Assignment5.ipynb

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      "name": "Evaluation Lab-1.ipynb",
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      "name": "python3",
      "display_name": "Python 3"
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    "language_info": {
      "name": "python"
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  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "LpespbSxJvW6"
      },
      "source": [
        "Name - Amitesh Kumar</br>\n",
        "Roll No - 1901023<br/>\n",
        "Email - amitesh.kumar@iiitg.ac.in"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "0awEmqQEKgj-"
      },
      "source": [
        "1. Implement the linear regression using the Gradient Descent\n",
        "Algorithm (batch mode) using the given data splittings:\n",
        "Dataset: Boston house pricing dataset."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "DVxKktGNJMZO"
      },
      "source": [
        "from sklearn import datasets\n",
        "boston = datasets.load_boston(return_X_y=False)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "yBEWXeseJw3E",
        "outputId": "f3958157-fc34-4c6d-fd6c-bac64fe5f622"
      },
      "source": [
        "print(boston)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "{'data': array([[6.3200e-03, 1.8000e+01, 2.3100e+00, ..., 1.5300e+01, 3.9690e+02,\n",
            "        4.9800e+00],\n",
            "       [2.7310e-02, 0.0000e+00, 7.0700e+00, ..., 1.7800e+01, 3.9690e+02,\n",
            "        9.1400e+00],\n",
            "       [2.7290e-02, 0.0000e+00, 7.0700e+00, ..., 1.7800e+01, 3.9283e+02,\n",
            "        4.0300e+00],\n",
            "       ...,\n",
            "       [6.0760e-02, 0.0000e+00, 1.1930e+01, ..., 2.1000e+01, 3.9690e+02,\n",
            "        5.6400e+00],\n",
            "       [1.0959e-01, 0.0000e+00, 1.1930e+01, ..., 2.1000e+01, 3.9345e+02,\n",
            "        6.4800e+00],\n",
            "       [4.7410e-02, 0.0000e+00, 1.1930e+01, ..., 2.1000e+01, 3.9690e+02,\n",
            "        7.8800e+00]]), 'target': array([24. , 21.6, 34.7, 33.4, 36.2, 28.7, 22.9, 27.1, 16.5, 18.9, 15. ,\n",
            "       18.9, 21.7, 20.4, 18.2, 19.9, 23.1, 17.5, 20.2, 18.2, 13.6, 19.6,\n",
            "       15.2, 14.5, 15.6, 13.9, 16.6, 14.8, 18.4, 21. , 12.7, 14.5, 13.2,\n",
            "       13.1, 13.5, 18.9, 20. , 21. , 24.7, 30.8, 34.9, 26.6, 25.3, 24.7,\n",
            "       21.2, 19.3, 20. , 16.6, 14.4, 19.4, 19.7, 20.5, 25. , 23.4, 18.9,\n",
            "       35.4, 24.7, 31.6, 23.3, 19.6, 18.7, 16. , 22.2, 25. , 33. , 23.5,\n",
            "       19.4, 22. , 17.4, 20.9, 24.2, 21.7, 22.8, 23.4, 24.1, 21.4, 20. ,\n",
            "       20.8, 21.2, 20.3, 28. , 23.9, 24.8, 22.9, 23.9, 26.6, 22.5, 22.2,\n",
            "       23.6, 28.7, 22.6, 22. , 22.9, 25. , 20.6, 28.4, 21.4, 38.7, 43.8,\n",
            "       33.2, 27.5, 26.5, 18.6, 19.3, 20.1, 19.5, 19.5, 20.4, 19.8, 19.4,\n",
            "       21.7, 22.8, 18.8, 18.7, 18.5, 18.3, 21.2, 19.2, 20.4, 19.3, 22. ,\n",
            "       20.3, 20.5, 17.3, 18.8, 21.4, 15.7, 16.2, 18. , 14.3, 19.2, 19.6,\n",
            "       23. , 18.4, 15.6, 18.1, 17.4, 17.1, 13.3, 17.8, 14. , 14.4, 13.4,\n",
            "       15.6, 11.8, 13.8, 15.6, 14.6, 17.8, 15.4, 21.5, 19.6, 15.3, 19.4,\n",
            "       17. , 15.6, 13.1, 41.3, 24.3, 23.3, 27. , 50. , 50. , 50. , 22.7,\n",
            "       25. , 50. , 23.8, 23.8, 22.3, 17.4, 19.1, 23.1, 23.6, 22.6, 29.4,\n",
            "       23.2, 24.6, 29.9, 37.2, 39.8, 36.2, 37.9, 32.5, 26.4, 29.6, 50. ,\n",
            "       32. , 29.8, 34.9, 37. , 30.5, 36.4, 31.1, 29.1, 50. , 33.3, 30.3,\n",
            "       34.6, 34.9, 32.9, 24.1, 42.3, 48.5, 50. , 22.6, 24.4, 22.5, 24.4,\n",
            "       20. , 21.7, 19.3, 22.4, 28.1, 23.7, 25. , 23.3, 28.7, 21.5, 23. ,\n",
            "       26.7, 21.7, 27.5, 30.1, 44.8, 50. , 37.6, 31.6, 46.7, 31.5, 24.3,\n",
            "       31.7, 41.7, 48.3, 29. , 24. , 25.1, 31.5, 23.7, 23.3, 22. , 20.1,\n",
            "       22.2, 23.7, 17.6, 18.5, 24.3, 20.5, 24.5, 26.2, 24.4, 24.8, 29.6,\n",
            "       42.8, 21.9, 20.9, 44. , 50. , 36. , 30.1, 33.8, 43.1, 48.8, 31. ,\n",
            "       36.5, 22.8, 30.7, 50. , 43.5, 20.7, 21.1, 25.2, 24.4, 35.2, 32.4,\n",
            "       32. , 33.2, 33.1, 29.1, 35.1, 45.4, 35.4, 46. , 50. , 32.2, 22. ,\n",
            "       20.1, 23.2, 22.3, 24.8, 28.5, 37.3, 27.9, 23.9, 21.7, 28.6, 27.1,\n",
            "       20.3, 22.5, 29. , 24.8, 22. , 26.4, 33.1, 36.1, 28.4, 33.4, 28.2,\n",
            "       22.8, 20.3, 16.1, 22.1, 19.4, 21.6, 23.8, 16.2, 17.8, 19.8, 23.1,\n",
            "       21. , 23.8, 23.1, 20.4, 18.5, 25. , 24.6, 23. , 22.2, 19.3, 22.6,\n",
            "       19.8, 17.1, 19.4, 22.2, 20.7, 21.1, 19.5, 18.5, 20.6, 19. , 18.7,\n",
            "       32.7, 16.5, 23.9, 31.2, 17.5, 17.2, 23.1, 24.5, 26.6, 22.9, 24.1,\n",
            "       18.6, 30.1, 18.2, 20.6, 17.8, 21.7, 22.7, 22.6, 25. , 19.9, 20.8,\n",
            "       16.8, 21.9, 27.5, 21.9, 23.1, 50. , 50. , 50. , 50. , 50. , 13.8,\n",
            "       13.8, 15. , 13.9, 13.3, 13.1, 10.2, 10.4, 10.9, 11.3, 12.3,  8.8,\n",
            "        7.2, 10.5,  7.4, 10.2, 11.5, 15.1, 23.2,  9.7, 13.8, 12.7, 13.1,\n",
            "       12.5,  8.5,  5. ,  6.3,  5.6,  7.2, 12.1,  8.3,  8.5,  5. , 11.9,\n",
            "       27.9, 17.2, 27.5, 15. , 17.2, 17.9, 16.3,  7. ,  7.2,  7.5, 10.4,\n",
            "        8.8,  8.4, 16.7, 14.2, 20.8, 13.4, 11.7,  8.3, 10.2, 10.9, 11. ,\n",
            "        9.5, 14.5, 14.1, 16.1, 14.3, 11.7, 13.4,  9.6,  8.7,  8.4, 12.8,\n",
            "       10.5, 17.1, 18.4, 15.4, 10.8, 11.8, 14.9, 12.6, 14.1, 13. , 13.4,\n",
            "       15.2, 16.1, 17.8, 14.9, 14.1, 12.7, 13.5, 14.9, 20. , 16.4, 17.7,\n",
            "       19.5, 20.2, 21.4, 19.9, 19. , 19.1, 19.1, 20.1, 19.9, 19.6, 23.2,\n",
            "       29.8, 13.8, 13.3, 16.7, 12. , 14.6, 21.4, 23. , 23.7, 25. , 21.8,\n",
            "       20.6, 21.2, 19.1, 20.6, 15.2,  7. ,  8.1, 13.6, 20.1, 21.8, 24.5,\n",
            "       23.1, 19.7, 18.3, 21.2, 17.5, 16.8, 22.4, 20.6, 23.9, 22. , 11.9]), 'feature_names': array(['CRIM', 'ZN', 'INDUS', 'CHAS', 'NOX', 'RM', 'AGE', 'DIS', 'RAD',\n",
            "       'TAX', 'PTRATIO', 'B', 'LSTAT'], dtype='<U7'), 'DESCR': \".. _boston_dataset:\\n\\nBoston house prices dataset\\n---------------------------\\n\\n**Data Set Characteristics:**  \\n\\n    :Number of Instances: 506 \\n\\n    :Number of Attributes: 13 numeric/categorical predictive. Median Value (attribute 14) is usually the target.\\n\\n    :Attribute Information (in order):\\n        - CRIM     per capita crime rate by town\\n        - ZN       proportion of residential land zoned for lots over 25,000 sq.ft.\\n        - INDUS    proportion of non-retail business acres per town\\n        - CHAS     Charles River dummy variable (= 1 if tract bounds river; 0 otherwise)\\n        - NOX      nitric oxides concentration (parts per 10 million)\\n        - RM       average number of rooms per dwelling\\n        - AGE      proportion of owner-occupied units built prior to 1940\\n        - DIS      weighted distances to five Boston employment centres\\n        - RAD      index of accessibility to radial highways\\n        - TAX      full-value property-tax rate per $10,000\\n        - PTRATIO  pupil-teacher ratio by town\\n        - B        1000(Bk - 0.63)^2 where Bk is the proportion of blacks by town\\n        - LSTAT    % lower status of the population\\n        - MEDV     Median value of owner-occupied homes in $1000's\\n\\n    :Missing Attribute Values: None\\n\\n    :Creator: Harrison, D. and Rubinfeld, D.L.\\n\\nThis is a copy of UCI ML housing dataset.\\nhttps://archive.ics.uci.edu/ml/machine-learning-databases/housing/\\n\\n\\nThis dataset was taken from the StatLib library which is maintained at Carnegie Mellon University.\\n\\nThe Boston house-price data of Harrison, D. and Rubinfeld, D.L. 'Hedonic\\nprices and the demand for clean air', J. Environ. Economics & Management,\\nvol.5, 81-102, 1978.   Used in Belsley, Kuh & Welsch, 'Regression diagnostics\\n...', Wiley, 1980.   N.B. Various transformations are used in the table on\\npages 244-261 of the latter.\\n\\nThe Boston house-price data has been used in many machine learning papers that address regression\\nproblems.   \\n     \\n.. topic:: References\\n\\n   - Belsley, Kuh & Welsch, 'Regression diagnostics: Identifying Influential Data and Sources of Collinearity', Wiley, 1980. 244-261.\\n   - Quinlan,R. (1993). Combining Instance-Based and Model-Based Learning. In Proceedings on the Tenth International Conference of Machine Learning, 236-243, University of Massachusetts, Amherst. Morgan Kaufmann.\\n\", 'filename': '/usr/local/lib/python3.7/dist-packages/sklearn/datasets/data/boston_house_prices.csv'}\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "5NoA1GOWJxVj",
        "outputId": "69d7531e-83c2-4f20-cce2-f93383882026"
      },
      "source": [
        "print(boston['feature_names'])"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "['CRIM' 'ZN' 'INDUS' 'CHAS' 'NOX' 'RM' 'AGE' 'DIS' 'RAD' 'TAX' 'PTRATIO'\n",
            " 'B' 'LSTAT']\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ItmE8zl-Ma6s",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "b7484bd2-d035-49c6-8d7f-02db169da7e5"
      },
      "source": [
        "print(boston['target'])"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[24.  21.6 34.7 33.4 36.2 28.7 22.9 27.1 16.5 18.9 15.  18.9 21.7 20.4\n",
            " 18.2 19.9 23.1 17.5 20.2 18.2 13.6 19.6 15.2 14.5 15.6 13.9 16.6 14.8\n",
            " 18.4 21.  12.7 14.5 13.2 13.1 13.5 18.9 20.  21.  24.7 30.8 34.9 26.6\n",
            " 25.3 24.7 21.2 19.3 20.  16.6 14.4 19.4 19.7 20.5 25.  23.4 18.9 35.4\n",
            " 24.7 31.6 23.3 19.6 18.7 16.  22.2 25.  33.  23.5 19.4 22.  17.4 20.9\n",
            " 24.2 21.7 22.8 23.4 24.1 21.4 20.  20.8 21.2 20.3 28.  23.9 24.8 22.9\n",
            " 23.9 26.6 22.5 22.2 23.6 28.7 22.6 22.  22.9 25.  20.6 28.4 21.4 38.7\n",
            " 43.8 33.2 27.5 26.5 18.6 19.3 20.1 19.5 19.5 20.4 19.8 19.4 21.7 22.8\n",
            " 18.8 18.7 18.5 18.3 21.2 19.2 20.4 19.3 22.  20.3 20.5 17.3 18.8 21.4\n",
            " 15.7 16.2 18.  14.3 19.2 19.6 23.  18.4 15.6 18.1 17.4 17.1 13.3 17.8\n",
            " 14.  14.4 13.4 15.6 11.8 13.8 15.6 14.6 17.8 15.4 21.5 19.6 15.3 19.4\n",
            " 17.  15.6 13.1 41.3 24.3 23.3 27.  50.  50.  50.  22.7 25.  50.  23.8\n",
            " 23.8 22.3 17.4 19.1 23.1 23.6 22.6 29.4 23.2 24.6 29.9 37.2 39.8 36.2\n",
            " 37.9 32.5 26.4 29.6 50.  32.  29.8 34.9 37.  30.5 36.4 31.1 29.1 50.\n",
            " 33.3 30.3 34.6 34.9 32.9 24.1 42.3 48.5 50.  22.6 24.4 22.5 24.4 20.\n",
            " 21.7 19.3 22.4 28.1 23.7 25.  23.3 28.7 21.5 23.  26.7 21.7 27.5 30.1\n",
            " 44.8 50.  37.6 31.6 46.7 31.5 24.3 31.7 41.7 48.3 29.  24.  25.1 31.5\n",
            " 23.7 23.3 22.  20.1 22.2 23.7 17.6 18.5 24.3 20.5 24.5 26.2 24.4 24.8\n",
            " 29.6 42.8 21.9 20.9 44.  50.  36.  30.1 33.8 43.1 48.8 31.  36.5 22.8\n",
            " 30.7 50.  43.5 20.7 21.1 25.2 24.4 35.2 32.4 32.  33.2 33.1 29.1 35.1\n",
            " 45.4 35.4 46.  50.  32.2 22.  20.1 23.2 22.3 24.8 28.5 37.3 27.9 23.9\n",
            " 21.7 28.6 27.1 20.3 22.5 29.  24.8 22.  26.4 33.1 36.1 28.4 33.4 28.2\n",
            " 22.8 20.3 16.1 22.1 19.4 21.6 23.8 16.2 17.8 19.8 23.1 21.  23.8 23.1\n",
            " 20.4 18.5 25.  24.6 23.  22.2 19.3 22.6 19.8 17.1 19.4 22.2 20.7 21.1\n",
            " 19.5 18.5 20.6 19.  18.7 32.7 16.5 23.9 31.2 17.5 17.2 23.1 24.5 26.6\n",
            " 22.9 24.1 18.6 30.1 18.2 20.6 17.8 21.7 22.7 22.6 25.  19.9 20.8 16.8\n",
            " 21.9 27.5 21.9 23.1 50.  50.  50.  50.  50.  13.8 13.8 15.  13.9 13.3\n",
            " 13.1 10.2 10.4 10.9 11.3 12.3  8.8  7.2 10.5  7.4 10.2 11.5 15.1 23.2\n",
            "  9.7 13.8 12.7 13.1 12.5  8.5  5.   6.3  5.6  7.2 12.1  8.3  8.5  5.\n",
            " 11.9 27.9 17.2 27.5 15.  17.2 17.9 16.3  7.   7.2  7.5 10.4  8.8  8.4\n",
            " 16.7 14.2 20.8 13.4 11.7  8.3 10.2 10.9 11.   9.5 14.5 14.1 16.1 14.3\n",
            " 11.7 13.4  9.6  8.7  8.4 12.8 10.5 17.1 18.4 15.4 10.8 11.8 14.9 12.6\n",
            " 14.1 13.  13.4 15.2 16.1 17.8 14.9 14.1 12.7 13.5 14.9 20.  16.4 17.7\n",
            " 19.5 20.2 21.4 19.9 19.  19.1 19.1 20.1 19.9 19.6 23.2 29.8 13.8 13.3\n",
            " 16.7 12.  14.6 21.4 23.  23.7 25.  21.8 20.6 21.2 19.1 20.6 15.2  7.\n",
            "  8.1 13.6 20.1 21.8 24.5 23.1 19.7 18.3 21.2 17.5 16.8 22.4 20.6 23.9\n",
            " 22.  11.9]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "HWp1Il_7MjGI",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "78e2f1ce-62dd-4ca1-c09a-04f71e90eb0c"
      },
      "source": [
        "print(boston['data'].shape)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(506, 13)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xsem8364MnpZ",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "12909941-6c33-4f6b-a383-94a1252e4a82"
      },
      "source": [
        "from sklearn.model_selection import train_test_split\n",
        "import random\n",
        "# train(30%), validation (10%), and test(60%)\n",
        "X_train, X_test, y_train, y_test = train_test_split(boston['data'],boston['target'] , test_size=0.7, random_state=random.randint(30,100))\n",
        "X_test, X_validation, y_test, y_validation = train_test_split(X_test, y_test, test_size=0.14, random_state=random.randint(30,100))\n",
        "print(\"Size of the train dataset:\",X_train.shape)\n",
        "print(\"Size of the test dataset:\",X_test.shape)\n",
        "print(\"Size of the validation dataset:\",X_validation.shape)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Size of the train dataset: (151, 13)\n",
            "Size of the test dataset: (305, 13)\n",
            "Size of the validation dataset: (50, 13)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vph5AByvOk2l"
      },
      "source": [
        "import numpy as np\n",
        "import random\n",
        "def gradient_descent(alpha, x, y, ep=0.0001, max_iter=10):\n",
        "    # Here we have to do H(Xi) = w0 + w1Xi\n",
        "    # ep mens the error value\n",
        "    # epoch menas the interations\n",
        "    converged = False\n",
        "    iter = 0\n",
        "    m = x.shape[0] # Number of samples/patterns (m=31)\n",
        "    # Initial Weights\n",
        "    weight = np.ones(x.shape[1]+1)\n",
        "    random.seed(10)\n",
        "    for i in range(0,x.shape[1]+1):\n",
        "      weight[i] = random.uniform(-0.3, 0.3)\n",
        "    #weight_0 = random.uniform(-0.3, 0.3) # Weights\n",
        "    #weight_1 = random.uniform(-0.3, 0.3) # Weights\n",
        "    total_error = 0\n",
        "    temp_in = 0\n",
        "    for i in range(0,m): # vary the row\n",
        "      for j in range(0,x.shape[1]):\n",
        "        temp_in = temp_in + weight[j+1]*x[i][j]\n",
        "      total_error = total_error + (weight[0] + temp_in - y[i])**2 # total error\n",
        "    print(\"Intial Error\",total_error)\n",
        "    # Iterate Loop\n",
        "    sum = np.ones(x.shape[1]+1)\n",
        "    while not converged:\n",
        "        error = 0\n",
        "        # Update the weight\n",
        "        temp_in = 0\n",
        "        temp_out = 0\n",
        "        for i in range(0,m): # fix the row\n",
        "          for j in range(0,x.shape[1]): # fix the column\n",
        "            temp_in = temp_in + weight[j+1]*x[i][j] #hypothesis calculation\n",
        "          temp_out = temp_out + (weight[0] + temp_in - y[j])*1\n",
        "        sum[0] = temp_out\n",
        "\n",
        "        for k in range(1,x.shape[1]):\n",
        "          temp_in = 0 \n",
        "          temp_out = 0\n",
        "          for i in range(0,m): # vary the row\n",
        "            for j in range(0,x.shape[1]):\n",
        "              temp_in = temp_in + weight[j+1]*x[i][j]\n",
        "            temp_out = temp_out + (weight[1] + temp_in - y[j])*x[i][k]\n",
        "          sum[k+1] = temp_out\n",
        "        # weight updatation\n",
        "        for j in range(0,x.shape[1]):\n",
        "          weight[j] = weight[j] - (alpha/m) * sum[j]\n",
        "        # Calculate the Mean Squared Error\n",
        "        for i in range(0,m): # vary the row\n",
        "          for j in range(0,x.shape[1]):\n",
        "            temp_in = temp_in + weight[j+1]*x[i][j]\n",
        "          error = error + (weight[0] + temp_in - y[i])**2 # total error\n",
        "        print(error)\n",
        "        if abs(total_error-error) <= ep:\n",
        "            print(total_error)\n",
        "            print(error)\n",
        "            print('Converged, iterations: ', iter, '!!!')\n",
        "            converged = True\n",
        "        total_error = error  # update error \n",
        "        iter += 1  # update iter\n",
        "        if iter == max_iter:\n",
        "            print('Max interactions exceeded!')\n",
        "            converged = True\n",
        "    return weight,error"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "u6nxMGfEPIxs",
        "outputId": "96f8edd6-b28d-42ed-995d-e65b06169655"
      },
      "source": [
        "alpha = [ 0.1, 0.001, 0.0001, 0.5, 1.0]\n",
        "mse = np.ones(5)\n",
        "weight = np.ones(14)\n",
        "for i in range(5):\n",
        "  weight,mse[i] = gradient_descent(alpha[i], X_train, y_train, ep=0, max_iter=1000)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Intial Error 857794668.9298929\n",
            "4.4502627804184193e+21\n",
            "2.2048209505702523e+34\n",
            "1.092306383865997e+47\n",
            "5.411474354478143e+59\n",
            "2.6809377955781222e+72\n",
            "1.3281828560845672e+85\n",
            "6.580047109286052e+97\n",
            "3.259868907513367e+110\n",
            "1.614995321108791e+123\n",
            "8.000965563958331e+135\n",
            "3.9638164345700107e+148\n",
            "1.9637430759287153e+161\n",
            "9.728722134117465e+173\n",
            "4.819776859969573e+186\n",
            "2.3878006442832255e+199\n",
            "1.1829576518767254e+212\n",
            "5.860576382220428e+224\n",
            "2.9034306914833694e+237\n",
            "1.4384096768744287e+250\n",
            "7.126129804286574e+262\n",
            "3.530407699834631e+275\n",
            "1.7490249082404247e+288\n",
            "8.664971271699611e+300\n",
            "inf\n",
            "inf\n",
            "inf\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:52: RuntimeWarning: overflow encountered in double_scalars\n",
            "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:54: RuntimeWarning: invalid value encountered in double_scalars\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "inf\n",
            "inf\n",
            "inf\n",
            "inf\n",
            "inf\n",
            "inf\n",
            "inf\n",
            "inf\n",
            "inf\n",
            "inf\n",
            "inf\n",
            "inf\n",
            "inf\n",
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            "inf\n",
            "inf\n",
            "inf\n",
            "inf\n",
            "inf\n",
            "inf\n",
            "inf\n",
            "inf\n",
            "nan\n",
            "nan\n",
            "nan\n",
            "nan\n",
            "nan\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:51: RuntimeWarning: overflow encountered in double_scalars\n",
            "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:34: RuntimeWarning: overflow encountered in double_scalars\n",
            "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:33: RuntimeWarning: overflow encountered in double_scalars\n",
            "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:43: RuntimeWarning: overflow encountered in double_scalars\n",
            "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:42: RuntimeWarning: overflow encountered in double_scalars\n",
            "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:43: RuntimeWarning: invalid value encountered in double_scalars\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
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            "Max interactions exceeded!\n",
            "Intial Error 857794668.9298929\n",
            "4.449313498169633e+17\n",
            "2.2041365773247915e+26\n",
            "1.0918701875282799e+35\n",
            "5.40883217746532e+43\n",
            "2.6793904493202935e+52\n",
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            "6.575079402129522e+69\n",
            "3.2571180572002712e+78\n",
            "1.6134889618373584e+87\n",
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            "3.9594147397335767e+104\n",
            "1.9613879097116884e+113\n",
            "9.71618985441785e+121\n",
            "4.813139961741158e+130\n",
            "2.384300496225497e+139\n",
            "1.1811185424669493e+148\n",
            "5.850944600178084e+156\n",
            "2.898401090448658e+165\n",
            "1.4357901937506335e+174\n",
            "7.112519682882705e+182\n",
            "3.52335156345135e+191\n",
            "1.7453739030840465e+200\n",
            "8.64611437918149e+208\n",
            "4.283053260152327e+217\n",
            "2.1217097559421822e+226\n",
            "1.051038129817734e+235\n",
            "5.206561110618118e+243\n",
            "2.579190785714106e+252\n",
            "1.2776619668491367e+261\n",
            "6.329194841167353e+269\n",
            "3.135313437892241e+278\n",
            "1.5531502190276355e+287\n",
            "7.693889783750898e+295\n",
            "3.8113467248239655e+304\n",
            "inf\n",
            "inf\n",
            "inf\n",
            "inf\n",
            "inf\n",
            "inf\n",
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            "inf\n",
            "inf\n",
            "inf\n",
            "inf\n",
            "nan\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:51: RuntimeWarning: invalid value encountered in double_scalars\n",
            "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:33: RuntimeWarning: invalid value encountered in double_scalars\n",
            "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:42: RuntimeWarning: invalid value encountered in double_scalars\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
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            "nan\n",
            "nan\n",
            "nan\n",
            "nan\n",
            "nan\n",
            "nan\n",
            "nan\n",
            "nan\n",
            "nan\n",
            "nan\n",
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            "nan\n",
            "nan\n",
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            "nan\n",
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            "nan\n",
            "nan\n",
            "nan\n",
            "nan\n",
            "nan\n",
            "nan\n",
            "nan\n",
            "nan\n",
            "nan\n",
            "nan\n",
            "nan\n",
            "nan\n",
            "nan\n",
            "nan\n",
            "nan\n",
            "nan\n",
            "nan\n",
            "nan\n",
            "nan\n",
            "nan\n",
            "nan\n",
            "nan\n",
            "nan\n",
            "nan\n",
            "nan\n",
            "nan\n",
            "nan\n",
            "nan\n",
            "nan\n",
            "nan\n",
            "nan\n",
            "nan\n",
            "nan\n"
          ]
        },
        {
          "output_type": "error",
          "ename": "KeyboardInterrupt",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-13-7511a06d1eb9>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      3\u001b[0m \u001b[0mweight\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mones\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m14\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      4\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mi\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mrange\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m5\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[0;32m----> 5\u001b[0;31m   \u001b[0mweight\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mmse\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mgradient_descent\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0malpha\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mX_train\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_train\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mep\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmax_iter\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m1000\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
            "\u001b[0;32m<ipython-input-11-56af683eb572>\u001b[0m in \u001b[0;36mgradient_descent\u001b[0;34m(alpha, x, y, ep, max_iter)\u001b[0m\n\u001b[1;32m     51\u001b[0m             \u001b[0mtemp_in\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtemp_in\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0mweight\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mj\u001b[0m\u001b[0;34m+\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mj\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     52\u001b[0m           \u001b[0merror\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0merror\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mweight\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0mtemp_in\u001b[0m \u001b[0;34m-\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m**\u001b[0m\u001b[0;36m2\u001b[0m \u001b[0;31m# total error\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 53\u001b[0;31m         \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0merror\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     54\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mabs\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtotal_error\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0merror\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m<=\u001b[0m \u001b[0mep\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     55\u001b[0m             \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtotal_error\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/ipykernel/iostream.py\u001b[0m in \u001b[0;36mwrite\u001b[0;34m(self, string)\u001b[0m\n\u001b[1;32m    398\u001b[0m             \u001b[0mis_child\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0;32mnot\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_is_master_process\u001b[0m\u001b[0;34m(\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[1;32m    399\u001b[0m             \u001b[0;31m# only touch the buffer in the IO thread to avoid races\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 400\u001b[0;31m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpub_thread\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mschedule\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;32mlambda\u001b[0m \u001b[0;34m:\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_buffer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mwrite\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mstring\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    401\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mis_child\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    402\u001b[0m                 \u001b[0;31m# newlines imply flush in subprocesses\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "d-stMMwLay5Z",
        "outputId": "9208a966-7466-4ac3-9715-f8a68466756a"
      },
      "source": [
        "weight,mse = gradient_descent(0.1, X_train, y_train, ep=0.5, max_iter=10)\n",
        "# validation mse\n",
        "temp_in = 0\n",
        "error=0\n",
        "for i in range(0,X_validation.shape[0]): # vary the row\n",
        "  for j in range(0,X_validation.shape[1]):\n",
        "    temp_in = temp_in + weight[j+1]*X_validation[i][j]\n",
        "  error = error + (weight[0] + temp_in - y_validation[i])**2 # total error"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Intial Error 878923581.0088302\n",
            "663493372623.4982\n",
            "663493372623.4982\n",
            "Converged, iterations:  1 !!!\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "7lY29iz5djUD",
        "outputId": "8dcb6220-55e3-43dd-f9fe-870ac8ad031f"
      },
      "source": [
        "print(error)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "260335832936.87228\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Z9cu4NlTblmq",
        "outputId": "64fcad7e-21a5-4691-fbaa-c35a706b69ec"
      },
      "source": [
        "weight,mse = gradient_descent(0.01, X_train, y_train, ep=0.6, max_iter=20)\n",
        "# validation mse\n",
        "temp_in = 0\n",
        "error=0\n",
        "for i in range(0,X_validation.shape[0]): # vary the row\n",
        "  for j in range(0,X_validation.shape[1]):\n",
        "    temp_in = temp_in + weight[j+1]*X_validation[i][j]\n",
        "  error = error + (weight[0] + temp_in - y_validation[i])**2 # total error\n",
        "print(error)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Intial Error 878923581.0088302\n",
            "343812550.5995353\n",
            "343812550.5995353\n",
            "Converged, iterations:  1 !!!\n",
            "2216168350.5295186\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "RUG_mT5-b1oM",
        "outputId": "54e8c669-58b6-4aef-bcbc-d99282501b1c"
      },
      "source": [
        "weight,mse = gradient_descent(0.02, X_train, y_train, ep=0.7, max_iter=30)\n",
        "# validation mse\n",
        "temp_in = 0\n",
        "error=0\n",
        "for i in range(0,X_validation.shape[0]): # vary the row\n",
        "  for j in range(0,X_validation.shape[1]):\n",
        "    temp_in = temp_in + weight[j+1]*X_validation[i][j]\n",
        "  error = error + (weight[0] + temp_in - y_validation[i])**2 # total error\n",
        "print(error)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Intial Error 878923581.0088302\n",
            "10015947820.52335\n",
            "10015947820.52335\n",
            "Converged, iterations:  1 !!!\n",
            "9700386319.888268\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "YxcUlb2gb_Xz",
        "outputId": "21ab13a9-aecd-444d-9416-fe9f2b81a962"
      },
      "source": [
        "weight,mse = gradient_descent(0.03, X_train, y_train, ep=0.8, max_iter=40)\n",
        "# validation mse\n",
        "temp_in = 0\n",
        "error=0\n",
        "for i in range(0,X_validation.shape[0]): # vary the row\n",
        "  for j in range(0,X_validation.shape[1]):\n",
        "    temp_in = temp_in + weight[j+1]*X_validation[i][j]\n",
        "  error = error + (weight[0] + temp_in - y_validation[i])**2 # total error\n",
        "print(error)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Intial Error 878923581.0088302\n",
            "35690870386.10229\n",
            "35690870386.10229\n",
            "Converged, iterations:  1 !!!\n",
            "22483540479.86128\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "RnyA_A3CcEPl",
        "outputId": "d3768a94-805f-4c77-b925-d1caffbb42fe"
      },
      "source": [
        "weight,mse = gradient_descent(0.001, X_train, y_train, ep=0.9, max_iter=50)\n",
        "# validation mse\n",
        "temp_in = 0\n",
        "error=0\n",
        "for i in range(0,X_validation.shape[0]): # vary the row\n",
        "  for j in range(0,X_validation.shape[1]):\n",
        "    temp_in = temp_in + weight[j+1]*X_validation[i][j]\n",
        "  error = error + (weight[0] + temp_in - y_validation[i])**2 # total error\n",
        "print(error)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Intial Error 878923581.0088302\n",
            "5321273945.453223\n",
            "5321273945.453223\n",
            "Converged, iterations:  1 !!!\n",
            "10962621.081855852\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "XjWZrssacLvP"
      },
      "source": [
        "Train MSE, Test MSE, Validation MSE considering the best\n",
        "set of hyperparameters\n",
        "III. Graph plotting for Train MSE vs. Epochs considering the\n",
        "best set of hyperparameters"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "wOrPsKSGeRGZ"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vSv1j6nkd0JQ"
      },
      "source": [
        "659316193277.154\n",
        "702428642.4330704\n",
        "27186543086.0606\n",
        "116406280648.20908\n",
        "202483337.3731066"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "3x4WyJrjeN3l",
        "outputId": "ffe71d24-7072-44b9-fd14-f2e2d7e4e9f2"
      },
      "source": [
        "# best hyper\n",
        "weight,mse = gradient_descent(0.001, X_train, y_train, ep=0.9, max_iter=50)\n",
        "print(\"train mse\",mse)\n",
        "# validation mse\n",
        "temp_in = 0\n",
        "error=0\n",
        "for i in range(0,X_validation.shape[0]): # vary the row\n",
        "  for j in range(0,X_validation.shape[1]):\n",
        "    temp_in = temp_in + weight[j+1]*X_validation[i][j]\n",
        "  error = error + (weight[0] + temp_in - y_validation[i])**2 # total error\n",
        "print(\"validation mse\",error)\n",
        "temp_in = 0\n",
        "error=0\n",
        "for i in range(0,X_test.shape[0]): # vary the row\n",
        "  for j in range(0,X_test.shape[1]):\n",
        "    temp_in = temp_in + weight[j+1]*X_test[i][j]\n",
        "  error = error + (weight[0] + temp_in - y_test[i])**2 # total error\n",
        "print(\"test mse\",error)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Intial Error 878923581.0088302\n",
            "5321273945.453223\n",
            "5321273945.453223\n",
            "Converged, iterations:  1 !!!\n",
            "train mse 5321273945.453223\n",
            "validation mse 10962621.081855852\n",
            "test mse 6606215603.940079\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Vj8T4FRuetX9",
        "outputId": "276d1727-4059-46fb-a188-6c74a17d8f56"
      },
      "source": [
        "itr = [10,20,30,40,50,60]\n",
        "ny_mse = np.ones(6)\n",
        "for i in range(0,6):\n",
        "  weight,mse = gradient_descent(0.3, X_train, y_train, ep=0.1, max_iter = itr[i])\n",
        "  ny_mse[i] = mse"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Intial Error 878923581.0088302\n",
            "6777967377414.37\n",
            "6777967377414.37\n",
            "Converged, iterations:  1 !!!\n",
            "Intial Error 878923581.0088302\n",
            "6777967377414.37\n",
            "6777967377414.37\n",
            "Converged, iterations:  1 !!!\n",
            "Intial Error 878923581.0088302\n",
            "6777967377414.37\n",
            "6777967377414.37\n",
            "Converged, iterations:  1 !!!\n",
            "Intial Error 878923581.0088302\n",
            "6777967377414.37\n",
            "6777967377414.37\n",
            "Converged, iterations:  1 !!!\n",
            "Intial Error 878923581.0088302\n",
            "6777967377414.37\n",
            "6777967377414.37\n",
            "Converged, iterations:  1 !!!\n",
            "Intial Error 878923581.0088302\n",
            "6777967377414.37\n",
            "6777967377414.37\n",
            "Converged, iterations:  1 !!!\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 294
        },
        "id": "F1taBaUHgxPU",
        "outputId": "85a0f6ae-1cf9-4d83-ab8a-ca05207341d6"
      },
      "source": [
        "import matplotlib.pyplot as plt\n",
        "plt.title(\"Mean Square Error vs Epoch\")\n",
        "plt.xlabel(\"Epoch\")\n",
        "plt.ylabel(\"Mean Square Error\")\n",
        "plt.plot(itr, ny_mse,'-o')\n",
        "plt.show()"
      ],
      "execution_count": null,
      "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",
      "metadata": {
        "id": "lXTXDPOgay_x"
      },
      "source": [
        "Learning rate : 0.01, rho : 0.6, epoch : 20"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "dgws-cH2YeFm"
      },
      "source": [
        "Learning rate : 0.1 , rho : 0.5, epoch : 10<br/>\n",
        "ii) Learning rate : 0.01, rho : 0.6, epoch : 20<br/>\n",
        "iii) Learning rate : 0.02, rho : 0.7, epoch : 30<br/>\n",
        "iv) Learning rate : 0.03, rho : 0.8, epoch : 40<br/>\n",
        "v) Learning rate : 0.001, rho : 0.9, epoch: 50<br/>\n",
        "Note: Same weights should be initialized every time; 4 marks for\n",
        "only correct coding without results"
      ]
    }
  ]
}