0216MPNeuronAndPerceptron.ipynb

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  "metadata": {
    "colab": {
      "name": "0216MPNeuronAndPerceptron.ipynb",
      "version": "0.3.2",
      "provenance": [],
      "collapsed_sections": [],
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/manushaurya/padhaibyonefourthlabs/blob/master/0216MPNeuronAndPerceptron.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "metadata": {
        "id": "01trAgGA3RtU",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "#Loading datasets"
      ]
    },
    {
      "metadata": {
        "id": "iQUI3TcQy03e",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "import sklearn.datasets\n",
        "import numpy as np"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "hm9sYFeHy_5p",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "breast_cancer = sklearn.datasets.load_breast_cancer()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "G-eZMhupzJ6s",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "X = breast_cancer.data\n",
        "Y = breast_cancer.target"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "qdXR2gugzle0",
        "colab_type": "code",
        "outputId": "4bc72663-71a7-42da-cc20-4b6b63adceb2",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "cell_type": "code",
      "source": [
        "print(X.shape,Y.shape)"
      ],
      "execution_count": 332,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "(569, 30) (569,)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "MkDd0OWizv7m",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "import pandas as pd"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "EC54U96pz8t6",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "data = pd.DataFrame(breast_cancer.data,columns=breast_cancer.feature_names)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "uWGoC4KA0Vpx",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "data['class'] = breast_cancer.target"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "-vYMjJv61RpP",
        "colab_type": "code",
        "outputId": "d1337b82-d5a0-4d21-ec42-e835f58f4329",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 290
        }
      },
      "cell_type": "code",
      "source": [
        "data.head()"
      ],
      "execution_count": 336,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>mean radius</th>\n",
              "      <th>mean texture</th>\n",
              "      <th>mean perimeter</th>\n",
              "      <th>mean area</th>\n",
              "      <th>mean smoothness</th>\n",
              "      <th>mean compactness</th>\n",
              "      <th>mean concavity</th>\n",
              "      <th>mean concave points</th>\n",
              "      <th>mean symmetry</th>\n",
              "      <th>mean fractal dimension</th>\n",
              "      <th>...</th>\n",
              "      <th>worst texture</th>\n",
              "      <th>worst perimeter</th>\n",
              "      <th>worst area</th>\n",
              "      <th>worst smoothness</th>\n",
              "      <th>worst compactness</th>\n",
              "      <th>worst concavity</th>\n",
              "      <th>worst concave points</th>\n",
              "      <th>worst symmetry</th>\n",
              "      <th>worst fractal dimension</th>\n",
              "      <th>class</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>17.99</td>\n",
              "      <td>10.38</td>\n",
              "      <td>122.80</td>\n",
              "      <td>1001.0</td>\n",
              "      <td>0.11840</td>\n",
              "      <td>0.27760</td>\n",
              "      <td>0.3001</td>\n",
              "      <td>0.14710</td>\n",
              "      <td>0.2419</td>\n",
              "      <td>0.07871</td>\n",
              "      <td>...</td>\n",
              "      <td>17.33</td>\n",
              "      <td>184.60</td>\n",
              "      <td>2019.0</td>\n",
              "      <td>0.1622</td>\n",
              "      <td>0.6656</td>\n",
              "      <td>0.7119</td>\n",
              "      <td>0.2654</td>\n",
              "      <td>0.4601</td>\n",
              "      <td>0.11890</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>20.57</td>\n",
              "      <td>17.77</td>\n",
              "      <td>132.90</td>\n",
              "      <td>1326.0</td>\n",
              "      <td>0.08474</td>\n",
              "      <td>0.07864</td>\n",
              "      <td>0.0869</td>\n",
              "      <td>0.07017</td>\n",
              "      <td>0.1812</td>\n",
              "      <td>0.05667</td>\n",
              "      <td>...</td>\n",
              "      <td>23.41</td>\n",
              "      <td>158.80</td>\n",
              "      <td>1956.0</td>\n",
              "      <td>0.1238</td>\n",
              "      <td>0.1866</td>\n",
              "      <td>0.2416</td>\n",
              "      <td>0.1860</td>\n",
              "      <td>0.2750</td>\n",
              "      <td>0.08902</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>19.69</td>\n",
              "      <td>21.25</td>\n",
              "      <td>130.00</td>\n",
              "      <td>1203.0</td>\n",
              "      <td>0.10960</td>\n",
              "      <td>0.15990</td>\n",
              "      <td>0.1974</td>\n",
              "      <td>0.12790</td>\n",
              "      <td>0.2069</td>\n",
              "      <td>0.05999</td>\n",
              "      <td>...</td>\n",
              "      <td>25.53</td>\n",
              "      <td>152.50</td>\n",
              "      <td>1709.0</td>\n",
              "      <td>0.1444</td>\n",
              "      <td>0.4245</td>\n",
              "      <td>0.4504</td>\n",
              "      <td>0.2430</td>\n",
              "      <td>0.3613</td>\n",
              "      <td>0.08758</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>11.42</td>\n",
              "      <td>20.38</td>\n",
              "      <td>77.58</td>\n",
              "      <td>386.1</td>\n",
              "      <td>0.14250</td>\n",
              "      <td>0.28390</td>\n",
              "      <td>0.2414</td>\n",
              "      <td>0.10520</td>\n",
              "      <td>0.2597</td>\n",
              "      <td>0.09744</td>\n",
              "      <td>...</td>\n",
              "      <td>26.50</td>\n",
              "      <td>98.87</td>\n",
              "      <td>567.7</td>\n",
              "      <td>0.2098</td>\n",
              "      <td>0.8663</td>\n",
              "      <td>0.6869</td>\n",
              "      <td>0.2575</td>\n",
              "      <td>0.6638</td>\n",
              "      <td>0.17300</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>20.29</td>\n",
              "      <td>14.34</td>\n",
              "      <td>135.10</td>\n",
              "      <td>1297.0</td>\n",
              "      <td>0.10030</td>\n",
              "      <td>0.13280</td>\n",
              "      <td>0.1980</td>\n",
              "      <td>0.10430</td>\n",
              "      <td>0.1809</td>\n",
              "      <td>0.05883</td>\n",
              "      <td>...</td>\n",
              "      <td>16.67</td>\n",
              "      <td>152.20</td>\n",
              "      <td>1575.0</td>\n",
              "      <td>0.1374</td>\n",
              "      <td>0.2050</td>\n",
              "      <td>0.4000</td>\n",
              "      <td>0.1625</td>\n",
              "      <td>0.2364</td>\n",
              "      <td>0.07678</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>5 rows × 31 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "   mean radius  mean texture  mean perimeter  mean area  mean smoothness  \\\n",
              "0        17.99         10.38          122.80     1001.0          0.11840   \n",
              "1        20.57         17.77          132.90     1326.0          0.08474   \n",
              "2        19.69         21.25          130.00     1203.0          0.10960   \n",
              "3        11.42         20.38           77.58      386.1          0.14250   \n",
              "4        20.29         14.34          135.10     1297.0          0.10030   \n",
              "\n",
              "   mean compactness  mean concavity  mean concave points  mean symmetry  \\\n",
              "0           0.27760          0.3001              0.14710         0.2419   \n",
              "1           0.07864          0.0869              0.07017         0.1812   \n",
              "2           0.15990          0.1974              0.12790         0.2069   \n",
              "3           0.28390          0.2414              0.10520         0.2597   \n",
              "4           0.13280          0.1980              0.10430         0.1809   \n",
              "\n",
              "   mean fractal dimension  ...    worst texture  worst perimeter  worst area  \\\n",
              "0                 0.07871  ...            17.33           184.60      2019.0   \n",
              "1                 0.05667  ...            23.41           158.80      1956.0   \n",
              "2                 0.05999  ...            25.53           152.50      1709.0   \n",
              "3                 0.09744  ...            26.50            98.87       567.7   \n",
              "4                 0.05883  ...            16.67           152.20      1575.0   \n",
              "\n",
              "   worst smoothness  worst compactness  worst concavity  worst concave points  \\\n",
              "0            0.1622             0.6656           0.7119                0.2654   \n",
              "1            0.1238             0.1866           0.2416                0.1860   \n",
              "2            0.1444             0.4245           0.4504                0.2430   \n",
              "3            0.2098             0.8663           0.6869                0.2575   \n",
              "4            0.1374             0.2050           0.4000                0.1625   \n",
              "\n",
              "   worst symmetry  worst fractal dimension  class  \n",
              "0          0.4601                  0.11890      0  \n",
              "1          0.2750                  0.08902      0  \n",
              "2          0.3613                  0.08758      0  \n",
              "3          0.6638                  0.17300      0  \n",
              "4          0.2364                  0.07678      0  \n",
              "\n",
              "[5 rows x 31 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 336
        }
      ]
    },
    {
      "metadata": {
        "id": "nl6mpZ7n1UH9",
        "colab_type": "code",
        "outputId": "8d036c2e-32ed-4f4f-97e0-f0aaf4d638ee",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 384
        }
      },
      "cell_type": "code",
      "source": [
        "data.describe()"
      ],
      "execution_count": 337,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>mean radius</th>\n",
              "      <th>mean texture</th>\n",
              "      <th>mean perimeter</th>\n",
              "      <th>mean area</th>\n",
              "      <th>mean smoothness</th>\n",
              "      <th>mean compactness</th>\n",
              "      <th>mean concavity</th>\n",
              "      <th>mean concave points</th>\n",
              "      <th>mean symmetry</th>\n",
              "      <th>mean fractal dimension</th>\n",
              "      <th>...</th>\n",
              "      <th>worst texture</th>\n",
              "      <th>worst perimeter</th>\n",
              "      <th>worst area</th>\n",
              "      <th>worst smoothness</th>\n",
              "      <th>worst compactness</th>\n",
              "      <th>worst concavity</th>\n",
              "      <th>worst concave points</th>\n",
              "      <th>worst symmetry</th>\n",
              "      <th>worst fractal dimension</th>\n",
              "      <th>class</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>569.000000</td>\n",
              "      <td>569.000000</td>\n",
              "      <td>569.000000</td>\n",
              "      <td>569.000000</td>\n",
              "      <td>569.000000</td>\n",
              "      <td>569.000000</td>\n",
              "      <td>569.000000</td>\n",
              "      <td>569.000000</td>\n",
              "      <td>569.000000</td>\n",
              "      <td>569.000000</td>\n",
              "      <td>...</td>\n",
              "      <td>569.000000</td>\n",
              "      <td>569.000000</td>\n",
              "      <td>569.000000</td>\n",
              "      <td>569.000000</td>\n",
              "      <td>569.000000</td>\n",
              "      <td>569.000000</td>\n",
              "      <td>569.000000</td>\n",
              "      <td>569.000000</td>\n",
              "      <td>569.000000</td>\n",
              "      <td>569.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>14.127292</td>\n",
              "      <td>19.289649</td>\n",
              "      <td>91.969033</td>\n",
              "      <td>654.889104</td>\n",
              "      <td>0.096360</td>\n",
              "      <td>0.104341</td>\n",
              "      <td>0.088799</td>\n",
              "      <td>0.048919</td>\n",
              "      <td>0.181162</td>\n",
              "      <td>0.062798</td>\n",
              "      <td>...</td>\n",
              "      <td>25.677223</td>\n",
              "      <td>107.261213</td>\n",
              "      <td>880.583128</td>\n",
              "      <td>0.132369</td>\n",
              "      <td>0.254265</td>\n",
              "      <td>0.272188</td>\n",
              "      <td>0.114606</td>\n",
              "      <td>0.290076</td>\n",
              "      <td>0.083946</td>\n",
              "      <td>0.627417</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>3.524049</td>\n",
              "      <td>4.301036</td>\n",
              "      <td>24.298981</td>\n",
              "      <td>351.914129</td>\n",
              "      <td>0.014064</td>\n",
              "      <td>0.052813</td>\n",
              "      <td>0.079720</td>\n",
              "      <td>0.038803</td>\n",
              "      <td>0.027414</td>\n",
              "      <td>0.007060</td>\n",
              "      <td>...</td>\n",
              "      <td>6.146258</td>\n",
              "      <td>33.602542</td>\n",
              "      <td>569.356993</td>\n",
              "      <td>0.022832</td>\n",
              "      <td>0.157336</td>\n",
              "      <td>0.208624</td>\n",
              "      <td>0.065732</td>\n",
              "      <td>0.061867</td>\n",
              "      <td>0.018061</td>\n",
              "      <td>0.483918</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>6.981000</td>\n",
              "      <td>9.710000</td>\n",
              "      <td>43.790000</td>\n",
              "      <td>143.500000</td>\n",
              "      <td>0.052630</td>\n",
              "      <td>0.019380</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.106000</td>\n",
              "      <td>0.049960</td>\n",
              "      <td>...</td>\n",
              "      <td>12.020000</td>\n",
              "      <td>50.410000</td>\n",
              "      <td>185.200000</td>\n",
              "      <td>0.071170</td>\n",
              "      <td>0.027290</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.156500</td>\n",
              "      <td>0.055040</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>11.700000</td>\n",
              "      <td>16.170000</td>\n",
              "      <td>75.170000</td>\n",
              "      <td>420.300000</td>\n",
              "      <td>0.086370</td>\n",
              "      <td>0.064920</td>\n",
              "      <td>0.029560</td>\n",
              "      <td>0.020310</td>\n",
              "      <td>0.161900</td>\n",
              "      <td>0.057700</td>\n",
              "      <td>...</td>\n",
              "      <td>21.080000</td>\n",
              "      <td>84.110000</td>\n",
              "      <td>515.300000</td>\n",
              "      <td>0.116600</td>\n",
              "      <td>0.147200</td>\n",
              "      <td>0.114500</td>\n",
              "      <td>0.064930</td>\n",
              "      <td>0.250400</td>\n",
              "      <td>0.071460</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>13.370000</td>\n",
              "      <td>18.840000</td>\n",
              "      <td>86.240000</td>\n",
              "      <td>551.100000</td>\n",
              "      <td>0.095870</td>\n",
              "      <td>0.092630</td>\n",
              "      <td>0.061540</td>\n",
              "      <td>0.033500</td>\n",
              "      <td>0.179200</td>\n",
              "      <td>0.061540</td>\n",
              "      <td>...</td>\n",
              "      <td>25.410000</td>\n",
              "      <td>97.660000</td>\n",
              "      <td>686.500000</td>\n",
              "      <td>0.131300</td>\n",
              "      <td>0.211900</td>\n",
              "      <td>0.226700</td>\n",
              "      <td>0.099930</td>\n",
              "      <td>0.282200</td>\n",
              "      <td>0.080040</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>15.780000</td>\n",
              "      <td>21.800000</td>\n",
              "      <td>104.100000</td>\n",
              "      <td>782.700000</td>\n",
              "      <td>0.105300</td>\n",
              "      <td>0.130400</td>\n",
              "      <td>0.130700</td>\n",
              "      <td>0.074000</td>\n",
              "      <td>0.195700</td>\n",
              "      <td>0.066120</td>\n",
              "      <td>...</td>\n",
              "      <td>29.720000</td>\n",
              "      <td>125.400000</td>\n",
              "      <td>1084.000000</td>\n",
              "      <td>0.146000</td>\n",
              "      <td>0.339100</td>\n",
              "      <td>0.382900</td>\n",
              "      <td>0.161400</td>\n",
              "      <td>0.317900</td>\n",
              "      <td>0.092080</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>28.110000</td>\n",
              "      <td>39.280000</td>\n",
              "      <td>188.500000</td>\n",
              "      <td>2501.000000</td>\n",
              "      <td>0.163400</td>\n",
              "      <td>0.345400</td>\n",
              "      <td>0.426800</td>\n",
              "      <td>0.201200</td>\n",
              "      <td>0.304000</td>\n",
              "      <td>0.097440</td>\n",
              "      <td>...</td>\n",
              "      <td>49.540000</td>\n",
              "      <td>251.200000</td>\n",
              "      <td>4254.000000</td>\n",
              "      <td>0.222600</td>\n",
              "      <td>1.058000</td>\n",
              "      <td>1.252000</td>\n",
              "      <td>0.291000</td>\n",
              "      <td>0.663800</td>\n",
              "      <td>0.207500</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>8 rows × 31 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "       mean radius  mean texture  mean perimeter    mean area  \\\n",
              "count   569.000000    569.000000      569.000000   569.000000   \n",
              "mean     14.127292     19.289649       91.969033   654.889104   \n",
              "std       3.524049      4.301036       24.298981   351.914129   \n",
              "min       6.981000      9.710000       43.790000   143.500000   \n",
              "25%      11.700000     16.170000       75.170000   420.300000   \n",
              "50%      13.370000     18.840000       86.240000   551.100000   \n",
              "75%      15.780000     21.800000      104.100000   782.700000   \n",
              "max      28.110000     39.280000      188.500000  2501.000000   \n",
              "\n",
              "       mean smoothness  mean compactness  mean concavity  mean concave points  \\\n",
              "count       569.000000        569.000000      569.000000           569.000000   \n",
              "mean          0.096360          0.104341        0.088799             0.048919   \n",
              "std           0.014064          0.052813        0.079720             0.038803   \n",
              "min           0.052630          0.019380        0.000000             0.000000   \n",
              "25%           0.086370          0.064920        0.029560             0.020310   \n",
              "50%           0.095870          0.092630        0.061540             0.033500   \n",
              "75%           0.105300          0.130400        0.130700             0.074000   \n",
              "max           0.163400          0.345400        0.426800             0.201200   \n",
              "\n",
              "       mean symmetry  mean fractal dimension     ...      worst texture  \\\n",
              "count     569.000000              569.000000     ...         569.000000   \n",
              "mean        0.181162                0.062798     ...          25.677223   \n",
              "std         0.027414                0.007060     ...           6.146258   \n",
              "min         0.106000                0.049960     ...          12.020000   \n",
              "25%         0.161900                0.057700     ...          21.080000   \n",
              "50%         0.179200                0.061540     ...          25.410000   \n",
              "75%         0.195700                0.066120     ...          29.720000   \n",
              "max         0.304000                0.097440     ...          49.540000   \n",
              "\n",
              "       worst perimeter   worst area  worst smoothness  worst compactness  \\\n",
              "count       569.000000   569.000000        569.000000         569.000000   \n",
              "mean        107.261213   880.583128          0.132369           0.254265   \n",
              "std          33.602542   569.356993          0.022832           0.157336   \n",
              "min          50.410000   185.200000          0.071170           0.027290   \n",
              "25%          84.110000   515.300000          0.116600           0.147200   \n",
              "50%          97.660000   686.500000          0.131300           0.211900   \n",
              "75%         125.400000  1084.000000          0.146000           0.339100   \n",
              "max         251.200000  4254.000000          0.222600           1.058000   \n",
              "\n",
              "       worst concavity  worst concave points  worst symmetry  \\\n",
              "count       569.000000            569.000000      569.000000   \n",
              "mean          0.272188              0.114606        0.290076   \n",
              "std           0.208624              0.065732        0.061867   \n",
              "min           0.000000              0.000000        0.156500   \n",
              "25%           0.114500              0.064930        0.250400   \n",
              "50%           0.226700              0.099930        0.282200   \n",
              "75%           0.382900              0.161400        0.317900   \n",
              "max           1.252000              0.291000        0.663800   \n",
              "\n",
              "       worst fractal dimension       class  \n",
              "count               569.000000  569.000000  \n",
              "mean                  0.083946    0.627417  \n",
              "std                   0.018061    0.483918  \n",
              "min                   0.055040    0.000000  \n",
              "25%                   0.071460    0.000000  \n",
              "50%                   0.080040    1.000000  \n",
              "75%                   0.092080    1.000000  \n",
              "max                   0.207500    1.000000  \n",
              "\n",
              "[8 rows x 31 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 337
        }
      ]
    },
    {
      "metadata": {
        "id": "pbaGnCL21g1M",
        "colab_type": "code",
        "outputId": "d7607954-f931-4579-bdd1-9e80487ba65a",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 69
        }
      },
      "cell_type": "code",
      "source": [
        "print(data['class'].value_counts()) #print(breast_cancer.target_names) will be tha class names 1 :malignant 0:benign"
      ],
      "execution_count": 338,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "1    357\n",
            "0    212\n",
            "Name: class, dtype: int64\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "NXzFQ3AN2D9s",
        "colab_type": "code",
        "outputId": "ec15b554-1ec7-4c4b-bf06-db04441e455d",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "cell_type": "code",
      "source": [
        "print(breast_cancer.target_names)"
      ],
      "execution_count": 339,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "['malignant' 'benign']\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "a3OsVq082k9g",
        "colab_type": "code",
        "outputId": "cf49dc08-d717-48e2-891a-b2d843211f94",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 228
        }
      },
      "cell_type": "code",
      "source": [
        "data.groupby('class').mean()"
      ],
      "execution_count": 340,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>mean radius</th>\n",
              "      <th>mean texture</th>\n",
              "      <th>mean perimeter</th>\n",
              "      <th>mean area</th>\n",
              "      <th>mean smoothness</th>\n",
              "      <th>mean compactness</th>\n",
              "      <th>mean concavity</th>\n",
              "      <th>mean concave points</th>\n",
              "      <th>mean symmetry</th>\n",
              "      <th>mean fractal dimension</th>\n",
              "      <th>...</th>\n",
              "      <th>worst radius</th>\n",
              "      <th>worst texture</th>\n",
              "      <th>worst perimeter</th>\n",
              "      <th>worst area</th>\n",
              "      <th>worst smoothness</th>\n",
              "      <th>worst compactness</th>\n",
              "      <th>worst concavity</th>\n",
              "      <th>worst concave points</th>\n",
              "      <th>worst symmetry</th>\n",
              "      <th>worst fractal dimension</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>class</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>17.462830</td>\n",
              "      <td>21.604906</td>\n",
              "      <td>115.365377</td>\n",
              "      <td>978.376415</td>\n",
              "      <td>0.102898</td>\n",
              "      <td>0.145188</td>\n",
              "      <td>0.160775</td>\n",
              "      <td>0.087990</td>\n",
              "      <td>0.192909</td>\n",
              "      <td>0.062680</td>\n",
              "      <td>...</td>\n",
              "      <td>21.134811</td>\n",
              "      <td>29.318208</td>\n",
              "      <td>141.370330</td>\n",
              "      <td>1422.286321</td>\n",
              "      <td>0.144845</td>\n",
              "      <td>0.374824</td>\n",
              "      <td>0.450606</td>\n",
              "      <td>0.182237</td>\n",
              "      <td>0.323468</td>\n",
              "      <td>0.091530</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>12.146524</td>\n",
              "      <td>17.914762</td>\n",
              "      <td>78.075406</td>\n",
              "      <td>462.790196</td>\n",
              "      <td>0.092478</td>\n",
              "      <td>0.080085</td>\n",
              "      <td>0.046058</td>\n",
              "      <td>0.025717</td>\n",
              "      <td>0.174186</td>\n",
              "      <td>0.062867</td>\n",
              "      <td>...</td>\n",
              "      <td>13.379801</td>\n",
              "      <td>23.515070</td>\n",
              "      <td>87.005938</td>\n",
              "      <td>558.899440</td>\n",
              "      <td>0.124959</td>\n",
              "      <td>0.182673</td>\n",
              "      <td>0.166238</td>\n",
              "      <td>0.074444</td>\n",
              "      <td>0.270246</td>\n",
              "      <td>0.079442</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>2 rows × 30 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "       mean radius  mean texture  mean perimeter   mean area  mean smoothness  \\\n",
              "class                                                                           \n",
              "0        17.462830     21.604906      115.365377  978.376415         0.102898   \n",
              "1        12.146524     17.914762       78.075406  462.790196         0.092478   \n",
              "\n",
              "       mean compactness  mean concavity  mean concave points  mean symmetry  \\\n",
              "class                                                                         \n",
              "0              0.145188        0.160775             0.087990       0.192909   \n",
              "1              0.080085        0.046058             0.025717       0.174186   \n",
              "\n",
              "       mean fractal dimension           ...             worst radius  \\\n",
              "class                                   ...                            \n",
              "0                    0.062680           ...                21.134811   \n",
              "1                    0.062867           ...                13.379801   \n",
              "\n",
              "       worst texture  worst perimeter   worst area  worst smoothness  \\\n",
              "class                                                                  \n",
              "0          29.318208       141.370330  1422.286321          0.144845   \n",
              "1          23.515070        87.005938   558.899440          0.124959   \n",
              "\n",
              "       worst compactness  worst concavity  worst concave points  \\\n",
              "class                                                             \n",
              "0               0.374824         0.450606              0.182237   \n",
              "1               0.182673         0.166238              0.074444   \n",
              "\n",
              "       worst symmetry  worst fractal dimension  \n",
              "class                                           \n",
              "0            0.323468                 0.091530  \n",
              "1            0.270246                 0.079442  \n",
              "\n",
              "[2 rows x 30 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 340
        }
      ]
    },
    {
      "metadata": {
        "id": "Zy8cWQvh3Zgc",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "#Test Train split"
      ]
    },
    {
      "metadata": {
        "id": "lLGhAxf82y-t",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "from sklearn.model_selection import train_test_split\n",
        "\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "CXAOaeZ176qz",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "X = data.drop('class',axis=1)\n",
        "#axis : {0 or ‘index’, 1 or ‘columns’}, default 0 Whether to drop labels from the index (0 or ‘index’) or columns (1 or ‘columns’).\n",
        "Y = data['class']"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "L1w-GXkr8VkS",
        "colab_type": "code",
        "outputId": "05599d54-2380-4fb0-da4d-c0b8333f8ffe",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "cell_type": "code",
      "source": [
        "type(X)"
      ],
      "execution_count": 343,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "pandas.core.frame.DataFrame"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 343
        }
      ]
    },
    {
      "metadata": {
        "id": "FKGZYgcA8YpR",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "X_train, X_test, Y_train, Y_test = train_test_split(X,Y,test_size=0.1,stratify=Y,random_state=1)\n",
        "#can add 3rd argument test_size=0.1 for 10% test data size,4th argument stratify=Y to maintain the constant ratio of 1s & 0s classes in test and train data\n",
        "#random_state=1 to fix the state of our data"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "3K4P1YzZ8tEe",
        "colab_type": "code",
        "outputId": "0911089a-8c20-4bc7-b195-23578c21537c",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "cell_type": "code",
      "source": [
        "print(X.shape,X_train.shape,X_test.shape)"
      ],
      "execution_count": 345,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "(569, 30) (512, 30) (57, 30)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "TmTO1-aG84qm",
        "colab_type": "code",
        "outputId": "8b7b875e-76de-45ff-a9f8-28c6c1f18f75",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "cell_type": "code",
      "source": [
        "print(Y.mean(),Y_train.mean(),Y_test.mean()) #train has slightly lesser 1's ie more malignant data"
      ],
      "execution_count": 346,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "0.6274165202108963 0.626953125 0.631578947368421\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "YdQcAIyf97_v",
        "colab_type": "code",
        "outputId": "6e434531-99ac-46cb-bebd-9987d8bcc0df",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1597
        }
      },
      "cell_type": "code",
      "source": [
        "print(X_test.mean(),X_train.mean(),X.mean())"
      ],
      "execution_count": 347,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "mean radius                 14.743807\n",
            "mean texture                19.109825\n",
            "mean perimeter              95.908246\n",
            "mean area                  715.896491\n",
            "mean smoothness              0.094496\n",
            "mean compactness             0.097130\n",
            "mean concavity               0.083923\n",
            "mean concave points          0.049159\n",
            "mean symmetry                0.173981\n",
            "mean fractal dimension       0.061169\n",
            "radius error                 0.418767\n",
            "texture error                1.306656\n",
            "perimeter error              2.909946\n",
            "area error                  43.944193\n",
            "smoothness error             0.006809\n",
            "compactness error            0.023659\n",
            "concavity error              0.027989\n",
            "concave points error         0.010293\n",
            "symmetry error               0.020169\n",
            "fractal dimension error      0.003618\n",
            "worst radius                16.942105\n",
            "worst texture               25.967544\n",
            "worst perimeter            111.783860\n",
            "worst area                 960.843860\n",
            "worst smoothness             0.130357\n",
            "worst compactness            0.225973\n",
            "worst concavity              0.241340\n",
            "worst concave points         0.106994\n",
            "worst symmetry               0.276719\n",
            "worst fractal dimension      0.079852\n",
            "dtype: float64 mean radius                 14.058656\n",
            "mean texture                19.309668\n",
            "mean perimeter              91.530488\n",
            "mean area                  648.097266\n",
            "mean smoothness              0.096568\n",
            "mean compactness             0.105144\n",
            "mean concavity               0.089342\n",
            "mean concave points          0.048892\n",
            "mean symmetry                0.181961\n",
            "mean fractal dimension       0.062979\n",
            "radius error                 0.403659\n",
            "texture error                1.206856\n",
            "perimeter error              2.861173\n",
            "area error                  39.935506\n",
            "smoothness error             0.007067\n",
            "compactness error            0.025681\n",
            "concavity error              0.032328\n",
            "concave points error         0.011963\n",
            "symmetry error               0.020584\n",
            "fractal dimension error      0.003815\n",
            "worst radius                16.194275\n",
            "worst texture               25.644902\n",
            "worst perimeter            106.757715\n",
            "worst area                 871.647852\n",
            "worst smoothness             0.132592\n",
            "worst compactness            0.257415\n",
            "worst concavity              0.275623\n",
            "worst concave points         0.115454\n",
            "worst symmetry               0.291562\n",
            "worst fractal dimension      0.084402\n",
            "dtype: float64 mean radius                 14.127292\n",
            "mean texture                19.289649\n",
            "mean perimeter              91.969033\n",
            "mean area                  654.889104\n",
            "mean smoothness              0.096360\n",
            "mean compactness             0.104341\n",
            "mean concavity               0.088799\n",
            "mean concave points          0.048919\n",
            "mean symmetry                0.181162\n",
            "mean fractal dimension       0.062798\n",
            "radius error                 0.405172\n",
            "texture error                1.216853\n",
            "perimeter error              2.866059\n",
            "area error                  40.337079\n",
            "smoothness error             0.007041\n",
            "compactness error            0.025478\n",
            "concavity error              0.031894\n",
            "concave points error         0.011796\n",
            "symmetry error               0.020542\n",
            "fractal dimension error      0.003795\n",
            "worst radius                16.269190\n",
            "worst texture               25.677223\n",
            "worst perimeter            107.261213\n",
            "worst area                 880.583128\n",
            "worst smoothness             0.132369\n",
            "worst compactness            0.254265\n",
            "worst concavity              0.272188\n",
            "worst concave points         0.114606\n",
            "worst symmetry               0.290076\n",
            "worst fractal dimension      0.083946\n",
            "dtype: float64\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "vAAA7m-SBKon",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "#Binarisation"
      ]
    },
    {
      "metadata": {
        "id": "e363KBpP_6sW",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "import matplotlib.pyplot as plt"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "yRNWCrr4BSSC",
        "colab_type": "code",
        "outputId": "27ecb95e-4858-47aa-8aa2-d7b35de9bf27",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 358
        }
      },
      "cell_type": "code",
      "source": [
        "plt.plot(X_train.T,'*') #X_Train.T will show the transpose thus showing how the features vary\n",
        "plt.xticks(rotation='vertical') #x axis labes are vertical for readability\n",
        "plt.show()"
      ],
      "execution_count": 349,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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srImbXBDZ959/lZLzzic/P4dNL7zK4NkXRJVNpJ7WprXZX9ts2JZH0Z4S9u1zXnxhexoN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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "metadata": {
        "id": "rJdMIWXtBak2",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "X_binarised_3_train = X_train['mean area'].map(lambda x:0 if x<1000 else 1 )"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "cSEervvrFkYh",
        "colab_type": "code",
        "outputId": "9807d4d0-4e9e-4962-c1fe-6404e30d231e",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 265
        }
      },
      "cell_type": "code",
      "source": [
        "plt.plot(X_binarised_3_train,'*')\n",
        "plt.show()"
      ],
      "execution_count": 351,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "metadata": {
        "id": "Gz7C6d02FtEE",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "X_binarised_train = X_train.apply(pd.cut,bins=2,labels=[1,0])#two levels 0 and 1 to classify our data #labels flipped after model not performing well\n",
        "X_binarised_test = X_test.apply(pd.cut,bins=2,labels=[1,0])"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "aejrN-D9GSvZ",
        "colab_type": "code",
        "outputId": "03611b34-fcb3-4feb-e07c-9bb30e7cfc56",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 358
        }
      },
      "cell_type": "code",
      "source": [
        "plt.plot(X_binarised_train.T,'*')\n",
        "plt.xticks(rotation='vertical')\n",
        "plt.show()"
      ],
      "execution_count": 353,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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LGXGAadPmtOItjbq6vkyaNLOqst6mt+ltepsdrc2s/80ixJBPAJaKPV4amFh6ELlZ/gqc\nqqqPtqqHjuM4TqsJ8ZE/CuwCICLrARNUNT6kjAQuVdVH2qF/juM4TgUqzshV9VkReUlEngUagMNE\nZCgwHfgbsDewmogcEP3LWFW9qr067DiO4zQnyEeuqiclnnot9neP6nXHcRzHyYtndjqO4xQcN+SO\n4zgFxw254zhOwXFD7jiOU3DckDuO4xQcN+SO4zgFxw254zhOwXFD7jiOU3DckDuO4xQcN+SO4zgF\nxw254zhOwXFD7jiOU3DckDuO4xQcN+SO4zgFxw254zhOwXFD7jiOU3DckDuO4xQcN+SO4zgFxw25\n4zhOwXFD7jiOU3DckDuO4xQcN+SO4zgFxw254zhOwXFD7jiOU3DckDuO4xQcN+SO4zgFxw254zhO\nwXFD7jiOU3DckDuO4xQcN+SO4zgFxw254zhOwakNERKRS4GNgUbgKFV9Ifbaj4HzgQXAw6p6Tnt0\ndNSDowA4dttjK8peefs1AByy+wFl5S59YDQAx/zq6Iptjr37dqiBPXbavazcdaNvBmC/o39bsc2r\nrjkDgIMOGF5W7taHTgJgz20urNjmebfZ53Tqb8p/TmffcgUAZ+51WMU2b7nmVgD2OmDPsnJfvvIE\n9X0Wo3a1TSq2ees99v577lj+/fO0GSp79YMP061bV4b+7GcV27zjvqsA+PX2B5WVe/ytifT5eBob\nLTegYptn3jMWgLN33KOs3N/GjqGGGn66xxEV23zywRfo2bM7G229Tlm5UY9cAsCxPz++Ypvnjbkd\ngFOPLH/P3/y36wH47c/2rdjmdQ+MpaYG9t22/LXfOOI6APYZtl/FNq++xfT4wL3K6/GTd5rcFrtW\n1vdRN4wE4Nihx5WXu+YGkztgaMU2r7z3LqCGQ3bYuaJsXioachHZAlhNVTcRkTWB64C4powBfgZ8\nCjwpIner6pvV7ujMPpOCZddecmaQ3Iy+nwe32TC5T5Dcap9rcJurvvdZkNx7vYOb5LMlJgfJzeg/\nLbjNOdMGB8lNuf8+ptbAymdUNrorD5lS9TZDZV9d8BosgKFUNuSr9Q+7l+5/ejw1XWrYaP+NK8rO\nqv0gqM2pU5cNkgN49Y3p1AAbbV1ebuZiU4PbnDWjW5Dc6gPmBLe55pDpQXIrTnk/uM01lqkPkhtY\ntyC4zS/6zw6S+2Dq0sFtrtXv02DZvNQ0NjaWFRCR4cBHqnpN9Ph/wEaqOkNEVgZuUtXNo9dOBmap\n6uVZ7U2aNLP8GyYY9eAoZvaZxBcN9iUs2aUrfWfVpc7Mr7z9GtZeciYrDDDl+3BaX974om+Lmfml\nD4xmRt/Pm7XZb+bg1Jn52Ltvp2FyH2ZOM0Ped8AsugyaxR47N5+lXDf6Zlb7XBk86RMAPq9blncG\nS+rM/KprzmDV9z5j2UnzAPikrjvvrrJUi5n5rQ+dxHu94fOGBgAGd+nCKrPTZ+bn3TaKz5aYTEOt\n3dRd6mtZasqgFjPzs2+5ghn9p/F17xkALDa7H/2mD0idmd9yza3MmTaYBQtsvO/atZ5eAz5vMTP/\n8pUnmHL/fSz4+EuTW25xlthuexb/v61atHnrPVew8pApLNPDHn86F96fuESLmXmeNkNlr37wYTPi\nfaNBbOYA1u26Dgdu+8sWbd5x31Ws1n8mgyKFnjy9N+9M79tiZv74WxO5/+nxzJjyFQD9lujJdpuv\nyI/WHNKizTPvGcus2g/4urcZs8Vm96dP/UotZuZ/GzuGqVOX5ctZAwFYvM9UBg78hJ/tcWSLNp98\n8AVefWM63aM52TzqWXft/myx7YbN5EY9cgkzF5vaXI++Hpg6Mz9vzO3MmtGNz2ttdTG4fhp9+s1v\nMTO/+W/Xs/qAOQzpZoPyxPlL8Pa0Xqkz8+seGMuaQ6azTFebkH26oI63JvZnv181v/YbR1zHilPe\nZ0ikRxPrlmX8EiunzsyvvmU0ayxTzwqL20Dy4Ze9+N+ntS1m5k/eOZqBdQvoH32X06f3Zuqkrqkz\n81E3jOSL/rOZ2X8WAH2n92HJ6b1bzMxHXXMDH0xdmtkN9rn37lLPSgMnpM7Mr7z3Ltbq9ykrDrDv\nffy0/vx3xjIcssMuLWTLUVfXtybrtRBDfhXwkKreFz1+CthfVd8WkU2B41V1x+i1/YFVVPWUrPbq\n6xc01tZ2zXUBp1x1Lu/2t9FslenLcsFBp2bKnjFmJNuvYTPd+3QIw49IdzGcdtX5vN3/Y7vGL5fj\nnIMzu8xlV/6R6e8uA0D/1T7lqN/9LlXu3DOvZKtX/wHAE+v+mNPOPiSzzfMuPpEtn3kXgHGbr8qp\nx1+UKnf5VUfzVP+5AGz+ZQ+OPHh0ZpsnjT6H94dMAGCVCUO44JgzUuWOvuQiJiw/HoClP16B0cNO\nymxzxMVXMOfz5QHoNfhDhp1weKrc1P+9zFsnngfAmhedzsA11s1uc8zFbLWGKfQTb9Ux7KgT2txm\nqOzYhx/j3pl3AbBjv934zS9aDgwlxlwzhs2Wt3vk2Q+X54gD010cL7w3ieF/eBaAMw/dlA1Wqcts\n88Trb+GDXs8AsPKczblw33R31V+uHcEbbwoA3/3O2+y4f/YS/99PvM4jD34IwC+2XZENt/puqtwp\n15/Nu71MN1adPYTz90u/PwCOPfuPvDPDBqPV+09k5Bnp9/yIa0ez1XKmm49/vDTH739MZpsjrv0D\nWy1nq5HHP1qZ4w9I14+LTx3FZm/YZ/TsWptz/PnZbQ6/5Hy2WccG5odfH8Dpw9L1+NbLL2ANsdXI\nWzqQvY44ObPNUy84m3dWjj6n95fi/JPPTJU755IR/Puz1QDYaMg7nD5sWGabI24cy1ZDXgFg3IT1\nOG7obzJly5BpyIN85KGNVXgNgGnTwpdhJRb0/5KVMP9CQ78vmTQpe7k7uGcj//rQXAGDF2vIlJ3f\nbyorNVqb8/pPK9tml/m9GLBCNIub1zNTdvDML3lzlQ0AWHJm+X72nTKJl9bob39PnpwpO3nx+awZ\n7UlPHTC/bJuf9ZlNr5nW5md9v8qUndX7a/pNWcr+7jW3bJuNswbQs/fM6O+BmbKfPPIgvbZah+7d\navnokQdYsMQqmW0usdQMXv+yR/T3zKq0GSr79/dfpTerUlMDf5/0Cj+etEFmm4N6zEI/t1npwB7Z\n/fzL0++x9vcG071bV/7y9Hus0G+xzDYn1X9EnxnLAfBF44eZbU79vJall3gPgClfdCn7HT3x2Lv0\n6Aldu3bhicffZsW1V0yVm99nBivQy/7uO6NsmwtmdGVlvgCgfnrXTNmBA7rw2iy7niUWp/w9v0Q3\nXpyzOgD9lsiW7TP7a15baX0Aes/Jvo8BBi0+n+c/tNXyEv3rs/vZZz5ffNrX/u5dXo9mLzafpScs\nAcBXi2XLfjR9KZbqaa999OXgsm3W1k/lqY9XAKArU8rKZlFX1zfztZColQnAUrHHSwMTM15bJnqu\nqtTM7cWwH53JRb8eQc28nmVlZ87rym47HMxhBw5j5rzsmX/Xeb0YtvWZXLT7CLpWaLOxez27/2ZH\njjhybxq7Z/vZZvboww4nH86Bo05mVo/yPvU5vXvzm2GXceRF1zGnd69MuT7z4PCtLuTs3a6g97yy\nTdJrbg8u2f5UbjjgQnrO7Z4pVzu/OxfseizXHHomtfOz5QBqe8xh6BG/4vjhe1DbI3sQXmyZFVh2\nz2NY67BT6LHM8mXbnPNVT7bd6kT23e1c5nydbfTytBkqW1c7gIt/fRDXHXYcg2oXL9vmrPnd+MnP\nj2CP357C7Ppsf/EKS/bh2F+uxdl7bMjyS5b/3msbe3PRDkdw3YGnUEv25kdtjzlsf+D+HHDK0dR2\n/6psm3V1PdnvqJ8w7OwdGDQo+17qUr8YJ2x5FpfsOpIu9T3KttmlO5x20u5cNvJAupS5RWbNreFX\nP9iX/XY6klnzys/jZs7uwk6b7c7BOxzIzDnZpuerbj3Z9dQjOHT0KXzVrbxuzvqqC7vucCyHHngm\ns77Kfv95X9ewwbbH8It9z2D+3PL97LGgK6fudSKjjzmX7guybUjfHrM5/6jtufrcvehbRjcAZizo\nyZ7b7cPR+x/OjAXlr6k1hLhWNgXOVtWfiMh6wJiSTzx6/b/ANsAnwHPAnqr6dlZ7eX3kcerq+gaP\nZKGy3qa36W16mx2tzYz/bb1rRVWfFZGXRORZoAE4TESGAtNV9R7gEOC2SPyOckbccRzHqT5BPnJV\nTe6GvRZ77Z80D0d0HMdxFiGe2ek4jlNw3JA7juMUHDfkjuM4BccNueM4TsGpGH7oOI7jdGx8Ru44\njlNw3JA7juMUHDfkjuM4BccNueM4TsFxQ+44jlNw3JA7juMUHDfkjuM4BafDG3IR6RH9HiAi2UfE\nLLr+7FPh9S4iUr7Qddj7VD7FtwMgIpVP3G2S/b92eP+W57S1QS6SDeqniLQ8zy1btuKhK+2J69G3\nmw6dECQilwMvAn8FHsfqnTeo6sEpsssCZwADVHVXEdkdeE5VP0zIjVHVlocfpr//BsBJwMDoqe7A\nUqq6akLuJGAaMBYYB0wB/qWqLc7SEpG9gW7AzcADUdvXqeqVCbn/Au8BtwL3qerXZfq5LrCkqj4q\nIqcD6wOXqOozCbm7o/YeVNWyx1SISG9ga6A/sZOfVPWmhNxtWL36/5VrL5J9HPipqpY9LTfnd/Qw\nsIeqflkNuZz9fFJVtwjsZ5Bszmu/U1V3DZBzPergetRWWnPU26JkHVU9QkSOAq5V1UtF5O8ZstcA\nl2E3DMAXwA1A8lDGGhE5CPg3sPBLUNU3U9q8HDgFuAiru74j8K8UuV+p6mYiciBwr6qeIyL/yOjn\nIcAPgF8Dr6nqCSLyGNDsBlTVtURkTWB74H4RmQiMVdW/pbR5BbCniPwEWBc4DLgR+HFCbmTU3oki\n8gZwq6o+ntHPfwDjsQNDSqSN+hsAb4jIbJo+z0ZVXTJFdjbwjoi8RvPPfreEXJ7vqB/wsYi8F8nW\nRO+/USvl8vRzoog8A7yQkEs7hHS8iIxNuaY/JOTyXPtUETk/RfbhhJzrUcfXozbR0Q15DxFZBtgL\n2FFEaoGs5VZXVf2riJwAoKqPi0jaqalrRz/x008bgR+lyM5R1SdEZK6qvgS8JCKPAA8m31tEugB7\nAKVZTtYBewtUtV5EdgHOjp5LPe9MVd8SkS+AqcC+wDAROQc4QVXHxUTnqur46NqvVNVPo/4k23sW\neBYWzpKuiD7fq4ERqjo7Jj5PVSueEKuqq1WSiTEiUC7Pd5R+enHr5SC8n3/N0eb70e/+FeTyXHt3\nYAhmVOKySUPuetTB9aitdHRDfgV2U45V1U9E5FzgrgzZ+SLyI+xmGIyN+i0OO1TVrQBEpJuqzq/w\n/nNEZDvgg2jm8x6QdiDkPcBnwJ2q+na0LHs+o82XReRd64q+KiJHAB8lhURkP2y20R9bam6vql+I\nyCDg70DcjztPRK7GDvg4QkR+ji07k232AraL2l0KuCP6+Qlwb/S7xAORX/lpYKGLQVWbHU4YLUdH\nA6sAXYE3gCMzXC2vAUdjs50GbLk/JimkqluJSB9gNWAB8I6qlju48uxEm+nHnofLBfUTOxlrD+y7\nWBDJ3Z7WoKqeLSJbxmUjg5CUC752Vd1XRFYG1olkX1HVj1NEXY86uB61lQ5tyCM/0k2xx6eVEd8f\nOAcYBDyC3QAtNuIiZboM6AGsISLnAU+q6qMpbe6BfVGHY4q9DrB3Sj8vwpaNRCP4DRkKhaoeKSJn\nquq06Kn7gD+miK4FHKuq/431faCqThaRsxKyu2F+uNNVdYGIzCd9Bvo68BfgDFX9T+z5G8TOZo1z\nMC3vj0Zg5cRzY4BjopkWIrIx8AfSZ2Y3Av8EhmOzyS2A64Fmfl4R2RM4C3gT+55WFpET1Y4WTHIt\ntpw+Nmpzy+i55OZmqFxwP6P/n4b5c0tyWwEHJhsUkUuxz+5JoBdwuoi8lLyn81y7iByPGZNnItmz\nROTqpJ/Y9agQetQmOrQhF5FJNPmTugN9gA8ylvNDVfWAgGaHY0amNCO5DLsJWtyAqjpTRNYG1lfV\n4SKytKpOSOlnaZPmVkxRp4jIc6raYsYn0SaNiNyELS0HYgbhj9HrtZhybACcFo3+YDODccD3VPW+\nRLPXqeousX4/JiL/AjZOyD2a4b9FVQ9KPF4t6s8AbGNsetr/AfUlIx79379EJMsH2FdVR8Ye/yvD\nB3o45tedE/WhD/A3bMaWpKsj1a7uAAAgAElEQVSq3h17fHvkY22tXJ5+Lquqv020meUrXV9Vfxh7\nfKGIPJkil+fadwC+r6oLItla7P5Lbvi5HnV8PWoTHdqQq2pd/LGIfA/z86WxZLRJkdx4Si5h5qvq\nlJKxiZZZDWkNisgl2BJwVWzJfFA0mid366uxSVOaTfwCmzVuBPyXpp3uBuwGjPdvZ2xTap3IB1iS\n7QK8kvLeCyRwg0pEfowtyb8Gukef0UGa2MEHvoxmhuOi9/8R5otMo6uIbKCqL0bv8X3SQ2AXxL83\nVZ0lIlkRJPNEZNfE+89tg1yefnaPGyWxiI8WS/GIbiLSs+QmEYtm6Joil+faa7D7okQDKRtprkeF\n0KM20aENeRJVfT1l6VJiG2yGEidtCfOBiAwHBonIr6P/SdtpB9gg8lk+Eb3/WSLyVIpc1TZpVPUB\nzK+2l6rektFGSfZu4G4RGaaqIRt0eTaohgNbqupEABFZDvMx/iAhNxQ4CjgtausFUpbiEYcBl4nI\ndyLZN6LnkjwrIg9is7IazA3yz4w294v6ehqmpC9g7oHWyuXp5ynAY5FydonazZrljwJeF5G3I9lV\ngeNT5PJc+x3Ai9GssQbz7V6VIbsQ16PmdBA9ahMd2pCLyJ00n2EsjYWGtUBVVw9s9iDsRnkau/Hv\nxxQijW4i0q3UB7ENkrSd8apt0kR+v7OB7cU2iJqhLUPgwIzJKFrGqu6XkBupqs0iBUQka0d9Xunm\ni9r6OPIZJjk3ZWaVxYaqunWA3InA5tiyuBE4r8wMZm9VzTLIrZHL08/eqrpmtGxu1PIx6u9im6er\nY9f0dsosF/Jd+58xd8b/RbIXaSLeG1yPCqJHbaJDG3Lg97G/G4EZWERBC0TkA1ouKxek+AHHqOrh\nwMJRWkTuwJZoSUZi8a7Li8hfgTWxzZpmxDdpIi5T1Rlp/UzZpLmf5ps090a/r8QiEUK4Bdt0/CTt\nRRHZEFtiHiki8WiBWuAELPoiyfsicgXNXRHvpcjliSf+aeTzrJQ8NE4teSZt1pYk1BUQKpenn4eL\nyLOx77IcI7Eko7Slepw81357JDu+gpzrURjfpB61iQ5pyEVk+2gjYm1a3lSbYlERSdaO/d0NW7pI\nrM2dMZ/Z2iKyUUI2y685HvghtvM9Dxv9W4RiRRs5o7BNsk2A/cUy+V5OkV0WOENEBqhl5W2CZdp9\niL1BScGuxTa57gIeV9VU/2PEx6r6pzKvfwbMwja64v7SBsw1ksZB2NJxc+w7+CfpM648y8z1seSh\nWTRPykkmD4Umz0C4KyBULk8/8yQZzSEsySjPtU+UMglJrkdAcfSoTXRIQ05TssKglNdSIyK0eRA+\nmH/sGKLkDlW9W0QewG6US2JyDcBE0inNov5dob+XA4fSpBh/w3yVm6fIhmbOrYnFo+6O+Wufw5ac\naRlpL0UbSk/RPFb14ej3x8CNIvIQMBMYoqrj0y5ERL6vqs8DP8VSpOPv9zNaJpvcrKrXpbWVwu5p\nSplCaPIMWOjjQ1WUg/B+ngB8GtjmKMJmhnmuvVJCkutRcfSoTXRUQ/6BiPwQeCL0H6IvIOkHbLZR\noqrzoptyF2AZVR0RzQI+y2g2NFW7Xi17rPT6m1k7+ARmzqnVhCht2KwOnIr5Q9N8i0tHv3eMPZeW\n4bc1cHr099oiMgZLTInXfdgS80um1fBIa/MnkXuhYq0VYISIVKxhAiyRw+9+mIg8U8E/nUcuTz8v\n0MBaK8CZgbJ5rn1bLV9rxfWoOHrUJjqqIT8i+j0A+C6WMdcVW/L+m/Rd/DdifzdiKbRpoUtXYaP3\nltgsY0vsy03brEjbwV4q5bkvxTLIeouFqu0YvUcaQZlzIrI5lj32M2zWdy+JKAcR6aGqc0mPqEjj\ncGA9mmYHJ2C+u3iySMlHeSBmVD4X06zvkD4DjNdamUu2GwK81sqirrXielQcPWoTHdKQl2YZInIP\nsIqqzooe98PqGaSxYbT5spCMzZfl1FKbS6FQvxeLL07jGewGWCJ63B04mZY+rn2xzZvJ2FLvebJ9\nZkGZc5gf8m4samF6dD2DEzLXY5ED/6X5LKqGdP/vgmg2VZLNiqMG2/i5XUReBe7Ernl3Ep9nyiZY\nObzWyiKsteJ6BBREj9pKhzTkMVag+Yc0B6vpsZAKmy/dU9rsLlbnuBQKtSaWAZbGnzFf2JbYrvhW\nWPp0M9SSNu7HYn+7RG2vR8qMR1UnisixmEKXZNM2iX6N+de2j5aapZt/4fWr6h7R75Wia6mUPfa0\niNwMLCsiJ2IzlayEi8Gqeq9Ytt3lqnq1pFTMk8CypxHPYEvN+HJck0Kar47HNGyGtKSqHi0iW5Ge\nxBEql6efN4rIJsAKqnq7iAzRWKhZQvbs6LNaUVWfjs0CW33tkSHtQRlfbYTrUQfXo7bS0Q357cDb\nYqUiG4E1sDoYC6mw+dIiDRhb/j0OrCYi/4vazUpJHqCqO4nIOLUyoItjIU43x4WizY8BWNhSKf60\ntENNQvZqLOus1LfSqJ9c4t9BwM0ftRmUPaaqp0VLzf9gij1MVZ/LuPZeIrIZlgG4ZXTtaUX6Qzed\nwGaBFZfjkq+Oxw1Y8aNtosdLYgkXyRoqoXJ5+pnMWDxY0jMWifmUe2Px5BeJyARVvbi11y6WiJP0\n1b6gqjcnRF2POr4etYkOfUJQdJOvi33wZwPfJyVBQK24e1dgqqp+GM0Ga0ipT6GqT6nqetiI/H1V\nXTP5RcXoISIrAPXRRslcYqFYMQao6qaqupuq7hr9pCUcgCVvLKeqG0U/G2b4aQeo6j5YTYwjsJ37\nbVLkoCl7bB1VXRP4OXBhUkhEVsRmOD2wmcxPRKRF0f6I0zHf34WqOhmbzaZVAOyqqn8lShVXq8uc\ndV8tp6onYjNCVPX3NG0wJa/nRzRFQVxGU/Zekr5qRaLmRW3eAfRsg1yefm6gqr/G4rJR1bNoXk0v\nzg6quhm2MgA4huabaiXyXHvJVzspenwCKX5e16NC6FGb6NAzchFZCQtHivvWtgCWSxF/BsvMugxY\nFvMbnprS5iHYBkR/bGMJAFVNiyc+HdvMOwfzh/bDRuwkT4vIWhqrsFaG1zC/3qQKcsmb/2PSb34I\nzx57GIun/bxSJ9VOSfkX0F8s+SHrRJOgTaeI0OV4cB0PoIuIrBJr8+ek1zAJlcvTz9CMRWLvVfKr\nLka6/uW59iBfretRIfSoTXRoQ44t/67HNkCGYzfVQWmCqnqL2LFOf8NmSD/UlApr2IxlO8K+hMdi\nD1fJFDTjdZyIzKAp/jQrcmMV4D2x9OJ6sqMn0m7+tAQOCM8e+1BTjs1KI7F0jS9zk/0M3XQCq01S\nWo6/Ffv/JGl1PLKU+3DgT8AGYqe/vEb6PRIql6efQRmLEWPFKiOuJiJXYkv80Slyea496av9FeY+\nSuJ61PH1qE109DM7H1PVrSV23qGIPKyqLfyaYucSro4VcBqEpfrer01hQCW564BTVDUr5rVdEZHV\niIWKRQzSWCnYmOxyGtVjFpE1NCNWW6xk52+wsLJS4ao7NCpvGpPbFasD/QrNEx6Gp7T5IhbBUPUb\nRESWxGY/qTHd0lQ4aVPss3oe+HPyetqbSv2MZHpTIWMxJrsipsDzgJc0pdZ23muPfLULZdN8ta5H\n3049itPRZ+Q1IrIFVpf4IGx0XClD9vnIBwaAWCLEMSlyrwMfisjnNB/JW13oXUR+CvyOlsV2fhST\nKdVHvgbzvZXkarGEhe8l2rwIGExT+NUwEZkS+W6T9MI2UErL/P7Y0j2ZpXcOgUtCwpeuuVHVrNjg\n0usNWNhW2ap17U2lfkYys7GY7JD2xlOhLkrea1fVp7HCVeVwPfoW6lGcjm7If4vFyR6JLQm3BYZl\nyN4hVoFsGbVSlGtiG0VJfofNoLLSiZshVrs5eWMld9Evw2Yw5dK14/WR48kdLeojR2yqqgtLXarq\nASKSVc70buBVzB1QKmd6DxZ2FecDLX86TJzQpavT8flG9UhETlPVcxPPjVTV4xKirketpKMb8hO1\nKZQrWUoySWim2XPAZG1ZU6IFYuFQA2l5AnbyRnhX00PjFqI56iNHdI1v/IhVXqvJkO2hqvFstbsk\nvSD/uyJyCzaDjC8J03yG+wT0sVRfeYiq/ltE9sL8kVeqaou460i+YtyzlInHzpDvg31PYBt5f1DV\nnyZk+gFLqZVH3QKLerhVVVvMlESkK5aN90W0QfYd4BG1dO+43OHY0jvXbCtyn/RLc9mIFYEKqaaY\nh29Ej0Rkp+j/fhhNiEp0wz7/pCHvtHrUVjq6Ic+TrhyaabYKtiR8j8oj5AC1KmypiMih0Z+fiMif\naXnAatoXO09E7lHVHaM2HgWuUtXkYbiHAVeKhQM0YBteh2R05fHoWh/DQv9+gB1P1ivqR6lU6+To\nJx7HmuW7C02guQU4Suyszv2wzaUxWCZfM8SShUozmawaFWBxz0E1TMTCvoZiERkfYXHdaRXs7sBi\nt7thBmo0tgG4bYrsrTRl490V/e9vaJmN1w+4T0S+xEqY/iXLsEnTMWZjsZnjFBH5V8qm2dPRvXkr\ncF9y8Ei0+TNsZtyPDFdExDeiR6r6FxF5GSujG49SaQBKm8iuR1WgoxvyXOnKEhYy9tuU57KoFA5V\nKmX5WfQT8sUeg/n2SmyHLeWa3YBqdavjZzyWI2vU35PmKcbDsZobzVxFGdxAWAJNvVph/0uA0ar6\nTDSjTeMwKtSoiJgo4TVMfqGqK4vIE2qn0KxHeqGiHqo6TkTOBi5V1bEikhVdk5aNl3YW5fnA+SIy\nBIsY+auIfAr8UVWT53EGHWOmqmtF9+72wP1iETZjNb1a32gsEiW1fnaMb0yPVHV8ZKiXSq7aMGMI\nrkdtpkMbco3SlQMpGzImIger1Ro+nPSbI81IlA2HUjuBBBE5QFWvif+jWPpwGl1pHmfdhZSbIZpp\nHp58XlNCsTRKLQ7gsej945t4qZlzRAk0IrJb9B53iMjvUuRqReRUzPCcHi1dyx3PFVKjIk8Nk0YR\nqYn60VNVXxaLgU6ymNgJ9btjIYgrkl33JC0bb2CaoIgsjc3Ud8DKlT4I7CsiO6pqPBQx+BgztQqA\nX2Bnn+6LbdCdA5ygquNiou9nGPhke9+0Ht1MmVWb61Hb6dCGPCdfq+p6EgsZi5YxJcZHv99o+a/p\naEpBKLFTZuJ//xTYLfKllugG7IalOye5HKsW+BZ2M6yO1SpJsjOwUogvPwe12vwk93KEJtDshaWe\n76CqX4vIythyP42guGfNUcMEm4EdjbkiXhOLokj7zA7FjOIhaqe6702TmydJs2w8ETmNlGw8sU2z\n7ph7aWe1zD2AW8XqXscJOsZMrPrfr7FBZiywfeSrH4R9VvHMUc3higil6npE+qptoe1xPapCh6rd\n4KJGRFbFMrXOj5bCNdHztZjyrQgQm7lUquEcb7tSRty/gPnYTvobNM0IGrDwqBao6s1i1ejWxJRP\nsTjgJP8jppxV4gYROY6W8a9pM4l4As1n2G5+WhLJVthMdAMR2SB67ruk+wFPBzbDalTMA47X9Ljn\n4Bomqjoq9n8PY6Fer6a89/vYJqiKbXZ2A7IOj3gMO5n988iwvIElOyW5S1WbGXgR+Y2q3oZtEjZr\nU5vHYl+GxSsn+T5wbNKdFw0oZyVkp0c/ba7d0Z56RNOqbTuaVm19Yq+7HrWRDm/IpXL4X0/M57Yk\nNnqXaCC9OE6lGs5xymbEqepMzMe7tjRFQjRgG3ipfvWAwaFEF2zG9TLNN5NSa09IWBW8fbDZwMax\n51KXhKr6FvDjMm2V+G7s725R22+Qnoo8Ti0hpVLcc+ip62nVFzfFBpZk9cXSZmctbdzslKazGw8T\nkbiR6IbVur5No8qGWQYykr2MyEDGWCPr3lE7ti3++GyxIlv/h50+9KKqPpv8PwkL/2tPPSqt2nZK\nW7W5HrWdDm3IJSD8T1X/A/xHRO5W1ZDlXtkazgnmq+r1IjJUVe8G7o5mfc18uCJyKbYR8iSWQHC6\niLysqi1qVBCeLv37lOdSkfBokC6qmnZsVlqbF2CuiGYFsJK+RW0erlUK3UtGDpQYL2GHK+SpYRJa\nfbGtm51xF1CesxvzGsiJErjRm7jvemH33UsaxThLjvC/dtajUfHZu1rBshZ0Zj1qKx3akFMh/C9O\n4M2Xp4YzhGfErZ/wmV0oIsmohRJBgwOWEXY0VrWuATvdJatqWmg0yN9F5ABaxr+mhaH9AqudnRn+\nBiBRaFaMIViZ1DTSDldI2zDLU8Mk6Mgv2r7ZGXdffBH58f9BUzXDVFphINM2erMiN8redxoY/pfo\nb3voUejsvdPqUVvp6IY8TzW0ICS8hjOEZ8R1E4uY+Cp6j95kb2iEDg43YiuP4TQtG68nPbQuNBqk\nNEuNn5aTFYb2d+zzeVnLnzwe/24aMZ/tyAzZ0LMjXyBx6jotXRAlQqsvVnOzs3SizNPYNdckfqel\nqe8UuYpK31HWkXhZJ/Skuaoq3ncaFv6Xm5x6FDp778x61CY6uiHPUw0tNJ0+67y9Fjegqn4qFtq2\noqruJyKLZYyslwKvi8jb2BJqVRLnAsb4LXZeYWlw2Ib0waGvqsYN4r8kPcsMwqvg/QW4XcMyERuw\n08RnipUoTTU8Gh6yBU1nSELT8v5FIldZ5EIZDFyHuShmRbKrYe6aeERDiaDqi1HUxAjstByAazTl\nhJ5I9lGxiJSlosfnJl7fI/rzSCzjs9IpRmDREytqdsJQuRN60k6+gfT7Lnf4X6Ifoen0efRo38gv\nvg7my39FUwqGZVxPp9CjttKhDblWCP9LPB+aTh983p40nerSB7sJLxSRiYnoA1T1z9H7r459ce9o\nUxZYks8xBdgKm82+SZMyxOkqIhuo6otRX75P9oENQdEgWNxyUCYitiQcqBnV/ETkSlU9REReIGXp\nrymZspqIcojcMtfGnloTMzSr07zUaKmQVBpDVTXrZJr4eyW/y4vSvstINm22meYr3REYJSLPYwPN\nX7MGBypET2j5E3qyjo+L33eNwNsZ913Z8L/omvOm0+fRo+OxjeJnsOSis0TkarWDPrKup1PoUbXo\n0IZc8hXED/Wnl0bd5aTpvL2sM/R2UMvGK7kEjsFOFU+W9FwXi4JYFbtJ3hCRo6Id6yRjI5nnsNH5\nAGwXfPeE3GHAZSLyHUxJ3yD7lO/3sZv4LuDxrCWc5stE/Ad2sMA7Ge95VvR7l4zXQ2jA6piU+vcU\n8JSI3KqqWbOmJEtGg3tyczBpAIK+y4iKp6RH77GfWJLPppjb4GQReS82Y4+TjJ4otbFb7O95sQEn\nfl5os1KxInKmWsTKnSQGURFp1mZEpfC/1vjTc+kRdorQgqiPtdiGZjND3kn1qCp0aENOjoL4BPrT\nNd95e6GnuowBjtGoFnK0hL2CdJ/ZsqraLN5VUqqxqeobIrKvqn4UyWTWUcZmsj/BbuLLxJJR7tSU\nrD8Jz0TcDluOZ7m1LozNxtJoUZxJRCbR5EsGMxJXJuWAz8VS4vuq6iYicjTwT1VNi/veJrqWOGl+\n6tDvEnLMNlW1QUTmRTJzsTM50wiNnggpWnVvzjbLhv+VyONPz6lHNURHAUY0kL6B2xn1qCp0dEMe\nujMNgf50sWp9O2GxvY3A0iIyXtMzB9NOdUlL/67XWEF7Vf1XGSP3bxHZUFVfiPrzf9hsshkicjEW\nsjY0emqYiEzVlDC0yG9fqgq3Oqb495EI2ZMcmYiqumpG/0uUQgy3w/ye47AZ0lZkG771kr5RsVoe\nScZgK7GSe+VRzMC1CPlS1RZ+cxEZmtJm6HcJgaeki8i12ArxJSxz8yK1mOi4zPZq8d9rk268kjO4\nikWrVPW16M/pWDGmR8UyRdenuVumRFD4X0SQPz2nHj0CvCh25FkXLP766hS5zqhHVaGjG/Lggvg5\n/Ol3YMuyW2mqOXw3KVlhqvqHaODYCDNO52ds0nwZ+QHH0XRE1NSMa9oFOFJEZmM3dc/o+vam+cCz\niQbWUY5mRtthyvYpNmNL2yQ6CBgfzcoGiMi6qlrKgtwyaitz2R71o1Qz4qFI/mhVjX/Ot4vIg4n+\nLdzEjIxs/DCAtE3MerV6I6X3fFMyzq0UyyY9kebut6WwWPJ4v0O/yzyzzfuAQ+N+cRHZR1VvjMks\nHv0elPZeKYQWrQKbre4Z3efrYi6DG2mZgJIneaeiPz0iWI+i51bBPoszgFs0/aDmTqdH1aKjG/Lg\ngvgS7k//Wu1U9BIvikhqJbLIl/gbmiJhto98kEm3wVCsIP5p2LLxBTLOrVTVZdOeTyFPHeVjMSU6\nT1WnR/KDU+QOw673r1hhpOdEpEFVD44Zo7zL9iVEZFvMV9kAbIj5BOPk3cT8UqzmSG+xzakdaV6g\nKM7lWKGni7DypDtiMejNiPyve1P5uyxli5ZOSV8MOyX9J9ryKK+JwC0ikhxEFhryklHXwCxMbBZY\nWjn8DzMCWZu5cyN3yAlYDfhPJb3yZJ7knYr+9IhgPVLVn4tFf30XM+qni8iKqprMNxhK59WjNtGh\nDbmGh/9BuD/9xejG/wdNNYf/J7YZkgzqvxW4kMpHOs3EZmdP0hRLvB4pKbsish12w2YeZxVxKFZH\nubSD/ybZdZR/jRUd2j6axXYHTqblQbfrqOoRInIUcK2qXirNMxYBdhCR7ckm6QrYG1t+XxBdz/9I\nZDdq0ybmg5GLbCHREj3Jvtj3OBnL2Hw+2WaMOar6hIjMjZblL4nII5jPMs6tmIugUslXsOX1IwGy\nYwgYRKByFmaJ6LMqFa2aWzIoGcwTO9x3E+AIsYJMLXRaw8P/INCfTg49EistvAlWR2ZxrG78nSlt\ndmY9ahMd2pBLYPhfRKg/fcPo9y8Sz19By6D+t4DrtfLBqXnKWl6C3UhlB4doqRZaYe0OTAm2BO7H\n/L9npcj1EJFlMGXdMVoyL56QKfn7NsLcAU9iirolpoDJfr5B7MAFsdT6PwAHprz/WSJSq1bKsysW\n0rY7pqxxZkfXUXrvTIUG5kRK/UHkPngPK7iV5GO18qshTFHVkwPkQgcRCMxaFJFDsM+uP+ZaBEDT\nz8LcDdgaOE1VF4jIfJonqZTaDAr/iwj1p+fRo3HY7Ppy4O+aHarXafWorXRoQ06+kLEgf7rmq818\nG/CKiLxO85Cx5HI8T1nLV4Fny6wsWsMAVd1JRMZFM4XFgT/SMjnjCmw5PVZVPxGRc2lZiP8KsBmP\nqi7c4BI7xLZZ0abo+f2xFdAgzJ/clXRDBjZrO01EfosZqnuxWVqSPAq9B+bOOBybxa+DrRKSvBT5\nfZ+i+XeZ5l54XEQOS5FNpmCHDiIQnrV4GObWCDnYtx/mOjwoWrmCxUEnXUBB4X8RQf70nHo0AHMp\nbQZcLSL9MR9zMgyw0+pRW+nohjxPyFieA2ZDORdzrVQ6PzJPWctHsOJRbydk00KsQukhIisA9dES\n8mMsmqAZagkt8Vjo08usNoaIyNraVHtjVdLT5A/Glp5/VatYuB3ZJ7TPw9Lna7Hv9CtsqZ8kWKHV\nokRKkSJJAxZn6ej3jrHnsvzEpc3bXRKyye9oD2wTtzSIfI/0QQTCsxb/jc30Q+pnh7qAQsP/IJ8/\nPZQGbJD/CvgaKzSWVuemM+tRm+johjw4ZCynPz2UNzVxYkkGwWUtMZ/qXoSdPh5ScgDMR70Blqr+\nV2ymVvFwgQouo2OAa6MbuwHbxU8zPF9HvtTuItJFVe+PVlBp39NzwEhVPT2aFR6HrbA2TsjlUegg\nIj9xP8KO5xqpqsnIm+Thw2Auv60jl81wETmZjBPgNTxr8XXsLMzPaV52Nc21EuoCCg3/y+tPD+VN\nrBTDk8AFqpqVHFN1PZLwkgPwzepRm+jQhlxzhIzl8afnMJCTxUKVXqS5QUnGoAaXtcSM0zhVLVvs\nXsJLDqCqj4nIctFns4qUT3oIQlUfI93tkeQFsdPkH8VcEh9jm3lpbKnRCfHR9V8kIrelyOVR6CBE\n5I/YOYmlLMnSZtpGMZlSnfEjRSTuIqnFsjuTfb2J5gbxdWzT/acp7/9TbHVXWhl8KCInavOj28A2\nFtciYKAn3AUUGv4X7E/PYyBVNS1XII2q6ZHkLznwTetRm+jQhlzCw/8gPJ0+2EBiM4iQ3eU8ZS1r\nsVTt18hI1Y4ILuEb+d0G0zzpYYqqnpgiGzqIhTICmKqqc6PPfhApyTMRy4hV8itlbB6Dfb7JzZ88\nCh3KBtjRceVmT/E644NonoE6NEW+p6r+ufRAVR+KDGEalwB7lpbY0fdwMzbpiPMcMDnQtRLkAtLw\n8D+o4E9vjYHMQdX0SFtRwjdUj3LO8hcJHdqQEx7+B+H+9Dw1zm+sLAXkK2uZlU2YJE8J3001IOkh\n5yAWyu1qp/6EDAiX0zxj82+kZ2zmUehQnseMc2bFumgmdqNYfPAK2pQ1+CPSS/B+KFZR8RnMZbE1\nLU8mKvFZzE+Kqr4uIuNT5FaJ2n2P5q6VtCJkW0X966ZlKjBKePgfVPCnt8ZA5qCqeqT5S/iW1aN2\nHsTaREc35KHhfxDuT696jfNok2+xyFc8ECuVmnZuJIQXus9Twjc06aHiICZRHHAWKcZ0ogSeaEN4\nxmYehS6LNFVn7Aq8JyLvUsFAYquMCTSlfG+BuXv2SciVnvtx1Oaz2BmjaXwUDaSPYUZ/c2B6ZGjQ\nplOSfpvj2rbE7vEewBoich7wpKo+mhAdR1j4HwT401thIINoJz0KLuFLBT3KM4i1Qo/aREc35KHh\nf3n86blqnIcgIpdjN//DRJlemPE4OEU8qNC95ijhi4WsXSlmIRuwsp5pSQ8hg9gVZV5LM6ZpdW+y\nCMrY1HyhbZVoTXXGFVR1YfSJqp4Zc9nF6YHN8J+PHnfBIlmS5W7BVkGfYCVQoelw6rqE3JI0dyeW\nSHMnDse+j1Lo22VYaFvSkIeG/0G4Pz2PgQyiPfSI8JIDEKBHOQaxvHrUJjq6IQ8N/wv2p+c0kKHE\nM72u0/RMrxJBhe4lR7Lv26oAABDuSURBVAlfVX2FsKSHioNYOSMqVpgp+d6h7idonrF5MmYAF85y\nReQeVd1RmqokJt8r92Crqh9Gbd+lqs2MejTrTEbMADSIyDbYDLsLpnRpm2r/AD6geaRK6upRVc8O\n7HIed+J8VZ0iUWEpVf0iY4UTGv6Xx5+ex0CGUnU9IrzkQB49qjiI5dWjttLRDXlo+B8EKkAeA5mD\nHlI506tEaKH74BK+InIGFsvcjKThyzOIidXNGI751ME+p0+w0KzWcnTaJhGRb1FVSzHeP9P0krW5\nETt55yRgHRH5gqZZbheaZsVJ9gHOAy7GQvD+Tfpm5zxNrz3eFvK4Ez8QkeHAILHDMHbAQv2ShIb/\n5fGnBxvIHLSHHoWWHAjWI3IMYu2kRy3o6IY8NPwPwhUgT43zUCpmesUILXSfp4TvzsBKlSIdcg5i\nZ2HL1BuxmfzONCXe5KIVm0QjROSnWiFEM4TYZzdMVUcE/s9HxHzVkl124IFIUZ+m+f2ZdapNCMHu\nROy+LZ0dujHmVvlzUkjDw/8g3J8ebCBz0B56lKeEb5AekW8QO4sq6VHZDlW7wSoTGv4H4QqQx0AG\noTkyvTS80H1wCV8qHCMWI88gNltVPxBL8pkCXBUtc9PivsvSikiH2cA7YqFl8Q3UtpT+vEtErscG\njtLm2JmaUj9bwssOHExLHUo71KIUxrcrzU/+0ZRok2B3InaIxQyaTsnpjhnYNB99KKH+9DwGMoh2\n0qM8JXxD9SjPIFY1PSpHhzbkOf2voQqQx0C2inKrAgkvdJ+n5EAXaHaMWCkiI2n48gxin4rVRHlF\nRG7BfMGt3hBW1fHRNYQQNHPOyTXYhtRxmMHbEjsvNK30alDZgZKrSkQGAA1avlLh1VQ++QfyuROf\nxGqmZ5X4bQ2h/vQ8BrJVVEmP8pQcCNWjPINYVfUoiw5tyHMSqgDtUZMlD5toQMy35is5EFrzOM8g\ntg/m17sNW74Pws4nXBQ8Q8rstY1tdtXmJXRvF5G0Co0QWHZARH6MrTC+xg6EaAAOyojyqHjyT0Qe\nd+IUVU2GRLaVUH96e9RkyUOoHuUpORCqR3kGsUWiR98mQx6kADkNZDASnjEZFPMt+Ur4hsbU5hnE\n7ohFedwU9SkryqPahM5e8zAvMpzjaDp9JutIutCyA8OxsgMTYWFt9bFYlcckoSf/5HEnXi8Wspes\nSdNq10qoPz2ngQxC8mVMhupRnhK+oXqUZxBbJHr0bTLkQQqQ00AGIfkyJkML3ecp4Rsam15xEEuJ\n8ijRhezkjGoTOnvNw37Y53Mq9t28AOyfJqiqx4lId7UDmJ/ANocfSxGdF/exq+rHYjXB0ziFppN/\nSvsCae9/BDYY3Jbmv09wIuZaiRvfkGiXNpPTQFZqqzUZk8F6RHgJ31A9qjiILWo9+tYY8hz+9DwG\nMpQ8af+hhe7zlPANjU2vOIi1JsqjHchzbmUQ0SB2HnbdDcDLWYZSRNYBzoyMRCNmJN7GEkTivC8i\nV9B8lv9eRhcWx0L6BmADwJcZcttj0RDXRIPuncDdqjojRXaSqu6V0U57k8dAlqUVm+F59ChPCd9Q\nPao4iC1qPUqLu/y2k8dAhvK0iKzVxjaSJEsOvAJclyHbVewQYqBsTO0OqroZTQfaHoMpZBovishV\nsTbvFpHQov9tpXRu5YZi51b+hTbWsRA7luzPmJvmF8B9YqfxpHE99llvgZULuJX0s0UPwo522xxL\nnvkn2dELO2EDwihgSxFJHZhU9VNVvVJVt8EyK3+HDRjXi8iQhPhLInKuiGwnIr8s/WS8f7XJYyAr\nEm2GHwp8papPYiGxO9MUKttaSiUHroj06CVsxZNGsB5hg9hRqvo7LJs8a0BdJHr0rZmR5yC4xnkO\nqp72rzlK+BIeU5tnEDuf5nU/DsUM6mbBF9FKVPUpEVkfi5hoUNVW1++IsT3hM8gp2rwe+f0ZG6N1\nQC9VPSpq82QsIqHFTD9yZXXBDP72wMki8p4mEorEYv13x+6pT7CV4gPYYJE8pb50f4UcllFtgmuc\n56Dqaf/kKOFLuB7lGcQWiR51OkOe00CGtpkr7T9kY1RylPDNEVObZxDrqqpxN0Fm1cBqIyJDscy3\naVikTV/gFFXNmkmFkEf5/icif6D5wcITSrPdWHRCcD3y6P8aRGQedt/NxeLAk9wWtftzVZ0ae/4J\nEXkUQER6qJ3WnmZkFhV5DGQoeTImgzZGNUcJ3xx6lGcQWyR61OkMeR4DmaPN4IzJHBujwTU3JDCm\nNucgdnd0oz6PzeQ3peXZhe3F0cC6agkUiEipxnlbDPkdmCviOSorXylLLxkmtivNZ7zB9chF5Frs\nnngJuAe4SO2YuiS7YQWZpkqsIJMaZ0Uy12OhbP+N+lOT+J12mlBVyWMgc1AxYzLvxqjkKOEbqkfk\nG8QWiR51OkNOvqJEoeTJmAzdGM1TcyMopjbnLP9iEfkLphz12MEI/QL6Ug0+pcmPDzCF7E3EIFT1\nMhG5j6bMzgs1KqiVIpt6LFxpphYjWY/8R2TXI78PODSaSQMgIvukbNKHFGTaI/pd1US2POQxkDmo\nmDHZio3RcYSX8A3So5yz/EWiR53RkOcxkKHkyZgMrYeep+ZGaD3yPLP8xTHjUVplrI8lN7SluFgo\nM4BXReRJzEBugh20ezFkJseUJc8gJiI3Y+6UUthYi2PhIuL1yBdgG59Z9cgnAreISHzVthQ2CYiT\n5l7oGhcQkQ/Idgs1qOqqGa9Vk3GEG8hQgjImNV899DwlfENj0/PM8heJHnVGQ57HQIaSJ2MydGM0\nT82N0JjaPIPYnVhY5u7YKT5bkFIZrp14JPop8UKWYA7yrMRWV9UVKwmpFfW6NvqpxBgslvwi7LvZ\nETP8SdLcC30TMmtjBuYULCZ5HE0rgtUD+lIN8hjIUPJkTIZujAaX8CVcj8YRPogtEj3qjIY8j4EM\nJThjMsfGaHDNjRwxtXkGsS5qBypsoaojReT3mJ/5vpA+tYUUd0M1yDWIRb7YV2n+OSVdK3mYo6pP\niMhcVX0J89c/QstiXCHuhdkAIrKZqp4Se2msZNfvrjZ5DGQoeTImQzdGg0v45tCjPIPYItGjzmjI\n8xQlCkJzpP3n2BjNU3MjlDyDWHexxJg50UDzPrAoluztRZ5BbH1sUI7P3tNcK3mYI1Z864No1vke\nsHxSKNp8vjT2uFxBprli9dyfxQzrhjSFmLY3wQYyFM2X9h9USlbzlfANJc8gtkj0qDMa8qobSMmX\n9h+6MZqn5kYoeQaxw7Ad/BOxEMUlaHu8/TdJnkFsVVVtYWTbyB7YCe2HY9/994C9y/5HZXbGZvBb\nYq4WpXlMebvRHgZS8qX9t0c99FDyDGKLRI86oyFvDwOZJ+0/aGM0r3tBwop25RnEtlHVC6K/q3q+\n4DdEnkHsLhHZGvODVuvAiD7A1qr6J2C4WPLQpxX+pyxR+GLulPgOTJ60/6rXQ88Rm55nEFsketTp\nDHk7+V/zZExWvR56jtj0PIPYktFS8AWabzy1xZh9k+QZxA6k5eyurfHZuZKHOil5kraqVg89b2x6\nThaJHnU6Q95O5MmYbI966EGx6TkHsW1oWYdlkSSbtBPBg1gpfE/CDowIJTh5qBOTJ2OyavXQWxGb\nnodFokc1jY2LpOrltx4RWZGmjMmXy2zSICLLYhujT5fbGM3x3hcDNwbEpoe0tbOq3i0iu8UNT9ER\nqxU+JIo7/i22oXmlqrY4sEISB0ZgCp11YETo+9+K+edLyUNbA721FQdDRJEamYrbxg3xb4woimdj\nLAHsDCwSJPMzz7ExGvr+y5MSm552jwS0tUj1yGfkVSBnsknV66FT3aJdF4idZH6YWGp8M1T1D23o\n5zfJLTTFHe9L+YJMeQ6MCCWePFSP7aFkJQ9V4o0yrxVWp/NkTObcGA2lmkW7FqkeFfZL72DkSTap\nej30HLHpIRyIxdJ2x8Kqvi0EF2Qi34ERofTACiY9Hz3ugkWy5D7NJ+4iEyufXApl7YGVyQ1JUOpw\n5MmYpIr10GPkuUcqsUj1yA15dciTbFL1eug5YtMrolYL+kkRuUdVy838ikZQ3HFEngMjQvkHdvBu\nPFKlTX5NEfkjdjrQGtim3/rAxW1p8xtmHOEZk1Wthx6R5x4py6LWIzfk1SFPskl71EPPU7QriG+Z\nEYd8cccHYa6yzWmK/mlreNs8TdQerwJrqeoPRGScqv4qcgGdXuX3WJTkyZhsj3roVY9NX1R65Ia8\nOgQnm2g71EMnX9GuTknOjMngAyNy8IBYPfOnqV5seq1YlUZEpC5yAa3Thva+afJkTLZHPfSqx6Yv\nKtyQV4fgZJM8G6M5qHpsetTXkCSjbyPtEfN9MC31ra1haJdj9csvB/4T+fEXVa2V9iBPXZT2qIde\ntdj0EqFJRm3FDXl1yJNs0h710Ksem54jyejbSNVjvksb0lWOTX9bVV+M2r0fq5JY2Bl5nozJnBuj\noVQtNr2dk4xa4Ia8OuTJmKx6PXTNUbQrB6EHYHwbyXNgRBDJ2HQRaXVsuoisCghwfuT2KVGLhcut\n2Ja+FoRxVLkeuuYr2lWprfZMMmqBG/IqkDNjsur10NspNj30AIxvI3kOjAilmrHpPbFklSWx4+dK\nNABnta2bhaHq9dCrHZuu+Q7AaBNuyBc97VEPveqx6VQ3yahQaL4DI0KpWmy6qv4H84nfDUwvzRpF\nRFqThVhQ2qMeenvEplczySgTN+SLnqrXQ6cdYtOrnGTktE9s+m+x0rhDo8fHi8gUVT2xje0WgarX\nQ6d9YtOrmWSUiRvyRU97HBhR9dj0aiYZOUD7xKZvqgGHBX8bybMxmoP2iE2vWpJRObpUu0GnIk9i\nS/bXgP/GflpNVLdhP2wn/BHgJ22sOQEWbvcmli34EFHhqDa22ZlZGJuuqkcDK2A+7rbQNUrRB8oe\nuu2EUYpN/zkW7394FWqi7AXMIUoywsJNq34Ahlc//BaQEpsOtHkD9TFV3VpEnlTVLaLnHlbVX7a5\nw50QsbM0ry6FNYrINsBRqtrq2HQRWRfztwo20P43arMzblBXhURs+g5YJFirY9NF5M54klF74a6V\nbwftEZveLklGnZj2iE1vcViwiJxGG1d4nZV2ik2vepJRGm7Ivx1UPTad9jkAozPTHrHpv8S+m4HR\nU92xBK5zM//JKcc4qhybThWTjMrhhvzbQdVj09spyagz0x6x6WdhceQ3YuGiOwMz29hmZ6bqsenV\nTDIqhxvybwdVj01vpySjTks7xabPVtUPRKSLqk4Brop88bdV8T06E//f3h2jNBREURg+i7CyEEHh\nYGFr5WaCCxE34BIUQXALFhauQOvpRLDMHiwmImRmQMO9ie/5f2XSTJPLI++eOeG76dEhoxEG+Txk\n7KZnhIwQ68O1tu7V9r3qfeezD2wlythNzwgZNRjk85Cxmx4eMkK4hepLuQfVtqE91X1lbCBpNz0j\nZNTghzkPv7m066cyCjAQ6+lrNVQbVMZhKzJCRg32yDFk+1DfBRgvGS9psDnbd6rXoq6vtk21IHt2\nbD+qDu+lagHGW0ABRoNBjq6MkBFi2b7sfV5Kudr2WTAWHTLq4a8VjGSEjBDA9m0pZSHpoJRysevz\nYCwpZNRgkGMkI2SEGCer0oIj26frX5ZSznZwJvQ9Kz5k1GCQYyQ8ZIQw55L2JV0ruDIM4cJDRj0M\ncoxkFGAgwCpc9K4a2MLfllGA0WCQYyQjZAT8NxkhowZbK+iyfSPpWLEhIwAJeCLHSEbICEACnsgB\nYOKoegOAiWOQA8DEMcgBYOIY5AAwcZ8vZRV2zKQddQAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "metadata": {
        "id": "qg-qu3LwGYMp",
        "colab_type": "code",
        "outputId": "80c84784-c395-4fd2-dba0-76f455d7d2f9",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 358
        }
      },
      "cell_type": "code",
      "source": [
        "plt.plot(X_binarised_test.T,'*')\n",
        "plt.xticks(rotation='vertical')\n",
        "plt.show()"
      ],
      "execution_count": 354,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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PPZYOFB+se3ZoztVH7catp+9Ljw7N0q+nyWLOPWgfrjnhl6zTJP07gvDvqXeX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\nOrVuOPTzAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "metadata": {
        "id": "uSsBM8PfG3dF",
        "colab_type": "code",
        "outputId": "11fa97d6-45f3-416c-8433-514633f67c06",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "cell_type": "code",
      "source": [
        "type(X_binarised_test)"
      ],
      "execution_count": 355,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "pandas.core.frame.DataFrame"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 355
        }
      ]
    },
    {
      "metadata": {
        "id": "zUG_ZhxhG9XF",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "X_binarised_test = X_binarised_test.values #converted to np array from dataframe\n",
        "X_binarised_train = X_binarised_train.values"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "7j1ceg59HKTh",
        "colab_type": "code",
        "outputId": "dbf9ad6a-9ec8-4dfe-cad3-f72c35e1832d",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "cell_type": "code",
      "source": [
        "type(X_binarised_test)"
      ],
      "execution_count": 357,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "numpy.ndarray"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 357
        }
      ]
    },
    {
      "metadata": {
        "id": "9XOxVKajIBH8",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "#MP Neuron Model"
      ]
    },
    {
      "metadata": {
        "id": "yk4SmyqGMjgT",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "from random import randint"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "Fk8rEj11HNA7",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 69
        },
        "outputId": "4ef81b9b-1640-4fd4-a162-846c1a3874df"
      },
      "cell_type": "code",
      "source": [
        "b = 3\n",
        "i= randint(0,X_binarised_train.shape[0])\n",
        "\n",
        "print('For row ',i)\n",
        "\n",
        "if(np.sum(X_binarised_train[100,:])>=b):\n",
        "  print('MP neuron inference is malignant')\n",
        "else:\n",
        "  print('MP neuron inference is benign')\n",
        "  \n",
        "if(Y_train[i]==1):\n",
        "  print('Ground truth is malignant')\n",
        "else:\n",
        "  print('Ground truth s benign')\n",
        "    "
      ],
      "execution_count": 359,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "For row  152\n",
            "MP neuron inference is malignant\n",
            "Ground truth is malignant\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "OhQPeksmMTjc",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "94a5acf3-4632-4fce-979b-d73063e88094"
      },
      "cell_type": "code",
      "source": [
        "b = 3\n",
        "\n",
        "Y_pred_train = []\n",
        "accurate_rows = 0\n",
        "\n",
        "for x,y in zip(X_binarised_train,Y_train):\n",
        "  y_pred = np.sum(x)>=b\n",
        "  Y_pred_train.append(y_pred)\n",
        "  accurate_rows += (y == y_pred)\n",
        "  \n",
        "print(accurate_rows,accurate_rows/X_binarised_train.shape[0])"
      ],
      "execution_count": 360,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "321 0.626953125\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "Ge_UvgOfPG5F",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 555
        },
        "outputId": "061d257b-0dce-4fc5-fe53-6c623d7ba01b"
      },
      "cell_type": "code",
      "source": [
        "for b in range(X_binarised_train.shape[1]+1):\n",
        "  Y_pred_train = []\n",
        "  accurate_rows = 0\n",
        "\n",
        "  for x,y in zip(X_binarised_train,Y_train):\n",
        "    y_pred = np.sum(x)>=b\n",
        "    Y_pred_train.append(y_pred)\n",
        "    accurate_rows += (y == y_pred)\n",
        "  print(b,accurate_rows,accurate_rows/X_binarised_train.shape[0])\n",
        "  \n",
        "  #if b=0, y_pred=1, and since 62% of our data is 1, @b=0 the accuracy of our model is 62% as seen below\n",
        "  #Also the model isn't performing well. Since the features of malignant class have lesser magnitude than benign class, they should be binarised 0:1 \n",
        "  #After flipping accuracy increases to 84.9%(b=28)"
      ],
      "execution_count": 361,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "0 321 0.626953125\n",
            "1 321 0.626953125\n",
            "2 321 0.626953125\n",
            "3 321 0.626953125\n",
            "4 321 0.626953125\n",
            "5 321 0.626953125\n",
            "6 321 0.626953125\n",
            "7 321 0.626953125\n",
            "8 321 0.626953125\n",
            "9 321 0.626953125\n",
            "10 321 0.626953125\n",
            "11 321 0.626953125\n",
            "12 321 0.626953125\n",
            "13 321 0.626953125\n",
            "14 323 0.630859375\n",
            "15 324 0.6328125\n",
            "16 329 0.642578125\n",
            "17 332 0.6484375\n",
            "18 336 0.65625\n",
            "19 340 0.6640625\n",
            "20 344 0.671875\n",
            "21 352 0.6875\n",
            "22 359 0.701171875\n",
            "23 371 0.724609375\n",
            "24 387 0.755859375\n",
            "25 402 0.78515625\n",
            "26 419 0.818359375\n",
            "27 433 0.845703125\n",
            "28 435 0.849609375\n",
            "29 417 0.814453125\n",
            "30 389 0.759765625\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "-pTyFXudUaW2",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "from sklearn.metrics import accuracy_score"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "Zpc_z3OQQR73",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "3437b298-0cc6-43d8-9dc1-768097f75906"
      },
      "cell_type": "code",
      "source": [
        "#Evaluated on test data\n",
        "b=28\n",
        "Y_pred_test = []\n",
        "\n",
        "\n",
        "for x in X_binarised_test:\n",
        "  y_pred = (np.sum(x)>=b)\n",
        "  Y_pred_test.append(y_pred)\n",
        "  \n",
        "accuracy = accuracy_score(Y_pred_test,Y_test)\n",
        "print(accuracy)"
      ],
      "execution_count": 363,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "0.7894736842105263\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "5PYQVvPuV_oa",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "#MP Neuron Class"
      ]
    },
    {
      "metadata": {
        "id": "3Xex2qxBUDMi",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "class MPNeuron:\n",
        "  \n",
        "  def __init__(self):\n",
        "    self.b=None\n",
        "    \n",
        "  def model(self,x):\n",
        "    #x is the vector of numbers\n",
        "    return (sum(x)>=self.b)\n",
        "  \n",
        "  def predict(self,X):\n",
        "    Y=[]\n",
        "    for x in X:\n",
        "      result = self.model(x)\n",
        "      Y.append(result)\n",
        "    return np.array(Y)\n",
        "  \n",
        "  def fit(self,X,Y):\n",
        "    accuracy={}\n",
        "    for b in range(X.shape[1]+1):\n",
        "      self.b=b\n",
        "      Y_pred = self.predict(X)\n",
        "      accuracy[b] = accuracy_score(Y_pred,Y)\n",
        "    best_b = max(accuracy,key=accuracy.get)\n",
        "    self.b=best_b\n",
        "    print('Optimal value of b is ',best_b)\n",
        "    print('Highest accuracy is ',accuracy[best_b])\n",
        "      \n",
        "    "
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "HGijwCpeagmP",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 52
        },
        "outputId": "e6ee3e45-e960-4b2e-dc36-9b7a0ef9c56d"
      },
      "cell_type": "code",
      "source": [
        "mp_neuron = MPNeuron()\n",
        "mp_neuron.fit(X_binarised_train,Y_train)"
      ],
      "execution_count": 365,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Optimal value of b is  28\n",
            "Highest accuracy is  0.849609375\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "vMD58ucRavZf",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "Y_pred_test = mp_neuron.predict(X_binarised_test)\n",
        "accuracy_test = accuracy_score(Y_pred_test,Y_test)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "OUmV0SN-hZof",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "4ff5bc90-a8fe-47b2-90c2-afc75c92e476"
      },
      "cell_type": "code",
      "source": [
        "print(accuracy_test)"
      ],
      "execution_count": 367,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "0.7894736842105263\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "M52nPodrl4n9",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "#Perceptron"
      ]
    },
    {
      "metadata": {
        "id": "86czPbaMmLqE",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "$y=1, \\mbox{if} \\sum_i w_i x_i >=b$\n",
        "\n",
        "$y=0, \\mbox{otherwise}$\n",
        "\n"
      ]
    },
    {
      "metadata": {
        "id": "fE7cH_r17NNX",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "X_train = X_train.values #converted to numpy arrays\n",
        "X_test = X_test.values"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "kri1zOvHtHul",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 265
        },
        "outputId": "2448919c-7e5e-4a77-d88e-e2945332c9a3"
      },
      "cell_type": "code",
      "source": [
        "plt.plot(perceptron.w)\n",
        "plt.show()"
      ],
      "execution_count": 369,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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432edr/PllmVPIFv2A+R8tsjlH7OCn1XioSdbb0gJag8OZ6bn+Y9nxzlvY+/i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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "metadata": {
        "id": "tT7V_WuShnKr",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "class Perceptron:\n",
        "  \n",
        "  def __init__(self):\n",
        "    self.w=None\n",
        "    self.b=None\n",
        "    \n",
        "  def model(self,x):\n",
        "    return 1 if(np.dot(self.w,x)>=self.b) else 0\n",
        "    \n",
        "  def predict(self,X):\n",
        "    Y = []\n",
        "    for x in X:\n",
        "      result = self.model(x)\n",
        "      Y.append(result)\n",
        "    return np.array(Y)\n",
        "    \n",
        "  def fit(self,X,Y,epochs=1,lr=1):\n",
        "    self.w = np.ones(X.shape[1])#1 means the number of features which will be same as number of weights\n",
        "    self.b = 0\n",
        "    accuracy ={}\n",
        "    max_accuracy=0\n",
        "    weight_matrix = [] #will be used for animation\n",
        "    \n",
        "    for i in range(epochs):\n",
        "      for x,y in zip(X,Y):\n",
        "        y_pred = self.model(x)\n",
        "        if y==1 and y_pred ==0:\n",
        "          self.w = self.w + lr*x #lr is learning rate, our second hyperparameter\n",
        "          self.b = self.b + lr*1 #can't use += due to inconsistent values\n",
        "        elif y==0 and y_pred==1:\n",
        "          self.w = self.w - lr*x\n",
        "          self.b = self.b - lr*1\n",
        "      weight_matrix.append(self.w)\n",
        "      accuracy[i]=accuracy_score(self.predict(X),Y)\n",
        "      if accuracy[i]>max_accuracy:\n",
        "        max_accuracy = accuracy[i]\n",
        "        checkpointw = self.w\n",
        "        checkpointb = self.b\n",
        "        \n",
        "    \n",
        "    self.w = checkpointw\n",
        "    self.b = checkpointb\n",
        "    print(max_accuracy)\n",
        "    plt.plot(accuracy.values())\n",
        "    plt.show()\n",
        "    return np.array(weight_matrix)\n",
        "  "
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "BmYd0XptsWLG",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "perceptron = Perceptron()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "UVs0W5B6sjL5",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 282
        },
        "outputId": "1ccef295-1bd2-4a82-abc8-b339134b72cf"
      },
      "cell_type": "code",
      "source": [
        "wt_matrix = perceptron.fit(X_train,Y_train,100,1) #No. of epochs=1000 is called the Hyperparameter(1) of the model, Learning rate is other such hyperparameter"
      ],
      "execution_count": 372,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "0.927734375\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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lFl2AyWatSGlFCGE3qQT5duAeAKXUOqBZax37NrIe2D/DbUtITggSQojxJk09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\nOAlyIYSwuP8PSLecOwOuFUAAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "metadata": {
        "id": "VpVCJ_kYdReU",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "5668a509-f82f-4bab-8c3e-162af5d98937"
      },
      "cell_type": "code",
      "source": [
        "type(wt_matrix)"
      ],
      "execution_count": 373,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "numpy.ndarray"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 373
        }
      ]
    },
    {
      "metadata": {
        "id": "4rLVi2g6tOjM",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "a2d641e1-24d5-4d00-acf2-525d13c6904e"
      },
      "cell_type": "code",
      "source": [
        "Y_pred_train = perceptron.predict(X_train)\n",
        "print(accuracy_score(Y_pred_train,Y_train))\n",
        "#Getting 37% accuracy, noting that we're doing it just one time through the entire X_train, i.e we're changing weights just once we need to do it again again until we get convergence\n",
        "#We'll use epoch for that"
      ],
      "execution_count": 374,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "0.927734375\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "LudKnJo-uDJX",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "7548628b-0855-45d7-9070-77b291509894"
      },
      "cell_type": "code",
      "source": [
        "Y_pred_test = perceptron.predict(X_test)\n",
        "print(accuracy_score(Y_pred_test,Y_test))"
      ],
      "execution_count": 375,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "0.9473684210526315\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "9ImFgNsnuRAn",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 265
        },
        "outputId": "1a490f2c-5db9-422c-8424-768cf1eb9ced"
      },
      "cell_type": "code",
      "source": [
        "plt.plot(perceptron.w) #shows how weights are applied on our features finally after training\n",
        "plt.show()"
      ],
      "execution_count": 376,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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432edr/PllmVPIFv2A+R8tsjlH7OCn1XioSdbb0gJag8OZ6bn+Y9nxzlvY+/i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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "metadata": {
        "id": "8ITjCHt9ehzu",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 265
        },
        "outputId": "b7084e36-a41d-430c-e329-f574999fd39e"
      },
      "cell_type": "code",
      "source": [
        "plt.plot(wt_matrix[-1,:]) #last row of weight matrix\n",
        "plt.show()"
      ],
      "execution_count": 377,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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UTCjVWBocluD1eAozl5rVawB7E7M9Vw4wEU9h3tTtNFRrZLI5jrwyQl80xFWX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\nypQda5R3VwAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "metadata": {
        "id": "CG2hDEKZbFWo",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "#Creating Weight animation"
      ]
    },
    {
      "metadata": {
        "id": "C6e48WMKahPt",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "%matplotlib inline\n",
        "from matplotlib import animation, rc\n",
        "from IPython.display import HTML"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "aXAmHbWHbLtx",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 269
        },
        "outputId": "581e07a4-348a-444c-97ba-acf4225320a8"
      },
      "cell_type": "code",
      "source": [
        "fig, ax = plt.subplots()\n",
        "\n",
        "ax.set_xlim(( 0, wt_matrix.shape[1]))\n",
        "ax.set_ylim((-15000, 25000)) #as per the range of weight_matrix\n",
        "\n",
        "line, = ax.plot([], [], lw=2)"
      ],
      "execution_count": 379,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "metadata": {
        "id": "g2S6srDLbWly",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "def animate(i):\n",
        "    x = list(range(wt_matrix.shape[1]))\n",
        "    y = wt_matrix[i,:]\n",
        "    line.set_data(x, y)\n",
        "    return (line,)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "IA1NhXXLbdzR",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "anim = animation.FuncAnimation(fig, animate, frames=100, interval=200, blit=True)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "0RHXh3_Pbgz8",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 309
        },
        "outputId": "895eaaae-03e0-4e38-88e7-68eab0f7abaa"
      },
      "cell_type": "code",
      "source": [
        "HTML(anim.to_html5_video())"
      ],
      "execution_count": 382,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<video width=\"432\" height=\"288\" controls autoplay loop>\n",
              "  <source type=\"video/mp4\" src=\"data:video/mp4;base64,AAAAHGZ0eXBNNFYgAAACAGlzb21pc28yYXZjMQAAAAhmcmVlAACNcW1kYXQAAAKtBgX//6ncRem9\n",
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              "  Your browser does not support the video tag.\n",
              "</video>"
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 382
        }
      ]
    },
    {
      "metadata": {
        "id": "fqbEvhwGhDqt",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    }
  ]
}