fuzzy_GA_lab.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "provenance": [],
      "authorship_tag": "ABX9TyNZGHyCZ/hnHZAGIQiMDSlg",
      "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/Madhusudan0626/soft-computing/blob/main/fuzzy_GA_lab.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": {
        "id": "6A3p1eZrK1SQ"
      },
      "outputs": [],
      "source": [
        "from matplotlib import pyplot as plt\n",
        "import numpy as np\n",
        "from scipy.stats import norm\n",
        "import math\n",
        "import random\n",
        "import skfuzzy.membership\n",
        "import pandas as pd\n",
        "import warnings\n",
        "warnings.filterwarnings('ignore')"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "A=[(1,0.8),(2,0.7),(3,0.1),(4,1.0),(5,0.9),(6,0.3)]\n",
        "B=[(1,0.3),(2,0.4),(3,0.4),(4,1),(5,0),(6,0)]"
      ],
      "metadata": {
        "id": "x0_A_tnRL6jS"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def algSum(A,B):\n",
        "    sum=[]\n",
        "    \n",
        "    for i in A:\n",
        "        for j in B:\n",
        "            if i[0]==j[0]:\n",
        "                sum.append((i[0],(i[1]+j[1]-i[1]*j[1])))\n",
        "    return sum\n",
        "\n",
        "                         \n",
        "def cart(A,B):\n",
        "    c=[]\n",
        "    \n",
        "    for i in A:\n",
        "        for j in B:\n",
        "            c.append(((i[0],j[0]), min(i[1],j[1])))\n",
        "    return c\n",
        "  \n",
        "    \n",
        "def union(A,B):\n",
        "    union=[]\n",
        "\n",
        "    for i in A:\n",
        "        for j in B:\n",
        "            if i[0] == j[0]:\n",
        "                union.append((i[0],max(i[1],j[1])))\n",
        "\n",
        "    return union\n",
        "     \n",
        "def intersect(A,B):\n",
        "    intersection=[]\n",
        "    for i in A:\n",
        "        for j in B:\n",
        "            if i[0] == j[0]:\n",
        "                intersection.append((i[0],min(i[1],j[1])))\n",
        "    return intersection\n",
        "      \n",
        "    \n",
        "def complement(A):\n",
        "    A_=[]\n",
        "    for i in A:\n",
        "        A_.append((i[0],1-i[1]))\n",
        "    return A_\n",
        "\n",
        "def diff(A,B):\n",
        "    d=[]\n",
        "    B_=complement(B)\n",
        "    for i in A:\n",
        "        for j in B_:\n",
        "            if i[0]==j[0]:\n",
        "                d.append((i[0],min(i[1],j[1])))\n",
        "    return d  "
      ],
      "metadata": {
        "id": "PJrIL8SKMbbr"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(\"Set A : \", A,\"\\n\")\n",
        "print(\"Set B : \", B,\"\\n\")\n",
        "\n",
        "asum=algSum(A,B)\n",
        "print(\"Algebraic sum: \",asum,\"\\n\")\n",
        "\n",
        "dif=diff(A,B)\n",
        "print(\"Algebraic difference: \",dif,\"\\n\")\n",
        "\n",
        "c=cart(A,B)\n",
        "print(\"Cartesian product: \",c,\"\\n\")\n",
        "\n",
        "u=union(A,B)\n",
        "print(\"Union: \",u,\"\\n\")\n",
        "\n",
        "inter=intersect(A,B)\n",
        "print(\"Intersection: \", inter,\"\\n\")\n",
        "\n",
        "com=complement(A)\n",
        "print(\"Complement of A: \",com,\"\\n\")\n",
        "\n",
        "com=complement(B)\n",
        "print(\"Complement of B: \",com,'\\n')\n",
        "\n",
        "print(\"complement(A) union complement(B): \", union(complement(A),complement(B)),\"\\n\")\n",
        "print(\"complement(A) intersection complement(B): \", intersect(complement(A),complement(B)))"
      ],
      "metadata": {
        "id": "wvOEd361M8b9"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def test(A,B):\n",
        "    sum=[]\n",
        "    \n",
        "    for i in A:\n",
        "        for j in B:\n",
        "            if i[0]==j[0]:\n",
        "                sum.append((i[0]+j[0],min(i[1],j[1])))\n",
        "            if i[0]+j[0]==i[0]:\n",
        "              sum.append((i[0],min(i[1],j[1])))\n",
        "            else:\n",
        "              sum.append((j[0],min(i[1],j[1])))\n",
        "    \n",
        "    return sum\n",
        "A=[(0,0.2),(1,1.0),(2,0.2)]\n",
        "print(test(A,A))\n"
      ],
      "metadata": {
        "id": "KkIcB_zAQUSm",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "8734d5be-3a06-4ec3-fc53-fe4c8fafa694"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[(0, 0.2), (0, 0.2), (1, 0.2), (2, 0.2), (1, 0.2), (2, 1.0), (1, 1.0), (2, 0.2), (2, 0.2), (1, 0.2), (4, 0.2), (2, 0.2)]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "a=float(input(\"Enter a: \"))\n",
        "b=float(input(\"Enter b: \"))\n",
        "m=(a+b)/2\n",
        "X=(a,m,b)\n",
        "Y=(0,1,0)\n",
        "plt.plot(X,Y)\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 465
        },
        "id": "fyxTlt_b1t9p",
        "outputId": "6fa551c6-d00c-4e10-ecff-86cad52e3e97"
      },
      "execution_count": null,
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Enter a: 2\n",
            "Enter b: 6\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Triangular MF\n",
        "x=float(input(\"Enter the element: \"))\n",
        "if x<=a or x>=m:\n",
        "    mem = 0\n",
        "mem1 = (x-a)/(m-a)\n",
        "mem2= (b-x)/(b-m)\n",
        "mem=max(min(mem1,mem2),0)\n",
        "    \n",
        "print(\"Membership : \",mem) "
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Cd4Lf1O42uMy",
        "outputId": "02932934-1c23-41cb-82cb-1731e6e1113a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Enter the element: 7\n",
            "Membership :  0\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(\"Enter a: \")\n",
        "a=input()\n",
        "a=float(a)\n",
        "print(\"Enter b: \")\n",
        "b=input()\n",
        "b=float(b)\n",
        "print(\"Enter c: \")\n",
        "c=input()\n",
        "c=float(c)\n",
        "print(\"Enter d: \")\n",
        "d=input()\n",
        "d=float(d)\n",
        "X=(a,b,c,d)\n",
        "Y=(0,1,1,0)\n",
        "plt.plot(X,Y)\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 569
        },
        "id": "BhM1jAGf4yDU",
        "outputId": "ad7c25c8-989d-446d-e2dd-fa788eae54dc"
      },
      "execution_count": null,
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Enter a: \n",
            "1\n",
            "Enter b: \n",
            "5\n",
            "Enter c: \n",
            "6\n",
            "Enter d: \n",
            "7\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Trapezodial MF\n",
        "x=float(input(\"Enter the element: \"))\n",
        "if x<=a or x>=d:\n",
        "    mem = 0\n",
        "elif x > b and x < c :\n",
        "    mem = 1\n",
        "else:\n",
        "  mem1 = (x-a)/(b-a)\n",
        "  mem2= (d-x)/(d-c)\n",
        "  mem=max(min(mem1,mem2),0)\n",
        "    \n",
        "print(\"Membership : \",mem) "
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "-PBaIuT25Fy6",
        "outputId": "28234310-8c22-49b1-ff82-12554681b331"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Enter the element: 4\n",
            "Membership :  0.75\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Gaussian Distribution\n",
        "c=float(input(\"Enter the mean: \"))\n",
        "var=float(input(\"Enter the variance: \"))\n",
        "sig=np.sqrt(var)\n",
        "x_axis = np.arange(-10, 10, 0.001)\n",
        "plt.plot(x_axis, norm.pdf(x_axis,c,sig))\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 465
        },
        "id": "9aPsgGxghT6R",
        "outputId": "bee29614-171d-4ab1-d70d-e30d6be15be4"
      },
      "execution_count": null,
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Enter the mean: 0\n",
            "Enter the variance: 2\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "x=float(input(\"Enter the element: \"))\n",
        "\n",
        "mem = np.exp((-1*(x-c)**2)/(2*var))\n",
        "    \n",
        "print(\"membership: \",mem) "
      ],
      "metadata": {
        "id": "AHSnBYGKjpi9",
        "outputId": "5d4d70f6-6b43-4116-f9af-5254b4bf08cf",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Enter the element: 2.4\n",
            "membership:  0.23692775868212176\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import random\n",
        "\n",
        "W = 5\n",
        "weights = [3, 2, 4, 1]\n",
        "values = [100, 20, 60, 40]\n",
        "\n",
        "items = [(weight, value) for weight, value in zip(weights, values)]\n",
        "\n",
        "\n",
        "def evaluate_fitness(individual):\n",
        "    total_weight = sum(individual[i] * items[i][0] for i in range(len(individual)))\n",
        "    total_value = sum(individual[i] * items[i][1] for i in range(len(individual)))\n",
        "    if total_weight > W:\n",
        "        return 0\n",
        "    else:\n",
        "        return total_value\n",
        "\n",
        "\n",
        "def initialize_population(population_size):\n",
        "    population = []\n",
        "    for _ in range(population_size):\n",
        "        individual = [random.randint(0, 1) for _ in range(len(items))]\n",
        "        population.append(individual)\n",
        "    return population\n",
        "\n",
        "\n",
        "def selection(population):\n",
        "    selected = random.choices(population, k=2)\n",
        "    return selected[0], selected[1]\n",
        "\n",
        "\n",
        "def crossover(parent1, parent2):\n",
        "    crossover_point = random.randint(1, len(items) - 1)\n",
        "    child1 = parent1[:crossover_point] + parent2[crossover_point:]\n",
        "    child2 = parent2[:crossover_point] + parent1[crossover_point:]\n",
        "    return child1, child2\n",
        "\n",
        "\n",
        "def mutation(individual, mutation_rate):\n",
        "    for i in range(len(individual)):\n",
        "        if random.random() < mutation_rate:\n",
        "            individual[i] = 1 - individual[i]\n",
        "    return individual\n",
        "\n",
        "\n",
        "def genetic_algorithm(population_size, num_generations, mutation_rate):\n",
        "    population = initialize_population(population_size)\n",
        "\n",
        "    for _ in range(num_generations):\n",
        "        next_generation = []\n",
        "        for _ in range(population_size // 2):\n",
        "            parent1, parent2 = selection(population)\n",
        "            child1, child2 = crossover(parent1, parent2)\n",
        "            child1 = mutation(child1, mutation_rate)\n",
        "            child2 = mutation(child2, mutation_rate)\n",
        "            next_generation.extend([child1, child2])\n",
        "\n",
        "        population = next_generation\n",
        "\n",
        "    best_individual = max(population, key=evaluate_fitness)\n",
        "    best_fitness = evaluate_fitness(best_individual)\n",
        "    return best_individual, best_fitness\n",
        "\n",
        "\n",
        "# Example usage\n",
        "population_size = 50\n",
        "num_generations = 100\n",
        "mutation_rate = 0.1\n",
        "\n",
        "best_individual, best_fitness = genetic_algorithm(population_size, num_generations, mutation_rate)\n",
        "\n",
        "print(\"Best individual:\", best_individual)\n",
        "print(\"Best fitness:\", best_fitness)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "dFLCQapf91N8",
        "outputId": "c765573a-9dc5-42e4-f50b-318c17379c19"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Best individual: [1, 0, 0, 1]\n",
            "Best fitness: 140\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import random\n",
        "import math\n",
        "\n",
        "def calculate_distance(city1, city2):\n",
        "    x1, y1 = city1\n",
        "    x2, y2 = city2\n",
        "    distance = math.sqrt((x2 - x1)**2 + (y2 - y1)**2)\n",
        "    return distance\n",
        "\n",
        "def tsp(n, cities):\n",
        "    # Generate a random permutation of cities\n",
        "    route = random.sample(range(n), n)\n",
        "    \n",
        "    # Calculate the total distance of the initial route\n",
        "    total_distance = 0\n",
        "    for i in range(n - 1):\n",
        "        city1 = cities[route[i]]\n",
        "        city2 = cities[route[i + 1]]\n",
        "        distance = calculate_distance(city1, city2)\n",
        "        total_distance += distance\n",
        "    \n",
        "    # Connect the last city with the first city\n",
        "    city1 = cities[route[n - 1]]\n",
        "    city2 = cities[route[0]]\n",
        "    distance = calculate_distance(city1, city2)\n",
        "    total_distance += distance\n",
        "    \n",
        "    return route, total_distance\n",
        "\n",
        "# Example usage\n",
        "cities = [(0, 0), (1, 5), (2, 3), (5, 2)]\n",
        "num_cities = len(cities)\n",
        "\n",
        "route, total_distance = tsp(num_cities, cities)\n",
        "\n",
        "print(\"Optimal Route:\", route)\n",
        "print(\"Total Distance:\", total_distance)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "C5YCPSNU-iRy",
        "outputId": "84852846-8738-498a-c9b7-74a092205d11"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Optimal Route: [2, 0, 1, 3]\n",
            "Total Distance: 16.866848449225152\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import random\n",
        "\n",
        "def is_valid(board):\n",
        "    n = len(board)\n",
        "    for i in range(n):\n",
        "        for j in range(i + 1, n):\n",
        "            if board[i] == board[j] or abs(board[i] - board[j]) == j - i:\n",
        "                return False\n",
        "    return True\n",
        "\n",
        "def queens(n=8):\n",
        "    board = random.sample(range(n), n)\n",
        "    print(\"Generated random sequence of placing 8 queen: \",board)\n",
        "    while not is_valid(board):\n",
        "        random.shuffle(board)\n",
        "    return board\n",
        "\n",
        "# Example usage\n",
        "n = 8\n",
        "solution = queens(n)\n",
        "print(\"Solution:\", solution)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "TjS_snnBPFDz",
        "outputId": "2c1d8e1e-1163-4ef6-f3f4-ebf75a2b88e9"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Generated random sequence of placing 8 queen:  [7, 0, 6, 5, 3, 4, 1, 2]\n",
            "Solution: [3, 5, 0, 4, 1, 7, 2, 6]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "8XedcopTVLGq"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}