colab-notebooks/04_MPNeuron.ipynb

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    "colab": {
      "name": "04_MPNeuron.ipynb",
      "provenance": [],
      "collapsed_sections": [
        "UgTAl0CULJ1Q",
        "cLykHjbhLNcn",
        "Tq5tyuaeNocz",
        "KyoV5wnLPLMn",
        "qtoG36F6Vbhw"
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      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
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  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/satyajitghana/PadhAI-Course/blob/master/colab-notebooks/04_MPNeuron.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ser0hOsOMi6P",
        "colab_type": "code",
        "outputId": "b880185b-1fb3-49f0-d830-6e1595e224d3",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 102
        }
      },
      "source": [
        "!pip install sklearn"
      ],
      "execution_count": 48,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Requirement already satisfied: sklearn in /usr/local/lib/python3.6/dist-packages (0.0)\n",
            "Requirement already satisfied: scikit-learn in /usr/local/lib/python3.6/dist-packages (from sklearn) (0.22.2.post1)\n",
            "Requirement already satisfied: joblib>=0.11 in /usr/local/lib/python3.6/dist-packages (from scikit-learn->sklearn) (0.14.1)\n",
            "Requirement already satisfied: numpy>=1.11.0 in /usr/local/lib/python3.6/dist-packages (from scikit-learn->sklearn) (1.18.2)\n",
            "Requirement already satisfied: scipy>=0.17.0 in /usr/local/lib/python3.6/dist-packages (from scikit-learn->sklearn) (1.4.1)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ELUQAoDTJFNq",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import sklearn.datasets\n",
        "import numpy as np\n",
        "import pandas as pd"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "UgTAl0CULJ1Q",
        "colab_type": "text"
      },
      "source": [
        "# Loading Dataset"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "lnsAyc3xJV4N",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "breast_cancer = sklearn.datasets.load_breast_cancer()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "YUofBk2fJgRP",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "X = breast_cancer.data\n",
        "Y = breast_cancer.target"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "eA2wp9pbJmF_",
        "colab_type": "code",
        "outputId": "56900082-7127-4d16-c172-a5186d647429",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "type(X), type(Y), X.shape, Y.shape"
      ],
      "execution_count": 52,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(numpy.ndarray, numpy.ndarray, (569, 30), (569,))"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 52
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "hQ9bskhcJ6Ro",
        "colab_type": "code",
        "outputId": "3f0ccca2-fe9c-47ee-bc91-e81547fd5383",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 921
        }
      },
      "source": [
        "breast_cancer"
      ],
      "execution_count": 53,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'DESCR': '.. _breast_cancer_dataset:\\n\\nBreast cancer wisconsin (diagnostic) dataset\\n--------------------------------------------\\n\\n**Data Set Characteristics:**\\n\\n    :Number of Instances: 569\\n\\n    :Number of Attributes: 30 numeric, predictive attributes and the class\\n\\n    :Attribute Information:\\n        - radius (mean of distances from center to points on the perimeter)\\n        - texture (standard deviation of gray-scale values)\\n        - perimeter\\n        - area\\n        - smoothness (local variation in radius lengths)\\n        - compactness (perimeter^2 / area - 1.0)\\n        - concavity (severity of concave portions of the contour)\\n        - concave points (number of concave portions of the contour)\\n        - symmetry \\n        - fractal dimension (\"coastline approximation\" - 1)\\n\\n        The mean, standard error, and \"worst\" or largest (mean of the three\\n        largest values) of these features were computed for each image,\\n        resulting in 30 features.  For instance, field 3 is Mean Radius, field\\n        13 is Radius SE, field 23 is Worst Radius.\\n\\n        - class:\\n                - WDBC-Malignant\\n                - WDBC-Benign\\n\\n    :Summary Statistics:\\n\\n    ===================================== ====== ======\\n                                           Min    Max\\n    ===================================== ====== ======\\n    radius (mean):                        6.981  28.11\\n    texture (mean):                       9.71   39.28\\n    perimeter (mean):                     43.79  188.5\\n    area (mean):                          143.5  2501.0\\n    smoothness (mean):                    0.053  0.163\\n    compactness (mean):                   0.019  0.345\\n    concavity (mean):                     0.0    0.427\\n    concave points (mean):                0.0    0.201\\n    symmetry (mean):                      0.106  0.304\\n    fractal dimension (mean):             0.05   0.097\\n    radius (standard error):              0.112  2.873\\n    texture (standard error):             0.36   4.885\\n    perimeter (standard error):           0.757  21.98\\n    area (standard error):                6.802  542.2\\n    smoothness (standard error):          0.002  0.031\\n    compactness (standard error):         0.002  0.135\\n    concavity (standard error):           0.0    0.396\\n    concave points (standard error):      0.0    0.053\\n    symmetry (standard error):            0.008  0.079\\n    fractal dimension (standard error):   0.001  0.03\\n    radius (worst):                       7.93   36.04\\n    texture (worst):                      12.02  49.54\\n    perimeter (worst):                    50.41  251.2\\n    area (worst):                         185.2  4254.0\\n    smoothness (worst):                   0.071  0.223\\n    compactness (worst):                  0.027  1.058\\n    concavity (worst):                    0.0    1.252\\n    concave points (worst):               0.0    0.291\\n    symmetry (worst):                     0.156  0.664\\n    fractal dimension (worst):            0.055  0.208\\n    ===================================== ====== ======\\n\\n    :Missing Attribute Values: None\\n\\n    :Class Distribution: 212 - Malignant, 357 - Benign\\n\\n    :Creator:  Dr. William H. Wolberg, W. Nick Street, Olvi L. Mangasarian\\n\\n    :Donor: Nick Street\\n\\n    :Date: November, 1995\\n\\nThis is a copy of UCI ML Breast Cancer Wisconsin (Diagnostic) datasets.\\nhttps://goo.gl/U2Uwz2\\n\\nFeatures are computed from a digitized image of a fine needle\\naspirate (FNA) of a breast mass.  They describe\\ncharacteristics of the cell nuclei present in the image.\\n\\nSeparating plane described above was obtained using\\nMultisurface Method-Tree (MSM-T) [K. P. Bennett, \"Decision Tree\\nConstruction Via Linear Programming.\" Proceedings of the 4th\\nMidwest Artificial Intelligence and Cognitive Science Society,\\npp. 97-101, 1992], a classification method which uses linear\\nprogramming to construct a decision tree.  Relevant features\\nwere selected using an exhaustive search in the space of 1-4\\nfeatures and 1-3 separating planes.\\n\\nThe actual linear program used to obtain the separating plane\\nin the 3-dimensional space is that described in:\\n[K. P. Bennett and O. L. Mangasarian: \"Robust Linear\\nProgramming Discrimination of Two Linearly Inseparable Sets\",\\nOptimization Methods and Software 1, 1992, 23-34].\\n\\nThis database is also available through the UW CS ftp server:\\n\\nftp ftp.cs.wisc.edu\\ncd math-prog/cpo-dataset/machine-learn/WDBC/\\n\\n.. topic:: References\\n\\n   - W.N. Street, W.H. Wolberg and O.L. Mangasarian. Nuclear feature extraction \\n     for breast tumor diagnosis. IS&T/SPIE 1993 International Symposium on \\n     Electronic Imaging: Science and Technology, volume 1905, pages 861-870,\\n     San Jose, CA, 1993.\\n   - O.L. Mangasarian, W.N. Street and W.H. Wolberg. Breast cancer diagnosis and \\n     prognosis via linear programming. Operations Research, 43(4), pages 570-577, \\n     July-August 1995.\\n   - W.H. Wolberg, W.N. Street, and O.L. Mangasarian. Machine learning techniques\\n     to diagnose breast cancer from fine-needle aspirates. Cancer Letters 77 (1994) \\n     163-171.',\n",
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              "        'mean smoothness', 'mean compactness', 'mean concavity',\n",
              "        'mean concave points', 'mean symmetry', 'mean fractal dimension',\n",
              "        'radius error', 'texture error', 'perimeter error', 'area error',\n",
              "        'smoothness error', 'compactness error', 'concavity error',\n",
              "        'concave points error', 'symmetry error',\n",
              "        'fractal dimension error', 'worst radius', 'worst texture',\n",
              "        'worst perimeter', 'worst area', 'worst smoothness',\n",
              "        'worst compactness', 'worst concavity', 'worst concave points',\n",
              "        'worst symmetry', 'worst fractal dimension'], dtype='<U23'),\n",
              " 'filename': '/usr/local/lib/python3.6/dist-packages/sklearn/datasets/data/breast_cancer.csv',\n",
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              " 'target_names': array(['malignant', 'benign'], dtype='<U9')}"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 53
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8jMMveLtJpTN",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "data = pd.DataFrame(breast_cancer.data, columns=breast_cancer.feature_names)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2UlyTzPrJ-6S",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "data['class'] = breast_cancer.target"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "72S-APuZKBch",
        "colab_type": "code",
        "outputId": "66c0ee9f-5fee-450b-ed97-c51228ce4407",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 258
        }
      },
      "source": [
        "data.head()"
      ],
      "execution_count": 56,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "  <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>radius error</th>\n",
              "      <th>texture error</th>\n",
              "      <th>perimeter error</th>\n",
              "      <th>area error</th>\n",
              "      <th>smoothness error</th>\n",
              "      <th>compactness error</th>\n",
              "      <th>concavity error</th>\n",
              "      <th>concave points error</th>\n",
              "      <th>symmetry error</th>\n",
              "      <th>fractal dimension error</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",
              "      <th>class</th>\n",
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              "      <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>1.0950</td>\n",
              "      <td>0.9053</td>\n",
              "      <td>8.589</td>\n",
              "      <td>153.40</td>\n",
              "      <td>0.006399</td>\n",
              "      <td>0.04904</td>\n",
              "      <td>0.05373</td>\n",
              "      <td>0.01587</td>\n",
              "      <td>0.03003</td>\n",
              "      <td>0.006193</td>\n",
              "      <td>25.38</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>0.5435</td>\n",
              "      <td>0.7339</td>\n",
              "      <td>3.398</td>\n",
              "      <td>74.08</td>\n",
              "      <td>0.005225</td>\n",
              "      <td>0.01308</td>\n",
              "      <td>0.01860</td>\n",
              "      <td>0.01340</td>\n",
              "      <td>0.01389</td>\n",
              "      <td>0.003532</td>\n",
              "      <td>24.99</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",
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              "    <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>0.7456</td>\n",
              "      <td>0.7869</td>\n",
              "      <td>4.585</td>\n",
              "      <td>94.03</td>\n",
              "      <td>0.006150</td>\n",
              "      <td>0.04006</td>\n",
              "      <td>0.03832</td>\n",
              "      <td>0.02058</td>\n",
              "      <td>0.02250</td>\n",
              "      <td>0.004571</td>\n",
              "      <td>23.57</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>0.4956</td>\n",
              "      <td>1.1560</td>\n",
              "      <td>3.445</td>\n",
              "      <td>27.23</td>\n",
              "      <td>0.009110</td>\n",
              "      <td>0.07458</td>\n",
              "      <td>0.05661</td>\n",
              "      <td>0.01867</td>\n",
              "      <td>0.05963</td>\n",
              "      <td>0.009208</td>\n",
              "      <td>14.91</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>0.7572</td>\n",
              "      <td>0.7813</td>\n",
              "      <td>5.438</td>\n",
              "      <td>94.44</td>\n",
              "      <td>0.011490</td>\n",
              "      <td>0.02461</td>\n",
              "      <td>0.05688</td>\n",
              "      <td>0.01885</td>\n",
              "      <td>0.01756</td>\n",
              "      <td>0.005115</td>\n",
              "      <td>22.54</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",
              "</div>"
            ],
            "text/plain": [
              "   mean radius  mean texture  ...  worst fractal dimension  class\n",
              "0        17.99         10.38  ...                  0.11890      0\n",
              "1        20.57         17.77  ...                  0.08902      0\n",
              "2        19.69         21.25  ...                  0.08758      0\n",
              "3        11.42         20.38  ...                  0.17300      0\n",
              "4        20.29         14.34  ...                  0.07678      0\n",
              "\n",
              "[5 rows x 31 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 56
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "FVTYdDfVKCEt",
        "colab_type": "code",
        "outputId": "3f9e7a7e-4fd6-4c15-9c74-c81eea9d680d",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 351
        }
      },
      "source": [
        "data.describe()"
      ],
      "execution_count": 57,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
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              "\n",
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              "        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>radius error</th>\n",
              "      <th>texture error</th>\n",
              "      <th>perimeter error</th>\n",
              "      <th>area error</th>\n",
              "      <th>smoothness error</th>\n",
              "      <th>compactness error</th>\n",
              "      <th>concavity error</th>\n",
              "      <th>concave points error</th>\n",
              "      <th>symmetry error</th>\n",
              "      <th>fractal dimension error</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",
              "      <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>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>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>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>0.405172</td>\n",
              "      <td>1.216853</td>\n",
              "      <td>2.866059</td>\n",
              "      <td>40.337079</td>\n",
              "      <td>0.007041</td>\n",
              "      <td>0.025478</td>\n",
              "      <td>0.031894</td>\n",
              "      <td>0.011796</td>\n",
              "      <td>0.020542</td>\n",
              "      <td>0.003795</td>\n",
              "      <td>16.269190</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>0.277313</td>\n",
              "      <td>0.551648</td>\n",
              "      <td>2.021855</td>\n",
              "      <td>45.491006</td>\n",
              "      <td>0.003003</td>\n",
              "      <td>0.017908</td>\n",
              "      <td>0.030186</td>\n",
              "      <td>0.006170</td>\n",
              "      <td>0.008266</td>\n",
              "      <td>0.002646</td>\n",
              "      <td>4.833242</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>0.111500</td>\n",
              "      <td>0.360200</td>\n",
              "      <td>0.757000</td>\n",
              "      <td>6.802000</td>\n",
              "      <td>0.001713</td>\n",
              "      <td>0.002252</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.007882</td>\n",
              "      <td>0.000895</td>\n",
              "      <td>7.930000</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>0.232400</td>\n",
              "      <td>0.833900</td>\n",
              "      <td>1.606000</td>\n",
              "      <td>17.850000</td>\n",
              "      <td>0.005169</td>\n",
              "      <td>0.013080</td>\n",
              "      <td>0.015090</td>\n",
              "      <td>0.007638</td>\n",
              "      <td>0.015160</td>\n",
              "      <td>0.002248</td>\n",
              "      <td>13.010000</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>0.324200</td>\n",
              "      <td>1.108000</td>\n",
              "      <td>2.287000</td>\n",
              "      <td>24.530000</td>\n",
              "      <td>0.006380</td>\n",
              "      <td>0.020450</td>\n",
              "      <td>0.025890</td>\n",
              "      <td>0.010930</td>\n",
              "      <td>0.018730</td>\n",
              "      <td>0.003187</td>\n",
              "      <td>14.970000</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>0.478900</td>\n",
              "      <td>1.474000</td>\n",
              "      <td>3.357000</td>\n",
              "      <td>45.190000</td>\n",
              "      <td>0.008146</td>\n",
              "      <td>0.032450</td>\n",
              "      <td>0.042050</td>\n",
              "      <td>0.014710</td>\n",
              "      <td>0.023480</td>\n",
              "      <td>0.004558</td>\n",
              "      <td>18.790000</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>2.873000</td>\n",
              "      <td>4.885000</td>\n",
              "      <td>21.980000</td>\n",
              "      <td>542.200000</td>\n",
              "      <td>0.031130</td>\n",
              "      <td>0.135400</td>\n",
              "      <td>0.396000</td>\n",
              "      <td>0.052790</td>\n",
              "      <td>0.078950</td>\n",
              "      <td>0.029840</td>\n",
              "      <td>36.040000</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",
              "</div>"
            ],
            "text/plain": [
              "       mean radius  mean texture  ...  worst fractal dimension       class\n",
              "count   569.000000    569.000000  ...               569.000000  569.000000\n",
              "mean     14.127292     19.289649  ...                 0.083946    0.627417\n",
              "std       3.524049      4.301036  ...                 0.018061    0.483918\n",
              "min       6.981000      9.710000  ...                 0.055040    0.000000\n",
              "25%      11.700000     16.170000  ...                 0.071460    0.000000\n",
              "50%      13.370000     18.840000  ...                 0.080040    1.000000\n",
              "75%      15.780000     21.800000  ...                 0.092080    1.000000\n",
              "max      28.110000     39.280000  ...                 0.207500    1.000000\n",
              "\n",
              "[8 rows x 31 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 57
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Ms4OsguSKKVS",
        "colab_type": "code",
        "outputId": "37c5dc77-e7e9-44c1-e379-72e34609a44b",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 68
        }
      },
      "source": [
        "data['class'].value_counts()"
      ],
      "execution_count": 58,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "1    357\n",
              "0    212\n",
              "Name: class, dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 58
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "kFoYg0KmKRAL",
        "colab_type": "code",
        "outputId": "376489ee-6297-4f8a-b93b-b6bf59575375",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "breast_cancer.target_names"
      ],
      "execution_count": 59,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array(['malignant', 'benign'], dtype='<U9')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 59
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "wgFdaun5KW9P",
        "colab_type": "code",
        "outputId": "fe163410-1532-40d7-c002-ee2d33c6c8c2",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 196
        }
      },
      "source": [
        "data.groupby('class').mean()"
      ],
      "execution_count": 60,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
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              "  <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>radius error</th>\n",
              "      <th>texture error</th>\n",
              "      <th>perimeter error</th>\n",
              "      <th>area error</th>\n",
              "      <th>smoothness error</th>\n",
              "      <th>compactness error</th>\n",
              "      <th>concavity error</th>\n",
              "      <th>concave points error</th>\n",
              "      <th>symmetry error</th>\n",
              "      <th>fractal dimension error</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",
              "      <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>0.609083</td>\n",
              "      <td>1.210915</td>\n",
              "      <td>4.323929</td>\n",
              "      <td>72.672406</td>\n",
              "      <td>0.006780</td>\n",
              "      <td>0.032281</td>\n",
              "      <td>0.041824</td>\n",
              "      <td>0.015060</td>\n",
              "      <td>0.020472</td>\n",
              "      <td>0.004062</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>0.284082</td>\n",
              "      <td>1.220380</td>\n",
              "      <td>2.000321</td>\n",
              "      <td>21.135148</td>\n",
              "      <td>0.007196</td>\n",
              "      <td>0.021438</td>\n",
              "      <td>0.025997</td>\n",
              "      <td>0.009858</td>\n",
              "      <td>0.020584</td>\n",
              "      <td>0.003636</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",
              "</div>"
            ],
            "text/plain": [
              "       mean radius  mean texture  ...  worst symmetry  worst fractal dimension\n",
              "class                             ...                                         \n",
              "0        17.462830     21.604906  ...        0.323468                 0.091530\n",
              "1        12.146524     17.914762  ...        0.270246                 0.079442\n",
              "\n",
              "[2 rows x 30 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 60
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9X_ZWLwCKdLt",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "cLykHjbhLNcn",
        "colab_type": "text"
      },
      "source": [
        "# Test Train Split"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "zP-K0LcULPb3",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from sklearn.model_selection import train_test_split"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "lb249Zp9LWOc",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "X = data.drop('class', axis=1)\n",
        "Y = data['class']"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "N8sd4mDuLbj-",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "X_train, X_test, Y_train, Y_test = train_test_split(X, Y)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-2woUpa0LiZt",
        "colab_type": "code",
        "outputId": "b8050d15-6438-4374-ee87-2cdd82a11323",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        }
      },
      "source": [
        "X.shape, X_train.shape, X_test.shape"
      ],
      "execution_count": 64,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "((569, 30), (426, 30), (143, 30))"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 64
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "mfNI9egFLl2h",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "X_train, X_test, Y_train, Y_test = train_test_split(X, Y, test_size=0.1)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "PwkfyuzaLuEd",
        "colab_type": "code",
        "outputId": "831d7d96-90ea-47c9-a96e-774e7ab8ac9c",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        }
      },
      "source": [
        "Y.mean(), Y_train.mean(), Y_test.mean()"
      ],
      "execution_count": 66,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(0.6274165202108963, 0.619140625, 0.7017543859649122)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 66
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "qsPXfzSSL3d6",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "X_train, X_test, Y_train, Y_test = train_test_split(X, Y, test_size=0.1, stratify = Y)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0-ip4ffhMJxq",
        "colab_type": "code",
        "outputId": "bc569a3a-f050-4b60-cc82-b99b3370c8a4",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        }
      },
      "source": [
        "X.mean(), X_train.mean(), X_test.mean()"
      ],
      "execution_count": 68,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(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, mean radius                 14.114576\n",
              " mean texture                19.265430\n",
              " mean perimeter              91.911504\n",
              " mean area                  653.970703\n",
              " mean smoothness              0.096504\n",
              " mean compactness             0.105124\n",
              " mean concavity               0.089436\n",
              " mean concave points          0.049089\n",
              " mean symmetry                0.182054\n",
              " mean fractal dimension       0.062922\n",
              " radius error                 0.408265\n",
              " texture error                1.216721\n",
              " perimeter error              2.891245\n",
              " area error                  40.674367\n",
              " smoothness error             0.007064\n",
              " compactness error            0.025810\n",
              " concavity error              0.032348\n",
              " concave points error         0.011861\n",
              " symmetry error               0.020714\n",
              " fractal dimension error      0.003839\n",
              " worst radius                16.255438\n",
              " worst texture               25.599727\n",
              " worst perimeter            107.189043\n",
              " worst area                 879.191406\n",
              " worst smoothness             0.132206\n",
              " worst compactness            0.254568\n",
              " worst concavity              0.271613\n",
              " worst concave points         0.114550\n",
              " worst symmetry               0.291290\n",
              " worst fractal dimension      0.084030\n",
              " dtype: float64, mean radius                 14.241509\n",
              " mean texture                19.507193\n",
              " mean perimeter              92.485789\n",
              " mean area                  663.138596\n",
              " mean smoothness              0.095066\n",
              " mean compactness             0.097307\n",
              " mean concavity               0.083082\n",
              " mean concave points          0.047392\n",
              " mean symmetry                0.173151\n",
              " mean fractal dimension       0.061684\n",
              " radius error                 0.377388\n",
              " texture error                1.218040\n",
              " perimeter error              2.639832\n",
              " area error                  37.307404\n",
              " smoothness error             0.006835\n",
              " compactness error            0.022501\n",
              " concavity error              0.027816\n",
              " concave points error         0.011214\n",
              " symmetry error               0.019001\n",
              " fractal dimension error      0.003399\n",
              " worst radius                16.392719\n",
              " worst texture               26.373333\n",
              " worst perimeter            107.909474\n",
              " worst area                 893.084211\n",
              " worst smoothness             0.133827\n",
              " worst compactness            0.251547\n",
              " worst concavity              0.277362\n",
              " worst concave points         0.115110\n",
              " worst symmetry               0.279167\n",
              " worst fractal dimension      0.083189\n",
              " dtype: float64)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 68
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "IL_fSvTqNQwD",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Tq5tyuaeNocz",
        "colab_type": "text"
      },
      "source": [
        "# Binarisation of Input"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "QYMxx4J-NqX6",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "sns.set()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "mIiJb2dlOCKr",
        "colab_type": "code",
        "outputId": "7e7cffb2-1781-48f2-b8cc-1089c957da18",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        }
      },
      "source": [
        "plt.plot(X_train.T, '.')\n",
        "plt.xticks(rotation='vertical')\n",
        "plt.show()"
      ],
      "execution_count": 70,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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YgBpBxB/nsx2dEiFO58qpM+DEa11DIocrp844OiWXQYXADgI8KsGDAWDgOYYAj0pHp0SI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\ngg==\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8_AvLy8NOGWB",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "X_binarised_3_train = X_train['mean area'].map(lambda x: 0 if x < 1000 else 1)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "nyWnyYAxOj0u",
        "colab_type": "code",
        "outputId": "85622c61-b757-460f-c257-6c676997e379",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        }
      },
      "source": [
        "plt.plot(X_binarised_3_train, '.')"
      ],
      "execution_count": 72,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[<matplotlib.lines.Line2D at 0x7f3793876ac8>]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 72
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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6XI7jZL7oPUtYg49lYl8m9iTRVzYxqadUa/D8S1YAMBQBDwCGIuABwFAEPAAYioAHAEMR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\nIuABwFD/H+o4h7YiLuxDAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6c8mIRBIOoIR",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "X_binarised_train = X_train.apply(pd.cut, bins=2, labels=[1, 0])"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "aT1C7M_mO00s",
        "colab_type": "code",
        "outputId": "9beef01c-d100-479e-c81d-ef76cdccdff1",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        }
      },
      "source": [
        "plt.plot(X_binarised_train.T, '.')\n",
        "plt.xticks(rotation='vertical')\n",
        "plt.show()"
      ],
      "execution_count": 74,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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kwN/fH7GxsdyyIUOGICgoCB07duT9N9HR0di9ezeio6Ordm0JIYSIJqqJRorLly/jq6++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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "o_SE7r6jO4vv",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "X_binarised_test = X_test.apply(pd.cut, bins=2, labels=[1, 0])"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "W87h2OUSO-q7",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "X_binarised_test = X_binarised_test.values\n",
        "X_binarised_train = X_binarised_train.values"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "gGXZoo8EPGcS",
        "colab_type": "code",
        "outputId": "3fe63eb8-43e3-42b6-bbc1-a90611ff1f3f",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        }
      },
      "source": [
        "type(X_binarised_train)"
      ],
      "execution_count": 77,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "numpy.ndarray"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 77
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "mO8wE-vHPI7n",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "KyoV5wnLPLMn",
        "colab_type": "text"
      },
      "source": [
        "# MP Neuron Model"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "DegZuy0JPrnC",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from random import randint"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7IzdGtxTPNY0",
        "colab_type": "code",
        "outputId": "1d6e613d-4dce-4be0-d8be-0570b1d3886e",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        }
      },
      "source": [
        "b = 3\n",
        "\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 is benign')"
      ],
      "execution_count": 79,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "For row 53\n",
            "MP Neuron inference is malignant\n",
            "Ground truth is benign\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "45WGsroEP4di",
        "colab_type": "code",
        "outputId": "75a0590d-0d1e-4e52-cd53-7e78628efe26",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        }
      },
      "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": 80,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "321 0.626953125\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "TYxLNbAAReTY",
        "colab_type": "code",
        "outputId": "019d4929-1a11-4b5d-c4a1-f34293162e48",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        }
      },
      "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",
        "\n",
        "    print(b, accurate_rows, accurate_rows/X_binarised_train.shape[0])"
      ],
      "execution_count": 81,
      "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",
            "13 322 0.62890625\n",
            "14 324 0.6328125\n",
            "15 326 0.63671875\n",
            "16 330 0.64453125\n",
            "17 335 0.654296875\n",
            "18 338 0.66015625\n",
            "19 341 0.666015625\n",
            "20 344 0.671875\n",
            "21 354 0.69140625\n",
            "22 363 0.708984375\n",
            "23 374 0.73046875\n",
            "24 395 0.771484375\n",
            "25 407 0.794921875\n",
            "26 422 0.82421875\n",
            "27 438 0.85546875\n",
            "28 437 0.853515625\n",
            "29 417 0.814453125\n",
            "30 384 0.75\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9XoxC6Z4UT1S",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from sklearn.metrics import accuracy_score"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "JeeivBGRSlV8",
        "colab_type": "code",
        "outputId": "f85e9dd3-f22e-4271-f4f5-c7a8d290edbf",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        }
      },
      "source": [
        "b = 28\n",
        "Y_pred_test = []\n",
        "accurate_rows = 0\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",
        "    \n",
        "print(b, accuracy)"
      ],
      "execution_count": 83,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "28 0.7192982456140351\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "qoOIsqqcUlFV",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "qtoG36F6Vbhw",
        "colab_type": "text"
      },
      "source": [
        "# MP Neuron Class"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8m0o0KWLVdSx",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "class MPNeuron:\n",
        "    \n",
        "    def __init__(self):\n",
        "        self.b = None\n",
        "        \n",
        "    def model(self, x):\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.asarray(Y)\n",
        "    \n",
        "    def fit(self, X, Y):\n",
        "        accuracy = {}\n",
        "        \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",
        "            \n",
        "        best_b = max(accuracy, key = accuracy.get)\n",
        "        self.b = best_b\n",
        "        \n",
        "        print('Optimal value of b is ', best_b)\n",
        "        print('Highest accuracy is ', accuracy[best_b])"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "HK6uQLPMWZ5Z",
        "colab_type": "code",
        "outputId": "842b8cd3-448f-4b60-ec69-9f5f68d4b112",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        }
      },
      "source": [
        "mp_neuron = MPNeuron()\n",
        "mp_neuron.fit(X_binarised_train, Y_train)"
      ],
      "execution_count": 85,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Optimal value of b is  27\n",
            "Highest accuracy is  0.85546875\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Af7shHYrWf_L",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "Y_test_pred = mp_neuron.predict(X_binarised_test)\n",
        "accuracy_test = accuracy_score(Y_test_pred, Y_test)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3shdqIegWsIE",
        "colab_type": "code",
        "outputId": "ba90a093-f326-4242-d8bd-f394baa48edc",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        }
      },
      "source": [
        "accuracy_test"
      ],
      "execution_count": 87,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.7719298245614035"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 87
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9RWxxyP9WuoV",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "pTD4MkNvWzky",
        "colab_type": "text"
      },
      "source": [
        "# Perceptron Class"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "l_nV92XUarqv",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "X_train = X_train.values\n",
        "X_test = X_test.values"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "47HJQ6tYW1T8",
        "colab_type": "code",
        "colab": {}
      },
      "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.asarray(Y)\n",
        "    \"\"\"\n",
        "    lr = learning rate\n",
        "    epochs = number of time to iterate over all the data\n",
        "    \"\"\"\n",
        "    def fit(self, X, Y, epochs = 1, lr = 1):\n",
        "        \n",
        "        self.w = np.ones(X.shape[1])\n",
        "        self.b = 0\n",
        "        \n",
        "        accuracy = {}\n",
        "        max_accuracy = 0\n",
        "        \n",
        "        wt_matrix = []\n",
        "                \n",
        "        for i in range(epochs):\n",
        "            wt_matrix.append(self.w)\n",
        "            for x, y in zip(X, Y):\n",
        "                y_pred = self.model(x)\n",
        "\n",
        "                if y == 1 and y_pred == 0:\n",
        "                    self.w = self.w + lr * x\n",
        "                    self.b = self.b - lr * 1\n",
        "                elif y == 0 and y_pred == 1:\n",
        "                    self.w = self.w - lr * x\n",
        "                    self.b = self.b + lr * 1\n",
        "            \n",
        "            accuracy[i] = accuracy_score(self.predict(X), Y)\n",
        "\n",
        "            if (accuracy[i] > max_accuracy):\n",
        "                max_accuracy = accuracy[i]\n",
        "                chkptw = self.w\n",
        "                chkptb = self.b\n",
        "                \n",
        "        self.w = chkptw\n",
        "        self.b = chkptb\n",
        "                \n",
        "        print('Highest Accuracy ', max_accuracy)\n",
        "        plt.plot(list(accuracy.values()), '-.')\n",
        "        plt.ylim([0, 1])\n",
        "        plt.show()\n",
        "        \n",
        "        \n",
        "        return np.asarray(wt_matrix)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "def82oq6ap0D",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "perceptron = Perceptron()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "HTzWC6_ga1QT",
        "colab_type": "code",
        "outputId": "8f012ddf-2ba3-4f8f-bd7a-de29600fd096",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 289
        }
      },
      "source": [
        "wt_matrix = perceptron.fit(X_train, Y_train, epochs=100, lr=1)"
      ],
      "execution_count": 91,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Highest Accuracy  0.92578125\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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77LOkpqayc+dO3nrrLY4dO8aePXtGvMFXgp4wb+2wkhQdzF1rpvW7WOrFXcf5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\nRK5CYII=\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "unLWqT2ta64C",
        "colab_type": "code",
        "outputId": "902948bf-1ce7-4df3-9db2-71a577fac634",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "Y_pred_train = perceptron.predict(X_train)\n",
        "accuracy_score(Y_pred_train, Y_train)"
      ],
      "execution_count": 92,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.92578125"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 92
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "q6Z90ibQbC-j",
        "colab_type": "code",
        "outputId": "617edaea-567c-4b45-f98e-48ee279f6418",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "Y_pred_test = perceptron.predict(X_test)\n",
        "accuracy_score(Y_pred_test, Y_test)"
      ],
      "execution_count": 93,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.8421052631578947"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 93
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_VNBlzGua3SI",
        "colab_type": "code",
        "outputId": "b8f9bc49-8fbf-4daa-f810-b1a2564fa974",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 268
        }
      },
      "source": [
        "plt.plot(perceptron.w)\n",
        "plt.show()"
      ],
      "execution_count": 94,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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Y7Sq2qQoAVq9ejeeff97oYtAK0dPhxAefTELTtKJHfw0Oh9Dd4ayIeQ61zmY1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\nRESUl/8PX3C5iH09FwkAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "GjBe2XjsbH8l",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "%matplotlib inline"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "kqnB6EXeff7K",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "from matplotlib import animation, rc\n",
        "from IPython.display import HTML\n",
        "import seaborn as sns\n",
        "sns.set()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "qkIKBrosfq7I",
        "colab_type": "code",
        "outputId": "429adf5f-c8f7-4d92-c363-f4cf4b1dfb7e",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 272
        }
      },
      "source": [
        "fig, ax = plt.subplots()\n",
        "\n",
        "ax.set_xlim((0, wt_matrix.shape[1]))\n",
        "ax.set_ylim((-15000, 25000))\n",
        "\n",
        "line, = ax.plot([], [], lw=2)"
      ],
      "execution_count": 97,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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w+0s1OHgt18swxsn5nJxNIl8hsyxXWi/OjW6XXbx4MfXzsWPHZFlWqnjq6urU1dUlSerr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\nAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "LY74e_2Jf-zb",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# animation function. This is called sequentially\n",
        "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": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "NPRAhqVmgTQv",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# call the animator. blit=True means only re-draw the parts that have changed.\n",
        "anim = animation.FuncAnimation(fig, animate, frames=100, interval=200, blit=True)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "PXZKOogagT6Q",
        "colab_type": "code",
        "outputId": "4f919cf3-535d-4082-bde7-33c873134da8",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 309
        }
      },
      "source": [
        "HTML(anim.to_html5_video())"
      ],
      "execution_count": 100,
      "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,AAAAHGZ0eXBNNFYgAAACAGlzb21pc28yYXZjMQAAAAhmcmVlAACGr21kYXQAAAKtBgX//6ncRem9\n",
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              "PTEgbWVfcmFuZ2U9MTYgY2hyb21hX21lPTEgdHJlbGxpcz0xIDh4OGRjdD0xIGNxbT0wIGRlYWR6\n",
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              "bWluPTAgcXBtYXg9NjkgcXBzdGVwPTQgaXBfcmF0aW89MS40MCBhcT0xOjEuMDAAgAAAEadliIQA\n",
              "Ev/+963fgU3AQO1rulc4tMurlDQ9UfaUpni2SAAAAwAH+v4QTPGoI5ASI4LdT3r7p1K8pBfs1voF\n",
              "5rTmoUAS+cVr015jkMVfg3niKHskFhOkbd8RoKca/IjU9lmRQsoibOluQ81DkQ2srInF+fDhmDiF\n",
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              "\">\n",
              "  Your browser does not support the video tag.\n",
              "</video>"
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 100
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "UTWYs_hnhbHf",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    }
  ]
}