diff --git a/doc/pub/week39/html/week39-reveal.html b/doc/pub/week39/html/week39-reveal.html index 6a4b2f801..91ca9a80b 100644 --- a/doc/pub/week39/html/week39-reveal.html +++ b/doc/pub/week39/html/week39-reveal.html @@ -163,6 +163,7 @@ MathJax.Hub.Config({ diff --git a/doc/pub/week39/html/week39-solarized.html b/doc/pub/week39/html/week39-solarized.html index 2b2180abc..8eb4c801d 100644 --- a/doc/pub/week39/html/week39-solarized.html +++ b/doc/pub/week39/html/week39-solarized.html @@ -249,6 +249,7 @@ MathJax.Hub.Config({ diff --git a/doc/pub/week39/html/week39.html b/doc/pub/week39/html/week39.html index faaccd860..823d1e6d5 100644 --- a/doc/pub/week39/html/week39.html +++ b/doc/pub/week39/html/week39.html @@ -254,6 +254,7 @@ MathJax.Hub.Config({ diff --git a/doc/pub/week39/ipynb/ipynb-week39-src.tar.gz b/doc/pub/week39/ipynb/ipynb-week39-src.tar.gz index 991e991b8..959001ce7 100644 Binary files a/doc/pub/week39/ipynb/ipynb-week39-src.tar.gz and b/doc/pub/week39/ipynb/ipynb-week39-src.tar.gz differ diff --git a/doc/pub/week39/ipynb/week39.ipynb b/doc/pub/week39/ipynb/week39.ipynb index f00e95f43..ec57e4a90 100644 --- a/doc/pub/week39/ipynb/week39.ipynb +++ b/doc/pub/week39/ipynb/week39.ipynb @@ -20,6 +20,8 @@ "\n", "* Thursday: Repetition of Logistic regression equations and classification problems and discussion of Gradient methods. Discussion of project 1 and examples on how to implement Logistic Regression\n", "\n", + "* [Video of Lecture](https://www.uio.no/studier/emner/matnat/fys/FYS-STK3155/h21/forelesningsvideoer/LectureSeptember30.mp4?vrtx=view-as-webpage)\n", + "\n", "* Friday: Stochastic Gradient descent with examples and automatic differentiation\n", "\n", "* Reading recommendations:\n", @@ -48,22 +50,12 @@ }, { "cell_type": "code", - "execution_count": 48, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], + "execution_count": null, + "metadata": { + "collapsed": false, + "editable": true + }, + "outputs": [], "source": [ "%matplotlib inline\n", "\n", @@ -132,23 +124,12 @@ }, { "cell_type": "code", - "execution_count": 50, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "GridSearchCV(estimator=Ridge(),\n", - " param_grid={'alpha': array([1.00000000e-04, 2.78255940e-04, 7.74263683e-04, 2.15443469e-03,\n", - " 5.99484250e-03, 1.66810054e-02, 4.64158883e-02, 1.29154967e-01,\n", - " 3.59381366e-01, 1.00000000e+00])})\n", - "Best estimated lambda-value: 1.0\n", - "MSE score: 1.0853702554682934\n", - "R2 score: -0.00029036626896195017\n" - ] - } - ], + "execution_count": null, + "metadata": { + "collapsed": false, + "editable": true + }, + "outputs": [], "source": [ "import numpy as np\n", "from sklearn.model_selection import train_test_split\n", @@ -181,7 +162,7 @@ "\n", "# Decide which values of lambda to use\n", "nlambdas = 10\n", - "lambdas = np.logspace(-4, 0, nlambdas)\n", + "lambdas = np.logspace(-4, 2, nlambdas)\n", "# create and fit a ridge regression model, testing each alpha\n", "model = Ridge()\n", "gridsearch = GridSearchCV(estimator=model, param_grid=dict(alpha=lambdas))\n", @@ -221,7 +202,10 @@ { "cell_type": "code", "execution_count": null, - "metadata": {}, + "metadata": { + "collapsed": false, + "editable": true + }, "outputs": [], "source": [ "import numpy as np\n", @@ -1034,7 +1018,10 @@ { "cell_type": "code", "execution_count": null, - "metadata": {}, + "metadata": { + "collapsed": false, + "editable": true + }, "outputs": [], "source": [ "import numpy as np\n", @@ -1069,7 +1056,10 @@ { "cell_type": "code", "execution_count": null, - "metadata": {}, + "metadata": { + "collapsed": false, + "editable": true + }, "outputs": [], "source": [ "pt.axis(\"equal\")\n", @@ -1087,7 +1077,10 @@ { "cell_type": "code", "execution_count": null, - "metadata": {}, + "metadata": { + "collapsed": false, + "editable": true + }, "outputs": [], "source": [ "x = guesses[-1]\n", @@ -1104,7 +1097,10 @@ { "cell_type": "code", "execution_count": null, - "metadata": {}, + "metadata": { + "collapsed": false, + "editable": true + }, "outputs": [], "source": [ "def f1d(alpha):\n", @@ -1126,7 +1122,10 @@ { "cell_type": "code", "execution_count": null, - "metadata": {}, + "metadata": { + "collapsed": false, + "editable": true + }, "outputs": [], "source": [ "pt.axis(\"equal\")\n", @@ -1504,7 +1503,10 @@ { "cell_type": "code", "execution_count": null, - "metadata": {}, + "metadata": { + "collapsed": false, + "editable": true + }, "outputs": [], "source": [ "x = 2*np.random.rand(m,1)\n", @@ -1670,33 +1672,12 @@ }, { "cell_type": "code", - "execution_count": 43, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Eigenvalues of Hessian Matrix:[0.27481859 4.57754488]\n", - "[[4.03064789]\n", - " [2.97999589]]\n", - "[[4.03064789]\n", - " [2.97999589]]\n" - ] - }, - { - "data": { - "image/png": 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\n", 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\n", 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" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], + "execution_count": null, + "metadata": { + "collapsed": false, + "editable": true + }, + "outputs": [], "source": [ "from random import random, seed\n", "import numpy as np\n", @@ -2061,7 +2015,10 @@ { "cell_type": "code", "execution_count": null, - "metadata": {}, + "metadata": { + "collapsed": false, + "editable": true + }, "outputs": [], "source": [ "import numpy as np \n", @@ -2123,7 +2080,10 @@ { "cell_type": "code", "execution_count": null, - "metadata": {}, + "metadata": { + "collapsed": false, + "editable": true + }, "outputs": [], "source": [ "import numpy as np \n", @@ -2162,7 +2122,10 @@ { "cell_type": "code", "execution_count": null, - "metadata": {}, + "metadata": { + "collapsed": false, + "editable": true + }, "outputs": [], "source": [ "# Importing various packages\n", @@ -2239,25 +2202,7 @@ ] } ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "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": "3.8.5" - } - }, + "metadata": {}, "nbformat": 4, "nbformat_minor": 4 } diff --git a/doc/src/week39/week39.do.txt b/doc/src/week39/week39.do.txt index 44b43ce15..03f964094 100644 --- a/doc/src/week39/week39.do.txt +++ b/doc/src/week39/week39.do.txt @@ -6,6 +6,7 @@ DATE: today ===== Plan for week 39 ===== * Thursday: Repetition of Logistic regression equations and classification problems and discussion of Gradient methods. Discussion of project 1 and examples on how to implement Logistic Regression +* "Video of Lecture":"https://www.uio.no/studier/emner/matnat/fys/FYS-STK3155/h21/forelesningsvideoer/LectureSeptember30.mp4?vrtx=view-as-webpage" * Friday: Stochastic Gradient descent with examples and automatic differentiation