{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Make sure you run this at the begining**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import sys\n",
    "import math\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# Append template path to sys path\n",
    "sys.path.append(os.getcwd() + \"/template\") "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from utils.load_data import load_data\n",
    "from utils.load_data import log\n",
    "from utils.visualize_tsp import plotTSP\n",
    "from utils.tsp import TSP\n",
    "from utils.tsp import TSP_Bench\n",
    "from utils.tsp import TSP_Bench_ALL"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Workshop Starts Here"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<img src=\"images/tsp.jpg\" alt=\"TSP\" style=\"width: 900px;\"/>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<img src=\"images/solutions.png\" alt=\"solutions\" style=\"width: 900px;\"/>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Get familiar with your dataset"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There are problems at different levels. **3 simple, 2 medium, 1 hard**."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "for root, _, files in os.walk('./template/data'):\n",
    "    if(files):\n",
    "        for f in files:\n",
    "            print(str(root) + \"/\" + f)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "ulysses16 = np.array(load_data(\"./template/data/simple/ulysses16.tsp\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "ulysses16[:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "plt.scatter(ulysses16[:, 0], ulysses16[:, 1])\n",
    "for i in range(0, 16):\n",
    "    plt.annotate(i, (ulysses16[i, 0], ulysses16[i, 1]+0.5))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Naive Solution: In Order"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "simple_sequence = list(range(0, 16))\n",
    "print(simple_sequence)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "plotTSP([simple_sequence], ulysses16, num_iters=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Naive Solution: Random Permutation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "random_permutation = np.random.permutation(16).tolist()\n",
    "print(random_permutation)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "plotTSP([random_permutation], ulysses16, num_iters=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Best Solution"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "best_ulysses16 = [0, 13, 12, 11, 6, 5, 14, 4, 10, 8, 9, 15, 2, 1, 3, 7]\n",
    "plotTSP([best_ulysses16], ulysses16, num_iters=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Calculate Fitness (Sum of all Distances)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "def dist(node_0, node_1, coords):\n",
    "        \"\"\"\n",
    "        Euclidean distance between two nodes.\n",
    "        \"\"\"\n",
    "        coord_0, coord_1 = coords[node_0], coords[node_1]\n",
    "        return math.sqrt((coord_0[0] - coord_1[0]) ** 2 + (coord_0[1] - coord_1[1]) ** 2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "print(\"Coordinate of City 0:\", ulysses16[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "print(\"Coordinate of City 1:\", ulysses16[1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "print(\"Distance Between\", dist(0, 1, ulysses16))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "def fitness(solution, coords):\n",
    "    N = len(coords)\n",
    "    cur_fit = 0\n",
    "    if(len(solution) != N):\n",
    "        return math.inf\n",
    "    for i in range(len(solution)):\n",
    "        cur_fit += dist(solution[i % N], solution[(i + 1) % N], coords)\n",
    "    return cur_fit"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "print (\"Order Fitness:\\t\", fitness(simple_sequence, ulysses16))\n",
    "print (\"Random Fitness:\\t\", fitness(random_permutation, ulysses16))\n",
    "print (\"Best Fitness:\\t\", fitness(best_ulysses16, ulysses16))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Naive Random Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import math\n",
    "import random\n",
    "import numpy as np\n",
    "from model.base_model import Model\n",
    "\n",
    "class MyRandomModel(Model):\n",
    "    def __init__(self):\n",
    "        super().__init__()\n",
    "\n",
    "    def init(self, nodes):\n",
    "        \"\"\"\n",
    "        Put your initialization here.\n",
    "        \"\"\"\n",
    "        super().init(nodes)\n",
    "\n",
    "    def fit(self, max_it):\n",
    "        \"\"\"\n",
    "        Put your iteration process here.\n",
    "        \"\"\"\n",
    "        random_solutions = []\n",
    "        for i in range(0, max_it):\n",
    "            solution = np.random.permutation(self.N).tolist()\n",
    "            random_solutions.append(solution)\n",
    "            self.fitness_list.append(self.fitness(solution))\n",
    "        self.best_solution = random_solutions[self.fitness_list.index(min(self.fitness_list))]\n",
    "        return self.best_solution, self.fitness_list"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "tsp_file = './template/data/simple/ulysses16.tsp'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "best_solution, fitness_list, time = TSP_Bench(tsp_file, MyRandomModel, max_it=100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.plot(fitness_list, 'o-')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Uniform Cost Search"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import math\n",
    "import random\n",
    "from model.base_model import Model\n",
    "\n",
    "class MyUCSModel(Model):\n",
    "    def __init__(self):\n",
    "        super().__init__()\n",
    "\n",
    "    def init(self, nodes):\n",
    "        \"\"\"\n",
    "        Put your initialization here.\n",
    "        \"\"\"\n",
    "        super().init(nodes)\n",
    "\n",
    "    def getMST(self, node):\n",
    "        MST = []\n",
    "        distances = []\n",
    "        for i in range(0, self.N):\n",
    "            if i != node:\n",
    "                MST.append(i)\n",
    "                distances.append(self.dist(node, i))\n",
    "        return [x for _,x in sorted(zip(distances, MST))]\n",
    "\n",
    "    def fit(self, max_it):\n",
    "        \"\"\"\n",
    "        Put your iteration process here.\n",
    "        \"\"\"\n",
    "\n",
    "        UCS_solutions = []\n",
    "        UCS_losses = []\n",
    "    \n",
    "        for i in range(0, self.N):\n",
    "            solution = [i]\n",
    "            UCS_solutions.append(solution)\n",
    "            UCS_losses.append(math.inf)\n",
    "    \n",
    "        MSTs = []\n",
    "        for i in range(0, self.N):\n",
    "            MSTs.append([-1] * self.N)\n",
    "\n",
    "        # Breadth First: Set each city as starting point, then go to next city simultaneously\n",
    "        min_loss = math.inf\n",
    "        while(len(UCS_solutions[ UCS_losses.index(min(UCS_losses)) ]) != self.N):\n",
    "            unvisited_list = list(range(0, self.N))\n",
    "            min_loss = min(UCS_losses)\n",
    "            # For each search path\n",
    "            for i in range(0, self.N):\n",
    "                if UCS_losses[i] == min_loss:\n",
    "                    cur_city = UCS_solutions[i][-1]\n",
    "                    unvisited_list = list( set(range(0, self.N)) - set(UCS_solutions[i]) )\n",
    "                    if MSTs[cur_city][0] == -1:\n",
    "                        MST = self.getMST(cur_city)\n",
    "                        MSTs[cur_city] = MST\n",
    "\n",
    "                    for j in MSTs[cur_city]:\n",
    "                        if(j in unvisited_list):\n",
    "                            UCS_solutions[i].append(j)\n",
    "\n",
    "                            N = len(UCS_solutions[i])\n",
    "                            cur_fit = 0\n",
    "                            for k in range(len(UCS_solutions[i])):\n",
    "                                coord_0, coord_1 = self.coords[UCS_solutions[i][k % N]], self.coords[UCS_solutions[i][(k + 1) % N]]\n",
    "                                cur_fit += math.sqrt((coord_0[0] - coord_1[0]) ** 2 + (coord_0[1] - coord_1[1]) ** 2)\n",
    "                            UCS_losses[i] = cur_fit\n",
    "#                             if(UCS_losses[i] < min_loss):\n",
    "#                                 min_loss = UCS_losses[i]\n",
    "                            break\n",
    "        self.best_solution = UCS_solutions[ UCS_losses.index(min(UCS_losses)) ]\n",
    "        self.fitness_list.append(self.fitness(self.best_solution))\n",
    "\n",
    "        return self.best_solution, self.fitness_list"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "tsp_file = './template/data/simple/ulysses16.tsp'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "best_solution, fitness_list, time = TSP_Bench(tsp_file, MyUCSModel)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## A star"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Heuristic"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import math\n",
    "import random\n",
    "from model.base_model import Model\n",
    "\n",
    "class MyASModel(Model):\n",
    "    def __init__(self):\n",
    "        super().__init__()\n",
    "\n",
    "    def init(self, nodes):\n",
    "        \"\"\"\n",
    "        Put your initialization here.\n",
    "        \"\"\"\n",
    "        super().init(nodes)\n",
    "\n",
    "    def getMST(self, node):\n",
    "        MST = []\n",
    "        distances = []\n",
    "        for i in range(0, self.N):\n",
    "            if i != node:\n",
    "                MST.append(i)\n",
    "                distances.append(self.dist(node, i))\n",
    "        return [x for _,x in sorted(zip(distances, MST))]\n",
    "\n",
    "    def fit(self, max_it):\n",
    "        \"\"\"\n",
    "        Put your iteration process here.\n",
    "        \"\"\"\n",
    "\n",
    "        MST_solutions = []\n",
    "    \n",
    "        for i in range(0, self.N):\n",
    "            solution = [i]\n",
    "            MST_solutions.append(solution)\n",
    "    \n",
    "        # Breadth First: Set each city as starting point, then go to next city simultaneously\n",
    "        for step in range(0, self.N - 1):\n",
    "#             print(\"[step]\", step)\n",
    "            unvisited_list = list(range(0, self.N))\n",
    "            # For each search path\n",
    "            for i in range(0, self.N):\n",
    "                cur_city = MST_solutions[i][-1]\n",
    "                unvisited_list = list( set(range(0, self.N)) - set(MST_solutions[i]) )\n",
    "                closest_neighbour = -1\n",
    "                min_f = math.inf\n",
    "                    \n",
    "                for j in unvisited_list:\n",
    "                    g = self.dist(cur_city, j)\n",
    "                    sub_unvisited_list = unvisited_list.copy()\n",
    "                    sub_unvisited_list.remove(j)\n",
    "\n",
    "                    sub_cur_city = self.getMST(j)[0]\n",
    "                    h = 0\n",
    "                    while len(sub_unvisited_list) > 0:\n",
    "                        if(len(unvisited_list) == 2):\n",
    "                            break\n",
    "                        else:\n",
    "                            for k in self.getMST(sub_cur_city):\n",
    "                                if k in sub_unvisited_list:\n",
    "                                    h = h + self.dist(sub_cur_city, k)\n",
    "                                    sub_cur_city = k\n",
    "                                    sub_unvisited_list.remove(k)\n",
    "                                    break\n",
    "                    # Get f(x) = g(x) + h(x)\n",
    "\n",
    "                    f = g + h\n",
    "                    if(f < min_f):\n",
    "                        closest_neighbour = j\n",
    "                        min_f = f\n",
    "                MST_solutions[i].append(closest_neighbour)\n",
    "\n",
    "        for i in range(0, self.N):\n",
    "            self.fitness_list.append(self.fitness(MST_solutions[i]))\n",
    " \n",
    "        self.best_solution = MST_solutions[ self.fitness_list.index(min(self.fitness_list)) ]\n",
    "\n",
    "        return self.best_solution, self.fitness_list"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "tsp_file = './template/data/simple/ulysses16.tsp'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "best_solution, fitness_list, time = TSP_Bench(tsp_file, MyASModel)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "plt.plot(fitness_list, 'o-')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Hill Climbing"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Heuristic Iteration"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import math\n",
    "import random\n",
    "import numpy as np\n",
    "from model.base_model import Model\n",
    "\n",
    "class MyHillClimbModel(Model):\n",
    "\n",
    "    def __init__(self):\n",
    "        super().__init__()\n",
    "\n",
    "    def init(self, nodes):\n",
    "        \"\"\"\n",
    "        Put your initialization here.\n",
    "        \"\"\"\n",
    "        super().init(nodes)\n",
    "\n",
    "    def random_tour(self):\n",
    "        return np.random.permutation(self.N).tolist()\n",
    "\n",
    "    def all_pairs(self, size, shuffle=random.shuffle):\n",
    "        r1 = list(range(size))\n",
    "        r2 = list(range(size))\n",
    "        if shuffle:\n",
    "            shuffle(r1)\n",
    "            shuffle(r2)\n",
    "        for i in r1:\n",
    "            for j in r2:\n",
    "                yield (i,j)\n",
    "\n",
    "    def move_operator(self, tour):\n",
    "        '''generator to return all possible variations\n",
    "          where the section between two cities are swapped'''\n",
    "        for i,j in self.all_pairs(len(tour)):\n",
    "            if i != j:\n",
    "                copy=tour[:]\n",
    "                if i < j:\n",
    "                    copy[i:j+1]=reversed(tour[i:j+1])\n",
    "                else:\n",
    "                    copy[i+1:]=reversed(tour[:j])\n",
    "                    copy[:j]=reversed(tour[i+1:])\n",
    "                if copy != tour: # no point returning the same tour\n",
    "                    yield copy\n",
    "\n",
    "    def fit(self, max_it=100):\n",
    "        \"\"\"\n",
    "        Put your iteration process here.\n",
    "        \"\"\"\n",
    "\n",
    "        self.best_solution = self.random_tour()\n",
    "        best_score = -self.fitness(self.best_solution)\n",
    "\n",
    "        num_evaluations = 0\n",
    "\n",
    "        while num_evaluations < max_it:\n",
    "            # examine moves around our current position\n",
    "            move_made = False\n",
    "            for next_solution in self.move_operator(self.best_solution):\n",
    "                if num_evaluations >= max_it:\n",
    "                    print(\"Max iteration reached:\", max_it)\n",
    "                    break\n",
    "\n",
    "                # see if this move is better than the current\n",
    "                next_score = -self.fitness(next_solution)\n",
    "                num_evaluations += 1\n",
    "                if next_score > best_score:\n",
    "                    self.best_solution = next_solution\n",
    "                    self.fitness_list.append(self.fitness(self.best_solution))\n",
    "                    best_score=next_score\n",
    "                    move_made=True\n",
    "                    break # depth first search\n",
    "\n",
    "            if not move_made:\n",
    "                break # we couldn't find a better move (must be at a local maximum)\n",
    "\n",
    "        return self.best_solution, self.fitness_list"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "tsp_file = './template/data/simple/ulysses16.tsp'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "best_solution, fitness_list, time = TSP_Bench(tsp_file, MyHillClimbModel)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.plot(fitness_list)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Your Smart Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import math\n",
    "import random\n",
    "from model.base_model import Model\n",
    "import numpy as np\n",
    "\n",
    "class MyModel(Model):\n",
    "    def __init__(self):\n",
    "        super().__init__()\n",
    "\n",
    "    def init(self, nodes):\n",
    "        \"\"\"\n",
    "        Put your initialization here.\n",
    "        \"\"\"\n",
    "        super().init(nodes)\n",
    "        self.log(\"Nothing to initialize in your model now\")\n",
    "\n",
    "    def fit(self, max_it):\n",
    "        \"\"\"\n",
    "        Put your iteration process here.\n",
    "        \"\"\"\n",
    "        self.best_solution = np.random.permutation(self.N).tolist()\n",
    "        self.fitness_list.append(self.fitness(self.best_solution))\n",
    "\n",
    "        return self.best_solution, self.fitness_list"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Test your Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "tsp_problem = './template/data/simple/ulysses16.tsp'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "best_solution, fitness_list, time = TSP_Bench(tsp_file, MyModel)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Test All Dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "for root, _, files in os.walk('./template/data'):\n",
    "    if(files):\n",
    "        for f in files:\n",
    "            print(str(root) + \"/\" + f)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "tsp_path = './template/data'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot_results(best_solutions, times, title):\n",
    "    fig = plt.figure()\n",
    "    nodes = [len(s) for s in best_solutions]\n",
    "    data = np.array([[node, time] for node, time in sorted(zip(nodes, times))])\n",
    "    plt.plot(data[:, 0], data[:, 1], 'o-')\n",
    "    fig.suptitle(title, fontsize=20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "print(\"Random Search\")\n",
    "best_solutions, fitness_lists, times = TSP_Bench_ALL(tsp_path, MyRandomModel)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "plot_results(best_solutions, times, \"Random Model\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "print(\"Uniform Cost Search\")\n",
    "best_solutions, fitness_lists, times = TSP_Bench_ALL(tsp_path, MyUCSModel)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "plot_results(best_solutions, times, \"Uniform Cost Search\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "print(\"A Star Search\")\n",
    "best_solutions, fitness_lists, times = TSP_Bench_ALL(tsp_path, MyASModel)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "plot_results(best_solutions, times, \"A Star Search\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "print(\"Hill-Climbing Search\")\n",
    "best_solutions, fitness_lists, times = TSP_Bench_ALL(tsp_path, MyHillClimbModel)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "plot_results(best_solutions, times, \"Hill-Climbing Search\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Conclusions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Simple\n",
    "# ulysses16:  77.12 (UCS-BFS),     77.02 (A-Star)\n",
    "# att48:      39236 (UCS-BFS),     47853 (A-Star)\n",
    "# st70:         761 (UCS-BFS),  time-out (A-Star)\n",
    "\n",
    "# Medium\n",
    "# a280:       3088 (UCS-BFS),   time-out (A-Star)\n",
    "# pcb442:    58952 (UCS-BFS),   time-out (A-Star)\n",
    "\n",
    "# Hard\n",
    "# dsj1000: time-out (UCS-BFS), 542,620,572 (Hill-Climbing)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<p style=\"font-size: 18px\"> 1. UCS is the slowest one, and gets the same result as BFS, DFS, DP </p>\n",
    "<p style=\"font-size: 18px\"> 1. A-Star can only solve problems with number of cities < 50. </p>\n",
    "<p style=\"font-size: 18px\"> 2. Hill-Climbing gets different results every time (Heuristic). </p>\n",
    "<p style=\"font-size: 18px\"> 3. Hill-Climbing is the fastest one till now. (faster than Dynamic Programming, but worse results). </p>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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