Travelers Problem.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Librerías Cargadas.\n",
      "CSV Cargado.\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import mlrose\n",
    "from matplotlib.animation import FuncAnimation\n",
    "import math\n",
    "import json\n",
    "import time\n",
    "from random import randint\n",
    "from matplotlib import pyplot as plt\n",
    "print ('Librerías Cargadas.')\n",
    "data = pd.read_csv('DataSet/data.csv', header = None, names=[\"id\", \"x\", \"y\"])\n",
    "print('CSV Cargado.')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "El Conjunto tiene: 634 lugares distintos\n"
     ]
    }
   ],
   "source": [
    "unique_locations = set()\n",
    "for i in range(0,980):\n",
    "    unique_locations.add((data['x'][i],data['y'][i]))\n",
    "print(\"El Conjunto tiene: \"+str(len(unique_locations))+\" lugares distintos\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "matrix = list(unique_locations) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 2880x2880 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(40,40));\n",
    "plt.plot([p[0] for p in matrix], [p[1] for p in matrix], 'o');\n",
    "plt.axis('scaled');\n",
    "plt.axis(\"off\");\n",
    "plt.show();"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Mapa de Luxemburgo con las 634 Ubicaciones\n",
    "![](Images/lux980.png)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "fitness_coords = mlrose.TravellingSales(coords = matrix)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "problem_fit = mlrose.TSPOpt(length = 634, fitness_fn = fitness_coords,maximize=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/nexudev/.local/lib/python3.6/site-packages/mlrose/algorithms.py:311: RuntimeWarning: overflow encountered in exp\n",
      "  prob = np.exp(delta_e/temp)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best Fitness:139445.19287257854, time: 7.554456949234009\n",
      "Best Fitness:139828.80027547653, time: 7.44458794593811\n",
      "Best Fitness:136331.3984124168, time: 7.314178228378296\n",
      "Best Fitness:139654.09924951702, time: 7.44690728187561\n",
      "Best Fitness:137093.32557962826, time: 7.394891738891602\n",
      "Best Fitness:137749.69056970463, time: 7.417880296707153\n",
      "Best Fitness:138884.20421469037, time: 7.4654316902160645\n",
      "Best Fitness:140164.41201608407, time: 7.368479251861572\n",
      "Best Fitness:136052.63831450138, time: 7.594430923461914\n",
      "Best Fitness:109078.85401106693, time: 20.555490732192993\n",
      "Best Fitness:106499.56539212355, time: 20.266963958740234\n",
      "Best Fitness:108577.65256551813, time: 20.296026945114136\n",
      "Best Fitness:106259.12465314817, time: 20.253825902938843\n",
      "Best Fitness:108418.8929383955, time: 20.39191436767578\n",
      "Best Fitness:108223.70253647096, time: 20.78437042236328\n",
      "Best Fitness:111901.31393180696, time: 20.551004648208618\n",
      "Best Fitness:103704.6857835826, time: 20.42961311340332\n",
      "Best Fitness:107849.56282792361, time: 20.215128898620605\n",
      "Best Fitness:110844.35389007119, time: 17.92518424987793\n",
      "Best Fitness:95450.98274085569, time: 32.58865022659302\n",
      "Best Fitness:94236.4005842141, time: 32.64347791671753\n",
      "Best Fitness:95986.88903754775, time: 30.220606565475464\n",
      "Best Fitness:97139.83495583481, time: 32.79759502410889\n",
      "Best Fitness:95520.40220746619, time: 32.834866523742676\n",
      "Best Fitness:94188.18067581342, time: 32.56837320327759\n",
      "Best Fitness:94605.77216150211, time: 32.71741437911987\n",
      "Best Fitness:92721.24475646202, time: 32.39089393615723\n",
      "Best Fitness:96279.43274353574, time: 27.690958261489868\n",
      "Best Fitness:89513.72068243736, time: 44.33031988143921\n",
      "Best Fitness:89365.58978973428, time: 44.276469230651855\n",
      "Best Fitness:84504.90367939172, time: 44.91366219520569\n",
      "Best Fitness:87575.07017553298, time: 44.51823401451111\n",
      "Best Fitness:88630.67388142676, time: 44.47810339927673\n",
      "Best Fitness:87489.38363972575, time: 44.65885925292969\n",
      "Best Fitness:88502.99814569978, time: 44.50018882751465\n",
      "Best Fitness:86477.60413585909, time: 44.30962252616882\n",
      "Best Fitness:98347.12280452017, time: 29.16854190826416\n",
      "Best Fitness:84123.37981514745, time: 54.027657985687256\n",
      "Best Fitness:84425.08254915506, time: 56.195974349975586\n",
      "Best Fitness:83323.10344288978, time: 55.79282259941101\n",
      "Best Fitness:82502.65482516367, time: 55.7491934299469\n",
      "Best Fitness:81901.81497837306, time: 56.24376821517944\n",
      "Best Fitness:83618.8829924783, time: 56.32650804519653\n",
      "Best Fitness:79693.046342236, time: 56.414979457855225\n",
      "Best Fitness:83536.8235012365, time: 56.19921541213989\n",
      "Best Fitness:120030.35273077752, time: 13.533130645751953\n",
      "Best Fitness:96030.12033796335, time: 34.27540707588196\n",
      "Best Fitness:82943.10084985322, time: 60.76105523109436\n",
      "Best Fitness:79651.63233765468, time: 68.00523710250854\n",
      "Best Fitness:79017.18602780845, time: 68.05787706375122\n",
      "Best Fitness:82597.76401035182, time: 68.10586261749268\n",
      "Best Fitness:78908.27363742092, time: 68.73914456367493\n",
      "Best Fitness:79510.54822919113, time: 68.51197028160095\n",
      "Best Fitness:79495.82886990477, time: 67.90269660949707\n",
      "Best Fitness:105220.02514232308, time: 20.87938642501831\n",
      "Best Fitness:81725.97337012424, time: 53.93358826637268\n",
      "Best Fitness:75202.87630799132, time: 69.82823848724365\n",
      "Best Fitness:73693.21190904855, time: 79.60692048072815\n",
      "Best Fitness:77214.63771723864, time: 79.78917384147644\n",
      "Best Fitness:77894.21032000799, time: 79.4358401298523\n",
      "Best Fitness:74424.15303476741, time: 79.38720703125\n",
      "Best Fitness:74982.41249895205, time: 79.98228597640991\n",
      "Best Fitness:74535.24023506622, time: 80.66439414024353\n",
      "Best Fitness:133294.83866748348, time: 8.541646242141724\n",
      "Best Fitness:92768.10352152456, time: 34.5063910484314\n",
      "Best Fitness:85987.3521835046, time: 47.0509467124939\n",
      "Best Fitness:71286.48837581572, time: 91.68818092346191\n",
      "Best Fitness:74217.08220078201, time: 90.97044634819031\n",
      "Best Fitness:73685.98243805404, time: 91.33136630058289\n",
      "Best Fitness:72110.27668318702, time: 91.54286861419678\n",
      "Best Fitness:73155.03220062707, time: 91.5482726097107\n",
      "Best Fitness:72663.82978632064, time: 91.46980786323547\n",
      "Best Fitness:98658.00682688093, time: 25.97947096824646\n",
      "Best Fitness:84253.18249874598, time: 48.18502473831177\n",
      "Best Fitness:66332.41865179219, time: 103.46301794052124\n",
      "Best Fitness:71020.0894773745, time: 102.27357935905457\n",
      "Best Fitness:73426.84092882271, time: 97.31928777694702\n",
      "Best Fitness:71054.47813877674, time: 103.32618737220764\n",
      "Best Fitness:70242.6357382142, time: 103.22716498374939\n",
      "Best Fitness:72236.70884815608, time: 103.69175958633423\n",
      "Best Fitness:69629.4429857947, time: 103.49117612838745\n",
      "Best Fitness:100123.76717752214, time: 26.646138191223145\n",
      "Best Fitness:95986.43566442897, time: 31.39280366897583\n",
      "Best Fitness:83915.2406807469, time: 57.99068307876587\n",
      "Best Fitness:76702.44881392804, time: 72.80533266067505\n",
      "Best Fitness:67355.21671474815, time: 115.40859198570251\n",
      "Best Fitness:76344.90796831608, time: 97.15346813201904\n",
      "Best Fitness:69514.39852600265, time: 114.64468002319336\n",
      "Best Fitness:65681.24312153555, time: 115.04752898216248\n",
      "Best Fitness:67046.12235807476, time: 115.24616289138794\n"
     ]
    }
   ],
   "source": [
    "schedule = mlrose.ExpDecay()\n",
    "arr = []\n",
    "for max_iters in range(1000,20000,2000):\n",
    "    for max_attempts in range(50,500,50):\n",
    "        random = randint(0,1e6)\n",
    "        start_time = time.time()\n",
    "        best_state, best_fitness =  mlrose.simulated_annealing(problem_fit, schedule = schedule, max_attempts=max_attempts, max_iters=max_iters, random_state=random)\n",
    "        elapsed_time = time.time() - start_time\n",
    "        data = {\"algorithm\":\"simulated_annealing\",\"max_attempts\": max_attempts, \"max_iters\":max_iters, \"best_fitness\":best_fitness, \"best_route\":best_state, \"time\": elapsed_time, \"randomize\": random}\n",
    "        print(\"Best Fitness:\"+str(best_fitness)+\", time: \"+str(elapsed_time))\n",
    "        arr.append(data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Se ha terminado el algoritmo con: p=0.1, max_attempt=10 duration (s)=34.517141580581665\n",
      "Se ha terminado el algoritmo con: p=0.1, max_attempt=30 duration (s)=76.05989456176758\n",
      "Se ha terminado el algoritmo con: p=0.1, max_attempt=50 duration (s)=105.97127056121826\n",
      "Se ha terminado el algoritmo con: p=0.1, max_attempt=70 duration (s)=298.03549098968506\n",
      "Se ha terminado el algoritmo con: p=0.1, max_attempt=90 duration (s)=489.20089530944824\n",
      "Se ha terminado el algoritmo con: p=0.2, max_attempt=10 duration (s)=41.35205388069153\n",
      "Se ha terminado el algoritmo con: p=0.2, max_attempt=30 duration (s)=66.3310034275055\n",
      "Se ha terminado el algoritmo con: p=0.2, max_attempt=50 duration (s)=211.37642788887024\n",
      "Se ha terminado el algoritmo con: p=0.2, max_attempt=70 duration (s)=257.1219172477722\n",
      "Se ha terminado el algoritmo con: p=0.2, max_attempt=90 duration (s)=187.82014083862305\n",
      "Se ha terminado el algoritmo con: p=0.3, max_attempt=10 duration (s)=24.280178785324097\n",
      "Se ha terminado el algoritmo con: p=0.3, max_attempt=30 duration (s)=101.49169158935547\n",
      "Se ha terminado el algoritmo con: p=0.3, max_attempt=50 duration (s)=257.7368748188019\n",
      "Se ha terminado el algoritmo con: p=0.3, max_attempt=70 duration (s)=208.37396717071533\n",
      "Se ha terminado el algoritmo con: p=0.3, max_attempt=90 duration (s)=237.5448088645935\n",
      "Se ha terminado el algoritmo con: p=0.4, max_attempt=10 duration (s)=35.15753674507141\n",
      "Se ha terminado el algoritmo con: p=0.4, max_attempt=30 duration (s)=122.70859169960022\n",
      "Se ha terminado el algoritmo con: p=0.4, max_attempt=50 duration (s)=131.9190797805786\n",
      "Se ha terminado el algoritmo con: p=0.4, max_attempt=70 duration (s)=222.23376846313477\n",
      "Se ha terminado el algoritmo con: p=0.4, max_attempt=90 duration (s)=292.47088623046875\n",
      "Se ha terminado el algoritmo con: p=0.5, max_attempt=10 duration (s)=27.959903955459595\n",
      "Se ha terminado el algoritmo con: p=0.5, max_attempt=30 duration (s)=90.95624041557312\n",
      "Se ha terminado el algoritmo con: p=0.5, max_attempt=50 duration (s)=175.6341106891632\n",
      "Se ha terminado el algoritmo con: p=0.5, max_attempt=70 duration (s)=168.52417039871216\n",
      "Se ha terminado el algoritmo con: p=0.5, max_attempt=90 duration (s)=234.15386652946472\n",
      "Se ha terminado el algoritmo con: p=0.6, max_attempt=10 duration (s)=30.091609716415405\n",
      "Se ha terminado el algoritmo con: p=0.6, max_attempt=30 duration (s)=167.05482864379883\n",
      "Se ha terminado el algoritmo con: p=0.6, max_attempt=50 duration (s)=158.9257402420044\n",
      "Se ha terminado el algoritmo con: p=0.6, max_attempt=70 duration (s)=141.2228376865387\n",
      "Se ha terminado el algoritmo con: p=0.6, max_attempt=90 duration (s)=342.25597977638245\n",
      "Se ha terminado el algoritmo con: p=0.7, max_attempt=10 duration (s)=33.67507338523865\n",
      "Se ha terminado el algoritmo con: p=0.7, max_attempt=30 duration (s)=100.13299131393433\n",
      "Se ha terminado el algoritmo con: p=0.7, max_attempt=50 duration (s)=160.78648972511292\n",
      "Se ha terminado el algoritmo con: p=0.7, max_attempt=70 duration (s)=250.8944170475006\n",
      "Se ha terminado el algoritmo con: p=0.7, max_attempt=90 duration (s)=499.6823296546936\n",
      "Se ha terminado el algoritmo con: p=0.8, max_attempt=10 duration (s)=26.60321569442749\n",
      "Se ha terminado el algoritmo con: p=0.8, max_attempt=30 duration (s)=91.81884169578552\n",
      "Se ha terminado el algoritmo con: p=0.8, max_attempt=50 duration (s)=178.89412999153137\n",
      "Se ha terminado el algoritmo con: p=0.8, max_attempt=70 duration (s)=288.5287890434265\n",
      "Se ha terminado el algoritmo con: p=0.8, max_attempt=90 duration (s)=311.7612714767456\n",
      "Se ha terminado el algoritmo con: p=0.9, max_attempt=10 duration (s)=53.66204500198364\n",
      "Se ha terminado el algoritmo con: p=0.9, max_attempt=30 duration (s)=102.1294195652008\n",
      "Se ha terminado el algoritmo con: p=0.9, max_attempt=50 duration (s)=178.97948598861694\n",
      "Se ha terminado el algoritmo con: p=0.9, max_attempt=70 duration (s)=200.96583318710327\n",
      "Se ha terminado el algoritmo con: p=0.9, max_attempt=90 duration (s)=261.42509174346924\n"
     ]
    }
   ],
   "source": [
    "for i in range(1,10):\n",
    "    mut_prob = i/10.0;\n",
    "    for max_at in range(10,100,20):\n",
    "        random = randint(0,1e6)\n",
    "        start_time = time.time()\n",
    "        best_state, best_fitness = mlrose.genetic_alg(problem_fit, random_state = random, mutation_prob = mut_prob, max_attempts = max_at)\n",
    "        elapsed_time = time.time() - start_time\n",
    "        data = {\"algorithm\":\"genetic_alg\",\"mutation_rate\": mut_prob, \"max_attempts\":max_at, \"best_fitness\":best_fitness, \"best_route\":best_state, \"time\": elapsed_time, \"randomize\": random}\n",
    "        print(\"Se ha terminado el algoritmo con: p=\"+str(mut_prob)+\", max_attempt=\"+str(max_at)+\" duration (s)=\"+str(elapsed_time))\n",
    "        arr.append(data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best_Fitness: 13093.245161922787, Elapsed_Time: 766.8402669429779\n"
     ]
    }
   ],
   "source": [
    "min_distance = 10000000000\n",
    "p_index = 0;\n",
    "j_index = 0;\n",
    "def select_nearest_point(start_index, matrix, visited_index):\n",
    "    min_index = 0;\n",
    "    min_distance = 10000000000\n",
    "    for i in range(0,len(matrix)):\n",
    "        if(i not in visited_index):\n",
    "            p_distance = get_distance(matrix[start_index][0],matrix[start_index][1],matrix[i][0],matrix[i][1])\n",
    "            if(min_distance > p_distance):\n",
    "                min_distance = min(p_distance, min_distance)\n",
    "                min_index = i\n",
    "    return min_index, min_distance\n",
    "start_time = time.time()\n",
    "for p in range(0,len(matrix)):\n",
    "    visited_index = list()\n",
    "    visited_index.append(p);\n",
    "    v_distance = 0;\n",
    "    flag = 1;\n",
    "    start_point = p;\n",
    "    while(len(visited_index) < 634 and flag):\n",
    "        min_index, new_distance = select_nearest_point(start_point,matrix, visited_index)\n",
    "        visited_index.append(min_index)\n",
    "        v_distance += new_distance\n",
    "        if(v_distance >= min_distance):\n",
    "            flag = 0\n",
    "        start_point = min_index\n",
    "    if(v_distance < min_distance):\n",
    "        route = visited_index\n",
    "        min_distance = v_distance\n",
    "elapsed_time = time.time() - start_time\n",
    "print('Best_Fitness: '+str(min_distance)+', Elapsed_Time: '+str(elapsed_time))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
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      "text/plain": [
       "<Figure size 2880x2880 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "route_nn = [351, 399, 166, 376, 36, 127, 304, 507, 525, 538, 79, 496, 2, 63, 56, 236, 541, 272, 5, 425, 381, 26, 438, 122, 587, 327, 527, 543, 307, 312, 70, 37, 278, 154, 218, 345, 328, 501, 547, 222, 46, 384, 75, 80, 553, 217, 366, 585, 557, 129, 414, 480, 603, 78, 298, 377, 32, 512, 242, 55, 499, 353, 494, 453, 532, 391, 176, 196, 160, 589, 570, 531, 106, 66, 208, 299, 302, 13, 277, 484, 470, 607, 287, 569, 167, 162, 519, 586, 186, 92, 58, 159, 119, 38, 540, 181, 210, 444, 72, 288, 223, 14, 560, 373, 103, 367, 65, 187, 49, 100, 441, 352, 168, 342, 284, 318, 271, 99, 292, 451, 379, 566, 609, 395, 321, 362, 633, 606, 234, 562, 206, 322, 184, 132, 24, 112, 142, 130, 189, 61, 478, 431, 354, 448, 407, 355, 9, 71, 415, 317, 516, 76, 398, 429, 513, 31, 314, 348, 410, 571, 593, 452, 11, 485, 121, 472, 139, 502, 16, 220, 363, 62, 10, 77, 123, 226, 87, 12, 375, 402, 111, 320, 227, 238, 261, 267, 219, 357, 256, 522, 500, 597, 535, 268, 595, 518, 621, 497, 207, 203, 559, 155, 232, 27, 247, 508, 233, 358, 529, 153, 458, 259, 418, 551, 264, 241, 537, 275, 421, 311, 316, 88, 350, 209, 487, 464, 457, 368, 382, 40, 113, 430, 3, 64, 432, 83, 428, 297, 200, 426, 7, 332, 231, 417, 28, 95, 491, 542, 289, 177, 482, 172, 308, 360, 91, 205, 25, 138, 118, 442, 4, 86, 463, 473, 337, 141, 605, 509, 608, 437, 201, 383, 615, 173, 182, 149, 276, 555, 340, 175, 273, 150, 611, 626, 580, 42, 147, 339, 578, 361, 199, 125, 406, 250, 625, 193, 604, 216, 544, 413, 137, 274, 213, 128, 423, 41, 313, 548, 528, 422, 1, 632, 393, 301, 158, 202, 303, 260, 45, 144, 459, 536, 296, 561, 174, 630, 243, 486, 471, 456, 498, 126, 249, 622, 380, 336, 331, 146, 263, 477, 575, 281, 572, 228, 192, 616, 145, 454, 568, 365, 554, 481, 404, 447, 131, 19, 489, 43, 109, 15, 96, 124, 21, 469, 412, 558, 230, 466, 347, 446, 17, 476, 579, 54, 221, 39, 23, 101, 524, 117, 602, 405, 266, 309, 300, 460, 588, 246, 191, 545, 573, 51, 344, 619, 136, 204, 440, 33, 386, 290, 165, 436, 18, 244, 151, 549, 490, 169, 254, 48, 325, 600, 135, 59, 628, 85, 282, 53, 483, 617, 599, 614, 330, 556, 171, 443, 411, 467, 349, 582, 338, 590, 539, 505, 29, 262, 102, 408, 178, 215, 371, 564, 224, 285, 108, 495, 534, 503, 388, 133, 449, 8, 576, 255, 68, 291, 251, 252, 60, 30, 211, 305, 433, 140, 577, 510, 104, 245, 35, 374, 450, 190, 198, 416, 89, 468, 214, 269, 67, 98, 409, 286, 552, 295, 225, 479, 455, 163, 420, 506, 514, 323, 179, 550, 90, 591, 419, 624, 400, 335, 270, 445, 81, 359, 343, 401, 517, 574, 148, 474, 52, 364, 592, 434, 623, 170, 583, 613, 326, 492, 523, 465, 629, 212, 197, 334, 257, 237, 239, 94, 180, 293, 84, 294, 439, 143, 596, 387, 195, 97, 0, 504, 341, 310, 229, 563, 183, 47, 565, 280, 488, 627, 329, 620, 610, 74, 240, 161, 424, 372, 188, 73, 134, 390, 248, 93, 396, 584, 110, 306, 283, 258, 346, 394, 435, 116, 511, 22, 156, 567, 546, 601, 397, 530, 50, 581, 493, 462, 475, 333, 427, 157, 403, 526, 533, 324, 594, 6, 370, 521, 44, 392, 57, 164, 515, 378, 279, 194, 598, 265, 82, 315, 107, 34, 389, 612, 114, 520, 385, 120, 631, 253, 319, 461, 20, 185, 356, 369, 115, 235, 69, 152, 105, 618]\n",
    "route_sa = [625, 608,   1, 260,  45, 429, 220,  31, 491, 410,  61,  24, 633,\n",
    "       560,  65, 284, 342, 416, 254, 540, 149, 182, 383, 201, 353,  80,\n",
    "       141, 391, 585, 137, 524, 554, 109, 213, 242, 499, 541, 142, 425,\n",
    "       626, 147, 327,  99, 271, 543, 459, 144, 326, 495, 588, 577, 231,\n",
    "        28,  95, 332, 238,  47, 324, 134, 591,  52, 256, 207, 493, 346,\n",
    "       511, 515, 618, 349, 155, 564, 212, 252, 170, 359, 372, 445,  81,\n",
    "       594,  73, 462, 419, 179, 164, 286, 402, 502, 318, 199, 337, 509,\n",
    "       339,   4, 621, 457, 394, 194, 258, 279, 153, 455, 262, 119, 309,\n",
    "       624, 248, 323, 239, 257, 343, 304, 482, 121, 289, 451, 292, 187,\n",
    "       433, 386, 204, 287, 454, 404, 145, 321, 441, 223, 460, 477, 523,\n",
    "        74, 465, 539,  30, 344,  85,  48,  59, 135, 352, 600, 193, 412,\n",
    "        43, 579, 604, 406,  25,  26, 132, 206, 562, 606, 567, 306,  50,\n",
    "       283, 546, 487, 267, 522, 597, 375, 443, 505, 590,  12, 395, 130,\n",
    "         2, 379,   7,  10,  67, 317, 214, 450, 190, 301, 365, 484, 250,\n",
    "       473, 166, 355, 288, 208,  68, 290, 617, 431, 516, 542, 496, 322,\n",
    "       202, 158, 616, 447,  94,   6, 161, 504, 565, 329, 627, 229, 310,\n",
    "       581, 479, 139,  71, 165, 400, 517, 574,  53, 227, 472, 513, 485,\n",
    "       348, 571, 268, 247, 171, 458, 508,  72, 437,  41, 423,  66, 345,\n",
    "       307, 312, 184,  35, 599, 432, 356, 316, 114, 385, 319, 368, 421,\n",
    "       350, 311, 241, 537, 120, 629, 126, 281, 576, 613, 174, 249, 211,\n",
    "       246, 545,  60, 408,  46, 218, 501, 328, 196, 176, 547, 611, 407,\n",
    "       398, 415, 399, 308, 507, 236, 127, 104, 245,  27, 518, 123, 426,\n",
    "       628, 282,  18, 614, 549, 367, 168,  14, 210,  92,  51, 503, 263,\n",
    "       186, 570,  13, 527, 129, 366, 568, 300, 243, 622, 630, 108, 335,\n",
    "       270, 235, 152, 265, 411, 494, 117, 413, 470, 277, 167, 532, 140,\n",
    "       510, 438,  42, 381, 278, 556, 233, 551, 521, 255, 133, 471, 536,\n",
    "       136, 364,  98, 448, 376, 478,  89, 444, 222, 274,  32, 605, 272,\n",
    "        63,  91, 351,  79, 234, 103, 436, 596, 293, 341, 280, 334, 561,\n",
    "       422, 146, 159, 330, 440,  78,  54, 466, 558, 230, 469, 347, 101,\n",
    "       580,   5,  70, 586, 632, 414,  39,  23, 405, 192, 572, 481, 217,\n",
    "       150, 555, 175, 340, 273, 154, 589, 181, 607, 531, 302, 373, 569,\n",
    "       106, 100,  49, 519, 191, 358, 467, 331, 160,  75, 125, 138, 276,\n",
    "       118,  86, 122, 162, 303,  58, 619, 456, 102, 592, 105, 370,  57,\n",
    "       392,  88, 264, 259, 559, 232,  96,  17,  15, 480,  38, 203, 464,\n",
    "       552, 148,  84, 294,   0, 488, 583,   8, 486, 449, 427,  97, 240,\n",
    "       534, 575, 557, 446, 128, 377, 266, 528,  55, 587, 205, 468, 198,\n",
    "       357,  44, 420,  69,  93, 157, 475, 403, 188, 180, 237, 563, 183,\n",
    "        20, 185, 631,  22, 275, 113, 209, 497, 409, 299, 553, 384, 112,\n",
    "       538, 189, 566, 609, 338, 225,  90, 506, 156, 533, 424, 195, 439,\n",
    "       143, 489, 221, 500, 315,  82,  34, 389, 115, 369,  40,  83, 219,\n",
    "       200, 363, 453, 216, 173, 442, 463, 169, 490, 520, 461, 253, 297,\n",
    "        62,  77, 428, 535, 529, 378, 110, 584, 418, 314, 417,  16,  76,\n",
    "       226, 151, 336, 131,  19, 548, 116, 601, 514, 526, 387, 396, 612,\n",
    "       595, 261, 320, 111, 593, 452, 177, 172,  36,  56, 360, 525, 362,\n",
    "        37, 393, 313, 124, 476, 228, 197, 620, 610, 390, 333, 397, 107,\n",
    "       530, 435, 598, 163, 474, 244, 578, 361, 602,  21, 512, 298, 603,\n",
    "       615, 251, 291, 492, 388, 224, 498, 296, 305,  33, 374, 325, 483,\n",
    "        11,   9, 354, 269,  87,  64, 430,   3, 382, 295, 623, 285, 215,\n",
    "        29, 371, 178, 401, 550, 434, 582, 573, 380, 544]\n",
    "route_ga = [566, 327, 545, 121, 466, 374, 524, 234, 243, 261, 532,  30, 536,\n",
    "       291, 196, 530, 208, 351,  46, 533, 507, 323, 209, 156,  34, 332,\n",
    "       472,  33, 537, 404, 205, 461, 520, 595, 567, 117,  12, 242, 583,\n",
    "       394,  36, 181, 109, 184,  49, 190, 441, 498, 143, 295, 240,  86,\n",
    "       509, 286, 260, 274, 371, 622, 283, 407, 225, 316, 623, 200, 188,\n",
    "        53, 451, 611, 605, 618, 581, 450, 193, 495, 500, 123, 277, 282,\n",
    "       627, 237, 264, 389, 603, 301,  26, 255, 410,  70, 202, 158, 570,\n",
    "       201, 413,   2,  65, 552,  88, 175, 482, 267,  38, 216, 139, 145,\n",
    "       182,  16, 213, 189, 630,  22,   6, 183, 120, 222, 485,  39, 458,\n",
    "       304, 339,  97, 551,  87, 442, 149, 555, 474,  81, 565, 419, 219,\n",
    "       116, 399,   1, 253,  93,  78, 345,   3, 311, 199, 414, 130, 104,\n",
    "       528, 539,  50, 325, 574, 115, 356,  28, 125,  11,  20, 350, 296,\n",
    "       430, 418, 527, 112,  75, 302, 553, 468, 490, 382, 247, 564, 338,\n",
    "       376, 228, 329, 613, 153, 108, 341, 275, 257, 195, 599, 220, 534,\n",
    "        45, 518, 320, 579, 560, 186,  74, 578, 322, 601, 146, 388,  68,\n",
    "       328, 473, 588, 248,  27,  55, 179, 438, 330, 514, 424, 366,  67,\n",
    "       434, 481, 521, 484, 210, 383, 499, 502,  59, 415, 159, 456, 572,\n",
    "       542, 214, 293, 544, 535, 235, 426, 515, 562, 217, 263, 128,  19,\n",
    "         0, 604, 453, 336, 226, 347, 608,  91, 197, 171, 550, 251, 617,\n",
    "       464,  52, 440, 262,  43, 198, 568, 459,  15,  73, 569, 469,  17,\n",
    "       161,  72, 308, 422, 266,  40, 529, 177, 134, 127, 289, 278, 409,\n",
    "       444, 349, 516, 543, 631,  37, 326, 633, 378, 391, 207, 460, 393,\n",
    "       471,  92, 331, 420, 147, 402, 157,  80, 437, 313,  21, 384, 416,\n",
    "       408, 204, 486, 164, 180,   7, 455, 272, 368, 575, 346,  51, 230,\n",
    "       400, 306, 467,  58, 594, 513, 548, 132, 491, 477, 401,  41, 348,\n",
    "       357,  82, 557, 483, 446, 489, 352,  89,  14, 593, 358, 480, 619,\n",
    "         4, 268, 380, 395, 429, 381,  98, 318, 279, 335, 411, 106,  63,\n",
    "       580, 523, 436, 287,  95, 185, 432, 148, 433, 377, 227,   5, 221,\n",
    "       606, 187, 454, 314, 541, 152, 517, 265, 105, 126, 137,  44, 343,\n",
    "       101,  54, 612, 531, 584, 602, 512,  25, 549, 614, 354, 554, 629,\n",
    "       246, 369, 452, 431,  23, 135, 373, 365, 252, 170, 150, 615, 167,\n",
    "        76, 239, 610, 540, 305, 160, 443, 162, 405, 576,  60,  64,  66,\n",
    "       494,  62, 556, 573, 621, 428, 144, 589,  18, 462, 585, 625,  96,\n",
    "       375, 385, 114, 259, 547, 470, 294, 412, 191, 449, 511,  29, 457,\n",
    "       487, 632, 176,  42, 592, 427, 559, 497, 111,  79, 582,  94, 151,\n",
    "       154, 609, 307, 142, 233,  85, 258, 508, 598, 256, 232, 250, 360,\n",
    "       445, 628, 624, 245, 525,  24, 379, 496, 155, 136, 597, 417, 280,\n",
    "       465, 290, 129, 192, 396,  90, 102, 303, 616, 131, 173, 299, 493,\n",
    "       141, 423,  35, 447, 118, 478, 492, 236, 386,  99, 212, 133, 577,\n",
    "       558, 166, 488, 372, 174, 561, 223,  71, 310, 479, 215, 333, 178,\n",
    "       224, 362, 273, 292,  47,  77, 270, 284, 110, 626, 476, 244, 100,\n",
    "       504, 390,   9, 398, 254, 363,  69, 298, 397, 403, 355, 334, 392,\n",
    "       309, 211, 238,   8, 122, 169,  84, 503, 218, 586, 288,  10, 519,\n",
    "       475, 344, 387,  32, 526, 319,  57, 107, 571, 361, 620, 591, 596,\n",
    "        61, 249, 113,  13, 276,  56, 406, 285, 600, 315, 538, 300, 448,\n",
    "       140, 163, 297, 367,  48, 172, 138, 321, 510, 317, 119, 203, 421,\n",
    "       269, 506, 425, 241, 324, 231, 124,  83, 370, 103, 439, 353, 463,\n",
    "       340, 364, 229, 607, 342, 206, 563, 590, 337, 501, 271, 587, 312,\n",
    "       281, 168, 165,  31, 522, 435, 194, 505, 359, 546]\n",
    "tour = []\n",
    "for i in range(0, len(route_sa)):\n",
    "    tour.append((matrix[route_sa[i]][0],matrix[route_sa[i]][1]))\n",
    "x = np.asarray([p[0] for p in tour])\n",
    "y = np.asarray([p[1] for p in tour])\n",
    "fig = plt.figure(figsize=(40,40));\n",
    "dist = 0\n",
    "graph, = plt.plot([], [], 'bo-', linewidth=1.5, label=dist, markersize=5)\n",
    "plt.plot(x,y,'o',color=\"#b3b3b3\",zorder=0)\n",
    "plt.axis('off')\n",
    "plt.axis('scaled')\n",
    "x_init = x[0]\n",
    "y_init = y[0]\n",
    "def animate(i):\n",
    "    global dist \n",
    "    global x_init\n",
    "    global y_init\n",
    "    dist += get_distance(x_init,y_init, x[i], y[i])\n",
    "    graph.set_label(dist)\n",
    "    x_init = x[i]\n",
    "    y_init = y[i]\n",
    "    graph.set_data(x[:i+1], y[:i+1])\n",
    "    graph.set_marker('>')\n",
    "    graph.set_markersize(15)\n",
    "    graph.set_markevery(every=[0,i])\n",
    "    graph.set_markerfacecolor('red')\n",
    "    plt.legend(prop={'size': 36})\n",
    "    return graph\n",
    "\n",
    "def get_distance(x1,y1,x2,y2):\n",
    "    return math.sqrt((x1-x2)**2+(y1-y2)**2)\n",
    "\n",
    "ani2 = FuncAnimation(fig, animate, frames=634, interval=100)\n",
    "ani2.save('best_route_route_sa.mp4',writer='ffmpeg',extra_args=['-loglevel','verbose'])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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