Copy_of_AI101.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "provenance": [],
      "authorship_tag": "ABX9TyNKR3nzrV5Dt9euZduTWdR4",
      "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/Kawin101/935306-AI/blob/main/Copy_of_AI101.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": 659
        },
        "id": "NW9s-wn1fcgc",
        "outputId": "27c64451-ff23-44d6-81bd-0e36df5a9c28"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "14. 633410020-9 นายพชรพล คนเพียร\n",
            "34. 633410077-0 นางสาวธิดารัตน์ มาทา\n",
            "50. 633410142-5 นายกวินภพ ชมนิกร\n",
            "                               \n",
            " ** The Shortest Path is :  ** \n",
            "A -> C = 8 km.\n",
            "C -> H = 2 km.\n",
            "Total: [A -> C -> H] = 10 km.\n",
            " ** The Big lane size is : **\n",
            "Max size Lane = A -> C 4 lane.\n",
            "Max size Lane = C -> H 4 lane.\n",
            "Total: [A -> C -> H] = 8 lane.\n",
            " ** The best road is : **\n",
            "A -> B = 7.6\n",
            "B -> E = 5.6\n",
            "Total : [A -> B -> E] = 13.2\n",
            " **Other** | Goal: A > C > E\n",
            "Step1: A -> C = 8 km.  ||  4 lane.\n",
            "Step2: A -> C -> A 16 km.  ||  4 lane.\n",
            "Step3: A -> C -> A -> B 26 km.  ||  4 lane.\n",
            "Step4: A -> C -> A -> B -> E 34 km.  ||  2 lane.\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 3 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "# Define\n",
        "\n",
        "# ** Point 2 **\n",
        "# Route 1 (km.)\n",
        "A = 0\n",
        "B = 0\n",
        "C = 0\n",
        "AtoB = 10\n",
        "BtoE = 8\n",
        "BtoF = 6\n",
        "\n",
        "AtoC = 8\n",
        "CtoG = 4\n",
        "CtoH = 2\n",
        "\n",
        "# Route 2 (lane.)\n",
        "LAtoB = 4\n",
        "LBtoE = 2\n",
        "LBtoF = 2\n",
        "\n",
        "LAtoC = 4\n",
        "LCtoG = 2\n",
        "LCtoH = 4\n",
        "\n",
        "# Set vule to fine min of position (หาตำแหน่งที่ใกล้ที่สุด)\n",
        "kmMin01 = min(AtoB, AtoC)\n",
        "kmMin02 = min(BtoE, BtoF, CtoG, CtoH)\n",
        "print('14. 633410020-9 นายพชรพล คนเพียร')\n",
        "print('34. 633410077-0 นางสาวธิดารัตน์ มาทา')\n",
        "print('50. 633410142-5 นายกวินภพ ชมนิกร')\n",
        "print('                               ')\n",
        "print(' ** The Shortest Path is :  ** ')\n",
        "print('A -> C =', kmMin01 , 'km.')\n",
        "print('C -> H =', kmMin02 , 'km.')\n",
        "print('Total: [A -> C -> H] =',kmMin01+kmMin02, 'km.')\n",
        "\n",
        "# Max lane size find\n",
        "laneMin01 = max(LAtoB, LAtoC)\n",
        "laneMin02 = max(LBtoE, LBtoF, LCtoG, LCtoH)\n",
        "print(' ** The Big lane size is : **')\n",
        "\n",
        "if AtoB > AtoC:\n",
        "  print('Max size Lane = A -> C', laneMin01, 'lane.')\n",
        "else:\n",
        "  print('going to A -> B')\n",
        "\n",
        "if CtoG > CtoH:\n",
        "  print('Max size Lane = C -> H', laneMin02, 'lane.')\n",
        "else:\n",
        "  print('goint to C -> G')\n",
        "\n",
        "if AtoB > AtoC:\n",
        "  if CtoG > CtoH:\n",
        "    print('Total: [A -> C -> H] =', laneMin01+laneMin02, 'lane.')\n",
        "\n",
        "# ** Point 3 **\n",
        "# Define\n",
        "SH01 = (60/100)\n",
        "SH02 = (40/100)\n",
        "\n",
        "# Route 1 (Fine best result)\n",
        "math01 = AtoB * SH01 + LAtoB * SH02\n",
        "math02 = AtoC * SH01 + LAtoC * SH02\n",
        "math03 = BtoE * SH01 + LBtoE * SH02\n",
        "math04 = BtoF * SH01 + LBtoF * SH02\n",
        "math05 = CtoG * SH01 + LCtoG * SH02\n",
        "math06 = CtoH * SH01 + LCtoH * SH02\n",
        "\n",
        "print(' ** The best road is : **')\n",
        "if math01 > math02: \n",
        "  print('A -> B =', math01)\n",
        "else:\n",
        "  print('A -> C =', math02)\n",
        "\n",
        "if math03 > math04:\n",
        "  print('B -> E =', math03)\n",
        "else:\n",
        "  print('B -> F =', math04)\n",
        "\n",
        "#if math05 > math06:\n",
        "  #print('C -> G =', math05, )\n",
        "#else:\n",
        "  #print('C -> H =', math06)\n",
        "\n",
        "if math01 > math02:\n",
        "  if math03 > math04:\n",
        "    print('Total : [A -> B -> E] =',math01+math03)\n",
        "\n",
        "# ** Point 4 **\n",
        "# Define (A -> C -> E)\n",
        "# Short Time\n",
        "# Best Road\n",
        "\n",
        "print(' **Other** | Goal: A > C > E')\n",
        "if AtoB > AtoC: # A->C\n",
        "  print('Step1: A -> C =', AtoC, 'km.', ' || ', LAtoC, 'lane.')\n",
        "  if AtoC < AtoB: # C->A\n",
        "    print('Step2: A -> C -> A', AtoC+AtoC, 'km.', ' || ', (LAtoC), 'lane.')\n",
        "    if AtoB > AtoC: \n",
        "      print('Step3: A -> C -> A -> B', AtoC+AtoC+AtoB, 'km.', ' || ', (C+LAtoB), 'lane.')\n",
        "      if BtoE > BtoF:\n",
        "        print('Step4: A -> C -> A -> B -> E', AtoC+AtoC+AtoB+BtoE, 'km.', ' || ', (LBtoE), 'lane.')\n",
        "\n",
        "# ** Point 1 **\n",
        "# Output\n",
        "plt.subplot(331),plt.plot([A,LAtoC,LCtoH],[A,AtoC,CtoH])\n",
        "plt.xlabel('x - lane.')\n",
        "plt.ylabel('y - km.')\n",
        "plt.title('ShortestPath&BigLane (A>C>H)')\n",
        "\n",
        "plt.subplot(335),plt.plot([A,LAtoB,LBtoE],[A,AtoB,BtoE])\n",
        "plt.xlabel('x - lane.')\n",
        "plt.ylabel('y - km.')\n",
        "plt.title('Best Road (A>B>E)')\n",
        "\n",
        "plt.subplot(339),plt.plot([A,LAtoC,(LAtoC+LAtoC)/2,LAtoB,LBtoE],[A,AtoC,AtoC+AtoC,AtoB,BtoE])\n",
        "plt.xlabel('x - lane.')\n",
        "plt.ylabel('y - km.')\n",
        "plt.title('Other (A>C>A>B>E)')\n",
        "\n",
        "plt.show()"
      ]
    }
  ]
}