Códigos de implementação das buscas supervisionadas e não supervisionadas no Trabalho de MC906.

proj1-busca-nao-supervisionada.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Buscas não supervisionadas"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Imports"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 211,
   "metadata": {},
   "outputs": [],
   "source": [
    "# imports necessarios\n",
    "from search import *\n",
    "from notebook import psource, heatmap, gaussian_kernel, show_map, final_path_colors, display_visual, plot_NQueens\n",
    "import networkx as nx\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from matplotlib.ticker import MultipleLocator\n",
    "import time\n",
    "from statistics import mean, stdev\n",
    "from math import sqrt\n",
    "from memory_profiler import memory_usage\n",
    "\n",
    "# Needed to hide warnings in the matplotlib sections\n",
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Criação do mapa e do grafo"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 212,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "# make the dict where the key is associated with his neighbors\n",
    "mapa = {}\n",
    "for i in range(0,60):\n",
    "    for j in range(0,60):\n",
    "        mapa[(i,j)] = {(i+1,j):1, (i-1,j):1, (i,j+1):1, (i,j-1):1}\n",
    "        \n",
    "grafo = UndirectedGraph(mapa)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Modelagem da classe problema"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 213,
   "metadata": {},
   "outputs": [],
   "source": [
    "class RobotProblem(Problem):\n",
    "\n",
    "    \"\"\"Problema para encontrar o goal saindo de uma posicao (x,y) com um robo.\"\"\"\n",
    "\n",
    "    def __init__(self, initial, goal, mapa, graph):\n",
    "        Problem.__init__(self, initial, goal)\n",
    "        self.mapa = mapa\n",
    "        self.graph = graph\n",
    "\n",
    "    def actions(self, actual_pos):\n",
    "        \"\"\"The actions at a graph node are just its neighbors.\"\"\"\n",
    "        neighbors = list(self.graph.get(actual_pos).keys())\n",
    "        valid_actions = []\n",
    "        for act in neighbors:\n",
    "            if act[0] == 0 or act[0] == 60 or act[1] == 0 or act[1] == 60:\n",
    "                i = 1\n",
    "            elif (act[0] == 20 and (0<= act[1] <= 40)):\n",
    "                i = 2\n",
    "            elif (act[0] == 40 and (20<= act[1] <= 60)):\n",
    "                i = 3\n",
    "            else:\n",
    "                valid_actions.append(act)\n",
    "                \n",
    "        return valid_actions\n",
    "\n",
    "    def result(self, state, action):\n",
    "        \"\"\"The result of going to a neighbor is just that neighbor.\"\"\"\n",
    "        return action\n",
    "\n",
    "    def path_cost(self, cost_so_far, state1, action, state2):\n",
    "        return cost_so_far + 1\n",
    "\n",
    "    def goal_test(self, state):\n",
    "        if state[0] == self.goal[0] and state[1] == self.goal[1]:\n",
    "            return True\n",
    "        else:\n",
    "            return False"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Busca nao supervisionada: DFS"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Calculo da memoria usada"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 214,
   "metadata": {},
   "outputs": [],
   "source": [
    "def calc_memory_dfs():\n",
    "    init_pos = (10,10)\n",
    "    goal_pos = (50,50)\n",
    "\n",
    "    robot_problem = RobotProblem(init_pos, goal_pos, mapa, grafo)\n",
    "    node = depth_first_graph_search(robot_problem)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 215,
   "metadata": {},
   "outputs": [],
   "source": [
    "mem_usage = memory_usage(calc_memory_dfs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 216,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Memória usada (em intervalos de .1 segundos): [31.50390625, 31.8671875, 32.0, 32.125, 32.2109375, 32.39453125, 32.45703125, 32.51171875, 32.5625, 32.6171875, 32.6328125, 32.546875]\n",
      "Maximo de memoria usada: 32.6328125\n"
     ]
    }
   ],
   "source": [
    "print('Memória usada (em intervalos de .1 segundos): %s' % mem_usage)\n",
    "print('Maximo de memoria usada: %s' % max(mem_usage))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Calculo do custo da busca e o caminho percorrido"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 198,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Custo da busca DFS: 1084\n"
     ]
    }
   ],
   "source": [
    "init_pos = (10,10)\n",
    "goal_pos = (50,50)\n",
    "\n",
    "robot_problem = RobotProblem(init_pos, goal_pos, mapa, grafo)\n",
    "node = depth_first_graph_search(robot_problem)\n",
    "print(\"Custo da busca DFS: \" + str(node.path_cost))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 199,
   "metadata": {},
   "outputs": [],
   "source": [
    "list_nodes = []\n",
    "for n in node.path():\n",
    "    list_nodes.append(n.state)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 200,
   "metadata": {},
   "outputs": [],
   "source": [
    "x = []\n",
    "y = []\n",
    "for nod in list_nodes:\n",
    "    x.append(nod[0])\n",
    "    y.append(nod[1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 201,
   "metadata": {},
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure()\n",
    "plt.xlim(0,60)\n",
    "plt.ylim(0,60)\n",
    "plt.title('Caminho percorrido pelo robo na busca DFS')\n",
    "plt.annotate(\"\",\n",
    "              xy=(0,0), xycoords='data',\n",
    "              xytext=(0, 60), textcoords='data',\n",
    "              arrowprops=dict(arrowstyle=\"-\",\n",
    "                              edgecolor = \"black\",\n",
    "                              linewidth=5,\n",
    "                              alpha=0.65,\n",
    "                              connectionstyle=\"arc3,rad=0.\"),\n",
    "              )\n",
    "plt.annotate(\"\",\n",
    "              xy=(0,0), xycoords='data',\n",
    "              xytext=(60, 0), textcoords='data',\n",
    "              arrowprops=dict(arrowstyle=\"-\",\n",
    "                              edgecolor = \"black\",\n",
    "                              linewidth=5,\n",
    "                              alpha=0.65,\n",
    "                              connectionstyle=\"arc3,rad=0.\"),\n",
    "              )\n",
    "\n",
    "plt.annotate(\"\",\n",
    "              xy=(60,0), xycoords='data',\n",
    "              xytext=(60, 60), textcoords='data',\n",
    "              arrowprops=dict(arrowstyle=\"-\",\n",
    "                              edgecolor = \"black\",\n",
    "                              linewidth=5,\n",
    "                              alpha=0.65,\n",
    "                              connectionstyle=\"arc3,rad=0.\"),\n",
    "              )\n",
    "\n",
    "plt.annotate(\"\",\n",
    "              xy=(0,60), xycoords='data',\n",
    "              xytext=(60, 60), textcoords='data',\n",
    "              arrowprops=dict(arrowstyle=\"-\",\n",
    "                              edgecolor = \"black\",\n",
    "                              linewidth=5,\n",
    "                              alpha=0.65,\n",
    "                              connectionstyle=\"arc3,rad=0.\"),\n",
    "              )\n",
    "plt.annotate(\"\",\n",
    "              xy=(40,20), xycoords='data',\n",
    "              xytext=(40, 60), textcoords='data',\n",
    "              arrowprops=dict(arrowstyle=\"-\",\n",
    "                              edgecolor = \"black\",\n",
    "                              linewidth=5,\n",
    "                              alpha=0.65,\n",
    "                              connectionstyle=\"arc3,rad=0.\"),\n",
    "              )\n",
    "plt.annotate(\"\",\n",
    "              xy=(20,0), xycoords='data',\n",
    "              xytext=(20, 40), textcoords='data',\n",
    "              arrowprops=dict(arrowstyle=\"-\",\n",
    "                              edgecolor = \"black\",\n",
    "                              linewidth=5,\n",
    "                              alpha=0.65,\n",
    "                              connectionstyle=\"arc3,rad=0.\"),\n",
    "              )\n",
    "plt.scatter(x,y)\n",
    "plt.scatter(10,10,color='r')\n",
    "plt.scatter(50,50,color='r')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Calculo do tempo gasto pela DFS com inicio em (10,10) e fim em (50,50)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "init_pos = (10,10)\n",
    "goal_pos = (50,50)\n",
    "\n",
    "robot_problem = RobotProblem(init_pos, goal_pos, mapa, grafo)\n",
    "\n",
    "times = []\n",
    "for i in range(0,1000):\n",
    "    start = time.time()\n",
    "    node = depth_first_graph_search(robot_problem)\n",
    "    end = time.time()\n",
    "    times.append(end - start)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Media do tempo gasto para a busca DFS: 1.009512909412384\n",
      "Desvio padrao do tempo gasto para a busca DFS: 0.14409295994680466\n",
      "Intervalo de confiança para a busca DFS: (1.0005819352271286,1.0184438835976395)\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "media_dfs = mean(times)\n",
    "desvio_dfs = stdev(times)\n",
    "intervalo_conf = '(' + str( media_dfs - 1.96 * (desvio_dfs / (len(times)) ** (1/2)) ) + ',' + str( media_dfs + 1.96 * (desvio_dfs / (len(times)) ** (1/2)) ) + ')'\n",
    "print(\"Media do tempo gasto para a busca DFS: \" + str(media_dfs))\n",
    "print(\"Desvio padrao do tempo gasto para a busca DFS: \" + str(desvio_dfs))\n",
    "print(\"Intervalo de confiança para a busca DFS: \" + intervalo_conf)\n",
    "fig = plt.figure()\n",
    "plt.hist(times,bins=50)\n",
    "plt.title('Histograma para o tempo de execucao da DFS')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Projecao da relacao entre distancia em linha reta e tempo para a DFS"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [],
   "source": [
    "goal_pos = (50,50)\n",
    "x = []\n",
    "y = []\n",
    "for i in range(5,50):\n",
    "    for j in range(5,50):\n",
    "        if i != 20 and i != 40:\n",
    "            init_pos = (i,i)\n",
    "            distancia_linha_reta = sqrt( (goal_pos[0] - init_pos[0]) ** 2 + (goal_pos[1] - init_pos[1]) ** 2)\n",
    "            robot_problem = RobotProblem(init_pos, goal_pos, mapa, grafo)\n",
    "            start = time.time()\n",
    "            node = depth_first_graph_search(robot_problem)\n",
    "            end = time.time()\n",
    "            x.append(distancia_linha_reta)\n",
    "            y.append(end - start)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure()\n",
    "plt.scatter(x,y)\n",
    "plt.ylim(0.2, 2)\n",
    "plt.title(\"Distancia em linha reta x Tempo DFS\")\n",
    "plt.xlabel(\"Distancia em linha reta entre os pontos inicial e final\")\n",
    "plt.ylabel(\"Tempo da busca DFS\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Busca nao supervisionada: BFS"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Calculo da memoria usada"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 219,
   "metadata": {},
   "outputs": [],
   "source": [
    "def calc_memory_bfs():\n",
    "    init_pos = (10,10)\n",
    "    goal_pos = (50,50)\n",
    "\n",
    "    robot_problem = RobotProblem(init_pos, goal_pos, mapa, grafo)\n",
    "    node = breadth_first_graph_search(robot_problem)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 220,
   "metadata": {},
   "outputs": [],
   "source": [
    "mem_usage = memory_usage(calc_memory_bfs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 221,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Memória usada (em intervalos de .1 segundos): [32.92578125, 32.92578125, 32.92578125, 32.92578125, 32.92578125, 32.92578125, 32.92578125, 32.92578125, 32.92578125]\n",
      "Maximo de memoria usada: 32.92578125\n"
     ]
    }
   ],
   "source": [
    "print('Memória usada (em intervalos de .1 segundos): %s' % mem_usage)\n",
    "print('Maximo de memoria usada: %s' % max(mem_usage))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Calculo do custo da busca e o caminho percorrido"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 203,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Custo da busca BFS: 124\n"
     ]
    }
   ],
   "source": [
    "init_pos = (10,10)\n",
    "goal_pos = (50,50)\n",
    "\n",
    "robot_problem = RobotProblem(init_pos, goal_pos, mapa, grafo)\n",
    "node = breadth_first_graph_search(robot_problem)\n",
    "print(\"Custo da busca BFS: \" + str(node.path_cost))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 205,
   "metadata": {},
   "outputs": [],
   "source": [
    "list_nodes = []\n",
    "for n in node.path():\n",
    "    list_nodes.append(n.state)\n",
    "    \n",
    "x = []\n",
    "y = []\n",
    "for nod in list_nodes:\n",
    "    x.append(nod[0])\n",
    "    y.append(nod[1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 207,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure()\n",
    "plt.xlim(0,60)\n",
    "plt.ylim(0,60)\n",
    "plt.title('Caminho percorrido pelo robo na busca BFS')\n",
    "plt.annotate(\"\",\n",
    "              xy=(0,0), xycoords='data',\n",
    "              xytext=(0, 60), textcoords='data',\n",
    "              arrowprops=dict(arrowstyle=\"-\",\n",
    "                              edgecolor = \"black\",\n",
    "                              linewidth=5,\n",
    "                              alpha=0.65,\n",
    "                              connectionstyle=\"arc3,rad=0.\"),\n",
    "              )\n",
    "plt.annotate(\"\",\n",
    "              xy=(0,0), xycoords='data',\n",
    "              xytext=(60, 0), textcoords='data',\n",
    "              arrowprops=dict(arrowstyle=\"-\",\n",
    "                              edgecolor = \"black\",\n",
    "                              linewidth=5,\n",
    "                              alpha=0.65,\n",
    "                              connectionstyle=\"arc3,rad=0.\"),\n",
    "              )\n",
    "\n",
    "plt.annotate(\"\",\n",
    "              xy=(60,0), xycoords='data',\n",
    "              xytext=(60, 60), textcoords='data',\n",
    "              arrowprops=dict(arrowstyle=\"-\",\n",
    "                              edgecolor = \"black\",\n",
    "                              linewidth=5,\n",
    "                              alpha=0.65,\n",
    "                              connectionstyle=\"arc3,rad=0.\"),\n",
    "              )\n",
    "\n",
    "plt.annotate(\"\",\n",
    "              xy=(0,60), xycoords='data',\n",
    "              xytext=(60, 60), textcoords='data',\n",
    "              arrowprops=dict(arrowstyle=\"-\",\n",
    "                              edgecolor = \"black\",\n",
    "                              linewidth=5,\n",
    "                              alpha=0.65,\n",
    "                              connectionstyle=\"arc3,rad=0.\"),\n",
    "              )\n",
    "plt.annotate(\"\",\n",
    "              xy=(40,20), xycoords='data',\n",
    "              xytext=(40, 60), textcoords='data',\n",
    "              arrowprops=dict(arrowstyle=\"-\",\n",
    "                              edgecolor = \"black\",\n",
    "                              linewidth=5,\n",
    "                              alpha=0.65,\n",
    "                              connectionstyle=\"arc3,rad=0.\"),\n",
    "              )\n",
    "plt.annotate(\"\",\n",
    "              xy=(20,0), xycoords='data',\n",
    "              xytext=(20, 40), textcoords='data',\n",
    "              arrowprops=dict(arrowstyle=\"-\",\n",
    "                              edgecolor = \"black\",\n",
    "                              linewidth=5,\n",
    "                              alpha=0.65,\n",
    "                              connectionstyle=\"arc3,rad=0.\"),\n",
    "              )\n",
    "plt.scatter(x,y)\n",
    "plt.scatter(10,10,color='r')\n",
    "plt.scatter(50,50,color='r')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Calculo do tempo gasto pela BFS com inicio em (10,10) e fim em (50,50)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [],
   "source": [
    "init_pos = (10,10)\n",
    "goal_pos = (50,50)\n",
    "\n",
    "robot_problem = RobotProblem(init_pos, goal_pos, mapa, grafo)\n",
    "\n",
    "times = []\n",
    "for i in range(0,1000):\n",
    "    start = time.time()\n",
    "    node = breadth_first_graph_search(robot_problem)\n",
    "    end = time.time()\n",
    "    times.append(end - start)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Media do tempo gasto para a busca BFS: 0.05700565814971924\n",
      "Desvio padrao do tempo gasto para a busca BFS: 0.009455976054136637\n",
      "Intervalo de confiança para a busca BFS: (0.056419570681830004,0.05759174561760847)\n"
     ]
    },
    {
     "data": {
      "image/png": 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NM2qyLJ61IOK8iJgdEbN7e3trVdbMzIavTtCvAlZFxHV5fiEp+O8dGJLJf++rlJ9Z2X4GsLo91TUzs+FqGfQRcQ+wUtLz86I5wG3AImBeXjYP+HaeXgQcm799cyDw0MAQj5mZjb2emuX+DviKpPHACuA40pvEJZKOB+4CjshlLwcOAZYDj+WyZmbWJbWCPiJuAmY3WTWnSdkAThplvczMrE38L2PNzArnoDczK5yD3syscA56M7PCOejNzArnoDczK5yD3syscA56M7PCOejNzArnoDczK5yD3syscA56M7PCOejNzArnoDczK5yD3syscA56M7PCOejNzArnoDczK5yD3syscA56M7PCOejNzArnoDczK5yD3syscA56M7PCOejNzArnoDczK5yD3syscA56M7PCOejNzArnoDczK5yD3syscA56M7PCOejNzArnoDczK1ztoJc0TtKNkr6T5/eQdJ2kZZIuljQ+L986zy/P6/s6U3UzM6tjOD369wBLK/NnAmdHxCxgLXB8Xn48sDYingecncuZmVmX1Ap6STOAQ4HP53kBrwEW5iIXAofl6bl5nrx+Ti5vZmZdULdH/wngVOCpPL8jsC4iNuT5VcD0PD0dWAmQ1z+Uy29E0gmSFkta3N/fP8Lqm5lZKy2DXtIbgPsi4obq4iZFo8a6ZxZEnBcRsyNidm9vb63KmpnZ8PXUKPNy4E2SDgEmANuTeviTJfXkXvsMYHUuvwqYCayS1APsADzY9pqbmVktLXv0EfGBiJgREX3AkcCPIuJo4MfA4bnYPODbeXpRniev/1FEPKtHb2ZmY2M036N/P/A+SctJY/Dn5+XnAzvm5e8DFoyuimZmNhp1hm6eFhFXA1fn6RXA/k3KPA4c0Ya6mZlZG/hfxpqZFc5Bb2ZWOAe9mVnhHPRmZoVz0JuZFc5Bb2ZWOAe9mVnhHPRmZoVz0JuZFc5Bb2ZWOAe9mVnhHPRmZoVz0JuZFc5Bb2ZWOAe9mVnhHPRmZoVz0JuZFc5Bb2ZWOAe9mVnhHPRmZoVz0JuZFc5Bb2ZWOAe9mVnhHPRmZoVz0JuZFc5Bb2ZWOAe9mVnhHPRmZoVz0JuZFc5Bb2ZWOAe9mVnhHPRmZoVz0JuZFc5Bb2ZWOAe9mVnhWga9pJmSfixpqaQlkt6Tl0+VdKWkZfnvlLxckj4labmkWyTt2+lGmJnZ4Or06DcAp0TEC4EDgZMk7Q0sAK6KiFnAVXke4GBgVn6cAJzb9lqbmVltLYM+ItZExK/y9MPAUmA6MBe4MBe7EDgsT88FLorkWmCypF3bXnMzM6tlWGP0kvqAPwGuA3aJiDWQ3gyAnXOx6cDKymar8rLGfZ0gabGkxf39/cOvuZmZ1VI76CVtC3wTODki1g9VtMmyeNaCiPMiYnZEzO7t7a1bDTMzG6ZaQS9pK1LIfyUiLs2L7x0Yksl/78vLVwEzK5vPAFa3p7pmZjZcdb51I+B8YGlE/Gdl1SJgXp6eB3y7svzY/O2bA4GHBoZ4zMxs7PXUKPNy4BjgVkk35WX/CJwBXCLpeOAu4Ii87nLgEGA58BhwXFtrbGZmw9Iy6CPiZzQfdweY06R8ACeNsl5mZtYm/pexZmaFc9CbmRXOQW9mVjgHvZlZ4Rz0ZmaFc9CbmRXOQW9mVjgHvZlZ4Rz0ZmaFc9CbmRXOQW9mVjgHvZlZ4Rz0ZmaFc9CbmRXOQW9mVjgHvZlZ4Rz0ZmaFc9CbmRXOQW9mVjgHvZlZ4Rz0ZmaFc9CbmRXOQW9mVjgHvZlZ4Rz0ZmaFc9CbmRXOQW9mVjgHvZlZ4Rz0ZmaFc9CbmRXOQW9mVriebldgc9a34LtdOe4dZxzaleOa2ebJPXozs8I56M3MCuegNzMrXEeCXtLrJf1G0nJJCzpxDDMzq6ftQS9pHPDfwMHA3sBRkvZu93HMzKyeTnzrZn9geUSsAJD0dWAucFsHjrVF6ta3fbqpW9802hLPdTdtic/zWLS5E0E/HVhZmV8FHNBYSNIJwAl59hFJv+lAXYZjJ+D+Ltehkzbr9unMlkU26/bVsEW0r8bzvDlr+hyOss271ynUiaBXk2XxrAUR5wHndeD4IyJpcUTM7nY9OsXt27y5fZu/braxEzdjVwEzK/MzgNUdOI6ZmdXQiaD/JTBL0h6SxgNHAos6cBwzM6uh7UM3EbFB0ruAK4BxwBciYkm7j9MBm8wwUoe4fZs3t2/z17U2KuJZw+dmZlYQ/8tYM7PCOejNzApXfNC3+jkGSVtLujivv05SX2XdSyT9QtISSbdKmjCWda9rpG2UtJWkC3Pblkr6wFjXvY4a7XulpF9J2iDp8IZ18yQty495Y1fr+kbaPkn7VK7PWyS9ZWxrXs9onr+8fntJd0s6Z2xqPDyjvD53k/SD/Pq7rZo/bRURxT5IN4N/B+wJjAduBvZuKHMi8Jk8fSRwcZ7uAW4BXprndwTGdbtNbW7jW4Gv5+mJwB1AX7fbNIL29QEvAS4CDq8snwqsyH+n5Okp3W5TG9u3FzArT08D1gCTu92mdrWvsv6TwFeBc7rdnna3D7ga+PM8vS0wsRP1LL1H//TPMUTEE8DAzzFUzQUuzNMLgTmSBLwOuCUibgaIiAci4skxqvdwjKaNAUyS1ANsAzwBrB+batfWsn0RcUdE3AI81bDtXwBXRsSDEbEWuBJ4/VhUehhG3L6I+G1ELMvTq4H7gN6xqXZto3n+kPSnwC7AD8aisiMw4vbl3wDriYgrc7lHIuKxTlSy9KBv9nMM0wcrExEbgIdIvfe9gJB0Rf7YdeoY1HckRtPGhcCjpJ7gXcBZEfFgpys8THXa14ltx0pb6ihpf1KP8ndtqle7jLh9kp4DfBz4hw7Uq11G8/ztBayTdKmkGyX9R/5RyLYrPejr/BzDYGV6gIOAo/Pfv5Q0p73Va4vRtHF/4EnSx/49gFMk7dne6o1arZ/U6MC2Y2XUdZS0K/Al4LiIeFavuMtG074TgcsjYmXLkt0zmvb1AK8A/h7YjzT8M7891dpY6UFf5+cYni6ThzB2AB7My38SEffnj1OXA/t2vMbDN5o2vhX4fkT8ISLuA64BNrXfGxnNT2psDj/HMao6Stoe+C7wwYi4ts11a4fRtO9lwLsk3QGcBRwr6Yz2Vm/URnt93piHfTYAl9GhjCk96Ov8HMMiYODbGIcDP4p0Z+QK4CWSJuZwfBWb5k8tj6aNdwGvUTIJOBC4fYzqXddoflLjCuB1kqZImkK673JFh+o5UiNuXy7/LeCiiPhGB+s4GiNuX0QcHRG7RUQfqdd7UURsav+R0Wiuz18CUyQN3Fd5DZ3KmG7fte70AzgE+C1p7PKf8rJ/Bd6UpycA3wCWA9cDe1a2fRuwBPg18O/dbku720i6y/+N3MbbgH/odltG2L79SL2jR4EHgCWVbd+e272cNLTR9fa0q335+vwDcFPlsU+329PO56+yj/lsgt+6acP1+eekb/fdClwAjO9EHf0TCGZmhSt96MbMbIvnoDczK5yD3syscA56M7PCOejNzArnoDczK5yD3syscP8PLPkELsb0AE0AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "media_bfs = mean(times)\n",
    "desvio_bfs = stdev(times)\n",
    "intervalo_conf = '(' + str( media_bfs - 1.96 * (desvio_bfs / (len(times)) ** (1/2)) ) + ',' + str( media_bfs + 1.96 * (desvio_bfs / (len(times)) ** (1/2)) ) + ')'\n",
    "print(\"Media do tempo gasto para a busca BFS: \" + str(media_bfs))\n",
    "print(\"Desvio padrao do tempo gasto para a busca BFS: \" + str(desvio_bfs))\n",
    "print(\"Intervalo de confiança para a busca BFS: \" + intervalo_conf)\n",
    "fig = plt.figure()\n",
    "plt.hist(times,bins=10)\n",
    "plt.title('Histograma para o tempo de execucao da BFS')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Projecao da relacao entre distancia em linha reta e tempo para a BFS"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {},
   "outputs": [],
   "source": [
    "goal_pos = (50,50)\n",
    "x = []\n",
    "y = []\n",
    "for i in range(5,50):\n",
    "    for j in range(5,50):\n",
    "        if i != 20 and i != 40:\n",
    "            init_pos = (i,i)\n",
    "            distancia_linha_reta = sqrt( (goal_pos[0] - init_pos[0]) ** 2 + (goal_pos[1] - init_pos[1]) ** 2)\n",
    "            robot_problem = RobotProblem(init_pos, goal_pos, mapa, grafo)\n",
    "            start = time.time()\n",
    "            node = breadth_first_graph_search(robot_problem)\n",
    "            end = time.time()\n",
    "            x.append(distancia_linha_reta)\n",
    "            y.append(end - start)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure()\n",
    "plt.scatter(x,y)\n",
    "plt.xlim(0,42)\n",
    "plt.ylim(0, 0.15)\n",
    "plt.title(\"Distancia em linha reta x Tempo BFS\")\n",
    "plt.xlabel(\"Distancia em linha reta entre os pontos inicial e final\")\n",
    "plt.ylabel(\"Tempo da busca BFS\")\n",
    "plt.show()"
   ]
  }
 ],
 "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": "3.7.0"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}

proj1-busca-supervisionada.ipynb