03_machine_learning_101.ipynb

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  "name": "03_machine_learning_101"
 },
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  {
   "cells": [
    {
     "cell_type": "heading",
     "level": 1,
     "metadata": {},
     "source": [
      "Machine Learning 101: General Concepts"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Here is where we start diving into the field of machine learning.\n",
      "\n",
      "By the end of this section you will\n",
      "\n",
      "- Know the basic categories of supervised learning, including classification and regression problems.\n",
      "- Know the basic categories of unsupervised learning, including dimensionality reduction and clustering.\n",
      "- Know the basic syntax of the Scikit-learn **estimator** interface.\n",
      "- Know how features are extracted from real-world data.\n",
      "\n",
      "In addition, we will go over several basic tools within scikit-learn which can be used to accomplish the above tasks."
     ]
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {},
     "source": [
      "Quick Review:"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "We saw before the basic definition of Machine Learning:\n",
      "\n",
      "Machine Learning (ML) is about building programs with **tunable parameters** (typically an\n",
      "array of floating point values) that are adjusted automatically so as to improve\n",
      "their behavior by **adapting to previously seen data.**\n",
      "\n",
      "In most ML applications, the data is in a 2D array of shape ``[n_samples x n_features]``,\n",
      "where the number of features is the same for each object, and each feature column refers\n",
      "to a related piece of information about each sample."
     ]
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {},
     "source": [
      "Supervised Learning, Unsupervised Learning, and Scikit-learn Estimators"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Machine learning can be broken into two broad regimes:\n",
      "*supervised learning* and *unsupervised learning*.\n",
      "We\u2019ll introduce these concepts here, and discuss them in more detail below."
     ]
    },
    {
     "cell_type": "heading",
     "level": 3,
     "metadata": {},
     "source": [
      "Supervised Learning"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "In **Supervised Learning**, we have a dataset consisting of both features and labels.\n",
      "The task is to construct an estimator which is able to predict the label of an object\n",
      "given the set of features. A relatively simple example is predicting the species of \n",
      "iris given a set of measurements of its flower. This is a relatively simple task. \n",
      "Some more complicated examples are:\n",
      "\n",
      "- given a multicolor image of an object through a telescope, determine\n",
      "  whether that object is a star, a quasar, or a galaxy.\n",
      "- given a photograph of a person, identify the person in the photo.\n",
      "- given a list of movies a person has watched and their personal rating\n",
      "  of the movie, recommend a list of movies they would like\n",
      "  (So-called *recommender systems*: a famous example is the [Netflix Prize](http://en.wikipedia.org/wiki/Netflix_prize)).\n",
      "\n",
      "What these tasks have in common is that there is one or more unknown\n",
      "quantities associated with the object which needs to be determined from other\n",
      "observed quantities.\n",
      "\n",
      "Supervised learning is further broken down into two categories, **classification** and **regression**.\n",
      "In classification, the label is discrete, while in regression, the label is continuous. For example,\n",
      "in astronomy, the task of determining whether an object is a star, a galaxy, or a quasar is a\n",
      "classification problem: the label is from three distinct categories. On the other hand, we might\n",
      "wish to estimate the age of an object based on such observations: this would be a regression problem,\n",
      "because the label (age) is a continuous quantity."
     ]
    },
    {
     "cell_type": "heading",
     "level": 3,
     "metadata": {},
     "source": [
      "Unsupervised Learning"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "**Unsupervised Learning** addresses a different sort of problem. Here the data has no labels,\n",
      "and we are interested in finding similarities between the objects in question. In a sense,\n",
      "you can think of unsupervised learning as a means of discovering labels from the data itself.\n",
      "Unsupervised learning comprises tasks such as *dimensionality reduction*, *clustering*, and\n",
      "*density estimation*. For example, in the iris data discussed above, we can used unsupervised\n",
      "methods to determine combinations of the measurements which best display the structure of the\n",
      "data. As we\u2019ll see below, such a projection of the data can be used to visualize the\n",
      "four-dimensional dataset in two dimensions. Some more involved unsupervised learning problems are:\n",
      "\n",
      "- given detailed observations of distant galaxies, determine which features or combinations of\n",
      "  features are most important in distinguishing between galaxies.\n",
      "- given a mixture of two sound sources (for example, a person talking over some music),\n",
      "  separate the two (this is called the [blind source separation](http://en.wikipedia.org/wiki/Blind_signal_separation) problem).\n",
      "- given a video, isolate a moving object and categorize in relation to other moving objects which have been seen.\n",
      "\n",
      "Sometimes the two may even be combined: e.g. Unsupervised learning can be used to find useful\n",
      "features in heterogeneous data, and then these features can be used within a supervised\n",
      "framework."
     ]
    },
    {
     "cell_type": "heading",
     "level": 3,
     "metadata": {},
     "source": [
      "Scikit-learn's interface"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "In scikit-learn, almost all operations are done through an estimator object.\n",
      "\n",
      "For example, a linear regression estimator can be instantiated as follows:"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "from sklearn.linear_model import LinearRegression\n",
      "model = LinearRegression()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 1
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "print model"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "LinearRegression(copy_X=True, fit_intercept=True, normalize=False)\n"
       ]
      }
     ],
     "prompt_number": 2
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Scikit-learn strives to have a uniform interface across all methods,\n",
      "and we\u2019ll see examples of these below. Given a scikit-learn *estimator*\n",
      "object named `model`, the following methods are available:\n",
      "\n",
      "- *Available in all Estimators*\n",
      "  + `model.fit()` : fit training data. For supervised learning applications,\n",
      "    this accepts two arguments: the data `X` and the labels `y` (e.g. `model.fit(X, y)`).\n",
      "    For unsupervised learning applications, this accepts only a single argument,\n",
      "    the data `X` (e.g. `model.fit(X)`).\n",
      "- *Available in supervised estimators*\n",
      "  + `model.predict()` : given a trained model, predict the label of a new set of data.\n",
      "    This method accepts one argument, the new data `X_new` (e.g. `model.predict(X_new)`),\n",
      "    and returns the learned label for each object in the array.\n",
      "  + `model.predict_proba()` : For classification problems, some estimators also provide\n",
      "    this method, which returns the probability that a new observation has each categorical label.\n",
      "    In this case, the label with the highest probability is returned by `model.predict()`.\n",
      "  + `model.score()` : for classification or regression problems, most (all?) estimators implement\n",
      "    a score method.  Scores are between 0 and 1, with a larger score indicating a better fit.\n",
      "- *Available in unsupervised estimators*\n",
      "  + `model.transform()` : given an unsupervised model, transform new data into the new basis.\n",
      "    This also accepts one argument `X_new`, and returns the new representation of the data based\n",
      "    on the unsupervised model.\n",
      "  + `model.fit_transform()` : some estimators implement this method,\n",
      "    which more efficiently performs a fit and a transform on the same input data."
     ]
    },
    {
     "cell_type": "heading",
     "level": 3,
     "metadata": {},
     "source": [
      "Diagrams of Supervised and Unsupervised Learning"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "%pylab inline"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "Welcome to pylab, a matplotlib-based Python environment [backend: module://IPython.zmq.pylab.backend_inline].\n",
        "For more information, type 'help(pylab)'.\n"
       ]
      }
     ],
     "prompt_number": 3
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "from figures import plot_supervised_chart, plot_unsupervised_chart"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 4
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "plot_supervised_chart(annotate=False)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "display_data",
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ANGrUiF27dhEeHs6lS5cYNmwYt27dUusdy2yO2IABA3j06BGenp5ERkZy9epV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\nniiEEUKIRPxIEKMARoh8/Q+QBgi2Mk5ExAAAAABJRU5ErkJggg==\n"
      }
     ],
     "prompt_number": 5
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "plot_supervised_chart(annotate=True)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "display_data",
       "png": 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5hMhLToaoqI/zvt4jO3fuzFA+AeDFixfs2LEDhUKhtWRZWlqSN29eQkJCdOaGh4ezYcOG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q6UttQobk6dOoUbN24U+Th8Ph9jxoyBvr6+DM6KwVAAUVHU/sLBgQqdrl4Vj5/T0CC7NH482TsA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\n"
      }
     ],
     "prompt_number": 6
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "plot_unsupervised_chart()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "display_data",
       "png": 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DqakpMTEx1KlT56OT6JVKZZqq19u3b9O0K1KkSJqYWrRowcyZM9O0TW9OmxBC\niLQkCRMil6tRowaXLl1Kk5ylunz5MrGxsUyePJly5coBKWvbvUtHRwcgzXp2JUuW5N69e2rbzp49\n+95jvRvTxo0bsbS0pFAh+WdECCE+hUzMFyKXGzduHOvWrcPPz48LFy5w+fJlNm/ezMiRI4GUife6\nurrMnz+f69evs3PnTsaOHavWR7ly5VAoFOzYsYOYmBhevHgBQP369dm9ezfbt28nKiqKoUOH8s8/\n/6hVx9KbIzZgwACePn2Kp6cn4eHhXL9+nQMHDtCnTx+eP3/+mc+IEELkDxpPwvz9/alVqxa1atXC\nyckJNzc3+vbty6ZNm0hISNB0eDnG39+fIUOGaDoMoUHp3ZUI4Obmxs6dO/ntt99Uy6lMnz5dVfUq\nWbIkwcHBbN26lWrVqvHzzz8TEBCg1leZMmUYP348Y8aMwczMDF9fXwC6d++u+nJxccHIyAgPDw+1\nfdOLy9zcnGPHjqFUKmnSpAlffPEF33//PXp6eujq6n6O0yOEEPmOIiIiQu2/uA4ODkRERGS6o0/d\nb/z48cTExDBhwgSSkpJ4/Pgx4eHhrFy5EgsLCxYtWoSenl6m+81r/P39efr0KQEBATlyPAcHh3yz\nlIBCocjRa/ZT90vdN7+cdyGEEFmj8ckcycnJaGtrq26bNzExoWLFijg5OeHl5UVISAi9e/fm33//\nZdasWRw9epTXr1/z5ZdfMnz4cLW5K+fPn2fBggVcvHgRLS0tqlSpws8//4yJiQl9+vTB2tqaH374\nQdX+v4lPnz59KF++PLq6uuzYsQOlUkmPHj3w8PBg1qxZ7Nu3DwMDA3x9fWncuLGqn4cPHzJnzhz+\n/PNPAKpXr86wYcOwsLAAICgoiIMHD9K9e3cWLlzIkydPqFWrFj/99BPFihUjKCiInTt3AqjuKluy\nZAk1atRg6dKlbNu2jdjYWIoWLYqTkxPjx4//jN8RIYQQQuQEjQ9Hvo+1tTW1a9fm4MGDAKrVwmfP\nnk1wcDB6enr4+vry+vVrAK5cuULfvn2xtLRkxYoVrFq1iiZNmqgNaWZkUcrdu3djYGBAcHAwPj4+\nzJo1i2HDhmFtbc2aNWto0aIFEyZMIDY2FoD4+Hj69u2Lnp4eQUFBrFy5EhMTE/r37098fLyq3+jo\naA4ePMisWbMIDAwkKiqKhQsXAtClSxcaNWqEo6Mje/fuZe/evVSvXp2DBw+yZs0aRo0aRWhoKHPm\nzOGLL76ZMrH0AAAgAElEQVTI/pMthBBCiByXa5MwSFmU8u7du9y5c4ejR48yZswY7O3tsbGxYcKE\nCbx48YI9e/YAEBISgq2tLaNHj6ZixYpYWVnh7u6OmZnZe/tPb8KxjY0NvXr1omzZsnTu3JlixYqh\nq6uLp6cnZcuWpWfPniQlJXH27FkgZTVzSJk8bWNjQ7ly5fjxxx95+fIlv//+u6rfxMRE/Pz8sLGx\nwc7ODg8PD06ePAmkLEugo6OjqggaGxtTqFAh7t+/j4mJCY6OjpQqVYoqVarIIrBCCCFEPqHx4cgP\nSU5ORqFQcOPGDZRKJXZ2dqr3DAwMsLGx4caNGwBERUWpno+XFTY2NmqvjY2N1bYVKlQIQ0NDHj9+\nDKQsD3D37l1cXV3V9nv9+jV3795VvTYzM1Nba8nExIS4uLgPxtKwYUM2bNiAu7s7Tk5OfPPNN7i6\nuqqeFyiEEEKIvCtXJ2E3btygbNmy730/NUmDlKHFD014Tm9RyvTuvkxvzaP0tiUlJan+rFy5MpMn\nT07TxtDQ8L19pBfvf4dGS5UqxZYtWzh58iQnTpwgICCAoKAg1XCsEEIIIfKuXDEcmd5t+deuXeOP\nP/6gQYMGlC9fnqSkJM6dO6d6//nz5/z999+UL18egMqVK3/wjrXixYvz6NEjtW1Xr15N99iZUaVK\nFe7cuUOxYsUoW7as2te7SdjHaGtrp5sU6ujo4OzszNChQwkJCeH69euqodC8rk3bdpw5c0bTYQgh\nhBAakSuSsNevXxMbG0tMTAxXrlxh7dq19O3bl6pVq+Ll5YWFhQV169Zl8uTJnDlzhmvXrjFu3DgM\nDAxo0qQJkDK5PSoqismTJ3P16lVu3rzJ1q1buX//PpCyNMCxY8c4cuQIN2/eZPbs2Tx48CBNNSqz\nywc0adIEY2Njhg0bxunTp7l79y6nT59mzpw53LlzJ8P9lC5dmr///ptbt27x5MkTEhIS2L59O2Fh\nYVy7do27d++ybds2tLW1sbS0zFSMudX/jhyhhbuH6iaHgq5Vq1Y0bNgw3fcuXbqEUqnkwIEDWTqG\nUqnk119/zVIfQgghsofGhyMVCgXh4eE0adIEpVJJ0aJFsbGxoU+fPnh4eKiG8fz8/Jg1axZDhw7l\n9evX2NvbM3/+fNXjWCpVqsSCBQtYuHAhPj4+6OjoULVqVerUqQOk/IK7du0aEyZMAKBDhw7Uq1eP\np0+fpoknM/T09Fi6dCmBgYGMGjWK58+fU7JkSRwcHNQqYen1++42Dw8PTp06RdeuXXn16hWLFy/G\n0NCQ4OBg5syZQ0JCAhUqVGDGjBmYm5sDsH37diZMmMD27ds/eANCblba/lvatPfk4L49Bf7xNz17\n9sTDw4Nbt26pFmJNtXz5cqysrN6bpGVGdqxT9vbtW5mbKIQQWaTxxVrFp1uyZAmHDh1i/fr1KJWZ\nK2rmhkVDjUua0m/NWbaO98atdnUCZqV9GHRG5JfFWhMTE7GwsKB37974+/urtr99+5ayZcvi6+tL\nmzZtGDFiBEePHkVfX58GDRoQEBBAqVKlVO2Dg4OZOXMmV69epVixYjRp0oRVq1ZhZWXF7du3Ve2s\nrKy4fv06kHItzZgxgzt37mBpacnIkSPp2bOnqq1SqSQwMJADBw6wb98++vfvz+TJkxk6dChbtmwh\nNjYWU1NTOnfuzJQpUz7p3AghREGTK4Yjxac5fvw4I0eOzHQClpsolVq0m7iB9ZtCWbduvabD0Sgt\nLS28vb1ZtWqVWqK2fft2YmNj8fHxwdXVlerVq3Py5EkOHjzI8+fPcXd3V7VfsmQJffv2pUePHly4\ncIE9e/bw5ZdfAqgSx2XLlnH//n3VEimhoaH4+voydOhQLl68yKBBg+jfvz87duxQi2/8+PG0aNGC\nCxcu0L9/f+bOncvWrVv55ZdfuHbtGr/88gu2trY5caqEECJfkEpYAZVbKmED1l3AwNiU6KvnWDWg\nAf87uB97e/tM9ZNfKmGQckNKpUqV2Lt3L40aNQKgefPmANSsWZPjx4+rzQt7/PgxJUqUIDw8HAcH\nB8qWLUvXrl3TvVsXUipamzdvpk2bNqptzs7OVKlShWXLlqm2devWjWvXrnH06FHVfr6+vsydO1fV\nZtCgQVy8eDHL89SEEKKgyrslFJGvmFesTvMRC2jZuk2BnqhvY2ND3bp1WbFiBQD37t1j37599OjR\ng1OnTnHkyBGKFi2q+rK0tEShUPD333/z8OFD7t27R4MGDTJ1zMuXL+Ps7Ky2zdnZmcjISLVtDg4O\naq99fHw4c+YMlSpV4vvvv2fXrl0aT+yFECIvkSRM5BrVG3Wg0rftadPeM93lOgqKHj16sHXrVh4/\nfsyqVasoUaIErVq1IikpiRYtWnD27Fm1r6tXr6qqZdnpvzeTvLvYMMBXX33FzZs3mTJlCklJSXh7\ne9OoUSNJxIQQIoMkCRO5SsP+k3kUr2TEyFGaDkVj2rZti56eHmvWrGHFihV07dqVQoUKUbNmTS5c\nuIClpSUVKlRQ+zIwMMDU1JQyZcp8cHhQW1ubxMREtW1VqlRRe8QWwO+//061atU+GquBgQFt27Zl\n4cKF7Ny5k0OHDvH3339/2gcXQogCpmCvCSDS9eTJE6p/VYP4V/Efb5wFTx/HoaWto7ZNqZUyUX+J\nTy0catSgc+dOnzWG3EhfX59OnTrh5+fHkydP6NGjBwADBgxg6dKleHp6MnLkSExMTLh+/TqbNm1i\n1qxZGBgYMGbMGIYMGUKpUqVo1qwZL1++5NChQwwdOhRIuSPywIED1KlTB11dXYoXL86IESNo3749\nNWvWpFGjRuzZs4d169YRGhr6wThnz55N6dKl+fLLL9HW1mbt2rUYGRl98CkXQggh/l+eTcL8/f15\n+vQpAQEBmg4l33n27BkvXsbTN/jz3mihpa2LftFiabYXNjKm4/RQBgxoQLVqVTM9UT8/6NmzJ4sW\nLcLZ2ZnKlSsDYG5uzrFjx/jxxx9p0qQJ8fHxWFpa0rhxY3R1dQHo27cvOjo6zJo1i5EjR2JsbKw2\nVJm61t7KlSspW7Ys169fx93dnfnz5zNz5kwGDx6MlZUVixYt+ugQp6GhITNmzFA9eaJGjRrs3r1b\nHqklhBAZlGeTMIVCkeVHDon3U2ppYViytMaO/+5E/TOnTlKiRAmNxaIJX331ler5pO+ysbFh06ZN\nH9y3e/fudO/ePd33WrRoQYsWLdJs79OnD3369Hlvn+nF0rNnT7W1xIQQQmROnk3C3p38m1oVs7e3\nZ926dbx+/Zp27drRv39/lixZwq+//opSqaRLly54eXmp9lu7di07duzgn3/+oWjRonzzzTcMHjwY\nAwMDVZuwsDCCgoJ4+vQpTk5OODk5MW3aNNUaSwBHjhwhKCiIGzduYGJiQuPGjendu7dqBfhDhw4R\nFBTEnTt30NPTw8bGhilTpmBsbJwDZyrvqt6oA/cvn5IV9YUQQuRL+WZi/unTp4mOjiYoKIgff/yR\nkJAQfH19SU5OZsWKFfTu3Zu5c+cSFRWl2kepVDJ8+HA2bdrExIkTuXjxItOnT1e9f+7cOSZNmoSn\npyfr16/HxcWFJUuWqFXg/vjjD8aOHUvHjh3ZuHEj48aN4+DBgyxYsACAR48eMXr0aFq2bMmWLVsI\nCgqiWbNmOXdi8jiZqC+EECK/yjdJWNGiRRk5ciTlypWjcePG2Nra8uTJE/r374+FhQVt27bF3Nxc\nrYL13XffUbNmTczMzKhRowa+vr5qd5Zt2LABJycnunbtioWFBa1bt6ZevXpqVbjUu9datGhBmTJl\nqFmzJr6+vmzZsgVIScISExNp0KABZmZmWFtb4+7uLlWwDEqdqL9+Uyhr167TdDhCCCFEtsk34zsV\nKlRQq1AZGxurPUA7ddvjx49Vr0+ePMnKlSu5desWz58/JzExkYSEBB49eoSJiQm3bt2ibt26an1U\nq1ZN7a6xS5cuERkZSXBwsGpbUlISb968ITY2lkqVKvH111/j6emJo6Mjjo6ONGjQgGLF0k5IF+mT\nifpCCCHyo3yThGlpaam9VigUaeYQKRQKVRUrOjqawYMH06ZNG/r374+RkRGXLl1izJgxmVooNDk5\nmd69e6e7SnmxYsVQKpUsWLCA8+fP8+effxIWFkZgYCBBQUFUrFjxEz5pwZQ6Ub+FuwdnTp3ExMRE\n0yEJIYQQWZJvkrDM3ikZGRlJQkICQ4cOVe175MgRtTZWVlZcuHBBbdvFixfVXtva2nLjxo2Pro1k\nZ2eHnZ0dvXr1okOHDuzfv1+SsExKnajftkNHmagvhBAiz8vTc8LenZv130elJCcnp7stlaWlJUlJ\nSaxbt467d++yd+9e1q9fr9a+Y8eOnDhxgtWrV3P79m3CwsL43//+p5bw9erViz179rBkyRKuXbvG\nzZs3OXjwIPPmzQPg/PnzLF++nMjISO7fv8/hw4d58OABFSpUyLbzUJA07D+ZK9dvs3r1ak2HIoQQ\nQmRJnk3C3l0nLL01w963LVXFihUZPnw4a9eupUOHDoSFhTF48GC1NnZ2dowZM4YNGzbw3Xffcfjw\nYbp27Yq2traqjZOTE3PnziUiIgIfHx+8vb0JCQnB3NwcSLlh4OzZswwZMoQ2bdowd+5cevbsSZMm\nTYCUBzTXqlWLnTt3Zu8Jyqf+2rUavULg4eGh6VCEEEKILFFERESolYscHByIiMj8Sumful9eM3v2\nbE6ePJmmavapIiIiGDRoEJs2baJ06ZxbHNXBweG9D1q+c+cONRy/Yfj2OzkWT0bcuXiStUOacezo\nYapWrararlAocvSazcq1/qHzLoQQomCRSTUfERISgqOjI4ULFyY8PJwtW7bw/fffZ1v/x48fx8fH\nJ0cTsLzoWewDfhnVlhXLlqolYEIIIUReJUnYR1y+fJm1a9fy/PlzypQpg6+vLx07dsy2/gcOHJht\nfeVXiQlv2Ti6Pb17+ODh0VrT4QghhBDZQpKwj5g8ebKmQyjwds8ZSnlTQ34e76/pUIQQQohsI0mY\nyNVObV/FPyf38ldEOEplnr2PRAghhEhDkjCRa925eJL980dw7OhhecKAyNeSk5OJjY3l3r17REdH\nq/68e/cujx49IiEhQfWlUCjQ1tamUKFC6OvrY2ZmRpkyZTA3N6d06dKYm5tjbm5O4cKFNf2xhBAf\nIUmYyJVkIr7Ir16+fMnZs2c5deoU4eHhnDp1imvXrlG4cGFKliyJiYkJJiYmlChRghIlSvDFF1+g\nra2NlpYWWlpaJCcnqx6xFh8fT1xcHH/++SexsbE8evSImJgYYmJiKFy4MNWrV6dWrVrUqlWLmjVr\npnm8mxBCsyQJS8e9e/dwd3dn9erV2NraajqcHKerq8vjh9FMb/Z579gspKvPgLVn0S1soLY94e0b\nNslEfJFPxMXFsWvXLvbv309ERATXr1/H2toaW1tbKleujJubG+XLl0dPTy/bjplaWYuKiuLy5csE\nBQVx6dIlXrx4gb29PbVr16ZFixY4OTmleeSbECLnSBL2AQV1PSdTU1Pu37/PmzdvPutxqn5hx9v4\nl2mSsD1zh1G+lJFMxBd51tWrVwkLCyMsLIwzZ87w9ddf4+joSKNGjbC2tkZHR+ezHl+hUKgqas7O\nzqrtcXFxXLp0iXPnztGzZ08ePXpEs2bNaN26NY0aNcLAwOADvQohsluBTsKCg4MJDQ0lJiYGCwsL\nvL29adq0Ke7u7gB07doVgJo1a7J48WIAduzYwerVq7lz5w5Fixaldu3a+Pv7a+ojfDY58YBsZTr/\nA5eJ+CKvunLlCsuXL2fr1q08efKEOnXq0K5dO6ZOnZqtVa6sMDY2xtnZGWdnZ/r168e9e/c4evQo\nM2bMoGvXrnzzzTd89913eHp6oq+vr+lwhcj3CmwStnDhQg4dOsSoUaMoV64c586dY+LEiRgaGhIc\nHIy3tzeBgYFUrFhR9ZiiLVu2MHv2bAYMGECdOnV4+fJlgXhKQE6Rifgir0lISGDHjh3Mnz+fs2fP\n0rx5c8aMGUOVKlXyxH8iSpcujaenJ56enjx//pzjx4+zcuVKhg0bhre3N/3798fGxkbTYQqRbxXI\nJOzVq1esXbuWBQsWYG9vD4C5uTkXLlxg48aNjBo1CgAjIyOMjY1V+y1fvpxOnTrRqVMn1bbKlSvn\nbPD5lEzEF3nJ/fv3Wbp0KYsXL8bU1JS2bdsyceLEzz7M+DkZGBjg5uaGm5sb//zzD6GhoTg5OVGj\nRg2+//57mjdvLvPHhMhmBTIJu379Om/evMHX11ftTqGEhIT3Pj4oLi6OmJgYatWqlVNhFhgyEV/k\nFbdv38bPz4/Q0FAaNmzIjBkz8uV/xMqWLYuvry+9e/fmwIED+Pn5MWDAAMaNG0e3bt0oVKhA/uoQ\nItsVyJ+k1An3c+bMoVSpUmrvFSpUiKSkJE2EVWDtmTNUJuKLXO3Ro0dMnjyZlStX0rZtW7Zu3UrR\nokU1HdZnp6urS/PmzWnevDnnz59n0aJFzJgxgylTptCmTRtZ7kKILMr9kxY+g/Lly6Ojo8O9e/co\nW7as2peZmZlqDlhiYqJqH2NjY0xNTQkPD9dU2PlSxPaV3D21n1/WrckTc2hEwfL8+XN+/vlnKleu\nzN27d9mwYQP9+vUrEAnYf9nZ2bFgwQIGDhzIuHHjqFWrFocOHdJ0WELkaQWyElakSBG8vLyYM2cO\nycnJfPXVV7x8+ZILFy6gVCpp1aoVurq6/PHHH5iZmaGrq4uBgQHdu3dn9uzZqjuM4uPjiYiIoHPn\nzgCMGzcOhULB+PHjNfwJ847fg6dw4o/jGBkZaToUIVSSkpJYtmwZY8eOpWbNmixfvhwLCwtNh6Vx\nCoWC2rVr4+joyP79++nWrRuVKlVi/vz5BXJNRSGyqkAmYQD9+vXD2NiYNWvWMHXqVIoUKULlypXp\n2rUrWlpajBgxgqVLl7J06VK++uorFi9eTNu2bSlUqBBr165l/vz5GBoa4uLiourzwYMHUp7PBBNj\nY6ZNnSoT8UWucvPmTbp160ZcXByzZ8+W5CIdSqWSxo0bU79+fbZs2YKzszMjR45k2LBhMnlfiExQ\nREREqK1I6uDg8EnLLnzqfkIzHBwcNL4YbWJiYrb8g61QKHL0ms3KtZ4bzrtIX1JSEosXL2bs2LF0\n6dKFTp06SUKRQXfv3mXSpEkkJycTEhIiiasQGSSTcITGyC+47PHPP/+wefNmXr9+relQ8qybN2/S\noEEDlixZwpIlS+jSpYtcn5lQpkwZAgMDadCgAc7OzkyfPl1tTq0QIn2ShAmRx/lPmEi/wSMwK1OW\nwUOHERUVpemQ8pQVK1ZQs2ZNvvzyS5YuXUr58uU1HVKepFQqadeuHStXrmTLli3Url2bmzdvajos\nIXI1ScKEyMOePXvGxo2/0Gvp7/Ra/ifnH2tT26Uu37h+y7p166U69gFv375lwIABTJw4kcWLF0v1\nK5ukVsVcXFxwdHTkyJEjmg5JiFxLkjAh8rD16zdQoUZdjEzLUKKsNW7fT2XY9tuUbz6ASYErpDr2\nHrGxsbi5uXHu3DlWrFhBhQoVNB1SvqJUKuncuTPjxo2jbdu2LFmyRNMhCZErSRImRB62YHEQNVr3\nUdtWSFuH6g3b03X+fqmOpePixYvUqlULS0tLZs2aVSDX/MopTk5OBAUFMX36dPr168fbt281HZIQ\nuYokYULkUadPnyb6wUMqOrm9t41Ux9Rt374dV1dXfHx8GDhwoAw/5gBLS0tWrFjBxYsXadiwIY8e\nPdJ0SELkGpKECZFHLVwcRA33XigzkEhoqjqWnJzM77///ln6zqylS5fSs2dPZs+eTfPmzTUdToFi\nYGDAzJkzsbKyonbt2ty9e1fTIQmRK0gSJkQelDohv0bLbpneNyerY8eOHaNOnTrcunUrW/vNrMDA\nQPz8/Fi8eDFffPGFRmMpqLS0tBg4cCDNmjXDxcVF49eEELmBJGFC5EHvTsj/VGmqY090sr06tmBx\nEHpFirJt2/Ys9/WpZs+ezfTp01m8eDGWlpYai0Ok6Nq1K+3atcPFxYXr169rOhwhNEqSMCHyoAVL\n0k7Iz4oSZa1xGzCFYdtvU6HF99lSHYuLi2PH9m00HTSTLWGaScICAwOZM2cOixYtokyZT09YRfbq\n2LEjnTt3pl69ety+fVvT4QihMZKECZHHnD59muj7H56Q/6kKaetg16BdtlTHQkJWY+vSHPvG33Hy\nxB/8+++/2R7vhyxdupTJkycTGBiImZlZjh5bfFz79u1p164d9erV4969e5oORwiNkCRMiDwmMxPy\nsyIr1bHk5OSUap17b3SLFKXCl9+wb9++zxrvu7Zt28bYsWNZsGCBVMBysU6dOtGsWTPc3Nx4/vy5\npsMRIsdJEiZEHpI6Ib9mq+45dsxPqY4dO3aMl28SKV/DFQBrl1Zs2botR+K9ePEi3bp1Y9q0aTIH\nLA/w9vamUqVKeHl5kZSUpOlwhMhRkoQJkYesX78B65rfYliytEaOn9Hq2ILFQXzl3huFQgFAlTot\n2bN7FwkJCZ81vtjYWFq0aMGgQYPkLsg8QqFQMGLECG7fvo2/v7+mwxEiR0kSJkQesmBJSnKjaR+q\nji1fvoId27dRo7m3qn0xMwuKlbLgjz/++GwxvX37lnbt2uHq6irrgOUxOjo6TJ06leXLl7Np0yZN\nhyNEjpEkTIg84vTp09x/EPNZJuRnxX+rY7OXr+erpl4UKVZCrZ2NSytCwz7fkOSQIUNISEhgwIAB\nn+0Y4vMpUaIE06dPp2/fvpw5c0bT4QiRIyQJEyKPWLg4iK9a9fzsE/I/1bvVseYjAtO8b1unJVs/\n03phK1asYNeuXfz888/yKKI8zNbWlhEjRtCqVSt5vJEoECQJEyIPSEpKYv26tWjp6vM2/pWmw/kk\npW1r8PTfZ9m+Kv+NGzcYMWIE06ZNk4dx5wONGjXC1dWV77//XtOhCPHZSRImRB6gVCrZsH4d8RcP\nMKOlBbtmD+bB9UhNh5UpSqUS2zots3X1/KSkJLp160aXLl2oUKFCtvUrNKtfv36cOHGC0NBQTYci\nxGclSZgQeUTLli05uG83Z09H8I2VAWt8G7KiTx1O71ydZ6pjlVxaZuvq+YsXL+bJkyd06tQp2/oU\nmqenp8dPP/1Ev379ZFhS5GuShAmRx1hZWTFl0kTu3rnF1DFDefT7ujxTHbN2qM+Fc2eIjY3Ncl83\nbtxg7NixjB07VuaB5UP29vY0atRIbrQQ+ZokYULkUdra2nh4eOSp6pi2nj4Va9Vj9+7dWern3WFI\nKyur7AlO5Dp9+/YlPDycX3/9VdOhCPFZSBImRD6Ql6pj1s6t2ByataUqli5dKsOQBcC7w5KPHz/W\ndDhCZLtC2dWRoaEhDg4O2dWd+MyKFy+u6RDyLC0trU++1j/3eU+tjnl4eHDz5k2WLF3Gct+GFC9r\njX2r3tg1aIe2nv5njeFjbF2aM3fuUN68eYOOjk6m93/+/Dnjxo0jICBAhiELAHt7e5ydnZk2bRpT\np07VdDhCZKtsS8IOHTqUXV2JTHBwcCA5OVnTYRQoiYmJeeKcp1bHJvj7sWPHDgIXBTFjzhDsm3pR\ns3VvSlWoqpG4ipYohVmFKhw+fJhGjRplev9Zs2bh4OBA5cqVP0N0Ijfq2bMnnTt3ZuDAgZQurZlH\ndgnxOchwpBD5XG6cO2bj3IrQT3igd0xMDHPnzqV3b80/uknknFKlStGqVSt5tqTIdyQJE6IAyS1z\nx2zrtCRs+/ZMVxQnTZqEm5sbZcuW/UyRidzK29ubLVu2cOXKFU2HIkS2kSRMiAJI09WxUtbVSETJ\n+fPnM7zPrVu3CA4Opnv37p8xMpFbGRkZ0alTJ0aPHq3pUITINpKECVHAaaI6plAoqOjcnK2ZeKD3\n2LFjadeuHSVKlPh4Y5EveXp6cvToUSIiIjQdihDZQpIwIQSQc9WxN/EvObRsAuf2rMM8g5Oso6Oj\n2bZtG507d86WGETepK+vT+fOnZk1a5amQxEiW0gSJoRI43NUx5KSkvhr1xrmtbelyKOLnP3rFL16\nZGxocenSpTRq1Ege0C1o2bIlu3fv5uHDh5oORYgskyRMCPFeH6qOnT+4JcP93Dp7nGU9axMVNo+t\nm9bz66ZfMrzSfUJCAosXL6ZNmzaf+ClEfmJoaEj9+vVZtmyZpkMRIsskCRNCZMi71TEvdzeOLPf/\n6D6P791k4xhPQsd64j/cl1Phf+Ls7Jyp427fvh1zc3MqVar0iZGL/KZt27YsWrSIxMRETYciRJZI\nEiaEyBRtbW0io65SvXm397aJf/4v+xb8yCLvmrSoXY2/r0bRpYsXSmXm/8kJDAyUKphQY2trS4kS\nJdi1a5emQxEiSyQJE0JkSlxcHDu2b6NG865p3ktKTCR86zLmdbDFLCGayPPnGO83jsKFC3/SsaKi\nojh37hz169fPatgin2nTpg2BgYGaDkOILMm2xxYJIQqGkJDV2Do3o0gxE7Xt104eYt/coZgbF2Xv\nzm3Z8izZ5cuX06JFi096xqTI3xo2bMicOXO4ffs2lpaWmg5HiE8ilTAhRIYlJyezYEkQNVr3UW2L\nuXWFdcPd2TO1JzMn/MQfvx/JlgQMIDQ0lAYNGmRLX3nd9u3bcXV1zXKb/EJXVxcXFxe2bcv846+E\nyC3SVMIMDQ2z7R9Q8fkVL15c0yHkWVpaWp90rRfkc37s2DFevkmkfA1XXv77mP8tm8DZPasZ9cMP\nDB70C3p6etl2rKioKJ49e4atrW229Znd/P392blzJ61atWLs2LFq782bN4/Vq1fj4uJCQEBAjsTj\n5uZGnTp1cuRYuYGLiwtbt27l+++/13QoQnySNEnYoUOHNBFHgebg4JDpZ+iJrEtMTJTznkkLFgfx\nZfNu/LExkCMrJ9LWw4NNlyIxNTXN9mNt27YNV1dXFApFtvedXRQKBaVKleLAgQOMGDFClYQmJCSw\ncweKssIAACAASURBVOdOzMzMcjR+XV1ddHV1c+x4mubk5MTPP//Mv//+i6GhoabDESLTZDhSCJEh\ncXFx/Lp5I8fXB/Dv6e0cOXSAZUGLP0sCBrB161ZcXFw+S9/ZqWLFilhYWLB//37VtmPHjqGnp0fN\nmjVVif7FixcZMGAADRs2pG7duvTs2TPNszOfP3/OlClTaNKkCc7OzrRv316tX4CTJ0/i6elJnTp1\n6Nu3L/fu3VO999/hyKCgIDw9Pdm7dy/u7u7UrVuX4cOH8+TJE7U+t23bRvv27XF2dqZt27asW7cu\nT/wHpUiRItjb27N3715NhyLEJ5EkTAiRIadOnaKCTUXWrlrOof17sbOz+2zHevToEefPn88zUyPc\n3d3V5iaFhYXRqlUrtSrYq1evaNGiBcuWLSMkJIRKlSoxaNAgnj59CqTMtxs0aBBnzpzB39+fzZs3\nM3z4cLWbEt68ecOqVavw8/Nj5cqVPHv2jClTpnwwtujoaA4ePMisWbMIDAwkKiqKhQsXqt4PDQ1l\n4cKF9OvXj82bNzN48GCCg4PZtGlTdp2ez8rZ2ZmwsDBNhyHEJ5G7I4UQGdKoUSMuXTj/8YbZYNeu\nXXz99de5fmgtOTkZhUJBkyZNmDNnDv/88w96enr8+eefjBw5kkWLFqna/jehHDFiBIcOHeL48eM0\nbdqU8PBwzp8/z8aNG1VPEzA3N1fbJzExkZEjR6ruBuzSpQsTJkz4YIyJiYn4+flRpEgRADw8PNi+\nfbvq/WXLljFo0CDVMiDm5uZ4e3uzefNmOnTo8GknJgfVqVOHrl27kpCQQKFC8itN5C1yxQohcp19\n+/bh6Oio6TAyrGjRotSrV4+wsDAMDAxwcHCgVKlSam3i4uJYvHgxp06dIjY2lqSkJF6/fs2DBw8A\nuHz5MiYmJh98nJOOjo7acgwmJia8ffuWZ8+evfe5mmZmZqoELHWfuLg4AB4/fszDhw+ZNGmSWkUt\nL61Eb2ZmRokSJThz5kyeqZwKkUqSMCFErhMREUHTpk01HUamtGrVCj8/PwoXLky/fv1U21OHJP39\n/Xn8+DFDhw6ldOnSaGtr069fP96+fZvhY2hpaam9Tu07KSnpvfv8tzqkUChU871S9xs9ejTVq1fP\ncBy5TdWqVTl16pQkYSLP+T/27jyuxvz///jjnEoxIVSqyZ5vyDJDMSYJIXsTM8zY17JlZ8aWMINh\nDDPI2JoRg7Eba0MMhbFE9n2ZQaGQmVBU5/eHX+fjKLRf59Trfrud203Xuc51vc5R1/U87+t9vd/S\nJ0wIoVfi4+P5559/qFSpktKlZEhqoKlbty6FChXi8ePHNGrUKM16p06dolOnTri6ulKhQgUKFy5M\nbGys9vkqVaoQGxvLzZs386hyKFWqFFZWVty+fRt7e/s0D0Ph6OjIsWPHlC5DiEyTljAhhF6JjIzE\nwcEBExMTpUvJtNWrVwO6rU+pIa1s2bLs2LEDJycnnj59yo8//qjzHuvWrUv16tUZM2YMI0aMoEyZ\nMty+fZuEhATc3d1zrWZfX19mzpyJubk5H3/8MUlJSVy6dImYmBh69uyZa/vNSVWrVs2zsdiEyEkS\nwoQQeiUiIgJHR0ely8gQlUqlcwfk63Nkvvq8v78/33zzDV27dsXa2hofHx+doSJUKhU//vgjP/zw\nAxMnTuTp06fY29vj4+Ojs056NWTm59eXeXl5YWZmxooVK1iwYAGmpqZUqlTJIDrlp6pcuTKXL18m\nMTFR72/mEOJVquPHj+v/YDD5nAzWmj0qlYrjx49n+nXyueunrl27UqFCBT755BOlSxEGpEuXLqxc\nuVL6hQmDIn3ChBB65fjx43o9VZHQT1WqVCEiIkLpMoTIFAlhQgi9kZKSwvXr16lYsaLSpQgDU6FC\nBc6fP690GUJkioQwIYTeiImJoWjRojqjxAuREVZWVty5c0fpMoTIFAlhQgi9ER0dnWtzUYr8zdLS\nkujoaKXLECJTjKUTo/JKlCihdAkGrUSJElnqjCufu/6JiorC0tJS6TKEAbKyspIQJgyOsdwdJgxd\n6hQswvBFR0dTqlQppcswaIsXLyY0NJTffvtN6VLylKWlJXfv3tXO5ymEIZDLkUIIvXHnzh2DCWEB\nAQEMHz5c6TLS6NatG0uWLFG6jDxXpEgRjIyM+Pfff5UuRYgMkxAmhNAbUVFRBhPCXh+oNbclJSVl\naL3ChQtTrFixXK5GP8klSWFoZMR8IYTeiI2NpWzZskqXkSEajeaNg/1ev36dH374gcjISExNTXFx\ncWHEiBHagHnu3DkCAwO5dOkSL168oHLlygwdOpQaNWpot+Hi4sKYMWM4evQof/31F59++imFCxcm\nNDSU3r17ExgYSFxcHC4uLkyYMAELCwsg7eXIgIAAHj9+TN26dVmxYgUJCQk0atSIMWPGYGZmBsCz\nZ8+YPn06f/75J0WKFKFLly5ERERQokQJJk2alJsfY44qUaIEDx48ULoMITJMWsKEEHojKSlJZ95F\nQxQbG0u/fv2oXLkyy5cvJzAwkGfPnjFy5EhtaHv27Blt2rRh6dKlBAcH83//938MHTqUx48f62xr\nyZIlNGjQgN9++43PPvsMeNlvLjQ0lNmzZzN//nwuXbpEYGDgW2s6efIkN27cIDAwkOnTp7Nv3z7W\nrFmjfX7OnDmcPHmS7777jsDAQC5cuEBkZKTB9a0yNjbmxYsXSpchRIYZ9tFOCJGv5IcQtn79ehwd\nHRk8eLB2WUBAAB4eHpw/fx4nJ6c0d/OOHj2avXv3cujQIVq2bKld3rx5c7y8vHTWTU5OZtKkSbz3\n3nsAeHt7s3Xr1rfWZG5uztixY1GpVJQvX56mTZty9OhRevbsydOnT9m6dStTpkyhbt26wMt5Llu1\napWtz0EJRkZGGb5sK4Q+MOyjnRAiX0lKSkKtNuwG+gsXLnDixAkaNmyos1ylUnHnzh2cnJx4+PAh\nP/30ExERETx48ICUlBQSExO5d++ezmuqVq2aZvs2NjbaAAYv7wp81x3CFStW1GnVsrS05OzZswDc\nvn2bpKQknJyctM+bmZlRqVKljL9pPWFkZCQtYcKgSAgTQugNlUpl8JOqazQa3NzcGDp0aJrnSpYs\nCbxsGXv06BEjRozAzs4OExMTBgwYkCZAFC5cOM02Xm8pzMhnZmRklOY1KSkp73wfhiYlJcXgQ7wo\nWCSECSH0hrGxMcnJyUqXkWHp9ZmqUqUKe/bswcbG5o2XVk+dOsXo0aNxdXUF4MGDB8TGxuZpnans\n7e0xNjbm3Llz2NnZAZCQkMC1a9cM5iaJVMnJyZiYmChdhhAZJiFMCKE3TExMDCqExcfHc/nyZZ1W\nI1dXVzZv3sy4cePo3r07FhYW3Llzh9DQUIYPH07hwoUpW7YsO3bswMnJiadPn/Ljjz/manh4W6tW\nkSJFaNeuHfPmzcPCwoJSpUqxbNkyg2wJS05ONvg+haJgkd9WIYTeKFy4MImJiUqXkSEqlYqTJ0/S\npUsXneUeHh4EBQUxb948hgwZQmJiIjY2NtSvX18btPz9/fnmm2/o2rUr1tbW+Pj4EBcXl+H9vmvZ\nqz+/aTyzV5cNGzaMZ8+eMWLECIoUKULnzp159OgRpqamGapJXyQmJmqH3RDCEKg0hvh1RwiRL40c\nOZIXL17Qo0cPpUsp0J4/f07btm3p3r17mpCpz9q2bctff/1lcJdRRcElLWFCCL1hZ2fHiRMnlC6j\nwLl06RI3btzAycmJJ0+eEBwczLNnz2jevLnSpWVYSkoKsbGx2NjYKF2KEBkmIUwIoTfs7OzYvXu3\n0mUUSKtWreLvv//GyMgIR0dHFi9ejJWVldJlZVhcXBzFihWjUKFCSpciRIZJCBNC6A1bW1uZdkYB\njo6OBAcHK11GtsTExFC6dGmlyxAiU2RAFSGE3rCzsyMmJkbpMoQBio2N1Q6xIYShkBAmhNAbtra2\n3L9/3yCHRxDKkhAmDJGEMCGE3ihatCgmJiZpJrI2JC4uLuzduzdP9tWuXTt+/fXXt66zePFiOnXq\nlCf1KOnu3btyV6QwOBLChBB6pUaNGly6dEnpMt7q4sWLuLi40KdPH0XrCA4OpkOHDtqf8zIA6psr\nV67wwQcfKF2GEJkiIUwIoVfq1q3LhQsXlC7jrTZv3ky1atU4e/YsN2/ezPP9p84xaWFhkWZw0oJ6\nKff8+fM4OzsrXYYQmSIhTAihV5ydnfW6JSwhIYGQkBB8fX1xdnZmy5Ytb13/7NmzdOnSBVdXV7p1\n68ahQ4dwcXHRGQ/txIkT9OjRA1dXVzw9Pfn+++9JSkrSPu/r68uMGTOYO3cuzZo1o2/fvsDLwUlT\nL0e2bdsWgK+++goXFxe8vLx06ggJCcHLywt3d3dGjRqlM0J/QEAAw4cPZ/ny5Xh6etKoUSPmz59P\nSkoKCxcupFmzZnh6erJy5crsfXi5JCYmhqSkJLkcKQyOhDAhhF6pU6eOXreEhYaGYm5uzscff0z7\n9u3Zvn27TmB61dOnTxk2bBgVK1Zk5cqVDB48mB9++EFnyqD79+8zZMgQqlatyq+//srEiRMJCQlh\n/vz5OtvauXMnKpWKpUuXMnnyZEB36qEVK1YAMGHCBEJCQli+fLn2uejoaEJDQ5k9ezbz58/n0qVL\nBAYG6mz/xIkTREdHs3jxYsaOHUtwcDB+fn5oNBqCgoLw8fHhhx9+0MuAfOHCBT788MO3TlQuhD6S\nECaE0CuVK1cmLi4uw3Mp5rXff/+ddu3aAeDu7o5KpWL//v3prrtz5040Gg0TJ06kQoUK1KtXj969\ne+tcMly/fj2lS5fmq6++onz58jRo0AA/Pz/Wrl2rM4/m+++/z9ChQylXrhzly5dPsy8LCwvg5c0N\nJUuW1P4MLye2njRpEg4ODtSoUQNvb2+OHTum8/qiRYvy5ZdfUq5cOTw9PalSpQpxcXEMHDiQMmXK\n0KFDB2xtbdO8Th+k9tETwtBICBNC6BW1Wk2tWrX0ssXl1q1bREZGai/9GRsb07p16zdekvz7779x\ncHDQGcXdyclJZ50bN25QvXp1nWW1atXixYsX3Lp1S7usSpUqWa7bxsaG9957T/uzpaUlDx8+1Fmn\nYsWKOi1JJUuWpFKlSjrrlCxZkkePHmW5jtxy+fJl6Q8mDJKMmC+E0Dv169cnMjKSevXqKV2Kjs2b\nN5OSkpKmvxW8vKxobW2dZvm7OsqrVKo3rvNqKCpcuHAmq/0fY2PdQ316+zQyMkqzTkZep7Tk5GRO\nnz5N3bp1lS5FiEyTljAhhN5p06YNhw4dUroMHUlJSWzbto3BgwezevVq7WPVqlU4ODjw+++/p3lN\n+fLluXbtms5lxXPnzqVZ5+zZszrhJjIyEhMTE+zt7TNVo7GxMSkpKZl8Zy8Zan+qM2fOYGtrS5ky\nZZQuRYhMkxAmhNA7rq6uREVFce/ePaVL0Tp48CCPHz/G29ubihUrah+VKlWiefPmbN26Nc1rWrRo\ngVqt5ptvvuH69escOXKEoKAg4H+h57PPPiMmJoYZM2Zw48YNwsPDmT9/Pp06dcLU1FS7rYy0QNnZ\n2XH06FFiY2P5999/M/X+Xt++RqPJ0DKlhYWF8cknnyhdhhBZIiFMCKF3jI2NadmyJWFhYUqXorVl\nyxZcXFwoVqxYmuc8PDyIjo7myJEjOsuLFCnCnDlzuHbtGl27dmXevHn4+voCaPuJWVlZ8eOPP3Lp\n0iW6dOnC1KlTadGiBYMGDdLZVkZaqoYNG8bx48dp06YN3bp1e+trX12mUqnSrJPRZUoLDw/X3igh\nhKFRafTta40QQgDr1q1j3rx5zJkzR+lSctT+/fsZPXo0u3fvpnjx4kqXY9D++ecf+vfvT3R0NGq1\ntCkIwyO/tUIIveTp6cnJkyd5+vSp0qVky7Zt2zh58iRRUVGEhYUxe/ZsGjZsKAEsB4SFhdG2bVsJ\nYMJgyd2RQgi9VKxYMerVq8fhw4fx8PBQupwse/jwIYsXLyY2NpZSpUrh5uaGn5+f0mXlC+Hh4fj7\n+ytdhhBZJpcjhRB6a+nSpaxatYpZs2YpXYrQM3fu3KFXr17cvn07W8N3CKEkacMVQuitL774gsjI\nSO7evat0KULPbNq0iR49ekgAEwZNQpgQQm+99957dOnShU2bNilditAjiYmJbN26lQEDBihdihDZ\nIiFMCKHXBg0axJYtW3jx4oXSpQg9ERoaygcffEDlypWVLkWIbJEQJoTQa1WqVKF69ers3btX6VKE\nnti4caPc3CDyBQlhQgi9N3jwYDZu3Kh0GUIPXLx4kZiYGFq3bq10KUJkm4QwIYTea9euHVFRUZw/\nf17pUoTCfvvtN/r3759mcnEhDJGEMCGE3jMxMWH8+PH89NNPSpciFHTt2jUOHz4slyJFviEhTAhh\nEHx8fIiOjubYsWNKlyIU8tNPP/Hll1/KbAMi35AQJoQwCCYmJkybNo3AwEBkjOmC59SpU1y+fJnB\ngwcrXYoQOUZCmBDCYHTs2BG1Wi13ShYwGo2GwMBAJk+ejJmZmdLlCJFjJIQJIQyGWq3m22+/ZdGi\nRSQlJSldjsgjhw4dIj4+nh49eihdihA5SkKYEMKgNG/enDJlyrB582alSxF5ICkpiQULFjBjxgy5\nI1LkOzKBtxDC4Jw9exZ3d3eCg4OxsbFRuhyRi5YtW8bVq1cJCQlBpVIpXY4QOUpawoQQBqd69eoM\nHz6c6dOnSyf9fOzKlSusXbuWZcuWSQAT+ZKEMCGEQfrqq694+vQpW7ZsUboUkQuSkpKYMmUKM2bM\noEyZMkqXI0SukMuRQgiDJZcl869ly5Zx7do1du3aJa1gIt+SljAhhMGSy5L5k1yGFAWFhDAhhEFL\nvSy5du1apUsROSAhIYHJkyfz7bffYm9vr3Q5QuQqud9XCGHQjI2NWbduHR999BGVKlXC2dlZ6ZJE\nFmk0GqZOnUqdOnXo3bu30uUIkeukJUwIYfAqVarEmjVrmDhxIrdv31a6HJFFP//8Mw8ePGDp0qVy\nGVIUCBLChBD5goeHBxMmTGD06NE8efJE6XJEJu3fv59NmzaxZcsWmZpIFBhyd6QQIt/QaDT069eP\n69ev8+2336JWy/dMQ3D16lUGDRrEjh07qFu3rtLlCJFn5AglhMg3VCoVgYGBJCYmsmjRIqXLERkQ\nFxfH6NGjmTNnjgQwUeBICBNC5CuFChVi06ZN7N27l9WrVytdjniL//77jyFDhtC1a1e6deumdDlC\n5Dm5O1IIke9YW1uzb98+3NzcKFSoEB06dFC6JPGaJ0+eMGzYMJo0acI333yjdDlCKEJCmBAiXypX\nrhz79u2jYcOGGBsb4+XlpXRJ4v978uQJI0aMwMXFhR9//FHuhBQFloQwIUS+ValSJf7880+aNGnC\nixcv+PTTT5UuqcD777//GDZsGC4uLixatEgCmCjQJIQJIfK1ypUrc+DAARo3bkxCQgJdu3ZVuqQC\nKy4ujiFDhtCkSRNpARMC6ZgvhCgAKlSoQFhYGDt37uTbb78lKSlJ6ZIKnKtXr9KrVy+8vLwkgAnx\n/0kIE0IUCGXKlOHo0aPEx8fj5+dHXFyc0iUVGPv372fQoEF88803TJs2TQKYEP+fhDAhRIFRrFgx\ntm7dSpMmTejVqxdXrlxRuqR8TaPREBQUxHfffceOHTtkGAohXiMj5gshCqRff/2VIUOGMHbsWBo3\nbqx0OflOQkICU6dO5cGDB2zZsgU7OzulSxJC70gIE0IUWMePH8fLy4tmzZrh4+ODqamp0iXlC1ev\nXiUgIIA6deqwdOlSmQtSiDeQy5FCiALL2dmZkydP8vjxY7p3787Zs2eVLsmgJSUlsWzZMgYOHMiI\nESNYsWKFBDAh3kJawoQQBZ5Go2Ht2rX4+fnRunVr+vXrJ61imXT16lWmTJmCnZ0dy5Yto2zZskqX\nJITek5YwIUSBp1Kp6NSpE2fOnCEuLk5axTLh1davYcOG8ccff0gAEyKDpCVMCCFe8WqrmLu7O337\n9sXS0lLpsvSORqPh0KFDBAYGUqZMGWn9EiILJIQJIUQ6Hjx4wLRp0wgKCqJ9+/Z069aNokWLKl2W\nXjhz5gyBgYE8fvyYGTNm8Mknn8jYX0JkgYQwIYR4i3/++YdJkyaxdetWunXrxqefflpgO5vfuHGD\nhQsXcvHiRSZPnkzPnj0xNpbZ74TIKglhQgiRAefPn2fs2LEcO3aMrl270rp1a8zNzZUuK09cvnyZ\nNWvWcOjQIcaMGcPgwYMpXLiw0mUJYfAkhAkhRCb89ddffPfdd+zZs4fmzZvToUMHHBwclC4rxz1/\n/py9e/eyceNGoqOj6d+/P0OGDMHCwkLp0oTINySECSFEFkRFRbFkyRIWLVqEnZ0d7du3p0mTJpiY\nmChdWrZER0ezadMmfv/9d2rWrMngwYNp27atXHYUIhdICBNCiGx48eIFv//+O/Pnz+fMmTM0bNiQ\nBg0aUK9ePYPpO3b79m3CwsI4ePAgly5donv37gwYMABHR0elSxMiX5MQJoQQOeT69ets2bKFLVu2\nEBERgbOzM66urri5uenVMBcpKSmcO3eOAwcOEB4ezuPHj2ndujWffPIJTZs2pUiRIkqXKESBICFM\nCCFywaNHj9i1axebN2/mjz/+wNbWlqpVq+Lo6EjVqlVxcHCgUKFCeVLLgwcPuHjxIhcuXODy5cuc\nPn0aa2trvLy8aNeuHXXr1kWtlrG7hchrEsKEECKXvXjxgoiICCIiIjh27BjHjh3j+vXrVKxYkSpV\nqlChQgWsrKywtLTUPjJzKVOj0fD48WNiY2OJiYkhNjaW6Ohorly5woULF3j27BkffvghdevWxdnZ\nmbp161KuXLlcfMdCiIyQECaEEAp4+vQpp0+f5vjx41y4cIGoqCiioqKIjo7m3r17mJmZYW1tjYWF\nBcbGxhgZGWFkZIRGoyEpKYnk5GQSExO1watIkSKULl0aOzs77OzsKFOmDB9++CHOzs5UqFBBBlMV\nQg9JCBNCCD2j0Wh4+PAh0dHRPHjwgKSkJJKSknjx4gVqtRoTExOMjY0xNTXF1tYWGxsbGbdLCAMk\nIUwIIYQQQgHSE1MIIYQQQgESwoQQQgghFCAhTAghhBBCARLChBBCCCEUIJOBCSGEHti5cydHjhzJ\n9nbUajUDBgzAysoqB6oSQuQmCWFCCKEHvps7j4fqEliWrZyt7ZzZtQJnZ2datWqVQ5UJIXKLhDAh\nhNATH7TsQhXX7IWn+xeO5lA1QojcJn3ChBBCCCEUICFMCCGEEEIBEsKEEEIIIRQgIUwIIYQQQgES\nwoQQQgghFCAhTAghhBBCARLChBBCCCEUICFMCCGEEEIBEsKEEEIIIRQgIUwIIYQQQgESwoQQQggh\nFCAhTAghhBBCARLChBBCCCEUICFMCCGEEEIBEsKEEEIIIRQgIUwIIYQQQgESwoQQQgghFCAhTAgh\nhBBCARLChBBCCCEUICFMCCGEEEIBEsKEEEIIIRQgIUwIIYQQQgESwoQQQgghFCAhTAghhBBCARLC\nhBBCCCEUICFMCCGEEEIBxiqVSukaCrwSJUrw8OFDpcswWCVLluTRo0eZfp187iKvLF0WxNoNm966\nTuSJ45Rrk/3vxRqVivH+Acydv/CN6xQyMWbxwgXY2dlle38ib2X1eCeU8a7zjOr48eOaPKxHpMPZ\n2RmNRv4bskqlUnH8+PFMv04+d5FX/vrrL9p4fUJxe0cadBlJel9+VWo1lVw8MDE1y9a+Ym5eIvaf\ny+k+9zjmDrt+GE2LFi1Y/esKzMyyty+R97J6vBPKeNd5xjgPaxFCiALpo48+IjLiOO28O3A2ZCVe\nE4IwLWKeK/uyKu+IVXnHNMtvRoaz9dv+jB8/jvFjv0o3CAoh8pb0CRNCiDxgb2/PobD9VLcrytI+\n9Ym9dTVP9qvRaPhrXSC/fdWeFUFLmTBurAQwIfSEhDAhhMgjZmZm/BK0lDFDB7C0nyuXDu3K1f29\nSExgyzd9Of97IEcPH6JVq5a5uj8hROZICBNCiDykUqkYPGggWzdtYNs3vdn/y/Rc6Zv4+P4dfu7v\njo36XyKO/oWDg0OO70MIkT0SwoQQQgENGjTgxPGj3P1rM2vHdSTxaXyObftmZDiLetWl1+efsHH9\nWszNc6f/mRAieySECSGEQlL7idV4v1iO9BOT/l9CGBYJYUIIoaDUfmJfDhuYrX5iLxIT2Px1Hy5s\nXSj9v4QwEBLChMiCnj17olar0zxOnz6d7W3/+eefqNVqGUi2AFGpVAwaOICtmzawfVqfTPcTe3zv\nNj/3d8fW6D+OHzks/b+EMBASwoTIApVKRbNmzbh7967Ow8nJKcf2kROdtZ8/f54DlYi8ktpP7N6R\nLRnuJ3bjZBiLetWl9xfe0v9LCAMjIUyILNBoNJiammJtba3zMDIyYuvWrdSpU4fChQtTsWJFJkyY\nwIsXL7SvXblyJS4uLhQrVozSpUvTsWNHoqKiALh58yZNmjQBwMrKCrVaTe/evQFo1KgRfn5+OnX0\n7NmTtm3ban9u1KgRAwcOZNSoUVhbW+Pm5gbA+fPnad26tXafnTt35t69e7n6GYmsef/99zkUtp+a\n9sXf2k8stf/XurGfsvKXIBmAVQgDJCFMiCxKr6UqJCSErl27MmTIEM6fP09QUBDr169n3Lhx2nVe\nvHjB1KlTOX36NNu2bSM2NpYvvvgCgLJly7JhwwbgZXC6e/cuP/zwA/Cy9e31k2x6y1auXIlKpSI8\nPJzg4GCio6Np2LAhNWvW5NixY4SGhhIfH4+Xl5dM26SnTE1N+XnZkv/1Ezu4U+f5V/t/HTl8kJYt\nWyhUqRAiO2TaIiGyaNeuXRQtWlT7s5ubG0+ePGH06NH06NEDgAoVKjBjxgy6devGrFmzAOjVq5f2\nNeXLlycwMJBq1aoRFRWFnZ0dJUqUAMDa2pqSJUu+tQaNRpMmSFWsWFG7LwB/f38++OADpk+fmayy\n6QAAIABJREFUrl22fPlySpUqxfHjx3FxccniJyByU2o/sVo1a9D+045EfzoY955j+ff+HX77qgM1\nK5dlz5HDcvlRCAMmIUyILHJ3d2fx4sXan83MzKhcuTJHjx7l22+/1S5PSUkhISGBe/fuUbp0aU6c\nOMHkyZM5deoUDx8+1Iaof/75Bzs7u2zVpFKpqFOnjs6yiIgIDhw4oBMYU9e9fv26hDA916BBA05G\nHKOddwdWnj5I9KWTjBg2hHFffSmXH4UwcBLChMii1D5fr9JoNAQEBPDZZ5+lWd/S0pInT57g6elJ\n8+bNWblyJdbW1sTExODm5vbOTvRqtTpNq9erfc1Svffee2lqatOmDd99912ada2trd+6T6EfUvuJ\nTfCfRJOxfnL5UYh8QkKYEDmodu3aXLhwIU04S3Xx4kUePHjAtGnTKFeuHABnz57VWadQoUIAJCcn\n6yy3srLSduBPderUqTfu69Wa1q5dS9myZTE2lj95Q2Vqasqsb2coXYYQIgdJx3whcpC/vz+rVq1i\n0qRJnD17losXL7J+/Xq+/PJL4GXHe1NTU+bNm8f169fZvn07EydO1NlGuXLlUKlUbNu2jZiYGJ48\neQJAkyZN2LlzJ1u3buXSpUuMGDGC27dv67SOpddHbNCgQTx+/JhOnTpx9OhRrl+/zp49e/D19SU+\nPuemyhFCCJE5EsKEyIL07koEaN68Odu3b2ffvn3Uq1ePevXqMXPmTG2rl5WVFcuXL2fz5s04OTkx\ndepU5syZo7Ot999/n8mTJzN+/HhsbGy0w1L07t1b+2jQoAHFixfH29tb57Xp1WVra8vBgwdRq9W0\naNGC6tWrM3jwYMzMzDA1Nc2Nj0cIIUQGqI4fPy73qCvM2dlZhgrIBpVKxfHjxzP9OvnchRCGJqvH\nO6GMd51npCVMCCGEEEIBEsKEEMJAJCYmSuutEPmIhDAhhDAA165do1KlSvTv35+UlBSlyxFC5AAJ\nYUIIoeeuXbuGu7s7HTt25NixYwwYMECCmBD5gIQwIYTQY6kBrHv37nTp0oU5c+ZIEBMin5AQJoQQ\neurVANahQwcAzM3NJYgJkU9ICBMiC9q1a0fTpk3Tfe7ChQuo1Wr27NmTrX2o1Wo2btyYrW0Iw5Ve\nAEslQUyI/EFCmBBZ0LdvX/bt28fff/+d5rlly5ZRvnz5N4a0zMiJO+HSm19S6Le3BbBUEsSEMHwS\nwoTIgtatW1O6dGl+/vlnneUvXrxgxYoV9O7dm/Pnz9O6dWuKFStG6dKl6dy5M/fu3dNZf/ny5dSo\nUQMzMzNsbGzo2bMnAOXLlwfgs88+Q61W68wPuWjRIhwcHDA1NaVy5cosXbpUZ5tqtZrAwEDat2+P\nubk548ePz/kPQOSajASwVBLEhDBsEsKEyAIjIyN69OjBL7/8otNatXXrVh48eEDPnj1p2LAhNWvW\n5NixY4SGhhIfH4+Xl5d2/UWLFtG/f3/69OnD2bNn2bVrF7Vq1QLQjoi9dOlS7t69y7FjxwDYtGkT\nfn5+jBgxgnPnzjF06FAGDhzItm3bdOqbPHkybdq04ezZswwcODAvPhKRAzITwFJJEBPCcMm0RXpA\nps/JHqWmLbp69Sr/93//R0hICM2aNQNetpAB1KlTh0OHDun0C3v06BGlSpXi6NGjODs7Y29vT/fu\n3Zk2bVq621er1axfv5727dtrl7m6ulK1alWd1q9evXpx9epVwsLCtK/z8/Pjhx9+yPJ7E3kvKwHs\nVfHx8QwfPhwXFxcWLlyIWi3fsfMjmbbIsMi0RULkEgcHB9zd3QkKCgIgKiqKP/74gz59+hAREcGB\nAwcoWrSo9lG2bFlUKhXXrl3j/v37REVF4eHhkal9Xrx4EVdXV51lrq6unD9/XmeZs7Nz9t6cyFPZ\nDWAgLWJCGCIJYUJkQ58+fdi8eTOPHj3il19+oVSpUrRr146UlBTatGnDqVOndB5XrlzRtpblJJVK\npfPze++9l+P7ELkjJwJYKgliQhgWCWFCZEOHDh0wMzNj5cqVBAUF0b17d4yNjalTpw5nz56lbNmy\nVKxYUedhbm6OtbU177///luHsTAxMSE5OVlnWdWqVQkPD9dZFh4ejpOTU668P5G7cjKApZIgJoTh\nkBAmRDYULlyYzp07M2nSJK5fv06fPn0AGDRoEI8fP6ZTp04cPXqU69evs2fPHnx9fYmPjwdg/Pjx\nzJ07l7lz53L58mUiIyP5/vvvtdsuX748e/bs4e7duzx69AiA0aNHs2LFCgIDA7ly5Qrz5s1j1apV\njBkzJu/fvMiW3AhgqSSICWEYJIQJkU19+/YlLi4OV1dXHB0dAbC1teXgwYOo1WpatGhB9erVGTx4\nMGZmZpiamgLQv39/FixYwJIlS6hRowYtW7bU6ds1e/Zs9u3bR9myZalTpw4AXl5ezJs3jzlz5uDk\n5MS8efNYuHBhrlziFLknNwNYKgliQui/TN8dGRAQwPbt27W31qeKiIigf//+7Nmzh+LFi+d4oa9r\n27Ytd+/efePzderU4aeffsry9n19falUqVKetDDI3ZHZo9TdkUJkRV4EsFfJXZP5i5J3R548eZKv\nv/6aDRs2AC/PwxMnTqRu3bqZ2k7btm3x9/fHxcWFoKAgoqKimDBhAlFRUXh5eXHkyJFc/z319fWl\nVatWeHl55ep+3nWeMc7sBlUqFYUKFSI4OJgOHTpgYWGRrQKzasWKFdpvdpcvX8bPz4/g4GBKly4N\nvOxPI4QQ+iSvAxj8r0Vs+PDhDBgwQIKYyJD0AtaHH36oDWDwMg+8flNQRrz6mt69e2evUAOXpb9E\nZ2dn7Ozs0ozU/brr168zdOhQ3N3dad68OePHj+fBgwcA3Lx5ExcXFx4+fAhAQkIC9evXZ8iQIdrX\nb968GW9v73S3bWFhQcmSJSlZsiTFihUDoESJEtpl169fx8fHhwYNGtCqVStmzJjBkydPgJetdh99\n9BERERHa7W3YsAF3d3fu3LlDQEAAJ06cYN26dbi4uODi4vLWVjchhHgXJQJYKrk0KTIrqwFLZE6m\nQ5hGo0GtVjN48GA2bNjA7du3010vNjaWfv36UblyZZYvX05gYCDPnj1j5MiRwMtOx6VKldI2q546\ndQpzc3NOnTqlPUBERERkabyjq1ev4ufnh7u7O2vWrGHmzJlcvnyZKVOmAC8vVXbv3h1/f3/+++8/\nbt68ydy5cxkzZgzvv/8+o0aNombNmrRr146QkBBCQkKwtrbOdB1CCAHKBrBUEsREdkVERLyx/+mN\nGzfw8vLijz/+ACAsLIzOnTvTuHFj+vTpw9WrV9N93eLFi/H399dZtnPnTtq0aUPTpk214zACPH/+\nnNmzZ9OyZUtatmzJ999/rzM37qZNm/D29sbDw4MRI0YQGxurfe7IkSN06NCBRo0aMXPmzCx/Bjkt\nSy1hKpUKV1dXatWqRWBgYLrrrF+/HkdHRwYPHkz58uVxcHAgICCAc+fOaTsf165dWxvCIiIi8PDw\noHjx4pw7dw6AEydOaDskZ0ZwcDDNmjWjS5cu2NvbU716db788kv27t1LXFwcAD4+PlhaWjJ16lTG\njx+Pm5ub9pfL3NwcY2NjzMzMtC1r0nwvhMgKfQhgqSSIidxw8eJF/Pz8GDNmDM2bN+fixYvac+ve\nvXtp3749I0aMICkpKUPbO3XqFBs3bmThwoUsXbqUmzdvAhAUFMS5c+dYvXo1q1ev5ty5cyxbtgyA\nY8eOsWDBAmbMmMGuXbuwtbVl3LhxAMTFxTFmzBgGDRpEaGgo9vb2REZG5spnkVlZShapncz8/PwI\nDQ3l4sWLada5cOECJ06coGHDhtpHmzZtUKlU2tazOnXqaC8JprZ6pS67desW9+/fz1IIu3jxIjt3\n7tTZd9++fXX2bWxszNdff82BAweIi4vT/mcJIURO0acAlkqCmMhJJ06cYOTIkUyZMkU7m8emTZto\n3749Tk5OqFQqWrdujYmJCWfOnMnQNvv160ehQoWoXLkylStX5sqVKwDs2rWLvn37YmFhgYWFBf36\n9WPHjh3Ay9YzLy8vHB0dMTExYfDgwZw+fZro6GgOHjxIpUqVaNKkCUZGRnTu3BlLS8vc+UAyKdMd\n81/l5OREkyZN+PHHH3XulISXQc3NzY2hQ4emeV3JkiWBlyFsxowZ3L59m4sXL+Ls7ExCQgK7du2i\nRIkS2NvbY2Vllem6NBoNn3zyCZ07d07z3KvbO336NBqNhvj4eB49eoS5uXmm9yWEEOnRxwCWSjrr\ni5yg0WjYuHEjderUoXbt2trl0dHRbN++nd9++027LCkpiZiYmAxtt1SpUtp/m5mZ8fTpUwBiYmKw\ntbXVPmdjY6PdZmxsLNWqVdM+V7hwYYoXL879+/eJjY1N06Uo9SY+pWX7r27QoEGcPHmSw4cP6yyv\nUqUK165dw8bGBnt7e51HkSJFgP/1CwsKCsLe3h4LCwtq167NqVOnOHLkSJbnv0vd9+v7tbe3147R\ndOfOHWbNmsVXX31F3bp1mThxos7o5OmNVi6EEBmhzwEslbSIiexSqVSMGzeO6OhonYGmbWxs6N27\nN/v27dM+wsLCaN68ebb2Z2VlRVRUlPbnu3fvasPV6889e/aMx48fU7p0aSwtLbl37572OY1Go/Oz\nkrIdwuzt7fH29mb16tU6yzt27Eh8fDzjxo3j7Nmz3L59myNHjjBt2jRtqoWX/cJ27NihDVx2dnZY\nWFiwd+/eLF2KBOjRowfnzp1j+vTpXLx4kVu3bhEWFsa0adMASE5Oxt/fnzp16uDt7c2ECRO4d+8e\nS5Ys0W7Dzs6Oc+fOER0dTVxcnIwnJYTIEEMIYKkkiIm3efHiBYmJidpHen26ihQpwrx58zh58iTz\n588HwNvbmw0bNnD27Fk0Gg3Pnj0jPDxc59yfFZ6engQFBREXF0dcXBxLliyhZcuW2ue2bt3K5cuX\nef78OQsWLKBGjRrY2Njg6urK9evX2bdvH0lJSaxZs0an076SMh3C0rtttV+/fhgbG+sst7S0ZNmy\nZahUKoYMGUKnTp2YOXMmhQoVolChQtr16tSpQ0pKik7gcnZ2TrMsI3WlcnBwYMmSJURHR+Pr60vn\nzp1ZsGCB9hrwzz//zJ07d5g4cSIAxYsXJyAggF9++YVTp04B0LVrV0xMTPjss89o3ry5NjW3bduW\nyZMnZ7guIUTBYUgBLJUEMfEmQ4cOpUGDBtrHqw0VrzI3N2fBggUcOnSIRYsWUbVqVSZMmMCsWbNo\n0qQJ3t7ebN++PUNDXrxtnT59+lC1alU+//xzPv/8c6pVq6btClW3bl369+/PmDFjaNmyJVFRUdqG\nFwsLC2bMmMH8+fNp2rQpt27d4oMPPsjCJ5LzMj1ifkGWkJCAh4cHkyZNynaz6qtk5PbskRHzhT4w\nxAD2KhlZ3zAoOWK+yLx3nWfkrywTjh8/To0aNXI0gOU3X477kt/W/vbuFYXIpOAVwfhP8X/3igow\n9AAG0iKWG+R4KN5FQlgmNGjQIFvzURYExyOP06NnD70Zg0XkH5cvX2bqpKn8uupXpUvRkR8CWCoJ\nYjlLjofiXSSEiRxXvWN12ni30ZuOjyL/qPFpDQYNHaQ3J7X8FMBSSRDLWXI8FG8jIUzkuBoda+DY\n0ZEOn3fI8AjJQmSETU0bWi9orRcntfwYwFJJEMs5cjwUbyMhTOSKptOaEmsUy+ivRitdishnanas\nqfhJLT8HsFQSxHKOHA/Fm0gIE7lCbaTm09WfsnrTar3rwyMMn5IntYIQwFJJEMsZBfF4uHXrVvr2\n7at0GXrvrSEsICAAFxcXXFxc+Oijj2jevDn9+/dn3bp1BapZNSAggOHDhytdhsEpUrIIn2/6XK/6\n8Ij8QamTWl4FsICAABYuXAjAyZMns7yv6dOnayc4zioJYjkjPx4PDx8+TL9+/XB3d6dZs2b4+vpy\n4MCBHN2Hi4uLds7n3Njm4sWLqVevHu7u7ri7u9OhQwdmzpyZZ90d3hrCVCoV9erVIyQkhG3btrFg\nwQLc3NxYtGgR/fr1IyEhIU+KFIbLtqat3vThEflLXp/U0gtgbdu2xdXVlYYNG+Lp6cnkyZN59uxZ\ntvf16qDYH374IRs2bHjna9JreRg7dmyaeX2zQoJYzjDU42F6/9+hoaF89dVXtGnThh07drB79258\nfX0JCwtToML0ZbSxyNPTk/3797N3715mzZrFgwcP6NatW578H701hGk0GkxMTChZsiSWlpZUrlyZ\nLl26sGjRIi5evEhwcDAA//77L5MmTaJJkya4uroycOBArl+/rrOtM2fO0L9/f9zc3GjUqBEDBgzQ\nvkFfX19mzpyps/7rrU++vr7MmDGDOXPm4OHhQbNmzVizZg2JiYlMmzaNRo0a0aZNG0JCQnS2c//+\nfcaNG0eTJk1o0qQJw4YN49atW9rnFy9eTKdOnQgJCcHLywt3d3dGjRpFXFyc9vnt27cTHh6ubRU8\nceJEZj/nAk0f+vCI/CmvTmpvagFTqVTMnTuXAwcOsHLlSs6fP5+m5Smrv/P6NpCwBLGcoS/Hwxs3\nbuDr60vjxo3p1KmTTgtWQEAA06dPZ8iQIbi5uREREaHzWo1Gw/fff0+/fv3w8vLivffeA15OQzh+\n/Pg0+4qKisLFxUXnd8bX15ctW7YAcOvWLXx8fGjUqBFNmzZl3LhxwMvZeAC++OILGjZsyJ49ewAI\nCwujc+fONG7cmD59+nD16lXtdtu2bcvy5cv5/PPPcXd3z9TvqZGRERUrVmT69OmUKFGCX3/N/VZ2\n46y8qFKlStSvX5/Q0FB8fHwICAjg1q1bfP/995ibmxMYGIifnx8bN27E1NSUy5cv079/f1q3bs3I\nkSMxMTHh1KlTOr+Ar09VkN70SDt37qRr164sX76c/fv3M3v2bMLDw3Fzc2PlypVs27aNKVOm4Ozs\nTKlSpUhISKB///588MEHLF68GBMTE1asWMHAgQNZt24dZmZmwMsZ30NDQ5k9ezbPnj1j3LhxBAYG\nMm7cOLp168bNmzf5999/mTJlCgDFihXLyseWKwYMGcCG9e/+lpxX4h7E0XN0zzTLm05ryspWKxn9\n1WjmfDcn7wvLQXFxcdSsXVNagvNY/L/xNA5onGZ5zY41uRtxlw6fdyB0VyjGxlk6rL1RRi9BWllZ\n4erqyrVr13BxcWHMmDGsWrWKlJQUtmzZQlhYGAsXLiQ6OpqKFSsyduxYHBwcALh48SJTp07l9u3b\nuLq66mw3IiICf39/tm/fDryctHj27NlERkaSkpKCp6cnHTt2ZPr06SQnJ9OwYUOMjY3Zu3cvAQEB\nlC5dmgEDBvDpp58ybNgwGjRoALwMhy1atGDBggU4Ojpy5swZ5syZw40bN7C1tWXkyJFppo5LDWLD\nhw9nwIABWR5Z/6+//sKrg5feBc2cpq/Hw6SkJIYPH84nn3zCggULiIyMZOTIkQQHB1OuXDkAQkJC\n+PHHH6lZsybPnz/Xef3ff//N/fv38fDwyJF6Fi5cSP369Vm8eDEvXrzg/PnzACxZsgQXFxdWr16N\nvb098L+/lTlz5lCtWjV27NjBiBEj2Lhxo/Zv/48//uDHH3/EwsIiS7+farUad3d3Dh8+nCPv722y\nfLSqUKECR48e1U6OvWTJEu1cTFOmTKFNmzbs2rULLy8vgoODqVKlijbdApQvX/6t29doNGn+QB0c\nHLTJuEuXLvzyyy+YmprSqVMnAPr27cvy5cs5deoUTZo00baK+fv/b5TtsWPH0rx5c8LDw2natCnw\nckLvSZMmadO8t7c3W7duBaBw4cIUKlRI2yKoby5fvUzjbxvj4OGgdCkAqNQqzEubp1me2odnkcsi\nnGs706VzFwWqyxn//fcfTxKe0P94f6VLKXDS+92C3DupZSSApR6n7t69y8GDB2ncuDHh4eHs37+f\n4OBgTE1N33ri0Gg0jBo1ii5dutCxY0f+/PNPxo8fT8+ePdPsKzk5meHDh1O3bl2mTp2KWq3mwoUL\nlC9fnnHjxrF582aWLl2qXf/VL7MtWrQgJCREG8L++usvSpYsiaOjI/fv32fYsGFMnTqVjz/+mCNH\njjBmzBg2bNiAhYWFTg05EcTu3r2LVXUrvH72ytTrDI2+Hg/PnDnDs2fPtL9jzs7ONGjQgJCQEHx8\nfABo1KgRNWvWBNCZ7xnQXilKnY85u0xMTIiOjub+/ftYW1tTq1atN667adMm2rdvj5OTEwCtW7cm\nKCiIM2fO8OGHH6JSqfj888+xtrbOVk2Wlpb8+++/2dpGRmQ5hGk0GlQqFTdu3ECtVlOjRg3tc+bm\n5jg4OHDjxg0ALl26RJMmTbJdbOq3xlQlS5bUWWZsbEyxYsV49OgR8DIx37lzh4YNG+q8LjExkTt3\n7mh/trGx0QYwePnhP3z4MNv15pUipYpQzE5/WufeRNuHx2MQTtWc9GYC1axQG6kN4jMvKHLjpJbR\nADZq1CiMjIwwNzfHzc2NXr16sWzZMnr16kXRokWBN584Tp8+DbwMV1988QUAHh4erFq1Kt39nTt3\njtjYWIYOHaoNPqknrDe1KqUu9/T0pGvXriQmJmJqasquXbvw9PQEXl5lcHV15eOPPwagXr16VKtW\njYMHD9K6des028yJIFaocKEC/Tek5PEwJiYGGxsbnWW2trbaS/oqleqtISY1mMfGxmJra5vteoYM\nGcJPP/1Ejx49KFasGF26dKFdu3bprhsdHc327dv57bf/TQeVlJRETEyM9ufSpUtnu6b79+9TvHjx\nbG/nXbIcwm7cuKFtHkxPakiDl/+hb53AUq1O83x618rTu8yQ3rLUa8ApKSk4OjpqZ1J/1auXFF/f\nRnr1ZmT2d/Fur/bhiTwWmWPfpITIyZNaRi9BqlQqZs+ejYuLS5rnXj0RvOnEERsbi0ajSXPCe/0E\nmerevXvY2tpm6RJLmTJlKF++PAcOHMDNzY2wsDD69++vrS80NFSnU3VycjLOzs5v3F5OXZosyJQ6\nHlpZWXH37l2d83R0dPQ7r1ClKleuHKVLlyY0NJSuXbu+c/3ChQsDkJCQQJEiRQB48OCB9vlSpUpp\n+5JFRkYyaNAgateunW7GsLGxoXfv3vTu3fuN+8vu+TolJYWwsDA++uijbG0nI975F5Pem7l69SqH\nDx/Gw8ODChUqkJKSov1GBxAfH8+1a9eoUKECAI6Ojm+d9b1EiRJpOtVeuXIl2x9k1apVuXXrFhYW\nFtjb2+s8MtOvy8TERDqU5yB96Zgq8h/bmra0mt+Ktu3b6hzkMyM3hqFIPXHs27dP+wgLC6N58+ZY\nWlpy//59nfXv3r2b7nZKly7N3bt3SU5OTvNcRo6Xnp6ehISEsH//fipUqKA9ydnY2NCqVSud+g4c\nOECPHj3euj3prJ99ShwPa9SogZmZGcHBwSQlJREREUF4eDjNmzcH3n1TiEqlYvjw4SxdupStW7cS\nHx9PSkoKkZGR6TZ6lChRAmtra3bs2EFycjJbtmzRGXZiz5493Lt3D0Dbepwa6EuWLKlz5crb25sN\nGzZw9uxZNBoNz549Izw8nKdPn2bvQ+HlF6MbN24wfvx4Hj58SOfOnbO9zXd5ZwhLTEzkwYMHxMTE\ncPnyZX799Vf69+9PtWrV6Nq1K2XKlMHd3Z1p06YRGRnJ1atX8ff3x9zcnBYtWgDQrVs3Ll26xLRp\n07hy5Qo3b95k8+bN2gONs7MzBw8e5MCBA9y8eZPvv/+ee/fupflFyGwnzhYtWlCyZElGjhzJiRMn\nuHPnDidOnGDu3Lk6d0i+i52dHdeuXePvv/8mLi5OgkMOaDqtKbFqGUFa5LxanWpR+dPKtO/UPtN/\nq7k1DtjbThy1atXCyMiINWvWkJSUxN69ezl37ly623FycsLS0pL58+eTkJBAYmIip06dAl6erO7f\nv6/znl8/ZjZv3pzDhw+zYcMGWrZsqV3esmVLDhw4wF9//UVycjKJiYlERESkCYfpkSCWfXl9PDQ2\nNmbOnDkcOnSIpk2bMnPmTKZMmaLtlJ/ejXGv8/DwYPr06fz++++0atUKT09PfvrpJ9zd3dPdxvjx\n41mxYgVNmzblxo0bOv2+zp8/T69evWjYsCEjR45k9OjR2NnZAeDj48OkSZNo3LgxoaGhVK1alQkT\nJjBr1iyaNGmCt7c327dvz1Sjzevr/vHHHzRs2JDGjRszcuRISpQowcqVK/OkZfKtlyNVKhVHjx6l\nRYsWqNVqihYtioODA76+vnh7e2sv402aNInZs2czYsQIEhMT+eCDD5g3b562M9///d//sWDBAgID\nA+nZsyeFChWiWrVquLm5AdCuXTuuXr2qvfuwY8eONG7cmMePH6epJzPMzMxYsmQJ8+fP56uvviI+\nPh4rKyucnZ11WsLS2+6ry7y9vYmIiKB79+48ffqURYsWUbt2bXx9fQFYtGhRpuoS/78Pz5r80VFf\n6J9m05uxsmXmOurnZAB7/Zjy6onjn3/+wdTUlA8//JDatWtjbGzMrFmz+Prrr1m4cCGurq5v7ENr\nZGTE999/z3fffUfr1q1RqVS0bNmSWrVqUbduXSpWrIinpydqtZrdu3enORFaWlpSq1YtTpw4wYwZ\nM7TLS5cuzezZs5k3bx7jx49HrVZTvXp1vvzyywy9X7k0mT1KHA8rVqz4xnPXpEmTMrSN+vXrU79+\n/XSfa9OmDW3atNH+/PHHH2uHpHjdkCFDGDJkSLrPdejQIc3f49v2+/vvv7+z7qNHj2r/7ePjo70Z\nQQmq48eP5+97hHNR27Zt+fTTT9/ZZP8uzs7OWb5V26OVB2UHl6VKqyrZqkEp0aej+cXjF/7c/WeW\n+/CoVKq3Xu5+k6x+7rdu3aL2x7UZdWtUpl8r8s7Th09Z5LKI2VNnv/OkVpCmIspN8fHxDB8+HBcX\nl7cGsc2bNzPllyl02twpjyvUbxk5Hmb1eCeU8a7zjHxVyaJr165RqFAhunSRFpzsSO2xaQ34AAAW\nfUlEQVTDY2gjSAv9l9ER9SWA5Ry5NJk9cjwseCSEZVGlSpXYsGFDjg8MWRDV6lQLKxcr/AP8372y\nEJlgW9OW+qPq03fAmycS7tSpk3bOOJF9qUFs7dq1eTLieH4jx8OCRUKYUNytY7e4ue8mgwcOVroU\nkc/8d+8/ji84zvgv006lkmrGjBmEhITIdGQ5JCUlhblz51K9enXat2+vdDkGR46HBYuEMKGo/+79\nx28dfiNoSRDVqlVTuhyRjyQ9T2LdZ+vw6emD9yfeb1yvadOmrFmzhrFjx0oQy6aUlBSmTZtGbGws\nu3bt0hkEW7ybHA8LHglhQjHJL5JZ+9nad54khciKXSN3UaF4BaYGTH3nuhLEsk8CWPbk5+Ohr68v\nrq6uNGzYkEaNGuHj46Mz6fbbpDf599atW+nb981dDAxJroSwgIAAhg8fnhubFvnIzhE7qVAsYydJ\nITIj4pcIbofcZs2KNRkeKsHQglh6J6c3ye2TlgSw7Msvx8M3/T6OGTOGAwcOsHfvXurUqZPhYTBS\n5eRk7/o01meuhLCMDPQmCrbUk+RvK3+T8YREjrp17Ba7R+9m++btaSaffpfcDGJt27alfv362smP\nU3Xp0gUXF5c3jpKv7ySAZZ++Hw9v3LiBr68vjRs3plOnThw4cED7XEBAANOnT2fIkCG4ubkRERHx\n1m2p1WqaNWvG9evXtcs0Gg2//PILn3zyCR4eHowdO1Y7eXa/fv0AaNy4Me7u7pw5c4bp06dz5swZ\nGjZsqB1T7/nz58ydO5c2bdrg6enJ9OnTSUxMBCAiIoJWrVqxfPlyPD09mTpVf4Jurvxvv5pYU1vF\nUt98o0aNmD9/PikpKSxcuJBmzZrh6enJypUrdbbx66+/8sUXX+Dm5karVq34+uuviY+P11lny5Yt\ntG7dmgYNGjBq1CjWr1+fZg63AwcO0LVrV1xdXfHy8iIwMFCvUnBBlJ2TpBBvkxN9anIriKlUKt5/\n/31CQkK0y65evUpCQoLBfmmVAJZ9+n48TEpKYvjw4dSvX5/du3czevRoJk6cyN9//61dJyQkhL59\n+xIWFqYzEn56Xrx4wa5du6hZs6Z22Zo1azhw4ABLliwhJCSEokWL8u233wKwdOlSAP7880/2799P\njRo1GDduHDVq1NC2rAHMnz+fW7dusXr1ajZt2kRMTIz2tfByrsr//vuP7du3M27cuBz7fLIrTyL3\niRMniI6OZvHixYwdO5bg4GD8/PzQaDQEBQXh4+PDDz/8wKVLl/5XmFrNqFGjWLduHV9//TXnzp1j\n5syZ2udPnz7NN998Q6dOnVi9ejUNGjRg0aJFOgezw4cPM3HiRD7//HPWrl2Lv78/oaGhLFiwIC/e\ntkiHdDwVuSWjHfEzIreCWMuWLdmxY4f2523bttG6dWvtF9f4+Hj8/f1p1qwZbdu2ZdmyZdrnUu86\nbNq0KV5eXoSHh+tsOz4+nilTptCiRQtatWrFwoULc3WcLglg2WcIx8MzZ87w7NkzevbsibGxMc7O\nzjRo0EDny0SjRo20oSp1ppzXzZo1S9uatW7dOp3L4xs3bmTAgAFYWVlhbGyMj48PoaGhpKSkpHsZ\nMr0pDTdt2sTw4cMpWrQoRYoUoWfPnvzxxx/addRqNb6+vhgbG2NqapqtzyQn5ckgV0WLFuXLL79E\npVJRrlw5fv31V+Li4hg4cCAAZcqUYfny5Rw7dgxHR0cAvvjiC+3rbWxs8PPzY9SoUdqpjdasWcNH\nH31E9+7dtds4f/48mzZt0r4uKCiI7t27a6dOeP/99/Hz88Pf35+hQ4fmxVsXr8jPHU+F8jLTET8j\nUoPY559/zvTp06ldu3a2t1mjRg127NjBzZs3KVOmDLt372bZsmUsXLgQjUbDzJkzefr0Kb///jtx\ncXEMHjwYS0tLvLy82LRpE+Hh4axatQozMzNGjx6t86UzICCAUqVKsXnzZp49e8awYcMoXbp0rgwT\nIQEs+wzleBgTE4ONjY3OMltbW+1gsiqVCmtr63duZ/To0Xh5eQEQGRnJiBEjWLx4MQ4ODkRFRTFq\n1CidS7FGRkY8fPgwQzU+evSIhIQEunXrpl2m0Wh0wlqJEiUwMTHJ0PbyUp6EsIoVK+ocLEqWLKkz\nd2PqskePHml/PnbsGD///DN///038fHxJCcnk5SURGxsLJaWlvz999/aiUJTOTk56YSwCxcucP78\neZYvX65dlpKSwvPnz3nw4AGlSpXK6bea54qYFWF91/UUMkv/20eeU0PnLZ2xr2Of5qn80vHU1NSU\nR9GPmGk3890rixzVdEZTandPG4ZS+9ScPHoyR/vU5EYQa9WqFdu3b+fDDz+kQoUKWFlZAZCcnMzu\n3btZvXo1hQsXpnDhwnTp0oUdO3bg5eXF7t276dy5s/aE17t3bwYPfjmW1IMHDzh06BD79u3D1NQU\nMzMzOnfuzKZNm3I8hOVEADMzM+PcrnP5/28oHxwPraysuHv3LhqNRnsej46Opnz58lne5gcffECZ\nMmU4cuQIDg4O2NjYMGnSJJ1LlKmio6PTLHv98r2FhQWmpqasW7fujZNu6+sl/zwJYUZGRjo/q1Sq\nNCPNq1QqbWqNjo5m2LBhtG/fnoEDB1K8eHEuXLjA+PHjM9WfS6PR4OPjg4eHR5rn9PHae1as/XWt\nTnhVWseuHYm/F///2rv3oCirx4/j711QFkQFNFgmQ2XG0SkpTVCUFAV1FVC8BQgFRBheIi8poXxJ\n6VcJGiupqOUlxMDK0CE1LNSE8jIKMSFechT4Tim4qGAhKnH5/eHwfNnA++Lu4nnNOMM++zwP5wE5\nz2fPOc85Lba31U1SH2xtbSkvL6e2tlbfRXmqJKxI4Mx/z7TY3jSm5nDO4Tb5u9ZlEJPJZHh7exMe\nHs7Fixe1uiKrqqqoq6vTanVQKpVUVFQAcOXKFezs7KT3mn9dVlZGXV0d48aNk7Y1NDS0aMF4XLpq\nAVOpVJSWlOr0iTdD1B7qQycnJxQKBampqQQFBfHbb7/xyy+/SIteP8rvsLCwkOLiYhwdHYE7i3Qn\nJycTFxeHUqmksrKSwsJC3N3dsba2Ri6X8+eff+Lg4ABAt27d0Gg01NXVYWpqilwuZ/LkySQmJhIV\nFYW1tTUajYbi4mJcXV1198NoA08khD1sAj19+jR1dXUsWLBAOrb50xgAvXr1oqioSGvbqVOntF73\n69ePkpISevRo+SmkvWj6xGwozBQt+9rb+iapD3f7tCW0nc6dO7fY9qTG1OgyiCmVSp599lmOHDnC\n++//b2kaKysrTE1NKSsro3fv3gCUl5dLLV/du3fXeoLy8uXLWufs0KEDBw4caLObui67IGUyGfb2\n9josnWFqD/Whqakpq1atIiEhgS+++AI7Ozs++OADevbsCTz4bAgrVqwgMTERuBOiZs+ezdChQ4E7\nw48aGxuZM2cOV65cwdramrFjx+Lu7o5CoSAsLIw333yTuro61qxZg4uLC46OjqhUKuRyOdnZ2URG\nRrJp0yZCQ0OpqqrC1taWadOmPb0hrHk6bm0QXWvbmjg4ONDQ0EB6ejojR46kqKiI7du3a+0fEBBA\neHg427Ztw93dnYKCAg4dOqT1n2HGjBnMmzcPe3t7PD09MTU15cKFC5w6dYp33nlHl5cr3EXTTXLz\n55sNduCpYJye9JgaXQax2NhYqqurUSgUUuu+iYkJY8aMYd26dcTFxXH9+nXS09OlcS5jxozh66+/\nZvjw4SgUClJSUqTzde/eHVdXV1atWsXMmTMxNzfn0qVLaDQanXShijFgumGs9aGjoyOfffZZq+89\nyHxfdzu2iUwmIygoiKCgoFbfj4iIICIiQmtbUlKS1uuOHTsye/Zsaax5c4MGDWLv3r33Lac+tPk8\nYa2l5Ltta9KnTx8WLlxIWloafn5+ZGZmMm/ePK19nJyciImJ4auvvmL69Onk5OQQHBysNfDO1dWV\nTz/9lLy8PEJDQwkJCSE1NVXrE9jnn3/eYloLQTfq/6lnh98OZoTMYMpksYacoFv6GFOjq6cme/To\nQb9+/aTXTXXiokWLMDc3x9fXl/DwcMaPH8/EiRMBmDRpEq6urkyfPp3XX38dDw8PrToxLi6Of/75\nBz8/Pzw8PHjvvfe4evWq1vkfhQhguiHqQ6E1sry8vHbTKa9Wqzlx4kSLVrN7Wbp0KZWVlaxevboN\nS3Zvzs7O7WZshKeXJw5vO9DPqx97IvdgUWJB1ndZbTruQSaTkZeX99DHtaef+9PgP7H/4XjH43jG\nepKfks+Jj09QcLxAL106+/fv1+lgfUMlAtjjaYv68FHrO0E/7nefeSJjwtpKamoqQ4YMwcLCguPH\nj5ORkSE9LfQgGhsbycvLY8OGDW1YyqeTMQ08FYyLIYypaYunJg2NCGC6I+pD4W6MOoSdPXuWtLQ0\nqqurpTnAAgICHvh4mUxmsP3ExuzPE3+StzbPaAaeCsaj+nK1wUxu2Z6DmAhguiPqQ+FejDqEffzx\nx/ougtCKg/93kB07duj9Jim0P8fWH2NxzGKDmdyyPQYxEcB0S9SHwr2IdlFBp6y7WLM4ZrEYeCro\nXJcuXVB5qwxucsu2XPT7SRMBTLdEfWi4li9fzubNm/VdjPY1MN9YtacB4vX19S0m521rYmD+06G+\nvh65XG6wM18b+2B9EcB0ry3qwwet7yZMmMC1a9cwMTHB3NycIUOGEBUVhaWlpU7Low/Lli3Dzs6O\nWbNmPdD+u3fvJjMzU2tB7yflfvcZ0RIm6NSTDmDC08PExMRgAxgYd4uYCGBtQ5/1oUwmIykpidzc\nXNLT0zl//jxbtmzR6fd4mBVshNYZfAi7dOkSLi4unD17Vt9FEQRBuCdjDGIigLV/3bp1w9XVlQsX\nLgBw8uRJwsLCGDVqFIGBgeTn50v7RkREsHbtWkJCQnB3d+fdd9/lr7/+Av53P87MzMTHx4c5c+YA\nkJmZyauvvoqHhweRkZFaKzuo1Wpp9vuAgACpDLW1tSQlJeHj44NKpWL58uXcvn0bgPz8fLy8vEhL\nS2Ps2LGMGzeO3bt3A7Bz50727dtHamoqI0aMYMGCBQCkpKQwadIk3N3d8fPz49ChQwCUlJQQHx/P\nyZMnGTFiBB4eHsCd1rT169dL5dy1axeTJ0/G09OTBQsWSAuUA7i4uJCRkcGUKVMYNWoUK1bobs1T\ngw9hTUS3kSAIxsCYgpgIYO1b033z8uXLHDlyhP79+6PRaJg3bx7h4eH89NNPzJ07l6ioKKqqqqTj\nvv/+e5YuXcoPP/yAiYkJn3zyidZ5CwoK+Pbbb1m9ejU5OTmkpKSwcuVK9u/fz4ABA4iJiQHg6NGj\nFBQUsHPnTnJycoiPj6dr164ArF27lj/++IPt27eza9cuKioqtLoLr169yo0bN8jKyiI2NpaEhASq\nq6uZMmUK48ePJyQkhNzcXNRqNQDPPfccmzZtIicnhxkzZhAbG8vVq1fp3bs3ixcvxsnJidzcXA4e\nPAhoT2B84sQJkpOTiY+PZ9++fdjb27NkyRKtaz58+DCpqals376d7Oxsjh49qpPfkcGEsK1btzJp\n0iTc3NwICAggKysLAF9fXwCCg4NxcXFh5syZ0jF79uzB39+fYcOGoVKpWLZsmT6KLgiCoMUYgpgI\nYO1bY2MjCxcuxN3dHR8fH3r06EFYWBhZWVm4ubkxbNgwAIYMGcLzzz/P4cOHpWO9vb1xdHREoVAw\na9YssrOztRpC3nrrLRQKBWZmZmRkZBAaGkqvXr2Qy+W88cYb/P7775SXl9OhQwdqamooLS2loaGB\nXr160b17dxobG9m1axfz58+nc+fOWFhYEBoayo8//ih9D1NTU8LDwzExMcHNzQ0LCwtKS0u1rq85\nT09PaU3fMWPG4ODgIK0vfb9GnKysLHx9fenbty8dOnTg7bffprCwUKtFLyQkBEtLS5RKJc7Ozpw7\nd+4hfyOtM4gpKtatW8fBgweJjo6mZ8+eFBYW8uGHH9KlSxe2bt1KSEgIa9eupU+fPtKyRBkZGajV\naubMmcPw4cOpqakRswgLgmAwDHn6ChHA2j+ZTEZiYiIuLi78+uuvzJ8/nzNnzlBWVsaBAwf4+eef\npX3r6+txdnaWXtvZ2UlfK5VK6urqtFrKmr9fVlZGYmJii7UcKyoqcHZ2xs/Pj4SEBMrKyvDw8GDu\n3Lncvn2bW7duSeuiQss1pa2srLQmtlUoFNy8efOu17t3717S09O5dOkSADU1NVy/fv2BflZXrlzR\nmkLE3Nycrl27otFoUCqVwJ0u3eZlqampeaBz34/eQ9jNmzdJS0sjOTmZAQMGAGBvb09RURHffPMN\n0dHRAHTt2hUbGxvpuM2bNxMYGEhgYKC0rW/fvk+28IIgCPdgiEFMBLCnz8svv4y/vz9r1qxh6NCh\neHl5SV2GrWneAlReXo6pqSlWVlZSCGr+gIxSqSQ8PByVStXqufz9/fH396eyspLo6Gi2bdtGREQE\nZmZm7NixQ2q9ehj/fkCnrKyMjz76iPXr1/Piiy9KC4I3hbr7PdDzzDPPSOEN7uSS69evY2tr+9Bl\ne1h6744sLi6mtraWyMhIRowYIf3LyMjg4sWLrR5z7do1KioqxMLbgiAYPEPqmhQB7OkVGBjIqVOn\nGDBgALm5uRw7doz6+npu375Nfn4+Go1G2jcrK4uSkhJu3brFhg0bGD169F2DzNSpU9myZQvFxcUA\nVFdXs3//fgBOnz5NUVERdXV1Uvdl0zQzkydPJjExkcrKSgA0Gg3Hjh17oGuxsbHRygc3b95EJpNh\nZWVFQ0MD3333HefPn9faX6PRaD3N2bzlTaVSsXv3bs6dO0dtbS3Jyck4OTlJrWD/pssx6npvCWu6\nmKSkJK0mTrjTJ9zQ0KCPYgmCIOiMIbSIiQD2dLOyssLHx4e0tDTUajWrV68mJiYGuVxO//79pV4n\nAC8vL5YtW0ZpaSmDBg3SGqT+7zA2cuRIampqWLJkCWVlZVhaWuLq6sro0aO5ceMGarWaixcv0rFj\nR4YNG0ZwcDAAkZGRbNq0idDQUKqqqrC1tWXatGm4urre91p8fX2Jjo5m1KhRODs7s3LlSoKCgggL\nC0Mmk+Ht7S31rAEMHjwYR0dHVCoVcrmc7OxsrYH5gwcPZubMmURFRfH333/z0ksvaa3I8+9rbn7s\n49L7ZK03btxg7NixREdHM2HChBbvV1RU4OXlRUpKCi+88IK03dvbm/Hjxz/Ugt2GSkwa+njEZK2C\nsdDXhK4igLUfj1rfPaiIiAi8vLykh+KEx3O/+4zeW8I6derEa6+9RlJSEo2NjQwcOJCamhqKioqQ\ny+VMnDgRMzMzjh49ilKpxMzMDEtLS8LCwlCr1djY2ODm5satW7fIy8sjKCgIgPfffx+ZTEZcXJye\nr1AQBOEOfbSIiQAmCIZL7yEMYNasWdjY2PDll18SHx9Pp06d6Nu3L8HBwZiYmLBo0SI2btzIxo0b\nGThwIBs2bGDq1KmYmpqSlpbGmjVr6NKlC6+88op0zsuXLxv07NqCIDydnmQQEwFMEAyb3rsjBdEt\n9rhEd6RgjNq6a1IEsPaprbsjBd0Sa0cKgiAYoLZ8alIEMEEwDiKECYIg6ElbBDERwATBeIgQJgiC\noEe6DGIigAmCcREhTBAEQc90EcREABME4yNCmCAIggF4nCAmApggGCcRwgRBEAzEowQxEcAEwXj9\nP0pdHTY4LLEVAAAAAElFTkSuQmCC\n"
      }
     ],
     "prompt_number": 7
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "*(Aside: these charts are generated in matplotlib.  You can see the code using the %load magic)*"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "%load figures/ML_flow_chart.py"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 8
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "\"\"\"\n",
      "Tutorial Diagrams\n",
      "-----------------\n",
      "\n",
      "This script plots the flow-charts used in the scikit-learn tutorials.\n",
      "\"\"\"\n",
      "\n",
      "import numpy as np\n",
      "import pylab as pl\n",
      "from matplotlib.patches import Circle, Rectangle, Polygon, Arrow, FancyArrow\n",
      "\n",
      "def create_base(box_bg = '#CCCCCC',\n",
      "                arrow1 = '#88CCFF',\n",
      "                arrow2 = '#88FF88',\n",
      "                supervised=True):\n",
      "    fig = pl.figure(figsize=(9, 6), facecolor='w')\n",
      "    ax = pl.axes((0, 0, 1, 1),\n",
      "                 xticks=[], yticks=[], frameon=False)\n",
      "    ax.set_xlim(0, 9)\n",
      "    ax.set_ylim(0, 6)\n",
      "\n",
      "    patches = [Rectangle((0.3, 3.6), 1.5, 1.8, zorder=1, fc=box_bg),\n",
      "               Rectangle((0.5, 3.8), 1.5, 1.8, zorder=2, fc=box_bg),\n",
      "               Rectangle((0.7, 4.0), 1.5, 1.8, zorder=3, fc=box_bg),\n",
      "               \n",
      "               Rectangle((2.9, 3.6), 0.2, 1.8, fc=box_bg),\n",
      "               Rectangle((3.1, 3.8), 0.2, 1.8, fc=box_bg),\n",
      "               Rectangle((3.3, 4.0), 0.2, 1.8, fc=box_bg),\n",
      "               \n",
      "               Rectangle((0.3, 0.2), 1.5, 1.8, fc=box_bg),\n",
      "               \n",
      "               Rectangle((2.9, 0.2), 0.2, 1.8, fc=box_bg),\n",
      "               \n",
      "               Circle((5.5, 3.5), 1.0, fc=box_bg),\n",
      "               \n",
      "               Polygon([[5.5, 1.7],\n",
      "                        [6.1, 1.1],\n",
      "                        [5.5, 0.5],\n",
      "                        [4.9, 1.1]], fc=box_bg),\n",
      "               \n",
      "               FancyArrow(2.3, 4.6, 0.35, 0, fc=arrow1,\n",
      "                          width=0.25, head_width=0.5, head_length=0.2),\n",
      "               \n",
      "               FancyArrow(3.75, 4.2, 0.5, -0.2, fc=arrow1,\n",
      "                          width=0.25, head_width=0.5, head_length=0.2),\n",
      "               \n",
      "               FancyArrow(5.5, 2.4, 0, -0.4, fc=arrow1,\n",
      "                          width=0.25, head_width=0.5, head_length=0.2),\n",
      "               \n",
      "               FancyArrow(2.0, 1.1, 0.5, 0, fc=arrow2,\n",
      "                          width=0.25, head_width=0.5, head_length=0.2),\n",
      "               \n",
      "               FancyArrow(3.3, 1.1, 1.3, 0, fc=arrow2,\n",
      "                          width=0.25, head_width=0.5, head_length=0.2),\n",
      "               \n",
      "               FancyArrow(6.2, 1.1, 0.8, 0, fc=arrow2,\n",
      "                          width=0.25, head_width=0.5, head_length=0.2)]\n",
      "\n",
      "    if supervised:\n",
      "        patches += [Rectangle((0.3, 2.4), 1.5, 0.5, zorder=1, fc=box_bg),\n",
      "                    Rectangle((0.5, 2.6), 1.5, 0.5, zorder=2, fc=box_bg),\n",
      "                    Rectangle((0.7, 2.8), 1.5, 0.5, zorder=3, fc=box_bg),\n",
      "                    FancyArrow(2.3, 2.9, 2.0, 0, fc=arrow1,\n",
      "                               width=0.25, head_width=0.5, head_length=0.2),\n",
      "                    Rectangle((7.3, 0.85), 1.5, 0.5, fc=box_bg)]\n",
      "    else:\n",
      "        patches += [Rectangle((7.3, 0.2), 1.5, 1.8, fc=box_bg)]\n",
      "    \n",
      "    for p in patches:\n",
      "        ax.add_patch(p)\n",
      "        \n",
      "    pl.text(1.45, 4.9, \"Training\\nText,\\nDocuments,\\nImages,\\netc.\",\n",
      "            ha='center', va='center', fontsize=14)\n",
      "    \n",
      "    pl.text(3.6, 4.9, \"Feature\\nVectors\", \n",
      "            ha='left', va='center', fontsize=14)\n",
      "    \n",
      "    pl.text(5.5, 3.5, \"Machine\\nLearning\\nAlgorithm\",\n",
      "            ha='center', va='center', fontsize=14)\n",
      "    \n",
      "    pl.text(1.05, 1.1, \"New Text,\\nDocument,\\nImage,\\netc.\",\n",
      "            ha='center', va='center', fontsize=14)\n",
      "    \n",
      "    pl.text(3.3, 1.7, \"Feature\\nVector\", \n",
      "            ha='left', va='center', fontsize=14)\n",
      "    \n",
      "    pl.text(5.5, 1.1, \"Predictive\\nModel\", \n",
      "            ha='center', va='center', fontsize=12)\n",
      "\n",
      "    if supervised:\n",
      "        pl.text(1.45, 3.05, \"Labels\",\n",
      "                ha='center', va='center', fontsize=14)\n",
      "    \n",
      "        pl.text(8.05, 1.1, \"Expected\\nLabel\",\n",
      "                ha='center', va='center', fontsize=14)\n",
      "        pl.text(8.8, 5.8, \"Supervised Learning Model\",\n",
      "                ha='right', va='top', fontsize=18)\n",
      "\n",
      "    else:\n",
      "        pl.text(8.05, 1.1,\n",
      "                \"Likelihood\\nor Cluster ID\\nor Better\\nRepresentation\",\n",
      "                ha='center', va='center', fontsize=12)\n",
      "        pl.text(8.8, 5.8, \"Unsupervised Learning Model\",\n",
      "                ha='right', va='top', fontsize=18)\n",
      "        \n",
      "        \n",
      "\n",
      "def plot_supervised_chart(annotate=False):\n",
      "    create_base(supervised=True)\n",
      "    if annotate:\n",
      "        fontdict = dict(color='r', weight='bold', size=14)\n",
      "        pl.text(1.9, 4.55, 'X = vec.fit_transform(input)',\n",
      "                fontdict=fontdict,\n",
      "                rotation=20, ha='left', va='bottom')\n",
      "        pl.text(3.7, 3.2, 'clf.fit(X, y)',\n",
      "                fontdict=fontdict,\n",
      "                rotation=20, ha='left', va='bottom')\n",
      "        pl.text(1.7, 1.5, 'X_new = vec.transform(input)',\n",
      "                fontdict=fontdict,\n",
      "                rotation=20, ha='left', va='bottom')\n",
      "        pl.text(6.1, 1.5, 'y_new = clf.predict(X_new)',\n",
      "                fontdict=fontdict,\n",
      "                rotation=20, ha='left', va='bottom')\n",
      "\n",
      "def plot_unsupervised_chart():\n",
      "    create_base(supervised=False)\n",
      "\n",
      "\n",
      "if __name__ == '__main__':\n",
      "    plot_supervised_chart(False)\n",
      "    plot_supervised_chart(True)\n",
      "    plot_unsupervised_chart()\n",
      "    pl.show()\n",
      "\n",
      "\n"
     ],
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {},
     "source": [
      "Returning to Feature Extraction"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Recall that previously, we have seen two types of features:\n",
      "\n",
      "- The iris dataset had measured features: (the lengths of petals, sepals, etc.)\n",
      "- The digits and faces datasets were pixel values (the images were pre-aligned)\n",
      "\n",
      "How might we handle other types of features?"
     ]
    },
    {
     "cell_type": "heading",
     "level": 3,
     "metadata": {},
     "source": [
      "Categorical Features"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Sometimes we have categorical features: for example, imagine the dataset included\n",
      "the colors:\n",
      "\n",
      "    color in [red, blue, purple]\n",
      "\n",
      "Often it is best for categorical features to have their own dimenions:\n",
      "\n",
      "The enriched iris feature set would hence be in this case:\n",
      "\n",
      "- sepal length in cm\n",
      "- sepal width in cm\n",
      "- petal length in cm\n",
      "- petal width in cm\n",
      "- color#purple (1.0 or 0.0)\n",
      "- color#blue (1.0 or 0.0)\n",
      "- color#red (1.0 or 0.0)"
     ]
    },
    {
     "cell_type": "heading",
     "level": 3,
     "metadata": {},
     "source": [
      "Unstructured data"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Most often, data does not come in a nice, structured, CSV file where every\n",
      "column measures the same thing.  In this case, we must be more imaginitive\n",
      "in how we extract features.\n",
      "\n",
      "Here is an overview of strategies to turn unstructed data items into arrays of numerical features."
     ]
    },
    {
     "cell_type": "heading",
     "level": 4,
     "metadata": {},
     "source": [
      "Text documents:\t"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "- Count the frequency of each word or pair of consecutive words in each document. This approach is called **Bag of Words**\n",
      "\n",
      "*Note:* we include other file formats such as HTML and PDF in this category:\n",
      "an ad-hoc preprocessing step is required to extract the plain text in\n",
      "UTF-8 encoding for instance.\n",
      "\n",
      "For a tutorial on text processing in scikit-learn, see\n",
      "http://scikit-learn.github.com/scikit-learn-tutorial/working_with_text_data.html"
     ]
    },
    {
     "cell_type": "heading",
     "level": 4,
     "metadata": {},
     "source": [
      "Images:\t"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "- Rescale the picture to a fixed size and take all the raw pixels values (with or without luminosity normalization)\n",
      "\n",
      "- Take some transformation of the signal (gradients in each pixel, wavelets transforms...)\n",
      "\n",
      "- Compute the Euclidean, Manhattan or cosine similarities of the sample to a set reference prototype images aranged\n",
      "  in a code book. The code book may have been previously extracted from the same dataset using an unsupervised\n",
      "  learning algorithm on the raw pixel signal.  Each feature value is the distance to one element of the code book.\n",
      "\n",
      "- Perform local feature extraction: split the picture into small regions and perform feature extraction locally in each area,\n",
      "  Then combine all the features of the individual areas into a single array."
     ]
    },
    {
     "cell_type": "heading",
     "level": 4,
     "metadata": {},
     "source": [
      "Sounds:\t"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Same type of strategies as for images; the difference its it's a 1D rather than 2D space.\n",
      "\n",
      "For more information on feature extraction in scikit-learn, see\n",
      "http://scikit-learn.org/stable/modules/feature_extraction.html"
     ]
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {},
     "source": [
      "Looking Ahead"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "In the next couple notebooks, we will explore a simple examples of classification,\n",
      "regression, dimensionality reduction, and clustering using the datasets we've\n",
      "seen."
     ]
    }
   ],
   "metadata": {}
  }
 ]
}