Hill_Climbing.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "Hill Climbing.ipynb",
      "provenance": [],
      "collapsed_sections": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "m80G2Qg0Pb88"
      },
      "source": [
        "# Hill Climbing Algorithem"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "gpy5Bm48JM7g"
      },
      "source": [
        "## librarires\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3vPK0XHvJL0Z"
      },
      "source": [
        "import numpy as np\n",
        "from copy import deepcopy\n",
        "from copy import copy\n",
        "import sys\n",
        "import numpy\n",
        "numpy.set_printoptions(threshold=sys.maxsize)\n",
        "import random\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "%matplotlib inline"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "iJJRdP8wOyzh"
      },
      "source": [
        "## Colab Setting"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "oHenjJF9LoZA"
      },
      "source": [
        "### Mount Drive on Colab"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "6o82mnz8HgHh",
        "outputId": "b4a94a28-dbf6-411e-e4ba-f99ac7c511f3"
      },
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Drive already mounted at /content/drive; to attempt to forcibly remount, call drive.mount(\"/content/drive\", force_remount=True).\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "NzhVkDlpO51q"
      },
      "source": [
        "## Pre_prosseing With DATA"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "pFgqpTmHOl8o"
      },
      "source": [
        "### Load Data"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "jkm0FYBSyEjR",
        "outputId": "7b60c609-bec7-4703-af20-659f2a33d015"
      },
      "source": [
        "with open('/content/drive/MyDrive/Colab Notebooks/AI Class Project 1/dj38.tsp') as f:\n",
        "    file = f.read().split(\"\\n\")\n",
        "    #print(file)\n",
        "    index=[]\n",
        "    x=[]\n",
        "    y = []\n",
        "    for i in file:\n",
        "        a , b, c = i.split()\n",
        "        index.append(int(a)-1) # in here I change city numbers from 1 to m into 0 to m-1 to be same as list index\n",
        "        x.append(float(b))\n",
        "        y.append(float(c))\n",
        "print(index)\n",
        "print(x)\n",
        "print(y)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37]\n",
            "[11003.6111, 11108.6111, 11133.3333, 11155.8333, 11183.3333, 11297.5, 11310.2778, 11416.6667, 11423.8889, 11438.3333, 11461.1111, 11485.5556, 11503.0556, 11511.3889, 11522.2222, 11569.4444, 11583.3333, 11595.0, 11600.0, 11690.5556, 11715.8333, 11751.1111, 11770.2778, 11785.2778, 11822.7778, 11846.9444, 11963.0556, 11973.0556, 12058.3333, 12149.4444, 12286.9444, 12300.0, 12355.8333, 12363.3333, 12372.7778, 12386.6667, 12421.6667, 12645.0]\n",
            "[42102.5, 42373.8889, 42885.8333, 42712.5, 42933.3333, 42853.3333, 42929.4444, 42983.3333, 43000.2778, 42057.2222, 43252.7778, 43187.2222, 42855.2778, 42106.3889, 42841.9444, 43136.6667, 43150.0, 43148.0556, 43150.0, 42686.6667, 41836.1111, 42814.4444, 42651.9444, 42884.4444, 42673.6111, 42660.5556, 43290.5556, 43026.1111, 42195.5556, 42477.5, 43355.5556, 42433.3333, 43156.3889, 43189.1667, 42711.3889, 43334.7222, 42895.5556, 42973.3333]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "j4C8o9jtOV7C"
      },
      "source": [
        "### Display data"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 262
        },
        "id": "6LJf2qWX-8tM",
        "outputId": "b60f6c95-83b9-416b-d9c8-6b3020d7e865"
      },
      "source": [
        "plt.style.use('seaborn-whitegrid')\n",
        "\n",
        "plt.plot(y, x, 'o', color='black');"
      ],
      "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": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "aGpAtSF2PF9x"
      },
      "source": [
        "## Defind Functions"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "niISKabxIy8D"
      },
      "source": [
        "### Computing distanatce between two cities (Function):"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "hew32W_5JDcC",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "0ce4f169-50ee-4f90-b284-af2e92ef2f97"
      },
      "source": [
        "def distance(x1,y1 ,x2,y2):\n",
        "  dis = np.sqrt(np.square(x1-x2) + np.square(y1-y2))\n",
        "  return dis\n",
        "\n",
        "print(f\"first ({x[0]},{y[0]}) and second ({x[1]},{y[1]})\")\n",
        "distance(x[0],y[0], x[1],y[1])"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "first (11003.6111,42102.5) and second (11108.6111,42373.8889)\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "290.99301545433866"
            ]
          },
          "metadata": {},
          "execution_count": 18
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "pxVgx9MUp18d"
      },
      "source": [
        "### Make matrix of distance:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ldMkVILIcuMX"
      },
      "source": [
        "def tsp_matrix(x,y):\n",
        "  tsp = []\n",
        "  row = []\n",
        "  for i in range(len(x)):\n",
        "    row.clear()\n",
        "    for j in range(len(x)):\n",
        "      row.append(distance(x[i],y[i] ,x[j],y[j]))\n",
        "    r = copy(row)\n",
        "    tsp.append(r)\n",
        "  return tsp"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "EmHnxQwjghI0",
        "outputId": "1122a376-922d-429d-c442-73d57f0b9f49"
      },
      "source": [
        "#test\n",
        "\n",
        "print(tsp_matrix(x,y))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[[0.0, 290.99301545433866, 794.0018312710164, 628.7062892741252, 850.0494347752532, 806.3010169484456, 881.9759098638978, 972.873183452112, 991.2811948915838, 437.07375847296123, 1237.9197337359349, 1186.9678817276742, 903.3931728506114, 507.7926916725464, 903.1807647058093, 1178.8418412907574, 1197.2193112261596, 1201.2191072966534, 1205.3779366004712, 901.7446864102617, 760.410092131903, 1032.2892417783694, 943.2215951621602, 1105.6400293858078, 998.6000054737141, 1011.2552138704898, 1527.0919609740627, 1338.9847283010597, 1058.8192781887758, 1205.6363263392866, 1793.625572849654, 1337.936789400792, 1714.40563803204, 1740.6001775427158, 1498.45358451041, 1852.3537304910717, 1624.7519408767353, 1858.0926127166258], [290.99301545433866, 0.0, 512.5409796925513, 341.88801268259067, 564.4124767084799, 515.311506988317, 591.0256944238062, 682.8768039571414, 701.258258572444, 457.1591933470562, 946.943476952674, 896.4363964995716, 622.3517788385158, 483.5144322280772, 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          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "PeivIjmnqJdl"
      },
      "source": [
        "### Make random sluoition"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "tZCcGmwKqP8V"
      },
      "source": [
        "def random_solution(tsp):\n",
        "  cities = list(range(len(tsp)))\n",
        "  solution = []\n",
        "\n",
        "  for i in range(len(tsp)):\n",
        "    random_city = cities[random.randint(0,len(cities)-1)]\n",
        "    solution.append(random_city)\n",
        "    cities.remove(random_city)\n",
        "\n",
        "  return solution"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "sSF6rl87sJm3",
        "outputId": "835848e8-5c30-4b5a-d0bf-a7aa6b7c11ef"
      },
      "source": [
        "# test of function\n",
        "tsp = tsp_matrix(x,y)\n",
        "print(random_solution(tsp), len(random_solution(tsp)))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[34, 24, 4, 1, 32, 13, 18, 30, 37, 3, 11, 27, 28, 9, 5, 17, 8, 26, 31, 22, 35, 21, 0, 29, 7, 19, 33, 23, 2, 14, 36, 16, 20, 10, 12, 6, 25, 15] 38\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "0X1kLNM-wlxL"
      },
      "source": [
        "### Find routelength of soluotion"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "T4U_dDptxfz4"
      },
      "source": [
        "def route_length(tsp, solution):\n",
        "  \"\"\"\n",
        "  Funciton input is tsp and solution which tsp is matrix of distance between cites and solution is list of cites\n",
        "\n",
        "  The output is length of solution from firt city to the goal (cost)\n",
        "  \"\"\"\n",
        "  length = 0\n",
        "  for i in range(len(solution)):\n",
        "    length += tsp[solution[i-1]][solution[i]]\n",
        "  return length"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "I-1IKqUs02x_",
        "outputId": "b8eccf47-a99f-4d28-9ef1-12c4bdff8798"
      },
      "source": [
        "# test\n",
        "tsp = tsp_matrix(x,y)\n",
        "rands = random_solution(tsp)\n",
        "print(route_length(tsp,rands))\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "28524.668671051088\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "4rr0VEkI2jmC"
      },
      "source": [
        "### Find neighbors of a solution\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xcbims2e3AkH"
      },
      "source": [
        "def find_neighbors(solution):\n",
        "  neighbors = []\n",
        "  for i in range(len(solution)): #This for loop will make new list from solution by changing element positions\n",
        "    for j in range(i+1,len(solution)):\n",
        "      a = solution.copy()\n",
        "      a[i] = solution[j]\n",
        "      a[j] = solution[i]\n",
        "      neighbors.append(a)\n",
        "  return neighbors"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "RpvdrwpU5avt",
        "outputId": "4f274955-9a2d-4c49-97f2-467b207719c2"
      },
      "source": [
        "# test\n",
        "solution = random_solution(tsp)\n",
        "print(find_neighbors(solution))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
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12, 18, 31, 23, 1, 14], [7, 36, 34, 20, 15, 8, 6, 35, 9, 29, 24, 11, 5, 37, 0, 32, 2, 26, 10, 17, 3, 21, 28, 33, 13, 22, 1, 16, 4, 30, 25, 27, 12, 18, 31, 19, 23, 14], [7, 36, 34, 20, 15, 8, 6, 35, 9, 29, 24, 11, 5, 37, 0, 32, 2, 26, 10, 17, 3, 21, 28, 33, 13, 22, 14, 16, 4, 30, 25, 27, 12, 18, 31, 19, 1, 23], [7, 36, 34, 20, 15, 8, 6, 35, 9, 29, 24, 11, 5, 37, 0, 32, 2, 26, 10, 17, 3, 21, 28, 33, 13, 22, 23, 4, 16, 30, 25, 27, 12, 18, 31, 19, 1, 14], [7, 36, 34, 20, 15, 8, 6, 35, 9, 29, 24, 11, 5, 37, 0, 32, 2, 26, 10, 17, 3, 21, 28, 33, 13, 22, 23, 30, 4, 16, 25, 27, 12, 18, 31, 19, 1, 14], [7, 36, 34, 20, 15, 8, 6, 35, 9, 29, 24, 11, 5, 37, 0, 32, 2, 26, 10, 17, 3, 21, 28, 33, 13, 22, 23, 25, 4, 30, 16, 27, 12, 18, 31, 19, 1, 14], [7, 36, 34, 20, 15, 8, 6, 35, 9, 29, 24, 11, 5, 37, 0, 32, 2, 26, 10, 17, 3, 21, 28, 33, 13, 22, 23, 27, 4, 30, 25, 16, 12, 18, 31, 19, 1, 14], [7, 36, 34, 20, 15, 8, 6, 35, 9, 29, 24, 11, 5, 37, 0, 32, 2, 26, 10, 17, 3, 21, 28, 33, 13, 22, 23, 12, 4, 30, 25, 27, 16, 18, 31, 19, 1, 14], [7, 36, 34, 20, 15, 8, 6, 35, 9, 29, 24, 11, 5, 37, 0, 32, 2, 26, 10, 17, 3, 21, 28, 33, 13, 22, 23, 18, 4, 30, 25, 27, 12, 16, 31, 19, 1, 14], [7, 36, 34, 20, 15, 8, 6, 35, 9, 29, 24, 11, 5, 37, 0, 32, 2, 26, 10, 17, 3, 21, 28, 33, 13, 22, 23, 31, 4, 30, 25, 27, 12, 18, 16, 19, 1, 14], [7, 36, 34, 20, 15, 8, 6, 35, 9, 29, 24, 11, 5, 37, 0, 32, 2, 26, 10, 17, 3, 21, 28, 33, 13, 22, 23, 19, 4, 30, 25, 27, 12, 18, 31, 16, 1, 14], [7, 36, 34, 20, 15, 8, 6, 35, 9, 29, 24, 11, 5, 37, 0, 32, 2, 26, 10, 17, 3, 21, 28, 33, 13, 22, 23, 1, 4, 30, 25, 27, 12, 18, 31, 19, 16, 14], [7, 36, 34, 20, 15, 8, 6, 35, 9, 29, 24, 11, 5, 37, 0, 32, 2, 26, 10, 17, 3, 21, 28, 33, 13, 22, 23, 14, 4, 30, 25, 27, 12, 18, 31, 19, 1, 16], [7, 36, 34, 20, 15, 8, 6, 35, 9, 29, 24, 11, 5, 37, 0, 32, 2, 26, 10, 17, 3, 21, 28, 33, 13, 22, 23, 16, 30, 4, 25, 27, 12, 18, 31, 19, 1, 14], [7, 36, 34, 20, 15, 8, 6, 35, 9, 29, 24, 11, 5, 37, 0, 32, 2, 26, 10, 17, 3, 21, 28, 33, 13, 22, 23, 16, 25, 30, 4, 27, 12, 18, 31, 19, 1, 14], [7, 36, 34, 20, 15, 8, 6, 35, 9, 29, 24, 11, 5, 37, 0, 32, 2, 26, 10, 17, 3, 21, 28, 33, 13, 22, 23, 16, 27, 30, 25, 4, 12, 18, 31, 19, 1, 14], [7, 36, 34, 20, 15, 8, 6, 35, 9, 29, 24, 11, 5, 37, 0, 32, 2, 26, 10, 17, 3, 21, 28, 33, 13, 22, 23, 16, 12, 30, 25, 27, 4, 18, 31, 19, 1, 14], [7, 36, 34, 20, 15, 8, 6, 35, 9, 29, 24, 11, 5, 37, 0, 32, 2, 26, 10, 17, 3, 21, 28, 33, 13, 22, 23, 16, 18, 30, 25, 27, 12, 4, 31, 19, 1, 14], [7, 36, 34, 20, 15, 8, 6, 35, 9, 29, 24, 11, 5, 37, 0, 32, 2, 26, 10, 17, 3, 21, 28, 33, 13, 22, 23, 16, 31, 30, 25, 27, 12, 18, 4, 19, 1, 14], [7, 36, 34, 20, 15, 8, 6, 35, 9, 29, 24, 11, 5, 37, 0, 32, 2, 26, 10, 17, 3, 21, 28, 33, 13, 22, 23, 16, 19, 30, 25, 27, 12, 18, 31, 4, 1, 14], [7, 36, 34, 20, 15, 8, 6, 35, 9, 29, 24, 11, 5, 37, 0, 32, 2, 26, 10, 17, 3, 21, 28, 33, 13, 22, 23, 16, 1, 30, 25, 27, 12, 18, 31, 19, 4, 14], [7, 36, 34, 20, 15, 8, 6, 35, 9, 29, 24, 11, 5, 37, 0, 32, 2, 26, 10, 17, 3, 21, 28, 33, 13, 22, 23, 16, 14, 30, 25, 27, 12, 18, 31, 19, 1, 4], [7, 36, 34, 20, 15, 8, 6, 35, 9, 29, 24, 11, 5, 37, 0, 32, 2, 26, 10, 17, 3, 21, 28, 33, 13, 22, 23, 16, 4, 25, 30, 27, 12, 18, 31, 19, 1, 14], [7, 36, 34, 20, 15, 8, 6, 35, 9, 29, 24, 11, 5, 37, 0, 32, 2, 26, 10, 17, 3, 21, 28, 33, 13, 22, 23, 16, 4, 27, 25, 30, 12, 18, 31, 19, 1, 14], [7, 36, 34, 20, 15, 8, 6, 35, 9, 29, 24, 11, 5, 37, 0, 32, 2, 26, 10, 17, 3, 21, 28, 33, 13, 22, 23, 16, 4, 12, 25, 27, 30, 18, 31, 19, 1, 14], [7, 36, 34, 20, 15, 8, 6, 35, 9, 29, 24, 11, 5, 37, 0, 32, 2, 26, 10, 17, 3, 21, 28, 33, 13, 22, 23, 16, 4, 18, 25, 27, 12, 30, 31, 19, 1, 14], [7, 36, 34, 20, 15, 8, 6, 35, 9, 29, 24, 11, 5, 37, 0, 32, 2, 26, 10, 17, 3, 21, 28, 33, 13, 22, 23, 16, 4, 31, 25, 27, 12, 18, 30, 19, 1, 14], [7, 36, 34, 20, 15, 8, 6, 35, 9, 29, 24, 11, 5, 37, 0, 32, 2, 26, 10, 17, 3, 21, 28, 33, 13, 22, 23, 16, 4, 19, 25, 27, 12, 18, 31, 30, 1, 14], [7, 36, 34, 20, 15, 8, 6, 35, 9, 29, 24, 11, 5, 37, 0, 32, 2, 26, 10, 17, 3, 21, 28, 33, 13, 22, 23, 16, 4, 1, 25, 27, 12, 18, 31, 19, 30, 14], [7, 36, 34, 20, 15, 8, 6, 35, 9, 29, 24, 11, 5, 37, 0, 32, 2, 26, 10, 17, 3, 21, 28, 33, 13, 22, 23, 16, 4, 14, 25, 27, 12, 18, 31, 19, 1, 30], [7, 36, 34, 20, 15, 8, 6, 35, 9, 29, 24, 11, 5, 37, 0, 32, 2, 26, 10, 17, 3, 21, 28, 33, 13, 22, 23, 16, 4, 30, 27, 25, 12, 18, 31, 19, 1, 14], [7, 36, 34, 20, 15, 8, 6, 35, 9, 29, 24, 11, 5, 37, 0, 32, 2, 26, 10, 17, 3, 21, 28, 33, 13, 22, 23, 16, 4, 30, 12, 27, 25, 18, 31, 19, 1, 14], [7, 36, 34, 20, 15, 8, 6, 35, 9, 29, 24, 11, 5, 37, 0, 32, 2, 26, 10, 17, 3, 21, 28, 33, 13, 22, 23, 16, 4, 30, 18, 27, 12, 25, 31, 19, 1, 14], [7, 36, 34, 20, 15, 8, 6, 35, 9, 29, 24, 11, 5, 37, 0, 32, 2, 26, 10, 17, 3, 21, 28, 33, 13, 22, 23, 16, 4, 30, 31, 27, 12, 18, 25, 19, 1, 14], [7, 36, 34, 20, 15, 8, 6, 35, 9, 29, 24, 11, 5, 37, 0, 32, 2, 26, 10, 17, 3, 21, 28, 33, 13, 22, 23, 16, 4, 30, 19, 27, 12, 18, 31, 25, 1, 14], [7, 36, 34, 20, 15, 8, 6, 35, 9, 29, 24, 11, 5, 37, 0, 32, 2, 26, 10, 17, 3, 21, 28, 33, 13, 22, 23, 16, 4, 30, 1, 27, 12, 18, 31, 19, 25, 14], [7, 36, 34, 20, 15, 8, 6, 35, 9, 29, 24, 11, 5, 37, 0, 32, 2, 26, 10, 17, 3, 21, 28, 33, 13, 22, 23, 16, 4, 30, 14, 27, 12, 18, 31, 19, 1, 25], [7, 36, 34, 20, 15, 8, 6, 35, 9, 29, 24, 11, 5, 37, 0, 32, 2, 26, 10, 17, 3, 21, 28, 33, 13, 22, 23, 16, 4, 30, 25, 12, 27, 18, 31, 19, 1, 14], [7, 36, 34, 20, 15, 8, 6, 35, 9, 29, 24, 11, 5, 37, 0, 32, 2, 26, 10, 17, 3, 21, 28, 33, 13, 22, 23, 16, 4, 30, 25, 18, 12, 27, 31, 19, 1, 14], [7, 36, 34, 20, 15, 8, 6, 35, 9, 29, 24, 11, 5, 37, 0, 32, 2, 26, 10, 17, 3, 21, 28, 33, 13, 22, 23, 16, 4, 30, 25, 31, 12, 18, 27, 19, 1, 14], [7, 36, 34, 20, 15, 8, 6, 35, 9, 29, 24, 11, 5, 37, 0, 32, 2, 26, 10, 17, 3, 21, 28, 33, 13, 22, 23, 16, 4, 30, 25, 19, 12, 18, 31, 27, 1, 14], [7, 36, 34, 20, 15, 8, 6, 35, 9, 29, 24, 11, 5, 37, 0, 32, 2, 26, 10, 17, 3, 21, 28, 33, 13, 22, 23, 16, 4, 30, 25, 1, 12, 18, 31, 19, 27, 14], [7, 36, 34, 20, 15, 8, 6, 35, 9, 29, 24, 11, 5, 37, 0, 32, 2, 26, 10, 17, 3, 21, 28, 33, 13, 22, 23, 16, 4, 30, 25, 14, 12, 18, 31, 19, 1, 27], [7, 36, 34, 20, 15, 8, 6, 35, 9, 29, 24, 11, 5, 37, 0, 32, 2, 26, 10, 17, 3, 21, 28, 33, 13, 22, 23, 16, 4, 30, 25, 27, 18, 12, 31, 19, 1, 14], [7, 36, 34, 20, 15, 8, 6, 35, 9, 29, 24, 11, 5, 37, 0, 32, 2, 26, 10, 17, 3, 21, 28, 33, 13, 22, 23, 16, 4, 30, 25, 27, 31, 18, 12, 19, 1, 14], [7, 36, 34, 20, 15, 8, 6, 35, 9, 29, 24, 11, 5, 37, 0, 32, 2, 26, 10, 17, 3, 21, 28, 33, 13, 22, 23, 16, 4, 30, 25, 27, 19, 18, 31, 12, 1, 14], [7, 36, 34, 20, 15, 8, 6, 35, 9, 29, 24, 11, 5, 37, 0, 32, 2, 26, 10, 17, 3, 21, 28, 33, 13, 22, 23, 16, 4, 30, 25, 27, 1, 18, 31, 19, 12, 14], [7, 36, 34, 20, 15, 8, 6, 35, 9, 29, 24, 11, 5, 37, 0, 32, 2, 26, 10, 17, 3, 21, 28, 33, 13, 22, 23, 16, 4, 30, 25, 27, 14, 18, 31, 19, 1, 12], [7, 36, 34, 20, 15, 8, 6, 35, 9, 29, 24, 11, 5, 37, 0, 32, 2, 26, 10, 17, 3, 21, 28, 33, 13, 22, 23, 16, 4, 30, 25, 27, 12, 31, 18, 19, 1, 14], [7, 36, 34, 20, 15, 8, 6, 35, 9, 29, 24, 11, 5, 37, 0, 32, 2, 26, 10, 17, 3, 21, 28, 33, 13, 22, 23, 16, 4, 30, 25, 27, 12, 19, 31, 18, 1, 14], [7, 36, 34, 20, 15, 8, 6, 35, 9, 29, 24, 11, 5, 37, 0, 32, 2, 26, 10, 17, 3, 21, 28, 33, 13, 22, 23, 16, 4, 30, 25, 27, 12, 1, 31, 19, 18, 14], [7, 36, 34, 20, 15, 8, 6, 35, 9, 29, 24, 11, 5, 37, 0, 32, 2, 26, 10, 17, 3, 21, 28, 33, 13, 22, 23, 16, 4, 30, 25, 27, 12, 14, 31, 19, 1, 18], [7, 36, 34, 20, 15, 8, 6, 35, 9, 29, 24, 11, 5, 37, 0, 32, 2, 26, 10, 17, 3, 21, 28, 33, 13, 22, 23, 16, 4, 30, 25, 27, 12, 18, 19, 31, 1, 14], [7, 36, 34, 20, 15, 8, 6, 35, 9, 29, 24, 11, 5, 37, 0, 32, 2, 26, 10, 17, 3, 21, 28, 33, 13, 22, 23, 16, 4, 30, 25, 27, 12, 18, 1, 19, 31, 14], [7, 36, 34, 20, 15, 8, 6, 35, 9, 29, 24, 11, 5, 37, 0, 32, 2, 26, 10, 17, 3, 21, 28, 33, 13, 22, 23, 16, 4, 30, 25, 27, 12, 18, 14, 19, 1, 31], [7, 36, 34, 20, 15, 8, 6, 35, 9, 29, 24, 11, 5, 37, 0, 32, 2, 26, 10, 17, 3, 21, 28, 33, 13, 22, 23, 16, 4, 30, 25, 27, 12, 18, 31, 1, 19, 14], [7, 36, 34, 20, 15, 8, 6, 35, 9, 29, 24, 11, 5, 37, 0, 32, 2, 26, 10, 17, 3, 21, 28, 33, 13, 22, 23, 16, 4, 30, 25, 27, 12, 18, 31, 14, 1, 19], [7, 36, 34, 20, 15, 8, 6, 35, 9, 29, 24, 11, 5, 37, 0, 32, 2, 26, 10, 17, 3, 21, 28, 33, 13, 22, 23, 16, 4, 30, 25, 27, 12, 18, 31, 19, 14, 1]]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "LUtJln2177Lv"
      },
      "source": [
        "### Find best neighbor"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "lQm1eEYH8D2y"
      },
      "source": [
        "def find_best_neighbor(tsp , neighbors):\n",
        "  best_neighbor_length = route_length(tsp, neighbors[0]) # Initial answer\n",
        "  best_neighbor = neighbors[0]\n",
        "\n",
        "  for i in neighbors:\n",
        "    current_length = route_length(tsp , i)\n",
        "    if current_length < best_neighbor_length :\n",
        "      best_neighbor_length = current_length\n",
        "      best_neighbor = i\n",
        "  \n",
        "  return best_neighbor, best_neighbor_length"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "HJW23h5BCeXV"
      },
      "source": [
        "### Plot Cost"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "TDadY8KBChr7"
      },
      "source": [
        "def plot_cost(cost_value):\n",
        "  iteration = []\n",
        "  for i in range(1,len(cost_value)+1):\n",
        "    iteration.append(i)\n",
        "    \n",
        "  plt.plot(iteration, cost_value, '-o', color='blue');"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "9xhMdqKzD2bu"
      },
      "source": [
        "### Hill Climbing Function"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vVD93ee7qDq_"
      },
      "source": [
        "def hill_climbing(tsp):\n",
        "  \n",
        "  cost_value = []\n",
        "\n",
        "  current_solution = random_solution(tsp) # This line of code will give us a random road to all cities\n",
        "\n",
        "  current_route_length = route_length(tsp,current_solution) # This our cost value for current route\n",
        "\n",
        "  print(f\"Current solution is {current_solution} with cost of: {current_route_length}\")\n",
        "\n",
        "  neighbors = find_neighbors(solution)\n",
        "\n",
        "  best_neighbor , best_neighbor_length = find_best_neighbor(tsp , neighbors)\n",
        "\n",
        "  while best_neighbor_length < current_route_length :\n",
        "    current_solution = best_neighbor\n",
        "    print(f\"Current solution is {current_solution} with cost of: {current_route_length}\")\n",
        "    current_route_length = best_neighbor_length\n",
        "\n",
        "    cost_value.append(current_route_length)\n",
        "\n",
        "    neighbors = find_neighbors(current_solution)\n",
        "    best_neighbor , best_neighbor_length = find_best_neighbor(tsp , neighbors)\n",
        "\n",
        "  return current_solution , current_route_length, cost_value"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "J-8XA7d6p_Rk"
      },
      "source": [
        "## Main program"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 886
        },
        "id": "qqOX5cIiDxbQ",
        "outputId": "7403cb06-6c6b-4711-8a26-1dbfdd3c9448"
      },
      "source": [
        "tsp = tsp_matrix(x,y) # This line will give us m*m matrix of distance between cites. m is total number of cities\n",
        "\n",
        "Best_solution, best_cost , cost_value= hill_climbing(tsp) # This line of code will find best solution with hill_climbing algorithm\n",
        "\n",
        "print(\"\\n\"+ f\"Best solution is {Best_solution} and best cost is {best_cost}\")\n",
        "\n",
        "plot_cost(cost_value)\n",
        "plt.xlabel(\"iteration\")\n",
        "plt.ylabel(\"Cost\")"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Current solution is [27, 37, 12, 25, 13, 11, 7, 34, 4, 20, 19, 0, 2, 31, 26, 16, 24, 3, 23, 10, 32, 28, 15, 18, 1, 21, 35, 17, 9, 6, 29, 22, 30, 36, 5, 33, 14, 8] with cost of: 29980.34369784497\n",
            "Current solution is [7, 36, 34, 20, 15, 8, 6, 0, 9, 29, 24, 11, 5, 37, 35, 32, 2, 26, 10, 17, 3, 21, 28, 33, 13, 22, 23, 16, 4, 30, 25, 27, 12, 18, 31, 19, 1, 14] with cost of: 29980.34369784497\n",
            "Current solution is [7, 36, 34, 20, 15, 8, 6, 0, 9, 29, 24, 11, 5, 37, 35, 32, 30, 26, 10, 17, 3, 21, 28, 33, 13, 22, 23, 16, 4, 2, 25, 27, 12, 18, 31, 19, 1, 14] with cost of: 24566.92700878591\n",
            "Current solution is [7, 36, 34, 33, 15, 8, 6, 0, 9, 29, 24, 11, 5, 37, 35, 32, 30, 26, 10, 17, 3, 21, 28, 20, 13, 22, 23, 16, 4, 2, 25, 27, 12, 18, 31, 19, 1, 14] with cost of: 21745.8661813856\n",
            "Current solution is [7, 36, 34, 33, 15, 8, 6, 0, 9, 29, 24, 11, 5, 37, 35, 32, 30, 26, 10, 17, 3, 31, 28, 20, 13, 22, 23, 16, 4, 2, 25, 27, 12, 18, 21, 19, 1, 14] with cost of: 19034.461905469176\n",
            "Current solution is [7, 36, 34, 33, 15, 8, 6, 0, 9, 29, 24, 11, 5, 37, 35, 32, 30, 26, 10, 17, 25, 31, 28, 20, 13, 22, 23, 16, 4, 2, 3, 27, 12, 18, 21, 19, 1, 14] with cost of: 18103.938595013307\n",
            "Current solution is [7, 36, 34, 33, 15, 8, 6, 0, 9, 29, 24, 11, 27, 37, 35, 32, 30, 26, 10, 17, 25, 31, 28, 20, 13, 22, 23, 16, 4, 2, 3, 5, 12, 18, 21, 19, 1, 14] with cost of: 17278.373167985355\n",
            "Current solution is [7, 36, 34, 33, 15, 8, 1, 0, 9, 29, 24, 11, 27, 37, 35, 32, 30, 26, 10, 17, 25, 31, 28, 20, 13, 22, 23, 16, 4, 2, 3, 5, 12, 18, 21, 19, 6, 14] with cost of: 15759.689600509178\n",
            "Current solution is [7, 36, 34, 33, 15, 8, 1, 0, 9, 29, 24, 25, 27, 37, 35, 32, 30, 26, 10, 17, 11, 31, 28, 20, 13, 22, 23, 16, 4, 2, 3, 5, 12, 18, 21, 19, 6, 14] with cost of: 15131.251534169498\n",
            "Current solution is [7, 36, 34, 33, 15, 8, 1, 0, 9, 29, 24, 25, 27, 37, 35, 32, 30, 26, 10, 17, 11, 31, 28, 20, 13, 22, 23, 16, 4, 2, 3, 5, 6, 18, 21, 19, 12, 14] with cost of: 14588.010618236814\n",
            "Current solution is [7, 36, 34, 37, 15, 8, 1, 0, 9, 29, 24, 25, 27, 33, 35, 32, 30, 26, 10, 17, 11, 31, 28, 20, 13, 22, 23, 16, 4, 2, 3, 5, 6, 18, 21, 19, 12, 14] with cost of: 14108.532645155774\n",
            "Current solution is [18, 36, 34, 37, 15, 8, 1, 0, 9, 29, 24, 25, 27, 33, 35, 32, 30, 26, 10, 17, 11, 31, 28, 20, 13, 22, 23, 16, 4, 2, 3, 5, 6, 7, 21, 19, 12, 14] with cost of: 13752.85082227598\n",
            "Current solution is [37, 36, 34, 18, 15, 8, 1, 0, 9, 29, 24, 25, 27, 33, 35, 32, 30, 26, 10, 17, 11, 31, 28, 20, 13, 22, 23, 16, 4, 2, 3, 5, 6, 7, 21, 19, 12, 14] with cost of: 13507.25983603927\n",
            "Current solution is [37, 36, 34, 18, 15, 8, 1, 0, 9, 13, 24, 25, 27, 33, 35, 32, 30, 26, 10, 17, 11, 31, 28, 20, 29, 22, 23, 16, 4, 2, 3, 5, 6, 7, 21, 19, 12, 14] with cost of: 13152.547366573575\n",
            "Current solution is [37, 36, 34, 18, 15, 8, 1, 0, 9, 13, 24, 25, 27, 33, 35, 32, 30, 26, 10, 17, 11, 20, 28, 31, 29, 22, 23, 16, 4, 2, 3, 5, 6, 7, 21, 19, 12, 14] with cost of: 12929.466584439575\n",
            "Current solution is [37, 36, 34, 18, 15, 8, 1, 0, 9, 13, 24, 25, 27, 33, 32, 35, 30, 26, 10, 17, 11, 20, 28, 31, 29, 22, 23, 16, 4, 2, 3, 5, 6, 7, 21, 19, 12, 14] with cost of: 12572.947593574596\n",
            "Current solution is [37, 36, 34, 18, 15, 8, 1, 0, 9, 13, 20, 25, 27, 33, 32, 35, 30, 26, 10, 17, 11, 24, 28, 31, 29, 22, 23, 16, 4, 2, 3, 5, 6, 7, 21, 19, 12, 14] with cost of: 12350.289825305232\n",
            "Current solution is [37, 36, 34, 18, 15, 8, 1, 0, 9, 13, 20, 28, 27, 33, 32, 35, 30, 26, 10, 17, 11, 24, 25, 31, 29, 22, 23, 16, 4, 2, 3, 5, 6, 7, 21, 19, 12, 14] with cost of: 12129.71756607393\n",
            "Current solution is [37, 36, 27, 18, 15, 8, 1, 0, 9, 13, 20, 28, 34, 33, 32, 35, 30, 26, 10, 17, 11, 24, 25, 31, 29, 22, 23, 16, 4, 2, 3, 5, 6, 7, 21, 19, 12, 14] with cost of: 11901.973919084763\n",
            "Current solution is [37, 36, 27, 18, 15, 8, 1, 0, 9, 13, 20, 28, 34, 33, 32, 35, 30, 26, 10, 11, 17, 24, 25, 31, 29, 22, 23, 16, 4, 2, 3, 5, 6, 7, 21, 19, 12, 14] with cost of: 11507.262647311205\n",
            "Current solution is [37, 36, 27, 18, 15, 16, 1, 0, 9, 13, 20, 28, 34, 33, 32, 35, 30, 26, 10, 11, 17, 24, 25, 31, 29, 22, 23, 8, 4, 2, 3, 5, 6, 7, 21, 19, 12, 14] with cost of: 11319.114637557277\n",
            "Current solution is [37, 36, 27, 18, 15, 16, 1, 0, 9, 13, 20, 28, 34, 33, 32, 35, 30, 26, 10, 11, 17, 24, 25, 31, 29, 22, 19, 8, 4, 2, 3, 5, 6, 7, 21, 23, 12, 14] with cost of: 11188.090367882893\n",
            "Current solution is [37, 36, 27, 18, 15, 16, 1, 0, 9, 13, 20, 28, 34, 33, 32, 35, 30, 26, 10, 11, 17, 24, 25, 31, 29, 22, 19, 8, 4, 2, 3, 5, 6, 7, 21, 14, 12, 23] with cost of: 11042.278209951768\n",
            "Current solution is [37, 36, 27, 18, 15, 16, 1, 0, 9, 13, 20, 28, 34, 33, 32, 35, 30, 26, 10, 11, 17, 24, 25, 31, 29, 22, 19, 8, 4, 2, 3, 5, 6, 7, 12, 14, 21, 23] with cost of: 10928.78581326363\n",
            "Current solution is [37, 36, 27, 18, 15, 16, 1, 0, 9, 13, 20, 28, 34, 33, 32, 35, 30, 26, 10, 11, 17, 24, 25, 31, 29, 22, 19, 6, 4, 2, 3, 5, 8, 7, 12, 14, 21, 23] with cost of: 10502.756107103463\n",
            "Current solution is [37, 36, 27, 18, 15, 16, 1, 0, 9, 13, 20, 28, 34, 32, 33, 35, 30, 26, 10, 11, 17, 24, 25, 31, 29, 22, 19, 6, 4, 2, 3, 5, 8, 7, 12, 14, 21, 23] with cost of: 10435.37822736176\n",
            "Current solution is [37, 36, 27, 18, 17, 16, 1, 0, 9, 13, 20, 28, 34, 32, 33, 35, 30, 26, 10, 11, 15, 24, 25, 31, 29, 22, 19, 6, 4, 2, 3, 5, 8, 7, 12, 14, 21, 23] with cost of: 10369.264269280133\n",
            "Current solution is [34, 36, 27, 18, 17, 16, 1, 0, 9, 13, 20, 28, 37, 32, 33, 35, 30, 26, 10, 11, 15, 24, 25, 31, 29, 22, 19, 6, 4, 2, 3, 5, 8, 7, 12, 14, 21, 23] with cost of: 10317.10385431073\n",
            "Current solution is [34, 36, 27, 18, 17, 16, 1, 0, 9, 13, 20, 28, 37, 32, 33, 35, 30, 26, 10, 11, 15, 24, 25, 31, 29, 22, 19, 6, 4, 2, 3, 5, 7, 8, 12, 14, 21, 23] with cost of: 10286.334973999461\n",
            "Current solution is [34, 36, 27, 18, 17, 16, 1, 0, 9, 13, 20, 28, 37, 32, 33, 35, 30, 26, 10, 11, 15, 24, 25, 31, 29, 22, 19, 5, 4, 2, 3, 6, 7, 8, 12, 14, 21, 23] with cost of: 10279.60034867597\n",
            "Current solution is [34, 36, 27, 18, 17, 16, 1, 0, 9, 13, 20, 28, 37, 32, 33, 35, 30, 26, 10, 11, 15, 24, 25, 31, 29, 22, 19, 5, 3, 2, 4, 6, 7, 8, 12, 14, 21, 23] with cost of: 10277.216572705187\n",
            "\n",
            "Best solution is [34, 36, 27, 18, 17, 16, 1, 0, 9, 13, 20, 28, 37, 32, 33, 35, 30, 26, 10, 11, 15, 24, 25, 31, 29, 22, 19, 5, 3, 2, 4, 6, 7, 8, 12, 14, 21, 23] and best cost is 10198.26885942377\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0, 0.5, 'Cost')"
            ]
          },
          "metadata": {},
          "execution_count": 16
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "IBd7tI1G6AHJ"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    }
  ]
}