extreme_learning_machines.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "extreme_learning_machines.ipynb",
      "provenance": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "code",
      "execution_count": 5,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 419
        },
        "id": "ly76n83Xas-4",
        "outputId": "8ee07a0e-6eba-4c75-ec3d-8f62e0935ece"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:2: FutureWarning: The pandas.datetime class is deprecated and will be removed from pandas in a future version. Import from datetime module instead.\n",
            "  \n"
          ]
        },
        {
          "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>Country</th>\n",
              "      <th>Date</th>\n",
              "      <th>Total Number</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>South Korea</td>\n",
              "      <td>1/22/2020</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>South Korea</td>\n",
              "      <td>1/23/2020</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>South Korea</td>\n",
              "      <td>1/24/2020</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>South Korea</td>\n",
              "      <td>1/25/2020</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>South Korea</td>\n",
              "      <td>1/26/2020</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>South Korea</td>\n",
              "      <td>1/27/2020</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>South Korea</td>\n",
              "      <td>1/28/2020</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>South Korea</td>\n",
              "      <td>1/29/2020</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>South Korea</td>\n",
              "      <td>1/30/2020</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>South Korea</td>\n",
              "      <td>1/31/2020</td>\n",
              "      <td>11</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "       Country       Date  Total Number\n",
              "0  South Korea  1/22/2020             1\n",
              "1  South Korea  1/23/2020             1\n",
              "2  South Korea  1/24/2020             2\n",
              "3  South Korea  1/25/2020             2\n",
              "4  South Korea  1/26/2020             3\n",
              "5  South Korea  1/27/2020             4\n",
              "6  South Korea  1/28/2020             4\n",
              "7  South Korea  1/29/2020             4\n",
              "8  South Korea  1/30/2020             4\n",
              "9  South Korea  1/31/2020            11"
            ]
          },
          "metadata": {},
          "execution_count": 5
        }
      ],
      "source": [
        "import pandas as pd\n",
        "from pandas import datetime\n",
        "def parser(x):\n",
        "    return datetime.strptime(x,\"%Y-%m-%d\")\n",
        "data = pd.read_csv('covid_numbers_korea.csv')\n",
        "data.head(10)"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import matplotlib.pyplot as plt\n",
        "%matplotlib inline\n",
        "num_data=data.iloc[:, 2]\n",
        "num_data = num_data.astype(float)\n",
        "\n",
        "num_data.plot(style='k.',ylabel='Numbers',xlabel='Day#')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 279
        },
        "id": "Fha7C6QWcQRg",
        "outputId": "11107056-1724-4f69-c977-912a36b191a9"
      },
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.metrics import mean_squared_error\n",
        "import math\n",
        "import numpy as np\n",
        "from scipy.linalg import pinv2\n",
        "num_data = np.array(num_data)\n",
        "num_data = np.reshape(num_data, (-1, 1))\n",
        "print(num_data.shape)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "7Owd-Uivc2jI",
        "outputId": "c15b3268-b4a3-495c-bcd5-6de35f32d0d1"
      },
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(619, 1)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "m = 14\n",
        "per = (1736 - m)/1826\n",
        "size = int(len(num_data) * per)\n",
        "d_train, d_test = num_data[0:size], num_data[size:len(num_data)]\n",
        "mean_train = np.mean(d_train)\n",
        "sd_train = np.std(d_train)\n",
        "d_train = (d_train-mean_train)/sd_train\n",
        "d_test = (d_test-mean_train)/sd_train"
      ],
      "metadata": {
        "id": "iiDAqxvfgsOZ"
      },
      "execution_count": 27,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "X_train = np.array([d_train[i][0] for i in range(m)])\n",
        "y_train = np.array(d_train[m][0])\n",
        "for i in range(1,(d_train.shape[0]-m)):\n",
        "    l = np.array([d_train[j][0] for j in range(i,i+m)])\n",
        "    X_train = np.vstack([X_train,l])\n",
        "    y_train = np.vstack([y_train,d_train[i+m]])\n",
        "X_test = np.array([d_test[i][0] for i in range(m)])\n",
        "y_test = np.array(d_test[m][0])\n",
        "for i in range(1,(d_test.shape[0]-m)):\n",
        "    l = np.array([d_test[j][0] for j in range(i,i+m)])\n",
        "    X_test = np.vstack([X_test,l])\n",
        "    y_test = np.vstack([y_test,d_test[i+m]])\n",
        "print(X_train.shape)\n",
        "print(y_train.shape)\n",
        "print(X_test.shape)\n",
        "print(y_test.shape)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "COIs6E5yg83H",
        "outputId": "20aff992-59a7-46c7-c0c5-d0cddcc81672"
      },
      "execution_count": 28,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(569, 14)\n",
            "(569, 1)\n",
            "(22, 14)\n",
            "(22, 1)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import scipy.stats as stats\n",
        "input_size = X_train.shape[1]\n",
        "hidden_size = 100   #no. of hidden neurons\n",
        "mu, sigma = 0, 1\n",
        "w_lo = -1 \n",
        "w_hi = 1\n",
        "b_lo = -1 \n",
        "b_hi = 1\n",
        "#initialising input weights and biases randomly drawn from a truncated normal distribution\n",
        "input_weights = stats.truncnorm.rvs((w_lo - mu) / sigma, (w_hi - mu) / sigma, loc=mu, scale=sigma,size=[input_size,hidden_size])\n",
        "biases = stats.truncnorm.rvs((b_lo - mu) / sigma, (b_hi - mu) / sigma, loc=mu, scale=sigma,size=[hidden_size])\n",
        "\n",
        "\n",
        "def relu(x):      #hidden layer activation function\n",
        "    return np.maximum(x, 0, x)\n",
        "\n",
        "def hidden_nodes(X):\n",
        "    G = np.dot(X, input_weights)\n",
        "    G = G + biases\n",
        "    H = relu(G)\n",
        "    return H\n",
        "output_weights = np.dot(pinv2(hidden_nodes(X_train)), y_train)\n",
        "def predict(X):\n",
        "    out = hidden_nodes(X)\n",
        "    out = np.dot(out, output_weights)\n",
        "    return out\n",
        "prediction = predict(X_test)"
      ],
      "metadata": {
        "id": "F8PgwHfthPIA"
      },
      "execution_count": 25,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "correct = 0\n",
        "total = X_test.shape[0]\n",
        "y_test = (y_test*sd_train) + mean_train\n",
        "prediction = (prediction*sd_train) + mean_train\n",
        "# evaluate forecasts\n",
        "rmse = math.sqrt(mean_squared_error(y_test, prediction))\n",
        "print('Test RMSE: %.3f' % rmse)\n",
        "mape_sum = 0\n",
        "for i,j in zip(y_test,prediction):\n",
        "    mape_sum = mape_sum + (abs((i-j)/i))\n",
        "mape = (mape_sum/total)*100\n",
        "mpe_sum = 0\n",
        "for i,j in zip(y_test,prediction):\n",
        "    mpe_sum = mpe_sum + ((i-j)/i)\n",
        "mpe = (mpe_sum/total)*100\n",
        "print('Test MAPE: %.3f' % mape)\n",
        "print('Test MPE: %.3f' % mpe)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 394
        },
        "id": "yfZX32I9hgez",
        "outputId": "eddcd09b-e55d-457c-d6ac-1408ed263841"
      },
      "execution_count": 26,
      "outputs": [
        {
          "output_type": "error",
          "ename": "TypeError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-26-9ef58176b40d>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      4\u001b[0m \u001b[0mprediction\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mprediction\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0msd_train\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0mmean_train\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m \u001b[0;31m# evaluate forecasts\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 6\u001b[0;31m \u001b[0mrmse\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmath\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msqrt\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmean_squared_error\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0my_test\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mprediction\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      7\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'Test RMSE: %.3f'\u001b[0m \u001b[0;34m%\u001b[0m \u001b[0mrmse\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      8\u001b[0m \u001b[0mmape_sum\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/sklearn/metrics/_regression.py\u001b[0m in \u001b[0;36mmean_squared_error\u001b[0;34m(y_true, y_pred, sample_weight, multioutput, squared)\u001b[0m\n\u001b[1;32m    422\u001b[0m     \"\"\"\n\u001b[1;32m    423\u001b[0m     y_type, y_true, y_pred, multioutput = _check_reg_targets(\n\u001b[0;32m--> 424\u001b[0;31m         \u001b[0my_true\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_pred\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmultioutput\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    425\u001b[0m     )\n\u001b[1;32m    426\u001b[0m     \u001b[0mcheck_consistent_length\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0my_true\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_pred\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msample_weight\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/sklearn/metrics/_regression.py\u001b[0m in \u001b[0;36m_check_reg_targets\u001b[0;34m(y_true, y_pred, multioutput, dtype)\u001b[0m\n\u001b[1;32m     87\u001b[0m         \u001b[0mthe\u001b[0m \u001b[0mdtype\u001b[0m \u001b[0margument\u001b[0m \u001b[0mpassed\u001b[0m \u001b[0mto\u001b[0m \u001b[0mcheck_array\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     88\u001b[0m     \"\"\"\n\u001b[0;32m---> 89\u001b[0;31m     \u001b[0mcheck_consistent_length\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0my_true\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_pred\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     90\u001b[0m     \u001b[0my_true\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcheck_array\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0my_true\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mensure_2d\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdtype\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     91\u001b[0m     \u001b[0my_pred\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcheck_array\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0my_pred\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mensure_2d\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdtype\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/sklearn/utils/validation.py\u001b[0m in \u001b[0;36mcheck_consistent_length\u001b[0;34m(*arrays)\u001b[0m\n\u001b[1;32m    326\u001b[0m     \"\"\"\n\u001b[1;32m    327\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 328\u001b[0;31m     \u001b[0mlengths\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0m_num_samples\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mX\u001b[0m \u001b[0;32min\u001b[0m \u001b[0marrays\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mX\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    329\u001b[0m     \u001b[0muniques\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0munique\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlengths\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    330\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0muniques\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m>\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/sklearn/utils/validation.py\u001b[0m in \u001b[0;36m<listcomp>\u001b[0;34m(.0)\u001b[0m\n\u001b[1;32m    326\u001b[0m     \"\"\"\n\u001b[1;32m    327\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 328\u001b[0;31m     \u001b[0mlengths\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0m_num_samples\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mX\u001b[0m \u001b[0;32min\u001b[0m \u001b[0marrays\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mX\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    329\u001b[0m     \u001b[0muniques\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0munique\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlengths\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    330\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0muniques\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m>\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/sklearn/utils/validation.py\u001b[0m in \u001b[0;36m_num_samples\u001b[0;34m(x)\u001b[0m\n\u001b[1;32m    267\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    268\u001b[0m             raise TypeError(\n\u001b[0;32m--> 269\u001b[0;31m                 \u001b[0;34m\"Singleton array %r cannot be considered a valid collection.\"\u001b[0m \u001b[0;34m%\u001b[0m \u001b[0mx\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    270\u001b[0m             )\n\u001b[1;32m    271\u001b[0m         \u001b[0;31m# Check that shape is returning an integer or default to len\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mTypeError\u001b[0m: Singleton array 316020.0 cannot be considered a valid collection."
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# plot forecasts against actual outcomes\n",
        "fig, ax = plt.subplots(figsize = (10,6))\n",
        "ax.plot(y_test,label = 'Actual')\n",
        "ax.plot(prediction, color='red',label = 'Predictions')\n",
        "ax.legend(loc='upper right', frameon=False)\n",
        "plt.xlabel('Days',fontname=\"Arial\", fontsize=20, style='italic', fontweight='bold')\n",
        "plt.ylabel('Cummulative Numbers',fontname=\"Arial\", fontsize=2, style='italic', fontweight='bold')\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 434
        },
        "id": "7KNuEv-shoID",
        "outputId": "6eb780a8-defe-474b-cb59-b193b8a5d346"
      },
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0, 0.5, 'Cummulative Numbers')"
            ]
          },
          "metadata": {},
          "execution_count": 21
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "findfont: Font family ['Arial'] not found. Falling back to DejaVu Sans.\n",
            "findfont: Font family ['Arial'] not found. Falling back to DejaVu Sans.\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 720x432 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    }
  ]
}