DBN_&_SHAP.ipynb

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  "metadata": {
    "colab": {
      "provenance": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "source": [
        "# Installation & Imports"
      ],
      "metadata": {
        "id": "50Hv99UFpQDR"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "id": "YKaXE4hp9Yb8",
        "outputId": "cc452316-eea8-4d68-8a40-fd78c25624ff"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'2.8.2'"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 4
        }
      ],
      "source": [
        "import tensorflow as tf\n",
        "tf.__version__"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install -U tensorflow==1.15.0"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "PmTOpwAk9fd6",
        "outputId": "54b87c5c-3bdc-4b12-d68b-f4deb834e33e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "Collecting tensorflow==1.15.0\n",
            "  Downloading tensorflow-1.15.0-cp37-cp37m-manylinux2010_x86_64.whl (412.3 MB)\n",
            "\u001b[K     |████████████████████████████████| 412.3 MB 23 kB/s \n",
            "\u001b[?25hRequirement already satisfied: wheel>=0.26 in /usr/local/lib/python3.7/dist-packages (from tensorflow==1.15.0) (0.37.1)\n",
            "Collecting gast==0.2.2\n",
            "  Downloading gast-0.2.2.tar.gz (10 kB)\n",
            "Collecting keras-applications>=1.0.8\n",
            "  Downloading Keras_Applications-1.0.8-py3-none-any.whl (50 kB)\n",
            "\u001b[K     |████████████████████████████████| 50 kB 5.8 MB/s \n",
            "\u001b[?25hRequirement already satisfied: keras-preprocessing>=1.0.5 in /usr/local/lib/python3.7/dist-packages (from tensorflow==1.15.0) (1.1.2)\n",
            "Requirement already satisfied: six>=1.10.0 in /usr/local/lib/python3.7/dist-packages (from tensorflow==1.15.0) (1.15.0)\n",
            "Requirement already satisfied: protobuf>=3.6.1 in /usr/local/lib/python3.7/dist-packages (from tensorflow==1.15.0) (3.17.3)\n",
            "Requirement already satisfied: google-pasta>=0.1.6 in /usr/local/lib/python3.7/dist-packages (from tensorflow==1.15.0) (0.2.0)\n",
            "Collecting tensorboard<1.16.0,>=1.15.0\n",
            "  Downloading tensorboard-1.15.0-py3-none-any.whl (3.8 MB)\n",
            "\u001b[K     |████████████████████████████████| 3.8 MB 32.3 MB/s \n",
            "\u001b[?25hRequirement already satisfied: numpy<2.0,>=1.16.0 in /usr/local/lib/python3.7/dist-packages (from tensorflow==1.15.0) (1.21.6)\n",
            "Collecting tensorflow-estimator==1.15.1\n",
            "  Downloading tensorflow_estimator-1.15.1-py2.py3-none-any.whl (503 kB)\n",
            "\u001b[K     |████████████████████████████████| 503 kB 36.6 MB/s \n",
            "\u001b[?25hRequirement already satisfied: termcolor>=1.1.0 in /usr/local/lib/python3.7/dist-packages (from tensorflow==1.15.0) (1.1.0)\n",
            "Requirement already satisfied: wrapt>=1.11.1 in /usr/local/lib/python3.7/dist-packages (from tensorflow==1.15.0) (1.14.1)\n",
            "Requirement already satisfied: astor>=0.6.0 in /usr/local/lib/python3.7/dist-packages (from tensorflow==1.15.0) (0.8.1)\n",
            "Requirement already satisfied: grpcio>=1.8.6 in /usr/local/lib/python3.7/dist-packages (from tensorflow==1.15.0) (1.47.0)\n",
            "Requirement already satisfied: absl-py>=0.7.0 in /usr/local/lib/python3.7/dist-packages (from tensorflow==1.15.0) (1.2.0)\n",
            "Requirement already satisfied: opt-einsum>=2.3.2 in /usr/local/lib/python3.7/dist-packages (from tensorflow==1.15.0) (3.3.0)\n",
            "Requirement already satisfied: h5py in /usr/local/lib/python3.7/dist-packages (from keras-applications>=1.0.8->tensorflow==1.15.0) (3.1.0)\n",
            "Requirement already satisfied: markdown>=2.6.8 in /usr/local/lib/python3.7/dist-packages (from tensorboard<1.16.0,>=1.15.0->tensorflow==1.15.0) (3.4.1)\n",
            "Requirement already satisfied: setuptools>=41.0.0 in /usr/local/lib/python3.7/dist-packages (from tensorboard<1.16.0,>=1.15.0->tensorflow==1.15.0) (57.4.0)\n",
            "Requirement already satisfied: werkzeug>=0.11.15 in /usr/local/lib/python3.7/dist-packages (from tensorboard<1.16.0,>=1.15.0->tensorflow==1.15.0) (1.0.1)\n",
            "Requirement already satisfied: importlib-metadata>=4.4 in /usr/local/lib/python3.7/dist-packages (from markdown>=2.6.8->tensorboard<1.16.0,>=1.15.0->tensorflow==1.15.0) (4.12.0)\n",
            "Requirement already satisfied: typing-extensions>=3.6.4 in /usr/local/lib/python3.7/dist-packages (from importlib-metadata>=4.4->markdown>=2.6.8->tensorboard<1.16.0,>=1.15.0->tensorflow==1.15.0) (4.1.1)\n",
            "Requirement already satisfied: zipp>=0.5 in /usr/local/lib/python3.7/dist-packages (from importlib-metadata>=4.4->markdown>=2.6.8->tensorboard<1.16.0,>=1.15.0->tensorflow==1.15.0) (3.8.1)\n",
            "Requirement already satisfied: cached-property in /usr/local/lib/python3.7/dist-packages (from h5py->keras-applications>=1.0.8->tensorflow==1.15.0) (1.5.2)\n",
            "Building wheels for collected packages: gast\n",
            "  Building wheel for gast (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for gast: filename=gast-0.2.2-py3-none-any.whl size=7554 sha256=dc53d0e6c08c65e8c953bd860f523a95a0a0b21bbb846f9ad804e0676b2c9790\n",
            "  Stored in directory: /root/.cache/pip/wheels/21/7f/02/420f32a803f7d0967b48dd823da3f558c5166991bfd204eef3\n",
            "Successfully built gast\n",
            "Installing collected packages: tensorflow-estimator, tensorboard, keras-applications, gast, tensorflow\n",
            "  Attempting uninstall: tensorflow-estimator\n",
            "    Found existing installation: tensorflow-estimator 2.8.0\n",
            "    Uninstalling tensorflow-estimator-2.8.0:\n",
            "      Successfully uninstalled tensorflow-estimator-2.8.0\n",
            "  Attempting uninstall: tensorboard\n",
            "    Found existing installation: tensorboard 2.8.0\n",
            "    Uninstalling tensorboard-2.8.0:\n",
            "      Successfully uninstalled tensorboard-2.8.0\n",
            "  Attempting uninstall: gast\n",
            "    Found existing installation: gast 0.5.3\n",
            "    Uninstalling gast-0.5.3:\n",
            "      Successfully uninstalled gast-0.5.3\n",
            "  Attempting uninstall: tensorflow\n",
            "    Found existing installation: tensorflow 2.8.2+zzzcolab20220719082949\n",
            "    Uninstalling tensorflow-2.8.2+zzzcolab20220719082949:\n",
            "      Successfully uninstalled tensorflow-2.8.2+zzzcolab20220719082949\n",
            "\u001b[31mERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.\n",
            "tensorflow-probability 0.16.0 requires gast>=0.3.2, but you have gast 0.2.2 which is incompatible.\n",
            "kapre 0.3.7 requires tensorflow>=2.0.0, but you have tensorflow 1.15.0 which is incompatible.\u001b[0m\n",
            "Successfully installed gast-0.2.2 keras-applications-1.0.8 tensorboard-1.15.0 tensorflow-1.15.0 tensorflow-estimator-1.15.1\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.colab-display-data+json": {
              "pip_warning": {
                "packages": [
                  "gast",
                  "tensorboard",
                  "tensorflow"
                ]
              }
            }
          },
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        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Import Libraries\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "import seaborn as sns\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "from sklearn.cluster import KMeans\n",
        "from sklearn.metrics import silhouette_score\n",
        "from sklearn.preprocessing import LabelEncoder, StandardScaler\n",
        "from yellowbrick.cluster import KElbowVisualizer"
      ],
      "metadata": {
        "id": "_OXHDMAj9nS0"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Data and Preprocessing"
      ],
      "metadata": {
        "id": "1Lt8fck5pU7Q"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df = pd.read_csv('/content/creditcard.csv')\n",
        "df.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 300
        },
        "id": "_yL2apxI-rrp",
        "outputId": "feaa60bf-f64a-4e7b-ef18-c4d737086871"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   Time        V1        V2        V3        V4        V5        V6        V7  \\\n",
              "0     0 -1.359807 -0.072781  2.536347  1.378155 -0.338321  0.462388  0.239599   \n",
              "1     0  1.191857  0.266151  0.166480  0.448154  0.060018 -0.082361 -0.078803   \n",
              "2     1 -1.358354 -1.340163  1.773209  0.379780 -0.503198  1.800499  0.791461   \n",
              "3     1 -0.966272 -0.185226  1.792993 -0.863291 -0.010309  1.247203  0.237609   \n",
              "4     2 -1.158233  0.877737  1.548718  0.403034 -0.407193  0.095921  0.592941   \n",
              "\n",
              "         V8        V9  ...       V21       V22       V23       V24       V25  \\\n",
              "0  0.098698  0.363787  ... -0.018307  0.277838 -0.110474  0.066928  0.128539   \n",
              "1  0.085102 -0.255425  ... -0.225775 -0.638672  0.101288 -0.339846  0.167170   \n",
              "2  0.247676 -1.514654  ...  0.247998  0.771679  0.909412 -0.689281 -0.327642   \n",
              "3  0.377436 -1.387024  ... -0.108300  0.005274 -0.190321 -1.175575  0.647376   \n",
              "4 -0.270533  0.817739  ... -0.009431  0.798278 -0.137458  0.141267 -0.206010   \n",
              "\n",
              "        V26       V27       V28  Amount  Class  \n",
              "0 -0.189115  0.133558 -0.021053  149.62    0.0  \n",
              "1  0.125895 -0.008983  0.014724    2.69    0.0  \n",
              "2 -0.139097 -0.055353 -0.059752  378.66    0.0  \n",
              "3 -0.221929  0.062723  0.061458  123.50    0.0  \n",
              "4  0.502292  0.219422  0.215153   69.99    0.0  \n",
              "\n",
              "[5 rows x 31 columns]"
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              "      <th></th>\n",
              "      <th>Time</th>\n",
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        "df.dropna(inplace = True)\n",
        "df.head()"
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      "execution_count": null,
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              "   Time        V1        V2        V3        V4        V5        V6        V7  \\\n",
              "0     0 -1.359807 -0.072781  2.536347  1.378155 -0.338321  0.462388  0.239599   \n",
              "1     0  1.191857  0.266151  0.166480  0.448154  0.060018 -0.082361 -0.078803   \n",
              "2     1 -1.358354 -1.340163  1.773209  0.379780 -0.503198  1.800499  0.791461   \n",
              "3     1 -0.966272 -0.185226  1.792993 -0.863291 -0.010309  1.247203  0.237609   \n",
              "4     2 -1.158233  0.877737  1.548718  0.403034 -0.407193  0.095921  0.592941   \n",
              "\n",
              "         V8        V9  ...       V21       V22       V23       V24       V25  \\\n",
              "0  0.098698  0.363787  ... -0.018307  0.277838 -0.110474  0.066928  0.128539   \n",
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              "4 -0.270533  0.817739  ... -0.009431  0.798278 -0.137458  0.141267 -0.206010   \n",
              "\n",
              "        V26       V27       V28  Amount  Class  \n",
              "0 -0.189115  0.133558 -0.021053  149.62    0.0  \n",
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              "      <td>-1.514654</td>\n",
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          "metadata": {},
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    {
      "cell_type": "code",
      "source": [
        "df1 = df[df['Class'] == 1]\n",
        "df1"
      ],
      "metadata": {
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          "height": 927
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              "      Time        V1        V2        V3        V4        V5        V6  \\\n",
              "541    406 -2.312227  1.951992 -1.609851  3.997906 -0.522188 -1.426545   \n",
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              "6609  8090 -1.783229  3.402794 -3.822742  2.625368 -1.976415 -2.731689   \n",
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              "6774 -4.716143  1.249803 -0.718326  ...  0.756053  0.140168  0.665411   \n",
              "6820 -4.235253  1.703538 -1.305279  ...  0.645103 -0.503529 -0.000523   \n",
              "6870 -4.261237  1.701682 -1.439396  ...  0.667927 -0.516242 -0.012218   \n",
              "6882 -4.577265  0.472216  0.472017  ...  0.481830  0.146023  0.117039   \n",
              "6899 -3.103570  1.778492 -3.831154  ...  0.734775 -0.435901 -0.384766   \n",
              "6903 -3.104557  1.823233 -3.878658  ...  0.716720 -0.448060 -0.402407   \n",
              "6971  5.431271 -1.946734 -0.775680  ... -1.052368  0.204817 -2.119007   \n",
              "\n",
              "           V24       V25       V26       V27       V28   Amount  Class  \n",
              "541   0.320198  0.044519  0.177840  0.261145 -0.143276     0.00    1.0  \n",
              "623  -0.293803  0.279798 -0.145362 -0.252773  0.035764   529.00    1.0  \n",
              "4920 -0.087330 -0.156114 -0.542628  0.039566 -0.153029   239.93    1.0  \n",
              "6108 -0.053502  0.252405 -0.657488 -0.827136  0.849573    59.00    1.0  \n",
              "6329 -1.632653  1.488901  0.566797 -0.010016  0.146793     1.00    1.0  \n",
              "6331  0.128940  1.488481  0.507963  0.735822  0.513574     1.00    1.0  \n",
              "6334 -0.759908  1.605056  0.540675  0.737040  0.496699     1.00    1.0  \n",
              "6336 -0.716522  1.415717  0.555265  0.530507  0.404474     1.00    1.0  \n",
              "6338 -0.219845  1.474753  0.491192  0.518868  0.402528     1.00    1.0  \n",
              "6427 -0.364700 -1.843078  0.351909  0.594550  0.099372     1.00    1.0  \n",
              "6446  0.473246 -1.959304  0.319476  0.600485  0.129305     1.00    1.0  \n",
              "6472 -0.372216 -2.032068  0.366778  0.395171  0.020206     1.00    1.0  \n",
              "6529 -0.425550  0.123644  0.321985  0.264028  0.132817     1.00    1.0  \n",
              "6609  0.461700  0.044146  0.305704  0.530981  0.243746     1.00    1.0  \n",
              "6641 -0.234649 -1.004881  0.435832  0.618324  0.148469     1.00    1.0  \n",
              "6717 -0.279642  1.106766  0.323885  0.894767  0.569519     1.00    1.0  \n",
              "6719 -0.263743  1.539916  0.523574  0.891025  0.572741     1.00    1.0  \n",
              "6734  0.083008 -1.911034  0.322188  0.620867  0.185030     1.00    1.0  \n",
              "6774  0.131464 -1.908217  0.334808  0.748534  0.175414     1.00    1.0  \n",
              "6820  0.071696  0.092007  0.308498  0.552591  0.298954     1.00    1.0  \n",
              "6870  0.070614  0.058504  0.304883  0.418012  0.208858     1.00    1.0  \n",
              "6882 -0.217565 -0.138776 -0.424453 -1.002041  0.890780     1.10    1.0  \n",
              "6899 -0.286016  1.007934  0.413196  0.280284  0.303937     1.00    1.0  \n",
              "6903 -0.288835  1.011752  0.425965  0.413140  0.308205     1.00    1.0  \n",
              "6971  0.170279 -0.393844  0.296367  1.985913 -0.900452  1809.68    1.0  \n",
              "\n",
              "[25 rows x 31 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-79004048-b9f5-4339-9132-fa7ba7b98cd5\">\n",
              "    <div class=\"colab-df-container\">\n",
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Time</th>\n",
              "      <th>V1</th>\n",
              "      <th>V2</th>\n",
              "      <th>V3</th>\n",
              "      <th>V4</th>\n",
              "      <th>V5</th>\n",
              "      <th>V6</th>\n",
              "      <th>V7</th>\n",
              "      <th>V8</th>\n",
              "      <th>V9</th>\n",
              "      <th>...</th>\n",
              "      <th>V21</th>\n",
              "      <th>V22</th>\n",
              "      <th>V23</th>\n",
              "      <th>V24</th>\n",
              "      <th>V25</th>\n",
              "      <th>V26</th>\n",
              "      <th>V27</th>\n",
              "      <th>V28</th>\n",
              "      <th>Amount</th>\n",
              "      <th>Class</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>541</th>\n",
              "      <td>406</td>\n",
              "      <td>-2.312227</td>\n",
              "      <td>1.951992</td>\n",
              "      <td>-1.609851</td>\n",
              "      <td>3.997906</td>\n",
              "      <td>-0.522188</td>\n",
              "      <td>-1.426545</td>\n",
              "      <td>-2.537387</td>\n",
              "      <td>1.391657</td>\n",
              "      <td>-2.770089</td>\n",
              "      <td>...</td>\n",
              "      <td>0.517232</td>\n",
              "      <td>-0.035049</td>\n",
              "      <td>-0.465211</td>\n",
              "      <td>0.320198</td>\n",
              "      <td>0.044519</td>\n",
              "      <td>0.177840</td>\n",
              "      <td>0.261145</td>\n",
              "      <td>-0.143276</td>\n",
              "      <td>0.00</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>623</th>\n",
              "      <td>472</td>\n",
              "      <td>-3.043541</td>\n",
              "      <td>-3.157307</td>\n",
              "      <td>1.088463</td>\n",
              "      <td>2.288644</td>\n",
              "      <td>1.359805</td>\n",
              "      <td>-1.064823</td>\n",
              "      <td>0.325574</td>\n",
              "      <td>-0.067794</td>\n",
              "      <td>-0.270953</td>\n",
              "      <td>...</td>\n",
              "      <td>0.661696</td>\n",
              "      <td>0.435477</td>\n",
              "      <td>1.375966</td>\n",
              "      <td>-0.293803</td>\n",
              "      <td>0.279798</td>\n",
              "      <td>-0.145362</td>\n",
              "      <td>-0.252773</td>\n",
              "      <td>0.035764</td>\n",
              "      <td>529.00</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4920</th>\n",
              "      <td>4462</td>\n",
              "      <td>-2.303350</td>\n",
              "      <td>1.759247</td>\n",
              "      <td>-0.359745</td>\n",
              "      <td>2.330243</td>\n",
              "      <td>-0.821628</td>\n",
              "      <td>-0.075788</td>\n",
              "      <td>0.562320</td>\n",
              "      <td>-0.399147</td>\n",
              "      <td>-0.238253</td>\n",
              "      <td>...</td>\n",
              "      <td>-0.294166</td>\n",
              "      <td>-0.932391</td>\n",
              "      <td>0.172726</td>\n",
              "      <td>-0.087330</td>\n",
              "      <td>-0.156114</td>\n",
              "      <td>-0.542628</td>\n",
              "      <td>0.039566</td>\n",
              "      <td>-0.153029</td>\n",
              "      <td>239.93</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6108</th>\n",
              "      <td>6986</td>\n",
              "      <td>-4.397974</td>\n",
              "      <td>1.358367</td>\n",
              "      <td>-2.592844</td>\n",
              "      <td>2.679787</td>\n",
              "      <td>-1.128131</td>\n",
              "      <td>-1.706536</td>\n",
              "      <td>-3.496197</td>\n",
              "      <td>-0.248778</td>\n",
              "      <td>-0.247768</td>\n",
              "      <td>...</td>\n",
              "      <td>0.573574</td>\n",
              "      <td>0.176968</td>\n",
              "      <td>-0.436207</td>\n",
              "      <td>-0.053502</td>\n",
              "      <td>0.252405</td>\n",
              "      <td>-0.657488</td>\n",
              "      <td>-0.827136</td>\n",
              "      <td>0.849573</td>\n",
              "      <td>59.00</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6329</th>\n",
              "      <td>7519</td>\n",
              "      <td>1.234235</td>\n",
              "      <td>3.019740</td>\n",
              "      <td>-4.304597</td>\n",
              "      <td>4.732795</td>\n",
              "      <td>3.624201</td>\n",
              "      <td>-1.357746</td>\n",
              "      <td>1.713445</td>\n",
              "      <td>-0.496358</td>\n",
              "      <td>-1.282858</td>\n",
              "      <td>...</td>\n",
              "      <td>-0.379068</td>\n",
              "      <td>-0.704181</td>\n",
              "      <td>-0.656805</td>\n",
              "      <td>-1.632653</td>\n",
              "      <td>1.488901</td>\n",
              "      <td>0.566797</td>\n",
              "      <td>-0.010016</td>\n",
              "      <td>0.146793</td>\n",
              "      <td>1.00</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6331</th>\n",
              "      <td>7526</td>\n",
              "      <td>0.008430</td>\n",
              "      <td>4.137837</td>\n",
              "      <td>-6.240697</td>\n",
              "      <td>6.675732</td>\n",
              "      <td>0.768307</td>\n",
              "      <td>-3.353060</td>\n",
              "      <td>-1.631735</td>\n",
              "      <td>0.154612</td>\n",
              "      <td>-2.795892</td>\n",
              "      <td>...</td>\n",
              "      <td>0.364514</td>\n",
              "      <td>-0.608057</td>\n",
              "      <td>-0.539528</td>\n",
              "      <td>0.128940</td>\n",
              "      <td>1.488481</td>\n",
              "      <td>0.507963</td>\n",
              "      <td>0.735822</td>\n",
              "      <td>0.513574</td>\n",
              "      <td>1.00</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6334</th>\n",
              "      <td>7535</td>\n",
              "      <td>0.026779</td>\n",
              "      <td>4.132464</td>\n",
              "      <td>-6.560600</td>\n",
              "      <td>6.348557</td>\n",
              "      <td>1.329666</td>\n",
              "      <td>-2.513479</td>\n",
              "      <td>-1.689102</td>\n",
              "      <td>0.303253</td>\n",
              "      <td>-3.139409</td>\n",
              "      <td>...</td>\n",
              "      <td>0.370509</td>\n",
              "      <td>-0.576752</td>\n",
              "      <td>-0.669605</td>\n",
              "      <td>-0.759908</td>\n",
              "      <td>1.605056</td>\n",
              "      <td>0.540675</td>\n",
              "      <td>0.737040</td>\n",
              "      <td>0.496699</td>\n",
              "      <td>1.00</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6336</th>\n",
              "      <td>7543</td>\n",
              "      <td>0.329594</td>\n",
              "      <td>3.712889</td>\n",
              "      <td>-5.775935</td>\n",
              "      <td>6.078266</td>\n",
              "      <td>1.667359</td>\n",
              "      <td>-2.420168</td>\n",
              "      <td>-0.812891</td>\n",
              "      <td>0.133080</td>\n",
              "      <td>-2.214311</td>\n",
              "      <td>...</td>\n",
              "      <td>0.156617</td>\n",
              "      <td>-0.652450</td>\n",
              "      <td>-0.551572</td>\n",
              "      <td>-0.716522</td>\n",
              "      <td>1.415717</td>\n",
              "      <td>0.555265</td>\n",
              "      <td>0.530507</td>\n",
              "      <td>0.404474</td>\n",
              "      <td>1.00</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6338</th>\n",
              "      <td>7551</td>\n",
              "      <td>0.316459</td>\n",
              "      <td>3.809076</td>\n",
              "      <td>-5.615159</td>\n",
              "      <td>6.047445</td>\n",
              "      <td>1.554026</td>\n",
              "      <td>-2.651353</td>\n",
              "      <td>-0.746579</td>\n",
              "      <td>0.055586</td>\n",
              "      <td>-2.678679</td>\n",
              "      <td>...</td>\n",
              "      <td>0.208828</td>\n",
              "      <td>-0.511747</td>\n",
              "      <td>-0.583813</td>\n",
              "      <td>-0.219845</td>\n",
              "      <td>1.474753</td>\n",
              "      <td>0.491192</td>\n",
              "      <td>0.518868</td>\n",
              "      <td>0.402528</td>\n",
              "      <td>1.00</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6427</th>\n",
              "      <td>7610</td>\n",
              "      <td>0.725646</td>\n",
              "      <td>2.300894</td>\n",
              "      <td>-5.329976</td>\n",
              "      <td>4.007683</td>\n",
              "      <td>-1.730411</td>\n",
              "      <td>-1.732193</td>\n",
              "      <td>-3.968593</td>\n",
              "      <td>1.063728</td>\n",
              "      <td>-0.486097</td>\n",
              "      <td>...</td>\n",
              "      <td>0.589669</td>\n",
              "      <td>0.109541</td>\n",
              "      <td>0.601045</td>\n",
              "      <td>-0.364700</td>\n",
              "      <td>-1.843078</td>\n",
              "      <td>0.351909</td>\n",
              "      <td>0.594550</td>\n",
              "      <td>0.099372</td>\n",
              "      <td>1.00</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6446</th>\n",
              "      <td>7672</td>\n",
              "      <td>0.702710</td>\n",
              "      <td>2.426433</td>\n",
              "      <td>-5.234513</td>\n",
              "      <td>4.416661</td>\n",
              "      <td>-2.170806</td>\n",
              "      <td>-2.667554</td>\n",
              "      <td>-3.878088</td>\n",
              "      <td>0.911337</td>\n",
              "      <td>-0.166199</td>\n",
              "      <td>...</td>\n",
              "      <td>0.551180</td>\n",
              "      <td>-0.009802</td>\n",
              "      <td>0.721698</td>\n",
              "      <td>0.473246</td>\n",
              "      <td>-1.959304</td>\n",
              "      <td>0.319476</td>\n",
              "      <td>0.600485</td>\n",
              "      <td>0.129305</td>\n",
              "      <td>1.00</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6472</th>\n",
              "      <td>7740</td>\n",
              "      <td>1.023874</td>\n",
              "      <td>2.001485</td>\n",
              "      <td>-4.769752</td>\n",
              "      <td>3.819195</td>\n",
              "      <td>-1.271754</td>\n",
              "      <td>-1.734662</td>\n",
              "      <td>-3.059245</td>\n",
              "      <td>0.889805</td>\n",
              "      <td>0.415382</td>\n",
              "      <td>...</td>\n",
              "      <td>0.343283</td>\n",
              "      <td>-0.054196</td>\n",
              "      <td>0.709654</td>\n",
              "      <td>-0.372216</td>\n",
              "      <td>-2.032068</td>\n",
              "      <td>0.366778</td>\n",
              "      <td>0.395171</td>\n",
              "      <td>0.020206</td>\n",
              "      <td>1.00</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6529</th>\n",
              "      <td>7891</td>\n",
              "      <td>-1.585505</td>\n",
              "      <td>3.261585</td>\n",
              "      <td>-4.137422</td>\n",
              "      <td>2.357096</td>\n",
              "      <td>-1.405043</td>\n",
              "      <td>-1.879437</td>\n",
              "      <td>-3.513687</td>\n",
              "      <td>1.515607</td>\n",
              "      <td>-1.207166</td>\n",
              "      <td>...</td>\n",
              "      <td>0.501543</td>\n",
              "      <td>-0.546869</td>\n",
              "      <td>-0.076584</td>\n",
              "      <td>-0.425550</td>\n",
              "      <td>0.123644</td>\n",
              "      <td>0.321985</td>\n",
              "      <td>0.264028</td>\n",
              "      <td>0.132817</td>\n",
              "      <td>1.00</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6609</th>\n",
              "      <td>8090</td>\n",
              "      <td>-1.783229</td>\n",
              "      <td>3.402794</td>\n",
              "      <td>-3.822742</td>\n",
              "      <td>2.625368</td>\n",
              "      <td>-1.976415</td>\n",
              "      <td>-2.731689</td>\n",
              "      <td>-3.430559</td>\n",
              "      <td>1.413204</td>\n",
              "      <td>-0.776941</td>\n",
              "      <td>...</td>\n",
              "      <td>0.454032</td>\n",
              "      <td>-0.577526</td>\n",
              "      <td>0.045967</td>\n",
              "      <td>0.461700</td>\n",
              "      <td>0.044146</td>\n",
              "      <td>0.305704</td>\n",
              "      <td>0.530981</td>\n",
              "      <td>0.243746</td>\n",
              "      <td>1.00</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6641</th>\n",
              "      <td>8169</td>\n",
              "      <td>0.857321</td>\n",
              "      <td>4.093912</td>\n",
              "      <td>-7.423894</td>\n",
              "      <td>7.380245</td>\n",
              "      <td>0.973366</td>\n",
              "      <td>-2.730762</td>\n",
              "      <td>-1.496497</td>\n",
              "      <td>0.543015</td>\n",
              "      <td>-2.351190</td>\n",
              "      <td>...</td>\n",
              "      <td>0.375026</td>\n",
              "      <td>0.145400</td>\n",
              "      <td>0.240603</td>\n",
              "      <td>-0.234649</td>\n",
              "      <td>-1.004881</td>\n",
              "      <td>0.435832</td>\n",
              "      <td>0.618324</td>\n",
              "      <td>0.148469</td>\n",
              "      <td>1.00</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6717</th>\n",
              "      <td>8408</td>\n",
              "      <td>-1.813280</td>\n",
              "      <td>4.917851</td>\n",
              "      <td>-5.926130</td>\n",
              "      <td>5.701500</td>\n",
              "      <td>1.204393</td>\n",
              "      <td>-3.035138</td>\n",
              "      <td>-1.713402</td>\n",
              "      <td>0.561257</td>\n",
              "      <td>-3.796354</td>\n",
              "      <td>...</td>\n",
              "      <td>0.615642</td>\n",
              "      <td>-0.406427</td>\n",
              "      <td>-0.737018</td>\n",
              "      <td>-0.279642</td>\n",
              "      <td>1.106766</td>\n",
              "      <td>0.323885</td>\n",
              "      <td>0.894767</td>\n",
              "      <td>0.569519</td>\n",
              "      <td>1.00</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6719</th>\n",
              "      <td>8415</td>\n",
              "      <td>-0.251471</td>\n",
              "      <td>4.313523</td>\n",
              "      <td>-6.891438</td>\n",
              "      <td>6.796797</td>\n",
              "      <td>0.616297</td>\n",
              "      <td>-2.966327</td>\n",
              "      <td>-2.436653</td>\n",
              "      <td>0.489328</td>\n",
              "      <td>-3.371639</td>\n",
              "      <td>...</td>\n",
              "      <td>0.536892</td>\n",
              "      <td>-0.546126</td>\n",
              "      <td>-0.605240</td>\n",
              "      <td>-0.263743</td>\n",
              "      <td>1.539916</td>\n",
              "      <td>0.523574</td>\n",
              "      <td>0.891025</td>\n",
              "      <td>0.572741</td>\n",
              "      <td>1.00</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6734</th>\n",
              "      <td>8451</td>\n",
              "      <td>0.314597</td>\n",
              "      <td>2.660670</td>\n",
              "      <td>-5.920037</td>\n",
              "      <td>4.522500</td>\n",
              "      <td>-2.315027</td>\n",
              "      <td>-2.278352</td>\n",
              "      <td>-4.684054</td>\n",
              "      <td>1.202270</td>\n",
              "      <td>-0.694696</td>\n",
              "      <td>...</td>\n",
              "      <td>0.743314</td>\n",
              "      <td>0.064038</td>\n",
              "      <td>0.677842</td>\n",
              "      <td>0.083008</td>\n",
              "      <td>-1.911034</td>\n",
              "      <td>0.322188</td>\n",
              "      <td>0.620867</td>\n",
              "      <td>0.185030</td>\n",
              "      <td>1.00</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6774</th>\n",
              "      <td>8528</td>\n",
              "      <td>0.447396</td>\n",
              "      <td>2.481954</td>\n",
              "      <td>-5.660814</td>\n",
              "      <td>4.455923</td>\n",
              "      <td>-2.443780</td>\n",
              "      <td>-2.185040</td>\n",
              "      <td>-4.716143</td>\n",
              "      <td>1.249803</td>\n",
              "      <td>-0.718326</td>\n",
              "      <td>...</td>\n",
              "      <td>0.756053</td>\n",
              "      <td>0.140168</td>\n",
              "      <td>0.665411</td>\n",
              "      <td>0.131464</td>\n",
              "      <td>-1.908217</td>\n",
              "      <td>0.334808</td>\n",
              "      <td>0.748534</td>\n",
              "      <td>0.175414</td>\n",
              "      <td>1.00</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6820</th>\n",
              "      <td>8614</td>\n",
              "      <td>-2.169929</td>\n",
              "      <td>3.639654</td>\n",
              "      <td>-4.508498</td>\n",
              "      <td>2.730668</td>\n",
              "      <td>-2.122693</td>\n",
              "      <td>-2.341017</td>\n",
              "      <td>-4.235253</td>\n",
              "      <td>1.703538</td>\n",
              "      <td>-1.305279</td>\n",
              "      <td>...</td>\n",
              "      <td>0.645103</td>\n",
              "      <td>-0.503529</td>\n",
              "      <td>-0.000523</td>\n",
              "      <td>0.071696</td>\n",
              "      <td>0.092007</td>\n",
              "      <td>0.308498</td>\n",
              "      <td>0.552591</td>\n",
              "      <td>0.298954</td>\n",
              "      <td>1.00</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6870</th>\n",
              "      <td>8757</td>\n",
              "      <td>-1.863756</td>\n",
              "      <td>3.442644</td>\n",
              "      <td>-4.468260</td>\n",
              "      <td>2.805336</td>\n",
              "      <td>-2.118412</td>\n",
              "      <td>-2.332285</td>\n",
              "      <td>-4.261237</td>\n",
              "      <td>1.701682</td>\n",
              "      <td>-1.439396</td>\n",
              "      <td>...</td>\n",
              "      <td>0.667927</td>\n",
              "      <td>-0.516242</td>\n",
              "      <td>-0.012218</td>\n",
              "      <td>0.070614</td>\n",
              "      <td>0.058504</td>\n",
              "      <td>0.304883</td>\n",
              "      <td>0.418012</td>\n",
              "      <td>0.208858</td>\n",
              "      <td>1.00</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6882</th>\n",
              "      <td>8808</td>\n",
              "      <td>-4.617217</td>\n",
              "      <td>1.695694</td>\n",
              "      <td>-3.114372</td>\n",
              "      <td>4.328199</td>\n",
              "      <td>-1.873257</td>\n",
              "      <td>-0.989908</td>\n",
              "      <td>-4.577265</td>\n",
              "      <td>0.472216</td>\n",
              "      <td>0.472017</td>\n",
              "      <td>...</td>\n",
              "      <td>0.481830</td>\n",
              "      <td>0.146023</td>\n",
              "      <td>0.117039</td>\n",
              "      <td>-0.217565</td>\n",
              "      <td>-0.138776</td>\n",
              "      <td>-0.424453</td>\n",
              "      <td>-1.002041</td>\n",
              "      <td>0.890780</td>\n",
              "      <td>1.10</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6899</th>\n",
              "      <td>8878</td>\n",
              "      <td>-2.661802</td>\n",
              "      <td>5.856393</td>\n",
              "      <td>-7.653616</td>\n",
              "      <td>6.379742</td>\n",
              "      <td>-0.060712</td>\n",
              "      <td>-3.131550</td>\n",
              "      <td>-3.103570</td>\n",
              "      <td>1.778492</td>\n",
              "      <td>-3.831154</td>\n",
              "      <td>...</td>\n",
              "      <td>0.734775</td>\n",
              "      <td>-0.435901</td>\n",
              "      <td>-0.384766</td>\n",
              "      <td>-0.286016</td>\n",
              "      <td>1.007934</td>\n",
              "      <td>0.413196</td>\n",
              "      <td>0.280284</td>\n",
              "      <td>0.303937</td>\n",
              "      <td>1.00</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6903</th>\n",
              "      <td>8886</td>\n",
              "      <td>-2.535852</td>\n",
              "      <td>5.793644</td>\n",
              "      <td>-7.618463</td>\n",
              "      <td>6.395830</td>\n",
              "      <td>-0.065210</td>\n",
              "      <td>-3.136372</td>\n",
              "      <td>-3.104557</td>\n",
              "      <td>1.823233</td>\n",
              "      <td>-3.878658</td>\n",
              "      <td>...</td>\n",
              "      <td>0.716720</td>\n",
              "      <td>-0.448060</td>\n",
              "      <td>-0.402407</td>\n",
              "      <td>-0.288835</td>\n",
              "      <td>1.011752</td>\n",
              "      <td>0.425965</td>\n",
              "      <td>0.413140</td>\n",
              "      <td>0.308205</td>\n",
              "      <td>1.00</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6971</th>\n",
              "      <td>9064</td>\n",
              "      <td>-3.499108</td>\n",
              "      <td>0.258555</td>\n",
              "      <td>-4.489558</td>\n",
              "      <td>4.853894</td>\n",
              "      <td>-6.974522</td>\n",
              "      <td>3.628382</td>\n",
              "      <td>5.431271</td>\n",
              "      <td>-1.946734</td>\n",
              "      <td>-0.775680</td>\n",
              "      <td>...</td>\n",
              "      <td>-1.052368</td>\n",
              "      <td>0.204817</td>\n",
              "      <td>-2.119007</td>\n",
              "      <td>0.170279</td>\n",
              "      <td>-0.393844</td>\n",
              "      <td>0.296367</td>\n",
              "      <td>1.985913</td>\n",
              "      <td>-0.900452</td>\n",
              "      <td>1809.68</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>25 rows × 31 columns</p>\n",
              "</div>\n",
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          },
          "metadata": {},
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df0 = df[df['Class'] == 0].sample(n=5000, random_state=1)\n",
        "df0"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 488
        },
        "id": "xNFhyT9_AWcv",
        "outputId": "a8e4a960-7c79-4e4d-c175-3e9f3f456d7c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "      Time        V1        V2        V3        V4        V5        V6  \\\n",
              "1775  1377  1.315384 -0.044160  0.030803 -0.374108 -0.182137 -0.369705   \n",
              "5381  5340 -1.548592 -0.064454  0.778314  2.354014  2.428965 -0.988154   \n",
              "1301  1021  0.928194 -1.109748  0.063417 -0.932666 -0.589244  0.531417   \n",
              "2365  1910 -1.129496  1.219960  0.309779  0.160574 -0.452048 -0.033863   \n",
              "2599  2127 -0.656716  1.067455  1.931015 -0.302225  0.221967 -0.631476   \n",
              "...    ...       ...       ...       ...       ...       ...       ...   \n",
              "6204  7232 -0.858963  1.416471  1.954935  1.356072 -0.389605 -0.603968   \n",
              "672    509 -0.404841  1.005372  1.372756  0.005994 -0.118849 -0.788473   \n",
              "3980  3630 -0.884235  0.993348  1.901040 -0.194557  0.330835 -0.254230   \n",
              "6156  7135 -0.481812  1.515797  0.208853  1.008474 -0.040537  1.401936   \n",
              "3216  2792 -0.416184  0.598640  1.188603 -2.092818  0.045647 -1.319317   \n",
              "\n",
              "            V7        V8        V9  ...       V21       V22       V23  \\\n",
              "1775 -0.156686 -0.025748  0.080582  ... -0.115442 -0.354745 -0.065918   \n",
              "5381 -0.578934  0.059530  1.122241  ... -0.402470 -0.532506 -0.471415   \n",
              "1301 -0.489887  0.167473  1.985469  ...  0.185421  0.396802 -0.441135   \n",
              "2365 -0.044711  1.006086 -0.965042  ... -0.239840 -1.137696  0.295929   \n",
              "2599  1.192656 -0.569361  0.163354  ... -0.381832 -0.415262 -0.107263   \n",
              "...        ...       ...       ...  ...       ...       ...       ...   \n",
              "6204  0.334771 -0.031155  1.033726  ... -0.232415 -0.262665 -0.045103   \n",
              "672   0.566918  0.114721 -0.521987  ... -0.207334 -0.619987  0.020848   \n",
              "3980  0.839262 -0.173872  1.085489  ... -0.346363 -0.675996 -0.303634   \n",
              "6156 -2.159645 -5.227156  0.216818  ... -2.727182  0.397706 -0.039062   \n",
              "3216  1.011039 -0.255301  0.703409  ...  0.114666  0.627161 -0.271867   \n",
              "\n",
              "           V24       V25       V26       V27       V28  Amount  Class  \n",
              "1775 -0.442702  0.313711  0.995331 -0.089888 -0.015565    1.85    0.0  \n",
              "5381 -0.602375  0.174808 -0.096288  0.321892 -0.025850    7.55    0.0  \n",
              "1301 -1.279042  0.654722 -0.434115  0.070377  0.048256  195.00    0.0  \n",
              "2365 -0.374796 -0.954603  0.001955 -0.243133 -0.022968   48.80    0.0  \n",
              "2599  0.508591 -0.230168 -0.034504 -0.013202 -0.432126    9.72    0.0  \n",
              "...        ...       ...       ...       ...       ...     ...    ...  \n",
              "6204  0.876798 -0.290150 -0.616472 -0.407526 -0.156628    0.75    0.0  \n",
              "672   0.456775 -0.249289  0.044937  0.234036  0.088356    8.28    0.0  \n",
              "3980 -0.140838  0.519798  0.169280 -0.153101  0.039810   30.36    0.0  \n",
              "6156 -0.921931  0.730772 -0.322860  0.068996  0.257444   39.99    0.0  \n",
              "3216  0.515345 -0.016253 -0.866379  0.229122 -0.023825    1.00    0.0  \n",
              "\n",
              "[5000 rows x 31 columns]"
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              "      <td>-0.115442</td>\n",
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              "      <td>-0.442702</td>\n",
              "      <td>0.313711</td>\n",
              "      <td>0.995331</td>\n",
              "      <td>-0.089888</td>\n",
              "      <td>-0.015565</td>\n",
              "      <td>1.85</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5381</th>\n",
              "      <td>5340</td>\n",
              "      <td>-1.548592</td>\n",
              "      <td>-0.064454</td>\n",
              "      <td>0.778314</td>\n",
              "      <td>2.354014</td>\n",
              "      <td>2.428965</td>\n",
              "      <td>-0.988154</td>\n",
              "      <td>-0.578934</td>\n",
              "      <td>0.059530</td>\n",
              "      <td>1.122241</td>\n",
              "      <td>...</td>\n",
              "      <td>-0.402470</td>\n",
              "      <td>-0.532506</td>\n",
              "      <td>-0.471415</td>\n",
              "      <td>-0.602375</td>\n",
              "      <td>0.174808</td>\n",
              "      <td>-0.096288</td>\n",
              "      <td>0.321892</td>\n",
              "      <td>-0.025850</td>\n",
              "      <td>7.55</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1301</th>\n",
              "      <td>1021</td>\n",
              "      <td>0.928194</td>\n",
              "      <td>-1.109748</td>\n",
              "      <td>0.063417</td>\n",
              "      <td>-0.932666</td>\n",
              "      <td>-0.589244</td>\n",
              "      <td>0.531417</td>\n",
              "      <td>-0.489887</td>\n",
              "      <td>0.167473</td>\n",
              "      <td>1.985469</td>\n",
              "      <td>...</td>\n",
              "      <td>0.185421</td>\n",
              "      <td>0.396802</td>\n",
              "      <td>-0.441135</td>\n",
              "      <td>-1.279042</td>\n",
              "      <td>0.654722</td>\n",
              "      <td>-0.434115</td>\n",
              "      <td>0.070377</td>\n",
              "      <td>0.048256</td>\n",
              "      <td>195.00</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2365</th>\n",
              "      <td>1910</td>\n",
              "      <td>-1.129496</td>\n",
              "      <td>1.219960</td>\n",
              "      <td>0.309779</td>\n",
              "      <td>0.160574</td>\n",
              "      <td>-0.452048</td>\n",
              "      <td>-0.033863</td>\n",
              "      <td>-0.044711</td>\n",
              "      <td>1.006086</td>\n",
              "      <td>-0.965042</td>\n",
              "      <td>...</td>\n",
              "      <td>-0.239840</td>\n",
              "      <td>-1.137696</td>\n",
              "      <td>0.295929</td>\n",
              "      <td>-0.374796</td>\n",
              "      <td>-0.954603</td>\n",
              "      <td>0.001955</td>\n",
              "      <td>-0.243133</td>\n",
              "      <td>-0.022968</td>\n",
              "      <td>48.80</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2599</th>\n",
              "      <td>2127</td>\n",
              "      <td>-0.656716</td>\n",
              "      <td>1.067455</td>\n",
              "      <td>1.931015</td>\n",
              "      <td>-0.302225</td>\n",
              "      <td>0.221967</td>\n",
              "      <td>-0.631476</td>\n",
              "      <td>1.192656</td>\n",
              "      <td>-0.569361</td>\n",
              "      <td>0.163354</td>\n",
              "      <td>...</td>\n",
              "      <td>-0.381832</td>\n",
              "      <td>-0.415262</td>\n",
              "      <td>-0.107263</td>\n",
              "      <td>0.508591</td>\n",
              "      <td>-0.230168</td>\n",
              "      <td>-0.034504</td>\n",
              "      <td>-0.013202</td>\n",
              "      <td>-0.432126</td>\n",
              "      <td>9.72</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6204</th>\n",
              "      <td>7232</td>\n",
              "      <td>-0.858963</td>\n",
              "      <td>1.416471</td>\n",
              "      <td>1.954935</td>\n",
              "      <td>1.356072</td>\n",
              "      <td>-0.389605</td>\n",
              "      <td>-0.603968</td>\n",
              "      <td>0.334771</td>\n",
              "      <td>-0.031155</td>\n",
              "      <td>1.033726</td>\n",
              "      <td>...</td>\n",
              "      <td>-0.232415</td>\n",
              "      <td>-0.262665</td>\n",
              "      <td>-0.045103</td>\n",
              "      <td>0.876798</td>\n",
              "      <td>-0.290150</td>\n",
              "      <td>-0.616472</td>\n",
              "      <td>-0.407526</td>\n",
              "      <td>-0.156628</td>\n",
              "      <td>0.75</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>672</th>\n",
              "      <td>509</td>\n",
              "      <td>-0.404841</td>\n",
              "      <td>1.005372</td>\n",
              "      <td>1.372756</td>\n",
              "      <td>0.005994</td>\n",
              "      <td>-0.118849</td>\n",
              "      <td>-0.788473</td>\n",
              "      <td>0.566918</td>\n",
              "      <td>0.114721</td>\n",
              "      <td>-0.521987</td>\n",
              "      <td>...</td>\n",
              "      <td>-0.207334</td>\n",
              "      <td>-0.619987</td>\n",
              "      <td>0.020848</td>\n",
              "      <td>0.456775</td>\n",
              "      <td>-0.249289</td>\n",
              "      <td>0.044937</td>\n",
              "      <td>0.234036</td>\n",
              "      <td>0.088356</td>\n",
              "      <td>8.28</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3980</th>\n",
              "      <td>3630</td>\n",
              "      <td>-0.884235</td>\n",
              "      <td>0.993348</td>\n",
              "      <td>1.901040</td>\n",
              "      <td>-0.194557</td>\n",
              "      <td>0.330835</td>\n",
              "      <td>-0.254230</td>\n",
              "      <td>0.839262</td>\n",
              "      <td>-0.173872</td>\n",
              "      <td>1.085489</td>\n",
              "      <td>...</td>\n",
              "      <td>-0.346363</td>\n",
              "      <td>-0.675996</td>\n",
              "      <td>-0.303634</td>\n",
              "      <td>-0.140838</td>\n",
              "      <td>0.519798</td>\n",
              "      <td>0.169280</td>\n",
              "      <td>-0.153101</td>\n",
              "      <td>0.039810</td>\n",
              "      <td>30.36</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6156</th>\n",
              "      <td>7135</td>\n",
              "      <td>-0.481812</td>\n",
              "      <td>1.515797</td>\n",
              "      <td>0.208853</td>\n",
              "      <td>1.008474</td>\n",
              "      <td>-0.040537</td>\n",
              "      <td>1.401936</td>\n",
              "      <td>-2.159645</td>\n",
              "      <td>-5.227156</td>\n",
              "      <td>0.216818</td>\n",
              "      <td>...</td>\n",
              "      <td>-2.727182</td>\n",
              "      <td>0.397706</td>\n",
              "      <td>-0.039062</td>\n",
              "      <td>-0.921931</td>\n",
              "      <td>0.730772</td>\n",
              "      <td>-0.322860</td>\n",
              "      <td>0.068996</td>\n",
              "      <td>0.257444</td>\n",
              "      <td>39.99</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3216</th>\n",
              "      <td>2792</td>\n",
              "      <td>-0.416184</td>\n",
              "      <td>0.598640</td>\n",
              "      <td>1.188603</td>\n",
              "      <td>-2.092818</td>\n",
              "      <td>0.045647</td>\n",
              "      <td>-1.319317</td>\n",
              "      <td>1.011039</td>\n",
              "      <td>-0.255301</td>\n",
              "      <td>0.703409</td>\n",
              "      <td>...</td>\n",
              "      <td>0.114666</td>\n",
              "      <td>0.627161</td>\n",
              "      <td>-0.271867</td>\n",
              "      <td>0.515345</td>\n",
              "      <td>-0.016253</td>\n",
              "      <td>-0.866379</td>\n",
              "      <td>0.229122</td>\n",
              "      <td>-0.023825</td>\n",
              "      <td>1.00</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>5000 rows × 31 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-9d229fa6-6248-4076-aabb-1fac027ea07e')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
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              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
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              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
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              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
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              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
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              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
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              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-9d229fa6-6248-4076-aabb-1fac027ea07e button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-9d229fa6-6248-4076-aabb-1fac027ea07e');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
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              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## PCA"
      ],
      "metadata": {
        "id": "Z7fB54iupdRY"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.decomposition import PCA\n",
        "\n",
        "scaling=StandardScaler()\n",
        "\n",
        "to_drop = [\"Class\",'Time']\n",
        "X0 = df0.drop(to_drop, axis=1).to_numpy()\n",
        "scaling.fit(X0)\n",
        "Scaled_data=scaling.transform(X0)\n",
        " \n",
        "# Set the n_components=3\n",
        "pca=PCA()\n",
        "pca.fit_transform(Scaled_data)\n",
        "var = pca.explained_variance_ratio_\n",
        "plt.figure()\n",
        "# Almost all the data can be extracted by taking 6th principal component\n",
        "plt.plot(np.cumsum(var))\n",
        "plt.xlabel(\"Number of Components\")\n",
        "plt.ylabel(\"Varience\")\n",
        "plt.title(\"Principal Components\")\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 376
        },
        "id": "2nc_z7voAZ61",
        "outputId": "b6c139bd-cd96-4365-c8c6-ba788574a917"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 576x396 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.decomposition import PCA\n",
        "\n",
        "scaling=StandardScaler()\n",
        "\n",
        "to_drop = [\"Class\",'Time']\n",
        "X1 = df1.drop(to_drop, axis=1).to_numpy()\n",
        "scaling.fit(X1)\n",
        "Scaled_data=scaling.transform(X1)\n",
        " \n",
        "# Set the n_components=3\n",
        "pca=PCA()\n",
        "pca.fit_transform(Scaled_data)\n",
        "var = pca.explained_variance_ratio_\n",
        "plt.figure()\n",
        "# Almost all the data can be extracted by taking 6th principal component\n",
        "plt.plot(np.cumsum(var))\n",
        "plt.xlabel(\"Number of Components\")\n",
        "plt.ylabel(\"Varience\")\n",
        "plt.title(\"Principal Components\")\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 376
        },
        "id": "h95yE7lcAeMw",
        "outputId": "3c14b7ef-6743-41ae-e4ed-b3dbbd177f55"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 576x396 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## UMAP"
      ],
      "metadata": {
        "id": "Fbmcjn1MphZ_"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install umap-learn"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "KmNsuYiSBhOd",
        "outputId": "2a82cca0-5f6e-4c3e-b990-1a1188df40b5"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "Collecting umap-learn\n",
            "  Downloading umap-learn-0.5.3.tar.gz (88 kB)\n",
            "\u001b[K     |████████████████████████████████| 88 kB 3.6 MB/s \n",
            "\u001b[?25hRequirement already satisfied: numpy>=1.17 in /usr/local/lib/python3.7/dist-packages (from umap-learn) (1.21.6)\n",
            "Requirement already satisfied: scikit-learn>=0.22 in /usr/local/lib/python3.7/dist-packages (from umap-learn) (1.0.2)\n",
            "Requirement already satisfied: scipy>=1.0 in /usr/local/lib/python3.7/dist-packages (from umap-learn) (1.7.3)\n",
            "Requirement already satisfied: numba>=0.49 in /usr/local/lib/python3.7/dist-packages (from umap-learn) (0.56.0)\n",
            "Collecting pynndescent>=0.5\n",
            "  Downloading pynndescent-0.5.7.tar.gz (1.1 MB)\n",
            "\u001b[K     |████████████████████████████████| 1.1 MB 34.3 MB/s \n",
            "\u001b[?25hRequirement already satisfied: tqdm in /usr/local/lib/python3.7/dist-packages (from umap-learn) (4.64.0)\n",
            "Requirement already satisfied: setuptools in /usr/local/lib/python3.7/dist-packages (from numba>=0.49->umap-learn) (57.4.0)\n",
            "Requirement already satisfied: llvmlite<0.40,>=0.39.0dev0 in /usr/local/lib/python3.7/dist-packages (from numba>=0.49->umap-learn) (0.39.0)\n",
            "Requirement already satisfied: importlib-metadata in /usr/local/lib/python3.7/dist-packages (from numba>=0.49->umap-learn) (4.12.0)\n",
            "Requirement already satisfied: joblib>=0.11 in /usr/local/lib/python3.7/dist-packages (from pynndescent>=0.5->umap-learn) (1.1.0)\n",
            "Requirement already satisfied: threadpoolctl>=2.0.0 in /usr/local/lib/python3.7/dist-packages (from scikit-learn>=0.22->umap-learn) (3.1.0)\n",
            "Requirement already satisfied: zipp>=0.5 in /usr/local/lib/python3.7/dist-packages (from importlib-metadata->numba>=0.49->umap-learn) (3.8.1)\n",
            "Requirement already satisfied: typing-extensions>=3.6.4 in /usr/local/lib/python3.7/dist-packages (from importlib-metadata->numba>=0.49->umap-learn) (4.1.1)\n",
            "Building wheels for collected packages: umap-learn, pynndescent\n",
            "  Building wheel for umap-learn (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for umap-learn: filename=umap_learn-0.5.3-py3-none-any.whl size=82829 sha256=e030762629e66453736e442f33478e226596c1bd03461ec8e22fdcd27f9f826a\n",
            "  Stored in directory: /root/.cache/pip/wheels/b3/52/a5/1fd9e3e76a7ab34f134c07469cd6f16e27ef3a37aeff1fe821\n",
            "  Building wheel for pynndescent (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for pynndescent: filename=pynndescent-0.5.7-py3-none-any.whl size=54286 sha256=a4c76d7048f97f7f3f4a74490af8a9bad5a60a50cf4af2da8574c6f0b97a103e\n",
            "  Stored in directory: /root/.cache/pip/wheels/7f/2a/f8/7bd5dcec71bd5c669f6f574db3113513696b98f3f9b51f496c\n",
            "Successfully built umap-learn pynndescent\n",
            "Installing collected packages: pynndescent, umap-learn\n",
            "Successfully installed pynndescent-0.5.7 umap-learn-0.5.3\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import umap\n",
        "scaling=StandardScaler()\n",
        "scaling.fit(X0)\n",
        "Scaled_data=scaling.transform(X0)\n",
        "reducer = umap.UMAP()\n",
        "Xumap = reducer.fit_transform(Scaled_data)\n",
        "from sklearn.cluster import DBSCAN\n",
        "sh_list = []\n",
        "for i in np.arange(0.10,1.0,0.01):\n",
        "    dbscan_umap = DBSCAN(eps=i, metric = 'euclidean').fit(Xumap)\n",
        "    sh = silhouette_score(Xumap, dbscan_umap.labels_, metric = 'euclidean')\n",
        "    sh_list.append([i,sh])\n",
        "    \n",
        "sh_arr = np.array(sh_list)\n",
        "_ = plt.plot(sh_arr[:,0],sh_arr[:,1])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 347
        },
        "id": "OUX5PMmvAhpA",
        "outputId": "9a94474b-7405-4276-f63e-ce0019d217a7"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 576x396 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "max_index = np.where(sh_arr[:,1] == max(sh_arr[:,1]))\n",
        "max_sh = sh_arr[max_index,0][0,0]\n",
        "dbscan_umap = DBSCAN(eps=max_sh, metric = 'euclidean').fit(Xumap)\n",
        "silhouette_score(Xumap, dbscan_umap.labels_, metric = 'euclidean')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ILX_Mf9NA09n",
        "outputId": "493e3317-22c2-4916-f357-c76c821996b9"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.59652543"
            ]
          },
          "metadata": {},
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import seaborn as sns\n",
        "dbscan_umap_data = np.concatenate([Xumap, dbscan_umap.labels_.reshape(len( dbscan_umap.labels_),1)],axis=1)\n",
        "dbscan_umap_data = pd.DataFrame(data = dbscan_umap_data,columns = ['X','Y','Lables'])\n",
        "_= sns.scatterplot(data=dbscan_umap_data, x='X', y=\"Y\", hue=\"Lables\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 361
        },
        "id": "7wlxryRvGOgj",
        "outputId": "164e9962-1df8-4b80-fb97-fe639fb4a1bd"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 576x396 with 1 Axes>"
            ],
            "image/png": 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2bNnC1q2nt82nIAiCILwdCzpoW0WLQ9u7Zj13aPuB0xoqTyaT/O3f/i1/8Rd/wZ/+6Z/ied4pX0sQBEEQ3o4FHbTziSyZifSs57KTafKJ2bft/FWqqqq4+OKLUVWV1tZWQqEQU1NTp9PUBcl1HDzXPdfNEARBWDAWdNAOVoSJVEdnPReuihKsCJ/SdTdv3sxLL72E67okEgny+TwVFRWn09QFpZjOkRudID86ydTeHiZ3d5MbmaCYyogRCUEQhNOwoBPRNJ9G2yXL2Pmz7TPOtV2yFM13ahuH1NXVccstt/Cbv/mbAHzmM59Blhf095+3xS4UKSYzuI6DUzDp/elzeE6ppy3JMhe98yps00b16fiioXPcWkEQhPlnQQdtgCvvuREozWFnJ9OEq6K0XbK0fPxU3X333dx9991nookLgmNajL++n2BdJarfR+9T28oBWw36cUyL/ideYvG7r6dYzACIwC0IgnCSFnzQlhWZqz54M5fffR35RJZgRfiUe9gXKtdxMDN5nKKJJEvo4SBqwI/rOOB6yJpKIZFm5KU3WHrXrThFEztnULmynXBzHWY6h+rX8VwXM5vFX1mBXSjguQ7++OzTF4IgCMJMCz5oH6X5NGL1Yt75ZLiOQ2EyVQrCRhFZU5EUicJkCn91HCuVxbUdtGgIp2Ai6xpmKoseDVG1Zime49L3+C/L19NCAdredQ22YeAYJramovh8aAHfOXyXgiAI88cFE7SFt8/MGThGgfxYgtzQKJOdB0snJIm6d6ymcnkb450HmHh1b+mwItN45cVUr1mKrCjYhSKxxS30/Oipade1cgaTnd1UrS0FdNd2MNMZEbQFQRDeJpE9JZQ5jkNudILc8DjZwVGcYrEcsBW/j+ZrLwUPRnfsJlARo/29NxJftgjPcRl8bgexJa0MPPsKg8/uoDCVmvU10r2D5IbGyQ6Oofh9WPni2XyLgiAI85roaQsAeJ5H9vAwvT9/gapV7diGiVM0y+ebrtrA0AuvYhulIDu1u5tgXRW1l6wCwEznSOztwcrmCdZVofj1WV9Hj4UJ1FRw8PuP46+MoYlkNEEQhLdN9LQFAMxUht6fPY+/MkagphLPsZE1BYBgfTXZofFywD4qPzqJlc1TtXoZ4eZaMv0jABSTaXzRMFpkZkCuWtmOnSughYNM7OxCCwXm/s0JgiAsEHMatLu6urjxxht5+OGHARgeHuYDH/gAW7Zs4Q/+4A8wTfNXXOH8lMvl+NjHPsYHPvAB7r77bp5//vlz3aRTFgwGsYsmtmmx9DduoXrdcmRVwV9TSbS1EQB/ZQxjdHLW5xcTaexCgVB9Db4jmeBO0cLzPBbdciWVK9vRo2HCTbW03rSJyd3dDD77ClWrl4DnIQGWIYbIBUEQ3o45C9r5fJ4vfvGLbNq0qXzsG9/4Blu2bOGRRx5h0aJFPProo3P18jMUCyZjA+MUC6f/ReGHP/whbW1tfPe73+XrX/86X/7yl89AC88e27QoTCXJDo/TFIxTmEphjCVIHjyMHg4y9OLrSB4UU1marr4Eu1jEVxmb9Vp6NIyiaSBL1KxfDpIEwNTeHpxCkUIiRay9GS0cpP/pl8kNjWHlDBSfTtXqJRz8j6c4+OjjJA/2Y2XzZ+X9O5ZNfmwSI5kmP5EgP5nEOMEcvCAIwvlkzoK2rus89NBD1NbWlo9t27aNG264AYDrrrvurOyO5dgOj/zN9/nMli/wp3d/ns9s+QKP/M33cWznlK9ZUVFBMpkEIJ1Oz6sSpoVECmMiiWM5uKYFeNhGATwPp2gysXM/ZirD8NbXKSZTKAEf1WuWUbN+BbI6PQVCj0WQdRVjYork/sPImspFt19NtK2Z/MgEik8nPzzB+Gv7SOzvxTtyz2VdQ4+EQJIwE2kKk0kO/eQZ0oeHyY2dXg1313UpprIYiRS5iSny41MYiXS5Bnoxk2NqTzee6zH60k4O/fgZhl94leJkkuzIJHZR9PrPF5ZhYOVyFJIJzFQKK392vtQJwvlszhLRVFVFfdOHvGEY6HopQamqqorx8fG3da3Ozs6Teu0dO3aU//38v29l5zN7yj9PjkzxxP99mrGxUa769U2zPf1Xqq+vZ9++fVx99dXkcjk++clPTnvN81V78yKciRSSqjD68i4Kk6UvHuGWeiqWXYQWDjG1u6f8+OSBPpIH+gBYfOf1tN1+NameAYzJJOHGGoL1NdhGgZGXduI5LtnBUWouXgFA09WXgKJQubKdqT3d09rRcPlaiukcQ89NLy87sasLf3UFuaXN9E+MntR7a65vIOoPYucLWDkDSZFBKhWC8VyT3GSCfKFAAIVgfTXdP3iinGhnprJk+oZpvWkTkgRvDLz179t8+L8+n7zd+1VTU0NdPI4kSSDL2NkMIAEeqBp2LofrOHR2zb5z30Ihfr9OzoV2v85Z9vjJbByxevVqfL63t5Z3x44dbNy4ESgNif/bl3846+MG94+yetUafCfIcn4rP/rRj1ixYgXf//732bdvH5/+9Kf5wQ9+cNLXOdvSh4fIpzIk9h/CTB3b4SzbP4KsyMSXLkIJ6LgZu3xO8Wm03HA5nu1QTOfw11ZStWYZxtgkoy/vIn/cXLeVzSMrCuneQYzxKVpv2oS/Ok7j5otJ9QwgKQqVKxejR0IY4wmiFzWS7h069vycQaihBt1yy/+Hb0chlcE2igw+u51M3zAAkqrQeMV6iok0kdZ6CiOTjG59o9yG4zPjATzHJXN4GDUUeMvXPv73S5jJ8zzsQgEcB8/z8DwPLRx+y9r8nudh5XN4jotn26XiO2YRLRKhMD6OMTQEsky0vR3PNNlw8cVIC7TWv/j9OjkL8X4Vi8W37Kie1d/8YDBIoVAAYHR0dNrQ+VxITaSYGkvMei4xOkVq4tTmMV999VU2b94MwIoVKxgbG8NxTn24/WzwPI/JzoNoQf+0gH1UuncIz/WoXrPs2EFJou32a8kOjtH/1DbG39iHkzOY2tsDEtMC9lGu6yKrClY2T+JAH3okxOj2PeixCHUbVzG5p5uDj/6Cwed3IOs6jVdtKD83uqiB3PA4rmXPuO5bsXIG6Z6BcsAG8GyHwed2oPp9uEWL/ie3YecLpfefnH1LVjOdBbEJ2Smz8nnMTAY7kya5Zw9Tb7yOMTyEnctiZrOYqRTOkb//ac/LZMD18BwHO5PBTEyB4+AWiyh+P76qKnBd0gcO4Nl2qXyuIFygzmrQvuKKK3jssccAePzxx7nqqqvm9PVi1TEqa2efb66oqyRWPXty1a+yaNEi3njjDQAGBwcJhUIoinLK7TwrXG9G7/LNPMehmMzQsGk9aihA7caVDL/0BhNv7MfOFzBTWYa3voEkgWs5NN94eTnx7ChfNMziO64j1FhDYk83ajhI45Xr8VdG6X9qG7n+0XJ7kl29GOMJIi31+OIRgnXV5Ecm8Fe//RwBx7RwjAJTuw/Ofr5YpHDcl5RCIk2wtnLWx4aa6lBFdbZTYuXzuK6Daxgkdu3CyqRxi0XyAwMkdu3CcxyS+/Yyvv0VzFQKM50i09tLtu8wnuOA55Ht6SZ98ADG6CjZw4dJHziA6g8QbGgsv04xmSiNmAvCBWrOhsc7Ozt54IEHGBwcRFVVHnvsMf7yL/+ST33qU3zve9+jsbGRO++8c65eHgCfX+fiq9fxxP99esa5i69ae0pD4wB33XUXn/70p7nnnnuwbZvPf/7zp9nSuefaNpUdiykmM2iREFYmN+18pKWe3PA4if296NEwzVdvRA34Gdu+e8a1JnZ20XzdO9CjIZb95i0Mb9tJpneI6nXLmdrbQ6ZvmMV3XEdmYJT80BiB+ip022U4/fqMayW7DrP4zuvJD48z+NwOWm++Al88csL3UUzncIom2YERMn3DxBY346+uQNZm/1WWZAVZOfYp75oWjmkSaqwlNzRWPu6vjBFpqUf2ndrvxIXKzudxPQ/PtpEVhdShnhmPcQwD1zSJLVtO5lAPZipF+sBx89KSRMXqNdiGMe15nuNQmBgn0NBAdMlS0gcPICmKGA0RLmhzFrRXr17Nd7/73RnH/+mf/mmuXnJWd33svQC89vxOEqNTVNRVcvFVa8vHT0UoFOLrX//6mWriWVFMZfBcF9so0HjFeka27aSYLG2RGWyooWHzxViZUjUzWddI9QwQbq6f9VquZaP4dMxUlqFfvs7id11LfOki0j0DpLr7AUjuP0zVumVY6SxO1phRmOUo78hweqCmkuX/7Tb0aBhZnTlqUUxnyQ+PM/baPlzTItbeQrixlv4ntxFuaaBmQwcDT7087TmypqJFgrhvWikwsm0XtZesonbjSgqTSXyxCL6KCHLQjz8sKrS9HXahgF0wkJBKKw/MIg4S4bbF5AcHMBPTp6U8y2Jq7x4q165j6rVXp1/M80h17SfU2ETmTUHfSqcJNjRiJpP4a2vxV1Vj53Io8fhcv0VBOC8t+DKmiqqw5b7f4Nc/cgepiRSx6tgp97DnMzOTp+8XW2m55Qp8lTGart6IXbRQAz7UoB9ZVQk2VJMdHCPV3U/9ZWtQAj4kRSkNXx4nUF2BJMsYE0kkWSbV049r2+WArfj00g5hiTSHf/o8rbdcQaCmEsWnzxiir1zZjhoMIPs0XNfFm6Ub5bku6UODDDx9LCgXplKEW+qpWrOUyV0HqLt0FfWXr2Nsx25cy8ZfFafp6o2o4SCuZRNqqiU3eKRnLUlo4QD+iij+6jiSJKNHgqWsZeFXckwTK50GRQHPI9PTg5VJI6kqoeZmQi0tOIUCznE9Z9nnw7MsrEymvPzueG6xiKTO/DhSQyHsfI7CxDiVRxLQ3twjF4QLyYIP2kf5/Dq1zTXnuhnnjGvZ1G5chaIoDL/wKq7rEmtrxhibpJjOUXfJSgpTKSo7FhNf0kL3D5/CF4/QdNVGBp59BY5k+8u6RuPmDRx+Yit1G1dh5w3yo1NUr1vOZGc3DZevRdY0FL+OGvBx0a9dRTGVJtXdT9vt11BIpChMpkh299O0+WLMTI7BZ14m1FhLZFED2eFx9GAAz3GwsgaKT0NSFEZeemPGe8r2j1CxdBGTQHZghKo1y4gubgbXRQn4sfJ5ZEnCXx2n7Z3XUExlcIsmWjiEHg+jzBIkhGNKQ9+ljG6QkDQNGXCPZHv74hVMvfH6kfPg2TbZ3l4i7UuILl9B5kAXsu4j1NKCa9vElq+YNTADSLJcGvqedlAi1NLC1JH8ETwPu1AK2J7rLtgMckF4K+JT6wLhq4xh5Qx6f3qs5GpucIyqtctQ/Tqu5SCrCh4wun0Prm1jTCSY2t/DkvfcgDGRQFIVJEmi74mt6KEATqGA57hEWuvB9VjynhtI948c2YLzAI5pE2tvoW7jSsxUju4fPonnugSqK1h8+9Uc+vGzWLnSh3C6d4jx1/fTdvvVJLv7GNm6s/RFQZJou/2aEw6vH80kDtZW4YuE8B1X79wfC5f/LasqWtA/B3d2bnieV+pRei6SUtp3/EyNBHiui5XP49kWAJKqIus+VP3YCJSZLWV0Zw8dojBRqqfgq6oi0t6Oa9lISDgFoxywj5cfGiS+ahX+6hr0eJx090ECtbVIioqsaQQbm8gPDU57TqilBSSJyJIlFCcnUfwBfJUVpLu6wPOQfT5k3Ue2r49Qcwue6+LaNrKmIUkSjmmWfh9dB7doIqkqSiCAFgyekXsmCOcLEbQvAMV0Fs+yGNm2c8a5yV0HaH/PDThFk6HnX6VmYwf50Yny+fzwBL2PvUjDpvXYhoExOkXNuuV4nsfw1jfQY2HCzXWMv7aPUGMNTr7A+Ov7ys83UxmSBw4zuetA+ZhjmqR6BsoB+yg7b5DqGcAXC5d79ngemb5h/NVxChPJ6Y2XJGRFwV8dJ1g3e0b4fORYJk4uj2Pbpd6kW0rk8lQV32kGIc/zKKZSeKZJuqcbJ59HCQSILlmKGwigRyLYVimYZ7q7KU4dW9ZXnJzEtSxiHSuRNA3PnT0jzHOc0khJJo2VyxJuXUT64AFc00TWNOKrVqGGwxjDQyDJBOrqsHNZsnv3oEYiRNuXkOk9hLG7tIZfUhRiy5eT6+8j2r4Ez/PIHDpEcXICf109/uoq8Eq/V7IsgyyVgnehgGlZyD4fqn/+fGEThBLPS4sAACAASURBVLcigvYC5dg2ZjKD57iM7thNqKHmSNnSN/E87EIRPRQk1t6ComkEaivRAn6C9dV4roedNxh6fgeLfu0qYu0t5EcnyQ2N03LDZQRrKun5r+douf4dpR72m5ZexZcuYmzH9Ax0PRqZGYCPKEwkCdVXTzs2tbeHRTdfQe/PXyiXQgWov3wtStDH4nddhx4Jv/lS85ZjGLi2jZ3LIcml3rVULKL4AxQ9D1/o1JPlbMNA8jymOnehBAKowSB2Pk9i104q11+Mmc3i2Day500L2EdZ6TSeaeKLRmedmwYI1NVRTCRBlgnU1JLtPUSkrQ3P85CQsPN5ZJ8fLRYjUN/A1OuvlXvsdra0PC/a3o59pGypGgiAouKrqCC5ZzeOYaBXVBK+qI30gS6KkxNE2peA4zDVuaucg6H4/cRXr8HO5XBMEy0QQNa0U753gnA+EEF7ATKmkky80cXEzi5arr+MZNdh/JVx1GAAOz+9dyspMq5pYSsFKjvasAsWjVdcTDGZwUxnmdrbg2s7tNx4OVbWoOeHT6JFQ1SuaMNfFefgD54gtrSVYjpHoCI6vTCKLKH49BmpZcVUmsqOdlI9AzPaHqiO47ypuIprWmSHx2i/8wYKkwmsrEGktQEtEsL/FsvD5iPP83BMCxwHSZbJdB8sByE9Hie6dBnFTBrV50c5MpxtGQZusQAeyLqOGjxxUl1pLX6SeMdKrGwGz/MItS6iMD5OYXSUYHMzsiTNSD58cxs918VzHGIrOkjt31ceGdFiMbRYHLdYwLJtzGyGUGsrqa4uOBLkJUUhvnIVjmGQH+gn3rGyFKwlUEIhzGSSbO+haa8ZWdxOYWIcO1daqlicnMBMJYkubifVtR9Jkkjs3YPnOKjBIIH6BiRFoTgxjhavQJYkiqkU/oqKmXPngjCPiKB9irq6uvjoRz/Khz70Ie655x6Gh4e5//77cRyHmpoavva1r5XrrJ9NxaxB+tAQE2/sR1IV7EJpLnhy90Gart7I4cdeRJIlajesRAsHUXz6keFEj+GXdhJd1Ejfy7uwcgayrlF36WqsnMHA0y+z+F3XsvQ3b6GYzKAGfFjZPI1XbUALBsgOjlKYSBBuqiU7OEbthg70WAQrm6Np80acYpHhl3bi2Q5WJo8WCqBFgliZY5tAaOEggdpK0ocGZ7yvQGWc4Rdfo/aSVci6jh4L44uWetd2voBVLJbm5TUFPRRE0edpj8rzkDUN13FKa5mPK/drJpMYw8P4G+pxisVSdTFJKgWzTIbCxARqKER8RQdaNDpr4PZsG8XvI7l3z7TjkbbFOJYJkoRbKODaFvGVKykmkqVh7CMUf2mlgZ3LIekalpGnYs1aXLOIJMtYmQzFyUlkRcHzPPRolNT+YwEbSl8c0t0HCbW0kOvtRdZ0jJFhPM8jsngx2b7DM9qd7T1EpH1JKWv9uPfiui7ykb+z6JIlpXlzVSXV04OTzeCrrMRXVU1yd2fpy0HBQAstnFEZ4cJzwQRtwygwMTZJdW0VgcDpzW+91bajt912G3/1V3/Fo48+ypYtW0632SfNzuXKG3R4toPqL1X4sjI58mNTtN1+DbKqMPjsdgpHtqOUFJnGKzdQ2bG4lKh2JFC4psXwi6+x6JYriS1uxi4UsbIGgeo4hVQGxyhijE1h5QtkDg8hKTKtN1xOuKWe3OAYY6/uLbdLjx3JRH/6ZRSfjhoKUH/ZWsx0jsJkklBDDYGaCmSfj1BjDenDQ9j5AmowQN2lq5jae6i07jocRHUcPKcUBIxEGrdQxExnkTUNWVdxjAJqJIw/Mv/WXJeyqGUcy5oWsI/Kj4ygRaO4toUWjWEMD2NOTZaGgld0kBsaJLlnNxVr1qKFpweno7XAM93dM66bOdxL9YaNJPfsLg9RA/hrawk2N5MfGEDWdWIrOkju24edzaBVVOCLV5DrO0ygrg5JVlDDYQpj4xiTEyBJSLJSTng7npPPgwdKMIgWDpM7Mo8uycq0AF9u+wmG4j3bIrp0WXnYHEo9+ejSZWQP91KcmsLK5Qg1t1CYmsRfVT3rdQRhvljwQdu2bf7Xl/+Opx9/gZGhMeoba7nu5s388f/4/Rm7kL1dR7cdfeihh8rHtm3bxp/92Z8BpW1H//Ef//GcBO03j0Urfp1AbSXG2BTjr+4h3FTL1J6ecsCG0mYZgy/soO32a2YNFFN7e/BVRJl4Y3/pgCTRfO2l5McThBqqy0VNStd5jaarNzDy0vSkNzOVwcoZtNx8BU6+UO5NW9kctRtXIWsq2aExHKNIuKmWmnXLUcNBFF2nMJHATGdpunojqQOHGd2+Gy0SpP2OG8gOjjL43PZyEI8saqB242rsdA7mYdCGUuA60XI0SZFxjmwfmtzdWQ5Udj5PcWqKeMdKUvv34RSLM4K2JElIgGvOLGer+HwUE4lpARtKvfuK1WsI1NbhSWAlU9jZDL7KKvw1NdhGnkB9A8VEolQkp64OvSKOrGn4KipOPIcslXbvCtY3gASVGzaC6+K5LmokgpPPTxuil46sCX8zLRYj19c3bU24d2SUIrK4nfSBLtxiEVlVMEbG8c/xfgfC3DCNIrZp448ETrj5TDaRObKaRCIUDy3YJZ0L810d5399+e/4///x0fLPQwMj5Z//5HP3ntI1z+S2o2eapChUrGhj5KU3qFm/AkmWqbt0NbKmMrGzC9d2SB6cOfyI6+GcaFmV7WDnj9vowfMYePpl2t9747TgD6D6dbIDY8wm3TuIvzJGYt8hZF1jyXtuAFmiMJXCzhWQ8Ag31SBrKsH6anLD44zt68TzoGrtUrRQgHBLPaPbd1OxvA27WJxWcAUgc3iYQE0lgZpKzJyBHgqc3A08D2jBIJ5tz1rYJtzSSm5okFBzy7RAdVR+aHDWwGQZBp5lIakqwaYmjOHhab1XPRajMDb9/y3Y1ITi95Pat7e0VK+uHiUYQFJV/DU1pblsKG2BGo2CrmOm0uT6DqP4/biWia+qGl9lJcWp6fukB+obUAMBMj09yJqGGo7gr6nGtSx8FRUodfVHssR7wHWJtC+ZkRgXbGlFVtQZ1degFLiPr1HueR5KKIwoXD6/mAWT8UOjeI6D4ziomkqwIky0No6iKMiyTGo0QTFXwLEcJFnCyBqkR5OEKsNUNFSd67dwxi3ooG0YBZ5+/IVZzz39ixf4+P2/e9pD5bM5mW1HzzTFp+GvjNF2+zUMv/g6Yzv2HDmu03rj5SihAIquYxszd1uSTzAPHFvczOj2mVvFFabSaOEAik/DKZaGN+1CET06ew/XFwuXl3n5YmFyIxMMPrcDXzxCwxXrcS0bK1ckfeggajAwbZnY8AuvUVjRhhYOUnNxB3o0TGF89h3cUgf7CDfX4VoWMP+CNlDai3zdelJd+7GzWSRZJtTSiuu6peVf1iwrAShlh/uqq5F9emmdt1zqmTu5HJmeHpyCgRaNEVvRQaanu7zrlhqO4No2lCrbokUiyKo6bSg9e7gXvbKS6JKl5AePSyL0PMxU6cubr6IS17ZxM6UL+aqq8FVXo4RCGENDSLJMsKkZX2UlxugIViZDoK4ONRgktX//tJ6+GgxSsWpVaYgdj1BLK8H6elzHRfH7MUaGS3PcsjzrkPrxc/qSXFpaJkL2/DLeM4wkSzz90M9IjZb+3uuWNHLN/3crSBKLaloYOTDIjh/+ksTgBNUX1XHJezczeGCQ2vZGfNEAwdDCWqu/oEsKTYxNMjI0e69vdGiMibGZS1pO1dnedvREfNEwvooIk7sOYEwcC2pO0eTwL7ZiJjJUr10243lqKIDnuNRfthZJOfZrUbl6Ca7t4BRmDqmqfg2nYNJwxcUgS1SubKdh0zoCNRWob/oyJMkysbZmClMpGjatp/7ydUiKQvXaZSi6RubwEH2P/7K0DnhxC5OdM3ftSuzvRY+E8FeUkqxOlAUsq6VkpHmbjAaoPh9mPkd81WqqNmwoJYUlE2R7ukvJZIHZv4zo8Ti+yiqSnbuxCwZOLodXLJbmfI9UE7PSKVL79hJdsqSUkb5kCZ7rEGpqLl8nUFdHbmBmdr85NYWsa7NusQmljWkkWcZfV0ds+QoUXylxTZJkwosuItTcQnFqisL4OL6qakKLFuGrrsG1zBlD83Y+j1MogizjFosUx8dxiiauZeLk8xjDwxQmxktD7G+iBIOlkrmSRGzZctRQGElVUMR67XmjkDWQZIlffPNH5YANMHpwiJceeQbPcchOpXniW/9JYrBUW2Kid5THv/5DGjtaeeXR58hNZM5R6+fOgu5pV9dWUd9Yy9DAyIxzdY21VNeeuaGTo9uO3nHHHWdl29G3IskyqVkysF3TQpIlwq311Csy4zt24xQtwq311KxbgaypeMDiO67Dyhq4lk1ueHzWDTzUoB8tFMRzHSb39NB+5/UkD/TR/+Q2FL9Oy/WXUZhMkTo0gB4JUrV6GVP7D9F8zSUMPLO9vPQsWFdF4+YN9P6sNCIy/MvXWXTr5lnnLzm61MgDY3yK+LKLZh1Crlq7DDXgQwvO0142pS8eweoarGy21Fs2zdLcrKbhr6tHDYfx19ZRGBs99hxNI9y6iMSunYQXL6Y4OYVrW6WkrzfxXBenaOKvrcMx8kiKQqbvMPGOleQGB0CeeV+PkfBVVWGMzPy7knWdSHs7xcmp8vC5EggQaVtM+uABtHCYYEM92f5+cgP96PE4/qqqGUPzRxUnJ7CNPPnjvkAowSDRpcvw19VRGB3FV1lJsKmJ/PAwuG6pcltbG3ahSNXGjaCoSJTm7WWx3GvesIsWZr5IdnJm4O3b1cPFd1zO/uc6Z+TxuI5L3xs9hKuiWMZbb0c8Hy3ooB0I+Lnu5s3T5rSPuu6mzac8NH4+bDv6ViRJQpLlWT90lYAPCYi3N+OviqH6dJJdffT+9DkaN29g8NntM6+3UqL5uncwsm0ndr5ApLWBylXtSJqKlcgTX9KKUzCZ3HUAxafTsGk9hckkxVSW6rXLCNRU0vfkSzS8Yw19T2yd1mvPj04y9vpeQg3VpLr7SyUyVRk16J8+jw6oAV8pIz4YINRQgx4Ps/hd1zLw7CsUE+nSErVLVhFqrClVVZvnFJ8PxefDzueRZJn4qtWlIV9VBc8l1NxMoKEeK5NB9QdQw2EmX3sVz7JQfH5Se/YQal2EdYKhdKdYRAkGkTWdTE9pGDyVTuOvrUPx+UqbdeSmb+GKLJd60jW1FBMJ3OKxPIhQcwt2sYBrGBQnj1XVcwyD9IEuwosuQlJVUvuOrSowEwlSpkmgro7C+MzArYbDM+bD1XAEWVEINTYRbl2EmU7hi0QJ1DeA65R2s8vnkTQdt1BECSpooZCoVT7P+CKBIwmLJyKRT2ZnPVPI5NGDfjTf/B1tO5EFHbQB/vh//D5QmsMeHRqjrrGW627aXD5+Ks6XbUdPRA+HqF6zdFo5UQAtEkRWVQpTadK9g6UEr5VtOKaJ57jkRycJN9eRHRid9rzE/sOEm+upXrMMxaeTG5nAc1wmOw+gBfwkug5Te/EKAJqu2sjQL18rB9zEvkMEaquoWNKKlc3POsye7hksZYYf2SXMsxwaNq1j4OlXjiVLyRL1m9ZhprLEl19UXqOth0O03X4NrmWXloRFQ6gLrOqVGgzO+od6tMhJoLq0EY6VzeAdKdtp50ofZoXxMYKNTWSyM6cb9HgcRdNIHFfIxHMcjOEhCuNjVKxeQ2LXzmlf/mLLluEByT27ibS1Iek+FE0FJFzHQQkEmNz+SunBsox0pFCLa1nIulbqDQOKP0CgoR5Z1TDTqdK6clWdVstcUhT0eAW5vr7yseiy5di5LJOvvQqehxIIEFu2HDOTIdtzbP7dV11NqKkZ2V8qQiMC9vyj6RrhqiiRmhiZ8ekJrxdtWEogFmTZ5lUMdPbOeG7r+naykxn88fk72nYiCz5oq6rKn3zuXj5+/++esXXa5zvXdalY0VYauu7sxnNdQk21NF/7DjJ9Qww9f2w/43RPP7UbVxJqqCF54DCtN26imMxgZUtDqpIss+iWK3Adl0zfMLJPo2b9ChRdJ9nVW6pI5nlokRCBmkpyoxMzesjG2CRVq9pPWKVLkqTyEFesvQUzZ+CLhml/zw2YmRyu46AFAyiaQqy9Bf24xBJFUwlUXZh7K795Xl/WfajhcKlM6JEvLo5hIGsaejyOmTxWOjbU0oqsaZiZdCkB7U0828YuGKX5bsfF81xkTacwNUmgvh5fVRV2Lo9s2aQH+vEcB8XvJ7Z8OXoshr+2DvDALQXW/MgwIOFaVilzPBgkN9CPa5roFRUgyVSsXUdhbAwzmUALh/Ed2Tv7KDUUwrUs8oPHpn4cwyDRuYuKtesIL16MeqTEa35gAMcslgq8xGJn+M4LZ0t1ay23/MGdPPWdnzLVX1qR07ymjYtvv4yXv/8ca2++hJvuvYO+17s5sHUvru3Qcd1agrEQ/kgA3bfwPusXfNA+KhDw07Ko6Vw346ywsvlSTfGiRcuNl6OFQ8iqQn5kfMb6aYDx1/fRuHkjueFxBp55mdpLVqOF/OB5+CrjpLr7sbJ56jetI907yKEfP4vnuVR2LCHYUEOVV0p0C7fM7KUfVZhKEWtvQQsHy18IjoovW0R+fIrGzRsIN9dhZvJY2RyyrhFsqCJQcWEG5ZOl6DrxjpVMvf5aKQFM13FNk8yhHuIdK0ECz7KPrBKQkGSJwtgY/poajCM94ONJQGr//mk9ZgB/RSXBxiYcwzi27AtwCgWmOjupWn/xtC07ASLtS1BDIYINDeB5pA8e6/nb+Typ/fuIr+gg0NCA59g4hkFyd+eR7Tw1fJUVBJuaSe3fR7itDVnTkSSwcjmKk5NIsoysKBiDQ0iaWtoJDMA7shvYAl2zeyHQQn5u/vidFDJ5JFkil8gyfngECYkfffkRPNejadUi7vzs+8H18EUCPP/Pj/OO37gaX9B3rpt/xonf5AXIA6ycQepgH9HWBjzXITswjqyp02uDH3284yKrpeFDp2iROTxExfLSsLk9PE5uaAwtFMTOGeSGxssf3sGaOH2PvYiZziEpMotu2YxTNDHGpma8hh4OkjrYR8v1lzG89XWM8UQps7e9hZp1yykk03i2g1M00UI+fNEQZi4Pjodj21jpLHbBQg368EXDYrjzBPRolOpL34FdLFCxdi3G8DBaNEpyz+5yUZVAXX15o5DI4sV4roeVyWJnjyT8SBKx5cvLQ9m4bjnXR4/HkVSVwsT4tJKiRwVqaskcPDBjy85M90H0+Eb0WBwzkya2bDmSroPjlEqiqkopOc4wpn2ByPR0E+/owMpksDMZIhe1kek+WC4wo0WjxFetJne4l8JxtREKo6NUrFqNq6kiaM9jnueB62IaRTzXw7UdItVR+l7vpnvbsS+Mg7sP8/R3fsrKG9ZT01bPVR+8iVj9wtn573jiN3kBkqRSdnB0USO5iQTxSIh07yCxxS3IujZjty9JVQjUVNJ68xWowQAc2at4cm83+aFjH4RHh88HplIoPg0rX0BWVRo2rUfWFKb29VDZ0Uay6/C0Lwf6kSA7sbOLaHsLVauWIKkKasCPmcoy/vo+oktaCNRUkh8ax3UcZFVFj4XxXJd0z2BpXbksoxl+iuks4fqaeb2kay6pgQBqIEAxkcBXVU1i187y+ujSpjF5PM/DzKQJSs34q+JokQh2Po9rWXiui5lKoQVDWKljc4mSohBedBFTu3YSrK+ftbSoFo1gjMzstQM4hdLSMzwPPV6BHouRPdx77PqyTMW69aWh/HQaPA/XsnDNItnDvcSWLStv8XmUlU6T7e2ddeol3dNNxdp1s69EEOaFxNAEVsGi64VO9j69E8eyaehoYdX16+l5pQszfywRMjE4Qbyuguf+98+58d47zmGr55YI2guQoutooSBKwEe4vpqpvT3o0TBTe7qpu3Q1wy++Nu3xdZesAqlU+az/ia3YRoHWm66YFrCPGn9jP1WrluCviOJ6HrH2FsZe3YNjWkTbmvA8WHTrlaR7hylOJQnWV6NHwwy9UJpHd4ulhCRjPEGqpxNF16m5eAVmIku6Z5BIcx3JfYcoTCVpuuoSFF1leOvrVCy7CEmRyfYPl+qP+3QUv44/Fj0r93RekiQ820ILhwk1t5Dt7yM/OIAWiZQKlTQ24hgGdiCA6vOh6DrFVIrsoR6Kk5ME6huIrViBlc0ia3ppa8x9e8F1S+cbG8m+aW11afhdn7VUqmfb5QAaqKubNrQOpS+a6QNdRNuX4BSLeO7RBDiptNzPm70Ea3FinOiyZTOyzx3DKGWTi172vOS5HrKicODFN+h8/FgezvDefjJjSS5+1+Vs+96z055jFkwcx2W8ZxhVU6loXHgV0cQY4wKkBf3Ifo3Y4hZUv490zwCR1gaKqQy5wVFabrycypWLqexYTNu7r8XMGOSGJxh4ahtWNo/nuDOSyY4qJFJE25rIDI/j5AuMvtJZKmLheaR7Bhj55esgybhHdpNKdfcz8PTLpexuVUVSZCZ3H2T8tb2YqSzG+BR9j/8SPI/88Dh9v9hK5crFSLJM3xNbMdM5Ii31mJkcasBPZUc70UWNZPtGyPaPkRudwHmLbSQvZJKqgCQRam4huW9vuXiJlcmQ3LuntNuVLGMMDWFm0qU18LaNr7L0QWeMDJPav5/i5FSpApokldd8Hx2e9tfVHXtBWUbSNSIXtc1oixaJTK8PfoINQOxs9sg+2h7pri4yPT2o4VDp2id8o7OfUUOhUpKd+P2Yl2zLwjSK7HnytRnnspMZfMHpSWaBWAiQ8IX8WIZJPpWb8byFQATtBUr1+/BVRNFCASKLGhh9pZPWGy4HSS5tuBEOUrOhg/4nt6GF/Iy+Mr1MqazN3jsJNdQwtbubQEWU8df3zjhfTGaQFIn4khbShwYpJo8VRqi/fC2OZZMbnLked/z1fVSsWAzA6MudNF65ATwPYyJJsKGGcGMtVjZPdnCU7h8+ydCLr9H/xFa6f/AkxuiZq2y3oEgyss9PMZmYOUTseRQmJ5A1jVx/H5M7dmAbBpKq4phFgk1N5cc5BYNgY2NpeiJ6bGQje+gQ/uoaYh0dRJctI7pkKcbQELZhEOtYiRoOI/t8BJtbiC5ZSra/77imzf7RI+s6nm2Xg7pn22S6e4guWYprWbNWNAvU1s78EiBJRJcsxSkWT7hqQTi/yYqCBNjmzDwcAEk+9v8aiAa5Yst1OKZF9aI6el89iOcuzGkREbQXKD0UBFXBdRxqN64slzEFj+iiRtSAn/HX9mLnDPyV0Rm1yHPDY8SXtk47JqsqNeuWkzx4GFlTy/XG38wtWOjhEC3XX0b95Wupe8caWm68nPShgbeomV1AOVIIwUxn0ePhci10LeBjdHsnocYaxrbvnvY8p2gy/Ms3KKQWXrnC0yUDdi4765AylIaaJVUpzWM7DvmhISRFwV9Vjes4xJYvJ7p0GbEVHWjRGLn+fqLtS6bt3JXtPYQWCmPnDXJ9fUiqih6PY+eyRJcsJbpsOXo8Tn5kGF9FRfl5VjaDXjkzUSjU0kp+ZHhaoDWTCdRgAElTiS5dhnrc7mW+mhqCzS2o4TDxVasJ1DcQam2l6uINGJMTyKp2wpKvwvnNtR0K+SIXXTJL2WVdJd5YydW/fQtXfehm1v3aO5joG0OSJYKxEJnxFJHqhTl1JiZ7FrKiRbZ/lMSBw1x022bMI8VNAlVxCskMrmnTdM0l6JEQsbbmcnETgKk9PVSvW85F77yaTP8Ieuj/sffeYZLd5Z3v5+RQOXbOPXk0MxoFlAAhgQQIAyKtuYBZm3W4a699A97HvsHG+O597Pvcvbu2l/v47vPY64CxsUnG2NiAJDIIlDU5dfd0TtWV6+Rz/zg91dPqHoTQaDShPv9ops6pU2dKVfX+3vf3vt+vgZ5Ps/L8GUI/oDG3RGKoh9rU5qYjQRJRkjEQBZREjMUnjuA7Ll7TQk3GMfKZ9U65zatgsyvXdgzT0gkC1ye9Y4hYdx4EAbtcu2TJvj67uKW5rgMgReVxLZPFXt1ajdCLRRoXzTy71QrxkRF8INbfj1utIQgCXr1Oc3YmUhqrVsgcOLhuxkKkBfDM08imiV7II66Pmkm6QX1yAkFRiA0MEEmf5pEMg9biEtbyMuk9e3HTGVqLC5H7WHc39tpauxHyAqKiRA1n56fR8jnMnt51r24BJZHEtyz8wEc1Y0iaim87uPUaei6HEot3Mu1rFEVXKc+tcuvDd1FZKLXntBVd5U3/7u2IokiyK43neiiqQrPS4Ol/+D6e7fKmf/cOUl2ZF3mFa5NO0L6O8V2PytlpWournPns11AScbRsAj2XQlQk0juHcOstph99nL7X3kpzqYRbW98HEgSUmEFjfhklbiLpGrPfeJLMrmFqk7OUz04z9MY7sdeqONXoOYIoMvimu/AtG6vewipV6L/3NsIw+nEXBIGlp47Te8/NmwReREWmcGg3048+DoJA7z2HacwvkT+wA7tURY4ZSLp6yW5xNREtEjpsRtZ1FDOG12yi5XKbArfe1YWoaVgXjVdp2RySLCPF47jNJs3ZmS0ypl6zSei6yIZBGASsPv0UgijiNVsIooi/shrNWVs2sYEB7HIZgpCQMJI/7e5BMgxCL/K81otFEiOjWGslqmfOoKbTJMbGNzWpxUdGaExPE3ou1sIC1kWa57nDt1A9fQo5HkcZGkbUNERNgzBSkusE7Gub4UPjPP2P3+dNv/J2WpUGnusTy8RRTB3VUFg4PcPskSn69g4RyyZ47YffRKKQum4DNnSC9nVOiHKRn7Rbq+PW6vgtm967D1OfXcBtWnTdup/63CL9b7gNAQHPspAUBUGSURIGreU11LiJZ9lImkqsp0Bjfpnpr/+Qrlv2oaXjhCGoyRgLP3iO2uQ8CAKZXcM0FlYwi1kERUWSRPIHdtAqVRl/9xtpLK4iqQpaOkn5zHkyu0aI9RYIH8nVWwAAIABJREFU/IDQ86MRo4FuvKZFz12HCMMQo5CJZrwvoueOA2ipxJV+c68J1GQSJImYaRLrH4gaBDWVMAwpPfVUu+IhatomH27FNEmOjVN67tlN15N0HTkej0rqnkdidIzQ85DjMfxmC9+2kQ0TNZvDXl5Gz2QRZAk9naF8/BjJnbuoXSSsUp+cRDIMzO4esgcPgSTRmp9HkCQkTcMcGEBJJAm8CYyeXkRFximXcavVSOlMFDF6+7CXl5F0DVnvlMKvJxKFFEOHxvnb3/zTTXvUt7zrbm55+51UxCav+9kHX8U7vPJ0gvZ1jKjIpMYHKZ+d3lSOjsw1ZHrvupnjx48zsGMI33YQVWVdR3oDp9Yg8HymvvJdBu+/g6VnjhHrLpLdO4YgiUiagqRpCKLA2c89slHWDEPWTkxQvGUvqycm6bvnZqyVNdZOnUeQBMyuHMmhPqYfe5zWUgmzmCX0A/RcitLxc5FAiyiQ3TeGWcyiJuPUZxfpv/c2KudmKJ+ZRjY0um7Zi5ZLI3XGerZFkCS0ZDLSKfd9hHXDD6/VIr13XyQZmkyipTPI5mbfYTWTJnvwENWzZwkcG71QiDy9XZfA86ieOoWaTiGIErUzZ1BSKQihcvIE2UM3Y60s0zg/hSBJJMbGyNx0YFvfa7/VojZxjlzmFvB9RMMgMTqGpGkErkPo+6R27qJx/jyBY6PlCyTHxhFkmcD3catV0vv2dQL2dUijXOeb/+1ftjSVPfn57zB+x55IfOUGo/NLdx2jZ1KEQcjQg3ez/OwJ7FIVsztP9+37237XzWYTUZYQ5Uv4MydiJAa6o3Gsr32PwqHdJAa6EWQJr2Xj1hpYa1XUeGxbDevS8XMUD+1BlERSowOkRgfax9x6k4E3vAa30cReqyIIAitHTmOtRBrZq8+dJjnUB+kUsq6SHhsgcD3UbDJaNMhSpI7WKYG+KIIgIFy0sLkgwGJ2d1/yOaKsoOfzqKkUYRAgKgqeZRG4Ll6zidHdTWthPlpsFQuIioqk67QW5mlMn0fWdfxmk9D3qZ46RWr3HkRV3XaOW88XqJ+fwq3VSI6PUz56FEnXMXp6kREiUZZ1mrMzOGsljHVJ1Ej6tDPWdT3iNGyqi+WtB0Jola/Pka4XoxO0r2NEWSLWlUOUJfpffxthGCIqMpKmoCV+fOtKPZOk7/W3kT+wE6fRAklEScaRTR0tFceu1i/ZCBYGIWoqjqRv1QBW4iaiKuNUattaggae1/bdbv+bFBlVkaHTEXzFuLhbnDBEUFWCtRL1qan2w43z5yNf6/4BBEnCrdXQc3nsUgnJNFETSZxKBXNwkNSu3VTPnG7PbavpDFo+R+VEtI9tLS+TvukmvGoNUZaoXeRCdgGv2USUFSonT5AYG+uo412nKIZCLBOnsRZpDPTuHWTk1p0IgoCRjiGVKi9yheuPTtC+AbgcLliyriL3FIi94PEwjPSAMXUESST0N5c/c/vH0YtZxIvcqC5GUlXUZGzb56bGBvEvMaPZ4VVCkhA8j8bMzJZDdqlEbHCIMAiiMTCrRXLnLnzLwimvRTPWjoOga8QGBpF0ncC2cKq1yJjkwnVWVlDTGepTkyTGxgis7acGLsxm16emyOXyhGGIb1kQhlFG39kyueaJZ5O89sNv4p//8+e55Z130ao2+d6nHiPwfLIDBe761/e/2rd4xenMaXd4WQiCQKw7j6AqDD/0OmRjI6NOjvRHnd2XUL+6gJZOMvTmezYJuhjFLInBboxCx+HrqsJ1CQP/kuXoMAgQBAG9WERNpmjOTNM4P4VbrWItLbH6zNPg+dhrJQLbonLyJK35uU09F6Km4VtRFu5WqpF15zZcsCW9II9an5hg+QeP4zk2bq1GY36O1tISTrWKv83WTYdrg4FDY7z7//gwCALHHn0mShKA8nyJ5/7hh1QW16iv1V4VMRXPdgle5PftctNZinZ42VjlKs5aldr5BQqH9yLKEoIo0phbYuaxHzD04N0/MtuXVAWzp8DYu96ItRqNCAVBgJ5NYRavP+3gaxHfcfBtm/LRI+jFYtslbBPrTW7ZQzfj1OsIsrz1nDCkMTVJfGwct1xG0o12gL5AfHCI2sQ5AKzVlWieu1ptz4YDxPoHsFaiuV0tlyPw/aiZ7dbbaC0s0JzZ0ByQ4/HI4jMe72Tf1yCqrhK4PvMnpnnDL7wVWVNI5JNYdYtT3znKk1/4Ljvu3Et9tUYinyKWfmE98PKzNrfCuR+eYvLJ02T68uy97xDFsR7EK+A+2PkEd3hZhEFA6eiZaAxsdonWytqWc1qr2zSSvAAtbqLFTdS4ie96KKaOvM0+eIcrj++6UYm7WsFrNGhMT5PevZvysWOb5ENTO3fSWl5Cjccjh7Hk9opUXqOBWykTeB7pfXtpzsxgra4iqirxwaGoa/xCo1oYUj19ivjICIIYbaEIkoS9uoK9soKkacSHh6mcPIlkmoSetylgQ6Rnbi0vRWpt8R+/l6PD1YHTskES2HH3XmqrVTK9OaaePsuTX/hu+5zjjz3LvT//FoQwfMWD9trcKv/8nz5Pz+5+7v7QGxElCc91WJlcoDja+4q+NnSCdoeXideyKB2fQMskMbpy2wZts+vHz5bVxCu/Su7w0vBtO8pq18uPoedRPXuW5I6dkclIGKImI2UyURDxbYdEf/8lS9JKKoW9soJdKtGcUcgcPIheKOLUqkBUZVGSybZfd+C61CcmSO7YgZKMOtkFWcbo7kZUtSijr9dJ7d2HU92+McleXcXounSnfIerlzAIkUSJVrnB9HMTGEmTp//h++3jgigwfHgHvucTClAvVYlnX5qEaRiElBdK1JYryKpMspAink9tOc9uWqxOr3DXB+7Hsx2+85ePsHRunmx/njvf/wYqy2VSr/CWXidod3hZCJKErGvMfftpRh56HdVzM5t0zGM9ebTM9akBfKMQ+j52qbRpPMxvtaicPIEgisSGhrHLa9TPnWsfr5+fInvgIEaxi9bSYvtxYX2MrHzsGBAFZGthEXuthG/bZA8eREAguWMnbq2GUy4jGwaSaSJqOko8GvELYrFoDE2WCcMQPZ8HQiR1++qMqKqXdAPrcPXSrDSoLK5h1Zoopsbi6Vl23BnNZ4/csoP+/cNkh4qc+d5xvv1nX0WN6dzyzjvZcece4rmtQbdeqrE0MY9ne4S+j2pqpHtyVJfW+Mof/H2U1QP9B4a5+wP3kx/q2vT85YkFJFnErjd59P/7R3w32l9fmVzkS7/3ad7xWx/oBO0OVzeyrtF9xwEmvvSNSIDlgTuxVitYa1USA91omQR6uhO0r2XcWg3ZNPFbFmo6jVO+aLtDENCyWVaffMHI3ro3dmr3HvSuIna5jKRpiJJE9SJFNAC3Xkfv7kGNx9tiGWHgo6ZSqOk0gRfZusqG3p7Jv3hvWgAS6y5isd4+BFmOmtMuItbf3wna1xi+6/HcP/0Az/UYv3MvE0+cBqIemPt+8SHOPn6Cb//F11AMlQNvvo3b3/s6Hv/bb/DdTz5KGMLNb3vNJg2HZqXB9//6Mfr2D/OtP/sKbivaglEMldf+6weQVBnWg/bMc5Mc7X6GWx6+i3gmUlt0bZfT3z/GzrtuYvboZDtgXyAMQ44/9iyZvhxm8pWrGHa6xzu8bOL9XQw9eDeh53Pu7x+jubhK8ebdxPqKxLryiPL2414drhECHy2ToTE7g5rOkBzfgdHVRXx4mNzNh3Fr2zusRbrlIWtHj+LV6oiKSuXkyS3CKkoqid9sELgOoqygZTKo6Uzkvy6KyLEYaiKBKF96FluJxUgMjxAKAtlDhyIHsfXnpvfvRzKMzfPmHa56qktlnv7S45x9/CRaXCfdc8EVLuSJz38nst8MQ5ymzROf+za+5/H6j7wZgKe+8N22wcgFKgtrJItpHv/0N9oBG8BtOfzw777Jvvtv3nT+me8do1mut/8e+AH5/iKSJNKqvKDBcp36arXd3f5K0cm0O7xsZF0ju2eU+EA3oR8g6Sqypr7at9XhMqGm0pSef47Uzl04lUj3W8/nkVMpqqdOYVxCVS0qSYsQBDjlNWKDA0iG0RZVuXCObBjYLYswBGt5CUnTkFQV8QWyqi+GrOvIuo7baJDatTuSbRUEQkFAUlWkTtC+prCbNoEfUF+tMnNkkv6bhjEzccIgpLKwtXfmuS8/wet+7kHG79jD2cdPsHhmDs/xKAx3I8oijVINLW7QKG1dZNZWqhiJzYJN0guSDc3USPVkaVbrdO3o49R3NtsEA4zfsQdBfmVz4U6m3eGyocZNtFS8E7CvM5REgvSevdQmJ3FrNURVQU4kwPfxWk3cWhVtG2/sxNgYBAGpPXtBECgfOUJ69x6SO3aiFwokxsZI792HpBvohQKh5+G3LJzKy1O5UmIxFNNETSRQ4nHUWKwTsK9BjGQM1Yx6FL75J/+C3XR46Nffi2ps//viWDZWrUnv3kGGD48ThgFf/S9/z9rsCgCxbPzSI1lC5FJ4MXvuPUgss9mIKNmVZnlikcJoN6O379p0bOCmYbp29hFLvrITCp1Mu0OHDj8SQRQjb+pbbyVwHARJIvR9PNtGTWdozs2RGB1FTaWxVpYRZJnYwACtuXkoFBAlicTYOLUzpyOnr127iedyeNUqreVlFMOgPn2ewLYj564wxHfdqDze2Ye+YUkWUtz7b97MV/7o7yGEL/7uX/G+3/s5Mn15JEXasqc8cNMI8ydn2HHPPnbcvRczFef1H3kzrVpUyk735Jg9Mc3w4XEmn9rcVzH2mj2U5qLgLikSe+49yNgdu4mlNwfgdFeW8Tv2cOSRp7jtPfew742HsaoNjFQMMx0j1b118Xq56QTtDh06/FhIqoqkRlmOtbKCKAro+TxOeY3auXOImoaWyaIkElGpe2WZwHNR0xnUTIb03r3IiUQkcWrbOPUaajy2ScLUqVRwjx0lc+gQQggIAqKidLyxb0AEUWDklp28+3d+hsmnzxB4AavnlyiMdHP/v307X/vEF9v7x4lCip337Ofbf/E1unf08Z1PPoKsKbzxl99OspAkCAKMpMme195E/55B4rkkJ775PAKw/4HD3PTgrYTAzrv2IcoiiVwCM7293W9uoMCd77uX6nIZLaFTGOvGiF05L4RO0O7QocNLJgxD3HoTSVWJj4xEKnauixyLIek65aNHAPAtm9D3EAhREgkkw8SzbfA8jEKR6ulTW64t6Tqh61I5dQrfsiJrz9FR1EwWu7SKKCuoqRRyLNYJ5Nc5sqbQvbOf3FCR5lod3/WJZRMYSZOf+l9+mtL5ZQRBwG5YfOvPvsJr3vs6nv/qU0AkMfrtP/8q9/3SQ+2yeCybIJZNUBzr5fA77oweyyQQpeh4cpvZ7O1QTW3LONiVorOn3eFlcSP62XaIsu7GusuXKEogikimCaKIvbYWGXcAWjaDU6sjKCphCG61ir28xMoTP8RaXNwkTXqB2MAga88/375G6PtUT5/Gq9dRYnGs5SWWf/gD3Je5993h2kHRVFLdWbIDBbSYjpmKY6ZM0j0ZJEVCi+vc9YH7OPmtI5TnVtvPq69WseutLdcTJZFEPkUin2oH7GuFTqa9jmdbhL4HIYQICISRP7B6+ZqqwiDAc2xEiDpaFXVL88O1QqtcZ+XsHJPfP46ZSTBy1z5S/TmkjrbzDYGgKCTHx6idPYMci2H29oEoAAJqKoVWKERBNplC0g0QRfxGnTAIqJ6Ksmt7rYSez9OY3pAdFVW17dT1QhqzMyR37iIxNo4wN0f55AlyNx++rN/RDtcOqq6RKGYQ5Wh/+0u//2l4wcdGEASM1PWlsnhtRozLgGu1cGoVmotzuPUafrOOV69jryzhrq1AGOI16/iOQ+D7+I69bVbw4+LbVnS9epXmwizW0jxes47b2roKvNqxGy2e+cw3Ofql76MnTVqVBo/9x79j5ez8q31rHa4Qsq6DKGL09JIYGSXwPPxWC7dawW81MXv7SO/bj6hpiIaOW6lQn5rCXi21r+E1GkiGgZLYEN8RRDFSS9mG0A9oLczjVMrEe3oiCdWX8Z3scG2jJQzsWgNZVZB1lfE79245Z/yuvSQKP17J+1rhhkuLfMch9L1IZUkUURIpfKuFtbxA4NjIsThaMkXgu4CAW68iadEPlNesIcoSSjyBKG28dZ5tbdhPiiKiJCNIUnu/LQwCfM/FXl3GrWzMFzprq8SHx/EA2bhyjQwvl/pShfx4L0YqxuLx8+jpOHf+wkM49RbV+RKJrgyC2NlrvJ6JOsrzeDGLwLaoT5xrl7MBjK4uzMEhREmKxq1EEWGbKkz19GliA4PEh4cj0RWBSzpxGYUCjblZAtsmvW8f6b372vacHW48WuUGP/jst7npgVtJ5JPc8s67SHVlOPq1pwCB/W86zPhde0heZ/a+N0zQ9lqt9f3XaHXurC4TAno2T3NmcuO8eg2/1cTo7qc5G+3ZCZKM2T+EbBiEYYDXbKLEE1FJ3fPA92gtzuHbFmoyjZbvAkKQFARJjLIBP9gUsC/Qmp8hNjh6Rd6Dy0UYBJz75vN03zTCrR98I54b+RnLqsK3P/EF9r/jbnJjPYiSFOlCJ80rYlnX4coiyjIhYC0ubQrYAIIo4VUr1CYnCV0XLZ8nOTaOb7VoLUQVGVFVMXv7EBUlKou7LtUTx9GyORKjo9QmJtplcr1QICQksCOZSbtUItYfQ+jMX9+wCKLAwqlZmmsNdr/hIPFMnP6bhuke7yVeSJEbKFyXjYo3RND2Wk3CIMBaWUI2TVrzMwCo6SzW6tKW80PfjzpeRQk5nkCJJ2gtzBK6LkoyhZYr4rWaQEjoezSmziIqCrH+YQLPpT51ltD3UFIZ9FyR+tRZjML2qlG+bUUzr1YLQZKvCREI13K49YP3EwZw9lvPs3punsxQF2Ovu4nX/MJDTP/gJK1KnTNffxaAgVt3MnrPTdu65nS4thEAu7x5MSrpBpJhbBrlspaWcGs1Mvv2kxgdo7W0SKx/gPrkRLtDPD48QnLnLqqnThJ4Lum9+yLrzyCIDEkmJtrXC30fBOFSlfQONwDxbJKb3nwrT33+u3znz7/afjyWifPu3/3wdRmw4QYI2mEYEgYBrcU5lHgCa3Fj31WQJILW9vaBoe8jqCpKLEFzbqNRximX8Bp1YsNjePU6hGE7U/atFtbSxvXdcgm/1UTL5uESmaao6fhWi5AA2UzgtxoQEjXBGTqCcPVlqKIsUV+p8uSnHsWzIg3f2uIac8+e5d7/+b3ImgIh7Lz/MK7loCcMJr5zlKE795AsZl7lu+9wWRFFJE3fJE1q9HTTeIGnNUTOYL5tE/geyfEdlJ59pp1Jh75P7ewZ0vv2k7vlVkLPQ5BlAsdh7fnntlxLy+aioN0pj9+wCKJAz6FBDodExiKOR++eAV73cw+SuI4ThOs+aAeuEzWV1auoiaiTVU2lQRAI/AAlkcS2rS3PE1UNJZbAKi1ve02/0Whn7ADx4R3Y22TtgW0hygpeo4aSymwpkRtdvQSei6THqE+eRlzvKPcdh1j/MLJhXnKP79WgsVrl+S98h67dA+2AfQHPdmmWasQLaZ7/wndoXSS2P37vQZyGTXOtjpl5ZWX+Olw5QtclPjxM6ZmNz7Uoy5ds2gwDHyWRjMxEtusQnz5PfGgYLZtBkGTcep3YwMCmDnOzfwAlmex0jXdgtb7G7e95LXvfcJDA9zHTcTRTf7Vv6xXl6okGrxChH7T/HGWvBs2FWQhDJE3H7BuK9rGtjUxBy3fhNWpImn7J7tQwDIiKg9EPT+BEZe6LEWRl/YcpxCmX0PNdKD0DuI0agqygJpK0VpYwu/tpLc1hdvfjNesEnodRyOA1agiSdJUF7QpazKBVbrDnzbeRHiyixnQQwK42MVIxSpU6nrP5fTvz9WfpOzSG07Q6Qfs6QhDAaTTIHjpEfWICz4qMP7R8Hnt564JXVFRCSUI1TbKHbqa5MI+1sNA+HgYBSirVdvSSDQO9uwe9q5vQcRBUBUFWUDoKaR3WkWSJVNeNU8G7eqLBK4UoICCjprN4VhN7deOHxLct6lNniY/sIPQ8Qs8FUSRwbARVw201kBPJbRvIorLcRqbgNerI8SRevYocS6BmsgS2jSCKSIaJ3tOPvTRPGIbE+odBEHDrNYx8F4igpbM0pjf27NxqGTWdJQxeWZu3l4ooSex84GZCL+TpT3+d4//8QxJdGQ7/9BuY+PZRls/MkiimOfDwPUw9foKVM7Pt5zZLNWS9kx1dT4iKirW0hDQ8RHx8B265jF1aJdbfj1utthvHAJK7dke65Y6D53tYpRJGsQuzp5fS05GKVax/AFnTNq4vy2jJZNREGobXrK5Bhw6Xi0sG7Q9/+MN8/OMfZ2ho6Erez2VHUlTseg0t30V98syW46Hv4bcaCIKItbKIqGqoyTTNxVnMngFEWWl7/V5AL3ThVsqbruNUyyRGdtAMQ5REkubM1MbBJQGzb4j48Dh2uRy9nqIhJ9MQBISOi+c4aIVuCAPcaoXAsXHKJbRsnsD3Ea+SvTvF0MAS+MYffRbf9Sju6mfvQ3fw7U/8PZ4dZdeVuVWe/NQj3PahN1GamCdYr3Yohtb+c4frA9kwSI6NsfrM0xjFImZfP2o62n7KHjwU9Wv4AbKuU5+doXryBACCLJMc30H19CliAwMkd+zArdXQcrltX0cQhCit79DhBueSy9aHH36Yn/3Zn+UTn/gE7mUUMHj88ce54447+NCHPsSHPvQhfvd3f/eyXXs7BFFENWNRl2l4iYARhjRmp9DzXbjVMq3lBbRMPhJAsVto+SJm7yBm/zCJsV3I8SRKIonZNxipPQFKMo1dWsUodNFanNtyfWtxDq9RR8tksZYXEUUJr7JGffpclGH7HkosjqTpqKkMRlcvAIHvbVowvNq4lsPquTl81yMz1EXvwTGWTky3A3abEGaeOk3X3mjRF8slkXW1bbX3crGbNsszy6zOreI52zcTdrgyqOk0hVtvQzZjtObnCT0P2TBwKxXWnnsOa3GR2vmpzWVwz4sCdv8A9YkJ1HSGxOhYJNrSoUOHS3LJTPud73wn999/P3/wB3/Aww8/zG/8xm9syroHBgZ+4he9/fbb+cM//MOf+PkvlWieNERNZbG3NJatr+DDEM9qIqpau3kMQcBvNnDWVkEQiA2M0jh/bqPJRhAwewYIBQHFjOG3mtFe9zYNNoHrIAgioeehFXtwKiXcWqSdHAL2ymJUnheiblyv1UBJZRBEKZoFvwoIgoBGqdq2xBu+cw/nvnWE4q7+bc+3qk2yw90MvWY3xZ0DSLKEFjdf9n2szq/y7GPPcvIHJ5k7PcvhBw5z7/vfQKbTmf6KEQaRPkFIZOhxsVytIAgoiQRKYsMVybcsaufOAqBmMtsag1wY2wpcNwr0yeSWczp06LCZH7mnnUgk+M3f/E1+67d+i1/7tV8jnU4ThiGCIPDII49cqXu8LEiygpbN4zsWXr0WPSiKmN397X3u0PcvGiEJoznupSg7UJNp7NLS5q7YMKQ5P01sYAS3XsOtrqFlC9vfgCgSEu3LyYZJY2mr5KdTLmH2DkQ/kPUqZt8QgesiaZcnO325BJ6PXWmiJwwQIAxCnEaLwngfpx99Zsv5g7ftorCzn2c/801qiyVSg4XouS+DpfOLPPPIs0wemaDQn+c1D72GRz/1KIEf8PZfeQeSLLE0tUh5pYLTdNBjOql8KvrvdSZneKVw6nWcchlBgMDzkHQD2TQjURRZ3rZRMgyCje9KGCCIYjRz/QIEgejzfZVs/3TocLXzI4P2E088wcc//nEOHDjAI488Qjp9eeTgzpw5wy/90i9RqVT4lV/5Fe6+++7Lct0XRRIxegYIXZfAiUwJrNXFdrOMbMRwyqXIw1fVcKvlyEQEkM04zfmts6eEIaHnRtKlogQCKOksbrm06TQ9V8St15DyJqH9o/XGfauFHEsgysr6yBp4VgtJ017VuW1ZVUh0ZTj25cc5+K7XIskSA7fuojS5yNBr9jD1+PH2udnhLnKjPbQqDfa+7Q7UmIaZeXmZ1NriGp/8nU+yMhOZ1U8emeS5bzzHw7/2Lj73/3yW17/vXhzbYWFigfNHp3juG88hSiKH33SYQ/fdjOu4ZLozSJ0A8WPjWi3s1RXcahVraWOkUTZN0vv305idRUulcFst1HgcJRZDVFUERUFNZ3DKa7SWlzF6emjOzm66tqhpBK5HYnQMsTO+1aHDj4UQXsJb8aMf/SgnT57kYx/7GLfccstle8HFxUWefPJJ3vKWtzA9Pc3P/MzP8JWvfAV1my+tbdscOXLksr22qqqMDw0iyzLW4ly7PA2gZvIIAthrq8QGhnGbLZyVjT04o7sPa2lh225us38IUVUJLBvpojK5U1oBQYi6wH0fSTfwbQtBELCWF9sLgguIqoaWKyJpGm6tCoCSSEUd8IJESEij2eLc+fOX7T15qQzmezj/3ROsnJnjpnfcidOwePJTj9J7cJzirn58x0VSZOorFTKDXaQH8gRhyMzKPPZFncQvFVEUCddCPvP7f7fl2P579mM1bR78Nw9gNxy+8elvcPqJzeXY295yG4fuvxlFVWjRolqv/sT3ciOxf2wUr96gst5AdjHxoSG0QhG/1URU1t25RIFAVpAVBREoPfssoeeSGB3Dsyxa83MQhijJJInRUUBAUBROTEzgXSXbQB06XA3s378fbZsq6yUz7Z07d/J7v/d7yJd5Rrirq4u3vvWtAAwODpLP51lcXPyRe+SXuvntePLJJ3/kIiMMAlzbQu/qRc3mCT0vKs+FAr7TIpHJR+U/I44oCDjl1cj8Q9HQCl1YL2gyEzUdUdEQEGjOTxMf3UUY+IRBiNk/AoFH4LmgqIiKQuP8BKIiY/b00ZidbjfHCbKC0dVLGAbtLndR05ENE9+2CFwXJZEkbhoc3L9/01jMy+HF3q/t0A2D/kNrbgKPAAAgAElEQVTjNNdqpPrydO8dYvqJk0w/cXLTeff9+vtQYxp6PEa6e/uu4JfCY3/92LaPL51f4uB9h1A1jepyjdNPnEIzNW5/6+2ki2mMpImiyMiKzMLkAo7lcPANB4klX7pl30/yfl3L2GtreJdworMrFfSubiTDjL5Hut4ugweWhQekdu+KjECCAD2XQ89mERUlKocHAYIso8TjHDx48Mr+w65SbrTP18vleny/XixZvWRE/oVf+IVX5Ia++MUvsry8zEc+8hGWl5dZXV2lq6vrFXmt7RBEEdW40AwVIwzDKKgGAZKuRz8oAJKEXuhCSWcIbBu3XkHL5IA+7JUFQt9HSaYxit2EgkjgOcSHxgldB1FR8FpNahOn0IvdSJqBqMiAgJKMXMVaSwuYPX2Imh5pna/LNnrrGTaiiFHopjE71W5sc8qrqOksWq6IS4iivTqdtolihkQxw+TjxzjxlScZfe0BVicWsKrN9jn7fupOfM9Hj18+L9ve8d5tH+/f3c/QviGWJpYQFRHN1Hj7L7+Dr//NY+y8dSficoUf/OPj2E2bkYMjPPDhB1mZWcHc0xHoeFFkCWmbjm41lcLs7aN89Aheo4GSSBIb6McqlVBiMQLHRSsWqRw7GmXggrDhhAckxsdRUim0i5rXOnTo8OJccXGV++67j49+9KM88sgjuK7Lxz72sW1L41cKQRCQlO1f/0KTjSdJ66NdIUoyhRKPRy3f61KooixAsG4esh5kJTOGUexGVLXIicj3ccolQt9DTWXWFwcCzblpgotkVJVUZr2ZLYzMTF6we+GUS8jxJJJgtJsCXy08y6X3pmGe+pvH2PXArUiqjGc5xHJJ1LhBqid7WV+ve6SbkQMjTDy3IUJjJk0O3nuIv/ztv+D2t97OgXsPcPe77uGxv34UQRBQdXVThj7x7AR//R8+xYd+52eolWokc52O5R+FKCuoySSCrETTDRBNTfT2UT52tH2eW6tSPnGC1K5d677X51GSSZRUKtIlf8HnWDZM1GSnMbBDh5fKFQ/a8XicP/7jP77SL/sT4TsO/rqKWhj4qOkcYeBv0hyXDBOzd5DQdXAqZfRiD5JmEPgebmUNr14lMbaL2sSp9g+X16gjxRMoZnxTwAZwK2uYfYORs1Fp5RI35hO6Dp4kvWrZNkBhRz/f/KPPsefB23FaNo2VCqm+PHoqRqonF8mbXkZS+RTv+/fvY+bUDKefOkOhv4CsSPzt738au2nz5FeeZPTmUYb2DvHoJx/h/g+9ke9/8XtbrlNdrbK2uIasyZ2g/SIohoHt+2QPHqQxNYVTKaMVilil1a0nBwFevRFt5SST68poA6jJFF69RmthIZIpTSbxWk382VnUdAqIjD9kwyBwXQRRvKqkezt0uJrofDN+BF6jtkla1GvUUZJp1HQ26jInsiEMfR9RN1ABa2WRMAhQUxm0XAE9X8RaWd6SaUhK1J2+HResCiXd2KSJ3kYUo9Eb/9WVOE12Z7nr59/GU3/zGI3VKsmeLIUdfaT78pFy2kvAdVxcyyUIAkRRxExuP8+dKqRJFdLsu3s/Z54+w5/+xp+0jzWrTb78X7/M2/77nwJA0RSs5lYzGADP8aiX6nQNXrmtmWsVLR7Ha7WIDw/jNZsgCDTnZrc9N3Bd5JhJGEQOdaVnniH0oiCe3ref0PcIHDeS8O3qxi6VCGwbNZ3Ba7Vw1svrkmkgqlokLxyGiKKIoCjrExTbV5fcVqut/y/KckeopcN1SSdoXwLPatFcmNnyuFstExsaQ01lovK452GtLCLrBtbKIgCSEUMyTALHRlI1fKu55Tqh76533G4NypKm47Ya6IXuTYsGADmexLeaKInUq65LLogCudEeXverD+M0bWRVxki/NDMQ3/dZmlzEd30qKxVqa3U0UyOVT5LtzZEuXHrMUNNVREncJI26NLXE5JFJuoa7OPvMWfbcsYcj39rc1CFKIrH05dtrvyEQBHzXQTIM3GYDLZPFWduqya+mkkiGiVevUT2zIRvsVquUjx8juWMntYlzZA8cZO3okahJDWhMT6Pl80i6Tvn4MfRikdjAIGEQ4JTLiJKEHIvh2zaiLKOs/xmiOW+nVouqT60WXr2BHIsRJJMImoZylegcdOhwOeio71+KILi0w5fvUZ86S33yDK2FGfR8EWd9fEzUNLRMDrdaRhAlAs/F6OpFy2/O6HzLQssXt1y7Le7ieXitJrGBYdRUBjmewOjuQzbXm+dcB8/aPou80mhxg0Qx/ZIDtt2ymXj2HLVyHUmViaXjHP/+cT77f3+Gz/7Hz3LmydPUK/VLPj9ZTHHv+9+w6TFBEEhkE7zn199Ls9pg5227KA5tvM+yIvOu/+ndHP/eMZL5Tmn8x8FtNPCazUiHANqz2Gpmc8+C0d2NHE/g1ms05+a2XCcy5fEwBwdpLsy3A/YF7JUVlFgcBAFraam9F944P0X1zGnWjh4F3yPwPWpTU6w88UNWnngCp9EgcBxqExNUTpygMTNN5eQJ1p5/jtC2sFZXCTrjZB2uEzqZ9qWQJERN37LnDFxs7kXoedSnzmH29NOcO4+WLeC1GoiKSnN2wzRENmMYPf34rSZyLI5v2wS2RWJ0J9bKIl6jgRxLoKUzNObOR9KltQo2IMfiKOkshCBKMoFjR2NmV5kD2I9LGIYsTi5y7HvHyPfn0U2dRqWB03JYnV0hCALWFtf43H/6HB9IfJB9d+/b9jqpXIqD9x2kb0cfJ75/HEmWGLt5jFQhhRk3+cBvf5DqcpX3/fv30ag0aNUt9LjOkW88z+0P3U6h/xLqdR3wbLvtUlebnMBavFBFMkiMjOK1mpi9PRhdRRAEZN1AUFXwfbRMdpP/9cWEgR+Zh1ykQ34xfquFqCgEjoPXqGPPlIkNDFKfnCD0XOpTU5gDg7Tm5za60n0fr9nEKW/ebvItC2t5GWt5mdjAIGZvb2daoMM1TydoXwJZ0zF7BtZnpjeitJYr4FZfUBYMg8haWxAiZTQzvilgA3jNBlquQCBJNGcvEkcRReKDoyipLJJuYM3PbNEa9xp1ZDOOU6sQ2BZmdz9us4ae25qpXwvMn53jL3/7L3nrLz7El//kn3jwww+iGiqu4/Kej74XPabxp7/5p9TX6nz9bx4jmUuS7ckSS20taRf6CqQLafp39Uez8QkTWd34WKfzaRr1BnpMp7xcgRBe977Xk+/PX8l/8lVN6PsEQdAOgIHj4DWbeFa0xyxqOuk9e6lNTeI3m9TOncPo7op08nUVUZYRZJnW0hKNqUlk00QvFGjObN1eElUVp1yJ9Ae2mf8WNa39+ZfMGN70NImRERKjo9SnprDX1jAHBpFjcbxGAzWbBUK8+vYVGXt1FSWRoHrmNEoyidoZMetwjdMJ2j8CQddIjO3CrVcInEjcxK1X22plm86VlMgnWxTxL2ibb8PFft4ABAHWyiJqKkvtzHFifUO4L1TrEqL5bjmWQJDESGVN0TZmyq8hXNvlsU89xu1vu51/+q//yDt/7WEe/atHmT6+sZC57a2387P/4ef43H/+LI1KA8/1WJhYYOzQ2LbXVFQFRb30exGLx4jFY2R7Xr7Ay/WGU6/hVqu05hcQZRmzrw8kifrUJF6ziVHsQtQ1WstLJIZHKB8/hm+1EFWN1vwcRl8/oqpir6xQXzcIcWs1jJ5elGQSt7r+WRYEkjt2IBtm9F3q6sJeK21q0JR0HcKAcF0zQdJ1wiAgDHyac3Okdu2mNjFB6HntXpDE2BiB6yHHt9+aubAPHnreFgXCDh2uRTpBexsC38erV2muj3Zp2Tx+q4HfikrYWxBFRFWlMXUW2YwjGdt3Pm9nmADg1WsYXb2Yvf04lTXMngFaKwuErouk6ejFHkJJRo29PLONq4FWo8X0iWn2v+4mkvkUC+fmNwVsgB/+0w/YcXict/3i2zj++HG+9Zlvcttbbqe6WiGZ68z2Xi6cVov6xCTW0mL7MWtlmfTefYiaTrKvn+bsLN7yMlo6jSBJGF3dtBbmo8KSKBLYNk6rRWN2c1Z9wXYzMTJK4PtRAPY8Qt9HzaQJg5DczYepn5/Cb7XQcjnUVIr69DTp/fsRRBF8n9jQEAgiYRjSnJsluWsnBCFutUpy127q5yawV5ZJ7twZSQlfvE8uCBjdPZSeeRpRURCkzs9dh2ufzqd4G/xmg/pUlDVIZgzfarVX9moqi5rK4FSiErmoqJh9g/itJoHr4FRKxDNZEMVNClDRudt3sUbGCS6CouI2avhWk9jAKIFtEbgOoqpdNyYXuqnTv6sfVVc58PqbeObRZ7c97+wzZ9n/2pu46fUH+MQv/xf23b2fwLuEH3qHn4jQsjYF7As05+dIjI7hVqsYXV005+ewVpZxKmVSe/bi1qp4loWWyxF6XmSt+cLxwzCkMX0eSddAEKkcP9Y+R8vlUNORjWp8aAi3WqO1ME/guCQGhyifPLFh4hOPo+fyhL6PUy6TkGTcVp3Uzl0A2CtR5aoxM0N63z5C18V3XbxGA6PYReVEZGKTGBu/JitTHTq8kE7QfgG+4xD4HnpXL265hG+10FIbc9mtxVmUZBqzdxBRVRBkhfrEGYyunvY1mrPnifUPYa8s4TUbiKqG0d2HICtIhonf2jwCZhR7sVeXUBJptGyBwLEjm1BRREmmEWQF8RKqbdcaqq7yhv/uPpamlxAEgWQuwXYTv7FUjGa1gaxFH9EwDNHinbnby8l2HdV6voCSTlN65um2VW18aDhy+VpZxrcs4iMj+JaNW28Q+j5urXrJPWw5nqD09FPtv4uKgtnTGzWbeR6RtG+S2sQEeqFI+cTxTdmyV69Tm5xAz+VpLS8R+n70/REl3HK0cFYSCcy+PirHj0caB7JCYmQEr9kkMTa+rlBodua2O1wXdEa+1gl9H6dWxWvWcdZWcStrKKkMiZGdiGYMNbPRaexWy9jlEoKiYa+urO+VCVEjDxC4Do3pKURNJzYwQmxoFEEU8a1o9jo+NIZkmMjxBGb/EGEYRpm5ICCqKlo2Mi1RkmkUM4Z8ndkW9oz10DvSy9DeIe54+51bjqu6SqqQxoibOE0HSZYoDhYxroPtgauJLZmnIKDlc9TOnG5nxaHvUzt3Fi2fi3QJXBfCaOxRTSaxFhfw6nVk00S92LpXFEmO78Ct10nt3h2JBcViZG46gNuoYy0vR9+TuVnCICA5NoYcM7eMgQGR4EoqidnVTX1yEntlBS2diuSBAbO/n9bCAnqxC6O7B8KA6ulIgbA2NYUUi3Ua0DpcN3Qy7XXcZp3QdWnMTAIgxxLIZpzAsQl8D0nXSIzuxGs2ECQ5Ek9Zt9kEsEvLmD0Dked2GEYNNYQIqoZXq9Ja2MgnRVXD7B3AXlulOXseLZNDzxYRRIEgCJFULXIeu04RRZGukS4qKxW0hM77/9f389inHmNlZoXBvYPc8sCttOotErkEf/G//zkP/4/vomu4o1x2uRF1jdjgEI3z0aSDmkphbyOYAuCsraEmk0iaSvnoEbKHbqZ66mS7T6N66hTpfftJjIziNuoIgkBzbg63VkPSdRKjY0iGweozT2/aNooNDNCcnUEvFrcN2BeQNJ3Q82nOR/Pfa88/T2b/TfjNyGde0nVaiwtImk5yx05aiwtYy0vIqkpgWXCJRrUO1x92y6ZariFJEtli5tW+nctOJ2gDge/hNep4613foqqjF7tpLczit5oIsoJe6MKplhEEkdbCLJJuYHT3I8fi2KVlfKuFvbqE2dMfXUMzCGyL0HU2BWyAwLEj8xDPbZuLIAgIioqiqIjXyf71i5HMJTn79FlShTTv+NV3srZQorZWx0gY9O7opVau8sGPfYjCQAFZ6XxULzeKbmD09KBlMlgryyiJJHaptO25YRii5fI4lSqSYaxPNCTxHSey5TQMlESC2rmzWEtLm57rWxaSrlM7d25Ln0djeprU7j2RcU88gWQYW0bB9EIRp1KmcZGPfOj7hL5PctcuKkePtNXRAtumUq2Q2r0Ha3EBJJHAdQk8r6Nnfp0ThiEzZ+dYOL/A1z7zdeymzR0P3M6+23czMN7/at/eZaPzKSba2xMEAd+JhFTMnj7qk2fbXteh59Kan4mMPIgya99qIUgSYeBj9g7QWpzHty1aC3PohS4gxKlVURPbq265tQparkjoeYjrmbt0gzXKCIJAtjvLtz73Lc4fn+ItP/9WCgNFnviXH9K/c4BbHri+fHKvRtR4HOJx5EQi0n1XlW2b04zunihzNU0kQ6d66iSCJJPasRNBUZBjMQRRxGs0tn8hQWgvipVkCrOnhzAMEAQxWgQQTW2k9+6jevpUe1RMyxfQ8nkqx49tuaTvOIi+3w7YF9OcmyM2OED15Eliff3trasO1xddxW6W51bwPB9JEjh75Bx/8X/9dfv4+VMzHLhrP+/5t++gf7TvVbzTy0cnaENUzpYkZDOO16gRuE47YF+Ms1ZCK6yXaYWoO1yQJBAF4sPjUQNZECKIAk65hCCAIG+fNQuygmzGUZJpREW94QL2BbK9We551z3MnRnlO5/9NqqucvtDr6F/5/WzMr4WkNe3YxzfIzE6Rm1yIsqKRZHEyAi+4yAIAk65jL264fBVrlZI7tqFns8TeB5aNrdt4JY0DVHTkE0TLZOhcupke0ZbTWeIDQ4iiCJus0ly127wvHX51EhCddt7jsfwL7FI8NeVDPViEUFRbpjq1fXKwvQitbU69XIdWZVZXVijd6iL575/jO/9yw8w4zoP/PT9nD+9VYnvue8e4d533kOmmCEW334c91qiE7SJ5qcVM44oKfiOFTXbbHee77VX7Fouj++5NGemiA0MgxTSXJjFKHTTmN4o48mx+GYv4nWMYjeCLKOYHeOKXG+OXG+OfXfvQxCFjtTkq4hsmISFPLlsltBzESQ5EjvRVJREAr/VxLOsTcGyfu4cSjyB12igF/K0Fhc27U+r2SxhEBAfHEKQpPYY1gWc8hpKIo5WKFI9fozc4VsIPI/61CTxoSFkw8Cr5zYWC6JIetduZMOEYLN73gX0XA5RUTC6ezb0/Dtck0yfmeHxrz7Bl//qq4Tr/78/+NF/xd/+v5/n3NFJANaW4L/9n5/k/f/De/jI//ZhqqUqX/3bRymvRJ4QldUqS+eXyfflSKSu7f6GTtAmMukIbJuQELNnIMoAlreep6Sy+M0merEHJZbAbdaJD40RhgEgoGVyWMvzm55jLc1j9g7gVMq4tQqirGB09SJqBrLe6Ya+GFHqDDO82viWhRBG2uNuo46Ry2OvlXBKJSTDQM/lSe3aTWt2hta6HnngulFDmhBtNWUP3UzgODi1GoKqYuay+I4bbRcltxfHsZaX0QvFaH9bVVHjcbKHbsZZXcVrNFEzGfR8AQiRDBO7vIYSpAh9H6Onh9b8xvdO1DSM7h48y0LNZFA6o17XLNW1Gstzq/zTX35l0+MCQjtgX8yX/vyfuf/d9/K1v3uMf/Wr7+aHjzxF73A3faM92JZNebmCLMsYsWv3M9EJ2oCkaoSeD45NfWYSNZNDL3RjLW+YGsixBEoiiW/bUWe3AFo6i6hG/r5uqxkpMr0gSw99n8b0JLGhcfR8F4gSoiRd193hHa5NfM8jDAICxyGwbfR0hvKxoxvl7nKZ1uIi6T17MfsHkBMJWssrxPt68er1qNlLUWnMTGOvrGD29qKaaXzHJfA91HTmklUUQZZBEHDrdcIwiDrZV0s0zk8ReB56oYCey1M+eYLE0DCt+XmUWDzSKQ9DUrv3EHiRgiBhSH1qEqOru1O1ucapVxqcePLUlsc9b3uzpNpaHSOm///t3XmcHWWZ8P1fVZ06+97dp9d0pztbZyEhCSABWQIoAg8jiEtEI84wozjKqyI8Ku+84kcG0BnHx9dxxlEEF1BBHMYRRxZRFsFAyEJC1iYJ2Xo/3ef02beqev44TZNOdwyBpLtP9/X9J5++q86pq+pzcq5T933XdZMaSvPL7z7MR276EPt3HqR7fy+P/fxJdIfO2pvXMGdJ66kO/ZSRpE15QhQ2rfwLPVJPrr8Xze0ZqSWu2vTyl4pmw+H2jPtFoOl2sCxU3V4eEz/K64sqaMNJXoipppTPY+ayxF55BUVRCLQvHDs+bZrkentx1dWROniQ0KLF5SUwj6iI5pnVjO71kdq/n2IqhWp34AiFsPv95ZXDNG1MBTXvrGYAHKEgqsNJfmCQ5N431uPO9fVRSqXwNrdQTCWxuVxYpoEFOCPliZ/68GS6wuDAyNi7t7UNX0uLdJFXKFVVcI1TVMmm21A1FdMYPfdozpI2Du0pP62TjKVIDCZ57Oe/x6bbuO5L13L/Nx/k7q/9mE/f+XcVO6Nc+iOH6Q4nis2OzefH0zIHZ3UtqsOJYneUC6E4Xdh0/ZgJV7XZQFVxNcwas81RVYNqd2BzOCVhiympkEyiGAZDu3YN//jUKWXHnwBWzKRBU3FWVZPct29MAk4fOogzUi5GlI9Gsfu8ZHt7yHR3l8ejFy5CHy52oth0/PPmY1oWAxs3MNTRgZHLUhiKj5nxXcpksLlcOKqqUex2FE1D1W2kDx8itnUr0Y0byPf24qyuIbxi5fDKYPsp5cauJiYqgz/so335fLSjJvSue+xFPvjp941qc3ldrH7feax/csNIm6oqzD2tjY/c9EFymTwfvflDrH7f+SRi468KVwnkTvsI+vCjJ0Y+h2WaKOqJdWPrThdFFHxt88nHB7EMA0cgjOpwztjZ4WLqM4bHpK1icWQCmVEooB1jzoXd58MqldC9XrLDxU6OZh1xB2RZ5SUygwsjlPI5jEym/NhXQyOWZaE5HMRe2YpvzhwwLTKHDqHa7QTbF5Lp6qQwNDTqvfMDUVw15WVpk3v3vvFct2mSPnwIyzJxVFVjD4XxKSpmvgAy37Miub1uQpEAf3/H3/Grf/813Qd6CFT5OfPildTUV/EPd9/C4b1dmKZFqVjiV//+awr58hBl05xGCvkip5+3lJ/+0y/KlScBX9DLZ+76BImhFP4KnJQmSXscmuOtT1LQnU4sy4HqdKOAFHQQU55lGMMTyYZL8VoWWBZGJoM9GKQQj4/sq9hsuJuaKKUz2DxuNIdj3OekFVUd2R/LRLXbUXU7+d5e9FAIvapcslRx2Em/9hqeWc3ko9FRCTrX30+gfSHFdLo8dq0oWKZBrr+fXH8/wcWLx12TO9vdjc3jxchm0H2+cgyiYtU11+EPB/jUP15PPlfA6XZS3VCFw2HnlVdeoW1xK4O9g6x7Yj3JWBJFUVh4xgLOfveZGEWD+//lASzLwu7QKRZLJOMp/uuHv+Xaz39QkrYoUxQFTb4oRKUwTRTAtCzcDQ1kOstjgqkD+/G2tuFuaCQfG8Tm8WAPhkju20dhcADV4cDb3FKu830E3R8oP1utKATbF1LMZPA0zSovyxkOozrLw0TJvXvwzZmLomnYPO5Rj0q+Ln3oIK66OjKdnfjnziPd+UZ1wWM+mmmagAUomMUCNk/lP5s707m9LtzescVRCoUCTac1oNlULl97Ke/64EUoisLWddv5ydd/xgdvvIaVq5ezcOUCUkNpXB4nmVSWR370KPns2B+blUAyixAznOpwYBSLqIqCsyaC5ijX8cayUBQFm9dLtr8PzeliaNeukcpmZj5PIRYj0L6wvLRmsYizthZ7IEgplSQwf0F5ha7qamz+AJrTiWZZWEq5W7uUyVCIx3DV1Y97xwzlcWz//PnYgyHyg4OUUkeMRVqMO6nNEQ5TiMfLC5hY8HoVQzF9eQMenvufF/jNvb8jn83zwU9fzdpbPkwoEuTQq4f5yTd+PrJvpLGGj968Br1CSyNXZtRCiJNG1bSR5KdoGjafF79/PoqmoejlwkDOcBWqpqF7vSNJGyAX7ScfGyS07HTyA1FUTWPw5c3ljcNjiKlUitBpS8urc0UimOnUSKGU9KFD2NwebO7x74btfj/Z7h5K6fSYO+b04UOEFi8htn3bSOLW3G5cdXUU4nFy/VF8bW2Ss2cAX9DHpWsuZuWFp5PP5vEFfQSrA+za1MFz/7Nu1L59nf0c2nOYRWcumKRo3x5J2kII7F4vxVwOyyhhs+tYhkkxkUBRVYZ27RxZzctRVYVvztxRj2NpLheKquCsqiYzfId+tEI8RjGRwBGpKdf6V9WR9xzavQtPczOuhgayXW9MbFNUFXdjE/FdO/HNbi3PPj+Cs7oaxeWkavkKSrkcVqmEVSph5PPDRYwCKKqK7W3MURGVQ9VUIo01o9oOvjp2jXeALc9t5dI1F09EWCedJG0hBMDYymGmSXTDS6Oa8gMD2DxebG43pWwW/7x5GNks8W3bUGw6nqYm9AU+Ert3jXqdatMxDQNQsAwTV30Dmc43vlDTBw/iaW4hvOx0sv39aHp5EZ3ka/uwuVxobjfB9nay/X1YxSKuunp0vx9V1ykmEmju8sRPyzDK5VdtOopNK5c6FTOWLzT+OuqhSAhnhVZFk6QthBhXMTX+s6y5vl6cNREcQLanZ2RFLsgxtGsn/vkL0H0+islyN7qiqqgOO46qKhRFKT+jbZm4GxvLd8+miT0YxBWJlCe3eb0YuVx5XHzefFSHA0UtrwbmCIfHxGMPBDANo7wUrs0GzvIjlrJIyMzWtb+HUqGIP+QjEUuO2nbF2ktxOO2TFNnbI0lbCDE+9RglR1UVV10dZqEw7ozv1P7XCMxfQGzbK+h+P56mWeTjMTyNTehuN6bdjqIqqE4XjupqVM2GoutoDnu5siDDd/3B4JsPVdNQZfEdcYRMMsMv//VhPvy5D7Dp2S3s3vwqVXVhLrzqnegOHVuFTkSTimhCiHHpHi+oY78i3A2NYLMd85Ers1BA1cuVzvztC1GdjnJpU085qao2G3Z/AFdNDc5QuFzi1OsdSdhCnAylUolsJsc9//hTCrkCF11zPrPbm3no3/5rskN7Wyrzp4YQ4pTTfT7CywqmZAYAACAASURBVE5naNdOjGwWRVXxzJqFPRyGUgntGKtn6f4A2Wg/ud5efH4/ntDYLm0YrvkvXdjiFIhF4xx6tZOzLl7JC0+8xLYXd7DtxR0AzFnSSqhm/NXmKoEkbSHEuBRFwRkOow0vtamoKpauQ6lEfOcO7IHAmJnkiqbha2sltnUr/gUL2HfoEKedQDe3ECdDIpak92Avs9ubsekaL/x+A6Zhcvo7l7L66vPw+Ct3KEWSthDiL9Ldbhh+jrqQTlFKpymlUpRSKVz19YSXnU4pkymvYudyke3rJbh4CfZQiEJ3z3HeXYiTK9Yf55F7f8emZ7YAMP/0uVzzyb8iUB0k2t2Poip4K7B86eskaQsh3hTLNFEsRq3+le3uJtvdjWLTUXUbwcWLcdXVU4hGUTSV9ra24cV33vr0GcswsCwLc/g5bFXXZT16cUymZfGBT7+P93/qKkzD5NGf/Z4H//VhAG76P5/BHx7/MbBKIUlbCPGmKKqKhYVtnNW/rFIRPRxC0WwU+vvAMhnatQtnTYSCouB4C13kRrFIMZEgfehguURqTQTd76eQTGJzu7H7fG/rx4CYfg7t7WT/zgNsfPplXB4nKy44nXevWU2oNsRvf/QoqqbR2Now2WG+LZK0hRBvmqJqaG43jnCY/ODgG+2ahre5hfSBA+Si/ag2G+6GBox8AbNYpJTLYTvGxLXxGMUi+WiU+I7tI23FRAJ7OIyvtQ0sk2Imje72SOIWAAz0DvLYz55k3WMvjrStf3IjH/vfH+asi1fy59+9QKgmWJ4AWcHk0y6EeNN0txvVpuFtbSOwoB1XXT2+1jaqlq8gsXcv2Z7ucinRXI7kvn2oug0UBbNYJD8UJx+PUUgkKKTTf/E4RjZLct/eMe2FwUFK6TSZzk6MbI7icd5HzByDvbFRCRvAsix+d9/j5NJZbvja9dS31E5SdCeP3GkLIU6I7vFimSaqXcceDqFqNorpFIXY4Jh904cPUxWJUEwmynfmFjhCITSXi4JlYfeOnhBk5HOUMuUVv4xcbtzjW4ZRrsSWTBJobz/hu3gxPaWGxv8B1981gFEyaZhdN8ERnRpypy2EOGGKqqK7PehuD5rDMWZ5zNdZpRJmvsDQzp3kenvJ9fUytHsXhXgci/LkMgCjUKCQSJDtjzLU0VF+f9/4E4YUrfy1VUqnMXKVuSayOPn8wfFnhNfOiuDyuI5Zh7zSSNIWQrxtmtMJ44wV2oNBsv19Y9rTBw9AsYRZKlFIDDGweRPRl9aT3PMqrpoaLMvC09wypiKbu7GRXDT6RoNljawWJmY2h8fJRddcMKpNURXe//dXUV1fNUlRnXzSPS6EeNt0j5fAgnaGdu0caVN1Hd+cuQxs3jRmf8s0sYwSZj7PwOZNI3fqlmGQfG0fVaEQyX17CcxfgFnIYxSK6B4PhaEh8kckbc3tRrXJ15iAprYGLrzqnSw+q50NT23G6/ewcvVyamdFcPvGPvFQqeTTLoR42xRVxVFVRdWKlZQyaVDUcrc55cpqR6+wragqiqpRTCbH7Vo3hiuwDe3aiep0Epi/gKFdOzELhZF9/PPmlZ/ZtkvNclHWNKeRcG2Y5vmzsNv1adMlfiRJ2kKIk8LmdJZnicdiWMUShaE4ejCIr20OuYEBHKEQxUyaXHc33tZWTNMct0sdID8QxTu7lfiO7Zi5HPEdO/DNno2i6ygKqA4nCsPV2oQ4gtvrwu2dPnfWR5OkLYQ4aXSvF29zC5muTtS0Dd/sVhRFoZTLku3twebxUrXyDFBVFE3DGp6IdjRF01A0jeDChRTi8fKKYoqCWSxgc3swC3lsAalpLmYeSdpCiJNGURTsfj82t5tiUxMUSwy+snWkW7uUSpHv7yN8+ulke3pw1dXhnz+fxJ49MDyhzB4MYnO7SXTsxtvaWh63NkwUXcfu8YCioLlcaDKWLWYg+dQLIU461WYD06KYTI4ah4byJLRMdw+KppLo2I1v/gL8c+ehDCfjUjpNoqMDgKGdO/G2tuGqKz9jqzmdqFIBTcxgkrSFEKeEoqoUU6lxtxmZNM7aOjKHD0OxSHLPq1imiauuHldtLcFFi7EMA83lQnU4ZOxaiGGStIUQp4RlWThCQbLdXWO22UMhiokhAEyzvIoXlB/50pwObG43lqJgcziktrgQR5CkLYQ4JXS3G8Wy0P1+ionESLvmdOEIVzGwaWN5fNruoGrFShRNRXO5ZaxaiL9A/ncIIU4JVdPojcWobV9IKZ2iOJRA83iwud3Ed+4AIDB/AdhsODyeSY5WiMogSVsIccr0RaPMamlBczrRQ2GsQoFSKoW3uQXd60XzeNB0fbLDFKJiSNIWQpxymq6jATgc2I+xEIgQ4vhkhocQQghRISRpCyGEEBVCkrYQQghRISZ8TPvOO+9ky5YtKIrCrbfeytKlSyc6hIphFIoUU2mKmSxGLldeSSkUwDLKyxpqTge6xyNLEwohxAwxod/269ev58CBAzz44IPs3buXW2+9lQcffHAiQ6gYxXQGI5/HLJbIDwyS6x/AWRUqz7RVoPuZddiDfqpXLC23y/KEQggx7U1o9/i6deu45JJLAJgzZw5DQ0OkjlHmcCazTAuzZFBMpuldt4HYtl1ke/uJ7eig94UN5TWEXU4K8QR96zZSTMo1FEKImWBC77Sj0SiLFy8e+TscDtPf34/X6/2Lr9u2bdsJHWfjxo1vKb6pormhEY9Np5BIUkpnRm0zC0XSnd2Eli4iun4zpWyWQjLNnu4u8vn8WzpepV+viSbX68TI9Toxcr1OzEy7XpM6GPp6veHjWbJkCQ6H403tu3HjRlauXPl2wpp0Rr5ALhYbcwetqCr2gJ9SJouvbTYNF59Hz9N/RlEUlixZ8paONR2u10SS63Vi5HqdGLleJ2Y6Xq98Pv8Xb1QnNGlHIhGi0ejI3319fdTU1ExkCBVBc9hRVA174I0iFP45s3GEg+Sig9jcLsx8jmxvlMiqM9C9b70EpKZpmIaBoqgoqoJlWViGgaJpKIpyMk5HCCHESTKhY9rnnnsujz/+OADbt28nEokct2t8pnp9Zrjd78PT1ABA/0svk3ztILHtu+n843PYPR4UTcPmfHO9EEcrpDO0t8wm29tP8sAhMr39ZHr6yfZFyccT5JPpk3lKQggh3qYJvdNesWIFixcvZs2aNSiKwm233TaRh68oDr8PyzCoPnMZmBZdTz0/egfTIrarg9DiBfS8sAH/7GbcDXVvOoEXM1nMXJ6+FzeSjw2NtPvbWrAsi2IqTfXy08jm82gOOza3C1XTTuYpCiGEOEETPqZ98803T/QhK5bu91HqK2AVi+NuL8QTaA4HhXiC3nUbqDlrOcF5bX/xPS3LwjJNStkcma6eUQkbILHvAJGzlpPcf5B0Zzeqw05uIIa/pQnLAldNFTaX86SdoxBCiDdPqnJMYZqmYXM7KZnGuNttXg+qrmNkcwAMbtmOt7Eem9sFgGkYWIaJZtcxTZNCfIhCPIFlWTjCQVIHD4/7voWhJDa3m3RnD86qEL7WZlRdR7XZKOXyGKUSdrcbRZOCekIIMZEkaU9xzoCfvKrgmdVI+lDnqG3Vy5cwuH3XyN9GvoBpGBjFEvnBGPGOvbgi1ThDQcxiie7nXsQqlcqvPWMZmtMJJMYcU3PaMYsldK8HV30ExYLoxi3kY0M4ggGqTl+CaZg4fB7pMhdCiAkkSbsCOHw+qpYtwtNYR/K1g2gOO/65raQ7u8kc7h7Zz91Qi83pIBsdoOup56g950ziu/dilUrEd746krABYtt2UbvqDLI9fW8cSFEILpiDPRQkfNpCHEE/lqrQ9eSfYPjxvHwsTtfTz9N48XmUbCp2mUgohBATRpJ2hXAE/DgCftyN9VimQbarl6Hde0e2a04H1ctPo1QoMLh1B+66WjKdPeSjg/hbZmHkC6Pez8jlKeUL1J9/NgNbdlBMpag9+wziHXuJ79oDgGLTiJy5HEfQP3rs27JIHjhMaOG8CTl3IYQQZZK0K4w+PDvcNnsWzuowxUQKxWbD7veiez3kYkMUkinCi9uJbn6l/CJ1/Oet+9ZtoPE9q6k790wsFJJ7XyPXPzCy3SoZ9L6wkaZ3XUB06w5yR9yVG5ksvLnaOEIIIU4SSdoVSrXZcAQDOIKBUe2KpuKqrsKyTBRVxTIMCvEEzuowuejgqH19LU0YqQyGAppdJ7Hv4NgDWRa5wRhVixcwCCPd6Z7mRtCk+IoQQkwkmf47zegeN4F5baQ7ewkMP/41tOc1/G0teJsbQVFQNI3A/Dl4Z8/CLBTo/fNLFFNpFNv4k8oUFDLdvYQWLwDA01SPI+jH7nnrldiEEEKcOEna04yqaeh+L4F5rbjrIwTmtoICfes3o9rtNL3rAiLvWIFpljAyWVIHDuOsqcYRDIwk+VHvZ9exLIt8PAGmScNF5xFetghnODQJZyeEEDObdI9PQ3afF81hp5jK4F8wF29zI8V0hlI+j4WFszqMsypE8sAhSsUikRVLOfz7ZwjMn0tgwRwSe/ZjGQb2UIDQwnlEN2/D39qMaRooioWCdIsLIcRkkKQ9TWl2O3ih78VNpA6Wn++2BwNku/vwtTbjb2sh2D4fT3MTg1u2Yxkm8Z0dOKvDVC1bBIqK7vXQ+8JGFFVB93rQXW76N24h1D5vzFj6TPFmV5sTQohTQZL2NFZMpUkd7ET3+wgvmk9uIEYpm8UsFMqPcFkWZqlEYSg58ppcdHBkwlpw0XxCi+ZhczqxeT0YhkGufwBlcftkndKEKWbzpHoG6N26l6p5s9B9bgCa/NVEdx/AsiDdO4CvoQZ/YzX6cBU6IYQ4lWRMe5op5csJOTcYA01DdTkJL15A3/rNDL26j/ThbqKbXqEQG6L72XWYxRLOqvHHp13VVTirwiiqQmzXqyRf3Ydq19H907ugSiGdpWdzB5t/+Aj+xhosLGwOG5gmpWwem8PO4J5DmIUSnet3cPC5VzAKheO/sRBCvE1ypz1NWKZFPpGgGE8wuG0XxWQKZ00VDeedzdDe17BM842dFTBLJUqZLEY+j7suQrqzB/OIxOOsqaKUzdL/7DoAbB43gfltBBfOw+6rrKRdzOZJ98WI7etEdzkJttbjqQmhHPX8eiGdY6DjIGbJYO8T6wm01FEqFPA11dC/7TX2P70JyzBRNJXZq1fgCHhxBn3s+s2fqF06B29d1SSdoRBippCkPU0UM2lKqTQ9z68facv29tP1x+doWH0uyb0HRtoVVcMslVcOG9q9l0D7HKpOX0wpk6WUSuOsDqP7fXQ99dzIazyNdfhmN1dUN3A2liSfTDP46mH2/3HjSLtq0zj9b/4XwZa6kTbLsujbuoeO3z7PvMvPoWpRC62rV4JlkR1I8NofNryxr2Hy2pMbOO2jlxLf34WnOkhuKCVJWwhxykn3+DRhGSZDHfvGtJulEvnBOOGli47Y10AbnlCVj8WJ73wVTAt7wE9gXhuK3U7X08+PVDyzuV04QkFy0YEx7z8VWaZFsjvKwO6DFJIZ9j+1cdR2s2TQ8chzFNLZkbbcUIp9f9iAoip4Z1Uz6x2LKSQzHHhqEz0vvzrucXq37qVq/mxMw0B3yQQ1IcSpJ3fa04aFkcuNu6WYzeJtasRdH6H76XUY+TzFZIrQknZi23ZRiCfo3/AyNo+b+vPORrPpRM5YTimTQXXYwbTo37gVm8uJ7vXiDAcn+NzevHwqQ6Z/iFTvAAef3cLsi5bjCvkpZnKUcm90/6e6ByhlC9g95Z4Dq2RQyuaZd8U55KIJSvkCh/+8DbvPjcM/fhEZRVXQPXaCrQ3YJGkLISaAJO1pQtE0PE0Noxf2GOaqrqIQj6PoOvWrzyHb249VMnDVVuMIBSllMticTnSfl9xgHMsoEd24FdVhxyqWRsbDS+kM2d4+VN02Jce1MwMJMv0x+rbvwzIt2q8+H4fPzazzl2EZBv6GGoxCiXwizcF121D1Nz7+uttFdXsLzqCXPY++wNzLV5EdTJCLp1jwV++k75W9Y45Xu3QupWKJxncsBkUhO5jANEzsHie62zmRpy6EmCEkaU8TiqLiaWog3dlNfjA+0h6Y10puMIbN4SC2cwdVpy+hMJSkmE7jqouge92odh0zn6eQSoNpoNrKHwvzqJXBXPURsv0D2P3+KZe00wNDZPpibH/wD7RdciZGvsDhddvwNVRT3d6C6rCR7ouz81dP4Y2EmXfpO0bdHetuB3MuW0XiUC/ZwQTK8Bw1yzSJ7jpA27vO5MCzWzDyBTSHnbZLzsAZ9NK1fgeoKpqucfiFHRj5Ar6mCAv+6lz8jZFJuhpCiOlKxrSnCd3jRrHr1Jy1nPrzV1G76gzqL3onut+H7naReO0AxWQKs1gkMLcVd001nb9/hu5nXyDb3QuKiqs6jKJppDp7CLbPHfX+msOOv7WF9OHu0TPRp4BMbAgjX2DfE+tpvWglXS/t5LU/bmRg90H2P7WJl3/8O9Ldg2g2Gys/9T6ygwm2/ORRUl39o97HUx1AtWnYfW4UVcNTGwYguusAvVv2MPvC5Sy85kJW/O2VeJtq2P/UJvp37sfucXLgmZdHlj9NHu7j5Xv/h2RXdMKvhRBiepOkPY04vB40pwPNYUfRVIx0hlz/AIWhBMH5c9D9PmwuJ6VcjsTwbHKbx40rUoPmcmIUCjjCQULz52CZFpF3rCC8dBGRs1dSf8E59L20GRSm3HPapUyBUq5APpHG5rSTicZHb8/mGdzbyWt/3EAxmeH0v74cyzLp2fIqxVx+1L6e2jDN71zGoT9vZe57zsY/q3y3nO6LMfjqYZwhHx2PrSPdFaVv2z4iS+bQvXH32JhyBZLdUZI9lTF5TwhRGaR7fJqxezzkSgaJ7bvJdPWMtCuqSsPqcymmM2R7+vDPnY17ViOqooAyPFG8VMICjFIRb0sTpWQKVdMopDNkunvBtKg/f9WU6xovZnKouo26FfNJHSNJpnoGcFcF6HtlL8HWBprPO510zwBmsQTON7rJPdUBLMPA4XeTjsZovXglxUweRVFxhrxYlsnQ3i4i7bPBstB0jVJ+/MIqpUye2L4uPDUhVE1+Hwsh3j5J2tOQkcmOSthQHpsd2LoDT0MdNo8bZ00VxfgQ+Xgcd10tAy9vJz8YQ3M5qVq2GM3pwOb1oPt9KDYNs1CietlibF4PijK1FgxRbRqqzUbd8vnE93aNu49/Vi2JQ704A16wLALNtbirA68/1TZCUVV89dW4qwIYxRIoYBRKANgcdob6B7E57RTSWZwhHwN7DlOzqJWul3Ye9UagOe0cem4L1QtbcIf8p+DMhRAzjfz8n4ZKmey47flYHLCIbd9N3/rNGPk8nvo6up9ZR34wBoCRzdH3wkaMbB6jUMBVU4UzFMRdW43u8065hA2ge5xodhuWYWJh4a4Z/UiazeWgZuFskof7Cc9tYqDjIJZl4akL4/SN/ziXZtexe1zY3S5cQR+uoA/d5aAr3s/pf30Fsb1dtF60kkx/HG9deKQbHco/IuZcejbdm3YDypS8ZkKIyiR32tOQ5rCP2+4IBigk0wCUUuV/80NJLMMYs298Zwc1Zy0/dUGeRJ7qIEOH+gBI9wzScEY7RqFEsrMfV1UATyRILp6kZkkbrrCfzhe303zeMlzhE7/7LRaL+JsinPbRd1NIZ1nxt39FbijFnPecjZEtkBtKAdC1cReprijzrzwXV9B3Us9XCDFzSdKehlSHvfx4VnffG42KQmBeG33rN480FeIJ7KHxC6WYhSKoldERo6gqiqZSyuQIzW3i8Lpt5BNpPDUhYvs6CcyKYHM7qTt9Hq8+to4FV74Td3UQTX/rH3+H143DW175y9dQTSaRJnW4j9f+uJFiOouiKjSevZjQ3KaTdZpCCCFJezpy+LyEFi3A29RAurMHm9uFq6aK2M6OUXfVdr8Pm8c97nv4WpvLa3JXCGfAy1CyPHs8NKcRZ9BbXtxDVXBW+THyRTTdxqL3X4TvFNQId/s9uBe14q4OUkzn0Bw2PDWht/XDQAghjibfKNOQ5nTirLKhuRw4I1WAQteTf8LIv/F4k+7zgqKgmCahxQuI7dg9UmvcGanG3ViP7qqcql4OnxtfbRXZWIJgSx2lfBHNbsMZ9OKrr56wOLyR8Zc5FUKIk0GS9jSl6Ta0QACAQjpDw+pzyfT0kRuI4YpU46ypQrEs8kMJXPW1uOtrMbI5VLteXiAkGJjkMzhxrrAfZ9BHIZNF1TRZxEMIMe1I0p4B7B43eNw4q0KYhoGiqlimSTGZQnE6yl3kponmdmH3eMasM11JFFUZGWsWQojppjJmGomTRtU0FEVB1TQcwQB7+3pweD04/D4cPm9FJ2whhJjuJGnPcOYUqyMuhBDi2CRpCyGEEBVCxrSFEG9aX3c/mUwWu92OUTKwLHB7XVRHwpMdmhAzgiRtIcSb0nWoh1KxRDyaYM+u1+g80E3rvGYWnDaXXDZHU0vDZIcoxLQnSVsIcVymaVIoFEjEUvz/X/sBnQe6R7a1LZjNF27/FPlcAYezcgryCFGJZExbCHFcyUSaQq7IlvXbRiVsgH2799OxfS+xgfgxXi2EOFkkaQshjiuTTGNZFts27Rp3+9aXtqPJmuFCnHLyv0wIcVyqqlHIFZg9r3nc7XPaW/EcY5lTIcTJI0lbCHFcFhaGYfDOd70Dt8c1aps/6GP5qtPGtAshTj6ZiCaEACCTynD4QBeZdI76pgjBcACHs1y/XVEUUskM4eogd37//+XxXz9Fx7a9nLZyEZe893wCIVkzXIiJIElbCEG0b5DNL7xCQ3MthVyBPzzyJ8I1IRpb6vD6vdQ3RTh8oJtYdIjBaIx3X7Wa97zvIvK5Ak6Hg0hdzWSfghAzgiRtIQRdB3vx+Nw8/eif+e+fPTrS7vV7uP3fvsThA91c8J5V/Pud99I6r5mObXvp7ernHecvp7GlfhIjF2JmkaQtxAyVHEqRTKQwSgY2XcMyrVEJGyCVSPOLu/+Lv/38R9BtOjf/46fpOdxHLpfn7AtWUttYg6rK1BghJookbSFmmFKxxPaXd/P7Xz/FgqXzaJnTjG630dvVP+7+L7+4jWw2h6qpGEMGC06bO8ERCyFeJz+RhZhh9nUc4IEf/hfnXnw2tQ01/OmJdTz7+DpmzW5g5TlLx+xf3xSh+3AfLo+THS93cGDf4UmIWggBcqctxIzzx//5E1d95DK6D/Xx73fdO9L+nz95hE/c8jH6ewY5eERi/sgN7yfaO8j3vv4Tnvv9C8xb3MYnb7mOJSvapWtciAkm/+OEmGHSiQwer4d7v/3zMdvu/96v+OvPfpg57a185FPv519+8jUOH+hm4bL5PP/kiwC8un0ft37yDl7dsW+iQxdixpOkLcQMc8Z5p5PNZMllc2O2ZVIZfEEvf/O5D7N90y7++dbvEh8YIpPO8PH/Z83IfoV8ga0bdhCLxuk61EssGiPaP4hhGBN5KkLMONI9LsQMM3dhG4lYAl23USyWRm3zB7xkklluu/EbWJYFwG9+8RgvPL2Bz972CYJhP/HBBADRnkGifVFME7oOpMiks9TUV1NdF6ImUj3h5yXETCBJW4gZprG5Dt2m8cHrr+Jn//GrUds+cct1PPSj/x5J2K/r644SH0iwZHk7z/1hPQALl83DNOBbt/0HB/YcYs7C2aiKyiVXnc9Z71xOfVPdhJ2TEDOFJG0hZqBIQw0XX3k+bfObefQ//4iiKrznfRcRDAfo6Rz/0a+SUSJQFQDgmuv+F/Wzannm0T9T21DDRz55DZqu4fd7sCzo6x4gk8rh8cgiIkKcTJK0hZihmlrqqW+KsHjFQnLZHNs37eLH9/+OpWcu4g+PPDuy3+Ll7Vx69Wr6uqP4/B7+6d7b8Pg8DA0O4fQ4Of/dZ9Pb1UdVTZhvf/vnHNrXCcDKVcv4+GfXHOvwQoi3QJK2EDOYpmkEQj76ewfY+tIOznv32bi9bras30a0d5D6WbWcf+kq/s9t/zHSZf7AD3/N33zuWp559M/UzYqw/Kwl9HZFuefbP6O/Z2DkvTeu24LDZeeGL36cusbIZJ2iENPKhM4ef/jhh7ngggtYu3Yta9eu5Xvf+95EHl4IMQ7LtFAVhXdfvZrtm3ZRP6uWL379Rr78T5/l07dez8/+41djxrh//v2Hedd7L+D5J9fzs+//J23tLaMS9uteeHojiXhyok5FiGlvwh/5uvzyy7nvvvu47777+NSnPjXRhxdCHGXHlg5u+tj/R+f+LprnNNF9sBdV1fj+P/+UnsO94ybdXDYHigLApnVb0fXxO+0sy0IZ3k8I8fbJc9pCzGCJoRQ/+OZPyecKRPti/OGRP5HL5vi3O+9hsD9GqVjC4bSPeZ1m07DZtJG/i4Ui4ZrQmP1WrT6DQNh/Ss9BiJlkwpP2+vXruf7667nuuuvYsWPHRB9eCHGERDxJx7a9APzy3l/zd19Yy5z2VvbtPgDAU797nqs+cvmY1112zcX8+Y8vATB77iwsC/72po9SP6t2ZJ+lZyxk7ac/iNcnM8iFOFkU6+jBqpPkoYce4qGHHhrVdsUVV9DS0sKFF17I5s2b+cpXvsIjjzxyzPfI5/Ns27btVIQnhADCvmr+4e/vYumZi3nvte9h60s7sDvs/PBb91PIFwB413svZN6iVp5/cj2mZXLuxe+g62APv/nFY0Tqq/mHb32BXDaHpmkoChTyRZwuB76Al2QmQTaXmeSzFKLyLFmyBIfDMab9lCXtN+Pcc8/l2WefRdO0cbe/nrSPFfx4Nm7cyMqVK09mmNOaXK8TM92u1+7te3F7XGSSabZu2MHd/3I/i5YvYM6C2TzywOMj+7k9Lt577XtYfcV5KMBgNI5pWdQ11NDYUo9lWUR7BzFNk3B1EN2uA9Pvep1qcr1OzHS8XsfLexPaPX733Xfz29/+FoCOjg7C4fAxE7YQ4tRKDqXQVJWHf/IIfOC0vwAACGRJREFU2zfv5pf3/gaAHZt343I7WfN3VxOuDuL2ujn/0lW848IzSA4lsdltLD/7NFauWkpjSz0AiqJQU1dFbUPNSMIWQpx8E/qc9pVXXsktt9zCAw88QKlU4o477pjIwwshjhAfjPPtr36fqkiIpWcuJp16oxv7l/f+N02zG7jsmotZsnIhXYd6cTodRBob8XpljFqIyTKhSbuuro777rtvIg8phBhHqVhiKJbknIvO5OUXt/HiM5u49Z8/xy/ufphXt5eX3Dy8v4sH7vk1d5yxiLb5zdQ21eDxuCc5ciFmNqmIJsQM1H24l5/864NseWn7SNvTjz3PLXd8mu987W6ymRw2m42bvnYDVbVhmlsb5XlrIaYASdpCzDDpZJq+nuiohA1gGia/vv9Rvvqv/5tsOktVTZi29hZ0XcaohZgqJGkLMcP0dPYxNLwm9tEO7D2Ez+8lFA7SNLse2zEqnQkhJof8jxRihsll81RFwuNua186D4/XRf0sWQtbiKlIypgKMcNU11XhdDm44LJzRrXbHXbW/v0H0GzyW16IqUr+dwoxw9TW19B9sJcP/vV7ufA95/LiMxuJ1FezYtVSauqqqKmrnuwQhRDHIElbiBlo6ZmL6DrYg9vj4iOffB92l4NQODjZYQkhjkOSthAzkKqqNM1uoGl2w2SHIoQ4ATKmLYQQQlQISdpCCCFEhZCkLYQQQlQISdpCCCFEhZCkLYQQQlQISdpCCCFEhZCkLYQQQlQISdpCCCFEhZjSxVUsywKgUCic0Ovy+fypCGfakut1YuR6nRi5XidGrteJmW7X6/V893r+O5piHWvLFJBMJuno6JjsMIQQQogJNX/+fHw+35j2KZ20TdMknU6j6zqKokx2OEIIIcQpZVkWxWIRj8eDqo4dwZ7SSVsIIYQQb5CJaEIIIUSFkKQthBBCVAhJ2kIIIUSFkKQthBBCVIhpk7TXr1/PqlWreOqpp0badu3axZo1a1izZg233XbbJEY3dT388MNccMEFrF27lrVr1/K9731vskOaku68804+9KEPsWbNGrZu3TrZ4Ux5L774ImefffbI5+r222+f7JCmpI6ODi655BLuv/9+ALq7u1m7di3XXnstn/3sZ0+4RsV0d/T1+tKXvsSVV1458jl7+umnJzfACTCli6u8WQcPHuRHP/oRK1asGNV+xx13cOutt7J06VK+8IUv8Mwzz3DBBRdMUpRT1+WXX84Xv/jFyQ5jylq/fj0HDhzgwQcfZO/evdx66608+OCDkx3WlHfWWWfxne98Z7LDmLIymQy33347q1atGmn7zne+w7XXXstll13Gt771LX71q19x7bXXTmKUU8d41wvgpptuYvXq1ZMU1cSbFnfaNTU1fPe73x31IHqhUKCzs5OlS5cCsHr1atatWzdZIYoKtm7dOi655BIA5syZw9DQEKlUapKjEpXObrdz9913E4lERtpefPFFLr74YkC+s4423vWaiaZF0na5XGiaNqotFovh9/tH/q6qqqK/v3+iQ6sI69ev5/rrr+e6665jx44dkx3OlBONRgmFQiN/h8Nh+Sy9CXv27OGGG27gwx/+MM8///xkhzPl2Gw2nE7nqLZsNovdbgfkO+to410vgPvvv5+PfexjfP7zn2dwcHASIptYFdc9/tBDD/HQQw+Narvxxhs577zz/uLrpIbM+Nfuiiuu4MYbb+TCCy9k8+bNfPGLX+SRRx6ZpAgrg3yWjm/27Nl85jOf4bLLLuPQoUN87GMf44knnhhJSOL45HN2fO9973sJBoMsXLiQH/zgB3z3u9/lK1/5ymSHdUpVXNL+wAc+wAc+8IHj7hcOh4nH4yN/9/b2zvhuleNdu+XLlzM4OIhhGGN6LmaySCRCNBod+buvr4+amppJjGjqq62t5fLLLwegubmZ6upqent7mTVr1iRHNrW53W5yuRxOp1O+s96EI8e3L7roIr761a9OXjATZFp0j49H13Xa2trYsGEDAE888cRx78Znorvvvpvf/va3QHlmZjgcloR9lHPPPZfHH38cgO3btxOJRPB6vZMc1dT2m9/8hnvuuQeA/v5+BgYGqK2tneSopr5zzjln5LMm31nHd+ONN3Lo0CGgPB9g3rx5kxzRqTctao8//fTT3HPPPezbt49wOExNTQ333nsve/bs4Stf+QqmabJs2TK+/OUvT3aoU05PTw+33HILlmVRKpVGZtuL0b75zW+yYcMGFEXhtttuo729fbJDmtJSqRQ333wziUSCYrHIZz7zGXly4yjbtm3jG9/4Bp2dndhsNmpra/nmN7/Jl770JfL5PA0NDdx1113ouj7ZoU4J412vj370o/zgBz/A5XLhdru56667qKqqmuxQT6lpkbSFEEKImWDado8LIYQQ040kbSGEEKJCSNIWQgghKoQkbSGEEKJCSNIWQgghKoQkbSEEAK+88gqXXHLJqLrqt99+O9/4xjcmMSohxJEkaQshADjttNO46qqr+PrXvw7Ahg0bWL9+PZ/73OcmOTIhxOskaQshRtxwww3s3r2bJ598kq9+9avcddddOByOyQ5LCDFMiqsIIUbZt28fV111FR//+Me56aabJjscIcQR5E5bCDFKR0cHTU1NbNq0SVaaEmKKkaQthBjR39/Pt771LX70ox8RiUT46U9/OtkhCSGOIN3jQogRn/jEJ7jsssu4+uqrGRwc5JprruHHP/4xLS0tkx2aEAK50xZCDHvggQcAuPrqq4HymvSf//zn+fKXv4xpmpMZmhBimNxpCyGEEBVC7rSFEEKICiFJWwghhKgQkrSFEEKICiFJWwghhKgQkrSFEEKICiFJWwghhKgQkrSFEEKICiFJWwghhKgQ/xfbvYw19AStTgAAAABJRU5ErkJggg==\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import umap\n",
        "scaling=StandardScaler()\n",
        "scaling.fit(X1)\n",
        "Scaled_data=scaling.transform(X1)\n",
        "reducer = umap.UMAP()\n",
        "Xumap1 = reducer.fit_transform(Scaled_data)\n",
        "\n",
        "from sklearn.cluster import DBSCAN\n",
        "sh_list = []\n",
        "for i in np.arange(0.10,0.5,0.01):\n",
        "    dbscan_umap1 = DBSCAN(eps=i, metric = 'euclidean').fit(Xumap1)\n",
        "    sh = silhouette_score(Xumap1, dbscan_umap1.labels_, metric = 'euclidean')\n",
        "    sh_list.append([i,sh])\n",
        "    \n",
        "sh_arr = np.array(sh_list)\n",
        "_ = plt.plot(sh_arr[:,0],sh_arr[:,1])"
      ],
      "metadata": {
        "id": "ZBI975jOGT7x"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "DBSCAN(eps=1, metric = 'euclidean').fit(X1)\n",
        "np.unique(dbscan_umap1.labels_)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "RgoqiCOZGgrt",
        "outputId": "7dbf64b1-9071-4ba6-fa62-c96a77c5e154"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([-1])"
            ]
          },
          "metadata": {},
          "execution_count": 33
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## TSNE"
      ],
      "metadata": {
        "id": "1-hBxW-spk0x"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.manifold import TSNE\n",
        "scaling=StandardScaler()\n",
        "scaling.fit(X1)\n",
        "Scaled_data=scaling.transform(X1)\n",
        "Xtsne1 = TSNE(n_components=2).fit_transform(Scaled_data)\n",
        "from sklearn.cluster import DBSCAN\n",
        "sh_list = []\n",
        "for i in np.arange(1.9,3.0,0.01):\n",
        "    dbscan_tsne1 = DBSCAN(eps=i, metric = 'euclidean').fit(Xtsne1)\n",
        "    sh = silhouette_score(Xtsne1, dbscan_tsne1.labels_, metric = 'euclidean')\n",
        "    sh_list.append([i,sh])\n",
        "    \n",
        "sh_arr = np.array(sh_list)\n",
        "_ = plt.plot(sh_arr[:,0],sh_arr[:,1])"
      ],
      "metadata": {
        "id": "uqGILu91IIdC"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "new_data = pd.read_csv('/content/new_data (1).csv')"
      ],
      "metadata": {
        "id": "q1Lq7IruJeyz"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "new_data = new_data.iloc[:,1:]\n",
        "new_data"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        },
        "id": "hVX7svIBEZlx",
        "outputId": "4e154596-26a8-443a-81db-7bdbe91b17bf"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "              X         Y  Lables\n",
              "0     14.241011  2.372252       0\n",
              "1     12.587422  3.434279       0\n",
              "2     10.477040  2.065177       0\n",
              "3      9.291985  4.902061       0\n",
              "4      4.509871 -1.385614       0\n",
              "...         ...       ...     ...\n",
              "2492  14.805978  1.928994       0\n",
              "2493   9.970026  4.018586       0\n",
              "2494   2.974874 -4.333831       1\n",
              "2495   1.513629 -0.817950       1\n",
              "2496  12.005279  8.446909       0\n",
              "\n",
              "[2497 rows x 3 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-dab6ac24-1952-4c83-9d87-ec7de5ecf3c0\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <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>X</th>\n",
              "      <th>Y</th>\n",
              "      <th>Lables</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>14.241011</td>\n",
              "      <td>2.372252</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>12.587422</td>\n",
              "      <td>3.434279</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>10.477040</td>\n",
              "      <td>2.065177</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>9.291985</td>\n",
              "      <td>4.902061</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>4.509871</td>\n",
              "      <td>-1.385614</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2492</th>\n",
              "      <td>14.805978</td>\n",
              "      <td>1.928994</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2493</th>\n",
              "      <td>9.970026</td>\n",
              "      <td>4.018586</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2494</th>\n",
              "      <td>2.974874</td>\n",
              "      <td>-4.333831</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2495</th>\n",
              "      <td>1.513629</td>\n",
              "      <td>-0.817950</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2496</th>\n",
              "      <td>12.005279</td>\n",
              "      <td>8.446909</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>2497 rows × 3 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-dab6ac24-1952-4c83-9d87-ec7de5ecf3c0')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-dab6ac24-1952-4c83-9d87-ec7de5ecf3c0 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-dab6ac24-1952-4c83-9d87-ec7de5ecf3c0');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Standardization"
      ],
      "metadata": {
        "id": "sQEnmvy_pn1C"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.preprocessing import MinMaxScaler\n",
        "sc = MinMaxScaler(feature_range = (0,1))\n",
        "X = new_data.iloc[:,:-1]\n",
        "Y = new_data['Lables']\n",
        "X = sc.fit_transform(X)"
      ],
      "metadata": {
        "id": "-mglMKTLMZA6"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Deep Belief Network"
      ],
      "metadata": {
        "id": "tt52SaxZoZRK"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "!git clone https://github.com/albertbup/deep-belief-network.git"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ZwnBQTCpMdCG",
        "outputId": "cccfcb32-efe9-48d7-f215-4191b38c443c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Cloning into 'deep-belief-network'...\n",
            "remote: Enumerating objects: 798, done.\u001b[K\n",
            "remote: Counting objects: 100% (35/35), done.\u001b[K\n",
            "remote: Compressing objects: 100% (26/26), done.\u001b[K\n",
            "remote: Total 798 (delta 13), reused 20 (delta 9), pack-reused 763\u001b[K\n",
            "Receiving objects: 100% (798/798), 183.45 KiB | 5.92 MiB/s, done.\n",
            "Resolving deltas: 100% (459/459), done.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "%cd deep-belief-network\n",
        "!pwd"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "0pqDKHJ8MfGq",
        "outputId": "5130aa2b-cf91-4536-e36f-e3d8ffdb7dee"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "/content/deep-belief-network\n",
            "/content/deep-belief-network\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Initial Model (DBN)"
      ],
      "metadata": {
        "id": "tcUt-R43oc8m"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from dbn.tensorflow import SupervisedDBNClassification\n",
        "\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.metrics import accuracy_score\n",
        "\n",
        "X_train, X_test, Y_train, Y_test = train_test_split(X, Y, test_size=0.3, random_state=0)\n",
        "\n",
        "classifier = SupervisedDBNClassification(hidden_layers_structure=[50, 50],\n",
        "                                         learning_rate_rbm=0.05,\n",
        "                                         learning_rate=0.09,\n",
        "                                         n_epochs_rbm=30,\n",
        "                                         n_iter_backprop=500,\n",
        "                                         batch_size=16,\n",
        "                                         activation_function='relu',\n",
        "                                         dropout_p=0.2)\n",
        "classifier.fit(X_train, Y_train)\n",
        "\n",
        "Y_pred = classifier.predict(X_test)\n",
        "print('Done.\\nAccuracy: %f' % accuracy_score(Y_test, Y_pred))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "tz-SwnF3MhJ1",
        "outputId": "4b82493b-7171-4b17-d7ea-89b38fce5743"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[START] Pre-training step:\n",
            ">> Epoch 1 finished \tRBM Reconstruction error 0.055238\n",
            ">> Epoch 2 finished \tRBM Reconstruction error 0.058672\n",
            ">> Epoch 3 finished \tRBM Reconstruction error 0.052639\n",
            ">> Epoch 4 finished \tRBM Reconstruction error 0.056952\n",
            ">> Epoch 5 finished \tRBM Reconstruction error 0.068054\n",
            ">> Epoch 6 finished \tRBM Reconstruction error 0.064140\n",
            ">> Epoch 7 finished \tRBM Reconstruction error 0.054566\n",
            ">> Epoch 8 finished \tRBM Reconstruction error 0.055805\n",
            ">> Epoch 9 finished \tRBM Reconstruction error 0.054793\n",
            ">> Epoch 10 finished \tRBM Reconstruction error 0.056554\n",
            ">> Epoch 11 finished \tRBM Reconstruction error 0.050124\n",
            ">> Epoch 12 finished \tRBM Reconstruction error 0.056809\n",
            ">> Epoch 13 finished \tRBM Reconstruction error 0.054958\n",
            ">> Epoch 14 finished \tRBM Reconstruction error 0.056694\n",
            ">> Epoch 15 finished \tRBM Reconstruction error 0.055558\n",
            ">> Epoch 16 finished \tRBM Reconstruction error 0.051133\n",
            ">> Epoch 17 finished \tRBM Reconstruction error 0.054393\n",
            ">> Epoch 18 finished \tRBM Reconstruction error 0.068293\n",
            ">> Epoch 19 finished \tRBM Reconstruction error 0.066963\n",
            ">> Epoch 20 finished \tRBM Reconstruction error 0.055759\n",
            ">> Epoch 21 finished \tRBM Reconstruction error 0.049313\n",
            ">> Epoch 22 finished \tRBM Reconstruction error 0.048298\n",
            ">> Epoch 23 finished \tRBM Reconstruction error 0.054003\n",
            ">> Epoch 24 finished \tRBM Reconstruction error 0.070293\n",
            ">> Epoch 25 finished \tRBM Reconstruction error 0.046014\n",
            ">> Epoch 26 finished \tRBM Reconstruction error 0.043820\n",
            ">> Epoch 27 finished \tRBM Reconstruction error 0.034412\n",
            ">> Epoch 28 finished \tRBM Reconstruction error 0.032284\n",
            ">> Epoch 29 finished \tRBM Reconstruction error 0.035739\n",
            ">> Epoch 30 finished \tRBM Reconstruction error 0.036787\n",
            ">> Epoch 1 finished \tRBM Reconstruction error 0.077449\n",
            ">> Epoch 2 finished \tRBM Reconstruction error 0.046385\n",
            ">> Epoch 3 finished \tRBM Reconstruction error 0.040832\n",
            ">> Epoch 4 finished \tRBM Reconstruction error 0.066873\n",
            ">> Epoch 5 finished \tRBM Reconstruction error 0.043085\n",
            ">> Epoch 6 finished \tRBM Reconstruction error 0.039994\n",
            ">> Epoch 7 finished \tRBM Reconstruction error 0.036944\n",
            ">> Epoch 8 finished \tRBM Reconstruction error 0.064276\n",
            ">> Epoch 9 finished \tRBM Reconstruction error 0.046007\n",
            ">> Epoch 10 finished \tRBM Reconstruction error 0.034535\n",
            ">> Epoch 11 finished \tRBM Reconstruction error 0.033871\n",
            ">> Epoch 12 finished \tRBM Reconstruction error 0.035013\n",
            ">> Epoch 13 finished \tRBM Reconstruction error 0.032970\n",
            ">> Epoch 14 finished \tRBM Reconstruction error 0.049914\n",
            ">> Epoch 15 finished \tRBM Reconstruction error 0.045550\n",
            ">> Epoch 16 finished \tRBM Reconstruction error 0.033083\n",
            ">> Epoch 17 finished \tRBM Reconstruction error 0.100189\n",
            ">> Epoch 18 finished \tRBM Reconstruction error 0.088831\n",
            ">> Epoch 19 finished \tRBM Reconstruction error 0.273092\n",
            ">> Epoch 20 finished \tRBM Reconstruction error 0.093056\n",
            ">> Epoch 21 finished \tRBM Reconstruction error 0.091010\n",
            ">> Epoch 22 finished \tRBM Reconstruction error 0.245397\n",
            ">> Epoch 23 finished \tRBM Reconstruction error 0.398102\n",
            ">> Epoch 24 finished \tRBM Reconstruction error 0.485908\n",
            ">> Epoch 25 finished \tRBM Reconstruction error 0.449327\n",
            ">> Epoch 26 finished \tRBM Reconstruction error 0.498909\n",
            ">> Epoch 27 finished \tRBM Reconstruction error 0.409496\n",
            ">> Epoch 28 finished \tRBM Reconstruction error 0.321492\n",
            ">> Epoch 29 finished \tRBM Reconstruction error 0.412246\n",
            ">> Epoch 30 finished \tRBM Reconstruction error 0.393452\n",
            "[END] Pre-training step\n",
            "[START] Fine tuning step:\n",
            ">> Epoch 0 finished \tANN training loss 0.477707\n",
            ">> Epoch 1 finished \tANN training loss 0.402728\n",
            ">> Epoch 2 finished \tANN training loss 0.336790\n",
            ">> Epoch 3 finished \tANN training loss 0.290777\n",
            ">> Epoch 4 finished \tANN training loss 0.246681\n",
            ">> Epoch 5 finished \tANN training loss 0.224470\n",
            ">> Epoch 6 finished \tANN training loss 0.215987\n",
            ">> Epoch 7 finished \tANN training loss 0.214611\n",
            ">> Epoch 8 finished \tANN training loss 0.211828\n",
            ">> Epoch 9 finished \tANN training loss 0.195194\n",
            ">> Epoch 10 finished \tANN training loss 0.192838\n",
            ">> Epoch 11 finished \tANN training loss 0.217064\n",
            ">> Epoch 12 finished \tANN training loss 0.209907\n",
            ">> Epoch 13 finished \tANN training loss 0.192013\n",
            ">> Epoch 14 finished \tANN training loss 0.202081\n",
            ">> Epoch 15 finished \tANN training loss 0.190726\n",
            ">> Epoch 16 finished \tANN training loss 0.321464\n",
            ">> Epoch 17 finished \tANN training loss 0.187387\n",
            ">> Epoch 18 finished \tANN training loss 0.166302\n",
            ">> Epoch 19 finished \tANN training loss 0.159199\n",
            ">> Epoch 20 finished \tANN training loss 0.168633\n",
            ">> Epoch 21 finished \tANN training loss 0.173402\n",
            ">> Epoch 22 finished \tANN training loss 0.166255\n",
            ">> Epoch 23 finished \tANN training loss 0.421134\n",
            ">> Epoch 24 finished \tANN training loss 0.243502\n",
            ">> Epoch 25 finished \tANN training loss 0.185364\n",
            ">> Epoch 26 finished \tANN training loss 0.178895\n",
            ">> Epoch 27 finished \tANN training loss 0.205123\n",
            ">> Epoch 28 finished \tANN training loss 0.190385\n",
            ">> Epoch 29 finished \tANN training loss 0.205449\n",
            ">> Epoch 30 finished \tANN training loss 0.183740\n",
            ">> Epoch 31 finished \tANN training loss 0.124378\n",
            ">> Epoch 32 finished \tANN training loss 0.178016\n",
            ">> Epoch 33 finished \tANN training loss 0.187526\n",
            ">> Epoch 34 finished \tANN training loss 0.135388\n",
            ">> Epoch 35 finished \tANN training loss 0.283580\n",
            ">> Epoch 36 finished \tANN training loss 0.316016\n",
            ">> Epoch 37 finished \tANN training loss 0.136560\n",
            ">> Epoch 38 finished \tANN training loss 0.172834\n",
            ">> Epoch 39 finished \tANN training loss 0.147023\n",
            ">> Epoch 40 finished \tANN training loss 0.168945\n",
            ">> Epoch 41 finished \tANN training loss 0.270920\n",
            ">> Epoch 42 finished \tANN training loss 0.279715\n",
            ">> Epoch 43 finished \tANN training loss 0.214580\n",
            ">> Epoch 44 finished \tANN training loss 0.126566\n",
            ">> Epoch 45 finished \tANN training loss 0.293214\n",
            ">> Epoch 46 finished \tANN training loss 0.174745\n",
            ">> Epoch 47 finished \tANN training loss 0.219307\n",
            ">> Epoch 48 finished \tANN training loss 0.287956\n",
            ">> Epoch 49 finished \tANN training loss 0.179467\n",
            ">> Epoch 50 finished \tANN training loss 0.276057\n",
            ">> Epoch 51 finished \tANN training loss 0.174561\n",
            ">> Epoch 52 finished \tANN training loss 0.168311\n",
            ">> Epoch 53 finished \tANN training loss 0.183292\n",
            ">> Epoch 54 finished \tANN training loss 0.164485\n",
            ">> Epoch 55 finished \tANN training loss 0.119422\n",
            ">> Epoch 56 finished \tANN training loss 0.164123\n",
            ">> Epoch 57 finished \tANN training loss 0.177243\n",
            ">> Epoch 58 finished \tANN training loss 0.166955\n",
            ">> Epoch 59 finished \tANN training loss 0.196538\n",
            ">> Epoch 60 finished \tANN training loss 0.205300\n",
            ">> Epoch 61 finished \tANN training loss 0.216518\n",
            ">> Epoch 62 finished \tANN training loss 0.167596\n",
            ">> Epoch 63 finished \tANN training loss 0.253012\n",
            ">> Epoch 64 finished \tANN training loss 0.407995\n",
            ">> Epoch 65 finished \tANN training loss 0.269456\n",
            ">> Epoch 66 finished \tANN training loss 0.130170\n",
            ">> Epoch 67 finished \tANN training loss 0.192937\n",
            ">> Epoch 68 finished \tANN training loss 0.321856\n",
            ">> Epoch 69 finished \tANN training loss 0.211418\n",
            ">> Epoch 70 finished \tANN training loss 0.262453\n",
            ">> Epoch 71 finished \tANN training loss 0.278257\n",
            ">> Epoch 72 finished \tANN training loss 0.229949\n",
            ">> Epoch 73 finished \tANN training loss 0.156356\n",
            ">> Epoch 74 finished \tANN training loss 0.287003\n",
            ">> Epoch 75 finished \tANN training loss 0.331673\n",
            ">> Epoch 76 finished \tANN training loss 0.270812\n",
            ">> Epoch 77 finished \tANN training loss 0.308296\n",
            ">> Epoch 78 finished \tANN training loss 0.147706\n",
            ">> Epoch 79 finished \tANN training loss 0.416781\n",
            ">> Epoch 80 finished \tANN training loss 0.216449\n",
            ">> Epoch 81 finished \tANN training loss 0.150492\n",
            ">> Epoch 82 finished \tANN training loss 0.268852\n",
            ">> Epoch 83 finished \tANN training loss 0.196720\n",
            ">> Epoch 84 finished \tANN training loss 0.212994\n",
            ">> Epoch 85 finished \tANN training loss 0.219238\n",
            ">> Epoch 86 finished \tANN training loss 0.200682\n",
            ">> Epoch 87 finished \tANN training loss 0.202750\n",
            ">> Epoch 88 finished \tANN training loss 0.184658\n",
            ">> Epoch 89 finished \tANN training loss 0.336457\n",
            ">> Epoch 90 finished \tANN training loss 0.312601\n",
            ">> Epoch 91 finished \tANN training loss 0.223158\n",
            ">> Epoch 92 finished \tANN training loss 0.160318\n",
            ">> Epoch 93 finished \tANN training loss 0.109189\n",
            ">> Epoch 94 finished \tANN training loss 0.241990\n",
            ">> Epoch 95 finished \tANN training loss 0.193337\n",
            ">> Epoch 96 finished \tANN training loss 0.228363\n",
            ">> Epoch 97 finished \tANN training loss 0.192900\n",
            ">> Epoch 98 finished \tANN training loss 0.218024\n",
            ">> Epoch 99 finished \tANN training loss 0.208040\n",
            ">> Epoch 100 finished \tANN training loss 0.229209\n",
            ">> Epoch 101 finished \tANN training loss 0.351150\n",
            ">> Epoch 102 finished \tANN training loss 0.297352\n",
            ">> Epoch 103 finished \tANN training loss 0.236643\n",
            ">> Epoch 104 finished \tANN training loss 0.146212\n",
            ">> Epoch 105 finished \tANN training loss 0.251087\n",
            ">> Epoch 106 finished \tANN training loss 0.264262\n",
            ">> Epoch 107 finished \tANN training loss 0.261318\n",
            ">> Epoch 108 finished \tANN training loss 0.263828\n",
            ">> Epoch 109 finished \tANN training loss 0.202968\n",
            ">> Epoch 110 finished \tANN training loss 0.116528\n",
            ">> Epoch 111 finished \tANN training loss 0.261005\n",
            ">> Epoch 112 finished \tANN training loss 0.198433\n",
            ">> Epoch 113 finished \tANN training loss 0.374400\n",
            ">> Epoch 114 finished \tANN training loss 0.240825\n",
            ">> Epoch 115 finished \tANN training loss 0.222776\n",
            ">> Epoch 116 finished \tANN training loss 0.226686\n",
            ">> Epoch 117 finished \tANN training loss 0.271866\n",
            ">> Epoch 118 finished \tANN training loss 0.227848\n",
            ">> Epoch 119 finished \tANN training loss 0.257261\n",
            ">> Epoch 120 finished \tANN training loss 0.277676\n",
            ">> Epoch 121 finished \tANN training loss 0.234170\n",
            ">> Epoch 122 finished \tANN training loss 0.328689\n",
            ">> Epoch 123 finished \tANN training loss 0.239727\n",
            ">> Epoch 124 finished \tANN training loss 0.346054\n",
            ">> Epoch 125 finished \tANN training loss 0.110970\n",
            ">> Epoch 126 finished \tANN training loss 0.278862\n",
            ">> Epoch 127 finished \tANN training loss 0.238065\n",
            ">> Epoch 128 finished \tANN training loss 0.223926\n",
            ">> Epoch 129 finished \tANN training loss 0.246304\n",
            ">> Epoch 130 finished \tANN training loss 0.199669\n",
            ">> Epoch 131 finished \tANN training loss 0.245187\n",
            ">> Epoch 132 finished \tANN training loss 0.252999\n",
            ">> Epoch 133 finished \tANN training loss 0.231760\n",
            ">> Epoch 134 finished \tANN training loss 0.222096\n",
            ">> Epoch 135 finished \tANN training loss 0.225995\n",
            ">> Epoch 136 finished \tANN training loss 0.271555\n",
            ">> Epoch 137 finished \tANN training loss 0.193884\n",
            ">> Epoch 138 finished \tANN training loss 0.319780\n",
            ">> Epoch 139 finished \tANN training loss 0.217539\n",
            ">> Epoch 140 finished \tANN training loss 0.283581\n",
            ">> Epoch 141 finished \tANN training loss 0.241666\n",
            ">> Epoch 142 finished \tANN training loss 0.275610\n",
            ">> Epoch 143 finished \tANN training loss 0.253090\n",
            ">> Epoch 144 finished \tANN training loss 0.331933\n",
            ">> Epoch 145 finished \tANN training loss 0.179420\n",
            ">> Epoch 146 finished \tANN training loss 0.270040\n",
            ">> Epoch 147 finished \tANN training loss 0.276629\n",
            ">> Epoch 148 finished \tANN training loss 0.274199\n",
            ">> Epoch 149 finished \tANN training loss 0.285331\n",
            ">> Epoch 150 finished \tANN training loss 0.253995\n",
            ">> Epoch 151 finished \tANN training loss 0.281509\n",
            ">> Epoch 152 finished \tANN training loss 0.284010\n",
            ">> Epoch 153 finished \tANN training loss 0.293003\n",
            ">> Epoch 154 finished \tANN training loss 0.272722\n",
            ">> Epoch 155 finished \tANN training loss 0.316520\n",
            ">> Epoch 156 finished \tANN training loss 0.307062\n",
            ">> Epoch 157 finished \tANN training loss 0.259811\n",
            ">> Epoch 158 finished \tANN training loss 0.222785\n",
            ">> Epoch 159 finished \tANN training loss 0.286123\n",
            ">> Epoch 160 finished \tANN training loss 0.323581\n",
            ">> Epoch 161 finished \tANN training loss 0.437853\n",
            ">> Epoch 162 finished \tANN training loss 0.328961\n",
            ">> Epoch 163 finished \tANN training loss 0.223645\n",
            ">> Epoch 164 finished \tANN training loss 0.249465\n",
            ">> Epoch 165 finished \tANN training loss 0.262341\n",
            ">> Epoch 166 finished \tANN training loss 0.349379\n",
            ">> Epoch 167 finished \tANN training loss 0.254849\n",
            ">> Epoch 168 finished \tANN training loss 0.251501\n",
            ">> Epoch 169 finished \tANN training loss 0.218147\n",
            ">> Epoch 170 finished \tANN training loss 0.364443\n",
            ">> Epoch 171 finished \tANN training loss 0.367672\n",
            ">> Epoch 172 finished \tANN training loss 0.191950\n",
            ">> Epoch 173 finished \tANN training loss 0.324114\n",
            ">> Epoch 174 finished \tANN training loss 0.211782\n",
            ">> Epoch 175 finished \tANN training loss 0.307540\n",
            ">> Epoch 176 finished \tANN training loss 0.228476\n",
            ">> Epoch 177 finished \tANN training loss 0.350017\n",
            ">> Epoch 178 finished \tANN training loss 0.241762\n",
            ">> Epoch 179 finished \tANN training loss 0.320988\n",
            ">> Epoch 180 finished \tANN training loss 0.312792\n",
            ">> Epoch 181 finished \tANN training loss 0.349813\n",
            ">> Epoch 182 finished \tANN training loss 0.200258\n",
            ">> Epoch 183 finished \tANN training loss 0.320975\n",
            ">> Epoch 184 finished \tANN training loss 0.282613\n",
            ">> Epoch 185 finished \tANN training loss 0.402546\n",
            ">> Epoch 186 finished \tANN training loss 0.305631\n",
            ">> Epoch 187 finished \tANN training loss 0.319741\n",
            ">> Epoch 188 finished \tANN training loss 0.320315\n",
            ">> Epoch 189 finished \tANN training loss 0.289053\n",
            ">> Epoch 190 finished \tANN training loss 0.304351\n",
            ">> Epoch 191 finished \tANN training loss 0.362568\n",
            ">> Epoch 192 finished \tANN training loss 0.194755\n",
            ">> Epoch 193 finished \tANN training loss 0.223215\n",
            ">> Epoch 194 finished \tANN training loss 0.231875\n",
            ">> Epoch 195 finished \tANN training loss 0.301175\n",
            ">> Epoch 196 finished \tANN training loss 0.359457\n",
            ">> Epoch 197 finished \tANN training loss 0.259188\n",
            ">> Epoch 198 finished \tANN training loss 0.182078\n",
            ">> Epoch 199 finished \tANN training loss 0.273349\n",
            ">> Epoch 200 finished \tANN training loss 0.242356\n",
            ">> Epoch 201 finished \tANN training loss 0.310432\n",
            ">> Epoch 202 finished \tANN training loss 0.295104\n",
            ">> Epoch 203 finished \tANN training loss 0.221042\n",
            ">> Epoch 204 finished \tANN training loss 0.346819\n",
            ">> Epoch 205 finished \tANN training loss 0.328789\n",
            ">> Epoch 206 finished \tANN training loss 0.415499\n",
            ">> Epoch 207 finished \tANN training loss 0.255853\n",
            ">> Epoch 208 finished \tANN training loss 0.276447\n",
            ">> Epoch 209 finished \tANN training loss 0.343463\n",
            ">> Epoch 210 finished \tANN training loss 0.355669\n",
            ">> Epoch 211 finished \tANN training loss 0.260305\n",
            ">> Epoch 212 finished \tANN training loss 0.264668\n",
            ">> Epoch 213 finished \tANN training loss 0.276155\n",
            ">> Epoch 214 finished \tANN training loss 0.317935\n",
            ">> Epoch 215 finished \tANN training loss 0.347306\n",
            ">> Epoch 216 finished \tANN training loss 0.286967\n",
            ">> Epoch 217 finished \tANN training loss 0.326192\n",
            ">> Epoch 218 finished \tANN training loss 0.249818\n",
            ">> Epoch 219 finished \tANN training loss 0.539354\n",
            ">> Epoch 220 finished \tANN training loss 0.281269\n",
            ">> Epoch 221 finished \tANN training loss 0.334605\n",
            ">> Epoch 222 finished \tANN training loss 0.325277\n",
            ">> Epoch 223 finished \tANN training loss 0.316366\n",
            ">> Epoch 224 finished \tANN training loss 0.375388\n",
            ">> Epoch 225 finished \tANN training loss 0.311976\n",
            ">> Epoch 226 finished \tANN training loss 0.396325\n",
            ">> Epoch 227 finished \tANN training loss 0.355571\n",
            ">> Epoch 228 finished \tANN training loss 0.292836\n",
            ">> Epoch 229 finished \tANN training loss 0.321989\n",
            ">> Epoch 230 finished \tANN training loss 0.360322\n",
            ">> Epoch 231 finished \tANN training loss 0.350521\n",
            ">> Epoch 232 finished \tANN training loss 0.264099\n",
            ">> Epoch 233 finished \tANN training loss 0.291973\n",
            ">> Epoch 234 finished \tANN training loss 0.363849\n",
            ">> Epoch 235 finished \tANN training loss 0.288421\n",
            ">> Epoch 236 finished \tANN training loss 0.314773\n",
            ">> Epoch 237 finished \tANN training loss 0.301511\n",
            ">> Epoch 238 finished \tANN training loss 0.142419\n",
            ">> Epoch 239 finished \tANN training loss 0.211674\n",
            ">> Epoch 240 finished \tANN training loss 0.302667\n",
            ">> Epoch 241 finished \tANN training loss 0.316888\n",
            ">> Epoch 242 finished \tANN training loss 0.259698\n",
            ">> Epoch 243 finished \tANN training loss 0.336362\n",
            ">> Epoch 244 finished \tANN training loss 0.255357\n",
            ">> Epoch 245 finished \tANN training loss 0.350699\n",
            ">> Epoch 246 finished \tANN training loss 0.229221\n",
            ">> Epoch 247 finished \tANN training loss 0.265723\n",
            ">> Epoch 248 finished \tANN training loss 0.362516\n",
            ">> Epoch 249 finished \tANN training loss 0.168399\n",
            ">> Epoch 250 finished \tANN training loss 0.347523\n",
            ">> Epoch 251 finished \tANN training loss 0.366411\n",
            ">> Epoch 252 finished \tANN training loss 0.317580\n",
            ">> Epoch 253 finished \tANN training loss 0.309178\n",
            ">> Epoch 254 finished \tANN training loss 0.380262\n",
            ">> Epoch 255 finished \tANN training loss 0.278626\n",
            ">> Epoch 256 finished \tANN training loss 0.322597\n",
            ">> Epoch 257 finished \tANN training loss 0.361763\n",
            ">> Epoch 258 finished \tANN training loss 0.311725\n",
            ">> Epoch 259 finished \tANN training loss 0.384924\n",
            ">> Epoch 260 finished \tANN training loss 0.277896\n",
            ">> Epoch 261 finished \tANN training loss 0.366051\n",
            ">> Epoch 262 finished \tANN training loss 0.294600\n",
            ">> Epoch 263 finished \tANN training loss 0.253992\n",
            ">> Epoch 264 finished \tANN training loss 0.273816\n",
            ">> Epoch 265 finished \tANN training loss 0.306643\n",
            ">> Epoch 266 finished \tANN training loss 0.410123\n",
            ">> Epoch 267 finished \tANN training loss 0.297938\n",
            ">> Epoch 268 finished \tANN training loss 0.514905\n",
            ">> Epoch 269 finished \tANN training loss 0.236530\n",
            ">> Epoch 270 finished \tANN training loss 0.295204\n",
            ">> Epoch 271 finished \tANN training loss 0.479775\n",
            ">> Epoch 272 finished \tANN training loss 0.460197\n",
            ">> Epoch 273 finished \tANN training loss 0.293037\n",
            ">> Epoch 274 finished \tANN training loss 0.353300\n",
            ">> Epoch 275 finished \tANN training loss 0.297628\n",
            ">> Epoch 276 finished \tANN training loss 0.357002\n",
            ">> Epoch 277 finished \tANN training loss 0.342541\n",
            ">> Epoch 278 finished \tANN training loss 0.245995\n",
            ">> Epoch 279 finished \tANN training loss 0.273864\n",
            ">> Epoch 280 finished \tANN training loss 0.389997\n",
            ">> Epoch 281 finished \tANN training loss 0.295023\n",
            ">> Epoch 282 finished \tANN training loss 0.301852\n",
            ">> Epoch 283 finished \tANN training loss 0.419867\n",
            ">> Epoch 284 finished \tANN training loss 0.365643\n",
            ">> Epoch 285 finished \tANN training loss 0.222013\n",
            ">> Epoch 286 finished \tANN training loss 0.265720\n",
            ">> Epoch 287 finished \tANN training loss 0.344854\n",
            ">> Epoch 288 finished \tANN training loss 0.315407\n",
            ">> Epoch 289 finished \tANN training loss 0.363609\n",
            ">> Epoch 290 finished \tANN training loss 0.186804\n",
            ">> Epoch 291 finished \tANN training loss 0.337564\n",
            ">> Epoch 292 finished \tANN training loss 0.427515\n",
            ">> Epoch 293 finished \tANN training loss 0.371233\n",
            ">> Epoch 294 finished \tANN training loss 0.296593\n",
            ">> Epoch 295 finished \tANN training loss 0.377896\n",
            ">> Epoch 296 finished \tANN training loss 0.266035\n",
            ">> Epoch 297 finished \tANN training loss 0.293724\n",
            ">> Epoch 298 finished \tANN training loss 0.449976\n",
            ">> Epoch 299 finished \tANN training loss 0.417082\n",
            ">> Epoch 300 finished \tANN training loss 0.370343\n",
            ">> Epoch 301 finished \tANN training loss 0.334383\n",
            ">> Epoch 302 finished \tANN training loss 0.429808\n",
            ">> Epoch 303 finished \tANN training loss 0.315719\n",
            ">> Epoch 304 finished \tANN training loss 0.388704\n",
            ">> Epoch 305 finished \tANN training loss 0.369013\n",
            ">> Epoch 306 finished \tANN training loss 0.228377\n",
            ">> Epoch 307 finished \tANN training loss 0.428413\n",
            ">> Epoch 308 finished \tANN training loss 0.491808\n",
            ">> Epoch 309 finished \tANN training loss 0.373035\n",
            ">> Epoch 310 finished \tANN training loss 0.173323\n",
            ">> Epoch 311 finished \tANN training loss 0.322155\n",
            ">> Epoch 312 finished \tANN training loss 0.271856\n",
            ">> Epoch 313 finished \tANN training loss 0.339705\n",
            ">> Epoch 314 finished \tANN training loss 0.341258\n",
            ">> Epoch 315 finished \tANN training loss 0.316874\n",
            ">> Epoch 316 finished \tANN training loss 0.381173\n",
            ">> Epoch 317 finished \tANN training loss 0.289303\n",
            ">> Epoch 318 finished \tANN training loss 0.379599\n",
            ">> Epoch 319 finished \tANN training loss 0.393210\n",
            ">> Epoch 320 finished \tANN training loss 0.277264\n",
            ">> Epoch 321 finished \tANN training loss 0.427267\n",
            ">> Epoch 322 finished \tANN training loss 0.442366\n",
            ">> Epoch 323 finished \tANN training loss 0.472304\n",
            ">> Epoch 324 finished \tANN training loss 0.354955\n",
            ">> Epoch 325 finished \tANN training loss 0.285307\n",
            ">> Epoch 326 finished \tANN training loss 0.355549\n",
            ">> Epoch 327 finished \tANN training loss 0.420041\n",
            ">> Epoch 328 finished \tANN training loss 0.352469\n",
            ">> Epoch 329 finished \tANN training loss 0.363013\n",
            ">> Epoch 330 finished \tANN training loss 0.550976\n",
            ">> Epoch 331 finished \tANN training loss 0.372721\n",
            ">> Epoch 332 finished \tANN training loss 0.349862\n",
            ">> Epoch 333 finished \tANN training loss 0.379568\n",
            ">> Epoch 334 finished \tANN training loss 0.280550\n",
            ">> Epoch 335 finished \tANN training loss 0.446556\n",
            ">> Epoch 336 finished \tANN training loss 0.243637\n",
            ">> Epoch 337 finished \tANN training loss 0.390513\n",
            ">> Epoch 338 finished \tANN training loss 0.373431\n",
            ">> Epoch 339 finished \tANN training loss 0.333878\n",
            ">> Epoch 340 finished \tANN training loss 0.390029\n",
            ">> Epoch 341 finished \tANN training loss 0.480201\n",
            ">> Epoch 342 finished \tANN training loss 0.390348\n",
            ">> Epoch 343 finished \tANN training loss 0.354495\n",
            ">> Epoch 344 finished \tANN training loss 0.465102\n",
            ">> Epoch 345 finished \tANN training loss 0.376642\n",
            ">> Epoch 346 finished \tANN training loss 0.319932\n",
            ">> Epoch 347 finished \tANN training loss 0.390937\n",
            ">> Epoch 348 finished \tANN training loss 0.362488\n",
            ">> Epoch 349 finished \tANN training loss 0.403449\n",
            ">> Epoch 350 finished \tANN training loss 0.313634\n",
            ">> Epoch 351 finished \tANN training loss 0.409661\n",
            ">> Epoch 352 finished \tANN training loss 0.449301\n",
            ">> Epoch 353 finished \tANN training loss 0.537556\n",
            ">> Epoch 354 finished \tANN training loss 0.248175\n",
            ">> Epoch 355 finished \tANN training loss 0.481624\n",
            ">> Epoch 356 finished \tANN training loss 0.500008\n",
            ">> Epoch 357 finished \tANN training loss 0.418877\n",
            ">> Epoch 358 finished \tANN training loss 0.361388\n",
            ">> Epoch 359 finished \tANN training loss 0.383851\n",
            ">> Epoch 360 finished \tANN training loss 0.337178\n",
            ">> Epoch 361 finished \tANN training loss 0.345723\n",
            ">> Epoch 362 finished \tANN training loss 0.391658\n",
            ">> Epoch 363 finished \tANN training loss 0.389739\n",
            ">> Epoch 364 finished \tANN training loss 0.406888\n",
            ">> Epoch 365 finished \tANN training loss 0.373243\n",
            ">> Epoch 366 finished \tANN training loss 0.571433\n",
            ">> Epoch 367 finished \tANN training loss 0.446829\n",
            ">> Epoch 368 finished \tANN training loss 0.326805\n",
            ">> Epoch 369 finished \tANN training loss 0.401634\n",
            ">> Epoch 370 finished \tANN training loss 0.373898\n",
            ">> Epoch 371 finished \tANN training loss 0.320242\n",
            ">> Epoch 372 finished \tANN training loss 0.385603\n",
            ">> Epoch 373 finished \tANN training loss 0.427287\n",
            ">> Epoch 374 finished \tANN training loss 0.313348\n",
            ">> Epoch 375 finished \tANN training loss 0.424009\n",
            ">> Epoch 376 finished \tANN training loss 0.321713\n",
            ">> Epoch 377 finished \tANN training loss 0.475972\n",
            ">> Epoch 378 finished \tANN training loss 0.294636\n",
            ">> Epoch 379 finished \tANN training loss 0.395554\n",
            ">> Epoch 380 finished \tANN training loss 0.504167\n",
            ">> Epoch 381 finished \tANN training loss 0.349064\n",
            ">> Epoch 382 finished \tANN training loss 0.359487\n",
            ">> Epoch 383 finished \tANN training loss 0.434941\n",
            ">> Epoch 384 finished \tANN training loss 0.357420\n",
            ">> Epoch 385 finished \tANN training loss 0.470596\n",
            ">> Epoch 386 finished \tANN training loss 0.349650\n",
            ">> Epoch 387 finished \tANN training loss 0.363187\n",
            ">> Epoch 388 finished \tANN training loss 0.453085\n",
            ">> Epoch 389 finished \tANN training loss 0.510586\n",
            ">> Epoch 390 finished \tANN training loss 0.389075\n",
            ">> Epoch 391 finished \tANN training loss 0.458078\n",
            ">> Epoch 392 finished \tANN training loss 0.405178\n",
            ">> Epoch 393 finished \tANN training loss 0.468654\n",
            ">> Epoch 394 finished \tANN training loss 0.476778\n",
            ">> Epoch 395 finished \tANN training loss 0.469238\n",
            ">> Epoch 396 finished \tANN training loss 0.382215\n",
            ">> Epoch 397 finished \tANN training loss 0.420707\n",
            ">> Epoch 398 finished \tANN training loss 0.465320\n",
            ">> Epoch 399 finished \tANN training loss 0.481541\n",
            ">> Epoch 400 finished \tANN training loss 0.297832\n",
            ">> Epoch 401 finished \tANN training loss 0.374265\n",
            ">> Epoch 402 finished \tANN training loss 0.593912\n",
            ">> Epoch 403 finished \tANN training loss 0.377072\n",
            ">> Epoch 404 finished \tANN training loss 0.426207\n",
            ">> Epoch 405 finished \tANN training loss 0.369840\n",
            ">> Epoch 406 finished \tANN training loss 0.391549\n",
            ">> Epoch 407 finished \tANN training loss 0.357741\n",
            ">> Epoch 408 finished \tANN training loss 0.299109\n",
            ">> Epoch 409 finished \tANN training loss 0.324915\n",
            ">> Epoch 410 finished \tANN training loss 0.340191\n",
            ">> Epoch 411 finished \tANN training loss 0.446082\n",
            ">> Epoch 412 finished \tANN training loss 0.437507\n",
            ">> Epoch 413 finished \tANN training loss 0.439334\n",
            ">> Epoch 414 finished \tANN training loss 0.316555\n",
            ">> Epoch 415 finished \tANN training loss 0.345881\n",
            ">> Epoch 416 finished \tANN training loss 0.593570\n",
            ">> Epoch 417 finished \tANN training loss 0.500808\n",
            ">> Epoch 418 finished \tANN training loss 0.354525\n",
            ">> Epoch 419 finished \tANN training loss 0.404104\n",
            ">> Epoch 420 finished \tANN training loss 0.435725\n",
            ">> Epoch 421 finished \tANN training loss 0.344466\n",
            ">> Epoch 422 finished \tANN training loss 0.396281\n",
            ">> Epoch 423 finished \tANN training loss 0.316690\n",
            ">> Epoch 424 finished \tANN training loss 0.455074\n",
            ">> Epoch 425 finished \tANN training loss 0.278830\n",
            ">> Epoch 426 finished \tANN training loss 0.431578\n",
            ">> Epoch 427 finished \tANN training loss 0.467565\n",
            ">> Epoch 428 finished \tANN training loss 0.475542\n",
            ">> Epoch 429 finished \tANN training loss 0.338550\n",
            ">> Epoch 430 finished \tANN training loss 0.361883\n",
            ">> Epoch 431 finished \tANN training loss 0.388717\n",
            ">> Epoch 432 finished \tANN training loss 0.391739\n",
            ">> Epoch 433 finished \tANN training loss 0.398139\n",
            ">> Epoch 434 finished \tANN training loss 0.390675\n",
            ">> Epoch 435 finished \tANN training loss 0.483541\n",
            ">> Epoch 436 finished \tANN training loss 0.395358\n",
            ">> Epoch 437 finished \tANN training loss 0.484493\n",
            ">> Epoch 438 finished \tANN training loss 0.356342\n",
            ">> Epoch 439 finished \tANN training loss 0.382996\n",
            ">> Epoch 440 finished \tANN training loss 0.086920\n",
            ">> Epoch 441 finished \tANN training loss 0.392244\n",
            ">> Epoch 442 finished \tANN training loss 0.383075\n",
            ">> Epoch 443 finished \tANN training loss 0.372008\n",
            ">> Epoch 444 finished \tANN training loss 0.366209\n",
            ">> Epoch 445 finished \tANN training loss 0.458624\n",
            ">> Epoch 446 finished \tANN training loss 0.473274\n",
            ">> Epoch 447 finished \tANN training loss 0.523930\n",
            ">> Epoch 448 finished \tANN training loss 0.362879\n",
            ">> Epoch 449 finished \tANN training loss 0.553960\n",
            ">> Epoch 450 finished \tANN training loss 0.361109\n",
            ">> Epoch 451 finished \tANN training loss 0.548368\n",
            ">> Epoch 452 finished \tANN training loss 0.414698\n",
            ">> Epoch 453 finished \tANN training loss 0.367064\n",
            ">> Epoch 454 finished \tANN training loss 0.556826\n",
            ">> Epoch 455 finished \tANN training loss 0.519012\n",
            ">> Epoch 456 finished \tANN training loss 0.532062\n",
            ">> Epoch 457 finished \tANN training loss 0.552419\n",
            ">> Epoch 458 finished \tANN training loss 0.400578\n",
            ">> Epoch 459 finished \tANN training loss 0.386572\n",
            ">> Epoch 460 finished \tANN training loss 0.527269\n",
            ">> Epoch 461 finished \tANN training loss 0.372911\n",
            ">> Epoch 462 finished \tANN training loss 0.610555\n",
            ">> Epoch 463 finished \tANN training loss 0.306089\n",
            ">> Epoch 464 finished \tANN training loss 0.479205\n",
            ">> Epoch 465 finished \tANN training loss 0.419495\n",
            ">> Epoch 466 finished \tANN training loss 0.330906\n",
            ">> Epoch 467 finished \tANN training loss 0.402741\n",
            ">> Epoch 468 finished \tANN training loss 0.526532\n",
            ">> Epoch 469 finished \tANN training loss 0.624816\n",
            ">> Epoch 470 finished \tANN training loss 0.374103\n",
            ">> Epoch 471 finished \tANN training loss 0.423888\n",
            ">> Epoch 472 finished \tANN training loss 0.413723\n",
            ">> Epoch 473 finished \tANN training loss 0.453781\n",
            ">> Epoch 474 finished \tANN training loss 0.292333\n",
            ">> Epoch 475 finished \tANN training loss 0.396359\n",
            ">> Epoch 476 finished \tANN training loss 0.541506\n",
            ">> Epoch 477 finished \tANN training loss 0.389510\n",
            ">> Epoch 478 finished \tANN training loss 0.354993\n",
            ">> Epoch 479 finished \tANN training loss 0.447845\n",
            ">> Epoch 480 finished \tANN training loss 0.441836\n",
            ">> Epoch 481 finished \tANN training loss 0.416488\n",
            ">> Epoch 482 finished \tANN training loss 0.331108\n",
            ">> Epoch 483 finished \tANN training loss 0.461209\n",
            ">> Epoch 484 finished \tANN training loss 0.526930\n",
            ">> Epoch 485 finished \tANN training loss 0.512195\n",
            ">> Epoch 486 finished \tANN training loss 0.590400\n",
            ">> Epoch 487 finished \tANN training loss 0.395392\n",
            ">> Epoch 488 finished \tANN training loss 0.449881\n",
            ">> Epoch 489 finished \tANN training loss 0.487489\n",
            ">> Epoch 490 finished \tANN training loss 0.482139\n",
            ">> Epoch 491 finished \tANN training loss 0.558296\n",
            ">> Epoch 492 finished \tANN training loss 0.369963\n",
            ">> Epoch 493 finished \tANN training loss 0.459119\n",
            ">> Epoch 494 finished \tANN training loss 0.439022\n",
            ">> Epoch 495 finished \tANN training loss 0.471945\n",
            ">> Epoch 496 finished \tANN training loss 0.300595\n",
            ">> Epoch 497 finished \tANN training loss 0.265878\n",
            ">> Epoch 498 finished \tANN training loss 0.474771\n",
            ">> Epoch 499 finished \tANN training loss 0.383709\n",
            "[END] Fine tuning step\n",
            "Done.\n",
            "Accuracy: 0.913333\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Confusion Matrix"
      ],
      "metadata": {
        "id": "ujLGIzDKorMp"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.metrics import accuracy_score,f1_score,classification_report,roc_curve,auc,confusion_matrix,ConfusionMatrixDisplay\n",
        "confusion_matrix = confusion_matrix(Y_test, Y_pred)\n",
        "cm_display = ConfusionMatrixDisplay(confusion_matrix = confusion_matrix, display_labels = [False, True])\n",
        "cm_display.plot()\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 361
        },
        "id": "WMCI-_XoMnZB",
        "outputId": "3b87c766-1819-427a-c3bf-c36b53219d45"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 576x396 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Classification Report"
      ],
      "metadata": {
        "id": "Z6lAJ2VyouM9"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "print(classification_report(Y_test, Y_pred, labels = [False, True]))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "C0VGDnSoNe8o",
        "outputId": "0094bfde-dbab-45c7-ccce-0063a8baf2a3"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "       False       0.99      0.90      0.94       605\n",
            "        True       0.70      0.98      0.81       145\n",
            "\n",
            "    accuracy                           0.91       750\n",
            "   macro avg       0.85      0.94      0.88       750\n",
            "weighted avg       0.94      0.91      0.92       750\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install scikit-plot"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "pZaDTy4DRX2p",
        "outputId": "6b74cfc6-3091-49c2-ae91-accad0ea34e8"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "Collecting scikit-plot\n",
            "  Downloading scikit_plot-0.3.7-py3-none-any.whl (33 kB)\n",
            "Requirement already satisfied: matplotlib>=1.4.0 in /usr/local/lib/python3.7/dist-packages (from scikit-plot) (3.2.2)\n",
            "Requirement already satisfied: scipy>=0.9 in /usr/local/lib/python3.7/dist-packages (from scikit-plot) (1.7.3)\n",
            "Requirement already satisfied: scikit-learn>=0.18 in /usr/local/lib/python3.7/dist-packages (from scikit-plot) (1.0.2)\n",
            "Requirement already satisfied: joblib>=0.10 in /usr/local/lib/python3.7/dist-packages (from scikit-plot) (1.1.0)\n",
            "Requirement already satisfied: pyparsing!=2.0.4,!=2.1.2,!=2.1.6,>=2.0.1 in /usr/local/lib/python3.7/dist-packages (from matplotlib>=1.4.0->scikit-plot) (3.0.9)\n",
            "Requirement already satisfied: kiwisolver>=1.0.1 in /usr/local/lib/python3.7/dist-packages (from matplotlib>=1.4.0->scikit-plot) (1.4.4)\n",
            "Requirement already satisfied: cycler>=0.10 in /usr/local/lib/python3.7/dist-packages (from matplotlib>=1.4.0->scikit-plot) (0.11.0)\n",
            "Requirement already satisfied: numpy>=1.11 in /usr/local/lib/python3.7/dist-packages (from matplotlib>=1.4.0->scikit-plot) (1.21.6)\n",
            "Requirement already satisfied: python-dateutil>=2.1 in /usr/local/lib/python3.7/dist-packages (from matplotlib>=1.4.0->scikit-plot) (2.8.2)\n",
            "Requirement already satisfied: typing-extensions in /usr/local/lib/python3.7/dist-packages (from kiwisolver>=1.0.1->matplotlib>=1.4.0->scikit-plot) (4.1.1)\n",
            "Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.7/dist-packages (from python-dateutil>=2.1->matplotlib>=1.4.0->scikit-plot) (1.15.0)\n",
            "Requirement already satisfied: threadpoolctl>=2.0.0 in /usr/local/lib/python3.7/dist-packages (from scikit-learn>=0.18->scikit-plot) (3.1.0)\n",
            "Installing collected packages: scikit-plot\n",
            "Successfully installed scikit-plot-0.3.7\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### ROC-AUC Curve"
      ],
      "metadata": {
        "id": "VIRSVN2Zoz8V"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "fpr, tpr, _ = roc_curve(Y_test, Y_pred)\n",
        "roc_auc = auc(fpr, tpr)\n",
        "plt.figure()\n",
        "plt.plot(fpr, tpr, label='ROC curve (area = %0.2f)' % roc_auc)\n",
        "plt.plot([0, 1], [0, 1], 'k--')\n",
        "plt.xlim([0.0, 1.0])\n",
        "plt.ylim([0.0, 1.05])\n",
        "plt.xlabel('False Positive Rate')\n",
        "plt.ylabel('True Positive Rate')\n",
        "plt.title('ROC Curve')\n",
        "plt.legend(loc=\"lower right\")\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 376
        },
        "id": "VwaYGCx6N3iM",
        "outputId": "78ef55d7-3793-4e89-e131-4cd1484e29d1"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 576x396 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "X =pd.DataFrame(X,index = new_data.index)\n",
        "X_train, X_test, Y_train, Y_test = train_test_split(X, Y, test_size=0.2, random_state=0)\n",
        "Y_pred = pd.DataFrame(classifier.predict(X_test),index = X_test.index)\n",
        "\n",
        "from sklearn.preprocessing import OneHotEncoder\n",
        "Y_pred = OneHotEncoder().fit_transform(Y_pred).toarray()"
      ],
      "metadata": {
        "id": "KD5fvri8Rs-S"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import scikitplot as skplt\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "skplt.metrics.plot_roc_curve(Y_test, Y_pred)\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 437
        },
        "id": "rzVEgnGYRF_N",
        "outputId": "0ae72347-263e-45e8-97a0-200369d5a8b7"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning: Function plot_roc_curve is deprecated; This will be removed in v0.5.0. Please use scikitplot.metrics.plot_roc instead.\n",
            "  warnings.warn(msg, category=FutureWarning)\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 576x396 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Saving and Testing"
      ],
      "metadata": {
        "id": "SunAe6BXo5CK"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "classifier.save('model.pkl')"
      ],
      "metadata": {
        "id": "3WV1kEuaI7WV"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "!pwd"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "gBIetScbtSJL",
        "outputId": "f511ba82-9094-4ba0-e0f4-2e6a2cbadf69"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "/content/deep-belief-network/deep-belief-network\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import pickle\n",
        "classifier = pickle.load(open('/content/model.pkl', 'rb'))"
      ],
      "metadata": {
        "id": "dPX7l40GnzR7"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "Y_pred = classifier.predict(X_test)\n",
        "print('Done.\\nAccuracy: %f' % accuracy_score(Y_test, Y_pred))"
      ],
      "metadata": {
        "id": "rcJ5zquappX-"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Final Model (DBN)"
      ],
      "metadata": {
        "id": "Jlg05C15oNgZ"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from dbn.tensorflow import SupervisedDBNClassification\n",
        "\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.metrics import accuracy_score\n",
        "\n",
        "X_train, X_test, Y_train, Y_test = train_test_split(X, Y, test_size=0.3, random_state=0)\n",
        "\n",
        "classifier = SupervisedDBNClassification(hidden_layers_structure=[50, 50],\n",
        "                                         learning_rate_rbm=0.05,\n",
        "                                         learning_rate=0.09,\n",
        "                                         n_epochs_rbm=30,\n",
        "                                         n_iter_backprop=500,\n",
        "                                         batch_size=16,\n",
        "                                         activation_function='relu',\n",
        "                                         dropout_p=0.2)\n",
        "classifier.fit(X_train, Y_train)\n",
        "\n",
        "Y_pred = classifier.predict(X_test)\n",
        "print('Done.\\nAccuracy: %f' % accuracy_score(Y_test, Y_pred))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ikOwvZdtqDuU",
        "outputId": "f637f9cd-6f46-4396-f645-9db94be4de38"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "WARNING:tensorflow:From /content/deep-belief-network/dbn/tensorflow/models.py:19: The name tf.Session is deprecated. Please use tf.compat.v1.Session instead.\n",
            "\n",
            "WARNING:tensorflow:From /content/deep-belief-network/dbn/tensorflow/models.py:127: The name tf.truncated_normal is deprecated. Please use tf.random.truncated_normal instead.\n",
            "\n",
            "WARNING:tensorflow:From /content/deep-belief-network/dbn/tensorflow/models.py:143: The name tf.placeholder is deprecated. Please use tf.compat.v1.placeholder instead.\n",
            "\n",
            "WARNING:tensorflow:From /content/deep-belief-network/dbn/tensorflow/models.py:149: The name tf.random_uniform is deprecated. Please use tf.random.uniform instead.\n",
            "\n",
            "WARNING:tensorflow:From /content/deep-belief-network/dbn/tensorflow/models.py:150: to_float (from tensorflow.python.ops.math_ops) is deprecated and will be removed in a future version.\n",
            "Instructions for updating:\n",
            "Use `tf.cast` instead.\n",
            "WARNING:tensorflow:From /content/deep-belief-network/dbn/tensorflow/models.py:178: The name tf.assign_add is deprecated. Please use tf.compat.v1.assign_add instead.\n",
            "\n",
            "WARNING:tensorflow:From /content/deep-belief-network/dbn/tensorflow/models.py:89: The name tf.variables_initializer is deprecated. Please use tf.compat.v1.variables_initializer instead.\n",
            "\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[START] Pre-training step:\n",
            ">> Epoch 1 finished \tRBM Reconstruction error 0.054479\n",
            ">> Epoch 2 finished \tRBM Reconstruction error 0.045588\n",
            ">> Epoch 3 finished \tRBM Reconstruction error 0.047428\n",
            ">> Epoch 4 finished \tRBM Reconstruction error 0.056079\n",
            ">> Epoch 5 finished \tRBM Reconstruction error 0.048722\n",
            ">> Epoch 6 finished \tRBM Reconstruction error 0.054994\n",
            ">> Epoch 7 finished \tRBM Reconstruction error 0.042097\n",
            ">> Epoch 8 finished \tRBM Reconstruction error 0.063729\n",
            ">> Epoch 9 finished \tRBM Reconstruction error 0.049360\n",
            ">> Epoch 10 finished \tRBM Reconstruction error 0.047487\n",
            ">> Epoch 11 finished \tRBM Reconstruction error 0.056010\n",
            ">> Epoch 12 finished \tRBM Reconstruction error 0.055190\n",
            ">> Epoch 13 finished \tRBM Reconstruction error 0.058265\n",
            ">> Epoch 14 finished \tRBM Reconstruction error 0.064287\n",
            ">> Epoch 15 finished \tRBM Reconstruction error 0.057355\n",
            ">> Epoch 16 finished \tRBM Reconstruction error 0.050835\n",
            ">> Epoch 17 finished \tRBM Reconstruction error 0.055175\n",
            ">> Epoch 18 finished \tRBM Reconstruction error 0.061674\n",
            ">> Epoch 19 finished \tRBM Reconstruction error 0.047808\n",
            ">> Epoch 20 finished \tRBM Reconstruction error 0.048252\n",
            ">> Epoch 21 finished \tRBM Reconstruction error 0.041893\n",
            ">> Epoch 22 finished \tRBM Reconstruction error 0.044020\n",
            ">> Epoch 23 finished \tRBM Reconstruction error 0.037592\n",
            ">> Epoch 24 finished \tRBM Reconstruction error 0.038229\n",
            ">> Epoch 25 finished \tRBM Reconstruction error 0.033718\n",
            ">> Epoch 26 finished \tRBM Reconstruction error 0.035020\n",
            ">> Epoch 27 finished \tRBM Reconstruction error 0.031931\n",
            ">> Epoch 28 finished \tRBM Reconstruction error 0.027052\n",
            ">> Epoch 29 finished \tRBM Reconstruction error 0.033172\n",
            ">> Epoch 30 finished \tRBM Reconstruction error 0.029771\n",
            ">> Epoch 1 finished \tRBM Reconstruction error 0.051771\n",
            ">> Epoch 2 finished \tRBM Reconstruction error 0.055515\n",
            ">> Epoch 3 finished \tRBM Reconstruction error 0.068085\n",
            ">> Epoch 4 finished \tRBM Reconstruction error 0.058166\n",
            ">> Epoch 5 finished \tRBM Reconstruction error 0.034115\n",
            ">> Epoch 6 finished \tRBM Reconstruction error 0.037931\n",
            ">> Epoch 7 finished \tRBM Reconstruction error 0.039403\n",
            ">> Epoch 8 finished \tRBM Reconstruction error 0.051766\n",
            ">> Epoch 9 finished \tRBM Reconstruction error 0.057049\n",
            ">> Epoch 10 finished \tRBM Reconstruction error 0.039860\n",
            ">> Epoch 11 finished \tRBM Reconstruction error 0.030545\n",
            ">> Epoch 12 finished \tRBM Reconstruction error 0.029806\n",
            ">> Epoch 13 finished \tRBM Reconstruction error 0.032123\n",
            ">> Epoch 14 finished \tRBM Reconstruction error 0.054593\n",
            ">> Epoch 15 finished \tRBM Reconstruction error 0.094177\n",
            ">> Epoch 16 finished \tRBM Reconstruction error 0.090491\n",
            ">> Epoch 17 finished \tRBM Reconstruction error 0.216450\n",
            ">> Epoch 18 finished \tRBM Reconstruction error 0.115941\n",
            ">> Epoch 19 finished \tRBM Reconstruction error 0.062192\n",
            ">> Epoch 20 finished \tRBM Reconstruction error 0.063678\n",
            ">> Epoch 21 finished \tRBM Reconstruction error 0.048738\n",
            ">> Epoch 22 finished \tRBM Reconstruction error 0.076935\n",
            ">> Epoch 23 finished \tRBM Reconstruction error 0.081762\n",
            ">> Epoch 24 finished \tRBM Reconstruction error 0.031617\n",
            ">> Epoch 25 finished \tRBM Reconstruction error 0.039994\n",
            ">> Epoch 26 finished \tRBM Reconstruction error 0.064581\n",
            ">> Epoch 27 finished \tRBM Reconstruction error 0.323023\n",
            ">> Epoch 28 finished \tRBM Reconstruction error 0.192202\n",
            ">> Epoch 29 finished \tRBM Reconstruction error 0.157968\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "WARNING:tensorflow:From /content/deep-belief-network/dbn/tensorflow/models.py:338: calling dropout (from tensorflow.python.ops.nn_ops) with keep_prob is deprecated and will be removed in a future version.\n",
            "Instructions for updating:\n",
            "Please use `rate` instead of `keep_prob`. Rate should be set to `rate = 1 - keep_prob`.\n",
            "WARNING:tensorflow:From /content/deep-belief-network/dbn/tensorflow/models.py:357: The name tf.train.GradientDescentOptimizer is deprecated. Please use tf.compat.v1.train.GradientDescentOptimizer instead.\n",
            "\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            ">> Epoch 30 finished \tRBM Reconstruction error 0.137217\n",
            "[END] Pre-training step\n",
            "[START] Fine tuning step:\n",
            ">> Epoch 0 finished \tANN training loss 0.461131\n",
            ">> Epoch 1 finished \tANN training loss 0.426664\n",
            ">> Epoch 2 finished \tANN training loss 0.355609\n",
            ">> Epoch 3 finished \tANN training loss 0.293998\n",
            ">> Epoch 4 finished \tANN training loss 0.247904\n",
            ">> Epoch 5 finished \tANN training loss 0.229439\n",
            ">> Epoch 6 finished \tANN training loss 0.226853\n",
            ">> Epoch 7 finished \tANN training loss 0.211948\n",
            ">> Epoch 8 finished \tANN training loss 0.210958\n",
            ">> Epoch 9 finished \tANN training loss 0.218600\n",
            ">> Epoch 10 finished \tANN training loss 0.183613\n",
            ">> Epoch 11 finished \tANN training loss 0.247566\n",
            ">> Epoch 12 finished \tANN training loss 0.193370\n",
            ">> Epoch 13 finished \tANN training loss 0.194585\n",
            ">> Epoch 14 finished \tANN training loss 0.174603\n",
            ">> Epoch 15 finished \tANN training loss 0.166434\n",
            ">> Epoch 16 finished \tANN training loss 0.199967\n",
            ">> Epoch 17 finished \tANN training loss 0.172581\n",
            ">> Epoch 18 finished \tANN training loss 0.162183\n",
            ">> Epoch 19 finished \tANN training loss 0.171048\n",
            ">> Epoch 20 finished \tANN training loss 0.316347\n",
            ">> Epoch 21 finished \tANN training loss 0.198928\n",
            ">> Epoch 22 finished \tANN training loss 0.183984\n",
            ">> Epoch 23 finished \tANN training loss 0.178952\n",
            ">> Epoch 24 finished \tANN training loss 0.160036\n",
            ">> Epoch 25 finished \tANN training loss 0.148609\n",
            ">> Epoch 26 finished \tANN training loss 0.183235\n",
            ">> Epoch 27 finished \tANN training loss 0.191376\n",
            ">> Epoch 28 finished \tANN training loss 0.145773\n",
            ">> Epoch 29 finished \tANN training loss 0.151515\n",
            ">> Epoch 30 finished \tANN training loss 0.163599\n",
            ">> Epoch 31 finished \tANN training loss 0.263714\n",
            ">> Epoch 32 finished \tANN training loss 0.144107\n",
            ">> Epoch 33 finished \tANN training loss 0.220055\n",
            ">> Epoch 34 finished \tANN training loss 0.183057\n",
            ">> Epoch 35 finished \tANN training loss 0.209443\n",
            ">> Epoch 36 finished \tANN training loss 0.151263\n",
            ">> Epoch 37 finished \tANN training loss 0.128082\n",
            ">> Epoch 38 finished \tANN training loss 0.286947\n",
            ">> Epoch 39 finished \tANN training loss 0.143208\n",
            ">> Epoch 40 finished \tANN training loss 0.132549\n",
            ">> Epoch 41 finished \tANN training loss 0.183746\n",
            ">> Epoch 42 finished \tANN training loss 0.261712\n",
            ">> Epoch 43 finished \tANN training loss 0.217068\n",
            ">> Epoch 44 finished \tANN training loss 0.135502\n",
            ">> Epoch 45 finished \tANN training loss 0.137832\n",
            ">> Epoch 46 finished \tANN training loss 0.166030\n",
            ">> Epoch 47 finished \tANN training loss 0.155902\n",
            ">> Epoch 48 finished \tANN training loss 0.181831\n",
            ">> Epoch 49 finished \tANN training loss 0.199912\n",
            ">> Epoch 50 finished \tANN training loss 0.224723\n",
            ">> Epoch 51 finished \tANN training loss 0.219353\n",
            ">> Epoch 52 finished \tANN training loss 0.305349\n",
            ">> Epoch 53 finished \tANN training loss 0.231326\n",
            ">> Epoch 54 finished \tANN training loss 0.175122\n",
            ">> Epoch 55 finished \tANN training loss 0.199030\n",
            ">> Epoch 56 finished \tANN training loss 0.175148\n",
            ">> Epoch 57 finished \tANN training loss 0.199453\n",
            ">> Epoch 58 finished \tANN training loss 0.210283\n",
            ">> Epoch 59 finished \tANN training loss 0.296110\n",
            ">> Epoch 60 finished \tANN training loss 0.215034\n",
            ">> Epoch 61 finished \tANN training loss 0.169197\n",
            ">> Epoch 62 finished \tANN training loss 0.190640\n",
            ">> Epoch 63 finished \tANN training loss 0.125982\n",
            ">> Epoch 64 finished \tANN training loss 0.187933\n",
            ">> Epoch 65 finished \tANN training loss 0.320731\n",
            ">> Epoch 66 finished \tANN training loss 0.137396\n",
            ">> Epoch 67 finished \tANN training loss 0.201476\n",
            ">> Epoch 68 finished \tANN training loss 0.214059\n",
            ">> Epoch 69 finished \tANN training loss 0.265405\n",
            ">> Epoch 70 finished \tANN training loss 0.186658\n",
            ">> Epoch 71 finished \tANN training loss 0.114703\n",
            ">> Epoch 72 finished \tANN training loss 0.217035\n",
            ">> Epoch 73 finished \tANN training loss 0.228232\n",
            ">> Epoch 74 finished \tANN training loss 0.198371\n",
            ">> Epoch 75 finished \tANN training loss 0.199350\n",
            ">> Epoch 76 finished \tANN training loss 0.315204\n",
            ">> Epoch 77 finished \tANN training loss 0.240987\n",
            ">> Epoch 78 finished \tANN training loss 0.167963\n",
            ">> Epoch 79 finished \tANN training loss 0.275005\n",
            ">> Epoch 80 finished \tANN training loss 0.227596\n",
            ">> Epoch 81 finished \tANN training loss 0.151468\n",
            ">> Epoch 82 finished \tANN training loss 0.265498\n",
            ">> Epoch 83 finished \tANN training loss 0.274547\n",
            ">> Epoch 84 finished \tANN training loss 0.206292\n",
            ">> Epoch 85 finished \tANN training loss 0.186512\n",
            ">> Epoch 86 finished \tANN training loss 0.208203\n",
            ">> Epoch 87 finished \tANN training loss 0.256958\n",
            ">> Epoch 88 finished \tANN training loss 0.180033\n",
            ">> Epoch 89 finished \tANN training loss 0.264124\n",
            ">> Epoch 90 finished \tANN training loss 0.251652\n",
            ">> Epoch 91 finished \tANN training loss 0.240862\n",
            ">> Epoch 92 finished \tANN training loss 0.117157\n",
            ">> Epoch 93 finished \tANN training loss 0.176007\n",
            ">> Epoch 94 finished \tANN training loss 0.264933\n",
            ">> Epoch 95 finished \tANN training loss 0.166526\n",
            ">> Epoch 96 finished \tANN training loss 0.242068\n",
            ">> Epoch 97 finished \tANN training loss 0.226020\n",
            ">> Epoch 98 finished \tANN training loss 0.244795\n",
            ">> Epoch 99 finished \tANN training loss 0.361492\n",
            ">> Epoch 100 finished \tANN training loss 0.343503\n",
            ">> Epoch 101 finished \tANN training loss 0.223186\n",
            ">> Epoch 102 finished \tANN training loss 0.256303\n",
            ">> Epoch 103 finished \tANN training loss 0.255395\n",
            ">> Epoch 104 finished \tANN training loss 0.168998\n",
            ">> Epoch 105 finished \tANN training loss 0.220761\n",
            ">> Epoch 106 finished \tANN training loss 0.265898\n",
            ">> Epoch 107 finished \tANN training loss 0.235904\n",
            ">> Epoch 108 finished \tANN training loss 0.308108\n",
            ">> Epoch 109 finished \tANN training loss 0.175883\n",
            ">> Epoch 110 finished \tANN training loss 0.248708\n",
            ">> Epoch 111 finished \tANN training loss 0.296387\n",
            ">> Epoch 112 finished \tANN training loss 0.270693\n",
            ">> Epoch 113 finished \tANN training loss 0.256725\n",
            ">> Epoch 114 finished \tANN training loss 0.241403\n",
            ">> Epoch 115 finished \tANN training loss 0.289634\n",
            ">> Epoch 116 finished \tANN training loss 0.204155\n",
            ">> Epoch 117 finished \tANN training loss 0.349358\n",
            ">> Epoch 118 finished \tANN training loss 0.236129\n",
            ">> Epoch 119 finished \tANN training loss 0.179126\n",
            ">> Epoch 120 finished \tANN training loss 0.225085\n",
            ">> Epoch 121 finished \tANN training loss 0.253433\n",
            ">> Epoch 122 finished \tANN training loss 0.285254\n",
            ">> Epoch 123 finished \tANN training loss 0.246495\n",
            ">> Epoch 124 finished \tANN training loss 0.223440\n",
            ">> Epoch 125 finished \tANN training loss 0.226047\n",
            ">> Epoch 126 finished \tANN training loss 0.198961\n",
            ">> Epoch 127 finished \tANN training loss 0.329098\n",
            ">> Epoch 128 finished \tANN training loss 0.398782\n",
            ">> Epoch 129 finished \tANN training loss 0.293910\n",
            ">> Epoch 130 finished \tANN training loss 0.225367\n",
            ">> Epoch 131 finished \tANN training loss 0.192907\n",
            ">> Epoch 132 finished \tANN training loss 0.248023\n",
            ">> Epoch 133 finished \tANN training loss 0.212997\n",
            ">> Epoch 134 finished \tANN training loss 0.296660\n",
            ">> Epoch 135 finished \tANN training loss 0.275679\n",
            ">> Epoch 136 finished \tANN training loss 0.218593\n",
            ">> Epoch 137 finished \tANN training loss 0.245471\n",
            ">> Epoch 138 finished \tANN training loss 0.272359\n",
            ">> Epoch 139 finished \tANN training loss 0.361874\n",
            ">> Epoch 140 finished \tANN training loss 0.278615\n",
            ">> Epoch 141 finished \tANN training loss 0.264036\n",
            ">> Epoch 142 finished \tANN training loss 0.269217\n",
            ">> Epoch 143 finished \tANN training loss 0.405090\n",
            ">> Epoch 144 finished \tANN training loss 0.281466\n",
            ">> Epoch 145 finished \tANN training loss 0.337948\n",
            ">> Epoch 146 finished \tANN training loss 0.309310\n",
            ">> Epoch 147 finished \tANN training loss 0.264547\n",
            ">> Epoch 148 finished \tANN training loss 0.201060\n",
            ">> Epoch 149 finished \tANN training loss 0.141334\n",
            ">> Epoch 150 finished \tANN training loss 0.247819\n",
            ">> Epoch 151 finished \tANN training loss 0.282556\n",
            ">> Epoch 152 finished \tANN training loss 0.235199\n",
            ">> Epoch 153 finished \tANN training loss 0.273244\n",
            ">> Epoch 154 finished \tANN training loss 0.437665\n",
            ">> Epoch 155 finished \tANN training loss 0.302969\n",
            ">> Epoch 156 finished \tANN training loss 0.244968\n",
            ">> Epoch 157 finished \tANN training loss 0.300341\n",
            ">> Epoch 158 finished \tANN training loss 0.197654\n",
            ">> Epoch 159 finished \tANN training loss 0.295299\n",
            ">> Epoch 160 finished \tANN training loss 0.126518\n",
            ">> Epoch 161 finished \tANN training loss 0.227002\n",
            ">> Epoch 162 finished \tANN training loss 0.168909\n",
            ">> Epoch 163 finished \tANN training loss 0.262102\n",
            ">> Epoch 164 finished \tANN training loss 0.280917\n",
            ">> Epoch 165 finished \tANN training loss 0.301190\n",
            ">> Epoch 166 finished \tANN training loss 0.292152\n",
            ">> Epoch 167 finished \tANN training loss 0.330334\n",
            ">> Epoch 168 finished \tANN training loss 0.311148\n",
            ">> Epoch 169 finished \tANN training loss 0.273020\n",
            ">> Epoch 170 finished \tANN training loss 0.296938\n",
            ">> Epoch 171 finished \tANN training loss 0.276355\n",
            ">> Epoch 172 finished \tANN training loss 0.195922\n",
            ">> Epoch 173 finished \tANN training loss 0.271884\n",
            ">> Epoch 174 finished \tANN training loss 0.274482\n",
            ">> Epoch 175 finished \tANN training loss 0.223221\n",
            ">> Epoch 176 finished \tANN training loss 0.336025\n",
            ">> Epoch 177 finished \tANN training loss 0.254729\n",
            ">> Epoch 178 finished \tANN training loss 0.238851\n",
            ">> Epoch 179 finished \tANN training loss 0.377813\n",
            ">> Epoch 180 finished \tANN training loss 0.371846\n",
            ">> Epoch 181 finished \tANN training loss 0.230757\n",
            ">> Epoch 182 finished \tANN training loss 0.334295\n",
            ">> Epoch 183 finished \tANN training loss 0.272888\n",
            ">> Epoch 184 finished \tANN training loss 0.292337\n",
            ">> Epoch 185 finished \tANN training loss 0.363615\n",
            ">> Epoch 186 finished \tANN training loss 0.508768\n",
            ">> Epoch 187 finished \tANN training loss 0.306026\n",
            ">> Epoch 188 finished \tANN training loss 0.356450\n",
            ">> Epoch 189 finished \tANN training loss 0.317918\n",
            ">> Epoch 190 finished \tANN training loss 0.443524\n",
            ">> Epoch 191 finished \tANN training loss 0.341968\n",
            ">> Epoch 192 finished \tANN training loss 0.367569\n",
            ">> Epoch 193 finished \tANN training loss 0.249841\n",
            ">> Epoch 194 finished \tANN training loss 0.252247\n",
            ">> Epoch 195 finished \tANN training loss 0.253698\n",
            ">> Epoch 196 finished \tANN training loss 0.257459\n",
            ">> Epoch 197 finished \tANN training loss 0.290175\n",
            ">> Epoch 198 finished \tANN training loss 0.259638\n",
            ">> Epoch 199 finished \tANN training loss 0.330696\n",
            ">> Epoch 200 finished \tANN training loss 0.332752\n",
            ">> Epoch 201 finished \tANN training loss 0.243298\n",
            ">> Epoch 202 finished \tANN training loss 0.353371\n",
            ">> Epoch 203 finished \tANN training loss 0.315563\n",
            ">> Epoch 204 finished \tANN training loss 0.322931\n",
            ">> Epoch 205 finished \tANN training loss 0.257663\n",
            ">> Epoch 206 finished \tANN training loss 0.364442\n",
            ">> Epoch 207 finished \tANN training loss 0.313248\n",
            ">> Epoch 208 finished \tANN training loss 0.369473\n",
            ">> Epoch 209 finished \tANN training loss 0.207742\n",
            ">> Epoch 210 finished \tANN training loss 0.272037\n",
            ">> Epoch 211 finished \tANN training loss 0.345555\n",
            ">> Epoch 212 finished \tANN training loss 0.372526\n",
            ">> Epoch 213 finished \tANN training loss 0.320288\n",
            ">> Epoch 214 finished \tANN training loss 0.328445\n",
            ">> Epoch 215 finished \tANN training loss 0.289561\n",
            ">> Epoch 216 finished \tANN training loss 0.297138\n",
            ">> Epoch 217 finished \tANN training loss 0.278741\n",
            ">> Epoch 218 finished \tANN training loss 0.304834\n",
            ">> Epoch 219 finished \tANN training loss 0.347491\n",
            ">> Epoch 220 finished \tANN training loss 0.377943\n",
            ">> Epoch 221 finished \tANN training loss 0.285919\n",
            ">> Epoch 222 finished \tANN training loss 0.271062\n",
            ">> Epoch 223 finished \tANN training loss 0.258106\n",
            ">> Epoch 224 finished \tANN training loss 0.337217\n",
            ">> Epoch 225 finished \tANN training loss 0.317195\n",
            ">> Epoch 226 finished \tANN training loss 0.333036\n",
            ">> Epoch 227 finished \tANN training loss 0.331826\n",
            ">> Epoch 228 finished \tANN training loss 0.271049\n",
            ">> Epoch 229 finished \tANN training loss 0.447681\n",
            ">> Epoch 230 finished \tANN training loss 0.290444\n",
            ">> Epoch 231 finished \tANN training loss 0.376408\n",
            ">> Epoch 232 finished \tANN training loss 0.365182\n",
            ">> Epoch 233 finished \tANN training loss 0.109040\n",
            ">> Epoch 234 finished \tANN training loss 0.382079\n",
            ">> Epoch 235 finished \tANN training loss 0.333863\n",
            ">> Epoch 236 finished \tANN training loss 0.325281\n",
            ">> Epoch 237 finished \tANN training loss 0.311530\n",
            ">> Epoch 238 finished \tANN training loss 0.257883\n",
            ">> Epoch 239 finished \tANN training loss 0.327970\n",
            ">> Epoch 240 finished \tANN training loss 0.264507\n",
            ">> Epoch 241 finished \tANN training loss 0.422446\n",
            ">> Epoch 242 finished \tANN training loss 0.395299\n",
            ">> Epoch 243 finished \tANN training loss 0.286191\n",
            ">> Epoch 244 finished \tANN training loss 0.282748\n",
            ">> Epoch 245 finished \tANN training loss 0.343136\n",
            ">> Epoch 246 finished \tANN training loss 0.296434\n",
            ">> Epoch 247 finished \tANN training loss 0.317719\n",
            ">> Epoch 248 finished \tANN training loss 0.305836\n",
            ">> Epoch 249 finished \tANN training loss 0.127666\n",
            ">> Epoch 250 finished \tANN training loss 0.252055\n",
            ">> Epoch 251 finished \tANN training loss 0.338029\n",
            ">> Epoch 252 finished \tANN training loss 0.318162\n",
            ">> Epoch 253 finished \tANN training loss 0.331593\n",
            ">> Epoch 254 finished \tANN training loss 0.483527\n",
            ">> Epoch 255 finished \tANN training loss 0.313039\n",
            ">> Epoch 256 finished \tANN training loss 0.357901\n",
            ">> Epoch 257 finished \tANN training loss 0.358938\n",
            ">> Epoch 258 finished \tANN training loss 0.253372\n",
            ">> Epoch 259 finished \tANN training loss 0.382404\n",
            ">> Epoch 260 finished \tANN training loss 0.131291\n",
            ">> Epoch 261 finished \tANN training loss 0.339507\n",
            ">> Epoch 262 finished \tANN training loss 0.293892\n",
            ">> Epoch 263 finished \tANN training loss 0.339836\n",
            ">> Epoch 264 finished \tANN training loss 0.312216\n",
            ">> Epoch 265 finished \tANN training loss 0.220110\n",
            ">> Epoch 266 finished \tANN training loss 0.461936\n",
            ">> Epoch 267 finished \tANN training loss 0.314547\n",
            ">> Epoch 268 finished \tANN training loss 0.298802\n",
            ">> Epoch 269 finished \tANN training loss 0.318230\n",
            ">> Epoch 270 finished \tANN training loss 0.352613\n",
            ">> Epoch 271 finished \tANN training loss 0.347418\n",
            ">> Epoch 272 finished \tANN training loss 0.394347\n",
            ">> Epoch 273 finished \tANN training loss 0.290314\n",
            ">> Epoch 274 finished \tANN training loss 0.321935\n",
            ">> Epoch 275 finished \tANN training loss 0.324387\n",
            ">> Epoch 276 finished \tANN training loss 0.262006\n",
            ">> Epoch 277 finished \tANN training loss 0.263906\n",
            ">> Epoch 278 finished \tANN training loss 0.413908\n",
            ">> Epoch 279 finished \tANN training loss 0.285468\n",
            ">> Epoch 280 finished \tANN training loss 0.376795\n",
            ">> Epoch 281 finished \tANN training loss 0.430827\n",
            ">> Epoch 282 finished \tANN training loss 0.342631\n",
            ">> Epoch 283 finished \tANN training loss 0.161838\n",
            ">> Epoch 284 finished \tANN training loss 0.395986\n",
            ">> Epoch 285 finished \tANN training loss 0.311589\n",
            ">> Epoch 286 finished \tANN training loss 0.267590\n",
            ">> Epoch 287 finished \tANN training loss 0.425066\n",
            ">> Epoch 288 finished \tANN training loss 0.319560\n",
            ">> Epoch 289 finished \tANN training loss 0.329089\n",
            ">> Epoch 290 finished \tANN training loss 0.289850\n",
            ">> Epoch 291 finished \tANN training loss 0.287731\n",
            ">> Epoch 292 finished \tANN training loss 0.340466\n",
            ">> Epoch 293 finished \tANN training loss 0.249838\n",
            ">> Epoch 294 finished \tANN training loss 0.311636\n",
            ">> Epoch 295 finished \tANN training loss 0.327815\n",
            ">> Epoch 296 finished \tANN training loss 0.288461\n",
            ">> Epoch 297 finished \tANN training loss 0.340465\n",
            ">> Epoch 298 finished \tANN training loss 0.302818\n",
            ">> Epoch 299 finished \tANN training loss 0.328909\n",
            ">> Epoch 300 finished \tANN training loss 0.350528\n",
            ">> Epoch 301 finished \tANN training loss 0.376036\n",
            ">> Epoch 302 finished \tANN training loss 0.425747\n",
            ">> Epoch 303 finished \tANN training loss 0.310167\n",
            ">> Epoch 304 finished \tANN training loss 0.386863\n",
            ">> Epoch 305 finished \tANN training loss 0.356836\n",
            ">> Epoch 306 finished \tANN training loss 0.351972\n",
            ">> Epoch 307 finished \tANN training loss 0.339756\n",
            ">> Epoch 308 finished \tANN training loss 0.323243\n",
            ">> Epoch 309 finished \tANN training loss 0.326256\n",
            ">> Epoch 310 finished \tANN training loss 0.350410\n",
            ">> Epoch 311 finished \tANN training loss 0.361241\n",
            ">> Epoch 312 finished \tANN training loss 0.361669\n",
            ">> Epoch 313 finished \tANN training loss 0.356332\n",
            ">> Epoch 314 finished \tANN training loss 0.390678\n",
            ">> Epoch 315 finished \tANN training loss 0.312666\n",
            ">> Epoch 316 finished \tANN training loss 0.402951\n",
            ">> Epoch 317 finished \tANN training loss 0.341596\n",
            ">> Epoch 318 finished \tANN training loss 0.378950\n",
            ">> Epoch 319 finished \tANN training loss 0.329626\n",
            ">> Epoch 320 finished \tANN training loss 0.411189\n",
            ">> Epoch 321 finished \tANN training loss 0.308706\n",
            ">> Epoch 322 finished \tANN training loss 0.358716\n",
            ">> Epoch 323 finished \tANN training loss 0.344672\n",
            ">> Epoch 324 finished \tANN training loss 0.372565\n",
            ">> Epoch 325 finished \tANN training loss 0.382960\n",
            ">> Epoch 326 finished \tANN training loss 0.323804\n",
            ">> Epoch 327 finished \tANN training loss 0.353239\n",
            ">> Epoch 328 finished \tANN training loss 0.319968\n",
            ">> Epoch 329 finished \tANN training loss 0.391110\n",
            ">> Epoch 330 finished \tANN training loss 0.348383\n",
            ">> Epoch 331 finished \tANN training loss 0.407014\n",
            ">> Epoch 332 finished \tANN training loss 0.432107\n",
            ">> Epoch 333 finished \tANN training loss 0.339145\n",
            ">> Epoch 334 finished \tANN training loss 0.437309\n",
            ">> Epoch 335 finished \tANN training loss 0.400334\n",
            ">> Epoch 336 finished \tANN training loss 0.243137\n",
            ">> Epoch 337 finished \tANN training loss 0.353450\n",
            ">> Epoch 338 finished \tANN training loss 0.338801\n",
            ">> Epoch 339 finished \tANN training loss 0.399129\n",
            ">> Epoch 340 finished \tANN training loss 0.326059\n",
            ">> Epoch 341 finished \tANN training loss 0.243953\n",
            ">> Epoch 342 finished \tANN training loss 0.322539\n",
            ">> Epoch 343 finished \tANN training loss 0.367407\n",
            ">> Epoch 344 finished \tANN training loss 0.306266\n",
            ">> Epoch 345 finished \tANN training loss 0.292094\n",
            ">> Epoch 346 finished \tANN training loss 0.424569\n",
            ">> Epoch 347 finished \tANN training loss 0.356681\n",
            ">> Epoch 348 finished \tANN training loss 0.353740\n",
            ">> Epoch 349 finished \tANN training loss 0.367979\n",
            ">> Epoch 350 finished \tANN training loss 0.387540\n",
            ">> Epoch 351 finished \tANN training loss 0.355286\n",
            ">> Epoch 352 finished \tANN training loss 0.316628\n",
            ">> Epoch 353 finished \tANN training loss 0.361699\n",
            ">> Epoch 354 finished \tANN training loss 0.350387\n",
            ">> Epoch 355 finished \tANN training loss 0.362561\n",
            ">> Epoch 356 finished \tANN training loss 0.266069\n",
            ">> Epoch 357 finished \tANN training loss 0.401496\n",
            ">> Epoch 358 finished \tANN training loss 0.404976\n",
            ">> Epoch 359 finished \tANN training loss 0.406612\n",
            ">> Epoch 360 finished \tANN training loss 0.364435\n",
            ">> Epoch 361 finished \tANN training loss 0.295846\n",
            ">> Epoch 362 finished \tANN training loss 0.289986\n",
            ">> Epoch 363 finished \tANN training loss 0.387193\n",
            ">> Epoch 364 finished \tANN training loss 0.415877\n",
            ">> Epoch 365 finished \tANN training loss 0.356195\n",
            ">> Epoch 366 finished \tANN training loss 0.403017\n",
            ">> Epoch 367 finished \tANN training loss 0.407004\n",
            ">> Epoch 368 finished \tANN training loss 0.522326\n",
            ">> Epoch 369 finished \tANN training loss 0.357060\n",
            ">> Epoch 370 finished \tANN training loss 0.364099\n",
            ">> Epoch 371 finished \tANN training loss 0.340095\n",
            ">> Epoch 372 finished \tANN training loss 0.374990\n",
            ">> Epoch 373 finished \tANN training loss 0.346778\n",
            ">> Epoch 374 finished \tANN training loss 0.357598\n",
            ">> Epoch 375 finished \tANN training loss 0.390262\n",
            ">> Epoch 376 finished \tANN training loss 0.331652\n",
            ">> Epoch 377 finished \tANN training loss 0.328741\n",
            ">> Epoch 378 finished \tANN training loss 0.321819\n",
            ">> Epoch 379 finished \tANN training loss 0.364643\n",
            ">> Epoch 380 finished \tANN training loss 0.342728\n",
            ">> Epoch 381 finished \tANN training loss 0.402488\n",
            ">> Epoch 382 finished \tANN training loss 0.345394\n",
            ">> Epoch 383 finished \tANN training loss 0.403338\n",
            ">> Epoch 384 finished \tANN training loss 0.336375\n",
            ">> Epoch 385 finished \tANN training loss 0.355336\n",
            ">> Epoch 386 finished \tANN training loss 0.384772\n",
            ">> Epoch 387 finished \tANN training loss 0.370730\n",
            ">> Epoch 388 finished \tANN training loss 0.384247\n",
            ">> Epoch 389 finished \tANN training loss 0.543334\n",
            ">> Epoch 390 finished \tANN training loss 0.384744\n",
            ">> Epoch 391 finished \tANN training loss 0.385680\n",
            ">> Epoch 392 finished \tANN training loss 0.338817\n",
            ">> Epoch 393 finished \tANN training loss 0.319543\n",
            ">> Epoch 394 finished \tANN training loss 0.394325\n",
            ">> Epoch 395 finished \tANN training loss 0.359654\n",
            ">> Epoch 396 finished \tANN training loss 0.343060\n",
            ">> Epoch 397 finished \tANN training loss 0.367198\n",
            ">> Epoch 398 finished \tANN training loss 0.343792\n",
            ">> Epoch 399 finished \tANN training loss 0.361611\n",
            ">> Epoch 400 finished \tANN training loss 0.355155\n",
            ">> Epoch 401 finished \tANN training loss 0.186899\n",
            ">> Epoch 402 finished \tANN training loss 0.414605\n",
            ">> Epoch 403 finished \tANN training loss 0.379710\n",
            ">> Epoch 404 finished \tANN training loss 0.402940\n",
            ">> Epoch 405 finished \tANN training loss 0.376250\n",
            ">> Epoch 406 finished \tANN training loss 0.367639\n",
            ">> Epoch 407 finished \tANN training loss 0.380343\n",
            ">> Epoch 408 finished \tANN training loss 0.349131\n",
            ">> Epoch 409 finished \tANN training loss 0.385347\n",
            ">> Epoch 410 finished \tANN training loss 0.319889\n",
            ">> Epoch 411 finished \tANN training loss 0.264976\n",
            ">> Epoch 412 finished \tANN training loss 0.316938\n",
            ">> Epoch 413 finished \tANN training loss 0.367919\n",
            ">> Epoch 414 finished \tANN training loss 0.314904\n",
            ">> Epoch 415 finished \tANN training loss 0.423460\n",
            ">> Epoch 416 finished \tANN training loss 0.346607\n",
            ">> Epoch 417 finished \tANN training loss 0.399076\n",
            ">> Epoch 418 finished \tANN training loss 0.413251\n",
            ">> Epoch 419 finished \tANN training loss 0.380301\n",
            ">> Epoch 420 finished \tANN training loss 0.371042\n",
            ">> Epoch 421 finished \tANN training loss 0.323383\n",
            ">> Epoch 422 finished \tANN training loss 0.354461\n",
            ">> Epoch 423 finished \tANN training loss 0.384813\n",
            ">> Epoch 424 finished \tANN training loss 0.423624\n",
            ">> Epoch 425 finished \tANN training loss 0.414443\n",
            ">> Epoch 426 finished \tANN training loss 0.396253\n",
            ">> Epoch 427 finished \tANN training loss 0.403678\n",
            ">> Epoch 428 finished \tANN training loss 0.296818\n",
            ">> Epoch 429 finished \tANN training loss 0.415492\n",
            ">> Epoch 430 finished \tANN training loss 0.418030\n",
            ">> Epoch 431 finished \tANN training loss 0.277177\n",
            ">> Epoch 432 finished \tANN training loss 0.365720\n",
            ">> Epoch 433 finished \tANN training loss 0.440242\n",
            ">> Epoch 434 finished \tANN training loss 0.434664\n",
            ">> Epoch 435 finished \tANN training loss 0.388156\n",
            ">> Epoch 436 finished \tANN training loss 0.359863\n",
            ">> Epoch 437 finished \tANN training loss 0.375852\n",
            ">> Epoch 438 finished \tANN training loss 0.462499\n",
            ">> Epoch 439 finished \tANN training loss 0.341851\n",
            ">> Epoch 440 finished \tANN training loss 0.431311\n",
            ">> Epoch 441 finished \tANN training loss 0.375286\n",
            ">> Epoch 442 finished \tANN training loss 0.361897\n",
            ">> Epoch 443 finished \tANN training loss 0.544477\n",
            ">> Epoch 444 finished \tANN training loss 0.335180\n",
            ">> Epoch 445 finished \tANN training loss 0.362392\n",
            ">> Epoch 446 finished \tANN training loss 0.395197\n",
            ">> Epoch 447 finished \tANN training loss 0.300996\n",
            ">> Epoch 448 finished \tANN training loss 0.336298\n",
            ">> Epoch 449 finished \tANN training loss 0.395432\n",
            ">> Epoch 450 finished \tANN training loss 0.457819\n",
            ">> Epoch 451 finished \tANN training loss 0.378670\n",
            ">> Epoch 452 finished \tANN training loss 0.425676\n",
            ">> Epoch 453 finished \tANN training loss 0.445744\n",
            ">> Epoch 454 finished \tANN training loss 0.384387\n",
            ">> Epoch 455 finished \tANN training loss 0.406497\n",
            ">> Epoch 456 finished \tANN training loss 0.449873\n",
            ">> Epoch 457 finished \tANN training loss 0.394542\n",
            ">> Epoch 458 finished \tANN training loss 0.365728\n",
            ">> Epoch 459 finished \tANN training loss 0.334056\n",
            ">> Epoch 460 finished \tANN training loss 0.409218\n",
            ">> Epoch 461 finished \tANN training loss 0.348977\n",
            ">> Epoch 462 finished \tANN training loss 0.390207\n",
            ">> Epoch 463 finished \tANN training loss 0.363446\n",
            ">> Epoch 464 finished \tANN training loss 0.364580\n",
            ">> Epoch 465 finished \tANN training loss 0.368125\n",
            ">> Epoch 466 finished \tANN training loss 0.409258\n",
            ">> Epoch 467 finished \tANN training loss 0.520297\n",
            ">> Epoch 468 finished \tANN training loss 0.366672\n",
            ">> Epoch 469 finished \tANN training loss 0.379454\n",
            ">> Epoch 470 finished \tANN training loss 0.398352\n",
            ">> Epoch 471 finished \tANN training loss 0.399621\n",
            ">> Epoch 472 finished \tANN training loss 0.354039\n",
            ">> Epoch 473 finished \tANN training loss 0.421149\n",
            ">> Epoch 474 finished \tANN training loss 0.412055\n",
            ">> Epoch 475 finished \tANN training loss 0.388750\n",
            ">> Epoch 476 finished \tANN training loss 0.449851\n",
            ">> Epoch 477 finished \tANN training loss 0.332681\n",
            ">> Epoch 478 finished \tANN training loss 0.457972\n",
            ">> Epoch 479 finished \tANN training loss 0.377253\n",
            ">> Epoch 480 finished \tANN training loss 0.403286\n",
            ">> Epoch 481 finished \tANN training loss 0.469071\n",
            ">> Epoch 482 finished \tANN training loss 0.413646\n",
            ">> Epoch 483 finished \tANN training loss 0.381995\n",
            ">> Epoch 484 finished \tANN training loss 0.350139\n",
            ">> Epoch 485 finished \tANN training loss 0.399957\n",
            ">> Epoch 486 finished \tANN training loss 0.499067\n",
            ">> Epoch 487 finished \tANN training loss 0.441058\n",
            ">> Epoch 488 finished \tANN training loss 0.399260\n",
            ">> Epoch 489 finished \tANN training loss 0.405014\n",
            ">> Epoch 490 finished \tANN training loss 0.400192\n",
            ">> Epoch 491 finished \tANN training loss 0.391585\n",
            ">> Epoch 492 finished \tANN training loss 0.483183\n",
            ">> Epoch 493 finished \tANN training loss 0.528284\n",
            ">> Epoch 494 finished \tANN training loss 0.451178\n",
            ">> Epoch 495 finished \tANN training loss 0.371941\n",
            ">> Epoch 496 finished \tANN training loss 0.432232\n",
            ">> Epoch 497 finished \tANN training loss 0.374550\n",
            ">> Epoch 498 finished \tANN training loss 0.406603\n",
            ">> Epoch 499 finished \tANN training loss 0.362541\n",
            "[END] Fine tuning step\n",
            "Done.\n",
            "Accuracy: 0.940000\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Confusion Matrix"
      ],
      "metadata": {
        "id": "9y7Y8pO-o9HH"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.metrics import accuracy_score,f1_score,classification_report,roc_curve,auc,confusion_matrix,ConfusionMatrixDisplay\n",
        "confusion_matrix = confusion_matrix(Y_test, Y_pred)\n",
        "cm_display = ConfusionMatrixDisplay(confusion_matrix = confusion_matrix, display_labels = [False, True])\n",
        "cm_display.plot()\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 361
        },
        "id": "ixJv15f6qFJK",
        "outputId": "0a4e1ec7-a2c9-41e2-d90a-630c738b0ac4"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 576x396 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "cf_matrix = np.array([[563,42],[3,142]])\n",
        "cf_matrix"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "U2OBNMfQ-fcE",
        "outputId": "bb842036-2ae6-43a6-9f93-906d1c5dc554"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[563,  42],\n",
              "       [  3, 142]])"
            ]
          },
          "metadata": {},
          "execution_count": 41
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import seaborn as sns\n",
        "\n",
        "ax = sns.heatmap(cf_matrix/np.sum(cf_matrix), annot=True, \n",
        "            fmt='.2%', cmap='Blues')\n",
        "\n",
        "ax.set_title('Confusion Matrix\\n\\n');\n",
        "ax.set_xlabel('\\nPredicted Values')\n",
        "ax.set_ylabel('Actual Values ');\n",
        "\n",
        "## Ticket labels - List must be in alphabetical order\n",
        "ax.xaxis.set_ticklabels([\"False\",\"True\"])\n",
        "ax.yaxis.set_ticklabels([\"False\",\"True\"])\n",
        "\n",
        "## Display the visualization of the Confusion Matrix.\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 415
        },
        "id": "64el0Ch99SBS",
        "outputId": "7f111c10-b58b-431d-8690-75b99a598a1a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 576x396 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Classification Report"
      ],
      "metadata": {
        "id": "GCojVeIFpCgM"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "print(classification_report(Y_test, Y_pred, labels = [False, True]))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "qTD4cpfbxG5e",
        "outputId": "7a7ca2b3-525a-4b61-afeb-c8e4376d1788"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "       False       0.99      0.93      0.96       605\n",
            "        True       0.77      0.98      0.86       145\n",
            "\n",
            "    accuracy                           0.94       750\n",
            "   macro avg       0.88      0.95      0.91       750\n",
            "weighted avg       0.95      0.94      0.94       750\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install scikit-plot"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "XFox_pHeyN2k",
        "outputId": "5d6a1f3c-64b6-4f8e-a37d-ffceb521a035"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "Collecting scikit-plot\n",
            "  Downloading scikit_plot-0.3.7-py3-none-any.whl (33 kB)\n",
            "Requirement already satisfied: scipy>=0.9 in /usr/local/lib/python3.7/dist-packages (from scikit-plot) (1.7.3)\n",
            "Requirement already satisfied: scikit-learn>=0.18 in /usr/local/lib/python3.7/dist-packages (from scikit-plot) (1.0.2)\n",
            "Requirement already satisfied: joblib>=0.10 in /usr/local/lib/python3.7/dist-packages (from scikit-plot) (1.1.0)\n",
            "Requirement already satisfied: matplotlib>=1.4.0 in /usr/local/lib/python3.7/dist-packages (from scikit-plot) (3.2.2)\n",
            "Requirement already satisfied: pyparsing!=2.0.4,!=2.1.2,!=2.1.6,>=2.0.1 in /usr/local/lib/python3.7/dist-packages (from matplotlib>=1.4.0->scikit-plot) (3.0.9)\n",
            "Requirement already satisfied: python-dateutil>=2.1 in /usr/local/lib/python3.7/dist-packages (from matplotlib>=1.4.0->scikit-plot) (2.8.2)\n",
            "Requirement already satisfied: cycler>=0.10 in /usr/local/lib/python3.7/dist-packages (from matplotlib>=1.4.0->scikit-plot) (0.11.0)\n",
            "Requirement already satisfied: numpy>=1.11 in /usr/local/lib/python3.7/dist-packages (from matplotlib>=1.4.0->scikit-plot) (1.21.6)\n",
            "Requirement already satisfied: kiwisolver>=1.0.1 in /usr/local/lib/python3.7/dist-packages (from matplotlib>=1.4.0->scikit-plot) (1.4.4)\n",
            "Requirement already satisfied: typing-extensions in /usr/local/lib/python3.7/dist-packages (from kiwisolver>=1.0.1->matplotlib>=1.4.0->scikit-plot) (4.1.1)\n",
            "Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.7/dist-packages (from python-dateutil>=2.1->matplotlib>=1.4.0->scikit-plot) (1.15.0)\n",
            "Requirement already satisfied: threadpoolctl>=2.0.0 in /usr/local/lib/python3.7/dist-packages (from scikit-learn>=0.18->scikit-plot) (3.1.0)\n",
            "Installing collected packages: scikit-plot\n",
            "Successfully installed scikit-plot-0.3.7\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### ROC-AUC Curve "
      ],
      "metadata": {
        "id": "FyXyToLdpGCR"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "fpr, tpr, _ = roc_curve(Y_test, Y_pred)\n",
        "roc_auc = auc(fpr, tpr)\n",
        "plt.figure()\n",
        "plt.plot(fpr, tpr, label='ROC curve (area = %0.2f)' % roc_auc)\n",
        "plt.plot([0, 1], [0, 1], 'k--')\n",
        "plt.xlim([0.0, 1.0])\n",
        "plt.ylim([0.0, 1.05])\n",
        "plt.xlabel('False Positive Rate')\n",
        "plt.ylabel('True Positive Rate')\n",
        "plt.title('ROC Curve')\n",
        "plt.legend(loc=\"lower right\")\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 376
        },
        "id": "IUETx4LiyJB9",
        "outputId": "82e65c8a-bfea-4346-ba5a-750cbe414471"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 576x396 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "X =pd.DataFrame(X,index = new_data.index)\n",
        "X_train, X_test, Y_train, Y_test = train_test_split(X, Y, test_size=0.2, random_state=0)\n",
        "Y_pred = pd.DataFrame(classifier.predict(X_test),index = X_test.index)\n",
        "\n",
        "from sklearn.preprocessing import OneHotEncoder\n",
        "Y_pred = OneHotEncoder().fit_transform(Y_pred).toarray()\n",
        "import scikitplot as skplt\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "skplt.metrics.plot_roc_curve(Y_test, Y_pred)\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 437
        },
        "id": "hmi0xeUHyTj4",
        "outputId": "4a8a5bb2-5bac-49aa-fa4e-7bf9de2dab3b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning: Function plot_roc_curve is deprecated; This will be removed in v0.5.0. Please use scikitplot.metrics.plot_roc instead.\n",
            "  warnings.warn(msg, category=FutureWarning)\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 576x396 with 1 Axes>"
            ],
            "image/png": 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74ZWLot+oUSPCw8P5/vvvueuuu/j222+pVasW9evXL+vUKiRFUVzdd4qdbCWbNHvadbWsrje+NI5xzWMV4hhOnMV/4vcV/y5LXbYOLHpXIbVq0VSyoDU5PIqZRqVBY9Nj/aURKpsOlU2LRq2m8oBTHoUpJ/7yr1W5uK46ilWDYtNQs0cKDQakehUyrUrLyoHVSTujwWFT4bSpGPFzJlXqaNCoNCTFJxFZKxKtSsvxX+Hj0bmt5aa365nwRYTXPjUqDXPmn2X76lR37LROQ+levwZKuMEjNiPFQecj37vjQgI0nNu6jqxnmqEJCfR4//N372IKm9yx49b1ZVboJFjY0+uUNgpewPGky+7t/5vflcZf3g1tPH8/bkw9QXeWubeNl/R0/a0m+uTemM1mzGYzmZmZmM1mdl68CHnaxRYFMtamsPfPvz3iMjKswL/yxLkK5GudX+KsMzcnlw5Ab499Xvgrns+2f+bjByUKMOaJVeCyHafViVardRdSjaYqiQnZ1HAaMQC11WpCdIE8+uijHgXXZDKxZ4+KzF12ap91YkBFE42GmCEPM3D0DHec0WjEYAhg5cq/MSw5gCHLTmCgDhqE883UR6Gq5ywzq9WBIz4Dw54k1CYtBOqgSiADbq7s9Y5Wrmzi4316q1s3jMlTu8BU38/HxcUxefJkfvjBdQmjbdu2zJs3j5tvvtnVI55SqMPkq8SL/sWLFxk6dKh7e9iwYWg0GpYuXcpjjz3G6tWrAZg9ezZTp07lnXfeoXLlysyaNaukUyuSZFsyJ7JOXLt4/MOWUmHjS7r15lA8PwmzpWzOeXmnUWk8fpFf8ysad8vN1/PmdDOVQisV6jVqNOjUvo+RfUlD2nk1ik2D06amcg0dtW7SexVHjUrDT/9JJe2CA4dVhd0CQyfUoXJEgFd+R/7IYPqEP7FZFWxWJy1aVWb+x119vo+Hh/zAiv/97T5Hn30xiMF3Rnmdu9TUbMJ6zHBvBwfrSZjh4zotMHvzb0xa95N7e1jbPsxp2dtn7C+Od7hkTnZvT9h/P01vagKRIcSmxBJd33UJZcvpU3zMUndcxCEdI56vAcv6e+1zRcgKtrPfvV33tXg62BvAC+094swaG5Bb9K3ZDoLfPULwo+1RwoOx2WyYM82kZqaSkZbq8VpLhpX4fSdY8/HH7oKb8zU5ORDIbZVbFIUXxk7iOw56xJnNlYHHcuMA86U0unbt6uNM3Qu4RourATsKx/86wne7vrsqTg2cpa22NpVUeoxX7uMeWbk6GlOoR+G1WiNQnU1iYHI9DCq4VaslMrIh04dO94gzGo1cvKii3qrLVD2UiT5QR62QAJZM/Jz/9r0JnU7nmYLNAfNjIUALARow6fj4wbE+3hOQbgWzzTViPkALOjXVfdx7/v77m8P9zX3vIw+9XgN1Q11/S8HmzZsZPnw46enpBAUF8fLLL/PII4+gVhff7PoSL/pVqlRh7dq1Pp/LKfgATZo04euvvy7pdK6L2WHmpt9uItmeXHCwn1CjRqPSoFbU6DS6/AuZj2JSYPErTFwB8aV9DEuWE7sF7FZw2qBWjVAMBu//PhcuZLJx40ksFgcWi52ICBP33tvU5zletGg3v/56BovFgS3xEpNe7U1MjHfvVmamlfr133bvU6fTkJ7+gs99zl25nbcmrHdvjx3bjqfm9fEZ+/KSd/n770vu7bfGDqJZeIRXXJbqNPt3537yq145lLqBdb3iAIwBnuNrLMcuw7l0qB3s8fjV585itsHETa5CWjnQ4zm93nOKlOXDfXAmG5bf43V8r/2+sg3qhkFkyLXjUiwoW8+RlJjoUUgzMzNJSorzjEXht42/8l3Kt3kKrpmMjEzgtjxxrl6zNlGt+CPzNA5H3g/TrdDRm2pqVws2XKXi2IHDjBgx3+s9wR3crr2V5toIDKiopFIRd+IshyyHropTCNCu4r+BD2JQqaiiUmFUq+nUqZNXV7ROZ6TbKQ0DdoaiVoNDp+Jop340HPJvj+7tnK9VFv6NLtWBxqQHo45to3ZBde8xV39s2cmtqpq5RTfMwOSrzr3bo74f9qLTwKS2BccBBOtdf29QzZo1Q6PR0K9fP2bMmEGtWrWK/Rjlonu/vDuRdYJkezKB6kBaBLUodOut2IpkUY+RJ/56i5/qyqfj8jLA7OzZVBITM7FaXYWvSZMq1KwZ7BVntzuZNWubu0A6nQozZnh3mQJ8881B5szZ7o4dMiSKF1/s4jO22S0LOXToonv7wIGnaN68qlfckSOXGDz4G/d2x46R+Rb93347x+ef57YgT53y3W+n02m4cCF3kJfDkf+1cq8CabZDhhWCvH8RehW+/x6Ef7eAxuHXjtt/Afosh7X3+9jnVcefsROsKnit8zXztDoUlCUHUD1xa8FF3+Yk40IKe3/99aqWrpmUlIsesVYFPpi/kHVLT5OQkIBGo8FsNnP5sgaDri1tlZoYVCpu1miwp2dTvXp1r/cEjWmnbc8gfRQGFXTQatmyfTszN/hqpOzly6BhtNRWw3Dl/5Az04rD4UCj0WAyma4U0zRu0+7ky0sD3a88EVyNRwY+4tEizvm+wzYtdf92ogRoIVDLlIHTmHR7TY/CHBgYiEathsV/ugrulcK7tf9o8NHaxepwXaTWq1GrVDQDmvl4RwBMj8zvGQ+OIC1EFy5WgMViYdmyZQwfPhydTkfVqlX55ZdfqFWrlvt3cHGTol8I8ZZ4ANqHtmdj63xGw96AFEUhO9uOxeIgw2LHarUQGel7FuiJE5eJjY1zF8gmTarQuXMdn7GvvrqZEydSsFhc+547txf161fyituzJ57+/f/rjmvduga//vqIz32+9NJmliz5w7398cd38+ijvtd/mDIl999IrVblW/STkjLZvv2ce/vcuTSfceCj8FkchYyz+4xzxV5VzA5chL+ToYln0dXpPLv27HYnzkd/RD2tM9S6ugV91T6XHoBzFvhhUMHHf3cPNK/qo+hfFZdpg325o6xtNpu7+CpKFmGBGgItruuqRlQc3X+YXz7+2+u68k03aWkZH0RluxYDKpzAw/cNYY/97FWt7QDqqVswy3QnBlTU06j5e99fdOnysPdJJYB5xgE8FdgBPaBSqfhkZywrs7f6iN3Npspvo1Hlnt/qVapiMAV6Fd7+acGMOKbHplGwaaFFk9a8dUcrrxax0Wik0do0TBkqNCYD6cEBbBq8k8AGVdDrr/rgZbbBsRQwuopzA5OOTyrlMwt7lOem9xXlPK4aEJYvve9FZkTp2LlzJ2PGjOHIkSNkZGQwdqzrksXVa9YUNyn6hRBndXXv1TTULPZ9nzhxmdTUbHcxbd26BsHB3lMQExIy+PTTfe4CWa2aiWefbedzn3Pm/MbKlX+7Y6dO7cJ993l/hlcUMBrf9HjM6XzJ5yfMn346zpNP5l5zHTmydb5Ff9WqI+zZE+/efvHF230W/Zz3lSMz08c0pCu8Ck8+xVSrVaNWq3A6Xa1hp1PBbnei1XpfE/NqQaZYICEDqgcVfPyP94FaDbdWu3bcwYtw6xL44+GC97noD9DoYGY3j8dVKhUGg8bjg4Zl1TECx0R7Ff3q1YO4rVkEhiOXMaigqUaDw2zlUlKSV7d1u3Y6OqSHUOOMHYMK6qg1/LBiFb/uW+oRl5aWzW3RKt47fjtG1ASpVGB3UiUsnNTMdOx2z3+LRwzt+ST8Iff2kg1bGLHS12AuWBI6jo4BuYuPnDh4hIP2k1dFZRJmOMF9eabmntAG06lTJ59d0Q1OVuHceTuKQYMSoGZQh0fo2Xoc58+f59Zbb/V4TeamCxiCjehDA1EF6IhvPwo0+V8/zcng9it/feqR78s9GXXQ0vtyivBvaWlpTJs2jU8++QRFUWjYsCG33XZbwS8sJlL0CyGnpX/gzXo8avjOXaC//vp+1GrvAvnllweYOHG9O27o0Ja89573ACGABx74H7t3514z3LlzBG3bel/HSUjIYPLkDe7tW26plm/RP378Mlu35k7rSEz0Nb3F1QrWatXY7bkj0W02p1cxBB8FMp+WLhS+QHvHFWGfuxNhdwLc5t0dazBoyMrKMzJ50Ldo59wBjSpdFXdVq/zbI5Boh40PeO0zNDSAMIMWg9WBQaVC/fkhuK2WV9EPDw/k/vubYVh1nAAgUqWG8xmuwUhXLd/50EMtaJXtwPTVEQyoaKnVcOlcIoe2bvXqtn7xRSMDvlTRIFGLAdCoVLw65SX2GZI84jIzM6mVEcjPYbkDug798RfNqj3o87zONQ1knDHGvT179TrmZW/yGds0vBPB6tzWqjUtC7tiR61W5+m2NhKsrkJCRiY2tRObViGkbg0ebvGwV4vYaDSi/KVnf5YWjcmAJtjAO7cvRV+nkkdhDgwMROtUQWKmu9u6QaCWrZp8hj9fJWe5Fp+Xqob7/uAqRElYs2YNkyZNIj4+Hq1Wy+jRo5k4cSIBAf90nb3Ck6JfCHEWV1E+8D8d+6y5XcxWq4OAAO9TaLHYOX8+3b3tmvbiW6kUyAJi8xZ9i8Xus+h7FcgLZp9d0T5jF+4F1NCp9rXjjiRD7ffhxEivrse6dcNoVS8Mw7kMDEDN5X9DYIDPov/cc51QPj+IIS4DPSq02+LgUjY08ozr1ashv34+CMOoDRhUUFmlguzc82+3290F9b33OhAyy0TEN7lzuf/Yvps9yi6vbusqVTKZE9aEQEfue4jp0IUL2alexfx+7a18GZJ7SeOr1esY/MV/vN4TQMeQZ4jS504L+vWnzfxs+9srzqKuDFf+WayKHadaoUqVKj67otXp1diedgmnQY1iUNPiphjebNzT53XlM0cdGEwB6ENNGMJMnI46hykkGJ1Od83rjw2Agfk+WwT5DQgT4gawceNG90y21q1b8/bbb9O8ecEzCIqbFP1CyCn6er2aLKtngfRV9At7/bcosT6vFedMTbnqF65X7PbzsPG01yIRACEhBtR2JwYFDGoVtn7fwLs94BbPQWr16oUxqFdD14ISqGi3JQ4e+RF+e8hrn5MmdWR4UOCVWIhadxo61/Eq+vXqhXHu3DgM7T/HYHZ1MWNxuArvVUV//PgOjK9fGUbmjky3Z2Rz+cIFr5Zuu3ZZtN6gpVpK7pzbzxYv4a+VaV7ToUIuwzJd7jX/s0dP0rJSJTIzM7HZPFcne914Jy8ac6eK/e+zr3jjo3Ve7x/glfA3CVTndr0f3LOfJCXdKy5FY+GAkkC2yo5Nq5AcrNCxRUevFrHJZCIuKZwf7WbUJj1qk54nmr3AU9WDvIt5QCAXVXqM4SEEBBlprlZzgTd85nm1ztd60vfQCCFEIXTr1o2ePXtyxx13MGLECDSashlTIUW/EOKtru59vUFDVkbeop9fgb6qpR2fATvioL33mIBGjSpx8WQKhmw7BpWK4Pf2gl0FfTynboWHBzJxYgcMyw5iyLJTJckGdRfB8cchxHMMwOOPt6ZfUACG2bvQq1TU2hgHIYd8Fv24uAkwaCX8cmVA2+FkuJztFdexYyQdP7oboj/NfTDLd69E376NcOxORLsjdy3puBNnObnN7HVd2Ww2M0KlwpRnHuozI54i3pHqVcw7pdVkMQPccT9++z13L73LZw6rQkZyl76Fe/vrT//L91bv+zRUVQVjC78Ds2IlCxuJjjRSUl2j6NVqtUdBjdM4We48jEOvxmlQo4mI5OHaD/u8rrz9sgGDMRBDiBF9qJFVtdZjDPbu3tZqXf8FY2Nj6RAdTQfgKZ/vSAhxIzl27BhTpkxh1qxZ1K1bF7VazZdffllio/ILS4p+IeS09Ke8FU2V71IwnEzFYFcI7vU1LOwFnTyvwffq1ZAz20dg6P8NBiDgYDo89RPsHe6170WL7oI3trsWnwD4NR46XfQq+mFhAcya1Qt+OAsX8iwrmWX3KvoNG4bT8LZaoMu9FJG329rhcHgU1CpWM3k7Trdu2MLRk3bMZjN///0333//PWazGfWlbKbT2B13Of4Cfdq18+qyzszM5AVDT1433emOXfL+Yl6c8z2+3FPpFUya3PHIP3yzilPOS15xwdosNhmPkIUNs2JlnyqeypUr++yK3m2zc0Z1FFWgDrXJQPta99K+8hCf15V/z/NYdaORS8bnMZlM6NYdLUMAACAASURBVPX6Mv8PKoS4sVitVt555x1mz56NxWJh2rRpLF68GKBc/D6Rol8ARVHcLf1Rj3QiaMtmSMqZrmSDNIvXa0wmPaYGlVyju3Nk5z91i6suEdjSs0i9eNGrRWw2m7ndYSXvkhjvzn6b89p0r+vKTRKMzKCjO27rT5u5q9IwzGYzVqvnGIMvgx/hAUPr3H3Oms9X1j1eaQapDIyr9BJZiqvoXrRl8vvvv/t8S78pp5jp2IRDr8KpU3GkSjYdKnXweV35K4cVgykFXUgg+lAjr4bMwZhnYJiv1wQGBnKfWs20/M+qEEKUqt27dzNmzBgOHXItnDRkyBCmTStfv6Wk6Bfgsv0yFqeFEE0IJo0Jh15N3s77U4ePExea4NXStV028xS5RT/7cgYj//1vn93bQ1JvZiq5I6jfnf02419d4TOfv8Km0ExbA4AsxcrCeQs47Ej0imumqU5bo9HdbX3QnkBKtqvbWqVSeRTR/+lOsFV3CZVRhypQR0ClxgwPa4HRaCQjI4P69eu7Y3+4qpW83TTYq+XsWvFL55WTEEL4o/T0dN544w0++ugjFEWhXr16zJ07l27dupV1al78uuhnZWWxf/9+ny1mX9/nfG2cZCTRmsp562VOhMXDa5BxOgOdTsfiwCE8HJA7Ve7VF6ayxLLT69ga1HQLe4EsbGQpNjKsrpWXfPlWk84lXbI79pjmEuHh4T67ot80HEFvOosuKABjkImBxseu2SLO+b6v0chLVx4zGAyF7mYqLyvyCSFEeXXu3Dn+85//oFarefrpp3nuuecwGo0Fv7AM+HXR79GjB7/99luRXqNDw/dVXOtfb7XZ2HjKyis7F+O0xoHDzHTrBpZoY93zhbOrq2gf1N5n4V1qsmM05hbhJcYl1yzMOd8X580VhBBCFL+UlBRCQ0NRqVTcfPPNzJ49m1tuuYWWLVuWdWrX5NdF/+DBgwB06tSJkJCQQrWIwy16mOgauPed1crsrGx40TW38uWXu/DKKzH5Hk8IIYR/UxSFL774gqlTpzJr1iwGDnStQjFs2LAyzqxw/LroZ2W5RrmvX7++8F0t+5IA1400Yq9aXvTmm2XJTCGEqKhOnjzJ+PHj2bLFddfJ9evXu4v+jcJv+5EVRcFicY2sL9ISh2oVdKqFclMYexye8/Cjo4t/7X0hhBDlm91uZ8GCBXTq1IktW7YQHh7O+++/z/vvv1/WqRWZ37b0s7NdC8wYDIaiXSNvEQErB2CzOpj+yV5mrl/GyQPZhCRH0qCB75vGCCGE8E9nzpxh2LBh7N/vug32v/71L15//XWqVKlSxpldH78t+jld+9d7IwO9XsOTT97GZ7eN5WTqNr69ZZPPm+sIIYTwXxEREWRkZFCnTh3mzJlD9+7dyzqlf8Rvi35OSz8wMPAf7SdnNb5Io/ed74QQQvifzZs306pVK0JDQwkMDOSLL76gVq1amEymgl9czvntNf2clv4/KfqKohBndRX9GvoaxZKXEEKI8unSpUs8+eSTDBw4kFdffdX9eOPGjf2i4IMft/T/afc+QIo9BYvTQrAmmCBtUHGlJoQQohxRFIXly5czZcoUkpOTCQgIoF69eiiKUi7Wyy9Oflv0i6N7P6drv4ZBWvlCCOGPTp8+zYQJE9i4cSMAXbp0Ye7cuTRo0KCMMysZflv0r7t7f8RakhwOtFVN6KokEhClo2aYTNUTQgh/c/78eTp16oTZbCYsLIxp06bx4IMP+l3rPi+/L/pF6t53OGHVMeZmZDIjK5v6ajWWjgNQPxwMsvy8EEL4lVq1anHnnXdis9l46623qFq1almnVOL8tuhfV/f+pWxQINbuWpTnpNMJW5sRMCCrgBcKIYQo77Kyspg5cyb9+vWjTZs2ALzzzjsV6q6gflv0r6t7/4IZRVG8lt+NurVycaYmhBCilP3yyy+MGzeOkydPsmHDBrZs2YJara5QBR/8eMpeTku/SN37kcFc/qAnkbVD0OQsxBNooWVTmaMvhBA3osuXL/PMM89w7733cvLkSW6++WbmzZtXYe9mKi39vEIMhN/XlH33NSUry0b7r+/hz5OnqB3Yu4SyFEIIURIURWHFihVMmTKFCxcuYDAYmDhxIs8++yx6vb6s0yszUvTzERioI+Omv6H2CZmyJ4QQN5iUlBQmTZpESkoKnTp1Yt68eTRq1Kis0ypzflv0r6t7Pw9FUXLn6ctqfEIIUe45HA4URUGr1VKpUiVmzpxJZmYmw4YNq7Dd+Vfz27PwT1v6qfZUsp3ZBGmCCNYGF2dqQgghitnBgwfp06cPCxcudD923333MXz4cCn4efjtmfinRT+nlV/TIAvzCCFEeZWdnc0bb7xBt27diI2NZdmyZdhstrJOq9zy26J/Xd37p1LhUhY45UY7QghR3m3bto0uXbowZ84c7HY7jz76KBs3bqxw0/CKwm+v6V9PS/+djkvZdimTaIOO5pU0VHutCjWrSUtfCCHKk6ysLF544QU+/fRTwHUXvPnz59O+ffsyzqz88/uiX+iWvlNhzaUM1lptfGW1Qjro9kdSo6O09IUQojwxGAwcPXoUnU7H+PHjGTt2LAaDoazTuiH4bdEv6jK8yuUsYm2eK/HZbj5HTb209IUQoqydP38eRVGoXbs2arWaBQsWYLPZaNq0aVmndkPx22v6Re3eP3f0MhcUxb0dqAYiL8ocfSGEKENOp5OPP/6YDh06MHbsWJQrv6cbNmwoBf86+G1Lv6jd+9VaV2fbtkfZsyee2J3n2ZD4E+c0iozeF0KIMnL48GHGjh3L77//Drh+n5vNZkwmUxlnduPy26Jf1O59vV5Dx46RdOwYCc9Aw98mQpZM2RNCiNJmsViYN28e8+bNw2azUa1aNWbMmMFdd93l1/e6Lw1+W/T/yTx9RVGIt8QDMmVPCCFKk81mo2fPnhw4cACAf//737zyyiuEhYWVcWb+we+L/vUsw5tqTyXLmSWr8QkhRCnT6XT07t2b7Oxs5s2bR6dOnco6Jb/itwP5itq9n1e8VVr5QghRWtasWcPatWvd2xMnTuSXX36Rgl8C/L6lfz1FX5bgFUKIkpeQkMDkyZP5/vvviYiIYOfOnYSFhcmc+xLkt0W/KMvwbt9+Fuuyv2gVbiIkMgS97jQhEQHUqCYtfSGEKG5Op5NPP/2UV155hbS0NEwmE+PHjyc4WC6nljS/LfpFaem/8cav/PDDUQAaa9QsMgUR8V6QtPSFEKKYHT16lHHjxvHbb78B0KtXL2bPnk3t2rXLOLOKwS+Lvt1ux263o1arC3XjhdjYePf3RxxOKqlVJIamyzV9IYQoRoqiMHz4cA4fPkxERARvvfUWAwYMkGl4pcgvB/Ll7dov6IcpPj6dhIQM97YBaGiEjACLtPSFEKIY5Kyip1KpmD59Og8++CA7duxg4MCBUvBLmV+29IvStW+xOBg2JIrYLac5HJ/OLdWD2dDjFKhk9L4QQvwT6enpvPHGGzidTmbOnAlAly5d6NKlSxlnVnGVStHfvn07M2fOxGw2U7NmTd566y2qV6/uEbN582bmzZuHxWIhLCyMKVOm0LJly+s6XlGKfr16YXz630EAZGZaSUrKpGd8G1mNTwgh/oH169czYcIEzp8/j06nY/To0XLdvhwo8e59s9nM+PHjef3111m3bh0xMTG8/PLLHjFpaWlMmDCBGTNmsHbtWkaNGsWzzz573ccsysj9vEwmPfXqhbmn7MnNdoQQomiSkpIYMWIEgwcP5vz589x6661s2LBBCn45UeJFf8eOHURGRtK8eXMABg0axLZt28jIyL2OfvbsWQIDA913TGrfvj0JCQmkpaVd1zH/yRz9NEcaWc4sTBoTwRqZPiKEEIW1bt062rdvz4oVKzAajUybNo3169fTokWLsk5NXFHiRf/UqVNERka6t00mE2FhYZw5c8b9WMOGDVGr1Wzfvh1w/eBERUUREhJyXcf8J0vwuhfm0deUASZCCFEEe/fuJSUlhTvuuINt27bx9NNPo9X65dCxG1aJ/2tkZWV5ra5kMBgwm83u7YCAAKZNm8YTTzxBQEAATqeTxYsXF2r/OTdlyGvfvn0AOBwOYmNji5TvLtsuAIKtwUV+rb+S81A65DyXPDnHxctut5OcnEzVqlUBePLJJ2nbti0xMTFcvHiRixcvlnGG4molXvSNRiMWi8XjsezsbI/7IScmJvLiiy+yfPlymjRpws6dO3nmmWdYt25dgfdNjoqK8vpQkZSUBEBERATR0dH5vtbpVFCrVZBlhwANqFQcij8EB6FxlcZER+X/2ooiNjb2mudQFA85zyVPznHx2rt3L5MmTcLhcLBp0yb0ej2xsbFMmjSprFPzWxaLxWdDtyhKvOg3aNCANWvWuLfT09NJTU2lbt267sf27t1L7dq1adKkCQDt2rVDrVZz/Pjx6xrBX9ju/fvu+5rDhy8SnWqjdaaDB2qF0r2KmeaP1KBGpAziE0KIq2VmZvLWW2/xwQcf4HQ6qVOnDmfPnqVhw4ZlnZoohBK/pt+uXTvi4uLYvXs3AEuWLCEmJgaj0eiOqVevHseOHePcuXMA/PXXX6Snp1OnTp3rOmZh77C3a1cchw5d5LO4VManZpAQl0GNg2rsaqdM1xNCiKv8/PPPdOrUiffeew+Ap59+mm3btknBv4GUeEs/ICCAuXPn8tprr5GVlUWdOnWYPn06iYmJPPbYY6xevZqmTZsyYcIEHn/8cZxOJ3q9nlmzZhEWFnZdxyzM6P2kpEzOncudHaAHojQaABJD02S6nhBC5PH888/z4YcfAtCiRQvmz59Pq1atyjgrUVSlMqyyXbt2rFq1yuvx1atXu78fMmQIQ4YMKZbjFaZ7/+DBCx7bLbQa9CoVVp2DFFMWNfXS0hdCiBzNmzcnICCA559/nqeeeqpQ9zUR5Y9fzqUoTPd+t271SE19nr1744mNjScsQAt9GzP4l/6uJXilpS+EqMBOnTrFvn37uOeeewAYOnQoMTExssjODc4vi35hF+cJCTHQtWs9unatB7huCrHu1F5wyhK8QoiKyW638/777zN9+nQURaFFixY0aNAAlUolBd8P+GXRv95leNMcaZidZoxqo6zGJ4SocP7880/GjBnjXutk4MCBBAfL70J/4pdF/3qX4Y23xAOuVr6sxieEqCjMZjMzZ85k4cKFOBwOateuzZw5c+jZs2dZpyaKmRT9PORGO0KIimjcuHEsX74clUrFE088wYsvvkhQUFBZpyVKgF8W/evt3o+3Xmnpy8h9IUQFMn78eI4dO8aMGTO47bbbyjodUYL8sugX1NJ/552dKApER9fg1jOZmMw2iDCiciYQogmQQXxCCL+lKAorVqzg559/ZuHChahUKpo0acKGDRvksmYFUCGL/pw52zl9OhVwLUm4NyyUllotD2Lg4xfrUKOldO8LIfzPuXPnmDBhAj/99BMAAwYMcF+3l4JfMZT4Mrxl4Vrd+5cumd0FH0CtgsZXVuIDSAxLl5a+EMKvOBwOPvjgAzp06MBPP/1EaGgo8+fPp3v37mWdmihlFa6lHxsb77EdpdMSkOcTbmJoOjX00tIXQviHgwcPMnr0aPbs2QPAPffcw/Tp06lWrVoZZybKQoUr+jfdFM7MmT3YsyeB2Ng42oQZoXN9uJjFnhPbuBSUKS19IYTf+Pbbb9mzZw81atRg9uzZ9O3bt6xTEmXIL4v+tbr369evxKRJndzbTqcCahWKotBlywMoDkWm7AkhbmipqamEhoYCMGHCBDQaDaNGjSIkJKSMMxNlzS+v6Rdlnr5a7eraT3ekk+nIxKg2EqKR/xhCiBtPSkoKY8aMoUOHDqSkpAC4b5IjBV+Anxf9oszTz1mNr4ahhoxiFULcUBRF4bvvvqN9+/YsW7aMS5cusWPHjrJOS5RDft29X5QV+XJW45Pr+UKIG8n58+d57rnn+PHHHwHXrcznz59PkyZNyjgzUR75ZdG/nmV446xXluCVkftCiBvEN998w7hx48jIyCAoKIhXX32V4cOHo1b7ZSeuKAZ+95OhKEq+3fvHjiWTlWXz+bq8N9sRQogbQeXKlcnIyKBfv37s2LGDRx55RAq+uCa/a+lbrVYAdDodmjyL7gD06fMZp06l0KxZBNHRNZmh1VN17wWICKR3oIP/9apHjUbS0hdClE8Wi4XNmzfTu3dvALp168aGDRto1aqVjEUSheJ3Hwnz69pPScnm+PHLOBwK+/cnsWzZPoITzXA6DXYn0uJXE6HmALnZjhCiXNqxYwddunRhyJAhHoP0WrduLQVfFJrftfTzm6O/Z4/nSnzNmkUQmGzxeCwxVJbgFUKUL2lpabz66qv85z//AeCmm27y6sUUorAqTEs/I8NKgwaV3NvR0TXhgtkjJjE0XRbmEUKUGz/88AMdOnTgP//5D1qtlgkTJrBlyxbatGlT1qmJG5TftfTzK/p3392Eu+9uwuXLWezZE0+lSoEwrxJczIIkM8O23MeFkAxp6QshyoUPP/yQ559/HoDbbruN+fPn06xZszLOStzo/K7oX2sJXoBKlQLp3r1B7gNhAaTX0/JZ1g4C1YGyGp8QolwYOHAg7733Hk8//TSPPvqodOmLYlFhuvevJe/CPDIgRghRFo4ePcqYMWOw2VzTiqtUqcKuXbt4/PHHpeCLYiNFH4i3XlmCVxbmEUKUMqvVyqxZs7j99ttZtmwZH3zwgfs5nU5XhpkJf1So7v0tW7awfv16srOzmTNnDlu3biU6OrpIhbW0FNS974sswSuEKAu7du1izJgxHD58GIChQ4cybNiwMs5K+LMCW/qLFi3i7bffpnHjxuzbtw+A/fv389JLL5V4ctfjulr6eW62I4QQJS09PZ3JkyfTp08fDh8+TIMGDfjuu+9YsGABYWFhZZ2e8GMFtvS//vprfvjhBwICAvjyyy8BePLJJ+nXr1+JJ3c9fBX9gQO/Ijw8kOjoGkRH16R16xpo/0iCJDNEBJKdmYTBqpWWvhCiVKxZs4aPPvoIjUbD6NGjmThxYrnsORX+p8Cir9Vq0WpdYTmD3BRFKdms/oGru/fT0ix8+62r6+zjj/eiUkFa2gsEffwn/O8IAC8SybEno6lxq7T0hRAlw2q1otfrAfjXv/7F3r17GTp0KFFRUWWcmahICuzev/322xk5ciQbNmwgOzubLVu28Oyzz9K5c+fSyK/Irm7p793ruRJf06ZVCArSu+bn55Ekc/SFECVAURQ+++wzWrVqxalTpwBXA2r69OlS8EWpK7DoP/fcc0RHR7No0SJ0Oh2LFy+mTZs2TJ48uTTyK7Kr77AXG+tZ9KOjrxR2r9X40mT0vhCiWB0/fpx7772X0aNHEx8fz+eff17WKYkKrsDu/Z9//pmnn36ap59+2uPxJUuW8PDDD5dUXtctp3s/p6X/8MO3EhVVldjYOGJj4+nRo74rsEsk1AiCC2bOnTtKQpisuy+EKB42m42FCxcyc+ZMsrOzqVy5Mm+++Sb33XdfWacmKrh8i35CQgLnz59n1qxZVK9e3eM6fnp6OgsWLCiXRf/q7v3w8EB69WpIr14NPQNfc12eSLenE7klhEB1IKHa0FLNVQjhf/766y+eeuopDhw4AMDgwYOZNm0alStXLuPMhLhG0T927BhLliwhKSmJCRMmeDyn0+kYOnRoiSd3Pa7u3i+Ie2EeQw1ZjU8I8Y9pNBr+/vtv6taty9y5c4mJiSnrlIRwy7fod+7cmc6dO/P2228zZswYr+fPnj1booldr6u79wviXphHL137QojrExsb676vfdOmTfnyyy9p164dRqOxrFMTwkOBA/nGjBlDYmIiu3fvZteuXezatYstW7bw4IMPlkZ+RVbUxXlkYR4hxPW6ePEiI0eOpGfPnqxYscL9eExMjBR8US4VOJBvyZIlzJkzh4iICC5cuEClSpXIzs7mgQceKI38iqyoy/DKErxCiKJSFIWvvvqK//u//yM5OZmAgADS0tLKOi0hClRg0f/ss89Ys2YNkZGR9O3blx9//JFvv/3W3aIub/K29L/66gCNGoUTFVUVg8H3W5Wb7QghiuLUqVOMHz+ezZs3A9C1a1fmzp1L/fr1yzYxIQqhwKKv0+mIjIwEwOl0AjBgwADuueeectnFn/thRM+QId+gKKDTqWnZshrbtz+GTqeB9/fCmpMQEUgvjYGdtzSkZjNp6Qshrm3Hjh0MGjSIrKwsKlWqxOuvv87gwYNlELC4YRR4Tb9WrVq89tprOBwOatSowVdffcX+/fu5fPlyaeRXZDnd+2fP2siZZWizOUlPt7oKPsBfl2BHHHx/nF4ra9A0rppc0xdCFOjWW2+lVq1aDBo0iB07djBkyBAp+OKGUmDRnzFjBmq1Go1Gw/jx4/nkk0947LHHGDlyZGnkV2Q5Lf0TJzwvP0RH5ynqVy/BG5ouo/eFEF7MZjPTp08nJSUFcI0V2rBhAx999BERERFlnJ0QRVdg937lypX5v//7PwBatmzJunXrSjypfyKn6NerV4l7721KbGwcZ8+meRZ9ryV406WlL4TwsHnzZsaPH8+pU6dISEhg/vz5AISEhJRxZkJcv2sW/eXLl3P48GFat25N//793Y+fPXuWWbNmsWDBghJPsKhyuvf79WvAM8/0ACApKROtNk+nxqJeEJdBVkIyU3aM42zNNMK0cg9rIQQkJyczdepUvvjiCwCaNWvGsGHDyjgrIYpHvkV/5syZxMbG0q5dOxYuXEhqair9+/fn3Xff5X//+x+DBg0qzTwLzdc8/apVTZ5BjSpBo0qcNWcxv/Im6gfUl+tyQlRwiqLwzTffMGXKFC5evIjBYOC5557jmWeeQafTlXV6QhSLfIv+zz//zIoVKzCZTDz00EMMGjSId955h27durF69Wpq1apVmnkWWlEW58lZmEfm6Ash/vjjD/dYpc6dOzN37lwaNWpUxlkJUbzyLfoqlQqTydVCrlatGjqdjqVLl9KwYcP8XlIuFGVxHlmYR4iKTVEUdy9fq1ateOKJJ2jWrBlDhw6V3j/hl/Idva9Wez4VGBhY7gu+w+HAarUCYDAYCoyXhXmEqLj++usvevXqxc6dO92PvfXWWwwbNkwKvvBbBU7ZKw7bt29nwIAB9O7dm0ceeYSEhASvmIyMDMaMGUO3bt3o06fPdc0SyNvKL8x/WmnpC1HxZGVlMW3aNGJiYoiNjWXmzJllnZIQpSbf7v0TJ04QFRXl3rbb7e7tnC6xnPtFX4vZbGb8+PEsXryY5s2b8+mnn/Lyyy+zaNEij7jp06cTERHBpk2bOHnyJC+//DLdu3dHqy1wVqFbTtHX66vx4IPfEBSkJzhYz003VebJJ29zBZ1Jg0tZEGHkQrrrw4dM1xOiYti6dSvjxo3j+PHjqFQqRowY4Z6SLERFcM2BfMVhx44dREZG0rx5cwAGDRrEzJkzycjIICgoCACr1coPP/zAhg0bUKlUNGjQgGXLlhX5WDmD+HS6cL74IvcDSceOkblFf9lfMD8WgKW0p9YDF6jZSlr6Qviz1NRU5s6dy48//ghAkyZNePvtt2nbtm0ZZyZE6cq36BfX6PxTp0651+4HMJlMhIWFcebMGZo1a+aOMRgMrFixgm+//Raj0cj48ePp2LFjkY6VW/Q9p+gFBelzNy54rsZ32WSWa/pC+Dmr1crWrVvR6/VMmDCBMWPGoNfrC36hEH6m8H3n1ykrK8trUJ3BYMBszl0VLy0tjfT0dAwGA2vWrOHXX39l9OjRbNiwgbCway+ak/cSw7FjxwBwOj3n1NrtmcTGulr3DY/Fk3ePiWHpJB1KIludfT1vr0LIOXeiZMl5Ll45twLPuUT4wgsvUK1aNerUqcP+/fvLODv/JT/H5VuJF32j0YjFYvF4LDs72z0dECA4OBiHw8GQIUMAuP3226lRowb79u2ja9eu19x/VFSU+0OFw+EAoFo1O3PmDCA93UJ6upVGjcKJjr7Z9YKWmZChwXkhE+fFDFLCsul2WzcZrZuP2NhYoqOjyzoNvyfnufg4nU4++eQTXnvtNcaNG8e4cePcz8k5Llnyc1yyLBZLocbSXUuJF/0GDRqwZs0a93Z6ejqpqanUrVvX/ViNGq7u9czMTHfLXqPReE0bLEjOQL7QUIWhQ1v6DnqzCwDHzUdpsq0JDQyyGp8Q/uLQoUOMHTuWXbt2ubfzzsUXoqIrsKparVZmzJhBjx49iImJAWDx4sWcPHmyUAdo164dcXFx7N69G4AlS5YQExOD0Wh0x4SEhNC5c2c++eQTAPbt28f58+dp0aJFkd5MUVfjU9QK1YzVi3QMIUT5Y7FYePPNN+nWrRu7du2ievXqfPrpp3z44YdS8IXIo8Ci/8ILL5CVlcU777zjHvhSr149XnrppUIdICAggLlz5/Laa6/Rs2dP/vjjD1566SUSExO588473XFvvPEGf/75J3fccQdTp05l3rx5BV7Pv1pRir7M0RfCPyQkJNClSxdmz56NzWbj4YcfZvv27R6/X4QQLgV27//xxx/u6XsajQaAHj16MG/evEIfpF27dqxatcrr8dWrV7u/r1atGkuWLCn0Pn0p0hK8VlfRl5H7QtzYqlat6r63/fz58+nQoUMZZyRE+VVg0dfr9Vy8eJEqVaq4H0tOTi6XXWZysx0hKobVq1cTFRVFvXr1UKvVfPzxx4SGhhbqA78QFVmBRf/hhx/m3nvvpW/fvly+fJmZM2fy008/8cQTT5RGfkWSU/Q1GiNZWTYCArT5fjjJ6d6X1fiEuHHEx8czefJkVq9eTbdu3fjmm29QqVRUq1atrFMT4oZQYNF/4IEHaNiwIZs2baJnz54YjUbefvtt98I65UlO9/769eEYjW+i0agIDjbw3XeD6dKlLqw7Cd8cgQgj3WzBnK7bgJq3SktfiPLO6XSydOlSXnnlFdLT0wkKCqJfvtVx2AAAIABJREFUv34yMl+IIiqw6E+YMIG+ffveECtY5bT07XbX2AOHQyElJRu93rXNviT49igAjxNF0r3nqTlcir4Q5dmRI0cYO3YsO3bsAKBPnz7MnDmT2rVrl3FmQtx4Chy937JlS5YuXUrnzp2ZNGkSGzduxGazlUZuRZZb9D3fVnDwlQ8rVy3BmxiaLt37QpRjqamp9OjRgx07dlC1alU++eQTPv/8cyn4QlynAov+8OHDWbZsGWvXrqV9+/YsX76cmJgYnn/++dLIr0hyuve1WlVu6x4IDr6yDHCS2SM+uVIWlbSVSi0/IUTRhIaGMmrUKIYOHcqOHTu49957pTtfiH+g0CvyhYeH06lTJ7Kzs7HZbGzZsqUk87ouOS39F18M55lnnsFqdZCRYSU09ErRH90a+jfg4vmz/OfPhSQ0tMsvECHKkbS0NF5//XXat2/PwIEDAZg8ebL8PxWimBRY9A8ePMjGjRv5+eefSUpKonv37jzyyCO0b9++NPIrkqun7On1GsLD80zfi64O0dU5eDmJ5/Z8R8fQot3FTwhRctauXcvEiROJi4vjhx9+4M4770Sv10vBF6IYFVj0R44cSa9evZg8eTJt27Yt8nr4pamwi/PEW11z9GVhHiHKXmJiIi+88AIrV64EoHXr1syfP7/cDxwW4kaUb9FPTk4mPDyc5cuXuwv9hQsXPGLK29zYwi7OI0vwClH2FEXhs88+46WXXiI1NRWTycSUKVMYOXKke/VPIUTxyrfoDx06lDVr1hATE4NKpUJRFI/nVSoVhw4dKvEEiyKn6BfY0r+yGp+M3Bei7FgsFhYsWOAeoT9nzhwiIyPLOi0h/Fq+RT/ndrj79+9Hp9N5PR8fH19yWV2nnO79Alv6VmnpC1EWbDYbFouFoKAgAgICWLBgAefPn2fQoEFy7V6IUlDgBfp77rnH6zGn0+keWVueuFr6YXz9dTzvvvs7S5f+P3t3HldT/j9w/HVbbhulUraxjEEMhS9jXytuRXbG1hhbM8Jo8kVjDEORPYx9mTGZYRCyRAkRDWbsExNmhkYSbdr37u+P++t8XZViptXn+Xj0eDinc+9531N6n89y3p8bhIREqL6ZnAXRKZCd+78SvGJMXxDKzNWrV7G2tmbevHnSvs6dOzNs2DCR8AWhjBTZ0t+/fz/bt28nKioKhUKh9r3U1FRMTExKPbjXpWrpm7N5831AVXnPzq6JqgSv331wCwbgQHV7NllXo25H0dIXhNKWkpLC4sWL2bZtG3l5eaSlpZGcnEz16tXLOzRBeOsUmfSHDx9Or169GDVqFB4eHuov0tLCwsKi1IN7XaqWvo7aPkPDgoV5jJP10MyTiTF9QShlQUFBzJw5k8jISDQ0NJg+fTpz5sxBX1+/vEMThLfSK2fvm5mZ8eOPPxb6/ZSUFAwMDEotsDehSvrqrQepBO9L1fhijdMw0ap4vRWCUBXk5OTg4uKCr68voCrnvXbtWlq3bl3OkQnC263Y2fs9e/asNLP3Vd37T5k2rQ25uVqkpGTRpcv/zwaWa4CpLsr4DGRKyDItetldQRD+GS0tLbS0tNDT02POnDm4uLigpVXiAqCCIJSSYmfvh4eHl1kw/5SqpR/LkiXWBccLPbqDR3cuxIQw4pwj79cSLQ5B+Dc9ePCAlJQULC0tAfD09GT27Nk0atSofAMTBEFS7Oz9P//8kx07dgBw//59Ro8ezZgxYypcK1+pVJaoOE9U3hOijZMwNjIvq9AEoUrLyclh3bp1dOvWDWdnZzIzMwHVeh0i4QtCxVJs0v/iiy+kZSwXLlxI9+7d+eSTT1i4cGGpB/c6cnJyyMvLQ1NT85XdiKIwjyD8e27evImtrS1ff/016enpWFlZSUlfEISKp9ikn5ycjEKhIC4ujvDwcCZPnkyPHj1ITU0ti/hKTJTgFYSyk5aWxvz587GxseHWrVvUr1+fffv2sWXLFgwNDcs7PEEQilDszBqZTEZ6ejr+/v507doVLS0tsrOzycrKKov4SqykSV8stiMI/4xSqcTR0ZHr16+joaHBlClT+OKLL6hWrVp5hyYIQjGKTfqjR4+WZvB///33APz3v//F1ta21IN7HSVdYU+09AXhn5HJZEycOJFNmzaxdu1a/vOf/5R3SIIglFCxSX/s2LEMHjwYHR0daax86tSpNGvWrNSDex35Lf24OAeaNFlH9eo6VK8uZ8eOATTNyAPfu2CmzweJhiSa1adOR9HSF4SSUCqVHDhwgMTERCZOnAjAqFGjGD58eKHrcgiCUHEVm/SVSiVnzpwhNDSUuLg4atasSc+ePSts0s/Jqc6ffyZI+3NzlfBbDGy8AcBSbPihmyF1x4uWviAU5++//2bmzJmcPn0aXV1d+vbtS/369ZHJZCLhC0IlVGzSX758OVeuXMHR0RFDQ0OeP3/O1q1b+eOPP5g2bVpZxFgi+d37SqX6R6pWTQ4x6Wr7Yo1SRTU+QXiF3NxctmzZwpIlS0hLS6NGjRp4eHhIT/IIglA5FZv0Q0JCOHjwIDo6/6tpP2LECIYPH16hkn5+Sz83V731Ub26HGLUS/Cmm8pENT5BKEJYWBiurq5cu3YNgMGDB+Pl5YW5uahtIQiVXbFJPzc3F7lcrrZPV1eXvLy8UgvqTeQn/R49brBly3ckJ2eSnJxF9eo6YP8u1NTjceQfnLsfQHSznHKOVhAqrq+++opr165Rt25dVq1aVWCVTUEQKq9ik37Hjh2ZMmUKI0aMkLr3fX196dSpU1nEV2L53fvGxlo0a2aq/s2u70DXdwh9+hdjwr5niNmQcohQECqu7OxsaYx++fLlfPvtt8ydO1csfysIVUyxSf/LL79k586d7Nixg/j4eGrWrEmvXr1wcnIqi/hKrEQleMXjeoKgJiEhgfnz5/PkyRP279+PTCajadOmeHl5lXdogiCUgmKTvlwux9nZGWdn57KI542V5Dl9qTCPKMErvOWUSiV+fn64u7sTExODXC7n999/5/333y/v0ARBKEVFluFNSEjAzc2Njh070r17dzw9PSt0Te3XaunLRUtfeHtFRkYyevRoJk6cSExMDJ07dyYkJEQkfEF4CxSZ9D09PalRowa7d+9m27ZtpKSksGLFirKM7bWUJOmLxXaEt93OnTvp0qULgYGBVK9endWrV3P06NEKV3dDEITSUWT3flhYGIGBgdL2okWLGDKk4k6AU3XvV+fRIz3OnXtI9eo61KplQL3a1UAmAw2ZGNMX3npRUVGkpKTQv39/li1bRp064gZYEN4mRSZ9DQ31TgC5XE5ubm6pB/SmVC39Ruzdq8nevao1AkaObMWeye1h1DEw1WOf/hCOW4VRp7v4Qye8HTIyMvjzzz9p2bIlADNnzuSDDz6gT58+5RyZIAjlocju/cKK11TkgjaqpK+jtq96dTnEpkOeEmLSaBVRm0axpphqmxb+JoJQhfz888/07NmTIUOGkJCgKk2to6MjEr4gvMWKbOk/efKECRMmvHLft99+W3qRvSZV9756EaHCqvGlmOZV6JsXQfinEhMT+frrr6VVMZs2bcrTp08xNjYu58gEQShvRSb9+fPnF9jn6OhYqsH8E6qWfiKWlroYGZmTnJxJgwZGkJildlyWqWb5BCgIZeDo0aPMmTOH6OhotLW1cXV1xc3NTa2MtiAIb68ik/7gwYPLMo5/TJX0b/Pll4348MMP1b/p1p6j4ftZ+Msc2jbuWi7xCUJpmzt3Lps3bwbggw8+YM2aNbRo0aKcoxIEoSIpcky/snllcR5dLf4yecbV9x6hU1+M5wtVk729PdWqVWP58uWcOHFCJHxBEAootiJfZVHcc/pRWarH9erIxcx9oWq4d+8ep0+fZsqUKQB0796d3377DSMjo3KOTBCEiuqtSfr5hXnEM/pCZZeVlcWaNWtYvXo1WVlZtGnThs6dOwOIhC8IwiuVKOmfO3eOkydPkpGRwapVq7hw4QLt2rV7ZfW7slZc7X1RmEeoCi5fvoyrqyt3794FwMnJSXTjC4JQYsWO6W/ZsoW1a9fSrFkzbt68CcBvv/1W6Oz+8lRsSz9/sR3RvS9UQklJScyaNQsHBwfu3r3Le++9x9GjR1m7di01atQo7/AEQagkim3p79u3D39/f3R1dfnpp58A+PTTT3FwcCj14F6HKukPws3tF2rXvk/16nIWunSgZkgkmOvTJEqXLGMz0dIXKqUlS5awY8cOtLS0+Oyzz/jvf//7yhUlBUEQClNs0tfS0kJLS3VYflEbpVJZulG9AVX3fhOCgh4BjwCYZ9sE5ocCcJjxnG/+J6ZjxOx9oXJQKpXS/7lZs2bx4MED5s+fL5XUFQRBeF3Fdu93794dZ2dnTp06RUZGBufOnWP69Ol069atLOIrsULL8KZmq20nmeSIanxChadUKvHx8aFfv35kZamKS5mamrJ3716R8AVB+EeKTfqzZ8+mXbt2bNmyBW1tbbZv384HH3zA7NmzS3ySixcvMnjwYBQKBePHjyc6OrrIY8PDw2nZsiWXL18u8fsDpKdnAtrStkwGBsnqST9dVCEVKrg///yTgQMH4urqyqVLlzh8+HB5hyQIQhVSbPe+XC5n6tSpTJ069Y1OkJaWhpubG9u3b6dly5b4+PiwYMECtmzZUuDYvLw8vv76a2rWrPla51AqlWRmZgC72LfvMKmp2aSnZyNrZQYTLHkUeY8Hf98m/r03+giCUOpycnJYvXo1K1asIDMzk5o1a+Ll5VWhl7MWBKHyKTbp9+3bt8gu8cDAwGJPcOnSJerXry91Sw4dOpTly5eTkpJCtWrV1I7ds2cPzZs3R1tbu7C3KpJqPF+Jjk4kw4e/1P3Z/R0OPbrJjHtrcann8lrvKwhl4dq1a7i4uPDgwQMARo0ahYeHByYmJuUcmSAIVU2xSd/T01NtOzExkaNHj9KlS5cSneDhw4fUr19f2jYwMKBGjRr8/fffvP/++9L+mJgYfHx82L9//2v3Kohn9IXK7Pfff+fBgwc0atSI1atX06tXr/IOSRCEKqrYpN+hQ4cC+6ytrRkzZgwjR44s9gTp6ekFVvjS0dEhLU19ydslS5YwdepUDA0Ni33PF4WFhRETEwOonjS4evVqwWNSwwDIfJLJ1fiC3xderbBrKvwz0dHR1K5dGwALCwtmzJiBra0turq64nqXInFtS5+4xhXbG5XhTUxMJCoqqkTH6uvrk5mZqbYvIyMDAwMDafv8+fM8f/6cAQMGvHYsrVq1IjIyEgBDQ0PatWtX4JjM65kQD10sutDOtOD3haJdvXq10GsqvJmYmBi++OILjh8/zoULF2jcuDGgehxWXOfSJX6XS5+4xqUrMzOTsLCwf/Qerz2mn5ubS0xMDEOHDi3RCRo3bszx48el7eTkZBITE2nYsKG0LygoiDt37tC1q2rZ28TERKZPn87cuXMZNGhQsecoafe+qMYnlBelUsmePXv46quvSEhIQE9Pj7CwMCnpC4IglIXXHtPX0NDA3NycBg0alOgEHTt2ZO7cuVy5coX27duzc+dOevfujb6+vnTMokWLWLRokbTt5OTEtGnT6NixY4nOoXpGXxcNDRPi49OpXl2Otram9H2x2I5Qnh48eICbmxvnzp0DoFevXqxevZpGjRqVb2CCILx1ik36+/fvZ8WKFW98Al1dXVavXs2iRYtIT0+nQYMGLF26lKdPnzJx4kSOHTv2xu+dT5X0Lbl9uw+mpssBcBljyYZbieSZ6fKDxoeENYzG1FpU4xPK1qFDh5g2bRrp6emYmJiwePFiRowYIYpECYJQLopN+tnZ2fzyyy+FTugrqY4dO3LkyJEC+4tK+Lt27Xqt91d178vV9unnAU9S0XiSigMtMU+vgYas2FpEgvCvatmyJXl5eYwYMQJPT8/XrkEhCILwbyo26UdHRzN58mR0dXULrOZVkuf0y0KhJXhfWh4gxSS37AIS3lqpqans37+fcePGIZPJaNasGZcvXy7xcJggCEJpKjLpp6Wloa+vj5ubW1nG80ZUST8bPb1s5PJqJCdnUT1XPetnmopWvlC6zpw5w8yZM4mIiMDAwIDhw4cDiIQvCEKFUWTSHzZsGMePH/9H3fplRdW9f55hwxrh4+ODUqkkLy0bYjPYF7aTH8O208WiD4ryDlSokuLi4pg3bx579+4FwNLSkqZNm5ZzVIIgCAUVmfQr4vK5RVG19EFPTw9QPfOsaSAHAznXs59wRPc3Pmg8ojxDFKogpVLJ/v37+fLLL4mLi0NXV5c5c+bg4uLy2qWkBUEQykKRST8zM5Pr16+/Mvn/5z//KZWgXtfLSf9FogSvUFr27NnDtGnTAOjRowerV68Wz90LglChFZn0nz17xn//+98ik75MJuP06dOlFtjreFVxnidZqmf0RWEe4d82ZMgQfHx8cHJyYvTo0eIxPEEQKrwik379+vU5ceJEWcbyxkRLXygLYWFheHh4sHnzZoyNjdHV1eXEiRMi2QuCUGlUiSntr2zp/381vjo6oqUvvJn09HQWLlxI7969CQoKYtWqVdL3RMIXBKEyKbKlX9ISuBWBqqVvTXAwaGiEUl1Xi/G1jaC2DmYR2mjUqE5NbVEURXh9ISEhfP755zx48ACZTIazszNz5swp77AEQRDeSJFJ/+uvvy7DMP4ZVdLvQmBgBoGBpwD4yNQEfZmMcOYRaZaIxoAq0akhlJGEhAS++uordu/eDUDz5s1Zu3YtH3zwQTlHJgiC8OaqRCZMTc3gxfsXDQ0ZL47uJxpnl3lMQuUWFhbG7t27kcvlzJ07l7Nnz4qELwhCpVdsGd7KICUlS227up6W2lhrmknlqTkglJ+kpCQMDQ0B6N69Ox4eHvTt21cU2hEEocqoEkk/MzMNOMmoUeOoU6chWk/TQFOHJ5F/kvo0lpR6VaJDQyglubm57NixA09PT/bu3Uvnzp0BmDp1ajlHJgiC8O+qEkk/JycN+JmJExdhY2Mj7V/3xxcsjViKR2MPepdfeEIFdufOHWbMmMHVq1cBOHHihJT0BUEQqpoqkfSLek5fKswjHtcTXpKRkcGqVatYu3YtOTk51KlTh+XLl9OvX7/yDk0QBKHUVOmkLxXmkYvCPML/3Llzh48//pg//vgDgAkTJjB//nxpPF8QBKGqqhJJv6jiPKIwj1AYc3Nz4uPjadq0KWvXrqVTp07lHZIgCEKZqBJJv9iWvijB+9YLCgqiZ8+eyOVyatasyaFDh2jWrBk6OjrlHZogCEKZqRLT2gtL+pl5mcTnxKMl0xLV+N5iUVFRODk58eGHH7Ju3Tppv6WlpUj4giC8dapESz8x0RLoSNu2uzCsrss0DW0mtDFha94okszz0LCuEvc2wmvIy8tj586dLFy4kOTkZKpVq4aZmVl5hyUIglCuqkTSz87WBHR48iSVJ09SSdTXQz82m8l04ZlpWnmHJ5Sx8PBwPv/8cy5fvgyAg4MDy5Yto169euUcmSAIQvmq9Ek/NzcXpVL9Y1R7oRpfsnEO5mUdlFBufvvtN2xtbcnOzqZWrVosW7YMR0dHsRqeIAgCVSDpZ2ZmAupjs9Vf+AOfYSr+2L9NWrVqRefOnWnUqBFff/01NWrUKO+QBEEQKoxKn/RVj+sdwdj4F27evENyVAq1n2fy498/EPrHKXpZONCyvIMUSk1SUhJLlizhk08+4d1330Umk7Fv3z7kcnl5hyYIglDhVPqkr2rp56GvL6N+fSOobwRA0J2/+L7xef7T/KPyDVAoNcePH2fWrFk8efKEP//8k/379wOIhC8IglCESp/08wvzFCjB+/+FecQz+lVPdHQ07u7uHDlyBIB27dqxcOHCco5KEASh4quySV8U5ql6lEolu3btYv78+SQlJWFgYMC8efOYNGkSmpqa5R2eIAhChVfpk76qe7+QErz5i+3IRQneqiIiIoLZs2eTlZVF3759WblyJe+88055hyUIglBpVPqkX1hLPzMvk7jsODRlmpjJRUGWyiw7OxstLS1kMhmNGjVi4cKFmJmZMXjwYPEYniAIwmuq9KXqVCV425CYWI+AgD8I3XGDmPA/qJauQ23t2mjIKv1HfGtduXKFXr16ceDAAWnfJ598wpAhQ0TCFwRBeAOVvqWfmpoJDOLmTbC3/xEtIMvUhGTZSlL1suFBHmiKxF+ZJCcns3jxYrZt24ZSqWTbtm0MHTpUJHpBEIR/qNJnw8RE9TK71WUyKTlk64iEX9mcPHmSLl26sHXrVjQ0NJgxYwZ+fn4i4VcAFhYW9OnTBzs7O+zs7OjTpw9z584lLe1//wefPXuGu7u7dNzAgQPZs2eP2vtkZWXh7e2NnZ0dCoUChUKBt7c3WVlZZf2R/rEff/yRrl27smnTphIdf/nyZfr06VPKUcHOnTuxt7dHoVDw5ZdfvvLarlixQm0xqorI39+f/v37o1AomD59OsnJyYUe5+fnR79+/ejVqxezZs1S+9xBQUH07dsXGxsbpk+fTkpKCuHh4QwYMEBatO1tUOkzYlJShtr2i9X4UkyUZR2O8IYSExOZNGkSI0eO5PHjx7Rp04YzZ86wYMGCAk9mCOVn165dBAQEEBAQgL+/P4mJiWzZsgWAtLQ0nJycqFOnDidOnCAgIIANGzawb98+1q9fL73H7NmzCQ8PZ9++fQQGBrJ3717Cw8P54osvyutjvbGTJ0/i6urKlClTyjsUyY0bN/Dx8WHv3r0EBASQnJzMrl27Cj32+vXrhISE4OLiUsZRllxUVBQeHh5s3bqVwMBA6tWrh7e3d4Hj7t27h5eXF9u3byc4OJi8vDy2bdsGwKNHj1i4cCHbtm3j1KlT1K5dm+DgYJo3b46trW2h71dVVfqkn5eXBdzg3XczsO1Yj67m1UionUOGdjZZNcVjXJWFjo4Ov/32G/r6+nh4eHDy5EksLS3LOyzhFeRyOd27d+f3338H4NChQ5iYmDBjxgy0tFQjh++88w5Lly5l+/btJCcnc//+fc6dO8eyZcswNDQEoEaNGixZsoRhw4YVep6tW7diY2ODQqHAy8sLpVLJwYMH+fjjj6VjXtx2d3fHy8sLR0dH1q9fT4cOHcjJyZGOdXFxYc+ePWRlZeHp6YlCocDa2prNmzcXev7MzEzmz5+PQqHA3t6epUuXkpuby/Lly7lx4wZr167lm2++KfA6Pz8/qSfj5VYnqOYjubq6SudftmyZ9L0TJ07Qv39/7O3tcXR0lBaPKmr/iwICAnBwcMDQ0BCZTMbQoUMJCAgo9LNt3LiRjz/+WPp5bdiwAYVCga2tLZ988glJSUkAfPPNN8ybN49hw4axc+dOlEol69evR6FQ0Lt3bzw9PcnNzQXgr7/+YtSoUdjb29OnTx+OHTtW6LlL6vTp03Tu3Jm6dVWPXw8bNqzQz3Pp0iU6depEnTp1kMlkjBs3jpMnTwJw5MgR+vbtS8OGDZHJZHz55Zc4OjoC4OTkxOHDh4mLi/tHcVYWlX5MXy7PBvwYPNiNVasmATD+znh2Ru3k28ZbaVy+4Qmv8Ndff2FiYkKNGjXQ1dVl69at1KhRg4YNG5Z3aEIJJCYmcuzYMbp27QrAL7/8Qu/evQscZ2FhgYmJCbdu3eLhw4e0adOmwJoIpqamdO7cucBrr1y5gq+vL4cPH0YulzN69OgiE9iLLl68iK+vLzo6Ohw/fpwrV66gra1Neno6ly5dwtPTk23btvHHH39w9OhRcnJyGDNmDBYWFgU+w/fff090dDT+/v7k5OQwduxYjh07xuzZs/ntt98YNmwYAwcOVHtNZGQky5Ytw8/PD3Nzc6ZPn46Pj4/ajeyePXtITU0lICCApKQkqeu5ffv2LFy4kAMHDlCvXj2uXLlCUFAQHTt2LHL/ix4+fIi1tbW0Xb9+ff76668C1yglJYWLFy+yYsUKAMLCwvjxxx85efIk+vr6TJw4kR9++EHqBTh37hyHDx/GxMQEPz8/AgIC8PX1RU9Pj6lTp7Jnzx5atGjB8uXL6d27N87Ozvz6669MmjQJhUKBtra22vlHjx5NfHy82j4jIyP27t1b4PM0aNBA2m7QoAFxcXEkJiZiZGQk7ZfJZOTl5Unb+vr6/P333wDcvXuXOnXqMH78eKKioujYsSNffPEFenp6GBsbY2lpyZkzZxg+fHiB61TVVPqWfmGP7EVlRoEMzA1FYZ6KKDs7mzVr1tCtWzfmz58v7W/durVI+C/p168fsv+fp1JaX/369StxPE5OTtjZ2WFjY4ONjQ2dOnVi8uTJgOomwNjYuNDX1axZk8TERBITEzE1NS3x+UJCQujZsyfVqlVDLpeza9cu+vbtW+zrOnfujI6OaiEuhULBmTNnADh//jxWVlaYmJgQHBzM6NGjkcvl6OvrM3DgQKll+KKzZ88yYsQItLS00NXVxdHRkdDQ0FeePzQ0lLZt21KrVi1kMhmrVq1S65kAmDBhAhs3bkQmk2FkZETTpk2JjIwEVDdBP/30E48fP6Z9+/bS0EdR+1+Unp6uVopaV1e30DHr27dvU7duXekGrFWrVpw9e5Zq1aqhoaFB27ZtefTokXR869atMTExASA4OJihQ4dSvXp1tLS0GD58uHTtNm7cyMSJEwFVtczMzExiYmIKnH/37t3SUFH+18sJv7DPI5fLkclkBT5T586dCQ0N5d69e+Tk5PDjjz9KdVySkpL4+eefWblyJYcOHeLRo0dqPTutW7fmxo0bBc5dFVX6ln5hxXnyC/OIanwVz/Xr15kxYwZhYWGA6gYgNzdXVNSrJHbt2kXt2rWJj4/Hzs4OBwcHqWvY2NiYZ8+eFfq62NhYTExMSExM5OnTpyU+X0JCAubm/1scu6TzO15sASoUCqZNm4ZCoeDUqVM4ODgAqqdEvLy8WL16NaCaYGhlZVXgveLj49Xez8jIqNiu4ISEBGn4ApBuQF708OFDli5dyl9//YU5KPjuAAAgAElEQVSGhgbR0dEMGTIEgE2bNrFp0yaGDBlCnTp1mDt3Lh06dChy/4v09PTUhhLS09PR19cvcP64uDgpiecf5+XlJQ0ZJCYm0qtXL7XPnS85OZkdO3ZISTo3N1d6r/Pnz7Np0yYSEhKQyWQolUq1Fvjr0tfXV/s8mZmZKJXKAp+pSZMmfPXVV7i5uSGXy6WbEoDq1avTpk0b6YZz1KhRbN26lc8//xwAExMTbt++/cYxViaVPukX2dJHVOOrSFJTU1myZAlbtmwhLy+PBg0asGrVKmxsbMo7tArN39+/vEMolImJCU5OTqxYsUKaud6jRw927drF1KlT1Y69d+8eiYmJWFlZYWZmhpeXF0+fPqVWrVrSMUlJSXz33Xd89tlnak9qGBsbk5CQIG3n/1tDQ0MaQ85/fVGaN2+OpqYmERERXLhwQWodm5ubM2HChEKHJF5Us2ZNnj9/Lm0/f/6cmjVrvvI1xsbGXL9+XdpOSUmR/lblW7RoES1btmTDhg1oamoycuRI6XsNGjTAy8uLvLw8/Pz8mDlzJufPny9y/4saN25MRESEtB0REUGTJk1eGS+ohjEePnzIwYMHMTAwwNvbu8gbNHNzc6ytrRk7dqza/suXL+Pq6sqaNWvo2bNnkTdSUPLu/XfffZdff/1V2n748CFmZmZqN1X5Bg8ezODBgwH49ddfadasGQB169YlJSVFOk5DQ+OtbWhUue59UY2v4klKSlJ7rMnFxYXQ0FCR8Cu58ePHc/36dX755RcABgwYQE5ODkuXLiU7OxtQzbx2d3fHxcUFfX193nvvPRwcHHBzcyM2NhZQJVE3NzepZfgia2trzpw5Q2JiIjk5OUydOpULFy5gbm7OgwcPyMzMJD09vdhxfoVCwYEDB2jRooU0BGFjY8P+/fvJzc1FqVSyceNGQkJCCry2V69e+Pr6kpubS1paGocPH6Znz56vPF/Pnj25du0akZGRKJVKFixYgK+vr9oxcXFxtGjRAk1NTUJDQ4mIiCAtLY34+HjGjx9PSkoKGhoatG7dGplMVuT+l9nb2+Pv709sbCw5OTn4+PgUOoRjYmKidkMVFxdH48aNMTAw4PHjx5w7d07tccwX2djYcPjwYamL/aeffuLQoUNkZmaSlpZGq1atANWNhLa2dqHvU9LufVtbWy5evCjNS9i5cyf9+/cvcFxERAQDBw4kKSmJ7OxsNm/eLPWc2Nvbc/z4caKjo8nNzcXX11dtDklCQoJar0dVVulb+i9370dnRgNQW14bTdnbeSdX0RgaGtKtWzd+++031qxZQ9u2bcs7JOFfUK1aNZydnVm2bBm+vr5oamry3XffsXLlSuzt7dHS0kJHR4exY8eqTZDy8PBg06ZNjBkzBplMhra2NgMGDJDGgV/Upk0bJk6cyKBBg6SnBfr3709eXh6tW7dGoVDwzjvvYGNj88pxdoVCwZYtW/D09JT2jR49msjISPr164dSqaRVq1aMGzeuwGudnJx49OiRNL/Czs4Oe3v7V16b2rVrs2jRIsaNG4empiaWlpaMHz9ebdx4ypQpeHl5sXHjRmxsbJg2bRrr1q2jRYsWdO/enaFDh6KpqYm2tjaLFy/GxMSk0P0vs7S0ZMKECYwZMwalUkmXLl0YNWpUgeNatWrF48ePSUpKwtDQkJEjR/LZZ5+hUCiwsLDA3d2d6dOns3PnzgKvtbW15f79+1KrukGDBixevJi///6bSZMmMWjQIExNTZkyZQq2trZ8+umnHDt2rNBhhuLUqlWLBQsWMHXqVHJzc3n//feZN28eALdu3WLt2rXs2LGDhg0bYmNjw8CBA6W5KvnxtWnThmnTpjF69Gi0tLRo164dzs7O0jlu3rxZorkiVYFMqVRWyofZMzMzCQsLw939J06disfKqjnNUvUY+L4ef9Q9gG4tE9znfg86IvH/E1evXqVdu3av9RqlUsn+/ftp2LChNLM4JSUFHR2dAjN4BZU3uc7C6xHXuKCJEyfi6OjIoEGD/pX3q4zXODExkb59++Lv71/ssE15y897rVq1KnSeSElU+pZ+YqIm0IBbt9K4RRo9ovX5Wk81UYd55RraWykiIoKZM2dy5swZmjZtSkhICDo6OlSrVq28QxME4SVTpkxh0aJFODo6vrVj3D/88AP9+/ev8An/31Lpx/SzstQ7Kgz/f4wrxTAHtN/OX+LykJOTw4YNG+jatStnzpyhRo0azJgxQ+1RG0EQKpb27du/Vhnhqubu3bsEBgbi5uZW3qGUmUrf0s/OVp/Ikl+GN91Ehmhblo3ffvuNGTNmSOOVQ4cOZcmSJZiZiYmUglDRzZkzp7xDKDcWFhYcOXKkvMMoU5U+6Zub3+XOnet4e66ldrw+jznN4qTb2DZ0QKSc0peRkcHw4cN59uwZ9erVY/Xq1WWyoIggCILw+ip90ofnQATWju9iZWWF3fVVBMYH0qb1p+UdWJWmVCqRyWTo6uqyaNEirl27xpdffikVwxAEQRAqnko/pv/yc/r51fhEYZ7SER8fz9SpU9VWpRoxYgRLly4VCV8QBKGCK5Okf/HiRQYPHoxCoWD8+PFER0cXOObq1asMHz4ce3t7hgwZolaB6VVefk4/vxqfKMH771IqlRw4cIBOnTqxZ88e1q1b98oqaIIgCELFU+pJPy0tDTc3Nzw9PQkMDKR3794sWLBA7ZisrCxcXFyYOXMmJ06cYMaMGSWeTfliSz8rL4vY7Fg00BDV+P5FkZGRjBw5ksmTJxMbG0vXrl05depUoWUwBUEQhIqr1JP+pUuXqF+/Pi1btgRUM7tDQ0PV6iBnZ2fj4eFBp06dANXKTM+ePStRS/LFpB+d9f/V+HRENb5/Q15eHocOHaJz584EBQVhaGiIt7c3hw8fLlEtb6FqsbCwoE+fPtjZ2WFnZ0efPn2YO3euWonVZ8+e4e7uLh03cOBA9uzZo/Y+WVlZeHt7Y2dnJ6037+3tXWC9+crgxx9/fK1H3i5fvlwmE13j4uIYP358ic61YsUK1q1bV+ox/RP+/v70798fhULB9OnTSU5OLvQ4Pz8/+vXrR69evZg1a5ba71RQUJC0fPH06dNJSUkhPDycAQMGFLoKYVVV6kn/4cOH1K9fX9o2MDCgRo0a0jrH+fteLIEYEhJCo0aNStSSzMgwAkyJv5vIH48fAmI8/98ik8kIDQ0lNTUVR0dHLl26xLhx49DQqPRTQYQ3tGvXLqlOur+/P4mJiWzZsgVQ9eo5OTlRp04dTpw4QUBAABs2bGDfvn2sX79eeo/Zs2cTHh7Ovn37CAwMZO/evYSHhxe6TGxFd/LkSVxdXZkyZUp5hyJ5/vw5Y8eOlRabeZXr168TEhKCi4tLGUT2ZqKiovDw8GDr1q0EBgZSr149tTlF+e7du4eXlxfbt28nODiYvLw8tm3bBsCjR49YuHAh27Zt49SpU9SuXZvg4GCaN2+Ora1toe9XVZX67P309PQC5QJ1dHSKXMghPDycJUuWsGrVqhKeYRSQToN226gpk5H4znKSjHK46X2RHGNRGOZ1ZWVlkZqaKi1K4urqyt9//02XLl14/Pgxjx8/LucIq66rV6+WdwjFunXrltrvQMOGDbl06RJXr17l5MmTyOVyunXrxs2bN6VjPvroIxYsWEDr1q2Jj48nODiYdevWcf/+femYDz/8kEePHhV6DY4cOcLp06fR1NSkbdu2jB07lpCQEC5cuMCXX34JwLlz56TtzZs3Y2BgQFhYGB06dCAgIIDNmzejqanJ1atXWbVqFa1bt6Znz57s3r2bmzdvkpOTg7W1daHlaLOysti1axd37txBJpPRpk0bRo8ezU8//cS1a9cIDw/n2rVrDBs2TO11ISEh+Pn5AaplXydPnsz9+/fJzMzk6tWrZGZmsmXLFiIiIsjJyaFDhw6MGTMGUPWQHjx4kLy8PDQ1NRk3bhzvv/9+kftflJKSgouLC8+fP5fOVZRly5bRq1cv6ed18OBBQkNDyc3NpV69eri4uGBgYICvry8JCQlERETQtWtX7OzsOHToEKGhoWRnZ9OuXTucnJzQ0NDg6NGjbN26lZSUFHJzcxk+fDhdunQpMobiBAYGYmFhwZMnT3jy5AktWrTA09OzwCJCAQEBWFhYEBUVRVRUFB06dGDHjh106tSJgwcP0qZNG2JjY4mNjcXOzg5Q/Z+zsrJi5syZdOrUSW354Kqq1JO+vr6+NNkuX0ZGBgYGBgWOvXbtGq6urixevFiq2f46qstkGKbrYZgO73T9APSqwBOJZSg0NBRXV1feeecdDh48KK3gNXDgwHKOrOqrLDXLraysqF27NqCqWe7t7U2vXr1o164dPj4+ODo6Fvgc7dq145tvvkFDQ4OUlBT+85//FLtKXb4rV65w8eJFAgICkMvljB49mpiYGBo1asStW7ekc0VEREjbpqam3LlzB39/f3R0dLhx44aUJN9//33Cw8NZv349e/bsISkpiaCgIHJychgzZgzW1tYFltrdunUrOTk5nDp1ipycHMaOHUtUVBSrVq3CycmJYcOGFfg/EhkZyb59+/Dz88Pc3Jzp06cTFhaGpaUlOjo6tGvXjm+//Ra5XE5wcDBJSUn07duX0aNH0759e6ZOncqBAweoV68eV65cISgoCCcnpyL3F+by5cvSuQqTkpLCnTt32Lp1KzVq1CAsLIzg4GBOnjyJvr4+EydOJCwsDBcXF37++WcuXLjA4cOHMTExwc/Pj5s3b3L06FH09PSYOnUqd+/epUWLFvj7++Po6IizszO//vorkyZN4tNPPy2w7kZJl9Y9fvw4bdq0kT6HpaUls2fPpkmTJmpJ+s6dO0RHR0vHGRkZsXTpUtq1a8f3339PvXr1WL9+PVFRUXTs2JEvvvhCeuqrbdu2xMXFYW1tXei1qijya+//E6WeFRs3bszx48el7eTkZBITE2nYsKHaceHh4cyYMQNvb2/at2//RufKr8aXqZ+Hjkj4Jfb8+XMWLFjArl27ANDS0iI2NlZU1KsA+t3ox/G448Uf+A84mDrg38a/RMc6OTmhqalJdnY2iYmJfPzxx0yePBlQ3QTk9xC9rGbNmiQmJpKYmIipqWmJYwsJCaFnz57S2g27du1CLpdz+PDhV76uc+fOUg+jQqHgzJkzKBQKzp8/j5WVFSYmJgQHB+Ps7IxcLkculzNw4EBOnjxZIOmfPXuWCRMmoKWlhZaWFo6OjoSGhr7yZjg0NJS2bdtSq1YtAFatWiX1NOSbMGECTk5OyGQyjIyMaNq0KZGRkbRv3x5TU1N++uknRo4cSfv27aW/iUXtfxO3b9+mbt261KhRA1Ctunf27FmpdHbbtm159OiRdHzr1q2l5WeDg4MZOnSo9Jju8OHD8fHxoUWLFmzcuJH8ddzatWtHZmYmMTEx1K2r/kTV7t27SxRnenq62rK3crkcmUxGenq6WtLv3Lkz3t7e3Lt3j8aNG/Pjjz9KDc6kpCQePHjAzp07pZuUzZs38/nnn0uf7caNG2qrQVZVpT4427FjR6Kiorhy5QqgWgu5d+/eakssKpVK3N3dWbBgwRv8Ej9DVyuFWloamGmqkn6GqZjEVxJKpZLDhw/TqVMn6Y+pu7s7Z8+eFQlfKFT+mP7+/fvR0NDAwcEBLS3VDbaxsTHPnj0r9HWxsbGYmJhgbGzM06dPS3y+hIQEtbk9enp6JVoY5sVkkJ/0AU6dOoWDg2pBruTkZLy8vKSJiT4+PoVO6IqPj1d7PyMjI+Li4l4rbh0dHek65Xv48CHTp0+nb9++2NnZERYWRl5eHgCbNm0iNjaWIUOGMGjQIH755ZdX7n8TcXFxask0PT0dT09PaXLl7t27eXER1hevQXJyMjt27JCu3bJly6QEe/78ecaMGYNCocDBwQGlUil9rjehr6+vNiEvMzMTpVJZYJneJk2a8NVXX+Hm5saIESNo0qSJdFNSvXp1bGxsMDU1RV9fn1GjRqktxWxiYlLsz7SqKPXmsK6uLqtXr2bRokWkp6fToEEDli5dytOnT5k4cSLHjh3jxo0b3L17l5UrV7Jy5UrptatWrZJm/RdtN42aGPH777/jcM0ek0ch7G/0Izal+7EqPaVSyYQJE6QWU8eOHVmzZg0WFhblHJnwopK2wMuaiYkJTk5OrFixQpq53qNHD3bt2sXUqVPVjr137x6JiYlYWVlhZmaGl5cXT58+lVrBoGqJfffdd3z22WfSsBKobiQSEhKk7fx/a2hokJubq/b6ojRv3hxNTU0iIiK4cOGCNGHQ3NycCRMmFGjZv6xmzZo8f/5c2n7+/HmxK7IZGxtz/fp1aTslJUV60ijfokWLaNmyJRs2bEBTU5ORI0dK32vQoAFeXl7k5eXh5+fHzJkzOX/+fJH7/w3ff/89Dx8+5ODBgxgYGODt7V3kDZq5uTnW1taMHTtWbf/ly5dxdXVlzZo19OzZk6ysLKysrAp9j5J277/77rtqdVsePnyImZlZoRO9Bw8ezODBgwH49ddfpcmMdevWVXtiTEND461dVbBMpmF37NiRI0eOEBQUxI4dOzAzM6NWrVocO3YMUHUj/f7779Ks4Pyv4hO+Sn5hnsfZUSRUS8P43Qal9lmqCplMRvPmzalWrRqrVq3C399fJHzhtYwfP57r169Lrc0BAwaQk5PD0qVLyc7OBlQzr93d3XFxcUFfX5/33nsPBwcH3NzciI2NBVRJ1M3NjYSEBLWED2Btbc2ZM2dITEwkJyeHqVOncuHCBczNzXnw4AGZmZmkp6cTEBDwylgVCgUHDhygRYsW0hCEjY0N+/fvJzc3F6VSycaNGwkJCSnw2l69euHr60tubi5paWkcPny42DkJPXv25Nq1a0RGRqJUKlmwYAG+vr5qx8TFxdGiRQs0NTUJDQ0lIiKCtLQ04uPjGT9+PCkpKWhoaNC6dWtkMlmR+9+UiYmJ2g1VXFwcjRs3xsDAgMePH3Pu3LkiJ1zb2Nhw+PBhqWfkp59+4tChQ2RmZpKWlkarVq0A1Y2EtrZ2oe+ze/fuAn/zX074ALa2tly8eJG//voLUPUW9+/fv8BxERERDBw4kKSkJLKzs9m8eTNDhgwBwN7enuPHjxMdHU1ubi6+vr507txZem1CQoJar0dVViUGvqUSvJn/X4JXRzyyV5jw8HCioqKkySqurq44OTkVGGsThJKoVq0azs7OLFu2DF9fXzQ1Nfnuu+9YuXIl9vb2aGlpoaOjw9ixY9XGSj08PNi0aRNjxoxBJpOhra3NgAEDmDhxYoFztGnThokTJzJo0CDkcjndu3enf//+5OXl0bp1axQKBe+88w42NjZq3bUvUygUbNmyBU9PT2nf6NGjiYyMpF+/fiiVSlq1asW4ceMKvNbJyYlHjx7Rr18/ZDIZdnZ22Nvbv/La1K5dm0WLFjFu3Dg0NTWxtLRk/Pjx0kqUoFrL3svLi40bN2JjY8O0adNYt24dLVq0oHv37gwdOhRNTU20tbVZvHgxJiYmhe5/2ZkzZ1i+fDkZGRnSTPVatWrx/fffqx3XqlUrHj9+TFJSEoaGhowcOZLPPvsMhUKBhYUF7u7uTJ8+nZ07dxY4h62tLffv35da1Q0aNGDx4sX8/fffTJo0iUGDBmFqasqUKVOwtbXl008/5dixYwW65EuiVq1aLFiwgKlTp5Kbm8v777/PvHnzANXTJGvXrmXHjh00bNgQGxsbBg4ciEwmo1+/flJ8bdq0Ydq0aYwePRotLS3atWuHs7OzdI6bN2+qPTZelcmULw7aVCL5sxgdHR1p0aIFJ4JOoBOsgwYaZFlnieI8L8jMzMTb2xtvb2+MjIy4dOlSie9qK8us8spOXOfSJ65xQRMnTsTR0bHQRxXfRGW8xomJifTt2xd/f/9ih23KW37ea9WqVYFH4UuqSlRZ0dXV5WmWauyplryWSPgvuHTpEj179mT58uVkZ2fTr1+/AhOKBEF4O02ZMoVvv/1WbX7E2+aHH36gf//+FT7h/1uqRNLX09MTC+28JCkpiZkzZ+Lg4MC9e/do2rQpx44dw9vbW9TMFwQBgPbt279WGeGq5u7duwQGBpZ4rZeqoAo0+azJvNiQXYMvMfjdvrRtXw/ez4Jqb3c1PicnJ86fP4+WlhYzZsxg5syZ0oRHQRCEfHPmzCnvEMqNhYUFR44cKe8wylQVSPpWHItKh6jn+PzaF6cjOmCX/tYn/dmzZ5ORkYG3t3eBMp2CIAjC26kKJP3/ya/Ih5le+QZSxvLy8vDx8eHPP//Ew8MDgK5duxIQEPCPHukRBEEQqpYql/SzdUH7LWrl379/H1dXVy5evAjAiBEjsLS0BBAJXxAEQVBT6SfyaRPC+BaaDP5PBtc73yLaruBCPlVRVlYWK1eupHv37ly8eBEzMzO2b98uFcUQBEEQhJdV+pZ+Ntdo9lE3fup9gkMpN+n9wTTql3dQpezXX3/F1dWV33//HYAxY8awaNGiIhc7EQRBEASoAkkfVM/pv02P7O3cuZPff/+dxo0b4+3tTffu3cs7JEEA1CukCeqsra1RKpVSUZXc3FxatGjBV199JS1wlZKSwpo1azh//jwymQxNTU0cHR2ZPHmyVCteqVSyc+dODhw4QHZ2Nrm5uXTr1o2ZM2dKC8xURK6urlJFwYpq586d7N27l7y8PNq3b8+CBQukVQdftHXrVvz8/EhLS0OhUODu7o5MJsPd3Z0LFy5Iq0ICLF++nOfPn7Nt2za+//57NDTKt4O90nfvA8j15MRkx6CBBuZy8/IOp1S8uKCIh4cHc+bM4fz58yLhCxWKlZWVSPivsGLFCrVa82ZmZixduhRQTch1dnaW6vsHBATwww8/cPnyZansLMDKlSs5fvw4O3bsIDAwkCNHjpCdnc0nn3xCRS2wevz4cZKTkyt0wr9x4wY+Pj7s3buXgIAAkpOTpeXGX3Tu3Dl8fX3Zs2cPJ0+e5Pbt22pLPbu5uan9jK2srOjRowd169Yt9P3KWpVI+pnVVEs6VsVqfE+fPmXChAn07dtXWrrSxMSEOXPmSGsOCEJpi4yMpFu3bmzbtk1aevXGjRs4OzvTvXt3aeW6y5cv06dPHwAyMjKYPXs21tbW2NvbS38Yv/nmG+bNm8ewYcPYuXMneXl5eHt7S8u0uru7F7nQy/Xr1xkyZAh2dnY4ODjw888/AzBs2DACAwOl406dOsWIESOkfzs6OjJjxgwmTJggrexWWBwLFy5EoVBgbW3NrFmzpIWDIiMjGTRoENbW1syfP59PPvmEgwcPAqrSs0OHDqVPnz6MGDFCbQ36V9HU1KRXr16Eh4cDEBISQnR0NIsWLZJqahgbG7Ny5UpOnDjBX3/9xfPnz9m1axdLly6VVinU19dn/vz5TJo0qdCk7+fnJ/3MZs2aRVZWltrP6eWf28vXxcrKSm01vMWLF7Ny5UqUSiXr169HoVDQu3dvPD09i1xCd8OGDUyePBngldfZ3d0dLy8vHB0dOXHiBFlZWdJyv9bW1mzevFl6z6J+F95UQEAADg4OGBoaIpPJGDp0aKELOf3888/Y2tpiZGSEXC5n9OjRnDx5stj3d3Z2Ztu2bWrLBJeHKpH00/VUKz1VpYV2lEolP/zwA506dcLPz4/IyEhu3rxZ3mEJ5cFsvfpXUXzC1I9zO1P0sTZ7i3+/lyQkJGBmZkZgYCAWFhZ8/vnnLF26lCNHjnDs2DH+/vtvteO//fZbsrOzOXPmDN999x0eHh7SUq3nzp1j69atfPzxx5w4cYKQkBAOHjyIv78/SUlJhS7yAjB//nwmTpxIQEAAzs7OLFiwAFAtqHPmzP8+b1BQEPb29jx69IjZs2ezatUq1q5dS8eOHfn666+l416MIygoiCtXrnDs2DFOnDjB7du3OX78OKDqou3atStnzpyhR48eUoJJSUlhypQpuLm5ERQUxEcffcSMGTNKdD0zMjI4cOAAbdu2BeCXX36hW7duBcpkm5qaYmlpyZUrV7h58ya1a9fmvffeUztGR0cHa2vrAl3HkZGRLFu2DB8fHwICAkhPT8fHx6fY2F68Lh07diQ4OFj63unTp6WbuICAAHx9fQkKCuLRo0cEBQUVeK8//viDmJgYOnToAPDK6wxw8eJFfH19sbe3Z9u2bfzxxx8cPXqUY8eOERgYKMVS1O/Ci7KysqSbyRe/PvvsswLHPnz4kAYN/rdCa/369aWV/V4kk8nUbm4MDAzUfvePHTvG0KFDcXBwYPPmzdKN2HvvvUf16tW5evVq0Re+DFSBMX0ZqbqpQNUZz//zzz9xc3OT1sm2trZm9erVar+QglDWcnJysLOzA5DWKc9fuMnMzIxnz56pHR8SEsKkSZMA1apz586dw8BA9XRN69atpdeePXuWQYMGSSuwDRkyhO+++w4XF5cCMfj5+UmPorZr105qVdvZ2TF8+HBpmdyzZ8/i6urKmTNn6NChA82aNePq1auMHDmSrl27SrXmX4wjv8Wqra0NgKWlpfT+V65cYcqUKYBqhTlzc9Uw4tWrV6lVqxZdu3YFoH///nz99ddERUUVunrlrFmz0NHRIS8vj+joaEaOHImrqyugWvglf2z/ZTVr1uT58+fo6Ohgampa6DGFCQ0NpW3btlKvwKpVq9DU1Cw28bx8Xc6cOcPQoUO5ffs2WlpatGzZkq1btzJ06FBpHsHw4cPZsGFDgfe6desWLVu2lG5IXnWdATp37izNewgODsbZ2Rm5XI5cLmfgwIGcPHmS3r17F/m78CK5XF7sssv50tPT1cbvdXV1paWDX9SlSxfmzZvHxx9/jJGREfv27ZN6YT/44APy8vIYMmQIz549Y/z48dSuXVta0Kh169Zcv35dbVnfslbpk74mn7HJMZP2evNZ2VkPNsRDs8q7LvL27duZP38+GRkZmJqasmTJEoYNGyaeuRfKnaamptTtrKGhobZMqqamZoFFW0K9AtQAACAASURBVBISEtQmluUnfAAjIyPp3/Hx8WrbRkZGxMXF8fTpU2mpWysrK5YvX87Ro0fx8fEhNTWVvLw8qRVVv3596tSpw/Xr18nOzubdd9+lTp06JCcnc+XKFezs7MjIyEBXV5dq1arx/PnzQuPw8PDgzp07yGQyYmNjpfMnJSWpHZufRJOSknj06JF0MwSqRBMfH19o0l+xYgXt27eXWqC9e/eWrqOxsXGBG6d8sbGxmJqaYmxsLPWWlERCQoLaWhslXZntxc9qa2vL0qVLyczM5NSpU9KywsnJyezYsYO9e/cCqomJhU16i4uLU1vV81XX+eVzJycn4+XlxerVqwFVy93KygqgyN+FN6Wnp6fW9Z6enl7oUsA9evTAyclJSvp9+vThyRPVsu4vzlmoU6cOH374IcHBwVLSNzExURsqKQ+VPunnIiMhT0lOhjYWN6qBRuVOjgYGBmRkZPDhhx/i6en5Wnf1glCRGBsbk5CQIG1HR0er/UHPl9+Kzff8+XNq1qxJrVq11FppT58+Zd68eezfv58WLVrw8OFDFAqF9H2FQsHp06fJzs6WEpO5uTldunRh3bp1xS776u3tjZaWFkePHkUulzNz5kzpewYGBmrzDGJiYqT3b9y4sTS+X1JyuZxp06axfPlyDhw4gIaGBj169MDNzY2srCy15BkfH09YWBheXl4YGhoSFxfH7du3admypXRMdnY269ev59NPP1Wb62NsbMz169el7ZSUFDIyMgrcpL04UfhlNWrUwMrKiosXL3Lq1ClWrFghfXZra2vGjh0rHVtYD8LLyfhV1/ll5ubmTJgwgd69e6vtL+53IV9WVhYDBgwosL9Zs2asW7dObV/jxo2JiIiQtiMiImjSpEmhcU2ePFmao+Dn54eFhQUA9+7do1GjRtLPLycnp8KtalolxvThxRK8Be/MKrLU1FTOnTsnbY8cOZKTJ0+yadMmkfAFlZhp6l9F+aiV+nGrrYs+9vSHxb/fP2RtbY2fnx9KpZKYmBgGDRqkdhOQr1evXhw5coT09HRycnLw9fWlZ8+eBY6Lj49HX1+fxo0bk5OTI7UwU1NVw3sKhYKLFy8SHBwstby7devGlStXpK7fW7du4enpWWi8cXFxNGvWDLlcTnh4ONevX5cSvZWVFSdOnABUXc75LfLWrVsTExMjzbd59OgRs2bNKlGrc+DAgWRmZkoTHDt06EDLli2ZM2eOdN7nz58za9YsBg8ezDvvvIOhoSGTJk1izpw5UoJKT09n/vz53Llzp8Dk3p49e3Lt2jUiIyNRKpUsWLAAX19fzMzMiImJIS4ujtzcXI4ePfrKWBUKBfv27SM7O5vmzZsDYGNjw+HDh6Uu8J9++omQkJACrzU1NVX7ub/qOr/MxsaG/fv3S8M2GzduJCQkpNjfhXz53fsvf72c8AHs7e3x9/cnNjaWnJwcfHx86NevX4HjLl++jJOTE1lZWaSkpLBz506pJT9//nxpzkRiYiKHDx+mV69e0msTEhLKvZ5KxboF+Qeqy2TkaoOmYeUpwXv69GlmzpzJ06dPOX/+PE2aNEEmk9G+ffvyDk0Q/rGPP/6YiIgIevfuja6uLnPmzCm0y9vOzo67d+8yZMgQlEolHTt25KOPPipwXPPmzenRowcKhQJTU1Pc3d25du0aTk5OHDx4kHfffZe8vDxq1aoldb+bm5vj4eHB1KlTSU5OxtTUlLlz5xYa74QJE5gzZw4HDx6kffv2zJkzhy+//BIrKytmzZrFzJkz8ff3p0ePHrRp0waZTIauri7r1q3Dw8OD1NRUtLW1mTFjRomG4zQ1NZkxYwZLlizB3t4eXV1dVq9ezZo1axg0aBAaGhpoamoycOBAaW4EwPTp0zEyMmLKlCnk5uaioaGBjY2N2gTFfLVr12bRokWMGzcOTU1NLC0tGT9+PDo6OgwdOpRBgwZRt25dBg4cKBX7KkyfPn1YuHAhzs7O0j5bW1vu37/P4MGDAWjQoAEffvhhgdfmD80olUpkMtkrr/PLRo8eTWRkJP369UOpVNKqVSvGjRuHvr7+K38X3oSlpSUTJkxgzJgx/9fenYdFVbZ/AP8OI4uCC6AombhUoGKKoriAIoIzg2EqSoFLKSJFior9VFJxXzByo5TcSsySzERzJRAXXEJJXkxNNFfckUWGfZy5f3/My3kdAR1JloH7c11cdYbnnHOfm3Huc84853lAROjTpw98fHwAqDsfxsfHY9myZejevTvatm0LqVQKkUgkdHYEgOXLl2Pu3Ln45ZdfoKenhyFDhsDDw0PYR0pKCgYPHlyh+F4b0lGFhYWUlJREb1i2op4/9KIOq9rR37GHqzssraSnp5O/vz+ZmpqSqakpOTs708WLF6s7rDIlJSVVdwh1Aue58v3bHKtUKuH/PT09KTY29t+GVOuUl2OZTEZnzpyp4mhqlmvXrpGjoyMVFRVVeBslda+wsLDC29D52/uEp7j25j/4+93raNS3fXWH80JEhKioKPTq1Qu//PIL6tevj/nz5yMuLo6nv2WsBlu+fDkWLFgAQP10zfXr13mei1cQEBCAjRs3VncY1WrTpk3w9fUts7NjVdL5og8x8Fj5WD0an37NHo1v8eLF+Oyzz5CZmQlnZ2ecOHECkydPrnEdPRhjmsaNG4ebN29i4MCB+OyzzzB37ly0aNGiusPSGe+//z6MjIwQHR1d3aFUixMnTuD27dsaTylUF92vNv/tDGxhYIF6ejX7cEaOHImoqCjMmTMH3t7e/BgeYzrCwsKi3AGDmHbK6jxXVzg5OcHJyam6wwBQG670m6j/UxMH5jl//jy++OILjRGZkpOT4ePjwwWfMcZYlavZl8ZaqNdMfQiWBjVnCN78/Hx8+eWXWLt2LZRKJbp37y4M2lDd3+cwxhiru3S+6Hd/JEHTH27B1vlt4B0FYKxfrfEcPXoU06ZNw82bNyESifDJJ5+UOWgEY4wxVtV0vuj/ccMa90+1wvxdDYG3HwJ936yWODIzMxESEoLt27cDADp27Ig1a9a8cAQwxhhjrCrpfNEv0VAkAppV31SzkZGR2L59OwwNDTFjxgxMmjRJmFCCMcYYqwlqWdGv2iF4FQqFUNg/++wz3LhxA5MnTy53vGbGGCsxZswY3LhxAyYmJgDUE9ZYWVlhzpw5aNu2LQD1Z8y6detw6NAhoUOwi4sLAgMDNSaD2b17N7Zs2YLCwkIoFArY2dlhxowZwsiENVFYWBgMDQ3LnOa2pti/fz8iIiKgUChgbW2NpUuXakwiVWL37t3YuHEj8vLy0KNHDyxZskTovxUbG4uwsDAolUp07NgRy5Ytw507dzBjxgz8/PPPpYZOrmw633vfvlk+epkXoaF1A8DUqEr2qVQqERERAQcHB2HGJENDQ4SHh3PBZ4xpbfr06cJ48LGxsejVqxe++OIL4ffBwcG4ePEioqKicOjQIURHRyM9PR0BAQHCScBPP/2EdevWYcWKFcK2WrdujdGjRwtTvtY0ycnJOH78eJnTJ9cU9+7dw6JFi7BhwwbExMSgZcuWWLVqVal2V65cwbJly7Bp0yYcOXIEKpVKGIgoLS0NCxYswMaNGxEXF4cWLVrgyJEjaN++Pdzc3MrcXqWr8Fh+1axkOMJWe1sR4kB3Cu5UyX4vXLhArq6uwhC6mzdvrpL9VhceHrZq1PQ8p6WlkaOjI23YsIEkEglJJBJKTk6mCRMmkJOTEwUHBwttd+zYQTKZjAYOHEgjR46kO3fU/zZVKhUtXbqUXFxcSCKR0MaNG4mI6Ndff6WJEyfSRx99RMuXLyciosjISHJ3dyepVEqffvopZWRklBnXtWvXyNvbm2QyGbm5udHevXuJiGjy5Mka/zYvXbpEDg4OpFQqKSkpiTw9PcnNzY28vLzo9u3b5cbxzTffkEQiIVdXV/L396cnT54QEVFWVhaNGTOGnJ2dKTAwkGbNmkXh4eFERHT16lUaNWoUSSQS8vDwoPPnz5cZ++jRo2n37t2ljqdLly5ERHTlyhXq2rUrZWdna7QpKioiJycnSkhIIKVSSX369KGEhIRS24+NjS1zyNdjx47RoEGDSCKRkL+/P2VlZVFaWhp16NBBaPPs8vN5cXR0pL/++kto+/3339PUqVOJiGj58uUklUrJxcWFgoKCqKCgoMxj9/Pzo507dwrL5eU5PDycZs+eTcOHD6fvv/+eVCoVff311ySRSKh///60aNEievr0qZC7st4LFbV161bhuIjUf9fevXuXahcZGUmTJ08WllNSUuj9998XjmvBggVlbj8zM5McHBzo8ePHWsf0Oobh1fmib3nYkkRxIlIoFZW6v/z8fFq4cCE1a9aMTE1NydbWlg4ePFip+6wJanoxqi1elGdgvsZPedavT9JoN2HCb+W27dZt/Uu396y0tDTq2LEjRUdHExFRYGAg9e/fnzIyMigzM5M6depEt27dosePH1OnTp3o/v37REQUHBxMs2bNIiKi3bt3k7e3NxUXF5NcLidnZ2dKSUmhX3/9lezs7OjGjRtERJScnEz9+vUTPgwXLlwobON5n3zyCa1fv56IiM6cOUOdO3em4uJi2r9/P40aNUpot2bNGgoMDCS5XE49evSgEydOEBHR3r17adiwYUREpeL466+/qHfv3iSXy0mpVNLYsWNp7dq1REQUGhpKU6ZMEdp16dKFwsPDSalUkkQioR07dhCR+u/q5ORECkXpz6fni75CoaClS5fS2LFjiYho27Zt5OvrW+ZxBwcH08qVK+nq1atka2urMS/Ai+Tl5ZGDgwOlpqYSEdHixYtp/vz5Ly36z+Zl3rx5tGbNGqHtqFGjKCYmhs6ePUvdu3enBw8eEBFRSEgIhYaGlopBLpeTra0tZWVlCfkrL8/h4eHk5OQknPRFR0fTe++9Rzk5OaRQKMjf359++OEHIir/vfA8Hx8fkkqlGj8ffPBBqXYLFy6klStXCstFRUVkbW1d6iRs69atNGnSJGH56tWrZGdnR0Tqfyclf1OJREIhISGUn58vtB0/frzwXtEGj70PAKLKH40vMTER/fr1w6pVq6BUKuHn54dTp04J03cyVhc8ffpUeM9bW1vj3XffhZmZGUxNTdGsWTM8evQI5ubm+PPPP4Uhart37y5Ma3v8+HFIpVLo6+vDxMQEBw4cwLvvvgsAaNOmDdq0aQNA/dhryexpAODl5YWTJ0+WGdO6deswfvx4AIC9vT2KioqQnp6O/v3749KlS8jOzgag/l61Z8+e+PPPP9G8eXM4OjoCADw8PHD79m3cu3evVBydOnXC0aNHYWJiAj09PXTt2lU4lqSkJGH2tE6dOgkzxF2/fh0ZGRkYMWKEEJOZmZnGnPbPCgsLg0wmg1QqhZ2dHXJycrBixQoA6qlZzczMylzP3Nwc2dnZyM7OhpmZmdaDfZ07dw4tWrSAtbU1APXXC89+nVCeZ/MilUoRHx8PQP3U0uXLl+Hs7Iz4+Hj07t1b6Efg4+OD33//vdS2Ll68iDfeeANNmqhHVntRngH19MUleThy5AiGDx+Ohg0bol69evDy8hL2Ud574Xk//fRTqal2S6bmfVZBQYHGuCoGBgYQiUTCVMIlevfujZMnT+LKlSt4+vQpfvzxR+FrlZycHJw6dQpfffUVoqOjkZaWhm+//Vbj2P7zn/+8KPWvXa3oyFfZo/EpFApcu3YNNjY2WL16tTCNImN1iVgshpGRut+Mnp6eRkcysVgMpVIJpVKJ8PBwxMfHQ6lUIi8vT+iUlpWVhUaNGgnrPLt+48aNhf/PzMyEhcX/5tFo1KgRMjIyAEA46WjevDkiIyORkJCAiIgIZGVlQSQSgYigUqnQoEED9OnTB0ePHoW9vT1ycnJgY2OD+/fvIy0tTeOE3cDAQOib82wcBQUFWLZsGRITEwGoi3DJ3Og5OTkabUsKXU5ODgoLC+Hu7i78Ljc3Vzj5eN706dMxZMgQAIC3tze6desmFDhTU1Nh38/LyMiApaUlTE1NkZGRgadPn2o1h8fzfwNtBwt79lgdHBzw8OFD3Lt3D6dOnYKzszMMDQ0hl8s1LoaICAqFoszYnz2ZeVGen9+3XC7H5s2bhSKtVCqFbZX3XqioBg0aoLi4WFguKioCEWm8bwHg7bffRkhICKZNmwYDAwPhpAQAGjZsCDs7O+EE1sfHBxs2bEBQUBAAwMzMDBcvXqxwjBVRK4r+6x6Nj4iQkpICOzs7AOpxk3/88Ue4urryiHqMvcCBAwcQHx+Pbdu2wczMDDt27MDevXsBqItYVlaW0Pbx48fCScSzmjZtqlEks7Oz0bRpUwDAoUOHhNcVCgWmTp2K1atXw9nZGcXFxRpzskulUsTGxiIrK0uY+9zCwgLt2rUrc871K1euaCxHRkbi5s2b2LVrF4yNjbFq1So8fPgQAGBsbIz8/HyhbXp6OqysrGBhYQFjY2ONOLUVFBSE//u//4OHhwfq168PR0dHhIWF4fHjx8LxA0BxcTFOnDiBlStXom3btjAzM0N8fDwkEonG9r755huMHDlSo8A+/zcoKCjAkydPIBaLoVKphDnvc3Jyyo1TLBbDzc0NR44cQUJCgnBXw8LCAv369cPKlStf6bhflOfnWVhYYMCAARg9erTG6y97Lzxr5MiRwkleicaNG5e62m/bti3Onj0rLN+8eRPNmjXTOGkqMWzYMAwbNgwAcPbsWeFOyhtvvIHc3FyhnZ6eHsRicXmpqBK6f3sfr/dK/86dOxg1ahQGDBigcUvR3d2dCz6rFkTzNH7K4+9vr9Fuw4bB5bb980//l26vIjIyMtCyZUuYmZkhKysLBw8eRF5eHgBgwIAB2L9/P4qLi5Gfn4+RI0eWKrQA0L9/f6FYA0BUVBScnZ1LtSsoKEB+fr4wxW1kZCT09fWFYuzi4oLk5GTExcUJV95dunRBeno6UlJSAKh7V0+fPl3oCf/8sbRr1w7Gxsa4e/cujh07Jmy7c+fOQmH/+++/cf78eQBAy5Yt0aJFC+F3mZmZmDZtmsYJQnl69uyJd955B5s3bwYAWFlZYejQoZg2bZpQpAoLCxESEoKOHTuiR48e0NPTw9SpU7F48WIhBoVCgVWrViEuLk54HLCEvb090tPThbbr1q3D2rVrYWpqCrFYjNTUVADqR9BepOQW/19//YV+/foBUP99z5w5I8QaFxeHDRs2lFq35L2hTZ6f5+rqij179gi32KOiohAdHf3S98KztL297+bmhtOnT+P69esAgC1btghf6Tzr1q1bGDJkCHJycqBQKPDtt9/C09MTgLpuHDhwAA8ePIBSqcTOnTvRu3dvYd2srKxyv8KpLDp/pd/+00ko1CMg6hHQpeJT66pUKnz33XdYuHAhcnNz0bBhQzx69Og1RspY7efh4YH9+/dj4MCBaNWqFaZOnYqAgACEhoZi5syZSE1NhUQigaGhIUaMGIFu3brh5s2bGtvo3Lkz/P39MWrUKKhUKnTo0AHz588vta9GjRrBz88PQ4cOhbm5OQICAuDm5oZPP/0U+/btg4mJCWxtbZGamgo7OzucO3cORkZGCA8Px6JFi5CXlwd9fX1MmTKlzO/Evb29MXnyZEilUtjY2CA4OBiBgYHYsmULAgICMGXKFAwcOBB2dnZwdXWFSCSCSCTCypUrMX/+fKxevRp6enoYN25cqVvC5QkKCsJHH32EDz/8EM2aNcOsWbPw7bffYuTIkUIbV1dXLFy4UFgePnw4DA0NERISgsLCQohEIjg4OCAyMrLUhUr9+vXx9ddfY/r06QCA1q1bIzQ0FEZGRggMDISfnx8sLCwwZsyYF8bZq1cvfP755+jXr5+wD1tbWwwdOhRjxoyBSqWCubk5FixYUGrdTp064e7du8jJyUGjRo1emOfnubm54erVq8JVtZWVFZYsWfLS94K2+X9W8+bNMW/ePEycOFF4xn7OnDkA1JOprVmzBps3b0br1q3h6uqKIUOGQCQS4b333hPis7Ozw6RJkzBy5EjUq1cP9vb28Pf3F/aRkpJS6g5NpatwF8BqJvTet1xOzvUWE8XfqvC2Ll26RBKJRHgMb8yYMXT37t3XGK3u4t77VYPzXPled46f7TEfGBhIW7Zsea3b10Xa5tjX11d4EqSuys7OJgcHB0pPT9d6He69/1//ZjS+/fv3o3///jh79ixatGiByMhIbN26FW+8UfOm6mWM1Qzbtm1DQEAAVCoVMjIycObMGXTt2rW6w9IZAQEB+O6776BUKqs7lGqzbds2eHh4aPTXqAq1o+jrVXzc/V69eqFJkyYYO3YsTp8+jcGDy/8elDHGAHXHLQMDA0gkEvj4+MDX17fcjmOstO7du8PR0RERERHVHUq1SE1NRUxMDKZNm1bl+9b57/Q7j/wDAU0+Acy1K/o5OTmIiIhAUFAQDAwMYG5ujsTEROGZUcYYexljY2OEh4dXdxg6bebMmdUdQrWxsbHBb7/9Vi371vmi/9egRPTuvwPQe/lNi3379mHmzJm4f/8+RCIRZsyYAQBc8BljjNUJOl/0TVQmLx2N7/79+5g5cyb27dsHQH1rqaxHLxhjjLHaTOeLfhOUf5WuUqmwdetWzJs3D3K5HCYmJggJCYGvr2+1D5DAGGOMVTWdL/qmMC33d7///rvQUUIqlSIsLAxvvvlmVYXGGGOM1Sg6X/TNxeYay/TfYSQBdaH38vKCTCbD0KFDtZ6UgjHGGKuNdP6RPbObb6AwTz0pwpkzZ+Dm5oZr164BAEQiEdavX49hw4ZxwWeMMVbnVUnRP336NIYNGwapVIpx48bhwYMHpdpcvnwZ3t7ekEql8Pb2xuXLl7Xa9q+zm+HYoiOYOXMm3N3dkZyc/MoTPjDGGGN1QaUX/fz8fEybNg2LFy9GTEwMXFxcMG9e6Uk+goKC4Ofnh5iYGEyYMEEYG/pl6tW7jclRk7Fx40aIxWIEBQXhq6++et2HwRhjjOm8Si/6f/zxB1q1agVbW1sA6skhTp48qTHdYGpqKuRyOdzc3ACoJ5TIyMgQbtO/iLHxYaTnpKNbt26Ij49HSEgI6tev2Oh8jDHGWG1W6R35bt68iVatWgnLxsbGaNKkCW7fvo2OHTsKbZ7vVd+qVStcv34db731Vpnbpf9OhdnUvDkCPgqA3xQ/iMViFBUVVdKR1F2c06rBea58nOPKxzmuPMXF6v5rVMZU0Nqq9KJfUFAAQ0NDjdcMDQ015jnWps3zFAoFAGD1mhUA1HNas8px4cKF6g6hTuA8Vz7OceXjHFc+hUIBIyOjCq1b6UW/QYMGpc78CgsLYWxs/EptnmdsbAxra2vo6+tzz3zGGGO1HhFBoVC8sDa+TKUX/Xbt2uHAgQPCslwux5MnT9C6dWuNNmlpacIyEeHWrVvl3toHAD09PTRs2LBygmaMMcZqoIpe4Zeo9I58PXv2xL1795CUlAQA2LJlC1xcXNCgQQOhzdtvvw0zMzPs3bsXABAdHY2WLVuibdu2lR0eY4wxVmeI6N/0CNBSYmIilixZgoKCAlhZWSE0NBQqlQrjx48XJsFJTU1FSEgIsrOzYW5ujsWLF7/wSp8xxhhjr6ZKij5jjDHGqp/OD8PLGGOMMe3U+KJfmUP4MjVtcvznn3/Cy8sL7u7u8PT0xNmzZ6shUt2mTZ5LXL58Gba2tkhMTKzCCHWfNjnOzc3FlClT0L9/f8hkMsTExFRDpLpLmxwfPXoUQ4YMgUwmg7e3N86fP18Nkeo2hUKB0NBQ2NjYlPtZUaHaRzVYXl4e9erViy5cuEBERJGRkeTv71+qnUwmo9jYWCIiiouLIw8PjyqNU5dpk+OioiJycHCg06dPExHR0aNHycnJqcpj1WXavpeJiJRKJX344YfUr18/+uOPP6oyTJ2mbY5nz55NixYtIpVKRdeuXaPRo0eTQqGo6nB1kjY5fvLkCXXr1o3+/vtvIiI6duwY9evXr8pj1XV+fn60Zs0asra2pvv375fZpiK1r0YX/cOHD5OXl5ewnJubS7a2tiSXy4XXLl++TI6Ojhrr9e7dm/75558qi1OXaZPj3NxciomJEZblcjlZW1vTkydPqjRWXaZNnkts27aN5s2bR6NHj+ai/wq0yXFRURHZ2dnR48ePqyNEnadNji9cuKDxmVxUVMSfFxVw7tw5IqJyi35Fa1+Nvr3/oiF8n21T3hC+7OW0ybGxsTEkEomwfPz4cbRp0waNGjWq0lh1mTZ5BoD09HRs3boV06ZNq+oQdZ62nxeGhobYtWsXBg0ahBEjRuDUqVPVEa5O0ibHb731FvT09HD69GkAQExMDDp16sSfF6+oa9euL/x9RWtfpQ/O829U1hC+7H9eNX+XL1/G0qVLsWLFiqoIr9bQNs9Lly7FxIkT+QOyArTJcU5ODuRyOQwNDXHgwAEkJCRg8uTJiIuLQ5MmTao6ZJ2jTY6NjIywaNEifPLJJzAyMoJKpcKmTZuqOtRar6K1r0Zf6VfWEL7sf14lf+fOnYO/vz+WLFmCnj17VlWItYI2eU5ISEB2djbef//9qg6vVtAmxw0bNoRSqYSPjw8AoG/fvrC0tERKSkqVxqqrtMnxw4cPMXv2bPzyyy84c+YM1q5di0mTJiEvL6+qw63VKlr7anTRb9euncZto9c1hC/7H21yDKiv8KdMmYKVK1fC2dm5qsPUedrkOTY2FpcuXYKjoyMcHR2RnJyMwMBA7N69uzpC1jna5NjS0hIANAqQWCyGnl6N/iisMbTJcXJyMt58803Y2NgAUI/Kqqenp9VU6Ux7Fa19NfqdzkP4Vj5tckxECA4Oxrx589C9e/fqClWnaZPnhQsXIjExESdPnsTJkyfRtWtXfP311xg6dGh1ha1TtMlxo0aN4OTkhO+++w4AkJKSgrt37+Ldd9+tlph1jTY5btOmDf755x/cuXMHAHDx4kXI5XJYWVlVS8y1VYVr32vqKH/kswAAB9BJREFUaFhp/vjjDxo8eDC5ubmRr68vPXr0iB48eEDvvfee0Oby5cvk5eVFAwcOJG9vb+65/4peluNz585R+/btSSqVavyUPLbDtKPNe/lZ3Hv/1WmT4wcPHtDHH39MLi4uNHjwYEpISKjGiHWPNjn+6aefSCaTkUQiIQ8PD+GxMqad9PR04XPW2tqa3NzcSCqVvpbax8PwMsYYY3VEjb69zxhjjLHXh4s+Y4wxVkdw0WeMMcbqCC76jDHGWB3BRZ8xxhirI7joM8YYY3VEjR57n7HaxsbGBlZWVhCLxcJrLVu2xObNm8td586dO5BIJLh06dK/3n9iYiLGjx8vTNShUqnQunVrzJ07V2MilVfx8ccfY8aMGbC1tcWOHTvwwQcflHr933o+b0qlEj169MCcOXM0BoYpS0pKCgwNDdG+fft/HQdjuo6f02esCtnY2ODYsWNo0aKF1uu87qI/Z84cxMbGCq9t2LABcXFx2LFjx7/atlKpRM+ePYXR2l6n5/NWXFyMoKAgvP322wgKCnrhunPnzoW9vT2GDBny2uNiTNfw7X3Gaojr16/Dx8cH7u7uGDhwIPbt21eqzcOHD/Hxxx9j0KBBcHNzw6pVqwCoh0r+5ptvIJVK4eLigsWLF0OpVGq139GjRyMlJQVyuRwqlQqrVq2CTCaDTCZDcHCwMGvXwYMH4eHhAXd3dwwePBiJiYkAgAEDBiApKQnjxo2DXC6HTCZDWlqa8PqIESMQExMj7C8uLk64GxAXF4fBgwfD1dUVvr6+yMzM1CpmAwMD9O3bF3///TcA9YxjU6dOhVQqxYABA7B8+XIAwPbt27Fnzx6EhYXh+++//1d5Yqw24KLPWA3x5ZdfwsXFBQcPHsTSpUsxe/ZsKBQKjTZbtmxBjx49cODAAezduxdpaWl49OgR9uzZg0OHDmHnzp2IjY1FWloatm/frtV+lUol9PT0oK+vj4MHD+L48ePYtWsX9u/fj5ycHGzZsgUAsGDBAqxfvx4HDx7EvHnzEB8fr7GdpUuXQiwW49ChQxpfFUilUo22sbGxcHd3R1paGmbMmIEVK1bg8OHD6NmzJ+bPn69VzE+ePMG+ffuEOce3b9+OvLw8HDp0CNHR0di1axeSkpLg4+ODzp07Y/r06Rg3bty/yhNjtQEXfcaq2JgxY4QraZlMhjlz5gAA1q1bh/HjxwMA7O3tUVRUhPT0dI11zc3NceLECSQlJcHAwAArV66EhYUFjhw5guHDh6Nhw4aoV68evLy88Pvvv780FqVSiU2bNqFv374wMjLC0aNHMXToUDRo0ABisRienp44efKksO+oqCjcvXsX3bt3xxdffKHV8cpkMhw7dgxKpRJPnz7F0aNHIZPJcPz4cTg4OMDa2hoA4O3tjfj4+HKvvEvy5urqCldXV/Tq1QsTJkwAAPj6+mLdunUQiURo3Lgx3nnnHWHCl2dVNE+M1RbckY+xKvbDDz+U+Z1+QkICIiIikJWVBZFIBCKCSqXSaDN27FioVCosWLAAjx49wqhRoxAYGAi5XI7Nmzfj559/BqAu5mZmZmXu//79+5DJZMJy586dERoaCgDIzMxE48aNhd81btwYGRkZAICIiAhERETA09MTlpaWmDVrFhwcHF56vK1atYKlpSWSk5OhUCjQtm1bWFpaQi6XIykpSSMWExMTZGdnw9zcvNy8ZWZmQiaTYdCgQahXT/0RdvPmTYSGhuL69evQ09PDgwcP4OnpWWobr5InxmojLvqM1QAKhQJTp07F6tWr4ezsjOLiYnTu3LlUu3r16sHf3x/+/v64ceMGJkyYAHt7e1hYWGDAgAEYPXr0S/dlaWmJQ4cOlfm7pk2bIjs7W1jOzs5G06ZNAQBWVlZYtmwZVCoVdu/ejc8//xwJCQlaHZ9UKsXhw4ehUCjg7u4OALCwsECfPn0QHh6u1TZKmJmZYcyYMQgLC0NERAQA9bTEtra2WLt2LcRiMby9vctc91XyxFhtxLf3GasBCgoKkJ+fj06dOgEAIiMjoa+vL3SiKzF37lzhdruVlRWaNm0KkUgEV1dX7NmzBwUFBQCAqKgoREdHv3Ic/fv3x2+//YaCggI8ffoUO3fuhLOzMzIzMzFu3Djk5uZCT08PXbp0gUgk0lhXX18fKpUKubm5pbYrlUpx+vRpHDlyRLiyd3JyQlJSEtLS0gAA58+fx+LFi7WKc9y4cUhOTsaZM2cAABkZGejQoQPEYjFOnjyJW7duCbmrV68e5HI5ALy2PDGmq/hKn7EaoFGjRvDz88PQoUNhbm6OgIAAuLm54dNPP8X69euFdt7e3pg7dy4WLVoEIsKAAQPQu3dvAMDVq1cxbNgwAOoTgiVLlrxyHDKZDKmpqfD09AQRoWfPnvjoo49gaGiIvn37Yvjw4RCLxdDX1y+1/WbNmsHe3h4uLi4aMQNA27ZtoVKp0Lx5czRv3hyA+qp70aJFmDhxIhQKBYyNjTFr1iyt4jQxMYG/vz+WL1+OnTt3IiAgAMuWLcO6devg6uqKSZMmITw8HB06dICbmxvCwsKQlpaG4ODg15InxnQVP6fPGGOM1RF8e58xxhirI7joM8YYY3UEF33GGGOsjuCizxhjjNURXPQZY4yxOoKLPmOMMVZHcNFnjDHG6ggu+owxxlgdwUWfMcYYqyP+HxsPS7nDRcueAAAAAElFTkSuQmCC\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Explainibility using SHAP"
      ],
      "metadata": {
        "id": "xrlNUCtCmVqi"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install shap"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "lP4cB82Q1M8Z",
        "outputId": "0bd90bbd-e3c3-47c0-d51d-7f25410a3a84"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "Collecting shap\n",
            "  Downloading shap-0.41.0-cp37-cp37m-manylinux_2_12_x86_64.manylinux2010_x86_64.whl (569 kB)\n",
            "\u001b[K     |████████████████████████████████| 569 kB 5.4 MB/s \n",
            "\u001b[?25hRequirement already satisfied: pandas in /usr/local/lib/python3.7/dist-packages (from shap) (1.3.5)\n",
            "Requirement already satisfied: numpy in /usr/local/lib/python3.7/dist-packages (from shap) (1.21.6)\n",
            "Requirement already satisfied: packaging>20.9 in /usr/local/lib/python3.7/dist-packages (from shap) (21.3)\n",
            "Requirement already satisfied: scikit-learn in /usr/local/lib/python3.7/dist-packages (from shap) (1.0.2)\n",
            "Requirement already satisfied: tqdm>4.25.0 in /usr/local/lib/python3.7/dist-packages (from shap) (4.64.0)\n",
            "Requirement already satisfied: cloudpickle in /usr/local/lib/python3.7/dist-packages (from shap) (1.5.0)\n",
            "Requirement already satisfied: numba in /usr/local/lib/python3.7/dist-packages (from shap) (0.56.0)\n",
            "Requirement already satisfied: scipy in /usr/local/lib/python3.7/dist-packages (from shap) (1.7.3)\n",
            "Collecting slicer==0.0.7\n",
            "  Downloading slicer-0.0.7-py3-none-any.whl (14 kB)\n",
            "Requirement already satisfied: pyparsing!=3.0.5,>=2.0.2 in /usr/local/lib/python3.7/dist-packages (from packaging>20.9->shap) (3.0.9)\n",
            "Requirement already satisfied: llvmlite<0.40,>=0.39.0dev0 in /usr/local/lib/python3.7/dist-packages (from numba->shap) (0.39.0)\n",
            "Requirement already satisfied: setuptools in /usr/local/lib/python3.7/dist-packages (from numba->shap) (57.4.0)\n",
            "Requirement already satisfied: importlib-metadata in /usr/local/lib/python3.7/dist-packages (from numba->shap) (4.12.0)\n",
            "Requirement already satisfied: zipp>=0.5 in /usr/local/lib/python3.7/dist-packages (from importlib-metadata->numba->shap) (3.8.1)\n",
            "Requirement already satisfied: typing-extensions>=3.6.4 in /usr/local/lib/python3.7/dist-packages (from importlib-metadata->numba->shap) (4.1.1)\n",
            "Requirement already satisfied: pytz>=2017.3 in /usr/local/lib/python3.7/dist-packages (from pandas->shap) (2022.2.1)\n",
            "Requirement already satisfied: python-dateutil>=2.7.3 in /usr/local/lib/python3.7/dist-packages (from pandas->shap) (2.8.2)\n",
            "Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.7/dist-packages (from python-dateutil>=2.7.3->pandas->shap) (1.15.0)\n",
            "Requirement already satisfied: joblib>=0.11 in /usr/local/lib/python3.7/dist-packages (from scikit-learn->shap) (1.1.0)\n",
            "Requirement already satisfied: threadpoolctl>=2.0.0 in /usr/local/lib/python3.7/dist-packages (from scikit-learn->shap) (3.1.0)\n",
            "Installing collected packages: slicer, shap\n",
            "Successfully installed shap-0.41.0 slicer-0.0.7\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "explainer = shap.Explainer(f, med)\n",
        "shap_values = explainer(X_valid.iloc[0:1000,:])"
      ],
      "metadata": {
        "id": "JBc9gnsH390e"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "row_to_show = 5\n",
        "data_for_prediction = X.iloc[row_to_show]  # use 1 row of data here. Could use multiple rows if desired\n",
        "data_for_prediction_array = data_for_prediction.values.reshape(1, -1)\n",
        "\n",
        "\n",
        "classifier.predict_proba(data_for_prediction_array)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "zZFJmyJW4nA8",
        "outputId": "f470092e-d19b-482b-8d59-2727e3dcff46"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[0.00349086, 0.99650913]], dtype=float32)"
            ]
          },
          "metadata": {},
          "execution_count": 23
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "f = lambda x: classifier.predict_proba(x)[:,1]\n",
        "med = X_train.median().values.reshape((1,X_train.shape[1]))\n",
        "\n",
        "explainer = shap.Explainer(f, med)\n",
        "shap_values = explainer(X_test.iloc[0:1000,:])\n"
      ],
      "metadata": {
        "id": "5cixQjWD45qU"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "shap.plots.beeswarm(shap_values)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 193
        },
        "id": "SX55okTp5Bpi",
        "outputId": "6731886f-626d-48d1-ce45-4bc439b8d1f7"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 576x165.6 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "shap.plots.heatmap(shap_values)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 252
        },
        "id": "bhgzvjja5iqO",
        "outputId": "fedbd166-19cf-48cd-d0cc-325e253ca49a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 576x252 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "shap.initjs()\n",
        "shap.force_plot(explainer, shap_values, X_train)"
      ],
      "metadata": {
        "id": "k9Tr50ol1Kxb"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}