fvFel9CZRDoqIiFTljLwOZ5Zod+K1M/OLueXtjeQVlTLy9PbcP6Fn3ScXFJh763z2mVkhdskSmDjRKe1qDTSCIiIiVbVgXgfQIhv6NYXNZjD9/S0Vxdhe/OMgfK11dJv5+TB58qm9dT75RMFJM2kERUREqrKnDklztdCGfk3x/Ko9rP4thQBfH167bjARbeooxpaXZxZdW7nS3JX400+1t44DaARFRESqaoG8jgqOzndxkBU7f2fuqj0APH1pPxI7hNV+YmYmnHeWGZwEWuCpREhsoKqsNIpGUEREpCZn5oxU1oJ1TBrr1+Qspr+/BYCpZ3XmssEdaz/xxAkYew5s2w3BwP0GxGwyR4TcYATI02kERUREXKel810akJZTyJ/e3EhuUSlndWvPjIv61H7ikSNw7rlmcBIKzAB64DYjQN5AAYqIiLhOC9UxaYzCklJufWcTR9Pz6dw+mHnXDsKvtqTYPXvg7LPht98g0gqPAp0rPe7iESBvoQBFRERcpyXzXephGAYzlmxnw8F0QgJ8+c/UIbSrLSn2l1/gnHPg8GHo0QNePBvi3WcEyJsoB0VERFyrpfJd6vGf7w6weNNRfCzw4jUDOT26lkqxP/wAF1wAGRnQvz98+SX47DVzTpqz4illXbUdl2cofwUFKCIi0sqt/vUETy/fBcCMC/swumd0zZNWrjTrnOTmwogRZr2T8HAgxhwBqhJgPNL4ESA3XmbtagpQRESk1dr9ezZ3vLcFmwF/HJrADSO71Dxp6VK46iqzjP24cWaF2DZtTj3enBGg2pZZW6zmcRePKrmaclBERKRVOplbxE1vbiCnsIRhXSN4/JJELBZL1ZMWLDDL1xcVwZQpZoXYysFJc7nhMmt3oQBFRESaxk1L1DdGQXEpN7+1kSMn8+kUEcyr/zMYf99KXaJhwFNPwQ03QGkpTJsGixaZe+w4kpsts3YnClBERMR+5bkTySsg/5h5v3K0RwQpNpvB3Yu2sulQOqGBtazYKS2Fv/wFZswwf3/gAXjjDfB1QlaEI5dZe3DAWBsFKCIiYj83LVHfGLM/38Xn25Pxt/rwr+uH0D2m0oqdggK48kqYNw8sFpg7F2bPNn92Bkcts/bggLEuSpIVERH7eWjuxIJ1B3j92wMA/OOKMxjerf2pB9PTzU3/vv0W/P3hnXfgiiuc3yhHLLP2wmRbjaCIiIj9PDB34qsdyTz+6U4A7p/Qk0kDOpx68OhRswDbt99CaKhZ46QlgpP62DNl46EBY30UoIiIiP3cqER9Y2w9ksGd72/BMOCPQztx2+jTTj24YwecdZZ5Hx9vBimjR7usrYD9UzYeGDA2RAGKiIjYz01K1DfG4bQ8blqwgYJiG6N7RvHkpL6nlhN/9525r87Ro9Crl1kt9owzXNtgsD/Hp6kBoxsn1ioHRUREmsYNStQ35GRuEdPmryctt4i+8aG8dM0gfMs3APzwQ7j2WigsNKvDLlsG7dvXf8GWYu+UTXnAaE9FWzevYqsARUREvFJOYQk3zF/P/tRcOoQH8ca0M2kb4GvWOJkzx1w+DHDJJfD++xAU5NoGVxbezwwYKgcpDU3Z2BswunliraZ4RETE6xSWlHLr25v4+Wgm7YL9ePPGM4kJDYTiYrj55lPByZ13wkcfuVdwAi2T4+PmibUKUERE5BQ3zklorFKbwT2Lfua7vakE+1uZf8NQc3fijAw4/3z4z3/AxwdefBFeeAGs1gav2eJaIsfHzRNrNcUjItJapKyrlqMwo2qugZvnJDSGYRg8+vF2PtuWhJ/VwmvXDWZAQjgcOAAXXgi7dkHbtuaUzoUXurq59XN2jk/iDPPva7Gemt5xo5VYGkEREWkNGrNs1YOrw5b754rdLPzpMBYL/POqAZzTPcpcmTNsmBmcdOhgrtxx9+CkJbj5SiyNoIiItAaNSYh085yEhsxfd4C5X+8F4MlJiVx0Rry5wd/UqeZKnYEDzd2IO3Ro4EqtiBuvxNIIiohIa9CY4MPNcxLq8/HWYzz+iVkl9p5xPfifYZ3g6afh6qvN4OTii2HtWgUnHkQBioiINytPei1MrflY9eDDw6rDlvtqRzL3fvAzANNGdOGOER3NUZOHHzZPmD4dliwxc0/EY2iKR0TEW1VPeq2stuCjKcW+XGz1bye4/d3NlNgMLh3YgUcHhmEZNQo2bDBX57zwAtx+u6ubKU2gAEVExFtVzzsp5xMAMWNqDz7cOCehunV7U7n17U0Ulxpc2C+OfyTk4zNsKCQlQUQEfPAB/OEPrm6mNJHDp3hmzpyJxWKpcouNja143DAMZs6cSXx8PEFBQYwePZodO3Y4uhkiIt7H3holteWdAAREmkGIG4+MNGT9gZP86c2NFJbYGNs7hheKfsZ3zGgzOOnbF9avV3Di4ZySg9K3b1+SkpIqbtu2nUrCmjNnDs899xwvvfQSGzZsIDY2lnHjxpGdne2MpoiIeAd7d7cFz0t6bWQAtuVwOjcu2EB+cSljTmvHq1sX4nvjjVBUBJMmmcuKTzut1ueK53BKgOLr60tsbGzFLSoqCjBHT55//nkefvhhpkyZQmJiIm+++SZ5eXm8++67zmiKiIh3aEqNEk9Kem1kALb9WCbXv7GenMISxsb48e/FM/F9/nnzwUcfNcvWh4S0dOvFCZwSoOzZs4f4+Hi6du3K1Vdfzf79+wE4cOAAycnJjB8/vuLcgIAARo0axffff++MpoiIeIem1ChpqBCXO5W1b0QA9ltyNtf95yeyC0qY5J/Jv16+HeuKFRAcDIsXw+OPmyXsxSs4PEl22LBhvPXWW/To0YPff/+dWbNmMWLECHbs2EFycjIAMTExVZ4TExPDoUOH6rxmYWEhhYWFFb9nZWU5utkiIu6tKbvbQu1JrynrYPO9kPbTqWOuLmvfQAD2a3IW177+E+l5xdycvo2H3nsaS3Y2dO4MS5fCgAEt3mRxLoeHmhMnTuSyyy6jX79+jB07ls8++wyAN998s+Ici8VS5TmGYdQ4Vtns2bMJCwuruCUkJDi62SIiLc+eEQxHTdeUT6VUDk7A9WXt68mX2XE8kz/+60fSs/P5++ZFPPyvB83gpHw5sYITr+T0sbA2bdrQr18/9uzZU7Gap3wkpdyJEydqjKpU9uCDD5KZmVlxO3LkiFPbLCLidPYmvTpq35TyqZTauLKsfR0B2LaoB7nm9Z+wpKay5OMnuGrF2+bjd90FX30FZTmO4n2cHqAUFhaya9cu4uLi6Nq1K7GxsaxYsaLi8aKiIr755htGjKj7f7KAgABCQ0Or3EREPFpTkl7Lp2suPdr0ZcJ1LT0G167wqSUA29J3Fdf8N5+u+7bz1dt303/3JmjTBt57D55/Hvz9XdNWaREOz0G57777uPjii+nUqRMnTpxg1qxZZGVlMXXqVCwWC9OnT+fpp5+me/fudO/enaeffprg4GCuueYaRzdFRMR9uWpjvtpyWSo4YYVPyrpqlWlnmMerH4saWSVfZtOhk0z9z3om/7iMx75+Hb/SEujZEz780KxzIl7P4QHK0aNH+eMf/0hqaipRUVEMHz6cH3/8kc6dOwPw17/+lfz8fG677TbS09MZNmwYX331FSFaFiYirUlTk16bK3GGmQxrsVZ97fbDYNBzji3eVr3UfkEyJK2A8pTD8mPVknPXHzjJba+t5YlP5zJlx2rz3MsugzfeAI2gtxoWwzDqmIx0X1lZWYSFhZGZmanpHhHxTNU77/Kk16bklTTltVtiv53VE83cmrqmlMpZrObUzpjP+X5vKk/882P+uXgWvVMOYlitWP7+d7jnHqhnMYV4Bnv6b+3FIyLiCq7cmK+l9tupL9+lsrKpra92JLPssZf44JN/ElqUhxETg2XRInO1jrQ6ClBERFzFgzbma5J6810qsVj5KP1Scq6/mZc2fwqAbcQIfBYvhvj4FmiouCOV3BMREeeodemw1byvdOy/O8bQ88HlXF8enNxzLz5r1ig4aeU0giIiIs5R1zRW2XJqI30bX3x6JhPf+YI2xQXkhrYj6N138LnwAle3XNyAkmRFRKTF2bJz2HrpdQxatRSAo/2H0eGzD7F06ODaholT2dN/a4pHRERaVPHWnznR+wwGrVpKqcWHn2+aTsdN6xScSBUKUEREpGUYBgUvzcMYOozYYwdIbtueH/61iP7//idYrQ0/X1oV5aCIiIjzpaVRcNPNBH68BIC1pw3B5603OXtEHxc3TNyVRlBERMS5VqygJLEfgR8vodjHyvMTbib86y8VnEi9FKCIiIhzFBTA3XfD+PH4JiexL6Ijf7nzZS5793nO6BTh6taJm9MUj4iION7PP8O118KOHQC8NfBCvph6Dy//6WzatdEuxNIwBSgiIuI4Nhs89xzGww9jKSoipU0490+8i+DJF/PGlQMI9HPDZNjadlwu27hQXEcBioiIOMaRIzB1KqxejQX4qvtwHjj/DqaMH8BDF/TGx8cNN/urbcflarsri2soB0VExNlS1pk7+y7paN6nrHN1ixzLMODdd6FfP1i9mgL/QP52/h3cOuVh7rjqLGZc1Mc9gxMwR07KgxMouzfKjosraQRFRMSZvP0benIy/PnPsHQpADs69uL2iXeTFt+Z+dcMYlSPKNe2ryG17bhctruyuJZGUEREnMlbv6GXj5r07QtLl2Lz9eOlUf/DpKufge7dWXLbSPcPTsDMObFUy4uxWM3j4lIaQRERcSZv/IZebdQkpXsfrj/nz+yK6sqI09oz79pBhAd7yEqdxBnmiJbFav5dLFbAUrapobiSRlBERJyppb6ht0Sei2HAwoXQpw8sXYrh58fyK/7MWZOeZldUV64b3pk3bxzqOcEJnNpxOXYcBHUw78d+A1EjXN2yxvHi/CbtZiwi4kzVc1DKv6E7shOs8zXWOC7PJTkZbr0VPv4YgKJ+/Zk+cTrLLVFYfSzMvLgP153VxTGvJY3TEn93B9NuxiIi7qIlvqE7M8/FMOCdd8xRk48/Bj8/9t/xN86a/DTLLVG0b+PP2zcNVXDiCt6a31RGOSgiIs4WNRLGfF7/Oc0pFuasPJf9+81RkxUrzEsOHMg7t8zk0YNWjCKDgZ3CmXftIOLCgpr3OtI03pjfVIkCFBERV2vuUuTwfuZzKndWzclzKS6G556Dxx+H/HwICCD/gYe4o8NYVu5LB+C64Z2ZcVFvAnzdsDJsa+Hov7ub0RSPiIirNXeoPnEGYDmVjNuclSjr18OQIfDAA2Zwct557Fn5PeODz2blvnQCfH149or+PDk5UcGJqzny7+6GFKCIiDiDPasrmjtU74g8l+xsuPNOGD4cfvkF2rfHmD+ft578Nxd+8TtHTuaTEBHER7eN4LLBHRt/XU/iaStiPH0FUgO0ikdExNHsXV2xeiIkr6g5VB87ruHcFUdYtgxuvx2OHjV/v+46sp58hvvXHufLHb8D8Ide0Tx7ZX/PWkJsDw9cEeOJtIpHRMSV7J2ycdVQ/aFDMGUKTJpkBifdusFXX7HpyReY+N6vfLnjd/ysFh65qA//njrEe4MT8PoVMZ5IAYqIeL+WHrq3d8qmpYfqCwpg1izo3RuWLAFfX3jgAWw//8I8v65c+doPHMvIp3P7YD788whuOrsrFoubbvbnKF6+IsYTaRWPiHg3V2zW15TVFY1ZiuwIy5ebuSb79pm/jxoFL71ESufu3LNoK9/uSQXg4v7xPH1pIiGBfs5vkzvw8hUxnkgjKCLi3VwxdO/K1RV1jRbt3w+XXAIXXmgGJ/Hx5mZ/q1fzpSWSCc+v5ds9qQT6+fDMlH7MvXqAewUnzR0Fa+j5Xr4ixhMpSVZEvNuSjpB/rObxoA5w6VHnvW6NwmuPOH91RW2JnkXA9uvhxXehsNCczpk+HR59lGy/QJ74ZCeLN5mfQ6/YEOb+cSA9YkKc2057NTeBtbHPd8XfrJWxp//WFI+IeDdXDd231JRNZZVHiwxgYym8A6TMNx8/7zx48UXo04ef9qdx7+INHE3Px2KBW849jbvHdW9cbZPmVL1t7vuCU0HG9lmN+4wb+3xX/M2kTgpQRMS7Jc4wc04s1qrfnr1x6L480fMg8C6wo+x4eyu88h5cfjmFpTaeW76Lf327H8OAju2CeO7KAQztGtG413BFTk9zE1jdMQG2pYM8D6QARUS8W/kKGW8cuq/eyeVHw+vH4RvDHEHxAyZa4JYxcMEVbD+WyX2Lf+bX5GwArhqSwCMX96FtgB1dQXNHM5qiuaNg7pYA64ogzwMpQBER7+eNQ/eVO7nCUnjvOCwzoLDs8bOAq30gyoeCgTOZ+8WvvLZ2P6U2g/Zt/HnmsjMY1yfG/td1xWhEc0fBqj8fzPuidPNzbOmgwBVBngfSKh4REU+0fRbYbPBdKdwHLC4LTnq1gReHwf0doN94NvZexQXvFjNvzT5KbQYXnhHHl3ef27TgBMxRB0u1PBVnj0Y0t05M+fMjhlQ9fnKjGeS1dEl7d5xyckMaQRER8UQ/bIQ3bFBWzoT2wNXAeWEw5UdyCkv4xxe/8tZXhzAMiAoJYNbkRCb0ja15LXvyIVyV09PcUbCokeDfruYoiitGLtxtyslNKUAREfEk27fDww/DMrOgGoHAJcBEIMAK7c5g7e4UHvxoG8cy8gEz1+ShC3oTFlxLXRN78yE8OafHXUYuWlPidjMoQBER8QQHD8Jjj8Hbb4NhgI8PjDLgCguE2cBi5ffiCGYdvJtPvlwPmCt0nplyBmd3j6z7uk3Jh/DUnB53Gbnw5CCvBSlAERFxZydOwFNPwSuvQHGxeezyy+HJJ6F9GmyfRUn6Dt7K+iPP7T+XnKJifCwwdUQX7p/Qk2D/Bv6Zd5dRhZbgTiMXnhrktSCHJ8nOnj2bM888k5CQEKKjo5k8eTK//fZblXOmTZuGxWKpchs+fLijmyIi4rmysswRk9NOg7lzzeDkD3+A9eth8WLo1QuiRrKp27tcfGQhT/x6LjlFMCAhnGV/OZvHLu7bcHACrkl6dZWW3pRRmsXhIyjffPMNt99+O2eeeSYlJSU8/PDDjB8/np07d9KmTZuK884//3zmz59f8bu/vxdv4y0i0lj5+fDqq/D005BalmcyZAjMng1jx1aclp5bxN+/+JX3NxwBICzIjwcm9uKqIQn4+Nix87A7jSq0BI1ceAyHByhffPFFld/nz59PdHQ0mzZt4txzz604HhAQQGxsLdnkIiKeqLmVQfPz4V//gmeegeRk81iPHub0zmWXgcUMOopLbSz88RDPr9pDRp455XPlkI787fxetG8bYH+7G5sPocqn0sKcnoOSmZkJQERE1TLKa9asITo6mvDwcEaNGsVTTz1FdHR0rdcoLCyksLCw4vesrCznNVhExF7NqQxaW2DSuTPMmAHTppmb+wGGYbDmtxRmfbaTfSm5gLm536zJiQzp0sgy9XVpaFTBlZVPFRi1Wk7dzdgwDCZNmkR6ejrffvttxfFFixbRtm1bOnfuzIEDB3jkkUcoKSlh06ZNBATU/AYwc+ZMHn/88RrHtZuxiBdoyQ7IWa+1eiIkr6i5OiR2XN0df34+vP66GZgkJZnHOnUyA5OpU6HStPfu37OZ9dku1u5OAaB9G3/uHd+Tq85MwGrPdE5TNeX9OUJzdzEWt2PPbsZODVBuv/12PvvsM7777js6duxY53lJSUl07tyZ999/nylTptR4vLYRlISEBAUoIp6uegdUrv0wGPSsYzshZ3Z2SzpC/rGax4M6wKVHqx6rKzB5+GFzxKRSYHIyt4h/rtjNu+sPU2oz8LNauHFkV24/73RCA2upaeIs9rw/R3J2YKTRmRZnT4DitCmeO+64g2XLlrF27dp6gxOAuLg4OnfuzJ49e2p9PCAgoNaRFRHxcNVrcJRL+8kMJhz5TdmZ+580pr5GVha89hr8858NBia5hSX857sD/GvtfnIKSwCY0DeGByf2pkvkqcUGLcZV9UOcuQRaG/a5PYcHKIZhcMcdd7BkyRLWrFlD165dG3xOWloaR44cIS4uztHNERF3VlsHVMFwbAny5nR2DX3Trm8lzIkT5jLhl1+GjAzz/IQEMzC54YYqgUlhSSnv/XSYF7/eS1puEQB940OZcWEfzjqtffPef3O4aqWPMwMjbdjn9hweoNx+++28++67fPzxx4SEhJBclvQVFhZGUFAQOTk5zJw5k8suu4y4uDgOHjzIQw89RGRkJJdeeqmjmyMi7qy2Dqico4uFNbWzq/5NO/8YJH1RdRqqtpUwoX+CJ96D//zHnNYB6NkT/vY3uPbaKoFJqc3g463HeG7Fbo6mm+d2aR/MveN7cmG/OPuWDTuDqyqfOjMwcvbojKaOms3hOSgWS+3/I82fP59p06aRn5/P5MmT2bJlCxkZGcTFxTFmzBiefPJJEhISGvUa9sxhiYgbK+/8jZKajzk6CbPOHJRqhbqqdy5F6eaut7UFURbfmlMCO3bA3/8O774LpWXPGTIEHnwQJk82S9SXsdkMvtr5O/9csZvffs8GIDokgLvGdufKIQn4WbXhfM3O3kGBkbPyW5TYWy+3SZJ1FgUoIl4kZR1svtfMOylXV/DgiNeqr7OrrXOpcwqKUx3a6OWwdi089xwsW3bq8bFjzcBkzJiKOiYAthPf8cWq95m7J5Ff88wvZqGBvvx59OlMG9GFIH9r9VfyTq4caWhswGovV6148hAKUETE8zjqm3JzOr3aOpf6lAAb28F3XWDLFvOYxQJTpsADD5gjJ5WU2gyWr1vDi6t2sbugMwBtffKYFvkpN1/xJ8ISzmnc63oDdxhpcMbojKtWPHkIt1jFIyJSr9oCieZ+w2zuyox6k3YryQZWlt0y0oF0CAoy65dMn27mmlRSUmrjs21JvPj1XvaeyAM6E+KTww2Ry7gxchnhfvmw9wQktKJv2O6QpOqMsvfusmOyF1CAIiItz1lLPJvS6VUOlGyFmHuo2k49brFCRNlIyM8/wefAOqBsY2FiI+HOe+B//xfaV11pk19UyuJNR3j92/0cOWkmv4Zac7kxcik3RC4jzJpb1k68c/fg+njrLsqtbW8jJ1KAIiItz1nfnu3t9GoUiisPTsruLVbz1xOXwjtfw1eVntsj1BwtuenhKityANJyCnnrh0O89cNB0sv2y2kX7MeNI7sytfQ+QtM+0zdsZ4w0uMPqGVetePJCClBExHnq6jCc9e3Z3k6vRqG4suAkIAKyfGFdCHyZBUcfKLuWxVyJc/fdcPbZVRJfAQ6l5fLvbw/wwcYjFJaYozAJEUHcfE43rhicYCa/pvwVVi73/m/Yzakd09TXc5fCa02dOnKHAMuNKEAREeeor8Nw1jy9vZ1e9UDJAH6zwepc+KkEiss274uIgBtvhD//Gbp1q3IJwzD4bm8qb35/kFW/nqB82UG/DmHcMqob5/eNxbfycuHW8A27McGCoz+H2kblANZOhnOXun9H704BlpvQKh4RcY76llsmznDOEk+wb2VGeRtzS828kpVA5YUWw4ebQckVV5hJsJXkFJbw0eajvPn9wYrdhQFG9YjillHdOKtb+zrrQnk9Vyy1rWv1DNRer8bdtJLlyVrFIyKuV980jjNHERozvJ6yDrY9CT/8CCtL4UegoOyxAOCKi+HumTBoUI2n7k/J4a2vf+DDbdlkl5h7hLXxg8uHdOa6s7pwenTb5r8HT+eKBNj6qhI7etsEZ/DWpOFmUIAiIs7R0DROXYGEs+fhd3wCT02Gb2xwvNLxDla4tDvc9TycPqHKUwqKS/liezLvrT/MTwdOlh0NoJv/Ua6PXM5lEasJGfE5RDkhOPHEvARXLLUtn96rjSd09FqeXIMCFBFxjqYkQTZnHr62jhzMY2m/wG+xsK4NfPUdlJbNbAcAQ4ExPjBqLJz3RZVL/pqcxfvrj7BkyzEy883VOD7YGB2yiamRn3BO2y34WAzn1e/w1LwEVyy1LR+VWzsZClOrPuYJHb2WJ9egHBQRcR57K3U2dR6+1hL1wDEDvjNgrQEZlc4/DRgNDAeCy46VVfrMzCvms21JfLDxCFuPnHpSh/AgrhySwBWpU4i3ba/ZBmdUCq3r84gYAv7t3HtUxVl76DTmdZ2V3+RsrvrMWpBK3YuIe2nsNEVTy4RX7sjTgR8wk14PVjonBDjHAuPCICa7SqdfZASwxvdGlhTfxKpdJygqNZcI+/pYGNcnhqvOTOCc7lFYfSwtm8xYb+JntW/a7j6q0pLcpaP3xOk5J1OSrIi4VuV/mIM7mrsBQ8PTFE2dh0/6GdaWrcTZgTl6AmAF+gPnAgMBXwP8faHYgoGVzbndWZJxHp9mnE1GaShgLivuFRvCpQM7MGVQR6JCAqq+VksOxdeX+OnKEvGu1JhO3xkl7O3lqdNzbkQBiog4VvV/mKuPANTXodrT+RcVwYoV8M47sOR3KKz0WHfgbGAY5shJ+Utj5RffC1jOdXy2I4Wj+ae+wUWHBDBpQDyXDuxIn/h6vtm1ZB2T2j6PuoIVd08CdQRP6vTdYa8hD6cARUQcq0Z11lrU1aE21PkXFcGqVfDBB7B0KWRknHpuHHC2BUYYEFOWg2IBw4Bf8rqxPPMcPsscydGiGMxoJpQgPyvnJ8Zy6cAOjAz7DevOe+GnRgzH1/cN3ZHD+rV9HkXp5ohUa1zt4UmdvpYNN5sCFBFpGnvK2FdXX4davfMvKoLly2Hx4ppBSUwMXH01/M//QKcC2PEUZGyjOLQ/G8L/yop9RazYlVplpCTIz8p5vaO5qF8co3tGl5WfXwcrx9Dsb+bO+IZf/fMof42WWu3hTnkUntTpa9lwsylAERH72VvGvjqj1BwJSFlXe2dXWGiOlNQWlMTGwmWXmdVdzz4brFYAsgqK+SbiP6z8/XdWf3uCrIKcsieE1h6UVOaob+Yt8Q2/JaeY3G1KxZM6fS0bbjYFKCJiv/o64lrzJoDQnpC189Q1Tm40O7/yzi493Rwp+fhj+OILyM4+dW4tQYlhGOw5kcM3v6WwZvcJftp/khLbqUWJ7dv4c16vaP7QO4ZRPaJqBiWVOeqbeUPXcdRoREslgbrblIondfqtYc8lJ1OAIiL2a0oZ++1PQvZvVTu7VB944k+wMx7WroWSklPXi4uDKVOqBCWZecWs23mCb35L4ZvdKSRnFVRpwmlRbRjbJ4ZxvWMY2KmduSy4MRz1zby+67jbaERjuNuUiqd1+u6wmsiDKUAREfs1pYx9xjYoLYUDwBZgE3DYBvxadgP69oVJk8zbkCHkFtvYdCidH1bs4cf9afx8JINKgyQE+PowvFt7zu0RxZieUXRraql5R30zr+8625/ErUYjGsMdp1TU6bcaClBEPI07JC3a06GnpsKXX8ICG/wEVJq5wQL0awdTZ8CkSeQldGbjwXR+3J/Gj6/+wC9HM6tM2wCcHt2WUT2iOLdHFMO6RhDoV8/UTWM56pt5fddxt9GIxvCkKRXxOqokK+JJ6izjvab2IKU8mEnbaAYDYJZJry2osTfwqataZ2kpbNgAn39u5pJs2GCu9S0XBCQCgy2kJYazedBHbM6MZf2Bk/x8JKNGQNIhPIjh3dozvFsEI06PpEN4UNPa62otWYHWkdylKqt4BZW6F/FW9nRy5cGMYQNslR7wAYtP1aDG3sCnMsOA/fth6Wvw6duw6QRk26qe078/pRPO52CveNaVHmJTehu25PXicH5YjcvFhwUy/LT2DO/WnrO6tSchIrjGOc1qr6t48h4xIg6iUvci3ihlHfy+uvHTBOUrMKgWLGADLFVzH+xdrZGcDF9/bS4FXrUKDh2q2qRgyO0TzJ7zbufbxAv5LtefX45lUPCbDXOnvlO6R7dlYKdwhnSO4KzT2tOxXRAWSwPJre62uqQxPC3B0x6eNpolHkEBiognqBgNKan5WG1Ji3UFM+WqBzUNLo9Nge++gzVrzIBkx44qp9p8fUjrHMq2bqezKuFMPo4cTY6ljfngjpyK80IDfRnYqR2DOrVjYKdw+ieEExbk1/D7r84T8znAOxM8PXF1kngEBSgiLaU53zIrRkNqUy1psb5gpuIp1YKa6qs10oDfLHDYBx7uA7t21bjEkS69+KnrAJZH9+aH+L7k+wdWeTzIUkDvNsdI7HceiR3CGNSpHd0i2+DT2KW/9XHH1SWtlSeOZolHUIAi0hKa+y2zrvLxPgEw+HlzCWvlvVrqDGYAfKgS1Nhs4P8/sPor2GXB+NXAkkrZNY5UPGtPZCd+6tiX7zv354dO/UgPPpU/EuZbwMDAn0kM3EvfoP30DdpH18Dfscb9Acbc2fD7s5dWl7gPTx3NErenAEWkJdT2LRMfWDvZDDIaGlGpa8Sg3QDYeAdVAp86S8xbIKA9+J5Bae5EcuZ8SumPD9N26yb8c7Irn0WJxYcdMaexPqEvGzr2ZUPHPqQHh+Hv68PpUW0ZFdOW7jEh9IgJoVdsCB1LNmNZdfWpdjg7YPDmfA5Po9EscRKt4hFxhIamb5Z0hPxjdT+/McuFa1sBEjG45s62QInhQ3Jxe47nRpJ9uA2W/RZCD9uIP5RC/O+Ha1w+zy+AX2K7s75jX9YnJPJrlz5ExUdxemg+PUq/pYdlMz1iQuh05l+wxpzdyM9AAUOroNVJYgctMxZpSY1Z8lrb8uDaWPzAv2zqpHq9krIAoDR9O6lBw/i9450kr/87v+cZ/F7cnpS8cPwOlxJ+NIuEY7/T9/cD9Eg5hL+tZi7K/nbx/NyxN4d7nEFW/8H4DTiDzjFhdI1sQ9fINkQXbMSy5T5I+6lS28re15C5cHSZVmzIKa0hONVKJYdQgCLSkhpTm6R6EFOLfFsAqSVhpJWEk1YSRlpJO06URPB7zI0kF7fjRFYByVkFpGQXElBYQO8TB+j3+14Sk/eR+PteuqcexteovqQYcoKCSerWk/SBIyg+cyhBZ48gplsHYkMDa9+rpt4kWx/zPVh8Wqb+iDqFptNn5zieWHfHTSlAEWlJtUzf2AwL2UY4WdYEMoP7k9HhRtLyDFJ3LyEtM7MsAAkntfRUQJJnC6pxad/SErqkH6dnyiF6phykZ+oheqYcolNGMj61JMIWtvWjoLM/1q42gk8rxKebBa5eA9F1TMvUprGjPeWcVQ1VnULT6bNzLE+tAuyGVKhNpKkqfes0wvqR0+NhMoIHkZlfTFZ+MZmVbhnlPx+6j6ycHDJL25BZ2paM0hCyS4OxUXmPmNyy+wm1vqzFsNEh6wR9U/cyMO03eqceoFvKMeJTUvEtrSNQiIuDwYOhZxSE/wztthMQXkSApbjSha2w4ymItuMf0bpWDNXFWSs2ak0sBlaOMafB6irZL1r662haqeQSClDEqxmGQe7xdWT+/AKZJ4+QGZRIZty1ZPqdZgYXaQfIPL6RzLx8MmztySooJbN0Cpml15FV2pbSrzKB1Q28Svc6HwmwFBJuzSHMmkN73ww6lJygR/ohuqQlEZ+WQmRKBqEncgk8UYhPUR0XCQQ6AglAggX6D4HrPoOoqKrn1ZaIW98/onVNAdS2KqMKH6pUp3XWio26AiWjGApTIekr9ygI5uipFEdcTx2qY2mlkksoQBGPUFhSagYUeebIRUZeMRl5RWSm7ibz8LdkZGeRWRpsjmIQTZZPPBlFvmTlF1FiA5hW6Wp5QOV/qOv/R8bfp4SwID/CSg4Tbs0mzJpDmG8uodYcwrpfTlj70wgvPUhY8geEFe4kPO8I7VLTCEnLxf9EKSRx6pZZzwtZgXjMQKQiIAHaY8YEABgQdLxmcAL2/SNafQog/xgkfQHth0G3qVVrjJTzK0veLc6iIkhx5nLiBgOlWkr2tzRHV1F11PXUoTqW6u64hHJQpEWVlNo4mVdEWk4R6blFp4KN/CIz+Mgrnz4pOvVzXjH5xXZMOdTC31JsBhTVbuHW7JrHfXNPBSLWHALbRJn/sJfPQZdiVlpNscDJNpACnGwLme3gSKpZFr4+4UBc+c0C8RYY/xiwDjI2n9p1GKDwJDVGK+qa97ZnuWd9eSYW36ordYI7mkuZ4VT9FmwQEFk2zeKkFRuNqYgL4B8J7Ye4JhnU0bkJjrqelv46XmtYqdQClCQrLcZmMziZV0RqTiEnc4pIzS3iZE4hablFpOUWcTKniLRc8/eTuWbQ0VQWC4QFWAi3HSPMN4dwa1bF9Il5y64UZOQRFnMGYXlbCSvZS5ClkIb2nwPM4qnZmAHIybL7zCBItUFK4aljNRfLVNUWiAaiMEdFKgISH+h4Jvi3a/gfuqZ0Mo35RzRlHaz6A9gKa79G9Q7RlQmCKevMYnaFqXWc4MRVRY2Zaqmrvk1QB7j0qP2v6cjrqUMVN6QkWWk2m80gLbeIE9kFnMgq5ER2Ab9Xvs8q4ER2ISnZhZTY7ItxLdiI8C+gXbAf7Yr3EGbNNoMN31zCfXIIC4sgzHaM8LBowntdS3j8mYQF+xES4IvPNxc0foVJznZzNMCn2Aw8cjCnWDLK7st/zuBU4HESqBFD5de8ti9m8BGFGYhEA9E+0Gc4XLkcirbD5ntrryMy6LnGdRRNqZba0GZ0jRmVsHcjQWeKGgnnLi1rs42qkWF5LoyP45NBGzvV4uiplLqmtWyFZpvsCbq8cWNCaVUUoLRSBcWlHMvI53hGPsfS8zmWUXYr+zk5s6DRgYcFg3a+ObQPKCKiXTTt20XRvk0AEW38aW8cpf2+x4mwZhDpm06Ebybh1hysFqjoXOoajiixwvb5EDgXflkGaRuhqNKURxFm0JGDOepRHoBkURaEGJBRfCoYaWCmoIowzNyPiLJb+0q/RwLtqJQXUs4GQYcgLAwYCRN+bP632Po6maYkU9a76WCZhjYSrH6Os+ttVA7U0jaemgKLGGJOPVUfXXFE8NTYVTCOzk0ov171/y8KT5oBk6sTgkVakEuneObNm8c//vEPkpKS6Nu3L88//zznnHNOg8/TFE/DbDaDpKwCDqXmcjAtj0MnczmclsexlBSOp2eTWhTc4DUs2Gjvn090WCgxERHEhAYSHRJAdGgg0cYBYo6+QHTut0T6ZeBnqdR5tR8Gg541f653eB4znyMfM2+18n0up4KPygFI5VsdMxT1aoMZfIRXu68ciETQtNDdYjU7zSrTN03srBvq9Jta56LRJfcrTSPVN9WE4dp6G86afrJnqsXRUyl1TWup7oZ4AY+Y4lm0aBHTp09n3rx5jBw5ktdee42JEyeyc+dOOnXq5JpGlf9DU/4tzVYMPn7mY21PM++z91V9rDHnNPf5dZxjAClFbdhT2In9edEcLIrjUGEMB4s6crigHUW2uv68ZnDSxiePDn4pdPA/QXzZfeXfo/zSTwUefmFQYsAJPzhUBLnZUIAZJBSV3ZffCn6C+Wef+r22AKT8vilBRmU+mPkeIT7Q1gYhmEFHOFUDkDAgqj3EnlnzW3hReq372djNoGoyaVNXYDRmeqGpdS5qnULwgYCISpsWVutg65tqWj2xae1wFGetrrBn6sbRUylRI82/RXVaJiytjMtGUIYNG8agQYN45ZVXKo717t2byZMnM3v27Hqf65QRlJR1sGKUufU8htnZlH8y1X+uft/Qscb+XD7FXlr1Z5sNUgrDOV4QzfHCKJIKIkkubM/vhREUlfpjtdnwtZXiY5Td22z4GqUElhYSzUmiSac9GUQYWYTacgix5dHWlo9faQmWEsypj+J67ssDkKbntzbMHwjCjJ3K79uW3UKq3Vf+uY0/jF19qrOsc2WKHatfKuc2NJgNW8YnwNxZuHqg05RvvY0ZFWhqMqWjV3c4Okm0KZyRDOrqVTCqXCpeyu1HUIqKiti0aRMPPPBAlePjx4/n+++/r3F+YWEhhYWnvmpnZWU5vlHbZ0GSAfe536ImHyCGDGLIYCC7Xd0cU0C1m3+13wMrHQuiZvARXO1YU6dUYs871WHUNX+PD/V+q65thKDjJDj6cVneSxplQ1p1tyNmjOOSSRtznaYmZzYl8bY+7lBvwxnJoI7+nOyluhsirglQUlNTKS0tJSYmpsrxmJgYkpOTa5w/e/ZsHn/8cec2KmNb2SoBJ7JUureUDZxYLNiwYMOHEh8rxRZfSnys2Hx8KLFYKfXxodTHis1iwcdq4Otbip9PCf6+xQT4FhHgW4yPj2EW+fIpu1kr3fth/pWr39d2rK5zqgcf/pXei92fgV9ZZ9bcz7qWoKO+ZMqmrH7pfqt5X/kbevWaIJU7ju1POqazbkyn35wOzJEdujd3pK5cBePqAEnEDbhkiuf48eN06NCB77//nrPOOqvi+FNPPcXbb7/Nr7/+WuX82kZQEhISHDvFs3oiHPsKcss6zrIgouLn6sdqO6een3NLA9mWfzpb83qyNa8HW/N7klwcWWtTYv1S6RO4n95BB+gTuJ9egQfpHJCEr8XJAZQzVR4i3/5k/UuFKyqY1jLFYvGrtA+Li/7BrmtKwVHTAo29jrvUuXCXdoiI23P7KZ7IyEisVmuN0ZITJ07UGFUBCAgIICCglqQxRyr/JhgCzf12bzMs7C1MYFNub37O78HWvB7sLuhUbfM48KGU0wOO0jdoH33KgpHeQQeI8HXCFFYFC3UuM/ULM5Nx254GxdmQtbPh65RXE+04CfYvqFr3o66Ko7V96zYwq4HmHT01xbLxDvN1aqwOcXHnV9c3a0d9623sddylzoW7tENEvIpLk2QHDx7MvHnzKo716dOHSZMmuSZJFpq8iqfU4s/OvE78VDSS9Rmd2JDRgfSSkBqXjw/IpH/wbga02U//4N30a3uUNpYcp670qfE+ygMFjMZVHK3r86hrBKOx36YbW/FU38xFRLyGR5S6X7RoEddddx2vvvoqZ511Fv/61794/fXX2bFjB507d673ua6ug1JUYmPbsQx+OnCS9QdOsvFgOjmFVauABflZGZAQzsBO4fRPCGdgQjjRoYEt3lYRERF34fZTPABXXXUVaWlpPPHEEyQlJZGYmMjy5csbDE5cwTAM9qfm8u3uFNbuSeXH/WnkFVXNnwgJ9OXMLhEM7RrBsK4RJHYIw89ao9SoiIiINII2C6xDZl4x3+9LZe2eFNbuTuVYRtX9WCLa+DO0PCDpFkGv2FCsPk1d2iIiIuL9PGIExR0dz8jng41HWLs7ha1HMqi8FY2/1Yczu7bjnO5RnNs9il6xIfgoIBEREXEKBSiVZOQV8/zKPRW/nx7dlnO6R3JujyiGdY0g2F8fl4iISEtQj1tJr9gQrhzSkUGd2nFOjyg6hAe5ukkiIiKtkgKUSnx8LMy5vL+rmyEiItLqaZmJiIiIuB0FKCIiIuJ2FKCIiIiI21GAIiIiIm5HAYqIiIi4HQUoIiIi4nYUoIiIiIjbUYAiIiIibkcBioiIiLgdBSgiIiLidhSgiIiIiNtRgCIiIiJuRwGKiIiIuB2P3M3YMAwAsrKyXNwSERERaazyfru8H6+PRwYo2dnZACQkJLi4JSIiImKv7OxswsLC6j3HYjQmjHEzNpuN48ePExISgsViadRzsrKySEhI4MiRI4SGhjq5hVJOn7tr6HN3DX3urqHP3TWa8rkbhkF2djbx8fH4+NSfZeKRIyg+Pj507NixSc8NDQ3Vf8AuoM/dNfS5u4Y+d9fQ5+4a9n7uDY2clFOSrIiIiLgdBSgiIiLidlpNgBIQEMBjjz1GQECAq5vSquhzdw197q6hz9019Lm7hrM/d49MkhURERHv1mpGUERERMRzKEARERERt6MARURERNyOAhQRERFxO14VoMybN4+uXbsSGBjI4MGD+fbbb+s9/5tvvmHw4MEEBgbSrVs3Xn311RZqqXex53P/6KOPGDduHFFRUYSGhnLWWWfx5ZdftmBrvYe9/72XW7duHb6+vgwYMMC5DfRS9n7uhYWFPPzww3Tu3JmAgABOO+003njjjRZqrfew93NfuHAh/fv3Jzg4mLi4OG644QbS0tJaqLXeYe3atVx88cXEx8djsVhYunRpg89xaL9qeIn333/f8PPzM15//XVj586dxl133WW0adPGOHToUK3n79+/3wgODjbuuusuY+fOncbrr79u+Pn5Gf/9739buOWezd7P/a677jL+/ve/G+vXrzd2795tPPjgg4afn5+xefPmFm65Z7P3cy+XkZFhdOvWzRg/frzRv3//lmmsF2nK537JJZcYw4YNM1asWGEcOHDA+Omnn4x169a1YKs9n72f+7fffmv4+PgYL7zwgrF//37j22+/Nfr27WtMnjy5hVvu2ZYvX248/PDDxocffmgAxpIlS+o939H9qtcEKEOHDjVuvfXWKsd69eplPPDAA7We/9e//tXo1atXlWO33HKLMXz4cKe10RvZ+7nXpk+fPsbjjz/u6KZ5taZ+7ldddZUxY8YM47HHHlOA0gT2fu6ff/65ERYWZqSlpbVE87yWvZ/7P/7xD6Nbt25Vjs2dO9fo2LGj09ro7RoToDi6X/WKKZ6ioiI2bdrE+PHjqxwfP34833//fa3P+eGHH2qcP2HCBDZu3EhxcbHT2upNmvK5V2ez2cjOziYiIsIZTfRKTf3c58+fz759+3jsscec3USv1JTPfdmyZQwZMoQ5c+bQoUMHevTowX333Ud+fn5LNNkrNOVzHzFiBEePHmX58uUYhsHvv//Of//7Xy688MKWaHKr5eh+1SM3C6wuNTWV0tJSYmJiqhyPiYkhOTm51uckJyfXen5JSQmpqanExcU5rb3eoimfe3XPPvssubm5XHnllc5ooldqyue+Z88eHnjgAb799lt8fb3if/sW15TPff/+/Xz33XcEBgayZMkSUlNTue222zh58qTyUBqpKZ/7iBEjWLhwIVdddRUFBQWUlJRwySWX8OKLL7ZEk1stR/erXjGCUs5isVT53TCMGscaOr+241I/ez/3cu+99x4zZ85k0aJFREdHO6t5Xquxn3tpaSnXXHMNjz/+OD169Gip5nkte/57t9lsWCwWFi5cyNChQ7ngggt47rnnWLBggUZR7GTP575z507uvPNOHn30UTZt2sQXX3zBgQMHuPXWW1uiqa2aI/tVr/gqFRkZidVqrRFNnzhxokY0Vy42NrbW8319fWnfvr3T2upNmvK5l1u0aBE33XQTixcvZuzYsc5sptex93PPzs5m48aNbNmyhb/85S+A2XEahoGvry9fffUV5513Xou03ZM15b/3uLg4OnToUGV7+d69e2MYBkePHqV79+5ObbM3aMrnPnv2bEaOHMn9998PwBlnnEGbNm0455xzmDVrlkbIncTR/apXjKD4+/szePBgVqxYUeX4ihUrGDFiRK3POeuss2qc/9VXXzFkyBD8/Pyc1lZv0pTPHcyRk2nTpvHuu+9qTrgJ7P3cQ0ND2bZtG1u3bq243XrrrfTs2ZOtW7cybNiwlmq6R2vKf+8jR47k+PHj5OTkVBzbvXs3Pj4+dOzY0ant9RZN+dzz8vLw8anavVmtVuDUN3pxPIf3q01KrXVD5cvQ/vOf/xg7d+40pk+fbrRp08Y4ePCgYRiG8cADDxjXXXddxfnly6HuvvtuY+fOncZ//vMfLTNuAns/93fffdfw9fU1Xn75ZSMpKanilpGR4aq34JHs/dyr0yqeprH3c8/OzjY6duxoXH755caOHTuMb775xujevbvxpz/9yVVvwSPZ+7nPnz/f8PX1NebNm2fs27fP+O6774whQ4YYQ4cOddVb8EjZ2dnGli1bjC1bthiA8dxzzxlbtmypWN7t7H7VawIUwzCMl19+2ejcubPh7+9vDBo0yPjmm28qHps6daoxatSoKuevWbPGGDhwoOHv72906dLFeOWVV1q4xd7Bns991KhRBlDjNnXq1JZvuIez97/3yhSgNJ29n/uuXbuMsWPHGkFBQUbHjh2Ne+65x8jLy2vhVns+ez/3uXPnGn369DGCgoKMuLg449prrzWOHj3awq32bKtXr67332tn96sWw9B4l4iIiLgXr8hBEREREe+iAEVERETcjgIUERERcTsKUERERMTtKEARERERt6MARURERNyOAhQRERFxOwpQRERExO0oQBERERG3owBFRERE3I4CFBEREXE7ClBERETE7fw/KIh/wWc0jeUAAAAASUVORK5CYII=", "text/plain": [ "
    " ] @@ -838,7 +838,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "0.005\n" + "0.004999999999999996\n" ] } ], diff --git a/doc/LectureNotes/_build/jupyter_execute/chapter10.ipynb b/doc/LectureNotes/_build/jupyter_execute/chapter10.ipynb index 88f43ba1d..70350cab6 100644 --- a/doc/LectureNotes/_build/jupyter_execute/chapter10.ipynb +++ b/doc/LectureNotes/_build/jupyter_execute/chapter10.ipynb @@ -1077,7 +1077,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_95354/953065564.py:4: RuntimeWarning: overflow encountered in exp\n", + "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_22411/953065564.py:4: RuntimeWarning: overflow encountered in exp\n", " return 1/(1 + np.exp(-x))\n" ] }, @@ -1655,7 +1655,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_95354/953065564.py:4: RuntimeWarning: overflow encountered in exp\n", + "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_22411/953065564.py:4: RuntimeWarning: overflow encountered in exp\n", " return 1/(1 + np.exp(-x))\n" ] }, @@ -1673,7 +1673,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_95354/953065564.py:4: RuntimeWarning: overflow encountered in exp\n", + "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_22411/953065564.py:4: RuntimeWarning: overflow encountered in exp\n", " return 1/(1 + np.exp(-x))\n" ] }, @@ -1691,7 +1691,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_95354/953065564.py:4: RuntimeWarning: overflow encountered in exp\n", + "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_22411/953065564.py:4: RuntimeWarning: overflow encountered in exp\n", " return 1/(1 + np.exp(-x))\n" ] }, @@ -1709,7 +1709,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_95354/953065564.py:4: RuntimeWarning: overflow encountered in exp\n", + "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_22411/953065564.py:4: RuntimeWarning: overflow encountered in exp\n", " return 1/(1 + np.exp(-x))\n" ] }, @@ -1727,7 +1727,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_95354/953065564.py:4: RuntimeWarning: overflow encountered in exp\n", + "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_22411/953065564.py:4: RuntimeWarning: overflow encountered in exp\n", " return 1/(1 + np.exp(-x))\n" ] }, @@ -1745,7 +1745,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_95354/953065564.py:4: RuntimeWarning: overflow encountered in exp\n", + "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_22411/953065564.py:4: RuntimeWarning: overflow encountered in exp\n", " return 1/(1 + np.exp(-x))\n" ] }, @@ -1763,7 +1763,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_95354/953065564.py:4: RuntimeWarning: overflow encountered in exp\n", + "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_22411/953065564.py:4: RuntimeWarning: overflow encountered in exp\n", " return 1/(1 + np.exp(-x))\n" ] }, @@ -1781,11 +1781,11 @@ "name": "stderr", "output_type": "stream", "text": [ - "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_95354/953065564.py:4: RuntimeWarning: overflow encountered in exp\n", + "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_22411/953065564.py:4: RuntimeWarning: overflow encountered in exp\n", " return 1/(1 + np.exp(-x))\n", - "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_95354/1630775253.py:43: RuntimeWarning: overflow encountered in exp\n", + "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_22411/1630775253.py:43: RuntimeWarning: overflow encountered in exp\n", " exp_term = np.exp(self.z_o)\n", - "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_95354/1630775253.py:44: RuntimeWarning: invalid value encountered in true_divide\n", + "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_22411/1630775253.py:44: RuntimeWarning: invalid value encountered in true_divide\n", " self.probabilities = exp_term / np.sum(exp_term, axis=1, keepdims=True)\n" ] }, @@ -1803,11 +1803,11 @@ "name": "stderr", "output_type": "stream", "text": [ - "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_95354/953065564.py:4: RuntimeWarning: overflow encountered in exp\n", + "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_22411/953065564.py:4: RuntimeWarning: overflow encountered in exp\n", " return 1/(1 + np.exp(-x))\n", - "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_95354/1630775253.py:43: RuntimeWarning: overflow encountered in exp\n", + "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_22411/1630775253.py:43: RuntimeWarning: overflow encountered in exp\n", " exp_term = np.exp(self.z_o)\n", - "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_95354/1630775253.py:44: RuntimeWarning: invalid value encountered in true_divide\n", + "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_22411/1630775253.py:44: RuntimeWarning: invalid value encountered in true_divide\n", " self.probabilities = exp_term / np.sum(exp_term, axis=1, keepdims=True)\n" ] }, @@ -1825,11 +1825,11 @@ "name": "stderr", "output_type": "stream", "text": [ - "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_95354/953065564.py:4: RuntimeWarning: overflow encountered in exp\n", + "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_22411/953065564.py:4: RuntimeWarning: overflow encountered in exp\n", " return 1/(1 + np.exp(-x))\n", - "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_95354/1630775253.py:43: RuntimeWarning: overflow encountered in exp\n", + "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_22411/1630775253.py:43: RuntimeWarning: overflow encountered in exp\n", " exp_term = np.exp(self.z_o)\n", - "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_95354/1630775253.py:44: RuntimeWarning: invalid value encountered in true_divide\n", + "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_22411/1630775253.py:44: RuntimeWarning: invalid value encountered in true_divide\n", " self.probabilities = exp_term / np.sum(exp_term, axis=1, keepdims=True)\n" ] }, @@ -1847,11 +1847,11 @@ "name": "stderr", "output_type": "stream", "text": [ - "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_95354/953065564.py:4: RuntimeWarning: overflow encountered in exp\n", + "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_22411/953065564.py:4: RuntimeWarning: overflow encountered in exp\n", " return 1/(1 + np.exp(-x))\n", - "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_95354/1630775253.py:43: RuntimeWarning: overflow encountered in exp\n", + "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_22411/1630775253.py:43: RuntimeWarning: overflow encountered in exp\n", " exp_term = np.exp(self.z_o)\n", - "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_95354/1630775253.py:44: RuntimeWarning: invalid value encountered in true_divide\n", + "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_22411/1630775253.py:44: RuntimeWarning: invalid value encountered in true_divide\n", " self.probabilities = exp_term / np.sum(exp_term, axis=1, keepdims=True)\n" ] }, @@ -1869,11 +1869,11 @@ "name": "stderr", "output_type": "stream", "text": [ - "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_95354/953065564.py:4: RuntimeWarning: overflow encountered in exp\n", + "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_22411/953065564.py:4: RuntimeWarning: overflow encountered in exp\n", " return 1/(1 + np.exp(-x))\n", - "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_95354/1630775253.py:43: RuntimeWarning: overflow encountered in exp\n", + "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_22411/1630775253.py:43: RuntimeWarning: overflow encountered in exp\n", " exp_term = np.exp(self.z_o)\n", - "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_95354/1630775253.py:44: RuntimeWarning: invalid value encountered in true_divide\n", + "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_22411/1630775253.py:44: RuntimeWarning: invalid value encountered in true_divide\n", " self.probabilities = exp_term / np.sum(exp_term, axis=1, keepdims=True)\n" ] }, @@ -1891,7 +1891,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_95354/953065564.py:4: RuntimeWarning: overflow encountered in exp\n", + "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_22411/953065564.py:4: RuntimeWarning: overflow encountered in exp\n", " return 1/(1 + np.exp(-x))\n" ] }, @@ -1909,11 +1909,11 @@ "name": "stderr", "output_type": "stream", "text": [ - "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_95354/953065564.py:4: RuntimeWarning: overflow encountered in exp\n", + "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_22411/953065564.py:4: RuntimeWarning: overflow encountered in exp\n", " return 1/(1 + np.exp(-x))\n", - "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_95354/1630775253.py:43: RuntimeWarning: overflow encountered in exp\n", + "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_22411/1630775253.py:43: RuntimeWarning: overflow encountered in exp\n", " exp_term = np.exp(self.z_o)\n", - "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_95354/1630775253.py:44: RuntimeWarning: invalid value encountered in true_divide\n", + "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_22411/1630775253.py:44: RuntimeWarning: invalid value encountered in true_divide\n", " self.probabilities = exp_term / np.sum(exp_term, axis=1, keepdims=True)\n" ] }, @@ -1931,11 +1931,11 @@ "name": "stderr", "output_type": "stream", "text": [ - "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_95354/953065564.py:4: RuntimeWarning: overflow encountered in exp\n", + "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_22411/953065564.py:4: RuntimeWarning: overflow encountered in exp\n", " return 1/(1 + np.exp(-x))\n", - "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_95354/1630775253.py:43: RuntimeWarning: overflow encountered in exp\n", + "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_22411/1630775253.py:43: RuntimeWarning: overflow encountered in exp\n", " exp_term = np.exp(self.z_o)\n", - "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_95354/1630775253.py:44: RuntimeWarning: invalid value encountered in true_divide\n", + "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_22411/1630775253.py:44: RuntimeWarning: invalid value encountered in true_divide\n", " self.probabilities = exp_term / np.sum(exp_term, axis=1, keepdims=True)\n" ] }, @@ -1953,25 +1953,132 @@ "name": "stderr", "output_type": "stream", "text": [ - "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_95354/953065564.py:4: RuntimeWarning: overflow encountered in exp\n", + "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_22411/953065564.py:4: RuntimeWarning: overflow encountered in exp\n", " return 1/(1 + np.exp(-x))\n", - "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_95354/1630775253.py:43: RuntimeWarning: overflow encountered in exp\n", + "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_22411/1630775253.py:43: RuntimeWarning: overflow encountered in exp\n", " exp_term = np.exp(self.z_o)\n", - "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_95354/1630775253.py:44: RuntimeWarning: invalid value encountered in true_divide\n", + "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_22411/1630775253.py:44: RuntimeWarning: invalid value encountered in true_divide\n", " self.probabilities = exp_term / np.sum(exp_term, axis=1, keepdims=True)\n" ] }, { - "ename": "KeyboardInterrupt", - "evalue": "", - "output_type": "error", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mKeyboardInterrupt\u001b[0m Traceback (most recent call last)", - "Cell \u001b[0;32mIn[8], line 11\u001b[0m\n\u001b[1;32m 8\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m j, lmbd \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28menumerate\u001b[39m(lmbd_vals):\n\u001b[1;32m 9\u001b[0m dnn \u001b[38;5;241m=\u001b[39m NeuralNetwork(X_train, Y_train_onehot, eta\u001b[38;5;241m=\u001b[39meta, lmbd\u001b[38;5;241m=\u001b[39mlmbd, epochs\u001b[38;5;241m=\u001b[39mepochs, batch_size\u001b[38;5;241m=\u001b[39mbatch_size,\n\u001b[1;32m 10\u001b[0m n_hidden_neurons\u001b[38;5;241m=\u001b[39mn_hidden_neurons, n_categories\u001b[38;5;241m=\u001b[39mn_categories)\n\u001b[0;32m---> 11\u001b[0m \u001b[43mdnn\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mtrain\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 13\u001b[0m DNN_numpy[i][j] \u001b[38;5;241m=\u001b[39m dnn\n\u001b[1;32m 15\u001b[0m test_predict \u001b[38;5;241m=\u001b[39m dnn\u001b[38;5;241m.\u001b[39mpredict(X_test)\n", - "Cell \u001b[0;32mIn[6], line 99\u001b[0m, in \u001b[0;36mNeuralNetwork.train\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 96\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mY_data \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mY_data_full[chosen_datapoints]\n\u001b[1;32m 98\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mfeed_forward()\n\u001b[0;32m---> 99\u001b[0m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mbackpropagation\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n", - "Cell \u001b[0;32mIn[6], line 64\u001b[0m, in \u001b[0;36mNeuralNetwork.backpropagation\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 61\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39moutput_weights_gradient \u001b[38;5;241m=\u001b[39m np\u001b[38;5;241m.\u001b[39mmatmul(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39ma_h\u001b[38;5;241m.\u001b[39mT, error_output)\n\u001b[1;32m 62\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39moutput_bias_gradient \u001b[38;5;241m=\u001b[39m np\u001b[38;5;241m.\u001b[39msum(error_output, axis\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m0\u001b[39m)\n\u001b[0;32m---> 64\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mhidden_weights_gradient \u001b[38;5;241m=\u001b[39m \u001b[43mnp\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mmatmul\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mX_data\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mT\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43merror_hidden\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 65\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mhidden_bias_gradient \u001b[38;5;241m=\u001b[39m np\u001b[38;5;241m.\u001b[39msum(error_hidden, axis\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m0\u001b[39m)\n\u001b[1;32m 67\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mlmbd \u001b[38;5;241m>\u001b[39m \u001b[38;5;241m0.0\u001b[39m:\n", - "\u001b[0;31mKeyboardInterrupt\u001b[0m: " + "name": "stdout", + "output_type": "stream", + "text": [ + "Learning rate = 10.0\n", + "Lambda = 0.0001\n", + "Accuracy score on test set: 0.07777777777777778\n", + "\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_22411/953065564.py:4: RuntimeWarning: overflow encountered in exp\n", + " return 1/(1 + np.exp(-x))\n", + "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_22411/1630775253.py:43: RuntimeWarning: overflow encountered in exp\n", + " exp_term = np.exp(self.z_o)\n", + "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_22411/1630775253.py:44: RuntimeWarning: invalid value encountered in true_divide\n", + " self.probabilities = exp_term / np.sum(exp_term, axis=1, keepdims=True)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Learning rate = 10.0\n", + "Lambda = 0.001\n", + "Accuracy score on test set: 0.07777777777777778\n", + "\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_22411/953065564.py:4: RuntimeWarning: overflow encountered in exp\n", + " return 1/(1 + np.exp(-x))\n", + "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_22411/1630775253.py:43: RuntimeWarning: overflow encountered in exp\n", + " exp_term = np.exp(self.z_o)\n", + "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_22411/1630775253.py:44: RuntimeWarning: invalid value encountered in true_divide\n", + " self.probabilities = exp_term / np.sum(exp_term, axis=1, keepdims=True)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Learning rate = 10.0\n", + "Lambda = 0.01\n", + "Accuracy score on test set: 0.07777777777777778\n", + "\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_22411/953065564.py:4: RuntimeWarning: overflow encountered in exp\n", + " return 1/(1 + np.exp(-x))\n", + "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_22411/1630775253.py:43: RuntimeWarning: overflow encountered in exp\n", + " exp_term = np.exp(self.z_o)\n", + "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_22411/1630775253.py:44: RuntimeWarning: invalid value encountered in true_divide\n", + " self.probabilities = exp_term / np.sum(exp_term, axis=1, keepdims=True)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Learning rate = 10.0\n", + "Lambda = 0.1\n", + "Accuracy score on test set: 0.07777777777777778\n", + "\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_22411/953065564.py:4: RuntimeWarning: overflow encountered in exp\n", + " return 1/(1 + np.exp(-x))\n", + "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_22411/1630775253.py:43: RuntimeWarning: overflow encountered in exp\n", + " exp_term = np.exp(self.z_o)\n", + "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_22411/1630775253.py:44: RuntimeWarning: invalid value encountered in true_divide\n", + " self.probabilities = exp_term / np.sum(exp_term, axis=1, keepdims=True)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Learning rate = 10.0\n", + "Lambda = 1.0\n", + "Accuracy score on test set: 0.07777777777777778\n", + "\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_22411/953065564.py:4: RuntimeWarning: overflow encountered in exp\n", + " return 1/(1 + np.exp(-x))\n", + "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_22411/1630775253.py:43: RuntimeWarning: overflow encountered in exp\n", + " exp_term = np.exp(self.z_o)\n", + "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_22411/1630775253.py:44: RuntimeWarning: invalid value encountered in true_divide\n", + " self.probabilities = exp_term / np.sum(exp_term, axis=1, keepdims=True)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Learning rate = 10.0\n", + "Lambda = 10.0\n", + "Accuracy score on test set: 0.07777777777777778\n", + "\n" ] } ], @@ -2016,7 +2123,52 @@ "collapsed": false, "editable": true }, - "outputs": [], + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_22411/953065564.py:4: RuntimeWarning: overflow encountered in exp\n", + " return 1/(1 + np.exp(-x))\n", + "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_22411/953065564.py:4: RuntimeWarning: overflow encountered in exp\n", + " return 1/(1 + np.exp(-x))\n", + "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_22411/953065564.py:4: RuntimeWarning: overflow encountered in exp\n", + " return 1/(1 + np.exp(-x))\n", + "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_22411/953065564.py:4: RuntimeWarning: overflow encountered in exp\n", + " return 1/(1 + np.exp(-x))\n", + "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_22411/953065564.py:4: RuntimeWarning: overflow encountered in exp\n", + " return 1/(1 + np.exp(-x))\n" + ] + }, + { + "data": { + "image/png": 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    " + ] + }, + "metadata": { + "filenames": { + "image/png": "/Users/mhjensen/Teaching/MachineLearning/doc/LectureNotes/_build/jupyter_execute/chapter10_59_2.png" + } + }, + "output_type": "display_data" + } + ], "source": [ "# visual representation of grid search\n", "# uses seaborn heatmap, you can also do this with matplotlib imshow\n", @@ -2083,7 +2235,626 @@ "collapsed": false, "editable": true }, - "outputs": [], + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/mhjensen/miniforge3/envs/myenv/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.\n", + " warnings.warn(\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Learning rate = 1e-05\n", + "Lambda = 1e-05\n", + "Accuracy score on test set: 0.18333333333333332\n", + "\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/mhjensen/miniforge3/envs/myenv/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.\n", + " warnings.warn(\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Learning rate = 1e-05\n", + "Lambda = 0.0001\n", + "Accuracy score on test set: 0.18611111111111112\n", + "\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/mhjensen/miniforge3/envs/myenv/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.\n", + " warnings.warn(\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Learning rate = 1e-05\n", + "Lambda = 0.001\n", + "Accuracy score on test set: 0.13055555555555556\n", + "\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/mhjensen/miniforge3/envs/myenv/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.\n", + " warnings.warn(\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Learning rate = 1e-05\n", + "Lambda = 0.01\n", + "Accuracy score on test set: 0.24444444444444444\n", + "\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/mhjensen/miniforge3/envs/myenv/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.\n", + " warnings.warn(\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Learning rate = 1e-05\n", + "Lambda = 0.1\n", + "Accuracy score on test set: 0.23333333333333334\n", + "\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/mhjensen/miniforge3/envs/myenv/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.\n", + " warnings.warn(\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Learning rate = 1e-05\n", + "Lambda = 1.0\n", + "Accuracy score on test set: 0.12777777777777777\n", + "\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/mhjensen/miniforge3/envs/myenv/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.\n", + " warnings.warn(\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Learning rate = 1e-05\n", + "Lambda = 10.0\n", + "Accuracy score on test set: 0.1527777777777778\n", + "\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/mhjensen/miniforge3/envs/myenv/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.\n", + " warnings.warn(\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Learning rate = 0.0001\n", + "Lambda = 1e-05\n", + "Accuracy score on test set: 0.9111111111111111\n", + "\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/mhjensen/miniforge3/envs/myenv/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.\n", + " warnings.warn(\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Learning rate = 0.0001\n", + "Lambda = 0.0001\n", + "Accuracy score on test set: 0.8888888888888888\n", + "\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/mhjensen/miniforge3/envs/myenv/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.\n", + " warnings.warn(\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Learning rate = 0.0001\n", + "Lambda = 0.001\n", + "Accuracy score on test set: 0.8722222222222222\n", + "\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/mhjensen/miniforge3/envs/myenv/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.\n", + " warnings.warn(\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Learning rate = 0.0001\n", + "Lambda = 0.01\n", + "Accuracy score on test set: 0.8305555555555556\n", + "\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/mhjensen/miniforge3/envs/myenv/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.\n", + " warnings.warn(\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Learning rate = 0.0001\n", + "Lambda = 0.1\n", + "Accuracy score on test set: 0.8888888888888888\n", + "\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/mhjensen/miniforge3/envs/myenv/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.\n", + " warnings.warn(\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Learning rate = 0.0001\n", + "Lambda = 1.0\n", + "Accuracy score on test set: 0.8805555555555555\n", + "\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/mhjensen/miniforge3/envs/myenv/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.\n", + " warnings.warn(\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Learning rate = 0.0001\n", + "Lambda = 10.0\n", + "Accuracy score on test set: 0.8944444444444445\n", + "\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/mhjensen/miniforge3/envs/myenv/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.\n", + " warnings.warn(\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Learning rate = 0.001\n", + "Lambda = 1e-05\n", + "Accuracy score on test set: 0.975\n", + "\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/mhjensen/miniforge3/envs/myenv/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.\n", + " warnings.warn(\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Learning rate = 0.001\n", + "Lambda = 0.0001\n", + "Accuracy score on test set: 0.9777777777777777\n", + "\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/mhjensen/miniforge3/envs/myenv/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.\n", + " warnings.warn(\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Learning rate = 0.001\n", + "Lambda = 0.001\n", + "Accuracy score on test set: 0.9805555555555555\n", + "\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/mhjensen/miniforge3/envs/myenv/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.\n", + " warnings.warn(\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Learning rate = 0.001\n", + "Lambda = 0.01\n", + "Accuracy score on test set: 0.9861111111111112\n", + "\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/mhjensen/miniforge3/envs/myenv/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.\n", + " warnings.warn(\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Learning rate = 0.001\n", + "Lambda = 0.1\n", + "Accuracy score on test set: 0.9805555555555555\n", + "\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/mhjensen/miniforge3/envs/myenv/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.\n", + " warnings.warn(\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Learning rate = 0.001\n", + "Lambda = 1.0\n", + "Accuracy score on test set: 0.9777777777777777\n", + "\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/mhjensen/miniforge3/envs/myenv/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.\n", + " warnings.warn(\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Learning rate = 0.001\n", + "Lambda = 10.0\n", + "Accuracy score on test set: 0.9444444444444444\n", + "\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/mhjensen/miniforge3/envs/myenv/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.\n", + " warnings.warn(\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Learning rate = 0.01\n", + "Lambda = 1e-05\n", + "Accuracy score on test set: 0.9861111111111112\n", + "\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Learning rate = 0.01\n", + "Lambda = 0.0001\n", + "Accuracy score on test set: 0.9888888888888889\n", + "\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Learning rate = 0.01\n", + "Lambda = 0.001\n", + "Accuracy score on test set: 0.9888888888888889\n", + "\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Learning rate = 0.01\n", + "Lambda = 0.01\n", + "Accuracy score on test set: 0.9861111111111112\n", + "\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Learning rate = 0.01\n", + "Lambda = 0.1\n", + "Accuracy score on test set: 0.9888888888888889\n", + "\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Learning rate = 0.01\n", + "Lambda = 1.0\n", + "Accuracy score on test set: 0.9722222222222222\n", + "\n", + "Learning rate = 0.01\n", + "Lambda = 10.0\n", + "Accuracy score on test set: 0.9527777777777777\n", + "\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Learning rate = 0.1\n", + "Lambda = 1e-05\n", + "Accuracy score on test set: 0.9027777777777778\n", + "\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Learning rate = 0.1\n", + "Lambda = 0.0001\n", + "Accuracy score on test set: 0.8583333333333333\n", + "\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Learning rate = 0.1\n", + "Lambda = 0.001\n", + "Accuracy score on test set: 0.8722222222222222\n", + "\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Learning rate = 0.1\n", + "Lambda = 0.01\n", + "Accuracy score on test set: 0.9055555555555556\n", + "\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Learning rate = 0.1\n", + "Lambda = 0.1\n", + "Accuracy score on test set: 0.8805555555555555\n", + "\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Learning rate = 0.1\n", + "Lambda = 1.0\n", + "Accuracy score on test set: 0.8722222222222222\n", + "\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Learning rate = 0.1\n", + "Lambda = 10.0\n", + "Accuracy score on test set: 0.8666666666666667\n", + "\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Learning rate = 1.0\n", + "Lambda = 1e-05\n", + "Accuracy score on test set: 0.08611111111111111\n", + "\n", + "Learning rate = 1.0\n", + "Lambda = 0.0001\n", + "Accuracy score on test set: 0.10555555555555556\n", + "\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Learning rate = 1.0\n", + "Lambda = 0.001\n", + "Accuracy score on test set: 0.10555555555555556\n", + "\n", + "Learning rate = 1.0\n", + "Lambda = 0.01\n", + "Accuracy score on test set: 0.17777777777777778\n", + "\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Learning rate = 1.0\n", + "Lambda = 0.1\n", + "Accuracy score on test set: 0.08333333333333333\n", + "\n", + "Learning rate = 1.0\n", + "Lambda = 1.0\n", + "Accuracy score on test set: 0.08888888888888889\n", + "\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Learning rate = 1.0\n", + "Lambda = 10.0\n", + "Accuracy score on test set: 0.09444444444444444\n", + "\n", + "Learning rate = 10.0\n", + "Lambda = 1e-05\n", + "Accuracy score on test set: 0.17222222222222222\n", + "\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Learning rate = 10.0\n", + "Lambda = 0.0001\n", + "Accuracy score on test set: 0.11666666666666667\n", + "\n", + "Learning rate = 10.0\n", + "Lambda = 0.001\n", + "Accuracy score on test set: 0.10555555555555556\n", + "\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Learning rate = 10.0\n", + "Lambda = 0.01\n", + "Accuracy score on test set: 0.1388888888888889\n", + "\n", + "Learning rate = 10.0\n", + "Lambda = 0.1\n", + "Accuracy score on test set: 0.11388888888888889\n", + "\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Learning rate = 10.0\n", + "Lambda = 1.0\n", + "Accuracy score on test set: 0.10555555555555556\n", + "\n", + "Learning rate = 10.0\n", + "Lambda = 10.0\n", + "Accuracy score on test set: 0.09444444444444444\n", + "\n" + ] + } + ], "source": [ "from sklearn.neural_network import MLPClassifier\n", "# store models for later use\n", @@ -2121,7 +2892,36 @@ "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/chapter10_63_1.png" + } + }, + "output_type": "display_data" + } + ], "source": [ "# optional\n", "# visual representation of grid search\n", @@ -2211,7 +3011,16 @@ "collapsed": false, "editable": true }, - "outputs": [], + "outputs": [ + { + "ename": "SyntaxError", + "evalue": "invalid syntax (2259440937.py, line 1)", + "output_type": "error", + "traceback": [ + "\u001b[0;36m Cell \u001b[0;32mIn[12], line 1\u001b[0;36m\u001b[0m\n\u001b[0;31m conda create -n tf tensorflow\u001b[0m\n\u001b[0m ^\u001b[0m\n\u001b[0;31mSyntaxError\u001b[0m\u001b[0;31m:\u001b[0m invalid syntax\n" + ] + } + ], "source": [ "conda create -n tf tensorflow\n", "conda activate tf" diff --git a/doc/LectureNotes/_build/jupyter_execute/chapter11.ipynb b/doc/LectureNotes/_build/jupyter_execute/chapter11.ipynb index bb7ddcacc..edd0b9ddb 100644 --- a/doc/LectureNotes/_build/jupyter_execute/chapter11.ipynb +++ b/doc/LectureNotes/_build/jupyter_execute/chapter11.ipynb @@ -2981,16 +2981,25 @@ "Cell \u001b[0;32mIn[9], line 143\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", "Cell \u001b[0;32mIn[9], line 120\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:32\u001b[0m, in \u001b[0;36mgrad\u001b[0;34m(fun, x)\u001b[0m\n\u001b[1;32m 29\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 30\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 31\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\u001b[0;32m---> 32\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mvjp\u001b[49m\u001b[43m(\u001b[49m\u001b[43mvspace\u001b[49m\u001b[43m(\u001b[49m\u001b[43mans\u001b[49m\u001b[43m)\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mones\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[43m)\u001b[49m\n", + "File \u001b[0;32m~/miniforge3/envs/myenv/lib/python3.9/site-packages/autograd/differential_operators.py:28\u001b[0m, in \u001b[0;36mgrad\u001b[0;34m(fun, x)\u001b[0m\n\u001b[1;32m 21\u001b[0m \u001b[38;5;129m@unary_to_nary\u001b[39m\n\u001b[1;32m 22\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mgrad\u001b[39m(fun, x):\n\u001b[1;32m 23\u001b[0m \u001b[38;5;250m \u001b[39m\u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m 24\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 25\u001b[0m \u001b[38;5;124;03m positional argument number `argnum`. The returned function takes the same\u001b[39;00m\n\u001b[1;32m 26\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 27\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---> 28\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 29\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 30\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 31\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", + "Cell \u001b[0;32mIn[9], line 80\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:81\u001b[0m, in \u001b[0;36mhessian\u001b[0;34m(fun, x)\u001b[0m\n\u001b[1;32m 78\u001b[0m \u001b[38;5;129m@unary_to_nary\u001b[39m\n\u001b[1;32m 79\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mhessian\u001b[39m(fun, x):\n\u001b[1;32m 80\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---> 81\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:64\u001b[0m, in \u001b[0;36mjacobian\u001b[0;34m(fun, x)\u001b[0m\n\u001b[1;32m 62\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 63\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---> 64\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:660\u001b[0m, in \u001b[0;36munbroadcast_f..\u001b[0;34m(g)\u001b[0m\n\u001b[1;32m 658\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21munbroadcast_f\u001b[39m(target, f):\n\u001b[1;32m 659\u001b[0m target_meta \u001b[38;5;241m=\u001b[39m anp\u001b[38;5;241m.\u001b[39mmetadata(target)\n\u001b[0;32m--> 660\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;01mlambda\u001b[39;00m g: \u001b[43munbroadcast\u001b[49m\u001b[43m(\u001b[49m\u001b[43mf\u001b[49m\u001b[43m(\u001b[49m\u001b[43mg\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mtarget_meta\u001b[49m\u001b[43m)\u001b[49m\n", - "File \u001b[0;32m~/miniforge3/envs/myenv/lib/python3.9/site-packages/autograd/numpy/numpy_vjps.py:653\u001b[0m, in \u001b[0;36munbroadcast\u001b[0;34m(x, target_meta, broadcast_idx)\u001b[0m\n\u001b[1;32m 651\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m axis, size \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28menumerate\u001b[39m(target_shape):\n\u001b[1;32m 652\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m size \u001b[38;5;241m==\u001b[39m \u001b[38;5;241m1\u001b[39m:\n\u001b[0;32m--> 653\u001b[0m x \u001b[38;5;241m=\u001b[39m \u001b[43manp\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43msum\u001b[49m\u001b[43m(\u001b[49m\u001b[43mx\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43maxis\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43maxis\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mkeepdims\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mTrue\u001b[39;49;00m\u001b[43m)\u001b[49m\n\u001b[1;32m 654\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m anp\u001b[38;5;241m.\u001b[39miscomplexobj(x) \u001b[38;5;129;01mand\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m target_iscomplex:\n\u001b[1;32m 655\u001b[0m x \u001b[38;5;241m=\u001b[39m anp\u001b[38;5;241m.\u001b[39mreal(x)\n", + "File \u001b[0;32m~/miniforge3/envs/myenv/lib/python3.9/site-packages/autograd/core.py:78\u001b[0m, in \u001b[0;36mdefvjp..vjp_argnums..\u001b[0;34m(g)\u001b[0m\n\u001b[1;32m 76\u001b[0m vjp_0 \u001b[38;5;241m=\u001b[39m vjp_0_fun(ans, \u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n\u001b[1;32m 77\u001b[0m vjp_1 \u001b[38;5;241m=\u001b[39m vjp_1_fun(ans, \u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n\u001b[0;32m---> 78\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;01mlambda\u001b[39;00m g: (\u001b[43mvjp_0\u001b[49m\u001b[43m(\u001b[49m\u001b[43mg\u001b[49m\u001b[43m)\u001b[49m, vjp_1(g))\n\u001b[1;32m 79\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m 80\u001b[0m vjps \u001b[38;5;241m=\u001b[39m [vjps_dict[argnum](ans, \u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs) \u001b[38;5;28;01mfor\u001b[39;00m argnum \u001b[38;5;129;01min\u001b[39;00m argnums]\n", + "File \u001b[0;32m~/miniforge3/envs/myenv/lib/python3.9/site-packages/autograd/numpy/numpy_vjps.py:660\u001b[0m, in \u001b[0;36munbroadcast_f..\u001b[0;34m(g)\u001b[0m\n\u001b[1;32m 658\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21munbroadcast_f\u001b[39m(target, f):\n\u001b[1;32m 659\u001b[0m target_meta \u001b[38;5;241m=\u001b[39m anp\u001b[38;5;241m.\u001b[39mmetadata(target)\n\u001b[0;32m--> 660\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;01mlambda\u001b[39;00m g: unbroadcast(\u001b[43mf\u001b[49m\u001b[43m(\u001b[49m\u001b[43mg\u001b[49m\u001b[43m)\u001b[49m, target_meta)\n", + "File \u001b[0;32m~/miniforge3/envs/myenv/lib/python3.9/site-packages/autograd/numpy/numpy_vjps.py:34\u001b[0m, in \u001b[0;36m\u001b[0;34m(g)\u001b[0m\n\u001b[1;32m 30\u001b[0m \u001b[38;5;66;03m# ----- Binary ufuncs -----\u001b[39;00m\n\u001b[1;32m 32\u001b[0m defvjp(anp\u001b[38;5;241m.\u001b[39madd, \u001b[38;5;28;01mlambda\u001b[39;00m ans, x, y : unbroadcast_f(x, \u001b[38;5;28;01mlambda\u001b[39;00m g: g),\n\u001b[1;32m 33\u001b[0m \u001b[38;5;28;01mlambda\u001b[39;00m ans, x, y : unbroadcast_f(y, \u001b[38;5;28;01mlambda\u001b[39;00m g: g))\n\u001b[0;32m---> 34\u001b[0m defvjp(anp\u001b[38;5;241m.\u001b[39mmultiply, \u001b[38;5;28;01mlambda\u001b[39;00m ans, x, y : unbroadcast_f(x, \u001b[38;5;28;01mlambda\u001b[39;00m g: \u001b[43my\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43m \u001b[49m\u001b[43mg\u001b[49m),\n\u001b[1;32m 35\u001b[0m \u001b[38;5;28;01mlambda\u001b[39;00m ans, x, y : unbroadcast_f(y, \u001b[38;5;28;01mlambda\u001b[39;00m g: x \u001b[38;5;241m*\u001b[39m g))\n\u001b[1;32m 36\u001b[0m defvjp(anp\u001b[38;5;241m.\u001b[39msubtract, \u001b[38;5;28;01mlambda\u001b[39;00m ans, x, y : unbroadcast_f(x, \u001b[38;5;28;01mlambda\u001b[39;00m g: g),\n\u001b[1;32m 37\u001b[0m \u001b[38;5;28;01mlambda\u001b[39;00m ans, x, y : unbroadcast_f(y, \u001b[38;5;28;01mlambda\u001b[39;00m g: \u001b[38;5;241m-\u001b[39mg))\n\u001b[1;32m 38\u001b[0m defvjp(anp\u001b[38;5;241m.\u001b[39mdivide, \u001b[38;5;28;01mlambda\u001b[39;00m ans, x, y : unbroadcast_f(x, \u001b[38;5;28;01mlambda\u001b[39;00m g: g \u001b[38;5;241m/\u001b[39m y),\n\u001b[1;32m 39\u001b[0m \u001b[38;5;28;01mlambda\u001b[39;00m ans, x, y : unbroadcast_f(y, \u001b[38;5;28;01mlambda\u001b[39;00m g: \u001b[38;5;241m-\u001b[39m g \u001b[38;5;241m*\u001b[39m x \u001b[38;5;241m/\u001b[39m y\u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39m\u001b[38;5;241m2\u001b[39m))\n", + "File \u001b[0;32m~/miniforge3/envs/myenv/lib/python3.9/site-packages/autograd/numpy/numpy_boxes.py:27\u001b[0m, in \u001b[0;36mArrayBox.__mul__\u001b[0;34m(self, other)\u001b[0m\n\u001b[0;32m---> 27\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21m__mul__\u001b[39m(\u001b[38;5;28mself\u001b[39m, other): \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43manp\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mmultiply\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mother\u001b[49m\u001b[43m)\u001b[49m\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:48\u001b[0m, in \u001b[0;36mprimitive..f_wrapped\u001b[0;34m(*args, **kwargs)\u001b[0m\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\u001b[0;32m---> 48\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mf_raw\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[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<__array_function__ internals>:180\u001b[0m, in \u001b[0;36msum\u001b[0;34m(*args, **kwargs)\u001b[0m\n", - "File \u001b[0;32m~/miniforge3/envs/myenv/lib/python3.9/site-packages/numpy/core/fromnumeric.py:2296\u001b[0m, in \u001b[0;36msum\u001b[0;34m(a, axis, dtype, out, keepdims, initial, where)\u001b[0m\n\u001b[1;32m 2293\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m out\n\u001b[1;32m 2294\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m res\n\u001b[0;32m-> 2296\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43m_wrapreduction\u001b[49m\u001b[43m(\u001b[49m\u001b[43ma\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mnp\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43madd\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43msum\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43maxis\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mdtype\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mout\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mkeepdims\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mkeepdims\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 2297\u001b[0m \u001b[43m \u001b[49m\u001b[43minitial\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43minitial\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mwhere\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mwhere\u001b[49m\u001b[43m)\u001b[49m\n", - "File \u001b[0;32m~/miniforge3/envs/myenv/lib/python3.9/site-packages/numpy/core/fromnumeric.py:86\u001b[0m, in \u001b[0;36m_wrapreduction\u001b[0;34m(obj, ufunc, method, axis, dtype, out, **kwargs)\u001b[0m\n\u001b[1;32m 83\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m 84\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m reduction(axis\u001b[38;5;241m=\u001b[39maxis, out\u001b[38;5;241m=\u001b[39mout, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mpasskwargs)\n\u001b[0;32m---> 86\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mufunc\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mreduce\u001b[49m\u001b[43m(\u001b[49m\u001b[43mobj\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43maxis\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mdtype\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mout\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[43mpasskwargs\u001b[49m\u001b[43m)\u001b[49m\n", "\u001b[0;31mKeyboardInterrupt\u001b[0m: " ] } diff --git 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b/doc/LectureNotes/_build/jupyter_execute/chapter1_9_0.png index 719be46df..e6ffd825c 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 24910e796..584d5ca09 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": [ - "-0.024792624800382218\n", - "3.9225384545636204\n", - "[[0.94623184 2.84401886]\n", - " [2.84401886 9.4477214 ]]\n" + "-0.009134699065945493\n", + "4.0244965108017645\n", + "[[0.85613835 2.50655379]\n", + " [2.50655379 8.3404509 ]]\n" ] } ], @@ -1845,10 +1845,10 @@ "name": "stdout", "output_type": "stream", "text": [ - "0.08536248571780691\n", - "1.8207274870895702\n", - "[[1. 0.74900488]\n", - " [0.74900488 1. ]]\n" + "0.07971187802560528\n", + "1.800782161095708\n", + "[[1. 0.59411814]\n", + " [0.59411814 1. ]]\n" ] } ], @@ -1905,36 +1905,30 @@ "name": "stdout", "output_type": "stream", "text": [ - "[[ 1.10115017 1.66431407]\n", - " [ 0.12043521 1.32305911]\n", - " [-1.30023144 -3.36154104]\n", - " [-0.25200841 -1.11277166]\n", - " [-1.55102329 -4.20158083]\n", - " [ 0.72770687 0.97206657]\n", - " [ 0.76533281 2.10747579]\n", - " [-0.20666447 0.79623487]\n", - " [-0.63919355 -2.48490503]\n", - " [ 1.23449607 4.29764815]]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ + "[[ 0.81395716 1.89155934]\n", + " [-1.34726166 -4.13453411]\n", + " [-0.46229544 -2.34061974]\n", + " [ 0.24429334 1.4051634 ]\n", + " [ 0.41971814 1.6405671 ]\n", + " [ 2.02456235 5.03973227]\n", + " [-1.97311824 -4.72521196]\n", + " [ 0.10738656 0.24578123]\n", + " [-0.52702419 -2.34023682]\n", + " [ 0.69978197 3.31779928]]\n", " 0 1\n", - "0 1.101150 1.664314\n", - "1 0.120435 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0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 \n", - "1 0.075289 0.077391 0.063786 0.064606 0.065654 0.066948 0.068512 \n", - "2 0.076321 0.079240 0.062216 0.063536 0.065121 0.067000 0.069203 \n", - "3 0.080549 0.081686 0.072380 0.072591 0.072997 0.073612 0.074454 \n", - "4 0.081527 0.083268 0.071437 0.072031 0.072849 0.073911 0.075239 \n", - "5 0.082742 0.085145 0.070552 0.071560 0.072826 0.074374 0.076232 \n", - "6 0.075842 0.075978 0.071436 0.071049 0.070805 0.070712 0.070780 \n", - "7 0.076448 0.077035 0.070622 0.070531 0.070605 0.070855 0.071294 \n", - "8 0.077280 0.078359 0.069907 0.070135 0.070552 0.071173 0.072015 \n", - "9 0.078359 0.079976 0.069293 0.069866 0.070655 0.071680 0.072961 \n", - "10 0.069907 0.069293 0.068353 0.067515 0.066778 0.066145 0.065619 \n", - "11 0.070135 0.069866 0.067515 0.066912 0.066425 0.066059 0.065821 \n", - "12 0.070552 0.070655 0.066778 0.066425 0.066205 0.066127 0.066199 \n", - "13 0.071173 0.071680 0.066145 0.066059 0.066127 0.066358 0.066766 \n", - "14 0.072015 0.072961 0.065619 0.065821 0.066199 0.066766 0.067539 \n" + "1 0.076334 0.073841 0.072973 0.070485 0.068088 0.065778 0.063554 \n", + "2 0.075334 0.072946 0.071789 0.069391 0.067077 0.064845 0.062693 \n", + "3 0.083645 0.080875 0.082361 0.079518 0.076777 0.074134 0.071588 \n", + "4 0.081405 0.078753 0.079982 0.077254 0.074622 0.072083 0.069637 \n", + "5 0.079080 0.076543 0.077541 0.074928 0.072404 0.069970 0.067623 \n", + "6 0.080793 0.078067 0.081296 0.078454 0.075712 0.073071 0.070527 \n", + "7 0.078170 0.075562 0.078544 0.075823 0.073198 0.070667 0.068229 \n", + "8 0.075609 0.073115 0.075864 0.073260 0.070747 0.068324 0.065989 \n", + "9 0.073115 0.070730 0.073260 0.070769 0.068364 0.066044 0.063809 \n", + "10 0.075864 0.073260 0.077608 0.074866 0.072223 0.069676 0.067224 \n", + "11 0.073260 0.070769 0.074866 0.072241 0.069711 0.067273 0.064924 \n", + "12 0.070747 0.068364 0.072223 0.069711 0.067288 0.064954 0.062705 \n", + "13 0.068324 0.066044 0.069676 0.067273 0.064954 0.062719 0.060565 \n", + "14 0.065989 0.063809 0.067224 0.064924 0.062705 0.060565 0.058503 \n" ] } ], diff --git a/doc/LectureNotes/_build/jupyter_execute/chapter3.ipynb b/doc/LectureNotes/_build/jupyter_execute/chapter3.ipynb index 3c06d6a3d..af11dfd5d 100644 --- a/doc/LectureNotes/_build/jupyter_execute/chapter3.ipynb +++ b/doc/LectureNotes/_build/jupyter_execute/chapter3.ipynb @@ -489,10 +489,10 @@ "name": "stdout", "output_type": "stream", "text": [ - "Runtime: 0.147545 sec\n", + "Runtime: 0.146141 sec\n", "Jackknife Statistics :\n", "original bias std. error\n", - " 99.977 99.967 0.152494\n" + " 100.139 100.129 0.148776\n" ] } ], @@ -917,7 +917,7 @@ "text": [ "Bootstrap Statistics :\n", "original bias std. error\n", - " 100.041 14.8133 100.041 0.149266\n" + " 99.989 15.1792 99.9878 0.152149\n" ] } ], @@ -975,7 +975,7 @@ "outputs": [ { "data": { - "image/png": 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", + "image/png": 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", "text/plain": [ "
    " ] @@ -1287,13 +1287,7 @@ "Error: 0.06547790180152355\n", "Bias^2: 0.06208238634231949\n", "Var: 0.0033955154592040936\n", - "0.06547790180152355 >= 0.06208238634231949 + 0.0033955154592040936 = 0.06547790180152359\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ + "0.06547790180152355 >= 0.06208238634231949 + 0.0033955154592040936 = 0.06547790180152359\n", "Polynomial degree: 4\n", "Error: 0.06844519414009445\n", "Bias^2: 0.06453579006728324\n", @@ -1324,29 +1318,23 @@ "Error: 0.017355848195593347\n", "Bias^2: 0.010331721306655127\n", "Var: 0.007024126888938232\n", - "0.017355848195593347 >= 0.010331721306655127 + 0.007024126888938232 = 0.01735584819559336\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ + "0.017355848195593347 >= 0.010331721306655127 + 0.007024126888938232 = 0.01735584819559336\n", "Polynomial degree: 9\n", "Error: 0.02660572763718093\n", "Bias^2: 0.010018312644137363\n", "Var: 0.016587414993043573\n", - "0.02660572763718093 >= 0.010018312644137363 + 0.016587414993043573 = 0.026605727637180936\n", - "Polynomial degree: 10\n", - "Error: 0.021592704588025025\n", - "Bias^2: 0.010516485576645508\n", - "Var: 0.011076219011379514\n", - "0.021592704588025025 >= 0.010516485576645508 + 0.011076219011379514 = 0.021592704588025022\n" + "0.02660572763718093 >= 0.010018312644137363 + 0.016587414993043573 = 0.026605727637180936\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ + "Polynomial degree: 10\n", + "Error: 0.021592704588025025\n", + "Bias^2: 0.010516485576645508\n", + "Var: 0.011076219011379514\n", + "0.021592704588025025 >= 0.010516485576645508 + 0.011076219011379514 = 0.021592704588025022\n", "Polynomial degree: 11\n", "Error: 0.07160048164233104\n", "Bias^2: 0.014436800088904942\n", @@ -1356,7 +1344,13 @@ "Error: 0.11547777218872497\n", "Bias^2: 0.01628578269596628\n", "Var: 0.09919198949275869\n", - "0.11547777218872497 >= 0.01628578269596628 + 0.09919198949275869 = 0.11547777218872497\n", + "0.11547777218872497 >= 0.01628578269596628 + 0.09919198949275869 = 0.11547777218872497\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ "Polynomial degree: 13\n", "Error: 0.22842468702219465\n", "Bias^2: 0.01975416527185249\n", @@ -1373,7 +1367,7 @@ }, "metadata": { "filenames": { - "image/png": "/Users/mhjensen/Teaching/MachineLearning/doc/LectureNotes/_build/jupyter_execute/chapter3_66_5.png" + "image/png": "/Users/mhjensen/Teaching/MachineLearning/doc/LectureNotes/_build/jupyter_execute/chapter3_66_4.png" } }, "output_type": "display_data" @@ -1737,9 +1731,9 @@ "name": "stderr", "output_type": "stream", "text": [ - "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_95419/626635268.py:73: RuntimeWarning: divide by zero encountered in log10\n", + "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_22458/626635268.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_95419/626635268.py:74: RuntimeWarning: divide by zero encountered in log10\n", + "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_22458/626635268.py:74: RuntimeWarning: divide by zero encountered in log10\n", " plt.plot(polynomial, np.log10(testerror), label='Test Error')\n" ] }, @@ -2081,7 +2075,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_95419/3817475779.py:63: RuntimeWarning: divide by zero encountered in log10\n", + "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_22458/3817475779.py:63: RuntimeWarning: divide by zero encountered in log10\n", " plt.plot(polynomial, np.log10(estimated_mse_sklearn), label='Test Error')\n" ] }, @@ -3755,7 +3749,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_95419/4162706317.py:7: UserWarning: set_ticklabels() should only be used with a fixed number of ticks, i.e. after set_ticks() or using a FixedLocator.\n", + "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_22458/4162706317.py:7: UserWarning: set_ticklabels() should only be used with a fixed number of ticks, i.e. after set_ticks() or using a FixedLocator.\n", " cb.ax.set_yticklabels(cb.ax.get_yticklabels(), fontsize=18)\n" ] }, @@ -4065,7 +4059,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_95419/3777801602.py:7: UserWarning: set_ticklabels() should only be used with a fixed number of ticks, i.e. after set_ticks() or using a FixedLocator.\n", + "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_22458/3777801602.py:7: UserWarning: set_ticklabels() should only be used with a fixed number of ticks, i.e. after set_ticks() or using a FixedLocator.\n", " cb.ax.set_yticklabels(cb.ax.get_yticklabels(), fontsize=18)\n" ] }, @@ -4142,7 +4136,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_95419/438060758.py:10: UserWarning: set_ticklabels() should only be used with a fixed number of ticks, i.e. after set_ticks() or using a FixedLocator.\n", + "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_22458/438060758.py:10: UserWarning: set_ticklabels() should only be used with a fixed number of ticks, i.e. after set_ticks() or using a FixedLocator.\n", " cb.ax.set_yticklabels(cb.ax.get_yticklabels(), fontsize=18)\n" ] }, @@ -4227,7 +4221,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_95419/3544313922.py:9: UserWarning: set_ticklabels() should only be used with a fixed number of ticks, i.e. after set_ticks() or using a FixedLocator.\n", + "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_22458/3544313922.py:9: UserWarning: set_ticklabels() should only be used with a fixed number of ticks, i.e. after set_ticks() or using a FixedLocator.\n", " cb.ax.set_yticklabels(cb.ax.get_yticklabels(), fontsize=18)\n" ] }, @@ -4288,7 +4282,7 @@ "output_type": "stream", "text": [ "\r", - " 0%| | 0/10 [00:00" ] @@ -107,7 +107,7 @@ }, { "data": { - "image/png": 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", + "image/png": 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    " ] @@ -1330,14 +1330,14 @@ "output_type": "stream", "text": [ "(426, 30)\n", - "(143, 30)\n", - "Test set accuracy with Logistic Regression: 0.94\n" + "(143, 30)\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ + "Test set accuracy with Logistic Regression: 0.94\n", "Test set accuracy with SVM: 0.63\n", "Test set accuracy with Decision Trees: 0.90\n", "Test set accuracy Logistic Regression with scaled data: 0.96\n", diff --git a/doc/LectureNotes/_build/jupyter_execute/chapter6_1_1.png b/doc/LectureNotes/_build/jupyter_execute/chapter6_1_1.png index 87477f968..2b27c01d5 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 81a773d64..de610b8d5 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/chapter8.ipynb b/doc/LectureNotes/_build/jupyter_execute/chapter8.ipynb index b05c6f737..f39f44f33 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": [ - "0.13035147135400782\n", - "4.25879315330607\n", - "[[0.86867512 2.59009792]\n", - " [2.59009792 8.82533209]]\n" + "-0.012423940191689783\n", + "4.101008878523571\n", + "[[0.89527291 2.65532045]\n", + " [2.65532045 8.81987609]]\n" ] } ], @@ -340,10 +340,10 @@ "name": "stdout", "output_type": "stream", "text": [ - "0.08690184845323\n", - "1.521422502348998\n", - "[[1. 0.69768266]\n", - " [0.69768266 1. ]]\n" + "0.08705631913312815\n", + "1.7026908764394864\n", + "[[1. 0.65870313]\n", + " [0.65870313 1. ]]\n" ] } ], @@ -397,30 +397,30 @@ "name": "stdout", "output_type": "stream", "text": [ - "[[ 1.14550854 1.96870431]\n", - " [ 0.79787194 3.11438414]\n", - " [-0.18497496 -1.31315504]\n", - " [-1.52706754 -4.97482498]\n", - " [-1.30190897 -3.11113486]\n", - " [-0.08421808 -1.70928399]\n", - " [ 0.11992194 -0.07776381]\n", - " [-0.90717653 -2.20404927]\n", - " [ 1.05201041 5.38762019]\n", - " [ 0.89003324 2.9195033 ]]\n", + "[[-1.7755649 -4.56778296]\n", + " [-0.81015037 -2.80072356]\n", + " [ 0.73628249 1.95206335]\n", + " [ 0.97366347 1.61130099]\n", + " [ 0.7271324 1.97965627]\n", + " [ 0.36881837 0.56037913]\n", + " [-1.33163086 -2.59391196]\n", + " [-0.68953877 -1.58298728]\n", + " [ 0.19982428 -1.08010965]\n", + " [ 1.60116388 6.52211567]]\n", " 0 1\n", - "0 1.145509 1.968704\n", - "1 0.797872 3.114384\n", - "2 -0.184975 -1.313155\n", - "3 -1.527068 -4.974825\n", - "4 -1.301909 -3.111135\n", - "5 -0.084218 -1.709284\n", - "6 0.119922 -0.077764\n", - "7 -0.907177 -2.204049\n", - "8 1.052010 5.387620\n", - "9 0.890033 2.919503\n", - " 0 1\n", - "0 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2.03447649]\n", - " [2.03447649 1.99774602]]\n" + "0 3.914672 1.954823\n", + "1 1.954823 1.963858\n", + "[[3.91467223 1.95482298]\n", + " [1.95482298 1.96385798]]\n" ] } ], @@ -949,13 +949,13 @@ "output_type": "stream", "text": [ "Centered covariance using own code\n", - "[[4.0321956 2.03447649]\n", - " [2.03447649 1.99774602]]\n" + "[[3.91467223 1.95482298]\n", + " [1.95482298 1.96385798]]\n" ] }, { "data": { - "image/png": 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", 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", "text/plain": [ "
    " ] @@ -1044,12 +1044,12 @@ "output_type": "stream", "text": [ "Eigenvalues of Covariance matrix\n", - "5.2895786617507\n", - "0.7403629637766833\n", + "5.123928000581825\n", + "0.7546022135629743\n", "First eigenvector\n", - "[0.85064942 0.52573336]\n", + "[0.85043503 0.5260801 ]\n", "Second eigenvector\n", - "[-0.52573336 0.85064942]\n" + "[-0.5260801 0.85043503]\n" ] }, { @@ -1057,7 +1057,7 @@ "output_type": "stream", "text": [ "Eigenvector of largest eigenvalue\n", - "[-0.85064942 -0.52573336]\n" + "[0.85043503 0.5260801 ]\n" ] } ], diff --git a/doc/LectureNotes/_build/jupyter_execute/chapter8_65_1.png b/doc/LectureNotes/_build/jupyter_execute/chapter8_65_1.png index 23112abab..96f175412 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/exercisesweek41.ipynb b/doc/LectureNotes/_build/jupyter_execute/exercisesweek41.ipynb index 07fb32b86..1a9bd516a 100644 --- a/doc/LectureNotes/_build/jupyter_execute/exercisesweek41.ipynb +++ b/doc/LectureNotes/_build/jupyter_execute/exercisesweek41.ipynb @@ -3,9 +3,7 @@ { "cell_type": "markdown", "id": "b18bcd06", - "metadata": { - "editable": true - }, + "metadata": {}, "source": [ "\n", @@ -15,9 +13,7 @@ { "cell_type": "markdown", "id": "7542d6aa", - "metadata": { - "editable": true - }, + "metadata": {}, "source": [ "# Exercises week 41\n", "**October 4-11, 2024**\n", @@ -28,9 +24,7 @@ { "cell_type": "markdown", "id": "80943a15", - "metadata": { - "editable": true - }, + "metadata": {}, "source": [ "# Overarching aims of the exercises this week\n", "\n", @@ -83,9 +77,7 @@ { "cell_type": "markdown", "id": "2095d197", - "metadata": { - "editable": true - }, + "metadata": {}, "source": [ "# Code examples from week 39 and 40" ] @@ -93,9 +85,7 @@ { "cell_type": "markdown", "id": "f428decb", - "metadata": { - "editable": true - }, + "metadata": {}, "source": [ "## Code with a Number of Minibatches which varies, analytical gradient\n", "\n", @@ -106,30 +96,27 @@ "cell_type": "code", "execution_count": 1, "id": "ba38d454", - "metadata": { - "collapsed": false, - "editable": true - }, + "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Own inversion\n", - "[[4.0846409]\n", - " [2.8352834]]\n", - "Eigenvalues of Hessian Matrix:[0.33241952 4.39292847]\n", + "[[3.96581896]\n", + " [3.03624103]]\n", + "Eigenvalues of Hessian Matrix:[0.26120167 4.71129677]\n", "theta from own gd\n", - "[[4.0846409]\n", - " [2.8352834]]\n", + "[[3.96581896]\n", + " [3.03624103]]\n", "theta from own sdg\n", - "[[4.03188133]\n", - " [2.82130663]]\n" + "[[3.99729015]\n", + " [3.02646648]]\n" ] }, { "data": { - "image/png": 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", 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", "text/plain": [ "
    " ] @@ -216,9 +203,7 @@ { "cell_type": "markdown", "id": "de04b41a", - "metadata": { - "editable": true - }, + "metadata": {}, "source": [ "In the above code, we have use replacement in setting up the\n", "mini-batches. The discussion\n", @@ -229,9 +214,7 @@ { "cell_type": "markdown", "id": "77fc1cca", - "metadata": { - "editable": true - }, + "metadata": {}, "source": [ "## Momentum based GD\n", "\n", @@ -244,9 +227,7 @@ { "cell_type": "markdown", "id": "441d1f36", - "metadata": { - "editable": true - }, + "metadata": {}, "source": [ "$$\n", "\\mathbf{v}_{t}=\\gamma \\mathbf{v}_{t-1}+\\eta_{t}\\nabla_\\theta E(\\boldsymbol{\\theta}_t) \\nonumber\n", @@ -256,9 +237,7 @@ { "cell_type": "markdown", "id": "47434945", - "metadata": { - "editable": true - }, + "metadata": {}, "source": [ "\n", "
    \n", @@ -274,9 +253,7 @@ { "cell_type": "markdown", "id": "f3ea5060", - "metadata": { - "editable": true - }, + "metadata": {}, "source": [ "where we have introduced a momentum parameter $\\gamma$, with\n", "$0\\le\\gamma\\le 1$, and for brevity we dropped the explicit notation to\n", @@ -293,9 +270,7 @@ { "cell_type": "markdown", "id": "923628c8", - "metadata": { - "editable": true - }, + "metadata": {}, "source": [ "$$\n", "\\Delta \\boldsymbol{\\theta}_{t+1} = \\gamma \\Delta \\boldsymbol{\\theta}_t -\\ \\eta_{t}\\nabla_\\theta E(\\boldsymbol{\\theta}_t),\n", @@ -305,9 +280,7 @@ { "cell_type": "markdown", "id": "5c94031c", - "metadata": { - "editable": true - }, + "metadata": {}, "source": [ "where we have defined $\\Delta \\boldsymbol{\\theta}_{t}= \\boldsymbol{\\theta}_t-\\boldsymbol{\\theta}_{t-1}$." ] @@ -315,9 +288,7 @@ { "cell_type": "markdown", "id": "f3f0e9c9", - "metadata": { - "editable": true - }, + "metadata": {}, "source": [ "## Algorithms and codes for Adagrad, RMSprop and Adam\n", "\n", @@ -329,9 +300,7 @@ { "cell_type": "markdown", "id": "92253eff", - "metadata": { - "editable": true - }, + "metadata": {}, "source": [ "## Practical tips\n", "\n", @@ -349,9 +318,7 @@ { "cell_type": "markdown", "id": "08209015", - "metadata": { - "editable": true - }, + "metadata": {}, "source": [ "## Using Automatic differentation with OLS\n", "\n", @@ -364,19 +331,16 @@ "cell_type": "code", "execution_count": 2, "id": "f1f7d4aa", - "metadata": { - "collapsed": false, - "editable": true - }, + "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Own inversion\n", - "[[3.89543945]\n", - " [3.14433067]]\n", - "Eigenvalues of Hessian Matrix:[0.30069427 4.55916434]\n" + "[[4.03656288]\n", + " [2.95497813]]\n", + "Eigenvalues of Hessian Matrix:[0.29560433 4.58257727]\n" ] }, { @@ -384,13 +348,13 @@ "output_type": "stream", "text": [ "theta from own gd\n", - "[[3.89543945]\n", - " [3.14433067]]\n" + "[[4.03656288]\n", + " [2.95497813]]\n" ] }, { "data": { - "image/png": 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", + "image/png": 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", "text/plain": [ "
    " ] @@ -458,9 +422,7 @@ { "cell_type": "markdown", "id": "1bc83f33", - "metadata": { - "editable": true - }, + "metadata": {}, "source": [ "## Same code but now with momentum gradient descent" ] @@ -469,10 +431,7 @@ "cell_type": "code", "execution_count": 3, "id": "dc2a3f65", - "metadata": { - "collapsed": false, - "editable": true - }, + "metadata": {}, "outputs": [ { "name": "stdout", @@ -481,73 +440,73 @@ "Own inversion\n", "[[4.]\n", " [3.]]\n", - "Eigenvalues of Hessian Matrix:[0.30043041 4.28093636]\n", - "0 [-13.544421] [-15.11052859]\n", - "1 [-0.26692247] [0.23040825]\n", - "2 [-0.2481902] [0.2142385]\n", - "3 [-0.23077255] [0.19920353]\n", - "4 [-0.21457724] [0.18522369]\n", - "5 [-0.19951849] [0.17222494]\n", - "6 [-0.18551655] [0.16013842]\n", - "7 [-0.17249725] [0.14890012]\n", - "8 [-0.16039162] [0.13845051]\n", - "9 [-0.14913555] [0.12873424]\n", - "10 [-0.13866942] [0.11969984]\n", - "11 [-0.12893778] [0.11129947]\n", - "12 [-0.1198891] [0.10348862]\n", - "13 [-0.11147544] [0.09622593]\n", - "14 [-0.10365224] [0.08947292]\n", - "15 [-0.09637807] [0.08319383]\n", - "16 [-0.08961438] [0.0773554]\n", - "17 [-0.08332537] [0.0719267]\n", - "18 [-0.0774777] [0.06687898]\n", - "19 [-0.07204042] [0.0621855]\n", - "20 [-0.06698472] [0.0578214]\n", - "21 [-0.06228382] [0.05376358]\n", - "22 [-0.05791283] [0.04999052]\n", - "23 [-0.05384858] [0.04648225]\n", - "24 [-0.05006956] [0.04322019]\n", - "25 [-0.04655574] [0.04018705]\n", - "26 [-0.04328852] [0.03736678]\n", - "27 [-0.04025059] [0.03474443]\n", - "28 [-0.03742586] [0.03230611]\n", - "29 [-0.03479936] [0.03003891]\n", + "Eigenvalues of Hessian Matrix:[0.27750534 4.13217667]\n", + "0 [-10.24816468] [-11.05638246]\n", + "1 [-0.1602625] [0.14404536]\n", + "2 [-0.14949972] [0.13437168]\n", + "3 [-0.13945974] [0.12534765]\n", + "4 [-0.13009402] [0.11692966]\n", + "5 [-0.12135727] [0.10907699]\n", + "6 [-0.11320726] [0.10175169]\n", + "7 [-0.10560458] [0.09491833]\n", + "8 [-0.09851247] [0.08854388]\n", + "9 [-0.09189665] [0.08259753]\n", + "10 [-0.08572513] [0.07705051]\n", + "11 [-0.07996807] [0.07187601]\n", + "12 [-0.07459764] [0.06704902]\n", + "13 [-0.06958788] [0.0625462]\n", + "14 [-0.06491455] [0.05834577]\n", + "15 [-0.06055507] [0.05442743]\n", + "16 [-0.05648836] [0.05077224]\n", + "17 [-0.05269476] [0.04736252]\n", + "18 [-0.04915593] [0.04418179]\n", + "19 [-0.04585476] [0.04121466]\n", + "20 [-0.04277528] [0.0384468]\n", + "21 [-0.03990261] [0.03586482]\n", + "22 [-0.03722287] [0.03345624]\n", + "23 [-0.03472308] [0.03120942]\n", + "24 [-0.03239118] [0.02911348]\n", + "25 [-0.03021588] [0.0271583]\n", + "26 [-0.02818667] [0.02533443]\n", + "27 [-0.02629373] [0.02363304]\n", + "28 [-0.02452792] [0.02204591]\n", + "29 [-0.02288069] [0.02056537]\n", "theta from own gd\n", - "[[3.89229722]\n", - " [3.09296935]]\n", - "0 [-0.03235719] [0.02793082]\n", - "1 [-0.03008641] [0.02597067]\n", - "2 [-0.02729375] [0.02356004]\n", - "3 [-0.02454051] [0.02118344]\n", - "4 [-0.02199232] [0.01898383]\n", - "5 [-0.01968447] [0.01699169]\n", - "6 [-0.01761069] [0.01520159]\n", - "7 [-0.01575266] [0.01359774]\n", - "8 [-0.01408975] [0.01216231]\n", - "9 [-0.01260207] [0.01087815]\n", - "10 [-0.01127138] [0.00972948]\n", - "11 [-0.01008116] [0.00870208]\n", - "12 [-0.00901661] [0.00778316]\n", - "13 [-0.00806447] [0.00696127]\n", - "14 [-0.00721287] [0.00622617]\n", - "15 [-0.00645121] [0.0055687]\n", - "16 [-0.00576997] [0.00498065]\n", - "17 [-0.00516067] [0.0044547]\n", - "18 [-0.00461571] [0.00398429]\n", - "19 [-0.0041283] [0.00356356]\n", - "20 [-0.00369236] [0.00318725]\n", - "21 [-0.00330245] [0.00285068]\n", - "22 [-0.00295372] [0.00254966]\n", - "23 [-0.00264181] [0.00228042]\n", - "24 [-0.00236284] [0.00203961]\n", - "25 [-0.00211332] [0.00182423]\n", - "26 [-0.00189016] [0.00163159]\n", - "27 [-0.00169056] [0.0014593]\n", - "28 [-0.00151204] [0.0013052]\n", - "29 [-0.00135237] [0.00116737]\n", + "[[3.92308585]\n", + " [3.06913112]]\n", + "0 [-0.02134409] [0.01918426]\n", + "1 [-0.01991068] [0.01789589]\n", + "2 [-0.01814351] [0.01630755]\n", + "3 [-0.01639489] [0.01473588]\n", + "4 [-0.01476927] [0.01327475]\n", + "5 [-0.01328972] [0.01194492]\n", + "6 [-0.01195336] [0.01074379]\n", + "7 [-0.0107497] [0.00966192]\n", + "8 [-0.00966668] [0.0086885]\n", + "9 [-0.00869259] [0.00781297]\n", + "10 [-0.00781659] [0.00702562]\n", + "11 [-0.00702885] [0.00631759]\n", + "12 [-0.00632049] [0.00568091]\n", + "13 [-0.00568352] [0.00510839]\n", + "14 [-0.00511073] [0.00459357]\n", + "15 [-0.00459568] [0.00413064]\n", + "16 [-0.00413253] [0.00371435]\n", + "17 [-0.00371605] [0.00334002]\n", + "18 [-0.00334155] [0.00300342]\n", + "19 [-0.00300479] [0.00270073]\n", + "20 [-0.00270197] [0.00242856]\n", + "21 [-0.00242967] [0.00218381]\n", + "22 [-0.00218481] [0.00196372]\n", + "23 [-0.00196462] [0.00176582]\n", + "24 [-0.00176663] [0.00158786]\n", + "25 [-0.00158859] [0.00142784]\n", + "26 [-0.00142849] [0.00128394]\n", + "27 [-0.00128453] [0.00115455]\n", + "28 [-0.00115508] [0.00103819]\n", + "29 [-0.00103867] [0.00093356]\n", "theta from own gd wth momentum\n", - "[[3.9959739 ]\n", - " [3.00347534]]\n" + "[[3.99663433]\n", + " [3.00302509]]\n" ] } ], @@ -610,9 +569,7 @@ { "cell_type": "markdown", "id": "0ef007d0", - "metadata": { - "editable": true - }, + "metadata": {}, "source": [ "## But noen of these can compete with Newton's method" ] @@ -621,27 +578,24 @@ "cell_type": "code", "execution_count": 4, "id": "0e498aa4", - "metadata": { - "collapsed": false, - "editable": true - }, + "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Own inversion\n", - "[[3.96414331]\n", - " [3.22481166]]\n", - "Eigenvalues of Hessian Matrix:[0.26674792 4.38123077]\n", - "0 [-15.60151468] [-17.64192473]\n", - "1 [-1.66342634e-14] [-1.21941484e-14]\n", - "2 [-6.41847686e-17] [1.63371136e-16]\n", - "3 [-6.76542156e-17] [-4.78340902e-17]\n", - "4 [-6.76542156e-17] [-4.78340902e-17]\n", + "[[3.81924504]\n", + " [3.09224244]]\n", + "Eigenvalues of Hessian Matrix:[0.31096156 4.57735536]\n", + "0 [-15.77829399] [-18.53684024]\n", + "1 [-3.15242624e-15] [-1.65243388e-16]\n", + "2 [3.85975973e-17] [5.35468713e-17]\n", + "3 [3.85975973e-17] [5.35468713e-17]\n", + "4 [3.85975973e-17] [5.35468713e-17]\n", "beta from own Newton code\n", - "[[3.96414331]\n", - " [3.22481166]]\n" + "[[3.81924504]\n", + " [3.09224244]]\n" ] } ], @@ -689,9 +643,7 @@ { "cell_type": "markdown", "id": "40292cf3", - "metadata": { - "editable": true - }, + "metadata": {}, "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**." @@ -701,19 +653,16 @@ "cell_type": "code", "execution_count": 5, "id": "fa819b9d", - "metadata": { - "collapsed": false, - "editable": true - }, + "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Own inversion\n", - "[[3.84668873]\n", - " [3.21776599]]\n", - "Eigenvalues of Hessian Matrix:[0.28766899 4.2084894 ]\n" + "[[3.84019294]\n", + " [3.22756518]]\n", + "Eigenvalues of Hessian Matrix:[0.2729933 4.56108455]\n" ] }, { @@ -721,13 +670,13 @@ "output_type": "stream", "text": [ "theta from own gd\n", - "[[3.84668873]\n", - " [3.21776599]]\n" + "[[3.84019294]\n", + " [3.22756518]]\n" ] }, { "data": { - "image/png": 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cPXvWcwUmIiIir+LxMFRSUoIuXbpgyZIltR67evUqdu3ahb///e/YtWsXtFotjh49irvuussDJSUiIiJvJAkhhKcLYSBJEjIyMjBmzBiL++zcuRO33HILTp48iZYtW9p03MLCQoSGhqKgoAAhISFOKi0RERG5kruu334uO7KLFBQUQJIkNG7c2OI+ZWVlKCsrM94vLCx0Q8mIiIhIiTzeTGaP0tJSzJw5ExMmTKgzIc6fPx+hoaHGW2xsrBtLSUREREqimDBUXl6O++67D5WVlXj33Xfr3HfWrFkoKCgw3nJyctxUSiIiIlIaRTSTlZeX45577kF2djY2bNhgtd0wMDAQgYGBbiodERERKZnsw5AhCB07dgxZWVkIDw/3dJGIiIjIi3g8DBUXF+P48ePG+9nZ2dizZw/CwsIQFRWFcePGYdeuXVi7di10Oh3y8vIAAGFhYQgICPBUsYmIiMhLeHxo/caNG5GSklJr+8SJEzFnzhzEx8ebfV5WVhaSk5Nteg0OrSciIlIe1QytT05ORl15TEbTIBEREZEXUsxoMiIiIiJXYBgiIiIiVWMYIiIiIlVjGCIiIiJVYxgiIiIiVWMYIiIiIlVjGCIiIiJV8/g8Q0RERFQPOh2wZQuQmwtERgIDBgC+vp4ulaIwDBERESmVVgs8+SRw+nTVtpgYYPFiQKPxXLkUhs1kRERESqTVAuPGmQYhADhzRr9dq/VMuRSIYYiIiEhpdDp9jZC5JasM29LT9fuRVQxDRERESrNlS+0aoeqEAHJy9PuRVQxDRERESpOb69z9VI5hiIiISGkiI527n8oxDBERESnNgAH6UWOSZP5xSQJiY/X7kVUMQ0RERErj66sfPg/UDkSG+4sWcb4hGzEMERERKZFGA3zxBRAdbbo9Jka/nfMM2YyTLhIREbmaq2aJ1miA0aM5A3U9MQwRERG5kqtnifb1BZKT638cFWMzGRERkatwlmhFYBgiIiJyBc4SrRgMQ0RERK7AWaIVg32GiIiIXMHW2Z/PnLFtP1d1wibWDBEREbmErbM/p6db7zuk1QJxcUBKCjBhgv5nXBz7HDmJJIS5xkzvUlhYiNDQUBQUFCAkJMTTxSEiIjXQ6fSB5cwZ8/2GqpMk07mBqtcCHTsGzJ5t/jmAV88p5K7rN8MQERGRqxhGkwF1ByJJ0g+3z84G1qypPRTflud5YZOZu67fbCYjIiJyFcMs0U2b1r2foTP1vHnmh+Jbe56zO2HrdMDGjcCqVfqfXj7ijR2oiYiIXEmjAa5dAx54wPq+ixdbb1Izx9bO2tbodPpAtngxcOlS1XZnThIpQ6wZIiIicrWa64dZUj2A2MPWztp10WqBiAh9/6Sa5fDySSIZhoiIiFxtwAB97UrNFeYNJAkIC7P/uJIExMbqj18fhr5N+fnmH/fySSIZhoiIiFzN11ffzATUDkSG+08+ad8xDc9btKh+nafrmim7Oi+eJJJhiIiIyB0MnalrNpnFxOi3v/BC3bVHNRmeV99+PNZmyq7JWf2TZIQdqImIiNxFowFGj7Y8k/TixfrmKkkyrakxBKQ5c4C2bZ07A7W94cYZ/ZNkhmGIiIjInXx9geRk848Zao9qzjMUE6NvDnPFaC57wo0z+ifJEMMQERGRnFirPXI2Q+duW2bKrm//JBvpruuwbekBrPrAxnXb6olhiIiISG7qqj1yxWtZap4zCA8Hli516TxD14uvY8Oifcj431VkHknAedEFQLzLXq86hiEiIiK1s9Q8Fx4OTJum79ztghqhkvMl+P71/dB+rsPaE4koQE/jY42lKxjW6md8dsLpL1sL1yYjIiIiveoLxLqoee7KyQKs/ecBaNf44rvcJFxDQ+NjLXzOYUyHI9CkNULytCRcq7imjrXJNm/ejFGjRiEqKgqSJCEzM9PkcSEE5syZg6ioKDRo0ADJyck4ePCgZwpLRETkzQzNc+PH6386KQjl7TuP9+/fjGFNf0WzuIZIe68fMnJ74xoaIs4vB0/32Iif3tuPM2XN8O+DAzFkZg/4N/R3ymvbwuPNZCUlJejSpQseeughjB07ttbjCxcuxJtvvonly5ejXbt2eOWVVzBkyBAcOXIEwcHBHigxEZGLueGvcyJXO7H1NDJePw5tVhP8VNgZAgONj3UKPAbNLWegmRKJLne3g+QT68GSyqyZTJIkZGRkYMyYMQD0tUJRUVFIT0/HjBkzAABlZWWIiIjAggUL8Nhjj9l0XDaTEZFiaLXmh1V78SKZpGDVgruIaIHDBVHQvpML7bYI7L6WYLLrLY0OQDPgIlLTW6HdMNs6Rrvr+u3xmqG6ZGdnIy8vD0OHDjVuCwwMxKBBg7Bt2zaLYaisrAxlZWXG+4WFhS4vKxFRvRnWh6r5N6phkUxnzDZM5CxaLcSTT0K6EdwlAMGIwS4sxm4kwwc6DGy8D5rBhRjzbFvE3pro2fLWQdZhKC8vDwAQERFhsj0iIgInT560+Lz58+dj7ty5Li0bEZFT1bU+lBD6Ic/p6fr5Z5TWZKaEZj8llFEmdNd1+P3xRei47BkI6EOQQTTOYDXGYkPKP5D0zuNoltDNU8W0i8c7UNtCqrFOixCi1rbqZs2ahYKCAuMtJyfH1UUkIqofa+tDKXWRTK0WiIsDUlKACRP0P+Pi9NvlQgll9LCywjJ8+/JOPNphM6IDLyBk2SII1A4RPtBfnwcfX4pm7Zp4oqgOkXUYatGiBYCqGiKD8+fP16otqi4wMBAhISEmNyIiWbN1fSglLZJpaParGfIMzX5yCBtKKKOHFOcV44unt2NC3E9oFlqGO2f3wn+ODEQH/I5YnLYcIBQY3GUdhuLj49GiRQusW7fOuO369evYtGkT+vbt68GSERE5ma3rQyllkUxrzX6AvtlPp3NrsUwooYxudumPy/jvX7diTOTPaBbpi7vf7INVJ/uhCCGI9MnD5MRNWDJ+m20HU1Bw93ifoeLiYhw/ftx4Pzs7G3v27EFYWBhatmyJ9PR0vPrqq2jbti3atm2LV199FQ0bNsSECRM8WGoiIieztj6UJOkfV8oimfY0+7lr2YmalFBGN8jdcw6ZC45A+0MjbLyUhAr0Nz7W2u8kxnbLRuoj4bj1L53g49cCeHmTbQdWSnCHDMLQr7/+ipSUFOP96dOnAwAmTpyI5cuX47nnnsO1a9cwefJkXL58Gbfeeit++OEHzjFERN6lrvWhDH0k3bRIplMoodlPCWV0kT83nkLGG39CuykM24sSIVDV9aRz0FFobj0LzdQodNa0heTTquqJOp1+jTJrlBTcIYMwlJycjLqmOpIkCXPmzMGcOXPcVygiIk+wtD5UTIw+CClpWL0Smv2UUEYnEZUCB9cch/ZfZ6DdHom9pe0BtDQ+3vum/UgdkI/Up+LQdkg7AO3MH2jLFn3tpTWPPqqc4A4ZhCEiIqpGo9EPn1f6MG8lNPspoYz1UFlRiV9XHIb2/QvQ7mqFY+VtAbQFAPiiAoOa7IPm9iKMea4dont2tu2gttaStW3rWKE9hGGIiEhuDOtDKZkSmv2UUEY7VZRWYMu7+6FdXojMQ21xWtfJ+FggSjE0Yi9S77yOUTM6omn77va/gCtr08zN9eQmslqOw1W4HAcRkYeYW14kNlZezX5KKGMdSq+UYv2i/dCuLMWa4x2RL8KNj92EIoyI3Q/NWGD4s50RHFXP/rY6nX4OJmu1adnZ9oVIC8vQFM6fj9C0NJdfvxmGiIjItdwxu3N9X0NhM1AXnS3Ct6/th3Y18HVOZxSjKuSES/kYffMhpN4XiNunJyGocZBzX9wwNxNgvjbN3mVjLC1DI0koFAKhAMOQMzAMERF5MaUubmtnAMs/dglfLTgE7Vp//HCuC8pQFXKifXKR2ukoNJNCMGByZ/gFubgXjLNq0ww1TRamOCgEGIachWGIiMhL1VGrAEC+i9vaGODO/JqLzIVHof0xGJsuJ0FXravvzf4noOl2Apq/NkWviR3h4+fmeZSdUZu2caN++RMLGIaciGGIiMgLWalVcLj/iqtZCXC5L/0bn+xsD+3mcOwoNh3l1SXoCDR9cqF5IhqdRt8MycfyOp2KsGqVfj04C9wVhjiajIiIlEmJM0hbWQKkEhLK587DTGSjEvoA1zd4H1IHXkLq9Hi0ua09gPbuLbMryWQOJ4YhIiJSJiXOIG0lwPlAoCVykB78H9w8IgGjn22HqO5Jbiygm1mb68lNGIaIiLyRwkZHOURhM0iXXy3HkaXbkGjDvm+8HwKMH+jyMnmctbme3BSQZL1qPREROUCr1felSUnR98dISdHf12o9XTLrdDp9p9pVq/Q/61ox3lCrIFnoNyNJ+hFOHpxBuvRKKb76+y94qO0WtLipCFNX9bXtiTIJcG5hWIYmOtp0e0wMsGKFW4rADtRERHLkaM2OUkdXAY4NkXf2nDdOUHi6EN8sPABthoRvTndGCW4yPtYc53DYrzOaVFyA2Qgn107f7mDmM19YUuKW6zfDEBGRJ9QVdhydN0epo6uA+oU4GcwgffFIPr785yFovwnEuvNdcB2BxsdifM9Ck3gMmodC0e+xRPh986XsApxcuev6zTBERORudYUdwPFQYGXOFqOsLPmMrgKcE+I80Efq9M5cZCw4ioz1Idh0Jck4+gsA2vlnQ9PjJDSPN0fPtITaQ+BlEOCUwF3Xb3agJiJyJ0s1IGfO6LeHhVkcdg1JAtLT9avam7vQK3F0FeCcIfJuWtz26PfZyFh0EtotTfFLSSKAqr493RochqbvOWimxSBhZBtIPvGWD6TR6N9Hb+/krhAMQ0RE7mJljhkAQH6+5edbCwUKG11l5MoQV88aI1EpsPfzo9C+kwvtL9E4WNYWgD7kSKhE3+AD0KRcQurTbRA/MAFAgu1lc1OAI+sYhoiI3MVaDYitLIUCa3O2GJqbPDi6yixXhTgH+15VVlRix4cHof0gH9q9rZFdUTXRoR/KcVv4XmiGXcXoGR3QIsnDcwCpYQoFN2AYIiJyF2c1T1kKBdbmbAH0fVLkdrF0RYiz1hxZo+9V+dVybHx7HzI+KUHG4fbIq6xaBiMI13BH5F5o7qrAyBmJaBLf094zdA2lLlArQ+xATUTkLrZ2cLbE1tFgSuyc68wh8jZ2yL722yH88NZBaD8rx1fZnXBZNIEPdBiALWiNP9Am4ioSHuiOYc91RaPmjRw+NZdQ8hQKduBoMidiGCIiWTBcpOuqAQkL0/cbslSzY+tFTonNJ84KcTaGzjvwDb7HcOP9SViGN/xmIKziQtVOcqxpUfIUCnbiaDIiIm9jSzPW0qX6n+aaP+wJBUrsnOusEVY2Nkc2wRW09D0NTdIfeLT7b0j46BlIFbY1q3mUEheolTmGISIidzIsPWAt7Hhq2LWna5ScEeJs7Gg95/nraPePaEgiEoh7wPEpDdxNqVMoyBjDEBFZ5+kLpLexpQbEEzU7nuqQ66TP15Fv/4R20SlkbgnHF4hBNM7AB5Y7ZLd/+QHARwI2KqymRalTKMgYwxAR1Y0jVlxDbs1Ydo6+clpArsfnS1QK7F71O7T/PoeMX2NwqOxmAK0BAOl4C1/gHgjAdA0wc6PqlFbTotQpFORMqEBBQYEAIAoKCjxdFCJlWb1aCEkSQv9fbtVNkvS31as9XUJyhooKIWJiar/P1d/v2Fj9fkLo3/ea+8fE2P95cODzVVFWIba8s1c81T1LtPLNMXmaP8rEHU1/EUvTNom8/efNlzM2tvZxs7Isn3v1W1aWQ79elzD87mr+/rzsu+mu6zdHkxGReSoasaJ69qxpdumSc4Z02/H5un5Nh6zF+5Dxv6vIPNIB5yqbG3drgKsYHrUPmtE6jJiRiMatQmu/jrUaLFtG+cnxs67EKRTsxNFkRORZHLGiHrY2/5w5A8yc6ZyOxjZ+vubFvIvX8h5EAaomOgxFAe5qvR+ae/wx9OnOaNi0t+Xj2NIcqdTJKrm+mdMwDBGReUrrR0GOs7Wj7YULzgvINn5uDuQ1RQFCEeFzHmPa/w5NWiMkP9EZATf1t63MtrJ1lJ/cyK3vmUIxDBGReRyxoh4XLljfJzYWaNbMtuPZEnRs/NwMaHcOU9P3offDneAbMNC213cUa1pUi2GIiMzjiBXls7W/zPTp1o/15ptA06a2va6VoHPyp9PIXFSJ8VIEmorzZoe/C0hAbAwmH3pCefMckeL4eLoARCRThn4UQFW/CQM596MgPa1W3yk4JQWYMEH/My5Ov706a313DJo2rQrINT8PBpKkr0EyE5APr/0D84ZsRI+GhxHXPwbpa27D4+JdAEAlan++JAmQ+PkiN2EYIiLLDP0ooqNNt8fEyGt5AjJlmDOoZsgxzBlUPRDZ0zfMjoAsKgV+/e8hPN93IxIC/0DHUW3w4o/J2HUtAT7QYVDoHiRrwpH/6vvwieHnizyLQ+uJyDrOQK0c9k6JYM+wekPzkYUh3brX38RP59pC+9EVZOxvg1O6GOPD/riOIc32QnNnKe6akYBmCdWa3Pj5Igu4ar0TMQwRkWrYG24cnWPnRoAp//MUdm25io+2t0fG0URcEFWdrBuhGMOj90OTKnDns50Q2jK09vGJ6sB5hoiIyH72TongwBw7JedL8N1r+6D93B9rT45GIapCThPpMu5qfRCae/0x5OkkNAjrU88TInI9hiEiIm/iyJQINsyxczn7Cr6afwAZa/3wXW4XlKIq5LTwOYfUhCPQPHgTBk3tDP+GTp4DiMjF2ExGROQKnuoHU5+lJWqUOS+0PTJfOwbt942QdSkJFfCHD3QYgC3o6rMP7W+uRNenknHrI0nw8eN4HHI+NpMRESlVPVZir7f6LC3h64tsnzbI+BTQZoVhW1EEBKpqkJ70W4K5Pi8j9PoFoBLAUQDzYoDmbjgvb8HO4rIk+5qhiooKzJkzB//73/+Ql5eHyMhITJo0CS+++CJ8fGz7S4Q1Q0TkEEcuXFotMHas5cdXr3ZPcLBxEU9RKXDoqz+gffs0tNtbYM+1DgBgrAEaEPgLOnYJwKChAYiaN7X+C7SqmSdDskK57fpd/4XvXeuVV14R4eHhYu3atSI7O1t8/vnn4qabbhKLFi2y+RgFBQUCgCgoKHBhSYnIq6xeLURMjBD6y7/+FhOj325JRYUQ4eGmz6l5Cw/X7+cOFRVCZGUJsXKl/ueN163UVYqfPzogZvbOEu38/zQpng8qxEsNXxOFDZqZltvX1/I5SZIQsbHuOy8lWr1a/3sy97uTpLo/Vyrmruu37GuGRo4ciYiICHz44YfGbWPHjkXDhg2xYsUKm47BmiEisoth0kJ7a0HWrwduv9368X/8ERg8uP7ltENFaQW2vncA2mUFyDjYFqd1UcbHAlCGoc33QjOiDGO7/YmQJx8y39/ImupzEVEVe+d+kiMPNe+xz9AN/fv3x3vvvYejR4+iXbt22Lt3L7Zu3YpFixZ5umhE5I10On1ThrkwIIT+wpWerl/Qs+bFYONG215jyRL9c118QSkrLMP6t/ZBu7IUa44l4KLoanwsBFcwvdknGNo9H0kP90QjzR36B+ImOBaEANuH9auNtSVPhABycvT7yTFMqqB5T/ZhaMaMGSgoKECHDh3g6+sLnU6HefPmYfz48RafU1ZWhrKyMuP9wsJCdxSViLyBOy5cmZn6mwsuKMV5xfh24X5oVwt8fSoRRehlfCxMuoTRNx/E5C7b0GPbvyCdPQN8D/0tJgZ49FHb1imzxNZh/Wpj79xPcmKpltSwtIuX9BWT/VjIzz77DJ988glWrlyJXbt24eOPP8brr7+Ojz/+2OJz5s+fj9DQUOMtNjbWjSUmIkWrz4XL3nBkbq0wB+Qfu4Tlj2zF6Mif0TTSD/e81QefnuqLIoQgyicXUzpvwvrXd+Pc1RB89M8L6Ll6lj4I1SzL7NmOFaCOBVoJjs39JAfWakkBfS2pTufWYrmC7PsMxcbGYubMmZgyZYpx2yuvvIJPPvkEv//+u9nnmKsZio2NZZ8hIrLe98GRtbqqHzsiAsjPt708DvYXObsrD5kLj0D7QzA2Xk6CrlpFfxu/kxjbPRupjzbFLZM6Vs0BZK3viiM4msy6+sz95En1+S44CfsM3XD16tVaQ+h9fX1RWVlp8TmBgYEIDAx0ddGISGls6fswYIB+m7ULl7laEF9fYOnSuofW12RHs9sfG04i481saDeFY3txZwAtjI8lBR2BpncuNFOjkJjaFpJPq9oHsNYEaAtfX9OagGqzVJMF9Zn7yZOU3LxnJ9mHoVGjRmHevHlo2bIlOnXqhN27d+PNN9/EX/7yF08XjYiUxNa+D/W9cGk0+rmEpk3TH9tWZi4oolLgQMYxaJechXZHJPaVtgdQFXL63LQfmkH5SJ0ejza3tQfQ3u7XsIskAatWAc2acdJAe9mw5InsKLV5zwGybyYrKirC3//+d2RkZOD8+fOIiorC+PHj8dJLLyEgIMCmY3BoPZHKOTK02cZJC+t8zS1b9MPtX3nF+v43mhoqKyrxy/JDyPjgIrS743C8PM64iy8qkNxkHzRDizD6mXaI7mnnRcjWZg9zmjUD3ntPnhdtJVHSDNQyaN5z1/Vb9mHIGRiGiFTO0b4Pzrhw2XBBEdHRyJqWAe2KEmQcbIezlVUhJxClGNZiLzQjyzHyuY4Ibxtm3+vbU5a6fPIJcP/9jr82KZOhRhUwX0vq4r5i7DNEROQsjvZ98PWtf8fQOprdBAAIgUmnX8F/n+tp3B6MQoxoeQCasRKGP9cZN7W4tX5lsKEsVkVHO6cMpCxKbN5zAMMQEXk/T/d9uHFBqZz6BHxyzxo35yAW6ViEDGgQLuVjTNtD0EwIwuCnkhAY0telZal1cbOkrg7jpA4ajX6SUaU07znArmaynJwcRc7Zw2YyIpXzYN+Hi0fy8dWCQ9B+HYj15zvhFuxEJHKRi0j8Kd2MMZ3/gOahUPR/PBF+QW78+7R6E+CxY3XPMfTss8DChe4rG9ENsmwm69ChA6ZPn46ZM2eiUaNGrioTEZFzuXlo8+mduchceBTaH0Ow6UoSKlFVq3LWvxX69ACe+msz9EyLgo9fjFNe0241mwCLi4HXXjO/7+uvA717m28SUVKHYCIL7KoZ2rZtG5566ink5ORg3rx5eOihh1xZNqdhzRARAaj/CLE6HFt3AhlvnYB2c1P8XJJo8ljXBr9D0ycPmmkx6DiqDSQfqV6v5XSOLiSqgjWryLNkPZrsv//9L1544QU0bdoUb731FpLluLBcNQxDRGTkpJoMUSmw74uj0L6TC+3P0ThQ1tb4mIRK9A0+AE3KJaQ+3QbxA2XevcCR0XaW5m3ijNTkRLJsJjN48MEHcffdd2P+/PkYMWIEhg4ditdeew0333yzs8tHRORc9RghVllRiZ8/OgjtB/nQ7onHnxVVEx36oRwpYfugGVaC0c+1R2TXJOeV2dXsHW1nbc0qSdKvWTV6NJvMSBEc7q0nhMDQoUNRVFSEt99+G99++y2mTJmCOXPmIDg42JllJCJPYX8QlF8tx6Yl+6H9bzEyD7dDbmVn42NBuIY7IvcidWQFRs7ohLA2PTxTyPq+T/aOtrO2rIcdS4wQyYFdYei9997Dzp07sXPnThw+fBi+vr5ISkrClClT0LVrV/zvf/9Dx44dkZGRgZ49e1o/IBHJl7f0B3EgKFy7dA3r3tgH7Wfl+PLPTrgsuhsfC0EBRrY6AM3dPrjj2SQ0at7bo2V1yvtk73psKlqzilRC2CEmJkaMGzdOvP7662Lr1q2itLS01j7z5s0TnTp1suewLldQUCAAiIKCAk8XhUgZVq8WQpKE0F8aq26SpL+tXu3pEtpm9WohYmJMzyEmxmz5C3IKxMqpP4lx0dtEIxSZPKWZdF482mGT+GbuL6K0oPb/e+4uq8lznPU+GY5V83jmjpWVVfs1zd2yshz9bRAJIdx3/Xb6chznzp1DVFQUdNVXNfYwdqAmsoOjI4vkxoYOvhcSBuLLBYeh/SYIP15IwnUEGneL9T0DTefj0PylMfo9lgjfABeeqyOdkV3xPtk62k4Ga1aROsh6NFldhBDYvHkzBg0a5MzD1gvDEJEdHF3HS06sBAUB4LwUgRhxChWoWvC5fcCf0PQ4Bc3jzdHjgQT3DIF3NNS46n2ytanOw2tWkTrIejRZXSRJklUQIiI7eUN/ECsdfCUAEeIc+mEbihpEQNPvHDRPxiJhZBsArd1WTACOd0Z21ftk62g7laxZRerAtcmIyJSn1/FyAnHmLGyp0/l89gE0m5MMIMHFJaqDo6FGDu+TCtasInVgGCIiU/aOLJKJyopKbP/gALQfXsKZ3X741IbnNEtOtL6TqzkaapzxPjlj6oR6zNtEJBcMQ0ScS8eUm9fxqo/rxdex8V/7oV1RgswjHXCuUj/RoQ90eB3RiMIZ+Jh7opwCnaOhpr7vk7dMnUDkBGb/nyBSDa1W33k1JQWYMEH/My5Ov13NDP1BoqNNtzdtqr+AhoXpQ6QHXL14FZmzfsaDbbYiIuQqhj3fA+8fHohzlc0RigI8EL8VXzy3E82WvQYfSaoKBgYyC3TGUAPYX1ZL71NMTN0dmA2dn2v2VTpzRr9d7Z9/Uh2njyaTI44mI7O4tpJ1hlqzNWuATz4BLl6seiwmBnjrLX1AcnGt2pWTBfh6wQFo1/jiu7OdcRWNjI81ly5gTPvD0DzQEClPJiHgpqrRYa5cmNXp6lNWe2o3vWXqhOpYu+u1FDu0Xo4YhqgWb7wguIpWC4wda9u+TmxmOX/wAtYsOAzttw2w/mIXlFcbAt/K9zQ0XY5D83AY+jzSqe45gJR0oXRHWb1h6oTq2Nzn1RQ7tJ5IEdyxtpKSLsKW6HTAX/9q+/6GZhYHa9VO/nQaGa//Ae2Gxtha2BkCA42PdQw8jtSep6H5WwS6je8AySfGtoMqqYOvO8rqDVMnGFiq3a3n55DUh2GI1MnVFwRv+Wt140YgP9/2/R1Ysfz3b/6EdtEpaH9qjt+udgRQFXJ6NjwETf/zSH2yJTrceTOAm+09A6pJDkPynUGn03/HzDVuOPA5JHVjGCJ1cuUFwZv+Wt240f7nWKlVE5UCu1b+Du2/zyHj11gcvl410aEPdOgfuh+a2wqQ+uzNaNmnI4CO9TkD5/CGWj4DhU6dUIs7andJNRiGSJ1cdUHgX6tVqtWq6a7rsG3pAWg/ugLtvjY4pUuAYaJDf1zH7c32QjO8FHc91wHNO3X1THkt8ZZaPgMFTZ1QJ29q7iOP49B6Uqf6DGeuiz1/rSpBPf6iLm/cFN+98iv+mrAZUUGXMPCJLli0exBO6WLQECUYG70d/5v8Ey6cvIZvzvfCIx8PQPNOzZxXdmfw1iHojg7JlxNvae4jWeBoMlI3Zw+9XrVKP1+RNStXAuPH2398d9PpgIgIu/oNCQCXfJujre4wLiPMuL2xdAV3xR+A5l5/DH0mCQ3CGrigwE6khhGHSm7+M7w/1mp3lfz+EEeTkQso+T8+V3H22kre9teqry+wdKnNQ+sFAAEJj+r+jQKEIlXSYkTUHnS5Mxpd3pgI/+D+ri2vM6mhT4qSRtrV5C3NfSQLrBlSC2/r9yBX1v5aBfQ1T0r7a1WrBSZOBIqLre76pjQdYfGhGH/pXQReOVf1gNI+b7bW8k2dqg+LSvnjwtv+KFLSxJpkN3ddv9lnSA28td+DHFXvi2TJffcp8uIjbAhCAPDUtEpMyp5jGoQA5X3ebK29W7JEOcu4yGn5GZ1OP1px1Sr9T0eXd9FogBMn9JNErlyp/5mdzSBEdmHNkLdTQ78Hczz91+9zzwGvvWb+MUlSRCdVUSlweO0fyFh8Eg9vuB8ROAfJ+tOAZs2ACxfMP6akz5sttXzVyX0ZFzktP8OaarIRa4bIObxtdJMtPP3Xr06n/2u3LunpHlvotC6iUmDnx4cwq89GJDTIRqfRN2PdBl+0cEYQApT1eatrxKE5hpAhx/fW2pQPgPvKzZpqkiGGIW+ntrk45PAfrcICqO66DpsW78GTXTehVcBZ3DKpI/65IxlHrrdGAMpwZ4gd5bz/ftv2U8rnzdIQdEtk9t4ayeUzKadQRlQNw5C387bRTXWRy3+0CgigZYVl+GbuTjzSfgsigy4hOb0r3t47CDm6aDRCMe6O2Y5VT2zDhZwyPLfGxokn587Vj8yzhZI+b9X7pEydattz5Bb25PKZlEsoI6qBQ+u9nbdMvW8LuQyFlmkALc4rxnev7Yd2dSXWnuyMIvQyPhYmXcJdbQ5Cc18gbn+qMxqE9alWTiufIUD/+AsvVP3b2z5v1YegL1lifX9H31tX9XWTy2dSLqGMqAaGIW9nz1wcnu50bA9zZZXLf7QyCqCX/riMr/55EBlr/fF9XhJKURVyonxyMabjUWgeDMbAKZ3h39BCeWz5DC1eXPVZ8ea5X1z53rqyU7FcPpNyCWVENQkVKCgoEABEQUGBp4viOatXCxETI4T+v0L9LTZWv93S4zExVY/LiaWyzp1rus3SLSvLPWWUJP2t+msbtrnw93p2d554975N4vawX4Uvyk1evrXfCfFsryyx/YP9Qleus+/A1j5Dju6rNK54bw3HrPlZdebnxYOfSaOKCv3nwty5GsoSG6vfj0i47/rNMKQmFRX6ILBypf6n4T8cd/xH7Cx1lRUQIjxcPv/RujEQ/JF1Urw2Ikv0Dd4rJOhMXrJz0BExe1CW2Pv5EVGpq6zfC1n6DNV3X6Vx5ntrCAiWwrszP7dyCKlyCGWkGO66fnOeIbVT0jxEtpQ1LAy4dEl/31wTjbvngHFR06OoFDi45ji0/zoD7fZI7C1tb/J475v2QzMwH6nT43Hz4Fb1fj2XUlLzbHXOKvfGjfrpH6zJynJOXzc5/L45azTZiGuTkXvIpdOxLWwpa36+flTTBx/U7nvhif9onbj2U2VFJXZ+fAgZH1yEdlcrHCtvC6Ct/mVQgeQm+5B6exHGPNcO0T07O+U1XU7Jk+856711d183OaxH5uw1AYnqiWFI7eTS6diZZWjbVj8U2gv+o60orcCWd/dDu7wQGQfb4UxlovGxQJRiaMReaEaWY9SMjghv292DJXWApRmRDXNCyXUmZ2dTa6diOYQyohsUEYbOnDmDGTNm4Ntvv8W1a9fQrl07fPjhh+jRo4eni6Z8SvqP2J6yKvg/2tIrpfjxzX3QflqGL493RL7oZnzsJhRhROx+aMYCw5/tjOCoWz1Y0nqwNieUJOnnhBo9WpEh1i5yGelFpGKyD0OXL19Gv379kJKSgm+//RbNmzfHH3/8gcaNG3u6aN5hwAAgPFzfvGSOnP4j9uKLRtHZInz72n5oVwNf53RGMW4xPhYu5WP0zYegmRCEwemdEdS4rwdL6iRKap51NXumvyAil5B9GFqwYAFiY2OxbNky47a4uDjPFcjbrFljOQgB+v+YH3nEfeWpi5ddNPKPXcJXCw5Bu9YfP5zrgjJUhZxon1ykdjoKzaQQDJjcGX5Bygt4dVJS86w7GJb9MNd/ip2KiVxO9qPJOnbsiGHDhuH06dPYtGkToqOjMXnyZDz66KMWn1NWVoaysjLj/cLCQsTGxnI0WU3WRmdVJ6dOrQoeiXLm11xkLjwK7Y/B2HQ5Cbpqf4/c7H8CY7ufgOaxZuiZlgAfPy9eLcfdI6iUQg4jvYhkxF2jyWQfhoKCggAA06dPx913341ffvkF6enpeP/99/Hggw+afc6cOXMwd+7cWtsZhmqw9YIEeG5ouiXOumi46uJT7bhnzkpY+WMzaLc2x45i01FeXYKOQNMnF5onotFp9M2QfGxaG175DEHcWpOnHKZ0ICKPYRi6ISAgAD179sS2bduM26ZNm4adO3di+/btZp/DmiEbrVoFTJhg+/7edoFy0bBu8cVqlD8+FQH5ecZtOYjBk1iMDGjQN3gfNIMuIfXp1mid3LI+Z6BshtFkgDzmhCIi2XFXGJJ9PXxkZCQ6duxosi0hIQGnTp2y+JzAwECEhISY3MgMe0eIedOK0oYLcc0mQsOwbq3WrsNVVlRi+9L9+O/NL0PcfTf8qgUhAIjGaazGWOS/9iF+KkzC018lyyMI6XT6GsJVq/Q/dTr3vbahn0x0tOn2mBgGISJyK9l3oO7Xrx+OHDlisu3o0aNo1Urms+oqgbXRWZYovVOrk4Z1l18tx+Z39kP73yJkHGqPc5UdcQJ3AhC1/srwAQBJQtjbc4GnJsmjZs2emjFHmhNteQ4n3yMiOXDpYh9O8Msvvwg/Pz8xb948cezYMfG///1PNGzYUHzyySc2H4Nrk9XB0jpBnl7o1JWyshw+z6v5V8Wa53eISTdvFmFSvsnuw7FWOb8/e9ajc2QRXyUt/EtEssWFWqv56quvRGJioggMDBQdOnQQS5cutev5DENWmLtwyWGhU1dZudK20LJypRBCiIKcArHqiZ/E3THbRCMUmezSVLogHmm/SXwz9xdx/cOP7Tqux9izMKgji/gqaeFfIpI1LtTqRFyo1QaGJo01a/RD1Gvypk6tNo6i+2bYYry7uzfWne+C6wg0bo/xPQtN4jFoHgpFv8cS4RfkZ9dxPT5c3NZy/vgjMGmSfYv4KmnhXyKSPS7U6i6c10PPsHxFcrL+d+DNk79Z6StVCQmnEY1R309BJfSfhXb+2Rjb8yQ0jzdHjwcSIPlE2X1c2cyQbWufr40b7Z8lmjNLE5ECqTsMKXnFbFdyRqdWe0KmuwNptZmsBSRIqAouldDXgKVjMbo0OApN33PQTItBwsg2kHzibT6urGfIdvY6c9XDFWeWJiIlcmkjnEyYbXP09n4NFRX6jrorV+p/urOfjz2dZ93c0bZSVyl2f/q7+PuALPGk37/EKZi+dp4UIb7sNVf8uemU4y9i7pxiY+XzmTL0GbLUad7QZ+jHH+3vEF6PzulERDWxz5AT1Wpz9PZ+DZ6s8TLM31PzY2Wuz5E9+9rKTC1TpZCw48OD0H6QD+3e1siuqJrfJwClmBbyMYYlnkH3v3RD2KS7nD4DtSybX22Z8HD0aPtniebM0kTkRG7r8+vSqCUTtZKlN//16skaL3tGKdmzrz3nXuOYl/yaib9IH5ocugFKRGrkdrHi8a3i0p+XXfbrkD1barAsTb1gy2gye55DRGQGa4acqFaytHUZipUrgfHjXV9AZ/F0jZc9o6kA54280umAefMgZs8GAFRf3cvQB+hBfAzEtYbmbl8Me6YzGjVvZP211cCWGixHFsZV8GK6RCQfHE3mSrZ2IHV2R1NX8/RInjNnbNvPns6zVvYteX8FMH06Gl29aBKCDHwgIACsiH4e0vETbJqpyTCKsC6OdKjnzNJEpCDqDENKGQJtL0+O5NFqgaeesm3fyEjb18AyE0jPH7yALxf+jrzM7Xi+cCaAuis3JQA4c5rDuevDltBk73Pk3q+KiFRDnWFIKUOg7eWpGi9LHaFrMoTMixetB6cagfTU9jPIeO04tBtCsbWgM4C+OIEJgJl1wCzicG754LQWRCQjsl+13mW8ccVsQ42XZK7BCPrtsbHOrfGqa9FTc+67D7jnnrqb826U/3TaLMy/cwt6NTqEVn2jkZ4xCJsLuqISvngkcAVicdq+D7DSmj29lSE81/wMnDmj367VeqZcRKRa6uxAXZ23VdXbMmTamUHP1k7TzZoB77wDTJ9edxACcMW/KV4Sc/GvisnGbRIqMSB0HzS3FWDM023Q6tQW2zrBAxzOLSee7uRPRIrCDtTu4khfCDkz1Hi5azkNW5ue3npLH4isBCEA0JR/iiwMhj+uY3DTvdAMv4a7nktARGLXqp3K7azlUWKzpzfydCd/IiIzGIa8kTtH8tja9BQdbXNwGtVkKx4ZH4QRMxIR2rKX+Z2sdYI3YD8UeeFyHUQkQwxD3spdNV42jsy7mtADv72/Arb0VnpKOwhI7lf3TnV1gjeYOxd44QV11gjJtfnXW6e1ICJFU28HanIOQygBanXcFpIEIQQWFE1G0xa+SP70MeQgxjgRYi32dvC21Ak+NhZYvRp46SXXBQCdTt9fatUq/U9bpwpwB61W3y8nJUXfryolRX9fDh2TPdHJn4jICoYhqj8LoeS0iMZYrMbMKzNxDQ3R0u8s1rd+FBL0QcmEo1MaaDTAiRP6mapXrtT/zM52bbOYnMOG3Edq1RGeFT2tBREpGkeTUb2d2HoaGa8fR+aGEPgUXUELnEMuIrEFA5AQ+Cc0t5xB6t9aoOu97SH5SMpeqsEVi8s6i5JGain5M0BEbuOu6zfDEDnk8No/oF2cA+1PEdh1LcHksV6NDkLT/wJSn2yJ9sNbmz+AXPu01EXuYcOeteHkMFJLiZ8BInIrDq0nWRGVAr99chja985D+1tLHLneBkAbAIAPdBjYeB9SbyvEmGduRss+nawfUIlTGsh9WLjSRmop8TNARF6JYYgs0l3X4af3D0D70RVo99+MHF1HAB0BAAEow+3N9kFzZynumpGAZgndPFtYd5B72Dh2zLb9OFKLiMgEw5C3V9XbeX5lhWXYsGgftCuvYc3RBFwQXYyPNUIx7ozZj9QxAnc+28nyHEDeSs7DwrVaYM6cuvdR6gLEREQupu4w5O2LRdp4fiXnS/Dda/ug/bwSa08mohBVIaeJdBl3tT4Izb3+GPJ0EhqE9XHnGciLjXMquT1s2Lo+nBAcqUVEZIZ6w5ClUUGGIchKXKy1ei3QsWP6mgIL51f8zsfQ7o6H9it/fJ+XhFJUhZxInzykdjyC1LRgDJraGf4N+7v3POSqrokePTks3FpfJoO5c5X3mSYicgN1hqG6/pI2bHvySf2SFkr5K9pcLZA5QqASwOXJz+MhnEAl9OfX2u8kNF2zoXk0HLf+pRN8/Fq4vsxK5O6132xhax+ltm1dWw4iIoVSZxiy5S/p06eBefP0sxjLnaVaLgt8AMTiNCb6r0Rsn1hopkQiaVw7SD6tXFtOb+HOtd9sIee+TERECqDOMGTrX9KzZwOJifJuWrC1v4gZH33sB4xPdn6Z1EBOw8Ll2peJiEgh1Lkchz1/Iaen13/dKVeuY2VrfxFzWFPgHbjEBRFRvagzDBn+kraFYRI9R7loHauK0gpsXLQHyx/cYP+TuRimvBdadYSlRWtjYpQ5GICIyI3UGYaq/yVtC0cn0XPyopllhWX4es5OPNxuCyIbXkHKU12xPOc2+8rEmgJ5L7RaH55YtJaIyAuoe22yl1/W9wuy5sUXgcGD7esk66R1rIrzivHtwv3Qrhb4+lQiilBV/jDpEsa02Ycl5+5FUPEFSLa8lWpfDFPOC60SEZEJLtTqRBZ/mTod0KqVvqbGFvZMyFiPRTPzj13CVwsOQbvWHz+c64IyBBkfi/LJRWqno9BMDMHAKZ3hF+RXdYEHas99I4R+fpm2bes/6knps3XLfaFVIiIywYVa3cHXF3j7bfNBwhx7JmS0cx2rs7vykLnwCLQ/BGPj5SToUDXR4c3+J6DpdgKavzZFr4kd4eNXo+OzO+a+8YbZuuW+0CoREXmEusMQYDlImCOEvvYgPd36hIw2jtT6dPE5vP3X/dhe3BlA1USHXYKOQNMnF6lTopCY2haST5z183DV3DfeMlu33BdaJSIij1B3M1l1hiag9euBV16xflAzzVu1jhcXZ3Hul0pIOI0YxCPbOAt0n5v2QzMoH6nT49HmNplMgOhNTUv1aLokIiL3YzOZuxkm0XNW7cGNEWti3DgAEiRUBaJK6DvrTscbSGmyF5qhRRjzXHtEde/sWNldyZualjg5IRERmcEwVJMTljaoKK3A5nf2Q/txOErxAWZjDmJRFSjyfZrjQMoTeP/fgxHeNqy+JXYtb2pakutCq0RE5FEMQzU5WHtQeqUU697Yh4zPyrDmeCdcEt1uPDIIX0CDpyM+wZDul9DlkVvQbPRQpCjlgutt617JcaFVIiLyKPYZMqeuoeqAscNw0dkifLNgP7QZwDc5nVGMYOOuTaWLGN32MDQTgjD4qSQEhgQ68YzcyErfJ0X1GapO6dMEEBGpgLv6DCluBur58+dDkiSkp6e77kXqWNqgcPEyfPRVM4yM+AVNowNw39t98X85fVGMYMT4nsUTSZuQ9dYe5F5tjP8cGYA7Z/dSbhACvHfdK0MfsfHj9T+VVn4iInIaRTWT7dy5E0uXLkVSUpLrX6zaUPX8rYewed11vLO3P7KmdTOO/gKAtv7ZGNvjJDSPN0fPtARIPlGuL5u7sWmJiIi8mGLCUHFxMe6//3588MEHeMWWoe+W2Ng8cmzdCWjfPIGMLU3xc8lkk8e6NTiM1D7noJkWg46j2kDyiXe8PErhynmMiIiIPEgxYWjKlCkYMWIEbr/9dqthqKysDGVlZcb7hYWF+n98+SUwa5bZWZTFmFTs++IotO/kQvtzNA6UtQUQBwCQUIm+wQegSbmE1KfbIH5gAoAE556gEhialqzxRH8c9gEiIiIHKSIMffrpp9i1axd27txp0/7z58/H3Llzaz+QllZrkzh9Bhg7FpN93sN7lY8BaA8A8EM5UsL2QTOsBKOfa4/Irm5omvMGnli2wxuWCiEiIo+R/WiynJwc9OzZEz/88AO6dOkCAEhOTkbXrl2xaNEis88xVzMUGxuLAgDm+qIbZoNOwCEMjTwAzV0VGDkjEU3iGzv9fLyaJ1aE5yr0RERei6vW35CZmYnU1FT4Vmvy0Ol0kCQJPj4+KCsrM3nMHOMvE+bDkME17TdokDrcOQVXG08s2+FNS4UQEVEtHFp/w+DBg7F//37s2bPHeOvZsyfuv/9+7Nmzx2oQskeD0itOO5bq2LNsh5Jfk4iIvI7s+wwFBwcjMTHRZFujRo0QHh5ea3u9KWUWZTnyxLId3rRUCBEReYzsw5BbcIHO+vPEsh3etlQIERF5hOz7DDmDSZ8hSwt0sqNt/Xhi2Q5vXSqEiIgAsM+Qa6xYYXaJDQYhJ/DEsh3eulQIERG5lbrC0F13ASdOAFlZwMqV+p/Z2QxCzlLHmm4uC5yeeE0iIvIq6momc3E1G93AGaiJiMgJ3HX9Zgdqcj5bl+1Q+msSEZFXUFczGREREVENDENERESkagxDREREpGoMQ0RERKRq7ECtZhyBRURExDCkWlot8OSTpgudxsToJzHk3DxERKQibCZTI60WGDeu9orvZ87ot2u1znkdnQ7YuBFYtUr/U6dzznGJiIiciGFIbXQ6fY2Qubk2DdvS0+sfXLRa/bphKSnAhAn6n3FxzgtaRERETsIwpDZbttSuEapOCCAnR7+fo9xV80REROQEDENqk5vr3P1qclfNExERkZMwDKlNZKRz96vJHTVPRERETsQwpDYDBuhHjUmS+cclCYiN1e/nCFfXPBERETkZw5Da+Prqh88DtQOR4f6iRY7PN+TqmiciIiInYxhSI40G+OILIDradHtMjH57feYZcnXNExERkZNx0kW10miA0aOdPwO1oeZp3Dh98KnekdoZNU9EREROxjCkZr6+QHKy849rqHkyN8P1okWc4ZqIiGSFYYhcw1U1T0RERE7GMESu46qaJyIiIidiB2oiIiJSNdYM6XRsyiEiIlIxdYchrdZ8J9/Fi9nJl4iISCXU20zGxUSJiIgIag1DXEyUiIiIblBnGOJiokRERHSDOsMQFxMlIiKiG9QZhriYKBEREd2gzjDExUSJiIjoBnWGIcNiokDtQMTFRImIiFRFnWEIqFpMNDradHtMjH475xkiIiJSBXVPusjFRImIiFRP3WEI4GKiREREKqfeZjIiIiIisGaoblzElYiIyOsxDFnCRVyJiIhUQfbNZPPnz0evXr0QHByM5s2bY8yYMThy5IhrX5SLuBIREamG7MPQpk2bMGXKFOzYsQPr1q1DRUUFhg4dipKSEte8IBdxJSIiUhVJCHNXffm6cOECmjdvjk2bNmHgwIE2PaewsBChoaEoKChASEhI3Ttv3AikpFg/aFYWR6ERERG5kF3X73pQXJ+hgoICAEBYWJjFfcrKylBWVma8X1hYaPsLcBFXIiIiVZF9M1l1QghMnz4d/fv3R2JiosX95s+fj9DQUOMtNjbW9hfhIq5ERESqoqhmsilTpuDrr7/G1q1bERMTY3E/czVDsbGxtlWz6XRAXJy+s7S5X40k6UeVZWdzmD0REZELuauZTDE1Q0888QS+/PJLZGVl1RmEACAwMBAhISEmN5txEVciIiJVkX0YEkJg6tSp0Gq12LBhA+Lj413/olzElYiISDVk34F6ypQpWLlyJdasWYPg4GDk5eUBAEJDQ9GgQQPXvTAXcSUiIlIF2fcZkmo2Vd2wbNkyTJo0yaZjuKvNkYiIiJyHQ+tvkHlWIyIiIoWTfZ8hIiIiIldiGCIiIiJVYxgiIiIiVWMYIiIiIlVjGCIiIiJVYxgiIiIiVWMYIiIiIlVjGCIiIiJVYxgiIiIiVWMYIiIiIlVjGCIiIiJVYxgiIiIiVWMYIiIiIlVjGCIiIiJVYxgiIiIiVWMYIiIiIlVjGCIiIiJVYxgiIiIiVWMYIiIiIlVjGCIiIiJVYxgiIiIiVWMYIiIiIlVjGCIiIiJVYxgiIiIiVWMYIiIiIlVjGCIiIiJVYxgiIiIiVWMYIiIiIlVjGCIiIiJVYxgiIiIiVWMYIiIiIlVjGCIiIiJVYxgiIiIiVWMYIiIiIlVjGCIiIiJVYxgiIiIiVWMYIiIiIlVjGCIiIiJVU0wYevfddxEfH4+goCD06NEDW7Zs8XSRiIiIyAsoIgx99tlnSE9PxwsvvIDdu3djwIABGD58OE6dOuXpohEREZHCSUII4elCWHPrrbeie/fu+Pe//23clpCQgDFjxmD+/PlWn19YWIjQ0FAUFBQgJCTElUUlIiIiJ3HX9Vv2NUPXr1/Hb7/9hqFDh5psHzp0KLZt2+ahUhEREZG38PN0Aay5ePEidDodIiIiTLZHREQgLy/P7HPKyspQVlZmvF9QUABAnzCJiIhIGQzXbVc3Ysk+DBlIkmRyXwhRa5vB/PnzMXfu3FrbY2NjXVI2IiIicp38/HyEhoa67PiyD0NNmzaFr69vrVqg8+fP16otMpg1axamT59uvH/lyhW0atUKp06dcukvU24KCwsRGxuLnJwcVfWV4nnzvNWA583zVoOCggK0bNkSYWFhLn0d2YehgIAA9OjRA+vWrUNqaqpx+7p16zB69GizzwkMDERgYGCt7aGhoar6EBmEhITwvFWE560uPG91Uet5+/i4touz7MMQAEyfPh1paWno2bMn+vTpg6VLl+LUqVN4/PHHPV00IiIiUjhFhKF7770X+fn5ePnll5Gbm4vExER88803aNWqlaeLRkRERAqniDAEAJMnT8bkyZMdem5gYCBmz55ttunMm/G8ed5qwPPmeasBz9u1562ISReJiIiIXEX2ky4SERERuRLDEBEREakawxARERGpGsMQERERqZoiw9C7776L+Ph4BAUFoUePHtiyZUud+2/atAk9evRAUFAQWrdujffee6/WPqtXr0bHjh0RGBiIjh07IiMjw1XFd5g9563VajFkyBA0a9YMISEh6NOnD77//nuTfZYvXw5JkmrdSktLXX0qdrHnvDdu3Gj2nH7//XeT/bzt/Z40aZLZ8+7UqZNxHyW835s3b8aoUaMQFRUFSZKQmZlp9Tne8P2297y95ftt73l7y/fb3vP2lu/3/Pnz0atXLwQHB6N58+YYM2YMjhw5YvV57viOKy4MffbZZ0hPT8cLL7yA3bt3Y8CAARg+fDhOnTpldv/s7GzceeedGDBgAHbv3o3nn38e06ZNw+rVq437bN++Hffeey/S0tKwd+9epKWl4Z577sHPP//srtOyyt7z3rx5M4YMGYJvvvkGv/32G1JSUjBq1Cjs3r3bZL+QkBDk5uaa3IKCgtxxSjax97wNjhw5YnJObdu2NT7mje/34sWLTc43JycHYWFhuPvuu032k/v7XVJSgi5dumDJkiU27e8t3297z9tbvt/2nreB0r/f9p63t3y/N23ahClTpmDHjh1Yt24dKioqMHToUJSUlFh8jtu+40JhbrnlFvH444+bbOvQoYOYOXOm2f2fe+450aFDB5Ntjz32mOjdu7fx/j333CPuuOMOk32GDRsm7rvvPieVuv7sPW9zOnbsKObOnWu8v2zZMhEaGuqsIrqEveedlZUlAIjLly9bPKYa3u+MjAwhSZI4ceKEcZsS3u/qAIiMjIw69/GW73d1tpy3OUr8fldny3l7y/e7Okfeb2/4fgshxPnz5wUAsWnTJov7uOs7rqiaoevXr+O3337D0KFDTbYPHToU27ZtM/uc7du319p/2LBh+PXXX1FeXl7nPpaO6W6OnHdNlZWVKCoqqrXYXXFxMVq1aoWYmBiMHDmy1l+WnlSf8+7WrRsiIyMxePBgZGVlmTymhvf7ww8/xO23315rlnY5v9+O8IbvtzMo8ftdH0r+fjuDt3y/CwoKAKDORVjd9R1XVBi6ePEidDpdrdXqIyIiaq1qb5CXl2d2/4qKCly8eLHOfSwd090cOe+a3njjDZSUlOCee+4xbuvQoQOWL1+OL7/8EqtWrUJQUBD69euHY8eOObX8jnLkvCMjI7F06VKsXr0aWq0W7du3x+DBg7F582bjPt7+fufm5uLbb7/FI488YrJd7u+3I7zh++0MSvx+O8Ibvt/15S3fbyEEpk+fjv79+yMxMdHifu76jitmOY7qJEkyuS+EqLXN2v41t9t7TE9wtIyrVq3CnDlzsGbNGjRv3ty4vXfv3ujdu7fxfr9+/dC9e3f861//wttvv+28gteTPefdvn17tG/f3ni/T58+yMnJweuvv46BAwc6dExPcbSMy5cvR+PGjTFmzBiT7Up5v+3lLd9vRyn9+20Pb/p+O8pbvt9Tp07Fvn37sHXrVqv7uuM7rqiaoaZNm8LX17dW2jt//nytVGjQokULs/v7+fkhPDy8zn0sHdPdHDlvg88++wwPP/ww/u///g+33357nfv6+PigV69esvlLoj7nXV3v3r1Nzsmb328hBD766COkpaUhICCgzn3l9n47whu+3/Wh5O+3syjt+10f3vL9fuKJJ/Dll18iKysLMTExde7rru+4osJQQEAAevTogXXr1plsX7duHfr27Wv2OX369Km1/w8//ICePXvC39+/zn0sHdPdHDlvQP8X46RJk7By5UqMGDHC6usIIbBnzx5ERkbWu8zO4Oh517R7926Tc/LW9xvQj9Y4fvw4Hn74YauvI7f32xHe8P12lNK/386itO93fSj9+y2EwNSpU6HVarFhwwbEx8dbfY7bvuM2d7WWiU8//VT4+/uLDz/8UBw6dEikp6eLRo0aGXvVz5w5U6SlpRn3//PPP0XDhg3FU089JQ4dOiQ+/PBD4e/vL7744gvjPj/99JPw9fUV//znP8Xhw4fFP//5T+Hn5yd27Njh9vOzxN7zXrlypfDz8xPvvPOOyM3NNd6uXLli3GfOnDniu+++E3/88YfYvXu3eOihh4Sfn5/4+eef3X5+lth73m+99ZbIyMgQR48eFQcOHBAzZ84UAMTq1auN+3jj+23wwAMPiFtvvdXsMZXwfhcVFYndu3eL3bt3CwDizTffFLt37xYnT54UQnjv99ve8/aW77e95+0t3297z9tA6d/vv/3tbyI0NFRs3LjR5HN79epV4z6e+o4rLgwJIcQ777wjWrVqJQICAkT37t1NhuVNnDhRDBo0yGT/jRs3im7duomAgAARFxcn/v3vf9c65ueffy7at28v/P39RYcOHUy+XHJhz3kPGjRIAKh1mzhxonGf9PR00bJlSxEQECCaNWsmhg4dKrZt2+bGM7KNPee9YMEC0aZNGxEUFCSaNGki+vfvL77++utax/S291sIIa5cuSIaNGggli5davZ4Sni/DUOnLX1uvfX7be95e8v3297z9pbvtyOfc2/4fps7ZwBi2bJlxn089R2XbhSQiIiISJUU1WeIiIiIyNkYhoiIiEjVGIaIiIhI1RiGiIiISNUYhoiIiEjVGIaIiIhI1RiGiIiISNUYhoiIiEjVGIaIiIhI1RiGiIiISNUYhohIkVatWoWgoCCcOXPGuO2RRx5BUlISCgoKPFgyIlIark1GRIokhEDXrl0xYMAALFmyBHPnzsV//vMf7NixA9HR0Z4uHhEpiJ+nC0BE5AhJkjBv3jyMGzcOUVFRWLx4MbZs2cIgRER2Y80QESla9+7dcfDgQfzwww8YNGiQp4tDRArEPkNEpFjff/89fv/9d+h0OkRERHi6OESkUKwZIiJF2rVrF5KTk/HOO+/g008/RcOGDfH55597ulhEpEDsM0REinPixAmMGDECM2fORFpaGjp27IhevXrht99+Q48ePTxdPCJSGNYMEZGiXLp0Cf369cPAgQPx/vvvG7ePHj0aZWVl+O677zxYOiJSIoYhIiIiUjV2oCYiIiJVYxgiIiIiVWMYIiIiIlVjGCIiIiJVYxgiIiIiVWMYIiIiIlVjGCIiIiJVYxgiIiIiVWMYIiIiIlVjGCIiIiJVYxgiIiIiVWMYIiIiIlX7fx9KaKO4gtvcAAAAAElFTkSuQmCC", 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", 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    " ] @@ -744,8 +693,8 @@ "output_type": "stream", "text": [ "theta from own sdg\n", - "[[3.86757762]\n", - " [3.17214797]]\n" + "[[3.85298956]\n", + " [3.28358004]]\n" ] } ], @@ -828,9 +777,7 @@ { "cell_type": "markdown", "id": "2ca466b4", - "metadata": { - "editable": true - }, + "metadata": {}, "source": [ "## Same code but now with momentum gradient descent" ] @@ -839,25 +786,22 @@ "cell_type": "code", "execution_count": 6, "id": "0d44a49c", - "metadata": { - "collapsed": false, - "editable": true - }, + "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Own inversion\n", - "[[3.90140097]\n", - " [3.0552878 ]]\n", - "Eigenvalues of Hessian Matrix:[0.32683063 4.01722503]\n", + "[[4.1356995 ]\n", + " [3.09209032]]\n", + "Eigenvalues of Hessian Matrix:[0.31362428 4.21949761]\n", "theta from own gd\n", - "[[3.90137221]\n", - " [3.055314 ]]\n", + "[[4.13531906]\n", + " [3.09242193]]\n", "theta from own sdg with momentum\n", - "[[3.83824223]\n", - " [3.10556487]]\n" + "[[4.25003261]\n", + " [3.05169046]]\n" ] } ], @@ -934,9 +878,7 @@ { "cell_type": "markdown", "id": "b82627f6", - "metadata": { - "editable": true - }, + "metadata": {}, "source": [ "## AdaGrad algorithm, taken from [Goodfellow et al](https://www.deeplearningbook.org/contents/optimization.html)\n", "\n", @@ -950,9 +892,7 @@ { "cell_type": "markdown", "id": "00d3aff0", - "metadata": { - "editable": true - }, + "metadata": {}, "source": [ "## Similar (second order function now) problem but now with AdaGrad" ] @@ -961,10 +901,7 @@ "cell_type": "code", "execution_count": 7, "id": "6b85aacc", - "metadata": { - "collapsed": false, - "editable": true - }, + "metadata": {}, "outputs": [ { "name": "stdout", @@ -981,9 +918,9 @@ "output_type": "stream", "text": [ "theta from own AdaGrad\n", - "[[2.00003645]\n", - " [2.99977853]\n", - " [4.00021916]]\n" + "[[2.00006114]\n", + " [2.99959609]\n", + " [4.00040218]]\n" ] } ], @@ -1041,9 +978,7 @@ { "cell_type": "markdown", "id": "d8ddde38", - "metadata": { - "editable": true - }, + "metadata": {}, "source": [ "Running this code we note an almost perfect agreement with the results from matrix inversion." ] @@ -1051,9 +986,7 @@ { "cell_type": "markdown", "id": "ff15b503", - "metadata": { - "editable": true - }, + "metadata": {}, "source": [ "## RMSProp algorithm, taken from [Goodfellow et al](https://www.deeplearningbook.org/contents/optimization.html)\n", "\n", @@ -1067,9 +1000,7 @@ { "cell_type": "markdown", "id": "66f96d12", - "metadata": { - "editable": true - }, + "metadata": {}, "source": [ "## RMSprop for adaptive learning rate with Stochastic Gradient Descent" ] @@ -1078,10 +1009,7 @@ "cell_type": "code", "execution_count": 8, "id": "888f1b4e", - "metadata": { - "collapsed": false, - "editable": true - }, + "metadata": {}, "outputs": [ { "name": "stdout", @@ -1098,9 +1026,9 @@ "output_type": "stream", "text": [ "theta from own RMSprop\n", - "[[1.99985591]\n", - " [3.00049636]\n", - " [3.99943837]]\n" + "[[1.99952851]\n", + " [3.00380258]\n", + " [3.995522 ]]\n" ] } ], @@ -1164,9 +1092,7 @@ { "cell_type": "markdown", "id": "2e0860f7", - "metadata": { - "editable": true - }, + "metadata": {}, "source": [ "## ADAM algorithm, taken from [Goodfellow et al](https://www.deeplearningbook.org/contents/optimization.html)\n", "\n", @@ -1180,9 +1106,7 @@ { "cell_type": "markdown", "id": "ab4a9859", - "metadata": { - "editable": true - }, + "metadata": {}, "source": [ "## And finally [ADAM](https://arxiv.org/pdf/1412.6980.pdf)" ] @@ -1191,10 +1115,7 @@ "cell_type": "code", "execution_count": 9, "id": "ccdd4d77", - "metadata": { - "collapsed": false, - "editable": true - }, + "metadata": {}, "outputs": [ { "name": "stdout", @@ -1211,9 +1132,9 @@ "output_type": "stream", "text": [ "theta from own ADAM\n", - "[[2.00008335]\n", - " [2.99969576]\n", - " [4.00033679]]\n" + "[[1.99984836]\n", + " [3.00097416]\n", + " [3.99895448]]\n" ] } ], @@ -1282,9 +1203,7 @@ { "cell_type": "markdown", "id": "25ac988c", - "metadata": { - "editable": true - }, + "metadata": {}, "source": [ "## Introducing [JAX](https://jax.readthedocs.io/en/latest/)\n", "\n", @@ -1298,9 +1217,7 @@ { "cell_type": "markdown", "id": "37d556d0", - "metadata": { - "editable": true - }, + "metadata": {}, "source": [ "### Getting started with Jax, note the way we import numpy" ] @@ -1309,10 +1226,7 @@ "cell_type": "code", "execution_count": 10, "id": "5b81d6e4", - "metadata": { - "collapsed": false, - "editable": true - }, + "metadata": {}, "outputs": [], "source": [ "import jax\n", @@ -1326,9 +1240,7 @@ { "cell_type": "markdown", "id": "c42db672", - "metadata": { - "editable": true - }, + "metadata": {}, "source": [ "### A warm-up example" ] @@ -1337,10 +1249,7 @@ "cell_type": "code", "execution_count": 11, "id": "98eb2f26", - "metadata": { - "collapsed": false, - "editable": true - }, + "metadata": {}, "outputs": [ { "name": "stderr", @@ -1353,7 +1262,7 @@ { "data": { "text/plain": [ - "[]" + "[]" ] }, "execution_count": 11, @@ -1415,9 +1324,7 @@ { "cell_type": "markdown", "id": "8a5f19b5", - "metadata": { - "editable": true - }, + "metadata": {}, "source": [ "### A more advanced example" ] @@ -1426,15 +1333,12 @@ "cell_type": "code", "execution_count": 12, "id": "d8f5eb38", - "metadata": { - "collapsed": false, - "editable": true - }, + "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "" + "" ] }, "execution_count": 12, @@ -1481,6 +1385,11 @@ } ], "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, "language_info": { "codemirror_mode": { "name": "ipython", diff --git a/doc/LectureNotes/_build/jupyter_execute/exercisesweek41_16_2.png b/doc/LectureNotes/_build/jupyter_execute/exercisesweek41_16_2.png index aef2a3345..e9439ac0a 100644 Binary files a/doc/LectureNotes/_build/jupyter_execute/exercisesweek41_16_2.png and b/doc/LectureNotes/_build/jupyter_execute/exercisesweek41_16_2.png differ diff --git a/doc/LectureNotes/_build/jupyter_execute/exercisesweek41_22_2.png b/doc/LectureNotes/_build/jupyter_execute/exercisesweek41_22_2.png index b97f16320..5840ca3f8 100644 Binary files a/doc/LectureNotes/_build/jupyter_execute/exercisesweek41_22_2.png and b/doc/LectureNotes/_build/jupyter_execute/exercisesweek41_22_2.png differ diff --git a/doc/LectureNotes/_build/jupyter_execute/exercisesweek41_5_1.png b/doc/LectureNotes/_build/jupyter_execute/exercisesweek41_5_1.png index f302d22ea..088571c87 100644 Binary files a/doc/LectureNotes/_build/jupyter_execute/exercisesweek41_5_1.png and b/doc/LectureNotes/_build/jupyter_execute/exercisesweek41_5_1.png differ diff --git a/doc/LectureNotes/_build/jupyter_execute/linalg.ipynb b/doc/LectureNotes/_build/jupyter_execute/linalg.ipynb index 0a0984204..1eb3ab202 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": [ - "[ 0.69465386 1.75956617 -0.23303727 -0.53125507 1.34598722 -1.09928714\n", - " 1.37013105 0.79898903 -0.23663482 0.99427512]\n" + "[ 0.03563709 0.57852915 0.83220985 1.27108866 -0.3587467 -0.38713573\n", + " -0.09584387 0.5223261 1.7663967 0.94027059]\n" ] } ], @@ -662,26 +662,26 @@ "name": "stdout", "output_type": "stream", "text": [ - "[[0.81947005 0.93619369 0.37449168 0.91933003 0.76855921 0.41820116\n", - " 0.96853248 0.60375434 0.96015381 0.30269539]\n", - " [0.46086448 0.16777789 0.9930742 0.10837392 0.69089532 0.94221383\n", - " 0.53629564 0.50327198 0.33734605 0.04757138]\n", - " [0.51069279 0.12363332 0.79171202 0.16791183 0.62617788 0.9288904\n", - " 0.85112594 0.86519139 0.61192712 0.90842732]\n", - " [0.82551764 0.67524588 0.02175561 0.1118933 0.42575338 0.45731379\n", - " 0.61069681 0.40184681 0.18702469 0.71838601]\n", - " [0.68655456 0.11747908 0.28253033 0.4591127 0.68072161 0.59372982\n", - " 0.95343966 0.24780663 0.98740373 0.06808421]\n", - " [0.99512017 0.14828178 0.02354386 0.90860768 0.891715 0.39039235\n", - " 0.48151166 0.43563433 0.52657934 0.73176319]\n", - " [0.77030637 0.00676256 0.37454707 0.5076963 0.51937727 0.46065811\n", - " 0.65917558 0.72962885 0.99370678 0.92341148]\n", - " [0.16292908 0.17214545 0.44995924 0.20367355 0.64885265 0.34225662\n", - " 0.4215795 0.27933134 0.02552966 0.62908496]\n", - " [0.8084934 0.51364117 0.4937346 0.05296475 0.69247718 0.56783103\n", - " 0.85276538 0.52635761 0.96461948 0.67374815]\n", - " [0.02137508 0.03177331 0.78186404 0.33096549 0.8423144 0.07745579\n", - " 0.4619526 0.61414743 0.38460453 0.51928402]]\n" + "[[0.79010601 0.87637891 0.68824222 0.4636751 0.50100007 0.22715479\n", + " 0.17865868 0.90903158 0.5736973 0.96961052]\n", + " [0.57293306 0.96660465 0.65525178 0.52480767 0.70159137 0.30894285\n", + " 0.11675128 0.60321647 0.68281739 0.64952115]\n", + " [0.21833102 0.74761815 0.52789388 0.27530242 0.69463406 0.89961861\n", + " 0.91864509 0.41794469 0.27403356 0.11416086]\n", + " [0.77596142 0.20224027 0.68830164 0.50895251 0.83741078 0.71957514\n", + " 0.78945959 0.94466211 0.06443054 0.29474356]\n", + " [0.39914635 0.22706777 0.23499891 0.9794096 0.33435637 0.28614301\n", + " 0.21641173 0.16925937 0.79086674 0.41259788]\n", + " [0.70408202 0.57833531 0.01817739 0.64689773 0.71380438 0.69311221\n", + " 0.09930135 0.90168941 0.47308061 0.445128 ]\n", + " [0.10100211 0.60575887 0.69824402 0.06423317 0.24582593 0.97235642\n", + " 0.21181534 0.72033728 0.77014839 0.13298019]\n", + " [0.25816519 0.81826799 0.19336703 0.34098895 0.10688434 0.34134773\n", + " 0.21635399 0.57016227 0.69925648 0.01418766]\n", + " [0.80374623 0.58202531 0.71460518 0.66363129 0.02553865 0.7204561\n", + " 0.34704885 0.52927353 0.02631244 0.02944974]\n", + " [0.57080764 0.04516434 0.15388662 0.99458998 0.2765068 0.05148401\n", + " 0.82259916 0.05648118 0.14249052 0.96164155]]\n" ] } ], @@ -800,13 +800,19 @@ "name": "stdout", "output_type": "stream", "text": [ - "0.07218473624441492\n", - "4.346000154268618\n", - "0.09048717285916912\n", - "[[ 1.0974119 3.2131067 3.36880327]\n", - " [ 3.2131067 10.51564682 9.75138064]\n", - " [ 3.36880327 9.75138064 17.26527089]]\n", - "[25.09487358 0.09070064 3.6927554 ]\n" + "0.22370461988004753\n", + "4.592766658048914\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "0.7163835161938888\n", + "[[ 1.11935351 3.19945294 3.19001247]\n", + " [ 3.19945294 10.52074297 8.75737136]\n", + " [ 3.19001247 8.75737136 16.29078236]]\n", + "[23.51532469 0.10801706 4.3075371 ]\n" ] } ], diff --git a/doc/LectureNotes/_build/jupyter_execute/project2.ipynb b/doc/LectureNotes/_build/jupyter_execute/project2.ipynb index 49590c6be..f54660f95 100644 --- a/doc/LectureNotes/_build/jupyter_execute/project2.ipynb +++ b/doc/LectureNotes/_build/jupyter_execute/project2.ipynb @@ -3,9 +3,7 @@ { "cell_type": "markdown", "id": "5b2f9dda", - "metadata": { - "editable": true - }, + "metadata": {}, "source": [ "\n", @@ -15,9 +13,7 @@ { "cell_type": "markdown", "id": "cacbd604", - "metadata": { - "editable": true - }, + "metadata": {}, "source": [ "# Project 2 on Machine Learning, deadline November 4 (Midnight)\n", "**[Data Analysis and Machine Learning FYS-STK3155/FYS4155](http://www.uio.no/studier/emner/matnat/fys/FYS3155/index-eng.html)**, Department of Physics, University of Oslo, Norway\n", @@ -30,9 +26,7 @@ { "cell_type": "markdown", "id": "acb32119", - "metadata": { - "editable": true - }, + "metadata": {}, "source": [ "## Classification and Regression, from linear and logistic regression to neural networks\n", "\n", @@ -76,9 +70,7 @@ { "cell_type": "markdown", "id": "027202f0", - "metadata": { - "editable": true - }, + "metadata": {}, "source": [ "### Part a): Write your own Stochastic Gradient Descent code, first step\n", "\n", @@ -132,9 +124,7 @@ { "cell_type": "markdown", "id": "9388fa74", - "metadata": { - "editable": true - }, + "metadata": {}, "source": [ "### Part b): Writing your own Neural Network code\n", "\n", @@ -170,9 +160,7 @@ { "cell_type": "markdown", "id": "49666354", - "metadata": { - "editable": true - }, + "metadata": {}, "source": [ "### Part c): Testing different activation functions\n", "\n", @@ -182,9 +170,7 @@ { "cell_type": "markdown", "id": "79aacf29", - "metadata": { - "editable": true - }, + "metadata": {}, "source": [ "### Part d): Classification analysis using neural networks\n", "\n", @@ -208,9 +194,7 @@ { "cell_type": "markdown", "id": "42e22900", - "metadata": { - "editable": true - }, + "metadata": {}, "source": [ "$$\n", "\\text{Accuracy} = \\frac{\\sum_{i=1}^n I(t_i = y_i)}{n} ,\n", @@ -220,9 +204,7 @@ { "cell_type": "markdown", "id": "82ae763d", - "metadata": { - "editable": true - }, + "metadata": {}, "source": [ "where $I$ is the indicator function, $1$ if $t_i = y_i$ and $0$\n", "otherwise if we have a binary classification problem. Here $t_i$\n", @@ -240,9 +222,7 @@ { "cell_type": "markdown", "id": "1d6b84d1", - "metadata": { - "editable": true - }, + "metadata": {}, "source": [ "### Part e): Write your Logistic Regression code, final step\n", "\n", @@ -262,9 +242,7 @@ { "cell_type": "markdown", "id": "0bce8832", - "metadata": { - "editable": true - }, + "metadata": {}, "source": [ "### Part f) Critical evaluation of the various algorithms\n", "\n", @@ -278,9 +256,7 @@ { "cell_type": "markdown", "id": "51b1b29b", - "metadata": { - "editable": true - }, + "metadata": {}, "source": [ "## Background literature\n", "\n", @@ -294,9 +270,7 @@ { "cell_type": "markdown", "id": "7e4ffbbd", - "metadata": { - "editable": true - }, + "metadata": {}, "source": [ "## Introduction to numerical projects\n", "\n", @@ -325,9 +299,7 @@ { "cell_type": "markdown", "id": "56112b03", - "metadata": { - "editable": true - }, + "metadata": {}, "source": [ "## Format for electronic delivery of report and programs\n", "\n", @@ -345,7 +317,25 @@ ] } ], - "metadata": {}, + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.18" + } + }, "nbformat": 4, "nbformat_minor": 5 } \ No newline at end of file diff --git a/doc/LectureNotes/_build/jupyter_execute/project2.py b/doc/LectureNotes/_build/jupyter_execute/project2.py new file mode 100644 index 000000000..50eb44cd8 --- /dev/null +++ b/doc/LectureNotes/_build/jupyter_execute/project2.py @@ -0,0 +1,234 @@ +#!/usr/bin/env python +# coding: utf-8 + +# +# + +# # Project 2 on Machine Learning, deadline November 4 (Midnight) +# **[Data Analysis and Machine Learning FYS-STK3155/FYS4155](http://www.uio.no/studier/emner/matnat/fys/FYS3155/index-eng.html)**, Department of Physics, University of Oslo, Norway +# +# Date: **Oct 8, 2024** +# +# Copyright 1999-2024, [Data Analysis and Machine Learning FYS-STK3155/FYS4155](http://www.uio.no/studier/emner/matnat/fys/FYS3155/index-eng.html). Released under CC Attribution-NonCommercial 4.0 license + +# ## Classification and Regression, from linear and logistic regression to neural networks +# +# The main aim of this project is to study both classification and +# regression problems by developing our own feed-forward neural network +# (FFNN) code. We can reuse the regression algorithms studied in project +# 1. We will also include logistic regression for classification +# problems and write our own FFNN code for studying both regression and +# classification problems. The codes developed in project 1, including +# bootstrap **and/or** cross-validation as well as the computation of the +# mean-squared error and/or the $R2$ or the accuracy score +# (classification problems) functions can also be utilized in the +# present analysis. +# +# The data sets that we propose here are (the default sets) +# +# * Regression (fitting a continuous function). In this part you will need to bring back your results from project 1 and compare these with what you get from your Neural Network code to be developed here. The data sets could be +# +# a. A simple one-dimensional function or the Franke function or the terrain data from project 1, or data sets your propose. It could be a simpler function than the Franke function. We recommend testing a simpler function (see below). But if you wish to try more complex function, feel free to do so. +# +# * Classification. Here you will also need to develop a Logistic regression code that you will use to compare with the Neural Network code. The data set we propose are the so-called [Wisconsin Breat Cancer Data](https://www.kaggle.com/uciml/breast-cancer-wisconsin-data) data set of images representing various features of tumors. A longer explanation with links to the scientific literature can be found at the [Machine Learning repository of the University of California at Irvine](https://archive.ics.uci.edu/ml/datasets/Breast+Cancer+Wisconsin+%28Diagnostic%29). Feel free to consult this site and the pertinent literature. +# +# You can find more information about this at the [Scikit-Learn site](https://scikit-learn.org/stable/modules/generated/sklearn.datasets.load_breast_cancer.html) or at the [University of California at Irvine](https://archive.ics.uci.edu/ml/datasets/breast+cancer+wisconsin+(original)). +# +# However, if you would like to study other data sets, feel free to +# propose other sets. What we list here are mere suggestions from our +# side. If you opt for another data set, consider using a set which has +# been studied in the scientific literature. This makes it easier for +# you to compare and analyze your results. Comparing with existing +# results from the scientific literature is also an essential element of +# the scientific discussion. The University of California at Irvine +# with its Machine Learning repository at +# is an excellent site to +# look up for examples and +# inspiration. [Kaggle.com](https://www.kaggle.com/) is an equally +# interesting site. Feel free to explore these sites. +# +# We will start with a regression problem and we will reuse our codes from project 1 starting with writing our own Stochastic Gradient Descent (SGD) code. + +# ### Part a): Write your own Stochastic Gradient Descent code, first step +# +# In order to get started, we will now replace in our standard ordinary +# least squares (OLS) and Ridge regression codes (from project 1) the +# matrix inversion algorithm with our own gradient descent (GD) and SGD +# codes. You can use the Franke function or the terrain data from +# project 1. **However, we recommend using a simpler function like** +# $f(x)=a_0+a_1x+a_2x^2$ or higher-order one-dimensional polynomials. +# You can obviously test your final codes against for example the Franke +# function. +# +# The exercise set for week 41 should help in solving this part of the project. +# +# You should include in your analysis of the GD and SGD codes the following elements +# 1. A plain gradient descent with a fixed learning rate (you will need to tune it) using the analytical expression for the gradient. +# +# 2. Add momentum to the plain GD code and compare convergence with a fixed learning rate (you may need to tune the learning rate). Keep using the analytical expression for the gradient. +# +# 3. Repeat these steps for stochastic gradient descent with mini batches and a given number of epochs. Use a tunable learning rate as discussed in the lectures from weeks 39 and 40. Discuss the results as functions of the various parameters (size of batches, number of epochs etc). Use the analytical gradient. +# +# 4. Implement the Adagrad method in order to tune the learning rate. Do this with and without momentum for plain gradient descent and SGD. +# +# 5. Add RMSprop and Adam to your library of methods for tuning the learning rate. +# +# The lecture notes from [weeks 39 and 40 contain more +# details](https://compphysics.github.io/MachineLearning/doc/pub/week39/html/week39.html) and code examples. Feel free to use these examples. +# 1. Replace thereafter your analytical gradient with either **Autograd** or **JAX** +# +# **Feel free to use codes on these methods from the lecture notes from week 39 and week 40**. +# +# In summary, you should +# perform an analysis of the results for OLS and Ridge regression as +# function of the chosen learning rates, the number of mini-batches and +# epochs as well as algorithm for scaling the learning rate. You can +# also compare your own results with those that can be obtained using +# for example **Scikit-Learn**'s various SGD options. Discuss your +# results. For Ridge regression you need now to study the results as functions of the hyper-parameter $\lambda$ and +# the learning rate $\eta$. Discuss your results. +# +# You will need your SGD code for the setup of the Neural Network and +# Logistic Regression codes. You will find the Python [Seaborn +# package](https://seaborn.pydata.org/generated/seaborn.heatmap.html) +# useful when plotting the results as function of the learning rate +# $\eta$ and the hyper-parameter $\lambda$ when you use Ridge +# regression. Since you will use different gradient descent methods, you can also add Lasse regression. This is however optional. How to code Lasso regression is discussed in the lecture notes from week 40. +# +# We recommend reading chapter 8 on optimization from the textbook of Goodfellow, Bengio and Courville at . This chapter contains many useful insights and discussions on the optimization part of machine learning. + +# ### Part b): Writing your own Neural Network code +# +# Your aim now, and this is the central part of this project, is to +# write your own Feed Forward Neural Network code implementing the back +# propagation algorithm discussed in the lecture slides from [week 41](https://compphysics.github.io/MachineLearning/doc/pub/week41/ipynb/week41.ipynb) and +# [week 42](https://compphysics.github.io/MachineLearning/doc/pub/week42/ipynb/week42.ipynb). +# +# We will focus on a regression problem first and study either the simple second-order polynomial from part a) or the +# Franke function or terrain data (or both or other data sets) from +# project 1. +# +# Discuss again your choice of cost function. +# +# Write an FFNN code for regression with a flexible number of hidden +# layers and nodes using the Sigmoid function as activation function for +# the hidden layers. Initialize the weights using a normal +# distribution. How would you initialize the biases? And which +# activation function would you select for the final output layer? +# +# Train your network and compare the results with those from your OLS and Ridge Regression codes from project 1 if you use the Franke function or the terrain data. +# You should test your results against a similar code using **Scikit-Learn** (see the examples in the above lecture notes from weeks 41 and 42) or **tensorflow/keras** or **Pytorch** (for Pytorch, see Raschka et al.'s text chapters 12 and 13). +# +# Comment your results and give a critical discussion of the results +# obtained with the Linear Regression code and your own Neural Network +# code. +# Make an analysis of the regularization parameters and the learning rates employed to find the optimal MSE and $R2$ scores. +# +# A useful reference on the back progagation algorithm is Nielsen's book at . It is an excellent +# read. + +# ### Part c): Testing different activation functions +# +# You should now also test different activation functions for the hidden layers. Try out the Sigmoid, the RELU and the Leaky RELU functions and discuss your results. You may also study the way you initialize your weights and biases. + +# ### Part d): Classification analysis using neural networks +# +# With a well-written code it should now be easy to change the +# activation function for the output layer. +# +# Here we will change the cost function for our neural network code +# developed in parts b) and c) in order to perform a classification analysis. +# +# We will here study the Wisconsin Breast Cancer data set. This is a typical binary classification problem with just one single output, either True or Fale, $0$ or $1$ etc. +# You find more information about this at the [Scikit-Learn +# site](https://scikit-learn.org/stable/modules/generated/sklearn.datasets.load_breast_cancer.html) or at the [University of California +# at Irvine](https://archive.ics.uci.edu/ml/datasets/breast+cancer+wisconsin+(original)). +# +# To measure the performance of our classification problem we use the +# so-called *accuracy* score. The accuracy is as you would expect just +# the number of correctly guessed targets $t_i$ divided by the total +# number of targets, that is + +# $$ +# \text{Accuracy} = \frac{\sum_{i=1}^n I(t_i = y_i)}{n} , +# $$ + +# where $I$ is the indicator function, $1$ if $t_i = y_i$ and $0$ +# otherwise if we have a binary classification problem. Here $t_i$ +# represents the target and $y_i$ the outputs of your FFNN code and $n$ is simply the number of targets $t_i$. +# +# Discuss your results and give a critical analysis of the various parameters, including hyper-parameters like the learning rates and the regularization parameter $\lambda$ (as you did in Ridge Regression), various activation functions, number of hidden layers and nodes and activation functions. +# +# As stated in the introduction, it can also be useful to study other +# datasets. +# +# Again, we strongly recommend that you compare your own neural Network +# code for classification and pertinent results against a similar code using **Scikit-Learn** or **tensorflow/keras** or **pytorch**. + +# ### Part e): Write your Logistic Regression code, final step +# +# Finally, we want to compare the FFNN code we have developed with +# Logistic regression, that is we wish to compare our neural network +# classification results with the results we can obtain with another +# method. +# +# Define your cost function and the design matrix before you start writing your code. +# Write thereafter a Logistic regression code using your SGD algorithm. You can also use standard gradient descent in this case, with a learning rate as hyper-parameter. +# Study the results as functions of the chosen learning rates. +# Add also an $l_2$ regularization parameter $\lambda$. Compare your results with those from your FFNN code as well as those obtained using **Scikit-Learn**'s logistic regression functionality. +# +# The weblink here compares logistic regression and FFNN using the so-called MNIST data set. You may find several useful hints and ideas from this article. + +# ### Part f) Critical evaluation of the various algorithms +# +# After all these glorious calculations, you should now summarize the +# various algorithms and come with a critical evaluation of their pros +# and cons. Which algorithm works best for the regression case and which +# is best for the classification case. These codes can also be part of +# your final project 3, but now applied to other data sets. + +# ## Background literature +# +# 1. The text of Michael Nielsen is highly recommended, see Nielsen's book at . It is an excellent read. +# +# 2. Goodfellow, Bengio and Courville, Deep Learning at . Here we recommend chapters 6, 7 and 8 +# +# 3. Raschka et al. at . Here we recommend chapters 11, 12 and 13. + +# ## Introduction to numerical projects +# +# Here follows a brief recipe and recommendation on how to write a report for each +# project. +# +# * Give a short description of the nature of the problem and the eventual numerical methods you have used. +# +# * Describe the algorithm you have used and/or developed. Here you may find it convenient to use pseudocoding. In many cases you can describe the algorithm in the program itself. +# +# * Include the source code of your program. Comment your program properly. +# +# * If possible, try to find analytic solutions, or known limits in order to test your program when developing the code. +# +# * Include your results either in figure form or in a table. Remember to label your results. All tables and figures should have relevant captions and labels on the axes. +# +# * Try to evaluate the reliabilty and numerical stability/precision of your results. If possible, include a qualitative and/or quantitative discussion of the numerical stability, eventual loss of precision etc. +# +# * Try to give an interpretation of you results in your answers to the problems. +# +# * Critique: if possible include your comments and reflections about the exercise, whether you felt you learnt something, ideas for improvements and other thoughts you've made when solving the exercise. We wish to keep this course at the interactive level and your comments can help us improve it. +# +# * Try to establish a practice where you log your work at the computerlab. You may find such a logbook very handy at later stages in your work, especially when you don't properly remember what a previous test version of your program did. Here you could also record the time spent on solving the exercise, various algorithms you may have tested or other topics which you feel worthy of mentioning. + +# ## Format for electronic delivery of report and programs +# +# The preferred format for the report is a PDF file. You can also use DOC or postscript formats or as an ipython notebook file. As programming language we prefer that you choose between C/C++, Fortran2008 or Python. The following prescription should be followed when preparing the report: +# +# * Use Canvas to hand in your projects, log in at with your normal UiO username and password. +# +# * Upload **only** the report file or the link to your GitHub/GitLab or similar typo of repos! For the source code file(s) you have developed please provide us with your link to your GitHub/GitLab or similar domain. The report file should include all of your discussions and a list of the codes you have developed. Do not include library files which are available at the course homepage, unless you have made specific changes to them. +# +# * In your GitHub/GitLab or similar repository, please include a folder which contains selected results. These can be in the form of output from your code for a selected set of runs and input parameters. +# +# Finally, +# we encourage you to collaborate. Optimal working groups consist of +# 2-3 students. You can then hand in a common report. diff --git a/doc/LectureNotes/_build/jupyter_execute/statistics.ipynb b/doc/LectureNotes/_build/jupyter_execute/statistics.ipynb index 1001b3e52..093a5fef7 100644 --- a/doc/LectureNotes/_build/jupyter_execute/statistics.ipynb +++ b/doc/LectureNotes/_build/jupyter_execute/statistics.ipynb @@ -1344,27 +1344,27 @@ "name": "stdout", "output_type": "stream", "text": [ - "3.613893902124586\n", - "[[18.33217312 13.09829902 1.98271562 18.53534008 -0.88860399 18.12957454\n", - " 7.18043393 1.59698459 17.87186187 10.81979166]\n", - " [13.09829902 9.35870701 1.41664613 13.24346138 -0.63490568 12.95354276\n", - " 5.13040489 1.14104212 12.76940759 7.73071831]\n", - " [ 1.98271562 1.41664613 0.21444055 2.00468914 -0.09610694 1.96080358\n", - " 0.77659961 0.17272182 1.93293067 1.17021424]\n", - " [18.53534008 13.24346138 2.00468914 18.74075864 -0.89845198 18.33049619\n", - " 7.26001134 1.61468323 18.0699274 10.93970238]\n", - " [-0.88860399 -0.63490568 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5.89755794\n", + " 18.2468382 -6.22809974 18.80853029 20.36255393]\n", + " [11.96799937 17.25659561 17.85035562 9.11320323 11.95881375 6.10047943\n", + " 18.8746702 -6.44239442 19.45568883 21.06318287]\n", + " [ 6.11006373 8.81006889 9.11320323 4.65259487 6.10537416 3.11449867\n", + " 9.63615006 -3.2890577 9.93277949 10.75345893]\n", + " [ 8.01793972 11.56102529 11.95881375 6.10537416 8.01178583 4.08700526\n", + " 12.64505146 -4.31607059 13.03430385 14.1112416 ]\n", + " [ 4.09014451 5.89755794 6.10047943 3.11449867 4.08700526 2.08487999\n", + " 6.45054585 -2.20173175 6.64911288 7.19848482]\n", + " [12.65476419 18.2468382 18.8746702 9.63615006 12.64505146 6.45054585\n", + " 19.95776346 -6.81208109 20.57212292 22.27186049]\n", + " [-4.31938579 -6.22809974 -6.44239442 -3.2890577 -4.31607059 -2.20173175\n", + " -6.81208109 2.32513272 -7.02177725 -7.60193996]\n", + " [13.04431557 18.80853029 19.45568883 9.93277949 13.03430385 6.64911288\n", + " 20.57212292 -7.02177725 21.20539421 22.95745476]\n", + " [14.12208052 20.36255393 21.06318287 10.75345893 14.1112416 7.19848482\n", + " 22.27186049 -7.60193996 22.95745476 24.85427641]]\n" ] } ], @@ -2001,15 +2001,15 @@ "name": "stdout", "output_type": "stream", "text": [ - "0.0973900819831327\n", - "4.559791959661649\n", - "0.6333349473431832\n", - "1.1064601390492845 11.271260217275557 17.072290639572326\n", - "3.390334838933238 3.4951301940723654 11.033233504714921\n", - "[[ 1.10646014 3.39033484 3.49513019]\n", - " [ 3.39033484 11.27126022 11.0332335 ]\n", - " [ 3.49513019 11.0332335 17.07229064]]\n", - "[26.5020612 0.075885 2.8720648]\n" + "0.061772005077293704\n", + "4.355734685106391\n", + "-0.03692132794542241\n", + "1.0765856870687196 10.882972378790114 11.598549916355722\n", + "3.274394546800107 2.662153330760193 7.985725003240627\n", + "[[ 1.07658569 3.27439455 2.66215333]\n", + " [ 3.27439455 10.88297238 7.985725 ]\n", + " [ 2.66215333 7.985725 11.59854992]]\n", + "[20.15422927 0.07415258 3.32972613]\n" ] } ], @@ -2638,7 +2638,7 @@ "outputs": [ { "data": { - "image/png": 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", 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BAADjEYgAAIDxCEQAAMB4BCIAAGA8AhEAADAegQgAABgvciD64osv9M0332hvb0+3bt3SnTt39Pjx4zhrAwAAGIrIgSibzerTTz/V1taWFhcXtb29rd3d3ThrAwAAGIrIgWh2dlaSVC6XdefOHUlSKpWKpyoAAIAh+knUT9zZ2VEQBNrZ2dEHH3yg58+f68WLF3HWBgAAMBSRR4hu376tZrOpRqOhvb09lUol+b4fY2kAAADDEXmE6MWLF/rXf/1XSdLe3p6y2awWFxdjKwwAAGBYIo8Qua4bfjwzM6OPPvqoYx0AAMBl0dcI0d7ensrlssbGxlSr1bq2NxoNffrpp7EVBwAAMAx9BaKZmRk5jqNisaidnR3Nzc11bF9bW4u1OAAAgGHoew7R3NycfvWrX+np06e6efNmx7Y//vGPcdUFAAAwNJEnVd+8eVPff/+9Wq1WuK5UKml7ezuWwgAAAIYlciC6ffu2fN+XZVnhuu+++y6OmgAAAIYqciBaWlrSyspKx7pHjx4NXBAAAMCwRb7sfn5+vqd1AAAASTfQW3eUSiVls1lJUhAEKpfLqtfrsRUHAAAwDJFHiEqlkubm5hQEgYIgkKTwfwAAgMsk8ghRsVjsuuzecZyBCwIAABi2yCNEx8OQJM3Ozg5UDAAAwChEHiH69ttvO5Z931epVNLvfve7gYsCAAAYpsiBKJ/Pa3FxMZw35LqulpaWYisMAABgWAaaQ/TRRx91rHv69OnABQEAAAxb5DlEx8OQJI2NjQ1UDAAAwChEHiH66quvOpZ3d3fl+75u3LgxcFEAAADDFHmE6De/+U14D6IgCGTbtu7fvx9nbQAAAEMR632IAAAALqPIgejmzZtqt9sql8uSpNu3b2t6ejq2wgAAAIYl8imz58+f68aNG3ry5ImePHmixcVFff/99zGWBgAAMByRR4gePXqkZ8+edaxbX1/XBx98MGhNAAAAQxV5hGhubq5rXSaTGagYAACAUYgciDzP61r3/PnzgYoBAAAYhcinzBzH0a1bt7S4uCjpzVt3FIvF2AoDAAAYlsiBaGFhQaVSSaVSSZK0tbWlhYWFWIpyXVee58m2bUlvwpf0ZlSqWq3Ktm15nqd8Pi/Lss7dBgAAcJbIgWhvb0+PHj3Sl19+qenpaT19+lTtdnvgS+9d11WlUlGpVJLneVpaWtLOzo4kaXl5WY1GQ9KbALSysqJKpXLuNgAAgLNEnkNULpf1ww8/hMs3b96U67oDF7S6uhqeerNtW7VaTVL3nCXbtsPHO2sbAADAeSIHovfee0/379+P9WaMnuep1WrJsiw1m035vh+eNnNdV6lUqmP/VCqlZrN55jYAAIDzRD5l9h//8R9aWlrSu+++G66r1+v68MMPIxfTbDaVSqVUrVblOI62trZk27ZyuZx83z/xc1qt1pnbjjs4ONDBwUG43G63JUlT41MaGx+LXDsGNzU+1fE/Rot+JAe9SA56kRyT45N6pVexHS9yIFpdXdXCwoLm5+fDEZ2jCdZRtVoteZ4nx3FkWZby+bxmZ2cVBMGpn3NaGDpt28bGhu7du9e1/uuff61r165FKRsxe/j+w1GXgLfQj+SgF8lBL0bv5cuX+lgfx3a8yIFobm5OjUZD5XJZvu/r/v37J96ssR+2bcuyrPDqsKP/m82mLMvqGvE5Or121rbj1tfXdffu3XC53W7r+vXr+vwPn+vVT+NLmujf1PiUHr7/UJ/8/hPtH+6Puhzj0Y/koBfJQS+SY/LHyViPFzkQSdLMzIxWVlbiqiWcL3QSx3FOHIHKZDKybfvUbcdNTExoYmKia/3+4b5eHRKIkmD/cJ9fNAlCP5KDXiQHvRi94PD0s0dRDBSI4mbbtjKZjHzfl2VZ4b2I0ul0176e5ymTyXSMKJ20DQAA4DyJCkSSVKlUVCgUtLi4qEajEV52//a2bDarer3ecZ+hs7YBAACcJXGByLKsUydn27Yd3qMol8v1vA0AAOAsPd2HaG9vT9lsNrxEHQAA4CrpKRA9e/ZMlUql4yaM33zzTdd+jx8/jq8yAACAIenplFkmk9HKyor+/u//PpyoXKlUuu7zU6vVBroxIwAAwCj0NEI0MzOjBw8eaG5uTi9evNCLFy8UBEHXv93d3YuuFwAAIHY9T6qemZnRRx99FC47jqOFhYWOfRzHia8yAACAIYl8ldnCwoLa7bbK5bIk6fbt210BCQAA4DKI/G73z58/140bN/TkyRM9efJEi4uL+v7772MsDQAAYDgijxA9evRIz54961i3vr6uDz74YNCaAAAAhiryCNFJb+R60nuHAQAAJF3kQOR5Xte658+fD1QMAADAKEQ+ZeY4jm7duqXFxUVJkuu64VtnAAAAXCaRR4gWFhZUKpXCexBtbW3pxo0bcdYGAAAwFAO9uevc3Jzu378fVy0AAAAjEXmECAAA4KogEAEAAOMRiAAAgPEIRAAAwHiRA1E2m9Xjx4/jrAUAAGAkIgeifD6vDz/8sGPdt99+O3BBAAAAwxb5svuxsTH98z//s+bn52XbtlqtliqVCvciAgAAl07kQHT//n05jqMffvhBP/zwgySp1WrFVhgAAMCwRA5EpVJJN2/e7Fj39OnTgQsCAAAYtshziG7evKlf/vKXunPnjqQ3YSibzcZWGAAAwLBEDkTr6+uyLEuO40h6E5Bc142tMAAAgGGJHIgymYxWVlZk23ac9QAAAAxd5ED0/PlzSW+uNjtSr9cHrwgAAGDIIk+qXlhYUCaT0XvvvadarSbXdVUsFuOsDQAAYCgGmlRdLpe1sLCgIAi0tbXFPYgAAMClFHmESJJs29aXX34pSZqeno6lIAAAgGGLPEK0t7enW7duybIszc7O6h//8R/VbrfjrA0AAGAoIgeijY0NFQoFHR4e6r//+791//59lcvlOGsDAAAYisinzLLZbMedqhcWFmIpCAAAYNgijxDNzs72tA4AACDpeh4hevz4ccdyrVZTs9mUZVmSJN/3Zdu2/vZv/zbO+gAAAC5cz4FobW1NS0tLmpmZkSTNzMx0vNO9JO3u7urDDz+Mv0oAAIAL1HMgOund7Y/b29sbuCAAAIBh63kO0UlhqN1u649//GP474svvoi1OAAAgGGIfJXZZ599Jtd1wzlE0pv3N/u3f/u3OOoCAAAYmsiBaH5+Xr/61a861j148GDgggAAAIYt8mX3juN0rVtaWhqoGAAAgFGIPEI0Ozurr776SrZty7Is+b6v7e1tbW9vx1kfAADAhYsciNbW1uT7fsccou+++y6OmgAAAIYqciBaWlrSyspKx7pHjx4NXBAAAMCwRZ5DND8/39M6AACApIs8QrSzs6NSqaRsNitJCoJA5XJZ9Xo9tuIAAACGIfIIUalU0tzcnIIgUBAEkhT+DwAAcJlEHiEqFotdd68+6VJ8AACApIs8QnTSW3nMzs4OVAwAAMAoRB4h+vbbbzuWfd9XqVTS7373u4GLAgAAGKbIgSifz2txcTGcN+S6LneqBgAAl9JAc4g++uijjnVPnz4duCAAAIBhizyH6HgYkqSxsbGBigEAABiFyCNEX331Vcfy7u6ufN/XjRs3Bi4KAABgmCKPEP3mN78J70EUBIFs29b9+/fjrA0AAGAoYr0PEQAAwGXU8wjRN99807FMGAIAAFdFzyNE9+/fl+/7siwrXBcEQTiR+mjbp59+GltxhUJB6+vr4WN6nqdqtSrbtuV5nvL5fE/bAAAAztJzIHIcR//yL//Stf67777T8vKyZmdn9eDBg9gKazab2tzc1Pr6erhueXlZjUZD0psAtLKyokqlcu42AACAs/R8yqxQKHSt++yzz5TJZPTZZ5+pXq/rgw8+iK0wz/Nk23bH8tts25bruuduAwAAOE/PgWhubi78+PHjx3rvvff0/Plz/ed//ueJI0eDqFaryuVyHetc11UqlepYl0ql1Gw2z9wGAABwnr6uMmu32/r000/luq6KxaJWVlZiL+j4PKW315+k1Wqdue24g4MDHRwchMvtdluSNDU+pbFxbiw5SlPjUx3/Y7ToR3LQi+SgF8kxOT6pV3oV2/F6DkTffPONVldXlcvl9Pz5c83MzHTt8/jxY3344YcDFVQul5XP53ve/7QwdNq2jY0N3bt3r2v91z//WteuXev5cXFxHr7/cNQl4C30IznoRXLQi9F7+fKlPtbHsR2v50CUz+eVz+c7Ji+/LQgCbWxsDBSIXNfV7du3T9xmWVbXiE+r1ZJlWWduO259fV13794Nl9vttq5fv67P//C5Xv00vqSJ/k2NT+nh+w/1ye8/0f7h/qjLMR79SA56kRz0Ijkmf5yM9Xg9B6K1tTV9+eWX4bvbH9dqtTQ7OztwQeVyOfzY8zxtbGzozp07chxHpVKpa/9MJiPbtk/ddtzExIQmJia61u8f7uvVIYEoCfYP9/lFkyD0IznoRXLQi9ELDk/OI1H1HIju3Lmj6enpU7fPzMyoWCwOVIzjOB3Lq6urWl1d7bja7IjnecpkMuEI0WnbAAAAztNzIFpYWIhln174vq+trS1Jb94iZHV1Vel0WpVKRYVCQdlsVvV6veM+Q2dtAwAAOEvk9zK7SJZlaW1tTWtrax3rbdsOR6GOX5Z/1jYAAICzRH63ewAAgKuCQAQAAIxHIAIAAMYjEAEAAOMRiAAAgPEIRAAAwHgEIgAAYDwCEQAAMB6BCAAAGI9ABAAAjEcgAgAAxiMQAQAA4xGIAACA8QhEAADAeAQiAABgPAIRAAAwHoEIAAAYj0AEAACMRyACAADGIxABAADjEYgAAIDxCEQAAMB4BCIAAGA8AhEAADAegQgAABiPQAQAAIxHIAIAAMYjEAEAAOMRiAAAgPEIRAAAwHgEIgAAYDwCEQAAMB6BCAAAGI9ABAAAjEcgAgAAxiMQAQAA4xGIAACA8QhEAADAeAQiAABgPAIRAAAwHoEIAAAYj0AEAACMRyACAADGIxABAADjEYgAAIDxCEQAAMB4BCIAAGA8AhEAADAegQgAABiPQAQAAIxHIAIAAMYjEAEAAOMRiAAAgPEIRAAAwHgEIgAAYDwCEQAAMN5PRl3Acc1mU67rSpLq9boePHggy7IkSZ7nqVqtyrZteZ6nfD7f0zYAAICzJC4Qua6rtbU1SdLm5qZu3rypRqMhSVpeXg4/9jxPKysrqlQq524DAAA4S6JOmTWbTW1sbITLuVxOzWZTnufJ87yOfW3bDkeSztoGAABwnkQFonQ6rQcPHoTLvu9LklKplFzXVSqV6tg/lUqFp9hO2wYAAHCexJ0yy+Vy4cfb29tyHEeWZYXh6LhWq3XmtuMODg50cHAQLrfbbUnS1PiUxsbHoheOgU2NT3X8j9GiH8lBL5KDXiTH5PikXulVbMdLXCA64vu+qtVqOC/orP362baxsaF79+51rf/651/r2rVr/ZaJC/Dw/YejLgFvoR/JQS+Sg16M3suXL/WxPo7teIkNRIVCQbVaLbxSzLKsrhGfVqsly7LO3Hbc+vq67t69Gy63221dv35dn//hc736aXxJE/2bGp/Sw/cf6pPff6L9w/1Rl2M8+pEc9CI56EVyTP44GevxEhmINjc3VSgUZNt2OMrjOI5KpVLXvplMRrZtn7rtuImJCU1MTHSt3z/c16tDAlES7B/u84smQehHctCL5KAXoxccBrEeL1GTqiWpWq0qnU6HYahcLsuyLNm23bGf53nKZDLnbgMAADhPokaIPM/T8vJyxzrLspTP5yVJlUpFhUJB2WxW9Xq94z5DZ20DAAA4S6ICkW3bCoLTh8Bs21axWJTUeTXaedsAAADOkrhTZgAAAMNGIAIAAMYjEAEAAOMRiAAAgPEIRAAAwHgEIgAAYDwCEQAAMB6BCAAAGI9ABAAAjEcgAgAAxiMQAQAA4xGIAACA8QhEAADAeAQiAABgPAIRAAAwHoEIAAAYj0AEAACMRyACAADGIxABAADjEYgAAIDxCEQAAMB4BCIAAGA8AhEAADAegQgAABiPQAQAAIxHIAIAAMYjEAEAAOMRiAAAgPEIRAAAwHgEIgAAYDwCEQAAMB6BCAAAGI9ABAAAjEcgAgAAxiMQAQAA4xGIAACA8QhEAADAeAQiAABgPAIRAAAwHoEIAAAYj0AEAACMRyACAADGIxABAADjEYgAAIDxCEQAAMB4BCIAAGA8AhEAADAegQgAABiPQAQAAIxHIAIAAMYjEAEAAOMRiAAAgPEIRAAAwHgEIgAAYDwCEQAAMB6BCAAAGO8noy4gLp7nqVqtyrZteZ6nfD4vy7JGXRYAALgErkwgWl5eVqPRkPQmHK2srKhSqYy4KgAAcBlciVNmnud1LNu2Ldd1R1QNAAC4bK5EIHJdV6lUqmNdKpVSs9kcUUUAAOAyuRKnzHzfP3F9q9XqWndwcKCDg4NweW9vT5I0+XryQmpD7ybHJ/Xy5UtN/jip4DAYdTnGox/JQS+Sg14kx+TrSb3SKwVBPH24EoHoNCcFpY2NDd27d697319274vheqVX+lgfj7oM/A/6kRz0IjnoRXK80itJ0u7urmZmZgY+3pUIRJZldY0GtVqtE68yW19f1927d8Nl3/f1N3/zN/qv//qvWJ5QRNdut3X9+nX96U9/0vT09KjLMR79SA56kRz0Ijn29vb013/9111TZqK6EoHIcRyVSqWu9ZlMpmvdxMSEJiYmutbPzMzwzZ0Q09PT9CJB6Edy0IvkoBfJMT4ez3ToKzGp2rbtjmXP85TJZLgPEQAA6MmVGCGSpEqlokKhoGw2q3q9zj2IAABAz65MILJtW8ViUZKUy+V6/ryJiQn94he/OPE0GoaLXiQL/UgOepEc9CI54u7FWBDX9WoAAACX1JWYQwQAADAIAhEAADAegQgAABjvykyqPovneapWq7JtW57nKZ/Pn3pJfj/7Ipp+nuNmsxm+UW+9XteDBw/oR4yifr8XCgWtr6/Tixj12wvXdeV5XnjbEcdxhlTp1dfva8bR+2l6nqdcLtd1KxhE12w2tbKyokajceZ+sbx2BwZIp9Phxzs7O0Eul4tlX0TTz3NcLBY7Pn77czG4KN/vjUYjkBS8ePHiAiszTz+9qNVqQT6fD/e1bfvC6zNJ1N9RQRCEfcHgKpVK+PvmPHG8dl/5U2ae53Us27YdjjgMsi+i6ec5bjab2tjYCJdzuZyazWbXMRBN1O/3t0clEI9+e7G6uhreZsS2bdVqtQutzyT99mJ7e/uiSzJWLpdTOp0+d7+4XruvfCA6Gsp8WyqVUrPZHGhfRNPPc5xOp/XgwYNw+ejNeuN63xrTRfl+r1arfd3nC73ppxee54Xv1dhsNuX7PgE1Rv3+XKRSKS0uLoanzpaWloZRJt4S12v3lQ9EJ73jvaSuN4Ptd19E0+9z/PaL7/b2thzHYd5KTPrthe/7PPcXpJ9eNJtNpVKpcL7E1taWqtXqBVdojn5/Lo7eFWF+fl6VSoU/GEYgrtduIyZVn+S0J3DQfRHNec+x7/uqVqvnTqzD4E7rRblcVj6fH24xhjupF61WS57nhX8c5PN5zc7OKuAeuxfqtJ8L13VVLBbleZ5WV1cl6cQ3G8fw9fvafeVHiCzL6kqJR8PNg+yLaKI+x4VCQbVajV7EqJ9euK6r27dvD6ky8/TTC9u2ZVlWuO3of07tx6OfXniep3q9LsdxlM/ntbOzo3K5zDzHIYvrtfvKB6LTLkXNZDID7YtoojzHm5ubKhQKsm1bvu8zYheTfntRLpe1tbWlra0teZ6njY0NXoRj0k8vmC90sfrpRbPZVDabDZdt29b6+jq/o4YsrtfuKx+Ijv/y8DxPmUym46+qozR/3r4YXD/9kN5M4k2n02EYKpfL9CMm/fTi6C/go3/SmyuderkCBOfr9/dUJpMJX3SPrvqjF/HopxfpdFr1er1j/93dXXpxAY6HzIt47TbizV09z1OpVFI2m1W9Xu+4odzy8rKy2azW1tbO3Rfx6LUfnudpfn6+43Mty9KLFy9GUPXV1M/PhvTml9LW1pYKhYLy+TyhKEb99ML3fRUKBS0uLqrRaIQjqIhHP71wXVfNZjPc7jgOvYiJ67qq1Wra3NzU2tqastlsOGn9Il67jQhEAAAAZ7nyp8wAAADOQyACAADGIxABAADjEYgAAIDxCEQAAMB4BCIAAGA8AhEAADAegQgAABiPQAQAAIxHIAIAAMYjEAEAAOP9ZNQFAEDcPM+T67ra2dnR6uqqms0mb9YM4EyMEAG4clzXVT6f19LSkpaXl5XL5VStVtVqtUZdGoCEYoQIwJVz+/ZtSVKz2dSdO3ckSTs7O6MsCUDCMUIE4Mo5Oi22vb2tXC4nSfJ9f3QFAUg8AhGAK2Vra0uFQkHNZlOe58m2bUlSuVwecWUAkmwsCIJg1EUAQFxc15XneUqlUrIsS57nSZLy+fyIKwOQZAQiAABgPE6ZAQAA4xGIAACA8QhEAADAeAQiAABgPAIRAAAwHoEIAAAYj0AEAACMRyACAADGIxABAADjEYgAAIDxCEQAAMB4/w/1s6YF98txbAAAAABJRU5ErkJggg==", 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    " ] @@ -2764,12 +2764,12 @@ "name": "stdout", "output_type": "stream", "text": [ - "-0.02214409916811925 1.073576975500551\n" + "-0.0370757046153366 1.001106660107757\n" ] }, { "data": { - "image/png": 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", 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", 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"[-0.60588173 1.59884169 -1.56922102 -1.32068736 -0.21631786 0.37600869\n", - " -0.14841322 -0.83655756 0.88809826 -0.85625134]\n" + "[ 1.51262599 0.63980912 -1.25680702 0.97680846 -1.33095972 -0.41396339\n", + " -0.81478187 -0.6087346 2.11164003 -1.21061589]\n" ] } ], @@ -1313,26 +1313,26 @@ "name": "stdout", "output_type": "stream", "text": [ - "[[0.32641434 0.35731585 0.64796751 0.41146753 0.67561021 0.53312817\n", - " 0.3910042 0.88157423 0.4576811 0.48004784]\n", - " [0.70469324 0.28496213 0.84862355 0.6988905 0.95890587 0.19849513\n", - " 0.89058005 0.51546825 0.88289263 0.06926192]\n", - " [0.38198866 0.33000573 0.80740939 0.54935794 0.38934047 0.87740526\n", - " 0.45053025 0.27231287 0.7070883 0.7989356 ]\n", - " [0.4198932 0.3727791 0.95400323 0.86459987 0.2666905 0.13564988\n", - " 0.97498674 0.9450635 0.6383903 0.57803254]\n", - " [0.13896095 0.13663125 0.68826552 0.13729154 0.91672129 0.08266769\n", - " 0.88639567 0.16407038 0.36353321 0.81007381]\n", - " [0.31849289 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[0.05080769 0.92665802 0.77039278 0.13455019 0.89692576 0.09621323\n", + " 0.48511333 0.8529175 0.32738537 0.12206812]\n", + " [0.98108401 0.73397147 0.62288579 0.66003032 0.18712313 0.63307537\n", + " 0.2032806 0.17418673 0.06061276 0.92991181]\n", + " [0.53480404 0.69484973 0.09821823 0.93019783 0.34478594 0.18646225\n", + " 0.11861803 0.25646067 0.55225408 0.84907109]\n", + " [0.50352245 0.92678221 0.27037635 0.9833205 0.84985833 0.82844656\n", + " 0.34112554 0.9306628 0.89155606 0.24149532]\n", + " [0.37137157 0.65751456 0.63693246 0.25068519 0.75674251 0.43724406\n", + " 0.34131583 0.74180248 0.63801791 0.76426396]\n", + " [0.2311959 0.77594586 0.52606333 0.54222783 0.86434639 0.72364915\n", + " 0.4008393 0.68827947 0.56408898 0.68640031]\n", + " [0.90137794 0.02599188 0.40848657 0.94114646 0.67199457 0.02124568\n", + " 0.32717946 0.59030403 0.59188296 0.81707832]]\n" ] } ], @@ -1446,13 +1446,13 @@ "name": "stdout", "output_type": "stream", "text": [ - "-0.050315327923114654\n", - "3.7539148284817965\n", - "-0.09246293455466892\n", - "[[ 0.90894281 2.8691787 2.58589719]\n", - " [ 2.8691787 10.23481471 8.16535252]\n", - " [ 2.58589719 8.16535252 12.44536331]]\n", - "[20.33742926 0.08458591 3.16710567]\n" + "-0.04372067685794603\n", + "3.777112373675558\n", + "-0.028188673105960467\n", + "[[ 1.0680225 3.36841864 2.42592679]\n", + " [ 3.36841864 11.49312535 7.50233673]\n", + " [ 2.42592679 7.50233673 7.94272259]]\n", + "[18.418656 0.05992237 2.02529207]\n" ] } ], @@ -1808,7 +1808,7 @@ "traceback": [ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[0;31mAttributeError\u001b[0m Traceback (most recent call last)", - "\u001b[0;32m/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_95519/1326197715.py\u001b[0m in \u001b[0;36m?\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 6\u001b[0;31m new_hobbit = {'First Name': [\"Peregrin\"],\n\u001b[0m\u001b[1;32m 7\u001b[0m \u001b[0;34m'Last Name'\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m\"Took\"\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 8\u001b[0m \u001b[0;34m'Place of birth'\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m\"Shire\"\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 9\u001b[0m \u001b[0;34m'Date of Birth T.A.'\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;36m2990\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;32m/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_22556/1326197715.py\u001b[0m in \u001b[0;36m?\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 6\u001b[0;31m new_hobbit = {'First Name': [\"Peregrin\"],\n\u001b[0m\u001b[1;32m 7\u001b[0m \u001b[0;34m'Last Name'\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m\"Took\"\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 8\u001b[0m \u001b[0;34m'Place of birth'\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m\"Shire\"\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 9\u001b[0m \u001b[0;34m'Date of Birth T.A.'\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;36m2990\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", "\u001b[0;32m~/miniforge3/envs/myenv/lib/python3.9/site-packages/pandas/core/generic.py\u001b[0m in \u001b[0;36m?\u001b[0;34m(self, name)\u001b[0m\n\u001b[1;32m 6200\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0mname\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_accessors\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 6201\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_info_axis\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_can_hold_identifiers_and_holds_name\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mname\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 6202\u001b[0m ):\n\u001b[1;32m 6203\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mname\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 6204\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mobject\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__getattribute__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mname\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m", "\u001b[0;31mAttributeError\u001b[0m: 'DataFrame' object has no attribute 'append'" ] diff --git a/doc/LectureNotes/_build/jupyter_execute/week35.ipynb b/doc/LectureNotes/_build/jupyter_execute/week35.ipynb index fd138fbff..04e32da79 100644 --- a/doc/LectureNotes/_build/jupyter_execute/week35.ipynb +++ b/doc/LectureNotes/_build/jupyter_execute/week35.ipynb @@ -1533,7 +1533,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "0.9952505213910134\n" + "0.9960887274532307\n" ] } ], @@ -1564,7 +1564,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "0.008753288788081405\n" + "0.010148621093080332\n" ] } ], @@ -1599,31 +1599,23 @@ "name": "stdout", "output_type": "stream", "text": [ - "[8.90304177e-02 3.21655059e-02 1.15924557e-02 1.83823317e-02\n", - " 8.19559737e-03 1.66018725e-02 1.79135536e-03 1.03313259e-01\n", - " 7.69182892e-04 1.29306584e-02 1.21565007e-02 2.83463634e-03\n", - " 3.03538673e-02 2.37415625e-02 1.56790195e-02 9.03400724e-03\n", - " 1.35613536e-02 5.35506347e-02 1.01123792e-02 4.10604579e-02\n", - " 2.63898112e-02 1.94766419e-02 3.81425129e-02 3.27482829e-02\n", - " 5.12829994e-03 6.02901273e-03 8.26321660e-02 4.04504728e-02\n", - " 2.20601797e-02 4.62113349e-03 9.03476611e-04 4.87494456e-02\n", - " 3.82060913e-03 2.53729411e-02 2.38612299e-02 1.59355752e-02\n", - " 3.60160003e-03 1.65738717e-02 2.98947674e-02 5.18501900e-03\n", - " 9.36303682e-03 4.81218742e-02 1.49392067e-02 4.88551766e-03\n", - " 2.17643975e-02 2.20608548e-04 1.90135464e-02 2.74291603e-02\n", - " 1.23344210e-02 6.03309191e-03 1.57252451e-02 9.02612988e-03\n", - " 3.32084559e-02 3.76692036e-03 2.87169607e-02 4.85551266e-02\n", - " 1.48826894e-02 6.41842093e-04 1.89017198e-02 3.49584063e-02\n", - " 1.77652198e-02 6.38298234e-03 1.05034088e-03 1.99753321e-02\n", - " 5.52031552e-03 8.22217237e-03 6.86192682e-02 8.40354798e-03\n", - " 1.29491144e-02 7.44658658e-03 1.00731392e-02 9.52284329e-02\n", - " 1.51437058e-02 2.00002585e-05 2.37700967e-02 1.95166920e-02\n", - " 4.82376174e-02 3.73986200e-02 4.84707251e-02 8.76887316e-02\n", - " 2.74724414e-02 5.14825560e-03 1.26254957e-02 2.81042619e-02\n", - " 2.11265643e-02 2.52301447e-03 3.13819592e-02 2.93900569e-02\n", - " 3.65720152e-02 1.02850506e-02 4.85945208e-02 2.79870689e-02\n", - " 3.12846660e-02 6.17869861e-02 9.09590269e-03 1.11715109e-02\n", - " 3.62863106e-02 1.21277816e-02 9.05665429e-03 4.85293303e-02]\n" + "[0.01006224 0.02667406 0.0272743 0.00100202 0.01480927 0.00410183\n", + " 0.02088307 0.02506901 0.00463124 0.00672505 0.0304378 0.00076521\n", + " 0.05364579 0.01232366 0.00969623 0.00677215 0.02239876 0.03858916\n", + " 0.00923474 0.00848739 0.01701429 0.07172932 0.08858739 0.04575775\n", + " 0.02988432 0.00988356 0.01546126 0.00033415 0.03201922 0.01425384\n", + " 0.00575681 0.01882218 0.05853984 0.00528252 0.03254614 0.03092729\n", + " 0.01094916 0.05696574 0.03412067 0.02444266 0.05749111 0.0689303\n", + " 0.00736737 0.02104639 0.00274212 0.00588836 0.05043958 0.01456865\n", + " 0.01813363 0.06019833 0.01078008 0.01059242 0.02369044 0.02692965\n", + " 0.00657601 0.01218403 0.03745816 0.05363722 0.00561212 0.03820746\n", + " 0.00988992 0.00774376 0.03426412 0.01323341 0.02387182 0.01151556\n", + " 0.01097287 0.07292363 0.02846506 0.04186033 0.00836649 0.00340452\n", + " 0.06200455 0.03246707 0.02987496 0.00355323 0.01740381 0.01196506\n", + " 0.02635861 0.07487128 0.08472879 0.0073544 0.01150437 0.00571884\n", + " 0.02025574 0.0014028 0.01512884 0.02146636 0.05097344 0.0284405\n", + " 0.06151386 0.00737863 0.04452918 0.03906948 0.01163942 0.07468007\n", + " 0.01647074 0.0096667 0.00369201 0.0168171 ]\n" ] } ], @@ -1677,15 +1669,15 @@ "name": "stdout", "output_type": "stream", "text": [ - "[ 2.05054319 -0.48521055 6.95338273 -2.63619709 1.0524497 ]\n", + "[ 1.97802245 0.57331229 2.49761526 3.47609206 -1.5187643 ]\n", "Training R2\n", - "0.9959308805732706\n", + "0.995702810640425\n", "Training MSE\n", - "0.009211602191395454\n", + "0.007370297974992432\n", "Test R2\n", - "0.9955318336036834\n", + "0.9950019477819025\n", "Test MSE\n", - "0.011818646101922625\n" + "0.009880124918446542\n" ] } ], @@ -2557,13 +2549,7 @@ "output_type": "stream", "text": [ "MSE before scaling: 0.00\n", - "R2 score before scaling 1.00\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ + "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", diff --git a/doc/LectureNotes/_build/jupyter_execute/week37.ipynb b/doc/LectureNotes/_build/jupyter_execute/week37.ipynb index d7e9643ea..e4a4034f2 100644 --- a/doc/LectureNotes/_build/jupyter_execute/week37.ipynb +++ b/doc/LectureNotes/_build/jupyter_execute/week37.ipynb @@ -1671,7 +1671,7 @@ "text": [ "Bootstrap Statistics :\n", "original bias std. error\n", - " 99.8485 15.0562 99.8488 0.149353\n" + " 100.257 14.853 100.257 0.149111\n" ] } ], @@ -1737,7 +1737,7 @@ "outputs": [ { "data": { - "image/png": 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I+MknRScxH5GRQHa2dlg4EekXixsiqrctW4C+fQFPT9FJzEdwMNCoEbumiAxBIToAEZm3vDxgzx5g4ULRSUxUaGilm2UAnpLeRMzynvjf/3w5ozORHvHMDRHVS1wcUFzMVcDrItLrIK4W+eLUKdFJiCwLixsiqpeYGKBLFyAoSHQS89PP7Ve4yu+ya4pIz1jcEFGdFRcD27ZxlFRd2dqU4EnPIyxuiPSMxQ0R1dm+fdprbljc1F2k10EkJwNXrohOQmQ5WNwQUZ1t3w40bgx06iQ6ifka5HECcrn22iUi0g8WN0RUZzt3AkOGgCN96sFVcQ+9e2vbkoj0g8UNEdVJaipw9qx2yQWqn8GDtcPp1WrRSYgsA4sbIqqTnTu1sxJzocz6GzIEyM8HfvlFdBIiy8DihojqJC4OePxxwM1NdBLz17Ur0LAhr7sh0hcWN0SkM7Ua2L1be8aB6s/GBhg0iNfdEOmL0OJmwYIFeOyxx+Ds7Axvb29ERkYiJSWlxsft27cPwcHBsLe3R7NmzbB8+XIjpCWiB44e1Q4B5/U2+jN4MHDyJHDjhugkROZPaHGzb98+TJo0CUeOHIFKpUJJSQkGDRqEe/fuVfmYy5cvY+jQoQgJCUFSUhLeffddvPHGG9i4caMRkxNZt507tYtkBgeLTmI5Bg3SflWpxOYgsgRCF86Me6SDefXq1fD29kZiYiL69u1b6WOWL1+OwMBALF68GADQtm1bnDhxAosWLcKzzz5bYf+ioiIUFRWV3c/LywMAqNVqqB8amvDgezWHK9QK20s3ltZeO3bIMXAgoNGUQqOpxQNsbXV6frVSWe6rxVOr4eEBdOmiwPbtEkaOLK3DU1jW75ihsb10YwrtpctryyRJkgyYRScXLlxAy5Ytcfr0aXTo0KHSffr27YuuXbvi008/Ldu2efNmPPfccygoKIDykTfD999/H3PmzKnwPOvWrYOjo6N+D4DICuTm2uKll4Zg8uQkDByYJjqORVm7ti127w7C6tVxsOEVkUTlFBQUYNSoUcjNzYWLi0u1+wo9c/MwSZIQFRWFJ554osrCBgAyMzPh4+NTbpuPjw9KSkpw69Yt+Pn5lfvZjBkzEBUVVXY/Ly8PAQEBGDRoULnGUavVUKlUCA8Pr1AgUUVsL91YUnutXy+DJMkwbVpH+Pl1rN2Dhg3T6TXUSiVU48cjfMUKKK3hk3VsLADAyUmGjRsV8Pcfim7ddHsKS/odMwa2l25Mob0e9LzUhskUN5MnT8apU6dw8ODBGveVPTId6oOTT49uBwA7OzvY2dlV2K5UKiv9B6pqO1WO7aUbS2ivPXu0yy0EBupwHMXFdXotpVoNZR0fa1b+/J0ICQGcnYE9e5R4/PG6PpX5/44ZE9tLNyLbS5fXNYkTn1OmTMHWrVsRHx+Pxo0bV7uvr68vMjMzy23LysqCQqGAp6enIWMSWT2N5q8lF0j/bG2BAQM43w1RfQk9cyNJEqZMmYLNmzcjISEBTZs2rfExvXr1ws8//1xu265du9C9e3dW30QGduqUdqhyuSHgoaHC8liiwYOBN94AcnMBV1fRaYjMk9AzN5MmTUJ0dDTWrVsHZ2dnZGZmIjMzE/fv3y/bZ8aMGXjxxRfL7k+YMAFXr15FVFQUzp49i1WrVmHlypWYNm2aiEMgsipxcUCDBkCfPqKTWK7Bg4GSEmDvXtFJiMyX0OJm2bJlyM3NRf/+/eHn51d2+/7778v2ycjIQGpqatn9pk2bYvv27UhISECXLl0wb948fPbZZ5UOAyci/dq5U3uippLL2EhPmjUDWrbkbMVE9SG8W6oma9asqbCtX79+OHnypAESEVFV8vOBgweBP6eYIgMaMgTYuhWQJKCScRJEVAOTuKCYiExffLy2u4RLLhje4MHA1avAH3+ITkJknljcEFGtxMUBzZsDLVqITmL5+vfXjpziqCmiumFxQ0S1snMnz9oYS4MG2jlveN0NUd2YzCR+RGS6LlwALl3i/DYGU8lw+iGpz2HWlXEo7DsC9vI/Z2mOjzdyMCLzxDM3RFSjuDjtJLqc0sZ4Bnscx32NPQ7kdhIdhcjssLghohrt3Ak88QTg5CQ6ifXo0OAy/G1vIi67h+goRGaH3VJEVLXQUBRplNh7cAtmNfkWCN0gOpHVkMmAwR4nsDP7MfwXy0THITIrPHNDRNU6lNsBBRoHDPE4LjqK1RnicQy/FTRFWmFD0VGIzAqLGyKqVlx2D/ja3kanBhdFR7E6Ye6JsEEpduU8JjoKkVlhcUNE1dqZ/RgGux/nTLkCeCjz0cPlHOKyWdwQ6YLX3BBZq1oMfbpe5IlT95rjncB1RghElRnsfhyfpj+LEo0N37CJaolnboioSruyu0MGDcLdT4iOYrWGeBzDnRJnHMtvKzoKkdlgcUNEVYrL7oHuzinwss0THcVqPeaSAndFHnaya4qo1ljcEFGlSiUbqHKCOUpKMLlMg3D3RM53Q6QDFjdEVKkT+a2RXeKKwSxuhBvscRzH81vj9m3RSYjMA4sbIqrUruzucJHfxePOv4uOYvUGexyHBBvs2SM6CZF5YHFDRJXanROMAe5JUNhoREexeo3sbqGt4xXs3i06CZF5YHFDRBXcLbHHL3ntEOZ+UnQU+lOY+0kWN0S1xOKGiCrYn9sZakmJMPdE0VHoT2Huibh8GbjIiaKJasTihogq2J0TjAC7G2jlkCY6Cv2pv1sy5HLw7A1RLbC4IaIKVDnBCHM/ySUXTIiLogCPP87ihqg2WNwQUTmZRe44c68Zu6RMUFgYsGcPUFoqOgmRaWNxQ0Tl7LkTDAAYyIuJTU54OJCTAyQliU5CZNpY3BBROarsYHRqcBE+tjmio9AjHn8ccHJi1xRRTVjcEFEZSdJeTMwuKdOkVAL9+gEqlegkRKaNxQ0RlUkpCEB6cUOuAm7CwsOBgweBggLRSYhMF4sbIiqjyukOpUyNELfToqNQFcLCgOJi4NAh0UmITBeLGyIqszsnGL1dfkMDeaHoKFSFdu0APz92TRFVh8UNEQEASjQ2iL/TBeEe7JIyZTKZ9uwNLyomqhqLGyICABzLb4v80gZcT8oMhIVph4PfuiU6CZFpYnFDRAC0XVKu8rsIdkoRHYVqEBam/bpnj9gcRKaKxQ0RAQB253TDAPeTUNhoREehGvj7a6+9YdcUUeVY3BAR8ksc8Etee3ZJmZGwMO1FxZIkOgmR6WFxQ0TYn9sZJZKCk/eZkfBw4OpV4OJF0UmITA+LGyKCKjsYgXaZaOlwTXQUqqV+/QC5nF1TRJVhcUNEfy65cBIymegkVFvOzkDPnixuiCrD4obIymUUeeC3gqZccsEMhYcDe/cCpaWikxCZFhY3RFZud04wAGCAe5LgJKSrsDAgJwc4yevAicphcUNk5XbnBKNzgwvwtr0jOgrpqEcPbfcUu6aIyqtTcbNmzRoUcElaIrMnSdr5bbjkgnlSKoH+/bnOFNGj6lTczJgxA76+vnjllVdw+PBhfWciIiM5WxCE68UNOb+NGQsL064Qzs+bRH+pU3Fz7do1REdHIycnB6GhoWjTpg3+85//IDMzU9/5iMiAducEw1ZWjBDXU6KjUB2FhQHFxcChQxzqRvRAnYobuVyOESNGYNOmTUhLS8Nrr72G7777DoGBgRgxYgS2bNkCjYZTuBOZut053dDH9Qwc5UWio1AdtW2rXY5hzx4WN0QP1PuCYm9vb/Tp0we9evWCjY0NTp8+jZdeegnNmzdHQkKCHiISkSGoNXIk3OnCLikzJ5Npz97s2cPxIUQPKOr6wBs3bmDt2rVYvXo1Ll26hMjISMTGxiIsLAz379/HzJkzMXbsWFy9elWfeYlIT47lt0V+aQMuuWBOQkMr3RyWGY5vz72L3FxbIwciMk11KvWHDx+OgIAArFmzBuPHj0d6ejrWr1+PsLAwAICDgwP++c9/Ii0tTa9hiUh/VNnBcFPkI9j5D9FRqJ4eFKinTjUUnITINNTpzI23tzf27duHXr16VbmPn58fLl++XOdgRGRYu3OCMcAtCXIZr48zd3522WjX4Ap+/ZXFDRFQxzM3/fr1Q7du3SpsLy4uxrfffgsAkMlkCAoKql86IjKIvDzgSF47dklZkDDPk/j114aQJNFJiMSrU3Ezbtw45ObmVtien5+PcePG1TsUERnWvn1AKeRcT8qCDPBMws2bjrhwQXQSIvHqVNxIkgRZJcsHX7t2Da6urvUORUSGpVIBQXaZaO5wXXQU0pO+7qcgl2s4aooIOl5z07VrV8hkMshkMgwcOBAKxV8PLy0txeXLlzFkyBC9hyQi/VKpgHCPE6jkMwqZKSdFIVq3zsbu3R6YMkV0GiKxdCpuIiMjAQDJyckYPHgwnJycyn5ma2uLJk2a4Nlnn9VrQCLSr2vXgHPngDnteL2Npenc+Sa2/eiAkn4joLCp5kLx+HjjhSISQKfiZvbs2QCAJk2aYOTIkbC3tzdIKCIyHJVKO/HbQE7eZ3G6dLmJ9evb4nh+G/Ry/V10HCJh6tQ5O3bsWBY2RGZq926gWzfAU5knOgrpWYsWd+CquAtVTnfRUYiEqnVx4+HhgVu3bgEA3N3d4eHhUeWNiEyTRqMtbsLDRSchQ5DLJfT3SIYqJ1h0FCKhat0t9b///Q/Ozs5l31c2WkpX+/fvx8KFC5GYmIiMjAxs3ry57LqeyiQkJCC0kunHz549izZt2tQ7D5GlO30ayMr6s7g5IjoNGUKYZxLeOvc68ksc4Ky4LzoOkRC1Lm7Gjh1b9v1LL72klxe/d+8eOnfujHHjxul0IXJKSgpcXFzK7jdsyFk5iWpDpQIcHIA+fUQnIUMZ6HkSJZICCXe6YLjXL6LjEAlR6+ImL6/2/fMPFx7ViYiIQERERK2f9wFvb2+4ubnVat+ioiIUFRWV3X9wHGq1Gmq1umz7g+8f3kZVY3vpxlTaa9cuOUJCABubUqhtTXeRRbVSWe4r1exBWwW63EQT+0zszOuBIf5VjIjj/1uT+T9pLkyhvXR5bZkk1W6ybhsbmxq7oh5M7ldaWlrrAGVBZLJad0s1adIEhYWFaNeuHWbOnFlpV9UD77//PubMmVNh+7p16+Do6KhzTiJzVVxsgzFjhmLUqLOIjLwoOg4Z0BdfdMbZs574/PO9oqMQ6U1BQQFGjRqF3NzcGk+i1PrMTbwJzIvg5+eHr776CsHBwSgqKsLatWsxcOBAJCQkoG/fvpU+ZsaMGYiKiiq7n5eXh4CAAAwaNKhc46jVaqhUKoSHh0PJT4s1YnvpxhTaa+9eGYqL5Zg0qTU6d24NDBsmJEdtqJVKqMaPR/iKFVDyk3WtPNxmBTd6YtS1mej0yQ9obH+r4s6xscYPaGJM4f+kOTGF9tKlB6nWxU2/fv3qFEafWrdujdatW5fd79WrF9LS0rBo0aIqixs7OzvY2dlV2K5UKiv9B6pqO1WO7aUbke0VHw94ewPduilhYwOguFhIDl0o1WoozSCnKVGq1RjkfBwyaJBwoxPG+cVVshP/zz7A9zDdiGwvXV631sXNqVOn0KFDB9jY2ODUqVPV7tupU6daB6ivnj17Ijo62mivR2SuVCogLAzawoYsmqcyD8HOf0CV073y4obIwtW6uOnSpQsyMzPh7e2NLl26QCaTobLLdep6zU1dJSUlwc/Pz2ivR2SObt0CkpLANYesSLh7Ir7OGAqNJIONrFaXVhJZjFoXN5cvXy4bcn358mW9vPjdu3dx4cKFcq+RnJwMDw8PBAYGYsaMGUhPT8e3334LAFi8eDGaNGmC9u3bo7i4GNHR0di4cSM2btyolzxElmrPHkCSOHmfNQlzT8SC1NE4dbcZujjzAnKyLrUuboKCgir9vj5OnDhRbqTTgwt/x44dizVr1iAjIwOpqallPy8uLsa0adOQnp4OBwcHtG/fHtu2bcPQoUP1kofIUqlUQNu2QKNGopOQsfRxPQMHm0KocrqzuCGro9PCmQ9LSUnBkiVLcPbsWchkMrRp0wZTpkwpd8FvTfr3719p19YDa9asKXd/+vTpmD59el0jE1klSdIWN9XMskAWyM5Gjb6up6DKCcbbgd+LjkNkVHW6tPCnn35Chw4dkJiYiM6dO6NTp044efIkOnTogB9//FHfGYmoHs6fB1JT2SVljcI9TuBAbicUlnI0EFmXOp25mT59OmbMmIG5c+eW2z579mz861//wt///ne9hCOi+lOpAIUCMIHZHMjIwt0TMe3iRBzK64iB7idFxyEymjqducnMzMSLL75YYfuYMWOQmZlZ71BEpD8qFdCrF/DnurdkRTo2uAQfZTZU2VwlnKxLnYqb/v3748CBAxW2Hzx4ECEhIfUORUT6UVKinbyPXVLWSSbTjppS5XQXHYXIqGrdLbV169ay70eMGIF//etfSExMRM+ePQEAR44cwY8//ljpOk5EJMaxY0BeHosbaxbukYh1WQNxq9gFXra1n76eyJzVuripbEHLpUuXYunSpeW2TZo0CRMmTKh3MCKqP5UKcHUFuvODu9UKc0+EBBvsuROMkd7i1wgkMoZad0tpNJpa3Yw5OzERVU+lAgYM0F5QTNapkd0ttHO8wutuyKpwlRkiC5WXBxw5wi4pAsLdT0CVE4xqphUjsih1/jx379497Nu3D6mpqSh+ZNXeN954o97BiKh+9u0DSktZ3JD2uptP0/+G8/cbo5XjNdFxiAyuTsVNUlIShg4dioKCAty7dw8eHh64desWHB0d4e3tzeKGSLTQUKjOT0ET+15o/uooQCY6EInUzzUZSpkaqpzuLG7IKtSpW+qtt97C8OHDkZ2dDQcHBxw5cgRXr15FcHAwFi1apO+MRFQHqpxghLsnQsbCxuo5KQrRy+V3XndDVqNOZ26Sk5Px5ZdfQi6XQy6Xo6ioCM2aNcPHH3+MsWPH4plnntF3TiLSwbVCL5wrCMLcJqtFRyETEe5+AgvTRqJEYwPFQwsWVymeI6vIfNXpzI1SqYTsz4+DPj4+ZSt3u7q6llvFm4jEUOV0hwwaDHBPEh2FTES4RyLySp1wLL+t6ChEBlenMzddu3bFiRMn0KpVK4SGhmLWrFm4desW1q5di44dO+o7IxHpSJXTHcHOf8BTyUnbSKu7cwrcFPlQ5QSjt+tvouMQGVSdztx8+OGH8PPzAwDMmzcPnp6eeP3115GVlYWvvvpKrwGJSDcaDbA7pxvC3RNFRyETIpdpMMAtCapszuhIlq9OZ266PzTdacOGDbF9+3a9BSKi+jl1Cripdke4+wnRUcjEhLufwOTzbyKvxBEuigLRcYgMpl6T+GVlZeHAgQM4ePAgbt68qa9MRFQPu3YBDjaF7HqgCsI9ElEKOfbmdBUdhcig6lTc5OXl4YUXXkCjRo3Qr18/9O3bF/7+/hgzZgxyc3P1nZGIdLBjBzDALQl2NmrRUcjENHe4jhYO1xCX3UN0FCKDqlNx8+qrr+Lo0aOIjY3FnTt3kJubi9jYWJw4cQLjx4/Xd0YiqqX8fODgQSDC85joKGSiIjyOYUd2Dy7FQBatTsXNtm3bsGrVKgwePBguLi5wdnbG4MGDsWLFCmzbtk3fGYmolvbsAUpKgCEeLG6ochEeR5Fa5ItzBYGioxAZTJ2KG09PT7i6ulbY7urqCnd393qHIqK62bEDaNlS2/1AVJn+bsmwtynCjuzHRUchMpg6FTczZ85EVFQUMjIyyrZlZmbi7bffxr///W+9hSOi2pMkbXETESE6CZkyB3kx+rn+ih287oYsWK2Hgnft2rVsVmIAOH/+PIKCghAYqD21mZqaCjs7O9y8eRP/93//p/+kRFSt338H0tL+LG5OiU5DpizC8ximX3wNd0vs4aQoFB2HSO9qXdxERkYaMAYR1VoV6wLFpf0d9javoN+HIwC5kTORWYnwOIqpFyYj/k5XDPf6RXQcIr2rdXEze/ZsQ+YgonracftxhLolwUFeLDoKmbiWDtfQzD4dcdk9WNyQRarTDMUPJCYm4uzZs5DJZGjXrh26duXEUEQi3C2xx4HcjljY/EvRUcgMyGTaIeHbsx+HJGnvE1mSOhU3WVlZeP7555GQkAA3NzdIkoTc3FyEhoZiw4YNaNiwob5zElE19t7phmLJFhEeR0VHITMxxOMYvrj+NP64H4DWjmmi4xDpVZ1GS02ZMgV5eXn47bffkJ2djZycHJw5cwZ5eXl444039J2RiGoQl90Dze3T0dIxXXQUMhOh7smwlRVjx22OmiLLU6fiJi4uDsuWLUPbtm3LtrVr1w5ffPEFduzYobdwRFQzSQJ2ZPdAhCfP2lDtNZAXop/br1yKgSxSnYobjUYDpVJZYbtSqYRGo6l3KCKqvZSCAFwp9EMEZyUmHUV4HEPCnS4oKLUTHYVIr+pU3AwYMABvvvkmrl//axbU9PR0vPXWWxg4cKDewhFRzXZkPw47WTH6uyWLjkJmJsLjKIokWyTc6SI6CpFe1am4+fzzz5Gfn48mTZqgefPmaNGiBZo2bYr8/HwsWbJE3xmJqBo7snugn9uvcJQXiY5CZqa1Yxqa2GdwtmKyOHUaLRUQEICTJ09CpVLh3LlzkCQJ7dq1Q1hYmL7zEVE17pXaY9+dzvhPs69ERyEzJJNpR03xuhuyNDoXNyUlJbC3t0dycjLCw8MRHh5uiFxEVAsJd7poh4B78nobqpsIj2NYfv0pXCjwRwtHLrhKlkHnbimFQoGgoCCUlpYaIg8R6WDH7R5oan8drRw4TwnVzQC3k9oh4VwlnCxInVcFnzFjBrKzs/Wdh4hqqWwIuMcxzjBLdeakKESI62led0MWpU7X3Hz22We4cOEC/P39ERQUhAYNGpT7+cmTJ/USjoiqdv5+Y1wqbIQIj89FRyEzF+F5FDMvv4L7pbZcm4wsQp2Km8jISMhkMkiSpO88RFRLcdk9YCsrRqh7sugoZOaGeBzHtIsTsT+3MwZ7HBcdh6jedCpuCgoK8PbbbyMmJgZqtRoDBw7EkiVL4OXlZah8RFSFHdk90NftFBrIC0VHITPXzvEKAuxuYMftHixuyCLodM3N7NmzsWbNGjz55JP4xz/+gd27d+P11183VDYiqsL9Uu3Ea5yVmPThwSrhvO6GLIVOZ242bdqElStX4vnnnwcAjB49Gn369EFpaSnkcrlBAhJRRQl3uqBQY8dVwElvIjyO4quM4bh03w/NHDJExyGqF53O3KSlpSEkJKTsfo8ePaBQKMotw0BEhheX3QNBdplo45gqOgpZiIHuJ6GQlXBCP7IIOhU3paWlsLW1LbdNoVCgpKREr6GIqHoPVgHnEHDSF2fFfTzBIeFkIXTqlpIkCS+99BLs7P5aQbawsBATJkwoNxx806ZN+ktIROVcvO+P8/cDsLD5l6KjkIWJ8DiGOVdeRGGpEvaiwxDVg07FzdixYytsGzNmjN7CEFHNdtzuAaVMjQFunE+K9CvC4yj+den/cCC3E7iwDpkznYqb1atXGyoHEdXSjuzHEeJ6Gs6K+6KjkIXp0OAyGtneRFx2DxY3ZNbqtPwCEYlRWAjE3+nCUVJkEDKZdrZiXndD5o7FDZEZ2b8fuK+x5yrgZDBDPI7jbEETXLkiOglR3bG4ITIjsbFAoF0m2jleER2FLFS4+wkoZWrExopOQlR3LG6IzIQkATExQKTXIQ4BJ4NxURRggFsSYmJEJyGqOxY3RGbi5EkgLQ2I9DooOgpZuEivg0hIAHJyRCchqhsWN0RmIiYGcHcHQlxPiY5CFm6E12GUlgLbtolOQlQ3LG6IzERMDDB8OKCw0YiOQhbO3+42Hn8c7Jois8XihsgMXLgAnDkDREaKTkLWIjISiIsD7nM6JTJDLG6IzMCWLYC9PTBokOgkZC0iI4F794A9e0QnIdKd0OJm//79GD58OPz9/SGTyRBTi3Og+/btQ3BwMOzt7dGsWTMsX77c8EGJBIuJ0RY2Dy3hRmRQbdpob+yaInMktLi5d+8eOnfujM8//7xW+1++fBlDhw5FSEgIkpKS8O677+KNN97Axo0bDZyUSJysLODQIXZJkfFFRgJbtwKlpaKTEOlGp7Wl9C0iIgIRERG13n/58uUIDAzE4sWLAQBt27bFiRMnsGjRIjz77LMGSkkk1s8/a6fFHzZMdBKyNpGRwEcfAb/8AjzxhOg0RLUntLjR1S+//IJBj1x0MHjwYKxcuRJqtRpKpbLCY4qKilBUVFR2Py8vDwCgVquhVqvLtj/4/uFtVDW2l27q016bNsnRpw/g5lYKtRqAra2e05ke9Z//l9WV/J+myum9zdRqdOkC+PkpsHGjBo8/blmj9PgephtTaC9dXtusipvMzEz4+PiU2+bj44OSkhLcunULfn5+FR6zYMECzJkzp8L2Xbt2wdHRscJ2lUqlv8BWgO2lG13b6/59OVSqCLzwwu/Yvv2SduPEiQZIZppU48eLjmB29NZm27cDADp37oT1673Rr99ui5wZm+9huhHZXgUFBbXe16yKGwCQPfK/S5KkSrc/MGPGDERFRZXdz8vLQ0BAAAYNGgQXF5ey7Wq1GiqVCuHh4ZWeAaLy2F66qWt7bdwog1otx7/+1QZNm7bRbrSC/im1UgnV+PEIX7ECSn6yrhW9t9mfi0spFDIMG6ZAYOBQdOxY/6c1FXwP040ptNeDnpfaMKvixtfXF5mZmeW2ZWVlQaFQwNPTs9LH2NnZwc7OrsJ2pVJZ6T9QVdupcmwv3ejaXrGxQOfOQKtWDz2muNgAyUyTUq2G0oqOVx/01mZ//p6GhwMuLsC2bUp061b/pzU1fA/Tjcj20uV1zWqem169elU4JbZr1y50796dv5xkcdRqbXHDUVIkkq0tMHQoh4STeRFa3Ny9exfJyclITk4GoB3qnZycjNTUVADaLqUXX3yxbP8JEybg6tWriIqKwtmzZ7Fq1SqsXLkS06ZNExGfyKD27QNyc1nckHiRkdqFW/98ayYyeUKLmxMnTqBr167o2rUrACAqKgpdu3bFrFmzAAAZGRllhQ4ANG3aFNu3b0dCQgK6dOmCefPm4bPPPuMwcLJIMTFAUJC2W4pIpIgIbS/Vli2ikxDVjtBrbvr37192QXBl1qxZU2Fbv379cPLkSQOmIhJPkrTFzd/+BoscoULmxcUFGDhQ+zs5ZYroNEQ1M6trboisRWIikJ7OLikyHZGR2q7S7GzRSYhqxuKGyATFxAAeHpwVlkzHiBHaZRi2bROdhKhmZjUUnMhaxMQAw4cDCv4PJVFCQ8vd9QPQ0+VzxETdxgurZms3xscbPxdRLfDMDZGJOX8e+O03dkmR6Yn0Ooi47Mdwv9TylwAh88bihsjEbNkCODgAjyyjRiRcpNchFGgcsDsnWHQUomqxuCEyMZs3awubSpY+IxKqtWMa2jheRcwtXgxGpo3FDZEJycwEfvmFXVJkuiK9DmLr7d4olfjng0wXfzuJTMjPP2vntbGCtTHJTEV6HcIttRsO57YXHYWoSixuiExITAwQEgJ4eYlOQlS5x5zPwc/2FrumyKSxuCEyEfn5wO7dwNNPi05CVDUbmYSnvA5h860nUM0E80RCsbghMhE7dgDFxcBTT4lOQlS9SK9DuFzoj1OnRCchqhynCCMyERs2AMFOKWgyboLoKETVCnVLgociFxs2uHJhVzJJPHNDZAJycrTT2o/22S06ClGNbG1K8PeG+7BuHaDRiE5DVBHP3BAZ07Bh2r6nR2y8PhQlxVF43nuvgFBEuhvjo8KXySNw8CDQt6/oNETl8cwNkQmIvhGOge4n4WfHJZfJPPR2/Q1BQcB334lOQlQRixsiwVILvbEvtwtG++wRHYWo1mxkEkaPBn74ASgqEp2GqDwWN0SCrc8aAAebQjztdUB0FCKdjB4N3LmjHelHZEpY3BAJ9t2NMIzwPAwXRYHoKEQ6adcO6NqVXVNkeljcEAl06m4znL7XHGN8VKKjENXJ6NHaZUNyc0UnIfoLixsigb67EQZPRS4GexwXHYWoTp5/XjsAcONG0UmI/sLihkgQjSTDuqyBGOkdD6VNqeg4RHXSqBEwYAAQHS06CdFfWNwQCbL/TidcK/LmxH1k9kaPBhISgGvXRCch0mJxQyRI9I1wNLW/jl4uv4mOQlQvzzwD2NkB69eLTkKkxeKGSIDCUiV+utkPo312QyYTnYaoflxdgeHDOWqKTAeLGyIBtmf3RG6pE0Z7s0uKLMOYMcCvvwJnzohOQsTihkiI6BthCHZKQZsGaaKjEOnFkCGAhwfP3pBpYHFDZGQ5aidsu92Tc9uQRbG1Bf7+d3ClcDIJLG6IjOynm/1QIsm5AjhZnDFjgNRU4OBB0UnI2rG4ITKy726EIcz9JHztckRHIdKr3r3BlcLJJChEByCyJqn3G2Jfbhd802aB6ChE9RcaWu6uDYDR0itYtmoEPjv3N9jZqIH4eDHZyKrxzA2REX2fGcoVwMmijfbZjZwSF+y43UN0FLJiLG6IjGhdxgA85XUIzor7oqMQGUS7BlfR1ekPfJcVJjoKWTEWN0RGcuWKC36725Rz25DFG+2zGz/f6o3ckgaio5CVYnFDZCT79jWGp5IrgJPle947HsWSAhtv9hUdhawUixsiI9BogP37G+Pvvvu4AjhZvEZ2tzDALQnf3WDXFInB4obICA4ckOH2bQeM8uPcNmQdRvvsRvydLlwpnIRgcUNkBKtW2cDP7y4edz0rOgqRUTzbcD8cbIqwapXoJGSNWNwQGdiNG8BPP8kQEXGZK4CT1XBRFOAFHxWWLwfUatFpyNqwuCEysBUrAKUSGDCAi2SSdZnUKAYZGcDmzaKTkLVhcUNkQGo1sGwZMGqUBk5O/PhK1qWj02X06wd8/rnoJGRtWNwQGVBMDHD9OjBhApdJJus0eTJw4ADw66+ik5A1YXFDZECffw707Qt06iQ6CZEYTz0FNGoEfPGF6CRkTVjcEBnIqVPA/v3aT65E1kqpBCZMAKKjgZwc0WnIWrC4ITKQL74A/P2ByEjRSYjEeu01oLQUWL1adBKyFgrRAYgsQmhoubs5aidE//ID3glcD+WgtVDb2gITJwoKRySWtzfw3HPagn/qVMCGH6vJwFjcEBnAmswhUEsKvOYfKzoKkVh/Fv6T89oi+tJSxHV5B0M9j5bfJz5eQDCyZKyfifRMI8nwRXoknmuYAB9bXmRABAA9nM+iu/M5fJ4eKToKWQEWN0R6FpfdAxcLG2FyI85cRvSATAZMbhSDHdk9cb6gkeg4ZOFY3BDp2efpkQh2SsHjLlxHiuhhIxvuhaciF0uvPyU6Clk4FjdEenShwB87snticqPNXEeK6BH2cjXG+8didUYE7pbYi45DFozFDZEeLb0eCU9FLkZ68wJJospM8P8Z+aUO+C4rXHQUsmAsboj05F6pPVZlROBVv21wkBeLjkNkkoLsb2CE12F8nh4JSRKdhiwVixsiPfnuRhjySx3weqOtoqMQmbTJjWJw5l4z7M/tLDoKWSgWN0R6IEnAkvSnMcLrMILsb4iOQ2TSBridRBvHqxwWTgbD4oZID/bndsaZe80wuVGM6ChEJu/BsPDNN0NwrdBLdByyQMKLm6VLl6Jp06awt7dHcHAwDhw4UOW+CQkJkMlkFW7nzp0zYmKiij5Pj0Qbx6sY4HZSdBQis/Ciz044ygvxZcZw0VHIAgktbr7//ntMnToV7733HpKSkhASEoKIiAikpqZW+7iUlBRkZGSU3Vq2bGmkxEQVXbsGbL4ZgsmNYjj8m6iWnBX38ZLvTnx1fRiKikSnIUsjtLj55JNP8Morr+DVV19F27ZtsXjxYgQEBGDZsmXVPs7b2xu+vr5lN7lcbqTERBUtWQI4ygvxos9O0VGIzMpE/xhkqT0QHS06CVkaYQtnFhcXIzExEe+880657YMGDcLhw4erfWzXrl1RWFiIdu3aYebMmQh9ZEXmhxUVFaHooY8FeXl5AAC1Wg21Wl22/cH3D2+jqrG9tDIzgSVLFHgzcAvsHUuhhm2l+6mVynJfqXpsL92ZY5s1t72BZ332Y+7cEIwcWQI7O+O9Nt/DdGMK7aXLa8skScxMA9evX0ejRo1w6NAh9O7du2z7hx9+iG+++QYpKSkVHpOSkoL9+/cjODgYRUVFWLt2LZYvX46EhAT07du30td5//33MWfOnArb161bB0dHR/0dEFmlr7/ugL17A/HVVyo4OfFNkkhXaWlOePPNAXj11VMYOvSK6DhkwgoKCjBq1Cjk5ubCxcWl2n2FFzeHDx9Gr169yrbPnz8fa9eurfVFwsOHD4dMJsPWrZXPLVLZmZuAgADcunWrXOOo1WqoVCqEh4dDaUaffERhewFpaUDbtgq8+64G7x4eWu2+aqUSqvHjEb5iBZT8pFgjtpfuzLnNxjXcgb17ZTh3rgQODsZ5Tb6H6cYU2isvLw9eXl61Km6EdUt5eXlBLpcjMzOz3PasrCz4+PjU+nl69uyJ6Go6bO3s7GBXyblOpVJZ6T9QVdupctbcXv/5D+DiAkRFyaFMqN2MxEq1Gspizl5cW2wv3Zljm82ZY4MNG4Cvv1YiKsq4r23N72F1IbK9dHldYRcU29raIjg4GCqVqtx2lUpVrpuqJklJSfDz89N3PKJqXbwIrFoFvPMO4OwsOg2ReWveHHj5ZWDBAuDuXdFpyBIIO3MDAFFRUXjhhRfQvXt39OrVC1999RVSU1MxYcIEAMCMGTOQnp6Ob7/9FgCwePFiNGnSBO3bt0dxcTGio6OxceNGbNy4UeRhkBWaOxdo2BB4/XXRSYgsw8yZwDffAJ99Brz7rug0ZO6EFjcjR47E7du3MXfuXGRkZKBDhw7Yvn07goKCAAAZGRnl5rwpLi7GtGnTkJ6eDgcHB7Rv3x7btm3D0KHVX+9ApE9nzwLR0do3YV6TTqQfgYHAhAnAwoXAxImAm5voRGTOhBY3ADBx4kRMnDix0p+tWbOm3P3p06dj+vTpRkhFVLXZs4GAAODVV0UnIbIQf07nMaPIHSvy1uGTLj9gbtPV5feJjxcQjMyV8OUXiMxJcjLw44/ArFkw6pwcRNbA1y4HUxptxv+u/Q03i11FxyEzxuKGSAezZgEtWgAvvig6CZFlmh64ATJI+DjtedFRyIyxuCGqpSNHgJ9/BubMARTCO3SJLJOnMg9vNf4Jn6c/jYwiD9FxyEyxuCGqpX//G+jQAXieHyiJDOqtxj/CwaYIH6aOFh2FzBSLG6JaSEgAdu/WDgG34f8aIoNyU97D2wHf48vrw3G1sPaTuhI9wLdpohpIkvasTbduQGSk6DRE1uGNxpvgrsjHB1fHiI5CZojFDVENdu4EDh4EPvgAkMlEpyGyDg3khZgRuA6rMyJwocBfdBwyMyxuiKohSdqZU3v3BoYMEZ2GyLpM8N8KX9tszLk6VnQUMjMc80HW7c/Jw6qy+WYIEn+bi/h4nrUhMjZ7uRozg9Zi4vmpmH4a6NhRdCIyFzxzQ1SFvBJHvHlhMiIigP79Rachsk4v++1Aa8c0vPYaUFoqOg2ZCxY3RFX416XXcKfECcuWiU5CZL1sbUqwsvVCHD0KLFkiOg2ZC3ZLEVUiIaczll9/Cp+3/BRBL8WIjkNk1Xq7/oYpU4D33gNGjACaNROdiEwdz9wQPaKg1A6vpryNENdf8br/FtFxiAjA/PmAtzcwfrz2Qn+i6rC4IXrErMvjcK2oIb5uvQg2Mr6LEpkCJyfgq6+AvXuBlStFpyFTx+KG6CHH8trgf9f+hjlNV6OV4zXRcYjoIeHhwMsvA//8J5CeLjoNmTIWN0R/KtIo8fK56ejqdAH/bPyD6DhEVIn//hdo0AB4/XV2T1HVWNwQ/enDq6ORcj8AK9sshMJGIzoOEVXCzQ1YuhT4+WdgwwbRachUsbghAnDqbjN8mDoaMwLXobPTRdFxiKgakZHAc88Bb7wB3LwpOg2ZIg4FJ6tXorHBy+emo5XDNbwXFC06DhFV5pHZxJcUu6HdnTV4o8MJrG/3gXZjfLyAYGSKeOaGrN7/rv0dSXdbYFWbj2FnoxYdh4hqwdv2Dj5t+Tk2ZA3E1lu9RcchE8PihqzaHwWNMevKOExtvBGPu5wVHYeIdDDKezeGehzB639MxR11A9FxyISwuCGrpdEAr6ZMg7/tLcxrukp0HCLSkUwGLG/1CfJLHfH2pQmi45AJYXFDVuvTT4EDuZ3xdetFcJQXiY5DRHUQYH8TC5t/ia8zhiE2VnQaMhUsbsgq7dgBTJsG/LPx9wh1TxYdh4jqYbxfLEZ4HsKoUcDp06LTkClgcUNW5/RpYORI4Mkngf80/0p0HCKqJxuZhO/afoDmzYFhw4DMTNGJSDQWN2RVMjO1b37NmwPr1gFyGSfrI7IETopC/PwzoFZr58G5f190IhKJ89yQ1bh/H3jqKe2b388/axfiIyLL0fiFUPzs3wp9jy/G2KCj2NBubsXFbzkXjlXgmRuyChoNMHYscOaMtrBp3Fh0IiIyhGDnPxDd9kP8dLMvZl0eJzoOCcLihqzCrFnATz8B0dFAcLDoNERkSE83PIiPmq3A/NQX8G3mINFxSAB2S5HF+/ZbYP584OOPgaefFp2GiIzh7YANSCkIwKsp09DUPgMhbhxGZU1Y3JDlCg3F/jud8Oqvi/CK7y5M27YI2C46FBEZg0wGLGv1P1wu9MXTZ+bhSLeJaOF4XXQsMhJ2S5HFulDgj6fPzEWI62ksbbUYMpnoRERkTLY2Jfip/fvwVObiydMLkKPmKAJrweKGLFJ2NvDk6QVoqMzFT+1nw9amRHQkIhLAQ5mP2I7v4pbaFX/7bQ6Ki0UnImNgcUMW5/p1ICwMuK12RWzHGXBX3hUdiYgEaumYjk0dZuNAbkdERgJ3+ZZg8VjckEX57TegVy8gKwvY2yWKfexEBADo5/YrtnWcgYMHgX79OIuxpWNxQxYjIQHo0wdwdQWOHAE6OV0SHYmITEi4RyIOHNAWNiEhCqSl8RocS8XRUmQR1q0Dxo0D+vbVzmfj6io6ERGZos5TQ3EksCEizvwH77wTgnbfvIMBTifL78RZjM0ez9yQWZMk4KOPgNGjgX/8A9i2jYUNEVUvwP4m4h/7J5o1y0VE4gJsuBEqOhLpGYsbMlslJcDEicCMGdoZiFevBmxtRaciInPgpryHWbN+wd989+MfZ2dhYepISFLNjyPzwG4pMkt3QyLw/O+zEJfdA1+3/gSv7N8ODBCdiojMiVIpYXWHhWiizMT0SxNwtdAHn7b8HHLRwajeWNyQ2cnMBIYlL0bK/QBs6zQDgz2Oi45ERGZKJgPmN1uJIPsbmPjHVKQVeeO7u4ATrzU2a+yWIrMhScCaNUD79sD1Yk/s7/ImCxsi0ovX/GOxteN72JPTDR06AHFxohNRfbC4IbNw4YJ2Yr5x44CICODX7q+iq/MF0bGIyIIM9TyKXx97FS1aaN9nRo/WzplF5ofFDZk0tRpYsADo2BG4dEn7aSo6Gmhomys6GhFZoOYO16FSAd98o32/adtWe8aYFxubFxY3ZLKOHgWCg4GZM4HJk4EzZ4DBg0WnIiJLJ5MBL74InDunPYMzbpz2zPEFniw2GyxuyOTk5wNvvqldRkGpBI4fBxYuBBo0EJ2MiKxJw4baM8Vxcdozxx07aufVUqtFJ6OacLQUmQy1GvjhB2DGKzdwW+2CRc1W4w2njVD8UyM6GhFZscGDtWeO338feO89YP16bZEzeDBgw1MEJonFDQl35w6wYgXw2WfAtWvAMM+L+KzFEjR14Mp2RCRAaMUZixsAWAjgH11bYuIfUzF0aDu0bQtERQFjxgD29kZPSdVgzUnCXLqk7X5q3Fh7XU14OHDqFPBzx/dY2BCRSermfB6/dJuE/V3eQKusg3htvAaBLjmY03Q1svo8rS2MHtxIGJ65IaOSJOCXX4BPPgE2bwbc3bWffCZOBHx9RacjIqqZTAaEuJ1GiNtpnC9ohE+vPYuPU5/Hgquj8aLvLrzV+Ee0bZAqOqZVY3FDRnHjBhAbC6x463cczW+H1g6pWNriJ7zgswuOB4qAA6ITEhHprqVjOj5v9RnmNl2NL68Px5L0p7EiYxgiPI7gpR+01+VwMV/jY3FDBiFJwNmzwNat2tuRI9rtA1zvI7bjDER4HIWNjBNHEJFl8FDmY0bQOvwz4AdsyBqAJelPY+RI7YjP/v2BESOA4cOBoCDRSa0Dr7khvSkpARIStN1MLVtql0n44APAz0+7YveNG8DuLtPwpOcRFjZEZJFsbUrwou8uHA9+HVevAv/7n7YbKyoKaNIE6NIFmDULOHEC0HAgqMHwzA3VWV6eLXbtkiE5WfsfdV9sHnJKXOBvexMjvA5jScfDCHVLgn22GlgD7Y2IyEoEBgKTJmlveXnAzp3Ali3AkiXAvHmAvz/Qty/Qvbv21rUr4OIiOrVlYHFDtZKTAyQmaosY7U2Bq1cjAGj7k4ODgSmNNmOE12F0c/oDMpngwEREoj00YsoFwN//vKk7yXEotwNib/fCke3tsPWHFijQOEAGDVo5XEN355SyW1fnC2iwf4eoIzBbwoubpUuXYuHChcjIyED79u2xePFihISEVLn/vn37EBUVhd9++w3+/v6YPn06JkyYYMTEFig0FBpJhvQiL1wu9MPlQj9cuv/wV19cL24IAHB21hYyzzyjgY1NEl55pTNat1ZqJ7IKXSP0MIiIzIHSphT93X9Ff/dfAQAlGhucKwhE4t3WOJHfCifyW2Pjrb4o1NjBBqUIaAI0awY0bVrxq7c3+GGyEkKLm++//x5Tp07F0qVL0adPH3z55ZeIiIjA77//jsDAwAr7X758GUOHDsX48eMRHR2NQ4cOYeLEiWjYsCGeffZZAUcgWA3zKKg1cuT8uBs3b6La27Wj3+JqoQ+KJduyx/ra3kYz+ww0tc/AAPcktJ7/Irp3115LY2MDqNUabN+ejhYtOnOGTiKielDYaNDB6Qo6OF3BWN+dALTv378XNEFifiucf3o6Ll0CTp/Wdmvdvv3XYx0dtUWOn5+20GnYsOqbqysglws6SCMTWtx88skneOWVV/Dqq68CABYvXoydO3di2bJlWLBgQYX9ly9fjsDAQCxevBgA0LZtW5w4cQKLFi0yieImLw8oKtJeJFZaWvXX0lLtUgMlJdrbg+8f/lpcrH2uwkLtrbLv752bjvxSB9wtdUB+iSPySx2RX+qg/VriiCLJFvApn1GhALy8/vpl9/YGungeQTP762jqkIlm9tfRxD4TDvLi8g8c/aLxGpKIyMopbUrR2ekiOjtdBBZML/ez/Hzg8mXtRKgPvmZmAunpQHKy9kPr7duVX7Ds4KA9A1/VzcFBO9uyvT1gZ/fX9wqFDOfONcb9+zI4OGhHgSkU2tuD7x/+6uUF+PhUfH1jEVbcFBcXIzExEe+880657YMGDcLhw4crfcwvv/yCQYMGlds2ePBgrFy5Emq1GkqlssJjioqKUFRUVHY/NzcXAJCdnQ31Q6ufqdVqFBQU4Pbt25U+T22MGCHH4cP6P41hZyfBzu6vXzRbW+33DgWecJbfRwP5HTR2vA4neREaKArgJC+Es1z71UV5D17KXHgq8+BlmwdXxb3ypzBvAmhV/vUKABQ8OpBu4MByd9UKBQpeegm3hw2DsqREu5GncKqkBrS/XwCUbKcasb10xzbTjVm11yPvvwDQ6M9buYs45AB8tbdSyQZ31A1wq9gFt9UuuFnsiruljrhb4oB8jQPuqh1wL8sBdzPscbfUARmlDjhf4oD7jVuiqEj7AfvBh2ntfRmAltD+hajZ66+XYt48/Q4Hy8/PBwBIUi1G20qCpKenSwCkQ4cOlds+f/58qVWrVpU+pmXLltL8+fPLbTt06JAEQLp+/Xqlj5k9e7YEgDfeeOONN954s4BbWlpajTWG8AuKZY9cCSVJUoVtNe1f2fYHZsyYgaioqLL7Go0G2dnZ8PT0LPeYvLw8BAQEIC0tDS4ci1cjtpdu2F66YXvpjm2mG7aXbkyhvSRJQn5+Pvz9/WvcV1hx4+XlBblcjszM8gskZmVlwaeKjjpfX99K91coFPD09Kz0MXZ2drCzsyu3zc3NrcpcLi4u/EXXAdtLN2wv3bC9dMc20w3bSzei28u1lmtZCOtotLW1RXBwMFQqVbntKpUKvXv3rvQxvXr1qrD/rl270L179zpfJ0NERESWRehVVFFRUfj666+xatUqnD17Fm+99RZSU1PL5q2ZMWMGXnzxr1E6EyZMwNWrVxEVFYWzZ89i1apVWLlyJaZNmybqEIiIiMjECL3mZuTIkbh9+zbmzp2LjIwMdOjQAdu3b0fQnyuLZWRkIDX1r2XjmzZtiu3bt+Ott97CF198AX9/f3z22Wd6GQZuZ2eH2bNnV+jCosqxvXTD9tIN20t3bDPdsL10Y27tJZOk2oypIiIiIjIPJj64n4iIiEg3LG6IiIjIorC4ISIiIovC4oaIiIgsikUWN/n5+Zg6dSqCgoLg4OCA3r174/jx42U/v3HjBl566SX4+/vD0dERQ4YMwfnz56t9zhUrViAkJATu7u5wd3dHWFgYjh07ZuhDMQpDtNfDNmzYAJlMhsjISAOkNz5DtdedO3cwadIk+Pn5wd7eHm3btsX27dsNeShGY6g2W7x4MVq3bg0HBwcEBATgrbfeQmFhoSEPRe/279+P4cOHw9/fHzKZDDExMeV+LkkS3n//ffj7+8PBwQH9+/fHb7/9Vm6foqIiTJkyBV5eXmjQoAFGjBiBa9eu1fjaS5cuRdOmTWFvb4/g4GAcOHBAn4dmEKLaa8GCBXjsscfg7OwMb29vREZGIiUlRd+HZxAif8ceWLBgAWQyGaZOnaqHI6qFGhdoMEPPPfec1K5dO2nfvn3S+fPnpdmzZ0suLi7StWvXJI1GI/Xs2VMKCQmRjh07Jp07d0567bXXpMDAQOnu3btVPueoUaOkL774QkpKSpLOnj0rjRs3TnJ1dZWuXbtmxCMzDEO01wNXrlyRGjVqJIWEhEhPPfWU4Q/GCAzRXkVFRVL37t2loUOHSgcPHpSuXLkiHThwQEpOTjbikRmOIdosOjpasrOzk7777jvp8uXL0s6dOyU/Pz9p6tSpRjyy+tu+fbv03nvvSRs3bpQASJs3by73848++khydnaWNm7cKJ0+fVoaOXKk5OfnJ+Xl5ZXtM2HCBKlRo0aSSqWSTp48KYWGhkqdO3eWSkpKqnzdDRs2SEqlUlqxYoX0+++/S2+++abUoEED6erVq4Y6VL0Q1V6DBw+WVq9eLZ05c0ZKTk6WnnzyyVq/D4omqs0eOHbsmNSkSROpU6dO0ptvvqnno6ucxRU3BQUFklwul2JjY8tt79y5s/Tee+9JKSkpEgDpzJkzZT8rKSmRPDw8pBUrVtT6dUpKSiRnZ2fpm2++0Vt2EQzZXiUlJVKfPn2kr7/+Who7dqxFFDeGaq9ly5ZJzZo1k4qLiw2WXRRDtdmkSZOkAQMGlNsWFRUlPfHEE/o9ACN69A+PRqORfH19pY8++qhsW2FhoeTq6iotX75ckiRJunPnjqRUKqUNGzaU7ZOeni7Z2NhIcXFxVb5Wjx49pAkTJpTb1qZNG+mdd97R09EYnjHb61FZWVkSAGnfvn31PxAjMnab5efnSy1btpRUKpXUr18/oxU3FtctVVJSgtLSUtjb25fb7uDggIMHD6KoqAgAyv1cLpfD1tYWBw8erPXrFBQUQK1Ww8PDQz/BBTFke82dOxcNGzbEK6+8ov/gghiqvbZu3YpevXph0qRJ8PHxQYcOHfDhhx+itLTUMAdiRIZqsyeeeAKJiYll3cOXLl3C9u3b8eSTTxrgKMS4fPkyMjMzMWjQoLJtdnZ26NevHw4fPgwASExMhFqtLrePv78/OnToULbPo4qLi5GYmFjuMQAwaNCgKh9jDgzVXpXJzc0FALP/G2DoNps0aRKefPJJhIWFGeYAqmBxxY2zszN69eqFefPm4fr16ygtLUV0dDSOHj2KjIwMtGnTBkFBQZgxYwZycnJQXFyMjz76CJmZmcjIyKj167zzzjto1KiR0f/B9M1Q7XXo0CGsXLkSK1asMOLRGJ6h2uvSpUv46aefUFpaiu3bt2PmzJn473//i/nz5xvx6AzDUG32/PPPY968eXjiiSegVCrRvHlzhIaG4p133jHi0RnWg4WCH11M2MfHp+xnmZmZsLW1hbu7e5X7POrWrVsoLS2t9nnNkaHa61GSJCEqKgpPPPEEOnTooIfk4hiyzTZs2ICTJ09iwYIFek5dM4srbgBg7dq1kCQJjRo1gp2dHT777DOMGjUKcrkcSqUSGzduxB9//AEPDw84OjoiISEBERERkMvltXr+jz/+GOvXr8emTZsqfBo1R/pur/z8fIwZMwYrVqyAl5eXkY/G8Azx+6XRaODt7Y2vvvoKwcHBeP755/Hee+9h2bJlRjwywzFEmyUkJGD+/PlYunQpTp48iU2bNiE2Nhbz5s0z4pEZh0wmK3dfkqQK2x5Vm33q8rzmwFDt9cDkyZNx6tQprF+/vs4ZTY2+2ywtLQ1vvvkmoqOjhfydtMjipnnz5ti3bx/u3r2LtLQ0HDt2DGq1Gk2bNgUABAcHIzk5GXfu3EFGRgbi4uJw+/btsp9XZ9GiRfjwww+xa9cudOrUydCHYhT6bq+LFy/iypUrGD58OBQKBRQKBb799lts3boVCoUCFy9eNObh6Z0hfr/8/PzQqlWrcn/M27Zti8zMTBQXFxv8mAzNEG3273//Gy+88AJeffVVdOzYEU8//TQ+/PBDLFiwABqNxliHZlC+vr4AUOHTcVZWVtknbV9fXxQXFyMnJ6fKfR7l5eUFuVxe7fOaI0O118OmTJmCrVu3Ij4+Ho0bN9ZTcnEM1WaJiYnIyspCcHBw2d+Bffv24bPPPoNCoTB4l7tFFjcPNGjQAH5+fsjJycHOnTvx1FNPlfu5q6srGjZsiPPnz+PEiRMVfv6ohQsXYt68eYiLi0P37t0NGV0IfbVXmzZtcPr0aSQnJ5fdRowYgdDQUCQnJyMgIMAYh2Nw+vz96tOnDy5cuFDuj/Iff/wBPz8/2NraGuwYjE2fbVZQUAAbm/JvYXK5HJJ2oIRB8htb06ZN4evrC5VKVbatuLgY+/btQ+/evQFoC0OlUllun4yMDJw5c6Zsn0fZ2toiODi43GMAQKVSVfkYc2Co9gK0ZykmT56MTZs2Ye/evbX6MGwODNVmAwcOrPB3oHv37hg9ejSSk5Nr3VNSZ0a5bNnI4uLipB07dkiXLl2Sdu3aJXXu3Fnq0aNH2UiUH374QYqPj5cuXrwoxcTESEFBQdIzzzxT7jleeOGFcqMG/vOf/0i2trbSTz/9JGVkZJTd8vPzjXpshmCI9nqUpYyWkiTDtFdqaqrk5OQkTZ48WUpJSZFiY2Mlb29v6YMPPjDqsRmKIdps9uzZkrOzs7R+/fqy523evLn03HPPGfXY6is/P19KSkqSkpKSJADSJ598IiUlJZUNyf7oo48kV1dXadOmTdLp06elf/zjH5UO023cuLG0e/du6eTJk9KAAQMqDNMdMGCAtGTJkrL7D4aCr1y5Uvr999+lqVOnSg0aNJCuXLlivIOvA1Ht9frrr0uurq5SQkJCub8BBQUFxjv4OhLVZo8y5mgpiyxuvv/+e6lZs2aSra2t5OvrK02aNEm6c+dO2c8//fRTqXHjxpJSqZQCAwOlmTNnSkVFReWeo1+/ftLYsWPL7gcFBUkAKtxmz55tpKMyHEO016MsqbgxVHsdPnxYevzxxyU7OzupWbNm0vz582s1h4Q5MESbqdVq6f3335eaN28u2dvbSwEBAdLEiROlnJwcIx2VfsTHx1f63vLgWDUajTR79mzJ19dXsrOzk/r27SudPn263HPcv39fmjx5suTh4SE5ODhIw4YNk1JTU8vtExQUVOH96osvvpCCgoIkW1tbqVu3bmYxrFlUe1X2mgCk1atXG/iI60/k79jDjFncyCTJQs7fEhEREcHCr7khIiIi68PihoiIiCwKixsiIiKyKCxuiIiIyKKwuCEiIiKLwuKGiIiILAqLGyIiIrIoLG6IiIjIorC4ISIiIovC4oaIiIgsCosbIiIisigsbojI7N28eRO+vr748MMPy7YdPXoUtra22LVrl8BkRCQCF84kIouwfft2REZG4vDhw2jTpg26du2KJ598EosXLxYdjYiMjMUNEVmMSZMmYffu3Xjsscfw66+/4vjx47C3txcdi4iMjMUNEVmM+/fvo0OHDkhLS8OJEyfQqVMn0ZGISABec0NEFuPSpUu4fv06NBoNrl69KjoOEQnCMzdEZBGKi4vRo0cPdOnSBW3atMEnn3yC06dPw8fHR3Q0IjIyFjdEZBHefvtt/PTTT/j111/h5OSE0NBQODs7IzY2VnQ0IjIydksRkdlLSEjA4sWLsXbtWri4uMDGxgZr167FwYMHsWzZMtHxiMjIeOaGiIiILArP3BAREZFFYXFDREREFoXFDREREVkUFjdERERkUVjcEBERkUVhcUNEREQWhcUNERERWRQWN0RERGRRWNwQERGRRWFxQ0RERBaFxQ0RERFZlP8HZ9A/qf9RWhgAAAAASUVORK5CYII=", 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", "text/plain": [ "
    " ] @@ -2075,13 +2075,7 @@ "Error: 0.08426840630693411\n", "Bias^2: 0.0796891867672603\n", "Var: 0.004579219539673834\n", - "0.08426840630693411 >= 0.0796891867672603 + 0.004579219539673834 = 0.08426840630693413\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ + "0.08426840630693411 >= 0.0796891867672603 + 0.004579219539673834 = 0.08426840630693413\n", "Polynomial degree: 2\n", "Error: 0.10398646080125035\n", "Bias^2: 0.1007711427354898\n", @@ -2091,7 +2085,13 @@ "Error: 0.06547790180152355\n", "Bias^2: 0.06208238634231949\n", "Var: 0.0033955154592040936\n", - "0.06547790180152355 >= 0.06208238634231949 + 0.0033955154592040936 = 0.06547790180152359\n", + "0.06547790180152355 >= 0.06208238634231949 + 0.0033955154592040936 = 0.06547790180152359\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ "Polynomial degree: 4\n", "Error: 0.06844519414009445\n", "Bias^2: 0.06453579006728324\n", @@ -2101,13 +2101,7 @@ "Error: 0.05227921801205686\n", "Bias^2: 0.0481872773043029\n", "Var: 0.004091940707753939\n", - "0.05227921801205686 >= 0.0481872773043029 + 0.004091940707753939 = 0.052279218012056844\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ + "0.05227921801205686 >= 0.0481872773043029 + 0.004091940707753939 = 0.052279218012056844\n", "Polynomial degree: 6\n", "Error: 0.037813671417389005\n", "Bias^2: 0.033657685071527665\n", @@ -2117,23 +2111,29 @@ "Error: 0.02760977349102253\n", "Bias^2: 0.022999498260366312\n", "Var: 0.004610275230656212\n", - "0.02760977349102253 >= 0.022999498260366312 + 0.004610275230656212 = 0.027609773491022525\n" + "0.02760977349102253 >= 0.022999498260366312 + 0.004610275230656212 = 0.027609773491022525\n", + "Polynomial degree: 8\n", + "Error: 0.017355848195593347\n", + "Bias^2: 0.010331721306655127\n", + "Var: 0.007024126888938232\n", + "0.017355848195593347 >= 0.010331721306655127 + 0.007024126888938232 = 0.01735584819559336\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "Polynomial degree: 8\n", - "Error: 0.017355848195593347\n", - "Bias^2: 0.010331721306655127\n", - "Var: 0.007024126888938232\n", - "0.017355848195593347 >= 0.010331721306655127 + 0.007024126888938232 = 0.01735584819559336\n", "Polynomial degree: 9\n", "Error: 0.02660572763718093\n", "Bias^2: 0.010018312644137363\n", "Var: 0.016587414993043573\n", - "0.02660572763718093 >= 0.010018312644137363 + 0.016587414993043573 = 0.026605727637180936\n", + "0.02660572763718093 >= 0.010018312644137363 + 0.016587414993043573 = 0.026605727637180936\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ "Polynomial degree: 10\n", "Error: 0.021592704588025025\n", "Bias^2: 0.010516485576645508\n", @@ -2143,13 +2143,7 @@ "Error: 0.07160048164233104\n", "Bias^2: 0.014436800088904942\n", "Var: 0.05716368155342608\n", - "0.07160048164233104 >= 0.014436800088904942 + 0.05716368155342608 = 0.07160048164233102\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ + "0.07160048164233104 >= 0.014436800088904942 + 0.05716368155342608 = 0.07160048164233102\n", "Polynomial degree: 12\n", "Error: 0.11547777218872497\n", "Bias^2: 0.01628578269596628\n", @@ -2171,7 +2165,7 @@ }, "metadata": { "filenames": { - "image/png": "/Users/mhjensen/Teaching/MachineLearning/doc/LectureNotes/_build/jupyter_execute/week37_139_5.png" + "image/png": "/Users/mhjensen/Teaching/MachineLearning/doc/LectureNotes/_build/jupyter_execute/week37_139_4.png" } }, "output_type": "display_data" @@ -2670,34 +2664,34 @@ "Mean squared error on test data: 1184.60929685\n", "Degree of polynomial: 23\n", "Mean squared error on training data: 0.00089193\n", - "Mean squared error on test data: 3892.17483760\n", - "Degree of polynomial: 24\n", - "Mean squared error on training data: 0.00083355\n", - "Mean squared error on test data: 1332.46736215\n" + "Mean squared error on test data: 3892.17483760\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ + "Degree of polynomial: 24\n", + "Mean squared error on training data: 0.00083355\n", + "Mean squared error on test data: 1332.46736215\n", "Degree of polynomial: 25\n", "Mean squared error on training data: 0.00079904\n", "Mean squared error on test data: 7577.76690383\n", "Degree of polynomial: 26\n", "Mean squared error on training data: 0.00075590\n", - "Mean squared error on test data: 1079.36895644\n", - "Degree of polynomial: 27\n", - "Mean squared error on training data: 0.00068091\n", - "Mean squared error on test data: 3207.25343155\n", - "Degree of polynomial: 28\n", - "Mean squared error on training data: 0.00063362\n", - "Mean squared error on test data: 674.79633065\n" + "Mean squared error on test data: 1079.36895644\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ + "Degree of polynomial: 27\n", + "Mean squared error on training data: 0.00068091\n", + "Mean squared error on test data: 3207.25343155\n", + "Degree of polynomial: 28\n", + "Mean squared error on training data: 0.00063362\n", + "Mean squared error on test data: 674.79633065\n", "Degree of polynomial: 29\n", "Mean squared error on training data: 0.00063866\n", "Mean squared error on test data: 3099.60342978\n" @@ -2707,9 +2701,9 @@ "name": "stderr", "output_type": "stream", "text": [ - "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_95542/626635268.py:73: RuntimeWarning: divide by zero encountered in log10\n", + "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_22575/626635268.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_95542/626635268.py:74: RuntimeWarning: divide by zero encountered in log10\n", + "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_22575/626635268.py:74: RuntimeWarning: divide by zero encountered in log10\n", " plt.plot(polynomial, np.log10(testerror), label='Test Error')\n" ] }, @@ -2844,7 +2838,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_95542/3817475779.py:63: RuntimeWarning: divide by zero encountered in log10\n", + "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_22575/3817475779.py:63: RuntimeWarning: divide by zero encountered in log10\n", " plt.plot(polynomial, np.log10(estimated_mse_sklearn), label='Test Error')\n" ] }, diff --git a/doc/LectureNotes/_build/jupyter_execute/week37_121_0.png b/doc/LectureNotes/_build/jupyter_execute/week37_121_0.png index 9571da176..59ff71db5 100644 Binary files a/doc/LectureNotes/_build/jupyter_execute/week37_121_0.png and b/doc/LectureNotes/_build/jupyter_execute/week37_121_0.png differ diff --git a/doc/LectureNotes/_build/jupyter_execute/week39.ipynb b/doc/LectureNotes/_build/jupyter_execute/week39.ipynb index 248b7b106..d48fadfff 100644 --- a/doc/LectureNotes/_build/jupyter_execute/week39.ipynb +++ b/doc/LectureNotes/_build/jupyter_execute/week39.ipynb @@ -1176,7 +1176,7 @@ { "data": { "text/plain": [ - "" + "" ] }, "execution_count": 1, @@ -1346,7 +1346,7 @@ { "data": { "text/plain": [ - "[]" + "[]" ] }, "execution_count": 5, @@ -2138,16 +2138,16 @@ "name": "stdout", "output_type": "stream", "text": [ - "Eigenvalues of Hessian Matrix:[0.24602146 5.15830902]\n", - "[[3.94388948]\n", - " [3.14880915]]\n", - "[[3.94388948]\n", - " [3.14880915]]\n" + "Eigenvalues of Hessian Matrix:[0.29950088 4.27458376]\n", + "[[3.66841959]\n", + " [3.26280614]]\n", + "[[3.66841959]\n", + " [3.26280614]]\n" ] }, { "data": { - "image/png": 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", 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", 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    " ] @@ -2232,9 +2232,9 @@ "name": "stdout", "output_type": "stream", "text": [ - "[[4.1567286 ]\n", - " [2.83641435]]\n", - "[4.12466453] [2.80907609]\n" + "[[4.06267057]\n", + " [2.86868711]]\n", + "[4.05285677] [2.86799623]\n" ] } ], @@ -2390,16 +2390,16 @@ "name": "stdout", "output_type": "stream", "text": [ - "Eigenvalues of Hessian Matrix:[0.29902518 4.33006628]\n", - "[[3.73252708]\n", - " [3.18093549]]\n", - "[[3.73265885]\n", - " [3.1808228 ]]\n" + "Eigenvalues of Hessian Matrix:[0.24719968 4.31175179]\n", + "[[4.07235641]\n", + " [3.05476114]]\n", + "[[4.06908518]\n", + " [3.05761244]]\n" ] }, { "data": { - "image/png": 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", + "image/png": 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", "text/plain": [ "
    " ] @@ -4896,14 +4896,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "theta from own gd" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", + "theta from own gd\n", "[[4.0586484]\n", " [3.0718316]]\n" ] @@ -4917,7 +4910,7 @@ }, "metadata": { "filenames": { - "image/png": "/Users/mhjensen/Teaching/MachineLearning/doc/LectureNotes/_build/jupyter_execute/week39_269_3.png" + "image/png": "/Users/mhjensen/Teaching/MachineLearning/doc/LectureNotes/_build/jupyter_execute/week39_269_2.png" } }, "output_type": "display_data" diff --git a/doc/LectureNotes/_build/jupyter_execute/week39_153_1.png b/doc/LectureNotes/_build/jupyter_execute/week39_153_1.png index 75cbb585c..717dbfc68 100644 Binary files a/doc/LectureNotes/_build/jupyter_execute/week39_153_1.png and b/doc/LectureNotes/_build/jupyter_execute/week39_153_1.png differ diff --git a/doc/LectureNotes/_build/jupyter_execute/week39_166_1.png b/doc/LectureNotes/_build/jupyter_execute/week39_166_1.png index e2318103e..b00dcb4a9 100644 Binary files a/doc/LectureNotes/_build/jupyter_execute/week39_166_1.png and b/doc/LectureNotes/_build/jupyter_execute/week39_166_1.png differ diff --git a/doc/LectureNotes/_build/jupyter_execute/week39_269_2.png b/doc/LectureNotes/_build/jupyter_execute/week39_269_2.png new file mode 100644 index 000000000..cc2a55be2 Binary files /dev/null and b/doc/LectureNotes/_build/jupyter_execute/week39_269_2.png differ diff --git a/doc/LectureNotes/_build/jupyter_execute/week40.ipynb b/doc/LectureNotes/_build/jupyter_execute/week40.ipynb index 57872f439..dc9559eed 100644 --- a/doc/LectureNotes/_build/jupyter_execute/week40.ipynb +++ b/doc/LectureNotes/_build/jupyter_execute/week40.ipynb @@ -145,17 +145,17 @@ "output_type": "stream", "text": [ "Parameters for OLS using gradient descent\n", - "[[4.14721582]\n", - " [2.54760497]\n", - " [5.2212296 ]]\n", + "[[4.26604611]\n", + " [2.29234681]\n", + " [5.33329216]]\n", "Parameters for Ridge using gradient descent\n", - "[[3.75424998]\n", - " [3.49608088]\n", - " [4.78010668]]\n", + "[[3.6161104 ]\n", + " [3.78762558]\n", + " [4.65410649]]\n", "Parameters for Lasso using gradient descent\n", - "[[3.62716271]\n", - " [3.82146046]\n", - " [4.64516328]]\n" + "[[4.24335376]\n", + " [2.36219301]\n", + " [5.29669411]]\n" ] } ], @@ -234,11 +234,11 @@ "[[4.]\n", " [3.]\n", " [5.]]\n", - "0 [-25.77106886] [-35.02189606]\n", - "1 [3.49285045e-13] [4.71505132e-13]\n", - "2 [1.24344979e-16] [3.86639832e-16]\n", - "3 [9.05941988e-16] [1.48786826e-15]\n", - "4 [-7.99360578e-16] [-1.35823372e-15]\n", + "0 [-26.28886314] [-34.64721597]\n", + "1 [-1.83231208e-13] [-1.80848093e-13]\n", + "2 [6.92779167e-16] [1.22835717e-15]\n", + "3 [-1.3500312e-15] [-1.8110093e-15]\n", + "4 [7.46069873e-16] [9.30442002e-16]\n", "beta from own Newton code\n", "[[4.]\n", " [3.]\n", @@ -689,14 +689,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "gamma_j after 500 epochs: 9.97108e-05" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n" + "gamma_j after 500 epochs: 9.97108e-05\n" ] } ], @@ -753,20 +746,20 @@ "output_type": "stream", "text": [ "Own inversion\n", - "[[4.16180248]\n", - " [2.8250103 ]]\n", - "Eigenvalues of Hessian Matrix:[0.26646852 4.68110474]\n", + "[[4.36014743]\n", + " [2.76030841]]\n", + "Eigenvalues of Hessian Matrix:[0.36278226 3.73204357]\n", "theta from own gd\n", - "[[4.16180248]\n", - " [2.8250103 ]]\n", + "[[4.36014743]\n", + " [2.76030841]]\n", "theta from own sdg\n", - "[[4.16257872]\n", - " [2.80576215]]\n" + "[[4.34582863]\n", + " [2.81684805]]\n" ] }, { "data": { - "image/png": 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", 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", 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    " ] @@ -2089,17 +2082,17 @@ "output_type": "stream", "text": [ "Own inversion\n", - "[[3.98246764]\n", - " [3.04018043]]\n", - "Eigenvalues of Hessian Matrix:[0.31541884 4.4735968 ]\n", + "[[3.85068028]\n", + " [3.09808149]]\n", + "Eigenvalues of Hessian Matrix:[0.31897935 3.95770298]\n", "theta from own gd\n", - "[[3.98246764]\n", - " [3.04018043]]\n" + "[[3.85068028]\n", + " [3.09808149]]\n" ] }, { "data": { - "image/png": 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iaen1hhohIWqvMy5LSzNsJwEGQ0RERCStrVtr1whVJwSQlwds2yZJcRgMERERkbTy8+3brqDAveW4hcEQERERSSsiwr7tWrVybzluYTBERERE0kpKMvQa02gsr9dogJgYYMAASYrDYIiIiIik5etr6D4P1A6IjM8XLZJsvCHZg6EtW7ZAq9UiMjISGo0Gq1evNq2rqKjAiy++iC5duqBRo0aIjIzEI488gvPnz8tXYCIiIqq/1FTg66+BqCjz5dHRhuVqGmeopKQE3bp1w5IlS2qtu3HjBvbs2YOXX34Ze/bsQUZGBo4dO4aUlBQZSkpEREQulZoKnDoFZGUB6emGf3NzJQ2EAEAjhKVO/vLQaDRYtWoVxo0bZ3WbnTt34s4778Tp06fRunVru/ZbXFyMkJAQFBUVoUmTJi4qLREREbmTVPdvj5uOo6ioCBqNBk2bNrW6TVlZGcrKykzPi4uLJSgZEREReSLZm8kcUVpaitmzZ+PBBx+sM0KcP38+QkJCTI+YmBgJS0lERESexGOCoYqKCvzxj39EVVUVPvjggzq3nTNnDoqKikyPvLw8iUpJREREnsYjmskqKiowYcIE5ObmYtOmTTbbDQMDAxEYGChR6YiIiMiTKT4YMgZCx48fR1ZWFsLCwuQuEhEREbmDXm+Ytyw/3zBKdbdukhxW9mDo+vXrOHHihOl5bm4u9u3bh9DQUERGRmL8+PHYs2cP1q5dC71ej4Jb85SEhoYiICBArmITERGRK2VkGGayrz6Ba2SkJIeWvWt9dnY2kpOTay2fPHky5s6di7i4OIuvy8rKwpAhQ+w6BrvWExERKVhGBjB+vGG2+mqKAYQAbr9/yx4MSYHBEBERkULp9UBsrHmN0C1SBUMe05uMiIiIvNDWrRYDISkxGCIiIiL55OfLXQIGQ0RERCSjiAi5S8BgiIiIiGSUlGSYqV6jka0IDIaIiIhIPr6+wOLFhv/LFBAxGCIiIiJ5paYCX38NREWZL6/53E3YtZ6IiIiUocYI1MXduiEkNNTt92/ZR6AmIiIiAmBoMqs+oHJxsSSHZTMZERERqRqDISIiIlI1BkNERESkagyGiIiISNUYDBEREZGqMRgiIiIiVWMwRERERKrGYIiIiIhUjcEQERERqRqDISIiIlI1BkNERESkagyGiIiISNUYDBEREZGqcdZ6IiIiqen1wNatQH4+EBEBJCUZZmwnWTAYIiIiklJGBjBzJnD27O1l0dHA4sVAaqp85VIxNpMRERFJJSMDGD/ePBACgHPnDMszMuQpl8oxGCIiIpKCXm+oERKi9jrjsrQ0w3YkKQZDREREUti6tXaNUHVCAHl5hu1IUswZIiIikkJ+vmu381bVk8ubNJHkkAyGiIiIpBAR4drtvJGl5HIJMBgiIiKSQlKSodfYuXOW84Y0GsP6pCTnj+HJXfaNyeWW3hs3Y84QERGRFHx9Dd3nAUPgU53x+aJFzgcvGRlAbCyQnAw8+KDh39hYz+ihVldyuQQYDBEREUklNRX4+msgKsp8eXS0Ybmz4wy5qsu+Xg9kZwMrVhj+lapnm63kcjfTCCFTGCah4uJihISEoKioCE0kSsYiIiKyypXNWXq9oQbIWjBhbH7Lza37GHIOBrlihaE2q4ZiACGA2+/fzBkiIiLneXKOipx8fYEhQ1yzL0e67Fs7prV8HWPNUn1qrewhc9I4m8mIiMg5npyj4k3q22VfCYNBGpPLa+ZSSYTBEBEROY7TSihHfbvsK2EwyLqSyyXAYIiIiByjhJoEus1WrYpGA8TEWO+yr5TBIK0ll0uAOUNERM5Sa76MK3JUyHWMtSrjxxsCn+pBqj1d9uUcDLLmNTR2rOFRfQTqMWNcf9waGAwRETlDzp43clNKTYKU5Ah8HTmmsVbF0ndy0aK6v5NSDAZpiT3XUHGxa49phezNZFu2bIFWq0VkZCQ0Gg1Wr15ttl4Igblz5yIyMhINGjTAkCFDcPDgQXkKS0QEMF9GbdNKyJEo7swxU1OBU6eArCwgPd3wb26u7eDc3YNBWqKwa0j2YKikpATdunXDkiVLLK5fsGAB3nnnHSxZsgQ7d+5Eq1atMHz4cFy7dk3ikhIRgfkyQP1zVDyJHDft+hzT2GV/4kTDv/YGMO4aDNISO68hUVGJY9/nuu64dVDUoIsajQarVq3CuHHjABhqhSIjI5GWloYXX3wRAFBWVobw8HC89dZbeOqpp+zaLwddJCKXyc42/Eq3JSvLu/NljDdswHKOirvHpZGCqwYzVPoxax7f3c2Bdl5DD/muQLr+HgDuv3/LXjNUl9zcXBQUFGDEiBGmZYGBgbjrrruwbds2q68rKytDcXGx2YOIyCXUmC9jiZQ1CXKRo8u53N3cna1ZcoSd10aVXsAfZa4/vgWKDoYKCgoAAOHh4WbLw8PDTessmT9/PkJCQkyPmJgYt5aTiFREbfkydXE2R8VTyBH4qiHYtvPa+NOEy8g9Uu7mwhgoOhgy0tRolxZC1FpW3Zw5c1BUVGR65OXlubuIRKQWasqXsYcUNQlykSPw9dJgW1+ux0//3I/Z/bKROCISeYhGFSxfQ0KjAVq0wF0pTRF8Yp8k5VN01/pWrVoBMNQQRVT74AsLC2vVFlUXGBiIwMBAt5ePiFSovmO6kOeQo8u5rWMCQGioIbdHr1f09+za+Wv4fmEOdKv0WPdbR1wUXU3rZuFtfIkHIIBaIZFGCODCBeDhhyUrq6JrhuLi4tCqVSts2LDBtKy8vBybN2/GgAEDZCwZEamaGvJlSJ4u5/ZMS3H5MjBsmCLngTuz/Rzen7AZdzffheZRARj/j/74z8lBuCiao6nmKh5s8xNWzNiGj0+NhOaFF6zUDUlP9t5k169fx4kTJwAAPXr0wDvvvIPk5GSEhoaidevWeOuttzB//nwsW7YM7du3x5tvvons7GwcPXoUwcHBdh2DvcmIyC3UOgK12lgaHDAmxvZghq4+Zk0K6LlXVVmFXZ8dhu7fF6DbFYFfS+PN1t/hfwopXU9B+3BTDHyyM/wb+htW2Oo1d0sxgBDA7fdv2YOh7OxsJFvoYjd58mQsX74cQgjMmzcPH330Ea5cuYK+ffvi/fffR2Jiot3HYDBERIrFgMozyDUCdXY2MGGCoTbIEnd3tbfgxsUb+GHhAehWlmHt8XgUVN1OW/GBHgOa5CBl0BVop7VG/N1x0PhYqP+xs3u9aoIhKTAYIiJFUvOUHmQfhYxrdX5PAda+cwy6DUHYVJiAPtiFCOQjHxHYh24YHnUEKWP0GPVsJzSPD7O9wxUrDCNr2yBVMKToBGoiIq9lHLSw5u9R4yjDzD0iQLau9qJKYN8XR6H7uAC6n1ti140EAK1wLzJwBPchBrcDeBEVBc277zr2fVVYbzjWDBERSU3uUYbJc0hYM1R6tRTZS3KQ+fkNrD3SDnn62x0ENKjCnwPfxfyyZ289r8aZ3CXjNVBXrzmwmcylGAwRkaIopOmDPICtoKGegXPhwQtY9/YR6Nb74/v8RJSgsWldQ5RgeKscaEeWY/TMO9Aq5U7XBvDWpnSphs1kRETeSg2jDCuFpyeou3hcK1ElcEh3EpkfnIVuWxh2XO8MgdvjJEX65EMbfxwpDzRA8oxENAjta1iRnW3/NCH2BvDGISpq5s35+ko+0TGDISIiqXnpKMOK4y0J6taChujo2t37LQR/5Tf12PrPHGT+7xp0B+OQW3kHgDtML+nZ4DC0d/4O7ePh6PlgR2h8LHzv3BXAp6YCY8eal3nAAGDbNsPzJk2AMWMc26cT2ExGRCQ1Nzd9EKwnqCtgbB6n2arlshD8XfRtgTT92/gfJpmWBaIUQ1scgHbYTYx5tj2i+9gRdMvUtCvV/ZvBEBGRHKzlS3jyzVop1JignpEBcd94AMIsudk4/9dj+AQ+7e+A9r4ADH82EY1aNnJs/zIF8FLdvxU9HQcRkdfilB7us3Wr/fktHqyytBJb3vsVf+69Efn3TYOoEQgBgM+tZf+OfgWfHB6AcfP7Oh4IAfJMTSIhBkNERHJJTQVOnTI0LaSnG/7NzWUgVF9enKBedKYIXz67DZPa/ojwhsW465lu+GW3LyJQYPWGroGA5qwLgj8vDuCZQE1EJCdfX3afdzVXJqgroDda7pY86Bb/Bl12Y2Rf7opK3J6oPFRzGRNaZAOFduzIFcGfpYRnT+uhZwGDISIi8i5JSYbaClv5LUlJtddVJ1NvNH25Hr8sPwTd8kvQ7YlCTll7ADGm9fEBvyGl2xloH2mG/o93ht+OIUDyPNs7dlXvRC8M4JlATURE3qe+CeoS90a7XnAdGxbmQLeqAutOdEShaGFa54tKDArJQcpdV6GdHov2w2PNX+zFvRPZm8yFGAwREamQpZqdmJjaY/PUJFFvtLM786F75zh0Gxtg04UuKEOQaV0IinB3zEGkaAXunpWA0HbN6t6Zl/ZOZDDkQgyGiIhUypmcHzeNqVNVWYU96Ueg+6QQup3h2Huzk9n6tn6noU08hZSHmyDp6UT4N/S3e98AnA/+5GTj85Hq/s2cISIi8l7O5Le4sDfazcs3sendHGR+cRNrj7XH+aoEAAkADJOf9g/OgXbAZaRMjUanUbHQ/JQL5B8BfilyPDHZ05KbFTRCOIMhIiKi6urZG61gfyHWvn0Uuu8DsKGgC26ij2ldI1zHyMgcaEdV4p5n49Gyc1fDiowMoG1y/QMDT0lutpaTde6cYbnEzXpsJiMiIqrOwYRkUSVwIOM4Mj88D92O5vilJNFs8xjfc9B2OoGUPzbCkBldENgk0Hx/3jh1SF0cyMkqLilhzpCrMBgiIiKH2EhIrvh0BbLy7kDmiuvQHWqHM/pos5f3aXQQ2r4XoH0yAt3u7wCNT82xoW9R49QhDuRkFffsyZwhIiIiWViZKb6kYQt8EJiGVyfdg+sINi0Pwk0MD98P7YgyjJkVj4june07jiNTh3hC85c9FDhCOIMhIiIiC8S4e3HENxH73vwGR/eXYUtpb2wuGYKqEkMNTYRPAcZ0OAbt+CAMnZmIhs37On4QBQYGbufKEcJdhMEQERHRLRU3KvDjhznQ/a8ImfvjcLKyA4AOpvXdGxyBtncBtI+1RK+HOsLHr1X9DqjAwKDebA1n4MgI4SUlkhSZwRARkRQUMMcVWXYl9yrWv3MIOp3At2c646roYVoXgDL8ofl+aIfewJiZd6B1/44AOrru4K6aOkQp7Oku7+treD5+vOH8LA0SuWiRpNcHE6iJiNxNQeOpkMGJjaehey8Xus0h2HK1C/TV6gaaay5iTLvD0N7rj+FpnREcGVzHnlxAiaNHOxO8O9orzo5BIjkCtQsxGCIi2ait27RC6cv12P6vg9D95zJ0+2JwuLyd2fqEwBNI6XEW2kfD0HdKAnwDJK61U9Lo0c4E7872ilPICNQMhoiI3EWN3aYV5Nr5a/ju7RzoVuvxTW5HXBTNTev8UIHBzQ4gZcg1aJ+JQ9shrWUs6S1KaEp1Nnh30xQmnI6DiMjTqbHbtMxO/3QWukUnoctqhOxLXVCO/qZ1zTRXMKr1IaSM1WDkrM5o2qanjCW1QO7Ro/V6Q42QpToSIQwBUVqaYcqPmkGah/eKYzBEROQuHn6DcIhMtRpVlVXY+Z9D0C27CN3uCOwvjQdwewDE9v650HY9jZRJTTHwqUT4BQ10e5k8Vn2Cdw/vFcdgiIiUTwnNB87w8BuE3SROEL9x8QZ+WHgAmV+XY+2JePxedXv6Cx/oMbBJDrSDriBlemvEj2oLIM7lZfBK9QnePbxXHIMhIlI2T+6J5eE3CLtINOHmuV35WLvwOHQ/BGFjYReU4vYAh8Eoxt3RB6Edrcc9zyUgrH23eh9PleoTvCuwu7wjmEBNRMrlDT2xlNht2lXcmCAuqgT2fXEUmUsLoPulJXbfSDBbH+uXB23Cb0h5KBiDpyYioHGAkydBJg5OUGuRi3vFsTeZCzEYIvJA3tQTS0ndpl3JxT2ISq+WIuvdA8j84ibWHr0DZ/WRpnUaVKFv44PQ9r8E7VORSLy3vfXJT8l5rgjeXdiszd5kRKRu3tQTKzXV0APHE/Oe6uKCBPHCgxew7u0jyFzvjw35iShBH9O6hijBiIgD0I6swOjnOiI8sUt9S0y2WJmgFtHR9gfvcveKcwKDISJSJm/rieWBNwibnMgxEVUCB9ecgO7Dc8jcFoafr3eGwO2cqSiffGg7Hod2QgP8YWYXBDXt5+pSky3eGrzXgcEQESmTWnpieTI7E8TLe/TFlgV7oEu/hsyDbXGqsj2A9qbNejU8BO2dhdA+Ho4eEztC4+Pln6kn9I70xuC9DgyGiEiZXN0TyxNuQJ6mjh5E4tbzd278Ca82LUcxbg9wGIhSDGu5H9phpRjzbHtE9U4AkGDhAF7Ik3tHejEmUBOR+zkbiDibzFnzeBcuALNm8QbkLhZu8GcQjTQsxioY3t+WmgvQtj8C7X0BGJaWiEYtG8lVWvl4Q+9IibE3mQsxGCKSUX1/CdfVE8tSXsOaNbW3t4Q3oHqrLK3ETx/lQPfZVaz7NRrhlWcRgXzkIwJbkYTOQSeR0us8tFOao8/kBPj4+bivMHLU/DlyTG/qHSkhBkMuxGCISCau+iVs6aZjKegJCwMuXbK/fHLcgDy8ua7oTBHWv30QukyBb04n4IpoZlrnj3IMCT2AlD9cx5iZ7RA7KLqOPbmQHE1Pjh7TTROZejvJ7t9C4SoqKsRLL70kYmNjRVBQkIiLixPz5s0Ter3e7n0UFRUJAKKoqMiNJSUiM5WVQkRHC2EIhWo/NBohYmIM2zlq5UrD663t29FHVpbLT99quWu+J9HRhuUKdjLrtFh0b7YY2my38EO5WfHDNBfFpLZbxVeztomiPBn+xlr7Lmg0hoc73ltnjpmebt93MT3d9eX1YFLdvxUfDL3++usiLCxMrF27VuTm5oqvvvpKNG7cWCxatMjufTAYIpJBVpZ7AhFbQZYzD0dvQJWVhnKnpxv+tSegk+Om7aTKskqx7aP9Yna/LNE58FitIncMOCFe6JMltr7/q6gscyKYdVlB3Rhwu/qY7roevJxU92/F9ybbvn07xo4di9GjRwMAYmNjsWLFCuzatUvmkhFRndw1TpCtwRid4Uj3fGeaZPR6w2ssZSUIYWiuS0sz5EDJ1GR2veA6NizMQWZGBdad7IgL4vYAh76oRFLTA9AOLoJ2eizaD28HoJ0s5TQjx8Cczh5TDfPUeTDFB0ODBg3Chx9+iGPHjqFDhw749ddf8eOPP2LRokVyF42I6uKucYJcOciiozcgZyclVeho2nk/n8faRSeQ+UMDbLrYFeW4PcBhCIowqvVBaMcIjHq+M5rF9ZCsXHaTY2BOZ4+plIlMPTxnzV0UHwy9+OKLKCoqQseOHeHr6wu9Xo833ngDEydOtPqasrIylJWVmZ4XFxdLUVQiqs5dv4RdNciiozeg+tTuKGQ07arKKuxJPwLdJ4XI3NkK+252BHB7/q92fqeh7XIK2oeaIOnpRPg3HODW8tSbHANz1ueYrpjqoj6+/hqYOtUw1ET1Y3OICeUnUK9YsUJER0eLFStWiP3794tPP/1UhIaGiuXLl1t9zSuvvCIA1HowZ4hIYl99ZT2vwtk8GWPORn0TqGNiHDt+fXI+ZMwXuXHphtC9/LN4suNmEeGTb/4xQC8GBv8q/n53lji45rio0le5/PhuZeu74M6cofoc05mcs/p64YW685wUlLNWHROob4mOjhZLliwxW/baa6+J+Ph4q68pLS0VRUVFpkdeXh6DISKpWeo55WwgYmnfxoCq5h91QIiwsNrH++qr+t2A6tMbqLJSiKgox5Nunbxp5v/6u/j4kS0ipdUO0QAlZodqjGJxX9Q2sfzxraLw0AXH3gMlquu74O7eZFIesz6+/NK+HwdSBGUOYgL1LTdu3ICPj/lAXb6+vqiqqrL6msDAQAQGBrq7aERkjbXcGqO3365ftbyt5oaxYw3jumRnG5YPGWJ41Cc3oj7NI2vWAKWllre31lznQKK2qBLY//Ux6JbmI3NHC+ws6QygpWl9jO85pCScgPaBRhgyowsCm/S371w8gRxNT3I3dzlCrzc0jdkiQ86aorg11HKByZMni6ioKFPX+oyMDNG8eXPx5z//2e59sGs9kYSk7O5srebEHeP5ONs8YmtMpLCw2uWyoxt+aVGpWP/6TjGtS7Zo7ZtXa9M+jXLEa0OzxL4vjnhe85cz5Gh6kuOYjrK3idZaraZUrLyXbCa7pbi4WMycOVO0bt1aBAUFibZt24qXXnpJlJWV2b0PBkNEEpJ7PBV3jufjaPOIPWMiRUeb30RtvKYKEBd8W4omuGK2qgFKhDZ8h/j4kS3i/N4C58+RvIu9zbvuvCZtqePHi1T3b07HQUSutWIF8OCDtrdLTwfq6BXqFFfN/1RX9+O65kqr2TzizBQMdr5mCLJwzKcjxnQ4hpT7g/CHZxLRsHlD28cidbH3O9iiheH7LnU3extT9hR/+ilCJk1y+/1b8TlDRORh7M2tOX7c9cd2xXg+tnJ1UlMtTxBr6SbiRJf6ylN5dv1h/vefdiL2vcHw8Wtl3zFInYxDXNgaqPSDD6QPhOwZrmL2bEmK4sYphIlIlZKSgKgo29t9/LHhj6Er1Xc8H+Ov1Jo3DuOgihkZhue+voZgauLEuhOz7QwMryEY6dN+wsQ22zBuSjPbLwDQ9oE+7p0FnryDcbBHY6K+JS+8YPh+S82eHy/nzklSFIeupLy8PHeVg4i8ha8v8OSTtrc7exaYO9dQje+qoKg+Pb5s/UoFDIMqOlJW469yKzciAeB3TTjCHhyOhz4YiM/PDMC3GIVziITV/rIajaFZjtM2kL2Mvd+io82Xt2gBfPklsGCBPOVy8yCjjnAoGOrYsSNefvlllJSUuKs8ROQN2re3b7vXXzfkM8TG3q51qQ8bwUedgYQjTWz2Mv4qByBqlKkKGgho8LT4ABUIROfA45jdLxs/fnQIrb5YDB+NpvZ5SDltA3mX1FTg1ClDflp6uuHf/Hzg/vvlK5MrRwavJ4eCoQ0bNuD7779H+/btsWzZMneViYg8naN/5Go2QzmrWvDhcCDhhikzis8W4+ufIvBuy9dwXpi/JxfRHBmBE/HgoDM4+UMuckrbY/72Iej/ZBf4Trg1z1nN5sboaOvznxHZYm/zrlTs+fFiT5O7CzjVm+zTTz/FSy+9hObNm2PhwoUYovBBmtibjEhixl5d1uYls8Tenl6WjlUzmXnNGvt7fBk50/PLgtM/nYVu0UlkbmqM7MtdUIEAAIAP9LgH32Bq8H+RXPEdgkqLbr/I2vxQnFSTvJ0xTw8w/1shcW8yp8cZunHjhnj55ZdFw4YNxbhx48Tx48dd1d3f5TjOEJEMrI3J48qxTuoaXNHRAfGcHFRRX6EXO/51QLw0MEt0CTpa62Ud/H8Tz/XKEtmL9orKFV+6bwwkIk9l6Tq+NWWP4scZunHjBvbs2YOVK1fi3Xffhb+/P6ZNm4a5c+ciODjYtRFbPbFmiEgmlrqp22Lv+EM2xidxqjnJxq9U4z5LCkvww6Ic6FaWY+2JePxedXvqCx/oMSjkALSDrkI7rTXiR7U1rHDVGEhE3shKLahU92+HgqEPP/wQO3fuxM6dO3H48GH4+vqia9eu6NevH7p3747//e9/OHbsGFatWoXevXu7rdCOYjBEsmATh4Hxfdi40ZAwbYuNZijTPt0VWFgZVPHS9Ffw9a/tofshCBsLu6AUDUyrg1GMUTE50I4WGDWrE8Lah9ber4ua4STB7y4phCKDoZiYGPTr18/06N27d60JUd98802kp6cjJyfH5YV1FoMhkpwDk2y6jNJvYLbyiBwJYNwdWOj1EJu34NTK3dj8kx/ePzoMu0oTzTaJ9ctDSuffoH0wGIOnJiKgcUDd+5RzZG5HyPHdlZPSrxuVk+z+7ep2t4KCAuHj4+Pq3dYLc4ZIUu6cG6uuY7p6YlJ3cHRuL2vsnW/JwYknb165KdbN/UX8KWGziPY9Z15E6EW/xvvFmyOyxIGMY45Pfir3nG32kOO7KydPuW5UzGMnaq2qqhLZ2dmu3m29KC4Y8oSZjsk5Us7YbuRpN7A6kiXt5sLAouBAofjk0S1iXMR20RDXzV7eENfFuIjt4t9TtojfcwqdPmUhhOEzDwuT9rvhaPmk/u7KydOuG5VSfAK1J1FUM5naqqDVRuq8EE9Nyq1v00Q9mtxElUDOquPQfXQeuu1h+Pl6Z4hqQ65F+eRD2/E4Uh5ogORnuiCoaZCTJ1lDRgZw3311b7NypXx/Bzwpp6m+PPW6USGp7t+cqFVK1nq/GAec42Bqns8NA/fVKTu7/hOTysE4+Ft9Xr94seG60Wgs9/yqNrhi+fVybF5yALoV16E71BanKjsA6GB6Sa+Gh5DStxDaJ1qh+wPx0Pi4eGRc41QfdQkLM0wAKxepv7tycsWEvuRVGAxJxZ7ZedPSDH8M+UvEc9VnbixHZWQATzxh37becAOryTjfkqWa1kWLcKnLEHzzpx+hW+eL784moBi9TJsE4SaGtjwA7bBSjHm2PaJ6JwBIcF9Zbd18AeDSJXlvvlJ+d+WmpsCP7MJgSCr8JaIOxuHlbTXf1HeSTWu1jNZ4ww3MktRUww+IW01uZ84IfLk5HJlTmuOn4hBUYZBp03CfQoy54yhSxgdg6MxENGp5p3Tl9ISbr1TfXSVQU+BHdmEwJBVP+GNI9edg841T6qplrMmbbmAWVJZW4qePcpD5GaDb3w/HK+LM1ncNOgptr3xopzRHn8kJ8PFraWVPbuYJN18pvrtKoabAj+zCYEgqnvDHkFzDRvNNvfPC7Glyqc5bbmC3FJ0pwvq3DyJzjcC3ZxJwRXQ3rfNHOZLD9kObXAJtWju0GRgPIF62spq4++brqrFy3P3dVQo1BX5kF/Ymk4orB5wjz+CuwdzsHbwvNBT4+GOvuIH9ln0GundzkZkdjC1XuqAS/qZ1YZpLGB13GNpxvhjxbGc0iVbowKp2TvXh1H5d3UNVLQMRWhlt3KsCPw+nyBGoPZUigiHAfX8MSRmkuoHY2wX6hx+AoUNdf3wJ6Mv1+HnZIeiWX0Lm3mgcKrvDbH2ngJPQds+DdnIo+j/eGb4BHnKjdvXN1x3zs6mNWgI/D8VgyIUUEwwB/CXiraQcP6q8HGjY0PBH3BpfX+DGDSDAxhQRCnK94Dq+fycHulUVWHeyIy6IFqZ1vqjE4KYHoL2rCNoZcbhjaBvLO/GEG5urysixckgFGAy5kKKCIcAz/mCT/aT+dS7F4HgSfUfzfj4P3cLj0G1siE0Xu6IcgfCBHknYinY4idjwG2h/X3eMfL4LmsU1rXtnahvQVE2DJJJqcdBFb1bfAedIOeQYP8rdPRPdGFRUVVZh9/+OQPdJIXS7WmHfzY4AIk3rn/b5CG/6voymFRcMC34HkBkNDF0MxNVxbDUOaCpHD1X+kCMvxWCIqD7cNX5UXTcdd/ZMdENQcePiDWxcnAPd16VYe6wD8qtuD3DoAz0GNMmBduAVPNj9EKL+Ph2aCgePrdYBTaXuoaq2mjdSF7fOfKYQipuolbyHvbOnp6XZv09bM2kbJ9S0NMlkfSbUdOFEnef3FoilkzYLbfgO0QAlZrtpjGIxPmqb+M8TW8WFIxfrf2xPmA3eHdz1PbCEk5qSTKS6f/vYCpaIqA72/upetMjwy9oWY81MzdomY+1IRsbtMVKA23lJRvUZI8WRWq6aq6oE9n1xFK8NzcadjQ8iskc4nvxsMHS/98VNNERr37OY3mUzvntzNy4WBeCrs/3xyNJBaB4fVu9jq3ZAU3d9D2qyVfMGGGre6krodwW93pAntWKF4V93H49UhcEQUX0YB9OzxdhUU9cfcEduOsbB8aKizLeLjnY+P8bBoKKsuAzrX9+FaV02o03AefT4Yzz+tmkIdpZ0BgDc2SgHrw/Lxq9fHcOp8ii8t/8ujJjTC4FNAut9bDNqHtDUHd+DmuoTqLpKRoah51xysmGMreRkw3N7fmAQ2YE5Q0T1Yfx1ft99dW9nT+6Qo/lHNeblqndCq53Bwvr/XsTHz+/A9+c74zp6m5Y3wA0Mb3UAKSPLMXpWPFp1TXT5sS1up/apFVz9PahJ7po3NSbHk+QYDBHVV2qqocZm0SLb29Z1w3DmpuPKnonGoMJKQCYA5CEGo7+ZiioYbrSRPvkY0+E4Uh5ogD88k4gGoX3rd2xnAhpOreDeHqpy1rypNTmeJMdmMiJXGDvWvu3qumHI3dzj6wtMnAgBQ+BTnfH5CvwR3Rocw98GZ2PXZ4eRVxaOjw4Pxui5fdAgtEH9jl2f/BcpmovUyhio1vxcjDQaw8Cx7qh5U0ITHakCB10kcgVXzD0n4/x1V3KvYv2C/RizVIvGVcWwdNsTAPThkfA7d8Z9v8LrO0I7x8FxD7mmErJ3Hr70dGDiRNcfn2Qn1f2bNUNEruCKnj1S9Q665fiGU3hnbDaGNN2HFm0b49CH2Qi2EggBgAaA3+/n3fsrPDUVOHXKMGpyerrh39xc+2+0xuaiiRMN/zIQcg25at7kri0l1WDNEJEruWLuOTfNX1dZWont/zoI3adXkPlraxwtb2ta5wM9LiEMTVFke0d//SuQkMCaFzWSuuZNxtpSUgbOTeZCDIZIUo7eMCxtD7jkplN8thjfvXMQujV6rMtNwGURalrnhwoMCd0P7ZDruH/w74hIe8Dh/XMEYnI7uZroSBEYDLkQgyFSLDdMcXDqx7PQLToJXVZjZF/uggrcnrm+meYKRscehHasD0Y+2xkhrUMMK+zNzaiJNySSgptqS0n5GAy5EIMhUiQXzXZfVVmFX5Yfgm7ZRej2ROJAaQez9R38c5HS7TS0jzTDgCc6wy/Iwoga9s6AbgmbKkgKTI5XJQZDLsRgiBTHmAthrduwjQCjpLAEGxbmQLeyHOtOxuP3qpamdT7QY1DIAaQkXYV2eht0GBlnf3ms5WbYIyvLfWPdEJEqSXX/5qCLRHJwYrb7szvzsXbhceh+aICNF7qgDLcHOGyCItwdcxApYwRGPZeA0HbdHStPXQMX2svb5v4iItVgMEQkBzsDh9yvduE/cwHdL+HYc7MTgNtdiOP8zkDbORcpDwUj6elEBDQeUL8yGbtP18zNaNECuHDB9uvZvZmIPJRHBEPnzp3Diy++iG+//RY3b95Ehw4d8Mknn6BXr15yF43IOXYGDlM+6I3NGAIA0KAK/RofhHbAJaRMjUaCth00Pq1dWy7jPFfZ2YYHAAweDEyZApw/r865v4jI6yk+GLpy5QoGDhyI5ORkfPvtt2jZsiVOnjyJpk2byl00IufZmIurChqcRTT2oAfujdgB7d0VGP1cR7Ts3MX9ZVuzpnbtUFjY7bmg6jP3F5NgiUiBFB8MvfXWW4iJicGyZctMy2JjY+UrEHk3CW7WokogZ/VJHA19AvednQsBDXyqzQZWBQ00ELj4wHQUfhiIoKb9XHr8Olnr4Xb5suHf0FDg0qXby6Oj7e/e7IZhBIiIXEHxvckSEhIwcuRInD17Fps3b0ZUVBSmTp2KJ554wuprysrKUFZWZnpeXFyMmJgY92aj8xev53Pjzbr8ejk2LzkA3YrryDzYDqf10QCAe5GBxZiJGNw+poiJgUaO8VPs6eEWFQUsXw4UFjr2PXfRMAJEpC7sWn9LUFAQAGDWrFm4//778csvvyAtLQ0fffQRHnnkEYuvmTt3LubNm1drudveTP7i9XxuuFlfOn4Z37x9CJlrffHduc64htvfvSDcxLCWB6AdXooxM9shsuS4fIG0MZDfuBF4/XXb2zvahb6ewwgQkXoxGLolICAAvXv3xrZt20zLnnnmGezcuRPbt2+3+BpJa4b4i9fzuehmLaoEjq7Phe79M8j8sRm2FSeiCre3D/cphLb9EWjvC8SwZ7ugYfOGLj4RJ1gK5G1xdIZwewd05DhFRFQDxxm6JSIiAgkJCWbLOnXqhJUrV1p9TWBgIAIDA91dNMNNdOZMyz1sjMmmaWmG3jn8xatcToz5Y1RZWokfP8yB7r9Xkbk/Ficq2gK4PQFqt6Cj0PbOh/b/WqD3pE7w8RvsnnNwhrVA3hZHu9DbO/4QxykiIpkoPhgaOHAgjh49arbs2LFjaNOmjUwlqqYeN1FSEAdv1ldPF2H92wehyxT45kxnXBXdTZsEoAzJYQeg/UMJxsxshzYD4wHEu77M9VVXIG+Ns13o7Q2eOE4REclE8cHQs88+iwEDBuDNN9/EhAkT8Msvv2Dp0qVYunSp3EXjL15vYedN+Ov3f8c/p+3BlitdUInbAxyGaS5hTNvD0N7rixHPJiI4sre7Suo6tgL5mhztQl+djWEEOE4REclN8cFQnz59sGrVKsyZMwevvvoq4uLisGjRIjz00ENyF42/eF1BCb3w7Bzz54GfZphygDoFnERKjzxoJ4ei32Od4RswSNoy15ejAbojXehrqmuqj/oEWfZSwneMiBRN8QnUruC2BCxbk1uyl0zdlNQLLyMD4lb+jKba4qpbzybgC1xuegdShhRDOyMW7f6ggGba+rA3qfmvfwWGDnVNAGHp846JcT7IcvaY7OlJ5DHYm8yF3PpmGpNQAcu/eNmbzDKF9MI7s/0cdAtPQLepEZpcOom38bzZmD8XfVviyIiZSPznNDRtE+L28khGrkBeyloahXzHiMh5DIZcyO1vphy/eD2ZjOPOVFVWYff/jiDzX4XQ7YrAr6Xmyc0d/E7imTgdkvvdRIcH+8BveLL31up5cyDPsY2IvAKDIReS5M1kXoL9JB535sbFG/hh4QHoVpZh7fF4FFSFm9b5QI8BTXKgHXgFKdNbI/7uOGh8NHXszct4ayDPsY2IvALHGfI0vr78o2ovCXrh5e/7HWvfOYrM74LwQ2EXlKKvaV0wijEy6iC09+hxz3Od0Dy+m9PH8XjGWerlDOTd8UOCPT2JyAEMhkh6buiFJ6oEfv3qGDI/yofu55bYdSMBwO0aoDa+Z6FNOAntxMa4a1oiApv0d7DQXkzOQN5dCc7s6UlEDmAzGUnPRcm7ZcVlyHr3ADI/v4G1R9ohTx9ltr5voxxo+19Eyp8ikXhve3U1f3kCdyY4s6cnkVdgzpALeWww5M15SE4m7144fBHr3j6CzG/88H1+IkrQ2LSuIUowvFUOtCPLMXpWPFp1benOM6D6kCLB2ZsTxIlUgsGQC3lkMKSG8VHsSN4VVQKHdCeh++dZZP4Uhh3XO0PAx7R5pE8+tPHHoZ3QAH94JhENQhtIfBLkFKkSnL01QZxIJZhA7alcUZtjrfng3DnDcm/5RWslebeirApb/t8e6NKvITMnDrmVdwC4w/Syng0OQ3vn79A+Ho6eD3aExod5Hx5HqgRnJSSIE5HisWbIlVxRm6PS8VEun7yCb98+BN1aDb7N64xi3B7gMBClGNriALTDbmLMs+0R3YfBj8dj13cisgObyVxIkjfTVcmgKrpJHPsuF7olp6Hb2hQ/FiVCX62isqXmAsbccQTa+wIwbGZnNG7VuI49uZA352kpCROcicgObCbzJHq9oUbI0h91IQx/2NPSDNX1tv6we/H4KJWlldj28UHoPr0C3f7WOFreFkCcaX1i4HGk9DoH7ZTmuPPRBPj4STyLuTfnaSktyJN78lYiomoYDLnC1q3Wm7UAwx/6vDzDdrZqc7xsfJSiM0X4buFB6NZU4ZtTCbgsbg9w6I9y3BV6ACnJ1zHmmbaIG9weQHt5CurNeVpKDfJSUw3vq6WyMcGZiCTEZjJXWLECePBB29ulpwMTJ9a9jRc0H+RuyYNu8W/QZTfG5stdUIEA07pQzWXcE3sIKeN8MXJWZzSJVkDvPm/O0/KEyUqVVmtFRIrBZjJP4sraHA9sPqiqrMIvyw8h898XodsThZyy9gBiTOvjA36DtusZpExuhv6Pd4Zf0CD5CmuJK2v2lMSVzbfuxKlsiEhmDIZcISnJUHNgqzYnyc4cGA9oPigpLMGGhTnIXFmOdSc6olAkmtb5ohKDQnKgTboK7fQ26DCyLYC2tXeilBoBb83T8tYgj4jIxdQXDLnjBuyO2hwFjo9ydmc+1i48jswfGmDThS4oqzb5aRMUYVTMQWjHCIx6LgGh7brXvbOvvwamTgUuXLi9TK48Fi/L0zLx1iCPiMjF1BUMZWYCc+a4J5HUHbU5MjcfiCqBPelHsPbj8zi74yyulwcgHxHYip6ogi/i/M4gJTEX2geDkfR0IgIaD7Bvx3/+M/D//l/t5WfPypOs7OqaPaXw1iCPiMjF1JVADaBW+pWrE0mV0vTjpJuXb2LTuznQfXkTuqPt0bdqOxZjJmJwO8ArDmyBq2mvIObNqY5PfvrVV8CECXVvExMjfbKyN85j5QXJ+ESkbhx00YXqDIYA1d8UCvYXYt07R5H5XQB+KEjEDTQCANyLDHyN8dBAwCzkcTZA0OuBVq2AixdtbyvHoJLeOI+VNwZ5RKQaDIZcyGYwZOTuG7BCao1ElcCBjOPQfXQemdub45eSRLP1Mb7nkNLxKP5f3kQEFRfCYt2PMwGkvaNrA/YNQ+AOCvmMXMobgzwiUgV2rZeDOxNJZR74rqy4DJvfz4FuxXXoDrXDaX0HAB1M6/s0Oght3wvQPhmBbvd3gGbLcSC50PoOnemJ5Mj7WzOPRaogxRu7eSswGZ+ISEkYDFXnrkRSmUY3vnj0Er55+zB03/hi/blEXEcv07og3MTw8P3QjijD6LQOiOzZ2fzF7uiJZO/726KFebKyUkdQ9iTeGOQREbkIm8kA9+YMSTi6sagSOPLNb9B9kAfdT82wrTgRVbi9z1Y+v0Pb4Si044MwdGYiGjZvaH1n7pgw1tZ7YfTVV7fzXDxhBGUiInIL5gy5kFkwZG0cIHfdVN08C33FjQr8+GEOdP8rgu5ALE5UxJqt7xZ0FCl98qF9rCV6PdQRPn4+9u3YXT2RrAU3Ri+8ACxYYF6G+gaS3pgHRESkAswZcofPPrM8zpArEkmt3XDd0Nx09XQRvv3HQeh0At+e6YyroodpXQDKkBx2ACnDSjBm5h1o3T8eQLyDJwP3TQtibTymFi2A998H7r//9jJXjKDMJjYiIrJBXcFQSoqhh5KrawnquuG6aOC7k5tOI/PdXOg2h2Dr1URU4vYAh801FzG67RGkpPpheFpnBEf2duYsanPXtCD2JvTWN5D05pnoiYjIZdTVTOaOajZbOS1ffgk8+6zDzU36cj12fHIQmcsvQ7cvBofL25m9LCHwBLTdzyJlShj6TkmAb4Abm33kamaqTxOjN89ET0SkEmwm8wT2zAo+axbwzjvAAw/YbG66dv4avl+Yg8wMPb7J7YiLoqtpUz9UYHCzA9DedQ3aGbFo94c7ANzh3nOTO8/G2Wky9Hrgvfc4SSkREdmFwVB92JvT0qKF1eamC0+/jC8/D0Pmk7uQfakLytHftLqp5iruaX0Q2hQN7n6uM5q26enGk6lGKXk2zuQtWSp7XThJKRGR6jEYqg9HclomTgTGjkVV9mb89uVubN7mjyXHR2DfXxLMNr3D/xRSup6C9uGmGPhkZ/g3HOiGgtehrjyb++4D0tIM+T5S1RQ5krdkq6eaJZyklIhI9ZgzVB925rSUrv4W3//SDLqVZVh7PB4FVeGmdT7QY2CTHGgHXYF2WmvE3x3n+OSnrmLvOECA9DVFtprtHCk7wJwhIiIPwJwhT2Ajp0UAuOgTjthxg3ADjU3Lg1GMu6MPQjtaj3ueS0BY+24SFroOtpr9qpO6R5atEZQdKXt9hgYgIiKvY+cIfGSRMacFgNCY1+ZU3Zrr/amqD3ADjdHG9yxmdN2M7+fvxsVrQfgyrz8mfTgIYe1D5Si5ZY7kzxiDv7Q0Q62M3Bwpe3Q0u9UTEZEJa4bqofRqKbJyYpAX/SrG5H2ASNy+IZ9FND4InIXeg0Mx76ljSLy3PTQ+0TKW1g6O5s8oqUeWvWVfuBCYMYM1QkREZMKcIQcVHryAdW8fgW69P77PT0TJreYvH+gxHN9jeMgudOoXgl5vTUB4t1auKL50bE3BYU16uiFBXE7umj6EiIhkw5whhRBVAod0J5H5wVnotoVhx/XOELg9rk2kTz608ceR8kADJM8Yggaho2QsbT3V1ZW9LkrokeWu6UOIiMjrsWbIgvLr5djyQQ506degOxiH3MrWZut7NjgM7Z2/I+WJcPSY2FG+3l/uYu9YPUqsbbFU9pgY18w/R0REkuKs9VbMnz8ff/nLXzBz5kwsWrTIrtfY82ZePnkF3759CJlrNVif1xnFCDGtC0QphrY4gJThNzHm2faI6q2AmhB3M3ZlX7PGEEjUZKxtUWIishJGzyYionpjM5kFO3fuxNKlS9G1a1fbG9vh2He5yHzvNHQ/NsVPRYnQ4/YAhy01FzDmjiPQ3heA4c8molHLPi45piLYEywYu7IPGWJY7+rJWt3JVjd8IiKiajwmGLp+/ToeeughfPzxx3j99ded2kdlaSW2/OdXZP7nCnT72+BYRRyAONP6LkHHoO15Hin/1xx9JifARzPAEDRszPSeGgZnptqwd5Z5IiIiD+QxwdC0adMwevRoDBs2zGYwVFZWhrKyMtPz4uJiAMAd4cW4gtsDHPqjHENCD0CbfB3atHaIHdQBQAfDSqXMz+VKdU21YWsARda2EBGRl/KIYOjzzz/Hnj17sHPnTru2nz9/PubNm1dr+RWEIkxzCffEHYZ2rC9GzuqMJtG9au+gPkGDUun1huDOUoqYEIYcIOO8Y6zxISIiFVF8AnVeXh569+6N77//Ht26GWp1hgwZgu7du1tNoLZUMxQTE4NvF/yIYTP6wi+ojhjQ1hxXSuxBZQ8751FDVpbtGiAmKBMRkQSYQH3L7t27UVhYiF69btfg6PV6bNmyBUuWLEFZWRl8a9yIAwMDERgYWGtfA57qUncgBNie40pJoy47wt7pKmxt543Nh0REpGqKD4aGDh2KAwcOmC2bMmUKOnbsiBdffLFWIFRvrgoalMbegRHr2s4bmw+JiEj1FB8MBQcHIzEx0WxZo0aNEBYWVmu5S7giaFCiAQMMTVl1Tarq42PYzhLmHBERkZfirPU1JSUZmn00VkaV1mgMIxonJVler1TbttmeXb6qCvj73y2vc6T5kIiIyIN4ZDCUnZ1t9+jTdtPrDUnGX34JPPHE7dqO6jx5jit7m/Xefddy0OStzYdERKR6im8mk4SlpOCwMMO/ly7dXqbkUZdtsbdZ79Ily8nh3tp8SEREqsdgyFpS8OXLhn/nzQPat/f8LuRJSUBo6O3zqoul2h1j8+G5c5bzhoxDDnha8yEREameRzaTuYytpGAA+Ne/gAkTDDUlcgRCxua7FSsM/9rK+7HG19dwrvawVLvj62voPg94V/MhERGpnrqDIaUnBWdkGAaATE4GHnzQ8G9srGG5M1566XbznyW2ksNTUw3d56OizJdHR7NbPREReSx1B0NKTgo2Nt/VDNaMY/o4ExD5+gJLl1ruKWdv7U5qKnDqlGGk6vR0w7+5uQyEiIjIY6k7GFJqUrA9zXdpac41mRlrd6KjzZc7UrtjnLR14kT5mg+JiIhcRPFzk7mC1blNjPOQ2UoKlnoeMlfOI2YN5xcjIiKF49xkUjAmBY8fbwh8qgdEciYFS9F8Z6zdISIiUjl1N5MBykwKVmrzHRERkRdSdzNZdUpqNlJq8x0REZGE2EwmtZrNRsbxfeQIjpTafEdEROSF2ExmiavH93GGEpvviIiIvBCbyWqyNj2HsUZG6kBESc13REREEpKqmYzBUHXGXB1ro1IzV4eIiEgyUgVDbCarTunTcxAREZHLMRiqTsnTcxAREZFbMBiqjuP7EBERqQ6DoeqSkgw5QZYmMgVsz+pOREREHofBUHXG8X2A2gERx/chIiLySgyGauL4PkRERKrCEagtSU0Fxo7l+D5EREQqwGDIGs7qTkREpApsJiMiIiJVYzBEREREqsZgiIiIiFSNwRARERGpGoMhIiIiUjUGQ0RERKRqDIaIiIhI1RgMERERkaoxGCIiIiJVYzBEREREqsZgiIiIiFSNwRARERGpGidqVQu9Hti6FcjPByIigKQkw2S0REREKsdgSA0yMoCZM4GzZ28vi44GFi8GUlPlKxcREZECsJnM22VkAOPHmwdCAHDunGF5RoY85SIiIlIIBkPeTK831AgJUXudcVlammE7IiIilVJ8MDR//nz06dMHwcHBaNmyJcaNG4ejR4/KXSzPsHVr7Rqh6oQA8vIM2xEREamU4oOhzZs3Y9q0adixYwc2bNiAyspKjBgxAiUlJXIXTfny8127HRERkRdSfAL1+vXrzZ4vW7YMLVu2xO7duzF48GCZSuUhIiJcux0REZEXUnwwVFNRUREAIDQ01Oo2ZWVlKCsrMz0vLi52e7kUKSnJ0Gvs3DnLeUMajWF9UpL0ZSMiIlIIxTeTVSeEwKxZszBo0CAkJiZa3W7+/PkICQkxPWJiYiQspYL4+hq6zwOGwKc64/NFizjeEBERqZpHBUPTp0/H/v37sWLFijq3mzNnDoqKikyPvLw8iUqoQKmpwNdfA1FR5sujow3LOc4QERGpnMc0k82YMQOZmZnYsmULoqOj69w2MDAQgYGBEpXMA6SmAmPHcgRqIiIiCxQfDAkhMGPGDKxatQrZ2dmIi4uTu0ieydcXGDJE7lIQEREpjuKDoWnTpiE9PR1r1qxBcHAwCgoKAAAhISFo0KCBzKUjIiIiT6cRwlI3I+XQ1Ez8vWXZsmV49NFH7dpHcXExQkJCUFRUhCZNmriwdEREROQuUt2/FV8zpPBYjYiIiDycR/UmIyIiInI1BkNERESkagyGiIiISNUYDBEREZGqMRgiIiIiVWMwRERERKrGYIiIiIhUjcEQERERqRqDISIiIlI1BkNERESkagyGiIiISNUYDBEREZGqMRgiIiIiVWMwRERERKrGYIiIiIhUjcEQERERqRqDISIiIlI1BkNERESkagyGiIiISNUYDBEREZGqMRgiIiIiVWMwRERERKrGYIiIiIhUjcEQERERqRqDISIiIlI1BkNERESkagyGiIiISNUYDBEREZGqMRgiIiIiVWMwRERERKrGYIiIiIhUjcEQERERqRqDISIiIlI1BkNERESkagyGiIiISNUYDBEREZGqMRgiIiIiVWMwRERERKrmMcHQBx98gLi4OAQFBaFXr17YunWr3EUiIiIiL+ARwdAXX3yBtLQ0vPTSS9i7dy+SkpIwatQonDlzRu6iERERkYfTCCGE3IWwpW/fvujZsyf++c9/mpZ16tQJ48aNw/z5822+vri4GCEhISgqKkKTJk3cWVQiIiJyEanu34qvGSovL8fu3bsxYsQIs+UjRozAtm3bZCoVEREReQs/uQtgy8WLF6HX6xEeHm62PDw8HAUFBRZfU1ZWhrKyMtPzoqIiAIYIk4iIiDyD8b7t7kYsxQdDRhqNxuy5EKLWMqP58+dj3rx5tZbHxMS4pWxERETkPpcuXUJISIjb9q/4YKh58+bw9fWtVQtUWFhYq7bIaM6cOZg1a5bp+dWrV9GmTRucOXPGrW+m0hQXFyMmJgZ5eXmqypXiefO81YDnzfNWg6KiIrRu3RqhoaFuPY7ig6GAgAD06tULGzZswL333mtavmHDBowdO9biawIDAxEYGFhreUhIiKq+REZNmjTheasIz1tdeN7qotbz9vFxb4qz4oMhAJg1axYmTZqE3r17o3///li6dCnOnDmDP/3pT3IXjYiIiDycRwRDDzzwAC5duoRXX30V+fn5SExMxDfffIM2bdrIXTQiIiLycB4RDAHA1KlTMXXqVKdeGxgYiFdeecVi05k343nzvNWA583zVgOet3vP2yMGXSQiIiJyF8UPukhERETkTgyGiIiISNUYDBEREZGqMRgiIiIiVfPIYOiDDz5AXFwcgoKC0KtXL2zdurXO7Tdv3oxevXohKCgIbdu2xYcfflhrm5UrVyIhIQGBgYFISEjAqlWr3FV8pzly3hkZGRg+fDhatGiBJk2aoH///vjuu+/Mtlm+fDk0Gk2tR2lpqbtPxSGOnHd2drbFczpy5IjZdt72eT/66KMWz7tz586mbTzh896yZQu0Wi0iIyOh0WiwevVqm6/xhuvb0fP2luvb0fP2luvb0fP2lut7/vz56NOnD4KDg9GyZUuMGzcOR48etfk6Ka5xjwuGvvjiC6SlpeGll17C3r17kZSUhFGjRuHMmTMWt8/NzcU999yDpKQk7N27F3/5y1/wzDPPYOXKlaZttm/fjgceeACTJk3Cr7/+ikmTJmHChAn4+eefpTotmxw97y1btmD48OH45ptvsHv3biQnJ0Or1WLv3r1m2zVp0gT5+flmj6CgIClOyS6OnrfR0aNHzc6pffv2pnXe+HkvXrzY7Hzz8vIQGhqK+++/32w7pX/eJSUl6NatG5YsWWLX9t5yfTt63t5yfTt63kaefn07et7ecn1v3rwZ06ZNw44dO7BhwwZUVlZixIgRKCkpsfoaya5x4WHuvPNO8ac//clsWceOHcXs2bMtbv/nP/9ZdOzY0WzZU089Jfr162d6PmHCBHH33XebbTNy5Ejxxz/+0UWlrj9Hz9uShIQEMW/ePNPzZcuWiZCQEFcV0S0cPe+srCwBQFy5csXqPtXwea9atUpoNBpx6tQp0zJP+LyrAyBWrVpV5zbecn1XZ895W+KJ13d19py3t1zf1TnzeXvD9S2EEIWFhQKA2Lx5s9VtpLrGPapmqLy8HLt378aIESPMlo8YMQLbtm2z+Jrt27fX2n7kyJHYtWsXKioq6tzG2j6l5sx511RVVYVr167Vmuzu+vXraNOmDaKjozFmzJhavyzlVJ/z7tGjByIiIjB06FBkZWWZrVPD5/3JJ59g2LBhtUZpV/Ln7QxvuL5dwROv7/rw5OvbFbzl+i4qKgKAOidhleoa96hg6OLFi9Dr9bVmqw8PD681q71RQUGBxe0rKytx8eLFOrextk+pOXPeNb399tsoKSnBhAkTTMs6duyI5cuXIzMzEytWrEBQUBAGDhyI48ePu7T8znLmvCMiIrB06VKsXLkSGRkZiI+Px9ChQ7FlyxbTNt7+eefn5+Pbb7/F448/brZc6Z+3M7zh+nYFT7y+neEN13d9ecv1LYTArFmzMGjQICQmJlrdTqpr3GOm46hOo9GYPRdC1Fpma/uayx3dpxycLeOKFSswd+5crFmzBi1btjQt79evH/r162d6PnDgQPTs2RPvvfce3n33XdcVvJ4cOe/4+HjEx8ebnvfv3x95eXn4xz/+gcGDBzu1T7k4W8bly5ejadOmGDdunNlyT/m8HeUt17ezPP36doQ3Xd/O8pbre/r06di/fz9+/PFHm9tKcY17VM1Q8+bN4evrWyvaKywsrBUVGrVq1cri9n5+fggLC6tzG2v7lJoz5230xRdf4LHHHsOXX36JYcOG1bmtj48P+vTpo5hfEvU57+r69etndk7e/HkLIfDvf/8bkyZNQkBAQJ3bKu3zdoY3XN/14cnXt6t42vVdH95yfc+YMQOZmZnIyspCdHR0ndtKdY17VDAUEBCAXr16YcOGDWbLN2zYgAEDBlh8Tf/+/Wtt//3336N3797w9/evcxtr+5SaM+cNGH4xPvroo0hPT8fo0aNtHkcIgX379iEiIqLeZXYFZ8+7pr1795qdk7d+3oCht8aJEyfw2GOP2TyO0j5vZ3jD9e0sT7++XcXTru/68PTrWwiB6dOnIyMjA5s2bUJcXJzN10h2jdudaq0Qn3/+ufD39xeffPKJOHTokEhLSxONGjUyZdXPnj1bTJo0ybT9b7/9Jho2bCieffZZcejQIfHJJ58If39/8fXXX5u2+emnn4Svr6/4+9//Lg4fPiz+/ve/Cz8/P7Fjxw7Jz88aR887PT1d+Pn5iffff1/k5+ebHlevXjVtM3fuXLF+/Xpx8uRJsXfvXjFlyhTh5+cnfv75Z8nPzxpHz3vhwoVi1apV4tixYyInJ0fMnj1bABArV640beONn7fRww8/LPr27Wtxn57weV+7dk3s3btX7N27VwAQ77zzjti7d684ffq0EMJ7r29Hz9tbrm9Hz9tbrm9Hz9vI06/vp59+WoSEhIjs7Gyz7+2NGzdM28h1jXtcMCSEEO+//75o06aNCAgIED179jTrljd58mRx1113mW2fnZ0tevToIQICAkRsbKz45z//WWufX331lYiPjxf+/v6iY8eOZheXUjhy3nfddZcAUOsxefJk0zZpaWmidevWIiAgQLRo0UKMGDFCbNu2TcIzso8j5/3WW2+Jdu3aiaCgINGsWTMxaNAgsW7dulr79LbPWwghrl69Kho0aCCWLl1qcX+e8Hkbu05b+9566/Xt6Hl7y/Xt6Hl7y/XtzPfcG65vS+cMQCxbtsy0jVzXuOZWAYmIiIhUyaNyhoiIiIhcjcEQERERqRqDISIiIlI1BkNERESkagyGiIiISNUYDBEREZGqMRgiIiIiVWMwRERERKrGYIiIiIhUjcEQERERqRqDISLySCtWrEBQUBDOnTtnWvb444+ja9euKCoqkrFkRORpODcZEXkkIQS6d++OpKQkLFmyBPPmzcO//vUv7NixA1FRUXIXj4g8iJ/cBSAicoZGo8Ebb7yB8ePHIzIyEosXL8bWrVsZCBGRw1gzREQerWfPnjh48CC+//573HXXXXIXh4g8EHOGiMhjfffddzhy5Aj0ej3Cw8PlLg4ReSjWDBGRR9qzZw+GDBmC999/H59//jkaNmyIr776Su5iEZEHYs4QEXmcU6dOYfTo0Zg9ezYmTZqEhIQE9OnTB7t370avXr3kLh4ReRjWDBGRR7l8+TIGDhyIwYMH46OPPjItHzt2LMrKyrB+/XoZS0dEnojBEBEREakaE6iJiIhI1RgMERERkaoxGCIiIiJVYzBEREREqsZgiIiIiFSNwRARERGpGoMhIiIiUjUGQ0RERKRqDIaIiIhI1RgMERERkaoxGCIiIiJVYzBEREREqvb/AcOxG1bHaLxSAAAAAElFTkSuQmCC", + "image/png": 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", "text/plain": [ "
    " ] @@ -2187,76 +2180,83 @@ "name": "stdout", "output_type": "stream", "text": [ - "Own inversion\n", + "Own inversion" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", "[[4.]\n", " [3.]]\n", - "Eigenvalues of Hessian Matrix:[0.32799141 4.41229845]\n", - "0 [-16.27698328] [-18.5653087]\n", - "1 [-0.44867801] [0.37354121]\n", - "2 [-0.41532521] [0.34577375]\n", - "3 [-0.38445171] [0.32007041]\n", - "4 [-0.35587321] [0.29627774]\n", - "5 [-0.32941912] [0.27425372]\n", - "6 [-0.30493151] [0.25386687]\n", - "7 [-0.2822642] [0.23499549]\n", - "8 [-0.26128189] [0.21752693]\n", - "9 [-0.24185931] [0.20135691]\n", - "10 [-0.22388052] [0.1863889]\n", - "11 [-0.2072382] [0.17253354]\n", - "12 [-0.191833] [0.15970814]\n", - "13 [-0.17757295] [0.14783612]\n", - "14 [-0.16437294] [0.13684661]\n", - "15 [-0.15215415] [0.12667402]\n", - "16 [-0.14084366] [0.11725761]\n", - "17 [-0.13037395] [0.10854118]\n", - "18 [-0.12068251] [0.1004727]\n", - "19 [-0.11171148] [0.09300398]\n", - "20 [-0.10340733] [0.08609047]\n", - "21 [-0.09572047] [0.07969087]\n", - "22 [-0.08860502] [0.07376699]\n", - "23 [-0.0820185] [0.06828347]\n", - "24 [-0.0759216] [0.06320757]\n", - "25 [-0.07027791] [0.05850899]\n", - "26 [-0.06505375] [0.05415968]\n", - "27 [-0.06021793] [0.05013368]\n", - "28 [-0.05574159] [0.04640695]\n", - "29 [-0.051598] [0.04295726]\n", + "Eigenvalues of Hessian Matrix:[0.35713539 3.88632765]\n", + "0 [-9.01615836] [-9.6932681]\n", + "1 [0.01454469] [-0.01357366]\n", + "2 [0.0132081] [-0.0123263]\n", + "3 [0.01199434] [-0.01119357]\n", + "4 [0.01089212] [-0.01016493]\n", + "5 [0.00989118] [-0.00923082]\n", + "6 [0.00898223] [-0.00838255]\n", + "7 [0.0081568] [-0.00761224]\n", + "8 [0.00740723] [-0.00691271]\n", + "9 [0.00672654] [-0.00627746]\n", + "10 [0.0061084] [-0.00570059]\n", + "11 [0.00554707] [-0.00517673]\n", + "12 [0.00503732] [-0.00470102]\n", + "13 [0.00457441] [-0.00426902]\n", + "14 [0.00415405] [-0.00387671]\n", + "15 [0.00377231] [-0.00352046]\n", + "16 [0.00342565] [-0.00319695]\n", + "17 [0.00311085] [-0.00290316]\n", + "18 [0.00282498] [-0.00263638]\n", + "19 [0.00256537] [-0.0023941]\n", + "20 [0.00232963] [-0.0021741]\n", + "21 [0.00211555] [-0.00197431]\n", + "22 [0.00192114] [-0.00179288]\n", + "23 [0.00174459] [-0.00162812]\n", + "24 [0.00158427] [-0.0014785]\n", + "25 [0.00143869] [-0.00134264]\n", + "26 [0.00130648] [-0.00121925]\n", + "27 [0.00118642] [-0.00110721]\n", + "28 [0.00107739] [-0.00100546]\n", + "29 [0.00097839] [-0.00091307]\n", "theta from own gd\n", - "[[3.85437905]\n", - " [3.12123488]]\n", - "0 [-0.04776242] [0.039764]\n", - "1 [-0.04421197] [0.03680811]\n", - "2 [-0.0398603] [0.03318519]\n", - "3 [-0.03559176] [0.02963147]\n", - "4 [-0.03166546] [0.02636268]\n", - "5 [-0.02813369] [0.02342235]\n", - "6 [-0.02498282] [0.02079913]\n", - "7 [-0.02218045] [0.01846605]\n", - "8 [-0.01969093] [0.01639344]\n", - "9 [-0.01748034] [0.01455304]\n", - "10 [-0.01551775] [0.0129191]\n", - "11 [-0.01377545] [0.01146857]\n", - "12 [-0.01222875] [0.01018089]\n", - "13 [-0.01085571] [0.00903778]\n", - "14 [-0.00963683] [0.00802302]\n", - "15 [-0.0085548] [0.00712219]\n", - "16 [-0.00759427] [0.00632251]\n", - "17 [-0.00674158] [0.00561262]\n", - "18 [-0.00598464] [0.00498243]\n", - "19 [-0.00531268] [0.004423]\n", - "20 [-0.00471617] [0.00392639]\n", - "21 [-0.00418664] [0.00348553]\n", - "22 [-0.00371656] [0.00309418]\n", - "23 [-0.00329927] [0.00274676]\n", - "24 [-0.00292882] [0.00243835]\n", - "25 [-0.00259997] [0.00216458]\n", - "26 [-0.00230805] [0.00192154]\n", - "27 [-0.0020489] [0.00170579]\n", - "28 [-0.00181885] [0.00151426]\n", - "29 [-0.00161463] [0.00134424]\n", + "[[4.00248779]\n", + " [2.9976783 ]]\n", + "0 [0.00088848] [-0.00082916]\n", + "1 [0.00080683] [-0.00075296]\n", + "2 [0.00070819] [-0.00066091]\n", + "3 [0.00061352] [-0.00057256]\n", + "4 [0.00052874] [-0.00049344]\n", + "5 [0.00045472] [-0.00042436]\n", + "6 [0.00039072] [-0.00036464]\n", + "7 [0.00033562] [-0.00031321]\n", + "8 [0.00028825] [-0.000269]\n", + "9 [0.00024755] [-0.00023102]\n", + "10 [0.00021259] [-0.0001984]\n", + "11 [0.00018256] [-0.00017038]\n", + "12 [0.00015678] [-0.00014631]\n", + "13 [0.00013464] [-0.00012565]\n", + "14 [0.00011562] [-0.0001079]\n", + "15 [9.92929225e-05] [-9.26639089e-05]\n", + "16 [8.52694246e-05] [-7.95766504e-05]\n", + "17 [7.32265127e-05] [-6.83377498e-05]\n", + "18 [6.28844641e-05] [-5.86861591e-05]\n", + "19 [5.40030606e-05] [-5.03976976e-05]\n", + "20 [4.63760101e-05] [-4.32798457e-05]\n", + "21 [3.98261559e-05] [-3.71672742e-05]\n", + "22 [3.42013616e-05] [-3.19180036e-05]\n", + "23 [2.93709777e-05] [-2.74101067e-05]\n", + "24 [2.52228066e-05] [-2.35388766e-05]\n", + "25 [2.1660497e-05] [-2.02143946e-05]\n", + "26 [1.86013055e-05] [-1.73594414e-05]\n", + "27 [1.59741748e-05] [-1.49077038e-05]\n", + "28 [1.37180834e-05] [-1.2802234e-05]\n", + "29 [1.17806281e-05] [-1.09941275e-05]\n", "theta from own gd wth momentum\n", - "[[3.99562995]\n", - " [3.00363823]]\n" + "[[4.00002833]\n", + " [2.99997356]]\n" ] } ], @@ -2341,24 +2341,30 @@ "output_type": "stream", "text": [ "Own inversion\n", - "[[4.10426556]\n", - " [2.93942872]]\n", - "Eigenvalues of Hessian Matrix:[0.31367041 4.07517385]\n", + "[[3.91650453]\n", + " [2.94495682]]\n", + "Eigenvalues of Hessian Matrix:[0.34862407 4.10453899]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ "theta from own gd\n", - "[[4.10426556]\n", - " [2.93942872]]\n" + "[[3.91650453]\n", + " [2.94495682]]\n" ] }, { "data": { - "image/png": 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", + "image/png": 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", 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    " ] }, "metadata": { "filenames": { - "image/png": "/Users/mhjensen/Teaching/MachineLearning/doc/LectureNotes/_build/jupyter_execute/week40_104_1.png" + "image/png": "/Users/mhjensen/Teaching/MachineLearning/doc/LectureNotes/_build/jupyter_execute/week40_104_2.png" } }, "output_type": "display_data" @@ -2368,8 +2374,8 @@ "output_type": "stream", "text": [ "theta from own sdg\n", - "[[4.08587209]\n", - " [2.96301764]]\n" + "[[3.8901199 ]\n", + " [2.92458892]]\n" ] } ], @@ -2473,21 +2479,15 @@ "output_type": "stream", "text": [ "Own inversion\n", - "[[4.33528448]\n", - " [2.81889188]]\n", - "Eigenvalues of Hessian Matrix:[0.30916402 4.51611732]\n", + "[[4.05785974]\n", + " [2.95842106]]\n", + "Eigenvalues of Hessian Matrix:[0.29678339 4.37215356]\n", "theta from own gd\n", - "[[4.33477019]\n", - " [2.81931348]]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ + "[[4.05738629]\n", + " [2.95882223]]\n", "theta from own sdg with momentum\n", - "[[4.40800396]\n", - " [2.78459458]]\n" + "[[4.07489511]\n", + " [2.90281987]]\n" ] } ], @@ -2595,9 +2595,9 @@ "output_type": "stream", "text": [ "theta from own AdaGrad\n", - "[[2.00003828]\n", - " [2.99979896]\n", - " [4.00019258]]\n" + "[[1.99994537]\n", + " [3.00034209]\n", + " [3.99966798]]\n" ] } ], @@ -2696,9 +2696,9 @@ "output_type": "stream", "text": [ "theta from own RMSprop\n", - "[[2.01129731]\n", - " [3.01249445]\n", - " [4.00885858]]\n" + "[[1.99975636]\n", + " [3.00348281]\n", + " [3.99607299]]\n" ] } ], @@ -2793,9 +2793,9 @@ "output_type": "stream", "text": [ "theta from own ADAM\n", - "[[2.00001716]\n", - " [2.99992107]\n", - " [4.00007706]]\n" + "[[2.00002276]\n", + " [2.99985884]\n", + " [4.00009004]]\n" ] } ], @@ -2999,7 +2999,7 @@ { "data": { "text/plain": [ - "[]" + "[]" ] }, "execution_count": 25, @@ -3080,7 +3080,7 @@ { "data": { "text/plain": [ - "" + "" ] }, "execution_count": 26, diff --git a/doc/LectureNotes/_build/jupyter_execute/week40_100_1.png b/doc/LectureNotes/_build/jupyter_execute/week40_100_1.png index a675ee5f0..31858288a 100644 Binary files a/doc/LectureNotes/_build/jupyter_execute/week40_100_1.png and b/doc/LectureNotes/_build/jupyter_execute/week40_100_1.png differ diff --git a/doc/LectureNotes/_build/jupyter_execute/week40_104_2.png b/doc/LectureNotes/_build/jupyter_execute/week40_104_2.png new file mode 100644 index 000000000..000dfbd57 Binary files /dev/null and b/doc/LectureNotes/_build/jupyter_execute/week40_104_2.png differ diff --git a/doc/LectureNotes/_build/jupyter_execute/week40_34_1.png b/doc/LectureNotes/_build/jupyter_execute/week40_34_1.png index 10f79836f..fcf8aaf89 100644 Binary files a/doc/LectureNotes/_build/jupyter_execute/week40_34_1.png and b/doc/LectureNotes/_build/jupyter_execute/week40_34_1.png differ diff --git a/doc/LectureNotes/gaussian.pdf b/doc/LectureNotes/gaussian.pdf index 6f21717a4..54868ad18 100644 Binary files a/doc/LectureNotes/gaussian.pdf and b/doc/LectureNotes/gaussian.pdf differ