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a/doc/pub/week34/ipynb/week34.ipynb +++ b/doc/pub/week34/ipynb/week34.ipynb @@ -819,10 +819,7 @@ { "cell_type": "code", "execution_count": null, - "metadata": { - "collapsed": false, - "editable": true - }, + "metadata": {}, "outputs": [], "source": [ "import numpy as np" @@ -838,10 +835,7 @@ { "cell_type": "code", "execution_count": null, - "metadata": { - "collapsed": false, - "editable": true - }, + "metadata": {}, "outputs": [], "source": [ "n = 10\n", @@ -860,10 +854,7 @@ { "cell_type": "code", "execution_count": null, - "metadata": { - "collapsed": false, - "editable": true - }, + "metadata": {}, "outputs": [], "source": [ "import numpy as np\n", @@ -882,10 +873,7 @@ { "cell_type": "code", "execution_count": null, - "metadata": { - "collapsed": false, - "editable": true - }, + "metadata": {}, "outputs": [], "source": [ "import numpy as np\n", @@ -909,10 +897,7 @@ { "cell_type": "code", "execution_count": null, - "metadata": { - "collapsed": false, - "editable": true - }, + "metadata": {}, "outputs": [], "source": [ "import numpy as np\n", @@ -934,10 +919,7 @@ { "cell_type": "code", "execution_count": null, - "metadata": { - "collapsed": false, - "editable": true - }, + "metadata": {}, "outputs": [], "source": [ "import numpy as np\n", @@ -955,10 +937,7 @@ { "cell_type": "code", "execution_count": null, - "metadata": { - "collapsed": false, - "editable": true - }, + "metadata": {}, "outputs": [], "source": [ "import numpy as np\n", @@ -976,10 +955,7 @@ { "cell_type": "code", "execution_count": null, - "metadata": { - "collapsed": false, - "editable": true - }, + "metadata": {}, "outputs": [], "source": [ "import numpy as np\n", @@ -1001,10 +977,7 @@ { "cell_type": "code", "execution_count": null, - "metadata": { - "collapsed": false, - "editable": true - }, + "metadata": {}, "outputs": [], "source": [ "import numpy as np\n", @@ -1022,10 +995,7 @@ { "cell_type": "code", "execution_count": null, - "metadata": { - "collapsed": false, - "editable": true - }, + "metadata": {}, "outputs": [], "source": [ "import numpy as np\n", @@ -1044,10 +1014,7 @@ { "cell_type": "code", "execution_count": null, - "metadata": { - "collapsed": false, - "editable": true - }, + "metadata": {}, "outputs": [], "source": [ "import numpy as np\n", @@ -1066,10 +1033,7 @@ { "cell_type": "code", "execution_count": null, - "metadata": { - "collapsed": false, - "editable": true - }, + "metadata": {}, "outputs": [], "source": [ "import numpy as np\n", @@ -1089,10 +1053,7 @@ { "cell_type": "code", "execution_count": null, - "metadata": { - "collapsed": false, - "editable": true - }, + "metadata": {}, "outputs": [], "source": [ "import numpy as np\n", @@ -1112,10 +1073,7 @@ { "cell_type": "code", "execution_count": null, - "metadata": { - "collapsed": false, - "editable": true - }, + "metadata": {}, "outputs": [], "source": [ "import numpy as np\n", @@ -1199,10 +1157,7 @@ { "cell_type": "code", "execution_count": null, - "metadata": { - "collapsed": false, - "editable": true - }, + "metadata": {}, "outputs": [], "source": [ "# Importing various packages\n", @@ -1225,10 +1180,7 @@ { "cell_type": "code", "execution_count": null, - "metadata": { - "collapsed": false, - "editable": true - }, + "metadata": {}, "outputs": [], "source": [ "%matplotlib inline\n", @@ -1271,10 +1223,7 @@ { "cell_type": "code", "execution_count": null, - "metadata": { - "collapsed": false, - "editable": true - }, + "metadata": {}, "outputs": [], "source": [ "import pandas as pd\n", @@ -1301,10 +1250,7 @@ { "cell_type": "code", "execution_count": null, - "metadata": { - "collapsed": false, - "editable": true - }, + "metadata": {}, "outputs": [], "source": [ "data_pandas = pd.DataFrame(data,index=['Frodo','Bilbo','Aragorn','Sam'])\n", @@ -1321,10 +1267,7 @@ { "cell_type": "code", "execution_count": null, - "metadata": { - "collapsed": false, - "editable": true - }, + "metadata": {}, "outputs": [], "source": [ "display(data_pandas.loc['Aragorn'])" @@ -1340,10 +1283,7 @@ { "cell_type": "code", "execution_count": null, - "metadata": { - "collapsed": false, - "editable": true - }, + "metadata": {}, "outputs": [], "source": [ "new_hobbit = {'First Name': [\"Peregrin\"],\n", @@ -1366,10 +1306,7 @@ { "cell_type": "code", "execution_count": null, - "metadata": { - "collapsed": false, - "editable": true - }, + "metadata": {}, "outputs": [], "source": [ "import numpy as np\n", @@ -1397,10 +1334,7 @@ { "cell_type": "code", "execution_count": null, - "metadata": { - "collapsed": false, - "editable": true - }, + "metadata": {}, "outputs": [], "source": [ "df.columns = ['First', 'Second', 'Third', 'Fourth', 'Fifth']\n", @@ -1433,10 +1367,7 @@ { "cell_type": "code", "execution_count": null, - "metadata": { - "collapsed": false, - "editable": true - }, + "metadata": {}, "outputs": [], "source": [ "b = np.arange(16).reshape((4,4))\n", @@ -1527,10 +1458,7 @@ { "cell_type": "code", "execution_count": null, - "metadata": { - "collapsed": false, - "editable": true - }, + "metadata": {}, "outputs": [], "source": [ "# Importing various packages\n", @@ -1659,10 +1587,7 @@ { "cell_type": "code", "execution_count": null, - "metadata": { - "collapsed": false, - "editable": true - }, + "metadata": {}, "outputs": [], "source": [ "import numpy as np\n", @@ -1704,10 +1629,7 @@ { "cell_type": "code", "execution_count": null, - "metadata": { - "collapsed": false, - "editable": true - }, + "metadata": {}, "outputs": [], "source": [ "import numpy as np \n", @@ -1880,10 +1802,7 @@ { "cell_type": "code", "execution_count": null, - "metadata": { - "collapsed": false, - "editable": true - }, + "metadata": {}, "outputs": [], "source": [ "import matplotlib.pyplot as plt\n", @@ -2087,10 +2006,7 @@ { "cell_type": "code", "execution_count": null, - "metadata": { - "collapsed": false, - "editable": true - }, + "metadata": {}, "outputs": [], "source": [ "# Common imports\n", @@ -2138,10 +2054,7 @@ { "cell_type": "code", "execution_count": null, - "metadata": { - "collapsed": false, - "editable": true - }, + "metadata": {}, "outputs": [], "source": [ "from pylab import plt, mpl\n", @@ -2174,10 +2087,7 @@ { "cell_type": "code", "execution_count": null, - "metadata": { - "collapsed": false, - "editable": true - }, + "metadata": {}, "outputs": [], "source": [ "\"\"\" \n", @@ -2205,10 +2115,7 @@ { "cell_type": "code", "execution_count": null, - "metadata": { - "collapsed": false, - "editable": true - }, + "metadata": {}, "outputs": [], "source": [ "# Read the experimental data with Pandas\n", @@ -2250,10 +2157,7 @@ { "cell_type": "code", "execution_count": null, - "metadata": { - "collapsed": false, - "editable": true - }, + "metadata": {}, "outputs": [], "source": [ "A = Masses['A']\n", @@ -2275,10 +2179,7 @@ { "cell_type": "code", "execution_count": null, - "metadata": { - "collapsed": false, - "editable": true - }, + "metadata": {}, "outputs": [], "source": [ "# Now we set up the design matrix X\n", @@ -2300,10 +2201,7 @@ { "cell_type": "code", "execution_count": null, - "metadata": { - "collapsed": false, - "editable": true - }, + "metadata": {}, "outputs": [], "source": [ "clf = skl.LinearRegression().fit(X, Energies)\n", @@ -2321,10 +2219,7 @@ { "cell_type": "code", "execution_count": null, - "metadata": { - "collapsed": false, - "editable": true - }, + "metadata": {}, "outputs": [], "source": [ "# The mean squared error \n", @@ -2361,10 +2256,7 @@ { "cell_type": "code", "execution_count": null, - "metadata": { - "collapsed": false, - "editable": true - }, + "metadata": {}, "outputs": [], "source": [ "\n", @@ -2412,10 +2304,7 @@ { "cell_type": "code", "execution_count": null, - "metadata": { - "collapsed": false, - "editable": true - }, + "metadata": {}, "outputs": [], "source": [ "from sklearn.neural_network import MLPRegressor\n", @@ -2827,12 +2716,161 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false, - "editable": true - }, - "outputs": [], + "execution_count": 1, + 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" + ], + "text/plain": [ + " 1 A A^(2/3) A^(-1/3) 1/A\n", + "A \n", + "1 1.0 1.0 1.000000 1.000000 1.000000\n", + "2 1.0 2.0 1.587401 0.793701 0.500000\n", + "3 1.0 3.0 2.080084 0.693361 0.333333\n", + "4 1.0 4.0 2.519842 0.629961 0.250000\n", + "5 1.0 5.0 2.924018 0.584804 0.200000\n", + ".. ... ... ... ... ...\n", + "264 1.0 264.0 41.153106 0.155883 0.003788\n", + "265 1.0 265.0 41.256962 0.155687 0.003774\n", + "266 1.0 266.0 41.360688 0.155491 0.003759\n", + "269 1.0 269.0 41.671089 0.154911 0.003717\n", + "270 1.0 270.0 41.774300 0.154720 0.003704\n", + "\n", + "[267 rows x 5 columns]" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "# Common imports\n", "import numpy as np\n", @@ -3333,11 +3371,8 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false, - "editable": true - }, + "execution_count": 2, + "metadata": {}, "outputs": [], "source": [ "# matrix inversion to find beta\n", @@ -3355,11 +3390,8 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false, - "editable": true - }, + "execution_count": 3, + "metadata": {}, "outputs": [], "source": [ "fit = np.linalg.lstsq(X, Energies, rcond =None)[0]\n", @@ -3375,12 +3407,22 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false, - "editable": true - }, - "outputs": [], + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], "source": [ "Masses['Eapprox'] = ytilde\n", "# Generate a plot comparing the experimental with the fitted values values.\n", @@ -3408,11 +3450,8 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false, - "editable": true - }, + "execution_count": 6, + "metadata": {}, "outputs": [], "source": [ "def R2(y_data, y_model):\n", @@ -3428,12 +3467,17 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false, - "editable": true - }, - "outputs": [], + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "0.9547578478889096\n" + ] + } + ], "source": [ "print(R2(Energies,ytilde))" ] @@ -3447,12 +3491,17 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false, - "editable": true - }, - "outputs": [], + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "0.03787596148305236\n" + ] + } + ], "source": [ "def MSE(y_data,y_model):\n", " n = np.size(y_model)\n", @@ -3470,12 +3519,29 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false, - "editable": true - }, - "outputs": [], + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "A \n", + "1 0 inf\n", + "2 1 1.123190\n", + "3 2 0.327631\n", + "4 6 0.344172\n", + "5 9 0.044402\n", + " ... \n", + "264 3304 0.009911\n", + "265 3310 0.009154\n", + "266 3317 0.007824\n", + "269 3338 0.011347\n", + "270 3344 0.009790\n", + "Name: Ebinding, Length: 267, dtype: float64\n" + ] + } + ], "source": [ "def RelativeError(y_data,y_model):\n", " return abs((y_data-y_model)/y_data)\n", @@ -3850,10 +3916,7 @@ { "cell_type": "code", "execution_count": null, - "metadata": { - "collapsed": false, - "editable": true - }, + "metadata": {}, "outputs": [], "source": [ "# Common imports\n", @@ -3972,10 +4035,7 @@ { "cell_type": "code", "execution_count": null, - "metadata": { - "collapsed": false, - "editable": true - }, + "metadata": {}, "outputs": [], "source": [ "import os\n", @@ -4133,10 +4193,7 @@ { "cell_type": "code", "execution_count": null, - "metadata": { - "collapsed": false, - "editable": true - }, + "metadata": {}, "outputs": [], "source": [ "x = np.random.rand(100,1)\n", @@ -4213,10 +4270,7 @@ { "cell_type": "code", "execution_count": null, - "metadata": { - "collapsed": false, - "editable": true - }, + "metadata": {}, "outputs": [], "source": [ "import os\n", @@ -4317,10 +4371,7 @@ { "cell_type": "code", "execution_count": null, - "metadata": { - "collapsed": false, - "editable": true - }, + "metadata": {}, "outputs": [], "source": [ "# split in training and test data\n", @@ -4337,10 +4388,7 @@ { "cell_type": "code", "execution_count": null, - "metadata": { - "collapsed": false, - "editable": true - }, + "metadata": {}, "outputs": [], "source": [ "scaler = StandardScaler()\n", @@ -4368,10 +4416,7 @@ { "cell_type": "code", "execution_count": null, - "metadata": { - "collapsed": false, - "editable": true - }, + "metadata": {}, "outputs": [], "source": [ "np.random.seed()\n", @@ -4403,7 +4448,25 @@ ] } ], - "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 }