lab6-churn.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "hide": true
   },
   "source": [
    "#Comparing and evaluating models\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false,
    "hide": true
   },
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "import numpy as np\n",
    "import scipy as sp\n",
    "import matplotlib as mpl\n",
    "import matplotlib.cm as cm\n",
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "pd.set_option('display.width', 500)\n",
    "pd.set_option('display.max_columns', 100)\n",
    "pd.set_option('display.notebook_repr_html', True)\n",
    "import seaborn as sns\n",
    "sns.set_style(\"whitegrid\")\n",
    "sns.set_context(\"poster\")\n",
    "from PIL import Image"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true,
    "hide": true
   },
   "outputs": [],
   "source": [
    "from sklearn.grid_search import GridSearchCV\n",
    "from sklearn.cross_validation import train_test_split\n",
    "from sklearn.metrics import confusion_matrix\n",
    "def cv_optimize(clf, parameters, X, y, n_jobs=1, n_folds=5, score_func=None):\n",
    "    if score_func:\n",
    "        gs = GridSearchCV(clf, param_grid=parameters, cv=n_folds, n_jobs=n_jobs, scoring=score_func)\n",
    "    else:\n",
    "        gs = GridSearchCV(clf, param_grid=parameters, n_jobs=n_jobs, cv=n_folds)\n",
    "    gs.fit(X, y)\n",
    "    print \"BEST\", gs.best_params_, gs.best_score_, gs.grid_scores_\n",
    "    best = gs.best_estimator_\n",
    "    return best\n",
    "def do_classify(clf, parameters, indf, featurenames, targetname, target1val, mask=None, reuse_split=None, score_func=None, n_folds=5, n_jobs=1):\n",
    "    subdf=indf[featurenames]\n",
    "    X=subdf.values\n",
    "    y=(indf[targetname].values==target1val)*1\n",
    "    if mask !=None:\n",
    "        print \"using mask\"\n",
    "        Xtrain, Xtest, ytrain, ytest = X[mask], X[~mask], y[mask], y[~mask]\n",
    "    if reuse_split !=None:\n",
    "        print \"using reuse split\"\n",
    "        Xtrain, Xtest, ytrain, ytest = reuse_split['Xtrain'], reuse_split['Xtest'], reuse_split['ytrain'], reuse_split['ytest']\n",
    "    if parameters:\n",
    "        clf = cv_optimize(clf, parameters, Xtrain, ytrain, n_jobs=n_jobs, n_folds=n_folds, score_func=score_func)\n",
    "    clf=clf.fit(Xtrain, ytrain)\n",
    "    training_accuracy = clf.score(Xtrain, ytrain)\n",
    "    test_accuracy = clf.score(Xtest, ytest)\n",
    "    print \"############# based on standard predict ################\"\n",
    "    print \"Accuracy on training data: %0.2f\" % (training_accuracy)\n",
    "    print \"Accuracy on test data:     %0.2f\" % (test_accuracy)\n",
    "    print confusion_matrix(ytest, clf.predict(Xtest))\n",
    "    print \"########################################################\"\n",
    "    return clf, Xtrain, ytrain, Xtest, ytest"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true,
    "hide": true
   },
   "outputs": [],
   "source": [
    "from matplotlib.colors import ListedColormap\n",
    "cmap_light = ListedColormap(['#FFAAAA', '#AAFFAA', '#AAAAFF'])\n",
    "cmap_bold = ListedColormap(['#FF0000', '#00FF00', '#0000FF'])\n",
    "cm = plt.cm.RdBu\n",
    "cm_bright = ListedColormap(['#FF0000', '#0000FF'])\n",
    "\n",
    "def points_plot(ax, Xtr, Xte, ytr, yte, clf, mesh=True, colorscale=cmap_light, cdiscrete=cmap_bold, alpha=0.1, psize=10, zfunc=False):\n",
    "    h = .02\n",
    "    X=np.concatenate((Xtr, Xte))\n",
    "    x_min, x_max = X[:, 0].min() - .5, X[:, 0].max() + .5\n",
    "    y_min, y_max = X[:, 1].min() - .5, X[:, 1].max() + .5\n",
    "    xx, yy = np.meshgrid(np.linspace(x_min, x_max, 100),\n",
    "                         np.linspace(y_min, y_max, 100))\n",
    "\n",
    "    #plt.figure(figsize=(10,6))\n",
    "    if mesh:\n",
    "        if zfunc:\n",
    "            p0 = clf.predict_proba(np.c_[xx.ravel(), yy.ravel()])[:, 0]\n",
    "            p1 = clf.predict_proba(np.c_[xx.ravel(), yy.ravel()])[:, 1]\n",
    "            Z=zfunc(p0, p1)\n",
    "        else:\n",
    "            Z = clf.predict(np.c_[xx.ravel(), yy.ravel()])\n",
    "        Z = Z.reshape(xx.shape)\n",
    "        plt.pcolormesh(xx, yy, Z, cmap=cmap_light, alpha=alpha, axes=ax)\n",
    "    ax.scatter(Xtr[:, 0], Xtr[:, 1], c=ytr-1, cmap=cmap_bold, s=psize, alpha=alpha,edgecolor=\"k\")\n",
    "    # and testing points\n",
    "    yact=clf.predict(Xte)\n",
    "    ax.scatter(Xte[:, 0], Xte[:, 1], c=yte-1, cmap=cmap_bold, alpha=alpha, marker=\"s\", s=psize+10)\n",
    "    ax.set_xlim(xx.min(), xx.max())\n",
    "    ax.set_ylim(yy.min(), yy.max())\n",
    "    return ax,xx,yy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false,
    "hide": true
   },
   "outputs": [],
   "source": [
    "def points_plot_prob(ax, Xtr, Xte, ytr, yte, clf, colorscale=cmap_light, cdiscrete=cmap_bold, ccolor=cm, psize=10, alpha=0.1):\n",
    "    ax,xx,yy = points_plot(ax, Xtr, Xte, ytr, yte, clf, mesh=False, colorscale=colorscale, cdiscrete=cdiscrete, psize=psize, alpha=alpha) \n",
    "    Z = clf.predict_proba(np.c_[xx.ravel(), yy.ravel()])[:, 1]\n",
    "    Z = Z.reshape(xx.shape)\n",
    "    plt.contourf(xx, yy, Z, cmap=ccolor, alpha=.2, axes=ax)\n",
    "    cs2 = plt.contour(xx, yy, Z, cmap=ccolor, alpha=.6, axes=ax)\n",
    "    plt.clabel(cs2, fmt = '%2.1f', colors = 'k', fontsize=14, axes=ax)\n",
    "    return ax "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##The churn example\n",
    "\n",
    "This is a dataset from a telecom company, of their customers. Based on various features of these customers and their calling plans, we want to predict if a customer is likely to leave the company. This is expensive for the company, as a lost customer means lost monthly revenue!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>State</th>\n",
       "      <th>Account Length</th>\n",
       "      <th>Area Code</th>\n",
       "      <th>Phone</th>\n",
       "      <th>Int'l Plan</th>\n",
       "      <th>VMail Plan</th>\n",
       "      <th>VMail Message</th>\n",
       "      <th>Day Mins</th>\n",
       "      <th>Day Calls</th>\n",
       "      <th>Day Charge</th>\n",
       "      <th>Eve Mins</th>\n",
       "      <th>Eve Calls</th>\n",
       "      <th>Eve Charge</th>\n",
       "      <th>Night Mins</th>\n",
       "      <th>Night Calls</th>\n",
       "      <th>Night Charge</th>\n",
       "      <th>Intl Mins</th>\n",
       "      <th>Intl Calls</th>\n",
       "      <th>Intl Charge</th>\n",
       "      <th>CustServ Calls</th>\n",
       "      <th>Churn?</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>KS</td>\n",
       "      <td>128</td>\n",
       "      <td>415</td>\n",
       "      <td>382-4657</td>\n",
       "      <td>no</td>\n",
       "      <td>yes</td>\n",
       "      <td>25</td>\n",
       "      <td>265.1</td>\n",
       "      <td>110</td>\n",
       "      <td>45.07</td>\n",
       "      <td>197.4</td>\n",
       "      <td>99</td>\n",
       "      <td>16.78</td>\n",
       "      <td>244.7</td>\n",
       "      <td>91</td>\n",
       "      <td>11.01</td>\n",
       "      <td>10.0</td>\n",
       "      <td>3</td>\n",
       "      <td>2.70</td>\n",
       "      <td>1</td>\n",
       "      <td>False.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>OH</td>\n",
       "      <td>107</td>\n",
       "      <td>415</td>\n",
       "      <td>371-7191</td>\n",
       "      <td>no</td>\n",
       "      <td>yes</td>\n",
       "      <td>26</td>\n",
       "      <td>161.6</td>\n",
       "      <td>123</td>\n",
       "      <td>27.47</td>\n",
       "      <td>195.5</td>\n",
       "      <td>103</td>\n",
       "      <td>16.62</td>\n",
       "      <td>254.4</td>\n",
       "      <td>103</td>\n",
       "      <td>11.45</td>\n",
       "      <td>13.7</td>\n",
       "      <td>3</td>\n",
       "      <td>3.70</td>\n",
       "      <td>1</td>\n",
       "      <td>False.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>NJ</td>\n",
       "      <td>137</td>\n",
       "      <td>415</td>\n",
       "      <td>358-1921</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>0</td>\n",
       "      <td>243.4</td>\n",
       "      <td>114</td>\n",
       "      <td>41.38</td>\n",
       "      <td>121.2</td>\n",
       "      <td>110</td>\n",
       "      <td>10.30</td>\n",
       "      <td>162.6</td>\n",
       "      <td>104</td>\n",
       "      <td>7.32</td>\n",
       "      <td>12.2</td>\n",
       "      <td>5</td>\n",
       "      <td>3.29</td>\n",
       "      <td>0</td>\n",
       "      <td>False.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>OH</td>\n",
       "      <td>84</td>\n",
       "      <td>408</td>\n",
       "      <td>375-9999</td>\n",
       "      <td>yes</td>\n",
       "      <td>no</td>\n",
       "      <td>0</td>\n",
       "      <td>299.4</td>\n",
       "      <td>71</td>\n",
       "      <td>50.90</td>\n",
       "      <td>61.9</td>\n",
       "      <td>88</td>\n",
       "      <td>5.26</td>\n",
       "      <td>196.9</td>\n",
       "      <td>89</td>\n",
       "      <td>8.86</td>\n",
       "      <td>6.6</td>\n",
       "      <td>7</td>\n",
       "      <td>1.78</td>\n",
       "      <td>2</td>\n",
       "      <td>False.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>OK</td>\n",
       "      <td>75</td>\n",
       "      <td>415</td>\n",
       "      <td>330-6626</td>\n",
       "      <td>yes</td>\n",
       "      <td>no</td>\n",
       "      <td>0</td>\n",
       "      <td>166.7</td>\n",
       "      <td>113</td>\n",
       "      <td>28.34</td>\n",
       "      <td>148.3</td>\n",
       "      <td>122</td>\n",
       "      <td>12.61</td>\n",
       "      <td>186.9</td>\n",
       "      <td>121</td>\n",
       "      <td>8.41</td>\n",
       "      <td>10.1</td>\n",
       "      <td>3</td>\n",
       "      <td>2.73</td>\n",
       "      <td>3</td>\n",
       "      <td>False.</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  State  Account Length  Area Code     Phone Int'l Plan VMail Plan  VMail Message  Day Mins  Day Calls  Day Charge  Eve Mins  Eve Calls  Eve Charge  Night Mins  Night Calls  Night Charge  Intl Mins  Intl Calls  Intl Charge  CustServ Calls  Churn?\n",
       "0    KS             128        415  382-4657         no        yes             25     265.1        110       45.07     197.4         99       16.78       244.7           91         11.01       10.0           3         2.70               1  False.\n",
       "1    OH             107        415  371-7191         no        yes             26     161.6        123       27.47     195.5        103       16.62       254.4          103         11.45       13.7           3         3.70               1  False.\n",
       "2    NJ             137        415  358-1921         no         no              0     243.4        114       41.38     121.2        110       10.30       162.6          104          7.32       12.2           5         3.29               0  False.\n",
       "3    OH              84        408  375-9999        yes         no              0     299.4         71       50.90      61.9         88        5.26       196.9           89          8.86        6.6           7         1.78               2  False.\n",
       "4    OK              75        415  330-6626        yes         no              0     166.7        113       28.34     148.3        122       12.61       186.9          121          8.41       10.1           3         2.73               3  False."
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#data set from yhathq: http://blog.yhathq.com/posts/predicting-customer-churn-with-sklearn.html\n",
    "dfchurn=pd.read_csv(\"https://dl.dropboxusercontent.com/u/75194/churn.csv\")\n",
    "dfchurn.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Lets write some code to feature select and clean our data first, of-course."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "dfchurn[\"Int'l Plan\"] = dfchurn[\"Int'l Plan\"]=='yes'\n",
    "dfchurn[\"VMail Plan\"] = dfchurn[\"VMail Plan\"]=='yes'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "colswewant_cont=[ u'Account Length', u'VMail Message', u'Day Mins', u'Day Calls', u'Day Charge', u'Eve Mins', u'Eve Calls', u'Eve Charge', u'Night Mins', u'Night Calls', u'Night Charge', u'Intl Mins', u'Intl Calls', u'Intl Charge', u'CustServ Calls']\n",
    "colswewant_cat=[u\"Int'l Plan\", u'VMail Plan']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##Asymmetry"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "First notice that our data set is very highly asymmetric, with positives, or people who churned, only making up 14-15% of the samples."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "14.491449144914492"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ychurn = np.where(dfchurn['Churn?'] == 'True.',1,0)\n",
    "100*ychurn.mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This means that a classifier which predicts that EVERY customer is a negative (does not churn) has an accuracy rate of 85-86%. \n",
    "\n",
    "But is accuracy the correct metric?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##Remember the Confusion matrix? We reproduce it here for convenience"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- the samples that are +ive and the classifier predicts as +ive are called True Positives (TP)\n",
    "- the samples that are -ive and the classifier predicts (wrongly) as +ive are called False Positives (FP)\n",
    "- the samples that are -ive and the classifier predicts as -ive are called True Negatives (TN)\n",
    "- the samples that are +ive and the classifier predicts as -ive are called False Negatives (FN)\n",
    "\n",
    "A classifier produces a confusion matrix which looks like this:\n",
    "\n",
    "![hwimages](./images/confusionmatrix.png)\n",
    "\n",
    "\n",
    "IMPORTANT NOTE: In sklearn, to obtain the confusion matrix in the form above, always have the observed `y` first, i.e.: use as `confusion_matrix(y_true, y_pred)`\n",
    "\n",
    "Consider two classifiers, A and B, as in the image below. Suppose they were trained on a balanced set. Let A make its mistakes only through false positives: non-churners(n) predicted to churn(Y), while B makes its mistake only through false negatives, churners(p), predicted not to churn(N). Now consider what this looks like on an unbalanced set, where the ps (churners) are much less than the ns (non-churners). It would seem that B makes far fewer misclassifications based on accuracy than A, and would thus be a better classifier."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![m:abmodeldiag](./images/abmodeldiag.png)\n",
    "\n",
    "However, is B reaslly the best classifier for us? False negatives are people who churn, but we predicted them not to churn.These are very costly for us. So for us. classifier A might be better, even though, on the unbalanced set, it is way less accurate!"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##Classifiers should be about the Business End: keeping costs down"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "####Establishing Baseline Classifiers via profit or loss."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Whenever you are comparing classifiers you should always establish a baseline, one way or the other.  In our churn dataset there are two obvious baselines: assume every customer wont churn, and assume all customers will churn.\n",
    "\n",
    "The former baseline, will on our dataset, straight away give you a 85.5% accuracy. If you are planning on using accuracy, any classifier you write ought to beat this. The other baseline, from an accuracy perspective is less interesting: it would only have a 14.5% correct rate.\n",
    "\n",
    "But as we have seen, on such asymmetric data sets, accuracy is just not a good metric. So what should we use?\n",
    "\n",
    "**A metric ought to hew to the business function that the classifier is intended for**.\n",
    "\n",
    "In our case, we want to minimize the cost/maximize the profit for the telecom.\n",
    "\n",
    "But to do this we need to understand the business situation. To do this, we write a **utility**, or, equivalently, **cost** matrix associated with the 4 scenarios that the confusion matrix talks about. \n",
    "\n",
    "![cost matrix](images/costmatrix.png)\n",
    "\n",
    "Remember that +ives or 1s are churners, and -ives or 0s are the ones that dont churn. \n",
    "\n",
    "Lets assume we make an offer with an administrative cost of \\$3 and an offer cost of \\$100, an incentive for the customer to stay with us. If a customer leaves us, we lose the customer lifetime value, which is some kind of measure of the lost profit from that customer. Lets assume this is the average number of months a customer stays with the telecom times the net revenue from the customer per month. We'll assume 3 years and \\$30/month margin per user lost, for roughly a $1000 loss."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "admin_cost=3\n",
    "offer_cost=100\n",
    "clv=1000#customer lifetime value"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- TN=people we predicted not to churn who wont churn. We associate no cost with this as they continue being our customers\n",
    "- FP=people we predict to churn. Who wont. Lets associate a `admin_cost+offer_cost` cost per customer with this as we will spend some money on getting them not to churn, but we will lose this money.\n",
    "- FN=people we predict wont churn. And we send them nothing. But they will. This is the big loss, the `clv`\n",
    "- TP= people who we predict will churn. And they will. These are the people we can do something with. So we make them an offer. Say a fraction f accept it. Our cost is\n",
    "\n",
    "`f * offer_cost + (1-f)*(clv+admin_cost)`\n",
    "\n",
    "This model can definitely be made more complex.\n",
    "\n",
    "Lets assume a conversion fraction of 0.5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "conv=0.5\n",
    "tnc = 0.\n",
    "fpc = admin_cost+offer_cost\n",
    "fnc = clv\n",
    "tpc = conv*offer_cost + (1. - conv)*(clv+admin_cost)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[    0.    103. ]\n",
      " [ 1000.    551.5]]\n"
     ]
    }
   ],
   "source": [
    "cost=np.array([[tnc,fpc],[fnc, tpc]])\n",
    "print cost"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can compute the average cost(profit) per person using the following formula, which calculates the \"expected value\" of the per-customer loss/cost(profit):\n",
    "\n",
    "\\begin{eqnarray}\n",
    "Cost &=& c(1P,1A) \\times p(1P,1A) + c(1P,0A) \\times p(1P,0A) + c(0P,1A) \\times p(0P,1A) + c(0P,0A) \\times p(0P,0A) \\\\\n",
    "&=& \\frac{TP \\times c(1P,1A) + FP \\times c(1P,0A) + FN \\times c(0P,1A) + TN \\times c(0P,0A)}{N}\n",
    "\\end{eqnarray}\n",
    "\n",
    "where N is the total size of the test set, 1P is predictions for class 1, or positives, 0A is actual values of the negative class in the test set. The first formula above just weighs the cost of a combination of observed and predicted with the out-of-sample probability of the combination occurring. The probabilities are \"estimated\" by the corresponding confusion matrix on the test set. (We'll provide a proof of this later in the course for the mathematically inclined, or just come bug Rahul at office hour if you cant wait!)\n",
    "\n",
    "The cost can thus be found by multiplying the cost matrix by the confusion matrix elementwise, and dividing by the sum of the elements in the confusion matrix, or the test set size.\n",
    "\n",
    "We implement this process of finding the average cost per person in the `average_cost` function below:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def average_cost(y, ypred, cost):\n",
    "    c=confusion_matrix(y,ypred)\n",
    "    score=np.sum(c*cost)/np.sum(c)\n",
    "    return score"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "####No customer churns and we send nothing\n",
    "\n",
    "We havent made any calculations yet! Lets fix that omission and create our training and test sets."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([False,  True,  True, ...,  True, False,  True], dtype=bool)"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "churntrain, churntest = train_test_split(xrange(dfchurn.shape[0]), train_size=0.6)\n",
    "churnmask=np.ones(dfchurn.shape[0], dtype='int')\n",
    "churnmask[churntrain]=1\n",
    "churnmask[churntest]=0\n",
    "churnmask = (churnmask==1)\n",
    "churnmask"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "testchurners=dfchurn['Churn?'][~churnmask].values=='True.'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[1145    0]\n",
      " [ 189    0]]\n"
     ]
    }
   ],
   "source": [
    "testsize = dfchurn[~churnmask].shape[0]\n",
    "ypred_dste = np.zeros(testsize, dtype=\"int\")\n",
    "print confusion_matrix(testchurners, ypred_dste)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "141.67916041979009"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dsteval=average_cost(testchurners, ypred_dste, cost)\n",
    "dsteval"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Not doing anything costs us 140 per customer."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "####All customers churn, we send everyone"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[   0 1145]\n",
      " [   0  189]]\n"
     ]
    }
   ],
   "source": [
    "ypred_ste = np.ones(testsize, dtype=\"int\")\n",
    "print confusion_matrix(testchurners, ypred_ste)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "166.54310344827587"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "steval=average_cost(testchurners, ypred_ste, cost)\n",
    "steval"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Make offers to everyone costs us even more, not surprisingly. The first one is the one to beat!"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Naive Bayes Classifier\n",
    "\n",
    "So lets try a classifier. Here we try one known as Gaussian Naive Bayes. We'll just use the default parameters, since the actual details are not of importance to us."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "using mask\n",
      "############# based on standard predict ################\n",
      "Accuracy on training data: 0.86\n",
      "Accuracy on test data:     0.87\n",
      "[[1059   86]\n",
      " [  88  101]]\n",
      "########################################################\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/anaconda/lib/python2.7/site-packages/IPython/kernel/__main__.py:17: FutureWarning: comparison to `None` will result in an elementwise object comparison in the future.\n"
     ]
    }
   ],
   "source": [
    "from sklearn.naive_bayes import GaussianNB\n",
    "clfgnb = GaussianNB()\n",
    "clfgnb, Xtrain, ytrain, Xtest, ytest=do_classify(clfgnb, None, dfchurn, colswewant_cont+colswewant_cat, 'Churn?', \"True.\", mask=churnmask)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1059,   86],\n",
       "       [  88,  101]])"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "confusion_matrix(ytest, clfgnb.predict(Xtest))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "114.36244377811094"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "average_cost(ytest, clfgnb.predict(Xtest), cost)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Ok! We did better! But is this the true value of our cost? To answer this question, we need to ask a question: what exactly is `clf.predict` doing?\n",
    "\n",
    "There is a caveat for SVM's though: we cannot repredict 1's and 0's directly for  `clfsvm`, as the SVM is whats called a \"discriminative\" classifier: it directly gives us a decision function, with no probabilistic explanation and no probabilities. (I lie, an SVM can be retrofitted with probabilities: see http://scikit-learn.org/stable/modules/svm.html#scores-probabilities, but these are expensive amd not always well callibrated (callibration of probabilities will be covered later in our class)).\n",
    "\n",
    "What do we do? The SVM does give us a measure of how far we are from the \"margin\" though, and this is an ordered set of distances, just as the probabilities in a statistical classifier are. This ordering on the distance is just like an ordering on the probabilities: a sample far on the positive side from the line is an almost very definite 1, just like a sample with a 0.99 probability of being a 1 is an almost very definite 1.\n",
    "\n",
    "For both these reasons we turn to ROC curves."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##Changing the Prediction threshold, and the ROC Curve"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Our dataset is a very lopsided data set with 86% of samples being negative. We now know that in such a case, accuracy is not a very good measure of a classifier.\n",
    "\n",
    "We have also noticed that, as is often the case in situations in which one class dominates the other, the costs of one kind of misclassification: false negatives are differently expensive than false positives. We saw above that FN are more costly in our case than FP. \n",
    "\n",
    "\n",
    "In the case of such asymmetric costs, the `sklearn` API function `predict` is useless, as it assumes a threshold probability of having a +ive sample to be 0.5; that is, if a sample has a greater than 0.5 chance of being a 1, assume it is so. Clearly, when FN are more expensive than FP, you want to lower this threshold: you are ok with falsely classifying -ive examples as +ive. We play with this below by chosing a threshold `t` in the function `repredict` which chooses a different threshold than 0.5 to make a classification.\n",
    "\n",
    "You can think about this very starkly from the perspective of the cancer doctor. Do you really want to be setting a threshold of 0.5 probability to predict if a patient has cancer or not? The false negative problem: ie the chance you predict someone dosent have cancer who has cancer is much higher for such a threshold. You could kill someone by telling them not to get a biopsy. Why not play it safe and assume a much lower threshold: for eg, if the probability of 1(cancer) is greater than 0.05, we'll call it a 1.\n",
    "\n",
    "One caveat: we cannot repredict for the linear SVM model `clfsvm`, as the SVM is whats called a \"discriminative\" classifier: it directly gives us a decision function, with no probabilistic explanation and no probabilities. (I lie, an SVM can be retrofitted with probabilities: see http://scikit-learn.org/stable/modules/svm.html#scores-probabilities, but these are expensive amd not always well callibrated).\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def repredict(est,t, xtest):\n",
    "    probs=est.predict_proba(xtest)\n",
    "    p0 = probs[:,0]\n",
    "    p1 = probs[:,1]\n",
    "    ypred = (p1 >= t)*1\n",
    "    return ypred"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "106.95127436281859"
      ]
     },
     "execution_count": 89,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "average_cost(ytest, repredict(clfgnb, 0.3, Xtest), cost)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([ 872.,  153.,   52.,   35.,   35.,   17.,   29.,   10.,   33.,   98.]),\n",
       " array([  1.30770108e-04,   1.00117692e-01,   2.00104613e-01,\n",
       "          3.00091535e-01,   4.00078456e-01,   5.00065377e-01,\n",
       "          6.00052299e-01,   7.00039220e-01,   8.00026142e-01,\n",
       "          9.00013063e-01,   9.99999985e-01]),\n",
       " <a list of 10 Patch objects>)"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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vrq5Oa2trNm3alMrKytTX12fr1q2pq6vL4sWLk7z4JKOWlpasWrUq3d3dqa2tTUdHRxoa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\nAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1098b4690>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(clfgnb.predict_proba(Xtest)[:,1])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Aha! At a 0.3 threshold we save more money!\n",
    "\n",
    "We see that in this situation, where we have asymmetric costs, we do need to change the threshold at which we make our positive and negative predictions. We need to change the threshold so that we much dislike false negatives (same in the cancer case). Thus we must accept many more false positives by setting such a low threshold.\n",
    "\n",
    "For otherwise, we let too many people slip through our hands who would have stayed with our telecom company given an incentive. But how do we pick this threshold?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "###The ROC Curve"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "ROC curves are actually a set of classifiers, in which we move the threshold for classifying a sample as positive from 0 to 1. (In the standard scenario, where we use classifier accuracy, this threshold is implicitly set at 0.5).\n",
    "\n",
    "We talked more about how to create a ROC curve in the accompanying lab to this one, so here we shall just repeat the ROC curve making code from there."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "from sklearn.metrics import roc_curve, auc"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "def make_roc(name, clf, ytest, xtest, ax=None, labe=5, proba=True, skip=0):\n",
    "    initial=False\n",
    "    if not ax:\n",
    "        ax=plt.gca()\n",
    "        initial=True\n",
    "    if proba:\n",
    "        fpr, tpr, thresholds=roc_curve(ytest, clf.predict_proba(xtest)[:,1])\n",
    "    else:\n",
    "        fpr, tpr, thresholds=roc_curve(ytest, clf.decision_function(xtest))\n",
    "    roc_auc = auc(fpr, tpr)\n",
    "    if skip:\n",
    "        l=fpr.shape[0]\n",
    "        ax.plot(fpr[0:l:skip], tpr[0:l:skip], '.-', alpha=0.3, label='ROC curve for %s (area = %0.2f)' % (name, roc_auc))\n",
    "    else:\n",
    "        ax.plot(fpr, tpr, '.-', alpha=0.3, label='ROC curve for %s (area = %0.2f)' % (name, roc_auc))\n",
    "    label_kwargs = {}\n",
    "    label_kwargs['bbox'] = dict(\n",
    "        boxstyle='round,pad=0.3', alpha=0.2,\n",
    "    )\n",
    "    for k in xrange(0, fpr.shape[0],labe):\n",
    "        #from https://gist.github.com/podshumok/c1d1c9394335d86255b8\n",
    "        threshold = str(np.round(thresholds[k], 2))\n",
    "        ax.annotate(threshold, (fpr[k], tpr[k]), **label_kwargs)\n",
    "    if initial:\n",
    "        ax.plot([0, 1], [0, 1], 'k--')\n",
    "        ax.set_xlim([0.0, 1.0])\n",
    "        ax.set_ylim([0.0, 1.05])\n",
    "        ax.set_xlabel('False Positive Rate')\n",
    "        ax.set_ylabel('True Positive Rate')\n",
    "        ax.set_title('ROC')\n",
    "    ax.legend(loc=\"lower right\")\n",
    "    return ax"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "collapsed": false,
    "figure_type": "m"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x10af88f10>"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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PEGpfj2WkKK4/kfJFZ9P68oPYtkXBjKU43PlYk1+FdkTb8b7tNP/jLlRVoeEdF1JAM/OK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\ngg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10af88790>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "make_roc(\"gnb\",clfgnb, ytest, Xtest, None, labe=50)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "OK. Now that we have a ROC curve that shows us different thresholds, we need to figure how to pick the appropriate threshold from the ROC curve. But first, let us try another classifier."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##Classifier Comparison"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "###Decision Trees"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Descision trees are very simple things we are all familiar with. If a problem is multi-dimensional, the tree goes dimension by dimension and makes cuts in the space to create a classifier.\n",
    "\n",
    "From scikit-docs:\n",
    "    \n",
    "<img src=\"http://scikit-learn.org/stable/_images/iris.svg\"/>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from sklearn.tree import DecisionTreeClassifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "reuse_split=dict(Xtrain=Xtrain, Xtest=Xtest, ytrain=ytrain, ytest=ytest)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We train a simple decision tree classifier."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "using reuse split\n",
      "BEST {'max_depth': 6} 0.935967983992 [mean: 0.86143, std: 0.01117, params: {'max_depth': 1}, mean: 0.87894, std: 0.00853, params: {'max_depth': 2}, mean: 0.89295, std: 0.01170, params: {'max_depth': 3}, mean: 0.91396, std: 0.00927, params: {'max_depth': 4}, mean: 0.93547, std: 0.01543, params: {'max_depth': 5}, mean: 0.93597, std: 0.01054, params: {'max_depth': 6}, mean: 0.92946, std: 0.01219, params: {'max_depth': 7}, mean: 0.92546, std: 0.00887, params: {'max_depth': 8}, mean: 0.92496, std: 0.01052, params: {'max_depth': 9}]\n",
      "############# based on standard predict ################\n",
      "Accuracy on training data: 0.97\n",
      "Accuracy on test data:     0.93\n",
      "[[1116   29]\n",
      " [  59  130]]\n",
      "########################################################\n"
     ]
    }
   ],
   "source": [
    "clfdt=DecisionTreeClassifier()\n",
    "clfdt, Xtrain, ytrain, Xtest, ytest  = do_classify(clfdt, {\"max_depth\": range(1,10,1)}, dfchurn, colswewant_cont+colswewant_cat, 'Churn?', \"True.\", reuse_split=reuse_split)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1116,   29],\n",
       "       [  59,  130]])"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "confusion_matrix(ytest,clfdt.predict(Xtest))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "###Compare!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x10ad28e50>"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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+yTRsWYsVnEeiqx6joIK27r6pbQuJdbfS2hFG1Q06mjfhqz4hP4ZMPEHWtOkM\nJwEHx7bJpmJ4/Ooex7DrMsexSMXjuJMuwEENTKG59m2U0GEkurbhKqjIn8M2isnEOsim4jiGm0RX\nHXOOOpVjj5jCkvnl2KkefrIhwOVnH5V/31PxLmrm7/xsDVdZaQllpSUj3m8wtm2TsTJE0zHqeqJ0\nJLswbZPg1DJeeuWvVB0xh66GHVRWTwXAperMn7WAX7d3Uu4qwuPysnH923zu8+fQ3d3Jf1x3KV+/\n9BoWLd5zEz7HcfjlL3/JFVdcQTgcBnKF648++qgEHEIIIYQ4eAyn8HXVmodwezxcc9kKli47kY0b\n1qGoKrf8/F7W/etNHn7gzvw++2tMr7/2MoWFRVx93feJRiN844J/3y3gGI6+ztVrNzbQEUljGB4c\n4J1/1eOr8OMrbceybRwUspaJ46iY6RRKb8dqVQFUN1YmhWk6+MprMJNdOA7YFti9QY9lQ9a080XQ\nsGtB9O7LspaD1nsO23Hwl9cQad3M1r/+AgWoWPQ5uhvewjYzhKYtpXzh6dS/cj9gU1R9LIpekB+D\nZeUChr4xW5ZD1nRw9RZoD3dcCmD2G5e3bCHR1k1s/esvAKha/Dm6tr2JY2UomraUSYefTuOr94Nj\nUzHnA8ysnpKfPap1R8+AwHNfMS2TjJUlY2XIWFnSvX/2/Zy1ctPWtnd3sLUrjKLlnpLNWLyQbRs3\nce+NP8Ktu1lx2ZVsX/sedsbk1I9/iq9ddAXfve4ybMfmo6d+kuKSUu5ecyuJeIxfP3I/v37kfgBu\n/NEqDMO927h+8IMf8N3vfjf/c3FxMatXr+YLX/jCPnhXxp8EHEIIIcQhajiFx/5ArgC3f+Hxsced\nAEBrawuBgvErPh/tmE5cfjInLP8wAI7tjKpGId+5uq6THR0xokkLtN67/7ZOKpnEZVq5NB3HwSGX\nHqW6PNi7dKxWXeM/A9SuFEWh4vAzAXqDEDACZfn1BZMW4CtfAIDWu76Py1fM9BMuZhxq13cbU+UR\nZ+aPqyrg8pfR91AtVFlD5fTFBLwGC2aWMK2yID971KSKKm5dfd+4jsdxHLJWtjeAyAUROwOK3M+W\nPbpSbU1VOf2cLxFw+1hUsQAFBebtfPJw7HEn5P+e9Lng4qu44OKrhnX88847j1tvvZVwOMxnP/tZ\nbr/9diZNmjSqsR4IJOAQQgghDlFDFfmqqrrHwmNN07jtJzfyyj9e5Prv/Wi/j6lvitdEIs6PbvoW\nXz7vwhFtS3vrAAAgAElEQVSft7k9TuOOCB3hFJFEFtNWwM7VDLiLphHdsZGCiiNIdm/DHaxEVUDR\nwCisIBvvQLOTKLqbVHcdk+adhKtvti1VRVFA11TonfDX0XJFzztTlWBg2tLuy9y9xdR72i536dzX\nBE9B6V2ioe7x2Kpio/QWY+9pu12XOY6DOYxxgYKigtvQ8HtdlBd7CQZclIXGFpj1pTv1BRC7Pp3I\nWNkxpdK5NB1Dc+HWDPCDQ4hoNoau6uiqjqIoTPKX5IKNcTZ58mTuuusuDMPgzDPPHPfj72sScAgh\nhBAHkb60n39tbiMcywxZ4AvsVmDb1dGG7g3lOzq/1xij8c9v8/x7OqoCkXiKn//XW5imnS88XvXY\nGzjAu28MLDxWqj/O4k8cz0033sCxn/4PPJ5c+lE83EmgsHbYhcfYFqlkDLc/uNfC41Skhc1bW7jl\nV6/nCqffqKW4UmVD+A1i4U7Wv3Avk+cv57WmIv6vtyDapask42G8vq2gaEOOq3/n6lTGRFV1+h6U\nFFTUEG/fzLaX14ADVUd9jmTr2yi2ybSFJ6Af/1lqX38A27aZPO94KirK8wXiUS1Bi65REvLsLBo3\n0viD/hEVjZsBC1s1cgXwg3TlHuz3bptZwEHVjT3uozhZNMWF5vKNqGhcVSBupPEU+IfsFt5/mUvX\nCAXczJxSyOwpoWH0xrBJZJJ7TXcaDVVRcGmufEBhaC6Mvj91A0N1DZhkIKT04OlIEreTtCe6ASjz\nFVHiKx71GAAs2yIWjVFYWIimDgxczj777DEd+0AiAYcQQghxkGjuiPHiG428vqGVrkiKVNrCtK38\nHVYHJ3df27FRUFAUJb8MIBGN4wm488sc32Saeot84131uAIVtHTEcXDoKzxubG5H6Vd4vOntv2Om\nIpTN/TBm1sR2oD2cQovlZqdKxRLEzPiA8+bPN8gyyzbJJOO4kxpOb+Fx0yCFxw4O2AVEu1ppaetA\n1d35MTVu30HdP+6iatGn0Upn0dadGPDa04k47oSCqupDjot+nauxwVYcdA303m2nHvkZNF3Ba7iY\nXB5g7rRlzJ1a1HvRfAzwlaF/cRedOvDnbITZM6tH9Ls3TZP179bhDw5/+uC6+joAZkyfMeQ2tm2T\ninWycP7MUaWiNTQ2E0mBxzOyxnoOuXSnrG2StUxMO0vGzgURWdskGumiKOQi1Z7Y+8EGoakqhmb0\nCyYGBhSu3icUw1UYLGB7cztFgZIxBxl9ttZu5aYbb8JwG/z0Jz9kVvXBmzK1NxJwCCGEEPtAc0sr\niWQ6Xzi8N23dCbY0hHnj3QbCkQyKvo502iKRNsmYFioKNg5ubyEouTux/Qt7c6kszoBlts3OQmHH\nGVDk21d43FfkW1jdW3j80n04/QqPCyqOoPmt31D7tzvAsSlf+EkU9HyHaHuEBdE4wy88LqxeStmC\n09n28v04zs4xta7/HXY2Seu7fwH+AsCUY7+KrruwHAeztyDasvc0rp2dq21NRVPB53YRKnDn79S7\nXSplId8I7tAPbjT9RnRdZ+G86by3ZRuWrQ4rVSgW6cr9GQ4OOQ6XzqiDDYDqqVU0NDYTjnX1FqPn\n2I6NaWfJWlYuqLBNsnaWrJXFtCxMxxzy74KmQnGRj8KiwccNA9OdjP5BhZ77WVfHd6plTdNYOH8m\n723ZRtYc26xnWcvk0Ud+xYO/fJBsNoNtmrz452UsufKKcRzxgUUCDiGEEGKCba1rIGW6cHuCw+q4\n296ToLbNobbdoiflJ4UbTA1TscFwMFyAkiuQTsQ68fiLUEdxgdW/yLd/4XHf9wWTFuDvLTzuW6Zq\nLqqO+tKAZeNpT4XHtgOBSQsIViwYMOZJNWdQefgZYx5LX+fqgKeAkDfL3JlVnHLc9DG/pv7S6SQl\nBYP3eNgbl8tFzWGzh729W8sAcNhhs0Z1vj0xbYuMmSFjZfAUGahBpTflKZfuZFsmKuAG3GiA1vvT\n3o003Wlf0TSNBfNGPk1vf2+99RZfO+881q5dm182c+ZMliw5+Ka6HQkJOIQQQogJ1NMTJpZW8PuH\nXyDb3pMkHMvQ0pUglbGwbLBta0AjM8UBRVXwB0swkz24g6X5ml3bdkDJXbj1b59gp1QMlzqS9g8j\nXuZoKm5DG/a+tgW2vsu4xjCWvrvm/V+701sQrWh7Hpeuq/jcLkpDHsqDKqX+LNlsFl0f++WSbduk\nUgmCXoWKSZVjPt5EchyHrG32BhSDTxk72tmdYGe601ABxUjTnQ4mzz//fD7YUBSFyy+/nJtuumnA\nZAjvRxJwCCGEEBMolkiOOL8dIJpIk0qZWLazc84fpbcwuPfmrq6o+LwGhs8iWFyw16LxsJHB0XR0\nXR+ysHesy+J6kkChb2SFx5EMngLfuI2l/2t36SoJI0UgWDCsguj+navnVwfp7gmTyY6ujqA/zdCY\nFAoSChWO+VhjtevsTrnpYvsXY2eHnfo3mL50p0FrKPTxT3c6mFxxxRU8/vjjRCIRHnjgAZYtW7a/\nh7RPSMAhhBBCTCDbtgfcrR1OJ+0HH7iHVMYmVH0M3oojyZo2Lf/6LZlYOygKc487m6rq6cyeHGLJ\n/HKCRpqF84YuDO5jmibvvFuHbwSFxyMRDXcyZ/pxBAIju1vb0NhMOOng9Y7/Xd5UKk7Qq1A9pWpU\n+1dWjC79aX/qS3eKZuOYjsn2cMuAdKfxmt0pF1C4BtZQaMZ+SXc6WGiaxpNPPklJSQkez8H32Rot\nCTiEEEKIfWiwTtqfO/9bvFPbSVNrhL8+9mMWfPQqHEVn4/M/Z3poDomuely6xuJPXk20fQtNG/7I\nh46/nsVzyygL+Ugn0ns/Mb2Fx/NnsGlrA1mzf27R2OSevNjMmV454mADcoXHjU0thKNdWNa4DAkA\nVXUIBX1MnXxgpzCNxEjSnZqTrQAYseE/Yeuf7jRYUbZLc71v053G04svvkgsFuP000/fbd3kyZP3\nw4j2Lwk4hBBCiH1o107aGze+w7otHTS1xdjeWIfmKyZp5hqw+YpnkOquY9rcJSz+xGmcctwMnvtz\nG+vMyXzk2GmjOr+u62MufJ0IUydXMnV/D+IAYDt2LnAYIqCQdKcDWyQS4dprr+Wuu+6itLSUDRs2\nUFZWtvcd3+ck4BBCCDFmtm2TTqexxun2tKIoGIaBy+Ua03EymQyZTGZcxgS5i3W32z2mO7y7dtK2\nbYXOcIKeWJpUIoGqezAt0BQHRTfIplOYlkM8ZU5Yd2+x75i2lQsizF1rKMae7qQoyoAAImnkumLP\nLZkh6U77wB//+EcuuOACGhsbAejo6GD16tXcdNNN+3lk+58EHEIIIcYkFouzqa4ZTXfDOF3MOI6D\nbWYpCbqZVj3y9APHcXh3cx1pUwVVG78UENvCMTNkVR9vbepk244I0Xhmj8XImUQY1R3IzTTlQG1D\nlJbn1vP3WgPTzHW1rmuO5orDdQMrm0Yhl6Zkm2k0lxfDpeL3uLjym9+le8XFXHnJV7nrgcdwuw+d\nHPCDQT7dycqQMbMDCrP7aijGa3an4aQ7pXbEAAh6Csb82sSe3XDDDXz/+9/P/+zxePjBD37A5Zdf\nvh9HdeCQgEMIIcSopdNpNtW3EAxNTMpANJWgqaWVyZUj68C7pXYbuAoIeMf2hGQw727r5JXX3yJi\n+eiKpElnrd4mYIPPt5qKRXH5lN4LQQX8k9m2ZS1mcB6JrnrcwQqylo3igNtXTjbeAXYSRXOT6qpj\nzqKTiTW9yZaYyinHXYTbyD1hURW5U72v7ct0p8GmjNU1uWw7UJ1yyinceOONOI7D8uXLue+++5gz\nZ87+HtYBQz65QgghRq0nHMXjHbob8Fh5PD7C0W5GWvObSNn4g6MPNt5r6OKfG1pp7UlgmfaAqVcj\n8QzxWBRVS6PoLnByfS+G6mBtWg6qmetibTsOgfIaoq2bqe3t7l25+HNEm9/CzmYomrGUqYvPYPv/\n3Y+iOMyoWc7UyeVMXVTJP194nGuvvBDTNLng4itxGcaoX58Y3L5MdxoYUEi608Fu2bJlXH/99VRV\nVXHhhRfK73IXEnAIIYQYtaxpousTe+E7mhvGY7nL/F5DF6+ub2FHV4J4ItvvCQaAgmXZWLaC5Zjo\nip5rLDeC4yuKQsXhZwI7e0b4Q5PQNJXiAg8Ljvoo0849iwXTSwCoq68D4N++c/OoX5M48NKdxMHJ\ncRxM0xy0vuwHP/jBfhjRwUECDiGEOAhlMhlSqVSuo/Q4UFUFj8eDMca75sPpMfHYrx5A1TQ+euon\nOOW0M/Lrerq7uOzr5/DDW9YweUr1mMYxljFRvIj27hSJlEk81sPmF1Yx9bivYQTKUBUHuy97ityf\nmgqKpqIpyqAdrA1Xrou2gjpod2xFU9BVhYDXRXmxl2DARVlo+F3JIVfvcajLpzvlU54GBhSS7iTG\nqqmpiQsvvJDq6mrWrFmzv4dzUJG/HUIIcZBpbWunqS2CbnhQxqmPgoODmemgosRP1QjrJfobrMfE\nd268Bcg1nbvvrlWsWvMQbo+Hay5bwdJlJxIqKsY0TX6xaiUez8gutIc7ps6eGMefeS0b31nPtd/+\nPgs//DVUBTTV4eUnfsxRp38TNIMHH76NWcd7yCpebMui8c0nUHd5gqMq4OigOAq6S8Xr1nG7dPwe\nbdCicSsIpqOC4hqyO7bbpVIW8jFzSiGzp4QoC42sM7mmvv8jjny60xA1FJLuJCaK4zjcf//9XHXV\nVUQiEQA+//nPs3z58v08soOHBBxCCHEQ6e7uobUrSbCwZPwP7vXTEYliGF2UlhSP6hC79pjYvGlj\nfl1jQx2VVVPxBwIALKhZxPp1b3HC8pN54J7VnPaJM/mvXz809texi5deeQ1P8Vy2bg+T1MoJt2+j\nI5wEHFLhFlRPMV0JB8jgKqymo3kTBRVHsGP9sxRNW0bnlhdQdQVdz13Uq4qCquiEAm7mzZjEEXPK\nWDSnjKrSwKDndxyHjZtqUY1CdH38/7cbDXcwb+boumgfSPoHEIPVUEi6k9gf6uvrWbFiBc8991x+\nWWlpaT7wEMMjAYcQQhxEunui+PwTV6Tt8xXQ0xMm4xj8Y20z/9rcRjiWGXrK12QE1fDnp3zd/K96\n6qNF/HPHG5imTSxp8pNHXsVw6XTv2EJDe5ofP/I6qgINtWHea3uHJ5/fQDoRJxzU2N4W5cFn1+MO\ntO58QpCKUFjcMOC8g40FyH8fj3ThKSjCNG3Wb26ioLIYn5LAwQFFwcxaaKpKNp1C1T04Vq7uQ9Xc\nWJkU4e3/RPf4KZg0l+7a/yXoNSgp9eefSCi2wsyqEIvnlnP47NIhgw3I3T0/bO5MausaSSetEafB\npWLduT/jhbseGU2FeTOr8PlG9kRkX5N0J3Gw+uEPfzgg2Dj77LNZvXq1NPMbIfkbKIQQB5GxXJQN\n147uBNtrU7y5sZWOcIpk2sxN29pbuOCgoODgoJCOR3F5AEVDwSFj63R0hcl64zgoWLZNRziDQppU\nHJLJBF3hJA4K4UgUn15CZ+1LALQ1biQVbmbt8w9SvfRcXJ4ADgqZeIxwxhhw3l3HkruGz80U5aCQ\njMbxpHP7OIpBJp3CbfW+d44DvVPKaroH20zvfH/NNKrupaf+JVRVobmnlmx0B53vPM5VX/4ZRUW5\nJ0vxRIzZU4rw+3c28NsTRVGYNXN0dSmakxvfYfMPvO7gfSTdSbxfrVy5kqeffhpFUbjzzjv51Kc+\ntb+HdFCSgEMIcUCxLIt4PE4mkx23Y3o8bnw+35guShKJBMlkqt9sRSPT1d0DQEdHJwCapuH3+ya8\nSPvJp//Af//Xw1g2TJq9jGkLTsAB/u/JH6IZXhTAFyxlwfJ/z9+9j3Z3oLiDpLIWKrmpXHF2Bju5\nKV9zF/eZrAO6g6bm9vUUTSPcshHfpCNI99TjLqjEtnLrVG8ZmVgHqUQc3WUQ66glNOODTPvA4fkn\nE/Uv30XF4WehuvxkTQdVcciYDlrWHnDeXceyU2591nTQevf3FE0n3rqRgsojSHZvwx2sRNcVNBQC\nRRU0xztQrBS6yyDZVcek+R9m8pyjCHgNyou9vPy7W/n6pd/MBxuHIkl3Eoeq4uJinnnmGWbPnk1R\nUdH+Hs5BSwIOIcQBI51Os2FzA5rhQ1PH75+nrJlAs1tYMG8mmqaNeP8tW7cRS4PL5R71dEA9yVyw\n09qTu9Nr22myTZ3MmFxCUVFoVMeEPRdpb6ht4zcP38mSj19LIgvv/GUVWmguqu7GtBxmLLsgP2NS\na2ci/30insHAxHHAVpyBsyrtRUFFDbH2zWx7aQ0KULHoc4Sb3sLKZghNW0r5wtPZ/ur9gE3h1GPR\nPROXHtYnWFlDsnMzDS+tAQVmHPv/SLeuR1MyTFtwItrxn6X2tftxHJsp849nStUkXLpGKOBm5pRC\n1rp1ioPjX8x+oBiQ7jRID4qMpDuJQ8C6detQFIWamprd1h1zzDH7YUTvL/K3XAhxQHAchw2btlEQ\nKh/3Y7vdbizLx6at2zhs7sjSUhoam8k4bgIFnjGNoe9Jhtvtzi/zen3UNXXi8bjxekd3QbunIu23\n3t6IO1BKytJIZ028xdMJ79iC7g1hmxm2/uMecGxK552Kt6gaVcnVQFi2Q2//uvwsrpqioPUWTTso\n6L2pTYZLwaUroKhoODioTD3yrPx2Cg6+0KR8ClRhVQ2hqoW7pUVpvcebdeJFuy0zdAXDpQ44r95v\nX32QlCqXnvtyUNAUmHb05/B5dKaUB5g9NcTcqUX9ZoI6CvjKkO/xsp/fvduy0T7p2h8GpDsNUkOR\nsUb/NFHSncTBLpPJ8KMf/Yibb76ZmpoaXn311UF7bIixkYBDCHFASKfTqLp77xuOkqZpJLMjv0iM\nJdMY3tE9Ru/frTqZSKGrCoanK5+6pCqgKjaOuR7DU7DHgui+ZV0dbejeUH5q1XfWbWNLT4iXG3OF\n2LGkycqHX0VTVXY0NJJ1XMSS2VwooLmxzBSGZlAy+4MEpx6LGW+n4dUHmHHSNfm6BkUBTVP6fkRX\nVAoCbkIBY7dxmQEbWzVw0Iac8nWsy2yvn2BRcGRF464UngL/uE07uyvFsUf1tGwiDEh3GqSGwrSt\nUR9bVVTc+sAAwi3pTuJ94o033uDcc89l3bp1ALz11lvceeedXHrppft5ZO8/EnAIIQ4IlmXlL3gn\nymhuSo/2RnZft+qWzgSJZIZExgQH1JjZvyccOA6ZZBhPQB2sJ9xuyxLRBJ6MO78sbWp0hyM4gSQ2\nDpZt0x1J4wAZR8fKprFtBwWwzDQetxdvqAxPYQmqpqAVlqG5fThmDMUbRENBVcGlawR8LkoKPRw5\nr5wPLpky6ExM6XSadzY3EiwsHd0btRfpdJKgF6ZOrhzRfpu21mOpflz6+N+pdBwHx8wMeFo1UWzH\nJmNnydomHYkuSXcSYpzcfPPNfO9738v9vwdQVZUrr7yS888/fz+P7P1J/iURQhyQRtOx2rZtVt96\nM03bG1BUlUuvvJ4pU6ft8zHZjkLBlKNxiheRNS3a3nue2I6N2LZF0fQPUFR99M4pX22HrOngMp0h\nC6L7L7Msp7eYOnc33x2aRnTHRvyTeguiCyrz6w1fOdl4B46VQFEN0t11zF58CtHWt4l0bmf20s+T\nTfag2Rmqp1RgWbljmi4fJWUBggE386cVccLiyUNO++p2u5kzrYJN9c1ougdlvIJGBexsllCBi6mT\np+x9+13MmTmNjZtqiSV1FHUcn0Q4Fo6ZYeH86eNyZ3846U7bY425jbtH9r9sSXcSYmgVFRX5YGPh\nwoU88MADHHvssft5VO9fEnAIIQ5Io+lYvWXze6RSKW75+b289cZrPPzAnVz/vZX7dEz/8cM7aWhP\nsWbl1Uw5djqJcCuJrm1UH38xlpmhe+uL4zYegEBFDYmOXJE2QOWizxFpegvHylA0bSmVh3+S7f/3\nAIricMwJp3D6aUuYN/UkfnbLTbS9eR8AN950M/MXHJ4/pp0JM3fW8AO1goIAixfMIp1OY49htqJd\nGYYx6lm8FEVhwbzcmLLZ8ZvxTNM0PB7PsIMNSXcS4sB03nnn8eSTT3LUUUfx7W9/e588sTyUScAh\nhDggjaZjdWFhEfF4DMdxSMRj6OOcTjOcMcWzGrGkTWjSLJJd9cS7GvEUVNL0+kPYZorKw09H03IF\n1qCgqjaOrqDrKgOTqGBgQlXvEw5NzRVp9xZVg8KUIz8DOOQu9R38ofLcsRUoqz6c6fOXUFHiZ9aU\nQspCXjRN5+rrvj+u742maQdk8zm32z1hFxK7ze40SEAxlnQnXdVx6y4Cuh+XqjO1sHLAlLGS7iTE\n6CmKwjPPPCNP+fYR+ddKCLHPrN3Uxmvv7GDbjgjReGZAAXAmnSSaSKMbPlQFNq/bRl20iP/b/jq2\nkyuG/tmvXwdFpaNpM03taW751eu4dJUttWE2tqynfNZxbG3s4LNnfZJsOs7hJ1/AqsfeyBchJ6I9\nFBVv22NR9q7Lurs60D3BYXfRfuL5zWRMi7SlYWSS2Jk4mUQ3Mz9wLk4mzNaXHuTYT38nfw5dUzBT\nNm6fb3hF41oS3ecdssAaGFCA3X961+EWS8v/fnMs2yJt7VIzYY7f7E4uVe99QrFrD4pd0p06clMp\nTwpIZ2MhRiIcDnPNNdewZMkSLrzwwt3WS7Cx70jAIYTYJ9ZuauPlt5vZ2hSmvStBKmNhs/Pur5lJ\nYVkWLnfuqjllar0dq5MAWLZNS2fu+1RKIZlM0N6T+znS27H6vX/+AVdhNZOXnYqZDPPOX+9m1slX\nofb29EglkiSd+IjGnYzGcPtzT0oytk57V5iMN54fU0c4Df26aEeTabBzr8cT9ODy+KmoqmbZwlKm\nlk3h/veCfO0TcygszPXecBwHt5qkekrVsMYTjkSp295JoGD0vTv2JBELM7WicEKOfaCZ6HQnQ3P1\nS3kaWEMh6U5CTKxnn32WCy+8kKamJgKBAKeddhrV1dX7e1iHLAk4hBBkMhl6whFS6cy4HdPjNigK\nFeJyuVi7qY1H/rCRHV1xUuneQMMB295ZEG2aNjhg9hY8u0PTiLRspKDiCOJduWJo08wFKC5fGelY\nB9lkHLRcx+qimcuJdzeh6m4sExzFi2NbmFkbVcsd08o6WL3F2TB4Ufauy7Kmg8ty8l20Iy25Au10\nz7beLtq5dVpvF+1MIoHLMEh01lI690N4PG466l5iatknwYySTiUJBkd/QV8YLGBqhUl9Uzu6buAM\ntyPfXig4mGaGqRXFY2pEeKDYV+lOQ3XIlnQnIfaPzs5OLrvsMh599NH8MsuyePPNNyXg2I/kX0Qh\nDnHJZJKNW7fj8Rai62NrbtdfNJylqbUeEy//fK+TaCKDaTrYdu8FvwJ7utwLVNQQ79hM/T/W4ABV\ni3PF0LaZoXj6UsoXnE7Dq7nu0IVTj8XlKaR41gfZsfa/aHj5Dhzbpuywj6Fq41fHMfwu2g7F05cS\nCpVQNXsajU4r9666Adtx+Pql3xzzne2S4iKKQoVkMplxK9JWFAW3233QpBjsLd0pa5ujbs43onQn\nIcQB5ZxzzuH3v/99/ucPfehD3HfffcyaNWs/jkpIwCHEIe7dLY0EJ6i7t9tdxn//+XWyWhDTtlEU\nUFWwzVzLDU0FTc0VS6uOimXZOwuiNae3Y/XOwmlfYd84FYomL6Rocg39C6w1r4/qZV/JHQ+FXQuw\ndZ1+xdkwWFH2rsssXelX5L2zi3bfen9oUn7fUG8XbVVRMVwqoQI3RUE3y8+9CMMOAzBj+owB79No\nL4pVVcXjGb8A8UCTtbIDAwpJdxJCDMPKlSv585//jMfj4ZZbbmHFihVyg+AAIAGHEIewbDaLoo5u\n2tGhtPck2Lo9zNbtPfTE0jRub0c1UtjkLvR0VQWXjdftwu3S8Xs0DJdG1vQQ7ong8g5dED3cZcCg\n6xOqh6IS34iKxnvcaTS3Z8Rj8HlcVJT6qZlZwrzqYurqw4O+X6Zp4vaO7+/gQGc7NlnL7A0o+tKd\n+oKJ8Ul3GhhQDJwyVtKdhHj/qqmp4eGHH+b4449n6tSp+3s4opf8qyvEIcy2beh3J3c0zfay2Syr\nb72Z5ubtOCiceNpXiJghWrrixJNZkhkbXcmiqTqoNoahM7eqiA8sqmLRnLIBDeXWbdiM4StG08ax\nUVuvdCaNR0kza+bIcnhb29pp7U7h8xWM+5gAUskwRePYnPBAYNkWmQFPKDL9AorxSXcyNGOXgGJn\nDYXczRTi/a+xsRGv10tpaelu684+++z9MCKxJxJwCCHy9tTYrqUjwi9+fisf/n/fIZaCXz7yY/7Z\nVEBb/ZvEuyPULL+IjrZmHr3vNmZ88LL8MR0H1L7HDgq4XSpTJwV3CzYAFsybyYb3ajFtbVy7Qzu2\nRcCrMWvmyC/sJ5WXYVmt7Ojs6J3tanSpNolYFP4/e3ceH1V5LnD8d7ZZs0xWEhIgrIKAUhFXahEX\nUKzXtbWtWkWtC7hQccf90rphVVQUhNZqa6/tbWu9VtteRUWu1qqogGHfEgLZJpNk9jnL/WNggLKH\nwAR4vn74kJw558xzQkzOc97nfR8g3L5ppMNxcOwkg/r16HBzu2w5EOVOW0Yotp1DIeVOQhzeHMdh\n1qxZTJ48me9+97vbTA4XXZckHEKIjJ01tmsMRfnk80W4c4oJhh3CsSSu/F7UrvqGcNN6ckoGsLE5\niqXkk4yFSMZjaC4POODYDpqq4nLpFOS46VWezzEDS7dLNiDdPG7okf2xLAvTNDvtugzD2Ken3t3L\nu1FeVkoqler4k/lUCICBfcuA9BwMw+jcxoSdQcqdhBBd1apVq7j66quZO3cuAL/97W/5wQ9+wDnn\nnJPlyMTuyE92IURGNBLB6/NnPldVDdu2aQzFCLa0YSku2mMp4gkLVBepZAwjp5zWjYvxlh5JtGUt\nViKCYyVRHE966rUKLl0jz++iOOCjZ7ccupf4dx4E6cRjf5RV7QtFUfZpJGLzsfur6/WeknInIcTB\naFMNdKQAACAASURBVPr06dx5551Eo9HMth/96EeccMIJWYxK7ClJOIQ4TNU1hZn76Rrmf7maFB5U\nBVbVhKn730XMXaZjO9AWjvPM6wuIxE1aGtqJRtPzMgDsVBw1z0tut8GkqhtY93/P4y2owpVTguH2\noerp4iND0Skr9BLI9zOwVwEjh1XscHRDdI7UpjKnxE56T0i5kxDiYLRy5cpMslFRUcELL7wgIxsH\nEUk4hDgM1TWF+fCLWj6t3kBjKAqagmVbOL4KalZ8iZ03gGhwHUZuGXVNkfQTb1cRyXATqXgETXcR\nDa6m6IhRpNpr8Jf2pfyoc4m11JBorcHn8+Dz6JQX5XBUby8XnH4Uui4/bvbV5nKnpJXcKqHYutzJ\nxHY63pdjc7nTzpaMlXInIUS2TJ06lTfffJPTTjuNxx9/nPz8jjdQFQee/PYQ4hBR1xTmoy/rWLo2\nSCic2OWSr/GESSSWIhKPY6UsDLcFDnhLBtNWv4xVH2xpthdc+wW2maSo9/F0G3wONf+cDdiU9juR\nirIyEvFclnwwh7ZVH6BqBkNOuZScPA/dCnwce2Q3ugfkifee2lzulE4oti53Sn/cWeVOO5pD4dIM\ntE6cqC+EEJ3J7/ezYMEC8vLysh2K6ABJOIQ4BNQ1hflwwXqWrAlSH4wSCiewTHvbHnZs+diyHEzb\nxrYcLNtBs9PbFUWhbOgFqMpWfSz8JeljFYVAxRDK+wwjx+tiSN8ixpxQlX7t6lE7ja29rWV/XfZB\nx7RNIsnoDsqd0gnF/ip32vyxlDsJIbqyZDLJ1KlTOe200zjllFO2e12SjYOXJBxCZJnjOOl+GPug\nZmMba+paaAyGaQ2nSCYtbNvBdhxURcmsKrT5YwXSyYcDyqYkRFNBy9yQbt+BW1FU3G4Vn0entNBL\nVXf5wb+13ZU7rWxbg4NNorFjIxRS7iSEOJR9+umnjB8/nsWLF/Paa6/x1Vdf4fV6sx2W6CTyG0qI\nLEkmkyxdsZaUraLsZW+H5bUtLFjSQH0ohpWySKQsInET27KxbQvV8OBy73wlKEVRUFXQdJ1EJIJb\nU/H5XOT69F120dY0NVMqdUTPwj0L1rEPifkb+1ru5LDzpFLKnYQQh6tYLMZ9993Hk08+mXn4tnLl\nSubOncvZZ5+d5ehEZzn47wKEOAilUikWL1tLbn4Je/v8Zum6IAtXx2kIq8RMNwnTwnEcdHe6DEoB\nEtE2LDWKy+PHwUEjvRTp5o8dHAxNI8fnwfCbDOpfxoghFZQEfJ16nclEnDx/1+s1sSOpTGnTphWe\nOrvcSTXQFZ1iX+EOEwopdxJCHG4cx2HMmDHMmzcvs23o0KHMmTOHY489NouRic4mCYcQWRBsacXt\n7dgKG4tWNbN6QxvxpJmeh2EDTrokStUAG3LyA2hWmOIi/w4njW/elpfjZmCvXlQELDxKlPbWeCde\npU2e30Xf3j078ZwdjGSrcqctCcWWpWLTzew6Z3WnHc2hMDSD6tb0CEVVQWVnXZYQQhzUFEXh5ptv\nZt68eRiGwT333MNdd921Tz2PRNckCYcQWRBPJDCMvR9NaAxFCYbimNamm2MbMlU8ioJL13DpKj3L\n8hjepzsXjBm+V+ff17kkWzuQDd62LnfaNqHo/NWdXLqR6Yot5U5CCLFvLrzwQqZMmcL3vvc9hg4d\nmu1wxH4iCYcQ2fBv9762bfP8M4+xetUKDMPg5lvvobz7tk/C4/E4999xA4NGXoqh+zFNk7oFvyMZ\nbcGxTcqPPIO8qqMJ5LrpXuynpHDvnxB11S7Quy53SmHaZofPvfXqTtt3yHZhaDqq0jW/LkIIcbAI\nhUL4/X4MY/sy24cffjgLEYkDSRIOIbqAj+d/gJlKMe2Zl1hSvYiXXniaex96PPP6p58tYOZzjxNs\nbiK3MYzm9dJS8zmGO4e+J16K6iRZ+PajDDrqeAb0LGDYgBIKvMksXtGe6wrlTkIIIfafv/zlL1x3\n3XXceOON3HXXXdkOR2SBJBxCdAHVi79m+IgTARg4aAjLl1VnXmsMRVlT18KwM69j/psvkjIdVMeh\nqOcwAoOP5+gBFQyocPOf831MuGhY5jgz0TUSDil3EkKIw1NjYyM333wzr732GgAPPvgg559/PgMH\nDsxyZOJAk4RDiCxZui7I59UN1IeifPPVGta0F/DZxs8xTZtwzOSxV/6Jy9CJxE3iCTfxpIVpb+6J\nAarqRlXdeHSbmU8/zOXjr8vKdWxd7pS0kiT2Y7nTvycUUu4khBBdj+M4vP7660ycOJGmpqbM9pEj\nR0pvjcOUJBxCZME3q5v5dEk79cEokViKlKPTHGzF9EYABdu2CbYmgQSW4+DY6SVvHcfBtGwURSHP\n78JvxPjtC/dx0cWX8J1Tz+z0OB3H2WZ0ImmlNiUUnVXupG3bEVt3bZVQSLmTEEIcrGbNmpVJNvLy\n8njyyScZP368LAF+mJKEQxyyUqkUltXx3gn/bkcT3TpqZW2IhmAyPXqRtPAUVBHa8A3ebkeRCK3B\nlVuOaaWb7znA5mf4CuDYDqoCbi3B/D8/yU8mTOaUkSd3KI5/L3faklDsn3Inl+bCLeVOQghxSFMU\nhVmzZjFkyBBGjx7NjBkzqKyUJcEPZ5JwiEOOaZp8s3Q1NgaK2nlPUmzLoqFuAz17lO/1sV8ua2Du\nZzXUNIYxUxYbN24kpfoBBQeHnLLBtDcsY+3851CAsqO/R+v6BVipJIFex6NulXGomoLHrdNQ/TaO\nmeCvf3qVv/7pVQAe+vlTuFzuzPse6HInl7a55EnKnYQQ4nDWu3dvFixYQP/+/WVUQ0jCIQ4tjuPw\nzdLVeHKK9ssSr3X1jdSs38igQYP2+JgvlzXwzserWbOhnXAsRSJpEYmlMDzptuAKoCoKFd+6AIUt\nP5R9gdLMx6qi4DJUjj17EmXFfk4YUs6AK44jZZmk7BSmZZKyTZoSIVJRE9M2iUYaCXvbOnytUu4k\nhBBiV2zbZubMmYwbN44ePXps9/qAAQOyEJXoiiThEIeURCKBo7r2Wz8Jl8tFLBzZ4Wt1TWEWrWhm\n8aomahvDJJIWqgJtkSThWArHAZt0jZTjKKDaqKoGDnhcOl63gdejYZrpUipFcdB1B0exMa0UimZR\nmK/Su0pB9beypCm4m3Knnc+tkHInIYQQ+2LFihVcffXVfPDBB5x11lm89dZbMpIhdkoSDnFIMU0T\nlP17o2zZ29/k1zWF+Xp5EwtXNLJyfSvhaIqUaWM7zqYEwtk0AYP0qIbhwU5FyC3IJ8en06dHHmXF\nHnqU+UjZJinLxHJ2Pv8ktZtSKCuZJN/vI8+dI+VOQgghOo1lWTz99NNMmTKFWCwGwNtvv838+fMZ\nOXJklqMTXZUkHKJLsm2bRCKBbe/dCkiRSIR4IoGqaZnzzHr+F6xbswqXy83Nk7ft4P3Pj+fxu1fn\noGoaZ479LmPO/g8sy2L6kz9jfe06FEVhwi130quqzy7ft64xQnNrnLX17bRFkpiWRco0QbWwNQtF\ntVA0G0ex0HQbQ7dRrBgOUVw5ASwN0G1CsT2bU6EpKoamY6hG+m/NQFd1DFXHTpkEAgp9+/Taq6+d\nEEIIsSuWZTF69Gg+/PDDzLbKykpmzpwpyYbYJUk4RJfT3h5m2Zo6dN2Ds5fDs9FIhA3NMTze9I37\n15//H63tUa6d/DPWrqjm2acfY+qjzwDp0ZCXXniKp557GbfHw203X8PxJ36b6m8Woqgqjz89i4Vf\nfcGv58zYpus3bEqIrCRfLKtj3lfrWLEhSDSZJJFK4LgtFMVG2VTupAFbDya4DR2Py0Wu349XdyjJ\n0+lepJKfYwJmptzJ0Ix0QqEbuDYlFW7VQN9FuZOmKvh9eeTl5e3V100IIYTYHU3TOPXUUzMJx3XX\nXcejjz4qv3PEbknCIbqUeDzO8rUbyd9qwvTeUFDwRhW8Xh8AtWtWcNQxJ+Lz+hk09Fh+9eLjNDQ1\nU1pcRM261ZR374E/JweAI4cczaKFCxh5ymkMP+5EYmacNbWrMbxu6sONJK0UddEG2qKNfLFhEavr\nWvl8ST0NwShR0yRlWzibEgsbUFUl3aBPUTB0Da9bo6TAR+/u+QyoLKZ7Yd425U7pORRS7iSEEKLr\nuvvuu/nyyy+55ZZbGDVqVLbDEQcJSThEl9ISasXnD+zzeVrjbbTE22hsbaCnNjCzXVM12sNRCgry\nCLYGMTxumqJBUnaKpGqxpmEdxU0rsR2b3814kcX/+ozLbrmJ5lgIgJSdSs/HAJaua6G2IUwyZWHa\nNsqmFadARXd0dNUg3+dNz53QXZTk5TC4dynD+pfRvThnn69RCCGE2F9M00TXt79NdLlc/PnPf85C\nROJgJgmH6FKSSRNNc+3TOdribURIYlomqsugNlhHccs6PLqHlGWyJlRHwh8nZEcItbfQEGkGoD3S\nRnFleaZz9iXXX0v7Dy5h+r33c9u0xzBc6bg0RSUeU4i0algxL6QUnKSC42hoikaez0vfinz6VObz\ngzMH7jROIYQQoiv65JNPGD9+PI899hjnnHNOtsMRhwBJOESXs/WyerZt8/wzj7F61QoMw+DmW3c+\n6fv0MeP41oknsba1jrdffg3dbRBtDbKqupruRw7EbGylpKKS9FJRUNq9nKaN9UTDEVweN6urlzL6\nu+fw1fyPaQuG+O7F3yenwIOu6fQKVODz+HGHNaK2F0+qlIAWw2PrxEwT1bHAUXC5dEoLvRTmexhU\nVXigv3RCCCFEh0UiEaZMmcLTTz+N4zhce+21LF68mEBg3ysPxOFNEg7RpX08/wPMVIppz7zEkupF\nvPTC05kJ3KZpMmvGUzzwi+mkSPHo3Xfi71VCJBbGwWH0jy7CcRw++fOf+OuM5zA0g3GXXs3yr76k\nfoWH0WPP5rJrruflx58Ex+Gccy7kuP4jOLrnUH7x2MM8ff/9mKbJ9RMnE9hU5qVulQwZuobPq5M0\nLRxHxTA0CnI99O6ez/GDyxg2oGPzUIQQQogDbe7cuVx99dWsWrUqs62kpISmpiZJOMQ+k4RDdGnV\ni79m+IgTARg4aAjLl1UTNxO0J8MsXfYNeSWFtNnpRnxVRwxg9ZJlJPUcrJTJh//1BtgOx59xBkce\nMYRibwGqopLsNYC+vSoAKPvOWE7/ztht3tPt9nDnvVN3GZeuKdiOg9/jSv/x6hzRs4BTjqmU+RlC\nCCEOKqlUaptkwzAM7rvvPu644w4Mw8hydOJQIAmH6JKao0Eaoy3UBTfQWxtIJBmhPRnBxmFF8xpU\nVaW1vRWPz5c5xuf1o1sOZSXdMU4fS99hQ2hpauIvL7/CKQ99e4crP9U3NhGLJ3fYzO/fhdrj/GvR\nWmrrGlDnNZI0LUzLwa1rVHbLIRJwaGl209rSSFlxAQUF8kRICCFE12cYBjNnzuT000/nuOOOY86c\nOQwePDjbYYlDSNYTjtdff52XXnqJ+vp6Bg0axJ133smwYcN2uv/XX3/NY489RnV1NQUFBZx33nlc\nd911O1xJQRycmqNB1rfVk7SS2Dos2bCMnIZSct052LaNqqYTB4/PRyqeoNhXSK7bjxcXPcuqCJSU\n0r/vIAzDRVWgkrm5b9DWGiIQ2HZOxfqN9cRTKi5XLrvrTd7SHmdlfYRgxCBhe9HIAc3G7dbwunRc\nPj8ufwC3rwCAdRtCOI5DYWHB/vgSCSGEEJ3qtNNO45133uH0009H03b3W1GIvZPVu/Q//elPPPDA\nA0yYMIGhQ4fyyiuvcNVVV/HGG29QWVm53f51dXVcccUVDB8+nOnTp7Nq1SqeeOIJIpEId9xxRxau\noGuzLItIJEIqtWfdq3dLUfC4Xfh8vm0mdu8r0zIJp6JEk1FWh2pZ2tKEaadjLuldyeqvq+l/zNEE\n122ke6+e+A0fuW4/VUMq+V3D83htA93RWLRwARd+71I++uBdFn6zjIsvvYZwezvhSASPx0MyEQcg\nmUjQHm4j2BLF58/JbN+V+uZW1tXW09jUhO4NYNkWoIBlY9k2bZEEClu+Jv7cALUbmiXhEEII0aW8\n9dZbjB49Gq/Xu91rY8aMyUJE4nCQtYTDcRymT5/O97//fSZMmADASSedxNixY/nVr37FlClTtjvm\nnXfewbIspk+fjsfj4aSTTqKxsZFXX31VEo5/E4vFWLKyFs3lQ1M670lFymzHUDYyaEDvzEjD3rBt\nm2gqRiQVJZKMEknFSJjJzOvhRHjTzXxa32FDqK1ewX8/MQOf4WXS7fey6otFxGMxxo47j2uuv4X7\n7rwZ27E5c+y5FBYVM+4/LmTJkgd5YdrdWDZcc+VVlOS5M+dM6CqFPggHXHg9e1abuqrOBEXDn1dE\n0nRwUEBx2Nx5w+fWycvZdjnfPajSEkIIIQ6IhoYGJk6cyO9//3tuv/12Hn300WyHJA4jWUs41q5d\nS11dHaNHj94SjK4zatQo5s2bt8Nj2tvb0XUdt3vLzWN+fj7RaJRkMonLtW/9Gw4Vtm1TvaKGvA52\n694VNx5M02TZyrUM7N97l/s6jkPMjBNNbkkwYmYCx9n1nXiBN59oKo5b03HpHi69/gbK/MUU+dIl\nUT17bHnf404YyXEnjNzmeE3Tuf3uh3d6/oTboaAgQGtcw+fzA7tffjc3J0YgN8qnbz1KxbcuwpVT\nhqKorHr/F3i8Pta7dVqqe3Hk3Q9suf5dXqUQQgix/zmOw29/+1tuuukmmpvTfaeeeOIJfvzjH3Pk\nkUdmOTpxuMhawrFmzRoAevXqtc32yspKampqcBxnu7KdsWPHMnv2bKZNm8Y111zD2rVrefnllznj\njDMk2dhKPB5H0z377fy6rhONbX87nTSTm0qj0glGNBXDsu1dnktVVHyGB7/Lh9/wUWQHSJg+WpNt\nNEZbACjxFWSSjc6g7qAabGfL7zaGony5rJH/++RzFs37LclYCBsFl6Hi1kHXVS6b+J8MG1BCScC3\n/YmFEEKILEkkEkyaNIn3338/sy0QCPDkk08yaNCg7AUmDjtZSzjC4TAAfr9/m+1+vz9ddhONbvfa\nEUccwcMPP8zdd9/NSy+9BMDgwYP52c9+1qEYqqurO3RcVxeJRKgLJndYn9lZ2lsbSVotxK0kMStO\n3EpgOdZujlJwqQYezY1Hc+PV3LhUHUdJEaaVMK3E43FWr19Obn4hbtIlW23hVtpo7ZS4k4kYPsMi\n3NrEhpZU5mv0yf/No6pPP1avWY3b62dJ9SIWLFrGstoItU0JQm0Rehx3GTWf/w6XppDrVSnWG1il\nmbzzm//kr5bFuRdcQlWf/pn3CrcF8WjJnYUiDjOxWAw4dH/uiP1HvndER2290ArA6NGjue+++ygt\nLWXJkiVZjEx0dZt/7nSWrM7hAHY6+XhH8wPmzp3LPffcw0UXXcTZZ59NfX09zzzzDNdeey2//OUv\nZZRjJ2zb5r9+M4e62rXousEPf/wTSkrLMq8v/PJz3vmfP6JqKieefConnTIa27b57cszaajfgKIo\nXPSjK8nvVkTSSpKwU7RGG1GjqV2+r67omeRi8x9tB0vTbs3j8VBZmkdtfRO6ywu72X+POQ62lSLH\no1BWWsra9UE+X9ZKMNZOPG6yZFUTdcnuLGmvQ1cVYkmHP360kUTKwbZBy+3J5qkwppX+3k1YGqeN\nOYeTvj2ahvoNPP/UI9w39RcdmtsihBBC7A933HEHq1evZuLEiYwdO7ZTF30RYk9lLeHIzc0F0k/j\nCwu3lMtEIhE0Tdvh0/lp06YxcuRIHnzwwcy2IUOGcPbZZ/Pmm29y4YUX7lUMh+pwYjgcxrW+NTM/\nYf68ufg8Hp598VWWVC/i96+9vE237p/dP5mnnnsZt8fDbTdfw+lnj2PJ0kVYmPz0kf9k4YIv+Otf\n/8Dlk27GwIMfcLsVKivLM++pqSp+w7epNMqLz+XDpXW8WdBwyyIej2NZuxs12TOqquJyuXC5XNQ1\nhalbGiUUDxNPKrTFwcZNNBbHiNqgpJO0eApse9NcDAc0J70Ola6pFObn0L2ohNOPOwOXy03vqt78\nrqiY/LxciovTc2faW/MYNKhvp8QvDn6bn04fqj93xP4j3zuio6qrq+nRowfLly+XpW7FXqmuriYa\njXba+bKWcGyeu1FTU0OPHj0y22tqaujde8eTkdeuXcu4ceO22danTx8CgQArV67cf8Ee5HbUrXuz\n1WtWUFJWTkRJ0BRupbxvL+Z+8i7+3Fxa21uJJuNEIxG0rfqcqIqCR3dT6i/C5/KSY/jwGJ07Z0TT\ntO1K6jpLXWOE9fVttEeSRJMqyZSFu6AXobpq/N2GEgmuxZVbjm05bF6HymHzqJyCz6MSyHVTv+oT\nXvrij9xw0+00NzUSjUYoLCzeLzELIYQQO7Ns2TImTpzI9OnTOeKII7Z7XZINkW1ZSziqqqooLy/n\nH//4ByeddBIAqVSK999/n1NPPXWHx1RWVvLFF19ss23t2rWEQqEd9u043G3drbuvNgjLsYibCVAU\n1oXWk7CSLNuwAlwaTdEgAIbHQzwaZciIYzH/kOLxW28jGo4w8e57KcspwWO48ehuokYLPQMVWb7C\njmlujbG+MUJbOImjGjiOQ27ZEMKNy1nz0XM4QPnR36OtbgGOlaSg9/Fgg65raCoU5boY2q+Yft+5\nhF88/jC3T7oWgEmTp0g5lRBCiAPGNE2efPJJ7r//fuLxOOPHj+fDDz+UBEN0OVlLOBRF4ZprruHh\nhx8mLy+PY445hldffZXW1lauuOIKANatW0cwGMx0Hr/++uu5/fbbmTJlCuPGjaOxsZFnn32WyspK\nzjvvvGxdSpcUjLbQ5sSIJWM4usKSjcvxrS8l1+3PNNqDdLfuxFYTg1KJBAV5hfzr7Xc5+qjhXHnV\nBFqam7hr8gSef+k1DL3jZVJdQV1TmJb2BLquki5jVVAUB0VVqPjWhcCW3hreQAm6ouEyVHL9Lgb3\nKeZ7pzxAIMegd890GeDkOx/c2VsJIYQQ+83ChQsZP348n332WWZbbW0tNTU1VFVVZS8wIXYgq53G\nf/jDH5JIJPj1r3/Nyy+/zKBBg5g9e3ZmtOL555/njTfeyNSvnnvuueTn5zNjxgwmTpxIXl4eJ598\nMj/96U/x+WRJ0q1tDDcRIU7cTFDcu4KVX39D328NJbhuA+U90yVsmqLSu1cfWhoayVf8FOQEWL98\nFVdddgP/85c/kJMbQFM1cnLysCwT27aAgzzhaIwQao/joKBpCqgqhqLiMnRchoqqgKGrOIBp2tgO\n5HgNBvQsYNiAEsKh+j1+L5mWJ4QQYn9ob2/nlFNOIRQKZbZNmDCBn//855k5skJ0JYqzuy5sh6jP\nP/+c4cOHZzuM/WJ1/Vr+suBTdHd61S7HcXj/tT/RvL4er+Hm+km3U7tqDWYyyVnjzufTTz7itVdm\nZ7p1jzv3QsLhdp56/GHaWkOYpsl/XHgJ3zn1zMx7hFubGTbk4JsQ/bdP1vDV8kYam9sJtbbi8Qfo\nVujjqP7FHFlVtNvjV69ZDUDvql03PQSItjVx1OB++xyzODTIxF/RUfK9I3bk6aef5pZbbqFfv37M\nnj2bU045Zbt95HtHdNTmSeOdda+c1REO0bmSVoq1oVo2tNaTY/iJk0JXNAK+PC67bgJlOVu6dQ/q\nOzhz3I66defk5DLlwccOaPwHgoJCrs9FsNXA0DVSiQiGnkNJoHN7lrS3NtOvV9nudxRCCCE6YOLE\niSiKwtVXXy1VHqLLk4TjENEcbaGmtQ7TttIrPLk8lPhLgHQ3707v1r2jdt17ob6hkYbmNqwdNCJv\nCsX4ankT6za20h5NZVaJsqx0iZOqpJem3Xabg8vlwu3LR1XAZWg4QCplZY5xGRrtsRSJhEksYWEY\nBgGPRbcci0JvCiux++aCZiw9fL2rfXVVoX9VGTk5+2eVLSGEEIePhQsXMmTIkO36Z2iaxk033ZSl\nqITYO5JwHORSVop1rXW0xLbcAPt9Pir9BqVFPffLqknJRJwcX8fnctQ3NFLfEseXs30JU2MoytIN\nYda3QGvSQ8zSNyUV/1b5Z277qYNDMhxGDUZx+/J2+t6O44CjoGkqCqAYPnr0KOeIfr32KHZ702T7\nPd1fCCGE6IhwOMw999zD9OnT+fWvf82ll16a7ZCE6DBJOLqAZDJJsCVEPJ5kTyfUKEDUjhFRIyj6\nlqSi0JtPz/wKKIFvlq7GQu/UpMO2LHK9Gn2qOn7D3dDctsNkA2BFbYgvl9XT0pbEtC0UFBzbyYxS\nbP03bPuxpucQDwfRXU4mQVEVBdtxMn8DaOqmkRFdIZWyaWiJ7SgUIYQQIiveffddrr76atasWQPA\nTTfdxBlnnEG3bt2yG5gQHSQJR5bF43GqV9Tg8QUwjJw9OsZyLDaGmwiGg8SjbVRW5JObk0fPQHcK\nvYHMfkcN7o9pmpimuYuz7TlFUTAMY58TmB2VUUF6dGNlbSvRuAk4YIOFwx5nYekg92CX9ApVLkPD\n7Za1yoUQQnQN4XCYn/70p8yaNSuzzeVycdttt1FY2Hll0UIcaJJwZNmS5TXkBkr2eP9wMsKG9gZS\ntomm6/jzCmnZGOX4E/vh0l3b7a/rOrredf+Zbdvm+WceY/WqFaQshaoR30NVclAUBUWF9poFtKya\nj6KqePLKKT/6fBRFRcMhGlxH/Td/pffI60jnMA6GrqDrKluylM0zQJTMNlVR8bg1cr0GPUpzGVQl\nP8SFEEJkn8vl4uOPP858fsIJJzBnzhxZZUoc9LrunehhwLZtHGXPnrBbjkVDpImWWFtmm6aodMsp\nxvCCYzkH5b/mx/M/oDkU5uQL7uCLBV/yr3dfo9cJV6KgoDoWTUv/zpCz7qAwkMPX786G9pUU9xzK\n+sX/S8OKT9F0N4X5XjbPYY9qCQoKfTudNL55m65r9CjN4dRjezBsQGk2vwRCCCEEkE445syZw2mn\nncZDDz3EjTfeKF3DxSHhILxFPXTYtr1NCdDWT/sNw+DmW++hvHslkVSUDe0NJK0UyUSCmT97tOR8\nBgAAIABJREFUhCsm3MS3Bn4LQzOIhNvS5zoIzf/4U7zFA1i9oQ3F151oSy2W7aBrCj6vl7GX3ceo\n4wZwRM9Cfr7svxk7ZgjfGj6C+fPC9O5zKdMeeYA7LhuROV+kLcjRg/tk8YqEEEKIjhsxYgTr1q0j\nEAjsfmchDhKdv4SR6LCP53+AmUox7ZmXuOLqCcx64Wnqw42sDa0naaWoWbmKGQ/9J61NzVTkl2Fo\nB2fX76ZQjH98upaX3ljI4hV1rGtIsqEpTDxpoSgKDhaO46CpCn16lnNEz0L+8qfXicdjfGv4cQCc\n/O1T5amPEEKIg9LGjRu59tpraW3d8RLrkmyIQ40kHF1I9eKvGT7iRAB69u/LkiWLaN7U9wHAQOO+\nh56gR4+qLEW47+qawny5rJFV60M0hmI4iotkIoZtgWU7OI6DoRsU5LrpUZZLebGPl158mq8W/It7\nHngk2+ELIYQQHeY4Dq+88gpHHnkkM2fO5Lbbbst2SEIcEJJwZJnjONiOje3YRCJh3F4v9eFGVgdr\nUBQFyzTBcSjxFfKd406lvFt3gMwxtmNj285BU1JV1xhhYzBCKJwgHEvhLepFuH4JAPHgWjz55bgN\nlUCuh7IiP2/91wxSyRRTHnwMl8ud5eiFEEKIjqmpqeGcc87h8ssvp6WlBYD//u//prGxMcuRCbH/\nyRyOLHAch6Ur1hCOJFlV04Tlqqcl3k59eysfL15Ay6ZVpUzTorkhRrG/kGA4TpCNAERjSdbUNhJN\npUuqYpEw8VgEr9dNrs+gb++eWbu23WlujdHYEiPhKJimRV75UCINy1k3/zlQoNeIHxDbuJBIQqeo\n17F8NPdtBg8dxl2TbwDgPy64hBNP/s6WE+7BMrhCCCFENtXX1zNkyBDa2rYs/HLBBRfw3HPPUVKy\n5ytVCnGwkoQjC6qXrUJx5ZGTr5Da0ERctbEMlcKqnqysrmbAiBMIrd9ARc8+9Cnrh8K2N9WapuH1\n5uDzb+6orZCbX4DH4yWRiLNy9boumXTUNYVpaU+gqqDYpK9LcehxzMX4PDqFeR76VQY4ZmApJQEf\nAG/+/eOdnq9bWXemPfPSgQpfCCGE6JBu3bpx8cUXM3v2bEpLS3nuuee46KKLsh2WEAeMJBwHkOM4\nrFlbw+qaJvw5KSzTZFVdLYbXRyjeRn73KuxF3/DatEfw6h4u+OENvPvOmySTcY498TRUVaEgkL/L\n93C5PbS3RQ7QFe2dusYIofY4oKBrKo4DhqLidRuUFHipKs9j2ICSTLLRETLgIYQQoit64okn8Hq9\n3H///RQXF2c7HCEOKEk4DhDHcahetoq4qYORi6P7cTBRdR8JVNRNXcZPOu8y/C4fFbllABSV900f\nD6Qcm9q6Rm6+67HdrNCkYllWl1vFqbk1RlskiaGC3+9GURTKinwM7VfMkVVF+3x+x3HQtL1pSy6E\nEEJ0rmAwuMOu4IFAgOnTp2chIiGyTxKOA2TNuvWornwMJbnNdq/uZu7//g9tTc2omsYpZ59PftmW\nRnSLv/oX//r4PVRVpbRbBWd+9xI2NDTz2i+fxeP1ApCXm8vkW2/JHOM4XfOmW0Eh1+cit6CI1mAT\nvtwCdE2lJODd53NblkUsHGTwEVX7HqgQQgixl0zT5PHHH2fq1Km8//77HHvssdkOSYguQxKOAySZ\nMtHdOqnUloRD03XWLP0G1VEYefH3iDYF+eqD9xh0+UQAUqkkH777JtfceC+6YfDGf81m5bJFVPRI\nz8+4cdJdAITbWjAM14G/qD1Q1xTmoy/rWLo2SG1jmETCJJYw0VQPrkSE7nm5dMu1wGrfp/fxugz6\nHFGFYRycvUmEEEIcvL766ivGjx/PF198AcD48eP57LPPcLm65u9mIQ40STgOkH8fc7Adm9//7mX+\n+dH/klPcC1dK5chBx/Hp/7wJwLKli/n044/wBbrx9VefccyxJ2I5Nrph0LCxjmQqyYxnHsc0k4wd\ncwaD+nfNSeIfLljPl8saaArFaA0nsWwbXVPRNB3N7aZ7RRl9qnpkO1QhhBBiryUSCaZOncrPf/5z\nTNMEQFEURo0ahWmaknAIsYkkHFnyyb8+orm9hYr+vcgJdOOf/3ibigsvJRUP09ZSzz/++kd+cOlV\nGC6DV345k1DjOsKhRkpKStlQu4oTTjyRE04YSVuogZnTH2XUqFNR1ey2Vdl6NCMUTtDanqAtmsSy\n0umW7TgogGU52LpDyrRpaIllNWYhhBCio1paWnj22WczycaAAQOYPXs2I0eOzHJkQnQtknBkQVui\nneXLq+nWpyfrl7eTV5JPzZKV9K3qgWpH6d+3Nw88NBVVUQm1thBpXk1wg8Itk+/DcLnoVuijb6/y\nTY3wqngtL59gsIni4tLdvvf+snk0o3p1Mw3BKKFwkmTKwtzckHDTEI+qKeiagsvQ8Hmk/EkIIcTB\nq6ysjKeeeoorr7ySyZMn88ADD+D17vu8RCEONZJwdLJUKoVlWdttTyaSWEqCRDJBY3uQSCSCXymk\nuEcPapcsAQVWLl9MRc/eAKiKylcLPuOXMx6hqLiU6376IJqaXnXqn/PfY+7bTdxw0+00NzUSjUYo\nLMzuEnt1jRHWbmhLl05F0smGZdngpJeqdRxABU1R8HkN8vxuuhf7GVS1/UoeQgghxMHisssu47jj\njmPgwIHZDkWILksSjk4Si8VYsqIGRXWhqNs3g1hb24zus2gMNbKsbj3tySR6MExFvwHULV1Fy8bV\nvPbLZ7lq4l386//eIx6P0avPABLRNsJtBg/feS15efmMHnsBJ4w8nf/5w2xun3QtAJMmTzmg5VR1\nTWEWrWhm8aomGltjJJLWjsunFCXd5E8Fxwa3oeH3ucjzu+hRmsOpx/Zg2IDsjcoIIYQQe6K9vZ2Z\nM2cyadKk7X7fKooiyYYQuyEJRydIJpNUr6wlL7Dzm2evr52IYtNihtEMg4KKcprW1lA15CiGnngG\nmuLnkstvxEyGGfqtE5g54ylOPGUMM179O6+/9jK9+/RjxPEnAxCPtjH5zgcP1OVto64pzNfLm/hi\naT3rNrYTjqVIJE0s28ZJD2igbP6jKRi6hsetU5Tn4dhB3fjOMZV0L87JSuxCCCHE3vrHP/7BNddc\nw9q1a3G5XNx4443ZDkmIg44kHJ0g1NqGx7vrDuAA69s30paMoCs6Ff3601q3kY9//3vcuotzL7qS\n6oWfE4m0cdZZZ3HscSfxzJNT0TWd7pU9OPa4k/YiIme/jXjUNUZYs6GVlbWtxJMmiaS1aVRDQVFA\n3TSaYRgauT4XHpdGfo6Lo/uXMHJYhSQbQgghDgqhUIhbb72VOXPmZLY9+OCDjB8/Hr/fn8XIhDj4\nSMLRCRLJFLru3unrDg6N0Wba4unRDSvVRkFuGaeNuxhFUeie2w2AouJuxBMxbMvmpJGjOGnkqL2O\nxXEcdNVGUbYv6+oMm7uF246D46TnZtg4qCgoioLbpeE1dAb0CnDaiF4cO6jbfolDCCGE2F9WrlzJ\nt7/9bTZs2JDZdvLJJzN79mxJNoToAEk49gPbtnn+mcdYvWoFhmFw0TVX056Mk+P2056IEFq3jk/f\nepfcQDEu3cWYs/4jM+nbtu3Mn12df0evp5JJUvF2Bg+s2l+XlukW7vPomKaNpikoioqqKOiGis9t\nUBzw0Kssj+4l8kNZCCHEwaeqqoqqqio2bNiAz+fjkUceYcKECVlffl6Ig5UkHPvBx/M/wEyl+Pkv\nZjDv8w94/VezGXvJj8hx+Sjw5vNV6APGXXwulaU98Gqbls9z0v0odCeKWzFwqTtPOBQtis9Ibrfd\n4zcIBHqj6/vvn7Uw30Ow3Y3b0DEMC01T8BgGLkPBdpDyKSGEEAc9TdOYM2cOt956K88++yy9e/fO\ndkhCHNQk4dgPqhd/zZDhw1kTqqG8dy9qV62iuDiA2aZQEigjuLGBJf9cwGdt7zNoyNGcMeaczLHR\nWITS0gAej2eH5w63h+jRZwBFhQUH6nK2oWsKpulQVuSnW6EPXVM4bURPWW1KCCHEQcmyLDRN2277\nwIEDeeutt7IQkRCHHkk4OlFzNEhjtIV1jTX4ehbR3U7341BVjf6lfVCKoHZDA4MHHcHIU0bj9niZ\nM3M6BXl+Bg89GoB4LEIsbGOntk84NA16lBVkLdkAMC2HWDzF2o2t4EBFaQ7mpqVwhRBCiIOF4zi8\n/PLLTJ06lY8++ohu3WTOoRD7iyQcnaQ5GqQx1kJboh3bgGB7C+2JMGU5JWioeF3p0ql+fXox/sor\n8fnT5UajThlJe1sTA/r2AiAcbmNAr2J8Pl/WrmVXVq0PEWxP4Pe4AGiPJFm1PiSTw4UQQhw01q1b\nx09+8hP+9re/AXDjjTfy+uuvZzkqIQ5dMvupkzRGW4gko7TG2ynvW8WaRUswbYtIXZCqPv0y+0XC\nYW645kfEYzEcx+GrLz+j34BBWYx874TaEyRT1nbbhBBCiK7Otm1mzJjB4MGDM8kGpJv3JRLyu0yI\n/UVGODpRJBUFoO+wIdQvW8tvH3kar+Fh0m338v57fyMeizF23HlccfUN3DX5BgzDYNgxx3HscSdm\nOfI9l+t3saE5QjiWnrRekOsm1+/KclRCCCHE7i1evJiJEydmVnrs1q0bM2bM4Pzzz89yZEIc2iTh\n6CQFnnxq29LrdRuawSU/+Qnd/EUU+QoBqKjsmdl31OgxjBo9Jitx7qvSAi/Va4LkeNNJhsetU1rg\nzXJUQgghxO4NHTqUSZMmMW3aNH784x/z5JNPUlhYmO2whDjkScLRCQxdx6Vo5HtyaY2343d5tkk2\n9orj7HC1jK4iP8dDvt9Fc2sbAFVlOeTn7HhFLSGEEKKreeihhxgzZgxnnHFGtkMR4rAhczg6QSA/\nl6ZQPbnuHCrzyxnabVDHkg3AsRK4XF23RKm5NYbbpdOzWy49u+Xices0t8ayHZYQQgiRkUqldrqk\nrc/nk2RDiANMRjj2QSwWIx5PYDsOjhahvrYdt9dPEXlElMhencs2LbBjDBvcH0VR9lPE+05h+9h2\ntE0IIYTIhgULFjB+/Hi+/PJL3n33XUaPHp3tkIQ47EnC0UErV6+jPeagGy6iZoy47SW3wMBlaziW\ng4O1+5NsxaXrGJ4ilq3ZSJ8epQTy8/ZT5B1X1xRm8apmqtc2E09a5PlclBf5KMyXkiohhBDZlUgk\nePjhh3nkkUewrPTv4GuvvZbq6mp0XW53hMgm+T+wA9bV1JGwXOTkpm+0WyMRXG43Lreb7rmlBDz5\n+3T+VTWNHOl27bTbeDbUNYV5//Ma1mxoJZmyURWFRNKirinCUf1Lsh2eEEKIw9iSJUu44IILqK6u\nzmwbOHAgc+bMkWRDiC5A5nB0QDiWwOXekgyEk1vKp3Jc/n0+v8ebS6i1fZ/P05kWrmji44UbaGqN\nkTJtbMvBtG1iCVM6jQshhMiqsrIyQqEQAJqmcffdd7NgwQJOPPHgWXZeiEOZJBwd4Gx1f520UiTM\ndE8Kr+FGV/f9SYqu66RMc5/P01nqmsKsrmsjnjRxHHAcBwcHn1snx9d1J7gLIYQ4PAQCAV544QWO\nPvpoPv30U6ZOndqlqgSEONzJOOM+akuE+e+X5rBhXQ1ej5fJt91PefdKAFpamnn0P6dk9l21cjlX\nXj2Bs845n9d/+yv++clHWKbJOeddzOlnjsvWJezWwhVNLK9poTWcwrItNFVF13Tchk6P0hy6l+z7\nqI4QQgixL84991zGjRvXpZeWF+JwJQnHPpo/7z0s02TiQ/dj1od56YWnufehxwEoKCjikWkzAKj+\nZiGv/PJFxo47j6+//Jzq6kVMe+Yl4rEYf3j9lWxewi7VNYVZtKqZaNzEbagkkg6WZYMCPbrlcsox\nlXQvzsl2mEIIIQ4D77zzDs8++yx//OMfd7iEvCQbQnRNUlK1D2zHZuk3izji6KPQVY2jhnyL5cuq\nt9vPcRxefHYaE26+HUVR+OKzT6jq3ZeH77uNB++9leNP/HYWot8zdY0RwtEkjuOgaSo+r0Fejpt+\nFQG+f+YASTaEEELsd8FgkCuuuIKzzjqLt956i0ceeSTbIQkh9oKMcHRQczTIurYNNIWaqND7ZiaL\nq6qGbduo6pZc7p8fz6NX7z5UVPYEoK21lcbGeh6YOo2NG+p46N7JvPjL17NyHXvCNG0s28G2bUAh\nx2tQVuSTZEMIIcR+96c//YkbbriBjRs3ZrbNmzdvu9+1QoiuSxKODmiKBGnFJpqM4vK6aWlvIWWl\nAHCc7X8Avv/u3/iPCy/JfJ6Xn0+PXlVomk5FZU8Ml4vW1hD5+YEDeh17QtcUPG4dXVPRvenh655l\nuRw3uCzLkQkhhDjUzZ07lwsuuCDzeU5ODo8++ijXXXedJBtCHEQk4eiA+vZGTMdNQ0szgbJurPhi\nIbVHn0Bd9Vq6V/aioal5m/2XVC9ifGn3zPaKnn34x9tvcPKoMbQEm4lEIlg2pMwUhm5k45IyUqkU\nra1tJJLpBOqfC1ayvq6JhuYoKFAS8FLg8VKSY7O+buNuzpbmcbvIz8+TtdCFEELslVGjRjFmzBj+\n9re/MWbMGF588UV69eqV7bCEEHtJ7gD3UiqVYn1tM95Ad5K2i8pBx7BhxXpenvYUbt3FZddM5sMP\n55GIxxh56tm0t4XweHOIpbZ8qQcMPYnFi7/hgbsn4Tg237/8RloiFo3BjfSqKNnhRLgDIR6P883y\nGtyeXHTDYOm6IMs3JrE1L3mBdEx5+V48/lzCyT1PjEKRJOs2rGbwgF5ZuzYhhBAHH0VRmDlzJu+9\n9x4//vGPURQl2yEJITpAEo69tGTFWsqLetKq2CiqhgJ856JL6FPQg3xPHgDlFT0z++cHCrnnZy9s\nd54Lf/iT7ba5DBdrauvp36dyv8W/K9XL15EXKM18XlMfAVRSZnpuCkA4ZqFp+l6tBKJpGm6Ph2+W\nrmXY0P6dHbYQQoiDnOM4LFmyhEGDBm33Ws+ePbniiisOfFBCiE4jBZB7ybQUCnwB8lw5KIACFHoD\nmWRjX6m6i2gkgtt1YEurUqkUirrt6ENTKEZ9MEpbNEUklsKyHdwujbycjo1SOIqGZVmdEa4QQohD\nxOrVqznzzDMZMWIEa9asyXY4Qoj9QEY49sLmVZrycry0BJOU+IuY+8abzG9sxmW4+MGlV1FcsmWE\n4PN/fcx7/3gb3TAYdsxxnHraGAAe/9l9eLxeAIqKS/nhZVdljnFQiUVaCfQ7sJOybdtGUbcMVVev\naeL/3p5D48Z1KKpGj+Hfx2V0o6zYT0nAy/wP3+MP//UKKAqnnjaGc8//PgA3XXc5Pn96xa6y8gpu\nmbyl8aGiqNi2LeukCyGEwLZtnnvuOe666y4ikQgA11xzDX//+9+ldEqIQ4wkHB1QUlJMfXsTn8z7\nmHi0nSuvvYGG2jpef20OV1w1AYBoJMKf//AbbrntXjxeLy9On0aPykpKy8pJpRJMuPm2zPmikXYA\nHMciEg5x/KBBWZ/r8Nd3/k4snmTAqTfR3ria9V+9QfEZ11FRnENhrptfzX6ep2e8jMfj5fqrLuHU\n08bi9ngAMs0OhRBCiB1ZsWIFV155JR999FFmW3l5ORMnTpRkQ4hDkCQcHRQozifcVMvgo/vRu6KI\ngT278/ILP6OqeyEAy5ZtpKp7IYP6pudjHDmgJ80bllMccJEK1/Py81OxbIsf/fgajjhiMACKqpJM\nBfBuGv3IlsZQlLUrviGnbCC6rlJQ3pd1n/6awjwPeTkuNE3jxV++jqqqtLQ0Y9sWumGwauVyEok4\n995xE5Ztcfn46xk4aEhWr0UIIUTXk0wm+fTTTzOfjx8/nieeeIKCgoIsRiWE2F/2OuGoqanhww8/\nZOPGjVx44YV4vV5qa2sZPnz4/oivy0rZKRLxODl5eeT4c1FR0XUDw+VCVVWqevdj/fp1xGJRPF4f\nixd+yUkjR5Gbm8/FP/gxY846l/W167j/7knM/NXvM+uJJ1OJLF8ZNIZiOGYCl+HJbFNUFa9boySQ\nToZUVWX+vLm88OwTjDj+ZNxuDx6Plwu+d+lOr00IIYQAOPLII7nvvvuYOXMms2bN4swzz8x2SEKI\n/WivEo5p06Yxe/bsdL2/onDSSScRiUSYOHEiZ555Jk888UTWS4EOhOZokFXBGlKaTXukDXXT3Put\nm/7l5uZxzfWT+NmDd5Kbl0/f/keQlx+gorIn3SvSox4VlT3JzcsnGGyiuLh0p+93oDS1xli4Jsrn\nS+qJJFWsWBS3aaOrCgrQv0chJQFfZv+Tv30qJ40cxS8ee4h3//FXRo0e02WvTQghRNdy++23c9NN\nN5Gbm5vtUIQQ+9keP3r+zW9+w6xZs7jiiit49dVXcRwHgBEjRnDllVfy97//nVmzZu23QLuKYDTI\nxnAjpm1S3rcX33yxgOZokCXfLKSqd7/MfpZlsnxpNY89NZM7p0xl9crlHP2tEfzv397kpReeBqC5\nqZFoNEJhYXG2LiejrinMippWlq4J0tKewFNYRduGajQFnEgdZRVV9K3MByAaCXPHT69Lr2ylKLg9\nXjRV7bLXJoQQIjs+++wzpkyZssPXDMOQZEOIw8Qej3D85je/YcyYMdx+++0Eg8HM9vz8fO644w5C\noRB/+ctfmDBhwn4JtKtoiobQ/emn/H2HDWHD0tXcN/lmvIaHSbfdy/vv/Y14LMbYceehaio3XX85\nmqpx1jnnU969gtJu5/KLxx/m9knXAjBp8pQuUXK0oSnC6rpW1jamSJk2ed2HEK5fytL3nkHTVC69\n+lYWfzEvc22nnjaWOyZdi67r9O7bn1NPPwvbtrrktQkhhDiwYrEYDz74II8//ji2bTN8+HDOP//8\nbIclhMiSPU44ampquPzyy3f6+rBhw3jrrbc6JaiubvPojqIonHX5DyjyFTCwuC+QLiXa7AeXXsUP\nLr1qm2M1TWfynQ8euGD30Or1rYTCCRxnU4JgQ4/hF5PnMygr8tO7TxlHVh2b2X/suPMYO+68bc7R\nVa9NCCHEgTN//nzGjx/PsmXLMtuee+45STiEOIzt8ePnwsJCamtrd/p6dXU1hYWFnRJUV1bsC2Bv\n9bmqKJT4Om9VjWwtBhgKJ9BVBUNX00sSKqAqEMj1UFbkz0wWF0IIIXbmz3/+M9/+9rczyYau60yZ\nMuWweSAphNixPU44zj77bH7zm9/w2WefbbdG9p///Gd+//vfc8YZZ3R6gF2JqqoU+Qso8gZQNv1X\n5C2gyNd5iZZjWxjGge0yDhBP2jSHIiRS6XRK1VQ87v9n777joyjzP4B/ZmZrdjdlCZBAKKEoVRA5\nBBEQUFBBsBwqnJ4Uo4IoiHp2T0ERxONoItJsnKAnov48PUSFQ8WCFJEmSA2EENKzJbs75fdHzEpM\nAptkN7NJPu97+XolM7szH7kY5rvP830eA9o0j0OPDk3KNItXl6YpMBi4EjMRUX01ZMgQtGnTBgBw\n8cUXY+vWrZgxYwbMZrPOyYhITyE//d13333YtWsXbr/9diQlleyCPXPmTBQUFCArKwsdO3bE/fff\nH7Gg0SLWZkKxR0VKXDIAoFFMfNiuLcsyTJJS6yt97TyQhSKvjEAgAKNRgSQZEWc3oe9FzdCrU3JY\n7uHz+2C3GrihExFRPRYTE4MVK1Zgy5YteOihh3T5AI2Iok/IBUdMTAzeeOMNrFu3Dl988QWsViv8\nfj/atm2LO++8E7feemuDWBK3TesWyNu7G+7CXAgQ4VYMMAXCcWUNFpOADu1Tw3Gx88rIdmH3rznY\nczgbuw/nwFMcgGiOh6cwB5JBglWyQ5TdKCrIPf/FzkOABpvVgPZtW4UhORERRYP8/HzEx5f/0G3A\ngAEYMGCADomIKFqFXHBkZGQgISEBo0aNwqhRo8qdLywsxK5du9CzZ88K3l2/tGyVDC1OhaIoaB2f\ngkRbzadUiaJYa5/+Z2S7sOtgNnYfysbx00VwewPwywpEQYAtvjHsVhOSG1mQ2qoZLurYtMb3kyQp\nDKmJiCga5OTkYOrUqfj222/x008/wWaz6R2JiKJcyD0cgwYNwueff17p+fXr1yMtLS0soaKdqpX0\nOUiSBIPBAEmSavxPbU41yjjjRk5BMU5muVDo9kNVNUADNA2QZRWSJCDWZkWLpNiw/LsREVH98N57\n76FTp05YtWoVDh06hKeeekrvSERUB1Q6wnHixAksW7YMgiAEl4F9//33sW3btnKvVVUV3377LazW\nhrGSkfrbnwcAiELd3GeiyONDgceHgKxAFAUYBBGqpkEQBCTYzejaNhHNEu16xyQioiiQmZmJe++9\nF++//37wmMPhQMeOHXVMRUR1RaUFR0pKCtLT07Fly5bgsW+//RbffvttudeKogin04kHH3wwMimj\nTOkIB1CyLG5dY5AEaBpgNRsQCKhQBA1mgwR7jBGtkmLR7YLG6NKukd4xiYgoSnz//fdlio1rrrkG\nr776Klq0aKFjKiKqK87Zw7Fy5crg1x06dMCLL76IESNGRDxUtNPOGuEQ6uAIh6xoSIg1w2w0wGgs\nGeGIjTGjTfNYdGrTCF3bcXSDiIh+N3LkSNxyyy347LPPMH/+fNx2221cdZCIQhZy0/jnn3+ORo34\nqTdQ90c4cgq8kGUNyY1saOqMgUEU0KVdIob2bq13NCIiilKLFi2CLMvBpfGJiEIVcsGRkpKCwsJC\nbN26FR6PB6r6+0O3oihwuVzYunUr5s6dG5Gg0aRswVH3RjiECvYzr+gYERE1LIcOHcL333+PMWPG\nlDuXmJioQyIiqg9CLjh27tyJCRMmwO12V/qahvLLqEzTeB16UC/de+N/O9KRmeOBp1hGjMWAts3j\n4Iyz6B2PiIh0oigKFi5ciMcffxyyLKNbt27o3Lmz3rGIqJ4IueD45z//CUEQMH36dAQCAcyYMQOL\nFi2Cz+fDmjVrkJ+fj7Vr10Yya9SoiyMcpXtvbNt/GifPuOAtViCIgD+g4FSOGwWuYr35e1V3AAAg\nAElEQVQjEhGRDvbv34/x48eXWRTmiSeewAcffKBjKiKqT0J+Wt69ezfGjBmDm2++GaNGjYLBYIAg\nCBg2bBhWrlwJQRCwdOnSSGaNGnWy4Djjxu7D2dh7JAdF7gAUTYWqalA1DYGAiqw8r94RiYiolv37\n3/9G9+7dyxQbaWlpeOONN3RMRUT1TchPy36/H61atQIAmEwmtGjRAvv27QMAGI1G3HDDDQ3m0xAN\ndW8fjsMn85GV60HpbLCSmkmAySghxmrUMxoREemkV69eMBpL/g5o3bo1NmzYgKVLlyIuLk7nZERU\nn4Q8pSopKQknT54Mfp+amor9+/cHv7dYLMjKygpvuih19ghHXVkWML/IB0kSYTZKkGUViqpBFIBY\nmxnNEm3o2Nqpd0QiIqplrVq1wosvvoh9+/Zh5syZsNu5JDoRhV/IBceVV16Jt956C6mpqbj22mvR\nq1cvzJ8/Hz/99BNSU1Px4YcfolmzZpHMGjVKm8ZFQagzBYfDZoLRIMJgEGE0ipAUDSaThNZJDgzs\n2QLdL2iid0QiIoogRVEgSVK54xMnTtQhDRE1JCHPB5o4cSLatm2Lhx9+GB6PB6NGjUJCQgJuueUW\nXHrppdi5cyfGjx8fyaxRo3SEo65MpwKAJglWaKqGBIcFzRs70KVdIv56bUc8MOYSFhtERPWY1+vF\nww8/jOuuu67MxrVERLUl5BGO2NhYrF69Grt27YLD4QAAvPvuu8EVqvr164cBAwZELGg0Kf2FXVdG\nN4CS3cUDsorsfA8MBgmtmzkQZ+dSuERE9dlXX32FCRMm4ODBgwCA119/HePGjdM5FRE1NCEXHEDJ\nA3a3bt2C3ycmJmLy5MnB73ft2oWLLroofOmiVF0b4cjIduFIRiEkSURifAwAQFE05BRwZSoiovqo\nqKgIjz32GF5++eXgMYPBgJycHB1TEVFDdd6CY9euXdi1axc0TUPHjh3Rs2fPcq9xu92YO3cu1qxZ\ngz179kQkaDSpcwXHGTdUVUVA/r3Z3eUNcHdxIqJ6avny5WWKjUsuuQQrV65sEB8KElH0qbTgcLlc\nmDJlCr755psyx/v27YvFixfDbDYDADZu3Ihnn30WmZmZwWVz6zNN08o0jUe7nQeysG7TQRzPLIKs\naLBaDIizmRBnN3N3cSKiemry5Ml48803sW/fPkyfPh3Tpk2DwVClSQ1ERGFT6W+f+fPn45tvvsGA\nAQMwcuRIWK1WfPXVV3jnnXcwe/ZsPPXUU3jhhRfw5ptvwmAw4O6778a9995bm9l1cXbDnRB6z70u\ndh7Iwn+/PYLTuR4EFBWqoqHYL8MRY4TTYUazxja9IxIRUQQYjUasWrUKBoMBF154od5xiKiBq7Tg\n2LhxI3r37o1XX301eGzgwIFo3LgxXnvtNTgcDrz55pu46KKL8Pzzz6N9+/bVCvDuu+9i+fLlOH36\nNDp27IhHH30U3bt3r/T1ubm5mDVrFv73v/9BVVX07NkTjz/+OFq0aFGt+1dV2V3Go3uEY9/RXBzL\ndMHrk4PHZFmFomhwxlrRLJHrrRMR1WVnzpzB4cOHcemll5Y717lzZx0SERGVV+lH9NnZ2Rg8eHC5\n40OGDEFhYSGWLl2KO++8E6tXr652sbFu3To888wzGDlyJBYuXAiHw4EJEybgxIkTFb4+EAhg3Lhx\n2L17N5577jm88MILSE9PR1paGgKBQLUyVFXZgiO6RzgK3X74Awo0raQ4MhhFWM0GtGhq53QqIqI6\nTNM0vPPOO+jUqROuv/565OXl6R2JiKhSlT4xFxcXIz4+vtzxhIQEAMDw4cPx0EMPVbiJUCg0TcPC\nhQtxyy234N5770X//v3xyiuvICEhAa+//nqF7/nggw9w7NgxrFy5EldddRWuvPJKvPTSS/B4PMEl\n/yJNxe9TqqJ9hCPBYUGMxQBJLMlpMoiwW01olRTL6VRERHXUqVOncMMNN+DWW29FdnY2MjMz8fjj\nj+sdi4ioUtXuIBs2bFiNbnzs2DFkZGRg0KBBv4cxGHDFFVfgq6++qvA9n3/+Ofr374+kpKTgsQ4d\nOmDz5s01ylIVdWmEo03zOBw+mQ93sQzZJ8NiNKBdSjz690jhdCoiojro/fffx4QJE5Cfnx88Nnz4\ncDzxxBM6piIiOrdqPzGXrlJVXUePHgWAcitbpaSkID09vcLdUA8cOIDU1FQsWrQIffv2RdeuXXH3\n3Xfj1KlTNcpSFXWp4DBIAkxGCRe2TED39o3Rt1szDOzJYoOIqK5yOp3BYsPpdGLVqlX46KOPkJKS\nonMyIqLKVXmEI1y7a7tcLgCAzVZ2ao/NZoOqqvB4POXO5eTkYO3atUhJScHMmTPh8Xjw0ksv4a67\n7sIHH3xQ7eldVVFmlaoon1IlKxoMkoisvJIN/lolOSAr5Qs5IiKqG6644gpMnDgR2dnZWLhwIZo2\nbap3JCKi8zpnwfHwww/j4YcfrvDcuHHjgl8LggBN0yAIAvbt2xfSjUsf3Ct7aBfF8qMHsixDlmUs\nX74cdnvJp/QtWrTAn//8Z3z22We45pprQrp3qVCzns0le3DSkwkA8JiK4LIUVPkatWX3/nzk5Hkh\n/fZnfep0FordebAhV+dkdZfXW1K8Vednhxo2/uxQdf3xZ2fixIkwGAzIzc1Fbi5/n1Pl+HuHqqv0\nZydcKi04rr/++ipfrCqf+DscDgAlu5Q7nc7gcbfbDUmSYLVay73HZrOhW7duwWIDALp06YLY2Fgc\nPHiwygVHdZw9whHtU6oAwOdX4S5WAAAxFhEJdqPOiYiI6FwURcGbb76JwsJCTJkypdx5buBHRHVN\npb+1Zs2aFdEbl/ZupKenl9lDIz09HampqRW+p2XLlvD7/eWOy7JcrelNHTt2rPJ7cj35EPNMAIAW\nccloam9c5WvUluMFR3Ei7zQ0seTPLCHegeTkeHTs2FrfYHVY6adE1fnZoYaNPzsUij179uDOO+/E\n999/D1EUMXbsWMTFxQHgzw5VHX/vUHXt27cPHo8nbNfT7SP61q1bIzk5GRs2bAgeCwQC2LRpE3r3\n7l3hey6//HJs374dWVlZwWM//PADPB4PLr744ohnBso2jQtRPsIhQECMxQBnrAXOWAscMSYIiO6+\nEyKihigQCOC5555Djx498P333wMAVFXFl19+qXMyIqKa021cVhAEpKWlYcaMGYiNjUWPHj2watUq\nFBQUYOzYsQCA48ePIzc3N7jz+B133IG1a9ciLS0N9913H7xeL1588UX06NEDl19+ea3krks7jTvj\nLOWaxrnhHxFR9Hn66afLzCxo06YNli9fjoEDB3L+PRHVebp+RD9mzBj87W9/w0cffYQpU6bA5XJh\nxYoVweX9Fi9ejNGjRwdf73Q6sXr1aqSkpOBvf/sbnnvuOVx++eVYunRprWWua8viyoqKBIcZCQ4z\nArIKgxTdRRIRUUM0bdo0NGrUCIIgYOrUqdi1axcGDhyodywiorDQvfNs3LhxZVa8OtusWbPK9ZK0\naNECL7/8cm1Eq1CZncajfHoSl8UlIqobGjdujDfeeANOpxN9+vTROw4RUVjpXnDUNXVphCOnwBsc\n4QCAgKwipyC8y5wREVHoPB4PcnJyyiyWUmrYsGE6JCIiirwqPzG7XC5s2rQJa9asQWZmJvLz82t1\np2+9qWrdKTgqahBn0zgRkT42bdqEiy66CKNGjYKiKHrHISKqNVV6Yl69ejUGDBiAe+65B88++yyO\nHDmCHTt2YPDgwZg9e3aZPSrqKw11Yx+OjGwXjp4qxLFTBTh8ogCZuR4YDSKbxomIallhYSEmTpyI\ngQMH4tChQ/j+++8xb948vWMREdWakJ+YP/30Uzz77LPo168f5syZEywuOnTogCuvvBKvvfYa/vWv\nf0UsaLRQzyqqqrP3R23IyHZh18Fs5BV64Zc1iJIATdWQV+hj0zgRUS367LPP0KVLFyxZsiR4rFev\nXrj66qt1TEVEVLtCLjiWLl2Kyy67DPPmzUPfvn2Dx5OTk7FgwQIMGjQI77zzTkRCRpO60MORccaN\no6cKkJ7lgqyokBUVflkBRLBpnIioFh08eBDp6ekAAIvFgpdeeglbtmxB586ddU5GRFR7Qn5iPnTo\nEAYPHlzp+f79++P48eNhCRXN6sI+HDkFXhS4/FDVklWqDJIIm9WI2Biz3tGIiBqUiRMnol+/fujf\nvz927dqFBx98EJIk6R2LiKhWhbxKld1uR15eXqXnjx8/DrvdHpZQ0UwrM6UqOkc4BAhwxBhhMkrw\nBUoaE20WI5xxZjRrbNM5HRFRwyGKIj744APEx8dDFKPz7wwiokgL+bff4MGD8a9//QvHjh0r17vw\nww8/4O2330b//v3DHjDa1IURDmecBQmxFgiiAEXVYDKIaJZoR7f2jdEssf4XhUREtUnTNLz99ttY\ns2ZNheedTieLDSJq0EIe4XjggQewdetWjBw5Ep06dQIALFu2DPPnz8fOnTuRnJyMqVOnRixotCgt\nOARBiNoeDoMkICCrSHLGAABMRhG9OjdlsUFEFGYnT57EPffcg48//hjx8fEYMGAAkpOT9Y5FRBRV\nQn5idjqdeO+99zB27FgUFRXBbDZj69atyMvLwx133IG1a9eiadOmkcwaFUpXqYrW0Q2gpDG82Cfj\neGYhjmcWwhdQ2CxORBRGmqZhxYoV6NSpEz7++GMAQH5+Pt566y2dkxERRZ+QRzhUVYXdbsfUqVMb\nxEhGZYIjHFXfM7HWHD6Zj9zCYtisJgBAkduPwyfz0bNj/S8IiYhqw5QpU7Bw4cLg94mJiVi0aBFu\nvvlmHVMREUWnkJ+a+/bti+nTp2Pbtm2RzBP16sIIR36RDwFZLXeMiIjC44477giuNnXrrbdi7969\nuOWWW6J2fyYiIj2FPMLRp08frFu3Dm+//TaaNWuGq6++GsOHDw/2czQUGkoe5KO1fyMj24XTuV4c\nP10En1+BxWRAU2cMHDaT3tGIiOqNSy65BLNmzUL79u0xcuRIveMQEUW1kAuOuXPnwufzYfPmzfj0\n00+xevVqrFy5Eq1bt8bw4cMxbNgwpKamRjJrVIjmEY7SHcYVRUFAViGKAjRNgwoVTRKsescjIqpz\nZFmGz+eDzVZ+SfGHHnpIh0RERHVPlT6mN5vNuOqqqzB37lx89913WLhwITp37ow333wT1157LW64\n4YZI5YwKqqYG9+GIxhGOr3eexH++Poz9x/LgLZYRkBXIqoo4mxlxdove8YiI6pSff/4Zffr0wX33\n3ad3FCKiOq3aT81msxmpqam48MIL0aZNG2iahvT09HBmizrqWZv+RVvBsfNAFnb8cgZFXj9UTYOm\nadAAOGKMaN7YoXc8IqI6w+/3Y/r06bjkkkvw448/4rXXXsNnn32mdywiojor5ClVpX755ResX78e\n69evx6FDh2A2m3HFFVdg4cKFGDBgQCQyRg3trE3/oq0xcN/RXBR5/FBUDaIgQNU0yLIKgyhxh3Ei\nohBt27YN48aNw88//xw81q5dO9jt3MeIiKi6Qi44/vnPf2L9+vU4evQoDAYD+vTpg7vuuguDBw9u\nML+Io3mEo9Dth8sbQEAumfYlAJBEAQmxZu4wTkQUouXLlweLDVEUMW3aNDz77LOIiYnRORkRUd0V\ncsHx6quvomfPnrjjjjswdOhQOJ3OSOaKSupZIxzRVHBkZLsgAhDwWzO7JMBkFNE6KQ7X9WvDYoOI\nKESzZ8/Gxx9/jLi4OKxcuRK9evXSOxIRUZ0XcsGxceNGJCcnRzJL1NPKjHBEz5SqjDNueHwyYqwG\n+GUFqqrBbjWhU5tG6H5BE73jERHVGbGxsdiwYQNSU1NhNpv1jkNEVC9UWnB88sknuPjii4NFxo4d\nO7Bjx47zXvDaa68NX7ooc/YIh4DoKThyCrwocPlhs5hgs5Tst9GmeSzaNI/TORkRUXT64osvEBcX\nh549e5Y716FDBx0SERHVX5UWHNOmTcOcOXNw3XXXBb8/H0EQGkzBIYrRM6VKgAC71QiXNwAAsFuN\nECCwUZyI6A8KCgrw8MMPY9myZejUqRO2b9/OkQwiogirtOB444030K5duzLfn0+0rdwUbtHaw+GM\nsyDWbkb6GRf8AQVxdhNSm8Wxd4OI6Cz/+c9/cPfdd+PkyZMAgL1792LlypWYOHGizsmIiOq3SguO\nSy+9tMz3oiiiTZs2aNSoUYWvP3XqFLZt2xbedFFGxVk9HFE0pcogCVAUFUnOklVU4h1mNHFyZ3Ei\nolJTpkzBggULgt9brVbMnDkTd911l46piIgahpA/pr/99tuxZcuWSs9v3rwZTzzxRFhCRatoHeGQ\nFQ2SJCKvyIe8Ih8kSYSsaOd/IxFRA3H2h2gDBw7Ezz//jKlTp0KSJB1TERE1DJWOcKSnp2P69OkA\nfl+dacWKFfjoo4/KvVZVVezevbveL5Ubrftw5BR4oSgqEhwl85AVRUVOgVfnVERE0WP06NH45JNP\n0K9fP6SlpUVVHx4RUX1XacHRokULJCUl4ZtvvgkeO336NAoLC8u9VhRFtG7dGpMmTYpMyiihRekI\nhwABqqIg4PcB0OD3afAXm+H1Vr/okCQJJpMpfCGJiGqBpmnQNK1cQSEIAlatWqVTKiKihu2c+3DM\nmDEj+HWHDh3w2GOPYcSIEREPFa3UKN2Hw+fJw5mcHJzKKf6ty8QBT7EZB46eqfY1NU2FpvjR6YJW\nXMGFiOqE9PR03HPPPbjyyivxwAMP6B2HiIh+E/LGf/v3749kjjqhzD4cUVJwpJ88BRkmmGPikIiS\nRnGj2QS7wwGbPbbG19978Di6dkiFwRDyjwoRUa3SNA3Lli3DQw89hKKiImzcuBEjRoxA27Zt9Y5G\nREQ4R8HxzDPP4KabbkLXrl2D34ci1NfVRdHYNF7oKoZkNEMSvSj0lOzDEeewQFHD0zRuMNngcrkR\nH89NBIko+hw6dAhpaWnYuHFj8Jjdbsfx48dZcBARRYlKC441a9bgkksuCRYca9asCemC9bng0KKw\naVxTNRS4fFBUFbExRgCAoqoocPnCcn1JMsDnD4TlWkRE4TZp0qQyxcZf/vIXzJs3D4mJiTqmIiKi\ns1VacPxxChWnVEXnlCoNJU3jQEnfxbYvVsGVdxJWixmtn34Wyc1Sgq9d995qbPjvR4iNiwcA3PfA\nY2ie0hIAkJ+XiymT7sDMOS8HjwHR8+9JRFSRBQsWoFu3bkhMTMSSJUswfPhwvSMREdEf1Hhi/rFj\nxyBJElJSUs7/4jouGqdUAYAGDflFPuz/6Vt4i30YOf5ZWORMLF8yH09NnxN83aFf9+PBR55B2/YX\nlnm/LMtYNG8WLBZuFkhEdcuFF16IDz/8EL1790ZcHKd+EhFFo5CfmjVNw9KlS/HUU08BKNl74+67\n78bQoUNx5ZVXIi0tDR6PJ2JBo0E07sNxJt+DQncAxX4Z7txjaNyiM1weP1LbdcTBA/vKvPbXA/vx\nzurX8bepd+Hd1W8Ej69cugDXXncjEpwV7yJPRKS3n376CZmZmRWeGzp0KIsNIqIoFvJT8/LlyzF3\n7lycPn0aAPDpp5/if//7H6655hpMnjwZW7duxcKFCyMWNBqUGeFAdEw1ys73IivPjfwiHzxuF1TR\nCEEQoKgaRFGCqv6eecCgIbhv6qOY+dJi7N39E3747mtsWP8xYuMS0KNnbwBl+1SIiPTm8/nw9NNP\no2fPnpg0aRJ/RxER1UEhT6l6//33MXToUMyfPx8A8PHHH8NqteKFF16AxWKB1+vFp59+ikceeSRi\nYfUWjSMcJ7NcyC3QIIoCjGYrZH9JAzlQ0tNx9uZXI2+4BTE2OwDgT5f2xeFfD2DH9h8gQMDO7T/g\n8KGDmDv7WTw1Yw4SEjjaQUT6+uGHHzB+/Hjs2bMHALBu3Tp89NFHGDlypM7JiIioKkJ+aj558iT6\n9esHoOQTp++++w69e/eGxWIBALRu3RpnzlR/o7m6QMNZIxxidBQcRR4/JKlktCW2SRvkZeyBz6eg\nIOsIWqe2C77O7XJhUtpfUOz1QtM0/LTzR7S7oCNmz12CWXNfwax/vII2bdvjwUefYbFBRLp77LHH\n0KdPn2CxIYoiHnnkEQwZMkTnZEREVFUhj3DExcUhJycHAPD111/D6/XiiiuuCJ7/9ddf0bhx47AH\njCalIxzRMroBADFWE4oCIhRVgyOpCwozf8GWD2bjlzgrHnj4KWz6cj2KvV5cPex6jL1zEh57aBKM\nRiO69+iFnr366B2fiKhCmqYFp4R27doVK1euRM+ePXVORURE1RFywdG7d2+8+eabMJvNWL16Ncxm\nM4YMGYLCwkKsXbsWq1evxs033xzJrLor7eEQo2ip2ASHCafyZThiTACAFlePQ9e2jdCrUzIAlFni\n9opBQ3HFoKGVXmvWP16JbFgiohD9/e9/xyeffIKbbroJjz32GEwmk96RiIiomkIuOJ544glMnToV\ns2bNQkxMDGbMmIGEhARs374ds2fPRq9evTB58uRIZtXd7wVH9IxwaKoGWVWRX1QMg0FEs8YxsMeE\n7y9mRZFhNHK5XCKqXVarFdu2bYPRaNQ7ChER1VDIBUd8fDxef/115OTkwOFwBD9t6tSpE9auXYvO\nnTtHLGS00IJTqqJjhCMj24WsvGKoiop4R0kvjaoibLuMA4Df54bDzh17iSj88vLy8OCDD2L06NG4\n6qqryp1nsUFEVD9UeeM/s9mMzZs3IyMjA0ajEU2bNkXv3r0jkS3qlI5wCFEywpFxxg1ZtCEz8xBE\noxkxFgvyjRqSE0woLvbW6NqKqkDxe3FBajP+pU9EYffRRx/hnnvuwalTp/Dll19i9+7dsNvtesci\nIqIIqFLB8e6772LWrFnlNvizWq14+OGHMWbMmLCGiyaapp3VNB4dIxyHT+aj0O1HvLMpvMVeFPv8\nMAoWtE6yoVlizaZBmYwGWCxJMBhqvBk9EVHQmTNnMGXKFKxevTp4LDs7G9u2bcOAAQN0TEZERJES\n8tPk559/jqeffhpdunTB+PHj0aZNG6iqiiNHjuC1117DjBkzkJSUhEGDBkUyr260KNyDI7/IB1EU\nYDCIcNhtMEoCLDEx6NCuGZwJ/KSQiKKLpmkYOnQoduzYETw2ePBgLFu2DKmpqTomIyKiSAq54Hj1\n1VfRpUsXrF69uswUm06dOuGqq67CmDFjsHz58npbcJTZZTxKCg4NgKdYhsfrhwYBjeOtSGoUg2aJ\nLDaIKPoIgoAZM2Zg+PDhiI2Nxdy5czF+/HgIUTJqTEREkRHyk/OBAwcwYsSICufzm0wmXHfdddi3\nb19Yw0WTswsOAfr/5ZiR7YIoCFBUDTFWE2xWI2wxRrRNidM7GhFRpYYNG4b58+dj7969mDBhAosN\nIqIGIOSCw2w2o7CwsNLzhYWF9bq5ONpGODLOuOEpDkAUgWK/DFXVEGczI85u0TsaERGOHz8On6/i\nFfPuv/9+NG/evJYTERGRXkJ+cr7sssuwatUqHD58uNy5Q4cOYdWqVfV6tSoV0dXDkVPgRV6RDzaL\nCY3jY5DgMMNmZYM3EelLVVW88sor6Ny5M2bOnKl3HCIiigIhP6E++OCDGDVqFEaMGIFBgwYFG/wO\nHz6MjRs3IiYmBg888EDEguqt7AiH/lMABAiIsRjhKZYBADarEQIENGts0zkZETVUv/76KyZMmIDN\nmzcDAGbOnIkbb7wR3bp10zkZERHpKeSCo0WLFnj33Xfxj3/8A5s3b8Znn30GoGRJ3EGDBuHBBx9E\nq1atIhZUb2qUrVKlQYPPLyO/qBgQBCTEmpHaLI4N40RU61RVxbx58/Dkk0/C6/19D6AxY8agRYsW\nOiYjIqJoUKU5OC1btsT8+fOhKAry8vKgaRqcTickSYpUvqihRdEIR0a2C3lFPvgCSnCHcavZgCbO\nmu29QURUHYIg4MsvvwwWGykpKVi6dCmuueYanZMREVE0OG/BsXPnTuzcuROKoqBTp07o06cPJElC\nYmJibeSLGtE0wpFxxo0TWUXIyvVAVlRYLQaIgh2yop3/zUREYSYIApYsWYIuXbpg9OjRmD17NmJj\nY/WORUREUaLSgsPr9eL+++/HV199VeZ4p06d8Morr6Bp06YRDxdNyiyLq/MIx+GT+Tid44EkiZAk\nEaoK+AKKrpmIqGFLSUnBwYMH0bhxY72jEBFRlKn0o/rFixfjq6++wrXXXosFCxZg0aJFuO2223Dg\nwAE8/vjjtZkxKkTTTuP5RT5I0u9Fj0ES4C2W2TBORBFVXFyMp59+GocOHarwPIsNIiKqSKUjHOvX\nr8eIESPw4osvBo9deeWVcDqdWLBgAfLz8xEfH18rIaNBtOzDsfNAFn45loesPA9kVYNREtEozswd\nxokoor777juMHz8e+/btw9dff40vvvhC99FeIiKqGyp9cs7MzMSf/vSncscHDx4MoGRTp4YkGqZU\n7TyQhS27MuCTZSiqBgGAJAmwx5i4wzgRRYTH48G0adNw2WWXYd++fQCAzZs348cff9Q5GRER1RWV\njnD4/X5YLOV3rS4dMvd4PJFLFYWiYYRj39FcpJ92we2VIQiAqmjwBxTuME5EESHLMnr16oU9e/YE\nj3Xr1g0rV65Ejx49dExGRER1SbWfnM/uaWgIomGn8UK3H57iADQNMBokmM0GxNpMSOZUKiKKAIPB\ngNtvvx0AYDQaMWPGDGzdupXFBhERVUmV9uFoyFT1rBEO6DOlKsFhgdkowSiJCCgqTAYJ8XYznHFm\nNowTUUQ8+OCD+PXXXzF16lR07txZ7zhERFQHnbPg2LBhA44dO1bmWOnGTh9++CG2bdtW7j2TJ08O\nY7zokJObh70HjiGvuAgAIJ8xwGwwn/d9GjRIggBnfAxaNE+u9v0zsl3Y/WsOfjqYhdN5bhT7FIiS\n+Nvu4vHo1r4xG8aJqEZcLhfs9vK/RwwGA5YtW6ZDIiIiqi/OWXB89tln+Oyzz2yoy40AACAASURB\nVCo898EHH1R4vL4VHHl5+UjPLIA1thH8JiMAwBHXKKSCo1SBxw2cPFWtoiMj24VdB7Ox45fTyMrz\nIqBoMBklWEwGJDlj0KtzUxYbRFQj69atw6RJk7Bw4UL8+c9/1jsOERHVM5UWHJ9//nlt5ohap7Pz\nYXfEI7/wVPBYVXs4LBYb8gpy0KJ51e//86/Z+N/2Ezh0sgCqpsIgiRBEQFZVeIpl7i5ORNWWlZWF\nyZMn49///jcA4N5778UVV1yBxMREnZMREVF9UmnBkZKSUps5opb6W3N8TZfFPasFJGQZ2S7sPpyD\nvCIfAA2aCsiaCtFkgN1qhD3GVPWLElGDp2kaVq9ejfvvvx85OTnB4927d4ff79cxGRER1Uf6bpld\nB5QuxlW6KtfxX3/Fkw/fV+5133/7FR64dxwevP9OrP/kw7DcO+OMG0VuPxTlt5ENoSSPJACNYq1o\n0cTOZnEiqjK/349nn302WGzEx8dj5cqV+O9//4tmzZrpnI6IiOobrlIVIg0aNn70MbZ//Q3iHWV3\nWJdlGcuXzMO8l9+A2WLBw1PScGmffohPcNbonodP5iM9q6hk3w2ULIULaIixGNGpjROXd2/O/g0i\nqjKz2YyVK1eiX79+GDFiBBYvXsxCg4iIIoYjHCFSNRWJSU0x9sGpvw97/Cb9+BEkN2sBm90Og8GA\nTl26YffPO2p0v50HsnD0VCE0VYMoAIIAWMwSLr6wCSbedBFuvvJCFhtEVG19+/bFjz/+iHXr1rHY\nICKiiGLBEYIcTy6O5Z9AwoUp8MrF5c573G7YbL9PbbLGxMDtdtfonht/TMf+o7nw+BQoigZN0yAK\nAlonx6H7BU1qdG0iahhUVcXy5cvhcrkqPN+jR49q9aQRERFVBQuO88h25+K0OweqqkGDhiKfBwFV\nLvMam80Oj8cT/N7r8cBud1T7njsPZOF4VhH8sgpRACRJgMVsQEpTB9o0j6v2dYmo4Thw4AAGDBiA\ntLQ0PP7443rHISKiBqxKBYfL5cLChQtx00034fLLL8ePP/6In3/+GU8++SROnDgRqYy6yvbkAQBK\nJ1EJggD5DwVHSsvWyDiZjqKiQgQCAez+eQc6dupa7XvuO5pbcq+zjqmahtgYE5vEieicZFnGnDlz\n0K1bN3z99dcAgEWLFuHgwYM6JyMiooYq5Kbx3NxcjB49GidOnEC7du2QnZ2NQCAAj8eDtWvX4ssv\nv8S//vUvpKamRjKvbkqXxS2ZfVBSCmz6cj2KvV5cPex6pE2ciqcfnQJVUzHk6hFwNqr+OvaFbj88\nxTJUTYOiqBBFAXarEV3bJrJvg4gq5Xa7MXDgQGzdujV4rGXLlli2bBnat2+vYzIiImrIQi44Xnrp\nJeTk5GDt2rVo0qQJLrvsMgBA//79sW7dOowfPx7//Oc/sWDBgoiF1UNiTALyNQXab2McCY0bY8Y/\nSv4drxg0NPi6Xr0vR6/el9f4fhnZJXOtVUWD0SDBaJDgiDHionaJ6NKuUY2vT0T1l81mwwUXXBAs\nOCZNmoRZs2bB4aj+FE8iIqKaCnlK1caNG/GXv/wFHTp0KHeuQ4cOuO2227B9+/awhosGje1OJMYk\nQPjtf42s8WgUU/XlbkNtzMw444bHK8NkEqGqKjRNgyPGhFZJcRzdIKLzmj9/Pvr27YtNmzbh5Zdf\nZrFBRES6C3mEw+PxICkpqdLzDocDRUVFYQkVTSwmA+wBE1LikgEAcZbYKl9D0zQYDNr5Xwggp8CL\nQrcPNosJNosJBklAyyQHnHGWKt+XiOovVVUhiuU/M2rUqFGwd4OIiCgahDzC0bZtW2zevLnCc6qq\n4tNPP0Xbtm3DFixapLZKQcCXB39xMTRNg1SFJSQ1TYPf74enMBsXtm0V0nsECLBZjMHvrRYDBAhs\nFieioG+++QbdunXDrl279I5CRER0XiEXHHfffTc2btyIJ598Ejt2lGxql5WVha+//hoTJkzA9u3b\ncccdd0QsqF4EQUC7ti0Q7wAMahFMmgdGzR3SPyZ4kBgronOHVBgMoQ0madDg9cvILypGvssHgyQg\ntRmnUxFRSVP4lClT0K9fP+zevRvjx4+HLMvnfyMREZGOQp5SNXToUDzzzDOYPXs23nvvPQDAI488\nAgAwGo2YNm0aRo4cGZmUOlM1DfHOkv0vUuKSkWRvHJH7ZGS7kFfkg6c4gHhHyRQqq9mAJk5rRO5H\nRHXHF198gbS0NBw5ciR4TFVVZGVlcadwIiKKaiEXHABw6623YtiwYdiyZQuOHz8OVVWRnJyMvn37\nolGj+ruCkqwpwa8NghSx+2SccePE6SLkFBRDVlRYLQaIgh2yElr/BxHVTwUFBbjxxhtRWFgIADCZ\nTHjmmWfw0EMPwWg0nufdRERE+qpSwQGUNIcPHTr0/C+sRxT194JDEiNXcBw+mY/TuR5IkghJEqGq\nQEBWzv9GIqrX4uLiMGfOHNx9993o06cPVqxYgY4dO+odi4iIKCQhFxx///vfz7m0q6ZpEAQBzzzz\nTDhyRZXaKjjyi3w4+4/YKAlwe2U2jBMR0tLSEB8fj5tuugmSFLnfQ0REROEWcsHxzjvvnPO80+ms\nt9OqZLV2plQ5bCaYjQbkKT7IigqLw4ykRjFsGCdqQL788ksMHDiw3Ac8giDg5ptv1ikVERFR9YVc\ncOzfv7/cMUVRkJOTg//+97945ZVXMGfOnLCGixaKVjsjHE0SrNitaUj4rWE83mFG25S4iN2PiKJH\nZmYmJk+ejLVr12Lp0qVIS0vTOxIREVFYhLwsbkUkSUKTJk3w17/+FcOGDcPzzz9f5Wu8++67GDJk\nCLp164Zbb70VO3fuDPm9ixYtqnDn83CrrSlVsqIhIKvIzvcg3+VDYpwFcXZu+EdUn2mahrfeegud\nOnXC2rVrAQAPPfQQsrKydE5GREQUHjUqOM52wQUXVHkTqnXr1uGZZ57ByJEjsXDhQjgcDkyYMAEn\nTpw473sPHDiAJUuWnLOvJFzKjHAIYfsjKyMj24UjGYWQRCAxPgbxdjNkRUNOgTci9yMi/eXl5WH4\n8OH461//iry8PABAQkICFi1ahMaNI7P8NhERUW0Ly9OzLMtYv3494uPjQ36PpmlYuHAhbrnlFtx7\n773o378/XnnlFSQkJOD1118/53sVRcHjjz9eaz0jpSMckihGrMDJOOOGoihQ1N+PFbl9EBD5goqI\n9OFwOJCZmRn8/sYbb8TevXtx++2318qHKURERLUh5B6OO++8s8K/AP1+Pw4dOoTs7GxMmjQp5Bsf\nO3YMGRkZGDRo0O9hDAZcccUV+Oqrr8753tdffx1erxe33XYb/vGPf4R8z+qS1ZIqwCBWeRXhkB0+\nmY/9x/NRUOSDwSAizmZCnMMCZxynVBHVVwaDAStXrsSwYcMwb948/PnPf9Y7EhERUdiF/AR9+PDh\nCo+LooiUlBRMnDgRo0ePDvnGR48eBQC0atWqzPGUlBSkp6cHl9n9o2PHjmHRokVYsWJFladwVVfp\nlKpITafaeSALR04WoLhYhigKUFUNAoB4u4lL4hLVc926dcPhw4dhMpn0jkJERBQRIRcca9asQZMm\nTcJ2Y5fLBQCw2co+UNtsNqiqCo/HU+6cpml48skncf3116NHjx61UnAoqgJNK9npOxIN4xnZLvz7\ni4M4eqoAAVmFJIowGkQEFBXOWCuXxCWqB/bv34/Jkyfjueeeq/A8iw0iIqrPQi44brrpJtxyyy2Y\nPHlyWG5c+hBf2TxlUSw/mrBmzRqkp6djyZIlYcmwb9++874moMo44UoHANgNNqhn/GG5NwBkF/ix\n81Ah0jML4fMp0ABomgqDKMEsKXAVZmHfPk/Y7kc15/WWNPGH8rNDJMsyVq5cicWLF8Pv9+P555+v\nt8uHU+Tw9w5VF392qLpKf3bCJeSCo7CwMKyrpjgcDgCA2+2G0+kMHne73ZAkCVartczrT506hTlz\n5mDWrFkwm82QZTlYtCiKAjFCDd1nr1AlhnlK1ZlCP45keuELKFA1DRAAVRWgaipizCIax/JTT6K6\nav/+/XjyySexd+/e4LEdO3agoKAAcXHcX4eIiBqOkAuOW2+9FatWrcIll1yCdu3a1fjGpb0b6enp\naNGiRfB4eno6UlNTy73+22+/hcfjwf3331/uXOfOnTF58uQqj7507NjxvK8p8rmgZJcUGk3tiWgR\n16xK9ziX4wVHoYluWC0apIACv6xAAJAQa8PlPdqiX+9W570G1a7ST4lC+dmhhuvkyZO49dZb4feX\njIgKgoC//OUvmDJlCnr27KlzOqpr+HuHqos/O1Rd+/btg8cTvlk2IRccJ06cwIkTJzB8+HDExcUh\nISGhzLSn0ibvTz75JKTrtW7dGsnJydiwYQMuu+wyAEAgEMCmTZswcODAcq8fNGhQcFOsUh9//DFe\ne+01rF27NmJr1kdy0z8BApwOC1yeAADAajbAHmNEny7J6NKudpb8JaLwa968Oe666y4sWrQIF1xw\nAVauXFlmJJeIiKghqdKUqi5duoTtxoIgIC0tDTNmzEBsbCx69OiBVatWoaCgAGPHjgUAHD9+HLm5\nuejevTvi4+PL7fOxdetWACUjHJEinzWlyiCEt+BwxlnQtFEMMvM88HsUmI0GtE6KQ/8eKWwWJ6rj\nXnjhBSQlJWHatGmwWq2cQ01ERA1WpQXHBx98gJ49eyIlJQUA8NZbb4X95mPGjIHP58Obb76JN954\nAx07dsSKFSuC91y8eDE+/PDDc/5FHenNsVT19534wj3CYZAEKIqGlk1L+lmcsRb8qVNTFhtEdciR\nI0cqnAZqt9vxxBNP6JCIiIgoulTaBf3oo49ix44dEQ8wbtw4bNy4ETt37sTq1avRrVu34LlZs2ad\ns9gYO3ZsxD81PHuEI5wFR0a2Cxt/PIGdv57BniM5SD9dBKNBgKxoYbsHEUVOUVERJk+ejPbt2+Ob\nb77ROw4REVHUisxOdvXI2T0c4ZpSlZHtwuYdJ3H8dCFURYMoCPD5FWSccSOnILzLkBFR+G3YsAFd\nu3bFyy+/DEVRMH78+LAvIUhERFRfsOA4j7MLjor2BqmOjDNuHM8shNevIKCUTNmSVRW5hcUQENkp\nYkRUfQUFBZgwYQKGDBmCY8eOAQDMZjMmTJgAo9GoczoiIqLodM6m8by8PGRkZFTpgs2ahW/Z2Ggg\nR2CEw+Vy4djxdOTkeaGoGqAJkCTAKtmQlxeDXXuV81/kD0QBMJtEtE1tGbbCiIjKCgQC+L//+7/g\n93379sWKFStw4YUX6piKiIgoup2z4Jg5cyZmzpwZ8sUEQah3K7EoYe7hKCgsQlZ2PmLjm8AtuyH/\n1pTuiDGhc5tGaJnSBDGOmGpdW5Zl7DtwBB0vSGXRQRQBiYmJWLRoEcaPH49Zs2Zh0qRJ/G+NiIjo\nPM5ZcFx11VW44IILQr5YpFeM0kPplCpBEMJScGSczobFHo/EeCC7wItAcclyuElOG3p0aILG8dUr\nNgDAYDAgELCisLAI8fHcyZgoEkaNGoX+/fsjKSlJ7yhERER1wjkLjiFDhuC6666rrSxRqXRKlRSm\n6VSKAhS4fFBUFUnOkuLCHmNEs8b2GhUbpYxGE7zFxYgHCw6i6jp16hTmzZuH559/HgZD2V+TgiCw\n2CAiIqqCkDf+a6hKp1QZwjZtQgs2hmuaim1frIIr9wSsVgtaP/0skpulBF+55etNePft1yEIAq66\n+jpce92NUBQFC+fOxMkTxyEIAu6d+ihatW4TfI8gCNC4si5RtWiahjfeeAMPPPAA8vPz0ahRI/zt\nb3/TOxYREVGdxsnH56BpGpTfeizCuQdHrN0ESRRxcM9W+Px+jBz3LEbcPB7Ll8wv87rlS+bhuRcX\nYs78ZVj33r/gchXhh+++hiCKmDN/GW4fdw/eXPlK2HIRNWTHjh3DNddcg3HjxiE/Px8AsGDBAhQX\nF+ucjIiIqG6rtOC4/vrr0aJFi9rMEnXOXhI3XFOqAEASBSiqCk/OEbRsexEUTUVqu444eKBsw70k\nGeB2FcHvK4amAaIgoE/fAZg89VEAwOnTp2B3OMKWi6ih2rt3L7p06YL169cHj918883Yvn07LBaL\njsmIiIjqvkqnVM2aNas2c0QlRVODXxvCOMKRW1iM/CIfCguLYG0sQRJEKKoGUZSgqmpw1ZsbR43B\nlIl3wGKx4rJ+AxFjswMAJEnC3Ben49uvN+Hxv78QtlxEDVWHDh3Qs2dPbNq0CU2bNsXixYtx4403\n6h2LiIioXuCUqnMoM8IRpoIjO9+LjDNuKIoGs9UGTfbDVexHgcsHTfu92Mg6nYn/++A9vPb2h1j5\nrw+Qn5eLrzd/EbzOtL89jaVv/BsL5r4An49TPohqQhRFLF++HBMmTMDevXtZbBAREYURC45zkMO8\nBwcAnMnzAgCKAzLim7ZFdvpueIsVnDx6AK1T2wVfFwj4IUoijEYTRFFEfEICXEVF+HLDJ3j37dcB\nAGaTGYIgQBT4fyNRqNxud4XH27Zti+XLl8PpdNZyIiIiovqNq1SdgxKBXcbz3T5k5gRwJs8LKeFC\naMf34Ot1s2CzGPHEU9Ox6cv1KPZ6cfWw6zH4qmF46P47YTKZkNw8BVddPRyyLOOfL87AI9PugSzL\nuPveaTCaTGHJRlSfBQIBzJ49GwsWLMCOHTvQvHlzvSMRERE1CCw4zuHsgkMMx6Z/2S5kZnvgKgZE\nUQAgoHXPm9GxjRP9uzdH4/gYNE9pGXz9DX8ejRv+PLrMNSTJgEefer7GWYgakh07dmD8+PHYuXMn\nAOCee+7BRx99VC83KyUiIoo2nItzDmdPqQrHCMfPv2bjYHou8ov88PlkyLIKUQQEICyb/gEo03RO\n1NAVFxfjiSeewJ/+9KdgsSEIAtq3bw9ZlnVOR0RE1DBwhOMcyjaN1+whPiPbhd2Hc+AuDkAyqxAk\nEYIAWCwG2CzGmkYN8hV74GiaGLbrEdVlBw8exIsvvghFKflvuWPHjlixYgX69OmjczIiIqKGgx+F\nn0M4V6nKOONGgcsHu92J4qJcqKoCaABUoHWz2BomLeH1uuCMNcJms4XlekR1XdeuXfHYY49BkiQ8\n/vjj2L59O4sNIiKiWsYRjnOQw9w0LssqTGYjbHFOeIvyIZokNLKK6JBsAAKFNbq2IAhIbmRDYiOu\nsEN0tieeeAI33XQTunXrpncUIiKiBokFxzkoYVwW1yAJsJoMMEgi4h0xcMbGoHVyHK66tCXatmlS\n06hEDVphYSHWrVuHO+64o9w5s9nMYoOIiEhHnFJ1DuGcUiUrGpIaxUAUBfgCCmxWI1olx6L7BSw2\niGri008/RZcuXTB27Fh88skneschIiKiP2DBcQ6KpgIAREGs8eZ6OQVeyIqGlk0duLBlAtq3SECc\nnftnEFVXbm4uxo4di2uvvRbp6ekAgAceeCDYIE5ERETRgVOqzqF0hMMQhj04BJRf77+iY0R0ftu3\nb8ewYcOQmZkZPNa/f38sX74ckhSeTTqJiIgoPDjCcQ6lTeM1nU4FAM44CwySgLwiH/KKfDAaBDjj\nLDW+LlFD1L59e5hMJSOEdrsdL7/8MjZu3Ij27dvrnIyIiIj+iAVHJVRNhfrblCqphtOpgJKmcVnR\nkOAwI8FhRkDWYJA4wkFUHQ6HA8uWLcPQoUOxe/duTJo0iRteEhERRSlOqapEOBrGM7Jd+HpnBn45\nlosTZ1zw+WWoGhAbY0JTpxWyooUrLlG9pWkaBKF8cT5kyBBcddVVFZ4jIiKi6MGPBCtRpuCoxh4c\nGdkubN5xEnsOZyPjjAv5hT54vAHIigqfX0HGGTdyCrzhjExUr2iahuXLl+Oyyy5DcXFxha9hsUFE\nRBT9WHBUQj5rD47qNI1nnHHjeGYhcgq8KHD7oWgqFLVk8z9ZVZFbWMymcaJKHDlyBEOGDEFaWhq+\n++47TJ8+Xe9IREREVE0sOCoRjilVLm8A7mIZsqICGnB2K4jNamTTONEfqKqKhQsXomvXrvj888+D\nx9PT06FpnIJIRERUF7GHoxKKqga/rk7BYZAExJglGEQRBkmEX1NhkATExpjQOMGKjq2daNbYFs7I\nRHXehg0bcP/99we/T05OxiuvvIKRI0fqmIqIiIhqgiMclSgzpaoaPRyyoqFZoh0mkwSjQYTNIiHe\nbkFKUwe6tW+M/j1S0CzRHs7IRHXekCFDcP311wMAxo8fjz179rDYICIiquM4wlEJtYZTqkp2FlfR\nsqkDQMkUqvYt4jG0d+twRSSqdwRBwOLFizFx4kQMGTJE7zhEREQUBhzhqIRcw4KDO4sTVc7v92PL\nli0VnktOTmaxQUREVI+w4KiEUsMpVc44C4r9Co5nFuJ4ZiH8fplN4kQAfvzxR/Ts2RODBw/GgQMH\n9I5DREREEcaCoxI1XaWqwFWMrDwvbFYTbFYT8t1+FLgq3kuAqCHwer149NFHcemll+Lnn39GcXEx\n7rrrLq4+RUREVM+xh6MSZaZUCVWryzKyXdiyKxMnThfBL6uwmAxobrUhK48b/VHDtGPHDowePRq/\n/PJL8Fjnzp3x4osvcvM+IiKieo4jHJU4e0pVVUY4MrJd2HUwG7mFXsiqBlEUoGkaZEVFodsfiahE\nUc/hcOD48eMAAIPBgKeeegrbtm1Dr169dE5GREREkcaCoxKlU6okUazSJ7AZZ9zIKSgu2ezvN7Kq\nwh9QkeBgDwc1TO3atcNzzz2Hiy++GFu3bsX06dNhNpv1jkVERES1gAVHJeTfNv6TqtEwnnGmCG5v\nAAFZQUBWIAoCmiTEoE3zuHDHJKozpkyZgu+//x7du3fXOwoRERHVIhYclSidUmWoYsN4gasY+S4f\nJFGE0SDBaJDQKsmBTm24szjVf//5z39wzz33VNgILkkSjEajDqmIiIhIT2war4CiKsEHpqquUPXr\niQK4vQH4AzJkFYixSBAEAd3aN+bO4lRv5eTkYOrUqVi1ahUAYMCAARg9erTOqYiIiCgacISjAmWW\nxK3ClKqMbBdO53gQkFXEWE2ItZnQxBmDJs4YFhtUb7333nvo1KlTsNgAgPfff1/HRERERBRNWHBU\nQD5707+qrFB1xg2LWYR61mySAJvFqR57++23MWrUKGRlZQEoWY1qyZIleOedd3RORkRERNGCBUcF\nVPX3FaaqOqXKZDRAFIFivwxFUdHEyWZxqr9uuukmdOzYEQBwzTXXYM+ePbj77rshivzVQkRERCXY\nw1EBWZWDX1el4DBIAjRVg81igs1igsUsIaWxnc3iVG+ZzWa89tpr+OWXX3D77bdzEz8iIiIqhx9D\nVkDRfh/hMFShh0NWNMQ5zAgoKtzFAZglCc5YK/s3qM5TVTW4cd8fXXrppfjrX//KYoOIiIgqxIKj\nAmWaxqswwpFT4IUsq0hyxqBlUwec8RZoKL88KFFdcvjwYVx55ZXo27cvCgsL9Y5DREREdQwLjgqc\n3TQuCaH/EQkQ4C4OILewGLmFxfB4AxDAT32pblIUBfPnz0fXrl2xceNGnDhxAo888ojesYiIiKiO\nYQ9HBao7wqFBw9kDGlrpMaI65pdffsH48eOxZcuW4LHmzZtj2LBhOqYiIiKiuogFRwWqW3AIEGCz\nGuGMLVkG12Y1coSD6qSjR4+WKTbS0tIwZ84cxMVxxTUiIiKqGk6pqoB8VsFRlaZxDRqyC7w4nlmI\nzFwPjKIIZxz34KC6Z+jQoRg3bhxat26Nzz//HEuXLmWxQURERNXCEY4KKFrVRzgysl3IK/IhIKuw\nWU0AgAK3DwaJIxxUN82bNw+iKMJu5yprREREVH0c4ahA6ZQqQRBCLjh+/jUbuw6ewaETBcjMccNd\n7IcgCJAV9nBQ9Prhhx+wePHiCs/Fxsay2CAiIqIaY8FRgdIpVVKI06kysl3YfTgH+UU+SJIAURTg\n96sIKMr530ykA6/X+//s3Xdc1WX7wPHPGRzmAUFEAReOHhIF3GK5MLeZpZm5lZw5SivryRxpZi4c\nOMuZCzUftTL9aVkPldlyPCUlihsHe8PhjN8feE4chuA8Ktf79eIl5zuv8+WA3+t7X/d98+abbxIS\nEsL48eP5/fffbR2SEEIIIR5TknAUw1xSpVaW7fLExWeSmZWHyWTCdLNBQ280kqszyizj4qETFRVF\nUFAQ8+fPx2g0YjAYmD9/vq3DEkIIIcRjShKOQkwmEwZj/kzjtzNClcFoRG80YTQaMRpN2GvUVKno\nJLOMi4fKmjVraN26NTExMQDY2dkxffp01q9fb9vAhBBCCPHYkk7jhVgNiVvGkiq1SoGjvRq1Son6\nZofx6lW0NAuocl9iFOJOdenSBTc3N1JTU2nSpAlr166lQYMGtg5LCCGEEI8xSTgKMZiMlu/L2sKh\nN5jw8XTm0o0MsnP1uLloqO1bgeAnvO5XmELcER8fH5YuXcrVq1eZOHEiarX8CRBCCCHE/SV3G4UU\nbOFQlzHhSEzNRm80Ub2yFsif8M/NRXNf4hOirLKysnByciqyfODAgTaIRgghhBDllfThKERvuv2S\nquJmE5cZxoWtxMfH069fPzp37ozRaCx9ByGEEEKI+0gSjkKs+nCUsYXDw82BnFw9F6+lcfFaGrk6\nvcwwLh44k8lEZGQk9erVY+vWrURFRbFy5UpbhyWEEEKIck5Kqgq5k4QjNSOHxNScAjOM60jNyLkv\n8QlRnKtXrzJ69Gj27NljWSYT9wkhhBDiYSAJRyEFS6rUZSypOnM5lRvJWWRm56FWqfBxdOZGcvb9\nClGIIiIjI62Sje7du7NixQqqVq1qw6iEEEIIIaSkqgjrFo7SL09cQgbXE7Msc2+oVAry9EbSMnX3\nM0whrIwbN45mzZpRsWJFNm/ezN69eyXZEEIIIcRDQVo4Crndkqq4+Ewc7JUYTf8s0+uNuGulD4d4\ncFQqFVu2bMHFxYXKlSvbOhwhhBBCCAtp4ShEb7z9kiq1SoVSCTk6PQaDzPF22QAAIABJREFUES93\nJ2r5ut2vEEU5dubMGQ4dOlTsutq1a0uyIYQQQoiHjiQchRhMt9fCoVYpMBpNODtoqFTBiSqezvhW\ncsankvP9DFOUMwaDgYULFxIYGMjLL79MfHy8rUMSQgghhCgTSTgKMRhvb6ZxvcFEBWd78gxGMnPy\ncNCo8HB1xMdTRgcS98aff/7JU089xaRJk8jOziYhIYEZM2bYOiwhhBBCiDKRPhyFmFs4lAolSkXp\n+VhiajZ5RiNVPPJndHbX2mPCVMpeQpTNmjVrGDNmDDrdP4MQjBo1itmzZ9swKiGEEEKIspOEoxBz\np/GyjFAFMsu4uL/q1atHXl4eALVq1WLNmjW0bdvWtkEJIYQQQtwGSTgKMXcaVyvLdmlMmEhIyeFa\nYgYajZpK7o4yy7i4Z0JCQpg4cSJGo5GZM2fi7Cx9g4QQQgjxaJGEowCjyYjRlN+HQ1WGcqq4hAyS\n03PJ0en/mWU8Ixe1Slo4xO0zmUwoFEU/O/PmzSt2uRBCCCHEo0A6jRdwu3Nw/O9MAidi4rl0PZ1r\niZlk5uhQKBToDdKHQ5RdVlYWkyZNYvz48cWul2RDCCGEEI8yaeEowCrhKGUOjriEDP6ITSQ5PRel\nMv+GUKczkqc33nI/IQr69ttveeWVVzh79iwAvXv3pk2bNjaOSgghhBDi3pEWjgL0BebgUJfSwhEX\nn0l6pg5DgQTDaDKRnaOXOThEqdLS0hg9ejTt2rWzJBsajYa//vrLxpEJIYQQQtxb0sJRwO2UVCWm\nZpOUlkNunh6DEVRKBU4OaqpUdJI5OESppk+fzsqVKy2vmzVrxtq1awkICLBhVEIIIYQQ9560cBRQ\n1kn/zJ3FVUoFSqUSO7USB42Kmt5uNAuo8iBCFY+4KVOmUKVKFRwcHJg/fz4//vijJBtCCCGEeCxJ\nC0cBViVVt+jDERefyZUb6WRm56HXGzApQOtkR01vV4Kf8HoQoYpHnIeHB9u2bcPHx4e6devaOhwh\nhBBCiPtGEo4CjGUsqYq9ksKN5GxUKiVaZ3sAqlXWUsvX7b7HKB4tN27cICUlhSeeeKLIOukcLoQQ\nQojyQEqqCijYwnGrhCMl3XquDaUCcnIN0llcWJhMJrZs2UK9evV46aWXLLOFCyGEEEKUN5JwFFCw\n0/itSqpMQFaOnqxsHZnZeSiVCuksLiyuXLlCjx496N+/P4mJiRw/fpyFCxfaOiwhhBBCCJuwecKx\nfft2OnbsSFBQEH379uX48eO33P73339n4MCBNG3alFatWjF58mQSExPvSSwFEw6lsvhLE5eQgVKh\nIEdnwMlRg7OjHa4uGmpXlXIqAZs2baJevXp88cUXlmXPPfccAwcOtGFUQgghhBC2Y9M+HP/5z3+Y\nPn06r776Kg0aNODTTz8lLCyMPXv2ULVq1SLbnz17liFDhvD000+zcOFCUlNTWbx4MWFhYezcuRO1\n+u7ejr4MLRz/i77AmXMXSIpPI1dvxN5OiVbtSmqSI3/F3NkM4yqlAu/Knri4SEnWoy43N5e0tDQA\nPD09iYiIoE+fPjJbuBBCCCHKLZslHCaTiaVLl/LSSy/x6quvAtCyZUs6d+7M+vXrmTJlSpF9Nm3a\nROXKlVm6dCkqVX5CUKNGDV588UV++OGHu+6EayilD8fFy3FEX0wjU++ESwUN5gIqJ1cX1E4VUDtU\nuONzx5y/Rt2aVSTpeMQNGzaMyMhIPD09Wbx4MZUqVbJ1SEIIIYQQNmWzhOPChQvExcURGhr6TzBq\nNW3btiUqKqrYferWrUvdunUtyQaAn58fkF83f7fMJVUqpbLIE+nc3FwSU3XojGrye3HcjFmlIDvH\nQKUKjnd1bq1bRc5fukr9J+vc1XGEbSkUCvbu3YuDg4OtQxFCCCGEeCjYLOE4f/48kN9CUVDVqlW5\ndOkSJpOpyE1/v379ihznm2++AaBWrVp3HZP+5sR/qmLKqXJyclDbaXB2sEOjVpGRrcdgMOKg0VCx\nggOVKjjd9fmNJpt3qRFloNfrmTt3Lh4eHrzyyitF1kuyIYQQQgjxD5slHBkZGQA4O1uXEDk7O2M0\nGsnKyiqyrrCrV68yd+5cGjRoQIsWLe46JnNJlbqYciqjMT8Bcne1x2gCF0c10d9v5WxqHGecHQl5\nYgbePv/0O/n2mwPs2r4ZO42Gp1u35/neLwOwfct6jv70PQa9nu49X+SZjt0s+5jurAuIeIBOnz7N\nlClT+OOPP3BxcaFjx45Ur17d1mEJIYQQQjy0bNqHAyixM21Jo0SZXb16lSFDhgDc8ZCj0dHRlu8N\nJiOX0i8C4KhyRJFosNo2NS2NhHQjGWlGMORyIfp3cnNzad97EmrdFZYs/JARY98AICMjnbWrlzJ5\n6hwcHZ1YPO99KnpVJjsri99++5mxE98lNzeHQ/s/p/YT9SznyEpPQqPMvaP3Iu4vnU7Hxx9/zKpV\nq9Dr9UB+0rxmzRr69u1r4+jEoyA7Oxuw/rsjRFnIZ0fcKfnsiDtl/uzcKzZLOLRaLQCZmZl4eHhY\nlmdmZqJSqXB0LLlPxOnTpxk+fDgGg4G1a9dSrVq1u47HaDJavlcpSk52MnMMqNVK8tIuULVWAHZ2\nSrRuNbl4IdayTcKN6/hWrYGTU34LjV+tupw5HU16Wio+vtVYHTGfnJxsevbuf9dxiwdj+vTp7N69\n2/K6evXqzJo1iyZNmtgwKiGEEEKIh5/NEg5z341Lly5ZJQyXLl2ydAQvzokTJ3jllVdwdXXl008/\nvatylieffNLyfXZeDrob+a0unk7u1HS3TmKSk1NwScgmJS+F1JwUMBlwca1AhQoV8HB1wM5OQ43q\nNVAqlXhWrMiWDStxd3PDwdGJ87ExhDzdloy0VOKvX2XWR4u5djWO9997g1XrtlvOkZnmxpNP3n1f\nFHHvzZw5ky+//BKDwcDgwYOJiIjAyenu++2I8sP8hLHg3x0hykI+O+JOyWdH3Kno6GiysrLu2fFs\nlnDUrFkTb29vDh48SMuWLQHIy8vj22+/pV27dsXuc+nSJYYPH46Xlxfr16+/p0OOFpz0r7ghcQES\nUrKIS8jkakIGOXoVyanpqFQKXF00mExGSxmYVuvK8NGvM3vG22hd3ahd91+4urqRnZVJteo1UanU\n+Fatjp1GQ2pqCm5udz6crngw6tevz/Lly9FqtQQGBkqyIYQQQghRRjZLOBQKBcOHD2fmzJm4urrS\nqFEjNm3aRGpqqqVvxsWLF0lKSiI4OBiA2bNnk5mZybRp07hy5YrVULi+vr53lYDoS5mD42pSBmcu\np5KUmo3eYMKtci0SLv1BemYbLp37i5p+/wxnazDoifk7mrmLVpOn0zF54ih6vzSISxfPsfc/kTzf\nux+JCfHk5mTj6iozlD9MMjIyyMvLw93dvci6V155RepghRBCCCFuk01nGu/Xrx+5ubls3LiRDRs2\n8OSTT7JmzRrLLOPLly9nz549REdHk5eXR1RUFEajkUmTJhU51uTJkxk6dOgdx2IoZZbx6wlZXE3I\nIDE1B4PRRAXfQFKv/s2XG9/H0V7Nu++9z7ffHCAnO5vO3XqiVCkZP3oQKqWKLt2fx9vHF28fX/74\n33Fef3UoRpORMePfkhmoHyJff/01r7zyCiEhIWzZssXW4QghhBBCPBYUJlP5HIz1t99+o3HjxpbX\nNzISuJgaB0BN96p4OnlYbf/ZwRP8fCqRxHS9Zdo/V2cNtXwrUMNbS72aFe86psy0JIICpA/Hg5aa\nmsobb7zBJ598Ylm2Z88eevToUWRbqYcVd0o+O+JOyWdH3Cn57Ig7Ze7DUfBe+W7YtIXjYVKwpKq4\nFg6FQolCCXqjCYPBiJ1aiUatwtXF7q5nGf/nHPfkMOI2fPnll4wcOdKqPK9FixbUrVvXhlEJIYQQ\nQjw+ZGrrm0rrNG5np0GjMOBor0alUqJQKHByUPNENfd7Mss4gFJRLhubbOrQoUOWZMPR0ZHw8HC+\n//57eRokhBBCCHGPSAvHTaUlHPb2DlRwsUNnMuLp5oKdnYpaPm73LNlIT02iVnWve3IsUXazZs1i\n79691KhRg48//pjatWvbOiQhhBBCiMeKJBw36UvpNO7h5oCTqwfXYmMw5OVRpZILjr526LKT7/rc\nKqWC2jW8cNW63PWxxO1xdnbmv//9L97e3qXObi+EEEIIIW6fJBw3GUoZFjc1I4ektBy8KuW3QtjZ\nKalQ0ZN6T9R4YDGKO2Mymdi0aRM1a9akVatWRdb7+vraICohhBBCiPJBEo6bzCVVCoWi2ITjzOVU\n4pOzSM3IRa1SUbmiEzeSsx90mOI2Xbp0iVGjRrFv3z7q1KnDiRMnZNI+IYQQQogHSGpIbjKYjACo\niimnikvI4HpiFnqDEXuNGpVKQW6egbRM3YMOU5SR0Whk1apVBAQEsG/fPgDOnDnD9u3bbRyZEEII\nIUT5Ii0cN5lbONTF1PHHxWfi5KBGbygwipTJhLvW4UGFJ27TgAED2Lp1q+W1l5cXy5Yto3fv3jaM\nSgghhBCi/JEWDvJr/M2dxosrp0pMzSY1Q0euTk9mdh4Gg5FKFRyp5ev2oEMVZVQwsRgwYACnTp2S\nZEMIIYQQwgakhYN/yqmgaElVXEIGyem56I0G7DVq7AFHjZpqlbX4VHJ+wJGKsnrhhRcYN24cHTt2\npHv37rYORwghhBCi3JKEg1vPwREXn8mVG+kkpuSQq9OjUitx19rj4eqIj6cMY2trer0eALW66Ed5\nyZIlDzocIYQQQghRiJRUUSjhKNTCEXslhRvJ2ajVSpwcNdjbqXFz0eDhJv03bO3EiRM0b96cefPm\n2ToUIYQQQghRAkk4AH2BOTjUhVo4UtJz0aiVmMz9xRWQqzNKOZUN5ebmMnXqVJo0acLvv//O9OnT\niY6OtnVYQgghhBCiGFJSxa1LqkxARk4eWdk6TChwc9FQpaKTlFPZyM8//8ywYcP4888/Lctq1qxJ\nVlaWDaMSQgghhBAlkRYOSk444hIyUCoU6PKMODlqcHa0w8XRjtpVZXQqW5k6daol2VAqlUyePJnj\nx4/TuHFjG0cmhBBCCCGKIy0cFCqpKtCHIy4+kzy9ETCRo9Njp1JSwcUeNxfpv2ErK1asoEGDBvj5\n+bF27VqaNm1q65CEEEIIIcQtSMJB4RaOfxp9ElOzScnIwdlBg7ODBgBXF/sHHp/4h5+fH19//TUN\nGzZEo9HYOhwhhBBCCFEKKami5JIqBQpcHP+5qbW3U6JUKKTD+ANw8OBBLl68WOy65s2bS7IhhBBC\nCPGIkIQD64n/CpZUebg5UMFFQ57BSGZOHmq1Ej8fN+kwfh+lpKQQFhZGx44dGTlyJCbL8GBCCCGE\nEOJRJAkHJbdwqFUK9EYTVTycqF5Zi4erA14ejrYIsVzYu3cv9erVY+3atQDs37+fzz//3MZRCSGE\nEEKIuyF9OAB9CRP/6Q0mcnL1XLyWBoCfjxt6gzxxv9dMJhNDhw5lw4YNlmVOTk7MmTOH7t272zAy\nIYQQQghxt6SFAzDcHKVKqVCiLNBpPPZKCsnpuTg7anB21JCamUvslRRbhfnYUigU1KpVy/K6ffv2\n/PHHH4wbN87q5yGEEEIIIR490sLBPyVVqkI3tynpueTk6ossE/fe22+/zddff82gQYMYNmwYCoXC\n1iEJIYQQQoh7QBIO/impKlhOBfmzjKdm5JKRnYdapaJqZRe0zjI60t0wmUzFJhMajYZvv/1WEg0h\nhBBCiMdMua9XMZqMGG+OUqUuNMu4AsgzmLDXqFGpFBgMJrzcpdP4nbpw4QJdunQpsSO4JBtCCCGE\nEI+fcp9wlDRCVVx8Jtm5epRKyNHpMRiMuDprZJbxO2A0GlmxYgX169fnwIEDjBo1ipQU6QsjhBBC\nCFEeSMJRwghVianZpGbk4uygoVIFJ9xdHXB2lAq023XmzBlCQ0MZM2YMGRkZABgMBmJiYmwcmRBC\nCCGEeBDKfcKhN/2TcKgLzTLu7Ghnee3sYIcCmWX8dphMJnr27Ml3331nWTZo0CBOnTpF06ZNbRiZ\nEEIIIYR4UMp9wmE0/jPLeMGSKhMmsnMMpKTnkJKRi1qtkFnGb5NCoWDx4sUAVK1alX379rFhwwY8\nPDxsHJkQQgghhHhQyn2NkL6YPhxxCRkkp+eSrdNTQZvfZ8PRXi2zjN+B9u3bs2nTJp599llcXV1t\nHY4QQgghhHjAyn0Lh6FgSdXNPhxx8ZlcvpFOfHIWCSlZZOboQKGQWcZv4cSJE2RnZxe7rn///pJs\nCCGEEEKUU5JwFNPCEXslhRtJWahUSuw1aoxG0OuNJR2iXMvJyeHdd9+lcePGTJs2zdbhCCGEEEKI\nh0y5TzgKdhpXKfIvR0p6LkrlP3NCqFUKsnP00mG8kJ9++olGjRoxe/ZsDAYDCxYs4LfffrN1WEII\nIYQQ4iFS7hOO4lo4TEBOrp6sbB2Z2XkolQqqVHSSDuM3GQwGJk6cSMuWLYmOjgZApVIxefJkAgIC\nbBydEEIIIYR4mJT7TuOFE464hAyUCgW5eUacHDUAuDprqF3VzVYhPnRUKhWXLl3CZMrv0xIUFMTa\ntWtp1KiRjSMTQgghhBAPm3LfwlFwlCq1QkVcfCY5ukIzjDvJDOOFRURE4O3tzcyZM/nll18k2RBC\nCCGEEMUq9wlHwVGqVErVzRnGdZYZxitoHdA6a2wY4cOpcuXKnDlzhilTpmBnZ1f6DkIIIR57AwcO\nxN/f3+orICCAkJAQxowZQ2xsbJF9UlJSmD9/Pp06dSIwMJCnn36a0aNH89NPP5V4nkOHDhEWFkbL\nli1p1KgRzz//PJs3b0av19/Pt/fA/fDDD3To0IHAwEBmzZpl63CKuHz5Mv7+/vzyyy93tP+3337L\noEGD7nFUD4dff/2VF198keDgYDp16sRnn31W6j43btywlKw/9dRTvPXWWyQlJVltM3PmzCK/Y/7+\n/pw5cwaATZs28c4779yX93Q3pKTqZguHSqlEoVBYZhhPy9QB4GSvLrczjCclJfHWW2/xxhtv4O/v\nX2S9k5OTDaISQgjxMGvcuDGTJ0+2vNbpdERHRxMREUFYWBgHDhxAo8l/kHf+/HmGDh2K0Whk6NCh\nBAQEkJyczO7duxkyZAhjx45l7NixVsefMWMGkZGR9OzZk379+uHk5MTPP//M3LlzOXr0KIsWLUKp\nfDyepy5YsABHR0c++eQTvL29bR3OPZWRkcGMGTOIiIiwdSj33NmzZ3nllVdo3749EyZMICoqinff\nfRcXFxc6depU7D56vZ5Ro0aRmZnJjBkzMBqNzJs3j9GjR7N161bLZ/qvv/6ia9euDBkyxGr/atWq\nAfDyyy/TtWtXfvjhB5566qn7+j5vR7lPOPQ3ZxpX3ZyDIyUjh6vxGVxPykKtVuLq7VouZxj/z3/+\nw5gxY7h27RqnTp0iKioKlUpV+o5CCCHKNa1WS2BgoNWyJk2a4ODgwHvvvceRI0do06YNBoOBcePG\nodFo2LZtG+7u7pbtO3bsyJIlS4iIiCAgIIB27doBsHv3brZu3crMmTN58cUXLduHhIRQt25dJk6c\nyOeff85zzz33YN7sfZaSkkLbtm1p1qyZrUO559avX0+tWrUey8FmVq9eTbVq1ViwYAEATz/9NMnJ\nySxbtqzEhOOvv/7i1KlTbNiwgebNmwPg4uJCWFgYp06don79+gDExMTQo0ePIr9jZiqViiFDhjBv\n3ryHKuF4PB4B3AVzSZVaqeL46Ruci0sjPScPJ0c7NHYqTFCuZhi/ceMGL730Ei+88ALXrl0D4H//\n+x9//vmnjSMTQghRmriEDH6Nvs6v0deJS8iwdThWnJ3zKwUUivxh5w8fPkxMTAxvvvmmVbJhNnbs\nWKpXr87KlSsty9asWYO/v79VsmHWtWtXhg4dioeHxy3jiIyMpFu3bgQFBdGlSxd27NhhWRcaGsrM\nmTOttv/ggw8IDQ21vPb392fVqlV069aNhg0bEhERgb+/P8eOHbPab/PmzQQHB1smxf3jjz8YPHgw\nwcHBhISEMGvWLHJycoqN0VyqFBcXx5YtWyzfAxw8eJBevXrRsGFD2rZty+LFizEY/ikPDw0NZcGC\nBfTp04c+ffqwe/fuYs+Rm5vLrFmzCAkJoXHjxkyZMoWFCxcWea//+c9/eP3112nUqBEtWrSwDIVf\n0OnTp3nppZcIDAzk2WefZf/+/SVef/O5t2zZQteuXa2Wnzx5kuHDh9O0aVPq169P586diYyMtKzf\ntWsXzZs355NPPqF58+a0bdvWcg03btxIx44dadCgAd27d2ffvn1Wx75x4wbvvPMOrVq1on79+rRq\n1YrZs2ej0+lKjLO4EkHzV/v27Uvc78cff6Rt27ZWy9q3b8/p06eJj48vdp/09HTgn98TADe3/AGL\n0tLSAIiLiyMtLY1//etfJZ4boFOnTsTExPDjjz/ecrsHqVy3cBiNRstIS8lpOvZ9HcP5a2nk5Rmw\nU6twclCTo9OXmxnGdTodzZo148KFC5ZlnTp1YtWqVdSoUcOGkQkhhChNXEIGl6//k2SYv3/QLfQm\nkwmDwWD5/zU3N5c//viD8PBwfHx8aNq0KZDfP0GpVPL0008XexylUkloaCjr168nJSUFnU5HTEwM\nI0eOLPHcBUu5irNu3Trmzp3LkCFDaN26NT///DPvvfcezs7Olptfc0JUUOFlK1as4N1338XNzY3G\njRuzY8cODhw4QMOGDS3b7Nu3j9DQUBwdHTlz5gwDBgygUaNGLF68mISEBBYsWMDly5etEiozLy8v\nIiMjefXVV2nSpAnDhg3D09OTyMhIpk2bRv/+/Zk0aRKnTp1i6dKlXL58mXnz5lm9z/Hjx/Pss8/i\n4+NT7LX497//zbfffsukSZPw8fFhzZo17N27l0qVKlltN3v2bJ577jmWL1/OL7/8wrJly/Dz8+Pl\nl1+2bPPhhx8SFhbG+PHj2b17N6+//jouLi4l/mx/+uknkpOT6dChg2VZXFwcgwYNol27dixZsgS9\nXs/mzZuZNm0aDRs25IknngDyS7G+/PJLFi5cSGZmJg4ODkRERLBy5UpGjBhBkyZNLO9LqVTSuXNn\njEYjr7zyCiqVimnTpqHVaomKiuKTTz6hevXqDBgwoNg4p0+fTmZmZrHrzGWBhWVlZREfH0/16tWt\nlptLns6fP1/kGkN+KWLVqlUJDw9n1qxZmEwm5s+fj4+PD40bNwbg77//BuCzzz5j7NixpKam0qxZ\nM6ZMmYKfn5/lWB4eHjRu3Jgvv/ySli1bFhvng1auEw69Mb9zWVJaDmfOZ3MjKZe8PAOYwGAwojcY\nUavLTxmRRqNh4sSJTJgwgQoVKhAeHs7gwYOL/eMrhBDi/khMzeZKfAaGmw+7Ll7Ov+HJUd645X6n\nLyZjNFk/IDsXl8oT1Yu2HpRGpVLgW8mFim6338L/3XffFSmTcXBwoGXLlrzzzjs4OuYf88qVK3h4\neODgUPIokFWrVgXg6tWr5OXlAZR4A10ao9HIypUr6dWrlyUxCQkJ4fLly/z2229FnrYXZCp0XZ96\n6imrVpauXbuyf/9+3n77bQCuX7/OsWPHWLp0KQDLly/Hy8uL1atXo1bn33rVqFGDAQMG8Ouvv9Kk\nSROr42s0GoKCgtBoNHh6ehIYGIjBYGDRokV069aN9957D4CWLVui1WqZNm0aw4cPt9yU16lThxEj\nRljmyirs3LlzfPnll8yZM4eePXsC0KJFi2Kf2jdq1IgpU6ZYtjl8+DDfffedVcLRr18/Xn/9dcu1\niY2NZdWqVbdMOHx9fXF1dbUsi4mJoVGjRsyfP99Swh0YGEjz5s355ZdfLO/NYDDw6quvWsqF0tLS\nWL16NcOHD2f8+PGW65KZmcmCBQvo3Lkz169fp0KFCkyZMsVynObNmxMVFcUvv/xSYsJRu3btYpff\nSkZGfqJfsKWi4Gvz+sI0Gg3Lli1j8ODBlhJCNzc3Nm3ahL29PfBPwpGdnU14eDgJCQlEREQwcOBA\n9u7da9W6V69ePQ4dOnTb8d8v5TrhMJjy+28kpeVwNT4LvUGJUqHAaDJZvlydNOWqw/jYsWO5fv06\nr7766h3/URdCCHHnriVmkZP7T8mKuZU9T2+85X56g6nIjbFRUfp+xcnT58dxJwlHkyZNLKPknD59\nmjlz5vDUU08xZ84cq6fCJpOp1L6BBdebvzcab//9QP5NdmpqquVmzqxgy0BZFXyaDPDss8+ybt06\nTpw4QVBQEAcOHECr1dK6dWsAjh49yjPPPANgGUkrODgYFxcXjhw5UiThKE5sbCzJycl06dLFannX\nrl2ZNm2a1U154fgKM48qZY4J8pPCNm3aFBkdLCgoyOq1l5dXkVKwzp07W70ODQ1lxYoVGI3GYjvw\nX7lypUgn+DZt2tCmTRtyc3OJiYnh/PnznDx5EsCSbJoVfH/Hjx9Hp9PRpk0bq1HKWrVqxWeffcaV\nK1fw9fVl48aNGI1Gzp8/z/nz5/nrr79ITEy85b1OwZa6whQKRbGfX/P2JT2sLWlAg8uXLxMWFkbd\nunV55ZVXAFi7di3Dhg1j8+bNVK9enR49ehAUFERISIhlP/MoWNu2bWPMmDGW5T4+Ply9erXE9/ag\nle+Eo8AcHNk5BlAoUakUmIwmTICLox0Nans+lh3Gr169SpUqVYr8QiiVSj744AMbRSWEEKJKRSer\nFg61Kv/vtJ361t0uK3s4Ep+SbbWsUgXHUvcrjkqloErFOxuJ0MXFxdLCERAQgLe3N0OHDsXOzo6P\nPvrIsp2vry9HjhxBp9OVWJ5y5coVAKpUqWK5kbvVTVR8fDyenp7F3uylpKQAULFixTt6XwUVPka9\nevXw8/Nj//79BAUF8dVXX9GhQwfLsPEpKSlERkZa9UeA/JvSkmr6C0tNTS323FqtFo1GY1X6U9p7\nTE5ORq1W4+JifX9T3H7mFikzpVJZJOnz9PQschy9Xk9WVlaRc0D++O4tAAAgAElEQVT+U/7CLVsG\ng4E5c+awfft28vLyqF69uiURK3zTXzBO88+1b9++Rc5jvr6+vr7s2LGDRYsWkZiYSKVKlQgKCsLe\n3r7EhAJgyJAhJQ756+vry9dff11kufn9Fi7FMr8u7npAfnIB+R3Ozdc8JCSELl26sGzZMj766CN8\nfHyKJEje3t7Url3b0vph5ujoiMFgICsr66EYVbRcJxx6k3lIXAUOajvUSiUmNWjs1FSq4ECjf3lR\nv87d/2F6mBiNRpYvX87bb7/NihUrGDhwoK1DEkIIUUBFN0erlgUHYyIAT/7Lq9R94xIyiIvPv7Hx\nqeT8UDwwa9GiBb1792bHjh107tzZ0sLQrl07tm3bxuHDh4sducdkMvHNN98QGBho6VRer149oqKi\nmDhxYrHnGjJkCJUqVWL9+vVF1mm1WoAi8xqcO3eOlJQUGjZsiEKhKHIznZWVVab32a1bN3bt2sWQ\nIUM4fvy4pbzHfO5nnnnGqgzJ/B6L6zBfnAoVKgCQmJhotTwtLQ2dTmdZXxaVK1dGr9eTkZFhdQNc\n+NqUlTkZMktISECj0ZR4c12hQgVLJ3izFStWsGPHDubOnUubNm1wcHAgJyeHnTt33vLc5p/rsmXL\nqFKlitU6k8mEn58fP//8M1OnTuXVV1+lf//+lmveu3fvWx77/fffL/HnX1KS7OzsTKVKlbh06ZLV\ncvPrklqfLly4QN26da0SPI1GQ0BAAGfPngXy5y1RKBS0adPGat/s7Owin6PU1FTs7OweimQDyvko\nVeYWDoPRhJe7MxqNCpPJhL1GhXdFF1o3qvpQ/LG+V06fPk2bNm0YN24cmZmZTJgwwTISlRBCiEef\nj6cLTZ6sTJMnKz9U/39NnDgRrVbLnDlzLOUxrVq1IjAwkLlz55KQkFBkn1WrVhEbG8uIESMsywYN\nGkR0dHSxN6G7d+/m7Nmz9OjRo9gYatWqhZubG4cPH7ZaHh4ezty5c4H8p8/Xr1+3rDMajRw7dqxM\nfRmfffZZ4uLiWLFiBZ6enrRo0cKyrnHjxpw9e5aAgADLl7e3N+Hh4cTExJR6bMi/UXV3d+err76y\nWm4ejalRo0ZlOg5Aw4YNUSqVVjX+Op2OqKioO+q3+d///tfyvclk4sCBA5bBAYpTpUqVIvcfx48f\np0GDBnTq1MnS+mE+7q1aIYKCglCr1SQmJlpd3zNnzrBixQpMJhPHjx9HoVAwevRoy4359evXOX36\n9C3fl5+fn9UxC37VrVu3xP1CQkL45ptvrJLXQ4cO8cQTT5Q4ilrVqlX566+/rBIcnU7HqVOnLH2Z\n9u3bx7vvvmtV0vb3339z8eLFIkMnX79+/aEqjS/XLRzmhCM9U4fR6Ej1yvlZsrvWnhrerg/VH+u7\nodfrCQ8PZ+rUqVYf0ueff/6WnfWEEEKIe8Hd3Z2RI0cyf/58Pv30U4YNG4ZSqWTBggWEhYXx/PPP\nExYWRr169UhLS+OLL75g//79jB492qqfQc+ePfnuu++YOnUqJ0+eJDQ0FIVCwffff8/WrVvp2rUr\nL7zwQrExqNVqRo0axbx583B3d6dFixYcPXqUgwcPsmzZMgBat27NunXr2LRpE7Vr12bbtm0kJSWV\n6SlxjRo1qF+/Pjt27KB///5WN+5jxoyhb9++TJgwgRdeeAGdTsfy5cu5fv069erVK9M1VKlUjB07\nlpkzZ+Lm5kZoaCh///03ERERdOnShTp16pTpOOZYn332WT744AOys7Px8fFh48aNJCQk4OvrW+r+\nhROAjRs34uzsTJ06dYiMjOTcuXO8//77Je7fsmVL1q5dy40bN/Dyym+5CwwMZPXq1WzevJm6devy\nv//9jzVr1uDo6HjLViYPDw8GDhzInDlzSE1NpUGDBvz1118sWrSI9u3b4+LiQmBgIEajkQ8++IBO\nnTpx9epVVqxYgZOTU5lbsG7HsGHD6N27NxMmTKB37978+OOPfP755yxZssSyTVJSEhcvXqROnTq4\nuLgwZMgQ9u7dy4gRIxg2bBgKhYJNmzYRHx/P8OHDLcf96quvePXVVxkyZAgJCQksWrSI+vXrFxn0\n4Pjx4w/NCFVQzhMOvclAUloOMZdSiI/LwpCrQeuswU6tRMHjMzKT0Whk48aNlmSjevXqfPzxx3Ts\n2NHGkQkhhCgvBg0axNatW1m5ciXPP/887u7uVKtWjZ07d7Jx40Z27tzJ5cuXcXZ2Jjg4mPXr11u1\nEpgtXLiQ7du3s2vXLg4cOIBer8fPz4+pU6eWWiIzdOhQ7O3t2bBhA+vXr6dmzZqEh4db5p4YNWoU\n8fHxhIeHo1aree655xg5ciSbNm0q03vs3r07f/75J927d7daHhAQwIYNGwgPD2fChAnY29tbRmQy\n33CXRf/+/XFwcGDt2rXs2LEDLy8vhg0bZtVZuKymT5+Og4MDixYtwmAw0K1bNzp37syZM2duuZ9C\nobBKphQKBTNmzGD16tXExMRQt25dPv74Y6shggtr1qwZbm5uREVF0atXLwBGjBhBfHw8ERER5OTk\n0LhxY9asWcPixYs5ceKE1fkKe+utt6hYsSLbt29nyZIleHl5MXjwYMss9S1atODtt9+2fM7q1KnD\n66+/bkk88vLyLP1t7gV/f39WrlzJ/PnzGTduHD4+PsyZM8fqvuvbb7/l3//+N59++ilNmzbFz8+P\nzz77jHnz5jFlyhSMRiMNGjQgMjISf39/y3HXr1/PokWLeO2117Czs6NDhw68+eabVudPSkoiOjq6\nxNJDW1CYbtVO9Rj77bffoKIb//0zhr8vJJGb7Epujgq1UkmNKlqebuhLpxY1bR3mPfPLL7/QsmVL\nRo4cyYcffmipeRS3xzzE4JNPPmnjSMSjRj474k7JZ0fcqZI+O8nJyXz//feEhoZaDd/at29fvLy8\nrJ7E3y8RERH8+OOPbNmy5b6fq7xZt24dn3/+Obt27brjY0RHR5OVlWWZA+RulesWjmtJGSQkZ5Gn\nN2I0KdDYqVAoIEunf6xaOACaNm3KmTNnZAI/IYQQopyzt7fn/fff58CBA7z00kuo1Wq++uorTp48\naRkt6X4bPHgw27Zt4+TJkwQGBj6Qc5YHOp2OzZs3M3XqVFuHYqVcdxpPTs8mITWHrJw88vLAeHMI\nQieNGg+3R69vQ05ODjNnzrQMEVeYJBtCCCGEcHJyYs2aNWRlZTFp0iTGjBnD6dOnWbFiRbFlbPeD\nVqtlxowZdzQPiijZtm3baNKkiWUOmIdFuW7hSM3MQaXKr0U0GcGkNOHq7EC92hUfucn+fvjhB8LC\nwiyjFXz88ce2DkkIIYQQD6nAwMAH1ppRkvbt2xc7u7m4c4MGDbJ1CMUq1y0cN5IzyM7Rgwkgv6Sq\nZhVX2jxCw+Gah7dt1aqVZdKXDRs2cP78edsGJoQQQgghBOW8hSM5I7+Fw9Feg8bkQI0qWlo19H1k\nko309HSCg4OJjY21LGvYsCHr1q2jZs2atgtMCCGEEEKIm8p1C4daBSYTlg7iujzjI1VKpdVqLUP5\n2dvbM3v2bI4ePUpQUJCNIxNCCCGEECJfuW7hUKryZxk36k04KBVUqej0yLRumM2fP5+kpCRmzZol\nQyYKIYQQQoiHTrlOOIwmIy6OdiiN9rirHKld1c3WIZUoNzcXe3v7Isvd3Nz47LPPbBCREEIIIYQQ\npSvXJVUJqdkkpGSTlpGHl7sDbi4P51C4O3fupFatWvzyyy+2DkUIIYQQQojbUq4TDgUK1GolSpSk\nZeWRmJpt65CsXLt2jd69e/Piiy8SFxdHWFgYOp3O1mEJIYQQQghRZuU64TAYTTf/hcTUnIdmdnGT\nycSnn35KvXr1rMqlqlatSnp6ug0jE0IIIUo2cOBA/P39rb4CAgIICQlhzJgxVqMqmqWkpDB//nw6\ndepEYGAgTz/9NKNHj+ann34q8TyHDh0iLCyMli1b0qhRI55//nk2b96MXq+/n2/vgfvhhx/o0KED\ngYGBzJo1676fb8iQIbzzzjuW1xEREWzevLnU/YxGI3369HksKzF0Oh2zZ8/m6aefplGjRowfP54b\nN26Uut+ePXvo3r07DRs25LnnnuOrr76yrDt69GiR35OCX1evXiUnJ4dOnTo9NtMclOs+HCaTCUwK\nMClxdnh4ZhdPSkpiwoQJJCcnA+Du7s7ixYsZMGAACsXDkRQJIYQQxWncuDGTJ0+2vNbpdERHRxMR\nEUFYWBgHDhxAo9EAcP78eYYOHYrRaGTo0KEEBASQnJzM7t27GTJkCGPHjmXs2LFWx58xYwaRkZH0\n7NmTfv364eTkxM8//8zcuXM5evQoixYtQql8PJ6nLliwAEdHRz755BO8vb3v+/kK32NERERY/SxL\nsmHDBipWrEjTpk3vV2g2M23aNL755hveeecdHB0dWbhwISNGjGDXrl0lfs4OHjzI5MmTGTp0KK1b\nt+bw4cO8/vrrODo60rZtWwICAti+fbvVPjk5OYwfP5769etbftajRo3i3XffLVPS97Ar1wmHQqEA\nBbg5OPBkTY+HZkjcihUrsmjRIgYPHswLL7zAsmXLqFKliq3DEkIIIUql1WoJDAy0WtakSRMcHBx4\n7733OHLkCG3atMFgMDBu3Dg0Gg3btm3D3d3dsn3Hjh1ZsmQJERERBAQE0K5dOwB2797N1q1bmTlz\nJi+++KJl+5CQEOrWrcvEiRP5/PPPee655x7Mm73PUlJSaNu2Lc2aNXvg5zaZTFb/liQjI4Ply5ez\nevXqBxHWA3Xx4kX27NnDggUL6NKlCwD+/v507tyZr7/+mg4dOhS732effUbTpk0tyVpISAgnT55k\n27ZttG3bFhcXlyK/Ix988AFKpZJ58+ZZlvXo0YPw8HAOHTrEM888c5/e5YPxeDwCuAsaOxU+FV1p\n/ZDNLj5w4EAOHz7MZ599JsmGEEKIMrmWfoPf4/7g97g/uJZeetnHg+TsnP9Qz/wU/fDhw8TExPDm\nm29aJRtmY8eOpXr16qxcudKybM2aNfj7+1slG2Zdu3Zl6NCheHh43DKOyMhIunXrRlBQEF26dGHH\njh2WdaGhocycOdNq+w8++MAy5xXk33CuWrWKbt260bBhQyIiIvD39+fYsWNW+23evJng4GCys/P7\nh/7xxx8MHjyY4OBgQkJCmDVrFjk5OcXGePnyZfz9/YmLi2PLli2W7yH/6XmvXr1o2LAhbdu2ZfHi\nxRgMBqv3sGDBAvr06UOfPn3YvXt3sefIzMxkypQpNG/enBYtWrBq1Sqr9eah9ufOnUv79u1LvJ47\nd+5Eq9XSsGFDy7K8vDyWLFlCp06daNCgAc2aNWPcuHFcu3at2DiDgoJYu3YtABcuXGDMmDE0atSI\npk2b8tZbb1kqPsz27t1Lr169CA4OJjg4mL59+/Lrr7+WGGNpJUwlXSNzWZ854QWoUaMGderUISoq\nqsTzZWRkWD7vZhUqVCA1NbXY7c+cOcOWLVt47bXXrH4XVCoVnTp1Ys2aNSWe61FRrhOOapW11PJ1\nw7+Gh02SDYPBwJYtWzAajUXWKRQK2rZt+8BjEkII8Wi6ln6Dy2nXMJqMGE1GLqdds0nSYTKZMBgM\n6PV69Ho9mZmZHD16lPDwcHx8fCxlNz/88ANKpZKnn3662OMolUpCQ0M5ceIEKSkp3Lhxg5iYGNq0\naVPiuSdPnkyrVq1KXL9u3TqmT59O69atWblyJZ07d+a9995j3759lm2KK10uvGzFihUMGTKEjz76\niJdffpnKlStz4MABq2327dtHaGgojo6OnDlzhgEDBqBSqVi8eDFvvPEG+/bt47XXXis2Ti8vLyIj\nI/H09KRz585s374dT09PIiMjGTduHMHBwSxbtowBAwawdu1a3n777SLv85lnnuGtt94qsXVk4sSJ\nHDp0iMmTJ/PBBx+wf/9+qz4YkZGRQP4D0GXLlpV4Tb/44osiT98//PBDNm/ezMiRI1m3bh2vvfYa\nR44cYfbs2cXGuWTJEkJDQ0lISKBfv35cu3aNuXPnMmPGDI4fP05YWBh5eXkA7N+/n8mTJ9OuXTs+\n/vhjZs+eTXp6Oq+99pplm8LMJUwlfbVu3brY/c6dO0elSpVwcLAuua9WrRrnzp0r8Zr06NGD77//\nnv3795Oens6+ffuIioqie/fuxW4fHh6On58fffr0KbKuQ4cOHDt2zCpZexSV65IqM5UNLkN0dDRh\nYWEcOXKExMRExo0b98BjEEII8fBJyk4hLu06RlP+w6iL6RcByCvlfuNM4nnLPmYXU65Qp2LN245B\nqVDi41oZD8cKt73vd999R0BAgNUyBwcHWrZsaamDB7hy5QoeHh5FbuYKqlq1KgBXr1613Ez6+Pjc\ndkyQ37F55cqV9OrVy6rU5fLly/z222907dq1xH0LlxU99dRTVq0sXbt2Zf/+/ZYb/+vXr3Ps2DGW\nLl0KwPLly/Hy8mL16tWo1fn3HDVq1GDAgAH8+uuvNGnSxOr4Go2GoKAgNBoNnp6eBAYGYjAYWLRo\nEd26deO9994DoGXLlmi1WqZNm8bw4cN54oknAKhTpw4jRowgOjq62Pfz119/8d133xEeHm4pFQoM\nDLRqyQgKCgLyr7e/v3+xx8nIyODUqVMMGDDAanlycjKTJ0/mhRdeAPJL6mJjY/niiy+stjPHabZg\nwQLy8vJYu3YtFSpUsMTVqVMnvvzyS3r27MnFixfp37+/Vd8eOzs7xo0bx4ULF6hTp06ROIsrYSqL\nzMxMnJyciix3cnK6ZQLQp08f/vjjD6uEsk+fPvTv37/ItpcuXeLw4cNFWtbM6tWrB+S30jzKpYLl\nOuHIyM7D1VlDRTfHB3bOvLw85s+fz/Tp0y1D3L7zzjv069ePihUrPrA4hBBCPJyuZ8STo8+1vNab\n8kde0hmKf3prlmfUF7kxViiMpe53qzjuJOFo0qSJZaSj06dPM2fOHJ566inmzJlj6SwO+TfxKpXq\nlscquN78fXFVAWVx7tw5UlNTrcpjAKua+bLy8/Ozev3ss8+ybt06Tpw4QVBQEAcOHECr1VqenB89\netTSCmAeSSs4OBgXFxeOHDlSJOEoTmxsLMnJyZYEwaxr165MmzaNX375xZJwFI6vsN9//x3A6sl+\npUqVCA4OLjWOgq5evYrRaCzSoT08PBzIT7xiY2OJjY3l999/L9ICUTjOo0ePEhQUhFartVynKlWq\nUKtWLX766Sd69uxpSVDS0tKIjY3l3LlzfPPNNwC3nDrgViOYmZPAwkwmU4mD9dxqYIIFCxawd+9e\n3njjDYKDgzlx4gQRERG4uLjw1ltvWW27Y8cO3Nzc6NGjR7HHcnFxwc3NjStXrpR4vkdBuU44XBzt\nMBhNaNS3/oN3r1y/fp2uXbtaftEBatasyccffyzJhhBCCAAqu1SyauFQK/L/q9ao7ErZz5OEzCSr\nZZ7OHqXuVxylQklll0q3vR/k3yCZWzgCAgLw9vZm6NCh2NnZ8dFHH1m28/X15ciRI+h0OqtEpCDz\nTVaVKlUsydTVq1dLPHd8fDyenp7F3iSmpKQA3JP/bwsfo169evj5+bF//36CgoL46quv6NChA3Z2\ndpZzR0ZGWsqUzBQKBfHx8WU6p7n+v/C5tVotGo2GzMzMEuMrLC0tDbVaXaSfQaVKt/czNw/Vb261\nMvv999+ZPn06p0+fRqvV8uSTT+Lg4FAkWSwcZ0pKCidPnizSQgb5ZWaQ/zN+9913iYqKws7Ojrp1\n6+Lr6wuU3MH96NGjDB48uMT3MWfOHHr27FlkuYuLi9V1NcvMzESr1RZ7rISEBNauXcukSZMYNmwY\nkJ+EOzs7M2PGDF5++WWqVatm2d7cIdz8WSmOg4PDIz8tQrlOOJLTc3F11mA0PpiuLJ6entjb2wP5\nf2TGjh3L7NmzcXF5eDqrCyGEsC0PxwpWLQt2N/vLPlnlyVL3vZZ+g7ib/TZ8tF5U0XrdlxhvR4sW\nLejduzc7duygc+fOlhaGdu3asW3bNg4fPkynTp2K7Gcymfjmm28IDAy0dKStV68eUVFRTJw4sdhz\nDRkyhEqVKrF+/foi68w3iElJ1knZuXPnSElJoWHDhigUiiI3xVlZWWV6n926dWPXrl0MGTKE48eP\nM378eKtzP/PMM7z88stF3mNxHeaLYy4xSkxMtFqelpaGTqezrC/rsfR6PRkZGVb3IMnJyVSuXPm2\njgNY3Qynp6czatQomjRpwrJlyyw313Pnzi2xxMtMq9XSpk0bq2sH+dfJnBxNmjSJGzduEBkZSf36\n9VEqlXz33Xf83//9X4nHrV+/vtW8ZoWZE5bCatasSUJCQpGk+PLlyyUOAXzp0iUMBoOlJM2sUaNG\nmEwmYmNjLdckLi6O2NjYIn1wCktLSyvz5+RhVa47jauVCrKy9SSn5Za+8T2gUqlYu3YtgYGB/Pe/\n/2XJkiWSbAghhLhnqmi9aORTn0Y+9R+KZMNs4sSJaLVa5syZYymradWqFYGBgcydO5eEhIQi+6xa\ntYrY2FirGv9BgwYRHR3Nzp07i2y/e/duzp49W2JpSq1atXBzc+Pw4cNWy8PDw5k7dy6Q/0T7+vXr\nlnVGo5Fjx46VaQ6sZ599lri4OFasWIGnpyctWrSwrGvcuDFnz54lICDA8uXt7U14eDgxMTGlHhvy\ny4/c3d2tJpADLB3eGzVqVKbjADRv3hzAqqN7amoqx48ft9qutPlMKleujFKptOrPEBsbS1paGoMH\nD7bcWBuNRn788cdS4zJfp7p161quU926dVm+fLllFLATJ07QrVs3AgMDLfGZR4wqqYXD2dnZ6toX\n/iopWQsJCcFgMPD1119blp0/f54zZ84QEhJS7D6+vr4oFAp+++03q+UnTpwA/umXBHDy5EmAW5ay\npaWlkZ2d/UDmYbmfynULB0CuznhfZhgvqe7P39+f48ePywR+Qgghyg13d3dGjhzJ/Pnz+fTTTxk2\nbBhKpZIFCxYQFhbG888/T1hYGPXq1SMtLY0vvviC/fv3M3r0aKsRkHr27Ml3333H1KlTOXnyJKGh\noSgUCr7//nu2bt1K165dLR2VC1Or1YwaNYp58+bh7u5OixYtOHr0KAcPHrSMwtS6dWvWrVvHpk2b\nqF27Ntu2bSMpKanYjsOF1ahRg/r167Njxw769+9v9f/8mDFj6Nu3LxMmTOCFF15Ap9OxfPlyrl+/\nbukUXBqVSsXYsWOZOXMmbm5uhIaG8vfffxMREUGXLl2K7SxdEj8/P3r06MHs2bPJzc3F29ubVatW\nWQ2vC/ktDr/++isNGzYs9qbY2dmZwMBAjh07Rq9evQCoXbs2zs7OLFu2DIPBQHZ2Nlu2bOHatWvk\n5t76Ae/QoUPZs2cPw4cPZ9CgQajVakvfGHOrR4MGDdi1axdPPPEErq6uHDx4kP379wNYhiC+V6pX\nr24ZySwjIwOtVsvChQvx9/e3+lyeOnUKe3t7ateujZeXF71797Z8poKCgjh16hQRERF06tSJ2rVr\nW/aLiYnB3d0dV1fXEmMwJ7wtW7a8p+/tQSvXCYfeYMTF0eGezzAeFRXFW2+9xe7du4ttmpRkQwgh\nRHkzaNAgtm7dysqVK3n++edxd3enWrVq7Ny5k40bN7Jz504uX76Ms7MzwcHBrF+/3qqVwGzhwoVs\n376dXbt2ceDAAfR6PX5+fkydOpXevXvfMoahQ4dib2/Phg0bWL9+PTVr1iQ8PNwyz8aoUaOIj48n\nPDwctVrNc889x8iRI9m0aVOZ3mP37t35888/iwx/GhAQwIYNGwgPD2fChAnY29vTqFEj5s+fb+mb\nUBb9+/fHwcGBtWvXsmPHDry8vBg2bBhjxowp8zHMPvjgAzw8PFi6dCl6vZ5evXoVKS0aN24cixYt\n4tdff+XIkSPFtnh06NDBaiZsFxcXli5dyty5cxk9ejSenp707t2b8ePH07dvX06ePFniiFHe3t5s\n2bKFefPm8eabb6JQKKhfvz7r1q2zjJT14YcfMn36dN555x00Gg3PPPMMe/bsoUuXLhw/fvyez3b+\n4Ycf8uGHHzJ//nyMRiMtW7ZkypQpVvdyY8eOpWrVqmzcuBGAGTNmULt2bXbu3MmKFSvw9vZmxIgR\nhIWFWR07KSnplskG5A8f3bBhw0e+r6/CVNoUko+p3377jXW/nsa/mhcvNGt+T+bhSE9P55133rFk\nteaaVfH4MNefmidEEqKs5LMj7pR8dsSdehCfnYyMDNq1a8eSJUtKLDMSd0an09GmTRtmzZp1y8kX\n74fo6GiysrJo3LjxPTleue7DYadWUsHZ6Z4kGwcPHqRBgwZWk+OcP3+etLS0uz62EEIIIcTDyMXF\nheHDh1tmChf3zp49e6hevfoDTzbuh3KdcGidNPek/8b58+fp0qULFy5cAMDe3p6PPvqII0eOlNpU\nJoQQQgjxKAsLCyM5OZmff/7Z1qE8NnJycli9ejUffvihrUO5J8ptHw69Xo9Br8dkMBWZiKYkJY2R\nXLNmTSZOnMi8efN46qmnWLNmDf/617/uZbhCCCGEEA8llUpV7Mhh4s45ODhw8OBBW4dxz5S7hOPi\n5TgSU7I5dyWF9JQcslyzORVT+uyNJhOYTAYqVnCkelWfIutnzJjBv/71L4YOHVrqMHJCCCGEEEKU\nF+Uq4bhy9TqpWSa0bhVx1t5A45iFq6sHztqyTZZjwsTR339HpVLh6209+pSjo2OR0QeEEEIIIYQo\n78pVwpGcmonKzoXLl69w7Xo8ackpXLt6g/Oa0i9DSnIyqz/+mJ9/+okFc6cTNrjfA4hYCCGEEEKI\nR1u5Sjhyc3TEXbmMi1slVA4V0DgY0OGCyt6txH1MmIiKimLTpk1kZ2Xj7O7NrA8X0Lf3czg7Oz/A\n6IUQQgghhHj0lKuE4/LVG7hW8rO8Tku4xsHDX9L039Ostvvjf8f4v317MZpMpGfmEnv+smWdi9aF\nF3v15/qNJGr5ScIhhBBCCCHErZSrhMNg+mcI3F+P/sDpE0FeV90AABpbSURBVL/j6mw9B4fBYGD3\nzi1MensGeXo9UyZPAAVggmbNmjFo8KD/b+/Og6I48z6Af4dLkcMlGq8glwbwAOQIghEComRR1JhY\nSJAEkIiiS8yhEYkxGIx4ICtBjQqIICi6a1w3tVlUYvCIvh6rhGjpi4rCqKgrKCKMMDP0+4fFvI6M\nAiMDE/x+qqYKnnme7l83vxr6N91PN7rriyCVN3Zw9EREREREfzydfjulXbt2wd/fH05OTggODkZR\nUdFz+5eUlCAsLAzOzs7w9fVFWlqaWus17WkKPV05bl07hxVL/oLiM8cBALdv3UDvV/vh0oXfsH7V\nIug3PoSJgQwfz/sYMTEx6Gn6+PIrAS/lA9qJiIiIiNqkUwuOPXv2ID4+HpMnT0ZqaipMTEwQGRmJ\n69evq+xfWVmJiIgI6OrqIiUlBUFBQVi7dq1aT7esr3sAA0Mj9LMajpiFicjbmgoAeCR5hG7dDPC3\nnI34ZNFK+I4Pwp+M9WD3+uAX2lYiIiIiopdRpxUcgiAgNTUV06ZNw9y5c+Ht7Y3vv/8eZmZm2Lp1\nq8oxubm5aGxsxPfffw9vb29ER0cjKioKmzZtgkwma9P67Ya7wXKEFxrlAm7cuIGHtbWQyWXobmiI\n6nt30afvABj2MIa0QQpzi0G4dPH3dthqIiIiIqKXS6cVHGVlZbh58ybGjBmjaNPT04OPjw+OHDmi\ncsyxY8fg6emJbt26Kdr8/PxQXV2Nc+fOtWn9BgbdoKNngAfVVVj9zed4UK+Pf/7zn+jbbwAq7/4X\n+t26QSaT4crl/0Wfvv3xSFKr3oYSEREREb3EOq3guHbtGgDA0tJSqd3c3BxisRiC0HyORFlZGSws\nLJTaBg4cqLS8tvj9dAGq75RChu7Q1TfEr4d/gQABY9+eiEsXz2NtUgI8RnlDEBrR46nJ5URERERE\n1LJOKzgePnwIAM2eZWFkZITGxkbU1dWpHKOq/5PLa63ahw9Qc+V3yHr0gkynB8aM+zOWr0yCnq4e\nRr81DkZGPRAdMx8eo7xx6eLvsBk8tE3LJyIiIiKiTrwtbtMZDJFIpPJ9HZ3mtZAgCM/s/6x25T7/\n//Pxwp8gyOXorlcH836voPzi/6DwQDdIG+rh4uGLMeODsCbhcwhCI5zd30Lto3rUVtwEANTXVUOo\n04cglbS4TupaJJLHf/MLFy50ciT0R8PcIXUxd0hdzB1SV1PutJdOKzhMTEwAALW1tXjllVcU7bW1\ntdDV1YWhoaHKMbW1ynMpmn5vWt7zdNPXhUwug56uHsYGBmNsYLDKfvX19bAcNBQfDBqq1AY8fk6H\njlwKySO5yrMw9HLg357UxdwhdTF3SF3MHepsnVZwNM3dEIvFinkYTb9bW1s/c0x5eblSm1gsBoBn\njnmS+YB+uFVVC4lUH+avmbfqrEgTQRAglUlhoCODlcUAyOurYT/YqtXjiYiIiIheRp1WcFhZWaF/\n//44cOAARo0aBQCQSqUoLCyEr6+vyjGenp7YuXMnJBKJ4gxIQUEBzMzMMGTIkBbXqasLWFu+BolE\ngkf1DVAxL/2ZRCIRunczgaGhIUQQQU/FJV9ERERERKSs0woOkUiEmTNnIiEhAaampnBxcUFOTg6q\nq6sRHh4OACgvL0dVVRVGjBgBAAgJCUFOTg6ioqIwY8YMXLx4EWlpaZg/fz709FrelNf69ca165Uw\nNjVDD8MeasdeU/1fDH3douWOREREREQvOZGg6v6zHSgzMxPZ2dm4d+8ehgwZgtjYWDg5OQEAYmNj\nsXfvXqXJTufOncO3336L8+fPo3fv3ggJCcFHH33U6vXdr36AituVkDeqs9ki6OoANpYDlJ4FQkRE\nREREqnV6wUFERERERF0XJyIQEREREZHGsOAgIiIiIiKNYcFBREREREQaw4KDiIiIiIg0hgUHERER\nERFpTJctOHbt2gV/f384OTkhODgYRUVFz+1fUlKCsLAwODs7w9fXF2lpaR0UKWmbtubOmTNn8MEH\nH+CNN96Al5cXFi5ciMrKyg6KlrRJW3PnSevWrYO9vb0GoyNt1da8qaqqwhdffIGRI0fijTfeQHR0\nNMRicQdFS9qkrblTXFyM0NBQuLq6YuzYsVi3bh1kMlkHRUva6Oeff4aLi0uL/V70OLlLFhx79uxB\nfHw8Jk+ejNTUVJiYmCAyMhLXr19X2b+yshIRERHQ1dVFSkoKgoKCsHbtWmzZsqWDI6fO1tbcuXLl\nCsLDw2FiYoLk5GQsXLgQZ86cQWRkJD/EXzJtzZ0nlZSUYOPGjRCJRB0QKWmTtuaNVCpFREQEzp07\nh2XLliExMRFisRgzZ86EVCrt4OipM7U1d27evInw8HAYGhoiNTUV4eHhSE9Px5o1azo4ctIWZ86c\nwYIFC1rs1y7HyUIX09jYKPj6+grx8fGKNqlUKvj5+QkJCQkqx6SkpAgeHh7Co0ePFG1r164V3N3d\nBalUqvGYSTuokzvx8fHC2LFjBZlMpmgrLi4W7OzshMLCQo3HTNpBndxpIpPJhPfee0/w9vYW7O3t\nNR0qaRF18mbXrl2Ck5OTUFFRoWi7cOGC4OXlJZw/f17jMZN2UCd3MjIyBEdHR0EikSjakpOTBRcX\nF43HS9qlvr5e2Lx5szB8+HDB3d1dcHZ2fm7/9jhO7nJnOMrKynDz5k2MGTNG0aanpwcfHx8cOXJE\n5Zhjx47B09NT6enhfn5+qK6uxrlz5zQeM2kHdXLn9ddfV1T9TaytrQEAN27c0GzApDXUyZ0mW7du\nhUQiQWhoKAQ+h/Wlok7eFBQUwNvbG/369VO02dvb4/Dhwxg6dKjGYybtoE7u1NTUQE9PT+lYp2fP\nnqirq0NDQ4PGYybtcfjwYaSlpWHhwoWt+t/THsfJXa7guHbtGgDA0tJSqd3c3BxisVjlTi0rK4OF\nhYVS28CBA5WWR12fOrkTEhKCkJAQpbaDBw8CAGxsbDQTKGkddXIHePzZs27dOiQkJEBfX1/TYZKW\nUSdvSkpKYG1tjXXr1uHNN9+Eg4MDZs2ahYqKio4ImbSEOrnz5z//GVKpFGvWrEF1dTWKi4uRlZWF\ncePGwcDAoCPCJi3h4OCAgwcPIjQ0tFX92+M4ucsVHA8fPgQAGBkZKbUbGRmhsbERdXV1Kseo6v/k\n8qjrUyd3nlZRUYFVq1bBwcEBHh4eGomTtI86uSMIAhYvXox33nmnVRP2qOtRJ28qKyuxe/duHD16\nFMuXL8eqVatw+fJlREVFQS6Xd0jc1PnUyR07OzskJCQgMzMTI0eORFBQEHr37o3ly5d3SMykPfr2\n7QtjY+NW92+P42S91of3x9BU1T9r8qWOTvMaSxCEZ/bnJM6Xhzq586SKigqEh4cDAJKTk9s1NtJu\n6uROXl4exGIxNm7cqNHYSHupkzcymQwymQzp6emKA4aBAwdi6tSp2L9/PwICAjQXMGkNdXLnl19+\nwZdffompU6di/PjxuH37Nr777jvMmjULmZmZPMtBz9Qex8ld7gyHiYkJAKC2tlapvba2Frq6ujA0\nNFQ5RlX/J5dHXZ86udOkpKQEwcHBqK2txZYtWxSnGunl0NbcqaiowOrVqxEXF4du3bpBJpMpDiDk\ncjnncrwk1PnMMTIygpOTk9K3k8OHD4epqSkuXbqk2YBJa6iTO2vWrMHo0aOxdOlSjBw5EpMmTcLm\nzZvxn//8Bz/++GOHxE1/TO1xnNzlCo6m6xmfvie5WCxWTOZVNaa8vLxZfwDPHENdjzq5AwC//fYb\npk+fDj09PWzfvh22trYajZO0T1tz5/jx46irq8PHH3+M4cOHY/jw4Vi5ciUAYNiwYVi/fr3mg6ZO\np85njoWFhcoJvjKZjGfkXyLq5E5ZWRmcnJyU2mxsbPCnP/0JV65c0Uyg1CW0x3Fylys4rKys0L9/\nfxw4cEDRJpVKUVhY+Mxr6j09PXH8+HFIJBJFW0FBAczMzDBkyBCNx0zaQZ3cabr/fZ8+fZCXl9ds\nUhW9HNqaO2PGjMHu3buVXhEREQCA3bt3IygoqMNip86jzmfO6NGjcebMGdy5c0fRdvLkSdTV1cHZ\n2VnjMZN2UCd3zM3NcebMGaW2srIy3L9/H+bm5hqNl/7Y2uM4WTc+Pj5eQ/F1CpFIBAMDA2zYsAFS\nqRQNDQ1ITEzEtWvXsGLFCpiamqK8vBxXr15V3FZw0KBB2LZtG44fPw4zMzPk5+dj48aNiImJgaur\naydvEXUUdXInNjYWly9fRlxcHADg1q1bipeurm6zSVbUNbU1d7p3744+ffoovS5fvoyjR4/im2++\nYd68JNT5zLGzs8MPP/yAgoICvPrqqzh//jy+/vpr2Nvb49NPP+3kLaKOok7umJqaIiMjA7du3YKh\noSHOnj2Lr776CiYmJli6dCnvlPeSOnnyJM6ePYvZs2cr2jRynNyWB4X8kWzZskXw8fERnJychODg\nYKGoqEjx3sKFC5s9YOv3338XgoODBQcHB8HX11dIS0vr6JBJS7Q2dxoaGoRhw4YJ9vb2gp2dXbPX\nli1bOmsTqJO09XPnSZmZmXzw30uqrXlTXl4uzJkzR3B2dhbc3d2F2NhYoaampqPDJi3Q1twpLCwU\npk2bJri4uAg+Pj7Cl19+KVRWVnZ02KRFUlNTmz34TxPHySJB4OxEIiIiIiLSjC43h4OIiIiIiLQH\nCw4iIiIiItIYFhxERERERKQxLDiIiIiIiEhjWHAQEREREZHGsOAgIiIiIiKNYcFBREREREQao9fZ\nARARdWWpqalYv379c/tcvHix1cs7ceIEwsLCkJycjPHjx79oeC2KjY3FP/7xD6U2XV1dGBkZYdiw\nYYiOjoa7u3u7r7dpv/3666/o1asXAKCmpgaNjY3o2bMnAOCDDz7A3bt38e9//7vd1/+069evY+zY\nsc3adXR0YGJiAltbW8ycORPe3t5qLf/OnTvo2bMnunXr9qKhEhFpHRYcREQdIC4uDmZmZp0dhtpW\nr16t+Fkul6OyshI5OTmYMWMGsrKy4Orq2q7r8/f3h5WVFUxMTAAA586dw+zZs7FhwwY4OjoCAKKj\no9HQ0NCu621NXOPGjVP8LpfLceXKFWzfvh2zZ89GTk4OXFxc2rTMQ4cO4fPPP8e+fftYcBBRl8SC\ng4ioA4wdOxYDBgzo7DDUNnHixGZtPj4+CAwMxIYNG5CRkdGu67Ozs4OdnZ3i95KSEty9e1epz6hR\no9p1na1ha2urcl+MGzcO06ZNw8aNG7F58+Y2LbO4uBgPHz5srxCJiLQO53AQEZFaBg0ahMGDB+O3\n337rsHUKgtBh62oLR0dHWFlZvdC+0NZtIyJ6USw4iIi0xIMHD7By5UqMGzcODg4OcHV1RVhYGIqK\nip477qeffsKUKVPg7OyMkSNHYs6cObh8+bJSn6qqKnz11VcYNWoUHB0dMWXKlHaZ+6Crqwu5XK7U\ntmPHDkyYMAEODg4YPXo0vv76a9y/f79NMaempsLe3h53795Famoq4uLiAADTpk3Dhx9+CODxHI6A\ngAAAwOLFi+Ho6Ija2lql9ZSWlsLe3h7btm1TtO3btw/vvvsunJyc4Onpibi4OFRVVb3wvjA0NGzW\nduTIEURERMDd3R3Dhw+Hn58fkpKSIJVKATyeI9M0x2f06NFYtGiRYuyJEycQGhoKZ2dnuLu74+OP\nP4ZYLH7hOImIOhoLDiKiDlBdXY2qqqpmryaCICAqKgp///vfMXHiRMTHx2P69Ok4f/48IiMj8eDB\nA5XLPXnyJObPn4/XXnsNcXFx+Oijj1BcXIwPP/xQcfD98OFDhISEoKCgACEhIVi4cCHMzMzw6aef\nYseOHWpv0507d1BaWoohQ4Yo2pYvX46lS5di4MCBWLRoESZMmIDdu3cjJCREcdlQa2JuIhKJ4O/v\nj6CgIABATEwMoqOjld4HgMDAQDQ0NOCXX35RGp+fnw9dXV1FYZKXl4d58+ahb9++iI2NRVBQEPbv\n34/333//hS5run37NkpKSpT2xaFDhzBz5kzo6Ojgs88+w6JFi2Bubo709HRFkREcHKyYE7JkyRIE\nBwcrxs6YMQMAMH/+fISHh+Ps2bOYNm0aKioq1I6TiKgzcA4HEVEHmDJlisr206dPw9jYGMXFxSgq\nKsLq1auV5giYm5tjyZIlKCoqUnkHpJ9++glGRkZYt26dos3e3h6rVq1CaWkpHBwckJ6ejlu3bmHv\n3r2wtLQEAEyfPh2ffPIJkpKSMHHiRBgbGz83/nv37iku+amvr8eVK1eQnJwMqVSqODC+dOkSsrOz\nMWnSJKxatUox1s3NDTExMcjIyMC8efNaFfOT7OzsMGLECOzatQteXl6KSeNPcnd3x6uvvop9+/Yh\nMDBQ0Z6fnw93d3f07t0bNTU1WLlyJaZOnYply5Yp+gQEBOC9995DZmYmYmJinrsfJBKJUqEolUpx\n5coVJCUlAQD+8pe/KN7LycmBjY0N0tLSoKPz+Pu9999/H35+fjh27Bg++eQTjBgxAra2tjhw4ADe\nfvtt9OrVC3K5HEuXLoWHh4fS3JipU6di/PjxSElJwYoVK54bJxGRNmHBQUTUAZKSkhS3d31S02U4\nTk5OOHXqFHr06KF4r6GhQXHpTV1dncrl9u/fHzU1NUhMTERISAgsLS3h5eUFLy8vRZ+ff/4ZQ4cO\nhampqdLBsp+fH/Lz83H69Gn4+Pg8N35PT89mbWZmZliyZInidrFNZxdmzpyp1G/cuHGwsbHBwYMH\nMW/evFbF3FY6OjoICAjArl27IJFIYGhoiNLSUpSUlCiKi2PHjkEikcDX11dpP/Tp0weDBw9GYWFh\niwVHRkaGygnyw4YNQ3p6Otzc3BRtGzduRG1traLYAB6fCTE2Nn7m3xMALly4gJs3byIyMlIpTj09\nPbi5uaGwsLDF/UFEpE1YcBARdQAXF5cW71Klo6ODbdu24eTJk7h69SrEYjFkMhkAoLGxUeWY6dOn\no7CwEFlZWcjKyoK1tTX8/PwQFBQECwsLAEB5eTnq6+tVFg0ikQi3bt1qMf7MzEzFz/r6+jAzM4ON\njY3ikiYAuHHjBkQikeIsypNsbGxw4sSJVsesjsDAQGRnZ6OwsBABAQHIz8+Hnp4e3n77bQCP9wMA\nzJ07V+X43r17t7iOd955B5MnTwYAXL16FZs3b4ahoSGWL1+udFct4PH8ltLSUvzwww+4dOkSysrK\nFAWEjY3NM9fRFGdCQgISEhKavS8SidDQ0AADA4MW4yUi0gYsOIiItMDdu3cRFBSEe/fu4c0338SE\nCRMwZMgQCIKgdJnO04yNjbFjxw6cPn0aBQUFOHToENLT05GVlYWtW7fC1dUVcrkco0aNanbmoYm1\ntXWL8akqVp72vLssyeVyxQFya2JWh6OjIywsLJCfn68oOLy8vBTP8mgq2lauXIk+ffo0G6+vr9/i\nOszNzRX7wtPTEz4+Ppg6dSrCwsKwa9cupYJp8+bNSE5Ohq2tLVxcXDBp0iS4uLggISHhuZPUm+Jc\nsGABhg4dqrKPrq5ui7ESEWkLFhxERFogLy8PN2/exM6dO+Hk5KRo/9e//vXccWKxGPfv34ebmxvc\n3NwQGxuLoqIihIaGYseOHXB1dcWAAQNQV1fXrGioqKjAxYsX0b1793bZBnNzcwiCgKtXrzb7tv/q\n1avo27dvq2NW1/jx45GdnY1Lly6hpKQEs2bNUrzXv39/AECvXr2a7YvDhw+3OI9FlQEDBmDZsmWY\nM2cO5s+fj7y8POjo6KC+vh7r16+Ht7d3s+dy3L17V+kyq6c1xWlsbNwszlOnTkEkErHgIKI/FN6l\niohIC9y/fx8ikUjpbINUKkVeXh4ANLv1bJNvv/0W0dHRkEgkijY7Ozvo6+tDT+/xd0q+vr4oKirC\nyZMnlcYmJiZi7ty5SmNVefKyqedpmgeSnp6u1F5QUIBr167hrbfeanXMT2s6QH/WfmgSGBiIuro6\nrF69GoaGhvDz81O8N3r0aOjr6yMjI0PpErWLFy9i1qxZ2LlzZ6u282ljxozBhAkTUFxcjOzsbACP\nJ5fX19c3O3v066+/4tq1a0rb8fS2OTo6olevXsjOzkZ9fb2i3+3btzF79mzFHa6IiP4oeIaDiEgL\neHl5IScnB1FRUZg8eTIePXqEPXv2KC5TetYtW8PCwhAZGYnQ0FBMmTIFIpEIP/74I6RSqeJWsrNm\nzcL+/fsRFRWFkJAQWFhY4PDhwzh48CAiIiIU36g/S2sfSGdra4vp06cjNzcXDx48gLe3N8rLy5Gb\nmwtLS0tERka2OuanNU24z83Nxb179zBmzBiVsQ0ePBi2trY4fPgwJkyYoHT25pVXXkFMTAySk5MR\nGhqKgIAA1NTUICcnB2ZmZpg9e3artlOVuLg4HD16FCkpKfD398eAAQPg6OiIvLw8dO/eHebm5jh/\n/jz27t0LKysr1NTUNNu2tLQ0+Pn5wcPDA4sWLcKCBQswdepUvPvuuxAEAbm5uZDL5fjss8/UjpOI\nqDPwDAcRkQaJRKJWnSF466238M033+D+/ftITExEbm4u/P39sXv3bvTu3RunTp1SWmYTT09PrF+/\nHgYGBkhJSUFSUhL09fWRnp4OFxcXAI8PtPPy8hAQEIC9e/ciMTERYrEYixcvxhdffNEu8Tf56quv\nsGjRIojFYqxYsQL79u1DcHAw/va3vykuWWpNzE+v18PDA/7+/jhw4AD++te/qtwXTQIDAyESiTB+\n/Phm70VFRWHlypV49OgRkpKSsH37dri5uSE3Nxfm5uat3s6n9erVC/Pnz4dEIsHSpUsBAGvXroWX\nlxfy8vKwfPlyiMViZGVlITw8HFVVVSgtLQXw+DIwd3d35OXlKSbnBwYGYtOmTTAxMcF3332HTZs2\nwdraGtnZ2c1uG0xEpO1EQmu/uiIiIiIiImojnuEgIiIiIiKNYcFBREREREQaw4KDiIiIiIg0hgUH\nERERERFpDAsOIiIiIiLSGBYcRERERESkMSw4iIiIiIhIY1hwEBERERGRxrDgICIiIiIijWHBQURE\nREREGvN/lzA0gmMi7i0AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10b22da50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax=make_roc(\"gnb\",clfgnb, ytest, Xtest, None, labe=60)\n",
    "make_roc(\"dt\",clfdt, ytest, Xtest, ax, labe=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "How do we read which classifier is better from a ROC curve. The usual advice is to go to the North-West corner of a ROC curve, as that is closest to TPE=1, FPR=0. But thats not our setup here..we have this asymmetric data set. The other advice is to look at the classifier with the highest AUC. But as we can see in the image below, captured from a run of this lab, the AUC is the same, but the classifiers seem to have very different performances in different parts of the graph\n",
    "\n",
    "![rocs](./images/churnrocs.png)\n",
    "\n",
    "And then there is the question of figuring what threshold to choose as well. To answer both of these, we are going to have to turn back to cost"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##Reprediction again: Now with Cost or Risk"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "You can use the utility or risk matrix to provide a  threshold to pick for our classifier. \n",
    "\n",
    "The key idea is that we want to minimize cost on our test set, so for each sample, simply pick the class which does that. \n",
    "\n",
    "Decision Theory is the branch of statistics that speaks to this: its the theory which tells us how to make a positive or negative prediction for a given sample.\n",
    "\n",
    "Do you remember the log loss in Logistic Regression and the Hinge Loss in the SVM? The former, for example, gave us a bunch of probabilities which we needed to turn into decisions about what the samples are. In the latter, its the values the decision function gives us.\n",
    "\n",
    "There then is a second cost or risk or loss involved in machine learning. This is the decision loss.\n",
    "\n",
    "What do we mean by a \"decision\" exactly? We'll use the letter g here to indicate a decision, in both the regression and classification problems. In the classification problem, one example of a decision is the process used to choose the class of a sample, given the probability of being in that class. As another example, consider the cancer story from the previous chapter. The decision may be: ought we biopsy, or ought we not biopsy. By minimizing the estimation risk, we obtain a probability that the patient has cancer. We must mix these probabilities with \"business knowledge\" or \"domain knowledge\" to make a decision.\n",
    "\n",
    "(As an aside, this is true in regression as well. there are really two losses there. The first one, the one equivalent to the log loss is the one where we say that at each point the prediction for y is a gaussian....the samples of this gaussian come from the bootstrap we make on the original data set...each replication leads to a new line and a distribution for the prediction at a point x. But usually in a regression we just quote the mean of this distribution at each point, the regression line E[y|x]. Why the mean? The mean comes from choosing a least squares decision loss...if we chose a L1 loss, we'd be looking at a median.)\n",
    "\n",
    "**The cost matrix we have been using above is exactly what goes into this decision loss!!**\n",
    "\n",
    "###Decision Theory Math\n",
    "\n",
    "To understand this, lets follow through with a bit of math:\n",
    "(you can safely skip this section if you are not interested)\n",
    "\n",
    "We simply weigh each combinations loss by the probability that that combination can happen:\n",
    "\n",
    "$$ R_{g}(x) = \\sum_y l(y,g(x)) p(y|x)$$\n",
    "\n",
    "That is, we calculate the **average risk** over all choices y, of making choice g for a given sample.\n",
    "\n",
    "Then, if we want to calculate the overall risk, given all the samples in our set, we calculate:\n",
    "\n",
    "$$R(g) = \\sum_x p(x) R_{g}(x)$$\n",
    "\n",
    "It is sufficient to minimize the risk at each point or sample to minimize the overall risk since $p(x)$ is always positive.\n",
    "\n",
    "Consider the two class classification case. Say we make a \"decision g about which class\" at a sample x. Then:\n",
    "\n",
    "$$R_g(x) = l(1, g)p(1|x) + l(0, g)p(0|x).$$\n",
    "\n",
    "Then for the \"decision\" $g=1$ we have:\n",
    "\n",
    "$$R_1(x) = l(1,1)p(1|x) + l(0,1)p(0|x),$$\n",
    "\n",
    "and for the \"decision\" $g=0$ we have:\n",
    "\n",
    "$$R_0(x) = l(1,0)p(1|x) + l(0,0)p(0|x).$$\n",
    "\n",
    "Now, we'd choose $1$ for the sample at $x$ if:\n",
    "\n",
    "$$R_1(x) \\lt R_0(x).$$\n",
    "\n",
    "$$ P(1|x)(l(1,1) - l(1,0)) \\lt p(0|x)(l(0,0) - l(0,1))$$\n",
    "\n",
    "This gives us a ratio `r` between the probabilities to make a prediction. We assume this is true for all samples.\n",
    "\n",
    "So, to choose '1':\n",
    "\n",
    "$$p(1|x) \\gt r P(0|x) \\implies r=\\frac{l(0,1) - l(0,0)}{l(1,0) - l(1,1)} =\\frac{c_{FP} - c_{TN}}{c_{FN} - c_{TP}}$$\n",
    "\n",
    "This may also be written as:\n",
    "\n",
    "$$P(1|x) \\gt t = \\frac{r}{1+r}$$.\n",
    "\n",
    "If you assume that True positives and True negatives have no cost, and the cost of a false positive is equal to that of a false positive, then $r=1$ and the threshold is the usual intutive $t=0.5$."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[    0. ,   103. ],\n",
       "       [ 1000. ,   551.5]])"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cost"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def rat(cost):\n",
    "    return (cost[0,1] - cost[0,0])/(cost[1,0]-cost[1,1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "def c_repredict(est, c, xtest):\n",
    "    r = rat(c)\n",
    "    print r\n",
    "    t=r/(1.+r)\n",
    "    print \"t=\", t\n",
    "    probs=est.predict_proba(xtest)\n",
    "    p0 = probs[:,0]\n",
    "    p1 = probs[:,1]\n",
    "    ypred = (p1 >= t)*1\n",
    "    return ypred"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.229654403567\n",
      "t= 0.18676337262\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "98.779610194902546"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "average_cost(ytest, c_repredict(clfdt, cost, Xtest), cost)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For reasons that will become clearer in a later lab, this value turns out to be only approximate, and we are better using a ROC curve or a Cost curve (below) to find minimum cost. However, it will get us in the right ballpark of the threshold we need. Note that the threshold itself depends only on costs and is independent of the classifier."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x10c3f7650>]"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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hgy6//PLAdcXFxSouLtaqVavk9/s1Y8YMFRUVdTsB+XSiUgAAAAD0IRQUFhaq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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10bd8c710>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(ts, [average_cost(ytest, repredict(clfdt, t, Xtest), cost) for t in ts] )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Note that none of this can be done for classifiers that dont provide probabilities. So, once again, we turn to ROC curves to help us out."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##Model selection from Cost and ROC"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Notice that the ROC curve has a very interesting property: if you look at the confusion matrix , TPR is only calculated from the observed \"1\" row while FPR is calculated from the observed '0' row. This means that the ROC curve is idenpendent of the class balance/imbalance on the test set, and thus works for all ratios of positive to negative samples. The balance picks a point on the curve, as you can read below.\n",
    "\n",
    "Lets rewrite the cost equation from before.\n",
    "\n",
    "\\begin{eqnarray}\n",
    "Cost &=& c(1P,1A) \\times p(1P,1A) + c(1P,0A) \\times p(1P,0A) + c(0P,1A) \\times p(0P,1A) + c(0P,0A) \\times p(0P,0A) \\\\\n",
    "&=& p(1A) \\times \\left ( c(1P,1A) \\times p(1P | 1A) + c(0P,1A) \\times p(0P | 1A) \\right ) \\\\\n",
    "&+& p(0A) \\times \\left ( c(1P,0A) \\times p(1P,0A) + c(0P,0A) \\times p(0P | 0A) \\right ) \\\\\n",
    "&=& p(1A) \\times \\left ( c(1P,1A) \\times TPR + c(0P,1A) \\times (1 - TPR)\\right ) \\\\\n",
    "&+& p(0A) \\times \\left ( c(1P,0A) \\times FPR + c(0P,0A) \\times (1 - FPR) \\right )\n",
    "\\end{eqnarray}\n",
    "\n",
    "\n",
    "This can then be used to write TPR in terms of FPR, which as you can see from below is a line if you fix the cost. So lines on the graph correspond to a fixed cost. Of course they must intersect the ROC curve to be acceptable as coming from our classifier.\n",
    "\n",
    "$$TPR = \\frac{1}{p(1A)(c_{FN} - c_{TP})} \\left ( p(1A) c_{FP} + p(0A) c_{TN} - Cost \\right ) + r \\frac{p(0A)}{p(1A)} \\times FPR$$"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There are three observations to be made from here.\n",
    "\n",
    "1. The slope is the reprediction ratio $r$ multiplied by the negative positive imbalance. In the purely asymmetric case the ratio r is the ratio of the false-positive cost to the false-negative cost. Thus for the balanced case, low slopes penalize false negatives and correspond to low thresholds\n",
    "2. When imbalance is included, a much more middling slope is achieved, since low $r$ usually comes with high negative-positive imbalance. So we still usually land up finding a model somewhere in the northwest quadrant.\n",
    "3. The line you want is a tangent line. Why? The tangent line has the highest intercept. Since the cost is subtracted, the highest intercept corresponds to the lowest cost!.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A diagram illustrates this for balanced classes:\n",
    "![asyroc](images/asyroc.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "So one can use the tangent line method to find the classifier we ought to use and multiple questions about ROC curves now get answered.\n",
    "\n",
    "(1) For a balanced data set, with equal misclassification costs, and no cost for true positives and true negatives, the slope is 1. Thus 45 degree lines are what we want, and hence closest to the north west corner, as thats where a 45 degree line would be tangent.\n",
    "(2) Classifiers which have some part of their ROC curve closer to the northwest corner than others have tangent lines with higher intercepts and thus lower cost\n",
    "(3) For any other case, find the line!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.229654403567\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "1.3912925507128404"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print rat(cost)\n",
    "slope = rat(cost)*(np.mean(ytest==0)/np.mean(ytest==1))\n",
    "slope"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "z1=np.arange(0.,1., 0.02)\n",
    "def plot_line(ax, intercept):\n",
    "    plt.figure(figsize=(12,12))\n",
    "    ax=plt.gca()\n",
    "    ax.set_xlim([0.0,1.0])\n",
    "    ax.set_ylim([0.0,1.0])\n",
    "    make_roc(\"gnb\",clfgnb, ytest, Xtest, ax, labe=60)\n",
    "    make_roc(\"dt\",clfdt, ytest, Xtest, ax, labe=1)\n",
    "    ax.plot(z1 , slope*z1 + intercept, 'k-')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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XH+NZSUfbll0dvLK9jYa2Afr648QTSTR/mGTaIuNqaEaKgABFFYCL5lPABZ8ewLUNNBUv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kSc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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10c2ac550>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from IPython.html.widgets import interact, fixed\n",
    "interact(plot_line, ax=fixed(ax), intercept=(0.0,1.0, 0.02))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As you can see our slope is actually on the rising part of the curve, even with the imbalance. (Since the cost ratio isnt too small..an analyst should play around with the assumptions that went into the cost matrix!)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##Cost curves"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The proof is always in the pudding. So far we have used a method to calculate a rough threshold from the cost/utility matrix, and seen the ROC curve which implements one classifier per threshold to pick an appropriate model. But why not just plot the cost/profit (per person) per threshold on a ROC like curve to see which classifier maximizes profit/minimizes cost? \n",
    "\n",
    "Just like in a ROC curve, we go down the sorted (by score or probability) list of samples. We one-by-one add an additional sample to our positive samples, noting down the attendant classifier's TPR and FPR and threshold. In addition to what we do for the ROC curve, we now also note down the percentage of our list of samples predicted as positive. Remember we start from the mostest positive, where the percentage labelled as positive would be minuscule, like 0.1 or so and the threshold like a 0.99 in probability or so. As we decrease the threshold, the percentage predicted to be positive clearly increases until everything is predicted positive at a threshold of 0. What we now do is, at each such additional sample/threshold (given to us by the `roc_curve` function from `sklearn`), we calculate the expected profit per person and plot it against the percentage predicted positive by that threshold to produce a profit curve. Thus, small percentages correspond to samples most likely to be positive: a percentage of 8% means the top 8% of our samples ranked by likelihood of being positive.\n",
    "\n",
    "As in the ROC curve case, we use `sklearn`'s `roc_curve` function to return us a set of thresholds with TPRs and FPRs."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def percentage(tpr, fpr, priorp, priorn):\n",
    "    perc = tpr*priorp + fpr*priorn\n",
    "    return perc\n",
    "def av_cost2(tpr, fpr, cost, priorp, priorn):\n",
    "    profit = priorp*(cost[1][1]*tpr+cost[1][0]*(1.-tpr))+priorn*(cost[0][0]*(1.-fpr) +cost[0][1]*fpr)\n",
    "    return profit\n",
    "def plot_cost(name, clf, ytest, xtest, cost, ax=None, threshold=False, labe=200, proba=True):\n",
    "    initial=False\n",
    "    if not ax:\n",
    "        ax=plt.gca()\n",
    "        initial=True\n",
    "    if proba:\n",
    "        fpr, tpr, thresholds=roc_curve(ytest, clf.predict_proba(xtest)[:,1])\n",
    "    else:\n",
    "        fpr, tpr, thresholds=roc_curve(ytest, clf.decision_function(xtest))\n",
    "    priorp=np.mean(ytest)\n",
    "    priorn=1. - priorp\n",
    "    ben=[]\n",
    "    percs=[]\n",
    "    for i,t in enumerate(thresholds):\n",
    "        perc=percentage(tpr[i], fpr[i], priorp, priorn)\n",
    "        ev = av_cost2(tpr[i], fpr[i], cost, priorp, priorn)\n",
    "        ben.append(ev)\n",
    "        percs.append(perc*100)\n",
    "    ax.plot(percs, ben, '-', alpha=0.3, markersize=5, label='cost curve for %s' % name)\n",
    "    if threshold:\n",
    "        label_kwargs = {}\n",
    "        label_kwargs['bbox'] = dict(\n",
    "        boxstyle='round,pad=0.3', alpha=0.2,\n",
    "        )\n",
    "        for k in xrange(0, fpr.shape[0],labe):\n",
    "            #from https://gist.github.com/podshumok/c1d1c9394335d86255b8\n",
    "            threshold = str(np.round(thresholds[k], 2))\n",
    "            ax.annotate(threshold, (percs[k], ben[k]), **label_kwargs)\n",
    "    ax.legend(loc=\"lower right\")\n",
    "    return ax"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
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+eCf/+pO/59bb/oqnd/2Cn//sJzidTj5266f5wt2buHPTR7l+w/upqq6Z7W6dc1MabvTS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sbTZPPmjO0jsQzjyULpQw1Ewzrr17bT1z80b9cjhBBCLGUyk7DERPQQE9lJoPTENDwPT0QXgktT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\n/D9tiRujTh4wCQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10c1ac750>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = plot_cost(\"gnb\",clfgnb, ytest, Xtest, cost, threshold=True, labe=50);\n",
    "plot_cost(\"dt\",clfdt, ytest, Xtest, cost, ax, threshold=True, labe=2);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Note the customers on the left of this graph are most likely to churn (be positive).\n",
    "\n",
    "This if you had a finite budget, you should be targeting them!\n",
    "\n",
    "Finding the best classifier has a real consequence: you save money!!!\n",
    "\n",
    "![costcurves](./images/costcurves.png)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[    0. ,   103. ],\n",
       "       [ 1000. ,   551.5]])"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cost"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The above graph is a snapshot of a run. One thing worth noticing is that classifiers perform differently in different regions. If you targeted only the top 20% of your users..and these are the ones most likely to churn so you should target them first, you would want to use the decision-tree classifier. And you might only get to target these top 20 given your budget. Remember that there is a cost associated with targeting predicted positives. That cost can be read of the graph above. Say we had a million customers. Now, at 10%, or 100,000 we are talking about a minimum budget of 10.3 million dollars. \n",
    "\n",
    "If 10-15 million is your budget, then you use the decision tree classifier on your left. If 40-60 million is your budget, roughly, you would use the gnb classifier instead."
   ]
  }
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