Pulsar Classification.ipynb

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   "source": [
    "# PULSAR Detection from HTRU2 Dataset"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
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   },
   "source": [
    "## 1. Introduction"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
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   "source": [
    "HTRU2 is a data set which describes a sample of pulsar candidates collected during the High Time Resolution Universe Survey.\n",
    "\n",
    "Pulsars are a rare type of Neutron star that produce radio emission detectable here on Earth. They are of considerable scientific interest as probes of space-time, the inter-stellar medium, and states of matter.\n",
    "\n",
    "As pulsars rotate, their emission beam sweeps across the sky, and when this crosses our line of sight, produces a detectable pattern of broadband radio emission. As pulsars \n",
    "rotate rapidly, this pattern repeats periodically. Thus pulsar search involves looking for periodic radio signals with large radio telescopes. \n",
    "\n",
    "Each pulsar produces a slightly different emission pattern, which varies slightly with each rotation. Thus a potential signal detection known as a 'candidate', is averaged over many rotations of the pulsar, as determined by the length of an observation. In the absence of additional info, each candidate could potentially describe a real pulsar. However in practice almost all detections are caused by radio frequency interference (RFI) and noise, making legitimate signals hard to find. \n",
    "\n",
    "Machine learning tools are now being used to automatically label pulsar candidates to facilitate rapid analysis. Classification systems in particular are being widely adopted, \n",
    "which treat the candidate data sets as binary classification problems. Here the legitimate pulsar examples are a minority positive class, and spurious examples the majority negative class. At present multi-class labels are unavailable, given the costs associated with data annotation."
   ]
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   "source": [
    "## 1.1 About the Dataset\n",
    "  "
   ]
  },
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   "source": [
    "**Dataset:** https://archive.ics.uci.edu/ml/datasets/HTRU2\n",
    "\n",
    "The data set shared here contains 16,259 spurious examples caused by RFI/noise, and 1,639 real pulsar examples. These examples have all been checked by human annotators. \n",
    "\n",
    "### Attribute Information\n",
    "\n",
    "Each candidate is described by 8 continuous variables, and a single class variable. The first four are simple statistics obtained from the integrated pulse profile (folded profile). This is an array of continuous variables that describe a longitude-resolved version of the signal that has been averaged in both time and frequency (see [3] for more details). The remaining four variables are similarly obtained from the DM-SNR curve (again see [3] for more details). These are summarised below: \n",
    "\n",
    "1. Mean of the integrated profile. \n",
    "2. Standard deviation of the integrated profile. \n",
    "3. Excess kurtosis of the integrated profile. \n",
    "4. Skewness of the integrated profile. \n",
    "5. Mean of the DM-SNR curve. \n",
    "6. Standard deviation of the DM-SNR curve. \n",
    "7. Excess kurtosis of the DM-SNR curve. \n",
    "8. Skewness of the DM-SNR curve. \n",
    "9. Class \n",
    "\n",
    "HTRU 2 Summary \n",
    "17,898 total examples. \n",
    "1,639 positive examples. \n",
    "16,259 negative examples."
   ]
  },
  {
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   "source": [
    "## 2. Loading the Dataset"
   ]
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  {
   "cell_type": "code",
   "execution_count": 1,
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   "source": [
    "import pandas as pd\n",
    "import os\n",
    "from sklearn.tree import DecisionTreeClassifier, export_graphviz\n",
    "import numpy as np\n",
    "\n",
    "# Models \n",
    "from sklearn.model_selection import train_test_split, GridSearchCV\n",
    "from sklearn import model_selection, metrics\n",
    "from sklearn import preprocessing\n",
    "from sklearn.utils import shuffle\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.naive_bayes import BernoulliNB\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "from sklearn.svm import SVC\n",
    "from sklearn.decomposition import PCA\n",
    "\n",
    "# Score functions\n",
    "from time import time\n",
    "from sklearn.pipeline import Pipeline\n",
    "from sklearn.metrics import roc_auc_score , classification_report\n",
    "from sklearn.pipeline import Pipeline\n",
    "from sklearn.metrics import precision_score, recall_score, accuracy_score, classification_report\n",
    "\n",
    "# Visualisation\n",
    "import matplotlib as mpl\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib.pylab as pylab\n",
    "import seaborn as sns\n",
    "\n",
    "# Configure visualisations\n",
    "%matplotlib inline\n",
    "mpl.style.use( 'ggplot' )\n",
    "sns.set_style( 'white' )\n",
    "pylab.rcParams[ 'figure.figsize' ] = 8 , 6"
   ]
  },
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   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
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   "source": [
    "# Function to plot Confusion Matrix\n",
    "def plotCM(Y_test,Y_pred):\n",
    "    print (metrics.classification_report(Y_test, Y_pred))\n",
    "    print (\"Confusion matrix\")\n",
    "    confusion_matrix = metrics.confusion_matrix(Y_test, Y_pred)\n",
    "    print (confusion_matrix)\n",
    "    #normalised_confusion_matrix = np.array(confusion_matrix, dtype=np.float32)/np.sum(confusion_matrix)*100\n",
    "\n",
    "    # Plot Results: \n",
    "    width = 5\n",
    "    height = 5\n",
    "    plt.figure(figsize=(width, height))\n",
    "    plt.imshow(\n",
    "        confusion_matrix, \n",
    "        interpolation='nearest', \n",
    "        cmap=plt.cm.rainbow\n",
    "    )\n",
    "    plt.title(\"Confusion matrix - Decision Tree Classifier \\n(normalised to % of total test data)\")\n",
    "    plt.colorbar()\n",
    "    tick_marks = np.arange(n_classes)\n",
    "    plt.xticks(tick_marks, LABELS, rotation=90)\n",
    "    plt.yticks(tick_marks, LABELS)\n",
    "    plt.tight_layout()\n",
    "    plt.ylabel('True label')\n",
    "    plt.xlabel('Predicted label')\n",
    "    plt.show()\n",
    "\n",
    "# Function to plot a correlation map\n",
    "def plot_correlation_map( df ):\n",
    "    corr = df.corr()\n",
    "    _ , ax = plt.subplots( figsize =( 12 , 10 ) )\n",
    "    cmap = sns.diverging_palette( 220 , 10 , as_cmap = True )\n",
    "    _ = sns.heatmap(\n",
    "        corr, \n",
    "        cmap = cmap,\n",
    "        square=True, \n",
    "        cbar_kws={ 'shrink' : .9 }, \n",
    "        ax=ax, \n",
    "        annot = True, \n",
    "        annot_kws = { 'fontsize' : 12 }\n",
    "    )\n",
    "\n",
    "# Function to depict the importance of a variable\n",
    "def plot_variable_importance( X , y ):\n",
    "    tree = DecisionTreeClassifier( random_state = 99 )\n",
    "    tree.fit( X , y )\n",
    "    plot_model_var_imp( tree , X , y )\n",
    "\n",
    "def plot_model_var_imp( model , X , y ):\n",
    "    imp = pd.DataFrame( \n",
    "        model.feature_importances_  , \n",
    "        columns = [ 'Importance' ] , \n",
    "        index = X.columns \n",
    "    )\n",
    "    imp = imp.sort_values( [ 'Importance' ] , ascending = True )\n",
    "    imp[ : 10 ].plot( kind = 'barh' )\n",
    "    print (model.score( X , y ))\n",
    "\n",
    "# Beautiful distribution of plots\n",
    "def plot_distribution( df , var , target , **kwargs ):\n",
    "    row = kwargs.get( 'row' , None )\n",
    "    col = kwargs.get( 'col' , None )\n",
    "    facet = sns.FacetGrid( df , hue=target , aspect=4 , row = row , col = col )\n",
    "    facet.map( sns.kdeplot , var , shade= True )\n",
    "    facet.set( xlim=( 0 , df[ var ].max() ) )\n",
    "    facet.add_legend()\n",
    "\n",
    "def plot_categories( df , cat , target , **kwargs ):\n",
    "    row = kwargs.get( 'row' , None )\n",
    "    col = kwargs.get( 'col' , None )\n",
    "    facet = sns.FacetGrid( df , row = row , col = col )\n",
    "    facet.map( sns.barplot , cat , target )\n",
    "    facet.add_legend()"
   ]
  },
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   "source": [
    "### 2.1 Reading the '.csv' Files"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
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   "outputs": [],
   "source": [
    "# read .csv from provided dataset\n",
    "pulsar_data=\"./Data/HTRU_2.csv\"\n",
    "data = pd.read_csv(pulsar_data)"
   ]
  },
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   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
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   "outputs": [
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    "data.head()"
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   "execution_count": 5,
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   "source": [
    "cols = list(data.columns)\n",
    "features = cols\n",
    "features.remove('C')"
   ]
  },
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   "cell_type": "code",
   "execution_count": 6,
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   "source": [
    "X = data[features]\n",
    "Y = data['C']"
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7f2HSR58AUDWyGs1vbsnePXvKXaJ4tc8RgFWrVjF27FiGDBlChw4drO3MZjMT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\nCGGUNIpCCCGEEMIoaRSFEEIIIYRR/wP6BHrq9aT3iQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1bc003e70f0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_correlation_map(X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": true,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "X = StandardScaler().fit_transform(X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": true,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "# Split dataset to 60% training and 40% testing\n",
    "X_train, X_test, Y_train, Y_test = model_selection.train_test_split(X, Y, test_size=0.4, random_state=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "df = pd.DataFrame({'M1':X[:,0],'ST1':X[:,1],'EX1':X[:,2],'SK1':X[:,3],'M2':X[:,4],'ST2':X[:,5],'EX2':X[:,6],'SK2':X[:,7]})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1.0\n"
     ]
    },
    {
     "data": {
      "image/png": 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AgEEIMwAABiHMAAAYhDADAGAQwgwAgEEIMwAABiHMAAAYhDADAGAQwgwAgEEIMwAABiHM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\nkgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1bc00d970b8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_variable_importance(df,Y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(10738, 8) (10738,)\n"
     ]
    }
   ],
   "source": [
    "print(X_train.shape, Y_train.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "___"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true,
    "deletable": true,
    "editable": true
   },
   "source": [
    "## 3. Clustering - A Unsupervised Learning Task"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "### TIPS: \n",
    "1. [Various Clustering Methods](http://scikit-learn.org/stable/modules/clustering.html#clustering)\n",
    "2. [What metrics are we going to use?](http://scikit-learn.org/stable/modules/classes.html#sklearn-metrics-metrics)\n",
    "3. [Comparing different clustering algorithms on Toy Datasets](http://scikit-learn.org/stable/auto_examples/cluster/plot_cluster_comparison.html#sphx-glr-auto-examples-cluster-plot-cluster-comparison-py)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "from sklearn import cluster\n",
    "from sklearn import metrics\n",
    "from sklearn.cluster import KMeans,AgglomerativeClustering"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "### Overview:\n",
    "Eventhough, we are provided with a labelled dataset for our analysis. We would for first part of our implementation, treat it as a unlabelled dataset and try to run clustering algorithms to find out the distinct group of data points namely:\n",
    "1. PCA\n",
    "2. Agglomerative Clustering\n",
    "3. KMeans\n",
    "4. Affinity Propogation\n",
    "5. MeanShift\n",
    "6. Mixture of Gaussian Models"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "### 3.1 PCA - Principal Component Analysis"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "It is a precursor step to any analysis that we may subject to our dataset. The above dataset has decent dimensional feature space consisting of **8 features**. In such a high-dimensional space, Euclidean distances tend to become **inflated and meaningless**. This can severely impact our algorithms performance. Such a situation demands more data to train our model and this problem is called the **'Curse of Dimensionality'**.\n",
    "\n",
    "The PCA algorithm solves this problem by finding out the features that explain the maximum variance. So, instead of training our models over 8 features we will be training them over **2** features that explain the maximum variance."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "8"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(features)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[ 4.1340467   2.1446051   0.80934896  0.45742482  0.25822692  0.1598793\n",
      "  0.02044194  0.01602625]\n"
     ]
    }
   ],
   "source": [
    "# Apply PCA with the same number of dimensions as variables in the dataset\n",
    "from sklearn.decomposition import PCA\n",
    "pca = PCA(n_components=8)\n",
    "pca.fit(X)\n",
    "\n",
    "# Print the components and the amount of variance in the data contained in each dimension\n",
    "\n",
    "# print(pca.components_)\n",
    "print(pca.explained_variance_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
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Hw73PdzXuIHRtE6s8G/edv4dr4MvGlVxjJahLcVVVj+PO/EYA+zduC2nisxTjqkf292I9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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1bc003a6198>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plt\n",
    "plt.plot(list(pca.explained_variance_ratio_),'-o')\n",
    "plt.title('Explained variance ratio as function of PCA components')\n",
    "plt.ylabel('Explained variance ratio')\n",
    "plt.xlabel('Component')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "### 3.1.1 Create 'reduced_data' - a Feature Dataframe containing 2 components explaining Maximum Variance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "# First we reduce the data to two dimensions using PCA to capture variation\n",
    "pca = PCA(n_components=2)\n",
    "reduced_data = pca.fit_transform(X)\n",
    "#print(reduced_data[:10])  # print upto 10 elements"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(17898, 2)"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "reduced_data.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "## 3.2 Applying KMeans on 'reduced_data'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "KMeans(algorithm='auto', copy_x=True, init='k-means++', max_iter=300,\n",
      "    n_clusters=2, n_init=10, n_jobs=1, precompute_distances='auto',\n",
      "    random_state=None, tol=0.0001, verbose=0)\n"
     ]
    }
   ],
   "source": [
    "kmeans = cluster.KMeans(n_clusters=2)\n",
    "clusters = kmeans.fit(reduced_data)\n",
    "print(clusters)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "# Plot the decision boundary by building a mesh grid to populate a graph.\n",
    "x_min, x_max = reduced_data[:, 0].min() - 1, reduced_data[:, 0].max() + 1\n",
    "y_min, y_max = reduced_data[:, 1].min() - 1, reduced_data[:, 1].max() + 1\n",
    "\n",
    "hx = (x_max-x_min)/1000.\n",
    "hy = (y_max-y_min)/1000.\n",
    "\n",
    "xx, yy = np.meshgrid(np.arange(x_min, x_max, hx), np.arange(y_min, y_max, hy))\n",
    "\n",
    "# Obtain labels for each point in mesh. Use last trained model.\n",
    "Z = clusters.predict(np.c_[xx.ravel(), yy.ravel()])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "*** K MEANS CENTROIDS ***\n",
      "[[-0.43861262 -0.1064617 ]\n",
      " [ 6.01191215  1.45923393]]\n",
      "*** CENTROIDS TRANSFERRED TO ORIGINAL SPACE ***\n",
      "[[ 0.19627924  0.13893976 -0.21639243 -0.2086499  -0.12520202 -0.13567283\n",
      "   0.11862305  0.0858532 ]\n",
      " [-2.69033199 -1.90439942  2.9660165   2.8598923   1.71610105  1.8596208\n",
      "  -1.62592531 -1.17676028]]\n"
     ]
    }
   ],
   "source": [
    "# Find the centroids for KMeans or the cluster means for Gaussian Mixed Model\n",
    "\n",
    "centroids = kmeans.cluster_centers_\n",
    "\n",
    "print('*** K MEANS CENTROIDS ***')\n",
    "print(centroids)\n",
    "\n",
    "# TRANSFORM DATA BACK TO ORIGINAL SPACE FOR ANSWERING 7\n",
    "\n",
    "print('*** CENTROIDS TRANSFERRED TO ORIGINAL SPACE ***')\n",
    "print(pca.inverse_transform(centroids))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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3AHgqjLC5AmZ9f6+1/mag/Q9h/n6OT9qfBvOlhhS8eBuMaDwXxov1WABPTjzE\nZ8EIvI/DiKuXAThea31NkbUTQkjbTExPZ0WuEEIIIYQQQghxoWeKEEIIIYQQQgpAMUUIIYQQQggh\nBaCYIoQQQgghhJACUEwRQgghhBBCSAGCpdGVUnMAHA1zFgpPJieEEEIIIYSMG7cCcA8Al1oVggek\nnTN1NMyJ8IQQQgghhBAyzjwC5riSIdLE1HUAsO7tH8Lt73SXuiZFCCEkYf4nnt/2FAghhBBi8YdD\n0/iYOQnvOt/1NDF1MwDc/k53wR3vevfqZ0YIIWSI28yZaHsKhBBCCJnBNBBIe2IBCkIIIYQQQggp\nAMUUIYR0hBvXX9z2FAghhBCSA4opQgghhBBCCCkAxRQhhBBCCCGEFIBiihBCOgRD/QghhJD+QDFF\nCCGEEEIIIQWgmCKEEEIIIYSQAlBMEUJIx2CoHyGEENIPKKYIIYQQQgghpAAUU4QQQgghhBBSAIop\nQgghhBBCCCkAxRQhhBBCCCGEFIBiihBCCCGEEEIKQDFFCCEdhBX9CCGEkO5DMUUIIYQQQgghBaCY\nIoSQjkLvFCGEENJtKKYIIYQQQgghpAAUU4QQQgghhBBSAIopQgghhBBCCCkAxRQhhHQY5k0RQggh\n3YViihBCCCGEEEIKQDFFCCEdh94pQgghpJtQTBFCCCGEEEJIASimCCGEEEIIIaQAFFOEENIDGOpH\nCCGEdA+KKUIIIYQQQggpAMUUIYQQQgghhBSAYooQQgghhBBCCkAxRQghhBBCCCEFoJgihBBCCCGE\nkAJQTBFCSE9gRT9CCCGkW1BMEUIIIYQQQkgBKKZIb9mwdnXbUyCEEEIIIWMMxRTpLRs3b217CoQ0\nDkP9CCGEkO5AMVUh9JQQQgghhBAyPlBMVQg9JYQQQgghhIwPFFOEENIzGOpHCCGEdAOKKUIIIYQQ\nQggpAMUUIYQQQgghhBSAYooQQnoIQ/0IIYSQ9qGYIoQQQgghhJACUEwRQgghhBBCSAEopgghpKcw\n1I8QQghpF4opQgghhBBCCCkAxRQhhBBCCCGEFIBiihBCCCGEEEIKQDFFCCGEEEIIIQWgmCKEEEII\nIYSQAlBMEUJIj2FFP0IIIaQ9KKYIIYQQQgghpAAUU4QQQgghhBBSAIqpjrJh7eq2pzD28BmQvsBQ\nP0IIIaQdRl5M9dUg3rh5a9tTGHv4DAghhBBCSBojL6ZoEBNCCCGEEELqYOTFFCGEjAMM9SOEEEKa\nh2KKEEKDIDT8AAAgAElEQVQIIYQQQgpAMUUIISMCvVOEEEJIs1BMEUIIIYQQQkgBKKYIIYQQQgjp\nMOsv2tP2FEiAkRdTfS2NTgghRWCoHyGEjB6nH7fI+zlFVvuMvJjqQ2l0Cj5CCCGEEJKXkMgizTHy\nYqoP9EHwEUIIIYQQQoahmCKEEEIIIYSQAlBMEUIIIYQQQkgBKKYIIYQQQgghpAAUU4QQMmKwoh8h\nhJCuMOoVBymmCCGEEEIIIbUw6hUHKaY6CsulE0IIIYQQ0m0opjpKWrl0Ci1CSBYM9SOEEELqh2Kq\nh/BcKkIIIYQQQtqHYooQQgghhBBCCkAxRQghIwpD/QghhJB6oZgihBBCCCGEkAJQTBFCCCGEEFID\no37GEqGYqhxW2iOEdAmG+hFCSHuM+hlLhGKqclhpjxBCCCGEkPGAYooQQgghhBBCCkAx5YGheoSQ\nUYKhfoQQQorAnK9sKKY8MFSPEEIIIYSMO8z5yoZiihBCCCGEEEIKQDFFCCGEEEIIIQWgmCoI86oI\nIYQQQggZbyimCsK8KkIIIYQQQsYbiilCCBkDWNGPEEIIqR6KKUIIIYQQUgiWzibjDsUUIYQQQggp\nBEtnk3GHYooQQsYEhvoRQggh1UIxRQghhBBCCCEFoJgihBBCCCGEkAJQTBFCyBjBUD9CCCGkOiim\negoPDe4PfFaEEEIIIaNJ78XUuBqqPDS4P/BZEUIIIcSGJeVHh96LKRqqhBCSD4b6EUJIuxQtKU8R\n1j16L6YIIYQQQggZB3iuV/egmCIAxjdcEhjvtZPxhd4pQgghpDwUUwTAeIdLxqydgosQQgghXWNU\nwv76vA6KqYqgsT3ajLPYJGQU6PP/URMyaozr32Md6x6VsL8+r4NiqiJobPcTimBCxoM+/x81IaPG\nuP49juu6Rx2KqQ5DQ58QQgghhJDuQjHVYejtmknVApN7TAghhBBCikIxRQrThueM4oeQ6mBFP0JI\n24xr/hQZHSimRpi6xQ6FTbswDJQQQkjfYR4R6TsUUyNMUbFDI70fUMwSQgghhLQLxRSZwTgZ6RSO\nhBBCCCGkKBRTpDb6IFSyhGPMGvqwTkJCMG+KEEKqocn8L+aadQeKKVIbdXq4mhIwMWsYJ08eIYQQ\nQvw0mf/FXLPuQDHVMejliIP5YIRUB71ThBBSDnqKxheKqY5BL0e9cH8JIYSQ0aErIoaeovGFYoqU\nwvX00PNDCCGEkKbouojJEnuh610RiSQbiilSCtfTM8qeHxajIKMMQ/0IIWn01bhvYt5pY2SJvdD1\nrotEchiKKUIiGbViFBR+hBBCYumrcd/EvPu6N6QaKKYIGVP6JPwIIYQQ0ix99UY2DcUUIT2C3iRS\nJwz1I2T8GGWDeZTX1gT0uMVBMUVIj6A3iRBCSJWMssE8ymsj3YFiikRBjwghhBBCCCHDUEyNAE0I\nHXpE4qDoJH2HoX6EkFGCoX6kbiimRgAKnXL4BFBRUcRnQQghJA809uuFoX6kbiimSKX00TPjE0Bd\nFEV93FtCCCHp0NjvBhS1pCgUUzUwzkZvF0UIIYQQQqphVEXHKIraUX1WXYNiqgYoKEgd8L0ihBDS\nNnWJDhr+1VP1s6ryGY3S86aYGhOKeMv64mHryzwJIYQQQsrSlhDJEmd55jVKnkCKqTGhiFejSk9I\nnYKHHhtCqoUV/QghTeMa16Pkuaia049b1Mn9GSWBlAeKKdIIFDyEEEKIny4axmUpu6ZxNcxjid2f\nUXy3ugbF1JjSRGgcw+8IIYSQbEZROIzimvpIm89hXIQcxdSYUpWnKE0wNemNonAjpFoY6kdIcUbF\niByVdbQB9258BDXFFClFV8L3ujIPQggh/aUqA7iMEenOocycGGrXHty78YFiqsfQG0MIIYRURxcM\nYHcOZebUhfV0naoEdGw/bXis6CWrF4qpHtMXb0wVoo/CMQz3htQFQ/0IiaMKY7XvBm9f51+V4Izt\npw2BS1FdLxRTI0xXjOwqRF+RPrqy/rrpi6gmhJBRJa+x6hMedRq8TQgdGuxkXKGYGmHG3cjOWv+4\niC1CCCHt0bRw8pFnvCY9TF3zZnVtPlUz6utrC4qpjsGQuDiqWOO4is1xeD9IdTDUj5By9M1j0+R8\n08YqYviz4EY6o76+tqCY6hhthcT1DYb9Faep94P7TQgZN55znk69XrdnYJQ8D0UMf4oF0gYT09PT\n3gtKqUUAfvrqM7fgjne9e6OTIoQQ0i1uc/qxbU+BEEIIaZzfH5zGe66cBoB7aa33uNfpmSKEEEII\nIZ1glLxrZDygmCKdhWFi2XCPCCGEjALrL9qD9RftYahexVCc1g/FVIv02RAuOvc8941D7ldZ0vbI\n3us+v2uEEOJjXI3EUV336cctopCqgSrK9pN0KKZapM9ioejc+7bmPosQe6/7tu+EEJJFXw3vvlec\n64qxnXceXZl31VS9rrbfrz5CMUVICqMoQvosEAkhJESdxnKVfYeM1arnX6XYsK/VZWznmW8oHFBC\nBX2Mqkjo6rrc5zCqYhZgNT9ChtiwdvVICihCqoAV/UhdMFemOzT9LPr47Ls+567Pr2+wmh8ZoqxX\nYtS9GlWcXzXqe9Qk3EtCxgMafoYufHvflOcsa7wu0/U5u/Prwns1ylBMjRllvS6++0fN4M27HndP\nNm7eGtXHqO1bHdBLSAhpmyYN0Rgjvcr55OmrCQFBo78eui7++g7FFCnNqBm8Vawnpo9R2zcy+ty4\n/uK2p0BI4wZ3miHahvFfpWGcp68m1nr6cYs6lWszDuJuHNZYNxRTJBex3pRQu3H1xjS57g1rV4/t\nPhNCRp8ufMsuBmjWXHyGat3Gax3915GDI/N8znl66HN3nDafdxfetbzkFaP2GimsisECFGSsYIEJ\nQsoxjkUomMzdT8bpuWWtNe9edGHvujAHQgAWoBhr6J2YiQipcdibcVgjqYY878o4hvo1nZBPqmGU\nDPE83oUi18u2r4MuzIGQGCimRphR9MBUIRDGxTtV1Ropykafcfh7qAMae+NHWwKa71o18AsQUgcU\nU6RXNFUcQqCQoKFNCOkOVRjDZfqgqAkTe+hv2sG6dc+Hz4/UAcUUqZSuio+i86pTSHR1rwjJYhxD\n/Uh+qjKY7X6qOD+HBrWfss8r7dnY104/blEjz4DPmTQFxRSplDa9GGnipIvelS7OiRBCqqIqYzat\nHxrM1RG7l7Giq4pcw6ICr40qimR8oZgiI4MtTqr0+tCDFKbM3si9de0vnxsh1UJjtFqa3s+844Xa\nlxWwee4vesaX7z4Kb1IXFFNjStOGpm+8OudQpdeHHqQwZfZG7q1LBPO51QtD/YYZB6ExqsZo1c8u\n9pwf2c8qx88rMNLo+vPu+vyqYhz+t6XvUEyNKU0bmr7x0ubQtPhyxxgnr4a91rbXTQFE+sq4GHZ9\nI8YQLfvs3DHyHjpb5btT1JNTFWXHCN1ft+DtMvzflu7DQ3vJyDIuJdAJyUOVfxfjeIAvIVmM0mGz\nMWsZpfVWDfdmNOChvWSkcT0p9u+xBmOWF6xtbw0hVcIvGAiplzaM57pKxsespalDrfvkTRIopMYD\niinSKllCJeu6axjmNRRD39Lbn9H4LA8FKSGkq8TmODVJ3jlUYbQX7aOuQhV19NeFZ5tG1+dH/FBM\njRFdNGizhErdQoZCKT9F3iPuMyGjSxsGYJVj5s1xaoIuzCELeQZdm2sTpdfromt7SeKgmBojaNBW\nx4a1qysRp20J3DLj8j0iAiv6EaAdA7CpQgvPOU83Mk5XKVsdsKiwKbO3MeeStSkEx+G9GTdYgIKQ\nDNIS9lnkov/wGZaDRSjIuMMiA4SMNixAQUhBxHuTZmhXaYR3MQyzK/TlTDJCSL2U+VZf7i3aR97c\noKo8LXnnQwhpFoopMnKUNbxjRFQd5D13qwh1iJImRCAFT3dhqF9/KGJ8d81g9wmX9RftSZ1nVSFd\nMSXCQ+3r8Fz12RtWt9Csk9j59m1dpDgUU6RXrFulah+jiOFelYALXbPn1LV8JwodQvpBEeO7CwZ7\nllF6+nGLZuTCuNfTfq+KLuxViBjDPkaQVkVVQrMNwRI73y6/D6RaKKZIr9i0LZwMLJQ17qW4RB7R\nUlaA5Qkl9LUNzZehg4SQpmkqjC6mbVkRUTdNjV3mvCj7Wtcq5dlz7rO3i/QbiilCg9th4+atg/+q\nJu9ex7YPzTetcEZT8P0afRjqR2yKfiNfh4eirIiom5AYSCOvUKizjHxVbasQ4HWHVRISgmKKNBam\nVWcoXB3jFSVt3I2bt+bKyarr2ZTpN+++MgyQkJmM8jfnXQ69SqPueaeVWQfqCx+rS1hkrSdETChm\nWtuY+4r01SVGIb9xnKCYIo1R1qiW+21jPku4VEFR8RC6r01xUWUhiy57nLo8t1GF3ql8dPmb87JG\nWV8N3LqfybnHH875bWrddY4j68k7xunHLYq+p8pn0qczpfqa3ziu8JwpQjoMz0AifYJnThEfMecw\njftZTXWsf9z3NA3uDckDz5kildL0N/5NjNdlL0bd5dLz9NHlfSKEdJeu5y3FULcnqej60+YVKiNf\ntL+uUmTOXXjf+rjXxA/FFMlF3WXD3bZNFFCock15Py9D1ryzyq3H9JFnvLRxux4WSEjfoCEWRxX7\nNEpejKxqh0UEWFmaDCvtEn2dN5kJw/wIsehCWF0Tcyg6htxXxRy7sNekehjql59RMtaroI394DMg\nhIRgmB9pjbpDyNrw9jRBVQf0FkXG9I1dVEiF+iKEdPcb6ra8XzH5VU2PWYQ694+eSUK6A8UUiabO\nkthFDOsmjfHYtWdVGuxCWfEsMSTXQm2yqhWm3dMEDCckpBqaFHl5xEFXxadLkXm2UeUuD106GLft\n8QkRKKZINOPoScibW2S3C3mY7J/XrTpcKrcp0jxfdQukJoTOOL6nhLRFVQZtG96oJvrOS9eEoiue\nmjgYt+uCkhAXiilSCV3wBkiRg5i5xM63KsPc9vbYfW7aFn/oYdV77PNQlSkyEfrc/qyIV6ssXXg3\nge7Mg3TLeO47TRm09jgxz28UPV1NIvtnnwnVxrMmpA+wAAUhNeOKFhZeqBbuZ7dgAYru02SxBRZ2\n6Ad1PacuPv+8c+riGkizsAAFGRnyfrPfpifA9pAV9f5UMYe2qGrsmH7K7Gfb3qK2x6+DG9df3PYU\nSAZNGobjZoT21etZ13Pq4vPPO6c6zgAjowXFFGmVPCF5Ukku9v6mvRVuOFvbRReaHt9dfxVCoe41\nVNl/kTPGRtmjRkOi+6y/aA+fU8V0UTz46ONz71veXF/eBVIehvmR3lEmrKuNkLC0MdesXIgt2/d6\n2xY5WLcuGEpH8sJwP+KDIVajje958RmSvsMwP1KYroYglTHq2xAEaWMuXro82LZq71aZ59mV0MSu\nvpOEkDh8hnae9kXpkyemT3N18T0vCiky6lBMkSD0ROSjKUO/6Diux6toDlqRcLZYst459zrFFSHN\nGN91jWFXi8saP3YOvnZ5vWFNY49Z5/lUhJDqoZjqGDQOm6fIgbx524Xurfuw4rRcqrxjp93nq1iY\nVqa+zHtuizoK/u7SdCGKLhiTZeZQ5t4mvvmvc4yYsuenH7coeg5lxUgbnpS0MWVuae9I370/Xfj7\n9dHVeZFuwZwp0ho+Y5gGsmHdKoVN2/RY7YfvPCr3emgvqtinUdvrOtaTt88q86aYd0GKkPbe1PlO\n8X2tH+4xaQrmTJHO4jPKQoe65vFkVOENqcJzUuZ+Ocy3bNlvey5umF5bYYkhz53kiIXegbQQPwqp\nmdSxnjb3qAmjqey30H3+FrvPc08j7b1pyts2CnTx/Whjj0P70MX9Ic1BMUU6SZmzmUJts/qINc7r\nFiFVCINQX7ZoaKt0e5qIzvosq+8814qMRfLRtzOnyhpnfTag3VC7UTUO2wrFzOq3KSEfm5/m0oV3\nuwvvZWgfurA/pD0opkgqfc/hyjP/WIM667yrOoskhLxKvvF9grQNkZg2bhUVBtPOtAqFC5LR3Aef\nodW28eWji3Oy8eUnjYrXrozRG3tv3rXKftv3FemjqnZtCYOYyo4ULaSLUEyRVNo8+LYK3KIIRcfI\nCjmLnUPR8YR9k8XD/7IKY+Q9aDc2fC+NssU0ipzFRS+UYRT3oS9lmWPmVJcHpWi/4+a1a6MgiH1f\n1ftVdD157+t6IRVC6oBiinSCqg6ojREDMsa6VSr63qZLdoc8KpJLVVWfRcIpJRcr5l7fZ2n7HhJM\noZymNgVBXZ6dOj1GbXij+hbq1yRpnrS0oglZtJUn1Cfynm/Vtmet7PhVCDxCiB+KKdIJingo0sp+\nx4zhCpM8QiJLADRt5Ps8cFnEtPPtta9IRJ7Ddn37niWYynqwfJ/XIXiroM53ZxS8UW0btXnJW846\ny3ilcVsNfdvHpufblgeTkD5CMUV6R50FFLLEgVtxzp1HnmIIeY35WLGXNp+8FfCy9tq9vmHtaq/n\nKW2sdatU4WfpO8sqtqw6DwDuJ30z1qqYb5OhVnnp84HBefru4ntX577kXe8ofclBSF4opgixCImH\nLG+HG6bo81zFhMWlHXQbg2+MOsvK+4RMHo9f2TLwaaXUY+eQpw0Zb2IOT62DvIZtk4Z/E7k9dQrS\nPH2XEbV15CxVufd1hhF2Ubh0URyT/kIxRUaWKj0NsZ4ZX3tX0GQZ/WXPlgrN0TfPtLZ2n1WUm8+6\nP2aPioZ4xs6nLHlCJ0k7MEF+mK4ZunUZ5VU8uzKitmglwKbeuXE6vJhnRZGqoZginaIJAZQ1dmgO\nbvia797Q5z5BEyuwfH1WuU+yrrTDdO3fQ3PyUfRsr6LiLWtedXmf8hRQGcWiGV3HNpLaMKpdumS0\n9WktXTPKY8mzR31dY4iq1lPle+abUx7R16W/X9INKKZIp2g71CrkhcmqpGcXUXA/D42T1c5n+Ep+\nURmx4Yq4tDC7NI+bLS5tgZinKEXRvKUsQVpVdchY2n5vY2lrnm1X9OuSkRRjtNU9j6IHt/oYNeO/\nDpoKgeuSkV91WGPdoixP/3zniQvFFOk1eb9pLxp2lpaXI6Iiz6G4MeF8vut5SqOHRI0ddljGuA7N\nxVeUApjp2fPttT23GA9cyJsV+xxi6aNHp49zrpus8J4mjKQuHJrahTnUzfqL9tRy3lbVVCn0u5S3\n1NVS7H1/r0k3mZienvZeUEotAvDTV5+5BXe8690bnRQhVZFVSS7rvrT7Y/qOHb/oPIvcX3RO7p6U\n3Vv5GchfGr8KD0to7Ka9WuPCbU4/tu0pkDGkizk74wD3nYwSvz84jfdcOQ0A99Ja73Gv0zNFRhaf\n0W5fSyOm/HqV+TExYX5lii+EcpPylE5396RISKGvDHpMNT67j6qEVMg7WKQICL1A2dQd6teF8tlN\n9dPV8aqmivmLQd/3vfCRZ01l15/3/jaOBCCkLSimyMiSFvYVm6MUix3KluesqbQ2oTLnrhiKGS8r\nxyqtuIbbV559snPJssqmx4rTtD2215FW4KNIFcKsKowUVO3Sh2/Bm55jn4pL+KjSIO/D+5GXOvJ8\n6s5Zih2vzJhtv7dk/KCYIr2hqLHq876UKVaQ5hnatE1nerNCRRpCBnxa7pZ4UbKq6aWJrrQiFL6x\nQ+PlqU4Ymk/MM/btsdxni7VQBcW8+2yLwTTKCCoKsfqo0rvRBbJygaqkzjOYyjCuxnIThUma8CgV\nra7JoiikqzBninSSqsK5qqBMPlKoYl2aZyzrfvvavkkdLASxbpUaupZnLvZ8hDJ5VrHt3XGLvgNu\niGeR59dE7lSX3vOmqDN3ymcMdiV3oyvz6AJt7sUoPIe219D2+IQ0DXOmSC/pkoGZJ58pT/5PzDhZ\nXq606n5yzVcpL6b/fZPhkulCWn+x3hzfZxs3b8W+SV3Yw+UKqdicuaxw0CKhjml06T0P0Sevmc/A\n64rR19Z5O1304sSUhpd5Vz3/ut+HKsvOh+6pcw32OHlC/rr4nhHSFBRTY06XDaUm5xZjbMcUZ4jp\nO01wpRWHsD/Lm7cUmmcoxwgYFmNFjP6yQkHC+bJylXwhfK64dfuJEb1p4tYN17TJE+7YB/qUD1aX\nIVvHPIqS15Cu0vBuMrxQ5h0z/7qEVxHqKjvf1JcC9jg8e4mQOCimxpwufzPe5NxixFGRinm+ynAx\nXqyQQR5TSU8KNMTmH7l922SJu6z8qTRhkZbLlkf4hPZH9sE+ByzGMxczjtwb6rNoiGBMIZA2qPJv\nMaaqX1GjuCvnJ3XNsKxKZJRZV+wcisw1j/Cqaw5dEHJp1DW/rq+bkCagmCKdIya0q87P6ybGi2WL\nsBjPgC1CfKF/diEF954QaV6rtM9Dnqws4ZK1ZvczuzCEb8wFi838swSjbx55vIZFhHdoDnkOZW6K\nJv5OXIOsi4UP+loeHeiGuIstUd6luVZ9T5Pl+31/U3WUUq/6eVGckT7CAhRk5HELMaQRWzAhNvQt\nrX0e70VW0YrYfkJ95SnYUCTsL6+nJuSZCuVCFSk2kYbvnaly3VmFP8q8E32Bh/iSIlRZ/CCmr7Lj\njVuxhib2lJCmYQGKMaMvuQ1N4jOKQ6R5gWzjNdaIzWofm4+Sdn8o3CwtVDAt3M03lu2lsu+PCedL\nm3/aGDZp3i537nlCC13kepaQiv07CwngskIq1Dcho0YZr2UVfeUdr8r59pGuhNoS0iQUUyMGDaxs\nYooa5L3PZ8C7OUChe9MM6yyBFFsUI6bgQhqhEuu+sLy0/QutAzgclue7xxVwIZGyZuXC1DGL/n24\n+5ennzzFSrr699vmlzSjHvYz6uvLIuvcrCoN777ny437u1In3FtSBoopUgl1G1tV9x8yxvOO4zOu\n04pPpM0hpjhCGaPenZs7Ztra814L7WXWfgnrVqlcBT/k/i3b90aJYbfQgy9c0DdeEe/UKNCmyBv1\nb7G7tL4mDoV1sSv3dYEye1D3/tV9oO44C4ouvYOkf1BMkUqow9iKyQeq0qCNWUMeL4Pbrmy+UGx/\naaF3WeF9bqGKNPHj/u628c09ViC5Z2TZ88tTmMPFJ4ZCYYnu/pXxcrn76s4h9DvJT5MGYVnDu2vG\n67gblGVzeYrc23RZ97QzokLz79p7SkjXoJgihanb8Mvy3Pja+MgzzzKiMHYcNxQtZg5ygG5MjpKv\n4MXGzVtneHvS9jOraMaGtaszq/3ZQixNuITyuXz3uCLHFoA+sgST/W9oTr42saLMbu8TwL7f+ySo\nujTXNIOwCmMw5FUpStc8MjGU3cfYin5tEfvuVHlwctVl3YuQJaL69p4S0jSs5kd6R9HclyaqpKWF\ni9mfuT9nFSWw788zlxhvSswc8twf219av3meQdb++PoqWq3Pvc83dtn3p8t5U1XPjRX9SFu0VVGO\nlewI6R+s5kdGjrJFBEKEjPJQnk1oDF+4W5oISRtT2hRZs9yzbpUazF3+9Ym8tDnI9bSQOLefNC9Y\nmrfJnbvbPhSCGBNGJ0ixC/se8f652H3Y4Yfi6fN5y4rS5ZysskKq7pDGot6OrPv6do7VKFPlHjUl\naPpa3Y/vIyHx0DNFSEIej0rV39IXycmpyoCPndu6VQoLFivsm9TRpcPLeLzyzC10bd+kHqoSGGor\na3M9imXzpNr0MrU9fgxteabG1TswTutmiNroM07vM2kXeqYIicT1qGTlwAhp+UvymdsmKyfH15/r\npQkVeQjlEMV6Auz7RZBsWLsam7ZpbNy8FZu26eD85V6fx8btN8sTmHct0lb2aePmrQOBlOXdk7XZ\n6/GtKW2fXKoQMmW8N10XUkB7337XlVcVQ5FxqsjbAcZLWIRy0uhx6R5Fn8k4vc+k21BMkZGkihCi\nWGM0KzzObRMTtucLfXNzdVzhkjVvV3iFBMnGzVsHIW8inmIKMITGtr1TaUUf3Hm499uficDL2qeQ\n2AyN5RO+7v76xGwWWSFuofc1Rth3gTxC3ebUi38y9Hseo6rqtnUYZlUZ7r65NWlIhtbRR2FStOKe\nu9Y239Uul28vAkUR6TsM8yMjS9nwsjyhdlmFGQAMwuNiCi6EQg6z7hVPkp3b45ubrwBDHvKEGIZC\n5uzPXKGUNrfYua9ZuRBbtu/1zsce193ntJ/ddbjrscMKY9+tPOF4ZZ9b2/jWGgr1YwhPvXR9f++5\n6im47pKLcMtNhzLbzjpiNu7x4OPwi23nNzCz+qnq2dT9jPsyT0LKkhXmRzFFRpYqc0aq6ivNIC/S\nzsZXaU5ynWIM8DziMqYtEM45ygrxsz+XfCb786z8tiyBaQvb0Dz6lB/VBcrsQdO5U1023ro8N5e6\n5nrPVU/Byleejl/v+DYu/dDJqYJq1hGzcfRrzsTdVjwK2z+6fiCoZG51zbHu/mPHL9OmT+8aIW3C\nnCky0sTmNZXFVzEulM/j4npx8oYP+sLyYsaV+33FImKQdnYOUtrc1qxc6A3Pk2qCMVXvfLlKMn93\nfNt4d8/uks9lXHcP3D59oX2+/Q6F5/nCDX3t817vE10Vk3nOh6oqN6kMbRu3edZblVfC5bpLLsKv\nd3wbd1vxKBz9mjMx64jZ3ra2kPr1jm/juksumlF4oq79rKP/qvc+q03b7xohowLFFOklRTw39n1F\n73HzfmKM7hgPjkuacAgdvuvLqfKNZZcAt8u9+wou2GLG7cvtf/HS5TP6Eg+Q24cvD8rt1yc87f21\n57tl+15vf76575vU3nW7/drjhbxgMblqafQlJ6pp2iqBXXVuUhfzU7KoysCOXbtvvFtuOoRLP3Ty\nDEFlt3WFlHiwYudvz68rxUcobgjpJwzzI2NLU6FcofC4tPC7tLF8Br4r3rL6DuUl2SXCs/KJQvPM\nCvGzf3dFhC+Mz+0rhH2PWxLd7R+YmVO1Ye1qTO7aOfhMxrY/ixnf97v9PLJCE2NCF0cRN9QvNgSp\n6VCl9Rftwc/+eBDnHp997lxd46d51vKIiTz71kZIWEgwhT5n2JqB+0BItTDMj5AAeT1GIS9UnnHs\nPgOeiGwAACAASURBVHzhd65XKKZv28iWf31CwsZX3lzu8/UZCmcLhepJtT1XDIX2LxT+mBYWad+/\nZuXCGd4w9163HxFItpdqy/a9Q2GNGzdvHXjc7NL09s8hwWf/Lvvthk762ofWO8pCykesMSjt5Fv/\nur0Mpx+3KFVIlan6Fjt+kWtl2sa2r3qtPg/Vrefd1iukCCGkLSimSGfoUqhTjOHqEzxpawh5bNxr\naUa1iBw7TM7XPiReQmFsvpyh0O+2UPLlj0looDtPG7nmm0tobnY/Ip7kd9tz5AoXXx6WLWZs4RkK\na5R+pT+3jXsGmG899nMqmsfWdbqwDikK0Ob4ab8D9Qi+2D7r2pu6vCGuoHrSpy/F3VY8Ct+6+ILK\nhVTW3jznvH5U0swqOpGHPoaqEtI0FFOkM/Thm/c070MaoRAt2wuU5b2JGd9uL7lVbgEJ1/Pim1Pa\n75u2aeybHBZLdjt7THecUI6QO29p6+6b/CzeInt8n6fLJ+Rs1q1SmNy1c0ZInt2fO2ffOFnl0F1P\nVBWio2gfdQqeMn/DN66/uPC9riHftqAC0o3QOkSHb82h4ht1eM/KriltDrfcdAjbP3LK0Gf7v7Bh\nhpAqO4es+8Uj2fa7VYY6PJKEjDsUU6S31GEUZvXpGv1ZYsf+3CeSQga6fGb3ZxvkPo+T29embRpr\nVi4ceGrcyna2aBABlBX66IYoyj2u4EgLYfSFuNnrdg8JtkWNW7XP97zSwgvtMEjxQi1eunyob9sD\nZe+pL3/MnbtvLLdP95rv9xhiwh/z3FclTXuo6jzU1ndgawzu+E0Y4DFesTztqibt8N+0Ocw6YjZW\nnnTG0GcrTzpjUOUvz5hdEI7E0GdRSogNC1CQsSMrkb/IwahZoXGh9r52oRwc3xxj7ncFgBRnEAEl\nP4f6CAk9u588eyFiyjd/N1/MHm/3lTuwZNkK78G49llUvrkCppCECCd7HMmxcvfC3WO3iIQ9RixZ\n70VbfbWJvY6mz5sqQtvJ/W2PX4Yic3eLTWz/yClYedIZnc+Z6vNzIoQMwwIUhOSkrJACso1sn5Dy\n5fjYn9u/u3k+bt9uaJy0ESEieU+uF8jtIzQnIN2DJB4o11smgsQWKT5Pkm8/z7l8X/C6e687932T\nGlu2750RimiXiXdxi3SIN8vnwQt5yNx/Q8/Vl3OVRZ4Q01jaCB3MWkcV315XWQa7qRwoH0UN9CLz\nq2NNZYXUpR86GX8+cIO3bHpRyhYuCd2f5yyzKqG3h5DmoZgiY0eeb/NjQvN8/bm5Or6ffWFkbttQ\n/75cKyFUKc4uriB5T9LenodvfPdze6526J4vFNAWN7ZACXmg5D8RfG6/aflJoWcr99j9y/zskD/B\nnoNPRNrtBHffXeGXJrh9BxPHEgqb9I0jhMRPFz1dRb/dl/A8Xz5Vnj5iaMoDUXScImtuO/QvVP4c\nMDlUj3vK30UJqtiznez1Fjk8N0/1yTqLhRQ5Z4sQUg6G+ZGxIW9YVCiErmj4Xkw7V1T5+kgLzQt9\nFhIwdpib/bkv1NH1bgn2mVWhMD2ZN3A4l0tC7uRa6HBguw85GyorTNK3p254oY07T59gs++Rs6ey\nxg/lVaXNN/ZaVrsmwgCrGMMN8+t6eJRvfvJZmbn3cd11MeuI2bjt89+LRx/7xNRQvjTB1Xe6/j4Q\nMk4wzI9EUSZMpy/kDYtyDeIYwzHrepbXyQ3PE2zPjy06fEUwbO/Lxs1bh85gcttt3LzVeyaVz5Mj\nfYiQm9y1c3C/zMPnhVm3Sg0JlA1rVw/lLvnGsyv6SWGIDWtXD0qgi9dKRFLIO2OLrgWLlbcQhG+f\n3TW498g8pG+7MIYrhn3eLbnuGydGgLnk8dL5xilKFWLN7uPG9RdXUrihzm/d04pdlDF+83hG2vAq\nVFluO+veezz4uEwhBcwsm36PBx9XaLwy7YoS6zEjhHQfeqbISFPkm/MsTwFQzoj0iTVXuFXpZbAL\nKWT17Y7j84KJdyhtLbbQcb08IS8KMOzlcuefhuuxcudue+DctbkFLey5ixfNPsfKR0iAZ3kZ3T6K\nPOMy3iv78yY8WTH0oQhFEbrqabDnVdcci/R7z1VPwXWXXDQQUml9zDpiNu7x4OPwi23nl5xpe9Sx\n91195wjpG/RMkbEmFKaVdU9a+e6QEAj96/7s9uH+bHtsBDu/KTSuTShvyhd+Zh/Cm5YfJN4nES1p\na5F/pUiFXehB9jc0f9sbZpd297Wz5+TO0y6QYXvgbG/Y5K6dOHhg/8DTJm3XrFw4aD977lyvB9Ce\nh0uaxzH2HnvNvrXLWst4r+zPuyCkmqLNfJGu5aoUzScrOkYsv9h2/pBHKq2PW246VEpIdeEg2zpK\n6TN/ipBmoJgiI0GMUMpjLGZ5QkLGsC/ULcbTFepLEMPenlfaeiT0Tu5xxYs9t6ywQREXbl6Uja8a\n3ZqVCweCx/aMScid3b+EwokAkv9EyIS8ab5y6LY3as3Khd5DgOWzLdv3YsmyFVi3Ss3wtokA3LRN\nB8MQpV/7X/dze39813zXpY2vbzfk0t2XvpP38F5fNbUmQqjyGKC2h4Cegu7RxYNsm3xPqirKUvQa\nIX2HYX5jRFfCeEaVMvvrhlr5iigAM8PI7LOeskLu3PEEX5jb5K6dmD137lDfIXHohsW5bUJzdefs\n+wwYLlzhrkuQghC+EEQZ+9DU1JAHy/UW2qGAvjHcNaaFH6bdV4Q+/O1mfWmQZ/5thfo1XclOxsw7\nXpXhW2X76nMoWRWFQ4qMRwjpF1lhfhRTZCxoyhhNEx1p9whZeUVZgsbGd7Cvz+tk5zW5eUV5PH6u\nqHIFhE+82GOGBKPdPpTf5BNlPkSY2euVvClXJNn75zso2KbIM/T1kfa+FHm3+sqo5k3VzSga61Ws\nyRZNQlafo7iXhJBiMGeKEKSXpY4lbyhhjLErIWw+ARHKE5K+Q9fkX58nxA6pE2yRYIsauwqg3GP/\n6/ZjHwRsjx/Kr5IwOnt8yVtyhZX8LJ6tkBfPFVcStmjvib0v7lzdz+RgX7cIhy93zV2fD5lbTM5U\n2vWqhVQd4YExfYba3Lj+4k7ksdRJFw7FbYsi5ziV6d8OsZT/suYTI7aKzIUQMnpQTJGxxidKXMT4\nTRNkeYoT2PjKksu8XOM8ZKzL3NIKCUj+koidrBLgdlEH23NkCw03jyg0Zkj4LVissPvKHQMRJiF6\noeIfbu6UiDF73fbPG9auxqGpqaHf7fwpV4hJfpWbqyZ5XG6uUui9sMdIK2Ri74Xv3yaJ+TvIwnd/\nVp9porDOPBafgevLvcrbRx6aCivrAiFxU0ffaf2H9qPoocYx+9sXgUsIKQ7FFBl7Yj0CIcPQFTm2\nUZynb/HM2MInNK4vx0o+t0WD/Lt46fLBeLbIcIszCHb1PdtbZReaELFgCwxbQCxeunzgaQrt2TmX\n7xvcJ3P0zcGehwgeW9CIGLOvb9xszoPauPnwWVUhISni0fZA2Wv0PSv7M3cMaecW3rDvsefg69O+\npypcz2dWmzy4fycxnro8VCEM0nKi8haIyHMuVFt0xZCvcx55+q5iHr7qh119/ln0dd6EdA2KKTK2\nxHikhDyGYZqHyDb+7c+k7Zbte2ccCBuaj/u7hKQBw9X/XC+XPS+77Lj0kSYaRcC4uVbSv5QUlzwj\nmbf9s7BulRqEEtpt7LnL5+I1kjkIbrihhCq649mFJ3x5XK5ws0WqXQ3RPQDZfpauGLT3zR3D9/x9\nHj5fzlVV+ESd7/Oy/RbFrepXRLysv2hPrjwZQrIoEhLYVfo6b0K6BgtQEBIgtgJbTPGBmMIDaQUG\nsrxcMgepageYsDXxDtlnQ9lt7LUKtoixD631rdMmdHDuwQP7cc7l+4YOyQUwo7Kfe0BwVgENex2u\n10fm7yusYY/jHsjrzj22Wp97b6i9L9fLN/cqBEmRfqoauyp8hShYGIDE0MR7wneRkPGABSgI8RAT\n7uQWUAhRJpwpzSgPhaMJbnEFCWtbt0oNnZtkCxxp465pwWKFQ1NTgzWLB2jdKjU468ldqx1aB5jD\nbdesXDjDy2MLKcEWNvKvHKIr83er54lXSH4XT5C9NvF2yZp8OU4i6CS0L5Q/JqGQvv12vX2hZ+SG\nvPnCI+1rVRErvl3KCqlQjlgsMXvQBeO1yHk6VYVUxfTTVPhW3eOU6b+rZ0GNemEVQsYReqZI63Tt\n2/AipHmVYu8L3RvyXMQYzL5wwBOOWjAkcMTDY5cE91Wvc8WN7f2RfpcsWzHw+Cxeutybi+WWHXfX\nbx+Wa/dje9rsvn25OqH9sPfB9r6F2tn9iUizPVlpZ2uF5hXzfhT9m4jxgBYZq82/0bLeqVDbUT9b\nqIo9KtpfW1Q9x7T+RmXPCCHZ8JwpQkpSJGQvts+QACgqqtasXDjjsF1bwACYIZhEpNjtRGRJXzKu\nTxjJXGwRZOciSRt7blKYwi2MIYhgctfrHqobwhWGdr+2p84VQ+6z8AkrEY3u56E+XGLauO3zCJlR\n+HLCpsozp6o8s6jJMbs0ThHa2PfY/rq8b4SQbsAwP0IiSAsvsg1ln7AKtY3F11/IGPb1bxcvcCvS\nAcMFHQQpTAEYT4t4mKTgAmDC9qSYgx12KGFpdn/Sl3iT5B47vM4WZnahDQm5k2IS9pyA4dCxQ1NT\n3mdwwlELBuGBbuigXUJ99ty5ADAIJ7SF3sbNppqirxCEPZdzLt83tA/2XNyzt9xn5e6X28YXJpcW\nPuij6pDBmDH7QtXV3Iq2ryPUK+bspLZoY9/ToIAihFQJxRQhiPMS+HJbskQPEDamQ4UV5HfXeLZF\nUyjPxueVsH+3q/fZ+U5u4Qe7pLktxnwV6VwBMD19y+Bz9ywowa7OJ9cXLFZDXiv7TCtZl3ir7BLt\ngMnLkgN53YN43dwpwHjExHMlHjMAQ5UU7X9FfNo5ZSFx4xNjNmliy/bqubi5aCHSwvnK0JS3q2ui\nrQuH6xYx/CkWwvjKmxNCSFEopsjYUJWRlpWTE/JWuddCYX5ZgkjaSDvJ5XH7BDAQJu7cNm7eOvD+\n2GLI9gz5DHspSiGfy9gb1q4eeI2WLFsx4zBiO6xw36QeKh5hz1UEkx0mKKLLPdNK5u2uEYA3D8qe\ns+zTgsVqaK323tv7Ih4vdz9k/W6FQteTZ8/DnWvasw6tKU3YxAj8LpM1z6Lipuh9dRjbeQ4ITjtg\nOK0N6Td8poT0B+ZMEdIQaYUjYnOn7HauQe7LE5LPXXHjnunkFptwS6L7Ckf4yo1L3xJe58ufsufp\n3m/fa8/Pxl6rOzdp75ZNd9d3aGpqRkl0dz3uHGOEr68IRdG8uDy5T6HiF6NAKGeqylAtuy9fv10K\nC+vSXKpiFNdECBkdmDNFRpqmQ4LyjBfyULmhfW7Yns9r4Yb3udeB4bA48QDZ7ez/Nm3TQ+Fv9rhS\nEtwuoe7mXUkfbhlzaSvXfZ4VEWqul2ty185BKKGE/Ln4hIv0Y3uFJGxPRJI9zqZteuD9sg8Odku3\np+UvyTU3r8snYtycsxCu6MojiELvhNt31thFrxchtk/38F4hr/Gd9k1/VthX2/lIoYOHR6XMdpVC\n6jnnxYXDNkFX97sLcG/IKEHPFCEB6vqG3zb8Y8ZwPRxuRTv780NTUzPKhougkDHFqPd5q0JlvgEM\nlVD34Xpwdl+5A+dcvm+oWqC7brcy3uSunZievmVQYt1uJ4UfAAz1KWux1+p+JuGQsjfuGt39dvfE\n51UMeZxiqPLdyttXXzxX9jxvc/qxtXkvivZbx3xi+6zbk+P237W9J4SQJqFnipCC5AmxytPGrqSX\nZsy7OTvSXirk2ca9eIIkF8ldx6GpqaFiCnYo3eSunZjctXMgruycIlswSN/STv49eGD/UNU+Ycmy\nFdiw1lTxkznI+kTczJk3HwAGlfzsCn9rVi7E5K6dWLNy4UBwSdEM6VPGFsS7JX3Y+ydnVIlHyhaW\nvjwz2St3L92ffeLSV5yiqHfHzonz9ZP1nrrzKyOkynio8t5rz/PG9RfXZnTb4X15vi2376t6LjHt\nmvxmP+/ex3qHui6kyu4xc9sIGQ8opggpSYxx6uY4+XBDxWxD3g7Jk7Ax2wtjj+Nes8WZW2RBQuJE\nnKxbpQYhcnZ/doU/6Xf3lTsAYCCI7OvSv4i0DWtXDwpe7L5yB044agEWL10+EFgS1rdm5UKcc/k+\nLFhsxM/suXOHvEnys9w3MTFraE6yTjdMUPbSLgMv48peyd5s2b53ECbpK0rhhmeGQgLte9Pyp7LE\nlp3b5d7rjucjrUJgnn5CY4fa5xV9dZBXHOU9P0ru6zM+g7/sms49Pj2ktS+U3Qf3/jIhmoSQ7kIx\nRYiHOnJEfIJKvCTATC+Ie66TGOeu58X2rtj92UjFOntcERSHpqaGDG67kISUMAfMuVMnHLVgUH58\nzrz5Ay+XXBeDWbxbdqlxuXdiYhbmzJs/OBRYvFJ2lT8Z3w5NtM+Pmp6+BRvWrsbipcux+8odODQ1\nNRCC9v6EKgHaYlIEkfxu76nvuYmI9OWryd7afdttfe+AXT4+r5fTHc933Z1rFj6hF+o3re+Y8NW8\n5DVA6xQ6RYRXle1D45fdozL9dlUgtJnjFqLvIpwQchiKKUIciuSUpHkXfAUlbKHkiii5R7wmNu68\nbDEDDIe52fe75c9tgbZ46fKB2LBD/cQ7JONs2qaxZNmKQdGGBYsVTjhqwWBc4eCB/Zg9dy7WrFw4\nVDpd/pXDe6enbxn0u2CxGoQ/bli7euDR2jepByLMXt/ExCzsm9SDcUVESXl2ud/eG/tsK9vDZD8L\nWY89H7lHhKzkbtkCz35u9t7KZ753wH6GoWt2v/bzs3HvKeoBKivC8pK377yhfm0Z9r5x6z5XKlSg\nwr0Wuycxawi16aKgalq4FB2vyLMihLQPxRQhDq6RF5Pz4nog3Hwj917fOUhZ/bnt7dwpADM8O3ao\nnV1Ewc5vskMKbSEg4XQi9uy1SG6WFJiwx5U8KBEx4pmStvZc58ybP5QPZIsTOfj30NTUIIxQxJ0I\nQDkjSs61sgtxSKiiiKFN2/QM75wbTrlh7Wqcc/m+GSLIV+xDPpd12mO72KGSIc9hFvZ74BPfVdB2\nUYoqvMFpoiKrfdFxXJow3N3x08a0r/3sjwej+o9ZQ6hN1r1ViYQmxUZTY6WV5yeEdBdW8yNjTZ2V\nzXx9p40n1+xqf3nuBWZ6vnyhg77PpAqg4MvvkfHtynqSb3RoamrgcbIr8klInF3VDzhceXDL9r1D\nFf3sfCY798s+g8pF5nfCUQsGoYf2fW47t5piaL322VdulUNfRUZfPpQ7Xgz2cw69Q75xYvoL/Z6n\nv7S+q8TXd+jMqb7SZaNZ5lbHuVtp93d5Twgh40lWNT+KKUI8FDUSswxht12sIesWMsjqzz0w1xYc\nbtnxNSsXDjw5vgNzbVEiIgjAoDS5LcTkXjlLSsLvRLABh71VIspscbZm5cIhIXbO5fu8pdxdgbVv\nUuPggf1D7Q8e2D/wasmc7DXbh/qmlUDPI4Z8YtV3zSdobNGWRsy7Gfv+lhFSbWELqjyG9yga6X1f\nU1Nl6fu+T4SQdqGYIqQCynwDH6rgllUZzefhADBDJIUM+JDAEnEg+UZydpR7TpTrdbELQsh87Lm4\nAs4VOvZ5Uj6Pzu4rd2DOvPkDcbb7yh1Dwk+u254n+1wtV0TZa/XNz7fPkm/lVtAL7a3sQ0hspYmr\nvELa/dlH1nh5vJ1F2uWlSL9Z3qmuGc5dm0/f4X42C/ebEJ4zRUgllDEk7apusW2lLDcwLKJEINjl\nx32hb65QkXZ2sQXxJkn+lZzhJH2vWblwaB4yjj2fE45aMAjlkz6k/PmhqSlM7to58EhJv3LWkxSX\nkGvnXL4PBw/sH8x3YmLW4JypyV07B0LLzv0S0SOer0NTU5iYmDW0VlmvPXfffgn2vqTl8cg1+9ww\n+Vzyo0TYhApVpM3Dnbv7s699KBevbGhfXZ4rXz5hWaoowFAlNESLk7fIRZ8rDnYVvr+EZEMxRVqn\namOqi6R5Q3ztbAPdFUu2qLJzeOR+EVsiWATxzNilw+UgW/vAXBFFkgMFmCIWu6/cMfA2iQiaM2/+\nUCid3D9n3vyBKJk9dy6WLFuBQ1NTOOGoBUMhhTIPGXPOvPmDEutbtu/F9PQtg3OwpC8RWHJeldwv\n7ULnMtlCQ0qx2x4yV6SmFf9Ie4b287Ofp29OoTFCxL5HLmntbMGVNZ79e56/27S2eYXajesvztXe\nNgb7ZBiOYoGFrLFjC2tkfd50gZC+C7S+z5+QtqGYGmO6ImL6lK9RlBhjNvR5lsHtVnizBZOEn9kl\n00WESQieVMYTQTa5a+fgMwkFnJ6+ZahK35x58wfhbZu26YE3Ss6NcvuWCntLlq3AwQP7B96sJctW\nYNM2PThzyvZ+SS6VlDw/NDU1dKbVxMSsgTfLLTax+8odQ2GL7pplb+Q8LPndrnQoe+HudWzulDy7\nrOdr95nVd5ZnKgZ3DWlju+9empcsdrxRpyrDtEnh5xvLt446jO4iYjdrHn0phd4V+j5/QtqGOVOE\nVExa3oqPPDlVAGZUxfNVmgMwI1fIznFy75Px7XvtKn22EFuwWA0Vhzh4YD8mJmYNBNehqamhn21P\nkczdHUvEFIBBWODsuXMHuVYiaiSXCjCCTvKj7GIUbn6Rmwfl5qWJaMt6DnZulJsv1pV8oqZpq4DF\nqFX1i2Uc81eqWDOrB85kXNdNSBGYM0VIwxQ5B8g1Ru3f7RC+DWtXD0TBxs3DB/7a4Wy+Sny+4gvS\nRsYR74x4srZs3zsQNycctWAgWqTUuTB77tyBF2nL9r2DAhDT07cMDu+V0EE7D+vggf2D/C3xqE1P\n3zLwaElVP/e8qokJ8z9dkh8FYGieLnYelOyTrHvJshVDuWLSTvbdXqdge7Vs71NMjlVMW2CmV6jI\nGEWJuV/2sgkh5c4nFOqX13PSt/CmMof5Vkkd53OFqMLgjz2HK0Tf3pMYKKQIqQ56pggpQJ1eg7yl\nr0N5ND7PjPxsh9QBwyXNXe+T9CXeLPtcKPHsiBfKPmtKPE1SstyuyCceJfua7QmT+8TrJJ4p6d+u\n5Cdhib6qg67g8xXrkHX4qhO6e+Qr8BAKvYupwBfzrPN4OvO+O01QZLyYe9K8U2lnJOWhjjOW2qTP\ncyfl4LMnpDgsjU7Gnq6HS/kqraXlqgCHvUj2NVtU2aF9dvlwewwRIXLIriAC6oSjFmBiYtbgPClB\nPECAESj2uVMiluy2Io5EiElIoJQ994kxwc6tsn+XeYngc8WYXYpd1mkLRnuf7T12QwBdYeUWrEgL\n9YspRd5WmFxRYtbU1PhVhvpVZWhm9VP3OKMm/gghpAswzI+MPV03VGPLQ9shXFItTu61K/fZHpUF\ni9VAMK1bpQaFGABTVGLj5q2DwhASPrdh7eqhynriFZJCEIKE4i1eunwQRnfO5ftwaGoKBw/sH+RL\n2eF351y+b1A8Qj6Tn2WMyV07B/8tWbYCExOzBtX3RKhJ2J+E6NlrlWqCsj9btu/F5K6dgxBG2UMJ\nN5zctXPoHXEr+rkFGuQ++5Bf3zMMFXRwC0/4xFWIKorG2AU28o6RVqSizHzSxnUFbl5iQrRssVEm\npCtLtFQVUpanyh2FVHFGMbyPEFI9FFOEtIjPgEzLn0rzAojAkup6NpKTBGBQeU88OiJ2JEdIyqSL\n+BChJDlP0p8rQPZNaqxZuXBQfGL23LnYsHb14P458+YPxps9d+7gUF7xbk1P34INa1cPecmkCqDM\n5dDUFObMm48TjlowGF9Ku4swkbWfcNSCoTwztzKfCCu3lLp95pbsue3NsisO+kL50sqHu5X7XGGT\nJayrqOaXleeUp0pfFdjevqLzqbIUdlb7ug3sJsVPmfOaukbdlQYJISQExRQhDeAzPLNKYvs8HWJ4\n2h4mGxEQ9vlQIjDk803bNCYmZg0Ow7Ur5Ilwscukb9y8dcjrIyFzcmDv5K6dOHhg/+CsJ+BwKJ0I\nmAWL1eBAXlu82GdEASaMT8IABfdcrENTU0OhfSLWZP3SfsmyFQORtW6VGvRpi0a5ZosaO98rZOTH\n5ECF7nXFkyuWXSEcIhQy2BQx72wWaSGSsTRt+KdVhesTaeF/bQvKIoxrIQlCSPtQTBHSADFFAdLu\nc0OhpAiDCANB8oREPIlwcQsYyOdbtu8dhM/ZVQAlP2jx0uWDwhGCbeyLBwrAQFQBhyv/yef7JvUg\nXE/mbM9VyqtLLpQc+nvwwP5BKJ4Uoli8dPngsF7xwh08sB/rVqmB90s+F2G4advhEEFZm5wpZYs1\nCQu089FCYXmbtumBByskit3nah8U7EPmZd8fG5JXhdcqhrxhgGmU8Ua9/op5AOoz/NdftAfPOS++\nMmebImv9RXtyj1NGfPTVYxN7nhYhhOSBYoqQHBTNNfFhG4z2WUa+b+ttAeOWQLdD28RYF4NcqtnZ\noks8W7PnzsWalQsH/axbZQSaiJ91q5Q3nwg4HCInB+kuWbZiKI/pnMv3DUL0pP309C2DfCoRXbbH\navbcuYPwPxlL+pF12zlcwGHRZgsl8ZrZ+V2yXzbSr4ghyYFyPT72zyKe5BnZXjEXX9ifzCHNq+OO\nHfI+pYXrVVUUIiYMtS3seZQtfR2679zj/dUf8/ZTN6cft6gWz0xfRVMexmGNhJB6oZginaHpMKUi\n5M01ybsm1+AXEeQzwu2QMPs+O5cn5JmS32fPnTsQUZKXJJ6Zgwf2DzxTc+bNHyriIN4sET+7r9wx\nFD53wlELhkSTzEeKXUh/GzdvxcTErIEY2rRND4pYSO7SwQP7MT19y6D/Ldv3Ds60Oufyfdh95Q4c\nPLB/kDMGYKiioHigfM9ChKPvui227LFDBSfkd/tcMJ+g8pEmfkLep7TcrKoET5vCKeZvp4tehabn\nlGe8LgqHLj5DQgjJA0ujE9ICafk0WZ/7jHT7d/vcJTHs7RA5yUvasn3voMS6CAepwGeXVAcwJuDT\n9wAAIABJREFUKJ9ue4BkXDkk1z43yi5EISF8dhl2tzz6V9//Juy4+ie46vIfAMBQezkfS9YzZ84c\nPP1pT8PjT944EJTueVD2eu0CB3Y/djW+mGcke5VWMMFuY/fjUkUFvCqFTsw+VD1mVVRZIp2QtmEp\ne0K6B0ujE9IwMaGAPmPbNlrT+rJDAu1QL7nfPlNJKuABxiu0b1IPKugBRjzZ+VCz584dhM6JmNl9\n5Y6hg3SlPym3vmTZikH+05x58wdV/5YsWzEQUtPTtwwVdgCMYJrctRMXnrkBn//85/C+jRswZ86c\noQIUi5cuH4Qqrlm5EIvvd39ctecXOHvTZ3DZuR/D7it34NDU1MDDZCNepDUrF87Yb/F62d4n+c8N\niZRnYHu+QkLKrvpnPy/3vyzs+fgoW3gijycrbc1Fxqr6/lHxbNjrKLOmUdmPKnH3pMt7RCFFSP+g\nZ4qQGinzbX7II+B6SADMuG4b9bbnRrwyCxarwQG7Pq+O/GsfwisH4bp5UzIP23tle7hsT40II6ka\nOLlrJ2bPPgL/9pWv4IlPeAJ+8ccDOO6Yx2Dy6isGhw6Lt2vd3y75/+3d34te233f8e8cp9JIDoRA\ncR2cQcfMSaaNlKiSrhqREALHOY5AKTkOSUbHapX2ItCLtBcndxWpUgj00Jv8A0NVpMbkxHYESoxN\nwdiopQRJiEoNQzX4iMGUUprEhkgzCj3Ti8l3z/dZs9Zea+299s/n/QIz0vM8e+/17FHC/pzvWt8l\nO//7L+UzP3RK7t+/L79983fl9evXC5vy6pi1smXHpw0r7H3RcYbuu3tvNy+uVffAnToZ+z3GXmsr\ndB2RvCA01gpUiFam2LC2jF/9w+0ia8Xa6uP3yb8PACmoTAEDatrFr+5Y+xAfanJg90nSgKGVJBGp\n9nhSWp3af/WyavCgHfj0nG+duyA7z55UlSldK6Xd/uy0Pp2at//qpTx/+liuXTojmxfXqg1+NbCt\nnz0vaz/2E/Jrv74p9+/fl8/80Cn5d7f+taz87X/k2X/1Uk6eOi2/+fM/JV/56lflMz90Sr77vVey\n+d4X5c8f/9lCcwq9L3cf7Vav6XooDVpaedK/6zhte3Q3SNm1UHcf7Tbumtf1msCcFu655xmzv37/\nGyLS34a1KVWNKVVCXGMIUiLtf59TuucApo0wBQwoNF3MTvGKPSTfun1Pdp49WXhYt13mdGqfbtyr\njRu069+t2/eqYKVBRLvu6Xm0s5923NMOetruXESqitTdR7vVFMSVlTfk7qPdatrezrMn1dTB/Vcv\nqw2E9/f35Qtf+BW5f/++XLlyRT788EP5jZ/+cTl56rT8h/+yLX/wn+7K5995R775rW/LT7z5GfnU\nj75ZfZ87D1/I5sW1au2Wdiq0FTq9llajlG277mssYf9sK3ix36E91jdNLqXZhO/voddSrj+EMTeV\nSX3Ydj+X8kDvfqar6kdO63YcV+L3QmgDQJgCBhJ60EyZPuaup9FGC77zbj3YXmhxfuPyRvU/d/8o\n27VOO+6JSNWVz35WN9vV6X/6Ge3Ap00odP8nrXLZDXk1lL3e25O9vVfy7rvv/m2g+kX58MMP5eTf\n+QH5we8+kc+/84786de+Jv/iX70v3/+rv5TnTx8fCz4aCDUIvt7bq6pSb527ULWAt+u2bt2+V03z\nc9vSu2ucbCix0wXtmjdf572cQOQGn5xqU9uOfiWCT8o5xhywXGOfBlaiihQKA03D5lj09Xsb878P\nAP0gTAGFpDSesGJ7DcUehjVM+Fp6u8dqgNB9pUSO2pBrFUnD0PrZ89VaJWU30RWRqrqjm+FqderO\nwxdVt74Tq6uysvJGFWzseyJSrT3SroEnT52Wrf/6XDbf+2IVqL73ve/JlStX5Jvf+rb82q9vyv94\n9N+qVuqbF9cWmmDoPVs/e76qhmn1zd6ztfWjipWdDqkNJnybIfvolEANnG4zEDtt0Pe7c89lf/re\n870fapveRF2795xz1P099FrbgKVT/XI0qTCpsQYIK3WMoe+YGhI++Nyb1bWmcF+6xj0Alg9hCigk\npSOaSPo6qdCDrW3vHaqaaKc6d0zrZ89X3fr0YV8DklZ2bt2+V03X06l0upZKw5GILFS1NJxoFUqD\nlwYqHz3+4OBj2d05vMaNyxvyqR99U278s3++8Nl/fPWq7O/vy8lTp6vKlnYl1GBn75cGLL1XGhQ0\nJOm9045/lq8jX124slMqfZWtGBu4QuEjNGWv9BS+0PnrOguWqDT1NRUxdS3THKbPNamYNA0Ceq3Y\nNUsHjTEGFypVwPKhmx9QUMlOaKFz2SYJ2snO8nX2s+fS47TbnsjReiDb4c/tjnfn4YuFbnwiR8HN\n7h2l0/Z0CqCIVJUqZVum609dr/UbP/3j8uWvfFk+/8471efv378v7777rsgbn1gYr+3kp+Nx79Xz\np4+rCpxv3ZPeF+3Up0HV7rMlcjS10Q28vrAT6qwX6gLo/n5Khou+1k6ljDv1uzUZM/tNoSup0z3H\nPi0UQDN08wN61OaBNbbmRd+3Hey0suLusSTiD1K2IqOBQVuUixyGHp3utvVguwpXut+TBiINJRqu\nbMVIQ4idJnhw8LG83tur1jOJSDW1TwPL5sU1+c2f/yn58MMPqzbpF/7Rz8o3v/VtuXLlinzlq1+V\nkydPVtfe3dmWE6ur1Xez1Tbbte/kqdMLQUqbXug91ECpYU7kMJTZkKav2eCk4Ujvqd2zKlRhSmli\n0aYDZIhv/Vao2pS77ikn+ORUaHP/b6nJVD90Y4wVo1S+sedMeQSwfKhMYXamtk9OKb59qUL34tql\nMwuVJhGp2oTbwKChQ6cA6nQ8u7Guvq/viSxWol7v7VUVKxGp9o3SqpG2U/+Dx7vyg999IleuXJH7\n9+/L5ntfPJzad/Kk/PEf/7H83M/+jHzzW9+Wz//CL8jBysqx/at0/HpOuy7KVsU0YLr7b21eXFuo\nYPn2pbLh1IaIzYtrC9f0/V5EZCHU1TWSSA1Uff077+P/pkpc43fe/rFqDQ8PtgCAEmKVKcIU0KGc\nB8Tch0lfeFLuxr7uFDV9zW7Eq3s6icixP/um6d19tCvXLp2pKji6dklDlg1cGph0ryr39ZMnT5oN\nef9ENt97T/b396vg8W//4z35e9//jvzcz/6M/OnXvibf//Q5+Sc/vVFdU9dO2emH9jvqeLRzn4YR\nbfeun1e+qZJKg5vvdxb6nTQJCqUD0zL8Rwam+o2DG2YJtwCmjGl+wIBS21iHPhubCuVWSZRvY1+3\n4qRtybWTn1af1tY3qlbiIoeBx07vW1vfkJWVN6pNeHX62sHBx7J+9nzVbc/S8z9/+lhOrK4uBLXV\n1VPyR3/05SpIfeELX5Dv/9VfLmwg/OsXz8gv/dIvyf37fyKff+cd+cHvPpH1f/CTC+HJbUax8+xJ\nFQrtVMVrl85U0/K0wYbeH/2fbZNup+bdvH51YR2Vr2GD/k5sp8Ccfwf2XKGpnjnnCY21jdD0wCm1\nPY8Z21S1sY2nTl/7bPVlSvceQP8IU0APUvYA8j2MhtpV68N+She4m9evytaDo7U9Op1NXbt0ptrE\nV+RwKp+2HNeqztr6YRVI31P6OT32+dPHsn72fFUl0mDldvTTboEnT52W//znu3Llyi/K/ft/Iu++\n+8uyv79fBbv1s+erqYb7+/vy7ru/XG3s+w///np1nc2La9U6JzsOPY8GpvWz56vP2MqdXWflvudO\nmXR/J27XPw1jWrGrC0C57chDgaiLoJQ7jibrnMYsNQD09aDdRSCZYkgYYsxTD4MAukWYAnqQujg/\n9JCq9AF868F29aAe23tIP2crTfoZDSA3Lm9U66fW1jfkzsMX1T5Uun7ozsMXVbtzkcNpcxqc9Hx3\nH+1W1Sc9j8jhNDxdy6QNKvR6lz/7d+Wf3vgNeffdX5at//pcTqyuys6zJ1XVTK+z9WBbXr/+G/nt\nm78r7733Rfmjr3x1YZx2/FoFc7vn2Xtp94fSn3Y9U5MOe3YPL/vT5Wtrb6tYQ1Z4fAF9ThWnOk0e\n1Kf8oF167H0EnSnfbwDzRJgCRi60lsduMmtDlX0Y1/U+Ou1M11LtPHuy8NC8/+plFUx2dw7bnW9e\nXJMblzeqKYFa0Vpb35ATq6tV5cWGH23EsP/qpTx/+nihwYVWiUQOK0d2bdUffOlL8vr131QdBrWt\n+ubFtSq8bV5cExGRnT//7/Lhl79c7Uul90g/d+v24Ua6dx/tevegUu50Pfczel9t8An9Xnx/D4Ul\n/d256jr61XXdiwWdEkFoDBWnlO/RtqNfyQf1visofV4vdK0xB50u788UK3wAyiFMAQNJbUsdmgpm\nq0tue3Q9xnaj07Akcnyj2bfOXajWE4kcVpI0/Nif7pQ+EammAurP13t78ta5C1W79P1XL6uKjwal\nu4925a1zF461Ubd7O+lURHft1v6rl9U1bCXJti7Xapv+9N1fX8XPBlNtDe+umdJj3d9LTkXJnRpY\n9747Vjte33uu3KYmY9V1oCv9QNx3sOjzemMOTSFdjnmK9wNAOXTzAybE1yXOclt22/e1456GFbvR\nrd2kVj9nm05o1zuRxQ13dTqdVqOUvq50zZQ95/Onj6vufvqe23lPw53t/mfHq9MIbSDxtTO37c71\n+u49C3VG1PPbapL9uz3Wbafum8IXEhvD0MYwvtRrN+nqR8e5RVO6H1MaK4DpoZsfMCO+akjoz24V\nZevBdhU+RI4689kgsvPsycJaJ5GjKXnuhrMrK2/I7s62rKy8UXX4EzmqKK2svFH9T+SoGqZd+k6e\nOi27O9tycPBxFbK0omUrZ7aRhI5X127dvH5VNi+uVQ0yth5sy+bFtYWqnb0nvvVTWm2z0+bcatHW\ng+0q2GmQciteofPbxhUuX2WraVDJrSrlfj61Elbqer7juwhxWpEKPYynVKz6mObV91SyKYWTKY0V\nwPwQpoCeNHmYTGmh7j6o+x56batv97itB9tVa3ENMW6YsK3MRQ43+NWGFCJStRlfWXmjaj2u1aQT\nq6tVAwr9/MHBx/J6b69qz762vlFNq9PPaOt1kcNAp6Fs68F21chC10aJSDWlT0Oedhp0p9TpdXSc\n9j7r331T+zS8Kbelep1Q9z/fVMPYMSG+tVkpn48pNfWvbWONrqphsQdx3QS4zTl8csNRH4GBtT8A\nkI8wBXQkpR16TMoDsvvQ7X7WtyZHm0zo+1rtef70sew8e1JVeXx07dTN61er6X7aNl0rTNpu3Va0\ntLqk4UfDlh574/JhFz67Rkun5WmY03Fr+LMBR6fYaVVt/ez5aoqiuynvW+cuVEFN11rp/dvd2T72\nu9I1VDoe957aRhj2p0g47Lrnr5sSmBPWSoeOkufrY3rgzetXWzeicHXRmIJqCgDMA2umgBHxTWWq\nm94UWsfieyDX13aePZH1s+ernxpCNi+uycrKG3JidbUKUrrWSOQwrGjVSNdEuWujNAjZZhiv9/aq\n9VV2jyqRw0B199GubF5cWzinXd/lW+ul47j7aHdhjNq1UOSwembf0/dT1pnt7mwvnDN0X+vWQrnn\nzKkEpXy26TnHuiartCbrpsZgzOt/xjw2AOgKa6aACYntM+V7z/dwbB+c7UO9hhmdlqd/v3n9qtx9\ntFsFFj1GA4VOv9OwtPPsSTWdTp08dbo63/6rl7L1YLta27Sy8sZCowm1svJGFeL0XPq5g4OPq+9x\nYnW16ranYzp56nRVEdLvplMDNUiF7plvjyn7vfU1N0jZ7xtrBqLnSPmce4wVaoGeE4hyqlwoL2f6\nXMq0wqGMeWxNzOm7ABgOYQqYMH1Q97X/1jBgKy/rZ89Xladbt+9Vf9aQYMOCiFTT/izdRFfbnq+s\nvCFvnbtQVbO0UnXt0hl569yFKlBpyNG/a+MJPZ+un7LX0e+l+1y93turugfqOisNV7ZRxbVLZ6o9\npnZ3tqupe9qcwgY6t7qk68h0KqN939dgItSYIfa6vbZvembsPO45fOdMkbs2K0fsfF22Ynen+oUe\nnMe40eyYqz9jHluuOX0XAMMhTAEj4XuoTX3YtGuCQtMEbcc5nQq3tr6xcC0bTjQMaUi5+2i3qmhp\noFG6lknDkYhUla+VlTeq9VhahdIql7Y318CjUwJ1DHaPKj2nve7mxbWFKpP97utnz1ff5/XeXlV9\n0+qWndJnG1XUVYBiUy5zXrfntmNv2mUv9PfUMYSObSN2vpzrpf7fROj90IPz3B6oS4ZDKjcAEEeY\nAkYi1pHPCu0lFfuzVkB0Op/bvc43Ne7508dV9Uv3h9LKkLYx1zVMtq3686ePqz/bAKRVIQ1mWnES\nkYXuffo5nd538tTpahNd3TNKw961S2eq76bH2gCpgU0Do70nej63kuc24MidnqfH2BbsTapFoddj\nTUn0e/S5Rio0tTJVXWBKbc2+7NMYS4bDuQVNAOgCDSiACXE3jw1xH+J9G8yKLE7r06lt+tlrl87I\nnYcv5Ob1q7Lz7IkcHHxcVZ20oYQ2g7Cb9mrb8hOrqwsb/mpgOjj4uApjvnVUdiwistCIQt/ThhXu\nxsMaiuyf7b2zFSmlDTjqmnb4zhm6x+57OWJd/UoHo5TzdRXG+gp5U21EAQAYBxpQADMSClL64G0b\nMujrdiqf2xBB1x1pmPBVqLTl+VvnLsiJ1VW58/BFVWl6vbdXrYvSDXp1jFsPtr3d+/Q4XVt1cPBx\nNQaRw6l9tjJ05+GLajNhXTvl+/5uxcm9N+6903vgroOyFSQ7/c4NYjYI6P27ef1qtQmwe33fmEKd\nBfV3lNI1sO46da/HzpfzmdTr+c6bO60x9Pudm7lMsZvL9wCAEMIUMGG+h2xfGLAhwdcK3A0Aul+T\nDSm2Jbn+XafTnTx1uqoubV5ckxOrq9VPt+okclSJ0p+615WGM33t9d7esWB38tTp6jP7r15WY/I1\ninArPbqRb6gLogZKnRKY8ztwr21fs5+NTYVz136556g7zl6nbg1d3d/b6KPa1OTe9KVkcJjLFLu5\nfA8ACCFMAQNo+gAbqmQobbjge8C0m9OKSBUYNADYzoA3r1+tKkAaMDTo6HQ3bX9u1x7pmicRWei4\nJyLVNEG7fso2l9BpgLa9unb/0/HqMc+fPq6ClT2f0rHbTXZ1jdba+kYVaGzw1I5/Ou0vtEGvvec2\nPPhCi7vGKRT06jQJP75AZt/z/T3lvE3WjcV0FYaGqFgRHMprElCphgHoE2umgAnIefD2TTFzO9jp\na7r+6O6j3YXjb1zeqDruueuF9D2Ro/VR62fPV++JLG7Uq7RxhZ5X11Lp2qk7D1/I5sW1Y+uv7F5X\nOp5rl85UXQX1NT3Wt0mv/f7aydBuAiyyuGbMrWjZz8R+J6GKlx3T1DSpOMU6H/Z1H0qtmepyw1rf\nuVOvx0a6zeXeO+41sJxYMwXMgC8g+X76qg5aDfG14b526UxV2blxeaNax7S2vlE1eLBBQIOU7i1l\nm0Ncu3SmqozpGqvdnaN1U9q8Qjfo1fVTGqquXTpThTqtYNkgdffRbjXuOw9fyK3b9xbCmohUFSWt\nMl27dObY99f9rkSkaseu989WrdyKn3v/3XudOn0v9rm2Hf9KHOergOaes+6YoQNlkz2nunyI9p07\n9XrL9nA/5FTKZbvXANJQmQJmyK6NClVJtDJlacXHHquVGd0TSuSoM57IYRjSQKVd/PSnsi3V9e+2\nOYVWqLSCZKf02T8rt7ugbanuBiz9rnq8Bhq3g5+uqbJTFX33yL3HdXyVQl83wDGZevc+n995+8e8\nD8JdVxrc81PZAIDpoTIFTFiTDm0ix7v2ue/5GiWISDV1zh7rCxSv9/aqkKOhyd1cV+2/eik7z57I\nW+cuyNaDwyYWeqwbXPZfvazGptPytIufG750jykNf1qpshW4nWdPqnO4HQ7tvdDr272tQvfIDWAi\n4aqUVvZsh76Uznwl5VawuhrXkMFxqADjXtcNVmMzpjGNaSwAUIcwhdGaa8vjHKlNBFy+1tv2gV/X\nDfmOsYHEssFHK1TKBh2dNqdNK7R6tLtzuCZJq1O+vaX0s/r5a5fOyPOnj6tKlE7hUzYg3bh8GLjs\nd7djtuPUUHPt0plqw1/bnt2eU//no+cJVa90eqENbW7TELdRhavt/x0MXf2K/QcB916kHFvi+iJH\n4WaIB/cxVqi6GFPs3obeH+P9AQAfwhRGa+iHwKmJPXhuPdg+1nwhhR6nQUWvo+uTdI3Urdv35ODg\n4+rc2lTC0oD1em/v2BS+a5fOyMHBx7J5cU1uXr9arcc6eeq03Hn4otrDSs+hYcuGK12Hpa+5P3XP\nKvt5O63RtojX76Yhy3b+0/vtTtuzP0X8FSu3YljXeU/ft8e61xi72H8QcO9FyrE+oXty6/Y9+ev3\nv1F7LA/u3YndW1/jDQCYEsIUMBN1D6VKw437vrYS19fdJgx3Hr6QE6urCw0ltCJjj7HrldzAphWr\n9bPnq/Clnj99LCdWV6s9pGwFSZtCrJ89LzevX62aZOi47Ga/J0+drqYl6rEiR5vq2vOIyEJTCxFZ\n2ODYfjedGugGp9jDvp36qMelBoS6pg5N/kPDEAEsVnUrif/4Mg8EWwBTQwMKYMbqHvp9TSpCDSs0\noNjz2TboGtLsOW3rc5fbECLUilzXSul5bBfCOw9fVI0ktHGGPZ8dp217rj999yL1/pVsJKHfxSf0\n+yA4xLm/o3//k6+Sj+2qUQQNKNrjHgLoGw0ogBnJaUhR99Ct09lCoeDG5Y1qLZHIUWXHflb3grId\n/jS8aKVJQ4td66Rd/9wq2ObFtWo6oVaxnj99XFWfdM3V5sW1qpW6G6R0bHbqn4Y2+9OdLueuY7Lc\naXbu+euOTaFjT20vXhekYuOY0vTAttxKbWyqn9XVwzohoDmd/pe7LxQAdI3KFLAkUtuli/j3VXJp\nxUg/r1Pw7Ia8tpqltCqlrdJtIPK1Rtd25boGy1bKdHy20iUi1R5TtuKjm/ra67lBUoOdPZcNTr6K\nlr2m7367n7XVsrZSq1RjrGaVHlPK+WIb+FL1WMT9AAAqUwD+VqibnH1PGy64a6bcbnQihxWV13t7\nVWDa3dmWk6dOy63b96q9pkSOqlK63kqn62mwuXF5owo9GqT0WA1lNkjp57Qb386zJ1XlSq+nQUob\nbty8frW6no5dW8Dbe+IGnOdPH1dNL/Q921TC7dTn3lM3bOnnfOcKCXW48zW/COlq36g26ip6TbYE\nKPEdCQ6LuB8AEEeYAmYk9SE01IFOqyba9GF3Z3vhIdV9ANYQpJUiPU7kaPqaDSK2HbqtWl27dEZu\n3b5X7TWl7+s5tGmE7bRnr+EGIX1d11Xp+HRs7p5UdtqjXZt199HusXuo446FCXvOkJRQFepwl9Jw\nxKfUVL8S4SW39X9qFS4kZ6ofAAApCFPASKU+9PqmmrktvHPaU9u1RTYcha4lsrimauvBUWXJnT4n\ncrSRrb6moUmnvr117kIVgnQcWtlyz7O7sy13Hr4IhpYblzdk/9VL7zopHYdeQ6trKQ/sde3MtYW6\nTidMqR7lTvlLXRul97nu38jc1lGNbTpjW0Ov+xn6+gAwdqyZAmaoxHoUDTduRzt3apxd36R0qp4b\nipRdB2XXVmnb85OnTlchyga1zYtrx6pFdp2XXbdlx6LXt2ur9M/uuia3MYYv6JRc7xM7V2ob9tLq\n1tj1dW33zyXE1k1hGljPBaAvrJkCllCT6VDu3zVEaBVGqxw7z55UVR3dy8n9rG7kqxUaW10SOZx+\np1UlO2YblDRI6bonkcOQ5bLX189ohUnXU+n5dW2V/tlWtNyH9romHHqcrbDVcSuF7rnqjgtVweoq\nSiWqTbl7WqXch9xr51w/ZeqlCFP95oIgBWAsCFPACKU+GKacJyS0FscNCPYcGpJsENFpfG7lym0K\nsfXg8H87z57ItUtnqql1OpVQr3v30W5VPbp5/erCBr/6+rVLZ2Tz4prsPHtSde7T0GWP1emD7ma8\n9v7abn12/Lbjnhu69DgNZTF2qmNKOMj5rGuozn0luhO2UTf1EgCArhCmgBGyD4ZtQlXTxgS+tVK2\nE53uQaVT79wHadvMwQana5fOyInV1apDn4YhO93OTgvU7n92fNr9z1ap1s+eX9hrSoOcbbXuVjt0\nDZYNTFboO+lYbEBz7597X32dAuu4Gx37zh1rbrEslvUetF3LNPW1UFMfP4D5IEwBI9B1y+fYNex1\nbGgKNbKwXfnuPto9Vkmz63xsZWn97PkqKJxYXV0IQCJHTS907ZROB7TjsmyXPw1Idh8nDTD6Xdyp\naPq+baHu3ivbwKGuFbo9zraWDwXj1CYSvu9et46qi5CVM+6+9V2Nev/rH43iQb7tNLe+psl1da+Y\n5gdgLGhAASy5UKOBuulivgd9nU7nnstOi7Mb7brX2nn2RESO2q3bZhLasELZv+ufta26hjt7fdvI\nQsdsP2M3+E2ZJmenN2rLdbeZRY6hpuY1vXbXx/g+m3p8yuc++cHbS9/AYCzffyzjAIAQGlAAE9a0\nCpBzXKjqsfPsifc8odfsNDZfAwE7dc2+b9db6bn0NZ3Kp5vsKm2Hfuv2Pbnz8EU1HfCtcxcWwqFe\ny47NVrr0M24jDHfTYve72uqd/nS/Q46U0Gr/nlLVSl13lxIcc5pmNL1O7LOpx6d+ruQDfF31ZQxV\nLJ+xBJixjAMAmiJMASPWtFrR9GHXBhsNLLabnn7OTgl0w5DI0TQ3d48jkaMgsHlxTUSOQtb62fNV\nMLJVJJ26p5vs7u4crdnSa7jTBUP3QIOVO43PN/0vVOGwlbhYR71Yx8SYUJOQ2O83Nh3RN57Qn+fY\n2MHX0c+GntwAFAoEc6q6jDUUAsDQCFPAkrMPzm7QEJEqqISCgK8ipefR5g/6ng1eWkXSc9v1WO6Y\n7Fh1k9+dZ0+q6XXu9X1BxoYmrUrZLoP2GvpeXSXI11BCrxEKM7alehdSz6tjCLUgrwsIpExuAAAd\n2klEQVRPbcZe16Qj55gu7p8NPaUCUFdByhdscsJOk2A01VBICATQNdZMAfCyne5Ejq890qrV+tnz\nx9ZNiSxuiutbq+T7rF1XpXSjXvc4t9GCPXddgwbfJsK+z4bG7N4T3zVKrvkZcj2Va0xjKWGoDXzn\nVLECgLljzRQwEWPqkuZr5a0tzEWO1h2dWF1d2KdJ95Fy1ynp8fZYXWdkQ5dvo1y7ka+GGXdvKLfj\nn90g2KWbCNspc/rZ0NogDVSv9/a8Ac49NqfRgstt3JHana+Pfz9zClJDIkgBwHwQpoCRGPpBNbVZ\ngQYtncbnTg2065d8XQJtRz89h12bZafBaTMK7fan19K9rvQa7l5U2mLdsuui3CYTOiY7tc9W2/Tv\ntlGFT909zG2qYEOVG7B8a7HaBLg2x4wl6PUhNmXMfZ8pZgAwf0zzA5ZEqXbWdVPo7Ht2Slxo+l1s\nSpuewzflLnSM/awGrhOrq8dCnzulUKcxikiwzbn7nWL3wTfm0HdJ+f3khibf/co5dkhtxpB6bNfT\n/JjOBwDTF5vmR5gC4JXyQGrDTt3aoVhosPs82fPaNVl2rZR7Xf2cyPG267YJhq9pROz71wU8X6jT\n79JVICl13ibTElPOOVQIa3rtodZNTRHhEMAyioWpH+h9RABGL7Wxgg0VdQ+zsU5xbpDSKXoaSkQW\nG0bsv3opIkfT8k6srh6r/Oj4LDe0+ehnQt9fN+m1bMXKF+p86u5X6v1PrWb5xtNF6BmymjV0Jc1n\nbuFjTt8FAEphzRSAY1JCkfuaW70Jsfsz6U/7mjv1bufZk+r8WmGyTSl0zZRd22TbkuvrtmLksuu0\n6sKWnlPXiqnQhsTud/OdyzcW2zyjroKUGthiUxGXmW/PqVL6bI3e5DNTNvfvB2A6CFMAirPhwg1O\nbic9keMByr6vAchdP+WeX/+sn3Wtnz1fu2dRLHDYUORb/1R3bA7talh37pxzuWMJnbfu3kzF0ONt\n+4CfenxKSJt7FWnu3w/AdBCmAHgftkOfiXV2cysrym0z7mtDbtcduRWZtfWN6ty2iuVeyw0jofVN\nPm4Is1UdbeXuG7OPrWLZ7oF6fd898B2fKuVcqVMxm46hb75/Q0P64HNvtgpUBAQAmB7CFIDomiYV\n+y//Gj600uROMbMBwgYR9zW7EbB9T89tj9Vrhfa18n0nN9yEKmTu93UDkT02FqrsuX1TI5tIaSLR\nRaOJZRYLSwQiAFguhCkASdy1SCG+qXZuIHI/7waf2LQ5W/1ypwX6pv25x9uGEb7xuGuNfOu5XLlB\npWnlyRcCc64Tq4hNSalw6K6bKrUe5/2vf8TaHgCYOVqjA4gq2Y47t4NdyrluXN6Q13t7CxUtFerg\nZwOVnk8kvP4pNu7c71a6jXhqB8CSuvx30Xeb9aFapM+t4x8AzE2sNTqVKQBRpdbvhKb8+a7ha/4Q\nOu/a+sax9up6Dm1V7l7XbWZhq1C2muVWwdyKm74XWpMVWmsWCj65jSBijSpSf3dNqlUp546dNxSa\nYtMnU6WsB/Tpq6JEkAKAaSNMAUiS8yCa+gCf0sBB5KiJQ2gd1M6zJwvT4Gw3Qd95YxUc9zra/MKu\nB7Pv2XP4jrdt3fVzviYUsWmUvntVqnpTcv1W7HM567jajit1PaCLkAMASME0PwCtdTElK7UNeV1l\nRqfyuVP6YtdKGZf9vO/8qfckNrYc9prun+14m56n7dhyxtAX/X5dTPPLncLHlD8AGB+m+QHoXEo1\nJbflulZpQs0r3E163fOJHG8YYatX9s++6YfueOoqSHodOw3QrbqFNjLODVKp7dTdMFQ3jdL9e9Nq\nTp2U5iWlpVTLdEwpm/fmTv3LDUYEKQCYHsIUgE6lTuMKhY/QOitfULLNIPQ1+1ndo8qGH52mZ8fh\nBrm672OvpdP+fGPXNVq+DoMp64p8189ZjxRbs5V73pTXhlY6vMXCDp37AGD5EKaAGen7gbb0Oiob\nYHzrnuqqG/Y996c9pz1+d+f4RryW3SA4Zx2YhjUNVW4VLTROO1b3nKHx+cTGHPu91a1lC503dswY\nw1ZpVJYAYPkQpoAZ6XsaVRfXy22Prj+bdI3TwOZ26VO+KXg3r1+Va5fOHBuvrwKk549N5Ys1ZAhV\nsELnreuOF3o/5RxtjhnbWimflKl+OahUAcD8EaYAtJI6JSy2Zsr9c2yNj34m1CBBA4+vkYVdR+RO\nBXTH45sOp23Yfd/NDS2hoBZqy+7jq2DVfd5+z6Zyj12GylOuLitVBDUAGAe6+QHoTBdd/txzN71G\n6LiUKXLutDxfyEnphlfXfa/Ud+uri17JroSpuvj3NdTmvaV12RmQroMAlgnd/AAMpssgZa+R08jB\nVwmr69SXUqmqu3bK1LdQc4icVuahMca66NV19cvRd5ASaf7vq+47lp7qN5Quww5BCgCOEKYAFJEy\njS/l2FCnuLqOdjZ86JS60LRAt2ufez7fMe6ffdfwTe+r+56hcTXhtpDPPbbu7z6hNu+usU79m8L6\nrbFhWiEA+BGmAPQiZ3+kUKXGPsSHgpBWSHwt1d0QFKpohf5uuwL6ptLZ68SaQNRdL/Z+Tse/1Gvk\nSK1Clfq+fRprAOxDXWBi/RcA+LFmCkBRKeuF+lhLlXuMyGGr8dSgENrDyVc1yx1Tl/dniOt0JbZe\nrY25rJ0CALTDmikAvUppvNBkLVDu9UXSpqPZzn51Qcq3Lim0f5T9jDumlO+UW9Fpur9TF0GqRGUn\n9fs02e8ql62aUEEBALgIUwAWlHoQreuUl3rtphUmlVJlCjWBqFvzpMfUnd83NdG+7l4jJSiFjq1r\nmZ7TTj0kZZyh6zW5Vtv9qkqGRDu9bWqNFwh/ANA9whSABV1O+4qdu82Df6wFecrrdu1TzpqnOqGN\nf911Ve65bdXLp+nvqcnaqjYNLmLaBuiu1HX1GzqkpF5fw9/Q4wWAOSNMATPWtMo0lkX4+hDfZmqc\n+56viUWs/bl7jlgDC8s2vrDn0pB14/KG9ztuPdjOntaXE3hyW7l3FXL6aJ/f5nhfECldocoNO7nX\nn1pFDQCmhAYUAAbRZ3MK95xu84icxhN1fBvXxpokpHzfnM9MvanEmNQ1oZjSxrVTGisAjA0NKABk\n6asqlbr+J0XKOp66PaG0CuTbPypUhfJVpWy1Sd/ztWj3NUloe99zN/pNlbNfVuq5mjbMaHK+ruSE\nk6Gn2RGkAKA7VKYAdCa1SpJTVfFVf3zXK1Gpcc9RV+GKnaMLpc/dtK18zj2ZUuUs1h69acWn60rR\nWMcFAFNEZQrAYFKbHaRUVfSzKR309M8pD/Ypr4XG1zZI5TbGKC21+UNKZ8PYOVLfn5KmwaPrwDLW\ncQHAHBGmAAymy4qNr2Jif4au7wtk7jlC19Sfvk58qcf6AlhoHKG27qli7evtdcbada9LdR39XENP\n5QMADINpfgDgyJmKltpBLzbtMPZ63fRGO46UsYxFrDnHGMSm+rXxq3+4LV/6lfjG0k0xbQ8A2mOa\nH4BJGLIde86mtK5bt+95Kze+z9mfsUqPG9LW1o83x/CNo4sgktqevoQme421aZLR9nu1qUgRpABg\n+ghTABoruY9VmxAQG0dq0GkzjpRAFbpmzufr1jW1CYWhc+o1c79fynlT3ku5T202WI59LjbVb6yB\nZehxMe0RwLIgTAForE3wSJXyAJ/S9CA3CNRVY0KVkLo1UrnVk9h6ppTPl1bqGnXBcOgpfin/TlKD\nQp+Bosm1uhzf0GEOAPpCmAIwaqkdAVPOk1PB8k2Z862PSu1ql7sGq0n1zldh01DYpKqU092vFG3e\nkaKvqYdudSo1KPQZKJpci8ADAO3RgALA6KQ2dSh9zSH2g2p63VLjHUPDhyb7dzU5b+p7Pl02ogAA\njBcNKAA0Vlcl6VJXjRRE6hs4dHFee+4m1aauxptznjYNHpqMoc0my+55QlW5oQMkAGAeCFMAguza\nliG77ZXSpOKREiRSQ6fvPsbWZrXdSyqn2UNIF2ubuugQGBrn2IJTF2uVaPgAAMMgTAFIMrYH0pAu\nmzTYzXh966tioTMU5urapdet00pZA1b3nXPXcZXURchJafrhvpf63XI28I1hrRIAzAdhCkCSNg/U\nfVa1mq4/cn/a4KSvuZvmhpolpEzN84Wk0Aa2oXbhOQ0p6qS2jh97dTI0XbBuml9u10RrTNUgAhoA\nDIMGFAAQMFRzhth1b1zeOBbsmmhynpR7MsR9S21i0XZsn/zg7WpD3JSNccewee4YxgAAU0UDCgBL\noaupaKXPnzL1rO5h31cha3p9PU/uZsM50+m6/FzduOzf3ddzuePRYJISUMYQYsYwBgCYK8IUgFHK\nbZwQCyCxc8a0mQ4We5jXZgyh8NY2DFgpzSRimxU33VMrdexdbQ7c9Pc/9HrBMU0nBAAsIkwBGKUS\njRPcz5dquZ07jljzCF8zBtvWu0TjjJzgVxeGYiG3RAUvFCRjYpXEoUPRsiMUApgjwhSAbH02Iujr\nWn1dp67C5Ws0kboPVN34tx5sF6sKxUJu6cDStInGnIJTyjS9KQQVphsCmCMaUACYla6aHwzVjKKL\nMaSszQop1fzCp+77pX73rn9Pn/zg7c7ODQAYHxpQAMg29hbYdVIfuHOk7OfU9hopSm2U26aCFAtS\npdelpbxnr+t+LtS+3j0uVdP9pqZQOQIA5CNMAThm6ApM13K+X0r4CDV1KB2yUkNdXfOKUDOGpuuU\nXEP92wldNxb+2jQWyaGt1F2ELACYNsIUMDFTrhqNTcq9bBMOSnTOs39PbbBQtxFw7HX7XiycpUj9\nfl1qeq3SY/StGSq5HopgBgD9I0wBEzP3qlEbuQ+/Y7iXTTeWza2uNRmH+17qhr0p5411Biwpd7+w\nWPfHlKl+v/qH5daVpTZuqPscQQsAukGYAjAbQ4WjUpvR1jWGaLNXUuzYUDfA1P2jcj8Ta4DRZciK\nVQZLXftLv1K/VqtvdNIDgG4QpgDMSqm9jnL00XY891qqbsNfG2pKNO5Ive8l9oMqNXUvNaSOaXot\nVSYAGA9aowNAhG+9UskqWN/nK9GCvEslxtDH90hpk/7+1z8arCo05LUBYC5ojQ5gsvpqOR7TZLpb\nm/PnCFV26u5TmxbkTceUw1aLmja8SG300bXUMNNFtYkgBQDdI0wBS25M05dcKd3wSuvjfpRcp9Ok\nW19M2zbpsapY3d/tvlDu9ENfd8HUaw9dbYsh+ADANBGmgCXX5CFzzAGsrVIP3XXNHmzlqMvpaE3X\nj6W0Vm8qtq+T7gvlC3SxLntdtWEPHd90A98usI4KAIbBmikAGLkh1jH5rhnrwlfqOnWf6WIMbcaW\nsm4KADBdrJkCRmzOFZ4mpnA/csZY6vuUXseUUrEq3Z7dNw49Z9353EBjp/6lVt6arr3L3VPLZatF\nVI4AYJ6oTAEoZgyd4NSYxlJabjWnyes5969N98C6Y0SaT0Pt83cfqk7RTQ8Apo/KFIDejCm8xNbm\nxIy5SpazH5XbtCG0x1OsY2HT7oAhttFE6JwplbDYPlUpTSu6QpACgPmjMgUAmeZc9XKVrFB1df6h\nfx+smwKA+aIyBQCFdd3xL/WYpnsw5Xy+TdBJaQGfcy+btj0v2YreJ7WrH+umAGB+CFMAMJAmoSyn\nbbmv2UMq28I9t1lD0/VOKY0iYu3PfePtetPlVEz7A4D5IUwBQMdKV0JSz5cStkLnsns6hdZZ+V6r\nq2aF1kCF1nPduLyRtJbLXjNlvD59rKmiMgUA88OaKQCYiLqgMvS6odAY3D2i6t6vO6e72XHKd+3z\nnrBuCgDmiTVTADATqUGqi0pYzlQ/X5dANwzVHeN7P1R5smMMHTsGWpWqq05RuQKA6SFMASgmtp4F\n/bTqLh0iQgEm5/qx43d3tpPOHWrA0WWYLEHXS9Wtm2JNFQBMD2EKQDFNu60tE7fSUvKcJcW6BjYN\nhb7qlIjI1oPtpPOlNOBo0lWwrdSOfimoUAHAdBCmAGBGSm1MHFufFAqFuZ3/Yu+VCD653fzadBXM\n5QtOboWKcAUA40WYAoAZaTIdz1dlSmneEFunVFfd0nVYOZWo1DGkvB8LkrHXfKHPDT0pIShlah/T\n/wBgvAhTADCAMa7rcafg1f35xuWNY8fX7e/kCyO3bt9bWCvVpPoTC1t9TT29dfue/M43/ufCa6kh\nKBa6qEwBwHgRpgAslbGEmL7XkdkqUJNNeN1gtLZevwdU6rV0rZR7fOh8VmqXwSGVqE5RmQKA8SJM\nAVgqXYeYPh7um1yjbq8n33vuRrh15/ONze4N5fucr7KVKmevqTaa3Ge3EQVBCADmjTAFAAU1ecBv\n0hGvqdTmD003xXXPEQo+tntf7rqmvqp6qftZ+TA1DwCWA2EKAAaWEw5SOs353vcFn6Ytz3MqQ00/\n42uGkbMxb0ro6bKK+MHn3iRQAcASWDk4OPC+sbGx8aaIfOe3fv+O/PCnPt3roAAAi1K66w19/dhn\n3CmA+ucux5TzudzPho775AdvZx8PABinv9g/kN97eiAi8tnt7e2P3PepTAHABISqSiWqKynrl1Kq\nRyH2PbsOKyfgpI6p7nMlugOmHFdyA18AwLgRpgBgwkLT32I0QN28fnWho16dWOvzJm3IU6Ytxrr6\npco5T5sGGQCA5UGYAoCelFyjk9sswqUBKvXYG5c3joWR3HVJvmvt7mwnBZfSHfzsea5dOnPs/VDA\nTP0dUp0CgOVAmALQytj3+RmTlAYQfcq5vhsuQpWouqqS77NbD7a953aV7JLovn7n4Yvkc+ZW2QAA\n80aYAtDKkE0RxqLpQ3TTKXq5mq45qlsTVXfO1O/UdbgMnb9Ny/Oc6wEA5o8wBQAttamalHwAb9Oo\nIRaW6vaTCl0vpWW7/Rk7rm3Tjbrzt22GAQBYToQpABhAF1WMtpv5uuubUvZwqgs+9pw5ASb2+pAV\nIKpPAACLMAUAHl1XIMZY4chtNa5/9jWjcINP3dS60uuQYsd0WQ0c4+8VANAdwhQAeHRdgSjZUKEk\ne43U9uBNq1gp96BJ0Crxu2u67ov9pgBguRCmAGAi+phiZqfmpe4/ZcX2onI/Z6taNy5vZO3v1GVn\nvbb3mgoVACyHlYODA+8bGxsbb4rId37r9+/ID3/q070OCgDQnq5bmqLY2Nt+t77uzSc/eLvzawAA\nuvMX+wfye08PREQ+u729/ZH7PpUpAJiQnIpHqbDQtMrirqWqO0+os19Xcs/f9B7oVL/3v/5Ro+MB\nAONGmAIwW1OcalWqeULJ7x66ZmxKXt16oljDiiZj6vL3PdUKHwCgW4QpALM1xQfgrsJSF0EjZU1V\naBPf1DAUeq3unCkVsBJyzvXB594sdl0AwHiwZgoAlsSNyxuytr7ReIpbXce+sQXXsY2JtVMAME2s\nmQKATFOcHphi68F2o4BR1/pc349Juad9TE0EAKAkwhQAOKb0IB4LIF0EQ7dZRIqUKXh1U/9KNcEA\nAKAkpvkBQIYxTR/rYix9f7/c643p/udiqh8ATA/T/ACgoC4e5JtWTkpNr6s7Z9dVndz7yWa6AIAx\nIUwBQE9CD/Khjnd1x6RKCR91Lc67rgJ1tZaq7l4DAFAK0/wAALViU+umPPWub0z1A4BpYZofAHRk\nWaaMxYJSXWVNDXWvur7usvwbAAD4EaYAoCGqMUdSAtfQugg+Y/heAIDhEKYAYAamUCEptfapaUv2\nWPDJOe8U7jcAoHusmQKAEWDdUVxqIBrzfWTNFABMC2umAKADpSsTbQLAslRJUu5R7n0s2SkQALB8\nCFMA0MCYqh9jGsuQNOQ0mQaYo839/uv3v9H4WADA+BCmAGBm2lROSh0b2zfr5vWrrddDuTTkEC4B\nAH1hzRQAoBcalMYadvpcb8XaKQCYBtZMAQA6lVpNunX7XnTz36645/Zda6whDwAwXoQpAFhCdcHl\nxuWNrHO1DSGpFau6QBQLYu65CU4AgBIIUwCwhHxhQkPU2npemOpiLCmfs3/3naNkpz46+AEAfAhT\nALDEbEjYerAtIvkVor7lTCvMFTqmdCWLrn4AMA+EKQBYYiVDQomQlTJ1jyl6AICxIEwBAIooEXJi\nU/fmhOoUAEwfYQoAJmyIKXcpISele14TsfMMPQURALBcCFMAMGFjrd606Z5XF4hu3b4XfR8AgL4Q\npgBgyY2tg12TwJQ7VipYAIASCFMAMGElQkGsg53vGiXDSIkNdEt/vq+wxbopAJg2whQAdKCvh/E+\nprX5rtHmuu69GePUvDGOCQAwPoQpAOjAMjyMN50eaO/NlKfbTXnsAIAyCFMA0KEhHrjrrtm0G97N\n61eTK0o5YWno6XZtzr8MgRkAUG/l4ODA+8bGxsabIvKd3/r9O/LDn/p0r4MCAEzLzetXOwkXbc/b\n1bhK+uQHbw89BABAwF/sH8jvPT0QEfns9vb2R+77VKYAYCBzmiZWcuqePb5tEBp7kBKhCQUATBlh\nCgAGUrKJw5iUDkA3Lm+0Op9rzPcOADAthCkAmKAxVVxKtE6v+/zWg+3sMdUZ070DAEwbYQoA0Frb\ndud1e1otA6b6AcA0EaYAAK3cun0vOTy5YWkKe04BABBCmAKAiGWtlrSl962uoUQf4YnfHwCgK4Qp\nAIgYY7VkCgFB71vTvaR8YazNOMaOqX4AMD2EKQCYoLEHhJwAFNv8d+zfFQCwvAhTADBhXVWoUs5b\n9xk3AE2hkgYAQC7CFABMWOmqjYaelPOmBqab169SXUrEVD8AmBbCFACgkht6UipOBCkAwFwRpgBg\nCbSZZhdqZ+5WnG7dvsd0PgDAUiFMAcASaFMdCrUz9220S6vz9pjqBwDTQZgCAASNcRof0wYBAGNB\nmAIABA0RXEpOSQQAoEuEKQCAiIwniJSckggAQJcIUwDQsbGElJi6IDKV7wAAQJ8IUwDQsTlUS6b6\nHQiBAIAuEaYAYET6fvgPtT2fi6mGQDr6AcA0EKYAYETaPPw3CUKhtuddXAsAgLkhTAHATJSqwtQF\nJX2v7bXsNQhmAICpIkwBwAyUCCQpQcm+1+aa9jxTnYrXNab6AcD4EaYAYAZKBJLccxCCAADLjjAF\nADPE1Ll5oDoFAONGmAKAGaJqBABA9whTAIAFVLUAAEhDmAKAiUkNO01DEVUtAADSEKYALJ2pV15S\nww6haB5YNwUA40WYArB0CBkAAKAEwhQAACNHdQoAxokwBQAAAAANEKYAYMlNfQ0ZAABDIUwBwJJL\nWUNG4AIA4DjCFAAgiqYdAAAcR5gCAAAAgAYIUwAATAAd/QBgfAhTAAAAANAAYQoAAAAAGiBMAQAw\nEUz1A4BxIUwBAAAAQAOEKQCYMfaHAgCgO4QpAJgx9oeaH6b6AcB4EKYAAAAAoAHCFAAAAAA0QJgC\nAPSC9VvlMNUPAMaBMAUA6AXrtwAAc0OYAgAAAIAGCFMAAEwQU/0AYHiEKQBAL1gzBQCYG8IUAKAX\nrJkCAMwNYQoAAAAAGiBMAcAEMWUOAIDhEaYAYIKYMgcAwPAIUwAATBQd/QBgWIQpAAAAAGiAMAUA\nAAAADRCmAACYMKb6AcBwCFMAAAAA0ABhCgAAAAAaIEwBADBxTPUDgGEQpgAAAACgAcIUAAAAADRA\nmAIAYAaY6gcA/SNMAQAAAEADhCkAgIiI3Lx+deghAAAwKYQpAICIiNy6fW/oIaAlpvoBQL8IUwAA\nAADQAGEKAIAZoToFAP0hTAEAAABAA4QpAAAAAGiAMAUAAAAADRCmAAAAAKABwhQAAAAANECYAgBg\nZujoBwD9IEwBAAAAQAOEKQAAAABogDAFAMAMMdUPALpHmAIAAACABghTAAAAANAAYQoAgJliqh8A\ndIswBQAAAAANEKYAAAAAoAHCFAAAM8ZUPwDoDmEKAAAAABogTAEAAABAA4QpAABmjql+ANANwhQA\nAAAANECYAgAAAIAGCFMAAAAA0ABhCgAAAAAaIEwBAAAAQAOEKQAAlgAd/QCgPMIUAAAAADRAmAIA\nAACABghTAAAsCab6AUBZhCkAAAAAaIAwBQAAAAANEKYAAFgiTPUDgHJ+oOa9T4iIfP///p+ehgIA\nAPqwv38w9BAAYBL+6nX1/y8/4Xu/Lkz9iIjI1r/5l4WHBAAAAACT8iMisuO+WBem/kxEfkZE/peI\n/L+OBgUAAAAAY/UJOQxSf+Z7c+XggFI/AAAAAOSiAQUAAAAANECYAgAAAIAGCFMAAAAA0ABhCgAA\nAAAa+P9xd8js55agdgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1bc00ea3400>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Put the result into a color plot\n",
    "Z = Z.reshape(xx.shape)\n",
    "plt.figure(1,figsize=(15,10))\n",
    "plt.clf()\n",
    "plt.imshow(Z, interpolation='Nearest',\n",
    "           extent=(xx.min(), xx.max(), yy.min(), yy.max()),\n",
    "           cmap=plt.cm.Paired,\n",
    "           aspect='auto', origin='lower')\n",
    "\n",
    "plt.plot(reduced_data[:, 0], reduced_data[:, 1], 'k.', markersize=2)\n",
    "plt.scatter(centroids[:, 0], centroids[:, 1],\n",
    "            marker='x', s=169, linewidths=3,\n",
    "            color='w', zorder=10)\n",
    "plt.title('Clustering on the seeds dataset (PCA-reduced data)\\n'\n",
    "          'Centroids are marked with white cross')\n",
    "plt.xlim(x_min, x_max)\n",
    "plt.ylim(y_min, y_max)\n",
    "plt.xticks(())\n",
    "plt.yticks(())\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "____"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "## 3.3 [Applying Agglomerative Clustering 'reduced_data'](http://scikit-learn.org/stable/modules/generated/sklearn.cluster.AgglomerativeClustering.html)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "Other Useful Tips:\n",
    "1. [What is a Connectivity Matrix?](https://people.hofstra.edu/geotrans/eng/methods/connectivitymatrix.html)\n",
    "2. [What is Precision & Recall? What is a Confusion Matrix?](http://www.dataschool.io/simple-guide-to-confusion-matrix-terminology/)\n",
    "3. [How to compute F-Score?](https://en.wikipedia.org/wiki/F1_score)\n",
    "4. [What is a ROC Curve? How is it useful?](http://www.dataschool.io/roc-curves-and-auc-explained/)\n",
    "5. [What is Cohen's Kappa?](https://en.wikipedia.org/wiki/Cohen's_kappa)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Cluster labels: [0 0 0 ..., 0 0 0]\n"
     ]
    }
   ],
   "source": [
    "ac = cluster.AgglomerativeClustering(n_clusters=2, affinity='euclidean', linkage='complete')\n",
    "labels = ac.fit_predict(X)\n",
    "print('Cluster labels: %s' % labels)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(17898,)"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.shape(labels)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true,
    "deletable": true,
    "editable": true
   },
   "source": [
    "___"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "### 3.3 [K Means Clustering](http://scikit-learn.org/stable/modules/generated/sklearn.cluster.KMeans.html#sklearn.cluster.KMeans)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "collapsed": true,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "n_classes = 2\n",
    "LABELS = [\" Noise \",\"Pulsars\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(10738,)\n",
      "[0 0 0 ..., 0 0 0]\n"
     ]
    }
   ],
   "source": [
    "clf = cluster.KMeans(init='k-means++', n_clusters=2, random_state=5)\n",
    "clf.fit(X_train)\n",
    "print(clf.labels_.shape)\n",
    "print(clf.labels_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "collapsed": true,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "# Predict clusters on testing data\n",
    "Y_pred = clf.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "             precision    recall  f1-score   support\n",
      "\n",
      "          0       0.98      0.95      0.97      6566\n",
      "          1       0.60      0.77      0.68       594\n",
      "\n",
      "avg / total       0.95      0.94      0.94      7160\n",
      "\n",
      "Confusion matrix\n",
      "[[6265  301]\n",
      " [ 134  460]]\n"
     ]
    },
    {
     "data": {
      "image/png": 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jbqJaZ6/0wHsCEZzzxN3MbWa2SrW0tfKkwC0iI9rMwD3XlOyB+/0FoUbgHm5dVVUiIiLd\n93BSRCSbfC7bEK2FzuvyrsAtIr2hL5etjiHL+CYtpqqSBpjZaDN72GKm6I5nZhPN7NL0++5mNqne\nNhmP8zmLgbgGWv/R1DU56/4/b2b/TO2vd6tYd2KGThvVjrG9mf3LzN4xs22qrB/UOQwmvZmtaGbF\n1DFqoPXbNnrsOsdaoqI3Zb30H0h5G9+i/Z9c+T9tuRE0Vknn5agzHU2MzNYRE4UOUjvHA/9/zN4d\nvGEWo/udS7Ql/irwKzNbLK1bhmhnPNRmhxCj6F1L9Lq8scr6wZ5D5nOu4nzm7AiU1XG0ttnpYPd/\nFNF+frEW5WdO+VyUugf76sDRAVVVUkfqCPJ1qjXo7wLe3vHAh/KJL405c3EacOgtYvS3yURzq1O8\nyoh2GSwM3JYGQ6pmsOfQzG95p+5ryPtPA5rdQPTorNo5SAamwF3f3sAdpZ6ANmuM5AOIITMXJhrM\n71k2iM46ROef9YhRyn5N9Pp7P43X8B2i08HniaFR5yJKfM8RXfenETN/vEx0NFmG6Jq8q7u/YzGG\n9BHEqHTLEp0QLibGIp6t/afNOR74j9M5LUG0GT2kNMKe1RjfOK0fS3RU2YjocPTbgd601KNuBeAU\nM9ve3cenIQCOS/uflyjp7ufu1Qa5mpTeu/UsRpZblBgIagzRoeojAx27Ih+1xj8vVfOcZ2aHu/uK\nQzmHAdKvR3RUWZ/4vj2U0t9VJ98TU57HpX2taDEe+AlEB5sicYewn7v/O21TdQzs1HZ4t5Sm6O5z\nBNnUa/Jk4k7mbSrGsEnnfSLRy3EhYnagY9z9vGr7r5W+bLeXA+ea2TFeMXRsS/TloS9L8+fOK3Gr\nqqS+zxFfznILE8FvO2JckvWJUmBpgPlbiKD4CaJL+a5Eb8CStYluresQvTIhugHPk5b9jujRd1Ta\ndjviC7BnSvv9tHw3YlCd7xEll8/XOpH0xf4eMTnEqkRg/kNZd+YBxzc2s7mIMa/fJsZlmJDyMZDt\niLHADwG2SxebG4ixUbYixrVeFviTVRn7Ol2ADiJ6xT1BfOlfJgLKCV4xmcYA51tv/POliYve/swa\ni3wo51CZfjQxaNCDRK/EDYj3r5FZmvYjemf+qixvZxH/788QQb1ITNk3ymqMgU1cuH5PDH609ADH\nOz29N1ul86gc//pCYgyQzYmqtyuAM1KArrb/WulLrifGG1+3gfdj6PJkrCoZltwNSgdmqXNYDPq+\nDtF1uFwfURK9393/RgzhWvpyfYMoOX/b3Z9IA/B8F9jPYlCikqPS4Dgvpr/fIcbKfob48s1LBKt7\n3P2vxMWgVHf6GLC7u9/s7s+7+6+JC0XVh1xlViKC2AupI8GRxJf0fZs1vvEu7v6ox5jTOxNdeVcn\nui+PAb7m7o95jHM94AQP6XlAPzEI0OvEIFSrAF9x97+n/X85vb9VR0Nz9zOIIUcXdfcjLcaV3hw4\n08wONbPnzezGVAqvpnz880fToFq7A9ub2Yc9xlsvEpMjV44rM+hzqJJ+/vQeHezuz3hMwFya4KCm\ndJczHXgnVSusTIx3vbO73+cxYe+uxEVkS+ICMnMMbHe/NL1Xj3pMkzaNGIRtjqm/0oV7J+IzfYe7\n3018jstdCXzD3R9OAzodnY63ygD7HzB92Tm+Swy7Wu2i2Xwj6OGkqkpqW4z44ldrlTHQmNmrAXf5\n7JMJ3JbWfzj9/V+fc97EF8qqOUp10pXjas8D4O5/MrNxZvYzonS1JjFD0GV1zudC4kHfkxbjSF8J\nnOfu01JwrjW+8copj+UPaAczMNBqaft/lxZ4THf2PHFB+mu1jTxmvik5jKh2+CgRWD5K3NGcQoxn\nUu2YtcY/f6rKNk07B3d/1WIii29azL5UGrc6SyQoPWB+suL/Mz/x/7maGmNg12HE5/P+smX3Mfvg\nR6cDO1gMnbwKs4aJrZxRaLDpJzPQaH7N1pcbOLe1dGDn8s67lHSW0ge32vs00JjZ1cZtLm3fVyNN\ntTq+gcbVPoyYrGEUUb2xDbMmYRhQGtDm48RclDcRJdIHzeyj1B/fuMiclX3Vxp4eSKPjWVdlZqsS\nVQ3nEbf0pecOf2bglheNjn/eqEGdg8XoiY8QF5XHiaqvvTIcF+L/M505/z+rEBM/1BwDu45SaCr/\n//anV2lWqWuJ6eAmE3eEA7Z2GWT6PmYfabN18hlL21k67bRY5+Wos0wmAurig9jmcWCDinrbjYgq\nimealK9vE7e1B6ZqkueJh2I1n6JYDEv7bXe/3t0PIEpabxH1mvXGN34EWLGijnIdaisvqzwOrGBl\nExRYjEg4hsbGs55AVB3NSPstfXbnYuDzbsb454M9h/L02xGfny3c/Xh3v46YABqrPqdlvWPPTUxa\nXfr/vEI8AFzFaoyBXWVflZy4KKxftmxNZr+L3JwYeW9CqiYrjRldOo/y/TeSvmRxNHP7oKmqpAZ3\nL5rZ/cSDpWoz2lRzGtFq5GSLAeNXJloCXODub1aphshiMrCNmd1CjCc9gagLnqfOdjngp6mVxh3E\nF3UpovrgJmqPb/wCETx+ndYvT7R8qeVt4CPpwdn1RIuKiy3mdcwRF4Un07oBmdmaRCApzRd6DzHW\n9rrEA9rKaepKao5/XifvWc+hPP1kYg7GbczsUWI8+ENTunr/q9K+xprZsu7uZnZFOpd9ieq7nxB3\nUI8Tz0QGGgO7tK81zGxFrxgoKbWwOQc40czeIKr+TmdWMH6T+DzsaGYXEaX80lC+pfOYuf8G02Mx\n686KlE0x11J9+Wz3WYMfl6rlVOKu7ypmDc5fV6r//CzxpPwh4kt0AbBvE/O0O/Gg8RGiyuR5orNK\nzafz7n4V0VLjKCLYHEuUwG/0OuMbp5Lu1kQp/C4iYM0xk32Fk4gJDv6S6vy/QEwucRMR6F4CNvc5\nx8iudARwROkZgMeY22elfWzAAGM5e3PGPx/sOcxMT7S0OJuYCPZhol5+LyIgNtKS4lfE+PMPWXRI\n2o2oe/4TcfGanyjNv+U1xsBO+5pIBPPHbNYkFeW+m9JfnvJ+AakqzN3/RbSi2pto4XMyEdgfLjuP\nmfsngna99BBVXi9TMWlFy/SRrVVJlmDfYhrWtQ4zW5YIcis3+KBHRBpgZr8FHnf3o1p8nBWB524Y\nm2e5uQffJvul6UU2f7oAGta1e6TSxkSi9CAiTZAe3I4nquOGR6bSdnp1GAXuxhxC1Nd1xSBTIl3g\nUGafQ1QGQQ8nG5A6QzRr4CCRnufuzXzm05h8PlvpOV9kuFosNkqBW0R6Q9Zqjw58ONmTgdvM5iHa\n875Mp11KRaSaPmIclHvd/b1se8hl677e13ntAXsycBNB+9Z2Z0JEBm0TYgiJwctnLHFrPO6O8TLA\nRS++yFIzWj+apAyvYxd+tn4i6SrT8q9wx8K7QPruZpJ1wChVlXSMfoClZsxgOQXuEWf+wnLtzoK0\njqo26d3ALSK9JvPDSVWViIi0Rz5jVUkH9nZR4BaR3pC5VUnnDQuiwC0ivSGfccb2DmxV0oE3ASIi\nUotK3CLSGzLXcauqRESkPdSqRESky6jELSLSZUoz4GTZrsMocItIb8hnnLFds7yLiMhQqcQtIr1B\nowOKiHSZzKMDdl7FhAK3iPQGNQcUEekyuVy2B425zgvcnXcPICIiNanELSK9QVUlIiJdRg8nRUS6\nTC7jsK4dWMetwC0ivUElbhGRLtPCiRTM7H5gSvrzOeBM4CRgBnCdux9hZnngdOBjwHvAnu7+tJlt\nUJm23vEUuEVEhsDM5gVy7j6+bNmDwBeBZ4GrzGxtYCVgXnffMAXrE4BtgTMq07r7A7WOqcAtIr0h\nn3HOyfol7o8B85vZdURMnQDM4+7PAJjZX4AtgKWBawHc/S4z+7iZLThAWgVuEZEWVpW8AxwPnAN8\nGLgGeLNs/VRgZWBB4K2y5f1p2ZQqaWtS4BaR3tC6h5NPAk+7exF40szeAhYtWz+aCOTzp99L8kTQ\nHl0lbU2d97hURKQVSiXuLK/avkbUV2NmyxAB+r9m9iEzywFbArcCtwNbp3QbAI+4+xRgepW0NanE\nLSIyNOcCE83sNqBIBPICcBExf8517n63md0LfNrM7gBywB5p+30q09Y7oAK3iPSGzHNO1t7G3acD\nO1VZtUFFugIRpCu3v6sybT0K3CLSG/JkfDjZ9JwMmQK3iPQG9ZwUEekyGqtERKTLaJZ3ERFpF5W4\nRaQ3aCIFEZEuM4LmnFTgFpHe0MJhXYebAreI9IYRVFWih5MiIl1GJW4R6Q0jqDmgAreI9IRiLkch\nQ311UQ8nRUTao5DPU8hQes6yTaspcItITyjkyVTiLnRe3FbgFpHe0J/P059hwKj+Dixxd16ORESk\nJpW4RaQnFPMZH06qA46ISHsU83mKGao9smzTagrcItITChlL3Fm2aTUFbhHpCYWM7bgLHdiOu/Pu\nAUREpCaVuEWkJ8TDySx13J1X4lbgFpGeoDpuEZEu05/L05/L0AEnwzatpsAtIj1hJJW4h/VSYmYT\nzOweMxtVtuwuM1uxxja/NLMxw5JBERmxSqMDDval0QHDisAPgaMaSezu+7c0NyIiXaYdgfs4YE8z\nu9LdHygtNLO5gPOBlYE+4ER3v8TMbgL2ARYDTgDeB94AdgbeBc4APkzcPRzq7jcN36mISLco5nMZ\ne052Xom7HbXubwN7AxPNbJ6y5d8A/uPuGwFbAEeb2eJl678AXA6MA84DFgH2BCa5+6eAbYHThiH/\nItKFslSTZK0Xb7W2PC5191uA64EjyxZ/BLglrZ8KPAZ8qGz9scCSwA3A9kTJe01g61QqvwwYVRHs\nRUSAWRMpZHl1mnbm6EfA1sDY9PfjwCYAZjaaCMrPlaXfBZjo7psC/yBK7U8AF7v7eGAr4A/A68OR\neRHpLkVy0e19kK8iKnHP5O7vAnsAC6VFZwGLmdltwE3AEe7+Wtkm9wAXmNnNwGbAr4EzgVXTsjuA\nF9y9MEynICLSFsP6cNLdJ1T8fR8wV9mi3apsM77sz3Wr7ParzcibiIxsI6kdtzrgiEhP0GTBIiJd\npj8H/Rk60/R3XoFbgVtEekMxY4lbM+CIiLRJkWzd19WqREREhkwlbhHpCWpVIiLSZQq5PIUMY2tn\n2abVFLhFpCcUM5a4O3GQKQVuEekJhVy2GdsLnRe39XBSRKTbqMQtIj2hP5enP0Ob7EbnnDSzJYG/\nA58GZgATgSLwKLCvuxfM7HBgm7R+f3e/x8zGVktb61gqcYtIT8gyMmDpVU+aCOZMYFpadCIxscsm\nQA7Y1szWIeYTWB/YkVnzB8yRtt7xFLhFpCcUMwbtBjvtHE/MxvXv9Pe6wM3p92uIyWE2Bq5z96K7\nv0jMH7DEAGlrUuAWkZ5QzOczv2oxs92J2bv+UrY45+7F9PtUYvjqBYG3ytKUlldLW5PquEWkJzRa\n7VFtuzq+BhTNbAtgLWKugCXL1o8G3gSmpN8rlxeqLKtJJW4RkSFw90+5+7g0d8CDxBwB15jZ+JRk\nK+BW4HZgSzPLm9kYIO/uk4AHqqStSSVuEekJw9yO+0DgbDObm5iW8VJ37zezW4E7iULzvgOlrbdz\nBW4R6QnFjFUlgxlRsGLGrnFV1k8AJlQse7Ja2loUuEWkJxTIOFZJB9YoK3CLSE9o4cPJYdd5lxIR\nEalJJW4R6Qn9+Rz9GUb6y7JNqylwi0hPKGYcj7uo8bhFRNqjmBtcC5Hy7TqNAreI9IQCOQoZJv7N\nsk2rKXCLSE9QqxIREWkblbhFpCfo4aSISJcpkLGqpJvquM3s5Fobuvt3mp8dEZHW6M/l6M8QuLNs\n02q1StyThy0XIiItNpIeTg4YuN39iNLvZjYfMBb4BzCPu08baDsREWmturXuZrY+8AxwFbAM8JKZ\nbdTqjImINFORXIwQOMhXsQPruBt5XHo8MXnlZHd/CdgVOKmluRIRabJimvg3y6vTNBK453f3x0p/\nuPvVqDWKiHSZLDO8Z60Xb7VGAvD7ZrYIUAQwM2ttlkREmq/XurwfDdwMLG1mFwOfAfZuaa5ERJqs\npzrguPuVZvYE8GmgDziqvOpERESGV6N11XMRQft9YHrrsiMi0hojqaqkkeaAewA3AesBGwO3mtkX\nW5wvEZFOouUtAAAUbUlEQVSmKuSyPqBsd87n1EiJ+wBgLXd/GcDMxgBXApe1MmMiIs3UT47+DKXn\nLNu0WiO17tNLQRvA3V8kqkxERLpGMWNTwE5sx11rkKl10q8PmdmpwJlAP7A7cHvrsyYiItXUqiqp\nrArZpuz3IqDRAUWkaxTJZeq+3old3msNMrXScGZERKSVCmRrx13owInC6j6cNLPFifFJPgDkiGaB\nY9195xbnTUSkaUZSc8BGWpX8HpgGrA78leiIc2srMyUi0mzFjIG7E6tKGrkHWMHdtwGuBk4FPgms\n2tJciYg0WYFZpe7BvTpPI4H7lfTzKWANd/8X0ZNSRETaoJGqktfM7HvAncARZjYFmL+12RIRaa6R\nNOdkIyXubwDvufttwH3AkcDBLc2ViEiTZasmyVYv3mqNjA74GnBy+v1gFLRFpAsV01RkWbbrNLV6\nTk4lTZ5Qjbsv2JIciYi0QE90wAHWGLZciIhIw2r1nHxhODPSDr/kOT7Acu3OhjTZlCUHvFGULvVu\nkRr3/43ptQ44IiJdT4FbRKTLFMkWhDvx/q2hwG1m8wFjgUeBed19WktzJSLSZD01kYKZbQA8A1wF\nLAu8ZGYbtTpjIiJSXSMNFH8ObAFMdveXiJECT2pprkREmqzUHDDLq9M0Erjnd/fHSn+4+9WoblxE\nukxP9ZwE3jezRUh19GZmrc2SiEjzFYo5+osZWpXU2cbM+oCzASOmd9yDmLtgIhE3HwX2dfeCmR1O\nzCY2A9jf3e8xs7HV0tY6ZiMl7qOBm4HlzOxi4I60TESka7SwxP0/AO7+SeAw4MT0OtTdNyGC+LZp\nHt9xwPrAjsBpafs50tY7YN3A7e5XAtsBhxOTBG/i7pXzUYqIdLRW1XG7+5+AvdOfKwCvAusSBV6A\na4jnhBsD17l70d1fBEaZ2RIDpK2pkVYliwKvA5cAvwVeSctERARw9xlmdgFwCnApkHP3UhPwqcBC\nwILAW2WblZZXS1tTI1Ulk4D/VLwebmA7EZGOEdUe+QyvxurF3X03YBWivnu+slWjgTeBKen3yuWF\nKstqaqSqJO/ufe7elzLzdeDCetuJiHSSIjkKxcG/6lWVmNmuZvbD9Oc7RCC+z8zGp2VbEfP03g5s\naWZ5MxsD5N19EvBAlbQ1DapZn7tPByaa2X3AD+ulFxHpFC3sOXk5cL6Z3UJM67g/8DhwtpnNnX6/\n1N37zexWYjaxPLBv2v7AyrT1Dlg3cFfUZ+eAjwOL1NtORKSTFIs5ihmaA9bbxt3/C3ypyqpxVdJO\nACZULHuyWtpaGilxTyLaF5Zy/xrwncEcREREmqeRwL2eu/+95TkREWmhXhvW9TfAR1qdERGRVmpV\nz8l2aCRwP2xmOwG3AW+XFrr76y3LlYhIk/VaiXtbYIeKZUWgr/nZERFpjVY9nGyHWrO8z+Pu77n7\nvMOZIRGRViiQrfRcc7SnNqnVAefOYcuFiIg0rFZVSefdH4iIZFRMPSGzbNdpagXuec1sbQYI4O5+\nf2uyJCLSfP0ZW5Vk2abVagXulYHLqB64i2m9iEhXyDoNWSdOXVYrcD/m7msPW05ERFqokLGqpBPb\ncTcyrKuIiHSQWiXuW4YtFyIiLdYTPSfdfb/hzIiISCsVgEKxbrKq23WaQY3HLSLSrXqi56SIyEgy\nkh5OKnCLSE8okq3Le4balZZTqxIRkS6jEreI9IT+Yo58D/ScFBEZMfRwUkSkyxQKOQqFDA8nM2zT\nagrcItITRlIHHD2cFBHpMipxi0hPKJCxHXeXjQ4oIjJi6OGkiEiXUc9JEZEuUyxmG2Sq2IFdJxW4\nRaQn9BeADE37+jtweEC1KhER6TIqcYtIT9DDSRGRLlMo5sjp4aSISPcoFHOZ6rgVuEVE2qRQzMEI\nKXHr4aSISJdRiVtEekKxEK8s23UaBW4R6QkjqapEgVtEekIx43jcRY3HLSLSHv1kaw5Y1OiAIiLt\nUczY5b0T67jVqkREpMuoxC0iPaFQhJxGBxQR6R6FQraekxRyHVc10bLAbWbjgd8DjwFFYD7gInc/\npUraFYHfufsGrcqPiPQ2Be7G3ejuOwKY2TyAm9mF7v5mi48rIjKbItnacVOnVYmZzQWcB6wIzAMc\nTRRYJxKF1keBfd29YGaHA9sAM4D93f0eMxtbLW2tYw5nVclooB+43sx2cfcnzGwfYCki0wCY2THA\nZkAf8Ft3/6WZjQMOT0nmB74KTAf+DEwGrgbeBnYDCsBt7v69YTkrEel1uwCT3X1XM1sUeDC9DnX3\nm8zsDGBbM3sBGAesDywPXAasB5xYmRb4Y60DtvoOYDMzu8nMbgQuAr5NBNhavgrsBGwCTEvLVgd2\ncffNgCuAHdLypYDPuPtxwB7Afu6+IfCsman+XkRmKhSyv+r4A/Dj9HuOKE2vC9ycll0DbAFsDFzn\n7kV3fxEYZWZLDJC2pmGrKikxs++X/VntHmRH4CdEUL4mLfsXcLKZvQ0sC9yelj/n7tPT73sAB5nZ\nz4A7B9i3iPSoYiGXqRdkrs427v42gJmNBi4FDgWOd/dSe5SpwELAgkQNARXLc1XS1tSOOvd3gaXT\n7+uUr0j14DsAXwE2BXY3sxWAs4E93H134N/MCsrl18K9gH3cfRywNrBRq05ARLpPfyGX+VWPmS0P\n/A240N1/y+yxaTTwJjAl/V65vFramtoRuE8GTjezvxD12DO5+3vA68BDxJtwHfAi8BvgbjO7nTix\nZars9xHgXjP7G/AacHfLzkBEuk6hGGOVDPpV54GmmX2QiFUHu/t5afEDqWUdwFbArURNwZZmljez\nMUDe3ScNkLamllWVuPtNwE1Vll9NPEystEFafyRwZMW6A9Kr6jZpu3OAc7LlVkRGuqzDulJ/m0OA\nRYAfm1mprns/onp3buBx4FJ37zezW4mq3Dywb0p7IHB2edp6B9QDPBGRIXD3/YhAXWlclbQTgAkV\ny56slrYWBW4R6QmFYv1qj2ryGo9bRKQ9omlflp6Tzc/LUClwi0hPaLBNdpUNm56VIVPgFpGeUCxm\na8dd7MCqkk4bO0VEROpQiVtEekLWOSfr9ZxsBwVuEekJ/YV4DZrquEVE2qOgEreISHcpFnIU+7NM\nFqzALSLSFoWMVSW5DqwqUasSEZEuoxK3iPSErHXcmXpbtpgCt4j0hEIxW8/JQrF+muGmwC0iPSFX\nyGVqIaJWJSIibdLXn61VSV+GbVpNDydFRLqMStwi0hPyhXhl2a7TKHCLSE/IFSGfpY5bDydFRNoj\n1x+vLNt1GgVuEekJfYUcZChx96lViYhIe+SL2WZ5z3dgVYlalYiIdBmVuEWkJ+T6c+QztMnOdWA7\nbgVuEekJ6jkpItJl+grZWoioHbeISJvkM7YqydL2u9X0cFJEpMuoxC0iPSHXD3l1wBER6R65jFUl\nejgpItImfQUoZilx6+GkiEh75AvZek4qcIuItEmuP5etM00HdsBRqxIRkS6jEreI9IRcEchS7dGB\ng0wpcItIT+jrz2Wr9ujAqhIFbhHpCfl+IEubbLXjFhFpj1wxW5vsYrHzakv0cFJEpMuoxC0iPSHr\nnJP0d16JW4FbRHpCX8Z23MX+XKbGKK2kwC0iPSFXyDbIVKHTojYK3CLSI/KFjJMiKHCLiLRH1i7v\nnTjnpFqViIh0GZW4RaQn9GWs4250dEAzWx/4mbuPN7OxwESiQcqjwL7uXjCzw4FtgBnA/u5+z0Bp\nax1LJW4R6QmlGXAG+2qkCaGZfR84B5g3LToRONTdNwFywLZmtg4wDlgf2BE4baC09Y6nwC0iPSFf\nyJHvz/BqrLflM8B2ZX+vC9ycfr8G2ALYGLjO3Yvu/iIwysyWGCBt7XNp7JRFRLpbrpD9VY+7Xwa8\nX344dy/125kKLAQsCLxVlqa0vFramhS4RUSarzzcjwbeBKak3yuXV0tbkwK3iPSEvv7srwweMLPx\n6fetgFuB24EtzSxvZmOAvLtPGiBtTWpVIiI9IZfquAerkG2W9wOBs81sbuBx4FJ37zezW4E7iULz\nvgOlrbdzBW4R6QmlViJZtmuEuz8PbJB+f5JoQVKZZgIwoWJZ1bS19Grg7gN4Z9Qr7c6HtMC7xU4b\ny02G6r3izO9qX9Z9tDpwD6deDdxLA9wwZud250NaQXF7JFuaaHo3aPmMVSUNNgccVr0auO8FNgFe\npiMnJhKRCn1E0L633RnpBD0ZuN39PeC2dudDRAYlU0m7JJexqiTT5Ast1pOBW0R6j+q4RUS6TD7j\nIFOZxvBuMQVuEekJpbFHsmzXadRzUkSky6jELSI9Ieuck42Oxz2cFLhFpCfk+yGfoY5BDydFpCnM\nLE8Mur8RcLe7T29zljqeAreItI2Z/Qx4FlgBWAd4FditrZnqAiOp56QeTo5wZrZou/MgTbexu58J\nbOjunwWWb3eGukErpy4bbipxj1BmNo6Y067PzP4AvODu57Y5W9IcfWb2CeD5NBTo4u3OkAwvlbhH\nrqOATwGvAMcC32xvdqSJLgBOAY4HjgNOam92ukOW0nbW3patphL3yFVw99fNrOju75rZ1HZnSJpm\nAXdfP/2+f1tz0kVGUs9JlbhHrqfN7CfAYmb2A+CFdmdImmZrM8s8LnWvUolbusE+wJ7EKIhvA3u1\nNzvSREsA/zaz54jRx4vuvlGb89TxcgXIM/gWIuqAI8NpHuBK4E9E0F4albpHis+1OwPSXgrcI9dF\nwHnA9sBjwFnAlm3NkTTLXMAO6WcOWAb4Rltz1AXy/ZDPMDuS6rhlOC0C/BlYzt1/SpTAZWT4dfq5\nMbASsFgb89I1RlIdtwL3yDU3cCDwdzNbDfhAm/MjzfOOu/8EeMnddwc+2Ob8dIVSq5JBv1TilmF0\nILAkcAywGWrHPZLkzGwp4ANmtgCg3rENGEk9JxW4RxgzWy79Ogk4hwje1wFvti1T0mxHAP8L/IYY\ns+Sa9manO4ykErceTo48B6TXmURTsVL7pyJR8pYu5+63ALcAmNmD7v5im7Mkw0yBe4Rx9wPSz03N\nbDHgQ8Cz7j6pvTmTZjGz7wDTgIWBPczs2tL/XQaWn5GtiiEPHRcpVVUyQpnZDsAdwCHAXWa2S5uz\nJM2zEzFeyVbuvhqwVpvz0xVKc05meXWaDruOSBMdAKzr7m+b2WjgRqJOVLpfgehQ9Wr6W80BG5Ar\nZGvHneu8uK0S9whWcPe3Adx9KvBum/MjzfM3oo77FDP7BXBpm/PTFUZSO26VuEeuZ83sBOIL/ing\nmTbnR5rE3X8E/AjAzO519/fbnCUZZgrcI9ceRDfoTwOPAz9ob3ZkqMzsTqJ1UOVyNMhUfflCtqZ9\nWeapbDUF7hHGzMaU/fnn9IKoE1Wzse62Y7sz0M2ytsnOUi/eagrcI88lzNl+eyywEBqvpKu5+wsA\nZnZYldVHDnN2uk4uY+DOKXBLq7n7hqXf03yERxLjlGzVtkxJs5Vak+SIWd478Ga+82SeAaf5WRky\nBe4Rysw+BkwErgfWc/f32psjaZY0w/tMZqYu7w3Iz8gYuFXillYzszzR6WYnYG93v63NWZImM7NV\nyv5cGlihXXmR9lDgHnnuJL7IxwGrpSFdAXD3s9qWK2kKM1uQ6Ej1NlFVMo0YCVLqyFxV0oEdcBS4\nR56r08/R6VXSgTd8Mhhm9i0iSPcDh7n7tW3OUlfJZexM04k9JxW4Rxh3P6LdeZCW2QkwYEHgQkCB\nexDezb2SqfT8bu6V5mdmiBS4RbrHu+4+HZiUWgxJY6YAb1w/ZudFhrCPN9J+OoICt0h36sAb+M7k\n7q+b2VjiTiWrKe7+erPyNFS5YlFVnyLdwMxeBW4ggvZm6XcA3H2nduVLhp9K3CLd40tlv5/RtlxI\n26nELSLSZTqxN6eIiNSgwC0i0mVUxy2DZmYrEhMzPFK2OAec5O7nDXHfVwKXuvtEM3sQGO/ubw6Q\ndiHgj+4+qNnrzWx74FvuPr5i+XjgVHdfo872RWCJwUzAbGYTgUfd/fjB5FWkGgVuyWqau8+cpNbM\nlgUeNbP73P3hZhygfP8DWAT4RDOOJdJNFLilKdz9X2b2FLCKma0DfB1YAHjL3Tc1s68D3ySq5yYT\nJd4nzGwZYsbyZYAXgCVL+ywv2ZrZD4HdgBnAU8DuwPnAfKlkvi6wCnASMXluH3By6Q7AzI4Edk7H\nfqre+aSBnE4jhsRdBngQ+LK7l+buPMbM1kvnc6i7X5m2q3qeg3ozRepQHbc0hZltSEzYcHdatDpR\nzbGpmY0jgu4m7r42MQDW5SndacBd7r468B1g1Sr7/jwRqDdM1RjPAd8ipmcrlfxzxKS5P3D3dYFx\nwEFmtoGZbQt8EVgL2IiYVKKevYAL0vjmY4GVgG3K1j/r7usAuwAXmNkSdc5TpGlU4pasSiVdiM/R\nJGBnd/+nmQE87O6lLsLbEMHvjrQOYFEzWxTYAjgIwN2fNrMbqxxrC+AP7v5GSncAzKxrL1kF+BBw\nXtkx5gPWBlYDLk+z3WNm5xEXiVoOBj5tZt9P+16GKH2XnJHy8qiZPQZsCGxc4zxFmkaBW7KaVqcO\n+u2y3/uAC939YJg5ZvgyxPgP5dOsQVSFVJpB2eiGZrYwsHBFmj7gzYp69w8CbxEl33rHqHQx8f34\nPXAVMKZiH+XjzOWA96l9niJNo6oSGQ7XAV8xs6XT3/swq7v2tcDeMHOi402rbH89sF0aixpgAnAA\nEYD7zCwHOPCume2S9rU88ChR930tsIOZLZyC6a4N5HlL4Eh3vyT9vT4RmEt2T8dZB/gwUUVU6zxF\nmkYlbmk5d/+Lmf0M+KuZFYhR1rZz96KZ7Qucb2aPAy8RDwErt786TQhxe6qC+AdRB/0OcE/6exNg\nW+CkVL0xF/Bjd78dwMzWBO4jSr8PAUvUyfYhwB/N7L9Eqf1mohqkZGUze4C4E9gxDUBU6zwH85aJ\n1KQu7yIiXUZVJSIiXUaBW0Skyyhwi4h0GQVuEZEuo8AtItJlFLhFRLqMAreISJdR4BYR6TL/HyhB\nqml8r5pVAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1bc00ec72b0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plotCM(Y_test, Y_pred)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "____"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "### 3.5 KMeans vs. Agglomerative Clustering"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "To understand the difference between these two clustering algorithms, let's visualise how differently they choose the centroids"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "pca = PCA(n_components=8)\n",
    "X1 = pca.fit_transform(X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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5roibgQGl1J8nuZBKxNQ53A9HoRFCCCkanZ1Sm+pN96zAjuRKqcccxzkPEhyp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\nz5xZ+L5Qzw5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      "text/plain": [
       "<matplotlib.figure.Figure at 0x1bc02442198>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from matplotlib.pyplot import cm \n",
    "n=2\n",
    "c = []\n",
    "color=iter(cm.rainbow(np.linspace(0,1,n)))\n",
    "for i in range(n):\n",
    "    c.append(next(color))\n",
    "\n",
    "\n",
    "n = 2\n",
    "f, (ax1, ax2) = plt.subplots(1, 2, figsize=(15,7.5))\n",
    "\n",
    "\n",
    "km = KMeans(n_clusters= n , random_state=0)\n",
    "y_km = km.fit_predict(X1)\n",
    "\n",
    "for i in range(n):\n",
    "    ax1.scatter(X1[y_km==i,0], X1[y_km==i,1], c=c[i], marker='o', s=40, label='cluster{}'.format(i))   \n",
    "ax1.set_title('K-means Clustering')\n",
    "\n",
    "\n",
    "ac = AgglomerativeClustering(n_clusters=n, affinity='euclidean', linkage='complete')\n",
    "y_ac = ac.fit_predict(X1)\n",
    "\n",
    "for i in range(n):\n",
    "    ax2.scatter(X1[y_ac==i,0], X1[y_ac==i,1], c=c[i], marker='o', s=40, label='cluster{}'.format(i))\n",
    "ax2.set_title('Agglomerative Clustering')\n",
    "\n",
    "\n",
    "# Put a legend below current axis\n",
    "plt.legend()\n",
    "    \n",
    "plt.tight_layout()\n",
    "#plt.savefig('./figures/kmeans_and_ac.png', dpi=300)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "____"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "# 4. Classification- A Supervised Learning Task"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "#### **TIP:**\n",
    "\n",
    "1. [Cross Validation](http://scikit-learn.org/stable/modules/generated/sklearn.model_selection.cross_val_score.html)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "## 4.1 Decision Tree Classifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "collapsed": true,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "dt = DecisionTreeClassifier(min_samples_split=20,random_state=99)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "--Cross Validation Result--\n",
      "[ 0.97122905  0.97234637  0.97402235  0.96899441  0.9765232 ]\n",
      "0.972623075218\n",
      "Time Elapsed:  0.9382190704345703\n"
     ]
    }
   ],
   "source": [
    "tt0=time()\n",
    "\n",
    "print (\"--Cross Validation Result--\")\n",
    "scores = model_selection.cross_val_score(dt, X,Y, cv=5)\n",
    "print (scores)\n",
    "print (scores.mean())\n",
    "tt1=time()\n",
    "print (\"Time Elapsed: \", tt1-tt0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "DECISION TREE: \n",
      "Acurracy:  0.970111731844\n",
      "Time Elapsed:  0.11507940292358398\n"
     ]
    }
   ],
   "source": [
    "t0=time()\n",
    "print (\"DECISION TREE: \")\n",
    "\n",
    "clf_dt= dt.fit(X_train,Y_train)\n",
    "\n",
    "print (\"Acurracy: \", clf_dt.score(X_test,Y_test))\n",
    "t1=time()\n",
    "print (\"Time Elapsed: \", t1-t0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "collapsed": true,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "# Predict clusters on testing data\n",
    "Y_pred = clf_dt.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "             precision    recall  f1-score   support\n",
      "\n",
      "          0       0.98      0.99      0.98      6566\n",
      "          1       0.83      0.80      0.82       594\n",
      "\n",
      "avg / total       0.97      0.97      0.97      7160\n",
      "\n",
      "Confusion matrix\n",
      "[[6468   98]\n",
      " [ 116  478]]\n"
     ]
    },
    {
     "data": {
      "image/png": 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ERlUEexERYMZEClkenaadKfopsBkwNv3/CLA+gJmNJoLy02Xb7wBMcvcNgH8R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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1bc02466320>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plotCM(Y_test, Y_pred)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "___"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "## 4.2 Random Forest Classifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "collapsed": true,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "rf = RandomForestClassifier(n_estimators=100,n_jobs=-1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "--Cross Validation Result--\n",
      "[ 0.97765363  0.98156425  0.97960894  0.97513966  0.9784796 ]\n",
      "0.978489215597\n",
      "Time Elapsed:  6.547849178314209\n"
     ]
    }
   ],
   "source": [
    "tt2=time()\n",
    "print (\"--Cross Validation Result--\")\n",
    "scores = model_selection.cross_val_score(rf, X,Y, cv=5)\n",
    "print (scores)\n",
    "print (scores.mean())\n",
    "tt3=time()\n",
    "print (\"Time Elapsed: \", tt3-tt2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "RANDOM FOREST: \n",
      "Acurracy:  0.980167597765\n",
      "Time Elapsed:  1.063206434249878\n"
     ]
    }
   ],
   "source": [
    "t2=time()\n",
    "print (\"RANDOM FOREST: \")\n",
    "clf_rf = rf.fit(X_train,Y_train)\n",
    "print (\"Acurracy: \", clf_rf.score(X_test,Y_test))\n",
    "t3=time()\n",
    "\n",
    "print (\"Time Elapsed: \", t3-t2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "collapsed": true,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "Y_pred = rf.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "             precision    recall  f1-score   support\n",
      "\n",
      "          0       0.98      0.99      0.99      6566\n",
      "          1       0.93      0.82      0.87       594\n",
      "\n",
      "avg / total       0.98      0.98      0.98      7160\n",
      "\n",
      "Confusion matrix\n",
      "[[6529   37]\n",
      " [ 105  489]]\n"
     ]
    },
    {
     "data": {
      "image/png": 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ccDawKPBV4GV3/xiwHXDqMKRfRLpQlmKSrOXirdaW6lJ3vwW4DjiibPGHgFvS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\nrkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1bc0215c668>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plotCM(Y_test, Y_pred)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "___"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "## 4.3 Naive Bayes Classifier\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "collapsed": true,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "nb = BernoulliNB()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "--Cross Validation Result--\n",
      "[ 0.95223464  0.9452514   0.94581006  0.95055866  0.95556177]\n",
      "0.949883302991\n",
      "Time Elapsed:  0.132066011428833\n"
     ]
    }
   ],
   "source": [
    "tt4=time()\n",
    "print (\"--Cross Validation Result--\")\n",
    "scores = model_selection.cross_val_score(nb, X,Y, cv=5)\n",
    "print (scores)\n",
    "print (scores.mean())\n",
    "tt5=time()\n",
    "print (\"Time Elapsed: \", tt5-tt4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "NAIVE BAYES: \n",
      "Acurracy:  0.949720670391\n",
      "Time Elapsed:  0.010007381439208984\n"
     ]
    }
   ],
   "source": [
    "t4=time()\n",
    "print (\"NAIVE BAYES: \")\n",
    "clf_nb=nb.fit(X_train,Y_train)\n",
    "print (\"Acurracy: \", clf_nb.score(X_test,Y_test))\n",
    "t5=time()\n",
    "print (\"Time Elapsed: \", t5-t4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "collapsed": true,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "Y_predict = nb.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "             precision    recall  f1-score   support\n",
      "\n",
      "          0       0.99      0.96      0.97      6566\n",
      "          1       0.65      0.86      0.74       594\n",
      "\n",
      "avg / total       0.96      0.95      0.95      7160\n",
      "\n",
      "Confusion matrix\n",
      "[[6287  279]\n",
      " [  81  513]]\n"
     ]
    },
    {
     "data": {
      "image/png": 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i2IuIAAsmUsjy6DTtzNE3gZ2Bcen/KcA2AGY2mgjKz5Sl3xuY7O7bAn8nSu2P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\nItJlFLhFRLqMAreISJdR4BYR6TL/H4LTo1vXYBkKAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1bc02367358>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plotCM(Y_test,Y_predict)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "___"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "## 4.4 KNN Classifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "KNN: \n",
      "Acurracy:  0.978770949721\n",
      "Time Elapsed:  0.1706843376159668\n"
     ]
    }
   ],
   "source": [
    "t6=time()\n",
    "print (\"KNN: \")\n",
    "knn = KNeighborsClassifier(n_neighbors=2)\n",
    "knn = KNeighborsClassifier()\n",
    "clf_knn=knn.fit(X_train, Y_train)\n",
    "print (\"Acurracy: \", clf_knn.score(X_test,Y_test) )\n",
    "t7=time()\n",
    "print (\"Time Elapsed: \", t7-t6)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "--Cross Validation Result--\n",
      "[ 0.97625698  0.97932961  0.98072626  0.97709497  0.97568474]\n",
      "0.977818512261\n",
      "Time Elapsed:  0.5076839923858643\n"
     ]
    }
   ],
   "source": [
    "tt6=time()\n",
    "print (\"--Cross Validation Result--\")\n",
    "scores = model_selection.cross_val_score(clf_knn, X,Y, cv=5)\n",
    "print (scores)\n",
    "print (scores.mean())\n",
    "tt7=time()\n",
    "print (\"Time Elapsed: \", tt7-tt6)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "Y_predict = clf_knn.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "             precision    recall  f1-score   support\n",
      "\n",
      "          0       0.98      0.99      0.99      6566\n",
      "          1       0.92      0.81      0.86       594\n",
      "\n",
      "avg / total       0.98      0.98      0.98      7160\n",
      "\n",
      "Confusion matrix\n",
      "[[6526   40]\n",
      " [ 112  482]]\n"
     ]
    },
    {
     "data": {
      "image/png": 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ccDawKPBV4GV3/xiwHXDqMKRfRLpQlmKSrOXirdaW6lJ3vwW4DjiibPGHgFvS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\nrkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1bc02173278>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plotCM(Y_test,Y_predict)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "___"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "## 4.5 SVM Classifier"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "### 4.5.1 SVM with a Linear Kernel "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "SVM (No Kernel): \n",
      "Acurracy:  0.97905027933\n",
      "time elapsed:  0.40779709815979004\n"
     ]
    }
   ],
   "source": [
    "t7=time()\n",
    "print (\"SVM (No Kernel): \")\n",
    "\n",
    "svc = SVC()\n",
    "clf_svc=svc.fit(X_train, Y_train)\n",
    "print (\"Acurracy: \", clf_svc.score(X_test,Y_test) )\n",
    "t8=time()\n",
    "print (\"time elapsed: \", t8-t7)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {
    "collapsed": true,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "Y_pred = svc.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "             precision    recall  f1-score   support\n",
      "\n",
      "          0       0.99      0.98      0.99      6648\n",
      "          1       0.80      0.93      0.86       512\n",
      "\n",
      "avg / total       0.98      0.98      0.98      7160\n",
      "\n",
      "Confusion matrix\n",
      "[[6532  116]\n",
      " [  34  478]]\n"
     ]
    },
    {
     "data": {
      "image/png": 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IaMZ5FBXjZSSDPYfM51zFuUSTuf9rwr6OJ9o4/7YJ+2rG/o8G7i19pi1K0+zyudn7uDas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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1bc00d59518>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plotCM(Y_pred,Y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "--Cross Validation Result--\n",
      "[ 0.97486034  0.97932961  0.98044693  0.97681564  0.97540525]\n",
      "0.97737155366\n",
      "Time Elapsed:  2.8686771392822266\n"
     ]
    }
   ],
   "source": [
    "tt7=time()\n",
    "print (\"--Cross Validation Result--\")\n",
    "scores = model_selection.cross_val_score(clf_svc, X,Y, cv=5)\n",
    "print (scores)\n",
    "print (scores.mean())\n",
    "tt8=time()\n",
    "print (\"Time Elapsed: \", tt8-tt7)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "### 4.5.2 SVM with Multiple Kernels "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 3 folds for each of 6 candidates, totalling 18 fits\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Done  18 out of  18 | elapsed:    7.6s finished\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best score: 0.980\n",
      "Best parameters set:\n",
      "\tC: 10\n",
      "\tkernel: 'rbf'\n",
      "             precision    recall  f1-score   support\n",
      "\n",
      "          0       0.98      0.99      0.99      6566\n",
      "          1       0.93      0.80      0.86       594\n",
      "\n",
      "avg / total       0.98      0.98      0.98      7160\n",
      "\n",
      "Confusion matrix\n",
      "[[6532   34]\n",
      " [ 116  478]]\n"
     ]
    },
    {
     "data": {
      "image/png": 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IaMZ5FBXjZSSDPYfM51zFuUSTuf9rwr6OJ9o4/7YJ+2rG/o8G7i19pi1K0+zyudn7uDas\nfo7bzL5HNE+cm2hiehMxmmGRaBK5v7sXUv+T7Yj2/wd6jEI5ptq6NU8ly2n0ktQR5MsMsot2p3D3\nt929WkAaDkO5xyyNOXORu19DDIRUmnDgMOCUaiPaZbAIcKu7T64YQ6ZksOfQzPvqTt3XkPfv7v8F\nrmPgHp1dI92VbEr0MRlHdP45kegwtjnx3mxvMcvQOGJExZ2ZNYrmHOvWO6Zy3PXtA9xe6glos8ZI\nPojIpS1CdMrZu2wQnbFE558NiVHKzic+mPfSeA3fJDodfIoYGnUuYpyNZ4iu+28TM3+8QHQ0WY7o\nmry7u79lMYb0kcSodMsTnRAuIsYinq3zgc05HvgP0jktSXQbPqw0wp7VGN84vT6GuIBtSnQ4GjB3\nlXrUrQScYmY7uvv4NATA8Wn/8xI53QPcvdogVy+n925Di5HlFiMGglqR6FD1gYGOXZGOWuOfl4p5\nzjGzI9x95aGcwwDrb0h0VNmI+L09mNa/s066J6Y0j0v7WtliPPCfER1sikTnlQPc/d9pm6pjYFuM\nP75HWqfo7nME2dRr8mTiTmYaFWPYpPM+kejluDAxO9Cx7n5Otf3XWr9st5cCZ5vZsV4xdGxL9OWh\nL0u/lbrXpK2Jjn5/InrifpvokXxTev1q4n1w4BqP0TOfM7NRZrYkMapm5bp/qnVA5bjr+yTx4yy3\nCBH8diDGJdmIyAWWBpi/mQiKHyY+wN2J3oAl6xNdpscSvTIhugHPk5b9jrjdOjptuwPxYe6d1v1O\nWr4HMajOt4mcy6dqnUj6YX+bmBxiDSIw/6GsO/OA4xub2VxEkcs0YryOCSkdA9mBGAv8MGCHdLG5\njhgbZRtiXOvliSKQOX4Z6QJ0CFHk8Bjxo3+BCCg/84rJNAY433rjny9LXPQOZNZY5EM5h8r1RxM/\nxAeIXokbE+9fI7M0HUD0zvxlWdp+TXzeHyeCepGYsm+U1RgDm7hw/Z6YZGPZAY53enpvtknnUTn+\n9QXEGCBbEkVUlwFnpABdbf+11i+5lhhvfIMG3o+hyxMVlIN91I+SSxC/iZ2InrcXAnmfNbxxaSKK\nhZh9CN3S8lyVdeueigzAYtD3sUTX4XJ9RE70Pne/gRjCtfTj2pfIOX/D3R9LA/B8CzjAYlCikqPT\n4DjPpf/fIsbKfor48c1LBKu73f1vxMWgNHzoo8Ce7n6Tuz/r7ucTF4qqlVxlViGC2OTUA+wo4kf6\nns0a33g3d3/EY8zpXYFPWIxvvBXRlf9L7v6oxzjXA07wkOoD+olBgF4hBqFaHfiCu/897f/z6f3d\naoB9nEEMObqYux9lMa70lsCvzOxwM3vWzK5PufBqysc/fyQNqrUnsKOZvd9jvPUiMTly5bgygz6H\nKuvPn96jQ939KY8JmEsTHNSU7nKmA295jJO+KnF7vau73+sxYe/uxEVka+ICMnMMbHe/JL1Xj3hM\nk/Y2MQjbHFN/pQv3LsR3+nZ3v4v4Hpe7AtjX3R9KAzodk463+gD7H3D9snN8hxh2tdpFs/laVzk5\nBfiru093dydGkiwPvqOJjNobzD5Of2l5ocqymhS4a1uc+OFXa5Ux0JjZawJ3+uyTCdyaXn9/+v9N\nn3PexMllxRylstbKcbXnAXD3PwP9ZvYTM/uzmT1FBIN6VS8XEMNwPm5m9xGDCD2eynbLxzeeZmbT\nmDX4/xpp/5MrKmgHMzDQmmn7f5cWuPvzlM1qU427v+kx4iPEmBg/Ji4w+xJ3Ln8hhjEd6Ji1xj8f\nrEGdQ/qMzwS+ZmZnm9ktRLFZlt9dqSL28bLPZwoxSNMazD4G9rMWI/DNqPI9q8aI7+d9ZcvuZfaA\ncjqwkZmdYmZ/ZdaIiAN95xpdfwoDjebXbFly26VHbbcSGZxcqjhfgBgIb3x6fRuiOPU2YjC6fMps\n5D3GX7q/yro1KXDXVvriVnufBhozu9q4zaXt+2qsU62Mb6BxtX9ITNYwiije2I5ZkzAMKH1JPkTM\nRXkjkSN9wMw+SP3xjYvMWdhXbezpgTQ6nnVVZrYGUdRwDnFLX6p3uJw5h2Ctd8zK8c8bNahzsBg9\n8WGismkSUfT1lQzHhfh8pjPn57M6MfFDzTGw6yhlMso/3/70KM0q9RdiOrgpxB3hQO/5YNfvY/aR\nNlsnnzG3XafTTrqrvp/IyFxOzHd7MHCkmd1BtDS5xGMAsFuIIrA/pvWotm69U1HlZG1TiIC6xCC2\nmQR8yszKy602JYooniImSB2qbxC3taVJc+clKsVqZg0shqVd3d1/AVxrMRPOU8RV/gFmjW9cqmRd\niRhp8UAiAK1sZkuX5eLGVh6jQvldxyRgJTNbrqwybQWi+KWR8awnEEVHM1KlYunXNBcDn/ckYjje\n+cpy3YMd/3yw51C+/g7E92er0nchveel4DbYY89NTFr9QNrHgkQF8XFm9hrwVXc/gKgAPdbMLiSK\nciZSfTKQEicuChsxq1JsHWa/i9wSWM3dn07HLo1rXjqP8v03sn7JEoyAmdvdvVp9z7gq600gvsvl\nyx6vtm4tCtw1uHsxFSmsS/UZbao5jWg1cnK6XV2VaAlwnru/ZvXnr2zEFGA7M7uZqPCYQJQFz1Nn\nuxzw49RK43bih7oMUXxwI7XHN55MBI/z0+vvI1q+1DIN+ECqOLuWCCgXWczrmCOGf308vTYgM1uH\nCCSl+ULvJsba3oCooK2cpq6k5vjnddKe9RzK159CzMG4nZk9QowHf3har95nVdrXGDNb3t3dzC5L\n57I/UXz3I+IOahJRJzLQGNilfa1tZit7xQh3qYXNWcCJZvYqUfR3OrOC8WvE92HndDFYnVlD+ZbO\nY+b+G1wfi1l3VqZsirmW6stnu8/qsAGmQEUljbiSQVwNU07sE0RN+YPEj+g8Zt0WNcOeREXjw0SR\nybNEZ5WatfPufiXRUuNoItgcR1SiXu91xjdOzbW2Je4c7iQC1hwz2Vc4iZjg4K8px/lpYnKJG4lA\n9zywpc85RnalI4EjS3UAHmNu/zrtY2MGGMvZmzP++WDPYeb6REuLM4kc70NEufxXiIDYSEuKXxJt\ngx+06JC0B1H2/Gfi4jU/kZt/3WuMgZ32NZEI5o/arEkqyn0rrX9pSvt5pKIwd/8X0YpqH6KFz8lE\nYH+o7Dxm7p8I2vXWhyjyeoGKSStapo+MZdzDkrpB0XjcdZjZ8kSQW7XBih4RaYCZ/RaY5O5Ht/g4\nKwPPXDcmzwpzD74f0vPTi2z5ZAE6aDxu5bjrSLmNiUTuQUSaIFXcjmdW78HWa12rkmGnwN2Yw4jy\nuq4YZEqkCxzO7HOIyiCocrIBqTNElna/IlKFuzezzqcx+Xy23HO+yHC1WGyUAreI9IasxR4dWDnZ\nk4HbzOYh2vO+QKddSkWkmj5iHJR73P3dbHvIZRtbu6/z2gP2ZOAmgnbdbqUi0nE2J7qYD14+Y447\n33mVk70auF8AuPC551hmRutHk5Th9QueaXcSpMneGvUi1624K6TfbiZZZ7NRUUnH6AdYZsYMVlDg\nHnEWZIV2J0FaR0Wb9G7gFpFek7lyUkUlIiLtkc9YVNKBvV0UuEWkN2RuVdJ5w4IocItIb8jnsrUQ\n6cBWJR14EyAiIrUoxy0ivSFzGbeKSkRE2kOtSkREuoxy3CIiXaY0A06W7TqMAreI9IZ8/RnbB9yu\nw3ReikREpCbluEWkN2h0QBGRLpN5dMDOK5hQ4BaR3qDmgCIiXSaXy1bRmOu8wN159wAiIlKTctwi\n0htUVCIi0mVUOSki0mVyGYd17cAybgVuEekNynGLiHQZTaQgIiLtohy3iPSGfMY5Jzswx63ALSK9\nYQQVlShwi0hvUOWkiEiXGUE57s67lIiISE3KcYtIb8g852Tn5W8VuEWkN+TJWFTS9JQMmQK3iPQG\nVU6KiHSZFo5VYmb3AW+kf58BfgWcBMwArnH3I80sD5wOrAu8C+zt7k+a2caV69Y7ngK3iPSGFs3y\nbmbzAjl3H1+27AHgs8DTwJVmtj6wCjCvu2+SgvXPgO2BMyrXdff7ax1TgVtEZGjWBeY3s2uImDoB\nmMfdnwIws78CWwHLAn8BcPc7zexDZrbQAOsqcIuItHAihbeAE4CzgPcDVwOvlb0+FVgVWAh4vWx5\nf1r2RpV1a1LgFpHe0Lo5Jx8HnnT3IvC4mb0OLFb2+mgikM+fnpfkiaA9usq6NXVedamISCuUek5m\nedT2JaK8GjNbjgjQb5rZamaWA7YGbgFuA7ZN620MPOzubwDTq6xbk3LcItIbWldUcjYw0cxuBYpE\nIC8AFwJ9REuRu8zsHuBjZnY7kAP2StvvV7luvQMqcIuIDIG7Twd2qfLSxhXrFYggXbn9nZXr1qPA\nLSK9oUXNAdtBgVtEekIxl6OQoQNOUZMFi4i0RyGfp5Ah95xlm1ZT4BaRnlDIkynHXei8uK3ALSK9\noT+fpz/DgFH9HZjj7rwUiYhITcpxi0hPKOYzVk524NRlCtwi0hOK+TzFDMUeWbZpNQVuEekJhYw5\n7izbtJoCt4j0hELGdtyFDmzH3Xn3ACIiUpNy3CLSE6JyMksZd+fluBW4RaQnqIxbRKTL9Ofy9Ocy\ndMDJsE2rKXCLSE8YSTnuYb2UmNkEM7vbzEaVLbvTzFausc0vzGzFYUmgiIxYpdEBB/vQ6IBhZeB7\nwNGNrOzuB7Y0NSIiXaYdgft4YG8zu8LdZ05Bb2ZzAecSMxz3ASe6+8VmdiMxa8TixLxu7wGvArsC\n7wBnEDMr54HD3f3G4TsVEekWxXwuY8/Jzstxt6PUfRqwDzFH2zxly/cF/uvumwJbAceY2RJlr38a\nuBQYB5wDLArsDbzs7h8BtgdOG4b0i0gXylJMkrVcvNXaUl3q7jcD1wJHlS3+AHBzen0q8CiwWtnr\nxwFLAdcBOxI573WAbVOu/I/AqIpgLyICzJpIIcuj07QzRd8npqofk/6fBGwOYGajiaD8TNn6uwET\n3f2jwD+IXPtjwEXuPh7YBvgD8MpwJF5EukuRXHR7H+SjiHLcM7n7O8T09AunRb8GFk9T3N8IHOnu\n/ynb5G7gPDO7CdgCOB/4FbBGWnY7MDnNpCwiMmINa+Wku0+o+P9eYK6yRXtU2WZ82b8bVNntF5uR\nNhEZ2UZSO251wBGRnqDJgkVEukx/DvozdKbp77wMtwK3iPSGYsYct2bAERFpkyLZuq+rVYmIiAyZ\nctwi0hPUqkREpMsUcnkKGcbWzrJNqylwi0hPKGbMcXfiIFMK3CLSEwq5bDO2FzovbqtyUkSk2yjH\nLSI9oT+Xpz9Dm2zNOSki0ial0f6ybNdpFLhFpCcUMwZuzTkpItImxXw+49RlKioREWmLkVRU0nmX\nEhERqUk5bhHpCSOpHbcCt4j0hFZXTprZUsDfgY8BM4CJQBF4BNjf3QtmdgSwXXr9QHe/28zGVFu3\n1rFUVCIiPaFAfuZ4JYN6NBAmzWwuYg7ct9OiE4HD3X1zIAdsb2ZjgXHARsDOwGkDrVvveArcItIT\nsszwPogKzROAM4B/p/83AG5Kz68GtgI2A65x96K7PweMMrMlB1i3JgVuEZEhMLM9gf+6+1/LFufc\nvZieTwUWBhYCXi9bp7S82ro1qYxbRHpCfz5Hf4aR/hrY5ktA0cy2AtYDzgeWKnt9NPAa8EZ6Xrm8\nUGVZTcpxi0hPKGYp387lKdYZq8TdP+Lu49x9PPAA8EXgajMbn1bZBrgFuA3Y2szyZrYikHf3l4H7\nq6xbk3LcItITirls3deL2ZoDHgycaWZzA5OAS9y938xuAe4gMs37D7RuvZ0rcItITyiQo5Bh4t/B\nbJNy3SXjqrw+AZhQsezxauvWosAtIj1BXd5FRKRtlOMWkZ5QqpzMsl2nUeAWkZ5QIGNRSYZy8VYb\nMHCb2cm1NnT3bzY/OSIirdGfy9GfIXBn2abVauW4pwxbKkREWmwkVU4OGLjd/cjSczObDxgD/AOY\nx93fHmg7ERFprbql7ma2EfAUcCWwHPC8mW3a6oSJiDRTkVyMEDjIR7EDy7gbqS49gRitaoq7Pw/s\nDpzU0lSJiDRZMZfL/Og0jQTu+d390dI/7n4Vao0iIl2mxcO6DqtGAvB7ZrYoMTsDZmatTZKISPMN\nR5f34dIA2U3EAAAUx0lEQVRI4D6GGOR7WTO7CPg4sE9LUyUi0mQ91QHH3a8ws8eIedT6gKPLi05E\nRGR4NVpWPRcRtN8DprcuOSIirTGSikoaaQ64F3AjsCExZ9otZvbZFqdLRKSpCrmsFZTtTvmcGslx\nHwSs5+4vAKSZG64A/tjKhImINFM/Ofoz5J6zbNNqjZS6Ty8FbYA0O/F7rUuSiEjzFTM2BezEdty1\nBpkam54+aGanAr8C+oE9ibnTRESkDWoVlVQWhWxX9rwIaHRAEekaRXKZuq93Ypf3WoNMrTKcCRER\naaUC2dpxFzpworC6lZNmtgQxPsmCQI5oFjjG3XdtcdpERJpmJDUHbKRVye+Bt4G1gL8RHXFuaWWi\nRESarZgxcHdiUUkj9wAruft2wFXAqcD/AGu0NFUiIk1WYFaue3CPztNI4H4x/X0CWNvd/0X0pBQR\nkTZopKjkP2b2beAO4EgzewOYv7XJEhFprpE052QjOe59gXfd/VbgXuAo4NCWpkpEpMmyFZNkKxdv\ntUZGB/wPcHJ6figK2iLShYppKrIs23WaWj0np5ImT6jG3RdqSYpERFqgJzrgAGsPWypERKRhtXpO\nTh7OhLTDT+d9mgX6V2h3MqTJnl+7ExtwyVC8WyjC1KHto9c64IiIdD0FbhGRLlMkWxAesKKvjRoK\n3GY2HzAGeASY193fbmmqRESarKcmUjCzjYGngCuB5YHnzWzTVidMRESqa6SB4k+BrYAp7v48MVLg\nSS1NlYhIk5WaA2Z5dJpGAvf87v5o6R93vwqVjYtIl+mpnpPAe2a2KKmM3systUkSEWm+QjFHfzFD\nq5IM27RaI4H7GOAmYBkzuwj4OLBPS1MlItJkPdUc0N2vMLPHiAkU+oCjy4tORES6wUjq8t5Iq5LF\ngFeAi4HfAi+mZSIi0gaNFJW8zJxt0F8A1FdcRLpGFJVkmSy4do7bzPqAMwED+oG9iPl5JxKx8xFg\nf3cvmNkRwHbADOBAd7/bzMZUW7fWMeuehbvn3b3P3fuA+YAvAxfU205EpJMUyVEoDv7RQFHJ/wK4\n+/8APwROTI/D3X1zIohvb2ZjgXHARsDOwGlp+znWrXfAQV1+3H26u08kyrtFRLpGqedklkct7v5n\nZjXYWAl4CdiAaNQBcDXRF2Yz4Bp3L7r7c8AoM1tygHVrqltUUlGenQM+BCxabzsRkU5SLOYoZmja\n18g27j7DzM4DPgPsCHzS3UtFzFOBhYGFgCllm5WW56qsW1MjOe6Xgf+W/T0P+F4D24mI9Ax33wNY\nnSjvnq/spdHAa8Ab6Xnl8kKVZTU1Ujm5obv/vYH1REQ6VqvacZvZ7sAK7v4j4C0iEN9rZuPd/UZg\nG+AG4EngeDM7gWjckXf3l83s/irr1tRI4P4N8IEG1hMR6Vgt7Dl5KXCumd0MzAUcCEwCzjSzudPz\nS9y938xuAe4gSjv2T9sfXLluvQM2ErgfMrNdgFuBaaWF7v5KA9uKiHSEVuW43f1N4HNVXhpXZd0J\nwISKZY9XW7eWRgL39sBOFcuKRC9KEZGu0MrKyeFWa5b3edz9XXefdzgTJCLSCgWyjTvSiTOY1mpV\ncsewpUJERBpWq6ik8+4PREQyKqaekFm26zS1Ave8ZrY+AwRwd7+vNUkSEWm+/oytSrJs02q1Aveq\nwB+pHriL6XURka4wkoZ1rRW4H3X39YctJSIiLVTIWFTSiTPgDH6MQxERaataOe6bhy0VIiIt1hNz\nTrr7AcOZEBGRVioAhcopYRrcrtM00nNSRKTr9UTPSRGRkWQkVU4qcItITyiSrct7htKVllOrEhGR\nLqMct4j0hP5ijnwP9JwUERkxVDkpItJlCoUchUKGyskM27SaAreI9ISR1AFHlZMiIl1GOW4R6QkF\nMrbj7rLRAUVERgxVToqIdBn1nBQR6TLFYrZBpood2HVSgVtEekJ/AcjQtK+/A4cHVKsSEZEuoxy3\niPQEVU6KiHSZQjFHTpWTIiLdo1DMZSrjVuAWEWmTQjEHIyTHrcpJEZEuoxy3iPSEYiEeWbbrNArc\nItITRlJRiQK3iPSEYsbxuIsaj1tEpD36ydYcsKjRAUVE2qOYsct7J5Zxq1WJiEiXUY5bRHpCoQg5\njQ4oItI9CoVsPScp5DquaKJlgdvMxgO/Bx4FisB8wIXufkqVdVcGfufuG7cqPSLS2xS4G3e9u+8M\nYGbzAG5mF7j7ay0+rojIbIpka8dNj7cqGQ30A9ea2W7u/piZ7QcsA0wsrWRmxwJbAH3Ab939F2Y2\nDjgirTI/8EVgOnA5MAW4CpgG7AEUgFvd/dvDclYi0tPMbC7gHGBlYB7gGKKkYSJR2vAIsL+7F8zs\nCGA7YAZwoLvfbWZjqq1b65itvgPYwsxuNLPrgQuBbxABtpYvArsAmwNvp2VrAbu5+xbAZcBOafky\nwMfd/XhgL+AAd98EeNrMVH4vIjMVCtkfdewGTHH3zYFPAKcCJwKHp2U5YHszGwuMAzYCdgZOS9vP\nsW69Aw5bUUmJmX2n7N9q9yA7Az8igvLVadm/gJPNbBqwPHBbWv6Mu09Pz/cCDjGznwB3DLBvEelR\nxUIuUy/IXP1t/gBcUlqdyE1vANyUll0NfBxw4Bp3LwLPmdkoM1tygHX/VOuA7ShzfwdYNj0fW/5C\nKgffCfgC8FFgTzNbCTgT2Mvd9wT+zaygXH4t/Aqwn7uPA9YHNm3VCYhI9+kv5DI/anH3ae4+1cxG\nEwH8cCCXAjTAVGBhYCHg9bJNS8urrVtTOwL3ycDpZvZXohx7Jnd/F3gFeBC4AbgGeA74DXCXmd1G\nlJUvV2W/DwP3mNkNwH+Au1p2BiLSdQrFGKtk0I8GKjTN7H1EzLrA3X/L7JnK0cBrwBvpeeXyauvW\n1LKiEne/EbixyvKriMrEShun148Cjqp47aD0qLpN2u4s4KxsqRWRkS7rsK7U2cbMliYymV939+vS\n4vvNbHyKg9sQQf1J4HgzOwFYAci7+8tmVm3dmlSBJyIyNIcBiwI/MLMfpGUHEPVycwOTgEvcvd/M\nbiHq4PLA/mndg4Ezy9etd0AFbhHpCYViY8UelfJ1tnH3A4hAXWlclXUnABMqlj1ebd1aFLhFpCdE\n074sPSebn5ahUuAWkZ7QYJvsKhs2PSlDpsAtIj2hWMzWjrvYgVOXddrYKSIiUody3CLSE7LOOdlA\nz8lhp8AtIj2hvxCPQVMZt4hIexSU4xYR6S7FQo5if5bJghW4RUTaopCxqCTXgUUlalUiItJllOMW\nkZ6QtYw7U2/LFlPgFpGeUChm6zlZKNZfZ7gpcItIT8gVcplaiKhViYhIm/T1Z2tV0pdhm1ZT5aSI\nSJdRjltEekK+EI8s23UaBW4R6Qm5IuSzlHGrclJEpD1y/fHIsl2nUeAWkZ7QV8hBhhx3n1qViIi0\nR76YbZb3fAcWlahViYhIl1GOW0R6Qq4/Rz5Dm+xcB7bjVuAWkZ6gnpMiIl2mr5CthYjacYuItEk+\nY6uSLG2/W02VkyIiXUY5bhHpCbl+yKsDjohI98hlLCpR5aSISJv0FaCYJcetykkRkfbIF7L1nFTg\nFhFpk1x/Lltnmg7sgKNWJSIiXUY5bhHpCbkikKXYowMHmVLgFpGe0Nefy1bs0YFFJQrcItIT8v1A\nljbZasctItIeuWK2NtnFYueVlqhyUkSkyyjHLSI9Ieuck/R3Xo5bgVtEekJfxnbcxf5cpsYoraTA\nLSI9IVfINshUodOiNgrcItIj8oWMkyI0uI2ZbQT8xN3Hm9kYYCJRyvIIsL+7F8zsCGA7YAZwoLvf\nPdC6Nc8lw2mIiHSdUpf3LI96zOw7wFnAvGnRicDh7r45kAO2N7OxwDhgI2Bn4LSB1q13PAVuEZGh\newrYoez/DYCb0vOrga2AzYBr3L3o7s8Bo8xsyQHWrUmBW0R6Ql8B+vozPBooKnH3PwLvlS3KuXup\nMcpUYGFgIeD1snVKy6utW5PKuEWkJ2SdASfLGN7MXjI+GngNeCM9r1xebd2alOMWkZ6QL+TI92d4\nZJsB534zG5+ebwPcAtwGbG1meTNbEci7+8sDrFuTctwi0hNyhWyTImScSOFg4EwzmxuYBFzi7v1m\ndgtwB5Fp3n+gdevtXIFbRKQJ3P1ZYOP0/HGiBUnlOhOACRXLqq5biwK3iPSEUmXjoGl0QBGR9sil\nMu7BKmiWdxGR9shnbFWSZZtW69XA3QfwVt+L7U6HtMC7HTi2hAzNu4WZv9W+rPtQ4O5+ywLcvPRu\n7U6HtMLUdidAWmhZopfioOUzFpVkbA7YUr0auO8BNgdeoCOrHkSkQh8RtO9pd0I6QU8Gbnd/F7i1\n3ekQkUHJlNMuydpzMtPkCy3Wk4FbRHqPyrhFRLpMPuNECpnG8G4xBW4R6QmlsUeybNdpNMiUiEiX\nUY5bRHpC1jknMw4y1VIK3CLSE/L9kM9QxqDKSRFpCjPLE/MTbgrc5e7T25ykjqfALSJtY2Y/AZ4G\nVgLGAi8Be7Q1UV1gJPWcVOXkCGdmi7U7DdJ0m7n7r4BN3P0TwPvanaBuUOqAM9iHOuDIsDGzccBp\nQJ+Z/QGY7O5ntzlZ0hx9ZvZh4Nk0a8oS7U6QDC/luEeuo4GPAC8CxwFfa29ypInOA04BTgCOB05q\nb3K6Q5bcdtbelq2mHPfIVXD3V8ys6O7vmJnGzBs5FnD3jdLzA9uaki4yknpOKsc9cj1pZj8CFjez\n7wKT250gaZptzSzzuNS9Sjlu6Qb7AXsToyBOA77S3uRIEy0J/NvMngGKQNHdN21zmjpergB5Bt9C\nRB1wZDjNA1wB/JkI2suiXPdI8cl2J0DaS4F75LoQOAfYEXgU+DWwdVtTJM0yF7BT+psDlgP2bWuK\nukC+H/LFDNt1YI5bZdwj16LA5cAK7v5jIgcuI8P56e9mwCrA4m1MS9cYSWXcCtwj19zAwcDfzWxN\nYME2p0ea5y13/xHwvLvvCSzd5vR0hVKrkkE/lOOWYXQwsBRwLLAFasc9kuTMbBlgQTNbAFDv2AaM\npJ6TCtwjjJmtkJ6+DJxFBO9rgNfalihptiOBzwC/IcYsubq9yekOIynHrcrJkeeg9PgV0VSs1P6p\nSOS8pcu5+83AzQBm9oC7P9fmJMkwU+AeYdz9oPT3o2a2OLAa8LS7v9zelEmzmNk3gbeBRYC9zOwv\npc9dBpafka2IIQ8dFylVVDJCmdlOwO3AYcCdZrZbm5MkzbMLMV7JNu6+JrBem9PTFUpzTmZ5dJoO\nu45IEx0EbODu08xsNHA9USYq3a9AdKh6Kf2v5oANyBWytePOdV7cVo57BCu4+zQAd58KvNPm9Ejz\n3ECUcZ9iZj8HLmlzerrCSGrHrRz3yPW0mf2M+IF/BHiqzemRJnH37wPfBzCze9z9vTYnSYaZAvfI\ntRfRDfpjwCTgu+1NjgyVmd1BtA6qXI4GmaovX8jWtC/LPJWtpsA9wpjZimX/Xp4eEGWiajbW3XZu\ndwK6WdY22VnKxVtNgXvkuZg522+PARZG45V0NXefDGBmP6zy8lHDnJyuk8sYuHMK3NJq7r5J6Xma\nj/AoYpySbdqWKGm2UmuSHDHLewfezHeezDPgND8pQ6bAPUKZ2brAROBaYEN3f7e9KZJmSTO8z2Rm\n6vLegPyMjIFbOW5pNTPLE51udgH2cfdb25wkaTIzW73s32WBldqVFmkPBe6R5w7ih3w8sGYa0hUA\nd/9121IlTWFmCxEdqaYRRSVvEyNBSh2Zi0o6sAOOAvfIc1X6Ozo9Sjrwhk8Gw8y+TgTpfuCH7v6X\nNiepq+QydqbpxJ6TCtwjjLsf2e40SMvsAhiwEHABoMA9CO/kXsyUe34n92LzEzNECtwi3eMdd58O\nvJxaDElj3gBevXbFXRcdwj5eTfvpCArcIt2pA2/gO5O7v2JmY4g7lazecPdXmpWmocoViyr6FOkG\nZvYScB0RtLdIzwFw913alS4Zfspxi3SPz5U9P6NtqZC2U45bRKTLdGJvThERqUGBW0Sky6iMWwbN\nzFYmJmZ4uGxxDjjJ3c8Z4r6vAC5x94lm9gAw3t1fG2DdhYE/ufugZq83sx2Br7v7+Irl44FT3X3t\nOtsXgSUHMwGzmU0EHnH3EwaTVpFqFLglq7fdfeYktWa2PPCImd3r7g814wDl+x/AosCHm3EskW6i\nwC1N4e7/MrMngNXNbCzwZWAB4HV3/6iZfRn4GlE8N4XI8T5mZssRM5YvB0wGlirtszxna2bfA/YA\nZgBPAHsC5wLzpZz5BsDqwEnE5Ll9wMmlOwAzOwrYNR37iXrnkwZyOo0YEnc54AHg8+5emrvzWDPb\nMJ3P4e5+Rdqu6nkO6s0UqUNl3NIUZrYJMWHDXWnRWkQxx0fNbBwRdDd39/WJAbAuTeudBtzp7msB\n3wTWqLLvTxGBepNUjPEM8HVierZSzj9HTJr7XXffABgHHGJmG5vZ9sBngfWATYlJJer5CnBeGt98\nDLAKsF3Z60+7+1hgN+A8M1uyznmKNI1y3JJVKacL8T16GdjV3f9pZgAPuXupi/B2RPC7Pb0GsJiZ\nLQZsBRwC4O5Pmtn1VY61FfAHd381rXcQzCxrL1kdWA04p+wY8wHrA2sCl6bZ7jGzc4iLRC2HAh8z\ns++kfS9H5L5LzkhpecTMHgU2ATarcZ4iTaPALVm9XacMelrZ8z7gAnc/FGaOGb4cMf5D+TRrEEUh\nlWZQNrqhmS0CLFKxTh/wWkW5+9LA60TOt94xKl1E/D5+D1wJrFixj/Jx5nLAe9Q+T5GmUVGJDIdr\ngC+Y2bLp//2Y1V37L8A+MHOi449W2f5aYIc0FjXABOAgIgD3mVkOcOAdM9st7et9wCNE2fdfgJ3M\nbJEUTHdvIM1bA0e5+8Xp/42IwFyyZzrOWOD9RBFRrfMUaRrluKXl3P2vZvYT4G9mViBGWdvB3Ytm\ntj9wrplNAp4nKgErt78qTQhxWyqC+AdRBv0WcHf6f3Nge+CkVLwxF/ADd78NwMzWAe4lcr8PAkvW\nSfZhwJ/M7E0i134TUQxSsqqZ3U/cCeycBiCqdZ6DectEalKXdxGRLqOiEhGRLqPALSLSZRS4RUS6\njAK3iEiXUeAWEekyCtwiIl1GgVtEpMsocIuIdJn/B48sqrRpV6zGAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1bc00d9a358>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.svm import SVC\n",
    "from sklearn.model_selection import cross_val_score\n",
    "from sklearn.pipeline import Pipeline\n",
    "from sklearn import model_selection\n",
    "\n",
    "svc = SVC()\n",
    "\n",
    "parameters = {'kernel':('linear', 'rbf'), 'C':[1, 10, 100]}\n",
    "\n",
    "grid = model_selection.GridSearchCV(svc, parameters, n_jobs=-1, verbose=1, scoring='accuracy')\n",
    "\n",
    "\n",
    "grid.fit(X_train, Y_train)\n",
    "\n",
    "print ('Best score: %0.3f' % grid.best_score_)\n",
    "\n",
    "print ('Best parameters set:')\n",
    "best_parameters = grid.best_estimator_.get_params()\n",
    "\n",
    "for param_name in sorted(parameters.keys()):\n",
    "    print ('\\t%s: %r' % (param_name, best_parameters[param_name]))\n",
    "    \n",
    "predictions = grid.predict(X_test)\n",
    "#print (classification_report(Y_test, predictions))\n",
    "plotCM(Y_test,Y_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 3 folds for each of 30 candidates, totalling 90 fits\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Done  42 tasks      | elapsed:   11.9s\n",
      "[Parallel(n_jobs=-1)]: Done  90 out of  90 | elapsed:   23.0s finished\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best score: 0.980\n",
      "Best parameters set:\n",
      "\tclf__C: 3\n",
      "\tclf__gamma: 0.3\n",
      "             precision    recall  f1-score   support\n",
      "\n",
      "          0       0.98      0.99      0.99      6566\n",
      "          1       0.93      0.80      0.86       594\n",
      "\n",
      "avg / total       0.98      0.98      0.98      7160\n",
      "\n",
      "Confusion matrix\n",
      "[[6532   34]\n",
      " [ 117  477]]\n"
     ]
    },
    {
     "data": {
      "image/png": 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IaMZ5FBXjZSSDPYfM51zFuUSTuf9rwr6OJ9o4/7YJ+2rG/o8G7i19pi1K0+zyudn7uDas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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1bc00dc9198>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "pipeline = Pipeline([\n",
    "    ('clf', SVC(kernel='rbf', gamma=0.01, C=100))\n",
    "])\n",
    "\n",
    "parameters = {\n",
    "    'clf__gamma': (0.01, 0.03, 0.1, 0.3, 1),\n",
    "    'clf__C': (0.1, 0.3, 1, 3, 10, 30),\n",
    "}\n",
    "\n",
    "grid_search = GridSearchCV(pipeline, parameters, n_jobs=-1, verbose=1, scoring='accuracy')\n",
    "\n",
    "grid_search.fit(X_train, Y_train)\n",
    "\n",
    "print ('Best score: %0.3f' % grid_search.best_score_)\n",
    "\n",
    "print ('Best parameters set:')\n",
    "best_parameters = grid_search.best_estimator_.get_params()\n",
    "\n",
    "for param_name in sorted(parameters.keys()):\n",
    "    print ('\\t%s: %r' % (param_name, best_parameters[param_name]))\n",
    "    \n",
    "predictions = grid_search.predict(X_test)\n",
    "plotCM(Y_test, predictions)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "___"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "## 4.6 Leveraging weak learners via Adaptive Boosting"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Decision tree train/test accuracies 0.976/0.977\n",
      "             precision    recall  f1-score   support\n",
      "\n",
      "          0       0.99      0.98      0.99      6609\n",
      "          1       0.82      0.89      0.85       551\n",
      "\n",
      "avg / total       0.98      0.98      0.98      7160\n",
      "\n",
      "Confusion matrix\n",
      "[[6504  105]\n",
      " [  62  489]]\n"
     ]
    },
    {
     "data": {
      "image/png": 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Vtq31eirjbszhxMhsHR+0q2jneOD/x4zdwRtmMbrfGURb4h2A35rZfGndIkQ746E2O4QY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\nIl1GgVtEpMsocIuIdBkFbhGRLvP/KCCzE+uabTUAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1bc00db3358>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.ensemble import AdaBoostClassifier\n",
    "\n",
    "tree = DecisionTreeClassifier(criterion='entropy', \n",
    "                              max_depth=1)\n",
    "\n",
    "ada = AdaBoostClassifier(base_estimator=tree,\n",
    "                         n_estimators=500, \n",
    "                         learning_rate=0.1,\n",
    "                         random_state=0)\n",
    "\n",
    "tree = tree.fit(X_train, Y_train)\n",
    "y_train_pred = tree.predict(X_train)\n",
    "y_test_pred = tree.predict(X_test)\n",
    "\n",
    "tree_train = accuracy_score(Y_train, y_train_pred)\n",
    "tree_test = accuracy_score(Y_test, y_test_pred)\n",
    "print('Decision tree train/test accuracies %.3f/%.3f'\n",
    "      % (tree_train, tree_test))\n",
    "plotCM(y_test_pred,Y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "AdaBoost train/test accuracies 0.979/0.980\n",
      "             precision    recall  f1-score   support\n",
      "\n",
      "          0       0.99      0.98      0.99      6640\n",
      "          1       0.82      0.93      0.87       520\n",
      "\n",
      "avg / total       0.98      0.98      0.98      7160\n",
      "\n",
      "Confusion matrix\n",
      "[[6531  109]\n",
      " [  35  485]]\n"
     ]
    },
    {
     "data": {
      "image/png": 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8WKIZ5xFUjJeRDPYcMp9zFecQTeb+rwn7Oo5o4/y7JuyrGfs/Eri39Jm2KE2zy+dm7+Pa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\ngg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1bc00ca1198>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ada = ada.fit(X_train, Y_train)\n",
    "y_train_pred = ada.predict(X_train)\n",
    "y_test_pred = ada.predict(X_test)\n",
    "\n",
    "ada_train = accuracy_score(Y_train, y_train_pred) \n",
    "ada_test = accuracy_score(Y_test, y_test_pred) \n",
    "print('AdaBoost train/test accuracies %.3f/%.3f'\n",
    "      % (ada_train, ada_test))\n",
    "plotCM(y_test_pred,Y_test)"
   ]
  }
 ],
 "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": 0
}