Assignment7/BibekGoswami_206102011/Assignment_7.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "Assignment 7.ipynb",
      "provenance": [],
      "collapsed_sections": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    }
  },
  "cells": [
    {
      "cell_type": "code",
      "metadata": {
        "id": "SBkCRN1LgBgw"
      },
      "source": [
        "from matplotlib import pyplot as plt\n",
        "import numpy as np"
      ],
      "execution_count": 1,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "T0T23L-QCV7T",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "a62f5ef3-e926-4dac-fbf0-78d3152c3ab6"
      },
      "source": [
        "# Minimum of f(x) = x^2+3x+2 using gradient descent\n",
        "\n",
        "# Function for giving value of the derivative for any value of x\n",
        "def derivative_of_fx(x):\n",
        "  return (2*x)+3\n",
        "\n",
        "iteration= 200 # Number of iteration\n",
        "precision = 0.0001 # To stop the loop when it's close to the minima\n",
        "i = 0 \n",
        "x = 0 # Intial value of x\n",
        "lr = 0.01 # Learning rate\n",
        "step_size = 1 # Previous step size\n",
        "\n",
        "# Finding x at which minima occurs\n",
        "while i<iteration and precision < step_size:\n",
        "  prev_x = x\n",
        "  x = x - (lr*derivative_of_fx(prev_x))\n",
        "  step_size = abs(x-prev_x)\n",
        "  i += 1\n",
        "\n",
        "print(\"The local minima occurs at \"+str(x))\n",
        "print(\"The minima of the function is \"+str((x**2)+(3*x)+2))"
      ],
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "The local minima occurs at -1.4736180800914178\n",
            "The minima of the function is -0.24930399430193706\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "h-P8OnMKCeQu",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "c83286b5-bf6b-4189-c09e-95d35fa98561"
      },
      "source": [
        "# Minimum of f(x1,x2) = x1^2+ x2^2 using gradient descent\n",
        "\n",
        "# Function for giving value of the derivative for any value of x\n",
        "def derivative_of_fx(x):\n",
        "  return (2*x)\n",
        "\n",
        "iteration= 500 # Number of iteration\n",
        "precision = 0.0001 # To stop the loop when it's close to the minima\n",
        "i = 0 \n",
        "x1 = 5 # Intial value of x1\n",
        "x2 = 5 # Intial value of x2\n",
        "lr = 0.01 # Learning rate\n",
        "step_size = 1 # Previous step size\n",
        "\n",
        "# Finding x at which minima occurs\n",
        "while i<iteration and step_size > precision:\n",
        "  prev_x1 = x1\n",
        "  prev_x2 = x2\n",
        "  x1 = x1 - (lr*derivative_of_fx(prev_x1))\n",
        "  x2 = x2 - (lr*derivative_of_fx(prev_x2))\n",
        "  step_size = min(abs(x1-prev_x1),abs(x2-prev_x2))\n",
        "  i += 1\n",
        "\n",
        "print(\"The local minima occurs at \"+str(x1)+\" \"+str(x2))\n",
        "print(\"The minima of the function is \"+str((x1**2)+(x2**2)))"
      ],
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "The local minima occurs at 0.004892309984953824 0.004892309984953824\n",
            "The minima of the function is 4.786939397775778e-05\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 319
        },
        "id": "3lGohaoKTqzc",
        "outputId": "eeab1e56-6752-4198-cace-7063dc6dcc57"
      },
      "source": [
        "# Implementation of AND using perceptron\n",
        "\n",
        "x=[[0,0],[0,1],[1,0],[1,1]] # The inputs\n",
        "y=[0,0,0,1] # The desired output\n",
        "lr=0.2 # Learning rate\n",
        "theta=0.5 # threshold\n",
        "weights=[0.5,0.5,0.5]\n",
        "i=0\n",
        "count=0\n",
        "max_iteration = 10000\n",
        "\n",
        "# Condition for iteration\n",
        "while(count<4 and i< max_iteration):\n",
        "    # Calculating the predicted output\n",
        "    yc= weights[0]+(weights[1]*x[i%4][0])+(weights[2]*x[i%4][1])\n",
        "    if (yc>theta):\n",
        "        yc=1\n",
        "    else:\n",
        "        yc=0\n",
        "    # Checking if the desired and predicted outputs are equal\n",
        "    if(yc != y[i%4]):\n",
        "        count=0\n",
        "        # Updating weights according to the condition\n",
        "        if(yc > y[i%4]):\n",
        "            weights[0]=weights[0]-lr*1\n",
        "            weights[1]=weights[1]-lr*x[i%4][0]\n",
        "            weights[2]=weights[2]-lr*x[i%4][1]\n",
        "        else:\n",
        "            weights[0]=weights[0]+lr*1\n",
        "            weights[1]=weights[1]+lr*x[i%4][0]\n",
        "            weights[2]=weights[2]+lr*x[i%4][1]\n",
        "        print(\"Iteration \",i,\" :\",weights)\n",
        "    else:\n",
        "        count+=1\n",
        "    i+=1\n",
        "\n",
        "# Final weights\n",
        "print(weights)\n",
        "\n",
        "x = np.array(x)\n",
        "# Plotting the input points\n",
        "plt.scatter(x[:,0],x[:,1],c=y,cmap='rainbow')\n",
        "\n",
        "# Calculating slope\n",
        "xintr=(theta-weights[0])/weights[1]\n",
        "yintr=(theta-weights[0])/weights[2]\n",
        "slope=-yintr/xintr\n",
        "\n",
        "# Plotting the decision boundary\n",
        "xx=plt.xlim()\n",
        "yeq = []\n",
        "for x in xx:\n",
        "    yeq.append(yintr+slope*x)\n",
        "plt.plot(xx,yeq)\n",
        "plt.show()"
      ],
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Iteration  1  : [0.3, 0.5, 0.3]\n",
            "Iteration  2  : [0.09999999999999998, 0.3, 0.3]\n",
            "[0.09999999999999998, 0.3, 0.3]\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "rhqMuij9xHOv",
        "outputId": "186c4f56-fefc-4b22-eb96-f12724e4f038"
      },
      "source": [
        "# Implementation of XOR using perceptron\n",
        "\n",
        "x=[[0,0],[0,1],[1,0],[1,1]] # The inputs\n",
        "y=[0,1,1,0] # The desired output\n",
        "lr=0.2 # Learning rate\n",
        "theta=0.5 # threshold\n",
        "weights=[0.5,0.5,0.5]\n",
        "i=0\n",
        "count=0\n",
        "max_iteration = 10000\n",
        "\n",
        "# Condition for iteration\n",
        "while(count<4 and i< max_iteration):\n",
        "    # Calculating the predicted output\n",
        "    yc= weights[0]+(weights[1]*x[i%4][0])+(weights[2]*x[i%4][1])\n",
        "    if (yc>theta):\n",
        "        yc=1\n",
        "    else:\n",
        "        yc=0\n",
        "    # Checking if the desired and predicted outputs are equal\n",
        "    if(yc != y[i%4]):\n",
        "        count=0\n",
        "        # Updating weights according to the condition\n",
        "        if(yc > y[i%4]):\n",
        "            weights[0]=weights[0]-lr*1\n",
        "            weights[1]=weights[1]-lr*x[i%4][0]\n",
        "            weights[2]=weights[2]-lr*x[i%4][1]\n",
        "        else:\n",
        "            weights[0]=weights[0]+lr*1\n",
        "            weights[1]=weights[1]+lr*x[i%4][0]\n",
        "            weights[2]=weights[2]+lr*x[i%4][1]\n",
        "        print(\"Iteration \",i,\" :\",weights)\n",
        "    else:\n",
        "        count+=1\n",
        "    i+=1\n",
        "\n",
        "# Final weights\n",
        "print(weights)\n",
        "\n",
        "x = np.array(x)\n",
        "# Plotting the input points\n",
        "plt.scatter(x[:,0],x[:,1],c=y,cmap='rainbow')\n",
        "\n",
        "# Calculating slope\n",
        "xintr=(theta-weights[0])/weights[1]\n",
        "yintr=(theta-weights[0])/weights[2]\n",
        "slope=-yintr/xintr\n",
        "\n",
        "# Plotting the decision boundary\n",
        "xx=plt.xlim()\n",
        "yeq = []\n",
        "for x in xx:\n",
        "    yeq.append(yintr+slope*x)\n",
        "plt.plot(xx,yeq)\n",
        "plt.show()"
      ],
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "\u001b[1;30;43mStreaming output truncated to the last 5000 lines.\u001b[0m\n",
            "Iteration  5001  : [0.7, -0.30000000000000004, 0.09999999999999998]\n",
            "Iteration  5002  : [0.8999999999999999, -0.10000000000000003, 0.09999999999999998]\n",
            "Iteration  5003  : [0.7, -0.30000000000000004, -0.10000000000000003]\n",
            "Iteration  5004  : [0.49999999999999994, -0.30000000000000004, -0.10000000000000003]\n",
            "Iteration  5005  : [0.7, -0.30000000000000004, 0.09999999999999998]\n",
            "Iteration  5006  : [0.8999999999999999, -0.10000000000000003, 0.09999999999999998]\n",
            "Iteration  5007  : [0.7, -0.30000000000000004, -0.10000000000000003]\n",
            "Iteration  5008  : [0.49999999999999994, -0.30000000000000004, -0.10000000000000003]\n",
            "Iteration  5009  : [0.7, -0.30000000000000004, 0.09999999999999998]\n",
            "Iteration  5010  : [0.8999999999999999, -0.10000000000000003, 0.09999999999999998]\n",
            "Iteration  5011  : [0.7, -0.30000000000000004, -0.10000000000000003]\n",
            "Iteration  5012  : [0.49999999999999994, -0.30000000000000004, -0.10000000000000003]\n",
            "Iteration  5013  : [0.7, -0.30000000000000004, 0.09999999999999998]\n",
            "Iteration  5014  : [0.8999999999999999, -0.10000000000000003, 0.09999999999999998]\n",
            "Iteration  5015  : [0.7, -0.30000000000000004, -0.10000000000000003]\n",
            "Iteration  5016  : [0.49999999999999994, -0.30000000000000004, -0.10000000000000003]\n",
            "Iteration  5017  : [0.7, -0.30000000000000004, 0.09999999999999998]\n",
            "Iteration  5018  : [0.8999999999999999, -0.10000000000000003, 0.09999999999999998]\n",
            "Iteration  5019  : [0.7, -0.30000000000000004, -0.10000000000000003]\n",
            "Iteration  5020  : [0.49999999999999994, -0.30000000000000004, -0.10000000000000003]\n",
            "Iteration  5021  : [0.7, -0.30000000000000004, 0.09999999999999998]\n",
            "Iteration  5022  : [0.8999999999999999, -0.10000000000000003, 0.09999999999999998]\n",
            "Iteration  5023  : [0.7, -0.30000000000000004, -0.10000000000000003]\n",
            "Iteration  5024  : [0.49999999999999994, -0.30000000000000004, -0.10000000000000003]\n",
            "Iteration  5025  : [0.7, -0.30000000000000004, 0.09999999999999998]\n",
            "Iteration  5026  : [0.8999999999999999, -0.10000000000000003, 0.09999999999999998]\n",
            "Iteration  5027  : [0.7, -0.30000000000000004, -0.10000000000000003]\n",
            "Iteration  5028  : [0.49999999999999994, -0.30000000000000004, -0.10000000000000003]\n",
            "Iteration  5029  : [0.7, -0.30000000000000004, 0.09999999999999998]\n",
            "Iteration  5030  : [0.8999999999999999, -0.10000000000000003, 0.09999999999999998]\n",
            "Iteration  5031  : [0.7, -0.30000000000000004, -0.10000000000000003]\n",
            "Iteration  5032  : [0.49999999999999994, -0.30000000000000004, -0.10000000000000003]\n",
            "Iteration  5033  : [0.7, -0.30000000000000004, 0.09999999999999998]\n",
            "Iteration  5034  : [0.8999999999999999, -0.10000000000000003, 0.09999999999999998]\n",
            "Iteration  5035  : [0.7, -0.30000000000000004, -0.10000000000000003]\n",
            "Iteration  5036  : [0.49999999999999994, -0.30000000000000004, -0.10000000000000003]\n",
            "Iteration  5037  : [0.7, -0.30000000000000004, 0.09999999999999998]\n",
            "Iteration  5038  : [0.8999999999999999, -0.10000000000000003, 0.09999999999999998]\n",
            "Iteration  5039  : [0.7, -0.30000000000000004, -0.10000000000000003]\n",
            "Iteration  5040  : [0.49999999999999994, -0.30000000000000004, -0.10000000000000003]\n",
            "Iteration  5041  : [0.7, -0.30000000000000004, 0.09999999999999998]\n",
            "Iteration  5042  : [0.8999999999999999, -0.10000000000000003, 0.09999999999999998]\n",
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          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "P_7oCC4_485P"
      },
      "source": [
        "XOR gate, as it is not linearly seperable, we cannot determine the decision boundary using a single layer perceptron"
      ]
    }
  ]
}