Linear-Regression.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "LinearRegression.ipynb",
      "provenance": [],
      "authorship_tag": "ABX9TyOqjPEFda2Q/ZwbQG9WOj9i",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/ThuanPhong0126/Machine-Learning-from-scratch/blob/main/Linear-Regression.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "WOXtoSNtUKL0"
      },
      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "HaHhGCYKV4_T"
      },
      "source": [
        "def load_data(file_name):\n",
        "  df = pd.read_csv(file_name, sep=',',  index_col=False)\n",
        "  df.columns = ['housesize','rooms', 'price']\n",
        "  data = np.array(df, dtype=float)\n",
        "  plot_data(data[:, :2], data[:, -1])\n",
        "  normalize(data)\n",
        "  return data[:, :2], data[:, -1]"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "86vyhOzeWda7"
      },
      "source": [
        "def normalize(data):\n",
        "  for i in range(0, data.shape[1]-1):\n",
        "    data[:, i] = (data[:, i] - np.mean(data[:, i])) / np.std(data[:, -i])\n",
        "\n",
        "    mu.append(np.mean(data[:, i]))\n",
        "    std.append(np.std(data[:, i]))"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7rRC2roCXsqf"
      },
      "source": [
        "def plot_data(x, y):\n",
        "  plt.plot(x[:, 0], y, 'bo')\n",
        "  plt.xlabel('House size')\n",
        "  plt.ylabel('Price')\n",
        "  plt.title('Plot of Data')\n",
        "  plt.show()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "WAZwJ9hgYzcd"
      },
      "source": [
        "def h(x, theta):\n",
        "  return np.matmul(x, theta)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "3d-oe27mbc2I"
      },
      "source": [
        "Cost function\n",
        "\n",
        "\n",
        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)"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "kOKnU8liZ9Qy"
      },
      "source": [
        "def cost_function(x, y, theta):\n",
        "  return ((h(x, theta) - y).T@(h(x, theta) - y)) / (2*y.shape[0])"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "1XNeX_c3cz3Z"
      },
      "source": [
        "Gradient Descent\n",
        "\n",
        "![gradient_descent](https://miro.medium.com/max/576/0*2D8fvpZJqoFYGDH2.png)"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "al2sCjFPcLpU"
      },
      "source": [
        "def gradient_descent(x, y, theta, learning_rate = 0.1, epochs = 10):\n",
        "  m = y.shape[0]\n",
        "  J_all = []\n",
        "\n",
        "  for _ in range(epochs):\n",
        "    h_theta = h(x, theta)\n",
        "    cost_ = (1/m) * (x.T@(h_theta - y))\n",
        "    theta = theta - (learning_rate*cost_)\n",
        "    J_all.append(cost_function(x, y, theta))\n",
        "  \n",
        "  return theta, J_all"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "poDL1HYAgiY7"
      },
      "source": [
        "def plot_cost(J_all, num_epochs):\n",
        "  plt.xlabel('Epochs')\n",
        "  plt.ylabel('Cost')\n",
        "  plt.title('Plot of Cost')\n",
        "  plt.plot(num_epochs, J_all, 'm', linewidth = '5')\n",
        "  plt.show()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0S4STYxwhG3X"
      },
      "source": [
        "def test(theta, x):\n",
        "  x[0] = (x[0] - mu[0])/std[0]\n",
        "  x[1] = (x[1] - mu[1])/std[1]\n",
        "\n",
        "  y = theta[0] + theta[1]*x[0] + theta[2]*x[1]\n",
        "  print('Price of house: ', y)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 659
        },
        "id": "12jG7u6weAvk",
        "outputId": "b50aa7fa-e731-4bcf-ffb8-408d01eb867e"
      },
      "source": [
        "mu, std = [], []\n",
        "\n",
        "x, y = load_data('/content/house_price_data.txt')\n",
        "y = np.reshape(y, (46, 1))\n",
        "x = np.hstack((np.ones((x.shape[0], 1)), x))\n",
        "theta = np.zeros((x.shape[1], 1))\n",
        "learning_rate = 0.1\n",
        "epochs = 50\n",
        "\n",
        "theta, J_all = gradient_descent(x, y, theta, learning_rate=learning_rate, epochs=epochs)\n",
        "J = cost_function(x, y, theta)\n",
        "print('Cost: ', J)\n",
        "print('Parameters: ', theta)\n",
        "\n",
        "n_epochs = []\n",
        "jplot = []\n",
        "count = 0\n",
        "for i in J_all:\n",
        "  jplot.append(i[0][0])\n",
        "  n_epochs.append(count)\n",
        "  count += 1\n",
        "jplot = np.array(jplot)\n",
        "n_epochs = np.array(n_epochs)\n",
        "plot_cost(jplot, n_epochs)\n",
        "\n",
        "test(theta, [1600, 2])"
      ],
      "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": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "text": [
            "Cost:  [[2.08146352e+09]]\n",
            "Parameters:  [[3.37371711e+05]\n",
            " [1.06205625e+05]\n",
            " [2.33256646e-01]]\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "text": [
            "Price of house:  [1.70342876e+08]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Qa5ygPLCfips"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    }
  ]
}