{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": true }, "outputs": [], "source": [ "%matplotlib inline" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Plotting Decision Regions" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "A function for plotting decision regions of classifiers in 1 or 2 dimensions." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "> from mlxtend.plotting import plot_decision_regions" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### References\n", "\n", "- " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Example 1 - Decision regions in 2D" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [ { "data": { "image/png": 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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "from mlxtend.plotting import plot_decision_regions\n", "import matplotlib.pyplot as plt\n", "from sklearn import datasets\n", "from sklearn.svm import SVC\n", "\n", "# Loading some example data\n", "iris = datasets.load_iris()\n", "X = iris.data[:, [0, 2]]\n", "y = iris.target\n", "\n", "# Training a classifier\n", "svm = SVC(C=0.5, kernel='linear')\n", "svm.fit(X, y)\n", "\n", "\n", "# Plotting decision regions\n", "plot_decision_regions(X, y, clf=svm,\n", " res=0.02, legend=2)\n", "\n", "# Adding axes annotations\n", "plt.xlabel('sepal length [cm]')\n", "plt.ylabel('petal length [cm]')\n", "plt.title('SVM on Iris')\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Example 2 - Decision regions in 1D" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "data": { "image/png": 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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "from mlxtend.plotting import plot_decision_regions\n", "import matplotlib.pyplot as plt\n", "from sklearn import datasets\n", "from sklearn.svm import SVC\n", "\n", "# Loading some example data\n", "iris = datasets.load_iris()\n", "X = iris.data[:, 2]\n", "X = X[:, None]\n", "y = iris.target\n", "\n", "# Training a classifier\n", "svm = SVC(C=0.5, kernel='linear')\n", "svm.fit(X, y)\n", "\n", "# Plotting decision regions\n", "plot_decision_regions(X, y, clf=svm, \n", " res=0.02, legend=2)\n", "\n", "# Adding axes annotations\n", "plt.xlabel('sepal length [cm]')\n", "plt.title('SVM on Iris')\n", "\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Example 3 - Decision Region Grids" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": true }, "outputs": [], "source": [ "from sklearn.linear_model import LogisticRegression\n", "from sklearn.naive_bayes import GaussianNB \n", "from sklearn.ensemble import RandomForestClassifier\n", "from sklearn.svm import SVC\n", "from sklearn import datasets\n", "import numpy as np\n", "\n", "# Initializing Classifiers\n", "clf1 = LogisticRegression(random_state=1)\n", "clf2 = RandomForestClassifier(random_state=1)\n", "clf3 = GaussianNB()\n", "clf4 = SVC()\n", "\n", "# Loading some example data\n", "iris = datasets.load_iris()\n", "X = iris.data[:, [0,2]]\n", "y = iris.target" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "data": { "image/png": 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JfqtoF921gerDR/LMA6/y1IdjsFqtbR6neFkx63eup89FfajdXkvvC3tT+m6p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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "import matplotlib.pyplot as plt\n", "from mlxtend.plotting import plot_decision_regions\n", "import matplotlib.gridspec as gridspec\n", "import itertools\n", "gs = gridspec.GridSpec(2, 2)\n", "\n", "fig = plt.figure(figsize=(10,8))\n", "\n", "labels = ['Logistic Regression', 'Random Forest', 'Naive Bayes', 'SVM']\n", "for clf, lab, grd in zip([clf1, clf2, clf3, clf4],\n", " labels,\n", " itertools.product([0, 1], repeat=2)):\n", "\n", " clf.fit(X, y)\n", " ax = plt.subplot(gs[grd[0], grd[1]])\n", " fig = plot_decision_regions(X=X, y=y, clf=clf, legend=2)\n", " plt.title(lab)\n", " \n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Example 4 - Highlighting Test Data Points" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "data": { "image/png": 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veXDSg+isdUijxKebD+++8C797ulH0/5NadK0CTnZOaRHp5N+Pp1W3q3QaE0x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\nn3sJITqVJUCl+luzZjp6fR7Llp1m5Mi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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "from mlxtend.plotting import plot_decision_regions\n", "from mlxtend.preprocessing import shuffle_arrays_unison\n", "import matplotlib.pyplot as plt\n", "from sklearn import datasets\n", "from sklearn.svm import SVC\n", "\n", "\n", "# Loading some example data\n", "iris = datasets.load_iris()\n", "X, y = iris.data[:, [0,2]], iris.target\n", "X, y = shuffle_arrays_unison(arrays=[X, y], random_seed=3)\n", "\n", "X_train, y_train = X[:100], y[:100]\n", "X_test, y_test = X[100:], y[100:]\n", "\n", "# Training a classifier\n", "svm = SVC(C=0.5, kernel='linear')\n", "svm.fit(X_train, y_train)\n", "\n", "# Plotting decision regions\n", "plot_decision_regions(X, y, clf=svm, res=0.02,\n", " legend=2, X_highlight=X_test)\n", "\n", "# Adding axes annotations\n", "plt.xlabel('sepal length [cm]')\n", "plt.ylabel('petal length [cm]')\n", "plt.title('SVM on Iris')\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Example 5 - Evaluating Classifier Behavior on Non-Linear Problems" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "collapsed": true }, "outputs": [], "source": [ "from sklearn.linear_model import LogisticRegression\n", "from sklearn.naive_bayes import GaussianNB \n", "from sklearn.ensemble import RandomForestClassifier\n", "from sklearn.svm import SVC\n", "\n", "# Initializing Classifiers\n", "clf1 = LogisticRegression(random_state=1)\n", "clf2 = RandomForestClassifier(n_estimators=100, \n", " random_state=1)\n", "clf3 = GaussianNB()\n", "clf4 = SVC()" ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# Loading Plotting Utilities\n", "import matplotlib.pyplot as plt\n", "import matplotlib.gridspec as gridspec\n", "import itertools\n", "from mlxtend.plotting import plot_decision_regions\n", "import numpy as np" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### XOR" ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "collapsed": true }, "outputs": [], "source": [ "xx, yy = np.meshgrid(np.linspace(-3, 3, 50),\n", " np.linspace(-3, 3, 50))\n", "rng = np.random.RandomState(0)\n", "X = rng.randn(300, 2)\n", "y = np.array(np.logical_xor(X[:, 0] > 0, X[:, 1] > 0), \n", " dtype=int)" ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [ { "data": { "image/png": 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46tRmIhSkyuVqKUgWS/z3boOesLLqqSgN65z7p5N+adplrFy5lOuvnxJ2vaFh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\nc1vS1w1Ug3cqFgxDTcNUMJVDBH1Wdu7EG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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "gs = gridspec.GridSpec(2, 2)\n", "\n", "fig = plt.figure(figsize=(10,8))\n", "\n", "labels = ['Logistic Regression', 'Random Forest', 'Naive Bayes', 'SVM']\n", "for clf, lab, grd in zip([clf1, clf2, clf3, clf4],\n", " labels,\n", " itertools.product([0, 1], repeat=2)):\n", "\n", " clf.fit(X, y)\n", " ax = plt.subplot(gs[grd[0], grd[1]])\n", " fig = plot_decision_regions(X=X, y=y, clf=clf, legend=2)\n", " plt.title(lab)\n", "\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Half-Moons" ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [ { "data": { "image/png": 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TgKFmdkaGxxIRH6pfNqrzB0gWWT4vtDzKLxHJi4x6tpxzHcBesx4ffH0m8Ki3/DhwFvBS\nJseT7DlnzhyaolEAdjc2kkgkiIdCjKur463GRsqBUXV1XbaVQ+cyQDiRIBQKMaquDoCaSIQlt91W\noE8khdClwAoY5VdwpecXJDOsAzrzK5VNQI/51T3nUpRhkiu5HLNVBzR5y1FgRA6PJf2QHlA7Gxp4\nKhQiHA4TByaUlzM5Hmd5JMLkxkbuBSZEIqxrbOzcdm/afgDLy8tZF48zIRJhw7ZtnNPQwOQrrug8\nnoKrOJXIeCzll8/0ll+nHH886xob+SJ05lcqm4Ae86t7zm3Yto14PM5H0jJM+SXZlMtiaz9Q4y1H\ngH3ddzCz2cBsgLlXXcXsGTNy2JzS1D2gxoRC1ITDEAoxIRzuDKRMxeNxxoRCLI9EOGfbNpricTYo\nuIpGiRRY6frML1CG5Vo+82tCOMwYoKapSfklWZfLYutF4FxgKXA+ML/7Ds65ecA8AOrrgzrBsu/0\nFlD3egE1OUsB1ZumeJzl4TDrgHBTk84YA6oEC6x0feYXKMNyQfklxSjjYsvMfg9MAk4xs7nAGc65\nK4HFwCfM7DlgpXNO4x1yqNdLhHkKqN7ojDF4gnRHYaaUX/6g/JJil3Gx5Zz7WLdVC7z1MeCyTN9f\n+qcpGmV5JALA5MbGPrvYa7wA2+kNJE10dBAPhZgcjfIWyVP5UdEou6Fz2/lpywBjOzqSA+SjUXYm\nEpxSXn70NqadMU5ItTVtkKsUVpAHuw+W8ssfMsmvMV5OdUBnfqWyCXrOr+45tzORSBZU4TBNvRxX\n+SWZ0KSmAdb9bHByY2NyPEMvwl6A7UwkwLsD55QsnZml2jLZC6513vHivQRXalzXzkRCZ4kFVEq9\nWOIvfsyvJq8tyi/JNhVbAZZ+NrjOOxvs3t2eHlCzshxQ6dLf75w5c/hi+pgLjjxj1Fli4ZT4WCzx\nCeWXlBIVWwHT/WxwXWMj4W5ng1262HMYUL3pHlw9nTGSFlyp2651lpg7KrDED5RfUqpUbAVMb2Mb\nctnFnomjnTGmd9WnBqEu11li1pXiWCzxJ+WXlCoVWwHQn7ENpxx/PABjolGW/+xneW9jf6QH1+Qr\nrujsfl/35psA7I7FmOwtp84SdYY4OOrJEr8olfwCjeWS3qnYCoDexjZ0vyMHkl/sIKiJRDrP/lJ3\nAnUAy70QTo2F0BniwGjAu/hNqeRXalyXxnJJT1Rs+VR/xjYs8c4GJ/v4bLA33c8Sl0cinb1aALFY\njHVvvqkzxH5QL5b4USrDUvkFye81Xo4VW34BnRmWyi9QL70kqdjyqf6MbZgcsLPB3qTOElNTRgA4\n0DiIo1CBJX6XyrBUfgGsjsWKNr+AzgxL5RfQmWHKr9JmzvnkCRN61AXQ9WxwjDcp31uxGLsqK5MP\nfT7pJCCYZ4N9ST9DXPfmm0wIhxnd3s64suQ5wc5EgjEjR5bsGWIxFlgzZ2KFbkPWKMN6fNROKr+A\nzgwrxvyCwxmWyi+gM8NS+QX+7aWvXzaqKHIlXwaSX+rZ8pnU2eC6tLPB42OxwI5tGIiexkGUU9rj\nuIqxwJLi1dP4rFR+AZ0ZVoz5BV176cd461IZpnFcpU3FVgCMKitjeRGfDab0NY6rVKjAkmKSyi8o\nzh75dKkM62kcl5Q2FVs+0X0waSwWY3UsRrnX/Ty5iM8Ge9LTOK6OeLx0Bs2rwJIA6WkwfEc8zjoo\nqvFZ/dXTOK5UfqXWadB8aVGx5RNHDCb1BsJPOOkkX889kyvpZ4gTuo3jKtZB85p8VIKqp8Hw66Dk\n8wsOZ1j6OC4Nmi89KrZ8JjX3DBT/+Ib+6G0+m2KhebGkmCi/jtTTOK5iyjDpH92NWEDd79x5KhQi\nHA53zqZc7OMbBio1DiI1SzME8w5FFViH6W7EYEu/fPiUd/d0KsOUX10FIb90N+LA6G7EgOhtLi05\nutQszRCcOxQ16F2KUY+XD5VhRxXE/JLMqdgqgPSzwcneYNKd8TiToeind8hE90HzV8RitADlaYPm\n/XKGCCqwpHh1z7DdsRijY7HD80np8uERgpZfkl0qtgqgp7m0JkNJTO+Qie6D5jvefJPVPjxD1GVC\nKXZHZJg3VksZ1rug5JfkhootkSxQL5aIBNkRGSZZpWIrz86ZM+eIubQAdoZCJTeX1mB1746PxWI4\n6DIHVz674zVlg5QKzQeYOb/lVxfKsJxRsZVnTdEoY0Khzq53SA4oHVNXp673fureHV+I+WvUkyWl\nSPMBZs4P+SX5l3GxZWY/BM4E3gQud851eOvPBu4D3gDizrkPZ3qsYpE+Fw0kB8Wf4sOzQeccT61a\nxUcmTcLMf3fo53v+Gr8UWM45Vq16ikmTPuLLv5cgUX4NTlDm0/JzhpXq/Fulml8ZFVtmdjow1jk3\nzcy+DXwa+FXaLg84567N5BjFaIk3j1bK5GjUl3egLF27lq/c9QD3XVPO9NNOK3RzjtDTc8iyzS8F\nVrq1a5dy113/zDXXDOG006YXujmBpfwavPQM8+OA+NT3ds2WP3H7wwu55qJRnHbie7N6jJlTdmf0\n+sHkVzGMqyrV/Mq0Z+tM4Elv+XHgy3QNq4vN7P3AIufcf2Z4rEDr6dlh6ROY+o1zjpsXPk1H/CJu\nXrSEaRMn+v4sZMO2bcTTzrYzGfvg1zsKnXMsXDiXePzjLFo0l4kTp/n+78XHlF/9FLT8AnB/8zcs\nfPQO4mWfYdHapUz89OzsfVeWLcvO+6TpK7+KYcLRUs6vTIutOmCntxwFRqRtWw6c4i0/bGbPO+dW\npL/YzGYDswHmXnUVs2fMyLA5/pUa63BOUxNfTH2hOjp82/2+dO1aXt85lDF11/Laji/z3Nq1vuzd\ngrTu+I4OxnizWJ9SXs6SAY598GMvVndr1y5l506oq/sOO3Z8gbVrnyups8Msyyi/oHQyLGj5BYP4\nruSggOqP/ubXzCm7qc9VG/OUdaWcX5kWW/uBGm85AuxLbXDOHUwtm1k9cDrQJaycc/OAeUDJPOrC\n793vcLhXKxS6mgOtfyQU+go3L/pP3/ZuZXo50a+9WN2lzgpDodm0th4gFJpdcmeHWZZRfnn7lVSG\nBSG/oPt35Q85+a7ULxuV8aVEGFh+pY4XxMuJpZ5fmRZbLwJzgHuB84EXUhvMrMY51+T9OhX4SYbH\nCqQgdr8/v24dKzc1Ao/RcOApRg4/jz+9sY8X1q9n6oQJhW7eUaU/dwyS3fHnzJlzxKXEoBRY6dat\ne55Nm97EbDVNTX+kpqaON97YzPr1LzBhwtRCNy+IlF/9kD5dTUo4HIaamqO8qrDWbVvFpk1vAvUc\nOPAow4df2Pd3ZaA5kINepv7mFxCY3Eop9fzKqNhyzq0ys91m9hywFfiRmc11zl0JXOJ1sceAF5xz\nS7PQ3sAJ4rPDxo4cyQ/+9v3cufgF2jsu5G01S/jGhWdy3IgRfb+4wNKfOwbJmZm/6HXFB7HASjdy\n5FhmzfoS9fU/49Ch0dTU7ObCCy9nxIjjCt20QFJ+9U+X6Wo8fs+wkcOP5W/PnM7iNS/Q0XERNTUv\nceGFXzr6d6VAlxHTHS2/jpCt9uYpC0s9vzKe+sE5d123VVd6638K/DTT9y8WQblVGmD86NGcevzx\ntHecyPjR32ZX40ucOm4c40ePLnTTjqomEmFDQwPr0taFveAqhsGlo0ePZ9y4U+noeAeRyH9z6NDX\nGDfuXYwePb7QTQss5Vf/BGW6mpSvnF/GzQtP4tCBDUQiH+LQoa0cf/xRvite4ZKNy4KDdbT86i5b\n7axfNir52fOQjaWeX6FCN6CYpbrfJ7/5ZmfXcE04zJiRI1n+s5/5croHODxmKxyeTXP7y+xtauL6\nBb/FOX8PSVly222MGTmyM6ASCejoiLPjLw1872eXFbZxWZAa8xCLXcrevTuJxS5l0aK5vv97kWA6\nZ84cJl9xBTsbGrrk1/KTTmLMyJG+zS9IfleeXbOIePwM9u79DvH4Gb1/V3xQaMHh/Jpw0kmdGRaP\nx5P/hlxxBefMmZP1Y+bzM5d6fqnYyqGmaJSnQiGWh8OdP00+736Hw2O2Wts3sHXP/8O5C1i1+S88\nv25d3y/2gY6OOO+0Mk4Nl3FqeQWjw2W0x5oL3ayMpcY8RKMriMcXE42u6BzzIJJtqSEQ6RkWhPyC\nZIa9tGE/0ZaHSSQ+TDS6kNdf39Trd6XQhVZ38XicCeEwE8JhxoRCLI9EaAr4rPKlnl96XE+Ohb3H\nWaT4vfsdkmO2/nXWB9i4fSn3PjOUIRVfpe3QSrY3NBS6ab1KjcdKlB3Dea6B0S6R3BBPMDxcRnsB\n25YtI0eO5UMfOpMlSx6hsvJy2tvv4UMfml4yYx6kMNIzLAjPP6xfNopdjTE+9O5TeGZ9lIqKORw6\ntJZzzjm11+9KJnf3ZbNQ62tW+SDehZhS6vmlYivHut91OMans8WnGz96NF85/3w+euOd1A27Bkcl\nVRVX8/Mld/DZaf65TbenebG+P2Ud37riRJZE3tZl01nRvXlsWW6MGvVXvPnmdsrLL6Gm5kra2t7F\n1q3zGDXqrwrdNCli6RkWlOcfjjrvE2x9cSHDhl0LDKGi4lq2br275+/KYMcr5WBAfT6eilEopZ5f\nKrZyJIi3S6frnP7B1tLQtIxjaob6YvqHgUw8umPbehLxGACNiTjfuuJEKiPH8v3b/i+XTcyZdeue\n57XXXqG1dSWx2C4qKk4uqVunJX+CnF8zp+zmlrX5mWYgl5cf02eUh2Sv4u2/npX1k/V89ZaVen6p\n2MqRpmiUU8rLO2dbhuSMy36/hJgyduRIfjDrA9xZP5/2QydxTM2bfOPCcwoy/cNAZ3avjBzLWdE9\nNHa0MzqU7II/ubyShyNv46zonlw1M+dGjDiOESNG0NBwpncr+8mYXVYy3fCSP4XIr2z+oz/yhGPy\nMs1Aqs3ZLrpqIhHOaWjonFEekrPKN/U0jU1AlHp+qdjKoaA8cLon40eP5tRx42jveCfjR9/DrsYL\nOXXs2LxO/zDYebFSPVc9XU4Msr/8ZRsHD45k9Oh/o6XlUo4//l0l86gLyb985le2i4f0aQZGj76P\nxsZzGTv21OxOM5DKpBxdTuzpUuJAHj/Wb3maEqeh4S06Oo4nEvkWHR1fK7n8UrGVA0Hugk85PP3D\nNV2mf3j5R6flZcxWZ/hmEAR7G3dxTuOurusyaVQBOedYsOAWmpo+T3V1C+HwlSX1qAvJn0LkVzZ7\nhuqXjeqcZiAcvpL29udpatrLggU38aMfTc/u92XZspxeShzQjPKDVJ+HebbSp33Yv38ntbWXllx+\nqdjKgSDOuNxdasxWZfkG9kYXedM/PMjz69YxbeLEnBwz2w+CLkvEWVZe2WXdCR3BvC9x7drn2Lx5\nG4nELvbuvZOampElNd5B8qcY8is1zUB5+Xqi0V/g3AVs3ryIdeueZ+LEaYVuXr8NaEZ5H0v9fbS0\nrCAe/zPR6KGSyy8VWzkStBmXu8vn9A+5eoyOhcK86g2QT18XRPv2bae6eiiVlRHa2+9h6tTpHHdc\n6Yx3kPwKWn51P1FLPhrmMrZv38gzz0Q6p39oaNies2PnYtzWUZ+IkS15uIxY6tM+gIqtnAnyeC3o\nNv3D8GsYNqSW3funs+DpZ7My/UM+nlNYWzeacd3GbNUGcAoI5xx/+MMjR9wy/eUv31oyXfCSX4HM\nr7QcGQ2cf/5XuPHGWQwffh1DhhzL/v1n8/TTjzBt2mez973Jw7itCd2L3FTPVoAeP1bq0z6Aiq2s\n63xET9p4h5qAjddKSV1KHFKxkf0HH6PhwJM0tzHo6R8K8SDoi7at50Ba71ZjIs735vxNoKZ/KPVb\npiW/3mps7JJfAG8VqC39NXPK7uTYo5QpUzovXVVUvMrBg/UcOLCYtraywH1vehq39b2fXcb3pwTj\niR6gDAMVW1nXFI2ypLy8y/woHwnQlA/pUpcSnXuNOxe/SHvHhYwdsYQxdXX9fo9CFFgplZFjea1h\nO0+mXToMlVfymYBN/1Dqt0xLfpUD93Zbd34hGjJAqct4qcxJXUp0zrF48f/S0XERI0a8RF3dmKO9\nja+kLiU+lTYFRLi8nE81Nx7lVf6jDFOxlRNBnDW+J+NHj2b2jBk8u2YN7R0n8vbR32Fn4zreamjg\n7WOOHlgTHc3JAAAgAElEQVSFLLJSvn/b//GtK0484lIiAbuUmJzyoUxTPkhejKqrO+LS1aiADMpO\nz53Ro8czY8Zs1qx5lo6O4xk9+t9obLyYhoa3GDPm7QVsZf/1eilxz8HCNGiQlGEqtrIuiF3wR5M+\nBcTB9nb2Nn2W6+fP4+UfTTxi3EO27ybMliBfSnTOMX/+TTQ17aW6+gVN+SA5VUzDIIBuU0C00NT0\nSebPvyX7U0DkUE//puxNFKgxg6AMSwr1vYsMRDiRYHk43OUnnAjQN6Ob1LittvaNbN1zN4nEblZt\n3s/z67qOF+gyL1bqxycOxGMsC5d1/jwZCtMekEuJa9c+x5tvbsK5C9iz5zu0t6/vHOsgkm1N0ShP\nhUJd8qspQNM+dL8jMDVuq719PXv23EkisYvNm7eybt3zBWrhwHX/N+VlK6PMBeffFGVYknq2siwU\nCh0xJ00oFNyatnMKiB1LuXfJJqoqL6O1vYLtDQ2+uFTYl8rIsexq2M6raetC4eD8b9/QsJ3q6gne\nretXMHXq65ryQXIqHA53ybB8TPuQq6kMUuO2duzYyJIlS71pB4ZmfwqIKVOoh867ErM5DUT6vymJ\nBBAKYcqwwAnO31hABHm8Q0+6TgHxb8TjH6TZtfDvv32BGz//aez97y90E48qyOO2nHM8/fQjlJef\nTSRyLG1t12nKB8m5go05zcEJ2+jR49OmgLiVqqoP0tJyKg899AOmTr0k+yfCU6ZkfRqI1L8p+5vL\nYOhQIDhT2CQSCR56aB7Dhn2P6upRtLaWboap2MqiYnhMT09++JudvLwxSkX4DQ7Fn+BA65O0JcpY\nP7yDgU8AkX/7G3fxVrfH9uwvUFsGQrdLS77tbmzskl8AuXsYTX6kTwFx6NAGDh3awM6dG/n973/M\nhRd+PSfHPFpP3UB7vVJ/J52jUUKhQOQXwKOP/jebNr1GTc1iOjo2Aq5kM0zFVhY1RaOcUl7OF9O7\n4AM67QOk3UI9PMasL33Fu4V6JR0NwbqFugP4Yg/r/E63S0u+FfS7ks0eoR4uJQJehi2jo2MWL7zw\nNBdc8LWsPysx2zr/TkKQcIBL0GH+H5rinOPFF5+mqmpWWn4ZUJoZlnGxZWY/BM4E3gQud851eOvD\nwN3AycAK59zVmR4rCAI583Kanu4oHA3MgM5bqMeMuZnm5i/w8ssP8YlPzPF9d/Db6kazpNtlxLN8\n3g3vnOPll3/X5c+7FG+XzjXlV1fj6upY3u3kcHIehkFk+1E36Q9XTk0BAXSZBmLHjotZu3Ypp532\nwaweO9ufpae/k4kBmPphzZpn2bGDkp7uIV1GxZaZnQ6Mdc5NM7NvA58GfuVtvhDY4Zy73MzuNrMz\nnHMvZdheXwvqtA/9mbIh/RZqsxCx2KXcf/9VvOMdk3n3u7MbVtkWxMuIa9Y8y/3330Uk8j+YhUr2\ndulcUn4dKagZBn3P7ZevaSDS25GNwiv90m7qUuJ+n9905ZxjwYJbaGr6PNXVLcovMu/ZOhN40lt+\nHPgyh8PqTODRtG1nAUUdVuFEguXl5V3Wje3w5wWrgc6J1XXcw6tEo3uJxWr4+c9v4tZb/T1njUvE\nObW8suu6jvYCtaZvyaC6iVgsQjS6ArOtACU71iGHlF/dBCnDBiqVYeXl64lGnyeRKOucBmLixGnZ\nOUgOnpWYSCSYUF5OLGEQBkIhX+cXJKd72Lx5G4nELvbuvZOampEln1+ZFlt1wE5vOQqM6LatqZdt\nAJjZbGA2wNyrrmL2jBkZNqewgjDtw2Cna0gf95C6jbqi4qvs2vWbnHTFZ1MsFGZK2qSmqXV+tWbN\ns+zefZCRI7/KoUP3cNZZ0znuuHdSqmMdciij/AJlmJ90f1xPd71NA7FixWNMmDA1eyeMy5Zl9VJi\nPBRicjyeHK9lIYgnfJ1fzjn+9KfHqKqqZsiQCO3t9zB16vSSnO4hXabF1n4gdatdBNjXz20AOOfm\nAfMAqK93Gbal4Pw+7UP9slGDvr06Ne7BOdd5GzX8NY2NVb6fkTlIY7ZSsy03Nx9k+PAJVFbeWrK3\nSudBRvkFyrAg6WkaiP37T+Thh6/ive8937fDIVJjttKnfvBrfkHyZPHhh39FJDKHSOTvaW19lzKM\nzIutF4E5JJ9bej7wQrdt5wJLvW3zMzyWDFKX2d0zlJeu+BJ2eLblz7Bnz3eIRP625Lvfc0j5VWBZ\nncy0H/IyHGLKlOQA/SxpbQ8nC60ASA6B+CGx2Eyi0YVAJWYaAgEZFlvOuVVmttvMngO2Aj8ys7nO\nuSuBxcAnvG0rS2FwaU0kcsSdOzUFmvYhV88pzFtXfBZVRo7lrG6P56mMHFug1vTOOceKFY9RVfUu\nKitLe7blfFB+HakgGZbHp0/0NByisvKr7Nv3ex566Lbs3V2dxc9Ueey4w/kVbUmu82l+PfTQf9DY\nOJSRI7/j5ddrGgLhMed80vNdBF3whZavB0GnLiXu2XOl1xX/GNHoVdx44wLfdsUHwZ///EduvPEy\nIpE51Nb+A62tzzJq1Dy+9737fVnEZsPMmRTPB1OGDVgmQxsyoQzLPuXX0QWjb1J6la8CK12Q70z0\nK3W/i+SPMiy7lF99U7EVQIUosNL11hXf0PBodrviS0Sq+33//mHqfhfJA2VY9ii/+kfFVkAUusBK\n19Odicmu+HZ+8YvbOfnkyb6eCsJv0icwra0t7Ye1iuSDMix7lF/9E4wJVEpY/bJRXe8mTP34QKor\n/tChV9m/fy7R6CI6OmayYMEP8c1YQJ/rPoFpNDqXQ4de7ex+F5HcUYZlRvnVf+rZ8rFsTtmQC+ld\n8du3b+SZZyJUVHyHxsavs3btUjo62pk06SM6u+lFqvt9374qjjnmq7S3awJTkXw6WobpcuLRKb8G\nRsWWz/jpcmFfjuyKv46qqlG0tn6d+fNvobFxD3PmDCnph48ezeHud03+J1IIvWXY/v2XB+bZr4Wi\n/BoYFVs+EKQCqydd7+zZgHMJdu/eSlXVB1i4cC6HDrXy3veepy8gybPBVaue4vTTz9XdOyI+0dvd\niQsW3MSsWW3KrzTOOVaufJL7779d+TUAKrYKJOgFVrr0rniAHTteZ8mSodTW/hNbtlzH7bf/Azfc\ncLd6uIC1a5dy113/zCc+8aru3hHxid7uTtyz57fKr27Wrl3KHXd8E+eOUX4NgIqtPCqmAitdqise\n6HJ3T3n52zl48BISiTt48MH/KekertTZ4MMP/5xYbCa//vU8hg+/i+pq3b0jUmg93Z04ZMh0tm2L\nkEjcwcKFP2HChKmsXv2HkhyHmt4jv3DhXA4e/Ajh8Kscc8yxtLUpv/pDdyPmiV/vKMy29Lt7Ghr+\ni1jsDRKJMG+88Rp33PFN1q59rtBNLIi1a5dy++3/wJYtrVRVXc3BgyNobl5MNDpPd++I+ERv+bVl\ny14WL76Lu+7655LMsFSP/OLFd7FlSyuJxN/R0RHmL3+5TvnVT+rZyqEuPVlFWlx1d7g73lFffz8w\nnbKyL9HU9Gucm8bChXOZOHFaSZ0BOed48MGf0NxcSzh8CcOGVVJT801qav6DCy882fuzUPe7SKH1\nlF/l5ZdhNowHH/x/hMOXsGhRaWWYc46FC+cSi83kwQfnMmTIHYwceSyHDn0lLcOUX31RsZVlpVhg\npUt1x69d+xxNTWVUVJzMoUOvEYsNBf6OLVv+pWSmhUh1vZeVVbB1614SiTCJxBs0Nc2joqKSpqYY\nJ544UYNJRXyip/wCaG9fwYEDIxkz5mp27PhqyUwLkZreYccOR1XV2eza9SCh0DrKyzdTUeGUYQOg\nYitLSr3I6i51huicY/Hi/wWupaLiWEKhr5bMtBCprvfa2lrKy69g5MgDdHRspKamngsvvBz1Zon4\nU/qA+WSGLQOupbx8CM3Nl5bMtBDp0zsMGXI8NTUfpabml15+qUd+IFRsZcjvE48WStczxDgVFbuA\n3xGPJ9i8OTktxKJFc4ty0GlqMPxDDy2gre18Nm/+NbW1UcxClJefTDT6IiecoLNBEb9Kv+knPcNa\nWhZ0eWj1v//7tKLMr96mp1F+DZ6KrUEo1rsKc+Fo00Ls2PFdFi++i4cfvpdrrhnCxInTWLXqqUAG\nVyqgJk36SOet0aHQaOrq7sFsNVOnvu7dGq2zQZEg6W1aiH37fl+0+aXpabJPxVY/qcAanN6mhaio\neAex2GwefPBrnYNOnUtw113/zDXXBO/yYiqgrr66kkWLDt8aXVc3hOHDdWu0SFD19tDqlpZTiza/\nND1N9mnqhz74+UHQQZN+W3U0Oo/m5noOHBhJVdXV7NiRfKBpPP5xFi2aSyKRYOXKJ339MNjU5cJE\nIsHChXOJxz/OggX/qlujRYpQqeTXzp1oepocCN94442FbkPSxo03FroJKfXLRrFx+zA2bh+WXDFl\nCowdW9hGFYFEIs6xx9YwcaIxYYJjy5bHgGsZOvRU2ttr2LPndxx77C/Zt++3wA7uu+823v72d/G2\nt53AqlVPMXr0+IKfVaW620ePHs/atUu5884bCIfbWbHiLYYPv51t226lvPw6hg07hbKyY6mpWcQn\nPvEe3vOe9/JXf3U6w4bVFbT9fnPKKXy/0G3IGh9lWFBs3D4sMNlaSvk1ZMjJOJfKr3czcaLxnvdM\nUoZ1M5D80mVEjy4T5l5fg07j8SG0t79EKNT75cVCjIvoaTzDNddUds49k+pub29/kVisioMHV1Nb\nu4OKCnRrtEiRKJX8ikZXYLZV+ZVlJd2z1WsPVkDOtIIs/SxxxIjX2bTpV1RWvhfnmmlvX82BA43U\n1d1JU9Ni1q9/jObm89ix4wmOOWYkd955Q07PGNPP/sys8wxw/PhTWbRoLo2NZ7Fhw6/5y1+GUFFx\nE/v2PUQo1Ew8vodQCMzqOeec0bz//SN1NtgH9WyVtiD1bKXzc35Bzz1Yyq/sG0h+mW+uKdfX56Uh\n6sHyn127NrFq1R+A1Jw299PUdC21tTNobl5CU9N1jBu3mpaWSxk+fA8NDdM56aQNXHzxV7jjjmu5\n5prbMj5j7H72d/vt3+x83xtvnMWWLacycuRSDhw4lurq+3jrrfOoqbmIoUOvZP/+x72ZlGd1HnvS\npHMZPXp8Vv+citHMmRTPaNs8ZVgxqV82KvAZ7If8Sh37yAz7DxYunKv8ypGB5FdGA+TN7Idm9pyZ\n3Wdm5d22nW1m28zsj2b2dCbHyRYNdPenVPf8jBmzOeGECd2651cQiyW752OxS9myZRe1td9m+/YE\nCxbc0jkgdc2aZzufW5Ya+Omc67IM9Lot1b2+Zs3SzsGiqffduRNqa7/Nli07icUupb29hVjsS0Sj\nC2luXkBFxa7O7vbU51BQ+V/Q8kv8Kdv5Bb3n1NGyracMmz//FnbscMovHxh0sWVmpwNjnXPTgFeB\nT/ew2wPOubOdcx8e7HGyRZOPBkNqTpvPfMaYOvV1ysrmU119Kq2tf+wSXPH4B9iypYPa2m8fcSdQ\nenClAigVYum/9xROqbtx6uq+0/m+4fCVXcYztLbeS3X1HsrKDjJt2ht85jPGrFmaeyZIgpZfEgzZ\nyK/0k7/uGdbbcur5hekZliywDhGPn6H88oFBX0Y0s68Czc65e83sfcCXnXNfT9t+NjAfeAtY5Jz7\nz6O+YQ664PUInWBL757fufM1Hn/8t4TDZ1BefioHDz5BPP4Vxoz5JO3tz3d21Tc3f4GamkYaGs7k\nxBM34FyCrVvfxUknbeC7372P73//C2zZcmqXbV27199DTc2PqK39KPv3P8b+/V+ntvY6EokGDh1a\nTzz+EjNmXMyYMe8A1N2eqUJdRsx6foEuIw5CMVxG7M1g8qul5VK+8Y0rWbRo7hE51dty6pLkf/3X\nvC4ZVlk5lZ07f0c4fDfDhp1PR8eryq8sG0h+ZXI3Yh2w01uOAiO6bV8OnOItP2xmzzvnVqTvYGaz\ngdkAc6+6itkzZmTQnCQVWMUj/e6fXbs2MWZM8qGwO3a8xhNPHKSycg+trfdy4EBj551AyTPGRxg3\n7tts2fJp4AB1db9gx44v8Oijd3X2Wm3ZcjFwgNra+9my5QFqar55xN04iUQH5eU1TJu2yQuntwHT\nFVDFIeP8gtxkmBSHweRXOHwlCxb8KwcOHNslp+rqftHr8vbts1iw4BbC4X/qkmHDh2+iurqVePwg\nZ5/dxJgx01F+FU6fxZaZjQZ+3cOmJ4EabzkC7Evf6Jw7mPYe9cDpwIpu+8wD5gEZnRWqwCp+3YPr\nuOOSwZU6Y6ysPIOWlj92njG2t7dw8OAlmN1LXZ0RCs3m17/+OsOH3wVY57YhQ15ICyeorj6VeHw+\nU6emzv6+qnAKsFzml7dfVjJMilt/8qu19VlCoZHs3r2J2tpvkp5TtbX0uFxXZ8TjZ7J580+orV1P\nItHQmWEf/KAyzE/6LLacc7uAs7uvN7NJwBzgXuB84IVu22ucc03er1OBn2Ta2HS6q7B09eeMsanp\np3R0RIFdNDTcAIzl4MEY4fCjtLS8QkfHPmAXBw7c1y2cdPZXTPyaX1K6essvSGVYhETiDRoa/qsz\np/bsuYaOjrojlhsabiAUqqG8vE098D436MuIzrlVZrbbzJ4DtgI/AjCzuc65K4FLvC72GPCCc25p\nNhqsXixJ19sZY1PTXrZtex2YyLhxEcwOsm3bZMaNq8XsQNq2k4hEFE6lplD5JZIuPb+g9wyrrS1j\n//4jl8eNi3j5dYPyy+cCMc+WerFEipPm2SptxTxAXopfvgbI54WmbBAREZEg82WxpZ4sERERKRa+\nKbZUYImIiEgx8k2xBajAEhERkaKT0bMRs0qFloiIiBQh/xRbIiIiIkVIxZaIiIhIDqnYEhEREckh\nFVsiIiIiOaRiS0RERCSHVGyJiIiI5JCKLREREZEcUrElIiIikkMqtkRERERySMWWiIiISA6p2BIR\nERHJIRVbIiIiIjmkYktEREQkh1RsiYiIiOSQii0RERGRHBp0sWVmETNbZmYHzey0HraHzeweM3vO\nzO7IrJkiItmj/BKRfMqkZ6sFuABY1Mv2C4EdzrlpwFAzOyODY4mIZJPyS0TyZtDFlnOuwzm39yi7\nnAk86S0/Dpw12GOJiGST8ktE8imXY7bqgCZvOQqM6L6Dmc02s+Vmtvzxx+flsCkiIgPSZ35B1wyb\n9/jjeWuciARLWV87mNlo4Nc9bPqcc27XUV66H6jxliPAvu47OOfmAfMA6utxfbZWRGQAcplf0DXD\nqK9XholIj/ostrxAOnsQ7/0icC6wFDgfmD+I9xARGTTll4j4QUaXEc3s98B5wN1mdpm3bq63eTFw\ngpk9B7Q5517K5FgiItmk/BKRfOmzZ+tonHMf62Hdld5/Y8Blmby/iEiuKL9EJF80qamIiIhIDqnY\nEhEREckhFVsiIiIiOaRiS0RERCSHVGyJiIiI5JCKLREREZEcUrElIiIikkMqtkRERERySMWWiIiI\nSA6p2BIRERHJIRVbIiIiIjmkYktEREQkh1RsiYiIiOSQii0RERGRHFKxJSIiIpJDKrZEREREckjF\nloiIiEgOqdgSERERySEVWyIiIiI5pGJLREREJIcGXWyZWcTMlpnZQTM7rYftZ5vZNjP7o5k9nVkz\nRUSyR/klIvmUSc9WC3ABsOgo+zzgnDvbOffhDI4jIpJtyi8RyZtBF1vOuQ7n3N4+drvYzJ4zs38c\n7HFERLJN+SUi+ZTLMVvLgVOADwMzzOx9OTyWiEg2Kb9EJGvK+trBzEYDv+5h0+ecc7t6e51z7mDa\ne9QDpwMrur33bGC29+v9zrlL+9NovzOz2c65eYVuRzbos/hTMX2WXMplfnnblGEZmDkz10conu9K\nsXwOKK7P0l/mnMvsDcwWAD9yzq3ptr7GOdfkLf8S+IlzbulR3me5c25yRo3xCX0Wf9Jnke6ylV/e\nfkXzd6LP4j/F8jmguD5Lf2V0GdHMfg+cB9xtZpd56+Z6my/x7vZ5EdjeV1CJiOST8ktE8qXPy4hH\n45z7WA/rrvT++1Pgp5m8v4hIrii/RCRf/DSpaTFdv9Vn8Sd9FsmlYvo70Wfxn2L5HFBcn6VfMh6z\nJSIiIiK981PPloiIiEjR8VWxZWa3mtmrZvaKmf3OzGoL3abBMrPPmNlaM0uYWSDvujCzGWa2wcxe\nN7NvFbo9g2Vm95jZHjNb0/fe/mZmx5vZM2a2zvv/SxNu+oTyy1+UX/5Tyvnlq2ILeAo4zTn3HmAj\n8E8Fbk8m1gCfAgJ5F5OZhYH/Bj4KTAA+b2YTCtuqQVsAzCh0I7IkBnzTOTcB+ADwtQD/vRQb5ZdP\nKL98q2Tzy1fFlnPuSedczPv1ZWBcIduTCefceufchkK3IwNTgNedc5ucc4dITgx5UYHbNCjebfv7\nCt2ObHDO7XTO/clbPgCsB8YWtlUCyi+fUX75UCnnl6+KrW4uBx4rdCNK2FhgW9rvb1EiX4qgMLOT\ngPcC/1vYlkgPlF+FpfzyuVLLr4zm2RoMM/sDMLqHTd92zj3s7fNtkt2Nv8hn2waqP59FJBfMbBjw\nG+Dq1EznknvKL5HMlWJ+5b3Ycs6de7Tt3kzOFwIfdj6fl6KvzxJw24Hj034f562TAjOzcpJB9Qvn\n3G8L3Z5SovwKDOWXT5VqfvnqMqKZzQCuBz7unGspdHtK3P8BJ5vZX5lZBfA54JECt6nkmZkBPwPW\nO+duK3R75DDll68ov3yolPPLV8UWcBcwHHjKzFaZ2U8K3aDBMrNPmtlbwBnAo2b2RKHbNBDeQN+v\nA0+QHMT4oHNubWFbNThm9ivgJeAUM3vLzK4odJsycBZwKXCO9x1ZZWZHPHZGCkL55RPKL98q2fzS\nDPIiIiIiOeS3ni0RERGRoqJiS0RERCSHVGyJiIiI5JCKLREREZEcUrElIiIikkMqtkRERERySMWW\niIiISA6p2BIRERHJIRVbIiIiIjmkYks6mZkzs/9I+/1aM7uxj9d83My+lYVjX2Zme73HN6w1s0Vm\nVp3p+4qIDJaZfdvLo1e8bPqemd3cbZ9JZrbeW37TzJ7rtn2Vma3JZ7vFf1RsSbp24FNmdkx/X+Cc\ne8Q5d0uWjv+Ac26Sc24icAj4bJbeV0RkQMzsDOBC4K+dc+8BzgWe4chc+hzwq7Tfh5vZ8d57vCsf\nbRX/U7El6WLAPOCa7hvMbKaZ/a+ZrTSzP5jZKG/9ZWZ2l5lFzGyLmYW89UPNbJuZlZvZ283scTNb\nYWbPmdmpR2uEmZUBQ4HG3o5tZiEze83M3ubtEzKz183sbd7Pb8zs/7yfs7x9Ppj28NOVZjY8m394\nIlJUxgB/cc61Azjn/uKcWwo0mtn70/a7hK7F1oMcLsg+322blCgVW9LdfwOzzCzSbf3zwAecc+8F\nfg1cn77RORcFVgEf9FZdCDzhnOsgWcB9wzn3PuBa4Me9HPuzZrYK2A6MAOp7O7ZzLgHcD8zy9jkX\nWO2c2wv8J3C7c+5vgIuBn3r7XAt8zTk3CZgGtPbzz0RESs+TwPFmttHMfmxmqWz7FcneLMzsA8A+\n59xraa/7DfApb3kmh3NMSlhZoRsg/uKcazKze4F/oGsxMg54wMzGABXA5h5e/gDJM7pnSIbRj81s\nGHAmsNDMUvtV9nL4B5xzX7fkjv8NXAfccpRj3wM8DNwBXA7M99afC0xIO16N144XgNvM7BfAb51z\nb/Xjj0RESpBz7qCZvY/kidmHSGbQt0jm3Itm9k2OvIQI0ECy9+tzwHqgJY/NFp9Sz5b05A7gCpKX\n8lL+C7jLOfdu4EpgSA+vewSYYWYjgPcBS0j+P7bfG4uV+jnqOAbnnCN5Njj9aMd2zm0DdpvZOcAU\n4DFv/xDJnrDU8cY65w56Y8v+DqgCXujrcqaIlDbnXNw590fn3PeArwMXe7mzmWQv/sUki6/uHiB5\nwqhLiAKo2JIeOOf2kRx3cEXa6gjJy3sAX+rldQeB/yN5GW+xF1RNwGYz+wyAJZ3ej2ZMBd7ox7F/\nSvJy4kLnXNxb9yTwjdQOZjbJ++/bnXN/ds790Gunii0R6ZGZnWJmJ6etmgRs8ZZ/BdwObOqlh/x3\nwL8DT+S2lRIUKrakN/8BpN+VeCPJS4ErgL8c5XUPAF+g69neLOAKM1sNrAUu6uW1n/UGr78CvBf4\n134c+xFgGIcvIULyEuhk73btdcDfe+uvNrM13vt3cLgnTESku2HAz81snZcZE0hmEcBCYCK99Fw5\n5w44537onDuUl5aK71nyio1IMJnZZJKD4acVui0iIiI90QB5CSxvsOpXOXxHooiIiO+oZ0tEREQk\nhzRmS0RERCSHVGyJiIiI5JCKLREREZEc8s8A+fp6DR4TKTUzZ1rfOwWEMkykV/XLRsGUKYVuRlbN\nnEm/80s9WyIiIpIzxVhoDZSKLREREZEcUrElIiIikkMqtkRERERySMWWiIiISA75527EozgUCtFW\nVUWiLBDN7VFZeztD29r6f+uCiBQFBzQPGUKssrLQTRm0UCzGkNZWKhKJQjdFJJB8X70cCoVoi0So\nrqggDIEsVhzQUlFBeyzGkFis0M0RkTxqLyvDhg8nEgoFNr/iJDOMaFQFl8gg+P4yYltVFdUVFZQR\nzEILku0eEgpxqKqq0E0RkTw7VFVFVUALLUjmVxlQXVFBmzJMZFB8X2wlysoIF7oRWRACEiHf/3GL\nSJYlAlxopQtDoIdyiBRSIP71L4agKobPICKDUwzf/2L4DCKFEohiS0RERCSoVGxlaF80yievv56h\nH/wgJ150Eb984olCN0lEpN+UYSK5pwvwGfrarbdSUV7O7sceY9XGjVwwZw6nn3wyE8ePL3TTRET6\npAwTyT31bGWgubWV3zzzDP965ZUMq65m6qRJfHzaNO577LFCN01EpE/KMMm1+mWjCt0EX1CxlYGN\nW7dSFg7zzhNO6Fx3+skns3bTpgK2SkSkf5RhkhdTphS6BQVX1JcRz7nsMpoaG7usq6mrY8mCBVl5\n/4pVCSkAACAASURBVIMtLdQMHdplXWTYMA60tGTl/UWkdOU6v0AZJpIvRV1sNTU2sry2tsu6yd3C\nKxPDqqtpam7ueszmZoZXV2ftGCJSmnKdX6AME8kXXUbMwDtPOIFYPM5rW7d2rlv92msaWCoigaAM\nE8kPFVsZGFpVxafOPpvvzptHc2srL6xezcNLl3LpRz9a6KaJiPRJGSaSHyq2MvTj66+ntb2dY2fM\n4PP/8i/8zw036KxQRAJDGSaSe0U9Zqumru6IMQ41dXVZPcaISISHbr01q+8pIpKP/AJlmEg+FHWx\nlc27dkRE8kn5JVI8dBlRREREJIcyKrbMLGJmy8zsoJmd1m3b2Wa2zcz+aGZPZ9ZMEZHsUn6JSL5k\n2rPVAlwALOpl+wPOubOdcx/O8DgiItmm/BKRvMio2HLOdTjn9h5ll4vN7Dkz+8dMjiMikm3KL5Hc\nql82So/q8eRyzNZy4BTgw8AMM3tf9x3MbLaZLTez5fMefzyHTRERGZA+8wuUYSLSPzm7G9E5dzC1\nbGb1wOnAim77zAPmAVBf73LVFhGRgehPfnn7KcNEpE8569kys5q0X6cCr+fqWCIi2aT8EpFsyrjY\nMrPfA+cBd5vZZWY219t0iXenz4vAdufc0kyPJSKSTcovEcmHjC8jOuc+1m3VAm/9T4GfZvr+fnfX\nwoUsWLyYP7/xBp8/7zwWfPe7hW6SiPST8kv5JZIPRT2DfD4cd8wxfOfyy3ni5ZdpbW8vdHNERPpN\n+SWSHyq2MvSpD30IgOXr1/PWnj0Fbo2ISP8pv0Tyo+gf1+Oc48mXX8Y53SgkIsGi/BIpDkVfbC1d\nuZKv3Hw3z61aVeimiIgMiPJLgqp+2ahCN8FXirrYcs5x888foSN2ETcveERnhyISGMovCTzNHt+p\nqIutpStX8vq2IYwZeQOvbavU2aGIBIbyS6R4FG2xlTorDIdnYxYiHJ6dk7PDWCxGW3s78XiceDxO\nW3s7sVgsq8cQkdKi/BIpLkVbbD2/ahUrN+ylrX0je/ffQ1v7Rv60YQ8vrF6d1ePcNH8+VdOnc8u9\n93L/449TNX06N82fn9VjiEhpUX6JFBfzzTiAXp4rtj8SoXbIkAG/3abt2/nDsmVHrD93yhTGjx07\n8PZlwf62Nmqj0YIcW8SXZs60Qjcha7KYYX7ML1CGSf/VLxtV9GO2Zs6k3/lVtPNsjR87ltmf/GSh\nmyEiMmDKL5HiUrSXEUVERET8QMWWiIiIZE0pXEIcKBVbIiIiIjkUiGLLJ0P4M1IMn0FEBqcYvv/F\n8BlECsX3xVYoFiNe6EZkQQIIJRKFboaI5FkokSiKQiVOMo9FZOB8X2wNaW2l5dAhYgT3zMoBbYkE\nFa2thW6KiORZRWsrrQEuuBwQA1oOHWKIMkxkUHw/9UNFIgHRKC1VVSTKfN/cXpW1t1Ops0KRklMZ\ni9F84ADRyspCN2XQQrEYQ1pbk3kschR6AHXPAlG9VCQSVDQ3F7oZIiIDZsCwtjZoayt0U0TyQ3ci\nHsH3lxFFREREgkzFloiIiEgOqdgSERERyaGMii0zi5jZMjM7aGanddsWNrN7zOw5M7sjs2aKiGSX\n8ktE8iXTAfItwAXArT1suxDY4Zy73MzuNrMznHMvZXg8yYFz5syhKRrt/P2txkbKgVF1dexubCSR\nSBAPhSiHzuVxdXUA1EQiLLnttsI0XCQzyq8ikZ5hbzU2Ek4kCIWSfQnKr/zRnYi9y6jYcs51AHvN\nrKfNZwKPesuPA2cBCqsC6i2QEokEY0IhasJhlhx/PJMbG7kXmBCJsK6xkQnl5UyOx5PrvOWapiaa\n4nE2NDQw+Yor2N3YSAd0hhgoyMTflF/B0lt+jaqrY2dDA6eUl3fm1/LyctbFk9Nh95RfyyMRztm2\nrUt+qRDLEt2J2KNcTv1QBzR5y1FgRPcdzGw2MBtg7lVXMXvGjBw2pzSlB9TOhobOomocHA6kUIgJ\n4TCT4/2fq78pHmd5OMw6DhdlX4TOEEsvxEDBJYHTZ36BMizX+pNfnfkzgPyCZIY9FQodcVLZ/UQS\nlF+SuVwWW/uBGm85AuzrvoNzbh4wD4D6+qBOsOw7vQXUvYMoqgYjvRALNzURj8f5iIJLgqXP/AJl\nWC4ov6QY5fJuxBeBc73l84EXcngsSdMUjbI8EmF5JMJToRDLw2Ga/n979x/fVn3fe/z1tSw7doh/\nxJDYxCk0NAskbKRbRikkjPIzW5PS3RW2NaWlsIbRdvdCWkZXuI9LV+7ajo52NHtcEtrGpXChJOug\nDiWQEkoChaVJCbuxE37l9+/g2HJsJ7Ykf+8flpwjRbZlS0c60nk/H488qkiKdISrtz/ncz7ne1wO\nqKFEo1FmBgI0lJQMbpNzPkzEo5RfeaL8kmKUcWfLGPNLYDYwwxizDPiotfY2YDXwSWPMBuANDZe6\nK3lvsLW9nUAgkNa/DQQCtEajHOzvZ04oxD4GfrtMDoU4DPSHw0RLSrjOcTvQ3z+w55fmewCDhxcP\n9vdrL1E8QfnlDZnkFwzk0MFweDC/poTDpwbkU+TXnFCIg/39BILB9LdR+SUZMNZ6pPOtFvyoDdVu\nH2yDxwdEY633xwOBgZZ4bBDUOWAKowuO5DMYnQPyB9vaWFtSQiD2fs7Wf3y7vhzbzoP9/TTU1Y36\n/aWwxc9aWvjNi1NOpxckZdio5DO/kt/fOSAf6O9Xfo1B88bJvhuOX7iQtPOrIK6NKKnF2+3AwIBn\ninkGZ9dqUSyUZmQhFIb791cuWcJnnSEKgyE6uO1JA/YAc9Se9xefBbMkymd+wdAZpvwSN6jYKjDp\ntNurYqF1sL+fhiwHVDqc7xPf3s749hI79OgIrrf27iWq9nxRO239HRVavqT8Er9SsVVgnHuDc2J7\ng61Je4Prpk4deDwUYtOPfpTzbUzYFkfgzLn11sG9QHbtGrx/cAgVTn027SUWvIQCS8WVoPwS/1Kx\nVQCS9wbntLdTlbQ3mDzkDgN7V15SVV09uG0HHQP20VjYHo5EmBMLsfheovYQC4e6V5KKX/IL/DtE\nr5XjR6ZiqwAMNduQ73b7aCW355PnIsIMDKACg7MQ2kP0rpQBqwJLkvglvxKG+/HhLJe++8NSseVR\n6cw2eKndPlrJ7flN1dWDXS2ASCRC665dvttD9DIVVzIa8QyL5xcMfK+J5Vix5RcwmGHx/AJ16WWA\nii2PGmq2oRDa7aMVb8/HW/MAFjQH4QE6NChjFc+weH4BvBmJFG1+wanDi/H8AgYzTPnlbyq2CsyM\n2N5gQwHuDQ4lvrfnHEBtdXS5JHfUvRI3BUtLmXnuuUWZX3Aqw/yUX35cX2ssVGx5jLP1PifWej8Y\njTKHgb2mhiLZG0wleQ+xgdhnVzveVepeSbakGoaP5xecyrBizC9I7NI3xO6LZ5hGIvxNxZbHxFvv\nrY7W+xxg07nnFuRsw2gMNceloXl3DBZZKq4kS1IOwzOQX1CY81mj4ezSO+e4fDs0L4NUbHlE8jBp\nJBLhzUiEYGnp4HxDse4NppJqjiscjWpoPgM6RChuSTUMH45GaYWims9KV6o5rnh+xe9Tl95fVGx5\nxGnDpLFB+GKbb0jXUHNcGppPn4oryZVUw/Ct4Pv8gsQ5rmIbmtf6WulTsSVSRDR/JSI5pYxJi4qt\nPEq1llaZj4ZJ06Gh+eGpeyX5lHz4MBiJcJFj/MHv+QWnD80fjkQIQ8LQfCHml7pao6NiK49SraX1\nQwZa71D8w6Tp0NB8IhVX4iXJhw9fc4w/KL8GJA/NF1V+KXvSpmLLIxIuXeGzYdJ0pRqaT15Vv9io\nuJJCEM8vUEd+KH7MLznFWGvzvQ0Dmps9siHuc7beG0pKgIGwWjd1qvYG0xDfQ4xf9BViAV9XV5Dt\n+GR+mrtauBCT723IGmWYMiwNxZJfWsx0dPmlzlYepFxLy3HleElP/KKvUATt+GQ+DzHxNmVY5go5\nv1RojZ6KrTyKX+cQ8OVaWmNVbO14P3WypLgow0av2PJL0qPDiDl25ZIlvLVjB2tjrXcY+KItqqpS\n632U4u34t/buJRoL/GsKoB2v4uoUHUYsLM7Dh/EMCwQCzNDhw1Er1PwCdbbicnoY0RjzHeBSYBdw\ni7U2HLv/CuCnwHtA1Fp7VabvVQw6QyEaSkoGW+/A4J6h11hrWbtlC9fMno0x3v2dGI1GPb1YYLaL\nK2stW7asZfbsazz9cykEyq/RSbl4qUfzCwojw7yeX8kyXfLBr/lVMvJThmaMuQiYYq2dB2wHPpX0\nlJ9Za69QUCWKn7kT/3NNf78nW+/rW1r4wtKfsaGlJd+bklK8HX9Nf//gf8sqj7TjmzdOHvwDDBRY\n8T8ZamlZz9KlX6elZUPGr+Vnyq+xc2bYNR4+fOjlDPNyfo0ogxzza35l2tm6FHghdnsN8HngCcfj\nf2GM+Qiwylr7rxm+V0Fztt8fTdF+91rL2FrLt1a+SDh6Pd9atY55s2Z5bi8kef2aeDu+NQ+LBeZq\niQZrLStXLiMa/QSrVi1j1qx5nvu5FBDlV5qSFy9divcPH3o9w7yUX+nKRlfLr/mVUWcLqAU6Y7dD\nwETHY5uAGcBVwHxjzB8l/2NjzGJjzCZjzKbla9ZkuCneFm+/xw8hzgwEBo/Te9H6lhbePTiehtqv\n8s6BSk/uGSaLt+NnBgI0lJSwqbp6cIX+bHN2rlJ2r1yaZ2hpWc/Bg1Bbey8HDljf7R1mWUb5Bf7J\nsELLLyi8DMtlfmUkw66WX/Mr085WB1AVu10NHIs/YK3tit82xjQDFwGbnf/YWrscWA74YrgUCmPx\nv/geYUnJHRw/8WtKSr7At1b9q+f2DOOSL4cBZL0d74XFReN7hSUlizlx4jglJYt9t3eYZRnlV+x5\nvsowr+ZX8vfTWst9T2zgRO/fUVLi7QzLRX55gd/zK9Ni6zfAEuBR4Drg1fgDxpgqa218r3Eu8HCG\n71WQCrH9/kprK2/saAeeo+34WuomXMvv3jvGq9u2MXfmzHxv3mmc7fiqzk46o1E6Y9dPPNjfz5VL\nlmTUik/oXOVRa+sr7NixC2PepLPz11RV1fLeezvZtu1VZs6cm9dtK1DKrzRcuWRJQn5B7Azq2lpP\n5Feq72drywZ2tHVA+DkOdTzPmVXXeDbD3M6vbMn0DES/51dGxZa1dosx5rAxZgOwB/iuMWaZtfY2\n4EZjzGIgArxqrV2fhe0tOIV29g7AlLo6/vHTH+Gh1a/SG17AWVXr+LsFl3L2xIkj/+M8cy4UCAOL\nBX52lK14L3SxUqmrm8KiRZ+juflH9PXVU1V1mAULbmHixLPzvWkFSfmVHi+dQT3kzFDS97Oubgqf\n/vTnWL36McJHFlIe/DUL/vgatu39Pdq7Bl5j4cWH3d7cUctGfnmV3/Mr46UfrLV3Jd11W+z+HwI/\nzPT1i4VX2++pTKuv5/ypU+kNn8O0+ns41P4a5zc2Mq2+Pt+bNqyq6mreamsbXCgQ0l8ssBDWvqqv\nn0Zj4/mEwx+iuvrf6Ov7Eo2NF1BfPy3fm1awlF/pceYXDGTYjBzmV8L3M43vZn39NKZOPZ++vkaq\nJ15NX/9+ps77M+ovvHzgCRs3JrymFwqvTPLLbZkOxoPySyvIuyjefp/T3j54X1UgAB5pvw8lPrMV\nCNxJd+/rHO3s5O+bfs7r373Q08fW1z34IHNuvZVAZ+fg8G40GuVgW9tprXivdq+GE595iERuoqPj\nIDU1N/lq5kFyyzkCQYrrH+b8LN9RfD9PnfV2CW1t91JT89nE74rztTxSeMXza6bjzMR4fnnizMQs\nrA/o5/xSseWizlCItUnt90K4flh8Zqs8+BZHQ6uw9uNs2fkUr7S2Mm/WrHxv3oiciwTCwEKBh490\nFUT3ajjxmYeens1Eo/+PUKjPVzMPklv5vP7hWIusuNbWV3jvvZ2cOLGX/v6rCIVW8u67kdTflSEK\nr3x2u7y00Gk2ulqg/FKx5TLntcMg9+33sZhSV8c3F13C2/vX8+hL4xlXdjsn+95gf1tbvjdtRFXV\n1VzT1sZkx3lhE0pKOQEFV1wlq6ubwsc+dinr1v2C8vJb6O39MR/72OW+mXmQ/Mj19Q+zcULKwHfl\nMl56aTtlZUvo62vhyivPH/m7En/PPBVdXjszMZsnB/k9v1RsuWzG1KkJf2/w4AKmyabV1/OF667j\nT+97iNoz7sRSTkXZHfxk3ff5y3nebfk2b5zMnX/1OF976HrWja+B8eMHH7ssdDSPW5Ydkyd/kF27\n9hMM3khV1W2cPHkBe/YsZ/LkD+Z706SIOTOsweUzqLP1y33y5A+yZ88Bzjjjq8A4ysq+yp49j6T/\nXclT0ZW80KknZGkn1e/5pWLLJc7TpeMCgQBUVQ3zr7xjcPkH00Jb50bOrBrvuVOnh5y7Ki+D8eM5\nsHcb/dEIAO39Ub526zmUV0/iGw/+Nsdbmh2tra/wzjv/xYkTbxCJHKKsbLqv2vCSO/nIr2xe3Dhr\nyww4iq5cc16gGsj5MhDZOnwY5/f8UrHlks5QiBnBIJ91flnCYc8fQoybUlfHPy66hIeaV9Dbdy5n\nVu3i7xZcmfflH9KZuyqvnsRloSO0h3upLxlowU8PlvNM9VlcFjqSi810xcSJZzNx4kTa2i6lquo1\nFiyYjjE3+6YNL7mT6/zKZqEFLiwzcPHFNMcKrlx0uKqqq7myrY2GklMXeZkRDOZ+Rfks/kz8nl8q\ntly0LukQohevgTiUafX1nN/YSG/49zivYQWh7s9zQZ6Xf0g3kOOdq6/deg7rqs9ye7Nypq1tH+Hw\nVBoavkV392eYOvUCLoyfyi6SZbnKr2x3UCBxmYGGhsfo7v5M5ssMxLKneeNG1wuu+JmJyYcSczUk\nn+3iF5RfKrZcUOiHECFx+QcwnAzP4Z9WvpjTy11kevbg0fZDXNl+KPG+TDcqT+KnTYfDiwBDIHCb\nr06bltzJZX65dXWG+PclELgNMITDf8jKlQ9n7fuSqzmuK/fupTPHhxLdKLT8fqkeULHlCi+tuDxW\n8ZmtcWVv09H1HG3HX6D7JK7ObGV7aYbS/igbg+UJ930g3JvRa+ZLa+srvP3225w48Vui0fcIBst9\nNe8guZOr/HLzMljxma2ysu10dTVz/PhqTp4szc73JYfD88WyorzfL9UDKrZck+8VlzMVX/7B2nd4\naPVv6A0vYMrEdTTU1mb1fTJdT2c4piTA9tiAvPO+QjQw71BCW9vvBudPwD/zDpJbbueX29cbHZjZ\nuhlrLatX/yfh8PVMnPgatbUNI//jdCUVXdkuuPKxorwbh3R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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "from sklearn.datasets import make_moons\n", "X, y = make_moons(n_samples=100, random_state=123)\n", "\n", "gs = gridspec.GridSpec(2, 2)\n", "\n", "fig = plt.figure(figsize=(10,8))\n", "\n", "labels = ['Logistic Regression', 'Random Forest', 'Naive Bayes', 'SVM']\n", "for clf, lab, grd in zip([clf1, clf2, clf3, clf4],\n", " labels,\n", " itertools.product([0, 1], repeat=2)):\n", "\n", " clf.fit(X, y)\n", " ax = plt.subplot(gs[grd[0], grd[1]])\n", " fig = plot_decision_regions(X=X, y=y, clf=clf, legend=2)\n", " plt.title(lab)\n", "\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Concentric Circles" ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [ { "data": { "image/png": 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klyh90vpVWVXFmCuvZFxRUaqfY6BfgNWwHsYaW4OI3MJj2sVoxYC3CkexvWY9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\nkkzKCONGytHBX1HmOiPqV4I4srLYGCqs5RfGzJp+DWcYjWiAjfP1M8H+n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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "from sklearn.datasets import make_circles\n", "X, y = make_circles(n_samples=1000, random_state=123, noise=0.1, factor=0.2)\n", "\n", "gs = gridspec.GridSpec(2, 2)\n", "\n", "fig = plt.figure(figsize=(10,8))\n", "\n", "labels = ['Logistic Regression', 'Random Forest', 'Naive Bayes', 'SVM']\n", "for clf, lab, grd in zip([clf1, clf2, clf3, clf4],\n", " labels,\n", " itertools.product([0, 1], repeat=2)):\n", "\n", " clf.fit(X, y)\n", " ax = plt.subplot(gs[grd[0], grd[1]])\n", " fig = plot_decision_regions(X=X, y=y, clf=clf, legend=2)\n", " plt.title(lab)\n", "\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Example 6 - Working with existing axes objects using subplots" ] }, { "cell_type": "code", "execution_count": 13, "metadata": {}, "outputs": [ { "data": { "image/png": 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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "import matplotlib.pyplot as plt\n", "from mlxtend.plotting import plot_decision_regions\n", "\n", "from sklearn.linear_model import LogisticRegression\n", "from sklearn.naive_bayes import GaussianNB \n", "from sklearn import datasets\n", "import numpy as np\n", "\n", "# Loading some example data\n", "iris = datasets.load_iris()\n", "X = iris.data[:, 2]\n", "X = X[:, None]\n", "y = iris.target\n", "\n", "# Initializing and fitting classifiers\n", "clf1 = LogisticRegression(random_state=1)\n", "clf2 = GaussianNB()\n", "clf1.fit(X, y)\n", "clf2.fit(X, y)\n", "\n", "fig, axes = plt.subplots(1, 2, figsize=(10, 3))\n", "\n", "fig = plot_decision_regions(X=X, y=y, clf=clf1, ax=axes[0], legend=2)\n", "fig = plot_decision_regions(X=X, y=y, clf=clf2, ax=axes[1], legend=1)\n", " \n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Example 7 - Decision regions with more than two training features" ] }, { "cell_type": "code", "execution_count": 14, "metadata": {}, "outputs": [ { "data": { "image/png": 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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "from mlxtend.plotting import plot_decision_regions\n", "import matplotlib.pyplot as plt\n", "from sklearn import datasets\n", "from sklearn.svm import SVC\n", "\n", "# Loading some example data\n", "X, y = datasets.make_blobs(n_samples=600, n_features=3,\n", " centers=[[2, 2, -2],[-2, -2, 2]],\n", " cluster_std=[2, 2], random_state=2)\n", "\n", "# Training a classifier\n", "svm = SVC()\n", "svm.fit(X, y)\n", "\n", "# Plotting decision regions\n", "fig, ax = plt.subplots()\n", "# Decision region for feature 3 = 1.5\n", "value = 1.5\n", "# Plot training sample with feature 3 = 1.5 +/- 0.75\n", "width = 0.75\n", "plot_decision_regions(X, y, clf=svm,\n", " filler_feature_values={2: value},\n", " filler_feature_ranges={2: width},\n", " res=0.02, legend=2, ax=ax)\n", "ax.set_xlabel('Feature 1')\n", "ax.set_ylabel('Feature 2')\n", "ax.set_title('Feature 3 = {}'.format(value))\n", "\n", "# Adding axes annotations\n", "fig.suptitle('SVM on make_blobs')\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Example 8 - Grid of decision region slices" ] }, { "cell_type": "code", "execution_count": 15, "metadata": {}, "outputs": [ { "data": { "image/png": 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xAbTevI+QH5pbREmW8rW8u5Dij4eaPxa+qqRsRV8fQ/5idrOYn+36FX/4zP3s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\nJF8wS59FgtPdPZapU29h375LgHFMm/YNent3cu65Vx53Ztj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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "from mlxtend.plotting import plot_decision_regions\n", "import matplotlib.pyplot as plt\n", "from sklearn import datasets\n", "from sklearn.svm import SVC\n", "\n", "# Loading some example data\n", "X, y = datasets.make_blobs(n_samples=500, n_features=3, centers=[[2, 2, -2],[-2, -2, 2]],\n", " cluster_std=[2, 2], random_state=2)\n", "\n", "# Training a classifier\n", "svm = SVC()\n", "svm.fit(X, y)\n", "\n", "# Plotting decision regions\n", "fig, axarr = plt.subplots(2, 2, figsize=(10,8), sharex=True, sharey=True)\n", "values = [-4.0, -1.0, 1.0, 4.0]\n", "width = 0.75\n", "for value, ax in zip(values, axarr.flat):\n", " plot_decision_regions(X, y, clf=svm,\n", " filler_feature_values={2: value},\n", " filler_feature_ranges={2: width},\n", " res=0.02, legend=2, ax=ax)\n", " ax.set_xlabel('Feature 1')\n", " ax.set_ylabel('Feature 2')\n", " ax.set_title('Feature 3 = {}'.format(value))\n", "\n", "# Adding axes annotations\n", "fig.suptitle('SVM on make_blobs')\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# API" ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "## plot_decision_regions\n", "\n", "*plot_decision_regions(X, y, clf, feature_index=None, filler_feature_values=None, filler_feature_ranges=None, ax=None, X_highlight=None, res=0.02, legend=1, hide_spines=True, markers='s^oxv<>', colors='red,blue,limegreen,gray,cyan')*\n", "\n", "Plot decision regions of a classifier.\n", "\n", "Please note that this functions assumes that class labels are\n", "labeled consecutively, e.g,. 0, 1, 2, 3, 4, and 5. If you have class\n", "labels with integer labels > 4, you may want to provide additional colors\n", "and/or markers as `colors` and `markers` arguments.\n", "See http://matplotlib.org/examples/color/named_colors.html for more\n", "information.\n", "\n", "**Parameters**\n", "\n", "- `X` : array-like, shape = [n_samples, n_features]\n", "\n", " Feature Matrix.\n", "\n", "- `y` : array-like, shape = [n_samples]\n", "\n", " True class labels.\n", "\n", "- `clf` : Classifier object.\n", "\n", " Must have a .predict method.\n", "\n", "- `feature_index` : array-like (default: (0,) for 1D, (0, 1) otherwise)\n", "\n", " Feature indices to use for plotting. The first index in\n", " `feature_index` will be on the x-axis, the second index will be\n", " on the y-axis.\n", "\n", "- `filler_feature_values` : dict (default: None)\n", "\n", " Only needed for number features > 2. Dictionary of feature\n", " index-value pairs for the features not being plotted.\n", "\n", "- `filler_feature_ranges` : dict (default: None)\n", "\n", " Only needed for number features > 2. Dictionary of feature\n", " index-value pairs for the features not being plotted. Will use the\n", " ranges provided to select training samples for plotting.\n", "\n", "- `ax` : matplotlib.axes.Axes (default: None)\n", "\n", " An existing matplotlib Axes. Creates\n", " one if ax=None.\n", "\n", "- `X_highlight` : array-like, shape = [n_samples, n_features] (default: None)\n", "\n", " An array with data points that are used to highlight samples in `X`.\n", "\n", "- `res` : float or array-like, shape = (2,) (default: 0.02)\n", "\n", " Grid width. If float, same resolution is used for both the x- and\n", " y-axis. If array-like, the first item is used on the x-axis, the\n", " second is used on the y-axis. Lower values increase the resolution but\n", " slow down the plotting.\n", "\n", "- `hide_spines` : bool (default: True)\n", "\n", " Hide axis spines if True.\n", "\n", "- `legend` : int (default: 1)\n", "\n", " Integer to specify the legend location.\n", " No legend if legend is 0.\n", "\n", "- `markers` : str (default 's^oxv<>')\n", "\n", " Scatterplot markers.\n", "\n", "- `colors` : str (default 'red,blue,limegreen,gray,cyan')\n", "\n", " Comma separated list of colors.\n", "\n", "**Returns**\n", "\n", "- `ax` : matplotlib.axes.Axes object\n", "\n", "\n", "\n" ] } ], "source": [ "with open('../../api_modules/mlxtend.plotting/plot_decision_regions.md', 'r') as f:\n", " print(f.read())" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] } ], "metadata": { "anaconda-cloud": {}, "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": "3.6.1" } }, "nbformat": 4, "nbformat_minor": 1 }