Adaline.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "Adaline.ipynb",
      "version": "0.3.2",
      "provenance": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    }
  },
  "cells": [
    {
      "cell_type": "code",
      "metadata": {
        "id": "AAwnTg-P__1O",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "import math"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Eu_pVvBdASjy",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "LEARNING_RATE=0.5"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "h0KJEXeVApjT",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def step(x):\n",
        "  if(x>0):\n",
        "    return 1\n",
        "  else:\n",
        "    return -1\n",
        "  "
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vNOcHrQgA8mz",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "INPUTS = np.array([[-1,-1,1],\n",
        "                   [-1,1,1],\n",
        "                   [1,-1,1],\n",
        "                   [1,1,1] ])\n",
        "OUTPUTS = np.array([[-1,1,1,-1]]).T\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "v45yQWPrBR-A",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "np.random.seed(1)\n",
        "b=0.51\n",
        "WEIGHTS=np.random.random((3,1))*2-1\n",
        "errors=[]"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "LO9paibRBn-b",
        "colab_type": "code",
        "outputId": "e330a241-a70c-459d-e7ce-bbd91346aff0",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 85
        }
      },
      "source": [
        "for iter in range(3000):\n",
        "\n",
        "    for input_item,desired in zip(INPUTS,OUTPUTS):\n",
        "        \n",
        "        # Feed this input forward and calculate the ADALINE output\n",
        "        ADALINE_OUTPUT = (input_item[0]*WEIGHTS[0]) + (input_item[1]*WEIGHTS[1]) + (input_item[2]*WEIGHTS[2])+b\n",
        "\n",
        "        # Run ADALINE_OUTPUT through the step function\n",
        "        ADALINE_OUTPUT = step(ADALINE_OUTPUT)\n",
        "\n",
        "        # Calculate the ERROR generated\n",
        "        ERROR = desired - ADALINE_OUTPUT\n",
        "        \n",
        "        # Store the ERROR\n",
        "        errors.append(ERROR)\n",
        "        \n",
        "        # Update the weights based on the delta rule\n",
        "        WEIGHTS[0] = WEIGHTS[0] + LEARNING_RATE * ERROR * input_item[0]\n",
        "        WEIGHTS[1] = WEIGHTS[1] + LEARNING_RATE * ERROR * input_item[1]\n",
        "        WEIGHTS[2] = WEIGHTS[2] + LEARNING_RATE * ERROR * input_item[2]\n",
        "        b=b+LEARNING_RATE*ERROR\n",
        "print(\"New Weights after training\", WEIGHTS)\n",
        "print(\"New BIAS after training\", b)\n"
      ],
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "New Weights after training [[-0.16595599]\n",
            " [-1.55935101]\n",
            " [-0.99977125]]\n",
            "New BIAS after training [0.51]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Fcf7jcvgDmjx",
        "colab_type": "code",
        "outputId": "f815fb1c-2131-445a-8e72-0b9b63148eec",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 85
        }
      },
      "source": [
        "for input_item,desired in zip(INPUTS, OUTPUTS):\n",
        "    # Feed this input forward and calculate the ADALINE output\n",
        "    ADALINE_OUTPUT = (input_item[0]*WEIGHTS[0]) + (input_item[1]*WEIGHTS[1]) + (input_item[2]*WEIGHTS[2])\n",
        "\n",
        "    # Run ADALINE_OUTPUT through the step function\n",
        "    ADALINE_OUTPUT = step(ADALINE_OUTPUT)\n",
        "\n",
        "    print(\"Actual \", ADALINE_OUTPUT, \"Desired \", desired)"
      ],
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Actual  1 Desired  [-1]\n",
            "Actual  -1 Desired  [1]\n",
            "Actual  1 Desired  [1]\n",
            "Actual  -1 Desired  [-1]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "h-7JYZTUFRTZ",
        "colab_type": "code",
        "outputId": "850539c2-792d-4fee-ca88-582618329404",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 299
        }
      },
      "source": [
        "ax = plt.subplot(111)\n",
        "ax.plot(errors, c='#aaaaff', label='Training Errors')\n",
        "ax.set_xscale(\"log\")\n",
        "plt.title(\"ADALINE Errors (2,-2)\")\n",
        "plt.legend()\n",
        "plt.xlabel('Error')\n",
        "plt.ylabel('Value')\n",
        "plt.show()"
      ],
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
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ll15buN86cXFVfTx4LiITgWvKuLmIHAl8C+8NgJeo6tdC9aPwBOptwABwsqo+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\nhAmHYRiGkYn/Dzw6RGkqcgckAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Gd6PVr2oFX7X",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    }
  ]
}