8.1 Decision Tree.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Iris Plants Database\n",
      "====================\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",
      "    ============== ==== ==== ======= ===== ====================\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",
      "\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"
     ]
    }
   ],
   "source": [
    "from sklearn.datasets import load_iris\n",
    "from sklearn import tree\n",
    "iris = load_iris()\n",
    "print(iris.DESCR)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "#iris.data\n",
    "#iris.target"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=4,\n",
       "            max_features=None, max_leaf_nodes=None,\n",
       "            min_impurity_split=1e-07, min_samples_leaf=1,\n",
       "            min_samples_split=2, min_weight_fraction_leaf=0.0,\n",
       "            presort=False, random_state=None, splitter='best')"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clf = tree.DecisionTreeClassifier(max_depth=2)\n",
    "clf.fit(iris.data, iris.target)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([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, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
       "       0, 0, 0, 0, 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,\n",
       "       1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 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, 1, 2, 2, 2, 2, 2, 2, 2, 2,\n",
       "       2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2])"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clf.predict(iris.data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "tree.export_graphviz(clf, out_file='tree.dot')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Populating the interactive namespace from numpy and matplotlib\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\IPython\\core\\magics\\pylab.py:161: UserWarning: pylab import has clobbered these variables: ['clf']\n",
      "`%matplotlib` prevents importing * from pylab and numpy\n",
      "  \"\\n`%matplotlib` prevents importing * from pylab and numpy\"\n"
     ]
    },
    {
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1YZKqEDt37kRLS4tqkFOzExnG0QuFQqov4La2trT95AkWrVbrtK+3HM+X6X0jqlRMUhWi\nq6vL6BA0kTprbeq4f16vF263G319fejr68PDDz8Mt9vN8xFVKLbuI1OZO3du1ruESCSClpYWjIyM\noK6uDsDkCOtLly7F8uXL0dTUxPMRVRjeSRlsdHQ06zOGzs5OZVkkEkEikYDb7VaWORyOrJ1nMx0v\n23MMedRxi8UCq9WqGjy1lCKRCKxWKxwOB0ZHR9PWDw8PAwDmz5+vLJs3bx6A9DsUno+oQhg0ZmBF\nm+oAs4FAQDVlerLkAUzlWWqj0agyIGy2qeDladaRMpBr6rJoNCokSRIej0cVS7ZBU5Fl8NXUVy7Z\ntkkeuBXvDcqaPHW8fP2Zjpc8K26hyv18hbzXmfbhALNUTpikdFDMKOjyKODJo4bH43FV4pJHLJel\nfknl+zvTMo/Hk3GbTAlTK7m+XOPxuAgGg8r74XK58u5XzJd1JZyPSYqqAZOUDopJUvIUGvIdjRCT\ndzWZ7mjC4bAydcV0k5Q8rcZU74amo9Dju1wu1R2EHkmjnM/HJEXVgM+kTKKpqQmSJGFgYEBZ9vjj\nj6c9LHe73bjmmmuUiQSnS27iLAqcYj31+Vm2lxbWrVunaoKd65oLmaK+2s9HVI6YpEyktbUVfr8f\no6OjiEQiWL58uWq91+vFli1bsGfPHixatEjTcyfP0ptLpmRWaIKbqrq6OtWXc6YZfuUZe5ctW8bz\nEVUgJikTkacf7+3txfDwMC688ELV+paWFgDQdPpxl8sFAOjr61NNGd/Z2anZOYqVSCRU08ivXr0a\nAHDkyBFl2bFjx1TreD6iysIkZSJz5syB3W5Hd3c3jh49qvSVkcm/tCORiOrOJxaLZZzTSf6VLm+b\n3OxZHungiiuuAAB0dHSgvr4eFosFDQ0Nqi/PUvB6vaqm75FIBPv371cSNzCZnF0uF3p7e5FIJJBI\nJNDb2wuXy6VK3HJz+lAoVJHnI6oqBjwHq3jFNJyQyQ0oxsbGsq6z2+0iGo0qrf2Sm5Yj6WF6OBxW\nGkb4fD4hhFCamyc3fQ6Hw0prM/l4ekKGB/7JzbOTm91nIm8rSZIIBAJp6+X3JVcz8XI+nyzT+5gP\n2HCCyoxFCA4CprUNGzYAAPr7+w2OxJzkhhV6Fz2r1Qqfz6frOYw8XzHvo8ViQX9/P1pbW/UKi0hT\nrO6jijQ6Ooobb7yxYs9HVC04dh9VnMHBQXz0ox/VvAWkWc5HVE2YpMgwelX7JTdGKIVSn49zSFE1\nYZKikuNj0Onh+0fVhM+kiIjItJikiIjItJikiIjItJikiIjItNhwQicDAwM4fvy40WFoamJiAjU1\n1VFkqulaicyMn0IdrF+/vuISFAA89dRTOOWUU3DuuecaHYquJiYm8Oijj2LJkiVYuHCh0eFoav36\n9SVvMk80HRwWiQrS39+PjRs34vHHH8eKFSuMDkd311xzDTweD55//nnMnTvX6HCIqhaTFOUVi8Ww\nePFiNDc346c//anR4ZTEG2+8gSVLluDcc8/F3r17jQ6HqGoxSVFezc3NOHDgAJ577jnMmjXL6HBK\n5rHHHsM//MM/4J577sHatWuNDoeoKjFJUU579+7FunXr8Oijj+KSSy4xOpyS27RpE/x+P55//nmc\nfvrpRodDVHWYpCir1157DYsXL8all16K22+/3ehwDJFIJHD22WdjxYoVuPvuu40Oh6jqsJ8UZfWd\n73wHM2bMMMVU8kapq6vDT3/6U/T398Pv9xsdDlHVYZKijH72s5/h7rvvRnd3N2bPnm10OIaSJAlf\n/epXYbPZ8Oc//9mwOGKxGLxeL6xWq2ExEJUaq/sojVzFdeGFF3J24fe89tprOOuss3DZZZfhtttu\nK3i/QqfVKORj2NbWhu7u7oK3J6oEvJOiNNu2bcM777yDW265xehQTOPDH/4w9uzZgzvuuAOPPfZY\nwfsJIRCPx1V/J78CgUDBx+rq6ppSzESVgHdSpMJm17kV2xw/1wSPFoul4DsjvSaKJDIr3kmR4o03\n3oDNZsOVV17JBJXFf/3Xf+Gvf/0rduzYMe1jZUo4iUQCbrcbFosFFosFDocDsVgs53E6OzthsVjg\ndrsRi8XSqhhjsZiyjdVqxeDg4LRjJyoZQfSeb33rW+LDH/6w+P3vf290KKbW19cnLBaLeOKJJwre\nB4BI/riFw2GR6eNns9kEABGNRpVtbDZb1uM4nU4RDoeFEELE43Fht9tV66PRqJAkSXg8HiGEEIFA\nQAAQwWCw8AsmMhCr+wgAsH//fnzxi1/EXXfdha9+9atGh2N6VqsVv/71rxEKhXDaaafl3T5bA4rU\nj5/D4cAf//hH5flT6t1Wpr+j0SjmzJkDYPKuqaGhQVnv9XrR0tKiOo/FYoHdbkd7e3vB10tkGAMT\nJJnEX//6V/HJT35SXHbZZUaHUjaOHj0q6uvrxXXXXVfQ9ijwTip5vdPpTNsv9W/5zsvj8Yh4PJ52\nHEmSlH1SX0TlgCWVxPXXXy/q6urEyy+/bHQoZaWnp0fMnDlTDA8P5902U2LIlihcLpeQJEmMjY3l\nTVJjY2OqROR0OvOel6icsPRWueHhYTFz5kzR09NjdChl58SJE+KSSy4Rn/rUp8T//d//5dy20GTh\n8XgEAOU5U74kJQsGg8pdVXKikrcfGxsr9LKITIVJqoq99dZbYvHixeLiiy8WJ06cMDqcshQOh8Ws\nWbPEDTfckHO7QpNUvqSU6e/kar5gMKha73K5BABht9uV7aLRaNodF5FZsQl6keQmwoW+zOjf/u3f\nEA6H4XK5TBuj2TU2NuLmm2/Gf/7nf+LQoUMZt0kkEhn/nYkkSQCASCSC8fFxZXksFlM1RU/+t9Pp\nRCQSAQDMnj0bTqdTWXfFFVcAADo6OlBfXw+LxYKGhgY0NzcXeolExjI6S5YrvPewOnVZ6lsqV9+Y\nzaFDh0RNTY3Ys2eP0aGUvRMnTogVK1aIJUuWiLffflu1Ti4Tqa9s5Dshu90uotGosNvtwmazKQ0t\nUo+B95qry40sMt0hhcNhpWm6fCyicsEm6EXKNEpAts6Z9fX1phoh4Pjx41i+fDlmzZqFoaEh3kVp\n4MUXX8RnPvMZfPe738XOnTuNDoeoYrC6r0jhcLig7erq6hAOhxGLxeD3+2G1WpFIJNDW1gaHw5Gx\nSjBbNaFWIwfcfPPNGBsbw2233cYEpZEzzjgD7e3tuOmmm/Dss88aHQ5R5TD0Pq7CIEdVTnIz4ZGR\nEaU1VjQazdqHJnmZViMHHD58WJx00kmis7OziCukXCYmJsQFF1wgzj33XHH8+HGjwyGqCKzu01C+\nwT/l9fF4HHV1dTn3S12mxcgB7777Lv7+7/8eM2bMwC9/+UvMnDmz0EujAv32t7/FOeecg3/913/F\n9773PaPDISp7rO4zQHKCKtTAwACA9KrAjo6Ogo/R2dmJUCiEnp4eJiidfOpTn4LD4cC//uu/4re/\n/a3R4RCVPd5JaajQO6nU9YXcSU13ioaxsTEsXboUDocDN954Y1HHoMJMTEzg/PPPx0knnYRf/OIX\n/EFANA1MUhoqRZIaGxvDokWLphTXiRMnsGLFCrz55ps4cOAAampqprQ/Td2zzz6Lc889F//xH/+B\na6+91uhwiMoWq/vKhMvlAgD09fUpHULl1n757N69G0899RRuv/12JqgSWbJkCW644QbY7Xa8+OKL\nRodDVL5K3FCjYiW30otGoznXp5LHXJPHVxsZGVG2lecSSt4/+ZWvY+YLL7wgTjvtNPH//t//0+Aq\naSrefvttsWTJErFixQoOO0VUJCYpDWRKHqnJKHm5JEmqdeFwWGmi7vP5hBBCaW6enPCmOnLAiRMn\nxKpVq8TZZ5+dNhIClcbTTz8tampqRFdXl9GhEJUlPpOqYLfeeiu+9a1vYXh4GMuXLzc6nKr1ve99\nD11dXXjuuefQ2NhodDhEZYVJqkK9/PLLWLx4Mdra2vDv//7vRodT1d566y2cc845aGxsxKOPPspR\nPoimgEmqQq1ZswYvvvgiQqEQTjnlFKPDqXojIyP4whe+gJ6eHnzjG98wOhyissHWfRXozjvvxM9/\n/nPcdtttTFAmccEFF2Dr1q3Ytm0bjh07pix/++234XA48KMf/cjA6IjMi3dSFebYsWM4++yz8bWv\nfQ27du0yOhxK8uabb6KpqQlnnXUWfD4fRkdHsXHjRrzwwgsAJvuzsSqQSI1JqsJ8+ctfRigUwuHD\nh3HaaacZHQ6lGBoawkUXXYQ1a9bgkUcewYwZMzAxMQEAeO6557B48WKDIyQyF1b3VRCv14sHHngA\nLpeLCcqkLBYLPvzhD+PRRx/FiRMnlARVU1ODJ554wtjgiEyISapC/OEPf8DWrVuxZcsWrFq1yuhw\nKMUbb7yBb33rW/jiF7+IeDyOd999V7VeCFH0/GBElYzVfRWipaUFv/zlL/HrX/8as2bNMjocSvLS\nSy/hE5/4RMbZnJPV19fjtdde43MpoiS8k6oA999/P7xeL1wuFxOUCb399tsAkDf5xONx/PrXvy5F\nSERlg0mqzL322mv45je/ia9//eu49NJLjQ6HMjjzzDPxyiuvYNmyZTmn7eBzKaJ0TFJlbtu2bbBY\nLOxnY3ILFizA8PAwvv3tb6smrUzG51JE6fhMqow98sgjWLNmDf77v/8bV155pdHhUIHuu+8+fO1r\nX8Px48dx/Phx1brZs2fjT3/6E59LEb2HSaoMHDx4EAsWLMD8+fOVZX/5y1+wePFifOELX4DH4zEw\nOirG//7v/+LKK6/E2NiY0gxdxv5SRO9jdZ/JHT9+HMuXL8eCBQvgdruV1mHf/e538dZbb2H37t0G\nR0jF+OQnP4mDBw+itbVVtZzPpYjUmKRM7uDBg8q/r776alx00UXo7++Hy+XC7t27cfrppxsYHU3H\nqaeeit7eXtxxxx04+eSTUVNTgxMnTuDxxx83OjQi02B1n8nddNNN+P73v4933nkHAFBbW4sZM2bg\ni1/8Ih5++GHMmMHfGZUgGAziyiuvxEsvvQSA4/gRyZikTO7iiy/G4OBgWifQGTNm4LzzzsOdd96J\nRYsWGRSdebz66qu47rrr0kZyKCfHjx/Hvn378MYbb0CSJI5gX0Y2btwISZKMDqMi8We4iR0/fhxP\nPvlkxlEKTpw4gaeffhpLlixBR0eHAdGZy+DgILxer9FhTEttbS0uvfRSrFmzhgmqjNx7771lX/bM\nrMboACi7gwcP4q233sq6Xm6+7HA48I1vfAMLFiwoVWimdc899xgdAlWZDRs2GB1CReOdlInt378f\ntbW1WdfX1tairq4OP/vZz5igiKgi8U7KxPbt25fWh0ZWU1ODxYsX48EHH0RjY2OJIyMiKg3eSZlU\ntudRcouvq6++GqOjo0xQRFTReCdlUocOHUp7HlVbW4uamhrcdtttaGlpMSgyIqLSYZIyqSeeeAK1\ntbVK44iamhr87d/+LR544AF86lOfMjg6IqLSYHWfSSU/j7JYLGhubsbTTz/NBFVB5NHQs42Krte+\nROWEScqEJiYmEAgEIIRAbW0turu7MTAwgA9+8INGh0YZpCYMi8WCzs5OZf3g4GDOpCKEUD17jMVi\ncDgcyraZ+uCk7mMWiUQCo6OjcLvdsFqtBe+T6T3MdO2hUEi1vq2tTY/LIBNJq+6rhJ775e61115T\n/r1ixQrs27cP+/btMzCi6Zs5cyZ+/OMfY+7cuUaHorl4PI5Dhw5h165d8Pv98Hg8WL9+vbJ+5cqV\nCAaDcDgcuPHGG3H++ednPVYsFsORI0fQ3t6O9vZ2eL1etLS04OjRo9i2bZvmsYdCIezduxft7e2a\nHM/pdALAlDqY/+Y3v8m6buXKlaq/Dxw4oPp7zZo1U4iOylHanVQl9Nwvdx/60Idw5pln4ktf+hJm\nz55tdDia8Hq9FTuhX11dHVauXImenh5IkoSBgQEkEgllfSQSgcPhQE9PT84EBQBHjhxRbSMnu+3b\nt2sWbywWg9frRVtbG37zm99g69atmh1bTq5T8dJLLyEcDit3h0IIRKNR2O12zJkzR7Xt3LlzVdtx\nKKLKl7XhBHvuk5aq4bnJnDlz0N7ejqVLl6Knp0e587n55pvR09OT9oWbSdXJzR0AACAASURBVGoS\nk5Od3W6fdnzyXRMArF27VnW3Z6SVK1emvTeDg4NYu3atalkkEoHVaoXdbsdll12WN+FTZWDrPiIN\nNTU1wefzwWq14pxzzsGLL76I73//+wUlqFSRSARutxvA5ACmxYjFYhgcHMTQ0BBWrFiBrVu3Zo2l\n0B8SWj8LyxTP0NBQWhINhUIAJqsSOzo6IElSwcmfyhcbThBpTJIkOJ1OrFq1CmeccUbRCWrhwoXK\nsx2/319ULA0NDTh69Ch++MMfYv369TljSa5Gy/XSWygUwooVK9KWS5KEeDyOYDAIu90Ov9+PBx98\nUPd4yFhMUkQ6aG5uhiRJ2LVrl+r5VKEaGxshhFC+kLdv367cVU1FNBrFggULsGPHDni9XsRisSkf\no9T27t2b1mBCVldXh6amJrS3t8PlchWdvKl8MEkRaSwWi+GFF17Anj174Pf70dPTU/SxmpqalKq+\nLVu2THn/OXPmYP369ejq6sKnP/1p7N69Gw6HQ6k6S5atGXgp+2TJSbSQu89169YxSVUBPpMi0tiD\nDz6IzZs3AwBcLhe2bNmCz3/+80U/6NdqUsumpiY0NTUpz6m6u7uxYsUKpeGCGfpdZWowkU1dXR1s\nNpvOEZHRKvpOyuFwwOFw6L4PETDZEq+zs1NJUACwefNmSJKEm266CePj40UfFwA8Ho8mcWa6uzKL\noaEhNDU1FbRtIpFAc3OzzhGR0So6SZmZ3++H1WqF1WqdUpVFsfuRvkKhEDZu3IiLL744bV1fXx/8\nfj/OPPPMvH3FrFYrOjs7EYlEAEx+ETudTtjtdl2ajMvPd7SU/Awu0/O4bD8EszWYANL72UUiEezf\nvz/rsyuqHBVd3VfMh0/rD2wmXq8XAwMD6OvrAwDs2LEDr776quoXuJb7kb6Sn9P4/X4EAgHly1Nu\npSdbtWoVgOzNuDdv3gyr1ap03nU6nWXVJyj1mVV9fT2Awpqt7927N2vH4tNOO0157+x2O9auXcuO\nvFWiopOUGUUiEbS0tGBkZAR1dXUAAJvNhqVLl2L58uVZqzqK3Y/0l+sLWG6lVyhJkkzxbKhYhcSe\n7Ydgrh+I5f6+UPHKurpvcHAQVqtVGdAzuXmtPPSLPMhl6t9+vx8WiwVWq1WpWkndRg/Dw8MAgPnz\n5yvL5s2bByB9XDIt9iMiKmdleyclP5sZGRmBz+eD1+tFQ0ODsl6SJNUzm02bNil/j46OQpIkhMNh\nLFy4EAsWLEBXV5dqm2ym2yt/aGgIAFQz6srNbf1+f9aqu2L3IyIqZxaR8m06MDCADRs2mP7WWk4W\nyXFaLBY4nU5lzLTUbbLtk28bveMu5LzF7mcWFosF/f39aG1t1eX45VJuk2WasqMU+5K2NmzYAADo\n7+83OJLKVLbVfdn6R2g5WjSRnqYz3FCphyoiMkrZJyl5WhG5B708n41eptsrP1eLpFwdE4vdj4io\nnJVtkpJHmz569CgsFgscDgc8Ho8uE8Mlm+4gnHKySW7kITfcWLZsmeb7ERGVs7JuOHHhhRdCkiTd\nE5OWVq9eDWBycju54cOxY8dU67Tcj8qf3PF1Kn34itmHyIzK9k7KarWivr4+rZqtra0NsVgsrTl6\n8t9yL/jk3vCZ9tFDY2MjXC4Xent7kUgkkEgk0NvbC5fLpWq5l9orv9D9iEqp2BFQQqFQ2ueWKJOy\nvZMKBoNwOBxpH4zu7m7VfwGomqYDk73ghRBKb/hM2zQ0NOj2QHrz5s3w+/2or6+HJEm49tprCxre\npdj9qLxV2sgpQHrfvjVr1ugSI5W/sk1Sp556Kvbs2QOfz6daPj4+jjPPPLOgBGNkq6h8Peizfcmw\n5z2ZwXRHQJk7dy7LMRWkLKv7vF4vFi1alLGaq6GhQbPRoolKoZpGTgEmE5zVaoXD4cDo6KhuMVKF\nECn6+/tFhsWmIkmScLlcIhwOq5aPjY0Jl8tlUFSUCwDR39+v2/HLodxm4vP5BAAxMjIihBDC4/EI\nAMpLkiTl30II1d/yPuFwWAAQNpstbZtsks+R65WNzWbLuF6OuZBrTr7GaDSa+40ysdbWVtHa2mp0\nGBWrLJNUPB4XHo9H+aAAEHa7XQQCAaNDoyyYpDLLlAwACKfTmXWbbPvk20ZL2Y5f6Hnj8bgIBoPC\nbrcLAGX945JJSl9lWd1XV1enTNom3uuX1N7ezkYEVHaqdeSUuro6ZS4rl8vFudEoq7JMUkSVotpG\nTslk3bp1TFKUFZMUkYGqbeSUTOrq6ji0F2XFJJVFKVpIEckjp2zbtg1CCPh8Pl2midda8ggosmJH\nQEkkEmhubtYuOKooTFJZ7Ny5Ey0tLWVRDVFI7/1iRwYgfVXbyClerxeDg4PK35FIBPv37+fzZMqK\nSSqLrq4uo0MoWL7e+16vF263G319fejr68PDDz8Mt9tdyhApi2AwmPH5Tnd3N3bu3KkaCaWhoUH1\ntzxiSurIKan76GXz5s1Ys2YN6uvrsXHjRjQ3N+cdbeK0007DqlWrlKrNP//5zzmfbxGV7YgT9L5c\nvfenOzIA6avaRk7hiCk0VYbfSXV2dsJiscDtdiMWi6laFCUSCbjdbqUKxOFwKNUX2Xret7W1KQ9w\nvV5v2rJYLKZUfQFQjt/W1obx8fG88cZiMSVmq9WqqrrIdz16yNd7fzojA5C+OHIKUQFSO06VslOk\n0+lURo2Ix+NKxz6Z3Fk3Go3m7FUfDAaFEEKMjIwo22TrjS/vg6Qe+/F4XDnX2NiYcn6kdEyMRqNC\nkiTh8XiEEEIEAgHV+fNdT6rkWHK9csnXe386IwNoCezMm4Yjp1QGdubVl6FJSk5Asmg0qjq33W5X\nkou8ffL6TF/ihSzLtE0wGMzb018esib12Ha7vaDr0Uuu3vvZEl0hCVBLTFLpOHJKZWCS0pehSUr+\ncHo8HhGPx7NuFw6HhdPp1DVJFbJd8t1bprudQq9HTy6XS3WHxCRFpC8mKX0Z+kzquuuugyRJaGlp\nQX19PTo7O9O2cbvduOaaa0zRAkhuui2ydHos5HqSTbfXfyapvfe1HBmAiKjUDE1SixYtgs/nQzAY\nhM1mw/bt21Vf7F6vF1u2bMGePXuwaNGiksRUyBd3tgYW+a4nVaZkly0BFiq1976WIwMQEZWaoUnK\nYrEgkUigqakJXV1dCAaDqoE1W1paAKAk06PLiSfXDKEulwsA0NfXp3SglFv7AfmvpxRSe+9rOTIA\nmR9HSqFKY3gTdKfTqfyynz17tmpgTfkuIBKJqO5esvW8z9TTPlfve3lQz0Qigb6+PkiSlPHOQ/73\nFVdcAQDo6OhQRgloaGhQJYVc16O1QnrvFzoyAFWGchkpJZFIYHR0FG63O2dCLWSkFI6mUuFSH1IZ\n0bpPbhSR3LJOiPdb3NntdhGNRpXWfnKzcqQ0XJjqsmAwqDSGcLlcqsYOmfYTYrIRh9yKTo6l0OvR\nWnLzc7vdrjSFz7WtJEmGtB4DG06UTGqZNSO73a58jrLF6vF4hCRJIh6PK91EUpvmF7KN3thwQl9l\nOenhdJXDh7jSMEmVTjmV72yxyj9E5b6MQrz/o1X+MVbINqXAJKUvw6v7iMoRR0rRVyEjpXA0lSqR\nmrUq/Rep3MEWKR1vSV+ooDspjpQy/ZFSssWa+h5m2l7uB2iW0VR4J6WvqktSxXyQaPoqKUml/sDh\nSCnFy/Y5LGT5VPfVC5OUvqquuk9Mow8SETDZl66hoQFerxeJRAJz5sxRlaX29nZ0dXUhEonk7dA9\nXfIo9rm6OgwMDABAWgfxjo4OAPmvh8hIVZekiKaLI6VoP1JKqkJGSuFoKtWBSYpoijhSivYjpaQq\nZKQUjqZSHUyRpMqpl7yWvxbLSbVedyYcKUV/hYyUwtFUqoMpklS59JJPlunXYigUUn2Rt7W1pe2n\nZe/4Uh6Lz/DUOFLK9MnXn/pvoLCRUjiaSpVIbUlhVOs+lElru1xxulwuVctBn8+nWq9l73ijjlXs\n/ydUYOs+jpRSvNQ4s5WrQkZKMXo0Fbbu0xeT1BTlijM1KSXTsne8kcdikjJOuXxGqg2TlL6mVd03\nOjqa9VmF3IPdYrEgEonk7IWfKtPxsj0PydeTvlQikQisViscDgdGR0fT1mvZO96sxyIi0tq0ktT5\n55+PQCAAALDb7apnFtu2bYPdbkcwGERjYyN27NiBLVu2IBqNIhwOo6OjAzt37sx43Gg0mrYsHA6n\nLYvFYti0aRMWLFgAIQSuvfZarFq1CqFQKONx9Ww6K5+zo6MDF1xwAaxWqyoJDw0NAVA/TJ8zZw4A\nTPl5klmPRfrJ9YyKqKKl3loVU20i13Un143Lw6skbzOVXvipf2dalq8nvR4yxSWLx+MiGAwq70fy\nc51s++U63lRjKMWxijmHvB+r+4onv+/Fvv+kH1b36UuT1n1r164FADzyyCPKskOHDinLAX164efr\nSV9qdXV1aGpqQnt7O1wuF+9ESDOCI6VQldIkSTU1NUGSJCVpAMDjjz+uDNki07oXfr6e9KlK0VNe\ntm7dOlWS0rJ3vFmPRUSkNc36SbW2tsLv92N0dBSRSATLly9XrdezF34hUxUApekpL6urq1N9yWvZ\nO96sxyIi0ppmSUqesry3txfDw8O48MILVev16IWfrye9kRKJhKqzpJa94816rGrDkVIqA98bc9Ms\nSc2ZMwd2ux3d3d04evQo6urqVOsL7YUv/1u+C5G3TW7WLY/kUEhP+lLwer2qpu+RSAT79+9XEjdQ\neO94uTl9thaKRh2L0lXKSClajlxS6lFXtDgfn/OZXGpLium0kpI7gSZPwJa6rtBe+OFwWOlVL3eS\nlSduS577JldPej0gQ+squce7fH25OtTm6x0vvy+FTNpWymPJMl1/IVChrfuKfT9KLVucWo5cIkRp\nR13R+nzF/r9k6z59mWbEiXJRqi8lLWcW1fJYTFJq5ZyktBy5RFaqUVf0OB+TlDmZYoBZUhsdHcWN\nN95oumNVCo6UMknr0UZKOeqKEecjg6RmLd5J5QadfzkHAoGM1aVGH0tW7PXDZHdSgUAga8fv5Cpb\nm82mDMAq/zLP1ildnnY9OY7k6mxZNBpVqq6TY8l2NyHvn++VS6Zt5GvLtG0xd9/J1d7yMZKr5s1+\nvmLLNu+k9MUkNUVT+WKoJNO9brMlKSE4Ukq2/5fTKdulGnVFj/MxSZkTq/umSFRpz/9KvG6OlKK9\nUo+6wlFeKh+TFFWtah8pRe/RRvQcdcUM56PSYJKiqlbNI6XoPdqInqOumOF8VBpMUlTVqnmkFL1H\nG9Fz1BUznI9Kg0mKqlo1j5Si5cglpR51RcvzkcmltqRg6z7SA0zYuk9WrSOlyLQYuaTUo65oeT5Z\nrvcoF7bu01fa/5H777+/4H4ZfPE1ldf999+vW0Hmj6vc5P8H06HlyCVmPF+x7xGTlL5qkOLyyy/H\nfffdh3fffTd1FVHRZs6cicsvv9zoMKhIpR65hCOlkCwtSdXU1ODLX/6yEbEQkQkNDg7iox/9qOat\nG81yPjK3tCRFRJVL7k8lptBkPbkxQimU+nycQ8rcmKSIqsBUklK14XtjbmyCTkREpsUkRUREpsUk\nRUREpsUkRUREpsUkRUREpsXWfVQRPvCBDwBgc2Iyxje+8Q2jQ6hYFsH2l1QBJiYm4PP5ynaklNdf\nfx3btm3D0qVL8c1vftPocErmV7/6FX74wx/in//5n/G5z33O6HCKdv755+NjH/uY0WFUJCYpIhNo\nbm7G6OgoDh8+jNmzZxsdTkldddVVeOihh3D48GE0NDQYHQ6ZDJ9JERns7rvvxn333Yc77rij6hIU\nAOzatQunnnoqrr76aqNDIRNikiIy0CuvvIJvf/vbuOaaa3DxxRcbHY4hPvShD+HOO++E3+/HHXfc\nYXQ4ZDKs7iMyiBACq1evRjgcxjPPPKM0/qhW119/PW677TYcPnwYCxcuNDocMgkmKSKD7NmzB9de\ney2efPJJnHfeeUaHY7i33noL5557LhoaGhAIBNhSkwCwuo/IEOPj4/je976HG264gQnqPaeccgp6\ne3vxy1/+Ert27TI6HDIJ3kkRldi7776Lz3/+8zh+/DhGR0dRW1trdEim0t7ejptuugmHDh3CWWed\nZXQ4ZDAmKaIS6+jowA9+8AM8/fTTWLx4sdHhmM7ExAQ+//nP48SJExgeHmYSr3Ks7iMqoWeeeQbt\n7e3o6OhggsqipqYGd911F55//nn84Ac/MDocMhjvpIhK5K233sJnP/tZfOQjH8Hjjz+OGTP4GzGX\nn/zkJ9i2bRuefPJJLF++3OhwyCBMUkQlsn37drhcLhw+fBgf//jHjQ7H9IQQuOSSS/Dyyy/jmWee\nwamnnmp0SGQA/pQjKoH9+/fjxz/+MXbt2sUEVSCLxYLbb78d0WgUO3bsMDocMgjvpIh09vrrr6Op\nqQlnn302fD6f0eGUnbvuugtf//rXsW/fPqxcudLocKjEmKSIdLZp0yY8+OCDePbZZzF37lyjwylL\nX/nKV3Dw4EEcPnwY9fX1RodDJcTqPiId+f1+3Hbbbbj11luZoKahu7sbx48fx3e+8x2jQ6ES450U\nkU7++Mc/YsmSJbjkkkvQ29trdDhlz+/3w2q14r777sOXv/xlo8OhEmGSItLJ2rVrceDAAVZRaeif\n/umf4Pf78eyzz3LuqSrBJEWkg76+PvzjP/4jHnvssaqdgkMPr7/+Oj7zmc9gyZIlbIRSJfhMikhj\nL7/8ctXPEaWXWbNm4c4778RDDz2E22+/3ehwqAR4J0WkIXZALQ12jK4eTFJEGtq9ezeuv/56DuWj\nM3mIqb/5m79BIBDgEFMVjP9niTQyPj6OHTt24IYbbmCC0tkpp5yCu+66C08++SR+/OMfGx0O6Yh3\nUkQakKeXmJiY4BxRJcRpTyofkxSRBjhRnzE4gWTlY3Uf0TT96le/UpIUE1RpzZw5E3fddRd++9vf\nor293ehwSAe8kyKaBj7AN4fdu3fjuuuuw5NPPonzzjvP6HBIQ0xSRNNw/fXXo6enh02hDSaEwOrV\nqxEOh/HMM8/gAx/4gNEhkUb4s4+oSE888QRuueUW3HLLLUxQBpPnnvrDH/7AuacqDO+kiIogD8/T\n1NSEBx54wOhw6D133303vva1r3E4qgrCJEVUBA50al5r167FU089hcOHD2P27NlGh0PTxOo+oiny\n+Xy4/fbb0d3dzQRlQrfeeismJibw7W9/2+hQSAO8kyKagj/84Q9YsmQJVq9ezTmiTOyhhx7C5Zdf\njr179+IrX/mK0eHQNPBOiiiDvXv3YubMmfj5z3+uWm6z2XDyySfjJz/5iUGRUSEuu+wybNq0CTab\nDa+++qqy/JVXXsGSJUuwdetWA6OjqWCSIsogEAjgxIkTuPTSS9HW1oY333wTd911F+6//37ccccd\nqKurMzpEyuNHP/oRZs2ahc2bNwMA+vv7cdZZZ+G5557Dnj17cOLECYMjpEKwuo8og4ULFyISiQAA\nampqMG/ePPz5z3/GVVddhVtuucXg6KhQv/jFL3DRRRdh6dKlOHToECwWC+SvvF/96lc455xzDI6Q\n8uGdFFGK3/3ud0qCAiYHj/3973+PN998EyeffDLeeecdA6OjqfjLX/6CD3zgAzh8+DAAKAmqtrY2\nrSqXzIlJiijFz3/+c8ycOVO1bGJiAkII/OhHP8KyZcvw3HPPGRQdFeL111/Hpk2bcPnll+PNN9/E\n8ePHVesnJibwyCOPGBQdTQWr+4hSXHnllfD7/Xj33Xczrq+pqcHExATuvvtubNiwocTRUT7Hjh3D\nggUL8m5XW1uLeDzOIZRMjndSREneffdd7Nu3L2uCAt6vMrJYLKUKi6ZA/n+X7//P8ePHMTQ0VIqQ\naBqYpIiSHDhwAG+88UbW9bW1tWhoaMDw8DBaW1tLGBkV6mMf+xj++Mc/Ys2aNTkT1UknnYT/+Z//\nKWFkVAwmKaIkjz32GE466aSM6ywWCy6//HI8//zzuOCCC0ocGU3FRz7yEfj9ftxyyy2ora1FTU1N\n2jbvvPMOHnroIQOio6ngMymiJOeddx4OHjyI5I9FTU0NZs6ciVtuuQVXX321gdFRMYLBINauXYtw\nOIyJiYm09UePHsX8+fMNiIwKwTspovckEgkcOnQoLUGdccYZePrpp5mgytTSpUsRDAaVRi7JVYAz\nZszAY489ZlRoVAAmKaL3PPHEE8ooBPIX2VVXXYVnnnkGZ599tpGh0TR98IMfxJ133on+/n6ceuqp\nqK2tBcAkVQ6YpIjeI3furK2txWmnnYa9e/fi1ltvxamnnmpwZKSV1tZWhEIhLF68GDNnzsTExAQe\nffRR8KmHefGZFNF75Lunz33uc7j33nuxcOFCgyMivRw/fhw33HADOjs7AQAHDx7EZz/7WYOjokyY\npAx24MABnHfeeUaHQRXoqaeewvLly3U5NoeHIj38y7/8Czo6OlTL0ttlUkm98MILAIB77rnH4Eio\nkqxbtw4vvPCCbknqnXfewZe+9CX2FSPNbNiwAb/73e/SljNJmURzc7PRIRBNSXNzM8staeaBBx7I\nuJwNJ4iIyLSYpIiIyLSYpIiIyLSYpIiIyLSYpIiIyLSYpCgvh8MBh8Oh+z5E08FyWpmYpKis+f1+\nWK1WWK1W+P3+gvZJJBIYHR2F2+2G1WrNu30oFFK2TZ2fSD6/xWKB1WqF1+st6jqoshVTTovZz+12\np5XRSCSCtrY2WCwWtLW1YXBwMOO+oVAIFotFebW1tRUcp64EGaq/v1/wf0NxPB6PkCRJxONxEY/H\nhc1mEy6XK+9+drtd2O12ASDve+90OoUkScLn84lwOJy2DoAIBoNCCCGCwaAAIJxOZ/EXpREAor+/\nv2yPX0mKLadT3U8uf8llOh6PC5/Pp/zb4/EIAMqyZC6XS9k/2zZ6am1tFa2trWnL+e1oMCap4oTD\nYQFAjIyMKMvkD6mcNPLJl6RsNpuw2+0iHo8XvD8AIUlSQefXE5OUORRbTqe6Xzwez/jDK1OiyVbu\nS52UUmVLUqzuIwwODipVVp2dnYjFYsq6WCwGr9erVIul/u33+5WqrkgkknEbPQwPDwOAarK6efPm\nAZgcD3G65OcU7e3tqKury7iN0+kEAIyOjgKAcv3t7e3TPj+lq6ZyOtX9enp6sHXr1rTlkiRlPL7N\nZlP9HYlEYLVa4XA4lPJsGgYkTEpi9J2Uz+dT/WKTqwPklyRJql9eyX/L+8i/+mw2W9o22SSfI9cr\nG5vNlnG9HHMhsp1D/sXq8/mUKhBJkkQgEEjbVv71OjIyIjwej4hGowWdW2+osDupaiunU9kvEAgo\n15gvnng8nrEqT35/k9/PUpdlVveZlNFJKlOhRspzldRtsu2TbxstZTv+VM6bbdvUZ03y84DkL7xk\n8rpcVYOlVmlJqtrKaaH7RaNR1XOqfMcNBALKc65U8XhcBINB5YdXIc/NtMTqPsoo9bZftn379hJH\nYh7ytTc1NQEA6urqlPept7dXtW1nZydWrFiBeDwOANi4cSMSiUQJo60OLKeZPfjgg9i8eXPB2+/a\ntQs33nhjxirsuro6NDU1ob29HS6Xa0qtEPXEJFXl5A+/3HQ6FAoBeP95i16Sm7rmemWTra4dyP6F\nNh1ywuru7laWeb1ebN++HZdeeinq6uqwceNG+P1+Truig2orp4Xs5/f7sXr16gKuYpLX64UkSTj/\n/PPzbrtu3TomKTKHpqYm+Hw+HD16FBaLBQ6HAx6PB9u2bdP1vGKyqjnvKxv5Q5z88Fx+IL5s2bJp\nxSZ/CWS6I0r+8mhpaQEA5VdpQ0MDAGDLli3TOj+lq7ZyWsh+VqsVCxcuzJgsUxNnKBTC888/X/Bd\nV3LtgdGYpKqc3+/HhRdeiG3btkEIAZ/Ph/Xr1xsdVl7yL8gjR44oy44dO6ZaVyx5jqSXXnpJWSYn\nrORJ/lJ/7crJKtevYCpOtZXTQvbLlSyT/x2LxbBv3z5Vq9NQKJSzs24ikTDNXGFMUlXOarWivr4+\nrfqira0NsVgsrZlv8t/yF3fyHUemffTQ2NgIl8uF3t5eJBIJJBIJ9Pb2wuVyobGxUdku27A3yTGn\n3jGtXLkSdrsdDodDif+ee+6BJEmqL8Zrr70WwPtVUHLTXXk5aafaymmh++UTi8WwadMmbN++XfW+\nLV26FGvWrAEwWX6TR6GIRCLYv38/Vq5cqcE7oAGdGmpQgYxu3RcMBlVNcZNfcqu1XC8h8jfT1ZPc\ndDZXE3G73a5aVmicyT3wXS5XxhZRgUBAeZ9sNlvGGIyACmvdV43ltJD9UqVeS673ZmxsTHUOvNdC\ntdDO8FrL1rrPIkSOClXS3cDAADZs2JCzXltP4+PjOOWUU9J+nY2Pj+PMM880LC6aHovFgv7+flX1\nZDkdPxXLaeXbsGEDAKC/v1+1nNV9Vczr9WLRokUZqw8aGhrg8XgMiIpIjeW0utUYHQAZZ2BgAK+/\n/jpWr16t+gIYHx/H0NDQlPpfEOmF5bS68U6qivX19WHWrFm4+eablQeqDocDr7zyCj/4ZBosp9WN\nd1JVrK6uDuvXr8f69evR1dVldDhEGbGcVjfeSRERkWkxSRERkWkxSZEplGJuHyItscyWBpMUmcLO\nnTvR0tJimkEtcwmFQmmjHuTidrtzDkJK5amcymyyfOUxFArB7XYrE0wajUmKTKGcHoinzowqDy+T\nSSgU4oCzFaqcyqwsX3ns7OyEw+HA3LlzsWfPHlN0kmbrPqIpmjt3bkEf3kQigb1795YgIqL88pXH\ntrY2nH766ejr68s435RReCdVZTo7O2GxWOB2uxGLxVS384lEQqkKkPuiyANvpta/+/1+papLnkLA\n6/WmLYvFYvD7/cp+8vHb2towPj6eN95YLKbEbLVaVQNh5rsePUQiEVitVjgcDmVA2Wx6enqwdetW\nXeOpBiyz2shVHuXBbdvb202VoABwgFmjlXKAWafTKcLhsBBicqpopzSfVQAACYlJREFUeZpomTwY\nZTQaFeFwWBm8UwihGtxTHoByZGRE2UaeVj11PyQNaClvkzwduzzIZfK2smg0KiRJEh6PRwgxOZhr\n8vnzXU8qZBloM/WVS/JgnHhv4M9oNJq2XSAQUK63kONqDRUywCzL7PTLrBxHtvIYDAYFAOHz+ZRB\nlQsd0FZL2QaYZZIyWCmTlPxhlkWjUdW57Xa78kGVt09en+kDUciyTNvIHwyn05l1O4/Hk/HY8mjR\n+a5HL/F4XASDQeULxuVyqdZHo1HVMiap6Z2HZXZ68pVHp9OpSqTJCVlObKXAJGVSpUxScsHzeDwZ\np52QhcNhpeDq9YEvZLtsUzPI2xR6PXpyuVxCkqS0ZcmYpIrHMjt9+cpjroSc/ANAb0xSJlXKJDU2\nNqb6ECX/IpTJX7pjY2OGf+DzfbkXcj2ZzpfvNRXxeFy1j8/nU6pzCr0OPVRKkmKZnV6ZLaQ8Fnqt\nemOSMikjJj0MBoPKL7rkD4lcVSEX6lJ84Aupqkl+BjCV6ymVTNegRQKcjkpJUjKW2eIUUh7lOFLv\n7ACk1RLoiUnKpEr9TCq5IMq39Mnrp/orsdgPvPyr1+fzZd1Ofohrt9uVuKPRqPKhznc9pRCPx/M+\nYOad1PTOwzKrrdSYUxt3CPF+DYHcAKQUsiUpNkGvMk6nU2lqO3v2bDidTmWdJEkAJptZJze1jcVi\nSrNeYLLZr7w8eZtsy2Rer1fZv6+vD5IkKefMtN8VV1wBAOjo6EB9fT0sFgsaGhrQ3Nxc0PVozev1\nqpoTRyIR7N+/HytXrtTtnMQyq7eVK1fCbrermu/fc889kCQJ69evNzQ2ACVO4ZTGiNZ98gPm1GoG\n+Ved3W4X0WhUaTklN9FNfsnHm8qyYDCo1Me7XC7VL8pM+wkx+UBcbkUnx1Lo9Wgtufm53W5X/fLM\nJfWaSgEVdCfFMqutbOVRvgvMdK2lkO1OyiKEEJpkOyrKwMAANmzYgEr+3yB3VqzkazQbi8WC/v5+\ntLa2luXxjcYyW3obNmwAAPT396uWs7qPiIhMi0mKdJWrvp/IjFhmzYVJinTV0NCQ8d9EZsUyay4c\nBZ10xTp9Kjcss+bCOykiIjItJikiIjItJqkqlDrPjpklT9NuhqmsS6Varzsbllnz0+u6maSq0M6d\nO9HS0gK/3290KAUTk0N4qZbJE9NZrVZNr0We5C5ZLBZTTa4nj0RQrFAopPpAt7W1qdZnut5qxjKr\nlkgkMDo6CrfbrXnizlT+AePKLJNUFerq6jI6hGnzer1wu93o6+tDX18fHn74Ybjd7mkfNxQKYcuW\nLapliUQCmzZtAjD5QYxGoxgYGFBmMy3GgQMHVH+vWbOm6GNVA5ZZNafTiYceeghbtmzRNHFnKv8y\no8osW/dR2YlEImhpacHIyIgy1bXNZsPSpUuxfPlyNDU1FXXcRCKBvXv3pi1/5JFH4Pf70dfXBwCY\nM2cO2tvbsXTpUlx00UVFjd03d+5c3ilVEa3LbHt7O4DJMQK1kq38y4wqs7yTKiOjo6NZ6307OzuV\nZZFIBIlEQlU9lTx4ZKpMx8tWtxyLxZRzWa1W1YCrpTI8PAwAmD9/vrJs3rx5ANJ/7U1FT08Ptm7d\nmrZ8YGAAAJQvFwD4+Mc/DgC49957p3yeSCQCq9UKh8OB0dHR4oItEyyzk/Qqs1rKVv4Bg8us7qMG\nUk5THWBWHlZfno46WfKgp/IcMdFoVBlsM9s8OPIU1kgZJDN1WTQaFZIkKcP3ZxriPxlSBuDM9sol\n0zbytWXattj5bwKBgDJVduo5s8VZSPyZJA9UK8ecPKW4FueAiQaYZZnVp8xmO1cxcpV/IUpTZjmf\nlEkVMwq6PMJy8ijF8Xhc9SUgjwYty/fFm6lgpS6TJ5hL3SbTl49WCokr3/J8otGoaort1OPIXzCp\nE9lN5wsiHo+LYDCo/L9MneJ7uucwU5ISgmVW6zKr1f5C5C//Mr3LLJOUSRWTpOTpCZInJAsEAhl/\nHYbDYWVagOl+4JOnvZ7KL8vpKMUHPvXDlnqckZER5Ve9/CUr/z/QYqoFefrzTColSbHMmjdJ5Sv/\n2fbRusxy0sMK0tTUBEmSlGclAPD444+nPXx1u9245pprlEnapktuRSTea2qa/Mok9VlEttdU5boe\nm802pWP5/X6sXr065zbnn38+AoEAjh49ivr6erjdbvzpT38CAFx88cVTOl8m69atK6um1cVgmdWu\nzGqpkPKfSSnLLFv3lanW1la0tLRgdHQU8+fPx/Lly1XrvV4vtmzZgnA4jMbGRk3PPT4+jkWLFuXd\nLtsXwXRJkgS/349YLIY5c+YAgDLT6bJly6Z0rFx9TCwWi3INK1euVLXi6+zshN1uL7olYbK6ujpD\nv6hKhWVWmzKrpULLf6pSllneSZUp+Quzt7cXw8PDuPDCC1XrW1paAEDTD7vL5QIA9PX1qabj7uzs\n1OwchZB/+R05ckRZduzYMdW6QuX6hZ3tA+r1ejE0NITt27dPNfSMEomEanrxSsUyq02Z1VIx5R8o\nbZllkipTc+bMgd1uR3d3N44ePapqHg28X70QiUQwPj6uLI/FYhnny5F/FcnbJjczlXuWX3HFFQAm\n+2bU19fDYrGgoaGh5F+wjY2NcLlc6O3tRSKRQCKRQG9vL1wul+oLTm52HAqFpn3ORCKBUCiEtrY2\nHD16FD6fL+09L+R8Xq9X1QQ6Eolg//79RfW1Kjcss9qWWTnppv67mGPlYniZnfLTLdJUMQ0nZPLD\n6NRWZ8nr7Ha7iEajSsup5Ga6SHrAGQ6HlYfMPp9PCCGUprvJTU3D4bDSukc+np6Q4yGs3CxWkiQR\nCATS1svXPNUmvqnnlP92uVxZmy4Xer7kprzJza8LjWUq12CmhhMyllltymzq+5HpnFqV/1KVWbbu\nM6npJKlqUGyBTzadfihGn6/SklQ1MFuZLXX51zpJsbqPKtro6ChuvPHGij0fVR4ty1AllEe27qOK\nNTg4iI9+9KMFteoqx/NR5dGyDFVKeWSSorIg900RU2giXOrGCFqer5rmIapURpfZUpd/vcoskxSZ\n2lQ+4JWkWq+7ElTr/zu9rpvPpIiIyLSYpIiIyLSYpIiIyLSYpIiIyLTYcMIkipnhlchI9957L2pr\na40OgyrEvffem3G4KiYpg/3d3/0dgMmh74m0JJctPZx00kl44IEH8MADD+h2Dqo+n/jEJ9KWWUS1\ntpckIiLT4zMpIiIyLSYpIiIyLSYpIiIyrRoA1xsdBBERUSb/H21snUdnshx+AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%pylab inline\n",
    "from IPython.display import Image\n",
    "Image('tree.png')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=2,\n",
       "            max_features=None, max_leaf_nodes=None,\n",
       "            min_impurity_split=1e-07, min_samples_leaf=1,\n",
       "            min_samples_split=2, min_weight_fraction_leaf=0.0,\n",
       "            presort=False, random_state=None, splitter='best')"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from itertools import product\n",
    "\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "from sklearn. datasets import load_iris\n",
    "from sklearn import tree\n",
    "\n",
    "iris = load_iris()\n",
    "X = iris.data[:, [2, 3]]\n",
    "y = iris.target\n",
    "\n",
    "clf = tree.DecisionTreeClassifier(max_depth=2)\n",
    "clf.fit(X, y)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0xa9a12b0>"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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pSyJlqKWOizFuTfOixS0yDCV1SSTtNc1HIS7GuDXNixa3yDCcJ0pJvhzAETN7NskJNVEq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\nlNRFRDyipC4i4pH/B6OmHS2bu1+QAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x981b748>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(X[:,0],X[:,1],color=\"black\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "x_min, x_max = X[:, 0].min() - 1, X[:, 0].max() + 1\n",
    "y_min, y_max = X[:, 1].min() - 1, X[:, 1].max() + 1\n",
    "xx, yy = np.meshgrid(np.arange(x_min, x_max, 0.1),\n",
    "                     np.arange(y_min, y_max, 0.1))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.0 7.9\n"
     ]
    }
   ],
   "source": [
    "print(x_min, x_max)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-0.9 3.5\n"
     ]
    }
   ],
   "source": [
    "print(y_min, y_max)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 0. ,  0.1,  0.2,  0.3,  0.4,  0.5,  0.6,  0.7,  0.8,  0.9,  1. ,\n",
       "        1.1,  1.2,  1.3,  1.4,  1.5,  1.6,  1.7,  1.8,  1.9,  2. ,  2.1,\n",
       "        2.2,  2.3,  2.4,  2.5,  2.6,  2.7,  2.8,  2.9,  3. ,  3.1,  3.2,\n",
       "        3.3,  3.4,  3.5,  3.6,  3.7,  3.8,  3.9,  4. ,  4.1,  4.2,  4.3,\n",
       "        4.4,  4.5,  4.6,  4.7,  4.8,  4.9,  5. ,  5.1,  5.2,  5.3,  5.4,\n",
       "        5.5,  5.6,  5.7,  5.8,  5.9,  6. ,  6.1,  6.2,  6.3,  6.4,  6.5,\n",
       "        6.6,  6.7,  6.8,  6.9,  7. ,  7.1,  7.2,  7.3,  7.4,  7.5,  7.6,\n",
       "        7.7,  7.8])"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.arange(x_min, x_max, 0.1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ -9.00000000e-01,  -8.00000000e-01,  -7.00000000e-01,\n",
       "        -6.00000000e-01,  -5.00000000e-01,  -4.00000000e-01,\n",
       "        -3.00000000e-01,  -2.00000000e-01,  -1.00000000e-01,\n",
       "        -2.22044605e-16,   1.00000000e-01,   2.00000000e-01,\n",
       "         3.00000000e-01,   4.00000000e-01,   5.00000000e-01,\n",
       "         6.00000000e-01,   7.00000000e-01,   8.00000000e-01,\n",
       "         9.00000000e-01,   1.00000000e+00,   1.10000000e+00,\n",
       "         1.20000000e+00,   1.30000000e+00,   1.40000000e+00,\n",
       "         1.50000000e+00,   1.60000000e+00,   1.70000000e+00,\n",
       "         1.80000000e+00,   1.90000000e+00,   2.00000000e+00,\n",
       "         2.10000000e+00,   2.20000000e+00,   2.30000000e+00,\n",
       "         2.40000000e+00,   2.50000000e+00,   2.60000000e+00,\n",
       "         2.70000000e+00,   2.80000000e+00,   2.90000000e+00,\n",
       "         3.00000000e+00,   3.10000000e+00,   3.20000000e+00,\n",
       "         3.30000000e+00,   3.40000000e+00])"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.arange(y_min, y_max, 0.1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "xx, yy = np.meshgrid(np.arange(x_min, x_max, 0.1),\n",
    "                     np.arange(y_min, y_max, 0.1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 0. ,  0.1,  0.2, ...,  7.6,  7.7,  7.8])"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "xx.ravel()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(44, 79)"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Z = clf.predict(np.c_[xx.ravel(), yy.ravel()])\n",
    "len(Z)\n",
    "#xx.shape\n",
    "Z = Z.reshape(xx.shape)\n",
    "Z.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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f3lPv/CAtlSUNY0xaQk42d81/ns/bf8iytl9SUN6dMYUnsiW0jnn57xBwQozbOhGf4+fD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\nVXNomJlZNYeGmZlV+wmbIC+YJL1k6gAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xab69dd8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure()\n",
    "plt.contourf(xx, yy, Z, alpha=0.4, cmap = plt.cm.RdYlBu)\n",
    "plt.scatter(X[:, 0], X[:, 1], c=y,  cmap = plt.cm.brg)\n",
    "plt.title('Decision Tree')\n",
    "plt.xlabel('Petal.Length')\n",
    "plt.ylabel('Petal.Width')\n",
    "plt.show()\n"
   ]
  },
  {
   "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": "3.6.0"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}