Mashkova_HW9_local_and_global_search.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Локальный и глобальный поиск"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Решите задачу коммивояжера на основе файла данных `hw9_data/tsp.csv`: найти минимальный НЕЦИКЛИЧЕСКИЙ путь (начинается и заканчивается в разных точках), соединяющий N точек. Файл содержит координаты каждого из городов на плоскости. Используйте L1 расстояние для расчетов и визуализации."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import random\n",
    "import operator\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import copy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [],
   "source": [
    "tsp_data = pd.read_csv('./hw9_data/tsp.csv', header=None)\n",
    "X = tsp_data[1].values\n",
    "Y = tsp_data[2].values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [],
   "source": [
    "class City:\n",
    "    \n",
    "    def __init__(self, x, y):\n",
    "        self.x = x\n",
    "        self.y = y\n",
    "    \n",
    "    def distance(self, city): # L_1 расстояние до города city\n",
    "        x_dist = abs(self.x - city.x)\n",
    "        y_dist = abs(self.y - city.y)\n",
    "        dist = x_dist + y_dist\n",
    "        return dist\n",
    "    \n",
    "    def __repr__(self): # привычное воспроизведение координат нашего города\n",
    "        return '(' + str(self.x) + ', ' + str(self.y) + ')'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [],
   "source": [
    "class Fitness:\n",
    "    \n",
    "    def __init__(self, route):\n",
    "        self.route = route # маршрут\n",
    "        self.distance = 0 # длина маршрута\n",
    "        self.fitness = 0 # величина, обратная длине маршрута\n",
    "    \n",
    "    def route_distance(self):\n",
    "        if self.distance == 0:\n",
    "            dist = 0\n",
    "            for i in range(len(self.route) - 1):\n",
    "                from_city = self.route[i]\n",
    "                to_city = self.route[i + 1]\n",
    "                dist += from_city.distance(to_city)\n",
    "            self.distance = dist\n",
    "        return self.distance\n",
    "    \n",
    "    def route_fitness(self):\n",
    "        if self.fitness == 0:\n",
    "            self.fitness = 1 / float(self.route_distance())\n",
    "        return self.fitness"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot_best_path(best_route):\n",
    "    plt.figure(figsize=(12, 8))\n",
    "    for i in range(len(best_route)):\n",
    "        if i < (len(best_route) - 1):\n",
    "            x1, y1 = [best_route[i].x, best_route[i + 1].x], [best_route[i].y, best_route[i].y]\n",
    "            x2, y2 = [best_route[i + 1].x, best_route[i + 1].x], [best_route[i].y, best_route[i + 1].y]\n",
    "            plt.plot(x1, y1, color='red')\n",
    "            plt.plot(x2, y2, color='red')\n",
    "        plt.scatter(best_route[0].x, best_route[0].y, color='blue', s=60)\n",
    "        plt.scatter(best_route[-1].x, best_route[-1].y, color='blue', s=60)\n",
    "        if i != 0 and i != len(best_route) - 1:\n",
    "            plt.scatter(best_route[i].x, best_route[i].y, color='black')\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [],
   "source": [
    "city_list = []\n",
    "for i in range(len(X)):\n",
    "    city_list.append(City(x=X[i], y=Y[i]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Задание 1 (1 балл) \n",
    "\n",
    "Реализуйте Монте-Карло поиск."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [],
   "source": [
    "def create_route(city_list): # создаём произвольный маршрут\n",
    "    route = random.sample(city_list, len(city_list))\n",
    "    return route\n",
    "\n",
    "def create_many_routes(city_list, n):\n",
    "    routes = []\n",
    "    for i in range(n):\n",
    "        routes.append(create_route(city_list))\n",
    "    return routes\n",
    "\n",
    "def rank_routes(routes, n): # ранжируем маршруты по величине fitness_route\n",
    "    fitness_ranking = {}\n",
    "    for i in range(n):\n",
    "        fitness_ranking[i] = Fitness(routes[i]).route_fitness()\n",
    "    return sorted(fitness_ranking.items(), key=operator.itemgetter(1), reverse=True)\n",
    "\n",
    "def monte_carlo(city_list, num_iterations):\n",
    "    routes = create_many_routes(city_list, num_iterations)\n",
    "    ranked_routes = rank_routes(routes, num_iterations)\n",
    "    best_route_index = ranked_routes[0][0]\n",
    "    best_route = routes[best_route_index]\n",
    "    print(f'Лучшее расстояние: {1 / ranked_routes[0][1]}')\n",
    "    return best_route"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Лучшее расстояние: 29640.0\n"
     ]
    }
   ],
   "source": [
    "best_route = monte_carlo(city_list, 1000)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 864x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_best_path(best_route)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Задание 2 (1 балл) \n",
    "\n",
    "Реализуйте поиск случайным блужданием."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {},
   "outputs": [],
   "source": [
    "def create_route(city_list): # создаём произвольный маршрут\n",
    "    route = random.sample(city_list, len(city_list))\n",
    "    return route\n",
    "\n",
    "def rank_routes(routes, n): # ранжируем маршруты по величине fitness_route\n",
    "    fitness_ranking = {}\n",
    "    for i in range(n):\n",
    "        fitness_ranking[i] = Fitness(routes[i]).route_fitness()\n",
    "    return sorted(fitness_ranking.items(), key=operator.itemgetter(1), reverse=True)\n",
    "\n",
    "def get_neighbours(route): # соседи - умножение на инверсию\n",
    "    neighbours = []\n",
    "    for i in range(len(route)):\n",
    "        for j in range(i + 1, len(route)):\n",
    "            neighbour = copy.deepcopy(route)\n",
    "            neighbour[i] = route[j]\n",
    "            neighbour[j] = route[i]\n",
    "            neighbours.append(neighbour)\n",
    "    return neighbours\n",
    "\n",
    "def random_walk(city_list, num_iterations):\n",
    "    curr_route = create_route(city_list)\n",
    "    routes = [curr_route]\n",
    "    for i in range(num_iterations):\n",
    "        neighbours = get_neighbours(curr_route)\n",
    "        ix = random.randint(0, len(neighbours) - 1)\n",
    "        routes.append(neighbours[ix])\n",
    "        curr_route = neighbours[ix]\n",
    "    ranked_routes = rank_routes(routes, num_iterations + 1)\n",
    "    best_route_index = ranked_routes[0][0]\n",
    "    best_route = routes[best_route_index]\n",
    "    print(f'Лучшее расстояние: {1 / ranked_routes[0][1]}')\n",
    "    return best_route"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Лучшее расстояние: 30740.0\n"
     ]
    }
   ],
   "source": [
    "best_route = random_walk(city_list, 100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_best_path(best_route)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Задание 3 (2 балла) \n",
    "\n",
    "Реализуйте Hill Climb."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 194,
   "metadata": {},
   "outputs": [],
   "source": [
    "def create_route(city_list): # создаём произвольный маршрут\n",
    "    route = random.sample(city_list, len(city_list))\n",
    "    return route\n",
    "\n",
    "def rank_routes(routes, n): # ранжируем маршруты по величине fitness_route\n",
    "    fitness_ranking = {}\n",
    "    for i in range(n):\n",
    "        fitness_ranking[i] = Fitness(routes[i]).route_fitness()\n",
    "    return sorted(fitness_ranking.items(), key=operator.itemgetter(1), reverse=True)\n",
    "\n",
    "def get_neighbours(route): # соседи - умножение на инверсию\n",
    "    neighbours = []\n",
    "    for i in range(len(route)):\n",
    "        for j in range(i + 1, len(route)):\n",
    "            neighbour = copy.deepcopy(route)\n",
    "            neighbour[i] = route[j]\n",
    "            neighbour[j] = route[i]\n",
    "            neighbours.append(neighbour)\n",
    "    return neighbours\n",
    "\n",
    "def hill_climb(city_list, num_iterations):\n",
    "    curr_route = create_route(city_list)\n",
    "    routes = [curr_route]\n",
    "    curr_fitness = Fitness(curr_route).route_fitness()\n",
    "    for i in range(num_iterations):\n",
    "        neighbours = get_neighbours(curr_route)\n",
    "        ranked_neighbours = rank_routes(neighbours, len(neighbours))\n",
    "        best_neighbour_index = ranked_neighbours[0][0]\n",
    "        best_neighbour = neighbours[best_neighbour_index]\n",
    "        best_neighbour_fitness = Fitness(best_neighbour).route_fitness()\n",
    "        if best_neighbour_fitness > curr_fitness:\n",
    "            print(f'Лучшее расстояние: {1 / best_neighbour_fitness}')\n",
    "            return best_neighbour\n",
    "        else:\n",
    "            routes.append(best_neighbour)\n",
    "            curr_route = best_neighbour\n",
    "            curr_fitness = best_neighbour_fitness\n",
    "    ranked_routes = rank_routes(routes, num_iterations + 1)\n",
    "    best_route_index = ranked_routes[0][0]\n",
    "    best_route = routes[best_route_index]\n",
    "    print(f'Лучшее расстояние: {1 / ranked_routes[0][1]}')\n",
    "    return best_route"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 195,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Лучшее расстояние: 33235.0\n"
     ]
    }
   ],
   "source": [
    "best_route = hill_climb(city_list, 1000)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 196,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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eUm4KqAO/CPx8CGGFdh/lD6QP+QDwhrT854HH+qi3JEmSdCj6moc5xvg+4H3bir8AvHWHba8C/7Cf55MkSZIOW7/TykmSJEkTzcAsSZIkZTAwS5IkSRkMzJIkSVIGA7MkSZKUwcAsSZIkZTAwS5IkSRkMzJIkSVIGA7MmVpIklMtlpqamKJfLJEmSd5UkSdIY6mulP2lUJUlCtVql2WwC0Gg0qFarAFQqlTyrJkmSxoyBWRNpcXGRZrPJM8AssALQbDK9sABnz+Zbuf1YXm5fnjmT7z6Gub9BGMU69WK317Hf1zcpx0G7W1mB2Vm4eDHvmkhHkoFZE2ltbQ1oh+WZrvKrrVYu9ZGkvmxs5F0D6UgzMGsiFYtFGo1Gu2UZeDC9LJVKrJ47l1OtDqDTUthPXQexj2HubxBGsU692O117Pf1Tcpx0O789UDKlYP+NJFqtRqFQmFLWaFQoFar5VQjSZI0rmxh1kTqDOybXljgaqtFqVSiVqs54E+SJB2YgVkTq1Kp3BjgNxbdMCRJ0kiyS4YkSepbkiQsLS1x7vx5577XxDEwS5KkvnTmvu/MRNSZ+97QrElhlwxJGlUXLsCrrzoPs0b+Pb5vaYknWy1OAZ0J8JrNJouLi44d0USwhVmSRtWrr8L163nXQtpTp2V5A1jvKu/MiS+NO1uYJWlU3XFH+9J5mDXi7/Ej5TKNRuOW8mKxmENtpMGzhVmSJPXFue816QzMkiSpL5VKhXq9TqlUIoRAqVSiXq/bf1kTwy4ZkiSpb5VKxYCsiWULsyRJkpTBwCxJkiRlMDBLkiRJGQzMkiRJUgYDsyRJkpTBwCxJkiRlMDBLkjIlSUK5XGZqaopyuUySJHlXSZIOlYFZkkZQkiRcuXKFr738cq4hNUkSqtUqjUaDGCONRoNqtWpolnSkuHCJJI2YTkh9MUaAGyEVyF4Y4v77YX0dTp4cWF3uW1riyVaLk8A68ADQbDZZXFx0kQpJR4aBWZJGzOLiIs1mkzuA24CnAJpNphcW4OzZWx+wvNy+fOUVuH4dNjYGVpe/1WoBMAPMduoC0GjAmTMDex7tofMee8wHa2UFZmfh4sW8a6IRZ2CWpBGztrYGwDXgWFf51TS87uqOO9qXp04NrC6fW1riaqvF9j1OHz8+sOeQcjPAL5eabAZmSRoxxWKRRqPB0+ntB9PLUqnE6rlztz5ge6vjTtv06K/S7iFPNps36lIoFKjX65y2S8bh6bzHA3xvhS322jcH/U0AR7BrWLrPraWlJS5fvpx3lY6EWq1GoVDYUlYoFKjVaodel0qlQr1ev9GiXCqVqNfr9l+WdKTYwjzmOoODmmnrz74HB0l72H5uXW21uPTss/ynJPHcGrLO8Z1eWOBqq0WpVKJWq+V23CuVyo2+0zu2cEvShDMwj7nO4KBnaA/IWYHswUFHzWEMlJnQQSPbz60ZgM1Njo3SubXT+zsh78dBQ+rly5d57rnnuNpq8Ui5nGvAlqRJY2Aec53BQTcCTWrPwUEanAkdNDK259aEvh9ZLl++zKVnn2VzcxPwlyZJGjQD85jrDA5aSW/vOTjoqDmMgTITOmhk+7nV8cgonVs7vb8T8n4kScJ96QwVe7UYb37+83xbGpY7XxecK1mSBsdBf2NulAYHabLsdG5NTU0duXMrj0G1nf7jndb8vVbX++L162zQDsvrXeWdXwkkSf2xhXnMjdrgIE2O7efW9PHjnDhxgu86QufWoQ+qTVfqu+/LX+bJdO7j/bQY//1SiUajcUt5sVgcfB0l6QgyME8AR7BrWLrPraOol0G13YPvOl8y5ubmsp/oE5+Ar/s6uOsu2Ni40bK83xbjWq22JdiDvzRJ0iAZmCVpFwcd+Lh98F1nKj4gOzRfv96+PHkSgEdWVw/UYtxpdV5cXGRtbY1isegvTZI0QAZmSUdP2vWhE1B38/8cO8bVVmtLWIbdl4V+7rnn2Nzc5CTtluEHADY3Kb3ySvavP3fdteVmLy3GlUrFgCxJQ+KgP6kPSZKwtLTEufPnXWVxxHUP3nvp05/m2le+sudjTpw4wdTU1o/JqakpTpw4seP2nZbnGdqt0h0HHXzXWV2vVCoRQnB1PUnKmS3MUo86A8Ke3DaTATj37ajZPnjv0vXrTLVaPP9TP5X5Xs0B/ylJOJYOfHwkHVS728DHR8plGo0GT20r72XwnS3GkjQ6DMx52+dPw3s6jBXtxtEQj8t9S0sHmsngQFZW2gtw9FPvQb32zn46Rukc2+k17lDWea86XSVeBjY3N/f1Xh1kUG2nKwUOvpOkiWJgztv6+pFcmWwSHHQmgwOZnd17G+1bd1cJaAdmGPw8xU7zKEmTycCct07Lcr/TwR3GinbjaIjHpfPz+3YDmfv24sX+9zGo1769RXmH/SVJks8MDVkr/XWVDbKrxF56nebx2rVrPLPPlf0kSYfLQX9Sj1xlsa3TP7jRaBBj3HNVujyM+qqF165do/nKK/te2U+SIJ+VSI+qEGPMuw67mp+fjxcuXMi7GsM16FbASWth7rePd6c/66lTg6tTl54WqTgsg3rt2/swb9vfUtoq+u3AMeCVtHwqBO68887+nnsvne5MMzPZZbRD6dQrr9y4PTU1xdQ3fEPvzzPA7ePL7U4i19O/T6Tl08ePc/r06f0957AN+d+SdrGy0u6i9brXtW9P2md83ob5f+eQ/1/ePpgZ2o02zqjTuxDC0zHG+Z3us0uGRtuI9/Gem5sbnYCck06r6DHgtq7yzX6/jP/N30CMMJXxQ1hnwY/uc2SnsrR+W2xu7v/c2mWfA9s+1XmlNyJpq3XrF5a8dF7LqNTnqNjYaH8O3nFH+/YoDbqdBMMcMD/kwfjbBzM/wAAHnusWBmaNtn77eE9qy/t+HFIf5u39gx9ML0ulEqurq70/7z33tMNCVovmTq2eWS2he7SWH+h5Brj99fPnAfjP28qnjx/n9Ki06NrCnA+/oGgX2wczdwx6MLPaDMyS+jK0qdT2+LKUJAn3deZHXl29OUgu64vCPgYw7ih9XPJTP7W/wY0H+LKSJAk/eP48kZtfNuDmT6unR6Wl6Ch/+cxTr+es9meMu2Qc5mBmOehPUp86q9J1los+jFXpOn33DnOQ3OXLlwc+uLHzOrZ3XnnDG95gP0RJmRx4frj6amEOIdwF/BbwrUAEfhK4BPwuUAZWgR+NMX41hBCA3wDeCTSBR2KMz/Tz/NKeBrEAyLjqpf/chQvw6qs3+0vCrf1w77rrlodVAF57DW67jdWvfQ3e+972Xz86z7vD8/29K1d4MUZmaA+Sewqg2WR6YQGmp9sb7fS6e12EZXmZO65c4ckYB9pfcHFxkWazyR20+393WoqmNzY4ffbsjenpRsJ+z6fOILVBTI0oaVfO+364+u2S8RvAf4wx/kgI4RhQAP4J8Ccxxl8JITwGPAb8IvAO4M3p33cAj6eX0vC4AMjBvPrqzQFrI6wzoPA6cK2r/GqrdTMwD+k5B9lfsPPYa2wdlNhpOR9LIzxIV5o0vc77roPrOTCHEF4HfBfwCECM8RpwLYTwEHAm3exDwDnagfkh4MOxPY/dUgjhrhDCm2KML/Vce2kvR7mVq5f+c1kLgXTstr9B99fL2N+pXRaNKZVKrJbLu9ejjz7Mn1ta4sFWa6D9BYvFIo1Gg6fT21sGTI7af377fX+P4q85kiZeP32YT9D+ZfL/DiFcDCH8Vgjh64G5rhD8RaAz59bdwPNdj38hLZOkA6nVahw7tnWiuGPHjg21796JEycG3l/QPoiSNB76Ccy30+7G93iM8X7g/6Pd/eKGtDX5QJOxhhCqIYQLIYQL6+vrfVRP0iTbvujSsBdhmpubG/jgxjwGTEqSDq6fPswvAC/EGD+Z3v592oH5cqerRQjhTcCX0vtfBO7tevw9adkWMcY6UIf2Sn991E86uu6/Hz772fbgvYP8RL7TwK79DpQb9CT9Gfu7b2mJP3711S0D8F599dX2ALxOl4whGEZ/QfsgStLo67mFOcb4ReD5EMK3pEVvAz4LPAE8nJY9DHw0vf4E8OOh7TTwsv2XpcFLkoSXPv1pXrt2jStXrnD58uW8qzRw3RP2dw/rdMJ+SdIw9DtLxv8MJOkMGV8AfoJ2CP+9EMIC0AB+NN32Y7SnlFuhPa3cT/T53JK26czr++T169wBLMfI311bo/5P/+n+fuYfk0F/TtgvSTpMfQXmGOMyML/DXW/bYdsI9Dkxq6QsnXl9T9JufT0FPNmZn3g/c/qOSZeMT95xB5empvi2zU2gPX/x1NQU/9Wrr9I6f55rwH9bLjsn6SFLkoT7lpbaqy96/Afm8uXLPPfccx5XKUcujS1NkE6XhHW2dlXY77y+165d4+rVqzxz/jzTx49z4sSJG9PcjJK5uXatpi5dYjNGpo8f5xvf8Aa++MUv8k205zTurMQHGC4OwY1fN7atvgge/35cvnyZS88+y2b65dDjKuUjDHtkeT/m5+fjhQsX8q7GcA3qZ+whr1mfm1F+XfffD+vrcPLk8J5jp5X3Mly5cuXGAht3AoH24h4At992W+ZjN2MkpP8pdy9dclu6n5sFu+yns+DJHs+zb/vZX9c2r6XXO1u/nF5OhcCdd97ZvrF9UY2Z7cuQ7KLzuJmZrdf3s/1B9z+q9qhj59zrrL74ibR8+vhxTp8+fRg1nEhX/uzPbvyb3uDmyPlSqcTq6mpe1Zocw/w/5rD+/xrl/yfHTAjh6RjjTj0nbGGWera+PvxVzQ648t709DTNV14BYJObo3qnpvYe37u5uXkjbGZG3r3qM+iVAvezv+vXb6nzjVgX4833afu+9vv+Xb8OIey93RGWufqietYdlrsnWnWAq3S4DMxSrzoty8P8Vn/AloNjwL9LEhYXF1lbW6NYLO67v+OxqSn+NEb+O+AVoNNz+RRw1+ted3PDU6d23kGnz/Fu9x/UfvbXtc0n076zna079d/Swrm9P/Z+67qy0l5m/eLF/b0nB23xGYcWoj3qmLn64ii/rhH3uelprrZaN1aB7HCAq3S4DMzShKlUKj31bSwWi9Bo3AjLnf+gP3H8OKe7g+UIzJKx0zZ/1elD22wC7foXCgXq9TqnO8ej16WxtadarUa1WqWZHn9w1cJBOHHiBJeefRbS7lLgcZXy0M9Kf5ImSK1Wu6XrRqFQ4MSJEznV6GBcNS9fneNfKpUIIXj8B2Rubo5v+eZv9rhKObOFWRLQDjyX/8W/YOrSJYiRUqlErVZjbj/T0Y0IV83LV6+/bijb3Nyc57OUM1uYpQFIkoRyuczU1BTlcpkkSfKuUk/m5ua48847OfPd383q6qrhR5IkbGGW+taZf7bTd9N5UiVJmiwGZqlPndX1nqG9WMgKwEFW18sy6NXz9vN8Gxvty85zjuBKf5nbZD1uv69lEPUa5TmVJUkHYpcMqU+d+VBn6Zr7F+efPdJmZtrT0EmSJoItzFKfisUijUaj3bLMzenYBjL/7GHPz3vmTLt19NSpm8+536nYcp5Wbl+PG8S0cuMwZ7IkaaBsYZb6VKvVKBQKW8qcJ1WSpMlhC7PUp87AvumFBa62WjemY3PAnyRJk8HALA2A8/9KkjS57JIhSZIkZTAwS5IkSRkMzJIkSVIGA7MkSZKUwcAsSZIkZTAwS5IkSRkMzJIkSVIGA7MkSZKUwcAsSZIkZTAwS5IkSRkMzJImRpIkLC0tce78ecrlMkmS5F0lSdIEMDBLmghJklCtVrnaagHQaDSoVquGZklS327PuwKSNAiLi4s0m01OAjPAUwDNJtMLC3D2bHuj5eWtDzpz5uBP1NlHL489zH0O2vIyzMzkXQtJyoWBWdJEWFtbA2B9W3mnxVl9mpmB2dm8ayFJuTAwS5oIxWKRRqPBA9vKS6USq+fOtW9sb8HtlB9EZx+9PPYw9ylJGhj7MEuaCLVajUKhsKWsUChQq9VyqtH+OFBRkkafgVnSRKhUKtTrdUqlEiEESqUS9XqdSqWSd9V25UBFSRoPdsmQNL5WVmBj40aXhgpQKZehXG7ff/bszQF/MHKD/u5bWuLJVotTwEZa1mw2WVxcHOmgL0lHjYFZ0vga80FonZblDbYOVuwMYJQkjQYDs6TxdfHiwQAXBdQAABfOSURBVLYfsUF/j5TLNBqNW8qLxWLf+5YkDY59mCUpJ+M6UFGSjhoDsyTlZBwHKkrSUWRg1shyui0dBZVKhdXVVTY3N1ldXTUsS9IIMjBrJDndliRJGhUO+tNIWlxcpNlschKYAZ4CaDaZXljYOk1YnrZPLzbA6cZ2fY5hW15uT9O2vHzr6+rorsvKSnumioMOvpMkaYwYmDWSOtNqrW8r77Q4a0RsbOy9jSRJY87ArJFULBZpNBo8sK28VCqxOoDpvAZi+/RiA5xubNfnGLYzZ9otyqdO3fq6Orrrclgt35Ik5cg+zBpJTrclSZJGhYFZI8nptiRJ0qiwS4ZGVqVSMSBLkqTc2cIsSZIkZTAwS5IkSRkMzJIkSVIGA7MkSZKUwcAsSZIkZTAwS5IkSRkMzJIkSVIGA7MkSZKUwcAsSZIkZTAwSyMqSRKWlpY4d/485XKZJEnyrpIkSUeSgVkaQUmSUK1WudpqAdBoNKhWq4ZmSZJycHveFZB0q8XFRZrNJieBGeApgGaT6YUFOHt2eE+8vAwbG+3LM2dulnXrlHffd+ZM+/rMzPDqJklSTgzM0ghaW1sDYH1beafFeSTNzMDsbN61kCRp4PoOzCGE24ALwIsxxh8MIZwAPgK8AXgaeHeM8VoI4TjwYeDbgb8G/lGMcbXf55cmUbFYpNFo8MC28lKpxOq5c8N74k5L8alT0Hme7hZluFnefd8w6yRJUs4G0Yf5Z4C/7Lr9q8CvxRhPAl8FFtLyBeCrafmvpdtJ2kGtVqNQKGwpKxQK1Gq1nGokaRwkSUK5XGZqasrBwtIA9RWYQwj3AH8X+K30dgC+B/j9dJMPAT+cXn8ovU16/9vS7SVtU6lUqNfrlEolQgiUSiXq9TqVSiXvqkkaUZ3Bwo1Ggxijg4WlAeq3S8avA78AfEN6+w3A12KMr6W3XwDuTq/fDTwPEGN8LYTwcrr9l/usw3Ddfz+sr8PJk8PZf/egqVHYj/Zv+zEf8HtQASrlMpTL7YKzZ4c74A/6G/Q3jLoMet9Zr+Wg+/Dfmg7DAc63+5aWeLLV4iTt8Q8PAM1mk8XFRb9sS33quYU5hPCDwJdijE8PsD6EEKohhAshhAvr69uHPOVgfb0dICRJGmGdQcEzQPfw284gYkm966eF+TuBHwohvBOYBu4EfgO4K4Rwe9rKfA/wYrr9i8C9wAshhNuB19Ee/LdFjLEO1AHm5+djH/UbjE7L8rAGNQ1q0JSDrw7f9mM+Ce/BKA36G8a+s17LQfcxzu+zxscBzrdHymUajUZ7GsouxWJx0LWSjpyeW5hjjL8UY7wnxlgG3gX8aYyxQnvK2B9JN3sY+Gh6/Yn0Nun9fxpjzD8QS5I0ARwsLA3PMFb6+0Xg50MIK7T7KH8gLf8A8Ia0/OeBx4bw3JIkHUmdwcLTx48DOFhYGqCBLFwSYzwHnEuvfwF46w7bXAX+4SCeT5Ik3apSqdwYHDzUOdulI2YYLcySJEnSxDAwS5IkSRkMzJKOlMuXL7O0tMS58+ddCU2StC8GZklAe5WwpaUlvvbyyywtLU1kkLx8+TKXnn32xny1roQmSdqPgQz6kzTeOkvqPpkGyautFtVqFWivODgpnnvuOTY3NzlJe3GHpwCaTaYXFva/iqIr/emgVlZgdhYuXsy7JpJ6ZGCWxOLiIs1m80aQPAU82QmS09NbN06D4uXLl7nj0iU2Y+Rz09OcOHGCubm5wVVqCMH0b6VfCG4DjtF+nQC0Wrcum72bzsqf+91e2thorxp7WEuxLy/DzMzBn0vSrgzMkm4snbvO1iV1r7ZatwZmbnZt+LZ07aGrrRaXnn0WYLChecCmQmAzRp7eVj59/DinT53a8TG36ASY/W4vHfaXq5mZdou2pIExMEuiWCzSaDR4AG4sq/sg7YUPVl977WaraqrTtQFgI92WzU1Kr7wyuLlfh7AE9St/+29z6dlneTCtO7RXQqvX65ze7+IOLo2tg+rnnPF8k0aCg/4kZS+pOzt7y8+7nUFzG7RbpTs6LdWjam5ujm/55m+mVCoRQnAlNEnSvtjCLOlGYFxcXIRGg+njx28GyU6Y7GrpeqRcptFo3LKfYrF4SDXu3dzcnCugSZIOxBZmSUA7NK+urnLmu7+b06dPZ7a6ZrZIS5I0YQzMkg6sUqlQr9ft2iBJOhLskiGpJ5VKxYAsSToSbGGWJEmSMhiYJUmSpAwGZkmSJCmDgVmSJEnKYGCWJEmSMhiYJUmSpAwGZkmSJCmDgVmSJO1LkiSUy2WmpqYol8skSZJ3laRD4cIlkiRpT0mSUK1WaTabADQaDarVKoCLGGniGZglSdKeFhcXaTabPAPMAisAzSbTCwtw9my+levV8nL78syZ8dp3Hs8zLCsrMDsLFy/mXZNMBmZJkrSntbU1oB2WZ7rKr7ZaudRHE2JjI+8a7IuBWZIk7alYLNJoNNoty8CD6WWpVGL13LmcatWnTqvsMOo/zH3n8TzDMiYt4w76kyRJe6rVahQKhS1lhUKBWq2WU42kw2MLsyRJ2lNnYN/0wgJXWy1KpRK1Ws0BfzoSDMySJGlfKpXKjQF+Y9sNQ+qBXTIkSZKkDAZmSZIkKYOBWZIkScpgYJYkSZIyGJglSZKkDAZmSZIkKYOBWZIkScpgYJYkSZIyGJglSZKkDAZmSZIkKYOBOUdJkrC0tMS58+cpl8skSZJ3lSRJkrSNgTknSZJQrVa52moB0Gg0qFarhmZJkqQRc3veFTiqFhcXaTabnARmgKcAmk2mFxbg7NmD73B5uX155szA6qg9bD/mk/Ie7PY6Dvv1DeP5lpdhZmZw+5MkHQkG5pysra0BsL6tvNPiLGkIZmZgdjbvWkiSxoyBOSfFYpFGo8ED28pLpRKr584dfIedVrheHqsDS5KE+xYWuNpq8cjqKrVajUrnl4Fxfw92O5cO+xzznJakifX+98N73pP+wg48GNqXjz8Ojz6aW7V2ZR/mnNRqNQqFwpayQqFArVbLqUbar936n1++fDnnmkmaJA4M16TqhOWdvOc97ftHjS3MOalUKkC7L/Pa2hrFYrHdSpmWa3Tt1v/8jkuX4KWXJrsPs/1/pUPR+WL+5LYv5oD/T2js7RaWu+8ftVZmA3OOKpWKH3xjaLf+55sxHn5lDpP9f9tWVmBjY/y/GOnw9DCA9b6lJZ5stTgFbKRlzWaTxcVF/9+QcmBglg5ot/7nnzh+nNOnTo1/n1v7DmfzS4MOQafL1wZbv5x3vrBLk2KZU3lXYV8MzNIB1Wo1qtUqzWbzRlmhUOBEsZhjrXRoLl7MuwYaNz18CX2kXKbRaNxSXvRzRhPm5/j1vKuwLw76y+CAC+2kUqlQr9cplUqEECiVStTrdebm5vKumqQJ4cBwabQYmHfhSnzKUqlUWF1dZXNzk9XVVfsUShqo3b6Y+1mjSfD44/3dn4cQR3ig0vz8fLxw4UIuz11Ofw67AhwDPpGWTx8/zunTp3OpU6bOoJJT49EXaCJNynswKq9jVOoh9WvSzuXOjDkvvJB3Tfo3zDEbhzUeZEzHnew2tVye8zCHEJ6OMc7vdJ99mHfRGVhxDLitq9yV+CRJR5oz5mgAHn109KaOy2Jg3kVnJoRX0tsPppc9r8Q3bGP6DXOiTMp7MCqvY1TqIfXLc1kae/Zh3oUDLiRJkgS2MO+qM7Bi6t3vZjNGSqWSK/FJkiQdQQbmDJVKBd77XgBWV1fzrYwkSZJyYZcMSZIkKYOBWZIkScrQc2AOIdwbQngqhPDZEMJnQgg/k5Z/Ywjh4yGEz6eXr0/LQwjhN0MIKyGEPw8hPDCoFyFJknQQruarg+inhfk14H+NMb4FOA28N4TwFuAx4E9ijG8G/iS9DfAO4M3pXxUYwXVcJEnSpHM1Xx1Uz4P+YowvAS+l1/8mhPCXwN3AQ8CZdLMPAeeAX0zLPxzbSwsuhRDuCiG8Kd2P+rWyAhsbN+f71OHrrOY17u/BqLyOUamH1C/P5ZFz39IST7ZanAI20rJms8ni4qKzYWlHA+nDHEIoA/cDnwTmukLwF4G59PrdwPNdD3shLdu+r2oI4UII4cL6+vogqnc0zM62V1+SJEmZOi3LG0B30uis8itt1/e0ciGEGeDfAz8bY7wSQrhxX4wxhhDiQfYXY6wDdYD5+fkDPfZIu3gx7xpoUlbzGpXXMSr1kPrluTxyHimXaTQat5QXi8UcaqNx0FcLcwjh62iH5STG+Adp8eUQwpvS+98EfCktfxG4t+vh96RlIytJEq5cucLXXn7ZAQGSJE0IV/PVQfUzS0YAPgD8ZYzx33Td9QTwcHr9YeCjXeU/ns6WcRp4eZT7L3cGBGzGdiO3AwIkSZoMlUqFer1OqVQihECpVKJer9t/Wbvqp4X5O4F3A98TQlhO/94J/ArwfSGEzwPfm94G+BjwBWAFOAv84z6ee+gWFxdpNptbyjoDAiQNl9M9SRq2SqXC6uoqm5ubrK6uGpaVqZ9ZMv4zEHa5+207bB+B9/b6fIet0/H/uV3KJQ1H59edJ7dN9wT4H5okKRd9D/qbVMVikUajwfbVVRwQoF1NytR+OU+B5XRPkqRR49LYu3BAgA7Mqf0GwumeJEmjxhbmXXRashYXF1lbW6NYLFKr1Wzh0u4mZWq/nKfAcronSdKosYU5gwMCpMPnrzuSpFFjYJY0UpzuSZI0auySIWnkVCoVA7IkaWTYwixJkiRlMDBLkiRJGQzMkiRJUgYDsyRJkpTBwCxJkiRlMDBLkiRJGQzMkiRJUgYDsyRJkpTBwCxJkiRlMDBLkiRJGQzMkiRJUgYDsyRJkpTBwCxJkiRlMDBLkiRJGQzMkiRJUgYDsyRJkpTBwCxJkiRlMDBLkiRJGQzMkiRJUgYDsyRJkpTBwCxJkiRlMDBLkiRJGQzMkiRJUgYDsyRJkpTBwCxJkiRlMDBLkiRJGQzMkiRJUgYDsyRJkpTBwCxJkiRlMDBLkiRJGQzMkiQNSZIkLC0tce78ecrlMkmS5F0lST0wMEuSNARJklCtVrnaagHQaDSoVquGZmkM3Z53BSSNmJUV2NiAM2fyrok01u5bWuLJVotTwEZa1mw2WVxcpFKp5Fk1SQdkYJa01exs3jWQJkKnZXkDWO8qX1tby6U+knpnYJa01cWLeddAmgiPlMs0Go1byovFYg61kdQP+zBLkjQEtVqNQqGwpaxQKFCr1XKqkaReGZglSRqCSqVCvV6nVCoRQqBUKlGv1+2/LI0hu2RIkjQklUrFgCxNAFuYJUmSpAwGZkmSJCmDgVmSJEnKYGCWJEmSMhiYJUmSpAwGZkmSJCmDgVmSJEnKYGCWJEmSMhiYJUmSpAwGZkmSJCmDgVmSJEnKYGCWJEmSMhiYJUmSBihJEpaWljh3/jzlcpkkSfKukvp06IE5hPD2EMKlEMJKCOGxw35+SZKkYUmShGq1ytVWC4BGo0G1WjU0j7kQYzy8JwvhNuBZ4PuAF4BPAT8WY/zsTtvPz8/HCxcuHFr9JEmS+lEul2k0GjwPzADLafn08eOcPn168E+4vAwzM/DCC4Pf9xETQng6xji/032H3cL8VmAlxviFGOM14CPAQ4dcB0mSpKFYW1sDYB3Y6CrvtDgP3MwMzM4OZ9+64fZDfr67gee7br8AfMch10GSJGkoisUijUaDB7aVl0olVs+dy6NKGoCRG/QXQqiGEC6EEC6sr6/nXR1JkqR9q9VqFAqFLWWFQoFarZZTjTQIhx2YXwTu7bp9T1p2Q4yxHmOcjzHOz/oTgyRJGiOVSoV6vU6pVCKEQKlUol6vU6lU8q6a+nDYg/5upz3o7220g/KngP8hxviZnbZ30J8kSZIOQ9agv0PtwxxjfC2E8NPAHwG3AR/cLSxLkiRJo+CwB/0RY/wY8LHDfl5JkiSpFyM36E+SJEkaJQZmSZIkKYOBWZIkScpgYJYkSZIyGJglSZKkDAZmSZIkKYOBWZIkScpgYJYkSZIyGJglSZKkDAZmSZIkKYOBWZIkScpgYJYkSZIyGJglSZKkDAZmSZIkKYOBWZIkScpgYJYkSZIyhBhj3nXYVQhhHWgM+WneCHx5yM9xlHl8h8vjOzwe2+Hy+A6Xx3e4PL7Dk+exLcUYZ3e6Y6QD82EIIVyIMc7nXY9J5fEdLo/v8Hhsh8vjO1we3+Hy+A7PqB5bu2RIkiRJGQzMkiRJUgYDM9TzrsCE8/gOl8d3eDy2w+XxHS6P73B5fIdnJI/tke/DLEmSJGWxhVmSJEnKcKQDcwjh7SGESyGElRDCY3nXZ9yEEO4NITwVQvhsCOEzIYSfScv/WQjhxRDCcvr3zq7H/FJ6vC+FEH4gv9qPhxDCagjh0+lxvJCWfWMI4eMhhM+nl69Py0MI4TfT4/vnIYQH8q39aAshfEvXObocQrgSQvhZz9/ehRA+GEL4UgjhL7rKDny+hhAeTrf/fAjh4Txey6jZ5dj+qxDC59Lj94chhLvS8nII4ZWuc/j9XY/59vQzZSU9/iGP1zNqdjm+B/4sMFfsbJfj+7tdx3Y1hLCclo/m+RtjPJJ/wG3AXwHfBBwD/l/gLXnXa5z+gDcBD6TXvwF4FngL8M+A/22H7d+SHufjwIn0+N+W9+sY5T9gFXjjtrJ/CTyWXn8M+NX0+juB/wAE4DTwybzrPy5/6efBF4GS529fx/G7gAeAv+gqO9D5Cnwj8IX08vXp9dfn/dry/tvl2H4/cHt6/Ve7jm25e7tt+/kv6fEO6fF/R96vbRT+djm+B/osMFcc7Phuu///AP739PpInr9HuYX5rcBKjPELMcZrwEeAh3Ku01iJMb4UY3wmvf43wF8Cd2c85CHgIzHGVozxOWCF9vugg3kI+FB6/UPAD3eVfzi2LQF3hRDelEcFx9DbgL+KMWYtlOT5u4cY458BX9lWfNDz9QeAj8cYvxJj/CrwceDtw6/9aNvp2MYY/zjG+Fp6cwm4J2sf6fG9M8a4FNvp48PcfD+OtF3O3d3s9llgrthF1vFNW4l/FPidrH3kff4e5cB8N/B81+0XyA57yhBCKAP3A59Mi346/Znwg52fYPGY9yICfxxCeDqEUE3L5mKML6XXvwjMpdc9vr17F1s/rD1/B+eg56vHuTc/SbvFreNECOFiCOF8COHvpGV30z6eHR7bvR3ks8Bztzd/B7gcY/x8V9nInb9HOTBrQEIIM8C/B342xngFeBy4DzgFvET7pxb15r+PMT4AvAN4bwjhu7rvTL9lO9VNH0IIx4AfAv5dWuT5OySer8MRQlgEXgOStOgloBhjvB/4eeDfhhDuzKt+Y8zPgsPxY2xtsBjJ8/coB+YXgXu7bt+TlukAQghfRzssJzHGPwCIMV6OMV6PMW4CZ7n5s7XH/IBijC+ml18C/pD2sbzc6WqRXn4p3dzj25t3AM/EGC+D5+8QHPR89TgfQAjhEeAHgUr6hYS0q8Bfp9efpt2v9ptpH8fubhse2ww9fBZ47h5QCOF24B8Av9spG9Xz9ygH5k8Bbw4hnEhbmN4FPJFzncZK2u/oA8Bfxhj/TVd5d7/Zvw90RsU+AbwrhHA8hHACeDPtDvzaQQjh60MI39C5TnuAz1/QPo6dmQMeBj6aXn8C+PF09oHTwMtdP4Vrd1taNzx/B+6g5+sfAd8fQnh9+hP496dl2iaE8HbgF4AfijE2u8pnQwi3pde/ifa5+oX0+F4JIZxOP79/nJvvh7bp4bPAXHFw3wt8LsZ4o6vFqJ6/tx/WE42aGONrIYSfpv1BfBvwwRjjZ3Ku1rj5TuDdwKc708EA/wT4sRDCKdo/va4C/xNAjPEzIYTfAz5L++fD98YYrx96rcfHHPCH6aw5twP/Nsb4H0MInwJ+L4SwADRoD5YA+BjtmQdWgCbwE4df5fGSfhH5PtJzNPUvPX97E0L4HeAM8MYQwgvA+4Bf4QDna4zxKyGEf047fAD8coxxv4OxJtYux/aXaM/U8PH0c2Ipxvgo7RkJfjmE8CqwCTzadQz/MfDbwB20+zx393s+snY5vmcO+llgrtjZTsc3xvgBbh0/AiN6/rrSnyRJkpThKHfJkCRJkvZkYJYkSZIyGJglSZKkDAZmSZIkKYOBWZIkScpgYJYkSZIyGJglSZKkDAZmSZIkKcP/DxGcszd+ubG7AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 864x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_best_path(best_route)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Задание 4 (3 балла) \n",
    "\n",
    "\n",
    "Реализуйте отжиг (simulated annealing). Температурный режим отжига выберите самостоятельно."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 191,
   "metadata": {},
   "outputs": [],
   "source": [
    "def create_route(city_list): # создаём произвольный маршрут\n",
    "    route = random.sample(city_list, len(city_list))\n",
    "    return route\n",
    "\n",
    "def simulated_annealing(city_list, T0, T_min, num_iterations, alpha):\n",
    "    start_route = create_route(city_list)\n",
    "    best_route = start_route\n",
    "    T = T0\n",
    "    iteration = 0\n",
    "    while T > T_min and iteration < num_iterations:\n",
    "        new_route = create_route(city_list)\n",
    "        delta_fitness = Fitness(new_route).route_fitness() - Fitness(best_route).route_fitness()\n",
    "        if delta_fitness > 0:\n",
    "            best_route = new_route\n",
    "        else:\n",
    "            p = np.exp(delta_fitness / T)\n",
    "            c = random.uniform(0, 1)\n",
    "            if c >= p:\n",
    "                best_route = new_route\n",
    "        iteration += 1\n",
    "        T = alpha * T\n",
    "    print(f'Лучшее расстояние: {1 / Fitness(best_route).route_fitness()}')\n",
    "    return best_route"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 192,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Лучшее расстояние: 32925.0\n"
     ]
    }
   ],
   "source": [
    "best_route = simulated_annealing(city_list, T0=4, T_min=1e-8, num_iterations=1000, alpha=0.8)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 193,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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kRZckSZImjIFZUytJEur1OmvdLgDtdpt6vW5oliRJezLQ1tjSOFtYWKDT6XAcmAUeB+h0OHr2LJw7V2xxO7l8uff99OnBX2d2duByJEk6zAzMmlrLy8sArGxqz3qcD4XZWZibK7oKSZImmoFZU6tcLtNutzm5qb1SqbB0/nwRJe1e1rM87nVKknQIOIZZU6vRaFAqlTa0lUolGo1GQRVJkqRJZGDW1KrVajSbTSqVCiEEKpUKzWaTWq1WdGmSJGmCOCRDU61WqxmQJUnSQOxhliRJQ5MkCdVqlZmZGde/19Swh1mSJA1Ftv59p9MBXl3/HvBqnyaagVkSLC7C6mr+us/DWhtaozFOv59xquUgHdbP3ee+VovHul2O01vS8yTQ6XRYWFgwMGuiGZgluVazpKHI1rnfvF1Sti6+NKkMzJLg0qWdH+Pa0ONtnH4/41TLQTqsn7vPmWqVdrvd21m1T7lcLqQeaVic9CdJkobC9e81rexhliRJQ5GNUz569ixr3S6VSoVGo+H4ZU08A7MkSRqaWq0G584BsHSIh6doujgkQ5IkScphYJYkSZJyGJglSZKkHAZmSZIkKYeBWZIkScphYJYkSZJyGJglSTtKkoRqtcrMzAzVapUkSYouSZIOjIFZksbUuITUJEmo1+u0221ijLTbber1uqFZ0qHhxiXSOFpchNVVOH266Epedfly7/s41TTFrl27xr1Xr/Lb6+u9hnabmfe8h2v//J9z7Nixm58wwt/Pfa0Wj3W7HAdWgJNAp9NhYWHBHdwkHQoGZmlQ998PKytw/PjwXvOFF+Cb33w1BI2D1dXe93GqaYrd8eKLvCVG7gCuA08A6+vrPP3001sH5hFa63YBmN3Uvry8fKB1SFJRDMzSoFZWXg2Tw3Lq1HBfbxiyoHziRLF1HBJPXrgAwAlgFXhb2h6uX2d9q+2Gs57lEWxFfKZapd1u8/im9nK5PPT3kqRxZGCWBpX1LI8gqIyVEQYy3WycQmqj0aBer0Onc6OtVCrRaDQOvBZJKoKT/qZAkiS0Wi3OX7jg7HUNVf+ks1arxbVr14ou6dBoNBqUSqUNbUWF1FqtRrPZ5OiRIwBUKhWazabjlyUdGvYwT7hs9vpj6RjDbPY64P/MNJDs3OqkvYpr3S5Xrl7lT5LEc+sAZMf46NmzrHW7VCoVGo1GYce+VqvBuXMALHmVQdIhE2KMRdewrVOnTsWLFy8WW8SYX4auppdtn6E3ISebjnX0yBEeeOCBAisbonEfOzvu9e1Tq9Virdvlu4HbgVvS9k4I3HnnnQVWNiFeegluu23w8ei7Pb8uX+b69es8ub7OWrfL0SNHmJ+fH+4EwT3UsqvHTZu8z724CHNzcOnSwdZUlDH/f6e0lRDCEzHGLf9o28M84bJZ6iub2rNZ7dJ+ZedQf1gGWB/jf2SPlVdeOdC3u379Op2XXmIt/Tm7IgAc+Koa2sKwJwZLOlAG5glXLpdpt9uc3NReqVSm57LpuPdUjHt9+7TdpLMzlQpLS0tFlDRZhnBeJEnCj7373dwaI5dardwe47W1XlTuXyuZ9XUqL700vL8Fu/1MU/rfxI7yPrfrl0sTzUl/E26cJgZpumx1bs3MzBzKc6uIHfeyMeS3xsgtvNpjvN3Ey6znfxaY62t3rWRJGpw9zBMumwC0sLDA8vIy5XK50IlBmh6bJ51lY2K/75CdW5snP458Ym26Ec59f/u3PNbtcgvwCuk6zDk9xl88enTLoViulSxJgzMwT4FarWZALkK2w182NnEKL7nWAI4e7X0BPP/8VH7OPPvZFjpJEu5LJ02eqVb39o/Y9JzKwu8r9Hb6y2zXYzw/P8+Vq1dZz7bSxqtNkjQsBmZpv0axw5/Gzl63hR54qcd0I5wzS0t72rgkG9v89NNPj8UydJI0TQzM0n5lO/xlpmGCU9ZrvvmzTYG/+Ru4+qWb29/4BviO79j+eUePHNkQmrMAe/T227fsbc96pLMtrWHnHumt7Gd3vWPHjt0IzlMz6VeSxoCT/qQBXLt2bbp2WZzSXvPtwjL02v/mb7Z/7vz8PDMzG/9UzszMMD8/v+Xjs3C9ysblHvc6+c7d9SRpfNjDLO3TtWvXNowZnYpdFrOe5f7eyc29qBPYc3l32OEBX4J4deu7jgF/kiTcnk5+PJMOddhu8mO2HN9m+5l85+56kjQeDMw7uXgRvvnNQzfRaaxku2eN2e/gjitXeEu6lNcgl951sD7E+zlxY0/MPqe3f07/5MelahV+8Rfh1399y13bsqEUnT0MpZAkjTeHZOzkm9888B27NBnW+8LyIJfeNYFWV3vDV7aQDaWoVCqEEBxKIUlTwB7mndxxR++7l0OLM6a7hmXr3r5tU/thXfc2SZKJWA/8H/NvtmyP53d4Yv95uMPVjv0u9bjlcnR7fhVJ0rDZwyzt01aTwQ7rpfdsKbV2u02M8cZ47omfBHmAsmO4tmk5uu129pOkTBG7kR42IaaXlcfRqVOn4sWLF4st4q67et9feKHYOg6zMe1h5vRprl27xve89NLoelUPepm3bLz4iRM3t2X670u10l7R7wZuB15K22dC4M477xxFpXvSvQ4vvbT9/XfcAUdu3+FFstVDZmc33h6SF198kfUYmaW3Wcn/k7afzI7hFsd9g138nga21fkxyOOmTd7nPmzH5LB93gJtnoAOvZV83vTGN95YZnIiLC7C3NyWc0MOSgjhiRjjqa3uc0iGNIBjx46NdvWC/S7zlk1WzYYU7Vb2Xv3ha/P7bw5mwH+R9op+CxCAW7I7YhyLZeqOALfNQN//T26YmYGZ62zcTm8r2VyG1dWNt4eklHZeZNcsTvS3r65uedw32MXvaWBbnR+DPG7a5H3uw3ZMDtvnLdAdL77IW2LkDnp/xp4A1tfXefrppycrMI/B/yvyGJilcbbVMm+7cc89vT8+e+3d2WcP8xfTHubsnuwZR48c4YEHHthbDSMyw4Bj0PqPzQh6zy6mx3CzkyFw5+ysPcyTwB7mVx22z1ugJy9cALixWVI2ryZcv876uF2ZzTNmK2FtZmCWptF+g/ZWw192sQ7zX2fbQadLqb2N3njuZrPJAyOa+HfgkwzT45D8w3/IfdmazEtLQ3nfJEn4uc99jq9uCsylUomnymXuPHZs59/lQayXvdvhUeM6jGrU8j73YTsmh+3zFihb+/3xTe2HdQL6qDjpT9LADnpXuqImGV67dm3LiXmDvG/2Wb761a9uaH/ta19Ls9mcrEuqkg5co9GgVCptaDusE9BHaaAe5hDCXcBvAd8FROCngSvA7wJVYAn4iRjj10MIAfgN4EGgA5yJMT45yPtLOgCLixvHlm1z2eymzT3OnbuxS92w3ddq8djmSYadDjPvfje8730jeU9WV3ntK6/wHNyYmPd4+r5Hz57d92fd8rMAM1/7Gne+732vHvtsAnJOfRvs9Pj92Gsto6hhnOV97sN2TCbt8770Etx2G5zacr7XYEY8mS3rmDiaXvmqpLuRjuOynpNs0CEZvwH8xxjjj4cQbgdKwD8B/jTG+KshhA8AHwB+EXgn8Ib063uAD6ffJY2zubne9zGakJH18N5O3wRDXt1MZtReYeMcwa3GHu9W0Z9FEqPdoOwA/nbWarUb/2gf6UT0Q2zfgTmE8G3A9wFnAGKM14HrIYSHeHWT2UeA8/QC80PAx2NvHbtWCOGuEMLrY4zP77t6SaOX9YrsZkziAY1b3DxmL5vkUqlUWFpaGs2bnj7NxVaL/2qLcFypVPb9P6kdP8texw1nHMN88BzD/KpJ+7yjrHfMJ7NpdwYZwzxPb0fgfxtCuBRC+K0QwrcAx/pC8JeBbADe3cAzfc9/Nm2TpD1pNBrcdtttG9puu+22kY/Zm5+fH/pYQccfStL4GyQw3wqcBD4cY7wf+P/oDb+4Ie1N3tN1xRBCPYRwMYRwcWVlZYDyJE2z3rSI7X8ehWPHjtFsNqlUKoQQhjK58aAnTEqS9m6QMczPAs/GGD+T/vz79ALztWyoRQjh9cBX0vufA+7te/49adsGMcYm0ITeTn8D1CdpSi0sLHD9+vUbE+UeB7h+faDJdztK15WtnTtHrVqFarXXPoTJjbkTJrP1bHe6rNu/DvMQdyCUJA3Qwxxj/DLwTAjhTWnT24EvAI8CD6dtDwOfTG8/Crwn9DwAfMPxy9LwJUlCq9Xi/IULVKvVkS+1VoTl5WXg5olyg0y+mxqzs69O1JQkDcWgq2T8j0CSrpDxFPBT9EL474UQzgJt4CfSx36K3pJyi/SWlfupAd9b0ibZmr6PbVonGJiqS/zlcpl2u31jCbYNE+VGNclo1JOYtnt9J9pJUuEGCswxxsvAVosWvn2Lx0ZgRAukSoLeUIVOp8NxemsF73md4LzL/7sZGrDb4QMD+swdd3BlZobZ9XWyxaBKpRIPPvgg1Wr14Hb/002SJOG+dJvvM9Wqv4MhOvDdLSXd4NbY0hTJhipsni67l6EK169f58k08Bw9coT5+fmx220uq+eVL36R6/R6lh988EEeeeQROun23NPauz7ODssVjiJkx9bzWyqGgVkalvvvh5UVOH58eK+5xx7b//v221nrdvkG8I2+9mwFhp1cv36dzksvsZb+vNbtcuXqVeDV9SHHxbFjx+CppzjyyissvfACL37kI/xvMTJLb1zYOkCnA//gH8DDD+e/2G5kGxvcOqI/m6+8AiHcvDPaOO2ut8N7/N0XX+S59HcwrJ0QJ8qgV2hyZDtCHqf3D+KTQKfTYWFhwcAsHQADszQsKyuF74Y3Pz/PlatXWV9fv9E2MzPD/Pz8rp6/ttaLyhu2aV5fZ+bKFZhJ5wj3r8awWfb58x4zTC+/DDHC6iqldGe8W3Z4ytgKofc1wbLdCYe5E6J6smO4ef2T7KqSpNEyMEvDkvUsD3PS1R4nch0D/mSLcY7ft8seqCfTwJaNgb4Re2PkdLZU2YkT279AFpTzHjMiF9NhJP09cDDE3f+KmlQ3TpP+dniPE+muhZuNdDLmOBnhTn+bd4TMlMvlfb2epL0xMEtTplar7fsS7dEjR1jrdllMf96w+kS27vAYbI29lb/eNMYT3DHvoDUaDX8HI5IdWzy2UiEG2elP0pSZn59nZmbjn4VJ+Z9ytmPeMHfh0974Oxgdd4SUimUPs6QbstUnnn76ada6XSqVyqtLV03ApK1Betc1HP4ORqf/v8NDMcRFGiMGZmkIpmnt2WPHjt0Izv5PWZIkh2RIA8vWR13btPbsNG5JLUnSYWQPszSggXfXy3NAO+fd9H6Z/vcdo53+ClHUZ9vt+16+DLObFx2TJA2DgVka0DB215MGNjsLc3NFVyFJU8nALA2oXC7TbrdvrPubGcraswe9TNvmXsz+991NLQUuKzdy474OsyRpZBzDLA2o0WhQKpU2tE3KUmySJGlnBmZpQK49K0nSdHNIhjQErj0rSdL0sodZkiRJymFgliRJknIYmCVJkqQcBmZJkiQph4FZkiRJymFgliRJknIYmCVJkqQcBmZJkiQph4FZkiRJymFgliRJknIYmCVNjSRJqFarzMzMUK1WSZKk6JIkSVPg1qILkKRhSJKEer1Op9MBoN1uU6/XAajVakWWJkmacAZmSbuzuAirq3D69PaPuXy59z3vMSNyX6vFY90ux4EV4CTQ6XRYWFgwMEuSBmJglrQ7c3NFV5BrrdsFYHZT+/Ly8sEXI0maKgZmSbtz6dLOj8l6ls+fH2UlWzpTrdJut3l8U3u5XD7wWiRJ08VJf5KmQqPRoFQqbWgrlUo0Go2CKto9JytK0nizh1nSVMjGKR89e5a1bpdKpUKj0Rj78ctOVpSk8WdgljQ1arUanDsHwFIBw0L2Y2FhgU6nw5PAHLAI0Olw9OzZ3mcpcCLlTcaplnGUd3yGdewm5XcwKXVmRlnvQR2Ly5dhdvMsDg2LQzIkqUDZpMQ5Nk5YzCYxStKuzM6O/eTsSWYPsyQVqFwu0263ez3LwNvS75VKpddLXuBEypuMUy3jKO/4DOvYTcrvYFLqzIyy3kk7FtqSPcySVKBJnqwoSYeFPcySVKBJnawoSYeJgVljLUkS7mu1WOt2OVOtGiQ0lSZxsqIkHSYOycP6lx8AABf1SURBVNDYypbbyiY/ZcttuUatJEk6SPYwa2xly20dp7d6wOOwcbmtoh3EUkEHvTRT9n6Z3bzv4mJvZvZudgKUJGkCGZg1trLltlY2tbvc1phZXS26AkmSRsrArLGVLbd1clP7jeW2inYQSwUd9HJEm3uUd/O+k7IxgSRJ++QYZo0tl9uSJEnjwMCssVWr1Wg2m1QqFUIIVCoVms2mq2RIkqQD5ZAMjbVarWZAliRJhbKHWZIkScphYJYkSZJyGJglSZKkHAZmSZIkKYeBWZIkScphYJYkSZJyGJglSZKkHAZmSZIkKYeBWZIkScphYJbGVJIktFotzl+4QLVaJUmSokuSJOlQMjBLYyhJEur1OmvdLgDtdpt6vW5oliSpALcWXYCkmy0sLNDpdDgOzAKPA3Q6HD17Fs6dG90bX7688efTp3f/nNOnN94uyqhqGPVn2+71x+GYZsaplnGUd3wGPXaLizA3B9/2bft7vqSBGJilMbS8vAzAyqb2rMdZ0iGzutr7bmCWCjFwYA4h3AJcBJ6LMf5ICGEe+ATwWuAJ4N0xxushhCPAx4HvBr4K/PcxxqVB31+aRuVymXa7zclN7ZVKhaXz50f3xpt7v3bzXtlzzp/feLsoo6ph1J9tu9cfh2OaGadaxlHe8Rn02NmrLxVqGGOYfw74q76ffw34UIzxOPB14Gzafhb4etr+ofRxkrbQaDQolUob2kqlEo1Go6CKJE2KJEmoVqvMzMw4YVgakoECcwjhHuC/AX4r/TkA3w/8fvqQR4AfS28/lP5Mev/b08dL2qRWq9FsNqlUKoQQqFQqNJtNarVa0aVJGmPZhOF2u02M0QnD0pAMOiTj3wC/AHxr+vNrgRdijC+nPz8L3J3evht4BiDG+HII4Rvp4/92wBqkqVSr1QzI0iRZXOyNNR7FpL9dTsi9r9XisW6X4/TmQJwEOp0OCwsL/j2RBrDvHuYQwo8AX4kxPjHEeggh1EMIF0MIF1dWNk95kiRpTM3NwexsoSVkE4Nngbm+9mwisaT9GaSH+XuBHw0hPAgcBe4EfgO4K4Rwa9rLfA/wXPr454B7gWdDCLcC30Zv8t8GMcYm0AQ4depUHKA+SZIOzqVL29837El/27zOmWqVdrvdW4qyT7lc3t/7SgIG6GGOMf5SjPGeGGMVeBfwZzHGGr0lY388fdjDwCfT24+mP5Pe/2cxRgOxJElD4oRhaTRGsdPfLwI/H0JYpDdG+aNp+0eB16btPw98YATvLUnSoZVNGD565AiAE4alIRnKxiUxxvPA+fT2U8Bbt3jMGvD3hvF+kiRpa7Va7caOoCNdt106REbRwyxJkiRNDQOzJEmSlMPALOlQcRc0SdJeDWUMs6TJlyQJ97VarHW7HD1yhPn5eY4VXdSQZbugdTodgBu7oAFOipIkbcvALOlGkHws3fRgrdvlytWr/EmSTFWQXFhYoNPp8CS9TR0WATodjp49e2OS1LYG3altJ9u9/uXLhW+GIUmHnUMyJN0IkseBE+nXd66vs7CwkPu8JElotVqcv3CBarXKtWvXDqLcfct2O5ujtxNaJtsdbSzNzvZ2kJMkFcYeZkk3gmT/ZvQr5G+nu7lXut1uc2Wm92/wcR3KUS6XabfbvZ5l4G3p90qlsvPyW4Pu1LaTUb++JGnfDMySbgTJk5vaKznb6fb3Ss/S2+LzLevrzFy5MrphC7uRM3TiM3fcwZWZGd6yvg70ap6ZmeFNd9yxc81FDckYJ5NQ47ga9Nhlz88Ufb4CLC72rn7kbQkuTQmHZEja13a6/b3Sq33t62O84/2xY8d40xvfyEwIABw9coQ3vfGNHDs2rn3i0hhbXYWVlZ0fJ00Be5gl3ZjYt7CwwPLyMuVymUajkTvhb6te6cfphdAHihxWsMPQhmN9j9lTnQ7JmIwax9Wgx25zT3HRQ4j630M6BOxhlgT0QvPS0hLr6+ssLS3tuDrGVr3SMzMzzM/Pj7JMSZIOnD3MkvZlq17pN91xh8MbJElTxx5mSfu2uVfasCxJmkYGZkmSJCmHgVmSJEnKYWCWJEmSchiYJUmSpBwGZkmSJCmHgVmSJEnKYWCWJEmSchiYJUnSniRJQqvV4vyFC1SrVZIkKbokaaQMzJIkadeSJKFer7PW7QLQbrep1+uGZk01t8aWpHGwuAirq3D6dNGVbO/y5d73ca5xXA167LLnZ3Z6nRH+ru5rtXis2+UEsJq2dTodFhYWqNVqQ38/aRwYmCVpHMzNFV2BtCtZz/IqsNLXvry8XEg90kEwMEvSOLh0qegKdpb1Vp4/X2QVk2nQY7e5p3in1xnh7+pMtUq73b6pvVwuD/29pHHhGGZJkrRrjUaDUqm0oa1UKtFoNAqqSBo9A7MkSdq1Wq1Gs9mkUqkQQqBSqdBsNh2/rKnmkAxJkrQntVrNgKxDxR5mSZIkKYeBWZIkScphYJYkSZJyGJglSZKkHAZmSZIkKYeBWZIkScphYJYkSZJyGJglSZKkHAZmSZIkKYeBWZIkScphYC5YkiRUq1VmZmaoVqskSVJ0SZIkSepza9EFHGZJklCv1+l0OgC0223q9ToAtVqtyNIkSZKUMjAXaGFhgU6nw5PAHLAI0Olw9OxZOHeu2OK0s8uXe99Pny60jLEyzGOyuAhzc3Dp0uCvJUnSAAzMBVpeXgZ6YXm2r32t2y2kHu3etWvXuOPFF1mPkS+2WszPz3Ps2LGiy5ouq6tFVyBJEmBgLlS5XKbdbvd6loG3pd8rlQpL588XVJV2kg2leSxGAN7W7VJaXqb5T/+pQ2mynuVhnL/23EuSxoSBuUCNRqM3ZjkdwwxQKpVoNBoFVqWdZENpjtO7MvA4OJQmM8whGft9rd08bz+v7RCc3jGYnd35cRqqJEm4r9Virdvl6JEjvStaRRclDVH/OX6mWqXRaIxdB5SrZBSoVqvRbDY5euQI0OtZbjabY3eSaKNsKM0K0D9owKE0mnqzs71x5Tow2RWt7O/LWrfLlatXXVFJU2PzOZ4tgDBu53iI6WXlcXTq1Kl48eLFYou4667e9xdeGN17DPMytkauWq3Sbrdvaq9UKiwtLR18QeNkFEMy9vpau3nefl7b/041iH2eP9nfm2fYONfli0eO8MADD2z/xOxqwLPP7rHQIbnnnt48hBMninn/vcquII2i3lG+9hRopT3LJ+h1Qt2bthfx/9QQwhMxxlNb3WcPs7RHjUaDUqm0oc2hNJJGYd9XtIq+GjA35/Ad7Up2Lq/SO88z2bk/LhzDLO1RNmRmYWGB5eVlyuXyWI63kjT5ssnhJ9OfH0+/nxn3yeGTthzkKK8geXUq15ltrtqWy+UCqtmePcw5kiThxRdf5IVvfMNd+LRBrVZjaWmJ9fV1lpaWDMuSRmKrK1ozMzNe0dLUmJSrtgbmbWSD0NfTMd7jOghdkjS9ssnhlUqFEAJHjxzhTW98o/9I19TYfI6P6wIIDsnYRrZ02B3ALYx46TCXapIkbaNWq70aHg7zsoaaWhvO8TFlD/M2ssHm14FX+tpHsnRY0ZMzJEmStC17mLeRTbR4Iv3ZXfgkSZIOJ3uYtzEpg9AlSZI0WvYwbyMbS3P07FnWul0qlYpLh0mSJB1CBuYctVrtxgQ/h2FIkiQdTg7JkCRJknLsOzCHEO4NITweQvhCCOHzIYSfS9u/PYTw6RDCl9Lvr0nbQwjhN0MIiyGEvwghnMx/B0mSJKl4g/Qwvwz8zzHGNwMPAO8LIbwZ+ADwpzHGNwB/mv4M8E7gDelXHfjwAO8tSZI0sCRJaLVanL9wwV19ta19B+YY4/MxxifT2/8Z+CvgbuAh4JH0YY8AP5befgj4eOxpAXeFEF6/78olSZIGkO3qm+2x4K6+2s5QJv2FEKrA/cBngGMxxufTu74MHEtv3w080/e0Z9O255E0HRYXYXV1OLuRXb7c+77X19rN8/bz2vutR4LhnT+eh0N1X6vFY90uJ4DVtK3T6bCwsOCqWNpg4El/IYRZ4N8D748xvth/X4wxAnGPr1cPIVwMIVxcWVkZtDxJB2luzm3eJU2MrGd5FehPHNluv1JmoB7mEMJt9MJyEmP8g7T5Wgjh9THG59MhF19J258D7u17+j1p2wYxxibQBDh16tSewrakgl26NLzXynrQ9rqk426et5/X3m89Egzv/PE8HKoz1Srtdvum9nK5XEA1GmeDrJIRgI8CfxVj/Nd9dz0KPJzefhj4ZF/7e9LVMh4AvtE3dGMsORFAkqTp5a6+2q1BhmR8L/Bu4PtDCJfTrweBXwV+MITwJeAH0p8BPgU8BSwC54B/NMB7j5wTASRJmm61Wo1ms0mlUiGEQKVSodlsOn5ZNwm9Ycbj6dSpU/HixYuFvHc1vUzzDDALpNMsOHrkCA888EAhNUmHxbVr17jjyhXWY+SLR44wPz/PsWPHdn4ivDop6sSJwR6z1XNmZ+HZZ3f/HCnjkIzDy9/ZxAghPBFjPLXVfe70t41swP8Kr86chVcnCEgajWvXrnHl6lXW03/Mr3W7XLl6lWvXrhVb2Oxsb1KjJOnQGcqyctOoXC7TbrfZvB1hpVJhyX8lSiPzPdUq7fX1G1d3ANbX13n66ad338s8Ozv8SX+SpEPLHuZtOBFAKsbAV3fsCZYkDZk9zNvIBvwvLCywvLxMuVym0Wg4EUAaMa/uSJLGjT3MOWq1GktLS6yvr7O0tGRYlg6AV3ckSePGwCxprLjMkyRp3DgkQ9LYqdVqBmRJ0tiwh1mSJEnKYWCWJEmSchiYJUmSpBwGZkmSJCmHgVmSJEnKYWCWJEmSchiYJUmSpBwGZkmSJCmHgVmSJEnKYWCWJEmSchiYJUmSpBwGZkmSJCmHgVmSJEnKYWCWJEmSchiYJUmSpBwGZkmSJCmHgVmSJEnKYWCWJEmSchiYJUmSpBwGZkmSJCmHgVmSJEnKYWCWJEmSchiYJUmSpBwGZkmSJCmHgVmSJEnKYWCWJEmSchiYJUmSpBwGZkmSJCmHgVmSJEnKYWCWJEmSchiYJUkaoSRJaLVanL9wgWq1SpIkRZckaY8MzJIkjUiSJNTrdda6XQDa7Tb1et3QLE2YW4suQJIO3OIirK7C6dNFV6Ipd1+rxWPdLieA1bSt0+mwsLBArVYrsjRJe2BglnT4zM0VXYEOiaxneRVY6WtfXl4upB5J+2NglnT4XLpUdAU6JM5Uq7Tb7Zvay+VyAdVI2i/HMEuSNCKNRoNSqbShrVQq0Wg0CqpI0n4YmCVJGpFarUaz2aRSqRBCoFKp0Gw2Hb8sTRiHZEiSNEK1Ws2ALE04e5glSZKkHAZmSZIkKYeBWZIkScphYJYkSZJyGJglSZKkHAZmSZIkKYeBWZIkScphYJYkSZJyGJglSZKkHAZmSZIkKYeBWZIkScphYJYkSZJyGJglSZJGIEkSWq0W5y9coFqtkiRJ0SVpnw48MIcQ3hFCuBJCWAwhfOCg31+SJGnUkiShXq+z1u0C0G63qdfrhuYJFWKMB/dmIdwCXAV+EHgW+CzwkzHGL2z1+FOnTsWLFy8eWH2SJI21e+6B1VU4caLoSrSDVqvFWrfLCWAVuDdtr1QqLC0tFVeYthVCeCLGeGqr+w66h/mtwGKM8akY43XgE8BDB1yDJEmTaW4OZmeLrkK7kPUsrwIrfe3Ly8uF1KPB3HrA73c38Ezfz88C33PANUiSNJkuXSq6Au3SmWqVdrt9U3u5XC6gGg1q7Cb9hRDqIYSLIYSLKysrOz9BkiRpzDQaDUql0oa2UqlEo9EoqCIN4qAD83O8OowH4J607YYYYzPGeCrGeGpubu5Ai5MkSRqGWq1Gs9mkUqkQQqBSqdBsNqnVakWXpn046El/t9Kb9Pd2ekH5s8DfjzF+fqvHO+lPkiRJByFv0t+BjmGOMb4cQvhZ4I+AW4CPbReWJUmSpHFw0JP+iDF+CvjUQb+vJEmStB9jN+lPkiRJGicGZkmSJCmHgVmSJEnKYWCWJEmSchiYJUmSpBwGZkmSJCmHgVmSJEnKYWCWJEmSchiYJUmSpBwGZkmSJCmHgVmSJEnKYWCWJEmSchiYJUmSpBwGZkmSJCmHgVmSJEnKYWCWJEmScoQYY9E1bCuEsAK0R/w2rwP+dsTvcZh5fEfL4ztaHt/R8viOlsd3tDy+o1XE8a3EGOe2umOsA/NBCCFcjDGeKrqOaeXxHS2P72h5fEfL4ztaHt/R8viO1rgdX4dkSJIkSTkMzJIkSVIOAzM0iy5gynl8R8vjO1oe39Hy+I6Wx3e0PL6jNVbH99CPYZYkSZLy2MMsSZIk5TjUgTmE8I4QwpUQwmII4QNF1zNpQgj3hhAeDyF8IYTw+RDCz6XtvxxCeC6EcDn9erDvOb+UHu8rIYQfLq76yRBCWAoh/GV6HC+mbd8eQvh0COFL6ffXpO0hhPCb6fH9ixDCyWKrH28hhDf1naOXQwgvhhDe7/k7mBDCx0IIXwkhfK6vbc/nbAjh4fTxXwohPFzEZxlH2xzffxlC+GJ6DP8whHBX2l4NIbzUdy5/pO85353+bVlMfwehiM8zbrY5vnv+m2C+2No2x/d3+47tUgjhcto+XudvjPFQfgG3AH8NfCdwO/D/Am8uuq5J+gJeD5xMb38rcBV4M/DLwP+yxePfnB7nI8B8evxvKfpzjPMXsAS8blPbvwA+kN7+APBr6e0Hgf8ABOAB4DNF1z8pX+nfgy8DFc/fgY/l9wEngc/1te3pnAW+HXgq/f6a9PZriv5s4/C1zfH9IeDW9Pav9R3fav/jNr3Of0qPeUh/B+8s+rONw9c2x3dPfxPMF3s7vpvu/1fA/5reHqvz9zD3ML8VWIwxPhVjvA58Anio4JomSozx+Rjjk+nt/wz8FXB3zlMeAj4RY+zGGJ8GFun9HrQ3DwGPpLcfAX6sr/3jsacF3BVCeH0RBU6gtwN/HWPM2yjJ83cXYox/DnxtU/Nez9kfBj4dY/xajPHrwKeBd4y++vG31fGNMf5xjPHl9McWcE/ea6TH+M4YYyv20sfHefV3cqhtc/5uZ7u/CeaLbeQd37SX+CeA38l7jaLO38McmO8Gnun7+Vnyw55yhBCqwP3AZ9Kmn00vD34su/yKx3w/IvDHIYQnQgj1tO1YjPH59PaXgWPpbY/v/r2LjX+kPX+Ha6/nrMd6/36aXo9bZj6EcCmEcCGE8HfStrvpHdOMx3dne/mb4Pm7P38HuBZj/FJf29icv4c5MGtIQgizwL8H3h9jfBH4MHAfcAJ4nt4lFu3Pfx1jPAm8E3hfCOH7+u9M/3XtUjcDCCHcDvwo8H+lTZ6/I+Q5OzohhAXgZSBJm54HyjHG+4GfB/5dCOHOouqbYP5NOBg/ycaOi7E6fw9zYH4OuLfv53vSNu1BCOE2emE5iTH+AUCM8VqM8ZUY4zpwjlcvW3vM9yjG+Fz6/SvAH9I7lteyoRbp96+kD/f47s87gSdjjNfA83dE9nrOeqz3KIRwBvgRoJb+o4R0qMBX09tP0BtX+0Z6x7J/2IbHN8c+/iZ4/u5RCOFW4L8DfjdrG7fz9zAH5s8CbwghzKc9TO8CHi24pomSjjf6KPBXMcZ/3dfeP272vwWy2bCPAu8KIRwJIcwDb6A3cF9bCCF8SwjhW7Pb9Cb2fI7eccxWDXgY+GR6+1HgPenKAw8A3+i7DK7tbejV8Pwdib2es38E/FAI4TXp5e8fStu0hRDCO4BfAH40xtjpa58LIdyS3v5OeufsU+kxfjGE8ED6d/w9vPo70Sb7+Jtgvti7HwC+GGO8MdRi3M7fW0f9BuMqxvhyCOFn6f0RvgX4WIzx8wWXNWm+F3g38JfZMjDAPwF+MoRwgt5l1yXgfwCIMX4+hPB7wBfoXTZ8X4zxlQOvenIcA/4wXS3nVuDfxRj/Ywjhs8DvhRDOAm16kyQAPkVv1YFFoAP81MGXPFnSf4j8IOk5mvoXnr/7F0L4HeA08LoQwrPAB4FfZQ/nbIzxayGEf0YveAD8SoxxtxOxpto2x/eX6K3U8On070UrxvheeisS/EoI4ZvAOvDevuP4j4DfBu6gN+a5f9zzobXN8T29178J5outbXV8Y4wf5eZ5JDBm5687/UmSJEk5DvOQDEmSJGlHBmZJkiQph4FZkiRJymFgliRJknIYmCVJkqQcBmZJkiQph4FZkiRJymFgliRJknL8/8u1bwjbIzPgAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 864x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_best_path(best_route)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Задание 5 (3 балла) \n",
    "\n",
    "Реализуйте генетический алгоритм."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [],
   "source": [
    "def create_route(city_list): # создаём произвольный маршрут - создание \"одного индивида\"\n",
    "    route = random.sample(city_list, len(city_list))\n",
    "    return route\n",
    "\n",
    "def initial_population(population_size, city_list): # создаём популяцию\n",
    "    population = []\n",
    "    for i in range(population_size):\n",
    "        population.append(create_route(city_list))\n",
    "    return population\n",
    "\n",
    "def rank_routes(population): # ранжируем маршруты популяции по величине fitness_route\n",
    "    fitness_ranking = {}\n",
    "    for i in range(len(population)):\n",
    "        fitness_ranking[i] = Fitness(population[i]).route_fitness()\n",
    "    return sorted(fitness_ranking.items(), key=operator.itemgetter(1), reverse=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [],
   "source": [
    "def selection(pop_ranked, elite_size): # селекция\n",
    "    selection_results = []\n",
    "    df = pd.DataFrame(np.array(pop_ranked), columns = ['Index', 'Fitness'])\n",
    "    df['cum_sum'] = df.Fitness.cumsum()\n",
    "    df['cum_percent'] = df.cum_sum*100 / df.Fitness.sum()\n",
    "    for i in range(elite_size):\n",
    "        selection_results.append(pop_ranked[i][0])\n",
    "    for i in range(len(pop_ranked) - elite_size):\n",
    "        pick = 100*random.random()\n",
    "        for j in range(len(pop_ranked)):\n",
    "            if pick <= df.iat[i, 3]:\n",
    "                selection_results.append(pop_ranked[i][0])\n",
    "                break\n",
    "    return selection_results\n",
    "\n",
    "def mating_pool(population, selection_results):\n",
    "    mat_pool = []\n",
    "    for i in range(len(selection_results)):\n",
    "        index = selection_results[i]\n",
    "        mat_pool.append(population[index])\n",
    "    return mat_pool"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [],
   "source": [
    "def breed(parent1, parent2): # ищем ребёнка\n",
    "    child = []\n",
    "    child1 = []\n",
    "    child2 = []\n",
    "    gene_A = int(random.random() * len(parent1))\n",
    "    gene_B = int(random.random() * len(parent2))\n",
    "    start_gene = min(gene_A, gene_B)\n",
    "    end_gene = max(gene_A, gene_B)\n",
    "    for i in range(start_gene, end_gene):\n",
    "        child1.append(parent1[i])\n",
    "    child2 = [item for item in parent2 if item not in child1]\n",
    "    child = child1 + child2\n",
    "    return child\n",
    "\n",
    "def population_breed(mating_pool, elite_size): # ищем детей\n",
    "    children = []\n",
    "    pool = random.sample(mating_pool, len(mating_pool))\n",
    "    for i in range(elite_size):\n",
    "        children.append(mating_pool[i])\n",
    "    for i in range(len(mating_pool) - elite_size):\n",
    "        child = breed(pool[i], pool[len(mating_pool) - i - 1])\n",
    "        children.append(child)\n",
    "    return children"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [],
   "source": [
    "def mutate(individual, mutation_rate): # вносим мутации - замены\n",
    "    for swapped in range(len(individual)):\n",
    "        if random.random() < mutation_rate:\n",
    "            swap_with = int(random.random() * len(individual))\n",
    "            city1 = individual[swapped]\n",
    "            city2 = individual[swap_with]\n",
    "            individual[swapped] = city2\n",
    "            individual[swap_with] = city1\n",
    "    return individual\n",
    "\n",
    "def mutate_population(population, mutation_rate):\n",
    "    mutated_pop = []\n",
    "    for individ in range(len(population)):\n",
    "        mutated_individ = mutate(population[individ], mutation_rate)\n",
    "        mutated_pop.append(mutated_individ)\n",
    "    return mutated_pop"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [],
   "source": [
    "def next_generation(current_generation, elite_size, mutation_rate):\n",
    "    pop_ranked = rank_routes(current_generation)\n",
    "    selection_results = selection(pop_ranked, elite_size)\n",
    "    mat_pool = mating_pool(current_generation, selection_results)\n",
    "    children = population_breed(mat_pool, elite_size)\n",
    "    next_gener = mutate_population(children, mutation_rate)\n",
    "    return next_gener"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [],
   "source": [
    "def genetic_algorithm(population, population_size, elite_size, mutation_rate, num_generations):\n",
    "    pop = initial_population(population_size, population)\n",
    "    progress = []\n",
    "    progress.append(1 / rank_routes(pop)[0][1])\n",
    "    print(f'Начальное расстояние: {1 / rank_routes(pop)[0][1]}')\n",
    "    for i in range(num_generations):\n",
    "        pop = next_generation(pop, elite_size, mutation_rate)\n",
    "        progress.append(1 / rank_routes(pop)[0][1])\n",
    "    print(f'Конечное расстояние: {1 / rank_routes(pop)[0][1]}')\n",
    "    best_route_index = rank_routes(pop)[0][0]\n",
    "    best_route = pop[best_route_index]\n",
    "    plt.figure(figsize=(12, 8))\n",
    "    plt.plot(progress)\n",
    "    plt.ylabel('Расстояние')\n",
    "    plt.xlabel('Поколение')\n",
    "    plt.show()\n",
    "    return best_route"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Начальное расстояние: 29420.0\n",
      "Конечное расстояние: 31410.0\n"
     ]
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 864x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "best_route = genetic_algorithm(population=city_list, population_size=100, elite_size=20, mutation_rate=0.01, num_generations=500)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_best_path(best_route)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}