11_Hierarchical_Clustering.ipynb

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  "metadata": {
    "colab": {
      "name": "11 Hierarchical Clustering.ipynb",
      "provenance": [],
      "collapsed_sections": [],
      "authorship_tag": "ABX9TyPg8Fjdl2F0FI/Rn1B8ikxd",
      "include_colab_link": true
    },
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    }
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  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/sandipanpaul21/Machine-Learning-in-Python/blob/master/11_Hierarchical_Clustering.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "u6dqL7f4XJgd"
      },
      "source": [
        "#### **Hierarchical Clustering**\n",
        "\n",
        "- Hierarchical clustering, also known as hierarchical cluster analysis, is an algorithm that groups similar objects into groups called clusters \n",
        "- The endpoint is a set of clusters, where each cluster is distinct from each other cluster, and the objects within each cluster are broadly similar to each other\n",
        "\n",
        "**Type of Hierarchical Clustering**\n",
        "1. **Agglomerative Clustering** which is a method of clustering which builds a hierarchy of clusters by merging together small clusters, it is a **bottom up approach**\n",
        "\n",
        "2. **Divisive Clustering**, is a **top down approach** where we consider all the observation as a single big cluster and then divide into small clusters  \n",
        "\n",
        "#### **Denodogram**\n",
        "\n",
        "- The dendrogram is a visual representation of the compound correlation data\n",
        "- The individual compounds are arranged along the bottom of the dendrogram and referred to as leaf nodes\n",
        "- Compound clusters are formed by joining individual compounds or existing compound clusters with the join point referred to as a node\n",
        "\n",
        "#### **Silhouette Score**\n",
        "\n",
        "- Clusters are well apart from each other as the silhouette score is closer to 1\n",
        "- Silhouette Coefficient score is a metric used to **calculate the goodness of a clustering technique** \n",
        "- Its value ranges from -1 to 1.\n",
        "    1. **Score = 1: Means clusters are well apart** from each other and clearly distinguished.\n",
        "    2. **Score = 0: Means clusters are indifferent**, or we can say that the distance between clusters is not significant.\n",
        "    3. **Score = -1: Means clusters are assigned in the wrong way**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "qcrOZvxZDrFS"
      },
      "source": [
        "# Importing Libraries\n",
        "\n",
        "from sklearn import datasets\n",
        "import matplotlib.pyplot as plt\n",
        "from sklearn.cluster import AgglomerativeClustering\n",
        "from sklearn.datasets import load_iris\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "from sklearn.metrics import silhouette_score\n",
        "from scipy.spatial import distance # To calculate distances\n",
        "from google.colab import files\n",
        "from IPython.display import Image\n",
        "import scipy.cluster.hierarchy as sch\n",
        "\n",
        "#uploaded = files.upload()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 193
        },
        "id": "rDJK5o0xclCc",
        "outputId": "67be27c3-ff6b-4ba2-eef7-de966252efb6"
      },
      "source": [
        "print('HIERARCHICAL CLUSTERING ALGORITHIM STEPS :')\n",
        "Image('F037A50D-FDEF-4187-962D-CB74158F4FFC.png')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "HIERARCHICAL CLUSTERING ALGORITHIM STEPS :\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<IPython.core.display.Image object>"
            ]
          },
          "metadata": {},
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 196
        },
        "id": "LRFl5EVBcnYd",
        "outputId": "eab0c160-225e-408a-81a8-c04bb38a1f07"
      },
      "source": [
        "print('HIERARCHICAL CLUSTERING ALGORITHIM TYPE :')\n",
        "Image('207B916C-6140-4038-AA53-0027F4E66C3C.png')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "HIERARCHICAL CLUSTERING ALGORITHIM TYPE :\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<IPython.core.display.Image object>"
            ]
          },
          "metadata": {},
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 515
        },
        "id": "Hkja8Ir-cipE",
        "outputId": "9a41a3f7-1a45-4714-df89-6ef4321b07a0"
      },
      "source": [
        "print('DIFFERENT TYPE OF LINKAGE :')\n",
        "Image('093C32DB-A817-4026-889C-CF26F7240891.jpeg')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "DIFFERENT TYPE OF LINKAGE :\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "image/jpeg": 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\n",
            "text/plain": [
              "<IPython.core.display.Image object>"
            ]
          },
          "metadata": {},
          "execution_count": 14
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-YThM0qSchtV",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 281
        },
        "outputId": "1b8359d1-af06-4ad5-f912-9ea912d3fc4d"
      },
      "source": [
        "# Lets take an small example\n",
        "x_axis = np.array([1,2,3,18,19,20])\n",
        "y_axis = np.array([1,2,3,18,19,20])\n",
        "\n",
        "data = pd.DataFrame({'x':x_axis, 'y':y_axis})\n",
        "\n",
        "plt.plot()\n",
        "plt.xlim([0,21])\n",
        "plt.ylim([0,21])\n",
        "plt.title('Dataset')\n",
        "plt.scatter(x_axis,y_axis)\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "_Bguht7t8iSt"
      },
      "source": [
        "#### **DENDOGRAM**\n",
        "\n",
        "- A dendrogram is a diagram that shows the hierarchical relationship between objects. \n",
        "- **It is a summary of the distance matrix.** For example, the dendrogram suggests that C and D are much closer to each other than is C to B\n",
        "- Dendrograms cannot tell you how many clusters you should have\n",
        "\n",
        "**A common mistake people make when reading dendrograms is to assume that the shape of the dendrogram gives a clue as to how many clusters exist.** \n",
        "\n",
        "- In the example above, the (incorrect) interpretation is that the dendrogram shows that there are two clusters, as the distance between the clusters (the vertical segments of the dendrogram) are highest between two and three clusters.\n",
        "- Interpretation of this kind is justified only when the ultrametric tree inequality holds, which, as mentioned above, is very rare.\n",
        "- In general, it is a mistake to use dendrograms as a tool for determining the number of clusters in data. Where there is an obviously “correct” number of clusters this will often be evident in a dendrogram. \n",
        "\n",
        "However, dendrograms often suggest a correct number of clusters when there is no real evidence to support the conclusion."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 272
        },
        "id": "6Az5h3SZ9Y1o",
        "outputId": "a648e64a-6af5-41dc-a4a3-abe0e2c036e8"
      },
      "source": [
        "print('DENDOGRAM EXAMPLE :')\n",
        "Image('70CA363E-F7FA-4C9D-871F-4D3FC35EC0B7.png')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "DENDOGRAM EXAMPLE :\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<IPython.core.display.Image object>"
            ]
          },
          "metadata": {},
          "execution_count": 16
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 426
        },
        "id": "14IS7waFAjth",
        "outputId": "a8f00411-dba9-46f1-c78e-38477c0077c7"
      },
      "source": [
        "print('DENDOGRAM OVERVIEW :')\n",
        "Image('EE76D8A0-92FA-4705-B1F6-748F38D83112.png')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "DENDOGRAM OVERVIEW :\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "image/png": 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eJe9i/OrhJzyEDU78P96BaDONLipWFJ/RLiGnM7GHw5F932qTkEBTS/7WgK2gzjxvHgDvhXPEr5j2hgPZVHZaOFU9XpC3FmLrnbjeM1bS1ZPW/xWWmmZ44Tw7PF5/HZtvZOCXBIDXiTEYT0bJR18qaXuSHj6iL7NpjwfmhveGdnpkPr5r06oMsIX5fOPXnv4ZK45cK1bszf/hpwKvkKUIgOen3ZeR5O9xTVXbRZLLk7xzIiMNnSYs1uFktu/cPbUli6M7Oamtwifm69HdAigoAwDAX8B8+asfUuYAjVe/AQC22o0Dz3mymXW4XBwNhauF2lR24Uo0NI+cJ9xxcrmm/vxM3jhXWbKEeq5YNLDItQjld3twLg4AgO2lg5A0BzFB3gHOgjvXzCXeFSvl7mx5zSvC73qyd62yy8gF5Ko4W70oQcMC598tqWlPWgxQWkZrxPoiXgjF3osyNQv4IQEA2FJ5Fzv24ZrxwJbDz7FnXJaYjmaG81aXhmZaPLheCn+rKe1CSWJT7AHk7ZymmTSk7pr0sxKlXmdqy2pE3fn5d08If3YBBWUAANhebDmFTbMsZ9sDthyP6fVCeOZEd7PsFqapYkt+V21Xi2+f85V9TeSoYYp87fFCNUrLVVzeWe75BffnZ1BQBgCA7aVSRqhD6s00p/71FL4SIKS+VVG+PcW89T16h1mnLvalnOBdYWfJtdndamj+cMnTxc+E488O1C81laUxi8v7km79r3iEvQcFZQAA2GJGQSXYbqPVCE7CFUHdAQZNFy8885Xw1sNe9Frke/fUdqzcDL2JhnssmHt9zbR8weV9xtLRqywz72LQgL4enw5U6TQjBb8kAADbSXBAnyato248AGjsQLknssWlmE/tLhee684MFiQvisME8fIqO6rbLARvKU8Olpq1Zinolrx6DJ8tqCyFR+/OVkTFzQVdCuBFZjNwLg4AgC3HHE7mc3sAu+6AzGXEP15qkT3vJfOxa7y0axCnrrIjN7o8To6smLPt8mue5JYnkF9wx19luWDB+7vcz8/MqLdfYJZVOBcHAAAAAFH+F1ZpfMBsHT6MRp32Xc2qiYX4msKO3OjxPDde4NGzRPOsVsyarbq7mZ6oDaewNEj1WJqOjmj9iUd9dYtt/V/CuTgAAAAASPbeZzizYjhivUC3tzV+cD3YSM8q7MiNC+5rE9U1Qhlr3lU1XipemhooLC9DhV7JlIPUgvekmUQdzsUBAAAAQJQ9WXw94irXI8VduKZaVHyzajuNSy07C7+MzRYCuQ5F2BPBV1j2QifeiFt/7bI+etRgCfXiAAAAACCGXGF1lzSZXljePe5G90T9FoWdJgT52hUpalLyTGA3NqdYb7BcsnT00pTjknv51tLDE4Me1IsDAAAAgDh8p32doi2WyCHnLuo09TvW1L2ZiFOP25F5AA6Ls+rvxM75rhupBKdVRQveoFdZYiu2Nk938GszuZNLPtQT+BkBAPhNzHwFvoQYRh4C8l/vH2laQl3ODFOjWWlJm7nih9VSa5dprsIOO9e0MGLdFYvmhbXsqeMJBEtZ455stAzaxKo7ngDUCp7olSSm77lwLg4AgD9AGjmg7zHGqPRixvL1/OvvdnHw/uLbfw5izdnz8+wb/PEsq+e9c5dy3UPLvF4s6hmmoL4+LxdsAX6vTkVfYaelegse5bZXv9zjjyj0FrPZmq+hW4ZRWwS6vM5stLSszGohiiinljNvVvDFf+YVQsMvrDyIrAOAN0mz0ag8hw2Td/QCHVVzNJmKiq/pjrpizGjeb3Qb/Tv2T+b9PP2HpiovSd6/6Du/2cNnPeAm1p7T9bM3+peoEJN8K1zoGDewP23iuhD/o2jJL4R9fFbhW5/I7QrLkJXBbjOsaEcAALxJ7hGaPIfNC5Z3u4sQumP/ME7byFHclO+3Wd468tnz/qUzNUDeI5zp+uHFx6ujaLvxhuUdAADg5TFmUp3uN59s8/fk/Qmj+W11D3S6YVMVbwWvFDMax29ude46XYTa+N5Ko4wQ6oO8xzo4roa9+Zs3770DAAC8PEZOmQhuBw2ebPP35P0Jo/lNzDIqzdkyqF1GqKGQd/MWFTsV5t2X6aJ8uotQGuQ9zKH+MfQ6c3wK3jsAAMC/R+s5BPXR8t56NnkfIjRnIt5po+LcVnnv+c6QXfYRGjPb0h8pDvuW5D2j659DDWf6KXjvAAAA/x7Oi5J359nkfSKG1EKoPPAbnESDShF12HUD3b4uebeyuVyKKXEuh5fRT3Jff3zIZULynsvRKHcjm+NB8Nn9868HqZigHxydH2VplLf5Xdc/5nKsN9/mp37qv86dEHlPHXz4dCYt3qu7BAAAAB5LczJ9GKfxIrTR9PXMtOfz+aRCXviaNUbIbvpw9TXSdw82Dw1X2dB3BvOpLb/Gb5qj++l4YKjl3UjHo85Dj5NGk5dGJC4rzYpymKqmxAEFcYJczW9RKx+IbbK8a13UkiwUN+TT4wfpWRsHkLfv5wPzeb33H7rOot6OdP29L+Q3pzjU/XBflvdDHvp+o1+Ri+oXfN9V5Mjb0TFpzuHeD3XCd/ru51PacIbl/Qe++fRbJrFLAAAA4HFUWLlyZ4o1tow3mX1G/ouiLz63RRoWzrzRZgO3tFt5pssxG8yQ3IecO6HgjpZmreO4vFcmtOuutG0dfpw8mgbqspumqJ3nl13lMFVNiQMKyXtlbIYbfkXem9Mu/a7nyQOYkK+z1BhNnk/ezaqu/6TXvl9dxefYjnd+Xvn/zSbK+4Gv3Idf/PtOv8sd/jeYGez4qn36yX91sbMTvNzZYYJ9seOb7eAGX96vaBf6t6QuAQAAgMeRLmMZaTgILyv7cmMXESp1nZJJ/eluKVCfks8DtrD9W4vdst9YTjOJitoEjINpQ7kbGIuJAApaS93gcWIZm785DR5ULrf9Bzyw5sjj5NF0hKZ3xS55g8hqbJiqpsQBKdT8n+TdLIl4eYW8NxB+VvAB+wkDCPb5UbEcDLTUec69d1/USYqVKjmcnv15lNE041zXL5Lk3fyiH38wNW3/WL9Kid7OiO9t7h/qp3h6sKvrP0LPu5D23omPf0Dv3dAlAAAA8DgGgdLgc2aDhyGRG0cWPyyfQyTtdtttVLozNc3oIFQ2tE02wb518SFYtU93UbvB30Sokab+aXEYlfc2aowCT/muyI/XxR8nRuNb0Cwzw2AOQd3sIg5pi9upmhIH9AvyPhCH5FXy/lC8temcij5LOYApQs7EH6AxCTT/GeX9SNc/4Yv3ur4vmo0r/UuSvO8z2f6h6yKvXepKP95l4h1YmBvlPYic18mG/lfyZHWXAAAAwCNpoNIkorGoK5auFfLuW4zI1R1CDxttzC5yqOJXWgjZvPdbk08s+lF5R7d0/zldYuvu8cdJo3HYfkEHIVQkW+o2eTtup2pKHNAvyHtLbE2o5N1ku/4j9izVACoOKtOJRrP7rPKeOaSx8Tv6oewz3+iHSfJ+ox+nmNN/wY0O2Dp7sCqgF1Te+zfJe6d9ZvGm/4YuAQAAgMcxia5IB3KT1hLkfcKUkSj4Rps7yY/N+2Jt0jdLeSGFjZj3zvPVdKj7rnicNJpbprb/Z+98XlNXojg+UIaB5AqF3E1pUcGqhSL0bmKDVNfVQjdmq5u7EQKCuI3isuDGldBNFboruOof+DI/YiZmJqbvtr776vnAvbXJnJlRL/ebM3PmnDGeecKT99k1hZ3iUvqEPizvpoUlSVeH1m3HGmgnsJYyA70eVN7RA2FOtKTM6KJUqOyR9xzJFTjR5j1bCgi3za8JKezz3jv80hU/PKfuEgAAAMjqvO+UcYkJlEreXexs010/8/tKGzdc/xZibYmbvnTR0Q9drXHHXDGcNJuleDnH2BZb7lTp1XaKS+kTolfGH5F3Y4xrc8nY1TcNe1ZNYCxN1GgfVN4LXFxvmBwjdJZ/F+HuafJu3JKI3ravl3C9nS2159GevXexBM/Pxmu6BAAAALLh7K5HW5LTrZR3RzrNbXHVVtq05WWBqtgkjzbLmVTW9EOH7q1iOGk2VY+njhniuhH8afBLr2o7xaX0CcXi4LPI+wzLmWw2stbvshGPEqoJxD662WHT2uRIz0AGX6M3f9JA9tve++g2Vd6bhLSKIVHim6dI3hdMvdO9d57WxrwgrRttlwAAAEAmDE9WVCE3dpq8BxbDfsiEa5PKphFFslPqXLFiLVdJeV/JsjbWDBdfS3BDj33OE8YtMe4r7VRdpU6oYQ0S+fNT5f0NY1cqBdaoY2eZTNRjTu3VcuNH8r47gYas+Mg/rLxfE1KhanxOZ5ojpMy8+LJG3h+ZvJu3ihpwCHXEEgBi6/T3aM/e+73svWu6BAAAADIxl8UllJtpmrzPcZw3jc0k3jNfMY+3VMi7dNqdHwhTDSfL+5AF1M2ZDPMx+GOBwk7VVdqEbIxxbYOyy/urFwUY8jeUrCvTHzrhuX0HaSYQ/1LWB5V382eLPFCnuolYlNvIFD69Wt6L4d67av28G0Xf83X6D3jvmi4BAACATNh4N79rLDOsQt6Z6Em8aWzseEEVdyvv1TR5n8ZcYVM9nCzv3OOna/Ph33y7QWGn6iptQrR9/QPyvvKwE69kNx3vyLvJCsd6bXcwcCN5352ATbMDbdkcOOf8iBQvhH7nuROP0FV8cT4Xpp4viX3xMnX5E0RJcoweaZ2qvXdypfTeNV0CAAAAmZiovPdUeU9aqG0snfdezey9tzXDxY7ptal+8t4nVOqn3EBhp+oqbULmdDlOmOjl3a5tj90JGm1cX0/kyMUZxm2f55591st7/HNYH1je7wl5EiHvi/C02kM8tG4kstSYZSHvFUJyTfGpSbsTj2HofJcH4ie99zALbtJ713QJAAAAZKGBFXvvqfKetFDb9OPtRPW0ffK+kqVwrBkuJu+02VwY0ti5DT/+rrBTdZU+IVR1MofWWXVc36n/utl9OLCk9ABDvbxXo6pz6OB77+iUhsoXWRGYny3S+nV3WhjtRM5fE5KrXFwV3rdR7VT/u6VmafFUlFLEFwL769Jp6Sa4y6Q6Ie/5oKeFedVMeu+aLgEAAIBMtBWR86nynrRQ2xg1ud1UKNY+eZf0V5xzUwwXk/cl9qprtipPldBBM+zqpqm4tEfesx+MU6h70ngo7T4M9PKOYpVkZ4cuCBuIcVi29UacTCs+xeT9LMcvt3phzvnmaHuKTdbiy/B4W5F58Unv/azHbv9CCe9d1yUAAACQBVdKqpIq71ZksXNSXmMzkHv2hSLvk/fIV56LXWvFcPJs6Dm4VVh5Nehg4ghpVNgpLu2T903GtDaTNq4nVv4TaW1mkbHppMj7WHouMJxPl/efp6m3S9v8sOj0hOpzcXRX4DIe1ntvPrDr5eblNgDuMsfKv/VuruS+CqziXNBQjLwr76jEysJ1qbwvtt57J61LAAAAIANLnMwMm5Rqebn4NVl5RWmzkdr168Kn3ifv3jSSQm+uGS6+eO1ivxYu6js4fI5Q2Sku7ZP39Kx11mzGB563cS25r5+Qdz8yXmO9vJtDaVV/8/lZ607uU2/HytKelSp3qq1veh0hU750WqqUmomGV6XCz9Thmqyw/A/lPXWXAAAAQBb3vfa8V6pRXcoI4+L6MoO8G+Ntu74b+tv75B0PhH/9WgtHTA4Xmw3LNl83tosEWwVV2Cku/ZG8D0UWm6mjUvekvD9vgwuseoq8sxy+4jkn6PrT5T1X/lBzTWDbj0TEm/HDMJXm/z40Tt0lAAAAsPf/bsvDeGbR6mS2v9LKu4trS/E/fWSBpmt3qJV306ph7E9MVF22t6vu++Q90DW7gdDUx1hkfDOSw8mzYQHxW7W3otcKO1VXfyTvA76X3ncwHloRE528T+u4/WoiczrzcB3r5Z0+N7RpQpzqa2Dx2fJeILew2g0AAPDtYblXPKftcVdULe/sDHjQpipZOPWwCKvaxrRpgxotaer5RiZ5H1ht2jOrE2/tTDAaLj4b1I4C7k1HypWXtFNc+iN5F3vkm518OWOdvNOz/LjO6tUPrDR5Z/XesUM/Orda+2R5f4IKqwAAAMfgvzd8h6mS59pJqRYSiqgHjvlvJgosPJ54zbeQ3gZN/ToOGtZdO6H9/JddeffRfODhGsbtWTU2wdhwsdmw0PLtdrHPMtJq7ZKXUieklPeo6kzD43v5qx15d7XyTku6U4V3nwNjJ20CtlvDNeyN/QYaO5/6fZ8VCS/eDgAAAHxzgUcNy15Z1dRGxmS5mkdRV8xiuv/RYWJbc/Mjm69m1bbs/u7V3eF2ZqNFMc1sM9+6014j5eZuyr84AzlEIHx7fXs5yfRxmHPLrn7Bt70ghJA7+FcPAABwHAr/f+z665knaspI+GkFXxGqtpOB+n8BZSrvL/BPHgAAADhejDp2bN1NF29STKeDtEeD/4wrlmmmB/HoAAAAwBFDg+EcV6nwDa+uXbhvDMYeTnfu/yO6kAgOAAAAOHrMIY1zV3vpz0utWZVG/s36f+EbeieE5oJ7gq8WAAAAOGYaq/Ww8WGj4dtq+he+GfOuJZLAn8I3CwAAAADfhJOwTMs9fBYAAAAA8E3IEXLLlufL8FkAAAAAwPegQNflCekRXWLaUqdTSuug24GMdwAAAADwd/FEi7QT8kh0iWlF3fWr32V1nbfHL8x4Z+bfu1/RFgAAAAC+NSwhbY6QsjYxrZD3S0KuDy7vqEeKxle0BQAAAIBvjEkT0napvFMvXrUIbwh5X+i8+y+V90eRbudmlL0tAAAAABw7ZbrnTuW9oEtMK+T97LJ7enh5L72wbDsXt63MbQEAAADg2KEJaUc0eL5M/1J5v6H3nuI0f321uXvSgu8KAADg+2NWG8f0do1q9Yt67rLz7kzeTzSJacPQukrliv9A6KJy/ZIv7ci7Ualw82al+3JeuQjtm4vzTndRqYS3zVK+0y0kVgIuCped8/xdOIRZuH4JfhQqdJjSO2nRDpq8ZeX8ZCEK3CXbhn1dls6kJrEZAwAAAH8pFm4fk74vcf2Len5n2eqYvN+pE9MK7/3H9kfr4oTnuXu4kuXdfAg6MRGqjPjdYp6bnxTDtDm83d0je93beZK4vOVtumwIo9Ljs3kkOYRuWnLinS5veUMfEMxEW6mvXgWJJjszBgAAOGL3eL5as8rlprXx3bE7G6ZkS7dnM15h5XVjKVS377qZtLi/9gdjOpS9N0LKwrh6RN/Gl8m7kHQm70Lqdd579IO2e6SS+iDL+xMhI+ov3xBym3unx+iZs/wSNH7qjILWud+/EEKForBuLeRBaE3aVi+Q5RIbYkFbtX4JyX7IBb/mAugjwQMhrdxj0PLRQIq2Ab/pw0WOpuq5RsoZAwAAHC32GGP8HLxY0ReMmq/T6H4b4zV9QaumtNeJw0mvmcqg2gMvHAq3fZD3g8i7WJDn8t5VJqYV3rsZ/eBuMdXpUiTvnUBE2bNB6f3yIrDqcC1ttkiOusz3hNywp8VHcntuIpS/JT3pUSJPSHER/F7o8iFa5OEOGWdbyX4I995/LAJdvwv6HbFAflXbe740YOaL7CiAYsYAAABHq+5UYl0bzd3gheu/LZ9nHsYzTesBFvLecJ3gpZ8UpwzyPgwG8JzB8G04a2M8UIu63wd5/1REOB2Xdx5mt997FxrZJSS/lffrQHUvYk8FPab6/7B3Pi+NM2EcDyxDoFEQspdFsULVCBLY99I0lOrZKHgx1/ayl0ChUHJtQ45CLz0VerGF3go9+Qe+mV/JTGbS1l3FXXy+wK7GmWcmhfKZZ+Z5nrnjKwbG3lMenv8i5tn96IpJeXiIn+znMt5rh+YZOYG/OjPPtG2PumbrO7cz1M4YBAKBvqgsH3kbvEG+QCi06bOJXwXpNUI+xTvmboyQXWowCfxdLLbwEiFl2wPWIrX1SwA0Bry/p3gyHMU7SZK72e29syVAwzS/cbxn4DwsnWsPCX6vebjeg9miT1vUae+IO+V18Rc8YPekAu/1PHvvwTQ7JCKg1LZeRBC8muYVaVKaMQgEAn1RJfl15utm/nDs6t33iYfSNMe7kXjFz/trgFCw28OPAO/vq2fm2DK8T7WFaRXv/Z4+v2J+c4b36a15KK4LThr1S4r3zOSlQPtspMM6lWm+5u1PpVOBy8IhV/Cetbym/X8Ry2rbc9Pkp/p0baHOGAQCgb6oFlp4RijWtK3FyG9HAtLDyk38atl9FPR2NwsA7++qH7wQLcP7D11hWtV7ZyuAE/OW4b2b9fsvN3r6ymLlMc9PWuYQ7wM1bs1f2FjLLNTNx3gxb+vSgE9VeP8m9DdPiWteavtSHLCflprwGYNAINAX1QoFmqcj5Gv97n7TEPGeKgfntV6O4qS5Wc4X43JkfEjj+LarPUOo2cvkcLxbk9VyNpZD+Wr2fN1ss3F74kC98oLAsWfrojuZpdXcbDaLtmpLfZ/earmyqf3JbDlvOlXT0Nm2ksVyvbHx2/S2jOY0Vxv8YX0Q3nNvneE99+a3e+93HJbceycsZ/2+4wD1Vvd12KLu+oNpHh6f35/RQPob07w94zoUdxE6yoBavGctW7mBU13b5+IVaB1ddcYgEAgEeC9kRTq8N/s4lC7cincbuZR9i5BHxsuH60kfxbty4azU5XH1KcV7M/SosZmA8JC06kd4wCZCTWGmqD8RYb9kOQHBhs/SN5o+eTRRbcnv4xsr2tLPxk7oW/VHjn4aGtvNPB8Bhy5WjbagPbxwsvgQvB9c8EtgOd7xWfz9Tu+dwtIqvHfzMXOZD48Mbop44hcU77Vn6mkf0iT0li54D/vk9fKAld57XZ2c2Pa+aHGO/3igzhgEAoEA74InGWji2R0fxbXdeKc76WvMqjiMPVRy1pcShysUe5h/XqY1MTmg1gKE0CBHePabG/tZQ982DCsQzwlSFFjyhgHu7vd5qL+NgqaLnwWepdqS36e/7qO+T8beJD7uFGft8iWK0rVke4mQG41Swvg4HOlHs3AugevjR97gQ/B+0mJUz/FOIunf7r13j3BUHgloa5h0Mx6bInj/8WCePT9f37EScoeqfYPtou/lvd+Z5rlmckJbITz+hafRg/cOAoFAVXjHse0r5WmK3LGxJ957LgpmeCvcskdynZtwP3olSDx750H9Cx+5Sb6V4M0zuNQy9Ps1fJzg5eM4npyxt3ZpKR6bd89MBgLJy7bk90FohC3PAuRmnbAhJ8rXLGpX2fbYRTF24q2Nyz8rzWhLHG2Y/Vhb4FXER+Dd6rx072S8vzwe37zde380jKsupWoG6mPy5yu2OX9utoR9d7wMuFRnUhcz8nTe+zOOgWfLh9ft3nsnb1HrmrdH4L2DQCDQNryPI76LLGrGEt73wvtMTZlj8rVBe7vwnjJnedzn4A6Rx/bf5witDWMi7BLM+Z47ZxsH/4R1xyZjR1xySLZKeGd2N3iDnh2nx/wt1K6y7RR5LI5wwCeldmkHyGerll78MXg3LMOqyXiv4Ydv9t5xOF69RU7XpxzUDyY/e5cq4WXdeYy9VYxkPRbh7lrv/SV32l+LWLqavm1u64nuKID3DgKBQDq8L2az9QhXlEuVILOeh0Jrb7xn4Krpx/MqythsxbuX4zJiC49FweGMtQGZV1jgOqwwG9HRMYKL5YdqS3ofn6HJCvIcwuz9vKqusu0430do92lvTZelUGZghT6s5rwh412j/bx3w/gvA/eJ8f3WvP2vc1Qfssh5/PjscTgcPlx3cuw/NW4a0+ezukT92/v6TeN8WBfcbUOshnM4ta5uSAi++Wva6dSPX4fidIq29WwK142jxk+TVMCxwHsHgUAggzuVgjcdsJq0G9X5C7kbKuJ9oHCU4X1ckFCWo6l0txvvI2FEn83CtwRn3SHONXOKJ1WD529rS7XyVFvS+wyKdq4lv6auq2Tb6heflk8tabrEwqNa8Il439N7J/vuD6TkPI2kf6Z47+Q3yrSI142ryTKJMXLf+K0xU61H/qNL/oiz705zg0O9957fTmOeTQ3w3kEgEMgQ3EsBdHEQ0Jj1cFJqtkRoZih43yB3osW7FSB3benxPng73lfCZoPLWJmKY9qG0fa4ZfEYvqQNXR3YUryfakt6n1nh+vvl6Wm6SrZFvAd0fpougfiZpH+V9z7lvjCNsud4x8fvd8bRNwzXs2GnToLoTrPfuhcXF/iOGVZ39vyQoLz7UypzV8e3xGRNG8R7n5aR3Xg0eRZf54IAvjWcitMR2hp1cmnd2cUNn39pxiAQCPQV1bYjpfysM57HCLlyeLtQxk7Ee4ZUf+Zo8G7YHkLxWlOOxtuvEo6M92IyC4r3Wh8Nelxjyv+U+cByEH0O2qS5mG1GOd4LkzpbWtxHxV6FQ6en6yrZFmDeo5+0pktbGnP0md4704H03xb9aFx2+CqucWsOWRb6PQ+6M4yjxmXjRl1HNOo3W4a4ubzMY/S+X9Y7R8aBVTmdq0b9O3yVQSAQSFBTvxFvGLM+kkrHt+PidxHvOBZd3m3PS8zhgvTIUwvKx5Xn4lvwnpTxPkGy1kaR+r5Qw/p6A59n0vuKSZ0t8X2SAu98o+OA4V3XVbJtjVDf5r1JRQBNl4m0IFj+BXj/LWVM54F0N8Jt8rXagfWHlq0/bgACgUBfDu+e9ph6Lmerj3C5Oh3ek7gK74a1ILe+hqUsd23BnF14d8p4JwsTQWvqKUd0hFJsvjXCE+kHYRSFOd4deZFTtmVoBi/wzr13XVe5hm4SIG/ZzhAesXWDpktTCvTf/Kt4fzBveThl3TSv4esFAoFAnyUrmcUVVWakzPGMP96cy0NR9i91SdsYXuO2HoeWlYyCDKtyBv0I9dt/jvexbtpL4h87bvmemxRfKk8L0s41eB9vK7Sjxzv33nVdZbxbZAEUeLjSXdVo8nn/P+u9P+VpbI0uXLYOAoFAnyvH1yeqSbllI6QoZJ5mmVWl+19qA1dw/Nm+wD63zO3Ae1tXuL7Xxw/XvCyuMKU8D32gwXt7WxH87d67rmv5BpwFqT/rR3blaI4Qvvd3nL3/lnDc/Ou3p+P7V3Iv+wF8uUAgEOgTNdCXmZEC6jdRGuUKUJz9O6jorFzvZrtyJpzjCVnsv413I9Dl15HYt7C8XhlIx+cq3vW29vLedV1Ln0Aze3+nV9s6mnSvbvqv4t2od3ka2+ETfLFAIBDoc7XR3hhXcyuRt1/NeRFXcizdcq/M96TYsNbiPUSBusc/QyiZKKkAafGGlq/Du9bWXt67rmvpE4jKYfyaLrGwSqr5/yzejYPp/a/hw8/jS/hagUAg0Ger6kJYuutup+niD/E+Knn4Gb/UaH1lHGHDWov3lS5/vhagwUAoEcMnEAhLCw3eV1ty8Xd475qupU8gLlW51XSxBsIZxwb9u3gHgUAg0N+H914iIYhCcqCelO+FdydHbKJcPjdDCJUvXlXHKfLjtXjHldtnmlVJ7Cu8nef+PE7G1+Bdb2sv713TVbHtz8ZJpnb1aI6HYvbpkzvpAO8gEAgEei+8RyicJ5jKbXxVu2ezh0IS9xvwHgcjG/OstwzUSPEBQsgfzPBg7eZyrR8nRC6GoFWB95qNK+Pb+Ew7WYac6GOEECpPOPFQsLIMK0n7yNPhXW9rL+9d07WE90Uejeink6rRBvgmexz1v8rmCngHgUAg0HviHWdk++SGdnrvqSGfCr8F7z5CCHmI2FI3vle0tr1LKuB6+nFITr4f9J0K751W1On7Ph4jn0Wsu+uO3D1PbpCPbK33rre1l/eudpVtjyMh3YDlEKijkfvekY8/ltBxAe8gEAgEeje8t1cRq+zmxiu2t/4/e2fv2rbWB2BDEcVSWhLcIeUNTUlz40Ix5BKQq5o0NFlat6VLtbTc+pLe0BgEgqIOISiux4KXToYskSFbwVP+wFfypyzJtuT4W8+zxI5+0jlRZD/6HZ2PnOaXs+qaQTVI75ojN0ttHUsrBi0Mm9MN22dOiGaYfcqpNu4BvHpPdfQu5UxFa6bFZqeMWuBqMlbzbkO9tAsK1HvgsQL0bnazd63UZ9feKXPsbFy35FKpIl8XteaYASkRUJpsn7CMczZyCUOZ6D8cvQMAxAHLNc2MVEhZ1VRBcm3sDE8LpNh/AnmpYlWv5VLfLunplJWy5It0/3LEi6pVEQdWPpeSrZS7Mb4cvJqMVJCrF0NmLvUdKzx9d83lBaNbn0tXp37/LlIlJaen8A9H7wAAccA3kMxtQXPwBPHpfLjl34ZihpuIfrhnQy5YMy2qvbct2TGdLvQOAABDELOdB+1+1L4LpzuU/MvNjcjgcsJz6V9NZqboguC6W5LQOwAATAun45miBho+p/VdOD2RKzqPz8eUdA8oJwqSETwF3yz17mpvT3kWm0XvAAAwMSTd6eIWnD1fVvvulnb6vZUL46nDgHKiUBfG1AowLmT3uIGCEmo6XvQOAADjSZ6tmh45e87pf6zS/PwNkpRIWBlByc3XmVVsvzdvgQo1JdxiOugdAACgRUpzRo1r1pxVq+KsBGuoRdUZCZeZB7ujdwAAWBzkxsh2ee7qVTKz7Unr9MJc1Ai9AwDA4lCx6sNGts8GqWTV6+0B/ugdAAAA0DsAAACgdwAAAPQOAAAA6B0AAADQOwAAzBIpPdKcMGI6zblD7wAAMKekhPwofq8KWc4degcAgLnVu+DLwwuqOkz56B29AwDAYun9eviSr+gdvQMAwLw53SwM0nsVvaN3AABYOHThYpDeK3llWMc59I7eAQBgzigO1nsI0Dt6BwCAOSOP3tE7AAAsF7mqIMgFm3RH71Llula9EFsBYiHd/SnJ9XrdavekT8vXjTXa0Dt6BwCAOUIqZ1prkQvllt5ltbk8eb7azugz6eZPJSErjU2Vxgar+SarViz0jt4BAGCOMLKO37M2f5p61523hpq3f+gtvQstvefljLMtn3UWVZd0TRAyiuL8Skfv6B0AAOaKktDz7F0Q1FQrN8+UevUu5AUl1Q6t2W8tMZEQLedOAL2jdwAAmGe9l6Xm6wtNMD16F4xOx7ucrfpS82XBQO/oHQAA5lrv2Y7Bi4Lq1Xsnd3eSd6v9+hq9o3cAAJhrvZudDbqgePRe7O5lCIrUfi3m0Tt6BwCAedb7daKblGc8er/s7pVvdbxrUI693sXKRW5xaoveAQBip3e5s8Hy6b27Lee+D0iYMde79CcrCNk6egcAgHnVe2mA3rvbKm7XJ2ox17vZnDngGr0DAMCc6j09QO/dbXJ7bpsG9Xjr/aI1MVA+PeuaiDt/7/53c/PX7rNHT9E7AAB6j6r3lLsXfdyzd72ld60823rsX3252+Hg+/ZT9A4AgN4j6T0tCNXuIWL+7N1sz+uryTOshXR1cLeXN+gdAAC9R9J7IivUum9i3nPeautdUGbXPP/0e0vqz788br74IqJ3AIBY6z0VXe+GYHRei0q89S4ZHb8XZ2b3ew2lf/i4b79ZfbL9/eDudgK9AwDEF3c7e2i9S7qr63w97rPWpbSO3//MqArvnMftj13N8S+3RfQOABBnskI5evaezgpGa5xcSYn9pLSdp+8jPX6Xbl+BN43W+P2Q0egdACAGqEKm2pJMeL073cXzVfsX6eusEPdJacWc0tF7thLR7VZRU0z5dopfdR63HzwJG47eAQBigOws6KrktXQkvTfWexcUZzVYNZ2J+6S0crd5XilE2rM1qi5/K8M3kvdfocPROwBAHKjmG3ln2je2vaV3LUjvttLUjJARNMPMJQwl7udQ7+jdtWpuCAqZ7n2BmRrV8H/Zufvzl+gdAADciBdVqyJG30+qpOQ0p69xKtTR/F4X3NiGH6Xwjed28n4VPh69Ayws62tHh+9Pj0/OjtbWORsAkyetdC2thvd7TfCg6BeRy/7ttM0/CR+P3gEWlL2vSRdfz15zSgAmzUXWlb+Hfv5uCX4iG37b6Vi3it4Blp33SQ/3T35yVgAmjKW5DB1W0LmsEIShR+mAf+WMiosQj94BFpIfyQDOVzkxAJPF/Rw9Ww25kxzsd8fwpbAF/7L1foPeAZad8yC9J7+SwANMmJ4H6WbIvooFMy/0M/yfXJgjrDgd599F0/u9Z714n9xLD5558U2a88QXsuEN2fSF+Crz2xvx0RfyyBvyyBfy0Rvy27Vx9wAWmXsDLoU3Qy+FOzu+EN9VuemNeOoL8fVt2fdG3PGOXVnxf0J8efaON+J/Az+67wP1nrx/xLcvwGTRe+QctoFetMp9U/hQx7D1fve/iHq/04v/y+uOF7/efSF+vXsjAvTuDQnQuzckQO/ekB6934VFxqX3be//+U20S6GJ78L1690XEuIT8nL4J8Svd2/EYL0fJvtwzrcvwBT9ntFDDzbsa/h8iE74K07j/Ifx6l16gN5h/vT+aKjeV6akd8mv9wfeT/X49b6+1c/v71f4+gWYXvu8YESYgT5XLQYZPswaNW/tr8DH0fR+s9OLL/HY2PHic/dLX4jvy2vfF+KrzBNvxObwEP8gwM1BIY7e375952X3rZdRQnwRu2MJ+eULuRpalV9j+YN2J/MHjfo39+p9lEvBvxTDztCQVV/IU98dwPBPyANfyMOhn5DNwZ/dzy2bb309Pn3173233/n2BZgoUl3rsXM5yhS1QYYvh9jvmZPibETS+2J3rYuYqOxGu/uJVjJJ02T5z613Tnbi0/mr07Nv7RvptZOu4T9zcgAmi9zbUy6rR5rXL3ddzPTsb4bYaYdpbaald5ip3sHH+kmnf91PzgbAZCkZvfl31ixF2Fus9+4epnl/4yDSijLoHdD78rDXTuBf0bgBMGFypqeBXStWQ3ayq3hHyeVDrTLzIdJ6sOgd0Psy+v2QcwEwaSzfUPZ8efiCr4Wa6utZZ4Yq76PTOh/e2Ogd0PvysNLpb8cSMwATJ10OGONWrg54DH+hq1pAx/lwXe83Hjt+30Hv6B29x5H28/cTTgXAFBJ4JUDWmlGs+ZbQlSrXptpn5rp8yBXgG33nD+6EfASA3gG9L1P6vvGi1bvu0+gHEddXp13vGRQ5dR6u06SyhOT0TJ+p6PKGWiybeq2mm+Wiajhj4bQ+oeWQhUk3jfk/brrjd1c3n6J39I7e4+H3dvP86ejHWEtuTVu2Myhy6hwlt7g+lxCpVBRuS+hpcfa/NPz+/N67Z7933nzc/fB8QFs9egf0vlSIrfQ9uXYL1yannWfOoMghfD5rcni0N6aa9dP768P3x1+/Hp+cfxO5fBcTWb2d3cO2zdu8/NDw+0F3Kk/0jt7Re1w4v3X6jt5tXrnX4fs8DvEG6/3HaXc6oi3mG1xUUsVMdKtno7bNO2xcPe6ZqPsAvaN39B4X1tpz27yOttv714ugd1c1J633rVOH5ny/xw8npPcz51/17+n54fnJll0Ml+/CUqgZkdyeKXbnvZMjlfTyzk0neX/8/W+evaN39B4XxK2Rlo47T35aBL27qjlpvbdT6fWzF2MZiRCgd/HUGeTQmkVc3Dv5weW7uEiJlKmEdLumXqYT8ght8+0UfvP39sdHvzf3B+6J3gG9LxnHLb2/EKPttRB6P56+3hOJ17bf/5mE3s+Tya09rtglUnzKNELk7ZeNBWjKo7TNRwG9A3pfMs7aj3K/RdnrxWLo/cUs9J74lkweTUDva/eTW6+5YJeM0mW5fxKfUU0r17oVGLFtHr2jd/QeW74lo09ts7pn3w2s22x0XLvy8+jw6FPvM+eHa58Pf6wGHUD8Z+/w8PPa/9k7Y15HcS2O02zhLLKCpiFSKpr5AEi7WrYiNGkotwlCQqCIbLXNSxFFs5MP8PqVXrUf9WEMxtgnyeXOvZm55P8vZu7Fsc853Eg/fDi2/X6MRr6nDyBXfbcDrK/hfeUlmdk6Mqm5qcyMFpQv1fp5wtXxJdlrQURp4d1dU686qPio4fw6yXYLxwktvMc43G+m+vzpf1/+/G1Ubffrf/7865+/tYzakJt/rxUTwDsEvM9M6x7v0YsPlhmqt/cda+tYXgq0Weu2lNdKe6JdV1qheSw+e+quBFkP8MjZdYNWHoH3ZUi0jk3qbpatmS5dwX31Y3XNVfOScMgjoqRm7yvOjKJ2Mj5quLCQhY7lxcL7tokVa+HmrN8/f/370y+/fPrv18/WH/rnL++dmwfeIeB9dip6CL44j11xwSXeaCNZK1bX8SoO9Aq9OhDXRCF5ZOJ5I/iV7lviVXEqJ6YsqErhStoDXA5Qccb4xsa7eKcQFKLD0GqY1N08Del8bRJcyuJzwlXrUmMy4XaUJN531iE9VHzEcG57IyNhlp9NvJ8mvj+B5qQhN/8v8A68A+/A+wvn4mz6uXE5G717ZyxuIRhGjHdFZUfB3YXjrBpiFeP0846z+CJglvF+uf2G71t0eUU3gCcn5q7jLAUGQwvvTI6xzIZW2+Tgpqdeh+dMrShb8jZmwlX7Eh0liXe3tB6V6Pis4U6idq4xtwoF9A28l4zjy/q0ev/cPPAOAe9zk9tvbDNlZxsT73tXzVvTnnfBRf6UGQ8O7l5ViDU4kx9aLLvGSzeAGPTQPRV4wZBZ10y2ra7btFbXTGpuBn1tgZj2c/kivpbNhKv2JTpKCu/53m4m47OGWwas6EDvVxbeiz5O6An1/rl54B0C3menTO229mq8DwfKHrqZcTgw3a1YZHCqp9/SLhY7yKcMT99ox0pLj1rP3fSdMKm5ue+dqNiedzP5c3uN6EdcIqPU8V5mjTbnfSkS66sbuZLD1eE2WpoisfDOv+VgAOhj6wG5eeAdAt5nJ1U6z1++15qB92GyemJF+3/JioX2/KBX1y202XxkvsV2u2o3T6/k97lR1u/pqYa+lTCpuZl0P14Yq2PZ243bjkQ/4hIZpY73QcGN7Wa0+OzhKlao8sZVNMa7KMfHJrRPK5Wb//X9qiuBdwh4n5tyxaV8Sh8d70Ppd9K9II40IHuM6cBb8Bt4b2jaT5a1eX1pzJbJVsKk5qbP2UmiNFidGF+KS9Jxoh9xiYxSx3tUClUBY4zv1zdyJdHVmxbod2NvzN4bb0/4sj6rHpCbB94h4H1ucpds+sY2Bt4HeoeSVCvGztteO2OXlwGeW71ozt3WYZKlCn+1jrrKxHtotlImdTdj+RBQNcYvsnsiV8/b/aihqChHeG+n1q7jrDcVYwXBdzM+a7jl6D6ldnJ+j2/rk+oRuXngHQLe5ye1PPzlW60ZeN+apLqwsUa1dalC20EtQd+ei96NHn+XocfJQJ03OsBWtlImdTfllF2SvWpJKR8LiH7UUFSUNt5bwrsHO49OxEfdNO2RZmPivcIRMk+rR+TmgXcIeJ+foukr4wy8+yap6m7BeS99ZDcPWHBaO87l0JtcpC37grg8xAT+RM3ZwsB7brZSJg03Q1FNF6z6f2WJH9GPGoqKksS7mIZXBprJ+Kibpj3SZCbeD9YLf+hZ9IjcPPAOAe/zk9rX5uXvdu/hfXcz059XjLGAqz1enD1jQeqtJNUie35+vjl7l62USd1NJxDW5Lx9J1Cfyw5EP2qoKXgX+YnRVn1kfNZw4xoFa/aeyooB6Pn0828PyM0D7xDwPj+pHWJffibsHby7y9vbo4dtoX5x8NQIla9YHQ1z7QGP99+9UyZHeBcf6966OwXbN6AVuCRcJb2fhPds9PhxJT5rOP/2u/cMtXXPqk+PyM0D7xDwPj+pRV0vX3h1b/buRLcGqzlL1/6wDO+k5doPCn8b3cODydrMaiVMjvAuKulObVZeQnbfvcsm+hGXJuH9NEqzX4nPHo7rMZuV844faEvloWfSQ3LzwDsEvM8Y7y+vzM61PDJJqpgFy6u9y8aSfn6NRrJFofA3FJJdzCeP0br3vpUwqbsplsaFcqW744rsftFNhol+xKVJeD+M8+h0fPZwlbY7n7nuXT4lYOX7M+oxuXngHQLegXenPWYuuQm+5FaqPzaK+LQ89GaoLB8mwPvxZFi25mYrYXI9Snc3COZ9Uj9iaT+zJ/oRl6bgvTY2+KXjs4c7a+/8M2vXOmdVMI4TYZ+Q7kNu/nfgHXiHgPcJql5x4nswfJgGX8x4ch3vRbLLG60VyvpTYbiG91I2u4m1HWu7I303Px5aCZOBHlN7yNyqJ+6AT6KffWkC3uuAsVFpHh2fPZwfsKpbL7AtbLyLpw52WOML+2z66yG5eeAdAt7np+IVeC8ZF8Ryr4LvKMZri8XzU2xMhUO1ojzai4l3zlmQuM5CHMbCFf44K+oG4fmZMX6xWNuQ8Gi0EiY1N7tjXJzhswfnaj/70l28H8QeOLkXbmJrDQIdHzGcODEuafi9ToImQAvv7QNKdE5ysXtOfdrgq/sc+vKQ3DzwDgHv81M0He/usT1uPRCMugK+Y3sWexFxxoz9WHYHbcsYLua5G7nQXCDXGzal5f3yOevAeI+VXsAYi4JRq2VSd1MG2hfcu5FWnUe4al26i3dNhZkMIOMjhluc23DFHyT2uY13NwnkTWtvS4Cv7nPo3/4s2HfNzQPvEPA+P/FXzN6dDjQm3r0BfMu0SwtE6QjPNWfBOazz/HJMDlxWg4ftR3mcOUuFv7VXcsYZCw5WvbjHUufSte61Vtvk4KbTlripcv1U3zeHcNW8dC1Klc3onlaieJ/Zq5eo+MjhjnEbVZUunaogbvv6JB4k2r9YhTq7Z9EfEu//vK8V4B0C3mcmcRTZK/DurHZheLl1xpzbMNKrQy83zEWsWqpf+9fS7rZOdgsd4AJ//vFYb68a8I9e7d8zedfNK/1o719/l434rmtx8er1LU99L/TC4w7v4J9Hi79+/emnP96Z7sA7BLzPTsOJcROng+5dXLkuMe0fb/kS0Hu1eMa+bzdwd8+k+yKqEq5S3j/okcvF1xLS9fPXz+9uA3iHgPe5yWPTd637Bp0Z0+C14LTVl+IdgiDgHXgH3iFKNZu+5/w36MTYeoTxBHiHIOAdeIeA97dWovD+kD1Tav0xwi/UkbDAOwQB78A7BLy/nTYK7+EjzC0jxs6yXs4/RddSBsA7BAHvwDsEvH+Lzgrvx0eYcy8BY7wqyzjijPErLwSAdwgC3oF3CHj/FqUK7/ljDG7ToN+07nSN4cA7BAHvwDsEvH/LbHrYQ275MJt5mGX17gbA/Sxb4m8DQcA78A4B76+V2k8Vm5xCEPAOvEPA+1ykTpSpcC8gCHgH3iHgfSZSW84fcC8gCHgH3iHgfR7yH7tpHQRBwDvwDgHv76/du+9qs/CXUzt87PNSbgb80YODgHfgHQLeP4TCd1/27rFgObED/9Cr4m4G/NGDg4B34B0C3j+EMoX3CbPKbVy+/NOT17B/l0Xv7ubkvRneb/iPFf0Q8A68Q8D7A6imNq2LJvRKpuxg+yHw7m7e7kgd4B0C3oF3CHj/3tr3eC/fFO9euv1R8a65NujCWbx4KwPAOwS8A+8Q8P6dpXa1SSd0ukTFHUad2e5Hxbvm2jB5jxm/vNnzA/AOAe/AOwS8f2dFPd43bzps+ePivSTwHr7lukDgHQLegXcIeP/OWqnKuvqNnxp+WLxHNt7d8i3r2YF3CHgH3iHg/TvrovC+nfJQMCzezuvslIW78XvrZchY7TdaK6C5l2ST7FbjYbxsc1zeJKDv6d1WvhjpmGVZKB1Y5OFmk3l+b4scdeTiyLVeO8b2mgVn2xg9uvIGJZusVu4sfY3Na/2XbeNI4i2cmwHfDM6KjgrOiM66IRAEvAPvwDvkaMveJx0ooxZvh2Xffa8tKzuojW73HdDqWF4KEg2IpbxW+tcIuD1V3ehZ31I4tdwk/yJ+rys2KKZHHbs4dq2fvKfdeMJC5CTSQtT4mnelCWkHz03jgNK5M9lQvz/jNjjdCPhOcGZ0RHBWdOYNgSDgHXgH3iHJq9ccKNMzSvQO4rIKxpveVVxAiDfayA+L1Xe8igN969s6ENcKzljkXSGgYGtQlYUq/PNYUHNxLQoW0jwv032LwSpOyVENF8eu9SoUqBvbokcRi5qELI+EsarpUi1u4N0rWiMivtP1gO8FZ0RHBGdHZ9wQCALegXfgHRL6P3vnr+MoDITxtBMhK3SJlMqvQEdHaNLwAkGREAiRF0iB0uTRj7/G2AMJ3O5pN/d9za3AHs/4Iv2wPba30aoLZTpGnQXJsJ5bdu5RsNMLXGm09k4UNDjyfBLX9umdSJwqJB0eRMmBJ+BJ5EWHz7aaSyQp6b8GLoKCeszqxKJ337Jqu6i71mk/7BtonI3qgqEkISmpHdjf+q8XFu+VH9RutrucrpMBvwxuHB0THNNn4w6BIOAdeAfeoVbpqgtlOkaFRBNkMfGeOx20FEgDks/2r9jM2lcE3PafDM+uWm0qUHP5OckuYaDs56Ytq7aLDN61bfzusIcgJqKknZR3sm52I+bwnpL0DP+ZgF8GN46OCY7ps3GHQBDwDrwD71Arf9WFMh2jYqLDW3iXikC37vgcb2C6k5HPE3DQrR3B1jQbaJ0ocu46/22rtosM3iMSx6Ftv5vmdqTqFScmMTl698zz7tiAXwc3js4OjuuzcYdAEPAOvAPvUI+ONRfKdIy6TH4VGHgfBrBlx8eUErVYHBvEYwhYtqNnVz9cb6uN+v129sG2arvI4P02QNvVWB2Q6K3dVbKBjffUvEGGDfh1cKPomOC4PnOXnTYIQcA78A68/xdyXIX3RTO8HaMcSc1a8Eu8D+njYTcM9rXMddf4uGAIGKvB6gDrrbAIaFu1XWTwnqrMupGzGvXPXSUO77554B8b8OvgRtExwXF95n7fPb4Q8A68Q8D775W6L04sqtYz6i6JktPxJd4HenttSweix7nXRcehSUDnXHhhHCm8a6fvDLg7t4vnnFXLRQbv2ZBY6GqT3dq8+r57zKy9HyzCcgG/Edw4Ois4Njr3q48jgoB34B0C3j9B0ap9cQOj3IyIRH5/gfezSbsnjXXiCXh+JEK/0M4lGvLQnYjo3oO42YfPWjVdZPCeDMNi3dnbQP0e78zo/WkRlgv4jeBG0dnBsdGNOgSCgHfgHXiHenSs2RenMcppT40J7rN435u0K7rN571YvG+jBn8ySG+Bwrs2s32VJMtqXP68dSTnrRousqP3nHP2PbwXlj0u4DeCG9ezgmOjwxG3EPAOvAPvEKNkzX1xJogekkiEi/B+mZtU7ivkRDJym8T3mMP75poRkRTK/UmruosvJ+ffxnvU4P3Cjd5f490Ozvx4MYJjowPeIeAdeAfeIVtbse6+OIMqh1IY6HmF991cSpirssKzvRooc3hvj9QVya1bLp+xOrj4MrVuDu+jtfdbU23Hrb2/xDsTnBmdERwbHfAOAe/AO/AOMaNatZDr/Q3eN5u7GI//X+HdTje3rZfauvKNw7tTCIqOe21f+5zV3kV2Y1z2Ht6dWD+bP5jOnH+JdyY4I+vOCo6LDniHgHfgHXiHbBUK75e/w/smHwbAHUPvs7QLzM3itvV8QOk2YUfvKeUbR685Z7V38Wrv8c9J7N4bvXtEKgl/J1uDgXaIzdt4Z4IzorOC46ID3iHgHXgH3iFLjtoXR8uuE7Wo4kSj3HvnqO11Y2kXzhyD21WIBgKeiMV7YK4pzFntXBy51ikelhZe4F1Lk3fK7h63cPLc2Rm8M8EZ0VnBcdEB7xDwDrwD75Ctx6rrYBVV9uq8mKs0cu8ln4w+0C4ws/Es67FaMriLKbwn4eVa6Tg8MawyLkr9Lth+ruHxHt4PUs1SXGR/TWtAIl6KdyY4C+9mcEyfAe8Q8A68A++QPaDN121776kSyOhew2dfSjOrOyVR48uZpN2diPImc/xaBg/O+lWQDJ3N9poTCRbv6rJ68vMnb5VxUXOtl69fCDuH901JlFZNba/NvrZAAfpWN3ooIu9NvDPBGdHZwTF9BrxDwDvwDrxDtoKeIAsPLu+okhARSZLMjXM1iWQi62ITtLsnTWK4L6zm9dvkhRBEdHM5vF9u2iEvomCtMi5qrg0g75MPXuF912xXa8zfyv6joGtUCqLTm3hnghvX44Kz+wx4h4B34B14h2ypbe/5Urw3V6yFabexTtysc+tC2R9lP0KQO9BuF3XN+5HLWa/Q2BQQQbzZ2Xh3CkHy4RXX6/Me3kSf32ZY5VwcXOtVqB4Y4z3S8N4dRHd4iNaphxMq/B/7RoP7ZjrgF8Hp9SaCs/oMeIeAd+AdeIdsiVW3vbcAaia3t08vDIvrjilwuHje8zBnoaKVW3juddJ6fSZ7eNny1Y8+ZUO7sba3b2yVcdF2LesOfn0r8mvhPremu8d71eh+QdetDG66zyAIeAfegXeo4cuR1p1q86a2jvPKg7+wHo5vOpfDRa7OctfiZV84znf/10wH9/1tQ8A78A4B779cwyUl4e9z/kGkkW4rVsxAaOP5hMTlU4ODIOAdeAfe/y/dVx5a9yNUjjbru3/3ieK4ggLnQ4ODIOAdeAfe/y8Ne69+4aXhBWkz1vtkweL51Hj59LHBQRDwDrwD7/+T4l+Md2fnEz3adPZ96es4XKVt+Yg/NjgIAt6Bd+D9f9LpN4/eN09JJLI0DXxBJP4agI7zwcFBEPAOvAPvGL3/lvH7OZL9uW7lp01ef3RwEPAOvEPA+z/Cu/c7A3CuXhwXl/1Hbhf76OAg4B14h4D371P4mzfGQRAEvAPvwDvEqfjeY20gCALegXcIeP/3Go61Qe4WBAHvwDsEvH+IhkNpI3QGBAHvwDsEvH+I5MoLYSEIAt6Bdwh4/7HKerxn621s97sv9emr7f3UNiEIeAfeIeD9m5T3eBfr9165JL+UjV9t76e2CUHAO/AOAe/fpFItvq+/QNwl+tJjV77a3k9t85UucZ5l6QM3u0PAO/AOAe8L5RRfcK4N8P4tevTzKtjUAAHvwDsEvC/UXuF94X3ibnT+WjQOBn803rW4v1cRkUzLMhWEq94h4B14h4D3pcpWps4/6PK1eB8M/mi8a3F/q0qipPHoIonu+J1CwDvwDgHvSweJreSy3Lr0q/Ge/g68p/8G7ztJ8tn+WQjK8TuFgHfgHQLel2jrqdn5Zdjyvxrv/u/Au/9v8B4PxwQ7EYkjfqkQ8A68Q8D7Eh3FmlPndx5Rsa90VGh0nuEpvBz0UntXf3TY1wDd2sVMg5P2Ngc3Pt137GfK1TudYnff+2S13jXfWDhO4f3ghrHxdtzk4KZqprfcvlfb5zlXx89m+yMnUmv8BW4EgIB34B0C3pcqXbH4flPfBHmHxiJoH0l189y57Jb1ZdwT1N+4VjHGIG+vspi2z1J7nF2oFIJKAdd60/yls5q5DN53nv3WaFJzM22aaVSS2Ks/s0lXzWdz/bHxtXOGthJnCkLAO/AOAe8LdVIH27w/A5yJmkqi0qlF44OIRBZILQM/qKmVpYk6z94lCoVVjDHI29sUsn6WCCLftWMQaZQ3RM+CiGu9ttpayGonTjbeSyKSSV1DvTWb1Nwsh9n8gChW30rplKvWs7n+OIwe3CjB7xQC3oF3CHhfpL0akcYLal1ptPZOFDTM8nwS3TEsJ5EXzduke8QXYwzyBe81drcV+R5EyXgy+yIoeFb/OrGg20TrrdXMdTabXU1Vz8I7tUZ2sXrLNKncrOGsPO9H1zvRTKNzrtrPZvrDeY7+Nx4k8TuFgHfgHQLelynVJ7XX4j3v8u4varS83XWvn90jvtgE3u2CgUolj8216Jxkt1BdEj351hurt461rjSDbdoc3mZTTQ5uyj6fvRxmPor2Leeq/WyuP+7q46FrYYffKQS8A+8Q8L5IoVq0dlfjXao15pu9Tnxrh9TzxcZ4twt6Wip5Rv6obqLYuLPnILrWa6virAGzMPE+vH20w3euycHNvHcio1y0MHYezTOuHvNsrj+8kX/x3xwZDEHAO/AOvP+fOqhLYW+r8R5pI83ELNvlm80XG+PdLphSstVwp2fXbbXRvG8d8dZnu7mkbR/fm3vJXT367i3X5OBm2P31JCqCrnIwWY95Ntcf4WifooeDbSDgHXiHgPelch4rhu8G3kMNTMIsG6vR6kyxMd7tgr7GY3eMu62Yw3vXel1JG9inxuwB95ZrcnBzL6hswSwPJYndpjnhN5yoxzyb648R3p2C/tFReRDwDrxDwPsH6SyW740z8H7XRpoap5xz4YVxpPA+UczGu1WwTiU/97qMVqZ1dp71nLlR603m/FAlNy64d0fJds1btknNzaDtrqxq/NnWDtvN80w97tlcfxSjD5gQo3cIeAfeIeB9uW5q+B6uxPuZ4dT5kfTfDf5MMRbvVsEnjaXn1jmRwt9NbUE3W6+tPoc6ZjK6O5q6KOu3bJOam+2QvSV79oe9s+dRXdfCcCrrJqAcUiBASppUFBENSsNUgISmuEi0F0BCIAQN6EojirnToKG6P2GaKahG01Psf3fiOB9O7Djh85CZ9Rbn7B0nay177z1PlrNsuy8Y5J2B9xzvmmg8dpG3jQ5k7yDAO+AdBHg/XVqAnVbW3WBjeK8ynFL6Ll3N9mzVDvBezYp35salt97cV6R0fmLK5qvuQHHlc5/1HqUpJqYSG4JJrJXrMhpm031PqPn/JSV+vOd410TjoccWxkFpHQjwDngHAd4vSd+zHl6SiveFA9e+5i41G10B78N4qXv0YbyhjamGe9iw3mP5uTh7d1u5Lul+m9gbyduHGPUT8gDvOd414Xio9PxEX1YV+EsKArwD3kGA91O1U0+dnk/Du3NtXg1geTneS+Jdd9yDcdTpSgssxL3zvq5HWRtv5bqk+43v8mfRpw7lR6TAjvcc75pwPFr0MoY57FoHArwD3kGA93MUHAsbbL5yId5fqfnk1RXw7vCunxhMcanKfb0abmXH8Y6tUglxO7YKMFo5T1p5Lul+40q6V3dWnqx4X3ilibznONeE47Eidl09n7RkEQQCvAPeAe8gX3or4Ps809bzE6qYm8epRTj5rUwz4n0r4l5bNkuJ0czkhUQfV8/xjq2GCwN28V3zIuvevVaeS7rfVVVutr3PGXhyf0pWynGf41wTjkeTmk3onLZfMAgEeAe8A95BXvpbDI99l2cZvvMWdWoan8epfgjYjpwB77RB7o1dmVnRTsOzE5uMYLy79YO7kP/yLs7aMOH3WjkuI/123PZVn8JOeu4n9rxQOdeE44FPifNeWEpzakM9EAjwDngHAd5P0UI+rbzODG/jcWoUZJ9bNQveaYP8G9uy2k3G+7Q7nDjyZh443l2Az7z2rhqf7nZ3pC/FWnkuqX67u837c+j484YpJYfKXhOPR9+fX1Bm2QseQSDAO+Ad8A6KpaXTkO9vGR6YySomaDGBUxNVNrtFSZksZFnNhPfQIP/GLX7xcIvhJ6/tWITh3ENrseN7d3eVl6dLB+GTN+eXO4a1pjzfRlt5Lql+k0NhpDC8lZQcKntNPB7PLYfvW6W0XFFlgiAQ4B3wDgK8n8h3Laye974hC7V1T0c3MaG4nOqQld4YelomvIcGE27cukexT1sqs73ecEXtGKMuud7d4jnVXz/HnBivyTMN773fMulWjkuq33hGPvhCXmxRX8h5oTLX0sbDdPuAT/IDuoMA74B3EOD9bI0SNoVLUJecRBPHu+ZzqulOB6jtkVTi4F1jt6YPDCbdWOp7MwytPk1nXDdvvjWXk8lu212p5Bg2xju2qmszVVZl2VwxwNTkvrTzWhdhK8dl2G+3hC+ob+/T2+bwQo1fSxuP55Ubznyhw99NUK7xXnhMYbwXQPkUxvujxvaQ/7r78kn5e23YbO5qgvmA52V3eMp2LKkGHURqy6YW28FNb8nzEv2S0uR6JzStbrfL5Dq16lZbxsnPuEwLUxAq75pAOiccUKKUOozBQ+L9P/8CgX6RHpLvCj3HneX7u1IsXjmCNIO85m70pDsz4dVEi54im/2rxZn9Lma8JrBQfJS/GWWjcX2T9lXtlb7tCrIam6uEnyW4G4wJ4B0EArzfSKU2xfdmXn4ovckyxUFFTXgzORfvIAdlqPLgJus2QpaB0OYqvvwHnr6P9xwTwDsIBHi/larzEO/tvPxQepVlPULxLuD91+H9iKyBIulj/ap43yPUA7xfrP/99Zj6758/f/4C5VN/HvgP70HxLlWpCvS8/FBa0pUC1WlwJCzg/UIAb0r5wbuBPpLjvwTvL4D3H6pi7f9QOZ9f/Rsq508WdS65mZd/paWWLL+RcrnqayuxKhDwfqI2qJwbvOsI1ZPjPxvvdbtRA7z/WL7DwrgcCxbGnZ68U3vb9PPzTmLKsjqfzdp4TbmaVPMPeD9R3/nBe6GHLEH8Z+P9jtMPgPf7C/AOeP9Fwvuf+prmZ7V18blv+pvWvSYiHPB+oho5yt73yBbED3gHvAPeAe+/XG8h3VuTXEVenDRHo+WwKlh7Vh2NSvBHfALiLAbvhd6+F9vIoDZ+yfwe+NR7CR9Pp6PQtDLe18V4p+JPDj/BSUJwCm2Af0+kj96N+5dx7fxRA7wD3kGA94u1VU899R30U/XRqCBkYB09lJW/GxZCyFqHteTKEV+q2MdopWjPMMoU3QzDAXHh5dNA7uPjKB33xjq8d2N8ik17dPxyQzHeia3NwfaC7fHjTwg/2YkfnL/+vewEqccMUHjfGAauwGP66Kj+abmX3FAO6V0DvAPeQYD366sUfng3NRiO3621hRFkOfpwUWbtDYSsRgP/d+BDsOFecm5cR+vabEQtFz+6ifUB47ixdjhcGUToOEDU7jAb9J5i2n19aGA64kbLXeb+7kRJYu3x4+eHL3AS4D34v1G2YwZCvA8q6Evi9VGSXgxkvX9/NkjLV2rXAO+AdxDg/QYK96RVmzAav15PiJ6cRwYiaeuLjYwnL6V1SFeQJOWjgtbR1J+eKrfdJWsD63PsodkopeI92TQ2YTlYf8Lz8w4ov8g1zuQ8FT83fJETFu+sgQDvLxYicw5MHyXdeapMmvw3HmHXAO+AdxDg/QYaqqccKAP6ZXj3QYq/aZMl5p/In4MfxJaH163w9y/IwklqoRY+vknCe8HHe7JpN0m2vSy99omscja8s+GLnHDwzhjw28YWeidf25k+Skdk6P6Li6Wndw3wDngHAd5voLBqfgWDAWKy9yf/N98kWe1V0MC/tI4logd08H/5Gf7Sv/Cdlr2LTEtjFDbWDM96evYeD1/ohJe9xw14bT0DrZm18V4fHbvBVwqDsFzYNcA74B0EeL+BlrdfEler3mNlmlIt3Xvs/gGXN1cxhvcPisFrgrCQywNkRerE9n6uKukW2kcNewgXZe8i09IBRd4HKnU+3otRvDPhC51w8M4YIG11GzXYfy/+LIQRsrxBEnph1wDvgHcQ4P0GCk6TUW9WVte9y054mmzeG7b/gMu7Z+97it024dUXhUNUpp8tGP7ZLhtkxNaJ7QngRNm7yLTPSe+N0fsMkJq9M+ELnXDwzhgo45109AayOQVyXh85eBd2DfAOeAcB3m9BqFNOgr0u3rf95nV7Ur3/4P24HXPi2XtYlD5GRgxdUsGKfUY++tBu0EX0kt7rjTfJePezd6FpIzIdYBNqpmbvTPhCJxy8MwYw3mtrZMQJTfWR6r1ioXFq1wDvgHcQ4P36WgT72dwuD03C+0yeA94fPXuvxfCmI29duSvqezh5oEJ42EMVj4uF/aHhrgpHqdm70HQ9eoib93U7NXuPhy92wsF7jcW7UTgg9ElPWsT6iEvrvBg+yCyGeNQA74B3EOD96ir5u7omnKZ6S7xPHK/bSwHbf84D3qkwc5a9Uzu0Ebz1EKrQioFqTSahv/zqsYGNEKoYjffDOjV7F5oeI0R/6z6QIra07J0JvyB0wuKdMYDx/o1BHj7I9FGqG8jGCXrtw3LvK6SMGuAd8A4CvF+fvHc45T0B73gr3MWFpt/kYR7wToWZs+ydwVtZcBS6S22crSr+XPSmguwj2a11IMjeP1y8C02Xz8reeXQWOsmCd4SMMTU7z/aR7GFjrxsGqhwlKX3UAO+AdxDg/doK5uZvuaENH+81PHGgXlitP8sH3mf5wXta9i4xJfFRuLqfyPfeerK6hRoliZSJC769S9/k27vIdA2d8+2dDV/oJCPe7R7epaZBagc5ffReAlDFPvhQF48a4B3wDgK8X1stf1Fc8e5478py5+KddFr5wHvr52Tv0Qp2Vt94Rfq7t/57gyrB+9tXHO8UmN/9ynmBaYNuVDJWznPwLnKSEe/Y/t7bkZbXR0nq2eio16m1AymjBngHvIMA79fV8Kz96qpat9MdBjVHZFm7sotcdO9bdkdDRZKaXLzP5JY0Tyyuqw1HnSXFTtdJcTsajZphwl9qyvKy6kgPWFtkw6hpo862xPWiTJqdzkir+jaYzpG+uRb0JLzXtO4o3hrxSYUZuInsBlAK1s9zYo1eShzsu2Xv73TazWqMLF33ysXDQnoJT6dH8D6mT2q3Cd6Fpj+jtXgkyDOyd5GTbHiveH0jCTmnj/gl5ytqOWXUAO+AdxDg/bo/yjv+mvfsee/z69w7fmYUQK4laW2yta0ZVug1yUk16mzHxfsEL8Vz/HNX209mxFxrFJJ0Ki2JxZ1/bRVsp7vwWLtkw3j2TM04XVzOw5Nw3eoDpnO4b0PP6Fzj4L3U5LRGfdJhzsIih9dw1F+9txxOrPFLCYN9TVHLtnh426fUfTfQIJh4/wih9hKrnNept4gBIngXmqYbFTt51zoqfi7eRU5OwbvifX7n9BEPwj45esA74B0EeL+5/A1pZ9kfwdvgmPMZJm0/gFzX4Y06b5vh8vkiLpz7m72z91FbCQK4K4v1WQ6OhA4iroirKEJXBNE4r3gcOUSDRMoASAiEoAFFQhSEBh2J8vT6FFH0rkh1uT7/3/PaXnu9O/YBuQ9zN1PkY9e7M+t8/Dy7M7OG3acdKwjvTn9PyRtgcF2NTtQfUohNikyJNTaoZtsKtjyHBn3CcOTMM8PVGTFDGVu0iZphS3Q+ox8fzZFL9GG1CS3OmdSbgRpjnMl4X9IRfTog7BV08mYuw+18R1cn+GMYxNgqNYEv+3alElZqi3F/KwlhYtpMvbhgM7RUdpnbq4qAd+UkyC0rVny8J059NA06tWuWdwfhPbQfND9JyS54Z8fvwBqpjmn9SHDfK6kNrkO8oyDeH5/3ztLiltuPOTNGY5czfWL0GHEIqbr0Kdus0eGYVS4oSqFMdch4L1gUafoEqvtWpqx0sK4t+sGXh6PEIn1T3gLgz95lMxZ0Kk1RCg75+8JmdtsgVboToHcMVm5fWpzpOea6omQpVcsS3ok3R7YT9so6QzPNIAOxF35VZQ33cASwVW4CV3m78lMN7k8H+eje3OZ+Y2nza/lEuUgvbPU/yl45BHTnmjm/EPA+U4+v3Pi71ola8vGeODXtbNHO3FT1z/ZBvIf2w3hPULIT3v3jd2CN7m1y3rW0lYtZYZu3hnhHvKMcBN6P3n37WzuEN7Vmu8Y7pJ9r2XBw4OGSkR+a1/Ybsw6LffTkhxDeax7nyqELG2oYEss3qDghZBwoqUr76yLeRTOUKrHWXlNHCjAYEcvPRl+yHX9pcXTSif9ZYFpi+qCrMuwdKjE6OTMttl2xDPMGxl43YKvcBK7ydv/21o/V0vTn9DSWj90KDQq/uKClXC6hz4PwNpmW8+TplF5yfpkT8K5N6V3oF25fcN970tRa1727fer8ePz7KBbvnP2w+QlKdsO78ls93kBrVIrfS2ogfgbdDW8N8Y54RzkAvD//mMlkXn9+l/431WFH73sFaU18n5c61vmw0XVJz0JPV6tBeB8Qq+A58VXAKh/5ul50oKkzrMnH9ALeJTPKIdP1IbGjg/sBHLPyR4a/OGdSI6hIs2TfGpzKsHflLxnQyZk5YlYMycjwPHl95bYB44AmaJW3vaezcSuwlUS81QM+aleVY5dclStgw3kTKdReceeathSl5OOdhdRpHgNLp9+VefBBkDh17tobMZ2HuioJ9seYH6+EGRf+LE/AtR1N3Sx/aY31inryezOv1+vz1s8Sy6BLfmuId8Q7Svrx/vJ1xpOPz96k/E01fbzvVxuWBYSZvBO5JH0PXmGqXcGW8d5jY5pcqJzCvF87+NygR/imrwRAmYB3yYwB6WvcV0PE+9c4d96WTrH9xZl85R0pUsDkL9FlvYBOzsya/8s1IeOqP7rqDgTGAU3QKm9dtHqrVU/ef3rbbW26OUW5+eqz3LxVj3sq5zDwaJepC3VoxJ3av40Ia5zyd8W2VLV7N1oR74h3xPs9y9dMKP99+ivNb6rKBZ7v4/vbDHJhCHeNGPQni+OlPpLwTqntUR3YYObHKnl/chPYxZfwLplhc0szhTMIzUjCeydwljv8lsNAxLvcC+jkzMwbZOmR2SosiZENlwiMA5qgVT6U6Lf+4BYj9Aewf8fJutEb4SrRlHc9ff8PIN5REO/byZcMLy/SvEm/R2Sd9z9UY1yudZoh3kNull3iZCM17JsS3rk9+aG3Sx+KsFNuedaZ4sY4hHfRjAIhqwaTtlhXP4Rngw+aiywuqnYk7HOYkWA7rxfSyZtZ9T4Cho7ytTe85mXPy+OgqYBVoqRMZuExAID3FAriHQXxvp18zAjy/ldKN+kbe1Wkbaz6LJE7wHtDIM46QsUzCe+01L3liXSbTTsK8qEHYZPm0d2Ed8CMiERi6/RmQMpJkIIuLs6MnB2IGX5mJB5g6fZCOnkzPZfdI7u3Nu+zABgHTQWsEiXVeC8ep7keLeIdBfH+J3h35EMqN+nHjBvr7cdoTRd/VnUwqYZ4zwvEGUfm7Eh4H0SwNRCtWkQeHUlK4vAOmOHmmzOJhs73LGItiw5FJwz88uIiNKUBg5qA957YC+kUzCy7HwoF9qMX4geMg6YCVomSMunysYXahV9/H/GOeEd5lHjPZF78+/dR2l7UPoHzI0KsplnwhsfiPXrOLXnvDu5GCyYDwS8347z33fHeBnf0w9G0oI1lhAV65MVF/fNk793rhXTyZioW1eatqk1R3/MGAOOgqRDv6Zejilpq+f/a51M19XvziHcUxPuW8j4TI+9/PU/Xi1pF9ti3EpOQYT4YHov3fPLZ+4rHXVkovtaIO3vfHe9ZMB5P4VUTYvQnZuzioNP1yNuQeiGdEbzTx/xTd6VPRkqHuAF2wDhoKsT7AUjuRFUrP6+vLi8qqlpKPd0R7yiI9z/FO92k/+dlil7UaPe73pecqz2Jx7ti8OfcYuR8NNldt4nNb3kXDD6WvhFGzu+Od8VOqvuijx1NxXwhaXFm5MC+yuXB+b0dqRfQGcE7jaRbevGEbsb7yD+cAMYBTYj3Q5BXVyd+TZvK92L6zUW8oyDe/xzvmcyLH+ep2aRnJ+CTXb4IAlJr/QS8DzmAS3nvtahXuhJi+ybEKnKev0fGWLwvksBXhWrecusfRdKUgMVF0u3XYhJfJO+d9QI6eTNpaly56gft0939vp+4AIwDmhDvhyFabjObberFgzAW8Y6CeL8NvNOCdl9TsknP0t53qGzK7bOfkXi807z24Ni4I1atG7BqrIGDHvnA6HCZeg4Lq0os3vUidwgAga+WeOtKVShTCyzO5AMPR2IQIu3tib2yzoiZVG3TYB80Nmkyzx6wFWhCvB+IHB0djq2IdxTE+y3h3ZEv39KwSc+uQ93h3rFOeHGKkYB3JW+RoR9y3ugLeO+JdXQGXGlX6u0PieHTsFFlwWuw9x6WcI8BXzWYCsR7v9buOVKMXRwF+MDvrhniPodbkT4r9gI6LX7F7iVzhWBzIng5wDi5CfGOgnhHvCPeU4x3R36cP3iunL17VZueQayarmi9ESFGAt7dG+NqDheLNYtYEbwHdWaDhrJgAoVrs60p+ZodOPYxeB8QgxJZjzNjQWvyubHwvWVV/IwpB6l59mgNL45WlSf9sYPwnmO2sZZYa5HhQugFdHJm+rfCKOGzEyV2nNyEeEdBvCPeEe8PjPcXAcg/xGzSP3RBO1a0rrPDmDMvF5tSyUzCu7aiz9i2e9GbweO9YIk17jVLqJ1Obzknhmtes5CI94V73bpF+2DwLdyr2Pu2Id9q355wuffGGFwcDZ4zWPqcdGO8SQYmtdK2Ir2yTs5M77OKBRvoNvf6AVulJsQ7CuId8Y54f2C8vw4wrr35FePSP+ytM/vgXSn3XRpWO0oWwrsZEGdRNYhByLCZVYY8vdvSzazUn29EGnpNd6xRHXOOMoR3peYtIh9rRrbZ9z30ZnTPYGwQa1Ue93rrRW1iePewSYtzJi2aA9cYayIZYJKmsvZ7R1yvrDM0U3FD+IJw/Sa/dMBWsSlmlSgoiHfEO+L9roVh/Uvgvp87re8+v4AJ/4C3zrDyq7WdRumNca291XX22toc7xc6rOvZ9nix3kZLoV0urxPq8ugOIs1x2RRL2hZtMswGv2PH7uLiPJrmF4txI1ZDfmGO8zfpvMnMBFtB81FQEO+Id8T7Q+H9c5DtrtArIP/69F9Mrtzndw8TZbsf3ncE9d0vQ9P1PWyoRSMALDgCIW7TYI9132hmnK360/2nnDs5feQrnKdhhYh3FMT7jnh/HgCcJcK9efYxrqDdQ2zSW/eA99TKihCOm5oB5w/sjfdHKvrb69/3MYbhXT1O3St4251d/bz+vtFuZba5WkG8I95RDg7vShBS92/Y++7z69QUtHvSeF8SUoxgvIZ430I2qlq/jzFpxXvu8pjVo2sh3hHvKE8W7+fB9jtPbu38RyYdt87Y+4TWPRYZ89l4+X5wJSzi/SZUzz3OzbI7j7k7vO9gzZ9I8XdJPZ5ez1qzy9LtXBWDeEe8oxwg3j8oR0G8/K/oIy+/fYnZpL/XgnZ96Br0JyJ61iZk5cXL5Zd2XPI/4l2QV5VTL0BwpuZ2HnN3eN/Bmj+C8bF60fV+WT9VS/VbmBHxjnhHOUS8K8+C3DgpOv7515hN+q/3F0g/3L2szSOStUWIMRwMqjSp3Ih5B4j3OLm+D6Bujffr+8G7MusGv6yfqFeId8Q7ylPF+8sgGe6T/NzR+Q8wV+7HvVla3b0o7WPy3xtNFnxgL+MYjniPk9NU4f30nvDOy6V6gXhHvKM8VbwrQW7cF/DRl/9ABe3ubX+e3Rg3eqp/U/ReudMZt/PxyWf5Tif7NN9NbjOvv+V+X+zOu7kwNiRXEoFa6M7FIgfCGE+07uaVrE6rb+paHN7fdrvSn0JEnWxNrABmAk1xBvGf599VKKftbX0ujwMmK3a7/7N3xryN41gcV0WcZMHjDCDYHljFugoGgoox1MQpknhHSBNAV17sAIENI2lkDGCkyKYZ2HvYw/RbGItz4Srr3t9vRZGUSIli7OyMI2Pfv5hJaD6SUYT8+MjHRwPwDngHHRbeP3B4T8/GnRXUfp9PaPf7vkbKkrIO4Z0BCQrC+L5yJ5xjdtozzVrFAePH0TcYRws3+tbGokfejI3rIFT3NhTmEhtC0mdcD9mzZJHbskOttY5LndDP4d3RrDXurO4tOQSL3fGjWdrL5IewvUryZSgdpqxIOSDRe8/ivTJd0uc2UDdmrTxc4m66A8A74B10mHjXkkw2/y/2As6+iYv07/Y10jHF+zm8MyBOxytMYNe1SXC4hVzLwwWY0sSfDh2MRCfSguDKxbDCFAuPKZdzNli+i0mK6zmThOA2runE3TlBDu/TqNj2vAjgXjolELvjRzNBNuP0Kg3YX6KVdJiyIuWAeHlomSkhzy2MBlsPVI3N8TeeF3XqzQHvgHfQgeL9bKsl99Nf+EX6sz0NFKd6J1vP8M6AUrVsVN/Ea93+5DhGlOci12e8IqjuIm453LIjdEVUNRZ1FNJqEhvNdyKsd/H6vIvQM/sQ2Yg4tlMP2d0M3vGHuKNu4KC6X9RdOprIgs4Cug5C64TEgdROWqQaEKcNQvPsooezHNBZjF0rbmxeR/bUiDrFRYB3wDvoQPGu/VeS2kamT3+yhHZfjH2N9JHdlgbvDCjVcwQfurDEEIXClqbA+xLZFgUc9ZilNtoKeZTQzSVyrKR1FoFuOWiRxTtak1GYvk1ccFl33Gg8tGGfRRSl0wrycd5OVqQcUKLuoo5W2cJKM7WbFDfmIpd0Wmm5CAHeAe+gA8X7L4lT/mLaWXLrzIf93RLbZ7ehQnA4KJHvZPK1WAjVfU2Bd78eO8exQuIES20GKK3XZLDG/m3iIa8z693Rh06y5T6hTUq640bzzHbEV2jjUE9+EpdJ7CRF6gHhz31/Onm2I2wr5uFLunAga4zP8jMHvAPeQQeLdyOJnPv1ZdPTs3ef95iatsPw/vUtf13NBswuyqQl8owsY5daBtUmj3c+xixATqXAJgIuF4sWwfqYtr7gCsNs1+vUYbaJYy7pLh2NOUX1eELQjdg+o57zKv5fYicpUg8o6sAlOWlDZSK+CT00J2tsxj8GD/AOeAcdHt5JMF3ld2lm2lLomuH97i1H0dPb8M6WSG52PdrinG6p9+5y+V0sUi61cfllgaaTLOOnW9jZQDOhmQjGy4LuuNEYDjEJoqYmpLmKHXchsZMUqQeEvXzbxoHwyH7uFj/EOUW4rDHhMawB74B30KHiXTv9WJCZ9u3V0F99p8zDVefH4b3/SHTR64Bjv3fZPFEponyl985bVAi1ZTaaLcSieYRyQs0B2y1Pu07TxGkb4v1KuuNHMyOTAOy5W/W47QFyunI7SZF6QFQtf4J3zeXH5lq+P5ikeM82ZgqPYQF4B7yDDhbvlT9L676b1VcnnR/qox+Hd5ZND2vUN+CN3qdaAlEporoq772F6KnzWMTfltkcixfHhWSpParZVOGdS1dDnHFZdzzeaa143530sYinBRI7WVPqAfEOupNbuNcq85Vrk8X7BO/ZxlrCY5gA3gHvoMPBe/ake5qZ9l3ZnhS7U+ZqV8ObyOjhB+K9fY91fovnH8MmvNL7U8XHiWgyjK1rCu8dW9R5BQU2A4T45HArsq0u1JTgnfvtB/Gnsu54vBOHOYixSRLLhfF2g8RO1pR6QLyC3Mm4wEMI1W13tgoTvOca84XHAOfeAe+gA8Z7mpn242m5HpT56qy0T98xla0M72y+Ed/lNoZXeo+ycpezy1DNA9WSXOcus7EKvHc13o+znq6sO+GYnodpTqLq4tX5Jgmgl9jJmtoe76QjTpM68jYk+WxQjHdxRSIAvAPeQQeM908f9p5udkuNX5mVtonvYqm2fjzeNe0y4vsNvNN7lJP1SV/w3iUWcpsmku+9q/Ge7hSYdO89350wGu0ZzejaPIkTnNJGJcOUFO2A95V4au7YQW6N5gqYFOPdqCMu6B723gHvoEPGu/atrO47S1u36z56T9cvvts18Wq8a3evifwDvV6SyHm19y6GgitsbL6ekUSyqfE+z+FU0p2Ad4xz5hTjbfcNPWEvsZMU7YD3mYj3CT2SR6YYhXinKfSSioB3wDvogPH+/l8ldd9ZXptdT6YN9RNtVDQpaBz1LnrXyZYpOdZufBUK43p3vf61gU/fK/He+qdeWPtGMlfIq+zmvc/yV6dJ8b4Uz72zc2hqvG9SF51mfct3J3rvhoMGS3LizYxab4WU4ZJhSop2Wpzf8N9uuMYUe+/RZ9ysIAS8A95BB4P3iuQSmcR9/3e53Pc7Fp++W/TaDSZu5L4f5T+6fBzRGUOflhzpJ9rROYnRb6d+eIeE9VWHX1/Ae7O6e+Qf6G9ojjIn46So1hwh9VqwDd75eoaXZK1T4z1N+rageWwl3fGjwdCe2GxR30UTdlmsxE7W1C6hdcLOPXdB7BQp8L5AdSstArwD3kGHh/c/tNK77zcM77vtbj9hg0ZVFvOGz7S1R8PbNBz/SNd7Edyro3O8YU8d8fg2m+oJjoyvPqnxfv3ddgFA22mG7MHLeOfjyma5I+BSm0qY1DPWLGntS3hPPGTfZhns8t2JUW4T5CbQXHBfS+wkRaoB1bhTmr6Tcf0D5NC2jj0V3nE+XtqOMQO8A95Bh4137Y9yuu81hvedDrk12zgWz7zX27XcZxfVcZwC7+hWr94wvOv6eezod05Y4aOutzvNqKlOW7I3IOB9qOvX8E7vU/G9bvF6zvFkUIj3FbJ9wSImljFdT4qnBLhegOtZYZJt9gW82xEL4x3twGGX0Ei6E0Yj3OPiozSvrcROUqQa0Npe05mPtXb4LuOnZdM78ibRFwq8430JcqeM73KTD8A74B1UdrwbMryX1X1vvyZtXY/U7+h6P//TM+J/Ze47xvvYTDzxuLDW1m/pgkFjpMT7zViHtfl9a+DhW9ln+N7yoNAT9+vICVehy1vMcE6XZTHejYGNkOOG0b/1TWUrvC99G+GxOHySuFx34mg0D6UB9y4XnZezkxWpBrSML3SfrfCjQW4m/Q9era+7oesgtLRUeNcW+KeZzaJGwpoNeAe8gw4E7xUp3lP3vVzB86PXpK0b6u0mceLPFbXu9XuG93YjLYzP4F3oOstpa/RkeB/2I108jYd48R7S2ux9ivqM8RXBKpR47w75wpzH6dkc0QJ5z75WaEOcXmyVXsgi1PRzeF9o1oykeF928wNk3WmaMBo+HH2DnJrCLl+kGlAzWDkkKZ3jBlol+9zmHvnhAk1zXFVjAzxdQXX32dBCF/AOeAcdiPd+KsV76r6XKnUdy2uzS2z6DfOnr5RXzT3SyPoj3v1+1G/JrOLWTP5iniiT0rYf4IV+C9UGwXxwrJ4E+EHgp5vRXWxhaXnoiWr686lf2Wksrel0nhsK665gNIXK2m0/ctqPNQiCqVXwqTUNtvvhKv50Hm86mCX4ZQPeQYD3v4H3krrv97unrcNRcYTq18p1875+wvCeLv339Cr+r83NJ8yxBO8nQ6xRnD1n3IJX+lBkfrdK2o9sySzjoADvgHfQgeK9nO771e5p67g1+RFZpc/9fbu86/T6VyneUwe8E+O9Jmz2Xyn23lsXI12/Bb6DQIB3wDuopHjn3PcSXRz3irR1PbxiTiSLybt8umX30CV4v8zg/atww/yFMnLevIfYOhAI8A54B5UI79+0Avf9t/I8qovd09YNdV4Zt9+4itnePh/en6d4b2Twfids2vfVB+Nqo52T6oFAIMA74B3w/r31UwHe09R1Hz6V5UmZvZ3T1t3o+viBaZhNiDOO0H511CTULsS7sF7/gvcebyA04KUGgQDvgHdQSfH+KXHffy3No+owvF9ua/HEZ5npZGLuI26PGknFQrw3tt57J/MEWfZbEAgEeAe8A973qaLFee7e9/K47w+7pq0TD7ubJ/oJfxbpkfPm74vxrlX5g/bjF/D+qDx/BwKBAO+Ad8D7XvSlKD9deu/7t7I8qmuG986WBj0xVd2TaMmR2rhV4H3EzRGk5955vN/r1Rq81CAQ4B3wDnrjnPOfCcK/5M+3/5Ysz/9cjidlJnfK9Le0GOpV/pjapU5z0xFx6+wXejHe8dH5JHS+r845b96JXYBAIMA74B3w/jb638cI4P95n//gp4+Sy2LfVJcM74/b1b/JZsCJcM9t2/cTZ/6hqsC71mjrI2p2eavG+11bOEUHAoEA74B3wPtb6fTnz/Ld9XeJ+35WjkfV0HfKSovdbiHMzewIM4Obqt7umZqBL4KpKvAe3xjXa2laq9fW2xK8319GujnqXJxvP/UAgUB/sXc+P24bVxzn6W1MbrfrAAStQqyTtQsIKUG0NQYwrLiu7YRIgRihgV7sMIWRreFeSBghfGh8KcgGTXPLIQchiA48KTpH/185/E1yqJXiXVLRfr8wbC3NeTOkR/5wHt97A7wD78D7QNwvlu9fKTsxoHGO980qx4xOmgVw5JO0inwmnkd/dMRz3/95dR3e5df8nDt3+F6x147W1Zy/8uwhZjQEAe/AO7TTeJe+LpbvX+/CcA4LvG9WdP5pa285vp6vJtXdesatHd3/VDoW4f1qhndJenX/KF7gXzl9dCydPmt0k1fOObpz//mnMiY0BAHvwDu043hXXu7WzjJb4n2TJ4aPXjx8uhGS5X9ffYFq8hAEvAPv0B7gXfqwWL7/uAur92vnjvctB4DZeuml6uZlulx/By8XeIeA9/PQk52qbVMkxiH3DGrJms2sxqHpcnVGI8dO5Tvqhngn7VLhnRjwDrxDe4n3D97apdo2TwdevUM7LNkkshvHQiJrfauACrG5A7wD78A7dFnwXu4sswu1bV4B71CXVkTktfHun4V3FsUKTKYRaZ1rfdU7Bt6Bd+Ad2iu8l6Vpnww/mIfbJcZBl4lDmha0Vu8TZo7OwnuO9InH2s8HuTxSgXfgHXiH9grvldK0Xw4+lgfblbWBLo/GjBbLFt7PVol3SYltGFPxaUvgHXgH3qF9w3tZmvbx4Mlxr3O8P8C0gWqaExvN3wzvkuQQdbx+N4F34B14h/YN75XaNoMnxz3fdksZ6JLIJsOR1uN94oSOKq/Fu2J0eOdVo4l3xQqtprGR429aGWFq+a3mY8d3VGUTW1boZH85Kj+uG0bNtBr6zmiDJo4MvAPv0H7jXflqZ5Lj7m+7ISx0OaTqNJOkqIV3Vc/gpC50IiLS3JnajXdJJ1Fw3czUiHSuVYp3dWkaRGS4lbB8ecWPaWzFKWrpulodhT6pfqH8eTIYw3WKEwJJjTR+cNKyVb+eQHIi3toILJ73lxjS7Y5hCExP5kbSd3I5kdTRmxoxfpK5mjrAO/AO7S/epb8Uy/fvBx7JSY73p5g2UEUuuYoQ75kn3TeIdNM1OQ3tbryPSbj+dw2OPyPWjJs0Qp2jjxszitNVMzlmELkco6xqdlVHZETJYJhGmp0P01QZP5g+jTRs1a5Hd3TSmKsT6c7U5W34efOOYbRM+zoZwXJupkNYdPSWXCBjBhELgXfgHdpjvEvf78jOcUXRuiuoDgtVl9dkqNIavMuM9DDxhTsLV+7G+yq109aUKs550ildtvux2WnhPjBsRZLkmUZuMqIKFRl3LZSyjTTB3jJJP85sMpNMSxLbql0PGdpqmhLYdIlxQ9N5kf7Xblo3PdbJVdMx5Nct6C3USPdlSZL5BQLvwDu0z3gvk+NeDrpjyouc7ieYNVAhxTJSrgddePe7q9tU8C57GnUkvtfxTov8s5GDe065N95OWDvRyoR7n4zaIlwZlc29wqZbPLI2bdXxnl9jSER51p9LQVfTuukV6dlnj4xxV28mmekhZWwSAe/AO7THeJd+LJbvvx5yGEVe3ClmDVRoZFKUbAfQiXebdKkT73Ofy/YWOhkzeRO860X23DJzi1ta2bWbLIKD0mE+z+Hb0pyWuU2teP5o26rh3Sx9AsV5dsZgQdOa6fhg8fyipywXNKnWAgqBd+Ad2m+8v/94J6LrnqPkPNTWIl+SduJd7Ux4qxalzfzWAh3W8V662r0Mv/OSujyIX+FcNI7Tn8cGhR12vQz8auXtucBWDe9eOfLimcXKThM0rZmWdCrukJlaEjQJKockRsA78A7tM94rO8cNGV33LMf7vzBroFy+poXSerxzSDldeGcuF9OJiHUVravjvaR1HnhmFv765IT4XFnPQeyR3vVKK8xAqlZD+tq2anj3S89BQeFRFhYvaFozLcC7oIlZzQ5cAu/AO7TfeJd+2IHoujKyDnlxUK6xnnm41+Gdh5tHvhjvubv62DOpw43eWL2Xzm4nW0FXsMmT5/3Ep+C2cV0dt2U5Xol3S4Tg3JYQ90uKiuNaeljQtGZaMim/WtlIHRqCJnrV2zAD3oF3aM/x/nYZXTdU7brDWwXeP8OsgTJFxEZn4p1HqROZ3ngN3hNidpS1qeN91MT7mLK8+ETpetnKmljtsD4ljMw0Db/Ee/E6X2SrivdROdhlA++iplXTSWidmmM78SkImoxrA/aAd+Ad2nO8V0rPD1a77lFO9zuYNFC2CE3K1Z2Nd0kKA43IWE7W4V02q6+du1bvlaK0Kd4Vi0iryq6slBfN5DbJZkSk6WYQuQXeC5tiW4LraeNd2LReQ3eiE+ML9NEsTTYQNbGIKk9ByHsH3qG9x3sluu7tgYZwisg6qMlvgwI/VwzU+He1A4eSutKJdGsN3nkCvXLm6r2F94YHvLbuLd/BF8c1Yqu0Iq3dxrvY1kZ4FzZtlMhXTSLGy/tke98KmtTf99vAO/AO7TveK9F1A+0M+9EVVJyHGlpRS1E33GTPaPK9jvewEcq22epdkkTB8ePkTXbY3IRuYpB5LCnZE0Ab71J3oP1ZeBc1be6AoxIRaSyyOnuTter7frx7B96h/ce78sPAO8MWWe9H1zBpoGxxGVWkkxn/7q2Bm2QZ9QpyDbx7WQj6lqv3erB5rjlPSYuqeWlpF1rh+16I8G52bjp/Jt4FTRt3wGK0Gk/ktb2x6juOBfAOvEN7j/dKdN177w7R/+c53j/HnIFESt+9H67De4yrYA3eF+L6N2eu3gPRO3ufjOnUaGbcryqnuiK8B+L3/5vgXdC0cQeWzTB+QRO3+kTi9oH38c1c316/MVGAd+AdeO9bZe26bwbo/dpRjveHmDNQN96ltXifNSLdanhX9ZRs6mzWeCNdJqgJ8R4K96JhZHstes7KIz6J8C62tRHeBU0bd8Bs+uIFTWakqeUF9oH3uwcH95JfiW7/9Ps/A+/AO/Deq95/OWTye+GbvzPCnIF+Jt6nZsNZXsW7amZh+F4rQa7cFUaIdykg1opqO5xRFDTeBSSl4bIzJ0yId6GtzfAuaNq4AwG5ltK4Ac0msk5R5r6Xg17wfuOgrnu3/wa8A+/Ae68qd4Z93Hvy++EpStZBPxPvgWsfp+tlt1meNse7rPpLI6f6grTGK/ioCMkT490yyPASJsr+Mn80UDUio/kuf6JnG7h58Qch3oW2NsN7u2kD736+tS0LvFFXbx5RuqeMZZLZ0+q9qd8C78A78N6rvin4/p++uy5q2pwgsA7aEu8mkcbcZIf25rI8IOK0MzQiIpa1b+7kIimWRoYbuWYX3iWH8T6CgPcxrxiPWqO0iTTTTXZpV4V4F9vaCO/tpnXT45lRphhkFW4EvfGz4iNMI/dYv3i8Kxzvf78b68b1b2/+43biob8OvAPvwHufeve9gu8f9NuzXCzeX2PGQJvj3eB/hEFGtWyn9dqyPFWM/7mdu61buerSYagTERkNXlplKJ68YMkjArGFVSG5IMktZIkpNx6sIcS70JYA72WUnFZ4G5pN68NlpK9C37Is344MMuWu3hyX3zDNXMiSa178Pxx3zv9KUhRFkg4Vafwx5/sfgHfgHXjvVV++NVBt2uLN+8kYMwbafoFohbbtW5uFbViV2LISuZZtW/LadlPHDp1qyxXpwjBw1betM+LDc1vK9teaNz1s/Y1L7qjqRXA6Ry4plh8mX7XDHv5x7h7U/g+Uv4h//gl4B96B935VJr//t89uPzvJ8f4AEwa6YHmdG7SfrRoNWS3rrvu8Nzhnm6ZOvWgPq/soDoe74Q28S3+Nf35HAd6Bd+C9V/3pN0O45+X7Od1PZUwY6IIVrakbt418kRdg0McWXerG+4C60cB7Ekl/DLwD78B7v/puCPf866Ie7VXMF+iipTPlXOxErd1kdgnvY+2cnmLOf/V+/WAt0IB3CHi/EClP+nfPPyzo/gjTBbrwKT47H+w5u8PPbEDVSD85IH26Ize8ifc/4t078A68D/FV7N89/6qoV3d6jOkC9THLz8GGqtM5eQHO7aoYGXl2gO8S7Ypvvumcv/tO/PNN4B14B957/z/iu56L29wqwuqOnmK2QL+E74jKAtcgw9+xcak6EYuWi3nAiIydoXt99T6+GeP74BMFeAfegff+v4xPeixuczh+dAUbwUK/MFlERLq/c+OaLPQsz5/NzjPD9Nh6o5BXjvd7/+P6+ItP+NL94HcTCXgH3oH3/lVxz3/4f/bO4MdtKg3gkUZ6U5yBDlUtk27cAQpo1FpWt5KlimlpoWCxalGDxKXCoBUsWi6OEBEHOK2SVtDeeughqppKOYWcm/9vYzuJn+1MMpPGdmL/fodqxvNl4s/zmp/f83vfS/mtap+9H9r9J9oKbIvfG0tXtueDonaazY691uoRas8QVvM1HuQHNefDXWXuf7AwHL0Dek+t+x4Oz69/a9i9/3377dt+7Y39337/QZL7G5/u01Zge9jQJZzVtd91HPpV+ER79YTjNeff3Plkmd5rURJJVWu104ck/4bphCSvVG1RSKD32gq/ZaMvS+01L8uKOdez5cNxc874LZXXawrS8PzzdfydJb4MVri//+8fP37rjQg/UmseYAPpTcb7+6v63X/2vvu9z/XJprD/XKz3N3eiXIr/yvM7cW4mxgwSIYkxjYN4xJXEyTyNhyQ3u7sYD0nul/NNPORpQu9X4iGJqvz1REKXEjdSG3RZFuYcsLM058PT5nx1t/BcXOGyeEzvqaXh+ZexkEQX+6P4L/nHoh7Pp28cw6fYHWATRylmu9S0Vx2fl2bOVz85uOo/fb/7WnrfKI+lpPePVtD7efSO3o/7H3J++qOw9nzc76+n9zvH2f1nRuYBNpH6bAs6zVytSF983Xv9lfck/hK9d3rv6D0tvVeP771XKs/Dx+/r1PtP8+X+FnPmATYUS9pjdrXFAvGyNoffjw/cW6T3VwdREs/q6wdxEpL6JBGS+PA6nwhJnns8InlbcmmFkJsJvR8sCvGpJUI2+rIsTygZcj4esZ8ISay5OIz8+KI3dfNKjJeP48QjrtyNR1xdJeTqCiF3EyGPjw+5F+j91Jcl2hSkrWGfREJqS//OCzZyrvw81+4/fMdnKOROSze5CHNoht13YXRXmbkXrzlfebm7aL5xdjPnq6cP2VtpBkL8RXvVhN43iMwuyyrvfIK2drG4/xnXlN4X4fD87fWd3O9z5P7xnb0KQP56FxYXYd5Hbl9IuCsM0Md77/6WcdeV/PW+GbAwbvP8V/T0HqaxOu6d9xNy/6xeQe+A3jeXWlv2u9E8bRcrUXO+8tR7hFhH7+gdveeT3o1fZn5/sbaTU76LPH3/+Kv/Ksgd0PuG+z3Sfxems8JnUkTvV7y5dRX0jt7Re07pnQtXx51d4/m98/adz776+Yeffr/z25eYHdD7FqAMZb1rWt9+rd579dXCLePQO6D3tNM7O9P7hc9pNoDeS0xXi3TgtXbnFHOp4nr3ZtbtXkHv6B2955Ze9UW4d9x7tBtA72W+OrqIog9OPEYfHZy/dM8rPX/9JnpH7+g9P71Lq+Oe024gNw6djqNGeos1p5WYmaV2WrZcV01xOsl9yRS7k9j9rO44CnpfhuqKONagdaI+vNd7f3ey6vfD+0Ftjg+OXwSF3gG9p5/e7XB13DMaDuTBnjrZ5FRzu5M1WcrINITQrFEoiL2G60cZbmfi/4EXI/T2tIup6m6lPvQPGq706FjtW0IIwxwdOeh9MV1DJA3fOUE/wd8Q9tat6YZxS2rSondA71mk9yeP3yFfWoYQuumanr0bgZBNT8eeqN1p7/ywL4TQTS+o6R+wTc//XozRnGhc6Krlv3AcZDSmv77jfWdZhicq9L4E76rG0CYXfOlnUoRfF9W+Qu+A3rNIT3nA43fIE8USejCLyxm4StANH9u5WqkoXU24QVBdF9rI79rbTd/4tjHW+pE3Pm8KMZjoXegi6La3LKFPRvE7mtC9AWbFO1YWvSutwchZqT6Y0kx24I3a0pfJG8K+e/8/dw8Wh6N3QO8ZpFe9Fj5+f1Gl7UDmnXchYouwekJXg68aQgQ10AdhMfSgkfaFNXlVrScMdaL3iejHXxuiG3xliuk2KXVTiHLoXfGr1PTrK71Y7Sf87pz+YwW9o3f0nn96Vak47VnaDmRNQ+jRA7Y2GaMf4wbdd9sQkRHiPUeEMTVd9Ke99+nEu72h6AWd9+kNQvB1OfTePflT8wR7DT2h99a6TxC9A3rPJr2HqRSfBzhZbzHeO+wJU3K/UfUPWdEJ3H0pptIU2mHwi7rSsWBYvy0HWuXQuzIT9OmK03g4ydnzwdVF7+gdvW9hesqjsPj8NVoPZIwlrIjfzdkQu69s1T/Ujb7GlHvzNcPvX45jw+7qdBZdJHBYDr070mPz4an2h3HaWtLuwdgIekfv6H0b05Mevz9QaD6QsY10ofWl8V89HHevVANzy4cmMZ3IDUIz0LstKU5PBnZLp3chjN6Je/CtuXIXmr32M0TvgN6zSk96/P6Q5gMZ46/GMpuTmWB1IYQ+w3/GXo+P3x9Gp+O5YhTovRbXez0S2CzJ4LwVLy97gjmztYYp5tNb/xmid0DvmaUXrn4/8xftB7Km4/UbjWGw5E0ITZNo+IcO4/1TeVp4Xwx9vWuVuN7tSGBZ1r134r1wq7tkjH5aWWgOhrr+E0TvgN4zS6/6gOo2kCfqSBdCtyuxMfbJD+OH1Pm994Telcmz+wmNsqx7byZG2Q23eaymnZGpJYvZTBmmcH7oHdB7dulJxeepbgN54BVU8f1uiMSCrvihmpj77D3Ze1c0eVVXtzRV61rWnH641e+2jqJx+51Re16/3Zq93jhM4fTQO6D3DNP7/ALT6yBfv9tBLRozWQU1MXNel2OU6cz5pN7H5m+EBwflKUp7NJw7TU5oumW2e4Nud9Brm5Y+P8gYNWdfj9I4O/QO6D3L9J4xvQ5yZiDaldhS9YC+sKKzw3rRde/+CPxcvbvyzDC3TDXn7fYxD9M1sRitpx7NevR6PY1zQ++A3jNN7wnT6yBfun4tmo6IL4NLHpIPKNa0at0cvXeFpobHyqR3Za/litOjeaVwurNvu6mcG3oH9J5pejceMb0O8uTIDLra7Vk9+RltoUeXxrmzGGU4WZk9V+9exdrJwyavEnvJdow7ZiX78QSr5OuzzrtVS+W80Dug92zTk6rbUL0OsqLtNvYDF7mT5e3+bnC+k5XWcLL9q3fIV81h04vxntMbDS9GdaeTu+fq3Ru5D/aUsU1hlm9DWHtonFzu+iiYRzeaHWmmc1boHdB7xundDofnH92gGUEm+Pu2u/4m7VObOJZ3rN32jvUih1xrut+MowthmK4uhDaqLtB7pWsIYbXblibcfb2E+70fNc2Tjcq7jckwhzq7I7BSmmWL3gG9Z52eNL3uOZvDQiZ02sZ0YHg2/l4bWMGosjWwY4c0dxKlBv1Sw52ufJP1XrXDfegc1wvUzIFScc1SXmJ7YC2TuzkKl8UPZ0cbKZ0Qegf0nnl60vS6v2lHkA1Vu9NotOzoY94jp9FxPOWE95n73iFpGXbN7rTs6oneoNWpl/sa283+sXXprF6k5I0967yndjOE3gG9Z56eNL3uzB80JMiMPS5B6qiNUduKzLUzzP6oE69b009vn3f0Dug9v/Sk6XVMnwco3C2UUldtp9NodBxbPZoX5cxuANzUzgS9A3rPIT2peh3T5wEKaPnFzzNm1XA0B72jd/ReqPT+CIfnf6H6PEC5sGdD8/303gS9A3rPJT1pc9gXTJ8HKBWzgnWajd7RO3ovWHrV51SfBygps81keim+CXoH9J5PevL0+bO0JoDyUJ0Ozhsqekfv6L146UnT51keB1AqeoHeB2m+B3oH9J5XetL0eZbHAZSJml+0rl9L8z3QO6D3vNKrfhF23y+fo0EBlAi12XXSnVSL3gG955fe2dDvX7P8HQDQO3pH78VIT6o+/4jl7wCA3tE7ei9EesqL0O8P2B0WANA7ekfvhUjvvUfsDgsA6B29o/eipXft69DvT2hUAIDe0Tt6L0R658LlcWf+pFUBAHpH7+i9EOndvkD5OgBA7+gdvRctPWn5+5lntCsAQO/oHb0XIr1nZyhPCwDoHb2j96Kl97fk9y9oWQCA3tE7ei9Eeg9DvV+4TdMCAPSO3tF7IdJ7jt8BAL2jd/RetPTk8nVsHwcA6B29o/dipHfjAX4HAPSO3tF70dKTy9Nexu8AgN7RO3ovRHrv/YLfAQC9o3f0XrT05PLzl8/RwAAAvaN39F6E9GS/X8DvADCPj3Z2duroHb2j921K79plxucBYKm3dz9A7+gdvW9VeufwOwAs5GD80bT7Cr2jd/S+XenJfmd9HAAkuOfpffcSekfv6H2r0tuL+P02rQwAItSv+3q/it7RO3rfsvTwOwAczze+3W+9W0Pv6B29b1l6Eb+zfxwAyNwfW+zl+NPpX+gdvaP3bUsPvwPAfBTvg+lD759f0Tt6R+9bl57s9zN/0dIAYMpjf1XcWN23bqJ39I7ety69iN+f0dQAIOD/7J3dSxtLA8YTCjhkI5EggjnSGtojknPTNwgUiBogIqUHJfUgzY0gvAj0SnoVgpdGPQERlVxJ6LWAd/0Dz04+Njszm0+zSXb7ezicmjg7u8803V+e2flYW19Y+LQZkb3zP8E7eAfvwbOXdK1fJ575rCGEmvpg35f+H4ls23+cxMA7eAfvwbPnXp9WXFt82hBCtn7Z96V715/gHbyD94DZs7Ku/eNEDb4jhFq3pWZqlyn+T/AO3sF7EO0pfH/K8XlDKNha2z9YfW0dNsIWvjQr+7SwsLsD3sE7eA+ivXTVxfdSmg8cQkHW7XE0uvVw8Ko6ltbt21JrxLxcmvYv8A7ewXsg7aVLLr5Xs3ziEAqu9qMtnT3ujF/J2+5mMhv2j4vgHbyD92DayzVcfC+yATxCwdVFtKOVq6/jdtL/cm0Fe2L/vAHewTt4D6a92JNgg1iEwqDjqEvjddJb27tyq7jtlmTv/N/gHbyD92Das6xLwQK1CIUN72N20n9ZcEmi/tMmeAfv4D2o9q7dfGeBWoQCqu9RXcejdtKvnizo+qc/3g8/qDLWsd35oGvH6DIwiqxpReLvjSLy7bVDlxZPNC0e6nptkXX7K8/h4cngWsYp4n0p//ZrFsu7WRS900uYGwV91ou8M4oY53mvl1gzihgfhWXl129cj4G6UDRqWdJKxIwiBkTjQ3i+14tsTLhZ2ngfuVmkNifYLAnBAnYIBV7lqIe2Hk5HqOLeoHvfwXU23hffqDJu+8tvdBn3t/dGkTUDUnqJ5pD+zYXpaneqR+7Ko36M0SzqX6lexPy+9o9exFzNyPgbMlC3aRQxPgrbyq8/en13ND8KOsciG3qJPwZ7Nr/1fNCLfJ5ws7TxPnKzSO1MslkqggVuEAq8HldMvK/ITvrNYWv4IZey+enoi82X3Xevw/t2iPA+A/XFezxseI9NBO+xIfD+diDe41PCu2X+C1meaLPspVx8bzABHqFA6vRhyyvBy0762DDHy4F1n3Y03P/bv3P+rSqD3ZtvdRnZZNsoYuD9vVHEwfuPtg4XDxfV/37oGqfIL6OIUck4RX4NUUQ+Kfl7tGYxM+g7vYgJqXu9iPmNzmh+s3PeKGI+p1F+/YdX57z5UVjyx/OGXmRjws3S6ZwftVmkNifbLIUME+ARCrri8aXyxYon4Y++DnH8G30dWtlZv77WD++zHFoXl3j/3+xb3a+K58OeX/Ice2aF2964n7BXNouygRwT4BEKrJYfv3kC/m7gPcI60XeRiZ14Rax5wXvI+fc74h17vihbZYIcQiFQLB7Zf/HqpH8cdOS9uQfsl+4idqR37IH3gNpTFqgVde6SCAVXXp30W4Oev/9p35F+qm/JXd8X3pPesQfeA20vVnPz/ZIB9AgFOPrFl++ONL4v9T9kR24Rpw/8kYvUfiS9Yw+8B9yee4EbBtAjFFxt377ocI9+H3DMX14d8XKLmfVV0jv2wHvA7d24+X7OAHqEAqjV/QfPwXW3A45b9LohNTeI/Ux6xx54D7g9K59ihxmEgqyDHnPfo8drAwK/zOlLxtsf++wrQ3rHHngPjj1lghwr0CMUrOBePov20tWgL/ejn470jj3wHiB7ygQ5cRmLIISCobXHo2hv7U/+hKR37IH3INnLNdx8LzHADqGAwH2rD9yjWz7MhSG9Yw+8B8qesgO8KPIAHqH512p/uEejdz6clPSOPfAeMHt1wQN4hIKksvdCtFFnbZuV7fDhnfQO/7A3svIZ5QF8jrsnQnOs0wsvsp89lE8fOq8u/Dgv6R174D1w9pLngi3kEAqEYnfH5gZxL2U5D27V6bEvhxDvpHf4h70xlFYG2GXYYgaheY3uxly4o4eD9u++DjvpnfROeod/v409S1mhVlyzBD1C86iyHt2/f3Wms8avOm+++HJu0jv2wHsg7VVSygw5OugRmr+v4Xca3C/c09t3Vnyc9E56xx54D6w9ZQU7kclzL0VovhR70eB+oPz6sfP+N39OT3rHHngPqD31Abx4poMeoXnS0pUC9zM9oztP5R9DiXfSO/zD3tiyngUd9AjNqdaU+XBbt/oS0gfO75ZJ76R3+Ic9VXsZRtAjNJ90/67sGLNjFPB30jvpHXvgPdj21C1mWOIGoTnRkpvuWx7z2n2e9E56xx54D7i9nLIEvThPcl9FaPZadffMn3n1vpcd9i+R3knv8A97HlJnyKXqjLBDaNZSxsxfefLb4f+DXxdBesceeA+4vaTaQd9ghB1CM5Z7vvtLzKvEsjPp/SCkeCe9wz/svVpaBz0j7BCarW5ddH/w7E+L+z3pnfSOPfAeCnvqCHpRS3ODRWhmOnCtRNur6/2bz5PeSe/YA+/hsJctKXwv5rnFIjSrWHc0kO5xZ9L7yg7pnfQO/7DXW1ZMXeJGXDNFDqHZyLVY3VWvka4vfk96J71jD7yHxJ4VyRfVKXIFbrMIzUCuB+8Xqz3KLPk+6Z30jj3wHh576ScCPEKz1rJD7ui3zV6FnL75rVXSO+kd/mFvoNQp8KJKgEdo2up2zW+d9ix06vukd9I79sB7mOxZ2gi7FAEeoenqa7drvl+/e3vJ2uPt8OKd9A7/sDdJvluJFE/gEZqZYmfRYYJ5fLs5Me54389rIb1jD7yHyZ6VPOcJPEKzUrn74H2pb8Gl8t1DedPXayG9Yw+8h8te7lowBx6hmWjVWa1mZX/mF0N6xx54D5k9q6BOkROXLGKH0FTUnRT3MvuLIb1jD7yHzp62CD2r0CM0FcWc8H68M/urIb1jD7yH0J62xo14Yhs5hHxX98n73RxcDekde+A9jPb0AM8+8Aj5ru/OlPc18E56h3/Ym1KArya5+yLkp5y16HzcBo70TnqHf9jTh9AzRw4hX+XsE3M8D+Gd9I498B5ae1ZBmwNfHG6InZXme0DvxomlXzERgaYNs5aOp7HULOmd9A7/sGcH+Gd1ETvRGAZNSZEBQj21JzLjH0zThlhxZ2DdyjJ4J73DP+z5/BFMVrUn8EPwPSnEiAG1XpB9BeOM3kskKpFIPpGYxtT8bKkxAbYOj3eP843etCg4unB2eZ+P6yG9Yw+8h9qeVVcD/KUPeM+Lhv3/qhh99l1WiGok8iTENNbG3xMiP028e5wPvIc4vK85ffNfwTvpHf5hbxqhtaHMkIv1YPp1emwGPYlEJFIQxWHLd8+VF+I5EjkXmWlM2+uDd5d7H/DerR28h1jlaezhTnonvcM/7Lk5k3HxvUf/dEIkx8V7OiXDd03Uhj2ge66EsH/MDdWnMIHvOcXz9MArmiDenfN1awfvIdbLfA2sI71jD/79DvZci9yUeibwsfF+I1JWJJcSN8On/aTzU7EZ4fPTaAVrmCuaIN4ts3bwHmIddfC+PycXRHrHHvz7HewVOkPseoG0OD7eS/I7w54YnpDdcxXFtQy3mVkvqVf0A+8etYP38OrU6ZuP+Xqe1ft/fywe/vj4jvROeod/2GtGydYQu0SPeG/TuZC2lesyKLtX30uqdyorWbkp6L37aSEZ/SQjfOedfP1mL9l8mXPPEm+9cJ0r2/xO0JDH65Um926Us2fzlXoln7R6vW5dXT2v09N1La5J52r1ivteRtOFvYqsR8N79yDXjHj7x5xzPnftvZsWBV2309kr7n59oandhcP3pHfswT/sNUFUuUz0yKi1lHtgvWRQvpQyNpvLNlrvPakIvRFiT/bNt7v9c9ftJ/2ZevOX592SCVnGfa6KfMNK6dE5XW/3NWQq7XfyneGBmcuk12vX1RVdzwjUa0mKVNqretW9t9F8a4WgVCObV/He6D7uqHejeUJOCGidT6m9V9Oi4Mt59H7r51l+2lxva/fTBukde/APe/1VTUnipGzdtPD+LF9WSxKOz51EWsjI987tksWkCjgbz3uiHcGTkoOZkjw04Yl397lqoiKH3OvjAUqyimpD1nTd+QZh19moyuupeLyOtIcPnldl3bVOntaupdMzrlevuvc0mpAFivKtVELFe6LL9JJzMXabPHXOp9Tu3bQoDDrr4N3PNW3etsB+uNiE/Po26R178A97A5QVyrN3IUqtlFwUqfZs9oJNpRvLpp/Np3N3n3iiVrMihVot3zzWZtl184jkTdYT7+5z7V3G7DB9mdcu5iZVK7Tx3Dx7OmVH3WZfduG6lDNfR5rD81I3ufZCvI22D+1aOnjXq1fdexmtC5HJ2ye08pLKCt6Tsu+iU4mEulQsJerd87lq925aFALFOrPej/wkiuyZX/+8Zv/0QQL+F+kde/APeyPi/bL783Unm2baNKqI3oPkGzYHFVL3xXurW8CoxGo/lrayrYSrD9szh/FZVZFpr4yTe+qMH9SvpYNbvXrtikyjuYw4b7+Vrmp4jxQ7bdVM+M6XjWRPvJtNi0Kg02ksWffTvil1uuQ/yPy+QXrHHvzD3kh4zzhPnWvtLJx3Mb3acwWbvD54bwi8/8fe2bWmkYVx3EMBxUmJSBC00kbaLcHedGWAAiYRUKSkJCSGQW8KwiLglfQqBC9j3A0ESUKuStjrALnbD7jndea8zYxGzevzN033jJ7nPM+snd88Z85LlH7TyfQYs8o4NL1Mj4h+cQLjkdUXc9j6bz5XX/bIEui1n6HTWwsV7yfiZIxSZ+Jzt/SYHe/mqQW9BPmbwU6W14ZDkvdDUdrBhV+QvUN4wD8Ibya830pIHvBUOOiRPw6d3nWjb5kyJ95ZjawE74SlTDLufUeqRO0bvph45w4pHlkC7UhBOPsa3nlXAukKqHX47ULHH6Jo4t08taCXIH9J2qPltfGBdM37S+JtfMOpfBGydwgP+AfhzYL3hkSvAv17X1pZLhu6Rvy+PlxsTrwfs9S4zJ6G+9LL+IDUbon5b/hi4p2bVzyyBFqWuwHONLzj1q5ZcGVnlfXOcwfseDdPLehF6HL5u8W9wdekf4LiL1z8Dtk7hAf8g/BmwXtA7xpjUDqVWq0IKZCS5Rhp9f3xXqnXGsdjxl8yln3wd5CL6+Wq2i7FsemLgnfZvOSRYwm0qoQ71pe16bA+9hG+LeAnpsEasuPdOLWgF5K+t5e+Iu0f6jXpCy5+hewdwgP+QXiz4L2iM6ii7isbMrbOTOvvh/fS6kDMFmf8pfvaFk5841pZa3fEp5jXQ/BumJc8sgSqWrrW8c5S9iwd0DeivfMn7OG/He8VwPtL5fvBXWt4vsztZDCvk8FaNiufYx6+Q/YO4QH/AO8G3ks6g2p87raQHe91A9z3wbszpvDd7/w+6/iD+Bq/6Zwyn7NKuaai/IbgvWa0wuOymJc8sgSqRnWs452Bna2se0t/D9iQeDveS4D3F3vBX3YD3/A1KRMUN3HxJ2TvEB7wD8KbC+/ZqbZkj8nex9Ph/QSzd8zWkT2WxuhXbslyMw1bOauupR+dvVvMh3Wf2w4Z2TtbOH8kRtPViLUa4B20cGXIyDrpNjhDVq6D7B3CA/5BeHPhvWo8ybapGv3s/WwqvOPmR8HirvIUvPRqQSGtKDsltV26qEw15Nm7zbzkkSXQkvLs/dbAO+mLFzcYA7rUbiENeActJV18q2Xz25C9Q3jAPwhvLryb49Ct2tdXalG6sjtT4X1Vej59ps2wrxfUBnjZUQ5XxMj5sQ3vNvPqyHkj0AIbGy/q6HgnI+mu+a53ZMb7CZ/PDngHLVSbOt7JNPgiZO8QHvAPwovBez0S7x19FrlVN9KqLbx6yq9WLft4j+roPwn46Qz0BXROtAXqefm37N3Yn/desuDdZl72yBLoSGrUmPdOs/vaDZ9Oh9vIDvjdgIT3OuAdtJBLkoL3n9ET3yF7h/CAf4B3dfKXlUGN0M1ktUT2Vr9tqElpeUdvyyJp5tl1Ssf7mA1K18vHknelAsueDV9YXDbzVXVZOj3QW4n+xylzv/dOalwQYe6nxiJZF+dRsg54B813SdrTs/cNyN4hPOAfhBepcrCai51BHXlYW6jwp+RB9Y5T9jPfbJnjXW7LomP/jqBeYPwtBbu8l8muLXqZrjkvvCt1BF41X3hcpnnNIzPQUjk14h36lYEF72S1+bLjdx2ID/jnsSyvMw94B91TW7bO+Qxk7xAe8A/Ci9RNADU7g+pkN5QsGTNWue6EJvLkU2dZurnauMbRd5Mla7bekuloHb0tiyoFuhW6UzlJpQqUv53yuE5Xg7vep2zWyxzVZDh8qYEPndl9YXGZ5jWPLIGukt3ZcYvVRjlVNvEubxRDG9XOY2Ad8A66t5yiPhHuaQ+tg+wd+AfhPQ0RLJUHZT623MYgstVqKjXYLwQbn9rskE8VBhiy7Al0mi5AwyqJJd6ltmz6m009JzWylL+04XKKTldftZSpn2Sr1gL5lbp17L7wuAzzukdmoM4qnSlPDHVKBQPviX3pEcS+P/TeP4+BdcD7DMpvwDlQlN5OJr9p6eP6kvHuzCHKv/iPpZ+f8kSkM+Vd/nkoM7Mo/zIvRkVNZPuG98UnI/MrtuBrR4Oi0cB7NmBQdTzgS72NI0a++5/iS87Q2WsYqDgRbojbgqAtq2oDVuM4UaX8bdyIRebYRu16mSX94wIBfqFTC/Mly/dr1c0bHlkCrXeo9dG4mhgNDIdP/L552jtf8k9dVbMedmofupfX7S3eZHuh9jKX7Rxye+cLcX8a55ZwThaun8qz9rQx1O5pZe+P0Xut3i9Y4Wa/vm5YtWXVGhM5/W82Y/RRvKbVB/LDX+zXVPpMXlPogvzgV6wOyYrHF9/Jy6r35GXoE/nxX+E6ZK9Y/en/8V/RejelyHaLP758eae9vvh/YvTmDf6ZQv9OpcOdHf3Qwv9lZBu1bMxNQzVbr2UrcZaqdfwpgTGHrO+ezToztOXgGg2lhlOpNRr1SjqsTCth5+oVJ8qXMPOmR2agTiVbry7z3D6g1lDuiZvcaCPkuSHbr83clqiQuZw85DlZuMgeMh+C4vukym/GEHKt/0yu39/x3cAeuxT7V1/pAitdQ8VlMbh2+VemHV9fd74G+ifQD0V/Mf1B9Gt9fX3vl6z/ZO1Jektein5qWv+5rulboG2i5KNoOzldw9tcs9nmFZOk3vYUbSSn9CZJjSaTM7k+fY3An+3Yk/dI/9+CE6wf2E4mH9qp9afy/M95uFom9Z3I8pIDXWpzgHdVE+QdpRPF3eJC8d5EqPuc8f6VdAerxR3p7eVfDbdnA5xJs+05sGnw5dGQDgIt9N/VegIEmq/7/DzzfPDeQlfh/s+D993njPcLdQ8ZssPMhfT2jP2TIBDoSegQ8ASaT+do67ngfaWI0Ea4//fG+1arl3/OeM+QofL+fvJkdNC3DHyxQSAQ6HXr8vngPdFFXoT/98b7A3Y/LEd/kfSdj+Eo7iUfeegcCAQCgZ6A+s8oe2+idoT/rxfvH8nYqR/0P/NknN32B/heg0Ag0OvWlmfg3ek2N7Xh/PndZnFaixm5ejwdI03nu80tqXhg4l3yP9z9kEZCnEvHB5Ax21hrNnfz9z9r8+kHGYnz68+PH76Q3B2SdxAIBHrluurlEHKJJhxla5d9DyHkDYOx5PlJDx/Kta/UKQJd112T6Oa2MGad5l0LydUFHQ/cYfDZc/cu2rT8JmoN6bi39NFpmzvbtfsf4n54I8I57Dv/e5jY0AxIeD93XeKJESO5ybjz6CHqyml8aItWZk8ecfsWFv4BgUCg162+RxDkYV1RlHlNF0Oq18f88g4EBPvkEGHVUMVGD0nTxSc0sb5DCLm9IeZw7kjFO+pLeEenMabp7UOP0JHA1qOr2JxiL5mvXbv/dvcjGvHx7v/tbrY1AwHeD3IsXiPGRAI36w0v73r0nf4kNrQldML8F9B9bxO+2CAQCPTalUFy5zxyEctJm23UyvCUFrlHOANNX+XQUE395a7yNp2yduTd7XI0s+qCjkcS3lcE3sNNExMe8o6K+M1dTE1+H2HpnJf8t7of1YiJd9OAj/emh1ifgxHjSrGFhrQj48gVnkaGtgylD9/y1P1dGr7VIBAIBHhX8e4n5Gsen2J+iVprAq6oKVfd8IJyE3kkSXXE0+ctD53HZu/hpkmyjlo8S8/fIW9zOryb7kc1YsG7YUC8t+uiU8ZNI8bEBLWKIjKvGB/akvTx4tOni4/wlQaBQCCQgXeR8xJAkWR1ZTMXrADb1xLRUySeoSfuRH+7yNAv0WVM9h5pOrGLkN+9nm/xhmLxrrsf3YgF74YB/t4mTtD1ufEixsQwWGzHpSyPDg0EAoFAoAfG+5WUYvcZ5QIuHyBPGSd2IHLVRNELYKxk6FHZe5TpxCnqyRVyW1Ph3XA/shEL3g0D7L2tNuqZg+BFL4QbBN9nCX1kaCAQCAQCPTDe/X7klSZqMV5NJByiNbmu0+K90xh0Le2J74FMR/uz9yjTiR6SdoXL88cAsXg33I9sxIJ3w8AaWUmn2ENtywA5cc/i+qn6Csd7ZGggEAj0tOSUqkuuAHp0vAeD0ncZ3qTMNOF42mPkiYC2jDNMw83u7jnLvqOy90jTrtId0GbUbMbh3XA/shEL3g0DBO/5of8k3RYjjl4k/WmXrV4fGRoIBAI9LWVT5eqMFQolOG3PC+95DW9FxOeVU+k7sW7mGA+7KMcnYzkHpz0XUfVisvdI0xvqJm5DdvsQm73r7kc3YsF73sS765yiYJCBJcbEBLnchyvk5mPPGggEAj01vKdKy60Aemy8Syu0Mbx1EcrJ0kDFsTsRo8eO2gihXKs/PB3GZu+Rprvq5jF3bBBbHN4N96MbMfFuGsB4vyQgD7JxI8bEVgv1SIKev/IYyWPOGggEAgHeQY+L9y2EohZJOaLP3NMtnp+e51Br0s0zEodm7zjHPY0xvbJ2r+zdpHOk/9PhHSF3d4j8FfrMGHErfYTaw76LcuwZRcxZA4FAoP/ZO3vf9JE0jns6C0cCWRFSrOhO6K6IfM2dRQnIhRE63YkTsEJQ0qSgQltFiDJALlKEjogK5Q9Ike7+wPWM38YzjweThcRhn2+TYHuet83+Psx4XgrA9HWtoHjnIruI8hYT75o0JT6lG/aSuU/qFUa1IZlW4h56du99G7x7V5m+IdC79+PxrnSSE++Dptaqk2kwbg/kGH4JINXRKvpKoq4aCoVCfb/KullQvHORXUR5C4r31Ax2WYzUq2Do/OqBVKMR9atNCu+lFJhXAd6VpgefmTkP4F3lJCfeW3QefbgjLZSjdtUckM1ti1s7cKBqKBQK9e3aFxbv+0vA+74IeG+q8L7iu92yumR4ezsMpotrG26tuvDuvcuf1D4K8K40/ZFe9x4wHJ4531ThXeUkH96rYW5BhxzIkX7J2aQtH6gaCoVCfbt6hcV77xLwXoAkIjbDeLvqEPU48z15eIg6sa8J+jrCzPnbZPH3VZ8Eu9YpTfe5m6Xw+wDUe+fih+isdHIM3kvB63coR7oyLu3jYNVQKBTqe9Vo67pX89VIaO20d20zvYeJYc6fvIYS7zWz/ZQ0M2r0hjefz92gmeG4u6e5WYt8gVYdd76bu6YhRZaKRbaU9h06z4g5XzCRiUOlkPJMhyIlIQfF53wejZKd2sDu74KMJormL2S1iiz0o73htbuRgHdtEK8tux2RkNZK09P4ZmlLqpNMvCfxg+GrnByDd/b6vQTmSDvri2b6P9GBqqFQKNS36tnSQz2GtHbHwSW7zRHoLbi2r2XhvbZbBmbseXRnpnkzdon1Xr2lnmgMW3Xfwvv2oylExkmyJPn2nfc0M8xjCXSecwTDm1CVIp2nGIqYhFzIVM5n0opOHVPwkZ7c9sKmlZU6W+mN8tUtPbA1eBt9dVcn9wxqD/4vAt5fSHVTYYgekGGId6VpdmIc/SJ0vSDh3u4g3pP4YbwrnByFdy14/Q7kyA6EDY6lHa0CVweqhkKhUN+rpUV5Y/l6Cmj9Tj8ux7b/4z3qcHo2vTbzn+yZGXgfU0At3yjn1uEd27XotZ5FOz0738J+/cjgtxyvQatP1Mb4bUl9z4XIEsmWJN80LJdZX/o2hPY5g/FNtK0cpUjnKYaSTgIoZDrnM2lSJcPFanGfyccuO3d9tZrW07u7hPogyWkyD/6T0wU95PyjJeC9NCWkPmX7wXw8RC2Uprv07PbpYuA/8mpk4z2JHw5f4eQ4vGsbUu1DOWq3r0MSK9zf7kDVUCgU6nvl6Kl377o+Zh/dnm45YV+XItLQNMNH/8yA8f5kPXvswixs5t/p6bPIsmnpY3Z5bun7DKs1S7fbbFDbW48bQmSJR9mS5DvIg3XbS5TR7kETcop5S5HOUw6FT0JuLeV8HvUHxNdQxNsk5uPNZlRl5Bpt5AFn+po52XO1c0+fqy8eNK0e4j2aUlcKGDicvmqdGHgq01etbT1oEU8OgPCexJ8RfraTKLjkp2yAu2Ys2Cp/KcfmiAxe+91mc9J9+BiS+9LhqqFQKFTB8P6Y/B72hce6HdJtrutPMN6N8GWz4YRdXWpqHA9BP8eb15Yjd5LVtkhzEO+AJck3c/7M+Gloph0Ov2vHBJO7FOk85VD4JOTWbf1rJt7dTPr9yY3ykUq33+/SmWUHjbU6D5OsmQLX/Y58LzKdEVmnaXxp/Hkk5DilZ8Ve+aLm+ySZqnharygUCnU+vNsckt/YT5dj+lLvwXhPtNefI+wl5JpFfNRK4SC0bNVHXukw3mVLsm/qPOpvM4a7B0wAKeYtRTpPORQuCaC1lPPZVGT+XBU9/m7qRLirES55R6FQPw/v71qCxRn7+caNyM8FnAN4LwedZf/OPr5m6Pou+r0XuJCtmiKuIbwDlmTf1FQyH69mpSfn5QomdylSeQKhcEnAref4J1h4vZA6/xHxjkKhfiDek0nibd0KEfjI49w7gPd53KtNwGVYPFHLGVbt4M20Gu+yJdm3QM19GsD5gslbiixAR6FwSUCFFHNGFR/vd1U8/hWFQv08vCf0dgOmlXS97ERKQU/Cu+O57fk6xjv3RSAhWy0YKoes0nnls6eGEu+yJcA3mzmf3HrUl2oTUDB5SyF+4RFDcZIZAlAhxZxRRdSEn1tYWpH6LdYEhUL9NLw7ItMcPa0nGO+18ixa492TTGnrGILhvDbQqkmXqlmPngrvkiXAN3XOtSzrttoEFEzeUqTzlENJkoALKeSMKqKMEan3w0GWzoLg2DwKhfqBeK+JTHPDhduRQLwbawa13nj/PI7xnpgyHFu3dw1Nc/Yh1jKstvdsPZqXiXfZEuBbYO6TrhtKE1AweUuRzlMOJUkiq5CpnFGF1PWAkMFqu/lY+b8Mke4oFOoS8A6MPst4f/SZtg72VZ0DePe90E6qbXG73mRYdd7p1i/tzN67ZAnwfaD3niuYvKVIPQeEkiSRXUguZ1Qx1drUwz1tRq93WA4UCnUJeG+oJneb8QTwZS2GKYR31nfV9dk+9KWwWipbAQhhvIuWAN8H3r3nCiZvKfjnoFCSJFSFjHNGFVVGs//y0JngW3cUCnUpeAfWn0lwK3OD4c8g3l1LXzdq3Bi5yqpnsW41jHfREuCbOufeIYzFpWt5gslbCv45KJTUzPnslKOcUUVWCXevQaFQPwrvnpJpY91uHMD7YzL8bcxAvO/Fg2FUVn17YyGybEuA79RidLqB3FptAgombyn456BQuCSUKYc5o1AoFAp1EjW4tW4g09q6tL5chNs64dpOB/E+FnazVVr17S2FyLItAb7ZVnJOwk1xECBPMHlLwT8HhcIloUw5zBmFQqFQqNPITvqyINN8GmZO+wobzON33Z6VhfdZ26QLvhvJFcFqMlru2EHn2xZ72ZAlwDfD+z6837bC7WGPCiZ3KfjnoFD4JOTWcs4oFAqFQp1Ebwl0YKZ59HQVk+6N7uzGZQhujsXORDecR1234Hfv8XLv3qMDWx3ba4/ytrbrhZh8g75XiJYA32zPeX1GrTnv/Pbz+YPJXQr+OSgUPgm5tZwzCoVCoVAnEYWOPbPDbd8BpmnejE0079GlZHuw7/oUrOmm900I7+Yzv6GLC1plH22drRwvi5EpLMm+2dQ6K1r8Jp5RnyuY3KVIPQeEkkpCai3njEKhUCjUadS2GedEvJsx07TGehb2dtemgHcrGOB22QPWeK41ALy7lm6XXc9xHK/9HB/FJlhtv0X7vT17UmQqS5Jv6rxh7i2KTftZ3BQ/VzC5S5H+GiOHkk5CbA3kfNlqBeenX7C69Sn+k4JCoQoiw2y75oGDSRum55pOtgnNh6WZcThKo6cvG5phaOGGL8kwdNqq4bjttueUsiMDLcm+A+bWPM9zPh3Mp0oBliGdhNBazvmLVdluvqRNoGtSLebXjn73RJY6ZIT/oqBQqB/1HeDzB5u103PXbW4cGrBqfM6SJuH9FMGcuhQZrb/x0LgOIZOvaFNovHcGJ4My4h2FQv1xVNZ1HmXW518z57SkwvvpgrkM+agOOq6tl8rRbc6H9yOi+f3fXF+rBPGOQqFQn4FyI8Xe9pktHcD7iYK5DLUG9zfslxfSOrrN+fB+RDS/uwILQj4Q7ygUCnV058jjp4XXZvFBrmezpMD76YK5NG2/Aqi58b79Mrx3BqTenyDeUSgU6mg1err+HswmYwu8d+e2pOq9ny6YC9O0UHiffhXeX6tkNNG6iHcUCoU6XiZdG7Z82497dNnY7uyWVHg/YTA/S61OZ1LhPt92O91WMqevNRSBetPtiOejCW3C5yYdAMWlSadZysJ7pdutiLdS7uRoMgWECVzKCkjbko+KEsoVqB1g7LbbLSHeUSjUH0zOOlrf3SvXzm9JifcTBvNz9LBg55UPF33K2PpCa62q9MKd/2FA+7D3/sc6Vbjk7WZzPySkOnoNYQ604Z4jg0U8144+ebedDpm7iYT3oXa9pc6qow/u4PS0Oz6abf0jeqhfH4XRlPq+EyhM6JIyIN+Uos9tdD4GJNUuw9j1ij43nG4qXcQ7LVzzr1TNi0us+cu//v3///z6319al5ZZ6S//+8fff/VT+9uf7i727/LP7M/yn/g/6MkJ787nrln7Eku1+bz0RcH8CN2tCCH16dRH0Atj7P31gF4YDKL+9HRIkTj09RrgaurD6p5ycnEXcllqQzW5pySltBu+xASvN0e/sXd+r4krXwDPcAEHY6mICLqyrXS3FO9LKQIF1AoosuziolmCvhSERSBP0icRH1fbLYis0qci3+cCvvUP/M6vJJNkEt1eL+udZF4axzlnzrG2nzlnfmFp3J3eceO91CwBUBp0EcA1e0jg7I63Zg5KJqeRF032uAEboZmiqkCDWPHB+wZ/boMJMjYzClLWQT7pXdxptxPhHZVfpzFcPsvlVfbDa8wsN3cVuVy7tVyLXV9W5PxaVunX8jb6A93/gP6QNKkh+uATeQ1kFiTa6M8vCKI0Awz6Jq8oqpOAS4enS6A0UhUlvsqACWsmkFH6OtBHedSuhTC/MN8EJUAD22YXaEkX3vGb+J9HcqSDTN+vO9uatLUJL4nouWR6NDASygmrggxikYsP3kf6psVGMUxOqKyTAVozi9Vo6N0I7+r/GAIv5Qr9jmJ8uf4gD9y/3Tpci93IGeDexSK8R0WyskQhs4n6BEUUmOSVALwvgZZmcSmLmIUyygxojNDZDdArJgBN0itpHazceAdLNrbqa2Dj1x1nzcDUNgKIovSxQt/2yomqAg0Kjt5Vc/9fVQdzf2UGGDBrLwwQ4T1/Z1JCquj93U3MxcBfsrh2GXOX63MJv5gfYhHeoyJZ8F4x0cQxNlNR/PGeqGTAyHzfoEGwUKYFgJXrzpqwRi3tCHkJNu6udWvV25yqFHVnWxNfAMMcSyx0FsnPAT663isnUhVsUHD0bn+IS5Y4ECnjTvlJNCO8n9lBrkx4/+xBYOw0L0m2JX3q9S0p3RezchrhPSryBe/duJuxGyUA70jCvhalA3TVT2YGuAtiEKyrTPuKqzTcXS+tF8kSDYQF3XHWNEGG/BfNI7bPWOQ8I1oEcoKqYIOCo3feu5nip2zG6+yGHe+fOFRcSuTXybWX798k8U0149rTUytFcSLdN9NaOBHhPSrSFMOdj05zQbcQ7wZY8I3TfjIDPi2Q1a00flOxg1nNv2tzgZygO84apJeIjJCqOWWnWiJVAjlBVbBBO0bvSgcYip8yx8ewDDfeVUeQK1VyniLw9uf7SuUdRf3NqzTO3SFvvp9XFeXiLwb4v2X7an6LRXjfy594oRxq9+OFg1qTrwHP8nXuRhgR3kuchEqpLZQpOTR3KeUcLVtevHOn1S9o+lzQHW8NDdXJj2qG6G4BPSmWE1QFG7RL9J6v9FtzlqgQKXN8DKtQ4/3iLiYt3nF6/uaS5uPfy8aJ6un3M9tN2TIvuFSuI7zvpaRCftJrAxYPyJq853o3hKhkEN7zgO06JwXQReoCmQun5gmNnFHLbBDe0/YrGoyLuuPxPic68mTenUbnKzIsEMiJVAUbtCV6VzuzATkxANh4dytzfMDxUajxbtL9q4wr59WPR2bGWiWsuLmW8Xd4EpNyU6O9pzHC+z/EO/yjeFevGk/2pa/1Y/GlMVfj+/V0fW/tkG+M63tKOhwW3vv4IBoXYzNKEN6RRIYvIx8Zl+YNjbIdLQV45y6jGYGST3c83mnAPCKaVoSyEzLdIJATqQo2KDh6H3UBABnNmMwmFt49ypwfQ7j3vbMJ6sszKRERt9ew0AUGRxLj/YNcXv1NxmNHEd7/63ivTSGEY/PVMRTCtnDPTq6DwxqtQI/Fp73shz8svFcF0Xsw3pGE51gLkUzaJ3oPxnvVHb2LunNs0xtgmtMUfTqD2mbpAnqBnEjVLnj3id7nGaAt+llKbV+8OzMS4Y7eyQTn9TdJI0Dui0b9+y6jbzQ5f30mlVNnZNz55WPo8J663zeLfw/v++6/hjndqzOKP0MR3tVGEVU/v4xf8FCAtC2vh+jxXj68K7p37j0Y714JsUwWiOfeg/HunXsXdOfA+wLMWG6erhNskqBfKCeoenv0XtWBkbRGIr54j2d4n1YhXzn/JXZ7oijncs7fWuW9pMvP8CJ6urTup1x5FzJbdF25Cx3ej2Hqj+J93/23YXFsBuH1NoQ9EWwfYe6FpOLVJwiHLCmfQqzfhy0HhveBd+X8FrwPXBvl/WQ08cr5YLw7Vs4vfbpz4B3jfMQUrYCBcL/xM1NQ9eboPTECGTPrnlj4450doWeNRcKN9+RnnMo4lzt6z7ND3mQ6+yV7fv7rw7ufzLNbuc6d/4vtY3wNHd6f/zDe99z/lTMxnxvXRbAtszthUbmH0Jycv8rt5X7YA8P7DHTVnfBesSWMnfC+ce57B9Vd8G7vXMuX6MBD0B1vjZItgf6GySHtFxPGcIGcoOrt0fuCcy9g7h29xx2VY4T+WBv8ZfslN97ZKW+vMvmU57c8vMp1Y84ZyUi8qsrX0OG9/Yfxvuf+axCWrYEDHKZQRTBs4zl4bD6v4Q/58N4EfHTph2pFdxy91tkF7x2uXbxrHfwSjPdS0nyxAnrapzvOGgLtucZeJwww0ulct0BOqOrN0fvKxnsTBOB9BTJpuyo6lNbCu2Qr563y6Ua+6WmVw/vr+6xUv6/kEU3NK6HDe7kBYb2ASlkpWNvVuZ3b6NGqTY2fav7cLtSeHhoplcN7md//zb24qo2fxjXSlO/f7ubBXsOuErH6eDyuld2y29j6Az4XdoDtFD7aEo8iz1IN5FncYZGaajzZdaxdvTHGlh0Y3vHJ8K3teO9yOfwJ6O6yHA+HqqxdfGleELMN71b43tfME+y83TmswafDWXoQcy2ACuQEVW+O3jvmMfrKRTcI71kNzNj/w+wkwrsie/Runl8nl3cc3r9eynVkHV0s+E4JHd4fzavPUdi6hj2z+smOv4/hlDJ5TZu2H4SjhPsiVVN8svH+AId2i2NTe23Neiz+SDn6d3TzXDAzAUOlPiRVKZcsBurjuu6P93Jjp1g6GO+Fpynrcmxa1FZSPWplkdtzV6NW5tZXtQPDO77XbU74U533fVE9A1rFKxFvLuf+eCc3xuExTnpinTa7Be9aCWzI0SCdEtCrft05rOEHBYkKsM+1FcgJqt4cvV+U2B15I/QQgHc8L2GQ+L1icIOPCO9y4v2Enbp7lJfJKz56j93EvkiUnT8xU/Ohw/s0hzmVQ+UBz1SbTO/ZE9hr+EwgifE9nOLWj57t4WpqSJar91Cj4y14fyAt19Mi2b3G948V1VF1bjpEle0Ug2mxhl4Vp+2c6pKlyp798U5t24Z3NQdfAvDew31OuWX1yLtGDluJvbVFj7EfbWx57vjA8K70u/hW9hm+t3zkj+oM0CezCaVYi9zjPpsZJXbWvFhGaeGLzo2Jhlqv1J3wvulrANuCBO0429Od0xplwC24H3D5d6+coOrt+95HSJUxwde4b6pBeFdWqIk2m2gZMElqEd4tvF9K6NoZo/ttWi6/8s7j9G+lGbyw1DyZSQnd3PuVtVo8Za0xu4IWNuN0tVkNUeuhjPmLOLd260ghqN2TtWqph6tgvBdyKOQlOe36fa/s6B/X4W4QJtQXCIcqM6qNZ9CFshjvvS143xq9p7i1eAK8P+QeSYIgNYS5K9Ye9UosqrVZHcl3FGvINrVWhPDA8J5IZBeI7ACAjNHyolpnTx0NN2GvTAnQXfQVfxklvSSHuulGy8t+TGkP3ldKeqZjEW3J/QNxd+e0Jr6w1cRX9ERaPzlvVaBBVvQ+EFJ/gBWVJiNFKQ2ClLUM7FNmsMgqxiCiu8TR+5m5tPxMMsfUi4vq2cknc+l87Issjl1yexhDjHelbWbISSRKY/QaeVudwiLLgpefIay5dKwR2pzA9MV7w733zIH3HiwyXI4hfDBh2iuYoHavwkt5l7r/dvT+yB2hK8C7yubX1SsWqqfsmQT8TEP6chEOmeWF6aHhnRA+2ep0WsG7XbL9TqdvL6ohEihESSjbpJqV3zsO6KLVbHpMMbvzscZ2ZYvc7pbvUqrNUX8n59RKs5lXoiI53iuy0p18h8kfz0fG94ocTp2zffyk0MzLNXo6CccfIofXH7BNH6bw0YzkX0jdmItwEcmmThU1aK8934Z3pCnui/caYzq1oW3C1HzfI4sX7Cn/MHqvQw7p4qV1rDzTN5FFxYI9NlibwXvD7vDw8L4j6hL/pnJ+sJFI/IvWHO7nGzK8S7dyXmV0v7mtyPu7i7PZh19y/Mo+e+/xxcD/K3R4Z9ExjlNrPQa6HolUe7CtcqB2BdEoeC/vinc+Fe7B+5pl5BnLC7T9M6d3vNWfY9fgY0v0rvZgzvbmxZPrV0QevHB1Q/Yx2a6q7UPEe1SiEkXve4rdj9Iy//LupDqS7zImLOHDewHSVPcxLKrHNDtfoFFpkSOaWWeXNv/uFrwjVWR2XYj3NrftPEUPi3Ug3S0rxvXj70TvPyCf3h9bc+mCMv4/e+fzoji2xfHcVQdTjRIkYFrokW4ejas3IcAAli0QEeiHhWYIunkgDAJZSa9EXKrWFIi04qoo3roAd/MHvtybX/dXUk4PTVWnzpfpHkc9996ka/qTc+455ybxhC41uBmtjIpf3AHeQYD34uE9pvtNu9B/eDeFwvsXwHukcRhqXgeUjSDWJaRusW5zk43Fa7xTnYt3nBs/u21J5tdKqlq2YkULiCivSG0FtXQhMyDfe79lEwXxFnpXUtpvOZ3uYpvgPV1RJ8R7i3ni2QLeQYD3wmXOx3T/q3A5FswFVX8rUnAe8K6nCMYuu04IGVaDr0iomyFaiH+W5s7leCed3VVz5YjzWyqr23Asyp/mbHk5+FM+fp+H966qrhma6+K5Mo3yLC7Oj/Fu8Xhnb8EJ8A4CvBfNe4/p/rV4GZR/vH2XBCTqkfP+piBd5399e/P2bfwrvLLfgv/4+Dr+R6TxHoIdx+bxNjT+fUZY12EBzrVudfi9+Hy8B1C9J7VlDj9/JyqBj3WriA1uGVtOmgzvOd671jHVARuMt9Yc3rUtYftgfH83TvDe4PHO3oIF4B0EeC8Y3qtxxdjbm5vkn4KcCPvHmzefv375/V8f3n/79y9x4XsRfy5fc2Ec3vzexs45Qb0Vhrp1NuL9Xd77ls5asx5x+5ouN78wkCLrX0/ZihfTXQtle9l4d0y1yT6YtJpq88S0ml0FaN+GPXAX2XgH7x0EEvBeqMx5aYrW28LgXdCvgPei4R3H4mOWzwKIL1QTt2ppsLvrXLF566K99zs2Kb1UNkMkUvO3JJnxsuNpEluJWgOuj1229y7SHafrC08q67Q9bybe2WzDR8A7CPBeMO/9TxnebwqL9/8U8ufydeMdZ9KdSFQ+rHhfhYlnmkkHrC0xc34rRTITpB7zNWdOOCibOf94Cd4TW5ke+ar8LLzrTdV0njIuUxvtd9l4Z594IHMeBHh/DXj/XFjv/aYCeC8I3lPINVS1E++sBxjTZxG17unK9q2k7r0hQzJ1NisOe/Ml5SvyDj3/mCugz8R7ZCvT4sKudTK64zPm+DcSY22Wg/c1tR6oewcB3guXOf9ngYPz/LX9911BGzC+OryzNV1jdWvGm9eBUx6DfEH1pWuYfNP5rsp63TEALWofvCx2h98SV7nF9nsrX4b3Le9mZzvrzBv63V24JEtGd7FrHVXjdlJz8P5IPaQsVMA76HXrfYiJ/xXokr4UODivfHj31+f4mn65+Vgr6s/l1yI9lF2kJp0oh7vNN7XES485pa2TZLbGWOj8roxV81ZEsqalHrveVKMjZZK2NAFg7/n5x0LOHA1T0bZ1erSewDvrvZejLjbWQDU7ytN4XyRPKI6Zh/dGU11HK7FmgHcQCPRzqXb9/tvH3799qCvKlQa3oyh6oJFKn5eCy8xi2mG44QTyRnegin3ZyTd1cl7atkMBMIDpAz6QxnrE1WUE7+Pm1iHt8E6DiJz0/HigFclct07jMg9T0XYhLibfe78L99IbAYIfnVR6Ft4tk5zprlkrVTVz8I4vtdlt4TPmm2oT8A4CgX5GwdkJxRJGanPWjJg1oALqAyqTnZxzauLf1Efx2Q4fE6uas8AAO8cJAEukC80As/0+qnvHXwz4R4rJy+L8ZCB1FprwMBVtT08fCMt677MwqL/gGuiss/BOjpg3yaH093oe3rUyKY8fkAPuzNeM97b9qk5E7ds+/B0CAoFeproE2hGzVklsnkTnU5BZWxOD1Rx3ZGO0trMQlLjhjB6fJhuWsGGjcoBdguvuQ9wC7s6RzZ8MNNjqPExF246YQS/x3mfpMqPngg6H93Em3pXOLLyCRWAs4l2P8a4ozpjcoPW2paxnL+yPuDaZ7zf7XV/8pHLYX/RMX91NLpyLORG9+OqhEfwVAgKBXqg0vdvRS099SWnpjmNl78q0HKejN3gjxXJ0nTHSrE6361ilzPmDeTq6/GQX3lazdG5BWkc8NZZitZg3wOiebdgTXoLldPXLjvy2dKf1Ev+A5y4KtamKdEIXcfuANoB3wDsIBAJR0NOkDwva37SQ24rf1IW2dZRyD3wl1Xvlf3SpygvMSSlNfITcwHff+wjZHN+vArz3LxikYiMj95is9q4CeAeBQCDQj3uaMNVB1pEzyjj3wHjrPu/R4KfVAXnHsNJl56Elj2V3ekkVzBwhdMz7wg61Ae8gEAgE+nHCyXCzBynhW2LbnPSz+7Wp8hX9hVBpH5P36niZry7KR24+xg6AdxAIBAL9UJEqPLmXftvNtGrgDLpVo9i3puSi3ffYTRDqGaiXy3/AOwgEAoF+qFqdU/lvp7i1Hm87VuFvzRIdsj9s94f9aknum08D05zkurbH412b9K75sWr93qXNLisS83q/129rl4w16U2iD2uTXr/+9DKYoau9Xr92gUm/BHgHgUAg0EvQhsd71XajF3ubJNcby6OQQ1ex0VGZI+9TxqjHqYGQjbUP8V49+B5CyFtSWfm1/TR4zxgdMUUnNp3kFyyCnlPrnUmqf2petZdKe2PgNz8JY7GXs1QmG2ztbSa47I9clDvPWIZk6PbZI3OTy9koGbNVwzn8faUPeAeBQCDQc2vEJ8jFofSejZDtL6cYh0POqETIThgvl+9h/HmBjnhEDw/mTX0XIS8Zq+rj9zAnl5/IQtJ6+9KRReQZIWRPlyMDGfN4ldOqixfoupKxmMtx+3ZAYjy33a8ssQ3+3jljGcLQwdK95eE8JUvw9xmzDfEFjvBboyHgHQQCgUDPrLrBszvCe81F7pAEw/v7pRCf9wkezzkgqyAqOI9sFPrdvRFyK7GTjOy5hlFukOx9hujcQ8fcO5MEwMk0Ng+o7aNp6suzYzGXg2xjj42GLpou0QgPVD8kGYWiKTt03UVLElaY2/Hzh2S2oYHcXi14q+ciGwHeQSAQCPSsKh2RXZHh/aqP0HWmGU6sw+DPSbtn8Z545lUvAvfVAblRNH5IRmtTmXo95DH7AVq88d32okxAPOYy3vwWxmLxjubJZygu+vOjvAGJKTO0skdu9HqHvHrWbD6aRgv+5CMEeAeBQCDQsyrAJZ9ZF3nvQ2Rnmx0igo3SGHc+3t1KahqaXBtonsYCsBO8SQc7ZybtHaL1BmMayeOHOBaD92kaE0hCFXEEXWLKDK0s0ziCHbJcYkK3AuoB3kEgEAj0zDoLXeti772d55lHm+5Xx8ihfRLvaah9h/zIBU4PXhkiTyO/Rw8BdU/Y7Y/99F0E/iq1ey4Zi8F7Uvm3QW5sch1+TWZKD42ZPkxZvssw2VBvKSMEeAeBQCDQc2qO0FyRe+8Bpdx+plnUj7ZqZFbNs3hPA+a9CLE+lUkXfKFKSvDn8SOAm3VKwDACaZXO9xPHYvDeS13/hMK1aHkSU2boAO9zFu8ykyl9Gw6AdxAIBAI9p3qeJAge431iI+MsB7yfmC2RfxHe03K4foR3yitWNA8j+Gofx9VpgFKqX0/6uyjUzowpjsXg/ToFb3q5RvgUIDFlhg7WEkceSnYYz5CY0G8pR8A7CAQCgZ5RAcD9SibeFXzwDJruxOj7BCHXD2VkHi/H4r3G472Oorp4ojCIMIlIPBGz+rThZhpW4ad4T5YuG4vGey3F+4HDu8yUHhqn1tntGNt2TW5SZ+7CHPAOAoFAoGfT1bWLRpK2NFQL2SFu7uId+KY2B0TrcAHeqaa0Ed4DhBu05pTXvheOuZmPEEKG6y83ywTvBv24IY4luRwJ3mWmbA/dtoum2EGvHb3wuUFiMoka4ER3DfAOAoFesDSrewoPQtf0xfZh/bAqZ3df76xWHfKiu3iZx5uDJBh3k/quLLwHcNu7CLmsg16x0aHf7/XJr41QWHcZ3tuyursd+Szdg0/eN5C7n9RCdop4b+fV8OXjXWbKtchv40q3pW8jY581G7vfD947CAR6weqs40NYyCsi8zED3Y2mqp7Ii+BbzZMGt+8nUHvEY1tOt9rOQ/Y1C2GDwKykKFe49H3+PXhXZMnxdbKT3eOfGNpeuomwk+BdyUy0fxLvMlPuBlxVSWfe0WaSOVvJoKsMYO8dBAK9YLpjTo8dRbEeglfj7W33doWPT5N/+16N8N56mAUvt3D/Xr4+TZE9eZKHRBOPbSHHVpZPM5LrnsL7VJZzf8YlaWc+3j9HRhL73svwPs059e4JvEtMuRtw/X/2zqU1dWiL49kgEDRwQilCSiedHZ0ExKGKQMWZoIFoh04EHB0nIiXDmlMKolQPCNqhSsGifsObl8lOsmP1Xs892cn6A63GJJrnL2vt9ZDQ+L6UPPltLn/DGPAOAoFCqzqbfU5ZnD82Sr/R0F0mzfzKsr9MvOtzDVn2BnZgyMXdB9Kd0L51jMfXc213CfpRQHAd7/iwiXhXSY8FuuF+L6Oa9/vtwjQMaeydvK7z8E5Y1LMD3r1h/IRFFDxTvgt4B4FAYVXO6Y7++mhPvblj3/zzpnJ37MubjXdGe/sH9mDIxXdRvnYGD01NXeb6uzspXaOxjr/b6dRDeYfSRLxXEcmj3kKDAQbz4/d3HP6T8E5e11l4Jyzq2QFd7wyERabWeIW5gYD3mF1N/VW/CbsBRInKLEsaZl+wQwLen9h6YYEh/SPIhw8KiwQFybVzeHh8FsC85XzeY81+6flinL9AjtMVhoh3RkEtn9nPTZGqeNvQcQ0kW46Any0i3onrOg/vhEU9O0BFSjHlsdW9iwgSUq30u6QCeI+X+mJC07oHewJEhSpsljT5N1v3T/xk2UcGx/sLycYHhYzu1XtHPIluqjIwp1e7rvK0I7wgu2WtNnRypj1JdiqSiqfw3paRPDKYmKy+H0ezb/VMPG+23s+81cBtoL0g4p24rvPw7l/Ug3e9l63R2ralmvORvm2EUNdYX7GLOgjwHiNlDLprOgDgQRTjfUHA+yPLfuoWO+CdHjWQWwqJh10j0byrt0l3hZJ1fUnpLX2K4pvcTiNZUZVOEN6ZWscISFe7eazIu4KQv5DeQJuxqxhd2ktEvJPXdRbe/Yu6V30/lZ09ZWUSEr5Nn0tSFSmNFF4CvMdHzVnCFgAeRC3eH7LswjutUGeHKcA7Xap68K56eCgbzwCqhTXZVZm25E+Em6I0z+T94eQNyVjcw8u2jXdGGLfSxle0xm2M5IRh9GpHny2vaF+dJ+KduC4C3p1xhbTtbfAu6lp1sYWkaaNWLLZrgy8ZdZJB31br6vsr3RkLTLcD51hcxO0TuHYAeBCdeF+wbMU77YW90+PkAe8RVKrYGAyqReGcedvHJjMu5LYbjfbpxflao1HDSsJwUySR6iZwpeqg/U1BBe+6iHfjSxftIkVgOE3a62QDG6bwL5IqVqv3cNrES5uER7sM7BQQdXi/0ej+4Z/R5PoT4D2KlskpIro18vnm/0u1yN1k/pVq7v5zrRMJ9mfuKVCU1E/4tQfAg+jBe7lSef2tl6558YbT57IW8QHvMZd6om7cJap6A/f+sUYoz5yNd1Dcnn97YiIBgAfRpR+uDLi6VZP22TffB5vN+fD+yT7BHoybJOk6pYjVa3kB/gref6bdzWZB8Ra/TQRo36d5u4Refz6HKIKIKvXkMsCH9fqdAfiPnHu+P/ZgPI73Z/YuB/swZmfM9CrGO9dOX8kLcC3zrI0woCdVlIfBdZCtXSJYS/oAz/Uyk9V6v7VcEnsejnD0VNBH2cueiQ83r0PNgHdNxqrY4XgvZNlflQfYj6CLdSuhTrgaEqVaKN+wflJVQeCbBzlaJU6KGsA3darvljNflGCkj15vsxQPk7jFyzyS/fCMEUdXx0bfk0PnLY53ozg99JUBXWY53LYURUZy2JzftxJCkvo+/lK1FzLQHWSrn/hO21ADns/055vDciYG/v4IhxA019YjTLz4ngrGO/PMsq/Ou996uToS3nNDwDvoUrWNsjG10P2u0jhvlQdoTX/CYQLZ1p8Pi3sC4ENqHmbWW/Hbp5PEKqrHjt/YWz+J11mbylWGAa3hmCxG7TLLZl+PyrJv2l+zU1why2b/3CTh+gddpGLj28z2f3RFFBujQbUNo+4gR5zgD6vr99YiFYDnVomzFFHvPDfHDtMhdqduoe4vT2foA8t8/8369HG08R/h8gddftVxIXaUwePqdY5xZLbk4KfhRPf5+gG/DF03ucx5dE+IkTwHJ64gg238LsJPUms4TUMM+89vmLLsUPv74+TCIBAozmjnJ3tRXK4nPfohz80xfB9fzPVPCIBfhmx7ud2ZeE9EMDmuv/RsoxC7C/GZ3FImdRc0pO4ua7OAWxkIBHLLcWcbjKd6WzK49/34amN+1tyIfrM+VBLPxfskaudgZh+n+MEABXSM+7Tc7jdvb+VTeIeqdSAQyGMzegZ8xf2GVsZzTQeQYs9G/fr4uRfwa1rxvo7WKdgjjKiYPpeY4v0hh09l60bs0w8WD5QHvINAoG9FKvGmMb7fpG5LBMzB22d6hEg03gX4sBnBeyLLZ8udl/vLKJ1/x1y4aD/CXIT3N/bpOaczvVB+Ys3ucPpENgd4p14C5HmBwmAz0sZ4DuPERnvrj9LiNMCv7O0VQza8y7lC68Tlbr2aZJocw8x9hyYyA9Mcvwk4/5axuxAdvC+MavO/hln9/6+yOfGXL3gO8E6b+PdWGuU7UKcF9M/xbtqOuxUtjMcouNej5makQHOOEY6AD11xG66/FMXtXqN6Hw9z5OyQcjF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            "text/plain": [
              "<IPython.core.display.Image object>"
            ]
          },
          "metadata": {},
          "execution_count": 20
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 322
        },
        "id": "2HmwV-Ek6Nkh",
        "outputId": "f90526da-4fde-4051-d908-cdaf886f415f"
      },
      "source": [
        "# Dendrogram (Median Linkage) \n",
        "\n",
        "Z = sch.linkage(data, method = 'median')\n",
        "plt.figure(figsize=(10,4))\n",
        "den = sch.dendrogram(Z)\n",
        "plt.title('Dendrogram (Median Linkage) for the clustering of the dataset')\n",
        "plt.xlabel('Data Points Number')\n",
        "plt.ylabel('Euclidean distance in the space with other variables')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0, 0.5, 'Euclidean distance in the space with other variables')"
            ]
          },
          "metadata": {},
          "execution_count": 3
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 720x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 322
        },
        "id": "K5tdJMnU7UaN",
        "outputId": "864d4ba1-1b52-450b-b58b-edf3bfc8ba6d"
      },
      "source": [
        "# Dendrogram (Single Linkage) \n",
        "\n",
        "Z = sch.linkage(data, method = 'single')\n",
        "plt.figure(figsize=(10,4))\n",
        "den = sch.dendrogram(Z)\n",
        "plt.title('Dendrogram (Single Linkage) for the clustering of the dataset')\n",
        "plt.xlabel('Data Points Number')\n",
        "plt.ylabel('Euclidean distance in the space with other variables')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0, 0.5, 'Euclidean distance in the space with other variables')"
            ]
          },
          "metadata": {},
          "execution_count": 4
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 720x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 322
        },
        "id": "JAuwPdc97VOs",
        "outputId": "efea686d-b1ce-4ceb-b8ce-d128c57a86ab"
      },
      "source": [
        "# Dendrogram (Complete Linkage) \n",
        "\n",
        "Z = sch.linkage(data, method = 'complete')\n",
        "plt.figure(figsize=(10,4))\n",
        "den = sch.dendrogram(Z)\n",
        "plt.title('Dendrogram (Complete Linkage) for the clustering of the dataset')\n",
        "plt.xlabel('Data Points Number')\n",
        "plt.ylabel('Euclidean distance in the space with other variables')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0, 0.5, 'Euclidean distance in the space with other variables')"
            ]
          },
          "metadata": {},
          "execution_count": 5
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 720x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 322
        },
        "id": "SlEkajEc7Vcc",
        "outputId": "8aade58a-3cd7-4343-a543-379c564f43eb"
      },
      "source": [
        "# Dendrogram (Average Linkage) \n",
        "\n",
        "Z = sch.linkage(data, method = 'average')\n",
        "plt.figure(figsize=(10,4))\n",
        "den = sch.dendrogram(Z)\n",
        "plt.title('Dendrogram (Average Linkage) for the clustering of the dataset')\n",
        "plt.xlabel('Data Points Number')\n",
        "plt.ylabel('Euclidean distance in the space with other variables')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0, 0.5, 'Euclidean distance in the space with other variables')"
            ]
          },
          "metadata": {},
          "execution_count": 6
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 720x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Tpls6dGi-IZ4",
        "outputId": "5bee69ec-e961-401f-94f5-1c5ccfafc073"
      },
      "source": [
        "# Building an Agglomerative Clustering Model : Initialise Model\n",
        "# We analyse the above-created dendrogram \n",
        "# decide that we will be making 2 clusters for this dataset\n",
        "\n",
        "cluster_H = AgglomerativeClustering(n_clusters=2,linkage= 'average')\n",
        "# Model Fit\n",
        "model_clt = cluster_H.fit(data)\n",
        "print(model_clt)\n",
        "print('\\n')\n",
        "\n",
        "data['clusters'] = model_clt.labels_\n",
        "print('Clusters assigned to each datapoints, cluster = 2 :')\n",
        "print(data['clusters'])"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "AgglomerativeClustering(affinity='euclidean', compute_full_tree='auto',\n",
            "                        connectivity=None, distance_threshold=None,\n",
            "                        linkage='average', memory=None, n_clusters=2)\n",
            "\n",
            "\n",
            "Clusters assigned to each datapoints, cluster = 2 :\n",
            "0    1\n",
            "1    1\n",
            "2    1\n",
            "3    0\n",
            "4    0\n",
            "5    0\n",
            "Name: clusters, dtype: int64\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "3jqLLZ7zBRWn",
        "outputId": "87f74c62-543a-40d0-a82d-0440058cd637"
      },
      "source": [
        "# Silhouette Score\n",
        "data = pd.DataFrame({'x':x_axis, 'y':y_axis})\n",
        "for k in range(2,6):# Maximum range should be 6, as it contains only 6 data points\n",
        "  cluster_H = AgglomerativeClustering(n_clusters=k,linkage= 'average')\n",
        "  model_clt = cluster_H.fit(data)\n",
        "  label = model_clt.labels_\n",
        "  sil_coeff = silhouette_score(data,label,metric = 'euclidean')\n",
        "  print('For cluster= {}, Silhouette Coefficient is {}'.format(k,sil_coeff))\n",
        "print('\\n')\n",
        "print('For Cluster = 2, it has highest Silhouette Value. So Number of Cluster = 2')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "For cluster= 2, Silhouette Coefficient is 0.9213643790849674\n",
            "For cluster= 3, Silhouette Coefficient is 0.5428175766885445\n",
            "For cluster= 4, Silhouette Coefficient is 0.16666666666666666\n",
            "For cluster= 5, Silhouette Coefficient is 0.08333333333333333\n",
            "\n",
            "\n",
            "For Cluster = 2, it has highest Silhouette Value. So Number of Cluster = 2\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        },
        "id": "IgadKpnrSlNM",
        "outputId": "d1f94191-6b67-49cb-d88c-7f247efc5cad"
      },
      "source": [
        "# Lets take another example : IRIS DATASET\n",
        "# Loading the Dataset\n",
        "\n",
        "iris = datasets.load_iris()\n",
        "iris_data = pd.DataFrame(iris.data)\n",
        "iris_data.columns = iris.feature_names\n",
        "iris_data['Type']=iris.target\n",
        "iris_data.head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>sepal length (cm)</th>\n",
              "      <th>sepal width (cm)</th>\n",
              "      <th>petal length (cm)</th>\n",
              "      <th>petal width (cm)</th>\n",
              "      <th>Type</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>5.1</td>\n",
              "      <td>3.5</td>\n",
              "      <td>1.4</td>\n",
              "      <td>0.2</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>4.9</td>\n",
              "      <td>3.0</td>\n",
              "      <td>1.4</td>\n",
              "      <td>0.2</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>4.7</td>\n",
              "      <td>3.2</td>\n",
              "      <td>1.3</td>\n",
              "      <td>0.2</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>4.6</td>\n",
              "      <td>3.1</td>\n",
              "      <td>1.5</td>\n",
              "      <td>0.2</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>5.0</td>\n",
              "      <td>3.6</td>\n",
              "      <td>1.4</td>\n",
              "      <td>0.2</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   sepal length (cm)  sepal width (cm)  ...  petal width (cm)  Type\n",
              "0                5.1               3.5  ...               0.2     0\n",
              "1                4.9               3.0  ...               0.2     0\n",
              "2                4.7               3.2  ...               0.2     0\n",
              "3                4.6               3.1  ...               0.2     0\n",
              "4                5.0               3.6  ...               0.2     0\n",
              "\n",
              "[5 rows x 5 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "YYotNIqgS1PM",
        "outputId": "ad06f864-04e8-4cf8-a9e8-4b36b50d9e7d"
      },
      "source": [
        "# Preparing Data\n",
        "# Here we have the target variable ‘Type’. \n",
        "# We need to remove the target variable, used to work in an unsupervised learning \n",
        "# The iloc function is used to get the features we require. \n",
        "# We also use .values function to get an array of the dataset. \n",
        "# Note : We transformed the dataset to an array so that we can plot the graphs of the clusters\n",
        "\n",
        "iris_X = iris_data.iloc[:, [0, 1, 2,3]].values\n",
        "print(iris_X[:5,:]) # Printing First 5 Rows"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[[5.1 3.5 1.4 0.2]\n",
            " [4.9 3.  1.4 0.2]\n",
            " [4.7 3.2 1.3 0.2]\n",
            " [4.6 3.1 1.5 0.2]\n",
            " [5.  3.6 1.4 0.2]]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "nQQGS8_dTvbU",
        "outputId": "207553ca-cf5d-4e49-c92b-e5ea79d4e865"
      },
      "source": [
        "# Now we will separate the target variable from the original dataset \n",
        "# And again convert it to an array by using numpy\n",
        "iris_Y = iris_data['Type']\n",
        "iris_Y = np.array(iris_Y)\n",
        "print(iris_Y)\n",
        "# Frequency count of the Output clusters\n",
        "unique, counts = np.unique(iris_Y, return_counts=True)\n",
        "freq_1 = dict(zip(unique, counts))\n",
        "freq_1"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n",
            " 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1\n",
            " 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 2 2 2 2 2 2 2 2 2 2 2\n",
            " 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2\n",
            " 2 2]\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{0: 50, 1: 50, 2: 50}"
            ]
          },
          "metadata": {},
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "7zdlBmMrilBn",
        "outputId": "d042052a-9f1b-46d2-cfb1-1b8a18503a66"
      },
      "source": [
        "# Filtering Setosa\n",
        "\n",
        "Setosa = iris_data['Type'] == 0\n",
        "print(\"Filtering Setosa, True means its Setosa and False means Non Setosa\")\n",
        "print(Setosa.head())\n",
        "print(\"Top 6 Rows of Setosa\")\n",
        "Setosa_v2 = iris_data[Setosa]\n",
        "print(Setosa_v2[Setosa_v2.columns[0:2]].head())\n",
        "print(\"Last 6 Rows of Setosa\")\n",
        "print(Setosa_v2[Setosa_v2.columns[0:2]].tail())"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Filtering Setosa, True means its Setosa and False means Non Setosa\n",
            "0    True\n",
            "1    True\n",
            "2    True\n",
            "3    True\n",
            "4    True\n",
            "Name: Type, dtype: bool\n",
            "Top 6 Rows of Setosa\n",
            "   sepal length (cm)  sepal width (cm)\n",
            "0                5.1               3.5\n",
            "1                4.9               3.0\n",
            "2                4.7               3.2\n",
            "3                4.6               3.1\n",
            "4                5.0               3.6\n",
            "Last 6 Rows of Setosa\n",
            "    sepal length (cm)  sepal width (cm)\n",
            "45                4.8               3.0\n",
            "46                5.1               3.8\n",
            "47                4.6               3.2\n",
            "48                5.3               3.7\n",
            "49                5.0               3.3\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 485
        },
        "id": "_wOmXWLJWzq5",
        "outputId": "fe936159-2fc9-4e6e-9807-c5bba5bd34b6"
      },
      "source": [
        "# Filtering Setosa for 2D Plot \n",
        "\n",
        "print(\"Setosa for 2D Plot\")\n",
        "print(\"X Axis points\")\n",
        "print(iris_X[iris_Y == 0,0])\n",
        "print(\"Y Axis Points\")\n",
        "print(iris_X[iris_Y == 0,1])\n",
        "print('\\n')\n",
        "# For Setosa in Target Column i.e, iris_Y = 0 \n",
        "# In other word it should range from (0,0) to (0,1)\n",
        "plt.scatter(iris_X[iris_Y == 0, 0], iris_X[iris_Y == 0, 1], \n",
        "            s = 80, c = 'orange', label = 'Iris-setosa')\n",
        "plt.xlim([4.5,8])\n",
        "plt.ylim([2,4.5])"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Setosa for 2D Plot\n",
            "X Axis points\n",
            "[5.1 4.9 4.7 4.6 5.  5.4 4.6 5.  4.4 4.9 5.4 4.8 4.8 4.3 5.8 5.7 5.4 5.1\n",
            " 5.7 5.1 5.4 5.1 4.6 5.1 4.8 5.  5.  5.2 5.2 4.7 4.8 5.4 5.2 5.5 4.9 5.\n",
            " 5.5 4.9 4.4 5.1 5.  4.5 4.4 5.  5.1 4.8 5.1 4.6 5.3 5. ]\n",
            "Y Axis Points\n",
            "[3.5 3.  3.2 3.1 3.6 3.9 3.4 3.4 2.9 3.1 3.7 3.4 3.  3.  4.  4.4 3.9 3.5\n",
            " 3.8 3.8 3.4 3.7 3.6 3.3 3.4 3.  3.4 3.5 3.4 3.2 3.1 3.4 4.1 4.2 3.1 3.2\n",
            " 3.5 3.6 3.  3.4 3.5 2.3 3.2 3.5 3.8 3.  3.8 3.2 3.7 3.3]\n",
            "\n",
            "\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(2.0, 4.5)"
            ]
          },
          "metadata": {},
          "execution_count": 13
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "vN6l86eLnvni",
        "outputId": "4defa0f4-6747-4749-e3f9-4741a75db935"
      },
      "source": [
        "# Filtering Versicolour\n",
        "\n",
        "Versi = iris_data['Type'] == 1\n",
        "print(\"Filtering Versicolour, True means its Versicolour and False means Non Versicolour\")\n",
        "print(Versi.head())\n",
        "print(\"Top 6 Rows of Versicolour\")\n",
        "Versi_v2 = iris_data[Versi]\n",
        "print(Versi_v2[Versi_v2.columns[0:2]].head())\n",
        "print(\"Last 6 Rows of Versicolour\")\n",
        "print(Versi_v2[Versi_v2.columns[0:2]].tail())"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Filtering Versicolour, True means its Versicolour and False means Non Versicolour\n",
            "0    False\n",
            "1    False\n",
            "2    False\n",
            "3    False\n",
            "4    False\n",
            "Name: Type, dtype: bool\n",
            "Top 6 Rows of Versicolour\n",
            "    sepal length (cm)  sepal width (cm)\n",
            "50                7.0               3.2\n",
            "51                6.4               3.2\n",
            "52                6.9               3.1\n",
            "53                5.5               2.3\n",
            "54                6.5               2.8\n",
            "Last 6 Rows of Versicolour\n",
            "    sepal length (cm)  sepal width (cm)\n",
            "95                5.7               3.0\n",
            "96                5.7               2.9\n",
            "97                6.2               2.9\n",
            "98                5.1               2.5\n",
            "99                5.7               2.8\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 485
        },
        "id": "M7mb70sXnTMg",
        "outputId": "14979811-1071-4346-b242-11b04fab32e3"
      },
      "source": [
        "# Filtering Versicolour for 2D Plot \n",
        "\n",
        "print(\"Versicolour for 2D Plot\")\n",
        "print(\"X Axis points\")\n",
        "print(iris_X[iris_Y == 1,0])\n",
        "print(\"Y Axis Points\")\n",
        "print(iris_X[iris_Y == 1,1])\n",
        "print('\\n')\n",
        "plt.scatter(iris_X[iris_Y == 1, 0], iris_X[iris_Y == 1, 1], \n",
        "            s = 80, c = 'yellow', label = 'Iris-versicolour')\n",
        "\n",
        "plt.xlim([4.5,8])\n",
        "plt.ylim([2,4.5])"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Versicolour for 2D Plot\n",
            "X Axis points\n",
            "[7.  6.4 6.9 5.5 6.5 5.7 6.3 4.9 6.6 5.2 5.  5.9 6.  6.1 5.6 6.7 5.6 5.8\n",
            " 6.2 5.6 5.9 6.1 6.3 6.1 6.4 6.6 6.8 6.7 6.  5.7 5.5 5.5 5.8 6.  5.4 6.\n",
            " 6.7 6.3 5.6 5.5 5.5 6.1 5.8 5.  5.6 5.7 5.7 6.2 5.1 5.7]\n",
            "Y Axis Points\n",
            "[3.2 3.2 3.1 2.3 2.8 2.8 3.3 2.4 2.9 2.7 2.  3.  2.2 2.9 2.9 3.1 3.  2.7\n",
            " 2.2 2.5 3.2 2.8 2.5 2.8 2.9 3.  2.8 3.  2.9 2.6 2.4 2.4 2.7 2.7 3.  3.4\n",
            " 3.1 2.3 3.  2.5 2.6 3.  2.6 2.3 2.7 3.  2.9 2.9 2.5 2.8]\n",
            "\n",
            "\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(2.0, 4.5)"
            ]
          },
          "metadata": {},
          "execution_count": 15
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "qD2TL0cwocaN",
        "outputId": "9c6d8ce2-0c1f-4bbd-e738-1866f12ff9ae"
      },
      "source": [
        "# Filtering Virginica\n",
        "\n",
        "Virginica = iris_data['Type'] == 2\n",
        "print(\"Filtering Virginica, True means its Virginica and False means Non Virginica\")\n",
        "print(Virginica.head())\n",
        "print(\"Top 6 Rows of Virginica\")\n",
        "Virginica_v2 = iris_data[Virginica]\n",
        "print(Virginica_v2[Virginica_v2.columns[0:2]].head())\n",
        "print(\"Last 6 Rows of Virginica\")\n",
        "print(Virginica_v2[Virginica_v2.columns[0:2]].tail())"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Filtering Virginica, True means its Virginica and False means Non Virginica\n",
            "0    False\n",
            "1    False\n",
            "2    False\n",
            "3    False\n",
            "4    False\n",
            "Name: Type, dtype: bool\n",
            "Top 6 Rows of Virginica\n",
            "     sepal length (cm)  sepal width (cm)\n",
            "100                6.3               3.3\n",
            "101                5.8               2.7\n",
            "102                7.1               3.0\n",
            "103                6.3               2.9\n",
            "104                6.5               3.0\n",
            "Last 6 Rows of Virginica\n",
            "     sepal length (cm)  sepal width (cm)\n",
            "145                6.7               3.0\n",
            "146                6.3               2.5\n",
            "147                6.5               3.0\n",
            "148                6.2               3.4\n",
            "149                5.9               3.0\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 485
        },
        "id": "wsnoGdx0objs",
        "outputId": "c200705a-8076-41e3-8de3-349f2ee6d5a6"
      },
      "source": [
        "# Filtering Virginica for 2D Plot\n",
        "\n",
        "print(\"Virginica for 2D Plot\")\n",
        "print(\"X Axis points\")\n",
        "print(iris_X[iris_Y == 2,0])\n",
        "print(\"Y Axis Points\")\n",
        "print(iris_X[iris_Y == 2,1])\n",
        "print('\\n')\n",
        "plt.scatter(iris_X[iris_Y == 2, 0], iris_X[iris_Y == 2, 1], \n",
        "            s = 80, c = 'green', label = 'Iris-virginica')\n",
        "plt.xlim([4.5,8])\n",
        "plt.ylim([2,4.5])"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Virginica for 2D Plot\n",
            "X Axis points\n",
            "[6.3 5.8 7.1 6.3 6.5 7.6 4.9 7.3 6.7 7.2 6.5 6.4 6.8 5.7 5.8 6.4 6.5 7.7\n",
            " 7.7 6.  6.9 5.6 7.7 6.3 6.7 7.2 6.2 6.1 6.4 7.2 7.4 7.9 6.4 6.3 6.1 7.7\n",
            " 6.3 6.4 6.  6.9 6.7 6.9 5.8 6.8 6.7 6.7 6.3 6.5 6.2 5.9]\n",
            "Y Axis Points\n",
            "[3.3 2.7 3.  2.9 3.  3.  2.5 2.9 2.5 3.6 3.2 2.7 3.  2.5 2.8 3.2 3.  3.8\n",
            " 2.6 2.2 3.2 2.8 2.8 2.7 3.3 3.2 2.8 3.  2.8 3.  2.8 3.8 2.8 2.8 2.6 3.\n",
            " 3.4 3.1 3.  3.1 3.1 3.1 2.7 3.2 3.3 3.  2.5 3.  3.4 3. ]\n",
            "\n",
            "\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(2.0, 4.5)"
            ]
          },
          "metadata": {},
          "execution_count": 17
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 286
        },
        "id": "HCShLm9HUWHn",
        "outputId": "291f8565-7464-4b58-c4af-f37eb8a106d1"
      },
      "source": [
        "# Visualise Classes all at once\n",
        "\n",
        "# In this Iris dataset, we have three classes (0,1,2)\n",
        "# We visualise these classes in a 2-D graph \n",
        "# This will help us in comparing the original classes with the clusters created \n",
        "# Plot the three type of flowers on a graph\n",
        "\n",
        "plt.scatter(iris_X[iris_Y == 0, 0], iris_X[iris_Y == 0, 1], \n",
        "            s = 80, c = 'orange', label = 'Iris-setosa')\n",
        "plt.scatter(iris_X[iris_Y == 1, 0], iris_X[iris_Y == 1, 1], \n",
        "            s = 80, c = 'yellow', label = 'Iris-versicolour')\n",
        "plt.scatter(iris_X[iris_Y == 2, 0], iris_X[iris_Y == 2, 1], \n",
        "            s = 80, c = 'green', label = 'Iris-virginica')\n",
        "plt.legend()\n",
        "\n",
        "# Inference : \n",
        "# We find that we have three classes with two types of Iris flowers overlapping each other"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.legend.Legend at 0x7fd427a585d0>"
            ]
          },
          "metadata": {},
          "execution_count": 18
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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2bNjAzp07scs9jY2NbN26lfXr17NwYc/7Tl1ted966y1uvfVWAObPn8+bb77Jnj17aGlpYf369b1eE7cwOVOfDMxWSh0Efg1co5T6ZbcxfwOGAkQuv5yBdcP0NLTWq7TWE7TWEwYNGpRQ4EIWENXb2yF6e9eoqa8hN8f+k1GOyqGmzn/Wu7GIPl9fX8+IESNO7u9LX/pS3LhuvPFGcnJyuPjiizl69GiP7bFseQE2btzIFVdcwZgxY3jttdd455137A4/qTgmda31Iq11qdZ6OHAL8JrW+ivdhj0P3B75/ouRMan7/CFkBqK395QjTUdo7bD/ZNTa0Upjk/+sd3uzDzv69+9/8nvTFNba2spdd93F2rVrefvtt5k7d24Pe10v6bNOXSm1VCk1O/Ljz4FzlFL7gfsQg4jMobkBtlfBxuutx+YG7/adiXp7H1McLCY/z/6TUX5ePkOC/rPeteOiiy7iwIEDHDx4EODkWX5fiGXLG03g5557Lk1NTSnvddqr285a603Apsj3D3Z5vhW4yc3AhBQTDsOmmXDk5dOff/dJKK6A6Rsgx4PatUzR26cBlaMqmbfe3no3rMNUjvan9W48CgoK+MlPfsJ1113HgAEDmDhxYp/ji2fLO3fuXMrLyykuLk5ofjcQ610hNq/N6JnQu1JcAde85F087SHxbE8AU0nj8q3LWbZlWcybpYWBQhZPXcyiKelnvdvU1EQwGERrzd13382FF17IN7/5zVSH5UhfJI1i6CX0pLnBPqGDtb3lCBQUexNToAguEIOpZLNwsvXJJ5ZOffHUxSe3pxtPPvkkv/jFL/jkk08YP348d955Z6pDShpypi70ZHuVdZnFibI7YdITyY9HSBjzJhkWobYQ6+rX0djUyJDgECpHVxLsJ5+MvEbO1AV3aD5sNu7jQ8mNQ3AVrbVxNWhR/yJuu1Q+GaWSvp5wi0uj0JPCUrNxA4YlNw7BNfLz8/nwww9TWukomKO15sMPPyQ/36kReE/kTF3oyZhqs8svY6qTHYngEqWlpRw+fNi2mlLwF/n5+ZSWGp5gdUGSutCTwhJL3eKkfvHqJqmQMIFAIGYFp5B5SFJPZz7aB29WnVKhTFwFZ4x0Z+7pG2Lr1OGUTt1N2kOWgVfrEcseYGilpXgRBJ8QagtRU1/DkaYjFAeLqRxVSVH/3r1H3ZjDCVG/pCOdnfDCKGja33NbsAxuqAe3rEabG2DPUuum6IBh1iUXN8/Q4zXB0J3SBEPwBW7YEbtpaSzql0wkXkIH6/kXRsHsv7izr8KS5MoWuzbBiBJtmL1nmfV4SfoVuwiZQ1c74ihRp8plW6z3qFNBlhtzmCJn6unGR/vghYucx31uPxR9KvnxJII0wRB8TqgtxOBHB5+WjLtTGCjk6P1H4+r43ZijK241yRD8wptVZuO2z01uHG4gTTAEn+OGHbGXlsYgST39aDliOM5DN8W+Ik0wBJ/jhh2xl5bGIEk9/TC9SVlQktw43ECaYAg+xw07Yq8tjSWppxsTV5mNu8KgeCjVSBMMwedUjqqkM2z/HnWyI3Zjjt4gST3dOGOkJVu0I1hmfpO0PQQHVsPeldZje6h32xNBmmAIPqeofxFLpi2hMBD7PRq1I7a7wenGHL1BJI3pyPV1sG4wfHK857Z+Z1vbnYinD39znpVoRy+Auofjb3dLPy5NMASf44YdsZeWxiJpTEfeWR7Rdsfo+h49u3XSdjvNMWgKHNua2D56gzTBEHyOG3bEbszhJGmUpJ5uuKHtNpnDCdGPC0JKEJ16puGGtttkDidEPy4IvkSSerrhhrbbZA4nRD8uCL5Eknq64Ya222QOJ0Q/Lgi+RJJ6uuGGtttkDidEPy4IvkQkjbHwwtvbZB/xxpQvcVa/2N3ANJnDRP0iN0kFF/DCYzybEPVLV7zw9jbZB9iPiach702cTnG4sQ9BsMFNj/FsQiSNvcEN/bcb+wCzONzQdjvNIfpxIUks37qcZVuW0dze830erbJ0y2M8k5CkbooX3t5G+yiwzqLDNuoU0YgLaY7bHuPZhOjUTfHC29tkH1oDDn9oRSMupDlee4xnE3KjNIoX3t4m+wh/4jyPaMSFNMdrj/FsQs7Uo3jh7W2yj5x+kBNIbhyCkGK89hjPJiSpR/HC29tkH0oBTsoV0YgL6Y3XHuPZhCT1KF54exvtYwmMedAsjuYG2F4FG6+3HptjtLBLph+6l/sQMgqvPcazCVG/dMUvOvVwGF4YBU37e/5+sAxm7oXXZ8GRl3tuL66A6RusOP1wLKIzFuIgOvW+kbCkUSmVD2wB+mPdWF2rtf7XbmPmAI8Af4s89e9a66fs5vVlUo/ihTbbbh+vzYidsKP0Ozt2g4woxRUweLo/NPdueq4LGYkbHuPZhBtJXQEDtNZNSqkAsBW4V2v9py5j5gATtNbzTQPzdVJPJc0NsO68xOfJzbdX2niiuRc9vSC4TcI6dW3RFPkxEPlKzTWbbODtanfmCXfYb/dCcy96ekHwHKMbpUqpXKVULfAB8IrWenuMYf+klHpLKbVWKTU0zjxVSqkdSqkdx44dSyDsDKb5sDvzOKlsvNDci55eEDzHKKlrrTu11uOAUmCSUqq825DfA8O11mOBV4BfxJlnldZ6gtZ6wqBBgxKJO3MpLHVnHqezaC8096KnFwTP6ZWkUWv9D2AjcF235z/UWrdFfnwKuNyd8LKQMdXuzJPjUCzsheZe9PSC4DmOSV0pNUgpdWbk+wLgs0B9tzFdT8dmA3VuBplVFJZY6hU7+p1tv724AsoNte59xQtdvyAIvcbE+2UI8AulVC7WH4HfaK3XK6WWAju01s8D9yilZgMdwHFgTrIC9gQ3mmQ0N1g3PZsPW5dUxlRbCduE6Rtg08z4OvSpL8CWG5x16gBvLwU0hNsj9gPKSrZR3/ZEjiM6R0ydei/24RkhoAY4AhQDlUDvXleThg7S9EFIJY5JXWv9FjA+xvMPdvl+EZD+guR4xTRvzjMvpgmHYyfkd588lXBzEizkVcrSoR/bYqlcdKcVb06e9XzXGJWKOD92+dkE0+O4ZBGMnO9zz3UNrAAeAnKBViAfmAcsARbiZM0Qr1Bm3vp5JwtlAMcxUkwjJBupKO2KG8U0ToVDxRVwzUuJzREsg5YGdxpt9DUGk+PwDcuBZUCMtaAQWIzTOYlJQwdAmj4ISUeaZJjiRjGNaeFQZSMUFCc2hx2JNtpw4zh8QwgYDNi8rhQCR4HYr6tJQ4eCvAK01rTayDyl6YPgBtIkwxQ3imlMC4fsxrlRfJRoow03jsM31GBdcrEjJzIuzgwGDR105J/tXqTpg+AB0iQjihvFNKaFQx8fSnwOOxJttOHGcfiGI1jX0O1oBeK/riYNHT7pdF5zafogeIGcqUdxo5jGtHBowLDE57Aj0UYbbhyHbyjGuilqRz6WyCvODAYNHfrl9iPgsObS9EHwAknqUdwopjEtHLIb50bxUaKNNtw4Dt9QCTi8roQj4+LMYNDQQUX+2e5Fmj4IHiBJPYobxTQmhUPFFfY3F03mCJa512ijrzE4HYdvKMKSLcZZi5Pql/ivq0lDhyXTlvDg9Ad90/Sh4UQDVb+v4vo111P1+yoaTsRooCJkJKJ+6YobTR/i6bvBXKfe2Zn6JhluHIdviKdT7yRRnXrXhg5ah5m5ZhQvH+j5ulWMKGPDrfXkONxwTZRwOMzMNTN5+UDP161iRAUbbt1ATtq8bkIsRNLYF9xoktHcAHuWWjcTBwyzLlWYntk66eUHTYFjW8006IkeSyLH4TtCwDqsm6JDsC659O51tWvosHzrDJZteZnm9p6/VxiAxVMrWDQludr+Gc/MiJnQo1SMqOCl29KlvkCIhST1dMNEL++ENKfwnFBbA4MfPY8WGxv7wgAcvb+RYL/k/FFsONHAeT9wri9o/FYjxcF0/cMsiE493TDRyzshzSk8p6a+mlyH/005CmrqqpMWQ/Vms7mrNyYvBiH1SFL3GyZ6eSekOYXnHGk6TKtDs6nWDmhsSp62//AJs/qCQyfSob5A6CuS1P2GiV7eCWlO4TnFwVLyHUr58vNgSDB52v7SgWb1BcMGpkN9gdBXJKn7DRO9vBPSnMJzKkdV0xm2HxPWUDm6OmkxVE8zm7v66uTFIKSezLMJcMML3WmORLzSnYjq5RNVv2TlTdLE/dL7SlH/EpZMq3BUvwT7FRNqa6CmvpojTYcpDpZSOaqaov6n3j999WMvGVhCxYgKR/VL9Cap037M4vDGo14wJ3PUL25ozJ3mGPUAbL4++dptJ434tBehfmVix5pRJK5DdyUKHWbFtpk8tPllcnOsa+j5edAZhiXTKlhw5Qs8/MYNNttf5OE3Vtpq4Z382E106kopW839gisX8PAbDzvEAV5o/8V/vifZI2l0wwvdaY6CktgFQVHc8hg3PRY39PQZQeJ+6W4SamtgXf1SGpsOMSQ4jMrR1QT7FTvq2KcMLWPr+w2u+LE3nGhg6ealHDpxiGEDh1F9dfXJM3Qnb/gpQ6ew9f2tDnGAFx714j/fk+xI6m54obuhD4fEPcbdOJasInG/dC8w0bE74YYfu4k3vHMcBRy9XxPsZ6fSStyjXvznY5MdOnU3vNDd0IdD4h7jbhxLVpG4X7oXmOjYnXDDj93EG945Dk1NndPJYOIe9eI/3zcy40apG17obujDIXGPcTeOJatI3C/dC0x07E644cdu4g3vHMcnNDY5jiJRj3rxn+8bmXGm7oYXuhv6cEjcY9yNY8kqEvdL9wITHbsTbvixm3jDO8fRjyFBB79+FzzqxX++b2RGUnfDC90NfTgk7jHuxrFkFYn7pXuBiY7dCTf82E284Z3jUFSOdlKlJO5RL/7zfSMzkrobXugmcwTL7OPo6jHeHoIDq2HvSuuxPXT62OYG2F4FG6+3Hpsb3DuWrCJxv3QviOrYC+Oc4BYGLHtet/zYQ237WL17Oiu3jWL17umE2vZF4nD2hq8YUeEQxxKC/R4k2R71XvrPZxKZoX4Bb7zQp74AW25IzMfcROueqBd61uEPnboT4XCnrd/6i1+uY+V/P5KQblvrTlZsG8VDm/fH0MKXsXByPZBjqw//9qfvZ/RPitn/v8d7zF921tnU332U3NxcRKeeGrJD0tiVRLTbpvpwO49xN7XuokPvJYn7pScTU122nWe78z4uZNmW/TZVrWUsmvIXIL43/IxnLoz5hydKxYgyXrrtL5GfkutRL/Qk+5J6X8kkrbvgO7zQZYfa9jH40YsMPN33E+z3qZjbG07s47wfXOS4r8Zv7ac4GHsOIblkh07dDTJJ6y74Di902TX1VYae7nPjbq/eXGW0r+qN8ecQUosk9SiZpHUXfIcXumxrH/ZjLE/3+E2oD584YrSvQ9LI2rdIUo+SSVp3wXd4ocu29mE/xvJ0j+8oWjrQ7LLfsIEuuZIKriNJPUomad0F3+GFLrty1CpDT/cn426vnrbKaF/VV8efQ0gtktSj+FHrLmQMXuiyi/qPZMm0Mlst/OKpZXFvkgKUDBxJxQj792jFiDK5Sepj0sv7xaQBRiJNMi5eaD3G1IcvPrXdZI63l1q/pztA5VnzlS8206m7cSyu4UbjiQagGjgMlEa+7/7x3Y1mC4k3n3CaIxEWTrbeG7F02YunLj65fd/f91G1vupknKtmrWLkuSO7Hy2x1svSoY9i6ab9aKA9DIEcSzG+eGpUp26/HhturbfV02+4tb7H8/EwW/PUN8lIlzhNcJQ0KqXygS1Af6w/Amu11v/abUx/YDVwOfAhcLPW+qDdvL2SNJoUFoF7BTuJ6MNNG1y89V2g04pP5QK5MPZf3T+WPuNGQU8YmAnE6sRTAWyIzJFoEYtTcwrn5hOgbedYOHkDSrnzwTaeLruzs5NRj49i///2TKZlZ5VRf3c9ubk5tuul9QOs2HY9D21+mbCG9k4I5Fqql+hxgH2TjGjRT8OJfSzdPI9DJxoYNrCE6qufND5DNyksAlJefJQucXYlYZ26sqIdoLVuUkoFgK3AvVrrP3UZcxcwVms9Tyl1C1Cptb7Zbt5eJXWToiBIvEmGG7w2I3ZCjxIsg5aGNDgWNxpPzCB2Qo9SAUxPeD9uNJ+ATY6t6PeVjlQAABf0SURBVBZNcaEBig0X/ujCmAk9StlZZfzlnq9ht17Lt5Y4FB9Za57s5hQmhVZAyptkpEucXXG1+EgpVYiV1L+utd7e5fmXgGqt9X8rpfKwPhMO0jaTGyd1k4KenALrZC7VjSWaG2DdeYnN4YtjcaPxRANgshb52FvnOjVbSLz5REFeAVq30GpzH9Mq2mkk2C859zv2/X0fFz3uXPSz/5/78amzP4m5LdQGgx/Fdi0K8kDrfFptpLeJF0E5F1oV5BUApLRJRrrE2R1Xio+UUrlKqVrgA+CVrgk9wnnA+wBa6w7gI+CcvoXcDaOCnrClTLHDi8YSrhQN+eFY3Gg8UW24L6ds7NRsIfHmE5p2HFs+KKipq05sRzZUrTcr+pn7fPz1qqnHcS00oB3WPPEiKOdCq7AOE3Z4nye7SUa6xNlbjP47aK07tdbjsO5yTVJKlfdlZ0qpKqXUDqXUjmPHjpn9kklBT7jd+rLDi8YSzYcTn8MXx+JG4wnTtXCSgDo1W0i8+cQnnZ20O/wdtYp2klcUdqTJrOinoSl+oEeacFyLTzqh3UFa6U4RlP37pz3cTnun/fs82U0y0iXO3tKrcxyt9T+AjcB13Tb9DRgKELn8cgbWDdPuv79Kaz1Baz1h0KBBZjs1KejJCVhfdnjRWKKwNPE5fHEsbjSeMF0Lp08ETs0WEm8+0S83l4DD/wSraCd5RWHRptBOlATjB1ocxHEt+uVCwOHs1J0iKPv3TyAnQCDX/n2e7CYZ6RJnb3FM6kqpQUqpMyPfFwCfBbprmp4Hbo98/0XgNbvr6b3CqKAnx7okYYcXjSVcKRryw7G40Xii2nBfThnZqdlC4s0nFAFnHY+GytHVie3IhlWzzIp+npwdf70qR+G4FgpQDmueeBGUc6FVjsohx+F9nuwmGekSZ28xOVMfAmxUSr0FvIl1TX29UmqpUmp2ZMzPgXOUUvuB+7B0aO5gUtAzZok/GksUlliyRTuCZWlwLG40nijBUrfYUQEk2mwh8eYTS6Yt4cHp9nMsnloRuUkawlLvrow8hnqMD7WFWL17NSu3rWT17tWE2rqP6TnHyHNHUnaWfdFP2VllfOrsauKtV1H/QsfioyXTKnhw+oNJLoJyLrRaMm2JcTGW83r6I06/4PjBVWv9FjA+xvMPdvm+FbjJ3dC6YFIUpDUc3RRbTjhoCoxekLTwTmP6BjOdukmBUyJFUAkT3UcsPfRizP5uvwiMAmLJ9Moi2xWwidjSxymA8+tmaa9ja8wXT61gwZUv8PAbN8TZPoWFkxcADwCxm0tYRTsvYMk8u6/HPKJ6eq1j65nnrZ8X0TMvQKmH485Rf3cdox4fbatTd1ovK854x3pKpw7ORVCJYFpoZTdmwZULWL51uc16Jq4PdyNON9bLTdLLT92uKMi0wYVX2DXSALMCJ180yUikCYKJ1h2DMWavW6itgXX1S2lsOsSQ4DAqR1dHzq6tOEJtzayrh8YmGBKEytEQ7Hd6HPHHTMFS88aPc/lWJz3zFBZNsZ8DFrHv7/uYt34eDU0NlARLeHL2k3zq7GjRj1n9QPy16LpeyW9OYbKPeGNMm4qkOk6vyY4mGW40uBBcxkTrXhB5TEQP71YcGmfFj81e2goiGnE7PTMcvR+C/eKOwP5Y3agfSA+8aCqSrmRHkww3GlwILmOidQ9Hvuxw0sO7EYeOfCWwl/owuTlOemaoqbMdgf2xulE/kB540VQkU0kvQ694uNHgQnAZE627gx4fcNbDuxFH7ArNXu2lqd2wQYXtCOyP1Y36gfTAi6YimUpmnKm70eBCcBkTrXsg8mWHkx7ejTj6GcThsJdggPw8Jz2zdZ3eZgT2x+pG/UB64EVTkUwlM5K6Gw0uBJcx0brn4PwWdNLDuxGHwtlx0mEvo3LoDDvpma0brzYjsD9WN+oH0gMvmopkKplx+SWqZXdSv8hNUg+Jat0tRUlNvVXGXhy0imSK+vdG/RJMwMv6VByJqXAs9Uu8Yynqv5gl05zVL8F+W2k40Uz1Zjh8AkoHQvU0KBnYVZMfz1ve9FhM3+dueOUnh6iG3En9Er1J6oXXecb4qScLV9UvYOa57qHnseDsc255lGvstOxa17Fi2yMJelmbeMOHHeLYy4pts2yPRWuYuWYmLx/oqSGvGFHBC19azw3PXmzTfKKOnJxHHOLE4FjcWI/U/1/xi9e5SRxp5aeeLFxP6lF8oe0WwMyretGUTdh5ri/fWsayLfZe6OZaZTvNvb33u0kcYH+mXhIssfVLrxhRxku3NWCm2U92/YCHNR0O2OnDvdCye6mXNyH7krrgC8x0xgUcvb8lrm7bxB/cHa2yvfe7mU+5s++2CY3fsi7rxMYNDXrmaN290LL7US+fHTp1wXeY6YzbbXXbJv7g7miVq223msRh4rttFMlGu61uaNAzR+vuhZY9HfXymXGjVPAdZjrjTlvdtok/uDtaZXvvd5M42sPtidYvAXDohN1WNzTomaN190LLno56eTlTF5KCmc4411a3beIP7o5W2d773SQOE99tE4YNtNvqhgY9c7TuXmjZ01EvL0ldSApmOuOArW7bxB/cHa1yte1WkzhMfLeNIrnabqsbGvTM0bp7oWVPR728JPWMx9n/OxmYeFUvnrqEYL/4nutF/Yn4g7vlZR1vLey9303iMPHddvJLrxhRRnEwEQ97E9zwyvcHZu+x5HvD+81PXZJ6xqKxpGuDgbuB70QeB0eeT77qaeHkBSyeOoWCPMuZMC/HeizI6+pjvoH4CbWCBz6zh5Jg7FhLgpoHPnO/QSQma2Efx8LJ9SyeupiCvAKC/YLk5eQR7BekIK/gpKf2gisXMGXolJgzTBk6hb1f3xs3sZedVcaLX67DSqgFWEk1L/JYgLmHvQkLPdpP8lk4eaHj65IO+3ATkTRmLH7QIpv4mEdjaACWAoeAYViXRIqZ8cw5vHzgeNw9VIw4m5du69EON2YcZmsRO44oiWimpwydwtb3txronRPRoPcGr/aTfPziDe8FolPPSvygRU48hoYTuzjvB5c77qnxW7spDo5NWhwmmOiZnchWf3Chd4hOPSvxgxY58RiqN3/ZaE/VG29JahwmmOiZnfCb3llITySpZyR+0CInHsPhE06XVSwOnTiW1DhMMNEzO+E3vbOQnkhSz0j8oEVOPIbSgecY7WnYwEFJjcMEEz2zE37TOwvpiST1jMQPWuTEY6ie9iujPVVf/eukxmGCiZ7ZCb/pnYX0RJJ6RuK1FjmW/ru3MfSco2TgZVSMONt2zxUjzra5SUqv4wi1hVi9ezUrt61k9e7VhNrMdP0meuaKERVppXcWTqev7w2vEfVLxuKFZ7bTPhYADzvEgO0c4fC3mLnmTF4+0FNVUjGigA23fkROjlN5vvNaaJ24L7eT7/aCKxfw8BsP+8aXWzBD/NQNkaTuFcnUIpvqv+1icJrD6jjUcKKZpZstw6thA61yeqv6sjd6+/hxuOmZ7aRn9oveWTBD/NQNkaSe7rih/zaZwwlvNOaiIc9O/PjeEJ26kCTc0H+bzOGENxpz0ZBnJ+n43pCkLvQRN/TfJnM44Y3GXDTk2Uk6vjckqQt9xA39t8kcTnijMRcNeXaSju8NSepCH3FD/20yhxPeaMxFQ56dpON7Q5K60Efc0MKbzFGR4D6cSUfPbMEb0vG9IT1KU0SoLURNfQ1Hmo5QHCymclQlRf2LejsL1k3CI1iXMiqxEqVXRHXmD2FpwT8B+mHp37v6ctvFaY0JtS2lpl5zpKmd4mCAylGKov6Lsde6u+f9HfXEjqVF7q1ndqitgZr6ao40HaY4WErlqGqK+pe4Eqd5DG68vwRw973hBY6SRqXUUKwSv8FY/3NXaa1/2G3MdOC/gL9Gnvqd1nqp3bzZKml0p5DBi8IiE6JxLI183w4EIvt+EJPio9OLfsK0drSTnxegM5zTbT288f5OREOudZgV22by0OaXyc2xmlXn51mt8JZMq2Dh5A0oF1re2cfgr0KZTMIv9QUJ69SVUkOAIVrrXUqpImAncKPWem+XMdOB+7XWs0wDy9ak7k4hgx8aYJjEYRUO2cW5fCu+KuxIhOVbZ7Bsy8s0t/fcVhiAxVMrWDTlpSTH4K9CGcF9XC8+Ukr9F/DvWutXujw3HUnqjrhTyOCHBhimcTjM0FbA4EfxVWFHXwm1NTD40fNo6Yg/pjAAR+9vJNivOP6ghGLwX6GM4D6uFh8ppYYD44HtMTZ/Rim1Wym1QSl1Sa+izBLcKWTwQwMM0zgcZqgPk5sTth3jt8KOeNTUV5Pr8L8pR0FNXXUSY0i/QhnBfYxvlCqlgsB/At/QWp/otnkXcL7WukkpdT3Wxc8LY8xRBVQBDBs2rM9BpyvuFDL4oQGGaRwOMzS102pzZgv+K+yIx5GmwwbHAo1Nh5IYQ/oVygjuY3SmrpQKYCX0NVrr33XfrrU+obVuinz/IhBQSp0bY9wqrfUErfWEQYPsGhtkJu4UMvihAYZpHA4zBAPk59k7LPqtsCMexcFS8h1OkfLzYEgweScz6VgoI7iPY1JX1q3ynwN1WuvvxxlTHBmHUmpSZF6zXmRZhDuFDH5ogGEah8MMo3LoDNu/Bf1W2BGPylHVdNpfSSKsoXJ0dRJjSL9CGcF9TM7UJwO3AdcopWojX9crpeYppeZFxnwR2KOU2g38CLhFp8r+0ce4U8jgdQOMROKwLxwq6r8k7Qo74lHUv4Ql0yoojPPBI6p+SdZNUiuG9CuUEdxHrHc9xh0ts1906p3AKGB/jG1lQB3wiG2cbjSn8AuiUxe8QPzUfYel7Q61NbOuHhqbYEgQKkdDsJ97TR+8YQbwss32CuAlTOL0S2GHG4TaGlhXv5TGpkMMCQ6jcnR1Us/QY8eQOespnI4kdV/hF425GzQA5xmMa8S6qSoIghtIkwxf4ReNuRtUuzxOEAQ3kKTuKX7RmLvBYcNxydNlC4LQE0nqnuIXjbkblBqOy74iM0FIJZLUPcUvGnM3qHZ5nCAIbpB9furtIXi/BlqPQH4xDK2EgFc+01Ftt5PDYvQmaar90u0owVK3OKlfsvEmqZ9fNyHTyR71i9awdwXseQhULnS2Qm4+6E4oXwIXLwRP9LsmGnMMxvhBa+ykU68nUdOv9MIv9QNCJuOkfsmeM/W9K2DPMujsIifsaLIe9yyzHi/xwmdaYenQ5xNfux31Ke8qfYzESiRWT/zSnViJJW2MRUNkux/i9IoVpMfrJmQy2XGm3h6C3w0+PaF3J7cQvnAUAqnWh6eLlj1d4vQKWQ/BG0SnDtY1dOVwGUDlwGE/6MPTRcueLnF6hayH4A+yI6m3HrGuodvR2QotftCHp4uWPV3i9ApZD8EfZEdSzy+2borakZsPBX7Qh6eLlj1d4vQKWQ/BH2RHUh9aaalc7NBhKPWDPjxdtOzpEqdXyHoI/iA7knqgyJIt5sbx9s4thPLFPrhJCv7xS3ciXeI8RaitgdW7q1i57XpW764i1BZPuWM7C7AaS9mzOvIzpON6CJlJ9kgaL47ov2Pq1Bef2u4LorHE0jsv7rI91aRHnPF8zuetf9IFD/t5nNKgp8d6CJlNdkgau9IegsPrrJuiBUOsSy6+OEOPRar90k3xd5zLt85g2ZaXaW7vuS3akWjRlJecZsG5EjiqQff3egjpjfipC1lNqK2BwY+eR0tH/DGFATh6f6NNIwvRoAv+QXTqQlZTU19NrsO7PEdBTV213SyIBl1IFySpCxnNkabDtNqcpYN1jb2xyc73XTToQvogSV3IaIqDpeQ7yAHy82BI0M73XTToQvogSV3IaCpHVdMZth8T1lA5utpuFkSDLqQLktSFjKaofwlLplVQGIi9Pap+iX+TFESDLqQTktSFjGfh5A0snlpBQR4E+0FejvVYkGcl9IWTN5jMgpW4C7CSd17ksQDRoAt+QiSNQtYQamtgXf1SGpsOMSQ4jMrR1Q5n6DFnQTToQiqRJhmCEKGofwm3XfpEorMAt7kRjiAkBbn8IgiCkEFIUhcEQcggJKkLgiBkEJLUBUEQMghJ6oIgCBmEqF98TQjLJOoIVql6JZb6QhAEITaOZ+pKqaFKqY1Kqb1KqXeUUvfGGKOUUj9SSu1XSr2llLosOeFmCxrLv3swcDfwncjj4MjzqaktEATB/5icqXcA39Ja71JKFQE7lVKvaK33dhkzE7gw8nUF8NPIo9AnVmA1ZOjq390UeVwWeVyEIAhCdxzP1LXWjVrrXZHvQ0AdcF63YZ8HVmuLPwFnKqXEsq5PhLDaocXqsEPk+WWcSvKCIAin6NWNUqXUcGA8sL3bpvOA97v8fJieiV8wQhoyCILQd4yTulIqCPwn8A2t9Ym+7EwpVaWU2qGU2nHs2LG+TJEFSEMGQRD6jlFSV0oFsBL6Gq3172IM+RswtMvPpZHnTkNrvUprPUFrPWHQoEF9iTcLkIYMgiD0HRP1iwJ+DtRprb8fZ9jzwFcjKphPAx9preVUsk9IQwZBEPqOifplMpYt3dtKqdrIc/8CDAPQWj8BvAhcD+zHupP3f90PNVuINmRYRuybpdKQQRCE+Dgmda31VkA5jNFYQmrBFaINFx7CumnainXJpRNpyCAIgh1SUepLFJYOfT7SkEEQhN4gSd3XSEMGQRB6hxh6CYIgZBCS1AVBEDIISeqCIAgZhLKEKynYsVLHgPdSsnOLc4G/p3D/vSFdYpU43SVd4oT0iTUT4jxfax23ejNlST3VKKV2aK0npDoOE9IlVonTXdIlTkifWLMhTrn8IgiCkEFIUhcEQcggsjmpr0p1AL0gXWKVON0lXeKE9Ik14+PM2mvqgiAImUg2n6kLgiBkHFmR1JVSuUqpPyul1sfYNkcpdUwpVRv5uiNFMR5USr0diWFHjO2+ae5tEOt0pdRHXdb0wRTFeaZSaq1Sql4pVaeU+ky37b5YU4M4/bKeF3WJoVYpdUIp9Y1uY1K+poZx+mVNv6mUekcptUcp9axSKr/b9v5Kqeci67k90n3OHq11xn8B9wG/AtbH2DYH+HcfxHgQONdm+/XABiy3r08D230c6/RYa52COH8B3BH5vh9wph/X1CBOX6xnt5hysdp0ne/HNTWIM+VritXy869AQeTn3wBzuo25C3gi8v0twHNO82b8mbpSqhS4AXgq1bEkiDT37gVKqTOAqVgNXtBaf6K1/ke3YSlfU8M4/ci1wLta6+4FhClf027Ei9Mv5AEFSqk8rGYJDd22fx7rjz7AWuDaSOOiuGR8UgceAx7AahcUj3+KfFRcq5QaajMumWjgZaXUTqVUVYztfmru7RQrwGeUUruVUhuUUpd4GVyEC4BjwNORS29PKaUGdBvjhzU1iRNSv57duQV4NsbzfljTrsSLE1K8plrrvwGPAoew/LU/0lq/3G3YyfXUWncAHwHn2M2b0UldKTUL+EBrvdNm2O+B4VrrscArnPqr6DVTtNaXATOBu5VSU1MUhwlOse7C+rh7KfBjLFN4r8kDLgN+qrUeD3yMP7uLmMTph/U8iVKqHzAb+G0q43DCIc6Ur6lS6iysM/ELgBJggFLqK4nOm9FJHasV32yl1EHg18A1Sqlfdh2gtf5Qa90W+fEp4HJvQzwZx98ijx8ANcCkbkOMmnt7gVOsWusTWuumyPcvAgGl1Lkeh3kYOKy13h75eS1W8uyKH9bUMU6frGdXZgK7tNZHY2zzw5pGiRunT9b0/wB/1Vof01q3A78Druw25uR6Ri7RnAF8aDdpRid1rfUirXWp1no41sew17TWp/0l7Ha9bzZQ52GI0RgGKKWKot8DFcCebsN80dzbJFalVHH0up9SahLW+8z2jeg2WusjwPtKqYsiT10L7O02LOVrahKnH9azG18i/iWNlK9pF+LG6ZM1PQR8WilVGInlWnrmn+eB2yPffxErh9kWF2Vl5yOl1FJgh9b6eeAepdRsoAM4jqWG8ZrBQE3kPZYH/Epr/Qel1DzwXXNvk1i/CHxdKdUBtAC3OL0Rk8Q/A2siH8MPAP/Xp2vqFKdf1jP6h/yzwJ1dnvPdmhrEmfI11VpvV0qtxboU1AH8GVjVLT/9HHhGKbUfKz/d4jSvVJQKgiBkEBl9+UUQBCHbkKQuCIKQQUhSFwRByCAkqQuCIGQQktQFQRAyCEnqgiAIGYQkdUEQhAxCkrogCEIG8f8BqIou+1H+90UAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        },
        "id": "Q4QWIYUC5XG7",
        "outputId": "7e8e2a26-eaca-4f6a-e17c-fa92ce908925"
      },
      "source": [
        "# Plotting of Dendrogram\n",
        "\n",
        "# We make use of dendrogram to decide the number of clusters required for our dataset\n",
        "# A dendrogram is a tree diagram which illustrates the arrangement of clusters.\n",
        "import scipy.cluster.hierarchy as sch\n",
        "\n",
        "# Pick the features from the original dataset\n",
        "# We don't need Target Column in Clustering\n",
        "iris_X_1 = iris_data[['sepal length (cm)','sepal width (cm)',\n",
        "                      'petal length (cm)','petal width (cm)']]\n",
        "iris_X_1.head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>sepal length (cm)</th>\n",
              "      <th>sepal width (cm)</th>\n",
              "      <th>petal length (cm)</th>\n",
              "      <th>petal width (cm)</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>5.1</td>\n",
              "      <td>3.5</td>\n",
              "      <td>1.4</td>\n",
              "      <td>0.2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>4.9</td>\n",
              "      <td>3.0</td>\n",
              "      <td>1.4</td>\n",
              "      <td>0.2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>4.7</td>\n",
              "      <td>3.2</td>\n",
              "      <td>1.3</td>\n",
              "      <td>0.2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>4.6</td>\n",
              "      <td>3.1</td>\n",
              "      <td>1.5</td>\n",
              "      <td>0.2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>5.0</td>\n",
              "      <td>3.6</td>\n",
              "      <td>1.4</td>\n",
              "      <td>0.2</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   sepal length (cm)  sepal width (cm)  petal length (cm)  petal width (cm)\n",
              "0                5.1               3.5                1.4               0.2\n",
              "1                4.9               3.0                1.4               0.2\n",
              "2                4.7               3.2                1.3               0.2\n",
              "3                4.6               3.1                1.5               0.2\n",
              "4                5.0               3.6                1.4               0.2"
            ]
          },
          "metadata": {},
          "execution_count": 19
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 383
        },
        "id": "TSWOuFF756Fi",
        "outputId": "f061b1ec-d5d6-4b48-974e-cc1f09b05eb3"
      },
      "source": [
        "# We finally plot a Dendrogram \n",
        "# which helps us in deciding what the threshold values should be for the clustering algorithm\n",
        "# Basically, we decide the number of clusters by using this dendrogram.\n",
        "\n",
        "Z = sch.linkage(iris_X_1, method = 'median')\n",
        "plt.figure(figsize=(20,7))\n",
        "den = sch.dendrogram(Z)\n",
        "plt.title('Dendrogram for the clustering of the dataset iris)')\n",
        "plt.xlabel('Type')\n",
        "plt.ylabel('Euclidean distance in the space with other variables')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0, 0.5, 'Euclidean distance in the space with other variables')"
            ]
          },
          "metadata": {},
          "execution_count": 20
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1440x504 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 380
        },
        "id": "1zjZaTB1aKCj",
        "outputId": "ae6e8a50-04e5-4c42-de03-0256ed8dd3d5"
      },
      "source": [
        "# Single Linkage - Nearest Point\n",
        "\n",
        "Z = sch.linkage(iris_X_1, method = 'single')\n",
        "plt.figure(figsize=(20,7))\n",
        "den = sch.dendrogram(Z)\n",
        "plt.title('Dendrogram for the clustering of the dataset iris)')\n",
        "plt.xlabel('Type')\n",
        "plt.ylabel('Euclidean distance in the space with other variables')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0, 0.5, 'Euclidean distance in the space with other variables')"
            ]
          },
          "metadata": {},
          "execution_count": 21
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1440x504 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 383
        },
        "id": "NpQnbDUMaLkt",
        "outputId": "2010e6bc-fef9-427c-e79f-fea0f3cd5eac"
      },
      "source": [
        "# Complete Linkage - Farthest Point \n",
        "\n",
        "Z = sch.linkage(iris_X_1, method = 'complete')\n",
        "plt.figure(figsize=(20,7))\n",
        "den = sch.dendrogram(Z)\n",
        "plt.title('Dendrogram for the clustering of the dataset iris)')\n",
        "plt.xlabel('Type')\n",
        "plt.ylabel('Euclidean distance in the space with other variables')\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0, 0.5, 'Euclidean distance in the space with other variables')"
            ]
          },
          "metadata": {},
          "execution_count": 22
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1440x504 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 380
        },
        "id": "zH_7oxCTaMbZ",
        "outputId": "83f47e4a-95c3-4897-a6b0-01a59bf0750b"
      },
      "source": [
        "# Average Linkage - Average Distance between all points\n",
        "\n",
        "Z = sch.linkage(iris_X_1, method = 'average')\n",
        "plt.figure(figsize=(20,7))\n",
        "den = sch.dendrogram(Z)\n",
        "plt.title('Dendrogram for the clustering of the dataset iris)')\n",
        "plt.xlabel('Type')\n",
        "plt.ylabel('Euclidean distance in the space with other variables')\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0, 0.5, 'Euclidean distance in the space with other variables')"
            ]
          },
          "metadata": {},
          "execution_count": 23
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1440x504 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "pJnhnUlt6Hw7",
        "outputId": "0eb3b621-2af7-4880-93d0-fe83c20f14d0"
      },
      "source": [
        "# Building an Agglomerative Clustering Model\n",
        "# Initialise Model\n",
        "# We analyse the above-created dendrogram \n",
        "# decide that we will be making 3 clusters for this dataset\n",
        "\n",
        "cluster_H = AgglomerativeClustering(n_clusters=3,linkage = 'average')\n",
        "\n",
        "# Fitting Model\n",
        "# After building Agglomerative clustering, we will fit our iris data set\n",
        "# Note that only the independent variables from the Iris dataset \n",
        "# are taken into account for the purpose of clustering\n",
        "\n",
        "model_clt = cluster_H.fit(iris_X_1)\n",
        "model_clt"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "AgglomerativeClustering(affinity='euclidean', compute_full_tree='auto',\n",
              "                        connectivity=None, distance_threshold=None,\n",
              "                        linkage='average', memory=None, n_clusters=3)"
            ]
          },
          "metadata": {},
          "execution_count": 24
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "RCNXJPX26oCP",
        "outputId": "b266e8b1-567e-473f-b39d-4611d5112162"
      },
      "source": [
        "# Predicting Output Class\n",
        "\n",
        "print('Output Clusters are')\n",
        "pred1 = model_clt.labels_\n",
        "print(pred1)\n",
        "# Frequency count of the Output clusters\n",
        "unique, counts = np.unique(pred1, return_counts=True)\n",
        "print(dict(zip(unique, counts)))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Output Clusters are\n",
            "[1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1\n",
            " 1 1 1 1 1 1 1 1 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n",
            " 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 2 0 2 2 2 2 0 2 2 2 2\n",
            " 2 2 0 0 2 2 2 2 0 2 0 2 0 2 2 0 0 2 2 2 2 2 0 2 2 2 2 0 2 2 2 0 2 2 2 0 2\n",
            " 2 0]\n",
            "{0: 64, 1: 50, 2: 36}\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 628
        },
        "id": "oMPtUa9C7B-T",
        "outputId": "57a1a007-f30e-4ba3-864d-ba63d228680a"
      },
      "source": [
        "# Visualizing Output\n",
        "\n",
        "print('Original Cluster')\n",
        "print(freq_1)\n",
        "# Frequency count of the Output clusters\n",
        "unique, counts = np.unique(pred1, return_counts=True)\n",
        "print('Hierarchical Clustering Output Cluster')\n",
        "print(dict(zip(unique, counts)))\n",
        "# Silhouette Score\n",
        "print('Silhouette Score for 3 Clusters')\n",
        "print(silhouette_score(iris_X,pred1))\n",
        "print('\\n')\n",
        "\n",
        "# In the above output we got value labels: ‘0’, ‘1’  and ‘2’\n",
        "# For a better understanding, we can visualize these clusters.\n",
        "\n",
        "# We use the above-found class labels and visualise how the clusters have been formed.\n",
        "\n",
        "plt.scatter(iris_X[pred1 == 0, 0], iris_X[pred1 == 0, 1], \n",
        "            s = 80, c = 'orange', label = 'Iris-setosa')\n",
        "plt.scatter(iris_X[pred1 == 1, 0], iris_X[pred1 == 1, 1], \n",
        "            s = 80, c = 'yellow', label = 'Iris-versicolour')\n",
        "plt.scatter(iris_X[pred1 == 2, 0], iris_X[pred1 == 2, 1], \n",
        "            s = 80, c = 'green', label = 'Iris-virginica')\n",
        "plt.legend()\n",
        "\n",
        "# Inference : \n",
        "# When compared to the original classes we find that the observations of the class label \n",
        "# \"1\" has been correctly formed into a separate well-defined cluster\n",
        "# however, for the other two classes, clusters are not as correct. \n",
        "# This is mainly because, in the original dataset, these two class labels were overlapping each other \n",
        "# which makes it difficult for the clustering algorithm as it works best for clear neat separate observations. \n",
        "# Still, the clusters have been formed, more or less correctly\n",
        "\n",
        "for k in range(2,10):\n",
        "  cluster_H = AgglomerativeClustering(n_clusters=k,linkage= 'average')\n",
        "  model_clt = cluster_H.fit(iris_X)\n",
        "  label = model_clt.labels_\n",
        "  sil_coeff = silhouette_score(iris_X,label,metric = 'euclidean')\n",
        "  print('For cluster= {}, Silhouette Coefficient is {}'.format(k,sil_coeff))\n",
        "print('\\n')\n",
        "print('For Cluster = 2, it has highest Silhouette Value')\n",
        "print('But according to Visualization and data, Number of Cluster is 3')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Original Cluster\n",
            "{0: 50, 1: 50, 2: 50}\n",
            "Hierarchical Clustering Output Cluster\n",
            "{0: 64, 1: 50, 2: 36}\n",
            "Silhouette Score for 3 Clusters\n",
            "0.5541608580282847\n",
            "\n",
            "\n",
            "For cluster= 2, Silhouette Coefficient is 0.6867350732769776\n",
            "For cluster= 3, Silhouette Coefficient is 0.5541608580282847\n",
            "For cluster= 4, Silhouette Coefficient is 0.4719936084994249\n",
            "For cluster= 5, Silhouette Coefficient is 0.4306699739542549\n",
            "For cluster= 6, Silhouette Coefficient is 0.3419903827982995\n",
            "For cluster= 7, Silhouette Coefficient is 0.3707424079292066\n",
            "For cluster= 8, Silhouette Coefficient is 0.3658753388418643\n",
            "For cluster= 9, Silhouette Coefficient is 0.3166806903618151\n",
            "\n",
            "\n",
            "For Cluster = 2, it has highest Silhouette Value\n",
            "But according to Visualization and data, Number of Cluster is 3\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    }
  ]
}