perceptron.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "provenance": [],
      "authorship_tag": "ABX9TyOc2YnZrPrvdc6dRf4/EbD7",
      "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/solmvz/MLActivities/blob/main/perceptron.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 530
        },
        "id": "rZl6elCqb_qU",
        "outputId": "33104365-223a-45f1-a251-4b62f92fb584"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0.5, 1.0, 'Random Classification Data with 2 classes')"
            ]
          },
          "metadata": {},
          "execution_count": 5
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 720x576 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "from sklearn import datasets\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "X, y = datasets.make_blobs(n_samples=150, n_features=2, centers=2, cluster_std=1.05, random_state=2)\n",
        "\n",
        "fig = plt.figure(figsize=(10,8))\n",
        "plt.plot(X[:, 0][y==0], X[:, 1][y==0], 'r^')\n",
        "plt.plot(X[:, 0][y == 1], X[:, 1][y == 1], 'bs')\n",
        "plt.xlabel(\"feature 1\")\n",
        "plt.ylabel(\"feature 2\")\n",
        "plt.title('Random Classification Data with 2 classes')"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "We want to learn a set of parameters theta to find an optimal hyperplane(straight line for our data) that separates the two classes. For logistic regression we had sigmoid(theta.X) and for perceptron we have unit step function."
      ],
      "metadata": {
        "id": "ALka-oDodMos"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "def step_func(z):\n",
        "  return 1.0 if (z>0) else 0.0"
      ],
      "metadata": {
        "id": "XDouSV1rdN8k"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def perceptron(X, y, lr, epochs):\n",
        "  # m -> # of examples\n",
        "  # n -> # of features\n",
        "  m, n = X.shape\n",
        "\n",
        "  # initializing parameters n (+ 1 for bias term)\n",
        "  theta = np.zeros((n+1, 1))\n",
        "  # saving n of updated values in every iteration\n",
        "  update_list = []\n",
        "\n",
        "  for epoch in range(epochs):\n",
        "    n_updated = 0\n",
        "    for idx, x_i in enumerate(X):\n",
        "      x_i = np.insert(x_i, 0, 1).reshape(-1, 1)\n",
        "      y_hat = step_func(np.dot(x_i.T, theta))\n",
        "      if (np.squeeze(y_hat) - y[idx]) != 0:\n",
        "        theta += lr*((y[idx] - y_hat)*x_i)\n",
        "        n_updated += 1\n",
        "        \n",
        "    update_list.append(n_updated)\n",
        "  return theta, update_list"
      ],
      "metadata": {
        "id": "mA6HwtnceMCj"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def plot_decision_boundary(X, theta):\n",
        "    \n",
        "    # X --> Inputs\n",
        "    # theta --> parameters\n",
        "    \n",
        "    # The Line is y=mx+c\n",
        "    # So, Equate mx+c = theta0.X0 + theta1.X1 + theta2.X2\n",
        "    # Solving we find m and c\n",
        "    x1 = [min(X[:,0]), max(X[:,0])]\n",
        "    m = -theta[1]/theta[2]\n",
        "    c = -theta[0]/theta[2]\n",
        "    x2 = m*x1 + c\n",
        "    \n",
        "    # Plotting\n",
        "    fig = plt.figure(figsize=(10,8))\n",
        "    plt.plot(X[:, 0][y==0], X[:, 1][y==0], \"r^\")\n",
        "    plt.plot(X[:, 0][y==1], X[:, 1][y==1], \"bs\")\n",
        "    plt.xlabel(\"feature 1\")\n",
        "    plt.ylabel(\"feature 2\")\n",
        "    plt.title('Perceptron Algorithm')\n",
        "    plt.plot(x1, x2, 'y-')"
      ],
      "metadata": {
        "id": "1-d3q9wreXYr"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "theta, update_list = perceptron(X, y, 0.5, 100)\n",
        "plot_decision_boundary(X, theta)\n",
        "#print(update_list)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 513
        },
        "id": "2_JF45x6glmd",
        "outputId": "7f27892d-8827-45c4-e73a-70adb1076891"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 720x576 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "We can see on the plot that in this case perceptron classifies the data without any misclassification (100% accuracy)."
      ],
      "metadata": {
        "id": "8uoD1KU6hveP"
      }
    }
  ]
}