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ca2/intro_2.ipynb

-{
- "metadata": {
-  "name": "intro_2"
- },
- "nbformat": 3,
- "nbformat_minor": 0,
- "worksheets": [
-  {
-   "cells": [
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "import numpy as np\n",
-      "import pylab as plt\n",
-      "from sklearn import datasets, neighbors, metrics"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [],
-     "prompt_number": 1
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "iris = datasets.load_iris()\n",
-      "# \u043e\u043f\u0438\u0441\u0430\u043d\u0438\u0435 \u043e\u0431\u044a\u0435\u043a\u0442\u043e\u0432\n",
-      "data = iris.data\n",
-      "# \u0437\u043d\u0430\u0447\u0435\u043d\u0438\u044f \u0446\u0435\u043b\u0435\u0432\u043e\u0433\u043e \u043f\u0440\u0438\u0437\u043d\u0430\u043a\u0430 (\u043d\u043e\u043c\u0435\u0440 \u043a\u043b\u0430\u0441\u0441\u0430)\n",
-      "target = iris.target\n",
-      "\n",
-      "N, n = data.shape"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [],
-     "prompt_number": 2
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "def build_knns(n_neighbors):\n",
-      "    # \u0441\u043f\u0438\u0441\u043a\u0438 \u043c\u0430\u0441\u0441\u0438\u0432\u043e\u0432\n",
-      "    knn_list = []     # \u043a\u043b\u0430\u0441\u0441\u0438\u0444\u0438\u043a\u0430\u0442\u043e\u0440\u044b\n",
-      "    score_list = []   # \u043f\u043e\u043a\u0430\u0437\u0430\u0442\u0435\u043b\u0438 \u043a\u0430\u0447\u0435\u0441\u0442\u0432\u0430 \u043a\u043b\u0430\u0441\u0441\u0438\u0444\u0438\u043a\u0430\u0442\u043e\u0440\u043e\u0432\n",
-      "    ij_list = []      # \u043f\u0430\u0440\u0430 \u0438\u043d\u0434\u0435\u043a\u0441\u043e\u0432 \u043f\u0440\u0438\u0437\u043d\u0430\u043a\u043e\u0432\n",
-      "    \n",
-      "    for i in range(n-1):\n",
-      "        for j in range(i+1,n):\n",
-      "            ij_list.append((i,j))\n",
-      "            \n",
-      "            knn = neighbors.KNeighborsClassifier(n_neighbors, weights='distance')\n",
-      "            X1 = data[:, i]\n",
-      "            X2 = data[:, j]\n",
-      "            X = np.c_[X1, X2]\n",
-      "            \n",
-      "            knn.fit(X, target)\n",
-      "            knn_list.append(knn)\n",
-      "            \n",
-      "            score = metrics.precision_score(target, knn.predict(X))\n",
-      "            score_list.append(score)\n",
-      "    \n",
-      "    return knn_list, score_list, ij_list\n",
-      "\n",
-      "knn_list, score_list, ij_list = build_knns(3)"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "output_type": "stream",
-       "stream": "stderr",
-       "text": [
-        "/Library/Frameworks/Python.framework/Versions/2.7/lib/python2.7/site-packages/sklearn/neighbors/classification.py:131: NeighborsWarning: kneighbors: neighbor k+1 and neighbor k have the same distance: results will be dependent on data order.\n",
-        "  neigh_dist, neigh_ind = self.kneighbors(X)\n",
-        "/Library/Frameworks/Python.framework/Versions/2.7/lib/python2.7/site-packages/sklearn/neighbors/classification.py:131: NeighborsWarning: kneighbors: neighbor k+1 and neighbor k have the same distance: results will be dependent on data order.\n",
-        "  neigh_dist, neigh_ind = self.kneighbors(X)\n",
-        "/Library/Frameworks/Python.framework/Versions/2.7/lib/python2.7/site-packages/sklearn/neighbors/classification.py:131: NeighborsWarning: kneighbors: neighbor k+1 and neighbor k have the same distance: results will be dependent on data order.\n",
-        "  neigh_dist, neigh_ind = self.kneighbors(X)\n",
-        "/Library/Frameworks/Python.framework/Versions/2.7/lib/python2.7/site-packages/sklearn/neighbors/classification.py:131: NeighborsWarning: kneighbors: neighbor k+1 and neighbor k have the same distance: results will be dependent on data order.\n",
-        "  neigh_dist, neigh_ind = self.kneighbors(X)\n",
-        "/Library/Frameworks/Python.framework/Versions/2.7/lib/python2.7/site-packages/sklearn/neighbors/classification.py:131: NeighborsWarning: kneighbors: neighbor k+1 and neighbor k have the same distance: results will be dependent on data order.\n",
-        "  neigh_dist, neigh_ind = self.kneighbors(X)\n",
-        "/Library/Frameworks/Python.framework/Versions/2.7/lib/python2.7/site-packages/sklearn/neighbors/classification.py:131: NeighborsWarning: kneighbors: neighbor k+1 and neighbor k have the same distance: results will be dependent on data order.\n",
-        "  neigh_dist, neigh_ind = self.kneighbors(X)\n"
-       ]
-      }
-     ],
-     "prompt_number": 3
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "def plot_map2d(clf, X):\n",
-      "    x_min, x_max = X[:, 0].min(), X[:, 0].max() # \u0432\u044b\u0447\u0438\u0441\u043b\u044f\u0435\u043c \u043c\u0438\u043d\u0438\u043c\u0430\u043b\u044c\u043d\u043e\u0435 \u0438 \u043c\u0430\u043a\u0441\u0438\u043c\u0430\u043b\u044c\u043d\u043e\u0435 \u0437\u043d\u0430\u0447\u0435\u043d\u0438\u0435 \u043f\u0440\u0438\u0437\u043d\u0430\u043a\u0430 \u0432 \u0441\u0442\u043e\u043b\u0431\u0446\u0435 0\n",
-      "    y_min, y_max = X[:, 1].min(), X[:, 1].max() # \u0432\u044b\u0447\u0438\u0441\u043b\u044f\u0435\u043c \u043c\u0438\u043d\u0438\u043c\u0430\u043b\u044c\u043d\u043e\u0435 \u0438 \u043c\u0430\u043a\u0441\u0438\u043c\u0430\u043b\u044c\u043d\u043e\u0435 \u0437\u043d\u0430\u0447\u0435\u043d\u0438\u0435 \u043f\u0440\u0438\u0437\u043d\u0430\u043a\u0430 \u0432 \u0441\u0442\u043e\u043b\u0431\u0446\u0435 1\n",
-      "    x_range = np.linspace(x_min, x_max, 100) # \u0441\u043e\u0437\u0434\u0430\u0435\u043c \u043e\u0434\u043d\u043e\u043c\u0435\u0440\u043d\u0443\u044e \u0441\u0435\u0442\u043a\u0443 \u043f\u043e \u043e\u0441\u0438 x\n",
-      "    y_range = np.linspace(y_min, y_max, 100)  # \u0441\u043e\u0437\u0434\u0430\u0435\u043c \u043e\u0434\u043d\u043e\u043c\u0435\u0440\u043d\u0443\u044e \u0441\u0435\u0442\u043a\u0443 \u043f\u043e \u043e\u0441\u0438 y\n",
-      "    xx, yy = np.meshgrid(x_range, y_range) # \u0441\u043e\u0437\u0434\u0430\u0435\u043c \u0434\u0432\u0443\u043c\u0435\u0440\u043d\u0443\u044e \u0441\u0435\u0442\u043a\u0443 \u043f\u043e \u0434\u0432\u0443\u043c \u043e\u0434\u043d\u043e\u043c\u0435\u0440\u043d\u044b\u043c\n",
-      "    #\n",
-      "    # np.c_[C1, C2] - \u0441\u043e\u0437\u0434\u0430\u0435\u0442 \u0434\u0432\u0443\u043c\u0435\u0440\u043d\u044b\u0439 \u043c\u0430\u0441\u0441\u0438\u0432, \u043a\u043e\u0442\u043e\u0440\u044b\u0439 \u043f\u043e\u043b\u0443\u0447\u0430\u0435\u0442\u0441\u044f \u0432 \u0440\u0435\u0437\u0443\u043b\u044c\u0442\u0430\u0442\u0435 \u043e\u0431\u044a\u0435\u0434\u0438\u043d\u0435\u043d\u0438\u044f \n",
-      "    # \u0434\u0432\u0443\u0445 \u0441\u0442\u043e\u043b\u0431\u0446\u043e\u0432 C1, C2\n",
-      "    # xx.ravel(), yy.ravel() - \u0441\u043e\u0437\u0434\u0430\u0435\u043c \u043e\u0434\u043d\u043e\u043c\u0435\u0440\u043d\u043e\u0435 \u043f\u0440\u0435\u0434\u0441\u0442\u0430\u0432\u043b\u0435\u043d\u0438\u0435 2-\u0445 \u043c\u0435\u0440\u043d\u044b\u0445 \u0441\u0435\u0442\u043e\u043a,\n",
-      "    # \u043a\u0430\u043a \u043a\u043e\u043d\u043a\u0430\u0442\u0435\u043d\u0430\u0446\u0438\u044e \u0441\u0442\u0440\u043e\u043a \u0441\u0435\u0442\u043a\u0438\n",
-      "    Z = clf.predict(np.c_[xx.ravel(), yy.ravel()]) # \u043f\u0440\u0435\u0434\u0441\u043a\u0430\u0437\u044b\u0432\u0430\u0435\u043c \u0441 \u043f\u043e\u043c\u043e\u0449\u044c\u044e \u043e\u0431\u0443\u0447\u0435\u043d\u043d\u043e\u0433\u043e \u043a\u043b\u0430\u0441\u0441\u0438\u0444\u0438\u043a\u0430\u0442\u043e\u0440\u0430\n",
-      "    # Put the result into a color plot\n",
-      "    Z = Z.reshape(xx.shape) # \u043e\u0434\u043d\u043e\u043c\u0435\u0440\u043d\u044b\u0439 \u043c\u0430\u0441\u0441\u0438\u0432 \u0437\u043d\u0430\u0447\u0435\u043d\u0438\u0439 \u043f\u0440\u0435\u0432\u0440\u0430\u0449\u0430\u0435\u043c \u0432 \u0434\u0432\u0443\u043c\u0435\u0440\u043d\u044b\u0439 \u0441 \u0444\u043e\u0440\u043c\u043e\u0439 \u043a\u0430\u043a \u0443 xx\n",
-      "    plt.winter()\n",
-      "    plt.pcolormesh(xx, yy, Z) # \u0432\u044b\u0432\u043e\u0434\u0438\u0441 \u0446\u0432\u0435\u0442\u043e\u0432\u0443\u044e \u043a\u0430\u0440\u0442\u0443\n"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [],
-     "prompt_number": 4
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "def plot_map():\n",
-      "    plt.figure(figsize=(10.0,12.0)) # \u0441\u043e\u0437\u0434\u0430\u0435\u043c \u043d\u043e\u0432\u044b\u0439 \u0440\u0438\u0441\u0443\u043d\u043e\u043a \u0441 \u0437\u0430\u0434\u0430\u043d\u043d\u044b\u043c\u0438 \u0440\u0430\u0437\u043c\u0435\u0440\u0430\u043c (\u0432 \u0434\u044e\u0439\u043c\u0430\u0445)\n",
-      "    for t, ij, knn in zip(range(1, 7), ij_list, knn_list):\n",
-      "        i, j = ij\n",
-      "        X = np.c_[data[:,i], data[:,j]]\n",
-      "        plt.subplot(3, 2, t)\n",
-      "        plot_map2d(knn, X)\n",
-      "        plt.scatter(X[:,0], X[:,1], c=target)\n",
-      "        plt.xlabel(str(i))\n",
-      "        plt.ylabel(str(j))\n",
-      "\n",
-      "plot_map()"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "output_type": "stream",
-       "stream": "stderr",
-       "text": [
-        "/Library/Frameworks/Python.framework/Versions/2.7/lib/python2.7/site-packages/sklearn/neighbors/classification.py:131: NeighborsWarning: kneighbors: neighbor k+1 and neighbor k have the same distance: results will be dependent on data order.\n",
-        "  neigh_dist, neigh_ind = self.kneighbors(X)\n",
-        "/Library/Frameworks/Python.framework/Versions/2.7/lib/python2.7/site-packages/sklearn/neighbors/classification.py:131: NeighborsWarning: kneighbors: neighbor k+1 and neighbor k have the same distance: results will be dependent on data order.\n",
-        "  neigh_dist, neigh_ind = self.kneighbors(X)\n"
-       ]
-      },
-      {
-       "output_type": "stream",
-       "stream": "stderr",
-       "text": [
-        "/Library/Frameworks/Python.framework/Versions/2.7/lib/python2.7/site-packages/sklearn/neighbors/classification.py:131: NeighborsWarning: kneighbors: neighbor k+1 and neighbor k have the same distance: results will be dependent on data order.\n",
-        "  neigh_dist, neigh_ind = self.kneighbors(X)\n",
-        "/Library/Frameworks/Python.framework/Versions/2.7/lib/python2.7/site-packages/sklearn/neighbors/classification.py:131: NeighborsWarning: kneighbors: neighbor k+1 and neighbor k have the same distance: results will be dependent on data order.\n",
-        "  neigh_dist, neigh_ind = self.kneighbors(X)\n"
-       ]
-      },
-      {
-       "output_type": "stream",
-       "stream": "stderr",
-       "text": [
-        "/Library/Frameworks/Python.framework/Versions/2.7/lib/python2.7/site-packages/sklearn/neighbors/classification.py:131: NeighborsWarning: kneighbors: neighbor k+1 and neighbor k have the same distance: results will be dependent on data order.\n",
-        "  neigh_dist, neigh_ind = self.kneighbors(X)\n",
-        "/Library/Frameworks/Python.framework/Versions/2.7/lib/python2.7/site-packages/sklearn/neighbors/classification.py:131: NeighborsWarning: kneighbors: neighbor k+1 and neighbor k have the same distance: results will be dependent on data order.\n",
-        "  neigh_dist, neigh_ind = self.kneighbors(X)\n"
-       ]
-      },
-      {
-       "output_type": "display_data",
-       "png": 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ArntSSimVUe4+gCKuiceL5oL9qTRiuBVhfmxceZykO91t0/HVp6Side0eFMwJ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wnU5mWQnAg3DkorZV7uYmjZCxa8ZsUgylSC1vekDSe85ApoeJMlfeEXWzW8u9Kt59Qr0npVZvwC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gnJMpRd0xEolXGEh8C5vVCvmwRPIEP9jfqCoxOc2Gy9cy84Br38TMETQE8/qJhPfmctxzZA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\n"
-      }
-     ],
-     "prompt_number": 5
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "def combine_knn(knn_list, score_list, ij_list):\n",
-      "    def predict(X):\n",
-      "        Y_list = []\n",
-      "        for knn, ij in zip(knn_list, ij_list):\n",
-      "            i, j = ij\n",
-      "            U = np.c_[X[:,i], X[:,j]]\n",
-      "            Y = knn.predict(U)\n",
-      "            Y_list.append(Y)\n",
-      "        Sa = np.array(score_list)\n",
-      "        Ya = np.column_stack(Y_list)\n",
-      "        Y1 = np.inner(Ya == 0, Sa)\n",
-      "        Y2 = np.inner(Ya == 1, Sa)\n",
-      "        Y3 = np.inner(Ya == 2, Sa)\n",
-      "        Yz = np.column_stack([Y1, Y2, Y3])\n",
-      "        Y = np.argmax(Yz, axis=1)\n",
-      "        return Y\n",
-      "    return predict\n",
-      "\n",
-      "knn2 = combine_knn(knn_list, score_list, ij_list)"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [],
-     "prompt_number": 6
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "print score_list\n",
-      "print metrics.precision_score(target, knn2(data))"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "[0.93548387096774188, 0.99346405228758161, 0.97530864197530864, 0.98113207547169812, 0.98113207547169812, 0.98717948717948734]\n",
-        "1.0\n"
-       ]
-      },
-      {
-       "output_type": "stream",
-       "stream": "stderr",
-       "text": [
-        "/Library/Frameworks/Python.framework/Versions/2.7/lib/python2.7/site-packages/sklearn/neighbors/classification.py:131: NeighborsWarning: kneighbors: neighbor k+1 and neighbor k have the same distance: results will be dependent on data order.\n",
-        "  neigh_dist, neigh_ind = self.kneighbors(X)\n",
-        "/Library/Frameworks/Python.framework/Versions/2.7/lib/python2.7/site-packages/sklearn/neighbors/classification.py:131: NeighborsWarning: kneighbors: neighbor k+1 and neighbor k have the same distance: results will be dependent on data order.\n",
-        "  neigh_dist, neigh_ind = self.kneighbors(X)\n",
-        "/Library/Frameworks/Python.framework/Versions/2.7/lib/python2.7/site-packages/sklearn/neighbors/classification.py:131: NeighborsWarning: kneighbors: neighbor k+1 and neighbor k have the same distance: results will be dependent on data order.\n",
-        "  neigh_dist, neigh_ind = self.kneighbors(X)\n",
-        "/Library/Frameworks/Python.framework/Versions/2.7/lib/python2.7/site-packages/sklearn/neighbors/classification.py:131: NeighborsWarning: kneighbors: neighbor k+1 and neighbor k have the same distance: results will be dependent on data order.\n",
-        "  neigh_dist, neigh_ind = self.kneighbors(X)\n",
-        "/Library/Frameworks/Python.framework/Versions/2.7/lib/python2.7/site-packages/sklearn/neighbors/classification.py:131: NeighborsWarning: kneighbors: neighbor k+1 and neighbor k have the same distance: results will be dependent on data order.\n",
-        "  neigh_dist, neigh_ind = self.kneighbors(X)\n",
-        "/Library/Frameworks/Python.framework/Versions/2.7/lib/python2.7/site-packages/sklearn/neighbors/classification.py:131: NeighborsWarning: kneighbors: neighbor k+1 and neighbor k have the same distance: results will be dependent on data order.\n",
-        "  neigh_dist, neigh_ind = self.kneighbors(X)\n"
-       ]
-      }
-     ],
-     "prompt_number": 7
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [],
-     "language": "python",
-     "metadata": {},
-     "outputs": [],
-     "prompt_number": 7
-    }
-   ],
-   "metadata": {}
-  }
- ]
+{
+ "metadata": {
+  "name": "intro_2"
+ },
+ "nbformat": 3,
+ "nbformat_minor": 0,
+ "worksheets": [
+  {
+   "cells": [
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "import numpy as np\n",
+      "import matplotlib.pyplot as plt\n",
+      "from sklearn import datasets, neighbors, metrics"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [],
+     "prompt_number": 1
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "iris = datasets.load_iris()\n",
+      "# \u043e\u043f\u0438\u0441\u0430\u043d\u0438\u0435 \u043e\u0431\u044a\u0435\u043a\u0442\u043e\u0432\n",
+      "data = iris.data\n",
+      "# \u0437\u043d\u0430\u0447\u0435\u043d\u0438\u044f \u0446\u0435\u043b\u0435\u0432\u043e\u0433\u043e \u043f\u0440\u0438\u0437\u043d\u0430\u043a\u0430 (\u043d\u043e\u043c\u0435\u0440 \u043a\u043b\u0430\u0441\u0441\u0430)\n",
+      "target = iris.target\n",
+      "\n",
+      "N, n = data.shape"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [],
+     "prompt_number": 2
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "print iris.DESCR"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "output_type": "stream",
+       "stream": "stdout",
+       "text": [
+        "Iris Plants Database\n",
+        "\n",
+        "Notes\n",
+        "-----\n",
+        "Data Set Characteristics:\n",
+        "    :Number of Instances: 150 (50 in each of three classes)\n",
+        "    :Number of Attributes: 4 numeric, predictive attributes and the class\n",
+        "    :Attribute Information:\n",
+        "        - sepal length in cm\n",
+        "        - sepal width in cm\n",
+        "        - petal length in cm\n",
+        "        - petal width in cm\n",
+        "        - class:\n",
+        "                - Iris-Setosa\n",
+        "                - Iris-Versicolour\n",
+        "                - Iris-Virginica\n",
+        "    :Summary Statistics:\n",
+        "    ============== ==== ==== ======= ===== ====================\n",
+        "                    Min  Max   Mean    SD   Class Correlation\n",
+        "    ============== ==== ==== ======= ===== ====================\n",
+        "    sepal length:   4.3  7.9   5.84   0.83    0.7826\n",
+        "    sepal width:    2.0  4.4   3.05   0.43   -0.4194\n",
+        "    petal length:   1.0  6.9   3.76   1.76    0.9490  (high!)\n",
+        "    petal width:    0.1  2.5   1.20  0.76     0.9565  (high!)\n",
+        "    ============== ==== ==== ======= ===== ====================\n",
+        "    :Missing Attribute Values: None\n",
+        "    :Class Distribution: 33.3% for each of 3 classes.\n",
+        "    :Creator: R.A. Fisher\n",
+        "    :Donor: Michael Marshall (MARSHALL%PLU@io.arc.nasa.gov)\n",
+        "    :Date: July, 1988\n",
+        "\n",
+        "This is a copy of UCI ML iris datasets.\n",
+        "http://archive.ics.uci.edu/ml/datasets/Iris\n",
+        "\n",
+        "The famous Iris database, first used by Sir R.A Fisher\n",
+        "\n",
+        "This is perhaps the best known database to be found in the\n",
+        "pattern recognition literature.  Fisher's paper is a classic in the field and\n",
+        "is referenced frequently to this day.  (See Duda & Hart, for example.)  The\n",
+        "data set contains 3 classes of 50 instances each, where each class refers to a\n",
+        "type of iris plant.  One class is linearly separable from the other 2; the\n",
+        "latter are NOT linearly separable from each other.\n",
+        "\n",
+        "References\n",
+        "----------\n",
+        "   - Fisher,R.A. \"The use of multiple measurements in taxonomic problems\"\n",
+        "     Annual Eugenics, 7, Part II, 179-188 (1936); also in \"Contributions to\n",
+        "     Mathematical Statistics\" (John Wiley, NY, 1950).\n",
+        "   - Duda,R.O., & Hart,P.E. (1973) Pattern Classification and Scene Analysis.\n",
+        "     (Q327.D83) John Wiley & Sons.  ISBN 0-471-22361-1.  See page 218.\n",
+        "   - Dasarathy, B.V. (1980) \"Nosing Around the Neighborhood: A New System\n",
+        "     Structure and Classification Rule for Recognition in Partially Exposed\n",
+        "     Environments\".  IEEE Transactions on Pattern Analysis and Machine\n",
+        "     Intelligence, Vol. PAMI-2, No. 1, 67-71.\n",
+        "   - Gates, G.W. (1972) \"The Reduced Nearest Neighbor Rule\".  IEEE Transactions\n",
+        "     on Information Theory, May 1972, 431-433.\n",
+        "   - See also: 1988 MLC Proceedings, 54-64.  Cheeseman et al\"s AUTOCLASS II\n",
+        "     conceptual clustering system finds 3 classes in the data.\n",
+        "   - Many, many more ...\n",
+        "\n"
+       ]
+      }
+     ],
+     "prompt_number": 3
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "print data\n",
+      "print target"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "output_type": "stream",
+       "stream": "stdout",
+       "text": [
+        "[[ 5.1  3.5  1.4  0.2]\n",
+        " [ 4.9  3.   1.4  0.2]\n",
+        " [ 4.7  3.2  1.3  0.2]\n",
+        " [ 4.6  3.1  1.5  0.2]\n",
+        " [ 5.   3.6  1.4  0.2]\n",
+        " [ 5.4  3.9  1.7  0.4]\n",
+        " [ 4.6  3.4  1.4  0.3]\n",
+        " [ 5.   3.4  1.5  0.2]\n",
+        " [ 4.4  2.9  1.4  0.2]\n",
+        " [ 4.9  3.1  1.5  0.1]\n",
+        " [ 5.4  3.7  1.5  0.2]\n",
+        " [ 4.8  3.4  1.6  0.2]\n",
+        " [ 4.8  3.   1.4  0.1]\n",
+        " [ 4.3  3.   1.1  0.1]\n",
+        " [ 5.8  4.   1.2  0.2]\n",
+        " [ 5.7  4.4  1.5  0.4]\n",
+        " [ 5.4  3.9  1.3  0.4]\n",
+        " [ 5.1  3.5  1.4  0.3]\n",
+        " [ 5.7  3.8  1.7  0.3]\n",
+        " [ 5.1  3.8  1.5  0.3]\n",
+        " [ 5.4  3.4  1.7  0.2]\n",
+        " [ 5.1  3.7  1.5  0.4]\n",
+        " [ 4.6  3.6  1.   0.2]\n",
+        " [ 5.1  3.3  1.7  0.5]\n",
+        " [ 4.8  3.4  1.9  0.2]\n",
+        " [ 5.   3.   1.6  0.2]\n",
+        " [ 5.   3.4  1.6  0.4]\n",
+        " [ 5.2  3.5  1.5  0.2]\n",
+        " [ 5.2  3.4  1.4  0.2]\n",
+        " [ 4.7  3.2  1.6  0.2]\n",
+        " [ 4.8  3.1  1.6  0.2]\n",
+        " [ 5.4  3.4  1.5  0.4]\n",
+        " [ 5.2  4.1  1.5  0.1]\n",
+        " [ 5.5  4.2  1.4  0.2]\n",
+        " [ 4.9  3.1  1.5  0.1]\n",
+        " [ 5.   3.2  1.2  0.2]\n",
+        " [ 5.5  3.5  1.3  0.2]\n",
+        " [ 4.9  3.1  1.5  0.1]\n",
+        " [ 4.4  3.   1.3  0.2]\n",
+        " [ 5.1  3.4  1.5  0.2]\n",
+        " [ 5.   3.5  1.3  0.3]\n",
+        " [ 4.5  2.3  1.3  0.3]\n",
+        " [ 4.4  3.2  1.3  0.2]\n",
+        " [ 5.   3.5  1.6  0.6]\n",
+        " [ 5.1  3.8  1.9  0.4]\n",
+        " [ 4.8  3.   1.4  0.3]\n",
+        " [ 5.1  3.8  1.6  0.2]\n",
+        " [ 4.6  3.2  1.4  0.2]\n",
+        " [ 5.3  3.7  1.5  0.2]\n",
+        " [ 5.   3.3  1.4  0.2]\n",
+        " [ 7.   3.2  4.7  1.4]\n",
+        " [ 6.4  3.2  4.5  1.5]\n",
+        " [ 6.9  3.1  4.9  1.5]\n",
+        " [ 5.5  2.3  4.   1.3]\n",
+        " [ 6.5  2.8  4.6  1.5]\n",
+        " [ 5.7  2.8  4.5  1.3]\n",
+        " [ 6.3  3.3  4.7  1.6]\n",
+        " [ 4.9  2.4  3.3  1. ]\n",
+        " [ 6.6  2.9  4.6  1.3]\n",
+        " [ 5.2  2.7  3.9  1.4]\n",
+        " [ 5.   2.   3.5  1. ]\n",
+        " [ 5.9  3.   4.2  1.5]\n",
+        " [ 6.   2.2  4.   1. ]\n",
+        " [ 6.1  2.9  4.7  1.4]\n",
+        " [ 5.6  2.9  3.6  1.3]\n",
+        " [ 6.7  3.1  4.4  1.4]\n",
+        " [ 5.6  3.   4.5  1.5]\n",
+        " [ 5.8  2.7  4.1  1. ]\n",
+        " [ 6.2  2.2  4.5  1.5]\n",
+        " [ 5.6  2.5  3.9  1.1]\n",
+        " [ 5.9  3.2  4.8  1.8]\n",
+        " [ 6.1  2.8  4.   1.3]\n",
+        " [ 6.3  2.5  4.9  1.5]\n",
+        " [ 6.1  2.8  4.7  1.2]\n",
+        " [ 6.4  2.9  4.3  1.3]\n",
+        " [ 6.6  3.   4.4  1.4]\n",
+        " [ 6.8  2.8  4.8  1.4]\n",
+        " [ 6.7  3.   5.   1.7]\n",
+        " [ 6.   2.9  4.5  1.5]\n",
+        " [ 5.7  2.6  3.5  1. ]\n",
+        " [ 5.5  2.4  3.8  1.1]\n",
+        " [ 5.5  2.4  3.7  1. ]\n",
+        " [ 5.8  2.7  3.9  1.2]\n",
+        " [ 6.   2.7  5.1  1.6]\n",
+        " [ 5.4  3.   4.5  1.5]\n",
+        " [ 6.   3.4  4.5  1.6]\n",
+        " [ 6.7  3.1  4.7  1.5]\n",
+        " [ 6.3  2.3  4.4  1.3]\n",
+        " [ 5.6  3.   4.1  1.3]\n",
+        " [ 5.5  2.5  4.   1.3]\n",
+        " [ 5.5  2.6  4.4  1.2]\n",
+        " [ 6.1  3.   4.6  1.4]\n",
+        " [ 5.8  2.6  4.   1.2]\n",
+        " [ 5.   2.3  3.3  1. ]\n",
+        " [ 5.6  2.7  4.2  1.3]\n",
+        " [ 5.7  3.   4.2  1.2]\n",
+        " [ 5.7  2.9  4.2  1.3]\n",
+        " [ 6.2  2.9  4.3  1.3]\n",
+        " [ 5.1  2.5  3.   1.1]\n",
+        " [ 5.7  2.8  4.1  1.3]\n",
+        " [ 6.3  3.3  6.   2.5]\n",
+        " [ 5.8  2.7  5.1  1.9]\n",
+        " [ 7.1  3.   5.9  2.1]\n",
+        " [ 6.3  2.9  5.6  1.8]\n",
+        " [ 6.5  3.   5.8  2.2]\n",
+        " [ 7.6  3.   6.6  2.1]\n",
+        " [ 4.9  2.5  4.5  1.7]\n",
+        " [ 7.3  2.9  6.3  1.8]\n",
+        " [ 6.7  2.5  5.8  1.8]\n",
+        " [ 7.2  3.6  6.1  2.5]\n",
+        " [ 6.5  3.2  5.1  2. ]\n",
+        " [ 6.4  2.7  5.3  1.9]\n",
+        " [ 6.8  3.   5.5  2.1]\n",
+        " [ 5.7  2.5  5.   2. ]\n",
+        " [ 5.8  2.8  5.1  2.4]\n",
+        " [ 6.4  3.2  5.3  2.3]\n",
+        " [ 6.5  3.   5.5  1.8]\n",
+        " [ 7.7  3.8  6.7  2.2]\n",
+        " [ 7.7  2.6  6.9  2.3]\n",
+        " [ 6.   2.2  5.   1.5]\n",
+        " [ 6.9  3.2  5.7  2.3]\n",
+        " [ 5.6  2.8  4.9  2. ]\n",
+        " [ 7.7  2.8  6.7  2. ]\n",
+        " [ 6.3  2.7  4.9  1.8]\n",
+        " [ 6.7  3.3  5.7  2.1]\n",
+        " [ 7.2  3.2  6.   1.8]\n",
+        " [ 6.2  2.8  4.8  1.8]\n",
+        " [ 6.1  3.   4.9  1.8]\n",
+        " [ 6.4  2.8  5.6  2.1]\n",
+        " [ 7.2  3.   5.8  1.6]\n",
+        " [ 7.4  2.8  6.1  1.9]\n",
+        " [ 7.9  3.8  6.4  2. ]\n",
+        " [ 6.4  2.8  5.6  2.2]\n",
+        " [ 6.3  2.8  5.1  1.5]\n",
+        " [ 6.1  2.6  5.6  1.4]\n",
+        " [ 7.7  3.   6.1  2.3]\n",
+        " [ 6.3  3.4  5.6  2.4]\n",
+        " [ 6.4  3.1  5.5  1.8]\n",
+        " [ 6.   3.   4.8  1.8]\n",
+        " [ 6.9  3.1  5.4  2.1]\n",
+        " [ 6.7  3.1  5.6  2.4]\n",
+        " [ 6.9  3.1  5.1  2.3]\n",
+        " [ 5.8  2.7  5.1  1.9]\n",
+        " [ 6.8  3.2  5.9  2.3]\n",
+        " [ 6.7  3.3  5.7  2.5]\n",
+        " [ 6.7  3.   5.2  2.3]\n",
+        " [ 6.3  2.5  5.   1.9]\n",
+        " [ 6.5  3.   5.2  2. ]\n",
+        " [ 6.2  3.4  5.4  2.3]\n",
+        " [ 5.9  3.   5.1  1.8]]\n",
+        "[0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n",
+        " 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1\n",
+        " 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 2 2 2 2 2 2 2 2 2 2 2\n",
+        " 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2\n",
+        " 2 2]\n"
+       ]
+      }
+     ],
+     "prompt_number": 4
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "\u0414\u043b\u044f \u043a\u0430\u0436\u0434\u043e\u0439 \u043f\u0430\u0440\u044b $0 < i < j < n-1$ \u0441\u0442\u0440\u043e\u0438\u043c \u043a\u043b\u0430\u0441\u0441\u0438\u0444\u0438\u043a\u0430\u0442\u043e\u0440 \u043f\u043e \u043f\u043e\u0434\u0442\u0430\u0431\u043b\u0438\u0446\u0435, \u0441\u043e\u0441\u0442\u0430\u0432\u043b\u0435\u043d\u043d\u043e\u0439 \u0438\u0437 \u0441\u0442\u043e\u043b\u0431\u0446\u043e\u0432 $i$ \u0438 $j$."
+     ]
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "def build_knns(n_neighbors):\n",
+      "    # \u0441\u043f\u0438\u0441\u043a\u0438 \u043c\u0430\u0441\u0441\u0438\u0432\u043e\u0432\n",
+      "    knn_list = []     # \u043a\u043b\u0430\u0441\u0441\u0438\u0444\u0438\u043a\u0430\u0442\u043e\u0440\u044b\n",
+      "    score_list = []   # \u043f\u043e\u043a\u0430\u0437\u0430\u0442\u0435\u043b\u0438 \u043a\u0430\u0447\u0435\u0441\u0442\u0432\u0430 \u043a\u043b\u0430\u0441\u0441\u0438\u0444\u0438\u043a\u0430\u0442\u043e\u0440\u043e\u0432\n",
+      "    ij_list = []      # \u043f\u0430\u0440\u0430 \u0438\u043d\u0434\u0435\u043a\u0441\u043e\u0432 \u043f\u0440\u0438\u0437\u043d\u0430\u043a\u043e\u0432\n",
+      "    \n",
+      "    for i in range(n-1):\n",
+      "        for j in range(i+1,n):\n",
+      "            ij_list.append((i,j))\n",
+      "            \n",
+      "            knn = neighbors.KNeighborsClassifier(n_neighbors, weights='weights')\n",
+      "            Xi = data[:, i]\n",
+      "            Xj = data[:, j]\n",
+      "            X = np.c_[Xi,Xj]\n",
+      "            \n",
+      "            knn.fit(X, target)\n",
+      "            knn_list.append(knn)\n",
+      "            \n",
+      "            score = metrics.precision_score(target, knn.predict(X))\n",
+      "            score_list.append(score)\n",
+      "    \n",
+      "    return knn_list, score_list, ij_list\n",
+      "\n",
+      "knn_list, score_list, ij_list = build_knns(3)"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "ename": "SyntaxError",
+       "evalue": "EOL while scanning string literal (<ipython-input-5-1dd1601dea0e>, line 11)",
+       "output_type": "pyerr",
+       "traceback": [
+        "\u001b[1;36m  File \u001b[1;32m\"<ipython-input-5-1dd1601dea0e>\"\u001b[1;36m, line \u001b[1;32m11\u001b[0m\n\u001b[1;33m    knn = neighbors.KNeighborsClassifier(n_neighbors, weights='weights\u001b[0m\n\u001b[1;37m                                                                     ^\u001b[0m\n\u001b[1;31mSyntaxError\u001b[0m\u001b[1;31m:\u001b[0m EOL while scanning string literal\n"
+       ]
+      }
+     ],
+     "prompt_number": 5
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "def plot_map2d(clf, X):\n",
+      "    x_min, x_max = X[:, 0].min(), X[:, 0].max() # \u0432\u044b\u0447\u0438\u0441\u043b\u044f\u0435\u043c \u043c\u0438\u043d\u0438\u043c\u0430\u043b\u044c\u043d\u043e\u0435 \u0438 \u043c\u0430\u043a\u0441\u0438\u043c\u0430\u043b\u044c\u043d\u043e\u0435 \u0437\u043d\u0430\u0447\u0435\u043d\u0438\u0435 \u043f\u0440\u0438\u0437\u043d\u0430\u043a\u0430 \u0432 \u0441\u0442\u043e\u043b\u0431\u0446\u0435 0\n",
+      "    y_min, y_max = X[:, 1].min(), X[:, 1].max() # \u0432\u044b\u0447\u0438\u0441\u043b\u044f\u0435\u043c \u043c\u0438\u043d\u0438\u043c\u0430\u043b\u044c\u043d\u043e\u0435 \u0438 \u043c\u0430\u043a\u0441\u0438\u043c\u0430\u043b\u044c\u043d\u043e\u0435 \u0437\u043d\u0430\u0447\u0435\u043d\u0438\u0435 \u043f\u0440\u0438\u0437\u043d\u0430\u043a\u0430 \u0432 \u0441\u0442\u043e\u043b\u0431\u0446\u0435 1\n",
+      "    x_range = np.linspace(x_min, x_max, 100) # \u0441\u043e\u0437\u0434\u0430\u0435\u043c \u043e\u0434\u043d\u043e\u043c\u0435\u0440\u043d\u0443\u044e \u0441\u0435\u0442\u043a\u0443 \u043f\u043e \u043e\u0441\u0438 x\n",
+      "    y_range = np.linspace(y_min, y_max, 100)  # \u0441\u043e\u0437\u0434\u0430\u0435\u043c \u043e\u0434\u043d\u043e\u043c\u0435\u0440\u043d\u0443\u044e \u0441\u0435\u0442\u043a\u0443 \u043f\u043e \u043e\u0441\u0438 y\n",
+      "    xx, yy = np.meshgrid(x_range, y_range) # \u0441\u043e\u0437\u0434\u0430\u0435\u043c \u0434\u0432\u0443\u043c\u0435\u0440\u043d\u0443\u044e \u0441\u0435\u0442\u043a\u0443 \u043f\u043e \u0434\u0432\u0443\u043c \u043e\u0434\u043d\u043e\u043c\u0435\u0440\u043d\u044b\u043c\n",
+      "    #\n",
+      "    # np.c_[C1, C2] - \u0441\u043e\u0437\u0434\u0430\u0435\u0442 \u0434\u0432\u0443\u043c\u0435\u0440\u043d\u044b\u0439 \u043c\u0430\u0441\u0441\u0438\u0432, \u043a\u043e\u0442\u043e\u0440\u044b\u0439 \u043f\u043e\u043b\u0443\u0447\u0430\u0435\u0442\u0441\u044f \u0432 \u0440\u0435\u0437\u0443\u043b\u044c\u0442\u0430\u0442\u0435 \u043e\u0431\u044a\u0435\u0434\u0438\u043d\u0435\u043d\u0438\u044f \n",
+      "    # \u0434\u0432\u0443\u0445 \u0441\u0442\u043e\u043b\u0431\u0446\u043e\u0432 C1, C2\n",
+      "    # xx.ravel(), yy.ravel() - \u0441\u043e\u0437\u0434\u0430\u0435\u043c \u043e\u0434\u043d\u043e\u043c\u0435\u0440\u043d\u043e\u0435 \u043f\u0440\u0435\u0434\u0441\u0442\u0430\u0432\u043b\u0435\u043d\u0438\u0435 2-\u0445 \u043c\u0435\u0440\u043d\u044b\u0445 \u0441\u0435\u0442\u043e\u043a,\n",
+      "    # \u043a\u0430\u043a \u043a\u043e\u043d\u043a\u0430\u0442\u0435\u043d\u0430\u0446\u0438\u044e \u0441\u0442\u0440\u043e\u043a \u0441\u0435\u0442\u043a\u0438\n",
+      "    Z = clf.predict(np.c_[xx.ravel(), yy.ravel()]) # \u043f\u0440\u0435\u0434\u0441\u043a\u0430\u0437\u044b\u0432\u0430\u0435\u043c \u0441 \u043f\u043e\u043c\u043e\u0449\u044c\u044e \u043e\u0431\u0443\u0447\u0435\u043d\u043d\u043e\u0433\u043e \u043a\u043b\u0430\u0441\u0441\u0438\u0444\u0438\u043a\u0430\u0442\u043e\u0440\u0430\n",
+      "    # Put the result into a color plot\n",
+      "    Z = Z.reshape(xx.shape) # \u043e\u0434\u043d\u043e\u043c\u0435\u0440\u043d\u044b\u0439 \u043c\u0430\u0441\u0441\u0438\u0432 \u0437\u043d\u0430\u0447\u0435\u043d\u0438\u0439 \u043f\u0440\u0435\u0432\u0440\u0430\u0449\u0430\u0435\u043c \u0432 \u0434\u0432\u0443\u043c\u0435\u0440\u043d\u044b\u0439 \u0441 \u0444\u043e\u0440\u043c\u043e\u0439 \u043a\u0430\u043a \u0443 xx\n",
+      "    plt.winter()\n",
+      "    plt.pcolormesh(xx, yy, Z) # \u0432\u044b\u0432\u043e\u0434\u0438\u0441 \u0446\u0432\u0435\u0442\u043e\u0432\u0443\u044e \u043a\u0430\u0440\u0442\u0443"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": []
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "names = iris.feature_names\n",
+      "def plot_map():\n",
+      "    plt.figure(figsize=(10.0,12.0)) # \u0441\u043e\u0437\u0434\u0430\u0435\u043c \u043d\u043e\u0432\u044b\u0439 \u0440\u0438\u0441\u0443\u043d\u043e\u043a \u0441 \u0437\u0430\u0434\u0430\u043d\u043d\u044b\u043c\u0438 \u0440\u0430\u0437\u043c\u0435\u0440\u0430\u043c (\u0432 \u0434\u044e\u0439\u043c\u0430\u0445)\n",
+      "    for t, ij, knn in zip(range(6), ij_list, knn_list):\n",
+      "        i, j = ij\n",
+      "        X = np.c_[data[:,i], data[:,j]]\n",
+      "        plt.subplot(3, 2, t+1)\n",
+      "        plot_map2d(knn, X)\n",
+      "        plt.scatter(X[:,0], X[:,1], c=target)\n",
+      "        plt.xlabel(names[i])\n",
+      "        plt.ylabel(names[j])\n",
+      "\n",
+      "plot_map()"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": []
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "print score_list"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": []
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "def combine_knn(knn_list, score_list, ij_list):\n",
+      "    def predict(X):\n",
+      "        Y_list = []\n",
+      "        for knn, ij in zip(knn_list, ij_list):\n",
+      "            i, j = ij\n",
+      "            U = np.c_[X[:,i], X[:,j]]\n",
+      "            Y = knn.predict(U)\n",
+      "            Y_list.append(Y)\n",
+      "        Sa = np.array(score_list)\n",
+      "        Ya = np.column_stack(Y_list)\n",
+      "        Y1 = np.inner(Ya == 0, Sa)\n",
+      "        Y2 = np.inner(Ya == 1, Sa)\n",
+      "        Y3 = np.inner(Ya == 2, Sa)\n",
+      "        Yz = np.column_stack([Y1, Y2, Y3])\n",
+      "        Y = np.argmax(Yz, axis=1)\n",
+      "        return Y\n",
+      "    return predict\n",
+      "\n",
+      "knn2 = combine_knn(knn_list, score_list, ij_list)"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": []
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "print score_list\n",
+      "print metrics.precision_score(target, knn2(data))"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": []
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [],
+     "language": "python",
+     "metadata": {},
+     "outputs": []
+    }
+   ],
+   "metadata": {}
+  }
+ ]
 }

ca2/intro_2_SVC.ipynb

+{
+ "metadata": {
+  "name": "intro_2_SVC"
+ },
+ "nbformat": 3,
+ "nbformat_minor": 0,
+ "worksheets": [
+  {
+   "cells": [
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "import numpy as np\n",
+      "import pylab as plt\n",
+      "from sklearn import datasets, svm, metrics"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [],
+     "prompt_number": 1
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "iris = datasets.load_iris()\n",
+      "# \u043e\u043f\u0438\u0441\u0430\u043d\u0438\u0435 \u043e\u0431\u044a\u0435\u043a\u0442\u043e\u0432\n",
+      "data = iris.data\n",
+      "# \u0437\u043d\u0430\u0447\u0435\u043d\u0438\u044f \u0446\u0435\u043b\u0435\u0432\u043e\u0433\u043e \u043f\u0440\u0438\u0437\u043d\u0430\u043a\u0430 (\u043d\u043e\u043c\u0435\u0440 \u043a\u043b\u0430\u0441\u0441\u0430)\n",
+      "target = iris.target\n",
+      "\n",
+      "N, n = data.shape"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [],
+     "prompt_number": 3
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "def build_cls():\n",
+      "    # \u0441\u043f\u0438\u0441\u043a\u0438 \u043c\u0430\u0441\u0441\u0438\u0432\u043e\u0432\n",
+      "    knn_list = []     # \u043a\u043b\u0430\u0441\u0441\u0438\u0444\u0438\u043a\u0430\u0442\u043e\u0440\u044b\n",
+      "    score_list = []   # \u043f\u043e\u043a\u0430\u0437\u0430\u0442\u0435\u043b\u0438 \u043a\u0430\u0447\u0435\u0441\u0442\u0432\u0430 \u043a\u043b\u0430\u0441\u0441\u0438\u0444\u0438\u043a\u0430\u0442\u043e\u0440\u043e\u0432\n",
+      "    ij_list = []      # \u043f\u0430\u0440\u0430 \u0438\u043d\u0434\u0435\u043a\u0441\u043e\u0432 \u043f\u0440\u0438\u0437\u043d\u0430\u043a\u043e\u0432\n",
+      "    \n",
+      "    for i in range(n-1):\n",
+      "        for j in range(i+1,n):\n",
+      "            ij_list.append((i,j))\n",
+      "            \n",
+      "            knn = svm.SVC(kernel='rbf')\n",
+      "            X1 = data[:, i]\n",
+      "            X2 = data[:, j]\n",
+      "            X = np.c_[X1, X2]\n",
+      "            \n",
+      "            knn.fit(X, target)\n",
+      "            knn_list.append(knn)\n",
+      "            \n",
+      "            score = metrics.precision_score(target, knn.predict(X))\n",
+      "            score_list.append(score)\n",
+      "    \n",
+      "    return knn_list, score_list, ij_list\n",
+      "\n",
+      "knn_list, score_list, ij_list = build_cls()"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [],
+     "prompt_number": 9
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "def plot_map2d(clf, X):\n",
+      "    x_min, x_max = X[:, 0].min(), X[:, 0].max() # \u0432\u044b\u0447\u0438\u0441\u043b\u044f\u0435\u043c \u043c\u0438\u043d\u0438\u043c\u0430\u043b\u044c\u043d\u043e\u0435 \u0438 \u043c\u0430\u043a\u0441\u0438\u043c\u0430\u043b\u044c\u043d\u043e\u0435 \u0437\u043d\u0430\u0447\u0435\u043d\u0438\u0435 \u043f\u0440\u0438\u0437\u043d\u0430\u043a\u0430 \u0432 \u0441\u0442\u043e\u043b\u0431\u0446\u0435 0\n",
+      "    y_min, y_max = X[:, 1].min(), X[:, 1].max() # \u0432\u044b\u0447\u0438\u0441\u043b\u044f\u0435\u043c \u043c\u0438\u043d\u0438\u043c\u0430\u043b\u044c\u043d\u043e\u0435 \u0438 \u043c\u0430\u043a\u0441\u0438\u043c\u0430\u043b\u044c\u043d\u043e\u0435 \u0437\u043d\u0430\u0447\u0435\u043d\u0438\u0435 \u043f\u0440\u0438\u0437\u043d\u0430\u043a\u0430 \u0432 \u0441\u0442\u043e\u043b\u0431\u0446\u0435 1\n",
+      "    x_range = np.linspace(x_min, x_max, 100) # \u0441\u043e\u0437\u0434\u0430\u0435\u043c \u043e\u0434\u043d\u043e\u043c\u0435\u0440\u043d\u0443\u044e \u0441\u0435\u0442\u043a\u0443 \u043f\u043e \u043e\u0441\u0438 x\n",
+      "    y_range = np.linspace(y_min, y_max, 100)  # \u0441\u043e\u0437\u0434\u0430\u0435\u043c \u043e\u0434\u043d\u043e\u043c\u0435\u0440\u043d\u0443\u044e \u0441\u0435\u0442\u043a\u0443 \u043f\u043e \u043e\u0441\u0438 y\n",
+      "    xx, yy = np.meshgrid(x_range, y_range) # \u0441\u043e\u0437\u0434\u0430\u0435\u043c \u0434\u0432\u0443\u043c\u0435\u0440\u043d\u0443\u044e \u0441\u0435\u0442\u043a\u0443 \u043f\u043e \u0434\u0432\u0443\u043c \u043e\u0434\u043d\u043e\u043c\u0435\u0440\u043d\u044b\u043c\n",
+      "    #\n",
+      "    # np.c_[C1, C2] - \u0441\u043e\u0437\u0434\u0430\u0435\u0442 \u0434\u0432\u0443\u043c\u0435\u0440\u043d\u044b\u0439 \u043c\u0430\u0441\u0441\u0438\u0432, \u043a\u043e\u0442\u043e\u0440\u044b\u0439 \u043f\u043e\u043b\u0443\u0447\u0430\u0435\u0442\u0441\u044f \u0432 \u0440\u0435\u0437\u0443\u043b\u044c\u0442\u0430\u0442\u0435 \u043e\u0431\u044a\u0435\u0434\u0438\u043d\u0435\u043d\u0438\u044f \n",
+      "    # \u0434\u0432\u0443\u0445 \u0441\u0442\u043e\u043b\u0431\u0446\u043e\u0432 C1, C2\n",
+      "    # xx.ravel(), yy.ravel() - \u0441\u043e\u0437\u0434\u0430\u0435\u043c \u043e\u0434\u043d\u043e\u043c\u0435\u0440\u043d\u043e\u0435 \u043f\u0440\u0435\u0434\u0441\u0442\u0430\u0432\u043b\u0435\u043d\u0438\u0435 2-\u0445 \u043c\u0435\u0440\u043d\u044b\u0445 \u0441\u0435\u0442\u043e\u043a,\n",
+      "    # \u043a\u0430\u043a \u043a\u043e\u043d\u043a\u0430\u0442\u0435\u043d\u0430\u0446\u0438\u044e \u0441\u0442\u0440\u043e\u043a \u0441\u0435\u0442\u043a\u0438\n",
+      "    Z = clf.predict(np.c_[xx.ravel(), yy.ravel()]) # \u043f\u0440\u0435\u0434\u0441\u043a\u0430\u0437\u044b\u0432\u0430\u0435\u043c \u0441 \u043f\u043e\u043c\u043e\u0449\u044c\u044e \u043e\u0431\u0443\u0447\u0435\u043d\u043d\u043e\u0433\u043e \u043a\u043b\u0430\u0441\u0441\u0438\u0444\u0438\u043a\u0430\u0442\u043e\u0440\u0430\n",
+      "    # Put the result into a color plot\n",
+      "    Z = Z.reshape(xx.shape) # \u043e\u0434\u043d\u043e\u043c\u0435\u0440\u043d\u044b\u0439 \u043c\u0430\u0441\u0441\u0438\u0432 \u0437\u043d\u0430\u0447\u0435\u043d\u0438\u0439 \u043f\u0440\u0435\u0432\u0440\u0430\u0449\u0430\u0435\u043c \u0432 \u0434\u0432\u0443\u043c\u0435\u0440\u043d\u044b\u0439 \u0441 \u0444\u043e\u0440\u043c\u043e\u0439 \u043a\u0430\u043a \u0443 xx\n",
+      "    plt.winter()\n",
+      "    plt.pcolormesh(xx, yy, Z) # \u0432\u044b\u0432\u043e\u0434\u0438\u0441 \u0446\u0432\u0435\u0442\u043e\u0432\u0443\u044e \u043a\u0430\u0440\u0442\u0443\n"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [],
+     "prompt_number": 10
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "def plot_map():\n",
+      "    plt.figure(figsize=(10.0,12.0)) # \u0441\u043e\u0437\u0434\u0430\u0435\u043c \u043d\u043e\u0432\u044b\u0439 \u0440\u0438\u0441\u0443\u043d\u043e\u043a \u0441 \u0437\u0430\u0434\u0430\u043d\u043d\u044b\u043c\u0438 \u0440\u0430\u0437\u043c\u0435\u0440\u0430\u043c (\u0432 \u0434\u044e\u0439\u043c\u0430\u0445)\n",
+      "    for t, ij, knn in zip(range(1, 7), ij_list, knn_list):\n",
+      "        i, j = ij\n",
+      "        X = np.c_[data[:,i], data[:,j]]\n",
+      "        plt.subplot(3, 2, t)\n",
+      "        plot_map2d(knn, X)\n",
+      "        plt.scatter(X[:,0], X[:,1], c=target)\n",
+      "        plt.xlabel(str(i))\n",
+      "        plt.ylabel(str(j))\n",
+      "\n",
+      "plot_map()"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "output_type": "display_data",
+       "png": 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mYhZJoBY4xijny2L+M24wgKOzXKMlsmYDBzc4FgzRkVCiDGSxMuqnJMjjX7Ym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fJTdx7A2p1t/3NQkEBek/+dskiVUo4QkPvihFYRX74IRKWGYMditlixHv+7xghHUUSaXsxoU0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\nAABJRU5ErkJggg==\n"
+      }
+     ],
+     "prompt_number": 11
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "print score_list"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "output_type": "stream",
+       "stream": "stdout",
+       "text": [
+        "[0.82692307692307687, 0.95999999999999996, 0.9667867146858744, 0.95999999999999996, 0.9604700854700855, 0.96678671468587429]\n"
+       ]
+      }
+     ],
+     "prompt_number": 12
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "def combine_knn(knn_list, score_list, ij_list):\n",
+      "    def predict(X):\n",
+      "        Y_list = []\n",
+      "        for knn, ij in zip(knn_list, ij_list):\n",
+      "            i, j = ij\n",
+      "            U = np.c_[X[:,i], X[:,j]]\n",
+      "            Y = knn.predict(U)\n",
+      "            Y_list.append(Y)\n",
+      "        Sa = np.array(score_list)\n",
+      "        Ya = np.column_stack(Y_list)\n",
+      "        Y1 = np.inner(Ya == 0, Sa)\n",
+      "        Y2 = np.inner(Ya == 1, Sa)\n",
+      "        Y3 = np.inner(Ya == 2, Sa)\n",
+      "        Yz = np.column_stack([Y1, Y2, Y3])\n",
+      "        Y = np.argmax(Yz, axis=1)\n",
+      "        return Y\n",
+      "    return predict\n",
+      "\n",
+      "knn2 = combine_knn(knn_list, score_list, ij_list)"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [],
+     "prompt_number": 13
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "print score_list\n",
+      "print metrics.precision_score(target, knn2(data))"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "output_type": "stream",
+       "stream": "stdout",
+       "text": [
+        "[0.82692307692307687, 0.95999999999999996, 0.9667867146858744, 0.95999999999999996, 0.9604700854700855, 0.96678671468587429]\n",
+        "0.966786714686\n"
+       ]
+      }
+     ],
+     "prompt_number": 14
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [],
+     "language": "python",
+     "metadata": {},
+     "outputs": [],
+     "prompt_number": 7
+    }
+   ],
+   "metadata": {}
+  }
+ ]
+}