Distance_Metrics.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "Distance_Metrics_MM.ipynb",
      "version": "0.3.2",
      "provenance": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    }
  },
  "cells": [
    {
      "metadata": {
        "id": "8jaCMS10dPvC",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "#Import required libraries\n",
        "\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.neighbors import KNeighborsClassifier\n",
        "from sklearn.metrics import accuracy_score"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "veQCG-lzQxeY",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "#Load the dataset\n",
        "\n",
        "url = \"IRIS.csv\"\n",
        "df = pd.read_csv(url)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "JPWaR7JKRmZL",
        "colab_type": "code",
        "outputId": "e0da30ee-1890-4eb3-c4f6-3d1b9f41f2a2",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        }
      },
      "cell_type": "code",
      "source": [
        "#quick look into the data\n",
        "\n",
        "df.head(5)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>sepal_length</th>\n",
              "      <th>sepal_width</th>\n",
              "      <th>petal_length</th>\n",
              "      <th>petal_width</th>\n",
              "      <th>species</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>5.1</td>\n",
              "      <td>3.5</td>\n",
              "      <td>1.4</td>\n",
              "      <td>0.2</td>\n",
              "      <td>Iris-setosa</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>4.9</td>\n",
              "      <td>3.0</td>\n",
              "      <td>1.4</td>\n",
              "      <td>0.2</td>\n",
              "      <td>Iris-setosa</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>4.7</td>\n",
              "      <td>3.2</td>\n",
              "      <td>1.3</td>\n",
              "      <td>0.2</td>\n",
              "      <td>Iris-setosa</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>4.6</td>\n",
              "      <td>3.1</td>\n",
              "      <td>1.5</td>\n",
              "      <td>0.2</td>\n",
              "      <td>Iris-setosa</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>5.0</td>\n",
              "      <td>3.6</td>\n",
              "      <td>1.4</td>\n",
              "      <td>0.2</td>\n",
              "      <td>Iris-setosa</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   sepal_length  sepal_width  petal_length  petal_width      species\n",
              "0           5.1          3.5           1.4          0.2  Iris-setosa\n",
              "1           4.9          3.0           1.4          0.2  Iris-setosa\n",
              "2           4.7          3.2           1.3          0.2  Iris-setosa\n",
              "3           4.6          3.1           1.5          0.2  Iris-setosa\n",
              "4           5.0          3.6           1.4          0.2  Iris-setosa"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 11
        }
      ]
    },
    {
      "metadata": {
        "id": "JMbTRA_ZR1Wz",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "#Separate data and label\n",
        "\n",
        "x = df.iloc[:,1:4]\n",
        "y = df.iloc[:,4]"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "SiYF_LYIZFu_",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "#Prepare data for classification process\n",
        "\n",
        "x_train, x_test, y_train, y_test = train_test_split(x, y, test_size=0.3, random_state=0)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "ExTl7eRUZvoJ",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "#Create a model\n",
        "\n",
        "KNN_Classifier = KNeighborsClassifier(n_neighbors = 3, p = 2, metric='minkowski')\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "A_yJvJlisoe3",
        "colab_type": "code",
        "outputId": "f39a9b68-5f54-4d9e-eab4-dd59c9bdac54",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "cell_type": "code",
      "source": [
        "#Train the model\n",
        "\n",
        "KNN_Classifier.fit(x_train, y_train)\n",
        "\n",
        "#Let's predict the classes for test data\n",
        "\n",
        "pred_test = KNN_Classifier.predict(x_test)\n",
        "\n",
        "#Find the accuracy score\n",
        "\n",
        "accuracy_score(y_test,pred_test)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.9777777777777777"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 16
        }
      ]
    },
    {
      "metadata": {
        "id": "6T3n2myqWeeA",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "#Import required libraries\n",
        "\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "from sklearn.cluster import KMeans\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "#Load the dataset\n",
        "url = \"IRIS.csv\"\n",
        "df = pd.read_csv(url)\n",
        "\n",
        "#quick look into the data\n",
        "df.head(5)\n",
        "\n",
        "#Separate data and label\n",
        "x = df.iloc[:,1:4].values"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "wMiKdNuxUvzv",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "#Creating the kmeans classifier\n",
        "KMeans_Cluster = KMeans(n_clusters = 3)\n",
        "y_class = KMeans_Cluster.fit_predict(x)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "8z9zHXNAaqPh",
        "colab_type": "code",
        "outputId": "a0e93baa-5915-43a5-986f-34de5ddab61a",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 69
        }
      },
      "cell_type": "code",
      "source": [
        "KMeans_Cluster"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "KMeans(algorithm='auto', copy_x=True, init='k-means++', max_iter=300,\n",
              "    n_clusters=3, n_init=10, n_jobs=None, precompute_distances='auto',\n",
              "    random_state=None, tol=0.0001, verbose=0)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 27
        }
      ]
    },
    {
      "metadata": {
        "id": "nYgUy02YU91j",
        "colab_type": "code",
        "outputId": "67ae7a71-17f2-47ee-82a1-f30703a6dc79",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 365
        }
      },
      "cell_type": "code",
      "source": [
        "#Visualising the clusters\n",
        "plt.scatter(x[y_class == 0, 0], x[y_class == 0, 1], c = 'yellow', label = 'Iris-setosa')\n",
        "plt.scatter(x[y_class == 1, 0], x[y_class == 1, 1], c = 'green', label = 'Iris-versicolour')\n",
        "plt.scatter(x[y_class == 2, 0], x[y_class == 2, 1], c = 'red', label = 'Iris-virginica')\n",
        "\n",
        "#Plotting the centroids and clustered data points of the clusters\n",
        "plt.scatter(KMeans_Cluster.cluster_centers_[:, 0], KMeans_Cluster.cluster_centers_[:,1], s = 100, c = 'black', label = 'Centroids')\n",
        "\n",
        "plt.legend()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.legend.Legend at 0x7fc3534dfdd8>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 28
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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CzJ8/F3a7HUVFhbjqqoGBdFsxBlqKKpqZR6PI1rR7T5B4+gKJ/HxhXyBFbZPXUtOtXKfT\nCb3eFVx1Ol2rLeyatAy+F2YP1uvb3qB9+eWX8OqrC9GtW3e88UbopoV465iiy7l5NDnceYfClaet\nG5GVpdoXyEC3yZOTkZGJI0cOAQC+/34n7Ha7X23p1q07Dh48AAB4+eU5+Pnnk6itrUF6ekdUV1dj\n3769aGxsDLyzCvCKlqJO03xZ7MYN0BcVwNFy5x2icORhIRYmTFDtC2Sg2+TJGTJkGNavX4epU6fg\nyiuvarUtni8ef/xJvPbaywCAPn36oVu37pg48U5MnToFWVldcM899yMvLxdDhw5Hamqqfx1ViLv3\nRCGO5zmCHoPgeIrF8QxQi60bW36BNL31F5SUa3+bvKqqSuzbtwfXXz8CJSW/4vHHp+KDD1aHullt\ncPceii5KA6WIeTRJAo7/CnCbPO0IwnOkmuRmIZYpTJ4NN5nM+PrrzfjggxVwOh147DHtJ6LwVXgc\nAaKWtJBwokUbUFgAC5NehJ4WzgstCPJCLFGMRiPmzHk51M1QRRSdfRQptJBwQgttoNZ4TEiruOqY\nwosWEk5ooQ3UGo8JaRgDLYUVLSRu10IbqDUeE9IynwLtunXrcOutt2LixInYunWryk0ics/T84LB\nSjihhTa0oXQnojDfyUiTx4ToHK+Btry8HG+99RY++OADLF26FFu2bAlGu4jkaSHhhBba0MRmg3nm\nM7AMGwTL4P6wDBsE88xnAJstOJ/XCi0dkxDavXsnnn32T5g69b/wzDN/wu7dO4XUm59/Ck899Tge\neuh+PPjgvfjznxegoaHBrzr+9a9vfU4Y4W4bvilT7sPp00V+/Vwt8LoYaseOHRg8eDDi4+MRHx+P\nl156KRjtInJLCwknWrbBUFQAe4iSXihdABRJC4i0cF6EiiRJmD79YWzevAl1deefmf3731fippvG\n4/XX34IpwC8bdrsdM2c+jSeeeApXXnkVnE4nFi58Fe++uwyPPPKoz/V8+OHf0L//QJ92z1G6DZ/W\neE1YkZubixMnTqCiogJVVVV47LHHMHjwYLfvZ8IK7YuY8dTC85KShDRbDUpC8RytJMEybJBs3mZ7\nVldYv9vluU1KP68SxeenFs6LIHvwwXvxj3+sc1t+8823Ii9vZUB179y5HevXr8NLL73S/Fp9fR10\nOj0+/3wtNm/+AjqdHsOGXY/f/e5e/N//vdNmO7vKygosWDAPvXr1xrPPzsLLL89B+/Ym3HHHb9G+\nfXvk5r4No9GItLSL8NxzL2Dz5i9x4sRxTJ/+BBYufBUHDuxHly5dcfz4MbzyyhvIzz+FZcveRmxs\nHDp0sGD27LkwBukRLtUSVlRUVGDx4sUoKirC/fffj2+++UY24TMAdOhggtHYdtcFpbx1hPwTGeOZ\nAHRND20TJANwusY1nsH+o378V8DNAiBDUQHSbDVAmofxUfp5FSk7PzVwXgTRtm3bsGXLJo/v2bJl\nE3766YDHiyR3yspO44or+l1wTBKQn5+Pbdu24pNPPgYA/O53v8OkSRNgNseiqOgU3n//Xfzzn//E\nhx9+iLfffhvvvpuL997LQ3l5OX766Si++eYbdOjQAWPHjsW7776LTp06Yc6cOdi5cysSEuJgMrVD\nZeUZHD58AJ9+uhpnzpzBqFGjYLGY8fbbqzFz5gwMGDAAX331FYxGG9LSOvjdt0D5e356DbQpKSm4\n8sorYTQa0aVLF5jNZlitVqSkpMi+v7xc/GKKiLkC0wiOpwAtkiMYCgtgD0VyBGM8LJ0zYCjIb1Nk\n75QBqzEe8HScjfGwuNnJyN450/vnVcLz0z95ee/j7FnPKRbPnj2L5cvfw6WX9vW7/traBkjS2TbH\nZNu273Hy5M+YPPluAEBVVRUOHjyG2tp6XHZZH5SUVCM2NgFWawVKSqphtztQWlqDyspadO6cAZvN\niOPHC+BwOGE0xqOkpBrZ2Zdj37596NmzFySpAfv27UfPnr1RVlYLozEenTtnwGqtxdCh12PGjFkY\nPXosRo4cAyAuaOdMIFe0XhdDXXvttdi5cyccDgfKy8shSRI6dAjeNwciLWq5yTZabLJtzpkRvEaY\nTHAkJ8sWOZKTvF9hcwFRRKisrPTpfVVVvr3vQl27dsOhQwdbvdbQ0ID8/FMYPHho8049f/3rR7ji\niv4APG9nB7Tcbk/XqryxsRE63fmw5HQCev35u6cOhwMAMHbsTVi0aCmSkpLxzDP/g19++TmgvgWL\n10Cbnp6OMWPG4Le//S0eeughzJw5U3bfP6KooZXkCJIEfWWFbJG+stKndtTmzIP08FTYs7rCaTDA\nntUV0sNTo2IBUaRISvJtlxt/d8NpMnDg1Thz5jT+9a9/AnAFuyVLFqGoqBD79u1FXV3duQVSr6G+\nvs5tPTqdvs3Wd4mJidDpdCgudj3n/J//7EOvXtnN5V26dMWRIz/C6XSiuPh084rj995bDoPBiAkT\nJmLEiNH4+ecTAfUtWHy6xzV58mRMnjxZ7bYQhQVfkiMEI9esqx2FbtpR6Fs73CSip/AxceKd+OCD\nla1WG18oLq49Jk26M6D69Xo9Xn99MRYsmId3312GmJgYDBx4NR577H+wdu1qPProQ9Dr9Rg+/HrE\nxsa5refKK/tj2rQpmDEjp9XrTz89Ey++OAMGgwEZGZkYMWI0vvpqIwDg0kt74OKLL8Ejj/weWVld\n0KNHTwBAenpHPPHENCQkJCIhIQGTJ98bUN+ChdvkRZtQrpKNFFpZrauVdgjG33f/qbnqmFpTZY6W\nIkSLxATo2TN8ExNogcmE+lFjZIvqR40OXnDjHCuds3hxLm6++VbExbVv9XpcXHvccccdWLw4N0Qt\nI4C790SNSEpMoAUxu3b49bpaojlJA51nMpmQl7cSe/bswqpVn6CqqhKJiYmYNOm3GDduJO8QhBhv\nHUeDCL3FGDJlpUjt2wO6CxZ2AIDTYEDpgWNASmpw2xRBSRr4+y4Wx1Ms3jomWdzZRCzjoYOATJAF\nANjtrvJwU1YK43ffAmWloW4JUcThreMo0LSzidwVLXc28Z+tdx/AYJAPtgaDqzxojTmfOENfWACH\nv4kz6uqQPH4EjIcPufpjMMCW3RsVG7YAce5XkBKR73hFGw24aEaslFTYsnvLFtmyewf1tnHLxBm6\nABJnJI8fgZgD+6Gz26EDoLPbEXNgP5LHj1C34URRhIE2SrRMTAAmJlCsYsMWNPbtB+e5DDhOgwGN\nffu5rgSDRWnijLJS15WsDOPhQ7yNTCQIbx1HixaJCdJsNa48trySDVxcHCq+3gaUlSKt6CRKO3cP\n+gIopYkzfJlrtg27TkRTiaIaA220MZlcO7JEwjZkZaWuYNC7T2BBTmE/du/eidWrP0F9vYR27UyY\nNOlODBx4TdDaoXTuXfhcs1bOCyKNYaAl3yldeCOK0gU8CvvhaZPtkSNHYfHiXN822VY6nufm3ls+\nH93Ep7n3c3PNMQf2t22aP3PNWjkviDTKkJOTkyOyQklqEFkdAMBsjlWl3mgV6HiaX3gOptwl0FdV\nQud0Ql9ViZi9e6CrrkLjjaNUaKm85NHXuRbwOJ2uBTxOJwy//op2m75A3QNTvH5eaT8eeeT3+Mc/\n1sF2QVYtm82Go0eP4KefjmLChImqtwMAGoffAF11FfS/lkBXWwNHZhfUTb7bFeR82Pyj7q570G7T\nF9CXlbm2SjEYYOvT1/WlxccgKfq84O+7WBxPseTG02yO9fgZJqyIQgGNp1aSXihNFqGwH99/vxOT\nJk3wmsB9zZp1GDDgatXaIVefotu2gd6GV+G84O+7WBxPsZiwglSjlaQXSpNFKO3HmjWfeAyyAFBX\ndxarVn2iajvaMJlcC58C/bKTkupa+OTnXLdWzgsiLWOgJZ80LbyRLQti0ovmBTxyfFjAo7QfojbZ\ndrUjw007MsImiYhWzgsiLWOgJd9oJemF0mQRCvshbJNtkwmOpGTZIkdSUvis2tXKeUGkYVwSSD7T\nyk4xFRu2uF917AMl/XBtsr0CdXV1bt8TFxfnfZNtSYK+okK2SF9R6Uo2ESZBSivnBZFWcTFUFFI8\nnlp5XjJEz9FOuXUsPt+53W35LdcMwf+t+8JjHfqTJ2AZ3B86h6NNmdNggHX7Xo/JJjRJ0HnB33ex\nOJ5icTEUBYfShTeiBLiAp1mA/Vj09jJMBND+gtfbA5h4rtwbzm0SRQ8GWiI/mTKz8GHfftgC4FEA\n9577/y0APuzbD6bMLB8qMaF+zDjZovoxY0P/JcYfNhvMM5+BZdggWAb3h2XYIJhnPgNc8JwxUbTi\nHC1RACo2bMGA8SNwzeFD0NntcPo5TxxJmnYQatK0gxAA1M6dH6pmEWkGr2iJAnFuU4HSA8eALVtQ\neuCYa5MBX/dwlSTEfrlRtij2yy+877yjFUp3ECKKAgy0REqkpAI33hi1iR4ipR9EamKgpdCQJOhP\nnojaKx7hi6FCNJ5c1EXkHQMtBRcXzriISvQQ6vFkwgoir7gYioKKC2fOE5HoQQvjyYQVRJ4xYUUU\nCtl4amUHIMFClgBEa+PJhBWaxPEUiwkrSNMicuFMWSnw9deu/w9UgIkzNDeeWklkQqQxDLQUNBG1\ncKauDsk3DkVq3x7AiBFI7dsDyTcOBTzkQBYtosaTKIIx0FLwRNDCmeTxIxBzYH/zBvQ6ux0xB/Yj\nefyI4DUigsaTKJJxMRQFVUQsnCkrde0cJMN4+JDrNnKg+Zf9FBHjSRThuBgqCmliPLWyA1AAjN99\ni+Q7boFOpswJoGL1567NDoIpjMfzQpo4PyMIx1MsLoai8GEtg3HP94C1LPA6ykph/O5bZQuRAmDr\n3QcwGOQLDQZXuT+iPHkHUaTjrWMKrpoaWAb0g75FgHVYUmDdsx+Ij/etjro69xu/+5prWImUVNh6\nZSPm4IE2RbZe2b7fNrbZYM6ZgdiN66EvLIAjIxP1425y3fY1+virKaIOIlIVr2gpqCwD+sFgLYMO\naP6fwVoGy4B+PtfRciGSDqFZiNR49RC/XpfTlGzCkH8KOoejOdmEOWdGUOsgInUx0FLwFOS3upJt\nSW8tAwryvdfhy0IktUkSYjd9IVsUu+lL324Bi9j1hjvnEIUFBlqfSdDrTwAI8z9eIhIsBKjdjm2K\nygHAeOig63axHLvdVa4yEYkitFIHEanP6yTOrl278Pjjj6NHjx4AgJ49e2LWrFmqN0w7bDCbZyA2\ndj30+gI4HJmor78JtbXzEFZT3BfMa6YGe14TQMPgoYrKgRYLkeSCbSALkQLgShSRAUN+2ytwR+cM\nnxJFNCWbkEuf6GuyCRF1EJH6fLqiHTRoEFasWIEVK1ZEWZAFzOYZMJmWwGA4BZ3OAYPhFEymJTCb\nw2sOTBMJFjKz4LCkyBY5LClAZpb3OlJSYcvuLVtky+4dnOdXTSY4kpJlixxJSb49XiMi2QQTVhCF\nBd469khCbKybObDYDQib28hamNc8x7pnP+yWFDiB5v/Zm1Yd+6hiwxY09u0Hp8HgqsNgQGPffq6r\n82CQJOgrKmSL9BWVPs+N1ubMg/TwVNizusJpMMCe1RXSw1P9SjYhog4iUpfXhBW7du3Ciy++iC5d\nuqCyshLTp0/H0KHub/HZbHYYjW6eMQw7xwH0BOCQKTMAOALgkqC2KCBffw2M8HDlumULcOONwWsP\nAJw6Bfzzn8Dw4UCXLoHVUVoK/PADcPnlQGpwMjEBAI4fB3r2BBwy54XBABw5Alzix3khScDp00Cn\nToFfhYqog4hU4XWSsVu3bpg+fTrGjRuH/Px83H///fjqq6/Qrl072feXl4u/ygtdZpN4WCyZMBhk\ntiGzZ8JqjQcQBhlXOndHqsHQfNu4JafBgNLO3YFgj68zFvqe/eBwxgb+syU79PEpcNTaAWcQ22+M\nh8XN3Ki9cyasxng/+yRBn1wDR201UOtmoZcvEi9yfb42DM5JD5jJSCyOp1iqZIZKT0/H+PHjodPp\n0KVLF6SmpuLMmTPKWho2TKivdzMHVj8eQJhcOaSkwpadLVtky/YjwYIINhvMM5+BZdggWAb3h2XY\nIJhnPgPYbMGtQwmTCfVjxskW1Y8Z68cVpQ1m8zOwWAbBYukPi2UQzOZnAASpH0QUFF6vaNetW4eS\nkhJMmTIFJSUlKCsrQ3p6ejDapgmu1cWuOdnzq47HN78eLhq/HYyYEQeA/QDscN357gc0bhns+neQ\nNCVYaNKUYAEAaufOD1odWtCCzj6aAAAfKElEQVS00K5J00I7AKitDZ9+EJFnXudoa2pq8OSTT6Kq\nqgqNjY2YPn06rrvOfcL0yN1UQIJeXwyHoyPC5kq2mQSLZZDrFngpgB8AXA4gFbDbu8Jq3YWg9EmS\nYBk2SP6Wa1ZXWL/b5f1qUEQdSglpQ4tjcmEdwTwmGqSN3/fIwfEUK5Bbx16vaOPj47F06VJlLYsI\nJjgcF4e6EQHR64uh159LbJAK4MaWZQXnvkCo3zdfEiw4untuh4g6lBLSj5bHpE1ZAMckgnbvIYo0\nfLwnCjgcHeFwZLopyzx3lR6EdpxLsCBb5leShgw3dXQOSpIGz23wMWGFqGMS6vlqIvKKgTYqaGRR\nl6AkDZjgZmX7BCk4V3MmExy3uElYcYuPCSsEHRNuKkCkfWGUQ5CUaLmoy2AogN0emkVdTYkUYjdu\ngL6oAI7OmagfN96PBAul0L9R7lrM9RmAfABZACYA+lfLgYpSuO6Pq0mC/o0KIEamDfMrgSoJvgRK\nxQvtvGwqUPv8bN5GJtIAr4uh/BW5i6EihYS0tBqUlMQjpIttApxTNBq/RXLyLdDp4ErMdRpAJwAm\nwOkEKio+h83mfrGeCHr9CVgs/aHTOWTaYIDVutfPOe/AFtrpT56AZXB/6GQSZzgNBli371V9vloN\n/H0Xi+MplirP0VKkMcGVzSrEVzomkysI+HnFZbP1getyFjJdMZwr91VgOzK1ml+9oA3hNud9XoTs\nTkWkQQy0FGZSYbO5Sb5hy4Zvt42VJooQNeetsB0mE+rHuUmcMc7XxBlMmkGkNs7RUthpbByMmJgD\nsq/7QkSiCBFz3kISVrwKoD3azBVjDoD6ILWBiDziFS2FGQmxsV/KlsTGfgXvtz5F7chkRG3t/HOJ\nJY7Aat11LjD5+t1VRDskxJo3AgsBHIRrj4uDABYCseYvfKgjQnanItI4BloKK74kelDz86KIaEer\nOi6YKw6nsSCKdAy0FFZcC5HcJItwZHhdiKT08+edn9sEevo9tykiYYXSOrSSyIQo0jHQUpgxweFw\nkyzCkQTvC5GUft6laW7TlavY0Ty3aTb7mihCxIIqpXVoJJEJUYTjYigKMxL0+grZEr2+Eq55RU8B\nQunnXXV4mtusrZ3tQx1idoZSWkek7E5FpGUMtBRWXPOKhW7KCr0m41f6+fN1iNgQwLWgqrZ2toKd\noZTWIaINROQJbx37LFIe6JcAHEe49kML85Li5zabdoYKZYAT0YZI+R0hEouB1qtIeaBf2eId7TCh\nvt5Nkob6sfBtXlLJ55vqGOumjjE+1iGKFs5PLbSBSLt469iLSHmgP1L6oRUxMdv9el0tWjiuWmgD\nkZbxitajSHmgP1L6Abj6slG2JDbW1yQNSj4PAKUwGg/LlrheL/WhDhG0cFy10AYibWOg9UD8A/2h\nmcOKlH4A2khYYTQeBGB3U2o/V+6PwMZTCwkntNAGIq1joPVA3KKX0M5hRUo/AG0shmq1g1Ab/uwg\npGw8tZBwQgttINI6BlqPxDzQ3zK5gU4XSHIDpSKlH4A2kjSkekh6kQxfN55XPp5aSDihhTYQaRsX\nQ3mh/IF+MckNlFK+24w2+gFoIUmDBNeWOXJMCLekF0ppoQ1EWqZzOp1OkRVeuPO8CHI72gefFNAD\n/Xr9CVgs/aHTOdqUOZ0GWK17fUxuIIqEtLQalJTEI7z7AQR6TJR+XsRYiB9PpWMhgpg2aOP3PXJw\nPMWSG8+0tASPn+GtY58F9kC/9uawLtjmxUfa6wegPMmCBL0+H/4uQmLSCy23gUh7eOtYda45rJbP\nGTYJrzms4PVj9+6dWL36E1RWViIxMQmTJt2JgQOvEVY/UIfk5BEwGg/BtXrYAJutNyoqtgCI8+Hz\nIsYiUs4LIvKGgTYIImUOS+1+SJKE6dMfxubNm1BXd7b59b//fSVGjhyFxYtzYTIpD0DJySMQE7O/\nxSt2xMTsR3LyCFRUbPOpDuVz3pFzXhCRZ5yjDSotzKOJGE91+vHgg/fiH/9Y57b85ptvRV7eSoU/\npRSpqT2g07V9DtbpNKC09Bh8XTXsEtic94V1aOG80IrI+X3XBo6nWJyj1bxImcMS34/vv9+JzZs3\neXzP5s2bsGfPLkU/R3yyicDmvC+sIzLOCyKSw0BLIdI6G9KaNZ+0ul0sp67uLFat+kTRTxWXbKJJ\nKYCvEby0i0QUbjhHS0Fmg9k8A7Gx61vMS96Eykr5zdgvVFVVqfDnp8Jmy0ZMzIG2LbNlw/fbxq0X\nVKWm+rugioiiBa9oKajcZUNKSTnk0+cTE5MUt6GxcbBfr8tpWlDVNNer051fUEVE1BIDLQWR+2xI\n995birg4z1eCcXHtMWnSnQLa8KVsSWzsV/B99x75Lwau13kbmYjOY6CloPG008u115Zi1KihHj8/\ncuQoDBhwdYtX/N/1Rpu79xBRJGOgpaBxZUPKcFOWgUWLluHmm29FXFzrPMJxce1x8823YvHi3HOv\nBL7rjbZ27yGiaMDFUBREJjgcyTAY8tuUOBxJMJlSkZe3Env27MKqVZ+gqqoSiYmJmDTpt62uZJvm\neZs0zfMCQG3tfK9tUJ6RKfVcP8pk+uH77j1EFB0YaCmIJOj18quL9fpKNO16M2DA1RfcIm5dh9Jd\nb7Sxew8RRQsGWgoa1/xooZuywnPZkbzteuN9jtX7rjdG1NbOR23t7AB37ymGXl/kpqzIxzYQUbTw\naY62rq4OI0eOxJo1a9Ruj4b5v/BGnTqUkgAcV9iGwPqhvV1vImVHJiLSMp8C7ZIlS5CUpPz5xfAU\n+MIbsXUodb4NQM8Q9cM1PyrHv11vxrmpY6yPdSgloh9EFC283jo+fvw4fvrpJ1x//fVBaI72KFt4\nI64OpbTSj0jZsUbE7j1EFB28XtHOnz8fzz77bDDaokGeF974dutURB1KaakfrvlRq3UXrNa9sFp3\nnQvSvi4XkBAbu9FNO77wox1Kne8HcCSAfhBRtPD4V2Ht2rW44oorkJWV5XOFHTqYYDS6e8YwcN62\nIVLHrwDkF94YDAVIS6sBkB6EOpTSYj/qAZQByADgz7HVwni2lAAgHWlpQfyRUSA0v++Ri+Mplr/j\n6THQbt26Ffn5+di6dSuKi4vRrl07dOzYEUOGDHH7mfJy8VcUodtPMR4WSyYMhlNtSuz2TFit8QC8\ntUtEHUrFw2LJkH1+1W7PCHI/WifjdyV48CcZvxbGszXu9ykWx1MsjqdYgexH6zHQLly4sPm/Fy1a\nhIyMDI9BNvKISG4gog6lPCeKCGY/mpLxn3c+GX9FxbagtYOIKFg4oeSFiMU7oV8A5FuiCG+U98OX\nZPzesyqFfjyJiHynczqdTpEVqnGLQhu3PqSAkhuIr8N/ev0JWCz9odM52pQ5nQZYrXv9TLAQWD+M\nxm+RnHwLdLq2ZU4nUFHxOWy261Rvh2jaOD8jB8dTLI6nWIHcOuamAj4LLLmB+Dr8TxYhPsFCYP0Q\nn4xfxHgSEamLgTZsKEkWoZUEC6mw2XrLlrheZzJ+Ioo8nKMNE0qTRWglwUJFxRYPq46JiCIPA21Y\nUL5jTctE+mlpNecegwnFLde4c6uLS2E0Hjx3u5hXskQUuXjrOAz4smON70wALkHo5zVTzy18YpAl\nosjGQBsGXIuZMtyUZXC3GCIiDWOgDQuuhBNyfE84QUREocBAGxZ8SThBRERaxEAbBlxztIVuygr9\nnKMlIqJgYqANA+ITTpCL/8k/iIj8xUAbFrSScCJSKEn+QUTkHz5HGyaYSF8cpck/iIj8wUAbNs4n\nnNBCIv3wJSL5BxGR78Lg1rEE4Dg4j9ZEC4n0w3duU2zyDyIi7zQcaM/PowE9OY+mCeE/t8mFZUQU\nbJoNtE3zaAbDKQCO5nk0s3lGqJsWtVoeE50uXI8JF5YRUXBpNNB6nkcLx1uW4S9yjklt7TxI0lTY\n7V3hdBpgt3eFJE3lwjIiUoUmF0P5Mo/mmqekYImsY8KFZUQUPJq8ouU8mvZE5jHRwsIyIop0mgy0\nnEfTIh4TIqJAaPLWMdA6QYPBUAC7nQkaQo1JM4iI/KfZQNtyHi0trQZWazx41RRqnNskIvKXRm8d\nt2QCcAki4w+6iEQPWkgWwblNIiJfafiKNpLYYDbPQGzs+ha3XG86d8vV10Mgog4iIgo2/oUOAhFJ\n7JkIn4goPIXBreNwJyLRQ+QkiyAiijYMtCoTkcSeifCJiMIXA63KRCR6iMxkEURE0YGBVnUiEj0w\nWQQRUbjiYqggEJHogckiiIjCEwNtUIhI9MBkEURE4YiBNqhMAna4EVEHEREFC+doiYiIVMRAS0RE\npCIGWiIiIhUx0BIREanI62Kos2fP4tlnn0VZWRnq6+sxbdo03HDDDcFoG6lCAvArAG47SEQUDF4D\n7TfffIO+ffvioYceQmFhIR588EEG2rB0fvcfoAAWC3f/ISIKBq9/YcePH9/836dPn0Z6erqqDSJ1\ncPcfIqLQ8PlSZvLkySguLsbSpUvVbA+pwvPuP7W1s8HbyERE6tA5nU6nr28+fPgwnn76aaxbtw46\nnU72PTabHUajQVgDSYTjAHoCcMiUGQAcAXBJUFtERBQtvF7RHjhwACkpKejUqROys7Nht9thtVqR\nkpIi+/7ycvF7o6alJaCkpFp4vdEjHhZLJgyGU21K7PZMWK3xADi+geL5KRbHUyyOp1hy45mWluDx\nM14f79mzZw/y8vIAAKWlpZAkCR06dFDQTAo+7v5DRBQqXq9oJ0+ejBkzZuDuu+9GXV0dXnjhBej1\nfPw23LTc/cdgKIDdzt1/iIiCwWugjYuLw+uvvx6MtpCqzu/+k5ZWc+52Ma9kiYjUxgcoo44JQDo4\nJ0tEFBy8B0xERKQiBloiIiIVMdASERGpiIGWiIhIRQy0REREKmKgJSIiUhEDLRERkYoYaImIiFTE\nQEtERKQiBloiIiIVMdASERGpiIGWiIhIRQy0REREKmKgJSIiUhEDLRERkYoYaImIiFTEQEtERKQi\nBloiIiIVMdASERGpiIGWiIhIRQy0REREKmKgJSIiUhEDLRERkYoYaImIiFTEQEtERKQiBloiIiIV\nMdASERGpiIGWiIhIRQy0REREKmKgJSIiUhEDLRERkYoYaImIiFTEQEtERKQiBloiIiIVMdASERGp\niIGWiIhIRUZf3rRgwQLs3bsXNpsNjzzyCEaPHq12u4iIiCKC10C7c+dOHDt2DB999BHKy8tx++23\nM9ASERH5yGugHThwIC6//HIAQGJiIs6ePQu73Q6DwaB644iIiMKd1zlag8EAk8kEAFi1ahWGDx/O\nIEtEROQjndPpdPryxs2bN+Odd95BXl4eEhIS3L7PZrPDaGQgJoo2UqOE09Wn0SmhE0wxplA3J6Q4\nFtSST4uhvvvuOyxduhTLly/3GGQBoLxcEtKwltLSElBSUi283mjF8RQr2sfT5rAhZ/sMbDyxHoU1\nBciIz8S4i29CzpB5MOp9+hPTSjiPp+ixECGcx1OL5MYzLc1zXPR65Kurq7FgwQK89957SE5OVtZC\nIoo4OdtnIPeHJc3/zq851fzvudfOD1WzQoJjQXK8ztFu2LAB5eXleOKJJ3DffffhvvvuQ1FRUTDa\nRkQaJzVK2HhivWzZxpMbIDWKv8OlVRwLcsfrFe1dd92Fu+66KxhtIaIwc0YqRmFNgWxZUU0BzkjF\n6J50cZBbFRocC3KHmaGIKGDppo7IiM+ULescn4l0U8cgtyh0OBbkDgMtEQXMFGPCuItvki0b1318\nVK245ViQO6FZBkdEESNnyDwArnnIopoCdI7PxLju45tfjyYcC5Lj83O0vlJjGTmXp4vF8RSL4+ki\nNUo4IxUj3dRR0dVbJIynqLEQIRLGU0sCebyHt46JCFKjhJOVJ7gyVkN4TCIHbx0TRTERCRa0mKQh\nVDieJMeQk5OTI7JCSWoQWR0AwGyOVaXeaMXxFCucx/OFbc8h94clqGqohBNOVDVUYu+ZPahuqMKN\nXUYFrY6WOJ4cTy2TG0+zOdbjZ3jrmChKiUiwwCQN53E8yR0GWqIo5UuChWDUESk4nuQOAy2RAlKj\nhOPW44quNEQsegmkDhEJFkQnaRAxnqGixfEkbWCgJQqAzWHDzH89g2F/H4Sei3pi2N8HYea/noHN\nYQuojsF/6x/0OkQkWDDFmDCq2xjZslFdR/v8aIuI8Qw1U4wJY7qPky0b022sz+PJpBeRh0vYiAIg\nYpcWLdQhIsHCrtM7/Hpdth3c9aYZk15EHiasiEIcT2WkRgnD/j4I+TWn2pRlJXTFd5N3eb3y0Eod\nLesKJMFC2dlS9H2vB+xOe5syg86AA//vGFLap3r92aL6EUqi+8EEINrEhBVEQaCVRS8iF86YYkzo\nnnSx33/QD5UdlA2yAGB32nGo7KDXOrS4AKigOh+fHPkQBdX5Pn9GdD8CPSaiRUrijFD2g7eOifzU\ntGBF7srFn0UvHU2dUCQVtinr2L6jXwtnlLRDqd4pfaCDDk60uDF2CsB+AHXAquMfIW5yLAYOvMZt\nHa5+ZCC/pm1Q6xyfEdQFQDUNNRiwsh+sdWXNr1niUrDn3v2Ibxfv8bNa6ocIkZI4Qwv94BUtkZ9E\nLSIqdnOFc1oqDpuFMyntU6Fr+jPSAOAjAH8FsBvAfuDvK1bijjsm4MEH74UkyV9JmGJMSIpLli1L\nik0K6hXdhUEWAKx1ZRiwsp/Xz2qpHyI0zZvn15yCA47mefOc7TNC3TS/aKEfDLREAcgZMg8PXz4V\nWQldYdAZkJXQFQ9fPtXnBSvHyo/CAflbrg7Ycaz8aFDaoVRBdf75fnwK4DCACxYK19WdxT/+sQ7T\npz8sW4fUKKHibIVsWUVdZdBu9RVU57cJsk2sdWVebyNrpR8iREriDK30I3yu/4k0xKg3Yu618/H8\n1bNhi6uBsS7eryuWTT9/4bW8R4eefrUjFLvF7Cja5vqPUwCOeX7v5s2bsGfPLgwYcHWr189IxSiq\nbXsLHQBO1xbijFSM7kkXC2itZ8198VB+52WT3ZZrpR8i+DLfHA590Uo/eEVLUUvE4ghrXRl2FOxw\neyXkzqhuYxWVixboWAzuPNT1H/vR5kr2QnV1Z7Fq1SdtXlcj6YWivgRYHknJJkT2JZSLkLRyTBho\nKeqISBRR01CDXnnd0X9FH9z36X3ov6IPeuV1R01DjU+f79GhJ2INcbJlsYY4n65mRfRF6eczE7Jg\nibUAdT69HVVVlW1ecyW9kP9iMarrmICSXgTcl7gU2TJLXAoyE7I8fl4Lc+aiiOiLiN8zpbRyTHjr\nmKKOiOQInhbN/PjgSZ/q2P/AUfR7vyfq7eejVKwhDvsf8G1+FhCQsELAWKSbOsIaZ/XpvYmJSbKv\n7zq93a/X5Yjoy55797tddexTGyIo2YTSvmglCYkWjgkTVkShaB5PEUkFCqrz0X9FH7fl++476PXq\np6Vj5Uex6ecvMKrbWJ+vZAHlfRExFs0JK36xu1Ybe7hYiYtrjzVr1rWZo9Vi0ouC6nzsKNqGwZ2H\n+nUsW7YnFHPmcpT+vgfSFy0mIVEzAQgTVmhIpDz4Hc5EJBXwZdGMP3p06IlpV/7BryALKO+LiLFo\nTljRBUAPz+8dOXJUmyDbqg4ZoUp6kZmQhTsvmxxQkAXEJJvQyt+LQPqixSQkoUwAwkAbBFqYqyAX\nEYsjlC6aESXd1BGd4zNkyzqbO3vti4ix6J3SBwadwfWP2wFko82EVFxce9x8861YvDjXbR066GTL\ndNChd4r7uwdNtLLoRYRI+HsRScdDBAbaINDCA9PkImJxRGZClseFTIFeBfnLFGOCZJO/2qm1SV77\nImIsUtqnIjult+sf7QDcBeB+AAOBpIHJePDBh7BmzTrk5a2EySRfX0r7VLQzxMqWtTPEer1tLKov\nWhEJfy8i6XiIwMVQKvP2wPTzV8+OupMu1JQujpAaJaTGpaKwtu2tsbS4VEiN3oOcCGVnS1FRVy5b\nVlFXjrKzpV6DlIiFIhsmbsH4NSNwuOwQ7E47DF0NyO7fGxsmbkGcUf4LyYX9sDkaZctsjkaf+iGq\nL6EWSX8vIuF4iMLFUCo7WXkCg//WHw442pQZdAZsv3tv0B/8DufxFCnQxRFaOabfFXyLO9bd4rZ8\n9a2fY1jmdT7VJWKhSNnZUhwqO4jeKX18CoxNRPYD0NZCJH+pcW6F+vc9nI+HHC6G0iDOVWhXoIsj\ntHJMW82PXsCgM/g0t9lExEKRlPapGJZ5nV9BFhDbD0A7u94EQo3kHcetx0O6oCqcj4coDLQq41xF\n5NHKMU1pn4pelmzZsl6WbL8DXqi0mue9QHZK77Dphwiizq2WC6p6LuoZlguqIokhJycnR2SFktQg\nsjoAgNkcq0q9wTI88wZUN1ThV6kEtY01yEzogsm97kbOkHnQ64L/XSfcx1MLtHJMf7Qewr9/3dfm\n9VsumYCRXccErR1K3XXZPdj0yxcoO1sGJ5ww6Azok9oXGyZuCast2UQQcW69sO055P6wBFUNlXDC\niaqGSuw9swfVDVW4scsolXsQ2eT+fprN8ov5mnCONoi0MlcRKeOpBVKjFNCmAqJ+ttaSAihVdrYU\nRfaT6GzoHlVXsnIC/XsRieeFlnCOVuM4V0EiaTEpgFIp7VNxY/cboz7IAoH/vYjE8yLcMdASBUAL\nc2BaWZRF2sLzQnsYaIkCoIWkAlpZlEXawvNCe6JrlQGRAFpKKsCkACSH54W2cDFUFOJ4KqOVhBUt\naWWhnQg8P8UJ5WK9SKXaYqijR49i5MiRWLlyZeCtI4oQWpwD40K787Sy640WmGJMuMRyCc+LEPMa\naCVJwksvvYTBgwcHoz1Emsc5MG2KhF1vKDJ5naNt164dli1bhmXLlgWjPURhgXNg2tO0QK1J0wI1\nAJh77fxQNYvIe6A1Go0wGrlmiqglo96IudfOx/NXz+YcmAZoaYEa0YWER9AOHUwwGuUThCvhbbKZ\n/MPxFCUBQHqoGxFx/D0/j1t/9ZikwRZXgzRL9B4n/r6L5e94Cg+05eXiFyBwFaJYHE+xOJ5iBTKe\nxsZ4ZMRnyqYd7ByfCWNdfNQeI56fYjEFIxFFJS5QIy3zekV74MABzJ8/H4WFhTAajfjyyy+xaNEi\nJCcnB6N9REQ+4QI10iomrIhCHE+xOJ5iKR3PSEreIQLPT7ECuXXM5cREFFGakncQaQXnaImIiFTE\nQEtERKQiBloiIiIVMdASERGpiIGWiIhIRQy0REREKmKgJSIiUhEDLRERkYoYaImIiFQkPAUjERER\nnccrWiIiIhUx0BIREamIgZaIiEhFDLREREQqYqAlIiJSEQMtERGRijS38fuCBQuwd+9e2Gw2PPLI\nIxg9enRz2fbt2/HGG2/AYDBg+PDhePTRR0PY0vDgaTxvvPFGdOzYEQaDAQDw2muvIT09PVRN1bSz\nZ8/i2WefRVlZGerr6zFt2jTccMMNzeU8N/3jbTx5bgamrq4ON998M6ZNm4aJEyc2v87zMzDuxtPv\n89OpITt27HD+13/9l9PpdDqtVqvzuuuua1U+btw4Z1FRkdNutzt/97vfOY8dOxaCVoYPb+N5ww03\nOGtqakLQsvCzfv16Z25urtPpdDoLCgqco0ePblXOc9M/3saT52Zg3njjDefEiROdq1evbvU6z8/A\nuBtPf89PTV3RDhw4EJdffjkAIDExEWfPnoXdbofBYEB+fj6SkpLQqVMnAMB1112HHTt24NJLLw1l\nkzXN03iSf8aPH9/836dPn2717ZXnpv88jScF5vjx4/jpp59w/fXXt3qd52dg3I1nIDQVaA0GA0wm\nEwBg1apVGD58eHNQKCkpgcViaX6vxWJBfn5+SNoZLjyNZ5PZs2ejsLAQV111Ff70pz9Bp9OFoqlh\nY/LkySguLsbSpUubX+O5GTi58WzCc9M/8+fPx6xZs7B27dpWr/P8DIy78Wziz/mpqUDbZPPmzVi1\nahXy8vJC3ZSI4G48//CHP2DYsGFISkrCo48+ii+//BJjx44NUSvDw4cffojDhw/jqaeewrp16/jH\nXyF348lz0z9r167FFVdcgaysrFA3JSJ4G09/z0/NBdrvvvsOS5cuxfLly5GQkND8+kUXXYTS0tLm\nf585cwYXXXRRKJoYVtyNJwDcdtttzf89fPhwHD16lH/M3Dhw4ABSUlLQqVMnZGdnw263w2q1IiUl\nhedmADyNJ8Bz019bt25Ffn4+tm7diuLiYrRr1w4dO3bEkCFDeH4GwNN4Av6fn5p6vKe6uhoLFizA\nO++8g+Tk5FZlmZmZqKmpQUFBAWw2G7755hsMHTo0RC0ND57Gs7q6GlOmTEFDQwMAYPfu3ejRo0co\nmhkW9uzZ03xHoLS0FJIkoUOHDgB4bgbC03jy3PTfwoULsXr1anz88ce48847MW3atOagwPPTf57G\nM5DzU1O793z00UdYtGgRunfv3vza1VdfjcsuuwyjRo3C7t278dprrwEARo8ejSlTpoSqqWHB23i+\n//77WLt2LWJjY9G7d2/MmjWLt0LdqKurw4wZM3D69GnU1dVh+vTpqKioQEJCAs/NAHgbT56bgVu0\naBEyMjIAgOenAHLj6e/5qalAS0REFGk0deuYiIgo0jDQEhERqYiBloiISEUMtERERCpioCUiIlIR\nAy0REZGKGGiJiIhUxEBLRESkov8P3i6XhET09kMAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<matplotlib.figure.Figure at 0x7fc353554cc0>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "metadata": {
        "id": "QTFqbNF9S_S0",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "import math\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as pyplot\n",
        "from sklearn.metrics.pairwise import cosine_similarity\n",
        "from sklearn.feature_extraction.text import TfidfVectorizer\n",
        "\n",
        "vectorizer = TfidfVectorizer()\n",
        "\n",
        "corpus = [\n",
        " 'the brown fox jumped over the brown dog',\n",
        " 'the quick brown fox',\n",
        " 'the brown brown dog',\n",
        " 'the fox ate the dog'\n",
        "]\n",
        "\n",
        "query = [\"brown\"]\n",
        "\n",
        "X = vectorizer.fit_transform(corpus)\n",
        "Y = vectorizer.transform(query)\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "22-EENP6UVu0",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 191
        },
        "outputId": "60179809-2bd6-4abf-8b65-74bf02fb8f06"
      },
      "cell_type": "code",
      "source": [
        "print(vectorizer.get_feature_names())\n",
        "print(X.toarray())\n",
        "print(Y)"
      ],
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "['ate', 'brown', 'dog', 'fox', 'jumped', 'over', 'quick', 'the']\n",
            "[[0.         0.54267123 0.27133562 0.27133562 0.42509967 0.42509967\n",
            "  0.         0.44366929]\n",
            " [0.         0.44181486 0.         0.44181486 0.         0.\n",
            "  0.69218835 0.3612126 ]\n",
            " [0.         0.84003859 0.4200193  0.         0.         0.\n",
            "  0.         0.3433933 ]\n",
            " [0.58680608 0.         0.37455072 0.37455072 0.         0.\n",
            "  0.         0.61243951]]\n",
            "  (0, 1)\t1.0\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "i6TF93H2Usap",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "results=[]\n",
        "results.append(cosine_similarity(Y, X.toarray()))"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "FxkOtFoAXcFs",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "aad76308-c677-4152-cd75-a4f697c5780b"
      },
      "cell_type": "code",
      "source": [
        "results"
      ],
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[array([[0.54267123, 0.44181486, 0.84003859, 0.        ]])]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 20
        }
      ]
    },
    {
      "metadata": {
        "id": "vvnghgs4X2mG",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "ea8d247b-5676-451d-81a5-fec23bee08d6"
      },
      "cell_type": "code",
      "source": [
        "cosine_similarity(Y, X.toarray())"
      ],
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[0.54267123, 0.44181486, 0.84003859, 0.        ]])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 21
        }
      ]
    }
  ]
}