lesson_6/pca/l6-pca-mini-project-solutions.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Connect Intensive - Machine Learning Nanodegree\n",
    "# Lesson 6: PCA Mini Project\n",
    "\n",
    "\n",
    "## Objectives\n",
    "  - Perform Principal Component Analysis (PCA) on a large set of features to explain as much of the variance as possible in the data using a smaller set of features (reducing the dimensionality).\n",
    "  - Introduce the `class_weight` parameter for `SVC()`, to see how correctly predicting targets from a smaller class size can be weighted more heavily.\n",
    "  - Visualize the eigenfaces (orthonormal basis of components) that result from PCA.\n",
    "  \n",
    "## Prerequisites\n",
    "  - You should have the following python packages installed:\n",
    "    - [matplotlib](http://matplotlib.org/index.html)\n",
    "    - [numpy](http://www.scipy.org/scipylib/download.html)\n",
    "    - [sklearn](http://scikit-learn.org/stable/install.html)\n",
    "  - For context, you should also have watched Sebastian & Katie's Udacity video lectures on PCA prior to working through this notebook.\n",
    "    \n",
    "## Acknowledgements\n",
    "  - The content of this notebook builds on the code from [pca/eigenfaces.py](https://github.com/udacity/ud120-projects/blob/master/pca/eigenfaces.py) from the [**ud120-projects**](https://github.com/udacity/ud120-projects) repo, and derives from [an eigenfaces/SVM example in the sklearn documentation](http://scikit-learn.org/stable/auto_examples/applications/face_recognition.html)\n",
    "  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## PCA Mini-Project\n",
    "\n",
    "\n",
    "PCA Mini-Project\n",
    "\n",
    "Our discussion of PCA spent a lot of time on theoretical issues, so in this mini-project we’ll ask you to play around with some sklearn code. The eigenfaces code is interesting and rich enough to serve as the testbed for this entire mini-project.\n",
    "\n",
    "The starter code can be found in pca/eigenfaces.py. This was mostly taken from the example found here, on the sklearn documentation.\n",
    "Take note when running the code, that there are changes in one of the parameters for the SVC function called on line 94 of pca/eigenfaces.py. For the 'class_weight' parameter, the argument string \"auto\" is a valid value for sklearn version 0.16 and prior, but will be depreciated by 0.19. If you are running sklearn version 0.17 or later, the expected argument string should be \"balanced\". If you get an error or warning when running pca/eigenfaces.py, make sure that you have the correct argument on line 98 that matches your installed version of sklearn."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "===================================================\n",
      "Faces recognition example using eigenfaces and SVMs\n",
      "===================================================\n",
      "\n",
      "The dataset used in this example is a preprocessed excerpt of the\n",
      "\"Labeled Faces in the Wild\", aka LFW_:\n",
      "\n",
      "  http://vis-www.cs.umass.edu/lfw/lfw-funneled.tgz (233MB)\n",
      "\n",
      "  .. _LFW: http://vis-www.cs.umass.edu/lfw/\n",
      "\n",
      "  original source: http://scikit-learn.org/stable/auto_examples/applications/face_recognition.html\n",
      "\n",
      "\n",
      "sklearn version:  0.18.1\n"
     ]
    }
   ],
   "source": [
    "\"\"\"\n",
    "===================================================\n",
    "Faces recognition example using eigenfaces and SVMs\n",
    "===================================================\n",
    "\n",
    "The dataset used in this example is a preprocessed excerpt of the\n",
    "\"Labeled Faces in the Wild\", aka LFW_:\n",
    "\n",
    "  http://vis-www.cs.umass.edu/lfw/lfw-funneled.tgz (233MB)\n",
    "\n",
    "  .. _LFW: http://vis-www.cs.umass.edu/lfw/\n",
    "\n",
    "  original source: http://scikit-learn.org/stable/auto_examples/applications/face_recognition.html\n",
    "\n",
    "\"\"\"\n",
    "\n",
    "print __doc__\n",
    "\n",
    "from time import time\n",
    "import logging\n",
    "import pylab as pl\n",
    "import numpy as np\n",
    "import sklearn\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.datasets import fetch_lfw_people\n",
    "from sklearn.model_selection import GridSearchCV\n",
    "from sklearn.metrics import classification_report\n",
    "from sklearn.metrics import confusion_matrix\n",
    "# from sklearn.decomposition import RandomizedPCA: deprecated \n",
    "from sklearn.decomposition import PCA\n",
    "from sklearn.svm import SVC\n",
    "\n",
    "from IPython.display import display, Image \n",
    "\n",
    "# Display progress logs on stdout\n",
    "logging.basicConfig(level=logging.INFO, format='%(asctime)s %(message)s')\n",
    "\n",
    "\n",
    "print \"sklearn version: \", sklearn.__version__\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2017-03-26 15:33:32,423 Loading LFW people faces from C:\\Users\\Ryan\\scikit_learn_data\\lfw_home\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total dataset size:\n",
      "n_samples: 1288\n",
      "n_features: 1850\n",
      "n_classes: 7\n"
     ]
    }
   ],
   "source": [
    "###############################################################################\n",
    "# Download the data, if not already on disk and load it as numpy arrays\n",
    "lfw_people = fetch_lfw_people(min_faces_per_person=70, resize=0.4)\n",
    "\n",
    "# introspect the images arrays to find the shapes (for plotting)\n",
    "n_samples, h, w = lfw_people.images.shape\n",
    "np.random.seed(42)\n",
    "\n",
    "# for machine learning we use the data directly (as relative pixel\n",
    "# position info is ignored by this model)\n",
    "X = lfw_people.data\n",
    "n_features = X.shape[1]\n",
    "\n",
    "# the label to predict is the id of the person\n",
    "y = lfw_people.target\n",
    "target_names = lfw_people.target_names\n",
    "n_classes = target_names.shape[0]\n",
    "\n",
    "print \"Total dataset size:\"\n",
    "print \"n_samples: %d\" % n_samples\n",
    "print \"n_features: %d\" % n_features\n",
    "print \"n_classes: %d\" % n_classes\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "###############################################################################\n",
    "# Split into a training and testing set\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.25, random_state=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false,
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Extracting the top 150 eigenfaces from 966 faces\n",
      "done in 0.247s\n",
      "Projecting the input data on the eigenfaces orthonormal basis\n",
      "done in 0.025s\n"
     ]
    }
   ],
   "source": [
    "###############################################################################\n",
    "# Compute a PCA (eigenfaces) on the face dataset (treated as unlabeled\n",
    "# dataset): unsupervised feature extraction / dimensionality reduction\n",
    "\n",
    "n_components = 150\n",
    "\n",
    "print \"Extracting the top %d eigenfaces from %d faces\" % (n_components, X_train.shape[0])\n",
    "t0 = time()\n",
    "pca = PCA(svd_solver='randomized',n_components=n_components, whiten=True).fit(X_train)\n",
    "print \"done in %0.3fs\" % (time() - t0)\n",
    "\n",
    "eigenfaces = pca.components_.reshape((n_components, h, w))\n",
    "\n",
    "print \"Projecting the input data on the eigenfaces orthonormal basis\"\n",
    "t0 = time()\n",
    "X_train_pca = pca.transform(X_train)\n",
    "X_test_pca = pca.transform(X_test)\n",
    "print \"done in %0.3fs\" % (time() - t0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting the classifier to the training set\n",
      "done in 25.941s\n",
      "Best estimator found by grid search:\n",
      "SVC(C=1000.0, cache_size=200, class_weight='balanced', coef0=0.0,\n",
      "  decision_function_shape=None, degree=3, gamma=0.001, kernel='rbf',\n",
      "  max_iter=-1, probability=False, random_state=None, shrinking=True,\n",
      "  tol=0.001, verbose=False)\n"
     ]
    }
   ],
   "source": [
    "###############################################################################\n",
    "# Train a SVM classification model\n",
    "\n",
    "print \"Fitting the classifier to the training set\"\n",
    "t0 = time()\n",
    "param_grid = {\n",
    "         'C': [1e3, 5e3, 1e4, 5e4, 1e5],\n",
    "          'gamma': [0.0001, 0.0005, 0.001, 0.005, 0.01, 0.1],\n",
    "          }\n",
    "# for sklearn version 0.16 or prior, the class_weight parameter value is 'auto'\n",
    "clf = GridSearchCV(SVC(kernel='rbf', class_weight='balanced'), param_grid)\n",
    "clf = clf.fit(X_train_pca, y_train)\n",
    "print \"done in %0.3fs\" % (time() - t0)\n",
    "print \"Best estimator found by grid search:\"\n",
    "print clf.best_estimator_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Predicting the people names on the testing set\n",
      "done in 0.072s\n",
      "                   precision    recall  f1-score   support\n",
      "\n",
      "     Ariel Sharon       0.56      0.69      0.62        13\n",
      "     Colin Powell       0.74      0.87      0.80        60\n",
      "  Donald Rumsfeld       0.76      0.81      0.79        27\n",
      "    George W Bush       0.93      0.87      0.90       146\n",
      "Gerhard Schroeder       0.76      0.76      0.76        25\n",
      "      Hugo Chavez       0.73      0.53      0.62        15\n",
      "       Tony Blair       0.88      0.83      0.86        36\n",
      "\n",
      "      avg / total       0.84      0.83      0.83       322\n",
      "\n",
      "[[  9   0   3   1   0   0   0]\n",
      " [  2  52   1   4   0   1   0]\n",
      " [  4   0  22   1   0   0   0]\n",
      " [  1  11   2 127   3   1   1]\n",
      " [  0   2   0   1  19   1   2]\n",
      " [  0   3   0   1   2   8   1]\n",
      " [  0   2   1   2   1   0  30]]\n"
     ]
    }
   ],
   "source": [
    "###############################################################################\n",
    "# Quantitative evaluation of the model quality on the test set\n",
    "\n",
    "print \"Predicting the people names on the testing set\"\n",
    "t0 = time()\n",
    "y_pred = clf.predict(X_test_pca)\n",
    "print \"done in %0.3fs\" % (time() - t0)\n",
    "\n",
    "print classification_report(y_test, y_pred, target_names=target_names)\n",
    "print confusion_matrix(y_test, y_pred, labels=range(n_classes))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "###############################################################################\n",
    "# Qualitative evaluation of the predictions using matplotlib\n",
    "\n",
    "def plot_gallery(images, titles, h, w, n_row=3, n_col=4):\n",
    "    \"\"\"Helper function to plot a gallery of portraits\"\"\"\n",
    "    pl.figure(figsize=(1.8 * n_col, 2.4 * n_row))\n",
    "    pl.subplots_adjust(bottom=0, left=.01, right=.99, top=.90, hspace=.35)\n",
    "    for i in range(n_row * n_col):\n",
    "        pl.subplot(n_row, n_col, i + 1)\n",
    "        pl.imshow(images[i].reshape((h, w)), cmap=pl.cm.gray)\n",
    "        pl.title(titles[i], size=12)\n",
    "        pl.xticks(())\n",
    "        pl.yticks(())\n",
    "        \n",
    "# plot the result of the prediction on a portion of the test set\n",
    "\n",
    "def title(y_pred, y_test, target_names, i):\n",
    "    pred_name = target_names[y_pred[i]].rsplit(' ', 1)[-1]\n",
    "    true_name = target_names[y_test[i]].rsplit(' ', 1)[-1]\n",
    "    return 'predicted: %s\\ntrue:      %s' % (pred_name, true_name)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAgEAAAIDCAYAAACdLs1qAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nKP5/aZKwJNhW0kAAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xa3ef048>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Predictions\n",
    "\n",
    "prediction_titles = [title(y_pred, y_test, target_names, i)\n",
    "                         for i in range(y_pred.shape[0])]\n",
    "\n",
    "plot_gallery(X_test, prediction_titles, h, w)\n",
    "pl.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAgAAAAH0CAYAAABLk5VoAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAALEgAACxIB0t1+/AAAIABJREFUeJzsvXuwZdld3/ddt+eh6Z6e6ceMRhoJZgIlCAQbURhPlcwj\nVVAGDIqMSQrsAouY4GCXZZE44aUgoEAmRTCPMsWjJCKSYIR4hJdVYJVTjl8EK3ZMBTCgCM1II41G\nMz3T3dPzlNR3549zvud+7z7fvdY+p/v23Wf291N1q0/vvc/ea++19jprfdfvUbquQwghhBDmxd5x\nFyCEEEIIN54MAEIIIYQZkgFACCGEMEMyAAghhBBmSAYAIYQQwgzJACCEEEKYIcc2ACilfGcp5W03\n4DqllPL2UsrFUsp7jvp6YXvSJoKS9hD6pE1cX8qLPQ5AKeULALwDwKd3XffMDbzu/QDeDuABAB8E\n8He6rvunN+r6YZhjbBPfB+AvA/gMAN/fdd333Khrh2GOoz2UUl4K4McAfBGAUwD+AMB/23Xdv7kR\n1w91jrGP+GcAPgvArQAeBPDmrut+/aiuN4clgPsAPHQjK3HJOwD8ewDnAbwJwC+XUu6+wWUInuNq\nE+8D8K0A3nWDrxvqHEd7uB3A/w3gcwGcA/C/AHhXKeX2G1iGMMxx9RHfAuCVXdfdAeBvAvi5UsrL\nj+xqXdcd2R+AewH8CoDHsRjN/F3Z9z0Afk7+/9cBfADAEwC+C8BDAL5kuW8PwLcD+NPl/l8EcG65\n734AHYDXYzHTvgDgTct93wjgeQBXATwN4HsBnAXwj5dlurj8/EopxzksZu6PLPf/muz7SgC/B+AS\ngN8B8GcH7vvTALwA4LRs+xcAvvkon/cu/M21TfSewc8B+J7jrosp/KU9HHoWTwH43OOuk+P+S5tY\nfe/PL8vx54/sWR9hJe4B+HcA3gzgFgCfAuD9AL60X5EAPnP5oD9/eewPAfi4VOQbAfwugFdiIY38\nNIB39CryrQBuA/DZWPz4fsZy/zcA+FdSrvMAvhrASQCnAfxSr7LeBeCdywq/GcAXLbd/DoDHsJD0\nTywbzkMAbjX3/lUA/qi37R8C+IfH/XId59+c20TvOWQAkPbQfxavxqKzv/O46yVt4njbBBaDi+eX\n5fttAHtH9ryPsCIfAPDB3rbvAPB2U5FvZsUs/38SwMekIv8IwBfL/pcvK/omqUgdjb0HwNe6ijTl\nfDWAi3LefQBnzXE/CeD7etv+hBXd2/71AH63t+0tAH72OF+u4/6bc5voHZMBQNqDHnMHgN8H8B3H\nXSfH/Zc2sTrmZgBfjoVdyJE975twdNwH4N5SyiXZdgLAvzTH3gvgYf6n67pnSylP9M71q6WUfdl2\nFcA98v9H5fOzWKyxrVFKOQngRwB8GRajNQA4XUo5AeCTADzZdd3Fgft5fSnlDbLtlmXZ+zyNxUut\n3AngiivTjJhzmwjrzL49lFJuA/CbWEwYfmDouBkx+zaxvJePA/itUsobSynv67ruN2rHb8tRDgAe\nBvBg13WvGnHsRwB8Ov+zfCnO9871N7qu+9f9Ly6t7Tfh7y2v9UDXdY+WUl6NhbFeWV7nXCnlTNd1\nl3rfexjAW7que8uIa/whgE8ppZzuuo4/+p8N4B9tWNYXG3NuE2GdWbeHUsqtAH4NwIcA/NcblvHF\nyqzbhOEmAJ+65XebHKUXwHsAXCmlfFsp5bZSyolSymeVUj7PHPvLAF5bSnlNKeUWLGSeIvt/CsBb\nSin3AUAp5e5Syuu2LNdpAM8BuFRKOQfgu7mj67qPAPgtAD9RSjlbSrm5lPKFy91vBfDNpZQHlj6i\np0opX1FKOd2/QNd178XC6OO7SykvKaX8FQB/BgvDljkz2zaxLOPNpZSXYPHe3bRsGye2LPOLgdm2\nh1LKzct7eg7A67uu2+8fM1Pm3Cb+41LKly/v++ZSytcB+EIA/3zLMjc5sgFA13VXsbB+fDUWlpwX\nALwNCym8f+wfAngDgF/AYlT3NBaGEy8sD/kxAL8B4N2llCtYGHY8sGXRfhQLo48Ly/P8dm//12Ox\nTvTHyzJ8y7KM/xbANwH4cSysPN+HxTrREF8L4M8tj/0BAP9513WPb1nmFwVpE3grFp3IX8XCNfS5\n5blnyczbw2uwuPe/iMWPytPLvy/YsswvCmbeJgoWg5jHsPA2eCOAr+m67v/ZssxNJhkIqCx8YS8B\neFXXdQ8ed3nC8ZM2EZS0h9AnbWJzJhMIqJTy2lLKyVLKKSzcOX4fC3eJMFPSJoKS9hD6pE1cG5MZ\nAAB4HRZBFB4B8Cos3DGmJ0+EG0naRFDSHkKftIlrYJJLACGEEEI4WqakAIQQQgjhBpEBQAghhDBD\nNgoEdPbs2e7eexcBjLh0cPXq1dV+ftZlBW7b3193c9Xj3FJEa9vY5Yux53b7Silr+3Vbbf/e3sH4\nip9PnDhw++ZnPa5/vkcffRSXL19ev8gEOHPmTPeyl70MwMEzc89Gt/E4V4+6je2lddw237kex21y\nL24bGfu8dNtTTz11oeu6SWaWvO2227rTpxcuzrX7dn2E9iV89tpvbPpsa9v7uGdP3Pupx7n97t1v\nHcdtm7aTy5cv49lnn51kH7G3t9exn6vVlft9cLj6cfuH+uja/rHn3qQ8m/6uuf5HcX2SK//Vq1dH\n9REbDQDuvfdevPOd7wQAfPzjHwcAPPXUU6v9ly9fBgA8//zzq23cr9t4E5/4xCe0wIf26TY9bugz\ncQ/SDUzcg3Rl0BeUDfmmmw4eGz/rw+dxt99+EFXy1KlTAAB2jgBw5513HtoHALfccsuhsvytv/W3\n1u5xKrzsZS/DW9/6VgAH7UGfza233rq2jff1sY99bLWN333hhRdW29he9Dh+HmoD/A7Pp9/R8/A6\nY7fp+fQzr+3K6O5Py8o2ou3GPS+2RT3u3e9+9wcwUU6fPo2v/uqvBnBwv+6HXZ/P008/DeCg/wAO\n6vLZZ59d29Z6tu4913e69iPt3m2+k8BBHblt+vm2225bbeNn3faSl7wEAHDy5MnVNrYJN+i5+eab\nV9v4mWX4mZ/5GUyVEydO4OzZReRc1pvr4/Xd5zY3ONIJFPfru8E603p0z063ue+4QZvb5t7j1m+K\nG/Cy/Wr/wmeix3GbPi+WQe/p8uXLo/qIjUMBszB8Ga9cOQhvzx97Ldxzzz136Hg9R+uh1AYK/e/X\nzs3vt2YT/X2AH+G1GhTRjoodgz4vVpx2IGyELPPUjTRZPr4U7tk49CXiOVz9uIGVe8H0nK3R/dgX\nmdv0Gu7cNQVHceXW77LOdZsbAOwKrsy8H+0POADQ94XPwg32XB8w1CZq77cbFLT6JJaxpQDoDxXf\ng1bfVqtjLRc/6/mmTP9ddj+WLQVg7Hs8dgCgA7hNBwB6nNZzv6x6X+73aOxkVt+L/u/D0HXHEhuA\nEEIIYYZspADs7++vZvScyV68eJAAidtUxqCEp6MYoiMWJ9+62bybJbZmBHrO/nFuFtBC78UpADq6\nJBy5UfoDgGeeeQbAYRmQx41dE5sKTobivWwzU6mNwIfW92prrUptzU9H9G6U3cKd081CeW69nlMU\ndhn3LvK+VQFwSztUEfU4vsdOOtXvundnm7Xa/jUUZ8sD+DbPcms/5FQBN8N0fVy/nU9dJSQ1BcAd\np+8Bn5d77k5taS3RbLME4Gy2XJ+k1NRNJ/e7cys8T2uZcCwvjp4mhBBCCBuRAUAIIYQwQ7ZeAqDh\nDv8PeAtF7neGdS2rbychKtw+1piiZRjoZB93Ht1PGUclJbd0UbMWrS0ZTH0pgPfD8jpJu+U2SZzc\nNmTc5c431iWz5cq1bfmHqH2ndU+u3UyZruuqhl18X5y0r9v42bkZO6Pglqum4urVGeX1jwcO2smQ\n8Z6T8WtLmXp/+p0xTL1vIP2lClc/rWfoZHrnpcF+WJdadT8/67KAW0pwbttO7m8tS9a80vRexi5V\nuvO55cSxRAEIIYQQZshGQ86rV6+ufHWpADg/XZ3ZuxG18/t2xjzOx9e56bSMftzszrkU1WaBgJ+V\nO+NG913eqyoF7tmwPBzBTnmUX0pZm/m3jNqca52Lr+CMn45rFnwU1625obqR/C4ZBvZVDfe+uHgK\nLQPgWrAUd1yLsTO5sYadWg6nSrbinrh7qZV7V9pEXwFwRtuuj2i5W7vZPPtN3eYM/pzr9bUEb3L3\nq7j2qe3BXY+/GU4BcCr0Nsagu9GCQgghhHBdyQAghBBCmCEbLwFcunQJgI/eVTN+c/LXWLnfyf5D\n53HyiwvX6453UbxafuYOJwNSznFRpFwYUz7XqUf76hvntJZRagZ41xqfexvpd0xZW2yT06J2nm1i\nU0yFruvW2qwLzdwyiBubr8FxLW2iJe+6OmrJ+O7++ExcDIGxZdiFtlFKWYsIqsaevBd9DpT2nXGf\nWwJwUf2G4rLws+6vLV9uYvBX27ZNdM/aO+BiJGyzXBwFIIQQQpghW7sBchTXMnbR75LaqHxoZE1a\nsdiJjubGZmNzriaKcw2plUHLr6PefhlrGcGmbgToRs+t7/TZdIY8lDGrlS2wX4ZWtsZaBkegnrSk\nRb+e9Xyt+OK7gjP6qhn3KWNjv7u6dC7H22RidMdtw1iFwx3n7nlXIv8Bi3L3E5zp83SR+9xsv9ZX\ntnIBDL2/fVoJo5zBImnlj1Fq5dbzqCtj/7t6DaeqjSUKQAghhDBDMgAIIYQQZsjG6YD7PowuhafK\nGLWUqmOTXmwTDc5F+VKczO+knVYyiJrBh9vncsvXIuBN2QhQ4wA4udxJfvpdUpPUxvqBu+8OXc8x\n1uiwlQikZlDUKuOLjbG+/GOXaZwBFJ/z0JJCzWCu1W9sGiGu/7nG2Ofg9o2NFzAV+s9RpX0+b2fI\n14rC5/rjFpsa6bplgdYS0yZ9SP87LoKkPq9aAqgsAYQQQghhFBsbAfbj/atxmzNs4cjGGfe543R0\n5OL1u5GPG4G7uP9KLbXjUIxmN0twLoa1Mrr701k+UyqTqY/yx8T2b80A+/sAH+O9FfVtU/eoljtP\ny+3HuY2yTt1MZSh+fP96LxZqRoCtCIhjUzFv46q5qcFfaxY4lrHufa3ZZN9YcMpqUilldd/OuM/1\nua5vbhn3ERdlT4/js2tt6+/Tc7fUy9Z+d5y7tjMMrOWw2SZ9+YurxwkhhBDCKDIACCGEEGbIxkaA\nlB7cEoCD8oTKgE7GcDEEar6yrkx6bGvZoGagNmTU42R8Z0zo/HlrMl0t2cPU/b/7spiTyFvLGLWI\nV63vuraxTTS+WiQ4Z9jYOvfYeBUtYx63jLIrOCPA2jKbk063eYeUWpvYRtofm4jHGSq2cEsmbomx\nlSZ9avSlbDX4oyF0S9onraiLLeNK16/U+tqxBsdDyztu2aBmcDp2ScFF20064BBCCCGMYiMFoOu6\nldufMzhwrgzODVDPN3QO/exyBuhnN8Jzs7bWiLnvgjd0L86AxMX51vPUXAw1NSXvmdt2RQEYu2+s\nQnMt1x/rYtaapW9jlDfWpejFihp9EaeYjY2e6AwIN4ksWHMbHptnYCgyKGm5hrpybZrG2EU23YX2\npJEAiasTfcY1V7eW4ue2udTjiuvXa/1Gqx5bEUZdBNH++fQ6zuhcowTy93Gb/ioKQAghhDBDMgAI\nIYQQZsjGSwBOciOUOVTSpmThJBJnGKgSvzM0dCl2x8p2Y+XdoShu7pw1SdstCzg5Sq/BZ+f2TY2u\n60b59bdiQPSPH/puS+6tlUVxxn3uu7VYEf3P/XMrY59N7by7FCOg/3ydwV+rjlziFWcA1Yrg6AxR\nN5V33bahFORO8q1F/HTJ1FyEVFfWXaFv+NlfEtBjgPHRPccaD49danZJg1oG5rUy6LnHxsZxCaOG\nyt0v61axKTb+RgghhBB2no0UgFLKaobKUZyOSJxrnTNk4IhFZ/v8Lmf1APDMM88AOKwA6Hec62DN\n5U9HnrfddhuAw8YUbtSn16uliFTciIznaRme7YrxH+nP1FppS8e6UbnRtlOJnIGobqvlUnBKj2u7\nQ6N7Z5TG8moZa2VtRfRqGaBNkf6Myiktbuarz2xT49JWVEfHWAO8lsrkIjy20tayLxo7w9Q2xkh6\nu9BHaL4Qh1N1aGg+dpbeMtTW/S7PAD/r7wN/F1rpgFtGhzVV0qmbSs1Y1R0XI8AQQgghjCIDgBBC\nCGGGbKQvnjhxArfffjuAA7nOGei1DGlqBjdOJh2SfB081qUpVinIJeJxMrbu5zmddDyUQKh/HicN\n6rIHn4OLADZF+vJTKwlOLcGOM5Jyz1rrRJ/dc889t7bfxYWoyYmtyI9uicAZeyrc72Rvlya7Va6p\nU4tm6Zb6WG/al9SSQSk1n/FrpdZWW0aHraiP7Es0MU5tuWqX6r8Py06JvZbEB/BLHM6AsJaaWbfp\nM+YSqxqq85y67eTJkxtdbyj5nPu9qsWz0P6DbcQZxrtloiwBhBBCCGEUGQCEEEIIM2RjL4C+9b+z\nsHRW0bqNUp/K9M5bgNLNkDTmlghqMpo7zskmQ6GH+1LWUBlrkq8+L55nl/2+++3AWXyrBOdkfBcr\ngt/RZ8htQ0kvnLzc/27/89B9KE661e+0fP/HWpg72Zv3P/WloBotrwwX88L1G9uEax4TtwTwS0Vu\nGYptx4Wu1e2ujTo52FmraxurxVHZBUop1X7ceZNRilcPLUryLsZMa4mm5U3AMvAa+ln7en52ywJD\ny4CsS7ds2Upsx/PQG07L75LmbRMaendaUgghhBCuGxs7GbsRD+HISEdANPDRUS0VAO4D6tH8hgxl\n3Iyvb0QHHB419s/tRvc6wnPnbsUd4H79LsvaipLo4gVMlVLK2ojaGVC6GZvWjxvBjk1w4YwOFTer\ndmlJXXSysbhy12a6gDcw7Jev/3lX6MczcO+LPm93XK3elFqcES1LLT5I/zNxqpBT7Vqpzp3xmGur\nTl1gf+CUgm3Sv95oVAFws1c+G53t09D89OnTq238zPgtwHrU1CFaEVlZvlOnTq228bOqAry2+z3R\n8+nv2tNPPw0AePbZZ9e+o23IqZL9NMrAwbNTg1lnjDyWKAAhhBDCDMkAIIQQQpghGy8B9CV2Z8jn\nfOd1WcCFbO2fQ68xFJqXcpxLnuFkOSejqRTJ/XpPej3nQ0pU9qE800pk4+D1dkX6HZv/nNR8/hW3\njEIZzS3L6Hm0Pbj2xzK4unc+604u1O9re2DZXBwKLXdtaWMXln+GUMnXGdaNbdctX/H+NhdnBPDS\n6thkM+54lV7d/ppxtDP4bCUbqxmt7QKllFXZ3bIO70WlfcrvLky7brvjjjsAHH7/XH+k12M/TWle\ncUs9vAYAnDlzBsDhZQGi/Yu2Ed7X5cuXV9t4bRczRGHb1Xtmv6LbtpH+SRSAEEIIYYZsrAD0jW6c\n4Y4aPPCzjoqcsQtnRy6Km46w9dw6qiJutM1zO3c0PfeVK1cO/avfBQ5GgArPqaO5WspbPZ8z+ONo\nliPVbVw7bhTqFtqf9elnl/DEGba4RFA6Uue2ISPMa0md6ozJnAGZG207NUNnafy+vgOqEJBaspFW\nBMypoDO+2mxM79+19ZprYEtZG5uy2c24XYQ1LWvLZZfvgfY1vI77rnNZdSmpdcbXv+7UFaO+4a9T\n1PT+nLsdVYE777xztc0ZBtbckYGDvl3r2f32OIWG11FVoGXE6xQHlkHrnp+1j+BnvRdnBKjq86ZE\nAQghhBBmSAYAIYQQwgzZOg4AJRSV5J2USwmFvp3AgWRx8eLFte+q3EapROUTvR7Po/6blNBdXnqV\n5Xg9lfv5+cKFC/Z6lJ+ccYozDnOSppMGdfmA1+PyxrUYeBw1avDF+2slb3F+0i6iYy0xzpDc64w9\nKduphO6WiZxBjkss5aQ33e/u2UnOzjfcLY/s2hIAsB4TwuV6d4l/HK3lENbRUG51fnbxPlyCKLeU\n11pyUJnYJURzEetcf8BzOwNSPV8thsSUcZFU2Ze6OADnz59fbePyq8YGoDGefpfPcyhOA3+btO5Z\n5245Rg3+uATpkgsNRR5kOfQ3itd2hvEa9c8tV7gljmuJC7FbLSiEEEII14WNFICu69Yinelo3Llc\ncfSiIxvO/FUB4Hd19MvvOCMywBuQuBElR3F6HjdLd5EFVX1wMZw5wnOpJNU4hdvczEDh/fO6UzYC\nBNYNkHQU6tzoapHZnBuOPkM+C60fbS8udrvb5nDR6HhuvZ6bfWoZ3AyxFodez9eKY74LqCrk3GFr\nz6KVCpXnc/3LkOsf25lT7Zw7nZsZurjrijNcVtyMneVxiqBTJlyb2JWU4cTNzp07NvvSoXwL/W16\nnHP7VCM5KqzOUN2l3XV1od9lv65lcH2gU/VchELXt7n2rjg1cSxRAEIIIYQZkgFACCGEMEO2jgTo\n5FhKdCqZcZv67FP6VymFcr+TU4dSudIgRA0MuU0leZbVpfZ18qSL9tVCJSonyXG/k6hc2uBdiwjo\nIjA6Iyon2VL+0+fgovo5A08n0TkZ1+GM07T9uVSebjmqZSQ2NtFM/7x6L7ti8LW3t7fmx61t3snh\nbjmR9aZ9hFuCc3Xu5HInO7u4IC6KpCtXa1lAcWWoyfbOiMxt24U2UUoZZaSm7wjrvJUeur8P8Ea/\nly5dWn1+4oknDv0L+Mh8Lq4Jz6ltkr89aiyoyxnOcLVmzKrnZrl0CcOlB3cGpWOZfgsKIYQQwnVn\nawXARQJ0kfmcaxZn6Tra5shNR2GteNmMyKTRoehu4RQANytzLkM6C9TruRS1NaMmNWBzRj/O2MVF\nQJsqpZQ1oz43qq3NgPWzi+Dm6t7NhvSzmxk5NzpX984I0KX7BbzbXk2xcbNQh4trvwuzPWBRXrZr\n1n8rBfbQefR4oP68tX9xUeW0DNzv4vUrfOYuaqNzP3Xl12PdjNapf67+XX+wK31FXx10RpNqJF5L\npe1SPevxTklWt+6PfOQjAA6rAlSWXN4XF8XSGavq9bT9uX7RqYQsg3Nzd27GLUVrLLvRq4QQQgjh\nupIBQAghhDBDNloC2N/fXzP0c8YNTt5zspxKFs5Yy0l+KtU5f3sXB8AtV7jIX30DvH4ZnXGgk45Z\nRmcYouWizOR8gXeFvjGlS5yjz7MW7a4VRbBl/MTruSUV/Y5bmqiVQQ1d3XdayxBOvtx0CWDq8SCU\nvn+289NuJdVxiWPcu8ZnNWRg53zFXblcTIBafzZkBFhbAhor1Y81YtylZaEh3DIbcffnfOc1VgvP\no4ZzTz755Oozpf9WfBe3rOOWbfiboNK9S07Uih/B56Dl5ja35NXqX8ayGy0ohBBCCNeVjSMB9qPl\nuRjVznDCjcpdDOfWTEdHOZxB6+jdxfnuX1c/6za6dAylm60pCa6MbkTp1Awt/zajuONCU7+6mSq3\ntWLbu1wAtec1lJ7XRRl07kNuZudc+lwkQGfA1EpD7CJ1OcXBuVL2j586ahjqZt9OkXH9hjN+c3XU\nSsfav+5QuXgdffbu3Xaqj8sxUssJMVQGp0yMiW45ZSNA7SNcvbj+wBld9o8HDmbf+rycK7dLKe7c\nS11ba+WQcDlsVAFwrsuuX6wZgA65uBJeexv1OApACCGEMEMyAAghhBBmyEaaQSllTb5wEYlURnOy\nLWUaJ204n0cXGQ04kGTU2G6shD5WGlactF+7nouA56QuZ4DkZNGpoYlfnFFobQlAn3s/wZR+10mk\nzjBMcfud3O9kaCcVu9TFeh5XR+69cEsXYxP/7GI64Jp83YqKV3s/Ha10rC0ffLf8UpPzWxE63T25\n5VIX6dJFKHTn3gUjQNdHuH7fLb0p7l3kebX/dMbd7l1UaktvbnnH0Sp/awnKLS+4WATuvYgRYAgh\nhBA2YmMjQI5AnAGei9Tm4vC70TMNMdTgyo1wnRudm1m1DP7crNPNDNQtg4YlzhDMGaO5dJZuhqHX\nc0YjU6av+ow15HLGby3DuZbrWG1W5kbHrp061zCdBejovmX81y+jmwm3XCSd69uUUSNAl89hrLJV\nc/lrzfDdc1ZqET1dtFBnrKW0jEldu3Wz+JpiMmUlsEXNUNgpxO59cWpizZjT9f/AQR/u2pBTbF1k\nWj0fDf6GokqONfZ0BnzOMNUZHV7Lb8Vu/MqEEEII4bqSAUAIIYQwQ7ZOBlRL6+n8uVXad37f3O+M\n/JzEo+d2RnlO9nEJGVxUJ5VX1J+UZXQ+7E6CdD7xzmCllt50ytKfk3udcZeT6Vv+9DUZcEj2r8mv\nTgpuGYC677p0sa79bVNvLlkK29euxAEA1g04dQmF7b9lCObqw/lFt9Ilu2W9Gq3oc26bk5PHJohq\nGZDWlkxqiYKmgosV4vz2XbwP1250CZiJ31y6aeWpp55afXbLUq5+XRlc2l0uAWiyqdayZW0JwNWl\nK9/1WiKOAhBCCCHMkAwAQgghhBmy8RJAX5JyUp5LcqChFxmOUeV1SkAaRpGyndsGHEg7Kh/xWCft\nuyUALYNbAtBlCu5veR3UQpE6idl914WGnCL9dtAK+9ryj+5/V9uXO26s1KqwTrWe+dnVtx7XCgPt\n9rmlCRefwMnV7rgpo5Iv/9VlL34e6xng5HDnJz8ULtW9R9zmluPGyuktCdZ5GGzzLte8YaYs/Sv9\ncrolALffeeTobwHjv7hQ8EMJmvg+6fPk0q7WGZcaVNrntc+cObPadvr06UPH98/jlrdcmOGah5Pr\nD8b2oy2iAIQQQggzZGMFoG+w1fK5JU4V0Nk3R3u6zY1+XUpelyBCR0i1mZyblbnZCXAwO2xFNeRn\nZyzoyuAM4nYlDkDfMK8V8Yqf3fNyiTDcDH8oPTRH60PJnIgzViVOOdI210ryUqOV+Me1yV2Z+RON\n/ObeJ5fKtuYnv4kC5LbV0k/ru+hmna587t0emyJacYqDUwTHxguYKqoIOQNJ54PvDPA4w9aorzQC\ndKnmXcRA/c4999yz2ubiQjAxnCoAPI8z+NNrKC6WhFMESasvqfWL27AbvzIhhBBCuK5kABBCCCHM\nkI1DAfeNklqSxVi/Wid/UQpSue3ixYurz05GY7loaAgAly9fBnBY8uW5KQkBB0YeWj6Vf1kO5/M/\nNrmJCzPBEs8qAAAgAElEQVTsjJooZe2KoY+rPydVOnnPSWHO+M3JaU6OU6MgyoPaHpyxqjMUdaGa\n9XMtf7uTnFtJkVrJqHaF/nNxSU9aUrsz+BubIEjfGRda2y1DuLLWlo9cu9TPY328He64WnjgKfcR\npZRD72gf9skuuY1+j++xGgFyv1sCcKHZgYP+vhWu1xmruhgfbgl4KHlY/3puqc/1Ea3w09fSb0QB\nCCGEEGbIxkaAfbc3N8N3RlhuZuhmBjrC434dRWlyHo0u2L+ec/HSkZtzF+EscGg05ww93EyGuGej\n52P5XYKZXRjdO9xs3ykv+ixrLnFudqXPVc/Dz7rNpRBmeWrul/p5yKjQGZg53Ajdbds1A1DH3t7e\nWpKuVoIlp6K52VYtbfSQ4aRT7Zy64AzUtkm7ez0i9rn25NSRXegb9vb2VoZ7bobqXN3YXpwC4JLP\n6bNhXzoU+dEZK9fO7RQhPbebuTsjx5ZRqIsCOjYN9dh+yLG7PU0IIYQQtiYDgBBCCGGGbLQEUEpZ\nk7ydlOdkTZWrnFEMpX/6X+q5h2R/fr8VOY1SUitpBL/r/E+13E5ecpHfWksATtLsy85TjwTYl+Vb\nBpK15R/3XFuS7FjJVsvl4gC4GA9kyDDM+eQ6A1cn0W1ar7uyLODiAKjvdi15ils+cu9GK2Kg4uIt\n8FlqX+IkX2es5eITtIyv3FKI++5YY65dWira29tbGd45mZv3oO+iW6JxSaSIi+aq53PtqvXctd8n\nLLe2G9dnuetdryRpjmtZEpp+CwohhBDCdWdjBaDvVtUy3Km5/LWiP/XPC/iYyy52vzPmcsYZjqFI\nc+47eu0aNXclHa06Q7ep0nXdquxjR7ouJahznXKzBYdTm1zUyVYbcS6GTq0YijHeP4/b1krr6Ubw\nu9AOFHX74nNupU91iqCb6TmDKueO7NQ4p9K0ogjWZlSu39PtY12yWm6ujv47NGVjwBMnTqwpAPou\n1tJnt6LMunwuzDmj12i5ltd+M5z656K5ujT2/WsTZxRai+3fMhSupTxvsVu9SwghhBCuCxkAhBBC\nCDNkoyUA9emkDOIMHtwSQEt+d8lynOTnkkGodEMDDSfnqNQ+1pDGJSdqGfLV/HjdsoeThndB3tvf\n3189Hy7NuCWcliTrDKtq8ruT+HW/aw+tZRaW25VhSM51PsA1f96Wfzev59rDlNuBon0En72LA6BL\nAGNl0pqR8ZAhpkv04owO3XG16KRDsShqcQlaKVxrfaUzot6FPkKXjWtxAJSasac7ruU77/prTU/v\nlg7Zdl2aaZfSXXHGni4OxdiUvq02WVtGaREFIIQQQpghGysAHLm7iF4uXaIzynBpTzlDUxcLGgw5\nNyLAuwG6mO48t7oTOuMN55Liog3qfp6nlTKW11EjxppBx5RH9USNAF28b3cvbrTtZkg1l6mhGbk7\n91gDPJdHwM0wXLldZLCWseBYQ6BtotEdJydOnMDp06cBHLzz+t6xnbiY7s4tyrngudlUq470O2yz\nrVS8LtWrqw9tq841dCgqXb+szmXalaFvMD3lvkJdx2tpcFsuss69u5YvQuunpc66fAS1tOWtNqJ9\nYS3yZSuPhWtrYyMLjmU3epUQQgghXFcyAAghhBBmyNZGgE8//TQAbyTh/CBV7qA06OQcJ5e5JA16\nbCv5gvM9r0WkG4pjQClTy+hSy7oUkLX4BioZ8fMuGPgAdVneLQE4Qxonzzu5zRlJObnXGYGN9a1u\nycJKLcmPkzSdtKttqXa9XVkC2NvbW0Xz5PLZlStXVvvdEgCXxVpLADVjWSfDK26JoFXXzuCvlhBG\nz+naZatea/2UWwLYlZTh/XfLPeOWIa3zy3eppd2ygDPuc7TSQ7ttzqjVyf0K76W1LE5c/+mWrhMH\nIIQQQgij2FoBcKP2Wmpc54qhoz4XUc/N6PR6zuCvdm1n5OFcxoYiknFmrwZ/LoqUO4+bDXObPjeO\nJKc+qgcW9TfGyK41OxvrHsT24ka/ur9lOMZRuzvOzb6UsQZ/LVXKGaHW6nyb0f1xoH0EFTM1fOXM\nny68wIGRryp9/K4+x5pxbWvW5Rj77BWnVDqjYKdu6v05A2BnMMZt+t2+IfaU+4q9vb2qGyBxCsBQ\nimfi3Dn57IaMMF3/6s7Tf8Z67laEQtc2WobQTiUk7ruOpAMOIYQQwigyAAghhBBmyMZLAJTw6OvL\npECAl03csoDzr65FvBpKuENZyKWDbEVr6p9DyzUUtZDlpQGklk1lHyfvOYmZz0nlUDWO2gX6kpVL\nptOKWjVWundGRPq8XBwKtgc9jvKyyne1RCVDywK1yH1DyxSkJoeOTSQzRVy0UPYVAPDUU08BOJz2\nm+1flwr0HSM1v+ghybcmj7dSubr6r6UNVlpJwmr9gZP79dnw+br4CVOjlLK6HydzO7m/tgTgtjkZ\nfkgOd4bezpjQLQu4tkaJ3y0D6XVa8T7c9Vy/UnsO2/Qb0205IYQQQjgyMgAIIYQQZshGSwCa67sf\n7hMALl68COCwXOVkLRe+04XQ7B/f/w6vrdINr62SjPPBp1yi5a8lctHtzn9TcZIS77+1BLBLiT4U\n5x/tZK3afbUSo7iwrnpu5yXg/LLppaHLV/ysPuu1MNZ6blcGJyc6DwIXL2Aba96poMuEvA99ppcu\nXQJwuM3fcccda9u4VKB+27XkSy7RF1BfNmhZXNeSrAx5P41d/uv78gN1uV/Dofc9sabeR/RlcPeM\nW55CLkGQC8Xt2oNbnnVLAC7Wgos74N53l2xs6P6c376LOcEythKejY114ogCEEIIIcyQrY0AOSq5\n8847V/v5+cknn1xt46he0y9ydK+za+ef2TLoYxmcsUQr+hP360iqZbhTS97jZh06uudz09E9VRQd\n3feTUEx9dE9qs1c30nWjbTcbbvkCKy7egzPSYT3rNpeOmp+1/Yw19nTbXLyAVuKiXfH/J5owjPeo\nRoDsI5wCwH8B4PLlywDasx/3Hjsj3lZa1/7xQ7iofm5G2Ioy6Aym+VkNVvmcdNsu9RGllLXyjY3A\nqLi+2c2+XWKfocih/fK4d829x86Aecjot5ZkyilCilO8XGI7/k66CLQtogCEEEIIMyQDgBBCCGGG\nbLwE0E/0oZLKmTNnABz28aU/r/rqq1FQDWfc4IxAnIyq8p4L4euMzJwBl17Phfl0PpiUt1Xeo8yv\nMiefky4L7FKub6WWt35sfnOX/KMl07rloVayDj5vldFc/u+aYarSqqOaX7Nru86Q0kmXU0SXCVkf\n7BeAA2NLfQ+4RKBLBXxfdOmw9v4NtQkyVgZ2Rr8teXpsDndnUKbvPj+r3M/P2peQXYgXoXEAWH/u\neeq91JY2nLTvltbcMoNeb2z8BRdi2i0BDIWsdjJ+Ley4+65K+3wf9L0YGxLfEQUghBBCmCEbuwH2\nR0s6i3KzXBr86XFnz54FcHhUpC5ZxBllOHeLVkrYlmFIH6cK6PfdiFNHmS4iHY15VB3h83Kj0V1x\n8SE1A5lWAiQ+L1ePLbfQ1vWc0WHN7aeVrKg1Q3Ruh8591M323QzXpa6dOnxv2YbV4I+fnfGwKgVU\nAzQioEu85XAqoeLalFN7as9+yDC5Zijskn45gz99XjzOGZntQptQ1/FaBDyFz92pHmqQ61RcMqTa\njEmuBHhXbqcS1ZQ8LVutzSnOzV1n+/ysv6fOXXksUQBCCCGEGZIBQAghhDBDNl4C6Bu4qcxNOYsS\nP3CwBKAyBiU/J+c7v18niernluTiZOCaAYniJEQtt8v1TelT5X4+G7fN+clzeWDqiT760f5aUqVL\nENT3G1dcexiSPp1vvUs85YzAnEzbStDB77g24pJMabuhlNlaAtjGt/e46RvmuT5Clwkp/Wu/wW26\nNOjq0sX4UMYmmnJtp5Z4xS0zAd5QjPudIarGAGHfoNt4nEtSxD5nyksBmhyK5XXG2O79dMuqrbgg\nbhnQLUG65+n62lZsEreU536PXHlcAivt7yjzMyYGcPA7qu8Ff2MTByCEEEIIo9jYDZCjOY5OdDTk\n0n++9KUvBeDdedQQiNs4mtFthwrciJ7kZmVudE9aLoRu5uAijakxjzOGdAqAKz9Hun1XqqnSHz2P\ndfFRxYT73baW4tMajbNNunj+bqTeMt5quSG5WOTODdVF7+LnVorqKdN13dosWZ8f27XOcl0kQH5W\nl2GXenvs8xky7O3jDDqdYaCL/qffcUauzihY+0r3bKgAOEO2qfcNwGG3UKJKLN8DfTdcPev5iMux\nUXsngYN3zKk/TtltqQK1nB56HWdw7KLaahk4y9ffThrFqnEs35EYAYYQQghhFBkAhBBCCDNkoyWA\nEydOrCRsyg0qtVDOUqMFGvZogiBKGyp1OamW0seQkUstMYeTaZyU2zIgbBnY9I32gIOlDZU0XRIU\n5+faN5iZsoGPwy2Z6H06ydZJ8k4mc8sCrfM44xoajuk2FyuiZjCk5dEy1pIPtZYAagaLu7IEAKzX\nsd6Pk8OdQRw/65KZi3jGz+oX3fK5dkt9Y7/rtrUSvbh4COwPXNQ/t3Soz8tFzpwqGj2Wz0bv2cnX\nfD9V5nZJd1xU0VaSN75vajzK/mlset4W7jwuxoDC+9NyObmfS+S6VM4U29sQBSCEEEKYIVtHAuSo\nTkcnHOnqCJYjsXPnzq22cWbvRkoKR+g6cxob57sVwc+lf63lBwC8yxFHc250f/78+dU2qiMuApWL\nNsVyTXnm51J99vcD9boFDp6hjtqdEQ4/DxkB1gxyWvkbtnnOtUheem6nOLhU1s5dsBYRcYrs7++v\n7o3tW+/RqUI8TuPis3/R94qzfG0nLjqgu57i2oTDxfWvRXYDDtq6uxeNdOhc/thvqmEg71/fIZaH\n7WrKbUONAF174H25ePdqAMrfmVa8/lq+CMUpxC4qYcuF2bk6636X4pn7nUrojPvcNp31U1F3inKL\nKAAhhBDCDMkAIIQQQpghGy0BdF23kp1qPqgqd1A2UfmL0odKN5RznNz26KOPrrbpcoCTe5y85wy8\nnM81GfIh5blVlqRsd/fdd6+2UfrXZQ9KXSpVOgPCvqHblOU9YF0Wc+l0XRQs59esSyxOfnWpcZ3R\noVKL+jbWN3wIZ5RItJ7dEoCLfOlSbE+9/vvs7++vJEtnfOvqn+3fxdLQd4P7W8smLb9vtinnP94y\n9KqlnNbParzojIJd6l+XMKzvQ6/l5r1NuY3osjHvT5+xSwTGJYCLFy+utvGeXTIgbSM839AzcdE9\nXeKfWsKwoXTxxEUw1G0st94LZXyV+10cABr/qaE924FbwmgRBSCEEEKYIRspAPv7+4dGI4A3pnCu\nEzpKu+eeewAcHoHzODeDdO5TwMEISkdSzsDLxUgemyJYy8MRrBr3vfzlLwcA3HXXXattvFc1BHKG\nJhwZ10aZU3cDrM2cagqAM65pzRSdwdBQzO/a9ZyBoYvhToYUoVquChd50EX4U/c1Z2S6zaj+OLl6\n9epqlsJ7VKO2Wr06N0CnAGj9850cUgDYXziV0LmPtaL6Obcufc9dhD/O/PW42nnU5Y/17yJG7oIC\noLD+XO4ErTMauj322GNr53ARAxX3DF10UpcevGVg6NQDFy3S4ZQ+vRf2Axrjn2qAc/nr/w73yz+W\nKAAhhBDCDMkAIIQQQpghGy0BXL16dSXPjE2U4SIqUapQWYTfUWmDx+lSgUp5Fy5cWCtjzbhPqfmU\nq0SlUh6NeZjgCDgw/msl8OA9uOURPa7vEz31hB/959yS5ElLMnPfdUY4rdSbtchuTspzz9stFQD1\nqH/OD1mPc0lQ2Eacz3erPU+Fq1evrmRK3o9LjetkWZUw3TKak2X5HX3/tL9wCWM2jQLqrueM94CD\n/kK3OUOwvmGc3p9bGtVy9aPZTb1t9JdXtJ5dP8zosZos7oknngBw2EiOvxX6bGhs7pZb9Nq6RMDP\nzphT635sgiBnAKzvOcuty38u9gGlf/X55/1rX+KWJsYy7V+XEEIIIRwJGxsB6kgG8CkbdSTFEa7O\n7DmCUtdAvQbhiObee+9dbdORIs+tRhIcVbmoa05J0JGbi1WtLjksr7rzuGh+TgFwI103e+m7n0zZ\nCFBTv/ZTwOpnNxt29+Vm5E6hGZqR94/Tzy3FwUX5cjNTPbczMGMba7kBukiAxLnC7kLcd+CwEaCD\n75ZTBVozbXdcLfY+4J+fixdfa79OhdAZpJbR9QdEy81+Rb/r4sE7BaiWLndq6G8G/9X+jvXslFb9\nfWCfqzll3Oyaz0n7aFVZ+DydwZxTs9276L6j7csZ/Gmd8rdQDf74zqgCwM96nFN7XIr1sUQBCCGE\nEGZIBgAhhBDCDCmb+JCWUh4H8IGjK04w3Nd13d3tw248aQ/HRtpEUNIeQp9RbWKjAUAIIYQQXhxk\nCSCEEEKYIRkAhBBCCDMkA4AQQghhhmQAEEIIIcyQDABCCCGEGZIBQAghhDBDMgAIIYQQZkgGACGE\nEMIMyQAghBBCmCEZAIQQQggzJAOAEEIIYYZkABBCCCHMkAwAQgghhBmSAUAIIYQwQzIACCGEEGZI\nBgAhhBDCDMkAIIQQQpghGQCEEEIIMyQDgBBCCGGGZAAQQgghzJAMAEIIIYQZkgFACCGEMEMyAAgh\nhBBmSAYAIYQQwgzJACCEEEKYIRkAhBBCCDPk2AYApZTvLKW87QZcp5RS3l5KuVhKec9RXy9sT9pE\nUNIeQp+0ietL6bruuMtwpJRSvgDAOwB8etd1zxzD9b8IwP8J4C1d1/0PN/r6YZ3jahOllIcA3APg\n6nLT73Rd9xdv1PWD5zj7iFLKGwF8C4CXAvgggNd1XffeG1mGsM5xtIlSyicD+A+9zacA/Hdd1/2D\no7jmTUdx0olxH4CHjunH/2YAPwbg39zoa4cqx9YmALy267p/egzXDcMcS3sopfxXAL4RwFcA+CMA\nnwLg4o0sQxjkhreJrus+COB2/r+U8h8BeB+AXznKix7ZH4B7l4V/HMCDAP6u7PseAD8n///rAD4A\n4AkA3wXgIQBfsty3B+DbAfzpcv8vAji33Hc/gA7A67EYQV8A8Kblvm8E8DwWM66nAXwvgLMA/vGy\nTBeXn18p5TgH4O0AHlnu/zXZ95UAfg/AJQC/A+DPNu7/2wH8IICfBfD9R/msd+Vvzm1Cy5+/ebeH\nZXkfBvDFx10HU/uba5swz+G7AfyzI33WR1iJewD+HYA3A7gFi9Ht+wF8ab8iAXzm8kF//vLYHwLw\ncanINwL4XQCvBHArgJ8G8I5eRb4VwG0APhvACwA+Y7n/GwD8KynXeQBfDeAkgNMAfqlXWe8C8M5l\nhd8M4IuW2z8HwGMAHgBwYtlwHgJw68D93wfgvViM6H4WGQCkTSz2fRSLTuTdAD77uOsk7eF42gOA\nT16W6Y1YDAQexOKHZu+46yVt4vj6CDlfwWLg8g1H+ryPsCIfAPDB3rbvAPB2U5FvZsUs/38SwMek\nIv8IMlIG8PJlRd8kFamjsfcA+FpXkaacrwZwUc67D+CsOe4nAXxfb9ufsKLN8b8O4GuWn38WGQCk\nTQB/AYvO5uTyvh8FcOa46yXt4ca3BwCvWZbpXQDOLMv4XgDfdNz1kjZxfH2EHPMFWAxubj/K532U\nNgD3Abi3lHJJtp0A8C/NsfdiMQoGAHRd92wp5YneuX61lLIv265iYVBFHpXPz0LWUpRSykkAPwLg\ny7AYrQHA6VLKCQCfBODJruvcOtx9AF5fSnmDbLtlWfb+NV4L4HTXde90ZZgxs20Ty3v41/LfHyil\nvB6LF/033fEzYM7t4bnlvz/Ydd0lAJdKKT8N4C9hMSudK3NuE8rrAfxK13VPN467Jo5yAPAwgAe7\nrnvViGM/AuDT+Z9Sym1YSC56rr/R60B57P0bluvvLa/1QNd1j5ZSXg3g32MhuTwM4Fwp5czypVQe\nxsKS/y0jrvHFAP5cKYWN604AV0spf6brutdtWN4XE3NuE45ueY25Muf28CdYzFY72dYNHDsn5twm\nWLbbAPwXAL5qwzJuzFHGAXgPgCullG8rpdxWSjlRSvmsUsrnmWN/GcBrSymvKaXcgoXMox3jTwF4\nSynlPgAopdxdStn2h/Q0FqPvS6WUc1gYWgAAuq77CIDfAvATpZSzpZSbSylfuNz9VgDfXEp5YOkj\neqqU8hWllNPmGt8F4NOwkIleDeA3lt//L7cs84uF2baJUsonl1L+QinlllLKS0op/z2AuwCsdU4z\nYrbtoeu6Z7FYM/7WUsrpUsorAfxNLIzL5sxs24TwVVgYEv6zLcs6miMbAHRddxUL68dXY2HgcgHA\n27CYDfeP/UMAbwDwC1iM6p7GwnDiheUhP4bFj+i7SylXsDDseGDLov0oFuuwF5bn+e3e/q/HYp3o\nj5dl+JZlGf8tgG8C8ONYVM77sFgnWqPruitd1z3KPywazjNd1z25ZZlfFMy5TWDRgfzk8rgPYyEl\nfnnXdU8MHP+iZ+btAQD+zvI+HgHwfwH4eQD/85ZlflGQNgFgIf//b93SGOAomWQgoFLK7Vi4TLyq\n67oHj7s84fhJmwhK2kPokzaxOZPJBVBKeW0p5WQp5RQW7hy/j4W7RJgpaRNBSXsIfdImro3JDAAA\nvA4LKewRAK/Cwh1jevJEuJGkTQQl7SH0SZu4Bia5BBBCCCGEo2VKCkAIIYQQbhAZAIQQQggzZKNA\nQHfccUd3zz33NI9zywpjlxpKqcdF0f3u2E23jT1+E9y97u/vr+3jNv6r+/nvhQsXcOXKlUkGi7n9\n9tu78+cXcTfcvezt7R36Vz+36tHBZ6LHH+cS1rW0P/e8rl69unbcTTctXtETJ06str3vfe+70HXd\n3ZuX+Og5e/Zsd++9iyBntfvWe/3EJz6xts29L67+Ha5NjG1jY/uXoeNa+8cc547Xe+of98gjj+DS\npUuT7CNOnz696iNYXq1n1+ZdHzH2N+Vafntax/X75n4Za9u0D+T+1jb3HNgPaH/gjvuDP/iDUX3E\nRgOAe+65Bz/8wz88WOBap8aXHPAvcu0BuG2A7xxrD6h1nNs29odKG4Xr0J5//nkAwMc+9rG1bc89\n99xqG/fzHG9+85sHr3ncnD9/Hm9605sAAE8/vYhY+eyzz67233bbbQCA06cPYl7ceuutAICbb755\ntY2faz8Y+nmoY2jtr6HXqdFqf7y2a38K65nPDQCeeuqptTKfO3cOAHDmzJnVtte+9rUfGFXYY+De\ne+/FO97xDgDALbfcAuBwfTzzzCK76qVLBwHTLl5cRFDl/etxL7zwwmob3yfXcbqBgtLqqF0f4eqX\n2/gvcLgtczvvXbdpufkdPY7bXHtxA2t+9+u+7uvWjp8K58+fx3d/9yJmDst95cqV1X7WvdbFyZMn\nARx+rqx7N3jQ+q4dp8e69tKanLlz136kAd8e2AeyfwSAl7zkJYf+1eP0u2fPLqIQ33HHHattPI+2\nyU/7tE8b1UdsHAq4/0PtHpA+yI9//OOH9rlzKdr49UG677gOf+zMvnbcUHlcZTvcuVk5OhByA5xr\nVR9uJF3XrX7IWL/60rIB81/goDFro3YdXm20PTRrrr3crYGc+66jtZ9ttlUGHud+KDgw1M/6Qzhl\nSilrP3ja5tle9B45ANZ7dIPoTWfNQH0w4N5t7URrAwC3bWg/P9faOVDvzxSnjkyVrutWvwFEJzys\nZ+0j3ITAPTui53cTztbgns/RKbFumyvLUJ2x7l0bGZrY1uC96j3zGtv8dsQGIIQQQpghWycDchIJ\nP+voi59r69yAH8k7CXHsepuem6Or1voQR2bb2CG0RuNOGnZyIkd21zKqu5H0VR8dyZ86dWptG+vU\nLR0pYyU4N9IfWxeO1ki8JS87ZYy4WaFTTHR0796pqaPtGThcds7yVQHg/bplQre80pqBterIncfN\n0mtLh1pvbr9rE62+y5Vh7NLUVFGVkPXSVwSA9lJI7buu3Wyijrj24BTrbZYJ+9cAfBvi5/67M1QG\nfQ78jp5vLFEAQgghhBmSAUAIIYQwQ7ZeAnBGEpRiVJ5wSwA11w+VT/jdrYwbGjKMM1hsufCMdUOq\n0TIQmbrkPwTvReV+ZxBFWssxtSWmTQz1aq6DLa8DMlQnzqVtrDGPq3taALdcKadMKWV1b64/6Hu5\nAP6Zb3rfm7gH19ywnHS/iWeSWzaoGR227sUdP3bZcSq4pTtCC3Za/gMH70FLknd9eM0QWLle/az7\nHXT7nYtrq63VPFycl4N7Ri12o1cJIYQQwnXlmo0AncGf+rpzVNJSAJyRnGPs7E/PUxsVOkOTodGj\nM9C4lpG8my2QXTH+6cdOcAqAc91U3Ei3ZpjjlIKh/ZuyyWyh1oZaRq/O7YzPzl1PjaSmTt9ds6UI\n8nhnxLSNS6djbNCVsYaBQy5qY10V3baaAZsr1y6ohV3Xrb3zzide/d9r8TWc2tbqU1tqgKNmKOr6\nn6G4HzWV0f0OtgL88NrOtXYblTAKQAghhDBDMgAIIYQQZshGSwAq57iIS5QidAmgZszVks5q39Uy\nbEMtgpiTb/vb+/tr0eWGrjcmjsGUDX329vbWDHY0vKWTzGrS51hJc0gCdvEeagZh20SQHGu82GoP\nbvmAEriTAbfx8T0u+m24FRekZoC3iVHmmDLpuVv9j2MbQ+CapD/WZ7y2XDHlpYBSyto9altmf9EK\nncy+fmz/WSsP0DYcHHMOoB5SXj/rUp9bwmkt+xBn8Ke/t5sSBSCEEEKYIRsbAfaNnJwC0IrO5mK6\nj43Wt43LR21Wdq20Znq1fbWR55Rn/qSUsjLicdGoau5KtXrSzy0jTLftWgwoW1HYxtZLS/WoRZnT\n0f21RPk6LvquSi0VbWyGyLFqm+KMx2rHuW03ygVzrFHbjS7XtaCqsYt253KD1PqNlpJDhlRjZ4BX\ne89bxrw1o3Mtr+sXXXlbuWKcUf21RAudfgsKIYQQwnUnA4AQQghhhmy8BNA3QnAGPk5ecT7QTm51\ncikTqbAAACAASURBVMdQRKixRl+1fa0IYe7Y1nlqOOnGSVDXw6f9qFEjwFp0tZYcXjP0GvK3dt91\n8piT7a5lieBaolKOjQ6n23ZxCaD/zo+NrdBaHnPybS2pjjI25bYzThzbxoaoGau14hPUUseONVw8\nbvoGr9qWKf07Cd2l+na4+tHYEy65VqtN1tpfq761rvrJ3fSzq2ddHqmlH1Zq6aZbRAEIIYQQZsjG\nboD9+N6qANRiM7cMfNxsvuWesWlM720iiG0TQ7wVH5pcS7z7KVBKWZuZOqVnaD9xbjE1daRfhv5n\nvUbNEM0pE2NnHfqdlpLlFACX8rQVBfPFQG3m7+qt1b84NyylFjVvrHFqyz1xbJ/VOo9rv7UIhbvQ\nRwDr99CKrOjeff7euKiSbpu2G91f+z0a6wJai2TbL7f7LRir7o41Htboq5sSBSCEEEKYIRkAhBBC\nCDNk4yUA+vq7iEQ138kWNXl97HeHvjNWdtzm3GNl7pqB4a4uAQDrSzebGO31cTJ9y2jSxZxwqTLd\nuVUadOVz0cecfKnLIM5Yj9taUh7ZxvBoyrTKPlYmrUm1rWXC1ndqy0JO4tc25tqRK0Mrsp0rV/8c\nQ9+ZKmoo7CJd1pbt9Lm6bS+88MLgcUOMjdHiqC35DUWorbWrltEhz+Ninajsz+e7TR8RBSCEEEKY\nIRsrAP2RSisKH2kZ09VcpWrlGSpDa3S1afmVseVqbXP7No1LfZxonG8XbdHNbrnfGem0okq645yL\nT0uBcpGznJFRy9iH966pTE+ePAkAuP3229e+64wAW8ajzhVtynRdtzZ7bxlq1qKzaR25unTGwy2j\nzLGuws5ozSkAtchueqzei3P74qxurIvaLigBLl+I3rMzhnTu5E5xdiqRc5vU641Nxetm6a4eW/1e\nLeKnltu5C/Kcuo1uk6oAaP6VTYkCEEIIIcyQDABCCCGEGbLREkApZU2+b0WyIs6PteWL2ZLWasY3\nLrKUfrcWLWxIWuP3ncSj1IyanNHkri4BAOvldDK34qT2Z599FgDw3HPPrbY988wza9uef/55AN43\nvP+5j5OF9Tw0KOI1gINliCH5nfd36tSp1bbTp08DAM6cObN2PSflOelaj3OR56ZOXxbdxrDXvUMu\nlXBr6XBsxEXXJ7ViDPTPp2hbdP7qPKcmwSFOslZ2qY8opazdY2sJgDhfd1ePen4n8btlvaE+pFYG\nXse1Lz1O74+fW+mOXRwS9kl6HJ+DW4LSpcixRAEIIYQQZsjGuQBIzUVmbGx+peYuobhRu5sFulGm\nUwWcMZKODlsRBWtlVWqqQG3blEf5avBFXP24mbab7V++fHm17dKlS4f2AQezc2f4x/IA7frhd1gW\nvQ5n/brfGeUBByNuzvq1bPoceNwdd9yxVgY9zs0GnfvT1OkbOanBEu9R1SFn9FUz5m29Qw7tD3ht\nnTH101rrZy2DUyFaBn9j3VNdWd1skexCOuBSyuoeavWnfTMVNfccFL4T+h47JU/7Gu7X95znce1P\n30m2F91GAzwa/+pxgI/4WTNCdv2P3h/v5cqVK6ttfF5ahrFMvwWFEEII4bqTAUAIIYQwQzZeAnCy\nO3E+yzWpXdkqipGR5Cm5qEzDbSo7On/esUaALdm55vvp5ETn/9yKaDUFdAnASZW1qGkq0T399NMA\nDowBAS91cf/QEoAzyKk9dy2DM0RUOY6onO3acS3xVMu/20VK43dUBpwymiCK9+YkU3123OakTq0j\nftbj+pFJh3CGVBqrgTKqyrfOUJNlGJKYx8aTqBkd6rlrfRyZ8jKhLgGwrvTZuDbiYmmwjbh3wxkU\ns08BDi8tsj/Rvsa9+yrPE7abs2fPrradP39+bZsuCfLe9Xy8jpaRS5BaLrdkwrah7cEtQ4wlCkAI\nIYQwQzIACCGEEGbIxksAfXl8bJjPbXIfOwnZyWkuP7hKtZRGVCJxIURrVvnAgdTkZGdnsapSnbP6\n7p9DP++CF4DGheC96nOoLf+M9RRxvrJDsSKcX7ArF8vgLIvdkpCiYTdp1a+SHyVk58uv0nWtLbll\nBidJTpG9vb1V+fns9Zny+TkvHecDrbKsk04pmepyjesP9F1k+bTeGLfBybdab6wjvZ4rt1uy0TKw\nXG7JTCVtSr76vNx3p8r+/n71mbj+k/WjyzH8rO9G7f1VKf2JJ55Yfb548SKAw8sCfMeeeuqp1TZt\nY+Tuu+8GANx1112rba94xSsAAOfOnVtt0z6C761bhlAPJ9eO3VIyy6rLRDy3Cz/eYvotKIQQQgjX\nnY0jAfZHnS0/XZcsoRadS2kld3EGNzxWZ/scQbUiJdV8MYGD0ZxLPqHnrqkZOmqtHbcLkd+6rlub\nmbbiIdRGtVqPLvGPm+E7/2EdCbsIYkRH0Yw7oN91kQfdTNLFGFDcLJ6fXUQyvT9+drOdqdKfoTqj\nTJesRd8rPludTVEB0Fkg60NnbFqvfKb6TrM+9Dw04lJjLqKzN51ZkrGRPJ26pM/BqTxsW/pu9ONF\nTFkl3N/fX9Ub+1J97u63wBm+urgJfFddPAeNxKmROvm7oG2S7762occffxzA4Tr55E/+ZACHVSKq\nAno97Q9ce3GKIJ+Ri/DnfjO0XM44dixRAEIIIYQZkgFACCGEMEOu2QiwlbOZOLm/ZUDojABd+ESV\nPpzUXiuPSrY8jws/q9d2CVxU6qEkpfKl8ymuhTKesqxHXChgpbaM0ZKwavK7Snp33nnn6rPzyXW5\nstluNMYAZUDndz6UWMQZNLpwoS4OhUuU5GA7Vtl0yugyoVv+axnBEucn75aK+jEH9Djg4L3UtsXv\n3HPPPattLqwz255Kwy6Jj7YxFxfExZNw9+KWeXg9Z5g8Nsz6cdJ13Vrsj1ZiJt6rM4hrGXyzXvS7\n+ly5X6/HNvKhD31ote2RRx4BcPi34P77718rQyt2DNuaSw7mwhGrISL3t347ef9bxdLZ+BshhBBC\n2Hk2VgD6hnmtxBwusp2L2EZ0xscRkDOeAvyIxxkeObc8N7J2o3KXerTl9uOMefoJUoboGxFNWQnQ\nqG+19MouEYuLbKajZDdz5+zMReICDtxzVBVwMwLO/FUBcG4/Dhc9UOFM0s3m1FiJxkg6k3Sz411T\nAIB141YX7c4ZAbooby6pTitJmHs/FdabU/e0nbgkVS5plJsRtlLeOvXIqQdjo0hOla7r1vpI7Yed\n4TXfCTXkpsut9gvunaCSR3c/wEfcu3Dhwmrbo48+CgD48Ic/vNrGz9p+Hn74YQDAfffdt9pGNVLb\niGuz+pvCfse5l2rbdQqkcxXmc9rGVTgKQAghhDBDMgAIIYQQZsg1Oxc7GUrlKmc4V4sD4Az+nOHH\n0HmcYYgzwnISLferzKTXcxKtM8hxCYmczOnKT3YhDoBbAnAREV2yI5XpKaPpc3fLNi7vtfrtc4nA\nLR+oDMhza/1w+UGv53LXKy6an/Npp4+vypzueTkZkIw1GpwStSUAZ7ilz4LyaEvWdMuOToJVuF+X\ncJ588sm1451c7BJXteJXuPbmYkM4mZ/Py/Ubu7AEsL+/v5Zsx/ngu3fIGXO6JTN9hqwzNabTumK7\n0uiALIO+ny5h2Ec/+lEAwB//8R+vnVv7JH1/WR63DKHwfdB3wMW5cUtMZJvfjCgAIYQQwgy5ZjfA\nsUYsDqcK6Exn04iBul8Nrlw8eGe0xlGYjihdlD5nYLjNNqd6kF1wByylrM1ktf5YBzqC5f24aH3O\ncEpxx7XSt7o26a7hYsa7+OMuxr9TqLRNOiMdPpOW26tzO5sy2iZ4Py6fg4vo6SKj6eyNMzRndNcv\nA2F9uvdI2yVnZXq+fgQ7/Y5LQQsc1J2LdNmKBOiMo52Rbc0wcGpcvXp1TTVxRtbOLdgZhPfP3T/O\nPROXi0RzTLAOtE/iZ333WW5VD4i+n1oeKhFqcOzc+/h97c/cLJ99iTOijQIQQgghhFFkABBCCCHM\nkOtqBOh88Md+10X+ovzlpFqgnjjHyf1uyaFlmKPU4g600gE7/+daIoxdkPdKKWuRtbT+KNHps6E/\nrz53V4+sA7c84pZl9LNKjC7xBiU4l2DFRYbUa7gUz7qf33E+364dO4NZt1yxCwZfpC9R67Pgs3eJ\nXrQ+uN8ZZulx/WsCbYM5F1Wulgis1XaGykFcTAN3PN8TFzfDGb/tgqFw13UrSZ//unLrc+eSgWs3\nLnmWWwJ2/THgDU4pq2t8EdavLgFQnnd9hJZL20utHSu8FzU6JFoGZ/h4LWmhowCEEEIIM2RjBaBv\nvOIM3dxovDWTcwqA2zbWZW7IcGvofvQ7ej5nzOPuwc3u3LNxs/1aquQpz/xKKav74oh0SK3R7+jx\n+tm5Czolx83w9PtuNK5uOPzsRuUuhr/SmnW5WapTAGq4e55yO1C6rltT0nRGREMw56rn2oSLl+4M\nxoaid5JWdD2nADiDPzfjc/fSinfvcpY4g0/2Sc442hlFTg3NF+JSMxOtMxev3/Wzzgi3ZnCpOAVA\nXZN5Hn3udC926uVQvhpXz07lrbVZZ/TqjMmTCyCEEEIIo8gAIIQQQpghGy8B9GUXJ3U5aaMmB+v+\nVpKQFrVyteITuChlLcneUTNEcWlLVf4Ze40poEsAziCulrxlKH0rcYaUROU29eclKtlS7tfIYPT5\ndmllNdWwM/pxBket9lkzTlTcO7CpYe0U6Kc7Vcm3tgTgDDqdL7iez8ngzojOLce5JQBtT1xecIaB\nQ3XJz60lQe7X9s1+x0Ux1XvaBeM/onEhapFUnVHo2PTZrt0MLbe594nPW999Vwbu1/pxS5oupbgz\n/Gstl/LaGmWQyxCtSJpjiQIQQgghzJCNhgxd161Gvs4Fwxk3OOONWoxwZxTTMp5ys8mWAuC+62bf\nY5WElqGGu2fnJrVro3uOPl3eBjezq8Uxd8/VpZvWWZqe2+3nbF+jtXGbztzY7nQmwpG3iw4IHIy8\nnWuqntsZk7mIcTyfMzKasqFXn36ZnVGmU27cu6HPjDNxFzd+yOirNvt2bdW1N2fg5QzZ9JzOkFjL\nxXak7cmlkq65Fe5KX9GP8upUXsWpKO49qLkKuxk+cPBO63NlXep7x7oYGxl0qE9yhqs193Y9N2f7\nqkwwQqGqAq6sY4kCEEIIIcyQDABCCCGEGbJ1HADnu9tfHlBa8QJq8vrQEoCThWo+li2/S+KiTenn\nln9qvyz9z/3z7bIRIGWnmi+3u/dWvAcX+csZYLnogWqEw/NoCk5+1uOcESDlNpUpNdWwM9pyKX3d\ne+EiUdYiVu5CeyD9MrtYDSqh1xK4OCld68PJ9G4poZZqVz+rjMrvaNtxfuataH4u0YvzKXdyuHsO\n/W1TXh7SOAB8xu45KO7dr0XPc/K6PldGHwUO0n67RFCtqLYuXTyleN3W6rNczBSeW58N+x+XpIip\nz/U72/QRUQBCCCGEGbK1AkCckY6LhKSjE2e0UIsE6KI/AX407uKpuxStY5UCnanUYro7w52aAY+W\n2xkBTnlUr/Tv39W93p8zmnTPgbNznX3R0Ebr2z0nF6tbIwFeunTp0DWAg7aoRoDcr6Ny3c9ZhioA\nTt2q5ZBotcn+eXcJ5w7r3kX3zroIanzeTlEYUoVqbnmtdNDEuVxpO2jlInEKgIvwVzN6dve0K0aA\nhPWmBmyMvqf37N59F4GROENaVfI46wcOZuquvbi03lo/rh6dUZ5zU20p5U4RrBkBOkPERAIMIYQQ\nwigyAAghhBBmyDXriipjuFSZlCXGRgJUCcTJxU46bhlYuEQffeM1/Y6TGPU8zuhnrOGWk/udoeEu\nLAVolC9KUu5eWsYpPE599a9cuQLgQK4HfDpR5//tIgVevnx57dx6HNudloGynhrc6Hdcm+V3XDtu\nxTno38fQNXaF2hKA4paKapHyFJXi++cDDp5zK76IS9jD7zj/cJWnh4yG+9dzRoetctVSZdeMbKeC\n9hHOQM9F12M/rc+GS3j6frplIr6rmtpXJXs+R203fI4ucqt+10WmdUs57jdDf3sYldT1lYp7911k\ny2uJCxEFIIQQQpghGQCEEEIIM2SjJQCVc5zVvpO0a/KeSmeUOZxErttcbm615ua1W1bT/YQlwIG8\nMpTbmdd24SidpbvzK3X35yTEXbDw1WRAxPnuuhC4+oxZf2qpT8lek/iwvp0niKLX4xLCY489ttqm\n8i2hlKeSn7NQdhbh2oZcPTs528m9bC8uZsY2YT6Pi77nj0vqottcHTqfa9aNk7y1D1C/aX7Hyc56\nHLfp9djedBslaG0HzjJdqfUHLYt/twy6S0sAGgeAz8z51qsVPbc5Dy33rLUu6POvSwBaz+wbdBuv\no2XgNtd29dz8PPR+sr1oqGAXr4S4387WEuq1/FZEAQghhBBmyMYKQH9GUoteBXg/XJfEwUV16h8P\neD9tHV25JCtOKeDITGcGLrmLm9HqNpdYxBkTOl9NN0PuJ46Z8ui+lLIafXMU6qK06fN0KhFn0Fq3\nLs2re9Z6bqcu8DwtgxvWlc72aFDk0nH2t/fZyifXRB9zxkhTpuu6VbuuGUk6gz59Zrxfl/RE3yvX\nD7lUw9r/cLavEeK4Tc/jkjg5o0NnyOcSgTm1R6mppYp7h6ZK13Wrfpd13jLadmm4nYF5zUhzyHiU\nM/+XvvSlq22sU1UbeT1XLm2T3K8qYMsQ2kWL5GfXRlqxalopkGtEAQghhBBmSAYAIYQQwgzZOA5A\n36dTpRbKGC5Xtgt56oztXNjfIaM8F2PAndtRS2bkkrvouZVa/nfnz6vUlgB4vikvAezt7a1ksZph\nS8v4yRm/sQ5avtpOGtcynD9/HsBheY/79dlSAubx+lnbgzMK0rp3hopO2q/lqdf7c+FHp0zXdatn\n4Iy+XPKUWrhwlyzJ9RFq1OXkdScJa7n43qmBqItFwf3an+m98DrOWNS1eWfApeXvL6fod3YhVghw\nUD62C12y5VIsDW6Bg2fnlmddfA2V0vlMWHd6nF5HY3s4A1DXh/OzLgPRuPjxxx9fbdPPbDvOUNj1\nKy5OhluGdsbk7rstogCEEEIIM2TrSIBjoxTVUvG2EoK4UbKexxkA1YwJndGFm5W5UZ/ub6UVrrms\nuNG9UwA4ypz66L4/23JqzNh0wDrLdVHkXPIdl05V64SzDZci2BmduTSbinPT0fNwhtgyFnOqgJu5\n1hLETJH9/f3VM3cJVWoKgFM+dBvr1b0vTj3Sa7s+SduEc0XlZz3O1ZtezykXLEPL5Y+4PrDV7qbK\n/v7+6vk5pY9tRVUB1886d1jnXulUmwsXLqw+0/BTjQDpyufeT21rbA86w//whz8MAHjkkUfWyqD3\noH0J24ZTz107VfXAKQDcto2r8G70KiGEEEK4rmQAEEIIIcyQjZcAKD24KF+UwlxkvpoBHeCjWjnJ\nrGU8VsvrrRKPWz5w33VxDloJfZw/r5P63HIFJSeX+GaK9A3XXP20Ive5unJSKj8PJZdxueZ5bjUS\nc23S+eG6qH6KW95y9+KMVd31aj7kuyD3AotnwuUP55PtIr+5WCFO3uV7ossrfF9a76zL0e6WAFSK\ndss4rg26nPHOeFGpvdet5Z5+vzHltqGRAPt9G3CwZKbGl7XYMm6fS/6lSYMUyvi61MNlAV2WYrvT\nslLaf/LJJ1fbuBzwxBNPrLa56LKt3yO2F2d07pKfbRMx0BEFIIQQQpghGykAXdetuUe4UYyblTm3\nKBcx0KXyHBoRu5FvzeXKjdrdDMylBNXtzgCjpXC4Ef+LIRJg3xVHn1ctnbOL5uZm32427NzuAJ/q\nkzjjxLEMpR92M3unIrnIkLVUuWPTBk+R/f391eyKMyqXUtUZybkIeE4ldLP5oZS8rg26NOL8rGVg\nWVtR3Jwrp+LqrmYY684xNi7+1NA+gveg5eaMXRUAKsn6jJ2hqMsZ0D8eONwe2DZV6WHeEdd3aVtz\nBsVsN9qeW0qWU0tr7abVX/VTsm9CFIAQQghhhmQAEEIIIcyQreMAECeXq/zpogLWjBWcXDxkTFdL\noKCynDPccd91PsNOOm7JsTXJxqU2biUcmipd163d69Czq1GTPp3cq9tcgif1w3UGZrX6cfEJhpK4\nuNgBNG5zqX+dgY87t3sOu9AegEX7Zj3wXxcpzy2RKDXp3hnNukic/e8PXWNom1s6bC0TusigtSQ/\nrYRhzue/nzJ8ysuEwPpzdsnd1GiPBrtDMj5x8UNcRFlnNKp14dKDu5gT7h1kO9YlAHceV253nIt6\n6wzj3e+WM5BsEQUghBBCmCEbDxn6I9KWgUzNPcwZQej5WsYzLnpgbdbWMsRwI/XWDKQ/Gu9fp49T\nAHRG21cFpj6679eRM7RszXyJm8W52Ow6w9eIX/ysLj7OSMzNnFxccRrV6AyDccMB4Ny5cwAOj/65\n37U1165akeDILqR+BRblZN4FPj+XPtW9+1pHtZm2S9nrUkDrdtcfuJm9UwmdmjOkKDkDw1pUUhfZ\nrsXU+wRFU8i7+6sZdroU8s7l1rmi6/NXg79+pFW9nn7HpTJ3Rr9u9q33yfM4w0HXRrQdO+XQGUfX\nIk22iAIQQgghzJAMAEIIIYQZsnEcgH4kQCe/txKcuCUA992xEfWcn7aTfFVecdKgk1cUd24nadeM\nPFT2oQzljFR2IR1w13Vrcp3Wt0voUvN/1nqkZOYicTEFJ3A4KheXAHSJwD3jWuIpl9hFowhq+Z2x\nEmn5gzvjLmfA6r47ZT7xiU+skq+4KIyUSluGbk5uZX24ZQGXhprlAXxdu/O4aI3Oz39oqdJJzC5d\nay2Kqbueaye7wN7e3mpZzEWxqxnbuXgr+l22L12Wo8HpkJFp7fehFU+gtl/PpwaNLtIh4w64Pklx\n0XbdkkMtlXCLKAAhhBDCDNlIAdCoTo6a24xzzXHqgRu5DUVicwZ4zuirZjjRShHsRqFutKYzRzfD\ncCNFN8tl+XfB6EvjvteMPfUZ83jnEtWKskdFQY3y9DON8u68887VNp5TjYxYBhedUuuRMwudwfIa\nAHDmzJm1Mtbico+NCOeivun7M2WuXr26UmL4LPX5uVSorv77xwNekSEuvjzgXW37Kibgo/4RZ9Sl\nszf3/rr6crHmxyoALeVzqpRSVvXmDDadKuCeHd8JfY/5bNTIlO/sXXfdtdrGdL/A4Togrl+vGau6\n6ICc1QOH8wJQodR3msc6Y3KnoLq8Em62v42rcBSAEEIIYYZkABBCCCHMkI3jAPSlERelqJWilTjf\n+bEJEvTaQ4lAiPMfHhutb2zCImVTf9dapKpdMQLcNC6Eynw1oyyFkrvKeK94xStWn90yUi2iXMsA\nr+VfW0uIpbi6rMUBcEZlu2L4pUsAfH4aKZESrS7T1JaA9N12ft+1dKvAgTzvIqy56w3dE3EpbV36\n87FR/1rJyGrLoP13b4rs7e2tvROtxEZuCcAlAnOpf/lZff/VSNAlnmL/q/XolgVc3BaeR7dpHBJX\nntpvlC5BuqRItTgUWQIIIYQQwigyAAghhBBmyMZeAP3whGOXAFxYRycXt+Qv5y86tgytcjmpTuUX\nWrM6CadVbpZVpSBKRS5MpPONnjJO+mTZnYTlYinoce58lPLUEl/lRRdPoCaxj/WtHlqWcu3FSftu\nycHVq7P4ZxsZkrinxtWrV1dlZt1cvHhxtZ/LOC48sD4TSqrO317lVpe3XSVhbtf6cBbUY+vItUul\n1odoW635brfCZPeXAKbsDVBKWd2rs3R3de/iALjfB96/en08/vjja9seffTRteu1krK5eA7Oi4Hl\ncgnd9Pu6LMDzOA8n9XDh703Lv/9afiOiAIQQQggzZGMjwL5xlo4+agk1dBTDUZGbubuRujMa6W8n\nYyMP1lKQuvID3oCkZqzn4hPo7MTFAeDnXVAAuq5b86l3I2E1bHHPvWZA6IyphiILuqh/fO76jGsJ\neJwRoJZfZ3G1SIc6i3fGWk6tcD7mPM9QutGpsb+/v5rt8FnqrIY+2WoE6GJyOANZZ+TkVAF99tzf\nMsob25e0jHNdH+JSxm6awMXNKnfBCLCUsrrvvrrJ/f1tfFe1HvkOulTzzgBP33eXzKllkOuMtp3x\nqGtfLSWBx2p74DuiyphTiVrtfVOiAIQQQggzJAOAEEIIYYZstASwt7e38sF2xjD8rJILZQ6XL1ml\ni5rPv0vkod9pJVlpyTSkZWzB79dkbMDL4ZRs1AjQSV3c5so8Nfb391f340Iws65c6NZW/ATWhcrv\nDheW0yXj0G1OxmWdqixHOW4TubcWeliphU7W77I8U24HihoB8n40DsBTTz0F4PB74JZauN/laG8Z\nXbaMzNx5arTkYte3OQMvJ0UrbmnChcplH8FnNOW2sbe3t3qPnPEbcX2zvjf83dE24ozJXVwB16+4\n5ZjWedySLevFtVP9vgszrGHM+YzcUp+LQ6FGhWyT+p6NJQpACCGEMEO2VgA46nDGDU4BcKN7ZzDm\nRvctN6xWulk3m64ZYemIa0h96JehFcWMM1B1T3GGTv1nM+XR/f7+/tpsxBkr6eybaoCri7GppfU4\nZ3DjIsVp++tHW9Tr6Ai85pqq33EuTGMjm2kZ+Az1/jgz2IXkUMDiftjGeW+qAHGfzqI4c3EGli6C\n2iZKUc0t072zzsDLqYlOZdDtzgjQRSJ1/YsrqzMo3jUFgP2B1infE5csTo+j+6hTANx3h/pwZ1jn\njHld5EHnzskyDkWVdHXj2qwzCuU59V1x6rn2r5sSBSCEEEKYIRkAhBBCCDNk4yWAvmGUk8VVnnDJ\nOvp+ocCB3OEM7JwMrMe6/c7Ay0k8reiALUMht9/5cztjNCeR07ijlixmSlCGo0ylSxxqqELuvvtu\nAIf9XfkMtY24+BH9KJRAu055TufP6wy53PKBXkNlu1qiK2cI5PyfXQwIF+3MJUeaIvv7+6v2zOdy\n5cqV1X7eo9aHiwTJ5+wMZN0y31Dsjlp0yCHDrX65xkaM1P3aBt1SkjuPe+drxmiUn6ccB2BvYDdx\nNgAAIABJREFUb2+1BOSWPNlHaB/IZ6dLAKwrNZzjc3UJ1oYiP7plHZ7bRSxtLe+5OCMusqtbSmj1\nbW6Jg8/LLY9sEyskCkAIIYQwQzZWADgCc8YuREdkzgDGjYBaBnikFTu7Not3x7nZoktfO3RuRy1l\nqDOS01lzf/Y0ZQWg67o1l53Lly+v9jMtrI74+fmlL33papum9yVOOXLP3Y3a9TvMH9CKDudmbs4t\nS8tQMxJzM0qXgtQpJloGPl/nSjlF1AiQz8q5RDrXuta76GburQicNTfAlsHfNrhoqGP7jZqRsd4z\nny9zLGyTBvZGcdNNN61mqLw/LS/bvL4HRPsF9ouuPej5nFGePnfnylf7jVKciy/LrWqnUwDcfTnF\n070X+mweeeSRtW1Eo2uOJQpACCGEMEMyAAghhBBmyMbpgGmcQ8lCjaIoyagEwuOcjNpKe+mSJrQS\n8bilCSfBjU0R7M7jInW1DEMofToZWOUjRkpz/qxTpF9OlaYoUfKegAODMF0WuOuuuwAcGLMAXs53\ncq7ijAld9MZaStchg1OHiwLmYgzwsxo6cXlEU+WyHThDQxdNcIpobAjeh4vy5vym9dlzqVHlVu53\n6X71+TgjwH4Z+9TiACiu73JLF614Ev3z6Xm0/vuJf4CDtvPRj34UwPSXAGj4y/vSumKqXu0rWb/6\nDClvq/EwcQZ9ej59xs4ouGYkrjijPF7HJSADDtqQlpvPwSVJc2mKn3zyydW2j3zkIwAOvwNc5uRz\n3oQoACGEEMIM2VgB4AiqbwwIeKOFmvuSm30796B+Gdzn/rZW6t/auYdGgs7FkKM0l56xFaGKozg1\nnOMM2Y0Ip0hf4XGz3AsXLqx9z+VEcCNnhSPdVhpXN4pupfDk895k9ujyH7h8BLw/Hck//vjjAA5m\ncVrWO+64Y7WNM5+ptwPSdd3aO+Ei2+kzcxE/afSo9ebcSp1roFNxaum/9ftOTXTtYMhgzMX9d+6k\nzoXZRU3lM9Q+grNm/nst6WCPmhMnTqzaMO9Lfx9oEKf3wHdC68KlkWZ/oK6BnIkPGeLVVGOXC6CV\n48W5Biqse1UA2La1np27Ktv7Y489ttpGBUDvj8rpNhEBowCEEEIIMyQDgBBCCGGGbL0EQPnCyW0q\n71IGVkms5u/aMrAbK+e7/S46V4taxC4tm5OZHGoswmej8h73O7/QKcNyOn/lJ554YrXN+f26JCFu\naYWfVfJzkbNc3IGWoVQtfsRQHTi/dN4X6xY4MIJUuZ/+vFwKALw/sktSNHX6cqbWoXunnaEmn4Wm\nOOXymC4F9NNR98/j5F0nv/ePV1rSvfPnbsUh4XHalnmvetzDDz8M4KC9AAcyMKXhKS8B7O3tre6L\n/+p7TmlcnzvfF5W5Kf3rEgC/qzEyXDRFJ40PGXH2cUbYLcNNxbVjLl3od3gdjZrJvkHrnp+1XC9/\n+csBZAkghBBCCCPZOsC4m5lwm47S+FkNoPid1siM6Gyh5Zrl4GjJuYc5d6SWm5kzMmvFC+dx6hLH\nmb/Ohl1c9F1CR9NOFeDM2EXOcqqAzubZXnTE33JD5WzDGWS2UkuTIcNTtkud0bBOtb3TCJIzNwD4\n8Ic/DODwc6Ch0/3337/aRiOpoRnG1FCVkDgDWReF0eUL0feAz8cZkDpjLd3u6rBl3FfLS+KM/PRz\ny+XPzQzZV6p6xFm+th1+ZruashugRo9lnZ47d261n/esSojrKx988EEAB20AOHh27nxDCg3bhjNA\n1zbplARn8OfSQ2sb4n2pYa9zZWTfpirhQw89BOBABQIOVAFnKLqNEhQFIIQQQpghGQCEEEIIM2Qj\nnbnrujXfXielqMRBH0WN8kYJT6U8F+ms5ndfK2P/O86n00k8Y5cAnE+58yl2aRzV4I+yj56vn0xk\nmyWPG8Xe3t5KiqLUpfI85WttI6xnlchdQg22M5WPX/ayl61dw0XNc6lTW/ElXGRI10Zcml9dyqIs\nq1IeZVzdRsNIlSdpzOPury+rT5W9vb2V1DvWYJfH0zgKOGg7zuBW+wpuc20MOKivlt/3pgnDnOHf\n0Hm4X5Pb9A3jFOfzr4ZglIFpMDblGBGllNXz4/1rPVPSV4mc7UGNh/nuUBYHDp6d9gFcAtClAn3u\n7AdaS7+unbokUmx/QynDeW39TWR70CVP3t/73//+1TYue2jds69xMQS2IQpACCGEMEM2UgD29/dX\no2tn/ObyA3A2owoARzHOmMcZ/A2NcFopV/u0XPrcDN+N5FvRCDnK1BktDXt0tsh71XNwtMwZtUv7\nOBVKKWvRy3Sky9GvzpD4vPU5uBSe/I4bqauRqbY1ZzjJduoiz/XvRf/V47QMbvapRlucselsnzM2\nndmxjehsiPelCgDbQSuV7FTQNuFc3Thzdi65ehyfi9Yb25bO7pwLocsV4Nx0naueUzRd1L8hNzKX\n6pxtVOvVRbXku67qGNUj3cZ+Zcruf6TrurV+WtUT/i6cP39+tY0ze75LwEHODBrPAgfGf6qi8Fnr\nNm1XTp11rsI1ZbelHqhhPNusthf2d6pw0NBP7499iBpDkrGGpy12o1cJIYQQwnUlA4AQQghhhpRN\nIs2VUh4H8IGjK04w3Nd13eZ5Hm8AaQ/HRtpEUNIeQp9RbWKjAUAIIYQQXhxkCSCEEEKYIRkAhBBC\nCDMkA4AQQghhhmQAEEIIIcyQDABCCCGEGZIBQAghhDBDMgAIIYQQZkgGACGEEMIMyQAghBBCmCEZ\nAIQQQggzJAOAEEIIYYZkABBCCCHMkAwAQgghhBmSAUAIIYQwQzIACCGEEGZIBgAhhBDCDMkAIIQQ\nQpghGQCEEEIIMyQDgBBCCGGGZAAQQgghzJAMAEIIIYQZkgFACCGEMEMyAAghhBBmSAYAIYQQwgzJ\nACCEEEKYIRkAhBBCCDMkA4AQQghhhhzbAKCU8p2llLfdgOuUUsrbSykXSynvOerrhe1Jm5g3qf/Q\nJ23iiOm67kX9B+ALAHwIwKkbfN1XA/iXAC4vr/9dx/0s8nfsbeI1AN4D4AqA/xfA5x/3s5jj3zHW\n//cB+H0AnwDwPWb/XwPwAQDPAPg1AOeO+1nN5W+KbQLAywH8BoBHAHQA7r/e15/DEsB9AB7quu6Z\nG3zdnwfwLwCcA/BFAP52KeU/u8FlCJ4b3iZKKecA/CaA/wnAGQA/COA3Sylnb1QZworj6hPeB+Bb\nAbyrv6OU8p8A+GkAXw/gHgDPAviJG1q6eTO5NgFgH8BvA/jqI7v6EY9u7gXwKwAeB/AggL8r+74H\nwM/J//86FqPfJwB8F4CHAHzJct8egG8H8KfL/b+I5egYwP1YjI5eD+CDAC4AeNNy3zcCeB7AVQBP\nA/heAGcB/ONlmS4uP79SynEOwNuxGHVdBPBrsu8rAfwegEsAfgfAn63c+7MAPlP+/0sAvuO4R7rH\n/TfXNrE87j/0tr0XwDced52k/m9MnyDf+Tmsz/b+PoCfl/9/KoCPATh93HWWNnE8bUL23YQjUgCO\nslL3APw7AG8GcAuATwHwfgBf2q9YAJ+5fPCfvzz2hwB8XCr2jQB+F8ArAdyKxUj5Hb2KfSuA2wB8\nNoAXAHzGcv83APhXUq7zWIyoTgI4jcUPs1beuwC8c9kAbgbwRcvtnwPgMQAPADixbEgPAbh14P7/\nPoD/cXmOT8dCXvq8437ZjvNvzm0CfgDw/wH4keOul9T/jekT5HxuAPDrAL6tt+0KgM897npLmzie\nNiH7dnIA8ACAD/a2fQeAt5uKfTMravn/k1iMflmxfwTgi2X/y5cVf5NUrI7O3gPga13FmnK+GsBF\nOe8+gLPmuJ8E8H29bX/CijfHvwYLeecTy/J973G8YFP6m3ObwKJDuQjga5cdxuuX5/3p466X1P/R\n13/vGDcA+D8AfHNv24cB/KfHXW9pE8fTJmTfkQ0AbsLRcR+Ae0spl2TbCSwM4/rcC+Bh/qfrumdL\nKU/0zvWrpZR92XYVi7Uy8qh8fhbA7a5QpZSTAH4EwJdhMXoDgNOllBMAPgnAk13XXRy4n9eXUt4g\n225Zlr1/jXNYrN38HSxsAV4G4JdLKR/tum7O63qzbRNd1z1RSvnLWMxafgLAPwHwT7FQhubCbOt/\nBE8DuKO37U4sVIAXM2kTx8hRDgAeBvBg13WvGnHsR7CQyQEApZTbsJgx6bn+Rtd1/7r/xVLK/RuW\n6+8tr/VA13WPllJeDeDfAyjL65wrpZzpuu5S73sPA3hL13VvGXGNTwFwteu6/3X5/w+VUn4BwF/C\nvA175twm0HXdPwfwecsy3oSF1PkPNizrLjPr+m/wh1jI0gCAUsqnYvHD8d7rcO4pkzZxjBylF8B7\nAFwppXxbKeW2UsqJUspnlVI+zxz7ywBeW0p5TSnlFixknyL7fwrAW0op9wFAKeXuUsrrtizXaQDP\nAbi0nKl/N3d0XfcRAL8F4CdKKWdLKTeXUr5wufutAL65lPLA0mf0VCnlK0opp8013rsoZvlrpZS9\nUsrLAHwNFq5fc2bObQKllM9Zfv8OLJSAh7uu+ydblnkXmXv931xKeQkW/e5NpZSXLGeUAPCPlvf7\nBaWUU1i4h/3vXde92BWAtInhNoHlvluX/711+f/rxpENALquu4qF4dOrsbDsvADgbVjIWv1j/xDA\nGwD8AhajvKexMKR4YXnIj2HhD/nuUsoVLAw9HtiyaD+KhRHIheV5fru3/+uxWDf642UZvmVZxn8L\n4JsA/DgWa7nvw2LdaI2u654C8FcA/DfLY38PwB8A+P4ty/yiYM5tYsm3Lq/xMBbriF+1ZXl3ktQ/\n3orFj8pfBfCm5eevX57rDwF8MxYDgccAnALwt7e8n50hbWK4TSx5Dov7xPJaz215P5ayNDKYFKWU\n27FwoXhV13UPHnd5wvGTNjFvUv+hT9rEtTOZQECllNeWUk4u5a8fwiI60kPHW6pwnKRNzJvUf+iT\nNnF9mcwAAMDrsAiq8AiAV2HhnjE9eSLcSNIm5k3qP/RJm7iOTHIJIIQQQghHy5QUgBBCCCHcIDaK\nA3DixInu5ptvPrStlAMvjL29PR63to3/6nfcNj2ffr4e28bu36QMtXKruuKO0/vvH8d9jz76KC5d\nulS/mWPi1ltv7U6dOnVom97fTTfddOhf/P/tnduWHEfRhaN7LE4GdLAkA4YFa3HBk/Am3PFsvAGv\nwsFgbEuWZUvjg4yl6f/Ca1fvyf4qs3oEqPrvvW+mV01XVVZmVHbmjh0Rdd026BzhJswUnaNjS6/X\nu8aSc/T56uqqe+zly5dVVfXvf//74BjB++jly5dPdrvdg26j3hDefvvt3Z07d64dI5t325d9+NxC\n70ZvbKi//TiN0euOde/YaP7p4dWrVwef/fkEHbu8vKwXL16sco7YbDYHneNj+73vfa+qqr7//e8f\nHPN5Q+eMbIm+d5M5vHfu6P+j+YxsVp997L/99ttrf/2zf49s4/LyctEccdQC4NatW/Wb3/ymqvYP\n4S/tD3/4w6qq+slP9iGPP/3pd8mtfIB1zg9+sA9p1KDrb9XeAGiy8OM3WYT0zvV7+Gd9l9pI7fKB\n0TH1kV+H7qfv/eEPf6i14u23367f//73VbW3Bx/ne/fuVVXVu+/uE3H9+MffJd6isXDQxC34mPn/\n9VLQS9SbQP3z6JhfW/f2Y/pBf/HixXRMn7/+eh/B8/nn3+UPef/996djT548Obge2dzjx4/3J60M\nd+7cqT/+8Y9VtX8Otwm9875wfPDgu3nq4cOH0zHZv9uGFkj0A+n97f2s4z6J6jq+4NJ1/BjZHk3U\nZFu0uXHQJkj3/uKLL6ZjT58+raqqb775Zjqm++nYn/70p4Prrwmai9VnP/rRj6b//fKXv6yqqt/+\n9rfTsV/96ldVtbeLqr09+Pyp69Cc6u+L/0apLX5Mc67/Zujz6Aee5n9aXLhdaY746quvpmOyU80L\nVVWPHj2qqqqPPvpoOvbBB98lDr283KeHkL37ff/85z8vmiOOzgSoB9HL4QbcWz0v3YGNVly0uqcf\nkNG1e4uCufvpfH/56Xt0bdp16DO1iyaVtWGz2Ry8UL74u3//flXtF4FV138MhN6KuL1fC9r5jZgX\njR+NBd2PFpNzbegtLP2YzvHdsl7qL7/cVyRVW19nR/m/hp5NbfbJUeN/+/Y+zFsLRbcTn8AF+uGm\nHdHox5lA7yItAImlIRv0Nsh+aK5x26LFnj7TAqC3c10T1F49K80Rvvh7553vkvu5PehcXzzoM/3Y\n+w/8TX7sl24uib2iMXUb0Xf92jrmNkK2q0UDbTB6DOIcogEIgiAIgjPEUQzAbrebVrY9//UcHTLd\ntOMbvokfhnZgtLsjatVBrMbo3romrShbvUTV9VWanp92rMf6rt8ENpvNROnqr+9otctzVw+trNUn\no90auQWIkh/tyEZ+Y3++FiO2SWO+1Oen3W9V1bNnz6rqui5AO7+brO7fFPRs2pn5+Iv6v3v37nSM\n7IS0Iu31q8baIdqB9Wh8YhjJVTBnqz2mj+ZFehb/nvrEGYBTY4X0/HoWZ39E8zvdL9vwnb2YI7kQ\n/Xq0+57THenz6BjNU/QbNfq9omsLZENuN95Pguh+dxOJFfB5YynCAARBEATBGSILgCAIgiA4Qxwt\nAmwFbiTwoZAOp0BI8EA06UiU1xPu0Dnehp5yl2i5URtJKe5tVT+N6CHRWj2XyFqw3W6nsZawxyk6\ntw0/p0VPgU/irjm3yFIxYft9/0zjTQJP/+7SMCMKf3Khk9wnJAI8Reg9dypXLgC3E9kQieR6Svwq\nVu/TZ7IJovu9v0l0OHJXHfu+0jPTnOriN3rmUwC5CeUC82NS9fu8qGP+27KUuicsDdVztOJL/0wu\nRj+H5j36TXS70jP7OEs0qYihqr3rMCLAIAiCIAgW4SgGYLPZHIQw+GpV/xvFzvdW94S57y0Ng+kJ\nwXxlRuE6JNAbtWHp9xTC5CIW2i2uFZvN5kDoRc/ioLFQP4zCu5bmBKBzaFW+NERsjmHqhSWOEo20\n+R6q9iyKi+Ek7DmFsNCq63OEbMNtgmK3iR3rCfBoLI8B2cmIXZq7RtuG3vs7EoaSnajvfOfbJohZ\nM0tYtZ9XNebOeukzCUApHJIYuNE7uTTnyE0E6EsTUflun+YkYjMoxFDvjwsE1a8uFF2K9f/KBEEQ\nBEHwH0cWAEEQBEFwhjhaBNhSZE5ZiKYhWnaUBYuyIxFGVPvStveywc3l6+/dj56PBEUOSm3atmHN\n9N52u53oJxJ2ChRv7TGr+ux9RBRw+7+qsZhnaRrW1+nnUZphyihI4iDRey6QU9/cJMb3TYByQzi9\nq2d0SptcgrIZqpVAudEpd4KfszS//kgYqM/ehqV0P/2fbJXshDLbqQ/XPEe4S4jSQLei56p9f1Ie\nFRLFjvp/5CbquVtHrryRy5bcTdQu+u0h9DIiehrhpQgDEARBEARniKMZAIGy3dEukMQNvd3tSJQ3\nyudP6LEQxzAKvdUcCbx890rP3NZVqDrcdaw9EyCFeQp6PupX37H1xF0U8jUXKkpjSivrY3dMc4wQ\niYd6osOR7dIOSRm/TkkE2OZbJ6Hw0nd2lJmvx6K1n72N/nd0jNrlGM0HvZDj0RzYy5pK4sm1we1B\n9u3sD2VD7VWQba/d4ibVAOkYzT9UR6DH5DiI3RpVDO3Zmv/uar5wpm0pwgAEQRAEwRkiC4AgCIIg\nOEMcnQegzVY1KujTy2i3VIBH15v7f6+gD9FCRF3P0TlLqbaegI1ifHt1ydfsAqg6zITl4qheyWj6\nHokAewLJFiSs6xWVWQqi/Bx0P8o859ch22jFUv75VLK+bTabA9fdXKnUHnqUKNHFfo9R4Seid3uu\nopu4CenYqEDZUjFaO/eu3QXQ5oVwFwAVz1KfeN/Q703P/TjnBqLMfep3f8d67mfqb3L3zmGpy5Ns\nl0BFt5YiDEAQBEEQnCGOFgG2q05fkfV27LRLWrqam1sZ04qsl2WJMMoaN8rh3Fvt+XV6Oz7fDbfh\nXmtnANqVK5Vcpn7wZxZopT4S3Y1yqffEVhSe6KttstNRNrelO9de+303+zqr+zeB3W53wMRQZtDe\nTtm/RxlEqW/nGJJe39O8MaozMNqNLRWc0ftC4cN0P5pT14rNZjPt+GXDoyyQS9+rXp59ul7Vvm9p\nZ7/02GiMl4aAjn576NqUJZGYw6VYvwUFQRAEQfAfRxYAQRAEQXCGOFoE2MZCOj0hSoMo0x6V5Z+P\nEW31hDt0nZFYRO2fo5h7GJUyJYpH1KLT/l9//fXs99eG3W53QL1SP4xKJRNoHEfuIcow2aPtiHIm\nW5pr69KiVr3iQxTzTeW0TyUTYNWhq2bkouvRrUSHkxCY+tHPd7fKyG3QguYDcnWNsLTc7CgWvH03\n1iwCvLi4mDJbqtiV57mg2Hp6z3slw0m8N3pnbyL2XFroa+QiIFefru2u0Z7w0d0o6l/v16UIAxAE\nQRAEZ4ijRYCt8MJXZFqJ+Q6GdlFL82kT/Hs6f8QaUFupXbTrHK32eszFaMVP/SAGQFh7Brg2rI/C\nQh3qu5uEhrX3bD/TLqGXLWxkcyTUcpBIjHL390I6vb8EF/P0xLFrRcvM0c6esgM6iCnq1RCZ2/Hp\nfi9evDi49kis3D5PFZeppt0dlXUd7Qz1LDT/UB8urZ/yJnFxcTGV/KVy1xQa2BsD+h0ZYWnWyVHI\nMc0bS8PIyR78HdBc89VXX03HNG9QvR2vF3L37t2qum7jSxEGIAiCIAjOEFkABEEQBMEZ4igXwG63\nm6hNokdJxESxiZTpiSjkUWGHXtGIUcbAHrU2RyHT/Xo0MRWtoaxy/r1W7LVmF8Butzt4Buo7cguQ\n0I2oVnePjOKyiWqnfhcoPp2yFpKI0Y/7mImGG7kAeq4JivFdM83r2O123WyW1M8kpiTboWO6B33P\nPxN17DZB9qYxdGq1nf/aZ+mJj8l1QdSwj38vH0b7nTViu90euAC8lG2vhLyD+pDmY6L7KdPqXHbZ\nJRhlq3WXluY5ymdBv39+THZHNuK/q+rX58+fH/UcVWEAgiAIguAscTQD0O7UfbWjVYkLFLQC8pU1\nhb/1RD8klPJ7UwgJlSmm1SPtJudEZr0QplEGJyqFqWMUtvTNN9/MXnct2Gw2BzsUfxZ6Zn2mbGA+\nFhLD+IpYAkkXyqif/Lu0uqdVue8WtEKn3YTbru8GKQyJdpL6PAoD7IU5nooI0OcIKnuqY8SQjNgj\nEuHS3DD6v9rn39MxCsl1YS5lsKQ2ut1SKCKFc9FuUeNO9/3yyy8PnmNteOuttyaR2p07d6rqeria\nzw0CZYHU97yv9ZlYXHonq/oZP0c1CqiUNbHe/vsntoPYJrcr+k1cWnpa7aK+HCEMQBAEQRCcIbIA\nCIIgCIIzxNEugDbbnws6RPHor85pQSJAUask2Jgr7EPUR1t60j+TW8BB2ZiIEnTaWRgVMpFQw+kh\nKperfiV3xBrRUtROaco2/JnlFvBx7BUS8mOiPD///PPpmAtfiCbVODvtqLHwfle7XFxDdKLbg1wR\nX3zxxcExcm/1hH9VLIYcucHWBp8jeiJXHzf17yium0Rf6jMSWflxagNlB6ScHO5yotwjI5GW3gO3\nQb0TPn/q3fF2kWCszSuw5qJAt27dqnfffbeqqm7fvl1V7OJoz6m63oea40k8TJjL8qi5m9yNTslT\n0R2Nn7+fGgvKR1K1/10j+3O70tzmc4naSi4Mcl3fxE24XssJgiAIguC/hiwAgiAIguAMceNiQKJD\nnO5/5513quo6dSMag/IBjGpviz5xyt3PEVXkFDPRNMKITlcbRMdUXacqRdmQEtxBFJZoH3+WNu1v\n1XVK8JQgas7br8/eD6KpiFYnqovoTe9/dwc8e/asqq73sdp17969g+uIkqza069OMeo+TtV99tln\n0+dPP/20qq7biO5NceJEX44KJVHhkLWj5wJQ//gYUfpkel90HbenttBM1XUbJOpY//f3T/3s99M8\n4N/Ts82lrlXbfF5s4+Cr9vY2qutOrh+9Q+qPNbuHPA+Annk0H1DkSy/enqKo5tzGRJNTZAqNM0Wm\n6JnmfqMoxS/Nd7qf2xoVAHudlMiEMABBEARBcIY4eunYZitzYQvt8rWC8pWXjpHYxVdPWg35CsdX\nivpMuQgoFpzEJ5SNyVdrLsrQjpDEZhTnSas0in8mYWCvgMgaoTEgoSUJbkj4SOPo/Sp7IDurYiGN\n4OMsNoAErJRlztvvK3SxD2ICqvY7AdoVjsRpJFyiGOW1o7XZXrEr/78zO/qes3H6nr/HPfFwFbMv\nFD8uEAvh16ZiVpRjgkSALpDtFcrydumcXlvXnCNiu90eMIFUCMz7uFdCfpQHQOd6n1DOEcrvQrk7\nHJQZUnY6V0KeWGcaS8qZ0SuBTH0TEWAQBEEQBIuQBUAQBEEQnCFuLAKklK5E2xLVLjjtQ9SZjvn3\nXGxBuQh6oiASixDFPpcHgNLT9mJwieb29ovK9PuJMjqVIjC9VLUUA6vPPj7uAhHUJ07nEwVHgk0/\nR/3obdBY+H0Vq0z0JNWSr+J8DxIJervILUBUMgmdTi0PgIOETURhalxpLMll4OOmc2lcqpYXjNF7\nR+JUH39KSeu2r/P9nSbxmGyGhKGU18Tb3M69a84DcHFxMYltSRRMhZkovbr+T78PVCjJ30nKN+Nj\nSnMtvXc65udS35Orz+c7PYuLhyVgdpA4lqh/tTupgIMgCIIgWISjthXb7XZadbZ/q/aCQBcGagXl\nwjoqxEPhTlRIwaHVoMIPq/ahXRS+4StKElxRCM+o/Kv+77tOXXuU5Y1Kx1IYy1qx2WymsVF7neGg\nIjgkDFTfeR9eXl5WVdWjR4+mYx9//HFVVX344YfTsU8++eTgOt4G2kHRqr1XZtPtwdst23/48OF0\n7OnTpwfPQtkBKcyRdh0kFlwznCWkAieCH9NO6PHjx9MxCSy9v6lolOYa7x8SHVJRqdFd34SsAAAf\nF0lEQVR46H4+x0lA6nOOz3f67CGmmsd8VyoWQyFy/v+eSMz/fwos4cXFxfQeUbEjEjKSuJvYMSrE\nQ6zTqNQw9Tf9Zug99vlF7zv9DnrbqOgTCYpHonO1hwqshQEIgiAIgmARsgAIgiAIgjPEjUWAomec\n2hDV8+DBg+mYqB0X+FBxDNGsTovonDmBj+hap9tIOEgZ3Sjusr1ue21RRRQr7LQtFQ2iDE4Eqnm+\nVrgLQPBn1jNQbgfP4EfFMSgnA2WCcwqVivfIJp2yVZuJhqacBXMiHN3P2yN3gN9PfeLPovuRwIzy\nVZyCPVR9Z9+tEI7yePh8oDwK7gJQn1MuDe8zYS7XgMafcgeQDTqNqnff2yB7o2yD/pnyBFDxoZGg\nmDKktgLSNdvGdrud5nk9n4+VxoLmeO93vUMultN1XBSqc92+qOiO2xCJNCk3gM6lQlYkHnaQwNCf\nj3JOtG3x67ioUO1OHoAgCIIgCBbhxuWAKUSGwhGonCXtnGjXrJUdCfGq9qsuXw3pfrTb97ZSPmYS\nrfnKjcJYqLSovkfZpHy1KkGRH2t3C2te3e92u4OwLtrt+ZhqDCi/OvWh76TVXy7K87HQjs5XzPq/\n24ju4+3SzoLEiXO1IXTcx5mEfLqf7xrbjI9+7ihj5ZpxdXU1jTGJkmQnxPRR9kS3f9r56zq+M6R3\nhjJ1ehtoJ+fspkDhmGTzo/lH1yZ2iURylClvKav4JuHCcfUDZVqlMF1i43x8qMy7+oIyrnobHLI1\nKilNvw+UPdaPua1RyXdiF2icKcSaRNSvM/5hAIIgCILgDJEFQBAEQRCcIY5yATi9pzht/a3aU2pO\n8YhWIZqMaAyiaJw+of/7sV5px1GpYWGusIOoGIrdplhUKgnrwse7d+9W1XUXgOgoKiCzRqiv9MxO\na6lvKZOeP5eocafIRRs6/UXZG/3asjUS0pAoi2LDR3ka3F7IRSA4VUzZ4/QMTjNTdkASLK4d7fvm\n7wHFuuuYu3bakrdV+z7w8aeyug4qPiUbHWXrI7qfBFc0P41ye9AxootpvmsL6KzZBbDZbCZ7plwh\nlC2UireRLVEWSKLSiUL3OVfuQX8XZRs+FuQ2JnfTKDsgjbPa5dfWMyzNbHkTO1j3r0sQBEEQBP8V\nHC0C1IpNopsnT55M/1dWNt/lanXlKzISTtDubpQFjVb9S0so9lZzc3m+exmjqA30LL7LpZWnvncq\nud/1rMTq0A5Fz0phVBTGQuPo3/MwQF2bVtEktvO2ahdPOzeHMz3aRVA4j4NsmzJfauxJBEZM1VrR\nvhOUx97FneoDZ3NojiBhLNkO2SBlCqSQYspJT+V53X5pvhjllW+ZsyoORVW73e5InLhWeOg4vb8C\nMQAECpukOcJBO20fP80h/t6RgJsySFKmUco8SO3yeYNE1LoPiWNp/rwJWxwGIAiCIAjOEFkABEEQ\nBMEZ4mgXgOgp0SEqhlC1dwc4LUtFNojuIHqoFbu013H6VOgV03EahuhpykA1R+20/6f/UVEXd4+I\nxqG8CadS/KV1rxD97mOqZ3Yas0dd+bkkwPI+VhucSqbYcYFKPbtNUWlXh57Bz9F3KXMb2ZI/i+g9\ntwcqSXxqcGpVQj8JYKv2rjDPzCeXjLs+iO4nsSD1Pbkbyb03ysugMff32HNRtPfwtvkcoTaOio21\nbfH20Py3Nni2UCpaQ3kuXidrqq5HeTiqWIRK99H5Pva9ojskFq/ieZzE5CRUl635/WiOeB2XUBiA\nIAiCIDhDHK00a0VfHvKnFbyXaNWOyFcsJOahECjaOVFbHJQ9acnz+P3msu/pmrRzWJr/mQQrlFnq\ndUI7/pdoWRMKa3JQeI1W2xSC5StwHaNwqioW6fTCRv1/2mlSGCOVOHbQ6t9X5WQ3S98BypJ4KtA4\nkWjTd83EpOj/PpYUKkw1Axw01sRK6nxvA+30aE6isrVuM7S7I7uk55PN01xyCnUiPAxQ7fZ3g9gT\nArEx9Nwj9kDjQswbhW2Pfh+IYSIG2W1k6dxG2UKpXkgrsjwGYQCCIAiC4AyRBUAQBEEQnCGOdgG0\n9JNTFnIHfPbZZ9Mx0V5O9YjGIBrQQUU7RhmjKOafMi8Jo1wEI5AIkGgoyk5HlFJLl63dBdBSZSTI\n8bEQBefPRfR2z0bmMgFKOOZuKcrw16PWfJx0rjL+tZ+peIsobs80pv+7jRNtR0JYyvy1ZnjBMMoN\nobHzOGYStUlk5+NLmUZHrqfefODQ/0kUTC66uWIs5CLQtcktQO12+5adkMtB/bZmF8B2u53Gmmxe\n/eDvVS/7Ic2zjpFwl0SC5JbUtSknQ8/10Laf5ohWFFm1fx+osJFD/yex6k2yhZ7GrBIEQRAEwX8U\nRzEA2+32IEf9aMek1RJl53IQo6AVjTMA/n/KBEilN2mFR9nHaDdBq9G5WgFzz0Ttq9rvCCjT0ylk\n+9rtdgerTgp18/7UWNAuzcdTn50B0DEqo1nFIkCNLwlz/NpkkzpGLIMf91U7PYvGkkKTKHzN7Yt2\npmvGbrebxkfjQKJMyg7oIGaNagHoM2X5rOKdIIVh9b7vaMOgq67bEe3YSdzXYxKoLC3VsjiFkuGb\nzWZ6Hhpn9QPtyGn3Tb8jzqzo/ZzLLEhtIJCgTm30uZlqoBDjRTt2P0f3mwtfbDEqObwUYQCCIAiC\n4AyRBUAQBEEQnCGOFgG2RWootpUKb/h5JIAhoQzFZpOYxym/XoYtB8VmU8zwqNwjXacHcnH4uadU\n9vUm6GU7G4mp9H+3B6fnaex1jERZLiYjeln/d5cWuQDoGZwWlsCH7JBETUQVn0pmyKpD1wm544ii\npTwJ5FIiF9zofSc3DRX+IVcLuWTI9eT3GblsKAdIT/TVKxq1dhdAWw7bXRx6Ln9fNC7+W0DuPb2f\n/k723KqOUZnppfZA36MxJeEqib/pd4To/pG9L0UYgCAIgiA4Q2QBEARBEARniBsXnSfFPKXFXVpA\nR5QGuQCc9qF0jRRLSzQwqUWd3qU0kZS2ktSZRBOSu6IXu+r/P4W4by/0MYqWECi+Vt8jdb8fG0WU\n9Gq/U74Hp6bJLaX/O31HVJ5D9uSFbVTsxm2J1OvUVipatWZ4FADRn3rvqKY60ZrkYqTvkfuoak87\nj1IB9+hpKu4yV/yll9vD3aA92pmKHY0K2qwVm83moJ0jyprmddmU29Lz588PjvXG1j9TQSL6PRq5\nl3WOX48ie/xYLzLJQa4qYfS7uxTrt6AgCIIgCP7jOLocsFZitLujYiZarfoqrLfiotXVXOnG3m5/\ntFIkgZLOJcFiVb9kMbWLVrDEYPQKx6xZ4OOguHbKrNhb6RL742NGK3AfK+20nTWQeOjy8nI6ps80\ntt5+nevt8v+TbQveBu16Pfsd3a8noj0lBqBleVykJYbEj2n3NOp77cb8mN7fkTCU2B4v6asx9DHS\n/6nYD5XwruoLFUfloHuFY0Y5RdaKq6urabwo22abM6KKWUIa+55AdlQO2EvW37t3r6qu9zGxxiRE\npCJF/q7Kzv1ZZIv0LKNy6mQ3VDBqKcIABEEQBMEZIguAIAiCIDhDHO0CEG0hyoPqJVMMNNX6dppG\n13FqjcQzDlExThMTxSiqzON1RcOQGG1OQEIxn1TwhED0NaXyPDXqn/pMoGP0faJp5bZxSozSAzuN\nqwI8cgVUVd2+fbuqqh4/fjwd0+enT59Ox2QbRCdSIStvD4mM3N415qMiNnTvXgGSNYJEX06Zyv3i\n4lv9n1wAFONNcdb+/vm1VZjM+56Eq3LDyV6qDuPtq3h8HT3R5kj0RXQ/uQ/aXARrzxFB7q72f06H\n03xAomBymQn+TvocoXeZxJf+PY2B265s0QW+5ILyc3quC4f+T6JmKiREqeKTCjgIgiAIgkU4igG4\nurqaVkFazVJok6++iAHohdH5Dot2dH6udgm+k9Nq0Hf7aoOvvCjcjlZXtPuj1fhIBEiZv3q7gFNB\nL7yRQCIWGjPaNav/vdTu/fv3p8+yE1/JS+xDQkvfBciG3Eb0vTnGoSdwdeiYX7u3Wqfd/k1CfN4E\nvPiLbMJ3aJS9jQqq0PdIpEslpx3agfl1NIc4U6AxpvAwGvO5YjO0U12a/VDPRYwSzTlr3/lXXc8E\nSII5ffadr8bed9IUEq5jJNL1PvR5hdgfne/2QKGpapczAM+ePbv2v/az7M7ZDBKKUviiQL9BlDkx\nIsAgCIIgCBYhC4AgCIIgOEMc7QIQvUFUCtEYJKShesiUQ4BEgFTb2eldfXbajoovEPVEFBXF/FNx\nhlF+Aso0J/QozTXHf+92uwPRE+V28D6mWuwk5CJxEAn1KHaX6nV7H8stMIoXJ5eWU8RyLZFtU74H\nB8VwE82vc0/JNdQ+r9uExpP62Y9pnhnNETrm/env/p07dw7aR/XmndZtv+fX05i7HVCxGaeqSaxG\ntC3NL/S9dn5Z8xyx3W4PXAA+fupPtxn1HYkAKTOff49ytfi1NYf4Oyv3n48p0elUCIoyynp79NnP\noeyOJDqkuZKE4+RSWIowAEEQBEFwhjg6DFArFMpxTDs+WrXTbpFWO7o27RaqeCWvnZLvAil7l3aB\nc3UG2mfyz0szcI1W5rQSPKWd3tXV1bSipj6m8RGIAaC+dlBmLB8zrcLJ1ihTnNuS7MXP1f1ote2f\nKcMbhZKOSkv3sr75jmXN2Gw2BwIqYgBcmEX5+mnXTCI5En3NiTbbNpCN+VyjEEJioYgJatshUCgf\n7fYpp3svA+Cp1Ilo7drbq/fO+5PEowT9n0IDHZQ5lOZcYlmorgTl9Z8rE94L/3Z7OLbOxVx23GMR\nBiAIgiAIzhBZAARBEATBGeLGxYCIohPdMaLVKRacKM7W3eD3rWIqj+hmolb1HH5fXe8YSonyCfTE\nGOT2oGIjp4Crqyuk0/3/VZw50anSntCNxHREyfr//RzKv0Cuqp5La84F0H7P/0/CTh9bembKMyFQ\n5q81YrvdThQ8lT0lgRc9W6+oDoHK71btXQD+7lP2No0/5Qrx9lPOAv+/xtApbaKdiQ5fChLNrhXb\n7fYgd4KPo2zF3SjqJyoZTTlkRplZPS7f2yX0XA2jIkz0m0cucCoeRWNPdkx2Q+6KZAIMgiAIgmAR\nXltxNsrpTlmwtCKmVTKttv16VDKWVvK0S3fQLpBEJf5/EpT1xCJLxWGjegNrRhuKRCvmkZhH51Ce\nfRLFULhY1X5cKO+2r9ApFzcJjyjMysdP7XUWSTsaz16pMaVdjo+9rkMCn1NhAFwESLnq9Tw+RmQ7\nJLhSPxIjSIJOvw7VGKGdHDF+xOZQtj6/j481CUz1zHM7xxbUh6dQC2C73U6ZO8XWkSDOx0zfJ/Eb\nZeqk3yBnBr2PCaMaMEKvn4lNrNo/HzEANG9Q6WLKnEi4SbbQMABBEARBcIbIAiAIgiAIzhBHuQBc\n4KO/TpuInnH6RZ89HpdoEcqEJKrXsyx5xi7Rv0QDO1Uk6sapmVbM6M8yl8VN7SZKmIQaDn1vlPlL\nWJpr4E1it9sdtNPpecoWKVAfOw147969qrpOeck2vPiTX5vcTVSOs+cmIpfDyH3gY0+Z54jmFMXo\nz6f3geyB4tnXipbipGx+5Fqby7gokDuH6N1RVs5ewSYSUlH2Uaf43d1DBal0jrdB7wkJVgkktiaR\n5dpAeSH890H2QC4AcolQxkd6j32epfL0Pn66H7no6N5+PxKqUwErv57sgcoUe1t75caXimNHCAMQ\nBEEQBGeIoxgAL/XZEywRAzDKBkdCMArNuby8nD4rU9enn346HRNbQLsE2nW0z9eCMoxRuVkSeSzd\nTfTKQq55de+ZISknO+Wt1nONQudu375dVbyj1v+qrq/axQwQS0T2Nwrpo3oEdO2lObhHNTKoJK36\nhPphjdhsNgdllClzHwnwvMwzhe4+f/68qq7vsHSMGB6/Dr2fbbvb+/XKrPozOQMg5sqP6bvebrWX\nxH0UIuttaEPP1jxHeLZQgXbslFmR2DbqL5+PVR7cd9wPHjyYPv/85z+vqqq7d+9OxzRWVDKcsvXR\n75v/3tBvlB+jOaknhCYRKonOb8IahwEIgiAIgjNEFgBBEARBcIY42gXQ0pTkAnA6TnQHxbuOhGAU\nC+73E43jlPCzZ8+q6rpwkNpAeQCoeA0VfnBKhmg7itWksrQkpNT91hzbK3hmSIqjp6x/JAykGF+B\nRHIulHEqTxSr020UK03uFRKLUV4BdwHos/+/bas/A4leyW78+USLO6W8Zmw2mwMxo79PRH3r2KhY\njq7jVDpljSP3AolvHWQTlO2UKPkRKHug2uvHqBgQib7aWPc1zxWvXr2a3DQ0n6u//T0nFwC5jmgs\ndB2fF379619Pn997772quu5uIlvTNclOqdgP/eZV7V1C7qaWq9LtuH2mKs4TQ1n/SEC/FGEAgiAI\nguAMkQVAEARBEJwhjs4DIHqGaG6iTUSXjGKuRaX4deUOePjw4XTMFZ+6tpT4VXvqn+ozU4pY/x65\nCgjURkrnS9QcxXRSMQthzQrfqsPCJKRE9WdS31GBGHfbSD1L8bNzMbAUR0+2JlAMtreVaFpvj2hE\nihzwY6L03b1FuQH02elJubcoLn6N8OIvajOpnUcFtyhVMhXB0fh7/xDVPnqPiE7uFS2j4kJVe7eQ\nzyG6NqnH/dnJVaRj3i7NY5qv1uwCuLq6OphXqZAN5YDwftAzU+E0KqDjx7zfHz9+XFX7+cXb4CDX\nSy/V+Jw9U76ZXgE5ci15+zTWS39vRggDEARBEARniKMYgIuLi0mMRCt0ym4m0OqEVkpUAtJFUQ6t\njDwWvBff723V/WjF5e2nEq6+ClXbKJafntmfhWLi2xLHa2cA2sxuVCiJhKJUbMPZmE8++eTadau4\nqIqjV+aXCjzRbo/ijGmHX1UH8e5VnE2SxH0kaNTO3xktiqVfO9o8GMR8+K6Z2BcS6lEGNSq3SsWi\nfF6hbHEU903lumk8/DoUKy6QuJjyglA2OBeWSVQnMdmaywK/evVq2m0TE0YMjuZz32lrTCkzqL9r\nes+9v7Trr9r32Win3cu/QMwhFa1yuE1Su4lt6uWC8e+9DgMUBiAIgiAIzhBZAARBEATBGeIoXvGt\nt96a4hpFSzg9JlqMYvmJWiXqyqk1USVzdAdRLb2UnqP0wHqWubrKdG0CFY2gvAlE3bSx8KfiAiDx\nGz2fjlHaTafJRHN6X+vaVFiqinMRECWo+7g9UFuJzvf7kUuChFyiPonu9WeRC8D7pn2OU0Cbatdd\ndPqfu3v0mdxH5E70PqPYeRp/yvlAYlIfS3JBkgvAP+scSiFLYjU/V+meKe2z95cobXKTrQ3ffvtt\nffzxx1W1z2XhMfrqT7cRffb3k1LuUp4Gwfvaz1kqnNT5SwvtzAmTyZXbS6NPLgByO9LvyE2KAoUB\nCIIgCIIzxNEiwF5REu1qKEyOQh5odU+iOzrXP9NqnI6Ndu4kuqDCIiPQ6rJXXpLKpaqta2YAvNSn\nxpJ21ST29HGkEDet1Gkn7fbgu8FeSWlihBy98qxzY0Bhh7J3F/JR+KL6zb8nBoBCmJwdWTv0zlDp\nXB0j5ob6eRT2JXuindPcObRjpmxwxC7Q94g1cNBuUnMJFbLxzKba+YsRq6p69OhRVZ0eAyBb9nHW\n7wmV4vW+obBJ9ScxR3OMEDE09DtDvxm9IlL+TN4emlcEEqb6Pci2NQdSaGDCAIMgCIIgWIQsAIIg\nCILgDHFjEaAoEorbHxU00OceXebX8XPnCgO155OrYHRtig9f6kog6pjoI4pH93jX1s2wZhfAdrud\nKG+106lqHSNKzKH+JgGh24hoQhLFVO3pdxJtUZyxZ9yjQh+UxdKvQ4V6RGlSPXhyAbhLTe3yGHJR\nwF6EaM3Y7XaTXVMcPdVeFygDJ7n/fDyob+kceo9IGEjUqoOeyd/ZpcItHXO6X+I4tzFlx/TxF6X+\n4YcfVtXNisD8r/Dy5cupEE5vfJ3ul224W8DfVYHcjkSlE6jgktukjo2uQ3Ocv7/0O6nvkgiQru22\npnaPxPBLEQYgCIIgCM4QR9cC0ErMV2eLbgSrZFoRz2XYEnx1TCI6faYStMRMjLIxzbEBLahkKIWD\nOHSMVoKnUOqTGCF/Tu1e/RjValAf+0qXwt409hRmOncdtcvtRv3tTAHl+Sbhkd+bGACJ+ijMkTLP\neRvUbhd8qbSx5y5fM3a73UEYLImYKIzJa0H0WDsfc7ENvsOiugCjctC98GHK8uk2QcJXajfNP247\nVFtC9ujjLxHgRx99dO0Z14irq6vJhnv24GJYMSEUckvzqIPm1JH4W/ZE88aISaZQxFEtErJJgQSd\nJAwkxiAiwCAIgiAIFiELgCAIgiA4Q9w4DwCVKaV4RIplJDEPiS162dmqOOsaFX8h90IvG9MczUSx\n+fTdXrljAsU1iwYeUV5vErdu3apf/OIXVbUX8TiNJurP+1jHiO6lfABOyQtz/a/v+nV6JZdJBOZ2\nKjeXj4+LlUTfukBJ/yd7Jtt1Olv39mdWP52SC0D9Szbf5o3QOVVM1ZIrj9yJRNW27WpB8eNkW0Sx\n0xw310Y9K4naKF+EZ63U++IFbf75z39W1d4VsOY8AF4OmDIiygXgYkh9n/rG+1pjQHk/fBxH+VvI\n/Ue5G+gYFbHzuU1uRL82ic17bmoS2vuYq19vIgYNAxAEQRAEZ4ijRYBaxbZMQNV+pUtCMMp2R7n5\nHVoVzZUJpZAPytRFoYhqK4UY0s7QP49KxupZvd1awdKOxqEVrFaRa2YAXASona/vkLV78T5USJDK\nclbtQ5z8XNqxUVY8yvPt1yExD6FXItYZBR8z2sVrhU7387GXqMnPld24QFL9paxvp4B2R+I2TH1K\nfa9z5kpzC/o/zQt+nZHgj8R7FOJFdknPR7nfXfAp9shZAZoXNe7a9VftwwD13qydAdD7qvfT26s+\n8XDYBw8eVNV1gSQxAL3QYw/F65WIVxvn/j8Kw6ZQbn9/KZSYBMCULZTGleyU7rEUYQCCIAiC4AyR\nBUAQBEEQnCGOcgFsNpsD4Z2XdhSN41Sd6BCnRUSXeLyz6KGRkIGEdZQHYCQgpBhfyuJF1L5D5ztd\n04vVJAGTn9v2w9ozAUqoo2d59913p/9rzL0/RWkrhrlqT2WS2MWfX9dzus2vrX50UR6Jssj1QgIf\nylRJtkbZHf1cyl+gz25/cpk43S/x15MnTw7asla0dk0uGyr+4mNE4mESSpGAdFT6l8pP635LadRR\nxkC/n8aa8kW4nai/3D3297//vaqq/vrXv07HZAskZl0bdrvd1LeUV0M0uLvt5FaUK6CKs3dS8S/6\nvXFRJY0vidfpGLmAKQ8F2ZXbg+yA5iF3Xait/l5Q/h3Nh3EBBEEQBEGwCEczAG0+fBcyiA3w1ZxW\nQNrdVO13fCTqGa2efNVEDACJfrz9AmXnopU8ZXjyHbtWfn6MagGQOFHfo2fWqm7Nq/uLi4sD8Z+v\nUIlR0c7/b3/723RMO17K5kaZ/hy+86MQGR0j8R6Fjrk9yLZ9Be7sg8bN3wGyIZ1PWf98p6Lc7i74\n0rFTYQC8RLRs2cOixPr5zpdCcikklMov6zNlB6ziTG0UdkjCXWL8SFBMpa19p6odH2X982fWvPiv\nf/1rOiYGwMMAZXe615qFwpvN5kAY6Tty2bWPhfrG2USq6UHZR7WD9nv4O6b/k8DOr6PPZEvEQs/Z\njWyD3n1iN/23QLbh74ra73bzOqXCwwAEQRAEwRkiC4AgCIIgOEMc7QJohThOT4i6odKNntVJdB0J\naZzaoCxKTr3pHBJmESVPscAOoqwdon68PeQCIGGS+olK3pKI5RSKATndq/G9f//+9H+iKEVpuwtA\nGc082536hGKBnS6k+Fuikt1uiALWmJEQze/hn8nW9NkFf+ojf1f0fHr2qn2f/OUvf5mOKebbXWhr\nhs8R6itvu0Sg/v7JbUTCST9G5cYFdwF4bhIq3iNQDhB/F9V+ys7m7fL/a9w9rl2iNhdM63suhP7g\ngw+qak/7+zH/nkCx8WvDZrOZ2knzOeUK0fd/9rOfTcc0pvQ74u+a5gsq613FYnMS/JELgN73kfuF\n3H+yfcr4SKWu/fn0LN5WEtYuRRiAIAiCIDhDHMUAVB3unClsikRRfozybmtVRSEWtOvyz7RD8xVe\nLzTQQe2i9owYB9rxaTXnq1Fdm4RlpxDi49Cuj/Kdv/fee9Ox3/3ud1V1nQGgXa76gUqC+oqY7MrH\nh3LFkz1QuKBAok+/H5WaHdUjEFvh4ZBtjveqPSvi4UFrhguF1Vdu3xK6OWQzFCZJLBqF/JF4s+r6\nTlxQ+0g46G3167RtcPbIbVRiWGfC9NnfDdm3j/U//vGPg2MUIqvn1253zbax3W6nftRfmlOd/dPY\na16oqnr48GFVXX+PNbZuD+p/H0ffaavffY6gTHpqFzG2/h4TE+6fSbyoaz979mw6JqGs2xUJYYmp\npPoaSxEGIAiCIAjOEFkABEEQBMEZYnMMxbzZbD6pqvf/e80JAL/e7XYPxl/73yP28MYQmwgcsYeg\nxSKbOGoBEARBEATB/w/EBRAEQRAEZ4gsAIIgCILgDJEFQBAEQRCcIbIACIIgCIIzRBYAQRAEQXCG\nyAIgCIIgCM4QWQAEQRAEwRkiC4AgCIIgOENkARAEQRAEZ4j/A3N3Ozv5gXTmAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xd3157b8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plot the gallery of the most significant eigenfaces\n",
    "\n",
    "eigenface_titles = [\"eigenface {}\".format(i) for i in range(eigenfaces.shape[0])]\n",
    "plot_gallery(eigenfaces, eigenface_titles, h, w)\n",
    "pl.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Explained Variance of Each PC\n",
    "\n",
    "We mentioned that PCA will order the principal components, with the first PC giving the direction of maximal variance, second PC has second-largest variance, and so on. \n",
    "\n",
    "**How much of the variance is explained by the first principal component? The second**\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Eigenface 1 explains 19.35% of the variance.\n",
      "Eigenface 2 explains 15.12% of the variance.\n"
     ]
    }
   ],
   "source": [
    "for eigen, var in enumerate(pca.explained_variance_ratio_[:2]):\n",
    "    print(\"Eigenface {} explains {:.2f}% of the variance.\".format(eigen+1, var*100.0))\n",
    "    "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Potential Issue \n",
    "\n",
    "Quiz: Explained Variance Of Each PC\n",
    "We found that sometimes the pillow module (which is being used in this example) can cause trouble. If you get an error related to the fetch_lfw_people() command, try the following:\n",
    "\n",
    "pip install --upgrade PILLOW\n",
    "\n",
    "If you run into a different error, note that there are changes in one of the parameters for the SVC function called on line 94 of pca/eigenfaces.py. For the 'class_weight' parameter, the argument string \"auto\" is a valid value for sklearn version 0.16 and prior, but will be depreciated by 0.19. If you are running sklearn version 0.17 or later, the expected argument string should be \"balanced\". If you get an error or warning when running pca/eigenfaces.py, make sure that you have the correct argument on line 98 that matches your installed version of sklearn."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## How Many PCs to Use?\n",
    "\n",
    "\n",
    "Now you'll experiment with keeping different numbers of principal components. In a multiclass classification problem like this one (more than 2 labels to apply), accuracy is a less-intuitive metric than in the 2-class case. Instead, a popular metric is the F1 score.\n",
    "\n",
    "We’ll learn about the F1 score properly in the lesson on evaluation metrics, but you’ll figure out for yourself whether a good classifier is characterized by a high or low F1 score. You’ll do this by varying the number of principal components and watching how the F1 score changes in response.\n",
    "\n",
    "**As you add more principal components as features for training your classifier, do you expect it to get better or worse performance?**\n",
    "\n",
    "\n",
    "\n",
    "- better\n",
    "\n",
    "- could go either way X\n",
    "\n",
    "- worse \n",
    "\n",
    "- doesn't change "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## F1 Score vs. No. of PCs Used\n",
    "\n",
    "Change n_components to the following values: [10, 15, 25, 50, 100, 250]. For each number of principal components, note the F1 score for Ariel Sharon. (For 10 PCs, the plotting functions in the code will break, but you should be able to see the F1 scores.) \n",
    "\n",
    "**If you see a higher F1 score, does it mean the classifier is doing better, or worse?**\n",
    "\n",
    "\n",
    "- better x\n",
    "- worse\n",
    "- need more information"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "PC #:  10\n",
      "                   precision    recall  f1-score   support\n",
      "\n",
      "     Ariel Sharon       0.10      0.15      0.12        13\n",
      "     Colin Powell       0.43      0.53      0.48        60\n",
      "  Donald Rumsfeld       0.26      0.33      0.30        27\n",
      "    George W Bush       0.66      0.58      0.62       146\n",
      "Gerhard Schroeder       0.17      0.20      0.18        25\n",
      "      Hugo Chavez       0.25      0.13      0.17        15\n",
      "       Tony Blair       0.50      0.39      0.44        36\n",
      "\n",
      "      avg / total       0.49      0.46      0.47       322\n",
      "\n",
      "PC #:  15\n",
      "                   precision    recall  f1-score   support\n",
      "\n",
      "     Ariel Sharon       0.24      0.46      0.32        13\n",
      "     Colin Powell       0.64      0.72      0.68        60\n",
      "  Donald Rumsfeld       0.44      0.59      0.51        27\n",
      "    George W Bush       0.82      0.68      0.74       146\n",
      "Gerhard Schroeder       0.39      0.44      0.42        25\n",
      "      Hugo Chavez       0.60      0.40      0.48        15\n",
      "       Tony Blair       0.50      0.50      0.50        36\n",
      "\n",
      "      avg / total       0.65      0.62      0.63       322\n",
      "\n",
      "PC #:  25\n",
      "                   precision    recall  f1-score   support\n",
      "\n",
      "     Ariel Sharon       0.56      0.69      0.62        13\n",
      "     Colin Powell       0.72      0.87      0.79        60\n",
      "  Donald Rumsfeld       0.48      0.52      0.50        27\n",
      "    George W Bush       0.86      0.82      0.84       146\n",
      "Gerhard Schroeder       0.56      0.56      0.56        25\n",
      "      Hugo Chavez       0.89      0.53      0.67        15\n",
      "       Tony Blair       0.70      0.64      0.67        36\n",
      "\n",
      "      avg / total       0.75      0.74      0.74       322\n",
      "\n",
      "PC #:  50\n",
      "                   precision    recall  f1-score   support\n",
      "\n",
      "     Ariel Sharon       0.62      0.77      0.69        13\n",
      "     Colin Powell       0.83      0.92      0.87        60\n",
      "  Donald Rumsfeld       0.67      0.59      0.63        27\n",
      "    George W Bush       0.87      0.89      0.88       146\n",
      "Gerhard Schroeder       0.74      0.68      0.71        25\n",
      "      Hugo Chavez       0.77      0.67      0.71        15\n",
      "       Tony Blair       0.83      0.69      0.76        36\n",
      "\n",
      "      avg / total       0.82      0.82      0.81       322\n",
      "\n",
      "PC #:  100\n",
      "                   precision    recall  f1-score   support\n",
      "\n",
      "     Ariel Sharon       0.69      0.69      0.69        13\n",
      "     Colin Powell       0.82      0.92      0.87        60\n",
      "  Donald Rumsfeld       0.77      0.63      0.69        27\n",
      "    George W Bush       0.89      0.95      0.92       146\n",
      "Gerhard Schroeder       0.86      0.72      0.78        25\n",
      "      Hugo Chavez       0.90      0.60      0.72        15\n",
      "       Tony Blair       0.82      0.75      0.78        36\n",
      "\n",
      "      avg / total       0.85      0.85      0.85       322\n",
      "\n",
      "PC #:  250\n",
      "                   precision    recall  f1-score   support\n",
      "\n",
      "     Ariel Sharon       0.57      0.62      0.59        13\n",
      "     Colin Powell       0.72      0.90      0.80        60\n",
      "  Donald Rumsfeld       0.85      0.63      0.72        27\n",
      "    George W Bush       0.92      0.90      0.91       146\n",
      "Gerhard Schroeder       0.68      0.68      0.68        25\n",
      "      Hugo Chavez       0.78      0.47      0.58        15\n",
      "       Tony Blair       0.81      0.81      0.81        36\n",
      "\n",
      "      avg / total       0.82      0.82      0.81       322\n",
      "\n",
      "PC #:  500\n",
      "                   precision    recall  f1-score   support\n",
      "\n",
      "     Ariel Sharon       0.59      1.00      0.74        13\n",
      "     Colin Powell       0.67      0.88      0.76        60\n",
      "  Donald Rumsfeld       0.64      0.59      0.62        27\n",
      "    George W Bush       0.87      0.75      0.81       146\n",
      "Gerhard Schroeder       0.55      0.44      0.49        25\n",
      "      Hugo Chavez       0.69      0.60      0.64        15\n",
      "       Tony Blair       0.70      0.72      0.71        36\n",
      "\n",
      "      avg / total       0.75      0.74      0.74       322\n",
      "\n"
     ]
    }
   ],
   "source": [
    "\n",
    "n_components_list =  [10, 15, 25, 50, 100, 250, 500]\n",
    "\n",
    "for n_components in n_components_list:\n",
    "\n",
    "    print \"PC #: \", (n_components)\n",
    "    pca = PCA(svd_solver='randomized',n_components=n_components, whiten=True).fit(X_train)\n",
    "\n",
    "    eigenfaces = pca.components_.reshape((n_components, h, w))\n",
    "  \n",
    "    X_train_pca = pca.transform(X_train)\n",
    "    X_test_pca = pca.transform(X_test)\n",
    "   \n",
    "    param_grid = {\n",
    "             'C': [1e3, 5e3, 1e4, 5e4, 1e5],\n",
    "              'gamma': [0.0001, 0.0005, 0.001, 0.005, 0.01, 0.1],\n",
    "              }\n",
    "   \n",
    "    clf = GridSearchCV(SVC(kernel='rbf', class_weight='balanced'), param_grid)\n",
    "    clf = clf.fit(X_train_pca, y_train)\n",
    "    \n",
    "    y_pred = clf.predict(X_test_pca)\n",
    "    print classification_report(y_test, y_pred, target_names=target_names)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Dimensionality Reduction and Overfitting\n",
    "\n",
    "**Do you see any evidence of overfitting when using a large number of PCs? Does the dimensionality reduction of PCA seem to be helping your performance here?**\n",
    "\n",
    "- no, more PCs always gives better performance \n",
    "- no, more PCs don't change performance \n",
    "- need more information \n",
    "- yes, performance starts to drop with many PCs x\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Selecting Principal Components\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": "cGNhX3BpYy5wbmc=\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Image('pca_pic.png')\n",
    "\n",
    "# train different # of PC "
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 2",
   "language": "python",
   "name": "python2"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.13"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}