From a0c33afd42ef374e70d2773a3c246ccbb9838af4 Mon Sep 17 00:00:00 2001 From: Morten Hjorth-Jensen Date: Fri, 3 Sep 2021 06:18:45 +0200 Subject: [PATCH] Update week35.ipynb --- doc/pub/week35/ipynb/week35.ipynb | 712 ++++++++++++++++++++++++------ 1 file changed, 565 insertions(+), 147 deletions(-) diff --git a/doc/pub/week35/ipynb/week35.ipynb b/doc/pub/week35/ipynb/week35.ipynb index 5da4560be..de001d307 100644 --- a/doc/pub/week35/ipynb/week35.ipynb +++ b/doc/pub/week35/ipynb/week35.ipynb @@ -400,10 +400,7 @@ { "cell_type": "code", "execution_count": null, - "metadata": { - "collapsed": false, - "editable": true - }, + "metadata": {}, "outputs": [], "source": [ "%matplotlib inline\n", @@ -946,10 +943,7 @@ { "cell_type": "code", "execution_count": null, - "metadata": { - "collapsed": false, - "editable": true - }, + "metadata": {}, "outputs": [], "source": [ "# matrix inversion to find beta\n", @@ -968,10 +962,7 @@ { "cell_type": "code", "execution_count": null, - "metadata": { - "collapsed": false, - "editable": true - }, + "metadata": {}, "outputs": [], "source": [ "fit = np.linalg.lstsq(X, Energies, rcond =None)[0]\n", @@ -988,10 +979,7 @@ { "cell_type": "code", "execution_count": null, - "metadata": { - "collapsed": false, - "editable": true - }, + "metadata": {}, "outputs": [], "source": [ "Masses['Eapprox'] = ytilde\n", @@ -1021,10 +1009,7 @@ { "cell_type": "code", "execution_count": null, - "metadata": { - "collapsed": false, - "editable": true - }, + "metadata": {}, "outputs": [], "source": [ "def R2(y_data, y_model):\n", @@ -1041,10 +1026,7 @@ { "cell_type": "code", "execution_count": null, - "metadata": { - "collapsed": false, - "editable": true - }, + "metadata": {}, "outputs": [], "source": [ "print(R2(Energies,ytilde))" @@ -1060,10 +1042,7 @@ { "cell_type": "code", "execution_count": null, - "metadata": { - "collapsed": false, - "editable": true - }, + "metadata": {}, "outputs": [], "source": [ "def MSE(y_data,y_model):\n", @@ -1083,10 +1062,7 @@ { "cell_type": "code", "execution_count": null, - "metadata": { - "collapsed": false, - "editable": true - }, + "metadata": {}, "outputs": [], "source": [ "def RelativeError(y_data,y_model):\n", @@ -1121,10 +1097,7 @@ { "cell_type": "code", "execution_count": null, - "metadata": { - "collapsed": false, - "editable": true - }, + "metadata": {}, "outputs": [], "source": [ "import os\n", @@ -1177,10 +1150,7 @@ { "cell_type": "code", "execution_count": null, - "metadata": { - "collapsed": false, - "editable": true - }, + "metadata": {}, "outputs": [], "source": [ "# equivalently in numpy\n", @@ -1252,10 +1222,7 @@ { "cell_type": "code", "execution_count": null, - "metadata": { - "collapsed": false, - "editable": true - }, + "metadata": {}, "outputs": [], "source": [ "import numpy as np\n", @@ -1275,10 +1242,7 @@ { "cell_type": "code", "execution_count": null, - "metadata": { - "collapsed": false, - "editable": true - }, + "metadata": {}, "outputs": [], "source": [ "from sklearn.datasets import load_boston\n", @@ -1300,10 +1264,7 @@ { "cell_type": "code", "execution_count": null, - "metadata": { - "collapsed": false, - "editable": true - }, + "metadata": {}, "outputs": [], "source": [ "boston = pd.DataFrame(boston_dataset.data, columns=boston_dataset.feature_names)\n", @@ -1321,10 +1282,7 @@ { "cell_type": "code", "execution_count": null, - "metadata": { - "collapsed": false, - "editable": true - }, + "metadata": {}, "outputs": [], "source": [ "# check for missing values in all the columns\n", @@ -1341,10 +1299,7 @@ { "cell_type": "code", "execution_count": null, - "metadata": { - "collapsed": false, - "editable": true - }, + "metadata": {}, "outputs": [], "source": [ "# set the size of the figure\n", @@ -1365,10 +1320,7 @@ { "cell_type": "code", "execution_count": null, - "metadata": { - "collapsed": false, - "editable": true - }, + "metadata": {}, "outputs": [], "source": [ "# compute the pair wise correlation for all columns \n", @@ -1388,10 +1340,7 @@ { "cell_type": "code", "execution_count": null, - "metadata": { - "collapsed": false, - "editable": true - }, + "metadata": {}, "outputs": [], "source": [ "plt.figure(figsize=(20, 5))\n", @@ -1419,10 +1368,7 @@ { "cell_type": "code", "execution_count": null, - "metadata": { - "collapsed": false, - "editable": true - }, + "metadata": {}, "outputs": [], "source": [ "X = pd.DataFrame(np.c_[boston['LSTAT'], boston['RM']], columns = ['LSTAT','RM'])\n", @@ -1439,10 +1385,7 @@ { "cell_type": "code", "execution_count": null, - "metadata": { - "collapsed": false, - "editable": true - }, + "metadata": {}, "outputs": [], "source": [ "from sklearn.model_selection import train_test_split\n", @@ -1466,10 +1409,7 @@ { "cell_type": "code", "execution_count": null, - "metadata": { - "collapsed": false, - "editable": true - }, + "metadata": {}, "outputs": [], "source": [ "from sklearn.linear_model import LinearRegression\n", @@ -1508,10 +1448,7 @@ { "cell_type": "code", "execution_count": null, - "metadata": { - "collapsed": false, - "editable": true - }, + "metadata": {}, "outputs": [], "source": [ "# plotting the y_test vs y_pred\n", @@ -1638,12 +1575,418 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false, - "editable": true - 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"collapsed": false, - "editable": true - }, + "metadata": {}, "outputs": [], "source": [ "# Common imports\n", @@ -2358,10 +2692,7 @@ { "cell_type": "code", "execution_count": null, - "metadata": { - "collapsed": false, - "editable": true - }, + "metadata": {}, "outputs": [], "source": [ "import numpy as np\n", @@ -3062,12 +3393,20 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false, - "editable": true - }, - "outputs": [], + "execution_count": 26, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "-0.22501987939529708\n", + "3.4265364730355325\n", + "[[1.06251574 3.06513214]\n", + " [3.06513214 9.90295583]]\n" + ] + } + ], "source": [ "# Importing various packages\n", "import numpy as np\n", @@ -3096,12 +3435,20 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false, - "editable": true - }, - "outputs": [], + "execution_count": 27, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "0.080263294546264\n", + "2.011002230765845\n", + "[[1. 0.7136592]\n", + " [0.7136592 1. ]]\n" + ] + } + ], "source": [ "import numpy as np\n", "n = 100\n", @@ -3143,12 +3490,40 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false, - "editable": true - }, - "outputs": [], + "execution_count": 28, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[[-0.31787985 -1.14338084]\n", + " [ 0.04529807 -0.18207237]\n", + " [ 1.11176315 3.45031813]\n", + " [-1.91097714 -5.2907413 ]\n", + " [-0.19918861 1.18654759]\n", + " [ 0.84454053 2.34712647]\n", + " [ 0.82299467 1.93582947]\n", + " [-1.64434822 -5.13289015]\n", + " [ 1.10923753 3.75337971]\n", + " [ 0.13855987 -0.92411672]]\n", + " 0 1\n", + "0 -0.317880 -1.143381\n", + "1 0.045298 -0.182072\n", + "2 1.111763 3.450318\n", + "3 -1.910977 -5.290741\n", + "4 -0.199189 1.186548\n", + "5 0.844541 2.347126\n", + "6 0.822995 1.935829\n", + "7 -1.644348 -5.132890\n", + "8 1.109238 3.753380\n", + "9 0.138560 -0.924117\n", + " 0 1\n", + "0 1.000000 0.968568\n", + "1 0.968568 1.000000\n" + ] + } + ], "source": [ "import numpy as np\n", "import pandas as pd\n", @@ -3176,12 +3551,49 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false, - "editable": true - }, - "outputs": [], + "execution_count": 29, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " 0 1 2 3 4 5 6 7 \\\n", + "0 0.0 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 \n", + "1 0.0 0.063613 0.068251 0.071169 0.070546 0.069425 0.067581 0.065899 \n", + "2 0.0 0.068251 0.073991 0.077286 0.077098 0.076290 0.073977 0.072493 \n", + "3 0.0 0.071169 0.077286 0.083456 0.083469 0.082851 0.081789 0.080312 \n", + "4 0.0 0.070546 0.077098 0.083469 0.083869 0.083597 0.082364 0.081188 \n", + "5 0.0 0.069425 0.076290 0.082851 0.083597 0.083643 0.082304 0.081413 \n", + "6 0.0 0.067581 0.073977 0.081789 0.082364 0.082304 0.082005 0.080988 \n", + "7 0.0 0.065899 0.072493 0.080312 0.081188 0.081413 0.080988 0.080245 \n", + "8 0.0 0.064203 0.070945 0.078766 0.079908 0.080389 0.079865 0.079371 \n", + "9 0.0 0.062545 0.069397 0.077214 0.078591 0.079301 0.078699 0.078431 \n", + "10 0.0 0.061809 0.068087 0.076517 0.077496 0.077870 0.078063 0.077474 \n", + "11 0.0 0.060077 0.066459 0.074794 0.076005 0.076605 0.076675 0.076314 \n", + "12 0.0 0.058442 0.064904 0.073149 0.074564 0.075366 0.075332 0.075177 \n", + "13 0.0 0.056909 0.063432 0.071590 0.073188 0.074169 0.074047 0.074077 \n", + "14 0.0 0.055478 0.062046 0.070123 0.071881 0.073022 0.072826 0.073022 \n", + "\n", + " 8 9 10 11 12 13 14 \n", + "0 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 \n", + "1 0.064203 0.062545 0.061809 0.060077 0.058442 0.056909 0.055478 \n", + "2 0.070945 0.069397 0.068087 0.066459 0.064904 0.063432 0.062046 \n", + "3 0.078766 0.077214 0.076517 0.074794 0.073149 0.071590 0.070123 \n", + "4 0.079908 0.078591 0.077496 0.076005 0.074564 0.073188 0.071881 \n", + "5 0.080389 0.079301 0.077870 0.076605 0.075366 0.074169 0.073022 \n", + "6 0.079865 0.078699 0.078063 0.076675 0.075332 0.074047 0.072826 \n", + "7 0.079371 0.078431 0.077474 0.076314 0.075177 0.074077 0.073022 \n", + "8 0.078726 0.077994 0.076758 0.075809 0.074861 0.073933 0.073033 \n", + "9 0.077994 0.077452 0.075974 0.075216 0.074443 0.073672 0.072915 \n", + "10 0.076758 0.075974 0.075342 0.074315 0.073305 0.072325 0.071384 \n", + "11 0.075809 0.075216 0.074315 0.073486 0.072655 0.071838 0.071044 \n", + "12 0.074861 0.074443 0.073305 0.072655 0.071987 0.071318 0.070658 \n", + "13 0.073933 0.073672 0.072325 0.071838 0.071318 0.070783 0.070245 \n", + "14 0.073033 0.072915 0.071384 0.071044 0.070658 0.070245 0.069818 \n" + ] + } + ], "source": [ "# Common imports\n", "import numpy as np\n", @@ -3471,10 +3883,7 @@ { "cell_type": "code", "execution_count": null, - "metadata": { - "collapsed": false, - "editable": true - }, + "metadata": {}, "outputs": [], "source": [ "x = np.random.rand(100)\n", @@ -3496,10 +3905,7 @@ { "cell_type": "code", "execution_count": null, - "metadata": { - "collapsed": false, - "editable": true - }, + "metadata": {}, "outputs": [], "source": [ "import os\n", @@ -3694,10 +4100,7 @@ { "cell_type": "code", "execution_count": null, - "metadata": { - "collapsed": false, - "editable": true - }, + "metadata": {}, "outputs": [], "source": [ "from mpl_toolkits.mplot3d import Axes3D\n", @@ -3815,10 +4218,7 @@ { "cell_type": "code", "execution_count": null, - "metadata": { - "collapsed": false, - "editable": true - }, + "metadata": {}, "outputs": [], "source": [ "def FrankeFunction(x,y):\n", @@ -3876,7 +4276,25 @@ ] } ], - "metadata": {}, + "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" + } + }, "nbformat": 4, "nbformat_minor": 4 }