Support_Vector_Machine.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "source": [
        "# Support Vector Machine"
      ],
      "metadata": {
        "id": "iRZO7_gA-S9G"
      }
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "JhdVc18lC041"
      },
      "source": [
        "We are going to build a SVM to identify each of a large number of black-and-white rectangular pixel displays as one of the 26 capital letters in the English alphabet. \n",
        "\n",
        "The character images were based on 20 different fonts and each letter within these 20 fonts was randomly distorted to produce a file of 20,000 unique stimuli. \n",
        "\n",
        "Each stimulus was converted into 16 primitive numerical attributes (statistical moments and edge counts) which were then scaled to fit into a range of integer values from 0 through 15. We typically train on the first 16000 items and then use the resulting model to predict the letter category for the remaining 4000. See the article (P. W. Frey and D. J. Slate. \"Letter Recognition Using Holland-style Adaptive Classifiers\". (Machine Learning Vol 6 No.2 March 91)) for more details."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "876mk1W6C045"
      },
      "source": [
        "## Import Libraries"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "id": "WCwb66ceC045"
      },
      "outputs": [],
      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "from sklearn import svm\n",
        "import matplotlib.pyplot as plt\n",
        "%matplotlib inline "
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "G2rYcQXvC046"
      },
      "outputs": [],
      "source": [
        "def getAccuracy(testSet, predictions):\n",
        "\tcorrect = 0\n",
        "\tfor x in range(len(testSet)):\n",
        "\t\tif testSet[x]== predictions[x]:\n",
        "\t\t\tcorrect += 1\n",
        "\treturn (correct/float(len(testSet))) * 100.0"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "metadata": {
        "id": "WuFdK_NCC047",
        "outputId": "56b5c695-170c-4d35-f967-cff843d807bc",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 363
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "  letter  xbox  ybox  width  height  onpix  xbar  ybar  x2bar  y2bar  xybar  \\\n",
              "0      T     2     8      3       5      1     8    13      0      6      6   \n",
              "1      I     5    12      3       7      2    10     5      5      4     13   \n",
              "2      D     4    11      6       8      6    10     6      2      6     10   \n",
              "3      N     7    11      6       6      3     5     9      4      6      4   \n",
              "4      G     2     1      3       1      1     8     6      6      6      6   \n",
              "5      S     4    11      5       8      3     8     8      6      9      5   \n",
              "6      B     4     2      5       4      4     8     7      6      6      7   \n",
              "7      A     1     1      3       2      1     8     2      2      2      8   \n",
              "8      J     2     2      4       4      2    10     6      2      6     12   \n",
              "9      M    11    15     13       9      7    13     2      6      2     12   \n",
              "\n",
              "   x2ybar  xy2bar  xedge  xedgey  yedge  yedgex  \n",
              "0      10       8      0       8      0       8  \n",
              "1       3       9      2       8      4      10  \n",
              "2       3       7      3       7      3       9  \n",
              "3       4      10      6      10      2       8  \n",
              "4       5       9      1       7      5      10  \n",
              "5       6       6      0       8      9       7  \n",
              "6       6       6      2       8      7      10  \n",
              "7       2       8      1       6      2       7  \n",
              "8       4       8      1       6      1       7  \n",
              "9       1       9      8       1      1       8  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-a9e6664b-1634-4d82-aa0c-62b075df5a10\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>letter</th>\n",
              "      <th>xbox</th>\n",
              "      <th>ybox</th>\n",
              "      <th>width</th>\n",
              "      <th>height</th>\n",
              "      <th>onpix</th>\n",
              "      <th>xbar</th>\n",
              "      <th>ybar</th>\n",
              "      <th>x2bar</th>\n",
              "      <th>y2bar</th>\n",
              "      <th>xybar</th>\n",
              "      <th>x2ybar</th>\n",
              "      <th>xy2bar</th>\n",
              "      <th>xedge</th>\n",
              "      <th>xedgey</th>\n",
              "      <th>yedge</th>\n",
              "      <th>yedgex</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>T</td>\n",
              "      <td>2</td>\n",
              "      <td>8</td>\n",
              "      <td>3</td>\n",
              "      <td>5</td>\n",
              "      <td>1</td>\n",
              "      <td>8</td>\n",
              "      <td>13</td>\n",
              "      <td>0</td>\n",
              "      <td>6</td>\n",
              "      <td>6</td>\n",
              "      <td>10</td>\n",
              "      <td>8</td>\n",
              "      <td>0</td>\n",
              "      <td>8</td>\n",
              "      <td>0</td>\n",
              "      <td>8</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>I</td>\n",
              "      <td>5</td>\n",
              "      <td>12</td>\n",
              "      <td>3</td>\n",
              "      <td>7</td>\n",
              "      <td>2</td>\n",
              "      <td>10</td>\n",
              "      <td>5</td>\n",
              "      <td>5</td>\n",
              "      <td>4</td>\n",
              "      <td>13</td>\n",
              "      <td>3</td>\n",
              "      <td>9</td>\n",
              "      <td>2</td>\n",
              "      <td>8</td>\n",
              "      <td>4</td>\n",
              "      <td>10</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>D</td>\n",
              "      <td>4</td>\n",
              "      <td>11</td>\n",
              "      <td>6</td>\n",
              "      <td>8</td>\n",
              "      <td>6</td>\n",
              "      <td>10</td>\n",
              "      <td>6</td>\n",
              "      <td>2</td>\n",
              "      <td>6</td>\n",
              "      <td>10</td>\n",
              "      <td>3</td>\n",
              "      <td>7</td>\n",
              "      <td>3</td>\n",
              "      <td>7</td>\n",
              "      <td>3</td>\n",
              "      <td>9</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>N</td>\n",
              "      <td>7</td>\n",
              "      <td>11</td>\n",
              "      <td>6</td>\n",
              "      <td>6</td>\n",
              "      <td>3</td>\n",
              "      <td>5</td>\n",
              "      <td>9</td>\n",
              "      <td>4</td>\n",
              "      <td>6</td>\n",
              "      <td>4</td>\n",
              "      <td>4</td>\n",
              "      <td>10</td>\n",
              "      <td>6</td>\n",
              "      <td>10</td>\n",
              "      <td>2</td>\n",
              "      <td>8</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>G</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>8</td>\n",
              "      <td>6</td>\n",
              "      <td>6</td>\n",
              "      <td>6</td>\n",
              "      <td>6</td>\n",
              "      <td>5</td>\n",
              "      <td>9</td>\n",
              "      <td>1</td>\n",
              "      <td>7</td>\n",
              "      <td>5</td>\n",
              "      <td>10</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>S</td>\n",
              "      <td>4</td>\n",
              "      <td>11</td>\n",
              "      <td>5</td>\n",
              "      <td>8</td>\n",
              "      <td>3</td>\n",
              "      <td>8</td>\n",
              "      <td>8</td>\n",
              "      <td>6</td>\n",
              "      <td>9</td>\n",
              "      <td>5</td>\n",
              "      <td>6</td>\n",
              "      <td>6</td>\n",
              "      <td>0</td>\n",
              "      <td>8</td>\n",
              "      <td>9</td>\n",
              "      <td>7</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>B</td>\n",
              "      <td>4</td>\n",
              "      <td>2</td>\n",
              "      <td>5</td>\n",
              "      <td>4</td>\n",
              "      <td>4</td>\n",
              "      <td>8</td>\n",
              "      <td>7</td>\n",
              "      <td>6</td>\n",
              "      <td>6</td>\n",
              "      <td>7</td>\n",
              "      <td>6</td>\n",
              "      <td>6</td>\n",
              "      <td>2</td>\n",
              "      <td>8</td>\n",
              "      <td>7</td>\n",
              "      <td>10</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>A</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>8</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>8</td>\n",
              "      <td>2</td>\n",
              "      <td>8</td>\n",
              "      <td>1</td>\n",
              "      <td>6</td>\n",
              "      <td>2</td>\n",
              "      <td>7</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>J</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>4</td>\n",
              "      <td>4</td>\n",
              "      <td>2</td>\n",
              "      <td>10</td>\n",
              "      <td>6</td>\n",
              "      <td>2</td>\n",
              "      <td>6</td>\n",
              "      <td>12</td>\n",
              "      <td>4</td>\n",
              "      <td>8</td>\n",
              "      <td>1</td>\n",
              "      <td>6</td>\n",
              "      <td>1</td>\n",
              "      <td>7</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>M</td>\n",
              "      <td>11</td>\n",
              "      <td>15</td>\n",
              "      <td>13</td>\n",
              "      <td>9</td>\n",
              "      <td>7</td>\n",
              "      <td>13</td>\n",
              "      <td>2</td>\n",
              "      <td>6</td>\n",
              "      <td>2</td>\n",
              "      <td>12</td>\n",
              "      <td>1</td>\n",
              "      <td>9</td>\n",
              "      <td>8</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>8</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-a9e6664b-1634-4d82-aa0c-62b075df5a10')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-a9e6664b-1634-4d82-aa0c-62b075df5a10 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-a9e6664b-1634-4d82-aa0c-62b075df5a10');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 3
        }
      ],
      "source": [
        "lData = pd.read_csv(\"letterdata.csv\")\n",
        "lData.head(10) # all fields except the target (\"letter\") are numeric. We do not know the scale. So normalize"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "oZdRQX-zC048"
      },
      "source": [
        "## Split Data"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "metadata": {
        "id": "4zIUdC9XC049"
      },
      "outputs": [],
      "source": [
        "X,y = np.array(lData)[ :, 1:16], np.array(lData.letter)[:]   # note for y, extracting all rows of \"letter\" col"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 6,
      "metadata": {
        "id": "sTUUcowwC049"
      },
      "outputs": [],
      "source": [
        "X_train = X[0:16000, 0:17]\n",
        "X_test = X[16001: , :]\n",
        "y_train = y[:16000]\n",
        "y_test = y[16001:]"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "7-HgcIZMC04-"
      },
      "source": [
        "## Build Model"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 8,
      "metadata": {
        "id": "azPHRTG5C04-"
      },
      "outputs": [],
      "source": [
        "clf = svm.SVC(gamma=0.025, C=3)    #support vector classifier in use"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "6iyYFHnHC04_"
      },
      "source": [
        "**gamma** is a measure of influence of a data point. It is inverse of distance of influence.\n",
        "\n",
        "**C** is penalty of wrong classifications. "
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 9,
      "metadata": {
        "id": "hUZOr4i_C04_",
        "outputId": "a0cfcee4-fc8f-490d-95af-31b8138b63db",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "SVC(C=3, gamma=0.025)"
            ]
          },
          "metadata": {},
          "execution_count": 9
        }
      ],
      "source": [
        "clf.fit(X_train , y_train)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 10,
      "metadata": {
        "id": "pkvUq1HdC04_"
      },
      "outputs": [],
      "source": [
        "y_pred = clf.predict(X_test)"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y_pred"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "9heIHjC1BIFi",
        "outputId": "fa20c7c3-c8dc-4624-ad74-f74640a34e0f"
      },
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array(['N', 'V', 'I', ..., 'T', 'S', 'A'], dtype=object)"
            ]
          },
          "metadata": {},
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "getAccuracy(y_test , y_pred)   # Calling getAccuracy function instead of usinig array comparison to get the %age accuracy"
      ],
      "metadata": {
        "id": "dnGQDqvHBj3A"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "execution_count": 15,
      "metadata": {
        "id": "p6R0btyVC05A"
      },
      "outputs": [],
      "source": [
        "y_grid = (np.column_stack([y_test, y_pred]))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 16,
      "metadata": {
        "id": "noSJYVycC05A",
        "outputId": "6102dfe4-c414-4467-cdec-666e56c87dfa",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[['N' 'N']\n",
            " ['V' 'V']\n",
            " ['I' 'I']\n",
            " ...\n",
            " ['T' 'T']\n",
            " ['S' 'S']\n",
            " ['A' 'A']]\n"
          ]
        }
      ],
      "source": [
        "print(y_grid)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "FFVIQ6z_C05A"
      },
      "source": [
        "### "
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "clf.score(X_test,y_test)*100"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "N3IBdjkfBvl4",
        "outputId": "d285354f-baf4-4aea-b1bf-8c619478eb6a"
      },
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "96.07401850462615"
            ]
          },
          "metadata": {},
          "execution_count": 22
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 24,
      "metadata": {
        "id": "SDF19ENDC05B"
      },
      "outputs": [],
      "source": [
        "np.savetxt(\"ocr.csv\", y_grid , fmt='%s')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 25,
      "metadata": {
        "id": "sHMTc_5oC05B"
      },
      "outputs": [],
      "source": [
        "import string\n",
        "lab= list(string.ascii_uppercase[0:26]) #label\n",
        "plab=[\"Predicted \"+s for s in lab] #predicted label"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "collapsed": true,
        "id": "33C70BazC05B"
      },
      "source": [
        "Filter those cases where the model committed mistake and analyze the mistake, which characters most mistakes occured on?"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 26,
      "metadata": {
        "scrolled": true,
        "id": "lse3uhpIC05C",
        "outputId": "0f536916-1388-42df-915e-2621f2ae5caa",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "confusion matrix\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fc76f330e10>"
            ]
          },
          "metadata": {},
          "execution_count": 26
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1440x1152 with 2 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "from sklearn import metrics\n",
        "import seaborn as sns\n",
        "\n",
        "print('confusion matrix')\n",
        "cm=metrics.confusion_matrix(y_test, y_pred, labels=lab)\n",
        "\n",
        "df_cm = pd.DataFrame(cm, index = [i for i in lab],\n",
        "                  columns = [i for i in plab])\n",
        "plt.figure(figsize = (20,16))\n",
        "sns.heatmap(df_cm, annot=True ,fmt='g')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "b8V9PCfgC05C"
      },
      "outputs": [],
      "source": []
    }
  ],
  "metadata": {
    "kernelspec": {
      "display_name": "Python 3",
      "language": "python",
      "name": "python3"
    },
    "language_info": {
      "codemirror_mode": {
        "name": "ipython",
        "version": 3
      },
      "file_extension": ".py",
      "mimetype": "text/x-python",
      "name": "python",
      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython3",
      "version": "3.7.3"
    },
    "colab": {
      "provenance": [],
      "toc_visible": true
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}