Classification via scikit-learn.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn import datasets\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "sklearn.utils.Bunch"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "iris = datasets.load_iris()\n",
    "type(iris)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
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       "        [5. , 3.6, 1.4, 0.2],\n",
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       "        [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, 0.2],\n",
       "        [5. , 3.2, 1.2, 0.2],\n",
       "        [5.5, 3.5, 1.3, 0.2],\n",
       "        [4.9, 3.6, 1.4, 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",
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       "        [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",
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       "        [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",
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       "        [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",
       " 'target': array([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,\n",
       "        0, 0, 0, 0, 0, 0, 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,\n",
       "        1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 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,\n",
       "        2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2]),\n",
       " 'target_names': array(['setosa', 'versicolor', 'virginica'], dtype='<U10'),\n",
       " 'DESCR': '.. _iris_dataset:\\n\\nIris plants dataset\\n--------------------\\n\\n**Data Set Characteristics:**\\n\\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                \\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\\nThe famous Iris database, first used by Sir R.A. Fisher. The dataset is taken\\nfrom Fisher\\'s paper. Note that it\\'s the same as in R, but not as in the UCI\\nMachine Learning Repository, which has two wrong data points.\\n\\nThis is perhaps the best known database to be found in the\\npattern recognition literature.  Fisher\\'s paper is a classic in the field and\\nis referenced frequently to this day.  (See Duda & Hart, for example.)  The\\ndata set contains 3 classes of 50 instances each, where each class refers to a\\ntype of iris plant.  One class is linearly separable from the other 2; the\\nlatter are NOT linearly separable from each other.\\n\\n.. topic:: 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",
       " 'feature_names': ['sepal length (cm)',\n",
       "  'sepal width (cm)',\n",
       "  'petal length (cm)',\n",
       "  'petal width (cm)'],\n",
       " 'filename': '/Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages/sklearn/datasets/data/iris.csv'}"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "iris"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Class labels: [0 1 2]\n"
     ]
    }
   ],
   "source": [
    "X = iris.data[:, [2, 3]]\n",
    "y = iris.target\n",
    "print('Class labels:', np.unique(y))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "## training and test\n",
    "\n",
    "from sklearn.model_selection import train_test_split\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.3, random_state = 1, stratify=y)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "verify if the training and test subsets have similar proportions of class labels as the input dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Labels counts in y: [50 50 50]\n",
      "Labels counts in y_train: [35 35 35]\n",
      "Labels counts in y_test: [15 15 15]\n"
     ]
    }
   ],
   "source": [
    "print('Labels counts in y:', np.bincount(y))\n",
    "print('Labels counts in y_train:', np.bincount(y_train))\n",
    "print('Labels counts in y_test:', np.bincount(y_test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "StandardScaler(copy=True, with_mean=True, with_std=True)"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# feature scaling\n",
    "\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "sc = StandardScaler()\n",
    "\n",
    "sc.fit(X_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train_std = sc.transform(X_train)\n",
    "X_test_std = sc.transform(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages/sklearn/linear_model/stochastic_gradient.py:152: DeprecationWarning: n_iter parameter is deprecated in 0.19 and will be removed in 0.21. Use max_iter and tol instead.\n",
      "  DeprecationWarning)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Perceptron(alpha=0.0001, class_weight=None, early_stopping=False, eta0=0.1,\n",
       "      fit_intercept=True, max_iter=None, n_iter=40, n_iter_no_change=5,\n",
       "      n_jobs=None, penalty=None, random_state=1, shuffle=True, tol=None,\n",
       "      validation_fraction=0.1, verbose=0, warm_start=False)"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# train a perceptron model, one-vs-rest rule for multiclass classification\n",
    "from sklearn.linear_model import Perceptron\n",
    "ppn = Perceptron(n_iter = 40, eta0 = 0.1, random_state = 1)\n",
    "ppn.fit(X_train_std, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Misclassification: 9\n"
     ]
    }
   ],
   "source": [
    "# make predictions\n",
    "y_pred = ppn.predict(X_test_std)\n",
    "print('Misclassification: %d' % (y_test != y_pred).sum())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.2"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "9/(15*3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 0.80\n"
     ]
    }
   ],
   "source": [
    "# performance matrics\n",
    "from sklearn.metrics import accuracy_score\n",
    "print('Accuracy: %.2f' % accuracy_score(y_test, y_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accurary: 0.800\n"
     ]
    }
   ],
   "source": [
    "print('Accurary: %0.3f' % ppn.score(X_test_std, y_test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "from matplotlib.colors import ListedColormap\n",
    "def plot_decision_region(X, y, classifier, test_idx = None, resolution = 0.02):\n",
    "    # setup marker generator and color map\n",
    "    colors = ('red', 'blue', 'lightgreen', 'gray', 'cyan')\n",
    "    markers = ('s', 'x', 'o', '^', 'v')\n",
    "    cmap = ListedColormap(colors[:len(np.unique(y))])\n",
    "    \n",
    "    # plot the decision surface\n",
    "    x1_min, x1_max = X[:, 0].min() - 1, X[:, 0].max() + 1\n",
    "    x2_min, x2_max = X[:, 1].min() - 1, X[:, 1].max() + 1\n",
    "    xx1, xx2 = np.meshgrid(np.arange(x1_min, x1_max, resolution),\n",
    "                           np.arange(x2_min, x2_max, resolution))\n",
    "    z = classifier.predict(np.array([xx1.ravel(), xx2.ravel()]).T)\n",
    "    z = z.reshape(xx1.shape)\n",
    "    \n",
    "    plt.contourf(xx1, xx2, z, alpha = 0.3, cmap = cmap)\n",
    "    plt.xlim(xx1.min(), xx1.max())\n",
    "    plt.ylim(xx2.min(), xx2.max())\n",
    "    \n",
    "    # plot class samples\n",
    "    for idx, cl in enumerate(np.unique(y)):\n",
    "        plt.scatter(x = X[ y == cl, 0],\n",
    "                    y = X[ y == cl, 1],\n",
    "                    alpha = 0.8,\n",
    "                    c = colors[idx],\n",
    "                    marker = markers[idx],\n",
    "                    label = cl,\n",
    "                    edgecolor = 'black')\n",
    "        \n",
    "    #highlight test samples\n",
    "    if test_idx:\n",
    "        #plot all samples\n",
    "        X_test, y_test = X[test_idx, :], y[test_idx]\n",
    "        \n",
    "        plt.scatter(X_test[:, 0], X_test[:, 1],\n",
    "                    c = '', edgecolor = 'black', alpha = 1.0,\n",
    "                    linewidth=1, marker ='o', s=100, label = 'test set')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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j70VHRBe6X2m1rijr873oKtWU0ZVr1/h5wwbOnj+PQqHA08ODt8eNw9HBQd9DE14kmzax61YrmDEa1xwI3AvnE46jtEoAEnB11W7WyEg/dw5W1lYkxCeQlpqW1zpC9xdy/tYRqfdSMbYwJvp8dF4rifDT4VStUTXvWLq2EtER0VhUsSA6IhrbaraPHFe3rX11e8KkMGJuxGBhY/HIcXT/fni/0mpdUdg4RauMx6sUdwiyLDNz3jw69O6NSqlk1tSpfDx5MgmJiTTv1IllP/743MdOSEzkp3Xrnnv/71atIi2t+EsQHg4I4PipU8U+jlBEXbrm/dPDA1xzPGkc25fM0Eac8W5ESAh6W2jH0sqStp3bsvvP3U9sHRG8OxiXti6E7A/B4xUPGrZvyPY520m5mVKgrUS/Qf046X+Soa8P5Y9ftU/1F9a6oppdNdp3ac/+ZfsfeW/k+JGF7ldarSvK+nwvukqRQ5i/ZAm7fX3x8fbGumrVAu9FRUfT/ZVX+HjyZN4aO/aZxxQeGUn/YcMIOXHimfcFcHZz4/ShQ9ja2Dx94yf4/OuvMTczY/rkyY+8J3IIJWzTJnY1mKaNBE8QotLeNVjn/k9blrmGwKOBTBkzhS3/bCHiesQjC9Qc2H2A0HOhuLZ1JTIskpycHNw7u9O8ZfMCVUZDRw9l69qtuDRwoZl7M2a8NQP7WvY0cmtUYKEbMwszUhJSuBpyFffO7ni+5FnoIjilUfXzpGOKKqOi5xAqfEBITEzEuXlzQo4fx6Fmzcducy44mP7Dh3Pj3Lm85FpRDX/jDXbs20fDevXo0aUL3yxYwDc//MCf27eTmZnJK/3788WsWaSmpvLa+PFE37qFWq1m7scfc/fePabPnUvDevWwtbHh0K5dBY79yeefs9PHB5VSSc9u3Vi6YAH3YmN556OPiIzWzoF+9+WXONSsSbsePVAqldjZ2LBi8WI6eT54YlQEhBJWxIAAEBio/W7Rd2/ez1xdy2Y66c9f/2TpZ0sZ8+4YBo8aTFXbqlwNvcqWtVvw9/Xnl52/0KhZI9LT0pn46kTS09N5c8qbdHi5A7JG5tC+Q6z/bj216tRi8NjBrFuzjqb9muL9hTeNOjQi/V46kz6cROL9RH5Y/AMJsQmM/XYs9nXs8VniU2Y9ih5XSVSW538RiLLTXFv/+oseXbsWGgwAWjRrhnOtWtq1Evr0eabjf/3554RcusS5o0cB2O/nx5Xr1wk8eBBZlhkwYgRHjh3jXlwcNWvUYM+f2sZfiYmJVKlSheUrV3Jo165H7hDi4uP5e88eQgMDkSSJhMREAKZ88glT332Xju3bExkVRa8hQ7h06hTvjB9f6B2CULJ23WoFDYq2bV7MiO0LaANECHsJIZSOza2A0nvq+bXxr+Hm7sbvq35nUIdBpCanUt2hOkPGDWFX4K68tQ1MTE1Yv3M9Pn/7sOHHDXz85scoFAqat2nOpFmT6DGgB++OejevBUazbs04/Oth/E/4M2nYJFQGKlr0a8H7s96nRv0aANr2GCs2l8kFOX97DuBBe44yOn9FUuEDwvXwcFq5uT11u5ZubtyIjHzqdk+z/9Ah9vv50bKztglaSmoqV65fp1P79kybM4eZ8+bRv1evAn/BP04VS0uMjYx4c/Jk+vfqRf9evQD4x9+fi5cv522XlJxMSkpKscctFM2uJRe1/yjC3cHjeHgAsX0JUR3H35/cKaUEmjiWTnBo1KwRi35axKKfFj1xOwMDA7xe88LrNW2n3ti7sWz9ZSvL5y1nzvtzSElOwbyhOdYO1lStWZVXZr/CgJkDWNprKQBvr3kbpUpJelI6x7Ye4+imo9y8eJOujbvSc2BPRk4YiVNdpxL9bDqFVRL9GfFnIXsIhanwSWUTExMSk56+Pm5iUhLGRkbFPp8sy8yaOpVzR49y7uhRrp49y5tjxtCgXj3O+vvTrEkT5ixaxPwlS554HJVKReDBgwwZMIDdvr70HjIEAI1Gw8kDB/KOf/PiRczNzYs9buEZzJhZ7EO45njmJaJvH2uEvz8EnE8oF+sunD52mr7ufbkddZsl65aw78w+mrdrTmRwJJ91+IywE9rWHbpqHV0lz52rd/is42eEHQ/jpXEv4dnTk9Xeq1EoFAzpPITd/9tdKuMtbLEeUUn07Cp8QOjXsyd//P03arW60G1SU1PZs38/vV9++ZmPb2FuTnK+v9B7devGL//3f3l/td+8dYuYe/e4dfs2piYmjB42jI8nT+bs+fOP3V8nJSWFxKQk+vbsybeLFnE+JASAnl27smLNmrztzgUHP/E4QvnXwtYF1xxPkvf2zatO8gvXz3MNt6Ju8e6wd1n2yzIW/bSI5m2aY1vNlnenvYtKrcLrYy9+HPMj53zPFagc2vPVHhZ7LabPlD70mdKHy/9cZvy742nYtCEzv5zJJt9NzP9oPv+d+q/ExywqiUpOhU8qA7Tr0YPRQ4fy/ttvP/b9T+fPJ/TKFf76/ffnGtfIt94i6MIF+nTvzjcLFvD96tWs++03AMzNzdn0889cvX6djz/7DIVCgYGBAauWLcO9ZUtWrFnDj2vXUrN69QJJ5dt37jBw1CgyMjKQZZnpkyczbsQIYuPimPTxx1y6fJkctZrO7duz+ttvCbt6lSHjxqFQKERSuRTtWnKxRO4QniQwEAxdrmPUKBRrm7KtTpo8ajLB54Ixq2KWV5GkqxYC7VRS8v1kDI0M6d6vO8mpyURHRJOWmEZCfAI169R8ZD9dZU/U9ShOHD7Byq0riz3OhyuHHnc+kT94QFQZ5XPtxg1e6t+fscOGMeWdd6hmr32KMiIyksXff8/BI0c4uncv9nZ2pTZ2fRIBoeSURUDIT1e6CqVfneS/35+JQyby1s9v0bp/a/zW+3Fs6zGGzh+KQ1MHQgJCOP7rcdy93Pl70d/Y1Lahw/AOdHuzGwu6L8BQYcisL2YBj/Yr8lniw7jXxzHzrZkEXA/AwtLiKaN58jhFVdGzEVVG+dStU4cTvr7M/+YbGnl44OLsjEajITI6mjHDhnHc1xcba2t9D1Mo7wIDgbLN17jmeEJsweokeDQ4JMQncNL/JOlp6Tg6OWJoZMiNKzcwMDTAo6MHdtUf/LFzKegSYRfCUCgVtG7fOq8dxeZfN5OTnUOrfq1QqpRcPnGZQQsHYV7DnMSERBp3a4y1gzUHvz9IakIqb/72JgFrA+gxsQdZaVn0+aRP3sI7j6v6+XvF31SxrkJCXEKxAoKoKio9lSIgANRydGTt99+zdP58wq5dQ6FQ0Kh+fczMzPQ9NOEFseuwOdTUT5sTXXUSwLnY64QQyi2bUKzM0vh97u8c/vswrdu3JiM9g7Mnz6JWq2nu3hxrO2vmTJrDS71eov9r/Vm9ZDV3bt3B3dOd7KxsPp/yOW06tmHu0rnaB8yszEi8k4hNLZu83kb3btwDwNDEEIdmDsRcj8HY3Bhnd2d2zNWuf2BaxRTTKqZ5U0uPq/rZemMrifGJWFR5/mAAoqqoNFWIpPKzTHtVqVKFNq1a0bpFi0oRDF6kKcEXwujR+h4BLWxdaBzbl/CDtZjScT63YzOZt3cV7Ue158qlK/zwfz+wYfcG7ty6Q+cenfG/7I9Go+Hd197l1bGvcjj0MN/99h0rt67kyJUjuLm78Vq317Cxs6FRx0YEbA4AwKa2DeGnwzE0NMTQ0JCs9CxuBt9EnaOmac+meT2RANoMasOBVQcKVB3lFxkciaHKkOZtmmNlbVWszy+qikrPCx8QjLOziUtKEhe+x5BlmbikJIyzs/U9FKEUhB3aSn2nZvR028eJ5SYsfn0JSnMjNvy+gTMnz+DU0Il5H85jzIAx7N+xH1MLU1YuWcnqpauZOGIi/Tz78dGEj2jasimj3h5F2v00MuMz2b9qP5HBkQV6G1WxqsIlv0t4f+JNwq0EXLu45vVEUueoqdWsFpeOXMKtudtjq352zt/Jncg7jP9gfJE/n/9+/7xxThwxEf/9/sDjq4q2ztjK/fj7j2wrPJsXfsrIMS6OaOBebKy+h1IuGWdn4xgXp+9hCCUsOzuLAwfW8+WX/tyN8eVk0HRcWtdnwm/TOPHnfry3bcNr7ivItjJ3rt5BZaJixDcj2DB5A//z/h/DvxyetwjO6iWrGf/GeNZ/t55Pv/mU337+jfld5+PSyIU+/fpw7tdz7Li2g7TkNO5G3cXe0Z7og9EMHjSY4FPBLP1zKY5OjkyfP501y9Yw7I1hDB0yFN8Vvmy9vhWlpCTmZgxvfPAGXft0ffqHo2gL22xesZk/I/7ExNgEIzMj+s3rJxbBKSa9BgRJkn4B+gMxsiy7Ps8xDDQa6ty7V7IDE4SH6SGh/CTR0aFYWFjj6NiQ3/6YjmVNC1r3b4e5sSnhp24waMFgLO2tcG7vwgW/i3gM9+DsnrMYmxvjPtQdcwdzlCplXkJ224pteHb1xEBlwI6jO7hw7gK/rviVjSs2olQqUavV9B7cm/GTx9O0RdNCx9VzQE82rtzIgmkLUOeoyc7KpnOvznz61ad4dn3y0/n5PS1xrPuCRxfBEUnm56fvO4QNwI/Ab3oehyAULjBQm1DuUrS/bsuCWp2NSqV9sj7mXjhVnM1QGWobM8ZFx+Lc2pnY8HsoFBJoZKydbLl+KpzsrGxsXWzJysoiLTGN438cJ/CvQCLPR2JZxRJLK0t6DepF0xZNWbp+KV///DVpKWmYmpuiUj39cuFU14nPln/GnKVzSE1OxcjECENDw2f+fM+SOBZJ5pKj1xyCLMtHgHh9jkEQiqSmw3P3LyoN1avXJSYmnKSkWOztnDGras7VQG1Jqo2jLeFnwjFQGXEz6BY2zjZc3H8Bq+rWZGfkkJWeza0Lt5nVZhZhx8NwH+RO83bN0ag1hAaF0qdVH8KvhgPaFiqWVpZFCgb5KRQKLKpYPFcwgGdLHIskc8kp90llSZLeliTptCRJp+/ldvwUhMrO3NyKdu1eYe/en+jVdRL3rycS+HcACXfvU69NE3bM/ZvYywmc/fssTbq7ciUgDBMzM+xqV+Of5QfYOnUrQxaP4qV3Xyb44CXc27rj4OTAtoBtvPHBG4zvP56UZP21QnmWdhSidUXJ0fuTypIkOQO7i5JDeNyTyoJQ6gID2RXWsFyUnOZ3584NZs70ZNy4xVhWsWP9r1O4FxOFa8uOmBnacipwOzIazKzMSYlPIiczBzNrc0yUVTE0NkQyNMHQ0IL0rDvEhEfg3KIRXu8O44Mxo3lv2Ht4dvVk9Dsl/5mLumDNsyxsIxbBeTLxpLIgVHDVq9dhwYJ/WLx4KEqlAS93eYNLl45xNtAHjUaNo6sz1etXJ/jAfxiZG9FmWBvObjtLbFoUbm7dqVmjHgEnNyNLGt5ZNw2VWRUOrPYmOQn6vNmHdfPWlXhAKEr1kE7+xPHTPMu2QuFEQBCEpwkLAxrqexSPVbt2U1asCOHsWR/++edX1Go1NnY1qdelLpIElwLO031qdxybO5J4O4lWg9xZNWwlV6+f5O69K3R7uw/dJ/RDZai9FJiYDGHPV4ep8ck4wm9E432maAv5JCclc+G/C6jVauo2rEt1h8dv+7TqoejwaCKuRWBoZIhrK1dMTE1K+DcmPIm+y063AF0AW0mSooF5siyv1+eYBKGATZu0K6TNKF/TRTrZ2Zn88cdCfH3XYGdXCzMzK+Jib2FxzYS+Hwwm2O8MR9cexczaDAMTQxJvJqDJ0SArZcysLOgxsT9KlTLveI5NnEhLuEONyJaYG1mjTrAqsJDPw51X78fdZ9lny9jjvYf6TepjYGjApfOX8OjkwUdffESDJgWXliusImjjhY2M6zuOi0EXadi0Ielp6URci2DwmMFMmTsFM/OK31WgPNBrQJBleYQ+zy8IRVKOyk3zy8rKZOFCLwwNTfjqK38cHBohSbDwm35Ua2vGqje/QWmoZOjSobj1cyMpJhkbBzu+6riQe9djke0loi9G4OTmknfM6IsR2Ns5c+jQ77RtO0jbXA8I3Av03Yt3nLYld80aYJdkx/Buw+nwcgd8/vOhWs1qAKSmpPLnr38yquco1u9Yj1vrBysW6iqCdHcIAId+OcSt8Fu8N+091vy1BiNjbTlt1I0ovv3iW8b2Gctv+34mkJfJAAAgAElEQVQTQaEM6D2p/CxEUlkoc5s2savBtHJVcgrg4wPbt/cgLiEAIwtjNNkyKmMjNOpsjI2NyUjJwKSKMfG347BxssbMxozMlCzMqppy5/JdshM1yLIGZ7cGDJgzGMcmTkRfjMB32V48m4xl06Y5LFlynKMBf+B3fC1packoJRUmJtVAAhsXC1JiU6jXsj7zVkzjst/lR5K6GWkZfD3raz779jO2btxKdEQ0JsYm5Bjk8Mq8V6jdrDbX/r3GNwO/Yfr86UyYOuGR5PCI10ewa+subOxt+HTxp/r+tb+wRFJZECooWYYTJ7/g7r1DDFkyjMQ7CVzwDcZrnhe1W9Uh+nw0P7/2E4YGZigVBqTfT6fntJ7Y1bXn3vV7HF8diGMjd/4N3IUmwYhDS44TG7cVG2tHHKu6s3nzPN566zuOBvzB0ZB1DPl+MBnJ6fivOUz7se7UdWvC3YtxrBq/lDaeE1n/8z8E7vubwZ8OKJAonvjuRCSFxJIFSxj+zfC897bO2MqeL/aQmpyKJEu4tnDNCwYPJ5x/XvIzQ14dwlczv2LqvKkip1DKyv1zCIKgT7tutdL3EB4hSRAVsxoLO0vcX/Pk7N9nGLRwEPU61kOhAvs69igNlKhMJJzqNMGqug3tevambt1WtOvRi1fnjSJDc58hQz4lIiKYC2ePk34/gwtnjxNzN5xPPvGmS5dR+B1fy8AFr1CvXT3+3RpI/zn9aNy9MakZ8aQnplLPoyERIT5c3PEvPab2xqpBDZIys6nRvAbdP+7Olg1bMDA2oKpTVeq0rJPXKmP4kuFUta7KnuN7qONShzHvjgEKJpzzt9XYv3c/zvWcCTodpOfffMUn7hAEoRC7llzU/qOMpouSkuI4eHADwcGHUKuzqV3blV693sbR8dEKp/T0VKxqVAEgMzmD2u5OSAoFkENOZjYmliakp6cgaWIxsTMosK9jEydi7m1l9NCv8fP7lYULD5GWloilpS0WFg8WikpLS8a5tTMA8VFxODRzQKFUotFoyMnKoUq1qsSEhQPg0nAYyhwlqamQpk7ByrkWl8O2kpGYjINFwTUk8reVyMrIwsRM+1f/k1pQ2FSxISsz67l/t0LRFOkOQZIkpSRJNSVJqq37Ku2BCUK5UEbLZR46tImJE+sRHh5Ez55vMWDAhxgYGPHppy+xevUk1Gp1ge3NzS2JvXGP9KR0jCyMiTwdgazRABIW9pakJaRhZGiCgdIYM6uCTfl0ieOwsEBq1mxA1arVcHBoUCAYAJiaWhB+JhwA61o23Ay+SWZaJgqFAjvn6lw7HYadrRP2ds5EX4wAwMwMTGRz4i7EUcXShZwckAyVZGgyyJa1bdjzt5Vwru9M0L/av/wLa0FR07EmoSGhONV1KpHftVC4pwYESZImA3eBA8Ce3K/dpTwuQag0/v13Nxs3zmTx4gCmTt1Iu3aDaN26D2PHfsnq1VeIirrIhg0f520vy+Bg+w4KlRLfb3bR6pXWbJ+znasBV9HkQOT5SAyMDbA1dyH5/n0y4rKJCLqOOkdNRNB1fJftpcdL77Bnz4/06vV2oePq5jmBHXP/5urJq7QZ7sHuhXu49M8lzIytURooSYtPoX7t9vTqOgnfZXsfOUcvzzdIv5/OzaD7XD4WRWJSFpfPXC7QVmLY+GH8b+P/SEtNK7QFRZ06dWjcrDG1XcTfoaXtqVVGkiRdBdrKsqz3pvqiykgoS7uWXCz1OwRZlpkypQXjxi2mdevenDnrg++hlcTcC8fezpleXSfRsEE73n7bhRUrQrCx0a5/7OMDhw5P5nLoTxhXMUUhKfOqjAwNLbCzeIWrV37G2bkZI0Z8wf9t+4m01HCcnJzp8dI7BJ7aya1bYcyf/w9KpbLQ8f3x58ICVUZWVtoqI3s7Z5xrtma/71q++MKX2LjoAuPu1HYMO7Yvp0WLHjRu0gHfQyuJig7HxsUC9z69adjOPW9d6FnvzOLOzTus3LqSf4/9W6DKqE3bNvy89Gd+9v6ZVu3KXz7nRVHUKqOiBIRDQA9ZlnNKanDPSwQEoSyVRUAICwtk+fLR/PRTKP+d28+OwwvoNa1vgTLQgV3mEnhqJzY2Drz22uy8fWUZLl06xuLFQ3ByakbnziMxM7Ni377zBAevxd6+MTk5YSgUTmg0Y2nWzBYHh0v4+q7BwaEhM2b8gZlZlWKN399/M6tXT6JVq160bTsIAwMjQkIOc/jwJnr0eIuxY79CoXh0IuJc7HWMGmm7s6pzcji+fi0n9p1g6LihNG/TnLTUNPZ67+W/U/+x9JelvNRLtKUojmIHBEmSPsr9Z1O0z+3vATJ178uyXOZXZhEQhDKjWwOhlAOCv/8WTp3azowZf/DlMi9emt6uwINiEUHX8V96Evdmg7h4MYChQ2eRnZ2JnV3tvIt5VlYGx455c/r0bjIz07C3r4NS+SYXLrghy9mkpu7A1PRvbGySsbV1pHv38dSv36bEPkNKSgJ+fhu5ePEoanUOTk6u9Ow5AXv7os3564JDFcMo/H/z5971exgaGdKxe0e8hnlhamZaYmOtrEriOQSL3O+RuV+GuV8AL87TbILwrMpwQRxDQ2PS05MB7UI3jk2GFXjfsYkTd2O2EBi4k+DgQ4SEHMbY2Iy4uJt4eHgxdOinODo2omvX0XTt+qC9hizDtGkgSQaYmw9h2bIhSFLpfAZzcysGDJjCgAFTnmv/FrYuBO51Qe15nGa9m6G0SsA1t/exqZEIBmWp0IAgy/IXAJIkDZVl+X/535MkaWhpD0wQ9KqMFsRxdX2JH354g4SEmLxqnQJ3COevcf9uDHcjohgx4nMGDdLeuCclxeHru5ZZszozZ84uGjZsm7ePLMP27TzyetAg7TMMskypBYfn5eEB5LbJOBd6nTOhYNQolBC0zfWe1FhPKDlFKTudVcSfCYLwjCwsrOnQYQibN8/DxuI9/ppfsFrnl/fXIautUCqtUaunoJvhtbCwQaX6hA4d1vP116+Sna2t0ddd/I8ehU6doEcPsLaGI0e0P9dotN99fEr2c5w568OXy7z48JNmfLnMizNnn/8ELWxdaGHrQuPYvmSGNiLgfAJ+4aH4hYeW4IiFxyn0DkGSpD5AX8BBkqQf8r1lCeg9wSwIFcXrr3/D7NldUKkySUicgPcn25DlrajVNbh/Ow4DA2N69drPsWNKFArtX/oPLvpeODh8x4kTf9G583AkCUxMtMFg4EDYsQPi47VBwcRE+1oXLErqTuHMWR9tMnx6XxybDCP6YgQ7li0AoHWr3sU6tm46KRkw9TyOd5w2KFjb8EjnVaH4npRDuAWcAQbkftdJBqaW5qAEoTIxN7fiyy/92bp1Pj4+00lKdEKSLMnMPIGBgTHLlx+jVq3GeUHg6FHtfp06aYODufkoAgN30rnzcAB6935wsR80SLvtkSOwf3/B/Upq2sj30Ep6Te+bN9Xl5OZCr2l98V26stgBAfLN3OV4QiwEBpLXebUoazUIRfekHMJ54LwkSf8ny7mPGApCRZeXUG7w9G1LkJlZFYYNm0taWiJ+fgeAHFQqR8zNk9i27SuMjMxo23YgstwTSdLO9Oou6ubmVcnISC1wPN3FXhcUdEEk/34lpbBkeMy9rSV3knw8PIDYvpyLvY5/wp28tRqaOIrgUFyF5hAkSQqWJCkIOCNJUtDDX2U4RkEoW2WUUM7vjz8WMmqUNUeObMfYuB2SZEJ2dhjx8bcIC/uX6tXrsWLFLCIjm5KVpe2xtH279k4gPDyo0BLPhxPM+fcrKflbV+jo2mOUpha2LrjmeNI4ti+3jzXC35+8fIPwfJ6UVO4PeAE+uV+jcr/2AXtLf2iCUHE8fAHO/3rXrhVs3vwZbdv+SLVq97CxyaZNmxaMGZOCgUELbt++jq/vXqysztK27SckJ3enZctIjhyBbduy2L9/Hd26vVHg+BrNownmZcu0348eLdmgUGjriq6TSuYERaALDsl7+xIfB95ntEno0EwRHJ7Fk6aMIgAkSeohy3LLfG/NlCTpLPBJaQ9OECoCHx9ITy9Y9rl9uzbJ27s3/P77p3h5fYiv73vk5ByhWrWLTJ8exP79huTkLEaWR3H7th9Nm4ZRr944UlNDSUlZjJXVd/j4jEeSPNiypQWzZ4NCoQ0GM2b4kJqzEo0cjkppwYkzavxPpWFv54yLyyRMTHqX2LSRLk/gu3QlMfe2Ym/nzMCuc0skf/CsdNNJgYHaZGd8372EEMqQ1iIBXRRFaX8tSZLUQZblY7kvPBHrKAhCkciyNhjo5vALVghBQIA3WVnpjBz5Nfv3g1q9hpiY95BlQ9avB1nuiULxPRrNGC5e7Elk5Ho8PNqzf/8IJOkA1as3xM5uK0FBsGgRzJ6tDQbJygX0ndWXBu69uXzmHMc3HKPXuH5Y2lrhu3QB7e0BSu6C3bpVb70EgMLkzfjF9iVEdRzvM6I6qSiKEhDeBH6RJKkKIAH3gTeevIsgvJh2HTaHmiV3vPyVPo+rEFq79ghWVtWIiQlj1arqvPHGBXJypjJkiHY7lQqcnEZy+/Z20tP9SEv7jKNHM9BoFHTsOJc33+yJkZGG5cshKAiGDQMDi5UM+6Yvrbu5cOfuVRp1a0xVh6r4fX+YCSun0mt6yVUAvQhcc6uTAEISHpSu6prrCQ88NSDIsnwGaJ4bEJBlObHURyUI+rBpE9AKRo9+6qbPorBKnxMntnHo0O+kpiYwZYobsiyjVKpQq9cDrQHYuhVmztSQkxMOpKLRnEejMUSWUzlx4i3++8+M7OxMXFxaolZPRqF4DQinRWdt1U92TiaGxlVxcHUkLlp7VSzNCqDyzjVf6WoI2ukkV1ewUoqnoeHJD6aNlmV5U74md7qfA/ppbicIpe4Z+hc9/GBXYQ96Pa7SZ968Lzh//gsaNmxPaOhx1qy5gUplxvjxXYHVaNuH7WbYMA0mJuPIyjqLStUHU9N5pKT0AsxxchrM/fsHWLjwMCtWXEWt/gKN5hAGshPnjkTQupsLBiojsjKyuHv5DjaOtkDZVACVd/lLV3VtMqxtEgBo4lh5g8OT7hDMcr9bPGEbQaiUnpYo1nm40kc7TXSa3bu/wNNzDQYGb3H9ujPffDOCyMjjwE6gJdpivm9QqyVSUo4DGtTqDSQltcDMzAkbmxFERn5MlSobmDnzVTSaS9Ss2ZPs7G5AO/Yu2Qv0pYG7NaF+2hxC13H98iqABnad+8yf+XFrNbzo004tbHP7RsW6ELhX+zR0QFwCrq4JlXI66UlVRj/n/nOxLMsZZTQeQSj3npYozn+nkL+VhC543LgxDSurpri5vcWZMyDLOwkLa41C0R7YjFK5EFmeBSxEo1EDqVhZLSEhYROQjVJphVr9AdnZoFa/jqHhzxgZ7cHYeCBubssICnob05zl+Cz4iUPG4ZiYWEC2Bb7f+Dx3BVBptqcoL3QN9gL3PphOAipVc72iJJVDJEm6CxzN/QoQeQShItp1qxUU4QHlpyWKH542yt9KAiAs7BTvvLOKHj207y1a5Ma5c2fIzh4I1EWSbDEyUpKWpv3PzNCwFRkZ+4AAoAcZGdvIzDTC0REMDSE5+XXS07fh5TWQgQM78s47Wbz/fi3q1NlVEr8WoPTbU5Qnuukk0K7VEEAooJ1OqujBoShJ5XqSJNUGOgH9gJWSJCXIstyi1EcnCGVk15KLz/SE8sOJ4tTUg/z33xKOHo3Hyqoaw4fPo0ED7SI09+5FcuTIVpKS7mFmVpXs7CxCQg5x69Zl6tRpwYwZrzB6tBtGRjfQaC7g5bUJtTqLHTuWU7u2K5mZM1EozImPV6PRTEWSjAFYsQI+/hiUymrIcmJuMJKwsqpGenrJ/s1W1u0pyosWti4Qqw2CIarjBJzXTidV1CT0U58nkCTJEeiANiC0BC4Af5TyuASh7D1DdZEuN5CVFcGNGzW5fbsHERF3sLS05c6da0yf3pYJE1z4+uuhfPhhS+7evUFKSgLbtn0NyJw964tSaYiv71pGjHBCrd4CgELRlKior3j5ZW1ld1xcMubmQzAx6UtOTi1ycsLyxvDBB9qH0LKzw1Aqa7B9O2RnZ3PnznWsrWuU6K9GX+0pyhPdk9BnvLVtMrzPaJ+ErkhPQxflAbNI4ENgnyzL7WVZ7ifL8lelPC5B0IsntZjI/7Pt2+HQoVhu3WqKo6Mtw4fHY2NznubN97Fy5SVWrbpBfPxtAgN3sWpVGB06jCAwcAeffOJNu3aDychI5fz5fzA03APsRZJmMGnSFtzctM8TzJnzEebm9cjOrkqdOvuxtgZZHgf8zLBhMnZ2EB0NWVkaDAzW0KnTOI4ehR9/3EnNmg2oXt3l0YEXQ3loT1EeeHg8aJORGdqIM96NCAnRBoc7OXf0PbxiK0oOoSXQERgpSdInwBXAX5bl9aU6MkEoY0WtHNIlimX5XczMzFCpznHsmIKuXbU/X7oUTp48jkLREghi5syvuH37Hxo2XIOfX0/S0pqQmenEjRuRWFn9hkr1No0b72TVqj4MHjyYq1c3cP/+AZo2/Zs7d7I5deo9mjQ5TMuWLxMaasLhw3Po338hu3fLyPJU7O2dmDChHb//fg0fnw+ZPn1tif9uylN7ivIif4VS/lzDi/w0tCQXocOVJEnmaINCJ2A0gCzLRVtBuwS5168vn14uHn8QSt7OxRfZ3mBmgfLQh8tFH04Wv/qqMWPGLObAgSlER4OjI3z3HYwaBRkZnTEw+JDu3Q+zb98GoDoWFqE0aKDg7FmQ5b1oe0cqgPnY2XUiM3MCycnJyPItWrSYj0o1h6AgqFbte5KSFuHkNJF7914mLW0Slpag0agxMjLlrbe+48yZvRw4sJ4xY76kV68JZf77Ex4IUR3PWxe6vJSu1jWqe0aWZfenbffUgCBJ0mnACDhObqWRrvFdWRMBQSgVuWsgyB/PzAsCOoUFg7S0FIYPt2D7djWyrOCDD7RTODqybIFKFY0k3SE7uxGGhhORpNXIMuTkaO8+FIoo1GonwATIBCQUihpYWGzH1rYVoF3pLD4esrMvkpi4Gln2xcgoAxMTS0AmPT0ZQ0MT3N370rv3Ozg4lO06DsLjnYu9jlGjB7kFfS/kU9SAUJQpoz6yLN8rgTEJQvmTtyBO12daTEah0KbfcnJyMDQ05IcfYPDgB++bmirIyclBljMB6NQph4AA7bFUef/VVUOtVrBtWzIzZyqIj1+IRpOKjU2rvHPOng3Tp4OhYRPs7H5g2bKSXdxGKB35q5MeXsinPE8nPTWpLIKBUOHllps+y2IyxsamGBmZ4e39JWo1TJ5c8P2MDE/U6j2o1csAG/z8fJDlnLw7hOxs0Gj2IEnt+PBDBRoNpKbuRq1uT0KC9pyyrO1gqju/bnz5XwvlX/6FfO5dsyrXazXotY21JEm9JUm6LEnS1dyEtSDoxbMuJuPjA7Vrj2DHjm95/fUMoqPB0hImTAAjI9Bo3kOt/hqFYisq1VRkuQ4KxUaaNdNd7LNRKJZgYvIeUVGQlHQQleo2Bgb9SE8He3uoWlVbcWRtDT16aL8fOaIdj0aj/e7jo5/fl/B8dKWrEb/3zatOKk/BQW8BQZIkJbAS6AM0AUZIktREX+MRKrfHtZgYNEj72sTk0SZ26emgVq9AozEiKakuEIJCAVlZUL06gDVwFYVCSfXqo5Gkn8jK+pTk5PVUqxaFQjEUlcoeV9ehKJU7iI8fQbdu63FxUeadz90d3Ny03zMytLkEa2swNoYdO7TBKj1d3Cm8aDw8tF+NY/sWCA7lYenPQpPKkiQNfuwbuWRZ/qtYJ5ak9sDnsiz3yn09K/e4hT7jIJLKQonbtIldeOU9lPasHUz9/ZOIju5EVlYwklQdlcoWtfoOGk0sjRp50rhxew4cWI+VlScJCeakpR1Ao7mPlVVTPDzaExJymLQ0Y0xNv8fUtCuyrA1Cr7yiPa9Go10FTXe+I0cejKewhLfwYtJVJ4F2IZ+S7LpaEkllrye8JwPFCgiAAxCV73U00LaYxxQqkf+uXePoxYuoNRqaOzvTpVmzvGRvkWzapO1f1OVBZc7DF9fCLrYPEtCW1K59nszMCBITv0atjkGl6soPP8ymalV7AIYPn0dg4G42bozBzMwLlcoJL68QQMPLL79Ow4btmT5dyjuuLhiANhgUPN+DMYhgULHkX6tB7Xkc/2ugtAot0/LVJ3U7HV8mI3gKSZLeBt4GqG1np+fRCOVB0I0bTPzpJ27Fx9O/TRtUSiUbDh4kIzub7996i96tWxf9YF26Frl/UX75E9CyDGlpThgYrMLOTnuRPnz4wQXb2Nic+PjhWFk92D8zs2Neg7zHJbIfvtgXlvAWQaHi0XVdBQp0Xi2LhXyKUnaKJEn9gKaAse5nsizPL+a5bwK18r12zP1ZAbIsrwHWgHbKqJjnFF5wweHh9PjsM74eN44xXbqiUikBkGWZf86dZ8y3y1n7/vt4PcdF/ml000cPpougc2ftz3bu1G7TqZP2te4v+YEDH8z3P/zAm+6YAQGPf+/hJ6YLO4YIChVXYQv5lFbp6lMDgiRJqwFToCuwDhgCBJbAuf8F6kuSVAdtIBgOjCyB4woV2OQ1a1gwahTZOW/w/S4VHw26nnsxlAgKH8y4bk68vfJDIlq2xNDAoMTO+3Bbi+vXtaWjugSwlxeEhMCNG/BR7hqDJibaKZ/HJat170Ph7z1pTYXHbSdUXPnbZFxib6mtC12UOwRPWZbdJEkKkmX5C0mSlqFdzqlYZFnOkSTpfcAXUAK/yLJ8objHFSqui5GRXL55k3HdXubHPSq2HHUA4KNB11m+3YUtRx0Y0QkaOTiy/dQpXuvYsUTO+/CCOAMHQloaxMTA6dPah8e2b4f796FZM+02+f9qf3g9BN0FXff6Se/pPO0YQuXROHethvzrQkPJrNVQlICQnvs9TZKkmkAcUCK9dWVtQ5e9JXEsoeI7FRZGjxYtMDI04KNB1wHYctQhLzCM6HSTjwZdRyO35uTly08NCMVZEEeWtSWh8fHaJ4lBO31U2EX6ScnqZ0lkF2U7oXJ43EI+1jYJNHF8/jYZRQkIuyVJsgK+Ac6irTBa98xnEoRikmUZhfSgGuejQdfzggGQN32kVCjQlVOnZmSw2d+f9QcOEHHvHqZGRni1acN7OTnAS8+9II4kPWgrkZNzm6SkNRw48Ad//61dIKdLl9F07/4GlpY2Jfo7EITH0bXKCEnQVSdpF/KBZ5tSKkqN3hJZlhNkWd4GOAGNgIXPM2hBKI5WdeviFxSEWq1GlmH59oI9/5dvd0GW4cC5c7SqW5eImBhaT53KzsBAPh8xgjPLl7NzzhxMjYzo4OfHwaqpRT73w1U+urYSqakHiYpqTk7OXVq02Mjy5Wd5552VRESEMHmyK1eunC6pjy8IT+Wa41lgIZ/zAdpWGUVVlDuEE0ArAFnbqStTkqSzup8JQllp4eKCg40NW48GcOf++Nycwc0COYRb8aGcuXaNP2fMoN3HHzOhZ0+mvfJK3gNeNW1s+HLsWMYmp9Lh0Crs2nbF1bUr+R9f0G0LD54C1lX1dOyovVNYtAjOn7+KRjOc+fO3ceVKZwICtNsMGlSTRo06Ehi4nYULvfjhhyAsLe3EFI9QZrQ3vi6Q40LgM0zKP+lJ5epoHx7bhLb6R/d/Z0tgtSzLZd6yTzypLASGhdFv/nyGdvgCl2rdmfbKjbzSzI/WpbPR7wN+fGc0hioV3+/axdGvv2b0shYkphmwY/a/KBTaC/7Ad2twVRHI3bSd1Kvnw+zZ5L23aBGYmkLTpg8qi3x9tYlkXcUPgLf3Bzg6WtKu3cK8FhKmptrvp09DmzZw5cpbVKvmgoHBp48stCMIZWXAAKlITyo/acqoF7AU7fMBy4FluV9TgU9LYpCC8Kw8GjRgx+zZ+AUt5bdD/Zn7f5v4YssWus7+lD8C3mXVe2MZ+dJL/H7oEBN79UKjgcQ0A/yCbBm4qI02GCxqw4G7bpgavUJSUiDnz99h0aIHwSAoCFJTtQFA19yuV68Hzxekp0PPnjIpKb/x3ntv51UgSRL07KkNBkFB8O+/0KPH2+za9ZvoOyS8EIqyQM6rufkDvRN3CIKOLMv4BQVx9MIFNLKMm7MzA9u2xSB3sQGPadP4YcIE2jVqlBcE/IJsAVBna3CrdpfZq2rxwQfNsLDYxNWrzfOO7eamTRhLEoUumJOVlc6IEVb89VdmgQfHtGN7sLCNRnOfiIg6jBuXIMpEBb0piTsEnWOSJK2XJGkfgCRJTSRJerPYIxSEYpAkiZebN+fzkSOZP2oUQzp0yAsGAFZmZtxJ0FZZKBSwY/a/BfafvaoWsqwmMTGGKVOsCr6XO32Uv9xUR3dRNzQ0RqFQkJwc/8h2ugokSQK1+jYKRRURDIQXQlECwq9oHx6rmfs6DPiw1EYkCCVgaIcObDh4ECDvDiG/RYvgzBkf7O2dWbvW6ZH3NJrC+wdpHxCT8PQcgp/fxkIrkGQZkpI2YG7+WqEL7QhCeVKUgGAry/KfgAa0TxgD6lIdlSAU08iXXiLwyhX+PnEyb7qom1ssyX/sw61KBOfPx7N06QzU6qkEBWmnif74Q/s9KAgWLoS//37ygjleXh/w119L+O23q3nbLV2qnS4KCgIjo7NoNL/Svfu7hS60IwjlSVECQqokSTZoH0hDkqR2QGKpjkoQisnM2Jgds2fz7qqfuH7nC9o1/I8ds/8lKycLT5s5SFI77OwGUqvW8LycgUKh/e7mBmZm2oqhJy2YU79+G0aNWsi+fZ2xsVlBjx6JKBTQtOk97OwWc+NGb95/fy1jxrg8dqEdQShvipJUbgWsAFyBEMAOGCLLclDpD68gkVQWnlVETAzf79rFRj8/ctRqMjIzaWpRl37vL8fDox9Q8LmDh18XZcGc0NCT7Nr1PadO7cDAwBC1OocOHV5jwIAPqVPHrdD9BKGsFDYdYFQAABHpSURBVDWp/NSAACBJkgpoiPZZhMuyLGcXf4jPTgQE4Xmp1WqSAgLwO2GFYbeez7UGwtPk5GSTnp6MqaklSmWROssLQpkoakAoSvtrY+A9oCPaaaOjkiStlmU5o/jDFISyoVQqqWpigqGjc6kEAwCVygALC+tSObYglIWi/BnzG5CMdtoItE8t/w4MLa1BCYIgCGWvKAHBVZblJvleH5Ik6WJpDUgQBEHQj6JUGZ3NrSwCQJKktoBo4Si8eMLC9D0CQSjXinKH0Bo4LklSZO7r2sBlSZKCAVmWZbdSG50glJRNm7QL4swYre+RCEK5VZSAIPozChVDl676HoEglGtPDQiyLEeUxUAEQRAE/SpKDkEQBEGoBERAEARBEAAREIRKYtctseKrIDyNeL6+kpFlmUNBQfx26BC379/HysyM1zp2ZICHR4H1BCqSXUsuQk2HUntCWRAqiop5BRAe6+79+wz68ktSMzKY0KsX9WrU4HZ8PN/v3MnMDRvYNXcujWvV0vcwS8doUW4qCE8jAkIlkZGVRe/PP6dfmzYsGDUKKV/rzTd69GDjwYP0+Owz/l22jBrWoh+PIFRG/9/evUdJUZ9pHP++3XPpYWBwBYwBzI6yAwuKizCiRgx4BVmPrMFbMK6snJgNuzGKCYLomhM3e7wbE2MQo4gB3XhDFw1y2UBYUDJiREUQggoOgoIiMxwYei797h9Vg4ADAzLdNd3zfM7h0F1VdL0FQz1d9at6S4HQRjy1eDFdOnbktiuu4OwbbqC6qolHWtTX88BLL/HzK6/MfIEiEjkNKrcRD8+dyw8vuAAzo7qqimUdO37p1xGxGL+dO5dUKhV1uSISAQVCG/HXjRsZ0KPHAZdJ5OVRU1vL9pqaDFWVfrsHlEWkWQqENiJRUNDsjj7lTrKujsL8/AxVlSEaUBY5KAqENuL8/v35/eLFB1xmWzLJ6b17kygoyFBVItKaKBDaiLHDh/PgH/7Ah1u27DV9dWUlK9et47UPPqBy+3bWr19P+ZgxnDVuXESVikhUdJVRG9G3tJQJI0cyeOJEOuTlMWDbNsyMjbW1lMRiVDY0cFQsxtouXTAzypu6CklEcpqOENqQ60aM4N4xYyjq0IHNQPFRR7E1FsNjMe7u0oUNxx671/0JWW/69KgrEMkqkRwhmNklwE+B3sBAd9cT2A5T1Y4dTF+4kL+89x5mxik9ezJq8GCKEwl27NrFk4sWsXT1alKpFFcOGcIpPXuyI5nkX++6i4n5+SxOJhmzeTMnFBRwVUlJ1Jtz+Coqgv5FegaCyEGL6pTRCuDbwEMRrT+nPPTyy0yYNo1z+/Xj3H79cHdeWraMCY8/zqhvfYsnFi1iUO/eDC8vJ2bG/Dff5JYZMxg1eDDvVVXxRCLBP7VvT4EZi2pq6LF+Pe2LinD37D5iUP8ikUMSSSC4+yogu3c2rcTU+fO549lnqbjnHsq6dt09/Zphw7jv+ef58dSp3DV6NOMuumj3vO8NHcr0BQu46v776ZJIMKfbF9fpjykpYUN9PT0rK7nruecYP3JkRrdHRKKjQeUslqyrY8K0acy/7Ta+f/fde7WjcHdWbN1Kj06d+MnUqdwxdSqN8ZsCPgcKgc01NXxt7drd8+KxYFipJC+PSb/7HU+8+CJ5sb2Hmko6duSP996b7s0TkQxLWyCY2Xzg6CZmTXL3Fw7hc64BrgH4RpcuLVRdbpj56qucWFpK39LS3e0oGs3duZOJ4Q1mMWAaMCw8InvanRuBYuBDM+6LxxkVj7OyoYE+paUAlFdVUVtdzRgzfrjH5zbOa/XWrAF6RV2FSFZJWyC4+zkt9DlTgCkA5WVl3hKfmSve+fBDBvXp0+S8lbW1nJpIsNidfGAlMCyc9x7wTeAtoL0ZK73pv9b2+fmsrK1t+cLTbfr0YEB5vO5QFjkUOmWUxfLz8qhJJr80vWLXLqZXV7O8thbMaACmA6e7c4oZcWBXuGzKnbz9jOX4Aea1erq6SOSQRXIfgpldZGYbgNOAl8xsThR1ZLsz+/Zl5tKl+B7f8Kdv386FmzZxbrt2dInHOfHII4kB5wEjgMfdGQD8EagHtrkzJNb0j8HnySRDiorSvyEi0ipEdZXRTGBmFOvOJYP69KEgL48nFy1iw5YtPLtlC9cCvwWO3baNecCKzz4jDnwHOBUYA0wGvgUsABwYWFdHDVAHrFy3DoD1qRS1wIji4kxvlohERKeMspiZ8fj113PerbeyA/g+kAD+C9hAsLNPAscAo4F4+P4qgn/47UAB0IPgUDEGHOXOVnc+S6X4h65dObW6+kvrLdlnkFlEcoMCIcv1O+44/ve22zj52mvZCgw1I2HG8lSK4QSniV4AlgBnAG8THAmcW1TE+/n5fJZMsquujjMSCQrMeGXXLsoKCihMJHhj8uToNuwwzNrYH3pGXYVI9lEg5IC+paV0isX4JJXiqnicfGBJKsVSYD7QDhgOND4NwQmODDbu3EncjMmdO5OIxWgAftapE30KCrLj0tIm7H4gju5QFjlkCoQc0hUoaGjgUncaCHb6+cBnBFcZGUEYAMyuqaEgHqfene9u3sxR8ThLunWjRy48C0EPxBH5StTtNIeMBEa6Y0AZMJvgbuQ5wJt8EQaji4tZ0r079e58vbiYraWldIvH6VtZycf19dEULyKR0xFCK3bWuHF7taNoVNKxI6srK6Gubve0nakUvwxfJ4DNBJeadgAuB/a8vWzajh3M3rGDBLCuuppe1dU4wb0JpevW0SkWg/z8A65frStEco8CoRXbtx1Fo/KqKqir46M9nn18dDJJCjgR+ISgV1E34E8EdyivJwiKY4C/Ah8DAwjGF64naD1bQhAKlaWlDNy+/cDrb40qKoD2UVchkrV0yijH/J6gtzjAToIWFR8SXG46E7gvnNd4Yuhs4I3wdYLgCqSPU6mM1NqiKiqYtbB9MKAsIl+JjhByTJJgvKAe+HSfeRfA7q6mMwi6nqb48reCrP2h6NpNA8oih0FHCDnECMYNzg1ffw34R4KB5TyggWBMAWAqsJbgHoXTwmk7gYQZnffTykJEclvWfhlsi86qrKS6oYFNqRQNqRTlYWO7krABXRmwBrgTuJvgKOF9grGBAuAjgrGFfIK7lJPh/PMIBp13ApcXFxNTIIi0SQqEVqykY8e9BnA31dUxLxYjnp/PrtpaCJvaDXUnSfCNH2A8wXhALUEAtAd+BNwczu8CfBcYRxAanYDGBhUrCwspr6ra3Z6iqQFkta4QyU0KhFZs30s7y8eMoU8TO+PuVVUse+QR4iNG8Mz48fzzL37BzvDooYBgZ38zQS+jYmAj8JPwfWFhIduSSf6+e3f+7/bb6VxSktZtShs9EEfksOncQA5JuTP0pJPY8fTTFAFHhr8KgUeBXwLfC5f9AcG3gd7du3PN0KGsevDB7A2Dioqgf5EGlEUOiwIhh8TMeHX1agAKzRgci7GpsJDjgX8BxhJ8hzbgV4WFdIrF6N+jB7265cClmrrcVOSwKRByyEnHHcctM2YAwRjCglSKj/d5POYDQL9wEDrlzjOvvMKlgwZluFIRaY00hpBF9h1k3nM6wN1XX81ZN9/MQ7NnEysooC6ZpLS2liLgaIKriLYDR7rTtbaWKjNGlpfTvXPnTG6GiLRSCoQs0lz/oCF9+/KzUaP4weTJnFxWxsOXXcZvXn6ZNR99RMqdTzZt4qZLLmFgWRn3PP88efE4k8eOzVD1aaQBZZEWYb7PKYXWrLyszJepqVqzFr79Nj9+9FHeeP99UmH30+JEgrqGBgD69+jB2PPP57IzziA/L8u/E0yfHgwoj78x6kpEWq0LL7TX3b28ueWyfG8gTRnSty/L7ruv+QVzxZAzo65AJCdoUFlERAAFgoiIhBQIIiICKBAky83a2D/qEkRyhgJBstasO1cGdygPHBh1KSI5QYEg2U39i0RajAJBREQABYKIiIQUCJKdKiqirkAk5ygQJPtUVDBrYXvdoSzSwhQIkp10dZFIi1MgiIgIoEAQEZGQAkFERICIAsHM7jKzd83sLTObaWZHRFGHZKk1a6KuQCQnRXWEMA84wd1PBNYAEyOqQ7JN4wNxdIeySIuLJBDcfa6714dvlwLdo6hDspQuNxVJi9YwhnA1MHt/M83sGjNbZmbLtjTxgHkREWkZaXuEppnNB45uYtYkd38hXGYSUA/M2N/nuPsUYAoEz1ROQ6kiIkIaA8HdzznQfDMbDVwAnO3u2tHLQZm1sT/0jLoKkdyUtkA4EDMbBowHBrv7zihqkOwz686VwQvdoSySFlGNITwAdADmmdlyM5scUR2SbcbfGHUFIjkrkiMEd/+7KNYrIiL71xquMhIRkVZAgSAiIoACQbKFHogjknYKBMkKsxa2D56BICJpo0CQ7KH+RSJppUAQERFAgSAiIiEFgrR+GlAWyQgFgrRuFRXBgLJaXouknQJBWr+u3dS/SCQDFAgiIgIoEEREJKRAEBERACybnk1jZluA9VHXEeoMfBp1ERFqy9uvbW+7snX7/9bduzS3UFYFQmtiZsvcvTzqOqLSlrdf2942tx1yf/t1ykhERAAFgoiIhBQIX92UqAuIWFvefm1725XT268xBBERAXSEICIiIQXCYTCzu8zsXTN7y8xmmtkRUdeUKWZ2iZm9Y2YpM8vZqy72ZWbDzGy1ma01swlR15MpZvaomW02sxVR15JpZnaMmS0ws5Xhz/yPoq4pXRQIh2cecIK7nwisASZGXE8mrQC+DSyKupBMMbM48GvgfKAP8B0z6xNtVRnzGDAs6iIiUg/c4O59gFOBf8vVf3cFwmFw97nuXh++XQp0j7KeTHL3Ve6+Ouo6MmwgsNbd33f3WuC/gRER15QR7r4I2Bp1HVFw903u/pfw9XZgFZCTz3NVILScq4HZURchadUNqNzj/QZydMcgTTOzUuAk4M/RVpIeeVEX0NqZ2Xzg6CZmTXL3F8JlJhEcVs7IZG3pdjDbLtJWmFl74FngOnevjrqedFAgNMPdzznQfDMbDVwAnO05dg1vc9veBn0EHLPH++7hNMlxZpZPEAYz3P25qOtJF50yOgxmNgwYD1zo7jujrkfS7jWgzMyONbMC4HLgfyKuSdLMzAx4BFjl7vdGXU86KRAOzwNAB2CemS03s8lRF5QpZnaRmW0ATgNeMrM5UdeUbuEFBP8OzCEYWHzK3d+JtqrMMLMngVeBXma2wczGRF1TBp0OXAmcFf4/X25mw6MuKh10p7KIiAA6QhARkZACQUREAAWCiIiEFAgiIgIoEEREJKRAkDbPzEabWdeDWO4xM7v4YKe3QF037fG6tC12GpXMUiCIwGig2UCIwE3NLyLSchQIklPCb9LvmtkMM1tlZs+YWbtw3gAz+5OZvW5mc8zs6+E3+3JgRnjDUZGZ/YeZvWZmK8xsSnin6sGu/0vrCKcvNLM7zKzCzNaY2Rnh9HZm9lTYa3+mmf3ZzMrN7HagKKypsUdW3MweDnvyzzWzopb925O2ToEguagX8KC79waqgbFhL5pfARe7+wDgUeDn7v4MsAy4wt37uXsN8IC7n+zuJwBFBL2qmrW/deyxSJ67DwSuA24Np40FPg977d8CDABw9wlATVjTFeGyZcCv3f14YBsw8tD/akT2T83tJBdVuvuS8PV04FrgZeAEgjYjAHFg037+/JlmNh5oBxwJvAPMOoj19mpmHY1N0V4HSsPXg4D7Adx9hZm9dYDP/8DdlzfxGSItQoEguWjffiwOGPCOu592oD9oZgngQaDc3SvN7KdA4iDX29w6kuHvDXy1/3vJPV43EBy9iLQYnTKSXPQNM2vcKY8CFgOrgS6N080s38yOD5fZTtCkEL7Y+X8a9r8/lKuHDrSO/VkCXBou3wfou8e8uvA0lEhGKBAkF60meO7tKuBvgN+Ej7y8GLjDzN4ElgPfDJd/DJhsZssJvoU/TPDM6DkELa8PSjPr2J8HCUJkJfCfBKenqsJ5U4C39hhUFkkrdTuVnBI+4vDFcEC41TOzOJDv7rvMrAcwH+gVhotIRmkMQSRa7YAF4akhA8YqDCQqOkIQERFAYwgiIhJSIIiICKBAEBGRkAJBREQABYKIiIQUCCIiAsD/A71bs1Z8WD5RAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "X_combined_std = np.vstack((X_train_std, X_test_std))\n",
    "y_combined = np.hstack((y_train, y_test))\n",
    "plot_decision_region(X= X_combined_std, y = y_combined, classifier = ppn, test_idx = range(105, 150))\n",
    "plt.xlabel('petal length')\n",
    "plt.ylabel('petal width')\n",
    "plt.legend(loc = 'upper left')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}