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\n", 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\n", + "Luiz Inacio Lula da Silva 48 Mahmoud Abbas 29 Megawati Sukarnoputri 33 \n", + "Michael Bloomberg 20 Naomi Watts 22 Nestor Kirchner 37 \n", + "Paul Bremer 20 Pete Sampras 22 Recep Tayyip Erdogan 30 \n", + "Ricardo Lagos 27 Roh Moo-hyun 32 Rudolph Giuliani 26 \n", + "Saddam Hussein 23 Serena Williams 52 Silvio Berlusconi 33 \n", + "Tiger Woods 23 Tom Daschle 25 Tom Ridge 33 \n", + "Tony Blair 144 Vicente Fox 32 Vladimir Putin 49 \n", + "Winona Ryder 24 Knn score: 0.23255813953488372\n", + "(1547, 100)\n", + "Knn score: 0.3003875968992248\n" + ] + }, + { + "data": { + "image/png": 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\n", 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import matplotlib.pyplot as plt\n", + "import numpy as np\n", + "from sklearn.datasets import fetch_lfw_people\n", + "people=fetch_lfw_people(min_faces_per_person=20, resize=0.7)\n", + "image_shape=people.images[0].shape\n", + "\n", + "fig, axes=plt.subplots(2,5, figsize=(15,8), subplot_kw={'xticks': (),'yticks': ()})\n", + "for target,image, ax in zip(people.target, people.images, axes.ravel()):\n", + " ax.imshow(image)\n", + " ax.set_title(people.target_names[target])\n", + "#plt.subtitle(\"some_faces\")\n", + "plt.show()\n", + "\n", + "print (people.images.shape)\n", + "print (len(people.target_names))\n", + "\n", + "#count how often each target appears\n", + "counts=np.bincount(people.target)\n", + "#prints counts next to target names:\n", + "for i, (count,name) in enumerate(zip(counts, people.target_names)):\n", + " print(\"{0:25} {1:3}\".format(name, count), end=' ')\n", + " if (i+i)%3==0:\n", + " print()\n", + " \n", + "mask=np.zeros(people.target.shape, dtype=np.bool)\n", + "for target in np.unique(people.target):\n", + " mask[np.where(people.target==target)[0][:50]]=1\n", + "X_people=people.data[mask]\n", + "y_people=people.target[mask]\n", + "#scale the grey-scale values between 0-1 instead of 0 and 255 for numerical stability\n", + "X_people=X_people/255\n", + "\n", + "#Use a kneighbor classifier\n", + "import mglearn\n", + "from sklearn.model_selection import train_test_split\n", + "from sklearn.neighbors import KNeighborsClassifier\n", + "from sklearn.decomposition import PCA\n", + "#split data into training and test sets\n", + "X_train, X_test, y_train, y_test=train_test_split(X_people, y_people, stratify=y_people, random_state=0)\n", + "#build a KNeighborsClassifier with one neighbor\n", + "knn=KNeighborsClassifier(n_neighbors=1)\n", + "knn.fit(X_train, y_train)\n", + "print (\"Knn score: \", knn.score(X_test, y_test))\n", + "\n", + "mglearn.plots.plot_pca_whitening()\n", + "pca=PCA(n_components=100, whiten=True).fit(X_train)\n", + "X_train_pca=pca.transform(X_train)\n", + "X_test_pca=pca.transform(X_test)\n", + "print(X_train_pca.shape)\n", + "\n", + "knn=KNeighborsClassifier(n_neighbors=1)\n", + "knn.fit(X_train_pca, y_train)\n", + "print (\"Knn score: \", knn.score(X_test_pca, y_test))\n", + "\n", + "pca.components_.shape\n", + "fig, axes= plt.subplots(3,5, figsize=(15,12),subplot_kw={'xticks': (), 'yticks': ()})\n", + "for i, (component, ax) in enumerate(zip(pca.components_, axes.ravel())):\n", + " ax.imshow(component.reshape(image_shape), cmap='viridis')\n", + " ax.set_title(\"%d. component\" % (i+i))" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [] + } + ], + "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": "3.7.0" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/doc/Programs/JupyterFiles/Examples/Intro to ML Examples/Faces.ipynb b/doc/Programs/JupyterFiles/Examples/Intro to ML Examples/Faces.ipynb index cd97d9c2b..3e2799162 100644 --- a/doc/Programs/JupyterFiles/Examples/Intro to ML Examples/Faces.ipynb +++ b/doc/Programs/JupyterFiles/Examples/Intro to ML Examples/Faces.ipynb @@ -2,34 +2,14 @@ "cells": [ { "cell_type": "code", - "execution_count": 24, + "execution_count": 2, "metadata": {}, "outputs": [ { "data": { - "image/png": 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CiMZ/gBshhIgHvgFi0H4MP5FSvnOq11XOD0aTiUVpB/jzWApGaeLSVu0Zn9gZ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\nQFV7y/Y2SkOgpwPHAD8G3rW9Rd5nzXa0d4XtC/J+N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\n", 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Qn4uwgK1cPiEq/tehy2IheeAWFyTgNSNAsur+aAsdrZ2UH2Z/IZO1WQU1AwZ9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0nXnuP5GP5Wuu4/v8GcIwTxezsY0yoWWoSvRX51x8vG29HsJGF72q7QIAmhrdVGXdy6a5lrG4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\nrmuE9gVl+K6QQKuc6AWP7/gxjKojQue2j5T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jOSCiitouLGPRR4968Xz43vYrn8m4dkSi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cyIiORY56wAF85Zq/IAG4gf0cdXLEDbj+cvGSss/cLtdubu+QbPIWTGfHa49K5NRBXrru+f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- "text/plain": [ - "" + "
" ] }, "metadata": {}, @@ -62,10 +42,30 @@ "Saddam Hussein 23 Serena Williams 52 Silvio Berlusconi 33 \n", "Tiger Woods 23 Tom Daschle 25 Tom Ridge 33 \n", "Tony Blair 144 Vicente Fox 32 Vladimir Putin 49 \n", - "Winona Ryder 24 Knn score: 0.232558139535\n", + "Winona Ryder 24 Knn score: 0.23255813953488372\n", "(1547, 100)\n", - "Knn score: 0.308139534884\n" + "Knn score: 0.3003875968992248\n" ] + }, + { + "data": { + "image/png": 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cDhWM1ROTEEFMgja2o6bayHfvLWHRT5vIyyqibedopt09kpFXuH7+XnvncN785692+0PC/Bl7ZcNJaBVFURSlvg05Sbx54GebfTlVRbywdybR3g/SOTDOTTVTTpfqpnTh1Ue+59t3l5CbWYiUksP70/nvoz8wr4FUF2Ov6s+9z1xBSJh/7b7u/eJ59Ys7CAhWSy0piqIoTTc7daXD/UZp4pcTq1u4NkpzUi1jaC1gHgbbdSsP7Epl7Z+7HR7/3XtLGX/NQDy9tB9f8r6TpOxLIyTcn8ShnfAw6Jl0wyAmTLuI44ez8QvwIiom5Ix/DkVRlPPRpv3H+W3tXnKLSukWH8W0kb1pFRro7mq1uKOlzmfjHy1RM/XPZRd0MPb7t2uZ8/lKTh7JJjQykIk3DeXaey9B76Fnx7pkp+cV5ZdyaN9J2nSM4qXHfmDburrEseGRgTzz5vV07d0GD4Oedp2jATh2OIvd247h5+/N4BGd8fbxPOOfT1EU5Vz30bx1fPp7XS7dTftT+XnVbj5+eArdEqLdWLOWF+kdQl5VscOyKG/7F/69hUmsy91AhbGSroGdGRY+GG+995mupnIK3BaMCSHigG+AKLQlRD6RUr7TUvf/7p0/+fatP2q387KKmPnmIk4eyeIfb92El7frYMnb15MPXppvE4gB5GQV8dz9M/n6z8fx8fWiurqGN56by4rFtXkg8fXz4vHnr2ToqK7N+6EURVHOI0cz8vhsof2iBiXllbzywzJmPnWDG2rlPpNjB7O/6LjDsitiB9tsf3XkW/7KWlG7vSlvC39mLOWf3Z4gyBB0JqupnAJ3jhmrAR6TUnYDBgH3CSG6tcSNS4vK+XnGModly+ZsJTUlk2Hje6L3cPzjie8YRWhkIKv+cNyNWVRQxopFuwCY+ckKm0AMoKy0klee/pkMJ2tiKoqiKLB0azLSyWqfe49mcjKnsGUr5GbjWw3g6rjh6BC1+zyEnrvaTWBAWJfafXsK99kEYhYZFZnMSrWfXKa4n9uCMSllupRym/n7YiAJaJG8D/t3HKOirMpp+Y51yYRHB3HXk5cjhLAp8/Hz4sEXp5KbVURNjfP8Lhkn8jEaTSz81XGS1+pqI7NmrmXn9mPk55We2gdRFEU5j1VU17gsr6xyXX4+uq/jZL4b/DQPdZrCo52v5qch/+SGhEtsjlmXs8Hp+RtyN2OSpjNdTaWJzooxY0KIBLR8PS2yyK6Pn5fLcm9frYty8q3D6NKnDX/M2kROZiHtu8Yw4fpBRMaEUFJUjqeXB1WVjh8Gce0iKC+rpLiw3K5MAtJDx/x5O5g/bwceHjrGXNaL+x8bj6fnWfFHoiiK4hZFpRWUlFUSFRbA4G7xfLFok8PjWoUFkhAd2sK1OztE+4RyZWvnK8NUmJwnHq8yVWGSJnRCJVM4m7j9N78Qwh/4BXhYSlnkoLzZF9rt0jeeiJgQstPsuwn1HjqW/radbesOMWZqf/oN7UTn3vb39Q/0Yczkfvw+y/5BER4ZyPCxPfAw6AmLCCA3u27ApRaI6UFX1+JWU2Ni0fwdCCF45MmJzfIZFUVRziVZecW8MXMZa7YfxmiSRIb6c9sVAxnWoy1r9hyxOVYIuG/yEHQ64eRqF7auAZ3ZnOe4V6ajf3s8dG7/1a/U49bQWAhhQAvEvpNSOuzIPhML7ep0OvyCHOf7MholuzYeZsWCHTxz++d88foip9e554kJjL68j80DIb5DJC99cjueXgZ0Oh1XXjfQ9iQhbAIxa0sW7aIgX3VZKopyYamsquG+V39m5dZDGE3aILGsvBJe+/ovhnWJ557LBxEdEoCnh54+7WN4697JTBioJkA5c3HEEKK9o+z269AxpfVkN9RIaYg7Z1MK4HMgSUr5ZkveO2XvSY4eyAChQxsdKoG6NSmRUvs/MPvTFYy6og9tu7Syu46nl4EnXpnGrQ9cyuH96QSH+dO1XivatFuGUlRQxtyfNlJdZQQXL3LV1UZSj+USHOLXjJ9WURTl7PbXpoMcz3A8oenb37cw5807uOeKwQ7LFXveem+e6foEs1J/YUPuZqplNR392zOl9WR6BLXIPDmlidzZVjkUuBnYLYTYYd73tJRy4Zm+8YnDWdo3luCrPqtgDGDl7zsdBmMWUTEhTpO6CiG486GxTLt1KPv3nGTDumQWzN1uH5SZZwyFRwY05aMoiqKc83anpDkty8wrJie/lKgw9WxsimDPIO5uP507292GURox6AzurpLigtuCMSnlGly2E505rdqEuT6gXoBWWVl92vcMCvZj4LBOzP11i+NPLaBjp2haqUz9iqJcYIL8fZyWeeh1+PmqJNmnSid0arD+OeCC/BPq3LsNnfu4mAxQLxjrN7Rjs9y3rLSSrZuOOC1v2z6yWe6jKIpyLpkwrBs6R70UwMj+HfD3cT0DXlHOdRdkMAbwzAe30LFHa/sCve2PpPeg9iQO79ws91yz8gDSWQZDQKdXM4MUpbkJIex+kzvap7hPm+gQHr9ltF1A1jY2jEdvGuWmWilKy7lg57dGtArm3XkPs3fLEU4eySYmPpyKimp++ng5+3ceJzDElzFXJXLdvaPtEr+eCpNJ8s0Xq1weM2BQBwCys4vw9DQQFOS46f7QkSxm/riebTuP4+1l4NKRXbnx2kH4+arfL4riwHqgXyP2KW409ZLeDOwRz6K1+ygsqaBnxxhGX9QRg4fe3VVTlDPugg3GLLontqV7Ytva7ca2gqWdyOP3X7Zw4lguMXGhTJzSn9bx4U6PP3wok8wM50t3REYHYURy2+2fcvx4LkJAYmJb7r9vDHFxdYkND6Zk8OATP1BeoY1jK6Sc72ZvZPP2o7z/+g14ealBmooCIISIRlvVw0cI0Ze60ZqBgOPcNopbtY4K5q4pQ9xdDUVpcRd8MHYqNq45yAtP/KSlqjCbP2sTT79yNUNGOs59U9v8bkmdUU+P3nG88NK82iIpYfPmIzzy6Hd8/tkdBJnzon0+c01tIGbtYEomS1ckMXFcr9P8dIpy3hgH3Aa0BqzT5xQDT7ujQoqiKI5csGPGTlV1dQ1v/uc3m0BM22/kzRfmUeVk5mXb9pHEtDbPlBTC7uvI8VyHC+Lm5ZXy+8KdgNbVuWmr1QQAnflLL0AHf/y1x/4CinKBklJ+LaUcBdwmpRxl9TXJWZJpRVEUd1DBWBPt2HSEAicLexcXlrNlfYrDMiEE9z00FoPBfvzDlGsGkHIk2+k99+49ab6GNs0b0P7krPOkCcGeA2kcSMkAYNf+kzz56hwm3/ERtz76NbMWbMVoVIvDKhekBUKIG4QQTwshnrV8ubtSiqIoFqqbshFS9qeza+sRfP28EDrX8Wt5WZXTsgGDO/DujNv4ddYmkg9mEBYewGWX92HE6K78sWwvxcUVDs8LDNQG8gshuHhIJ/5aleQwWa3JJPlu9kbGjenB06/9Vht85RaU8u6Xy9m9/yQvPD6psR9bUc4XvwGFwFbA+QrKiqIobqKCMReqqmp49emfWbs8qXafp6cHOr3AZLTvU9TpBb36J7i8ZodO0TzxT/uAaOzYHvzyyxaH54wb26P2+7tuG86KdQcwmiTWNbCEZlt2HiMlLddhK9jy9QfZvf8kPbvEuqwjQEVVNSVllYQE+qJvIABVlLNcaynleHdXQlEUxRkVjLnw7YzlNoEYaAGas1QXE6ckEhEVdEr3uv22i0nal8a+JNtlQa6/fhB9+sTXbreKCiIg0Jv8wnJth7kqliU2K6trOJ6W5/Q+67YedhmMlZRX8u4Pq/hjXRKVVTVEhPhxw/hEbris/yl9LkU5C6wTQvSUUu52d0UURVEcUcGYE0ajiYVztjosk1LSKzGBzPRCMtMKiIwOYtK1A5h6o+OFbMvLq1i/LpnS0kp69mpDQoJ9CgxfXy/++99rmfH5CjZvPYKnQc/Ey3pz9VUX2R1bWVWDrB8PCi0uk1K6XGRKr3OdM+2xN+ey48DJ2u3s/FLe+WElldU13D5poMtzFeUsNQy4TQhxBK2b0vxPRaqpx4qinBVUMOZEZUU1xZbWJwdCQv15fcbt1NQY8XCRlHDN6gO89t/fKS2tG6oyclRXnnzqCpvB/MXFFTz8xPccshrI/+Gny9mblMZzT09GZw6i8gpKKbPM2KwXV0kgPMwf/2BfDh7OdFifEYM7Oa3rln3HbQIxa98t3ML14/rhrfKYKeeey9xdAUVRFFfUYCAnfHw9iYoJdlqe0EFbR9JVIJaeXsCLL/xmE4gBrFiexNdfrbbZ9833a20CMYuVaw6wfJXWVZqbX8LrHyyuS4Fh7prEatPX34tO7SIxGHTUH9U29bK+dExwvv7lzoNpTsuKyyo5dCLHabminK2klMeAOGC0+fsy1LNPUZSziGoZc0IIwdSbhvDhawvtyvwDvBk/ueGVVBYt3El1tdFh2YIF27l9+nD05lQVf61IcngcwFsfLGHJyiQOHMkkN7/UpkFMmv8jBUgPSE7NITlVC5oC/L2ICA0gMsyfiaN7MnqI69UFAv1cL6cU6O/tslxRzkZCiOeARKAz8CVgAL4FhrqzXoqiKBYqGHNh8rUDKS4s5+eZa2tTVsQlhPP481cRGh7Q4PlpaflOy4qLKigrqyQgQEtb4Sirfu2xJRWs23LY4bu8AEyA1GOX7qK4tJIOCZH8759XN1hXgEsHdubdH1dR5SCA7NmhFXFRIbXbaTmFrN93DE+DnhG92hPopwI15ax1FdAX2AYgpUwTQjT8D7gBQog44BsgCu296BMp5Tune13l/GA0mVicepA/jydjlCYubd2BCQldMOjUWpuKPRWMNeCmu0dy1Q2DSN6Xhl+ANx27xjR4zsoVScz6aSMHDqQ7PSYs3B8/qwCmX+82rN3gOGGsltzVxQ0FDvOOAWzfm0pqej5xrUIcllsLCfTlmTvG8p9P/7RJjREe7Mczd4wFtAkCr/24gtkrd2Iy95d6Gzx4dNpwrh7Ru8F7KIobVEkppRBCAggh/JrpujXAY1LKbebgbqsQYomUcl8zXV85R1WbjNy9/FeWnzxUu2/+0SS+O7idry+9Fh8PNfZWsaWCsUbw8/emz4B2jTp2xsfLmPXTxgaPmzLlItIzCvjqu7WsXnuQaqMJdAJpkvbdkA2NbnE9QZK9B9MaFYwBjB/SlV4dY5i/ag85BaV0io/ksqFd8ffRujBnr9zFTyt22JxTUV3DKz8so2PrCHq3bzhYVZQWNksIMQMIFkLcBUwHPj3di0op04F08/fFQogktIXJVTB2gZuVsssmELPYlHWCL5I2c19PtRi6YksFY83oxIk8Zs9qOBAbf1kvRozqwt8fnqnlC7MOpnRCS08h0fbrzUseWbYdcLCkpU3Zfz5eRNLRLB65dVSjPkdMRBD3THU8nGb2yp2O7yPhl1W7VDCmnHWklG8IIcYARWjjxp6VUi5pznsIIRLQukIbfgAozWpfaiZzNuwht7iM7nFRXDW4B6H+vm6t02+H9zotm3t4nwrGFDsqGGtGa9ccdLjYd32XXtqdb35Ybx+IWQhhXgTcQe4KB7tctpwJkAhm/bGNbh2iGTe0q90hJpNkc9Jx8orK6JoQRUKrUKeXS8stOqUyRXEnc/DVrAGYhRDCH/gFeFhKWVSv7G7gboA2bdqcidtf0Gau2MYbc1fWbv+1K4WZK7fx6b1X0zHGPp9jQ3LLysgoKiEuOJBA71MfB1tS7XxZvFIXZcqFSwVjzagxgZjeQ8fBw1ksWtxAMvB61xLm68e1CiEyMoCS0kqSjmQiLQuGO7mEtBorOm/ZbrtgLOloJk9+uIC07MLafSP7deCFuy9zmFOsbXQo+445zmHWrlWY68+kKG4ghJgC/BeIxDzCEi3pa2AzXNuAFoh9J6X8tX65lPIT4BOAxMTERjwhlMY6mVvIm7+tstufX1LOi7OX8vVD1zX6WsUVlfzrz7/480AyNSYTXh56pvTszjOXjMDLo+m/JgdFt2FffpbTMkWpT+XaaUaDh3Ro8JiBgzsw48uVDR5X/6ltaRT77O1beOuFa3nj39PQe+ldB2Ie2JRn55XYHFNeWc1Db/5qE4gBrNiWwhvfLXd43ZsudZzSw+Ch55qRagC/clZ6DZgkpQySUgZKKQP5L7aqAAAgAElEQVSaKRATwOdAkpTyzdOupdIkf2w7UDuJqL4dR9I5mWv7XDueV8Cbi9fw2KyFfLB8A1nFdc/D++bM5/ekA9SYtIlLlTVGfti+i3/9sfSU6nZH14sI8fKx2+/n4cnfeww6pWsq5zcVjDWj+PhwJjnJP6bTCcaO70l0bDAmk/kB0sAMSUtOV8vjZkBiW3x8PAEICvDhkiFdbE6RgNSByQOkAbtuzo7xETbbizfuJ7/Y8SoDi9YnUVRaYbd//IAuPDz1Yvy8PWv3RQT78drdE+kQ2/RuAUVpAZlSSueJ/E7dUOBmYLQQYof5a8IZuI/iQFmV83RA9csX703m8ne/5tPVm1m4+wDvL1vPxHe+Zuuxk+xKz2D9sVSH15i3dz/pRcVNrlusfxCzx93I2LiO6IVAJwQjY9vx07gb6BisnpOKPdVN2cwefGgsnbu0YuGCHeTklBAbG8Kgwe0ZOaobYWH+vPT6AkBL0ooOLUlYfZYyUdfVKHSCtNwifl64jSvH9cFDr+OxOy8hr6CUzbuOadf0wH6cmQCkFgxeP9F2se/jmQVOP0dVjZHMvGKH+cNuGZvI1OG92JGShpdBT58OsXjoTy+ur6iuIauohPAAP3w91bRvpVltEUL8BMxFW5sSAEfdik0hpVxDg3OZlTNlQMc4PluyyWGZl0FPqJ82iL+0sopn5izWZqxbKams4qlf/uTWkc4TeBulZF9mFq0Cm56WrkNwOJ+Mmkq1yYiU4KlX+cUU51Qw1syEEIwf34vx4x2vQdypQxSLl+0zB1sCdNIuIHvs/jHkF5Yzf+kuMvK1pnQJHDuZx9ufL2Pr7uO8/MRk/Hy9ePvZaSSlZLBm6yG++G2Dk0pBv55xzFuzl68Xbia/pBwkBAf6OJ2l6emhJyrU+QPIz9uToT0SGvx5NKTaaOTtRWv5eeNuSiur8PE0cGViNx6bOBxvg/rrqTSLQLQlkMZa7ZPAaQVjinsN7NSGAR3j2JRs36pVUWPk0a/n8/UD17J8/2FKKh0Pmk/NL6SkzPWA+gj/00tLp5K8Ko2hftu1sPGX9uSrH9ZTUm5+QbcEZFbWbz3C8CGdCA7zrw3GrK3elMKWXce5qHc8AF07RFNQWg6/Ob/vxj3HISnVLvDSC4FRSrv9lw3u2iJZ9f/9y1/M3VI3Dby8qpof1u0kt7iMN2++/IzfXzn/SSlvd3cdlDPjP9ePZcKLX2A052fUsvpqX9uPpLHl0AmKKytdXqNDWBiR/n5klZTalXWJjKBXq+gzUXVFsaGCsRYWEODNvXeO5LX3/qzbWW8Q/prNh1i91fHyR3XHpNQGYwBtY8PQCeF0QKul27M+k5T4+XhSWlH3dqjTC37buI/s0jL+NmkwXeOjGvPRmiy9oJj52xznx1y8O5lDmbm0j1IzNJXTI4RoDbxH3VqUq4GHpJQn3FerC1PykSxm/76VlGM5RIb5M3lMbwb3b1xCbUeO5xZgRNYO6aj/jNuXmsmQbglOz/fy0NM/Ppb3r7qcu2f/RkFF3TjZVoEBvDXpslOuG2iZ+IurKgny9Eavcz2Uo7i6gnmpOzlYlEmUTyBXxvUhxjfY5TkmaWJtzk5WZm+nylRN3+DOjI0ehJ/Hqb9IVxrL2F+0mtKaAqJ9OtLWry/CyUQxpfmoYMwNxo7qzqczV5NfUGZX5uiB4kj9fxvR4YFcMqgTS9YfsL+mi+WSALwMHjx35zhenLmU/NJyjEKryeo9R9iQdIzPHr+Gnu1aAVq3YnFZJYG+3qc9TmxPagZGk/PZ/rtTM1QwpjSHL4HvgWnm7ZvM+8a4rUYXoDWbD/HPN36jpkYbl3HwcCZrNh/ijmuHcPs1p5YENSzAnNzVyeMt1N+XDpFhTOjZmYW77Z+NNw/uS7CvN319Y1hx7x0sTDpIamEh7cNCGd+54ymltQCoMNbwxvZV/JS8i+LqSqJ8/Lm9ayL3dB/gMLBJLsrkjnXfkFtZ1zr3ycHVvN5/KmNiujm8h0maeCXpa9bk1K2IsjlvHwvT1/J67wcJ9mz6OLfDJVuZe+IVKk11v5uivTtwbZsX8PUIavL1lMZTsyndwGDQ89A9l6KvP9geavOGmeMhp4YP6Gi376m7xjF+WFf05iBJoAVipgaGLNTUGDmckUd+WYU2AcDSiiag2mTigffnUFRazru/reGSp2Yw+qkZjHnmE2Ys2oDRPBW8rLKKFXsOsXz3IZtWNleCfe2nfjelXFEaKUJK+aWUssb89RUQ0dBJSvMxmSRvfba0NhCz9tXs9WTmnFrC6A7R4fRo47gbMdDHi0t6aemGXp06jntGDCDE/ExpFRTAE+OH8+iYYbXH+3l6Mq13Dx4dPpTJ3bueciAG8MCqeXy2bzPF1VoXaWZ5Ca9uW8Hr2+3zogE8tW2OTSAGWqvaU9vmUFxtP6sdYE3OTptAzOJEeRbfHlvU5DqXG4uZc+Jlm0AMIKMihUXp7zX5ekrTqJYxNxk1rDOtooL4Zf5W9h5I40R6gXmGpVWAZgnG6sVslwztTL+e9okDfbwNPHfvBO69fjip6fn4eBu46+WfqKo2uqxLjZR8u2Srw3sBFJVVMv3t2aSk59buyy8p56Pf15NXXEb72DDenreGUvMgWV8vAw9ePozrL+7j8r7928YSFxZEar18QAARAX4M7Rzv4CxFabJcIcRNwA/m7euBXBfHK80sKSWDzBzHKSKMJsnqTSlcPcH5rEZXXrlxPPd8/Ctp+XUBna+XgTduvRwf88xsg17Pw5cO5cHRQyirqsLPy/OMdb3tyc1kSWqyw7Iv92/lnh4DCfKs60ZMLsokqTDD4fHlxmoWp+1jarz9z2Zl1jandViZvZ37O17TpHrvK1xBlclxqqPk4g2U1uTj59G4NY6VplPBmBt16RjNM49OJDO7iGv+9imyXpedJev+xQPak5peQGCAN5eN7M7E0T1dXjcixJ+IEH8ApozqxY+Lt2sFTpZTKqmqghrhtJ1Ugk0gZu3nNbuoEbYTAMoqq3n1l+W0CQ9maNcEp/XU6QSv3TCBv30+h8Kyurc/f29PXr9xAgY1FVxpHtPRxoy9Zd5eC6hB/S3IZHKUw6eO0ei63JX4iBB+e/JWluxMJjk9h+jgACb270Kgr/24KZ1O4O/tdcr3aozNWY5zlgGU11SzOyeDYTEJtfsKqhwHQBaFTsorTc57ICqMrictOFJc7fz9RGKiRAVjZ5QKxs4CURGBXDKsC0tW2eel7NcjjleevKpR16msquHQyRz8fbxoE639o3no+hH4+3jxw5JtFFdU2XR/SmHpFm3gwi7KjSbpdHLAF39tdhmMAfSMi+aP/5vO/G1JHM3OJy4siEn9uxHk4EGqKKdCSnkMmOTuelzIurSPJjTYlzwH42SF4LQG8YM27vXyRPt1d90h0NP1syvQ0zYY7BIUjY/eQLnRcRLbvmFxjveHdGZr/n6HZf1Cujjc70qkd1unZZ46H0I8WzX5mkrjqWDsLPF/947F09ODxSv2UV1jRK8TXDywI0/cO9bh8TU1RiqravDz1f5hf/fHVj6bu57Scu1tKcjfm3/cPJqxg7pw/fh+zFm3m+LqKi2lWf0B/VKav0SzprDcfvgkhaUVBDWQIiPAx4sbhrru0lSUUyWEaAe8AwxCexVZDzwipTzs1opdQAwGPX+/aTgvf/CH3Rq+V47rQ5uYUPdU7AwYG9cRPw9PSmvsW646BIXRK9w2qAkweHNzu0F8krza7vhB4W3pG+p4Lcvx0YNZmLaWtIocm/1eOgM3xo9vcr07Bw4lJDuG/Ko0u7J+IRPx1KkxvGeSkI1Z3foskZiYKLds2eLuapxRBUVlpGUUEhkeQHiov115YXE57/+4isXrD1BZVUP7uHC6d2zF3JWOFx7/5x1j2ZZyggXrtBQSNrM1rQIyKaW2LbALyExgE7ZLqOvSlDhtGZPAI5Mu5vbRia4/tKK4IITYKqU85b9EQogNwAfUjRm7DnhASjmwOerXWBfC86shG7cf4cf5Wzh0LJuIsAAmj+nNFZf2bNL4LSkl65OO8ee2g1RW1zC4azzj+3fGq5mSREspWXP0GH8eTMYoJaPatSUy0J+Pt2xi/YlU/AwGJnXpyr2JAwn0ctzluejYAR5aPZ8qU9143WBPb7659Bq7YMxyz8+S1zDz8AZyK0vx0Ru4Iq4Xj3cfi5+Hdo8ak5G/MneyOlvLyzgsohv9Qtry4/ElrMzeRpWpmn4hnbm2zVhKqgs5WZ5JpHcYA0J7Y9A1blWTwqpMFqS9xfGyXQB4CC/6hUxgVNR0dEINGzkVjX1+qWDsHFJdY2T6s9+TfDy7dp8JnAZDAB6eOqqk/XgMLaCyPcmyRJPlb4S0HkMmbI9pTAuaFDC8e1vev/PKhg9WFCeaIRjbJaXsVW/fTilli65sf6E/v5qDlJJ/zfyTBZtsh3R0jYvkkwevJsDn9MaDmaTkkQUL+X2/bRoMoQOjh+3Y2B4Rkcyedr3TWZepJYXMSt7JydJiOgeHM61DT0K9fV3ev8ZkJLeylCBPH7z1dQFUpbGax3d8zvZ828bcPsHt+F/fO/AyH5tRkc1L+94jo6Lud0SIIYinut5HW3/H3Z2O5FelU1qTT7hXG7z19o0CSuM19vmluinPIcs3JdsEYrUtVE4CIwlUmUzOAydLa5gVk6VlzElLl6tWMGF9nPkagaf5cFSUZrBICPEk8CPaX89rgYVCiFAAKWWeOyun2MsvKuOPDfvJKyqjW9sohvdtj16nY9nOFLtADCApNYs3flnB41NHnlZANnfvPrtADECaACM2vzH3ZGcx7+B+pnXr4fBacf5BPNZ3eKPvvSnnCItO7qG8pppBEe24LLZ7bZD1y4l1doEYwI6Cw/xyYh03xI8A4K0Dn9kEYgD51YW8tv9j3u//H/SNbN0K8Wylxoi1MBWMnUO273eQMNxVC5WToMpSVL9NVLoIxBpzP5vrmY+7PNFxwkJFaUGWOf731Nt/Hdpf29MbPa40q2Vbknn2k4VUWqXkaR8bxvv/uJrfNzsesA4wd8M+Fu46wMT+XXlw4jCOZufhZfCgW2xko7tB5+61D/QshMn+mbni6BGnwVhT/GfnAn46WtdqOv/ELr45tJ4vh95GkKcPSzPs84lZLM3YwQ3xIzhUcozDpcft640krzqH304s4srWE9AJlV70bKSCsXOIn6+nzbZsxsH2NuPAToFA6zKVOsD88uXv7Ulmkf3amorSkqSUzqeJKTY2rz7A7M9Xc2h/OqERAYyfmsiVNw+pTSTdFLl5JaxZn0yN0cTAxHa0jmk4LUJeUZldIAZw6GQu//1mKaU4nnFoUVlt5JdNe5i7fR815nQa8eHBPDf1Uga0b7ibrsjVOpYORvR4NUP6ndWZyTaBmMWBokze37+cZ3pNoMLoPI1FuTmNRV5VQb0SiUFnRC+0is9Jm8vqnNU80PHvtPNPOO16K81LhcjnkHFDbKduNxiLSZwOapX1vpf6xlzQ5a20a1iuI6Cksop/zV7My3OXn/qFFeU0CSH0QohJQogHhRCPWr7cXa+zzdJ523n27zPZtfkIpcUVpB7O5tPXF/HGUz83+Vo//LyRa277mDc/WMK7H//FTXd9ytsfLqGhMcp/rE+yC8QsVu84TLc4F4t2C3O6Hj21gRjAsZwC7vvyN1Jz6wcr9hJbxzovdPDbckLHTg1esyHzT+xyXpa6E4D+oR2cHpMYqq3G0sY3FmH1EPcQdYGYRU5VLm8ceOeU8pApZ5ZbgzEhxHghxAEhRIp5TIfiQsc2Edw91WoNN4nTJZP8fDx5+9Er6dkhxr47EkuOMdG4rkkLV12UlrFkDur0/bodHG/Eg1BRzpD5wG1AGBBg9aWYGWuMfPnWnw6DpeW/7yR538lGX2vL9qN8/MVKm6WPpIQ5C7Yz/4+dLs/NLSx1WmY0SUZ0b0tksP2Acon2DJJOGqrKq6r5ab3zoMfitv59CXKSFLb+tSd06MTotu0bvGZDSpwsdwRQWlOFlJLr40cQaLAf/B9o8OW6Ntq4tCjvcAaF9bXU1i4QsyiuKWZD7qbTrrfSvNzWTSmE0KNNNx8DnAA2CyHmSSn3uatO54LpVw1iUK8EFq7ZS1FJBSYBSzcdxCRl7cD5AD8vxg3pyjdLtrI9JU17Y7RaWsmy/qUNE7Xdi84y9TsL3U3mGZa1A/gdLOO0dHcK00fWTSjJKCxm4e4DlFRWMaBtawa1c5xLR1GaQev6sykVW4cPZpCb5Xi5IoAtqw/SsZuLViMr8xY6H980b+EOJl3mPKdgl4Qop2WBfl50iY/ky4ev4Z15a/hrRwpGk6k2ebVsoGlhd6r9kkPZJaXsOJGOv5cnA+Jb0zooiO+vu4ZXV6xizdFjSCAxNpa7BiSyNzeLNcePUW0yMjSuDfcPGIyuGZZUSgxLYGWm4+WT+ofHI4QgxieUDxP/zmeHFrMmex8SybDwbtzZfhyxvmG1x9/b4RbSKzI5Vppq94i3llmRddr1VpqXO8eMDQBSLIkXhRA/ApMBFYw1oFv7aLq1r2uuv25cP56asYD0PO1hWlRZyazl5geiTtTmD5ONaAGrHdhvlaUfF+ksJNQFcfULrI43WaXX+GHTTl76fbmWvR/4aMVGBraN48ObJuPr2bh8OIrSBIuEEGOllIvdXZGzlaGBHF0eTcjhlZXtPKhzVQYwql8HElqFcjTdfoLrdWP64e1pIDY8iNemT6SquoYpr8/keHbjWt2rjXXdnyYpefnPFfy4dRfV5i7NmKAAXps8noviW/PltKmUVFVhMpkI9NaSVmeUl3AgP4eiykp2ZGXw4749PD98FJd3anq2e2tT4/vx/ZFNpJfbrtGrF4K/dxpRu53gF8WLvW6ubb10NCnBW+9FpakUnTA5mixfq5WP86BXcQ93dlPGAtaLeJ0w71Oa6Lul22oDMYvaf4ONzCMXEeRHj/hoEqJC8PU2aG+aHmjhupPxZJaugfqE9QFmI7tqE9YOZuTwwoJltYGYxcYjqby9dG2j6qooTbQBmCOEKBdCFAkhioUQRQ2edQFJ6BhFXLsIh2U6nWDYmO6NvlbbhPBTKgPw8NDzwT+mMrRX29pAIsDXi7smD+LOSYNsjvU0ePDGrRMJ9XeduwsACX3iY2o3Z6zZxMzNO2oDMYC0wmKmf/8Lb6xYw5ojxzDodLWB2PKjh/nniqU2A/xzy8t4ePFCtmekN3x/F4I8ffhm2O2Mj+mOh3mmY6+QWD4edBMDI+znngghXM4Oza8qRAjhbAQLQYZABoZedFp1VprfWT+bUghxN3A3QJs2qiurvtzCUpZtPeiwzC59hYPuR4uHp1zMhIHaBIGR//yYUifrpFmuIXQCE7JR4fzEPl3oEK09hH/etsdpfDh3+z7+b/xw9Do1r0RpVm8Cg4Hd8lzKct3C7v/XJJ699xsqy23/7d/w91G0imv8ckVTJ/Vn8bK92pix2jGp2oOnuLSStIwCYqKDnZ4fGRLA249cRXZ+CQUl5cRFBuPt5bjFvEtsJAv/OZ2F2/ZzOCOXX7bsobSq2vY5Z24hutG85JrRZOLbzfZdqVJABSZmbNjMjA2bCfDy5Nkxo7iyRzc+37HV4f2NUvL1zm30jZ7YqJ+NMzG+wfzvomlUGqupNhnxN5z62rwJfnGklBzBZB6fYr36na/el390fhgvvcr/eLZx52+9k4D1XOPW5n02pJSfSCkTpZSJERGO39wuZOm5RXatTM4IMOefqNun1+m4ffxFtYHYrLU7KSxzPqBUCNB5Cmp00ukYDcvYNW+Dnk6xEcRHBZNtTnGRU+x8gG5xRSUV1TWN+iyK0gSpwB4ViLnWe0A73p99H5NuGES3Pm0YNrYHL31yGzfde0mTrtOhXST/eXoy/gFedcMkzA4dzeaRZ2ZRUeE6RQVARIg/HeMinAZiFj6eHnRrHcmY3h15ZspobRyXZSKR+U98+shEYkODACiurCKn1HbBcsssTOsgrriyiv/7fTGbjp8gJc95XuDkvNwGP0tjeekNpxWIAVwRM878ncCEDiMCoxTo8eQ/3f9FvJ9q1DgbubNlbDPQUQjRFi0Iuw64wY31OSf5NPCgsiPMA+6Bfu1jePXOiUSGaLOTVu87wos/L7ObDWmdWd8kgBpZNwYNu5dQ7XQPKBNG9mdls39ZNl+t3cbHt15Jt5hIFu1x3JIXHxaMn5enwzJFOQ2HgRVCiEVAbT+TlPJN91Xp7BTXNoJ7n7nitK8z8KL2eHl7UlJmnx8rI6uQpSuTuHzc6c+p2HjwOC/O+ovjOdq4sRA/H24Z0pfUwiKSM3KIDgrgmsE9Gd+7c+05/l6eBPt4U1Be99IpnYyJNUnJ11u2ExsQQGap45yJrQODTvtz2N/XhAT0p5CgdUBoP+5sezM/n/iNguoiQNDaJ4bpbW8kykc1aJyt3BaMSSlrhBD3A3+ijUr6Qkq51131OVf98Nd2p72PtQPxzS+KJg9s2kK3HUvj6W8W8dlD0wB4a8FqraBedFW7VqXVBAApAL22TIiUVlktLPeoV6GSyiqe+vlPfvzbdXy5dit5peV29b3rYjWOQTkjjpi/PM1fyhmWl1dCbp7zhM8HDmVwOacXjB3LzufBT3+zaU3PLy1n5opt/O/2y7m0d0eH53nodFzXrxcfr7VK7+BiYlNKTi5/Gz6AbU7Ght3Ys/mWOD1cnM17+5eyMvMAEhgR1Zn7u1xCh4DIJl3nkqiLGRExmGNlJ/DUeRLnG9PwSYpbuXXMmJRyIbDQnXU41y3eorUy1c8mYdkOCfBlcI8ECiorWL3viN35W5JP8OqsZdw+dgAp6bbN7fXTWdisTWmJviyLi0vb7Pv1SeBYfiF3fTuX3nHRZBSWkJSuraEW5ufLPSMGMLX/6S8roij1SSn/DSCE8Ddvq2UhzjB/P28MHnqqaxwncA0N9mv0tXKLSvli4Sb+2qZl8x/aoy13ThzIT2t2Oh3W8NWyLU6DMYD7RwwivaiYebuTbF5aHYkJCuSqLt1Izsvlk22bMZp7uz11eh4bPJThbRIa/VlcOVGWz61rP6Owuu5FdVlGEltyj/LDxfcQ59f4cXsAHjoP2rs5035VTQZFFWvR6bwJ8h6JXtf4P/cLzVk/gF9xrbKqpm5MhiXXGIAQDOgSx3sPT8FDr2Po4x84vcbcDXvtHo7Wi33XbpsDsdqy+v931aJubknbk5ZZu+uWIX25snc32keEapMBFOUMEEL0AGYCoebtHOAW1RJ/5vj6ejLq4s4sXm6fqUinE4y/pHEvXoWlFdzx2ixSswpqnzvzN+xj+fYU4ts6D04OpmU7LQMw6PW8duV47r14IK8sXcmyFPsXVYsb+2otX08MuZibe/Vh+dEj6IXgkrbtCfdtxEzORvrm0FqbQMyiqLqcrw6t5V+9Tr/7uKVIKUkteJHM4q/QVlgHnfAnPvQFwv2ucmvdzlYqGDvHDewWz9rd5gdJvenOw/u0x8O8plxZhfO1zSqqakjP11Jj1AVz2L4p1g/EHCWFbWL+w282bKfCVMOS/SnklZXTOjiQ2wf156aL6pJC/nkgmZlbd3A0L5/4kBBuSezDuM7O33gVxYFPgEellMsBhBAjgU+BIa5OUk7PA3eNZtvO4+RYdVdKtEz6m7YfYfJ458lfLWav2MnxrAK7Z05JVRUpJ50PnI8KatwCCwlhIXwwbRJ3/jSHtUfNi2xb3Wf6Rf24tFNdlv1W/gHc0OPM5A/emHPYadkmF2Vno6ySmWQWf26zzyRLOJL7OD6Gjvh5ql6Q+lQwdo6754pBbDmQqrWQWUmIDmXS0O5sP3SS1XuP4ONloKzS8Qwmg4eeuIgg+4CqfuJXCwdBl8Cc0qyJAdlPW3bXtqidKCjihT+Wk19WzgMjBvPJhs28vmJN7bGZJaVsSj3B4yOHcc8gNb5MaTQ/SyAGIKVcIYRQ/SVnWE2NifyiMvs2bwEzvl7FuFHdG5wpuW7Pkdpz6qsorQYnGRquHtr4gKmsqortJ63Gg1lVeH9WTqOvU5+UklVpR1icmoxEMqZ1R0bEtnOatd9X73w4o4+LsrNRVvHXTkqMZJd8h1/oKy1an3OBCsbOcd3bRvPZP6bx6YKNbNh7DB8vA2Mv6swdEwfw3Ld/snRHSoPXuHxAVxZtP3Da61M6G3fhLDmsM1+s38pVvbvx3poNDsvfX7OBa3v3JNjn9KaAKxeMw0KIf6F1VQLchDbD8rQIIb4ALgeypJTqVb+ejduOUGM0OXwmlJRWsmNPKoP6t3N5DQ+93ukzR0ho5e9PdlmZzcLgOp1gwdYkQvx9mHRRN5tzaoxGZqzaxOZjJ4kK9OfBUUNYeeQIZdWOX1TXHz3OycIiYoMCXX/YeowmE/etmssfx+tmjn9/cAejY9szY9QUDDr7wbUTYnuxtzDN4fUmtO7ZpPu7W2XNcedl1c7LLmROgzEhRBzwOlpW/EXA61LKanPZXCnllS1TRaUh3RKieev+yTb7Zv61tVGBWO92rRiX2JmfP97j9BghG9nqJQETCF3dy2W4vy9ZZWXOz3Wwv6y6mv8tX0NFjePBuRU1Naw7eowJXTs7LFeUeqYD/wZ+Rfurudq873R9BbwPfNMM17rgiEa8/V3SryNbkk84LfeQOhY9dwfPfPcHG5K1BV2MSJLSs3n6xz/ZkHKcl68fD8CBjGyu+fRHKq2eK7/tTOKits4XfpFAbmlZk4OxH1N22gRiFstOpjB9xU+UGSvxNXgyqU13rkrohYdOx7UJA1iZdYBNObbj1y4KS+D6hIFNur+7eRvaUV59wGmZYs9Vy9gXwC9oS4ncAawUQlwhpcwF4luicsqpm7vB+djk+MhgRvfuwOjeHeiZ0IpfNzgPxGqZqBug7+xN1fz/v40cSLfYKPy8DFzUtjU3fj6LHSfsp4W7WitzQdJBlxMCdKeQf0e5MEkp84EHz8B1VwkhEpr7ume7E/HGfAMAACAASURBVKl5bNt0GE8vD4Zc3InAIMeD2AcntsPToKeq2n5GZWCANz27Npxu4cqLe/Dpwg3kldgPbAfonqCt0bvl8Mna3IfWvYDztiax41gaiZ3bMGfXXpvljyw2HzkJBuxmgkskBk8dz/y1BB+DgQmdOnF9r154eTTcofTrIUfPVInO08S67Lpga13mUZamJfPhkKl46j34aOAtLEnfy/KM/UgpGRXdlbEx3R22pDUHKSWF1bkYdF74eTRunF1jRAVM52je/9ntFxiI9L+52e5zPnH1typCSvmx+fsHhBA3AauEEJOwSwuqnG3yisuclgX4ePPQ5Itrt+PCnS9NAlZ5xizpK+ovNWK1GeTrzYpDh/lswxZ0OoFBrycqwK/2OjapN1x1aVqlzqidI2re52fwZFhb9T6gNI4QYgkwTUpZYN4OAX6UUo5zfaZizWSSvPPaQhbN3167pNl7nh78/eExXH5lf7vjg4N8uePGYXz01Uqb/VIHBn9PxtzyHkEBPkwc3YM7rhnicPyYl8GDdx+4itte+1Hr8rRi8NBz69j+bDh4nBqTyS4QA+2RcTynkCP5u5EuRjUEGTwpNNVNcpJIpCdUCRP7srWZmVvSTvLFjq30i42hXUgo13brSZS/v8PrFVVV2u0THtLhwt1LTx5kadpBxrXugkGnZ0JsLybEntokgXJjBdvy91JhrKJnUCcivcOcHrs9fy1/ZswmpyodgaBzQG+ujL2dcK/oU7q3tQj/a6mqOUl68Qyk1H4WHrow4kNfwsez02lf/3zkKhgzCCG8pZQVAFLKb4UQGWhJWtXg17NctzZRrN131GFZ9/gom+3E9rF0bBVOcrr9YFUJWnJXzMuFOGiQklLbL3WQV11BXnqF9hQ0Qnl1DUUVlVormKzL2u8oMSxO7iEQSFG3DqaXl57s0lL8VbZ+pXHCLYEYaC1lQoimZdE8RefT2rpzZm1i4bztNvuqqmp49/VFdOgUTZdu9t19108ZQHxcGF/+uI4TafkID0FheSVZudrs7cLicr7/bTMHD2fyznPXOLxvt/goPnpoKv+bvZL9qVkAdIgN595JQwjwrYuwnK2d3ZihsG2DQ4gID2BZ8iGMUuLhpaeauhY9KSRSD6mlRaQe1NaYn7FtMzMmTGZYG/sXw8TI1iQXWj9PJULnvA1jUep+xrXu4rKO2/MOk1KSToRXEMMiuuJRr7VsdfYWPj70A2VGbWUBHYKx0RdzV7tpdj0Juws38d3xd61qJ9lfvIOPDv2bxzq9jq+H4yCzKWKDHyUqYDrFlRvQCW8CvIegE+qZ7YyrYOwzYCBQ+1ojpVwqhJgGvHamK6acntvHXMSG/cfs1q308TRww4i+ACzfc4gvlm9m/8lsgv18CA/wJceqRc0m6auzPGKWZZE8bPc5OExr8XIUzFlfx1nvo2WdJQF55eX8fe48Ft1+C8LZE1hR6piEEG2klMcBhBDxtFDrvpTyE7TUGiQmJp6TPQonj+cyf/ZmFiywX1wbtJex3+ducxiMGf+fvfOOj6pK///73JlJ75UQQhIIvUnv0gQBERVFQext9buWXdeyu+5vi7q7uq59VXR1EXtXVEDpIB2poScBAiGV9DaZcs/vjzszmcncGQIEQZz36xXNnHPPuSchc+a5z3mez2NXWbx8Nwdyi5urgOi8Z3/MPsrW7KMM7KNvsA7s2oEPHptDYXkNFouVj9ft4o/vLqahyUposAkjApuff1Kh+o97HZSRyqOXjqG8voET9Q1c9+nHWJs0Y0wivepWghbb+uDSxay75U5MBk/D6I6eg/nmyF7qrL4lhdyxqfriuAAVTXU8vH0+e2uOudoSgqN4+qIb6RmtlXfOry/khYPzUWn2HqpIviteQ7uQBK5I9awvuqzkC917VVsr2FyxkrFJbaNpZjTEEBs2uU3mutDxGXgjpXxeSrlap327lHLi2V1WgDNlUJcOPHP7NDomNh9Bdu+QxKu/vor05Fi+2ryH++d9zY4jRZitNoqrajlR18DgrA48OXsSf7/+UjA1e7L8ZUMK8C4B0PJ792taNEmDo4ySn7CIlsG+ueXlbC7wHdgbIIAbjwFrhRDvCiHeA9YAfzjHa/pZsHVjHnfPfo0vP9xIk5/i3iXF1brt36/cw+oNboHsfh6efszOP+l62sdHMfe7TXy4eodLqqexyYpqlX7Na+FWNLwlJoPC/eNGsHj/QR769jt++/UizI1WXHaNn9jWsoZ61h7zXnfn6Hg+mDibYclO41IQ7acAeKeoOMx2/d/v3/d85mGIAZxoquHh7fNpcoz5vvgHD0PMncVFnh/jNtXK8UbfIrf5DQfJq93Ih0ce5IX90/lv7s1sPPEhdqmfUBWgbQhIW1zAjO+Xxbi+nckvrcRoUOjgiA2z2VVeWrxOd8yWvAIenDaafy5c5fJUnexxXrr/362AOI7X7nFlXjbbyZT7/VBYU3t6AwP8opBSfieEGAAMczT9Rkp5+gJSDoQQHwJjgQQhRAHwFynlW/5H/XxQVZUX//ENlibHh7Afz1J6pn4B6u9XeSvw+yI05ORHWPmllXy/3TtLT+Dwfuk80DlDLYTqLVodFRLMGzdcxUvrNvDmpq1ec57sIRGgpsk7Pgygb0IKH116PdUWM0g4Wl/J9SvfpcHWwugSkrkHf+CjI1u4q9so7ujarEVc1FjJ+hP7deevsNSxunQPk1IuorTJtwBuSVM5UkrXKYJBGAlWQmlS9ZMiVLWCLwv+4npdbS1mbdnblJhzuKLDn33eJ8CZETDGLnCq6s18u20/a/YeQhGCCX27MCCzPWU19T7HLNp+gB35juzHUzwFdBliLRX6nRu5M3bM+W0rDTHZMlMA6JqQQE2TmYKaGpLDI4hvw9IkAS4sHMbXt2085+y2nO98Y//u45QUukLtEKqK1MnqCwoyMn3GIN056htaGCqqBMV7U5HAN6t3ExsbxuXjfGtq7c4vdiUPuI9FaMZWUlQ4pfX17lENSEUz0pwGmwqM7NKRu8cOZXBGB/Irq3irhSGmTSnAJl3xrnp7oSIEg9r7lsYAiA7SPGJ9glP4dMLNzN23gbUlh6i2NCKFiqJogf3VVjPP7F5GlCmEazMHAFBq1vc4OilqrASgfWgyWyv1M+hTQ5M9wjmEEAyKvZh15d/rXC1psOXozpNTu47Cxn20D+3hd00BTo+AMXYBU17bwE0vfcSx8uY39N6CUjISY08y0tsX5kth3yuuDO9rXPUtW/S33FR9r0Z6ec+GpnXgo727+GzvHprsNoyKwpSsrjwx7hKign3IcgcIEKDVWFpU7BCqBJsdaVBcx41JyVH85tHLSEvXz9rr17MDB/JK3ObQguFbHldKBQpKq/jHG0uorGngpiv0dbViwkM9x7XwrJdV1xMWZOLWSwbRKSWeIZ3TWLTrAO9t2E5+eRUd42O4YfhFzB7aDyEE2wsKeXzJSp/ef4EgPiSUJsVGrc07/uuaHr1IjWy9Bln3mGReGH4lc/f/wPN7V+pe81bOegYldOD9I+vZWn4Yi13BICSK8M7GDFa0j/DJ7UbzXdEarDpHidNSxnm1TU6ZxdHGPI41eGpRjo4fT07tlz7Xf7jux4AxdpY4qTEmhEgG/gG0l1JOEUL0BIZfSO74C5V5K7Z4GGJOjpRVEh8ZRnmdt/yFIgTXjejHt9kHvPpdG1/LWAqnceVvMa30sDk9YC7Dzc2j5mzq2y6Z+Kgw3s/e6RpnU1W+Obif8sYG3rtqZutuFiBAAJ90792BsPBgGuqbvVtClaDaEYrg0SdmMHZibxQdT5eTmdMHsuD7HTQ5tMYEIFXAkVkohfAKVZi/YDPXXNqfMJ1jy6HdOpIcE0FJVZ1PeZxGi5UVu/L41STtVDotPpoe7ZNIjo6gX1oKE3pkIYTg/R938sTiFagKfo8ipSq5e+AQzNh4P3snFeZG4kJCmdOnH/cPGe57oB/2VZf47DtSV8Gcda/RpDqNYQWbBEWomAyecWFBBk0OpH1oEo92v5NXcj+g0qrt+UGKiSvaT2ByymhaEmoI476sJ9hTvYWcuj2EGELoHzOKYEX4NcaMgWzIs0ZrPGNvA/PQgmABDgIfAwFj7DxnebZvBf6YsBAarVavepW/njyc9MRY7pkwjCcXrPAeKPC9cal++pxjnZuuQ+bCaXRpJwH6WUvu1yMgv7aaXZUlusbf+mNH2VVSTN/kM9fKCfDzRggR569fSlnxU63lp6K6vJbv31lD7s58YpOiufSmi+nUO+205goJDeL6Oy7mzReXerQLYNJl/Rh/6clL9ISHBWsGlwMpAIPbkRk4qoc74rOAhkYLuw4U0iElhrCQIOKimsMPjAaFp26dyn1zF1BrbvL5kLe/oJT8sko+27qbt3740dW+Me8YH23axYtzLuepJau1PcTPviWRVJrNPLt2HXcOHsSm2++mpslMVHAIRuX0haeTQ30LrBqEwGy3eHnBVKmgStXjlDcuqFmCYmBcb94Y9AR7anIw25voGZVFpMm3CpUiFHpGD8Ss1pFdvYFvCg/SLbI/cUEZVFiO6IwQdIu6uJU/YYBTpTXGWIKU8hMhxB8ApJQ2IYTvPNwA5w3+PFVhQSY++90NfLRuJ/sKSkmMiuDqYb0ZkqVt3LOH9yMsyMSbq7ZwqKyCyJBgwkNNFNbV+Zyzf0Y7th8v9n3TFoH9LueXcGRVtvC0efwQbn1VZjOqIr21yhyxaNmlJQFjLADAVnyHnUvggqrLcnjPMX5/+TNUn2hObPn69eX8+tkbmHbH+NOac+aNI4mLj+Dz9zZwJK+UpHbRXHbNIK6e0zqPUM7hUiwW7ejMX41aITUPFIpAVeCvb31HRU0DQsCw3hk8fON4OiRpCUj9O6Wy6K+38cj8hWw44LvO4YGiMg9DzEl1o5m/frUMi93NW2dHV4HfXeT67e3bSI+LISMmhqEdTs/AdXJNen/ezd2MqrNLK4rNZ9KpqiooDu9YXFAEIxM9tcmMioF+Mf71ypzYpY23D/+TnLrmE4ZD9buJMcUTLMKwS8+TkZEJNxITlNKquQOcOq0xxuqFEPE44ySFGAb4jyoMcF4wtmcnPlirrw00tndn0uJjeHj6GJ/jrxjYkysG9qTRYiXYaMRiszPkH69gtXunUKvAjkLfhpivIwUPY0tPCFbgZVW6gvl9XBtsODulQwL8vJBSZp7rNfyUvHj/fA9DDEBKyWuPfMCwqf1JaH+yWFF9Jkztx4Sp/U5rbFRkCzkHP9IWQoJdAWkSVNRohoCUsCH7CPc89Skf/v0mIkK1eNCosBDumTzcpzHWPjaKPUWlPu915EQlBOPaQ4SK9jBnkK6HQqchJpFgBIti57GVmpcwPTqGV6ZeTs9E39rBq44f4n/7tpBXXU5qRDQ3dRvAtAwt3qprdBKP97+Mv+1cjNVNY2xIQjq7a/UD6N0JNwbzZN/rMSmnH/a9rXK1hyHmpMpazsDYCSQGBVFiziHcGEffmCl0DL/otO8V4OS05l/yQeBroLMQYh2QCFxzVlcVoE24dfxglu/OpaTK05uVkRTLrJGt31xDg7S4hJAgIwvvu5kZr71PXZN76RDA6GZw6bnkXBub59GkM+1JC+r1sQDnhqjgXY5J59oKs37KdoBfLo4SSF0Al3UgpVxz7lbUthQeKmX/ljzdPrvNzuovNnP1vW1X/am4qIqN6zSjYcSoriS1i9a9LisjiS6dksg55NswciIBTPpv7pKKWhau3ct1E/u72vpltmdC3yyW7/IMxxAC7ps2kuwS33FZrhu22IukBNmyKpNOVZD86ipuWfA5q2++g1CTdxmn9w5s50+bmrMVj9fXsLnkGAerTvDgRVoM18zMAYxL6cp3x/dSb7MwJCGd/vFpzFzzEnl1+r+vSSl9uCgunckp/Yk0hepe01qyqzb47DtQu4OZPd88o/kDnBp+jTEhhIK2eY0BuqH9zR6QUvpW/wtw3pAcE8H7D8zm7ZU/snrPIRRF4ZK+Wdw8diBRYX4KtfkhLT6GPuntWJ/jeCLVO1psaZA5+tUWgbrS+Z/WBPc7gmw99Mp8jC1r8C3bEeCXhxDiDuABoAOwA01vbANwemd35yENtf4fQBpq2u4B5b+vLuezDzeiOqp7vPbSEmbOHk6HzAQ2bT6E0ahw8cXdGDmiK4oieOz+qTz410+oqGpASu9MSidTx/Xmm02+dcn2HCoC+mOzq3y7eS+LtuynzmxhYEYqhdU1VNWb6ZWWzK0TBjGqZyaRB0OYv26b7lzSx54j7BCkKFgcBcX1MrmdnGhoYGHOAa7p2dujvdFm5eltq9zv5tqnXs5ey5G6cnrFJzMmpTPdY5K4ofMQj/G/6T6Z3259H5v0jAYaHJ9B/7gMkkOiCTWceSC9zc/HuE1tXeWAAG2HX2NMSqkKIV6RUvYH9EVMApzXJEVH8MiVY3nkyrGtHmOzq+wpLEERgl7tk72ypfLKKjziK3SPIIVnf0tDTO86v4aZ8H7pKyauV2Kyj54Av1AeAAYDG6WU44QQ3dEyxC8Y0nukEp0Q6XVM6aTv6NbFEZ2Mlcv28Mn7nh4V1S75+L312E0KGLQ3+fLlexk5sgt//ctVdM5I5INX7+D7VXv4cVc+G3YewWL1NDTumTOa6y4fxNLtBzFb9JXeYyJCUVXJQ299y6pdnl7A2IhQPv7dHNKTm49iR3VJZ2inNDYdOtZyKlRnolCLTSQrIZ6ImGC2FhZqDXrhEG4cqaryattYcpRaqzMDVdKiLCTfHNnHN/l7+ZdhJTMy+vD00GkobgbqyKSuvDX8Dubn/cCe6gIiTSE0qQ3sqs5lV7XmBWwXEsO/+t9El8j2vhd3ErpGXsShev2P9W5RA0573gCnR2vSQZYLIa4WgSKAvwgW7trPhGffZNYbH3Ht6x8y8fn/sWKf58bXIdZTV8d13OgLX4aYxyXC5xx6gq9unR6kRUUzJauL/5sF+KVhllKaAYQQwVLK/Wie/gsGU5CR6x+Zrtt30Zge9GsjY+zbr7zFUZ0ods8347p1OSxduhvQsipnTB3AP35/FR//5w5uvnoYwy7K4LJxvXntydnccNVQTEYDk4f71rDadaSYm//1oZchBlBZ18hLX691vd6SV8DHG3Yxe3A/7hozhOSoCIKMBgZlpBIREewzezI0yMiOoqLmBj9llADSY2K82hR3/72ffUtV4Ysj2cw/uMWru09MGv8eeD2Lxz9CSlg4pU2eRl+xuYqHts33W9PyZAyNn0R8kHeiU7ASxvikq0973gCnR2tixn6FFjdmE0KYcep/Stl6pbsAPws2Hz7GI599h+qmxnq8qob7P/6GEVnpxISFML5bZ64d0IetRws9B/vwajlrT/pDuu92LeZxBs/6LD7u9npoageemTCZYENAyziABwVCiBjgK2CpEKISyD/Ha2pzrrj7EkIjgvn4uUUczy0mPDqUSXNGc/OfZ7TZPcpKanx36qg4L1+xl8mT+3q0JcVHctfsUbpT3HvtaA4cLWXf4eZ4L2cWZvaRYq3QuI/9ZFV2HkWVNfzmnW/ZU9A8PjU2itdvvYquKQkA/OHrJXyxQ98j1KdDCjuq3ERqEVqWp84940PDmNbF06a3qnZyqsoRqqIdySoSqXiLtWqBsBIp4aO87dzarfmosqChkpyaYhJDoog2BbO5XD+gv7SpmrVl+xib3Fu3/2SEGsK5u/OTrCj9jOzqDVhVK90j+zM++RqSQ84sW9QfUtppMH9PU9OPKEosEWEzMBpTHX02VPNiVPNSQKKETEAJuQwhvOPyLjRO+qklpfQtiBLggmLeuq0ehphTXd+GZE3uEQC+3rWfEZ06ctfIwfxvw1ZsjtiKIEWhe/skdheVuOaIDA7GKuw02FtRYNaXwebnmMC50mCDgflXXM3Q1LO3gQT4+SKlvMrx7V+FECuBaOC7c7iks8akG0Yz6YbRNNaZCQoNwmA4fS0sPTI6JVFU6H00B+jGgjU26MceHTp2gt0HComJCmV4/06YTNoGcLS4ksTocI6GBIGAukaLdqToR1jWiSolj330vYchBnC8soZ75y1g0aO3YjQo3DNqCCsPHqKywTOOLiMuhtuGDuDjfdku2QsA7GibjZsGYlpUNK9MvdwjeN+uqty57EtWHT/k/IVo8RmqRBpV/VA5CcWNWoJVo83Cn3d+xZLCPa4H1IwIb8+bO85ySKdLpCmGK1Lv4IrUO85oHl9IKbHYizAo4RiVaOz2ckpOzMJi3e26pqrmX8TH/ouIsBlYK+5EWpo9nKp5ISLoY0xx8xDi9OKcfy60RoFfV+XtQspECqBxoLhF7WQfhtD6Q0cZnZXB8t/czg85R1AUwdgumcSFh3GssopN+QWEmUyM69qJF9as539b9INoW4MChJqCqLfqb+odoqN4evylZ2yINVgtvHdwOwvz92Ox2xmb2olbewwiKTTi5IMDnPc4CoWPQvtYXSelvKAjlEMjzs4H19WzhrJpfY4reN+JBFSjt+HXv3+663ubXaWuzszTry9hzebmDMjY6DAe/+00bAJ++9yXWG3NhpAAFDuozhJKfgRa+2W2Z/OhAt2+wsoa1uw7zPjenekYF8Mnt81i7trNrM45jNGgMLlHV+4aNRi7VIkJCaG0vt4t01ILoVDsgsfGjSErPp4RaR094rwAlh3LczPE3BGaZofRKQkkUYyqa/4Qg0JBfRVzD67g+8LdHiPz6yoJ81PdLTPi/I2PLa9fwPHq52myHQEUokPGEaXYPAwxDRvllQ9jshch3AwxJ9KyCXv92xgj7v4pln3OaM15zsNu34cAQ9DEFC+YTKQAGklRERRVuwUA+3kY/SZ7P7eOGMg1Azxd5GmxMaTFNj/NPTB6BNnFJWw+dlz3KFOifwRgMhj47YjhXNGjB4oieGLNSpbk5WJVVdKiorm0cxYTO2cxMCXVa1M8VdYXHeGxjd9zuLb5KXNfZSlfHdrDF1NuJCU8cCL/c0YI8WdgJvCFo2meEOJTKeWT53BZZ4zdbqepwUJY5JlJHJwK/fqn88ifpvPGK8upKNc8OtExYdRZrFhaGGhxceFceeVAqqobmPvOGpav2UeTxab5fNy8XZXVDTz69Fckpsd4GGJOhFvVb6E6yyl5XhNsMjKqTyY/FmnhE66VuIVvHa9slsfsGBfDP6ZPAqCsrp4gg4GokGCmz3+f0rp63RhXVUqKamq5pb9+cPuSo370wRyyGUJIFJOnl6zcUs/M5fNoFN5HwBKBza5gNHhrO3aKSGZofBdKzRV8XrCc7ZUHCFJMXJzYn+mpYwgxnLsavRUNCzlU/oBbi0qNeRkmxeLjY8VOXf2H+DqGUxu/hl+6MSalvNz9tRAiDXjhrK0owDnj2kF92Hms6OQXAvVN+o4FKSVrjxxlRe4hhICJXbJ4//qZ/HA4n0937WZpTi421VmXzpE2ruAVJHtz/4v41ZDmOIqXp1xOncVCg9VCYlg4bZFPkltdzq9Xf8WBqjLd/qKGWl7atZ5/Dp98xvcKcE6ZA/RzC+J/Ck3i4mdpjJkbmpj32Id8//ZK6qsbaJ/Vjmsfms5ld038Se4/4dI+jJnQk/17joMQ9OiZSm5eCf+bt4YffzyMwaAwalRXbr99DBGRIdz10Lsczm/2uru8XUiXQVZvtlB97ISPOzqMMMUx1qrFobpKpwkw2228tmgDCmB36oK1kNs52MLzvyrnEM+tWMeB0hMIoEdKEvtOlPn9VFyWl8cfxugLZUudmLkWVyAM+seVZeZ6jEYI0gmNarIZyIpM4mhDiUuxv19MBn/rO4tCcxkP73iBamuzluTh+uNsKt/NP/vdR5BybmKtCqtf9mo7SWIqqmz0HZIivesoX2icTqRzARAo234BMmNAL/YWlvLB5h1aLK4fqYkhGR282myqyr1ffcuynOZsp3e27uCy7l15fvpULu6UQc8XX0RaHU957k+fLe5j0Kn7llNZzru7t1NQU0OnmFhu6tOfngm+FbD9YVXt3LL8Uwrq/BeTWHLsYMAY+/lTiObVNzteBwPHz91yzoy/XPUvti3d5XpdmFvMC3e/QUOtmZm/u9zPyLbDaDTQu19H1+tu3VJ4+qnrsNnsCCFcsWqLV+z2MMScuAwyx9tcSkB1Vtbw/ZHtlNER0iGX4+ZVt6mqK3Fb1fG2f7l1D3eMG0x6QiwbDh/l/z7+GrvDgJLA3qJSzZjz86lYY27y2TexYxZf5PlQgFIkihEvmQt3VLsAXdtJcEPGOAbEd+RwXQntQmPp5DiefGbffA9DzMn+2iOsKNnC5JQRvm94lrCr9TRa93u1q4BdepQm9SAoqDdYN+v2KUGnV5D958RJozuFEC8LIV5yfP0H+AE4/SCgAOc1f5o2joX33czDl47mVxcPISLYW1wwIjiI20YM8mp/f9tOD0PMycL9B/lsl7ZJJYaFa4lEvpUsABjSwdPY+3hvNjM+f58vDuxlc1EBH+3LZvpn77Eo78Cp/YAOlh3LPakhBlpQboCfPdXAHiHE20KIecBuoMq5r53jtZ0Su9bs9TDE3Pnwn19gMZ/bUDij0eCRNLAj21vjy4kjLV/LkAwSGOyg2EDYpFdmphQOT7oBlyK+wQ7ChtdGIlS0T/4WSAkLt2tGwmtrN7sMsZZr8ld5uaKxkc92t4x50pjUsQuj22fo9EjQOWZsSUxwmG57QnAEl6T0JCkkmtKmSv659xNmrnuKv2S/zw9l2T7n21Cu/3dytlFEMIrQ+1kEjT5S642GjkRGPwlCJ2FBRGKIuLNtF3ke0hrPmHulVRvwoZRy3VlaT4DzgMzEODIT4wCY2rsrzy1fxw+ObMpRndP57YSRZCZ417n7Yrdv9ey3t26jpKGWyODg5oLgoGuR9U9JYUxGhut1TVMTf1u7wvNSATZU7l/+LZuKjjE6LYNRqemEGFvnlj9UU9Gq6yamBTTLLgC+dHw5WXWO1nHGRHojGQAAIABJREFU7Frt+z1WW1HHoV35dB9y/vzNhoX5VoqXgDTikSkpcHi91GZJHOl0d7mVVHNd77BxpNsnmXMOvYe9BoumOr/tWKFOr2O81e0IVIe5W7ZwTW9vOQmDovDmJTN4d/92vsrbS7m5gcigIA7WlTZ7xPycNtzVdSQbKvazpfywqy0lNJrnB81GAvdseZW9Nc21OIsaKwBBuFHBqHgbe6JVpU3aHiGMxIddQVn9h159jRhJDr+NxsYvUdUTgCA0eCzxsU9jMKYh4j/BXvtv1KYVgEQJHoch8kEUY6ef/Of4qWmNMRYjpXzRvUEI8UDLtgAXJt3aJfL6nCux2GxIqQXK+qKq0XkK1ELsUMKBshMcqDzhkstw9ku3yyXQr10y/5482SMmbOXRQzTY3Ep3uMZLbFIyf+925u/dTnRwCH8aNo6Z3U6uu2MQ/jT8NRJCwriv78iTzhXg/EZKOf9cr6GtCI/W9560tr817N1xlB++z6apycqAEV0YPq6Hl0TG5g25fPrhRvJyS0lIjGTq9IuYftUgr2odk8b25PNvfZQkUnAp9rdEUcFmkEiDcCvaLZqrfbjdRkjvoH5f5Y6GdtayrmNCQyir81E2zd/eIOBwZSUNVithOjUpgw1G7ug1mDt6DXa13b7yE1YU5mp1L4VAEd66Y0mhEXSMjOX2brewp7qQ/dVFJIVEMiIxi3JLLdete4Zyi56kiKDRbiRS8faIDo/vo/8znCYWex15tcuos5USF9SJjMgxGHzof3WI/T31ll00WD2PbVOjf0di9H3ImD9htR1BUWIwGppDTRRTFkrcXKSjFJQQJxGpvIBojTF2M9DS8LpFpy3ABUyQ0ftP5UhFJQv3HaTRamVERkcGdmjP8b3V3offjr2tpSHm6qN569t1ooRx7/2PLnHx/GboCKZmdfXU/HFtxN7q1tVNZh5ZvZj0qBhs2Pnfnq0cqi6nQ2QMN3XvzyUds9hdXsxD6xeyv1I/aB8g1GBiZlYf7uo1lA4R+gWQA5z/CCE+kVJeK4TIRufTVUrZV2fYec2Ya4fzxsPvYLN6n6V1HdSZtG6pPsdKKcnNPoa1yUaXfh0xBXm/p1/957d8/UFzuaNFn26hZ/90/j73ZkIdGgtLFu/i3//4xnWSWFvTyCvPLyHvYAm/+8M0j/l6dEnhhmuG8t5nmzzakxOj6Nw9mdXb9IubA83eMGj2iOlFgQtPT5jLk9aCgZmpjOqWAcBVfXvyxnpv5XvpHK+63avFPaOCgwnR2Q998erFM3h25xo+zttBjcWMagNhkCiKtmIhoKyplns3fsrUDj15bsgMesc0/zv+e98CypqqfEqtqVJBlZqR56RHVCbjkgfrDzgNChu2s6zwj1jU5vi0yPIUpqQ+T1SQ99+cUYmmZ7sFVDZ+T415AwYlkviwKwkL0kRyhQgiyNTV5/1+SUaYE59/UUKI2cD1QKYQ4mu3rkigdWc8AS5YXlu/mefXrHNtgK9v3ELvlCTfUYg6m5pHnwNVakZWTkU59y7+htemTmd0h3SMiqIJzAr/lcUl8MSmFeyuLHat7VBNJWuOH+bG7hfx1eHd1Np8xdUIRqWk88qYK4kOurAFBn8hOHPrp/m96mdEXLtY7nvlTl68+3UPra/ohEge/K/v1P9ta/bznz98TNERLZg+Oj6Cmx+dxpQ5zZ7fzWsOeBhiTvZuz+eDuSu5/cHJ2Gx23pq7Qk9sn+8W7uTq64aS0SnRo/2uGy9m+KDOLFm1h9q6Jvr2TGXyuN6s+jHHrzHm4d1yvu39lUVzXKMa8TSmJFyUnsJrt17p8rjfM3ooW48dZ6vbcaUEVBOgNGuLId2yvh33ntm79ynJ6QQbjPxxwHiMBpi7T/v9KgaJTo4Siwr2MiGlK5d31LxaVZZ61pft95+GCExMHsrBusMEKSbGJA7ksvaj2yyT0qaaWV74Jw9DDKDWWsSq4ieZ3vE13XFCGIkLu4y4sMvaZB0XOv7M+/VAEZAAPOvWXgucm8jAAOcFPx47znNrvMMGs4tK/Cvpt0IMvOXxwstbNvLtrBu5re8A3tix5aSbEkj2VJboHjK8u387KPr14mKCQnjnkln0TfCu1Rbg54mU0qnTogBFbtIWocD5q5Z5EqbeMYFeI7qy+K0VlBdVknVRJpNvG0d0gr4e3rHcYv526xtYGh0PIUJQXV7HS498RExCJMMv1RyEy7/Z7vOey77Zzu0PTibnQDEV5T6O94BNG3K9jDGAPj1S6dPD04MyfmhXXnpvFdV1Zq/rB/fuyJajhVhtds3GcqTiSSG8veuOl8HBRi7qlMrWowWYbXaXtlff9GQuG9Cd6gYz4Y6EpLAgE/NuuJofco+wcM8BFh/Iwe40ulogpNBkKwSMTk/nwRGnl6H4zdE9ruNJ4fTu6/D1sd0uY6zW1qjJWUiH+0+HrpGpPNj9+tNaU2s4UreGJlW/DFapeTeVTYeJDc48a/f/peDTGJNS5qPVb7vwc0oDnBLOzMhTwVcMhzuq4vYE6jh22F1WSoPVyh9HjGVFwSFyK8v9TyJwafHodkp9Y6zKYqZjZOBI8gLlU8D9E9TuaGu7c5yfmPSeadz97M2tuvbdZxZhaXCTZJAONXsh+Oy15S5jrKbKt5ZTXbVWOsioo7LvjrOsUWsICTbx9O+u5PfPLaCqtrk0UZf0RMaN7Mb6Q8fcjiYFQjbHh8kW9WolYLbY2Hggnz7p7bh6VB9W7T/EqgOH2XW8hF3HS/jnN6u4adQAhnZJ45XVG9l5vJiwIBPT+/Tg+Sun8rtF32HxUXi7XVgEL0yfyuAO3pI+raXewyPffETpjpRQUN8sPp0SEktCcBQnmmpQpfQ6qjQKA/d1PbuO3wab/4OwRnsFsQSMsTOlNeWQhgEvo2mLBaH5PuoDhcJ/uZxo8CPA5/8U0c+wFkr8jjkUIQgyaB0FtdWuq88Mt4wBx80iTcGEm3xnfgX4WWN0L38kpbQIIX4R/9iFR8pYt1DH4+U4Zzy8r/mYrteAdLZv1D827Okoa5TVtR3tU2MpPO5dE1FRBKMu7ubV7o9+3VL58uU7Wb0ll7KKOrpmJDK4dzpvfL3BwxBzRwDYQLqdwkk3b1l2fjFj+nRi2V7Pn0WVkrd/2Mq8jVtdOmQNFisfbd3FjoIiUqMjOVypX3czxGjyaYhtLTnOGzu3sKusmITQMK7t1ofre/Tz0kocmpjOkuMOKR6przkmBFRZmvdXo2LgpsyxPLf/a6QUmhSbw0MWYQzl5YF30TXKd5xgW5AQ4vvfVBEmYoMu/EzHn4LWVJH9DzAbyAFCgTuAV87mogKc3/Rtp3/CIxBkxsQSbPR+OvZX51cRAmlwBn24f2lerj1lpQBEB4d4ZWm2tMumd+pJVJCvMiASFIlQHI4Bt6zMmVl92FleyDO7VvJs9ir2VBb7XnCAnxtlQojpzhdCiCsA33LvFxBfvr4C1e5D40pKElKadZ0umzmUmLhwr8sUg8KsO8cCIITgvt9NxhTk/R6/8bbRJLU7de9ySJCJS0f24IbLBzOkTwZCCJJjHYVxfMRmCQDVsQW4B/o7+GqLH++9U5vMbf/YX1JGUrjvOrSD0/QNnhVH87ju64/4/kgORfW1ZJ8o4f+tW8aDqxZ5XHeoppwGixVpF0g7qDZFywDVocLSQGFDswbizI4jeLjHlSSHxCARKJiYkNyfD0c8fNYNMYD2Yf1JCtHPUO8WdRmhRm+ZowCnTmuMMaSUuYBBSmmXUs4DApLkv2Bm9e9DbKh+gPtDY0ax8JabmNmnN0EmA1Joatiqj7+0IIOByV2zmo8nPYJ0tZ1yV5lmGM3o2ktrdghAOr+cRJhMPHXxJP4waKy+c85hiLkjBJgMguPmCq5b8Q5z963n1b3rmL7kLf6y9btW/DYC/Ay4G/ijEOKoEOIY8Cjwq3O8pp+EPZv9ZCsCU+Y0n97GJkTwzLw7GTSyiyvQvVO3dvzlpTn0H9bZdd2gIZ149c3buWx6f7r3bM+oMd34x7OzuOGW0W227olDuhGik+3pjlS040o9TbBGi01/jDPA36NR+yo4UaXrdA8zmbh98EDd+Z7csAqbjlW1IHcf20s1r2N+bSXXLHmHH4qd+mECKRVsFoNuIgTAt8c8xVxnpA3j89GPsuDiP/DduD/zeN/ZxAX7Nh7bmkmpT5EZMRbhOL4wilB6x8xkeNIDHtdJaafavI3Kxo3YVe9YwAC+aU1+boPDpb9DCPEvtKD+VhlxAS5MkiIimD/rGv6yZDnbj2sx0ilRkfxm9HAu7aYJTtbZLZix6/6FjU5PR0ro164d4zMzufrLD3XT1Z0iZAmhmnbSff2HsaWogC2lBR6ZUtIRS/HEqImEmYKY3a0faZHR3Lvqa6qaHLEoioqvbGmbUFlW6F3k973crQxJ7MhlHXue6q8owHmElDIPGCaEiHC89q4fcwFQX91AcFgQRjctwPAo30XEI6LDuOJ2zzqLaZ0SeXLuLdTVNGK12IlN0P/Az+iUyG8emdo2C9chPCSIGyYN5M1F+uVxXB4xHx73nunJlOw/5DlGOLIlfYwpqqlr3q8c1xiE4LlpU+iSEO91/aGqCg5V+46nWnYkj/5J7Xlj3waqLI06VwhUm4LB5G3MLS3cz13dRnm0GYRCUkjrPY+qVDlQm0uTvYkukZ0JN56+Bl2IIZoJ7Z+gwVZBo62cSFN7ggyeXtTyhtXsL/8rTXbNCDUq0XSKuZ8OUTec9n1/SbTGGLsRzfi6F/gtkAZcfTYXFeD8p0dyIp/cOIvj1TU0WK10iot1xUiYbVaW5OX6HJsRE8Pfxk0AYNZXH/sOuBcQYQomt7qcKxe8h5SSqOBgT++Z43sVyeNblvHF4d2UN9VTbm6gfWQEKNLHRuh2G8V3DNpnh3f6NMZyakr4X+56tpcfJdIUwvS0fszKHIxJ+eVp5JzPCCGC0fasDMDo9PpIKR8/h8tqM5a9/wMfPr2AgoNFhIQHM+H6Udz+5CzCo8OYcM0Qdm/Ufy9e98ClKHr6CkCEHyPudLDZ7Bh1whd8kV9cyUfLtjcnG+gg7PoG2fDu6bSLiTyl9Ukct3JIYkghkQrYhORXXy+gQ1QUtw4cwC0DBrjG1Fp816kEMDhiM9YWHfF5jaoK3QT0MnMdpeYakkKaQ7OllB5i2C2xqFYqLTVEmyLIqcvjjbx3OGHRjMVgJYhp7S/lmg5nVrs0zBhHmDHOq73OkkN26b2oNCcp2NRqDlY8QZAhkaTwS8/ovr8ETmqMSSnzHangKVLKv7XFTYUQzwCXAxYgD7hVSqkfORngvCY12juPw2KzY/VT07HeUZKkyW5jS1GB3/nrbGae3bpWv1Np3qelFFQ0NfJD0WHX5lzSqDlAesQmER8SxtriQ57HlK4IYd+UN3knK5ww1/Hy/hV8cXS7povmYG91ERvKDvHK0Nl+N80APzkL0OpTbgX8f4L+zFj01gpe/PVbrtfm+iYW/nc5eTuP8PyqvzJx1nC2rtrH2m89g/gHju3B9NvGtJyuzVm3KZd3Pt7A/pxiwsOCmDS+F3fcMJqIcF9xnRrvLfmReledTcd7TAi3mpVasZ/Y4FCmDOvG3oIy9hwrxmyzs/ZgPvJwvrf8hQTRIvDf1UeLODRnqpqDgpoanli5isrGRu4fPpwn1q3ivb07mgfrMDlTEzX1JxDrS3ax1FzFpUufZ2L7nsSFmFhatIt6WxN9Y9O5I2s8Q+KzXNfbpZ338xeyqGgt9bZGghUTYMGoWFz7Y5Nq4fOCb4g1RTMh+WKf6zldjte+52GIuXOsZl7AGGsFrcmmvBz4N1omZaYQ4iLgcSnldP8j/bIU+IOU0iaEeBr4A1ocR4ALgKiQEHolJrkC71syPE0rSWK22XSL9TYjdXXLhPN80r0kilPHTGd321dZytSMrrR0WGmnoNKH3IXWUGau4bndy5nTeTDJoVHk1JRw67r5VFr0M0pXlxxkbWkuo5PPn/qAAeggpTwrca5CiMlo1UgMwJtSyqfOxn30sNvsvPO3T5Fu4q/Ov+P9m/PY/N0Ohk0dwB/fuJ1tq/ezftEOVLvK0Ev7MuSSXj69YmdKfX0TC7/byffL95B3uBSpCBBQ32Dhy2+3s+9AEa88Mwejj1JIADtyjjf/ONLtver8MQX8auowrh/fH5PRyKVPvonZbm/WM/RhIAm7tyyG4xZuF+nvOwBv/biVOmnh7extHjI8Lee7tlsfesZrZX4u69iDF7J/0J0vNEjBRks5DU2d3yZVVpRs9xCH3VF5hAd+fJvnBtzE8ETN2Hs99zMWFzc/sDapVkBgk0bCjFa3WWFR0dKzYozVWbzDPJr7Drb5/S5EWvNu/CswBKgCkFLugDMTFZFSLpFSOiMsNwKnL94S4LzkN8NH6KpUd0tIYFpXLVU6OjiE7nEJvic5meK2Tvalr4uXOlPK3acX2hGlK7tSuJ+IaHOWW2t5/eA6rlj+Bjk1pTy+c6FPQ8zJiuL9fvsD/OSsF0K0baE+QGg1W14BpgA9gdlCiJ8swHDjom1UFFd7NjpV46Vk15p9znUycGwP7vvXbB54dg7DJvU5a4ZYZVU9dz/wDq/+dyV5h0q1Zya7BLt0GVP7c4pZt9H3hzdAZJhngpBeAY8Zo/oQGRbCsuwcKuqaQxH8aRr67HKbXPr51TTabHyYvcttMucgz6+hKWmuMbd1H0LfuBSvuYYmdeTDcTcxJjnL9YAphMRoUBECDIq+Sr9dqszNWQpAhaWaJSXrdddqlwZUKZr3NqDQXIzqK43zDAgxev98zX3t2/x+FyKteUdapZQt3vFnLPTkzm3A4jacL8B5wIROnXhz+pX0b6e9ScNMJmb37sMHV88k2M1t/8BgP2rWJ/vr1Mm89HWdbw+c8BrqNMgMjk0RoNLSwOM7FrOt4uhJFuX03J06q0p38tD2udyy6V88uec9DtQcO615AngxCtgqhDgghNglhMgWQrRFFZEhQK6U8pBDx+wj4Io2mLdVfPGi/20zPOrMi4afKvPfX8+xAu+gdtHieWnrTv/vo6nDe/jt79c5hcQYLbmguKq21etzPcO5twn/BlhLGmzWFi3C66vOLZ4swhTMR5fcwN+HTGFs+85MSO3C00MvY/642fSJS+X1kddzf8/RGA2qyxADUIRvo2lfzXFqrY3k1h7F7mFcSQxCJUixEaTYsEvF5VAUQpMYarS3fZZj+8hZOq0SEzZMsoRtBRdxsPRmaswb2/zeFwqtCeDfI4S4HjAIIboA96OVSvKLEGIZoFdb5jEp5QLHNY+hqb6872eeu4C7ADp27NiK5QY4XxibmcnYzEwsdjsmRdGNo5rSuSvPjp/CQysXe6Z5n8yecT8eEO4NbYMQ3keXW07ko7ji1KRLfBEpnHraAExI6X7K95ub+w0fH13lep1fX8Lq0l083ucWhicEsjnPkClnad5UwN1iLgCGnqV7edBYZ2b3Wm9vrzvjZ4302w9gbrSw7vtsThRXk9kthUFjup2R12ylwxuniypdgoOhof7rJl45ug8b9xxh1XZPaQ4JhIUE8eB1Y11tXdo1e9elW5a13h7iksFwVBhyFRV3D3mwO5x4OuPToqM4bqvRYmL9CFyPSPX8rAoxmpid1Z/ZWf11r+8QHtsiT0Gb3CkRJ4RTfV9iEBKDIvlPzjf0jErxGGNSVAzCcyO1qgaMit2l9bijKpuRCW37ZxobMpgusX8gt/LfSDRjNUTYMAkVu1oGQLV5NdXmtWQlvEZs2KQ2vf+FQGuMsfuAx9ACXz8AvgeePNkgKeUl/vqFELegFfCdIKXvwCEp5RvAGwCDBg1qS49cgJ8Ip4K+L67u3ou4sFDuXPwlNtXNCHI+TXtteG415Nz7VXTjRfrGtuNAXSlW3VIn+uWR9Iw7CSSHRlFmrkZxz8AUEim1z5pLUnoyIrEzp0JBQxmfHF3t1W6Tdv5z8CuGxndH0ZPrDuAXIUSUlLIGrZ7uuVrDWXmYlFLiZ9uk++DOpHRK8jvHnq1HePyet6mpbD52T++SzBNv3U6imxjsqdDUpK/v1ZJLxvj3fBkNCs/833TW7z7Cu9//SH5JJSajkRG907n+kgF0TG4WGh3dI5POyXHklVQ4inx7xPlrOI0vN00ygeaxU1W0GDGHISekQLFIRIjw8KhHBAXx7ylT+HD/Lj4/sMenMXZ55+50ifUTfqHDxPY9+fee76mw1AMSg6I6nPbOzF+BlCohJhvOULvFRVtYXCRJCQ2jwd6A4jDUvBHYVQXFoFl2Nmmj2lrBmrLvyK3bQ5ASwsDYkQyOuxiDaI1JoE9a9C0kR0yjrGE5Zut+Kurm6Vxlp6DqKWJCJwaSnFrgc4cXQrzr+PZOKeVjUsrBjq8/OQvuni6OoNdHgOlSSv8BOAF+EYzr2IktN93DsNQ0N7V+gUBopZKcX87gWvd4Ddflnq/DjCbu7DmYjy6dw7299L0EQpE+Mue92/vEtmdWxkBPQ8zt1r1i2vHc4JmnvMmsLdvt+Bm9KTSXk1dXqNsX4KR84Pj/VuBHx/+3ur0+U46jSf046eBocyGlfENKOUhKOSgx0buA9ukSFhlKvzG+PaazHvF/WtpktvL4PfM9DDGA/JwSnn30k9Ne1+ABfsKJHe8Lk1EhNeXkqu1CCEb2yWTuQzNZ/MxdfP3P2/j9nAkehhhoZZjm3jWDgZ1SPQwtJNoDmopr69D9xHMq+DvftkLbebpGxnPHoIFM7dqVB4YPZ8mttzAoNZW/jh6veb6c87sMNm2POl5f5RJ8bS0hBhMvDZlNQnAEitCPFZMoqF7nqYISs40wQwiKj0Li2liBlGAQBlJDE3n2wB9YUfo1RxvyyK3bw8fH3uB/h58943iyIEMCqZHXEeSn2pjZdogmW/4Z3edCxN/j9kAhRHvgNiFErBAizv3rDO/7HyASWCqE2CGEmHuG8wW4AIgNDeOj6dex8Ya7+c/EacybOoP119+FYkTz4RpxM8T0aN6M2odHse3aB5iQlsXOikK6xiYgFMeu7NiZhePpU2+elgaXSSg82Gs8DXbfygi5tSU02vTTu/3hyxBzovrNOA3gCynlNKFZxmOklJ2klJluX21RUG8L0EUIkekQxp4FfN0G87aK2/8xixAdiYhBk/oy9LIBOiOaWfd9NjWV9bp9OzfkUph/etWibr5hJCEh3keQElyfNlabyrpNvnUIT4d2sZG8fe+1JEQ2x8m1jOTSiwuTeMezOTlYVk5SSDgvTruM+0cMJzlCi1GLDArmgyuu5fMZ13NHv4Fa8XRnWIMC20qLuGHRJ+RV+S+w3ZJ+cWksmfhbksN8K+vb7N6nDKpU6BHZlx5R/vLqtI1uSrtLWHdiEbW2lmHgsLdmO9nVW05pzT7vJvwfQ5+sH0BKFWk/jlR/GapX/oyxucByoDueT5Rn/FQppcySUqZJKS9yfN19JvMFuLBICo9gWufujOvYiVCT6dQiwRxPh0WN1Yz65mXmrHyfG1Z+wAMbvkAooBil9mVwZlE6Y7+ajTRnH2hvkIuTs3ig1xjWlO5nQ5n7h4jnLm6TKg32UzfGRiT08tmXHBxLVuTZrz93oeIIgVh4lua2oYlhfw/sAz6RUvopiti2dB+cxYs/PM4lc0aRnJ5Ap77p3PX0HP76+e8w+JGNACgvqfHbf6JllmYryeqUxMvPzqF3z9Rmf7YCGD1Slampbf3hSm5BGdl5hTRZT34EesMo/ZgsCa7i4EDzkaTApT+mt9H8c+UaJr45jwNlZV59A9u1p6ixViuH1CLVs95q5a3sU/+YNClGv94pX3uhWbVxVQffcVihhiB+1fkWrkub4dfg2lm1qZUr9U9cmO/qDOFB/Qg2+t/TZMMXyBOXIMvGIUuHoVb+Cmnzr0n5c8fnAbGU8iXgJSHEa1LKe37CNQUI4KKgrga/kbJOhASDc6vSSh9VNjWnu1tVVf84UoBi8G3uhRuDaVDrePnAUrdWzXPWHI4iUVVBx/B4EoP1lb/z6opotFvoGplKkOL5tksPT+aK1BEsOO6ZF6MIhXu6XI4hEC92pmwTQgyWUrbNY78bUspFwKKTXniWyOydxiPz/u+Ux3Xq4VuKwGgy0DEr+bTX1KVzMn//69Vcc+tcLD5qRPbpcfIHjN2Hinhy/lJyj2teuuiIEO6YNozZl/j2+t06ZhBHT1Tz5ZbdLsPFqe0s3L7Hvc/ZpDpiytwRkF9Vxe2ff8mKO2/3in/dVlLoOZHb/NtO8ajSSf+4DJYX69v0BsVpqDWnDEmgX0wmA2P7kBWRQW7dEY8xQYqJx3o+QLfITqhS9WvsqVIvrvbUCQvqSbipF/XWlj+HJNzk++ETQDZ+jaz5vfuqoGkl0pYD8d8gFO9i9hcCrVHgDxhiAc4ZqeFRGIXAppve5NgBDaqHsr5Q8BEH5jbGx8uW1Nqa2FVZ4KE/ZmgRZybQYs9mpA3wihfbXZ3P03s/43B9CQAxpnBu7XQJV6d5xrA90HUG3aPS+LZwEyeaqsmKaM+1HcfSN6YtTtN+8QwF5ggh8oF6nDHeUvY9t8s6dwwY1ZVO3VM4tL/Iq++SqwYSE39mRaijo0K5aupFfPyVt3do+ODOdD2JsVdaWcu9z39BXWNzWEB1nZlnP1pFVHgIlw1vjperrjfz7dZ9HC+vJjM5jkcvH8O0Ad257b+fNWdFCpoFX3F72zszK0GL/3KztaRbck9xbR3LcvOY2q2rxzpDjUbvPcRxz9jgUHaXF7O/qox2oZGMSEnX1V5sya2dL2Zt6QGaVE9DNtwYjGKwIpEoLWJavyr4gcLGfPbX5iMQKI5FhRvD+X2Pu+kWqe0jilDoHnUR+2o8KzI46Rnt/3i7tVhshZite1FQUR2/RO2fQVLZ8AUdYn+PQfGu3gIg617Vn9ReAOYFEHZ9m6zxfOP0UycCBPgJqLQ0khWXwP7yMnQtJ0XSKseR9A7Wd4ZiGYWiHTXo4BRa2hRFAAAgAElEQVStcJbIU4R+wL8QUNBY7tFWYq7id9veot5N16fKWs/zBxYQaQpjUrv+buMFk1OGMDllSCt+mACnSKAWSwuEEDzx1u08++gnbF+Xg5QSo8nAxBmDuPv/tY1U2t23jiUiIoTPv9lGVXUDoaEmpkzow923nFwB/vPVuzwMMXfe+W6Lyxj7MbeA+99aQJ25OTzglcUbmHv3VUzs04Ulu5vFZZ1B/S5R2Jb/d/RrWYzSI/MSIL/SM3apuslMWb1+3B1ScsJWy7SFb7uaMiJjeX3sDKptDXyUt51Scy09YtpxQ9ZA0iOaw7B7RqfyypBb+M+BJeyoPIpAMDwxi990n0x5UyX/L3seVulpqJU2VbGoqIKoIG3HsjsWXmNrZO2JbfSIai6fNLXdtRyq20eT6nlUnB6WxYAYP7qPp0CN+QfXKgwt9m1VNlBr3kRM2ESvcVKtBvshr3ZXv2U7ImCMBQjw0/Lege38efNSLYDdgEeIliIEnaJjya1rXaCxopM16XxtU+2OWDPRfJTh2My0QH6tVUr8Gn4/Vhzm3cOr2FddQHp4EvW2Bg9DzJ0PjqzyMMYCnD0c9XUHoIm/SmCdlHLbOV7WOScuKYq/z7uD4mMVnCippkNm4hl7xNxRFMFN1w3n+quHsH33MZDQs1sKwcEnD94+eMw7RstJ3vFy7KqKXVV5aP5ClyHm/Mgvr2/gppc/4VeThiCAZXtzsava+79TXCx5VZW+ox4EqAbpFQMGkBnrKffx6YFsaq36MaLGYEFOjefedKS2kpnfv0e9qHftPRtL8/n40DZeGHYVkUHBJASHkxmZwIC4DP43/C5qrY0oQiHcqCVq1FhrvAwxJ6pUsKkCY4vko1Wlm7mz07Wu1x3CMnmgyxN8dfwd8htyMAoTg+IuZkq7mRiVk//btAbFTzal1u+jNqkIBUIAHzGFysmzcH+uBIyxAOclx2qrmg0xJ44Nsm98O76YciNvH/iRv29b7jVWqiA8Qjt8aYk19ysCl4irdJPpbk4Xdxhk+J6qsLGcV3O+82hzql63JK+uGCllQGvnJ0AI8WdgJvCFo2meEOJTKeVJ9RJ/CbRLi6Nd2pkmyOuTc6iEf764mNzDmnEVGmJi5vRB3D5npN+//aQY30ZhSLCJBev2EBxqoqJOk+ZwxYI5pmy0Wnlh4Toyk2L59NdzqDab+TH/OF9s3+13L2gfGUlBoyO5we3N3iE6iglZnvqB20u9j3idA23oe9prLE0oJoFwxalKzGoT92760GVMDohL46lBV5EWHkukKdRj/Ikm/4kVdlW49iyBtv802s2oUnVpFTbZzXx5fD45dbu115hZe2IxBuxEmSKIMMbQK3oEIYbTr+AQHToBRYSiykavPqOSSETIMN1xQgQhQ6dB42f6/aFXnvaazncCxliA85IvD+/xKemwu6KEGouZGZm9eXHXD9R5yUkIrXiyWwKXvxgypUWMmWuMl9aYQKrCLVGgeQ4hpFtwrVuP1HTSWt4/PjgSIQT59cV8UbCCPTWHiTKFMSFpMJNTRgSC9tuWOUA/pz6iEOIpYAetEK8OcPrU1pl58M+fUl3T/IHcaLbyzicbCA8PYvZVvo/krxzdhy/W7EJvC2i0WHny/WWEBjk+vpwPXjrv8cOllTzy/iJG9+3EW+t+1AweP/I4ZqvN7RxTCybrnBjHq1dMx9QieD8uJNR7Auc6/D1juf1MiiIxGDzFbbZVHOP2de/y7SW/JkjxvGfnSP91HjV9ModQrGP9PaIyPUSjFxZ95DLEnAsKEmY2VyxwtSwumsd1HX9Hl8jTiyEzKFF0iP0bRysexf0HFpjoGPd3v54zEfkw0roHbPtatD+CMF241UgCO36A8xL3TMiWqFJSY2kiLiSM/469hviQlk9wzZubJl2h6m7qzf36fRJN6VxRVIwGO0aDHSEkEcYQj6tMBjtBBlXXAwYCVefe01OHsq/mCL/Z/ixLSjZxvLGUfTVH+E/up/xz39t+1dWdVFur+bZwMW8ffp+lxStosAX0k31QiHb24SSYFuKsAdqexct3exhi7ny6YCt2u++svh4ZyTxy/XiMLSQ63OtINlpsGCycNNk6r7iCt9ZqiQQuMVgfnPDYdzSranD7DnSO9/Yczuzmo/b8yd66bslAzXqG0uPrWH0Fi45lew3tHNGeQXFdvdoBjMLuVQpJAiXmUorNmmfSLu1sqVjtMS5EsXkp9zepDXx09BkabKdfvCIhYhbdkhcQFz6TiOChJETMoXu7hcSETfY7TiixiPjPEDEvQtgcCL8HkbAYEX7Haa/l50DAMxbgvGRwUgfm7duq29cuLIIOEdEADEtOZ/2V97KqMI/KpkaSQsPZWJrPodoKOkbE0CcumYe3fI2vHVL4Ua0GgdFg91DDFo4U+ZsyR1LYWMXh+kIKGn3Ht2izKOB2bHFxYm9uzBjH73f9B7PqHXOy7sROdlQdpH9sN59z7qrazYs5r2JxG//l8a95tPuDpIcHari2oBqtxu5StD+EicBmIcRLAFLK+8/l4i5UDh3x/b44UVFHTW0jsTG+ZQpmjruIQ0UVfLxyh5t6q6fVJQCh6gu6el3o/FaluYalG6oBXffEZ9l76JIcz5AOHejhVkWhb2I7fj/kYp7evMZjd+kcE09eY5m+gSgkwi2mS7hZhy0fCl/ct4IpHXoRbPCM4xoa14MfKw66tUiMikqoQT+WrMZWwys583miz0NY1CbMarPBKZAYfBypWlQzu6p+YFiCb82wkxEefBHhwRed8jghTBAyBRFytsrKnn8EjLEA5yWT0rrSPTaR/ZXeG/r/9R6O0c1CCjIYmJTW/LQ4LjXL4/r1pUf4Mn8nLVSFOLn2vb7XrNZm5t3DazEoqs/sSnce6nEFdmnHbLcwOL4L3aPSqLbWsbfmsM8xG8qzfRpjFtXCq7lveBhi2rrqeC3vTZ7q+7j/Bf3y+NLx5WTVOVrHL4rEBH3NPYCw0CDCdaoHuFNZ28CXa3c7Ai99Xzcgsz3bCopQWykPLQDs2jdSwKD0VNISovls717d622qyuPLV4ICo9PTeXnaNCKDtbXffdFQJmV0YUHuPuqsTQxL6cjywhzycvUNUcXgndEthLchBlBiruGrozu5LnOQW1slr+V943FdqMGKyY9WIsD+2jwKG0tICUkiISiZE5YSx+/C//5VYyv33RmgTQkcUwY4LzEqCu9PnMWVmT1dcROp4VE8OXQSN3U/tTiGpwdfznNDr6JnTAqu6sDCKYkhfB5hBhuE3yNM8BeLphFlCmN66hBmdhzFjZnj6R6V5n+Ac34/x5TbKndQb9c/kjzeWEhene/U8F8iUsr5/r7O9fouVKZe0kcrFaTDlAm9CTL59wVszzmO1eYQIfVja5iCDKiq1L1G4vCCtXD+OLeBMIOR12ZNZ0hGB5/zu+uN/ZCfzx+XLvXo7xQTx28HjeT/DR/PxIwsLKoKuoFjAtWm8ECvi+kYEUuwwUS0yUfcmYOlhZ4G4rLibV6irfaTugU1aqx1CCEYl3S5q031s/8BtAvJaNXcAc6cgDEW4LwlPiSMF0Zfzvbr7mfTNf/HDzPu5oZupy4HIYTg8v/P3pnHV1Feffz7zF2z7wmBbIQ1YRdQQGRXsAqK+161Vm2rtVpra7V2se1ba9++tVq31q24Ky4ogiCisiMgeyDsgUD2fbvbPO8fN9vNnXuTACFAnu/nkw/ceWaeOQO5M2fOc87vpA3lw+l3cGXGSJ83UUGTqnXrO5IkzGxmbEJ7jlPwG5lJaLxy3j2YNf9+clGWcLKD9JIbHx8gHwWodtUEtarGHUD7SKE4hSQnRfGbBy7F3kbK4txRGdz1/fa1xpqOC/a+k5IYxdq9h1tywVr9NCvvCxAu/Jw1kyb4/ZwZRIbYuXjwQJ9oezA+37OHoprA38EpfVp/rwWtHbMxCSncO2QSX37vJ+y48lcsvugezMbJpgB42txgqlz+322nbsKtB55DSrBpVlJDvV0XJsTPYE7vmwg1hQMCl1/bAS+x1l5kRxpXPSpOPmqZUnHaE2axEmYJrlvTEYQQ/Pmc2VzUezB/37mMPZVFzTlhQsrmRUsBOKSTCHPg0u7mhU4ZKDomEXiCRs7uyLyMh7c+i6PNcuOo6P7kVO1iW8V2RsUMYVjUYB8ZgAER/dtO1YxZmMkISw98UoXiFDJ14iDGjEzn69W5VNc2MCI7hexBwSsCmxg7KJW4yFBKq+oM2w3NHDOI9XlHfDe3bbCh0dRyFlzelkpj+6WQHB3B1aOH0T8xDoAahxO3Hkj4ubE6u9Fn8UhJflUVieHG8hsXpw9i9K5NbCz2rRGxaiYeHOXrhMbYQrkkZRgLDm81nGtqL99k/aHRfXn38Ndt9hLUuS1YNQ82U9t7jkBHcFGvSYS1up9NTbyUifEXcazhMFZh47uKpXxbuhiX9N6LMsKGcEXKT0+a7piifZQzpuhRCCGYmjyQ1LBoLlv+rI/0RVu/aXP5YbyB/KZScWjSG2vWHxM0Vmu2XpbwjksB35XvJznEWMMpK7Iv/xh1P/OPLGdn5X4iLGFEWjRyqnayp8a7PPHpsWWMih7CLwbfhaXxxpgRlsbI6OFsrvC/gU9NnESUxbjNiELRHUSE27n0os53nrKYTfzmlgt56PlPcbo9zdEukxD8+qbpXH7+UEb+/B+BJ2iKkglAg/jwUF64fS5ZvRP9dq1xGou3GtqlaaRGRwcc/yJvLw6XC6RXnNqsaUzsncG9wyYwKsHfEf3RoMmsKNxLudM39WBgZCJXpvumZEyIH0L/8N7srWnb91IEzD2bEHcON6bPNbgOK2mhXu20i0NuY0riNRQ3HCbMHEWcLXDvUkXXoJwxRY+kf2Qit/WfwGv7VgbYQ1LpqsCb8tIiBiuRrYq6JBZNb26fYlQVFWIKHtFLDe3FdWkzCDHZ2V9ziCd2Pee3z3cVO1iQv5QrU1uqmu7pfxdv5b3HypI1OHQH4eYwpidO5YqUOR39JzjrEUJ8QpBsIyml+sc6Ddm29yiLVucgBEwc2Y+3H7uJ+Su2caignJSEKK6cNJzM3t6I1sDkBHYfDVy12Ss6AqxQVFWLFPDJ5l2kxEYRYfctHth2rDC4Ua2+05cMGkR8qHHU/N09W3lo1aLmz7qUOHUPUpeGjhhAengcb0++g//krmRF0V6smpmZfbK5vf/5hFl87TQJjb+NvItn9nzM10VbcEkPibZodOpwSv/2UTGWSO4fdFuHdAtDTGGkhQ1udz9F1yA6omd0ujBmzBi5YYN/41mF4ni5Z90bfFO022+7WfNgNqhQaq2abzW5EaL14qYv4eYQPr7gUWwmC9WuOjaW56BLndExg4myRvBF4WreyfuMEmc5AFHmcGrcVYZvt7HWaF4Y8z9+2x0eB9XuGqIskc2Rs7MNIcRGKeWY9vf0O25y41+vAHoBrzd+vh4olFLef5JM7BDq/uXlyLFyduQeIyoyhDHD0320xH74x7fZssc36jM2O5X/e2CuYcL/wo05PPy6b9eLpqR9qQFWf298aJ8kXr/rGqxm73wOt5sL/vVvyuobDBPUpGhZokyOjGDJrbcSavH/rrl1nfPff47COuN8svcuvpGxSYELBQJR73ZS0FBBnC2CyFYJ//VuBzWeBmKtEeytyeMPO56lppXWYKjJzqPZdzMkKnBag6Lr6ej9S0XGFD2ax0dezu2rX2ZfTeu3a4lJM35JEcLbbmRARCIH6456HSfpjYqJxsaWOgKTMPFw9lXYTBYW5H/NqwcW4NBdAJiFiQnxQ1lb5tsesdJdAwhMsnW5uXfJs8pVyqJjS5ieNBVrK6fLZrJhMwWXCOipSCm/BhBC/G+bm+EnQgjlFZ1inC43f35mMctW7WoufEmIDed3D1zKiKwUHnv+Mz9HDODbnYd5fdFGbp9znt/YJaOzeG/1Njbuz2/uK9vU4DtAXjrb8wtZvG0P0REhvLRmA9uOFlDjdCG0RgdOeBNBm6soG33FpPAw3rvuOkNHDCC3oiSgIwaw4uiBTjljbt3DM7mf82Heeuo8TizCxEW9h/Ng1mzCLXZCzDZCGntWDorI4IUxv2N50XqO1hfRyx7PtMTziLAE1nFTnF6oakpFjybGFsYbF9zJ3LRRjWr8EpMWPPFeItlfW4AuBVJKTEJi1rx/mjSJWehMScxmcuIwNpfv5oV985sdMQC39LC6NFCfatFKDU1i09xYNQ9mTefNvHd5cPPD5NUdPmnX30MIE0JkNn0QQvQF1FPqFPP86yv4YuUunwrk4rIafvnnDykqreaL9bkBj53/5eaAY/2SvUuWOq0cMQj6dHt7/RbufOtD1h48TK3T5fW5dNA83oR9pDcdgcbesn1jonl8xgySIwJrp4WYzd5UhlY/vuOdi1w/ufMT3jiwkjqPN5/NJT0szP+OX373puH+4eZQZveewl39ruGyPtOUI3aGoZwxRY8n1GyjoKEMs0nHbNLRtOCSFaLV34xEX4WAlcXbKGwo59OjKwxmkAFaJzWNet/xLZrbb+5yVwVP73m+Q+2SFM3cD3wlhPhKCPE1sBz4WTfb1KNwOFx8usy/vQ9ATZ2DtxZ8iztIe6TKmgb+89k6rv/T61zz+Dye/mgl5dXeJblJWX1bIlitvy9BviI5hcWGw6Ix9VMgEFIgPKBLOFBewY8XfMJXBwILNX9+cA+a1EAXjT8tDpkmBJdm+OZjNXhcLMnfxUeHtnKszrcBeImjmgVH2nYgkWhCZ0NZLssLtqM4u1DLlAoFUNBQ4fM5cLu7VpWUjW6TETo6Sws2sq/mcKtYV8vzIbAkRuM5gjhsBQ2F7KrOJSsycLskRQtSysVCiAFA09Nwl5QG2c6Kk8r2nfl8szoXXdcZOKAXdfWBKxbLA/SwbEICzy5Y3fx5b34JSzbs5pVfXMcFWX2ZMCidVbmHmsebswcCfIfqdHdLg/G26LSMtXLw3LrO31asZEpff33ABfty+J9vv245Od6UBim9OaW/GD2J1IiWCszP83N4dMOnVLoaAG+F6LWZo3ls5Cw0IcitOoZbelqup/k6BCZN59Ft87i0dAwPZV/ZoeR8xemPcsYUCiAzPJEjdWXNnyUCXTY5Xi2SFWZN93Gigi1nri3ZRomzzHefxvJ8XQq/5rxNWDQPWjutXSqcFUHHFX6MBjLw3vNGND4o/9u9Jp2dSCn5y98/Y/GyHT7bLRYNl8n4LSS7Xy/2lpSRm2dcGVnf1L+oFfklVby2ZAM/v3oyT/9gDo+9s5RPN+9qHhce4x6UEwaks+LIIQJh0gRudO+SZxs/J6e4mMKaGpLaaIy9tKMxBbGNvERTsU/v0Ba5mf3VJTyw9gNcrZT0PVLy5r4NpIZG84NB44m1hrWao/lvSMCta5g1nU+PbiA5JJZbM6cHvJaO4NQb2FW1CafuoH/4UKKtCe0fFAQp3ZTVr8DpKSbcOpQIW/YJzddTUC61QgHckHF+s55Ya8yajknzYNY8WEy633Mk2Grh/lr/G36TnplHQp+QJN8xBDenX8aY2OHBZcdBNQPvBEKIecDfgInA2MafTldnKjrGvLfX+DliALpLR3j89w8PszFrcja/v+tiosLtfuMxsaFgNv5CLN+8FwCr2cwfr5tJalxU85gAhNvrlLVuh7Rm3yGi7f7nAe9y4txR2UgLhk9HgbcXblv2VXh7OAZ6OXtm65rmv7+9f5OPI9aaefu+BWBwVB/CzbYA8wl06R344PCaE0pZ2FKxij/tvJM38/7O+0f+xRO7fsJH+f/2a7nUUaocW1h3ZBrbi+4it/RRNh27nK0Ft+PWg3cNcTnWUFt+PzWl36eh+h/onsByJWcrKjKmUABj4zL53fAr+ceuRZQ7vS1Hoi2h1MvqgH5RsCa7keYQdCr8Uli8xwhmJE7gnoHXsal8J9srcwkx2ZmUMIbkkETgInZU7uKlA69R7PC/KZ0TM5LeIUqUsROMAbKlSrTrEtas2cPSz7dTXdNAdnYfXv9gfcB9I+02qnWXt5ckkBQfwe8fmE1kRAiRESF8+OQPWLhyB1v3HiU6IpTrLhrFI68spvig8TJm6/9Rs0njhTuu4ME3FrLzSJHvjq2WG6WEqqoGLCEarjZ5aj+ZNI7z+6fx7g7jnKyRycnEhPj3k0yJiCKnPLADsaeilPyaSj4+tJ35B4zV9gGO1lVy37p3mdF7UHNHECO8ItOSUmc1Tt2NzdR5WZvChsO8k/dPdFo8ZInO2tLPibP24oKE2UGO9sej17G98E5cernP9vKGlewp/R1ZCX8zPK6+6q84ap5u/ux2fImj9jXC497BZBloeMzZiHLGFIpGLu0zipnJw9hcnocARsSk8dDmV1lfusdwf7Nm/PYYb41AF1W429xLNbyVlkLA2rK1JB4O59rU2YyJHeo3x5Cowfxp2G957cDrrC37Fo/0YBImxsWdy60ZN57opfY0tuPVGTvW3YacbTz1j89Z8HFLZfCmjQe9LoRNwyjp0Wo28fbfb2HnngKiIuycMzQNUyudsfBQG9dedA7XXtSiPD9peCbbDxYYnn/KiEyfz2nx0bx17/WM/+2z1DpdXqmKVhGu5kplHZLDIsivqcLT6BjaTCaiQ+yM6t2bm0aM4PUtW/wi1DuLi1iTl8f4NN/I9C1Zo3h49RLjfyTAbjZz69dvsb+6DKHpBG6DKVmcn8PSYzuJsLsDztdEL3vMcTliAGtLl/g4Yq1ZU7qo085YUe1CP0esieLaRfSL/TVWk283Eo8rx8cRa0LqJdRXPkZ4/NudsuFMRjljCkUrLJqZsXEtN/gHBl3OPRtfoMRR5bOfSfjLX9g1C3NTLuCY4xCbykv85paNOR8CcEkX848sYkH+YvqFpzK7z0zOjfVtfRJisnN3/zu43nUNpY5S4m3xRFoCl9YrAhIP7BRCrAeaE/eVAv+JsW3bYR9HrAkBaC4d3ea/nJfSJ4beSdH0TgrcTqgtl5w3mLeWbaK8usHnJL1iI7h15lgAahocvPzlBhZ9t5vqegd1DS5vEn5TNAx8HCtdg7wq3wpGh8fD44uX0zcuhh+ddy5vbtuK3iaY2uBx89NFC7ksO4vMmFguGziYMKuVGwaPZF3BYT4+kGN4Df1io9hV7VX5l7pAGlRhAwghvRXdQuL0SCwBigyaiogGRyaztzqf/hF9jHcMQpkzcNeBMmeRj8B1R2hwHwk4JnHhcBf4OWPO+gUBj3E7V6F7itFMJ5bDdqagnDGFIgipYfHMG38/C/M3sKl8H+tKdyKEbljp+OTIu8iKSuPKVfcFnE+XolU1Jrikzt6a/fxf7nPclnEjF/Wa4ndMlCVS9Zs8MX7X3QacjXy13NjxAG/kyahk+Por/YVbW7M/v5TXPl3PxpzDhNgtTB87kK+2H6CiusEnSGU1mXj8tpkkRIfT4HRz+7Pvk9NqaVKAV1qiSfy1TSJ/IEFYgHnrNzN+UJqfIyaFRJqgxFnHS5u9shN/X7uS1y67kiEJSTw1ZTYpEVH8a+tan+PSIqKp1VsX7wp0XUPzKQbyahxaLS0veR5dwyQ8flE0IXSE8MpcrCrZyurSrZwfP4TfDLm5U1GyBFtvdlcb6x3GW3t3yhEDCLX4V5k224wVu9nfYZSynSradsbPJlQCv0LRDpGWUK7PmMSTo27jouQRho7Y8KhMhkb3xSM97dRBtsXb4FcT8Ebeu7haicMqTg6NSvy7gIjGn5wmdX7F8eNwBF5Ga2zd2oKEUcPTmHBev4DH7D5UxO1/eJNFq3MoKq/h0LFyXl6wjn0HivwqZZxuD+9/5c29+mTjTh9HrLUNmqvVBz8DjTlUVoHD7XttEtni1LWitL6eexYvbE6iN2saLR3KvT951eVUORt8D5QC3aPh8Wh4PN6qHrO5bYGQwOkx4XRrDI5M4dI+53Bb5iTCzBKrye1T2b2qZAf/2f9Z4IsyYFzcTMzC2HmbmHBJp+YCSAi9GKspyXCsV/hcLKYov+0W28SA82mmvmim1E7bcaainDGFohP8YvC1XJx8LhbhfbXWEFyQMIzHh98GeLWIguH/stkqSqa72Fqxs8O2lDqK2Fm1kaP1Bzt8TE9ECHENsB64GrgGWCeEuKp7rTrzGTMmcCQkJNTqjaxI70Nm+uTB/M/vrgg633Pvr/QuL7ZByMZIWxu+2brf++fOwEKsAQUDg7wxpcdGMykjw3fX5l5L/vsfrCjn26P5FNfX8uy2NS1RuFY/NQ6jlywB0vsy5n0hM5ShRZcaoVo403sNYXnRFhy6xK1rzRWVTSw6uh6n3uJEVrqqeO/wR/xh5xM8sespVpas9amSjLclc1P6g0SYW5aMzcLClIS5jIubGfgfKACaZmN40suEWga03kpi2KX0i33E8BizbRom67mG122PfLDT0bkzGbVMqVB0ApvJwkNZ13Jnv0s4Wl9Kgj2aBFvLG1+o2U4vezwFDf45Y6JRzLU1rRX8hYBjDQXAiKA2NHjqeefwc2yvXN9ccZUeOoAb0+8jNoBGkFt3cqh2CzoeUkOHYTf1qFYpjwBjpZRFAEKIBOAL4P1utaob2bFhP/t25BObGMl504dgsQZ/FDTUOykqqCQmLpyISG814QWTBpGV3Zucnb79JE0mjd/+di6JvaIoLK4iPTWOpMTgy+xuj87abYG1v3yEWJvO07h+ZzF1PqYgPI19KA24+dyRDE5I4PKsLD7MyWl0qESzOr9s8jBb+Qll9fUczq/AFeBlzOmUxETYqXK1iZAhEQEKgVpT1FDBQ5tfbbIeicCte3NXm/ro1noaqHHVEWuLpKihmN/vfIIKV0te3NbK7Wws38y9/e9EaxSKHRw5ml9lPc++mu04dQd9w7IIMx9/SkSYdQBj+yykqmEzTk8R4dZs7JbA/TiF0AiPnUdD9d9x1r+H1MsxWUZhj7gHi/3C47bjTEQ5YwrFcRBtDSfaGm44dmP6pfzv7ldp/XCSwtEAACAASURBVBotkJjaSGEIdExtXvn7hqW3e+73Dr/Atsp1PtsO1e3hpf1/4eeDnmy+0Taxo/IrlhY8T73HW4RgEXYmJtzAuPgeExzSmhyxRko5wVUBIcTVeHPRsoBzpZRnROPxqvJafv/Dl9i5oSWaFJMQwW+ev52s0Rl++3vcHl751zIWfrCBulonFouJSRcO4ScPfY+wcDt/ffI6Xp+3mqVLtlFd3cCw4anceNMERo70/h6np8V1yC4hQGjCK8DXQaad0x+AGcMHsHTrXsN9zGYNj9T9omNCAm7A3PItDbVa+Pm0iZyf6bX9N1OmsGjvHho8vkuWAq+yftOcmhAMTUxiQ3GwnrEC3SUYHZdKTmUBdW5no0Cs9C5Z6hIphfezAYWOYsPfWI/U0KQ3zyzGEk5UYz/Ktw/P93HEmlhftpHvKrYyOmZk8zaTMDMwYqTfvidCpL3j8wktlJCoRwmJerTTRQNnE8oZUyg6gVN3kVdbQJg5hOSQeMN9JiWMwa17eCvvM4ocpTQrjAnpbSyueaul2uaeRZojyIoMrqtT4Sxha2VTgrBsXj0BKHQcYXf1FrIiRzXvf7Q+l0/y/4ZspXrpkg0sL3qZKGsSWZEXdOr6z1AWCyE+B95q/HwtsOgE59wOXAG8cILznFL++fC7Po4YQHlxNb/74X94bdVj2EOsPmPP/e9iPnnv2+bPLpeHZZ9tpaSoir8+fyuhoTbuvGsqd9419YTsMmkak0ZlsnyDsVPVNoqVGB3OXbPHA3DRiIEs2JDDql0HffeJDOOlH1+NSYNLn5mH0+Mr4yB0ePKyWZjNJoQQnJ+ZRrjN1jz+aW6unyPWYhDNS6CXDcwiJTKScFsmdpM5wDGSSoeD9cfy+cO5F7K5Mo9P83Y0fncFSbZIHh01g7/lfEZRQ7XPkQMjEznqOBwgzU00d/O4IvUCTJoJj/SwoTxwY/V1pRt9nLHTiZ7qiIFyxhSKDvPBkS95L28pVW6vKOzgyAzuG3A9aWH+AqzTks5jSuJYlhas4uUDbyPx3qAlAqSGSdN9RB2tmpV7B9zlF9VqS7HjGBIdrVEoo+neJSV40Chy5JNFizO2sWyBjyPWmg2lH/cIZ0xK+QshxBV4FfgBXpRSfniCc+bAmfXwKC2sZM0S42bdVWW1rPh0Mxde3ZK/U1FWw+KPjKvttmw4yI4teQwZcfI6Qfz46gvYvDuf8mrfCroJwzMYN7IvSzfm4nC5GZedzsXnDebNr7+jrKaeGSMH8PQP5vDx+p0s+m439U4X4wamccPEkcRHeiNFK391Jw9/sITVe/NweTz0T4zjlxdPYlxmYPuPVPlHlpoQCGxmE9cNHcavzp8EQLQthJ+OmMBfN30T9Dpf3Lmeb+bezX1DJrO5NJ9oawjnJ2Vi1jTGJqTz1oF1rC7ei00zM7PPMBLtIfxm2+sB5zNrJq5OPZ8b0qcB3nZUwRT03bJ9/TLFqUc5YwpFB/js6Epe2v+Rz7ZdVQd5eOszPD/mESIsoX7HaEIjzhZBlNVMrcfbJFkgmBg/niv6XMqXRSsodpTQO6QXUxMvINYa064dMdYEb+5Zm+UMIcAkdUI0X3XwUkdg7Z9SZ+CxswkhRF/gMynlB42fQ4QQGVLKg91r2aml5FhFs/K9EUX5ZT6f9+UW4nIZi4IC5O48GtQZ27w1jwOHSkhMiOC8sf0wt5PblZYUzXO/upoFK3awKecwoSFWZo4bzOxJQzGbNK6b7n3JePqTVVz553nNxy1cn0NidDjv/uomrho/zGdOp9vN0fJqosNCeOaGzsnK9YuNNdwukWhmQWJEOJsKj/H2jm3cMGQ4FpPJTw6jBdEo9QFHair58MA25vYdRnq47znibOHcM3g69wxu6TdZ73ESZrZR6/bvbW/VzMwb9wB9QluWg82amezIweyoMpYeGRHtLzKt6H6UM6ZQtIOUkvePLDMcq3BVs6xwHZen+C/T5Ncf4+m9L/i8iUokK0pWMyA8k+vSgleXSSkpdOSjoZFo7w1ArDURq2bCY/B2KwRUuX0fqNHWZI415BrOH23pFfT8ZxHvARNaffY0bhsb7CAhxBd4lfvb8oiU8uOOnFgIcSdwJ0BaWvf2E01Oj8diNeNyGkdGtm88yBvPfMFFV44hITma6NjgRR7RMcbjZeW1/Pp389mV26Kan5QYyR8fm8uAfsbSB+8t+Y43PttIQUkV4aE2Lp00hLuvPh+7zUJxRQ1Lvs2luq6B8DAbL33u326pqKKGu5/5gLd/5e1OIaXkhS/XM2/VJirqGjBrGtOH9OPRy6YRG+7/4mTEpQMH8b+rV1FUW9u8TSKRZvAIyaGqCg5VwZaiAr7OO8g9Y8/jo707A1dwtprlwbWf8u9d6/jv1OtJDDHOPW0ixGTlngGX8tecD/xaJP14wPd8HLEmrkm9nD/l7MOpO322Z4alMz7OqHpR0d0oZ0yhaIcadx2FDaUBxxce+4bz4oY09pVs4YvCrwIuCSwuWMb0pMkB59xasZ4FR1+n1OnNO0+2p3JFn1vpHZJu6Ig1UeLwrWwbHXMJOVXfYFSTPzq2c+1OzmDMUsrmp5KU0imEsAY7oHG/GSd6Yinli8CLAGPGjOnW3piRMWHMuHIsi95aYzi+ee1+Nq87wDvPL+fhf9zA+BlD6D84mb27/LtIRUSFMGHKYMN5/vzkQh9HDKCwqIpf/+4D3nrlTsxm37LIVz9ex/PvrWr+XFPn4O3FmziYX8rkCQP5yxtf4m7sIamb8auqbGLXkSIKK2pIig7n2S/W8uyyFvFVt67z+bY95JVW8u49N6AZiQW2IcRi4bW5V3LfooXkljZ+/w20xgC+PLSfL/P3NibZe3XDMEljp6xRyiK3sphfrlvIK1OubdeWOSnnkhIax/uHV5NXW0yf0DiuTB3PuXHGOab9wzN5LPshPs7/jJ1Vu7Cb7EyIO485vS/Gqlkod5ZS5Cgg1hpPgs3YQVacWpQzplC0g91kI8Rko97jv0wAUNBQzENb/8YTwx+kdyuHzCtTYUywsTUlX/LukRfb7H+YF/c/wc8GPk6oKYI6T7XhsbHWlvPr0kOMNZkZSXfyddGruKTXfg0z4+KvZFj0dMM5zkKKhRBzpJQLAIQQlwH+2iM9gLt/Oxenw8XyjzehNzXJFgLM5mYRPJfTzZO/eIfXVzzCLx+/god/8l9Kilp+30JCrTzyP1djs/sLhh45Ws6G7w4anru4pJrV6/Yx6fwWB6K+wcXrC40LUdduO8Sq3Dz01r0l2/GhCsuriQy1MW/Vd4bjOUeLWJF7gOjwENYdPEyo1cqs7AHEhxtH+QbFx7P45u+z+dgx8quruGfpp4FPrgvQGsNiEm/81dzW/5YIU8u2b47t41hdFcmh/nISpY4aXtv/DcsLdyKRTEoczM8GzSHR7i+eakTfsHR+NvBHPtsaPPW8vP+fbK5okcXJihzOzel3E2Hp2LyKrkE5YwpFO1g0M9MSx7Lw2EqDUYlJSGrctbx/ZDE/HXBL80iSLZFtGIu4JtmN9cB2V2/l3SP/NhxzSScrSj7nvLgLWV70gd+4WVgYG+sN5mwonc+GsvnUuEsxCxvZkRfQO3Q4mjCRGXYO4RbjfJizlLuBN4QQ/8L7mDwC3BL8kOAIIeYCTwMJwEIhxGYpZeeVMk8xVruFB/9+I9//xSWsXrKN5x9fgFHX6vpaB6uXbmfG3NG8/MFP+Xrpdg7uKyKxVxTTLh5OZJTxUl9hYZXftmbXQ8BHn29m9eYDDMpM5KLJ2ew7UkpNnfFLDuCVumgVxRIysF6rAFITojlQXEZ1Q+A5/7Loaw5UVTR/fuLzr3nk4qlcN2Z4wGNGJiczIK5jMh3NtKq4BAlmHaH5Cj9LoKi+xs8ZK3fUctua58mvb2m8/c6htXxVmMNr4+8mwX58WmCvH3qBLRXf+mzLqdrKv/f/Hw8M+t1xzak4OShnTKHoALf2ncOhumNsr9zXaqvXEWu6uW4s2+FzzPSkySwvXoFH+idBX5g0zfA8H+b/l2Dy4EfqDnDVwMcpdxaxuaLFObSbwrgm5R5irAmsLXmTlcWvNY+5pYOdVV9Q6TrG9Rl/b/9izzKklPuAcUKI8MbPNSdhzg+BE6rI7E4SkqNJG5Bs6Ig1UV1ZB4DNbuGi2aMC7tea1JRYNK1RNwsal/W8XxAJbNh8CGkSLAT++/46HvxxO8KebfXBmsRaDSJk5/TvQ0x4CA3u4NWCB8sqfJ58Ll3n9wuXMTIlmcG9/F+S9peV8ea2rRysqKB3SAT5ddXGy49a2++tYFhcEoPi4pl/aIthY/AQk4W+Ef4vRm8dWu3jiDVR2FDJvAMreSDre0Gv0YhiRyFbK4yjkAdq97C/JpfM8ODSOoquQ7VDUig6QKjZzhMj7mNARDKa8Iq1WjS9Wf0avCXmrUkLTeFH/W4n1NRS4aihMavXdGYaOGPFjmMUNgSvcIwwR2ESZq5L+xk/H/gUV/S5ixvSHuDXWS+SHTUWl97At6XzDY/Nr99BXm1g/aGzFSFEkhDiJeA9KWWNECJbCPGD7raru+k/pLfhUmMTQ0YHbnfUFiklBw+WUF/nYPLEQX6OGDQK2UugsaKzpKyGN+evZ2B6osGMYNIEepv8MCFBuPF7XxnYJ55//dhbEJMcHcH4/oGLJYyahEvgvU3b/bYv3beX770xj5e/28SXB/ZzrKoGzS3wU4sRxvlhj4+/kCfGX8zAKGNNwhv6jyLSavfbvqJoV0D7vwkyFoyC+iN+BQCtOVofTLRW0dWoyJhC0Qlm9TqfF/a/Yzg2Ps4/ejA+7lxGRY9ga+V2HB4H2ZGDibN534Tz6/LYXLEej3STHTWSyKBtSLytlHqHJFHUcJhEeyoJ9j4k2Pv47FXiOIRDDxz4OVK3nbSw01PwsQt5FXgFb1skgFzgHeCl7jLodCAiKpTZN03g/f/490wfO3kwA4cFbmPTmrVr9/Lcs8s4csRbydunTwzJvSI5VmKc1yh0kEKCEGzNyecvj87l8f8soaKVvphJEzxwy1ReWLSO0qo6n+M1HX5xxRSkGUqr6pg1ehD9e/s6O7+/YgY/+M98Dpe1aIVZTBoNJn81/iaKqn2/Nw63m18tXeInFgtgx0xSZBjhVhs7ygv82iOB18FbcnAfIxN689rU6/nVuoWsKNiPxBsRu6H/KH450jhCHkxvUDtObbtoa/Bl1ph2xhVdS7c6Y0KInwN/AxKklD0yoVZxZjEtaTwrSjaws2qfz/ZkewJXpRinDNlNNs6NHd382ak7eePQi2wqb6lqW1r4CcOjxpBo60ORI58mdX0vErvmxiQk60oXsK50AYMixnBV6v3YTb65O3ZT8DJ5uymio5d6NhEvpXxXCPEwgJTSLYQILKDVg7jtwVmERthZ8N9VVJTWEBJmZcblo7n9oY4tg+3KOcpvH5uP290SKsrPL0faTWAKpBnvG9iKjw7jnb/eyidfbyc3r5jE2HBmTxpKeu9YRmWn8sf/LmXLPm9FZ1S4ndsuPrdZcywQfWKjWPDALXy+bQ87jhSSEBnGnFFZ3PLf9zlQ6r/8BzAoydehW5l3iPKGtr0kvTg9Hh47fxpjU/ow7LV/Nl9XE03Xd7i6goPVZbyUs55jtdUMiU7m/F4Z/DDrPGLtgSU2piVls6vqqOHY9F5DAh4XjNTQDNJCM8mr2+83FmtNICsycM6couvpNmdMCJEKXATkdZcNCkVnsWoWfjvkXr4sXMOq0u9w625GxwxhVvIkws3t6xctKVjAkoKPcOj+CcZbKzcwIW4KZc6jaLgbV3gkZnS/fJPd1Rv4OP85rk37uc/2GGsfeodkcbTeX/DRJCwMjgwsp3EWUyuEiKPxGSmEGAcEllfvQWiaxvU/msbVd0ymsqyWiOgQrLbAS5dtee+99T6OWBO6RwZ0xlo7YpHhdjJS4rDZLNx0qb/sW4jNwvRzBjBqQB+y0pOYNCITq6Vjjy2r2czsUVnMHpXVvO0HE8bw6CdL/faNtNu4ZrSvYGyt0xV0/jqXk3CLlaSwcApra3wXABsvPcJmZc6iV6l2tXzft5UVsK+qlOcnXRkwynVt+ni+KNhObrVv1XXfsARuyphoeExHuL3vT3lu718pbCWBE2OJ487MB9rt/qHoWrozMvZ/wENAh8QTFYrTBatmYVbyJGYlT+rUcV8VLeaTo8ZLnE3kVm/BLNzNN3YBhom/ADsrV1Pluo3INpWRM5Mf4N28X1LbSgBWw8TM5AcINUd3yuazhAeABUA/IcQqvBWQPaZLejD25xzl2KFSUjITSB/YeRHg3Fx/DTIAza3jsQZ4uLf6pb7+8rHYAjh/Ly5Yw38+Weujaj930jB+ffOM425DddU5Q6lqcPDCinVUNlZcDkyM549zLiQxwjeqPC41BbOm4db9nU2zpnGoupx/fLuKicnpzN+7w2+ZMtRsYV9dsY8j1sTSI3v4+ug+pvbpb2hnuMXOv8/7IfMPr2d54U50KZmcOJhr0scRYQkxPKYjxNkS+HX2E+RUbaWgIZ84ayLDokdhEipjqbvplv+BRp2ffCnlljOpt5tCcbzoUmdZ4cJ296v3lDZmOXsRQRJudXTKnQV+zlicLY3bM//DzsplFDXsI8wSy9Coi4i2+vfQ7AlIKTcJISYDg/A+MndLKYOHPc5ySgsr+Z97X2dHq6bho84fwC+fuomodpT3WxMbG87RoxV+24WEjMRojlXU4Gil+C80gS4kCXHhXH/ZWK6ePdrvWICVW/fz4gJ/cdoPv9nGoLRErpoyosM2tuX2CaO5YewIcgqKCLNZGZhonFyfGBbOjcOG89oW/6IXXdP52/oWodq4kFAq9Xrc0qtnEWWzM6vvAN47sDlgmdziw7kBnTHwOmTfz5zE9zM799LXHprQGBI1kiFRPS539LSmy5yxYK1EgF/jXaLsyDynTTsRheJ4qXFXUeEqa3c/Tfi29Q4m2S7QiLEaq2fbTGGMiu1cL76zDSHEWOCwlLKgMU9sNHAlcEgI8TspZfv/IWcpf7jrVXK3+lbPfbdqD0/c9zp/nndXh+e5+OIRbN9uXAF807XjGDdhAF+vyqWmtoERQ1PJTI+nutZBTFQopiC9Kj/8xrihedPYiThjAHaLmVGpvdvdr7DWN6lfIpGaRG8TQyirr2daeiaJUaG8v3c7Ve4G3t2zDTB5Kw5M0i/CLQP2sex6Curz0dHpZe/jszyZU7mSdaUfUuzII8qSwDkx32N07CXHHYlUdJwuc8YCtRIRQgwD+gJNUbEUYJMQ4lwppZ8s+enUTkShOF7splCsmg2nQa5YExoa0dYYypwtXwOJQEr/GzlAduR5RFqCV0A5PNV8V/oqe6uX4tLrSA4Zxai475MU0iOaBb8AzAAQQkwC/gLcC4zEe0/pkUuVOzce9HPEmvhu1R4O7j5GxqCORVFnzhrGzp35LFzoGz2aPXsUMy4cihCCS2f6JoYHWpZsTXFF4IrgonLjKs2TzYqDB1m8d6/vRo2AT82v8g6g2zz+1Zq6Buh+avwzUgacLFM7zO7qHbx/+DUKGvIBiLcmclmfGxgZM5ZvSxewpKCl80exI4/PC56n2HGIi3v/5JTb2tM45Rl7UsptUspEKWWGlDIDrxr2OUaOmEJxtmDVrIyJmRBwPNYaz+2Z93FB/CVtRgRuNNq+RPcPH8llKcFvkG7dwWdH7mN7xbs0eMrxSAdH6tby2ZH7KKjbcpxXckZhahX9uhZ4UUo5X0r5GyDw+tBZzuF9RUHHj+wv7vBcLpeH2NgwoqNCEUB8XAQ/+vF0fnb/rBOKpgxIMe5Q0d7YyWJrQQF3fuyfzixFkLQBGaQ9gC58vsOTkvsyPcgSZVdwrP4IL+x9stkRAyhxFvHKgX+yq2oL3xS9YXjcpvLFlDmMKzsVJw+VtadQnCIuT7mBgoaj7K/d3bzNJExcmnwN05O8SwG61Mmr28N3Fd807yPQmBB/GYMjR1HlKqWXPYNeIRntnm9f9ReUOvb4bfdIJxtK/8OloU+flOs6jTEJIcxSSjcwncZ0h0Z67L2vV2rwVli90jquN/W7xz5g3boWmZeSkmqe+9cyIsLtzJx1/FIJ100fxWdrc3C5fRVIhICbZ4457nk7ym+XLcPh8fiFK4QUQYRTAzQGbyTGGkqf8Ajm9h3KzQNHYwrS/aArWF60CJdBqqSOzqJj82nQawMcKdlXs5FYW/vLuorjp9tvSI3RMYXirCfEFMr9gx5jd9V29tXuJtQUxuiY8T4NejWhcW3aT5mYcCm7qjaiCRNDI8/zE3ftCPl16wOOFdRvxq07MGu247qWM4S3gK+FECVAPbACQAjRnx4sbTF8XD8yBvbiYK7/YkTWOen0H9Kx37WtW/J8HLHWvPLyN8y4cGjQvLBgDEhN4G8/mcNf3/yS/GLvf1V8VBj3XHkB44dmHNecHaHa4eCNTVvYerTQu0Hg62DptOo32QYtuDM2ODKRt2Zdf9Js7SyH6w4GHCtsKCAiiDdg1qwn3yCFD93ujCkUPY1BkUMZFBk4Z8sj3Ryo+ZacyiXUusvJq1nD+ISrGBhxXqfOYxKBb6AaZjRh0BfmLEJK+SchxDIgGVgiWzKmNby5Yz0SIQSPvXgbv7/zZQ7lFjZv7z+0Dw8/fXOH59m48UDAseLiavLySunb9/iXFM8f1pcP/3Q7u/OKcHs8ZKUnYTZ33e/sN/sPcu/HC6l1OhEIBKC7pfcp2ZEVV13g9daMdzaSyDiVRFqivK8kBkRb4wjVnFS7/bXXTcLMwIhxXWydQjljCsVpxgeH/0Ju9drmz0fqc3gv73Fm97mf4dHTOzxPZsR09lQtNhzLiJiM1gO0haSUaw225XaHLacTyWlxPLfoQbau3cexvFJS+iYw9NzMTs1htQZPxLdZT/z3S9MEWRnGFcMnk6qGBu79+FM/oVdNF+gu2fKkNBHEMRPgEWBqHTqTzUMX9M442WZ3ivFxU9lZZZwrOiF+Ksn2GOYf/hMe6dtofXrS7YSZowyPU5w8lOSuQnEacaQux8cRa81Xhf9Flx3v4pMSeh79I/wVZMLMiYyN77h8geLsRAjBiPH9mXXteZ12xACmTB0cUJB40KBe9O4Tc4IWnjo+zdkdUHFfSG8/TSGhf2zwfDvcrZ2wFkcMID6k4/ptXcHImLFMTbzYb/vY2IlMjJ/BgIix/CDzKc6JuZjU0GyGRE3m5oy/MDauZ0vknCrO/ldjheIMYn/NpoBj1e5Sih2HSLJ37MEphGByr0dJD7+gWdqid8goBkfPwW7qkUr8ipNInz6x3Hb7JF5+6Ruf7eHhdu67f1Y3WXV8FNUESl4HgSAxPIwbRg7nvPRUrv7wrcATSQEuwCBDoKQh8DlOFVek3MSEuKlsrliPjs6wqHNIDe3bPJ5gT1cyFt2EcsYUitMIc5A8r46Mt0UIQd+IKfSNmHICVvni1qsoqXmXqoY1mLRQ4sIuJ8o+TQlD9kBuvOl8hg1L5bPPtlBRXsuAgcnMuWwUCQmRp9yWosoaXvlqA1/v2I9J05g2tB+3ThlDTHj77YOykxIDjoVaLHx+2/eJsHmLXaalZ/LlIf9m2wi8SfwBVm/7R3W8SrUr6RXSh1khc7vbDEUblDOmUJxGZEVNZHnRfzESLEqyZxJnSzn1RrXC6S5kV9HVONx5zdvK6j4hPuxqMmL/qhyyHsjwEWkMH9F13VHyCsv5/Nvd1DtcnJuVxnlZaX6/Z4WVNdz41FsUVraIxb68fANfbt/H6z+9jqhQe9BzTOufSb/YWPaV+TdluGHU8GZHDODZmbN5cu1K5u3YjNPTmDYgYGJKOlFhVhbm7fKbIzU8igvTTr3Iq+LMQTljCsVpRIw1mUkJN/BNsa8Ao1ULYVbyj7rJqhaOVD7h44g1UVL7HrGhlxAVMuXUG6U4a3n5s/U8+9GqZsHU1xZvYOzgVP7vnssIaaXk/8ryb30csSYOFpfz1qrN3H1h8GpAs6bx6rVX8KtFS1h9MA8JhFjMXD9yOA9Onuizr91s4TcTp/LwhMkU1FZTXFdLQmgYKRFR1LtdmFYJPj24q7nB+dC4JJ6ePAeLdnZXLytODOWMKRSnGRckXk9q2BA2l39OjbucZHt/Rsd+j2irUavXk0ONcw8HK5+jvH4VmrCTGPY9MqLuwtIqt0xKnbK6wM3OS+sWKGfsLEPXdTau2UfOtsNExYQx5aKhRMWcmkT0rfuO8q8PV/lt/3bXYV78ZA23XnwulXUNJMdE8NUOg2XDRr7asb9dZwwgOTKC1669kvzKKopra+kXF+sTEWuLWdNIiYgiJaKl0jDEbOGfk+fw0OjJ7CovpldoOEPjuu57qzh7UM6YQnEakhE2nIyw41cw7ww1zt1sPHY9HtmSYHy46mXK6lcwOvkdzFoYDa6DVDZ8g5SBe2vqegARI8UZSVVFHY/cO4/cnS2tcP7z1BIe+sMVXDBjSJef/+NVOwKOvbXsO+Z98x1uXSc2IhSnKbCGl9bJpfM+UZHEh4Xy7ZF8PLrO2JQUQtuR8WhLSngUKeFKDkLRcZQzplD0cA5UPOPjiDVR69rD0er38bh3UFL7HoEb73mJtE8MOq44s/jXXxf6OGIAToebJx6dT/aIVOK6OEm/rKou4JjLrePRAAFl1XXoJgI+zaYN7dep836Ss4vHv1xOWZ335SLCZuP+iRO45ZxRnZpHoegMSmdMoejhlNZ/E3CsuGYeJbXv0p4jZrcMIC5MVWidLdRU17NyWY7hmMvl4YuFXd9oPjuI2Kts06ZIeEAzUGMd1DuB684f0eFzbj56jAcXLmp2xMDbIukPy5azbK9x66fTgXqPg8N1hdS5VXT6TEVFxhSKHo6GBZ0GwzHdcyTosSYRQWzYHPpE/RyTFtoV5im6gcqKOtzuwALDZcXVXW7DFZOG8fayzVTU+DsYeptceAFIh+TaKcPZcugYmiaYMWwA108cQbi9S+Nr+AAAGKtJREFU4/1X/7vpOzzS+MXjlY2bmN6/c1G2rsalu3hp/8csLVhLg+7EplmYlnQud2TOxW5S/STPJJQzplD0cBLCZnKs5n2DEQkEzhGLsk9jYOLLXWaXovtISIoiKjqUygrjpcL+g3t3uQ1xkWE8//Or+Msby9i817tcGh5qo8rp8LYlaoNJCO6+aBzxkcdfYLC31F/aool9Qca6i6dy32J50Ybmzw7dxaJjq6hwVvPokDu60TJFZ1HOmELRw8mM/inlDWtocOc3b9PQCTfZkdIZ8LgQi9JNOluxWs1cfsM4Xnv2S7+xpN7RTL6o6xP4AQakxPPSL6/lWGkVDU4XdpuF2b9/BbfHP2F/QnbGCTliAClRkewsKgo4djpRUF/K10UbDcfWlG4lr/YYaWHJp9gqxfGinDGFoodSVr+CgpoPcHnKSAy5EISJSsdmBCZ013qkDLwUpQk7CRE3Nn+W0o2uV6BpUQjRucozxenJ9bdPwuPW+fDNtdTWeJexR4zpy/2/mYPVdmr/j5PjWhyh3990Eb+dtwS33uKQpSVE8+h100/4PDeOHMGSPXsDjp1O7KnJQw+Sy7m5Ilc5Y2cQyhlTKHog+8ue5HDVv5s/VzSswaxFM6LXaxyr+CsVrsDLk1ZTbzJin8BuTkNKD5XVT1FV8wq6XoKmxRARdiPRkQ8pp+wMRwjBzXdN5epbzufwwRIio0NJSu7+nqaXjM1idP8UPl2fQ1l1HUPSkrhw1ACslhN/nJ2fkc6vp07mb9+sbFbXN2saPzx3DJcPyT7h+U8m0ZbwoOOvHpiPzaQxs9cFp8gixYmgnDGFoodR48zxccSacOsV7Cn9Pbprg8FRLQxKfBO7JQOAsorfUF37SvOYrpdTWf0Mbs9REmL/dVLtPp0QQjwJzAacwD7gNillRfda1TXYQ6wMyOr6HLHO0Csmgjtmntslc98+ZjRzh2Tz1b4DuKXOpL4ZJIUHd3y6gyFR/Ui2x3OsocRgVKKj88K+dxgQnkFmeOopt0/ROZS0hULRwyiq/SzgWJVjE3qQPDEwYTElAOD2FFJdO89wr9q6D3G5Tl8pgJPAUmColHI4kAs83M32KE4iMSEhzB2azdXDhp6WjhiAJjR+mXUrkZa2eXISs6YjBEgkSwv9uxgoTj+UM6ZQ9DD0ICr6XgSBdMXCrSMwad6bv8O5EXAHmEPS4Fx/vCae9kgpl0gpmy5+LdC9HdwVncaoCOBMY0BEGi+O+Q0moaMJHZPQsWg6WivJtVLHWRmwPetQy5QKRQ8j1j6R/KpXDcdCzH2JNIdT4/wW76Oq5a4uEGTG/bP5syaCt3vRtNOr+qwLuR14p7uNULSPy+Ph+W/W8+7GrRTX1JEZH8sPJozmynOGdrdpx02EJZSMsCQO1xcYjqeH9TnFFimOBxUZUyh6GDEhFxBtH28wotE35gHS4/4Xm6kPGhKBjkDHhIl+8c9is7QEgOy2cZhMxjd6TYshxH7i1W3diRDiCyHEdoOfy1rt8wje8OAbAea4UwixQQixobi4+FSZrgjAL+Yv4l9fr6W4xquftr+kjEcWLOWlVcHzJE93Lu8zw3B7qMnOzF6qTdmZgIqMKRQ9DCEEQxNfIK/yeQpq5uPylBNpG0Fa9N3Ehngrr7KTl1Net4A653YspiTiwq/CYkpqM4+JhNhnKCy5GSlrWm23Ex/7DJqwn9LrOtlIKY2fcI0IIW4FLgWmS2ks2y6lfBF4EWDMmDHBe0opupScY0Us3rnHcOz5b9Zzw9gRhARoCL7+yBH2l5eRHh3NuJRURCebj3c105LGU+2u5b3Di6n1eDsWpIT04if9byTeFtPN1ik6gnLGFIoeiEmz0zfmZ/SN+ZnhuKbZiQu/hjiuCTqP3TaOlF6rqa59G5d7LxZzOuGh12E2G1ffSakjxJkfkBdCzAIeAiZLKQN3tFacNqw9cDjgWLXDwbajhZyb4Zv6d7S6mrsWfMyO4hYh2IFxcfx7zuWkRgVfpgeodDRgNZkIMXe9zMtlfWYwq9ck9tXmEaLZ6KsqKM8olDOmUChOCJMpgejIe4Pu46lfjKf2WaRrO4gYTKFXY4q4DyFCTpGVJ51nABuwtDFKslZKeXf3mqQIRqCoVxOhBuP3LPzExxEDyC0t5a5PPuazm24JONfy/H38ffNKtpUWYBYaM1L788iYaaSGt+/AnQg2k5XsyP5deg5F13Dmv6IqFIrTGk/dB7grfux1xABkOZ7aF3GV3UGA1b3THillfyllqpRyZOOPcsROc2ZmDcBqMmhqCWTGxzK0t+8y/NbCAjYXGCfF7yopYf2RI4ZjK48e5I4v57Ot1HusW+oszsvl2sVvUOloOIErUJzNKGdMoVB0GVLquGv+bjzmXIN0rjzFFil6IjvyC/nF+4twuTx+qi1hVit/nOOfHniksironHmVlYbbn9q6Co/BS8bRumre3rOl40YrehRqmVKhUHQZ0nMQPEcDjuuOVWg21a5F0XXsLSrllpffo87p8m6QgACzSePascO44/yxJEdF+B3XNyZ44nu/WP9xXUo2FBlHzADWFx3mLs7rlP2KnoGKjCkUii6j3ZwwEXpqDFH0WF5auaHFEcOrnCckeNw6mhSGjhhAVkIC41OMk+BH9UpmVLJ/kYomBOEWW0BbIq1ndoWxoutQzphCoThudE8hbucWdN1Y5VuYkhGWMQGOFmghl3adcQoFsP5A4EjVuv2BKywB/vm9S5iQmuaz7dw+fXhu9uyAx1yROSTg2NwgY4qejVqmVCgUnUbXy6mveAhXwxJAB2xYQ68hJOq3COEbGTBH/AJ3+Q+R0jcHxxT+AJo589QZreiRhNusAcci7P5RrKqGBuwWC1aTibjQUF6/8ipyS0s4UF5OWlQ0WQkJQc/3wMgL2FCcz46yQp/ttw4ezaTefY/vIhRnPcoZUygUnaa29DY8ro2ttjhw1s0DXIRGPwmAlC5k9V+g7l3MOECYkSIa3ToOU9gtaNZAETOF4uQxe8Rg/neJcaHIpSMGN/99Yc5unlm1lr0lZdjMJi7JGsQvp04iNjSEgXHxDIyL79D5omx2Prj4Jj47tJuVxw4SarYwJyObsUmqfakiMMoZUygUncLtWNvGEWvBWTcfe8Qv0EyJyOq/Qt08n3EhKzC59yAso06FqYoeQJ3DxStffssnG3OoqXcwul8Kd0w/l2HpvQC4edwoVu09xNo2S5JTBvXlqtHenpQLduzi558sah5zuD18sG0nOwqK+ODWGwJKYgTCZjIzN3OIWpZUdBjljCkUik7hbtILM8SFx7ULIexQF6B3tmcfOJaDPWi3IYWiXdwenR+9+AHfHWip2F2+fR8rcw7ywt1XMKZfCjaLmX/fcgXLcvaybNc+NCG4MHsAUwdlomkCKSVPr1xjOP/u4hIW79rDnCGDDccVipOFcsYUCkWn0EyJ7YwngecgEETg0p0DKGdMcWIs27bHxxFrwuXx8PRnq3jt3msBr4zFzKEDmTl0oN++RTW1HCw3LkABWH/4iHLGFF2OqqZUKBSdwmK/CKHFGY6ZLOdgsgwCLRGviEAAtKTAYwpFB1mZczDg2HcHjlLT4Gh3jlCLBVOQxt+RtsBSFQrFyUI5YwqFolMIYScs5kWE8O2zp5nSCI35p3cfUy+wTQowQQTYv9fVZip6AJYguVyaEGgdaEofYbcxbUC/gOOXDck6LtsUis6gnDGFQtFpzLZziUxaR0jUk9jCf0ZozHNEJH6FyZzevI+I/DOY2yzviAhE9DMILfwUW6w4G7lwxICAYxOzMgi1BW8O3sSj0yeTEhXpt/3+SRMYlNixKkqF4kRQOWMKheK4EFoYtrDrAo+bEiDuI3CuANdO79KlfRZCCzuFVirOZsYNTOPS0YP5dOMun+0x4SE8MLvjbbZ6R0Xy6Q9u5qPtOWzKP0qU3c7coVkMS+51sk1WKAxRzphCoegyhNDANtn7o1CcZIQQ/OmGWVyQncmnG3Korm9gdL8Urjt/JEnRnYu+hlmt3HjOCG48Z0QXWatQBKbbnDEhxL3ATwAPsFBK+VB32aJQKBSKMxMhBBePGsTFowZ1tykKxXHTLc6YEGIqcBkwQkrpEEIEr5VXKBQKhUKhOEvprgT+HwF/kVI6AKSURd1kh0KhUCgUCkW30l3O2EDgAiHEOiHE10KIsd1kh0KhUCgUCkW30mXLlEKILwCjUpRHGs8bC4wDxgLvCiEypZTSYJ47gTsB0tLSuspchUKhUCgUim6hy5wxKWXAXidCiB8BHzQ6X+uFEDoQDxQbzPMi8GLjccVCiENdZHIT8UBJF5+jKzmT7T+TbQdlf1eR3v4uCoVCcebSXdWUHwFTgeVCiIGAlQ48BKSUCV1tmBBig5RyTFefp6s4k+0/k20HZb9CoVAojo/ucsZeBl4WQmwHnMD3jZYoFQqF4mxh48aNNUKI3d1tRxtOt2iosic4yp72Od1s6pDmSrc4Y1JKJ3BTd5xboVAouondp1vk8XSLhip7gqPsaZ/TzSYhxIaO7Kd6U/rzYncbcIKcyfafybaDsl+hUCgUx4FyxtrQWDBwxnIm238m2w7KfoVCoVAcH8oZUygUilPD6ejsnm42KXuCo+xpn9PNpg7ZI1TevD9nQ99MIcTPgb8BCVLK0ymZMShCiCeB2XgLO/YBt0kpK7rXqvYRQswCngJMwH+klH/pZpM6hBAiFfgvkARI4EUp5VPda5VCoVD0LFRkrA1t+mYOwevQnFE0PmAvAvK625bjYCkwVEo5HMgFHu5me9pFCGEC/gVcDGQD1wshsrvXqg7jBn4upczGK8L8kzPIdoVCoTgrUM6YP2dD38z/+//27j3Iq7KO4/j7o5IYatBokwm4CqYpKpKSpJmiOU45kgrV6JgMOiPjXTNltMxLNhjllKlZFmGKZJqkEgaW1wiEwOUmoobkZfCehgok8OmP5/nZj2WvuMvzW/i+ZnY45+w55/n+zu4envM95zxf4CJSpqNTsT3V9uo8OwPoWTKeVhoIPGt7SX5T+HekDn3Ns73M9pw8vRxYBOxcNqpNl6SrJM2TVC9pqqRPFY5njKSnckwTJXUvGU+OaZikhZLWSir2VpykoyUtlvSspFGl4sixjJX0ah4OqjhJvSQ9JOnJ/LM6t3A8XSXNlDQ3x3NFyXgqJG0p6QlJk1paNzpj6+vUdTMlDQFesj23dCztYARwf+kgWmFn4IWq+RfphB0aSXXA/sDjZSPZpI2xva/t/sAk4LLC8dRiJnoBcDzwaKkAajDbPQ44umD7DdVaRn0VMNj2fkB/4GhJBxWMp+Jc0gVui0oN+lpUe9XNLKWF+C8h3aKsWc3Fb/uevM6lpD/48Rszts2VpG2BPwDn2f5P6Xg2VQ2ObTcKZ69tT62anQEMLRVLhe1FAJJKhvFBtjvHUsl2P1kiGNuP5oulmmB7GbAsTy+XVMmolzo+Bt7Js13yV9G/LUk9ga8AVwMXtLT+ZtkZa6+6maU0Fb+kfYBdgbn5RNYTmCNpoO2XN2KIzWru+ANIGg4cAxxRS53gZrwE9Kqa75mXdQqSupA6YuNt3106nk2dpKuBbwJvk8rC1YoRwB2lg6gRjWW7P1colppWKxn1nM2cDfQFbrBdOsP/E9LjQtu1ZuW4Tbm+St1M2lI3sxbYnm/7E7brbNeRTiADaqkj1pL8VuJFwLG23ysdTyvNAnaXtKukjwDfAO4tHFOrKPXafw0ssn1t6Xg2BZL+ImlBI19DAGxfarsXKet7Vul48jobNRPdmphC7auljLrtNfn2f09goKR+pWKRdAzwqu3Zrd1ms8yMtSDqZpZ1PbA18EDO7s2wPbJsSM2zvVrSWcAU0tAWY20vLBxWax0MnAzMl1Sfl11ie3LBmDq1ljK/VcYDk4HvdWA4NZmJbsMxKqVTZ7s3hlrNqNt+S9JDpGfsSr3wcDBwrKQvA12B7SXdZrvJMpAxzlgIIWwkkna3/UyePhv4ou1iz2nlTPS1OY6aeRQDQNLDwIW2W1Xbr53b3or0QsMRpE7YLODEkhdZ+XbgJNvFMj4VOaN+C/Cm7fNqIJ4dgfdzR2wbYCpwje0W32LsaJIOI/0eH9PcenGbMoQQNp7R+XbcPNKLNkWHBCBlorcjZaLrJd1UOB4kHSfpRWAQ8CdJUzZ2DHl4nUq2exHw+8IdsQnAdGAPSS9KOrVULFkloz44/97U5yxQKTsBD+W/q1nAA7XQEWuLyIyFEEIIIRQUmbEQQgghhIKiMxZCCCGEUFB0xkIIIYQQCorOWGhXks6RtEhSm8crklQn6cSOiCvv/1BJcyStllR8pPEQQggBojMW2t8ZwJdsn7QB29YBbe6M5ZGXW+N5YDhwe1vbCCGEEDpKdMZCu8mvxe8G3C/pfEndJI2VNDNXrh+S16uT9FjOUs2R9Pm8i9GkIu31efvhkq6v2v+kPGYLkt6R9GNJc4FBkj6bC7vPljRF0k4N47O91PY8YG0HH4oQQgOS1lQNg1CfzwMHSLquDfvoLumMdojlSkkbNPCspMmSum/gtuM2h6y8pMOqzuuhFWIE/tBubI/Mg0gebvt1ST8AHrQ9Ip+8ZioVCX+VlD1bKWl3YAJwADCKqsHx8sjgTekGPG77W3kk6EeAIbZfk/R1UnHWER31WUMIbbYil6upthRYb1BXSVvlsb4a6k7Kvt+4oUFI2tL2ZRu6ve2S42l1FoeRCnf/vXAcnUZkxkJHOgoYlcvsPEwqC9Eb6ALcLGk+cCew1wbsew2pFAfAHkA/8sCVwHdI5UtCCDUsZ1Am5enLJd0qaRpwq6S9c1a9XtK8fOE2GuiTl41psK86SU9JGp+fW71L0kfz95ZKukbSHGBYdYYqf++KnKWfL2nPvHxbSb/Jy+ZJOqFq/R1aaO8ySbPyAL+/lFJtt2aOQ1+lep1zcxx9lIzJ+5ifLzIrx+wRSfdIWiJptKST8rGaL6lPXm+cpJsk/UPS00r1EpHUtepzPSGpUot5uKS7Jf1Z0jOSflgV31GSpufY7lSqSdnosVOqFDASOD//nL6w4b8hm4/IjIWOJOAE24vXWShdDrwC7Ee6IFjZxParWfeCoWvV9Erba6raWWh7UHsEHULoENvo//VPn7N9XCPr7AUcYnuFpJ8BP7U9XtJHSHVfRwH9GsmwVewBnGp7mqSxpCzaj/L33rA9AD4oA1XtddsD8i3QC4HTgO8Cb9veJ2/Tow3tXW/7yrzdraTan/c1c2zGA6NtT5TUlXTeOx7oTzpP7gDMkvRoXn8/4DPAm8AS4Fe2B0o6FzgbqJQoqgMGAn1II9T3Bc4EbHuf3PGcKunTef3+wP7AKmBx/hmsIF3gHmn7XUkXAxcAVzZ27GyfpvTIyju2K8c+tCAyY6EjTQHOrlwVSto/L/8YsMz2WlJJjcoD+MtJpVkqlgL9JW0hqRfppNKYxcCOkgbldrpI2rtdP0kI4cNaYbt//mqsIwZwr+0VeXo6cEn+z3+XquXNecH2tDx9G3BI1ffuaGa7SqHr2aQODMCRwA2VFWz/uw3tHS7pcaXs/2CgyfORpO2AnW1PzO2stP1e3tcE22tsv0J6FOPAvNks28tsrwL+SarFCDC/Kn5IZZzW5nqoS4A9835vy209BfwLqHTG/mr7bdsrgSeBXYCDSJ3kabkzfUpeXtHYsQttFJ2x0JGuIt2SnCdpYZ6H9LzHKUoP3+8JvJuXzwPW5FT9+cA04DnSSeE6YE5jjdj+LzAUuCbvsx5Y7+FRSQcq1bwbBvwixxRCqB2VcwG2bweOJWVmJksa3IrtG9b3q55/l6atyv+uoW13jNZrL2e2bgSG5qzazayb1W8Pq6qm11bNr2Xd+Js7Hi3tt3IsRKr1WOlI72X71Ea2aeuxC1XiwIV2ZbuuanoFcHoj6zwD7Fu16OK8/H3SVWS1RofIsL1tg/l64NAWYptFPEsWQqcgaTdgie3rJPUmnTPmsm72vKHekgbZnk4aJudvHyKEB0i39M7L8fRoJDvWWHuVjtfr+dmqocBdTTVie7lS8e+v2v6jpK1JdwseA06XdAvwcdL57dukC9jWGpa335X0pvvivN+TgAfz7cneefmAJvYxA7hBUl/bz0rqRsrkPd1Mu8uB7dsQ52YvMmMhhBBq0deABfnWWD/gt7bfIN0uW6AGD/Bni4EzJS0CegA//xDtfx/okduaCxzemvZsv0XKhi0gPaoxqxVtnQycI2ke6Q3ETwITSXcL5gIPAhfZfrmNn+F5YCZwPzAy3368Edgi30K9Axieb3c2yvZrpPEZJ+T4ptNyh/A+4Lh4gL/1ZLeUtQwhhBBqW36Lb5Ltfptie20laRwpviazcqF2RGYshBBCCKGgyIyFEEIIIRQUmbEQQgghhIKiMxZCCCGEUFB0xkIIIYQQCorOWAghhBBCQdEZCyGEEEIoKDpjIYQQQggF/Q/i2GbnEpQtOwAAAABJRU5ErkJggg==\n", 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" } ], "source": [ @@ -165,7 +165,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.6.3" + "version": "3.7.0" } }, "nbformat": 4, diff --git a/doc/Programs/JupyterFiles/Examples/Scikit-Learn Website Examples/.ipynb_checkpoints/Blobs-checkpoint.ipynb b/doc/Programs/JupyterFiles/Examples/Scikit-Learn Website Examples/.ipynb_checkpoints/Blobs-checkpoint.ipynb new file mode 100644 index 000000000..2acd80223 --- /dev/null +++ b/doc/Programs/JupyterFiles/Examples/Scikit-Learn Website Examples/.ipynb_checkpoints/Blobs-checkpoint.ipynb @@ -0,0 +1,256 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "(3, 2)\n", + "(3,)\n" + ] + }, + { + "data": { + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "----------Uncertainty Estimates-------\n", + "(25, 2)\n", + "(25,)\n", + "[ True False False False True True False True True True False True\n", + " True False True False False False True True True True True False\n", + " False]\n", + "['red' 'blue' 'blue' 'blue' 'red' 'red' 'blue' 'red' 'red' 'red' 'blue'\n", + " 'red' 'red' 'blue' 'red' 'blue' 'blue' 'blue' 'red' 'red' 'red' 'red'\n", + " 'red' 'blue' 'blue']\n" + ] + }, + { + "data": { + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "-------Predicting Probabilities---------\n" + ] + }, + { + "data": { + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import mglearn\n", + "from sklearn.model_selection import train_test_split\n", + "from sklearn.datasets import make_blobs\n", + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "from sklearn.svm import LinearSVC\n", + "X,y= make_blobs(random_state=42)\n", + "\n", + "linear_svm=LinearSVC().fit(X,y)\n", + "print(linear_svm.coef_.shape)\n", + "print(linear_svm.intercept_.shape)\n", + "plt.scatter(X[:, 0], X[:, 1], c=y, s=60, cmap=mglearn.cm3)\n", + "line = np.linspace(-15, 15)\n", + "for coef, intercept in zip(linear_svm.coef_, linear_svm.intercept_):\n", + " plt.plot(line, -(line * coef[0] + intercept) / coef[1])\n", + " plt.ylim(-10, 15)\n", + " plt.xlim(-10, 8)\n", + " \n", + "mglearn.plots.plot_2d_classification(linear_svm, X, fill=True, alpha=.7)\n", + "plt.scatter(X[:, 0], X[:, 1], c=y, s=60)\n", + "line = np.linspace(-15, 15)\n", + "for coef, intercept in zip(linear_svm.coef_, linear_svm.intercept_):\n", + " plt.plot(line, -(line * coef[0] + intercept) / coef[1])\n", + "plt.show()\n", + "\n", + "\n", + "print (\"----------Uncertainty Estimates-------\")\n", + "# create and split a synthetic dataset\n", + "from sklearn.ensemble import GradientBoostingClassifier\n", + "from sklearn.datasets import make_blobs, make_circles\n", + "# X, y = make_blobs(centers=2, random_state=59)\n", + "X, y = make_circles(noise=0.25, factor=0.5, random_state=1)\n", + "# we rename the classes \"blue\" and \"red\" for illustration purposes:\n", + "y_named = np.array([\"blue\", \"red\"])[y]\n", + "# we can call train test split with arbitrary many arrays\n", + "# all will be split in a consistent manner\n", + "X_train, X_test, y_train_named, y_test_named, y_train, y_test = \\\n", + " train_test_split(X, y_named, y, random_state=0)\n", + "# build the gradient boosting model model\n", + "gbrt = GradientBoostingClassifier(random_state=0)\n", + "gbrt.fit(X_train, y_train_named)\n", + "\n", + "print(X_test.shape)\n", + "print(gbrt.decision_function(X_test).shape)\n", + "# show the first few entries of decision_function\n", + "gbrt.decision_function(X_test)[:6]\n", + "print(gbrt.decision_function(X_test) > 0)\n", + "print(gbrt.predict(X_test))\n", + "# make the boolean True/False into 0 and 1\n", + "greater_zero = (gbrt.decision_function(X_test) > 0).astype(int)\n", + "# use 0 and 1 as indices into classes_\n", + "pred = gbrt.classes_[greater_zero]\n", + "#pred is the same as the output of gbrt.predict\n", + "np.all(pred == gbrt.predict(X_test))\n", + "decision_function = gbrt.decision_function(X_test)\n", + "np.min(decision_function), np.max(decision_function)\n", + "fig, axes = plt.subplots(1, 2, figsize=(13, 5))\n", + "mglearn.tools.plot_2d_separator(gbrt, X, ax=axes[0], alpha=.4, fill=True, cm=mglearn.cm2)\n", + "scores_image = mglearn.tools.plot_2d_scores(gbrt, X, ax=axes[1], alpha=.4, cm='bwr')\n", + "for ax in axes:\n", + " # plot training and test points\n", + " ax.scatter(X_test[:, 0], X_test[:, 1], c=y_test, cmap=mglearn.cm2, s=60, marker='^')\n", + " ax.scatter(X_train[:, 0], X_train[:, 1], c=y_train, cmap=mglearn.cm2, s=60)\n", + "plt.colorbar(scores_image, ax=axes.tolist())\n", + "plt.show()\n", + "\n", + "print (\"-------Predicting Probabilities---------\")\n", + "gbrt.predict_proba(X_test).shape\n", + "np.set_printoptions(suppress=True, precision=3)\n", + "# show the first few entries of predict_proba\n", + "gbrt.predict_proba(X_test[:6])\n", + "fig, axes = plt.subplots(1, 2, figsize=(13, 5))\n", + "mglearn.tools.plot_2d_separator(gbrt, X, ax=axes[0], alpha=.4,\n", + " fill=True, cm=mglearn.cm2)\n", + "scores_image = mglearn.tools.plot_2d_scores(gbrt, X, ax=axes[1], alpha=.4,\n", + " cm='bwr', function='predict_proba')\n", + "for ax in axes:\n", + " # plot training and test points\n", + " ax.scatter(X_test[:, 0], X_test[:, 1], c=y_test, cmap=mglearn.cm2, s=60, marker='^')\n", + " ax.scatter(X_train[:, 0], X_train[:, 1], c=y_train, cmap=mglearn.cm2, s=60)\n", + "plt.colorbar(scores_image, ax=axes.tolist())\n", + "plt.show()\n" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "---------Scaling training and test data same------\n" + ] + }, + { + "data": { + "image/png": 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pwSAiIiIiIhWpwSAiIiIiIhWpwSAiIiIiIhWpwSAiIiIiIhXF1mAws3Yzu9vM\nHjOzDWb26TLHnGpmL5vZQ+H2+bjiEZHGM7PZZvaEmW00s8vK7D80LCceNLNfm9mZScQpIvWn/BfJ\njjjvw7AbuNjdHzCzNmCdmd3p7o+WHPef7n5WjHGISALMbAxwDXA60AP8ysxuLSkDPgfc6O7/bmZH\nArcB0xserIjUlfJfJFtiG2Fw9+fd/YHw++3AY8Ahcb2fiKTOicBGd3/a3f8AXA+cU3KMA/uH3x8A\nbG5gfCISH+W/SIY05BoGM5sOHA/8d5nd7zSzh83sdjM7qso5LjSzbjPr7u3tjSlSyZOBAVi5Ejo7\nYerU4OvKlcHzUheHAJuKHvcwtNPgi8BHzKyHoHfxb8qdSPkv0nSU/yIZEnuDwcz+BLgZ+F/u/krJ\n7geAN7v7ccC/AT+sdB53X+7une7eOXny5PgCllwYGIBzz4WFC2HdOtiyJfi6cCHMndsEjYa+Ppg1\nK/iaXlbmOS95PB/4rrtPA84ErjOzIeWS8l+k6Sj/RTIk1gaDme1D0FhY6e63lO5391fc/dXw+9uA\nfcxsUpwxiQCsWgVr18KOHYOf37ED7rwTrr8+mbgi6+qCu+6CJUuSjqSaHqC96PE0hk45uAC4EcDd\nfwmMB1QGSKyacnSxOToJiin/JZWaMv9TIM5Vkgz4NvCYu5et1ZjZG8PjMLMTw3hejCsmkYKlS4c2\nFgp27Eh5PbyvD5YtA/cg0PRWIH4FHG5mM8xsX2AecGvJMb8D3gtgZm8jqDBozoHEpmlHF5ujk6CY\n8l9Sp2nzPwXiHGF4F/BR4D1Fy6aeaWYXmdlF4THnAY+Y2cPA1cA8dy8dshSpu02bqu/v6WlMHDXp\n6tpbqg0MpLYC4e67gU8BPyVY9OBGd99gZleZ2dnhYRcDfx2WAauA81UGSJyacnSxeToJ9lD+Sxo1\nZf6nhDVjbnZ2dnp3d3fSYUgT6+wMehUq6eiAVP6L9fXBtGmDS7vW1qCFM3EiELQhVq0KRlE2bYL2\ndli0CObPh5YKXQRmts7dOxvwE4ya8l9Goylz/3OfCxoKu3bBfvvBJZfAVVfV7fTKf8mLpsz/mEXN\nf93pWTJpuDmKixYF9exyWlth8eL6v+doDQzA+vO7eG3n4BN60SiDhltFqmu60cXC6MKuXcHjXbuG\njDJoTrZINE2X/xE0Kv/VYJDMiVJpnj8/uH6wtNHQ2gqnnw7z5tX/PUf7M330g30c9qNljPddg/bZ\nrl14WIHQcKvk3XAfnu3t1V8/bVp932/UiqcgFr+pOglEBomSi/XO/6Q1NP/dvem2jo4OF6lkxQr3\n1lb3YMLv4K211X3lyuC4/v4PTgUTAAAgAElEQVTg+44O96lTg68rVwbPx/Weo/mZvrLPFb6LcWXf\nZPfYce5XXukdHeVjKGyVUgfo9hTkdpRN+S+V9Pe7n3PO0FxsbXWfMyfYX89cjfJ+o/p5XtzmfxhX\nJdht2+ry8yj/pdlFzcV6f1YXypSODvcpU4KvK1aMPvejnruR+Z948teyqcCQamqtNKf5PTs63G9h\njm9hUtntpbGT3OfM8SlTqscxdWr586vCIFkQ5cOznpX8ODsK+vvdbziicifBwLjRdRIUU/5LsxtJ\nR2G98j/ODoOo525k/mtKkmROEnMU437PTZvgXNYwhd6y29sO6oU1azI33CoyElGWS25pgVtugeXL\ngwscp04Nvi5fDjffXHlhgFrfr1arVsH4pzawnTZ6mTRo28okXt+3Ddavz+ScbJGRipqL9cz/OKcA\nRz13I/N/bP1OJZIO7e3BPL5K4qg0x/2eUc+/aFEwd7FcwVnrxdwizSLqh2dLCyxYEGyNeL9aLF0K\n6/rXVNzfcQR0r4H2zsaXdyJpM5JcrFf+R2mk1PoeUc/dyPqORhgkc6qtgATw0kv1X0Gg2ntOmADv\neMfoLoqMuqpTvS/mFmkmjR5hi/P9olaA4ljxTaTZJDG6HmeHQRrzXw0GyZxKleaCZ56p/woCld5z\nwgTYf3/43vdGt4JB1IZAPYdbRZpNoyvPcb5f1AqQOglEkmk4x9lISWP+q/ogmVNcaZ4+HcyGHlPv\nZUYrVdTPPx+2bx/9HMeRNAQKw63d3fDCC8HXBQvUWJDsq/Th2dICu3cHw/j1HF2s9mE9axb099c+\nshi1AqROApHKuThuHOyzT5BP9V7yuFqOjhsXzGaIe1ZBQ/M/ypXRadu0SoJElcSKSWl6/6jQKimS\nEYXlkk84wX3cOPeWlvqvYFLu/YqXZ77uutGvnhL3kq3FlP+SBcW5OGWK+wEHBGVAXPlTKUfHjAm2\n0bxvGvNffQ+SaUmvILJpExxAH3cyiwPoG7JfK5iI1FdhhG3xYhg7dmivXhyji6UjemajXz1FIwci\nI1Oci0uWBKOKr78++Jh65n+5HJ0+PSh3+vtH975pzH8VOZJpSS8z2t4OF9PFe7iLxQxdY1ErmIjE\nI84lT6O899gd5TsKRvLeml4oUkVfXzAPqW9oZ1yj8r80Rw86aGgjpdb3TVv+q9iRTEt6BZHPXNjH\nYpbRgrOYJYMqD1rBRCQ+SY4ubtpUvaNAI4siddDVBXfdVbYWnlT+Jz2rIU5qMEimJb2CyHm/7WJs\nSzAnooWBPZUHrWAiEq8kRxffdnAfiyp0FMT93iK50NcHy5YFU/uXLBkyypBU/ic9qyFOajBIpiU6\nD7Cvj5Z/Xca4gV0ATGAXF9sS3n1cn+Yhi8QsydHFf5veRQtDOwoa8d4iudDVtfcCpYGBIaMMSeV/\n0rMa4qTqimReYvMAiwu0UOv4Ae45e4nmIYvELLHRxb4+jl67jAns7SgojDJoZFGkDgqjC7uCHGPX\nriGjDEnlf9KzGuKkKotIHEoLtIIyBZuI1F9io4tdXVhJR8EYBvjqG5doZFGkHsp0xpWOMiSV/2lc\n3aheLFiCtbl0dnZ6d3d30mGIVPa5zwWFWrnlEsaNg898Bq66qvFxVWBm69y9M+k4olD+S2r19QWT\nlMstz9LaGlzxOHFi4+MahvJfmkaT5liaRc3/Jm7rpM/AQHA3v1rv7CkZsmEDtLXBpElDt7Y2WL8+\n6QhFpN66uoLF38sp3GpaRGqnHEvM2LjfwMxmA/8KjAG+5e5fKdk/Dvg+0AG8CPyFuz8bd1z1NjAA\n5547+GY9W7bAwoWwenXzD0XJCK1Zk3QEkpCBAVi1KlgHfNOmYNWMRYuCua0qAzKu0FHQ1lZ+vzoK\nckFlQIyUY4mJtcFgZmOAa4DTgR7gV2Z2q7s/WnTYBcA2d3+rmc0D/gn4izjjisOqVcPf2XPBgmRi\nE5HGUMdBzqmjIPdUBsRMOZaYuP9tTwQ2uvvT7v4H4HrgnJJjzgG+F36/GnivmVnMcdXdcHcV/NjH\nNEVJJOuidByISHapDJCsirvBcAhQfN+7nvC5sse4+27gZeCg0hOZ2YVm1m1m3b29vTGFW7vh7u63\nezesWxf0Msydq0aDSBap40Ak31QGSFbF3WAoN1JQuixTlGNw9+Xu3ununZMnT65LcPU03N39CtTL\n0Px0cbtUoo4DkXxTGSBZFXeDoQcorkpPAzZXOsbMxgIHAC/FHFfdVbu7X6kdO3Qhf7MqzE9duDAo\n9LdsUeEve6njINvUWSDDURmQTcr9+BsMvwION7MZZrYvMA+4teSYW4GPhd+fB9zlTXhziEp396uk\npyfeeCQemp8q1ajjILvUWSBRqAzIHuV+INYGQ3hNwqeAnwKPATe6+wYzu8rMzg4P+zZwkJltBBYD\nl8UZU1xK7+43dpj1p6ZNa0xcUl/DzU9V4Z9v6jjILnUWSBQqA7JHuR+IfXEvd7/N3Y9w97e4+5fD\n5z7v7reG37/m7h9y97e6+4nu/vRo3i/JYaOWlmDp1O5u+O53KxcYra2weHH88Uj9DTc/VYV/eiRR\nFqjjILvUWSBRqAzIHuV+IFOrAadp2KhSL0NrK5x+Osyb17hYpH6Gm5+qwj8dkiwL1HGQTeosaC7q\nPJR6Ue4HMtVgqOew0WgLm9JehqlTg6/Ll+vGLc2s2vxUFf6DmdlsM3vCzDaaWdmphmb2YTN71Mw2\nmNkP6vXeaSkL1HGQHeosGJkk81+dh1JPyv2Quzfd1tHR4eV0dLhD5a3Cy4bo73c/5xz31tbBr29t\ndZ8zJ9gv+ZTV/w2g2+uYo8AY4CngMGBf4GHgyJJjDgceBA4MH0+Jcu5K+V8sTWVBf7/7ypXBe06d\nGnxdubJ5/1fyasWKof8Hxf8PK1cmHWHtspb/9fxb9fcH5+vocJ8yJfi6YsXI8ldlQHPLcu67R8//\nhlXy67lVKjCmTKleSZg6NdovL+v/HDI6WSz8Y6gwvBP4adHjy4HLS475Z+DjIz13lAqDygKpt6x2\nFrhnL//T1GEgzS/r/wdR8z9TE2PqNWykC1ykmuL5qS+8EHxdsEDTzEpEucv7EcARZvYLM7vPzGZX\nOtlI7/SuskDqTdNMRyTR/K/XnHOtjiOg3C/I1I9Zr/nlusBFZNSi3MF9LMG0hFOB+cC3zGxiuZP5\nCO/0rrJA4qDOgsgSzX91GEi9Kfcz1mCo18VFusBFRkp3gRwi6l3ef+Tuf3T3Z4AnCCoQo6ayQBpB\neV9RovmvDgOJWx5zP1MNhnoNG2klHImsrw+fNYuPfrAvFStypEiUu7z/EDgNwMwmEUxRGNV9WApU\nFkjc0rQSTwolmv/qMJA45TX3M9VggPoMG420sMljS1NCXV3ws7s4+s4lmudaxKPd5f2nwItm9ihw\nN3Cpu79YrxgaXRaoHMiX1d/q49P/ZxZjd/QNej7PeV+QdP4n0WGg/M+P3F7bEuXK6LRtUVZJqCTq\nEmlRV8KpdvV8R4f7CSfUvhSbpNy2bXv+8Ntp9QPYNqoVOZJEnVdJiXMbTf4Xq2dZkPVVNGSob77x\nCu/H/O+5smnzvkD5Xz7/o+a18j9f6rUKV1pEzf/Ek7+WrdYCI46krrbsYrllGKu9Tz3We5YGuuIK\n9/32cwffwX4VKw5Rl/BMUt4qDPUuC6qVA+PGuc+YET2nVQ40gW3b/FWqdxY0Q94XKP8r53+UDgPl\nf77Ua9nutFCDoYw41lQfrqUZ9X3UQ9FkikYXClulikMz9DbkrcJQ77JgJOVAtZxWOdAkrrjCd1n1\nzoJmyPsC5b/yX6LL6whD5q5hqCaOJdKGW0Uh6vvkdk5cs+rqGjI5tYUBFjP4j6sLY9Op3mXBSMqB\najmtcqAJ9PXBsmWM910ATGAXi1nCAey9lkF5n27KfxmNvC6GkasGQxxLpA23ikLU99F6z00krDCw\na9egp0srDiNdkUMap95lwUjLgUo5rXKgCQzTWaC8Tz/lv4xGvVbhaja5ajDUukRatdUPqrU0R/I+\nWu+5iXR1we7dZXftY7u5snVJLu8C2UzqXRZ8+tMjLwfK5bTKgZSr0llwsS3h3cf1Ke+bgPJfRiOv\nd34em3QAjbRoUbBObrkWfKVhpMJ6u8XDhFu2BOdZvRpuuinYyg0jllPpfdrbg/NWovWeU2TDBmhr\nC7YS44CLT17PxWsaH5ZEV++yYNasYItaDkD5nFY5kHJVOgta993NPWcvgQVXNTgoGSnlv4xWYdnu\nBQuSjqRxMtoOKq+WYaTh5hTeeGP5lmZHB0yYEP198jonrimtWQO9vZW3NWotpF29y4K1a+G88waX\nAzNmwLhx5d+/Uk6rHEi5QmfBpElDt7Y2WL8+6QglAuW/SA2iXBmdtm2092GIcn+Fglqvhh/p+2h1\nBEkSOVslxT3+sqCWnFY5IElQ/iv/Jb+i5r8FxzaXzs5O7+7ubsh7TZ1afYhw6tTgLrL1MDAQrIKw\nZEkwV3HatKBHYd687M6Jk3Qws3Xu3pl0HFE0Mv+L1VIW1JLTKgek0ZT/w1P+S1ZFzf9cXcNQi0bO\nKczjnDiRZlFLWVBLTqscEEkf5b/kXSztVTP7qpk9bma/NrM1ZjaxwnHPmtl6M3vIzBrfZRCB5hSK\nCKgsEMkz5b/kXVwDXHcCR7v7scBvgMurHHuau89M63BoXtfbFZHBVBaI5JfyX/IulgaDu9/h7oW1\n5+4DmnYxsLyutysig6ksEMkv5b/kXSOuYfgr4IYK+xy4w8wc+Ia7L690EjO7ELgQ4NBDD617kNVo\nTqGIgMoCkTxT/kue1dxgMLO1wBvL7LrC3X8UHnMFsBtYWeE073L3zWY2BbjTzB5395+XOzBsTCyH\nYJWEWuMWEREREZHoam4wuPusavvN7GPAWcB7vcLare6+Ofy6xczWACcCZRsMIiIiIiLSeHGtkjQb\n+CxwtrvvrHBMq5m1Fb4HzgAeiSMeERERERGpTVyX6XwNaCOYZvSQmV0LYGZvMrPbwmOmAv9lZg8D\n9wM/dvefxBSPiIjEaGAAVq6Ezs7ggtDOzuDxwEDSkYlI3JT/2RfLRc/u/tYKz28Gzgy/fxo4Lo73\nl2QMDMCqVbB0KWzaFNzoZtGiYDk6rSAhkl0DA3DuubB2LezYETy3ZQssXAirV2sVGZEsU/7ng/6E\nUheFAmPhQli3Ligs1q0LHs+dq14GkSxbtWpwZaFgxw648064/vpk4hKR+Cn/80ENBqkLFRgi+bV0\n6dDcL9ixA5YsaWw8ItI4yv98UINB6kIFhkh+bdpUfX9PT2PiEJHGU/7ngxoMUhcqMETyq729+v5p\n0xoTh4g0nvI/H9RgkLpQgSGSX4sWQWtr+X2trbB4cWPjEZHGUf7ngxoMUhcqMETya/58mDVraBnQ\n2gqnnw7z5iUTl4jET/mfD2owSF2owBDJr5YWuOUWWL4cTjgB9t8fJkyAMWOC6YqrVmmlNJGsUv7n\ngxoMUhcqMETyraUl6Bhob4f+fti5E155Rcsri+SB8j/71GCQulGBIZJvWl5ZJL+U/9mmBoPUlQoM\nkfzS8soi+aX8zzY1GKSuVGBIMTObbWZPmNlGM7usynHnmZmbWWcj45P60vLKUkz5ny/K/2zLVYNh\nYABWroTOTpg6Nfi6cqWmydSTCgwpMLMxwDXA+4EjgflmdmSZ49qAvwX+u1GxqSyIh5ZXlgLlf/4o\n/7MtNw2GgQE499xgLv26dbBli+bWx0EFhhQ5Edjo7k+7+x+A64Fzyhz3JeCfgdcaEZTKgvhoeWUp\novzPGeV/tuWmwaC59YPF1cOiAkOKHAIUjzn1hM/tYWbHA+3u/h/VTmRmF5pZt5l19/b2jioolQXx\n5b+WV5Yiyv+UUv5LTdy96baOjg4fqY4Od6i81XDKptXf737OOe6trYN/B62t7nPmBPvTeG6JD9Dt\ndc5T4EPAt4oefxT4t6LHLcA9wPTw8T1A53DnrSX/i+W9LIg7R/v73VeuDH6PU6cGX1euVO6nmfJf\n+a/8z6+o+Z+bEQbNrd8rzh6W4vsxdHQEvRcdHcHjm28O9ktu9ADFk9SmAZuLHrcBRwP3mNmzwEnA\nrXFf+Jj3siDuHtaWFliwALq74YUXgq8LFij3c0j5n0LKf6lVbv6Emlu/V9wrGanAkNCvgMPNbIaZ\n7QvMA24t7HT3l919krtPd/fpwH3A2e7eHWdQeS8LtJKZNIjyP4WU/1Kr3FThNLd+r7z3sEhjuPtu\n4FPAT4HHgBvdfYOZXWVmZycVV97LAuX/8LSKzugp/9NJ+V+dcr+y3DQYdDHOXnnvYYlKBcfouftt\n7n6Eu7/F3b8cPvd5d7+1zLGnxt27CCoL0pj/aco1raJTP8r/9FH+V49DuV9FlAsdatmALwLPAQ+F\n25kVjpsNPAFsBC6Lcu5aL3rSxTiBFSuGXvBUfOHTypWNj6m/P4iro8N9ypTg64oVyf1t8nbxNjFc\n9BjXNtqLHt3zXRakLf/Tlmtp+/00gvJf+a/8T9/vplGi5n9sSR02GC4Z5pgxwFPAYcC+wMPAkcOd\nux4FRp6lKUHTGI97/gqOvFUY8ixt+Za2XMvjKjrK//xQ/leWx9x3j57/SU9JinpjF6mjtK1klMZ1\nsXVhmGRVrfkf17SBtOWa5nhLlin/K1PuVzc25vN/ysz+EugGLnb3bSX7y93Y5R3lTmRmFwIXAhx6\n6KExhJovhZWMFiyIdvzAQFCxX7o0SKr29uDisfnzR9/AiFJgRI2zXlRwSJbVkv/nnju4Yb9lSzC3\nd/Xq0XU0pC3X2tuDn60SXePVZPr64Lzzgn/UiROTjiYVlP/lKferG1VVz8zWmtkjZbZzgH8H3gLM\nBJ4H/qXcKco85+Xey92Xu3unu3dOnjx5NGHLCMV9IVCaCoyCNF4YJpKUOEcB05ZreV9FJ3O6uuCu\nuzQsPAp5yX/lfnWjajC4+yx3P7rM9iN3/72797v7APBNgulHpYa7sYukQNxThtJUYBSo4BDZqy7T\nBvr6guVp+voGPZ22XMv7KjqZ0tcHy5YFU9CXLBnyvyfRxDltKE35r9yvLrbZ6mZ2cNHDPwceKXNY\n1Ru7SDrUtbAoU2lIU4FRoIJDZK+6jAJW6OlNW66l7RovGYWurr1D4AMDGmWoUZyzANKU/8r9YUS5\nMrqWDbgOWA/8mqARcHD4/JuA24qOOxP4DcFqSVdEObdWSWisKVOqrxwwdeoITnbFFe5m7ldeueep\ntK3aUBxXXpbeQ6ukSBWjXj1k27a9Cd7aGjwukqdcS6NM5n/x/1zxh0rJ/54ML+7Vg+LK/7Qt155W\nUfM/8eSvZVOFobHqVlhUqTSowpCsTFYYpG5GvfThFVe477df8IL99hvUYRCnuCoMWauIZDL/i//n\nClsD//eyJE1Ln0YVZ0dkXvM/8eSvZVOFobHqVlhkrNKQJZmsMEjdjOrDN6Ge3rgqDGkdER2NzOV/\nuf85jTLUrBn/5+Nq5DTj72I4UfM/7zOyJIK6zDEsXHy2a1fweNeuhlyEFvcKT2m5pb1InEY1t7d4\nHnlBA+aTx7VYQxrvGyMlurpg9+7y+3bv1rUMI9SMc/vjulA71/kfpVWRtk09jI036ilDCQ0PxzmU\nmqWeBrLWwyjpkGBPb1zzrrN4N9jM5f+cOe6TJlXe5syp9VclTaKu114WyXP+p7BdKGlUuNFLdze8\n8ELwdcGCiD0LpaMLBQ0YZYhzObhc9zSIRJFgT29cK7uk8b4xUmLNGujtrbytWZN0hBKzuJZrz3P+\nq8Eg8ctgpQHSdUt7kVTasAHa2mDSpKFbWxusXx/bW8dVYUjjfWNEZLC4lmvPc/6rwSDxy2ClAfLd\n0yASSYI9vXFVGNJ43xgRGSyu+zvkOf/VYJD4ZbDSAPnuaRBJu7gqDGm60ZSIlBfXhdp5zn81GCTT\n4kzuPPc0iKRdXBWGZlwxRiSPRnXtZZVz5jX/LbhAurl0dnZ6d3d30mFIkxgYCC5AXrIkmCY0bVpQ\nmZ83b3TJXViytfTC50JjpJkKDzNb5+6dSccRhfJfpL6U/yL5FTX/xzYiGJEkFXoZFiyo/3lvuSWe\nxoiIiIhIWqjBIDIKcTVGRERERNJCfaAiIiIiIlKRGgwiIiIiIlKRGgwiIhKrgQFYuRI6O4NVRTo7\ng8cDA0lHJiJxU/5ng65hEBGR2JRbTWzLFli4EFavbq7VxERkZJT/2aE/kzSEehhE8mnVqqFLD0Pw\n+M47g1XGRCSblP/ZoQaDxK7Qw7BwIaxbF/QurFsXPJ47V40GkSxbunRoZaFgx45gSWIRySblf3ao\nwSCxUw+DSH5t2lR9f09PY+IQkcZT/meHGgwSO/UwiORXe3v1/S++qCmKIlml/M8ONRgkduphEMmv\nRYugtbXy/t27NUVRJKuU/9kRS4PBzG4ws4fC7Vkze6jCcc+a2frwuO44YpHkqYchn8xstpk9YWYb\nzeyyMvsXm9mjZvZrM/uZmb05iTglXvPnw6xZ1SsNoCmKWaP8F1D+Z0ksDQZ3/wt3n+nuM4GbgVuq\nHH5aeGxnHLFI8tTDkD9mNga4Bng/cCQw38yOLDnsQaDT3Y8FVgP/3NgopRFaWuCWW2D5cujogLFV\nFvPWFMVsUP5LgfI/O2KdkmRmBnwYWBXn+0i6qYchl04ENrr70+7+B+B64JziA9z9bnffGT68D5jW\n4BilQVpaYMEC6O6GN7yh+rGaopgJyn/ZQ/mfDXFfw3AK8Ht3f7LCfgfuMLN1ZnZhtROZ2YVm1m1m\n3b29vXUPVOKjHoZcOgQovnqlJ3yukguA2yvtVP5nx3BTFKep2pgFyn8pS/nfvGpuMJjZWjN7pMxW\n3Iswn+qjC+9y9xMIhi0/aWZ/VulAd1/u7p3u3jl58uRaw5aEqIchd6zMc172QLOPAJ3AVyudTPmf\nHdWmKLa2wuLFjY1HYqH8l7KU/82r5gaDu89y96PLbD8CMLOxwLnADVXOsTn8ugVYQzCMKRmnHoZc\n6AGK/9LTgM2lB5nZLOAK4Gx3f71BsUmCKk1RbG2F00+HefOSiUvqSvkvZSn/m1ecU5JmAY+7e9n+\nYjNrNbO2wvfAGcAjMcYjKaEehlz4FXC4mc0ws32BecCtxQeY2fHANwgqC1sSiFESUDpFcerU4Ovy\n5XDzzcF+aXrKfylL+d+8qswmH7V5lExHMrM3Ad9y9zOBqcCa4LpoxgI/cPefxBiPpMT8+XDTTUPv\n/qwehuxw991m9ingp8AY4DvuvsHMrgK63f1WgikIfwLcFJYDv3P3sxMLWhqmMEVxwYKkI5E4KP+l\nGuV/c4qtweDu55d5bjNwZvj908Bxcb2/pFehh+H664MLnHt6gmlIixcHjQX1MGSDu98G3Fby3OeL\nvp/V8KBEpCGU/yLZEucIg0hF6mEQERERaQ7qyxURERERkYrUYBARERERkYrUYBARERERkYrUYBAR\nERGRzBoYgJUrobMzWMq1szN4PDCQdGTNQw0GERHJPFUYRPJpYADOPRcWLoR162DLluDrwoUwd67K\ngKjUYBARkUxThUEkv1atGnrfJwge33lnsMS7DE8NBskV9TKK5I8qDCL5tXTp0Nwv2LEjuB+UDE8N\nBskN9TKK5JMqDCLNpZ6de5s2Vd/f01NbjHmjBoPkhnoZRZpHrioMfX0wa1bwVSTn6t25195eff+0\nabXHmidqMEhuqJdRpDnkrsLQ1QV33aVCSIT6d+4tWgStreX3tbbC4sW1xZk3ajBIquWql7FAvY2S\nc7mqMPT1wbJl4B40GJT3knP17tybPz/4SC0tA1pb4fTTYd682uLMGzUYJLVy18tYoN5GyblcVRi6\nuvYWZgMDynvJvXp37rW0wC23wPLl0NERdD52dASPb7452C/D069JUitXvYwF6m0UyU+FoZDvu3YF\nj3ftUt5L7sXRudfSAgsWQHc3vPBC8HXBgvhzP0srM6rBIKmVq17GAvU2imSqwgBVKg1f7Rpac1De\nS841RedeBFlbmVENBkmt3PQyFqi3UQTIToUBKlcaPnNhH3/4p6J8L9i1i51fXsLhk/uaujdSpFZN\n0bkXwUhnSaR9NCLpKpJIRVnrZRxWl3obRSA7FQaoXGm4aGcX9O8u+5qWgd18dOuSpu6NFKlV6jv3\nIhrJLIlmGI1okl+75FGWehkLKvYgvFQyulCgUQbJoaxUGKBypeFoNrCdNraNnQSTJvFa2yS2Mole\nJrGdNo5hPaD7xEg+pbpzL6KRzJJohvtENdGvXvImS72MUL0H4eZ3duG7y/c2vr5zN/9yyJLUDU+K\nxCkLFQaoXGk4lzVMoZe3HdQLvb2cfEQvk+llSridy5o9x+o+MSLNZySzJJrhPlGjKnrN7ENmtsHM\nBsyss2Tf5Wa20cyeMLP3VXj9DDP7bzN70sxuMLN9RxOPZEuWehmheg/CuKc28Pq+bTAp6G30SZN4\ned+gx/EVb+OwnetTNzwpIsOLWmlomvvEiEgkI5kl0Qz5P9oq1yPAucDPi580syOBecBRwGzg62Y2\npszr/wlY6u6HA9uAC0YZj2RMVnoZoXoPwjn9azj5iKCnkd5efrCsl0P22dvjWOhtTNPwpIgML2ql\noWnuEyMikYxklkQz5P+oql3u/pi7P1Fm1znA9e7+urs/A2wETiw+wMwMeA+wOnzqe8Cc0cQjkmYj\n6UFohuFJERle1EpDFq/ZEsmz0lkSU6bA9OnB13vvhRNP3DvNuBnyP65+2kOA4upRT/hcsYOAPnff\nXeWYPczsQjPrNrPu3t7eugYr0ggj6UFohuFJERlecaXhhBNg//1hwgQYMybI81WrggpD1q7ZEpG9\nsyTuvx/e+c5gEsEzzwxdBekv/iL9+T9sg8HM1prZI2W2c6q9rMxzXsMxe3e4L3f3TnfvnDx58nBh\ni6TOSHoQmmF4UkSiaWkJPvDb26G/H3buhFdeGVxhgGxdsyUiew23CtKNN6Y//8cOd4C7z6rhvD1A\ncZVnGrC55JitwEQzGxwNMYAAAAqISURBVBuOMpQ7RiQz5s+Hm24aWmiU60FYtCioSJSblpSW4UkR\niS7KsokLFuzdRCQ7okwzTnv+x9VmuRWYZ2bjzGwGcDhwf/EB7u7A3cB54VMfA34UUzwiiRvJfEZN\nTxDJFl2XJNI4abtrchamGY92WdU/N7Me4J3Aj83spwDuvgG4EXgU+AnwSXfvD19zm5m9KTzFZ4HF\nZraR4JqGb48mHpG0izqfEdI/PCmSZqowiORTGu+anIVpxqNdJWmNu09z93HuPtXd31e078vu/hZ3\n/1N3v73o+TPdfXP4/dPufqK7v9XdP+Tur48mHpFmEWV6QpaWlBVpJFUYRNIvrkZ9Gu+a3AyrIA1H\nVQ+RBGh6gogqDAXNUmEQqZc4G/Vp/HwtnWZ8AH3cySzeNKGP008PFkNIy2hoJWowSOalbWoC5Gd6\ngpnNDu/2vtHMLiuzf1x4l/eN4V3fpzc+SklC3isMBYXrkpqhwjBSyn+pJM5GfRo/X0uvYfxCaxfv\n4S5uP30JAwPwiU+kZzS0EjUYJNPSODUB8jE9Iby7+zXA+4EjgfnhXeCLXQBsc/e3AksJ7v4uOZD3\nCkPhuqRrr6VpKgwjofyXauJs1Kf183XPNOO1fSxiGS04b/vJErrX9qVqNLQSNRgkUXH3/qdxagLk\nZnrCicDG8FqlPwDXE9wFvtg5BHd5h+Cu7+8N7wIvGZfrCkPRdUlm8LOfpa+MqgPlv1QUZ6M+9Z+v\nXV17Kjn9fxhg4c7yhV3apierwSCJaUTvfxqnJkBupidEueP7nmPC+7G8TLBimmRcrisMRdJaRtVB\n3fLfzC40s24z6+7t7Y0pXGmkOBv1qV6WvK8Pli2DXbsAGO+7WMwSDqCv7OFpmp6sBoMkphG9/2mc\nmgC5mZ5Qtzu+q8KQPbmtMJRIaxlVB3XLf3df7u6d7t45efLkugQnyYqzUV/p8zUVy5IXjS4UtDDA\nYsr3DKRperIaDJKYRvSspXVqAuRiekKUO77vOcbMxgIHAC+VnkgVhuzJbYWhRJrLqFGqW/5L9sTd\nqE/lsuQlowsFEyg/ypC20dAUFZuSN43oWWumqQmQuekJvwION7MZZrYvMI/gLvDFbiW4yzsEd32/\nK7wLvGRcLisMZTRbGTUCyn+pqJka9XXT1QW7d5fdNZbdg0YZ0jgaOjbpACS/2tuDKTeV1KNnbf58\nuOmmoVOf0piMkK3pCe6+28w+BfwUGAN8x903mNlVQLe730pwd/frwru9v0RQqZAcKFQYrr8+aAj3\n9AQ5v3hxkJeZrDCU0WxlVFTKfxlOoVG/YEHSkTTIhg3Q1hZsRRzgdTilZT1Tx6e3HLRmbMx3dnZ6\nd3d30mHIKK1cGczNL9ej3toa9DTUoyAZGGieSklnZ3DNQiUdHUFPab2Z2Tp376z/metP+S9Zk3QZ\npfwXya+o+a8RBklMo3rWmqkXY9Gi6o2oJp6eICIVNFMZJSL5lLL+VcmTXM5hHEYzre4iIiIi+aAR\nBkmUetYG07xuERERSRs1GERSRo0oERERSRP1V4qIiIiISEVqMIiIiIiISEVqMIiIiIiISEVqMIiI\niIiISEVNeeM2M+sFftugt5sEbG3Qe9WbYk9GM8b+ZnefnHQQUdSY/2n8m6QxJkhnXGmMCdIZVy0x\nKf+Tkca4FFM0aYwJYsz/pmwwNJKZdTfLHTBLKfZkNHPsWZXGv0kaY4J0xpXGmCCdcaUxpqSl9XeS\nxrgUUzRpjAnijUtTkkREREREpCI1GEREREREpCI1GIa3POkARkGxJ6OZY8+qNP5N0hgTpDOuNMYE\n6YwrjTElLa2/kzTGpZiiSWNMEGNcuoZBREREREQq0giDiIiIiIhUpAaDiIiIiIhUpAbDMMzsi2b2\nnJk9FG5nJh3TcMxstpk9YWYbzeyypOMZCTN71szWh7/r7qTjqcbMvmNmW8zskaLn3mBmd5rZk+HX\nA5OMMW+G+983s3FmdkO4/7/NbHoKYlpsZo+a2a/N7Gdm9uakYyo67jwzczNryPKBUeIysw+Hv68N\nZvaDpGMys0PN7G4zezD8G8b+GVGu7CnZb2Z2dRjzr83shLhjSgPlf/3iKjquYWWA8j9yTMnkv7tr\nq7IBXwQuSTqOEcQ7BngKOAzYF3gYODLpuEYQ/7PApKTjiBjrnwEnAI8UPffPwGXh95cB/5R0nHnZ\novzvA/8TuDb8fh5wQwpiOg2YEH7/iTTEFB7XBvwcuA/oTMnf73DgQeDA8PGUFMS0HPhE+P2RwLMN\n+F0NKXtK9p8J3A4YcBLw33HHlPSm/K9vXOFxDSs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pwSAiIiIiIhWpwSAiIiIiIhWpwSAiIiIiIhWpwSAiIiIiIhXF1mAws3Yzu9vM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\ngg==\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "print (\"---------Scaling training and test data same------\")\n", + "from sklearn.datasets import make_blobs\n", + "from sklearn.preprocessing import MinMaxScaler\n", + "# make synthetic data\n", + "X, _ = make_blobs(n_samples=50, centers=5, random_state=4, cluster_std=2)\n", + "# split it into training and test set\n", + "X_train, X_test = train_test_split(X, random_state=5, test_size=.1)\n", + "# plot the training and test set\n", + "fig, axes = plt.subplots(1, 3, figsize=(13, 4))\n", + "axes[0].scatter(X_train[:, 0], X_train[:, 1],\n", + " c='b', label=\"training set\", s=60)\n", + "axes[0].scatter(X_test[:, 0], X_test[:, 1], marker='^',\n", + " c='r', label=\"test set\", s=60)\n", + "axes[0].legend(loc='upper left')\n", + "axes[0].set_title(\"original data\")\n", + "# scale the data using MinMaxScaler\n", + "scaler = MinMaxScaler()\n", + "scaler.fit(X_train)\n", + "X_train_scaled = scaler.transform(X_train)\n", + "X_test_scaled = scaler.transform(X_test)\n", + "# visualize the properly scaled data\n", + "axes[1].scatter(X_train_scaled[:, 0], X_train_scaled[:, 1],\n", + " c='b', label=\"training set\", s=60)\n", + "axes[1].scatter(X_test_scaled[:, 0], X_test_scaled[:, 1], marker='^',\n", + " c='r', label=\"test set\", s=60)\n", + "axes[1].set_title(\"scaled data\")\n", + "# rescale the test set separately, so that test set min is 0 and test set max is 1\n", + "# DO NOT DO THIS! For illustration purposes only\n", + "test_scaler = MinMaxScaler()\n", + "test_scaler.fit(X_test)\n", + "X_test_scaled_badly = test_scaler.transform(X_test)\n", + "# visualize wrongly scaled data\n", + "axes[2].scatter(X_train_scaled[:, 0], X_train_scaled[:, 1],\n", + " c='b', label=\"training set\", s=60)\n", + "axes[2].scatter(X_test_scaled_badly[:, 0], X_test_scaled_badly[:, 1], marker='^',\n", + " c='r', label=\"test set\", s=60)\n", + "axes[2].set_title(\"improperly scaled data\")\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [] + } + ], + "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": "3.7.0" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/doc/Programs/JupyterFiles/Examples/Scikit-Learn Website Examples/.ipynb_checkpoints/Boston Housing-checkpoint.ipynb b/doc/Programs/JupyterFiles/Examples/Scikit-Learn Website Examples/.ipynb_checkpoints/Boston Housing-checkpoint.ipynb new file mode 100644 index 000000000..0eb2e8d15 --- /dev/null +++ b/doc/Programs/JupyterFiles/Examples/Scikit-Learn Website Examples/.ipynb_checkpoints/Boston Housing-checkpoint.ipynb @@ -0,0 +1,132 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "(506, 104)\n" + ] + }, + { + "data": { + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "training set score: 0.771865\n", + "test set score: 0.725968\n", + "training set score: 0.771686\n", + "test set score: 0.722415\n", + "training set score: 0.771863\n", + "test set score: 0.725626\n", + "--------------------\n", + "training set score: 0.771865\n", + "test set score: 0.725916\n", + "number of features used: 2\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/usr/local/lib/python3.7/site-packages/sklearn/linear_model/base.py:509: RuntimeWarning: internal gelsd driver lwork query error, required iwork dimension not returned. This is likely the result of LAPACK bug 0038, fixed in LAPACK 3.2.2 (released July 21, 2010). Falling back to 'gelss' driver.\n", + " linalg.lstsq(X, y)\n" + ] + } + ], + "source": [ + "#!pip install mglearn\n", + "import mglearn\n", + "import sklearn\n", + "import pandas as pd\n", + "import matplotlib.pyplot as plt\n", + "import numpy as np\n", + "import IPython\n", + "\n", + "from sklearn.datasets import load_boston\n", + "boston = load_boston()\n", + "X, y = mglearn.datasets.load_extended_boston()\n", + "print(X.shape)\n", + "mglearn.plots.plot_knn_classification(n_neighbors=3)\n", + "plt.show()\n", + "\n", + "from sklearn.model_selection import train_test_split\n", + "X, y=mglearn.datasets.make_forge()\n", + "\n", + "X_train, X_test, y_train, y_test=train_test_split(X, y, random_state=0)\n", + "\n", + "from sklearn.neighbors import KNeighborsClassifier\n", + "clf=KNeighborsClassifier(n_neighbors=3)\n", + "clf.fit(X_train, y_train)\n", + "KNeighborsClassifier(algorithm='auto', leaf_size=30, metric='minkowski')\n", + "clf.predict(X_test)\n", + "clf.score(X_test, y_test)\n", + "\n", + "from sklearn.linear_model import LinearRegression\n", + "lr=LinearRegression().fit(X_train, y_train)\n", + "\n", + "print(\"training set score: %f\" % lr.score(X_train, y_train))\n", + "print(\"test set score: %f\" % lr.score(X_test, y_test))\n", + "\n", + "from sklearn.linear_model import Ridge\n", + "ridge = Ridge().fit(X_train, y_train)\n", + "print(\"training set score: %f\" % ridge.score(X_train, y_train))\n", + "print(\"test set score: %f\" % ridge.score(X_test, y_test))\n", + "\n", + "ridge01 = Ridge(alpha=0.1).fit(X_train, y_train)\n", + "print(\"training set score: %f\" % ridge01.score(X_train, y_train))\n", + "print(\"test set score: %f\" % ridge01.score(X_test, y_test))\n", + "\n", + "print (\"--------------------\")\n", + "\n", + "from sklearn.linear_model import Lasso\n", + "lasso00001 = Lasso(alpha=0.0001).fit(X_train, y_train)\n", + "print(\"training set score: %f\" % lasso00001.score(X_train, y_train))\n", + "print(\"test set score: %f\" % lasso00001.score(X_test, y_test))\n", + "print(\"number of features used: %d\" % np.sum(lasso00001.coef_ != 0))" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [] + } + ], + "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": "3.7.0" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/doc/Programs/JupyterFiles/Examples/Scikit-Learn Website Examples/.ipynb_checkpoints/Diabetes-checkpoint.ipynb b/doc/Programs/JupyterFiles/Examples/Scikit-Learn Website Examples/.ipynb_checkpoints/Diabetes-checkpoint.ipynb new file mode 100644 index 000000000..9e1ff2f44 --- /dev/null +++ b/doc/Programs/JupyterFiles/Examples/Scikit-Learn Website Examples/.ipynb_checkpoints/Diabetes-checkpoint.ipynb @@ -0,0 +1,113 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 23, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Coefficients: \n", + " [ 945.4992184]\n", + "Mean squared error: 3471.92\n", + "Variance score: 0.41\n" + ] + }, + { + "data": { + "image/png": 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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import matplotlib.pyplot as plt\n", + "import numpy as np\n", + "from sklearn import datasets, linear_model\n", + "from sklearn.metrics import mean_squared_error, r2_score\n", + "\n", + "diabetes = datasets.load_diabetes()\n", + "diabetes_X = diabetes.data[:, np.newaxis, 2]\n", + "diabetes_X_train = diabetes_X[:-50]\n", + "diabetes_X_test = diabetes_X[-50:]\n", + "\n", + "#print (diabetes_X.shape)\n", + "\n", + "# Split the targets into training/testing sets\n", + "diabetes_y_train = diabetes.target[:-50]\n", + "diabetes_y_test = diabetes.target[-50:]\n", + "\n", + "# Create linear regression object\n", + "regr = linear_model.LinearRegression()\n", + "\n", + "# Train the model using the training sets\n", + "regr.fit(diabetes_X_train, diabetes_y_train)\n", + "\n", + "# Make predictions using the testing set\n", + "diabetes_y_pred = regr.predict(diabetes_X_test)\n", + "\n", + "# The coefficients\n", + "print('Coefficients: \\n', regr.coef_)\n", + "# The mean squared error\n", + "print(\"Mean squared error: %.2f\"\n", + " % mean_squared_error(diabetes_y_test, diabetes_y_pred))\n", + "# Explained variance score: 1 is perfect prediction\n", + "print('Variance score: %.2f' % r2_score(diabetes_y_test, diabetes_y_pred))\n", + "\n", + "# Plot outputs\n", + "plt.scatter(diabetes_X_test, diabetes_y_test, color='black')\n", + "plt.plot(diabetes_X_test, diabetes_y_pred, color='blue', linewidth=3)\n", + "\n", + "plt.xticks(())\n", + "plt.yticks(())\n", + "\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [] + } + ], + "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": "3.7.0" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/doc/Programs/JupyterFiles/Examples/Scikit-Learn Website Examples/.ipynb_checkpoints/Make Moons-checkpoint.ipynb b/doc/Programs/JupyterFiles/Examples/Scikit-Learn Website Examples/.ipynb_checkpoints/Make Moons-checkpoint.ipynb new file mode 100644 index 000000000..1c818c5b3 --- /dev/null +++ b/doc/Programs/JupyterFiles/Examples/Scikit-Learn Website Examples/.ipynb_checkpoints/Make Moons-checkpoint.ipynb @@ -0,0 +1,120 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "accuracy on test set: 0.880000\n" + ] + } + ], + "source": [ + "import matplotlib.pyplot as plt\n", + "import mglearn\n", + "from sklearn.model_selection import train_test_split\n", + "from sklearn.ensemble import RandomForestClassifier\n", + "from sklearn.datasets import make_moons\n", + "import numpy as np\n", + "X, y = make_moons(n_samples=100, noise=0.25, random_state=3)\n", + "X_train, X_test, y_train, y_test = train_test_split(X, y, stratify=y, random_state=42)\n", + "forest = RandomForestClassifier(n_estimators=5, random_state=2)\n", + "forest.fit(X_train, y_train)\n", + "RandomForestClassifier(bootstrap=True, class_weight=None, criterion='gini',\n", + " max_depth=None, max_features='auto', max_leaf_nodes=None,\n", + " min_samples_leaf=1, min_samples_split=2,\n", + " min_weight_fraction_leaf=0.0, n_estimators=5, n_jobs=1,\n", + " oob_score=False, random_state=2, verbose=0, warm_start=False)\n", + "\n", + "fig, axes = plt.subplots(2, 3, figsize=(20, 10))\n", + "for i, (ax, tree) in enumerate(zip(axes.ravel(), forest.estimators_)):\n", + " ax.set_title(\"tree %d\" % i)\n", + " mglearn.plots.plot_tree_partition(X_train, y_train, tree, ax=ax)\n", + "mglearn.plots.plot_2d_separator(forest, X_train, fill=True, ax=axes[-1, -1], alpha=.4)\n", + "axes[-1, -1].set_title(\"random forest\")\n", + "plt.scatter(X_train[:, 0], X_train[:, 1], c=np.array(['r', 'b'])[y_train], s=60)\n", + "plt.show()\n", + "\n", + "forest = RandomForestClassifier(n_estimators=100, random_state=0)\n", + "forest.fit(X_train, y_train)\n", + "print(\"accuracy on test set: %f\" % forest.score(X_test, y_test))\n" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/usr/local/lib/python3.7/site-packages/sklearn/neural_network/multilayer_perceptron.py:564: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n", + " % self.max_iter, ConvergenceWarning)\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "from sklearn.neural_network import MLPClassifier\n", + "mlp = MLPClassifier(hidden_layer_sizes=[100], activation='relu', random_state=0, learning_rate='constant')\n", + "mlp.fit(X_train, y_train)\n", + "mglearn.plots.plot_2d_separator(mlp, X_train, fill=True, alpha=.3)\n", + "plt.scatter(X_train[:, 0], X_train[:, 1], c=y_train, s=60, cmap=mglearn.cm2)\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [] + } + ], + "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": "3.7.0" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/doc/Programs/JupyterFiles/Examples/Scikit-Learn Website Examples/Blobs.ipynb b/doc/Programs/JupyterFiles/Examples/Scikit-Learn Website Examples/Blobs.ipynb new file mode 100644 index 000000000..2acd80223 --- /dev/null +++ b/doc/Programs/JupyterFiles/Examples/Scikit-Learn Website Examples/Blobs.ipynb @@ -0,0 +1,256 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "(3, 2)\n", + "(3,)\n" + ] + }, + { + "data": { + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "----------Uncertainty Estimates-------\n", + "(25, 2)\n", + "(25,)\n", + "[ True False False False True True False True True True False True\n", + " True False True False False False True True True True True False\n", + " False]\n", + "['red' 'blue' 'blue' 'blue' 'red' 'red' 'blue' 'red' 'red' 'red' 'blue'\n", + " 'red' 'red' 'blue' 'red' 'blue' 'blue' 'blue' 'red' 'red' 'red' 'red'\n", + " 'red' 'blue' 'blue']\n" + ] + }, + { + "data": { + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "-------Predicting Probabilities---------\n" + ] + }, + { + "data": { + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import mglearn\n", + "from sklearn.model_selection import train_test_split\n", + "from sklearn.datasets import make_blobs\n", + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "from sklearn.svm import LinearSVC\n", + "X,y= make_blobs(random_state=42)\n", + "\n", + "linear_svm=LinearSVC().fit(X,y)\n", + "print(linear_svm.coef_.shape)\n", + "print(linear_svm.intercept_.shape)\n", + "plt.scatter(X[:, 0], X[:, 1], c=y, s=60, cmap=mglearn.cm3)\n", + "line = np.linspace(-15, 15)\n", + "for coef, intercept in zip(linear_svm.coef_, linear_svm.intercept_):\n", + " plt.plot(line, -(line * coef[0] + intercept) / coef[1])\n", + " plt.ylim(-10, 15)\n", + " plt.xlim(-10, 8)\n", + " \n", + "mglearn.plots.plot_2d_classification(linear_svm, X, fill=True, alpha=.7)\n", + "plt.scatter(X[:, 0], X[:, 1], c=y, s=60)\n", + "line = np.linspace(-15, 15)\n", + "for coef, intercept in zip(linear_svm.coef_, linear_svm.intercept_):\n", + " plt.plot(line, -(line * coef[0] + intercept) / coef[1])\n", + "plt.show()\n", + "\n", + "\n", + "print (\"----------Uncertainty Estimates-------\")\n", + "# create and split a synthetic dataset\n", + "from sklearn.ensemble import GradientBoostingClassifier\n", + "from sklearn.datasets import make_blobs, make_circles\n", + "# X, y = make_blobs(centers=2, random_state=59)\n", + "X, y = make_circles(noise=0.25, factor=0.5, random_state=1)\n", + "# we rename the classes \"blue\" and \"red\" for illustration purposes:\n", + "y_named = np.array([\"blue\", \"red\"])[y]\n", + "# we can call train test split with arbitrary many arrays\n", + "# all will be split in a consistent manner\n", + "X_train, X_test, y_train_named, y_test_named, y_train, y_test = \\\n", + " train_test_split(X, y_named, y, random_state=0)\n", + "# build the gradient boosting model model\n", + "gbrt = GradientBoostingClassifier(random_state=0)\n", + "gbrt.fit(X_train, y_train_named)\n", + "\n", + "print(X_test.shape)\n", + "print(gbrt.decision_function(X_test).shape)\n", + "# show the first few entries of decision_function\n", + "gbrt.decision_function(X_test)[:6]\n", + "print(gbrt.decision_function(X_test) > 0)\n", + "print(gbrt.predict(X_test))\n", + "# make the boolean True/False into 0 and 1\n", + "greater_zero = (gbrt.decision_function(X_test) > 0).astype(int)\n", + "# use 0 and 1 as indices into classes_\n", + "pred = gbrt.classes_[greater_zero]\n", + "#pred is the same as the output of gbrt.predict\n", + "np.all(pred == gbrt.predict(X_test))\n", + "decision_function = gbrt.decision_function(X_test)\n", + "np.min(decision_function), np.max(decision_function)\n", + "fig, axes = plt.subplots(1, 2, figsize=(13, 5))\n", + "mglearn.tools.plot_2d_separator(gbrt, X, ax=axes[0], alpha=.4, fill=True, cm=mglearn.cm2)\n", + "scores_image = mglearn.tools.plot_2d_scores(gbrt, X, ax=axes[1], alpha=.4, cm='bwr')\n", + "for ax in axes:\n", + " # plot training and test points\n", + " ax.scatter(X_test[:, 0], X_test[:, 1], c=y_test, cmap=mglearn.cm2, s=60, marker='^')\n", + " ax.scatter(X_train[:, 0], X_train[:, 1], c=y_train, cmap=mglearn.cm2, s=60)\n", + "plt.colorbar(scores_image, ax=axes.tolist())\n", + "plt.show()\n", + "\n", + "print (\"-------Predicting Probabilities---------\")\n", + "gbrt.predict_proba(X_test).shape\n", + "np.set_printoptions(suppress=True, precision=3)\n", + "# show the first few entries of predict_proba\n", + "gbrt.predict_proba(X_test[:6])\n", + "fig, axes = plt.subplots(1, 2, figsize=(13, 5))\n", + "mglearn.tools.plot_2d_separator(gbrt, X, ax=axes[0], alpha=.4,\n", + " fill=True, cm=mglearn.cm2)\n", + "scores_image = mglearn.tools.plot_2d_scores(gbrt, X, ax=axes[1], alpha=.4,\n", + " cm='bwr', function='predict_proba')\n", + "for ax in axes:\n", + " # plot training and test points\n", + " ax.scatter(X_test[:, 0], X_test[:, 1], c=y_test, cmap=mglearn.cm2, s=60, marker='^')\n", + " ax.scatter(X_train[:, 0], X_train[:, 1], c=y_train, cmap=mglearn.cm2, s=60)\n", + "plt.colorbar(scores_image, ax=axes.tolist())\n", + "plt.show()\n" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "---------Scaling training and test data same------\n" + ] + }, + { + "data": { + "image/png": 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pwSAiIiIiIhWpwSAiIiIiIhWpwSAiIiIiIhWpwSAiIiIiIhXF1mAws3Yzu9vM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\n+F0NKXtK9p8J3A4YcBLw33HHlPSm/K9vXOFxDSs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pwSAiIiIiIhWpwSAiIiIiIhWpwSAiIiIiIhWpwSAiIiIiIhXF1mAws3Yzu9vM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\ngg==\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "print (\"---------Scaling training and test data same------\")\n", + "from sklearn.datasets import make_blobs\n", + "from sklearn.preprocessing import MinMaxScaler\n", + "# make synthetic data\n", + "X, _ = make_blobs(n_samples=50, centers=5, random_state=4, cluster_std=2)\n", + "# split it into training and test set\n", + "X_train, X_test = train_test_split(X, random_state=5, test_size=.1)\n", + "# plot the training and test set\n", + "fig, axes = plt.subplots(1, 3, figsize=(13, 4))\n", + "axes[0].scatter(X_train[:, 0], X_train[:, 1],\n", + " c='b', label=\"training set\", s=60)\n", + "axes[0].scatter(X_test[:, 0], X_test[:, 1], marker='^',\n", + " c='r', label=\"test set\", s=60)\n", + "axes[0].legend(loc='upper left')\n", + "axes[0].set_title(\"original data\")\n", + "# scale the data using MinMaxScaler\n", + "scaler = MinMaxScaler()\n", + "scaler.fit(X_train)\n", + "X_train_scaled = scaler.transform(X_train)\n", + "X_test_scaled = scaler.transform(X_test)\n", + "# visualize the properly scaled data\n", + "axes[1].scatter(X_train_scaled[:, 0], X_train_scaled[:, 1],\n", + " c='b', label=\"training set\", s=60)\n", + "axes[1].scatter(X_test_scaled[:, 0], X_test_scaled[:, 1], marker='^',\n", + " c='r', label=\"test set\", s=60)\n", + "axes[1].set_title(\"scaled data\")\n", + "# rescale the test set separately, so that test set min is 0 and test set max is 1\n", + "# DO NOT DO THIS! For illustration purposes only\n", + "test_scaler = MinMaxScaler()\n", + "test_scaler.fit(X_test)\n", + "X_test_scaled_badly = test_scaler.transform(X_test)\n", + "# visualize wrongly scaled data\n", + "axes[2].scatter(X_train_scaled[:, 0], X_train_scaled[:, 1],\n", + " c='b', label=\"training set\", s=60)\n", + "axes[2].scatter(X_test_scaled_badly[:, 0], X_test_scaled_badly[:, 1], marker='^',\n", + " c='r', label=\"test set\", s=60)\n", + "axes[2].set_title(\"improperly scaled data\")\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [] + } + ], + "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": "3.7.0" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/doc/Programs/JupyterFiles/Examples/Scikit-Learn Website Examples/Boston Housing.ipynb b/doc/Programs/JupyterFiles/Examples/Scikit-Learn Website Examples/Boston Housing.ipynb new file mode 100644 index 000000000..0eb2e8d15 --- /dev/null +++ b/doc/Programs/JupyterFiles/Examples/Scikit-Learn Website Examples/Boston Housing.ipynb @@ -0,0 +1,132 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "(506, 104)\n" + ] + }, + { + "data": { + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "training set score: 0.771865\n", + "test set score: 0.725968\n", + "training set score: 0.771686\n", + "test set score: 0.722415\n", + "training set score: 0.771863\n", + "test set score: 0.725626\n", + "--------------------\n", + "training set score: 0.771865\n", + "test set score: 0.725916\n", + "number of features used: 2\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/usr/local/lib/python3.7/site-packages/sklearn/linear_model/base.py:509: RuntimeWarning: internal gelsd driver lwork query error, required iwork dimension not returned. This is likely the result of LAPACK bug 0038, fixed in LAPACK 3.2.2 (released July 21, 2010). Falling back to 'gelss' driver.\n", + " linalg.lstsq(X, y)\n" + ] + } + ], + "source": [ + "#!pip install mglearn\n", + "import mglearn\n", + "import sklearn\n", + "import pandas as pd\n", + "import matplotlib.pyplot as plt\n", + "import numpy as np\n", + "import IPython\n", + "\n", + "from sklearn.datasets import load_boston\n", + "boston = load_boston()\n", + "X, y = mglearn.datasets.load_extended_boston()\n", + "print(X.shape)\n", + "mglearn.plots.plot_knn_classification(n_neighbors=3)\n", + "plt.show()\n", + "\n", + "from sklearn.model_selection import train_test_split\n", + "X, y=mglearn.datasets.make_forge()\n", + "\n", + "X_train, X_test, y_train, y_test=train_test_split(X, y, random_state=0)\n", + "\n", + "from sklearn.neighbors import KNeighborsClassifier\n", + "clf=KNeighborsClassifier(n_neighbors=3)\n", + "clf.fit(X_train, y_train)\n", + "KNeighborsClassifier(algorithm='auto', leaf_size=30, metric='minkowski')\n", + "clf.predict(X_test)\n", + "clf.score(X_test, y_test)\n", + "\n", + "from sklearn.linear_model import LinearRegression\n", + "lr=LinearRegression().fit(X_train, y_train)\n", + "\n", + "print(\"training set score: %f\" % lr.score(X_train, y_train))\n", + "print(\"test set score: %f\" % lr.score(X_test, y_test))\n", + "\n", + "from sklearn.linear_model import Ridge\n", + "ridge = Ridge().fit(X_train, y_train)\n", + "print(\"training set score: %f\" % ridge.score(X_train, y_train))\n", + "print(\"test set score: %f\" % ridge.score(X_test, y_test))\n", + "\n", + "ridge01 = Ridge(alpha=0.1).fit(X_train, y_train)\n", + "print(\"training set score: %f\" % ridge01.score(X_train, y_train))\n", + "print(\"test set score: %f\" % ridge01.score(X_test, y_test))\n", + "\n", + "print (\"--------------------\")\n", + "\n", + "from sklearn.linear_model import Lasso\n", + "lasso00001 = Lasso(alpha=0.0001).fit(X_train, y_train)\n", + "print(\"training set score: %f\" % lasso00001.score(X_train, y_train))\n", + "print(\"test set score: %f\" % lasso00001.score(X_test, y_test))\n", + "print(\"number of features used: %d\" % np.sum(lasso00001.coef_ != 0))" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [] + } + ], + "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": "3.7.0" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/doc/Programs/JupyterFiles/Examples/Scikit-Learn Website Examples/Diabetes.ipynb b/doc/Programs/JupyterFiles/Examples/Scikit-Learn Website Examples/Diabetes.ipynb new file mode 100644 index 000000000..9e1ff2f44 --- /dev/null +++ b/doc/Programs/JupyterFiles/Examples/Scikit-Learn Website Examples/Diabetes.ipynb @@ -0,0 +1,113 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 23, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Coefficients: \n", + " [ 945.4992184]\n", + "Mean squared error: 3471.92\n", + "Variance score: 0.41\n" + ] + }, + { + "data": { + "image/png": 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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import matplotlib.pyplot as plt\n", + "import numpy as np\n", + "from sklearn import datasets, linear_model\n", + "from sklearn.metrics import mean_squared_error, r2_score\n", + "\n", + "diabetes = datasets.load_diabetes()\n", + "diabetes_X = diabetes.data[:, np.newaxis, 2]\n", + "diabetes_X_train = diabetes_X[:-50]\n", + "diabetes_X_test = diabetes_X[-50:]\n", + "\n", + "#print (diabetes_X.shape)\n", + "\n", + "# Split the targets into training/testing sets\n", + "diabetes_y_train = diabetes.target[:-50]\n", + "diabetes_y_test = diabetes.target[-50:]\n", + "\n", + "# Create linear regression object\n", + "regr = linear_model.LinearRegression()\n", + "\n", + "# Train the model using the training sets\n", + "regr.fit(diabetes_X_train, diabetes_y_train)\n", + "\n", + "# Make predictions using the testing set\n", + "diabetes_y_pred = regr.predict(diabetes_X_test)\n", + "\n", + "# The coefficients\n", + "print('Coefficients: \\n', regr.coef_)\n", + "# The mean squared error\n", + "print(\"Mean squared error: %.2f\"\n", + " % mean_squared_error(diabetes_y_test, diabetes_y_pred))\n", + "# Explained variance score: 1 is perfect prediction\n", + "print('Variance score: %.2f' % r2_score(diabetes_y_test, diabetes_y_pred))\n", + "\n", + "# Plot outputs\n", + "plt.scatter(diabetes_X_test, diabetes_y_test, color='black')\n", + "plt.plot(diabetes_X_test, diabetes_y_pred, color='blue', linewidth=3)\n", + "\n", + "plt.xticks(())\n", + "plt.yticks(())\n", + "\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [] + } + ], + "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": "3.7.0" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/doc/Programs/JupyterFiles/Examples/Scikit-Learn Website Examples/Make Moons.ipynb b/doc/Programs/JupyterFiles/Examples/Scikit-Learn Website Examples/Make Moons.ipynb new file mode 100644 index 000000000..1c818c5b3 --- /dev/null +++ b/doc/Programs/JupyterFiles/Examples/Scikit-Learn Website Examples/Make Moons.ipynb @@ -0,0 +1,120 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "accuracy on test set: 0.880000\n" + ] + } + ], + "source": [ + "import matplotlib.pyplot as plt\n", + "import mglearn\n", + "from sklearn.model_selection import train_test_split\n", + "from sklearn.ensemble import RandomForestClassifier\n", + "from sklearn.datasets import make_moons\n", + "import numpy as np\n", + "X, y = make_moons(n_samples=100, noise=0.25, random_state=3)\n", + "X_train, X_test, y_train, y_test = train_test_split(X, y, stratify=y, random_state=42)\n", + "forest = RandomForestClassifier(n_estimators=5, random_state=2)\n", + "forest.fit(X_train, y_train)\n", + "RandomForestClassifier(bootstrap=True, class_weight=None, criterion='gini',\n", + " max_depth=None, max_features='auto', max_leaf_nodes=None,\n", + " min_samples_leaf=1, min_samples_split=2,\n", + " min_weight_fraction_leaf=0.0, n_estimators=5, n_jobs=1,\n", + " oob_score=False, random_state=2, verbose=0, warm_start=False)\n", + "\n", + "fig, axes = plt.subplots(2, 3, figsize=(20, 10))\n", + "for i, (ax, tree) in enumerate(zip(axes.ravel(), forest.estimators_)):\n", + " ax.set_title(\"tree %d\" % i)\n", + " mglearn.plots.plot_tree_partition(X_train, y_train, tree, ax=ax)\n", + "mglearn.plots.plot_2d_separator(forest, X_train, fill=True, ax=axes[-1, -1], alpha=.4)\n", + "axes[-1, -1].set_title(\"random forest\")\n", + "plt.scatter(X_train[:, 0], X_train[:, 1], c=np.array(['r', 'b'])[y_train], s=60)\n", + "plt.show()\n", + "\n", + "forest = RandomForestClassifier(n_estimators=100, random_state=0)\n", + "forest.fit(X_train, y_train)\n", + "print(\"accuracy on test set: %f\" % forest.score(X_test, y_test))\n" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/usr/local/lib/python3.7/site-packages/sklearn/neural_network/multilayer_perceptron.py:564: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n", + " % self.max_iter, ConvergenceWarning)\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "from sklearn.neural_network import MLPClassifier\n", + "mlp = MLPClassifier(hidden_layer_sizes=[100], activation='relu', random_state=0, learning_rate='constant')\n", + "mlp.fit(X_train, y_train)\n", + "mglearn.plots.plot_2d_separator(mlp, X_train, fill=True, alpha=.3)\n", + "plt.scatter(X_train[:, 0], X_train[:, 1], c=y_train, s=60, cmap=mglearn.cm2)\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [] + } + ], + "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": "3.7.0" + } + }, + "nbformat": 4, + 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+194,46 @@ Automatically generated HTML file from DocOnce source '___sec77'), ('Resampling methods: Jackknife', 2, None, '___sec78'), ('Resampling methods: Jackknife estimator', 2, None, '___sec79'), - ('Resampling methods: Jackknife sample code', - 2, - None, - '___sec80'), - ('Resampling methods: Bootstrap', 2, None, '___sec81'), - ('Resampling methods: Bootstrap background', 2, None, '___sec82'), + ('Resampling methods: Bootstrap', 2, None, '___sec80'), + ('Resampling methods: Bootstrap background', 2, None, '___sec81'), ('Resampling methods: More Bootstrap background', 2, None, - '___sec83'), - ('Resampling methods: Bootstrap approach', 2, None, '___sec84'), - ('Resampling methods: Bootstrap algorithm', 2, None, '___sec85'), - ('Resampling methods: Bootstrap steps', 2, None, '___sec86'), - ('Resampling methods: Blocking', 2, None, '___sec87'), - ('Blocking Transformations', 2, None, '___sec88'), - ('Blocking Transformations', 2, None, '___sec89'), - ('Blocking Transformations, getting there', 2, None, '___sec90'), + '___sec82'), + ('Resampling methods: Bootstrap approach', 2, None, '___sec83'), + ('Resampling methods: Bootstrap steps', 2, None, '___sec84'), + ('Resampling methods: Blocking', 2, None, '___sec85'), + ('Blocking Transformations', 2, None, '___sec86'), + ('Blocking Transformations', 2, None, '___sec87'), + ('Blocking Transformations, getting there', 2, None, '___sec88'), ('Blocking Transformations, final expressions', 2, None, - '___sec91'), + '___sec89'), ('"Code examples for Blocking, Jackknife and ' 'bootstrap":"https://github.com/CompPhysics/MachineLearning/tree/master/doc/Programs/ResamplingAnalysisScripts"', 2, None, - '___sec92')]} + '___sec90'), + ('The bias-variance tradeoff', 2, None, '___sec91'), + ('Training and testing data', 2, None, '___sec92'), + ('Procedure to find a predictor', 2, None, '___sec93'), + ('What we want', 2, None, '___sec94'), + ('The expected generalization error', 2, None, '___sec95'), + ('Elaborating a little bit more', 2, None, '___sec96'), + ('The bias', 2, None, '___sec97'), + ('The variance', 2, None, '___sec98'), + ('Summing up', 2, None, '___sec99'), + ('Logistic Regression', 2, None, '___sec100'), + ('Basics', 2, None, '___sec101'), + ('Linear classifier', 2, None, '___sec102'), + ('Some selected properties', 2, None, '___sec103'), + ('The cross-entropy as a cost function for logistic regression', + 2, + None, + '___sec104'), + ('Maximum likelihood', 2, None, '___sec105'), + ('Minimizing the cross entropy', 2, None, '___sec106')]} end of tocinfo --> @@ -337,19 +351,33 @@ MathJax.Hub.Config({

  • Resampling methods: Jackknife and Bootstrap
  • Resampling methods: Jackknife
  • Resampling methods: Jackknife estimator
    • -
  • Resampling methods: Jackknife sample code
    • -
  • Resampling methods: Bootstrap
    • -
  • Resampling methods: Bootstrap background
    • -
  • Resampling methods: More Bootstrap background
    • -
  • Resampling methods: Bootstrap approach
    • -
  • Resampling methods: Bootstrap algorithm
    • -
  • Resampling methods: Bootstrap steps
    • -
  • Resampling methods: Blocking
    • -
  • Blocking Transformations
    • -
  • Blocking Transformations
    • -
  • Blocking Transformations, getting there
    • -
  • Blocking Transformations, final expressions
    • -
  • "Code examples for Blocking, Jackknife and bootstrap":"https://github.com/CompPhysics/MachineLearning/tree/master/doc/Programs/ResamplingAnalysisScripts"
    • +
  • Resampling methods: Bootstrap
    • +
  • Resampling methods: Bootstrap background
    • +
  • Resampling methods: More Bootstrap background
    • +
  • Resampling methods: Bootstrap approach
    • +
  • Resampling methods: Bootstrap steps
    • +
  • Resampling methods: Blocking
    • +
  • Blocking Transformations
    • +
  • Blocking Transformations
    • +
  • Blocking Transformations, getting there
    • +
  • Blocking Transformations, final expressions
    • +
  • "Code examples for Blocking, Jackknife and bootstrap":"https://github.com/CompPhysics/MachineLearning/tree/master/doc/Programs/ResamplingAnalysisScripts"
    • +
  • The bias-variance tradeoff
    • +
  • Training and testing data
    • +
  • Procedure to find a predictor
    • +
  • What we want
    • +
  • The expected generalization error
    • +
  • Elaborating a little bit more
    • +
  • The bias
    • +
  • The variance
    • +
  • Summing up
    • +
  • Logistic Regression
    • +
  • Basics
    • +
  • Linear classifier
    • +
  • Some selected properties
    • +
  • The cross-entropy as a cost function for logistic regression
    • +
  • Maximum likelihood
    • +
  • Minimizing the cross entropy
    • @@ -384,7 +412,7 @@ MathJax.Hub.Config({
    [2] Department of Physics and Astronomy and National Superconducting Cyclotron Laboratory, Michigan State University

    -

    Sep 13, 2018

    +

    Sep 14, 2018


    @@ -408,7 +436,7 @@ MathJax.Hub.Config({

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  • »
    • diff --git a/doc/pub/Regression/html/._Regression-bs001.html b/doc/pub/Regression/html/._Regression-bs001.html index a8780ba49..24bad7b5b 100644 --- a/doc/pub/Regression/html/._Regression-bs001.html +++ b/doc/pub/Regression/html/._Regression-bs001.html @@ -194,32 +194,46 @@ Automatically generated HTML file from DocOnce source '___sec77'), ('Resampling methods: Jackknife', 2, None, '___sec78'), ('Resampling methods: Jackknife estimator', 2, None, '___sec79'), - ('Resampling methods: Jackknife sample code', - 2, - None, - '___sec80'), - ('Resampling methods: Bootstrap', 2, None, '___sec81'), - ('Resampling methods: Bootstrap background', 2, None, '___sec82'), + ('Resampling methods: Bootstrap', 2, None, '___sec80'), + ('Resampling methods: Bootstrap background', 2, None, '___sec81'), ('Resampling methods: More Bootstrap background', 2, None, - '___sec83'), - ('Resampling methods: Bootstrap approach', 2, None, '___sec84'), - ('Resampling methods: Bootstrap algorithm', 2, None, '___sec85'), - ('Resampling methods: Bootstrap steps', 2, None, '___sec86'), - ('Resampling methods: Blocking', 2, None, '___sec87'), - ('Blocking Transformations', 2, None, '___sec88'), - ('Blocking Transformations', 2, None, '___sec89'), - ('Blocking Transformations, getting there', 2, None, '___sec90'), + '___sec82'), + ('Resampling methods: Bootstrap approach', 2, None, '___sec83'), + ('Resampling methods: Bootstrap steps', 2, None, '___sec84'), + ('Resampling methods: Blocking', 2, None, '___sec85'), + ('Blocking Transformations', 2, None, '___sec86'), + ('Blocking Transformations', 2, None, '___sec87'), + ('Blocking Transformations, getting there', 2, None, '___sec88'), ('Blocking Transformations, final expressions', 2, None, - '___sec91'), + '___sec89'), ('"Code examples for Blocking, Jackknife and ' 'bootstrap":"https://github.com/CompPhysics/MachineLearning/tree/master/doc/Programs/ResamplingAnalysisScripts"', 2, None, - '___sec92')]} + '___sec90'), + ('The bias-variance tradeoff', 2, None, '___sec91'), + ('Training and testing data', 2, None, '___sec92'), + ('Procedure to find a predictor', 2, None, '___sec93'), + ('What we want', 2, None, '___sec94'), + ('The expected generalization error', 2, None, '___sec95'), + ('Elaborating a little bit more', 2, None, '___sec96'), + ('The bias', 2, None, '___sec97'), + ('The variance', 2, None, '___sec98'), + ('Summing up', 2, None, '___sec99'), + ('Logistic Regression', 2, None, '___sec100'), + ('Basics', 2, None, '___sec101'), + ('Linear classifier', 2, None, '___sec102'), + ('Some selected properties', 2, None, '___sec103'), + ('The cross-entropy as a cost function for logistic regression', + 2, + None, + '___sec104'), + ('Maximum likelihood', 2, None, '___sec105'), + ('Minimizing the cross entropy', 2, None, '___sec106')]} end of tocinfo --> @@ -337,19 +351,33 @@ MathJax.Hub.Config({
  • Resampling methods: Jackknife and Bootstrap
  • Resampling methods: Jackknife
  • Resampling methods: Jackknife estimator
    • -
  • Resampling methods: Jackknife sample code
    • -
  • Resampling methods: Bootstrap
    • -
  • Resampling methods: Bootstrap background
    • -
  • Resampling methods: More Bootstrap background
    • -
  • Resampling methods: Bootstrap approach
    • -
  • Resampling methods: Bootstrap algorithm
    • -
  • Resampling methods: Bootstrap steps
    • -
  • Resampling methods: Blocking
    • -
  • Blocking Transformations
    • -
  • Blocking Transformations
    • -
  • Blocking Transformations, getting there
    • -
  • Blocking Transformations, final expressions
    • -
  • "Code examples for Blocking, Jackknife and bootstrap":"https://github.com/CompPhysics/MachineLearning/tree/master/doc/Programs/ResamplingAnalysisScripts"
    • +
  • Resampling methods: Bootstrap
    • +
  • Resampling methods: Bootstrap background
    • +
  • Resampling methods: More Bootstrap background
    • +
  • Resampling methods: Bootstrap approach
    • +
  • Resampling methods: Bootstrap steps
    • +
  • Resampling methods: Blocking
    • +
  • Blocking Transformations
    • +
  • Blocking Transformations
    • +
  • Blocking Transformations, getting there
    • +
  • Blocking Transformations, final expressions
    • +
  • "Code examples for Blocking, Jackknife and bootstrap":"https://github.com/CompPhysics/MachineLearning/tree/master/doc/Programs/ResamplingAnalysisScripts"
    • +
  • The bias-variance tradeoff
    • +
  • Training and testing data
    • +
  • Procedure to find a predictor
    • +
  • What we want
    • +
  • The expected generalization error
    • +
  • Elaborating a little bit more
    • +
  • The bias
    • +
  • The variance
    • +
  • Summing up
    • +
  • Logistic Regression
    • +
  • Basics
    • +
  • Linear classifier
    • +
  • Some selected properties
    • +
  • The cross-entropy as a cost function for logistic regression
    • +
  • Maximum likelihood
    • +
  • Minimizing the cross entropy
    • @@ -405,7 +433,7 @@ A regression model aims at finding a likelihood function \( p(y\vert \hat{x}) \)
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    • diff --git a/doc/pub/Regression/html/._Regression-bs002.html b/doc/pub/Regression/html/._Regression-bs002.html index 852e64cdc..8714b8699 100644 --- a/doc/pub/Regression/html/._Regression-bs002.html +++ b/doc/pub/Regression/html/._Regression-bs002.html @@ -194,32 +194,46 @@ Automatically generated HTML file from DocOnce source '___sec77'), ('Resampling methods: Jackknife', 2, None, '___sec78'), ('Resampling methods: Jackknife estimator', 2, None, '___sec79'), - ('Resampling methods: Jackknife sample code', - 2, - None, - '___sec80'), - ('Resampling methods: Bootstrap', 2, None, '___sec81'), - ('Resampling methods: Bootstrap background', 2, None, '___sec82'), + ('Resampling methods: Bootstrap', 2, None, '___sec80'), + ('Resampling methods: Bootstrap background', 2, None, '___sec81'), ('Resampling methods: More Bootstrap background', 2, None, - '___sec83'), - ('Resampling methods: Bootstrap approach', 2, None, '___sec84'), - ('Resampling methods: Bootstrap algorithm', 2, None, '___sec85'), - ('Resampling methods: Bootstrap steps', 2, None, '___sec86'), - ('Resampling methods: Blocking', 2, None, '___sec87'), - ('Blocking Transformations', 2, None, '___sec88'), - ('Blocking Transformations', 2, None, '___sec89'), - ('Blocking Transformations, getting there', 2, None, '___sec90'), + '___sec82'), + ('Resampling methods: Bootstrap approach', 2, None, '___sec83'), + ('Resampling methods: Bootstrap steps', 2, None, '___sec84'), + ('Resampling methods: Blocking', 2, None, '___sec85'), + ('Blocking Transformations', 2, None, '___sec86'), + ('Blocking Transformations', 2, None, '___sec87'), + ('Blocking Transformations, getting there', 2, None, '___sec88'), ('Blocking Transformations, final expressions', 2, None, - '___sec91'), + '___sec89'), ('"Code examples for Blocking, Jackknife and ' 'bootstrap":"https://github.com/CompPhysics/MachineLearning/tree/master/doc/Programs/ResamplingAnalysisScripts"', 2, None, - '___sec92')]} + '___sec90'), + ('The bias-variance tradeoff', 2, None, '___sec91'), + ('Training and testing data', 2, None, '___sec92'), + ('Procedure to find a predictor', 2, None, '___sec93'), + ('What we want', 2, None, '___sec94'), + ('The expected generalization error', 2, None, '___sec95'), + ('Elaborating a little bit more', 2, None, '___sec96'), + ('The bias', 2, None, '___sec97'), + ('The variance', 2, None, '___sec98'), + ('Summing up', 2, None, '___sec99'), + ('Logistic Regression', 2, None, '___sec100'), + ('Basics', 2, None, '___sec101'), + ('Linear classifier', 2, None, '___sec102'), + ('Some selected properties', 2, None, '___sec103'), + ('The cross-entropy as a cost function for logistic regression', + 2, + None, + '___sec104'), + ('Maximum likelihood', 2, None, '___sec105'), + ('Minimizing the cross entropy', 2, None, '___sec106')]} end of tocinfo --> @@ -337,19 +351,33 @@ MathJax.Hub.Config({
  • Resampling methods: Jackknife and Bootstrap
  • Resampling methods: Jackknife
  • Resampling methods: Jackknife estimator
    • -
  • Resampling methods: Jackknife sample code
    • -
  • Resampling methods: Bootstrap
    • -
  • Resampling methods: Bootstrap background
    • -
  • Resampling methods: More Bootstrap background
    • -
  • Resampling methods: Bootstrap approach
    • -
  • Resampling methods: Bootstrap algorithm
    • -
  • Resampling methods: Bootstrap steps
    • -
  • Resampling methods: Blocking
    • -
  • Blocking Transformations
    • -
  • Blocking Transformations
    • -
  • Blocking Transformations, getting there
    • -
  • Blocking Transformations, final expressions
    • -
  • "Code examples for Blocking, Jackknife and bootstrap":"https://github.com/CompPhysics/MachineLearning/tree/master/doc/Programs/ResamplingAnalysisScripts"
    • +
  • Resampling methods: Bootstrap
    • +
  • Resampling methods: Bootstrap background
    • +
  • Resampling methods: More Bootstrap background
    • +
  • Resampling methods: Bootstrap approach
    • +
  • Resampling methods: Bootstrap steps
    • +
  • Resampling methods: Blocking
    • +
  • Blocking Transformations
    • +
  • Blocking Transformations
    • +
  • Blocking Transformations, getting there
    • +
  • Blocking Transformations, final expressions
    • +
  • "Code examples for Blocking, Jackknife and bootstrap":"https://github.com/CompPhysics/MachineLearning/tree/master/doc/Programs/ResamplingAnalysisScripts"
    • +
  • The bias-variance tradeoff
    • +
  • Training and testing data
    • +
  • Procedure to find a predictor
    • +
  • What we want
    • +
  • The expected generalization error
    • +
  • Elaborating a little bit more
    • +
  • The bias
    • +
  • The variance
    • +
  • Summing up
    • +
  • Logistic Regression
    • +
  • Basics
    • +
  • Linear classifier
    • +
  • Some selected properties
    • +
  • The cross-entropy as a cost function for logistic regression
    • +
  • Maximum likelihood
    • +
  • Minimizing the cross entropy
    • @@ -412,7 +440,7 @@ response is equal to \( \beta_j \).
  • 11
  • 12
  • ...
    • -
  • 94
    • +
  • 109
  • »
    • diff --git a/doc/pub/Regression/html/._Regression-bs003.html b/doc/pub/Regression/html/._Regression-bs003.html index dd043015f..30ab5017c 100644 --- a/doc/pub/Regression/html/._Regression-bs003.html +++ b/doc/pub/Regression/html/._Regression-bs003.html @@ -194,32 +194,46 @@ Automatically generated HTML file from DocOnce source '___sec77'), ('Resampling methods: Jackknife', 2, None, '___sec78'), ('Resampling methods: Jackknife estimator', 2, None, '___sec79'), - ('Resampling methods: Jackknife sample code', - 2, - None, - '___sec80'), - ('Resampling methods: Bootstrap', 2, None, '___sec81'), - ('Resampling methods: Bootstrap background', 2, None, '___sec82'), + ('Resampling methods: Bootstrap', 2, None, '___sec80'), + ('Resampling methods: Bootstrap background', 2, None, '___sec81'), ('Resampling methods: More Bootstrap background', 2, None, - '___sec83'), - ('Resampling methods: Bootstrap approach', 2, None, '___sec84'), - ('Resampling methods: Bootstrap algorithm', 2, None, '___sec85'), - ('Resampling methods: Bootstrap steps', 2, None, '___sec86'), - ('Resampling methods: Blocking', 2, None, '___sec87'), - ('Blocking Transformations', 2, None, '___sec88'), - ('Blocking Transformations', 2, None, '___sec89'), - ('Blocking Transformations, getting there', 2, None, '___sec90'), + '___sec82'), + ('Resampling methods: Bootstrap approach', 2, None, '___sec83'), + ('Resampling methods: Bootstrap steps', 2, None, '___sec84'), + ('Resampling methods: Blocking', 2, None, '___sec85'), + ('Blocking Transformations', 2, None, '___sec86'), + ('Blocking Transformations', 2, None, '___sec87'), + ('Blocking Transformations, getting there', 2, None, '___sec88'), ('Blocking Transformations, final expressions', 2, None, - '___sec91'), + '___sec89'), ('"Code examples for Blocking, Jackknife and ' 'bootstrap":"https://github.com/CompPhysics/MachineLearning/tree/master/doc/Programs/ResamplingAnalysisScripts"', 2, None, - '___sec92')]} + '___sec90'), + ('The bias-variance tradeoff', 2, None, '___sec91'), + ('Training and testing data', 2, None, '___sec92'), + ('Procedure to find a predictor', 2, None, '___sec93'), + ('What we want', 2, None, '___sec94'), + ('The expected generalization error', 2, None, '___sec95'), + ('Elaborating a little bit more', 2, None, '___sec96'), + ('The bias', 2, None, '___sec97'), + ('The variance', 2, None, '___sec98'), + ('Summing up', 2, None, '___sec99'), + ('Logistic Regression', 2, None, '___sec100'), + ('Basics', 2, None, '___sec101'), + ('Linear classifier', 2, None, '___sec102'), + ('Some selected properties', 2, None, '___sec103'), + ('The cross-entropy as a cost function for logistic regression', + 2, + None, + '___sec104'), + ('Maximum likelihood', 2, None, '___sec105'), + ('Minimizing the cross entropy', 2, None, '___sec106')]} end of tocinfo --> @@ -337,19 +351,33 @@ MathJax.Hub.Config({
  • Resampling methods: Jackknife and Bootstrap
  • Resampling methods: Jackknife
  • Resampling methods: Jackknife estimator
    • -
  • Resampling methods: Jackknife sample code
    • -
  • Resampling methods: Bootstrap
    • -
  • Resampling methods: Bootstrap background
    • -
  • Resampling methods: More Bootstrap background
    • -
  • Resampling methods: Bootstrap approach
    • -
  • Resampling methods: Bootstrap algorithm
    • -
  • Resampling methods: Bootstrap steps
    • -
  • Resampling methods: Blocking
    • -
  • Blocking Transformations
    • -
  • Blocking Transformations
    • -
  • Blocking Transformations, getting there
    • -
  • Blocking Transformations, final expressions
    • -
  • "Code examples for Blocking, Jackknife and bootstrap":"https://github.com/CompPhysics/MachineLearning/tree/master/doc/Programs/ResamplingAnalysisScripts"
    • +
  • Resampling methods: Bootstrap
    • +
  • Resampling methods: Bootstrap background
    • +
  • Resampling methods: More Bootstrap background
    • +
  • Resampling methods: Bootstrap approach
    • +
  • Resampling methods: Bootstrap steps
    • +
  • Resampling methods: Blocking
    • +
  • Blocking Transformations
    • +
  • Blocking Transformations
    • +
  • Blocking Transformations, getting there
    • +
  • Blocking Transformations, final expressions
    • +
  • "Code examples for Blocking, Jackknife and bootstrap":"https://github.com/CompPhysics/MachineLearning/tree/master/doc/Programs/ResamplingAnalysisScripts"
    • +
  • The bias-variance tradeoff
    • +
  • Training and testing data
    • +
  • Procedure to find a predictor
    • +
  • What we want
    • +
  • The expected generalization error
    • +
  • Elaborating a little bit more
    • +
  • The bias
    • +
  • The variance
    • +
  • Summing up
    • +
  • Logistic Regression
    • +
  • Basics
    • +
  • Linear classifier
    • +
  • Some selected properties
    • +
  • The cross-entropy as a cost function for logistic regression
    • +
  • Maximum likelihood
    • +
  • Minimizing the cross entropy
    • @@ -403,7 +431,7 @@ where \( \epsilon_i \) is the error in our approximation.
  • 12
  • 13
  • ...
    • -
  • 94
    • +
  • 109
  • »
    • diff --git a/doc/pub/Regression/html/._Regression-bs004.html b/doc/pub/Regression/html/._Regression-bs004.html index 8c712a55d..356f38c1f 100644 --- a/doc/pub/Regression/html/._Regression-bs004.html +++ b/doc/pub/Regression/html/._Regression-bs004.html @@ -194,32 +194,46 @@ Automatically generated HTML file from DocOnce source '___sec77'), ('Resampling methods: Jackknife', 2, None, '___sec78'), ('Resampling methods: Jackknife estimator', 2, None, '___sec79'), - ('Resampling methods: Jackknife sample code', - 2, - None, - '___sec80'), - ('Resampling methods: Bootstrap', 2, None, '___sec81'), - ('Resampling methods: Bootstrap background', 2, None, '___sec82'), + ('Resampling methods: Bootstrap', 2, None, '___sec80'), + ('Resampling methods: Bootstrap background', 2, None, '___sec81'), ('Resampling methods: More Bootstrap background', 2, None, - '___sec83'), - ('Resampling methods: Bootstrap approach', 2, None, '___sec84'), - ('Resampling methods: Bootstrap algorithm', 2, None, '___sec85'), - ('Resampling methods: Bootstrap steps', 2, None, '___sec86'), - ('Resampling methods: Blocking', 2, None, '___sec87'), - ('Blocking Transformations', 2, None, '___sec88'), - ('Blocking Transformations', 2, None, '___sec89'), - ('Blocking Transformations, getting there', 2, None, '___sec90'), + '___sec82'), + ('Resampling methods: Bootstrap approach', 2, None, '___sec83'), + ('Resampling methods: Bootstrap steps', 2, None, '___sec84'), + ('Resampling methods: Blocking', 2, None, '___sec85'), + ('Blocking Transformations', 2, None, '___sec86'), + ('Blocking Transformations', 2, None, '___sec87'), + ('Blocking Transformations, getting there', 2, None, '___sec88'), ('Blocking Transformations, final expressions', 2, None, - '___sec91'), + '___sec89'), ('"Code examples for Blocking, Jackknife and ' 'bootstrap":"https://github.com/CompPhysics/MachineLearning/tree/master/doc/Programs/ResamplingAnalysisScripts"', 2, None, - '___sec92')]} + '___sec90'), + ('The bias-variance tradeoff', 2, None, '___sec91'), + ('Training and testing data', 2, None, '___sec92'), + ('Procedure to find a predictor', 2, None, '___sec93'), + ('What we want', 2, None, '___sec94'), + ('The expected generalization error', 2, None, '___sec95'), + ('Elaborating a little bit more', 2, None, '___sec96'), + ('The bias', 2, None, '___sec97'), + ('The variance', 2, None, '___sec98'), + ('Summing up', 2, None, '___sec99'), + ('Logistic Regression', 2, None, '___sec100'), + ('Basics', 2, None, '___sec101'), + ('Linear classifier', 2, None, '___sec102'), + ('Some selected properties', 2, None, '___sec103'), + ('The cross-entropy as a cost function for logistic regression', + 2, + None, + '___sec104'), + ('Maximum likelihood', 2, None, '___sec105'), + ('Minimizing the cross entropy', 2, None, '___sec106')]} end of tocinfo --> @@ -337,19 +351,33 @@ MathJax.Hub.Config({
  • Resampling methods: Jackknife and Bootstrap
  • Resampling methods: Jackknife
  • Resampling methods: Jackknife estimator
    • -
  • Resampling methods: Jackknife sample code
    • -
  • Resampling methods: Bootstrap
    • -
  • Resampling methods: Bootstrap background
    • -
  • Resampling methods: More Bootstrap background
    • -
  • Resampling methods: Bootstrap approach
    • -
  • Resampling methods: Bootstrap algorithm
    • -
  • Resampling methods: Bootstrap steps
    • -
  • Resampling methods: Blocking
    • -
  • Blocking Transformations
    • -
  • Blocking Transformations
    • -
  • Blocking Transformations, getting there
    • -
  • Blocking Transformations, final expressions
    • -
  • "Code examples for Blocking, Jackknife and bootstrap":"https://github.com/CompPhysics/MachineLearning/tree/master/doc/Programs/ResamplingAnalysisScripts"
    • +
  • Resampling methods: Bootstrap
    • +
  • Resampling methods: Bootstrap background
    • +
  • Resampling methods: More Bootstrap background
    • +
  • Resampling methods: Bootstrap approach
    • +
  • Resampling methods: Bootstrap steps
    • +
  • Resampling methods: Blocking
    • +
  • Blocking Transformations
    • +
  • Blocking Transformations
    • +
  • Blocking Transformations, getting there
    • +
  • Blocking Transformations, final expressions
    • +
  • "Code examples for Blocking, Jackknife and bootstrap":"https://github.com/CompPhysics/MachineLearning/tree/master/doc/Programs/ResamplingAnalysisScripts"
    • +
  • The bias-variance tradeoff
    • +
  • Training and testing data
    • +
  • Procedure to find a predictor
    • +
  • What we want
    • +
  • The expected generalization error
    • +
  • Elaborating a little bit more
    • +
  • The bias
    • +
  • The variance
    • +
  • Summing up
    • +
  • Logistic Regression
    • +
  • Basics
    • +
  • Linear classifier
    • +
  • Some selected properties
    • +
  • The cross-entropy as a cost function for logistic regression
    • +
  • Maximum likelihood
    • +
  • Minimizing the cross entropy
    • @@ -403,7 +431,7 @@ $$
  • 13
  • 14
  • ...
    • -
  • 94
    • +
  • 109
  • »
    • diff --git a/doc/pub/Regression/html/._Regression-bs005.html b/doc/pub/Regression/html/._Regression-bs005.html index e6eddc939..443564f6c 100644 --- a/doc/pub/Regression/html/._Regression-bs005.html +++ b/doc/pub/Regression/html/._Regression-bs005.html @@ -194,32 +194,46 @@ Automatically generated HTML file from DocOnce source '___sec77'), ('Resampling methods: Jackknife', 2, None, '___sec78'), ('Resampling methods: Jackknife estimator', 2, None, '___sec79'), - ('Resampling methods: Jackknife sample code', - 2, - None, - '___sec80'), - ('Resampling methods: Bootstrap', 2, None, '___sec81'), - ('Resampling methods: Bootstrap background', 2, None, '___sec82'), + ('Resampling methods: Bootstrap', 2, None, '___sec80'), + ('Resampling methods: Bootstrap background', 2, None, '___sec81'), ('Resampling methods: More Bootstrap background', 2, None, - '___sec83'), - ('Resampling methods: Bootstrap approach', 2, None, '___sec84'), - ('Resampling methods: Bootstrap algorithm', 2, None, '___sec85'), - ('Resampling methods: Bootstrap steps', 2, None, '___sec86'), - ('Resampling methods: Blocking', 2, None, '___sec87'), - ('Blocking Transformations', 2, None, '___sec88'), - ('Blocking Transformations', 2, None, '___sec89'), - ('Blocking Transformations, getting there', 2, None, '___sec90'), + '___sec82'), + ('Resampling methods: Bootstrap approach', 2, None, '___sec83'), + ('Resampling methods: Bootstrap steps', 2, None, '___sec84'), + ('Resampling methods: Blocking', 2, None, '___sec85'), + ('Blocking Transformations', 2, None, '___sec86'), + ('Blocking Transformations', 2, None, '___sec87'), + ('Blocking Transformations, getting there', 2, None, '___sec88'), ('Blocking Transformations, final expressions', 2, None, - '___sec91'), + '___sec89'), ('"Code examples for Blocking, Jackknife and ' 'bootstrap":"https://github.com/CompPhysics/MachineLearning/tree/master/doc/Programs/ResamplingAnalysisScripts"', 2, None, - '___sec92')]} + '___sec90'), + ('The bias-variance tradeoff', 2, None, '___sec91'), + ('Training and testing data', 2, None, '___sec92'), + ('Procedure to find a predictor', 2, None, '___sec93'), + ('What we want', 2, None, '___sec94'), + ('The expected generalization error', 2, None, '___sec95'), + ('Elaborating a little bit more', 2, None, '___sec96'), + ('The bias', 2, None, '___sec97'), + ('The variance', 2, None, '___sec98'), + ('Summing up', 2, None, '___sec99'), + ('Logistic Regression', 2, None, '___sec100'), + ('Basics', 2, None, '___sec101'), + ('Linear classifier', 2, None, '___sec102'), + ('Some selected properties', 2, None, '___sec103'), + ('The cross-entropy as a cost function for logistic regression', + 2, + None, + '___sec104'), + ('Maximum likelihood', 2, None, '___sec105'), + ('Minimizing the cross entropy', 2, None, '___sec106')]} end of tocinfo --> @@ -337,19 +351,33 @@ MathJax.Hub.Config({
  • Resampling methods: Jackknife and Bootstrap
  • Resampling methods: Jackknife
  • Resampling methods: Jackknife estimator
    • -
  • Resampling methods: Jackknife sample code
    • -
  • Resampling methods: Bootstrap
    • -
  • Resampling methods: Bootstrap background
    • -
  • Resampling methods: More Bootstrap background
    • -
  • Resampling methods: Bootstrap approach
    • -
  • Resampling methods: Bootstrap algorithm
    • -
  • Resampling methods: Bootstrap steps
    • -
  • Resampling methods: Blocking
    • -
  • Blocking Transformations
    • -
  • Blocking Transformations
    • -
  • Blocking Transformations, getting there
    • -
  • Blocking Transformations, final expressions
    • -
  • "Code examples for Blocking, Jackknife and bootstrap":"https://github.com/CompPhysics/MachineLearning/tree/master/doc/Programs/ResamplingAnalysisScripts"
    • +
  • Resampling methods: Bootstrap
    • +
  • Resampling methods: Bootstrap background
    • +
  • Resampling methods: More Bootstrap background
    • +
  • Resampling methods: Bootstrap approach
    • +
  • Resampling methods: Bootstrap steps
    • +
  • Resampling methods: Blocking
    • +
  • Blocking Transformations
    • +
  • Blocking Transformations
    • +
  • Blocking Transformations, getting there
    • +
  • Blocking Transformations, final expressions
    • +
  • "Code examples for Blocking, Jackknife and bootstrap":"https://github.com/CompPhysics/MachineLearning/tree/master/doc/Programs/ResamplingAnalysisScripts"
    • +
  • The bias-variance tradeoff
    • +
  • Training and testing data
    • +
  • Procedure to find a predictor
    • +
  • What we want
    • +
  • The expected generalization error
    • +
  • Elaborating a little bit more
    • +
  • The bias
    • +
  • The variance
    • +
  • Summing up
    • +
  • Logistic Regression
    • +
  • Basics
    • +
  • Linear classifier
    • +
  • Some selected properties
    • +
  • The cross-entropy as a cost function for logistic regression
    • +
  • Maximum likelihood
    • +
  • Minimizing the cross entropy
    • @@ -425,7 +453,7 @@ $$
  • 14
  • 15
  • ...
    • -
  • 94
    • +
  • 109
  • »
    • diff --git a/doc/pub/Regression/html/._Regression-bs006.html b/doc/pub/Regression/html/._Regression-bs006.html index 6cf07c123..073b00967 100644 --- a/doc/pub/Regression/html/._Regression-bs006.html +++ b/doc/pub/Regression/html/._Regression-bs006.html @@ -194,32 +194,46 @@ Automatically generated HTML file from DocOnce source '___sec77'), ('Resampling methods: Jackknife', 2, None, '___sec78'), ('Resampling methods: Jackknife estimator', 2, None, '___sec79'), - ('Resampling methods: Jackknife sample code', - 2, - None, - '___sec80'), - ('Resampling methods: Bootstrap', 2, None, '___sec81'), - ('Resampling methods: Bootstrap background', 2, None, '___sec82'), + ('Resampling methods: Bootstrap', 2, None, '___sec80'), + ('Resampling methods: Bootstrap background', 2, None, '___sec81'), ('Resampling methods: More Bootstrap background', 2, None, - '___sec83'), - ('Resampling methods: Bootstrap approach', 2, None, '___sec84'), - ('Resampling methods: Bootstrap algorithm', 2, None, '___sec85'), - ('Resampling methods: Bootstrap steps', 2, None, '___sec86'), - ('Resampling methods: Blocking', 2, None, '___sec87'), - ('Blocking Transformations', 2, None, '___sec88'), - ('Blocking Transformations', 2, None, '___sec89'), - ('Blocking Transformations, getting there', 2, None, '___sec90'), + '___sec82'), + ('Resampling methods: Bootstrap approach', 2, None, '___sec83'), + ('Resampling methods: Bootstrap steps', 2, None, '___sec84'), + ('Resampling methods: Blocking', 2, None, '___sec85'), + ('Blocking Transformations', 2, None, '___sec86'), + ('Blocking Transformations', 2, None, '___sec87'), + ('Blocking Transformations, getting there', 2, None, '___sec88'), ('Blocking Transformations, final expressions', 2, None, - '___sec91'), + '___sec89'), ('"Code examples for Blocking, Jackknife and ' 'bootstrap":"https://github.com/CompPhysics/MachineLearning/tree/master/doc/Programs/ResamplingAnalysisScripts"', 2, None, - '___sec92')]} + '___sec90'), + ('The bias-variance tradeoff', 2, None, '___sec91'), + ('Training and testing data', 2, None, '___sec92'), + ('Procedure to find a predictor', 2, None, '___sec93'), + ('What we want', 2, None, '___sec94'), + ('The expected generalization error', 2, None, '___sec95'), + ('Elaborating a little bit more', 2, None, '___sec96'), + ('The bias', 2, None, '___sec97'), + ('The variance', 2, None, '___sec98'), + ('Summing up', 2, None, '___sec99'), + ('Logistic Regression', 2, None, '___sec100'), + ('Basics', 2, None, '___sec101'), + ('Linear classifier', 2, None, '___sec102'), + ('Some selected properties', 2, None, '___sec103'), + ('The cross-entropy as a cost function for logistic regression', + 2, + None, + '___sec104'), + ('Maximum likelihood', 2, None, '___sec105'), + ('Minimizing the cross entropy', 2, None, '___sec106')]} end of tocinfo --> @@ -337,19 +351,33 @@ MathJax.Hub.Config({
  • Resampling methods: Jackknife and Bootstrap
  • Resampling methods: Jackknife
  • Resampling methods: Jackknife estimator
    • -
  • Resampling methods: Jackknife sample code
    • -
  • Resampling methods: Bootstrap
    • -
  • Resampling methods: Bootstrap background
    • -
  • Resampling methods: More Bootstrap background
    • -
  • Resampling methods: Bootstrap approach
    • -
  • Resampling methods: Bootstrap algorithm
    • -
  • Resampling methods: Bootstrap steps
    • -
  • Resampling methods: Blocking
    • -
  • Blocking Transformations
    • -
  • Blocking Transformations
    • -
  • Blocking Transformations, getting there
    • -
  • Blocking Transformations, final expressions
    • -
  • "Code examples for Blocking, Jackknife and bootstrap":"https://github.com/CompPhysics/MachineLearning/tree/master/doc/Programs/ResamplingAnalysisScripts"
    • +
  • Resampling methods: Bootstrap
    • +
  • Resampling methods: Bootstrap background
    • +
  • Resampling methods: More Bootstrap background
    • +
  • Resampling methods: Bootstrap approach
    • +
  • Resampling methods: Bootstrap steps
    • +
  • Resampling methods: Blocking
    • +
  • Blocking Transformations
    • +
  • Blocking Transformations
    • +
  • Blocking Transformations, getting there
    • +
  • Blocking Transformations, final expressions
    • +
  • "Code examples for Blocking, Jackknife and bootstrap":"https://github.com/CompPhysics/MachineLearning/tree/master/doc/Programs/ResamplingAnalysisScripts"
    • +
  • The bias-variance tradeoff
    • +
  • Training and testing data
    • +
  • Procedure to find a predictor
    • +
  • What we want
    • +
  • The expected generalization error
    • +
  • Elaborating a little bit more
    • +
  • The bias
    • +
  • The variance
    • +
  • Summing up
    • +
  • Logistic Regression
    • +
  • Basics
    • +
  • Linear classifier
    • +
  • Some selected properties
    • +
  • The cross-entropy as a cost function for logistic regression
    • +
  • Maximum likelihood
    • +
  • Minimizing the cross entropy
    • @@ -408,7 +436,7 @@ $$
  • 15
  • 16
  • ...
    • -
  • 94
    • +
  • 109
  • »
    • diff --git a/doc/pub/Regression/html/._Regression-bs007.html b/doc/pub/Regression/html/._Regression-bs007.html index eda8e0b5f..4d4f6940a 100644 --- a/doc/pub/Regression/html/._Regression-bs007.html +++ b/doc/pub/Regression/html/._Regression-bs007.html @@ -194,32 +194,46 @@ Automatically generated HTML file from DocOnce source '___sec77'), ('Resampling methods: Jackknife', 2, None, '___sec78'), ('Resampling methods: Jackknife estimator', 2, None, '___sec79'), - ('Resampling methods: Jackknife sample code', - 2, - None, - '___sec80'), - ('Resampling methods: Bootstrap', 2, None, '___sec81'), - ('Resampling methods: Bootstrap background', 2, None, '___sec82'), + ('Resampling methods: Bootstrap', 2, None, '___sec80'), + ('Resampling methods: Bootstrap background', 2, None, '___sec81'), ('Resampling methods: More Bootstrap background', 2, None, - '___sec83'), - ('Resampling methods: Bootstrap approach', 2, None, '___sec84'), - ('Resampling methods: Bootstrap algorithm', 2, None, '___sec85'), - ('Resampling methods: Bootstrap steps', 2, None, '___sec86'), - ('Resampling methods: Blocking', 2, None, '___sec87'), - ('Blocking Transformations', 2, None, '___sec88'), - ('Blocking Transformations', 2, None, '___sec89'), - ('Blocking Transformations, getting there', 2, None, '___sec90'), + '___sec82'), + ('Resampling methods: Bootstrap approach', 2, None, '___sec83'), + ('Resampling methods: Bootstrap steps', 2, None, '___sec84'), + ('Resampling methods: Blocking', 2, None, '___sec85'), + ('Blocking Transformations', 2, None, '___sec86'), + ('Blocking Transformations', 2, None, '___sec87'), + ('Blocking Transformations, getting there', 2, None, '___sec88'), ('Blocking Transformations, final expressions', 2, None, - '___sec91'), + '___sec89'), ('"Code examples for Blocking, Jackknife and ' 'bootstrap":"https://github.com/CompPhysics/MachineLearning/tree/master/doc/Programs/ResamplingAnalysisScripts"', 2, None, - '___sec92')]} + '___sec90'), + ('The bias-variance tradeoff', 2, None, '___sec91'), + ('Training and testing data', 2, None, '___sec92'), + ('Procedure to find a predictor', 2, None, '___sec93'), + ('What we want', 2, None, '___sec94'), + ('The expected generalization error', 2, None, '___sec95'), + ('Elaborating a little bit more', 2, None, '___sec96'), + ('The bias', 2, None, '___sec97'), + ('The variance', 2, None, '___sec98'), + ('Summing up', 2, None, '___sec99'), + ('Logistic Regression', 2, None, '___sec100'), + ('Basics', 2, None, '___sec101'), + ('Linear classifier', 2, None, '___sec102'), + ('Some selected properties', 2, None, '___sec103'), + ('The cross-entropy as a cost function for logistic regression', + 2, + None, + '___sec104'), + ('Maximum likelihood', 2, None, '___sec105'), + ('Minimizing the cross entropy', 2, None, '___sec106')]} end of tocinfo --> @@ -337,19 +351,33 @@ MathJax.Hub.Config({
  • Resampling methods: Jackknife and Bootstrap
  • Resampling methods: Jackknife
  • Resampling methods: Jackknife estimator
    • -
  • Resampling methods: Jackknife sample code
    • -
  • Resampling methods: Bootstrap
    • -
  • Resampling methods: Bootstrap background
    • -
  • Resampling methods: More Bootstrap background
    • -
  • Resampling methods: Bootstrap approach
    • -
  • Resampling methods: Bootstrap algorithm
    • -
  • Resampling methods: Bootstrap steps
    • -
  • Resampling methods: Blocking
    • -
  • Blocking Transformations
    • -
  • Blocking Transformations
    • -
  • Blocking Transformations, getting there
    • -
  • Blocking Transformations, final expressions
    • -
  • "Code examples for Blocking, Jackknife and bootstrap":"https://github.com/CompPhysics/MachineLearning/tree/master/doc/Programs/ResamplingAnalysisScripts"
    • +
  • Resampling methods: Bootstrap
    • +
  • Resampling methods: Bootstrap background
    • +
  • Resampling methods: More Bootstrap background
    • +
  • Resampling methods: Bootstrap approach
    • +
  • Resampling methods: Bootstrap steps
    • +
  • Resampling methods: Blocking
    • +
  • Blocking Transformations
    • +
  • Blocking Transformations
    • +
  • Blocking Transformations, getting there
    • +
  • Blocking Transformations, final expressions
    • +
  • "Code examples for Blocking, Jackknife and bootstrap":"https://github.com/CompPhysics/MachineLearning/tree/master/doc/Programs/ResamplingAnalysisScripts"
    • +
  • The bias-variance tradeoff
    • +
  • Training and testing data
    • +
  • Procedure to find a predictor
    • +
  • What we want
    • +
  • The expected generalization error
    • +
  • Elaborating a little bit more
    • +
  • The bias
    • +
  • The variance
    • +
  • Summing up
    • +
  • Logistic Regression
    • +
  • Basics
    • +
  • Linear classifier
    • +
  • Some selected properties
    • +
  • The cross-entropy as a cost function for logistic regression
    • +
  • Maximum likelihood
    • +
  • Minimizing the cross entropy
    • @@ -414,7 +442,7 @@ The left-hand side of this equation forms know. Our error vector \( \hat{\epsilo
  • 16
  • 17
  • ...
    • -
  • 94
    • +
  • 109
  • »
    • diff --git a/doc/pub/Regression/html/._Regression-bs008.html b/doc/pub/Regression/html/._Regression-bs008.html index ee1a2bd6d..4e2a476b6 100644 --- a/doc/pub/Regression/html/._Regression-bs008.html +++ b/doc/pub/Regression/html/._Regression-bs008.html @@ -194,32 +194,46 @@ Automatically generated HTML file from DocOnce source '___sec77'), ('Resampling methods: Jackknife', 2, None, '___sec78'), ('Resampling methods: Jackknife estimator', 2, None, '___sec79'), - ('Resampling methods: Jackknife sample code', - 2, - None, - '___sec80'), - ('Resampling methods: Bootstrap', 2, None, '___sec81'), - ('Resampling methods: Bootstrap background', 2, None, '___sec82'), + ('Resampling methods: Bootstrap', 2, None, '___sec80'), + ('Resampling methods: Bootstrap background', 2, None, '___sec81'), ('Resampling methods: More Bootstrap background', 2, None, - '___sec83'), - ('Resampling methods: Bootstrap approach', 2, None, '___sec84'), - ('Resampling methods: Bootstrap algorithm', 2, None, '___sec85'), - ('Resampling methods: Bootstrap steps', 2, None, '___sec86'), - ('Resampling methods: Blocking', 2, None, '___sec87'), - ('Blocking Transformations', 2, None, '___sec88'), - ('Blocking Transformations', 2, None, '___sec89'), - ('Blocking Transformations, getting there', 2, None, '___sec90'), + '___sec82'), + ('Resampling methods: Bootstrap approach', 2, None, '___sec83'), + ('Resampling methods: Bootstrap steps', 2, None, '___sec84'), + ('Resampling methods: Blocking', 2, None, '___sec85'), + ('Blocking Transformations', 2, None, '___sec86'), + ('Blocking Transformations', 2, None, '___sec87'), + ('Blocking Transformations, getting there', 2, None, '___sec88'), ('Blocking Transformations, final expressions', 2, None, - '___sec91'), + '___sec89'), ('"Code examples for Blocking, Jackknife and ' 'bootstrap":"https://github.com/CompPhysics/MachineLearning/tree/master/doc/Programs/ResamplingAnalysisScripts"', 2, None, - '___sec92')]} + '___sec90'), + ('The bias-variance tradeoff', 2, None, '___sec91'), + ('Training and testing data', 2, None, '___sec92'), + ('Procedure to find a predictor', 2, None, '___sec93'), + ('What we want', 2, None, '___sec94'), + ('The expected generalization error', 2, None, '___sec95'), + ('Elaborating a little bit more', 2, None, '___sec96'), + ('The bias', 2, None, '___sec97'), + ('The variance', 2, None, '___sec98'), + ('Summing up', 2, None, '___sec99'), + ('Logistic Regression', 2, None, '___sec100'), + ('Basics', 2, None, '___sec101'), + ('Linear classifier', 2, None, '___sec102'), + ('Some selected properties', 2, None, '___sec103'), + ('The cross-entropy as a cost function for logistic regression', + 2, + None, + '___sec104'), + ('Maximum likelihood', 2, None, '___sec105'), + ('Minimizing the cross entropy', 2, None, '___sec106')]} end of tocinfo --> @@ -337,19 +351,33 @@ MathJax.Hub.Config({
  • Resampling methods: Jackknife and Bootstrap
  • Resampling methods: Jackknife
  • Resampling methods: Jackknife estimator
    • -
  • Resampling methods: Jackknife sample code
    • -
  • Resampling methods: Bootstrap
    • -
  • Resampling methods: Bootstrap background
    • -
  • Resampling methods: More Bootstrap background
    • -
  • Resampling methods: Bootstrap approach
    • -
  • Resampling methods: Bootstrap algorithm
    • -
  • Resampling methods: Bootstrap steps
    • -
  • Resampling methods: Blocking
    • -
  • Blocking Transformations
    • -
  • Blocking Transformations
    • -
  • Blocking Transformations, getting there
    • -
  • Blocking Transformations, final expressions
    • -
  • "Code examples for Blocking, Jackknife and bootstrap":"https://github.com/CompPhysics/MachineLearning/tree/master/doc/Programs/ResamplingAnalysisScripts"
    • +
  • Resampling methods: Bootstrap
    • +
  • Resampling methods: Bootstrap background
    • +
  • Resampling methods: More Bootstrap background
    • +
  • Resampling methods: Bootstrap approach
    • +
  • Resampling methods: Bootstrap steps
    • +
  • Resampling methods: Blocking
    • +
  • Blocking Transformations
    • +
  • Blocking Transformations
    • +
  • Blocking Transformations, getting there
    • +
  • Blocking Transformations, final expressions
    • +
  • "Code examples for Blocking, Jackknife and bootstrap":"https://github.com/CompPhysics/MachineLearning/tree/master/doc/Programs/ResamplingAnalysisScripts"
    • +
  • The bias-variance tradeoff
    • +
  • Training and testing data
    • +
  • Procedure to find a predictor
    • +
  • What we want
    • +
  • The expected generalization error
    • +
  • Elaborating a little bit more
    • +
  • The bias
    • +
  • The variance
    • +
  • Summing up
    • +
  • Logistic Regression
    • +
  • Basics
    • +
  • Linear classifier
    • +
  • Some selected properties
    • +
  • The cross-entropy as a cost function for logistic regression
    • +
  • Maximum likelihood
    • +
  • Minimizing the cross entropy
    • @@ -409,7 +437,7 @@ $$
  • 17
  • 18
  • ...
    • -
  • 94
    • +
  • 109
  • »
    • diff --git a/doc/pub/Regression/html/._Regression-bs009.html b/doc/pub/Regression/html/._Regression-bs009.html index 44887a614..9831e51af 100644 --- a/doc/pub/Regression/html/._Regression-bs009.html +++ b/doc/pub/Regression/html/._Regression-bs009.html @@ -194,32 +194,46 @@ Automatically generated HTML file from DocOnce source '___sec77'), ('Resampling methods: Jackknife', 2, None, '___sec78'), ('Resampling methods: Jackknife estimator', 2, None, '___sec79'), - ('Resampling methods: Jackknife sample code', - 2, - None, - '___sec80'), - ('Resampling methods: Bootstrap', 2, None, '___sec81'), - ('Resampling methods: Bootstrap background', 2, None, '___sec82'), + ('Resampling methods: Bootstrap', 2, None, '___sec80'), + ('Resampling methods: Bootstrap background', 2, None, '___sec81'), ('Resampling methods: More Bootstrap background', 2, None, - '___sec83'), - ('Resampling methods: Bootstrap approach', 2, None, '___sec84'), - ('Resampling methods: Bootstrap algorithm', 2, None, '___sec85'), - ('Resampling methods: Bootstrap steps', 2, None, '___sec86'), - ('Resampling methods: Blocking', 2, None, '___sec87'), - ('Blocking Transformations', 2, None, '___sec88'), - ('Blocking Transformations', 2, None, '___sec89'), - ('Blocking Transformations, getting there', 2, None, '___sec90'), + '___sec82'), + ('Resampling methods: Bootstrap approach', 2, None, '___sec83'), + ('Resampling methods: Bootstrap steps', 2, None, '___sec84'), + ('Resampling methods: Blocking', 2, None, '___sec85'), + ('Blocking Transformations', 2, None, '___sec86'), + ('Blocking Transformations', 2, None, '___sec87'), + ('Blocking Transformations, getting there', 2, None, '___sec88'), ('Blocking Transformations, final expressions', 2, None, - '___sec91'), + '___sec89'), ('"Code examples for Blocking, Jackknife and ' 'bootstrap":"https://github.com/CompPhysics/MachineLearning/tree/master/doc/Programs/ResamplingAnalysisScripts"', 2, None, - '___sec92')]} + '___sec90'), + ('The bias-variance tradeoff', 2, None, '___sec91'), + ('Training and testing data', 2, None, '___sec92'), + ('Procedure to find a predictor', 2, None, '___sec93'), + ('What we want', 2, None, '___sec94'), + ('The expected generalization error', 2, None, '___sec95'), + ('Elaborating a little bit more', 2, None, '___sec96'), + ('The bias', 2, None, '___sec97'), + ('The variance', 2, None, '___sec98'), + ('Summing up', 2, None, '___sec99'), + ('Logistic Regression', 2, None, '___sec100'), + ('Basics', 2, None, '___sec101'), + ('Linear classifier', 2, None, '___sec102'), + ('Some selected properties', 2, None, '___sec103'), + ('The cross-entropy as a cost function for logistic regression', + 2, + None, + '___sec104'), + ('Maximum likelihood', 2, None, '___sec105'), + ('Minimizing the cross entropy', 2, None, '___sec106')]} end of tocinfo --> @@ -337,19 +351,33 @@ MathJax.Hub.Config({
  • Resampling methods: Jackknife and Bootstrap
  • Resampling methods: Jackknife
  • Resampling methods: Jackknife estimator
    • -
  • Resampling methods: Jackknife sample code
    • -
  • Resampling methods: Bootstrap
    • -
  • Resampling methods: Bootstrap background
    • -
  • Resampling methods: More Bootstrap background
    • -
  • Resampling methods: Bootstrap approach
    • -
  • Resampling methods: Bootstrap algorithm
    • -
  • Resampling methods: Bootstrap steps
    • -
  • Resampling methods: Blocking
    • -
  • Blocking Transformations
    • -
  • Blocking Transformations
    • -
  • Blocking Transformations, getting there
    • -
  • Blocking Transformations, final expressions
    • -
  • "Code examples for Blocking, Jackknife and bootstrap":"https://github.com/CompPhysics/MachineLearning/tree/master/doc/Programs/ResamplingAnalysisScripts"
    • +
  • Resampling methods: Bootstrap
    • +
  • Resampling methods: Bootstrap background
    • +
  • Resampling methods: More Bootstrap background
    • +
  • Resampling methods: Bootstrap approach
    • +
  • Resampling methods: Bootstrap steps
    • +
  • Resampling methods: Blocking
    • +
  • Blocking Transformations
    • +
  • Blocking Transformations
    • +
  • Blocking Transformations, getting there
    • +
  • Blocking Transformations, final expressions
    • +
  • "Code examples for Blocking, Jackknife and bootstrap":"https://github.com/CompPhysics/MachineLearning/tree/master/doc/Programs/ResamplingAnalysisScripts"
    • +
  • The bias-variance tradeoff
    • +
  • Training and testing data
    • +
  • Procedure to find a predictor
    • +
  • What we want
    • +
  • The expected generalization error
    • +
  • Elaborating a little bit more
    • +
  • The bias
    • +
  • The variance
    • +
  • Summing up
    • +
  • Logistic Regression
    • +
  • Basics
    • +
  • Linear classifier
    • +
  • Some selected properties
    • +
  • The cross-entropy as a cost function for logistic regression
    • +
  • Maximum likelihood
    • +
  • Minimizing the cross entropy
    • @@ -412,7 +440,7 @@ $$
  • 18
  • 19
  • ...
    • -
  • 94
    • +
  • 109
  • »
    • diff --git a/doc/pub/Regression/html/._Regression-bs010.html b/doc/pub/Regression/html/._Regression-bs010.html index c448753ac..45a7f1427 100644 --- a/doc/pub/Regression/html/._Regression-bs010.html +++ b/doc/pub/Regression/html/._Regression-bs010.html @@ -194,32 +194,46 @@ Automatically generated HTML file from DocOnce source '___sec77'), ('Resampling methods: Jackknife', 2, None, '___sec78'), ('Resampling methods: Jackknife estimator', 2, None, '___sec79'), - ('Resampling methods: Jackknife sample code', - 2, - None, - '___sec80'), - ('Resampling methods: Bootstrap', 2, None, '___sec81'), - ('Resampling methods: Bootstrap background', 2, None, '___sec82'), + ('Resampling methods: Bootstrap', 2, None, '___sec80'), + ('Resampling methods: Bootstrap background', 2, None, '___sec81'), ('Resampling methods: More Bootstrap background', 2, None, - '___sec83'), - ('Resampling methods: Bootstrap approach', 2, None, '___sec84'), - ('Resampling methods: Bootstrap algorithm', 2, None, '___sec85'), - ('Resampling methods: Bootstrap steps', 2, None, '___sec86'), - ('Resampling methods: Blocking', 2, None, '___sec87'), - ('Blocking Transformations', 2, None, '___sec88'), - ('Blocking Transformations', 2, None, '___sec89'), - ('Blocking Transformations, getting there', 2, None, '___sec90'), + '___sec82'), + ('Resampling methods: Bootstrap approach', 2, None, '___sec83'), + ('Resampling methods: Bootstrap steps', 2, None, '___sec84'), + ('Resampling methods: Blocking', 2, None, '___sec85'), + ('Blocking Transformations', 2, None, '___sec86'), + ('Blocking Transformations', 2, None, '___sec87'), + ('Blocking Transformations, getting there', 2, None, '___sec88'), ('Blocking Transformations, final expressions', 2, None, - '___sec91'), + '___sec89'), ('"Code examples for Blocking, Jackknife and ' 'bootstrap":"https://github.com/CompPhysics/MachineLearning/tree/master/doc/Programs/ResamplingAnalysisScripts"', 2, None, - '___sec92')]} + '___sec90'), + ('The bias-variance tradeoff', 2, None, '___sec91'), + ('Training and testing data', 2, None, '___sec92'), + ('Procedure to find a predictor', 2, None, '___sec93'), + ('What we want', 2, None, '___sec94'), + ('The expected generalization error', 2, None, '___sec95'), + ('Elaborating a little bit more', 2, None, '___sec96'), + ('The bias', 2, None, '___sec97'), + ('The variance', 2, None, '___sec98'), + ('Summing up', 2, None, '___sec99'), + ('Logistic Regression', 2, None, '___sec100'), + ('Basics', 2, None, '___sec101'), + ('Linear classifier', 2, None, '___sec102'), + ('Some selected properties', 2, None, '___sec103'), + ('The cross-entropy as a cost function for logistic regression', + 2, + None, + '___sec104'), + ('Maximum likelihood', 2, None, '___sec105'), + ('Minimizing the cross entropy', 2, None, '___sec106')]} end of tocinfo --> @@ -337,19 +351,33 @@ MathJax.Hub.Config({
  • Resampling methods: Jackknife and Bootstrap
  • Resampling methods: Jackknife
  • Resampling methods: Jackknife estimator
    • -
  • Resampling methods: Jackknife sample code
    • -
  • Resampling methods: Bootstrap
    • -
  • Resampling methods: Bootstrap background
    • -
  • Resampling methods: More Bootstrap background
    • -
  • Resampling methods: Bootstrap approach
    • -
  • Resampling methods: Bootstrap algorithm
    • -
  • Resampling methods: Bootstrap steps
    • -
  • Resampling methods: Blocking
    • -
  • Blocking Transformations
    • -
  • Blocking Transformations
    • -
  • Blocking Transformations, getting there
    • -
  • Blocking Transformations, final expressions
    • -
  • "Code examples for Blocking, Jackknife and bootstrap":"https://github.com/CompPhysics/MachineLearning/tree/master/doc/Programs/ResamplingAnalysisScripts"
    • +
  • Resampling methods: Bootstrap
    • +
  • Resampling methods: Bootstrap background
    • +
  • Resampling methods: More Bootstrap background
    • +
  • Resampling methods: Bootstrap approach
    • +
  • Resampling methods: Bootstrap steps
    • +
  • Resampling methods: Blocking
    • +
  • Blocking Transformations
    • +
  • Blocking Transformations
    • +
  • Blocking Transformations, getting there
    • +
  • Blocking Transformations, final expressions
    • +
  • "Code examples for Blocking, Jackknife and bootstrap":"https://github.com/CompPhysics/MachineLearning/tree/master/doc/Programs/ResamplingAnalysisScripts"
    • +
  • The bias-variance tradeoff
    • +
  • Training and testing data
    • +
  • Procedure to find a predictor
    • +
  • What we want
    • +
  • The expected generalization error
    • +
  • Elaborating a little bit more
    • +
  • The bias
    • +
  • The variance
    • +
  • Summing up
    • +
  • Logistic Regression
    • +
  • Basics
    • +
  • Linear classifier
    • +
  • Some selected properties
    • +
  • The cross-entropy as a cost function for logistic regression
    • +
  • Maximum likelihood
    • +
  • Minimizing the cross entropy
    • @@ -428,7 +456,7 @@ $$
  • 19
  • 20
  • ...
    • -
  • 94
    • +
  • 109
  • »
    • diff --git a/doc/pub/Regression/html/._Regression-bs011.html b/doc/pub/Regression/html/._Regression-bs011.html index 079200895..c65457864 100644 --- a/doc/pub/Regression/html/._Regression-bs011.html +++ b/doc/pub/Regression/html/._Regression-bs011.html @@ -194,32 +194,46 @@ Automatically generated HTML file from DocOnce source '___sec77'), ('Resampling methods: Jackknife', 2, None, '___sec78'), ('Resampling methods: Jackknife estimator', 2, None, '___sec79'), - ('Resampling methods: Jackknife sample code', - 2, - None, - '___sec80'), - ('Resampling methods: Bootstrap', 2, None, '___sec81'), - ('Resampling methods: Bootstrap background', 2, None, '___sec82'), + ('Resampling methods: Bootstrap', 2, None, '___sec80'), + ('Resampling methods: Bootstrap background', 2, None, '___sec81'), ('Resampling methods: More Bootstrap background', 2, None, - '___sec83'), - ('Resampling methods: Bootstrap approach', 2, None, '___sec84'), - ('Resampling methods: Bootstrap algorithm', 2, None, '___sec85'), - ('Resampling methods: Bootstrap steps', 2, None, '___sec86'), - ('Resampling methods: Blocking', 2, None, '___sec87'), - ('Blocking Transformations', 2, None, '___sec88'), - ('Blocking Transformations', 2, None, '___sec89'), - ('Blocking Transformations, getting there', 2, None, '___sec90'), + '___sec82'), + ('Resampling methods: Bootstrap approach', 2, None, '___sec83'), + ('Resampling methods: Bootstrap steps', 2, None, '___sec84'), + ('Resampling methods: Blocking', 2, None, '___sec85'), + ('Blocking Transformations', 2, None, '___sec86'), + ('Blocking Transformations', 2, None, '___sec87'), + ('Blocking Transformations, getting there', 2, None, '___sec88'), ('Blocking Transformations, final expressions', 2, None, - '___sec91'), + '___sec89'), ('"Code examples for Blocking, Jackknife and ' 'bootstrap":"https://github.com/CompPhysics/MachineLearning/tree/master/doc/Programs/ResamplingAnalysisScripts"', 2, None, - '___sec92')]} + '___sec90'), + ('The bias-variance tradeoff', 2, None, '___sec91'), + ('Training and testing data', 2, None, '___sec92'), + ('Procedure to find a predictor', 2, None, '___sec93'), + ('What we want', 2, None, '___sec94'), + ('The expected generalization error', 2, None, '___sec95'), + ('Elaborating a little bit more', 2, None, '___sec96'), + ('The bias', 2, None, '___sec97'), + ('The variance', 2, None, '___sec98'), + ('Summing up', 2, None, '___sec99'), + ('Logistic Regression', 2, None, '___sec100'), + ('Basics', 2, None, '___sec101'), + ('Linear classifier', 2, None, '___sec102'), + ('Some selected properties', 2, None, '___sec103'), + ('The cross-entropy as a cost function for logistic regression', + 2, + None, + '___sec104'), + ('Maximum likelihood', 2, None, '___sec105'), + ('Minimizing the cross entropy', 2, None, '___sec106')]} end of tocinfo --> @@ -337,19 +351,33 @@ MathJax.Hub.Config({
  • Resampling methods: Jackknife and Bootstrap
  • Resampling methods: Jackknife
  • Resampling methods: Jackknife estimator
    • -
  • Resampling methods: Jackknife sample code
    • -
  • Resampling methods: Bootstrap
    • -
  • Resampling methods: Bootstrap background
    • -
  • Resampling methods: More Bootstrap background
    • -
  • Resampling methods: Bootstrap approach
    • -
  • Resampling methods: Bootstrap algorithm
    • -
  • Resampling methods: Bootstrap steps
    • -
  • Resampling methods: Blocking
    • -
  • Blocking Transformations
    • -
  • Blocking Transformations
    • -
  • Blocking Transformations, getting there
    • -
  • Blocking Transformations, final expressions
    • -
  • "Code examples for Blocking, Jackknife and bootstrap":"https://github.com/CompPhysics/MachineLearning/tree/master/doc/Programs/ResamplingAnalysisScripts"
    • +
  • Resampling methods: Bootstrap
    • +
  • Resampling methods: Bootstrap background
    • +
  • Resampling methods: More Bootstrap background
    • +
  • Resampling methods: Bootstrap approach
    • +
  • Resampling methods: Bootstrap steps
    • +
  • Resampling methods: Blocking
    • +
  • Blocking Transformations
    • +
  • Blocking Transformations
    • +
  • Blocking Transformations, getting there
    • +
  • Blocking Transformations, final expressions
    • +
  • "Code examples for Blocking, Jackknife and bootstrap":"https://github.com/CompPhysics/MachineLearning/tree/master/doc/Programs/ResamplingAnalysisScripts"
    • +
  • The bias-variance tradeoff
    • +
  • Training and testing data
    • +
  • Procedure to find a predictor
    • +
  • What we want
    • +
  • The expected generalization error
    • +
  • Elaborating a little bit more
    • +
  • The bias
    • +
  • The variance
    • +
  • Summing up
    • +
  • Logistic Regression
    • +
  • Basics
    • +
  • Linear classifier
    • +
  • Some selected properties
    • +
  • The cross-entropy as a cost function for logistic regression
    • +
  • Maximum likelihood
    • +
  • Minimizing the cross entropy
    • @@ -415,7 +443,7 @@ $$
  • 20
  • 21
  • ...
    • -
  • 94
    • +
  • 109
  • »
    • diff --git a/doc/pub/Regression/html/._Regression-bs012.html b/doc/pub/Regression/html/._Regression-bs012.html index 5e1ad8d4b..2434f9584 100644 --- a/doc/pub/Regression/html/._Regression-bs012.html +++ b/doc/pub/Regression/html/._Regression-bs012.html @@ -194,32 +194,46 @@ Automatically generated HTML file from DocOnce source '___sec77'), ('Resampling methods: Jackknife', 2, None, '___sec78'), ('Resampling methods: Jackknife estimator', 2, None, '___sec79'), - ('Resampling methods: Jackknife sample code', - 2, - None, - '___sec80'), - ('Resampling methods: Bootstrap', 2, None, '___sec81'), - ('Resampling methods: Bootstrap background', 2, None, '___sec82'), + ('Resampling methods: Bootstrap', 2, None, '___sec80'), + ('Resampling methods: Bootstrap background', 2, None, '___sec81'), ('Resampling methods: More Bootstrap background', 2, None, - '___sec83'), - ('Resampling methods: Bootstrap approach', 2, None, '___sec84'), - ('Resampling methods: Bootstrap algorithm', 2, None, '___sec85'), - ('Resampling methods: Bootstrap steps', 2, None, '___sec86'), - ('Resampling methods: Blocking', 2, None, '___sec87'), - ('Blocking Transformations', 2, None, '___sec88'), - ('Blocking Transformations', 2, None, '___sec89'), - ('Blocking Transformations, getting there', 2, None, '___sec90'), + '___sec82'), + ('Resampling methods: Bootstrap approach', 2, None, '___sec83'), + ('Resampling methods: Bootstrap steps', 2, None, '___sec84'), + ('Resampling methods: Blocking', 2, None, '___sec85'), + ('Blocking Transformations', 2, None, '___sec86'), + ('Blocking Transformations', 2, None, '___sec87'), + ('Blocking Transformations, getting there', 2, None, '___sec88'), ('Blocking Transformations, final expressions', 2, None, - '___sec91'), + '___sec89'), ('"Code examples for Blocking, Jackknife and ' 'bootstrap":"https://github.com/CompPhysics/MachineLearning/tree/master/doc/Programs/ResamplingAnalysisScripts"', 2, None, - '___sec92')]} + '___sec90'), + ('The bias-variance tradeoff', 2, None, '___sec91'), + ('Training and testing data', 2, None, '___sec92'), + ('Procedure to find a predictor', 2, None, '___sec93'), + ('What we want', 2, None, '___sec94'), + ('The expected generalization error', 2, None, '___sec95'), + ('Elaborating a little bit more', 2, None, '___sec96'), + ('The bias', 2, None, '___sec97'), + ('The variance', 2, None, '___sec98'), + ('Summing up', 2, None, '___sec99'), + ('Logistic Regression', 2, None, '___sec100'), + ('Basics', 2, None, '___sec101'), + ('Linear classifier', 2, None, '___sec102'), + ('Some selected properties', 2, None, '___sec103'), + ('The cross-entropy as a cost function for logistic regression', + 2, + None, + '___sec104'), + ('Maximum likelihood', 2, None, '___sec105'), + ('Minimizing the cross entropy', 2, None, '___sec106')]} end of tocinfo --> @@ -337,19 +351,33 @@ MathJax.Hub.Config({
  • Resampling methods: Jackknife and Bootstrap
  • Resampling methods: Jackknife
  • Resampling methods: Jackknife estimator
    • -
  • Resampling methods: Jackknife sample code
    • -
  • Resampling methods: Bootstrap
    • -
  • Resampling methods: Bootstrap background
    • -
  • Resampling methods: More Bootstrap background
    • -
  • Resampling methods: Bootstrap approach
    • -
  • Resampling methods: Bootstrap algorithm
    • -
  • Resampling methods: Bootstrap steps
    • -
  • Resampling methods: Blocking
    • -
  • Blocking Transformations
    • -
  • Blocking Transformations
    • -
  • Blocking Transformations, getting there
    • -
  • Blocking Transformations, final expressions
    • -
  • "Code examples for Blocking, Jackknife and bootstrap":"https://github.com/CompPhysics/MachineLearning/tree/master/doc/Programs/ResamplingAnalysisScripts"
    • +
  • Resampling methods: Bootstrap
    • +
  • Resampling methods: Bootstrap background
    • +
  • Resampling methods: More Bootstrap background
    • +
  • Resampling methods: Bootstrap approach
    • +
  • Resampling methods: Bootstrap steps
    • +
  • Resampling methods: Blocking
    • +
  • Blocking Transformations
    • +
  • Blocking Transformations
    • +
  • Blocking Transformations, getting there
    • +
  • Blocking Transformations, final expressions
    • +
  • "Code examples for Blocking, Jackknife and bootstrap":"https://github.com/CompPhysics/MachineLearning/tree/master/doc/Programs/ResamplingAnalysisScripts"
    • +
  • The bias-variance tradeoff
    • +
  • Training and testing data
    • +
  • Procedure to find a predictor
    • +
  • What we want
    • +
  • The expected generalization error
    • +
  • Elaborating a little bit more
    • +
  • The bias
    • +
  • The variance
    • +
  • Summing up
    • +
  • Logistic Regression
    • +
  • Basics
    • +
  • Linear classifier
    • +
  • Some selected properties
    • +
  • The cross-entropy as a cost function for logistic regression
    • +
  • Maximum likelihood
    • +
  • Minimizing the cross entropy
    • @@ -417,7 +445,7 @@ meaning that the solution for \( \hat{\beta} \) is the one which minimizes the r
  • 21
  • 22
  • ...
    • -
  • 94
    • +
  • 109
  • »
    • diff --git a/doc/pub/Regression/html/._Regression-bs013.html b/doc/pub/Regression/html/._Regression-bs013.html index c496eb542..5fa95b71d 100644 --- a/doc/pub/Regression/html/._Regression-bs013.html +++ b/doc/pub/Regression/html/._Regression-bs013.html @@ -194,32 +194,46 @@ Automatically generated HTML file from DocOnce source '___sec77'), ('Resampling methods: Jackknife', 2, None, '___sec78'), ('Resampling methods: Jackknife estimator', 2, None, '___sec79'), - ('Resampling methods: Jackknife sample code', - 2, - None, - '___sec80'), - ('Resampling methods: Bootstrap', 2, None, '___sec81'), - ('Resampling methods: Bootstrap background', 2, None, '___sec82'), + ('Resampling methods: Bootstrap', 2, None, '___sec80'), + ('Resampling methods: Bootstrap background', 2, None, '___sec81'), ('Resampling methods: More Bootstrap background', 2, None, - '___sec83'), - ('Resampling methods: Bootstrap approach', 2, None, '___sec84'), - ('Resampling methods: Bootstrap algorithm', 2, None, '___sec85'), - ('Resampling methods: Bootstrap steps', 2, None, '___sec86'), - ('Resampling methods: Blocking', 2, None, '___sec87'), - ('Blocking Transformations', 2, None, '___sec88'), - ('Blocking Transformations', 2, None, '___sec89'), - ('Blocking Transformations, getting there', 2, None, '___sec90'), + '___sec82'), + ('Resampling methods: Bootstrap approach', 2, None, '___sec83'), + ('Resampling methods: Bootstrap steps', 2, None, '___sec84'), + ('Resampling methods: Blocking', 2, None, '___sec85'), + ('Blocking Transformations', 2, None, '___sec86'), + ('Blocking Transformations', 2, None, '___sec87'), + ('Blocking Transformations, getting there', 2, None, '___sec88'), ('Blocking Transformations, final expressions', 2, None, - '___sec91'), + '___sec89'), ('"Code examples for Blocking, Jackknife and ' 'bootstrap":"https://github.com/CompPhysics/MachineLearning/tree/master/doc/Programs/ResamplingAnalysisScripts"', 2, None, - '___sec92')]} + '___sec90'), + ('The bias-variance tradeoff', 2, None, '___sec91'), + ('Training and testing data', 2, None, '___sec92'), + ('Procedure to find a predictor', 2, None, '___sec93'), + ('What we want', 2, None, '___sec94'), + ('The expected generalization error', 2, None, '___sec95'), + ('Elaborating a little bit more', 2, None, '___sec96'), + ('The bias', 2, None, '___sec97'), + ('The variance', 2, None, '___sec98'), + ('Summing up', 2, None, '___sec99'), + ('Logistic Regression', 2, None, '___sec100'), + ('Basics', 2, None, '___sec101'), + ('Linear classifier', 2, None, '___sec102'), + ('Some selected properties', 2, None, '___sec103'), + ('The cross-entropy as a cost function for logistic regression', + 2, + None, + '___sec104'), + ('Maximum likelihood', 2, None, '___sec105'), + ('Minimizing the cross entropy', 2, None, '___sec106')]} end of tocinfo --> @@ -337,19 +351,33 @@ MathJax.Hub.Config({
  • Resampling methods: Jackknife and Bootstrap
  • Resampling methods: Jackknife
  • Resampling methods: Jackknife estimator
    • -
  • Resampling methods: Jackknife sample code
    • -
  • Resampling methods: Bootstrap
    • -
  • Resampling methods: Bootstrap background
    • -
  • Resampling methods: More Bootstrap background
    • -
  • Resampling methods: Bootstrap approach
    • -
  • Resampling methods: Bootstrap algorithm
    • -
  • Resampling methods: Bootstrap steps
    • -
  • Resampling methods: Blocking
    • -
  • Blocking Transformations
    • -
  • Blocking Transformations
    • -
  • Blocking Transformations, getting there
    • -
  • Blocking Transformations, final expressions
    • -
  • "Code examples for Blocking, Jackknife and bootstrap":"https://github.com/CompPhysics/MachineLearning/tree/master/doc/Programs/ResamplingAnalysisScripts"
    • +
  • Resampling methods: Bootstrap
    • +
  • Resampling methods: Bootstrap background
    • +
  • Resampling methods: More Bootstrap background
    • +
  • Resampling methods: Bootstrap approach
    • +
  • Resampling methods: Bootstrap steps
    • +
  • Resampling methods: Blocking
    • +
  • Blocking Transformations
    • +
  • Blocking Transformations
    • +
  • Blocking Transformations, getting there
    • +
  • Blocking Transformations, final expressions
    • +
  • "Code examples for Blocking, Jackknife and bootstrap":"https://github.com/CompPhysics/MachineLearning/tree/master/doc/Programs/ResamplingAnalysisScripts"
    • +
  • The bias-variance tradeoff
    • +
  • Training and testing data
    • +
  • Procedure to find a predictor
    • +
  • What we want
    • +
  • The expected generalization error
    • +
  • Elaborating a little bit more
    • +
  • The bias
    • +
  • The variance
    • +
  • Summing up
    • +
  • Logistic Regression
    • +
  • Basics
    • +
  • Linear classifier
    • +
  • Some selected properties
    • +
  • The cross-entropy as a cost function for logistic regression
    • +
  • Maximum likelihood
    • +
  • Minimizing the cross entropy
    • @@ -412,7 +440,7 @@ where the matrix \( \hat{\Sigma} \) is a diagonal matrix with \( \sigma_i \) as
  • 22
  • 23
  • ...
    • -
  • 94
    • +
  • 109
  • »
    • diff --git a/doc/pub/Regression/html/._Regression-bs014.html b/doc/pub/Regression/html/._Regression-bs014.html index afa0587b2..cf83d8257 100644 --- a/doc/pub/Regression/html/._Regression-bs014.html +++ b/doc/pub/Regression/html/._Regression-bs014.html @@ -194,32 +194,46 @@ Automatically generated HTML file from DocOnce source '___sec77'), ('Resampling methods: Jackknife', 2, None, '___sec78'), ('Resampling methods: Jackknife estimator', 2, None, '___sec79'), - ('Resampling methods: Jackknife sample code', - 2, - None, - '___sec80'), - ('Resampling methods: Bootstrap', 2, None, '___sec81'), - ('Resampling methods: Bootstrap background', 2, None, '___sec82'), + ('Resampling methods: Bootstrap', 2, None, '___sec80'), + ('Resampling methods: Bootstrap background', 2, None, '___sec81'), ('Resampling methods: More Bootstrap background', 2, None, - '___sec83'), - ('Resampling methods: Bootstrap approach', 2, None, '___sec84'), - ('Resampling methods: Bootstrap algorithm', 2, None, '___sec85'), - ('Resampling methods: Bootstrap steps', 2, None, '___sec86'), - ('Resampling methods: Blocking', 2, None, '___sec87'), - ('Blocking Transformations', 2, None, '___sec88'), - ('Blocking Transformations', 2, None, '___sec89'), - ('Blocking Transformations, getting there', 2, None, '___sec90'), + '___sec82'), + ('Resampling methods: Bootstrap approach', 2, None, '___sec83'), + ('Resampling methods: Bootstrap steps', 2, None, '___sec84'), + ('Resampling methods: Blocking', 2, None, '___sec85'), + ('Blocking Transformations', 2, None, '___sec86'), + ('Blocking Transformations', 2, None, '___sec87'), + ('Blocking Transformations, getting there', 2, None, '___sec88'), ('Blocking Transformations, final expressions', 2, None, - '___sec91'), + '___sec89'), ('"Code examples for Blocking, Jackknife and ' 'bootstrap":"https://github.com/CompPhysics/MachineLearning/tree/master/doc/Programs/ResamplingAnalysisScripts"', 2, None, - '___sec92')]} + '___sec90'), + ('The bias-variance tradeoff', 2, None, '___sec91'), + ('Training and testing data', 2, None, '___sec92'), + ('Procedure to find a predictor', 2, None, '___sec93'), + ('What we want', 2, None, '___sec94'), + ('The expected generalization error', 2, None, '___sec95'), + ('Elaborating a little bit more', 2, None, '___sec96'), + ('The bias', 2, None, '___sec97'), + ('The variance', 2, None, '___sec98'), + ('Summing up', 2, None, '___sec99'), + ('Logistic Regression', 2, None, '___sec100'), + ('Basics', 2, None, '___sec101'), + ('Linear classifier', 2, None, '___sec102'), + ('Some selected properties', 2, None, '___sec103'), + ('The cross-entropy as a cost function for logistic regression', + 2, + None, + '___sec104'), + ('Maximum likelihood', 2, None, '___sec105'), + ('Minimizing the cross entropy', 2, None, '___sec106')]} end of tocinfo --> @@ -337,19 +351,33 @@ MathJax.Hub.Config({
  • Resampling methods: Jackknife and Bootstrap
  • Resampling methods: Jackknife
  • Resampling methods: Jackknife estimator
    • -
  • Resampling methods: Jackknife sample code
    • -
  • Resampling methods: Bootstrap
    • -
  • Resampling methods: Bootstrap background
    • -
  • Resampling methods: More Bootstrap background
    • -
  • Resampling methods: Bootstrap approach
    • -
  • Resampling methods: Bootstrap algorithm
    • -
  • Resampling methods: Bootstrap steps
    • -
  • Resampling methods: Blocking
    • -
  • Blocking Transformations
    • -
  • Blocking Transformations
    • -
  • Blocking Transformations, getting there
    • -
  • Blocking Transformations, final expressions
    • -
  • "Code examples for Blocking, Jackknife and bootstrap":"https://github.com/CompPhysics/MachineLearning/tree/master/doc/Programs/ResamplingAnalysisScripts"
    • +
  • Resampling methods: Bootstrap
    • +
  • Resampling methods: Bootstrap background
    • +
  • Resampling methods: More Bootstrap background
    • +
  • Resampling methods: Bootstrap approach
    • +
  • Resampling methods: Bootstrap steps
    • +
  • Resampling methods: Blocking
    • +
  • Blocking Transformations
    • +
  • Blocking Transformations
    • +
  • Blocking Transformations, getting there
    • +
  • Blocking Transformations, final expressions
    • +
  • "Code examples for Blocking, Jackknife and bootstrap":"https://github.com/CompPhysics/MachineLearning/tree/master/doc/Programs/ResamplingAnalysisScripts"
    • +
  • The bias-variance tradeoff
    • +
  • Training and testing data
    • +
  • Procedure to find a predictor
    • +
  • What we want
    • +
  • The expected generalization error
    • +
  • Elaborating a little bit more
    • +
  • The bias
    • +
  • The variance
    • +
  • Summing up
    • +
  • Logistic Regression
    • +
  • Basics
    • +
  • Linear classifier
    • +
  • Some selected properties
    • +
  • The cross-entropy as a cost function for logistic regression
    • +
  • Maximum likelihood
    • +
  • Minimizing the cross entropy
    • @@ -417,7 +445,7 @@ where we have defined the matrix \( \hat{A} =\hat{X}/\hat{\Sigma} \) with matrix
  • 23
  • 24
  • ...
    • -
  • 94
    • +
  • 109
  • »
    • diff --git a/doc/pub/Regression/html/._Regression-bs015.html b/doc/pub/Regression/html/._Regression-bs015.html index d409891ec..7aa00b66b 100644 --- a/doc/pub/Regression/html/._Regression-bs015.html +++ b/doc/pub/Regression/html/._Regression-bs015.html @@ -194,32 +194,46 @@ Automatically generated HTML file from DocOnce source '___sec77'), ('Resampling methods: Jackknife', 2, None, '___sec78'), ('Resampling methods: Jackknife estimator', 2, None, '___sec79'), - ('Resampling methods: Jackknife sample code', - 2, - None, - '___sec80'), - ('Resampling methods: Bootstrap', 2, None, '___sec81'), - ('Resampling methods: Bootstrap background', 2, None, '___sec82'), + ('Resampling methods: Bootstrap', 2, None, '___sec80'), + ('Resampling methods: Bootstrap background', 2, None, '___sec81'), ('Resampling methods: More Bootstrap background', 2, None, - '___sec83'), - ('Resampling methods: Bootstrap approach', 2, None, '___sec84'), - ('Resampling methods: Bootstrap algorithm', 2, None, '___sec85'), - ('Resampling methods: Bootstrap steps', 2, None, '___sec86'), - ('Resampling methods: Blocking', 2, None, '___sec87'), - ('Blocking Transformations', 2, None, '___sec88'), - ('Blocking Transformations', 2, None, '___sec89'), - ('Blocking Transformations, getting there', 2, None, '___sec90'), + '___sec82'), + ('Resampling methods: Bootstrap approach', 2, None, '___sec83'), + ('Resampling methods: Bootstrap steps', 2, None, '___sec84'), + ('Resampling methods: Blocking', 2, None, '___sec85'), + ('Blocking Transformations', 2, None, '___sec86'), + ('Blocking Transformations', 2, None, '___sec87'), + ('Blocking Transformations, getting there', 2, None, '___sec88'), ('Blocking Transformations, final expressions', 2, None, - '___sec91'), + '___sec89'), ('"Code examples for Blocking, Jackknife and ' 'bootstrap":"https://github.com/CompPhysics/MachineLearning/tree/master/doc/Programs/ResamplingAnalysisScripts"', 2, None, - '___sec92')]} + '___sec90'), + ('The bias-variance tradeoff', 2, None, '___sec91'), + ('Training and testing data', 2, None, '___sec92'), + ('Procedure to find a predictor', 2, None, '___sec93'), + ('What we want', 2, None, '___sec94'), + ('The expected generalization error', 2, None, '___sec95'), + ('Elaborating a little bit more', 2, None, '___sec96'), + ('The bias', 2, None, '___sec97'), + ('The variance', 2, None, '___sec98'), + ('Summing up', 2, None, '___sec99'), + ('Logistic Regression', 2, None, '___sec100'), + ('Basics', 2, None, '___sec101'), + ('Linear classifier', 2, None, '___sec102'), + ('Some selected properties', 2, None, '___sec103'), + ('The cross-entropy as a cost function for logistic regression', + 2, + None, + '___sec104'), + ('Maximum likelihood', 2, None, '___sec105'), + ('Minimizing the cross entropy', 2, None, '___sec106')]} end of tocinfo --> @@ -337,19 +351,33 @@ MathJax.Hub.Config({
  • Resampling methods: Jackknife and Bootstrap
  • Resampling methods: Jackknife
  • Resampling methods: Jackknife estimator
    • -
  • Resampling methods: Jackknife sample code
    • -
  • Resampling methods: Bootstrap
    • -
  • Resampling methods: Bootstrap background
    • -
  • Resampling methods: More Bootstrap background
    • -
  • Resampling methods: Bootstrap approach
    • -
  • Resampling methods: Bootstrap algorithm
    • -
  • Resampling methods: Bootstrap steps
    • -
  • Resampling methods: Blocking
    • -
  • Blocking Transformations
    • -
  • Blocking Transformations
    • -
  • Blocking Transformations, getting there
    • -
  • Blocking Transformations, final expressions
    • -
  • "Code examples for Blocking, Jackknife and bootstrap":"https://github.com/CompPhysics/MachineLearning/tree/master/doc/Programs/ResamplingAnalysisScripts"
    • +
  • Resampling methods: Bootstrap
    • +
  • Resampling methods: Bootstrap background
    • +
  • Resampling methods: More Bootstrap background
    • +
  • Resampling methods: Bootstrap approach
    • +
  • Resampling methods: Bootstrap steps
    • +
  • Resampling methods: Blocking
    • +
  • Blocking Transformations
    • +
  • Blocking Transformations
    • +
  • Blocking Transformations, getting there
    • +
  • Blocking Transformations, final expressions
    • +
  • "Code examples for Blocking, Jackknife and bootstrap":"https://github.com/CompPhysics/MachineLearning/tree/master/doc/Programs/ResamplingAnalysisScripts"
    • +
  • The bias-variance tradeoff
    • +
  • Training and testing data
    • +
  • Procedure to find a predictor
    • +
  • What we want
    • +
  • The expected generalization error
    • +
  • Elaborating a little bit more
    • +
  • The bias
    • +
  • The variance
    • +
  • Summing up
    • +
  • Logistic Regression
    • +
  • Basics
    • +
  • Linear classifier
    • +
  • Some selected properties
    • +
  • The cross-entropy as a cost function for logistic regression
    • +
  • Maximum likelihood
    • +
  • Minimizing the cross entropy
    • @@ -415,7 +443,7 @@ $$
  • 24
  • 25
  • ...
    • -
  • 94
    • +
  • 109
  • »
    • diff --git a/doc/pub/Regression/html/._Regression-bs016.html b/doc/pub/Regression/html/._Regression-bs016.html index f0d818dba..90dffb4bd 100644 --- a/doc/pub/Regression/html/._Regression-bs016.html +++ b/doc/pub/Regression/html/._Regression-bs016.html @@ -194,32 +194,46 @@ Automatically generated HTML file from DocOnce source '___sec77'), ('Resampling methods: Jackknife', 2, None, '___sec78'), ('Resampling methods: Jackknife estimator', 2, None, '___sec79'), - ('Resampling methods: Jackknife sample code', - 2, - None, - '___sec80'), - ('Resampling methods: Bootstrap', 2, None, '___sec81'), - ('Resampling methods: Bootstrap background', 2, None, '___sec82'), + ('Resampling methods: Bootstrap', 2, None, '___sec80'), + ('Resampling methods: Bootstrap background', 2, None, '___sec81'), ('Resampling methods: More Bootstrap background', 2, None, - '___sec83'), - ('Resampling methods: Bootstrap approach', 2, None, '___sec84'), - ('Resampling methods: Bootstrap algorithm', 2, None, '___sec85'), - ('Resampling methods: Bootstrap steps', 2, None, '___sec86'), - ('Resampling methods: Blocking', 2, None, '___sec87'), - ('Blocking Transformations', 2, None, '___sec88'), - ('Blocking Transformations', 2, None, '___sec89'), - ('Blocking Transformations, getting there', 2, None, '___sec90'), + '___sec82'), + ('Resampling methods: Bootstrap approach', 2, None, '___sec83'), + ('Resampling methods: Bootstrap steps', 2, None, '___sec84'), + ('Resampling methods: Blocking', 2, None, '___sec85'), + ('Blocking Transformations', 2, None, '___sec86'), + ('Blocking Transformations', 2, None, '___sec87'), + ('Blocking Transformations, getting there', 2, None, '___sec88'), ('Blocking Transformations, final expressions', 2, None, - '___sec91'), + '___sec89'), ('"Code examples for Blocking, Jackknife and ' 'bootstrap":"https://github.com/CompPhysics/MachineLearning/tree/master/doc/Programs/ResamplingAnalysisScripts"', 2, None, - '___sec92')]} + '___sec90'), + ('The bias-variance tradeoff', 2, None, '___sec91'), + ('Training and testing data', 2, None, '___sec92'), + ('Procedure to find a predictor', 2, None, '___sec93'), + ('What we want', 2, None, '___sec94'), + ('The expected generalization error', 2, None, '___sec95'), + ('Elaborating a little bit more', 2, None, '___sec96'), + ('The bias', 2, None, '___sec97'), + ('The variance', 2, None, '___sec98'), + ('Summing up', 2, None, '___sec99'), + ('Logistic Regression', 2, None, '___sec100'), + ('Basics', 2, None, '___sec101'), + ('Linear classifier', 2, None, '___sec102'), + ('Some selected properties', 2, None, '___sec103'), + ('The cross-entropy as a cost function for logistic regression', + 2, + None, + '___sec104'), + ('Maximum likelihood', 2, None, '___sec105'), + ('Minimizing the cross entropy', 2, None, '___sec106')]} end of tocinfo --> @@ -337,19 +351,33 @@ MathJax.Hub.Config({
  • Resampling methods: Jackknife and Bootstrap
  • Resampling methods: Jackknife
  • Resampling methods: Jackknife estimator
    • -
  • Resampling methods: Jackknife sample code
    • -
  • Resampling methods: Bootstrap
    • -
  • Resampling methods: Bootstrap background
    • -
  • Resampling methods: More Bootstrap background
    • -
  • Resampling methods: Bootstrap approach
    • -
  • Resampling methods: Bootstrap algorithm
    • -
  • Resampling methods: Bootstrap steps
    • -
  • Resampling methods: Blocking
    • -
  • Blocking Transformations
    • -
  • Blocking Transformations
    • -
  • Blocking Transformations, getting there
    • -
  • Blocking Transformations, final expressions
    • -
  • "Code examples for Blocking, Jackknife and bootstrap":"https://github.com/CompPhysics/MachineLearning/tree/master/doc/Programs/ResamplingAnalysisScripts"
    • +
  • Resampling methods: Bootstrap
    • +
  • Resampling methods: Bootstrap background
    • +
  • Resampling methods: More Bootstrap background
    • +
  • Resampling methods: Bootstrap approach
    • +
  • Resampling methods: Bootstrap steps
    • +
  • Resampling methods: Blocking
    • +
  • Blocking Transformations
    • +
  • Blocking Transformations
    • +
  • Blocking Transformations, getting there
    • +
  • Blocking Transformations, final expressions
    • +
  • "Code examples for Blocking, Jackknife and bootstrap":"https://github.com/CompPhysics/MachineLearning/tree/master/doc/Programs/ResamplingAnalysisScripts"
    • +
  • The bias-variance tradeoff
    • +
  • Training and testing data
    • +
  • Procedure to find a predictor
    • +
  • What we want
    • +
  • The expected generalization error
    • +
  • Elaborating a little bit more
    • +
  • The bias
    • +
  • The variance
    • +
  • Summing up
    • +
  • Logistic Regression
    • +
  • Basics
    • +
  • Linear classifier
    • +
  • Some selected properties
    • +
  • The cross-entropy as a cost function for logistic regression
    • +
  • Maximum likelihood
    • +
  • Minimizing the cross entropy
    • @@ -420,7 +448,7 @@ $$
  • 25
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  • ...
    • -
  • 94
    • +
  • 109
  • »
    • diff --git a/doc/pub/Regression/html/._Regression-bs017.html b/doc/pub/Regression/html/._Regression-bs017.html index e44aa8d35..0ebe59f22 100644 --- a/doc/pub/Regression/html/._Regression-bs017.html +++ b/doc/pub/Regression/html/._Regression-bs017.html @@ -194,32 +194,46 @@ Automatically generated HTML file from DocOnce source '___sec77'), ('Resampling methods: Jackknife', 2, None, '___sec78'), ('Resampling methods: Jackknife estimator', 2, None, '___sec79'), - ('Resampling methods: Jackknife sample code', - 2, - None, - '___sec80'), - ('Resampling methods: Bootstrap', 2, None, '___sec81'), - ('Resampling methods: Bootstrap background', 2, None, '___sec82'), + ('Resampling methods: Bootstrap', 2, None, '___sec80'), + ('Resampling methods: Bootstrap background', 2, None, '___sec81'), ('Resampling methods: More Bootstrap background', 2, None, - '___sec83'), - ('Resampling methods: Bootstrap approach', 2, None, '___sec84'), - ('Resampling methods: Bootstrap algorithm', 2, None, '___sec85'), - ('Resampling methods: Bootstrap steps', 2, None, '___sec86'), - ('Resampling methods: Blocking', 2, None, '___sec87'), - ('Blocking Transformations', 2, None, '___sec88'), - ('Blocking Transformations', 2, None, '___sec89'), - ('Blocking Transformations, getting there', 2, None, '___sec90'), + '___sec82'), + ('Resampling methods: Bootstrap approach', 2, None, '___sec83'), + ('Resampling methods: Bootstrap steps', 2, None, '___sec84'), + ('Resampling methods: Blocking', 2, None, '___sec85'), + ('Blocking Transformations', 2, None, '___sec86'), + ('Blocking Transformations', 2, None, '___sec87'), + ('Blocking Transformations, getting there', 2, None, '___sec88'), ('Blocking Transformations, final expressions', 2, None, - '___sec91'), + '___sec89'), ('"Code examples for Blocking, Jackknife and ' 'bootstrap":"https://github.com/CompPhysics/MachineLearning/tree/master/doc/Programs/ResamplingAnalysisScripts"', 2, None, - '___sec92')]} + '___sec90'), + ('The bias-variance tradeoff', 2, None, '___sec91'), + ('Training and testing data', 2, None, '___sec92'), + ('Procedure to find a predictor', 2, None, '___sec93'), + ('What we want', 2, None, '___sec94'), + ('The expected generalization error', 2, None, '___sec95'), + ('Elaborating a little bit more', 2, None, '___sec96'), + ('The bias', 2, None, '___sec97'), + ('The variance', 2, None, '___sec98'), + ('Summing up', 2, None, '___sec99'), + ('Logistic Regression', 2, None, '___sec100'), + ('Basics', 2, None, '___sec101'), + ('Linear classifier', 2, None, '___sec102'), + ('Some selected properties', 2, None, '___sec103'), + ('The cross-entropy as a cost function for logistic regression', + 2, + None, + '___sec104'), + ('Maximum likelihood', 2, None, '___sec105'), + ('Minimizing the cross entropy', 2, None, '___sec106')]} end of tocinfo --> @@ -337,19 +351,33 @@ MathJax.Hub.Config({
  • Resampling methods: Jackknife and Bootstrap
  • Resampling methods: Jackknife
  • Resampling methods: Jackknife estimator
    • -
  • Resampling methods: Jackknife sample code
    • -
  • Resampling methods: Bootstrap
    • -
  • Resampling methods: Bootstrap background
    • -
  • Resampling methods: More Bootstrap background
    • -
  • Resampling methods: Bootstrap approach
    • -
  • Resampling methods: Bootstrap algorithm
    • -
  • Resampling methods: Bootstrap steps
    • -
  • Resampling methods: Blocking
    • -
  • Blocking Transformations
    • -
  • Blocking Transformations
    • -
  • Blocking Transformations, getting there
    • -
  • Blocking Transformations, final expressions
    • -
  • "Code examples for Blocking, Jackknife and bootstrap":"https://github.com/CompPhysics/MachineLearning/tree/master/doc/Programs/ResamplingAnalysisScripts"
    • +
  • Resampling methods: Bootstrap
    • +
  • Resampling methods: Bootstrap background
    • +
  • Resampling methods: More Bootstrap background
    • +
  • Resampling methods: Bootstrap approach
    • +
  • Resampling methods: Bootstrap steps
    • +
  • Resampling methods: Blocking
    • +
  • Blocking Transformations
    • +
  • Blocking Transformations
    • +
  • Blocking Transformations, getting there
    • +
  • Blocking Transformations, final expressions
    • +
  • "Code examples for Blocking, Jackknife and bootstrap":"https://github.com/CompPhysics/MachineLearning/tree/master/doc/Programs/ResamplingAnalysisScripts"
    • +
  • The bias-variance tradeoff
    • +
  • Training and testing data
    • +
  • Procedure to find a predictor
    • +
  • What we want
    • +
  • The expected generalization error
    • +
  • Elaborating a little bit more
    • +
  • The bias
    • +
  • The variance
    • +
  • Summing up
    • +
  • Logistic Regression
    • +
  • Basics
    • +
  • Linear classifier
    • +
  • Some selected properties
    • +
  • The cross-entropy as a cost function for logistic regression
    • +
  • Maximum likelihood
    • +
  • Minimizing the cross entropy
    • @@ -413,7 +441,7 @@ $$
  • 26
  • 27
  • ...
    • -
  • 94
    • +
  • 109
  • »
    • diff --git a/doc/pub/Regression/html/._Regression-bs018.html b/doc/pub/Regression/html/._Regression-bs018.html index 66b3996df..29ee68a4b 100644 --- a/doc/pub/Regression/html/._Regression-bs018.html +++ b/doc/pub/Regression/html/._Regression-bs018.html @@ -194,32 +194,46 @@ Automatically generated HTML file from DocOnce source '___sec77'), ('Resampling methods: Jackknife', 2, None, '___sec78'), ('Resampling methods: Jackknife estimator', 2, None, '___sec79'), - ('Resampling methods: Jackknife sample code', - 2, - None, - '___sec80'), - ('Resampling methods: Bootstrap', 2, None, '___sec81'), - ('Resampling methods: Bootstrap background', 2, None, '___sec82'), + ('Resampling methods: Bootstrap', 2, None, '___sec80'), + ('Resampling methods: Bootstrap background', 2, None, '___sec81'), ('Resampling methods: More Bootstrap background', 2, None, - '___sec83'), - ('Resampling methods: Bootstrap approach', 2, None, '___sec84'), - ('Resampling methods: Bootstrap algorithm', 2, None, '___sec85'), - ('Resampling methods: Bootstrap steps', 2, None, '___sec86'), - ('Resampling methods: Blocking', 2, None, '___sec87'), - ('Blocking Transformations', 2, None, '___sec88'), - ('Blocking Transformations', 2, None, '___sec89'), - ('Blocking Transformations, getting there', 2, None, '___sec90'), + '___sec82'), + ('Resampling methods: Bootstrap approach', 2, None, '___sec83'), + ('Resampling methods: Bootstrap steps', 2, None, '___sec84'), + ('Resampling methods: Blocking', 2, None, '___sec85'), + ('Blocking Transformations', 2, None, '___sec86'), + ('Blocking Transformations', 2, None, '___sec87'), + ('Blocking Transformations, getting there', 2, None, '___sec88'), ('Blocking Transformations, final expressions', 2, None, - '___sec91'), + '___sec89'), ('"Code examples for Blocking, Jackknife and ' 'bootstrap":"https://github.com/CompPhysics/MachineLearning/tree/master/doc/Programs/ResamplingAnalysisScripts"', 2, None, - '___sec92')]} + '___sec90'), + ('The bias-variance tradeoff', 2, None, '___sec91'), + ('Training and testing data', 2, None, '___sec92'), + ('Procedure to find a predictor', 2, None, '___sec93'), + ('What we want', 2, None, '___sec94'), + ('The expected generalization error', 2, None, '___sec95'), + ('Elaborating a little bit more', 2, None, '___sec96'), + ('The bias', 2, None, '___sec97'), + ('The variance', 2, None, '___sec98'), + ('Summing up', 2, None, '___sec99'), + ('Logistic Regression', 2, None, '___sec100'), + ('Basics', 2, None, '___sec101'), + ('Linear classifier', 2, None, '___sec102'), + ('Some selected properties', 2, None, '___sec103'), + ('The cross-entropy as a cost function for logistic regression', + 2, + None, + '___sec104'), + ('Maximum likelihood', 2, None, '___sec105'), + ('Minimizing the cross entropy', 2, None, '___sec106')]} end of tocinfo --> @@ -337,19 +351,33 @@ MathJax.Hub.Config({
  • Resampling methods: Jackknife and Bootstrap
  • Resampling methods: Jackknife
  • Resampling methods: Jackknife estimator
    • -
  • Resampling methods: Jackknife sample code
    • -
  • Resampling methods: Bootstrap
    • -
  • Resampling methods: Bootstrap background
    • -
  • Resampling methods: More Bootstrap background
    • -
  • Resampling methods: Bootstrap approach
    • -
  • Resampling methods: Bootstrap algorithm
    • -
  • Resampling methods: Bootstrap steps
    • -
  • Resampling methods: Blocking
    • -
  • Blocking Transformations
    • -
  • Blocking Transformations
    • -
  • Blocking Transformations, getting there
    • -
  • Blocking Transformations, final expressions
    • -
  • "Code examples for Blocking, Jackknife and bootstrap":"https://github.com/CompPhysics/MachineLearning/tree/master/doc/Programs/ResamplingAnalysisScripts"
    • +
  • Resampling methods: Bootstrap
    • +
  • Resampling methods: Bootstrap background
    • +
  • Resampling methods: More Bootstrap background
    • +
  • Resampling methods: Bootstrap approach
    • +
  • Resampling methods: Bootstrap steps
    • +
  • Resampling methods: Blocking
    • +
  • Blocking Transformations
    • +
  • Blocking Transformations
    • +
  • Blocking Transformations, getting there
    • +
  • Blocking Transformations, final expressions
    • +
  • "Code examples for Blocking, Jackknife and bootstrap":"https://github.com/CompPhysics/MachineLearning/tree/master/doc/Programs/ResamplingAnalysisScripts"
    • +
  • The bias-variance tradeoff
    • +
  • Training and testing data
    • +
  • Procedure to find a predictor
    • +
  • What we want
    • +
  • The expected generalization error
    • +
  • Elaborating a little bit more
    • +
  • The bias
    • +
  • The variance
    • +
  • Summing up
    • +
  • Logistic Regression
    • +
  • Basics
    • +
  • Linear classifier
    • +
  • Some selected properties
    • +
  • The cross-entropy as a cost function for logistic regression
    • +
  • Maximum likelihood
    • +
  • Minimizing the cross entropy
    • @@ -435,7 +463,7 @@ This approach (different linear and non-linear regression) suffers often from bo
  • 27
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  • ...
    • -
  • 94
    • +
  • 109
  • »
    • diff --git a/doc/pub/Regression/html/._Regression-bs019.html b/doc/pub/Regression/html/._Regression-bs019.html index ccdd47bd6..07aacfa3a 100644 --- a/doc/pub/Regression/html/._Regression-bs019.html +++ b/doc/pub/Regression/html/._Regression-bs019.html @@ -194,32 +194,46 @@ Automatically generated HTML file from DocOnce source '___sec77'), ('Resampling methods: Jackknife', 2, None, '___sec78'), ('Resampling methods: Jackknife estimator', 2, None, '___sec79'), - ('Resampling methods: Jackknife sample code', - 2, - None, - '___sec80'), - ('Resampling methods: Bootstrap', 2, None, '___sec81'), - ('Resampling methods: Bootstrap background', 2, None, '___sec82'), + ('Resampling methods: Bootstrap', 2, None, '___sec80'), + ('Resampling methods: Bootstrap background', 2, None, '___sec81'), ('Resampling methods: More Bootstrap background', 2, None, - '___sec83'), - ('Resampling methods: Bootstrap approach', 2, None, '___sec84'), - ('Resampling methods: Bootstrap algorithm', 2, None, '___sec85'), - ('Resampling methods: Bootstrap steps', 2, None, '___sec86'), - ('Resampling methods: Blocking', 2, None, '___sec87'), - ('Blocking Transformations', 2, None, '___sec88'), - ('Blocking Transformations', 2, None, '___sec89'), - ('Blocking Transformations, getting there', 2, None, '___sec90'), + '___sec82'), + ('Resampling methods: Bootstrap approach', 2, None, '___sec83'), + ('Resampling methods: Bootstrap steps', 2, None, '___sec84'), + ('Resampling methods: Blocking', 2, None, '___sec85'), + ('Blocking Transformations', 2, None, '___sec86'), + ('Blocking Transformations', 2, None, '___sec87'), + ('Blocking Transformations, getting there', 2, None, '___sec88'), ('Blocking Transformations, final expressions', 2, None, - '___sec91'), + '___sec89'), ('"Code examples for Blocking, Jackknife and ' 'bootstrap":"https://github.com/CompPhysics/MachineLearning/tree/master/doc/Programs/ResamplingAnalysisScripts"', 2, None, - '___sec92')]} + '___sec90'), + ('The bias-variance tradeoff', 2, None, '___sec91'), + ('Training and testing data', 2, None, '___sec92'), + ('Procedure to find a predictor', 2, None, '___sec93'), + ('What we want', 2, None, '___sec94'), + ('The expected generalization error', 2, None, '___sec95'), + ('Elaborating a little bit more', 2, None, '___sec96'), + ('The bias', 2, None, '___sec97'), + ('The variance', 2, None, '___sec98'), + ('Summing up', 2, None, '___sec99'), + ('Logistic Regression', 2, None, '___sec100'), + ('Basics', 2, None, '___sec101'), + ('Linear classifier', 2, None, '___sec102'), + ('Some selected properties', 2, None, '___sec103'), + ('The cross-entropy as a cost function for logistic regression', + 2, + None, + '___sec104'), + ('Maximum likelihood', 2, None, '___sec105'), + ('Minimizing the cross entropy', 2, None, '___sec106')]} end of tocinfo --> @@ -337,19 +351,33 @@ MathJax.Hub.Config({
  • Resampling methods: Jackknife and Bootstrap
  • Resampling methods: Jackknife
  • Resampling methods: Jackknife estimator
    • -
  • Resampling methods: Jackknife sample code
    • -
  • Resampling methods: Bootstrap
    • -
  • Resampling methods: Bootstrap background
    • -
  • Resampling methods: More Bootstrap background
    • -
  • Resampling methods: Bootstrap approach
    • -
  • Resampling methods: Bootstrap algorithm
    • -
  • Resampling methods: Bootstrap steps
    • -
  • Resampling methods: Blocking
    • -
  • Blocking Transformations
    • -
  • Blocking Transformations
    • -
  • Blocking Transformations, getting there
    • -
  • Blocking Transformations, final expressions
    • -
  • "Code examples for Blocking, Jackknife and bootstrap":"https://github.com/CompPhysics/MachineLearning/tree/master/doc/Programs/ResamplingAnalysisScripts"
    • +
  • Resampling methods: Bootstrap
    • +
  • Resampling methods: Bootstrap background
    • +
  • Resampling methods: More Bootstrap background
    • +
  • Resampling methods: Bootstrap approach
    • +
  • Resampling methods: Bootstrap steps
    • +
  • Resampling methods: Blocking
    • +
  • Blocking Transformations
    • +
  • Blocking Transformations
    • +
  • Blocking Transformations, getting there
    • +
  • Blocking Transformations, final expressions
    • +
  • "Code examples for Blocking, Jackknife and bootstrap":"https://github.com/CompPhysics/MachineLearning/tree/master/doc/Programs/ResamplingAnalysisScripts"
    • +
  • The bias-variance tradeoff
    • +
  • Training and testing data
    • +
  • Procedure to find a predictor
    • +
  • What we want
    • +
  • The expected generalization error
    • +
  • Elaborating a little bit more
    • +
  • The bias
    • +
  • The variance
    • +
  • Summing up
    • +
  • Logistic Regression
    • +
  • Basics
    • +
  • Linear classifier
    • +
  • Some selected properties
    • +
  • The cross-entropy as a cost function for logistic regression
    • +
  • Maximum likelihood
    • +
  • Minimizing the cross entropy
    • @@ -428,7 +456,7 @@ We see that, as expected, a linear fit gives a seemingly (from the graph) good r
  • 28
  • 29
  • ...
    • -
  • 94
    • +
  • 109
  • »
    • diff --git a/doc/pub/Regression/html/._Regression-bs020.html b/doc/pub/Regression/html/._Regression-bs020.html index 05181327e..8b2d8379c 100644 --- a/doc/pub/Regression/html/._Regression-bs020.html +++ b/doc/pub/Regression/html/._Regression-bs020.html @@ -194,32 +194,46 @@ Automatically generated HTML file from DocOnce source '___sec77'), ('Resampling methods: Jackknife', 2, None, '___sec78'), ('Resampling methods: Jackknife estimator', 2, None, '___sec79'), - ('Resampling methods: Jackknife sample code', - 2, - None, - '___sec80'), - ('Resampling methods: Bootstrap', 2, None, '___sec81'), - ('Resampling methods: Bootstrap background', 2, None, '___sec82'), + ('Resampling methods: Bootstrap', 2, None, '___sec80'), + ('Resampling methods: Bootstrap background', 2, None, '___sec81'), ('Resampling methods: More Bootstrap background', 2, None, - '___sec83'), - ('Resampling methods: Bootstrap approach', 2, None, '___sec84'), - ('Resampling methods: Bootstrap algorithm', 2, None, '___sec85'), - ('Resampling methods: Bootstrap steps', 2, None, '___sec86'), - ('Resampling methods: Blocking', 2, None, '___sec87'), - ('Blocking Transformations', 2, None, '___sec88'), - ('Blocking Transformations', 2, None, '___sec89'), - ('Blocking Transformations, getting there', 2, None, '___sec90'), + '___sec82'), + ('Resampling methods: Bootstrap approach', 2, None, '___sec83'), + ('Resampling methods: Bootstrap steps', 2, None, '___sec84'), + ('Resampling methods: Blocking', 2, None, '___sec85'), + ('Blocking Transformations', 2, None, '___sec86'), + ('Blocking Transformations', 2, None, '___sec87'), + ('Blocking Transformations, getting there', 2, None, '___sec88'), ('Blocking Transformations, final expressions', 2, None, - '___sec91'), + '___sec89'), ('"Code examples for Blocking, Jackknife and ' 'bootstrap":"https://github.com/CompPhysics/MachineLearning/tree/master/doc/Programs/ResamplingAnalysisScripts"', 2, None, - '___sec92')]} + '___sec90'), + ('The bias-variance tradeoff', 2, None, '___sec91'), + ('Training and testing data', 2, None, '___sec92'), + ('Procedure to find a predictor', 2, None, '___sec93'), + ('What we want', 2, None, '___sec94'), + ('The expected generalization error', 2, None, '___sec95'), + ('Elaborating a little bit more', 2, None, '___sec96'), + ('The bias', 2, None, '___sec97'), + ('The variance', 2, None, '___sec98'), + ('Summing up', 2, None, '___sec99'), + ('Logistic Regression', 2, None, '___sec100'), + ('Basics', 2, None, '___sec101'), + ('Linear classifier', 2, None, '___sec102'), + ('Some selected properties', 2, None, '___sec103'), + ('The cross-entropy as a cost function for logistic regression', + 2, + None, + '___sec104'), + ('Maximum likelihood', 2, None, '___sec105'), + ('Minimizing the cross entropy', 2, None, '___sec106')]} end of tocinfo --> @@ -337,19 +351,33 @@ MathJax.Hub.Config({
  • Resampling methods: Jackknife and Bootstrap
  • Resampling methods: Jackknife
  • Resampling methods: Jackknife estimator
    • -
  • Resampling methods: Jackknife sample code
    • -
  • Resampling methods: Bootstrap
    • -
  • Resampling methods: Bootstrap background
    • -
  • Resampling methods: More Bootstrap background
    • -
  • Resampling methods: Bootstrap approach
    • -
  • Resampling methods: Bootstrap algorithm
    • -
  • Resampling methods: Bootstrap steps
    • -
  • Resampling methods: Blocking
    • -
  • Blocking Transformations
    • -
  • Blocking Transformations
    • -
  • Blocking Transformations, getting there
    • -
  • Blocking Transformations, final expressions
    • -
  • "Code examples for Blocking, Jackknife and bootstrap":"https://github.com/CompPhysics/MachineLearning/tree/master/doc/Programs/ResamplingAnalysisScripts"
    • +
  • Resampling methods: Bootstrap
    • +
  • Resampling methods: Bootstrap background
    • +
  • Resampling methods: More Bootstrap background
    • +
  • Resampling methods: Bootstrap approach
    • +
  • Resampling methods: Bootstrap steps
    • +
  • Resampling methods: Blocking
    • +
  • Blocking Transformations
    • +
  • Blocking Transformations
    • +
  • Blocking Transformations, getting there
    • +
  • Blocking Transformations, final expressions
    • +
  • "Code examples for Blocking, Jackknife and bootstrap":"https://github.com/CompPhysics/MachineLearning/tree/master/doc/Programs/ResamplingAnalysisScripts"
    • +
  • The bias-variance tradeoff
    • +
  • Training and testing data
    • +
  • Procedure to find a predictor
    • +
  • What we want
    • +
  • The expected generalization error
    • +
  • Elaborating a little bit more
    • +
  • The bias
    • +
  • The variance
    • +
  • Summing up
    • +
  • Logistic Regression
    • +
  • Basics
    • +
  • Linear classifier
    • +
  • Some selected properties
    • +
  • The cross-entropy as a cost function for logistic regression
    • +
  • Maximum likelihood
    • +
  • Minimizing the cross entropy
    • @@ -419,7 +447,7 @@ plt.show()
  • 29
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  • ...
    • -
  • 94
    • +
  • 109
  • »
    • diff --git a/doc/pub/Regression/html/._Regression-bs021.html b/doc/pub/Regression/html/._Regression-bs021.html index 8c7daea96..c8b59f874 100644 --- a/doc/pub/Regression/html/._Regression-bs021.html +++ b/doc/pub/Regression/html/._Regression-bs021.html @@ -194,32 +194,46 @@ Automatically generated HTML file from DocOnce source '___sec77'), ('Resampling methods: Jackknife', 2, None, '___sec78'), ('Resampling methods: Jackknife estimator', 2, None, '___sec79'), - ('Resampling methods: Jackknife sample code', - 2, - None, - '___sec80'), - ('Resampling methods: Bootstrap', 2, None, '___sec81'), - ('Resampling methods: Bootstrap background', 2, None, '___sec82'), + ('Resampling methods: Bootstrap', 2, None, '___sec80'), + ('Resampling methods: Bootstrap background', 2, None, '___sec81'), ('Resampling methods: More Bootstrap background', 2, None, - '___sec83'), - ('Resampling methods: Bootstrap approach', 2, None, '___sec84'), - ('Resampling methods: Bootstrap algorithm', 2, None, '___sec85'), - ('Resampling methods: Bootstrap steps', 2, None, '___sec86'), - ('Resampling methods: Blocking', 2, None, '___sec87'), - ('Blocking Transformations', 2, None, '___sec88'), - ('Blocking Transformations', 2, None, '___sec89'), - ('Blocking Transformations, getting there', 2, None, '___sec90'), + '___sec82'), + ('Resampling methods: Bootstrap approach', 2, None, '___sec83'), + ('Resampling methods: Bootstrap steps', 2, None, '___sec84'), + ('Resampling methods: Blocking', 2, None, '___sec85'), + ('Blocking Transformations', 2, None, '___sec86'), + ('Blocking Transformations', 2, None, '___sec87'), + ('Blocking Transformations, getting there', 2, None, '___sec88'), ('Blocking Transformations, final expressions', 2, None, - '___sec91'), + '___sec89'), ('"Code examples for Blocking, Jackknife and ' 'bootstrap":"https://github.com/CompPhysics/MachineLearning/tree/master/doc/Programs/ResamplingAnalysisScripts"', 2, None, - '___sec92')]} + '___sec90'), + ('The bias-variance tradeoff', 2, None, '___sec91'), + ('Training and testing data', 2, None, '___sec92'), + ('Procedure to find a predictor', 2, None, '___sec93'), + ('What we want', 2, None, '___sec94'), + ('The expected generalization error', 2, None, '___sec95'), + ('Elaborating a little bit more', 2, None, '___sec96'), + ('The bias', 2, None, '___sec97'), + ('The variance', 2, None, '___sec98'), + ('Summing up', 2, None, '___sec99'), + ('Logistic Regression', 2, None, '___sec100'), + ('Basics', 2, None, '___sec101'), + ('Linear classifier', 2, None, '___sec102'), + ('Some selected properties', 2, None, '___sec103'), + ('The cross-entropy as a cost function for logistic regression', + 2, + None, + '___sec104'), + ('Maximum likelihood', 2, None, '___sec105'), + ('Minimizing the cross entropy', 2, None, '___sec106')]} end of tocinfo --> @@ -337,19 +351,33 @@ MathJax.Hub.Config({
  • Resampling methods: Jackknife and Bootstrap
  • Resampling methods: Jackknife
  • Resampling methods: Jackknife estimator
    • -
  • Resampling methods: Jackknife sample code
    • -
  • Resampling methods: Bootstrap
    • -
  • Resampling methods: Bootstrap background
    • -
  • Resampling methods: More Bootstrap background
    • -
  • Resampling methods: Bootstrap approach
    • -
  • Resampling methods: Bootstrap algorithm
    • -
  • Resampling methods: Bootstrap steps
    • -
  • Resampling methods: Blocking
    • -
  • Blocking Transformations
    • -
  • Blocking Transformations
    • -
  • Blocking Transformations, getting there
    • -
  • Blocking Transformations, final expressions
    • -
  • "Code examples for Blocking, Jackknife and bootstrap":"https://github.com/CompPhysics/MachineLearning/tree/master/doc/Programs/ResamplingAnalysisScripts"
    • +
  • Resampling methods: Bootstrap
    • +
  • Resampling methods: Bootstrap background
    • +
  • Resampling methods: More Bootstrap background
    • +
  • Resampling methods: Bootstrap approach
    • +
  • Resampling methods: Bootstrap steps
    • +
  • Resampling methods: Blocking
    • +
  • Blocking Transformations
    • +
  • Blocking Transformations
    • +
  • Blocking Transformations, getting there
    • +
  • Blocking Transformations, final expressions
    • +
  • "Code examples for Blocking, Jackknife and bootstrap":"https://github.com/CompPhysics/MachineLearning/tree/master/doc/Programs/ResamplingAnalysisScripts"
    • +
  • The bias-variance tradeoff
    • +
  • Training and testing data
    • +
  • Procedure to find a predictor
    • +
  • What we want
    • +
  • The expected generalization error
    • +
  • Elaborating a little bit more
    • +
  • The bias
    • +
  • The variance
    • +
  • Summing up
    • +
  • Logistic Regression
    • +
  • Basics
    • +
  • Linear classifier
    • +
  • Some selected properties
    • +
  • The cross-entropy as a cost function for logistic regression
    • +
  • Maximum likelihood
    • +
  • Minimizing the cross entropy
    • @@ -456,7 +484,7 @@ plt.show()
  • 30
  • 31
  • ...
    • -
  • 94
    • +
  • 109
  • »
    • diff --git a/doc/pub/Regression/html/._Regression-bs022.html b/doc/pub/Regression/html/._Regression-bs022.html index 602d90b97..9a94a2a15 100644 --- a/doc/pub/Regression/html/._Regression-bs022.html +++ b/doc/pub/Regression/html/._Regression-bs022.html @@ -194,32 +194,46 @@ Automatically generated HTML file from DocOnce source '___sec77'), ('Resampling methods: Jackknife', 2, None, '___sec78'), ('Resampling methods: Jackknife estimator', 2, None, '___sec79'), - ('Resampling methods: Jackknife sample code', - 2, - None, - '___sec80'), - ('Resampling methods: Bootstrap', 2, None, '___sec81'), - ('Resampling methods: Bootstrap background', 2, None, '___sec82'), + ('Resampling methods: Bootstrap', 2, None, '___sec80'), + ('Resampling methods: Bootstrap background', 2, None, '___sec81'), ('Resampling methods: More Bootstrap background', 2, None, - '___sec83'), - ('Resampling methods: Bootstrap approach', 2, None, '___sec84'), - ('Resampling methods: Bootstrap algorithm', 2, None, '___sec85'), - ('Resampling methods: Bootstrap steps', 2, None, '___sec86'), - ('Resampling methods: Blocking', 2, None, '___sec87'), - ('Blocking Transformations', 2, None, '___sec88'), - ('Blocking Transformations', 2, None, '___sec89'), - ('Blocking Transformations, getting there', 2, None, '___sec90'), + '___sec82'), + ('Resampling methods: Bootstrap approach', 2, None, '___sec83'), + ('Resampling methods: Bootstrap steps', 2, None, '___sec84'), + ('Resampling methods: Blocking', 2, None, '___sec85'), + ('Blocking Transformations', 2, None, '___sec86'), + ('Blocking Transformations', 2, None, '___sec87'), + ('Blocking Transformations, getting there', 2, None, '___sec88'), ('Blocking Transformations, final expressions', 2, None, - '___sec91'), + '___sec89'), ('"Code examples for Blocking, Jackknife and ' 'bootstrap":"https://github.com/CompPhysics/MachineLearning/tree/master/doc/Programs/ResamplingAnalysisScripts"', 2, None, - '___sec92')]} + '___sec90'), + ('The bias-variance tradeoff', 2, None, '___sec91'), + ('Training and testing data', 2, None, '___sec92'), + ('Procedure to find a predictor', 2, None, '___sec93'), + ('What we want', 2, None, '___sec94'), + ('The expected generalization error', 2, None, '___sec95'), + ('Elaborating a little bit more', 2, None, '___sec96'), + ('The bias', 2, None, '___sec97'), + ('The variance', 2, None, '___sec98'), + ('Summing up', 2, None, '___sec99'), + ('Logistic Regression', 2, None, '___sec100'), + ('Basics', 2, None, '___sec101'), + ('Linear classifier', 2, None, '___sec102'), + ('Some selected properties', 2, None, '___sec103'), + ('The cross-entropy as a cost function for logistic regression', + 2, + None, + '___sec104'), + ('Maximum likelihood', 2, None, '___sec105'), + ('Minimizing the cross entropy', 2, None, '___sec106')]} end of tocinfo --> @@ -337,19 +351,33 @@ MathJax.Hub.Config({
  • Resampling methods: Jackknife and Bootstrap
  • Resampling methods: Jackknife
  • Resampling methods: Jackknife estimator
    • -
  • Resampling methods: Jackknife sample code
    • -
  • Resampling methods: Bootstrap
    • -
  • Resampling methods: Bootstrap background
    • -
  • Resampling methods: More Bootstrap background
    • -
  • Resampling methods: Bootstrap approach
    • -
  • Resampling methods: Bootstrap algorithm
    • -
  • Resampling methods: Bootstrap steps
    • -
  • Resampling methods: Blocking
    • -
  • Blocking Transformations
    • -
  • Blocking Transformations
    • -
  • Blocking Transformations, getting there
    • -
  • Blocking Transformations, final expressions
    • -
  • "Code examples for Blocking, Jackknife and bootstrap":"https://github.com/CompPhysics/MachineLearning/tree/master/doc/Programs/ResamplingAnalysisScripts"
    • +
  • Resampling methods: Bootstrap
    • +
  • Resampling methods: Bootstrap background
    • +
  • Resampling methods: More Bootstrap background
    • +
  • Resampling methods: Bootstrap approach
    • +
  • Resampling methods: Bootstrap steps
    • +
  • Resampling methods: Blocking
    • +
  • Blocking Transformations
    • +
  • Blocking Transformations
    • +
  • Blocking Transformations, getting there
    • +
  • Blocking Transformations, final expressions
    • +
  • "Code examples for Blocking, Jackknife and bootstrap":"https://github.com/CompPhysics/MachineLearning/tree/master/doc/Programs/ResamplingAnalysisScripts"
    • +
  • The bias-variance tradeoff
    • +
  • Training and testing data
    • +
  • Procedure to find a predictor
    • +
  • What we want
    • +
  • The expected generalization error
    • +
  • Elaborating a little bit more
    • +
  • The bias
    • +
  • The variance
    • +
  • Summing up
    • +
  • Logistic Regression
    • +
  • Basics
    • +
  • Linear classifier
    • +
  • Some selected properties
    • +
  • The cross-entropy as a cost function for logistic regression
    • +
  • Maximum likelihood
    • +
  • Minimizing the cross entropy
    • @@ -410,7 +438,7 @@ where \( x \) is defined as before.
  • 31
  • 32
  • ...
    • -
  • 94
    • +
  • 109
  • »
    • diff --git a/doc/pub/Regression/html/._Regression-bs023.html b/doc/pub/Regression/html/._Regression-bs023.html index d6e15f9f4..234177cd2 100644 --- a/doc/pub/Regression/html/._Regression-bs023.html +++ b/doc/pub/Regression/html/._Regression-bs023.html @@ -194,32 +194,46 @@ Automatically generated HTML file from DocOnce source '___sec77'), ('Resampling methods: Jackknife', 2, None, '___sec78'), ('Resampling methods: Jackknife estimator', 2, None, '___sec79'), - ('Resampling methods: Jackknife sample code', - 2, - None, - '___sec80'), - ('Resampling methods: Bootstrap', 2, None, '___sec81'), - ('Resampling methods: Bootstrap background', 2, None, '___sec82'), + ('Resampling methods: Bootstrap', 2, None, '___sec80'), + ('Resampling methods: Bootstrap background', 2, None, '___sec81'), ('Resampling methods: More Bootstrap background', 2, None, - '___sec83'), - ('Resampling methods: Bootstrap approach', 2, None, '___sec84'), - ('Resampling methods: Bootstrap algorithm', 2, None, '___sec85'), - ('Resampling methods: Bootstrap steps', 2, None, '___sec86'), - ('Resampling methods: Blocking', 2, None, '___sec87'), - ('Blocking Transformations', 2, None, '___sec88'), - ('Blocking Transformations', 2, None, '___sec89'), - ('Blocking Transformations, getting there', 2, None, '___sec90'), + '___sec82'), + ('Resampling methods: Bootstrap approach', 2, None, '___sec83'), + ('Resampling methods: Bootstrap steps', 2, None, '___sec84'), + ('Resampling methods: Blocking', 2, None, '___sec85'), + ('Blocking Transformations', 2, None, '___sec86'), + ('Blocking Transformations', 2, None, '___sec87'), + ('Blocking Transformations, getting there', 2, None, '___sec88'), ('Blocking Transformations, final expressions', 2, None, - '___sec91'), + '___sec89'), ('"Code examples for Blocking, Jackknife and ' 'bootstrap":"https://github.com/CompPhysics/MachineLearning/tree/master/doc/Programs/ResamplingAnalysisScripts"', 2, None, - '___sec92')]} + '___sec90'), + ('The bias-variance tradeoff', 2, None, '___sec91'), + ('Training and testing data', 2, None, '___sec92'), + ('Procedure to find a predictor', 2, None, '___sec93'), + ('What we want', 2, None, '___sec94'), + ('The expected generalization error', 2, None, '___sec95'), + ('Elaborating a little bit more', 2, None, '___sec96'), + ('The bias', 2, None, '___sec97'), + ('The variance', 2, None, '___sec98'), + ('Summing up', 2, None, '___sec99'), + ('Logistic Regression', 2, None, '___sec100'), + ('Basics', 2, None, '___sec101'), + ('Linear classifier', 2, None, '___sec102'), + ('Some selected properties', 2, None, '___sec103'), + ('The cross-entropy as a cost function for logistic regression', + 2, + None, + '___sec104'), + ('Maximum likelihood', 2, None, '___sec105'), + ('Minimizing the cross entropy', 2, None, '___sec106')]} end of tocinfo --> @@ -337,19 +351,33 @@ MathJax.Hub.Config({
  • Resampling methods: Jackknife and Bootstrap
  • Resampling methods: Jackknife
  • Resampling methods: Jackknife estimator
    • -
  • Resampling methods: Jackknife sample code
    • -
  • Resampling methods: Bootstrap
    • -
  • Resampling methods: Bootstrap background
    • -
  • Resampling methods: More Bootstrap background
    • -
  • Resampling methods: Bootstrap approach
    • -
  • Resampling methods: Bootstrap algorithm
    • -
  • Resampling methods: Bootstrap steps
    • -
  • Resampling methods: Blocking
    • -
  • Blocking Transformations
    • -
  • Blocking Transformations
    • -
  • Blocking Transformations, getting there
    • -
  • Blocking Transformations, final expressions
    • -
  • "Code examples for Blocking, Jackknife and bootstrap":"https://github.com/CompPhysics/MachineLearning/tree/master/doc/Programs/ResamplingAnalysisScripts"
    • +
  • Resampling methods: Bootstrap
    • +
  • Resampling methods: Bootstrap background
    • +
  • Resampling methods: More Bootstrap background
    • +
  • Resampling methods: Bootstrap approach
    • +
  • Resampling methods: Bootstrap steps
    • +
  • Resampling methods: Blocking
    • +
  • Blocking Transformations
    • +
  • Blocking Transformations
    • +
  • Blocking Transformations, getting there
    • +
  • Blocking Transformations, final expressions
    • +
  • "Code examples for Blocking, Jackknife and bootstrap":"https://github.com/CompPhysics/MachineLearning/tree/master/doc/Programs/ResamplingAnalysisScripts"
    • +
  • The bias-variance tradeoff
    • +
  • Training and testing data
    • +
  • Procedure to find a predictor
    • +
  • What we want
    • +
  • The expected generalization error
    • +
  • Elaborating a little bit more
    • +
  • The bias
    • +
  • The variance
    • +
  • Summing up
    • +
  • Logistic Regression
    • +
  • Basics
    • +
  • Linear classifier
    • +
  • Some selected properties
    • +
  • The cross-entropy as a cost function for logistic regression
    • +
  • Maximum likelihood
    • +
  • Minimizing the cross entropy
    • @@ -401,7 +429,7 @@ have not discussed a more rigorous approach to the cost function.
  • 32
  • 33
  • ...
    • -
  • 94
    • +
  • 109
  • »
    • diff --git a/doc/pub/Regression/html/._Regression-bs024.html b/doc/pub/Regression/html/._Regression-bs024.html index 4744279f4..3085ac4e7 100644 --- a/doc/pub/Regression/html/._Regression-bs024.html +++ b/doc/pub/Regression/html/._Regression-bs024.html @@ -194,32 +194,46 @@ Automatically generated HTML file from DocOnce source '___sec77'), ('Resampling methods: Jackknife', 2, None, '___sec78'), ('Resampling methods: Jackknife estimator', 2, None, '___sec79'), - ('Resampling methods: Jackknife sample code', - 2, - None, - '___sec80'), - ('Resampling methods: Bootstrap', 2, None, '___sec81'), - ('Resampling methods: Bootstrap background', 2, None, '___sec82'), + ('Resampling methods: Bootstrap', 2, None, '___sec80'), + ('Resampling methods: Bootstrap background', 2, None, '___sec81'), ('Resampling methods: More Bootstrap background', 2, None, - '___sec83'), - ('Resampling methods: Bootstrap approach', 2, None, '___sec84'), - ('Resampling methods: Bootstrap algorithm', 2, None, '___sec85'), - ('Resampling methods: Bootstrap steps', 2, None, '___sec86'), - ('Resampling methods: Blocking', 2, None, '___sec87'), - ('Blocking Transformations', 2, None, '___sec88'), - ('Blocking Transformations', 2, None, '___sec89'), - ('Blocking Transformations, getting there', 2, None, '___sec90'), + '___sec82'), + ('Resampling methods: Bootstrap approach', 2, None, '___sec83'), + ('Resampling methods: Bootstrap steps', 2, None, '___sec84'), + ('Resampling methods: Blocking', 2, None, '___sec85'), + ('Blocking Transformations', 2, None, '___sec86'), + ('Blocking Transformations', 2, None, '___sec87'), + ('Blocking Transformations, getting there', 2, None, '___sec88'), ('Blocking Transformations, final expressions', 2, None, - '___sec91'), + '___sec89'), ('"Code examples for Blocking, Jackknife and ' 'bootstrap":"https://github.com/CompPhysics/MachineLearning/tree/master/doc/Programs/ResamplingAnalysisScripts"', 2, None, - '___sec92')]} + '___sec90'), + ('The bias-variance tradeoff', 2, None, '___sec91'), + ('Training and testing data', 2, None, '___sec92'), + ('Procedure to find a predictor', 2, None, '___sec93'), + ('What we want', 2, None, '___sec94'), + ('The expected generalization error', 2, None, '___sec95'), + ('Elaborating a little bit more', 2, None, '___sec96'), + ('The bias', 2, None, '___sec97'), + ('The variance', 2, None, '___sec98'), + ('Summing up', 2, None, '___sec99'), + ('Logistic Regression', 2, None, '___sec100'), + ('Basics', 2, None, '___sec101'), + ('Linear classifier', 2, None, '___sec102'), + ('Some selected properties', 2, None, '___sec103'), + ('The cross-entropy as a cost function for logistic regression', + 2, + None, + '___sec104'), + ('Maximum likelihood', 2, None, '___sec105'), + ('Minimizing the cross entropy', 2, None, '___sec106')]} end of tocinfo --> @@ -337,19 +351,33 @@ MathJax.Hub.Config({
  • Resampling methods: Jackknife and Bootstrap
  • Resampling methods: Jackknife
  • Resampling methods: Jackknife estimator
    • -
  • Resampling methods: Jackknife sample code
    • -
  • Resampling methods: Bootstrap
    • -
  • Resampling methods: Bootstrap background
    • -
  • Resampling methods: More Bootstrap background
    • -
  • Resampling methods: Bootstrap approach
    • -
  • Resampling methods: Bootstrap algorithm
    • -
  • Resampling methods: Bootstrap steps
    • -
  • Resampling methods: Blocking
    • -
  • Blocking Transformations
    • -
  • Blocking Transformations
    • -
  • Blocking Transformations, getting there
    • -
  • Blocking Transformations, final expressions
    • -
  • "Code examples for Blocking, Jackknife and bootstrap":"https://github.com/CompPhysics/MachineLearning/tree/master/doc/Programs/ResamplingAnalysisScripts"
    • +
  • Resampling methods: Bootstrap
    • +
  • Resampling methods: Bootstrap background
    • +
  • Resampling methods: More Bootstrap background
    • +
  • Resampling methods: Bootstrap approach
    • +
  • Resampling methods: Bootstrap steps
    • +
  • Resampling methods: Blocking
    • +
  • Blocking Transformations
    • +
  • Blocking Transformations
    • +
  • Blocking Transformations, getting there
    • +
  • Blocking Transformations, final expressions
    • +
  • "Code examples for Blocking, Jackknife and bootstrap":"https://github.com/CompPhysics/MachineLearning/tree/master/doc/Programs/ResamplingAnalysisScripts"
    • +
  • The bias-variance tradeoff
    • +
  • Training and testing data
    • +
  • Procedure to find a predictor
    • +
  • What we want
    • +
  • The expected generalization error
    • +
  • Elaborating a little bit more
    • +
  • The bias
    • +
  • The variance
    • +
  • Summing up
    • +
  • Logistic Regression
    • +
  • Basics
    • +
  • Linear classifier
    • +
  • Some selected properties
    • +
  • The cross-entropy as a cost function for logistic regression
    • +
  • Maximum likelihood
    • +
  • Minimizing the cross entropy
    • @@ -410,7 +438,7 @@ dimensionless.
  • 33
  • 34
  • ...
    • -
  • 94
    • +
  • 109
  • »
    • diff --git a/doc/pub/Regression/html/._Regression-bs025.html b/doc/pub/Regression/html/._Regression-bs025.html index 3465c3057..4a8fc5145 100644 --- a/doc/pub/Regression/html/._Regression-bs025.html +++ b/doc/pub/Regression/html/._Regression-bs025.html @@ -194,32 +194,46 @@ Automatically generated HTML file from DocOnce source '___sec77'), ('Resampling methods: Jackknife', 2, None, '___sec78'), ('Resampling methods: Jackknife estimator', 2, None, '___sec79'), - ('Resampling methods: Jackknife sample code', - 2, - None, - '___sec80'), - ('Resampling methods: Bootstrap', 2, None, '___sec81'), - ('Resampling methods: Bootstrap background', 2, None, '___sec82'), + ('Resampling methods: Bootstrap', 2, None, '___sec80'), + ('Resampling methods: Bootstrap background', 2, None, '___sec81'), ('Resampling methods: More Bootstrap background', 2, None, - '___sec83'), - ('Resampling methods: Bootstrap approach', 2, None, '___sec84'), - ('Resampling methods: Bootstrap algorithm', 2, None, '___sec85'), - ('Resampling methods: Bootstrap steps', 2, None, '___sec86'), - ('Resampling methods: Blocking', 2, None, '___sec87'), - ('Blocking Transformations', 2, None, '___sec88'), - ('Blocking Transformations', 2, None, '___sec89'), - ('Blocking Transformations, getting there', 2, None, '___sec90'), + '___sec82'), + ('Resampling methods: Bootstrap approach', 2, None, '___sec83'), + ('Resampling methods: Bootstrap steps', 2, None, '___sec84'), + ('Resampling methods: Blocking', 2, None, '___sec85'), + ('Blocking Transformations', 2, None, '___sec86'), + ('Blocking Transformations', 2, None, '___sec87'), + ('Blocking Transformations, getting there', 2, None, '___sec88'), ('Blocking Transformations, final expressions', 2, None, - '___sec91'), + '___sec89'), ('"Code examples for Blocking, Jackknife and ' 'bootstrap":"https://github.com/CompPhysics/MachineLearning/tree/master/doc/Programs/ResamplingAnalysisScripts"', 2, None, - '___sec92')]} + '___sec90'), + ('The bias-variance tradeoff', 2, None, '___sec91'), + ('Training and testing data', 2, None, '___sec92'), + ('Procedure to find a predictor', 2, None, '___sec93'), + ('What we want', 2, None, '___sec94'), + ('The expected generalization error', 2, None, '___sec95'), + ('Elaborating a little bit more', 2, None, '___sec96'), + ('The bias', 2, None, '___sec97'), + ('The variance', 2, None, '___sec98'), + ('Summing up', 2, None, '___sec99'), + ('Logistic Regression', 2, None, '___sec100'), + ('Basics', 2, None, '___sec101'), + ('Linear classifier', 2, None, '___sec102'), + ('Some selected properties', 2, None, '___sec103'), + ('The cross-entropy as a cost function for logistic regression', + 2, + None, + '___sec104'), + ('Maximum likelihood', 2, None, '___sec105'), + ('Minimizing the cross entropy', 2, None, '___sec106')]} end of tocinfo --> @@ -337,19 +351,33 @@ MathJax.Hub.Config({
  • Resampling methods: Jackknife and Bootstrap
  • Resampling methods: Jackknife
  • Resampling methods: Jackknife estimator
    • -
  • Resampling methods: Jackknife sample code
    • -
  • Resampling methods: Bootstrap
    • -
  • Resampling methods: Bootstrap background
    • -
  • Resampling methods: More Bootstrap background
    • -
  • Resampling methods: Bootstrap approach
    • -
  • Resampling methods: Bootstrap algorithm
    • -
  • Resampling methods: Bootstrap steps
    • -
  • Resampling methods: Blocking
    • -
  • Blocking Transformations
    • -
  • Blocking Transformations
    • -
  • Blocking Transformations, getting there
    • -
  • Blocking Transformations, final expressions
    • -
  • "Code examples for Blocking, Jackknife and bootstrap":"https://github.com/CompPhysics/MachineLearning/tree/master/doc/Programs/ResamplingAnalysisScripts"
    • +
  • Resampling methods: Bootstrap
    • +
  • Resampling methods: Bootstrap background
    • +
  • Resampling methods: More Bootstrap background
    • +
  • Resampling methods: Bootstrap approach
    • +
  • Resampling methods: Bootstrap steps
    • +
  • Resampling methods: Blocking
    • +
  • Blocking Transformations
    • +
  • Blocking Transformations
    • +
  • Blocking Transformations, getting there
    • +
  • Blocking Transformations, final expressions
    • +
  • "Code examples for Blocking, Jackknife and bootstrap":"https://github.com/CompPhysics/MachineLearning/tree/master/doc/Programs/ResamplingAnalysisScripts"
    • +
  • The bias-variance tradeoff
    • +
  • Training and testing data
    • +
  • Procedure to find a predictor
    • +
  • What we want
    • +
  • The expected generalization error
    • +
  • Elaborating a little bit more
    • +
  • The bias
    • +
  • The variance
    • +
  • Summing up
    • +
  • Logistic Regression
    • +
  • Basics
    • +
  • Linear classifier
    • +
  • Some selected properties
    • +
  • The cross-entropy as a cost function for logistic regression
    • +
  • Maximum likelihood
    • +
  • Minimizing the cross entropy
    • @@ -409,7 +437,7 @@ the \( \chi^2 \) function becomes smaller.
  • 34
  • 35
  • ...
    • -
  • 94
    • +
  • 109
  • »
    • diff --git a/doc/pub/Regression/html/._Regression-bs026.html b/doc/pub/Regression/html/._Regression-bs026.html index 76fd2f8bd..9ebb79045 100644 --- a/doc/pub/Regression/html/._Regression-bs026.html +++ b/doc/pub/Regression/html/._Regression-bs026.html @@ -194,32 +194,46 @@ Automatically generated HTML file from DocOnce source '___sec77'), ('Resampling methods: Jackknife', 2, None, '___sec78'), ('Resampling methods: Jackknife estimator', 2, None, '___sec79'), - ('Resampling methods: Jackknife sample code', - 2, - None, - '___sec80'), - ('Resampling methods: Bootstrap', 2, None, '___sec81'), - ('Resampling methods: Bootstrap background', 2, None, '___sec82'), + ('Resampling methods: Bootstrap', 2, None, '___sec80'), + ('Resampling methods: Bootstrap background', 2, None, '___sec81'), ('Resampling methods: More Bootstrap background', 2, None, - '___sec83'), - ('Resampling methods: Bootstrap approach', 2, None, '___sec84'), - ('Resampling methods: Bootstrap algorithm', 2, None, '___sec85'), - ('Resampling methods: Bootstrap steps', 2, None, '___sec86'), - ('Resampling methods: Blocking', 2, None, '___sec87'), - ('Blocking Transformations', 2, None, '___sec88'), - ('Blocking Transformations', 2, None, '___sec89'), - ('Blocking Transformations, getting there', 2, None, '___sec90'), + '___sec82'), + ('Resampling methods: Bootstrap approach', 2, None, '___sec83'), + ('Resampling methods: Bootstrap steps', 2, None, '___sec84'), + ('Resampling methods: Blocking', 2, None, '___sec85'), + ('Blocking Transformations', 2, None, '___sec86'), + ('Blocking Transformations', 2, None, '___sec87'), + ('Blocking Transformations, getting there', 2, None, '___sec88'), ('Blocking Transformations, final expressions', 2, None, - '___sec91'), + '___sec89'), ('"Code examples for Blocking, Jackknife and ' 'bootstrap":"https://github.com/CompPhysics/MachineLearning/tree/master/doc/Programs/ResamplingAnalysisScripts"', 2, None, - '___sec92')]} + '___sec90'), + ('The bias-variance tradeoff', 2, None, '___sec91'), + ('Training and testing data', 2, None, '___sec92'), + ('Procedure to find a predictor', 2, None, '___sec93'), + ('What we want', 2, None, '___sec94'), + ('The expected generalization error', 2, None, '___sec95'), + ('Elaborating a little bit more', 2, None, '___sec96'), + ('The bias', 2, None, '___sec97'), + ('The variance', 2, None, '___sec98'), + ('Summing up', 2, None, '___sec99'), + ('Logistic Regression', 2, None, '___sec100'), + ('Basics', 2, None, '___sec101'), + ('Linear classifier', 2, None, '___sec102'), + ('Some selected properties', 2, None, '___sec103'), + ('The cross-entropy as a cost function for logistic regression', + 2, + None, + '___sec104'), + ('Maximum likelihood', 2, None, '___sec105'), + ('Minimizing the cross entropy', 2, None, '___sec106')]} end of tocinfo --> @@ -337,19 +351,33 @@ MathJax.Hub.Config({
  • Resampling methods: Jackknife and Bootstrap
  • Resampling methods: Jackknife
  • Resampling methods: Jackknife estimator
    • -
  • Resampling methods: Jackknife sample code
    • -
  • Resampling methods: Bootstrap
    • -
  • Resampling methods: Bootstrap background
    • -
  • Resampling methods: More Bootstrap background
    • -
  • Resampling methods: Bootstrap approach
    • -
  • Resampling methods: Bootstrap algorithm
    • -
  • Resampling methods: Bootstrap steps
    • -
  • Resampling methods: Blocking
    • -
  • Blocking Transformations
    • -
  • Blocking Transformations
    • -
  • Blocking Transformations, getting there
    • -
  • Blocking Transformations, final expressions
    • -
  • "Code examples for Blocking, Jackknife and bootstrap":"https://github.com/CompPhysics/MachineLearning/tree/master/doc/Programs/ResamplingAnalysisScripts"
    • +
  • Resampling methods: Bootstrap
    • +
  • Resampling methods: Bootstrap background
    • +
  • Resampling methods: More Bootstrap background
    • +
  • Resampling methods: Bootstrap approach
    • +
  • Resampling methods: Bootstrap steps
    • +
  • Resampling methods: Blocking
    • +
  • Blocking Transformations
    • +
  • Blocking Transformations
    • +
  • Blocking Transformations, getting there
    • +
  • Blocking Transformations, final expressions
    • +
  • "Code examples for Blocking, Jackknife and bootstrap":"https://github.com/CompPhysics/MachineLearning/tree/master/doc/Programs/ResamplingAnalysisScripts"
    • +
  • The bias-variance tradeoff
    • +
  • Training and testing data
    • +
  • Procedure to find a predictor
    • +
  • What we want
    • +
  • The expected generalization error
    • +
  • Elaborating a little bit more
    • +
  • The bias
    • +
  • The variance
    • +
  • Summing up
    • +
  • Logistic Regression
    • +
  • Basics
    • +
  • Linear classifier
    • +
  • Some selected properties
    • +
  • The cross-entropy as a cost function for logistic regression
    • +
  • Maximum likelihood
    • +
  • Minimizing the cross entropy
    • @@ -428,7 +456,7 @@ relative error.
  • 35
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    • -
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    • +
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  • »
    • diff --git a/doc/pub/Regression/html/._Regression-bs027.html b/doc/pub/Regression/html/._Regression-bs027.html index ffaf441a0..0a22af5d8 100644 --- a/doc/pub/Regression/html/._Regression-bs027.html +++ b/doc/pub/Regression/html/._Regression-bs027.html @@ -194,32 +194,46 @@ Automatically generated HTML file from DocOnce source '___sec77'), ('Resampling methods: Jackknife', 2, None, '___sec78'), ('Resampling methods: Jackknife estimator', 2, None, '___sec79'), - ('Resampling methods: Jackknife sample code', - 2, - None, - '___sec80'), - ('Resampling methods: Bootstrap', 2, None, '___sec81'), - ('Resampling methods: Bootstrap background', 2, None, '___sec82'), + ('Resampling methods: Bootstrap', 2, None, '___sec80'), + ('Resampling methods: Bootstrap background', 2, None, '___sec81'), ('Resampling methods: More Bootstrap background', 2, None, - '___sec83'), - ('Resampling methods: Bootstrap approach', 2, None, '___sec84'), - ('Resampling methods: Bootstrap algorithm', 2, None, '___sec85'), - ('Resampling methods: Bootstrap steps', 2, None, '___sec86'), - ('Resampling methods: Blocking', 2, None, '___sec87'), - ('Blocking Transformations', 2, None, '___sec88'), - ('Blocking Transformations', 2, None, '___sec89'), - ('Blocking Transformations, getting there', 2, None, '___sec90'), + '___sec82'), + ('Resampling methods: Bootstrap approach', 2, None, '___sec83'), + ('Resampling methods: Bootstrap steps', 2, None, '___sec84'), + ('Resampling methods: Blocking', 2, None, '___sec85'), + ('Blocking Transformations', 2, None, '___sec86'), + ('Blocking Transformations', 2, None, '___sec87'), + ('Blocking Transformations, getting there', 2, None, '___sec88'), ('Blocking Transformations, final expressions', 2, None, - '___sec91'), + '___sec89'), ('"Code examples for Blocking, Jackknife and ' 'bootstrap":"https://github.com/CompPhysics/MachineLearning/tree/master/doc/Programs/ResamplingAnalysisScripts"', 2, None, - '___sec92')]} + '___sec90'), + ('The bias-variance tradeoff', 2, None, '___sec91'), + ('Training and testing data', 2, None, '___sec92'), + ('Procedure to find a predictor', 2, None, '___sec93'), + ('What we want', 2, None, '___sec94'), + ('The expected generalization error', 2, None, '___sec95'), + ('Elaborating a little bit more', 2, None, '___sec96'), + ('The bias', 2, None, '___sec97'), + ('The variance', 2, None, '___sec98'), + ('Summing up', 2, None, '___sec99'), + ('Logistic Regression', 2, None, '___sec100'), + ('Basics', 2, None, '___sec101'), + ('Linear classifier', 2, None, '___sec102'), + ('Some selected properties', 2, None, '___sec103'), + ('The cross-entropy as a cost function for logistic regression', + 2, + None, + '___sec104'), + ('Maximum likelihood', 2, None, '___sec105'), + ('Minimizing the cross entropy', 2, None, '___sec106')]} end of tocinfo --> @@ -337,19 +351,33 @@ MathJax.Hub.Config({
  • Resampling methods: Jackknife and Bootstrap
  • Resampling methods: Jackknife
  • Resampling methods: Jackknife estimator
    • -
  • Resampling methods: Jackknife sample code
    • -
  • Resampling methods: Bootstrap
    • -
  • Resampling methods: Bootstrap background
    • -
  • Resampling methods: More Bootstrap background
    • -
  • Resampling methods: Bootstrap approach
    • -
  • Resampling methods: Bootstrap algorithm
    • -
  • Resampling methods: Bootstrap steps
    • -
  • Resampling methods: Blocking
    • -
  • Blocking Transformations
    • -
  • Blocking Transformations
    • -
  • Blocking Transformations, getting there
    • -
  • Blocking Transformations, final expressions
    • -
  • "Code examples for Blocking, Jackknife and bootstrap":"https://github.com/CompPhysics/MachineLearning/tree/master/doc/Programs/ResamplingAnalysisScripts"
    • +
  • Resampling methods: Bootstrap
    • +
  • Resampling methods: Bootstrap background
    • +
  • Resampling methods: More Bootstrap background
    • +
  • Resampling methods: Bootstrap approach
    • +
  • Resampling methods: Bootstrap steps
    • +
  • Resampling methods: Blocking
    • +
  • Blocking Transformations
    • +
  • Blocking Transformations
    • +
  • Blocking Transformations, getting there
    • +
  • Blocking Transformations, final expressions
    • +
  • "Code examples for Blocking, Jackknife and bootstrap":"https://github.com/CompPhysics/MachineLearning/tree/master/doc/Programs/ResamplingAnalysisScripts"
    • +
  • The bias-variance tradeoff
    • +
  • Training and testing data
    • +
  • Procedure to find a predictor
    • +
  • What we want
    • +
  • The expected generalization error
    • +
  • Elaborating a little bit more
    • +
  • The bias
    • +
  • The variance
    • +
  • Summing up
    • +
  • Logistic Regression
    • +
  • Basics
    • +
  • Linear classifier
    • +
  • Some selected properties
    • +
  • The cross-entropy as a cost function for logistic regression
    • +
  • Maximum likelihood
    • +
  • Minimizing the cross entropy
    • @@ -433,7 +461,7 @@ plt.show()
  • 36
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    • -
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    • +
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  • »
    • diff --git a/doc/pub/Regression/html/._Regression-bs028.html b/doc/pub/Regression/html/._Regression-bs028.html index 0c607c69f..3c69fce56 100644 --- a/doc/pub/Regression/html/._Regression-bs028.html +++ b/doc/pub/Regression/html/._Regression-bs028.html @@ -194,32 +194,46 @@ Automatically generated HTML file from DocOnce source '___sec77'), ('Resampling methods: Jackknife', 2, None, '___sec78'), ('Resampling methods: Jackknife estimator', 2, None, '___sec79'), - ('Resampling methods: Jackknife sample code', - 2, - None, - '___sec80'), - ('Resampling methods: Bootstrap', 2, None, '___sec81'), - ('Resampling methods: Bootstrap background', 2, None, '___sec82'), + ('Resampling methods: Bootstrap', 2, None, '___sec80'), + ('Resampling methods: Bootstrap background', 2, None, '___sec81'), ('Resampling methods: More Bootstrap background', 2, None, - '___sec83'), - ('Resampling methods: Bootstrap approach', 2, None, '___sec84'), - ('Resampling methods: Bootstrap algorithm', 2, None, '___sec85'), - ('Resampling methods: Bootstrap steps', 2, None, '___sec86'), - ('Resampling methods: Blocking', 2, None, '___sec87'), - ('Blocking Transformations', 2, None, '___sec88'), - ('Blocking Transformations', 2, None, '___sec89'), - ('Blocking Transformations, getting there', 2, None, '___sec90'), + '___sec82'), + ('Resampling methods: Bootstrap approach', 2, None, '___sec83'), + ('Resampling methods: Bootstrap steps', 2, None, '___sec84'), + ('Resampling methods: Blocking', 2, None, '___sec85'), + ('Blocking Transformations', 2, None, '___sec86'), + ('Blocking Transformations', 2, None, '___sec87'), + ('Blocking Transformations, getting there', 2, None, '___sec88'), ('Blocking Transformations, final expressions', 2, None, - '___sec91'), + '___sec89'), ('"Code examples for Blocking, Jackknife and ' 'bootstrap":"https://github.com/CompPhysics/MachineLearning/tree/master/doc/Programs/ResamplingAnalysisScripts"', 2, None, - '___sec92')]} + '___sec90'), + ('The bias-variance tradeoff', 2, None, '___sec91'), + ('Training and testing data', 2, None, '___sec92'), + ('Procedure to find a predictor', 2, None, '___sec93'), + ('What we want', 2, None, '___sec94'), + ('The expected generalization error', 2, None, '___sec95'), + ('Elaborating a little bit more', 2, None, '___sec96'), + ('The bias', 2, None, '___sec97'), + ('The variance', 2, None, '___sec98'), + ('Summing up', 2, None, '___sec99'), + ('Logistic Regression', 2, None, '___sec100'), + ('Basics', 2, None, '___sec101'), + ('Linear classifier', 2, None, '___sec102'), + ('Some selected properties', 2, None, '___sec103'), + ('The cross-entropy as a cost function for logistic regression', + 2, + None, + '___sec104'), + ('Maximum likelihood', 2, None, '___sec105'), + ('Minimizing the cross entropy', 2, None, '___sec106')]} end of tocinfo --> @@ -337,19 +351,33 @@ MathJax.Hub.Config({
  • Resampling methods: Jackknife and Bootstrap
  • Resampling methods: Jackknife
  • Resampling methods: Jackknife estimator
    • -
  • Resampling methods: Jackknife sample code
    • -
  • Resampling methods: Bootstrap
    • -
  • Resampling methods: Bootstrap background
    • -
  • Resampling methods: More Bootstrap background
    • -
  • Resampling methods: Bootstrap approach
    • -
  • Resampling methods: Bootstrap algorithm
    • -
  • Resampling methods: Bootstrap steps
    • -
  • Resampling methods: Blocking
    • -
  • Blocking Transformations
    • -
  • Blocking Transformations
    • -
  • Blocking Transformations, getting there
    • -
  • Blocking Transformations, final expressions
    • -
  • "Code examples for Blocking, Jackknife and bootstrap":"https://github.com/CompPhysics/MachineLearning/tree/master/doc/Programs/ResamplingAnalysisScripts"
    • +
  • Resampling methods: Bootstrap
    • +
  • Resampling methods: Bootstrap background
    • +
  • Resampling methods: More Bootstrap background
    • +
  • Resampling methods: Bootstrap approach
    • +
  • Resampling methods: Bootstrap steps
    • +
  • Resampling methods: Blocking
    • +
  • Blocking Transformations
    • +
  • Blocking Transformations
    • +
  • Blocking Transformations, getting there
    • +
  • Blocking Transformations, final expressions
    • +
  • "Code examples for Blocking, Jackknife and bootstrap":"https://github.com/CompPhysics/MachineLearning/tree/master/doc/Programs/ResamplingAnalysisScripts"
    • +
  • The bias-variance tradeoff
    • +
  • Training and testing data
    • +
  • Procedure to find a predictor
    • +
  • What we want
    • +
  • The expected generalization error
    • +
  • Elaborating a little bit more
    • +
  • The bias
    • +
  • The variance
    • +
  • Summing up
    • +
  • Logistic Regression
    • +
  • Basics
    • +
  • Linear classifier
    • +
  • Some selected properties
    • +
  • The cross-entropy as a cost function for logistic regression
    • +
  • Maximum likelihood
    • +
  • Minimizing the cross entropy
    • @@ -405,7 +433,7 @@ this function as being similar to the \( \chi^2 \) function defined above.
  • 37
  • 38
  • ...
    • -
  • 94
    • +
  • 109
  • »
    • diff --git a/doc/pub/Regression/html/._Regression-bs029.html b/doc/pub/Regression/html/._Regression-bs029.html index 9ad9d511d..0739933b0 100644 --- a/doc/pub/Regression/html/._Regression-bs029.html +++ b/doc/pub/Regression/html/._Regression-bs029.html @@ -194,32 +194,46 @@ Automatically generated HTML file from DocOnce source '___sec77'), ('Resampling methods: Jackknife', 2, None, '___sec78'), ('Resampling methods: Jackknife estimator', 2, None, '___sec79'), - ('Resampling methods: Jackknife sample code', - 2, - None, - '___sec80'), - ('Resampling methods: Bootstrap', 2, None, '___sec81'), - ('Resampling methods: Bootstrap background', 2, None, '___sec82'), + ('Resampling methods: Bootstrap', 2, None, '___sec80'), + ('Resampling methods: Bootstrap background', 2, None, '___sec81'), ('Resampling methods: More Bootstrap background', 2, None, - '___sec83'), - ('Resampling methods: Bootstrap approach', 2, None, '___sec84'), - ('Resampling methods: Bootstrap algorithm', 2, None, '___sec85'), - ('Resampling methods: Bootstrap steps', 2, None, '___sec86'), - ('Resampling methods: Blocking', 2, None, '___sec87'), - ('Blocking Transformations', 2, None, '___sec88'), - ('Blocking Transformations', 2, None, '___sec89'), - ('Blocking Transformations, getting there', 2, None, '___sec90'), + '___sec82'), + ('Resampling methods: Bootstrap approach', 2, None, '___sec83'), + ('Resampling methods: Bootstrap steps', 2, None, '___sec84'), + ('Resampling methods: Blocking', 2, None, '___sec85'), + ('Blocking Transformations', 2, None, '___sec86'), + ('Blocking Transformations', 2, None, '___sec87'), + ('Blocking Transformations, getting there', 2, None, '___sec88'), ('Blocking Transformations, final expressions', 2, None, - '___sec91'), + '___sec89'), ('"Code examples for Blocking, Jackknife and ' 'bootstrap":"https://github.com/CompPhysics/MachineLearning/tree/master/doc/Programs/ResamplingAnalysisScripts"', 2, None, - '___sec92')]} + '___sec90'), + ('The bias-variance tradeoff', 2, None, '___sec91'), + ('Training and testing data', 2, None, '___sec92'), + ('Procedure to find a predictor', 2, None, '___sec93'), + ('What we want', 2, None, '___sec94'), + ('The expected generalization error', 2, None, '___sec95'), + ('Elaborating a little bit more', 2, None, '___sec96'), + ('The bias', 2, None, '___sec97'), + ('The variance', 2, None, '___sec98'), + ('Summing up', 2, None, '___sec99'), + ('Logistic Regression', 2, None, '___sec100'), + ('Basics', 2, None, '___sec101'), + ('Linear classifier', 2, None, '___sec102'), + ('Some selected properties', 2, None, '___sec103'), + ('The cross-entropy as a cost function for logistic regression', + 2, + None, + '___sec104'), + ('Maximum likelihood', 2, None, '___sec105'), + ('Minimizing the cross entropy', 2, None, '___sec106')]} end of tocinfo --> @@ -337,19 +351,33 @@ MathJax.Hub.Config({
  • Resampling methods: Jackknife and Bootstrap
  • Resampling methods: Jackknife
  • Resampling methods: Jackknife estimator
    • -
  • Resampling methods: Jackknife sample code
    • -
  • Resampling methods: Bootstrap
    • -
  • Resampling methods: Bootstrap background
    • -
  • Resampling methods: More Bootstrap background
    • -
  • Resampling methods: Bootstrap approach
    • -
  • Resampling methods: Bootstrap algorithm
    • -
  • Resampling methods: Bootstrap steps
    • -
  • Resampling methods: Blocking
    • -
  • Blocking Transformations
    • -
  • Blocking Transformations
    • -
  • Blocking Transformations, getting there
    • -
  • Blocking Transformations, final expressions
    • -
  • "Code examples for Blocking, Jackknife and bootstrap":"https://github.com/CompPhysics/MachineLearning/tree/master/doc/Programs/ResamplingAnalysisScripts"
    • +
  • Resampling methods: Bootstrap
    • +
  • Resampling methods: Bootstrap background
    • +
  • Resampling methods: More Bootstrap background
    • +
  • Resampling methods: Bootstrap approach
    • +
  • Resampling methods: Bootstrap steps
    • +
  • Resampling methods: Blocking
    • +
  • Blocking Transformations
    • +
  • Blocking Transformations
    • +
  • Blocking Transformations, getting there
    • +
  • Blocking Transformations, final expressions
    • +
  • "Code examples for Blocking, Jackknife and bootstrap":"https://github.com/CompPhysics/MachineLearning/tree/master/doc/Programs/ResamplingAnalysisScripts"
    • +
  • The bias-variance tradeoff
    • +
  • Training and testing data
    • +
  • Procedure to find a predictor
    • +
  • What we want
    • +
  • The expected generalization error
    • +
  • Elaborating a little bit more
    • +
  • The bias
    • +
  • The variance
    • +
  • Summing up
    • +
  • Logistic Regression
    • +
  • Basics
    • +
  • Linear classifier
    • +
  • Some selected properties
    • +
  • The cross-entropy as a cost function for logistic regression
    • +
  • Maximum likelihood
    • +
  • Minimizing the cross entropy
    • @@ -412,7 +440,7 @@ $$
  • 38
  • 39
  • ...
    • -
  • 94
    • +
  • 109
  • »
    • diff --git a/doc/pub/Regression/html/._Regression-bs030.html b/doc/pub/Regression/html/._Regression-bs030.html index 1b8a64ebc..02be16afa 100644 --- a/doc/pub/Regression/html/._Regression-bs030.html +++ b/doc/pub/Regression/html/._Regression-bs030.html @@ -194,32 +194,46 @@ Automatically generated HTML file from DocOnce source '___sec77'), ('Resampling methods: Jackknife', 2, None, '___sec78'), ('Resampling methods: Jackknife estimator', 2, None, '___sec79'), - ('Resampling methods: Jackknife sample code', - 2, - None, - '___sec80'), - ('Resampling methods: Bootstrap', 2, None, '___sec81'), - ('Resampling methods: Bootstrap background', 2, None, '___sec82'), + ('Resampling methods: Bootstrap', 2, None, '___sec80'), + ('Resampling methods: Bootstrap background', 2, None, '___sec81'), ('Resampling methods: More Bootstrap background', 2, None, - '___sec83'), - ('Resampling methods: Bootstrap approach', 2, None, '___sec84'), - ('Resampling methods: Bootstrap algorithm', 2, None, '___sec85'), - ('Resampling methods: Bootstrap steps', 2, None, '___sec86'), - ('Resampling methods: Blocking', 2, None, '___sec87'), - ('Blocking Transformations', 2, None, '___sec88'), - ('Blocking Transformations', 2, None, '___sec89'), - ('Blocking Transformations, getting there', 2, None, '___sec90'), + '___sec82'), + ('Resampling methods: Bootstrap approach', 2, None, '___sec83'), + ('Resampling methods: Bootstrap steps', 2, None, '___sec84'), + ('Resampling methods: Blocking', 2, None, '___sec85'), + ('Blocking Transformations', 2, None, '___sec86'), + ('Blocking Transformations', 2, None, '___sec87'), + ('Blocking Transformations, getting there', 2, None, '___sec88'), ('Blocking Transformations, final expressions', 2, None, - '___sec91'), + '___sec89'), ('"Code examples for Blocking, Jackknife and ' 'bootstrap":"https://github.com/CompPhysics/MachineLearning/tree/master/doc/Programs/ResamplingAnalysisScripts"', 2, None, - '___sec92')]} + '___sec90'), + ('The bias-variance tradeoff', 2, None, '___sec91'), + ('Training and testing data', 2, None, '___sec92'), + ('Procedure to find a predictor', 2, None, '___sec93'), + ('What we want', 2, None, '___sec94'), + ('The expected generalization error', 2, None, '___sec95'), + ('Elaborating a little bit more', 2, None, '___sec96'), + ('The bias', 2, None, '___sec97'), + ('The variance', 2, None, '___sec98'), + ('Summing up', 2, None, '___sec99'), + ('Logistic Regression', 2, None, '___sec100'), + ('Basics', 2, None, '___sec101'), + ('Linear classifier', 2, None, '___sec102'), + ('Some selected properties', 2, None, '___sec103'), + ('The cross-entropy as a cost function for logistic regression', + 2, + None, + '___sec104'), + ('Maximum likelihood', 2, None, '___sec105'), + ('Minimizing the cross entropy', 2, None, '___sec106')]} end of tocinfo --> @@ -337,19 +351,33 @@ MathJax.Hub.Config({
  • Resampling methods: Jackknife and Bootstrap
  • Resampling methods: Jackknife
  • Resampling methods: Jackknife estimator
    • -
  • Resampling methods: Jackknife sample code
    • -
  • Resampling methods: Bootstrap
    • -
  • Resampling methods: Bootstrap background
    • -
  • Resampling methods: More Bootstrap background
    • -
  • Resampling methods: Bootstrap approach
    • -
  • Resampling methods: Bootstrap algorithm
    • -
  • Resampling methods: Bootstrap steps
    • -
  • Resampling methods: Blocking
    • -
  • Blocking Transformations
    • -
  • Blocking Transformations
    • -
  • Blocking Transformations, getting there
    • -
  • Blocking Transformations, final expressions
    • -
  • "Code examples for Blocking, Jackknife and bootstrap":"https://github.com/CompPhysics/MachineLearning/tree/master/doc/Programs/ResamplingAnalysisScripts"
    • +
  • Resampling methods: Bootstrap
    • +
  • Resampling methods: Bootstrap background
    • +
  • Resampling methods: More Bootstrap background
    • +
  • Resampling methods: Bootstrap approach
    • +
  • Resampling methods: Bootstrap steps
    • +
  • Resampling methods: Blocking
    • +
  • Blocking Transformations
    • +
  • Blocking Transformations
    • +
  • Blocking Transformations, getting there
    • +
  • Blocking Transformations, final expressions
    • +
  • "Code examples for Blocking, Jackknife and bootstrap":"https://github.com/CompPhysics/MachineLearning/tree/master/doc/Programs/ResamplingAnalysisScripts"
    • +
  • The bias-variance tradeoff
    • +
  • Training and testing data
    • +
  • Procedure to find a predictor
    • +
  • What we want
    • +
  • The expected generalization error
    • +
  • Elaborating a little bit more
    • +
  • The bias
    • +
  • The variance
    • +
  • Summing up
    • +
  • Logistic Regression
    • +
  • Basics
    • +
  • Linear classifier
    • +
  • Some selected properties
    • +
  • The cross-entropy as a cost function for logistic regression
    • +
  • Maximum likelihood
    • +
  • Minimizing the cross entropy
    • @@ -413,7 +441,7 @@ years etc.
  • 39
  • 40
  • ...
    • -
  • 94
    • +
  • 109
  • »
    • diff --git a/doc/pub/Regression/html/._Regression-bs031.html b/doc/pub/Regression/html/._Regression-bs031.html index ba24a353e..6c1ba4856 100644 --- a/doc/pub/Regression/html/._Regression-bs031.html +++ b/doc/pub/Regression/html/._Regression-bs031.html @@ -194,32 +194,46 @@ Automatically generated HTML file from DocOnce source '___sec77'), ('Resampling methods: Jackknife', 2, None, '___sec78'), ('Resampling methods: Jackknife estimator', 2, None, '___sec79'), - ('Resampling methods: Jackknife sample code', - 2, - None, - '___sec80'), - ('Resampling methods: Bootstrap', 2, None, '___sec81'), - ('Resampling methods: Bootstrap background', 2, None, '___sec82'), + ('Resampling methods: Bootstrap', 2, None, '___sec80'), + ('Resampling methods: Bootstrap background', 2, None, '___sec81'), ('Resampling methods: More Bootstrap background', 2, None, - '___sec83'), - ('Resampling methods: Bootstrap approach', 2, None, '___sec84'), - ('Resampling methods: Bootstrap algorithm', 2, None, '___sec85'), - ('Resampling methods: Bootstrap steps', 2, None, '___sec86'), - ('Resampling methods: Blocking', 2, None, '___sec87'), - ('Blocking Transformations', 2, None, '___sec88'), - ('Blocking Transformations', 2, None, '___sec89'), - ('Blocking Transformations, getting there', 2, None, '___sec90'), + '___sec82'), + ('Resampling methods: Bootstrap approach', 2, None, '___sec83'), + ('Resampling methods: Bootstrap steps', 2, None, '___sec84'), + ('Resampling methods: Blocking', 2, None, '___sec85'), + ('Blocking Transformations', 2, None, '___sec86'), + ('Blocking Transformations', 2, None, '___sec87'), + ('Blocking Transformations, getting there', 2, None, '___sec88'), ('Blocking Transformations, final expressions', 2, None, - '___sec91'), + '___sec89'), ('"Code examples for Blocking, Jackknife and ' 'bootstrap":"https://github.com/CompPhysics/MachineLearning/tree/master/doc/Programs/ResamplingAnalysisScripts"', 2, None, - '___sec92')]} + '___sec90'), + ('The bias-variance tradeoff', 2, None, '___sec91'), + ('Training and testing data', 2, None, '___sec92'), + ('Procedure to find a predictor', 2, None, '___sec93'), + ('What we want', 2, None, '___sec94'), + ('The expected generalization error', 2, None, '___sec95'), + ('Elaborating a little bit more', 2, None, '___sec96'), + ('The bias', 2, None, '___sec97'), + ('The variance', 2, None, '___sec98'), + ('Summing up', 2, None, '___sec99'), + ('Logistic Regression', 2, None, '___sec100'), + ('Basics', 2, None, '___sec101'), + ('Linear classifier', 2, None, '___sec102'), + ('Some selected properties', 2, None, '___sec103'), + ('The cross-entropy as a cost function for logistic regression', + 2, + None, + '___sec104'), + ('Maximum likelihood', 2, None, '___sec105'), + ('Minimizing the cross entropy', 2, None, '___sec106')]} end of tocinfo --> @@ -337,19 +351,33 @@ MathJax.Hub.Config({
  • Resampling methods: Jackknife and Bootstrap
  • Resampling methods: Jackknife
  • Resampling methods: Jackknife estimator
    • -
  • Resampling methods: Jackknife sample code
    • -
  • Resampling methods: Bootstrap
    • -
  • Resampling methods: Bootstrap background
    • -
  • Resampling methods: More Bootstrap background
    • -
  • Resampling methods: Bootstrap approach
    • -
  • Resampling methods: Bootstrap algorithm
    • -
  • Resampling methods: Bootstrap steps
    • -
  • Resampling methods: Blocking
    • -
  • Blocking Transformations
    • -
  • Blocking Transformations
    • -
  • Blocking Transformations, getting there
    • -
  • Blocking Transformations, final expressions
    • -
  • "Code examples for Blocking, Jackknife and bootstrap":"https://github.com/CompPhysics/MachineLearning/tree/master/doc/Programs/ResamplingAnalysisScripts"
    • +
  • Resampling methods: Bootstrap
    • +
  • Resampling methods: Bootstrap background
    • +
  • Resampling methods: More Bootstrap background
    • +
  • Resampling methods: Bootstrap approach
    • +
  • Resampling methods: Bootstrap steps
    • +
  • Resampling methods: Blocking
    • +
  • Blocking Transformations
    • +
  • Blocking Transformations
    • +
  • Blocking Transformations, getting there
    • +
  • Blocking Transformations, final expressions
    • +
  • "Code examples for Blocking, Jackknife and bootstrap":"https://github.com/CompPhysics/MachineLearning/tree/master/doc/Programs/ResamplingAnalysisScripts"
    • +
  • The bias-variance tradeoff
    • +
  • Training and testing data
    • +
  • Procedure to find a predictor
    • +
  • What we want
    • +
  • The expected generalization error
    • +
  • Elaborating a little bit more
    • +
  • The bias
    • +
  • The variance
    • +
  • Summing up
    • +
  • Logistic Regression
    • +
  • Basics
    • +
  • Linear classifier
    • +
  • Some selected properties
    • +
  • The cross-entropy as a cost function for logistic regression
    • +
  • Maximum likelihood
    • +
  • Minimizing the cross entropy
    • @@ -436,7 +464,7 @@ Using R, we can perform similar studies.
  • 40
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  • ...
    • -
  • 94
    • +
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  • »
    • diff --git a/doc/pub/Regression/html/._Regression-bs032.html b/doc/pub/Regression/html/._Regression-bs032.html index 77af91566..60ffafc76 100644 --- a/doc/pub/Regression/html/._Regression-bs032.html +++ b/doc/pub/Regression/html/._Regression-bs032.html @@ -194,32 +194,46 @@ Automatically generated HTML file from DocOnce source '___sec77'), ('Resampling methods: Jackknife', 2, None, '___sec78'), ('Resampling methods: Jackknife estimator', 2, None, '___sec79'), - ('Resampling methods: Jackknife sample code', - 2, - None, - '___sec80'), - ('Resampling methods: Bootstrap', 2, None, '___sec81'), - ('Resampling methods: Bootstrap background', 2, None, '___sec82'), + ('Resampling methods: Bootstrap', 2, None, '___sec80'), + ('Resampling methods: Bootstrap background', 2, None, '___sec81'), ('Resampling methods: More Bootstrap background', 2, None, - '___sec83'), - ('Resampling methods: Bootstrap approach', 2, None, '___sec84'), - ('Resampling methods: Bootstrap algorithm', 2, None, '___sec85'), - ('Resampling methods: Bootstrap steps', 2, None, '___sec86'), - ('Resampling methods: Blocking', 2, None, '___sec87'), - ('Blocking Transformations', 2, None, '___sec88'), - ('Blocking Transformations', 2, None, '___sec89'), - ('Blocking Transformations, getting there', 2, None, '___sec90'), + '___sec82'), + ('Resampling methods: Bootstrap approach', 2, None, '___sec83'), + ('Resampling methods: Bootstrap steps', 2, None, '___sec84'), + ('Resampling methods: Blocking', 2, None, '___sec85'), + ('Blocking Transformations', 2, None, '___sec86'), + ('Blocking Transformations', 2, None, '___sec87'), + ('Blocking Transformations, getting there', 2, None, '___sec88'), ('Blocking Transformations, final expressions', 2, None, - '___sec91'), + '___sec89'), ('"Code examples for Blocking, Jackknife and ' 'bootstrap":"https://github.com/CompPhysics/MachineLearning/tree/master/doc/Programs/ResamplingAnalysisScripts"', 2, None, - '___sec92')]} + '___sec90'), + ('The bias-variance tradeoff', 2, None, '___sec91'), + ('Training and testing data', 2, None, '___sec92'), + ('Procedure to find a predictor', 2, None, '___sec93'), + ('What we want', 2, None, '___sec94'), + ('The expected generalization error', 2, None, '___sec95'), + ('Elaborating a little bit more', 2, None, '___sec96'), + ('The bias', 2, None, '___sec97'), + ('The variance', 2, None, '___sec98'), + ('Summing up', 2, None, '___sec99'), + ('Logistic Regression', 2, None, '___sec100'), + ('Basics', 2, None, '___sec101'), + ('Linear classifier', 2, None, '___sec102'), + ('Some selected properties', 2, None, '___sec103'), + ('The cross-entropy as a cost function for logistic regression', + 2, + None, + '___sec104'), + ('Maximum likelihood', 2, None, '___sec105'), + ('Minimizing the cross entropy', 2, None, '___sec106')]} end of tocinfo --> @@ -337,19 +351,33 @@ MathJax.Hub.Config({
  • Resampling methods: Jackknife and Bootstrap
  • Resampling methods: Jackknife
  • Resampling methods: Jackknife estimator
    • -
  • Resampling methods: Jackknife sample code
    • -
  • Resampling methods: Bootstrap
    • -
  • Resampling methods: Bootstrap background
    • -
  • Resampling methods: More Bootstrap background
    • -
  • Resampling methods: Bootstrap approach
    • -
  • Resampling methods: Bootstrap algorithm
    • -
  • Resampling methods: Bootstrap steps
    • -
  • Resampling methods: Blocking
    • -
  • Blocking Transformations
    • -
  • Blocking Transformations
    • -
  • Blocking Transformations, getting there
    • -
  • Blocking Transformations, final expressions
    • -
  • "Code examples for Blocking, Jackknife and bootstrap":"https://github.com/CompPhysics/MachineLearning/tree/master/doc/Programs/ResamplingAnalysisScripts"
    • +
  • Resampling methods: Bootstrap
    • +
  • Resampling methods: Bootstrap background
    • +
  • Resampling methods: More Bootstrap background
    • +
  • Resampling methods: Bootstrap approach
    • +
  • Resampling methods: Bootstrap steps
    • +
  • Resampling methods: Blocking
    • +
  • Blocking Transformations
    • +
  • Blocking Transformations
    • +
  • Blocking Transformations, getting there
    • +
  • Blocking Transformations, final expressions
    • +
  • "Code examples for Blocking, Jackknife and bootstrap":"https://github.com/CompPhysics/MachineLearning/tree/master/doc/Programs/ResamplingAnalysisScripts"
    • +
  • The bias-variance tradeoff
    • +
  • Training and testing data
    • +
  • Procedure to find a predictor
    • +
  • What we want
    • +
  • The expected generalization error
    • +
  • Elaborating a little bit more
    • +
  • The bias
    • +
  • The variance
    • +
  • Summing up
    • +
  • Logistic Regression
    • +
  • Basics
    • +
  • Linear classifier
    • +
  • Some selected properties
    • +
  • The cross-entropy as a cost function for logistic regression
    • +
  • Maximum likelihood
    • +
  • Minimizing the cross entropy
    • @@ -419,7 +447,7 @@ plt.show()
  • 41
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  • ...
    • -
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    • +
  • 109
  • »
    • diff --git a/doc/pub/Regression/html/._Regression-bs033.html b/doc/pub/Regression/html/._Regression-bs033.html index ae1488206..f2748b8d1 100644 --- a/doc/pub/Regression/html/._Regression-bs033.html +++ b/doc/pub/Regression/html/._Regression-bs033.html @@ -194,32 +194,46 @@ Automatically generated HTML file from DocOnce source '___sec77'), ('Resampling methods: Jackknife', 2, None, '___sec78'), ('Resampling methods: Jackknife estimator', 2, None, '___sec79'), - ('Resampling methods: Jackknife sample code', - 2, - None, - '___sec80'), - ('Resampling methods: Bootstrap', 2, None, '___sec81'), - ('Resampling methods: Bootstrap background', 2, None, '___sec82'), + ('Resampling methods: Bootstrap', 2, None, '___sec80'), + ('Resampling methods: Bootstrap background', 2, None, '___sec81'), ('Resampling methods: More Bootstrap background', 2, None, - '___sec83'), - ('Resampling methods: Bootstrap approach', 2, None, '___sec84'), - ('Resampling methods: Bootstrap algorithm', 2, None, '___sec85'), - ('Resampling methods: Bootstrap steps', 2, None, '___sec86'), - ('Resampling methods: Blocking', 2, None, '___sec87'), - ('Blocking Transformations', 2, None, '___sec88'), - ('Blocking Transformations', 2, None, '___sec89'), - ('Blocking Transformations, getting there', 2, None, '___sec90'), + '___sec82'), + ('Resampling methods: Bootstrap approach', 2, None, '___sec83'), + ('Resampling methods: Bootstrap steps', 2, None, '___sec84'), + ('Resampling methods: Blocking', 2, None, '___sec85'), + ('Blocking Transformations', 2, None, '___sec86'), + ('Blocking Transformations', 2, None, '___sec87'), + ('Blocking Transformations, getting there', 2, None, '___sec88'), ('Blocking Transformations, final expressions', 2, None, - '___sec91'), + '___sec89'), ('"Code examples for Blocking, Jackknife and ' 'bootstrap":"https://github.com/CompPhysics/MachineLearning/tree/master/doc/Programs/ResamplingAnalysisScripts"', 2, None, - '___sec92')]} + '___sec90'), + ('The bias-variance tradeoff', 2, None, '___sec91'), + ('Training and testing data', 2, None, '___sec92'), + ('Procedure to find a predictor', 2, None, '___sec93'), + ('What we want', 2, None, '___sec94'), + ('The expected generalization error', 2, None, '___sec95'), + ('Elaborating a little bit more', 2, None, '___sec96'), + ('The bias', 2, None, '___sec97'), + ('The variance', 2, None, '___sec98'), + ('Summing up', 2, None, '___sec99'), + ('Logistic Regression', 2, None, '___sec100'), + ('Basics', 2, None, '___sec101'), + ('Linear classifier', 2, None, '___sec102'), + ('Some selected properties', 2, None, '___sec103'), + ('The cross-entropy as a cost function for logistic regression', + 2, + None, + '___sec104'), + ('Maximum likelihood', 2, None, '___sec105'), + ('Minimizing the cross entropy', 2, None, '___sec106')]} end of tocinfo --> @@ -337,19 +351,33 @@ MathJax.Hub.Config({
  • Resampling methods: Jackknife and Bootstrap
  • Resampling methods: Jackknife
  • Resampling methods: Jackknife estimator
    • -
  • Resampling methods: Jackknife sample code
    • -
  • Resampling methods: Bootstrap
    • -
  • Resampling methods: Bootstrap background
    • -
  • Resampling methods: More Bootstrap background
    • -
  • Resampling methods: Bootstrap approach
    • -
  • Resampling methods: Bootstrap algorithm
    • -
  • Resampling methods: Bootstrap steps
    • -
  • Resampling methods: Blocking
    • -
  • Blocking Transformations
    • -
  • Blocking Transformations
    • -
  • Blocking Transformations, getting there
    • -
  • Blocking Transformations, final expressions
    • -
  • "Code examples for Blocking, Jackknife and bootstrap":"https://github.com/CompPhysics/MachineLearning/tree/master/doc/Programs/ResamplingAnalysisScripts"
    • +
  • Resampling methods: Bootstrap
    • +
  • Resampling methods: Bootstrap background
    • +
  • Resampling methods: More Bootstrap background
    • +
  • Resampling methods: Bootstrap approach
    • +
  • Resampling methods: Bootstrap steps
    • +
  • Resampling methods: Blocking
    • +
  • Blocking Transformations
    • +
  • Blocking Transformations
    • +
  • Blocking Transformations, getting there
    • +
  • Blocking Transformations, final expressions
    • +
  • "Code examples for Blocking, Jackknife and bootstrap":"https://github.com/CompPhysics/MachineLearning/tree/master/doc/Programs/ResamplingAnalysisScripts"
    • +
  • The bias-variance tradeoff
    • +
  • Training and testing data
    • +
  • Procedure to find a predictor
    • +
  • What we want
    • +
  • The expected generalization error
    • +
  • Elaborating a little bit more
    • +
  • The bias
    • +
  • The variance
    • +
  • Summing up
    • +
  • Logistic Regression
    • +
  • Basics
    • +
  • Linear classifier
    • +
  • Some selected properties
    • +
  • The cross-entropy as a cost function for logistic regression
    • +
  • Maximum likelihood
    • +
  • Minimizing the cross entropy
    • @@ -415,7 +443,7 @@ non-random scalar. To specify the parameters of the distribution of
  • 42
  • 43
  • ...
    • -
  • 94
    • +
  • 109
  • »
    • diff --git a/doc/pub/Regression/html/._Regression-bs034.html b/doc/pub/Regression/html/._Regression-bs034.html index ff816f316..7302f85b0 100644 --- a/doc/pub/Regression/html/._Regression-bs034.html +++ b/doc/pub/Regression/html/._Regression-bs034.html @@ -194,32 +194,46 @@ Automatically generated HTML file from DocOnce source '___sec77'), ('Resampling methods: Jackknife', 2, None, '___sec78'), ('Resampling methods: Jackknife estimator', 2, None, '___sec79'), - ('Resampling methods: Jackknife sample code', - 2, - None, - '___sec80'), - ('Resampling methods: Bootstrap', 2, None, '___sec81'), - ('Resampling methods: Bootstrap background', 2, None, '___sec82'), + ('Resampling methods: Bootstrap', 2, None, '___sec80'), + ('Resampling methods: Bootstrap background', 2, None, '___sec81'), ('Resampling methods: More Bootstrap background', 2, None, - '___sec83'), - ('Resampling methods: Bootstrap approach', 2, None, '___sec84'), - ('Resampling methods: Bootstrap algorithm', 2, None, '___sec85'), - ('Resampling methods: Bootstrap steps', 2, None, '___sec86'), - ('Resampling methods: Blocking', 2, None, '___sec87'), - ('Blocking Transformations', 2, None, '___sec88'), - ('Blocking Transformations', 2, None, '___sec89'), - ('Blocking Transformations, getting there', 2, None, '___sec90'), + '___sec82'), + ('Resampling methods: Bootstrap approach', 2, None, '___sec83'), + ('Resampling methods: Bootstrap steps', 2, None, '___sec84'), + ('Resampling methods: Blocking', 2, None, '___sec85'), + ('Blocking Transformations', 2, None, '___sec86'), + ('Blocking Transformations', 2, None, '___sec87'), + ('Blocking Transformations, getting there', 2, None, '___sec88'), ('Blocking Transformations, final expressions', 2, None, - '___sec91'), + '___sec89'), ('"Code examples for Blocking, Jackknife and ' 'bootstrap":"https://github.com/CompPhysics/MachineLearning/tree/master/doc/Programs/ResamplingAnalysisScripts"', 2, None, - '___sec92')]} + '___sec90'), + ('The bias-variance tradeoff', 2, None, '___sec91'), + ('Training and testing data', 2, None, '___sec92'), + ('Procedure to find a predictor', 2, None, '___sec93'), + ('What we want', 2, None, '___sec94'), + ('The expected generalization error', 2, None, '___sec95'), + ('Elaborating a little bit more', 2, None, '___sec96'), + ('The bias', 2, None, '___sec97'), + ('The variance', 2, None, '___sec98'), + ('Summing up', 2, None, '___sec99'), + ('Logistic Regression', 2, None, '___sec100'), + ('Basics', 2, None, '___sec101'), + ('Linear classifier', 2, None, '___sec102'), + ('Some selected properties', 2, None, '___sec103'), + ('The cross-entropy as a cost function for logistic regression', + 2, + None, + '___sec104'), + ('Maximum likelihood', 2, None, '___sec105'), + ('Minimizing the cross entropy', 2, None, '___sec106')]} end of tocinfo --> @@ -337,19 +351,33 @@ MathJax.Hub.Config({
  • Resampling methods: Jackknife and Bootstrap
  • Resampling methods: Jackknife
  • Resampling methods: Jackknife estimator
    • -
  • Resampling methods: Jackknife sample code
    • -
  • Resampling methods: Bootstrap
    • -
  • Resampling methods: Bootstrap background
    • -
  • Resampling methods: More Bootstrap background
    • -
  • Resampling methods: Bootstrap approach
    • -
  • Resampling methods: Bootstrap algorithm
    • -
  • Resampling methods: Bootstrap steps
    • -
  • Resampling methods: Blocking
    • -
  • Blocking Transformations
    • -
  • Blocking Transformations
    • -
  • Blocking Transformations, getting there
    • -
  • Blocking Transformations, final expressions
    • -
  • "Code examples for Blocking, Jackknife and bootstrap":"https://github.com/CompPhysics/MachineLearning/tree/master/doc/Programs/ResamplingAnalysisScripts"
    • +
  • Resampling methods: Bootstrap
    • +
  • Resampling methods: Bootstrap background
    • +
  • Resampling methods: More Bootstrap background
    • +
  • Resampling methods: Bootstrap approach
    • +
  • Resampling methods: Bootstrap steps
    • +
  • Resampling methods: Blocking
    • +
  • Blocking Transformations
    • +
  • Blocking Transformations
    • +
  • Blocking Transformations, getting there
    • +
  • Blocking Transformations, final expressions
    • +
  • "Code examples for Blocking, Jackknife and bootstrap":"https://github.com/CompPhysics/MachineLearning/tree/master/doc/Programs/ResamplingAnalysisScripts"
    • +
  • The bias-variance tradeoff
    • +
  • Training and testing data
    • +
  • Procedure to find a predictor
    • +
  • What we want
    • +
  • The expected generalization error
    • +
  • Elaborating a little bit more
    • +
  • The bias
    • +
  • The variance
    • +
  • Summing up
    • +
  • Logistic Regression
    • +
  • Basics
    • +
  • Linear classifier
    • +
  • Some selected properties
    • +
  • The cross-entropy as a cost function for logistic regression
    • +
  • Maximum likelihood
    • +
  • Minimizing the cross entropy
    • @@ -422,7 +450,7 @@ Hence, \( Y_i \sim \mathcal{N}( \mathbf{X}_{i, \ast} \, \beta, \sigma^2) \).
  • 43
  • 44
  • ...
    • -
  • 94
    • +
  • 109
  • »
    • diff --git a/doc/pub/Regression/html/._Regression-bs035.html b/doc/pub/Regression/html/._Regression-bs035.html index 60c0f4c50..def0f657e 100644 --- a/doc/pub/Regression/html/._Regression-bs035.html +++ b/doc/pub/Regression/html/._Regression-bs035.html @@ -194,32 +194,46 @@ Automatically generated HTML file from DocOnce source '___sec77'), ('Resampling methods: Jackknife', 2, None, '___sec78'), ('Resampling methods: Jackknife estimator', 2, None, '___sec79'), - ('Resampling methods: Jackknife sample code', - 2, - None, - '___sec80'), - ('Resampling methods: Bootstrap', 2, None, '___sec81'), - ('Resampling methods: Bootstrap background', 2, None, '___sec82'), + ('Resampling methods: Bootstrap', 2, None, '___sec80'), + ('Resampling methods: Bootstrap background', 2, None, '___sec81'), ('Resampling methods: More Bootstrap background', 2, None, - '___sec83'), - ('Resampling methods: Bootstrap approach', 2, None, '___sec84'), - ('Resampling methods: Bootstrap algorithm', 2, None, '___sec85'), - ('Resampling methods: Bootstrap steps', 2, None, '___sec86'), - ('Resampling methods: Blocking', 2, None, '___sec87'), - ('Blocking Transformations', 2, None, '___sec88'), - ('Blocking Transformations', 2, None, '___sec89'), - ('Blocking Transformations, getting there', 2, None, '___sec90'), + '___sec82'), + ('Resampling methods: Bootstrap approach', 2, None, '___sec83'), + ('Resampling methods: Bootstrap steps', 2, None, '___sec84'), + ('Resampling methods: Blocking', 2, None, '___sec85'), + ('Blocking Transformations', 2, None, '___sec86'), + ('Blocking Transformations', 2, None, '___sec87'), + ('Blocking Transformations, getting there', 2, None, '___sec88'), ('Blocking Transformations, final expressions', 2, None, - '___sec91'), + '___sec89'), ('"Code examples for Blocking, Jackknife and ' 'bootstrap":"https://github.com/CompPhysics/MachineLearning/tree/master/doc/Programs/ResamplingAnalysisScripts"', 2, None, - '___sec92')]} + '___sec90'), + ('The bias-variance tradeoff', 2, None, '___sec91'), + ('Training and testing data', 2, None, '___sec92'), + ('Procedure to find a predictor', 2, None, '___sec93'), + ('What we want', 2, None, '___sec94'), + ('The expected generalization error', 2, None, '___sec95'), + ('Elaborating a little bit more', 2, None, '___sec96'), + ('The bias', 2, None, '___sec97'), + ('The variance', 2, None, '___sec98'), + ('Summing up', 2, None, '___sec99'), + ('Logistic Regression', 2, None, '___sec100'), + ('Basics', 2, None, '___sec101'), + ('Linear classifier', 2, None, '___sec102'), + ('Some selected properties', 2, None, '___sec103'), + ('The cross-entropy as a cost function for logistic regression', + 2, + None, + '___sec104'), + ('Maximum likelihood', 2, None, '___sec105'), + ('Minimizing the cross entropy', 2, None, '___sec106')]} end of tocinfo --> @@ -337,19 +351,33 @@ MathJax.Hub.Config({
  • Resampling methods: Jackknife and Bootstrap
  • Resampling methods: Jackknife
  • Resampling methods: Jackknife estimator
    • -
  • Resampling methods: Jackknife sample code
    • -
  • Resampling methods: Bootstrap
    • -
  • Resampling methods: Bootstrap background
    • -
  • Resampling methods: More Bootstrap background
    • -
  • Resampling methods: Bootstrap approach
    • -
  • Resampling methods: Bootstrap algorithm
    • -
  • Resampling methods: Bootstrap steps
    • -
  • Resampling methods: Blocking
    • -
  • Blocking Transformations
    • -
  • Blocking Transformations
    • -
  • Blocking Transformations, getting there
    • -
  • Blocking Transformations, final expressions
    • -
  • "Code examples for Blocking, Jackknife and bootstrap":"https://github.com/CompPhysics/MachineLearning/tree/master/doc/Programs/ResamplingAnalysisScripts"
    • +
  • Resampling methods: Bootstrap
    • +
  • Resampling methods: Bootstrap background
    • +
  • Resampling methods: More Bootstrap background
    • +
  • Resampling methods: Bootstrap approach
    • +
  • Resampling methods: Bootstrap steps
    • +
  • Resampling methods: Blocking
    • +
  • Blocking Transformations
    • +
  • Blocking Transformations
    • +
  • Blocking Transformations, getting there
    • +
  • Blocking Transformations, final expressions
    • +
  • "Code examples for Blocking, Jackknife and bootstrap":"https://github.com/CompPhysics/MachineLearning/tree/master/doc/Programs/ResamplingAnalysisScripts"
    • +
  • The bias-variance tradeoff
    • +
  • Training and testing data
    • +
  • Procedure to find a predictor
    • +
  • What we want
    • +
  • The expected generalization error
    • +
  • Elaborating a little bit more
    • +
  • The bias
    • +
  • The variance
    • +
  • Summing up
    • +
  • Logistic Regression
    • +
  • Basics
    • +
  • Linear classifier
    • +
  • Some selected properties
    • +
  • The cross-entropy as a cost function for logistic regression
    • +
  • Maximum likelihood
    • +
  • Minimizing the cross entropy
    • @@ -410,7 +438,7 @@ $$
  • 44
  • 45
  • ...
    • -
  • 94
    • +
  • 109
  • »
    • diff --git a/doc/pub/Regression/html/._Regression-bs036.html b/doc/pub/Regression/html/._Regression-bs036.html index 957c6145f..80da17377 100644 --- a/doc/pub/Regression/html/._Regression-bs036.html +++ b/doc/pub/Regression/html/._Regression-bs036.html @@ -194,32 +194,46 @@ Automatically generated HTML file from DocOnce source '___sec77'), ('Resampling methods: Jackknife', 2, None, '___sec78'), ('Resampling methods: Jackknife estimator', 2, None, '___sec79'), - ('Resampling methods: Jackknife sample code', - 2, - None, - '___sec80'), - ('Resampling methods: Bootstrap', 2, None, '___sec81'), - ('Resampling methods: Bootstrap background', 2, None, '___sec82'), + ('Resampling methods: Bootstrap', 2, None, '___sec80'), + ('Resampling methods: Bootstrap background', 2, None, '___sec81'), ('Resampling methods: More Bootstrap background', 2, None, - '___sec83'), - ('Resampling methods: Bootstrap approach', 2, None, '___sec84'), - ('Resampling methods: Bootstrap algorithm', 2, None, '___sec85'), - ('Resampling methods: Bootstrap steps', 2, None, '___sec86'), - ('Resampling methods: Blocking', 2, None, '___sec87'), - ('Blocking Transformations', 2, None, '___sec88'), - ('Blocking Transformations', 2, None, '___sec89'), - ('Blocking Transformations, getting there', 2, None, '___sec90'), + '___sec82'), + ('Resampling methods: Bootstrap approach', 2, None, '___sec83'), + ('Resampling methods: Bootstrap steps', 2, None, '___sec84'), + ('Resampling methods: Blocking', 2, None, '___sec85'), + ('Blocking Transformations', 2, None, '___sec86'), + ('Blocking Transformations', 2, None, '___sec87'), + ('Blocking Transformations, getting there', 2, None, '___sec88'), ('Blocking Transformations, final expressions', 2, None, - '___sec91'), + '___sec89'), ('"Code examples for Blocking, Jackknife and ' 'bootstrap":"https://github.com/CompPhysics/MachineLearning/tree/master/doc/Programs/ResamplingAnalysisScripts"', 2, None, - '___sec92')]} + '___sec90'), + ('The bias-variance tradeoff', 2, None, '___sec91'), + ('Training and testing data', 2, None, '___sec92'), + ('Procedure to find a predictor', 2, None, '___sec93'), + ('What we want', 2, None, '___sec94'), + ('The expected generalization error', 2, None, '___sec95'), + ('Elaborating a little bit more', 2, None, '___sec96'), + ('The bias', 2, None, '___sec97'), + ('The variance', 2, None, '___sec98'), + ('Summing up', 2, None, '___sec99'), + ('Logistic Regression', 2, None, '___sec100'), + ('Basics', 2, None, '___sec101'), + ('Linear classifier', 2, None, '___sec102'), + ('Some selected properties', 2, None, '___sec103'), + ('The cross-entropy as a cost function for logistic regression', + 2, + None, + '___sec104'), + ('Maximum likelihood', 2, None, '___sec105'), + ('Minimizing the cross entropy', 2, None, '___sec106')]} end of tocinfo --> @@ -337,19 +351,33 @@ MathJax.Hub.Config({
  • Resampling methods: Jackknife and Bootstrap
  • Resampling methods: Jackknife
  • Resampling methods: Jackknife estimator
    • -
  • Resampling methods: Jackknife sample code
    • -
  • Resampling methods: Bootstrap
    • -
  • Resampling methods: Bootstrap background
    • -
  • Resampling methods: More Bootstrap background
    • -
  • Resampling methods: Bootstrap approach
    • -
  • Resampling methods: Bootstrap algorithm
    • -
  • Resampling methods: Bootstrap steps
    • -
  • Resampling methods: Blocking
    • -
  • Blocking Transformations
    • -
  • Blocking Transformations
    • -
  • Blocking Transformations, getting there
    • -
  • Blocking Transformations, final expressions
    • -
  • "Code examples for Blocking, Jackknife and bootstrap":"https://github.com/CompPhysics/MachineLearning/tree/master/doc/Programs/ResamplingAnalysisScripts"
    • +
  • Resampling methods: Bootstrap
    • +
  • Resampling methods: Bootstrap background
    • +
  • Resampling methods: More Bootstrap background
    • +
  • Resampling methods: Bootstrap approach
    • +
  • Resampling methods: Bootstrap steps
    • +
  • Resampling methods: Blocking
    • +
  • Blocking Transformations
    • +
  • Blocking Transformations
    • +
  • Blocking Transformations, getting there
    • +
  • Blocking Transformations, final expressions
    • +
  • "Code examples for Blocking, Jackknife and bootstrap":"https://github.com/CompPhysics/MachineLearning/tree/master/doc/Programs/ResamplingAnalysisScripts"
    • +
  • The bias-variance tradeoff
    • +
  • Training and testing data
    • +
  • Procedure to find a predictor
    • +
  • What we want
    • +
  • The expected generalization error
    • +
  • Elaborating a little bit more
    • +
  • The bias
    • +
  • The variance
    • +
  • Summing up
    • +
  • Logistic Regression
    • +
  • Basics
    • +
  • Linear classifier
    • +
  • Some selected properties
    • +
  • The cross-entropy as a cost function for logistic regression
    • +
  • Maximum likelihood
    • +
  • Minimizing the cross entropy
    • @@ -439,7 +467,7 @@ This is equivalent to saying that the matrix \( \hat{X} \) has at least an eigen
  • 45
  • 46
  • ...
    • -
  • 94
    • +
  • 109
  • »
    • diff --git a/doc/pub/Regression/html/._Regression-bs037.html b/doc/pub/Regression/html/._Regression-bs037.html index b0966445c..b1123d4a1 100644 --- a/doc/pub/Regression/html/._Regression-bs037.html +++ b/doc/pub/Regression/html/._Regression-bs037.html @@ -194,32 +194,46 @@ Automatically generated HTML file from DocOnce source '___sec77'), ('Resampling methods: Jackknife', 2, None, '___sec78'), ('Resampling methods: Jackknife estimator', 2, None, '___sec79'), - ('Resampling methods: Jackknife sample code', - 2, - None, - '___sec80'), - ('Resampling methods: Bootstrap', 2, None, '___sec81'), - ('Resampling methods: Bootstrap background', 2, None, '___sec82'), + ('Resampling methods: Bootstrap', 2, None, '___sec80'), + ('Resampling methods: Bootstrap background', 2, None, '___sec81'), ('Resampling methods: More Bootstrap background', 2, None, - '___sec83'), - ('Resampling methods: Bootstrap approach', 2, None, '___sec84'), - ('Resampling methods: Bootstrap algorithm', 2, None, '___sec85'), - ('Resampling methods: Bootstrap steps', 2, None, '___sec86'), - ('Resampling methods: Blocking', 2, None, '___sec87'), - ('Blocking Transformations', 2, None, '___sec88'), - ('Blocking Transformations', 2, None, '___sec89'), - ('Blocking Transformations, getting there', 2, None, '___sec90'), + '___sec82'), + ('Resampling methods: Bootstrap approach', 2, None, '___sec83'), + ('Resampling methods: Bootstrap steps', 2, None, '___sec84'), + ('Resampling methods: Blocking', 2, None, '___sec85'), + ('Blocking Transformations', 2, None, '___sec86'), + ('Blocking Transformations', 2, None, '___sec87'), + ('Blocking Transformations, getting there', 2, None, '___sec88'), ('Blocking Transformations, final expressions', 2, None, - '___sec91'), + '___sec89'), ('"Code examples for Blocking, Jackknife and ' 'bootstrap":"https://github.com/CompPhysics/MachineLearning/tree/master/doc/Programs/ResamplingAnalysisScripts"', 2, None, - '___sec92')]} + '___sec90'), + ('The bias-variance tradeoff', 2, None, '___sec91'), + ('Training and testing data', 2, None, '___sec92'), + ('Procedure to find a predictor', 2, None, '___sec93'), + ('What we want', 2, None, '___sec94'), + ('The expected generalization error', 2, None, '___sec95'), + ('Elaborating a little bit more', 2, None, '___sec96'), + ('The bias', 2, None, '___sec97'), + ('The variance', 2, None, '___sec98'), + ('Summing up', 2, None, '___sec99'), + ('Logistic Regression', 2, None, '___sec100'), + ('Basics', 2, None, '___sec101'), + ('Linear classifier', 2, None, '___sec102'), + ('Some selected properties', 2, None, '___sec103'), + ('The cross-entropy as a cost function for logistic regression', + 2, + None, + '___sec104'), + ('Maximum likelihood', 2, None, '___sec105'), + ('Minimizing the cross entropy', 2, None, '___sec106')]} end of tocinfo --> @@ -337,19 +351,33 @@ MathJax.Hub.Config({
  • Resampling methods: Jackknife and Bootstrap
  • Resampling methods: Jackknife
  • Resampling methods: Jackknife estimator
    • -
  • Resampling methods: Jackknife sample code
    • -
  • Resampling methods: Bootstrap
    • -
  • Resampling methods: Bootstrap background
    • -
  • Resampling methods: More Bootstrap background
    • -
  • Resampling methods: Bootstrap approach
    • -
  • Resampling methods: Bootstrap algorithm
    • -
  • Resampling methods: Bootstrap steps
    • -
  • Resampling methods: Blocking
    • -
  • Blocking Transformations
    • -
  • Blocking Transformations
    • -
  • Blocking Transformations, getting there
    • -
  • Blocking Transformations, final expressions
    • -
  • "Code examples for Blocking, Jackknife and bootstrap":"https://github.com/CompPhysics/MachineLearning/tree/master/doc/Programs/ResamplingAnalysisScripts"
    • +
  • Resampling methods: Bootstrap
    • +
  • Resampling methods: Bootstrap background
    • +
  • Resampling methods: More Bootstrap background
    • +
  • Resampling methods: Bootstrap approach
    • +
  • Resampling methods: Bootstrap steps
    • +
  • Resampling methods: Blocking
    • +
  • Blocking Transformations
    • +
  • Blocking Transformations
    • +
  • Blocking Transformations, getting there
    • +
  • Blocking Transformations, final expressions
    • +
  • "Code examples for Blocking, Jackknife and bootstrap":"https://github.com/CompPhysics/MachineLearning/tree/master/doc/Programs/ResamplingAnalysisScripts"
    • +
  • The bias-variance tradeoff
    • +
  • Training and testing data
    • +
  • Procedure to find a predictor
    • +
  • What we want
    • +
  • The expected generalization error
    • +
  • Elaborating a little bit more
    • +
  • The bias
    • +
  • The variance
    • +
  • Summing up
    • +
  • Logistic Regression
    • +
  • Basics
    • +
  • Linear classifier
    • +
  • Some selected properties
    • +
  • The cross-entropy as a cost function for logistic regression
    • +
  • Maximum likelihood
    • +
  • Minimizing the cross entropy
    • @@ -416,7 +444,7 @@ where \( \hat{I} \) is the identity matrix.
  • 46
  • 47
  • ...
    • -
  • 94
    • +
  • 109
  • »
    • diff --git a/doc/pub/Regression/html/._Regression-bs038.html b/doc/pub/Regression/html/._Regression-bs038.html index afb524f53..a2c37a570 100644 --- a/doc/pub/Regression/html/._Regression-bs038.html +++ b/doc/pub/Regression/html/._Regression-bs038.html @@ -194,32 +194,46 @@ Automatically generated HTML file from DocOnce source '___sec77'), ('Resampling methods: Jackknife', 2, None, '___sec78'), ('Resampling methods: Jackknife estimator', 2, None, '___sec79'), - ('Resampling methods: Jackknife sample code', - 2, - None, - '___sec80'), - ('Resampling methods: Bootstrap', 2, None, '___sec81'), - ('Resampling methods: Bootstrap background', 2, None, '___sec82'), + ('Resampling methods: Bootstrap', 2, None, '___sec80'), + ('Resampling methods: Bootstrap background', 2, None, '___sec81'), ('Resampling methods: More Bootstrap background', 2, None, - '___sec83'), - ('Resampling methods: Bootstrap approach', 2, None, '___sec84'), - ('Resampling methods: Bootstrap algorithm', 2, None, '___sec85'), - ('Resampling methods: Bootstrap steps', 2, None, '___sec86'), - ('Resampling methods: Blocking', 2, None, '___sec87'), - ('Blocking Transformations', 2, None, '___sec88'), - ('Blocking Transformations', 2, None, '___sec89'), - ('Blocking Transformations, getting there', 2, None, '___sec90'), + '___sec82'), + ('Resampling methods: Bootstrap approach', 2, None, '___sec83'), + ('Resampling methods: Bootstrap steps', 2, None, '___sec84'), + ('Resampling methods: Blocking', 2, None, '___sec85'), + ('Blocking Transformations', 2, None, '___sec86'), + ('Blocking Transformations', 2, None, '___sec87'), + ('Blocking Transformations, getting there', 2, None, '___sec88'), ('Blocking Transformations, final expressions', 2, None, - '___sec91'), + '___sec89'), ('"Code examples for Blocking, Jackknife and ' 'bootstrap":"https://github.com/CompPhysics/MachineLearning/tree/master/doc/Programs/ResamplingAnalysisScripts"', 2, None, - '___sec92')]} + '___sec90'), + ('The bias-variance tradeoff', 2, None, '___sec91'), + ('Training and testing data', 2, None, '___sec92'), + ('Procedure to find a predictor', 2, None, '___sec93'), + ('What we want', 2, None, '___sec94'), + ('The expected generalization error', 2, None, '___sec95'), + ('Elaborating a little bit more', 2, None, '___sec96'), + ('The bias', 2, None, '___sec97'), + ('The variance', 2, None, '___sec98'), + ('Summing up', 2, None, '___sec99'), + ('Logistic Regression', 2, None, '___sec100'), + ('Basics', 2, None, '___sec101'), + ('Linear classifier', 2, None, '___sec102'), + ('Some selected properties', 2, None, '___sec103'), + ('The cross-entropy as a cost function for logistic regression', + 2, + None, + '___sec104'), + ('Maximum likelihood', 2, None, '___sec105'), + ('Minimizing the cross entropy', 2, None, '___sec106')]} end of tocinfo --> @@ -337,19 +351,33 @@ MathJax.Hub.Config({
  • Resampling methods: Jackknife and Bootstrap
  • Resampling methods: Jackknife
  • Resampling methods: Jackknife estimator
    • -
  • Resampling methods: Jackknife sample code
    • -
  • Resampling methods: Bootstrap
    • -
  • Resampling methods: Bootstrap background
    • -
  • Resampling methods: More Bootstrap background
    • -
  • Resampling methods: Bootstrap approach
    • -
  • Resampling methods: Bootstrap algorithm
    • -
  • Resampling methods: Bootstrap steps
    • -
  • Resampling methods: Blocking
    • -
  • Blocking Transformations
    • -
  • Blocking Transformations
    • -
  • Blocking Transformations, getting there
    • -
  • Blocking Transformations, final expressions
    • -
  • "Code examples for Blocking, Jackknife and bootstrap":"https://github.com/CompPhysics/MachineLearning/tree/master/doc/Programs/ResamplingAnalysisScripts"
    • +
  • Resampling methods: Bootstrap
    • +
  • Resampling methods: Bootstrap background
    • +
  • Resampling methods: More Bootstrap background
    • +
  • Resampling methods: Bootstrap approach
    • +
  • Resampling methods: Bootstrap steps
    • +
  • Resampling methods: Blocking
    • +
  • Blocking Transformations
    • +
  • Blocking Transformations
    • +
  • Blocking Transformations, getting there
    • +
  • Blocking Transformations, final expressions
    • +
  • "Code examples for Blocking, Jackknife and bootstrap":"https://github.com/CompPhysics/MachineLearning/tree/master/doc/Programs/ResamplingAnalysisScripts"
    • +
  • The bias-variance tradeoff
    • +
  • Training and testing data
    • +
  • Procedure to find a predictor
    • +
  • What we want
    • +
  • The expected generalization error
    • +
  • Elaborating a little bit more
    • +
  • The bias
    • +
  • The variance
    • +
  • Summing up
    • +
  • Logistic Regression
    • +
  • Basics
    • +
  • Linear classifier
    • +
  • Some selected properties
    • +
  • The cross-entropy as a cost function for logistic regression
    • +
  • Maximum likelihood
    • +
  • Minimizing the cross entropy
    • @@ -415,7 +443,7 @@ Summarize what you have learned about the relationship between model complexity
  • 47
  • 48
  • ...
    • -
  • 94
    • +
  • 109
  • »
    • diff --git a/doc/pub/Regression/html/._Regression-bs039.html b/doc/pub/Regression/html/._Regression-bs039.html index 77323b262..d06c05aa9 100644 --- a/doc/pub/Regression/html/._Regression-bs039.html +++ b/doc/pub/Regression/html/._Regression-bs039.html @@ -194,32 +194,46 @@ Automatically generated HTML file from DocOnce source '___sec77'), ('Resampling methods: Jackknife', 2, None, '___sec78'), ('Resampling methods: Jackknife estimator', 2, None, '___sec79'), - ('Resampling methods: Jackknife sample code', - 2, - None, - '___sec80'), - ('Resampling methods: Bootstrap', 2, None, '___sec81'), - ('Resampling methods: Bootstrap background', 2, None, '___sec82'), + ('Resampling methods: Bootstrap', 2, None, '___sec80'), + ('Resampling methods: Bootstrap background', 2, None, '___sec81'), ('Resampling methods: More Bootstrap background', 2, None, - '___sec83'), - ('Resampling methods: Bootstrap approach', 2, None, '___sec84'), - ('Resampling methods: Bootstrap algorithm', 2, None, '___sec85'), - ('Resampling methods: Bootstrap steps', 2, None, '___sec86'), - ('Resampling methods: Blocking', 2, None, '___sec87'), - ('Blocking Transformations', 2, None, '___sec88'), - ('Blocking Transformations', 2, None, '___sec89'), - ('Blocking Transformations, getting there', 2, None, '___sec90'), + '___sec82'), + ('Resampling methods: Bootstrap approach', 2, None, '___sec83'), + ('Resampling methods: Bootstrap steps', 2, None, '___sec84'), + ('Resampling methods: Blocking', 2, None, '___sec85'), + ('Blocking Transformations', 2, None, '___sec86'), + ('Blocking Transformations', 2, None, '___sec87'), + ('Blocking Transformations, getting there', 2, None, '___sec88'), ('Blocking Transformations, final expressions', 2, None, - '___sec91'), + '___sec89'), ('"Code examples for Blocking, Jackknife and ' 'bootstrap":"https://github.com/CompPhysics/MachineLearning/tree/master/doc/Programs/ResamplingAnalysisScripts"', 2, None, - '___sec92')]} + '___sec90'), + ('The bias-variance tradeoff', 2, None, '___sec91'), + ('Training and testing data', 2, None, '___sec92'), + ('Procedure to find a predictor', 2, None, '___sec93'), + ('What we want', 2, None, '___sec94'), + ('The expected generalization error', 2, None, '___sec95'), + ('Elaborating a little bit more', 2, None, '___sec96'), + ('The bias', 2, None, '___sec97'), + ('The variance', 2, None, '___sec98'), + ('Summing up', 2, None, '___sec99'), + ('Logistic Regression', 2, None, '___sec100'), + ('Basics', 2, None, '___sec101'), + ('Linear classifier', 2, None, '___sec102'), + ('Some selected properties', 2, None, '___sec103'), + ('The cross-entropy as a cost function for logistic regression', + 2, + None, + '___sec104'), + ('Maximum likelihood', 2, None, '___sec105'), + ('Minimizing the cross entropy', 2, None, '___sec106')]} end of tocinfo --> @@ -337,19 +351,33 @@ MathJax.Hub.Config({
  • Resampling methods: Jackknife and Bootstrap
  • Resampling methods: Jackknife
  • Resampling methods: Jackknife estimator
    • -
  • Resampling methods: Jackknife sample code
    • -
  • Resampling methods: Bootstrap
    • -
  • Resampling methods: Bootstrap background
    • -
  • Resampling methods: More Bootstrap background
    • -
  • Resampling methods: Bootstrap approach
    • -
  • Resampling methods: Bootstrap algorithm
    • -
  • Resampling methods: Bootstrap steps
    • -
  • Resampling methods: Blocking
    • -
  • Blocking Transformations
    • -
  • Blocking Transformations
    • -
  • Blocking Transformations, getting there
    • -
  • Blocking Transformations, final expressions
    • -
  • "Code examples for Blocking, Jackknife and bootstrap":"https://github.com/CompPhysics/MachineLearning/tree/master/doc/Programs/ResamplingAnalysisScripts"
    • +
  • Resampling methods: Bootstrap
    • +
  • Resampling methods: Bootstrap background
    • +
  • Resampling methods: More Bootstrap background
    • +
  • Resampling methods: Bootstrap approach
    • +
  • Resampling methods: Bootstrap steps
    • +
  • Resampling methods: Blocking
    • +
  • Blocking Transformations
    • +
  • Blocking Transformations
    • +
  • Blocking Transformations, getting there
    • +
  • Blocking Transformations, final expressions
    • +
  • "Code examples for Blocking, Jackknife and bootstrap":"https://github.com/CompPhysics/MachineLearning/tree/master/doc/Programs/ResamplingAnalysisScripts"
    • +
  • The bias-variance tradeoff
    • +
  • Training and testing data
    • +
  • Procedure to find a predictor
    • +
  • What we want
    • +
  • The expected generalization error
    • +
  • Elaborating a little bit more
    • +
  • The bias
    • +
  • The variance
    • +
  • Summing up
    • +
  • Logistic Regression
    • +
  • Basics
    • +
  • Linear classifier
    • +
  • Some selected properties
    • +
  • The cross-entropy as a cost function for logistic regression
    • +
  • Maximum likelihood
    • +
  • Minimizing the cross entropy
    • @@ -403,7 +431,7 @@ Summarize what you think you learned about the relationship of knowing the true
  • 48
  • 49
  • ...
    • -
  • 94
    • +
  • 109
  • »
    • diff --git a/doc/pub/Regression/html/._Regression-bs040.html b/doc/pub/Regression/html/._Regression-bs040.html index 8b04331e1..fab77a5f8 100644 --- a/doc/pub/Regression/html/._Regression-bs040.html +++ b/doc/pub/Regression/html/._Regression-bs040.html @@ -194,32 +194,46 @@ Automatically generated HTML file from DocOnce source '___sec77'), ('Resampling methods: Jackknife', 2, None, '___sec78'), ('Resampling methods: Jackknife estimator', 2, None, '___sec79'), - ('Resampling methods: Jackknife sample code', - 2, - None, - '___sec80'), - ('Resampling methods: Bootstrap', 2, None, '___sec81'), - ('Resampling methods: Bootstrap background', 2, None, '___sec82'), + ('Resampling methods: Bootstrap', 2, None, '___sec80'), + ('Resampling methods: Bootstrap background', 2, None, '___sec81'), ('Resampling methods: More Bootstrap background', 2, None, - '___sec83'), - ('Resampling methods: Bootstrap approach', 2, None, '___sec84'), - ('Resampling methods: Bootstrap algorithm', 2, None, '___sec85'), - ('Resampling methods: Bootstrap steps', 2, None, '___sec86'), - ('Resampling methods: Blocking', 2, None, '___sec87'), - ('Blocking Transformations', 2, None, '___sec88'), - ('Blocking Transformations', 2, None, '___sec89'), - ('Blocking Transformations, getting there', 2, None, '___sec90'), + '___sec82'), + ('Resampling methods: Bootstrap approach', 2, None, '___sec83'), + ('Resampling methods: Bootstrap steps', 2, None, '___sec84'), + ('Resampling methods: Blocking', 2, None, '___sec85'), + ('Blocking Transformations', 2, None, '___sec86'), + ('Blocking Transformations', 2, None, '___sec87'), + ('Blocking Transformations, getting there', 2, None, '___sec88'), ('Blocking Transformations, final expressions', 2, None, - '___sec91'), + '___sec89'), ('"Code examples for Blocking, Jackknife and ' 'bootstrap":"https://github.com/CompPhysics/MachineLearning/tree/master/doc/Programs/ResamplingAnalysisScripts"', 2, None, - '___sec92')]} + '___sec90'), + ('The bias-variance tradeoff', 2, None, '___sec91'), + ('Training and testing data', 2, None, '___sec92'), + ('Procedure to find a predictor', 2, None, '___sec93'), + ('What we want', 2, None, '___sec94'), + ('The expected generalization error', 2, None, '___sec95'), + ('Elaborating a little bit more', 2, None, '___sec96'), + ('The bias', 2, None, '___sec97'), + ('The variance', 2, None, '___sec98'), + ('Summing up', 2, None, '___sec99'), + ('Logistic Regression', 2, None, '___sec100'), + ('Basics', 2, None, '___sec101'), + ('Linear classifier', 2, None, '___sec102'), + ('Some selected properties', 2, None, '___sec103'), + ('The cross-entropy as a cost function for logistic regression', + 2, + None, + '___sec104'), + ('Maximum likelihood', 2, None, '___sec105'), + ('Minimizing the cross entropy', 2, None, '___sec106')]} end of tocinfo --> @@ -337,19 +351,33 @@ MathJax.Hub.Config({
  • Resampling methods: Jackknife and Bootstrap
  • Resampling methods: Jackknife
  • Resampling methods: Jackknife estimator
    • -
  • Resampling methods: Jackknife sample code
    • -
  • Resampling methods: Bootstrap
    • -
  • Resampling methods: Bootstrap background
    • -
  • Resampling methods: More Bootstrap background
    • -
  • Resampling methods: Bootstrap approach
    • -
  • Resampling methods: Bootstrap algorithm
    • -
  • Resampling methods: Bootstrap steps
    • -
  • Resampling methods: Blocking
    • -
  • Blocking Transformations
    • -
  • Blocking Transformations
    • -
  • Blocking Transformations, getting there
    • -
  • Blocking Transformations, final expressions
    • -
  • "Code examples for Blocking, Jackknife and bootstrap":"https://github.com/CompPhysics/MachineLearning/tree/master/doc/Programs/ResamplingAnalysisScripts"
    • +
  • Resampling methods: Bootstrap
    • +
  • Resampling methods: Bootstrap background
    • +
  • Resampling methods: More Bootstrap background
    • +
  • Resampling methods: Bootstrap approach
    • +
  • Resampling methods: Bootstrap steps
    • +
  • Resampling methods: Blocking
    • +
  • Blocking Transformations
    • +
  • Blocking Transformations
    • +
  • Blocking Transformations, getting there
    • +
  • Blocking Transformations, final expressions
    • +
  • "Code examples for Blocking, Jackknife and bootstrap":"https://github.com/CompPhysics/MachineLearning/tree/master/doc/Programs/ResamplingAnalysisScripts"
    • +
  • The bias-variance tradeoff
    • +
  • Training and testing data
    • +
  • Procedure to find a predictor
    • +
  • What we want
    • +
  • The expected generalization error
    • +
  • Elaborating a little bit more
    • +
  • The bias
    • +
  • The variance
    • +
  • Summing up
    • +
  • Logistic Regression
    • +
  • Basics
    • +
  • Linear classifier
    • +
  • Some selected properties
    • +
  • The cross-entropy as a cost function for logistic regression
    • +
  • Maximum likelihood
    • +
  • Minimizing the cross entropy
    • @@ -478,7 +506,7 @@ plt.show()
  • 49
  • 50
  • ...
    • -
  • 94
    • +
  • 109
  • »
    • diff --git a/doc/pub/Regression/html/._Regression-bs041.html b/doc/pub/Regression/html/._Regression-bs041.html index c74a5266a..602baaa2e 100644 --- a/doc/pub/Regression/html/._Regression-bs041.html +++ b/doc/pub/Regression/html/._Regression-bs041.html @@ -194,32 +194,46 @@ Automatically generated HTML file from DocOnce source '___sec77'), ('Resampling methods: Jackknife', 2, None, '___sec78'), ('Resampling methods: Jackknife estimator', 2, None, '___sec79'), - ('Resampling methods: Jackknife sample code', - 2, - None, - '___sec80'), - ('Resampling methods: Bootstrap', 2, None, '___sec81'), - ('Resampling methods: Bootstrap background', 2, None, '___sec82'), + ('Resampling methods: Bootstrap', 2, None, '___sec80'), + ('Resampling methods: Bootstrap background', 2, None, '___sec81'), ('Resampling methods: More Bootstrap background', 2, None, - '___sec83'), - ('Resampling methods: Bootstrap approach', 2, None, '___sec84'), - ('Resampling methods: Bootstrap algorithm', 2, None, '___sec85'), - ('Resampling methods: Bootstrap steps', 2, None, '___sec86'), - ('Resampling methods: Blocking', 2, None, '___sec87'), - ('Blocking Transformations', 2, None, '___sec88'), - ('Blocking Transformations', 2, None, '___sec89'), - ('Blocking Transformations, getting there', 2, None, '___sec90'), + '___sec82'), + ('Resampling methods: Bootstrap approach', 2, None, '___sec83'), + ('Resampling methods: Bootstrap steps', 2, None, '___sec84'), + ('Resampling methods: Blocking', 2, None, '___sec85'), + ('Blocking Transformations', 2, None, '___sec86'), + ('Blocking Transformations', 2, None, '___sec87'), + ('Blocking Transformations, getting there', 2, None, '___sec88'), ('Blocking Transformations, final expressions', 2, None, - '___sec91'), + '___sec89'), ('"Code examples for Blocking, Jackknife and ' 'bootstrap":"https://github.com/CompPhysics/MachineLearning/tree/master/doc/Programs/ResamplingAnalysisScripts"', 2, None, - '___sec92')]} + '___sec90'), + ('The bias-variance tradeoff', 2, None, '___sec91'), + ('Training and testing data', 2, None, '___sec92'), + ('Procedure to find a predictor', 2, None, '___sec93'), + ('What we want', 2, None, '___sec94'), + ('The expected generalization error', 2, None, '___sec95'), + ('Elaborating a little bit more', 2, None, '___sec96'), + ('The bias', 2, None, '___sec97'), + ('The variance', 2, None, '___sec98'), + ('Summing up', 2, None, '___sec99'), + ('Logistic Regression', 2, None, '___sec100'), + ('Basics', 2, None, '___sec101'), + ('Linear classifier', 2, None, '___sec102'), + ('Some selected properties', 2, None, '___sec103'), + ('The cross-entropy as a cost function for logistic regression', + 2, + None, + '___sec104'), + ('Maximum likelihood', 2, None, '___sec105'), + ('Minimizing the cross entropy', 2, None, '___sec106')]} end of tocinfo --> @@ -337,19 +351,33 @@ MathJax.Hub.Config({
  • Resampling methods: Jackknife and Bootstrap
  • Resampling methods: Jackknife
  • Resampling methods: Jackknife estimator
    • -
  • Resampling methods: Jackknife sample code
    • -
  • Resampling methods: Bootstrap
    • -
  • Resampling methods: Bootstrap background
    • -
  • Resampling methods: More Bootstrap background
    • -
  • Resampling methods: Bootstrap approach
    • -
  • Resampling methods: Bootstrap algorithm
    • -
  • Resampling methods: Bootstrap steps
    • -
  • Resampling methods: Blocking
    • -
  • Blocking Transformations
    • -
  • Blocking Transformations
    • -
  • Blocking Transformations, getting there
    • -
  • Blocking Transformations, final expressions
    • -
  • "Code examples for Blocking, Jackknife and bootstrap":"https://github.com/CompPhysics/MachineLearning/tree/master/doc/Programs/ResamplingAnalysisScripts"
    • +
  • Resampling methods: Bootstrap
    • +
  • Resampling methods: Bootstrap background
    • +
  • Resampling methods: More Bootstrap background
    • +
  • Resampling methods: Bootstrap approach
    • +
  • Resampling methods: Bootstrap steps
    • +
  • Resampling methods: Blocking
    • +
  • Blocking Transformations
    • +
  • Blocking Transformations
    • +
  • Blocking Transformations, getting there
    • +
  • Blocking Transformations, final expressions
    • +
  • "Code examples for Blocking, Jackknife and bootstrap":"https://github.com/CompPhysics/MachineLearning/tree/master/doc/Programs/ResamplingAnalysisScripts"
    • +
  • The bias-variance tradeoff
    • +
  • Training and testing data
    • +
  • Procedure to find a predictor
    • +
  • What we want
    • +
  • The expected generalization error
    • +
  • Elaborating a little bit more
    • +
  • The bias
    • +
  • The variance
    • +
  • Summing up
    • +
  • Logistic Regression
    • +
  • Basics
    • +
  • Linear classifier
    • +
  • Some selected properties
    • +
  • The cross-entropy as a cost function for logistic regression
    • +
  • Maximum likelihood
    • +
  • Minimizing the cross entropy
    • @@ -434,7 +462,7 @@ plt.show()
  • 50
  • 51
  • ...
    • -
  • 94
    • +
  • 109
  • »
    • diff --git a/doc/pub/Regression/html/._Regression-bs042.html b/doc/pub/Regression/html/._Regression-bs042.html index 9fa68ef0a..d069264fa 100644 --- a/doc/pub/Regression/html/._Regression-bs042.html +++ b/doc/pub/Regression/html/._Regression-bs042.html @@ -194,32 +194,46 @@ Automatically generated HTML file from DocOnce source '___sec77'), ('Resampling methods: Jackknife', 2, None, '___sec78'), ('Resampling methods: Jackknife estimator', 2, None, '___sec79'), - ('Resampling methods: Jackknife sample code', - 2, - None, - '___sec80'), - ('Resampling methods: Bootstrap', 2, None, '___sec81'), - ('Resampling methods: Bootstrap background', 2, None, '___sec82'), + ('Resampling methods: Bootstrap', 2, None, '___sec80'), + ('Resampling methods: Bootstrap background', 2, None, '___sec81'), ('Resampling methods: More Bootstrap background', 2, None, - '___sec83'), - ('Resampling methods: Bootstrap approach', 2, None, '___sec84'), - ('Resampling methods: Bootstrap algorithm', 2, None, '___sec85'), - ('Resampling methods: Bootstrap steps', 2, None, '___sec86'), - ('Resampling methods: Blocking', 2, None, '___sec87'), - ('Blocking Transformations', 2, None, '___sec88'), - ('Blocking Transformations', 2, None, '___sec89'), - ('Blocking Transformations, getting there', 2, None, '___sec90'), + '___sec82'), + ('Resampling methods: Bootstrap approach', 2, None, '___sec83'), + ('Resampling methods: Bootstrap steps', 2, None, '___sec84'), + ('Resampling methods: Blocking', 2, None, '___sec85'), + ('Blocking Transformations', 2, None, '___sec86'), + ('Blocking Transformations', 2, None, '___sec87'), + ('Blocking Transformations, getting there', 2, None, '___sec88'), ('Blocking Transformations, final expressions', 2, None, - '___sec91'), + '___sec89'), ('"Code examples for Blocking, Jackknife and ' 'bootstrap":"https://github.com/CompPhysics/MachineLearning/tree/master/doc/Programs/ResamplingAnalysisScripts"', 2, None, - '___sec92')]} + '___sec90'), + ('The bias-variance tradeoff', 2, None, '___sec91'), + ('Training and testing data', 2, None, '___sec92'), + ('Procedure to find a predictor', 2, None, '___sec93'), + ('What we want', 2, None, '___sec94'), + ('The expected generalization error', 2, None, '___sec95'), + ('Elaborating a little bit more', 2, None, '___sec96'), + ('The bias', 2, None, '___sec97'), + ('The variance', 2, None, '___sec98'), + ('Summing up', 2, None, '___sec99'), + ('Logistic Regression', 2, None, '___sec100'), + ('Basics', 2, None, '___sec101'), + ('Linear classifier', 2, None, '___sec102'), + ('Some selected properties', 2, None, '___sec103'), + ('The cross-entropy as a cost function for logistic regression', + 2, + None, + '___sec104'), + ('Maximum likelihood', 2, None, '___sec105'), + ('Minimizing the cross entropy', 2, None, '___sec106')]} end of tocinfo --> @@ -337,19 +351,33 @@ MathJax.Hub.Config({
  • Resampling methods: Jackknife and Bootstrap
  • Resampling methods: Jackknife
  • Resampling methods: Jackknife estimator
    • -
  • Resampling methods: Jackknife sample code
    • -
  • Resampling methods: Bootstrap
    • -
  • Resampling methods: Bootstrap background
    • -
  • Resampling methods: More Bootstrap background
    • -
  • Resampling methods: Bootstrap approach
    • -
  • Resampling methods: Bootstrap algorithm
    • -
  • Resampling methods: Bootstrap steps
    • -
  • Resampling methods: Blocking
    • -
  • Blocking Transformations
    • -
  • Blocking Transformations
    • -
  • Blocking Transformations, getting there
    • -
  • Blocking Transformations, final expressions
    • -
  • "Code examples for Blocking, Jackknife and bootstrap":"https://github.com/CompPhysics/MachineLearning/tree/master/doc/Programs/ResamplingAnalysisScripts"
    • +
  • Resampling methods: Bootstrap
    • +
  • Resampling methods: Bootstrap background
    • +
  • Resampling methods: More Bootstrap background
    • +
  • Resampling methods: Bootstrap approach
    • +
  • Resampling methods: Bootstrap steps
    • +
  • Resampling methods: Blocking
    • +
  • Blocking Transformations
    • +
  • Blocking Transformations
    • +
  • Blocking Transformations, getting there
    • +
  • Blocking Transformations, final expressions
    • +
  • "Code examples for Blocking, Jackknife and bootstrap":"https://github.com/CompPhysics/MachineLearning/tree/master/doc/Programs/ResamplingAnalysisScripts"
    • +
  • The bias-variance tradeoff
    • +
  • Training and testing data
    • +
  • Procedure to find a predictor
    • +
  • What we want
    • +
  • The expected generalization error
    • +
  • Elaborating a little bit more
    • +
  • The bias
    • +
  • The variance
    • +
  • Summing up
    • +
  • Logistic Regression
    • +
  • Basics
    • +
  • Linear classifier
    • +
  • Some selected properties
    • +
  • The cross-entropy as a cost function for logistic regression
    • +
  • Maximum likelihood
    • +
  • Minimizing the cross entropy
    • @@ -399,7 +427,7 @@ decision on its value. How do we do that? Much of the same considerations apply
  • 51
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  • ...
    • -
  • 94
    • +
  • 109
  • »
    • diff --git a/doc/pub/Regression/html/._Regression-bs043.html b/doc/pub/Regression/html/._Regression-bs043.html index 3d955be3f..b96b7a067 100644 --- a/doc/pub/Regression/html/._Regression-bs043.html +++ b/doc/pub/Regression/html/._Regression-bs043.html @@ -194,32 +194,46 @@ Automatically generated HTML file from DocOnce source '___sec77'), ('Resampling methods: Jackknife', 2, None, '___sec78'), ('Resampling methods: Jackknife estimator', 2, None, '___sec79'), - ('Resampling methods: Jackknife sample code', - 2, - None, - '___sec80'), - ('Resampling methods: Bootstrap', 2, None, '___sec81'), - ('Resampling methods: Bootstrap background', 2, None, '___sec82'), + ('Resampling methods: Bootstrap', 2, None, '___sec80'), + ('Resampling methods: Bootstrap background', 2, None, '___sec81'), ('Resampling methods: More Bootstrap background', 2, None, - '___sec83'), - ('Resampling methods: Bootstrap approach', 2, None, '___sec84'), - ('Resampling methods: Bootstrap algorithm', 2, None, '___sec85'), - ('Resampling methods: Bootstrap steps', 2, None, '___sec86'), - ('Resampling methods: Blocking', 2, None, '___sec87'), - ('Blocking Transformations', 2, None, '___sec88'), - ('Blocking Transformations', 2, None, '___sec89'), - ('Blocking Transformations, getting there', 2, None, '___sec90'), + '___sec82'), + ('Resampling methods: Bootstrap approach', 2, None, '___sec83'), + ('Resampling methods: Bootstrap steps', 2, None, '___sec84'), + ('Resampling methods: Blocking', 2, None, '___sec85'), + ('Blocking Transformations', 2, None, '___sec86'), + ('Blocking Transformations', 2, None, '___sec87'), + ('Blocking Transformations, getting there', 2, None, '___sec88'), ('Blocking Transformations, final expressions', 2, None, - '___sec91'), + '___sec89'), ('"Code examples for Blocking, Jackknife and ' 'bootstrap":"https://github.com/CompPhysics/MachineLearning/tree/master/doc/Programs/ResamplingAnalysisScripts"', 2, None, - '___sec92')]} + '___sec90'), + ('The bias-variance tradeoff', 2, None, '___sec91'), + ('Training and testing data', 2, None, '___sec92'), + ('Procedure to find a predictor', 2, None, '___sec93'), + ('What we want', 2, None, '___sec94'), + ('The expected generalization error', 2, None, '___sec95'), + ('Elaborating a little bit more', 2, None, '___sec96'), + ('The bias', 2, None, '___sec97'), + ('The variance', 2, None, '___sec98'), + ('Summing up', 2, None, '___sec99'), + ('Logistic Regression', 2, None, '___sec100'), + ('Basics', 2, None, '___sec101'), + ('Linear classifier', 2, None, '___sec102'), + ('Some selected properties', 2, None, '___sec103'), + ('The cross-entropy as a cost function for logistic regression', + 2, + None, + '___sec104'), + ('Maximum likelihood', 2, None, '___sec105'), + ('Minimizing the cross entropy', 2, None, '___sec106')]} end of tocinfo --> @@ -337,19 +351,33 @@ MathJax.Hub.Config({
  • Resampling methods: Jackknife and Bootstrap
  • Resampling methods: Jackknife
  • Resampling methods: Jackknife estimator
    • -
  • Resampling methods: Jackknife sample code
    • -
  • Resampling methods: Bootstrap
    • -
  • Resampling methods: Bootstrap background
    • -
  • Resampling methods: More Bootstrap background
    • -
  • Resampling methods: Bootstrap approach
    • -
  • Resampling methods: Bootstrap algorithm
    • -
  • Resampling methods: Bootstrap steps
    • -
  • Resampling methods: Blocking
    • -
  • Blocking Transformations
    • -
  • Blocking Transformations
    • -
  • Blocking Transformations, getting there
    • -
  • Blocking Transformations, final expressions
    • -
  • "Code examples for Blocking, Jackknife and bootstrap":"https://github.com/CompPhysics/MachineLearning/tree/master/doc/Programs/ResamplingAnalysisScripts"
    • +
  • Resampling methods: Bootstrap
    • +
  • Resampling methods: Bootstrap background
    • +
  • Resampling methods: More Bootstrap background
    • +
  • Resampling methods: Bootstrap approach
    • +
  • Resampling methods: Bootstrap steps
    • +
  • Resampling methods: Blocking
    • +
  • Blocking Transformations
    • +
  • Blocking Transformations
    • +
  • Blocking Transformations, getting there
    • +
  • Blocking Transformations, final expressions
    • +
  • "Code examples for Blocking, Jackknife and bootstrap":"https://github.com/CompPhysics/MachineLearning/tree/master/doc/Programs/ResamplingAnalysisScripts"
    • +
  • The bias-variance tradeoff
    • +
  • Training and testing data
    • +
  • Procedure to find a predictor
    • +
  • What we want
    • +
  • The expected generalization error
    • +
  • Elaborating a little bit more
    • +
  • The bias
    • +
  • The variance
    • +
  • Summing up
    • +
  • Logistic Regression
    • +
  • Basics
    • +
  • Linear classifier
    • +
  • Some selected properties
    • +
  • The cross-entropy as a cost function for logistic regression
    • +
  • Maximum likelihood
    • +
  • Minimizing the cross entropy
    • @@ -469,7 +497,7 @@ plt.show()
  • 52
  • 53
  • ...
    • -
  • 94
    • +
  • 109
  • »
    • diff --git a/doc/pub/Regression/html/._Regression-bs044.html b/doc/pub/Regression/html/._Regression-bs044.html index de10a9c5a..f8c3d7705 100644 --- a/doc/pub/Regression/html/._Regression-bs044.html +++ b/doc/pub/Regression/html/._Regression-bs044.html @@ -194,32 +194,46 @@ Automatically generated HTML file from DocOnce source '___sec77'), ('Resampling methods: Jackknife', 2, None, '___sec78'), ('Resampling methods: Jackknife estimator', 2, None, '___sec79'), - ('Resampling methods: Jackknife sample code', - 2, - None, - '___sec80'), - ('Resampling methods: Bootstrap', 2, None, '___sec81'), - ('Resampling methods: Bootstrap background', 2, None, '___sec82'), + ('Resampling methods: Bootstrap', 2, None, '___sec80'), + ('Resampling methods: Bootstrap background', 2, None, '___sec81'), ('Resampling methods: More Bootstrap background', 2, None, - '___sec83'), - ('Resampling methods: Bootstrap approach', 2, None, '___sec84'), - ('Resampling methods: Bootstrap algorithm', 2, None, '___sec85'), - ('Resampling methods: Bootstrap steps', 2, None, '___sec86'), - ('Resampling methods: Blocking', 2, None, '___sec87'), - ('Blocking Transformations', 2, None, '___sec88'), - ('Blocking Transformations', 2, None, '___sec89'), - ('Blocking Transformations, getting there', 2, None, '___sec90'), + '___sec82'), + ('Resampling methods: Bootstrap approach', 2, None, '___sec83'), + ('Resampling methods: Bootstrap steps', 2, None, '___sec84'), + ('Resampling methods: Blocking', 2, None, '___sec85'), + ('Blocking Transformations', 2, None, '___sec86'), + ('Blocking Transformations', 2, None, '___sec87'), + ('Blocking Transformations, getting there', 2, None, '___sec88'), ('Blocking Transformations, final expressions', 2, None, - '___sec91'), + '___sec89'), ('"Code examples for Blocking, Jackknife and ' 'bootstrap":"https://github.com/CompPhysics/MachineLearning/tree/master/doc/Programs/ResamplingAnalysisScripts"', 2, None, - '___sec92')]} + '___sec90'), + ('The bias-variance tradeoff', 2, None, '___sec91'), + ('Training and testing data', 2, None, '___sec92'), + ('Procedure to find a predictor', 2, None, '___sec93'), + ('What we want', 2, None, '___sec94'), + ('The expected generalization error', 2, None, '___sec95'), + ('Elaborating a little bit more', 2, None, '___sec96'), + ('The bias', 2, None, '___sec97'), + ('The variance', 2, None, '___sec98'), + ('Summing up', 2, None, '___sec99'), + ('Logistic Regression', 2, None, '___sec100'), + ('Basics', 2, None, '___sec101'), + ('Linear classifier', 2, None, '___sec102'), + ('Some selected properties', 2, None, '___sec103'), + ('The cross-entropy as a cost function for logistic regression', + 2, + None, + '___sec104'), + ('Maximum likelihood', 2, None, '___sec105'), + ('Minimizing the cross entropy', 2, None, '___sec106')]} end of tocinfo --> @@ -337,19 +351,33 @@ MathJax.Hub.Config({
  • Resampling methods: Jackknife and Bootstrap
  • Resampling methods: Jackknife
  • Resampling methods: Jackknife estimator
    • -
  • Resampling methods: Jackknife sample code
    • -
  • Resampling methods: Bootstrap
    • -
  • Resampling methods: Bootstrap background
    • -
  • Resampling methods: More Bootstrap background
    • -
  • Resampling methods: Bootstrap approach
    • -
  • Resampling methods: Bootstrap algorithm
    • -
  • Resampling methods: Bootstrap steps
    • -
  • Resampling methods: Blocking
    • -
  • Blocking Transformations
    • -
  • Blocking Transformations
    • -
  • Blocking Transformations, getting there
    • -
  • Blocking Transformations, final expressions
    • -
  • "Code examples for Blocking, Jackknife and bootstrap":"https://github.com/CompPhysics/MachineLearning/tree/master/doc/Programs/ResamplingAnalysisScripts"
    • +
  • Resampling methods: Bootstrap
    • +
  • Resampling methods: Bootstrap background
    • +
  • Resampling methods: More Bootstrap background
    • +
  • Resampling methods: Bootstrap approach
    • +
  • Resampling methods: Bootstrap steps
    • +
  • Resampling methods: Blocking
    • +
  • Blocking Transformations
    • +
  • Blocking Transformations
    • +
  • Blocking Transformations, getting there
    • +
  • Blocking Transformations, final expressions
    • +
  • "Code examples for Blocking, Jackknife and bootstrap":"https://github.com/CompPhysics/MachineLearning/tree/master/doc/Programs/ResamplingAnalysisScripts"
    • +
  • The bias-variance tradeoff
    • +
  • Training and testing data
    • +
  • Procedure to find a predictor
    • +
  • What we want
    • +
  • The expected generalization error
    • +
  • Elaborating a little bit more
    • +
  • The bias
    • +
  • The variance
    • +
  • Summing up
    • +
  • Logistic Regression
    • +
  • Basics
    • +
  • Linear classifier
    • +
  • Some selected properties
    • +
  • The cross-entropy as a cost function for logistic regression
    • +
  • Maximum likelihood
    • +
  • Minimizing the cross entropy
    • @@ -502,7 +530,7 @@ plt.show()
  • 53
  • 54
  • ...
    • -
  • 94
    • +
  • 109
  • »
    • diff --git a/doc/pub/Regression/html/._Regression-bs045.html b/doc/pub/Regression/html/._Regression-bs045.html index 564e1ae2e..77c4f0e49 100644 --- a/doc/pub/Regression/html/._Regression-bs045.html +++ b/doc/pub/Regression/html/._Regression-bs045.html @@ -194,32 +194,46 @@ Automatically generated HTML file from DocOnce source '___sec77'), ('Resampling methods: Jackknife', 2, None, '___sec78'), ('Resampling methods: Jackknife estimator', 2, None, '___sec79'), - ('Resampling methods: Jackknife sample code', - 2, - None, - '___sec80'), - ('Resampling methods: Bootstrap', 2, None, '___sec81'), - ('Resampling methods: Bootstrap background', 2, None, '___sec82'), + ('Resampling methods: Bootstrap', 2, None, '___sec80'), + ('Resampling methods: Bootstrap background', 2, None, '___sec81'), ('Resampling methods: More Bootstrap background', 2, None, - '___sec83'), - ('Resampling methods: Bootstrap approach', 2, None, '___sec84'), - ('Resampling methods: Bootstrap algorithm', 2, None, '___sec85'), - ('Resampling methods: Bootstrap steps', 2, None, '___sec86'), - ('Resampling methods: Blocking', 2, None, '___sec87'), - ('Blocking Transformations', 2, None, '___sec88'), - ('Blocking Transformations', 2, None, '___sec89'), - ('Blocking Transformations, getting there', 2, None, '___sec90'), + '___sec82'), + ('Resampling methods: Bootstrap approach', 2, None, '___sec83'), + ('Resampling methods: Bootstrap steps', 2, None, '___sec84'), + ('Resampling methods: Blocking', 2, None, '___sec85'), + ('Blocking Transformations', 2, None, '___sec86'), + ('Blocking Transformations', 2, None, '___sec87'), + ('Blocking Transformations, getting there', 2, None, '___sec88'), ('Blocking Transformations, final expressions', 2, None, - '___sec91'), + '___sec89'), ('"Code examples for Blocking, Jackknife and ' 'bootstrap":"https://github.com/CompPhysics/MachineLearning/tree/master/doc/Programs/ResamplingAnalysisScripts"', 2, None, - '___sec92')]} + '___sec90'), + ('The bias-variance tradeoff', 2, None, '___sec91'), + ('Training and testing data', 2, None, '___sec92'), + ('Procedure to find a predictor', 2, None, '___sec93'), + ('What we want', 2, None, '___sec94'), + ('The expected generalization error', 2, None, '___sec95'), + ('Elaborating a little bit more', 2, None, '___sec96'), + ('The bias', 2, None, '___sec97'), + ('The variance', 2, None, '___sec98'), + ('Summing up', 2, None, '___sec99'), + ('Logistic Regression', 2, None, '___sec100'), + ('Basics', 2, None, '___sec101'), + ('Linear classifier', 2, None, '___sec102'), + ('Some selected properties', 2, None, '___sec103'), + ('The cross-entropy as a cost function for logistic regression', + 2, + None, + '___sec104'), + ('Maximum likelihood', 2, None, '___sec105'), + ('Minimizing the cross entropy', 2, None, '___sec106')]} end of tocinfo --> @@ -337,19 +351,33 @@ MathJax.Hub.Config({
  • Resampling methods: Jackknife and Bootstrap
  • Resampling methods: Jackknife
  • Resampling methods: Jackknife estimator
    • -
  • Resampling methods: Jackknife sample code
    • -
  • Resampling methods: Bootstrap
    • -
  • Resampling methods: Bootstrap background
    • -
  • Resampling methods: More Bootstrap background
    • -
  • Resampling methods: Bootstrap approach
    • -
  • Resampling methods: Bootstrap algorithm
    • -
  • Resampling methods: Bootstrap steps
    • -
  • Resampling methods: Blocking
    • -
  • Blocking Transformations
    • -
  • Blocking Transformations
    • -
  • Blocking Transformations, getting there
    • -
  • Blocking Transformations, final expressions
    • -
  • "Code examples for Blocking, Jackknife and bootstrap":"https://github.com/CompPhysics/MachineLearning/tree/master/doc/Programs/ResamplingAnalysisScripts"
    • +
  • Resampling methods: Bootstrap
    • +
  • Resampling methods: Bootstrap background
    • +
  • Resampling methods: More Bootstrap background
    • +
  • Resampling methods: Bootstrap approach
    • +
  • Resampling methods: Bootstrap steps
    • +
  • Resampling methods: Blocking
    • +
  • Blocking Transformations
    • +
  • Blocking Transformations
    • +
  • Blocking Transformations, getting there
    • +
  • Blocking Transformations, final expressions
    • +
  • "Code examples for Blocking, Jackknife and bootstrap":"https://github.com/CompPhysics/MachineLearning/tree/master/doc/Programs/ResamplingAnalysisScripts"
    • +
  • The bias-variance tradeoff
    • +
  • Training and testing data
    • +
  • Procedure to find a predictor
    • +
  • What we want
    • +
  • The expected generalization error
    • +
  • Elaborating a little bit more
    • +
  • The bias
    • +
  • The variance
    • +
  • Summing up
    • +
  • Logistic Regression
    • +
  • Basics
    • +
  • Linear classifier
    • +
  • Some selected properties
    • +
  • The cross-entropy as a cost function for logistic regression
    • +
  • Maximum likelihood
    • +
  • Minimizing the cross entropy
    • @@ -409,7 +437,7 @@ once using the original training sample.
  • 54
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  • ...
    • -
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    • +
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  • »
    • diff --git a/doc/pub/Regression/html/._Regression-bs046.html b/doc/pub/Regression/html/._Regression-bs046.html index 1bdcb76a1..a0c49fccf 100644 --- a/doc/pub/Regression/html/._Regression-bs046.html +++ b/doc/pub/Regression/html/._Regression-bs046.html @@ -194,32 +194,46 @@ Automatically generated HTML file from DocOnce source '___sec77'), ('Resampling methods: Jackknife', 2, None, '___sec78'), ('Resampling methods: Jackknife estimator', 2, None, '___sec79'), - ('Resampling methods: Jackknife sample code', - 2, - None, - '___sec80'), - ('Resampling methods: Bootstrap', 2, None, '___sec81'), - ('Resampling methods: Bootstrap background', 2, None, '___sec82'), + ('Resampling methods: Bootstrap', 2, None, '___sec80'), + ('Resampling methods: Bootstrap background', 2, None, '___sec81'), ('Resampling methods: More Bootstrap background', 2, None, - '___sec83'), - ('Resampling methods: Bootstrap approach', 2, None, '___sec84'), - ('Resampling methods: Bootstrap algorithm', 2, None, '___sec85'), - ('Resampling methods: Bootstrap steps', 2, None, '___sec86'), - ('Resampling methods: Blocking', 2, None, '___sec87'), - ('Blocking Transformations', 2, None, '___sec88'), - ('Blocking Transformations', 2, None, '___sec89'), - ('Blocking Transformations, getting there', 2, None, '___sec90'), + '___sec82'), + ('Resampling methods: Bootstrap approach', 2, None, '___sec83'), + ('Resampling methods: Bootstrap steps', 2, None, '___sec84'), + ('Resampling methods: Blocking', 2, None, '___sec85'), + ('Blocking Transformations', 2, None, '___sec86'), + ('Blocking Transformations', 2, None, '___sec87'), + ('Blocking Transformations, getting there', 2, None, '___sec88'), ('Blocking Transformations, final expressions', 2, None, - '___sec91'), + '___sec89'), ('"Code examples for Blocking, Jackknife and ' 'bootstrap":"https://github.com/CompPhysics/MachineLearning/tree/master/doc/Programs/ResamplingAnalysisScripts"', 2, None, - '___sec92')]} + '___sec90'), + ('The bias-variance tradeoff', 2, None, '___sec91'), + ('Training and testing data', 2, None, '___sec92'), + ('Procedure to find a predictor', 2, None, '___sec93'), + ('What we want', 2, None, '___sec94'), + ('The expected generalization error', 2, None, '___sec95'), + ('Elaborating a little bit more', 2, None, '___sec96'), + ('The bias', 2, None, '___sec97'), + ('The variance', 2, None, '___sec98'), + ('Summing up', 2, None, '___sec99'), + ('Logistic Regression', 2, None, '___sec100'), + ('Basics', 2, None, '___sec101'), + ('Linear classifier', 2, None, '___sec102'), + ('Some selected properties', 2, None, '___sec103'), + ('The cross-entropy as a cost function for logistic regression', + 2, + None, + '___sec104'), + ('Maximum likelihood', 2, None, '___sec105'), + ('Minimizing the cross entropy', 2, None, '___sec106')]} end of tocinfo --> @@ -337,19 +351,33 @@ MathJax.Hub.Config({
  • Resampling methods: Jackknife and Bootstrap
  • Resampling methods: Jackknife
  • Resampling methods: Jackknife estimator
    • -
  • Resampling methods: Jackknife sample code
    • -
  • Resampling methods: Bootstrap
    • -
  • Resampling methods: Bootstrap background
    • -
  • Resampling methods: More Bootstrap background
    • -
  • Resampling methods: Bootstrap approach
    • -
  • Resampling methods: Bootstrap algorithm
    • -
  • Resampling methods: Bootstrap steps
    • -
  • Resampling methods: Blocking
    • -
  • Blocking Transformations
    • -
  • Blocking Transformations
    • -
  • Blocking Transformations, getting there
    • -
  • Blocking Transformations, final expressions
    • -
  • "Code examples for Blocking, Jackknife and bootstrap":"https://github.com/CompPhysics/MachineLearning/tree/master/doc/Programs/ResamplingAnalysisScripts"
    • +
  • Resampling methods: Bootstrap
    • +
  • Resampling methods: Bootstrap background
    • +
  • Resampling methods: More Bootstrap background
    • +
  • Resampling methods: Bootstrap approach
    • +
  • Resampling methods: Bootstrap steps
    • +
  • Resampling methods: Blocking
    • +
  • Blocking Transformations
    • +
  • Blocking Transformations
    • +
  • Blocking Transformations, getting there
    • +
  • Blocking Transformations, final expressions
    • +
  • "Code examples for Blocking, Jackknife and bootstrap":"https://github.com/CompPhysics/MachineLearning/tree/master/doc/Programs/ResamplingAnalysisScripts"
    • +
  • The bias-variance tradeoff
    • +
  • Training and testing data
    • +
  • Procedure to find a predictor
    • +
  • What we want
    • +
  • The expected generalization error
    • +
  • Elaborating a little bit more
    • +
  • The bias
    • +
  • The variance
    • +
  • Summing up
    • +
  • Logistic Regression
    • +
  • Basics
    • +
  • Linear classifier
    • +
  • Some selected properties
    • +
  • The cross-entropy as a cost function for logistic regression
    • +
  • Maximum likelihood
    • +
  • Minimizing the cross entropy
    • @@ -414,7 +442,7 @@ bootstrap is widely used.
  • 55
  • 56
  • ...
    • -
  • 94
    • +
  • 109
  • »
    • diff --git a/doc/pub/Regression/html/._Regression-bs047.html b/doc/pub/Regression/html/._Regression-bs047.html index a11c4d63b..392ab67be 100644 --- a/doc/pub/Regression/html/._Regression-bs047.html +++ b/doc/pub/Regression/html/._Regression-bs047.html @@ -194,32 +194,46 @@ Automatically generated HTML file from DocOnce source '___sec77'), ('Resampling methods: Jackknife', 2, None, '___sec78'), ('Resampling methods: Jackknife estimator', 2, None, '___sec79'), - ('Resampling methods: Jackknife sample code', - 2, - None, - '___sec80'), - ('Resampling methods: Bootstrap', 2, None, '___sec81'), - ('Resampling methods: Bootstrap background', 2, None, '___sec82'), + ('Resampling methods: Bootstrap', 2, None, '___sec80'), + ('Resampling methods: Bootstrap background', 2, None, '___sec81'), ('Resampling methods: More Bootstrap background', 2, None, - '___sec83'), - ('Resampling methods: Bootstrap approach', 2, None, '___sec84'), - ('Resampling methods: Bootstrap algorithm', 2, None, '___sec85'), - ('Resampling methods: Bootstrap steps', 2, None, '___sec86'), - ('Resampling methods: Blocking', 2, None, '___sec87'), - ('Blocking Transformations', 2, None, '___sec88'), - ('Blocking Transformations', 2, None, '___sec89'), - ('Blocking Transformations, getting there', 2, None, '___sec90'), + '___sec82'), + ('Resampling methods: Bootstrap approach', 2, None, '___sec83'), + ('Resampling methods: Bootstrap steps', 2, None, '___sec84'), + ('Resampling methods: Blocking', 2, None, '___sec85'), + ('Blocking Transformations', 2, None, '___sec86'), + ('Blocking Transformations', 2, None, '___sec87'), + ('Blocking Transformations, getting there', 2, None, '___sec88'), ('Blocking Transformations, final expressions', 2, None, - '___sec91'), + '___sec89'), ('"Code examples for Blocking, Jackknife and ' 'bootstrap":"https://github.com/CompPhysics/MachineLearning/tree/master/doc/Programs/ResamplingAnalysisScripts"', 2, None, - '___sec92')]} + '___sec90'), + ('The bias-variance tradeoff', 2, None, '___sec91'), + ('Training and testing data', 2, None, '___sec92'), + ('Procedure to find a predictor', 2, None, '___sec93'), + ('What we want', 2, None, '___sec94'), + ('The expected generalization error', 2, None, '___sec95'), + ('Elaborating a little bit more', 2, None, '___sec96'), + ('The bias', 2, None, '___sec97'), + ('The variance', 2, None, '___sec98'), + ('Summing up', 2, None, '___sec99'), + ('Logistic Regression', 2, None, '___sec100'), + ('Basics', 2, None, '___sec101'), + ('Linear classifier', 2, None, '___sec102'), + ('Some selected properties', 2, None, '___sec103'), + ('The cross-entropy as a cost function for logistic regression', + 2, + None, + '___sec104'), + ('Maximum likelihood', 2, None, '___sec105'), + ('Minimizing the cross entropy', 2, None, '___sec106')]} end of tocinfo --> @@ -337,19 +351,33 @@ MathJax.Hub.Config({
  • Resampling methods: Jackknife and Bootstrap
  • Resampling methods: Jackknife
  • Resampling methods: Jackknife estimator
    • -
  • Resampling methods: Jackknife sample code
    • -
  • Resampling methods: Bootstrap
    • -
  • Resampling methods: Bootstrap background
    • -
  • Resampling methods: More Bootstrap background
    • -
  • Resampling methods: Bootstrap approach
    • -
  • Resampling methods: Bootstrap algorithm
    • -
  • Resampling methods: Bootstrap steps
    • -
  • Resampling methods: Blocking
    • -
  • Blocking Transformations
    • -
  • Blocking Transformations
    • -
  • Blocking Transformations, getting there
    • -
  • Blocking Transformations, final expressions
    • -
  • "Code examples for Blocking, Jackknife and bootstrap":"https://github.com/CompPhysics/MachineLearning/tree/master/doc/Programs/ResamplingAnalysisScripts"
    • +
  • Resampling methods: Bootstrap
    • +
  • Resampling methods: Bootstrap background
    • +
  • Resampling methods: More Bootstrap background
    • +
  • Resampling methods: Bootstrap approach
    • +
  • Resampling methods: Bootstrap steps
    • +
  • Resampling methods: Blocking
    • +
  • Blocking Transformations
    • +
  • Blocking Transformations
    • +
  • Blocking Transformations, getting there
    • +
  • Blocking Transformations, final expressions
    • +
  • "Code examples for Blocking, Jackknife and bootstrap":"https://github.com/CompPhysics/MachineLearning/tree/master/doc/Programs/ResamplingAnalysisScripts"
    • +
  • The bias-variance tradeoff
    • +
  • Training and testing data
    • +
  • Procedure to find a predictor
    • +
  • What we want
    • +
  • The expected generalization error
    • +
  • Elaborating a little bit more
    • +
  • The bias
    • +
  • The variance
    • +
  • Summing up
    • +
  • Logistic Regression
    • +
  • Basics
    • +
  • Linear classifier
    • +
  • Some selected properties
    • +
  • The cross-entropy as a cost function for logistic regression
    • +
  • Maximum likelihood
    • +
  • Minimizing the cross entropy
    • @@ -405,7 +433,7 @@ MathJax.Hub.Config({
  • 56
  • 57
  • ...
    • -
  • 94
    • +
  • 109
  • »
    • diff --git a/doc/pub/Regression/html/._Regression-bs048.html b/doc/pub/Regression/html/._Regression-bs048.html index bbac72124..8a2145a5b 100644 --- a/doc/pub/Regression/html/._Regression-bs048.html +++ b/doc/pub/Regression/html/._Regression-bs048.html @@ -194,32 +194,46 @@ Automatically generated HTML file from DocOnce source '___sec77'), ('Resampling methods: Jackknife', 2, None, '___sec78'), ('Resampling methods: Jackknife estimator', 2, None, '___sec79'), - ('Resampling methods: Jackknife sample code', - 2, - None, - '___sec80'), - ('Resampling methods: Bootstrap', 2, None, '___sec81'), - ('Resampling methods: Bootstrap background', 2, None, '___sec82'), + ('Resampling methods: Bootstrap', 2, None, '___sec80'), + ('Resampling methods: Bootstrap background', 2, None, '___sec81'), ('Resampling methods: More Bootstrap background', 2, None, - '___sec83'), - ('Resampling methods: Bootstrap approach', 2, None, '___sec84'), - ('Resampling methods: Bootstrap algorithm', 2, None, '___sec85'), - ('Resampling methods: Bootstrap steps', 2, None, '___sec86'), - ('Resampling methods: Blocking', 2, None, '___sec87'), - ('Blocking Transformations', 2, None, '___sec88'), - ('Blocking Transformations', 2, None, '___sec89'), - ('Blocking Transformations, getting there', 2, None, '___sec90'), + '___sec82'), + ('Resampling methods: Bootstrap approach', 2, None, '___sec83'), + ('Resampling methods: Bootstrap steps', 2, None, '___sec84'), + ('Resampling methods: Blocking', 2, None, '___sec85'), + ('Blocking Transformations', 2, None, '___sec86'), + ('Blocking Transformations', 2, None, '___sec87'), + ('Blocking Transformations, getting there', 2, None, '___sec88'), ('Blocking Transformations, final expressions', 2, None, - '___sec91'), + '___sec89'), ('"Code examples for Blocking, Jackknife and ' 'bootstrap":"https://github.com/CompPhysics/MachineLearning/tree/master/doc/Programs/ResamplingAnalysisScripts"', 2, None, - '___sec92')]} + '___sec90'), + ('The bias-variance tradeoff', 2, None, '___sec91'), + ('Training and testing data', 2, None, '___sec92'), + ('Procedure to find a predictor', 2, None, '___sec93'), + ('What we want', 2, None, '___sec94'), + ('The expected generalization error', 2, None, '___sec95'), + ('Elaborating a little bit more', 2, None, '___sec96'), + ('The bias', 2, None, '___sec97'), + ('The variance', 2, None, '___sec98'), + ('Summing up', 2, None, '___sec99'), + ('Logistic Regression', 2, None, '___sec100'), + ('Basics', 2, None, '___sec101'), + ('Linear classifier', 2, None, '___sec102'), + ('Some selected properties', 2, None, '___sec103'), + ('The cross-entropy as a cost function for logistic regression', + 2, + None, + '___sec104'), + ('Maximum likelihood', 2, None, '___sec105'), + ('Minimizing the cross entropy', 2, None, '___sec106')]} end of tocinfo --> @@ -337,19 +351,33 @@ MathJax.Hub.Config({
  • Resampling methods: Jackknife and Bootstrap
  • Resampling methods: Jackknife
  • Resampling methods: Jackknife estimator
    • -
  • Resampling methods: Jackknife sample code
    • -
  • Resampling methods: Bootstrap
    • -
  • Resampling methods: Bootstrap background
    • -
  • Resampling methods: More Bootstrap background
    • -
  • Resampling methods: Bootstrap approach
    • -
  • Resampling methods: Bootstrap algorithm
    • -
  • Resampling methods: Bootstrap steps
    • -
  • Resampling methods: Blocking
    • -
  • Blocking Transformations
    • -
  • Blocking Transformations
    • -
  • Blocking Transformations, getting there
    • -
  • Blocking Transformations, final expressions
    • -
  • "Code examples for Blocking, Jackknife and bootstrap":"https://github.com/CompPhysics/MachineLearning/tree/master/doc/Programs/ResamplingAnalysisScripts"
    • +
  • Resampling methods: Bootstrap
    • +
  • Resampling methods: Bootstrap background
    • +
  • Resampling methods: More Bootstrap background
    • +
  • Resampling methods: Bootstrap approach
    • +
  • Resampling methods: Bootstrap steps
    • +
  • Resampling methods: Blocking
    • +
  • Blocking Transformations
    • +
  • Blocking Transformations
    • +
  • Blocking Transformations, getting there
    • +
  • Blocking Transformations, final expressions
    • +
  • "Code examples for Blocking, Jackknife and bootstrap":"https://github.com/CompPhysics/MachineLearning/tree/master/doc/Programs/ResamplingAnalysisScripts"
    • +
  • The bias-variance tradeoff
    • +
  • Training and testing data
    • +
  • Procedure to find a predictor
    • +
  • What we want
    • +
  • The expected generalization error
    • +
  • Elaborating a little bit more
    • +
  • The bias
    • +
  • The variance
    • +
  • Summing up
    • +
  • Logistic Regression
    • +
  • Basics
    • +
  • Linear classifier
    • +
  • Some selected properties
    • +
  • The cross-entropy as a cost function for logistic regression
    • +
  • Maximum likelihood
    • +
  • Minimizing the cross entropy
    • @@ -411,7 +439,7 @@ MathJax.Hub.Config({
  • 57
  • 58
  • ...
    • -
  • 94
    • +
  • 109
  • »
    • diff --git a/doc/pub/Regression/html/._Regression-bs049.html b/doc/pub/Regression/html/._Regression-bs049.html index fe63d19f6..58b5b53e7 100644 --- a/doc/pub/Regression/html/._Regression-bs049.html +++ b/doc/pub/Regression/html/._Regression-bs049.html @@ -194,32 +194,46 @@ Automatically generated HTML file from DocOnce source '___sec77'), ('Resampling methods: Jackknife', 2, None, '___sec78'), ('Resampling methods: Jackknife estimator', 2, None, '___sec79'), - ('Resampling methods: Jackknife sample code', - 2, - None, - '___sec80'), - ('Resampling methods: Bootstrap', 2, None, '___sec81'), - ('Resampling methods: Bootstrap background', 2, None, '___sec82'), + ('Resampling methods: Bootstrap', 2, None, '___sec80'), + ('Resampling methods: Bootstrap background', 2, None, '___sec81'), ('Resampling methods: More Bootstrap background', 2, None, - '___sec83'), - ('Resampling methods: Bootstrap approach', 2, None, '___sec84'), - ('Resampling methods: Bootstrap algorithm', 2, None, '___sec85'), - ('Resampling methods: Bootstrap steps', 2, None, '___sec86'), - ('Resampling methods: Blocking', 2, None, '___sec87'), - ('Blocking Transformations', 2, None, '___sec88'), - ('Blocking Transformations', 2, None, '___sec89'), - ('Blocking Transformations, getting there', 2, None, '___sec90'), + '___sec82'), + ('Resampling methods: Bootstrap approach', 2, None, '___sec83'), + ('Resampling methods: Bootstrap steps', 2, None, '___sec84'), + ('Resampling methods: Blocking', 2, None, '___sec85'), + ('Blocking Transformations', 2, None, '___sec86'), + ('Blocking Transformations', 2, None, '___sec87'), + ('Blocking Transformations, getting there', 2, None, '___sec88'), ('Blocking Transformations, final expressions', 2, None, - '___sec91'), + '___sec89'), ('"Code examples for Blocking, Jackknife and ' 'bootstrap":"https://github.com/CompPhysics/MachineLearning/tree/master/doc/Programs/ResamplingAnalysisScripts"', 2, None, - '___sec92')]} + '___sec90'), + ('The bias-variance tradeoff', 2, None, '___sec91'), + ('Training and testing data', 2, None, '___sec92'), + ('Procedure to find a predictor', 2, None, '___sec93'), + ('What we want', 2, None, '___sec94'), + ('The expected generalization error', 2, None, '___sec95'), + ('Elaborating a little bit more', 2, None, '___sec96'), + ('The bias', 2, None, '___sec97'), + ('The variance', 2, None, '___sec98'), + ('Summing up', 2, None, '___sec99'), + ('Logistic Regression', 2, None, '___sec100'), + ('Basics', 2, None, '___sec101'), + ('Linear classifier', 2, None, '___sec102'), + ('Some selected properties', 2, None, '___sec103'), + ('The cross-entropy as a cost function for logistic regression', + 2, + None, + '___sec104'), + ('Maximum likelihood', 2, None, '___sec105'), + ('Minimizing the cross entropy', 2, None, '___sec106')]} end of tocinfo --> @@ -337,19 +351,33 @@ MathJax.Hub.Config({
  • Resampling methods: Jackknife and Bootstrap
  • Resampling methods: Jackknife
  • Resampling methods: Jackknife estimator
    • -
  • Resampling methods: Jackknife sample code
    • -
  • Resampling methods: Bootstrap
    • -
  • Resampling methods: Bootstrap background
    • -
  • Resampling methods: More Bootstrap background
    • -
  • Resampling methods: Bootstrap approach
    • -
  • Resampling methods: Bootstrap algorithm
    • -
  • Resampling methods: Bootstrap steps
    • -
  • Resampling methods: Blocking
    • -
  • Blocking Transformations
    • -
  • Blocking Transformations
    • -
  • Blocking Transformations, getting there
    • -
  • Blocking Transformations, final expressions
    • -
  • "Code examples for Blocking, Jackknife and bootstrap":"https://github.com/CompPhysics/MachineLearning/tree/master/doc/Programs/ResamplingAnalysisScripts"
    • +
  • Resampling methods: Bootstrap
    • +
  • Resampling methods: Bootstrap background
    • +
  • Resampling methods: More Bootstrap background
    • +
  • Resampling methods: Bootstrap approach
    • +
  • Resampling methods: Bootstrap steps
    • +
  • Resampling methods: Blocking
    • +
  • Blocking Transformations
    • +
  • Blocking Transformations
    • +
  • Blocking Transformations, getting there
    • +
  • Blocking Transformations, final expressions
    • +
  • "Code examples for Blocking, Jackknife and bootstrap":"https://github.com/CompPhysics/MachineLearning/tree/master/doc/Programs/ResamplingAnalysisScripts"
    • +
  • The bias-variance tradeoff
    • +
  • Training and testing data
    • +
  • Procedure to find a predictor
    • +
  • What we want
    • +
  • The expected generalization error
    • +
  • Elaborating a little bit more
    • +
  • The bias
    • +
  • The variance
    • +
  • Summing up
    • +
  • Logistic Regression
    • +
  • Basics
    • +
  • Linear classifier
    • +
  • Some selected properties
    • +
  • The cross-entropy as a cost function for logistic regression
    • +
  • Maximum likelihood
    • +
  • Minimizing the cross entropy
    • @@ -420,7 +448,7 @@ selection of a large set of these numbers reproduces this PDF.
  • 58
  • 59
  • ...
    • -
  • 94
    • +
  • 109
  • »
    • diff --git a/doc/pub/Regression/html/._Regression-bs050.html b/doc/pub/Regression/html/._Regression-bs050.html index 2dfb1ea0b..1ee718d5e 100644 --- a/doc/pub/Regression/html/._Regression-bs050.html +++ b/doc/pub/Regression/html/._Regression-bs050.html @@ -194,32 +194,46 @@ Automatically generated HTML file from DocOnce source '___sec77'), ('Resampling methods: Jackknife', 2, None, '___sec78'), ('Resampling methods: Jackknife estimator', 2, None, '___sec79'), - ('Resampling methods: Jackknife sample code', - 2, - None, - '___sec80'), - ('Resampling methods: Bootstrap', 2, None, '___sec81'), - ('Resampling methods: Bootstrap background', 2, None, '___sec82'), + ('Resampling methods: Bootstrap', 2, None, '___sec80'), + ('Resampling methods: Bootstrap background', 2, None, '___sec81'), ('Resampling methods: More Bootstrap background', 2, None, - '___sec83'), - ('Resampling methods: Bootstrap approach', 2, None, '___sec84'), - ('Resampling methods: Bootstrap algorithm', 2, None, '___sec85'), - ('Resampling methods: Bootstrap steps', 2, None, '___sec86'), - ('Resampling methods: Blocking', 2, None, '___sec87'), - ('Blocking Transformations', 2, None, '___sec88'), - ('Blocking Transformations', 2, None, '___sec89'), - ('Blocking Transformations, getting there', 2, None, '___sec90'), + '___sec82'), + ('Resampling methods: Bootstrap approach', 2, None, '___sec83'), + ('Resampling methods: Bootstrap steps', 2, None, '___sec84'), + ('Resampling methods: Blocking', 2, None, '___sec85'), + ('Blocking Transformations', 2, None, '___sec86'), + ('Blocking Transformations', 2, None, '___sec87'), + ('Blocking Transformations, getting there', 2, None, '___sec88'), ('Blocking Transformations, final expressions', 2, None, - '___sec91'), + '___sec89'), ('"Code examples for Blocking, Jackknife and ' 'bootstrap":"https://github.com/CompPhysics/MachineLearning/tree/master/doc/Programs/ResamplingAnalysisScripts"', 2, None, - '___sec92')]} + '___sec90'), + ('The bias-variance tradeoff', 2, None, '___sec91'), + ('Training and testing data', 2, None, '___sec92'), + ('Procedure to find a predictor', 2, None, '___sec93'), + ('What we want', 2, None, '___sec94'), + ('The expected generalization error', 2, None, '___sec95'), + ('Elaborating a little bit more', 2, None, '___sec96'), + ('The bias', 2, None, '___sec97'), + ('The variance', 2, None, '___sec98'), + ('Summing up', 2, None, '___sec99'), + ('Logistic Regression', 2, None, '___sec100'), + ('Basics', 2, None, '___sec101'), + ('Linear classifier', 2, None, '___sec102'), + ('Some selected properties', 2, None, '___sec103'), + ('The cross-entropy as a cost function for logistic regression', + 2, + None, + '___sec104'), + ('Maximum likelihood', 2, None, '___sec105'), + ('Minimizing the cross entropy', 2, None, '___sec106')]} end of tocinfo --> @@ -337,19 +351,33 @@ MathJax.Hub.Config({
  • Resampling methods: Jackknife and Bootstrap
  • Resampling methods: Jackknife
  • Resampling methods: Jackknife estimator
    • -
  • Resampling methods: Jackknife sample code
    • -
  • Resampling methods: Bootstrap
    • -
  • Resampling methods: Bootstrap background
    • -
  • Resampling methods: More Bootstrap background
    • -
  • Resampling methods: Bootstrap approach
    • -
  • Resampling methods: Bootstrap algorithm
    • -
  • Resampling methods: Bootstrap steps
    • -
  • Resampling methods: Blocking
    • -
  • Blocking Transformations
    • -
  • Blocking Transformations
    • -
  • Blocking Transformations, getting there
    • -
  • Blocking Transformations, final expressions
    • -
  • "Code examples for Blocking, Jackknife and bootstrap":"https://github.com/CompPhysics/MachineLearning/tree/master/doc/Programs/ResamplingAnalysisScripts"
    • +
  • Resampling methods: Bootstrap
    • +
  • Resampling methods: Bootstrap background
    • +
  • Resampling methods: More Bootstrap background
    • +
  • Resampling methods: Bootstrap approach
    • +
  • Resampling methods: Bootstrap steps
    • +
  • Resampling methods: Blocking
    • +
  • Blocking Transformations
    • +
  • Blocking Transformations
    • +
  • Blocking Transformations, getting there
    • +
  • Blocking Transformations, final expressions
    • +
  • "Code examples for Blocking, Jackknife and bootstrap":"https://github.com/CompPhysics/MachineLearning/tree/master/doc/Programs/ResamplingAnalysisScripts"
    • +
  • The bias-variance tradeoff
    • +
  • Training and testing data
    • +
  • Procedure to find a predictor
    • +
  • What we want
    • +
  • The expected generalization error
    • +
  • Elaborating a little bit more
    • +
  • The bias
    • +
  • The variance
    • +
  • Summing up
    • +
  • Logistic Regression
    • +
  • Basics
    • +
  • Linear classifier
    • +
  • Some selected properties
    • +
  • The cross-entropy as a cost function for logistic regression
    • +
  • Maximum likelihood
    • +
  • Minimizing the cross entropy
    • @@ -412,7 +440,7 @@ $$
  • 59
  • 60
  • ...
    • -
  • 94
    • +
  • 109
  • »
    • diff --git a/doc/pub/Regression/html/._Regression-bs051.html b/doc/pub/Regression/html/._Regression-bs051.html index 336c97cf8..51cd38d97 100644 --- a/doc/pub/Regression/html/._Regression-bs051.html +++ b/doc/pub/Regression/html/._Regression-bs051.html @@ -194,32 +194,46 @@ Automatically generated HTML file from DocOnce source '___sec77'), ('Resampling methods: Jackknife', 2, None, '___sec78'), ('Resampling methods: Jackknife estimator', 2, None, '___sec79'), - ('Resampling methods: Jackknife sample code', - 2, - None, - '___sec80'), - ('Resampling methods: Bootstrap', 2, None, '___sec81'), - ('Resampling methods: Bootstrap background', 2, None, '___sec82'), + ('Resampling methods: Bootstrap', 2, None, '___sec80'), + ('Resampling methods: Bootstrap background', 2, None, '___sec81'), ('Resampling methods: More Bootstrap background', 2, None, - '___sec83'), - ('Resampling methods: Bootstrap approach', 2, None, '___sec84'), - ('Resampling methods: Bootstrap algorithm', 2, None, '___sec85'), - ('Resampling methods: Bootstrap steps', 2, None, '___sec86'), - ('Resampling methods: Blocking', 2, None, '___sec87'), - ('Blocking Transformations', 2, None, '___sec88'), - ('Blocking Transformations', 2, None, '___sec89'), - ('Blocking Transformations, getting there', 2, None, '___sec90'), + '___sec82'), + ('Resampling methods: Bootstrap approach', 2, None, '___sec83'), + ('Resampling methods: Bootstrap steps', 2, None, '___sec84'), + ('Resampling methods: Blocking', 2, None, '___sec85'), + ('Blocking Transformations', 2, None, '___sec86'), + ('Blocking Transformations', 2, None, '___sec87'), + ('Blocking Transformations, getting there', 2, None, '___sec88'), ('Blocking Transformations, final expressions', 2, None, - '___sec91'), + '___sec89'), ('"Code examples for Blocking, Jackknife and ' 'bootstrap":"https://github.com/CompPhysics/MachineLearning/tree/master/doc/Programs/ResamplingAnalysisScripts"', 2, None, - '___sec92')]} + '___sec90'), + ('The bias-variance tradeoff', 2, None, '___sec91'), + ('Training and testing data', 2, None, '___sec92'), + ('Procedure to find a predictor', 2, None, '___sec93'), + ('What we want', 2, None, '___sec94'), + ('The expected generalization error', 2, None, '___sec95'), + ('Elaborating a little bit more', 2, None, '___sec96'), + ('The bias', 2, None, '___sec97'), + ('The variance', 2, None, '___sec98'), + ('Summing up', 2, None, '___sec99'), + ('Logistic Regression', 2, None, '___sec100'), + ('Basics', 2, None, '___sec101'), + ('Linear classifier', 2, None, '___sec102'), + ('Some selected properties', 2, None, '___sec103'), + ('The cross-entropy as a cost function for logistic regression', + 2, + None, + '___sec104'), + ('Maximum likelihood', 2, None, '___sec105'), + ('Minimizing the cross entropy', 2, None, '___sec106')]} end of tocinfo --> @@ -337,19 +351,33 @@ MathJax.Hub.Config({
  • Resampling methods: Jackknife and Bootstrap
  • Resampling methods: Jackknife
  • Resampling methods: Jackknife estimator
    • -
  • Resampling methods: Jackknife sample code
    • -
  • Resampling methods: Bootstrap
    • -
  • Resampling methods: Bootstrap background
    • -
  • Resampling methods: More Bootstrap background
    • -
  • Resampling methods: Bootstrap approach
    • -
  • Resampling methods: Bootstrap algorithm
    • -
  • Resampling methods: Bootstrap steps
    • -
  • Resampling methods: Blocking
    • -
  • Blocking Transformations
    • -
  • Blocking Transformations
    • -
  • Blocking Transformations, getting there
    • -
  • Blocking Transformations, final expressions
    • -
  • "Code examples for Blocking, Jackknife and bootstrap":"https://github.com/CompPhysics/MachineLearning/tree/master/doc/Programs/ResamplingAnalysisScripts"
    • +
  • Resampling methods: Bootstrap
    • +
  • Resampling methods: Bootstrap background
    • +
  • Resampling methods: More Bootstrap background
    • +
  • Resampling methods: Bootstrap approach
    • +
  • Resampling methods: Bootstrap steps
    • +
  • Resampling methods: Blocking
    • +
  • Blocking Transformations
    • +
  • Blocking Transformations
    • +
  • Blocking Transformations, getting there
    • +
  • Blocking Transformations, final expressions
    • +
  • "Code examples for Blocking, Jackknife and bootstrap":"https://github.com/CompPhysics/MachineLearning/tree/master/doc/Programs/ResamplingAnalysisScripts"
    • +
  • The bias-variance tradeoff
    • +
  • Training and testing data
    • +
  • Procedure to find a predictor
    • +
  • What we want
    • +
  • The expected generalization error
    • +
  • Elaborating a little bit more
    • +
  • The bias
    • +
  • The variance
    • +
  • Summing up
    • +
  • Logistic Regression
    • +
  • Basics
    • +
  • Linear classifier
    • +
  • Some selected properties
    • +
  • The cross-entropy as a cost function for logistic regression
    • +
  • Maximum likelihood
    • +
  • Minimizing the cross entropy
    • @@ -427,7 +455,7 @@ qualitatively as the spread of \( p \) around its mean.
  • 60
  • 61
  • ...
    • -
  • 94
    • +
  • 109
  • »
    • diff --git a/doc/pub/Regression/html/._Regression-bs052.html b/doc/pub/Regression/html/._Regression-bs052.html index a00d7ded0..29ef55f38 100644 --- a/doc/pub/Regression/html/._Regression-bs052.html +++ b/doc/pub/Regression/html/._Regression-bs052.html @@ -194,32 +194,46 @@ Automatically generated HTML file from DocOnce source '___sec77'), ('Resampling methods: Jackknife', 2, None, '___sec78'), ('Resampling methods: Jackknife estimator', 2, None, '___sec79'), - ('Resampling methods: Jackknife sample code', - 2, - None, - '___sec80'), - ('Resampling methods: Bootstrap', 2, None, '___sec81'), - ('Resampling methods: Bootstrap background', 2, None, '___sec82'), + ('Resampling methods: Bootstrap', 2, None, '___sec80'), + ('Resampling methods: Bootstrap background', 2, None, '___sec81'), ('Resampling methods: More Bootstrap background', 2, None, - '___sec83'), - ('Resampling methods: Bootstrap approach', 2, None, '___sec84'), - ('Resampling methods: Bootstrap algorithm', 2, None, '___sec85'), - ('Resampling methods: Bootstrap steps', 2, None, '___sec86'), - ('Resampling methods: Blocking', 2, None, '___sec87'), - ('Blocking Transformations', 2, None, '___sec88'), - ('Blocking Transformations', 2, None, '___sec89'), - ('Blocking Transformations, getting there', 2, None, '___sec90'), + '___sec82'), + ('Resampling methods: Bootstrap approach', 2, None, '___sec83'), + ('Resampling methods: Bootstrap steps', 2, None, '___sec84'), + ('Resampling methods: Blocking', 2, None, '___sec85'), + ('Blocking Transformations', 2, None, '___sec86'), + ('Blocking Transformations', 2, None, '___sec87'), + ('Blocking Transformations, getting there', 2, None, '___sec88'), ('Blocking Transformations, final expressions', 2, None, - '___sec91'), + '___sec89'), ('"Code examples for Blocking, Jackknife and ' 'bootstrap":"https://github.com/CompPhysics/MachineLearning/tree/master/doc/Programs/ResamplingAnalysisScripts"', 2, None, - '___sec92')]} + '___sec90'), + ('The bias-variance tradeoff', 2, None, '___sec91'), + ('Training and testing data', 2, None, '___sec92'), + ('Procedure to find a predictor', 2, None, '___sec93'), + ('What we want', 2, None, '___sec94'), + ('The expected generalization error', 2, None, '___sec95'), + ('Elaborating a little bit more', 2, None, '___sec96'), + ('The bias', 2, None, '___sec97'), + ('The variance', 2, None, '___sec98'), + ('Summing up', 2, None, '___sec99'), + ('Logistic Regression', 2, None, '___sec100'), + ('Basics', 2, None, '___sec101'), + ('Linear classifier', 2, None, '___sec102'), + ('Some selected properties', 2, None, '___sec103'), + ('The cross-entropy as a cost function for logistic regression', + 2, + None, + '___sec104'), + ('Maximum likelihood', 2, None, '___sec105'), + ('Minimizing the cross entropy', 2, None, '___sec106')]} end of tocinfo --> @@ -337,19 +351,33 @@ MathJax.Hub.Config({
  • Resampling methods: Jackknife and Bootstrap
  • Resampling methods: Jackknife
  • Resampling methods: Jackknife estimator
    • -
  • Resampling methods: Jackknife sample code
    • -
  • Resampling methods: Bootstrap
    • -
  • Resampling methods: Bootstrap background
    • -
  • Resampling methods: More Bootstrap background
    • -
  • Resampling methods: Bootstrap approach
    • -
  • Resampling methods: Bootstrap algorithm
    • -
  • Resampling methods: Bootstrap steps
    • -
  • Resampling methods: Blocking
    • -
  • Blocking Transformations
    • -
  • Blocking Transformations
    • -
  • Blocking Transformations, getting there
    • -
  • Blocking Transformations, final expressions
    • -
  • "Code examples for Blocking, Jackknife and bootstrap":"https://github.com/CompPhysics/MachineLearning/tree/master/doc/Programs/ResamplingAnalysisScripts"
    • +
  • Resampling methods: Bootstrap
    • +
  • Resampling methods: Bootstrap background
    • +
  • Resampling methods: More Bootstrap background
    • +
  • Resampling methods: Bootstrap approach
    • +
  • Resampling methods: Bootstrap steps
    • +
  • Resampling methods: Blocking
    • +
  • Blocking Transformations
    • +
  • Blocking Transformations
    • +
  • Blocking Transformations, getting there
    • +
  • Blocking Transformations, final expressions
    • +
  • "Code examples for Blocking, Jackknife and bootstrap":"https://github.com/CompPhysics/MachineLearning/tree/master/doc/Programs/ResamplingAnalysisScripts"
    • +
  • The bias-variance tradeoff
    • +
  • Training and testing data
    • +
  • Procedure to find a predictor
    • +
  • What we want
    • +
  • The expected generalization error
    • +
  • Elaborating a little bit more
    • +
  • The bias
    • +
  • The variance
    • +
  • Summing up
    • +
  • Logistic Regression
    • +
  • Basics
    • +
  • Linear classifier
    • +
  • Some selected properties
    • +
  • The cross-entropy as a cost function for logistic regression
    • +
  • Maximum likelihood
    • +
  • Minimizing the cross entropy
    • @@ -420,7 +448,7 @@ $$
  • 61
  • 62
  • ...
    • -
  • 94
    • +
  • 109
  • »
    • diff --git a/doc/pub/Regression/html/._Regression-bs053.html b/doc/pub/Regression/html/._Regression-bs053.html index 50e4d750b..e7812f150 100644 --- a/doc/pub/Regression/html/._Regression-bs053.html +++ b/doc/pub/Regression/html/._Regression-bs053.html @@ -194,32 +194,46 @@ Automatically generated HTML file from DocOnce source '___sec77'), ('Resampling methods: Jackknife', 2, None, '___sec78'), ('Resampling methods: Jackknife estimator', 2, None, '___sec79'), - ('Resampling methods: Jackknife sample code', - 2, - None, - '___sec80'), - ('Resampling methods: Bootstrap', 2, None, '___sec81'), - ('Resampling methods: Bootstrap background', 2, None, '___sec82'), + ('Resampling methods: Bootstrap', 2, None, '___sec80'), + ('Resampling methods: Bootstrap background', 2, None, '___sec81'), ('Resampling methods: More Bootstrap background', 2, None, - '___sec83'), - ('Resampling methods: Bootstrap approach', 2, None, '___sec84'), - ('Resampling methods: Bootstrap algorithm', 2, None, '___sec85'), - ('Resampling methods: Bootstrap steps', 2, None, '___sec86'), - ('Resampling methods: Blocking', 2, None, '___sec87'), - ('Blocking Transformations', 2, None, '___sec88'), - ('Blocking Transformations', 2, None, '___sec89'), - ('Blocking Transformations, getting there', 2, None, '___sec90'), + '___sec82'), + ('Resampling methods: Bootstrap approach', 2, None, '___sec83'), + ('Resampling methods: Bootstrap steps', 2, None, '___sec84'), + ('Resampling methods: Blocking', 2, None, '___sec85'), + ('Blocking Transformations', 2, None, '___sec86'), + ('Blocking Transformations', 2, None, '___sec87'), + ('Blocking Transformations, getting there', 2, None, '___sec88'), ('Blocking Transformations, final expressions', 2, None, - '___sec91'), + '___sec89'), ('"Code examples for Blocking, Jackknife and ' 'bootstrap":"https://github.com/CompPhysics/MachineLearning/tree/master/doc/Programs/ResamplingAnalysisScripts"', 2, None, - '___sec92')]} + '___sec90'), + ('The bias-variance tradeoff', 2, None, '___sec91'), + ('Training and testing data', 2, None, '___sec92'), + ('Procedure to find a predictor', 2, None, '___sec93'), + ('What we want', 2, None, '___sec94'), + ('The expected generalization error', 2, None, '___sec95'), + ('Elaborating a little bit more', 2, None, '___sec96'), + ('The bias', 2, None, '___sec97'), + ('The variance', 2, None, '___sec98'), + ('Summing up', 2, None, '___sec99'), + ('Logistic Regression', 2, None, '___sec100'), + ('Basics', 2, None, '___sec101'), + ('Linear classifier', 2, None, '___sec102'), + ('Some selected properties', 2, None, '___sec103'), + ('The cross-entropy as a cost function for logistic regression', + 2, + None, + '___sec104'), + ('Maximum likelihood', 2, None, '___sec105'), + ('Minimizing the cross entropy', 2, None, '___sec106')]} end of tocinfo --> @@ -337,19 +351,33 @@ MathJax.Hub.Config({
  • Resampling methods: Jackknife and Bootstrap
  • Resampling methods: Jackknife
  • Resampling methods: Jackknife estimator
    • -
  • Resampling methods: Jackknife sample code
    • -
  • Resampling methods: Bootstrap
    • -
  • Resampling methods: Bootstrap background
    • -
  • Resampling methods: More Bootstrap background
    • -
  • Resampling methods: Bootstrap approach
    • -
  • Resampling methods: Bootstrap algorithm
    • -
  • Resampling methods: Bootstrap steps
    • -
  • Resampling methods: Blocking
    • -
  • Blocking Transformations
    • -
  • Blocking Transformations
    • -
  • Blocking Transformations, getting there
    • -
  • Blocking Transformations, final expressions
    • -
  • "Code examples for Blocking, Jackknife and bootstrap":"https://github.com/CompPhysics/MachineLearning/tree/master/doc/Programs/ResamplingAnalysisScripts"
    • +
  • Resampling methods: Bootstrap
    • +
  • Resampling methods: Bootstrap background
    • +
  • Resampling methods: More Bootstrap background
    • +
  • Resampling methods: Bootstrap approach
    • +
  • Resampling methods: Bootstrap steps
    • +
  • Resampling methods: Blocking
    • +
  • Blocking Transformations
    • +
  • Blocking Transformations
    • +
  • Blocking Transformations, getting there
    • +
  • Blocking Transformations, final expressions
    • +
  • "Code examples for Blocking, Jackknife and bootstrap":"https://github.com/CompPhysics/MachineLearning/tree/master/doc/Programs/ResamplingAnalysisScripts"
    • +
  • The bias-variance tradeoff
    • +
  • Training and testing data
    • +
  • Procedure to find a predictor
    • +
  • What we want
    • +
  • The expected generalization error
    • +
  • Elaborating a little bit more
    • +
  • The bias
    • +
  • The variance
    • +
  • Summing up
    • +
  • Logistic Regression
    • +
  • Basics
    • +
  • Linear classifier
    • +
  • Some selected properties
    • +
  • The cross-entropy as a cost function for logistic regression
    • +
  • Maximum likelihood
    • +
  • Minimizing the cross entropy
    • @@ -421,7 +449,7 @@ $$
  • 62
  • 63
  • ...
    • -
  • 94
    • +
  • 109
  • »
    • diff --git a/doc/pub/Regression/html/._Regression-bs054.html b/doc/pub/Regression/html/._Regression-bs054.html index 807aa151b..5b3846909 100644 --- a/doc/pub/Regression/html/._Regression-bs054.html +++ b/doc/pub/Regression/html/._Regression-bs054.html @@ -194,32 +194,46 @@ Automatically generated HTML file from DocOnce source '___sec77'), ('Resampling methods: Jackknife', 2, None, '___sec78'), ('Resampling methods: Jackknife estimator', 2, None, '___sec79'), - ('Resampling methods: Jackknife sample code', - 2, - None, - '___sec80'), - ('Resampling methods: Bootstrap', 2, None, '___sec81'), - ('Resampling methods: Bootstrap background', 2, None, '___sec82'), + ('Resampling methods: Bootstrap', 2, None, '___sec80'), + ('Resampling methods: Bootstrap background', 2, None, '___sec81'), ('Resampling methods: More Bootstrap background', 2, None, - '___sec83'), - ('Resampling methods: Bootstrap approach', 2, None, '___sec84'), - ('Resampling methods: Bootstrap algorithm', 2, None, '___sec85'), - ('Resampling methods: Bootstrap steps', 2, None, '___sec86'), - ('Resampling methods: Blocking', 2, None, '___sec87'), - ('Blocking Transformations', 2, None, '___sec88'), - ('Blocking Transformations', 2, None, '___sec89'), - ('Blocking Transformations, getting there', 2, None, '___sec90'), + '___sec82'), + ('Resampling methods: Bootstrap approach', 2, None, '___sec83'), + ('Resampling methods: Bootstrap steps', 2, None, '___sec84'), + ('Resampling methods: Blocking', 2, None, '___sec85'), + ('Blocking Transformations', 2, None, '___sec86'), + ('Blocking Transformations', 2, None, '___sec87'), + ('Blocking Transformations, getting there', 2, None, '___sec88'), ('Blocking Transformations, final expressions', 2, None, - '___sec91'), + '___sec89'), ('"Code examples for Blocking, Jackknife and ' 'bootstrap":"https://github.com/CompPhysics/MachineLearning/tree/master/doc/Programs/ResamplingAnalysisScripts"', 2, None, - '___sec92')]} + '___sec90'), + ('The bias-variance tradeoff', 2, None, '___sec91'), + ('Training and testing data', 2, None, '___sec92'), + ('Procedure to find a predictor', 2, None, '___sec93'), + ('What we want', 2, None, '___sec94'), + ('The expected generalization error', 2, None, '___sec95'), + ('Elaborating a little bit more', 2, None, '___sec96'), + ('The bias', 2, None, '___sec97'), + ('The variance', 2, None, '___sec98'), + ('Summing up', 2, None, '___sec99'), + ('Logistic Regression', 2, None, '___sec100'), + ('Basics', 2, None, '___sec101'), + ('Linear classifier', 2, None, '___sec102'), + ('Some selected properties', 2, None, '___sec103'), + ('The cross-entropy as a cost function for logistic regression', + 2, + None, + '___sec104'), + ('Maximum likelihood', 2, None, '___sec105'), + ('Minimizing the cross entropy', 2, None, '___sec106')]} end of tocinfo --> @@ -337,19 +351,33 @@ MathJax.Hub.Config({
  • Resampling methods: Jackknife and Bootstrap
  • Resampling methods: Jackknife
  • Resampling methods: Jackknife estimator
    • -
  • Resampling methods: Jackknife sample code
    • -
  • Resampling methods: Bootstrap
    • -
  • Resampling methods: Bootstrap background
    • -
  • Resampling methods: More Bootstrap background
    • -
  • Resampling methods: Bootstrap approach
    • -
  • Resampling methods: Bootstrap algorithm
    • -
  • Resampling methods: Bootstrap steps
    • -
  • Resampling methods: Blocking
    • -
  • Blocking Transformations
    • -
  • Blocking Transformations
    • -
  • Blocking Transformations, getting there
    • -
  • Blocking Transformations, final expressions
    • -
  • "Code examples for Blocking, Jackknife and bootstrap":"https://github.com/CompPhysics/MachineLearning/tree/master/doc/Programs/ResamplingAnalysisScripts"
    • +
  • Resampling methods: Bootstrap
    • +
  • Resampling methods: Bootstrap background
    • +
  • Resampling methods: More Bootstrap background
    • +
  • Resampling methods: Bootstrap approach
    • +
  • Resampling methods: Bootstrap steps
    • +
  • Resampling methods: Blocking
    • +
  • Blocking Transformations
    • +
  • Blocking Transformations
    • +
  • Blocking Transformations, getting there
    • +
  • Blocking Transformations, final expressions
    • +
  • "Code examples for Blocking, Jackknife and bootstrap":"https://github.com/CompPhysics/MachineLearning/tree/master/doc/Programs/ResamplingAnalysisScripts"
    • +
  • The bias-variance tradeoff
    • +
  • Training and testing data
    • +
  • Procedure to find a predictor
    • +
  • What we want
    • +
  • The expected generalization error
    • +
  • Elaborating a little bit more
    • +
  • The bias
    • +
  • The variance
    • +
  • Summing up
    • +
  • Logistic Regression
    • +
  • Basics
    • +
  • Linear classifier
    • +
  • Some selected properties
    • +
  • The cross-entropy as a cost function for logistic regression
    • +
  • Maximum likelihood
    • +
  • Minimizing the cross entropy
    • @@ -415,7 +443,7 @@ $$
  • 63
  • 64
  • ...
    • -
  • 94
    • +
  • 109
  • »
    • diff --git a/doc/pub/Regression/html/._Regression-bs055.html b/doc/pub/Regression/html/._Regression-bs055.html index 211303d6f..ee34d7d4d 100644 --- a/doc/pub/Regression/html/._Regression-bs055.html +++ b/doc/pub/Regression/html/._Regression-bs055.html @@ -194,32 +194,46 @@ Automatically generated HTML file from DocOnce source '___sec77'), ('Resampling methods: Jackknife', 2, None, '___sec78'), ('Resampling methods: Jackknife estimator', 2, None, '___sec79'), - ('Resampling methods: Jackknife sample code', - 2, - None, - '___sec80'), - ('Resampling methods: Bootstrap', 2, None, '___sec81'), - ('Resampling methods: Bootstrap background', 2, None, '___sec82'), + ('Resampling methods: Bootstrap', 2, None, '___sec80'), + ('Resampling methods: Bootstrap background', 2, None, '___sec81'), ('Resampling methods: More Bootstrap background', 2, None, - '___sec83'), - ('Resampling methods: Bootstrap approach', 2, None, '___sec84'), - ('Resampling methods: Bootstrap algorithm', 2, None, '___sec85'), - ('Resampling methods: Bootstrap steps', 2, None, '___sec86'), - ('Resampling methods: Blocking', 2, None, '___sec87'), - ('Blocking Transformations', 2, None, '___sec88'), - ('Blocking Transformations', 2, None, '___sec89'), - ('Blocking Transformations, getting there', 2, None, '___sec90'), + '___sec82'), + ('Resampling methods: Bootstrap approach', 2, None, '___sec83'), + ('Resampling methods: Bootstrap steps', 2, None, '___sec84'), + ('Resampling methods: Blocking', 2, None, '___sec85'), + ('Blocking Transformations', 2, None, '___sec86'), + ('Blocking Transformations', 2, None, '___sec87'), + ('Blocking Transformations, getting there', 2, None, '___sec88'), ('Blocking Transformations, final expressions', 2, None, - '___sec91'), + '___sec89'), ('"Code examples for Blocking, Jackknife and ' 'bootstrap":"https://github.com/CompPhysics/MachineLearning/tree/master/doc/Programs/ResamplingAnalysisScripts"', 2, None, - '___sec92')]} + '___sec90'), + ('The bias-variance tradeoff', 2, None, '___sec91'), + ('Training and testing data', 2, None, '___sec92'), + ('Procedure to find a predictor', 2, None, '___sec93'), + ('What we want', 2, None, '___sec94'), + ('The expected generalization error', 2, None, '___sec95'), + ('Elaborating a little bit more', 2, None, '___sec96'), + ('The bias', 2, None, '___sec97'), + ('The variance', 2, None, '___sec98'), + ('Summing up', 2, None, '___sec99'), + ('Logistic Regression', 2, None, '___sec100'), + ('Basics', 2, None, '___sec101'), + ('Linear classifier', 2, None, '___sec102'), + ('Some selected properties', 2, None, '___sec103'), + ('The cross-entropy as a cost function for logistic regression', + 2, + None, + '___sec104'), + ('Maximum likelihood', 2, None, '___sec105'), + ('Minimizing the cross entropy', 2, None, '___sec106')]} end of tocinfo --> @@ -337,19 +351,33 @@ MathJax.Hub.Config({
  • Resampling methods: Jackknife and Bootstrap
  • Resampling methods: Jackknife
  • Resampling methods: Jackknife estimator
    • -
  • Resampling methods: Jackknife sample code
    • -
  • Resampling methods: Bootstrap
    • -
  • Resampling methods: Bootstrap background
    • -
  • Resampling methods: More Bootstrap background
    • -
  • Resampling methods: Bootstrap approach
    • -
  • Resampling methods: Bootstrap algorithm
    • -
  • Resampling methods: Bootstrap steps
    • -
  • Resampling methods: Blocking
    • -
  • Blocking Transformations
    • -
  • Blocking Transformations
    • -
  • Blocking Transformations, getting there
    • -
  • Blocking Transformations, final expressions
    • -
  • "Code examples for Blocking, Jackknife and bootstrap":"https://github.com/CompPhysics/MachineLearning/tree/master/doc/Programs/ResamplingAnalysisScripts"
    • +
  • Resampling methods: Bootstrap
    • +
  • Resampling methods: Bootstrap background
    • +
  • Resampling methods: More Bootstrap background
    • +
  • Resampling methods: Bootstrap approach
    • +
  • Resampling methods: Bootstrap steps
    • +
  • Resampling methods: Blocking
    • +
  • Blocking Transformations
    • +
  • Blocking Transformations
    • +
  • Blocking Transformations, getting there
    • +
  • Blocking Transformations, final expressions
    • +
  • "Code examples for Blocking, Jackknife and bootstrap":"https://github.com/CompPhysics/MachineLearning/tree/master/doc/Programs/ResamplingAnalysisScripts"
    • +
  • The bias-variance tradeoff
    • +
  • Training and testing data
    • +
  • Procedure to find a predictor
    • +
  • What we want
    • +
  • The expected generalization error
    • +
  • Elaborating a little bit more
    • +
  • The bias
    • +
  • The variance
    • +
  • Summing up
    • +
  • Logistic Regression
    • +
  • Basics
    • +
  • Linear classifier
    • +
  • Some selected properties
    • +
  • The cross-entropy as a cost function for logistic regression
    • +
  • Maximum likelihood
    • +
  • Minimizing the cross entropy
    • @@ -421,7 +449,7 @@ value of a set of measurements.
  • 64
  • 65
  • ...
    • -
  • 94
    • +
  • 109
  • »
    • diff --git a/doc/pub/Regression/html/._Regression-bs056.html b/doc/pub/Regression/html/._Regression-bs056.html index 1a6ac22be..a34c9c040 100644 --- a/doc/pub/Regression/html/._Regression-bs056.html +++ b/doc/pub/Regression/html/._Regression-bs056.html @@ -194,32 +194,46 @@ Automatically generated HTML file from DocOnce source '___sec77'), ('Resampling methods: Jackknife', 2, None, '___sec78'), ('Resampling methods: Jackknife estimator', 2, None, '___sec79'), - ('Resampling methods: Jackknife sample code', - 2, - None, - '___sec80'), - ('Resampling methods: Bootstrap', 2, None, '___sec81'), - ('Resampling methods: Bootstrap background', 2, None, '___sec82'), + ('Resampling methods: Bootstrap', 2, None, '___sec80'), + ('Resampling methods: Bootstrap background', 2, None, '___sec81'), ('Resampling methods: More Bootstrap background', 2, None, - '___sec83'), - ('Resampling methods: Bootstrap approach', 2, None, '___sec84'), - ('Resampling methods: Bootstrap algorithm', 2, None, '___sec85'), - ('Resampling methods: Bootstrap steps', 2, None, '___sec86'), - ('Resampling methods: Blocking', 2, None, '___sec87'), - ('Blocking Transformations', 2, None, '___sec88'), - ('Blocking Transformations', 2, None, '___sec89'), - ('Blocking Transformations, getting there', 2, None, '___sec90'), + '___sec82'), + ('Resampling methods: Bootstrap approach', 2, None, '___sec83'), + ('Resampling methods: Bootstrap steps', 2, None, '___sec84'), + ('Resampling methods: Blocking', 2, None, '___sec85'), + ('Blocking Transformations', 2, None, '___sec86'), + ('Blocking Transformations', 2, None, '___sec87'), + ('Blocking Transformations, getting there', 2, None, '___sec88'), ('Blocking Transformations, final expressions', 2, None, - '___sec91'), + '___sec89'), ('"Code examples for Blocking, Jackknife and ' 'bootstrap":"https://github.com/CompPhysics/MachineLearning/tree/master/doc/Programs/ResamplingAnalysisScripts"', 2, None, - '___sec92')]} + '___sec90'), + ('The bias-variance tradeoff', 2, None, '___sec91'), + ('Training and testing data', 2, None, '___sec92'), + ('Procedure to find a predictor', 2, None, '___sec93'), + ('What we want', 2, None, '___sec94'), + ('The expected generalization error', 2, None, '___sec95'), + ('Elaborating a little bit more', 2, None, '___sec96'), + ('The bias', 2, None, '___sec97'), + ('The variance', 2, None, '___sec98'), + ('Summing up', 2, None, '___sec99'), + ('Logistic Regression', 2, None, '___sec100'), + ('Basics', 2, None, '___sec101'), + ('Linear classifier', 2, None, '___sec102'), + ('Some selected properties', 2, None, '___sec103'), + ('The cross-entropy as a cost function for logistic regression', + 2, + None, + '___sec104'), + ('Maximum likelihood', 2, None, '___sec105'), + ('Minimizing the cross entropy', 2, None, '___sec106')]} end of tocinfo --> @@ -337,19 +351,33 @@ MathJax.Hub.Config({
  • Resampling methods: Jackknife and Bootstrap
  • Resampling methods: Jackknife
  • Resampling methods: Jackknife estimator
    • -
  • Resampling methods: Jackknife sample code
    • -
  • Resampling methods: Bootstrap
    • -
  • Resampling methods: Bootstrap background
    • -
  • Resampling methods: More Bootstrap background
    • -
  • Resampling methods: Bootstrap approach
    • -
  • Resampling methods: Bootstrap algorithm
    • -
  • Resampling methods: Bootstrap steps
    • -
  • Resampling methods: Blocking
    • -
  • Blocking Transformations
    • -
  • Blocking Transformations
    • -
  • Blocking Transformations, getting there
    • -
  • Blocking Transformations, final expressions
    • -
  • "Code examples for Blocking, Jackknife and bootstrap":"https://github.com/CompPhysics/MachineLearning/tree/master/doc/Programs/ResamplingAnalysisScripts"
    • +
  • Resampling methods: Bootstrap
    • +
  • Resampling methods: Bootstrap background
    • +
  • Resampling methods: More Bootstrap background
    • +
  • Resampling methods: Bootstrap approach
    • +
  • Resampling methods: Bootstrap steps
    • +
  • Resampling methods: Blocking
    • +
  • Blocking Transformations
    • +
  • Blocking Transformations
    • +
  • Blocking Transformations, getting there
    • +
  • Blocking Transformations, final expressions
    • +
  • "Code examples for Blocking, Jackknife and bootstrap":"https://github.com/CompPhysics/MachineLearning/tree/master/doc/Programs/ResamplingAnalysisScripts"
    • +
  • The bias-variance tradeoff
    • +
  • Training and testing data
    • +
  • Procedure to find a predictor
    • +
  • What we want
    • +
  • The expected generalization error
    • +
  • Elaborating a little bit more
    • +
  • The bias
    • +
  • The variance
    • +
  • Summing up
    • +
  • Logistic Regression
    • +
  • Basics
    • +
  • Linear classifier
    • +
  • Some selected properties
    • +
  • The cross-entropy as a cost function for logistic regression
    • +
  • Maximum likelihood
    • +
  • Minimizing the cross entropy
    • @@ -417,7 +445,7 @@ interested in finding the few lowest moments, like the mean
  • 65
  • 66
  • ...
    • -
  • 94
    • +
  • 109
  • »
    • diff --git a/doc/pub/Regression/html/._Regression-bs057.html b/doc/pub/Regression/html/._Regression-bs057.html index 9bb2aac13..e9cc28318 100644 --- a/doc/pub/Regression/html/._Regression-bs057.html +++ b/doc/pub/Regression/html/._Regression-bs057.html @@ -194,32 +194,46 @@ Automatically generated HTML file from DocOnce source '___sec77'), ('Resampling methods: Jackknife', 2, None, '___sec78'), ('Resampling methods: Jackknife estimator', 2, None, '___sec79'), - ('Resampling methods: Jackknife sample code', - 2, - None, - '___sec80'), - ('Resampling methods: Bootstrap', 2, None, '___sec81'), - ('Resampling methods: Bootstrap background', 2, None, '___sec82'), + ('Resampling methods: Bootstrap', 2, None, '___sec80'), + ('Resampling methods: Bootstrap background', 2, None, '___sec81'), ('Resampling methods: More Bootstrap background', 2, None, - '___sec83'), - ('Resampling methods: Bootstrap approach', 2, None, '___sec84'), - ('Resampling methods: Bootstrap algorithm', 2, None, '___sec85'), - ('Resampling methods: Bootstrap steps', 2, None, '___sec86'), - ('Resampling methods: Blocking', 2, None, '___sec87'), - ('Blocking Transformations', 2, None, '___sec88'), - ('Blocking Transformations', 2, None, '___sec89'), - ('Blocking Transformations, getting there', 2, None, '___sec90'), + '___sec82'), + ('Resampling methods: Bootstrap approach', 2, None, '___sec83'), + ('Resampling methods: Bootstrap steps', 2, None, '___sec84'), + ('Resampling methods: Blocking', 2, None, '___sec85'), + ('Blocking Transformations', 2, None, '___sec86'), + ('Blocking Transformations', 2, None, '___sec87'), + ('Blocking Transformations, getting there', 2, None, '___sec88'), ('Blocking Transformations, final expressions', 2, None, - '___sec91'), + '___sec89'), ('"Code examples for Blocking, Jackknife and ' 'bootstrap":"https://github.com/CompPhysics/MachineLearning/tree/master/doc/Programs/ResamplingAnalysisScripts"', 2, None, - '___sec92')]} + '___sec90'), + ('The bias-variance tradeoff', 2, None, '___sec91'), + ('Training and testing data', 2, None, '___sec92'), + ('Procedure to find a predictor', 2, None, '___sec93'), + ('What we want', 2, None, '___sec94'), + ('The expected generalization error', 2, None, '___sec95'), + ('Elaborating a little bit more', 2, None, '___sec96'), + ('The bias', 2, None, '___sec97'), + ('The variance', 2, None, '___sec98'), + ('Summing up', 2, None, '___sec99'), + ('Logistic Regression', 2, None, '___sec100'), + ('Basics', 2, None, '___sec101'), + ('Linear classifier', 2, None, '___sec102'), + ('Some selected properties', 2, None, '___sec103'), + ('The cross-entropy as a cost function for logistic regression', + 2, + None, + '___sec104'), + ('Maximum likelihood', 2, None, '___sec105'), + ('Minimizing the cross entropy', 2, None, '___sec106')]} end of tocinfo --> @@ -337,19 +351,33 @@ MathJax.Hub.Config({
  • Resampling methods: Jackknife and Bootstrap
  • Resampling methods: Jackknife
  • Resampling methods: Jackknife estimator
    • -
  • Resampling methods: Jackknife sample code
    • -
  • Resampling methods: Bootstrap
    • -
  • Resampling methods: Bootstrap background
    • -
  • Resampling methods: More Bootstrap background
    • -
  • Resampling methods: Bootstrap approach
    • -
  • Resampling methods: Bootstrap algorithm
    • -
  • Resampling methods: Bootstrap steps
    • -
  • Resampling methods: Blocking
    • -
  • Blocking Transformations
    • -
  • Blocking Transformations
    • -
  • Blocking Transformations, getting there
    • -
  • Blocking Transformations, final expressions
    • -
  • "Code examples for Blocking, Jackknife and bootstrap":"https://github.com/CompPhysics/MachineLearning/tree/master/doc/Programs/ResamplingAnalysisScripts"
    • +
  • Resampling methods: Bootstrap
    • +
  • Resampling methods: Bootstrap background
    • +
  • Resampling methods: More Bootstrap background
    • +
  • Resampling methods: Bootstrap approach
    • +
  • Resampling methods: Bootstrap steps
    • +
  • Resampling methods: Blocking
    • +
  • Blocking Transformations
    • +
  • Blocking Transformations
    • +
  • Blocking Transformations, getting there
    • +
  • Blocking Transformations, final expressions
    • +
  • "Code examples for Blocking, Jackknife and bootstrap":"https://github.com/CompPhysics/MachineLearning/tree/master/doc/Programs/ResamplingAnalysisScripts"
    • +
  • The bias-variance tradeoff
    • +
  • Training and testing data
    • +
  • Procedure to find a predictor
    • +
  • What we want
    • +
  • The expected generalization error
    • +
  • Elaborating a little bit more
    • +
  • The bias
    • +
  • The variance
    • +
  • Summing up
    • +
  • Logistic Regression
    • +
  • Basics
    • +
  • Linear classifier
    • +
  • Some selected properties
    • +
  • The cross-entropy as a cost function for logistic regression
    • +
  • Maximum likelihood
    • +
  • Minimizing the cross entropy
    • @@ -415,7 +443,7 @@ $$
  • 66
  • 67
  • ...
    • -
  • 94
    • +
  • 109
  • »
    • diff --git a/doc/pub/Regression/html/._Regression-bs058.html b/doc/pub/Regression/html/._Regression-bs058.html index 29e806ffa..5804df5b1 100644 --- a/doc/pub/Regression/html/._Regression-bs058.html +++ b/doc/pub/Regression/html/._Regression-bs058.html @@ -194,32 +194,46 @@ Automatically generated HTML file from DocOnce source '___sec77'), ('Resampling methods: Jackknife', 2, None, '___sec78'), ('Resampling methods: Jackknife estimator', 2, None, '___sec79'), - ('Resampling methods: Jackknife sample code', - 2, - None, - '___sec80'), - ('Resampling methods: Bootstrap', 2, None, '___sec81'), - ('Resampling methods: Bootstrap background', 2, None, '___sec82'), + ('Resampling methods: Bootstrap', 2, None, '___sec80'), + ('Resampling methods: Bootstrap background', 2, None, '___sec81'), ('Resampling methods: More Bootstrap background', 2, None, - '___sec83'), - ('Resampling methods: Bootstrap approach', 2, None, '___sec84'), - ('Resampling methods: Bootstrap algorithm', 2, None, '___sec85'), - ('Resampling methods: Bootstrap steps', 2, None, '___sec86'), - ('Resampling methods: Blocking', 2, None, '___sec87'), - ('Blocking Transformations', 2, None, '___sec88'), - ('Blocking Transformations', 2, None, '___sec89'), - ('Blocking Transformations, getting there', 2, None, '___sec90'), + '___sec82'), + ('Resampling methods: Bootstrap approach', 2, None, '___sec83'), + ('Resampling methods: Bootstrap steps', 2, None, '___sec84'), + ('Resampling methods: Blocking', 2, None, '___sec85'), + ('Blocking Transformations', 2, None, '___sec86'), + ('Blocking Transformations', 2, None, '___sec87'), + ('Blocking Transformations, getting there', 2, None, '___sec88'), ('Blocking Transformations, final expressions', 2, None, - '___sec91'), + '___sec89'), ('"Code examples for Blocking, Jackknife and ' 'bootstrap":"https://github.com/CompPhysics/MachineLearning/tree/master/doc/Programs/ResamplingAnalysisScripts"', 2, None, - '___sec92')]} + '___sec90'), + ('The bias-variance tradeoff', 2, None, '___sec91'), + ('Training and testing data', 2, None, '___sec92'), + ('Procedure to find a predictor', 2, None, '___sec93'), + ('What we want', 2, None, '___sec94'), + ('The expected generalization error', 2, None, '___sec95'), + ('Elaborating a little bit more', 2, None, '___sec96'), + ('The bias', 2, None, '___sec97'), + ('The variance', 2, None, '___sec98'), + ('Summing up', 2, None, '___sec99'), + ('Logistic Regression', 2, None, '___sec100'), + ('Basics', 2, None, '___sec101'), + ('Linear classifier', 2, None, '___sec102'), + ('Some selected properties', 2, None, '___sec103'), + ('The cross-entropy as a cost function for logistic regression', + 2, + None, + '___sec104'), + ('Maximum likelihood', 2, None, '___sec105'), + ('Minimizing the cross entropy', 2, None, '___sec106')]} end of tocinfo --> @@ -337,19 +351,33 @@ MathJax.Hub.Config({
  • Resampling methods: Jackknife and Bootstrap
  • Resampling methods: Jackknife
  • Resampling methods: Jackknife estimator
    • -
  • Resampling methods: Jackknife sample code
    • -
  • Resampling methods: Bootstrap
    • -
  • Resampling methods: Bootstrap background
    • -
  • Resampling methods: More Bootstrap background
    • -
  • Resampling methods: Bootstrap approach
    • -
  • Resampling methods: Bootstrap algorithm
    • -
  • Resampling methods: Bootstrap steps
    • -
  • Resampling methods: Blocking
    • -
  • Blocking Transformations
    • -
  • Blocking Transformations
    • -
  • Blocking Transformations, getting there
    • -
  • Blocking Transformations, final expressions
    • -
  • "Code examples for Blocking, Jackknife and bootstrap":"https://github.com/CompPhysics/MachineLearning/tree/master/doc/Programs/ResamplingAnalysisScripts"
    • +
  • Resampling methods: Bootstrap
    • +
  • Resampling methods: Bootstrap background
    • +
  • Resampling methods: More Bootstrap background
    • +
  • Resampling methods: Bootstrap approach
    • +
  • Resampling methods: Bootstrap steps
    • +
  • Resampling methods: Blocking
    • +
  • Blocking Transformations
    • +
  • Blocking Transformations
    • +
  • Blocking Transformations, getting there
    • +
  • Blocking Transformations, final expressions
    • +
  • "Code examples for Blocking, Jackknife and bootstrap":"https://github.com/CompPhysics/MachineLearning/tree/master/doc/Programs/ResamplingAnalysisScripts"
    • +
  • The bias-variance tradeoff
    • +
  • Training and testing data
    • +
  • Procedure to find a predictor
    • +
  • What we want
    • +
  • The expected generalization error
    • +
  • Elaborating a little bit more
    • +
  • The bias
    • +
  • The variance
    • +
  • Summing up
    • +
  • Logistic Regression
    • +
  • Basics
    • +
  • Linear classifier
    • +
  • Some selected properties
    • +
  • The cross-entropy as a cost function for logistic regression
    • +
  • Maximum likelihood
    • +
  • Minimizing the cross entropy
    • @@ -410,7 +438,7 @@ and covariance \( \mathrm{cov}(X,Y) \).
  • 67
  • 68
  • ...
    • -
  • 94
    • +
  • 109
  • »
    • diff --git a/doc/pub/Regression/html/._Regression-bs059.html b/doc/pub/Regression/html/._Regression-bs059.html index 669ad5cd5..72e7d0808 100644 --- a/doc/pub/Regression/html/._Regression-bs059.html +++ b/doc/pub/Regression/html/._Regression-bs059.html @@ -194,32 +194,46 @@ Automatically generated HTML file from DocOnce source '___sec77'), ('Resampling methods: Jackknife', 2, None, '___sec78'), ('Resampling methods: Jackknife estimator', 2, None, '___sec79'), - ('Resampling methods: Jackknife sample code', - 2, - None, - '___sec80'), - ('Resampling methods: Bootstrap', 2, None, '___sec81'), - ('Resampling methods: Bootstrap background', 2, None, '___sec82'), + ('Resampling methods: Bootstrap', 2, None, '___sec80'), + ('Resampling methods: Bootstrap background', 2, None, '___sec81'), ('Resampling methods: More Bootstrap background', 2, None, - '___sec83'), - ('Resampling methods: Bootstrap approach', 2, None, '___sec84'), - ('Resampling methods: Bootstrap algorithm', 2, None, '___sec85'), - ('Resampling methods: Bootstrap steps', 2, None, '___sec86'), - ('Resampling methods: Blocking', 2, None, '___sec87'), - ('Blocking Transformations', 2, None, '___sec88'), - ('Blocking Transformations', 2, None, '___sec89'), - ('Blocking Transformations, getting there', 2, None, '___sec90'), + '___sec82'), + ('Resampling methods: Bootstrap approach', 2, None, '___sec83'), + ('Resampling methods: Bootstrap steps', 2, None, '___sec84'), + ('Resampling methods: Blocking', 2, None, '___sec85'), + ('Blocking Transformations', 2, None, '___sec86'), + ('Blocking Transformations', 2, None, '___sec87'), + ('Blocking Transformations, getting there', 2, None, '___sec88'), ('Blocking Transformations, final expressions', 2, None, - '___sec91'), + '___sec89'), ('"Code examples for Blocking, Jackknife and ' 'bootstrap":"https://github.com/CompPhysics/MachineLearning/tree/master/doc/Programs/ResamplingAnalysisScripts"', 2, None, - '___sec92')]} + '___sec90'), + ('The bias-variance tradeoff', 2, None, '___sec91'), + ('Training and testing data', 2, None, '___sec92'), + ('Procedure to find a predictor', 2, None, '___sec93'), + ('What we want', 2, None, '___sec94'), + ('The expected generalization error', 2, None, '___sec95'), + ('Elaborating a little bit more', 2, None, '___sec96'), + ('The bias', 2, None, '___sec97'), + ('The variance', 2, None, '___sec98'), + ('Summing up', 2, None, '___sec99'), + ('Logistic Regression', 2, None, '___sec100'), + ('Basics', 2, None, '___sec101'), + ('Linear classifier', 2, None, '___sec102'), + ('Some selected properties', 2, None, '___sec103'), + ('The cross-entropy as a cost function for logistic regression', + 2, + None, + '___sec104'), + ('Maximum likelihood', 2, None, '___sec105'), + ('Minimizing the cross entropy', 2, None, '___sec106')]} end of tocinfo --> @@ -337,19 +351,33 @@ MathJax.Hub.Config({
  • Resampling methods: Jackknife and Bootstrap
  • Resampling methods: Jackknife
  • Resampling methods: Jackknife estimator
    • -
  • Resampling methods: Jackknife sample code
    • -
  • Resampling methods: Bootstrap
    • -
  • Resampling methods: Bootstrap background
    • -
  • Resampling methods: More Bootstrap background
    • -
  • Resampling methods: Bootstrap approach
    • -
  • Resampling methods: Bootstrap algorithm
    • -
  • Resampling methods: Bootstrap steps
    • -
  • Resampling methods: Blocking
    • -
  • Blocking Transformations
    • -
  • Blocking Transformations
    • -
  • Blocking Transformations, getting there
    • -
  • Blocking Transformations, final expressions
    • -
  • "Code examples for Blocking, Jackknife and bootstrap":"https://github.com/CompPhysics/MachineLearning/tree/master/doc/Programs/ResamplingAnalysisScripts"
    • +
  • Resampling methods: Bootstrap
    • +
  • Resampling methods: Bootstrap background
    • +
  • Resampling methods: More Bootstrap background
    • +
  • Resampling methods: Bootstrap approach
    • +
  • Resampling methods: Bootstrap steps
    • +
  • Resampling methods: Blocking
    • +
  • Blocking Transformations
    • +
  • Blocking Transformations
    • +
  • Blocking Transformations, getting there
    • +
  • Blocking Transformations, final expressions
    • +
  • "Code examples for Blocking, Jackknife and bootstrap":"https://github.com/CompPhysics/MachineLearning/tree/master/doc/Programs/ResamplingAnalysisScripts"
    • +
  • The bias-variance tradeoff
    • +
  • Training and testing data
    • +
  • Procedure to find a predictor
    • +
  • What we want
    • +
  • The expected generalization error
    • +
  • Elaborating a little bit more
    • +
  • The bias
    • +
  • The variance
    • +
  • Summing up
    • +
  • Logistic Regression
    • +
  • Basics
    • +
  • Linear classifier
    • +
  • Some selected properties
    • +
  • The cross-entropy as a cost function for logistic regression
    • +
  • Maximum likelihood
    • +
  • Minimizing the cross entropy
    • @@ -421,7 +449,7 @@ true PDFs behind, which we usually do not have.
  • 68
  • 69
  • ...
    • -
  • 94
    • +
  • 109
  • »
    • diff --git a/doc/pub/Regression/html/._Regression-bs060.html b/doc/pub/Regression/html/._Regression-bs060.html index d413567a3..cfc8e28fa 100644 --- a/doc/pub/Regression/html/._Regression-bs060.html +++ b/doc/pub/Regression/html/._Regression-bs060.html @@ -194,32 +194,46 @@ Automatically generated HTML file from DocOnce source '___sec77'), ('Resampling methods: Jackknife', 2, None, '___sec78'), ('Resampling methods: Jackknife estimator', 2, None, '___sec79'), - ('Resampling methods: Jackknife sample code', - 2, - None, - '___sec80'), - ('Resampling methods: Bootstrap', 2, None, '___sec81'), - ('Resampling methods: Bootstrap background', 2, None, '___sec82'), + ('Resampling methods: Bootstrap', 2, None, '___sec80'), + ('Resampling methods: Bootstrap background', 2, None, '___sec81'), ('Resampling methods: More Bootstrap background', 2, None, - '___sec83'), - ('Resampling methods: Bootstrap approach', 2, None, '___sec84'), - ('Resampling methods: Bootstrap algorithm', 2, None, '___sec85'), - ('Resampling methods: Bootstrap steps', 2, None, '___sec86'), - ('Resampling methods: Blocking', 2, None, '___sec87'), - ('Blocking Transformations', 2, None, '___sec88'), - ('Blocking Transformations', 2, None, '___sec89'), - ('Blocking Transformations, getting there', 2, None, '___sec90'), + '___sec82'), + ('Resampling methods: Bootstrap approach', 2, None, '___sec83'), + ('Resampling methods: Bootstrap steps', 2, None, '___sec84'), + ('Resampling methods: Blocking', 2, None, '___sec85'), + ('Blocking Transformations', 2, None, '___sec86'), + ('Blocking Transformations', 2, None, '___sec87'), + ('Blocking Transformations, getting there', 2, None, '___sec88'), ('Blocking Transformations, final expressions', 2, None, - '___sec91'), + '___sec89'), ('"Code examples for Blocking, Jackknife and ' 'bootstrap":"https://github.com/CompPhysics/MachineLearning/tree/master/doc/Programs/ResamplingAnalysisScripts"', 2, None, - '___sec92')]} + '___sec90'), + ('The bias-variance tradeoff', 2, None, '___sec91'), + ('Training and testing data', 2, None, '___sec92'), + ('Procedure to find a predictor', 2, None, '___sec93'), + ('What we want', 2, None, '___sec94'), + ('The expected generalization error', 2, None, '___sec95'), + ('Elaborating a little bit more', 2, None, '___sec96'), + ('The bias', 2, None, '___sec97'), + ('The variance', 2, None, '___sec98'), + ('Summing up', 2, None, '___sec99'), + ('Logistic Regression', 2, None, '___sec100'), + ('Basics', 2, None, '___sec101'), + ('Linear classifier', 2, None, '___sec102'), + ('Some selected properties', 2, None, '___sec103'), + ('The cross-entropy as a cost function for logistic regression', + 2, + None, + '___sec104'), + ('Maximum likelihood', 2, None, '___sec105'), + ('Minimizing the cross entropy', 2, None, '___sec106')]} end of tocinfo --> @@ -337,19 +351,33 @@ MathJax.Hub.Config({
  • Resampling methods: Jackknife and Bootstrap
  • Resampling methods: Jackknife
  • Resampling methods: Jackknife estimator
    • -
  • Resampling methods: Jackknife sample code
    • -
  • Resampling methods: Bootstrap
    • -
  • Resampling methods: Bootstrap background
    • -
  • Resampling methods: More Bootstrap background
    • -
  • Resampling methods: Bootstrap approach
    • -
  • Resampling methods: Bootstrap algorithm
    • -
  • Resampling methods: Bootstrap steps
    • -
  • Resampling methods: Blocking
    • -
  • Blocking Transformations
    • -
  • Blocking Transformations
    • -
  • Blocking Transformations, getting there
    • -
  • Blocking Transformations, final expressions
    • -
  • "Code examples for Blocking, Jackknife and bootstrap":"https://github.com/CompPhysics/MachineLearning/tree/master/doc/Programs/ResamplingAnalysisScripts"
    • +
  • Resampling methods: Bootstrap
    • +
  • Resampling methods: Bootstrap background
    • +
  • Resampling methods: More Bootstrap background
    • +
  • Resampling methods: Bootstrap approach
    • +
  • Resampling methods: Bootstrap steps
    • +
  • Resampling methods: Blocking
    • +
  • Blocking Transformations
    • +
  • Blocking Transformations
    • +
  • Blocking Transformations, getting there
    • +
  • Blocking Transformations, final expressions
    • +
  • "Code examples for Blocking, Jackknife and bootstrap":"https://github.com/CompPhysics/MachineLearning/tree/master/doc/Programs/ResamplingAnalysisScripts"
    • +
  • The bias-variance tradeoff
    • +
  • Training and testing data
    • +
  • Procedure to find a predictor
    • +
  • What we want
    • +
  • The expected generalization error
    • +
  • Elaborating a little bit more
    • +
  • The bias
    • +
  • The variance
    • +
  • Summing up
    • +
  • Logistic Regression
    • +
  • Basics
    • +
  • Linear classifier
    • +
  • Some selected properties
    • +
  • The cross-entropy as a cost function for logistic regression
    • +
  • Maximum likelihood
    • +
  • Minimizing the cross entropy
    • @@ -411,7 +439,7 @@ means.
  • 69
  • 70
  • ...
    • -
  • 94
    • +
  • 109
  • »
    • diff --git a/doc/pub/Regression/html/._Regression-bs061.html b/doc/pub/Regression/html/._Regression-bs061.html index 7c8258557..51d926aac 100644 --- a/doc/pub/Regression/html/._Regression-bs061.html +++ b/doc/pub/Regression/html/._Regression-bs061.html @@ -194,32 +194,46 @@ Automatically generated HTML file from DocOnce source '___sec77'), ('Resampling methods: Jackknife', 2, None, '___sec78'), ('Resampling methods: Jackknife estimator', 2, None, '___sec79'), - ('Resampling methods: Jackknife sample code', - 2, - None, - '___sec80'), - ('Resampling methods: Bootstrap', 2, None, '___sec81'), - ('Resampling methods: Bootstrap background', 2, None, '___sec82'), + ('Resampling methods: Bootstrap', 2, None, '___sec80'), + ('Resampling methods: Bootstrap background', 2, None, '___sec81'), ('Resampling methods: More Bootstrap background', 2, None, - '___sec83'), - ('Resampling methods: Bootstrap approach', 2, None, '___sec84'), - ('Resampling methods: Bootstrap algorithm', 2, None, '___sec85'), - ('Resampling methods: Bootstrap steps', 2, None, '___sec86'), - ('Resampling methods: Blocking', 2, None, '___sec87'), - ('Blocking Transformations', 2, None, '___sec88'), - ('Blocking Transformations', 2, None, '___sec89'), - ('Blocking Transformations, getting there', 2, None, '___sec90'), + '___sec82'), + ('Resampling methods: Bootstrap approach', 2, None, '___sec83'), + ('Resampling methods: Bootstrap steps', 2, None, '___sec84'), + ('Resampling methods: Blocking', 2, None, '___sec85'), + ('Blocking Transformations', 2, None, '___sec86'), + ('Blocking Transformations', 2, None, '___sec87'), + ('Blocking Transformations, getting there', 2, None, '___sec88'), ('Blocking Transformations, final expressions', 2, None, - '___sec91'), + '___sec89'), ('"Code examples for Blocking, Jackknife and ' 'bootstrap":"https://github.com/CompPhysics/MachineLearning/tree/master/doc/Programs/ResamplingAnalysisScripts"', 2, None, - '___sec92')]} + '___sec90'), + ('The bias-variance tradeoff', 2, None, '___sec91'), + ('Training and testing data', 2, None, '___sec92'), + ('Procedure to find a predictor', 2, None, '___sec93'), + ('What we want', 2, None, '___sec94'), + ('The expected generalization error', 2, None, '___sec95'), + ('Elaborating a little bit more', 2, None, '___sec96'), + ('The bias', 2, None, '___sec97'), + ('The variance', 2, None, '___sec98'), + ('Summing up', 2, None, '___sec99'), + ('Logistic Regression', 2, None, '___sec100'), + ('Basics', 2, None, '___sec101'), + ('Linear classifier', 2, None, '___sec102'), + ('Some selected properties', 2, None, '___sec103'), + ('The cross-entropy as a cost function for logistic regression', + 2, + None, + '___sec104'), + ('Maximum likelihood', 2, None, '___sec105'), + ('Minimizing the cross entropy', 2, None, '___sec106')]} end of tocinfo --> @@ -337,19 +351,33 @@ MathJax.Hub.Config({
  • Resampling methods: Jackknife and Bootstrap
  • Resampling methods: Jackknife
  • Resampling methods: Jackknife estimator
    • -
  • Resampling methods: Jackknife sample code
    • -
  • Resampling methods: Bootstrap
    • -
  • Resampling methods: Bootstrap background
    • -
  • Resampling methods: More Bootstrap background
    • -
  • Resampling methods: Bootstrap approach
    • -
  • Resampling methods: Bootstrap algorithm
    • -
  • Resampling methods: Bootstrap steps
    • -
  • Resampling methods: Blocking
    • -
  • Blocking Transformations
    • -
  • Blocking Transformations
    • -
  • Blocking Transformations, getting there
    • -
  • Blocking Transformations, final expressions
    • -
  • "Code examples for Blocking, Jackknife and bootstrap":"https://github.com/CompPhysics/MachineLearning/tree/master/doc/Programs/ResamplingAnalysisScripts"
    • +
  • Resampling methods: Bootstrap
    • +
  • Resampling methods: Bootstrap background
    • +
  • Resampling methods: More Bootstrap background
    • +
  • Resampling methods: Bootstrap approach
    • +
  • Resampling methods: Bootstrap steps
    • +
  • Resampling methods: Blocking
    • +
  • Blocking Transformations
    • +
  • Blocking Transformations
    • +
  • Blocking Transformations, getting there
    • +
  • Blocking Transformations, final expressions
    • +
  • "Code examples for Blocking, Jackknife and bootstrap":"https://github.com/CompPhysics/MachineLearning/tree/master/doc/Programs/ResamplingAnalysisScripts"
    • +
  • The bias-variance tradeoff
    • +
  • Training and testing data
    • +
  • Procedure to find a predictor
    • +
  • What we want
    • +
  • The expected generalization error
    • +
  • Elaborating a little bit more
    • +
  • The bias
    • +
  • The variance
    • +
  • Summing up
    • +
  • Logistic Regression
    • +
  • Basics
    • +
  • Linear classifier
    • +
  • Some selected properties
    • +
  • The cross-entropy as a cost function for logistic regression
    • +
  • Maximum likelihood
    • +
  • Minimizing the cross entropy
    • @@ -410,7 +438,7 @@ And in particular we are interested in its variance \( \mathrm{var}(\overline X_
  • 70
  • 71
  • ...
    • -
  • 94
    • +
  • 109
  • »
    • diff --git a/doc/pub/Regression/html/._Regression-bs062.html b/doc/pub/Regression/html/._Regression-bs062.html index 84e3e15e6..435aa866e 100644 --- a/doc/pub/Regression/html/._Regression-bs062.html +++ b/doc/pub/Regression/html/._Regression-bs062.html @@ -194,32 +194,46 @@ Automatically generated HTML file from DocOnce source '___sec77'), ('Resampling methods: Jackknife', 2, None, '___sec78'), ('Resampling methods: Jackknife estimator', 2, None, '___sec79'), - ('Resampling methods: Jackknife sample code', - 2, - None, - '___sec80'), - ('Resampling methods: Bootstrap', 2, None, '___sec81'), - ('Resampling methods: Bootstrap background', 2, None, '___sec82'), + ('Resampling methods: Bootstrap', 2, None, '___sec80'), + ('Resampling methods: Bootstrap background', 2, None, '___sec81'), ('Resampling methods: More Bootstrap background', 2, None, - '___sec83'), - ('Resampling methods: Bootstrap approach', 2, None, '___sec84'), - ('Resampling methods: Bootstrap algorithm', 2, None, '___sec85'), - ('Resampling methods: Bootstrap steps', 2, None, '___sec86'), - ('Resampling methods: Blocking', 2, None, '___sec87'), - ('Blocking Transformations', 2, None, '___sec88'), - ('Blocking Transformations', 2, None, '___sec89'), - ('Blocking Transformations, getting there', 2, None, '___sec90'), + '___sec82'), + ('Resampling methods: Bootstrap approach', 2, None, '___sec83'), + ('Resampling methods: Bootstrap steps', 2, None, '___sec84'), + ('Resampling methods: Blocking', 2, None, '___sec85'), + ('Blocking Transformations', 2, None, '___sec86'), + ('Blocking Transformations', 2, None, '___sec87'), + ('Blocking Transformations, getting there', 2, None, '___sec88'), ('Blocking Transformations, final expressions', 2, None, - '___sec91'), + '___sec89'), ('"Code examples for Blocking, Jackknife and ' 'bootstrap":"https://github.com/CompPhysics/MachineLearning/tree/master/doc/Programs/ResamplingAnalysisScripts"', 2, None, - '___sec92')]} + '___sec90'), + ('The bias-variance tradeoff', 2, None, '___sec91'), + ('Training and testing data', 2, None, '___sec92'), + ('Procedure to find a predictor', 2, None, '___sec93'), + ('What we want', 2, None, '___sec94'), + ('The expected generalization error', 2, None, '___sec95'), + ('Elaborating a little bit more', 2, None, '___sec96'), + ('The bias', 2, None, '___sec97'), + ('The variance', 2, None, '___sec98'), + ('Summing up', 2, None, '___sec99'), + ('Logistic Regression', 2, None, '___sec100'), + ('Basics', 2, None, '___sec101'), + ('Linear classifier', 2, None, '___sec102'), + ('Some selected properties', 2, None, '___sec103'), + ('The cross-entropy as a cost function for logistic regression', + 2, + None, + '___sec104'), + ('Maximum likelihood', 2, None, '___sec105'), + ('Minimizing the cross entropy', 2, None, '___sec106')]} end of tocinfo --> @@ -337,19 +351,33 @@ MathJax.Hub.Config({
  • Resampling methods: Jackknife and Bootstrap
  • Resampling methods: Jackknife
  • Resampling methods: Jackknife estimator
    • -
  • Resampling methods: Jackknife sample code
    • -
  • Resampling methods: Bootstrap
    • -
  • Resampling methods: Bootstrap background
    • -
  • Resampling methods: More Bootstrap background
    • -
  • Resampling methods: Bootstrap approach
    • -
  • Resampling methods: Bootstrap algorithm
    • -
  • Resampling methods: Bootstrap steps
    • -
  • Resampling methods: Blocking
    • -
  • Blocking Transformations
    • -
  • Blocking Transformations
    • -
  • Blocking Transformations, getting there
    • -
  • Blocking Transformations, final expressions
    • -
  • "Code examples for Blocking, Jackknife and bootstrap":"https://github.com/CompPhysics/MachineLearning/tree/master/doc/Programs/ResamplingAnalysisScripts"
    • +
  • Resampling methods: Bootstrap
    • +
  • Resampling methods: Bootstrap background
    • +
  • Resampling methods: More Bootstrap background
    • +
  • Resampling methods: Bootstrap approach
    • +
  • Resampling methods: Bootstrap steps
    • +
  • Resampling methods: Blocking
    • +
  • Blocking Transformations
    • +
  • Blocking Transformations
    • +
  • Blocking Transformations, getting there
    • +
  • Blocking Transformations, final expressions
    • +
  • "Code examples for Blocking, Jackknife and bootstrap":"https://github.com/CompPhysics/MachineLearning/tree/master/doc/Programs/ResamplingAnalysisScripts"
    • +
  • The bias-variance tradeoff
    • +
  • Training and testing data
    • +
  • Procedure to find a predictor
    • +
  • What we want
    • +
  • The expected generalization error
    • +
  • Elaborating a little bit more
    • +
  • The bias
    • +
  • The variance
    • +
  • Summing up
    • +
  • Logistic Regression
    • +
  • Basics
    • +
  • Linear classifier
    • +
  • Some selected properties
    • +
  • The cross-entropy as a cost function for logistic regression
    • +
  • Maximum likelihood
    • +
  • Minimizing the cross entropy
    • @@ -414,7 +442,7 @@ $$
  • 71
  • 72
  • ...
    • -
  • 94
    • +
  • 109
  • »
    • diff --git a/doc/pub/Regression/html/._Regression-bs063.html b/doc/pub/Regression/html/._Regression-bs063.html index d66dd45c4..596568c7b 100644 --- a/doc/pub/Regression/html/._Regression-bs063.html +++ b/doc/pub/Regression/html/._Regression-bs063.html @@ -194,32 +194,46 @@ Automatically generated HTML file from DocOnce source '___sec77'), ('Resampling methods: Jackknife', 2, None, '___sec78'), ('Resampling methods: Jackknife estimator', 2, None, '___sec79'), - ('Resampling methods: Jackknife sample code', - 2, - None, - '___sec80'), - ('Resampling methods: Bootstrap', 2, None, '___sec81'), - ('Resampling methods: Bootstrap background', 2, None, '___sec82'), + ('Resampling methods: Bootstrap', 2, None, '___sec80'), + ('Resampling methods: Bootstrap background', 2, None, '___sec81'), ('Resampling methods: More Bootstrap background', 2, None, - '___sec83'), - ('Resampling methods: Bootstrap approach', 2, None, '___sec84'), - ('Resampling methods: Bootstrap algorithm', 2, None, '___sec85'), - ('Resampling methods: Bootstrap steps', 2, None, '___sec86'), - ('Resampling methods: Blocking', 2, None, '___sec87'), - ('Blocking Transformations', 2, None, '___sec88'), - ('Blocking Transformations', 2, None, '___sec89'), - ('Blocking Transformations, getting there', 2, None, '___sec90'), + '___sec82'), + ('Resampling methods: Bootstrap approach', 2, None, '___sec83'), + ('Resampling methods: Bootstrap steps', 2, None, '___sec84'), + ('Resampling methods: Blocking', 2, None, '___sec85'), + ('Blocking Transformations', 2, None, '___sec86'), + ('Blocking Transformations', 2, None, '___sec87'), + ('Blocking Transformations, getting there', 2, None, '___sec88'), ('Blocking Transformations, final expressions', 2, None, - '___sec91'), + '___sec89'), ('"Code examples for Blocking, Jackknife and ' 'bootstrap":"https://github.com/CompPhysics/MachineLearning/tree/master/doc/Programs/ResamplingAnalysisScripts"', 2, None, - '___sec92')]} + '___sec90'), + ('The bias-variance tradeoff', 2, None, '___sec91'), + ('Training and testing data', 2, None, '___sec92'), + ('Procedure to find a predictor', 2, None, '___sec93'), + ('What we want', 2, None, '___sec94'), + ('The expected generalization error', 2, None, '___sec95'), + ('Elaborating a little bit more', 2, None, '___sec96'), + ('The bias', 2, None, '___sec97'), + ('The variance', 2, None, '___sec98'), + ('Summing up', 2, None, '___sec99'), + ('Logistic Regression', 2, None, '___sec100'), + ('Basics', 2, None, '___sec101'), + ('Linear classifier', 2, None, '___sec102'), + ('Some selected properties', 2, None, '___sec103'), + ('The cross-entropy as a cost function for logistic regression', + 2, + None, + '___sec104'), + ('Maximum likelihood', 2, None, '___sec105'), + ('Minimizing the cross entropy', 2, None, '___sec106')]} end of tocinfo --> @@ -337,19 +351,33 @@ MathJax.Hub.Config({
  • Resampling methods: Jackknife and Bootstrap
  • Resampling methods: Jackknife
  • Resampling methods: Jackknife estimator
    • -
  • Resampling methods: Jackknife sample code
    • -
  • Resampling methods: Bootstrap
    • -
  • Resampling methods: Bootstrap background
    • -
  • Resampling methods: More Bootstrap background
    • -
  • Resampling methods: Bootstrap approach
    • -
  • Resampling methods: Bootstrap algorithm
    • -
  • Resampling methods: Bootstrap steps
    • -
  • Resampling methods: Blocking
    • -
  • Blocking Transformations
    • -
  • Blocking Transformations
    • -
  • Blocking Transformations, getting there
    • -
  • Blocking Transformations, final expressions
    • -
  • "Code examples for Blocking, Jackknife and bootstrap":"https://github.com/CompPhysics/MachineLearning/tree/master/doc/Programs/ResamplingAnalysisScripts"
    • +
  • Resampling methods: Bootstrap
    • +
  • Resampling methods: Bootstrap background
    • +
  • Resampling methods: More Bootstrap background
    • +
  • Resampling methods: Bootstrap approach
    • +
  • Resampling methods: Bootstrap steps
    • +
  • Resampling methods: Blocking
    • +
  • Blocking Transformations
    • +
  • Blocking Transformations
    • +
  • Blocking Transformations, getting there
    • +
  • Blocking Transformations, final expressions
    • +
  • "Code examples for Blocking, Jackknife and bootstrap":"https://github.com/CompPhysics/MachineLearning/tree/master/doc/Programs/ResamplingAnalysisScripts"
    • +
  • The bias-variance tradeoff
    • +
  • Training and testing data
    • +
  • Procedure to find a predictor
    • +
  • What we want
    • +
  • The expected generalization error
    • +
  • Elaborating a little bit more
    • +
  • The bias
    • +
  • The variance
    • +
  • Summing up
    • +
  • Logistic Regression
    • +
  • Basics
    • +
  • Linear classifier
    • +
  • Some selected properties
    • +
  • The cross-entropy as a cost function for logistic regression
    • +
  • Maximum likelihood
    • +
  • Minimizing the cross entropy
    • @@ -418,7 +446,7 @@ estimate of the PDF of each of the \( X_i \), estimating all properties of
  • 72
  • 73
  • ...
    • -
  • 94
    • +
  • 109
  • »
    • diff --git a/doc/pub/Regression/html/._Regression-bs064.html b/doc/pub/Regression/html/._Regression-bs064.html index 4ffcec412..bfa49be74 100644 --- a/doc/pub/Regression/html/._Regression-bs064.html +++ b/doc/pub/Regression/html/._Regression-bs064.html @@ -194,32 +194,46 @@ Automatically generated HTML file from DocOnce source '___sec77'), ('Resampling methods: Jackknife', 2, None, '___sec78'), ('Resampling methods: Jackknife estimator', 2, None, '___sec79'), - ('Resampling methods: Jackknife sample code', - 2, - None, - '___sec80'), - ('Resampling methods: Bootstrap', 2, None, '___sec81'), - ('Resampling methods: Bootstrap background', 2, None, '___sec82'), + ('Resampling methods: Bootstrap', 2, None, '___sec80'), + ('Resampling methods: Bootstrap background', 2, None, '___sec81'), ('Resampling methods: More Bootstrap background', 2, None, - '___sec83'), - ('Resampling methods: Bootstrap approach', 2, None, '___sec84'), - ('Resampling methods: Bootstrap algorithm', 2, None, '___sec85'), - ('Resampling methods: Bootstrap steps', 2, None, '___sec86'), - ('Resampling methods: Blocking', 2, None, '___sec87'), - ('Blocking Transformations', 2, None, '___sec88'), - ('Blocking Transformations', 2, None, '___sec89'), - ('Blocking Transformations, getting there', 2, None, '___sec90'), + '___sec82'), + ('Resampling methods: Bootstrap approach', 2, None, '___sec83'), + ('Resampling methods: Bootstrap steps', 2, None, '___sec84'), + ('Resampling methods: Blocking', 2, None, '___sec85'), + ('Blocking Transformations', 2, None, '___sec86'), + ('Blocking Transformations', 2, None, '___sec87'), + ('Blocking Transformations, getting there', 2, None, '___sec88'), ('Blocking Transformations, final expressions', 2, None, - '___sec91'), + '___sec89'), ('"Code examples for Blocking, Jackknife and ' 'bootstrap":"https://github.com/CompPhysics/MachineLearning/tree/master/doc/Programs/ResamplingAnalysisScripts"', 2, None, - '___sec92')]} + '___sec90'), + ('The bias-variance tradeoff', 2, None, '___sec91'), + ('Training and testing data', 2, None, '___sec92'), + ('Procedure to find a predictor', 2, None, '___sec93'), + ('What we want', 2, None, '___sec94'), + ('The expected generalization error', 2, None, '___sec95'), + ('Elaborating a little bit more', 2, None, '___sec96'), + ('The bias', 2, None, '___sec97'), + ('The variance', 2, None, '___sec98'), + ('Summing up', 2, None, '___sec99'), + ('Logistic Regression', 2, None, '___sec100'), + ('Basics', 2, None, '___sec101'), + ('Linear classifier', 2, None, '___sec102'), + ('Some selected properties', 2, None, '___sec103'), + ('The cross-entropy as a cost function for logistic regression', + 2, + None, + '___sec104'), + ('Maximum likelihood', 2, None, '___sec105'), + ('Minimizing the cross entropy', 2, None, '___sec106')]} end of tocinfo --> @@ -337,19 +351,33 @@ MathJax.Hub.Config({
  • Resampling methods: Jackknife and Bootstrap
  • Resampling methods: Jackknife
  • Resampling methods: Jackknife estimator
    • -
  • Resampling methods: Jackknife sample code
    • -
  • Resampling methods: Bootstrap
    • -
  • Resampling methods: Bootstrap background
    • -
  • Resampling methods: More Bootstrap background
    • -
  • Resampling methods: Bootstrap approach
    • -
  • Resampling methods: Bootstrap algorithm
    • -
  • Resampling methods: Bootstrap steps
    • -
  • Resampling methods: Blocking
    • -
  • Blocking Transformations
    • -
  • Blocking Transformations
    • -
  • Blocking Transformations, getting there
    • -
  • Blocking Transformations, final expressions
    • -
  • "Code examples for Blocking, Jackknife and bootstrap":"https://github.com/CompPhysics/MachineLearning/tree/master/doc/Programs/ResamplingAnalysisScripts"
    • +
  • Resampling methods: Bootstrap
    • +
  • Resampling methods: Bootstrap background
    • +
  • Resampling methods: More Bootstrap background
    • +
  • Resampling methods: Bootstrap approach
    • +
  • Resampling methods: Bootstrap steps
    • +
  • Resampling methods: Blocking
    • +
  • Blocking Transformations
    • +
  • Blocking Transformations
    • +
  • Blocking Transformations, getting there
    • +
  • Blocking Transformations, final expressions
    • +
  • "Code examples for Blocking, Jackknife and bootstrap":"https://github.com/CompPhysics/MachineLearning/tree/master/doc/Programs/ResamplingAnalysisScripts"
    • +
  • The bias-variance tradeoff
    • +
  • Training and testing data
    • +
  • Procedure to find a predictor
    • +
  • What we want
    • +
  • The expected generalization error
    • +
  • Elaborating a little bit more
    • +
  • The bias
    • +
  • The variance
    • +
  • Summing up
    • +
  • Logistic Regression
    • +
  • Basics
    • +
  • Linear classifier
    • +
  • Some selected properties
    • +
  • The cross-entropy as a cost function for logistic regression
    • +
  • Maximum likelihood
    • +
  • Minimizing the cross entropy
    • @@ -416,7 +444,7 @@ $$
  • 73
  • 74
  • ...
    • -
  • 94
    • +
  • 109
  • »
    • diff --git a/doc/pub/Regression/html/._Regression-bs065.html b/doc/pub/Regression/html/._Regression-bs065.html index 9fdc61b01..d24d157ae 100644 --- a/doc/pub/Regression/html/._Regression-bs065.html +++ b/doc/pub/Regression/html/._Regression-bs065.html @@ -194,32 +194,46 @@ Automatically generated HTML file from DocOnce source '___sec77'), ('Resampling methods: Jackknife', 2, None, '___sec78'), ('Resampling methods: Jackknife estimator', 2, None, '___sec79'), - ('Resampling methods: Jackknife sample code', - 2, - None, - '___sec80'), - ('Resampling methods: Bootstrap', 2, None, '___sec81'), - ('Resampling methods: Bootstrap background', 2, None, '___sec82'), + ('Resampling methods: Bootstrap', 2, None, '___sec80'), + ('Resampling methods: Bootstrap background', 2, None, '___sec81'), ('Resampling methods: More Bootstrap background', 2, None, - '___sec83'), - ('Resampling methods: Bootstrap approach', 2, None, '___sec84'), - ('Resampling methods: Bootstrap algorithm', 2, None, '___sec85'), - ('Resampling methods: Bootstrap steps', 2, None, '___sec86'), - ('Resampling methods: Blocking', 2, None, '___sec87'), - ('Blocking Transformations', 2, None, '___sec88'), - ('Blocking Transformations', 2, None, '___sec89'), - ('Blocking Transformations, getting there', 2, None, '___sec90'), + '___sec82'), + ('Resampling methods: Bootstrap approach', 2, None, '___sec83'), + ('Resampling methods: Bootstrap steps', 2, None, '___sec84'), + ('Resampling methods: Blocking', 2, None, '___sec85'), + ('Blocking Transformations', 2, None, '___sec86'), + ('Blocking Transformations', 2, None, '___sec87'), + ('Blocking Transformations, getting there', 2, None, '___sec88'), ('Blocking Transformations, final expressions', 2, None, - '___sec91'), + '___sec89'), ('"Code examples for Blocking, Jackknife and ' 'bootstrap":"https://github.com/CompPhysics/MachineLearning/tree/master/doc/Programs/ResamplingAnalysisScripts"', 2, None, - '___sec92')]} + '___sec90'), + ('The bias-variance tradeoff', 2, None, '___sec91'), + ('Training and testing data', 2, None, '___sec92'), + ('Procedure to find a predictor', 2, None, '___sec93'), + ('What we want', 2, None, '___sec94'), + ('The expected generalization error', 2, None, '___sec95'), + ('Elaborating a little bit more', 2, None, '___sec96'), + ('The bias', 2, None, '___sec97'), + ('The variance', 2, None, '___sec98'), + ('Summing up', 2, None, '___sec99'), + ('Logistic Regression', 2, None, '___sec100'), + ('Basics', 2, None, '___sec101'), + ('Linear classifier', 2, None, '___sec102'), + ('Some selected properties', 2, None, '___sec103'), + ('The cross-entropy as a cost function for logistic regression', + 2, + None, + '___sec104'), + ('Maximum likelihood', 2, None, '___sec105'), + ('Minimizing the cross entropy', 2, None, '___sec106')]} end of tocinfo --> @@ -337,19 +351,33 @@ MathJax.Hub.Config({
  • Resampling methods: Jackknife and Bootstrap
  • Resampling methods: Jackknife
  • Resampling methods: Jackknife estimator
    • -
  • Resampling methods: Jackknife sample code
    • -
  • Resampling methods: Bootstrap
    • -
  • Resampling methods: Bootstrap background
    • -
  • Resampling methods: More Bootstrap background
    • -
  • Resampling methods: Bootstrap approach
    • -
  • Resampling methods: Bootstrap algorithm
    • -
  • Resampling methods: Bootstrap steps
    • -
  • Resampling methods: Blocking
    • -
  • Blocking Transformations
    • -
  • Blocking Transformations
    • -
  • Blocking Transformations, getting there
    • -
  • Blocking Transformations, final expressions
    • -
  • "Code examples for Blocking, Jackknife and bootstrap":"https://github.com/CompPhysics/MachineLearning/tree/master/doc/Programs/ResamplingAnalysisScripts"
    • +
  • Resampling methods: Bootstrap
    • +
  • Resampling methods: Bootstrap background
    • +
  • Resampling methods: More Bootstrap background
    • +
  • Resampling methods: Bootstrap approach
    • +
  • Resampling methods: Bootstrap steps
    • +
  • Resampling methods: Blocking
    • +
  • Blocking Transformations
    • +
  • Blocking Transformations
    • +
  • Blocking Transformations, getting there
    • +
  • Blocking Transformations, final expressions
    • +
  • "Code examples for Blocking, Jackknife and bootstrap":"https://github.com/CompPhysics/MachineLearning/tree/master/doc/Programs/ResamplingAnalysisScripts"
    • +
  • The bias-variance tradeoff
    • +
  • Training and testing data
    • +
  • Procedure to find a predictor
    • +
  • What we want
    • +
  • The expected generalization error
    • +
  • Elaborating a little bit more
    • +
  • The bias
    • +
  • The variance
    • +
  • Summing up
    • +
  • Logistic Regression
    • +
  • Basics
    • +
  • Linear classifier
    • +
  • Some selected properties
    • +
  • The cross-entropy as a cost function for logistic regression
    • +
  • Maximum likelihood
    • +
  • Minimizing the cross entropy
    • @@ -428,7 +456,7 @@ measurements in the sample.
  • 74
  • 75
  • ...
    • -
  • 94
    • +
  • 109
  • »
    • diff --git a/doc/pub/Regression/html/._Regression-bs066.html b/doc/pub/Regression/html/._Regression-bs066.html index a02391274..75d55974c 100644 --- a/doc/pub/Regression/html/._Regression-bs066.html +++ b/doc/pub/Regression/html/._Regression-bs066.html @@ -194,32 +194,46 @@ Automatically generated HTML file from DocOnce source '___sec77'), ('Resampling methods: Jackknife', 2, None, '___sec78'), ('Resampling methods: Jackknife estimator', 2, None, '___sec79'), - ('Resampling methods: Jackknife sample code', - 2, - None, - '___sec80'), - ('Resampling methods: Bootstrap', 2, None, '___sec81'), - ('Resampling methods: Bootstrap background', 2, None, '___sec82'), + ('Resampling methods: Bootstrap', 2, None, '___sec80'), + ('Resampling methods: Bootstrap background', 2, None, '___sec81'), ('Resampling methods: More Bootstrap background', 2, None, - '___sec83'), - ('Resampling methods: Bootstrap approach', 2, None, '___sec84'), - ('Resampling methods: Bootstrap algorithm', 2, None, '___sec85'), - ('Resampling methods: Bootstrap steps', 2, None, '___sec86'), - ('Resampling methods: Blocking', 2, None, '___sec87'), - ('Blocking Transformations', 2, None, '___sec88'), - ('Blocking Transformations', 2, None, '___sec89'), - ('Blocking Transformations, getting there', 2, None, '___sec90'), + '___sec82'), + ('Resampling methods: Bootstrap approach', 2, None, '___sec83'), + ('Resampling methods: Bootstrap steps', 2, None, '___sec84'), + ('Resampling methods: Blocking', 2, None, '___sec85'), + ('Blocking Transformations', 2, None, '___sec86'), + ('Blocking Transformations', 2, None, '___sec87'), + ('Blocking Transformations, getting there', 2, None, '___sec88'), ('Blocking Transformations, final expressions', 2, None, - '___sec91'), + '___sec89'), ('"Code examples for Blocking, Jackknife and ' 'bootstrap":"https://github.com/CompPhysics/MachineLearning/tree/master/doc/Programs/ResamplingAnalysisScripts"', 2, None, - '___sec92')]} + '___sec90'), + ('The bias-variance tradeoff', 2, None, '___sec91'), + ('Training and testing data', 2, None, '___sec92'), + ('Procedure to find a predictor', 2, None, '___sec93'), + ('What we want', 2, None, '___sec94'), + ('The expected generalization error', 2, None, '___sec95'), + ('Elaborating a little bit more', 2, None, '___sec96'), + ('The bias', 2, None, '___sec97'), + ('The variance', 2, None, '___sec98'), + ('Summing up', 2, None, '___sec99'), + ('Logistic Regression', 2, None, '___sec100'), + ('Basics', 2, None, '___sec101'), + ('Linear classifier', 2, None, '___sec102'), + ('Some selected properties', 2, None, '___sec103'), + ('The cross-entropy as a cost function for logistic regression', + 2, + None, + '___sec104'), + ('Maximum likelihood', 2, None, '___sec105'), + ('Minimizing the cross entropy', 2, None, '___sec106')]} end of tocinfo --> @@ -337,19 +351,33 @@ MathJax.Hub.Config({
  • Resampling methods: Jackknife and Bootstrap
  • Resampling methods: Jackknife
  • Resampling methods: Jackknife estimator
    • -
  • Resampling methods: Jackknife sample code
    • -
  • Resampling methods: Bootstrap
    • -
  • Resampling methods: Bootstrap background
    • -
  • Resampling methods: More Bootstrap background
    • -
  • Resampling methods: Bootstrap approach
    • -
  • Resampling methods: Bootstrap algorithm
    • -
  • Resampling methods: Bootstrap steps
    • -
  • Resampling methods: Blocking
    • -
  • Blocking Transformations
    • -
  • Blocking Transformations
    • -
  • Blocking Transformations, getting there
    • -
  • Blocking Transformations, final expressions
    • -
  • "Code examples for Blocking, Jackknife and bootstrap":"https://github.com/CompPhysics/MachineLearning/tree/master/doc/Programs/ResamplingAnalysisScripts"
    • +
  • Resampling methods: Bootstrap
    • +
  • Resampling methods: Bootstrap background
    • +
  • Resampling methods: More Bootstrap background
    • +
  • Resampling methods: Bootstrap approach
    • +
  • Resampling methods: Bootstrap steps
    • +
  • Resampling methods: Blocking
    • +
  • Blocking Transformations
    • +
  • Blocking Transformations
    • +
  • Blocking Transformations, getting there
    • +
  • Blocking Transformations, final expressions
    • +
  • "Code examples for Blocking, Jackknife and bootstrap":"https://github.com/CompPhysics/MachineLearning/tree/master/doc/Programs/ResamplingAnalysisScripts"
    • +
  • The bias-variance tradeoff
    • +
  • Training and testing data
    • +
  • Procedure to find a predictor
    • +
  • What we want
    • +
  • The expected generalization error
    • +
  • Elaborating a little bit more
    • +
  • The bias
    • +
  • The variance
    • +
  • Summing up
    • +
  • Logistic Regression
    • +
  • Basics
    • +
  • Linear classifier
    • +
  • Some selected properties
    • +
  • The cross-entropy as a cost function for logistic regression
    • +
  • Maximum likelihood
    • +
  • Minimizing the cross entropy
    • @@ -424,7 +452,7 @@ cannot overlook the always present correlations.
  • 75
  • 76
  • ...
    • -
  • 94
    • +
  • 109
  • »
    • diff --git a/doc/pub/Regression/html/._Regression-bs067.html b/doc/pub/Regression/html/._Regression-bs067.html index b58a4e36f..3a0f4bac7 100644 --- a/doc/pub/Regression/html/._Regression-bs067.html +++ b/doc/pub/Regression/html/._Regression-bs067.html @@ -194,32 +194,46 @@ Automatically generated HTML file from DocOnce source '___sec77'), ('Resampling methods: Jackknife', 2, None, '___sec78'), ('Resampling methods: Jackknife estimator', 2, None, '___sec79'), - ('Resampling methods: Jackknife sample code', - 2, - None, - '___sec80'), - ('Resampling methods: Bootstrap', 2, None, '___sec81'), - ('Resampling methods: Bootstrap background', 2, None, '___sec82'), + ('Resampling methods: Bootstrap', 2, None, '___sec80'), + ('Resampling methods: Bootstrap background', 2, None, '___sec81'), ('Resampling methods: More Bootstrap background', 2, None, - '___sec83'), - ('Resampling methods: Bootstrap approach', 2, None, '___sec84'), - ('Resampling methods: Bootstrap algorithm', 2, None, '___sec85'), - ('Resampling methods: Bootstrap steps', 2, None, '___sec86'), - ('Resampling methods: Blocking', 2, None, '___sec87'), - ('Blocking Transformations', 2, None, '___sec88'), - ('Blocking Transformations', 2, None, '___sec89'), - ('Blocking Transformations, getting there', 2, None, '___sec90'), + '___sec82'), + ('Resampling methods: Bootstrap approach', 2, None, '___sec83'), + ('Resampling methods: Bootstrap steps', 2, None, '___sec84'), + ('Resampling methods: Blocking', 2, None, '___sec85'), + ('Blocking Transformations', 2, None, '___sec86'), + ('Blocking Transformations', 2, None, '___sec87'), + ('Blocking Transformations, getting there', 2, None, '___sec88'), ('Blocking Transformations, final expressions', 2, None, - '___sec91'), + '___sec89'), ('"Code examples for Blocking, Jackknife and ' 'bootstrap":"https://github.com/CompPhysics/MachineLearning/tree/master/doc/Programs/ResamplingAnalysisScripts"', 2, None, - '___sec92')]} + '___sec90'), + ('The bias-variance tradeoff', 2, None, '___sec91'), + ('Training and testing data', 2, None, '___sec92'), + ('Procedure to find a predictor', 2, None, '___sec93'), + ('What we want', 2, None, '___sec94'), + ('The expected generalization error', 2, None, '___sec95'), + ('Elaborating a little bit more', 2, None, '___sec96'), + ('The bias', 2, None, '___sec97'), + ('The variance', 2, None, '___sec98'), + ('Summing up', 2, None, '___sec99'), + ('Logistic Regression', 2, None, '___sec100'), + ('Basics', 2, None, '___sec101'), + ('Linear classifier', 2, None, '___sec102'), + ('Some selected properties', 2, None, '___sec103'), + ('The cross-entropy as a cost function for logistic regression', + 2, + None, + '___sec104'), + ('Maximum likelihood', 2, None, '___sec105'), + ('Minimizing the cross entropy', 2, None, '___sec106')]} end of tocinfo --> @@ -337,19 +351,33 @@ MathJax.Hub.Config({
  • Resampling methods: Jackknife and Bootstrap
  • Resampling methods: Jackknife
  • Resampling methods: Jackknife estimator
    • -
  • Resampling methods: Jackknife sample code
    • -
  • Resampling methods: Bootstrap
    • -
  • Resampling methods: Bootstrap background
    • -
  • Resampling methods: More Bootstrap background
    • -
  • Resampling methods: Bootstrap approach
    • -
  • Resampling methods: Bootstrap algorithm
    • -
  • Resampling methods: Bootstrap steps
    • -
  • Resampling methods: Blocking
    • -
  • Blocking Transformations
    • -
  • Blocking Transformations
    • -
  • Blocking Transformations, getting there
    • -
  • Blocking Transformations, final expressions
    • -
  • "Code examples for Blocking, Jackknife and bootstrap":"https://github.com/CompPhysics/MachineLearning/tree/master/doc/Programs/ResamplingAnalysisScripts"
    • +
  • Resampling methods: Bootstrap
    • +
  • Resampling methods: Bootstrap background
    • +
  • Resampling methods: More Bootstrap background
    • +
  • Resampling methods: Bootstrap approach
    • +
  • Resampling methods: Bootstrap steps
    • +
  • Resampling methods: Blocking
    • +
  • Blocking Transformations
    • +
  • Blocking Transformations
    • +
  • Blocking Transformations, getting there
    • +
  • Blocking Transformations, final expressions
    • +
  • "Code examples for Blocking, Jackknife and bootstrap":"https://github.com/CompPhysics/MachineLearning/tree/master/doc/Programs/ResamplingAnalysisScripts"
    • +
  • The bias-variance tradeoff
    • +
  • Training and testing data
    • +
  • Procedure to find a predictor
    • +
  • What we want
    • +
  • The expected generalization error
    • +
  • Elaborating a little bit more
    • +
  • The bias
    • +
  • The variance
    • +
  • Summing up
    • +
  • Logistic Regression
    • +
  • Basics
    • +
  • Linear classifier
    • +
  • Some selected properties
    • +
  • The cross-entropy as a cost function for logistic regression
    • +
  • Maximum likelihood
    • +
  • Minimizing the cross entropy
    • @@ -418,7 +446,7 @@ measurements. For uncorrelated measurements this second term is zero.
  • 76
  • 77
  • ...
    • -
  • 94
    • +
  • 109
  • »
    • diff --git a/doc/pub/Regression/html/._Regression-bs068.html b/doc/pub/Regression/html/._Regression-bs068.html index fdd71b3a6..d4406fa1e 100644 --- a/doc/pub/Regression/html/._Regression-bs068.html +++ b/doc/pub/Regression/html/._Regression-bs068.html @@ -194,32 +194,46 @@ Automatically generated HTML file from DocOnce source '___sec77'), ('Resampling methods: Jackknife', 2, None, '___sec78'), ('Resampling methods: Jackknife estimator', 2, None, '___sec79'), - ('Resampling methods: Jackknife sample code', - 2, - None, - '___sec80'), - ('Resampling methods: Bootstrap', 2, None, '___sec81'), - ('Resampling methods: Bootstrap background', 2, None, '___sec82'), + ('Resampling methods: Bootstrap', 2, None, '___sec80'), + ('Resampling methods: Bootstrap background', 2, None, '___sec81'), ('Resampling methods: More Bootstrap background', 2, None, - '___sec83'), - ('Resampling methods: Bootstrap approach', 2, None, '___sec84'), - ('Resampling methods: Bootstrap algorithm', 2, None, '___sec85'), - ('Resampling methods: Bootstrap steps', 2, None, '___sec86'), - ('Resampling methods: Blocking', 2, None, '___sec87'), - ('Blocking Transformations', 2, None, '___sec88'), - ('Blocking Transformations', 2, None, '___sec89'), - ('Blocking Transformations, getting there', 2, None, '___sec90'), + '___sec82'), + ('Resampling methods: Bootstrap approach', 2, None, '___sec83'), + ('Resampling methods: Bootstrap steps', 2, None, '___sec84'), + ('Resampling methods: Blocking', 2, None, '___sec85'), + ('Blocking Transformations', 2, None, '___sec86'), + ('Blocking Transformations', 2, None, '___sec87'), + ('Blocking Transformations, getting there', 2, None, '___sec88'), ('Blocking Transformations, final expressions', 2, None, - '___sec91'), + '___sec89'), ('"Code examples for Blocking, Jackknife and ' 'bootstrap":"https://github.com/CompPhysics/MachineLearning/tree/master/doc/Programs/ResamplingAnalysisScripts"', 2, None, - '___sec92')]} + '___sec90'), + ('The bias-variance tradeoff', 2, None, '___sec91'), + ('Training and testing data', 2, None, '___sec92'), + ('Procedure to find a predictor', 2, None, '___sec93'), + ('What we want', 2, None, '___sec94'), + ('The expected generalization error', 2, None, '___sec95'), + ('Elaborating a little bit more', 2, None, '___sec96'), + ('The bias', 2, None, '___sec97'), + ('The variance', 2, None, '___sec98'), + ('Summing up', 2, None, '___sec99'), + ('Logistic Regression', 2, None, '___sec100'), + ('Basics', 2, None, '___sec101'), + ('Linear classifier', 2, None, '___sec102'), + ('Some selected properties', 2, None, '___sec103'), + ('The cross-entropy as a cost function for logistic regression', + 2, + None, + '___sec104'), + ('Maximum likelihood', 2, None, '___sec105'), + ('Minimizing the cross entropy', 2, None, '___sec106')]} end of tocinfo --> @@ -337,19 +351,33 @@ MathJax.Hub.Config({
  • Resampling methods: Jackknife and Bootstrap
  • Resampling methods: Jackknife
  • Resampling methods: Jackknife estimator
    • -
  • Resampling methods: Jackknife sample code
    • -
  • Resampling methods: Bootstrap
    • -
  • Resampling methods: Bootstrap background
    • -
  • Resampling methods: More Bootstrap background
    • -
  • Resampling methods: Bootstrap approach
    • -
  • Resampling methods: Bootstrap algorithm
    • -
  • Resampling methods: Bootstrap steps
    • -
  • Resampling methods: Blocking
    • -
  • Blocking Transformations
    • -
  • Blocking Transformations
    • -
  • Blocking Transformations, getting there
    • -
  • Blocking Transformations, final expressions
    • -
  • "Code examples for Blocking, Jackknife and bootstrap":"https://github.com/CompPhysics/MachineLearning/tree/master/doc/Programs/ResamplingAnalysisScripts"
    • +
  • Resampling methods: Bootstrap
    • +
  • Resampling methods: Bootstrap background
    • +
  • Resampling methods: More Bootstrap background
    • +
  • Resampling methods: Bootstrap approach
    • +
  • Resampling methods: Bootstrap steps
    • +
  • Resampling methods: Blocking
    • +
  • Blocking Transformations
    • +
  • Blocking Transformations
    • +
  • Blocking Transformations, getting there
    • +
  • Blocking Transformations, final expressions
    • +
  • "Code examples for Blocking, Jackknife and bootstrap":"https://github.com/CompPhysics/MachineLearning/tree/master/doc/Programs/ResamplingAnalysisScripts"
    • +
  • The bias-variance tradeoff
    • +
  • Training and testing data
    • +
  • Procedure to find a predictor
    • +
  • What we want
    • +
  • The expected generalization error
    • +
  • Elaborating a little bit more
    • +
  • The bias
    • +
  • The variance
    • +
  • Summing up
    • +
  • Logistic Regression
    • +
  • Basics
    • +
  • Linear classifier
    • +
  • Some selected properties
    • +
  • The cross-entropy as a cost function for logistic regression
    • +
  • Maximum likelihood
    • +
  • Minimizing the cross entropy
    • @@ -411,7 +439,7 @@ have to be stored throughout the experiment.
  • 77
  • 78
  • ...
    • -
  • 94
    • +
  • 109
  • »
    • diff --git a/doc/pub/Regression/html/._Regression-bs069.html b/doc/pub/Regression/html/._Regression-bs069.html index a02876967..60edd32c7 100644 --- a/doc/pub/Regression/html/._Regression-bs069.html +++ b/doc/pub/Regression/html/._Regression-bs069.html @@ -194,32 +194,46 @@ Automatically generated HTML file from DocOnce source '___sec77'), ('Resampling methods: Jackknife', 2, None, '___sec78'), ('Resampling methods: Jackknife estimator', 2, None, '___sec79'), - ('Resampling methods: Jackknife sample code', - 2, - None, - '___sec80'), - ('Resampling methods: Bootstrap', 2, None, '___sec81'), - ('Resampling methods: Bootstrap background', 2, None, '___sec82'), + ('Resampling methods: Bootstrap', 2, None, '___sec80'), + ('Resampling methods: Bootstrap background', 2, None, '___sec81'), ('Resampling methods: More Bootstrap background', 2, None, - '___sec83'), - ('Resampling methods: Bootstrap approach', 2, None, '___sec84'), - ('Resampling methods: Bootstrap algorithm', 2, None, '___sec85'), - ('Resampling methods: Bootstrap steps', 2, None, '___sec86'), - ('Resampling methods: Blocking', 2, None, '___sec87'), - ('Blocking Transformations', 2, None, '___sec88'), - ('Blocking Transformations', 2, None, '___sec89'), - ('Blocking Transformations, getting there', 2, None, '___sec90'), + '___sec82'), + ('Resampling methods: Bootstrap approach', 2, None, '___sec83'), + ('Resampling methods: Bootstrap steps', 2, None, '___sec84'), + ('Resampling methods: Blocking', 2, None, '___sec85'), + ('Blocking Transformations', 2, None, '___sec86'), + ('Blocking Transformations', 2, None, '___sec87'), + ('Blocking Transformations, getting there', 2, None, '___sec88'), ('Blocking Transformations, final expressions', 2, None, - '___sec91'), + '___sec89'), ('"Code examples for Blocking, Jackknife and ' 'bootstrap":"https://github.com/CompPhysics/MachineLearning/tree/master/doc/Programs/ResamplingAnalysisScripts"', 2, None, - '___sec92')]} + '___sec90'), + ('The bias-variance tradeoff', 2, None, '___sec91'), + ('Training and testing data', 2, None, '___sec92'), + ('Procedure to find a predictor', 2, None, '___sec93'), + ('What we want', 2, None, '___sec94'), + ('The expected generalization error', 2, None, '___sec95'), + ('Elaborating a little bit more', 2, None, '___sec96'), + ('The bias', 2, None, '___sec97'), + ('The variance', 2, None, '___sec98'), + ('Summing up', 2, None, '___sec99'), + ('Logistic Regression', 2, None, '___sec100'), + ('Basics', 2, None, '___sec101'), + ('Linear classifier', 2, None, '___sec102'), + ('Some selected properties', 2, None, '___sec103'), + ('The cross-entropy as a cost function for logistic regression', + 2, + None, + '___sec104'), + ('Maximum likelihood', 2, None, '___sec105'), + ('Minimizing the cross entropy', 2, None, '___sec106')]} end of tocinfo --> @@ -337,19 +351,33 @@ MathJax.Hub.Config({
  • Resampling methods: Jackknife and Bootstrap
  • Resampling methods: Jackknife
  • Resampling methods: Jackknife estimator
    • -
  • Resampling methods: Jackknife sample code
    • -
  • Resampling methods: Bootstrap
    • -
  • Resampling methods: Bootstrap background
    • -
  • Resampling methods: More Bootstrap background
    • -
  • Resampling methods: Bootstrap approach
    • -
  • Resampling methods: Bootstrap algorithm
    • -
  • Resampling methods: Bootstrap steps
    • -
  • Resampling methods: Blocking
    • -
  • Blocking Transformations
    • -
  • Blocking Transformations
    • -
  • Blocking Transformations, getting there
    • -
  • Blocking Transformations, final expressions
    • -
  • "Code examples for Blocking, Jackknife and bootstrap":"https://github.com/CompPhysics/MachineLearning/tree/master/doc/Programs/ResamplingAnalysisScripts"
    • +
  • Resampling methods: Bootstrap
    • +
  • Resampling methods: Bootstrap background
    • +
  • Resampling methods: More Bootstrap background
    • +
  • Resampling methods: Bootstrap approach
    • +
  • Resampling methods: Bootstrap steps
    • +
  • Resampling methods: Blocking
    • +
  • Blocking Transformations
    • +
  • Blocking Transformations
    • +
  • Blocking Transformations, getting there
    • +
  • Blocking Transformations, final expressions
    • +
  • "Code examples for Blocking, Jackknife and bootstrap":"https://github.com/CompPhysics/MachineLearning/tree/master/doc/Programs/ResamplingAnalysisScripts"
    • +
  • The bias-variance tradeoff
    • +
  • Training and testing data
    • +
  • Procedure to find a predictor
    • +
  • What we want
    • +
  • The expected generalization error
    • +
  • Elaborating a little bit more
    • +
  • The bias
    • +
  • The variance
    • +
  • Summing up
    • +
  • Logistic Regression
    • +
  • Basics
    • +
  • Linear classifier
    • +
  • Some selected properties
    • +
  • The cross-entropy as a cost function for logistic regression
    • +
  • Maximum likelihood
    • +
  • Minimizing the cross entropy
    • @@ -422,7 +450,7 @@ starting always at \( 1 \) for \( d=0 \).
  • 78
  • 79
  • ...
    • -
  • 94
    • +
  • 109
  • »
    • diff --git a/doc/pub/Regression/html/._Regression-bs070.html b/doc/pub/Regression/html/._Regression-bs070.html index d52b589b5..97dd38c1d 100644 --- a/doc/pub/Regression/html/._Regression-bs070.html +++ b/doc/pub/Regression/html/._Regression-bs070.html @@ -194,32 +194,46 @@ Automatically generated HTML file from DocOnce source '___sec77'), ('Resampling methods: Jackknife', 2, None, '___sec78'), ('Resampling methods: Jackknife estimator', 2, None, '___sec79'), - ('Resampling methods: Jackknife sample code', - 2, - None, - '___sec80'), - ('Resampling methods: Bootstrap', 2, None, '___sec81'), - ('Resampling methods: Bootstrap background', 2, None, '___sec82'), + ('Resampling methods: Bootstrap', 2, None, '___sec80'), + ('Resampling methods: Bootstrap background', 2, None, '___sec81'), ('Resampling methods: More Bootstrap background', 2, None, - '___sec83'), - ('Resampling methods: Bootstrap approach', 2, None, '___sec84'), - ('Resampling methods: Bootstrap algorithm', 2, None, '___sec85'), - ('Resampling methods: Bootstrap steps', 2, None, '___sec86'), - ('Resampling methods: Blocking', 2, None, '___sec87'), - ('Blocking Transformations', 2, None, '___sec88'), - ('Blocking Transformations', 2, None, '___sec89'), - ('Blocking Transformations, getting there', 2, None, '___sec90'), + '___sec82'), + ('Resampling methods: Bootstrap approach', 2, None, '___sec83'), + ('Resampling methods: Bootstrap steps', 2, None, '___sec84'), + ('Resampling methods: Blocking', 2, None, '___sec85'), + ('Blocking Transformations', 2, None, '___sec86'), + ('Blocking Transformations', 2, None, '___sec87'), + ('Blocking Transformations, getting there', 2, None, '___sec88'), ('Blocking Transformations, final expressions', 2, None, - '___sec91'), + '___sec89'), ('"Code examples for Blocking, Jackknife and ' 'bootstrap":"https://github.com/CompPhysics/MachineLearning/tree/master/doc/Programs/ResamplingAnalysisScripts"', 2, None, - '___sec92')]} + '___sec90'), + ('The bias-variance tradeoff', 2, None, '___sec91'), + ('Training and testing data', 2, None, '___sec92'), + ('Procedure to find a predictor', 2, None, '___sec93'), + ('What we want', 2, None, '___sec94'), + ('The expected generalization error', 2, None, '___sec95'), + ('Elaborating a little bit more', 2, None, '___sec96'), + ('The bias', 2, None, '___sec97'), + ('The variance', 2, None, '___sec98'), + ('Summing up', 2, None, '___sec99'), + ('Logistic Regression', 2, None, '___sec100'), + ('Basics', 2, None, '___sec101'), + ('Linear classifier', 2, None, '___sec102'), + ('Some selected properties', 2, None, '___sec103'), + ('The cross-entropy as a cost function for logistic regression', + 2, + None, + '___sec104'), + ('Maximum likelihood', 2, None, '___sec105'), + ('Minimizing the cross entropy', 2, None, '___sec106')]} end of tocinfo --> @@ -337,19 +351,33 @@ MathJax.Hub.Config({
  • Resampling methods: Jackknife and Bootstrap
  • Resampling methods: Jackknife
  • Resampling methods: Jackknife estimator
    • -
  • Resampling methods: Jackknife sample code
    • -
  • Resampling methods: Bootstrap
    • -
  • Resampling methods: Bootstrap background
    • -
  • Resampling methods: More Bootstrap background
    • -
  • Resampling methods: Bootstrap approach
    • -
  • Resampling methods: Bootstrap algorithm
    • -
  • Resampling methods: Bootstrap steps
    • -
  • Resampling methods: Blocking
    • -
  • Blocking Transformations
    • -
  • Blocking Transformations
    • -
  • Blocking Transformations, getting there
    • -
  • Blocking Transformations, final expressions
    • -
  • "Code examples for Blocking, Jackknife and bootstrap":"https://github.com/CompPhysics/MachineLearning/tree/master/doc/Programs/ResamplingAnalysisScripts"
    • +
  • Resampling methods: Bootstrap
    • +
  • Resampling methods: Bootstrap background
    • +
  • Resampling methods: More Bootstrap background
    • +
  • Resampling methods: Bootstrap approach
    • +
  • Resampling methods: Bootstrap steps
    • +
  • Resampling methods: Blocking
    • +
  • Blocking Transformations
    • +
  • Blocking Transformations
    • +
  • Blocking Transformations, getting there
    • +
  • Blocking Transformations, final expressions
    • +
  • "Code examples for Blocking, Jackknife and bootstrap":"https://github.com/CompPhysics/MachineLearning/tree/master/doc/Programs/ResamplingAnalysisScripts"
    • +
  • The bias-variance tradeoff
    • +
  • Training and testing data
    • +
  • Procedure to find a predictor
    • +
  • What we want
    • +
  • The expected generalization error
    • +
  • Elaborating a little bit more
    • +
  • The bias
    • +
  • The variance
    • +
  • Summing up
    • +
  • Logistic Regression
    • +
  • Basics
    • +
  • Linear classifier
    • +
  • Some selected properties
    • +
  • The cross-entropy as a cost function for logistic regression
    • +
  • Maximum likelihood
    • +
  • Minimizing the cross entropy
    • @@ -422,7 +450,7 @@ $$
  • 79
  • 80
  • ...
    • -
  • 94
    • +
  • 109
  • »
    • diff --git a/doc/pub/Regression/html/._Regression-bs071.html b/doc/pub/Regression/html/._Regression-bs071.html index 6611fda95..46f64d394 100644 --- a/doc/pub/Regression/html/._Regression-bs071.html +++ b/doc/pub/Regression/html/._Regression-bs071.html @@ -194,32 +194,46 @@ Automatically generated HTML file from DocOnce source '___sec77'), ('Resampling methods: Jackknife', 2, None, '___sec78'), ('Resampling methods: Jackknife estimator', 2, None, '___sec79'), - ('Resampling methods: Jackknife sample code', - 2, - None, - '___sec80'), - ('Resampling methods: Bootstrap', 2, None, '___sec81'), - ('Resampling methods: Bootstrap background', 2, None, '___sec82'), + ('Resampling methods: Bootstrap', 2, None, '___sec80'), + ('Resampling methods: Bootstrap background', 2, None, '___sec81'), ('Resampling methods: More Bootstrap background', 2, None, - '___sec83'), - ('Resampling methods: Bootstrap approach', 2, None, '___sec84'), - ('Resampling methods: Bootstrap algorithm', 2, None, '___sec85'), - ('Resampling methods: Bootstrap steps', 2, None, '___sec86'), - ('Resampling methods: Blocking', 2, None, '___sec87'), - ('Blocking Transformations', 2, None, '___sec88'), - ('Blocking Transformations', 2, None, '___sec89'), - ('Blocking Transformations, getting there', 2, None, '___sec90'), + '___sec82'), + ('Resampling methods: Bootstrap approach', 2, None, '___sec83'), + ('Resampling methods: Bootstrap steps', 2, None, '___sec84'), + ('Resampling methods: Blocking', 2, None, '___sec85'), + ('Blocking Transformations', 2, None, '___sec86'), + ('Blocking Transformations', 2, None, '___sec87'), + ('Blocking Transformations, getting there', 2, None, '___sec88'), ('Blocking Transformations, final expressions', 2, None, - '___sec91'), + '___sec89'), ('"Code examples for Blocking, Jackknife and ' 'bootstrap":"https://github.com/CompPhysics/MachineLearning/tree/master/doc/Programs/ResamplingAnalysisScripts"', 2, None, - '___sec92')]} + '___sec90'), + ('The bias-variance tradeoff', 2, None, '___sec91'), + ('Training and testing data', 2, None, '___sec92'), + ('Procedure to find a predictor', 2, None, '___sec93'), + ('What we want', 2, None, '___sec94'), + ('The expected generalization error', 2, None, '___sec95'), + ('Elaborating a little bit more', 2, None, '___sec96'), + ('The bias', 2, None, '___sec97'), + ('The variance', 2, None, '___sec98'), + ('Summing up', 2, None, '___sec99'), + ('Logistic Regression', 2, None, '___sec100'), + ('Basics', 2, None, '___sec101'), + ('Linear classifier', 2, None, '___sec102'), + ('Some selected properties', 2, None, '___sec103'), + ('The cross-entropy as a cost function for logistic regression', + 2, + None, + '___sec104'), + ('Maximum likelihood', 2, None, '___sec105'), + ('Minimizing the cross entropy', 2, None, '___sec106')]} end of tocinfo --> @@ -337,19 +351,33 @@ MathJax.Hub.Config({
  • Resampling methods: Jackknife and Bootstrap
  • Resampling methods: Jackknife
  • Resampling methods: Jackknife estimator
    • -
  • Resampling methods: Jackknife sample code
    • -
  • Resampling methods: Bootstrap
    • -
  • Resampling methods: Bootstrap background
    • -
  • Resampling methods: More Bootstrap background
    • -
  • Resampling methods: Bootstrap approach
    • -
  • Resampling methods: Bootstrap algorithm
    • -
  • Resampling methods: Bootstrap steps
    • -
  • Resampling methods: Blocking
    • -
  • Blocking Transformations
    • -
  • Blocking Transformations
    • -
  • Blocking Transformations, getting there
    • -
  • Blocking Transformations, final expressions
    • -
  • "Code examples for Blocking, Jackknife and bootstrap":"https://github.com/CompPhysics/MachineLearning/tree/master/doc/Programs/ResamplingAnalysisScripts"
    • +
  • Resampling methods: Bootstrap
    • +
  • Resampling methods: Bootstrap background
    • +
  • Resampling methods: More Bootstrap background
    • +
  • Resampling methods: Bootstrap approach
    • +
  • Resampling methods: Bootstrap steps
    • +
  • Resampling methods: Blocking
    • +
  • Blocking Transformations
    • +
  • Blocking Transformations
    • +
  • Blocking Transformations, getting there
    • +
  • Blocking Transformations, final expressions
    • +
  • "Code examples for Blocking, Jackknife and bootstrap":"https://github.com/CompPhysics/MachineLearning/tree/master/doc/Programs/ResamplingAnalysisScripts"
    • +
  • The bias-variance tradeoff
    • +
  • Training and testing data
    • +
  • Procedure to find a predictor
    • +
  • What we want
    • +
  • The expected generalization error
    • +
  • Elaborating a little bit more
    • +
  • The bias
    • +
  • The variance
    • +
  • Summing up
    • +
  • Logistic Regression
    • +
  • Basics
    • +
  • Linear classifier
    • +
  • Some selected properties
    • +
  • The cross-entropy as a cost function for logistic regression
    • +
  • Maximum likelihood
    • +
  • Minimizing the cross entropy
    • @@ -414,7 +442,7 @@ measurements is very large.
  • 80
  • 81
  • ...
    • -
  • 94
    • +
  • 109
  • »
    • diff --git a/doc/pub/Regression/html/._Regression-bs072.html b/doc/pub/Regression/html/._Regression-bs072.html index 79ad09f91..3cf54fa93 100644 --- a/doc/pub/Regression/html/._Regression-bs072.html +++ b/doc/pub/Regression/html/._Regression-bs072.html @@ -194,32 +194,46 @@ Automatically generated HTML file from DocOnce source '___sec77'), ('Resampling methods: Jackknife', 2, None, '___sec78'), ('Resampling methods: Jackknife estimator', 2, None, '___sec79'), - ('Resampling methods: Jackknife sample code', - 2, - None, - '___sec80'), - ('Resampling methods: Bootstrap', 2, None, '___sec81'), - ('Resampling methods: Bootstrap background', 2, None, '___sec82'), + ('Resampling methods: Bootstrap', 2, None, '___sec80'), + ('Resampling methods: Bootstrap background', 2, None, '___sec81'), ('Resampling methods: More Bootstrap background', 2, None, - '___sec83'), - ('Resampling methods: Bootstrap approach', 2, None, '___sec84'), - ('Resampling methods: Bootstrap algorithm', 2, None, '___sec85'), - ('Resampling methods: Bootstrap steps', 2, None, '___sec86'), - ('Resampling methods: Blocking', 2, None, '___sec87'), - ('Blocking Transformations', 2, None, '___sec88'), - ('Blocking Transformations', 2, None, '___sec89'), - ('Blocking Transformations, getting there', 2, None, '___sec90'), + '___sec82'), + ('Resampling methods: Bootstrap approach', 2, None, '___sec83'), + ('Resampling methods: Bootstrap steps', 2, None, '___sec84'), + ('Resampling methods: Blocking', 2, None, '___sec85'), + ('Blocking Transformations', 2, None, '___sec86'), + ('Blocking Transformations', 2, None, '___sec87'), + ('Blocking Transformations, getting there', 2, None, '___sec88'), ('Blocking Transformations, final expressions', 2, None, - '___sec91'), + '___sec89'), ('"Code examples for Blocking, Jackknife and ' 'bootstrap":"https://github.com/CompPhysics/MachineLearning/tree/master/doc/Programs/ResamplingAnalysisScripts"', 2, None, - '___sec92')]} + '___sec90'), + ('The bias-variance tradeoff', 2, None, '___sec91'), + ('Training and testing data', 2, None, '___sec92'), + ('Procedure to find a predictor', 2, None, '___sec93'), + ('What we want', 2, None, '___sec94'), + ('The expected generalization error', 2, None, '___sec95'), + ('Elaborating a little bit more', 2, None, '___sec96'), + ('The bias', 2, None, '___sec97'), + ('The variance', 2, None, '___sec98'), + ('Summing up', 2, None, '___sec99'), + ('Logistic Regression', 2, None, '___sec100'), + ('Basics', 2, None, '___sec101'), + ('Linear classifier', 2, None, '___sec102'), + ('Some selected properties', 2, None, '___sec103'), + ('The cross-entropy as a cost function for logistic regression', + 2, + None, + '___sec104'), + ('Maximum likelihood', 2, None, '___sec105'), + ('Minimizing the cross entropy', 2, None, '___sec106')]} end of tocinfo --> @@ -337,19 +351,33 @@ MathJax.Hub.Config({
  • Resampling methods: Jackknife and Bootstrap
  • Resampling methods: Jackknife
  • Resampling methods: Jackknife estimator
    • -
  • Resampling methods: Jackknife sample code
    • -
  • Resampling methods: Bootstrap
    • -
  • Resampling methods: Bootstrap background
    • -
  • Resampling methods: More Bootstrap background
    • -
  • Resampling methods: Bootstrap approach
    • -
  • Resampling methods: Bootstrap algorithm
    • -
  • Resampling methods: Bootstrap steps
    • -
  • Resampling methods: Blocking
    • -
  • Blocking Transformations
    • -
  • Blocking Transformations
    • -
  • Blocking Transformations, getting there
    • -
  • Blocking Transformations, final expressions
    • -
  • "Code examples for Blocking, Jackknife and bootstrap":"https://github.com/CompPhysics/MachineLearning/tree/master/doc/Programs/ResamplingAnalysisScripts"
    • +
  • Resampling methods: Bootstrap
    • +
  • Resampling methods: Bootstrap background
    • +
  • Resampling methods: More Bootstrap background
    • +
  • Resampling methods: Bootstrap approach
    • +
  • Resampling methods: Bootstrap steps
    • +
  • Resampling methods: Blocking
    • +
  • Blocking Transformations
    • +
  • Blocking Transformations
    • +
  • Blocking Transformations, getting there
    • +
  • Blocking Transformations, final expressions
    • +
  • "Code examples for Blocking, Jackknife and bootstrap":"https://github.com/CompPhysics/MachineLearning/tree/master/doc/Programs/ResamplingAnalysisScripts"
    • +
  • The bias-variance tradeoff
    • +
  • Training and testing data
    • +
  • Procedure to find a predictor
    • +
  • What we want
    • +
  • The expected generalization error
    • +
  • Elaborating a little bit more
    • +
  • The bias
    • +
  • The variance
    • +
  • Summing up
    • +
  • Logistic Regression
    • +
  • Basics
    • +
  • Linear classifier
    • +
  • Some selected properties
    • +
  • The cross-entropy as a cost function for logistic regression
    • +
  • Maximum likelihood
    • +
  • Minimizing the cross entropy
    • @@ -421,7 +449,7 @@ The value of \( \lambda \) which minimizes \( \mbox{AIC}(\lambda) \) corresponds
  • 81
  • 82
  • ...
    • -
  • 94
    • +
  • 109
  • »
    • diff --git a/doc/pub/Regression/html/._Regression-bs073.html b/doc/pub/Regression/html/._Regression-bs073.html index c5576b98d..54925502a 100644 --- a/doc/pub/Regression/html/._Regression-bs073.html +++ b/doc/pub/Regression/html/._Regression-bs073.html @@ -194,32 +194,46 @@ Automatically generated HTML file from DocOnce source '___sec77'), ('Resampling methods: Jackknife', 2, None, '___sec78'), ('Resampling methods: Jackknife estimator', 2, None, '___sec79'), - ('Resampling methods: Jackknife sample code', - 2, - None, - '___sec80'), - ('Resampling methods: Bootstrap', 2, None, '___sec81'), - ('Resampling methods: Bootstrap background', 2, None, '___sec82'), + ('Resampling methods: Bootstrap', 2, None, '___sec80'), + ('Resampling methods: Bootstrap background', 2, None, '___sec81'), ('Resampling methods: More Bootstrap background', 2, None, - '___sec83'), - ('Resampling methods: Bootstrap approach', 2, None, '___sec84'), - ('Resampling methods: Bootstrap algorithm', 2, None, '___sec85'), - ('Resampling methods: Bootstrap steps', 2, None, '___sec86'), - ('Resampling methods: Blocking', 2, None, '___sec87'), - ('Blocking Transformations', 2, None, '___sec88'), - ('Blocking Transformations', 2, None, '___sec89'), - ('Blocking Transformations, getting there', 2, None, '___sec90'), + '___sec82'), + ('Resampling methods: Bootstrap approach', 2, None, '___sec83'), + ('Resampling methods: Bootstrap steps', 2, None, '___sec84'), + ('Resampling methods: Blocking', 2, None, '___sec85'), + ('Blocking Transformations', 2, None, '___sec86'), + ('Blocking Transformations', 2, None, '___sec87'), + ('Blocking Transformations, getting there', 2, None, '___sec88'), ('Blocking Transformations, final expressions', 2, None, - '___sec91'), + '___sec89'), ('"Code examples for Blocking, Jackknife and ' 'bootstrap":"https://github.com/CompPhysics/MachineLearning/tree/master/doc/Programs/ResamplingAnalysisScripts"', 2, None, - '___sec92')]} + '___sec90'), + ('The bias-variance tradeoff', 2, None, '___sec91'), + ('Training and testing data', 2, None, '___sec92'), + ('Procedure to find a predictor', 2, None, '___sec93'), + ('What we want', 2, None, '___sec94'), + ('The expected generalization error', 2, None, '___sec95'), + ('Elaborating a little bit more', 2, None, '___sec96'), + ('The bias', 2, None, '___sec97'), + ('The variance', 2, None, '___sec98'), + ('Summing up', 2, None, '___sec99'), + ('Logistic Regression', 2, None, '___sec100'), + ('Basics', 2, None, '___sec101'), + ('Linear classifier', 2, None, '___sec102'), + ('Some selected properties', 2, None, '___sec103'), + ('The cross-entropy as a cost function for logistic regression', + 2, + None, + '___sec104'), + ('Maximum likelihood', 2, None, '___sec105'), + ('Minimizing the cross entropy', 2, None, '___sec106')]} end of tocinfo --> @@ -337,19 +351,33 @@ MathJax.Hub.Config({
  • Resampling methods: Jackknife and Bootstrap
  • Resampling methods: Jackknife
  • Resampling methods: Jackknife estimator
    • -
  • Resampling methods: Jackknife sample code
    • -
  • Resampling methods: Bootstrap
    • -
  • Resampling methods: Bootstrap background
    • -
  • Resampling methods: More Bootstrap background
    • -
  • Resampling methods: Bootstrap approach
    • -
  • Resampling methods: Bootstrap algorithm
    • -
  • Resampling methods: Bootstrap steps
    • -
  • Resampling methods: Blocking
    • -
  • Blocking Transformations
    • -
  • Blocking Transformations
    • -
  • Blocking Transformations, getting there
    • -
  • Blocking Transformations, final expressions
    • -
  • "Code examples for Blocking, Jackknife and bootstrap":"https://github.com/CompPhysics/MachineLearning/tree/master/doc/Programs/ResamplingAnalysisScripts"
    • +
  • Resampling methods: Bootstrap
    • +
  • Resampling methods: Bootstrap background
    • +
  • Resampling methods: More Bootstrap background
    • +
  • Resampling methods: Bootstrap approach
    • +
  • Resampling methods: Bootstrap steps
    • +
  • Resampling methods: Blocking
    • +
  • Blocking Transformations
    • +
  • Blocking Transformations
    • +
  • Blocking Transformations, getting there
    • +
  • Blocking Transformations, final expressions
    • +
  • "Code examples for Blocking, Jackknife and bootstrap":"https://github.com/CompPhysics/MachineLearning/tree/master/doc/Programs/ResamplingAnalysisScripts"
    • +
  • The bias-variance tradeoff
    • +
  • Training and testing data
    • +
  • Procedure to find a predictor
    • +
  • What we want
    • +
  • The expected generalization error
    • +
  • Elaborating a little bit more
    • +
  • The bias
    • +
  • The variance
    • +
  • Summing up
    • +
  • Logistic Regression
    • +
  • Basics
    • +
  • Linear classifier
    • +
  • Some selected properties
    • +
  • The cross-entropy as a cost function for logistic regression
    • +
  • Maximum likelihood
    • +
  • Minimizing the cross entropy
    • @@ -419,7 +447,7 @@ some sense) is then selected.
  • 82
  • 83
  • ...
    • -
  • 94
    • +
  • 109
  • »
    • diff --git a/doc/pub/Regression/html/._Regression-bs074.html b/doc/pub/Regression/html/._Regression-bs074.html index 18b5ebc11..d94d7a150 100644 --- a/doc/pub/Regression/html/._Regression-bs074.html +++ b/doc/pub/Regression/html/._Regression-bs074.html @@ -194,32 +194,46 @@ Automatically generated HTML file from DocOnce source '___sec77'), ('Resampling methods: Jackknife', 2, None, '___sec78'), ('Resampling methods: Jackknife estimator', 2, None, '___sec79'), - ('Resampling methods: Jackknife sample code', - 2, - None, - '___sec80'), - ('Resampling methods: Bootstrap', 2, None, '___sec81'), - ('Resampling methods: Bootstrap background', 2, None, '___sec82'), + ('Resampling methods: Bootstrap', 2, None, '___sec80'), + ('Resampling methods: Bootstrap background', 2, None, '___sec81'), ('Resampling methods: More Bootstrap background', 2, None, - '___sec83'), - ('Resampling methods: Bootstrap approach', 2, None, '___sec84'), - ('Resampling methods: Bootstrap algorithm', 2, None, '___sec85'), - ('Resampling methods: Bootstrap steps', 2, None, '___sec86'), - ('Resampling methods: Blocking', 2, None, '___sec87'), - ('Blocking Transformations', 2, None, '___sec88'), - ('Blocking Transformations', 2, None, '___sec89'), - ('Blocking Transformations, getting there', 2, None, '___sec90'), + '___sec82'), + ('Resampling methods: Bootstrap approach', 2, None, '___sec83'), + ('Resampling methods: Bootstrap steps', 2, None, '___sec84'), + ('Resampling methods: Blocking', 2, None, '___sec85'), + ('Blocking Transformations', 2, None, '___sec86'), + ('Blocking Transformations', 2, None, '___sec87'), + ('Blocking Transformations, getting there', 2, None, '___sec88'), ('Blocking Transformations, final expressions', 2, None, - '___sec91'), + '___sec89'), ('"Code examples for Blocking, Jackknife and ' 'bootstrap":"https://github.com/CompPhysics/MachineLearning/tree/master/doc/Programs/ResamplingAnalysisScripts"', 2, None, - '___sec92')]} + '___sec90'), + ('The bias-variance tradeoff', 2, None, '___sec91'), + ('Training and testing data', 2, None, '___sec92'), + ('Procedure to find a predictor', 2, None, '___sec93'), + ('What we want', 2, None, '___sec94'), + ('The expected generalization error', 2, None, '___sec95'), + ('Elaborating a little bit more', 2, None, '___sec96'), + ('The bias', 2, None, '___sec97'), + ('The variance', 2, None, '___sec98'), + ('Summing up', 2, None, '___sec99'), + ('Logistic Regression', 2, None, '___sec100'), + ('Basics', 2, None, '___sec101'), + ('Linear classifier', 2, None, '___sec102'), + ('Some selected properties', 2, None, '___sec103'), + ('The cross-entropy as a cost function for logistic regression', + 2, + None, + '___sec104'), + ('Maximum likelihood', 2, None, '___sec105'), + ('Minimizing the cross entropy', 2, None, '___sec106')]} end of tocinfo --> @@ -337,19 +351,33 @@ MathJax.Hub.Config({
  • Resampling methods: Jackknife and Bootstrap
  • Resampling methods: Jackknife
  • Resampling methods: Jackknife estimator
    • -
  • Resampling methods: Jackknife sample code
    • -
  • Resampling methods: Bootstrap
    • -
  • Resampling methods: Bootstrap background
    • -
  • Resampling methods: More Bootstrap background
    • -
  • Resampling methods: Bootstrap approach
    • -
  • Resampling methods: Bootstrap algorithm
    • -
  • Resampling methods: Bootstrap steps
    • -
  • Resampling methods: Blocking
    • -
  • Blocking Transformations
    • -
  • Blocking Transformations
    • -
  • Blocking Transformations, getting there
    • -
  • Blocking Transformations, final expressions
    • -
  • "Code examples for Blocking, Jackknife and bootstrap":"https://github.com/CompPhysics/MachineLearning/tree/master/doc/Programs/ResamplingAnalysisScripts"
    • +
  • Resampling methods: Bootstrap
    • +
  • Resampling methods: Bootstrap background
    • +
  • Resampling methods: More Bootstrap background
    • +
  • Resampling methods: Bootstrap approach
    • +
  • Resampling methods: Bootstrap steps
    • +
  • Resampling methods: Blocking
    • +
  • Blocking Transformations
    • +
  • Blocking Transformations
    • +
  • Blocking Transformations, getting there
    • +
  • Blocking Transformations, final expressions
    • +
  • "Code examples for Blocking, Jackknife and bootstrap":"https://github.com/CompPhysics/MachineLearning/tree/master/doc/Programs/ResamplingAnalysisScripts"
    • +
  • The bias-variance tradeoff
    • +
  • Training and testing data
    • +
  • Procedure to find a predictor
    • +
  • What we want
    • +
  • The expected generalization error
    • +
  • Elaborating a little bit more
    • +
  • The bias
    • +
  • The variance
    • +
  • Summing up
    • +
  • Logistic Regression
    • +
  • Basics
    • +
  • Linear classifier
    • +
  • Some selected properties
    • +
  • The cross-entropy as a cost function for logistic regression
    • +
  • Maximum likelihood
    • +
  • Minimizing the cross entropy
    • @@ -400,7 +428,7 @@ The validation set approach is conceptually simple and is easy to implement. But
  • 83
  • 84
  • ...
    • -
  • 94
    • +
  • 109
  • »
    • diff --git a/doc/pub/Regression/html/._Regression-bs075.html b/doc/pub/Regression/html/._Regression-bs075.html index a90a5964c..3d12297dd 100644 --- a/doc/pub/Regression/html/._Regression-bs075.html +++ b/doc/pub/Regression/html/._Regression-bs075.html @@ -194,32 +194,46 @@ Automatically generated HTML file from DocOnce source '___sec77'), ('Resampling methods: Jackknife', 2, None, '___sec78'), ('Resampling methods: Jackknife estimator', 2, None, '___sec79'), - ('Resampling methods: Jackknife sample code', - 2, - None, - '___sec80'), - ('Resampling methods: Bootstrap', 2, None, '___sec81'), - ('Resampling methods: Bootstrap background', 2, None, '___sec82'), + ('Resampling methods: Bootstrap', 2, None, '___sec80'), + ('Resampling methods: Bootstrap background', 2, None, '___sec81'), ('Resampling methods: More Bootstrap background', 2, None, - '___sec83'), - ('Resampling methods: Bootstrap approach', 2, None, '___sec84'), - ('Resampling methods: Bootstrap algorithm', 2, None, '___sec85'), - ('Resampling methods: Bootstrap steps', 2, None, '___sec86'), - ('Resampling methods: Blocking', 2, None, '___sec87'), - ('Blocking Transformations', 2, None, '___sec88'), - ('Blocking Transformations', 2, None, '___sec89'), - ('Blocking Transformations, getting there', 2, None, '___sec90'), + '___sec82'), + ('Resampling methods: Bootstrap approach', 2, None, '___sec83'), + ('Resampling methods: Bootstrap steps', 2, None, '___sec84'), + ('Resampling methods: Blocking', 2, None, '___sec85'), + ('Blocking Transformations', 2, None, '___sec86'), + ('Blocking Transformations', 2, None, '___sec87'), + ('Blocking Transformations, getting there', 2, None, '___sec88'), ('Blocking Transformations, final expressions', 2, None, - '___sec91'), + '___sec89'), ('"Code examples for Blocking, Jackknife and ' 'bootstrap":"https://github.com/CompPhysics/MachineLearning/tree/master/doc/Programs/ResamplingAnalysisScripts"', 2, None, - '___sec92')]} + '___sec90'), + ('The bias-variance tradeoff', 2, None, '___sec91'), + ('Training and testing data', 2, None, '___sec92'), + ('Procedure to find a predictor', 2, None, '___sec93'), + ('What we want', 2, None, '___sec94'), + ('The expected generalization error', 2, None, '___sec95'), + ('Elaborating a little bit more', 2, None, '___sec96'), + ('The bias', 2, None, '___sec97'), + ('The variance', 2, None, '___sec98'), + ('Summing up', 2, None, '___sec99'), + ('Logistic Regression', 2, None, '___sec100'), + ('Basics', 2, None, '___sec101'), + ('Linear classifier', 2, None, '___sec102'), + ('Some selected properties', 2, None, '___sec103'), + ('The cross-entropy as a cost function for logistic regression', + 2, + None, + '___sec104'), + ('Maximum likelihood', 2, None, '___sec105'), + ('Minimizing the cross entropy', 2, None, '___sec106')]} end of tocinfo --> @@ -337,19 +351,33 @@ MathJax.Hub.Config({
  • Resampling methods: Jackknife and Bootstrap
  • Resampling methods: Jackknife
  • Resampling methods: Jackknife estimator
    • -
  • Resampling methods: Jackknife sample code
    • -
  • Resampling methods: Bootstrap
    • -
  • Resampling methods: Bootstrap background
    • -
  • Resampling methods: More Bootstrap background
    • -
  • Resampling methods: Bootstrap approach
    • -
  • Resampling methods: Bootstrap algorithm
    • -
  • Resampling methods: Bootstrap steps
    • -
  • Resampling methods: Blocking
    • -
  • Blocking Transformations
    • -
  • Blocking Transformations
    • -
  • Blocking Transformations, getting there
    • -
  • Blocking Transformations, final expressions
    • -
  • "Code examples for Blocking, Jackknife and bootstrap":"https://github.com/CompPhysics/MachineLearning/tree/master/doc/Programs/ResamplingAnalysisScripts"
    • +
  • Resampling methods: Bootstrap
    • +
  • Resampling methods: Bootstrap background
    • +
  • Resampling methods: More Bootstrap background
    • +
  • Resampling methods: Bootstrap approach
    • +
  • Resampling methods: Bootstrap steps
    • +
  • Resampling methods: Blocking
    • +
  • Blocking Transformations
    • +
  • Blocking Transformations
    • +
  • Blocking Transformations, getting there
    • +
  • Blocking Transformations, final expressions
    • +
  • "Code examples for Blocking, Jackknife and bootstrap":"https://github.com/CompPhysics/MachineLearning/tree/master/doc/Programs/ResamplingAnalysisScripts"
    • +
  • The bias-variance tradeoff
    • +
  • Training and testing data
    • +
  • Procedure to find a predictor
    • +
  • What we want
    • +
  • The expected generalization error
    • +
  • Elaborating a little bit more
    • +
  • The bias
    • +
  • The variance
    • +
  • Summing up
    • +
  • Logistic Regression
    • +
  • Basics
    • +
  • Linear classifier
    • +
  • Some selected properties
    • +
  • The cross-entropy as a cost function for logistic regression
    • +
  • Maximum likelihood
    • +
  • Minimizing the cross entropy
    • @@ -409,7 +437,7 @@ cross-validation (LOOCV).
  • 84
  • 85
  • ...
    • -
  • 94
    • +
  • 109
  • »
    • diff --git a/doc/pub/Regression/html/._Regression-bs076.html b/doc/pub/Regression/html/._Regression-bs076.html index 4fba9d52b..d594e2320 100644 --- a/doc/pub/Regression/html/._Regression-bs076.html +++ b/doc/pub/Regression/html/._Regression-bs076.html @@ -194,32 +194,46 @@ Automatically generated HTML file from DocOnce source '___sec77'), ('Resampling methods: Jackknife', 2, None, '___sec78'), ('Resampling methods: Jackknife estimator', 2, None, '___sec79'), - ('Resampling methods: Jackknife sample code', - 2, - None, - '___sec80'), - ('Resampling methods: Bootstrap', 2, None, '___sec81'), - ('Resampling methods: Bootstrap background', 2, None, '___sec82'), + ('Resampling methods: Bootstrap', 2, None, '___sec80'), + ('Resampling methods: Bootstrap background', 2, None, '___sec81'), ('Resampling methods: More Bootstrap background', 2, None, - '___sec83'), - ('Resampling methods: Bootstrap approach', 2, None, '___sec84'), - ('Resampling methods: Bootstrap algorithm', 2, None, '___sec85'), - ('Resampling methods: Bootstrap steps', 2, None, '___sec86'), - ('Resampling methods: Blocking', 2, None, '___sec87'), - ('Blocking Transformations', 2, None, '___sec88'), - ('Blocking Transformations', 2, None, '___sec89'), - ('Blocking Transformations, getting there', 2, None, '___sec90'), + '___sec82'), + ('Resampling methods: Bootstrap approach', 2, None, '___sec83'), + ('Resampling methods: Bootstrap steps', 2, None, '___sec84'), + ('Resampling methods: Blocking', 2, None, '___sec85'), + ('Blocking Transformations', 2, None, '___sec86'), + ('Blocking Transformations', 2, None, '___sec87'), + ('Blocking Transformations, getting there', 2, None, '___sec88'), ('Blocking Transformations, final expressions', 2, None, - '___sec91'), + '___sec89'), ('"Code examples for Blocking, Jackknife and ' 'bootstrap":"https://github.com/CompPhysics/MachineLearning/tree/master/doc/Programs/ResamplingAnalysisScripts"', 2, None, - '___sec92')]} + '___sec90'), + ('The bias-variance tradeoff', 2, None, '___sec91'), + ('Training and testing data', 2, None, '___sec92'), + ('Procedure to find a predictor', 2, None, '___sec93'), + ('What we want', 2, None, '___sec94'), + ('The expected generalization error', 2, None, '___sec95'), + ('Elaborating a little bit more', 2, None, '___sec96'), + ('The bias', 2, None, '___sec97'), + ('The variance', 2, None, '___sec98'), + ('Summing up', 2, None, '___sec99'), + ('Logistic Regression', 2, None, '___sec100'), + ('Basics', 2, None, '___sec101'), + ('Linear classifier', 2, None, '___sec102'), + ('Some selected properties', 2, None, '___sec103'), + ('The cross-entropy as a cost function for logistic regression', + 2, + None, + '___sec104'), + ('Maximum likelihood', 2, None, '___sec105'), + ('Minimizing the cross entropy', 2, None, '___sec106')]} end of tocinfo --> @@ -337,19 +351,33 @@ MathJax.Hub.Config({
  • Resampling methods: Jackknife and Bootstrap
  • Resampling methods: Jackknife
  • Resampling methods: Jackknife estimator
    • -
  • Resampling methods: Jackknife sample code
    • -
  • Resampling methods: Bootstrap
    • -
  • Resampling methods: Bootstrap background
    • -
  • Resampling methods: More Bootstrap background
    • -
  • Resampling methods: Bootstrap approach
    • -
  • Resampling methods: Bootstrap algorithm
    • -
  • Resampling methods: Bootstrap steps
    • -
  • Resampling methods: Blocking
    • -
  • Blocking Transformations
    • -
  • Blocking Transformations
    • -
  • Blocking Transformations, getting there
    • -
  • Blocking Transformations, final expressions
    • -
  • "Code examples for Blocking, Jackknife and bootstrap":"https://github.com/CompPhysics/MachineLearning/tree/master/doc/Programs/ResamplingAnalysisScripts"
    • +
  • Resampling methods: Bootstrap
    • +
  • Resampling methods: Bootstrap background
    • +
  • Resampling methods: More Bootstrap background
    • +
  • Resampling methods: Bootstrap approach
    • +
  • Resampling methods: Bootstrap steps
    • +
  • Resampling methods: Blocking
    • +
  • Blocking Transformations
    • +
  • Blocking Transformations
    • +
  • Blocking Transformations, getting there
    • +
  • Blocking Transformations, final expressions
    • +
  • "Code examples for Blocking, Jackknife and bootstrap":"https://github.com/CompPhysics/MachineLearning/tree/master/doc/Programs/ResamplingAnalysisScripts"
    • +
  • The bias-variance tradeoff
    • +
  • Training and testing data
    • +
  • Procedure to find a predictor
    • +
  • What we want
    • +
  • The expected generalization error
    • +
  • Elaborating a little bit more
    • +
  • The bias
    • +
  • The variance
    • +
  • Summing up
    • +
  • Logistic Regression
    • +
  • Basics
    • +
  • Linear classifier
    • +
  • Some selected properties
    • +
  • The cross-entropy as a cost function for logistic regression
    • +
  • Maximum likelihood
    • +
  • Minimizing the cross entropy
    • @@ -424,7 +452,7 @@ $$
  • 85
  • 86
  • ...
    • -
  • 94
    • +
  • 109
  • »
    • diff --git a/doc/pub/Regression/html/._Regression-bs077.html b/doc/pub/Regression/html/._Regression-bs077.html index 2dc6342d7..248fff400 100644 --- a/doc/pub/Regression/html/._Regression-bs077.html +++ b/doc/pub/Regression/html/._Regression-bs077.html @@ -194,32 +194,46 @@ Automatically generated HTML file from DocOnce source '___sec77'), ('Resampling methods: Jackknife', 2, None, '___sec78'), ('Resampling methods: Jackknife estimator', 2, None, '___sec79'), - ('Resampling methods: Jackknife sample code', - 2, - None, - '___sec80'), - ('Resampling methods: Bootstrap', 2, None, '___sec81'), - ('Resampling methods: Bootstrap background', 2, None, '___sec82'), + ('Resampling methods: Bootstrap', 2, None, '___sec80'), + ('Resampling methods: Bootstrap background', 2, None, '___sec81'), ('Resampling methods: More Bootstrap background', 2, None, - '___sec83'), - ('Resampling methods: Bootstrap approach', 2, None, '___sec84'), - ('Resampling methods: Bootstrap algorithm', 2, None, '___sec85'), - ('Resampling methods: Bootstrap steps', 2, None, '___sec86'), - ('Resampling methods: Blocking', 2, None, '___sec87'), - ('Blocking Transformations', 2, None, '___sec88'), - ('Blocking Transformations', 2, None, '___sec89'), - ('Blocking Transformations, getting there', 2, None, '___sec90'), + '___sec82'), + ('Resampling methods: Bootstrap approach', 2, None, '___sec83'), + ('Resampling methods: Bootstrap steps', 2, None, '___sec84'), + ('Resampling methods: Blocking', 2, None, '___sec85'), + ('Blocking Transformations', 2, None, '___sec86'), + ('Blocking Transformations', 2, None, '___sec87'), + ('Blocking Transformations, getting there', 2, None, '___sec88'), ('Blocking Transformations, final expressions', 2, None, - '___sec91'), + '___sec89'), ('"Code examples for Blocking, Jackknife and ' 'bootstrap":"https://github.com/CompPhysics/MachineLearning/tree/master/doc/Programs/ResamplingAnalysisScripts"', 2, None, - '___sec92')]} + '___sec90'), + ('The bias-variance tradeoff', 2, None, '___sec91'), + ('Training and testing data', 2, None, '___sec92'), + ('Procedure to find a predictor', 2, None, '___sec93'), + ('What we want', 2, None, '___sec94'), + ('The expected generalization error', 2, None, '___sec95'), + ('Elaborating a little bit more', 2, None, '___sec96'), + ('The bias', 2, None, '___sec97'), + ('The variance', 2, None, '___sec98'), + ('Summing up', 2, None, '___sec99'), + ('Logistic Regression', 2, None, '___sec100'), + ('Basics', 2, None, '___sec101'), + ('Linear classifier', 2, None, '___sec102'), + ('Some selected properties', 2, None, '___sec103'), + ('The cross-entropy as a cost function for logistic regression', + 2, + None, + '___sec104'), + ('Maximum likelihood', 2, None, '___sec105'), + ('Minimizing the cross entropy', 2, None, '___sec106')]} end of tocinfo --> @@ -337,19 +351,33 @@ MathJax.Hub.Config({
  • Resampling methods: Jackknife and Bootstrap
  • Resampling methods: Jackknife
  • Resampling methods: Jackknife estimator
    • -
  • Resampling methods: Jackknife sample code
    • -
  • Resampling methods: Bootstrap
    • -
  • Resampling methods: Bootstrap background
    • -
  • Resampling methods: More Bootstrap background
    • -
  • Resampling methods: Bootstrap approach
    • -
  • Resampling methods: Bootstrap algorithm
    • -
  • Resampling methods: Bootstrap steps
    • -
  • Resampling methods: Blocking
    • -
  • Blocking Transformations
    • -
  • Blocking Transformations
    • -
  • Blocking Transformations, getting there
    • -
  • Blocking Transformations, final expressions
    • -
  • "Code examples for Blocking, Jackknife and bootstrap":"https://github.com/CompPhysics/MachineLearning/tree/master/doc/Programs/ResamplingAnalysisScripts"
    • +
  • Resampling methods: Bootstrap
    • +
  • Resampling methods: Bootstrap background
    • +
  • Resampling methods: More Bootstrap background
    • +
  • Resampling methods: Bootstrap approach
    • +
  • Resampling methods: Bootstrap steps
    • +
  • Resampling methods: Blocking
    • +
  • Blocking Transformations
    • +
  • Blocking Transformations
    • +
  • Blocking Transformations, getting there
    • +
  • Blocking Transformations, final expressions
    • +
  • "Code examples for Blocking, Jackknife and bootstrap":"https://github.com/CompPhysics/MachineLearning/tree/master/doc/Programs/ResamplingAnalysisScripts"
    • +
  • The bias-variance tradeoff
    • +
  • Training and testing data
    • +
  • Procedure to find a predictor
    • +
  • What we want
    • +
  • The expected generalization error
    • +
  • Elaborating a little bit more
    • +
  • The bias
    • +
  • The variance
    • +
  • Summing up
    • +
  • Logistic Regression
    • +
  • Basics
    • +
  • Linear classifier
    • +
  • Some selected properties
    • +
  • The cross-entropy as a cost function for logistic regression
    • +
  • Maximum likelihood
    • +
  • Minimizing the cross entropy
    • @@ -413,7 +441,7 @@ the design matrix and the parameters \( \beta \).
  • 86
  • 87
  • ...
    • -
  • 94
    • +
  • 109
  • »
    • diff --git a/doc/pub/Regression/html/._Regression-bs078.html b/doc/pub/Regression/html/._Regression-bs078.html index 558264248..7f87a2cee 100644 --- a/doc/pub/Regression/html/._Regression-bs078.html +++ b/doc/pub/Regression/html/._Regression-bs078.html @@ -194,32 +194,46 @@ Automatically generated HTML file from DocOnce source '___sec77'), ('Resampling methods: Jackknife', 2, None, '___sec78'), ('Resampling methods: Jackknife estimator', 2, None, '___sec79'), - ('Resampling methods: Jackknife sample code', - 2, - None, - '___sec80'), - ('Resampling methods: Bootstrap', 2, None, '___sec81'), - ('Resampling methods: Bootstrap background', 2, None, '___sec82'), + ('Resampling methods: Bootstrap', 2, None, '___sec80'), + ('Resampling methods: Bootstrap background', 2, None, '___sec81'), ('Resampling methods: More Bootstrap background', 2, None, - '___sec83'), - ('Resampling methods: Bootstrap approach', 2, None, '___sec84'), - ('Resampling methods: Bootstrap algorithm', 2, None, '___sec85'), - ('Resampling methods: Bootstrap steps', 2, None, '___sec86'), - ('Resampling methods: Blocking', 2, None, '___sec87'), - ('Blocking Transformations', 2, None, '___sec88'), - ('Blocking Transformations', 2, None, '___sec89'), - ('Blocking Transformations, getting there', 2, None, '___sec90'), + '___sec82'), + ('Resampling methods: Bootstrap approach', 2, None, '___sec83'), + ('Resampling methods: Bootstrap steps', 2, None, '___sec84'), + ('Resampling methods: Blocking', 2, None, '___sec85'), + ('Blocking Transformations', 2, None, '___sec86'), + ('Blocking Transformations', 2, None, '___sec87'), + ('Blocking Transformations, getting there', 2, None, '___sec88'), ('Blocking Transformations, final expressions', 2, None, - '___sec91'), + '___sec89'), ('"Code examples for Blocking, Jackknife and ' 'bootstrap":"https://github.com/CompPhysics/MachineLearning/tree/master/doc/Programs/ResamplingAnalysisScripts"', 2, None, - '___sec92')]} + '___sec90'), + ('The bias-variance tradeoff', 2, None, '___sec91'), + ('Training and testing data', 2, None, '___sec92'), + ('Procedure to find a predictor', 2, None, '___sec93'), + ('What we want', 2, None, '___sec94'), + ('The expected generalization error', 2, None, '___sec95'), + ('Elaborating a little bit more', 2, None, '___sec96'), + ('The bias', 2, None, '___sec97'), + ('The variance', 2, None, '___sec98'), + ('Summing up', 2, None, '___sec99'), + ('Logistic Regression', 2, None, '___sec100'), + ('Basics', 2, None, '___sec101'), + ('Linear classifier', 2, None, '___sec102'), + ('Some selected properties', 2, None, '___sec103'), + ('The cross-entropy as a cost function for logistic regression', + 2, + None, + '___sec104'), + ('Maximum likelihood', 2, None, '___sec105'), + ('Minimizing the cross entropy', 2, None, '___sec106')]} end of tocinfo --> @@ -337,19 +351,33 @@ MathJax.Hub.Config({
  • Resampling methods: Jackknife and Bootstrap
  • Resampling methods: Jackknife
  • Resampling methods: Jackknife estimator
    • -
  • Resampling methods: Jackknife sample code
    • -
  • Resampling methods: Bootstrap
    • -
  • Resampling methods: Bootstrap background
    • -
  • Resampling methods: More Bootstrap background
    • -
  • Resampling methods: Bootstrap approach
    • -
  • Resampling methods: Bootstrap algorithm
    • -
  • Resampling methods: Bootstrap steps
    • -
  • Resampling methods: Blocking
    • -
  • Blocking Transformations
    • -
  • Blocking Transformations
    • -
  • Blocking Transformations, getting there
    • -
  • Blocking Transformations, final expressions
    • -
  • "Code examples for Blocking, Jackknife and bootstrap":"https://github.com/CompPhysics/MachineLearning/tree/master/doc/Programs/ResamplingAnalysisScripts"
    • +
  • Resampling methods: Bootstrap
    • +
  • Resampling methods: Bootstrap background
    • +
  • Resampling methods: More Bootstrap background
    • +
  • Resampling methods: Bootstrap approach
    • +
  • Resampling methods: Bootstrap steps
    • +
  • Resampling methods: Blocking
    • +
  • Blocking Transformations
    • +
  • Blocking Transformations
    • +
  • Blocking Transformations, getting there
    • +
  • Blocking Transformations, final expressions
    • +
  • "Code examples for Blocking, Jackknife and bootstrap":"https://github.com/CompPhysics/MachineLearning/tree/master/doc/Programs/ResamplingAnalysisScripts"
    • +
  • The bias-variance tradeoff
    • +
  • Training and testing data
    • +
  • Procedure to find a predictor
    • +
  • What we want
    • +
  • The expected generalization error
    • +
  • Elaborating a little bit more
    • +
  • The bias
    • +
  • The variance
    • +
  • Summing up
    • +
  • Logistic Regression
    • +
  • Basics
    • +
  • Linear classifier
    • +
  • Some selected properties
    • +
  • The cross-entropy as a cost function for logistic regression
    • +
  • Maximum likelihood
    • +
  • Minimizing the cross entropy
    • @@ -411,7 +439,7 @@ need for bootstrapping.
  • 87
  • 88
  • ...
    • -
  • 94
    • +
  • 109
  • »
    • diff --git a/doc/pub/Regression/html/._Regression-bs079.html b/doc/pub/Regression/html/._Regression-bs079.html index 34ebc9b2d..fdf9341b6 100644 --- a/doc/pub/Regression/html/._Regression-bs079.html +++ b/doc/pub/Regression/html/._Regression-bs079.html @@ -194,32 +194,46 @@ Automatically generated HTML file from DocOnce source '___sec77'), ('Resampling methods: Jackknife', 2, None, '___sec78'), ('Resampling methods: Jackknife estimator', 2, None, '___sec79'), - ('Resampling methods: Jackknife sample code', - 2, - None, - '___sec80'), - ('Resampling methods: Bootstrap', 2, None, '___sec81'), - ('Resampling methods: Bootstrap background', 2, None, '___sec82'), + ('Resampling methods: Bootstrap', 2, None, '___sec80'), + ('Resampling methods: Bootstrap background', 2, None, '___sec81'), ('Resampling methods: More Bootstrap background', 2, None, - '___sec83'), - ('Resampling methods: Bootstrap approach', 2, None, '___sec84'), - ('Resampling methods: Bootstrap algorithm', 2, None, '___sec85'), - ('Resampling methods: Bootstrap steps', 2, None, '___sec86'), - ('Resampling methods: Blocking', 2, None, '___sec87'), - ('Blocking Transformations', 2, None, '___sec88'), - ('Blocking Transformations', 2, None, '___sec89'), - ('Blocking Transformations, getting there', 2, None, '___sec90'), + '___sec82'), + ('Resampling methods: Bootstrap approach', 2, None, '___sec83'), + ('Resampling methods: Bootstrap steps', 2, None, '___sec84'), + ('Resampling methods: Blocking', 2, None, '___sec85'), + ('Blocking Transformations', 2, None, '___sec86'), + ('Blocking Transformations', 2, None, '___sec87'), + ('Blocking Transformations, getting there', 2, None, '___sec88'), ('Blocking Transformations, final expressions', 2, None, - '___sec91'), + '___sec89'), ('"Code examples for Blocking, Jackknife and ' 'bootstrap":"https://github.com/CompPhysics/MachineLearning/tree/master/doc/Programs/ResamplingAnalysisScripts"', 2, None, - '___sec92')]} + '___sec90'), + ('The bias-variance tradeoff', 2, None, '___sec91'), + ('Training and testing data', 2, None, '___sec92'), + ('Procedure to find a predictor', 2, None, '___sec93'), + ('What we want', 2, None, '___sec94'), + ('The expected generalization error', 2, None, '___sec95'), + ('Elaborating a little bit more', 2, None, '___sec96'), + ('The bias', 2, None, '___sec97'), + ('The variance', 2, None, '___sec98'), + ('Summing up', 2, None, '___sec99'), + ('Logistic Regression', 2, None, '___sec100'), + ('Basics', 2, None, '___sec101'), + ('Linear classifier', 2, None, '___sec102'), + ('Some selected properties', 2, None, '___sec103'), + ('The cross-entropy as a cost function for logistic regression', + 2, + None, + '___sec104'), + ('Maximum likelihood', 2, None, '___sec105'), + ('Minimizing the cross entropy', 2, None, '___sec106')]} end of tocinfo --> @@ -337,19 +351,33 @@ MathJax.Hub.Config({
  • Resampling methods: Jackknife and Bootstrap
  • Resampling methods: Jackknife
  • Resampling methods: Jackknife estimator
    • -
  • Resampling methods: Jackknife sample code
    • -
  • Resampling methods: Bootstrap
    • -
  • Resampling methods: Bootstrap background
    • -
  • Resampling methods: More Bootstrap background
    • -
  • Resampling methods: Bootstrap approach
    • -
  • Resampling methods: Bootstrap algorithm
    • -
  • Resampling methods: Bootstrap steps
    • -
  • Resampling methods: Blocking
    • -
  • Blocking Transformations
    • -
  • Blocking Transformations
    • -
  • Blocking Transformations, getting there
    • -
  • Blocking Transformations, final expressions
    • -
  • "Code examples for Blocking, Jackknife and bootstrap":"https://github.com/CompPhysics/MachineLearning/tree/master/doc/Programs/ResamplingAnalysisScripts"
    • +
  • Resampling methods: Bootstrap
    • +
  • Resampling methods: Bootstrap background
    • +
  • Resampling methods: More Bootstrap background
    • +
  • Resampling methods: Bootstrap approach
    • +
  • Resampling methods: Bootstrap steps
    • +
  • Resampling methods: Blocking
    • +
  • Blocking Transformations
    • +
  • Blocking Transformations
    • +
  • Blocking Transformations, getting there
    • +
  • Blocking Transformations, final expressions
    • +
  • "Code examples for Blocking, Jackknife and bootstrap":"https://github.com/CompPhysics/MachineLearning/tree/master/doc/Programs/ResamplingAnalysisScripts"
    • +
  • The bias-variance tradeoff
    • +
  • Training and testing data
    • +
  • Procedure to find a predictor
    • +
  • What we want
    • +
  • The expected generalization error
    • +
  • Elaborating a little bit more
    • +
  • The bias
    • +
  • The variance
    • +
  • Summing up
    • +
  • Logistic Regression
    • +
  • Basics
    • +
  • Linear classifier
    • +
  • Some selected properties
    • +
  • The cross-entropy as a cost function for logistic regression
    • +
  • Maximum likelihood
    • +
  • Minimizing the cross entropy
    • @@ -368,18 +396,18 @@ MathJax.Hub.Config({

    Resampling methods: Jackknife

    -The Jackknife works by making many replicas of the estimator \( \widehat{\vec{\theta}} \). -The jackknife is a resampling method, we explained that this happens by scrambling the data in some way. When using the jackknife, this is done by systematically leaving out one observation from the vector of observed values \( \vec{X} = (X_1,X_2,\cdots,X_n) \). -Let \( \vec{X}_i \) denote the vector +The Jackknife works by making many replicas of the estimator \( \widehat{\theta} \). +The jackknife is a resampling method, we explained that this happens by scrambling the data in some way. When using the jackknife, this is done by systematically leaving out one observation from the vector of observed values \( \hat{x} = (x_1,x_2,\cdots,X_n) \). +Let \( \hat{x}_i \) denote the vector $$ -\vec{X}_i = (X_1,X_2,\cdots,X_{i-1},X_{i+1},\cdots,X_n), +\hat{x}_i = (x_1,x_2,\cdots,x_{i-1},x_{i+1},\cdots,x_n), $$

    -which equals the vector \( \vec{X} \) with the exception that observation +which equals the vector \( \hat{x} \) with the exception that observation number \( i \) is left out. Using this notation, define -\( \widehat{\vec{\theta}}_i \) to be the estimator -\( \widehat{\vec{\theta}} \) computed using \( \vec{X}_i \). +\( \widehat{\theta}_i \) to be the estimator +\( \widehat{\theta} \) computed using \( \vec{X}_i \).

    @@ -407,7 +435,7 @@ number \( i \) is left out. Using this notation, define

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    • diff --git a/doc/pub/Regression/html/._Regression-bs080.html b/doc/pub/Regression/html/._Regression-bs080.html index c2691e555..ef1423fc5 100644 --- a/doc/pub/Regression/html/._Regression-bs080.html +++ b/doc/pub/Regression/html/._Regression-bs080.html @@ -194,32 +194,46 @@ Automatically generated HTML file from DocOnce source '___sec77'), ('Resampling methods: Jackknife', 2, None, '___sec78'), ('Resampling methods: Jackknife estimator', 2, None, '___sec79'), - ('Resampling methods: Jackknife sample code', - 2, - None, - '___sec80'), - ('Resampling methods: Bootstrap', 2, None, '___sec81'), - ('Resampling methods: Bootstrap background', 2, None, '___sec82'), + ('Resampling methods: Bootstrap', 2, None, '___sec80'), + ('Resampling methods: Bootstrap background', 2, None, '___sec81'), ('Resampling methods: More Bootstrap background', 2, None, - '___sec83'), - ('Resampling methods: Bootstrap approach', 2, None, '___sec84'), - ('Resampling methods: Bootstrap algorithm', 2, None, '___sec85'), - ('Resampling methods: Bootstrap steps', 2, None, '___sec86'), - ('Resampling methods: Blocking', 2, None, '___sec87'), - ('Blocking Transformations', 2, None, '___sec88'), - ('Blocking Transformations', 2, None, '___sec89'), - ('Blocking Transformations, getting there', 2, None, '___sec90'), + '___sec82'), + ('Resampling methods: Bootstrap approach', 2, None, '___sec83'), + ('Resampling methods: Bootstrap steps', 2, None, '___sec84'), + ('Resampling methods: Blocking', 2, None, '___sec85'), + ('Blocking Transformations', 2, None, '___sec86'), + ('Blocking Transformations', 2, None, '___sec87'), + ('Blocking Transformations, getting there', 2, None, '___sec88'), ('Blocking Transformations, final expressions', 2, None, - '___sec91'), + '___sec89'), ('"Code examples for Blocking, Jackknife and ' 'bootstrap":"https://github.com/CompPhysics/MachineLearning/tree/master/doc/Programs/ResamplingAnalysisScripts"', 2, None, - '___sec92')]} + '___sec90'), + ('The bias-variance tradeoff', 2, None, '___sec91'), + ('Training and testing data', 2, None, '___sec92'), + ('Procedure to find a predictor', 2, None, '___sec93'), + ('What we want', 2, None, '___sec94'), + ('The expected generalization error', 2, None, '___sec95'), + ('Elaborating a little bit more', 2, None, '___sec96'), + ('The bias', 2, None, '___sec97'), + ('The variance', 2, None, '___sec98'), + ('Summing up', 2, None, '___sec99'), + ('Logistic Regression', 2, None, '___sec100'), + ('Basics', 2, None, '___sec101'), + ('Linear classifier', 2, None, '___sec102'), + ('Some selected properties', 2, None, '___sec103'), + ('The cross-entropy as a cost function for logistic regression', + 2, + None, + '___sec104'), + ('Maximum likelihood', 2, None, '___sec105'), + ('Minimizing the cross entropy', 2, None, '___sec106')]} end of tocinfo --> @@ -337,19 +351,33 @@ MathJax.Hub.Config({
  • Resampling methods: Jackknife and Bootstrap
  • Resampling methods: Jackknife
  • Resampling methods: Jackknife estimator
    • -
  • Resampling methods: Jackknife sample code
    • -
  • Resampling methods: Bootstrap
    • -
  • Resampling methods: Bootstrap background
    • -
  • Resampling methods: More Bootstrap background
    • -
  • Resampling methods: Bootstrap approach
    • -
  • Resampling methods: Bootstrap algorithm
    • -
  • Resampling methods: Bootstrap steps
    • -
  • Resampling methods: Blocking
    • -
  • Blocking Transformations
    • -
  • Blocking Transformations
    • -
  • Blocking Transformations, getting there
    • -
  • Blocking Transformations, final expressions
    • -
  • "Code examples for Blocking, Jackknife and bootstrap":"https://github.com/CompPhysics/MachineLearning/tree/master/doc/Programs/ResamplingAnalysisScripts"
    • +
  • Resampling methods: Bootstrap
    • +
  • Resampling methods: Bootstrap background
    • +
  • Resampling methods: More Bootstrap background
    • +
  • Resampling methods: Bootstrap approach
    • +
  • Resampling methods: Bootstrap steps
    • +
  • Resampling methods: Blocking
    • +
  • Blocking Transformations
    • +
  • Blocking Transformations
    • +
  • Blocking Transformations, getting there
    • +
  • Blocking Transformations, final expressions
    • +
  • "Code examples for Blocking, Jackknife and bootstrap":"https://github.com/CompPhysics/MachineLearning/tree/master/doc/Programs/ResamplingAnalysisScripts"
    • +
  • The bias-variance tradeoff
    • +
  • Training and testing data
    • +
  • Procedure to find a predictor
    • +
  • What we want
    • +
  • The expected generalization error
    • +
  • Elaborating a little bit more
    • +
  • The bias
    • +
  • The variance
    • +
  • Summing up
    • +
  • Logistic Regression
    • +
  • Basics
    • +
  • Linear classifier
    • +
  • Some selected properties
    • +
  • The cross-entropy as a cost function for logistic regression
    • +
  • Maximum likelihood
    • +
  • Minimizing the cross entropy
    • @@ -369,8 +397,8 @@ MathJax.Hub.Config({

    To get an estimate for the bias and -standard error of \( \widehat{\vec{\theta}} \), use the following -estimators for each component of \( \widehat{\vec{\theta}} \) +standard error of \( \widehat{\theta} \), use the following +estimators for each component of \( \widehat{\theta} \) $$ \widehat{\mathrm{Bias}}(\widehat \theta,\theta) = (n-1)\left( - \widehat{\theta} + \frac{1}{n}\sum_{i=1}^{n} \widehat \theta_i \right) \qquad \text{and} \qquad \widehat{\sigma}^2_{\widehat{\theta} } = \frac{n-1}{n}\sum_{i=1}^{n}( \widehat{\theta}_i - \frac{1}{n}\sum_{j=1}^{n}\widehat \theta_j )^2. @@ -402,7 +430,7 @@ $$

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    • diff --git a/doc/pub/Regression/html/._Regression-bs081.html b/doc/pub/Regression/html/._Regression-bs081.html index 95dcbc7f5..edf652384 100644 --- a/doc/pub/Regression/html/._Regression-bs081.html +++ b/doc/pub/Regression/html/._Regression-bs081.html @@ -194,32 +194,46 @@ Automatically generated HTML file from DocOnce source '___sec77'), ('Resampling methods: Jackknife', 2, None, '___sec78'), ('Resampling methods: Jackknife estimator', 2, None, '___sec79'), - ('Resampling methods: Jackknife sample code', - 2, - None, - '___sec80'), - ('Resampling methods: Bootstrap', 2, None, '___sec81'), - ('Resampling methods: Bootstrap background', 2, None, '___sec82'), + ('Resampling methods: Bootstrap', 2, None, '___sec80'), + ('Resampling methods: Bootstrap background', 2, None, '___sec81'), ('Resampling methods: More Bootstrap background', 2, None, - '___sec83'), - ('Resampling methods: Bootstrap approach', 2, None, '___sec84'), - ('Resampling methods: Bootstrap algorithm', 2, None, '___sec85'), - ('Resampling methods: Bootstrap steps', 2, None, '___sec86'), - ('Resampling methods: Blocking', 2, None, '___sec87'), - ('Blocking Transformations', 2, None, '___sec88'), - ('Blocking Transformations', 2, None, '___sec89'), - ('Blocking Transformations, getting there', 2, None, '___sec90'), + '___sec82'), + ('Resampling methods: Bootstrap approach', 2, None, '___sec83'), + ('Resampling methods: Bootstrap steps', 2, None, '___sec84'), + ('Resampling methods: Blocking', 2, None, '___sec85'), + ('Blocking Transformations', 2, None, '___sec86'), + ('Blocking Transformations', 2, None, '___sec87'), + ('Blocking Transformations, getting there', 2, None, '___sec88'), ('Blocking Transformations, final expressions', 2, None, - '___sec91'), + '___sec89'), ('"Code examples for Blocking, Jackknife and ' 'bootstrap":"https://github.com/CompPhysics/MachineLearning/tree/master/doc/Programs/ResamplingAnalysisScripts"', 2, None, - '___sec92')]} + '___sec90'), + ('The bias-variance tradeoff', 2, None, '___sec91'), + ('Training and testing data', 2, None, '___sec92'), + ('Procedure to find a predictor', 2, None, '___sec93'), + ('What we want', 2, None, '___sec94'), + ('The expected generalization error', 2, None, '___sec95'), + ('Elaborating a little bit more', 2, None, '___sec96'), + ('The bias', 2, None, '___sec97'), + ('The variance', 2, None, '___sec98'), + ('Summing up', 2, None, '___sec99'), + ('Logistic Regression', 2, None, '___sec100'), + ('Basics', 2, None, '___sec101'), + ('Linear classifier', 2, None, '___sec102'), + ('Some selected properties', 2, None, '___sec103'), + ('The cross-entropy as a cost function for logistic regression', + 2, + None, + '___sec104'), + ('Maximum likelihood', 2, None, '___sec105'), + ('Minimizing the cross entropy', 2, None, '___sec106')]} end of tocinfo --> @@ -337,19 +351,33 @@ MathJax.Hub.Config({
  • Resampling methods: Jackknife and Bootstrap
  • Resampling methods: Jackknife
  • Resampling methods: Jackknife estimator
    • -
  • Resampling methods: Jackknife sample code
    • -
  • Resampling methods: Bootstrap
    • -
  • Resampling methods: Bootstrap background
    • -
  • Resampling methods: More Bootstrap background
    • -
  • Resampling methods: Bootstrap approach
    • -
  • Resampling methods: Bootstrap algorithm
    • -
  • Resampling methods: Bootstrap steps
    • -
  • Resampling methods: Blocking
    • -
  • Blocking Transformations
    • -
  • Blocking Transformations
    • -
  • Blocking Transformations, getting there
    • -
  • Blocking Transformations, final expressions
    • -
  • "Code examples for Blocking, Jackknife and bootstrap":"https://github.com/CompPhysics/MachineLearning/tree/master/doc/Programs/ResamplingAnalysisScripts"
    • +
  • Resampling methods: Bootstrap
    • +
  • Resampling methods: Bootstrap background
    • +
  • Resampling methods: More Bootstrap background
    • +
  • Resampling methods: Bootstrap approach
    • +
  • Resampling methods: Bootstrap steps
    • +
  • Resampling methods: Blocking
    • +
  • Blocking Transformations
    • +
  • Blocking Transformations
    • +
  • Blocking Transformations, getting there
    • +
  • Blocking Transformations, final expressions
    • +
  • "Code examples for Blocking, Jackknife and bootstrap":"https://github.com/CompPhysics/MachineLearning/tree/master/doc/Programs/ResamplingAnalysisScripts"
    • +
  • The bias-variance tradeoff
    • +
  • Training and testing data
    • +
  • Procedure to find a predictor
    • +
  • What we want
    • +
  • The expected generalization error
    • +
  • Elaborating a little bit more
    • +
  • The bias
    • +
  • The variance
    • +
  • Summing up
    • +
  • Logistic Regression
    • +
  • Basics
    • +
  • Linear classifier
    • +
  • Some selected properties
    • +
  • The cross-entropy as a cost function for logistic regression
    • +
  • Maximum likelihood
    • +
  • Minimizing the cross entropy
    • @@ -365,38 +393,24 @@ MathJax.Hub.Config({ -

    Resampling methods: Jackknife sample code

    +

    Resampling methods: Bootstrap

    +
    +
    +

    +Bootstrapping is a nonparametric approach to statistical inference +that substitutes computation for more traditional distributional +assumptions and asymptotic results. Bootstrapping offers a number of +advantages: -

    -Sample code for the Jackknife method +

      +
    1. The bootstrap is quite general, although there are some cases in which it fails.
      2. +
    2. Because it does not require distributional assumptions (such as normally distributed errors), the bootstrap can provide more accurate inferences when the data are not well behaved or when the sample size is small.
      3. +
    3. It is possible to apply the bootstrap to statistics with sampling distributions that are difficult to derive, even asymptotically.
      4. +
    4. It is relatively simple to apply the bootstrap to complex data-collection plans (such as stratified and clustered samples).
      5. +
    +
    +
    -

    - - -

    def jack(data, stat):
-    n = len(data); t = zeros(n); inds = arange(n); t0 = time()
-    # 'jackknifing' by leaving out an observation for each i
-    for i in range(n):
-        t[i] = stat(delete(data,i) )
-        return t
-# define a function which returns your chosen estimator theta-hat
-def stat(data):
-    theta-hat = mean(data)
-    return theta-hat
-# Return the Jackknife  sample
-t = jack(X, stat)
-
    -

    -Consider first the function jack(). This function repeatedly -estimates the function called statistic() under the resampled -data by systematically leaving out one observation from the data. The -function stat() is passed as an argument to -jack(). The array t is eventually returned, which -contains all the estimates \( \widehat{\vec{\theta}} \), and can be -plotted or analysed in other ways, such as by calling std(t) -from numpy to estimate the standard error of -\( \widehat{\vec{\theta}} \). The function std(t) is just the -estimator \( \widehat{\sigma}^2 \).

    @@ -424,7 +438,7 @@ estimator \( \widehat{\sigma}^2 \).

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    • diff --git a/doc/pub/Regression/html/._Regression-bs082.html b/doc/pub/Regression/html/._Regression-bs082.html index 34ab55eb2..7fdfd64a1 100644 --- a/doc/pub/Regression/html/._Regression-bs082.html +++ b/doc/pub/Regression/html/._Regression-bs082.html @@ -194,32 +194,46 @@ Automatically generated HTML file from DocOnce source '___sec77'), ('Resampling methods: Jackknife', 2, None, '___sec78'), ('Resampling methods: Jackknife estimator', 2, None, '___sec79'), - ('Resampling methods: Jackknife sample code', - 2, - None, - '___sec80'), - ('Resampling methods: Bootstrap', 2, None, '___sec81'), - ('Resampling methods: Bootstrap background', 2, None, '___sec82'), + ('Resampling methods: Bootstrap', 2, None, '___sec80'), + ('Resampling methods: Bootstrap background', 2, None, '___sec81'), ('Resampling methods: More Bootstrap background', 2, None, - '___sec83'), - ('Resampling methods: Bootstrap approach', 2, None, '___sec84'), - ('Resampling methods: Bootstrap algorithm', 2, None, '___sec85'), - ('Resampling methods: Bootstrap steps', 2, None, '___sec86'), - ('Resampling methods: Blocking', 2, None, '___sec87'), - ('Blocking Transformations', 2, None, '___sec88'), - ('Blocking Transformations', 2, None, '___sec89'), - ('Blocking Transformations, getting there', 2, None, '___sec90'), + '___sec82'), + ('Resampling methods: Bootstrap approach', 2, None, '___sec83'), + ('Resampling methods: Bootstrap steps', 2, None, '___sec84'), + ('Resampling methods: Blocking', 2, None, '___sec85'), + ('Blocking Transformations', 2, None, '___sec86'), + ('Blocking Transformations', 2, None, '___sec87'), + ('Blocking Transformations, getting there', 2, None, '___sec88'), ('Blocking Transformations, final expressions', 2, None, - '___sec91'), + '___sec89'), ('"Code examples for Blocking, Jackknife and ' 'bootstrap":"https://github.com/CompPhysics/MachineLearning/tree/master/doc/Programs/ResamplingAnalysisScripts"', 2, None, - '___sec92')]} + '___sec90'), + ('The bias-variance tradeoff', 2, None, '___sec91'), + ('Training and testing data', 2, None, '___sec92'), + ('Procedure to find a predictor', 2, None, '___sec93'), + ('What we want', 2, None, '___sec94'), + ('The expected generalization error', 2, None, '___sec95'), + ('Elaborating a little bit more', 2, None, '___sec96'), + ('The bias', 2, None, '___sec97'), + ('The variance', 2, None, '___sec98'), + ('Summing up', 2, None, '___sec99'), + ('Logistic Regression', 2, None, '___sec100'), + ('Basics', 2, None, '___sec101'), + ('Linear classifier', 2, None, '___sec102'), + ('Some selected properties', 2, None, '___sec103'), + ('The cross-entropy as a cost function for logistic regression', + 2, + None, + '___sec104'), + ('Maximum likelihood', 2, None, '___sec105'), + ('Minimizing the cross entropy', 2, None, '___sec106')]} end of tocinfo --> @@ -337,19 +351,33 @@ MathJax.Hub.Config({
  • Resampling methods: Jackknife and Bootstrap
  • Resampling methods: Jackknife
  • Resampling methods: Jackknife estimator
    • -
  • Resampling methods: Jackknife sample code
    • -
  • Resampling methods: Bootstrap
    • -
  • Resampling methods: Bootstrap background
    • -
  • Resampling methods: More Bootstrap background
    • -
  • Resampling methods: Bootstrap approach
    • -
  • Resampling methods: Bootstrap algorithm
    • -
  • Resampling methods: Bootstrap steps
    • -
  • Resampling methods: Blocking
    • -
  • Blocking Transformations
    • -
  • Blocking Transformations
    • -
  • Blocking Transformations, getting there
    • -
  • Blocking Transformations, final expressions
    • -
  • "Code examples for Blocking, Jackknife and bootstrap":"https://github.com/CompPhysics/MachineLearning/tree/master/doc/Programs/ResamplingAnalysisScripts"
    • +
  • Resampling methods: Bootstrap
    • +
  • Resampling methods: Bootstrap background
    • +
  • Resampling methods: More Bootstrap background
    • +
  • Resampling methods: Bootstrap approach
    • +
  • Resampling methods: Bootstrap steps
    • +
  • Resampling methods: Blocking
    • +
  • Blocking Transformations
    • +
  • Blocking Transformations
    • +
  • Blocking Transformations, getting there
    • +
  • Blocking Transformations, final expressions
    • +
  • "Code examples for Blocking, Jackknife and bootstrap":"https://github.com/CompPhysics/MachineLearning/tree/master/doc/Programs/ResamplingAnalysisScripts"
    • +
  • The bias-variance tradeoff
    • +
  • Training and testing data
    • +
  • Procedure to find a predictor
    • +
  • What we want
    • +
  • The expected generalization error
    • +
  • Elaborating a little bit more
    • +
  • The bias
    • +
  • The variance
    • +
  • Summing up
    • +
  • Logistic Regression
    • +
  • Basics
    • +
  • Linear classifier
    • +
  • Some selected properties
    • +
  • The cross-entropy as a cost function for logistic regression
    • +
  • Maximum likelihood
    • +
  • Minimizing the cross entropy
    • @@ -365,24 +393,18 @@ MathJax.Hub.Config({ -

    Resampling methods: Bootstrap

    -
    -
    -

    -Bootstrapping is a nonparametric approach to statistical inference -that substitutes computation for more traditional distributional -assumptions and asymptotic results. Bootstrapping offers a number of -advantages: - -

      -
    1. The bootstrap is quite general, although there are some cases in which it fails.
      2. -
    2. Because it does not require distributional assumptions (such as normally distributed errors), the bootstrap can provide more accurate inferences when the data are not well behaved or when the sample size is small.
      3. -
    3. It is possible to apply the bootstrap to statistics with sampling distributions that are difficult to derive, even asymptotically.
      4. -
    4. It is relatively simple to apply the bootstrap to complex data-collection plans (such as stratified and clustered samples).
      5. -
    -
    -
    +

    Resampling methods: Bootstrap background

    +

    +Since \( \widehat{\theta} = \widehat{\theta}(\hat{X}) \) is a function of random variables, +\( \widehat{\theta} \) itself must be a random variable. Thus it has +a pdf, call this function \( p(\vec{t}) \). The aim of the bootstrap is to +estimate \( p(\hat{t}) \) by the relative frequency of +\( \widehat{\theta} \). You can think of this as using a histogram +in the place of \( p(\hat{t}) \). If the relative frequency closely +resembles \( p(\vec{t}) \), then using numerics, it is straight forward to +estimate all the interesting parameters of \( p(\hat{t}) \) using point +estimators.

    @@ -410,7 +432,7 @@ advantages:

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    • diff --git a/doc/pub/Regression/html/._Regression-bs083.html b/doc/pub/Regression/html/._Regression-bs083.html index 7f07051a1..7c77bcd23 100644 --- a/doc/pub/Regression/html/._Regression-bs083.html +++ b/doc/pub/Regression/html/._Regression-bs083.html @@ -194,32 +194,46 @@ Automatically generated HTML file from DocOnce source '___sec77'), ('Resampling methods: Jackknife', 2, None, '___sec78'), ('Resampling methods: Jackknife estimator', 2, None, '___sec79'), - ('Resampling methods: Jackknife sample code', - 2, - None, - '___sec80'), - ('Resampling methods: Bootstrap', 2, None, '___sec81'), - ('Resampling methods: Bootstrap background', 2, None, '___sec82'), + ('Resampling methods: Bootstrap', 2, None, '___sec80'), + ('Resampling methods: Bootstrap background', 2, None, '___sec81'), ('Resampling methods: More Bootstrap background', 2, None, - '___sec83'), - ('Resampling methods: Bootstrap approach', 2, None, '___sec84'), - ('Resampling methods: Bootstrap algorithm', 2, None, '___sec85'), - ('Resampling methods: Bootstrap steps', 2, None, '___sec86'), - ('Resampling methods: Blocking', 2, None, '___sec87'), - ('Blocking Transformations', 2, None, '___sec88'), - ('Blocking Transformations', 2, None, '___sec89'), - ('Blocking Transformations, getting there', 2, None, '___sec90'), + '___sec82'), + ('Resampling methods: Bootstrap approach', 2, None, '___sec83'), + ('Resampling methods: Bootstrap steps', 2, None, '___sec84'), + ('Resampling methods: Blocking', 2, None, '___sec85'), + ('Blocking Transformations', 2, None, '___sec86'), + ('Blocking Transformations', 2, None, '___sec87'), + ('Blocking Transformations, getting there', 2, None, '___sec88'), ('Blocking Transformations, final expressions', 2, None, - '___sec91'), + '___sec89'), ('"Code examples for Blocking, Jackknife and ' 'bootstrap":"https://github.com/CompPhysics/MachineLearning/tree/master/doc/Programs/ResamplingAnalysisScripts"', 2, None, - '___sec92')]} + '___sec90'), + ('The bias-variance tradeoff', 2, None, '___sec91'), + ('Training and testing data', 2, None, '___sec92'), + ('Procedure to find a predictor', 2, None, '___sec93'), + ('What we want', 2, None, '___sec94'), + ('The expected generalization error', 2, None, '___sec95'), + ('Elaborating a little bit more', 2, None, '___sec96'), + ('The bias', 2, None, '___sec97'), + ('The variance', 2, None, '___sec98'), + ('Summing up', 2, None, '___sec99'), + ('Logistic Regression', 2, None, '___sec100'), + ('Basics', 2, None, '___sec101'), + ('Linear classifier', 2, None, '___sec102'), + ('Some selected properties', 2, None, '___sec103'), + ('The cross-entropy as a cost function for logistic regression', + 2, + None, + '___sec104'), + ('Maximum likelihood', 2, None, '___sec105'), + ('Minimizing the cross entropy', 2, None, '___sec106')]} end of tocinfo --> @@ -337,19 +351,33 @@ MathJax.Hub.Config({
  • Resampling methods: Jackknife and Bootstrap
  • Resampling methods: Jackknife
  • Resampling methods: Jackknife estimator
    • -
  • Resampling methods: Jackknife sample code
    • -
  • Resampling methods: Bootstrap
    • -
  • Resampling methods: Bootstrap background
    • -
  • Resampling methods: More Bootstrap background
    • -
  • Resampling methods: Bootstrap approach
    • -
  • Resampling methods: Bootstrap algorithm
    • -
  • Resampling methods: Bootstrap steps
    • -
  • Resampling methods: Blocking
    • -
  • Blocking Transformations
    • -
  • Blocking Transformations
    • -
  • Blocking Transformations, getting there
    • -
  • Blocking Transformations, final expressions
    • -
  • "Code examples for Blocking, Jackknife and bootstrap":"https://github.com/CompPhysics/MachineLearning/tree/master/doc/Programs/ResamplingAnalysisScripts"
    • +
  • Resampling methods: Bootstrap
    • +
  • Resampling methods: Bootstrap background
    • +
  • Resampling methods: More Bootstrap background
    • +
  • Resampling methods: Bootstrap approach
    • +
  • Resampling methods: Bootstrap steps
    • +
  • Resampling methods: Blocking
    • +
  • Blocking Transformations
    • +
  • Blocking Transformations
    • +
  • Blocking Transformations, getting there
    • +
  • Blocking Transformations, final expressions
    • +
  • "Code examples for Blocking, Jackknife and bootstrap":"https://github.com/CompPhysics/MachineLearning/tree/master/doc/Programs/ResamplingAnalysisScripts"
    • +
  • The bias-variance tradeoff
    • +
  • Training and testing data
    • +
  • Procedure to find a predictor
    • +
  • What we want
    • +
  • The expected generalization error
    • +
  • Elaborating a little bit more
    • +
  • The bias
    • +
  • The variance
    • +
  • Summing up
    • +
  • Logistic Regression
    • +
  • Basics
    • +
  • Linear classifier
    • +
  • Some selected properties
    • +
  • The cross-entropy as a cost function for logistic regression
    • +
  • Maximum likelihood
    • +
  • Minimizing the cross entropy
    • @@ -365,18 +393,24 @@ MathJax.Hub.Config({ -

    Resampling methods: Bootstrap background

    +

    Resampling methods: More Bootstrap background

    -Since \( \widehat{\vec{\theta}} = \widehat{\vec{\theta}}(\vec{X}) \) is a function of random variables, -\( \widehat{\vec{\theta}} \) itself must be a random variable. Thus it has -a pdf, call this function \( p(\vec{t}) \). The aim of the bootstrap is to -estimate \( p(\vec{t}) \) by the relative frequency of -\( \widehat{\vec{\theta}} \). You can think of this as using a histogram -in the place of \( p(\vec{t}) \). If the relative frequency closely -resembles \( p(\vec{t}) \), then using numerics, it is straight forward to -estimate all the interesting parameters of \( p(\vec{t}) \) using point -estimators. +In the case that \( \widehat{\theta} \) has +more than one component, and the components are independent, use the +same estimator on each component separately. If the probability +density function of \( X_i \), \( p(x) \), had been known, then it would have +been straight forward to do this by: + +

      +
    1. Drawing lots of numbers from \( p(x) \), suppose we call one such set of numbers \( (X_1^*, X_2^*, \cdots, X_n^*) \).
      2. +
    2. Then using these numbers, we could compute a replica of \( \widehat{\theta} \) called \( \widehat{\theta}^* \).
      3. +
    + +By repeated use of (1) and (2), many +estimates of \( \widehat{\theta} \) could have been obtained. The +idea is to use the relative frequency of \( \widehat{\theta}^* \) +(think of a histogram) as an estimate of \( p(\hat{t}) \).

    @@ -404,7 +438,7 @@ estimators.

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    • diff --git a/doc/pub/Regression/html/._Regression-bs084.html b/doc/pub/Regression/html/._Regression-bs084.html index df9d27df6..c9937e38d 100644 --- a/doc/pub/Regression/html/._Regression-bs084.html +++ b/doc/pub/Regression/html/._Regression-bs084.html @@ -194,32 +194,46 @@ Automatically generated HTML file from DocOnce source '___sec77'), ('Resampling methods: Jackknife', 2, None, '___sec78'), ('Resampling methods: Jackknife estimator', 2, None, '___sec79'), - ('Resampling methods: Jackknife sample code', - 2, - None, - '___sec80'), - ('Resampling methods: Bootstrap', 2, None, '___sec81'), - ('Resampling methods: Bootstrap background', 2, None, '___sec82'), + ('Resampling methods: Bootstrap', 2, None, '___sec80'), + ('Resampling methods: Bootstrap background', 2, None, '___sec81'), ('Resampling methods: More Bootstrap background', 2, None, - '___sec83'), - ('Resampling methods: Bootstrap approach', 2, None, '___sec84'), - ('Resampling methods: Bootstrap algorithm', 2, None, '___sec85'), - ('Resampling methods: Bootstrap steps', 2, None, '___sec86'), - ('Resampling methods: Blocking', 2, None, '___sec87'), - ('Blocking Transformations', 2, None, '___sec88'), - ('Blocking Transformations', 2, None, '___sec89'), - ('Blocking Transformations, getting there', 2, None, '___sec90'), + '___sec82'), + ('Resampling methods: Bootstrap approach', 2, None, '___sec83'), + ('Resampling methods: Bootstrap steps', 2, None, '___sec84'), + ('Resampling methods: Blocking', 2, None, '___sec85'), + ('Blocking Transformations', 2, None, '___sec86'), + ('Blocking Transformations', 2, None, '___sec87'), + ('Blocking Transformations, getting there', 2, None, '___sec88'), ('Blocking Transformations, final expressions', 2, None, - '___sec91'), + '___sec89'), ('"Code examples for Blocking, Jackknife and ' 'bootstrap":"https://github.com/CompPhysics/MachineLearning/tree/master/doc/Programs/ResamplingAnalysisScripts"', 2, None, - '___sec92')]} + '___sec90'), + ('The bias-variance tradeoff', 2, None, '___sec91'), + ('Training and testing data', 2, None, '___sec92'), + ('Procedure to find a predictor', 2, None, '___sec93'), + ('What we want', 2, None, '___sec94'), + ('The expected generalization error', 2, None, '___sec95'), + ('Elaborating a little bit more', 2, None, '___sec96'), + ('The bias', 2, None, '___sec97'), + ('The variance', 2, None, '___sec98'), + ('Summing up', 2, None, '___sec99'), + ('Logistic Regression', 2, None, '___sec100'), + ('Basics', 2, None, '___sec101'), + ('Linear classifier', 2, None, '___sec102'), + ('Some selected properties', 2, None, '___sec103'), + ('The cross-entropy as a cost function for logistic regression', + 2, + None, + '___sec104'), + ('Maximum likelihood', 2, None, '___sec105'), + ('Minimizing the cross entropy', 2, None, '___sec106')]} end of tocinfo --> @@ -337,19 +351,33 @@ MathJax.Hub.Config({
  • Resampling methods: Jackknife and Bootstrap
  • Resampling methods: Jackknife
  • Resampling methods: Jackknife estimator
    • -
  • Resampling methods: Jackknife sample code
    • -
  • Resampling methods: Bootstrap
    • -
  • Resampling methods: Bootstrap background
    • -
  • Resampling methods: More Bootstrap background
    • -
  • Resampling methods: Bootstrap approach
    • -
  • Resampling methods: Bootstrap algorithm
    • -
  • Resampling methods: Bootstrap steps
    • -
  • Resampling methods: Blocking
    • -
  • Blocking Transformations
    • -
  • Blocking Transformations
    • -
  • Blocking Transformations, getting there
    • -
  • Blocking Transformations, final expressions
    • -
  • "Code examples for Blocking, Jackknife and bootstrap":"https://github.com/CompPhysics/MachineLearning/tree/master/doc/Programs/ResamplingAnalysisScripts"
    • +
  • Resampling methods: Bootstrap
    • +
  • Resampling methods: Bootstrap background
    • +
  • Resampling methods: More Bootstrap background
    • +
  • Resampling methods: Bootstrap approach
    • +
  • Resampling methods: Bootstrap steps
    • +
  • Resampling methods: Blocking
    • +
  • Blocking Transformations
    • +
  • Blocking Transformations
    • +
  • Blocking Transformations, getting there
    • +
  • Blocking Transformations, final expressions
    • +
  • "Code examples for Blocking, Jackknife and bootstrap":"https://github.com/CompPhysics/MachineLearning/tree/master/doc/Programs/ResamplingAnalysisScripts"
    • +
  • The bias-variance tradeoff
    • +
  • Training and testing data
    • +
  • Procedure to find a predictor
    • +
  • What we want
    • +
  • The expected generalization error
    • +
  • Elaborating a little bit more
    • +
  • The bias
    • +
  • The variance
    • +
  • Summing up
    • +
  • Logistic Regression
    • +
  • Basics
    • +
  • Linear classifier
    • +
  • Some selected properties
    • +
  • The cross-entropy as a cost function for logistic regression
    • +
  • Maximum likelihood
    • +
  • Minimizing the cross entropy
    • @@ -365,24 +393,23 @@ MathJax.Hub.Config({ -

    Resampling methods: More Bootstrap background

    +

    Resampling methods: Bootstrap approach

    -In the case that \( \widehat{\vec{\theta}} \) has -more than one component, and the components are independent, use the -same estimator on each component separately. If the probability -density function of \( X_i \), \( p(x) \), had been known, then it would have -been straight forward to do this by: +But +unless there is enough information available about the process that +generated \( X_1,X_2,\cdots,X_n \), \( p(x) \) is in general +unknown. Therefore, Efron in 1979 asked the +natural question: What if we replace \( p(x) \) by the relative frequency +of the observation \( X_i \); if we draw observations in accordance with +the relative frequency of the observations, will we obtain the same +result in some asymptotic sense? The answer is yes. -

      -
    1. Drawing lots of numbers from \( p(x) \), suppose we call one such set of numbers \( (X_1^*, X_2^*, \cdots, X_n^*) \).
      2. -
    2. Then using these numbers, we could compute a replica of \( \widehat{\vec{\theta}} \) called \( \widehat{\vec{\theta}}^* \).
      3. -
    - -By repeated use of (1) and (2), many -estimates of \( \widehat{\vec{\theta}} \) could have been obtained. The -idea is to use the relative frequency of \( \widehat{\vec{\theta}}^* \) -(think of a histogram) as an estimate of \( p(\vec{t}) \). +

    +Instead of generating the histogram for the relative +frequency of the observation \( X_i \), just draw the values +\( (X_1^*,X_2^*,\cdots,X_n^*) \) with replacement from the vector +\( \hat{X} \).

    @@ -409,6 +436,8 @@ idea is to use the relative frequency of \( \widehat{\vec{\theta}}^* \)

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    • diff --git a/doc/pub/Regression/html/._Regression-bs085.html b/doc/pub/Regression/html/._Regression-bs085.html index 20e0a59e9..993bdf63f 100644 --- a/doc/pub/Regression/html/._Regression-bs085.html +++ b/doc/pub/Regression/html/._Regression-bs085.html @@ -194,32 +194,46 @@ Automatically generated HTML file from DocOnce source '___sec77'), ('Resampling methods: Jackknife', 2, None, '___sec78'), ('Resampling methods: Jackknife estimator', 2, None, '___sec79'), - ('Resampling methods: Jackknife sample code', - 2, - None, - '___sec80'), - ('Resampling methods: Bootstrap', 2, None, '___sec81'), - ('Resampling methods: Bootstrap background', 2, None, '___sec82'), + ('Resampling methods: Bootstrap', 2, None, '___sec80'), + ('Resampling methods: Bootstrap background', 2, None, '___sec81'), ('Resampling methods: More Bootstrap background', 2, None, - '___sec83'), - ('Resampling methods: Bootstrap approach', 2, None, '___sec84'), - ('Resampling methods: Bootstrap algorithm', 2, None, '___sec85'), - ('Resampling methods: Bootstrap steps', 2, None, '___sec86'), - ('Resampling methods: Blocking', 2, None, '___sec87'), - ('Blocking Transformations', 2, None, '___sec88'), - ('Blocking Transformations', 2, None, '___sec89'), - ('Blocking Transformations, getting there', 2, None, '___sec90'), + '___sec82'), + ('Resampling methods: Bootstrap approach', 2, None, '___sec83'), + ('Resampling methods: Bootstrap steps', 2, None, '___sec84'), + ('Resampling methods: Blocking', 2, None, '___sec85'), + ('Blocking Transformations', 2, None, '___sec86'), + ('Blocking Transformations', 2, None, '___sec87'), + ('Blocking Transformations, getting there', 2, None, '___sec88'), ('Blocking Transformations, final expressions', 2, None, - '___sec91'), + '___sec89'), ('"Code examples for Blocking, Jackknife and ' 'bootstrap":"https://github.com/CompPhysics/MachineLearning/tree/master/doc/Programs/ResamplingAnalysisScripts"', 2, None, - '___sec92')]} + '___sec90'), + ('The bias-variance tradeoff', 2, None, '___sec91'), + ('Training and testing data', 2, None, '___sec92'), + ('Procedure to find a predictor', 2, None, '___sec93'), + ('What we want', 2, None, '___sec94'), + ('The expected generalization error', 2, None, '___sec95'), + ('Elaborating a little bit more', 2, None, '___sec96'), + ('The bias', 2, None, '___sec97'), + ('The variance', 2, None, '___sec98'), + ('Summing up', 2, None, '___sec99'), + ('Logistic Regression', 2, None, '___sec100'), + ('Basics', 2, None, '___sec101'), + ('Linear classifier', 2, None, '___sec102'), + ('Some selected properties', 2, None, '___sec103'), + ('The cross-entropy as a cost function for logistic regression', + 2, + None, + '___sec104'), + ('Maximum likelihood', 2, None, '___sec105'), + ('Minimizing the cross entropy', 2, None, '___sec106')]} end of tocinfo --> @@ -337,19 +351,33 @@ MathJax.Hub.Config({
  • Resampling methods: Jackknife and Bootstrap
  • Resampling methods: Jackknife
  • Resampling methods: Jackknife estimator
    • -
  • Resampling methods: Jackknife sample code
    • -
  • Resampling methods: Bootstrap
    • -
  • Resampling methods: Bootstrap background
    • -
  • Resampling methods: More Bootstrap background
    • -
  • Resampling methods: Bootstrap approach
    • -
  • Resampling methods: Bootstrap algorithm
    • -
  • Resampling methods: Bootstrap steps
    • -
  • Resampling methods: Blocking
    • -
  • Blocking Transformations
    • -
  • Blocking Transformations
    • -
  • Blocking Transformations, getting there
    • -
  • Blocking Transformations, final expressions
    • -
  • "Code examples for Blocking, Jackknife and bootstrap":"https://github.com/CompPhysics/MachineLearning/tree/master/doc/Programs/ResamplingAnalysisScripts"
    • +
  • Resampling methods: Bootstrap
    • +
  • Resampling methods: Bootstrap background
    • +
  • Resampling methods: More Bootstrap background
    • +
  • Resampling methods: Bootstrap approach
    • +
  • Resampling methods: Bootstrap steps
    • +
  • Resampling methods: Blocking
    • +
  • Blocking Transformations
    • +
  • Blocking Transformations
    • +
  • Blocking Transformations, getting there
    • +
  • Blocking Transformations, final expressions
    • +
  • "Code examples for Blocking, Jackknife and bootstrap":"https://github.com/CompPhysics/MachineLearning/tree/master/doc/Programs/ResamplingAnalysisScripts"
    • +
  • The bias-variance tradeoff
    • +
  • Training and testing data
    • +
  • Procedure to find a predictor
    • +
  • What we want
    • +
  • The expected generalization error
    • +
  • Elaborating a little bit more
    • +
  • The bias
    • +
  • The variance
    • +
  • Summing up
    • +
  • Logistic Regression
    • +
  • Basics
    • +
  • Linear classifier
    • +
  • Some selected properties
    • +
  • The cross-entropy as a cost function for logistic regression
    • +
  • Maximum likelihood
    • +
  • Minimizing the cross entropy
    • @@ -365,23 +393,19 @@ MathJax.Hub.Config({ -

    Resampling methods: Bootstrap approach

    +

    Resampling methods: Bootstrap steps

    -But -unless there is enough information available about the process that -generated \( X_1,X_2,\cdots,X_n \), \( p(x) \) is in general -unknown. Therefore, Efron in 1979 asked the -natural question: What if we replace \( p(x) \) by the relative frequency -of the observation \( X_i \); if we draw observations in accordance with -the relative frequency of the observations, will we obtain the same -result in some asymptotic sense? The answer is yes. +The independent bootstrap works like this: -

    -Instead of generating the histogram for the relative -frequency of the observation \( X_i \), just draw the values -\( (X_1^*,X_2^*,\cdots,X_n^*) \) with replacement from the vector -\( \vec{X} \). +

      +
    1. Draw with replacement \( n \) numbers for the observed variables \( \hat{x} = (x_1,x_2,\cdots,x_n) \).
      2. +
    2. Define a vector \( \hat{x}^* \) containing the values which were drawn from \( \hat{x} \).
      3. +
    3. Using the vector \( \hat{x}^* \) compute \( \widehat{\theta}^* \) by evaluating \( \widehat \theta \) under the observations \( \hat{x}^* \).
      4. +
    4. Repeat this process \( k \) times.
      5. +
    + +When you are done, you can draw a histogram of the relative frequency of \( \widehat \theta^* \). This is your estimate of the probability distribution \( p(t) \). Using this probability distribution you can estimate any statistics thereof. In principle you never draw the histogram of the relative frequency of \( \widehat{\theta}^* \). Instead you use the estimators corresponding to the statistic of interest. For example, if you are interested in estimating the variance of \( \widehat \theta \), apply the esimator \( \widehat \sigma^2 \) to the values \( \widehat \theta ^* \).

    @@ -407,6 +431,9 @@ frequency of the observation \( X_i \), just draw the values

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    • diff --git a/doc/pub/Regression/html/._Regression-bs086.html b/doc/pub/Regression/html/._Regression-bs086.html index 9036f5cb6..d138d2b8b 100644 --- a/doc/pub/Regression/html/._Regression-bs086.html +++ b/doc/pub/Regression/html/._Regression-bs086.html @@ -194,32 +194,46 @@ Automatically generated HTML file from DocOnce source '___sec77'), ('Resampling methods: Jackknife', 2, None, '___sec78'), ('Resampling methods: Jackknife estimator', 2, None, '___sec79'), - ('Resampling methods: Jackknife sample code', - 2, - None, - '___sec80'), - ('Resampling methods: Bootstrap', 2, None, '___sec81'), - ('Resampling methods: Bootstrap background', 2, None, '___sec82'), + ('Resampling methods: Bootstrap', 2, None, '___sec80'), + ('Resampling methods: Bootstrap background', 2, None, '___sec81'), ('Resampling methods: More Bootstrap background', 2, None, - '___sec83'), - ('Resampling methods: Bootstrap approach', 2, None, '___sec84'), - ('Resampling methods: Bootstrap algorithm', 2, None, '___sec85'), - ('Resampling methods: Bootstrap steps', 2, None, '___sec86'), - ('Resampling methods: Blocking', 2, None, '___sec87'), - ('Blocking Transformations', 2, None, '___sec88'), - ('Blocking Transformations', 2, None, '___sec89'), - ('Blocking Transformations, getting there', 2, None, '___sec90'), + '___sec82'), + ('Resampling methods: Bootstrap approach', 2, None, '___sec83'), + ('Resampling methods: Bootstrap steps', 2, None, '___sec84'), + ('Resampling methods: Blocking', 2, None, '___sec85'), + ('Blocking Transformations', 2, None, '___sec86'), + ('Blocking Transformations', 2, None, '___sec87'), + ('Blocking Transformations, getting there', 2, None, '___sec88'), ('Blocking Transformations, final expressions', 2, None, - '___sec91'), + '___sec89'), ('"Code examples for Blocking, Jackknife and ' 'bootstrap":"https://github.com/CompPhysics/MachineLearning/tree/master/doc/Programs/ResamplingAnalysisScripts"', 2, None, - '___sec92')]} + '___sec90'), + ('The bias-variance tradeoff', 2, None, '___sec91'), + ('Training and testing data', 2, None, '___sec92'), + ('Procedure to find a predictor', 2, None, '___sec93'), + ('What we want', 2, None, '___sec94'), + ('The expected generalization error', 2, None, '___sec95'), + ('Elaborating a little bit more', 2, None, '___sec96'), + ('The bias', 2, None, '___sec97'), + ('The variance', 2, None, '___sec98'), + ('Summing up', 2, None, '___sec99'), + ('Logistic Regression', 2, None, '___sec100'), + ('Basics', 2, None, '___sec101'), + ('Linear classifier', 2, None, '___sec102'), + ('Some selected properties', 2, None, '___sec103'), + ('The cross-entropy as a cost function for logistic regression', + 2, + None, + '___sec104'), + ('Maximum likelihood', 2, None, '___sec105'), + ('Minimizing the cross entropy', 2, None, '___sec106')]} end of tocinfo --> @@ -337,19 +351,33 @@ MathJax.Hub.Config({
  • Resampling methods: Jackknife and Bootstrap
  • Resampling methods: Jackknife
  • Resampling methods: Jackknife estimator
    • -
  • Resampling methods: Jackknife sample code
    • -
  • Resampling methods: Bootstrap
    • -
  • Resampling methods: Bootstrap background
    • -
  • Resampling methods: More Bootstrap background
    • -
  • Resampling methods: Bootstrap approach
    • -
  • Resampling methods: Bootstrap algorithm
    • -
  • Resampling methods: Bootstrap steps
    • -
  • Resampling methods: Blocking
    • -
  • Blocking Transformations
    • -
  • Blocking Transformations
    • -
  • Blocking Transformations, getting there
    • -
  • Blocking Transformations, final expressions
    • -
  • "Code examples for Blocking, Jackknife and bootstrap":"https://github.com/CompPhysics/MachineLearning/tree/master/doc/Programs/ResamplingAnalysisScripts"
    • +
  • Resampling methods: Bootstrap
    • +
  • Resampling methods: Bootstrap background
    • +
  • Resampling methods: More Bootstrap background
    • +
  • Resampling methods: Bootstrap approach
    • +
  • Resampling methods: Bootstrap steps
    • +
  • Resampling methods: Blocking
    • +
  • Blocking Transformations
    • +
  • Blocking Transformations
    • +
  • Blocking Transformations, getting there
    • +
  • Blocking Transformations, final expressions
    • +
  • "Code examples for Blocking, Jackknife and bootstrap":"https://github.com/CompPhysics/MachineLearning/tree/master/doc/Programs/ResamplingAnalysisScripts"
    • +
  • The bias-variance tradeoff
    • +
  • Training and testing data
    • +
  • Procedure to find a predictor
    • +
  • What we want
    • +
  • The expected generalization error
    • +
  • Elaborating a little bit more
    • +
  • The bias
    • +
  • The variance
    • +
  • Summing up
    • +
  • Logistic Regression
    • +
  • Basics
    • +
  • Linear classifier
    • +
  • Some selected properties
    • +
  • The cross-entropy as a cost function for logistic regression
    • +
  • Maximum likelihood
    • +
  • Minimizing the cross entropy
    • @@ -365,26 +393,28 @@ MathJax.Hub.Config({ -

    Resampling methods: Bootstrap algorithm

    +

    Resampling methods: Blocking

    +The blocking method was made popular by Flyvbjerg and Pedersen (1989) +and has become one of the standard ways to estimate +\( V(\widehat{\theta}) \) for exactly one \( \widehat{\theta} \), namely +\( \widehat{\theta} = \overline{X} \). - -

            
-def boot(data, statistic, R):
-    t = zeros(R); n = len(data); inds = arange(n); t0 = time()
-    for i in range(R):
-        t[i] = statistic(data[randint(0,n,n)])
-        return t
-# define a function which returns your chosen estimator theta-hat
-def stat(data):
-    theta-hat = mean(data)
-    return theta-hat
-
-t = boot(X, stat, 2**9)
-

    -Consider first the function boot(). In the for loop, this function repeatedly estimates the function called statistic() under the resampled data in data(randint(0,n,n)). The function statistic() is passed as an argument to boot(). The array t is eventually returned, which contains all the estimates \( \widehat{\vec{\theta}} \), and can be plotted or analysed in other ways, such as by calling std(t) from numpy to estimate the standard error of \( \widehat{\vec{\theta}} \). The function std(t) is just the estimator \( \widehat{\sigma}^2 \). +Assume \( n = 2^d \) for some integer \( d>1 \) and \( X_1,X_2,\cdots, X_n \) is a stationary time series to begin with. +Moreover, assume that the time series is asymptotically uncorrelated. We switch to vector notation by arranging \( X_1,X_2,\cdots,X_n \) in an \( n \)-tuple. Define: +$$ +\begin{align*} +\hat{X} = (X_1,X_2,\cdots,X_n). +\end{align*} +$$ + +

    +The strength of the blocking method is when the number of +observations, \( n \) is large. For large \( n \), the complexity of dependent +bootstrapping scales poorly, but the blocking method does not, +moreover, it becomes more accurate the larger \( n \) is.

    @@ -409,6 +439,10 @@ Consider first the function boot(). In the for loop, this function

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    • diff --git a/doc/pub/Regression/html/._Regression-bs087.html b/doc/pub/Regression/html/._Regression-bs087.html index c5e259f99..c35f45312 100644 --- a/doc/pub/Regression/html/._Regression-bs087.html +++ b/doc/pub/Regression/html/._Regression-bs087.html @@ -194,32 +194,46 @@ Automatically generated HTML file from DocOnce source '___sec77'), ('Resampling methods: Jackknife', 2, None, '___sec78'), ('Resampling methods: Jackknife estimator', 2, None, '___sec79'), - ('Resampling methods: Jackknife sample code', - 2, - None, - '___sec80'), - ('Resampling methods: Bootstrap', 2, None, '___sec81'), - ('Resampling methods: Bootstrap background', 2, None, '___sec82'), + ('Resampling methods: Bootstrap', 2, None, '___sec80'), + ('Resampling methods: Bootstrap background', 2, None, '___sec81'), ('Resampling methods: More Bootstrap background', 2, None, - '___sec83'), - ('Resampling methods: Bootstrap approach', 2, None, '___sec84'), - ('Resampling methods: Bootstrap algorithm', 2, None, '___sec85'), - ('Resampling methods: Bootstrap steps', 2, None, '___sec86'), - ('Resampling methods: Blocking', 2, None, '___sec87'), - ('Blocking Transformations', 2, None, '___sec88'), - ('Blocking Transformations', 2, None, '___sec89'), - ('Blocking Transformations, getting there', 2, None, '___sec90'), + '___sec82'), + ('Resampling methods: Bootstrap approach', 2, None, '___sec83'), + ('Resampling methods: Bootstrap steps', 2, None, '___sec84'), + ('Resampling methods: Blocking', 2, None, '___sec85'), + ('Blocking Transformations', 2, None, '___sec86'), + ('Blocking Transformations', 2, None, '___sec87'), + ('Blocking Transformations, getting there', 2, None, '___sec88'), ('Blocking Transformations, final expressions', 2, None, - '___sec91'), + '___sec89'), ('"Code examples for Blocking, Jackknife and ' 'bootstrap":"https://github.com/CompPhysics/MachineLearning/tree/master/doc/Programs/ResamplingAnalysisScripts"', 2, None, - '___sec92')]} + '___sec90'), + ('The bias-variance tradeoff', 2, None, '___sec91'), + ('Training and testing data', 2, None, '___sec92'), + ('Procedure to find a predictor', 2, None, '___sec93'), + ('What we want', 2, None, '___sec94'), + ('The expected generalization error', 2, None, '___sec95'), + ('Elaborating a little bit more', 2, None, '___sec96'), + ('The bias', 2, None, '___sec97'), + ('The variance', 2, None, '___sec98'), + ('Summing up', 2, None, '___sec99'), + ('Logistic Regression', 2, None, '___sec100'), + ('Basics', 2, None, '___sec101'), + ('Linear classifier', 2, None, '___sec102'), + ('Some selected properties', 2, None, '___sec103'), + ('The cross-entropy as a cost function for logistic regression', + 2, + None, + '___sec104'), + ('Maximum likelihood', 2, None, '___sec105'), + ('Minimizing the cross entropy', 2, None, '___sec106')]} end of tocinfo --> @@ -337,19 +351,33 @@ MathJax.Hub.Config({
  • Resampling methods: Jackknife and Bootstrap
  • Resampling methods: Jackknife
  • Resampling methods: Jackknife estimator
    • -
  • Resampling methods: Jackknife sample code
    • -
  • Resampling methods: Bootstrap
    • -
  • Resampling methods: Bootstrap background
    • -
  • Resampling methods: More Bootstrap background
    • -
  • Resampling methods: Bootstrap approach
    • -
  • Resampling methods: Bootstrap algorithm
    • -
  • Resampling methods: Bootstrap steps
    • -
  • Resampling methods: Blocking
    • -
  • Blocking Transformations
    • -
  • Blocking Transformations
    • -
  • Blocking Transformations, getting there
    • -
  • Blocking Transformations, final expressions
    • -
  • "Code examples for Blocking, Jackknife and bootstrap":"https://github.com/CompPhysics/MachineLearning/tree/master/doc/Programs/ResamplingAnalysisScripts"
    • +
  • Resampling methods: Bootstrap
    • +
  • Resampling methods: Bootstrap background
    • +
  • Resampling methods: More Bootstrap background
    • +
  • Resampling methods: Bootstrap approach
    • +
  • Resampling methods: Bootstrap steps
    • +
  • Resampling methods: Blocking
    • +
  • Blocking Transformations
    • +
  • Blocking Transformations
    • +
  • Blocking Transformations, getting there
    • +
  • Blocking Transformations, final expressions
    • +
  • "Code examples for Blocking, Jackknife and bootstrap":"https://github.com/CompPhysics/MachineLearning/tree/master/doc/Programs/ResamplingAnalysisScripts"
    • +
  • The bias-variance tradeoff
    • +
  • Training and testing data
    • +
  • Procedure to find a predictor
    • +
  • What we want
    • +
  • The expected generalization error
    • +
  • Elaborating a little bit more
    • +
  • The bias
    • +
  • The variance
    • +
  • Summing up
    • +
  • Logistic Regression
    • +
  • Basics
    • +
  • Linear classifier
    • +
  • Some selected properties
    • +
  • The cross-entropy as a cost function for logistic regression
    • +
  • Maximum likelihood
    • +
  • Minimizing the cross entropy
    • @@ -365,19 +393,39 @@ MathJax.Hub.Config({ -

    Resampling methods: Bootstrap steps

    +

    Blocking Transformations

    + We now define +blocking transformations. The idea is to take the mean of subsequent +pair of elements from \( \vec{X} \) and form a new vector +\( \vec{X}_1 \). Continuing in the same way by taking the mean of +subsequent pairs of elements of \( \vec{X}_1 \) we obtain \( \vec{X}_2 \), and +so on. +Define \( \vec{X}_i \) recursively by: + +$$ +\begin{align} +(\vec{X}_0)_k &\equiv (\vec{X})_k \nonumber \\ +(\vec{X}_{i+1})_k &\equiv \frac{1}{2}\Big( (\vec{X}_i)_{2k-1} + +(\vec{X}_i)_{2k} \Big) \qquad \text{for all} \qquad 1 \leq i \leq d-1 +\tag{21} +\end{align} +$$

    -The independent bootstrap works like this: +The quantity \( \vec{X}_k \) is +subject to \( k \) blocking transformations. We now have \( d \) vectors +\( \vec{X}_0, \vec{X}_1,\cdots,\vec X_{d-1} \) containing the subsequent +averages of observations. It turns out that if the components of +\( \vec{X} \) is a stationary time series, then the components of +\( \vec{X}_i \) is a stationary time series for all \( 0 \leq i \leq d-1 \) -

      -
    1. Draw with replacement \( n \) numbers for the observed variables \( \vec{x} = (x_1,x_2,\cdots,x_n) \).
      2. -
    2. Define a vector \( \vec{x}^* \) containing the values which were drawn from \( \vec{x} \).
      3. -
    3. Using the vector \( \vec{x}^* \) compute \( \widehat{\theta}^* \) by evaluating \( \widehat \theta \) under the observations \( \vec{x}^* \).
      4. -
    4. Repeat this process \( k \) times.
      5. -
    - -When you are done, you can draw a histogram of the relative frequency of \( \widehat \theta^* \). This is your estimate of the probability distribution \( p(t) \). Using this probability distribution you can estimate any statistics thereof. In principle you never draw the histogram of the relative frequency of \( \widehat{\theta}^* \). Instead you use the estimators corresponding to the statistic of interest. For example, if you are interested in estimating the variance of \( \widehat \theta \), apply the esimator \( \widehat \sigma^2 \) to the values \( \widehat \theta ^* \). +

    +We can then compute the autocovariance, the variance, sample mean, and +number of observations for each \( i \). +Let \( \gamma_i, \sigma_i^2, +\overline{X}_i \) denote the autocovariance, variance and average of the +elements of \( \vec{X}_i \) and let \( n_i \) be the number of elements of +\( \vec{X}_i \). It follows by induction that \( n_i = n/2^i \).

    @@ -401,6 +449,11 @@ When you are done, you can draw a histogram of the relative frequency of \( \wid

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    • diff --git a/doc/pub/Regression/html/._Regression-bs088.html b/doc/pub/Regression/html/._Regression-bs088.html index 45a14ee9c..dc6cc66fd 100644 --- a/doc/pub/Regression/html/._Regression-bs088.html +++ b/doc/pub/Regression/html/._Regression-bs088.html @@ -194,32 +194,46 @@ Automatically generated HTML file from DocOnce source '___sec77'), ('Resampling methods: Jackknife', 2, None, '___sec78'), ('Resampling methods: Jackknife estimator', 2, None, '___sec79'), - ('Resampling methods: Jackknife sample code', - 2, - None, - '___sec80'), - ('Resampling methods: Bootstrap', 2, None, '___sec81'), - ('Resampling methods: Bootstrap background', 2, None, '___sec82'), + ('Resampling methods: Bootstrap', 2, None, '___sec80'), + ('Resampling methods: Bootstrap background', 2, None, '___sec81'), ('Resampling methods: More Bootstrap background', 2, None, - '___sec83'), - ('Resampling methods: Bootstrap approach', 2, None, '___sec84'), - ('Resampling methods: Bootstrap algorithm', 2, None, '___sec85'), - ('Resampling methods: Bootstrap steps', 2, None, '___sec86'), - ('Resampling methods: Blocking', 2, None, '___sec87'), - ('Blocking Transformations', 2, None, '___sec88'), - ('Blocking Transformations', 2, None, '___sec89'), - ('Blocking Transformations, getting there', 2, None, '___sec90'), + '___sec82'), + ('Resampling methods: Bootstrap approach', 2, None, '___sec83'), + ('Resampling methods: Bootstrap steps', 2, None, '___sec84'), + ('Resampling methods: Blocking', 2, None, '___sec85'), + ('Blocking Transformations', 2, None, '___sec86'), + ('Blocking Transformations', 2, None, '___sec87'), + ('Blocking Transformations, getting there', 2, None, '___sec88'), ('Blocking Transformations, final expressions', 2, None, - '___sec91'), + '___sec89'), ('"Code examples for Blocking, Jackknife and ' 'bootstrap":"https://github.com/CompPhysics/MachineLearning/tree/master/doc/Programs/ResamplingAnalysisScripts"', 2, None, - '___sec92')]} + '___sec90'), + ('The bias-variance tradeoff', 2, None, '___sec91'), + ('Training and testing data', 2, None, '___sec92'), + ('Procedure to find a predictor', 2, None, '___sec93'), + ('What we want', 2, None, '___sec94'), + ('The expected generalization error', 2, None, '___sec95'), + ('Elaborating a little bit more', 2, None, '___sec96'), + ('The bias', 2, None, '___sec97'), + ('The variance', 2, None, '___sec98'), + ('Summing up', 2, None, '___sec99'), + ('Logistic Regression', 2, None, '___sec100'), + ('Basics', 2, None, '___sec101'), + ('Linear classifier', 2, None, '___sec102'), + ('Some selected properties', 2, None, '___sec103'), + ('The cross-entropy as a cost function for logistic regression', + 2, + None, + '___sec104'), + ('Maximum likelihood', 2, None, '___sec105'), + ('Minimizing the cross entropy', 2, None, '___sec106')]} end of tocinfo --> @@ -337,19 +351,33 @@ MathJax.Hub.Config({
  • Resampling methods: Jackknife and Bootstrap
  • Resampling methods: Jackknife
  • Resampling methods: Jackknife estimator
    • -
  • Resampling methods: Jackknife sample code
    • -
  • Resampling methods: Bootstrap
    • -
  • Resampling methods: Bootstrap background
    • -
  • Resampling methods: More Bootstrap background
    • -
  • Resampling methods: Bootstrap approach
    • -
  • Resampling methods: Bootstrap algorithm
    • -
  • Resampling methods: Bootstrap steps
    • -
  • Resampling methods: Blocking
    • -
  • Blocking Transformations
    • -
  • Blocking Transformations
    • -
  • Blocking Transformations, getting there
    • -
  • Blocking Transformations, final expressions
    • -
  • "Code examples for Blocking, Jackknife and bootstrap":"https://github.com/CompPhysics/MachineLearning/tree/master/doc/Programs/ResamplingAnalysisScripts"
    • +
  • Resampling methods: Bootstrap
    • +
  • Resampling methods: Bootstrap background
    • +
  • Resampling methods: More Bootstrap background
    • +
  • Resampling methods: Bootstrap approach
    • +
  • Resampling methods: Bootstrap steps
    • +
  • Resampling methods: Blocking
    • +
  • Blocking Transformations
    • +
  • Blocking Transformations
    • +
  • Blocking Transformations, getting there
    • +
  • Blocking Transformations, final expressions
    • +
  • "Code examples for Blocking, Jackknife and bootstrap":"https://github.com/CompPhysics/MachineLearning/tree/master/doc/Programs/ResamplingAnalysisScripts"
    • +
  • The bias-variance tradeoff
    • +
  • Training and testing data
    • +
  • Procedure to find a predictor
    • +
  • What we want
    • +
  • The expected generalization error
    • +
  • Elaborating a little bit more
    • +
  • The bias
    • +
  • The variance
    • +
  • Summing up
    • +
  • Logistic Regression
    • +
  • Basics
    • +
  • Linear classifier
    • +
  • Some selected properties
    • +
  • The cross-entropy as a cost function for logistic regression
    • +
  • Maximum likelihood
    • +
  • Minimizing the cross entropy
    • @@ -365,28 +393,26 @@ MathJax.Hub.Config({ -

    Resampling methods: Blocking

    +

    Blocking Transformations

    -The blocking method was made popular by Flyvbjerg and Pedersen (1989) -and has become one of the standard ways to estimate -\( V(\widehat{\theta}) \) for exactly one \( \widehat{\theta} \), namely -\( \widehat{\theta} = \overline{X} \). - -

    -Assume \( n = 2^d \) for some integer \( d>1 \) and \( X_1,X_2,\cdots, X_n \) is a stationary time series to begin with. -Moreover, assume that the time series is asymptotically uncorrelated. We switch to vector notation by arranging \( X_1,X_2,\cdots,X_n \) in an \( n \)-tuple. Define: +Using the +definition of the blocking transformation and the distributive +property of the covariance, it is clear that since \( h =|i-j| \) +we can define $$ -\begin{align*} -\vec{X} = (X_1,X_2,\cdots,X_n). -\end{align*} +\begin{align} +\gamma_{k+1}(h) &= cov\left( ({X}_{k+1})_{i}, ({X}_{k+1})_{j} \right) \nonumber \\ +&= \frac{1}{4}cov\left( ({X}_{k})_{2i-1} + ({X}_{k})_{2i}, ({X}_{k})_{2j-1} + ({X}_{k})_{2j} \right) \nonumber \\ +&= \frac{1}{2}\gamma_{k}(2h) + \frac{1}{2}\gamma_k(2h+1) \hspace{0.1cm} \mathrm{h = 0} +\tag{22}\\ +&=\frac{1}{4}\gamma_k(2h-1) + \frac{1}{2}\gamma_k(2h) + \frac{1}{4}\gamma_k(2h+1) \quad \mathrm{else} +\tag{23} +\end{align} $$

    -The strength of the blocking method is when the number of -observations, \( n \) is large. For large \( n \), the complexity of dependent -bootstrapping scales poorly, but the blocking method does not, -moreover, it becomes more accurate the larger \( n \) is. +The quantity \( \vec{X} \) is asymptotic uncorrelated by assumption, \( \vec{X}_k \) is also asymptotic uncorrelated. Let's turn our attention to the variance of the sample mean \( V(\overline{X}) \).

    @@ -409,6 +435,12 @@ moreover, it becomes more accurate the larger \( n \) is.

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    • diff --git a/doc/pub/Regression/html/._Regression-bs089.html b/doc/pub/Regression/html/._Regression-bs089.html index f518760be..5015fae51 100644 --- a/doc/pub/Regression/html/._Regression-bs089.html +++ b/doc/pub/Regression/html/._Regression-bs089.html @@ -194,32 +194,46 @@ Automatically generated HTML file from DocOnce source '___sec77'), ('Resampling methods: Jackknife', 2, None, '___sec78'), ('Resampling methods: Jackknife estimator', 2, None, '___sec79'), - ('Resampling methods: Jackknife sample code', - 2, - None, - '___sec80'), - ('Resampling methods: Bootstrap', 2, None, '___sec81'), - ('Resampling methods: Bootstrap background', 2, None, '___sec82'), + ('Resampling methods: Bootstrap', 2, None, '___sec80'), + ('Resampling methods: Bootstrap background', 2, None, '___sec81'), ('Resampling methods: More Bootstrap background', 2, None, - '___sec83'), - ('Resampling methods: Bootstrap approach', 2, None, '___sec84'), - ('Resampling methods: Bootstrap algorithm', 2, None, '___sec85'), - ('Resampling methods: Bootstrap steps', 2, None, '___sec86'), - ('Resampling methods: Blocking', 2, None, '___sec87'), - ('Blocking Transformations', 2, None, '___sec88'), - ('Blocking Transformations', 2, None, '___sec89'), - ('Blocking Transformations, getting there', 2, None, '___sec90'), + '___sec82'), + ('Resampling methods: Bootstrap approach', 2, None, '___sec83'), + ('Resampling methods: Bootstrap steps', 2, None, '___sec84'), + ('Resampling methods: Blocking', 2, None, '___sec85'), + ('Blocking Transformations', 2, None, '___sec86'), + ('Blocking Transformations', 2, None, '___sec87'), + ('Blocking Transformations, getting there', 2, None, '___sec88'), ('Blocking Transformations, final expressions', 2, None, - '___sec91'), + '___sec89'), ('"Code examples for Blocking, Jackknife and ' 'bootstrap":"https://github.com/CompPhysics/MachineLearning/tree/master/doc/Programs/ResamplingAnalysisScripts"', 2, None, - '___sec92')]} + '___sec90'), + ('The bias-variance tradeoff', 2, None, '___sec91'), + ('Training and testing data', 2, None, '___sec92'), + ('Procedure to find a predictor', 2, None, '___sec93'), + ('What we want', 2, None, '___sec94'), + ('The expected generalization error', 2, None, '___sec95'), + ('Elaborating a little bit more', 2, None, '___sec96'), + ('The bias', 2, None, '___sec97'), + ('The variance', 2, None, '___sec98'), + ('Summing up', 2, None, '___sec99'), + ('Logistic Regression', 2, None, '___sec100'), + ('Basics', 2, None, '___sec101'), + ('Linear classifier', 2, None, '___sec102'), + ('Some selected properties', 2, None, '___sec103'), + ('The cross-entropy as a cost function for logistic regression', + 2, + None, + '___sec104'), + ('Maximum likelihood', 2, None, '___sec105'), + ('Minimizing the cross entropy', 2, None, '___sec106')]} end of tocinfo --> @@ -337,19 +351,33 @@ MathJax.Hub.Config({
  • Resampling methods: Jackknife and Bootstrap
  • Resampling methods: Jackknife
  • Resampling methods: Jackknife estimator
    • -
  • Resampling methods: Jackknife sample code
    • -
  • Resampling methods: Bootstrap
    • -
  • Resampling methods: Bootstrap background
    • -
  • Resampling methods: More Bootstrap background
    • -
  • Resampling methods: Bootstrap approach
    • -
  • Resampling methods: Bootstrap algorithm
    • -
  • Resampling methods: Bootstrap steps
    • -
  • Resampling methods: Blocking
    • -
  • Blocking Transformations
    • -
  • Blocking Transformations
    • -
  • Blocking Transformations, getting there
    • -
  • Blocking Transformations, final expressions
    • -
  • "Code examples for Blocking, Jackknife and bootstrap":"https://github.com/CompPhysics/MachineLearning/tree/master/doc/Programs/ResamplingAnalysisScripts"
    • +
  • Resampling methods: Bootstrap
    • +
  • Resampling methods: Bootstrap background
    • +
  • Resampling methods: More Bootstrap background
    • +
  • Resampling methods: Bootstrap approach
    • +
  • Resampling methods: Bootstrap steps
    • +
  • Resampling methods: Blocking
    • +
  • Blocking Transformations
    • +
  • Blocking Transformations
    • +
  • Blocking Transformations, getting there
    • +
  • Blocking Transformations, final expressions
    • +
  • "Code examples for Blocking, Jackknife and bootstrap":"https://github.com/CompPhysics/MachineLearning/tree/master/doc/Programs/ResamplingAnalysisScripts"
    • +
  • The bias-variance tradeoff
    • +
  • Training and testing data
    • +
  • Procedure to find a predictor
    • +
  • What we want
    • +
  • The expected generalization error
    • +
  • Elaborating a little bit more
    • +
  • The bias
    • +
  • The variance
    • +
  • Summing up
    • +
  • Logistic Regression
    • +
  • Basics
    • +
  • Linear classifier
    • +
  • Some selected properties
    • +
  • The cross-entropy as a cost function for logistic regression
    • +
  • Maximum likelihood
    • +
  • Minimizing the cross entropy
    • @@ -365,39 +393,24 @@ MathJax.Hub.Config({ -

    Blocking Transformations

    - We now define -blocking transformations. The idea is to take the mean of subsequent -pair of elements from \( \vec{X} \) and form a new vector -\( \vec{X}_1 \). Continuing in the same way by taking the mean of -subsequent pairs of elements of \( \vec{X}_1 \) we obtain \( \vec{X}_2 \), and -so on. -Define \( \vec{X}_i \) recursively by: - +

    Blocking Transformations, getting there

    +We have $$ -\begin{align} -(\vec{X}_0)_k &\equiv (\vec{X})_k \nonumber \\ -(\vec{X}_{i+1})_k &\equiv \frac{1}{2}\Big( (\vec{X}_i)_{2k-1} + -(\vec{X}_i)_{2k} \Big) \qquad \text{for all} \qquad 1 \leq i \leq d-1 -\tag{21} -\end{align} +\begin{align} +V(\overline{X}_k) = \frac{\sigma_k^2}{n_k} + \underbrace{\frac{2}{n_k} \sum_{h=1}^{n_k-1}\left( 1 - \frac{h}{n_k} \right)\gamma_k(h)}_{\equiv e_k} = \frac{\sigma^2_k}{n_k} + e_k \quad \text{if} \quad \gamma_k(0) = \sigma_k^2. +\tag{24} +\end{align} $$ -

    -The quantity \( \vec{X}_k \) is -subject to \( k \) blocking transformations. We now have \( d \) vectors -\( \vec{X}_0, \vec{X}_1,\cdots,\vec X_{d-1} \) containing the subsequent -averages of observations. It turns out that if the components of -\( \vec{X} \) is a stationary time series, then the components of -\( \vec{X}_i \) is a stationary time series for all \( 0 \leq i \leq d-1 \) +The term \( e_k \) is called the truncation error: +$$ +\begin{equation} +e_k = \frac{2}{n_k} \sum_{h=1}^{n_k-1}\left( 1 - \frac{h}{n_k} \right)\gamma_k(h). +\tag{25} +\end{equation} +$$ -

    -We can then compute the autocovariance, the variance, sample mean, and -number of observations for each \( i \). -Let \( \gamma_i, \sigma_i^2, -\overline{X}_i \) denote the autocovariance, variance and average of the -elements of \( \vec{X}_i \) and let \( n_i \) be the number of elements of -\( \vec{X}_i \). It follows by induction that \( n_i = n/2^i \). +We can show that \( V(\overline{X}_i) = V(\overline{X}_j) \) for all \( 0 \leq i \leq d-1 \) and \( 0 \leq j \leq d-1 \).

    @@ -419,6 +432,13 @@ elements of \( \vec{X}_i \) and let \( n_i \) be the number of elements of

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    • diff --git a/doc/pub/Regression/html/._Regression-bs090.html b/doc/pub/Regression/html/._Regression-bs090.html index b95ea5a46..6b0af05a0 100644 --- a/doc/pub/Regression/html/._Regression-bs090.html +++ b/doc/pub/Regression/html/._Regression-bs090.html @@ -194,32 +194,46 @@ Automatically generated HTML file from DocOnce source '___sec77'), ('Resampling methods: Jackknife', 2, None, '___sec78'), ('Resampling methods: Jackknife estimator', 2, None, '___sec79'), - ('Resampling methods: Jackknife sample code', - 2, - None, - '___sec80'), - ('Resampling methods: Bootstrap', 2, None, '___sec81'), - ('Resampling methods: Bootstrap background', 2, None, '___sec82'), + ('Resampling methods: Bootstrap', 2, None, '___sec80'), + ('Resampling methods: Bootstrap background', 2, None, '___sec81'), ('Resampling methods: More Bootstrap background', 2, None, - '___sec83'), - ('Resampling methods: Bootstrap approach', 2, None, '___sec84'), - ('Resampling methods: Bootstrap algorithm', 2, None, '___sec85'), - ('Resampling methods: Bootstrap steps', 2, None, '___sec86'), - ('Resampling methods: Blocking', 2, None, '___sec87'), - ('Blocking Transformations', 2, None, '___sec88'), - ('Blocking Transformations', 2, None, '___sec89'), - ('Blocking Transformations, getting there', 2, None, '___sec90'), + '___sec82'), + ('Resampling methods: Bootstrap approach', 2, None, '___sec83'), + ('Resampling methods: Bootstrap steps', 2, None, '___sec84'), + ('Resampling methods: Blocking', 2, None, '___sec85'), + ('Blocking Transformations', 2, None, '___sec86'), + ('Blocking Transformations', 2, None, '___sec87'), + ('Blocking Transformations, getting there', 2, None, '___sec88'), ('Blocking Transformations, final expressions', 2, None, - '___sec91'), + '___sec89'), ('"Code examples for Blocking, Jackknife and ' 'bootstrap":"https://github.com/CompPhysics/MachineLearning/tree/master/doc/Programs/ResamplingAnalysisScripts"', 2, None, - '___sec92')]} + '___sec90'), + ('The bias-variance tradeoff', 2, None, '___sec91'), + ('Training and testing data', 2, None, '___sec92'), + ('Procedure to find a predictor', 2, None, '___sec93'), + ('What we want', 2, None, '___sec94'), + ('The expected generalization error', 2, None, '___sec95'), + ('Elaborating a little bit more', 2, None, '___sec96'), + ('The bias', 2, None, '___sec97'), + ('The variance', 2, None, '___sec98'), + ('Summing up', 2, None, '___sec99'), + ('Logistic Regression', 2, None, '___sec100'), + ('Basics', 2, None, '___sec101'), + ('Linear classifier', 2, None, '___sec102'), + ('Some selected properties', 2, None, '___sec103'), + ('The cross-entropy as a cost function for logistic regression', + 2, + None, + '___sec104'), + ('Maximum likelihood', 2, None, '___sec105'), + ('Minimizing the cross entropy', 2, None, '___sec106')]} end of tocinfo --> @@ -337,19 +351,33 @@ MathJax.Hub.Config({
  • Resampling methods: Jackknife and Bootstrap
  • Resampling methods: Jackknife
  • Resampling methods: Jackknife estimator
    • -
  • Resampling methods: Jackknife sample code
    • -
  • Resampling methods: Bootstrap
    • -
  • Resampling methods: Bootstrap background
    • -
  • Resampling methods: More Bootstrap background
    • -
  • Resampling methods: Bootstrap approach
    • -
  • Resampling methods: Bootstrap algorithm
    • -
  • Resampling methods: Bootstrap steps
    • -
  • Resampling methods: Blocking
    • -
  • Blocking Transformations
    • -
  • Blocking Transformations
    • -
  • Blocking Transformations, getting there
    • -
  • Blocking Transformations, final expressions
    • -
  • "Code examples for Blocking, Jackknife and bootstrap":"https://github.com/CompPhysics/MachineLearning/tree/master/doc/Programs/ResamplingAnalysisScripts"
    • +
  • Resampling methods: Bootstrap
    • +
  • Resampling methods: Bootstrap background
    • +
  • Resampling methods: More Bootstrap background
    • +
  • Resampling methods: Bootstrap approach
    • +
  • Resampling methods: Bootstrap steps
    • +
  • Resampling methods: Blocking
    • +
  • Blocking Transformations
    • +
  • Blocking Transformations
    • +
  • Blocking Transformations, getting there
    • +
  • Blocking Transformations, final expressions
    • +
  • "Code examples for Blocking, Jackknife and bootstrap":"https://github.com/CompPhysics/MachineLearning/tree/master/doc/Programs/ResamplingAnalysisScripts"
    • +
  • The bias-variance tradeoff
    • +
  • Training and testing data
    • +
  • Procedure to find a predictor
    • +
  • What we want
    • +
  • The expected generalization error
    • +
  • Elaborating a little bit more
    • +
  • The bias
    • +
  • The variance
    • +
  • Summing up
    • +
  • Logistic Regression
    • +
  • Basics
    • +
  • Linear classifier
    • +
  • Some selected properties
    • +
  • The cross-entropy as a cost function for logistic regression
    • +
  • Maximum likelihood
    • +
  • Minimizing the cross entropy
    • @@ -365,24 +393,33 @@ MathJax.Hub.Config({ -

    Blocking Transformations

    -Using the -definition of the blocking transformation and the distributive -property of the covariance, it is clear that since \( h =|i-j| \) -we can define +

    Blocking Transformations, final expressions

    + +

    +We can then wrap up $$ \begin{align} -\gamma_{k+1}(h) &= cov\left( ({X}_{k+1})_{i}, ({X}_{k+1})_{j} \right) \nonumber \\ -&= \frac{1}{4}cov\left( ({X}_{k})_{2i-1} + ({X}_{k})_{2i}, ({X}_{k})_{2j-1} + ({X}_{k})_{2j} \right) \nonumber \\ -&= \frac{1}{2}\gamma_{k}(2h) + \frac{1}{2}\gamma_k(2h+1) \hspace{0.1cm} \mathrm{h = 0} -\tag{22}\\ -&=\frac{1}{4}\gamma_k(2h-1) + \frac{1}{2}\gamma_k(2h) + \frac{1}{4}\gamma_k(2h+1) \quad \mathrm{else} -\tag{23} +n_{j+1} \overline{X}_{j+1} &= \sum_{i=1}^{n_{j+1}} (\vec{X}_{j+1})_i = \frac{1}{2}\sum_{i=1}^{n_{j}/2} (\vec{X}_{j})_{2i-1} + (\vec{X}_{j})_{2i} \nonumber \\ +&= \frac{1}{2}\left[ (\vec{X}_j)_1 + (\vec{X}_j)_2 + \cdots + (\vec{X}_j)_{n_j} \right] = \underbrace{\frac{n_j}{2}}_{=n_{j+1}} \overline{X}_j = n_{j+1}\overline{X}_j. +\tag{26} +\end{align} +$$ + +By repeated use of this equation we get \( V(\overline{X}_i) = V(\overline{X}_0) = V(\overline{X}) \) for all \( 0 \leq i \leq d-1 \). This has the consequence that +$$ +\begin{align} +V(\overline{X}) = \frac{\sigma_k^2}{n_k} + e_k \qquad \text{for all} \qquad 0 \leq k \leq d-1. \tag{27} \end{align} $$

    -The quantity \( \vec{X} \) is asymptotic uncorrelated by assumption, \( \vec{X}_k \) is also asymptotic uncorrelated. Let's turn our attention to the variance of the sample mean \( V(\overline{X}) \). +Fyvbjerg and Petersen demonstrated that the sequence +\( \{e_k\}_{k=0}^{d-1} \) is decreasing, and conjecture that the term +\( e_k \) can be made as small as we would like by making \( k \) (and hence +\( d \)) sufficiently large. The sequence is decreasing (Master of Science thesis by Marius Jonsson, UiO 2018). +It means we can apply blocking transformations until +\( e_k \) is sufficiently small, and then estimate \( V(\overline{X}) \) by +\( \widehat{\sigma}^2_k/n_k \).

    @@ -403,6 +440,14 @@ The quantity \( \vec{X} \) is asymptotic uncorrelated by assumption, \( \vec{X}_

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    • diff --git a/doc/pub/Regression/html/._Regression-bs091.html b/doc/pub/Regression/html/._Regression-bs091.html index 614c2f164..fdf72a220 100644 --- a/doc/pub/Regression/html/._Regression-bs091.html +++ b/doc/pub/Regression/html/._Regression-bs091.html @@ -194,32 +194,46 @@ Automatically generated HTML file from DocOnce source '___sec77'), ('Resampling methods: Jackknife', 2, None, '___sec78'), ('Resampling methods: Jackknife estimator', 2, None, '___sec79'), - ('Resampling methods: Jackknife sample code', - 2, - None, - '___sec80'), - ('Resampling methods: Bootstrap', 2, None, '___sec81'), - ('Resampling methods: Bootstrap background', 2, None, '___sec82'), + ('Resampling methods: Bootstrap', 2, None, '___sec80'), + ('Resampling methods: Bootstrap background', 2, None, '___sec81'), ('Resampling methods: More Bootstrap background', 2, None, - '___sec83'), - ('Resampling methods: Bootstrap approach', 2, None, '___sec84'), - ('Resampling methods: Bootstrap algorithm', 2, None, '___sec85'), - ('Resampling methods: Bootstrap steps', 2, None, '___sec86'), - ('Resampling methods: Blocking', 2, None, '___sec87'), - ('Blocking Transformations', 2, None, '___sec88'), - ('Blocking Transformations', 2, None, '___sec89'), - ('Blocking Transformations, getting there', 2, None, '___sec90'), + '___sec82'), + ('Resampling methods: Bootstrap approach', 2, None, '___sec83'), + ('Resampling methods: Bootstrap steps', 2, None, '___sec84'), + ('Resampling methods: Blocking', 2, None, '___sec85'), + ('Blocking Transformations', 2, None, '___sec86'), + ('Blocking Transformations', 2, None, '___sec87'), + ('Blocking Transformations, getting there', 2, None, '___sec88'), ('Blocking Transformations, final expressions', 2, None, - '___sec91'), + '___sec89'), ('"Code examples for Blocking, Jackknife and ' 'bootstrap":"https://github.com/CompPhysics/MachineLearning/tree/master/doc/Programs/ResamplingAnalysisScripts"', 2, None, - '___sec92')]} + '___sec90'), + ('The bias-variance tradeoff', 2, None, '___sec91'), + ('Training and testing data', 2, None, '___sec92'), + ('Procedure to find a predictor', 2, None, '___sec93'), + ('What we want', 2, None, '___sec94'), + ('The expected generalization error', 2, None, '___sec95'), + ('Elaborating a little bit more', 2, None, '___sec96'), + ('The bias', 2, None, '___sec97'), + ('The variance', 2, None, '___sec98'), + ('Summing up', 2, None, '___sec99'), + ('Logistic Regression', 2, None, '___sec100'), + ('Basics', 2, None, '___sec101'), + ('Linear classifier', 2, None, '___sec102'), + ('Some selected properties', 2, None, '___sec103'), + ('The cross-entropy as a cost function for logistic regression', + 2, + None, + '___sec104'), + ('Maximum likelihood', 2, None, '___sec105'), + ('Minimizing the cross entropy', 2, None, '___sec106')]} end of tocinfo --> @@ -337,19 +351,33 @@ MathJax.Hub.Config({
  • Resampling methods: Jackknife and Bootstrap
  • Resampling methods: Jackknife
  • Resampling methods: Jackknife estimator
    • -
  • Resampling methods: Jackknife sample code
    • -
  • Resampling methods: Bootstrap
    • -
  • Resampling methods: Bootstrap background
    • -
  • Resampling methods: More Bootstrap background
    • -
  • Resampling methods: Bootstrap approach
    • -
  • Resampling methods: Bootstrap algorithm
    • -
  • Resampling methods: Bootstrap steps
    • -
  • Resampling methods: Blocking
    • -
  • Blocking Transformations
    • -
  • Blocking Transformations
    • -
  • Blocking Transformations, getting there
    • -
  • Blocking Transformations, final expressions
    • -
  • "Code examples for Blocking, Jackknife and bootstrap":"https://github.com/CompPhysics/MachineLearning/tree/master/doc/Programs/ResamplingAnalysisScripts"
    • +
  • Resampling methods: Bootstrap
    • +
  • Resampling methods: Bootstrap background
    • +
  • Resampling methods: More Bootstrap background
    • +
  • Resampling methods: Bootstrap approach
    • +
  • Resampling methods: Bootstrap steps
    • +
  • Resampling methods: Blocking
    • +
  • Blocking Transformations
    • +
  • Blocking Transformations
    • +
  • Blocking Transformations, getting there
    • +
  • Blocking Transformations, final expressions
    • +
  • "Code examples for Blocking, Jackknife and bootstrap":"https://github.com/CompPhysics/MachineLearning/tree/master/doc/Programs/ResamplingAnalysisScripts"
    • +
  • The bias-variance tradeoff
    • +
  • Training and testing data
    • +
  • Procedure to find a predictor
    • +
  • What we want
    • +
  • The expected generalization error
    • +
  • Elaborating a little bit more
    • +
  • The bias
    • +
  • The variance
    • +
  • Summing up
    • +
  • Logistic Regression
    • +
  • Basics
    • +
  • Linear classifier
    • +
  • Some selected properties
    • +
  • The cross-entropy as a cost function for logistic regression
    • +
  • Maximum likelihood
    • +
  • Minimizing the cross entropy
    • @@ -365,25 +393,235 @@ MathJax.Hub.Config({ -

    Blocking Transformations, getting there

    -We have -$$ -\begin{align} -V(\overline{X}_k) = \frac{\sigma_k^2}{n_k} + \underbrace{\frac{2}{n_k} \sum_{h=1}^{n_k-1}\left( 1 - \frac{h}{n_k} \right)\gamma_k(h)}_{\equiv e_k} = \frac{\sigma^2_k}{n_k} + e_k \quad \text{if} \quad \gamma_k(0) = \sigma_k^2. -\tag{24} -\end{align} -$$ +

    Code examples for Blocking, Jackknife and bootstrap

    -The term \( e_k \) is called the truncation error: -$$ -\begin{equation} -e_k = \frac{2}{n_k} \sum_{h=1}^{n_k-1}\left( 1 - \frac{h}{n_k} \right)\gamma_k(h). -\tag{25} -\end{equation} -$$ +

    -We can show that \( V(\overline{X}_i) = V(\overline{X}_j) \) for all \( 0 \leq i \leq d-1 \) and \( 0 \leq j \leq d-1 \). + +

    from sys import argv
+from os import mkdir, path
+import time
+import numpy as np
+import matplotlib.pyplot as plt
+from matplotlib.ticker import FormatStrFormatter
+from matplotlib.font_manager import FontProperties
 
+# Timing Decorator
+def timeFunction(f):
+    def wrap(*args):
+        time1 = time.time()
+        ret = f(*args)
+        time2 = time.time()
+        print '%s Function Took: \t %0.3f s' % (f.func_name.title(), (time2-time1))
+        return ret
+    return wrap
+
+class dataAnalysisClass:
+    # General Init functions
+    def __init__(self, fileName, size=0):
+        self.inputFileName = fileName
+        self.loadData(size)
+        self.createOutputFolder()
+        self.avg = np.average(self.data)
+        self.var = np.var(self.data)
+        self.std = np.std(self.data)
+
+    def loadData(self, size=0):
+        if size != 0:
+            with open(self.inputFileName) as inputFile:
+                self.data = np.zeros(size)
+                for x in xrange(size):
+                    self.data[x] = float(next(inputFile))
+        else:
+            self.data = np.loadtxt(self.inputFileName)
+
+    # Statistical Analysis with Multiple Methods
+    def runAllAnalyses(self):
+        if len(self.data) <= 100000:
+            print "Autocorrelation..."
+            self.autocorrelation()
+        print "Bootstrap..."
+        self.bootstrap()
+        print "Jackknife..."
+        self.jackknife()
+        print "Blocking..."
+        self.blocking()
+
+    # Standard Autocorrelation
+    @timeFunction
+    def autocorrelation(self):
+        self.acf = np.zeros(len(self.data)/2)
+        for k in range(0, len(self.data)/2):
+            self.acf[k] = np.corrcoef(np.array([self.data[0:len(self.data)-k], \
+                                            self.data[k:len(self.data)]]))[0,1]
+
+    # Bootstrap
+    @timeFunction
+    def bootstrap(self, nBoots = 1000):
+        bootVec = np.zeros(nBoots)
+        for k in range(0,nBoots):
+            bootVec[k] = np.average(np.random.choice(self.data, len(self.data)))
+        self.bootAvg = np.average(bootVec)
+        self.bootVar = np.var(bootVec)
+        self.bootStd = np.std(bootVec)
+
+    # Jackknife
+    @timeFunction
+    def jackknife(self):
+        jackknVec = np.zeros(len(self.data))
+        for k in range(0,len(self.data)):
+            jackknVec[k] = np.average(np.delete(self.data, k))
+        self.jackknAvg = self.avg - (len(self.data) - 1) * (np.average(jackknVec) - self.avg)
+        self.jackknVar = float(len(self.data) - 1) * np.var(jackknVec)
+        self.jackknStd = np.sqrt(self.jackknVar)
+
+    # Blocking
+    @timeFunction
+    def blocking(self, blockSizeMax = 500):
+        blockSizeMin = 1
+
+        self.blockSizes = []
+        self.meanVec = []
+        self.varVec = []
+
+        for i in range(blockSizeMin, blockSizeMax):
+            if(len(self.data) % i != 0):
+                pass#continue
+            blockSize = i
+            meanTempVec = []
+            varTempVec = []
+            startPoint = 0
+            endPoint = blockSize
+
+            while endPoint <= len(self.data):
+                meanTempVec.append(np.average(self.data[startPoint:endPoint]))
+                startPoint = endPoint
+                endPoint += blockSize
+            mean, var = np.average(meanTempVec), np.var(meanTempVec)/len(meanTempVec)
+            self.meanVec.append(mean)
+            self.varVec.append(var)
+            self.blockSizes.append(blockSize)
+
+        self.blockingAvg = np.average(self.meanVec[-200:])
+        self.blockingVar = (np.average(self.varVec[-200:]))
+        self.blockingStd = np.sqrt(self.blockingVar)
+
+
+
+    # Plot of Data, Autocorrelation Function and Histogram
+    def plotAll(self):
+        self.createOutputFolder()
+        if len(self.data) <= 100000:
+            self.plotAutocorrelation()
+        self.plotData()
+        self.plotHistogram()
+        self.plotBlocking()
+
+    # Create Output Plots Folder
+    def createOutputFolder(self):
+        self.outName = self.inputFileName[:-4]
+        if not path.exists(self.outName):
+            mkdir(self.outName)
+
+    # Plot the Dataset, Mean and Std
+    def plotData(self):
+        # Far away plot
+        font = {'fontname':'serif'}
+        plt.plot(range(0, len(self.data)), self.data, 'r-', linewidth=1)
+        plt.plot([0, len(self.data)], [self.avg, self.avg], 'b-', linewidth=1)
+        plt.plot([0, len(self.data)], [self.avg + self.std, self.avg + self.std], 'g--', linewidth=1)
+        plt.plot([0, len(self.data)], [self.avg - self.std, self.avg - self.std], 'g--', linewidth=1)
+        plt.ylim(self.avg - 5*self.std, self.avg + 5*self.std)
+        plt.gca().yaxis.set_major_formatter(FormatStrFormatter('%.4f'))
+        plt.xlim(0, len(self.data))
+        plt.ylabel(self.outName.title() + ' Monte Carlo Evolution', **font)
+        plt.xlabel('MonteCarlo History', **font)
+        plt.title(self.outName.title(), **font)
+        plt.savefig(self.outName + "/data.eps")
+        plt.savefig(self.outName + "/data.png")
+        plt.clf()
+
+    # Plot Histogram of Dataset and Gaussian around it
+    def plotHistogram(self):
+        binNumber = 50
+        font = {'fontname':'serif'}
+        count, bins, ignore = plt.hist(self.data, bins=np.linspace(self.avg - 5*self.std, self.avg + 5*self.std, binNumber))
+        plt.plot([self.avg, self.avg], [0,np.max(count)+10], 'b-', linewidth=1)
+        plt.ylim(0,np.max(count)+10)
+        plt.ylabel(self.outName.title() + ' Histogram', **font)
+        plt.xlabel(self.outName.title() , **font)
+        plt.title('Counts', **font)
+
+        #gaussian
+        norm = 0
+        for i in range(0,len(bins)-1):
+            norm += (bins[i+1]-bins[i])*count[i]
+        plt.plot(bins,  norm/(self.std * np.sqrt(2 * np.pi)) * np.exp( - (bins - self.avg)**2 / (2 * self.std**2) ), linewidth=1, color='r')
+        plt.savefig(self.outName + "/hist.eps")
+        plt.savefig(self.outName + "/hist.png")
+        plt.clf()
+
+    # Plot the Autocorrelation Function
+    def plotAutocorrelation(self):
+        font = {'fontname':'serif'}
+        plt.plot(range(1, len(self.data)/2), self.acf[1:], 'r-')
+        plt.ylim(-1, 1)
+        plt.xlim(0, len(self.data)/2)
+        plt.ylabel('Autocorrelation Function', **font)
+        plt.xlabel('Lag', **font)
+        plt.title('Autocorrelation', **font)
+        plt.savefig(self.outName + "/autocorrelation.eps")
+        plt.savefig(self.outName + "/autocorrelation.png")
+        plt.clf()
+
+    def plotBlocking(self):
+        font = {'fontname':'serif'}
+        plt.plot(self.blockSizes, self.varVec, 'r-')
+        plt.ylabel('Variance', **font)
+        plt.xlabel('Block Size', **font)
+        plt.title('Blocking', **font)
+        plt.savefig(self.outName + "/blocking.eps")
+        plt.savefig(self.outName + "/blocking.png")
+        plt.clf()
+
+    # Print Stuff to the Terminal
+    def printOutput(self):
+        print "\nSample Size:    \t", len(self.data)
+        print "\n=========================================\n"
+        print "Sample Average: \t", self.avg
+        print "Sample Variance:\t", self.var
+        print "Sample Std:     \t", self.std
+        print "\n=========================================\n"
+        print "Bootstrap Average: \t", self.bootAvg
+        print "Bootstrap Variance:\t", self.bootVar
+        print "Bootstrap Error:   \t", self.bootStd
+        print "\n=========================================\n"
+        print "Jackknife Average: \t", self.jackknAvg
+        print "Jackknife Variance:\t", self.jackknVar
+        print "Jackknife Error:   \t", self.jackknStd
+        print "\n=========================================\n"
+        print "Blocking Average: \t", self.blockingAvg
+        print "Blocking Variance:\t", self.blockingVar
+        print "Blocking Error:   \t", self.blockingStd, "\n"
+
+
+
+
+# Initialize the class
+if len(argv) > 2:
+    dataAnalysis = dataAnalysisClass(argv[1], int(argv[2]))
+else:
+    dataAnalysis = dataAnalysisClass(argv[1])
+
+# Run Analyses
+dataAnalysis.runAllAnalyses()
+
+# Plot the data
+dataAnalysis.plotAll()
+
+# Print Some Output
+dataAnalysis.printOutput()
+

    @@ -402,6 +640,15 @@ We can show that \( V(\overline{X}_i) = V(\overline{X}_j) \) for all \( 0 \leq i

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  • Resampling methods: Jackknife and Bootstrap
  • Resampling methods: Jackknife
  • Resampling methods: Jackknife estimator
    • -
  • Resampling methods: Jackknife sample code
    • -
  • Resampling methods: Bootstrap
    • -
  • Resampling methods: Bootstrap background
    • -
  • Resampling methods: More Bootstrap background
    • -
  • Resampling methods: Bootstrap approach
    • -
  • Resampling methods: Bootstrap algorithm
    • -
  • Resampling methods: Bootstrap steps
    • -
  • Resampling methods: Blocking
    • -
  • Blocking Transformations
    • -
  • Blocking Transformations
    • -
  • Blocking Transformations, getting there
    • -
  • Blocking Transformations, final expressions
    • -
  • "Code examples for Blocking, Jackknife and bootstrap":"https://github.com/CompPhysics/MachineLearning/tree/master/doc/Programs/ResamplingAnalysisScripts"
    • +
  • Resampling methods: Bootstrap
    • +
  • Resampling methods: Bootstrap background
    • +
  • Resampling methods: More Bootstrap background
    • +
  • Resampling methods: Bootstrap approach
    • +
  • Resampling methods: Bootstrap steps
    • +
  • Resampling methods: Blocking
    • +
  • Blocking Transformations
    • +
  • Blocking Transformations
    • +
  • Blocking Transformations, getting there
    • +
  • Blocking Transformations, final expressions
    • +
  • "Code examples for Blocking, Jackknife and bootstrap":"https://github.com/CompPhysics/MachineLearning/tree/master/doc/Programs/ResamplingAnalysisScripts"
    • +
  • The bias-variance tradeoff
    • +
  • Training and testing data
    • +
  • Procedure to find a predictor
    • +
  • What we want
    • +
  • The expected generalization error
    • +
  • Elaborating a little bit more
    • +
  • The bias
    • +
  • The variance
    • +
  • Summing up
    • +
  • Logistic Regression
    • +
  • Basics
    • +
  • Linear classifier
    • +
  • Some selected properties
    • +
  • The cross-entropy as a cost function for logistic regression
    • +
  • Maximum likelihood
    • +
  • Minimizing the cross entropy
    • @@ -365,33 +393,23 @@ MathJax.Hub.Config({ -

    Blocking Transformations, final expressions

    - -

    -We can then wrap up -$$ -\begin{align} -n_{j+1} \overline{X}_{j+1} &= \sum_{i=1}^{n_{j+1}} (\vec{X}_{j+1})_i = \frac{1}{2}\sum_{i=1}^{n_{j}/2} (\vec{X}_{j})_{2i-1} + (\vec{X}_{j})_{2i} \nonumber \\ -&= \frac{1}{2}\left[ (\vec{X}_j)_1 + (\vec{X}_j)_2 + \cdots + (\vec{X}_j)_{n_j} \right] = \underbrace{\frac{n_j}{2}}_{=n_{j+1}} \overline{X}_j = n_{j+1}\overline{X}_j. -\tag{26} -\end{align} -$$ - -By repeated use of this equation we get \( V(\overline{X}_i) = V(\overline{X}_0) = V(\overline{X}) \) for all \( 0 \leq i \leq d-1 \). This has the consequence that -$$ -\begin{align} -V(\overline{X}) = \frac{\sigma_k^2}{n_k} + e_k \qquad \text{for all} \qquad 0 \leq k \leq d-1. \tag{27} -\end{align} -$$ - -

    -Fyvbjerg and Petersen demonstrated that the sequence -\( \{e_k\}_{k=0}^{d-1} \) is decreasing, and conjecture that the term -\( e_k \) can be made as small as we would like by making \( k \) (and hence -\( d \)) sufficiently large. The sequence is decreasing (Master of Science thesis by Marius Jonsson, UiO 2018). -It means we can apply blocking transformations until -\( e_k \) is sufficiently small, and then estimate \( V(\overline{X}) \) by -\( \widehat{\sigma}^2_k/n_k \). +

    The bias-variance tradeoff

    +We begin with an unknown function \( y=f(x) \) and fix a \emph{hypothesis set} + \( \mathcal{H} \) consisting of all functions we are willing to consider, + defined also on the domain of \( f \). This set may be uncountably + infinite (e.g. if there are real-valued parameters to fit). +The + choice of which functions to include in \( \mathcal{H} \) usually depends + on our intuition about the problem of interest. The function \( f(x) \) + produces a set of pairs \( (x_i,y_i) \), \( i=1\dots N \), which serve as the + observable data. Our goal is to select a function from the hypothesis + set \( h\in\mathcal{H} \) which approximates \( f(x) \) as best as possible, + namely, we would like to find \( h\in\mathcal{H} \) such that \( h\approx + f \) in some strict mathematical sense which we specify below. If this + is possible, we say that we \emph{learned} \( f(x) \). But if the + function \( f(x) \) can, in principle, take any value on + \emph{unobserved} inputs, how is it possible to learn in any + meaningful sense?

    @@ -410,6 +428,16 @@ It means we can apply blocking transformations until

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    • diff --git a/doc/pub/Regression/html/._Regression-bs093.html b/doc/pub/Regression/html/._Regression-bs093.html index 5a73b8e3c..8490d539a 100644 --- a/doc/pub/Regression/html/._Regression-bs093.html +++ b/doc/pub/Regression/html/._Regression-bs093.html @@ -194,32 +194,46 @@ Automatically generated HTML file from DocOnce source '___sec77'), ('Resampling methods: Jackknife', 2, None, '___sec78'), ('Resampling methods: Jackknife estimator', 2, None, '___sec79'), - ('Resampling methods: Jackknife sample code', - 2, - None, - '___sec80'), - ('Resampling methods: Bootstrap', 2, None, '___sec81'), - ('Resampling methods: Bootstrap background', 2, None, '___sec82'), + ('Resampling methods: Bootstrap', 2, None, '___sec80'), + ('Resampling methods: Bootstrap background', 2, None, '___sec81'), ('Resampling methods: More Bootstrap background', 2, None, - '___sec83'), - ('Resampling methods: Bootstrap approach', 2, None, '___sec84'), - ('Resampling methods: Bootstrap algorithm', 2, None, '___sec85'), - ('Resampling methods: Bootstrap steps', 2, None, '___sec86'), - ('Resampling methods: Blocking', 2, None, '___sec87'), - ('Blocking Transformations', 2, None, '___sec88'), - ('Blocking Transformations', 2, None, '___sec89'), - ('Blocking Transformations, getting there', 2, None, '___sec90'), + '___sec82'), + ('Resampling methods: Bootstrap approach', 2, None, '___sec83'), + ('Resampling methods: Bootstrap steps', 2, None, '___sec84'), + ('Resampling methods: Blocking', 2, None, '___sec85'), + ('Blocking Transformations', 2, None, '___sec86'), + ('Blocking Transformations', 2, None, '___sec87'), + ('Blocking Transformations, getting there', 2, None, '___sec88'), ('Blocking Transformations, final expressions', 2, None, - '___sec91'), + '___sec89'), ('"Code examples for Blocking, Jackknife and ' 'bootstrap":"https://github.com/CompPhysics/MachineLearning/tree/master/doc/Programs/ResamplingAnalysisScripts"', 2, None, - '___sec92')]} + '___sec90'), + ('The bias-variance tradeoff', 2, None, '___sec91'), + ('Training and testing data', 2, None, '___sec92'), + ('Procedure to find a predictor', 2, None, '___sec93'), + ('What we want', 2, None, '___sec94'), + ('The expected generalization error', 2, None, '___sec95'), + ('Elaborating a little bit more', 2, None, '___sec96'), + ('The bias', 2, None, '___sec97'), + ('The variance', 2, None, '___sec98'), + ('Summing up', 2, None, '___sec99'), + ('Logistic Regression', 2, None, '___sec100'), + ('Basics', 2, None, '___sec101'), + ('Linear classifier', 2, None, '___sec102'), + ('Some selected properties', 2, None, '___sec103'), + ('The cross-entropy as a cost function for logistic regression', + 2, + None, + '___sec104'), + ('Maximum likelihood', 2, None, '___sec105'), + ('Minimizing the cross entropy', 2, None, '___sec106')]} end of tocinfo --> @@ -337,19 +351,33 @@ MathJax.Hub.Config({
  • Resampling methods: Jackknife and Bootstrap
  • Resampling methods: Jackknife
  • Resampling methods: Jackknife estimator
    • -
  • Resampling methods: Jackknife sample code
    • -
  • Resampling methods: Bootstrap
    • -
  • Resampling methods: Bootstrap background
    • -
  • Resampling methods: More Bootstrap background
    • -
  • Resampling methods: Bootstrap approach
    • -
  • Resampling methods: Bootstrap algorithm
    • -
  • Resampling methods: Bootstrap steps
    • -
  • Resampling methods: Blocking
    • -
  • Blocking Transformations
    • -
  • Blocking Transformations
    • -
  • Blocking Transformations, getting there
    • -
  • Blocking Transformations, final expressions
    • -
  • "Code examples for Blocking, Jackknife and bootstrap":"https://github.com/CompPhysics/MachineLearning/tree/master/doc/Programs/ResamplingAnalysisScripts"
    • +
  • Resampling methods: Bootstrap
    • +
  • Resampling methods: Bootstrap background
    • +
  • Resampling methods: More Bootstrap background
    • +
  • Resampling methods: Bootstrap approach
    • +
  • Resampling methods: Bootstrap steps
    • +
  • Resampling methods: Blocking
    • +
  • Blocking Transformations
    • +
  • Blocking Transformations
    • +
  • Blocking Transformations, getting there
    • +
  • Blocking Transformations, final expressions
    • +
  • "Code examples for Blocking, Jackknife and bootstrap":"https://github.com/CompPhysics/MachineLearning/tree/master/doc/Programs/ResamplingAnalysisScripts"
    • +
  • The bias-variance tradeoff
    • +
  • Training and testing data
    • +
  • Procedure to find a predictor
    • +
  • What we want
    • +
  • The expected generalization error
    • +
  • Elaborating a little bit more
    • +
  • The bias
    • +
  • The variance
    • +
  • Summing up
    • +
  • Logistic Regression
    • +
  • Basics
    • +
  • Linear classifier
    • +
  • Some selected properties
    • +
  • The cross-entropy as a cost function for logistic regression
    • +
  • Maximum likelihood
    • +
  • Minimizing the cross entropy
    • @@ -363,239 +391,19 @@ MathJax.Hub.Config({

     

     

     

    - + -

    Code examples for Blocking, Jackknife and bootstrap

    +

    Training and testing data

    +We will discuss the bias-variance tradeoff in the context of continuous predictions such as regression. However, many of the intuitions and ideas discussed here also carry over to classification tasks. Consider a dataset \( \mathcal{L} \) consisting of the data \( \mathbf{X}_\mathcal{L}=\{(y_j, \boldsymbol{x}_j), j=1\ldots N\} \). Let us assume that the true data is generated from a noisy model +$$ +y=f(\boldsymbol{x}) + \epsilon +$$ - -

    from sys import argv
-from os import mkdir, path
-import time
-import numpy as np
-import matplotlib.pyplot as plt
-from matplotlib.ticker import FormatStrFormatter
-from matplotlib.font_manager import FontProperties
+where \( \epsilon \) is normally distributed with mean zero and standard deviation \( \sigma_\epsilon \).
 
-# Timing Decorator
-def timeFunction(f):
-    def wrap(*args):
-        time1 = time.time()
-        ret = f(*args)
-        time2 = time.time()
-        print '%s Function Took: \t %0.3f s' % (f.func_name.title(), (time2-time1))
-        return ret
-    return wrap
-
-class dataAnalysisClass:
-    # General Init functions
-    def __init__(self, fileName, size=0):
-        self.inputFileName = fileName
-        self.loadData(size)
-        self.createOutputFolder()
-        self.avg = np.average(self.data)
-        self.var = np.var(self.data)
-        self.std = np.std(self.data)
-
-    def loadData(self, size=0):
-        if size != 0:
-            with open(self.inputFileName) as inputFile:
-                self.data = np.zeros(size)
-                for x in xrange(size):
-                    self.data[x] = float(next(inputFile))
-        else:
-            self.data = np.loadtxt(self.inputFileName)
-
-    # Statistical Analysis with Multiple Methods
-    def runAllAnalyses(self):
-        if len(self.data) <= 100000:
-            print "Autocorrelation..."
-            self.autocorrelation()
-        print "Bootstrap..."
-        self.bootstrap()
-        print "Jackknife..."
-        self.jackknife()
-        print "Blocking..."
-        self.blocking()
-
-    # Standard Autocorrelation
-    @timeFunction
-    def autocorrelation(self):
-        self.acf = np.zeros(len(self.data)/2)
-        for k in range(0, len(self.data)/2):
-            self.acf[k] = np.corrcoef(np.array([self.data[0:len(self.data)-k], \
-                                            self.data[k:len(self.data)]]))[0,1]
-
-    # Bootstrap
-    @timeFunction
-    def bootstrap(self, nBoots = 1000):
-        bootVec = np.zeros(nBoots)
-        for k in range(0,nBoots):
-            bootVec[k] = np.average(np.random.choice(self.data, len(self.data)))
-        self.bootAvg = np.average(bootVec)
-        self.bootVar = np.var(bootVec)
-        self.bootStd = np.std(bootVec)
-
-    # Jackknife
-    @timeFunction
-    def jackknife(self):
-        jackknVec = np.zeros(len(self.data))
-        for k in range(0,len(self.data)):
-            jackknVec[k] = np.average(np.delete(self.data, k))
-        self.jackknAvg = self.avg - (len(self.data) - 1) * (np.average(jackknVec) - self.avg)
-        self.jackknVar = float(len(self.data) - 1) * np.var(jackknVec)
-        self.jackknStd = np.sqrt(self.jackknVar)
-
-    # Blocking
-    @timeFunction
-    def blocking(self, blockSizeMax = 500):
-        blockSizeMin = 1
-
-        self.blockSizes = []
-        self.meanVec = []
-        self.varVec = []
-
-        for i in range(blockSizeMin, blockSizeMax):
-            if(len(self.data) % i != 0):
-                pass#continue
-            blockSize = i
-            meanTempVec = []
-            varTempVec = []
-            startPoint = 0
-            endPoint = blockSize
-
-            while endPoint <= len(self.data):
-                meanTempVec.append(np.average(self.data[startPoint:endPoint]))
-                startPoint = endPoint
-                endPoint += blockSize
-            mean, var = np.average(meanTempVec), np.var(meanTempVec)/len(meanTempVec)
-            self.meanVec.append(mean)
-            self.varVec.append(var)
-            self.blockSizes.append(blockSize)
-
-        self.blockingAvg = np.average(self.meanVec[-200:])
-        self.blockingVar = (np.average(self.varVec[-200:]))
-        self.blockingStd = np.sqrt(self.blockingVar)
-
-
-
-    # Plot of Data, Autocorrelation Function and Histogram
-    def plotAll(self):
-        self.createOutputFolder()
-        if len(self.data) <= 100000:
-            self.plotAutocorrelation()
-        self.plotData()
-        self.plotHistogram()
-        self.plotBlocking()
-
-    # Create Output Plots Folder
-    def createOutputFolder(self):
-        self.outName = self.inputFileName[:-4]
-        if not path.exists(self.outName):
-            mkdir(self.outName)
-
-    # Plot the Dataset, Mean and Std
-    def plotData(self):
-        # Far away plot
-        font = {'fontname':'serif'}
-        plt.plot(range(0, len(self.data)), self.data, 'r-', linewidth=1)
-        plt.plot([0, len(self.data)], [self.avg, self.avg], 'b-', linewidth=1)
-        plt.plot([0, len(self.data)], [self.avg + self.std, self.avg + self.std], 'g--', linewidth=1)
-        plt.plot([0, len(self.data)], [self.avg - self.std, self.avg - self.std], 'g--', linewidth=1)
-        plt.ylim(self.avg - 5*self.std, self.avg + 5*self.std)
-        plt.gca().yaxis.set_major_formatter(FormatStrFormatter('%.4f'))
-        plt.xlim(0, len(self.data))
-        plt.ylabel(self.outName.title() + ' Monte Carlo Evolution', **font)
-        plt.xlabel('MonteCarlo History', **font)
-        plt.title(self.outName.title(), **font)
-        plt.savefig(self.outName + "/data.eps")
-        plt.savefig(self.outName + "/data.png")
-        plt.clf()
-
-    # Plot Histogram of Dataset and Gaussian around it
-    def plotHistogram(self):
-        binNumber = 50
-        font = {'fontname':'serif'}
-        count, bins, ignore = plt.hist(self.data, bins=np.linspace(self.avg - 5*self.std, self.avg + 5*self.std, binNumber))
-        plt.plot([self.avg, self.avg], [0,np.max(count)+10], 'b-', linewidth=1)
-        plt.ylim(0,np.max(count)+10)
-        plt.ylabel(self.outName.title() + ' Histogram', **font)
-        plt.xlabel(self.outName.title() , **font)
-        plt.title('Counts', **font)
-
-        #gaussian
-        norm = 0
-        for i in range(0,len(bins)-1):
-            norm += (bins[i+1]-bins[i])*count[i]
-        plt.plot(bins,  norm/(self.std * np.sqrt(2 * np.pi)) * np.exp( - (bins - self.avg)**2 / (2 * self.std**2) ), linewidth=1, color='r')
-        plt.savefig(self.outName + "/hist.eps")
-        plt.savefig(self.outName + "/hist.png")
-        plt.clf()
-
-    # Plot the Autocorrelation Function
-    def plotAutocorrelation(self):
-        font = {'fontname':'serif'}
-        plt.plot(range(1, len(self.data)/2), self.acf[1:], 'r-')
-        plt.ylim(-1, 1)
-        plt.xlim(0, len(self.data)/2)
-        plt.ylabel('Autocorrelation Function', **font)
-        plt.xlabel('Lag', **font)
-        plt.title('Autocorrelation', **font)
-        plt.savefig(self.outName + "/autocorrelation.eps")
-        plt.savefig(self.outName + "/autocorrelation.png")
-        plt.clf()
-
-    def plotBlocking(self):
-        font = {'fontname':'serif'}
-        plt.plot(self.blockSizes, self.varVec, 'r-')
-        plt.ylabel('Variance', **font)
-        plt.xlabel('Block Size', **font)
-        plt.title('Blocking', **font)
-        plt.savefig(self.outName + "/blocking.eps")
-        plt.savefig(self.outName + "/blocking.png")
-        plt.clf()
-
-    # Print Stuff to the Terminal
-    def printOutput(self):
-        print "\nSample Size:    \t", len(self.data)
-        print "\n=========================================\n"
-        print "Sample Average: \t", self.avg
-        print "Sample Variance:\t", self.var
-        print "Sample Std:     \t", self.std
-        print "\n=========================================\n"
-        print "Bootstrap Average: \t", self.bootAvg
-        print "Bootstrap Variance:\t", self.bootVar
-        print "Bootstrap Error:   \t", self.bootStd
-        print "\n=========================================\n"
-        print "Jackknife Average: \t", self.jackknAvg
-        print "Jackknife Variance:\t", self.jackknVar
-        print "Jackknife Error:   \t", self.jackknStd
-        print "\n=========================================\n"
-        print "Blocking Average: \t", self.blockingAvg
-        print "Blocking Variance:\t", self.blockingVar
-        print "Blocking Error:   \t", self.blockingStd, "\n"
-
-
-
-
-# Initialize the class
-if len(argv) > 2:
-    dataAnalysis = dataAnalysisClass(argv[1], int(argv[2]))
-else:
-    dataAnalysis = dataAnalysisClass(argv[1])
-
-# Run Analyses
-dataAnalysis.runAllAnalyses()
-
-# Plot the data
-dataAnalysis.plotAll()
-
-# Print Some Output
-dataAnalysis.printOutput()
-

    -

    diff --git a/doc/pub/Regression/html/._Regression-bs094.html b/doc/pub/Regression/html/._Regression-bs094.html new file mode 100644 index 000000000..2cc5fa744 --- /dev/null +++ b/doc/pub/Regression/html/._Regression-bs094.html @@ -0,0 +1,460 @@ + + + + + + + +Data Analysis and Machine Learning: Linear Regression and more Advanced Regression Analysis + + + + + + + + + + + + + + + + + + + + + + + +
    +
    + + Data Analysis and Machine Learning: Linear Regression and more Advanced Regression Analysis +
    + +
    + +
    +
    + + +
    + +

     

     

     

    + + + + +

    Procedure to find a predictor

    + +

    +We have a statistical procedure (e.g. least-squares regression) for +forming a predictor \( \hat{g}_{\mathcal{L}}(\boldsymbol{x}) \) that gives the +prediction of our model for a new data point \( \boldsymbol{x} \). This estimator +is chosen by minimizing a cost function which we take to be the +squared error + +$$ + \mathcal{C}( \boldsymbol{X}, \hat{g}(\boldsymbol{x})) = \sum_i (y_i - \hat{g}_\mathcal{L}(\boldsymbol{x}_i))^2. +$$ + +

    +

    + +

    + + +
    + + + + + + + +
    + +
    + + + + + + diff --git a/doc/pub/Regression/html/._Regression-bs095.html b/doc/pub/Regression/html/._Regression-bs095.html new file mode 100644 index 000000000..220acc43c --- /dev/null +++ b/doc/pub/Regression/html/._Regression-bs095.html @@ -0,0 +1,461 @@ + + + + + + + +Data Analysis and Machine Learning: Linear Regression and more Advanced Regression Analysis + + + + + + + + + + + + + + + + + + + + + + + +
    +
    + + Data Analysis and Machine Learning: Linear Regression and more Advanced Regression Analysis +
    + +
    + +
    +
    + + +
    + +

     

     

     

    + + + + +

    What we want

    + +

    +We are interested in the generalization error on all data drawn from +the true model, not just the error on the particular training dataset +\( \mathcal{L} \) that we have in hand. This is just the expectation of +the cost function over many different data sets +\( \{\mathcal{L}_j\} \). Denote this expectation value by +\( E_{\mathcal{L}} \). In other words, we can view \( \hat{g}_{\mathcal{L}} \) +as a stochastic functional that depends on the dataset \( \mathcal{L} \) +and we can think of \( E_{\mathcal{L}} \) as the expected value of the +functional if we drew an infinite number of datasets \( \{\mathcal{L}_1, +\mathcal{L}_2, \ldots \} \). + +

    +

    + +

    + + +
    + + + + + + + +
    + +
    + + + + + + diff --git a/doc/pub/Regression/html/._Regression-bs096.html b/doc/pub/Regression/html/._Regression-bs096.html new file mode 100644 index 000000000..2f5d184ab --- /dev/null +++ b/doc/pub/Regression/html/._Regression-bs096.html @@ -0,0 +1,470 @@ + + + + + + + +Data Analysis and Machine Learning: Linear Regression and more Advanced Regression Analysis + + + + + + + + + + + + + + + + + + + + + + + +
    +
    + + Data Analysis and Machine Learning: Linear Regression and more Advanced Regression Analysis +
    + +
    + +
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    + + +
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    + + + + +

    The expected generalization error

    + +

    +We would also like to average over different instances of the +"noise" \( \epsilon \) and we denote the expectation value over the +noise by \( E_\epsilon \). Thus, we can decompose the expected +generalization error as + +$$ +\begin{align} +E_\mathcal{L, \epsilon}[\mathcal{C}( \boldsymbol{X}, \hat{g}(\boldsymbol{x})) ]&= E_\mathcal{L,\epsilon}\left[ \sum_i ({y}_i - \hat{g}_\mathcal{L}(\boldsymbol{x}_i))^2 \right] \nonumber \\ + &= E_\mathcal{L, \epsilon}\left[ \sum_{i}({y}_i -f(\boldsymbol{x}_i) +f(\boldsymbol{x}_i)- \hat{g}_\mathcal{L}(\boldsymbol{x}_i))^2\right] \nonumber \\ + &= \sum_i E_\epsilon[ ({y}_i -f(\boldsymbol{x}_i))^2 ]+ E_\mathcal{L, \epsilon}[(f(\boldsymbol{x}_i)- \hat{g}_\mathcal{L}(\boldsymbol{x}_i))^2] + 2E_\epsilon[{y}_i -f(\boldsymbol{x}_i)]E_\mathcal{L}[f(\boldsymbol{x}_i)- \hat{g}_\mathcal{L}(\boldsymbol{x}_i)] \nonumber \\ + &=\sum_i \sigma_\epsilon^2 + E_\mathcal{L}[(f(\boldsymbol{x}_i)- \hat{g}_\mathcal{L}(\boldsymbol{x}_i))^2], +\tag{28} +\end{align} +$$ + +

    +where in the last line we used the fact that our noise has zero mean +and variance \( \sigma_\epsilon^2 \) and the sum over \( i \) applies to all +terms. + +

    +

    + +

    + + +
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    + + + + + + diff --git a/doc/pub/Regression/html/._Regression-bs097.html b/doc/pub/Regression/html/._Regression-bs097.html new file mode 100644 index 000000000..d5eb595d4 --- /dev/null +++ b/doc/pub/Regression/html/._Regression-bs097.html @@ -0,0 +1,463 @@ + + + + + + + +Data Analysis and Machine Learning: Linear Regression and more Advanced Regression Analysis + + + + + + + + + + + + + + + + + + + + + + + +
    +
    + + Data Analysis and Machine Learning: Linear Regression and more Advanced Regression Analysis +
    + +
    + +
    +
    + + +
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    + + + + +

    Elaborating a little bit more

    + +

    +It is also helpful to further decompose the second term as +follows: + +$$ +\begin{align} +E_\mathcal{L}[(f(\boldsymbol{x}_i)- \hat{g}_\mathcal{L}(\boldsymbol{x}_i))^2] &=E_\mathcal{L}[(f(\mathbf{x}_i)-E_\mathcal{L}[\hat{g}_\mathcal{L}(\boldsymbol{x}_i)]+ E_\mathcal{L}[\hat{g}_\mathcal{L}(\boldsymbol{x}_i)]- \hat{g}_\mathcal{L}(\boldsymbol{x}_i))^2] \nonumber \\ +&=E_\mathcal{L}[(f(\boldsymbol{x}_i)-E_\mathcal{L}[\hat{g}_\mathcal{L}(\boldsymbol{x}_i)])^2] + E_\mathcal{L}[( \hat{g}_\mathcal{L}(\boldsymbol{x}_i)-E_\mathcal{L}[\hat{g}_\mathcal{L}(\boldsymbol{x}_i)])^2] \nonumber \\ +&+2E_\mathcal{L}[(f(\boldsymbol{x}_i)-E_\mathcal{L}[\hat{g}_\mathcal{L}(\boldsymbol{x}_i)])( \hat{g}_\mathcal{L}(\boldsymbol{x}_i)-E_\mathcal{L}[\hat{g}_\mathcal{L}(\boldsymbol{x}_i)])] \nonumber \\ +&=(f(\boldsymbol{x}_i)-E_\mathcal{L}[\hat{g}_\mathcal{L}(\boldsymbol{x}_i)])^2+E_\mathcal{L}[( \hat{g}_\mathcal{L}(\boldsymbol{x}_i)-E_\mathcal{L}[\hat{g}_\mathcal{L}(\boldsymbol{x}_i)])^2]. +\tag{29} +\end{align} +$$ + +

    +

    + +

    + + +
    + + + + + + + +
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    + + + + + + diff --git a/doc/pub/Regression/html/._Regression-bs098.html b/doc/pub/Regression/html/._Regression-bs098.html new file mode 100644 index 000000000..14c31a5dc --- /dev/null +++ b/doc/pub/Regression/html/._Regression-bs098.html @@ -0,0 +1,455 @@ + + + + + + + +Data Analysis and Machine Learning: Linear Regression and more Advanced Regression Analysis + + + + + + + + + + + + + + + + + + + + + + + +
    +
    + + Data Analysis and Machine Learning: Linear Regression and more Advanced Regression Analysis +
    + +
    + +
    +
    + + +
    + +

     

     

     

    + + + + +

    The bias

    +The first term is called the bias +$$ +Bias^2= \sum_i (f(\boldsymbol{x}_i)-E_\mathcal{L}[\hat{g}_\mathcal{L}(\boldsymbol{x}_i)])^2 +$$ + +and measures the deviation of the expectation value of our estimator (i.e. the asymptotic value of our estimator in the infinite data limit) from the true value. + +

    +

    + +

    + + +
    + + + + + + + +
    + +
    + + + + + + diff --git a/doc/pub/Regression/html/._Regression-bs099.html b/doc/pub/Regression/html/._Regression-bs099.html new file mode 100644 index 000000000..1a31a51e5 --- /dev/null +++ b/doc/pub/Regression/html/._Regression-bs099.html @@ -0,0 +1,467 @@ + + + + + + + +Data Analysis and Machine Learning: Linear Regression and more Advanced Regression Analysis + + + + + + + + + + + + + + + + + + + + + + + +
    +
    + + Data Analysis and Machine Learning: Linear Regression and more Advanced Regression Analysis +
    + +
    + +
    +
    + + +
    + +

     

     

     

    + + + + +

    The variance

    +The second term is called the variance +$$ +Var=\sum_i E_\mathcal{L}[( \hat{g}_\mathcal{L}(\boldsymbol{x}_i)-E_\mathcal{L}[\hat{g}_\mathcal{L}(\boldsymbol{x}_i)])^2], +$$ + +

    +and measures how much our estimator fluctuates due to finite-sample effects. Combining these expressions, we see that the expected out-of-sample error of our model can be decomposed as +$$ +E_\mathrm{out}=E_\mathcal{L, \epsilon}[\mathcal{C}( \boldsymbol{X}, \hat{g}(\boldsymbol{x})) ] = Bias^2 + Var + Noise. +$$ + +

    +The bias-variance tradeoff summarizes the fundamental tension in +machine learning, particularly supervised learning, between the +complexity of a model and the amount of training data needed to train +it. Since data is often limited, in practice it is often useful to +use a less-complex model with higher bias – a model whose asymptotic +performance is worse than another model – because it is easier to +train and less sensitive to sampling noise arising from having a +finite-sized training dataset (smaller variance). + +

    +

    + +

    + + +
    + + + + + + + +
    + +
    + + + + + + diff --git a/doc/pub/Regression/html/._Regression-bs100.html b/doc/pub/Regression/html/._Regression-bs100.html new file mode 100644 index 000000000..d8ba91e24 --- /dev/null +++ b/doc/pub/Regression/html/._Regression-bs100.html @@ -0,0 +1,465 @@ + + + + + + + +Data Analysis and Machine Learning: Linear Regression and more Advanced Regression Analysis + + + + + + + + + + + + + + + + + + + + + + + +
    +
    + + Data Analysis and Machine Learning: Linear Regression and more Advanced Regression Analysis +
    + +
    + +
    +
    + + +
    + +

     

     

     

    + + + + +

    Summing up

    + +

    +The above equations tell us that in +order to minimize the expected test error, we need to select a +statistical learning method that simultaneously achieves low variance +and low bias. Note that variance is inherently a nonnegative quantity, +and squared bias is also nonnegative. Hence, we see that the expected +test MSE can never lie below \( Var(\epsilon) \), the irreducible error. + +

    +What do we mean by the variance and bias of a statistical learning +method? The variance refers to the amount by which our model would change if we +estimated it using a different training data set. Since the training +data are used to fit the statistical learning method, different +training data sets will result in a different estimate. But ideally the +estimate for our model should not vary too much between training +sets. However, if a method has high variance then small changes in +the training data can result in large changes in the model. In general, more +flexible statistical methods have higher variance. + +

    +

    + +

    + + +
    + + + + + + + +
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    + + + + + + diff --git a/doc/pub/Regression/html/._Regression-bs101.html b/doc/pub/Regression/html/._Regression-bs101.html new file mode 100644 index 000000000..6e003593c --- /dev/null +++ b/doc/pub/Regression/html/._Regression-bs101.html @@ -0,0 +1,466 @@ + + + + + + + +Data Analysis and Machine Learning: Linear Regression and more Advanced Regression Analysis + + + + + + + + + + + + + + + + + + + + + + + +
    +
    + + Data Analysis and Machine Learning: Linear Regression and more Advanced Regression Analysis +
    + +
    + +
    +
    + + +
    + +

     

     

     

    + + + + +

    Logistic Regression

    + +

    +So far we have focused on learning from datasets for which there is a +continuous output. In linear regression we have been +concerned with learning the coefficients of a polynomial to predict +the response of a continuous variable \( y_i \) on unseen data based on +its independent variables \( {\bf x}_i \). + +

    +Classification problems, +however, are concerned with outcomes taking the form of discrete +variables (i.e. categories). For example, we may want to detect if +there's a cat or a dog in an image. Or given a specific system, +we'd like to identify its state, say whether it is an ordered or disordered system (typical situation in solid state physics). +(e.g. ordered/disordered). + +

    +Logistic regression deals with binary, dichotomous outcomes (e.g. True or +False, Success or Failure, etc.). It is worth noting that logistic +regression is also commonly used in modern supervised Deep Learning +models, as we will see later. + +

    +

    + +

    + + +
    + + + + + + + +
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    + + + + + + diff --git a/doc/pub/Regression/html/._Regression-bs102.html b/doc/pub/Regression/html/._Regression-bs102.html new file mode 100644 index 000000000..2fcd8e032 --- /dev/null +++ b/doc/pub/Regression/html/._Regression-bs102.html @@ -0,0 +1,456 @@ + + + + + + + +Data Analysis and Machine Learning: Linear Regression and more Advanced Regression Analysis + + + + + + + + + + + + + + + + + + + + + + + +
    +
    + + Data Analysis and Machine Learning: Linear Regression and more Advanced Regression Analysis +
    + +
    + +
    +
    + + +
    + +

     

     

     

    + + + + +

    Basics

    + +

    +We consider the case where the dependent variables \( y_i\in\mathbb{Z} \) +are discrete and only take values from \( m=0,\dots,M-1 \) (i.e. \( M \) +classes). + +

    +The goal is to predict the +output classes from the design matrix \( X\in\mathbb{R}^{n\times p} \) +made of \( n \) samples, each of which bears \( p \) features. Of cours e the +primary goal is to identify the classes to which new unseen samples +belong. + +

    +

    + +

    + + +
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    + + + + + + diff --git a/doc/pub/Regression/html/._Regression-bs103.html b/doc/pub/Regression/html/._Regression-bs103.html new file mode 100644 index 000000000..0aa98b52c --- /dev/null +++ b/doc/pub/Regression/html/._Regression-bs103.html @@ -0,0 +1,455 @@ + + + + + + + +Data Analysis and Machine Learning: Linear Regression and more Advanced Regression Analysis + + + + + + + + + + + + + + + + + + + + + + + +
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    + + Data Analysis and Machine Learning: Linear Regression and more Advanced Regression Analysis +
    + +
    + +
    +
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    + + + + +

    Linear classifier

    + +

    +Let us start by considering a slightly simpler classifier: a linear classifier that categorizes examples using a weighted linear-combination of the features and an additive offset +$$ +\begin{equation} +s_i = \boldsymbol{x}_i^T\boldsymbol{w} + b_0 \equiv \mathbf{x}_i^T\mathbf{w}, +\tag{30} +\end{equation} +$$ + +where we use the short-hand notation +\( \mathbf{x}_i = (1,\boldsymbol{x}_i) \) and \( \mathbf{w}_i = (b_0,\boldsymbol{w}_i) \). + +

    +

    + +

    + + +
    + + + + + + + +
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    + + + + + + diff --git a/doc/pub/Regression/html/._Regression-bs104.html b/doc/pub/Regression/html/._Regression-bs104.html new file mode 100644 index 000000000..b210240fd --- /dev/null +++ b/doc/pub/Regression/html/._Regression-bs104.html @@ -0,0 +1,453 @@ + + + + + + + +Data Analysis and Machine Learning: Linear Regression and more Advanced Regression Analysis + + + + + + + + + + + + + + + + + + + + + + + +
    +
    + + Data Analysis and Machine Learning: Linear Regression and more Advanced Regression Analysis +
    + +
    + +
    +
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    + +

     

     

     

    + + + + +

    Some selected properties

    + +

    +This function takes values on the entire real axis. In the case of logistic regression, however, the labels \( y_i \) are discrete variables. One simple way to get a discrete output is to have sign functions that map the output of a linear regressor to \( \{0,1\} \), \( f(s_i)= \) sign$(s_i) = 1$ if \( s_i\ge 0 \) and 0 if otherwise. Indeed, this is commonly known as the "perceptron" in the machine learning literature. This model is extremely simple, and it is favorable in many cases (e.g. noisy data) to have a ``soft" classifier that outputs the probability of a given category. For example, given \( \mathbf{x}_i \), the classifier outputs the probability of being in category \( m \). One such function is the logistic (or sigmoid) function: +$$ +\begin{equation} +f(s) = \frac{1}{1+\mathrm e^{-s}}. +\tag{31} +\end{equation} +$$ + +Note that \( 1-f(s)= f(-s) \), which will be useful shortly. + +

    +

    + +

    + + +
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    + + + + + + diff --git a/doc/pub/Regression/html/._Regression-bs105.html b/doc/pub/Regression/html/._Regression-bs105.html new file mode 100644 index 000000000..1d815b130 --- /dev/null +++ b/doc/pub/Regression/html/._Regression-bs105.html @@ -0,0 +1,459 @@ + + + + + + + +Data Analysis and Machine Learning: Linear Regression and more Advanced Regression Analysis + + + + + + + + + + + + + + + + + + + + + + + +
    +
    + + Data Analysis and Machine Learning: Linear Regression and more Advanced Regression Analysis +
    + +
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    + +

     

     

     

    + + + + +

    The cross-entropy as a cost function for logistic regression

    + +

    +The perceptron is an example of a ``hard classification": each datapoint is deterministically assigned to a category (i.e \( y_i=0 \) or \( y_i=1 \)). In many cases, it is favorable to have a "soft" classifier that outputs the probability of a given category rather than a single value. For example, given \( \mathbf{x}_i \), the classifier outputs the probability of being in category \( m \). +Logistic regression is the most canonical example of a soft classifier. In logistic regression, the probability that a data point \( \boldsymbol{x}_i \) belongs to a category \( y_i=\{0,1\} \) is is given by +$$ +\begin{eqnarray} +P(y_i=1|\boldsymbol{x}_i,\boldsymbol{\theta)} &=& \frac{1}{1+\mathrm{e}^{-\mathbf{x}^T_i\mathbf{w}}},\nonumber\\ +P(y_i=0|\boldsymbol{x}_i,\boldsymbol{\theta)} &=& 1 - P(y_i=1|\boldsymbol{x}_i,\boldsymbol{\theta)}, +\end{eqnarray} +$$ + +where \( \boldsymbol{\theta}=\mathbf{w} \) are the weights we wish to learn from the data. + +

    +Notice that in terms of the logistic function, we can write +$$ +P(y_i=1) =f(\mathbf{x}_i^T\mathbf{w})=1-P(y_i=0). +$$ + +

    +

    + +

    + + +
    + + + + + + + +
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    + + + + + + diff --git a/doc/pub/Regression/html/._Regression-bs106.html b/doc/pub/Regression/html/._Regression-bs106.html new file mode 100644 index 000000000..7f367c12c --- /dev/null +++ b/doc/pub/Regression/html/._Regression-bs106.html @@ -0,0 +1,462 @@ + + + + + + + +Data Analysis and Machine Learning: Linear Regression and more Advanced Regression Analysis + + + + + + + + + + + + + + + + + + + + + + + +
    +
    + + Data Analysis and Machine Learning: Linear Regression and more Advanced Regression Analysis +
    + +
    + +
    +
    + + +
    + +

     

     

     

    + + + + +

    Maximum likelihood

    + +

    +We now define the cost function for logistic regression using Maximum +Likelihood Estimation (MLE). Recall, that in MLE we choose parameters +to maximize the probability of seeing the observed data. Consider a +dataset \( \mathcal{D}=\{(y_i,\boldsymbol{x}_i)\} \) with binary labels +\( y_i\in\{0,1\} \) where the data points are drawn independently. The +likelihood of the seeing the data under our model is just: +$$ +\begin{align} +P(\mathcal{D}|\mathbf{w})& = \prod_{i=1}^n \left[f(\mathbf{x}_i^T\mathbf{w})\right]^{y_i}\left[1-f(\mathbf{x}_i^T\mathbf{w})\right]^{1-y_i}\nonumber \\ +\tag{32} +\end{align} +$$ + +from which we can readily compute the log-likelihood: +$$ +\begin{equation} +l(\mathbf{w}) = \sum_{i=1}^n y_i\log f(\mathbf{x}_i^T\mathbf{w}) + (1-y_i)\log\left[1-f(\mathbf{x}_i^T\mathbf{w})\right]. +\tag{33} +\end{equation} +$$ + +

    +

    + +

    + + +
    + + + + + + + +
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    + + + + + + diff --git a/doc/pub/Regression/html/._Regression-bs107.html b/doc/pub/Regression/html/._Regression-bs107.html new file mode 100644 index 000000000..0ff989da8 --- /dev/null +++ b/doc/pub/Regression/html/._Regression-bs107.html @@ -0,0 +1,452 @@ + + + + + + + +Data Analysis and Machine Learning: Linear Regression and more Advanced Regression Analysis + + + + + + + + + + + + + + + + + + + + + + + +
    +
    + + Data Analysis and Machine Learning: Linear Regression and more Advanced Regression Analysis +
    + +
    + +
    +
    + + +
    + +

     

     

     

    + + + +The maximum likelihood estimator is defined as the set of parameters that maximize the log-likelihood where we maximize with respect to \( \theta \) +$$ +\hat{\mathbf{w}} = \sum_{i=1}^n y_i\log f(\mathbf{x}_i^T\mathbf{w}) + (1-y_i)\log\left[1-f(\mathbf{x}_i^T\mathbf{w})\right]. +$$ + +Since the cost (error) function is just the negative log-likelihood, for logistic regression we have that +$$ +\begin{eqnarray} +\mathcal{C}(\mathbf{w}) &=& - l(\mathbf{w}) \\ +&=& \sum_{i=1}^n -y_i\log f(\mathbf{x}_i^T\mathbf{w}) - (1-y_i)\log\left[1-f(\mathbf{x}_i^T\mathbf{w})\right].\nonumber +\end{eqnarray} +$$ + +This equation is known in statistics as the \emph{cross entropy}. Finally, we note that just as in linear regression, +in practice we usually supplement the cross-entropy with additional regularization terms, usually \( L_1 \) and \( L_2 \) regularization as we did for Ridge and Lasso regression. + +

    +

    + +

    + + +
    + + + + + + + +
    + +
    + + + + + + diff --git a/doc/pub/Regression/html/._Regression-bs108.html b/doc/pub/Regression/html/._Regression-bs108.html new file mode 100644 index 000000000..24f214d02 --- /dev/null +++ b/doc/pub/Regression/html/._Regression-bs108.html @@ -0,0 +1,455 @@ + + + + + + + +Data Analysis and Machine Learning: Linear Regression and more Advanced Regression Analysis + + + + + + + + + + + + + + + + + + + + + + + +
    +
    + + Data Analysis and Machine Learning: Linear Regression and more Advanced Regression Analysis +
    + +
    + +
    +
    + + +
    + +

     

     

     

    + + + + +

    Minimizing the cross entropy

    + +

    +The cross entropy is a convex function of the weights \( \mathbf{w} \) and, +therefore, any local minimizer is a global minimizer. Minimizing this +cost function leads to the following equation + +$$ +\begin{equation} +\boldsymbol{0}=\boldsymbol{\nabla} \mathcal{C}(\mathbf{w}) = \sum_{i=1}^n\left[f(\mathbf{x}_i^T\mathbf{w})-y_i\right]\mathbf{x}_i, +\tag{34} +\end{equation} +$$ + +

    +where we made use of the logistic function identity \( \partial_z f(z) = +f(z)[1-f(z)] \). This equation defines a transcendental equation for +\( \mathbf{w} \), the solution of which, unlike linear regression, cannot +be written in a closed form. +Here we need gradient descent methods! + +

    + +

    + + +
    + + + + + + + +
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    + + + + + + diff --git a/doc/pub/Regression/html/Regression-bs.html b/doc/pub/Regression/html/Regression-bs.html index 546bbbdcf..546bcf7b6 100644 --- a/doc/pub/Regression/html/Regression-bs.html +++ b/doc/pub/Regression/html/Regression-bs.html @@ -194,32 +194,46 @@ Automatically generated HTML file from DocOnce source '___sec77'), ('Resampling methods: Jackknife', 2, None, '___sec78'), ('Resampling methods: Jackknife estimator', 2, None, '___sec79'), - ('Resampling methods: Jackknife sample code', - 2, - None, - '___sec80'), - ('Resampling methods: Bootstrap', 2, None, '___sec81'), - ('Resampling methods: Bootstrap background', 2, None, '___sec82'), + ('Resampling methods: Bootstrap', 2, None, '___sec80'), + ('Resampling methods: Bootstrap background', 2, None, '___sec81'), ('Resampling methods: More Bootstrap background', 2, None, - '___sec83'), - ('Resampling methods: Bootstrap approach', 2, None, '___sec84'), - ('Resampling methods: Bootstrap algorithm', 2, None, '___sec85'), - ('Resampling methods: Bootstrap steps', 2, None, '___sec86'), - ('Resampling methods: Blocking', 2, None, '___sec87'), - ('Blocking Transformations', 2, None, '___sec88'), - ('Blocking Transformations', 2, None, '___sec89'), - ('Blocking Transformations, getting there', 2, None, '___sec90'), + '___sec82'), + ('Resampling methods: Bootstrap approach', 2, None, '___sec83'), + ('Resampling methods: Bootstrap steps', 2, None, '___sec84'), + ('Resampling methods: Blocking', 2, None, '___sec85'), + ('Blocking Transformations', 2, None, '___sec86'), + ('Blocking Transformations', 2, None, '___sec87'), + ('Blocking Transformations, getting there', 2, None, '___sec88'), ('Blocking Transformations, final expressions', 2, None, - '___sec91'), + '___sec89'), ('"Code examples for Blocking, Jackknife and ' 'bootstrap":"https://github.com/CompPhysics/MachineLearning/tree/master/doc/Programs/ResamplingAnalysisScripts"', 2, None, - '___sec92')]} + '___sec90'), + ('The bias-variance tradeoff', 2, None, '___sec91'), + ('Training and testing data', 2, None, '___sec92'), + ('Procedure to find a predictor', 2, None, '___sec93'), + ('What we want', 2, None, '___sec94'), + ('The expected generalization error', 2, None, '___sec95'), + ('Elaborating a little bit more', 2, None, '___sec96'), + ('The bias', 2, None, '___sec97'), + ('The variance', 2, None, '___sec98'), + ('Summing up', 2, None, '___sec99'), + ('Logistic Regression', 2, None, '___sec100'), + ('Basics', 2, None, '___sec101'), + ('Linear classifier', 2, None, '___sec102'), + ('Some selected properties', 2, None, '___sec103'), + ('The cross-entropy as a cost function for logistic regression', + 2, + None, + '___sec104'), + ('Maximum likelihood', 2, None, '___sec105'), + ('Minimizing the cross entropy', 2, None, '___sec106')]} end of tocinfo --> @@ -337,19 +351,33 @@ MathJax.Hub.Config({
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  • Blocking Transformations, final expressions
    • +
  • "Code examples for Blocking, Jackknife and bootstrap":"https://github.com/CompPhysics/MachineLearning/tree/master/doc/Programs/ResamplingAnalysisScripts"
    • +
  • The bias-variance tradeoff
    • +
  • Training and testing data
    • +
  • Procedure to find a predictor
    • +
  • What we want
    • +
  • The expected generalization error
    • +
  • Elaborating a little bit more
    • +
  • The bias
    • +
  • The variance
    • +
  • Summing up
    • +
  • Logistic Regression
    • +
  • Basics
    • +
  • Linear classifier
    • +
  • Some selected properties
    • +
  • The cross-entropy as a cost function for logistic regression
    • +
  • Maximum likelihood
    • +
  • Minimizing the cross entropy
    • @@ -384,7 +412,7 @@ MathJax.Hub.Config({
    [2] Department of Physics and Astronomy and National Superconducting Cyclotron Laboratory, Michigan State University

    -

    Sep 13, 2018

    +

    Sep 14, 2018


    @@ -408,7 +436,7 @@ MathJax.Hub.Config({

  • 9
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    • diff --git a/doc/pub/Regression/html/Regression-reveal.html b/doc/pub/Regression/html/Regression-reveal.html index a37027b27..ef7046053 100644 --- a/doc/pub/Regression/html/Regression-reveal.html +++ b/doc/pub/Regression/html/Regression-reveal.html @@ -148,7 +148,7 @@ MathJax.Hub.Config({
    [2] Department of Physics and Astronomy and National Superconducting Cyclotron Laboratory, Michigan State University

     
    -

    Sep 13, 2018

    +

    Sep 14, 2018


    @@ -2745,20 +2745,20 @@ need for bootstrapping.

    Resampling methods: Jackknife

    -The Jackknife works by making many replicas of the estimator \( \widehat{\vec{\theta}} \). -The jackknife is a resampling method, we explained that this happens by scrambling the data in some way. When using the jackknife, this is done by systematically leaving out one observation from the vector of observed values \( \vec{X} = (X_1,X_2,\cdots,X_n) \). -Let \( \vec{X}_i \) denote the vector +The Jackknife works by making many replicas of the estimator \( \widehat{\theta} \). +The jackknife is a resampling method, we explained that this happens by scrambling the data in some way. When using the jackknife, this is done by systematically leaving out one observation from the vector of observed values \( \hat{x} = (x_1,x_2,\cdots,X_n) \). +Let \( \hat{x}_i \) denote the vector

     
    $$ -\vec{X}_i = (X_1,X_2,\cdots,X_{i-1},X_{i+1},\cdots,X_n), +\hat{x}_i = (x_1,x_2,\cdots,x_{i-1},x_{i+1},\cdots,x_n), $$

     

    -which equals the vector \( \vec{X} \) with the exception that observation +which equals the vector \( \hat{x} \) with the exception that observation number \( i \) is left out. Using this notation, define -\( \widehat{\vec{\theta}}_i \) to be the estimator -\( \widehat{\vec{\theta}} \) computed using \( \vec{X}_i \). +\( \widehat{\theta}_i \) to be the estimator +\( \widehat{\theta} \) computed using \( \vec{X}_i \). @@ -2767,8 +2767,8 @@ number \( i \) is left out. Using this notation, define

    To get an estimate for the bias and -standard error of \( \widehat{\vec{\theta}} \), use the following -estimators for each component of \( \widehat{\vec{\theta}} \) +standard error of \( \widehat{\theta} \), use the following +estimators for each component of \( \widehat{\theta} \)

     
    $$ @@ -2779,43 +2779,7 @@ $$

    -

    Resampling methods: Jackknife sample code

    - -

    -Sample code for the Jackknife method - -

    - - -

    def jack(data, stat):
-    n = len(data); t = zeros(n); inds = arange(n); t0 = time()
-    # 'jackknifing' by leaving out an observation for each i
-    for i in range(n):
-        t[i] = stat(delete(data,i) )
-        return t
-# define a function which returns your chosen estimator theta-hat
-def stat(data):
-    theta-hat = mean(data)
-    return theta-hat
-# Return the Jackknife  sample
-t = jack(X, stat)
-
    -

    -Consider first the function jack(). This function repeatedly -estimates the function called statistic() under the resampled -data by systematically leaving out one observation from the data. The -function stat() is passed as an argument to -jack(). The array t is eventually returned, which -contains all the estimates \( \widehat{\vec{\theta}} \), and can be -plotted or analysed in other ways, such as by calling std(t) -from numpy to estimate the standard error of -\( \widehat{\vec{\theta}} \). The function std(t) is just the -estimator \( \widehat{\sigma}^2 \). -

    - - -
    -

    Resampling methods: Bootstrap

    +

    Resampling methods: Bootstrap

    @@ -2837,26 +2801,26 @@ advantages:

    -

    Resampling methods: Bootstrap background

    +

    Resampling methods: Bootstrap background

    -Since \( \widehat{\vec{\theta}} = \widehat{\vec{\theta}}(\vec{X}) \) is a function of random variables, -\( \widehat{\vec{\theta}} \) itself must be a random variable. Thus it has +Since \( \widehat{\theta} = \widehat{\theta}(\hat{X}) \) is a function of random variables, +\( \widehat{\theta} \) itself must be a random variable. Thus it has a pdf, call this function \( p(\vec{t}) \). The aim of the bootstrap is to -estimate \( p(\vec{t}) \) by the relative frequency of -\( \widehat{\vec{\theta}} \). You can think of this as using a histogram -in the place of \( p(\vec{t}) \). If the relative frequency closely +estimate \( p(\hat{t}) \) by the relative frequency of +\( \widehat{\theta} \). You can think of this as using a histogram +in the place of \( p(\hat{t}) \). If the relative frequency closely resembles \( p(\vec{t}) \), then using numerics, it is straight forward to -estimate all the interesting parameters of \( p(\vec{t}) \) using point +estimate all the interesting parameters of \( p(\hat{t}) \) using point estimators.

    -

    Resampling methods: More Bootstrap background

    +

    Resampling methods: More Bootstrap background

    -In the case that \( \widehat{\vec{\theta}} \) has +In the case that \( \widehat{\theta} \) has more than one component, and the components are independent, use the same estimator on each component separately. If the probability density function of \( X_i \), \( p(x) \), had been known, then it would have @@ -2864,19 +2828,19 @@ been straight forward to do this by:

    1. Drawing lots of numbers from \( p(x) \), suppose we call one such set of numbers \( (X_1^*, X_2^*, \cdots, X_n^*) \).
      2. -

    2. Then using these numbers, we could compute a replica of \( \widehat{\vec{\theta}} \) called \( \widehat{\vec{\theta}}^* \).
      3. +

    3. Then using these numbers, we could compute a replica of \( \widehat{\theta} \) called \( \widehat{\theta}^* \).

    By repeated use of (1) and (2), many -estimates of \( \widehat{\vec{\theta}} \) could have been obtained. The -idea is to use the relative frequency of \( \widehat{\vec{\theta}}^* \) -(think of a histogram) as an estimate of \( p(\vec{t}) \). +estimates of \( \widehat{\theta} \) could have been obtained. The +idea is to use the relative frequency of \( \widehat{\theta}^* \) +(think of a histogram) as an estimate of \( p(\hat{t}) \).

    -

    Resampling methods: Bootstrap approach

    +

    Resampling methods: Bootstrap approach

    But @@ -2892,44 +2856,20 @@ result in some asymptotic sense? The answer is yes. Instead of generating the histogram for the relative frequency of the observation \( X_i \), just draw the values \( (X_1^*,X_2^*,\cdots,X_n^*) \) with replacement from the vector -\( \vec{X} \). +\( \hat{X} \).

    -

    Resampling methods: Bootstrap algorithm

    - -

    - - -

            
-def boot(data, statistic, R):
-    t = zeros(R); n = len(data); inds = arange(n); t0 = time()
-    for i in range(R):
-        t[i] = statistic(data[randint(0,n,n)])
-        return t
-# define a function which returns your chosen estimator theta-hat
-def stat(data):
-    theta-hat = mean(data)
-    return theta-hat
-
-t = boot(X, stat, 2**9)
-
    -

    -Consider first the function boot(). In the for loop, this function repeatedly estimates the function called statistic() under the resampled data in data(randint(0,n,n)). The function statistic() is passed as an argument to boot(). The array t is eventually returned, which contains all the estimates \( \widehat{\vec{\theta}} \), and can be plotted or analysed in other ways, such as by calling std(t) from numpy to estimate the standard error of \( \widehat{\vec{\theta}} \). The function std(t) is just the estimator \( \widehat{\sigma}^2 \). -

    - - -
    -

    Resampling methods: Bootstrap steps

    +

    Resampling methods: Bootstrap steps

    The independent bootstrap works like this:

      -

    1. Draw with replacement \( n \) numbers for the observed variables \( \vec{x} = (x_1,x_2,\cdots,x_n) \).
      2. -

    2. Define a vector \( \vec{x}^* \) containing the values which were drawn from \( \vec{x} \).
      3. -

    3. Using the vector \( \vec{x}^* \) compute \( \widehat{\theta}^* \) by evaluating \( \widehat \theta \) under the observations \( \vec{x}^* \).
      4. +

    4. Draw with replacement \( n \) numbers for the observed variables \( \hat{x} = (x_1,x_2,\cdots,x_n) \).
      5. +

    5. Define a vector \( \hat{x}^* \) containing the values which were drawn from \( \hat{x} \).
      6. +

    6. Using the vector \( \hat{x}^* \) compute \( \widehat{\theta}^* \) by evaluating \( \widehat \theta \) under the observations \( \hat{x}^* \).

    7. Repeat this process \( k \) times.

    @@ -2939,7 +2879,7 @@ When you are done, you can draw a histogram of the relative frequency of \( \wid

    -

    Resampling methods: Blocking

    +

    Resampling methods: Blocking

    The blocking method was made popular by Flyvbjerg and Pedersen (1989) @@ -2953,7 +2893,7 @@ Moreover, assume that the time series is asymptotically uncorrelated. We switch

     
    $$ \begin{align*} -\vec{X} = (X_1,X_2,\cdots,X_n). +\hat{X} = (X_1,X_2,\cdots,X_n). \end{align*} $$

     
    @@ -2967,7 +2907,7 @@ moreover, it becomes more accurate the larger \( n \) is.

    -

    Blocking Transformations

    +

    Blocking Transformations

    We now define blocking transformations. The idea is to take the mean of subsequent pair of elements from \( \vec{X} \) and form a new vector @@ -3006,7 +2946,9 @@ elements of \( \vec{X}_i \) and let \( n_i \) be the number of elements of
    -

    Blocking Transformations

    +

    Blocking Transformations

    + +

    Using the definition of the blocking transformation and the distributive property of the covariance, it is clear that since \( h =|i-j| \) @@ -3030,7 +2972,7 @@ The quantity \( \vec{X} \) is asymptotic uncorrelated by assumption, \( \vec{X}_

    -

    Blocking Transformations, getting there

    +

    Blocking Transformations, getting there

    We have

     
    $$ @@ -3056,7 +2998,7 @@ We can show that \( V(\overline{X}_i) = V(\overline{X}_j) \) for all \( 0 \leq i

    -

    Blocking Transformations, final expressions

    +

    Blocking Transformations, final expressions

    We can then wrap up @@ -3091,7 +3033,7 @@ It means we can apply blocking transformations until

    -

    Code examples for Blocking, Jackknife and bootstrap

    +

    Code examples for Blocking, Jackknife and bootstrap

    @@ -3323,6 +3265,379 @@ dataAnalysis.printOutput()

    +
    +

    The bias-variance tradeoff

    +We begin with an unknown function \( y=f(x) \) and fix a \emph{hypothesis set} + \( \mathcal{H} \) consisting of all functions we are willing to consider, + defined also on the domain of \( f \). This set may be uncountably + infinite (e.g. if there are real-valued parameters to fit). +The + choice of which functions to include in \( \mathcal{H} \) usually depends + on our intuition about the problem of interest. The function \( f(x) \) + produces a set of pairs \( (x_i,y_i) \), \( i=1\dots N \), which serve as the + observable data. Our goal is to select a function from the hypothesis + set \( h\in\mathcal{H} \) which approximates \( f(x) \) as best as possible, + namely, we would like to find \( h\in\mathcal{H} \) such that \( h\approx + f \) in some strict mathematical sense which we specify below. If this + is possible, we say that we \emph{learned} \( f(x) \). But if the + function \( f(x) \) can, in principle, take any value on + \emph{unobserved} inputs, how is it possible to learn in any + meaningful sense? +
    + + +
    +

    Training and testing data

    + +

    +We will discuss the bias-variance tradeoff in the context of continuous predictions such as regression. However, many of the intuitions and ideas discussed here also carry over to classification tasks. Consider a dataset \( \mathcal{L} \) consisting of the data \( \mathbf{X}_\mathcal{L}=\{(y_j, \boldsymbol{x}_j), j=1\ldots N\} \). Let us assume that the true data is generated from a noisy model +

     
    +$$ +y=f(\boldsymbol{x}) + \epsilon +$$ +

     
    + +where \( \epsilon \) is normally distributed with mean zero and standard deviation \( \sigma_\epsilon \). +

    + + +
    +

    Procedure to find a predictor

    + +

    +We have a statistical procedure (e.g. least-squares regression) for +forming a predictor \( \hat{g}_{\mathcal{L}}(\boldsymbol{x}) \) that gives the +prediction of our model for a new data point \( \boldsymbol{x} \). This estimator +is chosen by minimizing a cost function which we take to be the +squared error + +

     
    +$$ + \mathcal{C}( \boldsymbol{X}, \hat{g}(\boldsymbol{x})) = \sum_i (y_i - \hat{g}_\mathcal{L}(\boldsymbol{x}_i))^2. +$$ +

     
    +

    + + +
    +

    What we want

    + +

    +We are interested in the generalization error on all data drawn from +the true model, not just the error on the particular training dataset +\( \mathcal{L} \) that we have in hand. This is just the expectation of +the cost function over many different data sets +\( \{\mathcal{L}_j\} \). Denote this expectation value by +\( E_{\mathcal{L}} \). In other words, we can view \( \hat{g}_{\mathcal{L}} \) +as a stochastic functional that depends on the dataset \( \mathcal{L} \) +and we can think of \( E_{\mathcal{L}} \) as the expected value of the +functional if we drew an infinite number of datasets \( \{\mathcal{L}_1, +\mathcal{L}_2, \ldots \} \). +

    + + +
    +

    The expected generalization error

    + +

    +We would also like to average over different instances of the +"noise" \( \epsilon \) and we denote the expectation value over the +noise by \( E_\epsilon \). Thus, we can decompose the expected +generalization error as + +

     
    +$$ +\begin{align} +E_\mathcal{L, \epsilon}[\mathcal{C}( \boldsymbol{X}, \hat{g}(\boldsymbol{x})) ]&= E_\mathcal{L,\epsilon}\left[ \sum_i ({y}_i - \hat{g}_\mathcal{L}(\boldsymbol{x}_i))^2 \right] \nonumber \\ + &= E_\mathcal{L, \epsilon}\left[ \sum_{i}({y}_i -f(\boldsymbol{x}_i) +f(\boldsymbol{x}_i)- \hat{g}_\mathcal{L}(\boldsymbol{x}_i))^2\right] \nonumber \\ + &= \sum_i E_\epsilon[ ({y}_i -f(\boldsymbol{x}_i))^2 ]+ E_\mathcal{L, \epsilon}[(f(\boldsymbol{x}_i)- \hat{g}_\mathcal{L}(\boldsymbol{x}_i))^2] + 2E_\epsilon[{y}_i -f(\boldsymbol{x}_i)]E_\mathcal{L}[f(\boldsymbol{x}_i)- \hat{g}_\mathcal{L}(\boldsymbol{x}_i)] \nonumber \\ + &=\sum_i \sigma_\epsilon^2 + E_\mathcal{L}[(f(\boldsymbol{x}_i)- \hat{g}_\mathcal{L}(\boldsymbol{x}_i))^2], +\tag{28} +\end{align} +$$ +

     
    + +

    +where in the last line we used the fact that our noise has zero mean +and variance \( \sigma_\epsilon^2 \) and the sum over \( i \) applies to all +terms. +

    + + +
    +

    Elaborating a little bit more

    + +

    +It is also helpful to further decompose the second term as +follows: + +

     
    +$$ +\begin{align} +E_\mathcal{L}[(f(\boldsymbol{x}_i)- \hat{g}_\mathcal{L}(\boldsymbol{x}_i))^2] &=E_\mathcal{L}[(f(\mathbf{x}_i)-E_\mathcal{L}[\hat{g}_\mathcal{L}(\boldsymbol{x}_i)]+ E_\mathcal{L}[\hat{g}_\mathcal{L}(\boldsymbol{x}_i)]- \hat{g}_\mathcal{L}(\boldsymbol{x}_i))^2] \nonumber \\ +&=E_\mathcal{L}[(f(\boldsymbol{x}_i)-E_\mathcal{L}[\hat{g}_\mathcal{L}(\boldsymbol{x}_i)])^2] + E_\mathcal{L}[( \hat{g}_\mathcal{L}(\boldsymbol{x}_i)-E_\mathcal{L}[\hat{g}_\mathcal{L}(\boldsymbol{x}_i)])^2] \nonumber \\ +&+2E_\mathcal{L}[(f(\boldsymbol{x}_i)-E_\mathcal{L}[\hat{g}_\mathcal{L}(\boldsymbol{x}_i)])( \hat{g}_\mathcal{L}(\boldsymbol{x}_i)-E_\mathcal{L}[\hat{g}_\mathcal{L}(\boldsymbol{x}_i)])] \nonumber \\ +&=(f(\boldsymbol{x}_i)-E_\mathcal{L}[\hat{g}_\mathcal{L}(\boldsymbol{x}_i)])^2+E_\mathcal{L}[( \hat{g}_\mathcal{L}(\boldsymbol{x}_i)-E_\mathcal{L}[\hat{g}_\mathcal{L}(\boldsymbol{x}_i)])^2]. +\tag{29} +\end{align} +$$ +

     
    +

    + + +
    +

    The bias

    +The first term is called the bias +

     
    +$$ +Bias^2= \sum_i (f(\boldsymbol{x}_i)-E_\mathcal{L}[\hat{g}_\mathcal{L}(\boldsymbol{x}_i)])^2 +$$ +

     
    + +and measures the deviation of the expectation value of our estimator (i.e. the asymptotic value of our estimator in the infinite data limit) from the true value. +

    + + +
    +

    The variance

    +The second term is called the variance +

     
    +$$ +Var=\sum_i E_\mathcal{L}[( \hat{g}_\mathcal{L}(\boldsymbol{x}_i)-E_\mathcal{L}[\hat{g}_\mathcal{L}(\boldsymbol{x}_i)])^2], +$$ +

     
    + +

    +and measures how much our estimator fluctuates due to finite-sample effects. Combining these expressions, we see that the expected out-of-sample error of our model can be decomposed as +

     
    +$$ +E_\mathrm{out}=E_\mathcal{L, \epsilon}[\mathcal{C}( \boldsymbol{X}, \hat{g}(\boldsymbol{x})) ] = Bias^2 + Var + Noise. +$$ +

     
    + +

    +The bias-variance tradeoff summarizes the fundamental tension in +machine learning, particularly supervised learning, between the +complexity of a model and the amount of training data needed to train +it. Since data is often limited, in practice it is often useful to +use a less-complex model with higher bias – a model whose asymptotic +performance is worse than another model – because it is easier to +train and less sensitive to sampling noise arising from having a +finite-sized training dataset (smaller variance). +

    + + +
    +

    Summing up

    + +

    +The above equations tell us that in +order to minimize the expected test error, we need to select a +statistical learning method that simultaneously achieves low variance +and low bias. Note that variance is inherently a nonnegative quantity, +and squared bias is also nonnegative. Hence, we see that the expected +test MSE can never lie below \( Var(\epsilon) \), the irreducible error. + +

    +What do we mean by the variance and bias of a statistical learning +method? The variance refers to the amount by which our model would change if we +estimated it using a different training data set. Since the training +data are used to fit the statistical learning method, different +training data sets will result in a different estimate. But ideally the +estimate for our model should not vary too much between training +sets. However, if a method has high variance then small changes in +the training data can result in large changes in the model. In general, more +flexible statistical methods have higher variance. +

    + + +
    +

    Logistic Regression

    + +

    +So far we have focused on learning from datasets for which there is a +continuous output. In linear regression we have been +concerned with learning the coefficients of a polynomial to predict +the response of a continuous variable \( y_i \) on unseen data based on +its independent variables \( {\bf x}_i \). + +

    +Classification problems, +however, are concerned with outcomes taking the form of discrete +variables (i.e. categories). For example, we may want to detect if +there's a cat or a dog in an image. Or given a specific system, +we'd like to identify its state, say whether it is an ordered or disordered system (typical situation in solid state physics). +(e.g. ordered/disordered). + +

    +Logistic regression deals with binary, dichotomous outcomes (e.g. True or +False, Success or Failure, etc.). It is worth noting that logistic +regression is also commonly used in modern supervised Deep Learning +models, as we will see later. +

    + + +
    +

    Basics

    + +

    +We consider the case where the dependent variables \( y_i\in\mathbb{Z} \) +are discrete and only take values from \( m=0,\dots,M-1 \) (i.e. \( M \) +classes). + +

    +The goal is to predict the +output classes from the design matrix \( X\in\mathbb{R}^{n\times p} \) +made of \( n \) samples, each of which bears \( p \) features. Of cours e the +primary goal is to identify the classes to which new unseen samples +belong. +

    + + +
    +

    Linear classifier

    + +

    +Let us start by considering a slightly simpler classifier: a linear classifier that categorizes examples using a weighted linear-combination of the features and an additive offset +

     
    +$$ +\begin{equation} +s_i = \boldsymbol{x}_i^T\boldsymbol{w} + b_0 \equiv \mathbf{x}_i^T\mathbf{w}, +\tag{30} +\end{equation} +$$ +

     
    + +where we use the short-hand notation +\( \mathbf{x}_i = (1,\boldsymbol{x}_i) \) and \( \mathbf{w}_i = (b_0,\boldsymbol{w}_i) \). +

    + + +
    +

    Some selected properties

    + +

    +This function takes values on the entire real axis. In the case of logistic regression, however, the labels \( y_i \) are discrete variables. One simple way to get a discrete output is to have sign functions that map the output of a linear regressor to \( \{0,1\} \), \( f(s_i)= \) sign$(s_i) = 1$ if \( s_i\ge 0 \) and 0 if otherwise. Indeed, this is commonly known as the "perceptron" in the machine learning literature. This model is extremely simple, and it is favorable in many cases (e.g. noisy data) to have a ``soft" classifier that outputs the probability of a given category. For example, given \( \mathbf{x}_i \), the classifier outputs the probability of being in category \( m \). One such function is the logistic (or sigmoid) function: +

     
    +$$ +\begin{equation} +f(s) = \frac{1}{1+\mathrm e^{-s}}. +\tag{31} +\end{equation} +$$ +

     
    + +Note that \( 1-f(s)= f(-s) \), which will be useful shortly. +

    + + +
    +

    The cross-entropy as a cost function for logistic regression

    + +

    +The perceptron is an example of a ``hard classification": each datapoint is deterministically assigned to a category (i.e \( y_i=0 \) or \( y_i=1 \)). In many cases, it is favorable to have a "soft" classifier that outputs the probability of a given category rather than a single value. For example, given \( \mathbf{x}_i \), the classifier outputs the probability of being in category \( m \). +Logistic regression is the most canonical example of a soft classifier. In logistic regression, the probability that a data point \( \boldsymbol{x}_i \) belongs to a category \( y_i=\{0,1\} \) is is given by +

     
    +$$ +\begin{eqnarray} +P(y_i=1|\boldsymbol{x}_i,\boldsymbol{\theta)} &=& \frac{1}{1+\mathrm{e}^{-\mathbf{x}^T_i\mathbf{w}}},\nonumber\\ +P(y_i=0|\boldsymbol{x}_i,\boldsymbol{\theta)} &=& 1 - P(y_i=1|\boldsymbol{x}_i,\boldsymbol{\theta)}, +\end{eqnarray} +$$ +

     
    + +where \( \boldsymbol{\theta}=\mathbf{w} \) are the weights we wish to learn from the data. + +

    +Notice that in terms of the logistic function, we can write +

     
    +$$ +P(y_i=1) =f(\mathbf{x}_i^T\mathbf{w})=1-P(y_i=0). +$$ +

     
    +

    + + +
    +

    Maximum likelihood

    + +

    +We now define the cost function for logistic regression using Maximum +Likelihood Estimation (MLE). Recall, that in MLE we choose parameters +to maximize the probability of seeing the observed data. Consider a +dataset \( \mathcal{D}=\{(y_i,\boldsymbol{x}_i)\} \) with binary labels +\( y_i\in\{0,1\} \) where the data points are drawn independently. The +likelihood of the seeing the data under our model is just: +

     
    +$$ +\begin{align} +P(\mathcal{D}|\mathbf{w})& = \prod_{i=1}^n \left[f(\mathbf{x}_i^T\mathbf{w})\right]^{y_i}\left[1-f(\mathbf{x}_i^T\mathbf{w})\right]^{1-y_i}\nonumber \\ +\tag{32} +\end{align} +$$ +

     
    + +from which we can readily compute the log-likelihood: +

     
    +$$ +\begin{equation} +l(\mathbf{w}) = \sum_{i=1}^n y_i\log f(\mathbf{x}_i^T\mathbf{w}) + (1-y_i)\log\left[1-f(\mathbf{x}_i^T\mathbf{w})\right]. +\tag{33} +\end{equation} +$$ +

     
    +

    + + +
    +The maximum likelihood estimator is defined as the set of parameters that maximize the log-likelihood where we maximize with respect to \( \theta \) +

     
    +$$ +\hat{\mathbf{w}} = \sum_{i=1}^n y_i\log f(\mathbf{x}_i^T\mathbf{w}) + (1-y_i)\log\left[1-f(\mathbf{x}_i^T\mathbf{w})\right]. +$$ +

     
    + +Since the cost (error) function is just the negative log-likelihood, for logistic regression we have that +

     
    +$$ +\begin{eqnarray} +\mathcal{C}(\mathbf{w}) &=& - l(\mathbf{w}) \\ +&=& \sum_{i=1}^n -y_i\log f(\mathbf{x}_i^T\mathbf{w}) - (1-y_i)\log\left[1-f(\mathbf{x}_i^T\mathbf{w})\right].\nonumber +\end{eqnarray} +$$ +

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

    + + +
    +

    Minimizing the cross entropy

    + +

    +The cross entropy is a convex function of the weights \( \mathbf{w} \) and, +therefore, any local minimizer is a global minimizer. Minimizing this +cost function leads to the following equation + +

     
    +$$ +\begin{equation} +\boldsymbol{0}=\boldsymbol{\nabla} \mathcal{C}(\mathbf{w}) = \sum_{i=1}^n\left[f(\mathbf{x}_i^T\mathbf{w})-y_i\right]\mathbf{x}_i, +\tag{34} +\end{equation} +$$ +

     
    + +

    +where we made use of the logistic function identity \( \partial_z f(z) = +f(z)[1-f(z)] \). This equation defines a transcendental equation for +\( \mathbf{w} \), the solution of which, unlike linear regression, cannot +be written in a closed form. +Here we need gradient descent methods! +

    + +
    diff --git a/doc/pub/Regression/html/Regression-solarized.html b/doc/pub/Regression/html/Regression-solarized.html index cb8f3afa2..42ef73561 100644 --- a/doc/pub/Regression/html/Regression-solarized.html +++ b/doc/pub/Regression/html/Regression-solarized.html @@ -214,32 +214,46 @@ div { text-align: justify; text-justify: inter-word; } '___sec77'), ('Resampling methods: Jackknife', 2, None, '___sec78'), ('Resampling methods: Jackknife estimator', 2, None, '___sec79'), - ('Resampling methods: Jackknife sample code', - 2, - None, - '___sec80'), - ('Resampling methods: Bootstrap', 2, None, '___sec81'), - ('Resampling methods: Bootstrap background', 2, None, '___sec82'), + ('Resampling methods: Bootstrap', 2, None, '___sec80'), + ('Resampling methods: Bootstrap background', 2, None, '___sec81'), ('Resampling methods: More Bootstrap background', 2, None, - '___sec83'), - ('Resampling methods: Bootstrap approach', 2, None, '___sec84'), - ('Resampling methods: Bootstrap algorithm', 2, None, '___sec85'), - ('Resampling methods: Bootstrap steps', 2, None, '___sec86'), - ('Resampling methods: Blocking', 2, None, '___sec87'), - ('Blocking Transformations', 2, None, '___sec88'), - ('Blocking Transformations', 2, None, '___sec89'), - ('Blocking Transformations, getting there', 2, None, '___sec90'), + '___sec82'), + ('Resampling methods: Bootstrap approach', 2, None, '___sec83'), + ('Resampling methods: Bootstrap steps', 2, None, '___sec84'), + ('Resampling methods: Blocking', 2, None, '___sec85'), + ('Blocking Transformations', 2, None, '___sec86'), + ('Blocking Transformations', 2, None, '___sec87'), + ('Blocking Transformations, getting there', 2, None, '___sec88'), ('Blocking Transformations, final expressions', 2, None, - '___sec91'), + '___sec89'), ('"Code examples for Blocking, Jackknife and ' 'bootstrap":"https://github.com/CompPhysics/MachineLearning/tree/master/doc/Programs/ResamplingAnalysisScripts"', 2, None, - '___sec92')]} + '___sec90'), + ('The bias-variance tradeoff', 2, None, '___sec91'), + ('Training and testing data', 2, None, '___sec92'), + ('Procedure to find a predictor', 2, None, '___sec93'), + ('What we want', 2, None, '___sec94'), + ('The expected generalization error', 2, None, '___sec95'), + ('Elaborating a little bit more', 2, None, '___sec96'), + ('The bias', 2, None, '___sec97'), + ('The variance', 2, None, '___sec98'), + ('Summing up', 2, None, '___sec99'), + ('Logistic Regression', 2, None, '___sec100'), + ('Basics', 2, None, '___sec101'), + ('Linear classifier', 2, None, '___sec102'), + ('Some selected properties', 2, None, '___sec103'), + ('The cross-entropy as a cost function for logistic regression', + 2, + None, + '___sec104'), + ('Maximum likelihood', 2, None, '___sec105'), + ('Minimizing the cross entropy', 2, None, '___sec106')]} end of tocinfo --> @@ -281,7 +295,7 @@ MathJax.Hub.Config({
    [2] Department of Physics and Astronomy and National Superconducting Cyclotron Laboratory, Michigan State University

    -

    Sep 13, 2018

    +

    Sep 14, 2018












    @@ -2710,18 +2724,18 @@ need for bootstrapping.

    Resampling methods: Jackknife

    -The Jackknife works by making many replicas of the estimator \( \widehat{\vec{\theta}} \). -The jackknife is a resampling method, we explained that this happens by scrambling the data in some way. When using the jackknife, this is done by systematically leaving out one observation from the vector of observed values \( \vec{X} = (X_1,X_2,\cdots,X_n) \). -Let \( \vec{X}_i \) denote the vector +The Jackknife works by making many replicas of the estimator \( \widehat{\theta} \). +The jackknife is a resampling method, we explained that this happens by scrambling the data in some way. When using the jackknife, this is done by systematically leaving out one observation from the vector of observed values \( \hat{x} = (x_1,x_2,\cdots,X_n) \). +Let \( \hat{x}_i \) denote the vector $$ -\vec{X}_i = (X_1,X_2,\cdots,X_{i-1},X_{i+1},\cdots,X_n), +\hat{x}_i = (x_1,x_2,\cdots,x_{i-1},x_{i+1},\cdots,x_n), $$

    -which equals the vector \( \vec{X} \) with the exception that observation +which equals the vector \( \hat{x} \) with the exception that observation number \( i \) is left out. Using this notation, define -\( \widehat{\vec{\theta}}_i \) to be the estimator -\( \widehat{\vec{\theta}} \) computed using \( \vec{X}_i \). +\( \widehat{\theta}_i \) to be the estimator +\( \widehat{\theta} \) computed using \( \vec{X}_i \).











    @@ -2730,8 +2744,8 @@ number \( i \) is left out. Using this notation, define

    To get an estimate for the bias and -standard error of \( \widehat{\vec{\theta}} \), use the following -estimators for each component of \( \widehat{\vec{\theta}} \) +standard error of \( \widehat{\theta} \), use the following +estimators for each component of \( \widehat{\theta} \) $$ \widehat{\mathrm{Bias}}(\widehat \theta,\theta) = (n-1)\left( - \widehat{\theta} + \frac{1}{n}\sum_{i=1}^{n} \widehat \theta_i \right) \qquad \text{and} \qquad \widehat{\sigma}^2_{\widehat{\theta} } = \frac{n-1}{n}\sum_{i=1}^{n}( \widehat{\theta}_i - \frac{1}{n}\sum_{j=1}^{n}\widehat \theta_j )^2. @@ -2740,43 +2754,7 @@ $$











    -

    Resampling methods: Jackknife sample code

    - -

    -Sample code for the Jackknife method - -

    - - -

    def jack(data, stat):
-    n = len(data); t = zeros(n); inds = arange(n); t0 = time()
-    # 'jackknifing' by leaving out an observation for each i
-    for i in range(n):
-        t[i] = stat(delete(data,i) )
-        return t
-# define a function which returns your chosen estimator theta-hat
-def stat(data):
-    theta-hat = mean(data)
-    return theta-hat
-# Return the Jackknife  sample
-t = jack(X, stat)
-
    -

    -Consider first the function jack(). This function repeatedly -estimates the function called statistic() under the resampled -data by systematically leaving out one observation from the data. The -function stat() is passed as an argument to -jack(). The array t is eventually returned, which -contains all the estimates \( \widehat{\vec{\theta}} \), and can be -plotted or analysed in other ways, such as by calling std(t) -from numpy to estimate the standard error of -\( \widehat{\vec{\theta}} \). The function std(t) is just the -estimator \( \widehat{\sigma}^2 \). - -

    -









    - -

    Resampling methods: Bootstrap

    +

    Resampling methods: Bootstrap

    @@ -2797,26 +2775,26 @@ advantages:











    -

    Resampling methods: Bootstrap background

    +

    Resampling methods: Bootstrap background

    -Since \( \widehat{\vec{\theta}} = \widehat{\vec{\theta}}(\vec{X}) \) is a function of random variables, -\( \widehat{\vec{\theta}} \) itself must be a random variable. Thus it has +Since \( \widehat{\theta} = \widehat{\theta}(\hat{X}) \) is a function of random variables, +\( \widehat{\theta} \) itself must be a random variable. Thus it has a pdf, call this function \( p(\vec{t}) \). The aim of the bootstrap is to -estimate \( p(\vec{t}) \) by the relative frequency of -\( \widehat{\vec{\theta}} \). You can think of this as using a histogram -in the place of \( p(\vec{t}) \). If the relative frequency closely +estimate \( p(\hat{t}) \) by the relative frequency of +\( \widehat{\theta} \). You can think of this as using a histogram +in the place of \( p(\hat{t}) \). If the relative frequency closely resembles \( p(\vec{t}) \), then using numerics, it is straight forward to -estimate all the interesting parameters of \( p(\vec{t}) \) using point +estimate all the interesting parameters of \( p(\hat{t}) \) using point estimators.











    -

    Resampling methods: More Bootstrap background

    +

    Resampling methods: More Bootstrap background

    -In the case that \( \widehat{\vec{\theta}} \) has +In the case that \( \widehat{\theta} \) has more than one component, and the components are independent, use the same estimator on each component separately. If the probability density function of \( X_i \), \( p(x) \), had been known, then it would have @@ -2824,18 +2802,18 @@ been straight forward to do this by:

    1. Drawing lots of numbers from \( p(x) \), suppose we call one such set of numbers \( (X_1^*, X_2^*, \cdots, X_n^*) \).
      2. -
    2. Then using these numbers, we could compute a replica of \( \widehat{\vec{\theta}} \) called \( \widehat{\vec{\theta}}^* \).
      3. +
    3. Then using these numbers, we could compute a replica of \( \widehat{\theta} \) called \( \widehat{\theta}^* \).
    By repeated use of (1) and (2), many -estimates of \( \widehat{\vec{\theta}} \) could have been obtained. The -idea is to use the relative frequency of \( \widehat{\vec{\theta}}^* \) -(think of a histogram) as an estimate of \( p(\vec{t}) \). +estimates of \( \widehat{\theta} \) could have been obtained. The +idea is to use the relative frequency of \( \widehat{\theta}^* \) +(think of a histogram) as an estimate of \( p(\hat{t}) \).











    -

    Resampling methods: Bootstrap approach

    +

    Resampling methods: Bootstrap approach

    But @@ -2851,44 +2829,20 @@ result in some asymptotic sense? The answer is yes. Instead of generating the histogram for the relative frequency of the observation \( X_i \), just draw the values \( (X_1^*,X_2^*,\cdots,X_n^*) \) with replacement from the vector -\( \vec{X} \). +\( \hat{X} \).











    -

    Resampling methods: Bootstrap algorithm

    - -

    - - -

            
-def boot(data, statistic, R):
-    t = zeros(R); n = len(data); inds = arange(n); t0 = time()
-    for i in range(R):
-        t[i] = statistic(data[randint(0,n,n)])
-        return t
-# define a function which returns your chosen estimator theta-hat
-def stat(data):
-    theta-hat = mean(data)
-    return theta-hat
-
-t = boot(X, stat, 2**9)
-
    -

    -Consider first the function boot(). In the for loop, this function repeatedly estimates the function called statistic() under the resampled data in data(randint(0,n,n)). The function statistic() is passed as an argument to boot(). The array t is eventually returned, which contains all the estimates \( \widehat{\vec{\theta}} \), and can be plotted or analysed in other ways, such as by calling std(t) from numpy to estimate the standard error of \( \widehat{\vec{\theta}} \). The function std(t) is just the estimator \( \widehat{\sigma}^2 \). - -

    -









    - -

    Resampling methods: Bootstrap steps

    +

    Resampling methods: Bootstrap steps

    The independent bootstrap works like this:

      -
    1. Draw with replacement \( n \) numbers for the observed variables \( \vec{x} = (x_1,x_2,\cdots,x_n) \).
      2. -
    2. Define a vector \( \vec{x}^* \) containing the values which were drawn from \( \vec{x} \).
      3. -
    3. Using the vector \( \vec{x}^* \) compute \( \widehat{\theta}^* \) by evaluating \( \widehat \theta \) under the observations \( \vec{x}^* \).
      4. +
    4. Draw with replacement \( n \) numbers for the observed variables \( \hat{x} = (x_1,x_2,\cdots,x_n) \).
      5. +
    5. Define a vector \( \hat{x}^* \) containing the values which were drawn from \( \hat{x} \).
      6. +
    6. Using the vector \( \hat{x}^* \) compute \( \widehat{\theta}^* \) by evaluating \( \widehat \theta \) under the observations \( \hat{x}^* \).
    7. Repeat this process \( k \) times.
    @@ -2897,7 +2851,7 @@ When you are done, you can draw a histogram of the relative frequency of \( \wid











    -

    Resampling methods: Blocking

    +

    Resampling methods: Blocking

    The blocking method was made popular by Flyvbjerg and Pedersen (1989) @@ -2910,7 +2864,7 @@ Assume \( n = 2^d \) for some integer \( d>1 \) and \( X_1,X_2,\cdots, X_n \) is Moreover, assume that the time series is asymptotically uncorrelated. We switch to vector notation by arranging \( X_1,X_2,\cdots,X_n \) in an \( n \)-tuple. Define: $$ \begin{align*} -\vec{X} = (X_1,X_2,\cdots,X_n). +\hat{X} = (X_1,X_2,\cdots,X_n). \end{align*} $$ @@ -2923,7 +2877,7 @@ moreover, it becomes more accurate the larger \( n \) is.











    -

    Blocking Transformations

    +

    Blocking Transformations

    We now define blocking transformations. The idea is to take the mean of subsequent pair of elements from \( \vec{X} \) and form a new vector @@ -2960,7 +2914,9 @@ elements of \( \vec{X}_i \) and let \( n_i \) be the number of elements of











    -

    Blocking Transformations

    +

    Blocking Transformations

    + +

    Using the definition of the blocking transformation and the distributive property of the covariance, it is clear that since \( h =|i-j| \) @@ -2982,7 +2938,7 @@ The quantity \( \vec{X} \) is asymptotic uncorrelated by assumption, \( \vec{X}_











    -

    Blocking Transformations, getting there

    +

    Blocking Transformations, getting there

    We have $$ \begin{align} @@ -3004,7 +2960,7 @@ We can show that \( V(\overline{X}_i) = V(\overline{X}_j) \) for all \( 0 \leq i











    -

    Blocking Transformations, final expressions

    +

    Blocking Transformations, final expressions

    We can then wrap up @@ -3035,7 +2991,7 @@ It means we can apply blocking transformations until











    -

    Code examples for Blocking, Jackknife and bootstrap

    +

    Code examples for Blocking, Jackknife and bootstrap

    @@ -3265,6 +3221,344 @@ dataAnalysis.plotAll() dataAnalysis.printOutput()

    +









    + +

    The bias-variance tradeoff

    +We begin with an unknown function \( y=f(x) \) and fix a \emph{hypothesis set} + \( \mathcal{H} \) consisting of all functions we are willing to consider, + defined also on the domain of \( f \). This set may be uncountably + infinite (e.g. if there are real-valued parameters to fit). +The + choice of which functions to include in \( \mathcal{H} \) usually depends + on our intuition about the problem of interest. The function \( f(x) \) + produces a set of pairs \( (x_i,y_i) \), \( i=1\dots N \), which serve as the + observable data. Our goal is to select a function from the hypothesis + set \( h\in\mathcal{H} \) which approximates \( f(x) \) as best as possible, + namely, we would like to find \( h\in\mathcal{H} \) such that \( h\approx + f \) in some strict mathematical sense which we specify below. If this + is possible, we say that we \emph{learned} \( f(x) \). But if the + function \( f(x) \) can, in principle, take any value on + \emph{unobserved} inputs, how is it possible to learn in any + meaningful sense? + +

    + + +

    Training and testing data

    + +

    +We will discuss the bias-variance tradeoff in the context of continuous predictions such as regression. However, many of the intuitions and ideas discussed here also carry over to classification tasks. Consider a dataset \( \mathcal{L} \) consisting of the data \( \mathbf{X}_\mathcal{L}=\{(y_j, \boldsymbol{x}_j), j=1\ldots N\} \). Let us assume that the true data is generated from a noisy model +$$ +y=f(\boldsymbol{x}) + \epsilon +$$ + +where \( \epsilon \) is normally distributed with mean zero and standard deviation \( \sigma_\epsilon \). + +

    +









    + +

    Procedure to find a predictor

    + +

    +We have a statistical procedure (e.g. least-squares regression) for +forming a predictor \( \hat{g}_{\mathcal{L}}(\boldsymbol{x}) \) that gives the +prediction of our model for a new data point \( \boldsymbol{x} \). This estimator +is chosen by minimizing a cost function which we take to be the +squared error + +$$ + \mathcal{C}( \boldsymbol{X}, \hat{g}(\boldsymbol{x})) = \sum_i (y_i - \hat{g}_\mathcal{L}(\boldsymbol{x}_i))^2. +$$ + +

    +









    + +

    What we want

    + +

    +We are interested in the generalization error on all data drawn from +the true model, not just the error on the particular training dataset +\( \mathcal{L} \) that we have in hand. This is just the expectation of +the cost function over many different data sets +\( \{\mathcal{L}_j\} \). Denote this expectation value by +\( E_{\mathcal{L}} \). In other words, we can view \( \hat{g}_{\mathcal{L}} \) +as a stochastic functional that depends on the dataset \( \mathcal{L} \) +and we can think of \( E_{\mathcal{L}} \) as the expected value of the +functional if we drew an infinite number of datasets \( \{\mathcal{L}_1, +\mathcal{L}_2, \ldots \} \). + +

    +









    + +

    The expected generalization error

    + +

    +We would also like to average over different instances of the +"noise" \( \epsilon \) and we denote the expectation value over the +noise by \( E_\epsilon \). Thus, we can decompose the expected +generalization error as + +$$ +\begin{align} +E_\mathcal{L, \epsilon}[\mathcal{C}( \boldsymbol{X}, \hat{g}(\boldsymbol{x})) ]&= E_\mathcal{L,\epsilon}\left[ \sum_i ({y}_i - \hat{g}_\mathcal{L}(\boldsymbol{x}_i))^2 \right] \nonumber \\ + &= E_\mathcal{L, \epsilon}\left[ \sum_{i}({y}_i -f(\boldsymbol{x}_i) +f(\boldsymbol{x}_i)- \hat{g}_\mathcal{L}(\boldsymbol{x}_i))^2\right] \nonumber \\ + &= \sum_i E_\epsilon[ ({y}_i -f(\boldsymbol{x}_i))^2 ]+ E_\mathcal{L, \epsilon}[(f(\boldsymbol{x}_i)- \hat{g}_\mathcal{L}(\boldsymbol{x}_i))^2] + 2E_\epsilon[{y}_i -f(\boldsymbol{x}_i)]E_\mathcal{L}[f(\boldsymbol{x}_i)- \hat{g}_\mathcal{L}(\boldsymbol{x}_i)] \nonumber \\ + &=\sum_i \sigma_\epsilon^2 + E_\mathcal{L}[(f(\boldsymbol{x}_i)- \hat{g}_\mathcal{L}(\boldsymbol{x}_i))^2], +\label{_auto17} +\end{align} +$$ + +

    +where in the last line we used the fact that our noise has zero mean +and variance \( \sigma_\epsilon^2 \) and the sum over \( i \) applies to all +terms. + +

    +









    + +

    Elaborating a little bit more

    + +

    +It is also helpful to further decompose the second term as +follows: + +$$ +\begin{align} +E_\mathcal{L}[(f(\boldsymbol{x}_i)- \hat{g}_\mathcal{L}(\boldsymbol{x}_i))^2] &=E_\mathcal{L}[(f(\mathbf{x}_i)-E_\mathcal{L}[\hat{g}_\mathcal{L}(\boldsymbol{x}_i)]+ E_\mathcal{L}[\hat{g}_\mathcal{L}(\boldsymbol{x}_i)]- \hat{g}_\mathcal{L}(\boldsymbol{x}_i))^2] \nonumber \\ +&=E_\mathcal{L}[(f(\boldsymbol{x}_i)-E_\mathcal{L}[\hat{g}_\mathcal{L}(\boldsymbol{x}_i)])^2] + E_\mathcal{L}[( \hat{g}_\mathcal{L}(\boldsymbol{x}_i)-E_\mathcal{L}[\hat{g}_\mathcal{L}(\boldsymbol{x}_i)])^2] \nonumber \\ +&+2E_\mathcal{L}[(f(\boldsymbol{x}_i)-E_\mathcal{L}[\hat{g}_\mathcal{L}(\boldsymbol{x}_i)])( \hat{g}_\mathcal{L}(\boldsymbol{x}_i)-E_\mathcal{L}[\hat{g}_\mathcal{L}(\boldsymbol{x}_i)])] \nonumber \\ +&=(f(\boldsymbol{x}_i)-E_\mathcal{L}[\hat{g}_\mathcal{L}(\boldsymbol{x}_i)])^2+E_\mathcal{L}[( \hat{g}_\mathcal{L}(\boldsymbol{x}_i)-E_\mathcal{L}[\hat{g}_\mathcal{L}(\boldsymbol{x}_i)])^2]. +\label{_auto18} +\end{align} +$$ + +

    +









    + +

    The bias

    +The first term is called the bias +$$ +Bias^2= \sum_i (f(\boldsymbol{x}_i)-E_\mathcal{L}[\hat{g}_\mathcal{L}(\boldsymbol{x}_i)])^2 +$$ + +and measures the deviation of the expectation value of our estimator (i.e. the asymptotic value of our estimator in the infinite data limit) from the true value. + +

    +









    + +

    The variance

    +The second term is called the variance +$$ +Var=\sum_i E_\mathcal{L}[( \hat{g}_\mathcal{L}(\boldsymbol{x}_i)-E_\mathcal{L}[\hat{g}_\mathcal{L}(\boldsymbol{x}_i)])^2], +$$ + +

    +and measures how much our estimator fluctuates due to finite-sample effects. Combining these expressions, we see that the expected out-of-sample error of our model can be decomposed as +$$ +E_\mathrm{out}=E_\mathcal{L, \epsilon}[\mathcal{C}( \boldsymbol{X}, \hat{g}(\boldsymbol{x})) ] = Bias^2 + Var + Noise. +$$ + +

    +The bias-variance tradeoff summarizes the fundamental tension in +machine learning, particularly supervised learning, between the +complexity of a model and the amount of training data needed to train +it. Since data is often limited, in practice it is often useful to +use a less-complex model with higher bias – a model whose asymptotic +performance is worse than another model – because it is easier to +train and less sensitive to sampling noise arising from having a +finite-sized training dataset (smaller variance). + +

    + + +

    Summing up

    + +

    +The above equations tell us that in +order to minimize the expected test error, we need to select a +statistical learning method that simultaneously achieves low variance +and low bias. Note that variance is inherently a nonnegative quantity, +and squared bias is also nonnegative. Hence, we see that the expected +test MSE can never lie below \( Var(\epsilon) \), the irreducible error. + +

    +What do we mean by the variance and bias of a statistical learning +method? The variance refers to the amount by which our model would change if we +estimated it using a different training data set. Since the training +data are used to fit the statistical learning method, different +training data sets will result in a different estimate. But ideally the +estimate for our model should not vary too much between training +sets. However, if a method has high variance then small changes in +the training data can result in large changes in the model. In general, more +flexible statistical methods have higher variance. + +

    + + +

    Logistic Regression

    + +

    +So far we have focused on learning from datasets for which there is a +continuous output. In linear regression we have been +concerned with learning the coefficients of a polynomial to predict +the response of a continuous variable \( y_i \) on unseen data based on +its independent variables \( {\bf x}_i \). + +

    +Classification problems, +however, are concerned with outcomes taking the form of discrete +variables (i.e. categories). For example, we may want to detect if +there's a cat or a dog in an image. Or given a specific system, +we'd like to identify its state, say whether it is an ordered or disordered system (typical situation in solid state physics). +(e.g. ordered/disordered). + +

    +Logistic regression deals with binary, dichotomous outcomes (e.g. True or +False, Success or Failure, etc.). It is worth noting that logistic +regression is also commonly used in modern supervised Deep Learning +models, as we will see later. + +

    + + +

    Basics

    + +

    +We consider the case where the dependent variables \( y_i\in\mathbb{Z} \) +are discrete and only take values from \( m=0,\dots,M-1 \) (i.e. \( M \) +classes). + +

    +The goal is to predict the +output classes from the design matrix \( X\in\mathbb{R}^{n\times p} \) +made of \( n \) samples, each of which bears \( p \) features. Of cours e the +primary goal is to identify the classes to which new unseen samples +belong. + +

    +









    + +

    Linear classifier

    + +

    +Let us start by considering a slightly simpler classifier: a linear classifier that categorizes examples using a weighted linear-combination of the features and an additive offset +$$ +\begin{equation} +s_i = \boldsymbol{x}_i^T\boldsymbol{w} + b_0 \equiv \mathbf{x}_i^T\mathbf{w}, +\label{_auto19} +\end{equation} +$$ + +where we use the short-hand notation +\( \mathbf{x}_i = (1,\boldsymbol{x}_i) \) and \( \mathbf{w}_i = (b_0,\boldsymbol{w}_i) \). + +

    +









    + +

    Some selected properties

    + +

    +This function takes values on the entire real axis. In the case of logistic regression, however, the labels \( y_i \) are discrete variables. One simple way to get a discrete output is to have sign functions that map the output of a linear regressor to \( \{0,1\} \), \( f(s_i)= \) sign$(s_i) = 1$ if \( s_i\ge 0 \) and 0 if otherwise. Indeed, this is commonly known as the "perceptron" in the machine learning literature. This model is extremely simple, and it is favorable in many cases (e.g. noisy data) to have a ``soft" classifier that outputs the probability of a given category. For example, given \( \mathbf{x}_i \), the classifier outputs the probability of being in category \( m \). One such function is the logistic (or sigmoid) function: +$$ +\begin{equation} +f(s) = \frac{1}{1+\mathrm e^{-s}}. +\label{eq:log_fun} +\end{equation} +$$ + +Note that \( 1-f(s)= f(-s) \), which will be useful shortly. + +

    +









    + +

    The cross-entropy as a cost function for logistic regression

    + +

    +The perceptron is an example of a ``hard classification": each datapoint is deterministically assigned to a category (i.e \( y_i=0 \) or \( y_i=1 \)). In many cases, it is favorable to have a "soft" classifier that outputs the probability of a given category rather than a single value. For example, given \( \mathbf{x}_i \), the classifier outputs the probability of being in category \( m \). +Logistic regression is the most canonical example of a soft classifier. In logistic regression, the probability that a data point \( \boldsymbol{x}_i \) belongs to a category \( y_i=\{0,1\} \) is is given by +$$ +\begin{eqnarray} +P(y_i=1|\boldsymbol{x}_i,\boldsymbol{\theta)} &=& \frac{1}{1+\mathrm{e}^{-\mathbf{x}^T_i\mathbf{w}}},\nonumber\\ +P(y_i=0|\boldsymbol{x}_i,\boldsymbol{\theta)} &=& 1 - P(y_i=1|\boldsymbol{x}_i,\boldsymbol{\theta)}, +\end{eqnarray} +$$ + +where \( \boldsymbol{\theta}=\mathbf{w} \) are the weights we wish to learn from the data. + +

    +Notice that in terms of the logistic function, we can write +$$ +P(y_i=1) =f(\mathbf{x}_i^T\mathbf{w})=1-P(y_i=0). +$$ + +

    + + +

    Maximum likelihood

    + +

    +We now define the cost function for logistic regression using Maximum +Likelihood Estimation (MLE). Recall, that in MLE we choose parameters +to maximize the probability of seeing the observed data. Consider a +dataset \( \mathcal{D}=\{(y_i,\boldsymbol{x}_i)\} \) with binary labels +\( y_i\in\{0,1\} \) where the data points are drawn independently. The +likelihood of the seeing the data under our model is just: +$$ +\begin{align} +P(\mathcal{D}|\mathbf{w})& = \prod_{i=1}^n \left[f(\mathbf{x}_i^T\mathbf{w})\right]^{y_i}\left[1-f(\mathbf{x}_i^T\mathbf{w})\right]^{1-y_i}\nonumber \\ +\label{_auto20} +\end{align} +$$ + +from which we can readily compute the log-likelihood: +$$ +\begin{equation} +l(\mathbf{w}) = \sum_{i=1}^n y_i\log f(\mathbf{x}_i^T\mathbf{w}) + (1-y_i)\log\left[1-f(\mathbf{x}_i^T\mathbf{w})\right]. +\label{_auto21} +\end{equation} +$$ + +

    +









    +The maximum likelihood estimator is defined as the set of parameters that maximize the log-likelihood where we maximize with respect to \( \theta \) +$$ +\hat{\mathbf{w}} = \sum_{i=1}^n y_i\log f(\mathbf{x}_i^T\mathbf{w}) + (1-y_i)\log\left[1-f(\mathbf{x}_i^T\mathbf{w})\right]. +$$ + +Since the cost (error) function is just the negative log-likelihood, for logistic regression we have that +$$ +\begin{eqnarray} +\mathcal{C}(\mathbf{w}) &=& - l(\mathbf{w}) \\ +&=& \sum_{i=1}^n -y_i\log f(\mathbf{x}_i^T\mathbf{w}) - (1-y_i)\log\left[1-f(\mathbf{x}_i^T\mathbf{w})\right].\nonumber +\end{eqnarray} +$$ + +This equation is known in statistics as the \emph{cross entropy}. Finally, we note that just as in linear regression, +in practice we usually supplement the cross-entropy with additional regularization terms, usually \( L_1 \) and \( L_2 \) regularization as we did for Ridge and Lasso regression. + +

    +









    + +

    Minimizing the cross entropy

    + +

    +The cross entropy is a convex function of the weights \( \mathbf{w} \) and, +therefore, any local minimizer is a global minimizer. Minimizing this +cost function leads to the following equation + +$$ +\begin{equation} +\boldsymbol{0}=\boldsymbol{\nabla} \mathcal{C}(\mathbf{w}) = \sum_{i=1}^n\left[f(\mathbf{x}_i^T\mathbf{w})-y_i\right]\mathbf{x}_i, +\label{_auto22} +\end{equation} +$$ + +

    +where we made use of the logistic function identity \( \partial_z f(z) = +f(z)[1-f(z)] \). This equation defines a transcendental equation for +\( \mathbf{w} \), the solution of which, unlike linear regression, cannot +be written in a closed form. +Here we need gradient descent methods! diff --git a/doc/pub/Regression/html/Regression.html b/doc/pub/Regression/html/Regression.html index 8cc2430b9..aae956a02 100644 --- a/doc/pub/Regression/html/Regression.html +++ b/doc/pub/Regression/html/Regression.html @@ -219,32 +219,46 @@ div { text-align: justify; text-justify: inter-word; } '___sec77'), ('Resampling methods: Jackknife', 2, None, '___sec78'), ('Resampling methods: Jackknife estimator', 2, None, '___sec79'), - ('Resampling methods: Jackknife sample code', - 2, - None, - '___sec80'), - ('Resampling methods: Bootstrap', 2, None, '___sec81'), - ('Resampling methods: Bootstrap background', 2, None, '___sec82'), + ('Resampling methods: Bootstrap', 2, None, '___sec80'), + ('Resampling methods: Bootstrap background', 2, None, '___sec81'), ('Resampling methods: More Bootstrap background', 2, None, - '___sec83'), - ('Resampling methods: Bootstrap approach', 2, None, '___sec84'), - ('Resampling methods: Bootstrap algorithm', 2, None, '___sec85'), - ('Resampling methods: Bootstrap steps', 2, None, '___sec86'), - ('Resampling methods: Blocking', 2, None, '___sec87'), - ('Blocking Transformations', 2, None, '___sec88'), - ('Blocking Transformations', 2, None, '___sec89'), - ('Blocking Transformations, getting there', 2, None, '___sec90'), + '___sec82'), + ('Resampling methods: Bootstrap approach', 2, None, '___sec83'), + ('Resampling methods: Bootstrap steps', 2, None, '___sec84'), + ('Resampling methods: Blocking', 2, None, '___sec85'), + ('Blocking Transformations', 2, None, '___sec86'), + ('Blocking Transformations', 2, None, '___sec87'), + ('Blocking Transformations, getting there', 2, None, '___sec88'), ('Blocking Transformations, final expressions', 2, None, - '___sec91'), + '___sec89'), ('"Code examples for Blocking, Jackknife and ' 'bootstrap":"https://github.com/CompPhysics/MachineLearning/tree/master/doc/Programs/ResamplingAnalysisScripts"', 2, None, - '___sec92')]} + '___sec90'), + ('The bias-variance tradeoff', 2, None, '___sec91'), + ('Training and testing data', 2, None, '___sec92'), + ('Procedure to find a predictor', 2, None, '___sec93'), + ('What we want', 2, None, '___sec94'), + ('The expected generalization error', 2, None, '___sec95'), + ('Elaborating a little bit more', 2, None, '___sec96'), + ('The bias', 2, None, '___sec97'), + ('The variance', 2, None, '___sec98'), + ('Summing up', 2, None, '___sec99'), + ('Logistic Regression', 2, None, '___sec100'), + ('Basics', 2, None, '___sec101'), + ('Linear classifier', 2, None, '___sec102'), + ('Some selected properties', 2, None, '___sec103'), + ('The cross-entropy as a cost function for logistic regression', + 2, + None, + '___sec104'), + ('Maximum likelihood', 2, None, '___sec105'), + ('Minimizing the cross entropy', 2, None, '___sec106')]} end of tocinfo --> @@ -286,7 +300,7 @@ MathJax.Hub.Config({

    [2] Department of Physics and Astronomy and National Superconducting Cyclotron Laboratory, Michigan State University

    -

    Sep 13, 2018

    +

    Sep 14, 2018












    @@ -2715,18 +2729,18 @@ need for bootstrapping.

    Resampling methods: Jackknife

    -The Jackknife works by making many replicas of the estimator \( \widehat{\vec{\theta}} \). -The jackknife is a resampling method, we explained that this happens by scrambling the data in some way. When using the jackknife, this is done by systematically leaving out one observation from the vector of observed values \( \vec{X} = (X_1,X_2,\cdots,X_n) \). -Let \( \vec{X}_i \) denote the vector +The Jackknife works by making many replicas of the estimator \( \widehat{\theta} \). +The jackknife is a resampling method, we explained that this happens by scrambling the data in some way. When using the jackknife, this is done by systematically leaving out one observation from the vector of observed values \( \hat{x} = (x_1,x_2,\cdots,X_n) \). +Let \( \hat{x}_i \) denote the vector $$ -\vec{X}_i = (X_1,X_2,\cdots,X_{i-1},X_{i+1},\cdots,X_n), +\hat{x}_i = (x_1,x_2,\cdots,x_{i-1},x_{i+1},\cdots,x_n), $$

    -which equals the vector \( \vec{X} \) with the exception that observation +which equals the vector \( \hat{x} \) with the exception that observation number \( i \) is left out. Using this notation, define -\( \widehat{\vec{\theta}}_i \) to be the estimator -\( \widehat{\vec{\theta}} \) computed using \( \vec{X}_i \). +\( \widehat{\theta}_i \) to be the estimator +\( \widehat{\theta} \) computed using \( \vec{X}_i \).











    @@ -2735,8 +2749,8 @@ number \( i \) is left out. Using this notation, define

    To get an estimate for the bias and -standard error of \( \widehat{\vec{\theta}} \), use the following -estimators for each component of \( \widehat{\vec{\theta}} \) +standard error of \( \widehat{\theta} \), use the following +estimators for each component of \( \widehat{\theta} \) $$ \widehat{\mathrm{Bias}}(\widehat \theta,\theta) = (n-1)\left( - \widehat{\theta} + \frac{1}{n}\sum_{i=1}^{n} \widehat \theta_i \right) \qquad \text{and} \qquad \widehat{\sigma}^2_{\widehat{\theta} } = \frac{n-1}{n}\sum_{i=1}^{n}( \widehat{\theta}_i - \frac{1}{n}\sum_{j=1}^{n}\widehat \theta_j )^2. @@ -2745,43 +2759,7 @@ $$











    -

    Resampling methods: Jackknife sample code

    - -

    -Sample code for the Jackknife method - -

    - - -

    def jack(data, stat):
-    n = len(data); t = zeros(n); inds = arange(n); t0 = time()
-    # 'jackknifing' by leaving out an observation for each i
-    for i in range(n):
-        t[i] = stat(delete(data,i) )
-        return t
-# define a function which returns your chosen estimator theta-hat
-def stat(data):
-    theta-hat = mean(data)
-    return theta-hat
-# Return the Jackknife  sample
-t = jack(X, stat)
-
    -

    -Consider first the function jack(). This function repeatedly -estimates the function called statistic() under the resampled -data by systematically leaving out one observation from the data. The -function stat() is passed as an argument to -jack(). The array t is eventually returned, which -contains all the estimates \( \widehat{\vec{\theta}} \), and can be -plotted or analysed in other ways, such as by calling std(t) -from numpy to estimate the standard error of -\( \widehat{\vec{\theta}} \). The function std(t) is just the -estimator \( \widehat{\sigma}^2 \). - -

    -









    - -

    Resampling methods: Bootstrap

    +

    Resampling methods: Bootstrap

    @@ -2802,26 +2780,26 @@ advantages:











    -

    Resampling methods: Bootstrap background

    +

    Resampling methods: Bootstrap background

    -Since \( \widehat{\vec{\theta}} = \widehat{\vec{\theta}}(\vec{X}) \) is a function of random variables, -\( \widehat{\vec{\theta}} \) itself must be a random variable. Thus it has +Since \( \widehat{\theta} = \widehat{\theta}(\hat{X}) \) is a function of random variables, +\( \widehat{\theta} \) itself must be a random variable. Thus it has a pdf, call this function \( p(\vec{t}) \). The aim of the bootstrap is to -estimate \( p(\vec{t}) \) by the relative frequency of -\( \widehat{\vec{\theta}} \). You can think of this as using a histogram -in the place of \( p(\vec{t}) \). If the relative frequency closely +estimate \( p(\hat{t}) \) by the relative frequency of +\( \widehat{\theta} \). You can think of this as using a histogram +in the place of \( p(\hat{t}) \). If the relative frequency closely resembles \( p(\vec{t}) \), then using numerics, it is straight forward to -estimate all the interesting parameters of \( p(\vec{t}) \) using point +estimate all the interesting parameters of \( p(\hat{t}) \) using point estimators.











    -

    Resampling methods: More Bootstrap background

    +

    Resampling methods: More Bootstrap background

    -In the case that \( \widehat{\vec{\theta}} \) has +In the case that \( \widehat{\theta} \) has more than one component, and the components are independent, use the same estimator on each component separately. If the probability density function of \( X_i \), \( p(x) \), had been known, then it would have @@ -2829,18 +2807,18 @@ been straight forward to do this by:

    1. Drawing lots of numbers from \( p(x) \), suppose we call one such set of numbers \( (X_1^*, X_2^*, \cdots, X_n^*) \).
      2. -
    2. Then using these numbers, we could compute a replica of \( \widehat{\vec{\theta}} \) called \( \widehat{\vec{\theta}}^* \).
      3. +
    3. Then using these numbers, we could compute a replica of \( \widehat{\theta} \) called \( \widehat{\theta}^* \).
    By repeated use of (1) and (2), many -estimates of \( \widehat{\vec{\theta}} \) could have been obtained. The -idea is to use the relative frequency of \( \widehat{\vec{\theta}}^* \) -(think of a histogram) as an estimate of \( p(\vec{t}) \). +estimates of \( \widehat{\theta} \) could have been obtained. The +idea is to use the relative frequency of \( \widehat{\theta}^* \) +(think of a histogram) as an estimate of \( p(\hat{t}) \).











    -

    Resampling methods: Bootstrap approach

    +

    Resampling methods: Bootstrap approach

    But @@ -2856,44 +2834,20 @@ result in some asymptotic sense? The answer is yes. Instead of generating the histogram for the relative frequency of the observation \( X_i \), just draw the values \( (X_1^*,X_2^*,\cdots,X_n^*) \) with replacement from the vector -\( \vec{X} \). +\( \hat{X} \).











    -

    Resampling methods: Bootstrap algorithm

    - -

    - - -

            
-def boot(data, statistic, R):
-    t = zeros(R); n = len(data); inds = arange(n); t0 = time()
-    for i in range(R):
-        t[i] = statistic(data[randint(0,n,n)])
-        return t
-# define a function which returns your chosen estimator theta-hat
-def stat(data):
-    theta-hat = mean(data)
-    return theta-hat
-
-t = boot(X, stat, 2**9)
-
    -

    -Consider first the function boot(). In the for loop, this function repeatedly estimates the function called statistic() under the resampled data in data(randint(0,n,n)). The function statistic() is passed as an argument to boot(). The array t is eventually returned, which contains all the estimates \( \widehat{\vec{\theta}} \), and can be plotted or analysed in other ways, such as by calling std(t) from numpy to estimate the standard error of \( \widehat{\vec{\theta}} \). The function std(t) is just the estimator \( \widehat{\sigma}^2 \). - -

    -









    - -

    Resampling methods: Bootstrap steps

    +

    Resampling methods: Bootstrap steps

    The independent bootstrap works like this:

      -
    1. Draw with replacement \( n \) numbers for the observed variables \( \vec{x} = (x_1,x_2,\cdots,x_n) \).
      2. -
    2. Define a vector \( \vec{x}^* \) containing the values which were drawn from \( \vec{x} \).
      3. -
    3. Using the vector \( \vec{x}^* \) compute \( \widehat{\theta}^* \) by evaluating \( \widehat \theta \) under the observations \( \vec{x}^* \).
      4. +
    4. Draw with replacement \( n \) numbers for the observed variables \( \hat{x} = (x_1,x_2,\cdots,x_n) \).
      5. +
    5. Define a vector \( \hat{x}^* \) containing the values which were drawn from \( \hat{x} \).
      6. +
    6. Using the vector \( \hat{x}^* \) compute \( \widehat{\theta}^* \) by evaluating \( \widehat \theta \) under the observations \( \hat{x}^* \).
    7. Repeat this process \( k \) times.
    @@ -2902,7 +2856,7 @@ When you are done, you can draw a histogram of the relative frequency of \( \wid











    -

    Resampling methods: Blocking

    +

    Resampling methods: Blocking

    The blocking method was made popular by Flyvbjerg and Pedersen (1989) @@ -2915,7 +2869,7 @@ Assume \( n = 2^d \) for some integer \( d>1 \) and \( X_1,X_2,\cdots, X_n \) is Moreover, assume that the time series is asymptotically uncorrelated. We switch to vector notation by arranging \( X_1,X_2,\cdots,X_n \) in an \( n \)-tuple. Define: $$ \begin{align*} -\vec{X} = (X_1,X_2,\cdots,X_n). +\hat{X} = (X_1,X_2,\cdots,X_n). \end{align*} $$ @@ -2928,7 +2882,7 @@ moreover, it becomes more accurate the larger \( n \) is.











    -

    Blocking Transformations

    +

    Blocking Transformations

    We now define blocking transformations. The idea is to take the mean of subsequent pair of elements from \( \vec{X} \) and form a new vector @@ -2965,7 +2919,9 @@ elements of \( \vec{X}_i \) and let \( n_i \) be the number of elements of











    -

    Blocking Transformations

    +

    Blocking Transformations

    + +

    Using the definition of the blocking transformation and the distributive property of the covariance, it is clear that since \( h =|i-j| \) @@ -2987,7 +2943,7 @@ The quantity \( \vec{X} \) is asymptotic uncorrelated by assumption, \( \vec{X}_











    -

    Blocking Transformations, getting there

    +

    Blocking Transformations, getting there

    We have $$ \begin{align} @@ -3009,7 +2965,7 @@ We can show that \( V(\overline{X}_i) = V(\overline{X}_j) \) for all \( 0 \leq i











    -

    Blocking Transformations, final expressions

    +

    Blocking Transformations, final expressions

    We can then wrap up @@ -3040,7 +2996,7 @@ It means we can apply blocking transformations until











    -

    Code examples for Blocking, Jackknife and bootstrap

    +

    Code examples for Blocking, Jackknife and bootstrap

    @@ -3270,6 +3226,344 @@ dataAnalysis.plotAll() dataAnalysis.printOutput()

    +









    + +

    The bias-variance tradeoff

    +We begin with an unknown function \( y=f(x) \) and fix a \emph{hypothesis set} + \( \mathcal{H} \) consisting of all functions we are willing to consider, + defined also on the domain of \( f \). This set may be uncountably + infinite (e.g. if there are real-valued parameters to fit). +The + choice of which functions to include in \( \mathcal{H} \) usually depends + on our intuition about the problem of interest. The function \( f(x) \) + produces a set of pairs \( (x_i,y_i) \), \( i=1\dots N \), which serve as the + observable data. Our goal is to select a function from the hypothesis + set \( h\in\mathcal{H} \) which approximates \( f(x) \) as best as possible, + namely, we would like to find \( h\in\mathcal{H} \) such that \( h\approx + f \) in some strict mathematical sense which we specify below. If this + is possible, we say that we \emph{learned} \( f(x) \). But if the + function \( f(x) \) can, in principle, take any value on + \emph{unobserved} inputs, how is it possible to learn in any + meaningful sense? + +

    + + +

    Training and testing data

    + +

    +We will discuss the bias-variance tradeoff in the context of continuous predictions such as regression. However, many of the intuitions and ideas discussed here also carry over to classification tasks. Consider a dataset \( \mathcal{L} \) consisting of the data \( \mathbf{X}_\mathcal{L}=\{(y_j, \boldsymbol{x}_j), j=1\ldots N\} \). Let us assume that the true data is generated from a noisy model +$$ +y=f(\boldsymbol{x}) + \epsilon +$$ + +where \( \epsilon \) is normally distributed with mean zero and standard deviation \( \sigma_\epsilon \). + +

    +









    + +

    Procedure to find a predictor

    + +

    +We have a statistical procedure (e.g. least-squares regression) for +forming a predictor \( \hat{g}_{\mathcal{L}}(\boldsymbol{x}) \) that gives the +prediction of our model for a new data point \( \boldsymbol{x} \). This estimator +is chosen by minimizing a cost function which we take to be the +squared error + +$$ + \mathcal{C}( \boldsymbol{X}, \hat{g}(\boldsymbol{x})) = \sum_i (y_i - \hat{g}_\mathcal{L}(\boldsymbol{x}_i))^2. +$$ + +

    +









    + +

    What we want

    + +

    +We are interested in the generalization error on all data drawn from +the true model, not just the error on the particular training dataset +\( \mathcal{L} \) that we have in hand. This is just the expectation of +the cost function over many different data sets +\( \{\mathcal{L}_j\} \). Denote this expectation value by +\( E_{\mathcal{L}} \). In other words, we can view \( \hat{g}_{\mathcal{L}} \) +as a stochastic functional that depends on the dataset \( \mathcal{L} \) +and we can think of \( E_{\mathcal{L}} \) as the expected value of the +functional if we drew an infinite number of datasets \( \{\mathcal{L}_1, +\mathcal{L}_2, \ldots \} \). + +

    +









    + +

    The expected generalization error

    + +

    +We would also like to average over different instances of the +"noise" \( \epsilon \) and we denote the expectation value over the +noise by \( E_\epsilon \). Thus, we can decompose the expected +generalization error as + +$$ +\begin{align} +E_\mathcal{L, \epsilon}[\mathcal{C}( \boldsymbol{X}, \hat{g}(\boldsymbol{x})) ]&= E_\mathcal{L,\epsilon}\left[ \sum_i ({y}_i - \hat{g}_\mathcal{L}(\boldsymbol{x}_i))^2 \right] \nonumber \\ + &= E_\mathcal{L, \epsilon}\left[ \sum_{i}({y}_i -f(\boldsymbol{x}_i) +f(\boldsymbol{x}_i)- \hat{g}_\mathcal{L}(\boldsymbol{x}_i))^2\right] \nonumber \\ + &= \sum_i E_\epsilon[ ({y}_i -f(\boldsymbol{x}_i))^2 ]+ E_\mathcal{L, \epsilon}[(f(\boldsymbol{x}_i)- \hat{g}_\mathcal{L}(\boldsymbol{x}_i))^2] + 2E_\epsilon[{y}_i -f(\boldsymbol{x}_i)]E_\mathcal{L}[f(\boldsymbol{x}_i)- \hat{g}_\mathcal{L}(\boldsymbol{x}_i)] \nonumber \\ + &=\sum_i \sigma_\epsilon^2 + E_\mathcal{L}[(f(\boldsymbol{x}_i)- \hat{g}_\mathcal{L}(\boldsymbol{x}_i))^2], +\label{_auto17} +\end{align} +$$ + +

    +where in the last line we used the fact that our noise has zero mean +and variance \( \sigma_\epsilon^2 \) and the sum over \( i \) applies to all +terms. + +

    +









    + +

    Elaborating a little bit more

    + +

    +It is also helpful to further decompose the second term as +follows: + +$$ +\begin{align} +E_\mathcal{L}[(f(\boldsymbol{x}_i)- \hat{g}_\mathcal{L}(\boldsymbol{x}_i))^2] &=E_\mathcal{L}[(f(\mathbf{x}_i)-E_\mathcal{L}[\hat{g}_\mathcal{L}(\boldsymbol{x}_i)]+ E_\mathcal{L}[\hat{g}_\mathcal{L}(\boldsymbol{x}_i)]- \hat{g}_\mathcal{L}(\boldsymbol{x}_i))^2] \nonumber \\ +&=E_\mathcal{L}[(f(\boldsymbol{x}_i)-E_\mathcal{L}[\hat{g}_\mathcal{L}(\boldsymbol{x}_i)])^2] + E_\mathcal{L}[( \hat{g}_\mathcal{L}(\boldsymbol{x}_i)-E_\mathcal{L}[\hat{g}_\mathcal{L}(\boldsymbol{x}_i)])^2] \nonumber \\ +&+2E_\mathcal{L}[(f(\boldsymbol{x}_i)-E_\mathcal{L}[\hat{g}_\mathcal{L}(\boldsymbol{x}_i)])( \hat{g}_\mathcal{L}(\boldsymbol{x}_i)-E_\mathcal{L}[\hat{g}_\mathcal{L}(\boldsymbol{x}_i)])] \nonumber \\ +&=(f(\boldsymbol{x}_i)-E_\mathcal{L}[\hat{g}_\mathcal{L}(\boldsymbol{x}_i)])^2+E_\mathcal{L}[( \hat{g}_\mathcal{L}(\boldsymbol{x}_i)-E_\mathcal{L}[\hat{g}_\mathcal{L}(\boldsymbol{x}_i)])^2]. +\label{_auto18} +\end{align} +$$ + +

    +









    + +

    The bias

    +The first term is called the bias +$$ +Bias^2= \sum_i (f(\boldsymbol{x}_i)-E_\mathcal{L}[\hat{g}_\mathcal{L}(\boldsymbol{x}_i)])^2 +$$ + +and measures the deviation of the expectation value of our estimator (i.e. the asymptotic value of our estimator in the infinite data limit) from the true value. + +

    +









    + +

    The variance

    +The second term is called the variance +$$ +Var=\sum_i E_\mathcal{L}[( \hat{g}_\mathcal{L}(\boldsymbol{x}_i)-E_\mathcal{L}[\hat{g}_\mathcal{L}(\boldsymbol{x}_i)])^2], +$$ + +

    +and measures how much our estimator fluctuates due to finite-sample effects. Combining these expressions, we see that the expected out-of-sample error of our model can be decomposed as +$$ +E_\mathrm{out}=E_\mathcal{L, \epsilon}[\mathcal{C}( \boldsymbol{X}, \hat{g}(\boldsymbol{x})) ] = Bias^2 + Var + Noise. +$$ + +

    +The bias-variance tradeoff summarizes the fundamental tension in +machine learning, particularly supervised learning, between the +complexity of a model and the amount of training data needed to train +it. Since data is often limited, in practice it is often useful to +use a less-complex model with higher bias – a model whose asymptotic +performance is worse than another model – because it is easier to +train and less sensitive to sampling noise arising from having a +finite-sized training dataset (smaller variance). + +

    + + +

    Summing up

    + +

    +The above equations tell us that in +order to minimize the expected test error, we need to select a +statistical learning method that simultaneously achieves low variance +and low bias. Note that variance is inherently a nonnegative quantity, +and squared bias is also nonnegative. Hence, we see that the expected +test MSE can never lie below \( Var(\epsilon) \), the irreducible error. + +

    +What do we mean by the variance and bias of a statistical learning +method? The variance refers to the amount by which our model would change if we +estimated it using a different training data set. Since the training +data are used to fit the statistical learning method, different +training data sets will result in a different estimate. But ideally the +estimate for our model should not vary too much between training +sets. However, if a method has high variance then small changes in +the training data can result in large changes in the model. In general, more +flexible statistical methods have higher variance. + +

    + + +

    Logistic Regression

    + +

    +So far we have focused on learning from datasets for which there is a +continuous output. In linear regression we have been +concerned with learning the coefficients of a polynomial to predict +the response of a continuous variable \( y_i \) on unseen data based on +its independent variables \( {\bf x}_i \). + +

    +Classification problems, +however, are concerned with outcomes taking the form of discrete +variables (i.e. categories). For example, we may want to detect if +there's a cat or a dog in an image. Or given a specific system, +we'd like to identify its state, say whether it is an ordered or disordered system (typical situation in solid state physics). +(e.g. ordered/disordered). + +

    +Logistic regression deals with binary, dichotomous outcomes (e.g. True or +False, Success or Failure, etc.). It is worth noting that logistic +regression is also commonly used in modern supervised Deep Learning +models, as we will see later. + +

    + + +

    Basics

    + +

    +We consider the case where the dependent variables \( y_i\in\mathbb{Z} \) +are discrete and only take values from \( m=0,\dots,M-1 \) (i.e. \( M \) +classes). + +

    +The goal is to predict the +output classes from the design matrix \( X\in\mathbb{R}^{n\times p} \) +made of \( n \) samples, each of which bears \( p \) features. Of cours e the +primary goal is to identify the classes to which new unseen samples +belong. + +

    +









    + +

    Linear classifier

    + +

    +Let us start by considering a slightly simpler classifier: a linear classifier that categorizes examples using a weighted linear-combination of the features and an additive offset +$$ +\begin{equation} +s_i = \boldsymbol{x}_i^T\boldsymbol{w} + b_0 \equiv \mathbf{x}_i^T\mathbf{w}, +\label{_auto19} +\end{equation} +$$ + +where we use the short-hand notation +\( \mathbf{x}_i = (1,\boldsymbol{x}_i) \) and \( \mathbf{w}_i = (b_0,\boldsymbol{w}_i) \). + +

    +









    + +

    Some selected properties

    + +

    +This function takes values on the entire real axis. In the case of logistic regression, however, the labels \( y_i \) are discrete variables. One simple way to get a discrete output is to have sign functions that map the output of a linear regressor to \( \{0,1\} \), \( f(s_i)= \) sign$(s_i) = 1$ if \( s_i\ge 0 \) and 0 if otherwise. Indeed, this is commonly known as the "perceptron" in the machine learning literature. This model is extremely simple, and it is favorable in many cases (e.g. noisy data) to have a ``soft" classifier that outputs the probability of a given category. For example, given \( \mathbf{x}_i \), the classifier outputs the probability of being in category \( m \). One such function is the logistic (or sigmoid) function: +$$ +\begin{equation} +f(s) = \frac{1}{1+\mathrm e^{-s}}. +\label{eq:log_fun} +\end{equation} +$$ + +Note that \( 1-f(s)= f(-s) \), which will be useful shortly. + +

    +









    + +

    The cross-entropy as a cost function for logistic regression

    + +

    +The perceptron is an example of a ``hard classification": each datapoint is deterministically assigned to a category (i.e \( y_i=0 \) or \( y_i=1 \)). In many cases, it is favorable to have a "soft" classifier that outputs the probability of a given category rather than a single value. For example, given \( \mathbf{x}_i \), the classifier outputs the probability of being in category \( m \). +Logistic regression is the most canonical example of a soft classifier. In logistic regression, the probability that a data point \( \boldsymbol{x}_i \) belongs to a category \( y_i=\{0,1\} \) is is given by +$$ +\begin{eqnarray} +P(y_i=1|\boldsymbol{x}_i,\boldsymbol{\theta)} &=& \frac{1}{1+\mathrm{e}^{-\mathbf{x}^T_i\mathbf{w}}},\nonumber\\ +P(y_i=0|\boldsymbol{x}_i,\boldsymbol{\theta)} &=& 1 - P(y_i=1|\boldsymbol{x}_i,\boldsymbol{\theta)}, +\end{eqnarray} +$$ + +where \( \boldsymbol{\theta}=\mathbf{w} \) are the weights we wish to learn from the data. + +

    +Notice that in terms of the logistic function, we can write +$$ +P(y_i=1) =f(\mathbf{x}_i^T\mathbf{w})=1-P(y_i=0). +$$ + +

    + + +

    Maximum likelihood

    + +

    +We now define the cost function for logistic regression using Maximum +Likelihood Estimation (MLE). Recall, that in MLE we choose parameters +to maximize the probability of seeing the observed data. Consider a +dataset \( \mathcal{D}=\{(y_i,\boldsymbol{x}_i)\} \) with binary labels +\( y_i\in\{0,1\} \) where the data points are drawn independently. The +likelihood of the seeing the data under our model is just: +$$ +\begin{align} +P(\mathcal{D}|\mathbf{w})& = \prod_{i=1}^n \left[f(\mathbf{x}_i^T\mathbf{w})\right]^{y_i}\left[1-f(\mathbf{x}_i^T\mathbf{w})\right]^{1-y_i}\nonumber \\ +\label{_auto20} +\end{align} +$$ + +from which we can readily compute the log-likelihood: +$$ +\begin{equation} +l(\mathbf{w}) = \sum_{i=1}^n y_i\log f(\mathbf{x}_i^T\mathbf{w}) + (1-y_i)\log\left[1-f(\mathbf{x}_i^T\mathbf{w})\right]. +\label{_auto21} +\end{equation} +$$ + +

    +









    +The maximum likelihood estimator is defined as the set of parameters that maximize the log-likelihood where we maximize with respect to \( \theta \) +$$ +\hat{\mathbf{w}} = \sum_{i=1}^n y_i\log f(\mathbf{x}_i^T\mathbf{w}) + (1-y_i)\log\left[1-f(\mathbf{x}_i^T\mathbf{w})\right]. +$$ + +Since the cost (error) function is just the negative log-likelihood, for logistic regression we have that +$$ +\begin{eqnarray} +\mathcal{C}(\mathbf{w}) &=& - l(\mathbf{w}) \\ +&=& \sum_{i=1}^n -y_i\log f(\mathbf{x}_i^T\mathbf{w}) - (1-y_i)\log\left[1-f(\mathbf{x}_i^T\mathbf{w})\right].\nonumber +\end{eqnarray} +$$ + +This equation is known in statistics as the \emph{cross entropy}. Finally, we note that just as in linear regression, +in practice we usually supplement the cross-entropy with additional regularization terms, usually \( L_1 \) and \( L_2 \) regularization as we did for Ridge and Lasso regression. + +

    +









    + +

    Minimizing the cross entropy

    + +

    +The cross entropy is a convex function of the weights \( \mathbf{w} \) and, +therefore, any local minimizer is a global minimizer. Minimizing this +cost function leads to the following equation + +$$ +\begin{equation} +\boldsymbol{0}=\boldsymbol{\nabla} \mathcal{C}(\mathbf{w}) = \sum_{i=1}^n\left[f(\mathbf{x}_i^T\mathbf{w})-y_i\right]\mathbf{x}_i, +\label{_auto22} +\end{equation} +$$ + +

    +where we made use of the logistic function identity \( \partial_z f(z) = +f(z)[1-f(z)] \). This equation defines a transcendental equation for +\( \mathbf{w} \), the solution of which, unlike linear regression, cannot +be written in a closed form. +Here we need gradient descent methods! diff --git a/doc/pub/Regression/ipynb/Regression.ipynb b/doc/pub/Regression/ipynb/Regression.ipynb index 65af1be9d..1615fc1bc 100644 --- a/doc/pub/Regression/ipynb/Regression.ipynb +++ b/doc/pub/Regression/ipynb/Regression.ipynb @@ -10,7 +10,7 @@ " \n", "**Morten Hjorth-Jensen**, Department of Physics, University of Oslo and Department of Physics and Astronomy and National Superconducting Cyclotron Laboratory, Michigan State University\n", "\n", - "Date: **Sep 13, 2018**\n", + "Date: **Sep 14, 2018**\n", "\n", "Copyright 1999-2018, Morten Hjorth-Jensen. Released under CC Attribution-NonCommercial 4.0 license\n", "\n", @@ -3520,9 +3520,9 @@ "\n", "## Resampling methods: Jackknife\n", "\n", - "The Jackknife works by making many replicas of the estimator $\\widehat{\\vec{\\theta}}$. \n", - "The jackknife is a resampling method, we explained that this happens by scrambling the data in some way. When using the jackknife, this is done by systematically leaving out one observation from the vector of observed values $\\vec{X} = (X_1,X_2,\\cdots,X_n)$. \n", - "Let $\\vec{X}_i$ denote the vector" + "The Jackknife works by making many replicas of the estimator $\\widehat{\\theta}$. \n", + "The jackknife is a resampling method, we explained that this happens by scrambling the data in some way. When using the jackknife, this is done by systematically leaving out one observation from the vector of observed values $\\hat{x} = (x_1,x_2,\\cdots,X_n)$. \n", + "Let $\\hat{x}_i$ denote the vector" ] }, { @@ -3530,7 +3530,7 @@ "metadata": {}, "source": [ "$$\n", - "\\vec{X}_i = (X_1,X_2,\\cdots,X_{i-1},X_{i+1},\\cdots,X_n),\n", + "\\hat{x}_i = (x_1,x_2,\\cdots,x_{i-1},x_{i+1},\\cdots,x_n),\n", "$$" ] }, @@ -3538,16 +3538,16 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "which equals the vector $\\vec{X}$ with the exception that observation\n", + "which equals the vector $\\hat{x}$ with the exception that observation\n", "number $i$ is left out. Using this notation, define\n", - "$\\widehat{\\vec{\\theta}}_i$ to be the estimator\n", - "$\\widehat{\\vec{\\theta}}$ computed using $\\vec{X}_i$. \n", + "$\\widehat{\\theta}_i$ to be the estimator\n", + "$\\widehat{\\theta}$ computed using $\\vec{X}_i$. \n", "\n", "## Resampling methods: Jackknife estimator\n", "\n", "To get an estimate for the bias and\n", - "standard error of $\\widehat{\\vec{\\theta}}$, use the following\n", - "estimators for each component of $\\widehat{\\vec{\\theta}}$" + "standard error of $\\widehat{\\theta}$, use the following\n", + "estimators for each component of $\\widehat{\\theta}$" ] }, { @@ -3563,50 +3563,6 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "## Resampling methods: Jackknife sample code\n", - "\n", - "Sample code for the Jackknife method" - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "def jack(data, stat):\n", - " n = len(data); t = zeros(n); inds = arange(n); t0 = time()\n", - " # 'jackknifing' by leaving out an observation for each i\n", - " for i in range(n):\n", - " t[i] = stat(delete(data,i) )\n", - " return t\n", - "# define a function which returns your chosen estimator theta-hat\n", - "def stat(data):\n", - " theta-hat = mean(data)\n", - " return theta-hat\n", - "# Return the Jackknife sample\n", - "t = jack(X, stat)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Consider first the function **jack()**. This function repeatedly\n", - "estimates the function called **statistic()** under the resampled\n", - "data by systematically leaving out one observation from the data. The\n", - "function **stat()** is passed as an argument to\n", - "**jack()**. The array **t** is eventually returned, which\n", - "contains all the estimates $\\widehat{\\vec{\\theta}}$, and can be\n", - "plotted or analysed in other ways, such as by calling **std(t)**\n", - "from **numpy** to estimate the standard error of\n", - "$\\widehat{\\vec{\\theta}}$. The function **std(t)** is just the\n", - "estimator $\\widehat{\\sigma}^2$.\n", - "\n", - "\n", - "\n", "## Resampling methods: Bootstrap\n", "Bootstrapping is a nonparametric approach to statistical inference\n", "that substitutes computation for more traditional distributional\n", @@ -3625,32 +3581,32 @@ "\n", "## Resampling methods: Bootstrap background\n", "\n", - "Since $\\widehat{\\vec{\\theta}} = \\widehat{\\vec{\\theta}}(\\vec{X})$ is a function of random variables,\n", - "$\\widehat{\\vec{\\theta}}$ itself must be a random variable. Thus it has\n", + "Since $\\widehat{\\theta} = \\widehat{\\theta}(\\hat{X})$ is a function of random variables,\n", + "$\\widehat{\\theta}$ itself must be a random variable. Thus it has\n", "a pdf, call this function $p(\\vec{t})$. The aim of the bootstrap is to\n", - "estimate $p(\\vec{t})$ by the relative frequency of\n", - "$\\widehat{\\vec{\\theta}}$. You can think of this as using a histogram\n", - "in the place of $p(\\vec{t})$. If the relative frequency closely\n", + "estimate $p(\\hat{t})$ by the relative frequency of\n", + "$\\widehat{\\theta}$. You can think of this as using a histogram\n", + "in the place of $p(\\hat{t})$. If the relative frequency closely\n", "resembles $p(\\vec{t})$, then using numerics, it is straight forward to\n", - "estimate all the interesting parameters of $p(\\vec{t})$ using point\n", + "estimate all the interesting parameters of $p(\\hat{t})$ using point\n", "estimators. \n", "\n", "\n", "## Resampling methods: More Bootstrap background\n", "\n", - "In the case that $\\widehat{\\vec{\\theta}}$ has\n", + "In the case that $\\widehat{\\theta}$ has\n", "more than one component, and the components are independent, use the\n", "same estimator on each component separately. If the probability\n", "density function of $X_i$, $p(x)$, had been known, then it would have\n", "been straight forward to do this by: \n", "1. Drawing lots of numbers from $p(x)$, suppose we call one such set of numbers $(X_1^*, X_2^*, \\cdots, X_n^*)$. \n", "\n", - "2. Then using these numbers, we could compute a replica of $\\widehat{\\vec{\\theta}}$ called $\\widehat{\\vec{\\theta}}^*$. \n", + "2. Then using these numbers, we could compute a replica of $\\widehat{\\theta}$ called $\\widehat{\\theta}^*$. \n", "\n", "By repeated use of (1) and (2), many\n", - "estimates of $\\widehat{\\vec{\\theta}}$ could have been obtained. The\n", - "idea is to use the relative frequency of $\\widehat{\\vec{\\theta}}^*$\n", - "(think of a histogram) as an estimate of $p(\\vec{t})$.\n", + "estimates of $\\widehat{\\theta}$ could have been obtained. The\n", + "idea is to use the relative frequency of $\\widehat{\\theta}^*$\n", + "(think of a histogram) as an estimate of $p(\\hat{t})$.\n", "\n", "## Resampling methods: Bootstrap approach\n", "\n", @@ -3667,51 +3623,17 @@ "Instead of generating the histogram for the relative\n", "frequency of the observation $X_i$, just draw the values\n", "$(X_1^*,X_2^*,\\cdots,X_n^*)$ with replacement from the vector\n", - "$\\vec{X}$. \n", - "\n", - "\n", - "## Resampling methods: Bootstrap algorithm" - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - " \n", - "def boot(data, statistic, R):\n", - " t = zeros(R); n = len(data); inds = arange(n); t0 = time()\n", - " for i in range(R):\n", - " t[i] = statistic(data[randint(0,n,n)])\n", - " return t\n", - "# define a function which returns your chosen estimator theta-hat\n", - "def stat(data):\n", - " theta-hat = mean(data)\n", - " return theta-hat\n", - "\n", - "t = boot(X, stat, 2**9)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Consider first the function **boot()**. In the **for** loop, this function repeatedly estimates the function called **statistic()** under the resampled data in **data(randint(0,n,n))**. The function **statistic()** is passed as an argument to **boot()**. The array **t** is eventually returned, which contains all the estimates $\\widehat{\\vec{\\theta}}$, and can be plotted or analysed in other ways, such as by calling **std(t)** from **numpy** to estimate the standard error of $\\widehat{\\vec{\\theta}}$. The function **std(t)** is just the estimator $\\widehat{\\sigma}^2$.\n", - "\n", - "\n", + "$\\hat{X}$. \n", "\n", "## Resampling methods: Bootstrap steps\n", "\n", "The independent bootstrap works like this: \n", "\n", - "1. Draw with replacement $n$ numbers for the observed variables $\\vec{x} = (x_1,x_2,\\cdots,x_n)$. \n", + "1. Draw with replacement $n$ numbers for the observed variables $\\hat{x} = (x_1,x_2,\\cdots,x_n)$. \n", "\n", - "2. Define a vector $\\vec{x}^*$ containing the values which were drawn from $\\vec{x}$. \n", + "2. Define a vector $\\hat{x}^*$ containing the values which were drawn from $\\hat{x}$. \n", "\n", - "3. Using the vector $\\vec{x}^*$ compute $\\widehat{\\theta}^*$ by evaluating $\\widehat \\theta$ under the observations $\\vec{x}^*$. \n", + "3. Using the vector $\\hat{x}^*$ compute $\\widehat{\\theta}^*$ by evaluating $\\widehat \\theta$ under the observations $\\hat{x}^*$. \n", "\n", "4. Repeat this process $k$ times. \n", "\n", @@ -3736,7 +3658,7 @@ "source": [ "$$\n", "\\begin{align*}\n", - "\\vec{X} = (X_1,X_2,\\cdots,X_n).\n", + "\\hat{X} = (X_1,X_2,\\cdots,X_n).\n", "\\end{align*}\n", "$$" ] @@ -3804,6 +3726,7 @@ "$\\vec{X}_i$. It follows by induction that $n_i = n/2^i$. \n", "\n", "## Blocking Transformations\n", + "\n", "Using the\n", "definition of the blocking transformation and the distributive\n", "property of the covariance, it is clear that since $h =|i-j|$\n", @@ -3979,7 +3902,7 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": 13, "metadata": { "collapsed": false }, @@ -4209,6 +4132,538 @@ "# Print Some Output\n", "dataAnalysis.printOutput()" ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## The bias-variance tradeoff\n", + "We begin with an unknown function $y=f(x)$ and fix a \\emph{hypothesis set}\n", + " $\\mathcal{H}$ consisting of all functions we are willing to consider,\n", + " defined also on the domain of $f$. This set may be uncountably\n", + " infinite (e.g. if there are real-valued parameters to fit). \n", + "The\n", + " choice of which functions to include in $\\mathcal{H}$ usually depends\n", + " on our intuition about the problem of interest. The function $f(x)$\n", + " produces a set of pairs $(x_i,y_i)$, $i=1\\dots N$, which serve as the\n", + " observable data. Our goal is to select a function from the hypothesis\n", + " set $h\\in\\mathcal{H}$ which approximates $f(x)$ as best as possible,\n", + " namely, we would like to find $h\\in\\mathcal{H}$ such that $h\\approx\n", + " f$ in some strict mathematical sense which we specify below. If this\n", + " is possible, we say that we \\emph{learned} $f(x)$. But if the\n", + " function $f(x)$ can, in principle, take any value on\n", + " \\emph{unobserved} inputs, how is it possible to learn in any\n", + " meaningful sense?\n", + "\n", + "\n", + "## Training and testing data\n", + "\n", + "We will discuss the bias-variance tradeoff in the context of continuous predictions such as regression. However, many of the intuitions and ideas discussed here also carry over to classification tasks. Consider a dataset $\\mathcal{L}$ consisting of the data $\\mathbf{X}_\\mathcal{L}=\\{(y_j, \\boldsymbol{x}_j), j=1\\ldots N\\}$. Let us assume that the true data is generated from a noisy model" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "$$\n", + "y=f(\\boldsymbol{x}) + \\epsilon\n", + "$$" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "where $\\epsilon$ is normally distributed with mean zero and standard deviation $\\sigma_\\epsilon$.\n", + "\n", + "## Procedure to find a predictor\n", + "\n", + "We have a statistical procedure (e.g. least-squares regression) for\n", + "forming a predictor $\\hat{g}_{\\mathcal{L}}(\\boldsymbol{x})$ that gives the\n", + "prediction of our model for a new data point $\\boldsymbol{x}$. This estimator\n", + "is chosen by minimizing a cost function which we take to be the\n", + "squared error" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "$$\n", + "\\mathcal{C}( \\boldsymbol{X}, \\hat{g}(\\boldsymbol{x})) = \\sum_i (y_i - \\hat{g}_\\mathcal{L}(\\boldsymbol{x}_i))^2.\n", + "$$" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## What we want\n", + "\n", + "We are interested in the generalization error on all data drawn from\n", + "the true model, not just the error on the particular training dataset\n", + "$\\mathcal{L}$ that we have in hand. This is just the expectation of\n", + "the cost function over many different data sets\n", + "$\\{\\mathcal{L}_j\\}$. Denote this expectation value by\n", + "$E_{\\mathcal{L}}$. In other words, we can view $\\hat{g}_{\\mathcal{L}}$\n", + "as a stochastic functional that depends on the dataset $\\mathcal{L}$\n", + "and we can think of $E_{\\mathcal{L}}$ as the expected value of the\n", + "functional if we drew an infinite number of datasets $\\{\\mathcal{L}_1,\n", + "\\mathcal{L}_2, \\ldots \\}$.\n", + "\n", + "\n", + "## The expected generalization error\n", + "\n", + "We would also like to average over different instances of the\n", + "\"noise\" $\\epsilon$ and we denote the expectation value over the\n", + "noise by $E_\\epsilon$. Thus, we can decompose the expected\n", + "generalization error as" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "$$\n", + "E_\\mathcal{L, \\epsilon}[\\mathcal{C}( \\boldsymbol{X}, \\hat{g}(\\boldsymbol{x})) ]= E_\\mathcal{L,\\epsilon}\\left[ \\sum_i ({y}_i - \\hat{g}_\\mathcal{L}(\\boldsymbol{x}_i))^2 \\right] \\nonumber\n", + "$$" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "$$\n", + "= E_\\mathcal{L, \\epsilon}\\left[ \\sum_{i}({y}_i -f(\\boldsymbol{x}_i) +f(\\boldsymbol{x}_i)- \\hat{g}_\\mathcal{L}(\\boldsymbol{x}_i))^2\\right] \\nonumber\n", + "$$" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "$$\n", + "= \\sum_i E_\\epsilon[ ({y}_i -f(\\boldsymbol{x}_i))^2 ]+ E_\\mathcal{L, \\epsilon}[(f(\\boldsymbol{x}_i)- \\hat{g}_\\mathcal{L}(\\boldsymbol{x}_i))^2] + 2E_\\epsilon[{y}_i -f(\\boldsymbol{x}_i)]E_\\mathcal{L}[f(\\boldsymbol{x}_i)- \\hat{g}_\\mathcal{L}(\\boldsymbol{x}_i)] \\nonumber\n", + "$$" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "

    \n", + "\n", + "$$\n", + "\\begin{equation} \n", + " =\\sum_i \\sigma_\\epsilon^2 + E_\\mathcal{L}[(f(\\boldsymbol{x}_i)- \\hat{g}_\\mathcal{L}(\\boldsymbol{x}_i))^2],\n", + "\\label{_auto17} \\tag{28}\n", + "\\end{equation}\n", + "$$" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "where in the last line we used the fact that our noise has zero mean\n", + "and variance $\\sigma_\\epsilon^2$ and the sum over $i$ applies to all\n", + "terms. \n", + "\n", + "## Elaborating a little bit more\n", + "\n", + "It is also helpful to further decompose the second term as\n", + "follows:" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "$$\n", + "E_\\mathcal{L}[(f(\\boldsymbol{x}_i)- \\hat{g}_\\mathcal{L}(\\boldsymbol{x}_i))^2] =E_\\mathcal{L}[(f(\\mathbf{x}_i)-E_\\mathcal{L}[\\hat{g}_\\mathcal{L}(\\boldsymbol{x}_i)]+ E_\\mathcal{L}[\\hat{g}_\\mathcal{L}(\\boldsymbol{x}_i)]- \\hat{g}_\\mathcal{L}(\\boldsymbol{x}_i))^2] \\nonumber\n", + "$$" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "$$\n", + "=E_\\mathcal{L}[(f(\\boldsymbol{x}_i)-E_\\mathcal{L}[\\hat{g}_\\mathcal{L}(\\boldsymbol{x}_i)])^2] + E_\\mathcal{L}[( \\hat{g}_\\mathcal{L}(\\boldsymbol{x}_i)-E_\\mathcal{L}[\\hat{g}_\\mathcal{L}(\\boldsymbol{x}_i)])^2] \\nonumber\n", + "$$" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "$$\n", + "+2E_\\mathcal{L}[(f(\\boldsymbol{x}_i)-E_\\mathcal{L}[\\hat{g}_\\mathcal{L}(\\boldsymbol{x}_i)])( \\hat{g}_\\mathcal{L}(\\boldsymbol{x}_i)-E_\\mathcal{L}[\\hat{g}_\\mathcal{L}(\\boldsymbol{x}_i)])] \\nonumber\n", + "$$" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "
    \n", + "\n", + "$$\n", + "\\begin{equation} \n", + "=(f(\\boldsymbol{x}_i)-E_\\mathcal{L}[\\hat{g}_\\mathcal{L}(\\boldsymbol{x}_i)])^2+E_\\mathcal{L}[( \\hat{g}_\\mathcal{L}(\\boldsymbol{x}_i)-E_\\mathcal{L}[\\hat{g}_\\mathcal{L}(\\boldsymbol{x}_i)])^2].\n", + "\\label{_auto18} \\tag{29}\n", + "\\end{equation}\n", + "$$" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## The bias\n", + "The first term is called the bias" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "$$\n", + "Bias^2= \\sum_i (f(\\boldsymbol{x}_i)-E_\\mathcal{L}[\\hat{g}_\\mathcal{L}(\\boldsymbol{x}_i)])^2\n", + "$$" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "and measures the deviation of the expectation value of our estimator (i.e. the asymptotic value of our estimator in the infinite data limit) from the true value. \n", + "\n", + "## The variance\n", + "The second term is called the variance" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "$$\n", + "Var=\\sum_i E_\\mathcal{L}[( \\hat{g}_\\mathcal{L}(\\boldsymbol{x}_i)-E_\\mathcal{L}[\\hat{g}_\\mathcal{L}(\\boldsymbol{x}_i)])^2],\n", + "$$" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "and measures how much our estimator fluctuates due to finite-sample effects. Combining these expressions, we see that the expected out-of-sample error of our model can be decomposed as" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "$$\n", + "E_\\mathrm{out}=E_\\mathcal{L, \\epsilon}[\\mathcal{C}( \\boldsymbol{X}, \\hat{g}(\\boldsymbol{x})) ] = Bias^2 + Var + Noise.\n", + "$$" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The bias-variance tradeoff summarizes the fundamental tension in\n", + "machine learning, particularly supervised learning, between the\n", + "complexity of a model and the amount of training data needed to train\n", + "it. Since data is often limited, in practice it is often useful to\n", + "use a less-complex model with higher bias - a model whose asymptotic\n", + "performance is worse than another model - because it is easier to\n", + "train and less sensitive to sampling noise arising from having a\n", + "finite-sized training dataset (smaller variance). \n", + "\n", + "\n", + "## Summing up\n", + "\n", + "The above equations tell us that in\n", + "order to minimize the expected test error, we need to select a\n", + "statistical learning method that simultaneously achieves low variance\n", + "and low bias. Note that variance is inherently a nonnegative quantity,\n", + "and squared bias is also nonnegative. Hence, we see that the expected\n", + "test MSE can never lie below $Var(\\epsilon)$, the irreducible error.\n", + "\n", + "\n", + "What do we mean by the variance and bias of a statistical learning\n", + "method? The variance refers to the amount by which our model would change if we\n", + "estimated it using a different training data set. Since the training\n", + "data are used to fit the statistical learning method, different\n", + "training data sets will result in a different estimate. But ideally the\n", + "estimate for our model should not vary too much between training\n", + "sets. However, if a method has high variance then small changes in\n", + "the training data can result in large changes in the model. In general, more\n", + "flexible statistical methods have higher variance.\n", + "\n", + "\n", + "## Logistic Regression\n", + "\n", + "So far we have focused on learning from datasets for which there is a\n", + "**continuous** output. In linear regression we have been \n", + "concerned with learning the coefficients of a polynomial to predict\n", + "the response of a continuous variable $y_i$ on unseen data based on\n", + "its independent variables ${\\bf x}_i$. \n", + "\n", + "Classification problems,\n", + "however, are concerned with outcomes taking the form of discrete\n", + "variables (i.e. categories). For example, we may want to detect if\n", + "there's a cat or a dog in an image. Or given a specific system,\n", + "we'd like to identify its state, say whether it is an ordered or disordered system (typical situation in solid state physics).\n", + "(e.g. ordered/disordered). \n", + "\n", + "**Logistic regression deals with binary, dichotomous outcomes (e.g. True or\n", + "False, Success or Failure, etc.). It is worth noting that logistic\n", + "regression is also commonly used in modern supervised Deep Learning\n", + "models**, as we will see later.\n", + "\n", + "\n", + "\n", + "## Basics\n", + "\n", + "We consider the case where the dependent variables $y_i\\in\\mathbb{Z}$\n", + "are discrete and only take values from $m=0,\\dots,M-1$ (i.e. $M$\n", + "classes).\n", + "\n", + "The goal is to predict the\n", + "output classes from the design matrix $X\\in\\mathbb{R}^{n\\times p}$\n", + "made of $n$ samples, each of which bears $p$ features. Of cours e the\n", + "primary goal is to identify the classes to which new unseen samples\n", + "belong.\n", + "\n", + "\n", + "## Linear classifier\n", + "\n", + "Let us start by considering a slightly simpler classifier: a linear classifier that categorizes examples using a weighted linear-combination of the features and an additive offset" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "
    \n", + "\n", + "$$\n", + "\\begin{equation}\n", + "s_i = \\boldsymbol{x}_i^T\\boldsymbol{w} + b_0 \\equiv \\mathbf{x}_i^T\\mathbf{w},\n", + "\\label{_auto19} \\tag{30}\n", + "\\end{equation}\n", + "$$" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "where we use the short-hand notation \n", + "$\\mathbf{x}_i = (1,\\boldsymbol{x}_i)$ and $\\mathbf{w}_i = (b_0,\\boldsymbol{w}_i)$. \n", + "\n", + "## Some selected properties\n", + "\n", + "This function takes values on the entire real axis. In the case of logistic regression, however, the labels $y_i$ are discrete variables. One simple way to get a discrete output is to have sign functions that map the output of a linear regressor to $\\{0,1\\}$, $f(s_i)=$ sign$(s_i) = 1$ if $s_i\\ge 0$ and 0 if otherwise. Indeed, this is commonly known as the \"perceptron\" in the machine learning literature. This model is extremely simple, and it is favorable in many cases (e.g. noisy data) to have a ``soft\" classifier that outputs the probability of a given category. For example, given $\\mathbf{x}_i$, the classifier outputs the probability of being in category $m$. One such function is the logistic (or sigmoid) function:" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "
    \n", + "\n", + "$$\n", + "\\begin{equation}\n", + "f(s) = \\frac{1}{1+\\mathrm e^{-s}}.\n", + "\\label{eq:log_fun} \\tag{31}\n", + "\\end{equation}\n", + "$$" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Note that $1-f(s)= f(-s)$, which will be useful shortly. \n", + "\n", + "## The cross-entropy as a cost function for logistic regression\n", + "\n", + "The perceptron is an example of a ``hard classification\": each datapoint is deterministically assigned to a category (i.e $y_i=0$ or $y_i=1$). In many cases, it is favorable to have a \"soft\" classifier that outputs the probability of a given category rather than a single value. For example, given $\\mathbf{x}_i$, the classifier outputs the probability of being in category $m$. \n", + "Logistic regression is the most canonical example of a soft classifier. In logistic regression, the probability that a data point $\\boldsymbol{x}_i$ belongs to a category $y_i=\\{0,1\\}$ is is given by" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "$$\n", + "\\begin{eqnarray}\n", + "P(y_i=1|\\boldsymbol{x}_i,\\boldsymbol{\\theta)} &=& \\frac{1}{1+\\mathrm{e}^{-\\mathbf{x}^T_i\\mathbf{w}}},\\nonumber\\\\\n", + "P(y_i=0|\\boldsymbol{x}_i,\\boldsymbol{\\theta)} &=& 1 - P(y_i=1|\\boldsymbol{x}_i,\\boldsymbol{\\theta)},\n", + "\\end{eqnarray}\n", + "$$" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "where $\\boldsymbol{\\theta}=\\mathbf{w}$ are the weights we wish to learn from the data. \n", + "\n", + "\n", + "Notice that in terms of the logistic function, we can write" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "$$\n", + "P(y_i=1) =f(\\mathbf{x}_i^T\\mathbf{w})=1-P(y_i=0).\n", + "$$" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "## Maximum likelihood\n", + "\n", + "We now define the cost function for logistic regression using Maximum\n", + "Likelihood Estimation (MLE). Recall, that in MLE we choose parameters\n", + "to maximize the probability of seeing the observed data. Consider a\n", + "dataset $\\mathcal{D}=\\{(y_i,\\boldsymbol{x}_i)\\}$ with binary labels\n", + "$y_i\\in\\{0,1\\}$ where the data points are drawn independently. The\n", + "likelihood of the seeing the data under our model is just:" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "$$\n", + "P(\\mathcal{D}|\\mathbf{w}) = \\prod_{i=1}^n \\left[f(\\mathbf{x}_i^T\\mathbf{w})\\right]^{y_i}\\left[1-f(\\mathbf{x}_i^T\\mathbf{w})\\right]^{1-y_i}\\nonumber\n", + "$$" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "
    \n", + "\n", + "$$\n", + "\\begin{equation} \n", + "\\label{_auto20} \\tag{32}\n", + "\\end{equation}\n", + "$$" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "from which we can readily compute the log-likelihood:" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "
    \n", + "\n", + "$$\n", + "\\begin{equation}\n", + "l(\\mathbf{w}) = \\sum_{i=1}^n y_i\\log f(\\mathbf{x}_i^T\\mathbf{w}) + (1-y_i)\\log\\left[1-f(\\mathbf{x}_i^T\\mathbf{w})\\right].\n", + "\\label{_auto21} \\tag{33}\n", + "\\end{equation}\n", + "$$" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The maximum likelihood estimator is defined as the set of parameters that maximize the log-likelihood where we maximize with respect to $\\theta$" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "$$\n", + "\\hat{\\mathbf{w}} = \\sum_{i=1}^n y_i\\log f(\\mathbf{x}_i^T\\mathbf{w}) + (1-y_i)\\log\\left[1-f(\\mathbf{x}_i^T\\mathbf{w})\\right].\n", + "$$" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Since the cost (error) function is just the negative log-likelihood, for logistic regression we have that" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "$$\n", + "\\begin{eqnarray}\n", + "\\mathcal{C}(\\mathbf{w}) &=& - l(\\mathbf{w}) \\\\\n", + "&=& \\sum_{i=1}^n -y_i\\log f(\\mathbf{x}_i^T\\mathbf{w}) - (1-y_i)\\log\\left[1-f(\\mathbf{x}_i^T\\mathbf{w})\\right].\\nonumber\n", + "\\end{eqnarray}\n", + "$$" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "This equation is known in statistics as the \\emph{cross entropy}. Finally, we note that just as in linear regression, \n", + "in practice we usually supplement the cross-entropy with additional regularization terms, usually $L_1$ and $L_2$ regularization as we did for Ridge and Lasso regression.\n", + "\n", + "## Minimizing the cross entropy\n", + "\n", + "The cross entropy is a convex function of the weights $\\mathbf{w}$ and,\n", + "therefore, any local minimizer is a global minimizer. Minimizing this\n", + "cost function leads to the following equation" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "
    \n", + "\n", + "$$\n", + "\\begin{equation}\n", + "\\boldsymbol{0}=\\boldsymbol{\\nabla} \\mathcal{C}(\\mathbf{w}) = \\sum_{i=1}^n\\left[f(\\mathbf{x}_i^T\\mathbf{w})-y_i\\right]\\mathbf{x}_i,\n", + "\\label{_auto22} \\tag{34}\n", + "\\end{equation}\n", + "$$" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "where we made use of the logistic function identity $\\partial_z f(z) =\n", + "f(z)[1-f(z)]$. This equation defines a transcendental equation for\n", + "$\\mathbf{w}$, the solution of which, unlike linear regression, cannot\n", + "be written in a closed form. \n", + "Here we need gradient descent methods!" + ] } ], "metadata": {}, diff --git a/doc/pub/Regression/ipynb/ipynb-Regression-src.tar.gz b/doc/pub/Regression/ipynb/ipynb-Regression-src.tar.gz index d69711e829e6dafeaecadfc6fd60bf12efceba0b..db1df18132a57a1d845b80d5269c353b9f15f0ef 100644 GIT binary patch literal 211 zcmb2|=3tnuGdr4r`R)0GSxkl^t%=v|j@C9Q8cSX-U8uK9L*yf?lJMLurW-w3p1+k9 zeY5Irr^o;N;vG)}-o>7oAAj#zsO6NCXEm1k+&-l_|IC{?t9G7AFAeryTbk{YEVb6P z_ppz@%iX-C^1owpq{3g?$DGjnb4xn=^;h}-F@6)X?K)E5Z{D+ye`&v?lTM!Ky{Pz68u0sX_#|qEe9kp@v%w3zFXj>uRQq$d&_(hi5qvoIh7`A19-^?ITh HG#D5F>JngW diff --git a/doc/pub/Regression/pdf/Regression-beamer-handouts2x3.pdf b/doc/pub/Regression/pdf/Regression-beamer-handouts2x3.pdf index 52f1686f3ec03534f25c85d9cc7fcf34c55ae059..025f01c9e745b1b74f2d1a91c1838566d9fe7e0d 100644 GIT binary patch delta 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We switch to vector notation by arranging $X_1,X_2,\cdots,X_n$ in an $n$-tuple. Define: !bt \begin{align*} -\vec{X} = (X_1,X_2,\cdots,X_n). +\hat{X} = (X_1,X_2,\cdots,X_n). \end{align*} !et @@ -2412,6 +2357,7 @@ $\vec{X}_i$. It follows by induction that $n_i = n/2^i$. !split ===== Blocking Transformations ===== + Using the definition of the blocking transformation and the distributive property of the covariance, it is clear that since $h =|i-j|$ @@ -2700,6 +2646,293 @@ dataAnalysis.printOutput() !ec +!split +===== The bias-variance tradeoff ===== +We begin with an unknown function $y=f(x)$ and fix a \emph{hypothesis set} + $\mathcal{H}$ consisting of all functions we are willing to consider, + defined also on the domain of $f$. This set may be uncountably + infinite (e.g.~if there are real-valued parameters to fit). +The + choice of which functions to include in $\mathcal{H}$ usually depends + on our intuition about the problem of interest. The function $f(x)$ + produces a set of pairs $(x_i,y_i)$, $i=1\dots N$, which serve as the + observable data. Our goal is to select a function from the hypothesis + set $h\in\mathcal{H}$ which approximates $f(x)$ as best as possible, + namely, we would like to find $h\in\mathcal{H}$ such that $h\approx + f$ in some strict mathematical sense which we specify below. If this + is possible, we say that we \emph{learned} $f(x)$. But if the + function $f(x)$ can, in principle, take any value on + \emph{unobserved} inputs, how is it possible to learn in any + meaningful sense? + +!split +===== Training and testing data ===== + +We will discuss the bias-variance tradeoff in the context of continuous predictions such as regression. However, many of the intuitions and ideas discussed here also carry over to classification tasks. Consider a dataset $\mathcal{L}$ consisting of the data $\mathbf{X}_\mathcal{L}=\{(y_j, \boldsymbol{x}_j), j=1\ldots N\}$. Let us assume that the true data is generated from a noisy model +!bt +\[ +y=f(\boldsymbol{x}) + \epsilon +\] +!et +where $\epsilon$ is normally distributed with mean zero and standard deviation $\sigma_\epsilon$. + +!split +===== Procedure to find a predictor ===== + +We have a statistical procedure (e.g. least-squares regression) for +forming a predictor $\hat{g}_{\mathcal{L}}(\boldsymbol{x})$ that gives the +prediction of our model for a new data point $\boldsymbol{x}$. This estimator +is chosen by minimizing a cost function which we take to be the +squared error + +!bt +\[ + \mathcal{C}( \boldsymbol{X}, \hat{g}(\boldsymbol{x})) = \sum_i (y_i - \hat{g}_\mathcal{L}(\boldsymbol{x}_i))^2. +\] +!et + +!split +===== What we want ===== + +We are interested in the generalization error on all data drawn from +the true model, not just the error on the particular training dataset +$\mathcal{L}$ that we have in hand. This is just the expectation of +the cost function over many different data sets +$\{\mathcal{L}_j\}$. Denote this expectation value by +$E_{\mathcal{L}}$. In other words, we can view $\hat{g}_{\mathcal{L}}$ +as a stochastic functional that depends on the dataset $\mathcal{L}$ +and we can think of $E_{\mathcal{L}}$ as the expected value of the +functional if we drew an infinite number of datasets $\{\mathcal{L}_1, +\mathcal{L}_2, \ldots \}$. +!split +===== The expected generalization error ===== +We would also like to average over different instances of the +``noise'' $\epsilon$ and we denote the expectation value over the +noise by $E_\epsilon$. Thus, we can decompose the expected +generalization error as + + +!bt +\begin{align} +E_\mathcal{L, \epsilon}[\mathcal{C}( \boldsymbol{X}, \hat{g}(\boldsymbol{x})) ]&= E_\mathcal{L,\epsilon}\left[ \sum_i ({y}_i - \hat{g}_\mathcal{L}(\boldsymbol{x}_i))^2 \right] \nonumber \\ + &= E_\mathcal{L, \epsilon}\left[ \sum_{i}({y}_i -f(\boldsymbol{x}_i) +f(\boldsymbol{x}_i)- \hat{g}_\mathcal{L}(\boldsymbol{x}_i))^2\right] \nonumber \\ + &= \sum_i E_\epsilon[ ({y}_i -f(\boldsymbol{x}_i))^2 ]+ E_\mathcal{L, \epsilon}[(f(\boldsymbol{x}_i)- \hat{g}_\mathcal{L}(\boldsymbol{x}_i))^2] + 2E_\epsilon[{y}_i -f(\boldsymbol{x}_i)]E_\mathcal{L}[f(\boldsymbol{x}_i)- \hat{g}_\mathcal{L}(\boldsymbol{x}_i)] \nonumber \\ + &=\sum_i \sigma_\epsilon^2 + E_\mathcal{L}[(f(\boldsymbol{x}_i)- \hat{g}_\mathcal{L}(\boldsymbol{x}_i))^2], +\end{align} +!et + +where in the last line we used the fact that our noise has zero mean +and variance $\sigma_\epsilon^2$ and the sum over $i$ applies to all +terms. + +!split +===== Elaborating a little bit more ===== + +It is also helpful to further decompose the second term as +follows: + +!bt +\begin{align} +E_\mathcal{L}[(f(\boldsymbol{x}_i)- \hat{g}_\mathcal{L}(\boldsymbol{x}_i))^2] &=E_\mathcal{L}[(f(\mathbf{x}_i)-E_\mathcal{L}[\hat{g}_\mathcal{L}(\boldsymbol{x}_i)]+ E_\mathcal{L}[\hat{g}_\mathcal{L}(\boldsymbol{x}_i)]- \hat{g}_\mathcal{L}(\boldsymbol{x}_i))^2] \nonumber \\ +&=E_\mathcal{L}[(f(\boldsymbol{x}_i)-E_\mathcal{L}[\hat{g}_\mathcal{L}(\boldsymbol{x}_i)])^2] + E_\mathcal{L}[( \hat{g}_\mathcal{L}(\boldsymbol{x}_i)-E_\mathcal{L}[\hat{g}_\mathcal{L}(\boldsymbol{x}_i)])^2] \nonumber \\ +&+2E_\mathcal{L}[(f(\boldsymbol{x}_i)-E_\mathcal{L}[\hat{g}_\mathcal{L}(\boldsymbol{x}_i)])( \hat{g}_\mathcal{L}(\boldsymbol{x}_i)-E_\mathcal{L}[\hat{g}_\mathcal{L}(\boldsymbol{x}_i)])] \nonumber \\ +&=(f(\boldsymbol{x}_i)-E_\mathcal{L}[\hat{g}_\mathcal{L}(\boldsymbol{x}_i)])^2+E_\mathcal{L}[( \hat{g}_\mathcal{L}(\boldsymbol{x}_i)-E_\mathcal{L}[\hat{g}_\mathcal{L}(\boldsymbol{x}_i)])^2]. +\end{align} +!et + +!split +===== The bias ===== +The first term is called the bias +!bt +\[ +Bias^2= \sum_i (f(\boldsymbol{x}_i)-E_\mathcal{L}[\hat{g}_\mathcal{L}(\boldsymbol{x}_i)])^2 +\] +!et +and measures the deviation of the expectation value of our estimator (i.e. the asymptotic value of our estimator in the infinite data limit) from the true value. + +!split +===== The variance ===== +The second term is called the variance +!bt +\[ +Var=\sum_i E_\mathcal{L}[( \hat{g}_\mathcal{L}(\boldsymbol{x}_i)-E_\mathcal{L}[\hat{g}_\mathcal{L}(\boldsymbol{x}_i)])^2], +\] +!et + +and measures how much our estimator fluctuates due to finite-sample effects. Combining these expressions, we see that the expected out-of-sample error of our model can be decomposed as +!bt +\[ +E_\mathrm{out}=E_\mathcal{L, \epsilon}[\mathcal{C}( \boldsymbol{X}, \hat{g}(\boldsymbol{x})) ] = Bias^2 + Var + Noise. +\] +!et + +The bias-variance tradeoff summarizes the fundamental tension in +machine learning, particularly supervised learning, between the +complexity of a model and the amount of training data needed to train +it. Since data is often limited, in practice it is often useful to +use a less-complex model with higher bias -- a model whose asymptotic +performance is worse than another model -- because it is easier to +train and less sensitive to sampling noise arising from having a +finite-sized training dataset (smaller variance). + +!split +===== Summing up ===== + +The above equations tell us that in +order to minimize the expected test error, we need to select a +statistical learning method that simultaneously achieves low variance +and low bias. Note that variance is inherently a nonnegative quantity, +and squared bias is also nonnegative. Hence, we see that the expected +test MSE can never lie below $Var(\epsilon)$, the irreducible error. + + +What do we mean by the variance and bias of a statistical learning +method? The variance refers to the amount by which our model would change if we +estimated it using a different training data set. Since the training +data are used to fit the statistical learning method, different +training data sets will result in a different estimate. But ideally the +estimate for our model should not vary too much between training +sets. However, if a method has high variance then small changes in +the training data can result in large changes in the model. In general, more +flexible statistical methods have higher variance. + +!split +===== Logistic Regression ===== + +So far we have focused on learning from datasets for which there is a +_continuous_ output. In linear regression we have been +concerned with learning the coefficients of a polynomial to predict +the response of a continuous variable $y_i$ on unseen data based on +its independent variables ${\bf x}_i$. + +Classification problems, +however, are concerned with outcomes taking the form of discrete +variables (i.e. categories). For example, we may want to detect if +there's a cat or a dog in an image. Or given a specific system, +we'd like to identify its state, say whether it is an ordered or disordered system (typical situation in solid state physics). +(e.g. ordered/disordered). + +_Logistic regression deals with binary, dichotomous outcomes (e.g. True or +False, Success or Failure, etc.). It is worth noting that logistic +regression is also commonly used in modern supervised Deep Learning +models_, as we will see later. + + +!split +===== Basics ===== + +We consider the case where the dependent variables $y_i\in\mathbb{Z}$ +are discrete and only take values from $m=0,\dots,M-1$ (i.e. $M$ +classes). + +The goal is to predict the +output classes from the design matrix $X\in\mathbb{R}^{n\times p}$ +made of $n$ samples, each of which bears $p$ features. Of cours e the +primary goal is to identify the classes to which new unseen samples +belong. + + +!split +===== Linear classifier ===== + +Let us start by considering a slightly simpler classifier: a linear classifier that categorizes examples using a weighted linear-combination of the features and an additive offset +!bt +\begin{equation} +s_i = \boldsymbol{x}_i^T\boldsymbol{w} + b_0 \equiv \mathbf{x}_i^T\mathbf{w}, +\end{equation} +!et +where we use the short-hand notation +$\mathbf{x}_i = (1,\boldsymbol{x}_i)$ and $\mathbf{w}_i = (b_0,\boldsymbol{w}_i)$. + +!split +===== Some selected properties ===== + +This function takes values on the entire real axis. In the case of logistic regression, however, the labels $y_i$ are discrete variables. One simple way to get a discrete output is to have sign functions that map the output of a linear regressor to $\{0,1\}$, $f(s_i)=$ sign$(s_i) = 1$ if $s_i\ge 0$ and 0 if otherwise. Indeed, this is commonly known as the ``perceptron" in the machine learning literature. This model is extremely simple, and it is favorable in many cases (e.g. noisy data) to have a ``soft" classifier that outputs the probability of a given category. For example, given $\mathbf{x}_i$, the classifier outputs the probability of being in category $m$. One such function is the logistic (or sigmoid) function: +!bt +\begin{equation} +f(s) = \frac{1}{1+\mathrm e^{-s}}. +\label{eq:log_fun} +\end{equation} +!et +Note that $1-f(s)= f(-s)$, which will be useful shortly. + +!split +===== The cross-entropy as a cost function for logistic regression ===== + +The perceptron is an example of a ``hard classification'': each datapoint is deterministically assigned to a category (i.e $y_i=0$ or $y_i=1$). In many cases, it is favorable to have a ``soft'' classifier that outputs the probability of a given category rather than a single value. For example, given $\mathbf{x}_i$, the classifier outputs the probability of being in category $m$. +Logistic regression is the most canonical example of a soft classifier. In logistic regression, the probability that a data point $\boldsymbol{x}_i$ belongs to a category $y_i=\{0,1\}$ is is given by +!bt +\begin{eqnarray} +P(y_i=1|\boldsymbol{x}_i,\boldsymbol{\theta)} &=& \frac{1}{1+\mathrm{e}^{-\mathbf{x}^T_i\mathbf{w}}},\nonumber\\ +P(y_i=0|\boldsymbol{x}_i,\boldsymbol{\theta)} &=& 1 - P(y_i=1|\boldsymbol{x}_i,\boldsymbol{\theta)}, +\end{eqnarray} +!et +where $\boldsymbol{\theta}=\mathbf{w}$ are the weights we wish to learn from the data. + + +Notice that in terms of the logistic function, we can write +!bt +\[ +P(y_i=1) =f(\mathbf{x}_i^T\mathbf{w})=1-P(y_i=0). +\] +!et + +!split +===== Maximum likelihood ===== + +We now define the cost function for logistic regression using Maximum +Likelihood Estimation (MLE). Recall, that in MLE we choose parameters +to maximize the probability of seeing the observed data. Consider a +dataset $\mathcal{D}=\{(y_i,\boldsymbol{x}_i)\}$ with binary labels +$y_i\in\{0,1\}$ where the data points are drawn independently. The +likelihood of the seeing the data under our model is just: +!bt +\begin{align} +P(\mathcal{D}|\mathbf{w})& = \prod_{i=1}^n \left[f(\mathbf{x}_i^T\mathbf{w})\right]^{y_i}\left[1-f(\mathbf{x}_i^T\mathbf{w})\right]^{1-y_i}\nonumber \\ +\end{align} +!et +from which we can readily compute the log-likelihood: +!bt +\begin{equation} +l(\mathbf{w}) = \sum_{i=1}^n y_i\log f(\mathbf{x}_i^T\mathbf{w}) + (1-y_i)\log\left[1-f(\mathbf{x}_i^T\mathbf{w})\right]. +\end{equation} +!et + +!split +The maximum likelihood estimator is defined as the set of parameters that maximize the log-likelihood where we maximize with respect to $\theta$ +!bt +\[ +\hat{\mathbf{w}} = \sum_{i=1}^n y_i\log f(\mathbf{x}_i^T\mathbf{w}) + (1-y_i)\log\left[1-f(\mathbf{x}_i^T\mathbf{w})\right]. +\] +!et +Since the cost (error) function is just the negative log-likelihood, for logistic regression we have that +!bt +\begin{eqnarray} +\mathcal{C}(\mathbf{w}) &=& - l(\mathbf{w}) \\ +&=& \sum_{i=1}^n -y_i\log f(\mathbf{x}_i^T\mathbf{w}) - (1-y_i)\log\left[1-f(\mathbf{x}_i^T\mathbf{w})\right].\nonumber +\end{eqnarray} +!et +This equation is known in statistics as the \emph{cross entropy}. Finally, we note that just as in linear regression, +in practice we usually supplement the cross-entropy with additional regularization terms, usually $L_1$ and $L_2$ regularization as we did for Ridge and Lasso regression. + +!split +===== Minimizing the cross entropy ===== + +The cross entropy is a convex function of the weights $\mathbf{w}$ and, +therefore, any local minimizer is a global minimizer. Minimizing this +cost function leads to the following equation + +!bt +\begin{equation} +\boldsymbol{0}=\boldsymbol{\nabla} \mathcal{C}(\mathbf{w}) = \sum_{i=1}^n\left[f(\mathbf{x}_i^T\mathbf{w})-y_i\right]\mathbf{x}_i, +\end{equation} +!et + +where we made use of the logistic function identity $\partial_z f(z) = +f(z)[1-f(z)]$. This equation defines a transcendental equation for +$\mathbf{w}$, the solution of which, unlike linear regression, cannot +be written in a closed form. +Here we need gradient descent methods!