BN_psychiatric-diseases.ipynb

{
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  "metadata": {
    "colab": {
      "name": "BN_psychiatric-diseases.ipynb",
      "provenance": [],
      "collapsed_sections": []
    },
    "kernelspec": {
      "display_name": "Python 3",
      "language": "python",
      "name": "python3"
    },
    "language_info": {
      "codemirror_mode": {
        "name": "ipython",
        "version": 3
      },
      "file_extension": ".py",
      "mimetype": "text/x-python",
      "name": "python",
      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython3",
      "version": "3.7.9"
    }
  },
  "cells": [
    {
      "cell_type": "code",
      "metadata": {
        "id": "epxdIC818iv-"
      },
      "source": [
        "'''\n",
        "Alex Costanzino, Xiaowei Wen\n",
        "MSc student in Artificial Intelligence\n",
        "@ Alma Mater Studiorum, University of Bologna\n",
        "June, 2021\n",
        "'''"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "apnjkQSb8n6V"
      },
      "source": [
        "# **Bayesian Network Model for Diagnosis of Psychiatric Diseases**\n",
        "*Fundamentals of Artificial Intelligence and Knowledge Representation - Mod. 3*"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "43i9OAED-mIv"
      },
      "source": [
        "Because of numerous possible causes involved, it isn't easy for general physicians to identify the precise reason of the psychiatric diseases and to decide the correct treatment. \n",
        "Bayesian networks are recognized as efficient graphical models with significant capabilities for investigating biomedical data either to obtain relationships between variables, either for medical predictions."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "sH0SNkdFATiP"
      },
      "source": [
        "## Libraries and imports"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "f9SDR0sE-lIZ"
      },
      "source": [
        "import sys\n",
        "!{sys.executable} -m pip install pgmpy==0.1.14\n",
        "!{sys.executable} -m pip install --upgrade pip"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "dxjEWg3_ALvP"
      },
      "source": [
        "from pgmpy.models import BayesianModel\n",
        "from pgmpy.factors.discrete import TabularCPD\n",
        "from pgmpy.inference import VariableElimination\n",
        "\n",
        "import matplotlib.pyplot as plt\n",
        "import networkx as nx\n",
        "import numpy as np\n",
        "\n",
        "from pgmpy.sampling import BayesianModelSampling\n",
        "from pgmpy.factors.discrete import State\n",
        "from pgmpy.sampling import GibbsSampling"
      ],
      "execution_count": 3,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "tp8sbOklARNm"
      },
      "source": [
        "## 1. Model\n",
        "**Bayesian networks** are data structures used to represent dependencies among variables. They can easily represents any full joint probability distribution in a concise way.\n",
        "\n",
        "A bayesian network is a directed graph in which each node is annotated with quantitative probability information:\n",
        "* Each node corresponds to a random variable, which may be discrete or continuous;\n",
        "* A set of directed links connects pairs of nodes. If there is a link from node $X$ to node $Y$, $X$ is said to be *parent* of $Y$. The graph has no directed cycles (it is, indeed, a directed acyclic graph, or DAG);\n",
        "* Each node $X_i$ has a conditional probability distribution $P(X_i | \\text{Parents}(X_i))$ that quantifies the effect of the parents on the node.\n",
        "\n",
        "The topology of the network specifies the conditional independence relationships that hold in the domain.\n",
        "\n",
        "| Node Name | Paper Variable Number|\n",
        "------------ | -------------\n",
        "| Sex |  X1 |  \n",
        "| Nr. of Parents suffering from schizophrenia  | X2 |  \n",
        "| Mother with virosis during pregnancy|  X12  |  \n",
        "| Genetic Influence schizophrenia   |  X11  |  \n",
        "| Simple Schizophrenia  | X14  |  \n",
        "| Social life deterioration|  X20  |  \n",
        "| Grimaces, Mannerisms, puerility| X19  |   \n",
        "| Personality Deterioration | X21 |"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "nQfbiX5ph7U-"
      },
      "source": [
        "### 1.1 Nodes name definition"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Dh0v9bHO3qET"
      },
      "source": [
        "sex = 'Sex'\n",
        "parents = 'Parents suffering from schizophrenia'\n",
        "mother_with_virosis = 'Mother with virosis during pregnancy'\n",
        "genetic_influence = 'Genetic influence'\n",
        "simple_schizophrenia = 'Simple schizophrenia'\n",
        "social_life_deterioration = 'Social life deterioration'\n",
        "mannerism = 'Mannerism'\n",
        "personality_deterioration = 'Personality deterioration'\n",
        "\n",
        "variables = [sex, parents, mother_with_virosis, genetic_influence, simple_schizophrenia, social_life_deterioration, mannerism, personality_deterioration]"
      ],
      "execution_count": 4,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "vXsz3-EfDvFI"
      },
      "source": [
        "### 1.2 Model definition"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "KidjrFebAQgt",
        "outputId": "b04dc4c5-0bed-46fb-8843-126a5fc31c61"
      },
      "source": [
        "model = BayesianModel([(mother_with_virosis, simple_schizophrenia),\n",
        "                       (parents, genetic_influence),\n",
        "                       (sex, genetic_influence),\n",
        "                       (genetic_influence, simple_schizophrenia),\n",
        "                       (simple_schizophrenia, personality_deterioration),\n",
        "                       (simple_schizophrenia, social_life_deterioration),\n",
        "                       (simple_schizophrenia, mannerism)])\n",
        "\n",
        "nodes = list(model.nodes())\n",
        "edges = list(model.edges())\n",
        "\n",
        "print('The nodes of the net are: {}'.format(nodes))\n",
        "print('The edges of the net are: {}'.format(edges))"
      ],
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "The nodes of the net are: ['Mother with virosis during pregnancy', 'Simple schizophrenia', 'Parents suffering from schizophrenia', 'Genetic influence', 'Sex', 'Personality deterioration', 'Social life deterioration', 'Mannerism']\n",
            "The edges of the net are: [('Mother with virosis during pregnancy', 'Simple schizophrenia'), ('Simple schizophrenia', 'Personality deterioration'), ('Simple schizophrenia', 'Social life deterioration'), ('Simple schizophrenia', 'Mannerism'), ('Parents suffering from schizophrenia', 'Genetic influence'), ('Genetic influence', 'Simple schizophrenia'), ('Sex', 'Genetic influence')]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "umpYZkKSDwjM"
      },
      "source": [
        "### 1.3 Model representation"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 683
        },
        "id": "z2AgPlMtDsgX",
        "outputId": "6fdb5351-3f66-4eea-fae6-019a64c1591c"
      },
      "source": [
        "DAG = nx.DiGraph()\n",
        "DAG.add_edges_from(edges)\n",
        "\n",
        "fig = plt.figure(figsize = (16,12))\n",
        "pos = nx.spring_layout(DAG) \n",
        "\n",
        "nx.draw_networkx_nodes(DAG, pos = pos, node_color = 'yellow')\n",
        "nx.draw_networkx_labels(DAG, pos = pos)\n",
        "nx.draw_networkx_edges(DAG, pos = pos, edge_color = 'purple', width = 1.5, arrows = True)\n",
        "\n",
        "plt.show()"
      ],
      "execution_count": 28,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
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edlrNnm+mTpM6yd7J3uhYuAllEQAAAECx2v/Tfi0euVgySU9HPq2H+jxkdCQUgLIIAAAAoFhkpmVq5biV2jF1h6o9Vk1hEWFyr+ludCzcAmURAAAAQJE7H3tekf0idW7vObX6f63U/oP2srX/6+MwYE0oiwAAAACKjNls1u7pu7V8zHLZu9hr4PKBqtOljtGxUAiURQAAAABFIj0lXUufX6q9P+xVzSdqKnRWqNx83IyOhUKiLAIAAAB44P7Y9Yci+0Uq8Uii2r3XTm3Gt5GNrY3RsXAXKIsAAAAAHhiz2axt/9mm1S+vlrOXs4auHSq/x/2MjoV7QFkEAAAA8ECkXUrTwmcWKm5hnOp1r6de03rJ2dPZ6Fi4R5RFAAAAAPftxKYTmhc+T6lnUtXps05q8Y8WMplMRsfCfaAsAgAAALhn5hyzNn68UWvfXit3P3eN2DxCPs18jI6FB4CyCAAAAOCepJ5J1fzB83X056Nq0K+Buk/tLscKjkbHwgNCWQQAAABw146sPqL5g+crPTldPb7pocYjGrPstJShLAIAAAAotJysHK19e602frxRXoFeGvLzEFVuWNnoWCgClEUAAAAAhXL5xGXNC5+nk5tPqvGIxur6ZVfZO9sbHQtFhLIIAAAA4I4OLjiohc8sVE5WjkJnh+rh8IeNjoQiRlkEAAAAcEtZ6Vla/cpqbfv3Nnk39VZYRJgq1alkdCwUA8oiAAAAgAJdPHRRkf0jdWbXGT32j8fU4eMOsitHhSgr+EoDAAAAyOe3H37T0tFLZetgq/6L+qt+j/pGR0IxoywCAAAAsMi4kqHlLyzX7mm7VaN1DYXODlUF3wpGx4IBKIsAAAAAJEln955VZL9IXTh4QW3ebKN277STjZ2N0bFgEMoiAAAAUMaZzWbt+HqHVv5jpRzdHTV49WDVerKW0bFgMMoiAAAAUIZdS7qmxc8u1oGfDqh2p9p66vun5FrF1ehYsAKURQAAAKCMOr3ttCL7Reryyct68uMnFfxKsEw2JqNjwUpQFgEAAIAyxpxj1pbPtmjN62vkVs1NwzcMl29LX6NjwcpQFgEAAIAy5Mr5K1owdIEOLz+swNBA9fi2h5wqOhkdC1aIsggAAACUEcejjytqYJSuXryqkP8LUbPnm8lkYtkpCkZZBAAAAEq5nOwcrX9/vda/v16V6lTSgKUDVLVRVaNjwcpRFgEAAIBSLPl0sqIGRil+XbyChgQp5P9C5ODqYHQslACURQAAAKCU+n3Z71owdIEy0zL11IynFDQkyOhIKEEoiwAAAEApk52RrTXj12jLpC2q8kgVhc0Nk2d9T6NjoYShLAIAAAClSOLRREX2j1TC9gQ1+1szdZ7UWXaO/NiPu8d3DQAAAFBK7J+7X4tHLZZM0tORT+uhPg8ZHQklGGURAAAAKOEy0zK1ctxK7Zi6Q9VbVFefH/vIvaa70bFQwlEWAQAAgBLsfOx5RfaL1Lm95xT8arCeeP8J2drbGh0LpQBlEQAAACiBzGazdk/breUvLJe9i70GLh+oOl3qGB0LpQhlEQAAAChh0lPStXT0Uu2dvVc1n6ip0FmhcvNxMzoWShnKIgAAAFCC/LHzD0X2i1Ti0US1e6+d2oxvIxtbG6NjoRSiLAIAAAAlgNls1rZ/b9PqV1bL2ctZQ6OHyq+Nn9GxUIpRFgEAAAArl3YpTQufWai4hXGq172eek3vJWcPZ6NjoZSjLAIAAABW7MSmE5oXPk+pZ1LV+fPOeux/HpPJZDI6FsoAyiIAAABghcw5Zm38eKPWvr1W7jXdNWLzCPk08zE6FsoQyiIAAABgZVLPpGr+4Pk6+vNRNezfUN2ndle58uWMjoUyhrIIAAAAWJEjq49o/qD5Sk9JV49veqjxiMYsO4UhKIsAAACAFcjOzFb0O9Ha+PFGeQV6acgvQ1S5QWWjY6EMoywCAAAABkuKT1LUgCid3HxSjUc2Vtf/7Sp7Z3ujY6GMoywCAAAABjq44KAWDl+onOwc9fmxjxr2b2h0JEASZREAAAAwRNa1LK16ZZW2/2e7vJt6KywiTJXqVDI6FmBBWQQAAACK2cVDFxXZL1Jndp9Ri3Et1OHjDrJ1sDU6FpAHZREAAAAoRr/N+k1Ln18qWwdbhS8OV73u9YyOBBSIsggAAAAUg4wrGVo+Zrl2T9+tGm1qqM/sPipfvbzRsYBboiwCAAAARezsb2cV2S9SF+IuqM2bbdTunXaysbMxOhZwW5RFAAAAoIiYzWbtmLpDK/6xQk4VnTTk5yHyb+9vdCygUCiLAAAAQBG4lnRNi0ct1oHIA6rdubZ6f99bLpVdjI4FFBplEQAAAHjATm09pXn95yn5VLI6/KuDWr3cSiYbk9GxgLtCWQQAAAAeEHOOWVs+26I1r6+RWzU3Dd8wXNVbVDc6FnBPKIsAAADAA3Dl/BUtGLpAh5cfVmBooHp820NOFZ2MjgXcM8oiAAAAcJ+ORx9X1MAoXb14VSH/F6JmzzeTycSyU5RslEUAAADgHuVk52jde+u0/v318qjroQFLB6hqo6pGxwIeCMoiAAAAcA+STycrakCU4tfHK2hIkEL+L0QOrg5GxwIeGMoiAAAAcJcOLT2kBUMXKOtalp6a8ZSChgQZHQl44CiLAAAAQCFlZ2Tr59d/1q+f/aoqQVUUNidMnvU9jY4FFAnKIgAAAFAIiUcTFdk/UgnbE9T8783VaWIn2Tny4zRKL767AQAAgDvYP3e/Fo9aLJONSX3n9VVgaKDRkYAiR1kEAAAAbiEzLVMr/rFCO7/eqeotqqvPj33kXtPd6FhAsaAsAgAAAAU4f+C8IvtF6ty+cwp+NVhPvP+EbO1tjY4FFBvKIgAAAHATs9ms3dN2a9mYZXJwddDAFQNVp3Mdo2MBxY6yCAAAAFyXnpyupc8v1d7Ze+Xf3l+9Z/WWm7eb0bEAQ1AWAQAAAEkJOxI0r/88JR5N1BPvP6HWr7eWja2N0bEAw1AWAQAAUKaZzWZt/XKrVr+yWq5VXDU0eqj82vgZHQswHGURAAAAZVbapTQtHL5QcYviVK9HPfWa1kvOHs5GxwKsAmURAAAAZdKJjSc0b8A8pZ5JVecvOuuxsY/JZDIZHQuwGpRFAAAAlCk52Tna+PFGRb8TLfea7hqxeYR8mvkYHQuwOpRFAAAAlBmpZ1IVNShKx9YcU8P+DdV9aneVK1/O6FiAVaIsAgAAoEw4suqI5g+er/SUdPX4pocaj2jMslPgNiiLAAAAKNWyM7O19u212vTxJnk18NKQX4aocoPKRscCrB5lEQAAAKVWUnyS5oXP06ktp9RkVBN1+aKL7J3tjY4FlAiURQAAAJRKBxcc1MLhC5WTnaM+EX3UsF9DoyMBJQplEQAAAKVK1rUsrXpllbb/Z7t8mvmoT0QfVapdyehYQIlDWQQAAECpcfHQRUX2i9SZ3WfUYlwLdfi4g2wdbI2OBZRIlEUAAACUCr/N+k1LRi+RXTk7hS8OV73u9YyOBJRolEUAAACUaBmpGVo2Zpn2zNijGm1qqM/sPipfvbzRsYASj7IIAACAEuvsb2f1U9+fdPHQRT3+1uNq+3Zb2djZGB0LKBUoiwAAAChxzGazYqbEaOW4lXKq6KQhPw+Rf3t/o2MBpQplEQAAACXKtaRrWjRykWLnxap259rq/X1vuVR2MToWUOpQFgEAAFBinNp6SvP6z1PyqWR1+KSDWr3USiYbk9GxgFKJsggAAACrZ84xa/Okzfpl/C8qX728hm8YruotqhsdCyjVKIsAAACwalfOX9GCoQt0ePlhBfYJVM9ve8rR3dHoWECpR1kEAACA1Tq29piiBkYp7VKaQr4KUbPRzWQysewUKA6URQAAAFidnKwcrXt/nda/v14e9Tw0cPlAVQ2qanQsoEyhLAIAAMCqJJ9KVtTAKMWvj1fQ0CCF/CdEDq4ORscCyhzKIgAAAKzGoSWHtGDYAmVdy9JT3z+loMFBRkcCyizKIgAAAAyXnZGtn1//Wb9+9quqBFVR2Jwwedb3NDoWUKZRFgEAAGCoxKOJiuwfqYTtCWo+prk6fdpJdo78mAoYjf8LAQAAYJh9c/ZpybNLZLIxqW9UXwX2DjQ6EoDrKIsAAAAodplXM7XiHyu085udqt6iuvpE9JG7n7vRsQDchLIIAACAYnX+wHn91Pcnnd9/XsGvBuuJ95+Qrb2t0bEA/AVlEQAAAMXCbDZr1393afkLy+Xg6qCBKwaqTuc6RscCcAuURQAAABS59OR0LRm9RPt+3Cf/J/3Ve2ZvuXm7GR0LwG1QFgEAAFCkEnYkKLJfpJKOJemJD55Q69day8bWxuhYAO6AsggAAIAiYTabtfXLrVr9ymq5VnHVsHXDVKN1DaNjASgkyiIAAAAeuKsXr2rRM4sUtyhO9XvWV8//9pSzh7PRsQDcBcoiAAAAHqgTG09oXvg8pZ5NVecvOuuxsY/JZDIZHQvAXaIsAgAA4IHIyc7Rxo83KvqdaLnXdNeILSPk09TH6FgA7hFlEQAAAPct5Y8UzR88X8fWHFPD8IbqPqW7ypUvZ3QsAPeBsggAAID7cnjlYS0YskDpKenq8W0PNX6mMctOgVKAsggAAIB7kp2ZrbVvrdWmf22SVwMvDflliCo3qGx0LAAPCGURAAAAdy0pPknzwufp1JZTavJsE3X5vIvsne2NjgXgAaIsAgAA4K7Ezo/VomcWyZxjVp+IPmrYr6HRkQAUAcoiAAAACiXrWpZWvbxK2/9vu3ya+ShsTpgq1qpodCwARYSyCAAAgDu6eOiiIvtF6szuM2oxroU6fNxBtg62RscCUIQoiwAAALitPTP3aOnzS2XnaKfwxeGq172e0ZEAFAPKIgAAAAqUkZqhZWOWac+MPfJ73E+hP4SqfPXyRscCUEwoiwAAAMjnzJ4ziuwXqYuHLurxtx9X27faysbOxuhYAIoRZREAAAAWZrNZMZNjtPLFlXKq5KQha4bI/wl/o2MBMABlEQAAAJKka0nXtGjkIsXOi1WdLnX01Iyn5FLZxehYAAxCWQQAAIBObT2lef3nKflUsjp+2lEtX2wpk43J6FgADERZBAAAKMPMOWZtnrRZv4z/ReWrl9fwjcNV/bHqRscCYAUoiwAAAGXUlXNXtGDoAh1ecViBfQLV89uecnR3NDoWACtBWQQAACiDjv1yTFGDopR2KU0hX4Wo2ehmMplYdgrgBsoiAABAGZKTlaN1763T+g/Wy6OehwYuH6iqQVWNjgXAClEWAQAAyojkU8maN2CeTmw4oUbDGqnrf7rKwcXB6FgArBRlEQAAoAw4tOSQFgxboKxrWeo9s7ceGfSI0ZEAWDnKIgAAQCmWnZGtn1/7Wb9+/quqNqqqsDlh8qjnYXQsACUAZREAAKCUunTkkub1n6eEmAQ1H9NcnT7tJDtHfvwDUDj8aQEAAFAK7ZuzT4tHLZaNrY36RvVVYO9AoyMBKGEoiwAAAKVI5tVMrfjHCu38Zqeqt6yuPj/2kbufu9GxAJRAlEUAAO7ZOUnTJf0m6bKkCpIekTRckpdxsVBmndt/TpH9InV+/3kFvxasJ957Qrb2tkbHAlBCURYBALhr2yV9JGn59Y+v3bQtStI7krpKel1S8+KNhjLJbDZr13e7tHzscpVzK6dBKwepdqfaRscCUMJRFgEAuCuTJb0sKU2SuYDtadf/u0DSSkkTJT1fPNFQJqUnp2vJc0u0L2Kf/J/0V+isULlWdTU6FoBSgLIIAECh/VkUrxZiX/P1/V6+/jGFEQ9ewo4ERfaLVNLxJLX/Z3sFvxosG1sbo2MBKCX40wQAyoBhw4bpzTffNDqGGjRooOjo6Ftub9eunb799tv7HufEiRNydXVVdnb2fZ/rhu364Yd/qFOnwhTFm/1ZGGMsr0yYMEGDBg265yQffvihRo4cec/Ho+Qzm8369Ytf9V3L75Sdka1h0cPUZnwbiiKAB4o/UQDAitSsWVMODg66cOFCntcbN24sk8mk48eP3/Ec06dPV+vWrYso4f3Zv3+/2rVrJ+n+C9Pt1KhRQ6mpqbK1fZA39vhIAwdmatWqezk2TbnvcXwwxo8f/0BKNUqmqxevKqJXhFaOW6m6Xetq9O7RqtG6hnm47qMAACAASURBVNGxAJRClEUAsDL+/v768ccfLR/v3btXV6/e7WxW0Xmws3XFz2w2Kycn5y6POqfcm9kU9B7FQo0qaZmk8/d4/A1ZWVn3fY4HxZqylBXxG+I1tdFUHV5xWJ2/6Kx+C/rJqZKT0bEAlFKURQCwMoMHD9b3339v+XjGjBkaMmRInn0uX76sIUOGyMvLS35+fvrggw+Uk5Oj2NhYjR49Wlu2bJGrq6vc3W88Wy0xMVHdunWTm5ubHnvsMR05csSy7eDBg+rYsaMqVaqk+vXra+7cuZZtw4YN0/PPP6+QkBC5uLho7dq1ebKsXbtWDz/8sOXjjh07qnnzG3cAbdOmjRYsWCApd+b0559/1ooVK/Thhx9qzpw5cnV1VVBQkGX/+Ph4BQcHy83NTZ06dco3y/qnwMBALVmyxPJxVlaWvLy8tHPnTh0/flwmk8lSZtq1a6c33nhDwcHBcnZ21tGjR7V582Y1b95cFSpUUPPmzbV582bLuaZPn65atWrJzc1N/v7++uGH/7n+uvTnpK3ZLI0bJ1WuLJUvLz38sLRvX4FRdeyY1LZthtzcqqtjx455Pqfo6GhVr149z/5/XicpdwY2LCxMgwYNUvny5TV9+vQ8s7J/fq4zZsxQjRo15OnpqX/+85+Wc6WlpWno0KGqWLGiAgMD9cknn+Qb72Ymk0lffvmlatWqJU9PT73yyiuWcj19+nQFBwdr3Lhx8vDw0IQJE5Senq6XX35ZNWrUUJUqVTR69GilpaVZzvfJJ5/I29tbPj4++vbbb2UymXT48GFJud9bf//732/5ffk///M/8vX1Vfny5dW0aVNt2LDBsm3ChAnq27evhgwZIjc3NzVo0EAxMTeW+p48eVKhoaHy8vKSh4eHxowZo4yMDFWqVEl79+617Hfu3Dk5Ozvr/Pn7L/JFKSc7R+s/WK8Z7WbIztFOI7aMUIv/aSGTyWR0NAClGGURAKxMixYtlJycrNjYWGVnZysiIiLfcs0XXnhBly9f1tGjR7Vu3Tp9//33mjZtmgIDAzVlyhS1bNlSqampSkpKshwTERGhd955R4mJiapTp47eeOMNSdKVK1fUsWNHDRgwQOfOnVNERIT+9re/6cCBA5ZjZ8+erTfeeEMpKSn5lri2aNFCv//+uy5cuKDMzEz99ttvSkhIUEpKitLS0hQTE6M2bdrkOaZLly4aP368+vXrp9TUVO3ZsyfPWNOmTdO5c+eUkZGhiRMnFnidwsPD88zArly5Up6enmrSpEmB+8+cOVNff/21UlJS5Obmpm7dumns2LG6ePGiXnzxRXXr1k0XL17UlStXNHbsWC1fvlwpKSnavHmzGjVKUd7HY0irVknr10uHDkmXL0tz50oeHgUOrQEDpKZNc3ThQpjeeustzZgxo+Adb2HhwoUKCwtTUlKSBg4cWOA+GzduVFxcnNasWaP33ntPsbGxkqR3331Xx48f19GjR7V69WrNmjXrjuPNnz9fMTEx2rlzpxYuXKj//ve/lm1bt25VrVq1dPbsWb3xxht67bXXdOjQIe3evVuHDx/W6dOn9d5770mSVqxYoc8++0w///yzDh8+XOD7VW/1fSlJzZs31+7du3Xp0iUNGDBATz/9tK5du/F1WLRokfr376+kpCT17NlTY8aMkZQ7+929e3f5+fnp+PHjOn36tPr37y8HBwf1798/zzX48ccf9eSTT8rLy3qfi5nyR4pmdZqltW+tVcP+DfXsjmfl09TH6FgAygDKIgBYoT9nF1evXq3AwEBVq1bNsu3PAvnRRx/Jzc1NNWvW1EsvvaSZM2fe9py9e/fWo48+Kjs7Ow0cOFC7d++WJC1ZskQ1a9bU8OHDZWdnp8aNG6tPnz766aefLMf26tVLwcHBsrGxkaOjY57zOjk5qXnz5lq/fr127NihoKAgBQcHa9OmTfr1119Vt25dedyqRRVg+PDhqlevnpycnNS3b19Lzr8aMGCAFi1aZFmiO3v2bIWHh9/yvMOGDVODBg1kZ2enVatWqW7duho8eLDs7OwUHh6ugIAALV68WJJkY2Ojffv2KS0tTd7e3mrQIP/sjb29lJIiHTyYO8sYGCh5e+cf98QJaft26f33pXLlkvX444+rR48ehb4ektSyZUs99dRTsrGxkZNTwUsO33nnHTk5OSkoKEhBQUGWAj537lyNHz9eFStWVPXq1TV27Ng7jvfqq6+qUqVKqlGjhv7xj3/kKeU+Pj564YUXZGdnJ0dHR3399df6/PPPValSJbm5uWn8+PGKiIiwjD18+HA1aNBAzs7OmjBhQr6xbvV9KUmDBg2Sh4eH7Ozs9NJLLyk9PV1xcXGW7a1bt1ZISIhsbW01ePBgy+e8bds2JSQk6NNPP5WLi4scHR0t/8gxdOhQ/fjjjzKbc5cUz5w5U4MHD77jNTHK4ZWHNSVoik5uOame3/VU71m9Va58OaNjASgjeHQGAFihwYMH6/HHH9exY8fyLUH9cwbPz8/P8pqfn59Onz5923NWrVrV8ntnZ2elpqZKyl32uXXr1jxLVrOysvL8AO3r63vbc7dt29aynLJt27aqWLGi1q1bp3Llyqlt27Z3/oQLkfOv6tSpo8DAQC1evFg9evTQokWLtGvXrlue9+bPISEhIc/1k25cQxcXF82ZM0cTJ07UiBEjFBwcrEmTbBQQkPd87dtLY8ZIf/+7FB8vhYZKEyfmLkm9WUKCVLGi5OIiSRUtY508efLOF6OA7Ldyq+uWkJCQ5/jCnOvmffz8/JSQkFDgtvPnz+vq1atq2rSp5TWz2Wx5X2tCQoKaNWt227Fv9/WeOHGivvvuOyUkJMhkMik5OTnPEt6/Hnvt2jVlZWXp5MmT8vPzk51d/h9zHnvsMTk7Oys6Olre3t46fPiwevbsecdrUtyyM7O19q212vSvTarcsLLCosPk9ZD1zn4CKJ2YWQQAK+Tn5yd/f38tW7ZMoaGhebZ5enrK3t5e8fHxltdOnDhhmX282/cw+fr6qm3btkpKSrL8Sk1N1eTJky373Omcf5bF9evXq23btmrbtq3WrVundevW3bIsPoj3Wv25FHXhwoV66KGHVKdOnVvue/N4Pj4+ea6flPcadu7cWatXr9Yff/yhgIAAjRq1T1LeGVVJGjtW2rFDOnAgdznqp5/mH9fbW0pMlK5ccZT0sGWsP7m4uOS5gVF2dna+98/dz7Xy9vbWqVOnLB8XpqTevM+JEyfk43NjyePNWTw9PeXk5KT9+/dbvncuX75sKXz3MvafNmzYoE8++URz585VYmKikpKSVKFCBcuM4O34+vrqxIkTt7wBz9ChQzVr1izNnDlTYWFh+WbLjZYUn6Tpbadr0782qcmzTTRy20iKIgBDUBYBwEp99913+uWXX+SSOyVlYWtrq759+1reQxgfH6/PPvvM8r7GKlWq6NSpU8rIyCjUON27d9ehQ4c0c+ZMZWZmKjMzU9u3b7e8560wWrVqpbi4OG3btk2PPvqoGjRoYJmxfPzxxws8pkqVKjp+/Pg93Jn0hv79+2vVqlWaPHmyBgwYUOjjQkJCdOjQIc2ePVtZWVmaM2eODhw4oO7du+vs2bNauHChrly5onLlysnV1VU2NvnXl27fLm3dKmVm5s4aOjpKNgX8rernJzVrJr3zTqYyMgZo48aNluWuklSvXj1du3ZNS5cuVWZmpj744AOlp6ff0/UoSN++ffXRRx8pMTFRp0+f1n/+8587HvPpp58qMTFRJ0+e1P/+7/+qX79+Be5nY2OjUaNGady4cTp37pwk6fTp01q5cqVl7GnTpik2NlZXr17V+++/X+jcKSkpsrOzk5eXl7KysvTee+8pOTm5UMc++uij8vb21muvvaYrV67o2rVr2rRpk2X7oEGDNH/+fM2aNSvfzL3RYqNiNbXRVJ3ff15hc8LUY2oP2TvZGx0LQBlFWQQAK1W7du08S/hu9u9//1suLi6qVauWWrdurQEDBuiZZ56RJLVv314NGjRQ1apV5enpecdx3NzctGrVKkVERMjHx0dVq1bVq6++eleFxcXFRU2aNFGDBg3k4OAgKfd9dn5+fqpcuXKBxzz99NOSJA8Pj1velOZOvL291bJlS23evPmWhaYgHh4eWrJkiSZNmiQPDw998sknWrJkiTw9PZWTk6PPPvtMPj4+qlSpktatW6fJk7+V1DXPOZKTpVGjcpeY+vnl3tzmlVcKHm/2bGnrVndVqlRf7777bp6CUqFCBX311VcaOXKkqlWrJhcXl9verfRuvf3226pevbr8/f3VoUMHhYWFqVy527/nrVevXmratKkaNWqkbt26acSIEbfc91//+pfq1KmjFi1aqHz58urQoYPlfYVdu3bV2LFj9cQTT1j2kXTH8aXc2d0uXbqoXr168vPzk6OjY6GW0Eq5/6CyePFiHT58WDVq1FD16tU1Z84cy3ZfX181adJEJpMp382XjJJ1LUvLxizT3D5zValOJT236zk16NvA6FgAyjjT7ZZzNGvWzHzzbagBACi7tktqJ+lennnpLGmdpILLf3GaPHmyIiIitG7dugK3m0wm/f7777dd0nuvYmNj1bBhQ6Wnpxf4fsLi9Mwzz8jHx0cffPCBoTkk6ULcBUX2i9TZPWfV4sUW6vBRB9k62BodC0AZYTKZdpjN5gL/gmJmEQCAQmkuaaJyi9/dcL5+nDFF8Y8//tCmTZuUk5OjuLg4TZo0Sb179y628efPn6/09HQlJibq1VdfVY8ePQwvisePH1dUVNRtZ0yLy57v9+jrpl8r+VSywpeEq/OkzhRFAFaDsggAQKE9rxuF8U43nTHpRlF8vohz3VpGRoaee+45ubm5qX379urVq5f+9re/Fdv4U6dOVeXKlVW7dm3Z2trmuXGSEd566y01bNhQr7zyivz9/Q3LkZGaoQVDF2jB0AXyaeqj0btHq163eoblAYCCsAwVAIC7FiPpI0nLlFsK0yxbMq/aybacrWxsu0l6Xdaw9BTW5cyeM4rsF6mLhy6q7dtt9fhbj8vGln+/B2CM2y1D5TmLAADctWaS5kk6L2m6pL2SEmXOcdemTy4pJaGPenz9jJEBYYXMZrNiJsdo5Ysr5VTJSUN/Gaqa7WoaHQsAbomyCADAPfOSdOMWqCYbKSVhsfb9uE9dv8ySnSN/zSLXtaRrWjRykWLnxapO1zp6asZTcvFyufOBAGAg1jwAAPAABYYGKiM1Q0fXHDU6CqzEqV9PaUqjKYpbGKeOn3bUgCUDKIoASgTKIgAAD5B/e3+VK19OsVGxRkeBwcw5Zm36ZJOmtZkmk8mk4RuHq9XLrWSyudPNkQDAOrA+BgCAB8jWwVb1etRT3MI45UzNkY0d/y5bFl05d0Xzh8zXkZVH9FDYQ+rxTQ85ujsaHQsA7gp/gwEA8IAFhgYq7WKa4tfHGx0FBjj2yzFNCZqi49HH1W1yN4XNDaMoAiiRKIsAADxgtTvXlp2THUtRy5icrBz98tYv+r7D93J0d9SobaPUbHQzmUwsOwVQMlEWAQB4wBxcHFSnSx0dnH9Q5pxbP88YpUfyqWTNaD9DGz7YoEZDG2lUzChVeaSK0bEA4L5QFgEAKAKBoYFKSUjR6W2njY6CInZoySFNaTRFZ3adUe+ZvdVrWi85uDgYHQsA7htlEQCAIlCvez3Z2NmwFLUUy87I1opxK/Rjjx9VwbeCnt3xrB4Z9IjRsQDggaEsAgBQBBzdHeX/pL9io2JlNrMUtbS5dOSSvmv1nbZ+sVXNxzTXiC0j5FHPw+hYAPBAURYBACgigaGBSjySqHN7zxkdBQ/Qvoh9mtp4qhKPJKpvVF+F/DtEdo48jQxA6UNZBACgiAQ8FSCZxFLUUiLzaqYWjVqkeeHzVOXhKnpu93MK7B1odCwAKDKURQAAiohLZRf5tfGjLJYC5/af0zePfqNd3+1S69dba2j0ULn7uRsdCwCKFGURAIAiFBAaoHN7z+ni7xeNjoJ7YDabtfPbnfqm+Te6ev6qBq0cpCc/fFK29rZGRwOAIkdZBACgCP25TPHg/IMGJ8HdSk9OV9SAKC0etVg1gmto9J7Rqt2xttGxAKDYUBYBAChCFWpUkE8zH5ailjAJMQma2mSq9v+0X+3/2V6DVg6Sa1VXo2MBQLGiLAIAUMQCQgN0eutpJZ9KNjoK7sBsNuvXL37Vd62+U3ZGtoZFD1Ob8W1ksjEZHQ0Aih1lEQCAIhYYen0p6gKWolqzqxevKqJnhFaOW6m6Xetq9O7RqtG6htGxAMAwlEUAAIqYZ31PeT3kxVJUKxa/IV5TgqboyKoj6vK/XdRvQT85VXIyOhYAGIqyCABAMQgIDVD8unhdvXDV6Ci4SU52jta9v04z2s2QvZO9RmwZocfGPiaTiWWnAEBZBACgGASGBsqcY1bcojijo+C6lD9SNKvTLEW/Ha2G/Rvq2Z3PyruJt9GxAMBqUBYBACgGVRtVlXtNd5aiWonDKw9rStAUnfr1lHr+t6d6z+qtcm7ljI4FAFaFsggAQDEwmUwKCA3Q0dVHlZ6cbnScMis7M1urX12tH7r8INcqrhoVM0qNhzdm2SkAFICyCABAMQkMDVR2RrYOLT1kdJQyKel4kqY/Pl2bP9msps811chtI+UV6GV0LACwWnZGBwAAoKzwbekr16quOhh1UA+HP2x0nDIlNipWi0YskjnHrLA5YWrQt4HRkQDA6lEWAQAoJiYbkwJ6B2jPjD3KTMuUvZO90ZFKvaxrWVr50krFfBUjn+Y+CosIU8VaFY2OBQAlAstQAQAoRoGhgcq8mqkjq44YHaXUuxB3Qd+2+FYxX8Wo5Ust9czGZyiKAHAXKIsAABQjv7Z+cqzoqINRB42OUqrtnrFbXzf9WsmnkhW+JFydJnaSrYOt0bEAoERhGSoAAMXI1t5W9XvWV9zCOGVnZsvWngLzIGWkZmjZ35dpz/d75NfWT6E/hKp8tfJGxwKAEomZRQAAillgaKCuJV3T8ejjRkcpVc7sOaOvm32t32b9prbvtNWQNUMoigBwHyiLAAAUs1oda8nexV6xUbFGRykVzGaztn+1Xd8+9q3Sk9M1ZM0QtZvQTja2/JgDAPeDP0UBAChm9k72qhtSVwfnH1ROdo7RcUq0tMQ0/RT2k5b9fZn82/tr9J7RqtmuptGxAKBUoCwCAGCAwNBAXTl7Rad+PWV0lBLr5JaTmtp4quIWxanjpx01YMkAuXi5GB0LAEoNyiIAAAaoG1JXtg62LEW9B+Ycszb+a6OmtZkmk8mk4RuHq9XLrWSyMRkdDQBKFcoiAAAGKFe+nGp1rKWDUQdlNpuNjlNiXDl3RT+E/KA1r61RYGigntv1nKo/Vt3oWABQKlEWAQAwSGBooJKOJ+nMrjNGRykRjv1yTFOCpih+Xby6TemmsDlhcnR3NDoWAJRalEUAAAxSv2d9mWxMip3PUtTbycnK0S9v/aLvO3wvR3dHjdw2Us2eayaTiWWnAFCUKIsAABjE2dNZfm39dDDqoNFRrFbyqWTNaD9DGz7YoEbDGmlUzChVebiK0bEAoEygLAIAYKDA0ECdP3BeFw5eMDqK1YlbHKcpQVN0ZtcZ9Z7VW73+20sOLg5GxwKAMoOyCACAgQJ6B0gSS1FvkpWepRXjViiiZ4Qq+FXQszue1SMDHzE6FgCUOZRFAAAMVL5aeVVvUV2x8yiLknTp8CX9N/i/2vrFVj36wqMasWWEPOp5GB0LAMokyiIAAAYLCA3QHzv+UFJ8ktFRDLX3x72a2mSqEo8mqt/8fur6ZVfZlbMzOhYAlFmURQAADBbYO1CSdHB+2bzRTebVTC0auUhRA6JU5eEqGr17tAKeCjA6FgCUeZRFAAAMVqlOJVV5pIpio8reUtRz+8/pm+bfaNd/d6n1+NYaGj1UFWpUMDoWAECURQAArEJAaIBObDyh1LOpRkcpFmazWTu+2aFvmn+jqxevatDKQXryn0/K1t7W6GgAgOsoiwAAWIHA0EDJLMUtjDM6SpFLT07XvPB5WvLsEtUIrqHRu0erdsfaRscCAPwFZREAACtQuWFlVapTqdQvRU2ISdDUxlN1IPKA2v+zvQatHCTXqq5GxwIAFICyCACAFTCZTAoIDdCxNcd0Lema0XEeOLPZrC2fb9F3rb5Tdma2hq0bpjbj28hkYzI6GgDgFiiLAABYicDQQOVk5ejQkkNGR3mgrl64qoieEVr14irVDamr0btHq0ZwDaNjAQDugLIIAICVqNa8mtyquZWqpajx6+M1pdEUHVl1RF2+7KJ+8/vJqZKT0bEAAIXAk24BALASJhuTAnoHaNd3u5RxJUMOLg5GR7pnOdk52vDhBq2bsE4Va1XUiC0j5N3E2+hYAIC7wMwiAABWJDA0UFlpWTqy8ojRUe5Zyh8pmtVplqLfjlbD8IZ6duezFEUAKIGYWQQAwIr4tfGTk4eTYqNicx+nUcIcXnFY84fMV+aVTPX8b081GtZIJhM3sQGAkoiyCACAFbGxs1H9XvUVGxmr7Ixs2TqUjIfUZ2dm65c3f9HmTzarcsPKCpsbJq9AL6NjAQDuA8tQAQCwMoGhgUpPTtexX44ZHaVQko4naVqbadr8yWY1fa6pRm4bSVEEgFKAmUUAAKxMrSdrycHNQQfmHVCdLnWMjnNbB+Yd0KIRiySzFDY3TA2ebmB0JADAA0JZBADAytg52qlet3qKWxCnnCk5srG1voVAWdeytPLFlYqZHCOf5j4KiwhTxVoVjY4FAHiArO9vHwAAoIDQAF29cFUnNp4wOko+Fw5e0LePfauYyTFq+XJLPbPxGYoiAJRCzCwCAGCF6natK9tytoqNilXNtjWNjmOxe8ZuLfvbMtk722vA0gGqG1LX6EgAgCLCzCIAAFbIwdVBdTrX0cGogzKbzUbHUUZqhuYPma+Fwxaq2qPV9Nzu5yiKAFDKURYBALBSAaEBSj6VrISYBENznNl9Rl83/Vp7f9irthPaavDPg1W+WnlDMwEAih7LUAEAsFL1e9SXydak2KhYVWterdjHN5vN2v7Vdq16aZWcPZw1ZM0Q1WxXs9hzAACMwcwiAABWyqmSk/yf8FfsvNhiX4qalpimuX3mavmY5ar1ZC09t/s5iiIAlDGURQAArFhAaIAu/X5J5w+cL7YxT245qamNpurQ4kPqOLGjwheHy8XLpdjGBwBYB8oiAABWLOCpAMkkxUbFFvlY5hyzNn68UdPaTJPJ1qRnNj2jVi+1ksnGVORjAwCsD2URAAAr5ubtJt+WvjoYdbBIx0k9m6ofuv6gNa+vUWBooJ7b9ZyqPVr875MEAFgPyiIAAFYusE+gzuw+o+h3ozV/8HxdPnn5gZ7/6JqjmtpoquLXx6v71O4KmxMmxwqOD3QMAEDJw91QAQCwYvsi9mnP93skSesmrJMkNQxvqAq+Fe773DlZOYqeEK0NH26QZ31PDVo1SFUernLf5wUAlA6URQAArNiWz7bo7J6zeV7zqOdx3+e9fPKyogZE6cTGE2o0vJG6/rurHFwc7vu8AIDSg2WoAABYsbCIMLl6u0rX7zFjY2cj95ru93XOuEVxmtpo6v9n797jc67/P44/rp3Pmx2cZzPHRQ6ROVOYsBQlx2qKkrNIRSKJiPp9q29JRM5JfcmcVVZDcow5H+d8GHYydrx+f1yMZbONbdcOz/vtttuuw/vzeb8+19zG0/vzfr+5sPsCneZ34pnvnlFQFBGReygsioiIFGAl/EoQHBqMfQl7AOw97LGwerC/vpMTklkzdA2Ln1mMq48rr+18jVo9a+VmuSIiUoQoLIqIiBRwHlU86B3WGwsri7TQmFNXj17lu8bfsfU/W2kwuAGvbnkVjyoPfzuriIgUXZqzKCIiUgh4+Xsx+MRg7NxigCnAHiAacAVqAb0BrwyP3btoLyGvh2BhZUHXZV2p/kz1/CpbREQKMYVFERGRQmEbruUnAatvPb9513s/A2OBdsC7wOMAJMUnsXrwanbN2oV3E2+eW/gcrhUefhVVEREpHhQWRURECryvgRHADcCYwfs3bn1fBqwFpnIp/DmWdl3K5QOXaTqqKU988MQDz3UUEZHiSWFRRESkQLsdFOOz0dYIxJOa/Cbbv1lN/JXmvLjuRfxa++VtiSIiUiTpvxhFRKTIWrBgAYGBgXly7uDgYN577708Ofcd24ARnDwZj8EAyckZt5o4Efr0ufPcwuomgZ+spX94g1wLigaDgaNHj+bKubLSr18/Pvzww3zpS0REMqewKCIihVpYWBiNGzfG1dUVd3d3mjRpwrZt2wDo2bMn69atM3OFD2MSd24xzdyoUTBzZvrXrOyScPD8T96UlcemT5/OmDFjzF2GiEixp9tQRUSk0IqJiSEoKIivv/6aF154gcTERP78809sbW3NXVouuIRpMZuM5ihmhxFYBVwms1VS80pycjJWVvonhohIYaeRRRERKbQOHz4MQPfu3bG0tMTe3p7AwEBq1TJtND9nzhyaNm2a1t5gMPDVV19RpUoVnJ2dGTNmDMeOHaNx48a4uLikBU6AjRs3Ur58eSZOnIinpye+vr4sWLAg01pCQkKoU6cObm5uNG7cmD179mTYzmg0MmzYMEqWLImLiwuPPvoo4eHhANy4cYPhw4fj4+ODq2sFmjZN4MZdA4sLFkCFCuDpCR99dOf1ceOgVy/T44EDwcnp9tdNrKxKM27cOAAOHDhAy5YtcXNzo0aNGvzyyy9p5wgODqZfv360adMGZ2dnWrRoQURERLraN2zYQJUqVXBzc2PAgAEYjca0z7lJkyYMGzYMDw8Pxo0bR0JCAiNGjKBChQqUKlWKfv36cePWxdz+bKdNm0bJkiUpU6YMs2fPTlfL7Vt8r127RlBQEF5eXpQoUYKgoCDOnDmT6c9BRERyj8KiiIgUWlWrVsXS0pKXX36Z1atXc+3atSyPWbt2LTt2wgRZ6AAAIABJREFU7OCvv/5iypQpvPbaa8yfP5/Tp08THh7OokWL0tpeuHCByMhIzp49y/fff89rr73GoUOH7jnnrl27eOWVV/jmm2+4cuUKr7/+Oh07diQhIeGetuvWreOPP/7g8OHDREdHs2TJEjw8PAAYMWIEO3bsYPPmzVy92pkpU4xY3PU3dVgYHDoEv/4K48fDgQP3Xt+XX0JcnOkrLAxKlLDmmWeeISkpiaeffprAwEAuXbrEF198Qc+ePdNdz4IFCxgzZgyRkZHUqVOHnj17pjt3SEgI27ZtY8+ePSxZsoS1a9emvbd161b8/Py4ePEio0eP5p133uHw4cPs3r2bo0ePcvbsWcaPH5/us42Ojubs2bPMmjWLAQMGZPjzS01NpXfv3kRERHDq1Cns7e0ZOHBgBj9ZERHJbQqLIiJSaLm4uBAWFobBYKBv3754eXnRsWNHLl68mOkxI0eOxMXFhRo1alCzZk0CAwPx8/PD1dWVdu3asWvXrnTtP/zwQ2xtbWnRogUdOnRgyZIl95xzxowZvP766wQEBGBpaUmvHr2wsbJh/c/rOb/rPCdDT3Lol0Psmb+HYyuPcSniEnMHzSWkfwgHJhzg976/M6vZLL79+lsaHGnA/Efnc/K3f2jcGO6+o3bsWLC3h9q1TV///JP5Z3P5Mjz7LHzxxSPUrVuXv/76i7i4ON555x1sbGx48sknCQoKSheOO3ToQPPmzbG1teWjjz5iy5YtnD59Ou39d955Bzc3NypUqMATTzzB7t27094rW7YsgwYNwsrKCjs7O2bMmMFnn32Gu7s7zs7OjBo1isWLF6e1t7a25v3338fa2pr27dvj5OSUYRD38PDgueeew8HBAWdnZ0aPHk1oaGjmFy4iIrlGEwpERKRQ8/f3Z86cOQAcPHiQXr16MXTo0HQh6G6lSpVKe2xvb5/uuZ2dHefOnCPmTAxRJ6NwdXbl/J/nSYhJ4Gb0TawuWbFt+TbWXFnDqbBTxFjFMPevuYRtC+NQ7CGmfjQVjGDESAopzOkxhx3suKeGmtRk8oLJRBFFLYdadC7dGRsPG5KMSdRpXgc3TzdcynsD+9MdV7r0nccODqbRw4wkJcHzz0OPHtCtW00Azp07h7e3NxZ3DVX6+Phw9uzZtOfe3t5pj52cnHB3d087ztT/nQIcHByIu6uAu4+9fPky8fHx1KtXL+01o9FISkpK2nMPD4908xr/fb7b4uPjGTZsGGvWrEkbeYyNjSUlJQVLS8uMPwAREckVCosiIlIkJCck4+3uTZcOXZizYA4nfjvB+Z3nuX7pOlu/2EpCtOmW0F/f+5V/DP9wM/omF/dc5Lf3fuPa+9dIiEngz+g/iSOOz+Z+xglOcI1rzG43GxtsANjDHkpSkt1HdhOXHIetoy3JHsl4uXpR06cmPRr1wNbFFjtXO2xdbe88drG957mVnRWXLl2iUflGhBwLoeWxllhhxf6d+2nStgle/k8CocDNHH8WgwaBiwtMmGAHPAqYRv5Onz5NampqWmA8deoUVatWTTvu7lHEuLg4rl69StmyZbPVp8FgSHvs6emJvb09+/bto1y5cjmu/27Tpk3j0KFDbN26ldKlS7N7927q1q2bNl9SRETyjsKiiIiYVWpKKomxiWmjdwnRCXcexySke54Yk5iuTcTlCP65+g/+if44JTkRTTRLWYoXXsxtNZdd7OIKV1gzeE1afyc2nCDRPRE7VzsMFgYcSzniV8sPGxcbwneGE50YTVCfIPac2cP8CfOJ7BrJmJFjCD8WzpSXpvDjth/xf8Sfg8EHKV++PK9MeIVa22vRqVMnhr4ylAYNGhAfH8/GjRtp3rw5zs7O6a5327ZtpKam8thjj2FvZ4+DowOGKAMWWFCXuiw8vBAuQ+BnA/j77zE89ljOPs9vvoHQUNi6lVvzHYMBCAgIwMHBgSlTpjB8+HA2bdrEihUr0rYZAVi1ahVhYWE0aNCAMWPG0LBhw3QjhtllYWFB3759GTZsGF9++SUlS5bk7NmzhIeH07Zt2xydKzY2Fnt7e9zc3Lh69SoffPBBjusREZEHo7AoIiIPxGg0knwj+Z5gl1HIu/v5v9skxiVm2ZfBwnDPyJxTGSd8K/gSuieU2Zdmc53rODs40/yx5rz72rt4lPHAdaMrF1Zc4K01b2HrYss423H0+asPlStXBuD7pt8T0CeAZ4KfAWDTe5u4cOEC9frWI3ZjLKVnlaZCjQrUaV0HBwcHpk+fjv8j/vfUV79+fb799lsGDhzIkSNHsLe3p2nTpjRv3vyetpHnIhkyeAinz53GIsWCSsZKNKYxAIEEstl9M9/ZfMcX7l9Qu7Yza9deydHPZdEiOH4cTAOCSUBFRo0axahRo1ixYgX9+/dn0qRJlCtXjrlz51K9evW0Y3v06MEHH3zAli1beOyxx5g/f36O+r7b5MmTGT9+PA0bNiQyMpJy5crxxhtv5DgsDh06lB49euDp6UnZsmUZPnw4y5Yte+C6REQk+wz3u42jfv36xu3bt+djOSIikh9Sk1MfOuQlxCSQmpyaZV/WjtYZ3o5p65r5LZrpnrvaYu1gne42x/ywceNGevXq9dDbNBiNRiIPRnJk5REOhxzmVNgpjClG7N3tqdyuMuUCyqWNfPq09KH7L92xdb69qs02oCUQ/wA9O2C6jbV+tloHBwdTvnx5JkyY8AB9iYhIYWUwGHYYjcYM/7LQyKKISCFiNBpJup6UcbDLQchLik/Ksi8LK4t7Ap1rBVfsXO2wcbHJ1rw8WxdbLKyK38LbyTeTORl6ksMhhzmy8ghRJ6IAKFWrFE1GNqFKhyqUb1geC0vTZ3N83XGsnax5dvazWNnd/Vfz48BUYAQ5C4wOt47LXlAUERHJiMKiiEg+SUlMyXHI+3ebhJgEjKlZL+xh42yTbmTO3t2eEhVL3Al52RjZs7KzyvfRvMIs5mwMR1Ye4cjKIxzfcJyk+CSs7K3wa+VnCojtq+BawTXDY7uv6H6fM79x6/sI4AaQ+c8/NcWAMdUWS+updx0nIiLyYHQbqohIFoypRhJiMwhwmYzaZRYEUxJSsuzL0tbyntCW0e2b92tj42STNmIleSc1JZWzf59NC4gXdl8AwNXHlSodqlC1Q1V8n/DF2t46l3rcDkwCVgEGTMHxNnvAyKlNNdnwzuM8t2BSpsFURETkbroNVUSKJaPRSPLN5AcLeXe1SYzNegEWDNwT4BxLOuJRxeO+Ie/f2yxY2erXckF2M+omR9ce5cjKIxxdfZT4yHgMFga8m3jT6uNWVO1QFa8aXnk0Ilsf+Am4DMwB9gLXgBKYtscIxqWcNed3/JfVg1fTbVm3PKhBRESKE/2rREQKpNTkVNNo3kOEvISYBFKTsl6Axcre6p4A51zG+c7zbIQ8G0cbDBa6ZbOoMRqNRB6I5PDKwxwJOcKpTXctTvNUZaoEVaFy28rYu9vnY1VewFsZvuPmCy3HtWTD2xs4uPwg1Z+pnmE7ERGR7FBYFJFcZTQaSYpPuu9eeRnum/evNknXs16AxWBpuCe0uZRzwdY/ZytuWlpb5sMnIw/vEqYRtT1ANOAK1AJ6YwpQuSP5ZjInN55MC4hRJ++/OE1B03BYQ/bM38PqQavxa+WHjZONuUsSEZFCSnMWRSRNSlLKQ4e8hJgEjClZL8Bi7Wh935G6jF77d3tzbKcg5rAN01y91bee37zrPdNcPWgHvItp9dCcizkTw5FVpq0tTvx6It3iNFU6VLnv4jQF0enNp/muyXc0fLMhbaflbF9DEREpXjRnUaSIM6YaSYxLzDzk3T3/7j4hL/lGcpZ9WVhb3BPa3Hzdsr34iq2LLbbOxXM7BXkQX3P/VUBvL/KyDFiLabuIrFcBvb04ze2tLS7+cxEwLU5TO7h2HixOk7+8G3vz2GuPsfU/W6n9Ym1K1ylt7pJERKQQ0siiiJklJyRnuSF6lnP0YhLut5q+iQFsnTMftcvOvDw7VzssbS01mie5YuLEiRw/fpyZM2dm0uJ2UEy/v2C7dtCtG7z8ckbH3N5f0BQYL168SJcuXdi1axevvPQKSReT2LdjH0FxQabFaSwNeDf2Nq1eGlQVr0fyanGa/Hfj2g3+W/2/uPm68crmVwrsbbMiImJe9xtZVFjMNfkzl0YKjtSUVBJjMx+ly3CPvAzapCRmvZ2ClZ1V5oEumyHPxkkLsAgsXryYzz77jPDwcBwdHalYsSIvv/wyb7zxRp6GpI0bN9KrVy/OnDmTzSO2AS3J2Ub0tzkAoRiN9Xh38Lts+X0Lwe7BnN58mt9SfiPGJob3urxH1aCqVGpbCfsS+bk4Tf7as2AP/+v1P9r/tz2P93+wW3RFRKRo022oeep+c2l+BsbysHNpJHcZjUaSbyRnuSF6uucZvJYYl/V2CgYLwz0Bzqm0Ex7VPO4f8v51+6aljRZgkYc3bdo0pkyZwn//+1/atm2Lk5MTu3fvZurUqbz66qvY2tqau8S7TCL9PoLZZzTe4PL+N1kU9BzrTq7DCScSaiXQZGQTrl28xqWbl+g8v3PulltAPdrjUXbP3s2v7/5K9U7VcS7jbO6SRESkENHI4kPJai7NbQZMizBkby6NZC41OfWhQ15CTAKpyVlvp2DtYJ1poMvOCpt2rnZYO2oBFikYoqOjKVu2LHPnzuW5557LtF1CQgKjR49myZIlJCQk0KlTJz777DPs7e3TRgeHDRvG5MmTsbS0ZOLEifTu3fu+x6ampuLp6UlCQgIODg4AHD58mBkzZnD06FHmz58PQFhYGCNHjmT//n04O8fw4YcQHHxvjS1bQq9e0KcPzJkDM2dCw4Ywaxa4ucFXX0GbJ61p41+PPyO2YWFpgY2NDcuWLSMsLCytz4xGO319fZk5cyatW7cmNTWVKVOm8O233xIVFUWrVq2YPn067u7unDx5kooVKzJnzhzGjBlDfHw8w4YNY/To0QCkpKQwefJkZs2axaVLl6hatSrLli3D29ubgwcPMmjQIHbs2IGXlxcffvghL7zwQq78nDNy5fAVvn70a6p3qs7zi5/Ps35ERKRw0shinsh4Lk3GjLfajbj1/E5gzHrOTtFgNBpJup5033l4Gc3L+/drSfFZb6dgYWVxT4BzreCao5Bn66IFWKRo2bJlCwkJCTzzzDP3bffOO+9w7Ngxdu/ejbW1NT169GD8+PFMmjQJgAsXLhAdHc3Zs2dZv349zz//PM8++ywlSpS477GrV6++722oERERtGvXjhkzZvD88yeJifmA06cTsnVtW7ea5i9GRsKMGfDqq3D2rCW/H+9McHA1ypcvz4QJEwBTIM2uL774gmXLlhEaGoqXlxeDBw9mwIABLFq0KK1NWFgYhw4d4vDhwzRo0IDOnTvj7+/Pp59+yqJFi1i1ahVVq1Zlz549ODg4cP36ddq0acP48eNZvXo1e/fupU2bNtSsWZNHHnkk27XlhEdVD5qOakrouFDq9K5D5baV86QfEREpeopcWPT19eXcuXOcO3cOT0/PtNfr1q3L7t27OXHiBL6+vg/ZyzayHxTvdjswPg6YwvuoUaMespa8l5KYkuU2ClmFvISYBIypWW+nYONsky6w2bvbp1tp837bKNx+bmVvpdE8kX+JjIzE09MTK6s7v/YbN27M/v37SUhIYO3atTRr1owZM2awZ88e3N3dAdPvqB49eqSFRWtra95//32srKxo3749Tk5OHDp0iICAgCyPvZ+FCxfSunVrunfvDvTCwyMBD4/sXZuPD/Tta3r88svQvz9cvHiT0qX3Ag/+nz7Tp0/nyy+/pHz58gCMGzeOChUqMG/evLQ2Y8eOxd7entq1a1O7dm3++ecf/P39mTlzJlOmTKFatWoA1K5dG4AffvgBX1/ftNHYunXr8txzz/Hjjz8yduzYB641K03faUr4wnBW9V/FG+FvFNpVXkVEJH8VubAIULFiRRYtWsSgQYMA2Lt3L/HxD7JIQmYefC6N6bhJwE8kJydjZWXFpfBLhI4Pxf85f2p2rZlrVRpTjSTEZrCwyn1CXkZtUhKyXoDF0sbynlG7En4lTAuruNhkK+TZONtotT6RPOLh4UFkZGTa7x2AzZs3A1C+fHlSU1O5fPky8fHx1KtXL+04o9FISkpKuvPcHTgdHByIi4vL1rH3c/r0aSpVqnTrWXSOrq30XbtC3LrLlbg4gGtANhNnBiIiIujUqRMWFnd+L1laWnLx4sW7+r7T+e3PAv59PenPuXXrVtzc3NJeS05O5sUXX3zgOrPDytaKDtM7MPfJufwx4Q9afdQqT/sTEZGioUiGxRdffJG5c+emhcXvv/+el156iffeew+AlStX8t5773Hs2DFcXV159dVXGTduHECW81DGjXuL/fuXY2dn5H//gwoV4Pvvof6tu3zPnYNBg+CPP8DJCYYNg8GDuXUshIcbsbNbxi+/OPPxB5MJWxjGvu376ExnklKT+HjFx6xevZqUlBQq+VZi/lfzcbVxpfOrnalTtQ5bdm3hyOkj1PGtw5ut3+TLtV/yd8TflHYozeuVXsc5wfnOpumxWS/AgoF7ApxjSUc8qnjcCXnZuH3TyrZI/lESKTIaNWqEra0ty5cvz3TOoqenJ/b29uzbt49y5crl6PxZHZvVaL+3tzd///33rWeuOeo7cyXu+66jo2O6/0hMSUnh8uXL6Wr67rvvaNKkyT3Hnjx58r7n9vb25tixY9SsWfOe11u0aMH69euzUX/uqvhERWq9WIvNn2ymVs9aeD2ilbpFROT+iuS/8Bs2bMi8efM4cOAAVatWZfHixWzatCktLDo6OjJ37lxq1KhBeHg4bdq0oU6dOjz77LNp58hsHgrs5pdfUvn5Z5g9G957DwYOhL/+gtRUePppeOYZWLQIzpyB1q2hWjVo29Z03uXL4ccfrRjTvRsLnr3AueRzGG8tjvPDLz9whCP0N/bHItmCC/9cYG6TudhhxwUusGTPEl7kRdrTnpmHZ9LvSD86u3amU8VOLL66mJALIQxqNOiehVjud/umjaO2UxApDtzc3Bg7diz9+/fHaDTStm1bHB0d2bNnD9evXwfAwsKCvn37MmzYML788ktKlizJ2bNnCQ8Pp+3tX2KZyOrYUqVKceXKFaKjo3F1vTcM9uzZk4kTJ7JkyRI6d65BdLQtp08nUKfOg16xLfAosC/TFlWrVuXmzZusXLmSwMBAJk6cSELCnXmS/fr1Y/To0Xz//ff4+Phw+fJlNm/enOW8T4A+ffowZswYHnnkESpXrszevXspV64cQUFBvPPOO8ybN49u3boBsHv3bpycnG79HZO3AqcGcjjkMCH9QggODdYt+yIicl9FMizCndHFFi1a4O/vn+5/ulu2bJn2uFatWnTv3p3Q0NB0YTGzeShwkaZNoX372/3A//2f6fG2bXD5Mrz/vum5n59pHs3ixXfCYqNG8OyziRxduw/L5PLparaxscHoZMS7vTf+VfzThbzVY1bTrWk33n7zbWxdbbn54U0OHjrIjNUzTH2t8GPMmDG88FPeragnIoXbyJEjKVeuHFOmTOGll17C0dERPz8/Jk+eTOPGjQGYPHky48ePp2HDhkRGRlKuXDneeOONLMNiVsdWr16d7t274+fnR0pKCvv37093bIUKFVi1ahUjRoygT5/9uLomMGECDxEWjUAw8FamLVxdXfnqq6/o06cPKSkpjBw5Mm1+IsCQIUMwGo0EBgZy7tw5SpYsSdeuXbMVFt98800SEhIIDAwkMjKS6tWr87///Q8PDw/WrVvHm2++yZtvvklqaiq1a9fm008/fdALzRHHko60mdKGFX1XsHvObur2rpsv/YqISOFU5LbOuL3seZUqVWjevDmNGjWiQ4cOdO/eHWtra06cOMHFixd55513CA8PJzExkYSEBLp06cK8efPSbkNNSkpKm5fTsmVLevXqRZ8+fRg3ripHjx7h1krvnDwJFStCUhL8/DP06GG6/fS2lBRo1gxWrTLdhnrkCCxYABDEhX++ZWjwUA7sOUDn1M6UqFaCa92v8cMPPxAVFUWvXr346KOPsLa2TlcDwHvvvceZM2eYM2cOABs2bKBfv34cPXo0vz5qEZE81BlYxv23JcqMAegE/JSrFRUVxlQjs5vPJvJgJAMPDsTB08HcJYmIiBndb+uMIruaiI+PDxUrVmTVqlV07px+8+UePXrQsWNHTp8+TXR0NP369eN+oTk9u0zf8fY2BceoqDtfsbGmoHjbnTt+SlC6dmmqP1Md/87+tPq4FS1GtWDs2LHs37+fzZs3ExISwty5c3N03SIiRcO7mPanzTmj0Z6U5JG5W04RYrAwEPRNEAnRCax/K//nToqISOFRZMMiwKxZs/jtt99wdHRM93psbCzu7u7Y2dnx999/s3DhwhyctRSZfWwNGoCzM0yeDDdumEYVw8NNt6emZ49pLo2Jla0VTd9uylXvq+zdu5eUlBRcXFywtrZOtwqfiEjx8TgwFcjpqJcD699qy0e2a1n0zCJ2z9lN3MW4PKivcCtZoySNRjRi95zdnAw9ae5yRESkgCrSSaRSpUrUr3/viOpXX33F+++/j7OzM+PHj+eFF3Iyzy/zCTSWlhASArt3m0YYPT2hTx+IvmcV+NtzadK7cOECzz//PC4uLvj7+9OiRYs8X05dRKTgeoM7gTGrhVgMt9pN5fKB5zCmGjn8y2GW917OtDLT+N9L/8vrYgudFmNa4FbRjZX9VpKckGzuckREpAAqcnMW84fm0oiI5J/tmPanXYXpd+jd+9zaYzQaOba+GgbDu1Rq05Wz284ys8HMdGeo+2pdOs7smH8lFxJHVh9hYfuFPPHhEzR/r7m5yxERETMolnMW89aDz6UxHfduLtYiIlLU1cf0H2yngA+AF4GgW98/4FL4Nha07cT8tgfZPn07ZeuXxaOaR9rRljaWPD7gcXMUXuBVaVeFR7o8wh8T/uDq0avmLkdERAoYhcUH8uBzaUzHZRjcRUTkvrwwbYUxF1hx6/tbpCS4m942wso3VhLyegh1XjZNGShVuxT27vbMbTWXM3+dMU/ZBdxT//cUVrZWrOy/MgeLvYmISHGgsPjAHmwujek4ERHJLUk3ktI93/ntTi6GX6T1lNa8/PvLvLrlVRw8HJjbai7H1h8zU5UFl3NZZ5786EmOrz9O+OJwc5cjIiIFiMLiQ3kDCMU0B9GOe29Ntb/1eqdb7RQURURyW1J8+rDo4OlAtY7VaPJWE+xL2OPm60bvsN64V3FnYYeF7F+630yVFlz136hP2fplWTtsLTejbpq7HBERKSAUFh/a/efSmF7/Cd16KiKSN2wcbQBw9XEFoOGwhtTsWjNdG6dSTgRvDKZcg3Is7bqUnTN35nudBZmFpQVB3wQRfzmeDe9uMHc5IiJSQGg1VBERKdSMRiNXj17FvZI789vO5/L+yww5MQRLG8t72ibFJ7Hk+SUcXX2U1pNb02RkEzNUXHCtGbqGrZ9v5dXNr1K+YXlzlyMiIvlAq6GKiEiRZTAY8KjigcHCQKMRjYg9F8veRXszbGvtYE23Zd2o2a0mG97ewIZ3NmhRl7s88eETOJd1JuT1EFKSUsxdjoiImJnCooiIFBmVAitR8tGSbJm6JdMQaGljSaf5najXrx6bJm8ipF8IqSmp+VxpwWTrbEu7z9txcc9Ftv5nq7nLERERM1NYFBGRIsNgMNB4RGMuhV/i2NrMVz61sLSgw1cdaDa6GTtn7OSn7j+RkqiRNIDqnapTNagqG8duJCoiytzliIiIGSksiohIkVKzW02cyzmz+ZPN921nMBh4csKTtJnahv0/7mfR04tIvJ6YT1UWXAaDgXZftgNg9aDVuk1XRKQYU1gUEZEixdLGkoAhAZz47QTnd57Psn3j4Y3pOKsjxzccZ16bedy4diMfqizY3HzcaDGuBYdXHObgsoPmLkdERMxEYVFERIqceq/Vw8bZhs1T7z+6eFvdV+rS5ccunN9xnjkt5hB7PjaPKyz4Gg5tSMlHS7Jm8BoSYhPMXY6IiJiBwqKIiBQ5dq521HutHvuW7Mv2vDv/zv70WNWDa8evMbvpbK4dv5bHVRZsltaWBH0TRMzZGDaO3WjuckRExAwUFkVEpEgKGBKAwWDI0aqefq38eOnXl7gZdZPvmn7HpfBLeVhhwefdyJt6r9Vj63+2cn5X1rf0iohI0aKwKCIiRZKrtys1utZg57c7uRl1M9vHlQ8oT/AfwRgMBmY3n82Zv87kYZUFX6tJrXDwdCDkdW0xIiJS3CgsiohIkdV4RGMS4xLZ/s32HB1XskZJXtn0Cvbu9sxtNZdj6zPfhqOosy9hT9vP2nJu2zm2T8/Z5ygiIoWbwqKIiBRZpeuUxq+1H1v/s5XkhOQcHevm68YrYa/gXtmdhR0Wsn/p/jyqsuCr2b0mfq39+G3Ub8Se0+I/IiLFhcKiiIgUaY3fakzc+TjCF4Xn+Fin0k4EhwZTrkE5lnZdys6ZO/OgwoLPYDDQ4esOJCcks3bYWnOXIyIi+URhUUREijS/Nn6UqlWKzVM3P9AG83Zudry47kUqBVZiRd8VbJqyKQ+qLPjcK7vTbHQz9i3Zx9E1R81djoiI5AOFRRERKdIMBgONRjTi8r7LDxxyrB2s6ba8GzW61mDD2xvY8M6GBwqehV2TkU3wqObByv4rSYpPMnc5IiKSxxQWRUSkyKvZtSbO5ZzZ/MnmBz6HpY0lnRd0pl6/emyavKlYrg5qZWtF0PQgok5E8ceEP8xdjoiI5DGFRRERKfIsbSwJGBLAyd9Pcm7HuQc+j4WlBR2+6kDTUU3Z+e1Ofur+U44XzinsfFv6Uvvl2mz+ZDOX9hXvfShFRIo6hUURESkW6r1WDxtnG7ZM3fJQ5zEYDLT6qBVtprZh/4/7WdxxMYnXE3OpysKhzSdtsHWxZWW/lRhTi9/tuCIixYXV37noAAAgAElEQVTCooiIFAt2rnbUe60e+37cR9TJqIc+X+Phjek4qyPHNxxnXpt53Lh2IxeqLBwcvRxpPaU1p8JOsWv2LnOXIyIieURhUUREio2AIQEYDAb++s9fuXK+uq/UpcuPXTi/4zxzWswh9nzx2YOwbu+6VGhagQ0jN3D98nVzlyMiInlAYVFERIoNV29Xanaryc5vd+baSKB/Z396rOzBtePXmN10NteOX8uV8xZ0BgsDHaZ3ICEmgfUj1pu7HBERyQMKiyIiUqw0GtGIpOtJ7PhmR66d06+1Hy/9+hI3o27yXdPvuBRePBZ+KVmjJI3fasw/c//hxO8nzF2OiIjkMoVFEREpVkrXLo1fGz+2/mdrrq5kWj6gPMF/BGMwGJjdfDZn/jqTa+cuyJq/1xy3im6s7Ley2K0MKyJS1CksiohIsdN4RGPiLsSxd+HeXD1vyRol6R3WG3t3e+a2msux9cdy9fwFkbWDNR2+6sCVw1fYNHmTucsREZFcpLAoIiLFjl8bP0rVKsWWqVtyfeuHEhVL8ErYK7hXdmdhh4XsX7o/V89fEFV+qjI1XqjBnxP/5MqRK+YuR0REconCooiIFDsGg4FGIxpxef9ljq45muvndyrtRHBoMOUalGNp16XsnLkz1/soaNr+X1usbK1Y1X8VRqP2XhQRKQoUFkVEpFiq2a0mzuWc2Tx1c56c387NjhfXvUilwEqs6LuCTZ8U7Vs0ncs48+TEJzm+4Tjhi8LNXY6IiOQChUURESmWLK0taTi0ISd/P8m5HefypA9rB2u6Le9Gja412DByAxve3VCkR93q96tP2cfLsnbY2lzbmkRERMxHYVFERIqteq/Vw9bFli1Tt+RZH5Y2lnRe0Jl6/eqx6eNNhPQLITUlNc/6MycLSwuCvgkiPjKeX9/91dzliIjIQ1JYFBGRYsvWxZbHXnuMfT/uI+pkVJ71Y2FpQYevOtB0VFN2ztjJT91/IiUxJc/6M6cydcsQMCSAHd/s4PSW0+YuR0REHoLCooiIFGsBgwMwGAz89X9/5Wk/BoOBVh+1os3UNuz/cT+LOi4i8XpinvZpLk+MfwKX8i6EvB5CSlLRDMUiIsWBwqKIiBRrrt6u1Oxek50zd+bLPLvGwxvTcVZHjq8/zrw284rk3D4bJxvafdGOS3sv5XkIFxGRvKOwKCIixV6j4Y1Iup7E9unb86W/uq/UpcuPXTi/4zxzWswh9nxsvvSbn6o/W51qHasROi6UqIi8u8VXRETyjsKiiIgUe6Vrl6ZSYCX+/vxvkhOS86VP/87+9FjZg2vHrzG76WyuHb+WL/3mp3ZftAMDrB64ukivAisiUlQpLIqIiACNRjQi7kIcexfszbc+/Vr78dKvL3Ez6ibfNf2OS+GX8q3v/OBawZWWH7TkcMhhDv7voLnLERGRHFJYFBERwRTcStUuxeapmzGm5t8oWPmA8gT/EYzBYGB289mc+etMvvWdHwIGB1CqVilWD15NQmyCucsREZEcUFgUERHBtFpp4xGNiTwQyZHVR/K175I1StI7rDf27vbMbT2XY+uP5Wv/ecnS2pKgb4KIPRfL72N+N3c5IiKSAwqLIiIit9ToWgOX8i5smbol3/suUbEEr4S9gnsldxZ2WMj+pfvzvYa8Ur5heeq9Xo+/v/ib8zvPm7scERHJJoVFERGRWyytLQkYGsDJjSc5t/1cvvfvVNqJ4NBgyjUox9KuS9k5a2e+15BXWk9qjYOXAyGvh5CakmruckREJBsUFkVERO5Sr289bF1s2Tx1s1n6t3Oz48V1L1IpsBIr+qxg0yebMmh1CZgC9AKevvV9CnA5HyvNGTs3O9p+1pZz28+x7att5i5HRESyQWFRRETkLrYuttR7vR77f9xP1Enz7A9o7WBNt+XdqNG1BhtGbmDDuxtubT2xDegM+ABjgQVAyK3v44AKt94vmGGsZrea+LXx47fRvxFzNsbc5YiISBYUFkVERP4lYHAABgsDWz7L/7mLt1naWNJ5QWfq9avHpo83cXn/h0BLYBlw89bX3W7cem3ZrXZf52O12WMwGOjwVQdSElNYO2ytucsREZEsKCyKiIj8i0t5F2p2r8muWbu4cfWG2eqwsLSgw1cd6H/AAq9HJgPxQFbbehhvtRtBQQyM7pXdaf5ec/b/uD/fV50VEZGcUVgUERHJQOMRjUm6nsT26dvNVkNYWBhNmtSmcsD7eHjE06QJbMv2Haa3A6P56s9M47ca41ndk1X9V5EUn2TuckREJBMKiyIiIhkoVasUlQIrsfXzrSQnJOd7/zExMQQFBTFokC1Xr8LZszB2LNja5uQsN4BJeVThg7OytaLD9A5EnYwi9MNQc5cjIiKZUFgUERHJROO3GnP94nX2Ltib730fPnwYMNK9eziWlmBvD4GBUKuW6f3vvgN/fyhRAtq2hYgI0+uTJ0NAACQnAxj5+utfqFGjOjdv/nuOo3n5tvClTnAdtkzdwqXwS+YuR0REMqCwKCIikomKrSpSuk5pNk/djDE1q7mCuatq1apYWibz8suJrF4N167deW/5cpg4EX7+GS5fhmbNoHt303tvvWUafZwwAY4cgVGjkpk//yns7Ozytf7saPNJG2xdbQnpF5Lvn6+IiGRNYVFERCQTBoOBRiMaEXkgMt8XY3FxcSEs7EkMhlT69gUvL+jYES5ehOnT4d13TSOLVlYwahTs3m0aXbSwgLlz4fPPTe1HjoS6da/ma+3Z5eDpQJtP2nB602l2fbfL3OWIiMi/KCyKiIjcR40XauBS3oXNn2zO9779/WHOHDhzBsLD4dw5GDrUFAqHDAE3N9OXuzsYjaZ5jQC+vvDEE3DyJAwYAHAt0z7MrU5wHXya+7B+5HquX7pu7nJEROQuCosiIiL3YWltScDQACJCIzi77Ww+9+6a9qh6dQgONoVGb2/45huIirrzdeMGNG5sartyJWzZAq1amW5LhRL5XHf2GQwGOkzvQGJcIutGrDN3OSIicheFRRERkSzU61sPWxdbdn67E7gETAF6AU/f+j4FuJyrfR48eJBp06I4c8a0/Onp07BoETRsCP36waRJsG+fqW10NPz4o+lxZCT06QMzZ8L338OKFbBqlVWu1pbbvPy9aDKyCXvm7eHEbyfMXY6IiNyisCgiIpIFWxdbeq6uypMTvgR8gLHAAiDk1vdxQAWgM5DtjRDvy9nZma1bLQkISMDR0RQSa9aEadOgUyd4+23o1g1cXEyvr15tOu611+CZZ6B9e/DwgFmzrOnTZxVXrlzJlbrySrPRzShRqQQr31hplq1KRETkXgajMfPVx+rXr2/cvr3gbeYrIiKSv77GtMH9DeB+q3YaAHtgKvBGLvXdGViWRb/3q6cT8FMu1ZK3jq49yoKnFtBiXAtajm1p7nJERIoFg8Gww2g01s/oPY0sioiIWW3atIkqVarg5OTEsmXLuHjxIs2bN8fZ2Znhw4fn+HynTp3CycmJlJSUXKrwdlCM58YNI08/Da6u0KVLRm2NQPyt9l/nUv/vYgqgD8IeX98tbNiwIcN3//zzT6pVq/bAlWVHy5YtmTlzZrbaVm5bmRpdaxA2MYwrh3M+ErpgwQICAwNzfJyIiGRMYVFEpADw9fXF3t4eJycnSpUqRXBwMHFxcfleh8Fg4OjRo/na5/vvv8/AgQOJi4vj2WefZcaMGXh6ehITE8O0adNyfL4KFSoQFxeHpaVlLlS3jdtBEWDpUtPWFVeu3JkjmLHbgTE37s55HNNIpUMOj3O4dZxNpi2aNWvGoUOHHry0PND2s7ZY2Vmx8o2V3O/up4z07NmTdeu0SI6ISG5RWBQRKSBWrFhBXFwcO3fuZPv27UyYMCFHxxuNRlJTU/OourwTERFBjRo10j1/5JFHMBgMOT5XcnJuz3WbhOnWU5OICKha1bS3Ycb93/3sxq3jc8Mb3AmMWX0uBu4Exdy6FTZ/JCcn41zGmVaTWnHitxPsXbDX3CWJiBRrCosiIgVMuXLlaNeuHeHh4Vy7do2goCC8vLwoUaIEQUFBnDlzJq1ty5YtGT16NE2aNMHBwYHjx49z8OBB2rRpg7u7O9WqVWPJkiVp7YODgxkwYAAdOnTA2dmZgIAAjh07BkDz5s0BqF27Nk5OTvzwww9ERkYSFBSEm5sb7u7uNGvWLMNAajQaGTZsGCVLlsTFxYVHH32U8PDwtBrvvg1xzpw5NG3aFIBKlSpx/Phxnn76aZycnOjevTvff/89U6ZMwcnJiQ0bNpCamsrHH39MpUqV8PDw4IUXXuDqVdMm8ydPnsRgMDBr1iwqVKjAk08+mfba7eDYsmVLxowZQ5MmTXB2diYwMJDIyMi0eubOnYuPjw8eHh58+OGH+Pr63rpt8xKwmttzBceOhfHj4YcfwMkJZs0y7YHYpAkMG2ZaTGbcONPKpC+9BF5eRnx8fmbChHfTPrM5c+bQpEkThg0bhpubG35+fmzevJk5c+bg7e1NyZIl+f777zP5k/EGc+aMwM/PHmdnqFjRwIIFd9799ltr/P0NODtb8sgjpdi5MyDtvd27d1OrVi1cXV3p2rUrN2/eBGDjxo2UL18egB9++AEnJ6e0L1tbW1q2bAlAdHQ0L730El5eXvj4+DBhwoR7rmngwIG4urpSvXp1fv3113SVR0REZPj5Z/TzA/jH5h+m202n4UsNafNkGyIiItLOZTAYmD59OlWqVMHNzY0BAwakjUDe/WcLYMiQIXh7e+Pi4kK9evX4888/M/lsRUQkIwqLIiIFzOnTp1m1ahV169YlNTWV3r17ExERwalTp7C3t2fgwIHp2s+bN48ZM2YQGxuLl5cXbdq0oUePHly6dInFixfTv39/9u/fn9Z+8eLFjB07lmvXrlG5cmVGjx4NwB9//AHAP//8Q1xcHF27dmXatGmUL1+ey5cvc/HiRSZOnJjhiN+6dev4448/OHz4MNHR0SxZsgQPD48sr/XYsWNUqFAhbVR10aJF9OzZk5EjRxIXF0fr1q354osvWLZsGaGhoZw7d44SJUowwLTTfJrQ0FAOHDjA2rVrM+xn4cKFzJ49m0uXLpGYmMjUqVMB2L9/P/3792fBggWcP3+e6Ohozt7e2Z456c7xwQcwahR07QpxcfDqq6bXt24FPz/T7amjR8OgQabAePw4hIbaMnfuTGbPnp12nq1bt1KrVi2uXLlCjx496NatG9u2bePo0aPMnz8/7Zbcf7t+/TqDB3/G6tU7iY29xObNo6lT52kgiB9/bM64cfbMnbuGmJhEfvllXbrPf8mSJaxZs4YTJ06wZ88e5syZc8/5u3btSlxcHHFxcZw7dw4/Pz+6d+8OwKBBg4iOjub48eOEhoYyd+7ce66pUqVKREZG8sEHH9C5c+e0QH+/zz+jn9/y5cv5+OOP+WHBD7xleItSMaXS6rgtJCSEbdu2sWfPHpYsWZLpz/3xxx9n9+7dXL16lR49etClS5e0oCwiIllTWBQRKSCeffZZ3NzcaNq0KS1atGDUqFF4eHjw3HPP4eDggLOzM6NHjyY0NDTdccHBwdSoUQMrKyvWrFmDr68vvXv3xsrKirp16/Lcc8/x410T7Dp16kSDBg2wsrKiZ8+e7N69O9OarK2tOX/+PBEREVhbW9OsWbMMw6K1tTWxsbEcPHgQo9GIv78/ZcqUyZXPZfr06Xz00UeUL18eW1tbxo0bx9KlS9Pdcjpu3DgcHR2xt894IZjevXtTtWpV7O3teeGFF9KueenSpTz99NM0bdoUGxsbxo8ff9f17QGyDhZly5oCopUV2NjA4sWmPRCdncHXN4Hhwysxb968tPYVK1akd+/eWFpa0rVrV06fPs3777+Pra0tgYGB2NjYZDpv1MLCgvDwcG7ccKJMmQ+pUeMXYAUzZ9oxcuR4Hn88EIPBQOXKlfHx8Uk7bvDgwZQtWxZ3d3eefvrp+/7MU1NT6dGjBy1btuT1118nJSWFxYsXM2nSJJydnfH19WX48OHprqlkyZIMHToUa2trunbtSrVq1Vi5cmWWn/9td//8pk+fzrvvvkvzzs1pPLQxlXZUYtfOXelGF9955x3c3NyoUKECTzzxRKbX06tXLzw8PLCysmL48OEkJCQUuDmaIiIFmcKiiEgBsWzZMqKiooiIiOCrr77C3t6e+Ph4Xn/9dXx8fHBxcaF58+ZERUWlW+nT29s77XFERARbt27Fzc0t7WvBggVcuHAhrU3p0qXTHjs4ONx3IZ233nqLypUrExgYiJ+fHx9//HGG7Z588kkGDhzIgAEDKFmyJK+99hoxMTEP83Gku6ZOnTqlXY+/vz+WlpZcvHgxrc3dn0FGMrvmc+fOpTvWwcHhrhG56GzVd3fXkZGQlAR35TR8fIx3jVZCqVKl0h7fDrf/fi2jn4mjoyM//PAD06dPp0yZMnTo0IGDBw8CptHoSpUqZVpjTn7mo0ePJjY2ls8///zWNUWSlJSULnz6+Piku6Zy5cql+08EHx8fzp07l+3+//1neMiQIbi5udF1VlemGKaQkpjCqYhTOb6eqVOn4u/vj6urK25ubkRHR6e7BVlERO5PYVFEpACbNm0ahw4dYuvWrcTExKTdKnr3KpF3/yPd29ubFi1aEBUVlfYVFxfH118/2DYOzs7OTJs2jePHj/PLL7/w6aef3jMf7bbBgwezY8cO9u/fz+HDh/nkk08AU8iJj49Pa3d3cM0Ob29vVq9ene6abt68Sbly5dLaPMhiOABlypRJNwf0xo0bd21e75qtc9zdtacnWFubFsK57dQpQ7paH0bbtm1Zv34958+fp3r16vTt2xcwfUa3554+jMWLF7No0SKWLl2KtbU1AJ6enlhbW6cb2Tt16lS6azp79my6P5OnTp2ibNmy2e7333+Gv/nmm7Sf9a7/7WK0cTQWf+Xsnyx//vknU6ZMYcmSJVy7do2oqChcXV1zvMKqiEhxprAoIlKAxcbGYm9vj5ubG1evXuWDDz64b/ugoCAOHz7MvHnzSEpKIikpiW3btnHgwIFs9VeqVCmOHz+e9jwkJISjR49iNBpxdXXF0tISC4t7/+rYtm0bW7duJSkpCUdHR+zs7NLa1alTh59//pn4+HiOHj3KrFmzcvAJQL9+/Rg9enRaWLl8+TLLly/P0Tky8/zzz7NixQo2b95MYmIi48aNuytM1ALscnQ+S0t44QXT3MXYWIiIsOXTT4/Tq1evh6714sWLLF++nOvXr2Nra4uTk1PaZ9ynTx+mTp3Kjh07MBqNHD16NF24y45du3YxaNAgli1bhpeX113XZMkLL7yQNuIYERHBp59+mu6aLl26xOeff05SUhI//vgjBw4coH379g90nf369WPSpEns27cPgDIty3Ct/jU2jttI1MmobJ8nNjYWKysrvLy8SE5OZvz48bk22i0iUlwoLIqIFGBDhw7lxo0beHp60rBhQ5566qn7tnd2dmbdunUsXryYsmXLUrp0ad5++20SEhKy1d+4ceN4+eWXcXNzY8mSJRw5coTWrVvj5OREo0aN6N+/P0888cQ9x8XExNC3b19KlCiRtrLoW2+9BcCwYcOwsbGhVKlSvPzyy/Ts2TNHn8GQIUPo2LEjgYGBODs707BhQ7Zu3Zqjc2SmRo0afPHFF3Tr1o0yZcrg5OREyZIlsbW1BYIf6JxffAGOjqZFb5o2TaBHj9688sorD11ramoqn376adrcw9DQ0LQR4y5dujB69Gh69OiBs7Mzzz77bLoFZrJj+fLlXLt2jaZNm6atiNquXbtb1/QFjo6O+Pn50bRpU3r06JHumgICAjhy5Aienp6MHj2apUuXZmuBo4x06tSJt99+m27duuHi4kLNmjWJ9IvEYGFg1cBV2T5P27Zteeqpp6hatSo+Pj7Y2dllebuyiIikZ7jf7Rj169c3bt+eGxsKi4iIFHxxcXG4ublx5MgRKlasCHQGlnF7+4ycMQCdgJ9ys8QCZ86cOcycOZOwsLA87WfLp1tYN3wdXZZ24ZHnHsnTvkREihODwbDDaDTWz+g9jSyKiEixtmLFCuLj47l+/TojRozg0UcfxdfX99a77wIZr7CaNftbx0tuCBgcQOk6pVkzeA0JMdkbKRcRkYejsCgiIsXa8uXLKVu2LGXLluXIkSMsXrz4rgVXHgemAg45PKvDreMy/I9aeQAWVhYEfRNE7PlYfhvzm7nLEREpFnQbqoiISJa+BkYAN7j/LakGTCOKU4E38qGu4mflgJXsmL6DPn/3oWy97K+4KiIiGdNtqCIiIg/lDSAU0xxEO+69NdX+1uudbrVTUMwrrSa2wrGkIyGvh5CakmruckREijSFRRERkWypj2mxmlPAB8CLQNCt7x/cev0ndOtp3rJztaPt/7Xl/I7zbPvvNnOXIyJSpFmZuwAREZHCxQt4y9xFFGs1XqjB7tm7+e293/B/zh+Xci7mLklEpEjSyKKIiIgUKgaDgfb/bU9qUiprhqwxdzkiIkWWwqKIiIgUOu6V3Gn2XjMO/HSAwysPm7scEZEiSWFRRERECqUmbzXB09+TVQNWkXg90dzliIgUOQqLIiIiUihZ2lgSND2I6IhoQseHmrscEZEiR2FRRERECi2f5j7U6V2Hvz79i4t7L5q7HBGRIkVhUURERAq1NlPaYOtqS8jrIRhTjeYuR0SkyFBYFBERkULNwdOBwKmBnNlyhp0zd5q7HBGRIkNhUURERAq92i/XxqeFDxve3sD1S9fNXY6ISJGgsCgiIiKFnsFgIGh6EInXE1k3fJ25yxERKRIUFkVERKRI8KzuSZO3m7Bn/h6O/3rc3OWIiBR6CosiIiJSZDQb1YwSlUqw8o2VJN9MNnc5IiKFmsKiiIiIFBnW9tZ0+LoDV49cJezjMHOXIyJSqCksioiISJFSqU0lanavSdikMCIPRZq7HBGRQkthUURERIqctp+2xcreilX9V2E0au9FEZEHobAoIiIiRY5TaSdaf9yaE7+dYM/8PeYuR0SkUFJYFBERkSKp3mv1KN+wPOuGr+PG1RvmLkdEpNBRWBQREZEiyWBhIOibIG5cvcH6t9ebuxwRkUJHYVFERESKrFK1StFwWEN2zdzFqbBT5i5HRKRQUVgUERGRIq3l2Ja4eLsQ0i+ElMQUc5cjIlJoKCyKiIhIkWbjZEP7L9tzed9ltny6xdzliIgUGgqLIiIiUuRV61iN6s9WJ3R8KNdOXDN3OSIihYLCooiIiBQLT33+FBaWFqwaoL0XRUSyQ2FRREREigVXb1dajm/J0dVH2b90v7nLEREp8BQWRUREpNgIGBRA6TqlWTNkDTejb5q7HBGRAk1hUURERIoNCysLgr4JIu5CHL+P+d3c5YiIFGgKiyIiIlKslGtQjsf7P87fX/7Nue3nzF2OiEiBpbAoIiIixc6THz2JU2knQl4PITU51dzliIgUSAqLIiIiUuzYudrx1P89xfmd5/n7v3+buxwRkQJJYVFERESKpUe6PELlpyrz+3u/E3MmxtzliIgUOAqLIiIiUiwZDAba/7c9qcmprBmyxtzliIgUOAqLIiIiUmyV8CtB8/ebc+D/27v3sKjrRI/jnwEEuYoIkgjeMqM009REMy+EF2TWzd2eLkfbbE8eSXNTuxzTLrhdNV233PXSbnuoczZdT2efLiAqmhey1HTDskRNE0lSUREYUWBgzh8YXgYNcOA7wPv1PD7azG9mPqP/9H5+w/z+uUf7UvaZngMAboVYBAAAzdrAJwYq7OYwrZqySqVnSk3PAQC3QSwCAIBmzdPbU9ZlVhUcLtCmOZtMzwEAt0EsAgCAZq/DoA7q/e+99fkfPtexr46ZngMAboFYBAAAkBQ3N06+rX2VMilFjgqH6TkAYByxCAAAIMmvjZ9GLBihH7b+oJ1/2Wl6DgAYRywCAACc1/PBnuo0tJPWz1wv2zGb6TkAYBSxCAAAcJ7FYlHCkgSVninV2hlrTc8BAKOIRQAAgIuERodq0MxB+vq9r3Ug/YDpOQBgDLEIAABwmTtn3amQriFaNXmV7OfspucAgBHEIgAAwGW8WnopYUmCTn13ShmvZJieAwBGEIsAAADV6BLXRbf82y369LVPdSLrhOk5ANDgiEUAAIArGPGHEfL291bqo6lyOLj2IoDmhVgEAAC4goDwAN312l06tPGQvvrvr0zPAYAGRSwCAABcRZ+JfRQ5IFJrn1ir4pPFpucAQIMhFgEAAK7C4mGRdZlVZ/PPat1/rjM9BwAaDLEIAADwM8JvCdeAGQP05dtfKjsj2/QcAGgQxCIAAEANDHlhiFp1bKXUxFSVl5abngMA9Y5YBAAAqAFvf2+N/tNo5X2bp88WfGZ6DgDUO2IRAACghrpZu+mmX92kzb/frPyD+abnAEC9IhYBAABqYdQbo+Th5aFVU1Zx7UUATRqxCAAAUAtBkUEa9tIwfbf6O337v9+angMA9YZYBAAAqKXbp9yudre10+rHV+tcwTnTcwCgXhCLAAAAteTh5aGEpQmyHbPpk9mfmJ4DAPWCWAQAAKiD9v3aq9+Ufvpi8Rc6sv2I6TkA4HLEIgAAQB3FvhSrgOsClDIpRRX2CtNzAMCliEUAAIA6atmqpUa9MUpHM49q26JtpucAgEsRiwAAANfg5ntuVtf4rtrw3AYV5BSYngMALkMsAgAAXAOLxaLRfx4tR4VDq3+32vQcAHAZYhEAAOAate7cWkOeH6KsD7K096O9pucAgEsQiwAAAC4w4IkBCuseprSpaSo9U2p6DgBcM2IRAADABTxbeMq6zKqCwwXamLTR9BwAuGbEIgAAgIt0uKODej/SW1sXbtXRXUdNzwGAa0IsAgAAuNDwucPlG+Kr1MRUOSocpucAQJ0RiwAAAC7kG+KrEQtG6IetP2jnWztNzwGAOiMWAQAAXKzn+J7qHNtZ62auk+2ozfQcAKgTYhEAAMDFLBaLRkAU88cAABfJSURBVC8eLftZu9bMWGN6DgDUCbEIAABQD0JvDNWgZwZp9/LdOrD2gOk5AFBrxCIAAEA9GTRzkEJuCFHq5FSVnS0zPQcAaoVYBAAAqCdeLb1kXWpV/oF8ZbySYXoOANQKsQgAAFCPOsd2Vs/xPbVl7hbl7ckzPQcAaoxYBAAAqGfD5w+Xt7+3Uh9NlcPBtRcBNA7EIgAAQD0LCA9Q3Nw4ZW/K1q53dpmeAwA1QiwCAAA0gNseuU1RA6O09sm1Kj5RbHoOAPwsYhEAAKABWDwsSliaoJKCEqU/nW56DgD8LGIRAACggYTfEq6YGTHK/K9MZW/ONj0HAK6KWAQAAGhAQ54folYdWyklMUXlpeWm5wDAFRGLAAAADcjb31uj/zxaJ/ac0GfzPzM9BwCuiFgEAABoYN0SuummX9+kzS9u1qkDp0zPAYBqEYsAAAAGjHpjlDxaeGjVlFVcexGAWyIWAQAADAhqH6TYl2J1YM0BfbPyG9NzAMAJsQgAAGBIvyn91K5PO62ZtkbnTp8zPQcALkEsAgAAGOLh6SHrMqvOHD+j9bPXm54DAJcgFgEAAAyK6BOhfo/1044lO3Rk+xHTcwCgCrEIAABgWOyLsQpsF6iUSSmqsFeYngMAkohFAAAA43yCfDTqzVE6mnlU297cZnoOAEgiFgEAANzCTb+6STck3KANz29QweEC03MAgFgEAABwBxaLRaP/NFqOCofSfpdmeg4AEIsAAADuIrhTsIYmDdXeD/cq68Ms03MANHPEIgAAgBuJmR6jtj3aKm1qmkptpabnAGjGiEUAAAA34tnCU9ZlVhXmFGrDCxtMzwHQjBGLAAAAbiZqYJRum3ibtr2xTUczj5qeA6CZIhYBAADcUNxrcfIN8a289mI5114E0PCIRQAAADfkG+KrkX8YqSPbj2jnWztNzwHQDBGLAAAAbuqWcbeo812dtf6Z9bIdtZmeA6CZIRYBAADclMViUcLiBNnP2rVm+hrTcwA0M8QiAACAG2vTrY0GzRqk3St268DaA6bnAGhGiEUAAAA3N2jmILXp1kapj6aq7GyZ6TkAmgliEQAAwM15+XgpYWmC8g/mK+PlDNNzADQTxCIAAEAj0HlYZ/V8sKe2zNuivD15pucAaAaIRQAAgEZixPwR8g7wVmpiqhwOh+k5AJo4YhEAAKCR8G/rr+Hzhit7c7YykzNNzwHQxBGLAAAAjUjv3/ZW1B1RSn8qXcUnik3PAdCEEYsAAACNiMXDIusyq0oKSpT+VLrpOQCaMGIRAACgkWnbva0GPDlAmcmZOrTpkOk5AJooYhEAAKARGvLcEAV3ClZqYqrsJXbTcwA0QcQiAABAI9TCr4VG/3m0TmSd0Gevf2Z6DoAmiFgEAABopG4YfYNuvudmbX5ps059d8r0HABNDLEIAADQiI16Y5Q8vT2VOplrLwJwLWIRAACgEQuMCFTsy7E6mH5Qu1fsNj0HQBNCLAIAADRy/Sb3U0TfCK2ZvkbnTp8zPQdAE0EsAgAANHIenh6yLrOqOK9Y62etNz0HQBNBLAIAADQB7W5rp9un3q4dS3foh20/mJ4DoAkgFgEAAJqIYS8OU2BEoFImpajCXmF6DoBGjlgEAABoInwCfRT/ZryO7TqmrW9sNT0HQCNHLAIAADQh0WOj1c3aTRuf36iCwwWm5wBoxIhFAACAJsRisSj+T/GSpLSpaYbXAGjMiEUAAIAmJrhjsIYkDdHej/Yq64Ms03MANFLEIgAAQBMUMy1GbW9pq7SpaSopKjE9B0AjRCwCAAA0QZ4tPGVdZlXhkUJtfGGj6TkAGiFiEQAAoImKGhClPv/RR9ve2KYfv/zR9BwAjQyxCAAA0ITd9epd8gv1q7z2YjnXXgRQc8QiAABAE+bb2lcjF45U7he52rF0h+k5ABoRYhEAAKCJ6/FAD3WJ66JPZn2iotwi03MANBLEIgAAQBNnsVg0evFo2UvsWjN9jek5ABoJYhEAAKAZaHNDG9056059s/Ibfbf6O9NzADQCxCIAAEAzccd/3qE2N7ZR6uRUlRWXmZ4DwM0RiwAAAM2El4+XEpYk6PT3p7X55c2m5wBwc8QiAABAM9J5WGfd+ptb9dnrnynv2zzTcwC4MWIRAACgmRk+f7h8An2UkpgiR4XD9BwAbopYBAAAaGb8w/wVNy9OhzMOKzM50/QcAG6KWAQAAGiGej/cWx0GdVD6U+k6k3fG9BwAbohYBAAAaIYsHhYlLE1QSWGJ0p9KNz0HgBsiFgEAAJqptt3bauBTA7XrnV06tPGQ6TkA3AyxCAAA0IwNfnawgjsHKyUxRfYSu+k5ANwIsQgAANCMtfBroYTFCTq596S2zNtieg4AN0IsAgAANHNdR3VV93u7K+PlDJ3cf9L0HABuglgEAACARv5xpLx8vLRq8io5HFx7EQCxCAAAAEmB7QIV+0qsDq47qN3Ld5ueA8ANEIsAAACQJPVN7KuIfhFaM32NzuafNT0HgGHEIgAAACRJHp4esi6zqvhEsdY/s970HACGEYsAAACo0q53O/V/vL92LtupnM9zTM8BYBCxCAAAgEsMnTNUQZFBSpmUovKyctNzABhCLAIAAOASPoE+GvXmKB3/+ri2/nGr6TkADCEWAQAA4CT67mh1+0U3bUrapNPZp03PAWAAsQgAAAAnFotF8YviJUlpU9O49iLQDBGLAAAAqFZwx2ANnTNU+z7ep6wPskzPAdDAiEUAAABcUf/H+yu8Z7jSpqappKjE9BwADYhYBAAAwBV5tvCUdZlVRblF2vD8BtNzADQgYhEAAABXFRkTqT6T+mj7m9v1479+ND0HQAMhFgEAAPCz4l6Nk1+Yn1ImpaiivML0HAANgFgEAADAz2oZ3FIjF45U7o5c7Viyw/QcAA2AWAQAAECN9Li/h7oM76L1s9arKLfI9BwA9YxYBAAAQI1YLBYlLE5QeWm5Vk9bbXoOgHpGLAIAAKDGQrqGaPCzg/Xt/36r/Wn7Tc8BUI+IRQAAANTKwKcGKjQ6VKsmr1JZcZnpOQDqCbEIAACAWvHy8VLC0gSdPnRam17cZHoOgHpCLAIAAKDWOg3ppF4Teunz+Z/r+O7jpucAqAfEIgAAAOpk+OvD5dPKRymJKXJUOEzPAeBixCIAAADqxC/UT8NfH66cLTn68m9fmp4DwMWIRQAAANRZrwm91OHODkp/Ol1n8s6YngPAhYhFAAAA1JnFYpF1qVWltlKlP5lueg4AFyIWAQAAcE3Cbg7TwKcGate7u/T9hu9NzwHgIsQiAAAArtngZwerdZfWSk1Mlb3EbnoOABcgFgEAAHDNWvi20OjFo3Vy30ltmbvF9BwALkAsAgAAwCW6juyq7vd1V8YrGTq5/6TpOQCuEbEIAAAAlxm5cKS8fLyU+miqHA6uvQg0ZsQiAAAAXCawXaDuevUufb/+e3393tem5wC4BsQiAAAAXKrPpD5qf3t7rZ2xVmfzz5qeA6COiEUAAAC4lIenh6zLrCo+Wax1M9eZngOgjohFAAAAuNx1va5T/8f7619v/Us5n+WYngOgDohFAAAA1Ithc4YpKCpIKZNSVF5WbnoOgFoiFgEAAFAvvAO8Fb8oXsd3H9fWhVtNzwFQS8QiAAAA6k30L6N14y9v1MakjTp96LTpOQBqgVgEAABAvYpfFC+Lh0WrHlvFtReBRoRYBAAAQL1qFdVKw34/TPtT92vPP/eYngOghohFAAAA1Lv+v+uv8FvDtfp3q1VSWGJ6DoAaIBYBAABQ7zy8Kq+9WPRjkTY8v8H0HAA1QCwCAACgQUT2j1TfxL7avmi7fvzXj6bnAPgZxCIAAAAazF2v3CX/tv5KmZSiivIK03MAXAWxCAAAgAbTMrilRi4cqdwdufpi8Rem5wC4CmIRAAAADar7fd11/Yjr9cnsT5Sdka1/PvhPHUg/YHoWgMsQiwAAAGhQFotFIxeOlP2sXclDkvX1/3ytb1Z+Y3oWgMt4mR4AAACA5uXM8TNaPma5Kuznf2bRIp05esbsKABOOLMIAACABlV6plTFJ4ov3OCQCo8UmhsEoFrEIgAAABpU686tNXX/VN064daq207uP2lwEYDq8DFUAAAANDj/MH/d/V93q/fDvbX8l8tVZiu76N7jkpIlfSWpQFIrST0lPSwprMG3As2VxeFwXPHOvn37Onbs2NGAcwAAANDclJeV68zxMwpqv1fSq5LSzt9z7qKjfCU5JMVLekZSv4YdCTRRFotlp8Ph6FvdfXwMFQAAAEZ5tvBUUPu/Sxoq6QNVRuK5y446e/62D84ft6QBFwLNE7EIAADQhCUmJurFF1+s0bFDhw7VX//612rvS0pK0vjx4yVJhw8fVkBAgMrLyyVJx44d0+DBgxUYGKgnnniiDiuXSHpSUrEkhywW6bvvrnSs4/xxT8oVwXj5e6lvGRkZuvHGGxvktYBrRSwCAAC4iU8//VQDBw5Uq1atFBISojvuuENffPHFNT3n0qVL9dxzz7loYaUOHTrIZrPJ09NTkvTWW28pNDRUhYWFWrBgQS2f7QtdCMXaKNbGjdMVGRley8dd6vL34moWi0XfXVS+d955p/bu3VsvrwW4Gl9wAwAA4AYKCwtltVq1ZMkS3XvvvSotLVVGRoZ8fHxMT/tZ2dnZuvnmm2WxWOrw6FdV+RHTuiiRVPdLbtjtdnl51f1/h6/18YC748wiAACAG9i3b58k6YEHHpCnp6d8fX01YsQI9ezZU5JUUVGhl156SR07dlTbtm31m9/8RgUFBVWP/+msZHBwsKKiopScnCxJmjBhgp599llJUn5+vqxWq8LCwtS6dWtZrVb98MMPtd566NAhWSwW2e12TZgwQe+8847mzZungIAArVu3ThUVFXrttdd0/fXXq02bNrr33nt16tSpap7puKQ0vf66Q+3aSRER0t/+dukRJSXSk09KHTpI4eFSYqJ09qx05owUHy/l5p5TQIC/AgIClJube9XX/mn322+/rQ4dOig2NvaS9yJJubm5GjNmjEJCQtS1a1f95S9/qdqSlJSke+65R+PHj1dQUJCSk5O1fft2DRgwQMHBwWrXrp0ee+wxlZaWSpIGDx4sSbr11lsVEBCgf/zjH9q4caMiIyOrnnPPnj0aOnSogoOD1b17d3300UdV902YMEFTpkxRQkKCAgMD1b9/fx04cKDW/15AXRGLAAAAbqBbt27y9PTUQw89pLS0NOXn519yf3JyspKTk7VhwwYdPHhQNptNjz32mKTKM3vx8fGaOnWq8vLylJmZqV69ejm9RkVFhR5++GFlZ2fr8OHD8vX1rXqOukpOTta4ceP09NNPy2azKS4uTosWLdIHH3ygTZs2KTc3V61bt9aUKVOqe7RWry7X/PlSerq0f7+0bt2lR8ycKe3bJ2VmVv4c45Ej0u9/L/n7S2lplYFpsyXJZrMpIiKiRq+9adMm7dmzR2vWrHFadP/99ysyMlK5ubl6//33NWvWLH3yySdV93/44Ye65557dPr0aY0bN06enp5auHChTpw4oc8//1zr16/X4sWLJUmbN2+WJO3atUs2m0333XffJa9VVlamX/ziFxoxYoSOHz+uRYsWady4cZd8THXFihV64YUXlJ+fr65du2r27Nm1+ecBrgmxCAAA4AaCgoL06aefymKxaOLEiQoLC9OYMWN07NgxSdLf//53zZgxQ126dFFAQIBeffVVrVixQna7Xe+9957i4uL0wAMPqEWLFmrTpk21sdimTRv9+te/lp+fnwIDAzV79mxt2rTJ5e9l6dKlevnllxUZGSkfHx8lJSXp/fffrzp7d8FXWrmyTA8/LPXoURmASUkX7nU4pLfekhYulEJCpMBAadYsacWKy1/x61q9dlJSkvz9/eXr63vJs+Tk5GjLli2aO3euWrZsqV69eumRRx7Ru+++W3XMgAEDdPfdd8vDw0O+vr7q06ePYmJi5OXlpU6dOmnSpEk1/jvdunWrbDabZs6cKW9vb8XGxspqtWr58uVVx4wdO1a33367vLy8NG7cOGVmZtbouQFX4EPWAAAAbuKmm26q+vhoVlaWxo8fr2nTpmn58uXKzc1Vx44dq47t2LGj7Ha7jh07ppycHF1//fU/+/zFxcWaPn26Vq9eXXXmsqioSOXl5S79gpfs7GyNHTtWHh4Xzkt4enrq2LFjat++/UVHFig3V+rT58ItF71F5eVJxcWX3u9wSM5fXHrhLOzVXvsnUVFR1e7Ozc1VSEiIAgMDL9rTURdfd/zyx+7bt08zZszQjh07VFxcLLvdrj4XD76K3NxcRUVFXbK1Y8eOOnLkSNV/X3fddVV/9vPzk81mq9FzA67AmUUAAAA3FB0drQkTJmj37t2SpIiICGVnZ1fdf/jwYXl5eSk8PFxRUVE1+lm2BQsWaO/evdq2bZsKCwurPibpcDhcuj0qKkppaWk6ffp01a9z585dFoqS1Ert2kk5ORduOXz4wp9DQyVfX+mbb6TTpyt/FRRIP/XShe/TaV2r177SF/FERETo1KlTKioqumjP4as+9tFHH1V0dLT279+vwsJCvfLKKzX++4yIiFBOTo4qKiqu+HqAScQiAACAG8jKytKCBQuqvnAmJydHy5cvV0xMjKTKL75ZuHChvv/+e9lsNs2aNUv33Xdf1ccT161bp5UrV8put+vkyZPVflyxqKhIvr6+Cg4O1qlTpzRnzpx6eS+JiYmaPXt2Vdzm5eXpww8/rObInrr33hZKTpa+/bbyLOLFkzw8pIkTpenTpePHK287ckT66UcNw8OlkyelgoKudXhtZ1FRURo4cKCeeeYZnTt3Tl999ZXefvvtqutLVqeoqEhBQUEKCAhQVlaWliy59NqP4eHhOnjwYLWP7d+/v/z8/DRv3jyVlZVp48aN+vjjj3X//ffXaC9Q34hFAAAANxAYGKht27apf//+8vf3V0xMjHr06FF13cLf/va3evDBBzV48GB17txZLVu21KJFiyRVXitw1apVWrBggUJCQtSrVy/t2rXL6TWmTZums2fPKjQ0VDExMRo1alS9vJfHH39cY8aM0YgRIxQYGKiYmBht27atmiMnKD7eU9OmSbGxUteulb9fbO7cyttjYqSgICkuTvrp+1+io6UHHvBUly5/VHBwsHJzc2vx2tVbvny5Dh06pIiICI0dO1Zz5sxRXFzcFY+fP3++3nvvPQUGBmrixIlOX2KTlJSkhx56SMHBwVq5cuUl93l7e+vjjz9WWlqaQkNDNXnyZL377ruKjo6u8V6gPlmudpq8b9++jos/ow0AAAC41q8kfSCpLh+FtUgaK+n/XLoIaE4sFstOh8PRt7r7OLMIAAAAg56R5PuzR1XP9/zjAdQHYhEAAAAG9ZM0X5JfLR/nd/5x1Z4QAeACXDoDAAAAhj16/vcnJZ3V1T+SalHlGcX5Fz0OQH3gzCIAAADcwKOSNqnyZxBbyvmjqb7nbx97/jhCEahvnFkEAACAm+iryi+ryZOULOlrSfmqvI7iLZImSAoztA1ofohFAAAAuJkwSU+ZHgE0e3wMFQAAAADghFgEAAAAADghFgEAAAAATohFAAAAAIATYhEAAAAA4IRYBAAAAAA4IRYBAAAAAE6IRQAAAACAE2IRAAAAAOCEWAQAAAAAOCEWAQAAAABOiEUAAAAAgBNiEQAAAADghFgEAAAAADghFgEAAAAATohFAAAAAIATYhEAAAAA4IRYBAAAAAA4IRYBAAAAAE6IRQAAAACAE2IRAAAAAOCEWAQAAAAAOCEWAQAAAABOiEUAAAAAgBNiEQAAAADghFgEAAAAADghFgEAAAAATohFAAAAAIATYhEAAAAA4IRYBAAAAAA4IRYBAAAAAE6IRQAAAACAE2IRAAAAAOCEWAQAAAAAOCEWAQAAAABOiEUAAAAAgBNiEQAAAADghFgEAAAAADghFgEAAAAATohFAAAAAIATYhEAAAAA4IRYBAAAAAA4IRYBAAAAAE6IRQAAAACAE2IRAAAAAOCEWAQAAAAAOLE4HI4r32mx5EnKbrg5AAAAAIAG1NHhcIRVd8dVYxEAAAAA0DzxMVQAAAAAgBNiEQAAAADghFgEAAAAADghFgEAAAAATohFAAAAAICT/wezkecEmYj4BQAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 1152x864 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Yz3Qy_wZ-e07"
      },
      "source": [
        "### 1.4 Graph structure\n",
        "![network.png](data:image/png;base64,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)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "5CKtnyWw7IZ4"
      },
      "source": [
        "## 2. CPD definition\n",
        "*Conditional probability distributions* (CPDs) are a form of tabular representation suitable for discrete variables.\n",
        "Each row in a CPD contains the conditional probability of each node value for a conditioning case.\n",
        "\n",
        "A conditioning case is just a possible combination of values for the parent nodes."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Yq2uuGfJ-kFi"
      },
      "source": [
        "### 2.1 Simple CPD"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "72dOJSRy7H-J",
        "outputId": "3e061a03-5c69-4c1f-da7c-16df051338ce"
      },
      "source": [
        "cpds = {}\n",
        "\n",
        "# P(X1)\n",
        "cpd_sex = TabularCPD(variable = sex, \n",
        "                     variable_card = 2,\n",
        "                     values = [[0.5], \n",
        "                               [0.5]],\n",
        "                     state_names = {sex : ['Male', 'Female']})\n",
        "\n",
        "print(\"Sex P(X1)\")\n",
        "print(cpd_sex)\n",
        "\n",
        "cpds[sex] = cpd_sex\n",
        "\n",
        "# P(X2)\n",
        "cpd_parents = TabularCPD(variable = parents, \n",
        "                         variable_card = 3,\n",
        "                         values = [[0.88], \n",
        "                                   [0.119],\n",
        "                                   [0.001]],\n",
        "                         state_names = {parents : ['Zero', 'One', 'Both']})\n",
        "\n",
        "print(\"Parents suffering from schizophrenia P(X2)\")\n",
        "print(cpd_parents)\n",
        "\n",
        "cpds[parents] = cpd_parents\n",
        "\n",
        "# P(X12)\n",
        "cpd_virosis = TabularCPD(variable = mother_with_virosis, \n",
        "                         variable_card = 2,\n",
        "                         values = [[0.12], \n",
        "                                   [0.88]],\n",
        "                         state_names = {mother_with_virosis : ['True', 'False']})\n",
        "\n",
        "print(\"Mother with virosis during pregnancy P(X12)\")\n",
        "print(cpd_virosis)\n",
        "\n",
        "cpds[mother_with_virosis] = cpd_virosis"
      ],
      "execution_count": 29,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Sex P(X1)\n",
            "+-------------+-----+\n",
            "| Sex(Male)   | 0.5 |\n",
            "+-------------+-----+\n",
            "| Sex(Female) | 0.5 |\n",
            "+-------------+-----+\n",
            "Parents suffering from schizophrenia P(X2)\n",
            "+--------------------------------------------+-------+\n",
            "| Parents suffering from schizophrenia(Zero) | 0.88  |\n",
            "+--------------------------------------------+-------+\n",
            "| Parents suffering from schizophrenia(One)  | 0.119 |\n",
            "+--------------------------------------------+-------+\n",
            "| Parents suffering from schizophrenia(Both) | 0.001 |\n",
            "+--------------------------------------------+-------+\n",
            "Mother with virosis during pregnancy P(X12)\n",
            "+---------------------------------------------+------+\n",
            "| Mother with virosis during pregnancy(True)  | 0.12 |\n",
            "+---------------------------------------------+------+\n",
            "| Mother with virosis during pregnancy(False) | 0.88 |\n",
            "+---------------------------------------------+------+\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "AhefTCJ2joQA"
      },
      "source": [
        "### 2.2 CPD with evidence"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "7KX9AIA7jgJ8",
        "outputId": "fa56a5c1-42ed-40a4-96fa-a885b4d091a0"
      },
      "source": [
        "# P(X11|X1,X2)\n",
        "cpd_genetic_influence = TabularCPD(variable = genetic_influence,\n",
        "                                   variable_card = 2,\n",
        "                                   values = [[0.01, 0.05, 0.30, 0.01, 0.06, 0.46],\n",
        "                                             [0.99, 0.95, 0.70, 0.99, 0.94, 0.54]],\n",
        "                                   evidence = [sex, parents],\n",
        "                                   evidence_card = [2,3],\n",
        "                                   state_names = {genetic_influence:['True','False'],\n",
        "                                                  sex : ['Male', 'Female'],\n",
        "                                                  parents : ['Zero', 'One', 'Both']\n",
        "                                                  }\n",
        "                                   )\n",
        "\n",
        "print(\"Genetic Influence P(X11|X1,X2)\")\n",
        "print(cpd_genetic_influence)\n",
        "\n",
        "cpds[genetic_influence] = cpd_genetic_influence\n",
        "\n",
        "# P(X14|X11,X12)\n",
        "cpd_schizophrenia = TabularCPD(variable = simple_schizophrenia, \n",
        "                              variable_card = 2,\n",
        "                              values = [[0.25, 0.15, 0.08, 0.01],\n",
        "                                        [0.75, 0.85, 0.92, 0.99]],\n",
        "                              evidence = [genetic_influence, mother_with_virosis],\n",
        "                              evidence_card = [2,2],\n",
        "                              state_names = {simple_schizophrenia : ['True', 'False'], \n",
        "                                             genetic_influence : ['True','False'], \n",
        "                                             mother_with_virosis : ['True', 'False']}\n",
        "                              )\n",
        "\n",
        "print(\"Simple Schizophrenia P(X14|X11,X12)\")\n",
        "print(cpd_schizophrenia)\n",
        "\n",
        "cpds[simple_schizophrenia] = cpd_schizophrenia\n",
        "\n",
        "# P(X19 | X14)\n",
        "cpd_mannerism = TabularCPD(variable = mannerism, \n",
        "                           variable_card = 2,\n",
        "                           values = [[0.85, 0.03],\n",
        "                                     [0.15, 0.97]],\n",
        "                           state_names = {mannerism : ['Present', 'Absent'], \n",
        "                                          simple_schizophrenia: ['True', 'False']},\n",
        "                           evidence = [simple_schizophrenia],\n",
        "                           evidence_card = [2]\n",
        "                           )\n",
        "\n",
        "print(\"Mannerism P(X19|X14)\")\n",
        "print(cpd_mannerism)\n",
        "\n",
        "cpds[mannerism] = cpd_mannerism\n",
        "\n",
        "# P(X20|X14)\n",
        "cpd_social_life_deterioration = TabularCPD(variable=social_life_deterioration,\n",
        "                     variable_card = 2,\n",
        "                     values = [[0.88, 0.10],\n",
        "                               [0.12, 0.90]],\n",
        "                     evidence = [simple_schizophrenia],\n",
        "                     evidence_card = [2],\n",
        "                     state_names = {simple_schizophrenia : ['True', 'False'], \n",
        "                                    social_life_deterioration : ['Present', 'Absent']}\n",
        "                     )\n",
        "\n",
        "print(\"Social Life Deterioration P(X20|X14)\")\n",
        "print(cpd_social_life_deterioration)\n",
        "\n",
        "cpds[social_life_deterioration] = cpd_social_life_deterioration\n",
        "\n",
        "# P(X21|X14)\n",
        "cpd_personality_deterioration = TabularCPD(variable=personality_deterioration,\n",
        "                                           variable_card = 2,\n",
        "                                           values = [[0.87, 0.05],\n",
        "                                                     [0.13, 0.95]],\n",
        "                                           evidence =[simple_schizophrenia],\n",
        "                                           evidence_card = [2],\n",
        "                                           state_names = {personality_deterioration : ['Present', 'Absent'], \n",
        "                                                          simple_schizophrenia : ['True', 'False']}\n",
        "                                           )\n",
        "\n",
        "print(\"Personality deterioration P(X21|X14)\")\n",
        "print(cpd_personality_deterioration)\n",
        "cpds[personality_deterioration] = cpd_personality_deterioration"
      ],
      "execution_count": 30,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Genetic Influence P(X11|X1,X2)\n",
            "+--------------------------------------+--------------------------------------------+-------------------------------------------+--------------------------------------------+--------------------------------------------+-------------------------------------------+--------------------------------------------+\n",
            "| Sex                                  | Sex(Male)                                  | Sex(Male)                                 | Sex(Male)                                  | Sex(Female)                                | Sex(Female)                               | Sex(Female)                                |\n",
            "+--------------------------------------+--------------------------------------------+-------------------------------------------+--------------------------------------------+--------------------------------------------+-------------------------------------------+--------------------------------------------+\n",
            "| Parents suffering from schizophrenia | Parents suffering from schizophrenia(Zero) | Parents suffering from schizophrenia(One) | Parents suffering from schizophrenia(Both) | Parents suffering from schizophrenia(Zero) | Parents suffering from schizophrenia(One) | Parents suffering from schizophrenia(Both) |\n",
            "+--------------------------------------+--------------------------------------------+-------------------------------------------+--------------------------------------------+--------------------------------------------+-------------------------------------------+--------------------------------------------+\n",
            "| Genetic influence(True)              | 0.01                                       | 0.05                                      | 0.3                                        | 0.01                                       | 0.06                                      | 0.46                                       |\n",
            "+--------------------------------------+--------------------------------------------+-------------------------------------------+--------------------------------------------+--------------------------------------------+-------------------------------------------+--------------------------------------------+\n",
            "| Genetic influence(False)             | 0.99                                       | 0.95                                      | 0.7                                        | 0.99                                       | 0.94                                      | 0.54                                       |\n",
            "+--------------------------------------+--------------------------------------------+-------------------------------------------+--------------------------------------------+--------------------------------------------+-------------------------------------------+--------------------------------------------+\n",
            "Simple Schizophrenia P(X14|X11,X12)\n",
            "+--------------------------------------+--------------------------------------------+---------------------------------------------+--------------------------------------------+---------------------------------------------+\n",
            "| Genetic influence                    | Genetic influence(True)                    | Genetic influence(True)                     | Genetic influence(False)                   | Genetic influence(False)                    |\n",
            "+--------------------------------------+--------------------------------------------+---------------------------------------------+--------------------------------------------+---------------------------------------------+\n",
            "| Mother with virosis during pregnancy | Mother with virosis during pregnancy(True) | Mother with virosis during pregnancy(False) | Mother with virosis during pregnancy(True) | Mother with virosis during pregnancy(False) |\n",
            "+--------------------------------------+--------------------------------------------+---------------------------------------------+--------------------------------------------+---------------------------------------------+\n",
            "| Simple schizophrenia(True)           | 0.25                                       | 0.15                                        | 0.08                                       | 0.01                                        |\n",
            "+--------------------------------------+--------------------------------------------+---------------------------------------------+--------------------------------------------+---------------------------------------------+\n",
            "| Simple schizophrenia(False)          | 0.75                                       | 0.85                                        | 0.92                                       | 0.99                                        |\n",
            "+--------------------------------------+--------------------------------------------+---------------------------------------------+--------------------------------------------+---------------------------------------------+\n",
            "Mannerism P(X19|X14)\n",
            "+----------------------+----------------------------+-----------------------------+\n",
            "| Simple schizophrenia | Simple schizophrenia(True) | Simple schizophrenia(False) |\n",
            "+----------------------+----------------------------+-----------------------------+\n",
            "| Mannerism(Present)   | 0.85                       | 0.03                        |\n",
            "+----------------------+----------------------------+-----------------------------+\n",
            "| Mannerism(Absent)    | 0.15                       | 0.97                        |\n",
            "+----------------------+----------------------------+-----------------------------+\n",
            "Social Life Deterioration P(X20|X14)\n",
            "+------------------------------------+----------------------------+-----------------------------+\n",
            "| Simple schizophrenia               | Simple schizophrenia(True) | Simple schizophrenia(False) |\n",
            "+------------------------------------+----------------------------+-----------------------------+\n",
            "| Social life deterioration(Present) | 0.88                       | 0.1                         |\n",
            "+------------------------------------+----------------------------+-----------------------------+\n",
            "| Social life deterioration(Absent)  | 0.12                       | 0.9                         |\n",
            "+------------------------------------+----------------------------+-----------------------------+\n",
            "Personality deterioration P(X21|X14)\n",
            "+------------------------------------+----------------------------+-----------------------------+\n",
            "| Simple schizophrenia               | Simple schizophrenia(True) | Simple schizophrenia(False) |\n",
            "+------------------------------------+----------------------------+-----------------------------+\n",
            "| Personality deterioration(Present) | 0.87                       | 0.05                        |\n",
            "+------------------------------------+----------------------------+-----------------------------+\n",
            "| Personality deterioration(Absent)  | 0.13                       | 0.95                        |\n",
            "+------------------------------------+----------------------------+-----------------------------+\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "eKfhGwcgzQ2O"
      },
      "source": [
        "values = list(cpds.values())\n",
        "# print(values)\n",
        "\n",
        "[model.add_cpds(x) for x in values];"
      ],
      "execution_count": 31,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "r-ObPUnXSqXY"
      },
      "source": [
        "Check if the CPDs are consistent with the nodes and the model:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "SUY7tSk_5f1s",
        "outputId": "c092d143-fe9f-4050-bda5-3fd44c665045"
      },
      "source": [
        "if model.check_model():\n",
        "  print(\"Yeahhh!\")\n",
        "else: \n",
        "  print(\"Booooo!\")"
      ],
      "execution_count": 32,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Yeahhh!\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "XuhQTYf8MDMm"
      },
      "source": [
        "## 3. Independencies\n",
        "$A$ and $B$ are independent, denoted $P \\models (A⊥B)$, iff:\n",
        "* $P(A|B) = P(A)$;\n",
        "* $P(B|A) = P(B)$;\n",
        "* $P(A, B) = P(A)P(B)$.\n",
        "\n",
        "$X$ and $Y$ are conditionally independent, given $Z$, denoted $P \\models (X⊥Y, Z)$, iff:\n",
        "$$P(X,Y|Z) = P(X|Z) P(Y,Z)$$\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "N-IXH1rl7Dsj",
        "outputId": "dbb2232d-980c-4155-9547-1c383ca47664"
      },
      "source": [
        "print('There can be made {} valid independence assertions, with respect to the all possible given evidence.'.format(len(model.get_independencies().get_assertions())), \"\\n\")\n",
        "independencies = model.get_independencies() \n",
        "# print(independencies) # Output is not shown for compactness sake\n",
        "\n",
        "for variable in variables:\n",
        "  print(\"The local independencies for \\'{}\\' are:\".format(variable), \"\\n\" ,model.local_independencies(variable), \"\\n\")"
      ],
      "execution_count": 33,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "There can be made 642 valid independence assertions, with respect to the all possible given evidence. \n",
            "\n",
            "The local independencies for 'Sex' are: \n",
            " (Sex ⟂ Parents suffering from schizophrenia, Mother with virosis during pregnancy) \n",
            "\n",
            "The local independencies for 'Parents suffering from schizophrenia' are: \n",
            " (Parents suffering from schizophrenia ⟂ Mother with virosis during pregnancy, Sex) \n",
            "\n",
            "The local independencies for 'Mother with virosis during pregnancy' are: \n",
            " (Mother with virosis during pregnancy ⟂ Genetic influence, Parents suffering from schizophrenia, Sex) \n",
            "\n",
            "The local independencies for 'Genetic influence' are: \n",
            " (Genetic influence ⟂ Mother with virosis during pregnancy | Parents suffering from schizophrenia, Sex) \n",
            "\n",
            "The local independencies for 'Simple schizophrenia' are: \n",
            " (Simple schizophrenia ⟂ Parents suffering from schizophrenia, Sex | Genetic influence, Mother with virosis during pregnancy) \n",
            "\n",
            "The local independencies for 'Social life deterioration' are: \n",
            " (Social life deterioration ⟂ Personality deterioration, Parents suffering from schizophrenia, Genetic influence, Mother with virosis during pregnancy, Sex, Mannerism | Simple schizophrenia) \n",
            "\n",
            "The local independencies for 'Mannerism' are: \n",
            " (Mannerism ⟂ Personality deterioration, Parents suffering from schizophrenia, Genetic influence, Mother with virosis during pregnancy, Sex, Social life deterioration | Simple schizophrenia) \n",
            "\n",
            "The local independencies for 'Personality deterioration' are: \n",
            " (Personality deterioration ⟂ Genetic influence, Parents suffering from schizophrenia, Mother with virosis during pregnancy, Sex, Social life deterioration, Mannerism | Simple schizophrenia) \n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "4e66PQ4A5DxP"
      },
      "source": [
        "### 3.1 Markov blanket\n",
        "The **Markov blanket** of a given node (in *red*) consists in the set (in *yellow*) of: \n",
        "* Its parents; \n",
        "* Its children; \n",
        "* Its children’s other parents. \n",
        "\n",
        "Each node is conditionally independent of all others, given its Markov Blanket."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "l2WrVOXk5TpT",
        "outputId": "6e7b2940-8c76-4c8d-ed39-ffc9b79fe913"
      },
      "source": [
        "leaves = model.get_leaves()\n",
        "markov_blanket = {}\n",
        "\n",
        "for node in nodes:\n",
        "    if not node in leaves:\n",
        "        markov_blanket[node] = model.get_markov_blanket(node)\n",
        "        print('The node \\'{}\\' has Markov blanket: {}.'.format(node, markov_blanket[node]))\n",
        "        print('\\n')\n",
        "        \n",
        "        nx.draw_networkx_nodes(DAG, pos, nodelist = [node], node_color = 'red')\n",
        "        nx.draw_networkx_nodes(DAG, pos, nodelist = markov_blanket[node], node_color = 'yellow')\n",
        "        nx.draw_networkx_nodes(DAG, pos, \n",
        "               nodelist = [n for n in nodes if n not in markov_blanket[node] and n != node], \n",
        "               node_color = 'grey')\n",
        "        nx.draw_networkx_labels(DAG, pos)\n",
        "        nx.draw_networkx_labels(DAG, pos)\n",
        "        nx.draw_networkx_edges(DAG, pos, edge_color = 'purple', width = 1.5, arrows = True)\n",
        "        plt.show()\n",
        "\n",
        "        print('\\n')\n",
        "        print(\"❈\"*85,\"\\n\")"
      ],
      "execution_count": 34,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "The node 'Mother with virosis during pregnancy' has Markov blanket: ['Genetic influence', 'Simple schizophrenia'].\n",
            "\n",
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\n",
            "\n",
            "❈❈❈❈❈❈❈❈❈❈❈❈❈❈❈❈❈❈❈❈❈❈❈❈❈❈❈❈❈❈❈❈❈❈❈❈❈❈❈❈❈❈❈❈❈❈❈❈❈❈❈❈❈❈❈❈❈❈❈❈❈❈❈❈❈❈❈❈❈❈❈❈❈❈❈❈❈❈❈❈❈❈❈❈❈ \n",
            "\n",
            "The node 'Simple schizophrenia' has Markov blanket: ['Personality deterioration', 'Mannerism', 'Genetic influence', 'Mother with virosis during pregnancy', 'Social life deterioration'].\n",
            "\n",
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\n",
            "\n",
            "❈❈❈❈❈❈❈❈❈❈❈❈❈❈❈❈❈❈❈❈❈❈❈❈❈❈❈❈❈❈❈❈❈❈❈❈❈❈❈❈❈❈❈❈❈❈❈❈❈❈❈❈❈❈❈❈❈❈❈❈❈❈❈❈❈❈❈❈❈❈❈❈❈❈❈❈❈❈❈❈❈❈❈❈❈ \n",
            "\n",
            "The node 'Parents suffering from schizophrenia' has Markov blanket: ['Genetic influence', 'Sex'].\n",
            "\n",
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\n",
            "\n",
            "❈❈❈❈❈❈❈❈❈❈❈❈❈❈❈❈❈❈❈❈❈❈❈❈❈❈❈❈❈❈❈❈❈❈❈❈❈❈❈❈❈❈❈❈❈❈❈❈❈❈❈❈❈❈❈❈❈❈❈❈❈❈❈❈❈❈❈❈❈❈❈❈❈❈❈❈❈❈❈❈❈❈❈❈❈ \n",
            "\n",
            "The node 'Genetic influence' has Markov blanket: ['Parents suffering from schizophrenia', 'Sex', 'Simple schizophrenia', 'Mother with virosis during pregnancy'].\n",
            "\n",
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\n",
            "\n",
            "❈❈❈❈❈❈❈❈❈❈❈❈❈❈❈❈❈❈❈❈❈❈❈❈❈❈❈❈❈❈❈❈❈❈❈❈❈❈❈❈❈❈❈❈❈❈❈❈❈❈❈❈❈❈❈❈❈❈❈❈❈❈❈❈❈❈❈❈❈❈❈❈❈❈❈❈❈❈❈❈❈❈❈❈❈ \n",
            "\n",
            "The node 'Sex' has Markov blanket: ['Genetic influence', 'Parents suffering from schizophrenia'].\n",
            "\n",
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\n",
            "\n",
            "❈❈❈❈❈❈❈❈❈❈❈❈❈❈❈❈❈❈❈❈❈❈❈❈❈❈❈❈❈❈❈❈❈❈❈❈❈❈❈❈❈❈❈❈❈❈❈❈❈❈❈❈❈❈❈❈❈❈❈❈❈❈❈❈❈❈❈❈❈❈❈❈❈❈❈❈❈❈❈❈❈❈❈❈❈ \n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "MfqK2ISa73dY"
      },
      "source": [
        "## 4. Queries\n",
        "The basic task for any probabilistic inference system is to compute the posterior probability distribution for a set of **query variables**, given some observed event, that is some assignment of values to a set of **evidence values**."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "e_QQFAv1-d6x"
      },
      "source": [
        "### 4.1 Exact inference with variable elimination\n",
        "The **variable elimination** is one of the most widespread method for exact inference. \n",
        "It's an improvement of the **inference by enumeration**, where the *repeated subexpression* of the query are evaluated only once (*dynamic programming*).\n",
        "\n",
        "Different orderings of the variables cause different intermediate factors to be generated during the calculation, but every choice yields a valid algorithm.\n",
        "\n",
        "In general, the time and space requirements of variable elimination are dominated by the size of the largest factor constructed during the operation of the algorithm. \n",
        "This in turn is determined by the order of elimination of variables and by the structure of the network.\n",
        "\n",
        "It is intractable to determine the optimal ordering, but several good heuristics are available: \n",
        "* MinFill: the number of edges that need to be added to the graph due to its elimination;\n",
        "* MinNeighbors: the number of neighbors it has in the current graph.\n",
        "* MinWeight: the product of weights, domain cardinality, of its neighbors.\n",
        "* WeightedMinFill: the sum of weights of the edges that need to be added to the graph due to its elimination, where a weight of an edge is the product of the weights, domain cardinality, of its constituent vertices.\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Yrnh_W1P79Fy"
      },
      "source": [
        "exact_inference_variable_elimination = VariableElimination(model)"
      ],
      "execution_count": 35,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "K1viTT0RQYTh"
      },
      "source": [
        "The absolute probability of having a simple schizophrenia is (fortunately) kinda low:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "K5XD0rlF-_Xg",
        "outputId": "caa2383c-5f42-42ef-9559-d6ae91dc4998"
      },
      "source": [
        "print('P(Simple schizophrenia)')\n",
        "query1 = exact_inference_variable_elimination.query([simple_schizophrenia], show_progress = False);\n",
        "print(query1)"
      ],
      "execution_count": 36,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "P(Simple schizophrenia)\n",
            "+-----------------------------+-----------------------------+\n",
            "| Simple schizophrenia        |   phi(Simple schizophrenia) |\n",
            "+=============================+=============================+\n",
            "| Simple schizophrenia(True)  |                      0.0207 |\n",
            "+-----------------------------+-----------------------------+\n",
            "| Simple schizophrenia(False) |                      0.9793 |\n",
            "+-----------------------------+-----------------------------+\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "tC47-rmRQi_o"
      },
      "source": [
        "By the way, if we start considering genetic causes and congenital issues, we will see that the probability will start to rise:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "9gcwcpMwQjbX",
        "outputId": "d7b25595-fe67-42b7-c0a8-dca9007f8cec"
      },
      "source": [
        "print('P(Simple schizophrenia|Genetic influence = True, Mother with virosis = True)')\n",
        "query2 = exact_inference_variable_elimination.query([simple_schizophrenia], {genetic_influence : 'True', mother_with_virosis : 'True'}, show_progress = False);\n",
        "print(query2)"
      ],
      "execution_count": 37,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "P(Simple schizophrenia|Genetic influence = True, Mother with virosis = True)\n",
            "+-----------------------------+-----------------------------+\n",
            "| Simple schizophrenia        |   phi(Simple schizophrenia) |\n",
            "+=============================+=============================+\n",
            "| Simple schizophrenia(True)  |                      0.2500 |\n",
            "+-----------------------------+-----------------------------+\n",
            "| Simple schizophrenia(False) |                      0.7500 |\n",
            "+-----------------------------+-----------------------------+\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "wLABU9i4RWoV"
      },
      "source": [
        "Two important variables involved in obtaining the probability of a disease is the genetic influence: patients sex and number of relatives diagnosed with the illness.\n",
        " \n",
        "It is known that women are more liable to have or to develop a psychiatric disease than men. Also there is a chance of schizophrenia for those whose mothers had some kind of virosis during pregnancy.\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "nixwvhJKFwEY",
        "outputId": "52cc8aab-1926-4eae-9bb2-802c9944ea4a"
      },
      "source": [
        "print('P(Genetic influence)')\n",
        "query3 = exact_inference_variable_elimination.query([genetic_influence], show_progress = False)\n",
        "print(query3)"
      ],
      "execution_count": 38,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "P(Genetic influence)\n",
            "+--------------------------+--------------------------+\n",
            "| Genetic influence        |   phi(Genetic influence) |\n",
            "+==========================+==========================+\n",
            "| Genetic influence(True)  |                   0.0157 |\n",
            "+--------------------------+--------------------------+\n",
            "| Genetic influence(False) |                   0.9843 |\n",
            "+--------------------------+--------------------------+\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "URHW7G77FwnQ",
        "outputId": "d3301d46-7e73-4ae2-d564-dd74454e34df"
      },
      "source": [
        "print('P(Social life deterioration)')\n",
        "query4 = exact_inference_variable_elimination.query([social_life_deterioration], show_progress = False)\n",
        "print(query4)"
      ],
      "execution_count": 39,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "P(Social life deterioration)\n",
            "+------------------------------------+----------------------------------+\n",
            "| Social life deterioration          |   phi(Social life deterioration) |\n",
            "+====================================+==================================+\n",
            "| Social life deterioration(Present) |                           0.1161 |\n",
            "+------------------------------------+----------------------------------+\n",
            "| Social life deterioration(Absent)  |                           0.8839 |\n",
            "+------------------------------------+----------------------------------+\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "qjBNCclDIRwa"
      },
      "source": [
        "The probability of having a simple schizophrenia, given the virosis during the pregnancy, gets higher as the number of parents with schizophrenia gets higher.\n",
        "\n",
        "That makes sense with the concept of familiarity of the disease."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "QoaVRU9hHIYX",
        "outputId": "85c9cc3a-da80-4492-866d-518723925e21"
      },
      "source": [
        "print('P(Simple schizophrenia|Parents suffering from schizophrenia = Zero, Mother with virosis during pregnancy = True)')\n",
        "query5a = exact_inference_variable_elimination.query([simple_schizophrenia], {parents : 'Zero', mother_with_virosis : 'True'}, show_progress = False)\n",
        "print(query5a, \"\\n\")\n",
        "\n",
        "print('P(Simple schizophrenia|Parents suffering from schizophrenia = One, Mother with virosis during pregnancy = True)')\n",
        "query5b = exact_inference_variable_elimination.query([simple_schizophrenia], {parents : 'One', mother_with_virosis : 'True'}, show_progress = False)\n",
        "print(query5b, \"\\n\")\n",
        "\n",
        "print('P(Simple schizophrenia|Parents suffering from schizophrenia = Both, Mother with virosis during pregnancy = True)')\n",
        "query5c = exact_inference_variable_elimination.query([simple_schizophrenia], {parents : 'Both', mother_with_virosis : 'True'}, show_progress = False)\n",
        "print(query5c)"
      ],
      "execution_count": 40,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "P(Simple schizophrenia|Parents suffering from schizophrenia = Zero, Mother with virosis during pregnancy = True)\n",
            "+-----------------------------+-----------------------------+\n",
            "| Simple schizophrenia        |   phi(Simple schizophrenia) |\n",
            "+=============================+=============================+\n",
            "| Simple schizophrenia(True)  |                      0.0817 |\n",
            "+-----------------------------+-----------------------------+\n",
            "| Simple schizophrenia(False) |                      0.9183 |\n",
            "+-----------------------------+-----------------------------+ \n",
            "\n",
            "P(Simple schizophrenia|Parents suffering from schizophrenia = One, Mother with virosis during pregnancy = True)\n",
            "+-----------------------------+-----------------------------+\n",
            "| Simple schizophrenia        |   phi(Simple schizophrenia) |\n",
            "+=============================+=============================+\n",
            "| Simple schizophrenia(True)  |                      0.0893 |\n",
            "+-----------------------------+-----------------------------+\n",
            "| Simple schizophrenia(False) |                      0.9106 |\n",
            "+-----------------------------+-----------------------------+ \n",
            "\n",
            "P(Simple schizophrenia|Parents suffering from schizophrenia = Both, Mother with virosis during pregnancy = True)\n",
            "+-----------------------------+-----------------------------+\n",
            "| Simple schizophrenia        |   phi(Simple schizophrenia) |\n",
            "+=============================+=============================+\n",
            "| Simple schizophrenia(True)  |                      0.1446 |\n",
            "+-----------------------------+-----------------------------+\n",
            "| Simple schizophrenia(False) |                      0.8554 |\n",
            "+-----------------------------+-----------------------------+\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "9DcoK09rJE5M"
      },
      "source": [
        "As we inferred before, the local independencies for 'Genetic influence' are:\n",
        "\n",
        "(Genetic influence ⟂ Mother with virosis during pregnancy | Sex, Parents suffering from schizophrenia).\n",
        "\n",
        "Indeed, if, for example, we calculate:\n",
        "* $P(\\text{Genetic influence} | \\text{Sex} = \\text{Male}, \\text{Parents suffering from schizophrenia} = \\text{Zero}, \\text{Mother with virosis during pregnancy} = \\text{True})$\n",
        "\n",
        "* $P(\\text{Genetic influence} | \\text{Sex} = \\text{Male}, \\text{Parents suffering from schizophrenia} = \\text{Zero}, \\text{Mother with virosis during pregnancy} = \\text{False})$\n",
        "\n",
        "we will have the same result, despite the virosis."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "9zI95GZtHvxf",
        "outputId": "38b0d506-1ebd-4a01-b4a6-0d2d98756460"
      },
      "source": [
        "print('P(Genetic influence|Sex = Male, Parents suffering from schizophrenia = Zero, Mother with virosis during pregnancy = True)')\n",
        "query6a = exact_inference_variable_elimination.query([genetic_influence], {sex : 'Male', parents : 'Zero', mother_with_virosis : 'True'}, show_progress = False)\n",
        "print(query6a, \"\\n\")\n",
        "\n",
        "print('P(Genetic influence|Sex = Male, Parents suffering from schizophrenia = Zero, Mother with virosis during pregnancy = False)')\n",
        "query6b = exact_inference_variable_elimination.query([genetic_influence], {sex : 'Male', parents : 'Zero', mother_with_virosis : 'False'}, show_progress = False)\n",
        "print(query6b)"
      ],
      "execution_count": 41,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "P(Genetic influence|Sex = Male, Parents suffering from schizophrenia = Zero, Mother with virosis during pregnancy = True)\n",
            "+--------------------------+--------------------------+\n",
            "| Genetic influence        |   phi(Genetic influence) |\n",
            "+==========================+==========================+\n",
            "| Genetic influence(True)  |                   0.0100 |\n",
            "+--------------------------+--------------------------+\n",
            "| Genetic influence(False) |                   0.9900 |\n",
            "+--------------------------+--------------------------+ \n",
            "\n",
            "P(Genetic influence|Sex = Male, Parents suffering from schizophrenia = Zero, Mother with virosis during pregnancy = False)\n",
            "+--------------------------+--------------------------+\n",
            "| Genetic influence        |   phi(Genetic influence) |\n",
            "+==========================+==========================+\n",
            "| Genetic influence(True)  |                   0.0100 |\n",
            "+--------------------------+--------------------------+\n",
            "| Genetic influence(False) |                   0.9900 |\n",
            "+--------------------------+--------------------------+\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "zgDWLi3J-emk"
      },
      "source": [
        "### 4.2 Approximate inference"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "peJydSfGwAE_",
        "outputId": "c2201490-9b81-4ec5-f51b-170f6458d726"
      },
      "source": [
        "''' Reference query for comparison'''\n",
        "reference_query = exact_inference_variable_elimination.query([simple_schizophrenia], {sex : 'Female', mother_with_virosis : 'True'}, show_progress = False).values[1]\n",
        "print('P(Simple schizophrenia = False|Sex = Female, Mother with virosis during pregnancy = True) = {}.'.format(reference_query))"
      ],
      "execution_count": 42,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "P(Simple schizophrenia = False|Sex = Female, Mother with virosis during pregnancy = True) = 0.917212.\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "wx_DEqwp4Va8"
      },
      "source": [
        "inference = BayesianModelSampling(model)\n",
        "\n",
        "evidences = [State(sex, 'Female'), State(mother_with_virosis, 'True')]\n",
        "verbose = False\n",
        "sample_size = 10\n",
        "\n",
        "def prob_LW(samples, variable):\n",
        "    return round(np.sum(np.dot(samples[variable], samples['_weight']))/np.sum(samples['_weight']), 2)\n",
        "\n",
        "def relative_error(exact_value, approx_value):\n",
        "    return np.absolute((exact_value - approx_value)/exact_value)\n",
        "\n",
        "def absolute_error(exact_value, approx_value):\n",
        "    return np.absolute(exact_value - approx_value)\n",
        "\n",
        "def run_experiment(sample_size):\n",
        "    # Sample\n",
        "    samples_LW = inference.likelihood_weighted_sample(evidence = evidences, \n",
        "                                                      size = sample_size, \n",
        "                                                      return_type = 'recarray')\n",
        "    \n",
        "    samples_RS = inference.rejection_sample(evidence = evidences, \n",
        "                                            size = sample_size, \n",
        "                                            return_type = 'recarray')\n",
        "    \n",
        "    # Statistics\n",
        "    query_LW = prob_LW(samples_LW, simple_schizophrenia)\n",
        "    query_RS = np.recarray.mean(samples_RS[simple_schizophrenia], axis = 0)\n",
        "\n",
        "      \n",
        "    # Return results\n",
        "    return np.array([(sample_size, \n",
        "                      query_RS, absolute_error(reference_query, query_RS),\n",
        "                      query_LW, absolute_error(reference_query, query_LW))],\n",
        "                    dtype = [('sample_size', '<i8'), \n",
        "                             ('query_RS', '<f8'),('e_query_RS', '<f8'),\n",
        "                             ('query_LW', '<f8'),('e_query_LW', '<f8')])"
      ],
      "execution_count": 43,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "tFl6DXXGty7g"
      },
      "source": [
        "results = np.array([], dtype = [('sample_size', '<i8'), \n",
        "                                ('query_RS', '<f8'), ('e_query_RS', '<f8'),\n",
        "                                ('query_LW', '<f8'), ('e_query_LW', '<f8')])\n",
        "\n",
        "starting_size_ = 1 \n",
        "final_size = 5    \n",
        "experiments = 10\n",
        "\n",
        "for size in np.logspace(starting_size_, final_size, num = experiments, dtype = '<i8'):\n",
        "  results = np.append(results, run_experiment(size))\n",
        "    \n",
        "print(results)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0N_n5My79EaR",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "bba1c505-0631-4629-9b67-0e104c120256"
      },
      "source": [
        "''' Comparison '''\n",
        "plt.figure(figsize = (15, 10))\n",
        "# Plot 1\n",
        "\n",
        "plt.xlabel(\"Number of samples\")\n",
        "plt.ylabel(\"Probability\")\n",
        "plt.title(f'P({simple_schizophrenia} | {sex} = Female, {mother_with_virosis} = True)')\n",
        "LWCplot, = plt.semilogx(results['sample_size'],\n",
        "                        results['query_LW'],\n",
        "                        '-bo',\n",
        "                        label = \"Likelihood Weighting\")\n",
        "\n",
        "RSCplot, = plt.semilogx(results['sample_size'],\n",
        "                        results['query_RS'],\n",
        "                        '-go',\n",
        "                        label = \"Rejection Sampling\")\n",
        "\n",
        "VECplot, = plt.semilogx(results['sample_size'],\n",
        "                        reference_query*np.ones(results.size),\n",
        "                        'r',\n",
        "                        label = \"Reference value\")\n",
        "\n",
        "plt.legend(handles = [LWCplot, RSCplot, VECplot])\n",
        "\n",
        "plt.show()\n",
        "# Plot 2\n",
        "plt.figure(figsize = (15, 10))\n",
        "\n",
        "plt.xlabel(\"Number of samples\")\n",
        "plt.ylabel(\"Absolute error\")\n",
        "plt.title(f'Absolute error on P({simple_schizophrenia} | {sex} = Female, {mother_with_virosis} = True)')\n",
        "ELWCplot, = plt.semilogx(results['sample_size'],\n",
        "                         results['e_query_LW'],\n",
        "                         'bo-',\n",
        "                         label = \"Likelihood Weighting\")\n",
        "\n",
        "ERSCplot, = plt.semilogx(results['sample_size'],\n",
        "                         results['e_query_RS'],\n",
        "                         'go-',\n",
        "                         label = \"Rejection Sampling\")\n",
        "\n",
        "plt.legend(handles = [ELWCplot, ERSCplot])\n",
        "\n",
        "plt.show()"
      ],
      "execution_count": 45,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1080x720 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1080x720 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "oY6EVRgp-M77"
      },
      "source": [
        "As we can see from the graphs, the approximate inference method converges to the reference value after a certain number of samples.  \n",
        "In our study case, the approximate method is not very useful since the developed model is kinda small, so the exact inference method is very fast."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "PCNGQr49PbEA"
      },
      "source": [
        "## 5. Conclusion and future developments"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "kFzE2afEGWNd"
      },
      "source": [
        "During the development of the model, due to the lack of data, we could not develop the whole network proposed by the paper, but only a subgraph, whose data were present in the paper.\n",
        "  \n",
        "Indeed, one of the future development could be the collection of the missing data and implement all other nodes, making this network more compliant for diagnosis purposes.\n",
        "\n",
        "Then, we can also inquire more specifically about the type of causes, such as the timing of the virosis, and how they influence other nodes: could we use a continuous variable or a discrete to represents time? Can different types of virus influence the probability of other nodes as well? Does it depend on the RNA or DNA basis?.\n",
        "\n",
        "\n"
      ]
    }
  ]
}