{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Bayesian Neural Networks in PyMC3 with Stochastic Gradient Algorithms\n",
    "\n",
    "To learn more about using bayesian methods, to train deep learning method please refer to this post by Thomas Wiecki http://twiecki.github.io/blog/2016/06/01/bayesian-deep-learning/. This notebook\n",
    "follows a similar structure."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Stochastic Gradient Fisher Scoring\n",
    "\n",
    "Author: [__shkr__](https://www.github.com/shkr)\n",
    "\n",
    "```\n",
    "Can we approximately sample from a Bayesian posterior distribution if we are only allowed to touch a small mini-batch of data-items for every sample we generate?\n",
    "```\n",
    "\n",
    "\n",
    "Stochastic Gradient Fisher scoring is an algorithm which at high mixing rates (or epsilon valuees) samples from a normal approximation of the \n",
    "posterior, while for slow mixing rates it will mimic the behaviour of SGLD with a preconditioner matrix that is as parameter `B`.\n",
    "\n",
    "The mixing rate is controlled by epsilon which is $ \\epsilon = 2^{-\\frac{t}{step\\_size\\_decay}}*step\\_size $\n",
    "\n",
    "\n",
    "Here we run the sampling algorithm with a minibatch of the training samples of X_train.\n",
    "The `total_size` is `X_train.shape[0]`. If the preconditioner matrix is not specified, an identity matrix is used.\n",
    "\n",
    "Reference: https://www.slideshare.net/hustwj/austerity-in-mcmc-land-cutting-the-computational-budget"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Generating data\n",
    "\n",
    "First, lets generate some toy data -- a simple binary classification problem that's not linearly separable."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/lib/python3.5/dist-packages/sklearn/cross_validation.py:44: DeprecationWarning: This module was deprecated in version 0.18 in favor of the model_selection module into which all the refactored classes and functions are moved. Also note that the interface of the new CV iterators are different from that of this module. This module will be removed in 0.20.\n",
      "  \"This module will be removed in 0.20.\", DeprecationWarning)\n"
     ]
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "import theano\n",
    "floatX = theano.config.floatX\n",
    "import pymc3 as pm\n",
    "import theano.tensor as T\n",
    "import sklearn\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "sns.set_style('white')\n",
    "from sklearn import datasets\n",
    "from sklearn.preprocessing import scale\n",
    "from sklearn.cross_validation import train_test_split\n",
    "from sklearn.datasets import make_moons"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "X, Y = make_moons(noise=0.2, random_state=0, n_samples=1000)\n",
    "X = scale(X)\n",
    "X = X.astype(floatX)\n",
    "Y = Y.astype(floatX)\n",
    "X_train, X_test, Y_train, Y_test = train_test_split(X, Y, test_size=.5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
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Wro4kXMY2lHRADiea4Ua7C6MU+RvqTIKUVztzXH9Un9dm6OQRy6zUIv8O1GIy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\nyBvZGwa/CHdTjMFXW56G/8BFaV0fAMqrm5lVyDos/UuNQc7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cfVOu+4blYHP0RCvm/ufb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MbXLbfYu449e8SrKjsv2b2JJq0tEGDGUWQofL6QgRateM0eCKPxsKNIV13NXsaJU16e2ndnzbI2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\nAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7feffeac17f0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots()\n",
    "ax.scatter(X[Y==0, 0], X[Y==0, 1], label='Class 0')\n",
    "ax.scatter(X[Y==1, 0], X[Y==1, 1], color='r', label='Class 1')\n",
    "sns.despine(); ax.legend()\n",
    "ax.set(xlabel='X', ylabel='Y', title='Toy binary classification data set');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Store training data in Theano shared variables"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "ann_input = theano.shared(X_train)\n",
    "ann_output = theano.shared(Y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Model specification\n",
    "\n",
    "A neural network is quite simple. The basic unit is a [perceptron](https://en.wikipedia.org/wiki/Perceptron) which is nothing more than [logistic regression](posterior_predictive.ipynb#Prediction). We use many of these in parallel and then stack them up to get hidden layers. Here we will use 2 hidden layers with 5 neurons each which is sufficient for such a simple problem."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "total_size = len(Y_train)\n",
    "n_hidden = 5\n",
    "\n",
    "# Initialize random weights between each layer\n",
    "init_1 = np.random.randn(X.shape[1], n_hidden).astype(floatX)\n",
    "init_2 = np.random.randn(n_hidden, n_hidden).astype(floatX)\n",
    "init_out = np.random.randn(n_hidden).astype(floatX)\n",
    "\n",
    "def build_network(ann_input, ann_output):\n",
    "    with pm.Model() as model:\n",
    "        # Weights from input to hidden layer\n",
    "        weights_in_1 = pm.Normal('w_in_1', 0, sd=1, \n",
    "                                 shape=(X.shape[1], n_hidden), \n",
    "                                 testval=init_1)\n",
    "\n",
    "        # Weights from 1st to 2nd layer\n",
    "        weights_1_2 = pm.Normal('w_1_2', 0, sd=1, \n",
    "                                shape=(n_hidden, n_hidden), \n",
    "                                testval=init_2)\n",
    "\n",
    "        # Weights from hidden layer to output\n",
    "        weights_2_out = pm.Normal('w_2_out', 0, sd=1,\n",
    "                                  shape=(n_hidden,), \n",
    "                                  testval=init_out)\n",
    "\n",
    "        # Build neural-network using tanh activation function\n",
    "        act_1 = pm.math.tanh(pm.math.dot(ann_input, weights_in_1))\n",
    "        act_2 = pm.math.tanh(pm.math.dot(act_1, weights_1_2))\n",
    "        act_out = pm.math.sigmoid(pm.math.dot(act_2, weights_2_out))\n",
    "\n",
    "        # Binary classification -> Bernoulli likelihood\n",
    "        out = pm.Bernoulli('out', \n",
    "                           act_out,\n",
    "                           observed=ann_output,\n",
    "                           total_size=total_size)\n",
    "        \n",
    "    return model, out"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/laoj/Documents/Github/pymc3/pymc3/step_methods/sgmcmc.py:107: UserWarning: Warning: Stochastic Gradient based sampling methods are experimental step methods and not yet recommended for use in PyMC3!\n",
      "  warnings.warn(EXPERIMENTAL_WARNING)\n",
      "100%|██████████| 5000/5000 [00:12<00:00, 408.54it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 22.3 s, sys: 1min 11s, total: 1min 33s\n",
      "Wall time: 14.7 s\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    }
   ],
   "source": [
    "from six.moves import zip\n",
    "\n",
    "def train_sgfs(model, B=None):\n",
    "    \n",
    "    # Tensors and RV that will be using mini-batches\n",
    "    minibatch_tensors = [ann_input, ann_output]\n",
    "    minibatch_RVs = [out]\n",
    "\n",
    "    # Generator that returns mini-batches in each iteration\n",
    "    def create_minibatch(data):\n",
    "        rng = np.random.RandomState(0)\n",
    "\n",
    "        while True:\n",
    "            # Return random data samples of set size 50 each iteration\n",
    "            ixs = rng.randint(len(data), size=50)\n",
    "            yield data[ixs]\n",
    "\n",
    "    minibatches = zip(\n",
    "        create_minibatch(X_train), \n",
    "        create_minibatch(Y_train),\n",
    "    )\n",
    "\n",
    "    total_size = len(Y_train)\n",
    "\n",
    "    with model:\n",
    "        draws = 4500\n",
    "        step_method = pm.SGFS(batch_size=50, \n",
    "                              total_size=total_size, \n",
    "                              B=B, \n",
    "                              step_size=.1, \n",
    "                              step_size_decay=100, \n",
    "                              minibatches=minibatches, \n",
    "                              minibatch_tensors=minibatch_tensors)\n",
    "        trace = pm.sample(draws=draws, step=step_method)    \n",
    "        return step_method, trace\n",
    "    \n",
    "neural_network, out = build_network(ann_input, ann_output)\n",
    "%time step_method, trace = train_sgfs(neural_network)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A smaller wall time than mini-batch ADVI"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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rF4yuU+l1X8syaa1rGaaqanZK1yKrUUGWqoRUOwdXleivup+qXCw6XI/abSRb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bfP/bW1tVFdXZ27X11dzZ49e0b9OoQQQhy45FwlxNixih2AEBPdjBkziMfjvPjiixx33HHMnDmT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\nI6MzTznYPQkAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7feffeac1668>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "pm.traceplot(trace);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 500/500 [00:00<00:00, 2345.24it/s]\n"
     ]
    }
   ],
   "source": [
    "# Replace shared variables with testing set\n",
    "ann_input.set_value(X_test)\n",
    "ann_output.set_value(Y_test)\n",
    "\n",
    "# Creater posterior predictive samples\n",
    "ppc = pm.sample_ppc(trace, model=neural_network, samples=500, random_seed=0)\n",
    "\n",
    "# Use probability of > 0.5 to assume prediction of class 1\n",
    "pred = ppc['out'].mean(axis=0) > 0.5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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5AZ58+cNeZTuNzsmqGdTofKtPNSM5MWRJuNixAqjPR9TMz2JLSblrTTm0z566\n4AwrC+BkXStue/gt1DW1R115USNrBfnQiViEnmQHcVpTFI305WlvKUkhZqWrKUVDe42HJXBYqU9O\nV7Qyqvd979N7UdvQZkm4uFVVThSlIchf9lbobrd7La1Ul1M4fTZ1L1rKixpZK6jWORHL0BPsIE5X\nfxLNcWVVuNKrWmYWLyta8c63tT2Mmvo22+fnZFU5M2zbfgiluyu4ee/Vp5pRVduMto4w2jrCPf8v\ngpXqciyMcssjjdHz7ub7QBCRhjRtB3E6BxYwNusaafeLZ43Ce4f0a1u/d6ia2UxDwY1z4qF3vk0t\nHbrVzqKldrWIFqxYEmrq23oiu9s6wsJaomh1uUAAgARkZ6TgFKNPu5k65l4j8rxTrXMilqGn10Hc\naKNpZNY10oQrqhottVxUcLM1qJ7PUXu+7Z1duO3htyyP3w+IaMGt7eEeH7R6gSJqshYtljN3aiGK\nLz8faSkhrH10V9SVF3X7eScIv0NPr8O4Vf2J5W820oQLczNsa8pOn5OIz1E5X0XbjDbhooZ3jwIB\nICkUQFsHv7GIiJaovU9KxH9be1dP0Rj1NXa7T7sbePG8E4Sf8eebGcV4HfBkpAln9k+2PDmrq5Jd\nddlILJ41ylR1MhZmIuzd1PS9gncO/zYxD7veP264DxEtUe/ZA8B8DqOxvKibzztBRAP0BLuEl9Wf\njCZfs5OzogmLFBUxC88n+bf3juH7c0ajX2qSqfOLBljn8P05o/Gv/z1l6Is2oyWqrRS8hWOkI+qt\n4vTzThDRBNUejyHaOsKoPtUCQMLQgX0jpEXzVree7aHNwk6N6qraZtzywJtgPXUXj8vF2u9/PWJ5\nt7xj2Dk+r5a60fUGzF3zeEl5ojxtIh6hJzlG6Orqxgulh4X8xDxEIp3tROFmZyQjJzOFWet8T3kV\nVjy4o6dDl1rIeFWbW6/muVUhaLRfoLdmWFPXihSbdcHdKAVrFzcEqNHzQLXOiVgkIk/0xx9/jBUr\nVuCGG27AddddF4khxBxmJmreBCoS6WwnCjclKYTx5w/qVWFNS019m+dChnf9AFgWgiL3xawvmodb\npWCtEi9aP+ECLS1AVRWQmwukpUV6NL7B87empaUF9913Hy6++GKvDx2zGE3USqEMpYPWyod24pYH\n3sTKh3Zia0k5urrORS4r0bk87EbhLisei9RkY8HhVZEP3vXbU34CewSurdn96v1WXfjFahEYM8Vw\nzBZwsQLvw9DxAAAgAElEQVQVOiFMEw4Da9YARUXAqFHyf9eskT8nvBfaSUlJ2Lp1KwYPHuz1oWMW\n0YlaZALlVSVTsBuFm5aahNlT8g2/Z1RxzSmhw79+bahlBIkZjc/LanIKvEWXstgSWbxpsXKtzS5a\nCAIAsG4d8NhjQEWFHAFbUSH/vW5dpEfmCzw3j4dCIYRC5GdyEpGqZWbMpmZaUlpF7cdlRU6zNHqn\nTa7865cCJCSgxkLer9fV5ACxFDlt4BvP3G/nWptteEMQaGkBSkr0t732GrB+fdybyumNiQFEJuqq\n2mbhCdRMS0ojWP5z9TG0XcS0Y9fidKAV7/pdPG4YAFjK+41Ujjkv5cmsz9vOtY7EooXwGKf9zlVV\nQCUj3qWyUt5+3nn2jxPFkND2AU5E1hrlplqZQM20pNTSk+t9sAq1da3IyU7FxToaWkpSCKsWT0S/\n1EShvFq3Aq1Ecnut5P1GImeYl399sq5VePHW3NqBv713TPe7Itc6FgrjEAzCYdlc/dprwOefA/n5\nwNVXAxs2AHYsqbm58r4qKvpuGzFC3h7n0FsTQZw08xoVyrA7gZpdWDz9+sFe7UBrzmpo3ZKEWxaO\nNzV2NW6ZXI3GYLUISSQLmOilPJlZvG0pOajbqAXgX2v1s0KFTmIUxe+soPidAeDRR63vNy1NFv7q\nfStcfXXcm8aBCAjtgwcP4sEHH8QXX3yBUCiEv/71r9i0aROysrK8HkrEcSOflpebamUCtbKwaOsI\nY8c+fRPXjn2VzM5iInm1bptceWOwk/frZc4wb4GVkhTCpDFDULq7os/v1Iu3to4ws486AAzMTOlz\nrXnPSrRVXSM4uO133rDh3L4qK2UNW9HiCe+F9tixY/HCCy94fVjfEYl8Witan5WFRfWp5p6OVVpa\n28OoPtWMwtxMS+dAJlc2RgssZXvZ4S8BoCfAcFBWSk8xG4W6xnbUcvL1x58/qM+1NnpWvAw6o2po\nLuK23zkUkrX19espT1sHepojRCQja0W1PusLiwSDPRtt56OtIJadkRwTJle7gsZIaGq3d5/N8Jp8\nwdA+CzCeRSM1OYRlxWP7jN0PRV2omIsHeOV3TkuL+6AzPegp1sGLohMi+bSRpK0jjCPH6izlGQ8d\nmIbU5KDuttTkIIYONF418+5BMBjA0vlFmDRmCAZkpKCuqR1lh7/Etu2HuLnGfsVK3rSCcp0azrRz\nhSZve9nhL3ULvbDy9WdPyUeapqlLJHLS9dhyNp2Nirm4iOJ31oP8zq5DmrYKL1fpfjXzqq/BybpW\nBALQbe7BW1ikJIUwc3J+r0A0hZmT87nnJnoPtm0/1Msv64f62lax4oLQXqcB6Sk41ahfz722vhUV\nVY2mLTtmYiAind7V1dWNLSXl+MveCt3tkSjhGtOQ3zli0BOswutGC05E1oqaVEW/xzKhalG0sKra\nZt193rRgLAIJCdhTfgK19W3I0fGbihxf7x74xRTrBFbPRXudWAIbkIVmYW6GaaHKytfv7Orus4gV\nWYRqn0En/c7aRZwWZWGSnWGtpjuhgfzOEYOe2rNES2CYglbTyslKxbjzcrCseGyvftRmrAe8axAI\nAJDkCX5K0VB0SxJWPrSTuU8r5yZ6D7yIB/AqkMnKuYh0YlMzdWwuMvsnW7bsJAYD+NM7Rw2foaXz\nixDu6sbeg1Woa2zHoLMV9JbMG4OtJeW9ft8/NRFNLR2obWizbdESuR45Wako2fUpyg5/Sb5uJyG/\ns+eQ0D5LNASGqdFqWjV1rdhZVok95Scwe0pBz2RkxnrAuwZSN3Df8kswuiAbL5QeFt6nmXMTvQdu\nmmK9DmSyci5GndgGZCSjvqm9j+VGz7Iz9rwcfH/OaO4YRZ4hdWR6XVM7BmSkYNKYIVg6v0j39+rz\ntWvREulM1z81MWbcKUR8Q0vMs/g9MEwNT7Nobe/qCbwx27CBdw0GZadidEF2z29F92kG0XvAC5Ky\nGw/gdVcqK+fCu06Ds1Ox8fbpePLOWdh8xwzcXDyuj/Vj0+3fxPSLRkCSJLy1vxK3Pfx3ZuCb6DOk\nvW6nGttQursCW0rKsaf8hNC1sPr88K5HIADMmVqAppYOR49JEJGChPZZ7AgCL6LN1YhoFnsPVqH6\nVIupiN6UpBCmFA3V/f6UoqHCpmmrmLkHS+cXYcG0kRicnYpAgiysFkwbaToeQH3fItWVyuy5GF2n\nzP7J3Lae/99fj2BHWSVq6tsMFyYi95t33XaePY4IVp8f3vWYO7UQi6Z/FbUN7CA9ryLbCcIJyDyu\nwmxgWKRyQnkmVQW5MIbkuBnZ7Shh0XvgZDyAct+uuKQwIi4SK+diNYiRJ2D/9t7nuHbO6F7pXCL3\nmyfY2zr0y6Dqoe5IZ/ae8q5HZ1c3NS4hYgYS2irMTp5eR5sr8CJ1FXKyUjF0YD9TwUdtHWG8d6ha\nd3/vHaruKT/qZqqa2XvgRDyAct/CEZ7czZyL1UULT8C2toexpeQg1nzv673GZHS/szNguIgUoWjk\nQDx71i9udhHMux7BYMCX6ZUEYQV6WnUQmTwjnXakaBZ/e+9z3ZKhymRkRiMTDQTzogmEW7W6efet\n7PCXQnW5/YTZ65SdkYycrFTd/uAA8OGnNWjrCPdKybr2bKAa636LLCKNCAUS8Nb+470+s7IIZl0P\nalxCxAr+m4WihEhGmwPnNItr54zGlpKD+PDTGpxqaOszGZnRyERN35HsXGUXo/s2f9pIhIIBy5O7\n32tepySFMO68HOzU6V8OAKca2lBb34o/767o4z7YePs30djcqXtuWqE4MDMFZ1rDzBr0WsLdOhV8\nzuLEIjian1mCUENPrUUiXQFKIS01CWu+93VDYSGikZk1fXvZucopjO5bTlaqpcndbzWvec/DsuKx\n2FN+QrftZk5WKra/fdR0epSeUNSmBlrFyUVwND6zBKGGosct4mbakRaR6HR5MjoXMaz3G5H9OBGV\n7WdE75v2ehrhdaoYC5E65v1SkzB7SoHu7yeNGdLTBUyLSAS9+rrpPUusmvQ8jBbBelkAVrI5vM4C\nIQgr0JLTBm77yaxob3q/UdK43jtUbbifeDAjOn3fIh3foEY0OJJ1Da64pBB/3lOhu2+zGq/es/Ss\nQblRPViL4D5VATNTkJ6WhDOtnaasHX6zkhAEj9iajT3GbQFnJTpd7zfaxh0i+9EzI/rdXyuK0/ct\n0vENCmYWD6xr0NYRdtztk5IUwuDsQK9e3iIMzuYvpvpUBaxv65UTLhrIFqksEMKAlhaqa64DLSMd\nwKwpVQQrhT7M1qQWLRhip3Wkn3HqvnlVTc/IfGul8I32Grjl9lG7D3goFcye+PGMPhXd1Jh51nnP\neXNrB/723jHTvyNcJBwG1qwBioqAUaPk/65ZI39OkKbtV6xobyKV0kT2o4U0ET5u566Lmm+dCo70\n0n2gZe7UQty6aILh98w867znfEvJQd2APKPfES6ybh3w2GPn/q6oOPf3o496OxYfavv0NPoUKxOw\nSKU0kf2o8ZO/1s+4Gd8gumhyavHgpfsAABKAno5gotfLzLPOes7bOsIo/6yW+buBmSlULc1rWlqA\nkhL9ba+9JrcC9UJ4hsPy4uG114DPPwfy88/1Cw9Fdr6j2dZnqP3GZidgs0UuRCZyv/hr/Y5b8Q1m\nF01OLh5EiwzZyf8fnJ2K//w/UzF0YFqvzAejfZp51lnPeV1j+9lyv/qMP38QLUi9pqoKqNSvIYDK\nSnm7F61A/aTta6An0iewor6vuuwreO9QtfAEvGTeGBz8rBYV1Y3o7pZ9hPlDM3DBVwag7F9fmp7I\n/ZKPHi04nQdsdtHkVfS/2YjrsYyCLlPH5qIwN6PXPveUn0BNfRsGZaXg4nHDmPvUK+iiRI+LPOe8\nZzs1OYRlxWNNXRPCAXJzZa22oqLvthEj5O1u4xdtnwEJbZ/AivpeMG0kNt8xQ3gCfr70MI6eaOz5\nu7sbqDjRiPHn5Zjaj4Lb/tq4woJ/zOqiyc0ysHWN7SjZ9alhARa1YD9Z13o2RzsB7R1hXYH69OsH\ne2U61NS34fW3j6JbknDLwvF9xsKLgBetW896tmdPye/VOIXwiLQ02Qyt1nIVrr7aG2HpF22fAc24\nLiMygRiZQGd/o6CX+ZB3LCNTam5OP9NjprrNNrHhH/PLokmrWSck6H9PbbLXLkSVgK+Zk0Zg+aLx\nvcbe1hHGjn2f6+7zzfeO9TSr0UO7QDGzYKFn24ds2CD/97XXZCE5YsS598UL/KDtcyCh7RJmzIc8\nE+jJulas2vBWr5xV9e/VAtau/5k35lgvuOIqNv1jfhAsWgEsMUqFK89ZdgaYC0i94K/qUy3MKO62\njm5s/uMBrLnmQseLncRDMaGoIxSS34v16yMTue0HbZ8DPZ0uYSZNSiQSVvt7PQE7acwQ5GSm9Cow\noSDifzYac8zUbfYyjcOGf0y9IIukYDGTsiXSY1t/AcluGAIAf/+f48jol8RMMbRb+Cdmnu1YIi0t\ncmboSGv7HKi4ik1YNb7NFEbhFbRg/V6v1nXp7gqkp+n74YxMqVaKuUQdkSjaIOIf08AqZpMYDDhe\nxEcEMznR53psixWcUd6f7PQUhAIMm/tZ9J7DWC38EzW0tACffSb/N5ZQtP1Dh4AjR+T/PvpoxNO9\nANK0LcMzJVsxU6tNoDX1rVzzY/WpFqaAPdPaiXmXFKLssLlI8UikdnleFjUSaRwW/GN+K2bDswQF\nAoDU3TfP2sgXnxgMYGtJ+bm64VmpkAy0bb3n0G/XKm7wcR6zo0RS22cQQ1fXW3iTxfXzxpiO+FX7\n1qpPNePep/cyzdyAxBWwxZefjxvPLh5EBaKXqV12GjRYFvROpHFYMaub9I/5sZgNTwDPnVqI4svP\nF+qxrV5A9qkbbqFIih+vVdxgtAD2YSWxWIGeaAuITBZWI35TkkIozM3ExeOGMX8/dGA/QwHLavhR\nfaoZQEKfaHTexNw/NRGJGmFqR0u2oh3Z7sRkJ42DpVXcey9QU2M8MZnwj/m1mA1PALOuPy8ly0yN\nfAXtu+PXaxXz8BbAJSVAZydQWhrbGngEoatoAZHJwm7Er9EkaWZR0NXVjadfP4gd+yrR2i77BFOT\ng5g5OR83LRjbM+kunV+Eg5/V9srzBoCjJxqxbfshZgCcGeFpVTuybQa1ksahaAuPPAI8/vi5zxWt\nYts2oLnZeGIyEQ3r12I2dqKstQtIszXyU5NDmD0lv8+7w7tW2RkpSEsRn95ipYOdJ/AWwMeO6b8r\nQMQricUK9HRaQGRi1U5yaSkhtLSF0dnVLSTcjCZJM4uCbdsP9WnP2drehT+9878IJCT0CL3Orm6c\nae3UHY8iUF8oPWxLeFrRjhwxg5oxU6s162PHZMetHk1N8n9ZE5PWRCjgH/NLXjYLJ6KsjSqR9U8N\n4VRDGwZmpmD8+YOwrHisbqET3rU61dCGtY/ustR/nnppG8BbAAeDQJdO6p4PKonpEoVm/LgT2k6s\nqM1MrInBAP70zlHLkwJrkhTVfNo6wtjDMUXuKT/RI/SMBGr1qWbbwtOKJumYGVTUTK311+lNQnoo\nE1NSkq0gnaUzv4L0qkrsqgrjRIsUcwU/jCqRmdHm1YtX7TNltf88BbIZwFsAs96VSFYS0xPMURxI\n5+/ROYjTK2pRTdftScFI86lrbEctJ8intr6tR+gZCdSOzm5mLrmo8LSiSTpmMhYxU/P8dUYoE9Om\nTdai1M9OJMHXXsM1n3+OxSNGoGXOlQjd+zBS0lKsjcmnGLl/RLV5ZfG6eNYo3PbwWzit0zOctaD0\nIpAtZs3uegvgefOAN96QrVNaIlFJjCeYfdwQxIgYeor4OC08RTRdP0S3ZmckIyc7lRmdm5N1rv2g\nUTDaA/+1j3kcN/s1O24y5pmpef46I0aMADIzrUepayaSwLFj6L/lcSA10fcTiVmcrkTW0hZGXVNf\ngQ1Y6z9vN5AtZszuLPMxawGcmOifSmIswdzZKS8u9PCrGV9FXAhtN4UnT9P1Q3RrSlIIF3NaGF48\nbpihr7x/amKf4DQtbvdrdryUJ2syMvLXAfL3FX+2mquvBhoarEWp+7yzkFs4VYnM6f7zdoP+ot7s\nLmo+1i6A/VJJzOh90ilqBMAXDUGMiKIln3VEhKcbiFaFsoteVTY1S+cX4arLvoLU5HMvW2pyEFdd\n9pU+Qk8RqJvvmIEn75yFR9ZczgxOA4BBWSlYMG2kjX7NYhW+tOPafMcM3Fw8zrzWYlQVTfHX6XHL\nLXJ1pOPHgdWrgcJCWZAXFsp/b9hwTujrwTMRWqicRpyDV1XQqP+8md+IEBPVBRUttaLibKvACvnv\nm27iVz/zSyUx3vukLNb18EFDECPiQtN2ekXtROs/JyKBRU1wwWAAtywcjyVXXsDM09Ybe25OCFW1\nzcwFT0ICcPdNU1GYm2nrPMxgWzMT8WXxtAVl8mH5xkMha80GfN5ZyCnc9PFasca40YzFDxY2W/C0\n1OefB956C1i4UD9oS23BiqS2ynuf8vNl/7s6NU3BBw1BjPDxk+McTglPK34qNzs0mTXBKYVbzMBb\n8AzKSsXQgcatPn2DqAlaNK+a5Ru3YiL0eWchuzjh4zUS+FbcLm50+fJrrr0wRnEdn3/ed6Hrt2hs\no/dpwwbZ/x5pM74FEiSJVeU68hw/fhwzZ87Ejh07kJeXZ2tf6klDtKKTlq0l5bqCf8G0kYZ+Kqc1\njLaOMFY+tFN3YhicnYrNd8wQOo7IuOyct6/47DPZJN6t00wiGJTNeU5qB2ZzQNUTH0vDj1LsPEPR\nGNQV1e9MS4vsNtLTUtUUFsrm77Q02cWkJyBXr45cEKXI+xSFedpxI7QVrApPp4Sk3XEoVNU245YH\n3tRtLBJIAJ68cxZyc9hasJmJ0IkFjy/gTUbqCSjSROFEwsPuu2NXALLeNTdN9VH/zrCEsBploZub\nC1xwgX6qlx/eqxh7n6J7+W4Bqz5Rp/xUrD7Y86eNRE5WqvDkYdcEZ8a07oYJMSK4aYJ2cmLwsLOQ\nF3nEdt6dhjPteOfAF7rbjDI/WAvTJfPG4PnSw45o7qzrp/fOAPJ7FhXvj2ImfvVV2dythxJrYaeu\nvxf4sFOXHXz+5PgHq0JS+1LrCcvS3RUo3V2Bwdnik4cdP73VFDin0nMiitbfPHw4MH263PzDCn7z\n5Qnilcm5rSOM9s4u5GTp1wpgvTtdXd3YUlKO3R+eQP2ZDt19Gwl81sJUW1/fSjqW6PVLSQphcHbA\n3+Z9vQWnOq5j5Urguef6/k5Z6MZJEKVfYD4xS5YswTE9c0ecYjY9pKurG1tLyrHyoZ245YE3sfKh\nnXji5QPYU36CeQxl8ti2/ZDQmJbOL8KCaSMxODsVgQTZ1GiUftXWEcaRY3WGlc1iFmUyOnAAuP56\n+bMXXgAmTOid+iUKKzVm3TqnR+4oikA7WSf3bjf77Bmhfv5ve/gtnGnRF7ysd2fto7tQuruCKbAB\n48Uya2FaUa1fc0CbjsVLpTRz/dy+1oa0tMjxHNpULaP0R0AWylu3slMcle+w0iRjIIjSbzBVgYUL\nF+LGG2/EokWLsGzZMiQmJno5Ll9itkmHnkYtgmjBFzNma61mEAjox2NFRXSrE9x9d2/twUoZwygt\niOJFpT7t89/aLtekTk0Oob0jzH13tpSUGxbzAfgWJZ5JXu+5B84tWI00YzPXL6JVEY2sQKKlPEWy\nKdwoqmLF5RRj/ms9mE9LcXExZs6cicceewwLFy7EnXfeiYKCgp7tI0aM8GSAfsJMkw7Wi8oSlmrM\n5nKKmK21kygr/NAPnaRcxylh63dfHgO384h5fuj+qSE8tOoyDB2oX1SnrSOMdw9Wc/c/ICMZl00Y\nzrUo8dxZRgtWo3gPM9cvojnbPKG8fr35d4DnGzbRftYQKy6nKHVTWYF7Nunp6fjJT36Cu+++G6tX\nr0ZWVhYkSUJCQgJ27Njh1Rg9wUxAjkiTDrOrfDV62q6dgCGjRYTUDQzKjq1OUlycErZR6stzK49Y\nseb888AJ3cYdgNwyMzkxxNWQTze1MY+R1T8JG2+fjsz+/DHyYj4Kh2boavKK+8tIMzZz/SKWs220\nML3pJvY7UFEhbxs92vxxnQj6stLMI4obgJiFO/uXlZXh3nvvxfjx47Fjxw5kZWV5NS7PcCMgh/ei\nDs5ORVpyCBXVOrWrz6LWdp0YH28RAQm4b/klGF2QHfsatoJTwjZKC6K4ValPq6HqYSSoeO8OAFwy\nfpihwFZgubPU0eNaN9fJulYBzbif8PWLWH90o4UpwH4HAGDjRmDzZleGxsWKFSxK3VRWYT4x69at\nw5EjR/DTn/4UF110kZdj8hQ3CvvzXtQLRw9G2b++1P1dIADMnVrYS9t1YnxGq/1ICOyItix0Utj6\npUGCSZyu1Mez5qgxElS8d2fksAwsM/FO8txZrM9FNWMz18/NqohMjBamI0eyS3kCQGmpLAy9FnZW\nrGBR6qayCvPtGTVqFB544AGEYswfoMbNIBFWt6z3DlWhrokRESsBxZef36NBt3WEmdHme8pPCI8v\nYqt9HXxT3copYeukL89DnM6951pzIOaHVtC+O9kZKfhG0VAsUzWIccKdpfe56Lti5vpFpM6ByML0\nttvYQtuMsHMy+MuKFSxK3VRWYT45y5Yt83IcEcHNIBHti1qy61PD6HGt6VAen75/r6a+zdT4IrLa\n18E3LQu1wjYzU26r2dGhH7ji1MTks+hWL1pjDsxMwWNrvyls1uYJOS8WfWbeFTPXz/M6B0YL0xEj\n5PQtPWGXlgYMGsTfvxvBX0lJAMsNy7KCRambyiqxq0YL4EWQSEpSCNkZQNlhfZO4Gq3Wm5YSYka6\nBgLydlH8UNUsoukvLJKSgE2b2BOP0cQkOnE1Nsq5rTt3yq09Yyy6laehXmrCD63dp1bIebHo88O7\n4ghGViCesGtqktMieUFcbgR/rVsHfPBB388nTuRbwaLUTWUFH5TjiRxu9dPVYmQ6HJip35O6pS3M\njDbv7pa3m8VMD2uniVRfcy5GxVHsblcKWOTlyXnhn3/ufBEWVvEMj7FS7McMbvWpZhVRieS74ihK\nRLeexnnvvUB6uv7vXnuN/UwZBX9ZeRZ5+6yvl61gLPzSx9sDYu+MTGLVbGzGp8bT6AdkJDNNh9kZ\nyRiczY5Cj7YiKBFJf+GZo40mnv/3/+xtX78euOsufuMFO9GtPstNdVtDddqd5Zv4ikhSUwM0N+tv\n4/m13Qj+4u3z2DGxNLQYqzOuh+dv9vr163HgwAEkJCTgrrvuwvjx470eQi/MTjRWXnSe6fCyCcOZ\npkM/BZA5gafnIyLQjCaeDz+0t/3oUbZQV3/PanSrT3NTrfhuRRbBTi/6fBNfEUmsBnG5EfzF26ck\n8dPQfBYr4iaeLiffe+89HDt2DL///e9x//334/777/fy8FxETWFW6wizTIfXzhnNrG/M+120FkHx\n7HxEaoIrk4QeI0YA48fb2w6whbr6e1YmODfMkxFAr0b/1pJydHX19Qs56c5yy9QedfDqhmdlyTEf\nZn9nJ/jrssvY25Q0NDUi9dNjDE9VtT179mDWrFkAgPPOOw8NDQ04c+YM+vfv7+UwLGMnkEqr0Wf0\nS8Rv/3oEqx7+O1djj5mgmLO4cj7aVTZPoL30kmzWzskxjjrNyWFvnzdPjjZn5bpefbWcC8srYKF8\nz8oEFyO5qWa1XaeyICJaXtRvbNgA7NrVNwDsgw/kRa7aaqN+15wK/lJbxXjvit5z7VNrk5t4qmnX\n1tYiOzu75+8BAwagpqbGyyHYwolAKkWj/+1fj5jS2GMmKOYsjpwPa5VdWckWaF98IUeiKqvxDRv4\nHYy02wsK5N+/8YZ8zDfekP8uKOj7e542kpHR+zhmMbISREFuqhVtV1n0bb5jBp68cxY23zEDN6vy\nt0VRTO16xE3THIWODqCuTn+bYrVR3rUxY+Tn/mtfA26+WQ5ksxv8pbaK8dA+1zFibTJLRKMtJFbX\nCp/i1ItOpjmHYJnAN25kCzRAFtyKqdwo6lS7/corZQ3k2DH5mMeOyX9feaX+77VCPz8fWLJEXlTY\niW6NgXaIdhbBdhd9XmWORAUiVpu1a+V3Rsl+qKyUsyHy8uRgy4KCc1YuM5kMPMGrRftc88b9+edy\nTEkM4qnQHjx4MGpra3v+PnnyJAYZJfD7CKdedF+mPkUbvJe9tFQ2WxuhXo3z0mKU7bm5smbNOqZe\nEIxW6B8+LE92GRnG4zPCyErgc4wWwWkpIW68h11iLV7EMkZWm8zM3m1s1TQ1ycJ87VprvmWe4AX4\nzzVv3N3d8kI6Bv3bni4nL730UmzatAnXXHMNDh06hMGDB0eNP1vBCZ9axDr/xAKKT621la8d3HYb\nkJgo+7C/0G8Tadr3a8eP7EYqSgRKqDpZM56XTdA/NRFrH93laiqWa/Ei0RbJbBTbUV0tC2cezz8v\nFxBSEPUt8yLG8/PlRfLIkeYroQGyth2D/m1PhfbXv/51FBUV4ZprrkFCQgLuueceLw/vCE686ClJ\nIfRPTdQV2v1TE+PLNCeKNoUrL09ehetVn+nXT9YQNmyQJ9BnntH/nlnfr19rHHuQm+pWTjOrRr+6\ndabbqViOlRf1Wd68KXhBZR99ZPz7xr6tTnv2x6tDkJYGzJ8vVyXUcvXVwNixYuN+9VX5mlsZQ5Th\n+ZO0zokKUFFOW0cYTS361X2aWjrQ1hEmwa1FGyXKekEBOadT+c3WrezvmfX9xlmNYzVu5TRrF8Fp\nKSGsfXSX7ncjVupWlGiOZOZZbUaOlKumGWnberiRyaC1ZDz6qNwffMIE/cV5FGVTiBAnZX+co6ur\nG0+8fADLf7HDMK+URV1jO2ob9BuBnGpoI5+2FjPBKoBc4YlX2CQQAK69Vo58NUuU+5Gt4EXgpBJY\n1tIWjs54j1iJZGbFdixcyP9dYqL+50YWqJYW4PXX9bdt3977uvFyspX0SitjiDJIaJugq6sbax/d\nhbE+huYAACAASURBVNLdFTjV2GaquIoaSjcxiVGwihbl5WX9prsb+N3vgHHjgBtvZJv29IijGscK\nXgZORu27IRLvEG2oheQLL8jathKDFAgACQnnUiA7O/X3YWSBMnPdeAWTYiCbQhQS2ibYUlLey9em\nxozGQekmJuFFieoxbx5/5Q3IL/3nn59LWzEbZarVSKw07fBJow8j3Bak6oYdUftuxEDefB/UQlKS\nZPP4mTNyyuKXXwKffAKUlbFzvDMyjK1ZotdNxJIRJ1YwEtoGKBNKw5l2pokQMK9xaNNNBmWlYOak\nEbh2jkFB/HiEt4pWCpskJMgvKiBHnN51lxzgIoKStqKOtxAVqFbKKDpZetEDwe+WIGWVMF0yb0z0\npWLFmqbHE5K7dp1btDY0sDXl5ma5IQkP0esmopHHiRUsQfJxhZPjx49j5syZ2LFjB/Ly8jw9tjZa\ndkB6Ck416vuhAbm95pN3zjQ9gTW3dmBLyUGUf1aL2njtNCSCOjJXG926erV+KdFVq2QznhLNy+pz\nqlBYCBw4IPcRFo0AXrNGPzBt9Wp28JGV32jxOFJZ/T5oUx2tPqdbS8p1070WTBuJm4vHOZpe5gm8\nZzTaBMdnn8kLSr13JhiUheJ558nCvahIP6OisFAWnEYLFu11Gz4cmD5dLpKk1DNw4jgxAgltBqwJ\nhcW8Swpx66IJjh1HmbisEnUTnih6dcYvuECuTKZFyfMcOlTONb3ySn7UeTAIXH+9fiEJPYHKOzZr\nIrHyGy0tLcDKleLjdBCnnqu2jjBWPrST2XZ28x0zove5jbY8bT3MCEknFqGAHFuyejWwcydw/Hjf\nhahTx4lySJXTgRctq8fIYRlYZkHAuhGVa6ZrUlSi9SUblTKcMAGYPBl4+mm2GU5h+HB5wtBDLwLY\nSvCRnYAldf1nVoUqlyOVnaqBzwtuO1nXilrGtqjAqLpeNGDG3O+UL/nuu+XnWrGKaTvzxYnP2ggS\n2jrwJhQAGJCRjISz/513SSEeWXO5JROhG1G5VluHRi1GQWrqlx+QX3JWCdHp0+UVvh56AtVK8BHv\nN0OGyKkzLB+1EhjEsxZESaQyL7gNALabsHIRLiEqJJ3wJYsEmsWJz9oIEto68CaUwdmp2Hj7dDz1\nk1l46iezcOuiCZZ9ek5H5cZlIxKeRqBl+3a5eERlpRwBm5/fezLiNRrRE8JWgo94vzlxQh7L+efL\nJnR1cJpornqURCqnJIUwacwQ5vayw1869ryqo9MJE5gVkurKfGaDI81YoGLBkmGD+FqiCMKriTx1\nbC4y+ycjs7/9fFGj45g1QcZtj2B1CUZewJm6MtJzz+n7Hs1WPLPSU1jZ9uyzfXPElRCTY8d6V9MS\nzVWPokjlOVMLULq7QnebE8+rW6VXfYfbPnTRMrl2giP9WiLYh8TQk+ssbnYAUq/8nTxO1BamsIta\nIzhwQFxb1luxm/WbWTHZhUKyxp+ebnxuimnQyA1QUBA1/j0l7uLnz77H/I4Tz2vMu4qcTB10Al7x\nEyN4Fqh58/y3EI1gjYUYVLucwY0OQLyVP+s4ZqJ1ndbc1URFNHpamtxgYOFC6/XBrXbOMtu0o6pK\nNocbobYOsKwAS5bIKW9+m9gYaOuY6+HE88pzFfm6hrkofqp1buSTFmnYoSw4S0pkS1MwCHR1yRkg\niYn+SJ3zQVOYKH9q3cexDkAwbrqgPo5V054TrUPVRKWJ0YrJWovbnbNyc+Vx8YLKgN7WAd55hUK+\nTDXSLvaMMjMGZaXg4nHDbFu0YsJVxLufTghJJ7HTtlZBWTB3dsqL0K4u+XOtq8gNRN8dHyyUfP7U\nxg5mV/5Wuyo5bSFwq7uTq3jZZ9qqoExLY1sE1KitA6zzUsykPmoJyVrsXXFJIVOYJiQAd980FYW5\nmbaPH9U960W0OSeEpJM45ZNuaZE1az3cWIyY0Zx9slDyqaoUe5hJ73IiCtyJfNqoj0Z3M8rUjj9R\n8Yfde69ctU3t205IkP/L81Frz8uOL9ElWP7k7W8fZcZdDMpKxdCB/Rw5ftTWMAfE7mekap2zfLlO\nlXH1uvGKmXfHJ01hSGh7hJkgMS+7KvHwyzh8iRVBqRX0EybIZVaPHwfKy+V/J08Cn34K/OtfYjmo\nPmwJyVvslR3+kpnqNfa8HEfH4WYwqWuI3k+va52LLFKdKH7i5WLE7Lvjk6YwPl5uxhZmgsTMmvbc\nChKLahOjm1g1k4n6w3JMCC+/mUlhvNibP20kQsFAT9xFclIIgISdZZU4+FmtYzETbgSTCmEntsDM\n/XQidkMUkWfXCbeUshixGkhqBrPvjpdj40CatoeIrvxFTXtulyyNahOjm1gxk7mlEftk9a/GyKqU\nk5WKm4vHYfMdMzD9ohFobQ+jtV0OOnIjLcup0quGOJGCZeZ+OlkhjJfCZPbZteuW8qpcqZV3xwel\nVEloe4iy8t98xww8eecsbL5jBm4uHqerUSydX4SrLvsKUpODPZ+lJofQLUk9QtmLPNSoNDG6jZWX\n3S1/mA9bQppZ7JV/Vqv7PXXMRNRUNHMitsBqlT2rQlJkofHxx/oNbgB3fLlelSu1cq19UEo1TlWl\nyCKSRhYMBhBISOjRQACgtT2MP73zvwgkJOD6eWM8yUONmInRz1gxk7lZ8clLM6kgIqmHRmb02vpW\n/Hl3RXSkGzoZWewXs/eGDfL2Z55h/95Na47baZeA9WvtxdgYxPns619aWjvwt/f0c3j3HqzC7G8U\neJqH6mS+ekxg9mV30x/mZYqbICKLPaOYie1vH+1V5tTX6YZOxhZ4dT+NFhpKvjQPP1YrM4MP3x0j\nfLZcJRS2lBxEa7u+OVBuWyjFZ8lSv8Azk7H8g1b9YaIlE33YSIHnT+aZ0SeNGYKyw1/qbvNluqEb\nsQVO3k+9Z8hooWHUoCYhAbjtNvtj8wM+fHdYkNB2CTt+uLaOMD78tIa5PedsPisFifkA9ctu5B80\n6w/zW21pF2DFTMyfNjK60g19GFsAgP8M8RYaubnGvuqCAnlBQngKzewO40TZz7rGdtQ2tDG3jz0v\nBylJIcdLlhIm0EvrEU3pEvWHRapkooflUFlm9LaOcPSlG/owtsDwGWK5bBYsAEpL9WMwFKKoo1ws\nkSBJSi9A/3H8+HHMnDkTO3bsQF5eXqSHI8TWknLdXOwF00YK++HaOsJY+dBO3QkrNTmIZ//zW+iX\nmtTr+xQk5hGssof33guMH68fZVtYKGvVygQnIhRbWuSe2iL70/vt0bPP4MiR4hOrD5ohqHHiXYoI\nfqkBL/IMJSWdu+fahYZW4CukpwNLl/qjgUccQuZxi+iZv50q+8nz9c2eUtBLYCvf9yQPlWCn9axe\nbRyIZMbcbSVFLByWfYxDhwLjxsn/hg6VPxMxqfusHGrUphv6xT8q8gzxXDbaGIyCAuCGG+QKfh6n\nORHnIE1bEEWbzeiXiN/+9Yiu+ftkXStueeBN6F3RQALw5J2zkJsjVltZbWbXmr59l+4SL/A0l4IC\nQJL0u3YpWs1dd4m31mxpkYW6nnmSpWmvWcNuQLJ6Nd+kbkezdxmyJFnEyjPE2o8fLAcEAPJpG6L1\nUackBXvlTqvTUK6fN8YxPxzlR/sQnuZy/Dhw3XXA88/33aYEKLGicZ9/HnjrLbnrl2JyNJsi1tIC\nvPIKe+wlJfxcYR+WQ1WgdEMdRASpU2mGEcxJJvpCKpsB2qpjaoGtRjGLOx3RTaZvj+GlVxml9Wzc\nyE7p4glFQNbQtaZokRQxZbxHj8oLBxbHj/OjgX1YDpXQwWxGgQ/KbhLOQkKbA89HrUVJQ4laP1y8\nIzIZGqX1ZGTI2uz27cAHH/T2D/KEohp1LWeev1E73iuvBPpxXC95eXzB64eUJdF89HjGbNyBD8pu\nEs5Cd44Dr8yiFsX8TWbtKEU0vUovrWfePGDZMmDlSjlNRht53dEha7nz5hlXmGJ1F9KaJ7Xj1fOl\nqykuNha8kUpZ8lnUum+xUyqVTNwxAwWiceClXmnxfRoKwcZKEFZLiyzYNm7k57NOnAjU18vCaMQI\nIDsbOHWKbSoXCRDijTc9XQ6IO3NG/jsjQw50e+QRcQHodeARI4Cu84erkLhpo/vHjxY++0y2qnTr\ndPELBmVNOtKCmYLWXIfM4xx4qVepySFHzd9R08koFrGSXpWWBjzxhKw58wpQfPDBOVPmsWPy3/Pn\ny6kzeoiYonnjbWkB9uwBysvlf1VV8sLCjMbqZcoSR3us/80fsO337znWajbq8XPcgRPV+8g9IgTZ\nngxgVR27ds5oNDR32jZ/O1FBjbBJZqY84X3xRd9trMmQZ6o0orQUOHBAPq4VU7RRxzAzBVUiDWcB\nkt1Qgz3//T/oSk4lKxbgbtMZu9ip3kfuEVPQFTGA56NO0xQ5sYISna7g605GsYZ6stAT2AB7MjSK\nBudRWQnU1FjvLuTnydssnAVIbXoO6voNcLTVbNTjx1KpdtuSRqpcb5RCqpwgbqReOVVBjbCIOhJX\ni1FqTG6udXOkWnu3aoqOlVQeTtT6u+dNQXtisj8bhEQKP0aDW3EvKRgJfDKV94GWrhGEF53uRk9s\nQgVvssjLA/btA3Jy2L9XhI1RNLgeTmjDUdgHmMmGDejs6kb9b/6A7IYa1Kbn4N3zpmDb5TcC8HGD\nkEhiNRrcjUAxI3cNb3Hr46I+foU07QiSnZFMPbEjBW+yqKoCPvzQeJX/2GNykxAWBQXAihVixVGs\nahR+qXNtBu05h0JI3LQR2598HbfeuBk/XLIJT0+/Cd2BIIA4bjXrZGCWm21e7eT4+zm4zqeQ0HYA\nq5HfvOj0uJ2ovII3WUgSMHOm8cQWCgH797MFd3ExsHmzWHEU3rF4k3c0RNwqY2xs5J7zku9MwpT5\nlyJzcJY7hYmi4VoB7ghYt5vBKO6a/HwgEACGDZMXrEYLVD8U9Yk2JB9TWVkpjRo1SqqsrIz0UHQJ\nh7ukLa9+KC2976/S/NtLpKX3/VXa8uqHUjjcZWkfCyzug7DI6tWSJIto/r/Vq/n76eyUpBUrJGn4\ncEkKBCSpsFD+TWen+WMvWSJJzc3n9rt6tbw/7X5527yiuVmSPv303Hi1aMeYni50fVvbO6UTNWek\n1naHzsUP18oMrGdDfZ2Mrr2a5mZJKijQ32dhodg+jOjslKRVqySpf/9z+05Plz9rbeVff/X9CQb9\nf38iDAltG2x59UPpqrUlff5tefVD0/tyfKIijFFPFgkJbKEtOrGJTqS8SRSQpPx8eVyrVrEnb5GJ\n3S1EhaDoosgpwcEiktfKLEYCtqHB/ALk00/l7+rtMxiUt2vHUF4u/xO9L7x7PXGi2PU3sxCJY6gi\nmkV41dIGZ6di8x0zyLwdLdTWAmPHAl9+qb89EAA+/ti5gBheZSs16elAU1PfzwsK5N/q+eS9aKPJ\nagGqbv/Jq9qmxc1qXj5uOaqLUdUzVic5XutV0Rad4TCwdi3w3HPnnrv0dLkQEK+iXksLMGYMu5Ru\nIKB/Pn68/lEA+bQtIhL5TUQJDQ1y3jQLO+ldrP2JNA/RE9iA3LGL1dHLKMXGLqIpOmby2N0MOLKT\njhQJeM9GXh6wc6f+Nl56lKjfeN06YNOm3s9dU5P8Gc/3XVXF7zDHWpz68fpHASS0LUKR3zGEkRAV\nDYgRDXRKS5Obh1glL08WdHq4HXErIgRbWoDWVnmcIrgZcBRt0ck8ATt9OrsIkJEANMrrb2wEnnmG\n/fuSEvZznZvLv9cBhpjx4/WPAkhoW4Qiv2MI3kQ5caK+KVihpUU27a5cKRbtq0QGv/GG/DdrQuNR\nXAwsXKi/TS0A3YiWNlrgfPvbsjl6wgSgro79vYQEbwrC8O5tVhaQZL+qoeOwBOxjj1lfgBgVZVm9\n+lyTGT14/djT0tjPI8DOrqDocGtE2qnOw++BaBT5HUNoI1jz8uSIcHWAjzpQRv19M1HnosFZyr+M\nDDkwTRtVy4u4dTta2uw56P0bPlySamqcGY8RnZ3RGQylNxY3guqam+VnjHe/Cgr410SJHs/I4EeP\nW4kO99M98QEktB2AIr9jCL0JQk8IsoQALyraKGqcFd1bXs6etLya2NUoKW7BoHWhrRe17BYiKU/R\nkhbmRnoUL7rc7LPDizw3K3yj5Z54DAltgjDCqmapFUwik6OR4DfCi5xcSZInZV6anNPnZQeRlKdo\nSguTJGe1z+ZmtsUoIUGSli93Rys2+m603ROPIJ82QfCw04JT62fk+YPT0/U/N+v3sxItbcb3rfjk\nr7xSnkKt4qU/0ygYLTPT26YVdmMNWlqAo0flYD+n+OY39T+/9Va5b7xRQxIzVdxEvst7715+WU7T\njFcivWrgQZo2EXGsaMc8jYClPaxa5YzZk6c1abVbK+ZHK1aHiRMjX+2Kp7WZKT5iR8O1a+5V/Mbq\nynKK35gVe6GFFZeRkCDvKyPD+D7ZdcmIfNfovRs+PG5N5SS0CYIHTwjyTL+sCcWo5KkTZk/RCdSs\n+dHIJ19QIAvogoK+E78T56Xso6bG/L54gYYiCx0n/Kt2zb28BZMyFtazZSYuQ11Kl3UN1ftvaBB3\nyYi6b0Tfuzg0lZPQjkMocM4krMlSrUHm5UnSsmWS9NFH4rW4hw/vG6HuBCLBSlZ83zztJxCQ/dzK\nvp2M9lXORxmvEgBXUGBeU1WEWkJC7+tiJFDtCly7sQbNzZI0YgRbeOXnS9L48ewxmrGQsMbD2scN\nN4hbKsxYNUTG7GVshE/wXGi/++670tSpU6WdO3cafpeEtrM40eAkLmEJwdZWthDQw+vAGp7wNBLA\nO3b01noUDVfU9O4kRpO3XU1VLbj1FjpOBPcZCStlwcP7vdXAv4ICvsBnjUf97PCuQUEBO2VMT9M2\n677Jy+OP1assBJ/gqdA+duyYtHz5cmnFihUktCOAkw1O4hKtEDQjhL2K6haFN3kGg7KAUZu7jUyq\nyjk7rWWLpMmJaqqiZlnt+M023GAdn2fuVZrEsBZ8RrnUPP9vMGhO4Keny8dSm8A/+oh/DZYsEX8X\nzC5ea2rkhbFf3p0I42n0+KBBg/DrX/8a6axIWcI12jrC2HtQv6LR3oNVpnuBxyVpaXJTi7Q08Rrc\nCnZqYLtR2YxXKayrS64XfewY8MEH8n+VHswffCBXidNW63rgAed7QANiNcxFaliLXn/1PVZwohQq\n73oDcrMNXn9ro6pjvOYzQ4awy97q0dQkj0fdd3vjRv412LiRXyZVjVFJVS05OcB3vqO/LQ6rqnkq\ntFNTUxEMBr08JHEWanDiMGaFsJWJX50a89WvysJw5Ur7glBBPXkGAvIEKkJ9PbBvX+9ymHfeKU/u\nFRW9J3teowkRRJqriAhOO4JXtOGGEcr1Lihgf4eXYrZhA7BqVe/0wIwMYPly/j55ZW9FKS1l18u/\n+mp5HLwyqWqMSqrqYVbQxzCuCe0//vGPWLx4ca9/b7/9tluHIwygBicOY1YIWJn41607JwglSW4W\n8fjjwOTJfMEtqpmrJ8+//U0+hgiVlXJnNKtWBzMYaaiAmOC0K3idEBrK9f7Tn+Ta63rwrAahkKzR\nVlcD5eXyv6oqOY+6uFj/N0rtfO34hw8XH7cyrttuM74GepYKFma+a0XQxyqRsMn/+Mc/Jp92BCCf\ntsOwfHMrVuj7dc2UoDTy5a5Y0fc3dtKSzKS2BYO9o96d8Pny6OyUpJwc/f3n5JiLHrebC+9U6prT\nAX2imQkiQYVW/P5UH9wzSGjHEdTgxGG0QkAvcEtPKIhMcEbRwnl5fX9vNjBOtEAG6586+MzNqPKa\nGnad82DQfOMRJ3PGre7DrUwCM+Myc79Z46L64J7jqdB+6623pOuuu0665JJLpKuuukq68cYbud+P\nRqEdDTnQ0TDGqEKZKFescG4ibmiQpH79+NqutlKXSHQ0b5LVW4SMH8/WotX7dTOdbccOvkDZscP+\nMURxSki50fjDLHpjWLVK/ic6LqoP7jkR0bRFiSahTTnQcY7TKV1GWpB2n6ImahGTvlpb42n86v2a\nNf2b0VKd1rTt4LSQsqOxW/mtqGlbZN9+S2OME6hhiENs234Ir799FCfrWiFJwMm6Vrz+9lFs234o\n0kMjvMBOSpcWkSYl6uCplha5eURenv53lcA43n6fekqOUC8qAu66S45GTkuTf8eKTFYH3IkECplp\nKqEmJwcYN05/W1GRvN0peEF8bgTcmQnGUrByHVm/aWyUn83c3N5jEBmXk888IQwJbQegHGjCkVxe\nBaPc5CVL5Ihd9UQ8YQJQV6f/fUXA8/bb1SXrSNpULbNR17zJXh0NbzYtbM8eORI6oJmyGhqcyQcX\nEYR+EVKrV5u/jqxrn5dnPa/eyWeeEIaEtgNQDjThWC4vYNzCc+NGWYPVTsRNTee+o5eSI5LzrKDW\nHPXSnVaskNs2imqXdrXU7m7gpZeAG27o/fmxY87kg4ssKCItpMJhOU//qaf0t7OuI+/aNzVZz6t3\n8pknhCGh7QCUAx1FONHLmPV7pwpA8CbDpibg7rv5E/GAAXLlMq2JWiTnWUGtOapN34cOyUU2SkuB\nCy4Q19CsaqlaDfj55/W/ZycfXHRBEWkhtW6dnKff1aW/nXUdRarKKZi9jlT0xHsi7VTnEU2BaJQD\n7XOc6GUs+nsnUooaGnr3TdYG+ZSXW8uN1p6HSGS4+nysBmJZTQsTTUuykw9uJs/cTtS33aAzqzXY\nzebgW7mOlKftGSS0HYJyoH2OW72M3UptEekKxZqI8/Pl7bwWoUp3Mtbkre18FQjI++UtJIwmbKf7\ndzsVrWxlQWFGSDmRJsZ7HkSeRdHFD0V9+x4S2g5DOdA+xIlexl6ntogIEtZEnJ7OFw68CVykx7RV\nDc1IS21ulhcbyoJDRFCpBZZompLed9xclDmxb6OubEZ92bXXPiPD20Wo+jxII7cFCW0i9rFbZtPt\nMp0sjCZ77UTM0oLVEzFvAZKXdy7n2YyWa3bxop24Ozvlgh7q8aenS9Ly5ewxKO1DtQVB1IuVhoZz\nxzHSdp0sdqI+P951zMiQxygKL8fe7NgaGrwt7kKV0xyDhDYR+9gts+l2mU4WooKkoUGSrr2WXYBE\nPUbRBYgZLdeuhsbT6Fn9u9XFYEQsDkZ9wBXsaILq+5WQILsfrr2Wfx2XLLG2f6cErVeaL1VOcwwS\n2kR8EG0+bTVGE6uRGVstjEUXILzvpafL2qMTgqO5WfaVs8Y+YoQsoHkmdTMWATcXXaz7kJjIPn5+\nvvnj2xW0XpuoqXKao5DQJuIDu1qKH2pF6yEitLQ+T9EFCO97Tk38Rhp9INC7pKr2eGYtArwFjR2s\nLh7cdK9oiZSJOlLupRiFhDYRX9gVNjU1coMKL+td8zAbrCVJ4gsQve+tWCFJ77/Pj043g5GmbaSJ\nmklnclPTM+rKZnR8p7VfM13cvAg+i4R7KUah4ipEfGGl1jNwrsjH5MnA7Nnyf50on2mX3FzxSlxK\n4QyROuFA36Iqc+cCL7wAXHihXAt86FDgttvsXYO0NGDhQvb2hQv598pMwRg9nCqKkpsLDBtm/ndX\nXinXejdbj50Fr8a403XTRYl0UZpYI9KrBh6kaRO+wc+BNKyWoE6aInl+c7vXQIkeV6chpafLn4mY\nbvV+r/dv4sS+1gV1hLkeZjRg3n1gRfZPmODsNWXdpyVLImui9qt7KQohoU0QRvg9kKazkx0d7cRY\n7ZqwzRxHydOuqTFnLmYJq4SE3gJCL+XJKAVM1P/Luw8soc0yqVu5V7znND+ffQ95x/LCbE+YgoQ2\nQRgRDYE0olXO1IhOoKLBYmbRO74VYckTVsOH68cfsIT8smXymFhas5EGrNyHvLxzGiVPy2X9s/Jc\nGT2nP/iB/rZVq/TPg/KqfQkJbYIwIpoCaZqbJemjj/hpUmYn5IYGSerf3zlNWzl+fr6saebnG1dh\n4wlLs4sq0Yh7O/dbW2DFbLBcQYE1TZv3nC5fLi60/ewOinNIaBOECNE4iTlVstMoD1ykWYh6HKtW\n6e+HVwHNyITL+l0g0LfEp500MauWFTMlYQFJuuEG88fgHWfFCvFr63d3UJxDQpsgRIiFQBrFZ2zG\nt2mklS5fzr4Gehr9ihVsrb1fP+tuCCOfvraUq9U0MatCS/v8FBSw/dzp6fzypjy3Bus5/egj8Wsb\nDe6gOIaENkGYIRoDabTlNc1okXYmcLPaJSBJw4aZF5Yi5m7t762MTSv8raB+fsxaPOy0hzXj4okm\nd1AcQkKbIGIdO20ZrU7gViuEXXedeWEpYu7W64u9YgXbd63+nVuBWGrfvtL6lHcML0vxRqM7KE6g\n4ioEYZWWFuCzz9wtTGH3OC0t7KIaWvQKXVgtjFFVBVRWio8TAPr3B375S2DVKiA9/dzn6elAdze7\n4EhuLpCfz993v369i9CEQsDmzcAtt/B/d8stwMcfswvQAN48B7z7KFocZcMGYPVqoLAQCAbl/65e\nLX9u57uEt0R61cCDNG3Cl3iVDuPEcYy0UBH/vBV/vlHDEZ62L9qRS42RNSE9ne8DVqwCwaDsQigo\nEMvLtnN/zGizTvqZzbh4otEdFOOQ0CYIs3hlOnTiODzhmZ9vroa42QmcNf5Vq3oLStF/PHN8Z6cc\ncc36rVEuuXJuZoq62Lk/ZiO0yc9MnIXM4wRhBifMlF4eh2feXrgQGDv2nImbZ+ZtaZFN3rm54rWi\nWSbWRx6RTc1lZcDw4WL7AmRze1WV/jbF3M0yk+fn82u0KzXpc3LEatPbvT8894HeeVL9buIsJLQJ\nwgxmJ1s/HMfIP8lqMhEO87cZYdSYpKHB3HmMGGEseFnNR5wWbHbvD88PzzpP8jMTAPm0CcIUXpkp\n3TiOlWIrbroCzOZLK8e0kqfsdLyBE/fH6rUlP3NcQ5o2QZjBKzOlG8fRa0vKM/OWlACvvKK/zQlX\ngJm2mhMnAg88YKz162n369cDx445G90ten94LgczmrN6P6LtZb3KbiC8JdKrBh6kaRO+xCttY4xR\nIwAACmlJREFUzovjGEUls4qxOFUZSxuBzav5bbaJh9tR/rz7Y6cQit1zoGYfMQ0JbYKwildmSjeP\nwzPzFhRYa+dodRw7drAXEIEAu4MZayxeRfnr3R9eDXAnIvCtpL9RYZSYgMzjBGEVUTOln4/DM/MW\nF3sX2JWWBkydyg7Oys1lB3fpBX55FeUP9L0/vGM/9RTw1a+KBfRZOQcvz5uICCS0CSLe4flWvYxY\nTksD5s/X3zZ/vrloa6+i/PXgHburS9Z7KyqAxx4D1q2zth/WOUTyvAlPIKFNEPEOLzXLKG3LCUQC\nphIT2RaBb36z72dWUqqcQqSsqgJP++XtJy9P/xwied6EJ5DQJghChmeGV7YBzkUka3PAx4wBnntO\n/7vbtwP33ttb68/IkOuSP/98X3NzJIuRmImK52m/vP2cPg3cdVdf8zoVYYl9Iu1U50GBaAThE9yI\nSDbTHlMdrd7cLElLlhgHW0WyB7r22LyoeF5QmrKfjAzx4LJY6P1OMEmQJEmK9MKBxfHjxzFz5kzs\n2LEDeXl5kR4OQcQva9bIPlgtq1fL5nKztLQAF1wg50+LUFgom+bT0vi/VX9PfSyzJVidQjn2I48A\njz/ed7vI9WtpAb72NX1ftd75ao8difMmXIPM4wRB8HEjItls6061addK3W6no+9FC5cox37sMesB\nfVVVwBdf6G8zMq97kd1AeAoJbYIg+LgRkcwLmEpPBwoK2MItksFWVmux2wnoo+AyQgUJbYKINZwu\nX+mG0OAFTC1dCvzrX2zhFslgq3XrZK25ogLo7hZL3VJjRful4DJCBQltgogV7HTk4uGW0ODlgBsJ\nt0h0vIpk4RLq8EWchQLRiP+/vfsJiTr/4zj+atUx22prYCY32o3o0qVL1EHCThIEIopMFl06SSwI\n1aGwDgWSqYgQumKUmaHo8PWwHjoUYkGHQiSW0kNKsilRpJRRa7Go/A6zM79cR5tx5jvf+cz3+Tjl\n9wt9398xes3nz/f9RaZI9maxb83Ph0aT/f2hKfFffgkFdmNj4s9sJ7JhKpWbrV6+DH0ZWlxcfi4r\nKzQzEH4szi5sLnM9QhvIBPHuqE7kOiaEhh11zs2FZi/++mv5uWR+xslmyu8MMWF6HMgEtK8MiWeJ\nIN61/9WWCbZskTyetddtB7uWS+AoQhvIBHbvMDYlAGLZKJbIvTQ2ht7t/V9//hn7ZrRUSXTTHNIS\noQ1kArt3GJsQALFuFEvkXv75R/rwIfq5P/5In7dorfZZpFOdiBuhDWQKu3YYm/K6x1iWCBK9l9Wu\n8eqV9Ntvic0+JOtxvTdvVu42NznpnuWSDERoA5nCrjdymbJeHssSQaL38r03eHV2rm32IdnLDz/9\nFPriFs0PP4TOw0iENpBpkt2+0pSOXLEsESR6L7G8wWstsw/JXn74+DH07u5oFhZC52EkQhvA6kzq\nyPW9JYJk3Etjo3Ty5Mrn4519sGP54eefQ61go9m5M32+aCFuhDaA7zOlI1csSwSJ3kt2tvT779LG\njdHP//hjfKFox/LDhg1SaWn0c6Wl6fVFC3GhuQqA2P23UYfJjTsS7cSWny99+rT83KZN0tu3sf+d\ndjVtsbOLHRzDSBtA7MLr5R6PGc9tryaRtf83b6TPn6Of+/vv+EbHdi0/2LUxEY5K6W9vfn5eFy9e\n1OTkpBYWFnTu3Dnt378/lSUASIbwxqmw8MYpKfE+5yYIrxlHGx3/+mv8a8bhqfloo+JEhb+cICOk\ndKTd39+vvLw89fT06MqVK6qrq0vl5QEkgynPbdsp2aNjRsWIUUr/RZSUlKi4uFiS5PV6NTs7m8rL\nA0iGWDZOuWFkZ8fomFExviOloZ2TkxP5c2dnZyTAARgk/KxztKnhdHpu227h0XFtrbmb8WAc20Lb\nsixZlrXkWFVVlQoLC9Xd3a3R0VG1tbXZdXkAdglPDUd7d3e6PbedCoyOkUK2hXYgEFAgEFh23LIs\nDQ4OqrW1dcnIG4BB7Nw4BWBFKZ0en5qaUm9vr7q6upSbm5vKSwNIJqaGAUekNLQty9Ls7KwqKysj\nx9rb2+VJt5fHA4gNU8NAStERDQAAQ9ARDQAAQxDaAAAYgtAGAMAQhDYAAIYgtAEAMAShDQCAIQht\nAAAMQWgDAGAIQhsAAEOk9RvWFxYWJElv3751uBIAAFInPz9f2dnLIzqtQ3t6elqSdOLECYcrAQAg\ndVZq353Wvce/fv2qkZER+Xw+ZWVlOV0OAAApsdJIO61DGwAA/B8b0QAAMAShDQCAIQhtAAAMQWgD\nAGAIV4f2/Py8zp8/r+PHj+vo0aMaHh52uiRXGRoaUkFBgR48eOB0Ka5SW1uriooKHTt2TM+ePXO6\nHNcZGxtTUVGRurq6nC7FdRoaGlRRUaHy8nLdv3/f6XLWJK2f07Zbf3+/8vLy1NPTo/HxcVVXV6uv\nr8/pslxhcnJSHR0d2rdvn9OluMrQ0JBevXqlYDColy9f6sKFCwoGg06X5Rpzc3OqqalRQUGB06W4\nzpMnTzQ+Pq5gMKgPHz6orKxMhw8fdrqsuLl6pF1SUqLq6mpJktfr1ezsrMMVuYfP51NLS4s2bdrk\ndCmu8vjxYxUVFUmSdu/erY8fP+rz588OV+UeHo9HN27ckN/vd7oU1zlw4ICuXbsmSdq8ebO+fPkS\n6bppEleHdk5OjnJzcyVJnZ2dKi4udrgi98jLy6NhjgNmZma0devWyM9erzfSeRD2y87O1vr1650u\nw5WysrK0YcMGSVJfX58OHTpk5P9BrpketyxLlmUtOVZVVaXCwkJ1d3drdHRUbW1tDlWX2Vb77OEs\neivBbQYGBtTX16dbt245XcqauCa0A4GAAoHAsuOWZWlwcFCtra3KyclxoLLMt9Jnj9Tz+/2amZmJ\n/Pzu3Tv5fD4HKwJS59GjR2pra9PNmzeNXZpz9fT41NSUent71dLSEpkmBzLZwYMHde/ePUnS6Oio\n/H6/Nm7c6HBVgP0+ffqkhoYGXb9+XVu2bHG6nDVzde/xpqYm3b17V9u3b48ca29vl8fjcbAqd3j4\n8KHa29s1MTEhr9crn89n7HSVaRobGzU8PKx169bp0qVL2rNnj9MlucbIyIjq6+v1+vVrZWdna9u2\nbWpubjY6REwRDAbV3NysXbt2RY7V19cv+f/fBK4ObQAATOLq6XEAAExCaAMAYAhCGwAAQxDaAAAY\ngtAGAMAQhDYASdLz589VVFS0pBd5TU2N6uvrHawKwLcIbQCSpL1796q0tFR1dXWSpOHhYQ0NDen0\n6dMOVwYgjNAGEHHq1Cm9ePFCAwMDunz5sq5evUq3QCCN0FwFwBITExMqLS3VyZMndfbsWafLAfAN\nRtoAlhgbG9OOHTv09OlT3gIGpBlCG0DE9PS0mpqa1NHRIb/frzt37jhdEoBvMD0OIKKyslJHjhxR\nWVmZ3r9/r/Lyct2+fVs7d+50ujQAYqQN4F+9vb2SpLKyMkmS1+vVmTNnVF1drcXFRSdLA/AvRtoA\nABiCkTYAAIYgtAEAMAShDQCAIQhtAAAMQWgDAGAIQhsAAEMQ2gAAGILQBgDAEP8DXGQ+CehIq68A\nAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7feffbd84f60>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots()\n",
    "ax.scatter(X_test[pred==0, 0], X_test[pred==0, 1])\n",
    "ax.scatter(X_test[pred==1, 0], X_test[pred==1, 1], color='r')\n",
    "sns.despine()\n",
    "ax.set(title='Predicted labels in testing set', xlabel='X', ylabel='Y');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy = 97.2%\n"
     ]
    }
   ],
   "source": [
    "print('Accuracy = {}%'.format((Y_test == pred).mean() * 100))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Lets look at what the classifier has learned\n",
    "\n",
    "For this, we evaluate the class probability predictions on a grid over the whole input space."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "grid = np.mgrid[-3:3:100j,-3:3:100j].astype(floatX)\n",
    "grid_2d = grid.reshape(2, -1).T\n",
    "dummy_out = np.ones(grid.shape[1], dtype=np.int8)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 500/500 [00:03<00:00, 148.52it/s]\n"
     ]
    }
   ],
   "source": [
    "ann_input.set_value(grid_2d)\n",
    "ann_output.set_value(dummy_out)\n",
    "\n",
    "# Creater posterior predictive samples\n",
    "ppc = pm.sample_ppc(trace, model=neural_network, samples=500, random_seed=0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Probability surface"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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Cpi+FMAPHDy9hIUsQNeqFCCLXsFoEpTMKNJJb4wlrqaqqAgD09/fjpZdewrFjx7By5Urs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\nIm35/7FGovj0LHHGAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7feff91ff4a8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "cmap = sns.diverging_palette(250, 12, s=85, l=25, as_cmap=True)\n",
    "fig, ax = plt.subplots(figsize=(10, 6))\n",
    "contour = ax.contourf(grid[0, :], grid[1, :], ppc['out'].mean(axis=0).reshape(100, 100), cmap=cmap)\n",
    "ax.scatter(X_test[pred==0, 0], X_test[pred==0, 1])\n",
    "ax.scatter(X_test[pred==1, 0], X_test[pred==1, 1], color='r')\n",
    "cbar = plt.colorbar(contour, ax=ax)\n",
    "_ = ax.set(xlim=(-3, 3), ylim=(-3, 3), xlabel='X', ylabel='Y');\n",
    "ax.set_title('$B \\propto I_t $')\n",
    "cbar.ax.set_ylabel('Posterior predictive mean probability of class label = 0');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Uncertainty in predicted value\n",
    "\n",
    "So far, everything I showed we could have done with a non-Bayesian Neural Network. \n",
    "The mean of the posterior predictive for each class-label should be identical to maximum likelihood predicted values. \n",
    "However, we can also look at the standard deviation of the posterior predictive to get a sense for the uncertainty in our predictions. Here is what that looks like:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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lCY7jcPfdd6Oqqgq33nornnrqKfz6178mHu8Ki1D5whpUVlfpWk70Aoetan9g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AC4traUJHgACP74h7Du3pMce9GH8GWX9HsCFRcBFgvcTzyZnDk292sILb8aQlER3L/7\nQ2oe2Q9Wwf3UM0nPi5d/E/y9azMmQ0qizsaimJIfxVcdSPgLTR1RJpGC9/+CmtY6FEa6k8SJiXH5\nj1/A7xZ8D5wtDwxnTStfTO1J5Lfno7akGq9VLyCH1GYILeNEgG6eGPp/N0rtrkiESC6M2C8YJiU8\nD4gCeIcDrKLqKNhscH65B2PX/AiWjvb+itO3rkvsRw527Zk+Ky0dU7qkYyDJQzrbHi4TbacrPv74\nY/z85z+HzWbDm2++ifvvvx9z587FBRdcYGj9HBEaAsjGuLxZmE2bB1TZYiIHJx9DRaQHU4KpZfRM\nMFiVH607ftKFzejvpXDaGE1NjhLqJHhYLPD/8XdSGv3WDyTSU17WX/2xWBItsBRfoKYmuF7eCOfL\nGyG6XEmj9nIemXXn50kki2tqAp75HQCA/897MyZD6urH2QU+HKyYhG0zLoXIcpizYxNqju+kbn9u\ndwP+sPcThDPIF1N7EhVFunFB3343TlhoeDtmqkEkMkQzT6ys/RjCXV/A0tmu2e4yqtVhA92wHz+K\nyMjREDz5xOVLXvwzfO+kWjNw0Sicx/s9q9QVp3SzyzIJLs0WAcpWJUZvG0baW7mq0MDj0Ucfxfr1\n6/GjH/0IALBq1SqsWrUqR4SGK0gCXKNtMeV6tHX0hM+ktHmgX3hdefQEIpwFdj5uuBI0lKfElMjE\nSVr+MgzefhvA83C+8qrk00MA29Tcb3wow2JB8M5/Q/D226gtLy1fIAbQnD7TyhZjXnsd3SuWU0XT\nRjBt+yb4VNWPmi4pnX772RdhTP1e3W2UxHvhi4fRyFrSEkbTPImmttZh07h5iHFW4uvZBs08kQuH\nwPVpnrTaXbpaHacbo+7+CRzHjyV8oMIjR+HYL+4DbP3+TmbDfYHkipOZ7LJMyYAZsmBEWJ0pBovA\n5PyDMofL5UJxcb9jvs/nSxJN6yE3Pj8EoSQx6WqDaBUMmvBZTpvXQt3RLlggws3HDJGg3SeaTwmR\ntKnfg8WC0IprtPO80DfWrjQ+NAi2rR1scxqfpxYh63OizgS0i+2Y+j0oaz4Ed9BPfF2JVosLHZb0\nCRnNk6gwEkB+lGxnoMTUEWWmzg2tcXnZPNEo1KPpslaHhJ7ps1D1X/fCeewIGBaiQRAAACAASURB\nVEGQCLAgwHnsCEbd94ukZc2G+wL9FadsYyBiKfTG8DPdvjzZpXfs2ZiayyF9OBwObNu2DQDQ1dWF\n//u//4PdRBUzR4QGECRfH7Prml1HCS0yJAufSaClzZv110mXAGWi78mEBOlVg2i/D/UXZXtHDPEi\nbdFeyuh6OAz26FG4/+th+K69Hr5rroPv2uvhfvRxIN4nRI/H4XxxHWAiVTkBlvynnuJEnQZoF9u8\noB8XbX7WUN1wX/lEnFE5HjXlY5L+GUXFiInw28ntoShnQY+GX1G6oJ0vsnmiUZDIR+uK69Fx4SWI\nFpdCZFlEi0vRceElaL/oMjiOHSFux378GNhAd+KxXFkyA9J0mB6Gc0XD6Bi72fdohpjlyFJmWLt2\nLf7whz+gtrYWS5YswdatW3HvvfcaXj/XGjMBLd+YTJHNqTAj26EJn+W0+UxNBTOpAg2H6TA1lOXt\njn2NAGX6JjZhvCSUDofBNrfA+ZcNsH/4MdimpqTYDFnfA0jCavcTT8L10itpHV+8ehxRiB05fx4A\ngD1+QtuLSG/blDYOAzkKhEyFRADt9nzs7hM0k0BypCa9HgNQW1Kd0AQp4eRjuPjIR9g4YSGsfAz5\n0SC6be6kVpnRSpDR9mlKirynEJZwL6zRVJsEIvlQxZrIWp2KJ/6bGK8CSJUh+/GjCJ0pJW/TpsC0\nQJsO0/I0Gqj2zkALqJXbN9IKM5oZliM2g4tjx47hmWeeSXrunXfewYgRdGsRGTkiZAJmSZDR5Qdq\nNJ4GpfBZnTY/XEnQQPsGGf19yl+W/N13orfQ1e/fU1wkiZ//5bb+qS8V+SHBvvUDBG+8QVMbRIII\nAAzTL7j+wSq4n3o62UvovPMAQYDv2uuTJ8yUo/1G9pXGxVZGp9WNR2Zdh16bS3dZdV4Z6ff9t7Fz\ncU7DHjiFaMprU1sOghUETGk/DG+kO2mibHKVsYt4puaJ4zZvJEZs0MiHUqvDRCJwHk41n+zfJ4vI\nyNFJzyUCc3f8Uwr3ZVkwgoCYrxh8Xh64YBDWznb6dJgBTyOacDgbBobpBJeazUEzs13a+83WZ5AD\nHcePH0d9fT0efPBB/OxnP4PYl0kYj8dx//33Y/HixYa2kyNCpym0HKeHKwkygmybJ6qRcldssfT7\n9yjEz+qpLz2wzS2wHKzTzAwjQSgvg/+/H4IwojJR5VEfi/upZ+Ba/1JiHXUFygzali1HwdZ/gDNh\nCgkABbFeOPkoeqFPhAB9spsXC8FGIEEA4IsGML+hf5JLnigrynPh06qluvtOV0ivNE+Uq0Rlh3en\njKYbqThbujph6dS+cYqWVaROj/VVltqXXgn78aOIlpaD5flEVUdZ5QEkAbe64mPG0ygbF/xsTGGZ\nrfaol9MjSmaJlJkA2hyMobW1FW+88QZOnDiBJ598MvE8y7JYsWKF4e3kiNBpDqXj9MkkQZlC7wKZ\nKQnKqCXqcPRPh1GmvrQglJUiPr4aQmkp0Z+IhMj88yFUj9M+FspxpIz2K80ZSeB5lD7/bJJHjVF0\n2j3otrn1FzSIbpsbfns+iiLdKa+JYEBq0Y2u34vtZ1+U8D0yC5JztBZElsOhhVeAv3gxuEA3wuPG\nQ/DkJ6rCTXubUFUMzZF13ulC3FsIK4EMCRyHY2vuSd0prZqDvopTUYnmMkw8rimG9+zYZs7TyACM\n6nWMOEibWcfM/rOFHAHKDNOnT8f06dOxYMECw9UfEnJEaIAhf8FpXUhPRluMhIyDRjPEydYF0e72\nzbbEaKClwWshcv48wOvV9CeKTRgPtqcnKT5DL8+MdhzyJJlQXpZi3OiePw/137gqyfOm5MU/w/vh\nFlPvSUbj2BpDI+1auh41YpxVUyfEauiU8oJ+uEIB9Do9if9JpEjtF2TEOVoJRuBR/fZfULn7kwRp\nFBxOdM1bAObyb6F03XNwf7kn2dywj4hYOtpQ+PbfkLdrp2ZFyL/oGxA9UgCysspTvOEF3WpO1d9f\ngktjGf+FF2mK4S3tbSh97vdo/t4qU7EfNBjRG5klQcrXzFasSIGwOQwtWK1WvPrqq7j88svxb//2\nb6itrcXq1auxZMkSQ+vniNAAgjQGn25lQV5vqBCn4QS9atBgkSCgPw1er7IjAhAqypNITSIz7P2t\n/aRn/vnS8/E4PXzVxHHIk2TuJ55MNW5cvwFV6G+ddX5xRNenRgAQyfehh7PDHg0jL+hHj9uLo1WT\nsW3GpZjKcprVRFYQsLRuC2pa6+CNdKPblofa4mpsnLAQgsYknCy6rmk9CG8kAL/dgz1F4zCl/TCx\nUtTj9mLKl+9j9Il9cAf9CLq9ONJ3bGpCoyRDNOfog0uWp+yn+p2XULX9vaTnuHAIvnfehHfz22D5\n/mlNmYg49+2FJRiEpb2VqCUTAcSKS/u1PSnVnyKwQbInk+wXBECzOpi3czval15JFcN7P9wCweU2\nFfuhh3T1Rtme/iLtK+f7M/Twm9/8Bk899RS2bNkCQRDwyiuvYNWqVTkidLKhRViUOoB0jBKznTuW\nrUpQxtoglkvbTdjsuL0RrYcZwqr3ZZ30OiUNXgQglJcjetY09F63AsKoKm1Sox6hV7bfjMDhgHj5\nNxPu0krIk2SarbPN70nhsV4vinxWWDrJPkQigKapc3Bk3iWI5vtwoCEELh6lVl3UWFq3Jam6Uxjt\nwfyGXRjb3YBHZ35bkwwlDqDvf4Flsbt4HBac+Dz1/docmLr/k8RjT7ATNV9JZpCfziJrh2jO0cX7\nPsehhVcktcnYWBTldV8QlweQRIKUcGqMycuIFfpw7K61YHkeTDxOqP5oewgpR/a1vKmsHW3gQr26\nYnijsR9mp8DUf0eZkiAz0PMGypGhoQOHwwGfz4ctW7bg8ssvh9vtBkv7blBh0H2Etm3bhnPPPReb\nN28e7F0PKvQupGbIzEBVgU52OwyQrlXWSbPgOH8pHOdfAcf5S2GdNCs9r5yTgHS+DIO334be5VeD\nryiHyHHgK8rRe8XlCF+0BBBFON5+B96froH7qWf6PYSARIWGa2oGIwjgmprhWr8B7ieepOxNG/y9\naxG/5fvScbCsdBzLr044W2u2zlrb4Fv5Pbj/+xGgtxeCRshhON+H/Zd8B+GicghWG6pHF4C32BDw\nFKWQIFLVzsrHUNNKnpCq6mnFFQfI3yEyeSqKBsABKIoGEmTqvZHT0e0uhAAG3e5C7Dnja3BEyCaM\no+v3gotHE6aJSvNEmnO0o7sDE/72f2AUhL6qGKaNDY3A2tmB0ff+AmN/egfGrvkRCramTqRpQR7Z\np3kNxXzF8Mw+E/zddyJ0ycWaflBGTRgzEVIbGVk/mZNZRvZ/so/xVEUkEsHvf/97vP/++zj33HNx\n5MgRBALkqCgSBrUidOzYMTz77LOYMWPGYO72pEGrepOuwWI2kU0SlEk1aJ+3GNaxkxOPGZcHbN/j\n2FfbdddPRyRNC8o0g7TvCAnTZO6nnoFzY3/6fMoElxlxM0BPpu9Dd91h4LsrgRXLU5altc4YAFxr\nK1wvvQLnS69AdJEnvkLnfA0A4OhsTQiJaen0U0eUJZ1L+dEgvIRWloya9jq8zs9P0gzRyNOUtkN4\nfdmdOAwkaYLO3P8pcfm8Xj8m+QCSBFx2jnZ2pVbDGACVtZ+Ad7hwcMlyVE7wIR6JSO2lLJMhBoDV\nLxEQWvWHBHlk3zexArGvL4CVUKXsmT4LHUc74ZtYgZ47fwzbZzvAEapHQlmpYRPGgSYCetUazWqt\nxjIk6G1fr0qcqyhlF/feey/Wr1+PBx54AHa7HR988AFWr15teP1BrQiVlJTg17/+NTx9gr5TEWpH\n6MEKUTWDoUKCBIYFV1ZFfI0rqwIIglOj0ItK0PsM9Iin0dFa6nKKdhaN5MikRk/cDACIx+F+9HHJ\nnXr5tfB961q4/+vhpMqS5nEoCVNfC08PDJAIeeUdzn4H5K9fBIgC5v7hXpzzm7sx55n/wPi31kuC\nYQoJVf7Oum1udNvyNJfNjwRTYjNo5KkwEoArFEiqSvU6PQi6vcTlI/k+RPPIx2rEObrs8G6MGCVN\nxIl2OwIz51CXH0jwDieiRSVJDtWtK65PysfrXX51iou1PFnWsa9ROicWnE/cftfUGVmdHMsUWkRG\n/Xw6MR3pEBgtrVEO2cGECRNwww03oKOjA2+//TYWLVqEuXPnGl5/UCtCTmd2Le6HGrTE0UNF6Jzt\nVlim4/J7u1rgcJBHpxmHW9IMpRG+OdB+QUZh9AvT0ASXAXEzgFSBc1sbXC9vhLV2N/x//F2qUSKl\ncpQQZ29+D2xrm67xo+By4dgv7kWspCxFq6IWEutVhgDp/NrjG4d5TWRtDWn0njY+H3R70etMvgnj\nLTYcqfr/7Z15fBT1/f9feySbY3NtyMFhASMF5KbWlh8i2uKFR6UaCCoWtAeteNS2UrBWLYrKz6+V\nL1K1FtQqX0AQvtiKSvmBqAgiopwKERRCgBzkgCRkkz1+f2xmMzv7mZnP3LO7n+fj4UMyM5+Zz+wx\nn9e+zwujMUF86r87QjId/vCEm+Dyt6Hnnu3E14bfJR7oKmwYCiFv6xY4u5quhl0uYnxQMCMTHcUl\nsjFCtDSPu5zYTT5qmeiyUjb++CeiHecbDp4E7r4L7Y1t8H6+M64OEmB8JWglxFV7N/haYteWG8cs\nQ9pZvnw5XnrpJQwbNgzhcBhPPvkkZs2ahUmTJlGNN0wIrVq1CqtWrYrZdvfdd2PcOPIvimQl2cQP\nh1YRxAVIh9tb4ciKtxCG21sjgdMSiLnF9lXXGJ4ppucDTFLkFPoQ8mZLBllH+5ZJuM/SKr9G9rP/\njdbf3x/ZEAjEpcbHVZXmXHgzfgbf7XfAVRefNcTH3XAaBe/8C7W3zhDNJOtxaHdcILEYQ3uXYODn\n4qUG9hWVxaXSS6XPHz3vQmKA9o7R1wKIxATxM9pOdxU/FCPsdKHy6ltQ8O0hZJ6J/57Htc1wuVA3\n7Q7UT74VaXU10WN6rH2jW1gUFKJt0IWovXUGwh4Pila8Ftl3ug5hhwNOiUa+HMGMTASzvTHVousn\nTZYWOBwyHecbDtfB95c5aG5vx9lPv4yeT0vdHrXQiBy9r29ExWgmhrSzbt06vPPOO9FGq21tbZgx\nY4b1Qqi8vBzl5eVGnZ4hghkB0LqIIAAIBRGsqYrGBPEJ1lRJZo/JxQbRiCESSlyZulWFlRA5zrp6\n+Gb8Av5LL4Hr2acRKCiA85134ag+gXDvXghdczVab7slcqxMZ3rPhx+hddZvItWtRVLjge6YpKil\nKD8f/svHy1bD5lKp4RAPDPacaUB6SzPaC4okrUIAkN7eAl8TWQiFAGz8Drk7O5c+P7TuMHz+szGp\n+iTCThc+uegG7Bx5NbLOnYU/zQNPpx9t3zQg6E6Xb646cKSithlhjwcdfb4T/ZvUT4y0r+Ddf6Ng\n0wbRuXDEWH+8ueix9g30f+gPoq0xlMIt3DnjRskea4QY0qP1hl7jacfIvQ4x+3zydbUYsbjd7phu\n81lZWUhLo38dWfq8CErr/lht+QG0iaAAHPC73PAEA3BL9ArXTQR10XnwMwCRmCBHRjbC7a0I1lRF\nt5PQ0p0eIFuDrI7lirqhPvwIzpPd/ccc6BYpgYICBJ+Yh+BDc4CaWqCkGMjKQrShwnm9ES4pgUOk\nRpGz/nRU3IhZjjK2bQeCwUgTWL6l6NczifMjkf3lfgQKComBwfy4G2dnB4bkduCrBhAtNb6mU3CC\nbAFxAPhhVhinSrvdaBwhpxP/O+ByfHPJTxWl6oecLlx4cCv6VR2IrScUuhbn9xf/fAibqwYKi8R7\ndvHvQdDAVMwSw+2rvXUGwi53jPUomJ1N7hXmcqGzuBRFy16hbo2hBJqAYKObsIrt0yKIrHbnAUDT\nEWUFVxlAaWkp5s2bF40L+uijj9CzJ/3nz1Qh9P7772PJkiU4cuQI9u/fj9deew1Lly41cwrUJJIA\n0kIIwP7sIpzMyME5pxuZoQB6tp/FkNY6cyLpw2F0frUTnYc+p6ojRCuClFiDtIgg3R72FG4o5zvv\nRkRQVhbQv1/8ObKyEPb5ABEhxMUSScUkOaqOI6vqePRvToQ5WlrR8of7I5luNTXIevmfyHhvA1EQ\nuRsb0DxmHLHadP13RyDscuGCDW9EKzKPzPPh656D4goYNuSXIgQnXAQxFIIDDfndwoH0fgcBnM2J\nbwsiVsvo4l1vx8QKcfWE8nM8+Lp/fIFEDn5zVan2GN0Tk29gyiEUS/U3T0Xz+B8BADqLSuJ6hfGv\n6/D7RV2UtHV/tKCnqFDyPVMqiJRYdUjb5Bq6Km0S669jFiGlzJs3D6+99hrWrFkDh8OBESNGYNo0\n6R8ifEwVQpdddhkuu+wyMy9JDU3DQ+44O6LWGrQ/uwhHsrvv+5wrLfr3sNbYxdjQXmKhoKrAaBJS\nIkiPtHkOI37xOlta4TxNLlDoOF6Nlm07EOrTG7kDB8Qf0NYGx1nxdHP/mB9GMsS82Qj5fHDVE9xX\nTidAiEPJWP8O0j/bBf/4SCXrlj/+Aelf7CamUnf6eqD21hkIZWXHBdUevuhaYkXmYc3xBQw7Mrxo\nKChBUSNhIXE6MGrfJmL1ZzEcoSAu3vV2vMVn9LVwhoLoV3WAOI42rimUlo7OYvlnSNGyV+Db9F70\nb6KVhlAhOpDthbulBe7G2BYcYtYkd3OjqItSGMhtNkqsL2q/Z2YFSguvo0YEsRghbWRmZmLKlCn4\nwQ9+AIfDgfPPPx8ZFBX2OZhrDN3iRkoM2VUAaSEAB05mkEsZnPR4Mbi1Puom00MEmdFPzKyMMaMe\nXLTZYWcOVsbsyx04AKiphaP6BPG8YQDnfvpTZP91YSRAmiSCAKIIArpcdDU1MXFE/vHjiHFDLcNH\nIZyVRYx9cRw4JVqR+YKTX2FnILb56b+u+g2uf3cxCptOxVgoXaGQbPVnIWIWHwA4MHAsslubiOP4\ncU1SyP6Q6mpOm//+RuJuvpUmvtt7fUydIBoXF1cokdQaIy6QWwVC95jSRd+I7xDJZWe2IOLPRc04\nhnJeeeUVPP/88+jfvz9CoRCOHTuGe+65B7fccgvVeNMrS9sRPatAW4UaS4ff5cY5J1kLn3Olwe9K\nLp2slzVI9MHV3g7n8WqAtgs76XiJ+j3R7LAucgcOiP4HACgpRrh3L+LYUGkpMtes6apMHRvjEwYi\nVaV/eiNCpfIWAq62UbRCdmkJwgDCzkhb07wDu1G07BUgGIxaKzgXzHk9IFqR2XOmAYN83e+TIxTE\n9794Fxkd7aIxSVz1ZzlcgQ5Ri0/fqgPwp3ngzyM/B6TqCSmhaMVrKNi0AQ4RsclZaaRcWkK8n++E\nw+8n7gt7PDg7mhxQLhbIrQWa2lq0i76a+j7cfrHjtQoOpUKKCRzzWLt2LTZu3IgVK1bgjTfewHvv\nvYeVK1dSj0+ulU4jQkGUCAKIj1wWjhBPMIDMUADnCN28M4Od8AQlivClKMSHG00quoLjYwKnu5qr\nhm+4Hq55DyOXdD6OrCyE8/IBXoxP98QL4Pl4e/x2AKHiIjQsfQnIzwf+ulA2O4xf2+hc+c2AvwNZ\n696KWpNcp07Bd4psrQjkFSBY2ANOgpVCKDiEFhwSXOd4UiwQn6xzZ0UtPt7WJng6/aj/7ghi9pdY\nPSFnZwfSW5rR4c1D6YXSApJG3HBWGimXlhA5FxcXsC1W90crQguMHtYXPXt86ZbZaSDCekdNTfKt\nShix9OjRI6ZQc15eHvr06UM9PuWEkFDccOJHbdFDvZugkpASN1qsHG6E0bP9bEyMEEdPf4tk9pgV\n0ARKq02b14JsKrrS4wUtOLxjLo4ESMvR1gZHE3mxdzQ2wiGSWu+sq4ezpRWh/PyICAuFkLH+XTja\n2oiWmFBxETJXrIxkltXUiPaFy9u3C/X+2IBczkpBauBZf8HQqLCQsuDwaSEUSSTBVZDOaY1fZLhz\nCLO//Lk+1H93RHQ7hyMURNnGN6PB3v48H85d/APJlHQaccNZaaRcWkJkXVwuF4JddX+c9adxuqFT\nd0uQlDtKeJwQNe4k0vVorUVGIXZvtLFQdshWS2TOO+88/OY3v8HYsWMRDofxySefID8/H6tXR56r\nN998s+T4lBNCevT/ElqO7FI5Wg1DugKiT3q8OOdKQ2awEz39LdHtgD3ig4xImVcK8Velih5g1Mdz\nrS9oRBAQiRE6QY4RctTWIVxaCsdJwgPX4UDmipVove+eiAXL6Yy2zSARyslB1ptruzeEyYLZWVOL\nQl8a6mO7YMRZKdpzCtDpyUSPyr3o89kH8HvzMKDfd0UtOHyO9R4omiIvzA4TqyDNFVrkZ39xgoxk\nCSIFe2dqiNcJOZ1ouuyK6OsiJRaF0Li4OOEQ6tMbBaQfyYLiiHpjRlFDo86vtmI0aSzDOPx+P/Ly\n8rBv3z4AgNfrRSgUwmefRcqwJL0QUlrvh3SsFhGkdLwaSC4v0sKuJnPMiUh22ODWeqo6QkoxI0Ca\nj5g1SK9Gq0Ko2mN09RNTczzQHRhNzBTjU1KMcO/ecFRVxe0K9+6F0IQJcC59OW6fIxSKCBuXC62/\n/pWoUAs7nTh33UR4dsg3wwV4wd1CK4zLFQ2kPr37W5y3fSP67OpOtc9oaUKvfTsQdLqIpRTCAM5m\n5sGfkYW+1Qdx4aFP0Jqdj2O9B2L/wEvQlunF9/b8Jy477NORVwOIryC9Y/S1MZ+NUFp6XGC0s7MD\nGY11cAY60eNgfNVqAPDu+lQ0JV1K3DRfNgF1t98Zsy3OpSVVM4gC4gLe5aJN+39b0ENDsUXaBqc0\n4+QsKGorO6uxUJlRFZsES5+nZ/fu3RgxYgSeeOIJyWPkSAghVPNtE0pGxpp/heJDrNGpGqEkhhUi\niIMTQ3oJICFuhOEOdsZt12INsosIkkLr54I2y0vt8XyImWJ8srIQmng1nC++FH/ua65GcN7D6Gho\nQOb/riO6vDwffIRzN1wvKtQAwH/FBGT+W95SAcQHdwsJezzo8Oah8PA+4n6XSD2pQ+d/DwF3GoYc\n6o55ymltxJBD2zH40HZ0uj3wBPwx+/gZZlwFaZpCi45QEGX/WYXSPdvh7pAOgk87XadfvA5PLLqb\nGxHMzILrXFv0/2qsN8J4GaGLVk2xRT1FEH8fbWFGLQ1Spa5jlQhKZubPn4/du3fD4XBg7ty5GD58\neHSf3+/Hn//8Z1RWVmLNmjXR7W+99Rb+8Y9/wO1245577okrv7N48WIMHjwY06dPR0FBrEZobGzE\nK6+8gq+++govvvii5NwSQggJoRUfpCaogD6uLCvcYEaJIDEMrRukM5Y1WqXpAableAn4wogTRcF5\nDwNAXBuO4LyHcebwN/B2+EUzsJynTiHrtdcRKioi1gcKlRQjcEGZqJALO53R4/zjLokGfUuR3tKM\nDJEsMgDodKcjkOWF52wj/LmRwoufDb8CN729kHwPQIwI4nPBya/Q0GsKKk/EFlqUshSWbXwT5+18\nX/Y+gMj9BzMl3JgCcUMjZsJuN/L/8y65+KIIVHEpEi5ao4st0ggBrWKBdrya4G4tvcFSVQTt2LED\nR48excqVK3H48GHMnTs3JqtrwYIFGDx4MCoru59pjY2NWLx4Md588020tbVh0aJFcULohRdewMsv\nv4xrr70WvXv3jlaTPnHiBE6dOoU77rgDzz//vOz8ElIIyQUoy4kUtSImEWOA1JJsIkirW0zqAUbK\n8pISAtHjP/iw+/hLx1EJBxIxliG3G8En5qGxYnJsV/nD3wDt7Uj/jOzWASK1gjLf+w86B1xAFEL+\ncZdE+o6JCbmxY9A6/WcInd8/KujkFpjCEf0QKChAWiP5u+UKdGDXlLsQSktHhzcPlSfOIefsaar4\nISFcPaCyvkXRHxBSnwtnZwd6HPyC+vyOUAiuc20Ip3skhY5UOw0h8fWE5K02pPo+ws+vlIuWttii\nmkVdbyFAsuLoMS8pgSTm0hM7XmpcqrBt2zZMmDABAFBWVobm5ma0tLTA6/UCAH7729+iqakJb731\nVsyYMWPGwOv1wuv1Yt68eXHndTqduPPOOzF9+nTs3bsXJ7tiIHv27Ilhw4bBReniTUghBJiTrZWK\n2FEAGRUorVt/MUGWV1R8mAApbujMwcruQGseUotfzHFnW9D20xvh2badKOyEfdHCbjccgQA8H26F\nZ+s2BMrOR9PfX0DDUfnvZ9jjQcuoi0SbifrzCtFeUMQLWj4nmQEmRUtWfjQ9n0YYp7c0w0PoKi9G\nZ0Eh0la+iZ5f74OnuQHBQuUxN/x2GQBUt8jgix/SAiznotVabJF/XbO6qxvZ24xWZIkJolQWQQBQ\nX1+PIUOGRP/2+Xyoq6uLCiGv14smQcbr8ePH0d7ejpkzZ+LMmTO4++67MWbMGOL5XS4XRo4ciZEj\nR6qaX0IIoZJ++cTtqS6G9HaLMRFEhvohRhAfJOLT52sk0+1JiAVOC+OI+IR6FCLUowexjxkfZ20t\nzk2tQOvddxGFXe6QwcALz6Fh917kz/g50r49yrtICGmVX8P7s5+j4S8LqO6l9tYZyPj6EDKPfRu3\nj1/Dh/u8B93pONp7MIYe+pjq/BwnS/rjyLFm9CuLD4QmZYl1ePPgz/Uhk1IMOTo7cN5n3UHfTiUx\nN4T+Y62DLjSuRYaEi7Z56Ghd3WLJIAKU3gNNcLbdXpf884vhK+qheJya4O6wSKapkKamJjz33HM4\nceIEbr/9dmzevBkOkXIdWkgIIUQKluYgBUanAkbGBumBWYHSZvUV0w2l6fYikOKDpEQQgO7Fj5/6\nTiAatC0QdkLxldunF9zH4jPUACDj+DE4z55BKCdXek4A4HLh2MNPoGjZK/B+/inSmhrRnlOAxr7f\nxTfjr48exn8/876S7ybvd3uQFvCj0x1Z1Acc+Ry9ar5B04WjorWBhPWAuLpBYacLobR01A8cSSyy\nSMLdcpa4nSbmhuQCy9+6BUFPBlz++CDtzoLCGKuNWONVKYQuXT2KLSZCIynPQQAAIABJREFUAUM1\n6HFfyfaaKKG4uBj1vLY+tbW1KCqSbllTWFiIUaNGwe124zvf+Q6ys7PR0NCAwkLp4qlqSAghxOjG\nKAGkpzXI7GwxpejmElOBmvR5OWQFEI/W++5B2p69SKv8WvSYaNB2VxE+0YKO+78U7U2GUAg90ILA\nwIEAxGMn+At43e13ov7mqSj+n5eR/eV+lO79BAXHKmPECdAVu1O5l3i+MID2XB/qB47E/+t/Kcbs\n/BcGHvksuj+ntRE5n26Cy9+GkNsTk7af2Xw6Knq+vjLSbf7whJuAcCgua4z0m1Tsd6qc9Uaq6rQj\nEJ/JCQDB7OyI4JHqZC8Hz6WrRx0h/ntshiAyS3SxekDaGTt2LBYtWoSKigrs378fxcXFUbeYGJdc\ncgn++Mc/4he/+AWam5vR1tYWlxnGUV5ejsmTJ2PixInIzs5WPL+EEUK03eGTBbtbfMTQUwRpdYvZ\nES3p87rgdqNp6UtdzVc/hLP+dLTjfKi0NNLq49czu5uz1tYi3Ls3QhMjmWfRliGBAFxr/1f8Ok4n\nAheURf+MLlZc8T5vLnqsfQM5n+3oXsC/dzEQDiH/I2lxkt7SLNqvLAxgz5S70FbcG/07O/Cd974l\nHtdzz3aERaQLv9t82OnC11dV4MiPfhqtIzT0zb9Tu8sAIFDgk4y5kao67QiSSwikd7TD4fejx+rl\nxGDqjIIstF4o7maNW9w1dqEXEwtmxAeJXYM2BV/u3DTXYkgzevRoDBkyBBUVFXA4HHj44YexZs0a\n5OTk4IorrsA999yDU6dO4ZtvvsG0adMwefJkXH/99bjqqqsweXLke/+nP/0JTie5PepDDz2Ed955\nB5MnT8aIESNw8803Y/To0dTzSxghBIjXChLuS1SsEj96WYPsbgmyBTqmz6vG7UbrH36H1rvvQm5u\nDs7U1kVabHS5w7IF/cYcVVXR2kTBJyKZG66HHoX71ddFLxEoOz/Suyy6obu/Wo/aWoTS0+HiNZtN\na6iH7z/rERS5/5Jv9uHcd2Yg7PHg1IEO+PN8yGw+TTy2984t+PrqKZKCyQHAIVI4lNRtPpSWjrbi\niKXu3A9+GK0kTUPr4CExlhahG0tJSw0OZ20tCrNDyNu7i3wPEm5WZuGIRQ/BxJDn97//fczfgwYN\niv77v//7v4ljKioqUFFRIXvu4cOHY/jw4Zg9eza++OILLFiwAM3NzZg+fTrKy8tlxyeUEOKTDMKH\nI9EFEGCNCFITH2RIkLRClKbbG0XuiGGRf3R0IsSJlqYmeDZvIR7vfOddBB+aE/n3+neJx4SBaNYY\nn7gA8XZycUKnyPa0hnqc3v1tRJykpYs2SHUC6PP5Bwi73Thy+Y3w5xYost4A8t3mhYURAwU+OFtb\niPcUzMhE7S0zuv4Qd2OJVZ0OZWTC1X4ubnunLxLUqtTNqpcIIqWb06aamwVtny8+wgaojMShuroa\na9euxfr16zFw4ED86le/wpYtWzBnzhzJytNAAguhZMEKEWTH7DAhVrrFDH94d8VmuJ5ZANTUAiXF\naK2qNvaaAviBz7kDB+DM/i8jFptN78NZL+KmqT4RmS8AR7XIfB0OnHlyPpDBizWRCBCnpT2nIEac\nfDP+epTu3oq0DnLxxNIvtuKb8dejsd93kblnO/EYMcS6zQPdQlpYGFHoouJoHnc5wl3xVZI1gUSq\nTiMcgm9jvOhsGXURTrc6kVtQSO5fJnCz6rmoKy1WaDRarkN6XZgASjymTZuG2tpalJeX4/XXX4fP\nF/mejh8/Pupak4IJIQtJ1Dggo6ERQUZli5n18I4Kkf79In+f1xuoqcWZM2f1cY91BTqTahqRUu+F\nFhsS4d69gJLirn+Te5qFSkvi4pxo6xdJ0enJRJhXiyetrQXujg7R49M6/bjgvZWovGoKir76AmkS\n7TFCDicQDsGfVxjTbV4srZ6DXxgxKmR2fdotZHhBy1IB0VxWGbHqdDAIOJzkthwul6glSQ83q5qU\ncTOzxsyoUM1IDH71q1/hkksuidm2ceNGTJgwAc8995zseCaEGLbC6i7zZgVDRhuplvWH66FH4Vz/\nLhzV1Sjs3RvtY34QcZO5VXw9ebE4ztpahIqLIwHQUudra0PGtk9kTx265upo9lj7mB8giyCESAuw\nVIA48TouF5yCIOHc2uMo2/hmNGC6w5uHdhm3V8HRQ4DThVMj/o9kCnz16HE4/oMJUcHjCAVxwYY3\n4tLq22b+QnbuXNyRMP5IKiCayyrLGTcKANBwkGdNk2nLIdq/7KqbAI2WDTXfhUQTHnJ9zexe+yfV\nOX78OKqqqvD000/D7XZH6xN1dnZi/vz5mDBhAoqLi2XPkxBCqKRfPoJ1+nVET3WG9i7R3T02rLQf\nC5ZWQXDWffAIApM5gSFWXFGqblB8scZTMcUaiYUYa2pFXV1hAKHiIvgvG4/W224BuGsqiXOSCBAX\n4oB4phQ/m4umxo/nbCPSW5qjFp4eB7+IVIp2OOEIh9DOswBxqflAV38x3nm5zLWG/AzRwojxbq/6\nmGKKUgHRnb4eMVllpNgW0bYcKvqXpSpiMUxKGrPqeV0xmo5os56mEnV1dVi/fj2qq6uxePHi6Han\n00kVZM2REEIISL4q0la6xYyKEdIqhmitQWJuMVsWUJRCRXHFuNgevhiiOB+pECNKisVdXUU90PDq\n0kgGWHs7nKdqou621t/ei9YZP4P768ORVPl8cgV4IBIg3t7Y1m25KChEMDsb7rNn4G5sEK3BE3MP\ngmyuwxNuAoIB9Nn1AXE8F/Qcdrrw9ZWTceTyG5He0oyAJxNu/zkUjuiHsMcDbkk6UdkAZ2cHSo+Q\naxSJFUakcXuFPR5RN1bLqIvizqm0E7qS/mUkWIBwLMIWJWpeF6kxNCn/DHlGjRqFUaNGYfz48dFe\nZmpIGCEEJI8YSkYRlCzWIDNN33oXV5Q8X21d3Pn4oihbzNV1+WWA1xtTVyhUXAz/JWMBhOH56GM6\nF5zbjeBf5qCZF7vUcLQR6cePod9DfwAoSu4HfYVwdnbA2dnRXePnmlvggCOmMCKHMOg5lJYeFVGB\nLG+c+Og1wIe02lOK21rQuL06i0vF3Vhd28Xia4wWKfzr2cH1Y3U6O3d9qX5tNHOUE1D895sJIG30\n7dsX8+fPR3Nzc0z7jgUL6Nr8JIQQkmqxkWgwEWQch482S1qFpFLnrXjgKi2uSHJr8a1CkucrLpIs\n1iiV0k90t61+M2a80AUnCq9th29gTzT6/dQ1dJytLbj4H4/FVk92udB6/kw0rMhG5o5P4DnTAD+h\nPQcJ7kcV/3OhxIWleAylG6txz7co9KXFBbortRKZjdbO62IZXGJjlQZmK40Hop2n1BxpRI4d38tE\n47777sM111yDwYMHqxqfEEIISI66QWaIoH3VNUTXkd1FkFa3mBYs+xWsoLiiWJNVteeLg9duISbb\nTGHqu5J+aQBQMLwfOn88HmkU8UNcnZ40YTPTLoHhmDQ52p6j5z5yew4S/Kr1NC4sYUFENWOIbixB\nnaFQSYmolU3tIm4UNIu91PdMy/yVBHXLiSqaa0ltJ52fWXyMp0ePHpg1a5bq8QkjhBjqsbsIMgO7\ntmehLa545mAlUQwJA6a5cRnbP4Gj+kQk0FlJsUZBo1Wlqe9qXHrc3Nz/2YQ0ynghID5mp8faN2Tb\nc9Ag6sIqvwVFy16JKYjYMmIUGq+4BvWTJlOP4Vuz+AgDrvlWtqqJ3fMXW2wB8SwnOy/Edp6bEuSC\nroHkuVe7cemll+Kjjz7CxRdfDDfvR4NYSw4hTAjphNDaY1XgrhEWE6Mx0hpEI4Cs6pjNubWIlhgC\nYmIoBs6yI1FHSAmKU9/V9EvrmjNm/Ay+2++Aq46u1QQ//kYqYJnfnoOD5BaLIuLCKlr2SlxBxIJN\nG5C/aQMChUU4O/r7+PYvC+BuOSM5Jsaa1YXU/N2bPoDjxz+Jzl/OFSO33YjPu9xCr/ZaNOJCD2it\nNqT7pHFZkmKNmCjSj+effx4tLS0x2xwOB7788kuq8UwIaUDK1cXt4wuisr55psYIJUIFaT3Ro4YQ\nh9GiiBM00RgfgSVGDL4Ykuw6T3m+uMwzwnloU98BjYX8vF6E8vOohRA//oY2YJmDRiDzXViSHeJB\nFji02WRq5q+l3pWRn22l56YVTmZ8N5W4sJRmfJHeL7u5NxOZnTt3ahrPhJCJJFMlabNjg5IFqlgf\nGSSFiwLixJgIRPfd2K6ssa0f69Mvrb0d3v/7DNIqv47b1enNQVrL2bjtbYO6AyPVBDkrQUqo8OEL\nHElxc7pb3PgG9gT6FiBUUkK0vInN3ypLppFYfS9GxvPIiVcWS6ScN998EzfddBMWLlxI3H/vvRKJ\nGzyYENIAZ4EQEzh8CwUTQfHoJYL0dkNa5SIz+5rUcxALpAbQetevI9u82XC2tAKBgLKK2Fwl7C0f\nwllDtmCGPRlovHgMsvd8gbSGeoTSPQDCyPtoC7K/PCDbuJRUp0cptB3i+dYbyTEOIP/dt1F36/To\nAilmeZObvx6CyKyK6kKUZnzZpXWH2DgtQeNMBCmHiwFyucSTIWhgQkgHSIIo4Yr7UWInEUTzGvMz\ngmiw6hepmWJIzCpF4yaLc7e53chctVpZSw8eND3O0hpPo/Hq61A3ZRqK//kP5G/tDoimaVzKbdeC\nVGYYH771RmqMIxSCb9N70XgkIN7ypnT+tO4jNZlPdkDNvLSm9CuFJiiaFNDORJA6Jk2aBADEjLGn\nnnqK+jxMCCmAFPfDR7g92WoGJVKWmBq0PBj54kKtoBEKFK3CSI0bjir+iIdcSw9JKFPz+eIi+6sD\nxGMyd3wCh1jjUoUIU905YoRWfS0xu01ovamrmAZHMID89zfCEQrFHc+50jhLQetv78XxH/9Ec7sM\nqyw8iYSRcUdKxA0TQdrZunUrnnnmGTQ1NQEAOjo6kJ+fj9mzZ1ONZ0JIIVJWiGRyfwnRUwSpsQap\nzYajsQbp+RCUEhB6xAfRYsq1VLQI4UObms+JC6mqzxlnG6MuKdXtJgR1fOJS3fnZZA2nkf+fd+Dd\n87m09cnlQuPV1yF/0wbiJfmutOiCqLFdhhypJJBoBYlRmXRKg6+ZKFLHs88+i4ceegjz58/H448/\njvXr1+Oiiy6iHs+EkALERFAyCyA7YFTavJ7IWVBoagDpYVUSu5aac8jNQWuLEKnU/DCAUGkpmoeN\njooLowOi4xunklPdwx4POnv2Qt3td6JexHrEJ5BXgEBhkS7zVprmrSep0Ild73ukeb+ELU78dWma\nrpmKeL1ejBw5EmlpaRgwYADuvfde/PznP8fYsWOpxtNVG0pBDh9tjvlP6hi7obdbzEqXmFIR1GuA\nLyFigkgi48zByuh/cuhi8WlrA775NvJ/FXBChriPpp5QV2o+ifaJV6Nh+WsI/mUOfBf2gW9gz2jM\nDQmtAdFyqe4Ov5+4j7M+SV2bq55NQo9AbqtIFOuF1iByK6/PoCMQCGDnzp3Izc3F2rVrsWfPHhw/\nfpx6PLMIJRHJVjdIjSVIaXC0GbEUSkULbeA0KZ5H7Foxx5T1h+uhR+Fc/y4c1dUI9+6N0MSrEZz3\nsLKMLy0tPbqQrKzdNRf+YmRUQLTSOj5K0RoIrQRSfRqtn/FEET1iqA1IZiImMXj00UdRX1+PBx54\nAPPmzcPp06cxc+ZM6vFMCBGgsfLYzRKUbCIIEO+bBuiblWdktoySDC3+sXJiiF9YUU5oCc8TnHUf\nPDzx4qiqgvPFl9DR2BgNcM4dOIBKkLXefRcQDEYW+PrTyusJSaTmA4QFWKTqs8Pvh/t0HdFFJRb8\nzMdIt1v0Hgj36SPdo8SclbhZ+McbWU1aKVZlp8m1ITH62qT3jgktffjyyy9x7bXXAgCWLl0KAFi+\nfDn1+KQUQnJdyOXGCrFTNpiQRBNAZhRQVGoVMgolMT9yneVJ0LTciBtPGeBMFaPE1QDaug3OunqE\nCgvhH/ND6tT5GASp+XILfjQgOhgU7+UFSAc/C84nluoezMpCWOn9iEEoQRCzQEcDtnfA3XAaAV8h\nzo6+mDhn0jk4pFLk9ar+rAb+ucys5M5Haxo+Ey/24cCBA9i/fz+WLl2Kc+fORbcHAgEsXrwYU6dO\npTpPUgohNSJIrl2GHesCMRFkf7TG+8ilswvdYnoHOEtdPy51vr4eWWv+t7t3mAZoXRlSAc4AqIKf\nOeoqpiHz4AFkHvs2ZnvmsW9RtOI14hg98Q3sCddDj8O38d3otrTT9ZE5h0Oou+0O4hijIb0XRluW\n7F7TCNA+RzvfW6Lg8Xhw+vRpnD17Fp999ll0u8PhwAMPPEB9nqQUQkqgte5wYsgu1qBEE0FqMMMt\nxmHEQ0nPAok09X1orieVqcUFOFNZpzSmzuuBZIDzrk/hQJi8T9DnK3q+QADu1lbimLwPN6N+0mSE\ns7JUzZXq89XejvyPPyBf/6MtCD74O+rXVK45qFL0+n7QnEdoNZKrxGxFtWkxy5bY8dy2RBB4iURZ\nWRnKyspw8cUXY/To0arPk9JZY3YRNUrYV11jqgiyUyVpWuTcYon8ENKcLSaRqeUfdwlyRwyTvDZ3\nfRrLklbk3ifZAOcG8hy44Gcl53O1n0PxspdF5xn3X98C9MgOwde3gPrz5qw+AYdIBp+z/Ryc1Seo\nziM2N46Ggydj/jMKpdcRO46mbYWZaLkX4XvB0Idnn31W0/iUtwgpwWrhlApWIA611iApESSs15Eo\n6N16QzJTqyvuSO6aNJYlPZAqNCcX4OxAGGmn44WNWPBz5HyFxDEAkPXVATj8/hhLUtzniIubUtly\nxGjEhIbe3we9xUmiVspO1HknGr169cK0adMwYsQIpKV112FK6aardo3p0YIVIsiq+kFqq0jLYceH\nkpnVpqPIZGpRCS8dUueVQIpTkQpwbumqN6SkCWvY40Hr4CHI/2hL3D4g0vNMLo1erOVIe2Mbgn+Z\nIzoOAEK9eyGclUW0CoWzshDq3UtyvBxmWk7UfM/k4sKE31+9vstK3VW0VaDt9qxJZvr06YM+ffqo\nHp9UQohvsRFabxJZGKWSCDIaO4khGhFkZCPW3BHDNJ1f0rJkEnUV05BRkAX3pg9E6wopqTlUe8sM\n5G77CM5gMG5fKN0TZ0mK+TxJxE15P9+Jb/d8i4Lh/cRvJiMD7ROvRtbqNXG72iderUlcaumKruY6\nRnVw1/v7S4r1USqIhOehaXhrl2dQsjBr1iw0Njbi+PHjGDZsGEKhULQzPQ1JI4Tk3Fap0BleL+wq\ngrS4xfgYIYaUdo+3xBJEuK6mrvckyxIA56maOCuTHhCzly7sg9YL7wV+/Suc/fTL+EapSpuwulwI\np6UBBCEkh1TcFBeX1HDQE70XEq333g04nfB88GG3u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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fefcc66a828>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "cmap = sns.cubehelix_palette(light=1, as_cmap=True)\n",
    "fig, ax = plt.subplots(figsize=(10, 6))\n",
    "contour = ax.contourf(grid[0, :], grid[1, :], ppc['out'].std(axis=0).reshape(100, 100), cmap=cmap)\n",
    "ax.scatter(X_test[pred==0, 0], X_test[pred==0, 1])\n",
    "ax.scatter(X_test[pred==1, 0], X_test[pred==1, 1], color='r')\n",
    "cbar = plt.colorbar(contour, ax=ax)\n",
    "_ = ax.set(xlim=(-3, 3), ylim=(-3, 3), xlabel='X', ylabel='Y')\n",
    "ax.set_title('$B \\propto I_t $')\n",
    "cbar.ax.set_ylabel('Uncertainty (posterior predictive standard deviation)');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Next steps\n",
    "\n",
    "This section was added with the introduction of the first approximate MCMC sampling algorithm in pymc3, Stochastic Gradient Fisher Scoring.\n",
    "The file [sgmcmc.py]() defines a BaseStochasticGradient class that can be extended to other approximate algorithms by only implementing `_initialize_values` and `mk_training_fn` methods"
   ]
  }
 ],
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